{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aplicações de Matrizes em Situações do Dia a Dia\n",
    "#### Vamos apresentar dois problemas rotineiros: um sobre um planejamento de atividades físicas e outro sobre planejamento financeiro de uma empresa. Ambos podem ser solucionados utilizando conhecimentos sobre matrizes em Álgebra Linear."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Leon, Steven J., 1943 - Álgebra Linear com Aplicações/ Steven J. Leon; tradução Valéria de Magalhães Iório - [reimpr]. Rio de Janeiro: LTC, 2008."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aplicação 1: Queimando calorias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alysson pesa 81 quilos (os dados são meramente ilustrativos...). Ele quer perder peso por meio de um progama de dieta e exercícios. Após consultar a **tabela 1**, ele monta o programa de exercícios na **tabela 2**. quantas calorias ele vai queimar por dia se seguir esse progama?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tabela 1. Calorias Queimadas por Hora"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Atividades Esportivas**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|**Peso**|**Andar a 3km/h**|**Correr a 9km/h**|**Andar de bicicleta a 9km/h**|**Jogar Tênis(Moderado)**|\n",
    "|:------:|:---------------:|:----------------:|:----------------------------:|:-----------------------:|\n",
    "|69      |213              |651               |304                           |420                      |\n",
    "|73      |225              |688               |321                           |441                      |\n",
    "|77      |237              |726               |338                           |468                      |\n",
    "|81      |249              |764               |356                           |492                      |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importando a biblioteca numpy do Python\n",
    "import numpy as np\n",
    "# importando a biblioteca de funções do Python matplotlib\n",
    "import matplotlib \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# definindo a matriz A a partir da tabela 1\n",
    "A = np.array([[213, 651, 304, 420],\n",
    "              [225, 688, 321, 441],\n",
    "              [237, 726, 338, 468],\n",
    "              [249, 764, 356, 492]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A matriz A é\n",
      "\n",
      " [[213 651 304 420]\n",
      " [225 688 321 441]\n",
      " [237 726 338 468]\n",
      " [249 764 356 492]]\n"
     ]
    }
   ],
   "source": [
    "# imprimindo a matriz A\n",
    "print(\"A matriz A é\\n\\n\", A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tabela 2. Horas por Dia para Cada Atividade"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Programa de Exercícios**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|**Dias da Semana**|**Andar**|**Correr**|**Andar de Bicicleta**|**Jogar Tênis**|\n",
    "|:----------------:|:-------:|:--------:|:--------------------:|:-------------:|\n",
    "|Segunda-feira     |1.0      |0.0       |1.0                   |0.0            | \n",
    "|Terça-feira       |0.0      |0.0       |0.0                   |2.0            |\n",
    "|Quarta-feira      |0.4      |0.5       |0.0                   |0.0            |\n",
    "|Quinta-feira      |0.0      |0.0       |0.5                   |2.0            |\n",
    "|Sexta-feira       |0.4      |0.5       |0.0                   |0.0            |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# definindo a matriz B a partir da tabela 2\n",
    "B = np.array([[1.0, 0.0, 1.0, 0.0],\n",
    "              [0.0, 0.0, 0.0, 2.0],\n",
    "              [0.4, 0.5, 0.0, 0.0],\n",
    "              [0.0, 0.0, 0.5, 2.0],\n",
    "              [0.4, 0.5, 0.0, 0.0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A matriz B é\n",
      "\n",
      " [[1.  0.  1.  0. ]\n",
      " [0.  0.  0.  2. ]\n",
      " [0.4 0.5 0.  0. ]\n",
      " [0.  0.  0.5 2. ]\n",
      " [0.4 0.5 0.  0. ]]\n"
     ]
    }
   ],
   "source": [
    "# imprimindo a matriz B\n",
    "print(\"A matriz B é\\n\\n\", B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Solução:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A informação pertinente para Alysson está localizada na quarta linha da **matriz A**, pois são os dados referentes a quem pesa 81 quilos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos criar uma nova matriz **x** do tipo **vetor-coluna** baseado nos dados contidos na 4º linha da **matriz A**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x recebe a quarta linha de A\n",
    "x = A[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x é transposta para se tornar um vetor-coluna\n",
    "x = np.transpose(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para resolver o problema, basta multiplicar a matriz $B_{5_{x}4}$ pela matriz $x_{4_{x}1}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# realizando a multiplicação da matriz B com a matriz x\n",
    "Calorias = np.dot(B,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A matriz com valores de calorias gastas é:\n",
      "\n",
      " [ 605.   984.   481.6 1162.   481.6]\n"
     ]
    }
   ],
   "source": [
    "# imprimindo a matriz de calorias gastas por semana\n",
    "print(\"A matriz com valores de calorias gastas é:\\n\\n\", Calorias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# esquematizando as etiquetas do gráfico no eixo x\n",
