Statistical_Process_Control_templates.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bf2e208-2441-4106-ab69-721b5c89bfd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Quality Control Charts with Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cba82e44-203e-4fe4-81d1-784a02200afd",
   "metadata": {},
   "outputs": [],
   "source": [
    "##################################################################################################################\n",
    "#\n",
    "#                                                       x chart and mR chart\n",
    "#\n",
    "#################################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "13982d6f-b095-45c5-88a9-4a3f25fc4e59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d279a3eb-34ba-4976-b4b6-e9617c5145f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All points within control limits.\n",
      "All points within control limits.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Create dummy data\n",
    "x = pd.Series(np.random.normal(loc=10, scale=2, size=10))\n",
    "\n",
    "# Define list variable for moving ranges\n",
    "MR = [np.nan]\n",
    "\n",
    "# Get and append moving ranges\n",
    "i = 1\n",
    "for data in range(1, len(x)):\n",
    "    MR.append(abs(x[i] - x[i-1]))\n",
    "    i += 1\n",
    "\n",
    "# Convert list to pandas Series objects    \n",
    "MR = pd.Series(MR)\n",
    "\n",
    "# Concatenate mR Series with and rename columns\n",
    "data = pd.concat([x,MR], axis=1).rename(columns={0:\"x\", 1:\"mR\"})\n",
    "\n",
    "# Plot x and mR charts\n",
    "fig, axs = plt.subplots(2, figsize=(15,15), sharex=True)\n",
    "\n",
    "# x chart\n",
    "axs[0].plot(data['x'], linestyle='-', marker='o', color='black')\n",
    "axs[0].axhline(statistics.mean(data['x']), color='blue')\n",
    "axs[0].axhline(statistics.mean(data['x'])+3*statistics.mean(data['mR'][1:len(data['mR'])])/1.128, color = 'red', linestyle = 'dashed')\n",
    "axs[0].axhline(statistics.mean(data['x'])-3*statistics.mean(data['mR'][1:len(data['mR'])])/1.128, color = 'red', linestyle = 'dashed')\n",
    "axs[0].set_title('Individual Chart')\n",
    "axs[0].set(xlabel='Unit', ylabel='Value')\n",
    "\n",
    "# mR chart\n",
    "axs[1].plot(data['mR'], linestyle='-', marker='o', color='black')\n",
    "axs[1].axhline(statistics.mean(data['mR'][1:len(data['mR'])]), color='blue')\n",
    "axs[1].axhline(statistics.mean(data['mR'][1:len(data['mR'])])+3*statistics.mean(data['mR'][1:len(data['mR'])])*0.8525, color='red', linestyle ='dashed')\n",
    "axs[1].axhline(statistics.mean(data['mR'][1:len(data['mR'])])-3*statistics.mean(data['mR'][1:len(data['mR'])])*0.8525, color='red', linestyle ='dashed')\n",
    "axs[1].set_ylim(bottom=0)\n",
    "axs[1].set_title('mR Chart')\n",
    "axs[1].set(xlabel='Unit', ylabel='Range')\n",
    "\n",
    "# Validate points out of control limits for x chart\n",
    "i = 0\n",
    "control = True\n",
    "for unit in data['x']:\n",
    "    if unit > statistics.mean(data['x'])+3*statistics.mean(data['mR'][1:len(data['mR'])])/1.128 or unit < statistics.mean(data['x'])-3*statistics.mean(data['mR'][1:len(data['mR'])])/1.128:\n",
    "        print('Unit', i, 'out of cotrol limits!')\n",
    "        control = False\n",
    "    i += 1\n",
    "if control == True:\n",
    "    print('All points within control limits.')\n",
    "    \n",
    "# Validate points out of control limits for mR chart\n",
    "i = 0\n",
    "control = True\n",
    "for unit in data['mR']:\n",
    "    if unit > statistics.mean(data['mR'][1:len(data['mR'])])+3*statistics.mean(data['mR'][1:len(data['mR'])])*0.8525 or unit < statistics.mean(data['mR'][1:len(data['mR'])])-3*statistics.mean(data['mR'][1:len(data['mR'])])*0.8525:\n",
    "        print('Unit', i, 'out of control limits!')\n",
    "        control = False\n",
    "    i += 1\n",
    "if control == True:\n",
    "    print('All points within control limits.')\n",
    "    \n",
    "plt.savefig(\"C:/Users/renea/Desktop/Statistical Process Control/x-and-mR_Chart.png\", dpi=300) # dpi=1500 is recommended for professional reports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47433ab4-4773-4108-9365-74f675a3bfaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "##################################################################################################################\n",
    "#\n",
    "#                                                       x-bar chart and S chart\n",
    "#\n",
    "#################################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c3e0507b-99a9-40ff-974b-c7ca12ef9785",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "882b1f64-b13f-47a5-806e-da58a8a894ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Group 7 out of mean control limits!\n",
      "All points within control limits.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Create dummy data\n",
    "x = np.array([list(np.random.normal(loc=10, scale=2, size=11)),\n",
    "        list(np.random.normal(loc=10, scale=2, size=11)),\n",
    "        list(np.random.normal(loc=10, scale=2, size=11)),\n",
    "        list(np.random.normal(loc=10, scale=2, size=11)),\n",
    "        list(np.random.normal(loc=10, scale=2, size=11)),\n",
    "        list(np.random.normal(loc=10, scale=2, size=11)),\n",
    "        list(np.random.normal(loc=10, scale=2, size=11)),\n",
    "        list(np.random.normal(loc=13, scale=2, size=11)),\n",
    "        list(np.random.normal(loc=10, scale=2, size=11)),\n",
