{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Onda Quadrada\n",
    "\n",
    "Uma onda quadrada é uma forma de onda periódica não-senoidal ao qual a amplitude alterna em uma frequência constante entre os valores mínimo e máximo fixos, com a mesma duração no mínimo e no máximo. Em uma onda quadrada ideal, as transições entre o mínimo e o máximo são instantâneas.\n",
    "\n",
    "Ondas quadradas são freqüentemente encontradas em eletrônicos e processamento de sinais, particularmente eletrônicos digitais e processamento de sinais digitais.\n",
    "\n",
    "A seguir temos algumas características das ondas quadradas:\n",
    "\n",
    "- Ondas Quadradas são formas de onda periódicas.\n",
    "- Entretanto, ondas quadradas são não-sinusoidais. \n",
    "- A transição entre os valores de pico é instantâneo em uma onda quadrada.\n",
    "- O período da onda quadrada é também chamado de largura do pulso.\n",
    "\n",
    "Para desenhar uma onda quadrada utilizando **matplotlib**, **NumPy** e **SciPy** é necessário alguns detalhes:\n",
    "\n",
    "- Frequência da onda quadrada, por exemplo: 10Hz, que é 10 ciclos por segundo.\n",
    "- A frequência de amostragem, ou seja, quandos pontos de dados no qual a onda está sendo construída, quanto mais elevado os pontos de dados, mais \"suave\" o quadrado é.\n",
    "- Ondas Quadradas possuem um ciclo de trabalho de 50%, ou seja, a porcentagem da forma de onda que ocorre acima do eixo zero é de 50%.\n",
    "- Por padrão, a função **signal.square** da biblioteca **SciPy** recebe um valor de trabalho de **0.5**.\n",
    "\n",
    "Começamos importando as bibliotecas necessárias:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scipy import signal\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definimos a taxa de amostragem 1000Hz/segundo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = np.linspace(0, 1, 1000, endpoint=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculamos o sinal da onda quadrada:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = signal.square(2 * np.pi * 5 * t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos então plotar o gráfico:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sukPNKxsfqIyrBgCgdL0uuGb2STWXXt2WUjpgmo9PSPqamktfpOal7N+zcEcIAGVLKc3lucAAFlBKKedy+6kep+bS8q3UXIr90pTSrYFfHwCK0uslymb2LDXPmfnMLAvu21JKxy30sQEAAAAAytLri0yllM5T88IYAAAAAABkKeFVlA8zs4vbVx3cv++DAQAAAAAM09BfZOpCSXumlFaa2bFq/uHzfaf7RDM7UdKJkrR48eKn7L777tN92iCsXbtWG21UwrkF1Iw5xFAwixgC5hBDwBxiKIY+i1dfffUdKaVHTfex3v+ZIDPbS9I3pnsO7jSfe4Okpe2/zTajpUuXpgsuuCDk+ObDaDTSxMRE34eBRzjmEEPBLGIImEMMAXOIoRj6LJrZz1JKS6f72HDXcklmtouZWfv2oWqO985+jwoAAAAAMER9/zNBn5c0IWknM1su6SRJiyQppXSKpJdK+lMzWyNplaQTUt8POQMAAAAABqnXBTel9IoNfPxkSScv0OEAAAAAAAo26EuUAQAAAACYKxZcAAAAAEAVWHABAAAAAFVgwQUAAAAAVIEFFwAAAABQBRZcAAAAAEAVWHABAAAAAFVgwQUAAAAAVIEFFwAAAABQBRZcAAAAAEAVWHABAAAAAFVgwQUAAAAAVIEFFwAAAABQBRZcAAAAAEAVWHABAAAAAFVgwQUAAAAAVIEFFwAAAABQBRZcAAAAAEAVWHABAAAAAFVgwQUAAAAAVIEFFwAAAABQBRZcAAAAAEAVWHABAAAAAFVgwQUAAAAAVIEFFwAAAABQBRZcAAAAAEAVWHABAAAAAFVgwQUAAAAAVIEFFwAAAABQBRZcAAAAAEAVWHABAAAAAFVgwQUAAAAAVIEFFwAAAABQBRZcAAAAAEAVWHABAAAAAFVgwQUAAAAAVIEFFwAAAABQBRZcAAAAAEAVWHABAAAAAFVgwQUAAAAAVIEFFwAAAABQBRZcAAAAAEAVWHABAAAAAFVgwQUAAAAAVIEFFwAAAABQBRZcAAAAAEAVWHABAAAAAFVgwQUAAAAAVIEFFwAAAABQBRZcAAAAAEAVWHABAAAAAFVgwQUAAAAAVIEFFwAAAABQBRZcAAAAAEAVWHABAAAAAFVgwQUAAAAAVIEFFwAAAABQBRZcAAAAAEAVWHABAAAAAFVgwQUAAAAAVIEFFwAAAABQBRZcAAAAAEAVWHABAAAAAFVgwQUAAAAAVIEFFwAAAABQBRZcAAAAAEAVWHABAAAAAFVgwQUAAAAAVIEFFwAAAABQBRZcAAAAAEAVWHABAAAAAFVgwQUAAAAAVIEFFwAAAABQhV4XXDP7pJndZmaXzfBxM7MPm9m1ZnaJmR2y0McIAAAAACjDJj3/+p+WdLKkz8zw8WMk7dv+9zRJH23/X6Rrr5W++13p5z9/tK6+uu+jKdfmm0sveIG0/fZ9H0m5HnxQ+v73d2IOMx1wgHTYYZJZ30dSrmuukb7+de4Tcyxe3Nwnbrdd30dSrtWrpfPO4z4x14EHSk97GveJOW66abFOPbXvoyjbFls094nbbtv3kZTprLOkm26Srr760dpnH2mPPfo+ou56XXBTSueZ2V6zfMrxkj6TUkqSfmxm25nZo1NKty7IAQa74ALp9a+XpMf1fSjFe8ITpMsv55uo15veJP3zPx/Q92FU4Utfkl760r6Poky/+IX0+MdLa9dyn5jrSU+SLr6Y+0SvE0+UPvMZ7hMjfPWr0vHH930UZbrmGuk1ryn2cZxBOeSQ5u/d3Cd2d/LJ0rJlkvQ4PfvZLLjzYTdJN4/9eHn7vt9acM3sREknStKSJUs0Go0W4vg6ueKKnSU9se/DqMKVV0pf+coPtMMOD/V9KEU6++xDJW3R92FU4fTTf6mddrqm78Mo0re+tURr1z6h78OowqWXSl//+ve1zTZr+j6UIp1zztMlbd73YVThc5+7Wdtue13fh1GkZct2kfT4vg+jChdeKJ199ve0ePHDfR9Kce6880mSdpQkXXLJJdpyy7v6PSCHoS+40513SdN9YkrpVEmnStLSpUvTxMTEPB6Wz5ZbSr/6lXTLLbdo11137ftwivTZz0oPPNC8ffjhR2iXXfo9nlJtttnk2y9/OZfxdHX55dIPf9i8veuuu2liYrd+D6hQN944+fbv/I501FH9HUupTjutecqBJB1xxDO04479Hk+pNt108u0TTpC23rq/YynRpZdKP/5x8/Zuu+2uiYnd+z2gQl177eTb++0nHXlkf8dSqk99SlrTnud7xjOeyW3ZYfwpgAcddKAGuFJt0NAX3OWSxu8lHyPplp6OJdtTn9r8NxpdrYkJFlyPM8+cXHAR433va5YLzN1HPjK54CLG4YeL5505fPnL0l3lnVwftL//+zIvyevThz40ueAixjOfyX2ix+c/L61c2fdR1KPUS7yH/s8EnSnpD9tXU366pHtKff4t4qVpH8vHXNAuDi39aBeLnn60i0NLP9rFoqdPDd16fQTXzD4vaULSTma2XNJJkhZJUkrpFEnLJB0r6VpJ90t6XT9HiqEYP5NUww2wL+PtSj071yfmMAZzmI9ZjMEs5mEOYzCH+ZjFfDXMYd+vovyKDXw8SXrTAh0OClDqDW3IaNodzeLR1Idu8WjaHc3i0dSHbrFK7Tn0S5SBGXFmzo92cWjpR7tY9PSjXRxa+tEuFj19aujGgouicOlJjBouP+kTcxiDOczHLMZgFvMwhzGYw3zMYqxS55AFF0Up9YY2ZDTtjmbxaOpDt3g07Y5m8WjqQ7d8NZwYYMFFsWq4AfaFdnFo6Ue7WPT0o10cWvrRLhY9fWq4koAFF0Up9YY2ZDTtjmbxaOpDt3g07Y5m8WjqQ7dYpfZkwUWxODPnR7s4tPSjXSx6+tEuDi39aBeLnj41dGPBRVF48YAYNVx+0ifmMAZzmI9ZjMEs5mEOYzCH+ZjFfDXMIQsuilLqDW3IaNodzeLR1Idu8WjaHc3i0dSHbrFK7cmCi2JxZs6PdnFo6Ue7WPT0o10cWvrRLhY9fWroxoKLonDpSYwaLj/pE3MYgznMxyzGYBbzMIcxmMN8zGK+GuaQBRdFKfWGNmQ07Y5m8WjqQ7d4NO2OZvFo6kO3WKX2ZMFFsTgz50e7OLT0o10sevrRLg4t/WgXi54+NXRjwUVRuPQkRg2Xn/SJOYzBHOZjFmMwi3mYwxjMYT5mMV8Nc8iCi6KUekMbMpp2R7N4NPWhWzyadkezeDT1oVusUnuy4KJYnJnzo10cWvrRLhY9/WgXh5Z+tItFT58aurHgoiilnkkamhouP+kTzeLR1Idu8WjaHc3i0dSHbrFK7cmCi6Lw3Ip4pd559Yk5jMGJlnzMYgxmMQ9zGIM5zMcs5quhGwsuilXDDbAvtItDSz/axaKnH+3i0NKPdrHo6VPDiRYWXBSl1BvakNG0O5rFo6kP3eLRtDuaxaOpD91ildqTBRfF4sycH+3i0NKPdrHo6Ue7OLT0o10sevrU0I0FF0XhuRUxarj8pE/MYQzmMB+zGINZzMMcxmAO8zGL+WqYQxZcFKXUG9qQ0bQ7msWjqQ/d4tG0O5rFo6kP3WKV2pMFF8XizJwf7eLQ0o92sejpR7s4tPSjXSx6+tTQjQUXReHSkxg1XH7SJ+YwBnOYj1mMwSzmYQ5jMIf5mMV8NcwhCy6KUuoNbcho2h3N4tHUh27xaNodzeLR1IdusUrtyYKLYnFmzo92cWjpR7tY9PSjXRxa+tEuFj19aujGgouilHomaWhquPykTzSLR1MfusWjaXc0i0dTH7rFKrUnCy6KwnMr4pV659Un5jAGJ1ryMYsxmMU8zGEM5jAfs5ivhm4suChWDTfAvtAuDi39aBeLnn60i0NLP9rFoqdPDSdaWHBRlFJvaENTw51Xn2gWj6Y+dItH0+5oFo+mPnSLVWpPFlwUizNzGALm0I92sejpR7s4tPSjXSx6+tTQjQUXReG5FTF4BDcPcxiDOczHLMZgFvMwhzGYw3zMYr4a5pAFF0Up9YY2ZDTtjmbxaOpDt3g07Y5m8WjqQ7dYpfZkwUWxODPnR7s4tPSjXSx6+tEuDi39aBeLnj41dGPBRVG49CRGDZef9Ik5jMEc5mMWYzCLeZjDGMxhPmYxXw1zyIKLopR6QxsymnZHs3g09aFbPJp2R7N4NPWhW6xSe7LgolicmfOjXRxa+tEuFj39aBeHln60i0VPnxq6seCiKKWeSRqaGi4/6RPN4tHUh27xaNodzeLR1IdusUrtyYKLovDcinil3nn1iTmMwYmWfMxiDGYxD3MYgznMxyzmq6EbCy6KVcMNsC+0i0NLP9rFoqcf7eLQ0o92sejpU8OJFhZcFKXUG9rQ1HDn1SeaxaOpD93i0bQ7msWjqQ/dYpXakwUXxeLMHIaAOfSjXSx6+tEuDi39aBeLnj41dGPBRVF4bkUMHsHNwxzGYA7zMYvxmMXumMMY3CfmYxbz1TCHLLgoSqk3tCGjaXc0i0dTH7rFo2l3NItHUx+6xSq1JwsuisWZOT/axaGlH+1i0dOHbrHo6Ue7WPT0qaEbCy6KwqUnMWq4/KRPzGEM5jAfs5iPbvmYwxjcJ+ZjFvPVMIcsuChKqTe0IaNpdzSLR1MfusWipw/d4tHUh26xSu3JgoticWbOj3ZxaOlHu1j09KFbLHr60S4WPX1q6MaCi6Jw6UmMGi4/6RNzGIM5zMcs5mMO8zGHMZjFfMxirFLnkAUXRSn1hjZkNO2OZvFo6kO3WPT0oVs8mvrQLV8NJwZYcFGsGm6AfaFdHFr60S4WPX3oFouefrSLRU+fGq4kYMFFUUq9oQ0ZTbujWTya+tAtFj196BaPpj50i1VqTxZcFIszcxgC5tCPdrHo6UO3WPT0o10sevrU0I0FF0XhxQPyTe1W6tm5PjGHMWq4DKpvzGI+5jAfcxiDWczHLOarYQ5ZcFGUUm9oQ8Idfj7mMB5NfegWi54+dItHUx+6xSq1JwsuisWihiFgDv1oF4uePnSLRU8/2sWip08N3VhwURQuPclXw6UnfWMOYzCL+ZjFfMxhPuYwBrOYj1nMV8McsuCiKKXe0IaKnj50i0dTH7rFoqcP3eLR1IdusUrtyYKLYnFmzodusejpR7tY9PShWyx6+tEuFj19aujGgouicOlJvhouPekbcxiDWczHLOZjDvMxhzGYxXzMYqxS55AFF0Up9YY2VPT0oVs8mvrQLRY9fegWj6Y+dMtXw4kBFlwUq4YbYB/oFouefrSLRU8fusWipx/tYtHTp4YrCVhwUZRSb2hDUsMdV9/oFo+mPnSLRU8fusWjqQ/dYpXakwUXxeLMHIaAOfSjXSx6+tAtFj39aBeLnj41dGPBRVF48YB8PIKbjzmMwSzmYxbzMYf5mMMYzGI+ZjFfDXPIgouilHpDG5Ia7rj6Rrd4NPWhWyx6+tAtHk196Bar1J4suCgWZ+YwBMyhH+1i0dOHbrHo6Ue7WPT0qaEbCy6KwqUn+XgENx9zGINZzMcs5mMO8zGHMZjFfMxivhrmkAUXRSn1hjZU9PShWzya+tAtFj196BaPpj50i1VqTxZcFIszcz50i0VPP9rFoqcP3WLR0492sejpU0M3FlwUhUtP8tVw6UnfmMMYzGI+ZjEfc5iPOYzBLOZjFmOVOocsuChKqTe0oaKnD93i0dSHbrHo6UO3eDT1oVu+Gk609LrgmtnzzOznZnatmb1zmo9PmNk9ZnZR+9//6uM4MUycmfOhWyx6+tEuFj196BaLnn60i0VPnxq6bdLXL2xmG0v6iKSjJS2XdL6ZnZlSumLKp34vpXTcgh8gBqnUM0lDUsOZub7RLR5NfegWi54+dItHUx+6xSq1Z5+P4B4q6dqU0vUppQclfUHS8T0eDwpTwxkmlI859KNdLHr60C0WPf1oF4uePjV063PB3U3SzWM/Xt6+b6rDzOxiMzvLzPZfmEPDUPHiAfl4BDcfcxiDWczHLOZjDvMxhzGYxXzMYr4a5rC3S5QlTZds6iheKGnPlNJKMztW0lcl7TvtFzM7UdKJkrRkyRKNRqPAQ421cuXKQR/fkN1115Mk7ShJuuSSS7Tllnf1e0AFWrlyE0nPkCStXbtGo9H3+y0naMEAACAASURBVD2gAl1xxTaSDpEk3XvvvRqNLuz3gAq1fPnvSHqMJOnaa6/RaPTLfg+oQCtWHChpB0nSxRdfrE03vbvfAyrQihWLJB0hSVqz5kGNRj/s94AKdMkl20p6siTpnntWaDS6qN8DKtQtt+yrdY/1XHPN1RqNbun3gAp0zz0HS9pOknTRRRdJWtHr8ZTooYcOl7SpJOmHP/yBtt/+oX4PyKHPBXe5pN3HfvwYSevdklNK9469vczM/snMdkop3TH1i6WUTpV0qiQtXbo0TUxMzMtBRxiNRhry8Q3ZjjtOvv2kJx0oMna3Yuy+fuONN2EWHTbffPLtrbfehoZOX/7y5Nv77ruvJiamPX+JWWy//eTbBx54EPeJDrfdNvn2okWbcnt22Hjjybe32WY7Gjp94QuTb++3336amNivv4Mp1HbbTb590EEHc5/osMnYdnjEEUdo5537OxavPi9RPl/Svma2t5ltKukESWeOf4KZ7WLWPDhuZoeqOd47F/xIMRhcepKvhktP+sYcxmAW8zGL+ZjDfMxhDGYxH7OYr4Y57O0R3JTSGjN7s6RzJG0s6ZMppcvN7A3tx0+R9FJJf2pmayStknRCSozrI1mpN7QhqeGOq290i0dTH7rFoqcP3eLR1IdusUrt2eclykopLZO0bMr7Thl7+2RJJy/0caEMnOrAEDCHfrSLRU8fusWipx/tYtHTp4ZufV6iDHTGpSf5eAQ3H3MYg1nMxyzmYw7zMYcxmMV8zGK+GuaQBRdFKfWGNlT09KFbPJr60C0WPX3oFo+mPnSLVWpPFlwUizNzPnSLRU8/2sWipw/dYtHTj3ax6OlTQzcWXBSl1DNJQ1LDpSd9o1s8mvrQLRY9fegWj6Y+dItVak8WXBSrhjNMKB9z6Ee7WPT0oVssevrRLhY9fWroxoKLovDiAfl4BDcfcxiDWczHLOZjDvMxhzGYxXzMYr4a5pAFF0Up9YY2JDXccfWNbvFo6kO3WPT0oVs8mvrQLVapPVlwUSzOzGEImEM/2sWipw/dYtHTj3ax6OlTQzcWXBSFS0/y8QhuPuYwBrOYj1nMxxzmYw5jMIv5mMV8NcwhCy6KUuoNbUhquOPqG93i0dSHbrHo6UO3eDT1oVusUnuy4KJYnJnDEDCHfrSLRU8fusWipx/tYtHTp4ZuLLgoCpee5OMR3HzMYQxmMR+zmI85zMccxmAW8zGL+WqYQxZcFKXUG9qQ1HDH1Te6xaOpD91i0dOHbvFo6kO3WKX2ZMFFsTgzhyFgDv1oF4uePnSLRU8/2sWip08N3VhwUZRSzyQNCY/g5qNbPJr60C0WPX3oFo+mPnSLVWpPFlwUhedWxCr1jqtvzGEMTrbkYxbzMYf5mMMYzGI+ZjFfDd1YcFGsGm6AfaBbLHr60S4WPX3oFouefrSLRU+fGk60sOCiKKXe0IakhjuuvtEtHk196BaLnj50i0dTH7rFKrUnCy6KxZk5DAFz6Ee7WPT0oVssevrRLhY9fWroxoKLovDcinw8gpuPOYzBLOZjFvMxh/mYwxjMYj5mMV8Nc8iCi6KUekMbkhruuPpGt3