Convolução_no_Tempo_Discreto_(LSTI).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Alm3ida/calculo_numerico/blob/master/Convolu%C3%A7%C3%A3o_no_Tempo_Discreto_(LSTI).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ijnaDg2IuZxc"
      },
      "source": [
        "# **Convolução de Sinais no Tempo Discreto (Discrete Time Convolution)**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_uhHdVAAueAO"
      },
      "source": [
        "---\n",
        "\n",
        "## **Desafio:**\n",
        "\n",
        "Implementar um código para a calcular numericamente a integral de convolução entre dois sinais. Os seguintes requisitos devem ser atendidos:\n",
        "\n",
        "- O usuário do código deve especicar os sinais que serão convoluidos através de funções em arquivos diferentes.\n",
        "- O usuário deve definir o intervalo de tempo para o cálculo da convolução.\n",
        "- O resultado da convolução deve ser apresentado de forma gráca.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LwUiaJWXu1Px"
      },
      "source": [
        "\n",
        "# **Convolução de Sinais no Tempo Discreto:**\n",
        "\n",
        "O cálculo da convolução no tempo discreto por definição é:\n",
        "\n",
        "\n",
        "$$\n",
        "y[n] = x[n]*h[n] = \\displaystyle\\sum_{\\tau = -\\infty}^{\\infty} x[\\tau]h[n-\\tau]\n",
        "$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ACBLa0ccBYtP"
      },
      "source": [
        "## **Como utilizar?**\n",
        "\n",
        "1. Crie um documento de texto (.txt), já com o nome da função que irá utilizar no código, como por exemplo, `sinal_1.txt`. Acesse os sinais de exemplo através deste [link](https://drive.google.com/drive/folders/1V5TXD6nTvpYQPEh4RC5DFDQxucu9xjkx?usp=sharing).\n",
        "> Caso opte por utilizar os materiais disponibilizados no link acima, faça o Upload deles na opção \"Arquivos\", localizada no menu lateral do painel no Google Colab."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "052r2QqFPKL4"
      },
      "source": [
        "<div>\n",
        "<center>\n",
        "<img width = '30%' src=\"https://i.imgur.com/qSxpzdX.png\">\n",
        "</center>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aJ237cKJPNar"
      },
      "source": [
        "\n",
        "2. Execute todas as células de código em sequência pelo menos uma vez. Pode-se utilizar a opção `Ambiente de execução > Executar Tudo`.\n",
        "3. Na célula de execução da convolução, indique os sinais a serem utilizados, passando o mesmo nome utilizado na definição.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SZizIC2Yychh"
      },
      "source": [
        "## **Importação das Bibliotecas**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "sEuqmJoNO0eD"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import scipy as s\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "from math import (cos, sin, exp, pow, pi)\n",
        "import glob\n",
        "import os"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nSLC3QUHtQN7"
      },
      "source": [
        "## **Programação Dinâmica: Funções de Sinais**\n",
        "\n",
        "Essa seção do projeto transforma todos os arquivos de texto que descrevem os sinais em funções internas do Python. Utilizando o método `exec()`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "4WFJCeUutSZs"
      },
      "outputs": [],
      "source": [
        "def conversao_sinal(path, nome_funcao):\n",
        "    \"\"\"Converte a função do sinal descrita dentro de um artigo no formato (.txt)\n",
        "    e retorna um código em Python que é executado dinamicamente.\"\"\"\n",
        "\n",
        "    # Realiza a abertura do arquivo txt e lê os dados\n",
        "    with open(path, 'r') as sinal:\n",
        "        funcoes_sinal = sinal.read()\n",
        "\n",
        "    # Separa cada seção da função descritas nas linhas \n",
        "    funcoes_sinal = funcoes_sinal.split('\\n')\n",
        "\n",
        "    # Constrói o código python a partir das linhas lidas\n",
        "    funcao_dinamica = \"\"\"\"\"\"\n",
        "\n",
        "    for pos, funcao in enumerate(funcoes_sinal):\n",
        "\n",
        "        imagem, intervalo = funcao.split(',')\n",
        "\n",
        "        imagem = imagem.replace(\" \", \"\")\n",
        "        intervalo = intervalo.replace(\" \", \"\")\n",
        "\n",
        "        limite_inferior = intervalo.split(';')[0].replace('[', '')        \n",
        "        limite_superior = intervalo.split(';')[1].replace(')', '')\n",
        "\n",
        "        if pos == 0:\n",
        "          funcao_parcial = f\"\"\"\\tif {limite_inferior} <= x < {limite_superior}:\\n\\t\\treturn {imagem}\\n\"\"\"\n",
        "        else:\n",
        "          funcao_parcial = f\"\"\"\\telif {limite_inferior} <= x < {limite_superior}:\\n\\t\\treturn {imagem}\\n\"\"\"\n",
        "        \n",
        "        funcao_dinamica += funcao_parcial\n",
        "    \n",
        "    funcao_dinamica += f\"\"\"\\telse:\\n\\t\\treturn 0\"\"\"\n",
        "    sinal = F\"\"\"def {nome_funcao}(x):\\n{funcao_dinamica}\"\"\"\n",
        "\n",
        "    print(sinal)\n",
        "    return sinal"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Leitura dos sinais nos arquivos de texto**"
      ],
      "metadata": {
        "id": "KVWvqd2K5eNe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "-Ujfu95stT6t"
      },
      "outputs": [],
      "source": [
        "# Acessa todos os sinais descritos nos arquivos .txt da pasta indicada\n",
        "\n",
        "path = \"./*.txt\"\n",
        "\n",
        "nomes_sinais = {}\n",
        "\n",
        "for file in glob.glob(path):\n",
        "    nome_sinal = os.path.basename(file).split('.')[0]\n",
        "    nomes_sinais[nome_sinal] = nome_sinal\n",
        "    exec(conversao_sinal(file, nome_sinal))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path2 = \"/content/drive/MyDrive/Colab Notebooks/signals/*.txt\"\n",
        "\n",
        "for file in glob.glob(path2):\n",
        "    nome_sinal = os.path.basename(file).split('.')[0]\n",
        "    nomes_sinais[nome_sinal] = nome_sinal\n",
        "    exec(conversao_sinal(file, nome_sinal))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1yuq-RwNKoU8",
        "outputId": "59ca5b21-cd34-4599-8acc-cc9576386191"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "def sinal2(x):\n",
            "\tif -np.inf <= x < -2:\n",
            "\t\treturn 0\n",
            "\telif 2 <= x < np.inf:\n",
            "\t\treturn 1\n",
            "\telse:\n",
            "\t\treturn 0\n",
