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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "CarlosEduardo06.ipynb",
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "FRRQA1IvR32v"
},
"source": [
"#Carregando os dados"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ke7ZgDs6OFj7",
"outputId": "843312b7-284e-4654-e630-7ff237091b74"
},
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Mounted at /content/drive\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "VD0bSW8TOu96"
},
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import scipy.stats as st\n",
"import math"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "imITLJbeOUBq"
},
"source": [
"df = pd.read_excel('/content/drive/MyDrive/2021.2/tec controle qualidade/T6.xlsx')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 80
},
"id": "ESYUppkxOqgc",
"outputId": "6fbd590c-d244-4056-c8f1-856acd19abf8"
},
"source": [
"df.head(1)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Amostra</th>\n",
" <th>Medida_1</th>\n",
" <th>Medida_2</th>\n",
" <th>Medida_3</th>\n",
" <th>Medida_4</th>\n",
" <th>Medida_5</th>\n",
" <th>Medida_6</th>\n",
" <th>Medida_7</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>61.439</td>\n",
" <td>65.578</td>\n",
" <td>59.981</td>\n",
" <td>59.372</td>\n",
" <td>58.882</td>\n",
" <td>58.837</td>\n",
" <td>63.279</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Amostra Medida_1 Medida_2 ... Medida_5 Medida_6 Medida_7\n",
"0 1 61.439 65.578 ... 58.882 58.837 63.279\n",
"\n",
"[1 rows x 8 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "rtc-qzz0R_3j"
},
"source": [
"#importando a tabela de parâmetros gráficos\n",
"parametros_graf = pd.read_excel(\"/content/drive/MyDrive/2021.2/tec controle qualidade/TEP00120 - TABELAS.xlsx\", \n",
" header = 2,\n",
" sheet_name = \"Parâmetros gráficos\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "MvgOTqEzR79b"
},
"source": [
"#Funções do programa anterior"
]
},
{
"cell_type": "code",
"metadata": {
"id": "rYjraqBySFIp"
},
"source": [
"def chamar_parametro(parametro,n):\n",
" \"\"\" atribuir o valor do parametro gráfico para determinado n a uma var\"\"\"\n",
" #usamos n-2 porque o index começa no zero e a coluna n no dois \n",
" var_grafica = parametros_graf[parametro].loc[n-2]\n",
" return var_grafica\n",
"\n",
"def df_mean_r_S(df):\n",
" \"\"\"retorna uma cópia do df com as colunas Média, Amplitude e S\"\"\"\n",
"\n",
" #criando uma lista com o nome das colunas que contém os dados coletados, sem a coluna Amostra\n",
" global colunas\n",
" colunas = [\"Medida_\"+ str(n) for n in range(1,len(df.columns))]\n",
"\n",
" df1 = df.copy()\n",
" #criando uma coluna com a média das amostras linha a linha\n",
" df1['Média'] = df[colunas].mean(axis=1)\n",
" #criando uma coluna com a amplitude de cada linha\n",
" df1['Amplitude'] = (df[colunas].max(axis=1) - df[colunas].min(axis=1))\n",
" #criando uma coluna com a amplitude de cada linha\n",
" df1['S'] = df[colunas].std(axis=1)\n",
" return df1\n",
"\n",
"def plot_controle(df1,limites,grafico):\n",
" \"\"\"plotar grafico de controle\"\"\"\n",
" if grafico == 'Xbarra':\n",
" plt.plot(df1['Amostra'], df1['Média'])\n",
" if grafico == 'S':\n",
" plt.plot(df1['Amostra'], df1['S'])\n",
" if grafico == \"R\":\n",
" plt.plot(df1['Amostra'], df1['Amplitude'])\n",
" plt.hlines(limites[0], xmin=df1['Amostra'][0], xmax = df1['Amostra'].iloc[-1], colors ='g')\n",
" plt.hlines(limites[1], xmin=df1['Amostra'][0], xmax = df1['Amostra'].iloc[-1], colors = 'y')\n",
" plt.hlines(limites[2], xmin=df1['Amostra'][0], xmax = df1['Amostra'].iloc[-1], colors = 'r')\n",
"\n",
" plt.title(grafico)\n",
" plt.xlabel(\"Amostra\")\n",
" plt.legend((grafico,'LIC', 'LM', 'LSC'),loc='best')\n",
" plt.show()\n",
"\n",
"def limites_de_controle(df):\n",
" \n",
" \"\"\"Calcula os limites de controle de Xbarra e R para amostras menores que 11 e Xbarra e S caso contrário\"\"\"\n",
" #tamanho das amostras\n",
" n = len(colunas)\n",
"\n",
" if n >= 11:\n",
" \n",