    "DiasdaSemana = ['Segunda-feira', 'Terça-feira', 'Quarta-feira', 'Quinta-feira', 'Sexta-feira']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dessa forma, vemos que Alysson, seguindo sua rotina de atividades físicas, queimara as respectivas calorias durante a semana:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|**Dias da Semana**|**Calorias Gastas**|\n",
    "|:----------------:|:-----------------:|\n",
    "|Segunda-feira     |605.0 calorias     |\n",
    "|Terça-feira       |984.0 calorias     |\n",
    "|Quarta-feira      |481.6 calorias     |\n",
    "|Quinta-feira      |1162.0 calorias    |\n",
    "|Sexta-feira       |481.6 calorias     |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assim, podemos ter o seguinte gráfico:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Gráfico de Calorias Gastas por Alysson')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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caer7TBjT+s+FOt3y5cudDkHVcs0/vjbDH37fnCirdDoUVYe7vyvAatOCfabPXD5Y2+/e28LWA8ebtUxVVRX+/v4NPj+iTxd+c/nIFsVjjOGhhx5ixYoV+Pn5kZOTQ15eHr169fpunq+++oo5c+bg7+9PdHQ05557LqtWraJLly5Mnjy53kvgGlrv6NGjueeee7j//vu57LLLOPvssxuN7+OPP+b+++/n2LFjvPrqq0yZMoXFixczf/58KisrOXjwIFu3bmXEiBGEhITwox/9iMsuu4zLLrus3vVt3ryZRx55hGPHjlFcXMyMGTMAOPPMM7npppuYPXs2V199NQDl5eVkZ2czYMCARt+/uioqKrj99ttZv349/v7+TfbFUKoj2nbwOKszjgLw3oYDzJ4U63BEyhPpqYF2MnLkSNasWVPvcwsWLODQoUOsWbOG9evXEx0d3azL2hoavaqh9Q4ZMoS1a9cyevRoHnnkER599NFTluvSpQthYWHs27cPgBkzZrB+/XpGjRpFeXk5+/bt46mnnmLZsmVs3LiRSy+9lNLSUgICAli5ciWzZs3i/fffr/dwPsBNN93Ec889x6ZNm/jNb37z3Wt94YUXeOyxx8jKymLixIkUFBTw5ZdfctZZZzX42ouKiti/fz9Dhgw5Zfpf//pXoqOj2bBhA6tXr6a8vNzl91OpjiIlLYPgAD+iQ4WU9BaNR6N8gE8eEWjJL/fWFhQ6//zzeeihh5g/fz633HILABs3bqSwsJDCwkJ69uxJYGAgy5cvJyPj9C/s2WefzT//+U9uvPFGjhw5wooVK/jzn//M9u3bG9xmQ+s9cOAA3bp1Izk5mcjISP71r3+dtuyDDz7IrbfeyqJFi4iMjMQY890O+/jx43Tu3JmIiAjy8vL46KOPmDp1KsXFxZw4cYJLLrmEM88887tf8eHh4RQVFZ3yXvbu3ZuKigoWLFhA3759AdizZw+JiYkkJiby0UcfkZWVxdKlS7n44ovrfX3FxcX88pe/5Morr6Rr164UFhae8tpjYmLw8/Pj5ZdfpqpKz5Eq31JUWsHb63K4bEwfOp3MJ2VbIRuyjjE2NtLp0JSH8clEwAkiwltvvcXdd9/NH//4R0JCQujXrx/PPPMM8+bN4/LLL2f06NEkJCQwbNiw05a/6qqr+Pbbbxk7diwiwp/+9Cd69erVaCLQ0Ho3bdrEfffdh5+fH4GBgTz//POnLXvrrbdSUlJCYmIiwcHBhIWFceaZZzJ+/HgiIiIYP348w4YNIzY2ljPPPBOwdvAzZ86ktLQUYwxPP/00ANdffz0333wzzz77LEuWLOH3v/89iYmJ9OjRg8TExO+ShPvuu49du3ZhjGHatGmMHTuWm2+++bQjFueddx7GGCorK7nmmmv41a9+dVr8t912G9dccw3/+9//uOiii3QsduVz3l6XQ0l5FclJceTuPMKbe6pIScvQRECdRuo7v+rtEhISzOrVq0+Ztm3bNoYPH97idWqJ4faXnZ3NzTffzEcffVTv8+5ok9Z+LtTp3D3Gumo+YwwXPfMlAf7C+3ecxRdffMHHR7rz1rps0h+cTkSojq/hCdz9XRGRNcaYhOYup30ElMeKiYlpMAlQSjVsdcZRduQVcUNS/HcVNJOT4iitqGbJ2myHo1OeRhMBpZTqYFLSMggPCeCKcX2+mzayTwQT4iJZkJ5R75U2yndpIqCUUh1IQXEZH23K5ZoJMYQGndoNLDkpnr2HSvh2T4FD0SlP5FOJgGbBqjb9PKiOaPHqbMqrqpmXGHfac5eM7k3X0EC9lFCdwmcSgZCQEAoKCvSfvwKsJKCgoICQkBCnQ1HKbaqrDa+uzCCxfzcGR5/ekTYk0J9rE2L5ZEse+cd1CG5l8ZnLB2NiYsjOzubQoUMtWr60tFR3Gh6mtW0SEhJCTEyMGyNSyllf7DpE1pGT/HLG6Zcg15g7OY75K/ayaFUWd04b3I7RKU/lM4lAYGBgvWV4XZWamsr48ePdGJFqLW0TpU61IC2DqLBgZozs1eA8/aI6c/bgKBauzOS2qQMJ8PeZA8OqAfoJUEqpDiD76AmWbc/nukkxBAU0/q89OSmeg4WlLNue307RKU+miYBSSnUAC1dmAjBn8umdBOuaNqwnvSNCSEnTToNKEwGllPJ65ZXVvLYqi2nDehLTNbTJ+QP8/ZgzOY4vdx1m/+GSdohQeTJNBJRSyst9vCWXw8XlzEuKd3mZ6yfFEuAnvGofSVC+SxMBpZTycilpGcR268S5g3u4vEzPLiFcODKa11dnUVqho3P6Mk0ElFLKi+3KKyJ93xHmTo7Hz0+atWxyYjxHT1Tw4aaDbRSd8gZtlgiIyH9EJF9ENteadq2IbBGRahFJqDP/gyKyW0R2iMiMWtMvsqftFpEH2ipepZTyRgvSMwny92N2QvNrYpwxsDsDenTWToM+ri2PCLwEXFRn2mbgamBF7YkiMgK4HhhpL/MPEfEXEX/g/4CLgRHAHHtepZTyeSfKK3ljTTYXj+5F97DgZi8vIsxLjGdt5jG2HChsgwiVN2izRMAYswI4UmfaNmPMjnpmnwksMsaUGWP2AbuByfZttzFmrzGmHFhkz6uUUj7v3fUHKCqrJLkZnQTrmjUhhpBAP1LStNOgr/KUPgJ9gaxaj7PtaQ1NV0opn2aM4ZW0DIZGh5MQ37XF64kIDeTyMX14Z30ORaUVboxQeYsOU2JYRG4BbgGIjo4mNTXVresvLi52+zpV62ibeCZtl/ax51gVWw6UcsOIIL744otG522qTYYHVfF6eRV/fC2V6fGBbo5UNcRTviuekgjkALG1HsfY02hk+imMMfOB+QAJCQlm6tSpbg0wNTUVd69TtY62iWfSdmkf7y3eQOegg/xy9lTCQxrfeTfVJlOBt7O+YuWRKn7/g3MQad7VB6plPOW74imnBt4FrheRYBHpDwwGVgKrgMEi0l9EgrA6FL7rYJxKKeW4YyfKeX/jAa4c37fJJMBVyYnx7MwrZtX+o25Zn/IebXn54ELgW2CoiGSLyI9E5CoRyQbOAD4QkY8BjDFbgMXAVmAp8DNjTJUxphK4HfgY2AYstudVSimftWRNNmWV1a3qJFjX5WP70CUkQC8l9EFtdmrAGDOngafeamD+x4HH65n+IfChG0NTSimvVV1tWJCeycT4rgzv3cVt6+0U5M81E2NIScvgcPEIolpwOaLyTp5yakAppZQLvtlTwL7DJSQnNT3KYHPNS4ynosqweHVW0zOrDkMTAaWU8iIpaRl0DQ3k4lG93b7uQT3DOGNAd15Nz6Sq2rh9/cozaSKglFJeIrewlE+35TE7IZaQQP822UZyUjzZR0/yxc78Nlm/8jyaCCillJdYuNL6pT430f2nBWpcODKaHuHBWmnQh2gioJRSXqCiqppFqzI5d0gP4rt3brPtBPr7cf2kWJbvyCfryIk2247yHJoIKKWUF1i2LY+842VuvWSwIXMmxyFYRyBUx6eJgFJKeYGUtEz6RIRw/rCebb6tPpGdmDY8msWrsyivrG7z7SlnaSKglFIebt/hEr7afZg5k+Pw92uf8r/JSfEcLi5n6Zbcdtmeco4mAkop5eEWpGUQ4CdcNzm26Znd5OxBUcR3D9VKgz5AEwGllPJgpRVVvL4mmxkje9EzPKTdtuvnJ8ydHMfKfUfYmVfUbttV7U8TAaWU8mDvbzxI4ckK5rVBJcGmXJsQS1CAHwv0qECHpomAUkp5sFfSMhjYozNnDOje7tvu1jmIS0f35o21OZSUVbb79lX70ERAKaU81OacQjZkHWNeYjwi7dNJsK7kpDiKyyp5Z/0BR7av2p4mAkop5aFS0jIICfTjmokxjsUwIa4rw3qFk5KWgTE6/kBHpImAUkp5oOOlFbyz/gAzx/YlolOgY3GICMlJ8Ww9eJx1Wccci0O1HU0ElFLKA725JpuTFVXtUkmwKVeO70tYcIBeSthBaSKglFIexhhDSnomY2MiGB0T4XQ4hAUHcNX4vry/8SBHS8qdDke5mSYCSinlYdL3HWF3fjHzPOBoQI3kpHjKK6tZsibb6VCUm2kioJRSHiYlLYMuIQFcPqaP06F8Z2ivcCb168qC9Ayqq7XTYEeiiYBSSnmQ/KJSPt6Sy6yJsXQK8nc6nFMkJ8Wzv+AEX+0+7HQoyo00EVBKKQ+yeFUWFVXGkUqCTbloVC+6dw7SToMdjCYCSinlIaqqDQtXZjFlYHcG9ghzOpzTBAf4c21CLJ9ty+Ng4Umnw1FuoomAUkp5iOXb88k5dpIbPKiTYF3zEuMwwMKVWU6HotxEEwGllPIQKekZ9AwPZvqIaKdDaVBst1DOHdKDRSszqaiqdjoc5QaaCCillAfIOnKCL3Ye4vrJcQT6e/a/5huS4skvKuOzrXlOh6LcwLM/bUop5SMWpGfiJ8KcybFOh9KkqUN70jeyEynp2mmwI9BEQCmlHFZWWcXi1VlMG9aT3hGdnA6nSf5+wtzEOL7eXcDeQ8VOh6Naqc0SARH5j4jki8jmWtO6icinIrLL/tvVni4i8qyI7BaRjSIyodYyN9rz7xKRG9sqXqWUcsrSzbkcKSn3iHEFXDU7IZZAf2FBeqbToahWassjAi8BF9WZ9gCwzBgzGFhmPwa4GBhs324BngcrcQB+AyQCk4Hf1CQPSinVUaSkZRDfPZSzBkU5HYrLeoQHM2NkL15fncXJ8iqnw1Gt0GaJgDFmBXCkzuSZwMv2/ZeBK2tN/5+xpAGRItIbmAF8aow5Yow5CnzK6cmFUkp5re25x1m1/yjzEuPw8xOnw2mW5KR4jpdW8t7GA06HolqhvfsIRBtjDtr3c4Gaa2T6ArUvSs22pzU0XSmlOoSUtAyCAvy4dqLndxKsK7F/Nwb3DGOBVhr0agFObdgYY0TEbSNXiMgtWKcViI6OJjU11V2rBqC4uNjt61Sto23imbRdXHey0rBk1QkmRQewYdU3bbadtmyTyd0rWLCtnJfeWUa/CM8aG8HTecp3pb0TgTwR6W2MOWgf+s+3p+cAtdPhGHtaDjC1zvTU+lZsjJkPzAdISEgwU6dOrW+2FktNTcXd61Sto23imbRdXJeSlkFp1WbuuXIyE+LarvtTW7bJhNIK3nx8GdsqenDT1DFtso2OylO+K+19auBdoKbn/43AO7Wm/8C+eiAJKLRPIXwMXCgiXe1Oghfa05RSyqsZY0hJy2BE7y6Mj410OpwW6xISyJXj+/DOhhwKT1Y4HY5qgba8fHAh8C0wVESyReRHwJPABSKyC5huPwb4ENgL7AZeBG4DMMYcAX4PrLJvj9rTlFLKq63NPMr23CKSk+IR8a5OgnXNS4yntKKaN9dmOx2KaoE2OzVgjJnTwFPT6pnXAD9rYD3/Af7jxtCUUspxKWmZhAUHMHNcH6dDabVRfSMYFxvJgvRMbprSz+sTG1+jlQWVV/psax6Ltpdh5ZBKeZcjJeV8sPEgV0/oS+dgx/psu1VyUjy784tJ26sHbb2NJgLK6+zOL+KOhetYur+SDzflOh2OUs32+uosyquqvaqSYFMuG9ObiE6BpOilhF5HEwHlVUorqrj91XV0CvKnT2fhj0u3U1apVc2U96iuNixIz2Ryv24MiQ53Ohy3CQn059qJMXy8JZf846VOh6OaQRMB5VUe+2Ar23OL+MvsscwdHkTmkRO88q3+AlHeY8WuQ2QeOcG8pDinQ3G7eUnxVFYbXluV1fTMymNoIqC8xkebDpKSlskt5wzgvKE9GRUVwDlDevDssl0cO1HudHhKuSQlLZOosCAuGtXL6VDcrn9UZ84aFMXClZlUVWv/HW+hiYDyCllHTvDLNzYyNjaSey8c+t30hy8ZTnFZJc8u2+1gdEq5JufYST7fnsfshFiCAzpmFb7kpDgOFJby+fb8pmdWHkETAeXxKqqquWPhOgCemzOeoIDvP7ZDe4UzOyGWV9L2s/9wiVMhKuWSRSszMcCcyR3vtECN6cOjie4SrJ0GvYgmAsrjPfXJDtZnHePJq8cQ2y30tOd/ccEQAv39+NPH2x2ITinXVFRVs2hVFucN7Vnv57ijCPD3Y87kOKsvRMEJp8NRLtBEQHm01B35/POLvcxNjOPSMb3rnadnlxB+cs5APtyUy+r9eg2z8kyfbMnjUFEZyR2wk2Bd10+Kw0+EBSv1qIA30ERAeay846Xcs3gDw3qF8+vLRjQ6783n9KdneDCPfbBNiwwpj5SSlkHfyE6cO6Sn06G0uV4RIVwwPJrFq7IordDLez2dJgLKI1VVG37+2npOlFfx3NzxhAQ23rEqNCiAey8cyvqsY3yw6WA7RamUa3bnF/Ht3gLmJsbh7+cb5XeTk+I5eqKCjzbr99HTaSKgPNI/lu/mmz0F/O6KkQzq6VrRlWsmxjCsV7gWGVIeJyUtk0B/4bpJsU3P3EFMGdid/lGdSUnLdDoU1QRNBJTHWbnvCH/9bCczx/Xh2oQYl5fz9xMevnQ4WUdO8r9v9Nyk8gwnyit5Y202F43qTVRYsNPhtBs/P2FeYhxrMo6y7eBxp8NRjdBEQHmUoyXl3LVoHXHdQnn8qtHNHsXs7ME9OHdID/7++S6OlmiRIeW89zYcoKi0khs60LgCrpo1MYbgAD+9lNDDaSKgPIYxhvuWbORwcRl/nzOBsBaOyvZQTZGhz3e5OUKlmi8lLZMh0WFM6tfV6VDaXWRoEJeN6cPb63IoLqt0OhzVAE0ElMf479f7+WxbHg9ePJzRMREtXs/QXuFcNymWV77NYJ8WGVIO2pB1jE05hSQnxTf76FZHkZwUR0l5FW+ty3E6FNUATQSUR9iUXcgfPtrG9OHR/PDMfq1e388vGEJQgB9/WqpFhpRzUtIyCA3y56rxfZ0OxTHjYiMZ1bcLC9Iy9NJeD6WJgHJcUWkFty9cS1RYMH+eNcYtv5x6hofw03MH8tHmXFZpkSHlgMITFby38QAzx/UlPCTQ6XAcIyIkJ8azPbeINRlHnQ5H1UMTAeUoYwyPvL2ZrCMn+Nv14+naOcht6/7x2f2J7qJFhpQzlqzNprSi2icqCTblinF9CA8J4BXtNOiRNBFQjnp9dTbvrD/Az6cPYXL/bm5dd02RoQ1Zx3h/oxY1Ue3HGMOCtAzGx0Uysk/L+7t0FKFBAVwzIYaPNuVSUFzmdDiqDk0ElGN25xfx63c3M2Vgd247b1CbbOPqCTEM792FPy7drqVOVbv5Zk8Bew+XkJzoe5cMNmReYhzlVdUsXp3tdCiqDk0ElCNKK6r42YJ1dA4K4JnrxrVZ2VV/P+HhS4aTffQk//t2f5tsQ6m6UtIyiAwNbHCgLF80ODqcxP7deHVlBtXVeqrOkzQrERCRriIypq2CUb7j0fe3siOviL/MHkvPLiFtuq2zBkdx3tAe/P3z3RzRIkOqjeUdL+WTrXnMTohtcowMX5OcFE/WkZN8seuQ06GoWppMBEQkVUS6iEg3YC3woog83fahqY7qg40HeTU9k5+cO4CpQ9tnJLYHLxlOSVklzy7TIkOqbS1amUVVtWHuZO0kWNeMkb2ICgtmgXYa9CiuHBGIMMYcB64G/meMSQSmt21YqqPKOnKCB97YyLjYSO69cGi7bXdIdDjXTYojJU2LDKm2U1lVzcKVmZw9OIp+UZ2dDsfjBAX4cd2kGD7fnk/OsZNOh6NsriQCASLSG5gNvN/G8agOrLyymtsXrgOBv88ZT6B/+3ZR+fkFgwkO8OPJj7a163aV71i2PZ/c46Uk++C4Aq6aYx8pWZiuoxJ6Clf+Ez8KfAzsNsasEpEBgB5fVc321Cc72JB1jD9dM4bYbqHtvv2aIkMfb8lj5T4tMqTcLyUtg94RIUwb1j6nvLxRTNdQzh/Wk0WrsiivrHY6HIULiYAx5nVjzBhjzG32473GmGvaPjTVkSzfkc/8FXtJTorj4tHO9aT+8dkD6NUlhMc/2Ko9l5Vb7T9cwpe7DnP9pDgC2vlol7eZlxTP4eIyPt6S63QoCtc6C4aIyM9E5B8i8p+aW2s2KiJ3ichmEdkiInfb07qJyKcissv+29WeLiLyrIjsFpGNIjKhNdtW7S/veCn3LN7AsF7hPHLpCEdj6RTkz70zhrIhu5D3Nh5wNBbVsSxIz8DfT7h+cqzToXi8cwf3ILZbJx2e2EO4kra+AvQCZgBfADFAUUs3KCKjgJuBycBY4DIRGQQ8ACwzxgwGltmPAS4GBtu3W4DnW7pt1f6qqg13L1rPyfIqnps7wSMup7p6fF9G9O7Cn5bu0CJDyi1KK6p4fU02F46IJrqNL4ftCPz8hLmT40nfd4RdeS3enSg3cSURGGSM+RVQYox5GbgUSGzFNocD6caYE8aYSqzk4mpgJvCyPc/LwJX2/ZlYVysYY0waEGl3XlRe4P+W7+bbvQU8OnMkg3qGOR0OYP0TeuTS4eQcO8nL3+x3OhzVAXyw8SDHTlRwg3YSdNnshBiC/P1YoJ0GHedKIlBh/z1m/5qPAFrTE2YzcLaIdBeRUOASIBaINsbUFITPBaLt+32BrFrLZ9vTlIdL31vAM5/t5MpxfZg1McbpcE4xZVAU5w/ryXPLtciQar2U9AwG9OjMGQO7Ox2K1+geFszFo3vxxppsTpRXOh2OTwtwYZ759vn6R4B3gTDgVy3doDFmm4j8EfgEKAHWA1V15jEi0qyeXCJyC9apA6Kjo0lNTW1piPUqLi52+zo7sqJyw6+/PkmPTsKMqGN88cUXbt9Ga9tkWlQ1qTsq+eXLy0keEey+wHycr31XMo5XsS6zlDnDgtrkc+4OntomI4OreKeskj+/tpxzY31vqGZPaRdXEoFlxpijwApgAICI9G/NRo0x/wb+ba/rCaxf+Xki0tsYc9A+9J9vz56DdcSgRow9re465wPzARISEszUqVNbE+JpUlNTcfc6OypjDD9+eTUllaW8cssURvVtm9HX3NEmWys28dqqLB66NoEBPTzj1IW387XvyoNvbiIkMJsHZk8lItQzd2ae2ibnGsMb+79k1THh18lnIdI2Y454Kk9pF1dODbxRz7QlrdmoiPS0/8Zh9Q94Fetow432LDcC79j33wV+YF89kAQU1jqFoDzQf77ez7Lt+Tx4ybA2SwLc5e7pQ+wiQ9udDkV5oaLSCt5Zn8PlY/p4bBLgyUSE5DPi2ZxznA3ZhU6H47MaTAREZJiIXANEiMjVtW43Aa3tFvuGiGwF3gN+Zow5BjwJXCAiu7BKGD9pz/shsBfYDbwI3NbKbas2tDH7GE9+tI0LRkRz05R+TofTpB7hwdw6dSCfbM0jfW+B0+EoL/PWuhxOlFdpJcFWuGp8XzoH+fPKt3opoVMaOzUwFLgMiAQurzW9COvyvxYzxpxdz7QCYFo90w3ws9ZsT7WPotIK7li4jh5hwfx51hivOcz3o7MGsCA9kyc+3MZbt52JXxsNiaw6FmMMKWkZjO4bwdjYSKfD8VphwQFcOb4vS9Zk86vLhhMZGuR0SD6nwSMCxph3jDE/BC4zxvyw1u1OY8w37Rij8gLGGB56azPZR0/y7JzxXvVl7hTkz70XapEh1Twr9x1hZ14xyUk6ymBrJSfFU1ZZzZI12U6H4pNc6SNwlT0McaCILBORQyKS3OaRKa+yeHUW7204wM+nDyahXzenw2m2q8b3ZWQfLTKkXJeSnkl4SACXj+3jdCheb3jvLkyM78qC9Ewt/e0AVxKBC+1hiC8D9gODgPvaMijlXXbmFfGbd7dw5qDu3Dp1kNPhtIifn/DwJVaRof9+vd/pcJSHO1RUxtLNB5k1MYbQIFcuvlJNSU6KY9/hEr7Zo3112