staff_scheduling_optimization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PJIOWA - Staff Scheduling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Processing and Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 01. Mathematical Formulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let n be the total no of shifts = 9\n",
    "\n",
    "#### Decision Variables\n",
    "\n",
    "- For each shift $i$ (where $i$ ranges from 0 to n-1):\n",
    "  - $ft_i$: Number of Staff working 4 shifts starting Shift $i$ to Shift $i+4$ (Except last starting shift = 5 shifts)\n",
    "  - $pt1_i$: Number of Staff working 1 shift starting Shift $i$ to Shift $i$\n",
    "  - $pt2_i$: Number of Staff working 2 shifts starting Shift $i$ to Shift $i+1$\n",
    "  - $pt3_i$: Number of Staff working 3 shifts starting Shift $i$ to Shift $i+2$\n",
    "\n",
    "#### Objective Function\n",
    "\n",
    "The objective is to Minimize the total number of staff required daily:\n",
    "- $\\text{Minimize} \\sum_{i=0}^{n-1} (ft_i + pt1_i + pt2_i + pt3_i)$\n",
    "\n",
    "#### Constraints\n",
    "\n",
    "##### 1. No employee starts after the 5th shift (or last_start_shift):\n",
    "\n",
    "  - For $i$ in range(5, n):\n",
    "    - $ft_i + pt1_i + pt2_i + pt3_i = 0$ <br>\n",
    "\n",
    "##### 2. Minimum FT Staff Constraints:\n",
    "\n",
    "  - For $i$ in range(0, n):\n",
    "    - $\\sum_{j=max(0, i-3)}^{i} ft_j \\geq z_{fti}$ <br>\n",
    "\n",
    "##### 3. Minimum PT Staff Constraints:\n",
    "\n",
    "  - For $i$ in range(0, n):\n",
    "    - $\\sum_{j=max(0, i-0)}^{i} pt1_j + \\sum_{j=max(0, i-1)}^{i} pt2_j + \\sum_{j=max(0, i-2)}^{i} pt3_j \\geq z_{pti}$ <br>  \n",
    "\n",
    "##### 4. Maximum PT Staff Constraints:\n",
    "\n",
    "  - For $i$ in range(0, n):\n",
    "    - $\\sum_{j=max(0, i-0)}^{i} pt1_j + \\sum_{j=max(0, i-1)}^{i} pt2_j + \\sum_{j=max(0, i-2)}^{i} pt3_j \\leq z_{i}$ <br>\n",
    "    \n",
    "\n",
    "##### 5. Maximum Individual 1-shift, 2-shift, 3-shift Staff Constraints:\n",
    "\n",
    "  - For $i$ in range(0, n):\n",
    "    - $pt1_i \\leq z_{pti}/4$ <br><br>\n",
    "  \n",
    "  - For $i$ in range(0, n):\n",
    "    - $\\sum_{j=max(0, i-1)}^{i} pt2_j\\leq z_{pti}$ <br><br>\n",
    "  \n",
    "  - For $i$ in range(0, n):\n",
    "    - $\\sum_{j=max(0, i-2)}^{i} pt3_j \\leq z_{pti}$ <br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 02. User Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([6, 3, 6, 6, 4, 3, 5, 8, 3],\n",
       " [4, 2, 4, 4, 2, 2, 3, 8, 3],\n",
       " [2, 1, 2, 2, 2, 1, 2, 0, 0])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "######## CHOOSE THE DAY #######\n",
    "day = 'Tuesday' \n",
    "######## CHOOSE THE DAY #######\n",
    "\n",
    "rec_shift_req = pd.read_excel('PJIOWA_data.xlsx', sheet_name='rec_shift_req')\n",
    "rec_shift_req = rec_shift_req.iloc[:9]\n",
    "mm_shift_req = pd.read_excel('PJIOWA_data.xlsx', sheet_name='min_shift_req')\n",
    "\n",
    "emp_max_hard = mm_shift_req['Maximum']\n",
    "emp_min_hard = mm_shift_req['Minimum']\n",
    "\n",
    "rec_shift_req['min'] = mm_shift_req['Minimum']\n",
    "rec_shift_req['max'] = mm_shift_req['Maximum']\n",
    "rec_shift_req = rec_shift_req.fillna(0)\n",
    "rec_shift_req[day] = rec_shift_req[day].astype(int)\n",
    "rec_shift_req[day] = rec_shift_req.apply(lambda x: x['min'] if x[day] < x['min'] else x[day], axis=1)\n",
    "rec_shift_req[day] = rec_shift_req.apply(lambda x: x['max'] if x[day] > x['max'] else x[day], axis=1)\n",
    "\n",
    "# 2/3 FT and 1/3 PT - just a cap\n",
    "z_ft = [int(2*max(rec_shift_req.loc[i, day], emp_min_hard[i])/3) if rec_shift_req.loc[i, day] < rec_shift_req.loc[i, 'min'] else int(2*min(rec_shift_req.loc[i, day], emp_max_hard[i])/3) for i in range(9)]\n",
    "z_pt = [min(rec_shift_req.loc[i, day] - z_ft[i], emp_max_hard[i]-z_ft[i]) for i in range(9)]\n",
    "\n",
    "#shift 5-8 E [0,8] move PTs to FT\n",
    "for i in range(7,9):\n",
    "    z_ft[i] += z_pt[i]\n",
    "    z_pt[i] = 0\n",
    "\n",
    "z = list(rec_shift_req[day])\n",
    "\n",
    "list(rec_shift_req[day]), z_ft, z_pt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 03. Technique & Application"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Technique:\n",
    "\n",
    " - The technique employed in this scheduling model is a mathematical optimization approach, specifically a <b>Integer Linear Programming (ILP)</b> formulation. ILP is a powerful technique for solving optimization problems where the objective function and constraints are linear, and all of the decision variables are required to take integer values.\n",
    "\n",
    "#### Application:\n",
    "\n",
    " - In this case, the ILP technique is applied to the problem of scheduling staff at PJIOWA. The decision variables represent the number of staff starting different types of shifts (1, 2, 3, and 4 shifts). The objective is to maximize the total number of staff assigned to shifts, subject to various constraints in order to meet demand.\n",
    "\n",
    " - The constraints include minimum and maximum staffing requirements for both full-time and part-time employees for each shift. Additionally, constraints ensure that no employee starts after the 5th shift and enforce hard limits on the maximum number of employees for consecutive shifts.\n",
    "\n",
    " - The Pyomo library in Python is utilized to implement and solve the ILP model. Pyomo allows for the easy expression of optimization models and interfaces with solvers like GLPK to find the optimal solution.\n",
    "\n",