" \"\"\"definir os limites de controle de S\"\"\"\n",
" #salvando os parametros gráficos \"B3\" e \"B4\" em vars com o mesmo nome. \n",
" B3 = chamar_parametro(\"B3\",n)\n",
" B4 = chamar_parametro(\"B4\",n)\n",
" #definindo LMs = Sbarra\n",
" LMs = df[\"S\"].mean(axis=0)\n",
" #Definindo LSCs = B3*Sbarra\n",
" LSCs = B4*LMs\n",
" #Definindo LICx = B4*Sbarra\n",
" LICs = B3*LMs\n",
" limites_aux = [LICs, LMs, LSCs]\n",
"\n",
" \"\"\"definir os limites de controle de Xbarra (S)\"\"\"\n",
" #salvando o parametro gráfico \"A3\" em uma var com o mesmo nome. \n",
" A3 = chamar_parametro(\"A3\",n)\n",
" Sbarra = df[\"S\"].mean(axis=0)\n",
" #definindo LMx = Xbarra-barra\n",
" LMx = df[\"Média\"].mean(axis=0)\n",
" #Definindo LSCx = Xbarra-barra + A3*Sbarra\n",
" LSCx = LMx + A3*Sbarra\n",
" #Definindo LICx = Xbarra-barra - A3*Sbarra\n",
" LICx = LMx - A3*Sbarra\n",
" limites_x = [LICx, LMx, LSCx]\n",
"\n",
" print('Amostras maior ou igual a 11, retornando os limites de Xbarra e S')\n",
"\n",
" elif n < 11:\n",
" \"\"\"definir os limites de controle de R\"\"\"\n",
" #salvando os parametros gráficos \"D3\" e \"D4\" em variáveis com o mesmo nome. \n",
" D3 = chamar_parametro(\"D3\",n)\n",
" D4 = chamar_parametro(\"D4\",n)\n",
" #definindo LM = Rbarra\n",
" LM = df[\"Amplitude\"].mean(axis=0)\n",
" #Definindo LSC = Xbarra-barra + A2*Rbarra\n",
" LSC = LM*D4\n",
" #Definindo LIC = Xbarra-barra - A2*Rbarra\n",
" LIC = LM*D3\n",
" #se LIC < 0, deve-se considerar = 0\n",
" if LIC < 0:\n",
" LIC = 0\n",
" limites_aux = [LIC, LM, LSC]\n",
"\n",
" \"\"\"definir os limites de controle de Xbarra (R)\"\"\"\n",
" #definindo Rbarra\n",
" Rbarra = df[\"Amplitude\"].mean(axis=0)\n",
" #salvando o parametro gráfico \"A2\" em uma var com o mesmo nome. \n",
" A2 = chamar_parametro(\"A2\",n)\n",
" #definindo LMx = Xbarra-barra\n",
" LMx = df[\"Média\"].mean(axis=0)\n",
" #Definindo LSCx = Xbarra-barra + A2*Rbarra\n",
" LSCx = LMx + A2*Rbarra\n",
" #Definindo LICx = Xbarra-barra - A2*Rbarra\n",
" LICx = LMx - A2*Rbarra\n",
" limites_x = [LICx, LMx, LSCx]\n",
"\n",
" print('Amostras menores que 11, retornando os limites de Xbarra e R')\n",
" return limites_x, limites_aux"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "SqqulvrWO4uv"
},
"source": [
"#a) plote os gráficos de controle para os dados do arquivo anexo referente a um processo sob controle;\n"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "u3JMc83jTf7l",
"outputId": "eb4d7bf9-a406-4705-e1fb-dd1572c00dce"
},
"source": [
"#definindo var n com o tamanho das amostras n, sendo a contagem das colunas sem a primeira col \"Amostra\"\n",
"n = len(df.iloc[:,1:].columns)\n",
"n"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"7"
]
},
"metadata": {
"tags": []
},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "09XqTKhfPOu0"
},
"source": [
"#criando as colunas \"Média\", \"Amplitude\" e \"S\"\n",
"df1 = df_mean_r_S(df)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OrGXVbvGTuA7",
"outputId": "76fede18-55c1-4914-9664-5bb986ae0b5b"
},
"source": [
"#obtendo limites de controle para Xbarra e R se n menor que 11 e Xbarra e S se maior que 11\n",
"limites_x, limites_aux = limites_de_controle(df1)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Amostras menores que 11, retornando os limites de Xbarra e R\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"id": "wH1llNEaU9_I",
"outputId": "feaae305-7c57-41cd-9716-c1ca22a7f142"
},
"source": [
"#Plotar Xbarra\n",