    "        list(np.random.normal(loc=10, scale=2, size=11))])\n",
    "        \n",
    "# Define list variable for groups means\n",
    "x_bar = []\n",
    "\n",
    "# Define list variable for groups standard deviation\n",
    "s = [] \n",
    "\n",
    "# Get and append groups means and standard deviations\n",
    "for group in x:\n",
    "    x_bar.append(group.mean())\n",
    "    s.append(np.std(group))\n",
    "    \n",
    "# Plot x-bar and s charts\n",
    "fig, axs = plt.subplots(2, figsize=(15,15))\n",
    "\n",
    "# x-bar chart\n",
    "axs[0].plot(x_bar, linestyle='-', marker='o', color='black')\n",
    "axs[0].axhline((statistics.mean(x_bar)+0.927*statistics.mean(s)), color='red', linestyle='dashed')\n",
    "axs[0].axhline((statistics.mean(x_bar)-0.927*statistics.mean(s)), color='red', linestyle='dashed')\n",
    "axs[0].axhline((statistics.mean(x_bar)), color='blue')\n",
    "axs[0].set_title('x-bar Chart')\n",
    "axs[0].set(xlabel='Group', ylabel='Mean')\n",
    "\n",
    "# s chart\n",
    "axs[1].plot(s, linestyle='-', marker='o', color='black')\n",
    "axs[1].axhline((1.649*statistics.mean(s)), color='red', linestyle='dashed')\n",
    "axs[1].axhline((0.321*statistics.mean(s)), color='red', linestyle='dashed')\n",
    "axs[1].axhline((statistics.mean(s)), color='blue')\n",
    "axs[1].set_title('s Chart')\n",
    "axs[1].set(xlabel='Group', ylabel='Range')\n",
    "\n",
    "# Validate points out of control limits for x-bar chart\n",
    "i = 0\n",
    "control = True\n",
    "for group in x_bar:\n",
    "    if group > statistics.mean(x_bar)+0.927*statistics.mean(s) or group < statistics.mean(x_bar)-0.927*statistics.mean(s):\n",
    "        print('Group', i, 'out of mean control limits!')\n",
    "        control = False\n",
    "    i += 1\n",
    "if control == True:\n",
    "    print('All points within control limits.')\n",
    "    \n",
    "# Validate points out of control limits for s chart\n",
    "i = 0\n",
    "control = True\n",
    "for group in s:\n",
    "    if group > 1.649*statistics.mean(s) or group < 0.321*statistics.mean(s):\n",
    "        print('Group', i, 'out of standard deviation cotrol limits!')\n",
    "        control = False\n",
    "    i += 1\n",
    "if control == True:\n",
    "    print('All points within control limits.')\n",
    "    \n",
    "plt.savefig(\"C:/Users/renea/Desktop/Statistical Process Control/x-bar_s_Chart.png\", dpi=300) # dpi=1500 is recommended for professional reports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edbd11c1-bb51-4424-ac5d-13a56b51dab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "##################################################################################################################\n",
    "#\n",
    "#                                                        c-chart\n",
    "#\n",
    "#################################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fd82f734-5238-4551-9a20-73c0fecd1479",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "066556a0-2694-48ef-bfa4-4e823d5a51f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All points within control limits.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Create dummy data\n",
    "c = {'defects':np.random.randint(0,5,10).tolist(),\n",
    "    'group_size':np.repeat(10,10).tolist()}\n",
    "\n",
    "# Convert data to data frame\n",
    "c = pd.DataFrame(c)\n",
    "\n",
    "# Plot c-chart\n",
    "plt.figure(figsize=(15,7.5))\n",
    "plt.plot(c['defects'], linestyle='-', marker='o', color='black')\n",
    "plt.axhline(statistics.mean(c['defects'])+3*np.sqrt(statistics.mean(c['defects'])), color='red', linestyle='dashed')\n",
    "plt.axhline(statistics.mean(c['defects'])-3*np.sqrt(statistics.mean(c['defects'])), color='red', linestyle='dashed')\n",
    "plt.axhline(statistics.mean(c['defects']), color='blue')\n",
    "plt.ylim(bottom=0)\n",
    "plt.title('c Chart')\n",
    "plt.xlabel('Group')\n",
    "plt.ylabel('Defect Count')\n",
    "\n",
    "# Validate points out of control limits\n",
    "i = 0\n",
    "control = True\n",
    "for group in c['defects']:\n",
    "    if group > statistics.mean(c['defects'])+3*np.sqrt(statistics.mean(c['defects'])) or group < statistics.mean(c['defects'])-3*np.sqrt(statistics.mean(c['defects'])):\n",
    "        print('Group', i, 'out of defects cotrol limits!')\n",
    "        control = False\n",
    "    i += 1\n",
    "if control == True:\n",
    "    print('All points within control limits.')\n",
    "    \n",
    "plt.savefig(\"C:/Users/renea/Desktop/Statistical Process Control/c_Chart.png\", dpi=300) # dpi=1500 is recommended for professional reports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4b0e161-ea9c-400e-97a5-5501ff47ab82",
   "metadata": {},
   "outputs": [],
   "source": [
    "##################################################################################################################\n",
    "#\n",
    "#                                                        u-chart\n",
    "#\n",
    "#################################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ab560057-201d-4a92-bce0-f7f8a0bf9176",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "218a14be-8a9d-4245-9c25-0fd6eebb05ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All points within control limits.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Create dummy data\n",
    "u = {'defects':np.random.randint(1,5,10).tolist(),\n",