g09aFbLHr60C0eTX3oFqvUniy4KBZn5jAEzKEf7WLR04dusejpR7tY9PSpoRsLLorCpSf5eAQ3H3MYg1nMxyzmYw7zMYcxmMV8zGK+GuaQBRdFKfWGNiQ13HH1jW7xaOpDt1j09KFbPJr60C1WqT1ZcFEszsxhCJhDP9rFoqcP3WLR0492sejpU0M3FlwUpdQzSUPCI7j56BaPpj50i0VPH7rFo6kP3WKV2pMFF0XhuRWxSr3j6htzGIOTLfmYxXzMYT7mMAazmI9ZzFdDNxZcFKuGG2Af6BaLnn60i0VPH7rFoqcf7WLR06eGEy0suChKqTe0IanhjqtvdItHUx+6xaKnD93i0dSHbrFK7cmCi2JxZg5DwBz60S4WPX3oFouefrSLRc9HLhZcFIXnVuTjEdx8zGEMZjEfs5iPOczHHMZgFvMxi3mmNit1DllwUZRSb2hDwjfQfHSLR1MfusWipw/d4tHUh26QWHBRMM7MYQiYQz/axaKnD91i0dOPdrHo2V0tzVhwURQuPcnHI7j5mMMYzGI+ZjEfc5iPOYzBLOZjFvOsP4PlBmTBRVG4w8/HN9B8dItHUx+6xaKnD93i0dSHbnFKbsmCi2JxZg5DwBz60S4WPX3oFouefrSLRc/uamnGgouilHw2aSh4BDcf3eLR1IdusejpQ7d4NPWhGyQWXBSG51bkY8HNxxzGYBbzMYv5mMN8zGEMZjEfs5inlmYsuChWLTdClI059KNdLHr60C0WPf1oF4ue3fEiU0APOKOZjzPE+egWj6Y+dItFTx+6xaOpD93ilNySBRfF4swchoA59KNdLHr60C0WPf1oF4ue3dXSjAUXReG5Ffl4BDcfcxiDWczHLOZjDvMxhzGYxXzMYp5aZpAFF0Up+cY2FLXcefWJbvFo6kO3WPT0oVs8mvrQDRILLgrGmTkMAXPoR7tY9PShWyx6+tEuFj27q6UZCy6KwqUn+XgENx9zGINZzMcs5mMO8zGHMZjFfMxiHl5FGegBd/j5+Aaaj27xaOpDt1j09KFbPJr60C1OyS1ZcFEszsxhCJhDP9rFoqcP3WLR0492sejZXS3NWHBRFC49yccjuPmYwxjMYj5mMR9zmI85jMEs5mMW45Q8gyy4KErJN7ah4BtoPrrFo6kP3WLR04du8WjqQ7c8tZwUYMFFsWq5EaJszKEf7WLR04dusejpR7tY9OyulmYsuCgKZ+by8QhuPrrFo6kP3WLR04du8WjqQ7c4vIoy0INazjKhbMyhH+1i0dOHbrHo6Ue7WPTsrpZmLLgoCi8ekI9HcPMxhzGYxXzMYj7mMB9zGINZzMcs5qllBje44JrZFmb2P83s4+2P9zWz4+b/0IDfVvKNbShqufPqE93i0dSHbrHo6UO3eDT1oVucklvO5RHcT0laLemw9sfLJb133o4ImCPOzGEImEM/2sWipw/dYtHTj3ax6NldLc3msuA+NqX0AUkPSVJKaZWkgnd6lIxLT/LxCG4+5jAGs5iPWczHHOZjDmMwi/mYxTy1NJvLgvugmS2WlCTJzB6r5hFdYMFxh5+Pb6D56BaPpj50i0VPH7rFo6kP3eKU/CrKm8zhc06SdLak3c3sdElHSHrtfB4UMBe1nGVC2ZhDP9rFoqcP3WLR0492sejZXS3NNrjgppT+zcwulPR0NZcm/7eU0h3zfmTANLj0JB+P4OZjDmMwi/mYxXzMYT7mMAazmI9ZzFPLDM644JrZIVPedWv7/z3MbI+U0oXzd1jA9Eq+sQ1FLXdefaJbPJr60C0WPX3oFo+mPnSLU3LL2R7B/WD7/80lLZV0sZpHcA+U9BNJz5jfQwNmx5k5DAFz6Ee7WPT0oVssevrRLhY9u6ul2YwvMpVSOiqldJSkGyUdklJamlJ6iqQnS7p2oQ4QGFfy2aSh4BHcfHSLR1MfusWipw/d4tHUh26Q5vYqyo9PKV267gcppcskHTx/hwTMTS1nmRYa3WLR0492sejpQ7dY9PSjXSx6dldLs7m8ivKVZvbPkj6n5p8K+gNJV87rUQEz4MUDYnGm04c5jMHVBPmYxXzMYT7mMAazmI9ZzLP+DJYbcC4L7usk/amk/9b++DxJH523IwJmwR1+Pr6B5qNbPJr60C0WPX3oFo+mPnSLU3LLufwzQQ9I+j/tf8BgcGYOQ8Ac+tEuFj196BaLnn60i0XP7mpptsEF18x+oebS5PWklPaZlyMCZsGlJ/l4BDcfcxiDWczHLOZjDvMxhzGYxXzMYp5aZnAulygvHXt7c0kvk7TD/BwOMLuSb2xDUcudV5/oFo+mPnSLRU8fusWjqQ/dIM3hVZRTSneO/ffLlNKHJD17AY4NmBVn5jAEzKEf7WLR04dusejpR7tY9OyulmZzuUT5kLEfbqTmEd2t5+2IgFlw6Uk+HsHNxxzGYBbzMYv5mMN8zGEMZjEfs5jnkfQqyh8ce3uNpF9Ievn8HA4wO+7w8/ENNB/d4tHUh26x6OlDt3g09aFbnJJbzmXB/eOU0vXj7zCzvefpeIA548wchoA59KNdLHr60C0WPf1oF4ue3dXSbIPPwZX0L3N8HzDvSj6bNBQ8gpuPbvFo6kO3WPT0oVs8mvrQLU7JLWd8BNfMHi9pf0nbmtmLxz60jZpXU85mZs+T9I+SNpb0zymlv5vycWs/fqyk+yW9NqV0YcSvjfLVcpZpodEtFj39aBeLnj50i0VPP9rFomd3tTSb7RLlx0k6TtJ2kl4w9v77JP1J7i9sZhtL+oikoyUtl3S+mZ2ZUrpi7NOOkbRv+9/TJH20/T8eoXjxgFgln53rE3MYg6sJ8jGL+ZjDfMxhDGYxH7OYp5ZmljbwOzGzw1JKPwr/hc0Ok/TulNJ/aX/8F5KUUnr/2Od8TNIopfT59sc/lzSRUrp1tq+99dZbp6c85SnRhxxmxYoV2m677fo+jCLdcIN0443N23vtJe25Z59HU6a775YuuaR5e7vtpIMO6vd4SpSSdN55kz8+8sj+jqVk//Ef0r33Nm8ffLC07bb9Hk+Jrr9euvnm5u2995b22KPf4ynRXXdJl17avL399tKBB/Z7PCVau1b63veatzfaSHrmM/s9nlJdeKF0333N24ccIm3Nv1nS2XXXScuXN2/vs4+0++79Hk9pVq+Wfvzj5u1Fi9bq8MPn8mzWfpx77rk/Syktne5js12i/I6U0gckvdLMXjH14ymlt2Qe126Sbh778XL99qOz033ObpJ+a8E1sxMlnShJixYt0ooVKzIPb/48/PDDgz6+IXvggc217gr5Vase0IoVD/R7QAVauXITSVtJktasWaMVK1b2e0AFas4LTp6k4vbss2bNVlr3bWjlypVKaU2/B1Sg1au5T8y1cuUiSVtK4j7Ra+1ak9ScoUopacWKe/o9oEKtWbO1mmftSffdd58efvjhfg+oQKtXL5a0mSRp1apVWrFidb8HVJgHH9xIzbNRm7/rlPr3m9kuUb6y/f8F8/RrT3fxxdSHk+fyOc07UzpV0qmStHTp0nTBBfN12PlGo5EmJib6PowinXSS9J73NG+/4Q3Su9/d6+EU6dvflo4+unn70EOl73yn3+Mp0Zo10qJFzdsbbSRddFG/x1Oqww6bPFN82mnS4Yf3ezwl+su/lN7fXvf05jdL73pXv8dTomXLpOc/v3n7iCOks87q93hKtGqVtMUWzdubbsp9otfSpdLPfta8ffrp0lOf2u/xlOhtb5M+2P4Dp299q/T2t/d7PKW58cbmCklJ2mGHB3TRRSEvuzQvbJbr+GdccFNKX2//f9o8HJPUPBo7fuHAYyTd4vgcPILwnJR8PMcnH93i0dSHbrHo6UO3eDT1oVucklvOdony1zXDo6WSlFJ6Yeavfb6kfdt/U/eXkk6Q9Mopn3OmpDeb2RfUXL58z4aef4tHjlqeCI+yMYd+tItFTx+6xaKnH+1i0bO7WprNdonyP8znL5xSWmNmb5Z0jponHHwypXS5mb2h/fgpkpap+SeCrlXzzwS9bj6PCcPHq+Pl4xHcfMxhDGYxH7OYjznMxxzGYBbzMYt5amk22yXK565728w2lfR4NY/o/jyl9GDEL55SWqZmiR1/3yljbydJb4r4tVAH7vDz8Q00H93i0dSHbrHo6UO3eDT1oVscs3K33dkewZUkmdnzJZ0i6To1L/q0t5m9PqXEyzCgV7WcZQIeqbgNx6KnD91i0dOPdrHo2V0tzTa44Er6oKSjUkrXSpKZPVbSNyWx4GLBcWYuH4/g5qNbPJr60C0WPX3oFo+mPnSLU3LLufzrvbetW25b10u6bZ6OB5gVz63Ix4Ibj1n0YRbzcZ+YjznMxxzGYBbzMYt5amk2l0dwLzezZZLOUPMc3JdJOt/MXixJKaV/ncfjA2ZUy40Q5UuJv4x4cBuORU8fusWipx/tYtGzu1pOssxlwd1c0q8lHdn++HZJO0h6gZqFlwUXC6bkG9tQ1HLn1TczvnlGYhZ96BaLnj50i0dTH7pBmsOCm1Lin+bBILFc+NAtHk196BaLnj50i0VPP9rFomd3tTSby6so7y3pzyTtNf75KaUXzt9hAdPjuRWxONPpN/4ILrPow9UE+bhPzMcc5mMOYzCL+ZjFPOvPYLkB53KJ8lclfULS1yWtnd/DAWbHHX4+voHGoF0sevrQLRY9fegWj6Y+dItTcsu5LLgPpJQ+PO9HAnTEmTkMBbPoQ7dY9PShG4aCWYxFz+5qaTaXBfcfzewkSd+StHrdO1NKF87bUQEz4NKTfDyCG4NZzMcs5mMO8zGH+aZ245XlfZjFfNwn5qllBuey4D5J0qslPVuTlyin9sfAgir5xjYUtdx59Y12sejpQ7dY9MRQMIs+dIM0twX3RZL2SSk9ON8HA3TBmTkMBbPoQ7dY9PShWzwewfVhFmPRs7tamm00h8+5WNJ2830gwFzwDTMfj+DGoF0sevrQLRY9/WgXi54+dItT+6soL5F0lZmdr8nn4KaU0vHzd1jA9HhuRT4W3BjMYj5mMR9zmI85jME/nZaPWczHfWKeWprNZcE9aextk/QMSa+Yn8MB5q6WGyHKxyz60C0WPX3oFo+mPnSLRc/uajnJssFLlFNK50q6R9LzJX1a0nMknTK/hwVMr+Qb21DUcufVN9rFoqcP3WLR0492sejpQzdIszyCa2b7STpBzaO1d0r6oiRLKR21QMcGzIozcz50i0dTH7rFoqcP3eLR1IdusejZXS3NZrtE+SpJ35P0gpTStZJkZm9dkKMCZsBzK2JxptOPWczH1QT5mMN8zGEMZjEfs5iPOcxTywzOdonySyT9StK/m9nHzew5ap6DC/Sm5BvbUNRy59U32sWipw/dYtHTj3ax6OlDtzglv4ryjAtuSukrKaXfl/R4SSNJb5W0xMw+ama/u0DHB8yIM3M+dItHUx+6xaKnD93i0dSHbrHo2V0tzebyIlO/SSmdnlI6TtJjJF0k6Z3zfmTANLj0JBZnOv2YxXxcTZCPOczHHMZgFvMxi/mYwzy1zOAGF9xxKaW7UkofSyk9e74OCJhNyTe2oajlzqtvtItFTx+6xaKnH+1i0dOHbpA6LrjAkHBmDkPBLPrQLRY9fegWj6Y+dItFz+5qacaCi6JwZi4fj+DGoF0sevrQLRY9/WgXi54+dItTcksWXBSF51bkY8GNwSzmYxbzMYf5mMMYzGI+ZjEfc5inlmYsuChWLTdClI9Z9KFbLHr60C0eTX3oFoue3a1/kqXcgCy4KApnNPNxhjgG7WLR04dusejpR7tY9PShW5ySW7LgolicmfOhWzya+tAtFj196BaPpj50i0XP7mppxoKLovDcilgln53rG7OYj6sJ8jGH+ZjDGMxiPmYxH3OYp5ZmLLgoCnf4+fgGGoN2sejpQ7dY9PSjXSx6+tAtTsktWXBRrFrOMi00usWjqQ/dYtHTh27xaOpDt1j07K6WZiy4KAqXnsQq+exc35jFfFxNkI85zMccxmAW8zGL+ZjDPLyKMtAD7vDz8Q00Bu1i0dOHbrHo6Ue7WPT0oVuckluy4KJYnJnDUDCLPnSLRU8fusWjqQ/dYtGzu1qaseCiKFx6ko9HcGMwi/mYxXzMYT7mMAazmI9ZzMccQmLBRWG4w8/HN9AYtItFTx+6xaKnH+1i0dOHbnlq+TsiCy6KxZk5DAWz6EO3WPT0oVs8mvrQLRY9u6ulGQsuilLy2aShqOXsXN9oF4uePnSLRU8/2sWipw/d4vAqykAPajnLtNDoFo+mPnSLRU8fusWjqQ/dYtGzu1qaseCiKLx4QCzOdPoxi/m4miAfc5iPOYzBLOZjFvMxh3lqmUEWXBSl5BvbUNRy59U32sWipw/dYtHTj3ax6OlDN0gsuCgYZ+Z86BaPpj50i0VPH7rFo6kP3WLRs7tamrHgoihcehKLM51+zGI+ribIxxzmYw5jMIv5mMV8zGGeWmaQBRdFKfnGNhS13Hn1jXax6OlDt1j09KNdLHr60C0Or6IM9IAzcz50i0dTH7rFoqcP3eLR1IdusejZXS3NWHBRFC49icWZTj9mMR9XE+RjDvMxhzGYxXzMYj7mME8tM8iCi6KUfGMbilruvPpGu1j09KFbLHr60S4WPX3