"plot_controle(df1,limites_x,'Xbarra')"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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HExQkZCZFcf7ENJauO6wrcwOMpa6FqLBgosN7voSYEhdh29zYw++0h3wCr6xvIf9QFT86fzyLpg1s3+Ws5GgOVTa69Ne0ot62405PCXzUsEhE/DOBf7a3ks3F1SxZMLZzp23HTIlPi7SMMhiO8smlU9M7j313XhbHG1p5e/NRH0am+qu3VZgOjvKKp8soQz6BF5XbRo5TRw38av+Y5GhaO6wcre57/nZPqzAdwkOCSY+L8MsE/sRHRQyPi+DKM0d1HhuREMm41BjWaB18ULqWTxzmjkli4vBY/vrZAS1RBZDernE5eGtrtSGfwPfaE/j41JgBv0Z2su25+10oo7hyhTojMcrvauDr9ley/sBxbjt3DOEhwSfdN398CusPHNe3+gPUvXziICLcfHYWu0rr+GJ/pQ8jVP3R2zUuB29tbnxaJPDosGDS451P63NFdrKth8kBS991YFcSuD8u5vnTx3tJjgnj2lmnXiOYPyGZlnYr6w4c90Fkgc9Z+cRh8YyRJEaH8cJnB70fmBoQRyfC3vjVCFxEEkRkuYjsEpGdIjJXRB62394qIm+IiF+uSCgqr2NcaoxLUwZ7khwTRmx4iEsXMh39DxybGTuTmRhFeV0LTa3+MaItOFzFp0UVfP+cMUSGBZ9y/5zsJMJCgnQ64QA5K584RIQG863ZmXy4s8zv3pWpU7W0d1DT1PMkBYeI0GDiIkIo9/A0UVdH4I8D7xtjJgLTgZ3ASuAMY8w0YA9wn2dCHJy95fWMS43t+4G9EBGyU6I54MIvmKWuhWFRoYSF9HxqHVMJS6r84xf2Tx/tJSEqlOu/Mtrp/ZFhwczJTtQEPgA9lU+6+s7c0QSL8OLnB70bnOq3yj4mKXSVEhvu+xG4iMQD84HnAYwxrcaYamPMCmOMY8uJL4FRPb2Gr9Q0tVFW28K4QdS/HWxTCV0rofS0CtPBMZXwkB+MuAqP1LBqVznfOzv7lN4tXc0fn0JReb1LF3LVCat29lw+cUiLi+CSqem8ll9MvRd2cVED58oiHgdvLKd3ZQSeDViAF0SkQESeE5Hobo/5LvCesyeLyK0iki8i+RaLd0dw7riA6ZCVFE1JVRMt7b2XPVy5wOFPc8H//PFeYsNDuOGsrF4fp9MJB+Y/23oun3T13XnZ1LW0szy/2EuRqYFw5RqXQ2pshF9MIwwBZgJPGmNygQbgXsedIvILoB1Y6uzJxphnjDF5xpi8lBTvdl/b50jgaYNP4GNSojGm7xWUrswRTYwOIzos2OcJfE9ZHe8VlnLT2Vl9dlqckBZDWlw4n+zRviiucqV84jAjI4HczARe/PygWzcXUe7lyipMB38ZgZcAJcaYdfbby7EldETkJmARcL3xw4msReV1hIUEMWpY1KBfyzETpbephMYYyl1I4CJCZlI0xT5O4E+u3kdUWDDfPTu7z8eKCOeMT2Ht3gqPLw8eKlwpn3R189nZHKxs5OPd5R6OTA2UIyEnxfTdnTPVvrmxJ8tifSZwY0wpUCwiOfZD5wM7RORrwD3AZcYY39cCnCgqr2dsSky/NmPoSZZjKmEvCby2uZ3WdqtL9bHMxEgO+TCBV9a38M7Wo1ydl+Fyq9j5E1KoaWpjS0m1h6MbGlwtnzhcfMZwhsdF6JRCP2apayEhKvSUtRLOeGNvTFdnofwAWCoiW4EZwEPAn4BYYKWIbBaRpzwU44DtLa93S/0bIC4ilOSYcA5Yek7gnasw41xJ4FEUH2/02dvl5RtLaOswXD/H9d4w54xLRgSdjeKC/pRPHEKDg/jO3NGs3VtBUVmdhyNUA+HKKkwHv0ngxpjN9jr2NGPM5caYKmPMOGNMhjFmhv3f7R6LcgAaW9spqWpyywwUhzF9NLXqzxXqzKRoWtp901bWajW8sv4ws7MSGZ/m+hTLYdFhTBsZz6dFWgfvS3/LJw5X52UQHCS8UXDEQ5GpwXBlkoKDYzaaJ/fGHLIrMfeV2xKtu0bgAFnJURzoZTcdxzfKlW+wL2eifL6vkkOVjVz/lf53Zpw/IYXNxdXUNLUNKobNxdU89uGeQb2Gp1Q3trJ+kKtO+1s+cUiJDefsccm8tfmoT/qjfL6vgsIjNXohtQeuTFJw8JsReCDaa7G9BXXHDBSH7OQYLHUt1DU7T179mWLky7ngS9cdIjE6jK+dMbzfz50/IYUOq+HzQe7S8/cvD/HYh0VUN7YO6nU84Tf/3sE1z3zB7tKBlTEGUj7pavH0ERypbmLjoaoBff6BOlbTxLeeXceiP67lzP9ZyZ1LN/HKusMcqmzQZlt2riyjd0iIDCU02LObGw/ZBF5UVk9IkDA6qfuU9YFzzEQ5WOE86VrqWwgLDnJp8+ORCZEE+aCtbHltMyt2lHHlmaNcuhDT3YyMBGLDQ/hkkPPBC4/UAAw4SXpKaU0zb285ijHw6MqBvUMYaPnEYeEZw4kIDeLNzd4to2w6ZLs4ffcFE/jqxDQ2Hqri529s49yHV3PO7z/m3te3smJ7qVdj8icNLe00tXW4PAIPChKSYzy7M8+Q3dR4b3k9WcnRnX2t3WFMimMqYT1TR8Wfcr/j7ZUrfVfCQoJIj4/0+lTCf+YX02E1A9rYAmwX2s4al8Qneyowxgyox0xzW0fnIqs9ZXUubxbtDX/74iAdxvDNmaN4fVMJhUdqOGPkqd/r3gy0fOIQEx7CBZPS+M/WY/zq61Pc+jPcm4LDVYSHBHXuxmSMYX9FA5/treCzvRX8Z9sxlm0o5oWbZ3FeTqpXYvIn/bnG5eDpueBDdgTuzhkoDpmJUYj0PJXQUtdCsot/nR2v580d6jushn+sL+bscUmd7yYGYv6EFI5UN7Gvlxk5vdlTVke7vca6y49G4I2t7byy/jALJw/nV5dNJj4ytN+j8C3F1Xy4s4zFuSMGVD5xuHzGSKoa27y68rWguJqpI+M7+/iICGNTYrhhbhZPfyeP/F9eQGx4CO9tO+a1mPxJfxbxOKTGenYEPiQTeEt7BwcrG9w6AwVsHcZGJkT2msD789fZ1lZ24L1FLHUt/N+K3S53Nfxkj4Uj1U1cP8d50ypXzR+f0vl6A1F4pBaAEfER7PGj6XKvbzpCdWMbt5yTTVxEKLfOH8OqXeUUHHatFt3WYeXef20jJSacO88bN6hY5k9IISEqlDcLvLNbT2u7lW1HasjN7LmpaHhIMOdNTOXDneW0d1i9EldX9S3tPq3F9+cal4OOwAfgYEUjVoPbEzj0vj9mf65Qg60rYUV9C42tA1uptWz9YZ74aC8/f2ObSz/YS9cdIjkmnAsnpw3o8zlkJEYxJjl6wHXw7UdriI0I4byJqewqrfOLC2RWq+Gvaw8wPSOBM0fbSh83nZVFYnQY/+fiKPyvaw+w81gtv1k8hbiIvq+D9CYsJIhLpqazckcZDV5ocLXzWC2t7VZy+yj7LJwynOMNreR7+QJrTVMbcx9axd++8N0m0ANL4BEe3dx4SCbwonL7DJRBtpF1xpHAuyedtg4rxxtb+5fA7TNRigc4Cl+9x0JYcBBvFBzpc3fzo9VNfLSrnGtmjXJLTXX+hBS+3F85oF16Co/WMmVEHBPT46hrbqfUwz2TXfHRrnIOVDRwy7zszrp+dHgIt587hk+LKthwsPdphcXHG3n0wz1cMCmNhVP6P7vHmctnjKSprYOVO8rc8nq9cbzL6G0EDnBuTgphIUF84OWLmV/ur6SupZ3XN5V49fN2ZalrIThIGBbl2splsCV7T25uPDQTeFk9IicuOrpTdnI0dc3tVDacPP3teEMrxvS8F6YzJ6YS9r+WXNXQSsHhKm47dwxfnZjKb97ZwaZe3uov21CMAac77gzE/AnJNLdZyT/Yv5FYe4eVXcdqOWNEPDn2RUT+UAd/bu1+RsRHcHG3qZXf+UoWKbHh/GHF7h6fa4zhF28WEizCbxZPGdTmIV3ljR7GyIRIr8xGKSiuZnhcBOnxkb0+LiY8hHnjklmxvcyr75zW2hePbS2p8dnGF5a6FpKiw/rVmsPTmxsPyQS+11JPZmIUEaH9nybXl+weeqIM5O3VYBbzfFJkwWrgqxNTefTqGaTHR3LH3zdR4eQvfXuHlVc3HObcCSlkJA6+sRfYdukJDhK+7OdejvssDbS0Wzlj5IkEvsfHCbzwSA1f7j/OTWdnEdLt3UlkWDB3LBjLl/uP9zj3/e0tR/lkj4WfLsxhRELvCbA/goKEr08fwadFFU6/r+5UcLi6z9G3w8IpaRypbmL70VqPxtTVZ3srmDjc9vPybqFvLqL2ZxWmg6Othqfq4EMzgZe5fwaKwxj7Bsfde6L0ZxWmQ0JUKEnRYWwpqel3HKt3WxgWFcq0UQnER4Xy5LdnUtXYyg9eKTjlAtOqXeWU1bYM+uJlV9HhIZwxMr7fKxYd87+njIgjPiqU4XERPp8L/vzaA0SHBXNND+9OrpudSXp8BH9YueeUUWd1YysPvrOD6aPiuWFulttjuzx3BB1Ww7senPlRUd/C4eONLifwCyalESR4bU74keom9lc0cFVeBtMzEvjPVh8l8H5e44ITI3BPJfCAmQdeULDApcd1WIV9lruZPOxDCgp+5vY4OqxCSNDdfLH9WcaHfNJ5fOP+qcDXsBy+kYJK10cmM9MuYuX2SXy54WeEh7h2scpqYNWOO5kx/CBbtzzUefzWmVP44/pL+NnLD3DD9BOxPbXmSpIikxnWdC0FBe5725sVdS7vFs1kXf75hAW7VgtfveU8woKnU1NyGQVHDelR32TzgWgKCv7LbXH1R2VjDG9vvpWLxxWwf9f/6/Fxl42bztMbL+Kv793MzPSDncf/vH4hxxvO4OdnPcPWLb/0SIyj429i6doVTI14xSOvv+HIWOAbRDffT4GLPVgmJl/LmxstnJdym0di6urD/WcAF5PU9jNmJGbz0pYFvPfpYobH9H/gMxhHj99OSuhBCgrucfk5rR3BwI/ZsudPXD3rd26PaciNwEsbEmi3BjMqrn9v7V0VHGQYHlPF0bqTr9ZXNdtKKwkR/atnz8vcRXN7GBuPjXH5OXuPD6e2JYoz0/efdPyr2dtZOHYzb+yawxcl4wEorY9nc2kWF47ZSnCQe2uWU1JKaLOGsPe46xft9lWlkZ1Q3hlLZnwFxbVJdFjdUzfur3f35mIQLp2wqdfHnZ+9jdToav6xbR6OQXhh+Sg+PDCNxTkbyB7mufna80fvYHflSErr+7egyFW7K9MJlg7GDnP9YumckUUcrknhWJ3n9zLfUpbFsIh6MuMrOCvDdi3i8+KcPp7lXlYD1c1RJET27/c7LLiD6NBmqpo8UxEImBF4bu5qlx5Xvr0U2MgFs37L9AzP/HBN3JrP4eMN5Obe3XnszcOFxEUcYU7eqn691tQOK3/MX8X2utu4M/dMl56zeuUeRIq44cLHT+nl/cTUDq5++kv+kn8lC79yNis2liCyjx9d9t99XqDqrzE5bfzvZyuoCnmA3NzxfT7eajUcfnMFV+SOJDf3VgD2W0t4a/cWhmX+m7Epnvkh70ljazur3v6IhVOSuPict/p8/E9NMfe8vpWK8GWcm5PCTx7/lIxEKw9961dEhrn/eotDanYTL2/9iH1tj3CxC+e5vx7O/5LJI9r5yqwPXX5O8uhGXtj8MSX8gUtyx7o9Jger1bDznQ9ZMCmFmTNXAzB9y2cUVH6dB3N/77HP211VQysdZiVnjP0uubkP9uu5wz9aDZGXeCSuITcCdyzRHuuhGjjYZrccrDy5l/dALnAAhATb5vuu2lnu8s4dq3eXk5uR4HQjhvCQYP5y/UzCQoK4/eWNvJZfzPmT0tyevAHio0LJSYtlnYt18MPHG6lvaeeMkXGdx3LsF6Z8UQd/fWMJNU22hTuu+MbMkWQlRfF/K/fw54/2st/SwP9cPtWjyRtsfXNmZyXypgc6FHZYDVuKXb+A6ZCRGMXk9Dg+2O7ZKY47S2upbGhl3rjkzmOLpqZTeKTWq6uYB7IK0yE1NoLyWr2I6ZK95fWMiI/odYf1wcpOjqa13crRmhPzt8trB5bAARZNG0FLu5VVO/v+Zaiob2FLSQ0LeulFMTIhkieuzWWfpZ6K+tZ+bdrQX7OzE9l4qMqllXmFRx0XME+UAsalxhAk3k/gVv6sbrAAACAASURBVKvh+bUHmJGR4HLPkpDgIH50wXh2ldbxxEd7WTxjBOdO8M4+r4tzR7C3vN7tMz+KyutoaO3odwIH26KeTYerPNrv+jP7zJ+zuyTwi6faSnb/8eKS/oHMMnNIiQ33WN9/lxK4iCSIyHIR2SUiO0VkrohcJSLbRcQqInkeiW4AisrrGNePTQoGwtlUQtsIPGJAr5c3ehjD4yL495a+fyAdy9f7aiY0b3wyv75sChdNTutc+u4Js7MTaWztcCmxbD9aS2iwnNTiNyI0mKykaK8n8FW7yjlY2cgt52T3a972ZdNHMi41hvjIUP570WQPRniyS6emExosvOXmOeEFh20dCHMz+t94a+EZaRgDH+7w3B6enxZVMD41huHxJ363Rg2LYoaXZ6MMOoH7eBrh48D7xpiJwHRgJ1AIfAP4pLcnusPrG0u4719b+3yc1WrYV97gsSmEDmOcJfB+9kHpKihIuHRaOmv2lPe5UcLHuy0kx4QzZURcr48D+M7cLJ65IW9QTZX6MjsrEaDPlYpgm0I4IS32lDa2OcNjvd4T5blP9zMyIZKv9XPVZHCQ8Motc3j7rrNd7gvtDglRYZw7IZW3txx167LsgsNVDIsKZXRS/9cH5KTFkpkY5bFVmc1tHWw4eJx545NPuW/RtHS2H63lYC87ZLnTYBK4Jzc37jOBi0g8MB94HsAY02qMqTbG7DTG9Lw8zY1Ka5v5x/pith/tfdrQkeommto6PNIDpauU2HCiw4LZb58L3tDSTmNrh0t7YfZk0bR02jpMr3Nr2zusfLLHwoKcFI8m5f5IjYsgOzm6zzq4MYbt9iX03U1Ii+VgZcOAluUPxLaSGtYdOM7NThbuuCI1LsKtfeZdtXjGCMpqW1h3wH0zrGwLeIYNaPWoiLBwShqf76ugtodNTgZj06EqmtusJ9W/HS6291r3VhnFUt9CeEgQsQMozXpyZx5XfnqzAQvwgogUiMhzIuLyT6+I3Coi+SKSb7EMbKrVt+eMJjosmKfX7O/1cY4LmJ4egYsI2SknmlqVD6BPcHczMhIYNSySd3p5W+jYymxBjnfqrq6alTWMDQeP97oNV2ltM8cbWp321p44PBarOfH987Sn1uwjNjyEq2dleOXzucsFk9KIDgvmLTd1KKxpaqOovJ6ZA6h/OyycMpy2DsPHu9xfRvl0bwUhQeK0X/zIhEhyMxM8usCpq/70+u+uc29MD/T8cSWBhwAzgSeNMblAA3Cvq5/AGPOMfUPkvJSUgSWe+KhQvjUnk3e2Hu11AwRHAvD0CBwgK+lEAh/M2ysHEWHRtBGs3VvB8Qbn24yt3m0hOEg4Z5x/JfDZ2UlUN9qSQU8cLWSdjsCHe68nyoGKBt4tPMZ35o4edMdAb4sMC2bhGcN5d9sxSqoG3w9kS7G9/j3AjSccz02OCWeFBxpufba3gtzMhB4nJFw61XtllIGswnTISIzkgkmpHpmt5EoCLwFKjDHr7LeXY0voXvW9eWMIDhKe/bTnUXhReR3JMeEk9KNb2ECNSY6mpKqR1narWxI42MooHVbD+4XOyygf7y7nzMxhxEf5V+KZk22rg6/v5a399qM1iMCk9FMTeFZSNGEhQV6pgz+9Zh+hwUHcfLZrUwf9zR0LxoHATS9sGPR+ogWHqxGBaU52l3JVcJBw4eRUVu8q77MEtmaPxeULj1UNrWw7UsO8XgYrl3ixjFJc1divRnVdjU6K5rkbZzFtlPvXpfSZwI0xpUCxiDiWPp0P7HB7JH0YHh/BFbkj+Wd+cY+tGYs8sAtPT7JTorEa29xmywD6oDgzZUQc2cnR/HvLqW+Ry2qb2X60lgUT/Wv0DTBqWCTp8RGs76UzYeGRWsamxBAVdupoKjhIGJ8a4/EReGlNM69vKuHqvFGD/l75yrjUGJ69IY/DlY18/2/5g7puUFBcxYTUWGIH+U7koinDaWjt4PN9zpt9tXVYeejdndz41/X8cFmBS3+ov9hfiTEwb3zP2+2NSIhkZqbnZ6Pss9RzqLKRuX609Z+Dq1dwfgAsFZGtwAzgIRG5QkRKgLnAf0TkA08F6XDr/LG0tFt56fODp9xnjLFto+bGXeh7k+1oalXRgKXe1ic4cZAjfxHh69PSWXeg8pS5tWt2uzZ90BdEhNnZiaw/UNnjQpPtR2t6nTmTMzzW410J//rZAawGbpvvuZWD3vCVMUn83zXT2XCwirtf3TygWSnGmH51IOzNWWOTiAkP4YPCU8soZbXNXP/sOp75ZD/XzsogOiyYB9/Z0eeCpE+LKogJD2F6H6PWS6ams+NYbY+brLjDCvtipQvd1OfdnVxK4MaYzfY69jRjzOXGmCpjzBvGmFHGmHBjTJoxZqGngx2XGsOFk9J46YtDp+xSUl7XQl1zu1fq3wDZSY6phPWU17aQHBPmlpkhi6aPwGrgvW0nl1FW7ylneFxEZ0tNfzMrK5Gy2hanrXEr61s4VtPMGSN6fquekxZLaW0zNY3un80AUNPYxtIvD7FoWrrbWur60qJpI/jvRZN5r7CU3/x7e79XaB6oaKCmqc0tCfzEVmtlJ/0x+XxfBZc+8SnbjtTw+LUz+H/fnMaPLpjAp0UVrNrZ+0XPz/ZW8JUxSX3OEnKUUTx5MfOD7aVMHRnPSDe2CnaXgFuJefuCsdQ0tfGP9YdPOl5U5r0LmGC7sJoUHdY5AnfXW/IJabHkpMWeVEZp67Dy6Z4KFuSkuG2zAHdz1MGdTSd0LPLpawQOsNtDdfC/fXGQhtYObj83sEffXX1vXjbfPyebl744xNOf9D5Dq7vOBTyDuIDZ1UWT06hsaGXjoSqsVsOfP97Lt59bR3xkKG/fdTaLZ4wE4Ia5oxmbEs1v391Ja7vz1buHKxs5fLyRc5zM/+5uREIkZ44e1uvsrcEoq21mc3E1C6cMbhtCTwm4BD4zcxizsxN5fu2Bk34A9npwG7WeZCVHs9/SMKhFPM4smpZO/qEqjlbblupvPFRFXUt7r8vnfW1cagyJ0WFO+4M7W0Lf3YmeKO7fJKCptYMXPj/IVyemOr2IGsjuu3gSX58+gv/33i7edLEVLNjq37HhIYxzUwOxBTkphAUH8Vp+Md//Wz4Pf7CbS6eN4O275jG+y8ro0OAgfrloMgcqGpyWQgHW2pfPO1vA48wlU9PZeayW/Rbns6Aq6lt4fu0BHnxnR783Y3bMrrnID8snEIAJHGDJuWM5VtPM211GqUXl9cRHhpIc4/kZKA6O/TEHM8XImUXTRwAn3hZ+vLuc0GDh7HH+dxHFQUSYlTXMaQLffrSWjMTIXmfPDI+LIC4ixCMj8Fc3HOZ4QytLFgyd0bdDUJDwyFXTmDsmiZ8t39K59VhfCg5XMz0jwW0LwmIjQjl7XBKvbSzhkyILv1k8hSeunUG0kymA5+WksiAnhSdWFTndaWjtXgvp8RGdK577com9N0rXMkpLewfvbjvG917cwJyHVvHgOzt4fu0BVu/u31qUFdtLyU6O9trkiP4KyAS+ICeFicNjeXrNvs7FI44ZKN4sMWQnR1Ne14KlvqVzsr67XveMkXGdZZTVuyzMykoc9GwBT5udncTh442U1px8AXb7kRqmpPc+VU1EyBke6/aeKG0dVp799AB5o4cxy77sf6gJDwnmqe+cydiUGG7/+8Y+Vyw3trazq7TOLfXvrm48K4vpGQm8dvtZ3DA3q9ffxV9eOpmmtg7+sGLPScc7rIbP91Uyb1yyy7/L6fGR5NnLKJsOV/GLN7Yx+7eruGPpJgqP1nDLOdm8+8NzSIkN55Vupdfe1DS18cW+Si6anOa3pcuATOAiwu3njqWovJ6P7CvA9nlxBoqDY4RgzOCnEHa3aNoItpTUsG5/JbvL6vxu9aUzjr4o67v0RalrbuNgZeNJLWR74kjgrlyQ21xc7dLmtm9vPsqR6ibuOG/ojb67io8M5cWbZxMXEcINz69nRy/NxbaV1NBhNW5P4AtyUnnrzrOZ4UIf/nGpMdwwN4tlGw6f9Adn+9EaqhvbXC6fOFwyNZ1dpXV84y+f8/qmEhbkpPC3787m83vP576LJzF5RBzX5GXw8e5ylxdBfbyrnHar8dvyCQRoAgdbnXhkQiRPrdlHZX0LlQ2tXt8QILvLrvfuTuCX2q+u3/fGNsA/pw92Nyk9lpjwkJMW9DgSyRQnS+i7y0mLpba5ndI+lhwfq2ni6qe+4MJH1/DMJ/t6nEZntRqeWrOPnLTYgDh/gzU8PoK/3zKHsJAgrnv2SzbbV1p2V2A/PmMAHQjd6UfnjychMpTf/PvEtEJH/fussf1L4N+YOZKr80bxu29OZcMvLuDxa3OZPyHlpB3kr51ta53w6oZil15zxY5SUmLDyfXQxjDuELAJPCQ4iO+fk03+oSqW2b8h4z3cRra7rCTPJfCMxChyMxPYb2lgZEKk12bXDEZIcBBnjj65Dl7owgwUh5zhtsf0VUZ5cvU+rMYwd2wSD727i28++TlFTmrnH+4so6i8niULxvrtW2B3G5MSwz9vm0t8ZCjffm4d6/afujq24HAVWUlRJDrZEMSb4qNC+fFFOaw7cLxz9fHaItvu8/39fUqICuP3V07nmlmZPZYaRw2LYsGEFJZtKKatj4uZzW0drN5t4cLJaX7TOM6ZgE3gAFfPymBYVChPrCoCPN/EqruI0GBG2PsUu3MWisOiabaLmedN9N/pg93Nzk5kT1k9VfZ+LtuP1pAaG+7SNYKctL535zlW08Sy9cVclTeKF26axRPX5XKosoFLn1jLnz/e2znLwBjDX1bvY9SwSBZNS3fDVxY4MhKj+Odtc0mLC+fGF9Z39pAH23nZZO9A6A+um5VBTlosv313JzWNbeQfrHJp+uBAXT9nNJa6lj43T1lbVEFjawcL/bh8AgGewKPCQrjxrCxa2q1EhwWTHu++C4mucpRRPLE0++vT0xmTEs0VuaPc/tqe4pgP7ugPvv2I8xayzsRHhTI8LqLXmSiO0fcdC8YhIlw2fQQrf3wuF05O4+EPdnPFXz5nV2ktX+4/zubiam6bP2ZALWMD3fD4CF69bS7ZyTHc8lJ+Z5viozXNWOpa3F7/HqiQ4CDu//pkSqqauOsfm2jtsDLPgxuQLMhJIT0+gqXrer+YuWJHKbHhIX65fL6rgP/JvnFuFpGhwYz18gwUh5y0OBKjw5xOlxqs1NgIPvrJAs4c7R+jJVdMHRVPWEgQ6w8cp7mtg72WeqctZHsyoZeZKF1H311XUybHhPPn62fyl+tncrS6ia//cS0/W76F5JgwrsoLrJax7pQcE86y73+FySPiWLJ0E29tPkLBYVu/moHswOMpZ49L5qLJaXxaVEFYcFDnxXBPCAkO4tpZmXxaVNHjnprtHVY+3FnOeRNTCQvx7xTp39G5YFh0GH+4ejo/vnCCTz7/jy4Yz2u3z/XJ5/ZH4SHB5GYksP7gcXaV1tFhNb0u4Olu4vBYisrrnV6Y7Dr6duaSqems/PG5XHxGOiVVTXxv3hgiQj274bC/i48K5e+3zOHM0cP4r1c386eP9hIeEsTEdP9qyfCLSycRZr+G4ulNoq+ZlUFwkPCP9c4vZm48VMXxhla/L5/AEEjgYPvF9dUqxfjIUK/PfvF3c7ITKTxS03kBzdUSCthaCbS2WznYbXTU0+i7u8ToMJ64LpePfnIut80fM7AvYIiJCQ/hpZtnc874FHaV1jFtVDyhflZWGp0UzbM35nH/1z2/z+jw+AjOn5jKa/nFTpfzf7C9jLCQIM4NgKm7/vVdVEPC7OwkrAb+vu4Q8ZGhjBrmehMgR7Ou7p0JHaPvO89zPvrubkxKjF/PHvC2yLBgnr3hTG6Zl8335vnnH7ZzJ6R4rdXBt+ZkUtnQesp+nsYYPtheyrxxyT1uJOFPNIErt8vNTCA4SCg+3sSUEXH9ujYxLjWGIDl5d54To+8MRg0L/E6CvhIeEswvF03ma2f4f2nA0+aPT2HUsEhe6XYxc8exWo5UN/lt86ruNIErt4sOD+m8cNmfC5hgm5qZlRR9UtP/v3zsGH0P7dWUynuCgoTrZmfyxf5K9nVpgvXB9jKCBM6fpAlcncYc0wn7U/92mJB2YibK0eomXt2go2/lflfljSIkSPhHl1H4iu2l5I1OJNkD6zo8QRO48ogLJ9t2UB9IA6mc4bEcrGygua2jS+1bR9/KvVJjI1g4ZTjLN5XQ3NbB4cpGdpXWcVGAlE/AxQQuIgkislxEdonIThGZKyKJIrJSRIrs//vPxFLlc7OyEtn+m68xYgC7mOQMj8VqbNtq6ehbedK35mRS3djGe4XHOi9oBsL0QQdXR+CPA+8bYyYC04GdwL3AKmPMeGCV/bZSg+bY3OG/3yzU0bfyqLljkshOjuaVdYdZsaOUSelxAbXlXp8JXETigfnA8wDGmFZjTDWwGHjJ/rCXgMs9FaQ6vYxOjCIsJIjS2mYdfSuPsl3MzGDDwSryD1Vx0eTAKZ+AayPwbMACvCAiBSLynIhEA2nGGMcWGKWA069cRG4VkXwRybdY+rcbhjo9hQQHMT41hpAg0dG38rgrz8wgLDgIYwKrfAKuJfAQYCbwpDEmF2igW7nE2Jr5Om3KbIx5xr6jfV5Kiv+vbFL+YcmCsfx68RQdfSuPS4wO44rckeSkxTLJz1oM9MWVpUYlQIkxZp399nJsCbxMRNKNMcdEJB0o91SQ6vTjaKWrlDf89oozaLeagGnb7NDnCNwYUwoUi0iO/dD5wA7gbeBG+7Ebgbc8EqFSSnlYSHBQQDY+c3Wx/w+ApSISBuwHbsaW/P8pIt8DDgFXeyZEpZRSzriUwI0xm4E8J3ed795wlFJKuUpXYiqlVIDSBK6UUgHK5w1v29raKCkpobm52deh+IWIiAhGjRpFaKjznbWVUsrB5wm8pKSE2NhYsrKyAm4Kj7sZY6isrKSkpITs7Gxfh6OU8nM+L6E0NzeTlJR02idvABEhKSlJ340opVzi8wQOaPLuQs+FUspVfpHAlVJK9d9pn8CLi4vJzs7m+PHjAFRVVZGdnc2LL77IokWLfBydUkr17LRP4BkZGSxZsoR777X157r33nu59dZbycrKGtTrdnR09HpbKaUGy+ezULr69b+3s+NorVtfc/KIOH719Sm9Pubuu+/mzDPP5LHHHmPt2rX86U9/4rPPPqO2tpZLL72UvXv3ct555/GXv/yFoKAglixZwoYNG2hqauLKK6/k17/+NQBZWVlcc801rFy5knvuuYd77733pNt1dXU888wztLa2Mm7cOF5++WWiorTbnlJqYE77EThAaGgoDz/8MHfffTePPfZY5xzs9evX88c//pEdO3awb98+/vWvfwHw29/+lvz8fLZu3cqaNWvYunVr52slJSWxadMmrr322lNuf+Mb32DDhg1s2bKFSZMm8fzzz3v/i1VKDRl+NQLva6TsSe+99x7p6ekUFhZy4YUXAjB79mzGjBkDwHXXXcfatWu58sor+ec//8kzzzxDe3s7x44dY8eOHUybNg2Aa6655qTX7Xq7sLCQX/7yl1RXV1NfX8/ChQu99NUppYYiv0rgvrJ582ZWrlzJl19+ybx58zpHz92n9IkIBw4c4JFHHmHDhg0MGzaMm2666aR529HR0Sc9p+vtm266iTfffJPp06fz4osvsnr1as99UUqpIe+0L6EYY1iyZAmPPfYYmZmZ/OxnP+OnP/0pYCuhHDhwAKvVyquvvsq8efOora0lOjqa+Ph4ysrKeO+991z+XHV1daSnp9PW1sbSpUs99SUppU4Tp/0I/NlnnyUzM7OzbHLHHXfwwgsvsGbNGmbNmsVdd93VeRHziiuuICgoiNzcXCZOnEhGRgZnn322y5/rwQcfZM6cOaSkpDBnzhzq6uo89WUppU4DYtvO0jvy8vJMfn7+Scd27tzJpEmTvBZDINBzopTqSkQ2GmNO2ZPBpRG4iBwE6oAOoN0Ykyci04GngBjgIHC9Mca9cwCVUkr1qD818POMMTO6/BV4DrjXGDMVeAP4mdujU0op1aPBXMScAHxi/3gl8M3Bh6OUUspVriZwA6wQkY0icqv92HZgsf3jq4AMZ08UkVtFJF9E8i0Wy+CiVUop1cnVBD7PGDMTuBi4U0TmA98F7hCRjUAs0OrsicaYZ4wxecaYvJSUFLcErZRSysUEbow5Yv+/HFu9e7YxZpcx5iJjzJnAP4B9ngtTKaVUd30mcBGJFpFYx8fARUChiKTajwUBv8Q2IyUgxcTEnHLsgQce4JFHHum8/cgjjzBx4kRmzJjBrFmz+Nvf/ubNEJVS6hSujMDTgLUisgVYD/zHGPM+cJ2I7AF2AUeBFzwXpm899dRTrFy5kvXr17N582ZWrVqFN+fPK6WUM33OAzfG7AemOzn+OPC4uwNa8OICt77e6ptWD/o1HnroIVavXk1cXBwAcXFx3HjjjYN+XaWUGozTvhdKX2pra6mrq+vsSqiUUv7C73qhuGPErJRSpwMdgfchLi6OmJgY9u/f7+tQlFLqJJrAXXDfffdx5513Ultra/VSX1+vs1CUUj7ndyUUX2hsbGTUqFGdt3/84x+fdP+SJUuor69n1qxZhIaGEhoayk9+8hNvh6mUUifRBA5YrdZe7xcR7rnnHu655x4vRaSUUn3TEopSSgUoTeBKKRWgNIErpVSA0gSulFIBShO4UkoFKE3gSikVoDSB03M7WRFh7969nccee+wxRIT8/HxvhqeUUk5pAu/F1KlTWbZsWeft1157jSlTpvgwIqWUOsHvFvIUFCxw6+vl5q4e8HMvv/xy3nrrLX75y1+yb98+4uPjCQ0NdV9wSik1CDoC70VcXBwZGRkUFhaybNkyrrnmGl+HpJRSnVwagYvIQaAO6ADajTF5IjID2zZqEUA7cIcxZv1gAxrMiNkTrr32WpYtW8YHH3zAqlWreOGFIbvxkFIqwPRnBH6eMWaGMSbPfvv3wK+NMTOA++23h5xFixbx8ssvk5mZ2bkjj1JK+YPB1MAN4Mho8dj2xRxyoqKi+N3vfseECRN8HYpSSp3E1QRugBUiYoCnjTHPAP8FfCAij2AbyZ/l7IkicitwK0BmZubgI/aAvtrJXnvttd4OSSml+uRqAp9njDkiIqnAShHZBVwJ3G2MeV1ErgaeBy7o/kR7sn8GIC8vzy+3cu+rnWxXq1ev9lwgSinVDy7VwI0xR+z/lwNvALOBG4F/2R/ymv2YUkopL+kzgYtItIjEOj4GLgIKsdW8z7U/7KtAkaeCVEopdSpXSihpwBsi4nj8K8aY90WkHnhcREKAZux1bqWUUt7RZwI3xuwHpjs5vhY40xNBKaWU6puuxFRKqQClCVwppQKUJnCct5PdvXs3CxYsYMaMGUyaNIlbbz1R4l+/fj3z588nJyeH3NxcbrnlFhobG70ZslJK+V83Qn/xwx/+kLvvvpvFixcDsG3bNgDKysq46qqrWLZsGXPnzgVg+fLl1NXVERUV5bN4lVKnH/9L4AsWuPf1Brjw5tixYyetzpw6dSoAf/7zn7nxxhs7kzfAlVdeOagQlVJqILSE0oO7776br371q1x88cU8+uijVFdXA1BYWMiZZ+rkG6WU7/nfCNxPlqrffPPNLFy4kPfff5+33nqLp59+mi1btvg6LKWU6qQj8F6MGDGC7373u7z11luEhIRQWFjIlClT2Lhxo69DU0opTeA9ef/992lrawOgtLSUyspKRo4cyV133cVLL73EunXrOh/7r3/9i7KyMl+FqpQ6TflfCaUHuyt2e+y1GxsbGT5ieOftm5bcROnRUpbctYTwiHAAfnz/j6kJqQHgd0/9jjt/dCeVFZUEBQWRNzePMXljqK6odks8pfWlLHlxiVteSynlH1bftNrtrxkwCdyTdpbvdHr8vgfvc3o8d1YuS99Z6smQlFKqTwGTwHOSc3wdgtdYLVaP/LVWSg0tWgNXSqkA5RcJ3Bi/3KjHJ/RcKKVc5fMEHhERQWVlpSYubMm7srKSiIgIX4eilAoAPq+Bjxo1ipKSEiwWi69D8QsREREnLeFXSqme+DyBh4aGkp2d7eswlFIq4LiUwEXkIFAHdADtxpg8EXkVcEwNSQCqjTEzPBKlUkqpU/RnBH6eMabCccMYc43jYxH5A1DjzsCUUkr1btAlFLHtdnw1tp3plVJKeYmrCdwAK0TEAE8bY57pct85QJkxpsjZE0XkVk7sWF8vIj2tiU8GKnq4z9c0toHR2AZGYxs4f45vMLGNdnZQXJm+