    "    'group_size':np.random.randint(10,15,10).tolist()}\n",
    "\n",
    "# Convert data to data frame\n",
    "u = pd.DataFrame(u)\n",
    "\n",
    "# Add 'u' column to data frame\n",
    "u['u'] = u['defects']/u['group_size']\n",
    "\n",
    "# Plot u-chart\n",
    "plt.figure(figsize=(15,7.5))\n",
    "plt.plot(u['u'], linestyle='-', marker='o', color='black')\n",
    "plt.step(x=range(0, len(u['u'])), y=u['u'].mean()+3*np.sqrt(u['u'].mean()/u['group_size']), color='red', linestyle='dashed')\n",
    "plt.step(x=range(0, len(u['u'])), y=u['u'].mean()-3*np.sqrt(u['u'].mean()/u['group_size']), color='red', linestyle='dashed')\n",
    "plt.axhline(statistics.mean(u['u']), color='blue')\n",
    "plt.ylim(bottom=0)\n",
    "plt.title('u Chart')\n",
    "plt.xlabel('Group')\n",
    "plt.ylabel('Fraction Defective')\n",
    "\n",
    "# Validate points out of control limits\n",
    "i = 0\n",
    "control = True\n",
    "for group in u['u']:\n",
    "    if group > u['u'].mean()+3*np.sqrt(u['u'].mean()/u['group_size'][i]) or group < u['u'].mean()-3*np.sqrt(u['u'].mean()/u['group_size'][i]):\n",
    "        print('Group', i, 'out of fraction defective cotrol limits!')\n",
    "        control = False\n",
    "    i += 1\n",
    "if control == True:\n",
    "    print('All points within control limits.')\n",
    "\n",
    "plt.savefig(\"C:/Users/renea/Desktop/Statistical Process Control/u_Chart.png\", dpi=300) # dpi=1500 is recommended for professional reports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa12b0ea-0c71-471e-b16f-8f3642ee3666",
   "metadata": {},
   "outputs": [],
   "source": [
    "##################################################################################################################\n",
    "#\n",
    "#                                                        p-chart\n",
    "#\n",
    "#################################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aed59c4d-cf50-4c0a-aef7-4358870fd45b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7c36e50b-006b-4d63-b49f-909eb7002fbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All points within control limits.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Create dummy data\n",
    "p = {'defects':np.random.randint(1,5,10).tolist(),\n",
    "    'group_size':np.random.randint(10,15,10).tolist()}\n",
    "    \n",
    "# Convert data to data frame\n",
    "p = pd.DataFrame(p)\n",
    "\n",
    "# Add 'p' column to data frame\n",
    "p['p'] = p['defects']/p['group_size']\n",
    "\n",
    "# Plot p-chart\n",
    "plt.figure(figsize=(15,7.5))\n",
    "plt.plot(p['p'], linestyle='-', marker='o', color='black')\n",
    "plt.step(x=range(0,len(p['p'])), y=statistics.mean(p['p'])+3*(np.sqrt((statistics.mean(p['p'])*(1-statistics.mean(p['p'])))/(p['group_size']))), color='red', linestyle='dashed')\n",
    "plt.step(x=range(0,len(p['p'])), y=statistics.mean(p['p'])-3*(np.sqrt((statistics.mean(p['p'])*(1-statistics.mean(p['p'])))/(p['group_size']))), color='red', linestyle='dashed')\n",
    "plt.axhline(statistics.mean(p['p']), color='blue')\n",
    "plt.ylim(bottom=0)\n",
    "plt.title('p Chart')\n",
    "plt.xlabel('Group')\n",
    "plt.ylabel('Fraction Defective')\n",
    "\n",
    "# Validate points out of control limits\n",
    "i = 0\n",
    "control = True\n",
    "for group in p['p']:\n",
    "    if group > (statistics.mean(p['p'])+3*(np.sqrt((statistics.mean(p['p'])*(1-statistics.mean(p['p'])))/statistics.mean(p['group_size'])))) or group < (statistics.mean(p['p'])-3*(np.sqrt((statistics.mean(p['p'])*(1-statistics.mean(p['p'])))/statistics.mean(p['group_size'])))):\n",
    "        print('Group', i, 'out of fraction defective cotrol limits!')\n",
    "        control = False\n",
    "    i += 1\n",
    "if control == True:\n",
    "    print('All points within control limits.')\n",
    "    \n",
    "plt.savefig(\"C:/Users/renea/Desktop/Statistical Process Control/p_Chart.png\", dpi=300) # dpi=1500 is recommended for professional reports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75fd48bf-b53f-4e11-b6bf-cdb4bfa0dc16",
   "metadata": {},
   "outputs": [],
   "source": [
    "##################################################################################################################\n",
    "#\n",
    "#                                                        np-chart\n",
    "#\n",
    "#################################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b1f31525-6500-49bd-94ed-a6a04210f8fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c50a6b83-0e6e-4200-a630-4b6e02c3bb2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All points within control limits.\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAODSnPUUTTNbkPrnITM7mObrkJkdzMjGU0/vXG9mG82s6znWq2JmyWb2zIX/FeA348aNU0REhMLCwhQREaFx48Z5HQkAQgqfowCQfZ31CJ5z7p7UPwtezIbNLFzS25IekrRT0lIzm+6cW5vOev0kzbqY/cBfxo0bp+joaB09elSStG3bNkVHR0uSGjRo4GU0AAgJfI4CQPaWkYusjM3Ia+moKmmjc26zc+6EUubxPZnOem0lTZX0Swa2CZ+LiYn545eS044ePaqYmBiPEgFAaOFzFACyt4zc6PzmtE9Sb3ReOQPfd42kHWme70x9Le22rpH0d0nDz7UhM4s2s2VmtmzPnj0Z2DVC1fbt2y/odQDAn/E5CgDZ27nm4HUzs0OSbk07/07Sz5I+ycC2LZ3X3BnP/yOpi3Mu+Vwbcs7FO+einHNRJUqUyMCuEYoWL16s8PDwdJeVKlUqi9MAQOjZsmWLcuXKle4yPkcBIHs4a8FzzvVJnX83wDlXKPWroHOumHOuWwa2vVPStWmel5T04xnrREmaaGZblXKPvaFm9tQF/Q0Q8k6cOKHY2FjdfffdKlSokHLnzv2n5fny5VNcXJxH6QAg+DnnNHLkSN16660ys7+UPD5HASD7yMgpmkvMrPDpJ2ZWJIMlbKmkcmZWxsxySaoraXraFZxzZZxzEc65CElTJL3gnJuW0fAIfYmJibrjjjsUFxenxo0ba/PmzXr33XdVunTpP9aJi4vjwgAAcBa7d+/W448/rubNm6tq1apav369Ro0a9afP0datW/M5CgDZREYK3