            "def degrau_unitario(x):\n",
            "\tif -np.inf <= x < 0:\n",
            "\t\treturn 0\n",
            "\telif 0 <= x < np.inf:\n",
            "\t\treturn 1\n",
            "\telse:\n",
            "\t\treturn 0\n",
            "def sinal3(x):\n",
            "\tif -np.inf <= x < 0:\n",
            "\t\treturn 0\n",
            "\telif 0 <= x < np.inf:\n",
            "\t\treturn x**2\n",
            "\telse:\n",
            "\t\treturn 0\n",
            "def sinal5(x):\n",
            "\tif -np.inf <= x < 0:\n",
            "\t\treturn 0\n",
            "\telif 0 <= x < 3:\n",
            "\t\treturn 1\n",
            "\telse:\n",
            "\t\treturn 0\n",
            "def sinal6(x):\n",
            "\tif -np.inf <= x < 0:\n",
            "\t\treturn 0\n",
            "\telif 0 <= x < 4:\n",
            "\t\treturn 1\n",
            "\telse:\n",
            "\t\treturn 0\n",
            "def sinal4(x):\n",
            "\tif -np.inf <= x < 0:\n",
            "\t\treturn 0\n",
            "\telif 0 <= x < pi:\n",
            "\t\treturn 3*sin(x)\n",
            "\telse:\n",
            "\t\treturn 0\n",
            "def sinal1(x):\n",
            "\tif -np.inf <= x < 0:\n",
            "\t\treturn 0\n",
            "\telif 0 <= x < 4:\n",
            "\t\treturn cos(x)+2*x+sin(x**2)\n",
            "\telif 4 <= x < 8:\n",
            "\t\treturn x\n",
            "\telif 8 <= x < np.inf:\n",
            "\t\treturn 0\n",
            "\telse:\n",
            "\t\treturn 0\n",
            "def exp_neg(x):\n",
            "\tif -np.inf <= x < 0:\n",
            "\t\treturn 0\n",
            "\telif 0 <= x < np.inf:\n",
            "\t\treturn exp(-x)\n",
            "\telse:\n",
            "\t\treturn 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "5FogeLJetOy2"
      },
      "outputs": [],
      "source": [
        "def inverse_time_domain(sinal_data):\n",
        "  \"\"\"Recebe um sinal e retorna o sinal invertido no tempo\"\"\"\n",
        "  \n",
        "  new_data = sinal_data[:]\n",
        "  new_data[0] = -new_data[0][::-1]\n",
        "  new_data[1] = new_data[1][::-1]\n",
        "  \n",
        "  return new_data  "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Sinal de teste no tempo discreto**"
      ],
      "metadata": {
        "id": "Yka2yrbDaHQ_"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VsWyHN5j7OGJ"
      },
      "source": [
        "<div>\n",
        "<center>\n",
        "<img width = \"70%\" src = \"https://i.imgur.com/QAEhQM9.png\">\n",
        "</center>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "3Ut3yTqPtsXd"
      },
      "outputs": [],
      "source": [
        "# Descreve os dois sinais da figura acima, utilizada como exemplo de teste da convolução gráfica\n",
        "sinal_1 = [np.arange(0, 7, 1), np.array([ 2, 1, 1, 1, 0, 0, 0])]\n",
        "sinal_2 = [np.arange(0, 7, 1), np.array([ 1, 2, 0, 0, 0, 0, 0])]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "yPALhY48tsZf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 801
        },
        "outputId": "2e21d051-baa9-4924-b2c8-10ddc56dfdc9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1120x980 with 3 Axes>"
            ],
            "image/png": 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ht2q1Wtbr9arDkCRJkqSuExG3Z2att7G27aoqSZIkSRqeLBxbdOedMGlS0UqSJElSN7FwbNGECXDBBUUrSZIkSd1kwM1xVHj1q+GKK6qOQpIkSZI6zxnHFr3wAjzzTNFKkiRJUjexcGzR4sUwalTRSpIkSVI3sXBs0eTJ8I//WLSSJEmS1E18xrFFEyfCpZdWHYUkSZIkdZ4zji3auRNWrChaSZIkSeomFo4tWrIEpk8vWkmSJEnqJhaOLXrNa+Cmm4pWkiRJkrqJzzi26IgjYM6cqqOQJEmSpM5zxrFFTzwBN95YtJIkSZLUTSwcW3T//XDuuUUrSZIkSd3EpaotOvnkomh85SurjkSSJEmSOsvCsUWHHQbHH191FJIkSZLUeQMuVY2IGyLiyYi4ro/x10XEiohYExF/3dR/TETcVfZfFRHRzsA7beNGuOKKolX3WrQIZs2C8eOLdtGiqiNSlcwHNZgLkqThrpVnHD8PvKOf8S8CfwZMBc6JiBPK/s8Al2XmFOBI4IDek3TtWnj/+4tW3WnRomJn3Xodtm4t2jlz/AtitzIf1GAuSJK6wYCFY2beCmzvbSwiJgIjM3NZZu4CvgG8uZxdPA1YWJ56DfCWtkRckVmz4De/KVp1p3nzYMeO3ft27Cj61X3MBzWYC5KkbjDUXVUnAhua3m8AJgETgCcyM/fof5GImBsR9Yiob9q0aYjh7DsHHQQHH1y06k4rV/bev2pVZ+PQ/sF8UIO5IEnqBpWXQZm5IDNrmVnr6empOpw+PfQQXHpp0ao79bU50tSpnY1D+wfzQQ3mgiSpGwy1cNzI7jOJk8q+LcARTRviNPoPWFu2wHe/W7TqTvPnw+jRu/eNHl30q/uYD2owFyRJ3WBIhWNmbgR2RcSJETECeDtwY7lEtc7vNsS5ELhxSJFWbOZMWLeuaNWdZs+GhQuhVit2TqzVivezZ1cdmapgPqjBXJAkdYP43WOIfZwQcQtwEjAaeAK4APg4MDczN0ZEDfgycChwdWZeVl53LMVmOYcD/w5ckpkv9HevWq2W9Xp9SF9IkiRJkjR4EXF7ZtZ6G2tlV9U/zszfz8zDMvPlmbk4M88pZxvJzHpmTsvMYxpFY9m/OjNPLfvfM1DRuL+7775ie/X77qs6ElXNXFAz80EN5oIkaTirfHOcA8WuXcX26rt2VR2JqmYuqJn5oAZzQZI0nA24VLWTXKoqSZIkSdUY0lJVSZIkSVJ3s3Bs0T33FL/Vdc89VUeiqpkLamY+qMFckCQNZxaOLRo7Fk4/vWjV3cwFNTMf1GAuSJKGM59xlCRJkiT5jKMkSZIkae9ZOLbo5z+Hgw8uWnU3c0HNzAc1mAuSpOHMwrFFr3gFXHZZ0aq7mQtqZj6owVyQJA1nPuMoSZIkSfIZx3Z49llYt65o1d3MBTUzH9RgLkiShjMLxxbddRe88pVFq+5mLqiZ+aAGc0GSNJxZOLbouOPg618vWnU3c0HNzAc1mAuSpOHMZxwlSZIkST7j2A7btsGttxatupu5oGbmgxrMBUnScNZS4RgRb46IVRGxOiLm7jE2NiKWNr22RcT7yrHLImJ909iZ++JLdMLy5fDGNxatupu5oGbmgxrMBUnScDZyoBMiYiTw98AbgW3A3RFxQ2ZuAcjM7cCM8twAHga+1/QR8zPzC22Ou+NOPBHuuAOOP77qSFQ1c0HNzAc1mAuSpOFswMIReB2wIjM3AETED4Gzga/3cu4s4NHMXNu+EPcPY8fCzJlVR6H9gbmgZuaDGswFSdJw1spS1YnAhqb3G4BJfZz7p8A39+j7QEQsi4grI2LMXsS4X3jsMfjyl4tW3c1cUDPzQQ3mgiRpOGvb5jjlMtXzgG81dV8JHAucDOwEPtHLdXMjoh4R9U2bNrUrnLZbswbmzi1adTdzQc3MBzWYC5Kk4ayVpaob2X2GcRJwRy/nnQH8MjPXNzoy87f/7hoRXwG+uOdFmbkAWADFz3G0Fnbnve518Pjj8Hu/V3Ukqpq5oGbmgxrMBUnScNbKjOMdwPSImFQuNX0T8KNeznvRMtWIeFnT27cCK/Y20KodfDBMmFC06m7mgpqZD2owFyRJw9mAhWNmPg98EPgJsBT4u8zcEhE/iIiJABFxEPAnwHV7XH55RNwbEcuAU4CPtzX6DvrlL+FjHytadTdzQc3MBzWYC5Kk4aylZxwz8/uZeVxmTsnML5V952TmxvL4hcx8eWY+ssd1F2fmCZl5Ymaen5lPtP8rdMYjj8AXv1i06m7mgpqZD2owFyRJw1lk7j+PFdZqtazX61WHIUmSJEldJyJuz8xab2Nt21VVkiRJkjQ8WTi2aNUqePvbi1bdzVxQM/NBDeaCJGk4s3Bs0dNPw+rVRavuZi6omfmgBnNBkjSc+YyjJEmSJMlnHCVJkiRJe8/CsUW/+AXMnFm06m7mgpqZD2owFyRJw5mFY4sOOQSOPrpo1d3MBTUzH9RgLkiShjOfcZQkSZIk+YyjJEmSJGnvWTi26PbbYcKEolV3MxfUzHxQg7kgSRrOLBxbdNRR8O53F626m7mgZuaDGswFSdJw5jOOkiRJkiSfcWyH55+HrVuLVt3NXFAz80EN5oIkaTizcGzR7bfD+PE+uyJzQbszH9RgLkiShjMLxxYdcwx86UtFq+5mLqiZ+aAGc0GSNJy19IxjRLwZ+DuKQvMzmblgj/FbgZcCz5RdszLz6Yg4Evg28HLgXuDPM/MZ+uAzjpIkSZJUjSE94xgRI4G/B/4QOBn4UERM6OXU8zNzRvl6uuybB1yfmccCDwFz9+ob7Aeeegruvrto1d3MBTUzH9RgLkiShrNWlqq+DliRmRsy8yngh8DZLX7+ucDV5fE1wFsGH+L+4Re/gNe+tmjV3cwFNTMf1GAuSJKGs1YKx4nAhqb3G4BJvZz3rxGxJCI+0NQ3LjO3DXDdAWHaNLjllqJVdzMX1Mx8UIO5IEkazka26XMuzMwNETEO+H5ErMrMha1cGBFzKZewTp48uU3htN/hh8Mf/VHVUWh/YC6omfmgBnNBkjSctTLjuJHdZwonlX2/lZkbynYb8C1gZjm0rSwme72uvGZBZtYys9bT0zPI8Dvn8cfh298uWnU3c0HNzAc1mAuSpOGslcLxDmB6REyKiDHAm4AfNQYjYmS5eyoR8ZJyfEU5fBNwcXl8EXBjuwLvtFWr4E//tGjV3cwFNTMf1GAuSJKGswGXqmbm8xHxQeAnFIXm5Zm5JSJ+QLHEdBvwo4g4GBhBURxeV17+aeC6iHgfsBz4+D74Dh1x6qmwdi289KVVR6KqmQtqZj6owVyQJA1nLf2OY6f4O46SJEmSVI0h/