JyIjjTFHRCQVWAn8wBjzif2+J4G9xpg/DDAwx+fIN8bkDeY1PEVjGxiNbWA0toHz5/g8EZtL88CNMUfs/5cDbwCz7QGFAN8AXnVnUEoppfrWZwIXkWgRiXV8DFwEFNrvvgDYZYwp8VyISimlnHGlBp4GvGG7VkkI8Iox5n37fdcC/3BTLM/0/RCf0dgGRmMbGI1t4Pw5PrfH5lINXCmllP/xeS8UpZRSA6MJXCmlApRfJHAR+ZqI7BaRvSJyr6/j6UpEDorINhHZLCL5Po7lryJSLiKFXY4lishKESmy/z/Mj2J7QESO2M/dZhG5xEexZYjIxyKyQ0S2i8iP7Md9fu56ic3n505EIkRkvYhsscf2a/vxbBFZZ/99fVVEwvwothdF5ECX8+az9h4iEiwiBSLyjv22+8+bMcan/4BgYB8wBggDtgCTfR1Xl/gOAsm+jsMey3xgJlDY5djvgXvtH98L/M6PYnsA+KkfnLd0YKb941hgDzDZH85dL7H5/NwBAsTYPw4F1gFfAf4JXGs//hSwxI9iexG40tc/c/a4fgy8Arxjv+328+YPI/DZ2BYC7TfGtALLgMU+jskvGdviqePdDi8GXrJ//BJwuVeDsushNr9gjDlmjNlk/7gO2AmMxA/OXS+x+ZyxqbffDLX/M9jaZiy3H/fVeespNr8gIqOAS4Hn+NuLUgAABJRJREFU7LcFD5w3f0jgI4HiLrdL8JMfYDtHG4GN9rYA/ibNGHPM/nEptmmf/uQuEdlqL7H4pLzTlYhkAbnYRmx+de66xQZ+cO7sZYDNQDm2Vdj7sHUebbc/xGe/r91jM8Y4zttv7eftUREJ90VswGPAPYDVfjsJD5w3f0jg/m6eMWYmcDFwp4jM93VAPTG292Z+MwoBngTGAjOAY8Cg2i0MlojEAK8D/2WMqe16n6/PnZPY/OLcGWM6jK1N9Chs75Yn+iIOZ7rHJiJnAPdhi3EWkAj8f96OS0QWAeXGmI2e/lz+kMCPABldbo+yH/MLpoc2An6kTETSAez/l/s4nk7GmDL7L5kVeBYfnjsRCcWWIJcaY/5lP+wX585ZbP507uzxVAMfA3OBBHsbDfCD39cusX3NXpIyxpgW4AV8c97OBi4T2z4Ky7CVTh7HA+fNHxL4BmC8/QptGLbVnW/7OCagzzYC/uJt4Eb7xzcCb/kwlpM4kqPdFfjo3Nnrj88DO40x/9flLp+fu55i84dzJyIpIpJg/zgSuBBbjf5j4Er7w3x13pzFtqvLH2TBVmP2+nkzxtxnjBlljMnCls8+MsZcjyfOm6+v1NqvyF6C7er7PuAXvo6nS1xjsM2K2QJs93Vs2NoWHAPasNXQvoettrYKKAI+BBL9KLaXgW3AVmzJMt1Hsc3DVh7ZCmy2/7vEH85dL7H5/NwB04ACewyFwP3242OA9cBe4DUg3I9i+8h+3gqBv2OfqeKrf8ACTsxCcft506X0SikVoPyhhKKUUmoANIErpVSA0gSulFIBShO4UkoFKE3gSikVoDSBq4AlIpeLiBERj60OFJEZvuqiqFRfNIGrQHYdsNb+v6fMwDYv+xRdVtUp5RM6D1wFJHvvkN3AecC/jTE5IrIA+DVQDUzF1r5zG/AjIBK43Bizz9406q9AMmABbjbGHBaRq4BfAR1ADbZNu/fan3sE+F9gErYeJWOAw9h6b7wMRNtDu8sY87knv3alHHQErgLVYuB9Y8weoFJEzrQfnw7cji3RfgeYYIyZja2t5w/sj/kj8JIxZhqwFHjCfvx+YKExZjpwmbG1N74feNUYM8MY86r9cZOBC4wx12Hrn3KhsTU8u6bLaynlcZrAVaC6DlujIOz/O8ooG4ytoVELttYMK+zHtwFZ9o/nYmu0D7bR8zz7x58BL4rI97FtNNKTt40xTfaPQ4FnRWQbtuXRkwf8FSnVT1rDUwFHRBKxdXibKiIGW7I1wH+Ali4PtXa5baWPn3djzO0iMgdbI/6NXUb13TV0+fhuoAzbyD8IaO7fV6PUwOkIXAWiK4GXjTGjjTFZxpgM4ABwjovP/xxblziA64FPAURkrDFmnTHmfmy18QygDttWZz2JB44ZW9vX79D7yF0pt9IErgLRddh6s3f1Oq7PRvkBcLOIbMWWdH9kP/6w2DawLsSW5LdgawE62b5B7jVOXusvwI0isgXbRgINTh6jlEfoLBSllApQOgJXSqkApQlcKaUClCZwpZQKUJrAlVIqQGkCV0qpAKUJXCmlApQmcKWUClD/P70nD/3woFaKAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"id": "yX2FTS6yVWQh",
"outputId": "4985904e-c184-4094-a2d0-fde7191081e1"
},
"source": [
"#Plotar R\n",
"plot_controle(df1,limites_aux,\"R\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "iXz2xrPkpBXl"
},
"source": [
"df1.head(10)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "6gvJPPEOPAQO"
},
"source": [
"#b) calcule o percentual fora das especificações (PFE) do processo para um cliente que deseja peças com dimensão entre 60 e 65;\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "un-yCbyMWvDj"
},
"source": [
"O percentual fora das especificações do processo é dado por:"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WARJiGziXPCz"
},
"source": [
""
]
},
{
"cell_type": "code",
"metadata": {
"id": "TyTtkiaVU8UT"
},
"source": [
"#Definindo os limites de especificação \n",
"LIE = 60\n",
"LSE = 65\n",
"\n",
"def get_PFE(df,LIE,LSE):\n",
" #definindo a média u\n",
" u_0 = df['Média'].mean(axis=0)\n",
" #definindo o sigma = Rbarra/d2\n",
" Rbarra = df[\"Amplitude\"].mean(axis=0)\n",
" d2 = chamar_parametro('d2',n)\n",
" sigma = Rbarra/d2\n",
" #primeiro termo do PFE\n",
" plus_z = (LIE-u_0)/sigma\n",
" #segundo termo do PFE\n",
" minus_z = (LSE-u_0)/sigma\n",
" #calculando as probabilidades\n",
" PFE = st.norm.cdf(plus_z)+(1-st.norm.cdf(minus_z))\n",
" print(f\"O percentual fora das especificações do processo de Limite inferior\",\n",
" f\"= {LIE} e Limite Superior = {LSE} é igual a {np.round(PFE*100,2)}%\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "94gWv7F5fgWY",
"outputId": "91cbff00-c499-42ee-87a8-8bef06a42721"
},
"source": [