psriUBNV9hLgdeNMdq1U32ntKKK219dS1hwAH+9bhz+Xnx+fcqgKKYN68k/lu+moLjM6XCUB1u8OouKKsO8RO0k6C4Xj+pN19BAHX/AAa4kAu+JyHZgIrBMRHoApW0blvIWv3tvKzvzivnL7HH0DPf+GusPXjKMExVVPLtMR9pW9auqNryanskZA7p7TNnsjiAk0J/ZCbF8ui2P3ELdxbQnV4YhfgCYAiQYYyqwqgHObOvAlOd7f+MBFq7M5KfnDuTcIT2cDsctBvUMZ87kWBakZ7LnULHT4SgP9MXOfHKOndRLBtvA3MQ4qqoNi1bp+APtydVBs/sA14jID4BZwIVtF5LyBpkFJ3jwjU1MiIvknguHOB2OW909fQghgf5aZEjVKyUtkx7hwVw4MrrpmVWzxHfvzLlDerBwZSYVVdVOh+MzmkwEROQ3wN/t23nAn4Ar2jgu5cHKK6u5Y+FaROBv148n0N/VfNI7RIVZRYY+3ZpHmhYZUrVkHTnB8h35XD8ptsN97j1FclI8ecfLWLYtz+lQfIYrn+RZWIV+cu26AmOxRiBUPurPH29nQ3Yhf5o1hthuoU6H0yZ+dFZ/ekeE8PgH2/RyJvWdhSszEWDOZK0d0FbOH9aTPhEhpKTp6YH24koicNIYUw1UikgXrMGAYptYRnVQn2/P48Uv93FDUjwXjertdDhtJiTQn/tmDGVTTiHvbtAiQwrKKqt4bVUW5w+Lpk9kJ6fD6bD8/YQ5k+P4avdh9h0ucTocn+BKIrBaRCKx6vyvAdYC37ZlUMoz5RaWcu/rGxnWK5yHLx3udDht7spxfRnVtwt//liLDClYujmXgpJyrSTYDq6bHEuAn7BALyVsF65cNXCbMeaYMeYF4ALgRvsUgfIhVdWGu19bx8nyKp6bO4GQQH+nQ2pzfn7CQ3aRof98vc/pcJTDFqRlEtctlHMGd4wrZDxZz/AQZozsxetrsjUJbweNjT7oLyJhtR4nAXFApIiEt0dwynP8/fNdpO09wu+vHOVT105PGRjF9OE9+cfyPVpkyIftyC1i5f4jzEuM00Gp2sm8pDgKT1bw/kYddb6tNXZE4I+cOuTvQqzSwr8CHmnLoJRnSdtbwLPLdnH1+L7MmhjjdDjt7oGLh3Oyooq/aZEhn7UgPYOgAD+uTdDuUe3ljAHdGdijs1YabAeNJQLTgKdrPT5mjLkcq4bAmW0alfIYR0rKuWvROuK7d+bRK0c5HY4jBvUMY+7kOBakZ7I7X4sM+ZqSskreXJvDpaN7062z94yq6e1EhHmJ8azPOsbmHK1s35YaSwT8jDGVtR7fD2CMMYDvHBv2YcYY7n19A0dLKnhu7njCgn13cJW7pg+mkxYZ8knvrD9AcVmldhJ0wDUTYwgJ9GNBuh4VaEuNJQJBtfsCGGM+ARCRCMD7i8qrJv37q318vj2fhy8dzsg+vl06IiosmNvOG8hn2/L4VkdH8xnGGFLSMhjWK5wJcV2dDsfnRHQKZObYvry97gDHSyucDqfDaiwReBF4TUS+S4NFJB6rr8C/2jow5awNWcf449LtXDgimh+coTXVAf7fmf3pG9mJxz/cqkWGfMTazGNsPXic5KR4RLSToBOSk+I5WVHFm2uynQ6lw2owETDGPA28C3wlIgUiUgCsAN4zxjzVXgGq9ne8tII7Fq6jZ3gIf5o1Rv8B2mqKDG3OOc47G3KcDke1gwVpGXQO8ufK8X2dDsVnjY6JYGxMBCnpmVhnppW7NVpHwBjzgjEmDugH9DPGxBtjnm+XyJQjjDE89OYmco6d5Nk544gM1c5RtV0xtg+j+0bw56VaZKijO1pSzvubDnLVhL4+3T/GE8xLimd3fjHp+444HUqH5NKoGcaYImNMUVsHo5z32qos3t94kF9cMISJ8d2cDsfj+PkJD186nAOFpfz7Ky0y1JG9viaL8spqHW7YA1w+pg9dQgL0UsI2osNnqe/szCvit+9t4axBUdx67kCnw/FYSQO6c8GIaJ5P3cNhLTLUIVVXGxakZzKpX1eG9eridDg+r1OQP7MmxvLxllwOFel3zt00EVAAnCyv4mcL1hIWHMDT143V6mlNeODiYZysqOKZz3Y6HYpqA1/tPkxGwQk9GuBB5iXFUVFlWLw6y+lQOpwmEwERubbmMkIReURE3hSRCW0fmmpPj76/hV35xfz1unH0DNerQ5sysEcY8xLjWLgyi935etaso0lJy6B75yAuGtXL6VCUbWCPMKYM7M6r6ZlU6VU7buXKEYFfGWOKROQsYDrwb0A7DHYg7204wMKVWdw6dSBn64AqLrtr2mBCtchQh3Ow8CSfbcvj2oRYggM6/uBa3iQ5KZ6cYydZvj3f6VA6FFcSgZqu0ZcC840xHwDalbyDyCgo4cE3NzEhLpJfXDDE6XC8SvewYG47bxCfbcvnmz2HnQ5HucnClVkYYF6iVhL0NBeMiKZneDApWmnQrVxJBHJE5J/AdcCHIhLs4nLKw5VXVnPHwnX4CTw7ZzyB/tqszfXDM/tZRYY+2KZFhjqAiqpqFq3M5NwhPYjtFup0OKqOQH8/rp8cxxc7D5F15ITT4XQYrvznnw18DMwwxhwDumGNQthiIvJzEdkiIptFZKGIhIhIfxFJF5HdIvKaiATZ8wbbj3fbz/drzbbV9/60dDsbswv506yxxHTVf3otERLozy8vGsqWA8d5e70WGfJ2n27NI7+ojORE7SToqeZMjsVPhAXpmU6H0mE0mQgYY04YY94ECu1yw4FAi0+Kikhf4E4gwRgzCvAHrsca9vivxphBwFHgR/YiPwKO2tP/as+nWunz7Xn866t9/OCMeO0Q1UqXj+nDmJgI/vzxDk6Wa5Ehb5aSlkHfyE6cN6yn06GoBvSO6MS0YT1ZvDqLskr9vrmDK1cNXCEiu4B9wBf2349aud0AoJOIBAChwEHgfGCJ/fzLwJX2/Zn2Y+znp4nWvG2Vg4UnuWfxBkb07sJDlwx3Ohyv5+cnPHzJcA4WlvKfr7XIkLfac6iYb/YUMDcxDn+9fNajJSfFc6SknKWbc50OpUNw5dTA74EkYKcxpj/WlQNpLd2gMSYHeArIxEoACoE1wLFawx5nAzXFvfsCWfaylfb83Vu6fV9XVW24a9F6yiqr+fvc8YQEaq9od0gc0J0LR0Tzj+W7teCJl1qQlkmgvzA7IdbpUFQTzhoURXz3UK006CauFNCuMMYUiIifiPgZY5aLyDMt3aCIdMX6ld8fOAa8DlzU0vXVWu8twC0A0dHRpKamtnaVpyguLnb7Op3w1q5yVu6r4ObRQWRtWY03l+bwtDY5v3s1y7ZV8cv/pXLjyGCnw3GMp7WLK8qqDIvSTzC+hz9b1nzrdDhu541t0pSkqEpe23GCV977nNhw7+zo7Cnt4koicExEwrBGHlwgIvlASSu2OR3YZ4w5BCAibwJnApEiEmD/6o8Banpe5QCxQLZ9KiECOG1AeGPMfGA+QEJCgpk6dWorQjxdamoq7l5ne/t2TwHvfZzG1RP68vDscU6H02qe2Cbbq7bwSloGD82ayODocKfDcYQntktTFq/O4kTlRn5xxSSSBnS8A47e2CZNGTupnLf+sIydVT25Yeoop8NpEU9pF1fSqJnASeDnwFJgD3B5K7aZCSSJSKh9rn8asBVYDsyy57kReMe+/679GPv5z42ORdlsBcVl3P3aOvp178zvZ3rnl8Yb3KlFhrzSgrQMBvcMI7G/DrTlLbp2DuKy0b15c202xWWVTS+gGuTKVQMlxpgqY0ylMeZlY8yzxpjTfpG7yhiTjtXpby2wyY5hPnA/8AsR2Y3VB+Df9iL/Brrb038BPNDSbfuq6mrDva9v4OiJCv4+dzyddUjVNtOtcxA/O38Qy7bn881uLTLkDTZmH2NDdiHzEuPQfsjeJfmMeErKq3h7nV662xoNJgIi8pX9t0hEjte6FYnI8dZs1BjzG2PMMGPMKGPMDcaYMmPMXmPMZGPMIGPMtcaYMnveUvvxIPv5va3Zti/691f7WL7jEI9cOpyRfSKcDqfDu2mKVWToMS0y5BVS0jLoFOjP1RNjnA5FNdP42EhG9O5CSloGeqC45RpMBIwxZ9l/w40xXWrdwo0xOi6nl1ifdYw/Lt3OjJHR3KAjqbWLmiJDWw8e5y39peLRCk9U8O6GA8wc14cuIYFOh6OaSURITopne24RazOPOh2O12r01ICI+IuInuz0UsdLK7hj4Vqiu4Twp2vG6mHPdnT5mD6M1SJDHu+NtdmUVlTrcMNebOa4PoQFB5CSppUGW6rRRMAYUwXssCsKKi9ijOHBNzdx4Fgpz84ZR0So/tppT35+wsOXjiD3eCn//krPZnkiYwwL0jMYFxvJqL56ysxbdQ4O4OoJfflg40GOlJQ7HY5XcuWqga7AFhFZJiLv1tzaOjDVOgtXZvHBxoPcc+EQJsZrT2gnTO7fjRkjo3k+dQ/5RaVOh6Pq+HZvAXsOlejRgA4gOSme8qpqXl/tzZVRnONKIvAr4DLgUeAvtW7KQ+3ILeJ3723h7MFR/PScgU6H49Puv2gYZZXVPPPZLqdDUXUsSMskolMgl43p7XQoqpWGRIczuV83Xl2ZqR10W8CVywe/wBpkKNy+bbOnKQ90sryK219dS3hIIE/PHoef1kx31IAeYSQnxbNoZSa78oqcDkfZ8o+X8vGWXK6dGKNltjuIeUlxZBSc4Eu9bLfZXBl0aDawErgWa0jidBGZ1fhSyim/fXcLuw8V88x14+gR7rtlbj3JndMG0zk4gCc+3OZ0KMr22qosKqsN8/S0QIdx0ahedO8cxCvf6vgDzeXKqYGHgUnGmBuNMT8AJmOdLlAe5p31Oby2Oovbpg7krMFRToejbN06B3H7eYNYvuMQX+3SXytOq6yqZuHKTM4aFEX/qM5Oh6PcJDjAn+smxfL59jxyjp10Ohyv4koi4GeMya/1uMDF5VQ72n+4hIff2szE+K78fPoQp8NRddw4pR8xXTvx+IfbqNJzmI76fHs+BwpLSU7Si6E6mjmT4zDAopV6KWFzuLJDXyoiH4vITSJyE/AB8GHbhqWao6yyijsWrsPfT3h2zngC/DVP8zRWkaFhbDt4nDfXZjsdjk9LSc8kuksw04dHOx2KcrPYbqGcN7Qni1ZlUVFV7XQ4XsOVzoL3YY0FMMa+zTfG3N/WgSnX/fGjHWzKKeRPs8bQN7KT0+GoBlw+pjfjYiN56hMtMuSUjIISVuw8xJzJcZowd1DJSXEcKirjky15TofiNVz6Jhhj3jDG/MK+vdXWQSnXfbY1j/98vY+bpvRjxsheToejGiEiPHzpcPKOl/GvL7XIkBNeTc/E30+4fpKeFuiozh3Sk76RnUhJ006Drmps0KG6gw25bdAh5R4HC09y75INjOjdhQcuHuZ0OMoFk/p146KRvXj