Y6jCuvXw/z5RavuZi6omfmgBnNBkjScWTi26Je/hI99rGjV3cwFNTMf1GAuSJKGM5eqSpIkSZJcqipJkiRJ2nsWji1aswbe/e6iVXczF9TMfFCDuSBJGs4sHFu0bRvcemvRqruZC2pmPqjBXJAkDWc+4yhJkiRJ8hlHSZIkSdLes3Bs0fLl8Ed/VLTqbuaCmpkPajAXJEnDmYVjiyLg4IOLVt3NXFAz80EN5oIkaTjzGUdJkiRJks84SpIkSZL2noVji+66C44+umjV3cwFNTMf1GAuSJKGMwvHFo0fD3PmFK26m7mgZuaDGswFSdJw5jOOkiRJkiSfcWyHTNi1q2jV3cwFNTMf1GAuSJKGs5YKx4h4c0SsiojVETF3j7HDIuKHEbEyIlZExKVNY5dFxPqIWFq+zmz3F+iUn/8cRo4sWnU3c0HNzAc1mAuSpOFs5EAnRMRI4O+BNwLbgLsj4obM3NJ02vzMXBQRY4C7IuKHmbmmaewLbY+8w44+Gj73uaJVdzMX1Mx8UIO5IEkazgZ8xjEiTgM+lJl/Ur6/Arg9M7/ex/nfAz6XmT+LiMuAx1stHH3GUZIkSZKqMdRnHCcCG5rebwAm9XGjVwAnAvc0dX8gIpZFxJXljOSe18yNiHpE1Ddt2tRCONV4+mlYvbpo1d3MBTUzH9RgLkiShrO2bY4TEYcA36SYndxRdl8JHAucDOwEPrHndZm5IDNrmVnr6elpVzhtd889cNxxRavuZi6omfmgBnNBkjSctVI4bmT3GcZJZd9vRUQAXwN+kJnXNfoz87HM3JWZu4CvADOHHnI1jj8ebrihaNXdzAU1Mx/UYC5IkoazVp5xHAncD7yBcnMc4LTmzXEiYj5wVGb+33tc+7LMfKQ8/igwKTP/oq97+YyjJEmSJFVjSM84ZubzwAeBnwBLgb/LzC0R8YOImBgRLwc+Aryu6Wc3/q/y8ssj4t6IWAacAny8Ld+oAk8+Cf/2b0Wr7mYuqJn5oAZzQZI0nLX0jGNmfj8zj8vMKZn5pbLvnMzcmJnrMzMyc1pmzihfPyrPuTgzT8jMEzPz/Mx8Yl9+mX3pvvvgTW8qWnU3c0HNzAc1mAuSpOFswN9xVGHGDFi2DF796qojUdXMBTUzH9RgLkiShjMLxxaNHg0nnFB1FNofmAtqZj6owVyQJA1nbfs5juHukUfgi18sWnU3c0HNzAc1mAuSpOHMwrFFDz0E/+N/FK26m7mgZuaDGswFSdJw5lLVFtVq8NRTcOihVUeiqpkLamY+qMFckCQNZxaOLRoxonh+RTIX1Mx8UIO5IEkazlyq2qK1a+GDHyxadTdzQc3MBzWYC5Kk4czCsUWbN8O11xatupu5oGbmgxrMBUnScBaZWXUMv1Wr1bJer1cdhiRJkiR1nYi4PTNrvY054yhJkiRJ6peFY4vuvx/e9raiVXczF9TMfFCDuSBJGs4sHFv03HPFcyvPPVd1JKqauaBm5oMazAVJ0nDmM46SJEmSJJ9xlCRJkiTtPQvHFi1ZAiecULTqbuaCmpkPajAXJEnDmYVji0aPhhkzilbdzVxQM/NBDeaCJGk4a6lwjIg3R8SqiFgdEXN7GX9dRKyIiDUR8ddN/cdExF1l/1UREe0MvpOOOw6uvrpo1d3MBTUzH9RgLqhh0SKYNQvGjy/aRYuqjkhVMRfU7EDPhwE3x4mIkcB9wBuBbcDdwGmZuaXpnDuB/xdYAdwGvDsz742I64CvZuZNzcd93cvNcSRJ0oFs0SKYMwd27Phd3+jRsHAhzJ5dXVzqPHNBzQ6UfBjq5jivA1Zk5obMfAr4IXB204dPBEZm5rLM3AV8A3hzObt4GrCwPPUa4C1D+B6VWry4+B938eKqI1HVzAU1Mx/UYC4IYN683f9iCMX7efOqiUfVMRfUbDjkQyuF40RgQ9P7DcCkFsYnAE/k76Y097wOgIiYGxH1iKhv2rRpMLF31KRJ8KEPFa26m7mgZuaDGswFAaxc2Xv/qlWdjUPVMxfUbDjkQ+Wb42TmgsysZWatp6en6nD69MpXwmWXFa26m7mgZuaDGswFARx/fO/9U6d2Ng5Vz1xQs+GQD60UjhvZfaZwUtk30PgW4IimDXH2vE6SJGlYmT//xTvrjh5d9Ku7mAtqNhzyoZXC8Q5gekRMiogxwJuAHzUGM3MjsCsiToyIEcDbgRvLJap1YE556oXAjW2NXpIkaT8ye3ax2UWtVuycWKvtf5tfqDPMBTUbDvkw4K6qABFxLvA5ikLz8sz8UkT8AJibmRsjogZ8GTgUuDozLyuvO5Zis5zDgX8HLsnMF/q6j7uqSpIkSVI1+ttVdWQrH5CZ3we+v0ffOU3HdWBaL9etBk4dVLSSJEmSpP1K5ZvjSJIkSZL2bxaOkiRJkqR+WThKkiRJkvpl4ShJkiRJ6ldLu6p2SkQ8CjxcdRz9+H1gc9VBaL9gLqiZ+aAGc0HNzAc1mAtqtj/nw9GZ+dLeBvarwnF/FxH1vranVXcxF9TMfFCDuaBm5oMazAU1O1DzwaWqkiRJkqR+WTgOzoKqA9B+w1xQM/NBDeaCmpkPajAX1OyAzAeXqkqSJEmS+uWMoyRJkiSpXxaOkiRJkqR+WTi2ICLeHBGrImJ1RMytOh5VJyJuiIgnI+K6qmNR9SLiFRFxa0TcFxHLIuKCqmNSNSLi8Ii4KyKWRsTyiHh31TGpWhFxWET8MiI+V3UsqlZEPFz+GbE0In5SdTyqVkRMjoiflH93uDciRlcdU6t8xnEAETESuA94I7ANuGC9mcQAACAASURBVBs4LTO3VBqYKhERbwDGAu/MzPMrDkcVi4iXAUdl5tKIeCnFfx+Oy8wdFYemDouIEcAhmbmz/EvAcuC1/lnRvSLiU8AUYF1m/q+q41F1IuJhYHpmPlV1LKpeRCwC/iozfxYRRwC/zsznq46rFc44Dux1wIrM3FD+H/6HwNkVx6SKZOatwPaq49D+ITMfycyl5fGjwOPAEdVGpSpk5q7M3Fm+PQSI8qUuFBHHAsdT/J1BkgCIiGnAc5n5M4DMfOJAKRrBwrEVE4ENTe83AJMqikXSfioiTgVGZOa6qmNRNcrlqr8A1gOfzczHq45Jlfkc8JdVB6H9RgKLIuLOiLiw6mBUqWOBpyLixoi4JyI+WnVAg2HhKElDVC41+RrwnqpjUXUyc2tmngRMBv48Io6qOiZ1XkS8FXggMx+oOhbtN87IzFOBc4GPRsSJVQekyowEzgT+OzALOCsizqo2pNZZOA5sI7vPME4q+ySJiDgE+C4wPzN/XnU8ql5mPgb8guIvB+o+NeDt5XNtnwPeHRF/XW1IqlJmbijbR4AfAKdUG5EqtAG4KzPXZeazFPkwo+KYWmbhOLA7gOkRMSkixgBvAn5UcUyS9gMREcBXgf/IzKsrDkcVioijImJseTwO+ANgVbVRqQqZ+ZeZ+YrMPBr4X8D/l5l/W3FYqkhEjG76b8MY4A+BFdVGpQrdCfRExPiIOIjiz4r7K46pZSOrDmB/l5nPR8QHgZ9QFNqXu0te94qIW4CTgNERsR64IDMXVxyWqnM68N+AZRHxtrLv4sy8t8KYVI1XAV8q/zEhgH8yDyQBRwE3FP9pYATFPyTcWW1IqkpZV3wU+CnFnxU/zsybKg6rZf4chyRJkiSpXy5VlSRJkiT1y8JRkiRJktQvC0dJkiRJUr8sHCVJkiRJ/bJwlCRJkiT1y8JRkiRJktQvC0dJkiRJUr8sHCVJkiRJ/bJwlCRJkiT1y8JRkiRJktQvC0dJkiRJUr8sHCVJkiRJ/bJwlCQNaxGxIiJmteFzHo6IM9oR0wD3iYh4KiKO6mP8nyLiwj7GRkbEvX1dK0nS3rJwlCQd8CLiDyKiHhHbImJLRPxHREwGyMxpmbl4H977kIj4SkT8KiJ+XcYxlEJ1MvCbzHysl3u9DDgH+Eb5vlFk9gBk5vPAl4EPD+H+kiS9iIWjJOmAFhHjgO8BlwPjgVcB/wTs6lAII4GHgTOAw4ErgBsjYsxeft504P4+xi4GvpeZje82GdiZmZuazvkGcHFEHLyX95ck6UUsHCVJB7rjgGcz8zuZ+UJmPpWZN2Tmr2D3JaYRkRHx3ohYGxGPR8RfNj4kIj4aEb8sZw0XR8SJrdw8M3dk5t9m5q/K+38D+A0wdS+/z3RgQ0R8NSK2RsR9EXFMOfZfgZ+V8b4GuA8YX8463lnG8yjwJPDavby/JEkvYuEoSTrQPQC8JCIWRMRZEfF7A5z/h8AJwBuATzQVZSspiq0JwM3A1/YmmIg4FjgCWLM311MUjqcB/1zGshz4f8qxE4DVAJl5P/A3wJWZOSYzZzZ9xkqgpcJXkqRWWDhKkg5ombkN+APgEOBqYHNEXBMRY/u4ZH45K7kcWEZRjFHOWG7OzOeA/wOcONjlphExCrgG+HQZ196YDvxVZi4ul6SuBqIcOxx4quncE4F7e/mM7cC4vby/JEkvYuEoSTrgZebyzLw4M19KMVt3GvCxPk5v3nRmJzAGICLeXe7Aug14lKJYm9BqDOUzhd+mmGn82z7OubBcVvpURFzVy/hIiiWuP2jqnkYxgwiwrRFv6USK4ndPY8tzJUlqCwtHSdKwkpl3A9+hmLlrSUQcTbGpzTspNth5GZD8bqZvoOsPopjtTOCdmZl9xHZtuax0TGZe0sspxwFP7LHZTXNxeC9wbHnPl5THy3v5nKn0PhMpSdJesXCUJB3QIuL4iHh/REws3x8HvAW4YxAfMwZ4AdhMsUvq3wwyjH+mKDYvKH8SY29Np2kGsVxuO4liExyAf6PYvRWKWUWAlzR/QPkbjhOAu4YQhyRJu7FwlCQd6LZTLE29OyJ2ALcAC4H5rX5A+bzjP1MUbQ8Dayl2Rh1QRLwKmAu8Dni8aSnqmYP5EqXdCsfy/QOZ2YjlauBtETEiM7cAXwd+FRH1pmveDlzddI0kSUMWfaymkSRJ+6GI+ALw88z8117GRgJLgLPKn+WQJKktLBwlSZIkSf1yqaokSZIkqV8WjpIkSZKkflk4SpIkSZL6NbLqAJodddRROXny5KrDkCRJkqSuc/vttz+WmS/tbWy/KhwnT55MvV4f+ERJkiRJUltFxMN9jblUVZIkSZLULwtHSZIkSVK/LBwlSZIkSf2ycJQkSZIk9cvCUZIkSZLUrwELx4h4RUTcGhH3RcSyiLigl3OOiYi7ImJNRFwVEVH2HxkRP4mI1RHxnYg4dF98CalTFi1axKxZsxg/fjyzZs1i0aJFVYckSZIk7XOtzDg+D7wvM/8LcDZwRUSM3uOczwCXZeYU4EhgTtk/D7g+M48FHgLmtidsqfMWLVrEnDlzqNfrbN26lXq9zpw5cyweJUmSNOwNWDhm5iOZubQ8fhR4HDiiMV7OLp4GLCy7rgHeUh6fC1zdS790wJk3bx47duzYrW/Hjh3MmzevoogkSZKkzhjUM44RcSowIjPXNXVPAJ7IzCzfbwAmlcfjMnNbL/3Nnzk3IuoRUd+0adPgopc6aOXKlb32r1q1qsORSJIkSZ3VcuEYEUcAXwPe084AMnNBZtYys9bT09POj5ba6vjjj++1f+rUqR2ORJIkSeqslgrHiDgE+C4wPzN/vsfwFuCIxoY4FLOKG8vjbRExrpd+6YAzf/58Ro/e/fHe0aNHM3/+/IoikiRJkjqjlV1VA/gq8B+ZefWe4+US1Tq/2xDnQuDG8vgm4OLy+KKmfumAM3v2bBYuXMjMmTMZO3YsM2fOZOHChcyePbvq0CRJkqR9Kn73aGIfJ0ScAfwUWNbUfTHwP4GrMvOuiDgW+AZwOPDvwCWZ+UJE/D5wHcVs43LgzzLz6b7uVavVsl6vD+X7SPvcbbfdxhlnnMF//ud/cvrpp1cdjiRJktQWEXF7ZtZ6Gxs50MWZ+Z/0PjM5t+mc1cCpvVy7GXA6RsPKtGnT+PGPf8y0adOqDkWSJEnqiAELR0m7O/zwwznrrLOqDkOSJEnqmEH9HIckePzxx7n++ut5/PHHqw5FkiRJ6ggLR2mQVq1axfnnn+/vN0qSJKlruFRVGqRTTjmFNWvWMHHixKpDkSRJkjrCwlEapFGjRnHMMcdUHYYkSZLUMS5VlQZpw4YNfPazn2XDhg1VhyJJkiR1hIWjNEgPP/wwH/7wh3n44YerDkWSJEnqCJeqSoN0+umnk5lVhyFJkiR1jDOOkiRJkqR+WThKg7RmzRouueQS1qxZU3UokiRJUkdYOEqDtG3bNm6++Wa2bdtWdSiSJElSR/iMozRIp556Kg8++GDVYUiSJEkd44yjJEmSJKlfFo7SIC1fvpyzzjqL5cuXVx2KJEmS1BEWjpIkSZKkfvmMozRI06dP5+abb646DEmSJKljBpxxjIgbIuLJiLiul7GxEbG06bUtIt5Xjl0WEeubxs7cF19AkiRJkrRvtbJU9fPAO3obyMztmTkjM2cAJwNbge81nTK/MZ6ZPxt6uFL17r77bo455hjuvvvuqkORJEmSOmLAwjEzbwW2t/BZs4BHM3PtUIOS9mfjxo3jrLPOYty4cVWHIkmSJHVEOzfH+VPgm3v0fSAilkXElRExpreLImJuRNQjor5p06Y2hiPtG1OmTOGqq65iypQpVYciSZIkdURbCseICOA84FtN3VcCx1IsYd0JfKK3azNzQWbWMrPW09PTjnAkSZIkSW3UrhnHM4BfZub6RkdmPpaZuzJzF/AVYGab7iVV6rbbbiMiuO2226oORZIkSeqIdhWOL1qmGhEva3r7VmBFm+4lVeroo4/m8ssv5+ijj646FEmSJKkjIjP7PyHiFuAkYDTwBHAB8HFgbmZujIiDgF8BMzPzkabrrgZmAAk8ALwnM5/o7161Wi3r9foQvo4kSZIkaW9ExO2ZWettbORAF2fmH/fSfU7T+AvAy3u57uLBBCkdKJ5++mk2btzIxIkTGTVqVNXhSJIkSftcO3dVlbrCPffcw5QpU7jnnnuqDkWSJEnqCAtHaZCmTp3Kddddx9SpU6sORZIkSeqIAZeqStrdkUceyXnnnVd1GJIkSVLHOOMoDdLWrVu5+eab2bp1a9WhSJIkSR1h4SgN0ooVKzj77LNZscJfmJEkSVJ3cKmqNEgnnXQSS5YsYcqUKVWHIkmSJHWEhaM0SGPGjGHGjBlVhyFJkiR1jEtVpUF69NFHueqqq3j00UerDkWSJEnqCAtHaZAefPBB3vve9/Lggw9WHYokSZLUES5VlQapVqvx61//msMOO6zqUCRJkqSOsHCUBmnEiBGMHTu26jAkSZKkjnGpqjRIa9eu5UMf+hBr166tOhRJkiSpIywcpUHatGkTX/3qV9m0aVPVoUiSJEkd4VJVaZBe//rXs3nz5qrDkCRJkjrGGUdJkiRJUr8GLBwj4oaIeDIirutj/NaIWBkRS8vXqLL/yIj4SUSsjojvRMSh7Q5eqsLKlSs577zzWLlyZdWhSJIkSR3Ryozj54F3DHDO+Zk5o3w9XfbNA67PzGOBh4C5Q4hT2m88++yzbNiwgWeffbbqUCRJkqSOGLBwzMxbge178dnnAleXx9cAb9mLz5D2OyeddBL1ep2TTjqp6lAkSZKkjmjXM47/GhFLIuIDTX3jMnNbebwBmNSme0mSJEmSOqgdheOFmXki8AbgrRExZzAXR8TciKhHRN2fN9CBYOnSpcyYMYOlS5dWHYokSZLUEUMuHDNzQ9luA74FzCyHtkXEuPJ4ErCxj+sXZGYtM2s9PT1DDUfa50aNGsW0adMYNWpU1aFIkiRJHTGkwjEiRkbEkeXxS4A3ASvK4ZuAi8vji4Abh3IvaX8xdepUrr32WqZOnVp1KJIkSVJHtPJzHLcA3wbOiYj1ETErIn4QEROBQ4AfRcQyYAmwHGj8bMengQsiYg0wBViwT76BJEmSJGmfGjnQCZn5x710n9N0fGof120GZu9lXNJ+a/HixZx99tn8+Mc/ZtasWVWHI0mSJO1z7dpVVeoaEydO5H3vex8TJ06sOhRJkiSpIyIzq47ht2q1Wtbr9arDkCRJkqSuExG3Z2attzFnHKVBeu6559i8eTPPPfdc1aFIkiRJHWHhKA3SHXfcQU9PD3fccUfVoUiSJEkdYeEoDdKUKVP4l3/5F6ZMmVJ1KJIkSVJHDLirqqTdHXXUUbzrXe+qOgxJkiSpY5xxlAZp+/bt1Ot1tm/fXnUokiRJUkdYOEqDtGzZMmbNmsWyZcuqDkWSJEnqCAtHaZCmT5/OT3/6U6ZPn151KJIkSVJH+IyjNEjjxo3jzDPPrDoMSZIkqWOccZQGafPmzVx77bVs3ry56lAkSZKkjrBwlAbpgQce4KKLLuKBBx6oOhRJkiSpI1yqKg3Sa1/7WtavX8+RRx5ZdSiSJElSR1g4SoN0yCGHMGnSpKrDkCRJkjrGparSIK1bt45PfvKTrFu3rupQJEmSpI6wcJQGad26dXzqU5+ycJQkSVLXGLBwjIgbIuLJiLiul7HDIuKHEbEyIlZExKVNY5dFxPqIWFq+/P0CDQunnXYazzzzDKeddlrVoUiSJEkd0cqM4+eBd/QzPj8zjwdeD/xFREzZY2xG+frZUAKVJEmSJFVjwMIxM28FtvcxtjMzF5XHTwGrgJe1M0Bpf7N69Wre9a53sXr16qpDkSRJkjqibc84RsQrgBOBe5q6PxARyyLiyogY0657SVXavn07d955J9u39/rvKZIkSdKw05bCMSIOAb4JfCgzd5TdVwLHAicDO4FP9HHt3IioR0R906ZN7QhH2qdOOeUUVqxYwSmnnFJ1KJIkSVJHDLlwjIgAvgb8IDN/u4FOZj6WmbsycxfwFWBmb9dn5oLMrGVmraenZ6jhSJIkSZLarB0zjp8Gdmbm/27ujIjmZx3fCqxow72kyt17772ceeaZ3HvvvVWHIkmSJHXEyIFOiIhbgJOA0RGxHrgA+Dgwl6Lw/AhwX0QsLS/5SGb+CLg8ImYACTwAvGcfxC913IgRIzj88MMZMWJE1aFIkiRJHRGZWXUMv1Wr1bJer1cdhiRJkiR1nYi4PTNrvY21bVdVSZIkSdLwZOEoDdKdd97JpEmTuPPOO6sORZIkSeoIC0dpkCZMmMAFF1zAhAkTqg5FkiRJ6ogBN8eRtLtXv/rVXHHFFVWHIUmSJHWMM47SIL3wwgs888wzvPDCC1WHIkmSJHWEhaM0SIsXL2bUqFEsXry46lAkSZKkjrBwlAZp8uTJ/OM//iOTJ0+uOhRJkiSpI3zGURqkiRMncumll1YdhiRJktQxzjhKg7Rz505WrFjBzp07qw5FkiRJ6ggLR2mQlixZwvTp01myZEnVoUiSJEkdYeEoDdJrXvMabrrpJl7zmtdUHYokSZLUET7jKA3SEUccwZw5c6oOQ5IkSeoYZxylQXriiSe48cYbeeKJJ6oORZIkSeoIC0dpkO6//37OPfdc7r///qpDkSRJkjrCparSIJ188sncf//9vPKVr6w6FEmSJKkjLBylQTrssMM4/vjjqw5DkiRJ6pgBl6pGxA0R8WREXNfH+OsiYkVErImIv27qPyYi7ir7r4qIaGfgnbRo0SJmzZrF+PHjmTVrFosWLao6JFVo48aNXHHFFWzcuLHqUCRJkqSOaOUZx88D7+hn/IvAnwFTgXMi4oSy/zPAZZk5BTgSOCC3oVy0aBFz5syhXq+zdetW6vU6c+bMsXjsYmvXruX9738/a9eurToUSZIkqSMGLBwz81Zge29jETERGJmZyzJzF/AN4M3l7OJpwMLy1GuAt7Ql4g6bN28eO3bs2K1vx44dzJs3r6KIVLVZs2bxm9/8hlmzZlUdiiRJktQRQ91VdSKwoen9BmASMAF4IjNzj/4XiYi5EVGPiPqmTZuGGE77rVy5stf+VatWdTgS7S8OOuggDj74YA46yE2JJUmS1B0q/5tvZi7IzFpm1np6eqoO50X62gRl6tSpHY5E+4uHHnqISy+9lIceeqjqUCRJkqSOGGrhuJHdZxInlX1bgCOaNsRp9B9w5s+fz+jRo3frGz16NPPnz68oIlVty5YtfPe732XLli1VhyJJkiR1xJAKx8zcCOyKiBMjYgTwduDGcolqnd9tiHMhcOOQIq3I7NmzWbhwIbVajfHjx1Or1Vi4cCGzZ8+uOjRVZObMmaxbt46ZM2dWHYokSZLUEfG7xxD7OCHiFuAkYDTwBHAB8HFgbmZujIga8GXgUODqzLysvO5Yis1yDgf+HbgkM1/o7161Wi3r9fqQvpAkSZIkafAi4vbMrPU21squqn+cmb+fmYdl5sszc3FmnlPONpKZ9cyclpnHNIrGsn91Zp5a9r9noKJxf3ffffcxZ84c7rvvvqpDUcXMBUmSJHWbyjfHOVDs2rWLHTt2sGvXrqpDUcXMBUmSJP3/7d19uF11fef999ckghxSiGiCZKBBJKTlwSiCJ4CNlFvvG0PR+/Kh1Ie73jOhlzPVGUdKB2VqmXZ6N6O0ddpy6VjscEmptuKNlSetT4nacAIhkJAAh0RCBkIkASGSEBHid/5YO7A5nHOyCTlrrd/Z79d1neu391prn/U5Z/1W9vlm/X5r95u9DlWtk0NVJUmSJKkZL2qoqiRJkiSpv1k49mjVqlXMmzePVatWNR1FDbMvSJIkqd9YOPZo+vTpnH766UyfPr3pKGqYfUGSJEn9xjmOkiRJkiTnOEqSJEmS9p2FY4+WL1/OtGnTWL58edNR1DD7giRJkvqNhWOPjjzySC655BKOPPLIpqOoYfYFSZIk9RvnOEqSJEmSnOO4Pzz55JPcf//9PPnkk01HUcPsC5IkSeo3Fo49WrlyJUcddRQrV65sOooaZl+QJElSv7Fw7NHcuXP50pe+xNy5c5uOoobZFyRJktRvnOMoSZIkSXKO4/6wfft2li5dyvbt25uOoobZFyRJktRvLBx7tHbtWs4880zWrl3bdBQ1zL4gSZKkftNT4RgR50TEcESsj4jFI9ZNj4jbu762R8RHO+suiYgHuta9aSJ+iDqcdNJJ3HzzzZx00klNR1HD7AuSJEnqN3ud4xgRU4E7gTOB7cCtwGmZ+cgo2wZwH/DmzNwYEZcAD2fmX/cSxjmOkiRJktSMFzvH8VRgXWZuzswdwI3AW8fYdgHw48zcuG9R2+uhhx7iC1/4Ag899FDTUdQw+4IkSZL6TS+F4xHA5q7nm4HZY2z7HuAfRiz7WESsiYjPRsTBI18QEYsjYigihrZu3dpT6CZs2LCBxYsXs2HDhqajqGH2BUmSJPWb/XZznM4w1XcC/9i1+LPAscDrgCeAPxz5usy8PDMHM3Nw5syZ+yvOfnfqqafy8MMPc+qppzYdRQ2zL0iSJKnf9FI4PshzrzDO7iwb6QxgU2Y+sGdBZj6Umbszczfwt8ApLyZsk6ZNm8Zhhx3GtGnTmo6ihtkXJEmS1G96KRxvBk6IiNmdoaZnA98cZbvnDVONiFd1PX07sG5fgzZt06ZNXHzxxWzatKnpKGqYfUGSJEn9Zq+FY2Y+DVwAfA+4HfizzHwkIm6IiCMAIuIlwP8NXD3i5Z+KiDsiYg3weuAP9mv6Gm3ZsoXLLruMLVu2NB1FDbMvSJIkqd/s9eM46uTHcUiSJElSM17sx3FIkiRJkvqYhWOPhoeHOe+88xgeHm46ihpmX5AkSVK/sXDs0a5du1i/fj27du1qOooaZl+QJElSv3GOoyRJkiTJOY6SJEmSpH1n4dij1atXc8opp7B69eqmo6hh9gVJkiT1GwvHHh1wwAHMmTOHAw44oOkoaph9QZIkSf3GOY6SJEmSJOc4SpIkSZL2nYVjj1asWMFhhx3GihUrmo6ihtkXJEmS1G8sHHs0a9Yszj//fGbNmtV0FDXMviBJkqR+4xxHSZIkSZJzHPeHp59+mscee4ynn3666ShqmH1BkiRJ/cbCsUcrVqxgxowZzmuTfUGSJEl9x8KxR8cccwyf//znOeaYY5qOoobZFyRJktRveprjGBHnAH9GVWj+t8y8fMT6pcDhwM86ixZk5q6IeAXwFeBfAXcA783MnzEG5zhKkiRJUjNe1BzHiJgK/Dnw68DrgAsj4rBRNn1XZs7vfO3qLLsI+GpmHgvcCyzep5+gBXbs2MGtt97Kjh07mo6ihtkXJEmS1G96Gap6KrAuMzdn5g7gRuCtPX7/c4ErO4//DviNFx6xHVavXs0b3vAGVq9e3XQUNcy+IEmSpH7TS+F4BLC56/lmYPYo2/19RNwWER/rWnZIZm7fy+uKcPzxx/Ptb3+b448/vukoaph9QZIkSf1m6n76Pu/LzM0RcQjw9YgYzszre3lhRCymM4T16KOP3k9x9r9DDz2Us846q+kYagH7giRJkvpNL1ccH+S5Vwpnd5Y9IzM3d9rtwD8Cp3RWbe8Uk6O+rvOayzNzMDMHZ86c+QLj1+fhhx/mK1/5Cg8//HDTUdQw+4IkSZL6TS+F483ACRExOyIOBs4GvrlnZURM7dw9lYh4aWf9us7q64APdB6/H7h2fwWv2/DwMO95z3sYHh5uOooaZl+QJElSv9nrUNXMfDoiLgC+R1VofiozH4mIG6iGmG4HvhkR04ApVMXh1Z2X/ylwdUR8FFgL/MEE/Ay1OPnkk9m4cSOHH35401HUMPuCJEmS+k1Pn+NYFz/HUZIkSZKa8aI+x1GVBx54gCVLlvDAAw80HUUNsy9IkiSp31g49mjTpk1cfPHFbNq0qekoaph9QZIkSf1mf30cx6R3+umns3v37qZjqAXsC5IkSeo3XnGUJEmSJI3LwrFHGzZs4Pzzz2fDhg1NR1HD7AuSJEnqNxaOPdq+fTtLly5l+/btTUdRw+wLkiRJ6jd+HIckSZIkyY/jkCRJkiTtOwvHHq1du5azzjqLtWvXNh1FDbMvSJIkqd9YOPYoIpg2bRoR0XQUNcy+IEmSpH7jHEdJkiRJknMcJUmSJEn7zsKxRytXrmTOnDmsXLmy6ShqmH1BkiRJ/cbCsUczZsxg0aJFzJgxo+koaph9QZIkSf3GOY6SJEmSJOc47g+Zye7du2lToa1m2BckSZLUb3oqHCPinIgYjoj1EbF4xLqDIuLGiLg7ItZFxEe61l0SEQ9ExO2drzft7x+gLsuXL2fq1KksX7686ShqmH1BkiRJ/Wbq3jaIiKnAnwNnAtuBWyPimsx8pGuzJZm5LCIOBlZGxI2ZuaFr3V/v9+Q1mzNnDpdeeilz5sxpOooaZl+QJElSv9lr4QicCqzLzM0AEXEj8FbgSwCZ+QSwrPN4R0QMA68CNoz+7co0e/ZsLrjggqZjqAXsC5IkSeo3vQxVPQLY3PV8MzB7tA0j4kjgJGBV1+KPRcSaiPhs54