    "This technique provides an automated and efficient way to determine the optimal staffing schedule that meets the staffing requirements."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 04. Optimization Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_call(start_end):\n",
    "\n",
    "    # Importing the required module(s)\n",
    "    import pyomo.environ as pyo\n",
    "    # Creating a Pyomo model object\n",
    "    ns = pyo.ConcreteModel(\"PJIOWA Staff Scheduling\")\n",
    "\n",
    "    # Let I be the set of items indexed from 0 to n-1. Note that both bounds in RangeSet are inclusive.\n",
    "    ns.I = pyo.RangeSet(0, n-1)\n",
    "\n",
    "    # Create separate decision variables for part-time staff working 1, 2, and 3, 4 shifts\n",
    "    # Note FT in 5th shift covers 5 shifts\n",
    "    ns.pt1 = pyo.Var(ns.I, within=pyo.NonNegativeReals, name=\"#Staff starting 1 shifts\")#, initialize=0, bounds = (0, max(z_pt)))\n",
    "    ns.pt2 = pyo.Var(ns.I, within=pyo.NonNegativeReals, name=\"#Staff starting 2 shifts\")#, initialize=0, bounds = (0, max(z_pt)))\n",
    "    ns.pt3 = pyo.Var(ns.I, within=pyo.NonNegativeReals, name=\"#Staff starting 3 shifts\")#, initialize=0, bounds = (0, max(z_pt)))\n",
    "    ns.ft = pyo.Var(ns.I, within=pyo.NonNegativeReals, name=\"#Staff starting 4 shifts\")#, initialize=1, bounds = (1, max(z_ft)))\n",
    "\n",
    "    # Modify the objective function to include the new decision variables\n",
    "    ns.obj = pyo.Objective(expr = sum([ns.ft[i] + ns.pt1[i] + ns.pt2[i] + ns.pt3[i] for i in ns.I]), sense=pyo.minimize, name=\"DailyReqStaff\")\n",
    "\n",
    "    ######################################### CONSTRAINTS START HERE ############################################\n",
    "\n",
    "    # NO START AFTER SHIFT 'start_end' (defined as 5 or 6, if 5 fails, then auto tries 6)\n",
    "    for i in range(start_end, len(ns.I)):\n",
    "        ns.add_component(f\"no_start_after_5_shift{i}\", pyo.Constraint(expr = (ns.ft[i] + ns.pt1[i] + ns.pt2[i] + ns.pt3[i]) == 0))\n",
    "\n",
    "    # MIN FT TOTAL (LHS[i]: FT[working in shift i]; RHS: Total FT Req)\n",
    "    ns.min_ft_sh1 = pyo.Constraint(expr = (ns.ft[0]) >= z_ft[0], name = \"min_ft_shift1\")\n",
    "    ns.min_ft_sh2 = pyo.Constraint(expr = (ns.ft[1]+ns.ft[0]) >= z_ft[1], name = \"min_ft_shift2\")\n",
    "    ns.min_ft_sh3 = pyo.Constraint(expr = (ns.ft[2]+ns.ft[1]+ns.ft[0]) >= z_ft[2], name = \"min_ft_shift3\")\n",
    "    ns.min_ft_sh4 = pyo.Constraint(expr = (ns.ft[3]+ns.ft[2]+ns.ft[1]+ns.ft[0]) >= z_ft[3], name = \"min_ft_shift4\")\n",
    "    ns.min_ft_sh5 = pyo.Constraint(expr = (ns.ft[4]+ns.ft[3]+ns.ft[2]+ns.ft[1]) >= z_ft[4], name = \"min_ft_shift5\")\n",
    "    ns.min_ft_sh6 = pyo.Constraint(expr = (ns.ft[4]+ns.ft[3]+ns.ft[2]) >= z_ft[5], name = \"min_ft_shift6\")\n",
    "    ns.min_ft_sh7 = pyo.Constraint(expr = (ns.ft[4]+ns.ft[3]) >= z_ft[6], name = \"min_ft_shift7\")\n",
    "    ns.min_ft_sh8 = pyo.Constraint(expr = (ns.ft[4]) >= z_ft[7], name = \"min_ft_shift8\")\n",
    "    ns.min_ft_sh9 = pyo.Constraint(expr = (ns.ft[4]) >= z_ft[8], name = \"min_ft_shift9\")\n",
    "\n",
    "    # MIN PT TOTAL (LHS[i]: PT1+PT2+PT3; RHS: Total PT Req)\n",
    "    for i in ns.I:\n",
    "        ns.add_component(f\"min_pt_shift{i}\", pyo.Constraint(expr = (sum(ns.pt1[j] for j in range(max(0, i-0), i+1)) + \\\n",
    "                                                                    sum(ns.pt2[j] for j in range(max(0, i-1), i+1)) + \\\n",
    "                                                                    sum(ns.pt3[j] for j in range(max(0, i-2), i+1))) >= z_pt[i]))\n",
    "\n",
    "    # MAX PT TOTAL (LHS[i]: PT1+PT2+PT3; RHS: Total Req)\n",
    "    for i in ns.I:\n",
    "        ns.add_component(f\"max_pt_shift{i}\", pyo.Constraint(expr = (sum(ns.pt1[j] for j in range(max(0, i-0), i+1)) + \\\n",
    "                                                                    sum(ns.pt2[j] for j in range(max(0, i-1), i+1)) + \\\n",
    "                                                                    sum(ns.pt3[j] for j in range(max(0, i-2), i+1))) <= z[i]))\n",
    "\n",
    "    # THIS PRIORITIZES 3 and 2 shift workers over 1 shift\n",
    "    # Add MAX constraints for staff working 1 shift\n",
    "    for i in ns.I:\n",
    "        ns.add_component(f\"max_pt1_shift{i+1}\", pyo.Constraint(expr = (ns.pt1[i]) <= int(z_pt[i]/4)))\n",
    "\n",
    "    # Add MAX constraints for staff working 2 shifts\n",
    "    for i in ns.I:\n",
    "        ns.add_component(f\"max_pt2_shift{i+2}\", pyo.Constraint(expr = (ns.pt2[i] + ns.pt2[i-1] if i > 0 else ns.pt2[i]) <= z_pt[i]))\n",
    "\n",
    "    # Add MAX constraints for staff working 3 shifts\n",
    "    for i in ns.I:\n",
    "        ns.add_component(f\"max_pt3_shift{i+3}\", pyo.Constraint(expr = sum(ns.pt3[j] for j in range(max(0, i-2), i+1)) <= z_pt[i]))\n",
    "\n",
    "    ######################################### CONSTRAINTS END HERE ############################################\n",
    "\n",
    "    # Solving using pyomo - glpk\n",
    "    opt = pyo.SolverFactory('glpk')\n",
    "    res = opt.solve(ns) \n",
    "    # ns.display()\n",
    "    \n",
    "    return res, ns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 05. Run Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##########################\n",
      "Logging 1 .... \n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model did not converge/No Optimal Solution found: last_start_shift =  5\n",
      "Relaxing last start shift =  6\n",
      "\n",
      "##########################\n",
      "Logging 2 .... \n",
      "\n",
      "Optimal Solution found: last_start_shift =  6\n"
     ]
    }
   ],
   "source": [
    "from pyomo.opt import SolverStatus, TerminationCondition\n",