"get_PFE(df1,60,65)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"O percentual fora das especificações do processo de Limite inferior = 60 e Limite Superior = 65 é igual a 49.45%\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qI8pxzdkqvhV",
"outputId": "4a91d95e-f47b-406e-b0be-098791a025be"
},
"source": [
"#LM de Xbarra\n",
"limites_x[1]"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"61.289989285714285"
]
},
"metadata": {
"tags": []
},
"execution_count": 126
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yj2om8IjrqLX",
"outputId": "70bc3246-7460-4b4c-89d6-9b473df61405"
},
"source": [
"#LM de R\n",
"limites_aux[1]"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"9.308125000000002"
]
},
"metadata": {
"tags": []
},
"execution_count": 127
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tIX3d1nrqgxv"
},
"source": [
"O valor alto do PFE é condizente com os gráficos de Xbarra e R.\n",
"\n",
"- Olhando para Xbarra, vemos que o Limite Médio (LM) é 61.3 e Xbarra fica entre 60 e 61 em muitas amostras. \n",
"- Olhando para R, vemos que a amplitude das amostras tem média de 9.3.\n",
"\n",
"Pensando nos dois gráficos como um conjunto, as amostra tem média (61.29) muito próxima do limite inferior de especificação (60) e, cosiderando que a amplitude média de é de 9.3, é coerente pensar que frequentemente itens prodizidos estarão abaixo limite inferior de especificação. Soma-se a isso os itens que estarão acima do limite superior de 65 e temos que 49.45% (PFE) dos itens produzidos estarão abaixo de 60 ou acima de 65."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DyimnknBPC9S"
},
"source": [
"#c) calcule os índices de capacidade do processo para as especificações do cliente;"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TamZCnVVuKdm"
},
"source": [
"Os índices de capacidade são métricas para avaliar o quanto o processo está atendendo às especificações."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vSscUGfauWfu"
},
"source": [
""
]
},
{
"cell_type": "code",
"metadata": {
"id": "sh-aikNptlW-"
},
"source": [
"def calcula_ICP(df,LIE,LSE):\n",
" #definindo a média u\n",
" u_0 = df['Média'].mean(axis=0)\n",
" #definindo o sigma = Rbarra/d2\n",
" Rbarra = df[\"Amplitude\"].mean(axis=0)\n",
" d2 = chamar_parametro('d2',n)\n",
" sigma = Rbarra/d2\n",
" \n",
" cp = (LSE-LIE)/6*sigma\n",
" \n",
" cpk = min((LSE-u_0)/3*sigma,(u_0-LIE)/3*sigma)\n",
" \n",
" d= (LSE+LIE)/2\n",
" cpm = (LSE-LIE)/(6*math.sqrt(sigma**2+(d-u_0)**2))\n",
"\n",
" return cp,cpk,cpm"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "4Qre0iZZvcF3"
},
"source": [
"cp,cpk,cpm = calcula_ICP(df1,LIE,LSE)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "py0o4nbGw7kN",
"outputId": "57bc2f81-de93-4d3b-d90b-f0a903bcdd17"
},
"source": [
"print(\"Índices:\\nCP = {}\\nCpk = {}\\nCpm = {}\".format(np.round(cp,2),np.round(cpk,2),np.round(cpm,2)))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Índices:\n",
"CP = 2.87\n",
"Cpk = 1.48\n",
"Cpm = 0.23\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pe5ZIWl9PFLp"
},
"source": [
"#d) classifique o processo de acordo com os índices de capacidade do processo calculados."
]
},
{
"cell_type": "code",
"metadata": {
"id": "0sosijaYPGa5"
},
"source": [
"def classifica_processo(cp,cpk):\n",
" \n",
" if cp<1:\n",
" print('Cp < 1 -> Processo inviável')\n",
" elif cp>3:\n",
" print('Cp > 3 -> Processo de Alta Precisão')\n",
" elif cp>1 and cp<3:\n",
" print('CP entre 1 e 3 - > proceso viável')\n",
"\n",
" if cpk<1:\n",
" print('Cpk < 1 -> Processo incapaz')\n",
" if cpk>=1 and cpk<=1.33:\n",
" print('Cpl entre 1 e 1.33 - > Processo razoavelmente capaz')\n",
" if cpk>1.33:\n",
" print('Cpk > 1.33 -> Processo capaz')\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ovQlxVGTy3GM",
"outputId": "6ab256cb-1b3e-4104-99c9-8c1a24a01d79"
},
"source": [
"classifica_processo(cp,cpk)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"CP entre 1 e 3 - > proceso viável\n",
"Cpk > 1.33 -> Processo capaz\n"
],
"name": "stdout"
}
]
}
]
}
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