rkyNcsjoihiBjaunXrC4her127drF+/Xp27drVdBQ1zL4gSZKkfrPfbo4TEQcA/wBcmJk7O4s/CxwLvA54AvjDka/LzMszczAzB2fOnLm/4ux3q1atYu7cuaxatWrvG2tSsy9IkiSp3/RSOD7Ic68wzu4se0ZEBPBF4IbMvHrP8sx8KDN3Z+Zu4G+BU1585GbMmzePa665hnnz5jUdRQ2zL0iSJKnf7PVzHDs3x7kLeDOdm+MAp3XfHCcilgCzMvP/HfHaV2Xmls7jTwCzM/N3x9qXn+MoSZIkSc14UZ/jmJlPAxcA3wNuB/4sMx+JiBsi4oiI+FfAfwJO7frYjf+z8/JPRcQdEbEGeD3wB/vlJ2rAo48+yje+8Q0effTRpqOoYfYFOG2paQAAIABJREFUSZIk9Zte7qpKZn4d+PqIZW/rehpjvO4D+x6tXe68807OPvtsfvjDH3L66ac3HUcNsi9IkiSp3/RUOArmz5/PmjVrePWrX910FDXMviBJkqR+Y+HYo4GBAU488cSmY6gF7AuSJEnqN/vt4zgmuy1btnDZZZexZcuWpqOoYfYFSZIk9RsLxx7de++9fPjDH+bee+9tOooaZl+QJElSv3Goao8GBwfZsWMHBx54YNNR1DD7giRJkvqNhWOPpkyZwsDAQNMx1AL2BUmSJPUbh6r2aOPGjVxwwQVs3Lix6ShqmH1BkiRJ/cbCsUfbtm3jqquuYtu2bU1HUcPsC5IkSeo3kZlNZ3jG4OBgDg0NNR1DkiRJkvpORKzIzMHR1nnFUZIkSZI0LgvHHt1111284x3v4K677mo6ihpmX5AkSVK/sXDs0VNPPcW2bdt46qmnmo6ihtkXJEmS1G+c4yhJkiRJco6jJEmSJGnfWTj26LbbbuPEE0/ktttuazqKGmZfkCRJUr+xcOzRwMAA8+fPZ2BgoOkoaph9QZIkSf2mp8IxIs6JiOGIWB8Ri0dZf2pErIuIDRHxya7lx0TEys7yz0VE7M/wdZo7dy5XXnklc+fObTqKGmZf0B7Lli1jwYIFzJgxgwULFrBs2bKmI6kh9gV1sz9oD/uCupXeH/Z6c5yImArcCZwJbAduBU7LzEe6trkF+DfAOuBfgPMz846IuBq4IjOv63481r68OY6kUixbtoxFixaxc+fOZ5YNDAxw/fXXs3DhwgaTqW72BXWzP2gP+4K6ldIfXuzNcU4F1mXm5szcAdwIvLXrmx8BTM3MNZm5G/gycE7n6uJpwPWdTf8O+I0X8XM06qabbmJgYICbbrqp6ShqmH1BABdddNFz/vEH2LlzJxdddFFDidQU+4K62R+0h31B3SZDf+ilcDwC2Nz1fDMwu4f1hwE/yWcvaY58HQARsTgihiJiaOvWrS8ke61mz57NhRdeyOzZz/sR1GfsCwK4++67R10+PDxccxI1zb6gbvYH7WFfULfJ0B8avzlOZl6emYOZOThz5sym44zpqKOO4pJLLuGoo45qOooaZl8QwLx580Zdftxxx9WcRE2zL6ib/UF72BfUbTL0h14Kxwd57pXC2Z1le1v/CPDyrhvijHydJBVryZIlz7uz7sDAAEuWLGkokZpiX1A3+4P2sC+o22ToD70UjjcDJ0TE7Ig4GDgb+OaelZn5ILA7Ik6KiCnAecC1nSGqQ8CizqbvA67dr+klqSELFy7k+uuvZ3BwkBkzZjA4ONi6Ce6qh31B3ewP2sO+oG6ToT/s9a6qABFxLnApVaH5qcz8fETcACzOzAcjYhD4AnAgcGVmXtJ53bFUN8s5FPgO8KHM/MVY+/GuqpIkSZLUjPHuqjq1l2+QmV8Hvj5i2du6Hg8Bx4/yuvXAyS8orSRJkiSpVRq/OY4kSZIkqd0sHCVJkiRJ47JwlCRJkiSNy8JRkiRJkjSunu6qWpeI+DFwX9M5xvFKYFvTIdQK9gV1sz9oD/uCutkftId9Qd3a3B/mZObho61oVeHYdhExNNbtadVf7AvqZn/QHvYFdbM/aA/7grqV2h8cqipJkiRJGpeF4wtzedMB1Br2BXWzP2gP+4K62R+0h31B3YrsDw5VlSRJkiSNyyuOkiRJkqRxWThKkiRJksZl4diDiDgnIoYjYn1ELG46j5oTEddExKMRcXXTWdS8iDgyIpZGxJ0RsSYi3t10JjUjIg6NiJURcXtErI2I85vOpGZFxEERsSkiLm06i5oVEfd13iNuj4jvNZ1HzYqIoyPie52/He6IiIGmM/XKOY57ERFTgTuBM4HtwK3AaZn5SKPB1IiIeDMwHfjtzHxXw3HUsIh4FTArM2+PiMOp/n2Ym5k7G46mmkXEFOCAzHyi80fAWuANvlf0r4j4E+A1wP2Z+XtN51FzIuI+4ITM3NF0FjUvIpYB/zkzfxARLwd+mplPN52rF15x3LtTgXWZublzwt8IvLXhTGpIZi4FHm86h9ohM7dk5u2dxz8GHgZe3mwqNSEzd2fmE52nBwDR+VIfiohjgXlUfzNIEgARcTzwVGb+ACAzf1JK0QgWjr04Atjc9XwzMLuhLJJaKiJOBqZk5v1NZ1EzOsNVVwMPAJ/OzIebzqTGXAp8vOkQao0ElkXELRHxvqbDqFHHAjsi4tqIWBURn2g60Ath4ShJL1JnqMkXgd9pOouak5mPZeZrgaOB90bErKYzqX4R8Xbgnsy8p+ksao0zMvNk4FzgExFxUtOB1JipwJuAfwcsAN4SEW9pNlLvLBz37kGee4VxdmeZJBERBwBfA5Zk5vKm86h5mfkQsJrqjwP1n0HgvM68tkuB8yPik81GUpMyc3On3QLcALy+2URq0GZgZWben5lPUvWH+Q1n6pmF497dDJwQEbMj4mDgbOCbDWeS1AIREcAVwHcz88qG46hBETErIqZ3Hh8C/Bow3GwqNSEzP56ZR2bmHOD3gL/JzD9qOJYaEhEDXf82HAz8OrCu2VRq0C3AzIiYEREvoXqvuKvhTD2b2nSAtsvMpyPiAuB7VIX2p7xLXv+KiG8DrwUGIuIB4N2ZeVPDsdSc04HfBNZExDs6yz6QmXc0mEnN+GXg853/TAjgr+wHkoBZwDXVPw1MofqPhFuajaSmdOqKTwDfp3qv+OfMvK7hWD3z4zgkSZIkSeNyqKokSZIkaVwWjpIkSZKkcVk4SpIkSZLGZeEoSZIkSRqXhaMkSZIkaVwWjpIkSZKkcVk4SpIkSZLGZeEoSZIkSRqXhaMkSZIkaVwWjpIkSZKkcVk4SpIkSZLGZeEoSZIkSRqXhaMkSR0R8VcR8b4x1k2NiDsiYtY+fu+IiB1jvX4i9y1J0otl4ShJEhARrwLeBny583xPoTcTIDOfBr4A/P443+ODEfHmMVYfDfw8Mx/a275H7r+XfUuSNJEsHCVJqnwA+KfM3N15fjTwRGZu7drmy8AHImJa9wsj4r0R8ZvPPo2PRMRZI77/CcBdPe57tP2Pum9Jkupg4ShJ6hsR8dcR8T86j18SEf8UEX/RWf1/AT/orPsV4E5gRueq3y0Amflj4FHgDSO+9T8AhwMfBf4/4LHM/M6IbU4ANkfEFRHxWETcGRHHjNz3WPsfZ9+SJE04C0dJUj/5U+C8iJgNfBoI4ILOuhOB9QCZeRfwX4DPZubBmXlK1/e4GzhplO+dXe3uUdafAJwG/A/gMGAt8K9H7nsv+x9r35IkTaipTQeQJKkumbk5Iq4ErussOiMzf9F5fCiwo2vzk4DvjvJtHgcOGbHsN4GtwGeATcCJEXHWiKuOJwD/OTNvAoiI9VSF62j7Hmv/o+1bkqQJ5xVHSVK/WQ3MB/5dZu7sWr4dOLjr+UnAmlFeP72z7TMy8+8z88vPPs2/7C4aI2IqcBxwQ9fLjqe6gjjavsfa//P2LUlSHSwcJUl9IyLeCHwS+Crw2yNW3wEc29nupZ3Ha0f5Nsd1tn2ezLwiM5eOsmou8JMRN9rpLgyf2fde9j/mviVJmkgWjpKkvhARRwFfAd4P/AfgvZ1le3wDOKPzeHqnfemI7zGLan7iyhe4+xPounoYEdOB2VQ3wBm571H3/yL2LUnSi2bhKEma9DqF2nXAf8nMZZm5GbgK+ETXZlcC74iIKZn5CPAl4H9FxFDXNucBV2bmz19ghOcUjp3n93R9n2f2DTDG/vd135IkvWiRmXvfSpKkPhARfw0sz8y/H2XdVOA24C2dj8aYNPuWJGlvLBwlSZIkSeNyqKokSZIkaVwWjpIkSZKkcVk4SpIkSZLGZeEoSZIkSRrX1KYDdJs1a1YeffTRTceQJEmSpL6zYsWKhzLz8NHWtapwPProoxkaGtr7hpIkSZKk/Soi7htrnUNVJUmSJEnjsnCUJEmSJI3LwlGSJEmSNC4LR0mSJEnSuCa8cIyI+yJiTUTcHhHfm+j9qeWWLYMFC2DGjKpdtqzpRJIkSZL2oq67qp6WmTtq2pfaatkyWLQIdu6sng8NVc+vvx4WLmw2myRJkqQxOVRV9bnoomeLxj127qyWS5IkSWqtOgrHBJZFxC0R8b6RKyNicUQMRcTQ1q1ba4ijxtx99+jLh4frzSFJkiTpBamjcDwjM08GzgU+EREnda/MzMszczAzB2fOnFlDHDVm3rzRlx93XL05JEmSJL0gE144ZubmTrsFuAF4/UTvUy21ZAkMDDx32cBAtVySJElSa01o4RgRAxExvfP4YODXgXUTuU+12MKF1Y1wTjkFpk+vWm+MI0mSJLXeRF9xnAX8MCJWA0PAFzPzlgnep9ps4UL4i7+Axx+vWotGSZIkqfUm9OM4MvNe4LUTuQ8V6Pjj4Z//uWolSZIktV5dn+MoPevQQ+Etb2k6hSRJkqQe+TmOqt/DD8NXv1q1kiRJklrPwlH1Gx6Gd73Lz2+UJEmSCuFQVdXv9a+HDRvgiCOaTiJJkiSpBxaOqt/LXgbHHNN0CkmSJEk9cqiq6rd5M3z601UrSZIkqfUsHFW/++6D3//9qpUkSZLUeg5VVf1OPx0ym04hSZIkqUdecZQkSZIkjcvCUfXbsAE+9KGqlSRJktR6Fo6q3/bt8K1vVa0kSZKk1nOOo+p38snwox81nUKSJElSj7ziKEmSJEkal4Wj6rd2LbzlLVUrSZIkqfUsHCVJkiRJ43KOo+p3wgnVzXEkSZIkFcErjpIkSZKkcVk4qn633grHHFO1kiRJklrPwlH1O+SQ6uY4hxzSdBJJkiRJPXCOo+r3mtfA5z7XdApJkiRJPfKKoyRJkiRpXBaOqt+//AtEVK0kSZKk1rNwVP3mzIFPfapqJUmSJLWecxxVv9mz4cILm04hSZIkqUdecVT9du2CH/2oaiVJkiS1noWj6rdqVXVn1VWrmk4iSZIkqQcWjqrfccfB1VdXrSRJkqTWc46j6veKV8A739l0CkmSJEk98oqj6vfYY/Ctb1WtJEmSpNazcFT91q2Dt761aiVJkiS1nkNVVb/XvhZuu626QY4kSZKk1qvlimNEHBQRmyLi0jr2p5Y7+GCYP79qJUmSJLVeXUNVLwaGatqX2u7HP4bPfa5qJUmSJLXehBeOEXEsMA+4caL3pUL86Efwb/9t1UqSJElqvTrmOF4KXAicNtrKiFgMLAY4+uija4ijxg0Owk9/Cgcd1HQSSZIkST2Y0CuOEfF24J7MvGesbTLz8swczMzBmTNnTmQctcWUKTB9etVKkiRJar2JHqo6CJwXEfdRXXk8PyI+OcH7VNtt3AgXXli1kiRJklpvQgvHzPx4Zh6ZmXOA3wP+JjP/aCL3qQJs3QpXXFG1kiRJklrPz3FU/d74Rti2rekUkiRJknpUW+GYmVfUtS9JkiRJ0v5T1+c4Ss+6+2545zurVpIkSVLrWTiqfk8+CZs3V60kSZKk1nOOo+r32tfC0FDTKSRJkiT1yCuOkiRJkqRxWTiqfrffDvPnV60kSZKk1rNwVP1e9jI4/viqlSRJktR6znFU/Y47Dq66qukUkiRJknrkFUdJkiRJ0rgsHFW/m26C6dOrVpIkSVLrWTiqfkccAR/9aNVKkiRJaj3nOKp+v/zL8Md/3HQKSZIkST3yiqPq99RTsG1b1UqSJElqPQtH1e/mm2HmzKqVJEmS1HoWjqrfa14D//N/Vq0kSZKk1nOOo+o3axZ88INNp5AkSZLUI684qn6PPw5DQ1UrSZIkqfUsHFW/NWtgwYKqlSRJktR6Fo6q3wknwPe/X7WSJEmSWs85jqrfIYfAm97UdApJkiRJPfKKo+q3bRtcdVXVSpIkSWo9C0fV75574P3vr1pJkiRJredQVdXvDW+ABx6AV7yi6SSSJEmSemDhqPodcADMnt10CkmSJEk9cqiq6nf//fDHf1y1kiRJklrPwlH1u/9++JM/sXCUJEmSCuFQVdXvtNPgZz9rOoUkSZKkHnnFUZIkSZI0LgtH1W/9evjgB6tWkiRJUutZOKp+jz8Ot9xStZIkSZJazzmOqt/rXw/r1jWdQpIkSVKPJvSKY0QcGhErI+L2iFgbEedP5P4kSZIkSfvfRA9VfRz4tcycD7wR+EREHDbB+1Tb3XEHvOlNVdtGy5bBggUwY0bVLlvWdKLRlZJTkiRJxZvQwjEzd2fmE52nBwDR+VI/mzIFDj20attm2TJYtAiGhuCxx6p20aL2FWWl5JQkSdKkMOE3x+kMV10NPAB8OjMfnuh9quV+9Vfh2murtm0uugh27nzusp07q+VtUkpOSZIkTQoTXjhm5mOZ+VrgaOC9ETGre31ELI6IoYgY2rp160THkcZ3992jLx8erjfH3pSSU5IkSZNCbR/HkZkPAauBN41YfnlmDmbm4MyZM+uKoybdcgvMnl21bTNv3ujLjzuu3hx7U0pOSZIkTQoTfVfVWRExvfP4EODXAC+J9LvDDoN3v7tq22bJEhgYeO6ygYFqeZuUklOSJEmTwkRfcfxl4AedOY4/AP4qM1t6K03V5tWvhs98pmrbZuFCuP56GBys7lY6OFg9X7iw6WTPVUpOSZIkTQqRmU1neMbg4GAODQ01HUMT7Re/gJ//HF76UnhJbaOlX5gSMkI5OSVJktR6EbEiMwdHW+dfmqrfTTfBy15WtW1VQkYoJ6ckSZKKZuGo+h19NPzlX1ZtW5WQEcrJKUmSpKI5VFWSJEmS5FBVtcwTT8C6dVXbViVkhHJySpIkqWgWjqrfbbfBCSdUbVuVkBHKySlJkqSiWTiqfr/yK3DddVXbViVkhHJySpIkqWjOcZQkSZIkOcdRLfOTn8C111ZtW5WQEcrJKUmSpKJZOKp+d90F555btW1VQkYoJ6ckSZKKNrXpAOpDr3tdVegcdVTTScZWQkYoJ6ckSZKKZuGo+h10EMyb13SK8ZWQEcrJKUmSpKI5VFX1e/BB+MxnqratSsgI5eSUJElS0SwcVb+NG+E//seqbasSMkI5OSVJklQ0h6qqfgsWwM9/DlOmNJ1kbCVkhHJySpIkqWgWjqrfS15SfbVZCRmhnJySJEkqmn9xqn733gsf+UjVtlUJGaGcnJIkSSqahaPq98gj8LWvVW1blZARyskpSZKkokVmNp3hGYODgzk0NNR0DEmSJEnqOxGxIjMHR1vnFUdJkiRJ0rgsHFW/O++ERYuqtq1KyAjl5JQkSVLRLBxVv927YefOqm2rEjJCOTklSZJUNOc4SpIkSZKc4yhJkiRJ2ncWjqrfqlUwb17VtlUJGaGcnJIkSSqahaPqN306nH561bZVCRmhnJySJEkqmnMcJUmSJEnOcZQkSZIk7TsLR9Vv+XKYNq1q26qEjFBOTkmSJBXNwlH1O/JIuOSSqm2rEjJCOTklSZJUNOc4SpIkSZKc46iWefJJuP/+qm2rEjJCOTklSZJUtAktHCPiyIhYGhF3RsSaiHj3RO6vry1bBgsWwIwZVbtsWdOJxrZyJRx1VNW2VQkZoYycJfVNSZIkjWpCh6pGxKuAWZl5e0QcDtwKzM3MnaNt71DVfbRsGSxaBDu7fq0DA3D99bBwYXO5xrJtG3znO3DWWfDKVzadZnQlZIT25yytb0qSJPWx8Yaq1jrHMSJWA+dk5v2jrbdw3EcLFsBov7fBQbjppvrzSHvYNyVJkorRijmOEXEyMGVk0RgRiyNiKCKGtm7dWlecyeXuu0dfPjxcb45ebd8OS5dWbVuVkBHan7O0vilJkqRR1VI4RsTLgS8CvzNyXWZenpmDmTk4c+bMOuJMPvPmjb78uOPqzdGrtWvhzDOrtq1KyAjtz1la35QkSdKoJrxwjIgDgK8BSzLTTymfCEuWVPPGug0MVMvb6KST4Oabq7atSsgI7c9ZWt+UJEnSqCb6rqoBXAF8NzOvnMh99bWFC6ubjQwOVneuHBxs981Hpk+HU06p2rYqISO0P2dpfVOSJEmjmui7qp4BfB9Y07X4A5l5x2jbe3OcF+mhh+C66+Ccc2DWrKbTjK2EnCVkBHNKkiRpv2ns5jiZ+cPMfElmzu/6GrVo1H6wYQMsXly1bVZCzhIygjklSZJUi1o/jmNvvOL4Ij31FPz0p/BLvwTTpjWdZmwl5CwhI5hTkiRJ+814Vxyn1h1GE2jaNDjssKZT7F0JOUvICOaUJElSLWr7HEfVYNMmuPjiqm2zEnKWkBHMKUmSpFpYOE4mW7bAZZdVbZuVkLOEjGBOSZIk1cI5jpIkSZKk5u6qKkmSJEkqn4XjZDI8DOedV7VtVkLOEjKCOSVJklQLC8fJZNcuWL++atushJwlZARzSpIkqRbOcZQkSZIkOcdRkiRJkrTvLBwnk9Wr4ZRTqrbNSshZQkYwpyRJkmph4TiZHHAAzJlTtW1WQs4SMoI5JUmSVAvnOEqSJEmSnOMoSZIkSdp3Fo6TyYoVcNhhVdtmJeQsISOYU5IkSbWwcJxMZs2C88+v2jYrIWcJGcGckiRJqoVzHCVJkiRJznHsG08/DY89VrVtVkLOEjKCOSVJklQLC8fJZMUKmDGj/fPISshZQkYwpyRJkmph4TiZHHMMfP7zVdtmJeQsISOYU5IkSbVwjqMkSZIkyTmOfWPHDrj11qptsxJylpARzClJkqRaWDhOJqtXwxveULVtVkLOEjKCOSVJklQLC8fJ5Pjj4dvfrto2KyFnCRnBnJIkSaqFcxwlSZIkSc5x7BsPPwxf+UrVtlkJOUvICOaUJElSLSwcJ5PhYXjPe6q2zUrIWUJGMKckSZJqMbXpANqPTj4ZNm6Eww9vOsn4SshZQkYwpyRJkmph4TiZHHggzJnTdIq9KyFnCRnBnJIkSaqFQ1UnkwcegCVLqrbNSshZQkYwpyRJkmox4YVjRFwTEY9GxNUTva++t2kTXHxx1bZZCTlLyAjmlCRJUi0m/OM4IuLNwHTgtzPzXeNt68dxSJIkSVIzGv04jsxcCjw+0fuRJEmSJE0M5zhOJhs2wPnnV22blZCzhIxgTkmSJNWi8cIxIhZHxFBEDG3durXpOGXbvh2WLq3aNishZwkZwZySJEmqxYTPcYRn5jl+2DmOkiRJktROjc5xlCRJkiSVrY6P4/g28BXgbRHxQEQsmOh99q21a+Gss6q2zUrIWUJGMKckSZJqMXWid5CZ/8dE70MdETBtWtW2WQk5S8gI5pQkSVItapnj2CvnOEqSJElSM5zjKEmSJEnaZxaOk8nKlTBnTtW2WQk5S8gI5pQkSVItLBwnkxkzYNGiqm2zEnKWkBHMKUmSpFo4x1GSJEmS5BzHvpEJu3dXbZuVkLOEjGBOSZIk1cLCcTJZvhymTq3aNishZwkZwZySJEmqhYXjZDJnDlx6adW2WQk5S8gI5pQkSVItnOMoSZIkSXKOY9/YtQvWr6/aNishZwkZwZySJEmqhYXjZLJqFcydW7VtVkLOEjKCOSVJklQLC8fJZN48uOaaqm2zEnKWkBHMKUmSpFo4x1GSJEmS5BzHvvHoo/CNb1Rtm5WQs4SMYE5JkiTVwsJxMrnzTjj77KptsxJylpARzClJkqRaTG06gPaj+fNhzRp49aubTjK+EnKWkBHMKUmSpFo4x1GSJEmS5BzHvrFlC1x2WdW2WQk5S8gI5pQkSVItLBwnk3vvhQ9/uGrbrIScJWQEc0qSJKkWDlWdTHbvhp/9DA48EKZMaTrN2ErIWUJGMKckSZL2m/GGqnpznMlkyhQYGGg6xd6VkLOEjGBOSZIk1cKhqpPJxo1wwQVV22Yl5CwhI5hTkiRJtbBwnEy2bYOrrqraNishZwkZwZySJEmqhXMcJUmSJEl+HIckSZIkad9ZOE4md90F73hH1bZZCTlLyAjmlCRJUi0sHCeTp56q5pA99VTTScZXQs4SMoI5JUmSVAvnOEqSJEmSnOMoSZIkSdp3Fo6TyW23wYknVm2blZCzhIxgTkmSJNXCwnEyGRiA+fOrts1KyFlCRjCnJEmSajHhhWNEnBMRwxGxPiIWT/T+JsSyZbBgAcyYUbXLljWdaHRz58KVV1Ztm5WQs4SMYM79pZRzvIScJWSEMnKWkBHKyFlCRigjZwkZoYycJWSEMnKWkBHKyTmWzJywL2AqcA8wGzgYGAYOG2v7N77xjdk6S5dmDgxkwrNfAwPVcknlK+UcLyFnCRkzy8hZQsbMMnKWkDGzjJwlZMwsI2cJGTPLyFlCxsxicgJDOVZtN9aK/fEFnAZc0/X8M8BvjbV9KwvHwcHnHuA9X4ODTSd7vuXLMw86qGrbrIScJWTMNOf+UMo5XkLOEjJmlpGzhIyZZeQsIWNmGTlLyJhZRs4SMmaWkbOEjJnF5ByvcJzooapHAJu7nm/uXH18RkQsjoihiBjaunXrBMfZB3ffPfry4eF6c/Ri9my48MKqbbMScpaQEcy5P5RyjpeQs4SMUEbOEjJCGTlLyAhl5CwhI5SRs4SMUEbOEjJCOTnH0fjNcTLz8swczMzBmTNnNh3n+ebNG335ccfVm6MXRx0Fl1xStW1WQs4SMoI594dSzvEScpaQEcrIWUJGKCNnCRmhjJwlZIQycpaQEcrIWUJGKCfnOCa6cHyQ515hnN1ZVo4lS55/J8iBgWq5pPKVco6XkLOEjFBGzhIyQhk5S8gIZeQsISOUkbOEjFBGzhIyQjk5xzPWGNb98UV1c5z1lHxznMxq0urgYOaMGVXbskmskl6kUs7xEnKWkDGzjJwlZMwsI2cJGTPLyFlCxswycpaQMbOMnCVkzCwiJ+PMcYxq/cSJiHOBS6mubn4qMz8/1raDg4M5NDQ0oXkkSZIkSc8XESsyc3C0dVMneueZ+XXg6xO9H0mSJEnSxGj85jiSJEmSpHazcJQkSZIkjcvCUZIkSZI0LgtHSZIkSdK4Jvyuqi9ERPwYuK/pHON4JbCt6RAak8en3Tw+7eWxaTePT7t5fNrN49NuHp+wQAG3AAAFxklEQVT2mZOZh4+2olWFY9tFxNBYt6dV8zw+7ebxaS+PTbt5fNrN49NuHp928/iUxaGqkiRJkqRxWTi+MJc3HUDj8vi0m8envTw27ebxaTePT7t5fNrN41MQh6pKkiRJksblFUdJkiRJ0rgsHCVJkiRJ47JwlCRJkiSNy8JxFBFxTkQMR8T6iFg8yvpTI2JdRGyIiE82kbFfRcSREbE0Iu6MiDUR8e5RtrkiIu6NiNs7X8c0kbVfRcR9nWNze0R8b5T1x0TEys7587mIiCZy9puIOK7rnLg9InZFxDtGbOO5U7OIuCYiHo2Iq7uW7fU9xvNo4o08NhFxUETcGBF3d47PR8Z43SUR8UDXefSmepP3hzHOnaWd47Pnd/+yUV73ioj4XudvvP8/Ig6sN3l/GOX8mT7iPWh7RHx0lNd5/rSYheMIETEV+HPg14HXARdGxGEjNrsM+C3gOOBtEXFivSn72tPARzPzV4G3Ap+JiIFRtvv3mTm/8/WjeiMKOK3zuz9zlHX/DbgkM18DvAJYVG+0/pSZw3vOCeAMYCfwrVE29dyp138H/p8Ry3p5j/E8mnijHZslmTkPeCPwuxHxmjFeu6TrPPrBhKbsX6MdH4B3df3ud42y/iLgq5l5LHAv8LwLBNovnnN8MvPxrveg1wGPAf80xms9f1rKwvH5TgXWZebmzNwB3EhVoAAQEUcAUzNzTWbuBr4MnNNM1P6TmVsy8/bO4x8DDwMvbzaVetW5KnIacH1n0d8Bv9Fcor51LvCdzNzZdJB+l5lLgcf3PO/lPcbzqB4jj01mPpGZyzqPdwDDwKuaSaeRx+cFOBe4svPYc2eC7OX4LAB+nJkb60uk/cHC8fmOADZ3Pd8MzH4B61WTiDgZmJKZ94+y+tKIWB0RfxoRU+rO1ucSWBYRt0TE+0asOwz4ST77OUCeP814D/APY6zz3GlWL+8xnkcNi4gjgZOAVWNs8rHOkP3PRsTBNUYT/H1E3BYRHxtj/SGZub3z2HOnGeO9B4HnT2tZOKpIEfFy4IvA74yy+uPAr1ANJXo18KEaownOyMyTqf5X9xMRcVLTgfSsiPglqqtVN4yy2nNH2ouIOIDqj94Lx7hq/1ngWKrheE8Af1hjvH73vsw8CXgz8PaIcAh3y3RGTLwT+McxNvH8aTELx+d7kOf+79PszrJe12uCdd60v0Y1Bn75yPWd4ayZmT+jKi5PqTtjP8vMzZ12C1Vx8vqu1Y8AL++6kYfnT/3eDvxz5/x4Ds+dVujlPcbzqCGd3/kXgRsy8+rRtsnMhzJzd2eo8d/ieVSbrvef7VSFyWi/++0RcUjnsedO/c4ANmXmA6Ot9PxpNwvH57sZOCEiZncuj58NfHPPysx8ENgdESd1hnGdB1zbTNT+03nTvgL4bmZeOcY2r+q0L6G66rWutoB9LiIGImJ65/HBVDeZeub33xlaN8SzN/J4H54/dRtziJDnTvN6eY/xPGrUnwJPZOZ/HWuDPedRx9vxPKpFREyNiFd0Hr+U6u+30X731wEf6Dx+P547dRt3mKrnT7vFs1MktEdEnAtcSlVYfyozPx8RNwCLM/PBiBgEvgAcCFyZmZc0l7a/RMQZwPeBNV2LPwD8B+BzmbkyIr5LdZfBl1D9cfW7mflk7WH7UES8Grim83QK8DeZ+d8j4nKePT7HUt3w41DgO8CHMvMXzSTuL53/Zb8HODIzf95Z1n1sPHdqFhHfBl4LDAA/Ad5NNU/4ee8xnkf1GuXY/BbV+8+dwFOdzf5TZn4zIv4IWJmZX4+IK4H5VMfxHuB3MvMntf8Ak9wox+c3gb8EplG9/1wLfDwzc8TxeSVwNdXVxrXAb41x91W9CGP827YC+F/AKZ1RSXu29fwphIWjJEmSJGlcDlWVJEmSJI3LwlGSJEmSNC4LR0mSJEnSuCwcJUmSJEnjsnCUJEmSJI3LwlGSJEmSNC4LR0mSJEnSuP435hE/KwRlyfAAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "limite_pontos_convolucao = 20\n",