    "n = len(z_pt)\n",
    "\n",
    "last_start_shifts = [5, 6]\n",
    "attempt = 1\n",
    "for last_start_shift in last_start_shifts:\n",
    "    print(\"##########################\")\n",
    "    print(f'Logging {attempt} .... \\n')\n",
    "    res, ns = model_call(last_start_shift)\n",
    "    if (res.solver.status == SolverStatus.ok) and (res.solver.termination_condition == TerminationCondition.optimal):\n",
    "        print('Optimal Solution found: last_start_shift = ', last_start_shift)\n",
    "        break\n",
    "    else:\n",
    "        attempt += 1\n",
    "        print('Model did not converge/No Optimal Solution found: last_start_shift = ', last_start_shift)\n",
    "        print('Relaxing last start shift = ', last_start_shift+1, end='\\n\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total Staff working Shift i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1bt1aLpdL0pXT2woUKKD8+fN7vFerV68uSe736s2+p0NCQiRJy5Yt08WLF6/5Oq62ZcsWHT9+XM8//7zHdX4NGjRQ/vz5491Gb/Qeu5tOnTqlb7/9Vs2bN9e5c+fc/fj3339Vp04d7d27132aYMqUKbVr1y7t3bs33nUdPXpU27dvV7t27dz9k6RatWrFez3dzXyuh4SEqEmTJvr888/dn9XR0dGaPXu2mjZtqmTJkt2RPgD3EgM3APdYcHCwJOncuXN37Tliw0OskJAQBQUFKW3atHGm//vvv3fseRcvXqxhw4Zp+/btHtcXxO4g3Y4WLVpo4cKF2rhxoypUqKD9+/dr69atHkM87927V2amvHnzxruOG40G53K5VKFCBa1du1YxMTFav3690qdPrzx58ki6EpImTpwoSe6wFBuSDh06JD8/P/eysTJmzKiUKVPq0KFDHtNz5sx5zTo+/fRTnT9/Xu+//368O8nXEhQUFOe0rlSpUnlcb3Lo0CFlypRJSZMm9Vju6rqvJTg4+Ka32djXnC9fPo/piRMnVq5cueL05FZcvW2nSpVKkm7q2pqXXnpJK1euVJkyZZQnTx7Vrl1brVq1cl9bdi2HDh1S3rx5PQKfdCVcx86/XWnTplXNmjVvuFx8v+OQkBBlzZo1zvsrJCQk3n5c/f5Injy5MmXKdN3rpm7ltbdt21bdu3fXoUOHlCNHDs2dO1dRUVEew6Dv3btXu3fvvuZpiMePH3cvdzPv6Zw5c6p3794aM2aMZs6cqcqVK6tx48buazKv97qkuNuoJOXPnz/OYC038x67m/bt2ycz08CBAzVw4MB4lzl+/LiyZMmi119/XU2aNNEjjzyiwoULq27dumrTpo0effRRSf977fH1Nl++fHG+1LrZz/W2bdtq9uzZ+v7771WlShWtXLlS//zzz3WHwQcSMkIScI8FBwcrc+bMcc7dvpZrBYzrXTAc3whx1xo1znmE5naeK9b333+vxo0bq0qVKnrvvfeUKVMmBQQEaNq0aTd1Yfy1NGrUSEmTJtWcOXNUoUIFzZkzR35+fmrWrJl7mZiYGLlcLn3zzTfxvs6bGWK3UqVK+u9//6sdO3a4r0eKVaFCBfXr10+HDx/WunXrlDlz5jgDStxsELzeSHYVK1bU9u3bNXHiRDVv3lypU6e+qXXeixEB8+fPr+3btysyMtLjSI63rrfN3e52fC0FChTQb7/9psWLF2vp0qWaP3++3nvvPQ0aNEhDhw69tcLvsWu9bm/6cae1bNlSvXr10syZM/XKK6/os88+U6lSpTyCSExMjIoUKaIxY8bEu45s2bK5l7vZ9/To0aP1zDPP6KuvvtLy5cv1wgsvaMSIEfrhhx+UNWvWO/La7tZ77GY/c2MHjunbt+81j0THfuFRpUoV7d+/392Pjz76SGPHjtUHH3zgcTTyZtzK53qdOnWUIUMGffbZZ6pSpYo+++wzZcyY8aa+BAASIkIS4AMNGzbUlClTtHHjRpUvX/66y8Z+U371hcTefHt9N55r/vz5CgoK0rJlyzzuczRt2rQ4y97KkaVkyZKpYcOGmjt3rsaMGaPZs2ercuXKHveRyZ07t8xMOXPm1COPPHLT63Zy3i9p/fr1HvfYKVmypAIDA7VmzRpt2rRJ9evXd8/LkSOHYmJitHfvXve369KVm66eOXNGOXLkuOka8uTJo7ffflvVqlVT3bp1tWrVqjt2WmaOHDm0evVqXbx40eNoknNkuOtp1KiRNm7cqPnz59/wKFfsa/7tt988wmRkZKQOHDjgsdOUKlWqeC+wP3To0DVHNryR621fyZIlU4sWLdSiRQtFRkbqiSee0PDhwzVgwIBrDq+eI0cO/fLLL4qJifE4orJnzx73/PvB3r17Pe73df78eR09etRje77arbz21KlTq0GDBpo5c6Zat26t9evXx7mpb+7cufXzzz+rRo0a1/093ep7ukiRIipSpIhee+01bdiwQRUrVtQHH3ygYcOGXfN1SVe20dhT/WL99ttv9+x3erOfubHvhYCAgJsKHalTp1b79u3Vvn17nT9/XlWqVNGQIUPUsWNH92uL73S83377zePnW/lc9/f3V6tWrTR9+nSNHDlSCxcuVKdOne75bR2AO4VrkgAf6N+/v5IlS6aOHTvqn3/+iTN///797pGjgoODlTZt2jij0L333nt3vK7cuXNLksdzRUdH39QITv7+/nK5XB7fgB48eFALFy6Ms2yyZMluOPKUU4sWLXTkyBF99NFH+vnnn+OMgvXEE0/I399fQ4cOjfMNupnd1CmFpUqVUlBQkGbOnKnDhw97HEkKDAxUiRIlNGnSJF24cMHj/kixO5hX7wzGflPeoEGDm36dkvToo49qyZIl2r17txo1ahRnlLPbVadOHUVFRXncyygmJsY9jPSNPPfcc8qUKZP69Omj33//Pc7848ePu3dIa9asqcSJE+vdd9/1+H18/PHHOnv2rEdPcufOrR9++EGRkZHuaYsXL76poduvJVmyZPEO/3z1dpA4cWIVLFhQZnbdEffq16+vY8eOafbs2e5ply9f1oQJE5Q8eXJVrVr1tmu9l6ZMmeLxOt9//31dvnxZ9erVu+ZjbvW1t2nTRr/++qv69esnf3//OCM8Nm/eXIcPH473nlqXLl3ShQsXJN38ezosLEyXL1/2mF+kSBH5+fnFO6R4rFKlSil9+vT64IMPPJb75ptvtHv37lt+396uHDlyyN/f/4af7+nTp1e1atU0efJkHT16NM56Tpw44f731dt58uTJlSdPHvfrzJQpk4oVK6ZPPvnE432yYsUK/frrrx6PvZXPdenK7//06dPq0qWL+6bTwP2KI0mAD+TOnVuzZs1SixYtVKBAAbVt21aFCxdWZGSkNmzYoLlz53rcO6Zjx45666231LFjR5UqVUpr166Nd0fVW4UKFVK5cuU0YMAAnTp1SqlTp9YXX3wRZyckPg0aNNCYMWNUt25dtWrVSsePH9ekSZOUJ08e/fLLLx7LlixZUitXrtSYMWPcN3MtW7bsNdddv359pUiRQn379pW/v7+efPJJj/m5c+fWsGHDNGDAAB08eFBNmzZVihQpdODAAS1YsECdO3dW3759r1t/4sSJVbp0aX3//fcKDAxUyZIlPeZXqFDBfV8pZ0gqWrSo2rVrpylTpujMmTOqWrWqfvzxR33yySdq2rSpxzf3N6tcuXL66quvVL9+ff3nP//RwoULb3hd1Y00bdpUZcqUUZ8+fbRv3z7lz59fixYtcg/ZfKOje6lSpdKCBQtUv359FStWTE8//bS7Rz/99JM+//xz91HRdOnSacCAARo6dKjq1q2rxo0b67ffftN7772n0qVLe+w4dezYUfPmzVPdunXVvHlz7d+/X5999pk7sN+OkiVLavbs2erdu7dKly6t5MmTq1GjRqpdu7YyZsyoihUrKkOGDNq9e7cmTpyoBg0aXPeIXefOnTV58mQ988wz2rp1q0JDQzVv3jz3kRJvjvYdPnxYn332WZzpyZMnV9OmTW97vfGJjIxUjRo11Lx5c/fvo1KlSmrcuPE1H3Orr71BgwZKkyaN5s6dq3r16il9+vQe89u0aaM5c+boueee0+rVq1WxYkVFR0drz549mjNnjvseYjf7nv7222/VvXt3NWvWTI888oguX76sGTNmxPs54RQQEKCRI0eqffv2qlq1qp566in9888/Gj9+vEJDQ9WrVy/vmn2TQkJC1KxZM02YMEEul0u5c+fW4sWL3ddmOU2aNEmVKlVSkSJF1KlTJ+XKlUv//POPNm7cqL///ls///yzJKlgwYKqVq2aSpYsqdSpU2vLli2aN2+eunfv7l7XiBEj1KBBA1WqVEkdOnTQqVOn3PcQO3/+vHu5W/lcl6TixYurcOHC7gE6SpQocRe6Btwj93o4PQD/8/vvv1unTp0sNDTUEidObClSpLCKFSvahAkTPIZ5vXjxoj377LMWEhJiKVKksObNm9vx48evOQT4iRMnPJ6nXbt2lixZsjjPX7Vq1ThDIu/fv99q1qxpgYGBliFDBnvllVdsxYoVNzUE+Mcff2x58+a1wMBAy58/v02bNs1dk9OePXusSpUqliRJEpPkHhL3WkOQm5m1bt3aJFnNmjWv2c/58+dbpUqVLFmyZJYsWTLLnz+/devWzX777bdrPsZpwIABJskqVKgQZ96XX35pkixFihR2+fJlj3lRUVE2dOhQy5kzpwUEBFi2bNlswIABHr9Ds+sPDy3HEOCxvvrqK0uUKJG1aNHCoqOjrzkEeHy/2/j6fuLECWvVqpWlSJHCQkJC7JlnnrH169ebJI9hpK/nyJEj1qtXL3vkkUcsKCjIkiZNaiVLlrThw4fb2bNnPZadOHGi5c+f3wICAixDhgzWtWtXO336dJx1jh492rJkyWKBgYFWsWJF27JlyzWHAL96qOf4enL+/Hlr1aqVpUyZ0mNI48mTJ1uVKlUsTZo0FhgYaLlz57Z+/frFqTs+//zzj7Vv397Spk1riRMntiJFisQ7bPqdGgLc+d66lfdvfDXEvq++++4769y5s6VKlcqSJ09urVu3tn///TfOOp19v5XXHuv55583STZr1qx450dGRtrIkSOtUKFCFhgYaKlSpbKSJUva0KFD4/wubvSe/uOPP6xDhw6WO3duCwoKstSpU9tjjz1mK1euvGZ9TrNnz7bixYtbYGCgpU6d2lq3bm1///23xzK38h67kfhuf3DixAl78sknLWnSpJYqVSrr0qWL7dy5M96h+ffv329t27a1jBkzWkBAgGXJksUaNmxo8+bNcy8zbNgwK1OmjKVMmdKSJEli+fPnt+HDh3sM/252pbcFChSwwMBAK1iwoH355Zdefa7Hih1a3jlkPXA/cpn54OpOAECCsHDhQj3++ONat27dDUd5w/1p+vTpat++vTZv3qxSpUrd9efr1auXPv74Yx07dizOaIp48I0fP169evXSwYMH44xGCdxPuCYJAB4SV1/fFB0drQkTJig4OJjTYnBHhIeH67PPPtOTTz5JQHoImZk+/vhjVa1alYCE+x7XJAHAQ6JHjx66dOmSypcvr4iICH355ZfasGGD3nzzzesOTQ7cyPHjx7Vy5UrNmzdP//77r1588UVfl4R76MKFC1q0aJFWr16tHTt26KuvvvJ1SYDXCEkA8JCoXr26Ro8ercWLFys8PFx58uTRhAkTPC7oBm7Hr7/+qtatWyt9+vR69913VaxYMV+XhHvoxIkTatWqlVKmTKlXXnnluoOBAPcLrkkCAAAAAAeuSQIAAAAAB0ISAAAAADg88NckxcTE6MiRI0qRIsUNb5YIAAAA4MFlZjp37pwyZ84sP79rHy964EPSkSNHlC1bNl+XAQAAACCB+Ouvv5Q1a9Zrzn/gQ1KKFCkkXWlEcHCwj6vxraioKC1fvly1a9dWQECAr8u579A/79A/79A/79A/79A/79FD79A/79C//wkLC1O2bNncGeFaHviQFHuKXXBwMCEpKkpJkyZVcHDwQ/8GuR30zzv0zzv0zzv0zzv0z3v00Dv0zzv0L64bXYbDwA0AAAAA4EBIAgAAAAAHQhIAAAAAODzw1yQBAADg/hUdHa2IiAglSpRI4eHhio6O9nVJ952oqKiHpn8BAQHy9/f3ej2EJAAAACQ4ZqZjx47pzJkzMjNlzJhRf/31F/e9vA0PW/9SpkypjBkzevVaCUkAAABIcGIDUvr06RUUFKQLFy4oefLk170BKOIXExOj8+fPP/D9MzNdvHhRx48flyRlypTpttdFSAIAAECCEh0d7Q5IadKkUUxMjKKiohQUFPRA7+TfLTExMYqMjHwo+pckSRJJ0vHjx5U+ffrbPvXuwe4SAAAA7jtRUVGSpKRJk/q4EtyPYreb2O3odhCSAAAAkCA9DNfP4M67E9sNIQkAAAAAHAhJAAAAwH3M5XJp4cKFvi5DkjRkyBAVK1bsmvOnT5+ulClT3rN6bhcDNwAAAOC+MatQoXv2XK127bql5W90mtfgwYM1ZMiQeOcdPHhQOXPm1LZt264bMm7VBx98oH79+unAgQPuaefPn1eqVKlUsWJFrVmzxj19zZo1euyxx7Rv3z7lzp37jtXg1KJFC9WvX/+urPtOIiQBAAAAd8DRo0fd/549e7YGDRqk3377zT0tefLk97ymxx57TOfPn9e2bdtUo0YNSdL333+vjBkzatOmTQoPD1dQUJAkafXq1cqePfttBSQzu6kb1SZJksQ9Al1Cxul2AAAAwB2QMWNG938hISFyuVzun9OnT68xY8Yoa9asCgwMVLFixbR06VL3Y3PmzClJKl68uFwul6pVqyZJ2rx5s2rVqqW0adMqJCREVatW1U8//XTTNeXLl0+ZMmXSunXr3NPWrFmjJk2aKGfOnPrhhx88pj/22GOSpIiICL3wwgvu+1RVqlRJmzdv9ljW5XLpm2++UcmSJRUYGOjxHLH279+vXLlyqXv37jKzOKfbxZ6eN2PGDIWGhiokJEQtW7bUuXPn3MucO3dOrVu3VrJkyZQpUyaNHTtW1apVU8+ePW+6D7eKkAQAAADcZePHj9fo0aM1atQo/fLLL6pTp44aN26svXv3SpJ+/PFHSdLKlSt19OhRffnll5KuBIR27dpp3bp1+uGHH5Q3b17Vr1/fI0TcSLVq1TwCzOrVq1WtWjVVrVpVq1evliRdunRJmzZtcoek/v37a/78+frkk0/0008/KU+ePKpTp45OnTrlse6XX35Zb731lnbv3q1HH33UY94vv/yiSpUqqVWrVpo4ceI1T0fcv3+/Fi5cqMWLF2vx4sX67rvv9NZbb7nn9+7dW+vXr9eiRYu0YsUKff/997cUFG+HT0PS2rVr1ahRI2XOnDneC87MTIMGDVKmTJmUJEkS1axZ070hAQAAAPeLUaNG6aWXXlLLli2VL18+jRw5UsWKFdO4ceMkSenSpZMkpUmTRhkzZlTq1KklSdWrV9fTTz+t/Pnzq0CBApoyZYouXryo77777qafu1q1atq0aZMuX76sc+fOadu2bapataqqVKniviZp48aNioiI0GOPPaYLFy7o/fff1zvvvKN69eqpYMGC+vDDD5UkSRJ9/PHHHut+/fXXVatWLeXOndtdsyRt2LBB1apVU9++fTVs2LDr1hcTE6Pp06ercOHCqly5stq0aaNVq1ZJuhISP/nkE40aNUo1atRQ4cKFNW3atJs6tc8bPg1JFy5cUNGiRTVp0qR457/99tt699139cEHH2jTpk1KliyZ6tSpo/Dw8HtcKQAAAHB7wsLCdOTIEVWsWNFjesWKFbV79+7rPvaff/5Rp06dlDdvXoWEhCg4OFjnz5/Xn3/+edPPX61aNV24cEGbN2/W999/r0ceeUTp0qVT1apV3dclrVmzRrly5VL27Nm1f/9+RUVFedQbEBCgMmXKxKm3VKlScZ7vzz//VK1atTRo0CD16dPnhvWFhoYqRYoU7p8zZcqk48ePS5L++OMPRUVFqUyZMu75ISEhypcv302//tvh04Eb6tWrp3r16sU7z8w0btw4vfbaa2rSpIkk6dNPP1WGDBm0cOFCtWzZ8l6WCgAAANxz7dq107///qvx48crR44cCgwMVPny5RUZGXnT68iTJ48yZ86sNWvW6MyZM6pataokKXPmzMqWLZs2bNig1atXq3r16rdcX7JkyeJMS5cunTJnzqzPP/9cHTp0UHBw8HXXERAQ4PGzy+VSTEzMLddyJyXY0e0OHDigY8eOqWbNmu5pISEhKlu2rDZu3HjNkBQREaGIiAj3z2FhYZKkqKgoRUVF3d2iE7jY1/+w9+F20T/v0D/v0D/v0D/v0D/v0cNbExUVJTNTTEyMYmJiZGaS5P7/veLNjnrsY2NiYpQ8eXJlzpxZ69atU+XKld3LrF+/XqVLl1ZMTIwSJbqyWx4VFeXxvOvXr9fEiRNVt25dSdJff/2lkydPuvvjfL5r1Wtmqly5sjsk9enTx71s5cqVtWTJEv3444/q0qWLYmJilDNnTiVOnFjff/+9smXL5q5r8+bNevHFFz2e6+rnNTMlSZJEixYtUsOGDVWnTh0tXbrUfaTI+bjY5Z0/Xz0tNDRUAQEB2rRpk7JmzSpJOnv2rH7//XdVrlw53tccu81ERUXJ39/fY97NvgcTbEg6duyYJClDhgwe0zNkyOCeF58RI0Zo6NChcaYvX75cSZMmvbNF3qdWrFjh6xLua/TPO/TPO/TPO/TPO/TPe/Tw5iRKlEgZM2bU+fPnPY6Y3MpgBXdC7JfttyM8PFxm5l5H9+7dNWLECGXKlElFihTRzJkztX37dr3//vsKCwtTUFCQkiRJoq+++kohISEKDAxUSEiIcuXKpU8++UT58+fXuXPnNGjQICVJkkTh4eEe9V26dOm69VauXFn9+vVTVFSUSpQo4V62TJky6t+/vyIjI1WqVCn39A4dOqh///4KCgpS1qxZ9e677+rChQtq1qyZwsLCdPHiRUlXfid+fv+7giciIkLR0dGKjo7WzJkz1axZM9WpU0fz5s1T8uTJ4/Qldnln7eHh4YqJiXFPe+qpp9SvXz8FBQUpbdq0euutt+Tn56eoqKh4X3NkZKQuXbqktWvX6vLlyx7zYuu+kQQbkm7XgAED1Lt3b/fPYWFhypYtm2rXrn3DQ30PuqioKK1YsUK1atWKc1gTN0b/vEP/vPMg9G9u2bK+e/LEiZVswABdGDFCuoVTVO6kZps2efV4+udd/yR6eCd6eK+Eh4frr7/+UvLkyRUUFCQz07lz5zyuW7kXvNl3DAoKksvlcq+jX79+ioiI0KBBg3T8+HEVLFhQCxcuVPHixd2PGTdunIYNG6YRI0aocuXK+vbbbzV16lQ999xzqlatmrJly6Zhw4a5w4uzviRJklyz3tgjSZcuXVL+/PmVJ08e97w6deqoa9euypcvnx555BH39NGjRytRokTq2rWrzp07p1KlSmnp0qXKnj27JLkPPqRIkcLjeQMDA+Xv76/g4GAFBwdr2bJlqlevnlq3bq3FixfH6YtzeWfv/Pz83NPeffddde3aVS1btlRwcLD69eunY8eOuZ/jauHh4UqSJImqVKnivgdUrJsNvi6718ctr8HlcmnBggVq2rSppCsXaeXOnTvOXYerVq2qYsWKafz48Te13rCwMIWEhOjs2bOEpKgoLVmyRPXr179vd7J8if55h/5550Ho36xChXz35IGBSjZ4sC4MHSo5Tsm+l1rt2uXV4+mfd/2T6OGd6OG9Eh4ergMHDihnzpwKCgpyH1UIDg72OGqBm/Og9e/ChQvKkiWLRo8erWeffTbO/Ku3H6ebzQYJtks5c+ZUxowZ3cP/SVde1KZNm1S+fHkfVgYAAADgXtm2bZs+//xz7d+/Xz/99JNat24tSe7B3e4Gn55ud/78ee3bt8/984EDB7R9+3alTp1a2bNnV8+ePTVs2DDlzZtXOXPm1MCBA5U5c2b30SYAAAAAD75Ro0bpt99+U+LEiVWyZEl9//33Sps27V17Pp+GpC1btrjv6ivJfS1Ru3btNH36dPXv318XLlxQ586ddebMGVWqVElLly6Nc9gMAAAAwIOpePHi2rp16z19Tp+GpGrVql13KEeXy6XXX39dr7/++j2sCgAAAMDDLMFekwQAAICHWwIZXwz3mTux3RCSAAAAkKDEjuJ5s/e0AZxitxtvRoN94O6TBAAAgPubv7+/UqZMqePHj0u6ct+cyMhIhYeHPxBDWN9rMTExD0X/zEwXL17U8ePHlTJlSvn7+9/2ughJAAAASHAyZswoSTp+/LjMTJcuXVKSJEnkcrl8XNn952HrX8qUKd3bz+0iJAEAACDBcblcypQpk9KnT69Lly7pu+++U5UqVe7bG2r7UlRUlNauXftQ9C8gIMCrI0ixCEkAAABIsPz9/RUYGKjLly8rKCjogd/Jvxv8/f3p3y16cE9KBAAAAIDbQEgCAAAAAAdCEgAAAAA4EJIAAAAAwIGQBAAAAAAOhCQAAAAAcCAkAQAAAIADIQkAAAAAHAhJAAAAAOBASAIAAAAAB0ISAAAAADgQkgAAAADAgZAEAAAAAA6EJAAAAABwICQBAAAAgAMhCQAAAAAcCEkAAAAA4EBIAgAAAAAHQhIAAAAAOBCSAAAAAMCBkAQAAAAADoQkAAAAAHAgJAEAAACAAyEJAAAAABwISQAAAADgQEgCAAAAAAdCEgAAAAA4EJIAAAAAwIGQBAAAAAAOhCQAAAAAcCAkAQAAAIADIQkAAAAAHAhJAAAAAOBASAIAAAAAB0ISAAAAADgQkgAAAADAgZAEAAAAAA6EJAAAAABwICQBAAAAgAMhCQAAAAAcCEkAAAAA4EBIAgAAAAAHQhIAAAAAOBCSAAAAAMCBkAQAAAAADoQkAAAAAHAgJAEAAACAAyEJAAAAABwISQAAAADgQEgCAAAAAAdCEgAAAAA4EJIAAAAAwIGQBAAAAAAOhCQAAAAAcCAkAQAAAIADIQkAAAAAHAhJAAAAAOBASAIAAAAAB0ISAAAAADgQkgAAAADAgZAEAAAAAA6EJAAAAABwICQBAAAAgAMhCQAAAAAcCEkAAAAA4EBIAgAAAAAHQhIAAAAAOBCSAAAAAMCBkAQAAAAADoQkAAAAAHAgJAEAAACAQ4IOSdHR0Ro4cKBy5sypJEmSKHfu3HrjjTdkZr4uDQAAAMADKpGvC7iekSNH6v3339cnn3yiQoUKacuWLWrfvr1CQkL0wgsv+Lo8AAAAAA+gBB2SNmzYoCZNmqhBgwaSpNDQUH3++ef68ccffVwZAAAAgAdVgg5JFSpU0JQpU/T777/rkUce0c8//6x169ZpzJgx13xMRESEIiIi3D+HhYVJkqKiohQVFXXXa07IYl//w96H20X/vEP/vPNA9C8w0HfPnTix5/99wOvfHf3zfiX00GfP7a0H4jPQh+jf/9xsD1yWgC/wiYmJ0SuvvKK3335b/v7+io6O1vDhwzVgwIBrPmbIkCEaOnRonOmzZs1S0qRJ72a5AAAAABKwixcvqlWrVjp79qyCg4OvuVyCDklffPGF+vXrp3feeUeFChXS9u3b1bNnT40ZM0bt2rWL9zHxHUnKli2bTp48ed1GPAyioqK0YsUK1apVSwEBAb4u575D/7xD/7zzIPRvbtmyvnvyxImVbMAAXRgxQoqM9EkJzTZt8urx9M+7/kn0kG3QO/TPO3fiPXwnhIWFKW3atDcMSQn6dLt+/frp5ZdfVsuWLSVJRYoU0aFDhzRixIhrhqTAwEAFxnM4PSAg4L7dsbjT6IV36J936J937uv+Ob7A8pnISJ/V4fXvjf55vxJ66N0K6J93K6B/Pnneq91sHQl6CPCLFy/Kz8+zRH9/f8XExPioIgAAAAAPugR9JKlRo0YaPny4smfPrkKFCmnbtm0aM2aMOnTo4OvSAAAAADygEnRImjBhggYOHKjnn39ex48fV+bMmdWlSxcNGjTI16UBAAAAeEAl6JCUIkUKjRs3TuPGjfN1KQAAAAAeEgn6miQAAAAAuNcISQAAAADgQEgCAAAAAAdCEgAAAAA4EJIAAAAAwIGQBAAAAAAOhCQAAAAAcCAkAQAAAIADIQkAAAAAHAhJAAAAAOBASAIAAAAAB0ISAAAAADgQkgAAAADAgZAEAAAAAA6EJAAAAABwICQBAAAAgAMhCQAAAAAcCEkAAAAA4EBIAgAAAAAHQhIAAAAAOBCSAAAAAMCBkAQAAAAADoQkAAAAAHAgJAEAAACAAyEJAAAAABwISQAAAADgQEgCAAAAAAdCEgAAAAA4EJIAAAAAwIGQBAAAAAAOhCQAAAAAcCAkAQAAAIADIQkAAAAAHAhJAAAAAOBASAIAAAAAB0ISAAAAADgQkgAAAADAIZGvCwBw78wqVMh3Tx4YqGSDB2tu2bJSRIRPSmi1a5dPnhcAANxfOJIEAAAAAA6EJAAAAABwICQBAAAAgAMhCQAAAAAcCEkAAAAA4EBIAgAAAAAHQhIAAAAAOBCSAAAAAMCBkAQAAAAADoQkAAAAAHAgJAEAAACAAyEJAAAAABwISQAAAADgQEgCAAAAAAdCEgAAAAA4EJIAAAAAwIGQBAAAAAAOhCQAAAAAcCAkAQAAAIADIQkAAAAAHAhJAAAAAOBASAIAAAAAB0ISAAAAADgQkgAAAADAgZAEAAAAAA6EJAAAAABwICQBAAAAgAMhCQAAAAAcCEkAAAAA4EBIAgAAAAAHQhIAAAAAOBCSAAAAAMCBkAQAAAAADoQkAAAAAHAgJAEAAACAAyEJAAAAABwISQAAAADgQEgCAAAAAAdCEgAAAAA4JPiQdPjwYT399NNKkyaNkiRJoiJFimjLli2+LgsAAADAAyqRrwu4ntOnT6tixYp67LHH9M033yhdunTau3evUqVK5evSAAAAADygEnRIGjlypLJly6Zp06a5p+XMmdOHFQEAAAB40CXokLRo0SLVqVNHzZo103fffacsWbLo+eefV6dOna75mIiICEVERLh/DgsLkyRFRUUpKirqrteckMW+/oe9D7frgehfYKDvnjtxYs//+8D9/Ltj+/PSg7D90T/vV0IPvVsB/fNuBfTPZ8/tdLN1uMzM7nItty0oKEiS1Lt3bzVr1kybN2/Wiy++qA8++EDt2rWL9zFDhgzR0KFD40yfNWuWkiZNelfrBQAAAJBwXbx4Ua1atdLZs2cVHBx8zeUSdEhKnDixSpUqpQ0bNrinvfDCC9q8ebM2btwY72PiO5KULVs2nTx58rqNuFfmli3ruydPnFjJBgzQhREjpMhIn5TQbNMmrx5P/+ifN+ifd+ifd+ifd7ztn0QP2Qa9Q/+8cyfew3dCWFiY0qZNe8OQlKBPt8uUKZMKFizoMa1AgQKaP3/+NR8TGBiowHgOZwYEBCggIOCO13jLHAHOZyIjfVaH178D+ufdCuifdyugf96tgP55twL65/1K6KF3K6B/3q2A/vnkea92s3Uk6CHAK1asqN9++81j2u+//64cOXL4qCIAAAAAD7oEHZJ69eqlH374QW+++ab27dunWbNmacqUKerWrZuvSwMAAADwgErQIal06dJasGCBPv/8cxUuXFhvvPGGxo0bp9atW/u6NAAAAAAPqAR9TZIkNWzYUA0bNvR1GQAAAAAeEgn6SBIAAAAA3GuEJAAAAABwICQBAAAAgAMhCQAAAAAcCEkAAAAA4EBIAgAAAAAHQhIAAAAAOBCSAAAAAMDhtkJSrly59O+//8aZfubMGeXKlcvrogAAAADAV24rJB08eFDR0dFxpkdEROjw4cNeFwUAAAAAvpLoVhZetGiR+9/Lli1TSEiI++fo6GitWrVKoaGhd6w4AAAAALjXbikkNW3aVJLkcrnUrl07j3kBAQEKDQ3V6NGj71hxAAAAAHCv3VJIiomJkSTlzJlTmzdvVtq0ae9KUQAAAADgK7cUkmIdOHDgTtcBAAAAAAnCbYUkSVq1apVWrVql48ePu48wxZo6darXhQEAAACAL9xWSBo6dKhef/11lSpVSpkyZZLL5brTdQEAAACAT9xWSPrggw80ffp0tWnT5k7XAwAAAAA+dVv3SYqMjFSFChXudC0AAAAA4HO3FZI6duyoWbNm3elaAAAAAMDnbut0u/DwcE2ZMkUrV67Uo48+qoCAAI/5Y8aMuSPFAQAAAMC9dlsh6ZdfflGxYsUkSTt37vSYxyAOAAAAAO5ntxWSVq9efafrAAAAAIAE4bauSQIAAACAB9VtHUl67LHHrnta3bfffnvbBQEAAACAL91WSIq9HilWVFSUtm/frp07d6pdu3Z3oi4AAAAA8InbCkljx46Nd/qQIUN0/vx5rwoCAAAAAF+6o9ckPf3005o6deqdXCUAAAAA3FN3NCRt3LhRQUFBd3KVAAAAAHBP3dbpdk888YTHz2amo0ePasuWLRo4cOAdKQwAAAAAfOG2QlJISIjHz35+fsqXL59ef/111a5d+44UBgAAAAC+cFshadq0aXe6DgAAAABIEG4rJMXaunWrdu/eLUkqVKiQihcvfkeKAgAAAABfua2QdPz4cbVs2VJr1qxRypQpJUlnzpzRY489pi+++ELp0qW7kzUCAAAAwD1zW6Pb9ejRQ+fOndOuXbt06tQpnTp1Sjt37lRYWJheeOGFO10jAAAAANwzt3UkaenSpVq5cqUKFCjgnlawYEFNmjSJgRsAAAAA3Ndu60hSTEyMAgIC4kwPCAhQTEyM10UBAAAAgK/cVkiqXr26XnzxRR05csQ97fDhw+rVq5dq1Khxx4oDAAAAgHvttkLSxIkTFRYWptDQUOXOnVu5c+dWzpw5FRYWpgkTJtzpGgEAAADgnrmta5KyZcumn376SStXrtSePXskSQUKFFDNmjXvaHEAAAAAcK/d0pGkb7/9VgULFlRYWJhcLpdq1aqlHj16qEePHipdurQKFSqk77///m7VCgAAAAB33S2FpHHjxqlTp04KDg6OMy8kJERdunTRmDFj7lhxAAAAAHCv3VJI+vnnn1W3bt1rzq9du7a2bt3qdVEAAAAA4Cu3FJL++eefeIf+jpUoUSKdOHHC66IAAAAAwFduKSRlyZJFO3fuvOb8X375RZkyZfK6KAAAAADwlVsKSfXr19fAgQMVHh4eZ96lS5c0ePBgNWzY8I4VBwAAAAD32i0NAf7aa6/pyy+/1COPPKLu3bsrX758kqQ9e/Zo0qRJio6O1quvvnpXCgUAAACAe+GWQlKGDBm0YcMGde3aVQMGDJCZSZJcLpfq1KmjSZMmKUOGDHelUAAAAAC4F275ZrI5cuTQkiVLdPr0ae3bt09mprx58ypVqlR3oz4AAAAAuKduOSTFSpUqlUqXLn0nawEAAAAAn7ulgRsAAAAA4EFHSAIAAAAAB0ISAAAAADgQkgAAAADAgZAEAAAAAA6EJAAAAABwICQBAAAAgAMhCQAAAAAcCEkAAAAA4EBIAgAAAAAHQhIAAAAAOBCSAAAAAMCBkAQAAAAADoQkAAAAAHAgJAEAAACAAyEJAAAAABwISQAAAADgQEgCAAAAAAdCEgAAAAA4EJIAAAAAwIGQBAAAAAAOhCQAAAAAcCAkAQAAAIADIQkAAAAAHAhJAAAAAOBASAIAAAAAB0ISAAAAADgQkgAAAADA4b4KSW+99ZZcLpd69uzp61IAAAAAPKDum5C0efNmTZ48WY8++qivSwEAAADwALsvQtL58+fVunVrffjhh0qVKpWvywEAAADwAEvk6wJuRrdu3dSgQQPVrFlTw4YNu+6yERERioiIcP8cFhYmSYqKilJUVNRdrfOmBAb67rkTJ/b8vw94/Tugf96tgP55twL6590K6J93K6B/3q+EHnq3Avrn3Qron8+e2+lm63CZmd3lWrzyxRdfaPjw4dq8ebOCgoJUrVo1FStWTOPGjYt3+SFDhmjo0KFxps+aNUtJkya9y9UCAAAASKguXryoVq1a6ezZswoODr7mcgk6JP31118qVaqUVqxY4b4W6UYhKb4jSdmyZdPJkyev24h7ZW7Zsr578sSJlWzAAF0YMUKKjPRJCc02bfLq8fSP/nmD/nmH/nmH/nnH2/5J9JBt0Dv0zzt34j18J4SFhSlt2rQ3DEkJ+nS7rVu36vjx4ypRooR7WnR0tNauXauJEycqIiJC/v7+Ho8JDAxUYDyHMwMCAhQQEHDXa74hR4DzmchIn9Xh9e+A/nm3Avrn3Qron3croH/erYD+eb8SeujdCuifdyugfz553qvdbB0JOiTVqFFDO3bs8JjWvn175c+fXy+99FKcgAQAAAAA3krQISlFihQqXLiwx7RkyZIpTZo0caYDAAAAwJ1wXwwBDgAAAAD3SoI+khSfNWvW+LoEAAAAAA8wjiQBAAAAgAMhCQAAAAAcCEkAAAAA4EBIAgAAAAAHQhIAAAAAOBCSAAAAAMCBkAQAAAAADoQkAAAAAHAgJAEAAACAAyEJAAAAABwISQAAAADgQEgCAAAAAAdCEgAAAAA4EJIAAAAAwIGQBAAAAAAOhCQAAAAAcCAkAQAAAIADIQkAAAAAHAhJAAAAAOBASAIAAAAAB0ISAAAAADgQkgAAAADAgZAEAAAAAA6EJAAAAABwICQBAAAAgAMhCQAAAAAcCEkAAAAA4EBIAgAAAAAHQhIAAAAAOBCSAAAAAMCBkAQAAAAADoQkAAAAAHAgJAEAAACAAyEJAAAAABwISQAAAADgQEgCAAAAAAdCEgAAAAA4EJIAAAAAwIGQBAAAAAAOhCQAAAAAcCAkAQAAAIADIQkAAAAAHAhJAAAAAOBASAIAAAAAB0ISAAAAADgQkgAAAADAgZAEAAAAAA6EJAAAAABwICQBAAAAgAMhCQAAAAAcCEkAAAAA4EBIAgAAAAAHQhIAAAAAOBCSAAAAAMCBkAQAAAAADoQkAAAAAHAgJAEAAACAAyEJAAAAABwISQAAAADgQEgCAAAAAAdCEgAAAAA4EJIAAAAAwIGQBAAAAAAOhCQAAAAAcCAkAQAAAIADIQkAAAAAHAhJAAAAAOBASAIAAAAAB0ISAAAAADgQkgAAAADAgZAEAAAAAA6EJAAAAABwICQBAAAAgAMhCQAAAAAcCEkAAAAA4EBIAgAAAAAHQhIAAAAAOCTokDRixAiVLl1aKVKkUPr06dW0aVP99ttvvi4LAAAAwAMsQYek7777Tt26ddMPP/ygFStWKCoqSrVr19aFCxd8XRoAAACAB1QiXxdwPUuXLvX4efr06UqfPr22bt2qKlWq+KgqAAAAAA+yBB2Srnb27FlJUurUqa+5TEREhCIiItw/h4WFSZKioqIUFRV1dwu8GYGBvnvuxIk9/+8DXv8O6J93K6B/3q2A/nm3Avrn3Qron/croYferYD+ebcC+uez53a62TpcZmZ3uZY7IiYmRo0bN9aZM2e0bt26ay43ZMgQDR06NM70WbNmKWnSpHezRAAAAAAJ2MWLF9WqVSudPXtWwcHB11zuvglJXbt21TfffKN169Ypa9as11wuviNJ2bJl08mTJ6/biHtlbtmyvnvyxImVbMAAXRgxQoqM9EkJzTZt8urx9I/+eYP+eYf+eYf+ecfb/kn0kG3QO/TPO3fiPXwnhIWFKW3atDcMSffF6Xbdu3fX4sWLtXbt2usGJEkKDAxUYDyHMwMCAhQQEHC3Srx5jgDnM5GRPqvD698B/fNuBfTPuxXQP+9WQP+8WwH9834l9NC7FdA/71ZA/3zyvFe72ToSdEgyM/Xo0UMLFizQmjVrlDNnTl+XBAAAAOABl6BDUrdu3TRr1ix99dVXSpEihY4dOyZJCgkJUZIkSXxcHQAAAIAHUYK+T9L777+vs2fPqlq1asqUKZP7v9mzZ/u6NAAAAAAPqAR9JOk+GVMCAAAAwAMkQR9JAgAAAIB7jZAEAAAAAA6EJAAAAABwICQBAAAAgAMhCQAAAAAcCEkAAAAA4EBIAgAAAAAHQhIAAAAAOBCSAAAAAMCBkAQAAAAADoQkAAAAAHAgJAEAAACAAyEJAAAAABwISQAAAADgQEgCAAAAAAdCEgAAAAA4EJIAAAAAwIGQBAAAAAAOhCQAAAAAcCAkAQAAAIADIQkAAAAAHAhJAAAAAOBASAIAAAAAB0ISAAAAADgQkgAAAADAgZAEAAAAAA6EJAAAAABwICQBAAAAgAMhCQAAAAAcCEkAAAAA4EBIAgAAAAAHQhIAAAAAOBCSAAAAAMCBkAQAAAAADoQkAAAAAHAgJAEAAACAAyEJAAAAABwISQAAAADgQEgCAAAAAAdCEgAAAAA4EJIAAAAAwIGQBAAAAAAOhCQAAAAAcCAkAQAAAIADIQkAAAAAHAhJAAAAAOBASAIAAAAAB0ISAAAAADgQkgAAAADAgZAEAAAAAA6EJAAAAABwICQBAAAAgAMhCQAAAAAcCEkAAAAA4EBIAgAAAAAHQhIAAAAAOBCSAAAAAMCBkAQAAAAADoQkAAAAAHAgJAEAAACAAyEJAAAAABwISQAAAADgQEgCAAAAAAdCEgAAAAA4EJIAAAAAwIGQBAAAAAAOhCQAAAAAcCAkAQAAAIADIQkAAAAAHAhJAAAAAOBASAIAAAAAB0ISAAAAADgQkgAAAADAgZAEAAAAAA6EJAAAAABwICQBAAAAgAMhCQAAAAAc7ouQNGnSJIWGhiooKEhly5bVjz/+6OuSAAAAADygEnxImj17tnr37q3Bgwfrp59+UtGiRVWnTh0dP37c16UBAAAAeAAl+JA0ZswYderUSe3bt1fBggX1wQcfKGnSpJo6daqvSwMAAADwAErk6wKuJzIyUlu3btWAAQPc0/z8/FSzZk1t3Lgx3sdEREQoIiLC/fPZs2clSadOnVJUVNTdLfgmXEzkw5b7+cl18aIu+vlJPqrj33//9erx9I/+eYP+eYf+eYf+ecfb/kn0kG3QO/TPO3fiPXwnnDt3TpJkZtddzmU3WsKHjhw5oixZsmjDhg0qX768e3r//v313XffadOmTXEeM2TIEA0dOvRelgkAAADgPvLXX38pa9as15yfoI8k3Y4BAwaod+/e7p9jYmJ06tQppUmTRi6Xy4eV+V5YWJiyZcumv/76S8HBwb4u575D/7xD/7xD/7xD/7xD/7xHD71D/7xD//7HzHTu3Dllzpz5ussl6JCUNm1a+fv7659//vGY/s8//yhjxozxPiYwMFCBgYEe01KmTHm3SrwvBQcHP/RvEG/QP+/QP+/QP+/QP+/QP+/RQ+/QP+/QvytCQkJuuEyCHrghceLEKlmypFatWuWeFhMTo1WrVnmcfgcAAAAAd0qCPpIkSb1791a7du1UqlQplSlTRuPGjdOFCxfUvn17X5cGAAAA4AGU4ENSixYtdOLECQ0aNEjHjh1TsWLFtHTpUmXIkMHXpd13AgMDNXjw4DinI+Lm0D/v0D/v0D/v0D/v0D/v0UPv0D/v0L9bl6BHtwMAAACAey1BX5MEAAAAAPcaIQkAAAAAHAhJAAAAAOBASLqPcTkZAAAAcOcRku5TMTExcrlcOnbsmGJiYnxdzn2JvgEAACA+hKT7lJ+fn/bt26csWbKocePGio6O9nVJ95WYmBj5+fnpwIEDHjcrhvc4wol7iS87vPP9999r+/btvG9vAz0DHmyEpPvYunXrZGbatWuXGjVqRFC6BX5+fjpy5IiKFSumtm3b6quvvvJ1Sfel2J2Ev//+W8ePH1dYWJhcLhc7rjcpvj7Ru1vj53flz9jFixclseN6KxYtWqSqVauqZ8+e2rVrl6/Lua/Ens0hSefPn9elS5d8XBEeRrGfd7t27dKJEyd8XM2Dh5B0HytatKhy5Mihxx9/XJGRkQSlW7R37175+/srT548GjVqlObMmePrku47LpdLCxYsUPny5VW9enXVrFlTe/fulZ+fHzv7NxB7NFOSVqxYofnz52v//v3uabh506dPV6lSpXT58mX3jituLHny5EqWLJk2btyoDh06aOfOnb4u6b4R+z4dOnSomjZtqooVK2rRokWKiIjwcWUJX+zfBr7Q8J7L5dKiRYvUsGFD/fbbb/T0DuOv8X3CucN5+fJlSVLx4sXVokULbdmyRU899ZT++ecfNW3alKB0k6pWraqyZcvq3LlzKlq0qEaPHq158+b5uqz7yoEDB9S7d2+99NJL6tWrlzJmzKhSpUppy5YtBKUbiN3JGjBggB5//HG99NJLKliwoKZMmaLz58/7uLr7S5YsWZQ0aVJt3bpVEkfjboaZqWjRomrVqpXGjBmjRIkSqXnz5hxRugUffPCBPvjgA1WvXl358+fXE088ofHjx+vs2bO+Li3Bcn45dODAAR9Xc/+KDUOnT5/WF198oV69eqlSpUp8SXSHEZLuA7EfKseOHdOlS5eUKFEi97yGDRsqderUKlGihAYOHKiDBw8SlOJx9U5TZGSkpCs7qDlz5lTZsmWVN29evfXWW5o/f74vSrxvOL+pCggIUIsWLdS9e3c9++yzmjJliurWrasaNWoQlK4htn9mpj/++ENr167V8uXLtXHjRg0ePFjPPfecJk+erHPnzvm40oQpvm9Ky5Ytq8jISH388ceSxNG4m+ByuZQmTRqlSZNGn376qZYsWaKQkBC1bNmSoHQNV3+WJUqUSGPHjtUrr7yiWbNmadSoUXr55Zf1wQcfKCwszEdVJlzz5s3TjBkzJEl9+vTRs88+qwsXLvi4qvuTy+XSd999pzp16ujw4cMqV66cJI7O3XGG+8K+ffssMDDQChYsaNOmTbPvv//ePa9WrVrWqVMnMzObO3euFS1a1Jo2bWqXL1/2VbkJSnR0tJmZHTp0yL7++muPeXv37rVSpUrZF198YX/++ac99dRTVqpUKZs3b54vSk3wYmJizMxs6dKl1qtXL2vSpInVrl3bwsLC3MscPXrUmjdvbmnSpLGNGzf6qtQEKXZbNDM7ffq0/f777zZgwACP6SNHjjSXy2WjR4/26Cs8nT9/3uPnb775xrJnz27r1q3zUUUJ33fffWc//vijHT9+3D0tOjraKleubF988YWFhYVZkSJF7NFHH7WdO3f6sNKEJ/azz8xs3rx5NnHiRKtfv77NmjXLY7mxY8eay+WykSNH2unTp+9xlQnbyy+/bC6Xy+rXr28pUqSwX375xdcl3ddOnz5t2bNnN5fLZTNnzvR1OQ8kQtJ9IDo62j766CMLCQmxwMBA6927t+XMmdN69epl27dvt59++slq165tv/76q4WHh9ucOXMse/bs1rJlS1+XnmD8+eeflipVKnO5XPbcc8/Zhx9+aOfOnTMzs+nTp1vBggXtxIkTtm3bNmvdurWVL1+eD51rWL58uQUFBVn16tWtXLlyFhgYaF999ZXHMseOHbO6deta9uzZLTw83EeVJlyvvvqqlStXzlKmTGklSpSwvXv3esx/++23LVGiRDZkyBC7cOGCj6pMuN5++22rVq2ajRgxwiIjI83M7J9//rEyZcrY+PHjzcwzkMJswYIF5nK5rECBAlauXDlbtGiR/fnnn2Zm1r9/f2vVqpWZmYWFhVnRokWtePHitm3bNh9WnHA4A9KAAQMsMDDQSpcubS6Xy1q2bGl//PGHx/Ljx483l8tlM2bMuNelJngFCxa0RIkS2fDhw31dygPhzJkzlidPHnv00Ud5v94FhKT7xPHjx+2DDz6w7Nmz23PPPWe7d++2xo0bW40aNSx//vyWNWtWmzx5spmZhYeH24IFC2z//v0+rjrhWLlypVWtWtVy585ttWrVsg4dOlj27Nntk08+sYULF9qzzz5rS5YsMTOzzZs3W5MmTax69ep8k3+VEydO2CuvvGIffPCBmV35gO7QoYMFBwfb0qVLPZb9559/7O+///ZFmQmOc4d95syZljFjRhs7dqw9//zzljRpUnv55ZfdO6yxBg4caBUrVvTYQcMVW7dutc6dO1vx4sUtd+7c9tZbb9mxY8fsvffes3Tp0tnRo0d9XWKCs2PHDgsICLCCBQta165dLV++fPbEE0/YyJEjbceOHRYSEuI+0h4WFmaZMmWytm3b+rjqhOXHH3+0pk2b2oYNGyw6Otref/99y5Qpk73yyit28OBBj2Vnz55tUVFRPqo04Yn9DGzWrJm1a9fOEiVKZNOnT/dxVfeP2L8D27Zts08//dQ+/fRT95ka//77r4WGhlrZsmXt559/9mWZDxxCUgLn3EE6efKkTZgwwUJCQmz06NFmduUUso4dO1qWLFnss88+81WZCU583yIvXbrUnnzySStTpoxt2bLFPv74Y2vatKk9+uij5nK5rGbNmu5lt23bZocPH76XJSdoMTExtnPnTgsKCrI8efJ4nGISExNj7du3txQpUtjy5ct9WGXC991339nzzz/v8V4dM2aMZc2a1V577TX766+/PJaPff8/zEHp6vdy7M8RERF27tw5e+2116xevXoWHBxszz77rKVOndref//9eB/7sIo99Xr79u2WJEkSe/HFF23JkiU2f/58y5s3r9WrV89cLpf179/fIiIizMzswoULnLLt8Mknn1iDBg2sQYMG7h6ZmU2YMMEyZ85sAwYMsEOHDsV53MMclGKP8pqZXbp0yWPeSy+9FG9Q4jTPa5s3b55lypTJKlWqZLVr17akSZPatGnTzOzKqXehoaFWsWJF++mnn3xb6AOEkJTAxP5Rv3DhgvtDxfmH6tSpU/buu+9aSEiIvfzyy+7pV+9cPcxie/jnn3/arFmzbMKECbZ7924zM1u1apXVqlXLKlWqZEeOHLHo6GhbvXq11a5dm1MjbkKPHj3M5XLZoEGDPE4Di4mJsY4dO5rL5bJVq1b5sMKEa8uWLZYrVy4LDg62SZMmecyLDUqDBg2yAwcOeMwjIF3x+eef25AhQ+z111+Pcy3D2bNn7csvv7R69epZ6tSprUKFCve61AQr9u9H7M76jz/+aEFBQda6dWs7ffq0RUVF2fz5861Lly7unStn3wlKV0yYMMFCQ0Mtc+bMcb6tnzhxomXPnt26detmx44d81GFCceuXbs8fn7vvfesc+fO9tJLL9mvv/7qnv7yyy9b4sSJbcqUKXbo0CF7/PHH7fHHH7/X5d4Xtm3bZmnTpnV/AbR161ZzuVzWp08f93v79OnTliJFCqtZs6ZHkMftIyQlQL/99puVLFnSnn76adu+fXucjf3ff/+1d99911KlSmV9+/Z1T+eP2f/+uP/888+WLVs2K1y4sLlcLgsNDbWPP/7YzK4cUapVq5aVL1/e/WF+8eJFn9V8v+nevbsFBgbaZ5995tG3mJgY69atm+3Zs8eH1SUc8YWbjz/+2HLnzm1169b12FkwMxs3bpz5+/vbhx9+eK9KvG/079/fsmfPbrVq1bLGjRtbSEiIxwANsb0+e/asbdiwwbJmzer+hvVhtX37do+jGDExMe6fN2/ebEmSJLEnn3wyzk79wxzKY13rCOSMGTOsYMGC9swzz8Q54vHWW29ZkyZNHvr+9ezZ00qXLm1r1641M7Phw4dbsmTJrG3btpYyZUqrXLmyzZ0717384MGDzeVyWaFChaxw4cIeR5/wv21x/vz51qBBAzMzO3jwoGXLls2ef/5593Kx17WeOXMmzjWuuH2EpAQmJibG3nvvPXvkkUesX79+ljlzZnv++efdO06xH8AnTpywd99919KlS2c9evTwZckJRuyHyS+//GJJkiSxIUOG2N9//21Hjhyx0qVLW4ECBdw7BF9//bXVqVPHypUr595Z5dSc/4ndzn777TfbvHmzbd682WN+586dLUmSJPbZZ5/FOY0CntvS1X/0J0+ebMWKFbOuXbvGCZRffPEFX3Zc5f3337esWbO6t8GZM2eay+WyoKAg++abb8zsSr9jt9kLFy5Yw4YNbdCgQT6r2dcWLlxoLpfLKlWqZFOmTInz/jX7X1Bq0aJFvKeJPayc793Vq1fbqlWr3Dv8ZmYfffSRFS9e3Dp27BjniAmnx145ivToo49a3bp17euvv7aWLVu6v9A4deqU1axZ06pUqWJz5sxx92nt2rX2zTffxDnq+bCJ3fbi+/vx6aef2mOPPWY7duywbNmyWefOnd3LrV692nr06GFHjhy590U/4AhJCdCuXbssQ4YMtmPHDvvll19sxIgRljlzZmvWrJmNGzfOPfTtpUuX7K233rIcOXLYP//881B/MMc6dOiQJUuWzJo1a+YxffXq1RYUFGTfffede9qSJUusYcOGVqBAAfv999/vdakJVux29OWXX1q2bNncoxF1797dtm7d6l6uS5cuFhwcbB9//DFBycH5Phw1apTVq1fPmjdvbq+99pp7+vvvv2/FixePNyiZcVQ41tmzZ613797uo8D//e9/LUWKFDZ69Ghr06aNJU2a1NasWWNmnn1v0qSJPfnkk3b58uWH8nNx/fr1VrduXevSpYt169bNUqdObYMHD44zuMqmTZssefLkVqtWLU4TM89tqGfPnpY+fXpLmzatZcmSxWrXru3+2ztlyhQrWbKkdenSxbZv337NdTxsYj+3fv/9dytYsKB7BFTn5QBHjhxxB6XZs2fH6dfD/tm3f/9++/bbb83MbM6cOVajRg2LiIiwdevWWfHixS1t2rT27LPPejzmhRdesGbNmtnZs2d9UfIDjZCUwMR+YAwaNMieeuop9+hqkZGRFhwcbKlSpbIsWbLYpEmT3CObnDp1ymf1JjSHDx+2XLlyWcOGDW3VqlXub2GWLFliKVOm9NjJN7vyjet//vOfONeBPOyWLVtmqVKlcp//PHv2bPP397c2bdrYjz/+6F6uVatWlilTJj6c/5/zD/6IESMsRYoU1qtXL2vbtq2lS5fOqlSp4h56fuLEiVaqVClr2bIl3+T/v6NHj9ovv/xiM2bMsB07dtilS5fs0KFDtnfvXvv1118tT548NmHCBDP735DWLpfLNmzY4F7Hpk2brHDhwg/1cLh//PGHNWrUyP773/+a2ZUvPNq1a2dly5a1li1b2tq1a92haOPGjVa9evWH/ki68727bt06K1SokP3www+2a9cuW7lypeXNm9fKlSvnXubDDz+0rFmz2ltvveWLchOcq7ef3bt3W4kSJSxJkiQ2f/58j3nHjh2zOnXqWMGCBbmG9f8NGzbMNm3aZE888YQlTZrUBg4caP7+/h6nDfft29dcLpdNnjzZDh06ZH///bf179/f0qRJw4AXdwkhKYFaunSpFS1a1D2EcufOnd0XjL788stWvHhxy5EjB0NUO8R+SB84cMCKFStmNWrUsG3bttnBgwctY8aMHtdvOT/Qr74p5cMuLCzMnn32WRs4cKCZXdnhypMnj9WtW9fSpEljTZs29QhKDLcc14YNG6xz584e39zv2LHD8uTJ4zGK4ujRo619+/YP/Q6q2ZVz7uvXr28ZM2a04OBgCwoKssaNG7u/2JgzZ46VL1/e/aXQmjVrrEuXLjZp0iSP03POnTvncbPUh9WIESMsT5487lE6jx07ZhkyZLDUqVNb+fLlrUSJEjZx4kSPx7AdXtnOmjVrFufb+r1791q2bNnsmWeecU9bvHjxQ3/k42qrV692Hznat2+fPfroo1arVi33Ed9YR44csZ49e9I/u3I9m8vlcvct9h5c/fr1i7Ns+/btLV++fJY8eXIrV66c5c2bl9Hs7iJCUgLWqFEja9u2rT3zzDOWMWNG27Jli3ver7/+ys5pPGI/cA8cOGBFixa1ihUrWvr06e25555zLxO7I/AwnxZxPREREbZ48WLbv3+//fvvv1asWDHr0KGDmZl99tlnFhQUZE8++aQ7KNFHT1999ZU9+uijli1bNvcoWLHb3MaNGy1dunQ2e/Zs9/Kx/XuYd1CnTJliqVKlslGjRtnKlSvt9OnT9vrrr1v+/PktX7589uOPP9qSJUvM5XLZzp077eTJk9aoUSPr1KmTex1RUVFsi+Z53Wr9+vVt+fLldvnyZStatKjVqlXLTp48aStXrrT27dtbxYoVH+rt7monTpywpk2bWtq0aa1OnTru6bEh/J133rHSpUvbyZMnPR73MO/oO7eflStXWr58+WzAgAHuI5V79uyxwoULW926dT1Od3d6mPt39uxZq1y5so0ZM8bMrpzFkStXLitatKhlzZrVli9fHucarS1btti8efNs48aN7AfeZYSkBCj2A2PlypWWJk0ay5cvn/ubAnYCbswZlMqUKWMZM2a0FStWuOfTwxuLPbo2ffp0K1eunPvb6JkzZ1qxYsWsePHi3EfqGn766Sdr0aKFBQQExDkV559//rE8efK4b/wc62HeJqdMmWKJEyeOc0qO2ZVv9R999FGrXLmy/fTTT/bkk0+ay+WyvHnzeoyE9TD373o6duxoVatWtcKFC1u1atXiXNjNQANx7dmzx9q1a2epUqWKc6Rt+vTplj9/fjtx4oSPqktYnNvNpEmT3Kd+pUqVyl577TX39rZnzx4rUqSINWjQgHvpxaNjx46WK1cumzBhgqVKlcp9mmytWrUsc+bMcYIS1wDfO4SkBOzs2bNWqlQpa9euna9Lue/EBqVDhw5ZsWLFrFatWrZ69WrfFnUfGjVqlJUoUcJ9N/lXXnnFJk6cyCmKN/Dbb7/ZU089ZUWLFvUY0js8PNwKFCjgvq7mYbd69WpzuVw2dOhQM7uy0+Ucqtrsyv1pkiVLZvPnz7eIiAibP3++zZkz56EfCet6nEeTsmTJYsWLF7czZ87EmX/1v3HF77//bm3atLFSpUrZqFGjLDw83A4dOmQ1a9a0OnXq0LOrDB061IKDg23u3Lm2bNkya9OmjeXLl89effVV95GOPXv2WPr06a13794+rjbhiN2ODh06ZEWLFjU/P784X6zVqlXLsmbNasuWLbPIyEgbNmyY1a1b12NET9w9hKQEKvYQ9oIFCyxr1qwe9wTBzYndifrjjz+sVKlSHvduwM359ttvLVmyZFa9enWrXr26pUiRIs6NPBG/nTt3WqtWrSxr1qzWpUsXe/PNN61p06aWN29eduz/3++//26VK1e2Jk2axHlvOk/jKVy4sHXv3j3O4x/m03RuJDo62i5dumSdO3e2p556ip2qW7R7925r06aNJUqUyEJDQ6158+ZWr14997f4D+tpis7h5GNiYuzUqVNWokQJGzt2rMdyffv2tUyZMtnAgQPdQenQoUMP/Xs2vu1m8+bNljJlSitUqJAVK1Yszmi79erVs3Tp0lmlSpUsODjY45pg3F1+wj0XExMjSTIzj387+fld+dWUKFFCAQEBWrlypaKjo+9toQnYzfTQ399f0dHRypkzp7744gsFBQUpR44c97zW+9ljjz2m2bNnK0+ePMqbN682btyoIkWK+Lqs+0KhQoX02muvqXLlypo5c6ZWrFihhg0b6vfff1eiRIl4P0vKmzevPv74Y0VERGj48OFat26de57L5ZIkhYWFKTw8XOnTp4/zeH9//3tWa0Li/Ky7cOFCvMv4+fkpKChILVq00Lx58/TNN9+4e/owu5neSVL+/Pk1ePBgPfXUU0qdOrWKFCmiJUuWKCgoSBEREe6/0Q+Tfv366cMPP3T30OVyKTAwUAEBAe5eXr58WZL0zjvvqECBApo6daref/99nThxQtmzZ3f/XX5Y+fn5ac+ePXr11Vd16NAhSVKOHDn09ddfa+rUqUqXLp3+85//aN++fe7HLFmyRIMHD9bjjz+uH3/8UaVLl/ZV+Q8dl129Z4m7Yu/evcqbN6/756VLl2rBggUKCwtT9+7dVbFixWs+dsyYMapXr54KFChwL0pNsG63h9HR0fL399fly5eVKFGie1VugmVmcrlc2r59u3799VcVKFBAOXPmVMqUKeNd7lo/P6xiYmJuaQdpz549GjZsmE6ePKlWrVqpbdu2kuin0969e/XCCy/IzDRw4EBVrFjRYzvt1auXXnnlFdWqVeuh75tz+3v//fd17Ngxde3aVRkzZoyzbGyvatasqdDQUH300Uf3utwE5VZ6F2vPnj0aMWKE9u3bp86dO6tdu3b3qtwE54cfflCpUqWUKFEiHTx4UKGhoZKkJ554Qn/++ae+//57JUmSxP03t0ePHtq0aZNiYmLUr18/tWjR4qF//0ZFRalixYrasmWL8uTJo4YNG6py5cp6/PHHJUmbNm