oBokFFwXjzByGgln0oVssevrQLR5NfegWi57d1dKMBRdF4cxcPh7BjUG7WPT0oVssevrRLhY9fegWp+SWLLgoVi1nmRYa3eLR1IdusejpQ7d4NPWhWyx6dldLMxZcFIUXD4hV8tm5vjGL+biaIB9zmI85jMEs5mMW8zGHedafwXIDsuCiKNzh5+MbaAzaxaKnD91i0dOPdrHo6UO3OCW3ZMFFsTgz50O3eDT1oVssevrQLR5NfegWi57d1dKMBRdF4dKTWCWfnesbs5iPqwnyMYf5mMMYzGI+ZjEfc5inlmYsuCgKd/j5+AYag3ax6OlDt1j09KNdLHr60C1OyS1ZcFGsWs4yLTS6xaOpD91i0dOHbvFo6kO3WPTsrpZmLLgoCpeexCr57FzfmMV8XE2QjznMxxzGYBbzMYv5mMM8vIoy0APu8PPxDTQG7WLR04dusejpR7tY9PShW5ySW7LgolicmfOhWzya+tAtFj196BaPpj50i0XP7mppxoKLopR8NmmI6OlHu1j09KFbLHr60S4WPX3oBokFFwWr5SzTQqNbPJr60C0WPX3oFo+mPnSLRc/uamnGgoui8OIBsTjT6ccs5uP54PmYw3zMYQxmMR+zmI85zFPLDLLgoigl39iGopY7r77RLhY9fegWi55+tItFTx+6xeFVlIEecGbOh27xaOpDt1j09KFbPJr60C0WPburpRkLLorCpSexONPpxyzm42qCfMxhPuYwBrOYj1nMxxzmqWUGWXBRlJJvbENRy51X32gXi54+dItFTz/axaKnD90gseCiYJyZ86FbPJr60C0WPX3oFo+mPnSLRc/uamnGgouicOlJLM50+jGL+biaIB9zmI85jMEs5mMW8zGHeWqZQRZcFKXkG9tQ1HLn1TfaxaKnD91i0dOPdrHo6UO3OLyKMtADzsz50C0eTX3oFouePnSLR1MfusWiZ3e1NGPBRVE4MxeLnn60i0VPH7rFoqcf7WLR04ducUpuyYKLYtVylmmh0S0eTX3oFouePnSLR1MfusWiZ3e1NNukj1/UzHaQ9EVJe0m6QdLLU0p3T/N5N0i6T9LDktaklJYu3FFiiHjxgFgln53rG7OYj+eD52MO8zGHMZjFfMxiPuYwTy3N+noE952SvpNS2lfSd9ofz+SolNLBLLeQuMOPwDfQGLSLRU8fusWipx/tYtHTh25xSm7Z14J7vKTT2rdPk/R7PR0HClbLWaaFRrd4NPWhWyx6+tAtHk196BaLnt3V0qyvBXdJSulWSWr/v/MMn5ckfcvMfmZmJy7Y0WGwuPQkVsln5/rGLObjaoJ8zGE+5jAGs5iPWczHHOZZfwbLDThvz8E1s29L2mWaD72rw5c5IqV0i5ntLOnfzOyqlNJ5M/x6J0o6UZKWLFmi0WjU9ZAXzMqVKwd9fEN22WU7SnqSJOnOO+/QaHRZvwdUoOuu20PSPpKkm2++SaPR9f0eUKF+85ulkraSJF1wwQVasWJlvwdUoJSepXXnWc8771wtWlTuN9O+XH75TpIOkCTdccftGo0u7/eACvSLX+wpaW9J0k033aDR6IZej6dU99//VElbSpJ++tOf6vbb7+/3gIo08Z9vnXvuiCXX4cord5b0REnSbbfdptHoin4PqDCXXTb5PeXhh9cUu6/M24KbUnruTB8zs1+b2aNTSrea2aMl3TbD17il/f9tZvYVSYdKmnbBTSmdKulUSVq6dGmamJjI/B3Mn9FopCEf35Ddd9/k2zvssBMdHX70o8m399hjD01M7NHfwRRsq60m337KU5bqyU/u71hqcOSRR2rTTfs+ivLcddfk2zvu+CjuEx3OG/tbxZ577qWJib16O5aSbbnl5NtPfeqhOuCA/o6lBhMTEyy4Dr/61eTbj3rUzpqYmOkiUUznzjsn395kk02K/Z7S1yXKZ0p6Tfv2ayR9beonmNmWZrb1urcl/a4kHq57hOPSk1h88/RjFvNxOV4+5jAfcxiDWcwztRmz6MMc5qmlWV8L7t9JOtrMrpF0dPtjmdmuZras/Zwlkr5vZhdL+qmkb6aUzu7laDEY3OHn4y9zMWgXi54+dItFTz/aYQiYwzglt+zl38FNKd0p6TnTvP8WSce2b18v6aAFPjQUpJazTAuNbvFo6kO3WPT0oVs8mnZHs3g07a6WZn09ggu4lHw2aYjo6Ue7WPT0oVssevrRLg4t/WgXp+RXUWbBRVF4bkU+LlGOwSzmYxbzMYf5mMMYzGIe5jAGc5inlmYsuChWLTfChUa3eDT1oVssevrQLR5Nu6NZPJp2V0szFlwUhbOasejpR7tY9PShWyx6+tEuDi39aBen5JYsuChWLWeZFhrd4tEUQ8Ac+tAtHk27o1k8mnZXSzMWXBSF51bEKvnsXN+YxTz8m48xmMN8PPcxBrOYhzmMwRzmqWUOWXBRlJJvbENRy51X32iHIWAOY9HTj3ZxaOlHuzi8ijLQA87M+dAtHk27o1k8mvrQLR5Nu6NZPJp2V0szFlwUhUtPYnGm049ZzLP+lQQE9GIO83FVSwxmMQ9zGIM5zFNLMxZcFIU7/Xx8E41Buzi09KNdLHr60S4OLf1oF6fkliy4KFYtZ5kWGt3i0bQ7msWjqQ/d4tG0O5rFo2l3tTRjwUVRSj6bNET09KMdhoA5jEVPP9rFoaUf7eKU3JIFF0XhuRX5uEQ5BrOYh+fgxmAO83GfGINZzMMcxmAO89TyvZkFF8XijsuHbvFowfAtcgAAE/BJREFU2h3N4tHUh27xaNodzeLRtLtamrHgoiic1YxFTz/axaGlH+1i0dOPdnFo6Uc7SCy4KFgtZ5kWGt3i0bQ7msWjqQ/d4tG0O5rFo2l3tTRjwUVReG5FLM50+jGLeXi+WQzmMB+zGINZzMMcxmAO89Qyhyy4KErJN7ahqOXOq2+0wxAwh7Ho6Ue7OLT0o12ckluy4KJYnJnzoVs8mnZHs3g09aFbPJp2R7N4NO2ulmYsuCgKl57EKvnsXN+YxTy1/FMEfWMO83FVSwxmMQ9zGIM5zFPL92YWXBSFO/18fBONQbs4tPSjXSx6+tEuDi39aAeJBRcF48ycD93i0bQ7msWjqQ/d4tG0O5rFo2l3tTRjwUVRODMXi55+tItDSz/axaKnH+3i0NKPdnFKbsmCi6Lw3Ip8XKIcg1nMQ7MYzGE+7hNjMIt5mMMYzGGeWuaQBRfF4o7Lh27xaNodzeLR1Idu8WjaHc3i0bS7Wpqx4KIoJZ9NGiJ6+tEuTsmv1Ng35jAWPf1oF4eWfrSLU/L3ZhZcFKuWs0wLjW7xaNodzeLR1Idu8WjaHc3i0bS7Wpqx4KIoPLciXy3Pr+gbs5iHOYzBHOZjFmMwi3mYwxjMYZ5amrHgoijc6ceipx/t4tDSj3ax6OlHuzi09KNdnJJbsuCiWLWcZVpodItH0+5oFo+mPnSLR9PuaBaPpt3V0owFF0Xh0pNYJZ+d6xuzmIdmMZjDfFwaGoNZzMMcxmAO89Qyhyy4KErJN7ahqOXOq2+0i1PyKzX2jTmMRU8/2sWhpR/t4pT8vZkFF8XizJwP3eLRtDuaxaOpD93i0bQ7msWjaXe1NGPBRVG49CQWZzr9mMU8XEkQgznMxyzGYBbzMIcxmMM8tTRjwUVRuNPPxzfRGLSLQ0s/2sWipx/t4tDSj3ZxSm7Jgoti1XKWaaHRLR5Nu6NZPJr60C0eTbujWTyadldLMxZcFKXks0lDRE8/2mEImMNY9PSjXRxa+tEuTsktWXBRrFrOMi00usWjaXc0i0dTH7rFo2l3NItH0+5qacaCi6Lw4gH5eA5uDGYxz/pzSEAv5jAf94kxmMU8zGEM5jBPLd+bWXBRFO70Y9HTj3ZxaOlHu1j09KNdHFr60Q4SCy4Kxpk5H7rFo2l3NItHUx+6xaNpdzSLR9PuamnGgouicOlJPi6DisEs5mEOYzCH+ZjFGMxiHuYwBnOYp5Y5ZMFFUUq+sQ0RPf1ohyFgDmPR0492cWjpR7s4JbdkwUWxODPnQ7d4NO2OZvFo6kO3eDTtjmbxaNpdLc1YcFEULj2JVfLZub4xi3lqeaXGvjGH+Wq5JK9vzGIe5jAGc5inlu/NLLgoCnf6+fgmGoN2cWjpR7tY9PSjXRxa+tEOEgsuCsaZOR+6xaNpdzSLR1MfusWjaXc0i0fT7mppxoKLonBmLhY9/WgXh5Z+tItFTz/axaGlH+3ilNySBRfFquUs00KjWzyadkezeDT1oVs8mnZHs3g07a6WZiy4KAovHpCP5+DGYBbz0CwGc5iP+8QYzGIe5jAGc5inljlkwUVRSr6xDRE9/WgXp+RXauwbcxiLnn60i0NLP9rFKfl7MwsuisWZOR+6xaNpdzSLR1MfusWjaXc0i0fT7mppxoKLonDpSb5aLj/pG7OYhzmMwRzmYxZjMIt5mMMYzGGeWpqx4KIo3OnHoqcf7eLQ0o92sejpR7s4tPSjXZySW7Lgoli1nGVaaHSLR9PuaBaPpj50i0fT7mgWj6bd1dKMBRdF4dKTWCWfnesbs5iHZjGYw3xcGhqDWczDHMZgDvPUMocsuChKyTe2oajlzqtvtItT8is19o05jEVPP9rFoaUf7eKU/L2ZBRfF4sycD93i0bQ7msWjqQ/d4tG0O5rFo2l3tTRjwUVRODMXi55+tItDSz/axaKnH+3i0NKPdpBYcFGwWs4yLTS6xaNpdzSLR1MfusWjaXc0i0fT7mppxoKLovDiAfl4Dm4MZjEPcxiDOczHLMZgFvMwhzGYwzy1zCELLopS8o1tiOjpRzsMAXMYi55+tItDSz/axSm5JQsuisWZOR+6xaNpdzSLR1MfusWjaXc0i0fT7mppxoKLonDpSb5aLj/pG7OYZ/05JKAXc5iP+8QYzGIe5jAGc5inlu/NLLgoCnf6sejpR7s4tPSjXSx6+tEuDi39aAeJBRcF48ycD93i0bQ7msWjqQ/d4tG0O5rFo2l3tTRjwUVRuPQkH5dBxWAW8zCHMZjDfMxiDGYxD3MYgznMU8scsuCiKCXf2IaInn60wxAwh7Ho6Ue7OLT0o12ckluy4KJYnJnzoVs8mnZHs3g09aFbPJp2R7N4NO2ulmYsuChKyWeThoiefrSLU/IrNfaNOYxFTz/axaGlH+3ilPy9mQUXReG5FflqeX5F35jFPMxhDOYwH7MYg1nMwxzGYA7z1NKMBRfFquVGuNDoFo+m3dEsHk196BaPpt3RLB5Nu6ulGQsuisJZzVj09KMdhoA5jEVPP9rFoaUf7eKU3LKXBdfMXmZml5vZWjNbOsvnPc/Mfm5m15rZOxfyGDF8tZxlWmh0i0fT7mgWj6Y+dItH0+5oFo+m3dXSbJOeft3LJL1Y0sdm+gQz21jSRyQdLWm5pPPN7MyU0hULc4gYovGzSatXS9/7Xn/HUqrbb598u+Szc30bb/fznzOLXV1//eTbzKHfeLtVq5hDjzvumHybWfQbb3fVVcxiV1dfPfk2c+g33u7++5nDrm66afLtkuewlwU3pXSlJNns5Q6VdG1K6fr2c78g6XhJLLiPYOMjc+ed0rOe1d+x1KDkO6++jbf7wAea/+BT8is19m18Dm+7jfvEXNwn+o23+9u/bf6DD3PoN97ul7/kPjFHyd+b+3oEdy52k3Tz2I+XS3raTJ9sZidKOlGSlixZotFoNK8Hl2PlypWDPr4he+CBjbR48eFatWrIo1uO22+/UKPRvX0fRpEefngvSXv1fBR12GGH+zUaXdD3YRTp/vs31mabHa7Vqzfu+1Cq8OtfX6DRaGXfh1GktWv3lrRn34dRhcWL79BodFnfh1GklSs30aJFh+uhh3iZoVzbbHNPsfvKvG0JZvZtSbtM86F3pZS+NpcvMc37ZjyVkFI6VdKpkrR06dI0MTExl8PsxWg00pCPb+g++1nplFOkBx7o+0jKZSbtt9/1euMbD+FMsdP++0ubbSZdwTUlWXbZRTrmmBu5T8zwmc9Ip57aPG0DPmbSE594nU48cSn3iU5PeIK0eHFzeTL8NtnkNp188s7af/+Jvg+lWKedJn3iE9wn5nj606XnPve+Yr83z9uCm1J6buaXWC5p97EfP0bSLZlfExV4yUua/5BnNLpJZvv0fRjFetSjpI/N+CoC6GI0+k3fh1C0l7+8+Q95RqObZfbYvg+jWEuWSB//eN9HUb7R6Artv//OfR9G0V7xiuY/5BmN1vZ9CG5Dfvz+fEn7mtneZrappBMkndnzMQEAAAAABqqvfyboRWa2XNJhkr5pZue079/VzJZJUkppjaQ3SzpH0pWSzkgpXd7H8QIAAAAAhq+vV1H+iqSvTPP+WyQdO/bjZZKWLeChAQAAAAAKNeRLlAEAAAAAmDMWXAAAAABAFVhwAQAAAABVYMEFAAAAAFSBBRcAAAAAUAUWXAAAAABAFVhwAQAAAABVYMEFAAAAAFSBBRcAAAAAUAUWXAAAAABAFVhwAQAAAABVYMEFAAAAAFSBBRcAAAAAUAUWXAAAAABAFVhwAQAAAABVYMEFAAAAAFSBBRcAAAAAUAUWXAAAAABAFVhwAQAAAABVYMEFAAAAAFSBBRcAAAAAUAUWXAAAAABAFVhwAQAAAABVYMEFAAAAAFSBBRcAAAAAUAUWXAAAAABAFVhwAQAAAABVYMEFAAAAAFSBBRcAAAAAUAUWXAAAAABAFVhwAQAAAABVYMEFAAAAAFSBBRcAAAAAUAUWXOD/b+/+Q6696zqAv9+6ipniwpGWZhOxHyabrDmzjDTD3P5oLFqYkiGJiCX1R2TQr5X/FBlE1LSQIQXpH+n8gebcAp1mY86Ye1xrMWboWGArM6bR2vbpj/ssHp+e+ZynPefHfd2vF9xwznV97+t8Dnw4h/e5vtf3AgAAFkHABQAAYBEEXAAAABZBwAUAAGARBFwAAAAWQcAFAABgEQRcAAAAFkHABQAAYBEEXAAAABZBwAUAAGARBFwAAAAWQcAFAABgEQRcAAAAFkHABQAAYBEEXAAAABZBwAUAAGARBFwAAAAWQcAFAABgEQRcAAAAFkHABQAAYBEEXAAAABZBwAUAAGARBFwAAAAWQcAFAABgEQRcAAAAFkHABQAAYBEEXAAAABZBwAUAAGARBFwAAAAWQcAFAABgEQRcAAAAFkHABQAAYBEEXAAAABZBwAUAAGARBFwAAAAWQcAFAABgEQRcAAAAFkHABQAAYBEEXAAAABZhJwG37RVtb2v7UNuLvsa4f2p7rO0tbW/eZo0AAAAcLmft6HU/k+THk/zJGmNfPDP3brgeAAAADrmdBNyZuT1J2u7i5QEAAFigfb8Gd5J8uO2n2r5218UAAACwvzZ2Brft9UmecpJdvzoz713zMD8wM/e0/eYk17X9h5m54RFe77VJHg7B97W94/Sr3ppzk5h2za7pQ/aFXmQf6EP2gT5kX+x7L377I+3ozGyzkK9+8fYjSX5pZk65gFTbK5PcNzNv3nRdm9b25pl5xMW1YBv0IftCL7IP9CH7QB+yLw5zL+7tFOW239j2CQ8/TvLSHCxOBQAAAP/Hrm4TdHnbu5O8IMkH2l672v6tbT+4GvbkJB9v++kkNyX5wMx8aBf1AgAAsP92tYryNUmuOcn2e5Jcunp8V5ILtlzatvzprguA6EP2h15kH+hD9oE+ZF8c2l7c6TW4AAAAcKbs7TW4AAAAcDoE3A1q+7K2d7S9s+2vnGR/2/7hav+tbS/cRZ0s2xp9+MpV/93a9hNtl3ppADt0qj48btzz2j7Y9ie2WR9Hxzq92PZFbW9pe1vbj267RpZvje/mJ7Z9f9tPr/rw1buok2Vre3XbL7Q96UK+hzWrCLgb0vaxSf44ySVJnp3kp9o++4RhlyR51urvtUnestUiWbw1+/CzSX5oZs5P8qYc4msu2E9r9uHD4343ybXbrZCjYp1ebHtOkquS/NjMfE+SK7ZeKIu25mfizyX5+5m5IMmLkvx+26/faqEcBW9P8rKvsf9QZhUBd3MuTnLnzNw1M/cneWeSy04Yc1mSP5sDNyY5p+23bLtQFu2UfTgzn5iZL66e3pjkaVuukeVb5/MwSd6Q5F1JvrDN4jhS1unFVyR598x8LklmRj9ypq3Th5PkCW2b5PFJ/i3JA9stk6WbmRty0FuP5FBmFQF3c56a5PPHPb97te10x8Cjcbo99rNJ/mqjFXEUnbIP2z41yeVJ3rrFujh61vlM/I4k39T2I20/1fZVW6uOo2KdPvyjJN+d5J4kx5L8wsw8tJ3y4H8dyqyyk9sEHRE9ybYTl6xeZww8Gmv3WNsX5yDgvnCjFXEUrdOHf5DkjTPz4MEJC9iIdXrxrCTfm+QlSc5O8rdtb5yZf9x0cRwZ6/Thjya5JckPJ3lmkuvafmxm/mPTxcFxDmVWEXA35+4k33bc86fl4Fe40x0Dj8ZaPdb2/CRvS3LJzPzrlmrj6FinDy9K8s5VuD03yaVtH5iZ92ynRI6Idb+b752ZLyf5ctsbklyQRMDlTFmnD1+d5Hfm4H6ed7b9bJLvSnLTdkqEJIc0q5iivDmfTPKsts9YLQrw8iTvO2HM+5K8arVC2fcl+dLM/PO2C2XRTtmHbZ+e5N1JftoZCjbklH04M8+YmfNm5rwkf5nk9cItG7DOd/N7k/xg27PaPi7J85PcvuU6WbZ1+vBzOZhFkLZPTvKdSe7aapVwSLOKM7gbMjMPtP35HKwG+tgkV8/MbW1ft9r/1iQfTHJpkjuTfCUHv9bBGbNmH/5GkicluWp19uyBmbloVzWzPGv2IWzcOr04M7e3/VCSW5M8lORtM3PSW2jA/8ean4lvSvL2tsdyME30jTNz786KZpHaviMHq3Sf2/buJL+Z5OuSw51VejDzAQAAAA43U5QBAABYBAEXAACARRBwAQAAWAQBFwAAgEUQcAEAAFgEtwkCgB1q+6Qkf716+pQkDyb5l9Xzi2fm/p0UBgCHkNsEAcCeaHtlkvtm5s27rgUADiNTlAFgT7X9mbY3tb2l7VVtH9P2rLb/3vb32v5d22vbPr/tR9ve1fbS1f++pu01q/13tP214477y20/s/p7w+7eIQCcWQIuAOyhts9JcnmS75+Z5+bgsqKXr3Y/McmHZ+bCJPcnuTLJS5JckeS3jzvMxav/uTDJK9o+t+3FSV652veCJK9ve/7m3xEAbJ5rcAFgP/1Ikuclubltkpyd5POrff85M9etHh9L8qWZeaDtsSTnHXeMa2fmi0nS9j1JXpjkG5K8a2a+csL2Wzf7dgBg8wRcANhPTXL1zPz6V21sz8rBWduHPZTkv457fPx3+4kLbczquACwSKYoA8B+uj7JT7Y9NzlYbbnt00/zGC9te07bxyW5LMnfJLkhyeVtz277+NX2j53JwgFgV5zBBYA9NDPH2v5WkuvbPibJfyd5XZJ7TuMwH0/yF0memeTPZ+aWJGn7jiSfXI15y8wcO3OVA8DuuE0QACxQ29ckec7M/OKuawGAbTFFGQAAgEVwBhcAAIBFcAYXAACARRBwAQAAWAQBFwAAgEUQcAEAAFgEARcAAIBFEHABAABYhP8BBGY5XYkI550AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "plt.plot(t, s, linewidth=3, color='blue')\n",
    "plt.title('Onda Quadrada - 5Hz amostrado em 1000Hz/segundo')\n",
    "plt.xlabel('Tempo')\n",
    "plt.ylabel('Amplitude')\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.ylim(-1.5, 1.5)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}