+Cy0y1N5KK6pYvDqLC4ZH0ysixOlwVBvx9xPmJsbx7d4CducXOx2OV2hs0KG6gw3poEMepLKqmrsWrqe8sprn5o7Xa6G9yAMXD6O8spq/fqpFhtrTR5sPcvREhVYS9AHXTYol0F9YkK5HBVzh8kkyEekpInE1t7YMSjXt2c93s3L/ER6/ahQDeoQ5HY5qhn5RnbnhjHheW5XJTi0y1G5S0jLpH9WZKQO7Ox2KamNRYcFcNKo3S9Zkc6K80ulwPJ4rBYWuEJFdwD7gC2A/8FEbx6Ua8c2ew/z9811cMyGGq8brGOre6M7zBxOmRYbazbaDx1mTcZR5iXFabdNHJCfGUVRayXsbDjgdisdz5YjA74EkYKcxpj8wDUhr06hUgw4Xl3H3ovX0j+rMozNHOh2OaqGunYO44/zBpO44xJe7DjkdToeXkpZBcIAfsyZq4uwrJvfvxpDoMB2e2AWuJAIVxpgCwE9E/Iwxy4GENo5L1aO62nDP4g0cO1nB/82dQOfgAKdDUq3wgynxVpGhD7TIUFsqKq3grXU5XDamD5GhQU6Ho9qJiJCcFM+mnEI2ZB1zOhyP5koicExEwoAVwAIR+RtQ0rZhqfr866u9fLHzEL+6bATDe2t/TW8XHODP/RcNY3tuEW9okaE28/a6HE6UV2klQR901fi+hAb566WETXAlEZgJnAB+DiwF9gCXt2VQ6nTrMo/yp6U7uHhUL5IT9R9aR3GZXWToL5/s0E5NbcAYQ0paJqP6dmFcbKTT4ah2Fh4SyMxxfXlv4wEKT1Q4HY7HaqyOwCAROdMYU2KMqTbGVBpjXgbWApHtFqGi8GQFdyxcR3SXEJ68Zgwi2tmpoxARfnWZVWToxRX7nA6nw1mdcZQdeUUkJ8br98ZHJSfFUVpRzRI96tagxo4IPAPUVzio0H5OtQNjDA+9uYmDhaX8fe54IjoFOh2ScrOJ8d24ZHQv/rliD/nHtciQO6WkZRAeEsAV4/o4HYpyyMg+EYyPi2RBegbGaF+c+jSWCEQbYzbVnWhP69dmEalTvLoykw82HeTeC4cyIa6r0+GoNvLLGcOoqKrmr5/tdDqUDqOguIyPNuVyzYQYQoO0Y60vS06MZ++hEr7dU+B0KB6psUQgspHndGSbdrA99ziPvreVc4b04CfnDHA6HNWG+kV15oakfry2KosduVpkyB0Wr86mvKqaedqnxuddOqY3kaGBvKKdBuvVWCKwWkRurjtRRH4MrGm7kBTAifJKbn91HV06BfL07LFaBMUH3DltkBYZcpOqasOrKzNI7N+NwdHhToejHBYS6M+1E2P4ZGseeXr67TSNJQJ3Az8UkVQR+Yt9+wL4EXBXu0Tnw3777hb2HCrmmevGERUW7HQ4qh1EhgZx57TBfLHzECt2apGh1lix8xBZR07quALqO/MS46mqNixameV0KB6nsUGH8owxU4DfYZUV3g/8zhhzhjEmt33C803vrM9h8epsfjZ1EGcOinI6HNWObjgjnrhuoTzxoRYZao2UtAyiwoJ1aG71nX5RnTl7cBQLV2ZSWVXtdDgepck6AsaY5caYv9u3z1u7QREZKiLra92Oi8jdItJNRD4VkV323672/CIiz4rIbhHZKCITWhuDJ9t3uISH3tzEpH5duXv6YKfDUe3slCJDa/Ryp5bIPnqCz3fkc92kGIICXB5XTfmA5KR4co+Xsmx7vtOheJR2/5YYY3YYY8YZY8YBE7GKFb0FPAAsM8YMBpbZjwEuBgbbt1uA59s75vZSVlnFHQvXEuDvx9+uH0+Av/4T80WXjO7F+LhIntIiQy2ycGUmAsyZrJ0E1ammDetJ74gQrTRYh9N7mmnAHmNMBlYFw5ft6S8DV9r3ZwL/M5Y0IFJEerd7pO3gyY+2sznnOE9dO5Y+kXphhq8SER65dDj5RWXMX7HX6XC8SnllNa+tyuL8YT2J6RrqdDjKwwT4+3H9pDi+3HWY/Ye1Un4NpxOB64GF9v1oY8xB+34uEG3f7wvU7t2RbU/rUD7dmsd/v97PTVP6ccGI6KYXUB3axPhuXDq6N//8Yq8WGWqGj7fkcri4nHnaSVA14PrJsfj7Ca+u1FEJazhWZUNEgoArgAfrPmeMMSLSrJ5SInIL1qkDoqOjSU1NdUeY3ykuLnb7OmsUnKzm19+cJL6LH2eG5ZOaqj3GXdGWbeIJzu1azdLKKu793xf8v1Hec+WIk+3yXPpJenQSzIEtpB7c6kgMnqijf1eaa3wPPxZ8u5eE4FyC/J27NNtT2sXJclsXA2uNMXn24zwR6W2MOWgf+q/pzZEDxNZaLsaedgpjzHxgPkBCQoKZOnWqW4NNTU3F3esEqKyqZs6LaSDlvHTL2fSP6uz2bXRUbdUmnmRH9Vb++/U+Hpp1BsN6eceIk061y668InYsXcH9Fw3j/KkD2337nswXvivNERhzmHn/SqcocjDXTIxxLA5PaRcnTw3M4fvTAgDvAjfa928E3qk1/Qf21QNJQGGtUwhe72/LdrFq/1Eev2q0JgHqNHecP4jwkECe+HC706F4vAXpmQT5+zE7wbl/7Mo7TBnYnQFRnUlJ106D4FAiICKdgQuAN2tNfhK4QER2AdPtxwAfAnuB3cCLwG3tGGqb+nr3YZ5bvptrJ8Zw5fgO1+1BuUFkaBB3nD+IFTsP8YUWGWpQSVklb6zJ5uLRveiuBbhUE0SEuYlxrMs8xpYDhU6H4zhHEgF7aOPuxpjCWtMKjDHTjDGDjTHTjTFH7OnGGPMzY8xAY8xoY8xqJ2J2t8PFZdz92noGRHXmdzNHOh2O8mA/OKMf8d1D+YMWGWrQuxsOUFRWqZUElcuunRhLSKAfKWnaadDpqwZ8UnW14ReLN1B4soLn5k7QkdFUo4IC/L4rMrRkjZZHrcsYQ0paBkOjw0mI1xE6lWsiQgO5fEwf3lmfQ1FphdPhOEoTAQfM/3IvK3Ye4teXjWB4b+/oAKacdfGoXkyM78pTn+ykpEyLDNW2PusYWw4cJ/mMeER0cC7luuSkeE6UV/HWutP6n/sUTQTa2drMozz18Q4uGd1Lh0dVLhMRHr50OIe0yNBpUtIy6Rzkz1Xaz0Y109jYSEb3jSAlLQNjfPe0myYC7ajwZAV3LlxHr4gQ/nD1GP31opplQlxXLh3Tm/kr9upQqrZjJ8p5f+MBrhzfl7BgPcWmmi85KY6decWs2n/U6VAco4lAOzHG8MAbG8ktLOXvc8YT0SnQ6ZCUF7p/xjCqqg1/+WSH06F4hCVrsimrrNZOgqrFLh/bh/CQAF7x4fEHNBFoJwvSM/locy73zRjK+Djt0KRaJq57KDdOief1NdlsO3jc6XAcVV1tWJCeycT4rtrXRrVYaFAA10yIYenmgxwqKnM6HEdoItAOth08zqPvb+WcIT24+ewBToejvNzt5w2mS0ggT3y4zelQHPXNngL2HS4hOUn72qjWSU6Ko6LKsHi1b16Vo4lAGztRXsntr64lslMgT88ei5+f9gtQrRMRGsid0wbz5a7DpO7w3XHVX0nbT9fQQC4e1SEHI1XtaFDPcJIGdOPV9EyfrNWhiUAb+/U7W9h7uIRnrhtHlFY8U25yQ1I88d1DeeLDbVRWVTsdTrvLLSzls235zE6IJSTQ3+lwVAdwQ1I/co6d5IudvpdcayLQht5al82SNdncft4gpgyKcjoc1YEEBfjxwEXD2JlXzJI12U6H0+4WrrR+uc3VS3CVm1w4Mpoe4cE+WWlQE4E2su9wCY+8tZnJ/bpx17TBToejOqCLRvUiIb4rf/nUt4oMVVRVs2hVJucO6UF8dx2oS7lHoL8f10+KZfmOfLKOnHA6nHaliUAbKKus4vZX1xIY4Mff5owjwF/fZuV+tYsM/dOHigwt25ZH3vEyvWRQud2cyXEI1hEnX6J7qDbwhw+3s+XAcZ6aNZbeEZ2cDkd1YOPjunLZmN7MX7GH3ELfKDKUkpZJn4gQzh/W0+lQVAfTJ7IT5w+LZvHqLMoqq5wOp91oIuBmn2zJ5aVv9vPDM/sxfUS00+EoH3D/RcOorsYnigztO1zCV7sPM2dyHP56BY5qA8lJcRwuLmfp5lynQ2k3mgi4Uc6xk9y3ZCOj+nbhgYuHOR2O8hGx3UK56cx+LFmbzdYDHbvI0IK0DAL8hOsmxzodiuqgzhncg7huoSzwoU6Dmgi4SWVVNXctXEdlVTXPzZlAcIBe0qTaz8+mDiKik1VkqKMOnlJaUcXra7KZMbIXPcNDnA5HdVB+fsLcxDhW7j/Cjtwip8NpF5oIuMkzn+1idcZRnrh6NP2itCezal8RoYHcNW0wX+0+TOrOQ06H0ybe23CAwpMVzNNKgqqNXTsxhiB/Pxak+8b4A5oIuMFXuw7zf6m7mZ0Qw8xxOhSqcsa8xHj6dQ/liQ86ZpGhlPRMBvbozBkDujsdiurguocFc+mY3ry5NscnLs3VRKCVDhWVcfdr6xnYI4zfXjHS6XCUDwsK8OOBi4exK7+Yxas7VpGhzTmFbMg6xrzEeB2+W7WL5KQ4issqeWf9AadDaXOaCLRCdbXhF4vXU1RawXNzxxMapOOhK2fNGNmLSf268vSnOynuQL9kUtIyCAn045qJMU6HonzEhLiuDOsVTkpaRoftd1NDE4FW+OeKvXy56zC/vnwEw3rpMKjKeSLCQ5cM53BxGfO/2ON0OG5xvLSCd9YfYObYvkR0CnQ6HOUjRITkpHi2HjzOuqxjTofTpjQRaKE1GUd56pMdXDq6N3Mna+cl5TnGx3Xl8rF9mP/lXg4WnnQ6nFZ7c002JyuqtJKgandXju9L5yB/UtI6dqdBTQRaoPBEBXcuXEfviBD+cM1oPWepPM4vZwy1iwztdDqUVjHGkJKeydiYCEbHRDgdjvIxYcEBXDWhL+9vPMjRknKnw2kzmgg0kzGG+9/YSN7xUp6bO4EuIXqoUnme2G6h/PDMfryxNpstBwqdDqfF0vcdYXd+MfP0aIBySHJSPOWV1by+JsvpUNqMJgLNlJKWwdItufzyoqGMi410OhylGnTbeYOI9PIiQylpGXQJCeDyMX2cDkX5qGG9upAQ35UF6ZlUV3vn96gpmgg0w9YDx/n9B9uYOrQHPz5rgNPhKNWoiE5WkaGvdxeQusP7igzlF5WydHMusybG0ilIK3Uq5yQnxZNRcIKvdh92OpQ2oYmAi0orDbcvXEtkp0D+cu1Y/HTAE+UF5ibG0z+qM0986H1FhhavyqKy2mglQeW4i0f3olvnoA7baVATARe9srWcfYdLeOb6cXQPC3Y6HKVcUrvI0GurveccZ1W1YeHKLM4c1J2BPcKcDkf5uOAAf2YnxPLZtrwOcSVOXY4kAiISKSJLRGS7iGwTkTNEpJuIfCoiu+y/Xe15RUSeFZHdIrJRRCa0d7xvrs3m6wOV3HH+YKYMjGrvzSvVKheOiGZyv2781YuKDC3fnk/OsZMkJ2onQeUZ5iXGYYCFK70noXaVU0cE/gYsNcYMA8YC24AHgGXGmMHAMvsxwMXAYPt2C/B8ewaad7yUR97ezNCuftx5/qD23LRSbiEiPHzpcA4Xl/NPLykylJKeQc/wYKaPiHY6FKUA60qcc4f0YNHKTCq87DRbU9o9ERCRCOAc4N8AxphyY8wxYCbwsj3by8CV9v2ZwP+MJQ2IFJHe7RVvz/BgfnfFSH4yNpgAfz2TorzT2NhIZo7rw4teUGQo68gJvth5iOsnxxGo3znlQZIT48kvKuPTrXlOh+JWThTH7w8cAv4rImOBNcBdQLQx5qA9Ty5Q81OgL1D7WEy2Pe1grWmIyC1YRwyIjo4mNTXVbQH3AIorT7h1nar1iouLtU2a4awu1XxQVc29L6/g5jFt18+lte2yeEc5AvSryiY1teMP+NIe9LviHn7G0D1E+PtH6wkt2NHq9XlKuziRCAQAE4A7jDHpIvI3vj8NAIAxxohIsy7YNMbMB+YDJCQkmKlTp7opXEtqairuXqdqHW2T5tst25i/Yi8PXpPEqL5tU6mvNe1SVlnFL778nOnDo7n6ogT3BubD9LviPj80u3jqk53EjkxodUdWT2kXJ467ZQPZxph0+/ESrMQgr+aQv/03334+B4ittXyMPU0p1Uw/8/AiQx9tyuVISbmOK6A81uxJsQT4CQvSMp0OxW3aPREwxuQCWSIy1J40DdgKvAvcaE+7EXjHvv8u8AP76oEkoLDWKQSlVDN0CQnk7ulD+GZPAct35De9QDtLScsgvnsoZw3Sq3OUZ+oZHsKMUb1YsiaLk+VVTofjFk71xLkDWCAiG4FxwBPAk8AFIrILmG4/BvgQ2AvsBl4Ebmv3aJXqQOYmxjEgqjNPfLjdo4oMbc89zuqMo8xLjNOCXcqj3ZAUz/HSSt7b2DH6sDiSCBhj1htjEowxY4wxVxpjjhpjCowx04wxg40x040xR+x5jTHmZ8aYgcaY0caY1U7ErFRHEehvFRnanV/MolWec010SloGQQF+XDsxtumZlXJQYv9uDO4ZxoIOUmlQr81RygddMCKayf278cxnOykqrXA6HIrLKnlrbQ6XjelN185BToejVKNEhHmJcWzILmRTtveO7llDEwGlfJCI8IhdZOgFDygy9Pa6HErKq7SToPIaV0+MoVOgf4cYf0ATAaV81JiYSK4c14d/fbmPA8ecKzJkjCElLYMRvbswXof2Vl6iS0ggM8f14Z0NORSecP6oWmtoIqCUD7t3xlAM8NQnrS+O0lJrM4+yPbeI5KR4RLSToPIeyUnxlFZU88babKdDaRVNBJTyYTFdQ/nRWf15c20Om3OcOdeZkpZJWHAAM8f1cWT7SrXUqL4RjI2NZEF6hkfW5XCVJgJK+bhbpw6kW+cgHvtga7v/MysoLuODjQe5ekJfOgc7UehUqdZJToxjz6ESvt1b4HQoLaaJgFI+zioyNJi0vUf4fHv7Fhl6fU025VXV2klQea3Lx/YholOgV1ca1ERAKcWcyXEM6NGZJz7c1m5DrFZXG15Nz2Ryv24MiQ5vl20q5W4hgf7MmhjDx1tyyT9e6nQ4LaKJgFKKQH8/Hrx4OHsOlbRbkaEVuw6ReeQEyWfo0QDl3eYlxlFZbXjNgwp0NYcmAkopAKYP70li/24882n7FBlKScskKiyIi0b2avNtKdWWBvQI46xBUSxcmUlVtfd1GtREQCkF1BQZGkFBSTnPp7ZtkaGcYyf5fHsesxNiCQrQf0PK+yUnxXGgsLTd+9m4g34DlVLfGR0TwVXj+/Lvr/aR04ZFhhatzMRg9U1QqiOYPjya6C7BvOKFlQY1EVBKneLeGdYI4X/5uG2KDFVUVbNoVRbnDe1JbLfQNtmGUu0twN+P6yfFsWLnITIKSpwOp1k0EVBKnaJvZCeryNC6nDYZUOWTLXkcKiojOUmPBqiOZc7kOPz9hFfTvetSQk0ElFKnuXXqQLp3DuLxD91fZOiVtP30jezEuUN6unW9SjmtV0QI04f3ZPHqLEorqpwOx2WaCCilThMeEsjdFwwhbe8Rlm1zX+en3flFpO09wtxE65eTUh1NclI8R09U8NHmg06H4jJNBJRS9bp+UiwDe3TmiY/cV2QoJS2TQH/hukmxblmfUp7mzIFR9OseSooXVRrUREApVa+aIkN7D5WwaGXr/6mdKK/kjbXZXDyqN1FhwW6IUCnP4+cnJCfFsybjKNsOHnc6HJdoIqCUatC04T05Y0B3/vrZLo63ssjQexsOUFRaqeMKqA5v1sQYggP8SPGSSwk1EVBKNUhEePjS4RxxQ5GhlLRMhkSHMalfVzdFp5RnigwN4rIxfXhrXU67VOlsLU0ElFKNGtU3gqtbWWRoQ9YxNuUUkpwUj4h2ElQdX3JSHCfKq3h7XY7ToTRJEwGlVJPunTEUAf68dHuLlk9JyyA0yJ+rxvd1b2BKeahxsZGM7NOFlLRMt1+C626aCCilmtQnshM/Prs/b68/wMbsY81atvBEBe9uOMDMcX0JDwlsmwCV8jAiVqfBHXlFrM446nQ4jdJEQCnlkp+eO5CosCAe/2Bbs37hvL4mi7LKaq0kqHzOzHF9CA8O8PhOg5oIKKVcEh4SyN3Th5C+7wifbs1zaRljDK+mZzI+LpKRfSLaOEKlPEtoUABXT+jLR5tyKSguczqcBmkioJRyWU2RoSc/2u5SkaFv9hSw93AJyYl6yaDyTfOS4imvqmbx6mynQ2mQJgJKKZcF+Pvx0CXD2Xu4hIUuFBlKScsgMjSQS8f0bofolPI8Q6LDSezfjVdXZlBd7ZmdBh1JBERkv4hsEpH1IrLantZNRD4VkV323672dBGRZ0Vkt4hsFJEJTsSslLKcP6wnUwZ255kmigzlHS/lk615zE6IJSTQvx0jVMqzJCfFk3XkJF/sOuR0KPVy8ojAecaYccaYBPvxA8AyY8xgYJn9GOBiYLB9uwV4vt0jVUp9R0R46JLhHD1Rzj+WN1xkaNHKLKqqDXMnaydB5dtmjOxFVFgwKd96ZqdBTzo1MBN42b7/MnBlren/M5Y0IFJE9DijUg6yigzF8J+v95F99MRpz1dWVbNwZSZnD46iX1RnByJUynMEBfhx3aQYPt+RX+/3xWlOJQIG+ERE1ojILfa0aGNMzbiNuUC0fb8vkFVr2Wx7mlLKQffOGGIVGfp4x2nPLdueT+7xUh1XQCnbHPvImCt9a9pbgEPbPcsYkyMiPYFPReSUcmXGGCMizepVYScUtwBER0eTmprqtmABiouL3b5O1TraJs67MM6fd9YfYGynIwyIsPoBFBcX88Kq9XQLEQLytpF6qGXVCJX76HfFM4yJ8ueVr/cwPvAgAX7iMe3iSCJgjMmx/+aLyFvAZCBPRHobYw7ah/7z7dlzgNqDl8fY0+qucz4wHyAhIcFMnTrVrTGnpqbi7nWq1tE2cV7CGZV8++flLM0N5bUrkhARFn3wOZsLTvLz6UOYdv5gp0NU6HfFU5he+fzwpVWc7D6Uy8f28Zh2afdTAyLSWUTCa+4DFwKbgXeBG+3ZbgTese+/C/zAvnogCSisdQpBKeWgsOAAfn7BEFbuO8IndpGh1KwK/P2E6yfHNrG0Ur7lnCE9iOnayeMqDTrRRyAa+EpENgArgQ+MMUuBJ4ELRGQXMN1+DPAhsBfYDbwI3Nb+ISulGnJdQiyDe4bx5EfbKS6r5MucSi4cEU10lxCnQ1PKo/j7CXMT40jfd4RdeUVOh/Oddj81YIzZC4ytZ3oBMK2e6Qb4WTuEppRqgZoiQz98aRX/76VVlFTADdpJUKl6zU6I5ZlPd7EgPZOpXZyOxuJJlw8qpbzU1KE9OHNQd1buO0KvzsIZA7s7HZJSHikqLJiLR/fijTXZlFV6RqVBTQSUUq1WU2QowE+YHheIiDgdklIeKzkpnqKySlbnVTodCqCJgFLKTUb2ieCbB85nWpxTVyUr5R0S4ruy8OYkzujjGd8VTQSUUm7Ts0uIHg1Qqgki1ukzPw/5rmgioJRSSvkwTQSUUkopH6aJgFJKKeXDNBFQSimlfJgmAkoppZQP00RAKaWU8mGaCCillFI+TBMBpZRSyodpIqCUUkr5ME0ElFJKKR8m1ii/HYuIHAIy3LzaKOCwm9epWkfbxDNpu3gebRPP5O52iTfG9GjuQh0yEWgLIrLaGJPgdBzqe9omnknbxfNom3gmT2kXPTWglFJK+TBNBJRSSikfpomA6+Y7HYA6jbaJZ9J28TzaJp7JI9pF+wgopZRSPkyPCCillFI+zOMTARF5WES2iMhGEVkvIokOxVHczPmDReQzO+brGpnvURGZ3voIPYeIdLdf93oRyRWRnFqPg9pwu8PsbawTkYGNzPehiES2VRyeRERiROQdEdklIntF5DkRCXbTum8SkT4tWO5OEdkmIgsamSdBRJ5tXYSeqyXt4srnVtvEPdyx3xGRh1q47YX2dn/eyDw/FZEftGT99a7Pk08NiMgZwNPAVGNMmYhEAUHGmAMOxFJsjAlrxvxJwGPGmBbt5EXE3xhT1ZJlPYmI/BYoNsY85cK8AcaYylZs6wEgwBjzWAuWFazvQ3VLt+9p7NeUDjxvjPmviPhjnZMsNsbc1cp1+wPLgHuNMaubuex2YLoxJrsF223VZ8QTtHG7pKJt0iru2u80d59hL9ML+MoYM6g5y9VavmVtYYzx2BtwNfBePdMnAl8Aa4CPgd729EnARmA98Gdgsz39JuC5Wsu/j9XIAMXA48AGIA2Itqf3B74FNgGPYX1JAcKw/gGutZ+bWU98PYHdQKEdy8BGYn4JmGXf3w/80V739cDNwCo7tjeAUKfbpAVt+Fvg3kZefyrwDLAauMduw2/s15wOBAOT7bZYZz83tJ7tXALkAjnAcntaMrDSboN/Av613ucooB+wA/gfsAWIB563Y9kC/M7p96+V7/00YEWdaV2Ao8DtjXwn6n0P6nw+k+3vzg77/e0E/Nr+vG7G2rFJPTG9AJTb352fA52B/9jttK7m+wRMBd6v9Rl6BfgaWGi325d2HGuBKU6/1+3ULrU/t9uAF+02+sR+/2dpm7ilfVze7wAR9vs91J5nIdb/7SeBKrsdFtjPvW0vuwW4pYFtbwRO2sudjbXvWGov9yUwrNb7f699P5VT/4dejvW/cx3wGfY+rdHX7PSb3kSDhNlvyE7gH8C5QCDWzqCHPc91wH/s+5uBM+z7T+JaImCAy+37fwIese+/C/zAvv8zvk8EAoAu9v0orB1+fV+u2l+axmJ+iVMTgV/WWkf3WvcfA+5wuk1a0Ia/Be5r5PWnAv+w7wcBe4AJ9uMIrNNXXbB+6QNMB95oZFs1X47hwHtAoP34H7Xacz/f/0OtBpJqraOb/dffjm2M0+9hK977O4G/