        "\n",
        "# Inverte o segundo sinal no tempo\n",
        "sinal_2_inv = inverse_time_domain(sinal_2)\n",
        "\n",
        "# Seção do código que calcula a convolução.\n",
        "y_response = []\n",
        "\n",
        "# Define-se um número limite de pontos da convolução \n",
        "for n in range(limite_pontos_convolucao):\n",
        "\n",
        "  # Uma lista para receber a soma das operações\n",
        "  sum_response = []\n",
        "\n",
        "  # Para cada iteração sobre o tamanho do primeiro sinal (referência)\n",
        "  for i in range(len(sinal_1[0])):\n",
        "  \n",
        "    # Tentamos fazer o produto com o segundo sinal atrasado e invertido no tempo\n",
        "    try:\n",
        "      sig2_i = i - n\n",
        "      sum_response.append(sinal_1[1][i]*sinal_2_inv[1][sig2_i])\n",
        "\n",
        "    # Se isso não for possível, a convolução é zero, não há interseção\n",
        "    except:\n",
        "      sum_response.append(0)\n",
        "  \n",
        "  # Anexa essa soma para um índice dentro da resposta ao sinal\n",
        "  y_response.append(sum(sum_response))\n",
        "\n",
        "x = np.arange(0, len(y_response), 1)\n",
        "y = y_response\n",
        "\n",
        "# Plot dos gráficos\n",
        "fig, ax = plt.subplots(3, figsize=(16, 14), dpi = 70)\n",
        "\n",
        "markerline, stemlines, baseline = ax[0].stem(sinal_1[0], sinal_1[1], use_line_collection = True, linefmt = 'blue', basefmt = \" \")\n",
        "markerline.set_markerfacecolor('blue')\n",
        "markerline.set_markeredgecolor('blue')\n",
        "stemlines.set_linestyle('dotted')\n",
        "ax[0].set_title(\"Sinal 1 - $x(t)$\")\n",
        "\n",
        "markerline, stemlines, baseline = ax[1].stem(sinal_2[0], sinal_2[1], use_line_collection = True, linefmt = 'black', basefmt = \" \")\n",
        "markerline.set_markerfacecolor('black')\n",
        "markerline.set_markeredgecolor('black')\n",
        "stemlines.set_linestyle('dotted')\n",
        "ax[1].set_title(\"Sinal 2 - $h(t)$\")\n",
        "\n",
        "markerline, stemlines, baseline = ax[2].stem(x, y, use_line_collection = True, linefmt = 'red', basefmt = \" \")\n",
        "stemlines.set_linestyle('dotted')\n",
        "markerline.set_markerfacecolor('red')\n",
        "markerline.set_markeredgecolor('red')\n",
        "ax[2].set_title(\"$x(t) * h(t)$\")\n",
        "\n",
        "plt.plot()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Convolução gráfica**"
      ],
      "metadata": {
        "id": "g_Oe9hI81BH5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "type(sinal3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nwBaDLXkKjWM",
        "outputId": "066c5994-60ea-4dcb-e2be-0e02ed33c81a"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "function"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Domínio de existência das funções, garanta que essa número seja menor que o limite do espaço\n",
        "\n",
        "dominio_x_funcoes = np.arange(0, 10, 1)\n",
        "limite_pontos_convolucao = 40 # Parâmetro de plotagem e iteração dos pontos da convolução\n",
        "\n",
        "## Altere aqui o nome dos sinais utilizados nos arquivos .txt\n",
        "sinal1_nome = 'sinal3'\n",
        "sinal2_nome = 'sinal4'\n",
        "\n",
        "x_sinal1 = dominio_x_funcoes\n",
        "exec(f\"y_sinal1 = [{sinal1_nome}(x) for x in x_sinal1]\")\n",
        "sinal_1 = [x_sinal1, y_sinal1]\n",
        "\n",
        "x_sinal2 = dominio_x_funcoes\n",
        "exec(f\"y_sinal2 = [{sinal2_nome}(x) for x in x_sinal2]\")\n",
        "sinal_2 = [x_sinal2, y_sinal2]"
      ],
      "metadata": {
        "id": "iZ8Mdxm-mMqx"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Inverte o segundo sinal no tempo\n",
        "sinal_2_inv = inverse_time_domain(sinal_2)\n",
        "\n",
        "y_response = []\n",
        "\n",
        "# Parametro (20) que vai gerar a quantidade de pontos computados da convolução\n",
        "for n in range(limite_pontos_convolucao):\n",
        "    sum_response = []\n",
        "  \n",
        "    for i in range(n):\n",
        "        try:\n",
        "            sum_response.append(sinal_1[1][i]*sinal_2_inv[1][i - n])\n",
        "        except:\n",
        "            sum_response.append(0)\n",
        "\n",
        "    y_response.append(sum(sum_response))\n",
        "\n",
        "\n",
        "x = np.arange(0, len(y_response), 1)\n",
        "y = y_response"
      ],
      "metadata": {
        "id": "GsbvKJzJroPu"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 709
        },
        "id": "fv_l2O_x23Ry",
        "outputId": "4d7229d0-cc7d-44cf-e8b0-30064f9680dc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x840 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot dos gráficos\n",
        "fig, ax = plt.subplots(3, figsize=(16, 14), dpi = 60)\n",
        "\n",
        "markerline, stemlines, baseline = ax[0].stem(sinal_1[0], sinal_1[1], use_line_collection = True, linefmt = 'blue', basefmt = \" \")\n",
        "markerline.set_markerfacecolor('blue')\n",
        "markerline.set_markeredgecolor('blue')\n",
        "stemlines.set_linestyle('dotted')\n",
        "ax[0].set_title(f\"{sinal1_nome} - $x(t)$\")\n",
        "\n",
        "markerline, stemlines, baseline = ax[1].stem(sinal_2[0], sinal_2[1], use_line_collection = True, linefmt = 'black', basefmt = \" \")\n",
        "markerline.set_markerfacecolor('black')\n",
        "markerline.set_markeredgecolor('black')\n",
        "stemlines.set_linestyle('dotted')\n",
        "ax[1].set_title(f\"{sinal2_nome} - $h(t)$\")\n",
        "\n",
        "markerline, stemlines, baseline = ax[2].stem(x, y, use_line_collection = True, linefmt = 'red', basefmt = \" \")\n",
        "stemlines.set_linestyle('dotted')\n",
        "markerline.set_markerfacecolor('red')\n",
        "markerline.set_markeredgecolor('red')\n",
        "ax[2].set_title(\"$x(t) * h(t)$\")\n",
        "\n",
        "plt.savefig(\"convolution_cover.jpeg\")\n",
        "plt.plot()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Convolução gráfica**\n",
        "\n",
        "\n",
        "Nesse trecho de código, a escala do gráfico da convolução perde o sentido porque os pontos não são tratados no tempo, mas sim somente na sequência numérica e ao tentar misturar ambos, a escala é multiplicada pelo limite dos intervalos dividido por cada tamanho, ou seja, é multiplicada pela razão entre a quantidade de pontos inteiros e os pontos desejados pela função."