qvOud9OP3HOHZD06vm+yTmXJKmNUq6OuU7SZOfcGjNraWYtLzIvfCI5OVkDBw5U5cqVtWvXLk2bNk2jRo1S4cKF1aBBA23dulU7duxQzpw5tWHDBq/jAkBQmjJliipWrKgvv/xS//3vf/W///1PpUqV+uNz9Pjx4ypVqpQWLFgg586cJQEA8KOMFLz01jnvDdIlyTn3mXOuvHOurHMuLvW14c65v1xUxTn3L+fclIxsF6Ft8+bNuv/++9WpUyfVrl1biYmJevLJv15gtWTJkmrSpIneffdd7dq1y4OkABCcfv31VzVo0EB16tRRmTJltGLFCr344osKC/vzj+xcuXKpS5cu+uabbzRnzhyP0gIAslJGCt4yM3vDzMqa2XVm9qak5YEOBv85PUckMjJSq1at0pgxYzR16lRdfvnlZ/2erl27/nG0DwAgzZ49W7fccosmT56s119/XYsWLdINN9xw1vWff/55XXXVVerVq1cWpgQAeCUjBa+tpBOSJkmaLOl3Sa0DGQr+89NPP/1pjsjq1av13HPPySy9i63+f2XKlFHDhg01YsQI/fILt0oEkH0dOXJErVu31iOPPKJChQrpm2++0SuvvKKcOXOe8/vy5MmjTp06ac6cOVq4cGEWpQUAeOW8Bc85d8Q511VSjdRbFXR3zh3JgmzwiQ8//PCPOSKDBw/+Y45IRnXr1k3Hjh3Tm2++GcCUABC8vvnmG1WqVEnDhg3Tyy+/rOXLlysqKirD3x8dHa3ixYtzJU0AyAbOW/DM7C4zWytpberzSDMbGvBkCHmn54j885//VNmyZbVixQq1bdv2L3NEzqdChQr65z//qSFDhmj//v0BSgsAwefEiROKiYnRPffcoxMnTuirr77SG2+8obx5817QdvLnz6/27dvr888/1/LlzLIAAD/LyG/ab0p6RNI+SXLOrZJUPZChEPrSzhH597//fd45IucTExOjw4cPa/DgwZmYEgCC1+rVq1W1alX17t1bjRs31urVq1WjRo2L3l7r1q1VpEgR5uIBgM9l6FCKc27HGS8lByALfODMOSKLFy9Wjx49lCNHhi68ela33HKLnnrqKf33v//VwYMHMyktAASf5ORk9e/fX1FRUfrpp5/0ySefaNSoUSpUqNAlbbdQoUJ66aWXNG3aNK1evTqT0gIAgk1GCt4OM7tLkjOzXGbWUSn3tQP+JO0ckfbt22v58uWqXLlypm0/JiZGBw4c0NChnCEMwJ82bdqkGjVqqEuXLn/cRuaJJ57ItO2/+OKLKlCggHr37p1p2wQABJeMFLyWSrlq5jWSdkqqJK6iiTROnDih7t2765577tHJkyc1Z84cDRo06ILniJxPVFSUatasqUGDBunIEa7zA8A/nHOKj49XZGSkEhIS9P7772vq1KkqUaJEpu6naNGiat26tSZNmqT169dn6rYBAMHhrAXPzPqlPrzfOdfAOXeFc+5y51xD59y+LMqHIHd6jkifPn3UpEkTJSQk6L777gvY/mJjY7V371698847AdsHAGSln376SbVr11aLFi105513KjExUY0aNTrvbWQuVvv27ZUnTx717ds3INsHAHjrXEfwHjWznJK6ZVUYhI4z54hMnz5dI0eOvOQ5Iudz9913q0aNGurfv7+OHTsW0H0BQKBNnjxZFStW1Ndff63Bgwdr9uzZuvbaawO6z8svv1zR0dEaO3astmzZEtB9AQCy3rkK3heS9kq61cwOmtmhtH9mUT4EoU2bNum+++5Tly5d9NhjjykxMVGPP/54lu2/R48e+umnnzR69Ogs2ycAZKb9+/erfv36evbZZ3X99ddf9G1kLlanTp0UHh6ufv36nX9lAEBIOetPEudcJ+dcYUmfOucKOecKpv0zCzMiSKSdI5KYmKixY8dqypQpmT5H5Hzuv/9+VatWTX379tXJkyezdN8AcKlmzZqlW265RR9++KF69uyphQsXqkKFClma4ZprrtHzzz+v0aNHa+fOnVm6bwBAYJ13qNA596SZlTazv0mSmeU1s4KBj4Zg8uOPP/4xR6RatWpavXq1GjZsGLA5IudiZoqNjdX27dv1wQcfZPn+AeBiHDlyRK1atVLNmjVVpEgRffvtt4qNjb3k28hcrC5duig5OVkDBw70ZP8AgMA4b8Ezs+aSpkgakfpSSUnTApgJQWbSpEl/zBF56623NGvWrIDPETmfWrVq6bbbblPv3r2VnMxtGQEEt0WLFikyMlIjRoxQhw4dtHz5ct1+++2eZoqIiFCjRo0UHx+vn3/+2dMsAIDMk5GT/VtLulvSQUlyzv0g6fJAhkJw2L9/v+rVq6e6deuqfPnyWrlypdq0aZNlc0TO5fRRvI0bN2rSpElexwGAdB0/flzdunXTvffeq+TkZM2ZM0cDBw5Unjx5vI4mSerWrZuOHz+uN954w+soAIBMkpHf1I87506cfmJmOSS5wEVCMPjiiy9UsWJFTZkyRb169dKCBQtUvnx5r2P9yVNPPaWbb75ZcXFxOnXqlNdxAOBPEhISVLVqVfXt21fPP/98wG8jczHKly+vZ599VkOHDtW+fdwBCQD8ICMFb66ZdZeU18wekvShpBmBjQWvHD58WK1atVKtWrVUtGhRffvtt4qJifFsjsi5hIWFKSYmRmvXrtW0adO8jgMAklJuI9OvXz9FRUXp559/1owZM/TOO++oYMHgnL7evXt3HT58WIMHD/Y6CgAgE5hz5z4YZ2ZhkppKeliSSZolaaQ73zcGSFRUlFu2bJkXu/a9hQsXqnHjxtq8ebM6dOignj17Bs1pRGeTnJysG2+8UQUKFNDy5cs9uegLAJy2adMmPffcc1q0aJH+8Y9/aPjw4SpevLjXsc7r6aef1pw5c7R161YVLlzY6zgAgPMws+XOuaj0lmXkKpqnlHJRlRecc884597xqtwhME7PEalevbqSk5P19ddfa8CAAUFf7iQpPDxc3bp104oVK/T55597HQdANuWc0/DhwxUZGak1a9bogw8+0IcffhgS5U6SYmJidODAAQ0dOtTrKACAS3TWI3iWcijkVUltlHLkziQlS3rLOffvLEt4Bo7gZa6EhAQ1atRICQkJatasmd54442gPY3obE6ePKly5crpqquu0qJFiziKByBL/fjjj2ratKm++OIL/e1vf9Po0aNVsmRJr2NdsEcffVRLly7V1q1blT9/fq/jAADO4WKP4LVTytUzqzjnijnnikq6Q9LdZvZy5sdEVkpOTlbfvn1DZo7IueTMmVNdu3bV4sWL9dVXX3kdB0A2MnHiRFWsWFFz587VkCFDNGvWrJAsd5LUo0cP7d27VyNGjDj/ygCAoHWuI3grJD3knNt7xuslJM12zt2WBfn+giN4l27jxo1q3LixFi1apGeeeUbDhg0LmdOIzubYsWMqW7asypcvrzlz5ngdB4DP7du3T61bt9akSZN0xx136P333w+6Kw1fjAcffFBr167Vli1bQuI0fQDIri72CF7OM8udJDnn9kjKmVnhkHXSzhFZu3atxo0bp8mTJ4d8uZOkPHnyqFOnTvr666+1YMECr+MA8LHPP/9ct9xyi6ZOnRq0t5G5WLGxsdq9e7dGjRrldRQAwEU6V8E7cZHLEIR27dqlWrVqqVWrVrr77ru1evVq1a9f31fz1Zo3b64SJUooLi7O6ygAfOjw4cNq2bKlHn30URUrVkxLliwJ2tvIXKwaNWrorrvuUr9+/XTiBD/qASAUnavgRZrZwXS+Dkm6JasC4tJNnDhRt9xyi+bNm6e33347pOeInEv+/PnVvn17ffHFF1q6dKnXcQD4yMKFCxUZGan4+Hh16tRJS5cu1W23eTJTIaDMTLGxsdq+fbvGjh3rdRwAwEU4733wgg1z8DJu3759euGFFzR58mTdeeedev/991WuXDmvYwXUwYMHFRERoerVq3PzcwCX7Pjx43rllVc0YMAARUREaMyYMbr33nu9jhVQzjlVqVJFBw4c0Pfff++rI5QA4BeXdB88hKbPPvtMFStW1Mcff6y4uDjNnz/f9+VOkgoVKqSXXnpJn3zyiRISEryOAyCErVq1SlWqVFH//v3VrFkzrVq1yvflTvr/R/E2bdqkSZMmeR0HAHCBKHg+c/jwYbVo0UK1a9dW8eLFtWTJEnXv3j1bjcC2bdtWBQsWVO/evb2OAiAEJSUlqU+fPqpSpYr27NmjmTNnKj4+PiRvI3OxnnjiCVWsWFFxcXE6deqU13EAABeAgucjCxYsUGRkpN555x117txZy5YtU6VKlbyOleWKFi2q1q1ba/LkyVq/fr3XcQCEkB9++EHVq1dX9+7d9dRTTykxMVG1a9f2OlaWCwsLU0xMjNatW6ePP/7Y6zgAgAtAwfOB48ePq0uXLqpevbqcc5o7d6769eun3Llzex3NMy+//LLy5MnDUTwAGeKc07Bhw1SpUiWtW7dO48eP16RJk1SsWDGvo3mmTp06Kl++vHr16qVQm68PANnZeQuemT1tZj+Y2W+nr6JpZgezIhzOb+XKlYqKilL//v3VvHnzbDNH5Hwuv/xytWzZUuPGjdPmzZu9jgMgiJ2+jcwLL7yge+65R4mJiapXr56vbiNzMcLDw9W9e3etXLlSn376qddxAAAZlJEjeP0lPeGcK+ycK+ScK+icKxToYDi3pKQk9e7dW1WrVtXevXv16aefasSIEdlqjsj5dOzYUeHh4erXr5/XUQAEIeecJkyYoIoVK2r+/PkaOnSovvjiC11zzTVeRwsa9evXV0REBEfxACCEZKTg/eycWxfwJMiwH374Qffee69iYmL097//XYmJiXr00Ue9jhV0rr76ajVt2lSjR4/Wzp07vY4DIIjs27dPdevWVf369XXjjTdq5cqVatWqVbY/anemnDlzqmvXrvr222/15Zdfeh0HAJABGSl4y8xskpnVSz1d82kzezrgyfAXzjkNHTpUlSpV0vfff68JEyZk+zki59O5c2c55zRgwACvowAIEp9++ukft5Hp06dPtrmNzMX617/+pauvvlq9evXyOgoAIAMyUvAKSToq6WFJj6d+PRbIUPirnTt3qmbNmmrdurXuvfdeJSYmqm7dul7HCnoRERFq1KiR4uPjtXv3bq/jAPDQoUOHFB0drccee0wlSpTQ0qVL1bVrV4WHh3sdLajlzp1bnTt31ty5czV//nyv4wAAzsNC7Zz6qKgot2zZMq9jZBnnnMaPH682bdroxIkTGjRokFq0aMFpRBfghx9+0A033KAOHTqof//+XscB4IH58+ercePG2rp1qzp37qzXX389W19p+EIdPXpUZcqUUaVKlTRr1iyv4wBAtmdmy51zUekty8hVNEua2cdm9ouZ/WxmU82sZObHxJn27t2rf/7zn2rYsKFuvPFGrVq1Si1btqTcXaBy5cqpbt26Gjp0qPbt2+d1HABZ6NixY+rcubPuu+8+mZnmzZunvn37Uu4uUL58+dShQwfNnj1bS5Ys8ToOAOAcMnKK5mhJ0yVdLekaSTNSX0MAffrpp7rlllv0ySef/DFH5Prrr/c6Vsjq3r27jhw5ov/+979eRwGQRVauXKkqVapowIABio6O1qpVq3TPPfd4HStktWrVSpdddpni4uK8jgIAOIeMFLwSzrnRzrmk1K/3JJUIcK5s69ChQ2revDlzRDLZzTffrKefflqDBw/Wb7/95nUcAAGU9jYy+/bt02effabhw4erQIECXkcLaQULFlS7du00ffp0rVq1yus4AICzyEjB22tmDc0sPPWroSTOcwuAefPmKTIyUqNGjVKXLl20dOlSRUZGeh3LN2JiYvTbb79pyJAhXkcBECBpbyPz9NNPKzExUbVq1fI6lm+0bdtWBQsW5CgeAASxjBS85yX9U9JuST9Jeib1NWSSY8eOqVOnTqpRowZzRALo9ttvV+3atfXmm2/q8OHDXscBkIlOnTqlt99+W5GRkVq/fr0mTJigiRMnqmjRol5H85XLLrtMbdu21ZQpU7RuHbfIBYBgdN6C55zb7px7wjlXwjl3uXPuKefctqwIlx2sWLFCUVFRGjhwoFq0aKFVq1bp7rvv9jqWb8XExGjfvn0aMWKE11EAZJLTt5Fp06aN7rvvPm4jE2Dt2rVT3rx51adPH6+jAADScdaCZ2adU/98y8wGn/mVdRH9KSkpSb169VLVqlW1f/9+ffbZZxo2bBhzRAKsWrVqevDBBzVw4ED9/vvvXscBcAmccxo3bpwqVqyohQsXavjw4frss8909dVXex3N10qUKKGWLVtq/Pjx2rRpk9dxAABnONcRvNPnXiyTtDydL1yk9evX65577lGPHj30zDPPMEcki8XGxmr37t0aNWqU11EAXKS0t5G5+eabtWrVKu4RmoU6dOigHDlyqF+/fl5HAQCc4awFzzk3I/XhUefcmLRfko5mTTx/OXXqlIYMGaLbbrtNGzZs0IQJEzRhwgTmiGSx++67T3fffbf69eunEydOeB0HwAWaOXOmKlasqE8++UR9+/bVvHnzuI1MFrv66qvVtGlTvffee9q+fbvXcQAAaWTkIivdMvgazmHHjh165JFH1LZtW9WoUYM5Ih4yM/Xo0UM7duzQ+++/73UcABl08OBBNWvWTI8//riuuOIKLVu2TF26dOE2Mh7p0qWLnHMaMGCA11EAAGmYcy79BWa1JD2qlCtoTkqzqJCkm5xzVQMf76+ioqLcsmXLvNj1RTk9R6RNmzZKSkrSoEGDFB0dzWlEHnPO/TH/cf369cqRI4fXkQCcw7x589S4cWNt375dXbp00auvvsqVhoNAs2bN9MEHH2jr1q268sorvY4DANmGmS13zkWlt+xcR/B+VMr8u2P689y76ZIeyeyQfrRnzx7VqVNHjRo1UsWKFZkjEkTMTLGxsdq8ebMmTpzodRwAZ3Hs2DF17NhRNWrUUHh4uObPn6/evXtT7oJE165ddfLkSQ0aNMjrKACAVGc9gvfHCmaFJB1xziWnPg+XlNs558k8vFA5gjd9+nQ1b95cBw4cUM+ePdWhQwdOIwoyp06dUqVKlXTy5EmtWbNGYWEZOWMZQFb57rvv1KhRI61du1atWrVS//79udJwEGrYsKGmTZumrVu3qnjx4l7HAYBs4WKP4J02W1LeNM/zSvq/DO64ppmtN7ONZtY1neVPmlmCma00s2Vmdk9GthvMDh48qKZNm+rJJ5/UlVdeqaVLl6pz586UuyAUFham2NhYff/995o6darXcQCkOn0bmTvuuEMHDhzQF198oaFDh1LuglT37t119OhR/ec///E6CgBAGSt4eZxzh08/SX2c73zflHqk721JtSTdJKmemd10xmpfSop0zlWS9LykkRnMHTTGjRuniIgIhYWF6corr9R1112n9957T926ddOSJUt06623eh0R5/CPf/xDFSpUUK9evXS+o9nAaWn/3UdERGjcuHFeRwppaf97XnPNNapQoYJ69OihOnXqaPXq1XrkEWYFBLObbrpJ//jHP/TWW2/pwIEDXscBsi1+NuG0jFxZ4oiZ3e6c+06SzKyypIzcIbqqpI3Ouc2p3zdR0pOS1p5eIW1xlJRfUkj9hj1u3DhFR0fr6NGUs1V//vlnmZleeeUVvfbaa96GQ4aEh4ere/fuaty4sWbOnKnHH3/c60gIcmf+u9+2bZuaNWumTZs2qWbNmh6nCz1ffPGF+vTpo2PHjkmSfvzxR0lSmzZt9NZbb3kZDRcgJiZGU6ZM0ZAhQxQbG+t1HCDbSe9nU3R0tCSpQYMGXkaDBzIyB6+KpIlKueiKJF0l6Vnn3Dlvdm5mz0iq6Zxrlvq8kaQ7nHNtzljv75L6SLpcUm3n3Dfn2m4wzcGLiIjQtm3b/vJ66dKltXXr1qwPhIty8uRJVahQQSVKlNDixYu5CA7O6Wz/7pG5+BwNPY8//rgWLVqkbdu2cTotkMX4nTT7OdccvPMWvNQN5JRUQZJJ+t45dzID31NH0iNnFLyqzrm2Z1m/uqRXnHN/S2dZtKRoSSpVqlTlYPnlKiwsLN3T+sxMp06d8iARLlZ8fLxatGih2bNn66GHHvI6DoLYuf7dz5w504NEoe2xxx7jc9Qnvv32W915550aMGCAOnbs6HUcIFvhd9LsJzMKXkWlzKPLc/o159w57xBtZtUkveaceyT1ebfU7+tzju/ZIqmKc27v2dbhCB4C4fjx47r++utVpkwZzZs3z+s4CGKXXXZZuvOM+Hd/cfgc9ZeHH35YCQkJ2rJli/LmzXv+bwBwyZKTk5U/f34dP378L8vy5s2rzZs3c59KH7qkq2ia2auS3kr9ul9Sf0lPZGC/SyWVM7MyZpZLUl2l3EMv7bavt9Tz4czsdkm5JO3LwLaDQlxcnPLl+/P1ZvLly6e4uDiPEuFi5c6dW507d9b8+fMpeDirb775Rr/99ttfrorLv/uLx+eov8TGxurnn3/WyJEhd800IGT169dPx48fV65cuf70es6cOXXy5ElVrFhRU6ZM8SgdvJCRq2g+I+lBSbudc00kRUo67x1mnXNJktpImiVpnaTJzrk1ZtbSzFqmrvYPSYlmtlIpV9x81oXQpQwbNGig+Ph4lS5dWmam0qVLKz4+nsmsIapZs2a6/PLL1atXL6+jIAgdOHBA9erVU0REhIYPH86/+0zC56i/VK9eXffee6/69++f7tEEAJnrm2++0SuvvKK6detq1KhRf/osHT16tBISEhQREaE6deqoYcOG+vXXX72OjCyQkYusLHHOVTWz5Uo5gndIUqJz7uasCHimYDpFE/4zYMAAde7cWYsXL9Ydd9zhdRwECeec6tWrp6lTp2rBggW8N4BzmD17th555BHFx8erefPmXscBfOvAgQOqVKmSwsLCtGLFChUuXDjd9U6ePKnevXurZ8+euvLKKzVq1Cg9/PDDWZwWme1Sb3S+zMyKSHpH0nJJ30laknnxgODRsmVLFS1alNPD8CejR4/WpEmT1LNnT8odcB4PPfSQqlSpoj59+igpKcnrOIAvOefUsmVL7dq1SxMmTDhruZNSTtV89dVXtXjxYhUsWFCPPPKIWrdurSNHjmRhYmSlcxa81PlxfZxzB5xzwyU9JKlx6qmagO8ULFhQL7/8smbMmKGVK1d6HQdBYN26dWrbtq0efPBBde7c2es4QNAzM/Xo0UNbtmzR+PHjvY4D+NKoUaMueOAxKipK3333ndq1a6ehQ4eqUqVK+uabc96dDCEqI6doLnfOVc6iPOfFKZoItAMHDqh06dJ6+OGH9eGHH3odBx46duyY7rzzTu3atUsJCQm66qqrvI4EhATnnG677TYdO3ZMa9as+cuFiQBcvHXr1ikqKkrVqlXT7NmzFRaWkRPy/mzOnDn617/+pZ07d6pr16569dVX/3KRFgS3Sz1Fc3Hqzc6BbKFIkSJq27atpk6dqrVr13odBx7q0qWLVq1apTFjxlDugAtgZoqJidH69es1depUr+MAvnHs2DHVq1dP+fLl09ixYy+q3EnS/fffr4SEBDVu3Fi9e/dW1apVtXr16kxOC69k5F1xv1JK3iYzSzCz1WaWEOhggJfatWunvHnzqk+fs962ET43Y8YMDR48WO3atdOjjz7qdRwg5Dz99NO64YYb1KtXL260DGSSzp07Z9rAY+HChTVq1ChNmzZNP/74o6KiojRgwAAlJydnUlp45awFz8xKpT6sJek6SQ9IelzSY6l/Ar5VvHhxtWrVSuPHj9emTZu8joMstmvXLjVp0kS33Xab+vbt63UcICSFh4ere/fuWr16tWbOnOl1HCDkzZgxQ2+99VamDzw++eSTSkxMVO3atdW5c2fVqFFDmzdvzrTtI+uddQ6emX3nnLs99fFU59w/sjTZWTAHD1ll9+7dioiIUMOGDblpbzaSnJyshx56SEuWLNF3332n8uXLex0JCFlJSUmqUKGCihYtqiVLlijl2m0ALtSuXbsUGRmpUqVK6ZtvvlHu3Oe9JfUFc85p7Nixatu2rZKTk/XGG2+oefPm/LsNUhc7By/t/83rMjcSEPyuvPJKNW/eXGPGjNH27du9joMs0rdvX82ZM0dDhgyh3AGXKEeOHOrWrZuWLVum2bNnex0HCEnJyclq2LChjh07pokTJwak3Ekpc2efe+45rV69WnfccYdatGih2rVr66effgrI/hA45yp47iyPgWyjU6dOMjP179/f6yjIAosWLdKrr76q+vXrq3Hjxl7HAXzhueeeU8mSJdWzZ0+d78rdAP6qb9+++vrrr7Ns4LFUqVL63//+p8GDB2vOnDmqWLGiJk+eHPD9IvOcq+BFmtlBMzsk6dbUxwfN7JCZHcyqgICXSpUqpcaNG2vkyJGMYPncgQMHVL9+fZUqVUrDhg3jlBQgk+TKlUtdunTRwoULNW/ePK/jACHl9MBjvXr1snTgMSwsTG3bttWKFStUtmxZPfvss6pfv77279+fZRlw8c5a8Jxz4c65Qs65gs65HKmPTz8vlJUhAS917dpVSUlJGjRokNdRECDOOUVHR2vXrl2aOHGiChXiIw7ITE2bNtUVV1yhXr16eR0FCBlpBx6HDx/uycDjDTfcoEWLFunf//63PvzwQ91yyy2aNWtWlufAhbm4m2cA2UjZsmVVv359DRs2THv27PE6DgLg3Xff1Ycffqi4uDhVrVrV6ziA7+TNm1edOnXS//3f/2nx4sVexwGCXtqBxwkTJng68JgjRw716NFDixcvVuHChVWzZk298MILOnLkiGeZcG4UPCADunXrpt9//13/+c9/vI6CTLZu3Tq9+OKL+tvf/qaOHTt6HQfwrRYtWqhYsWIcxQMy4PTAY69evXTHHXd4HUeSVLlyZS1fvlzt27fX8OHDFRkZqUWLFnkdC+mg4AEZcOONN+qZZ57RW2+9pV9//dXrOMgkx44dU926dVWgQAG9//77CgvjIxEIlAIFCujll1/Wp59+qhUrVngdBwhaaQceO3Xq5HWcP8mbN68GDRqkOXPmKCkpSffee6+6deum48ePex0NafDbDJBBMTExOnTokIYMGeJ1FGSSTp06KSEhQWPGjNFVV13ldRzA99q0aaPChQsrLi7O6yhAUAqVgcf77rtPCQkJatKkifr27auqVasqISHB61hIFZzvGiAIRUZG6vHHH9d//vMfHTp0yOs4uETTp0/XkCFD1L59e9WqVcvrOEC2ULhwYbVt21ZTp07VmjVrvI4DBJ1QGngsVKiQRo4cqenTp2v37t2KiopSv379lJyc7HW0bI+CB1yA2NhY7d+/X8OGDfM6Ci7Brl271KRJE91+++3q3bu313GAbKVdu3bKnz8///aAM4TqwOPjjz+uxMREPf744+ratavuu+8+bdq0yetY2RoFD7gAVatW1cMPP6xBgwbp6NGjXsfBRUhOTlaDBg10/PhxTZgwQblz5/Y6EpCtFCtWTC+88IImTpyoH374wes4QFDYuXNnSA88lihRQlOmTNHYsWOVmJioyMhIDR8+XM45r6NlSxQ84ALFxsbql19+0ciRI72OgovQp08fzZ07V0OHDlX58uW9jgNkS+3bt1euXLnUt29fr6MAnktOTlbDhg1DfuDRzNSwYUOtXr1a1apVU6tWrfToo4/qxx9/9DpatkPBAy7Qvffeq+rVq6t///5cNSrELFy4UK+99poaNGigRo0aeR0HyLauvPJKNW/eXO+//762bdvmdRzAU6cHHt9++21fDDxee+21mjVrlt566y3NnTtXFStW1MSJE72Ola1Q8ICLEBsbq127dmnMmDFeR0EG/frrr6pfv74iIiI0dOhQmZnXkYBsrVOnTjIz9evXz+sogGfSDjw+99xzXsfJNGFhYWrTpo1Wrlyp8uXLq169eqpbt6727dvndbRswULt3NioqCi3bNkyr2Mgm3POqVq1avr555+1YcMG5cyZ0+tIOAfnnOrUqaNPPvlEixYtUpUqVbyOBEApNz9/7733tGXLFl199dVexwGy1K+//qpKlSopR44cWrFihQoVKuR1pIBISkpSv3799Nprr6lEiRJ69913Q+oiMsHKzJY756LSW8YRPOAimJliY2O1detWjR8/3us4OI+RI0dq6tSp6t27N+UOCCJdunRRcnKyBg4c6HUUIEs559S8eXP9+OOPmjBhgm/LnSTlyJFDMTExWrJkiYoWLapHH31ULVu21OHDh72O5lshdwSvYMEoV7kyR/AQDJyWLVuuU6eSVaVKVU75C1JHjx7R8uXLVbhwYd16662S+P8EBJPvv1+nPXv26M4771TOnLm8jgNkiZ9++lEbNmzQddddp2uvLeV1nCxz6tQpbdmyRTt37lCePHl0ww03qnDhwl7HCklz53IEDwgAU+nSpfX7779rz549XodBOk6dStbatWsVHh6uG264UZQ7IPiUKlVap06d0s6dO72OAmSJo0ePaOPGjbrssst07bXXeh0nS4WFhals2bKqVKmSnJNWrlyhzZs36dSpU15H85UcXge4UBUqSF9/7XUKIMWpU8VUseILCg8P11dfrVJYGGMmwaRNm5c0f/7b+vzzz1WzJkcGgOCUT88+O0yff/65VqzYqqJFi3odCAiY33//XXfcUU3Fi/+sVatW6cors+vAYxEdOnSz2rdvr5EjR6pIkVv0/vvvq1KlSl4HCxnnOnGM30aBSxAWFqaYmBglJiZq+vTpXsdBGp988onefvttdejQQTVr1vQ6DoBziImJ0aFDh/TWW295HQUIqE6dOmn16tUaM2aMrrzySq/jeKpgwYJ65513NHPmTP3yyy+qWrWq+vTpo6SkJK+jhbyQm4PHVTQRbJKSknTDDTeoSJEiWrp0KXPxgsDOnTsVGRmpMmXKaNGiRcqVi6N3QLB76qmnNG/ePG3dutXXF5xA9jVt2jT9/e9/V4cOHbiw0Bn27t2rVq1aacqUKapWrZrGjBmjcuXKeR0rqHEVTSCAcuTIoW7dumn58uWaNWuW13GyveTkZDVo0EAnTpzQhAkTKHdAiIiJidGvv/6qYcOGeR0FyHQ7duzQ888/r8qVK6t3795exwk6xYsX1+TJkzVu3DitW7dOlSpV0rBhwxRqB6KCBQUPyASNGjXStddeq549e/Jh5LHevXtr3rx5Gjp0KKN/QAipUqWKHnnkEQ0aNEhHjx71Og6QaZKTk9WwYUOdPHmSgcdzMDPVr19fq1ev1t13360XXnhBtWrV0q5du7yOFnIoeEAmyJUrl7p06aJFixZp7ty5XsfJthYsWKDXXntNDRs2VKNGjbyOA+ACxcbGas+ePYqPj/c6CpBp4uLiGHi8ACVLltSsWbP09ttva968eapYsaLGjx/PAPoFYA4ekEmOHTumMmXK6KabbtKXX37pdZxs59dff1VkZKRy586t7777TgULFvQ6EoCLcP/992vDhg3atGmT8uTJ43Uc4JIsWLBA9913n+rXr6+xY8d6HSfk/PDDD3ruuee0ePFi1alTR8OGDVOxYsW8jhUUmIMHZIE8efKoU6dO+uqrr7Ro0SKv42Qrzjk1b95cP/30kyZMmEC5A0JYbGysfvzxR7333nteRwEuya+//qr69eurTJkyGjp0qNdxQlK5cuU0f/589e7dW9OmTVPFihX16aefeh0r6FHwgEzUokULFStWTHFxcV5HyVbi4+M1depU9enTR1FR6Q5mAQgRDzzwgO6880717dtXJ0+e9DoOcFGcc2rWrJl++uknTZw4kYHHS3D6YnZLlixR8eLF9dhjjyk6OlqHDh3yOlrQouABmSh//vxq3769PvvsM3333Xdex8kW1qxZo3bt2umRRx5R+/btvY4D4BKZmWJjY7Vt2zaNGzfO6zjARYmPj9dHH33EwGMmqlSpkpYtW6bOnTtr5MiRioyM1Pz5872OFZSYgwdkst9++00RERF64IEHNHXqVK/j+Nrvv/+uqlWr6pdfflFCQoKuuOIKryMByATOOVWuXFmHDx/WunXrFB4e7nUkIMPWrFmjqKgo3Xffffrss88UFsbxlMy2YMECPffcc9q6das6dOignj17Zrs5u8zBA7JQ4cKF9eKLL+qjjz5SYmKi13F8rUOHDkpMTNT7779PuQN85PRRvB9++EGTJ0/2Og6QYb///rvq1q2rQoUKacyYMZS7ALnnnnu0atUqNW/eXAMHDlRUVJRWrFjhdaygwbsOCIAXX3xRBQoU4GamAfTxxx9r2LBh6tixox555BGv4wDIZE899ZRuuukmxcXF6dSpU17HATKEgcesU7BgQY0YMUKffvqp9u3bp6pVqyouLk5JSUleR/McBQ8IgGLFiumFF17QpEmTtGHDBq/j+M6OHTvUtGlTRUVFcUEbwKfCwsIUExOjNWvW6JNPPvE6DnBeDDx649FHH1ViYqKefvppxcbG6p577sn2v3sxBw8IkJ9//lkRERGqV6+eRo0a5XUc30hOTtb999+vFStWaMWKFbr++uu9jgQgQJKSknTjjTeqUKFCWrZsmczM60hAunbs2KHIyEiVLVtWCxcuVK5cubyOlC1NnDhRL7zwgo4dO6YBAwaoVatWvj1Nljl4gAeuuOIKRUdHa+zYsdq6davXcXyjV69emj9/voYNG0a5A3zu9OXRv/vuO33++edexwHSlZSUpAYNGujkyZOaMGEC5c5DdevW1erVq1W9enW1adNGNWvW1M6dO72OleUoeEAAderUSWFhYerXr5/XUXxh/vz5+ve//63nnntODRs29DoOgCzQqFEjlSpVSj179lSonXWE7CEuLo6BxyByzTXX6PPPP9ewYcO0cOFCVaxYUePGjctWnx8UPCCASpYsqSZNmmjUqFHatWuX13FC2v79+9WgQQNdd911GjJkiNdxAGSRnDlzqmvXrlq8eLHmzJnjdRzgT04PPDZq1IiBxyBiZmrZsqVWrVqlm266SQ0bNlSdOnW0d+9er6NlCQoeEGBdunRRcnKyBg4c6HWUkOWcU7NmzbR7925NnDhRBQsW9DoSgCzUpEkTXXXVVerVq5fXUYA/pB14fPvtt72Og3Rcf/31mj9/vvr06aPp06erYsWKmjFjhtexAo6CBwRYmTJl1LBhQ40YMUK//PKL13FC0ogRI/Txxx+rb9++qly5stdxAGSxPHnyqFOnTpozZ44WLlzodRyAgccQEh4erq5du2rp0qW64oor9MQTT6hZs2Y6ePCg19EChoIHZIFu3brp2LFjevPNN72OEnISExP18ssvq2bNmmrXrp3XcQB4JDo6WsWLF+coHoLC6YHHPn36MPAYIiIjI7VkyRJ17dpVo0ePVmRkpObOnet1rICg4AFZoEKFCnr22Wc1ZMgQ7d+/3+s4IePo0aOqW7euChcurPfee8+3lzoGcH758+dXhw4d9MUXX4jbJcFLaQceX375Za/j4ALkzp1bffr00bx58xQeHq77779fHTt21LFjx7yOlqn4bQnIIt27d9fhw4c1ePBgr6OEjA4dOmjNmjUaO3asrrjiCq/jAPDYCy+8oCJFiiguLs7rKMimGHj0h7vvvlsrV65UixYtNGjQIFWuXFnfffed17EyDe9KIIvccssteuqpp/Tf//7X1+d9Z5aPPvpIw4cPV+fOnfXQQw95HQdAEChUqJBeeuklTZs2TatXr/Y6DrKh0wOP77//PgOPIa5AgQIaNmyYPv/8c/3666+644471LNnTyUlJXkd7ZIFtOCZWU0zW29mG82sazrLG5hZQurXIjOLDGQewGsxMTE6cOCAhg4d6nWUoLZ9+3Y1bdpUVapUUc+ePb2OAyCIvPjiiypQoIB69+7tdRRkM6cHHjt16qSHH37Y6zjIJDVr1lRiYqLq1KmjV155RXfffbfWr1/vdaxLErCCZ2bhkt6WVEvSTZLqmdlNZ6y2RdJ9zrlbJfWUFB+oPEAwiIqKUs2aNTVo0CAdOXLE6zhBKSkpSQ0aNFBycrImTJigXLlyeR0JQBApWrSoWrdurUmTJoX8L2EIHWkHHrnQj/8ULVpU48eP18SJE/XDDz/otttu03PPPafSpUsrLCxMERERGjdunNcxMyyQR/CqStronNvsnDshaaKkJ9Ou4Jxb5Jz7NfXpYkklA5gHCAo9evTQ3r17FR/PeEZ6evXqpQULFmj48OEqW7as13EABKH27dsrT5486tOnj9dRkA0w8Jh9PPvss0pMTFS5cuU0duxYbd++Xc45bdu2TdHR0SFT8gJZ8K6RtCPN852pr51NU0mfBzAPEBTuuusu3X///RowYIDvrtp0qebNm6eePXuqcePGql+/vtdxAASpyy+/XC1atNAHH3ygLVu2eB0HPnd64HHYsGEMPGYDV199tQ4cOPCX148ePaqYmJisD3QRAlnwLJ3XXLormt2vlILX5SzLo81smZkt27NnTyZGBLwRGxurn376SaNHj/Y6StDYt2+fGjRooLJly2rIkCFexwEQ5Dp27Kjw8HD169fP6yjwsblz5/4x8NigQQOv4yCL7NixI93Xt2/fnsVJLk4gC95OSdemeV5S0o9nrmRmt0oaKelJ59y+9DbknIt3zkU556JKlCgRkLBAVrr//vtVrVo19e3bVydPnvQ6juecc2rWrJl+/vlnTZw4UQUKFPA6EoAgd8011+j555/X6NGjtXPnTq/jwIf27dunhg0bqmzZsnrrrbe8joMsVKpUqQt6PdgEsuAtlVTOzMqYWS5JdSVNT7uCmZWS9JGkRs65DQHMAgQVM1NsbKy2b9+uDz74wOs4nhs+fLimTZumfv366fbbb/c6DoAQ0aVLF506dUoDBgzwOgp8Ju3A44QJE1SwYEGvIyELxcXFKV++fH96LV++fCFzD86AFTznXJKkNpJmSVonabJzbo2ZtTSzlqmrvSKpmKShZrbSzJYFKg8QbGrVqqXbb79dvXv39sU9Vy7W6tWr9fLLL6tWrVp66aWXvI4DIIRERESoUaNGio+P188//+x1HPjIsGHD/hh4rFy5stdxkMUaNGig+Ph4lS5dWmam0qVLKz4+PmRO0zXn0p0WF7SioqLcsmX0QPjDxx9/rKefflrjxo3LlhcVOXr0qKpUqaL9+/dr1apVuvzyy72OBCDEbNiwQTfeeKM6duzIfDxkitWrV6tKlSp64IEHNHPmTIWFBfS20cBFMbPlzrmodJdR8ADvnDp1Srfeequcc1q9enW2+yHSsmVLxcfHa/bs2frb3/7mdRwAIap+/fqaMWOGtm7dqmLFinkdByHs9MDjvn37lJCQwMAjgta5Cl72+m0SCDJhYWGKiYnR2rVrNW3aNK/jZKmpU6dqxIgR6ty5M+UOwCXp3r27Dh8+rMGDB3sdBSGuffv2Wrt2rcaOHUu5Q8jiCB7gseTkZN14440qUKCAli9fLrP07jDiL9u2bVOlSpVUvnx5LViwQDlz5vQ6EoAQ9/TTT+urr77Stm3bVLhwYa/jIARNnTpVzzzzjLp06aK+fft6HQc4J47gAUEsPDxc3bt314oVK/TZZ595HSfgkpKS1KBBAyUnJ2vChAmUOwCZIjY2Vr/99pvefvttr6MgBG3btk3NmjVT1apV1bNnT6/jAJeEggcEgQYNGqh06dLq2bOnQu2o+oX697//rYULF2rEiBG67rrrvI4DwCduv/12Pfroo3rjjTd05MgRr+MghDDwCL+h4AFBIGfOnOratau+/fZbffXVV17HCZivv/5avXr10r/+9S/Vq1fP6zgAfCY2Nlb79u3TiBEjvI6CEMLAI/yGOXhAkDh27JjKli2r8uXLa86cOV7HyXT79u1TZGSk8ufPr+XLl6tAgQJeRwLgQw8++KDWrl2rLVu2KE+ePF7HQZD7+uuv9cADD6hx48YaPXq013GADGMOHhAC8uTJo06dOunrr7/WggULvI6TqZxzatq0qfbs2aOJEydS7gAETGxsrHbv3q13333X6ygIcvv27VPDhg1Vrlw5vfXWW17HATINBQ8IItHR0SpRooR69erldZRMNXToUH3yySfq16+fbrvtNq/jAPCxGjVq6O6771a/fv104sQJr+MgSDnn9PzzzzPwCF+i4AFBJF++fOrQoYNmzZqlpUuXeh0nUyQkJKhDhw6qXbu2XnrpJa/jAPA5M1NsbKx27NihsWPHeh0HQWro0KGaPn06A4/wJebgAUHm4MGDioiIUPXq1UP+5udHjx5VVFSUfv31VyUkJKhEiRJeRwKQDTjnVKVKFR04cEDff/+9cuTI4XUkBJGEhARVrVpVDz74oGbOnJkt7j8L/2EOHhBCChUqpJdeekmffPKJEhISvI5zSdq1a6fvv/9eH3zwAeUOQJY5fRRv06ZNmjRpktdxEESOHDmiunXr6rLLLtPo0aMpd/AlCh4QhF588UUVLFhQvXv39jrKRfvwww/1zjvvqGvXrnrwwQe9jgMgm3niiSdUsWJFxcXF6dSpU17HQZB4+eWX/xh4vPzyy72OAwQEBQ8IQpdddpnatGmjyZMn6/vvv/c6zgXbtm2bmjdvrjvuuEOvv/6613EAZENhYWGKjY3VunXr9NFHH3kdB0Hg9MBjly5dGHiErzEHDwhSe/bsUenSpVWnTh2NGTPG6zgZlpSUpPvuu0+JiYlauXKlypQp43UkANlUcnKybrrpJuXNm1crVqzgdLxsbNu2bYqMjNQNN9yg+fPnK2fOnF5HAi4Jc/CAEFSiRAm1bNlS48aN0+bNm72Ok2Gvv/66Fi1apBEjRlDuAHgqPDxc3bt316pVq/Tpp596HQceSUpKUv369eWc04QJEyh38D0KHhDEOnbsqPDwcPXr18/rKBny9ddfKy4uTs8//7zq1q3rdRwAUP369RUREaFevXop1M5aQuY4PfA4fPhwBh6RLVDwgCB29dVXq2nTpho9erR27tzpdZxz2rt3rxo0aKDy5ctr8ODBXscBAElSzpw51bVrV3377bf6v//7P6/jIIudHnhs0qSJ6tWr53UcIEswBw8Ictu2bdP111+vVq1aBW1xcs7pySef1KxZs/Ttt9+qUqVKXkcCgD8cP35cZcuWVdmyZTV37lyv4yCL7N27V5GRkSpYsKCWL1+u/Pnzex0JyDTMwQNCWOnSpfXcc8/pnXfe0e7du72Ok663335bM2bM0IABAyh3AIJO7ty51blzZ82bN0/z5s3zOg6ygHNOzz//vPbu3asJEyZQ7pCtUPCAENC1a1edOHFCb7zxhtdR/mLVqlXq2LGjateurbZt23odBwDS1axZM11++eWKi4vzOgqywOmBx/79++u2227zOg6QpSh4QAgoV66c6tatq6FDh2rfvn1ex/nDkSNHVLduXRUtWlSjR4/mEuQAgla+fPnUoUMHzZ49W0uWLPE6DgIo7cDjiy++6HUcIMtR8IAQ0b17dx05ckT//e9/vY7yh3bt2mn9+vX64IMPVKJECa/jAMA5tWrVSkWLFlWvXr28joIAYeARoOABIePmm2/WP/7xDw0ePFi//fab13E0efJkjRw5Ut26ddMDDzzgdRwAOK+CBQuqXbt2mjFjhlauXOl1HATA6YHHsWPHMvCIbIuCB4SQmJgY/fbbbxoyZIinObZu3aro6Gjdeeedeu211zzNAgAXom3btipUqJB69+7tdRRkskmTJmnkyJHq2rWrHnzwQa/jAJ6h4AEh5LbbblPt2rX15ptv6vDhw55kOHnypOrVqyfnnCZMmKCcOXN6kgMALkaRIkXUpk0bTZkyRevWrfM6DjLJli1b/hh4fP31172OA3iKggeEmJiYGO3bt08jRozwZP+vv/66Fi9erPj4eEVERHiSAQAuRbt27ZQ3b1716dPH6yjIBCdPnlT9+vUlSePHj2fgEdkeBQ8IMdWqVdODDz6ogQMH6vfff8/SfX/11Vfq3bu3mjZtqmeffTZL9w0AmaVEiRJq2bKlxo8fr02bNnkdB5co7cBjmTJlvI4DeI6CB4SgHj16aPfu3Xr33XezbJ979+5Vw4YNVaFChaC6kicAXIyOHTsqR44c6tu3r9dRcAkYeAT+ypxzXme4IFFRUW7ZsmVexwA85ZxT9erVtXXrVm3atEm5cuUK+P6eeOKJP+4fFRkZGdD9AUBWaNOmjeLj47Vx40aVKlXK6zi4QHv37tWtt96qQoUKafny5cqfP7/XkYAsY2bLnXNR6S3jCB4QgsxMsbGx2rlzp95///2A7++tt97SzJkzNXDgQModAN/o3LmznHMaMGCA11FwgZxzatKkifbt26eJEydS7oA0KHhAiHr44YcVFRWlPn36KCkpKWD7WblypTp16qTHH39cbdq0Cdh+ACCrlSpVSo0bN9Y777yj3bt3ex0HFyDtwGOlSpW8jgMEFQoeEKJOH8XbvHmzJk6cGJB9HDlyRHXr1lXx4sU1atQomVlA9gMAXunatatOnjypgQMHeh0FGXR64PGxxx5j4BFIB3PwgBB26tQp3XbbbTpx4oQSExMVHh6eqdtv2rSpRo8erS+//FL3339/pm4bAIJFo0aN9NFHH2nbtm0qXry413FwDkeOHFHlypV18OBBJSQk8P8L2RZz8ACfCgsLU0xMjL7//nt99NFHmbrtiRMnatSoUerevTvlDoCvdevWTb///rv+85//eB0F5/Hiiy9qw4YNGjduHOUOOAuO4AEhLjk5WTfffLNy586tlStXZspplFu2bFGlSpV08803a968ecqRI0cmJAWA4FWnTh3Nnj1b27ZtU5EiRbyOg3RMnDhR9erVU0xMjHr16uV1HMBTHMEDfCw8PFzdu3dXQkKCZs6cecnbO3nypOrXry8z0/jx4yl3ALKFmJgYHTx4UEOGDPE6CtKxZcsWtWjRQtWqVdOrr77qdRwgqFHwAB+oV6+eypQpo169eulSj8q/+uqrWrx4sd555x1FRERkTkAACHKVKlXSY489pjfffFOHDh3yOg7SOHPgMWfOnF5HAoIaBQ/wgZw5c6pbt25asmSJ/ve//130dr788kv17dtXzZs3V506dTIxIQAEv9jYWO3fv1/Dhw/3OgrSYOARuDDMwQN84vjx47r++utVpkwZzZs374K/f8+ePYqMjFSRIkW0bNky5cuXLwApASC4Pfzww1q1apW2bt2qvHnzeh0n2/vyyy/10EMPqWnTpnrnnXe8jgMEDebgAdlA7ty51blzZ82fP/+CC55zTk2aNNH+/fs1YcIEyh2AbCs2Nla//PKLRo4c6XWUbG/Pnj1q1KiRKlSowBVOgQtAwQN8pFmzZrr88ssv+OpigwcP1qeffqqBAwcqMjIyQOkAIPhVr15d9957r/r376/jx497HSfbSjvwOHHiROXPn9/rSEDIoOABPpI3b1517NhR//vf//Ttt99m6HtWrFihzp0764knnlDr1q0DnBAAgl9sbKx27typMWPGeB0l22LgEbh4zMEDfObw4cMqXbq07rrrLs2YMeO861auXFlHjhzRqlWrVKxYsSxKCQDByzmnO++8U7/88os2bNjAVRuz2IoVK3TnnXfqkUce0SeffJIp93cF/IY5eEA2UqBAAb388suaOXOmVqxYcc51X3zxRf3www/64IMPKHcAkMrMFBsbq61bt2rChAlex8lWDh8+rLp166p48eIaNWoU5Q64CBQ8wIfatGmjQoUKqXfv3mddZ8KECRo9erRiY2NVo0aNrAsHACHgscceU2RkpHr37q3k5GSv42Qbpwcex40bp+LFi3sdBwhJFDzAh4oUKaK2bdtq6tSpWrt27V+Wb968WS1atNBdd92lV155xYOEABDczEwxMTFav369pk6d6nWcbOH0wGNMTAwDj8AlYA4e4FN79+5VRESE/v73v2vs2LF/vH7y5Endc8892rBhg1auXKnSpUt7mBIAgldycrIqVqyonDlzauXKlQoLY1w8UDZv3qxKlSrplltu0dy5c5UjRw6vIwFBjTl4QDZUvHhxtWrVSuPHj9fGjRv/eP2VV17RkiVL9M4771DuAOAcwsPDFRMTo9WrV5/3olW4eCdPnlS9evUUFham8ePHU+6ASxTQgmdmNc1svZltNLOu6Sy/wcy+MbPjZtYxkFmA7KhDhw7KmTOn+vbtK0n6v//7P/Xr10/R0dF65plnPE4HAMGvbt26