1jN9HXB3I9+Jet+Dej6fqUBC3ffOvv9Kzfeqnu3vB6Ls+08Ayfb9SKzvemdO3+msATrZj0OBEPv+YGC10+91O7VL7c9tJTDOnr641nuobdL69mnufucCrB8q1wNLa62nuM56a75XnbD2Vd3r2XY/7P2W/XgZMNi+nwh8Xuv9r50I/KPWMl35/mj/j4G/NPWaA/BgxphiEZmIlRmdB7yGtUMcBXxqHWHDHzhonzsLN8Z8ay/+KnCZC5spx/qygfXBvsC+fyZwjX3/FaxfQgACPCEi52DtRPoC0Vi/RhsytL6YG5j3tVr3R4nIY1hfxjCsLNQbBdP46695zUOBg8aYtQDGmEIAEYkAXhaRwViJW6AL25yGlcGvsrfZCcivZ74MY0xarcezReQWrISvNzACK0v3JY29B681uBScJyK/xNopdMP65fNeE9u6ELhCRO61H4cAcfXM964x5qR9PxB4TkTGYf3qGtLENjqifcaY9fb9NVg7kPpomzRTc/Y79vyfisi1wP8BYxtZ9Z0icpV9PxYrYSpoaGYRCQOmAK/b2wTrf2l9an8vY4DXRKQ31o+rfY3EBODZiQCAsc6TpwKpIrIJ69f5FmPMGbXna6ITTSWndowMqXW/wtipE9YHuPZ7YjjdPKAHMNEYUyEi+4EQEfkZ1iEhsA5TnxJefTE3oKTW/ZeAK40xG0TkJqyM3Bs19fpLGphe4/dYh/uvEpF+WJ8HROS/wHjggDGmvvf8ZWPMg02s+7tti0h/rNMYk4wxR0XkJU79rHibrViHi78jIl2AXlj/gGr/sw6xn2/qPai3rUQkBOvXU4IxJsvuGxIiIrF8v+N5wRjzQt1FgWuMMTvqrC+6zny1t/tzIA/rn64fUFpfTB6s2e1Sj7Ja96uwEt1TaJu0nKv7HQAR8cM6AnkC69f4af0sRGQq1tHMM4wxJ+y+HCF2YvAbe7Yfc+q4A37AMWPMOBdCrt0WfweeNsa8a2/3t00t7NFXDYjIUPtXYI1xWOfGetgdOhCRQBEZaYw5BhTV6t15fa3l9gPjRMTP/hJMdmHzX9dax7xa0yOAfDsJOA/rvDLGmP8zxoyzb3U7leyoL2YXYgjHOtoRWCcGb1OGa69/B9BbRCbY80XYX7IIrHP/YJ3mAcAY80P7/a6bBIB1SG2WiPS019VNROKbiLML1heq0P6nd7HLr9AzLQNCa3oX253S/gI8h/Urob7vRHPegyKszyh8v8M6bP+SmQVgjMmq9b2ou8MB6yjXHXYHOkRkvAuvKwLryFE1cAPWrzNv0pJ2cZW2SSs1Z79jP/9z+/m5wH/t/9cAFbXuRwBH7SRgGJAEYIx5q1ZbnNLB0xhzHNhnH21ALI0dcahR+//lja68Zo9OBLAOh78sIltFZCPWIcpfY32g/ygiG7DO5Uyx5/8R8KKIrMc6p1VoT/8a6wu2FXgWqzNLU+4CfmZng31rTV8AJNjTfwBsb2pFxpjyRmJuzK+wOn187cp2PFg1Lrx++326HnheRA5gdZIJxOq78QcRWYeLR7GMMVuBR4BP7M/Op1iHuRtbZgPWedrtWKeWvnZlW57KPtJ1FVZCtAvr12a1MeZxGvhONPM9eAl4wf6+lWF1XtuMtSNZ5WKYv8dq440issV+3JR/ADfan6VhNH1EyaO0pF2a4SW0TVrL5f2OiAzF+iV/jzHmS2AF1v8dsDpnbhTrksylQICIbMPqv1b7dGRj5gE/sre5BZjpwjK/xTqdsAYXRzb06MsHm0tEwowxxfb9B7B6pt/lcFiqBUTkfuBNY8wup2PpKERkClav5qtq+mEo52m7KKd1tETgOuBBrF+NGcBNxphDzkalmktE/gJcidXDeavD4SilVIfWoRIBpZRSSjWPp/cRUEoppVQb0kRAKaWU8mGaCCillFI+TBMBpbyMiFSJNSLaFhHZICL32PUW2mSEOLFGtHuuGfOHisgCEdkkIptF5Cv7OnallAfy+MqCSqnTnKypNmYXTHoVqxDQb+yiJM0aea4N3AXkGWNGg1WgBahwNiSlVEP0iIBSXswYkw/cAtxuVx6bKiLvA4jIZBH5VkTWicg39g4ZERkpIivtowob61RRw57nhyKyU0RWYo27UTP9chFJt9f5mZxedhaswk01lc0wxuwwxpTZyyfX2vY/7ap6iEixiPzZPsrxmR17qojsFZEr7Hn6iciXIrLWvk2xp0+1510iItvtoxE1VfF+LSKr7CMT82umK6W+p4mAUl7OGLMXq6RrzzpPbQfONsaMx6qM9oQ9/afA3+yjCgnUqY0u1mAlv8NKAM7CqqxW4yus0RrHA4uAX9YT0n+A++0k5LGaRENEhmON2namve0qvi+d3RlrZLWRWGVyH8MaAOwq4FF7nnzgAmPMBHs9tU+BjMcauW8EMIDvk5fnjDGTjDGjsOrxuzIQmVI+RU8NKNVxNTRq47fAwyISQ/3VGxOB1JpiXCLyGt8PhNPkyGbGmPUiMgBrFLvpWCNAnkHjI0KWY5VhBdgElNnjeWzi+5H1GhvhbqUxJtuOd729zFe0bPQ9pXyKHhFQysvZO90qTh9muWbUxlHA5diD0BhjXgWuAE4CH4rI+c3Y3N+xfmWPBn5CA6PjGWOKjTFvGmNuA1KwRuSsGRGyZpCVocaY39qL1B4FtBp7dD17EJuaHyy1R7hLwEpEatQdjS9Avh99b5Yd74sNxauUL9NEQCkvJiI9gBewds51y4TWO2qjnTjsNcY8C7wDjKmzXDpwroh0F2v0tGsbWGe9I5uJyJki0tW+H4R1uD6Dlo0IWff1NGeEu3pH31NKnUoTAaW8T6eayweBz4BPsM7p19XQqI2zgc32IfRRwP9qL2SMOYg1gtm3WKPhbav19G9pemSzgcAX9mH9dVhXMbzRkhEh62jWCHf20OQtGX1PKZ+iYw0opZRSPkyPCCillFI+TBMBpZRSyodpIqCUUkr5ME0ElFJKKR+miYBSSinlwzQRUEoppXyYJgJKKaWUD9NEQCmllPJh/x8MhJAZwosnpQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# definindo as dimensões do gráfico\n",
    "plt.figure(figsize=(8,5))\n",
    "# definindo a legenda do eixo x\n",
    "plt.xlabel('Dias da Semana')\n",
    "# definindo as legendas do eixo y\n",
    "plt.ylabel('Calorias Gastas')\n",
    "# plotando o gráfico e definindo uma legenda\n",
    "plt.plot(DiasdaSemana, Calorias, label = 'Calorias Gastas/Dia')\n",
    "# chamando a legenda para ser exposta no gráfico\n",
    "plt.legend()\n",
    "# definindo um input diferente de 0 para entrada verdadeira na grade do gráfico\n",
    "plt.grid(True)\n",
    "# definindo o título do gráfico\n",
    "plt.title('Gráfico de Calorias Gastas por Alysson')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A visualização não ficou muito adequada com esse gráfico. Talvez um gráfico de barras fosse mais adequado para essa situação."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Gráfico de Calorias Gastas por Alysson')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# definindo as dimensões do gráfico\n",
    "plt.figure(figsize=(8,5))\n",
    "# definindo a legenda do eixo x\n",
    "plt.xlabel('Dias da Semana')\n",
    "# definindo as legendas do eixo y\n",
    "plt.ylabel('Calorias Gastas')\n",
    "# plotando o gráfico e definindo uma legenda\n",
    "plt.barh(DiasdaSemana, Calorias, label = 'Calorias Gastas/Dia')\n",
    "# chamando a legenda para ser exposta no gráfico\n",
    "plt.legend()\n",
    "# definindo um input diferente de 0 para entrada verdadeira na grade do gráfico\n",
    "plt.grid(True)\n",
    "# definindo o título do gráfico\n",
    "plt.title('Gráfico de Calorias Gastas por Alysson')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aplicação 2: Gestão Financeira de uma Empresa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uma empresa fabrica três produtos. Suas despesas de produção estão divididas em três categorias. Em cada uma dessas categorias, faz-se uma estimativa do custo de produção de um único exemplar de cada produto. Faz-se, também, uma estimativa da quantidade de cada produto a ser fabricado por trimestre. Essas estimativas são dadas na **tabela 3** e na **tabela 4**. A empresa gostaria de apresentar a seus acionistas uma única tabela mostrando o custo total por **trimestre** de cada uma das três categorias: **matéria- prima**, **pessoal** e **despesas gerais**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tabela 3. Custo de Produção por Item (em reais)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|**Gastos**     |**Produto A**|**Produto B**|**Produto C**|\n",