      ],
      "metadata": {
        "id": "jCpcQK7--N81"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Domínio de existência das funções, garanta que essa número seja menor que o limite do espaço\n",
        "\n",
        "dominio_x_funcoes = np.arange(0, 10, 0.01)\n",
        "limite_pontos_convolucao = 50 # Parâmetro de plotagem e iteração dos pontos da convolução\n",
        "\n",
        "## Altere aqui o nome dos sinais utilizados nos arquivos .txt\n",
        "sinal1_nome = 'sinal3'\n",
        "sinal2_nome = 'sinal4'\n",
        "\n",
        "\n",
        "x_sinal1 = dominio_x_funcoes\n",
        "exec(f\"y_sinal1 = [{sinal1_nome}(x) for x in x_sinal1]\")\n",
        "sinal_1 = [x_sinal1, y_sinal1]\n",
        "\n",
        "x_sinal2 = dominio_x_funcoes\n",
        "exec(f\"y_sinal2 = [{sinal2_nome}(x) for x in x_sinal2]\")\n",
        "sinal_2 = [x_sinal2, y_sinal2]\n",
        "\n",
        "# Inverte o segundo sinal no tempo\n",
        "sinal_2_inv = inverse_time_domain(sinal_2)\n",
        "\n",
        "y_response = []\n",
        "\n",
        "# Parametro (20) que vai gerar a quantidade de pontos computados da convolução\n",
        "for n in range(int(40/0.01)):\n",
        "    sum_response = []\n",
        "  \n",
        "    for i in range(n):\n",
        "        try:\n",
        "            sum_response.append(sinal_1[1][i]*sinal_2_inv[1][i - n])\n",
        "        except:\n",
        "            sum_response.append(0)\n",
        "\n",
        "    y_response.append(sum(sum_response) * 0.01)\n",
        "\n",
        "\n",
        "x = np.arange(0, 40, 0.01)\n",
        "y = y_response\n",
        "\n",
        "# Plot dos gráficos\n",
        "fig, ax = plt.subplots(3, figsize=(16, 14), dpi = 60)\n",
        "\n",
        "markerline, stemlines, baseline = ax[0].stem(sinal_1[0], sinal_1[1], use_line_collection = True, linefmt = 'blue', basefmt = \" \")\n",
        "markerline.set_markerfacecolor('blue')\n",
        "markerline.set_markeredgecolor('blue')\n",
        "stemlines.set_linestyle('dotted')\n",
        "plt.setp(stemlines, linewidth = 1.25)\n",
        "plt.setp(markerline, markersize = 3)\n",
        "stemlines.set_alpha(0.3)\n",
        "ax[0].set_title(f\"{sinal1_nome} - $x(t)$\")\n",
        "\n",
        "markerline, stemlines, baseline = ax[1].stem(sinal_2[0], sinal_2[1], use_line_collection = True, linefmt = 'black', basefmt = \" \")\n",
        "markerline.set_markerfacecolor('black')\n",
        "markerline.set_markeredgecolor('black')\n",
        "stemlines.set_linestyle('dotted')\n",
        "plt.setp(stemlines, linewidth = 1.25)\n",
        "plt.setp(markerline, markersize = 3)\n",
        "stemlines.set_alpha(0.3)\n",
        "ax[1].set_title(f\"{sinal2_nome} - $h(t)$\")\n",
        "\n",
        "markerline, stemlines, baseline = ax[2].stem(x, y, use_line_collection = True, linefmt = 'red', basefmt = \" \")\n",
        "markerline.set_markerfacecolor('red')\n",
        "markerline.set_markeredgecolor('red')\n",
        "stemlines.set_linestyle('dotted')\n",
        "plt.setp(stemlines, linewidth = 1.25)\n",
        "plt.setp(markerline, markersize = 3)\n",
        "stemlines.set_alpha(0.3)\n",
        "ax[2].set_title(\"$x(t) * h(t)$\")\n",
        "\n",
        "plt.plot()\n"
      ],
      "metadata": {
        "id": "pZfwml-_-qtq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 709
        },
        "outputId": "ec67fdb9-b6e5-4a70-817d-0ad1eeb69e1f"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x840 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Teste 2"
      ],
      "metadata": {
        "id": "r8EwX79mA16j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Domínio de existência das funções, garanta que essa número seja menor que o limite do espaço\n",
        "\n",
        "dominio_x_funcoes = np.arange(0, 10, 0.01)\n",
        "limite_pontos_convolucao = 50 # Parâmetro de plotagem e iteração dos pontos da convolução\n",
        "\n",
        "## Altere aqui o nome dos sinais utilizados nos arquivos .txt\n",
        "sinal1_nome = 'sinal1'\n",
        "sinal2_nome = 'sinal4'\n",
        "\n",
        "\n",
        "x_sinal1 = dominio_x_funcoes\n",
        "exec(f\"y_sinal1 = [{sinal1_nome}(x) for x in x_sinal1]\")\n",
        "sinal_1 = [x_sinal1, y_sinal1]\n",
        "\n",
        "x_sinal2 = dominio_x_funcoes\n",
        "exec(f\"y_sinal2 = [{sinal2_nome}(x) for x in x_sinal2]\")\n",
        "sinal_2 = [x_sinal2, y_sinal2]\n",
        "\n",
        "# Inverte o segundo sinal no tempo\n",
        "sinal_2_inv = inverse_time_domain(sinal_2)\n",
        "\n",
        "y_response = []\n",
        "\n",
        "# Parametro (20) que vai gerar a quantidade de pontos computados da convolução\n",
        "for n in range(int(40/0.01)):\n",
        "    sum_response = []\n",
        "  \n",
        "    for i in range(n):\n",
        "        try:\n",
        "            sum_response.append(sinal_1[1][i]*sinal_2_inv[1][i - n])\n",
        "        except:\n",
        "            sum_response.append(0)\n",
        "\n",
        "    y_response.append(sum(sum_response) * 0.01)\n",
        "\n",
        "\n",
        "x = np.arange(0, 40, 0.01)\n",
        "y = y_response\n",
        "\n",
        "# Plot dos gráficos\n",
        "fig, ax = plt.subplots(3, figsize=(16, 14), dpi = 60)\n",
        "\n",
        "markerline, stemlines, baseline = ax[0].stem(sinal_1[0], sinal_1[1], use_line_collection = True, linefmt = 'blue', basefmt = \" \")\n",
        "markerline.set_markerfacecolor('blue')\n",
        "markerline.set_markeredgecolor('blue')\n",
        "stemlines.set_linestyle('dotted')\n",
        "plt.setp(stemlines, linewidth = 1.25)\n",
        "plt.setp(markerline, markersize = 3)\n",
        "stemlines.set_alpha(0.3)\n",
        "ax[0].set_title(f\"{sinal1_nome} - $x(t)$\")\n",
        "\n",
        "markerline, stemlines, baseline = ax[1].stem(sinal_2[0], sinal_2[1], use_line_collection = True, linefmt = 'black', basefmt = \" \")\n",
        "markerline.set_markerfacecolor('black')\n",
        "markerline.set_markeredgecolor('black')\n",
        "stemlines.set_linestyle('dotted')\n",
        "plt.setp(stemlines, linewidth = 1.25)\n",
        "plt.setp(markerline, markersize = 3)\n",
        "stemlines.set_alpha(0.3)\n",
        "ax[1].set_title(f\"{sinal2_nome} - $h(t)$\")\n",
        "\n",
        "markerline, stemlines, baseline = ax[2].stem(x, y, use_line_collection = True, linefmt = 'red', basefmt = \" \")\n",
        "markerline.set_markerfacecolor('red')\n",
        "markerline.set_markeredgecolor('red')\n",
        "stemlines.set_linestyle('dotted')\n",
        "plt.setp(stemlines, linewidth = 1.25)\n",
        "plt.setp(markerline, markersize = 3)\n",
        "stemlines.set_alpha(0.3)\n",
        "ax[2].set_title(\"$x(t) * h(t)$\")\n",
        "\n",
        "plt.plot()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 709
        },
        "id": "h96835oIA5U3",
        "outputId": "f9966836-017b-4344-9ef5-f958d37e67f0"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x840 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Domínio de existência das funções, garanta que essa número seja menor que o limite do espaço\n",
        "\n",
        "dominio_x_funcoes = np.arange(0, 10, 0.01)\n",
        "limite_pontos_convolucao = 50 # Parâmetro de plotagem e iteração dos pontos da convolução\n",
        "\n",
        "## Altere aqui o nome dos sinais utilizados nos arquivos .txt\n",
        "sinal1_nome = 'degrau_unitario'\n",
        "sinal2_nome = 'exp_neg'\n",
        "\n",
        "\n",
        "x_sinal1 = dominio_x_funcoes\n",
        "exec(f\"y_sinal1 = [{sinal1_nome}(x) for x in x_sinal1]\")\n",
        "sinal_1 = [x_sinal1, y_sinal1]\n",
        "\n",
        "x_sinal2 = dominio_x_funcoes\n",
        "exec(f\"y_sinal2 = [{sinal2_nome}(x) for x in x_sinal2]\")\n",
        "sinal_2 = [x_sinal2, y_sinal2]\n",
        "\n",
        "# Inverte o segundo sinal no tempo\n",
        "sinal_2_inv = inverse_time_domain(sinal_2)\n",
        "\n",
        "y_response = []\n",
        "\n",
        "# Parametro (20) que vai gerar a quantidade de pontos computados da convolução\n",
        "for n in range(int(40/0.01)):\n",
        "    sum_response = []\n",
        "  \n",
        "    for i in range(n):\n",
        "        try:\n",
        "            sum_response.append(sinal_1[1][i]*sinal_2_inv[1][i - n])\n",
        "        except:\n",
        "            sum_response.append(0)\n",
        "\n",
        "    y_response.append(sum(sum_response) * 0.01)\n",
        "\n",
        "\n",
        "x = np.arange(0, 40, 0.01)\n",
        "y = y_response\n",
        "\n",
        "# Plot dos gráficos\n",
        "fig, ax = plt.subplots(3, figsize=(16, 14), dpi = 60)\n",
        "\n",
        "markerline, stemlines, baseline = ax[0].stem(sinal_1[0], sinal_1[1], use_line_collection = True, linefmt = 'blue', basefmt = \" \")\n",
        "markerline.set_markerfacecolor('blue')\n",
        "markerline.set_markeredgecolor('blue')\n",
        "stemlines.set_linestyle('dotted')\n",
        "plt.setp(stemlines, linewidth = 1.25)\n",
        "plt.setp(markerline, markersize = 3)\n",
        "stemlines.set_alpha(0.3)\n",
        "ax[0].set_title(f\"{sinal1_nome} - $x(t)$\")\n",
        "\n",
        "markerline, stemlines, baseline = ax[1].stem(sinal_2[0], sinal_2[1], use_line_collection = True, linefmt = 'black', basefmt = \" \")\n",
        "markerline.set_markerfacecolor('black')\n",
        "markerline.set_markeredgecolor('black')\n",
        "stemlines.set_linestyle('dotted')\n",
        "plt.setp(stemlines, linewidth = 1.25)\n",
        "plt.setp(markerline, markersize = 3)\n",
        "stemlines.set_alpha(0.3)\n",
        "ax[1].set_title(f\"{sinal2_nome} - $h(t)$\")\n",
        "\n",
        "markerline, stemlines, baseline = ax[2].stem(x, y, use_line_collection = True, linefmt = 'red', basefmt = \" \")\n",
        "markerline.set_markerfacecolor('red')\n",
        "markerline.set_markeredgecolor('red')\n",
        "stemlines.set_linestyle('dotted')\n",
        "plt.setp(stemlines, linewidth = 1.25)\n",
        "plt.setp(markerline, markersize = 3)\n",
        "stemlines.set_alpha(0.3)\n",
        "ax[2].set_title(\"$x(t) * h(t)$\")\n",
        "\n",
        "plt.plot()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 709
        },
        "id": "Fn5ZOGTuBEUO",
        "outputId": "efefb81d-f7ef-4866-8756-d79b4b6f4c2c"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x840 with 3 Axes>"
            ],
            "image/png": 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\n"
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            "needs_background": "light"
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        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "Yka2yrbDaHQ_"
      ],
      "provenance": [],
      "mount_file_id": "1kD0ol4vNGtLsUXeKchiMSQx5NHoLWctq",
      "authorship_tag": "ABX9TyNqDXkM6E575mvgCA7+8N+e",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}