uu6669SrVy+F2llPoYKBRyBzBazgmVm4pLcl1ZJ0k6R6ZnbTGavtl/SipIGBygFkZ1deeaWqV6+ud999V2FhYXrkkUd01VVX6c033/Q6GgCEhBw5cqhbt25atmyZrrzySoWFhSkiIkLjxo3zOlpIGzdunCIiIhQWFqa+ffvq/vvvV506dbyOBfhCII+BV5W00Tm3WZLMbKKkJyX9ccUH59wvkn4xs9oBzAFkW+PGjdOCBQskpdzXyTmn/fv36+OPP1aDBg08TgcAoSFnzpwyM/3yyy+SpG3btql58+Y6ceKEnn32WY/ThZ5JkyapdevW+v333/94bfHixRo3bhw/m4BMELCLrJjZM5JqOueapT5vJOkO51ybdNZ9TdJh59x5j+RxkRUg4yIiIrRt27a/vF66dGlt3bo16wMBQAg622cpMhc/m4CMO9dFVgJ5BC+9O1NeVJs0s2hJ0ZJUqlSpS8kEZCvbt2+/oNcBAH91rs/Mfv36ZWESf+jSpUu6r/OzCcgcgSx4OyVdm+Z5SUk/XsyGnHPxkuKllCN4lx4NyB5KlSqV7qgzAyUAkHFn+ywtXbq0Onfu7EGi0DZ06FB+NgEBFMiraC6VVM7MyphZLkl1JU0P4P4AnCEuLk758uX702v58uVTXFycR4kAIPTwWZq5+O8JBFbACp5zLklSG0mzJK2TNNk5t8bMWppZS0kysyvNbKek9pJizWynmRUKVCYgu2nQoIHi4+NVunRpmZlKly6t+Ph4JrEDwAXgszRz8d8TCKyAXWQlULjICgAAAIDs7FwXWQnojc4BAAAAAFmHggcAAAAAPkHBAwAAAACfoOABAAAAgE9Q8AAAAADAJyh4AAAAAOATFDwAAAAA8AkKHgAAAAD4BAUPAAAAAHyCggcAAAAAPkHBAwAAAACfoOABAAAAgE9Q8AAAAADAJyh4AAAAAOATFDwAAAAA8AkKHgAAAAD4BAUPAAAAAHyCggcAAAAAPkHBAwAAAACfoOABAAAAgE9Q8AAAAADAJyh4AAAAAOATFDwAAAAA8AkKHgAAAAD4BAUPAAAAAHyCggcAAAAAPkHBAwAAAACfoOABAAAAgE9Q8AAAAADAJyh4AAAAAOATFDwAAAAA8AkKHgAAAAD4BAUPAAAAAHyCggcAAAAAPkHBAwAAAACfoOABAAAAgE9Q8AAAAADAJyh4AAAAAOATFDwAAAAA8AkKHgAAAAD4BAUPAAAAAHyCggcAAAAAPkHBAwAAAACfoOABAAAAgE9Q8AAAAADAJyh4AAAAAOATFDwAAAAA8AkKHgAAAAD4BAUPAAAAAHwioAXPzGqa2Xoz22hmXdNZbmY2OHV5gpndHsg8AAAAAOBnASt4ZhYu6W1JtSTdJKmemd10xmq1JJVL/YqWNCxQeQAAAADA7wJ5BK+qpI3Ouc3OuROSJkp68ox1npT0vkuxWFIRM7sqgJkAAAAAwLcCWfCukbQjzfOdqa9d6DoAAAAAgAzIEcBtWzqvuYtYR2YWrZRTOCXpsJmtv8RsgVBc0l6vQwDnwHsUwY73KEIB71MEO96j2UPpsy0IZMHbKenaNM9LSvrxItaRcy5eUnxmB8xMZrbMORfldQ7gbHiPItjxHkUo4H2KYMd7FIE8RXOppHJmVsbMckmqK2n6GetMl/Rc6tU075T0m3PupwBmAgAAAADfCtgRPOdckpm1kTRLUrikUc65NWbWMnX5cEmfSXpU0kZJRyU1CVQeAAAAAPC7QJ6iKefcZ0opcWlfG57msZPUOpAZslBQn0IKiPcogh/vUYQC3qcIdrxHszlL6VgAAAAAgFAXyDl4AAAAAIAsRMHLBGZW08zWm9lGM+vqdR4gLTO71szmmNk6M1tjZi95nQlIj5mFm9kKM5vpdRbgTGZWxMymmNn3qZ+n1bzOBKRlZi+n/pxPNLMJZpbH60zwBgXvEplZuKS3JdWSdJOkemZ2k7epgD9JktTBOXejpDslteY9iiD1kqR1XocAzuK/kr5wzt0gKVK8VxFEzOwaSS9KinLOVVTKBQ7repsKXqHgXbqqkjY65zY7505ImijpSY8zAX9wzv3knPsu9fEhpfxSco23qYA/M7OSkmpLGul1FuBMZlZIUnVJ70qSc+6Ec+6Ap6GAv8ohKa+Z5ZCUT+ncWxrZAwXv0l0jaUea5zvFL88IUmYWIek2Sd96HAU4038kdZZ0yuMcQHquk7RH0ujU04hHmll+r0MBpznndkkaKGm7pJ+Ucm/p2d6mglcoeJfO0nmNS5Mi6JhZAUlTJbVzzh30Og9wmpk9JukX59xyr7MAZ5FD0u2ShjnnbpN0RBJz7hE0zOwypZxBVkbS1ZLym1lDb1PBKxS8S7dT0rVpnpcUh8QRZMwsp1LK3Tjn3Ede5wHOcLekJ8xsq1JOc3/AzD7wNhLwJzsl7XTOnT77YYpSCh8QLP4maYtzbo9z7qSkjyTd5XEmeISCd+mWSipnZmXMLJdSJrRO9zgT8AczM6XMG1nnnHvD6zzAmZxz3ZxzJZ1zEUr5DP3KOcfIM4KGc263pB1mViH1pQclrfUwEnCm7ZLuNLN8qT/3HxQXAsq2cngdINQ555LMrI2kWUq5YtEo59waj2MBad0tqZGk1Wa2MvW17s65z7yLBAAhp62kcamDuZslNfE4D/AH59y3ZjZF0ndKuXr2Cknx3qaCV8w5posBAAAAgB9wiiYAAAAA+AQFDwAAAAB8goIHAAAAAD5BwQMAAAAAn6DgAQAAAIBPUPAAANmWmV1hZuPNbLOZLTezb8zs717nAgDgYlHwAADZUurNgKdJmuecu845V1kpN1ovecZ63DMWABAyuA8eACBbMrMHJb3inLsvnWX/klRbUh5J+SU9I2mUpOskHZUU7ZxLMLPXJB12zg1M/b5ESY+lbuYLSd9Kuk3SBknPOeeOBvLvBAAAR/AAANnVzZK+O8fyapIaO+cekPS6pBXOuVsldZf0fga2X0FSfOr3HJT0wiXmBQDgvCh4AABIMrO3zWyVmS1Nfel/zrn9qY/vkTRWkpxzX0kqZmaFz7PJHc65hamPP0jdBgAAAUXBAwBkV2sk3X76iXOutaQHJZVIfelImnUtne93kpL055+lec5Yfub6AAAEFAUPAJBdfSUpj5m1SvNavrOsO09SA0kysxqS9jrnDkraqtSSaGa3SyqT5ntKmVm11Mf1JC3IrOAAAJwNF1kBAGRbZnaVpDcl3SFpj1KO2g2XlFdSlHOuTep6RSWNVkqBS3uRlbySPpF0uaSlSjkNs1bq5j9TSjG8S9IPkhpxkRUAQKBR8AAAyGRmFiFppnOuotdZAADZC6doAgAAAIBPcAQPAAAAAHyCI3gAAAAA4BMUPAAAAADwCQoeAAAAAPgEBQ8AAAAAfIKCBwAAAAA+QcEDAAAAAJ/4f2wKH7NTitNBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Create dummy data\n",
    "data = {'defects':np.random.randint(1,5,10).tolist(),\n",
    "        'group_size':np.repeat(10,10).tolist()}\n",
    "\n",
    "# Convert data to data frame\n",
    "data = pd.DataFrame(data)\n",
    "\n",
    "# Add 'np' column to data frame\n",
    "data['np'] = data['defects']/data['group_size']\n",
    "\n",
    "# Plot np-chart\n",
    "plt.figure(figsize=(15,7.5))\n",
    "plt.plot(data['np'], linestyle='-', marker='o', color='black')\n",
    "plt.axhline(statistics.mean(data['np'])+3*(np.sqrt((statistics.mean(data['np'])*(1-statistics.mean(data['np'])))/statistics.mean(data['group_size']))), color='red', linestyle='dashed')\n",
    "plt.axhline(statistics.mean(data['np'])-3*(np.sqrt((statistics.mean(data['np'])*(1-statistics.mean(data['np'])))/statistics.mean(data['group_size']))), color='red', linestyle='dashed')\n",
    "plt.axhline(statistics.mean(data['np']), color='blue')\n",
    "plt.ylim(bottom=0)\n",
    "plt.title('np Chart')\n",
    "plt.xlabel('Group')\n",
    "plt.ylabel('Fraction Defective')\n",
    "\n",
    "# Validate points out of control limits\n",
    "i = 0\n",
    "control = True\n",
    "for group in data['np']:\n",
    "    if group > (statistics.mean(data['np'])+3*(np.sqrt((statistics.mean(data['np'])*(1-statistics.mean(data['np'])))/statistics.mean(data['group_size'])))) or group < (statistics.mean(data['np'])-3*(np.sqrt((statistics.mean(data['np'])*(1-statistics.mean(data['np'])))/statistics.mean(data['group_size'])))):\n",
    "        print('Group', i, 'out of fraction defective cotrol limits!')\n",
    "        control = False\n",
    "    i += 1\n",
    "if control == True:\n",
    "    print('All points within control limits.')\n",
    "\n",
    "plt.savefig(\"C:/Users/renea/Desktop/Statistical Process Control/np_Chart.png\", dpi=300) # dpi=1500 is recommended for professional reports"
   ]
  },
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   "id": "3a53fe2f-8b07-4789-aa73-7043c9967428",
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