    "|:-------------:|:-----------:|:-----------:|:-----------:|\n",
    "|Matéria-prima  |0.10         |0.30         |0.15         |\n",
    "|Pessoal        |0.30         |0.40         |0.25         |\n",
    "|Despesas-gerais|0.10         |0.20         |0.15         |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# definindo a matriz C baseada na tabela 3\n",
    "C = np.array([[0.10, 0.30, 0.15],\n",
    "              [0.30, 0.40, 0.25], \n",
    "              [0.10, 0.20, 0.15]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A matriz C é:\n",
      "\n",
      " [[0.1  0.3  0.15]\n",
      " [0.3  0.4  0.25]\n",
      " [0.1  0.2  0.15]]\n"
     ]
    }
   ],
   "source": [
    "# imprimindo a matriz C\n",
    "print(\"A matriz C é:\\n\\n\", C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Tabela 4. Quantidade Produzida por Trimestre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|**Produto**|**Verão**|**Outono**|**Inverno**|**Primavera**|\n",
    "|:---------:|:-------:|:--------:|:---------:|:-----------:|\n",
    "|A          |4000     |4500      |4500       |4000         |\n",
    "|B          |2000     |2600      |2400       |2200         |\n",
    "|C          |5800     |6200      |6000       |6000         |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# definindo a matriz D baseado na tabela 4\n",
    "D = np.array([[4000, 4500, 4500, 4000], \n",
    "              [2000, 2600, 2400, 2200],\n",
    "              [5800, 6200, 6000, 6000]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A matriz D é:\n",
      "\n",
      " [[4000 4500 4500 4000]\n",
      " [2000 2600 2400 2200]\n",
      " [5800 6200 6000 6000]]\n"
     ]
    }
   ],
   "source": [
    "print(\"A matriz D é:\\n\\n\", D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se fizermos o produto **CD**, teremos o custo de cada categoria por trimestre."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# realizando a multiplicação da matriz C com a matriz D\n",
    "Custos = np.dot(C, D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A matriz de Custos é:\n",
      "\n",
      " [[1870. 2160. 2070. 1960.]\n",
      " [3450. 3940. 3810. 3580.]\n",
      " [1670. 1900. 1830. 1740.]]\n"
     ]
    }
   ],
   "source": [
    "# imprime a matriz de custos por trimeste em cada categoria, em reais\n",
    "print(\"A matriz de Custos é:\\n\\n\", Custos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|**Custos de cada Categoria por Trimeste**|**Verão**|**Outono**|**Inverno**|**Primavera**|\n",
    "|:---------------------------------------:|:-------:|:--------:|:---------:|:-----------:|\n",
    "|Matéria-prima                            |1870     |2160      |2070       |1960         |     \n",
    "|Pessoal                                  |3450     |3940      |3810       |3580         |\n",
    "|Despesas Gerais                          |1670     |1900      |1830       |1740         |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# esquematizando as etiquetas do gráfico no eixo x\n",
    "Trimestres = ['Verão', 'Outono', 'Inverno', 'Primaveira']\n",
    "# definindo os valores gastos apenas na categoria Matéria-Prima\n",
    "MateriaPrima = Custos[0]\n",
    "# definindo os valores gastos apenas na categoria Pessoal\n",
    "Pessoal = Custos[1]\n",
    "# definindo os valores gastos apenas na categoria Despesas Gerais\n",
    "DespesasGerais = Custos[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Gráfico de Custos por Categoria em cada Trimestre')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# definindo as dimensões do gráfico\n",
    "plt.figure(figsize=(12,8))\n",
    "# definindo a legenda do eixo x\n",
    "plt.xlabel('Trimestres')\n",
    "# definindo a legenda do eixo y\n",
    "plt.ylabel('Dinheiro(R$)')\n",
    "# plotando o gráfico para a categoria Matéria-Prima, com sua respectiva legenda\n",
    "plt.plot(Trimestres, MateriaPrima, label = 'Matéria-Prima')\n",
    "# plotando o gráfico para a categoria Pessoal, com sua respectiva legenda\n",
    "plt.plot(Trimestres, Pessoal, label = 'Pessoal')\n",
    "# plotando o gráfico para a categoria Despesas Gerais, com sua respectiva legenda\n",
    "plt.plot(Trimestres, DespesasGerais, label = 'Despesas Gerais')\n",
    "# chamando a legenda para ser exposta no gráfico\n",
    "plt.legend()\n",
    "# definindo um input diferente de 0 para entrada verdadeira na grade do gráfico\n",
    "plt.grid(True)\n",
    "# definindo o título do gráfico\n",
    "plt.title('Gráfico de Custos por Categoria em cada Trimestre')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Desse modo, observe que a álgebra linear pode estar presente em diversas situações do nosso dia a dia, das mais simples até as situações mais complexas da engenharia. Por esse motivo, a disciplina deve ser levada a sério e merece ser estudada com um maior grau de profundidade por parte dos estudantes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alguma Dúvida? Entre em Contato Comigo: [Me envie um e-mail](mailto:alysson.barbosa@ee.ufcg.edu.br)"
   ]
  }
 ],
 "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": "3.8.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}