3SK6+8ohMnTujLL79Unjx5fFzxQ+5eH7p6GC1dutRcLpctWLDA/XPSpEntiSeesEqVKllAQIC9//77Fh4e7vE4To34n9vtoRP9/J/58+dbcHCw5cqVy9KkSWMvvPCC7d6929dlJXjObWjevHn29ttv2+rVq2845PTPP/9srVu3tsqVK8cZtAFX/P7771a3bl2rU6eO+9S7qKgoq1+/vjVs2PChPb3JydmD/fv3W40aNSxr1qw2cuTI6w4msHLlSk5zus3emV059a59+/ZWsGBBmzFjxt0uNcH74osvrFSpUvbNN9+Y2ZUBabJly2Z169a1kydPuv8ON2vWzJYtW2aNGze2UqVK+bLkBOXtt9+2MWPG2PLly23w4MGWKlUqa9WqlX344YcWExNj69evtyZNmlixYsX4u+xjhKR7pEuXLpYiRQr7+uuvbcSIEe6bdJqZvfnmm+bv728TJ060iIgIH1aZsNFD7zgvEm3YsKFNmTLFzp8/b++++66VL1/e2rRpY7t27fJxlQmXMyD179/fUqdObQUKFLBMmTJZ165d7ddff73u43fu3GmNGjWyunXrelxEj/+JDUr169e3devW2RNPPGEFCxZ0j2JHULqiZ8+eVqZMGWvWrJmVKFHCEidObCNGjLB//vnHY7mrvxh62IOS2c337mo7duyw55577qG88fjy5cutf//+7p8XLVrk/kJj2bJlZma2detWy5Ejh+XJk8eqVKlixYsXt9y5c5uZ2XvvvWfFixfnb/P/W716tQUHB7uv7zpy5IgNGTLEAgMDrXLlyvbhhx/ae++9Z48//rhVqFDBIiMj+ZLXRwhJ99Bzzz1nSZIkscKFC9sXX3zhMe/NN980Pz8/e++99657NORhRw+98+OPP1qPHj3sySef9LjXx9SpU61ChQrWpk2bG+7sP+x+/PFHa9CggW3atMnMzCZPnuzu3Y1C5q+//srQ6Tfw+++/W4MGDSwgIMDy5cvnDkgMdnHFggULLGXKlLZ9+3b3Tme/fv0sTZo09uabb3Ij3evwtncP405+RESE9e/f3/Lnz+++wbjZlfv5NGzY0GrUqGGrVq0ysytDU7/xxhvWv39/GzRokPs926ZNG2vUqBF/lx369u1rrVu3dg8E0qJFC8ufP7+1adPGatasaQEBAfb888/z98LHCEl32dXpv1+/fuZyuWz06NFx5o8cOdJcLpfHkMGgh3fS66+/bmnTprXMmTPbH3/84TFv6tSpVqVKFWvatKnt2bPHRxUmbDNmzLD//Oc/1rx5c49v5WNDZtu2bQmZd8Du3butR48e7p0sAtL/zJgxw32vHuc22LNnTwsMDLSRI0e6b+QJT/Tu9hw7dswGDRpkxYsXtwEDBrinL1261Bo0aGA1atSwpUuXxnncn3/+aS+++KKlSZPGduzYcS9LTvDmzp1r5cuXt+joaHv22WctQ4YMtnPnTjO78mXaxIkT3T/DdwhJd1HszvuWLVts0aJF7ukvvPCCBQUF2VdffRXnMWPGjGEny4Ee3nljx461nDlzWrdu3ezPP//0mPfee+9ZnTp1+PbqGl555RVLnz695c2bN06Ppk2bZpUrV7aGDRs+lKfk3C0Pc0CK735G06dPt9SpU7uvo7lw4YKZmR08eNCCg4Mtd+7cNmnSJLt8+fJDfYoOvbuzjh49aq+88kq8QalRo0ZWu3Zt901Qza4Eq0mTJlmxYsVs+/btvig5watSpYr5+flZ5syZ6VECRUi6S2I/YOfNm2cZM2a0l19+2eMCvOeff96SJEkS704+rqCH3ont35EjR+zIkSMeO+5vvvmmFS9e3Pr06WN//fWXx+O4XuaKa13/MmbMGMuTJ4+9+OKLcULmxIkTrUuXLlw7A685t6GrryUqVqyYlS1b1mPanj177Pnnn7fnnnvOUqZMaYcOHbondSZE9M47c+bMsddff91mz55tFy9edJ8SduzYMRs4cKAVLVrUXn75Zffyy5Yts/Lly9uLL77osZ5///3X/v3333tZ+n0h9m/z119/bY888oh7QCqCecJDSLqLvv/+ewsODrbJkyfHey7u888/b8HBwTZnzhwfVHd/oIe3J/bD9ssvv7TixYtbtmzZrFChQvb888+7lxk+fLgVL17c+vfvbwcPHvRVqQmScyfr559/tl9++cV+/vln97Q333zTihUrFm/IjO09QQm3y7mz9O6771qzZs2sf//+7lOafvzxR8ubN689+uijtnz5clu+fLnVrVvXnnrqKTMzS506tY0fP94ntfsavfPO7t27zeVymcvlsuTJk1uxYsWsbt26NnPmTPv9998tIiLCXnnlFatWrZpHUNq0aZP7M4+d/Ztz7Ngxy5Mnj7322mu+LgXXQEi6C2I/IAYOHGjNmzf3mHb1t1qtW7e2zJkz27lz5+5tkQkcPfTeypUrLSgoyN59912bO3euffDBB5YyZUp7/PHH3cu8+eabFhoaagMHDmTkq//n/AP/8ssv2yOPPGJp06a17NmzW4cOHdzzhg0bZiVKlLB+/frFCZnsJOB2ObedYcOGWcqUKa19+/ZWpEgRK1++vH300UdmduXoR82aNS1TpkyWI0cOq1ixooWHh9uFCxesQIECtnDhQl+9BJ+hd3fG4MGDLXPmzDZw4EAbOnSo9ezZ07Jnz27p06e3Zs2aWfv27a1FixaWJ08ej6BkxpdDt2rGjBmWLFky90BASFgISXfRs88+a3Xq1Il33tatW93/Pnr06L0q6b5DD29f7969rXXr1h7TNm3aZCEhIdarVy/3tLFjx8YZxAFmo0ePttSpU9uaNWts7dq1NmvWLEuTJk2ckJklSxabOHGiDyvFg8L5RcXmzZvtxRdftDVr1pjZlR375557zooVK+Zxr61du3bZn3/+6Q4Ir776quXOnfuhO2WM3nnP2cM+ffpYvnz5bMyYMWZ25ajHDz/8YB06dLC6detaokSJzOVyWY0aNfhSyAt///23VatWLc4ZCUgYCEl3QewHxssvv2yhoaFx7r9w/vx569Gjhy1fvtwX5d0X6KF3oqOjrWHDhlavXj33tNg/gKNGjbLSpUszitN1XL582Vq2bBnnNIgffvjBkidP7jF9xowZHIWDV15//XWPHc0vv/zSHn30UStcuLDHDnvszn6JEiVs0qRJHuv4+eefrVOnTpY6dWrbtm3bvSrd5+jdneX8LOvbt69lzZrVRo8e7R4ePXYglXXr1tkXX3zh/pmgdPtir/lCwuMneM3MJEkHDhzQwYMH9ddff0mSBg0aJD8/PzVv3lxHjx5VVFSUoqOjNXz4cC1evFj58+f3ZdkJCj28s/z8/PSf//xH+/bt06pVqyRJ/v7+kqRUqVLp7NmzCgwM9GWJCVpMTIz27NmjI0eOuKdFR0erbNmy6tq1qzZv3qzz589Lkp5++mn5+/srOjraV+XiPrZgwQLt2rVLMTEx7mkpU6ZUaGioDh48qLVr17qn58uXT7169VL58uU1YsQILVy40D0vMDBQxYsX14YNG1SsWLF7+Ap8h97dec7PsnfeeUetWrXS+PHjNWPGDJ04cUKJEiWSJFWsWFEtWrRQokSJdPnyZblcLl+WfV8LCgrydQm4Fl+ntAfFvHnzLEeOHJYlSxarWrWqTZ061cyufEOVL18+y549u5UpU8Zq165tadKksZ9++snHFSc89PD2xH6Dd+LECTt8+LD7m8B9+/ZZrVq1rFmzZrZixQr3sn379rUqVarY2bNnfVZzQnKtc+hHjRplRYoUsZUrV3pMHz58uFWsWNF9k1PAG5cuXXJvgwsWLHD/e/Pmzda0aVOrUKGCzZ8/3+Mxu3btsnfeeSfOEcyH7Ygmvbt7nP3o37+/hYaG2tixY+Oc1QE8yFxm//8VPm6Zmcnlcunw4cOqWLGiXn31VaVIkULr1q3TokWL9PLLL+v555+XJI0ePVpnzpxRcHCwHn/8ceXJk8fH1ScM9NA7sf1buHChBg8erHPnzsnM1L59e3Xv3l1//PGHXnvtNe3Zs0dZs2ZVihQptHHjRq1Zs+ah/8ZUunLEyM/vygH133//XefPn1f+/PmVNGlS7dmzRz169FBISIg6deqkOnXq6PTp02rZsqUyZsyoTz75xMfV434X+/6VpJ9++klPPvmkypUrp5kzZ8rPz08bNmzQ2LFjdfz4cfXs2VOPP/54nHVER0e7jxI/TOidd5z9uxZnfwYMGKCxY8dq+vTpatmy5b0oEfA5QpKXNm7cqCVLligsLEzjx4+XJB06dEiTJ0/WJ598on79+qlnz56+LTKBo4feWbVqlRo2bKghQ4aoTJkyWr58uVauXKmCBQtq3LhxOnv2rLZu3aqvv/5auXPn1pNPPslpivLcSXjllVc0Z84cnTlzRoGBgWrTpo369eunAwcOaPDgwdq6datSp06tgIAASdKWLVsUEBBwUzsaQHyu3nbOnz+vTz75RNOnT1e+fPn06aefys/PT+vXr9e4ceP077//qmPHjmrVqpUPq04Y6J13nF8OHTlyRGFhYcqbN69cLpf8/Pw85juD0nvvvacuXbo8tMESDyEfHL16IMTExNjZs2etY8eOFhwcbHXr1vWYf/DgQRswYIDlyJHDRo4c6fE4XEEPvRMTE2PR0dHWqVMna9eunce8qVOnWrFixeztt9/2TXH3kTFjxljatGlt8eLF9vvvv9sbb7xh5cuXt3bt2tmZM2fsyJEjtnr1ahs2bJhNnz7dfaFy7P+BW+U8xTM6Otp94XZYWJi99957Vrx4cWvdurV7ufXr11v16tU97nP2sKJ33nH+/XzttdesVKlSFhwcbPXq1bMhQ4bEO4gApybiYUVIukVX76D/8MMP1rFjRwsKCrLZs2d7zDt06JC98MILVrBgQTt16hQ79/+PHt5ZzzzzjD3xxBNm5vnH68UXX7RHHnnEV2UleDExMRYREWH169e3gQMHesz7+OOPrWDBgjZlypR4H8tOAu6EkSNH2hNPPGGNGze2b7/91syujNwZu7P/9NNPu3f2f/nlF+5B40DvvDN8+HBLly6dLVmyxP7991+rXbu25ciRg2t9AQdGt7tFLpdLa9as0WOPPSZJKlu2rLp166annnpKQ4YM0bx589zLZs+eXf369dOaNWuUKlUqTsv5f/TwzrD/P1M2Y8aM+vHHH3Xs2DH5+/u7R3qqVKmSAgMDdfbsWV+WmWC5XC4FBAQoOjpaYWFhkuQe1alDhw4qUaKEpkyZEu9jOd0Et8M5Ctsbb7yhUaNGKX369IqIiFCNGjU0ffp0JUuWTG3btlXnzp21e/duNWrUSGamIkWKuE+FehjRuzvDzHTixAktX75cEyZMUL169bRt2zatX79er732mooXL67IyEh6BUhK5OsC7keXL1/W1q1bVadOHS1btkzFihXT888/Lz8/P/eQ1U888YQkKWvWrD6uNmGih7fO/v88/P3798vM5O/vr5w5c2rEiBFavHixmjZtqi+//FLp0qWTn5+f1qxZo5CQEPeQrQ8753n2sVwul3LkyKGvvvpKr732mtKmTevuc/HixXX8+PGH+uJu3Fmx29+ff/4pf39/zZs3T1WqVFF4eLhGjBihjh07KiYmRh06dFCbNm10/vx57dq1y+ManKu34YcFvbs9ZqaYmBj3Z5jL5VLy5Ml16dIlVa5cWf/973/VqlUrjRo1Sh07dlR4eLi++OILFS9eXEWLFvVx9YCP+eoQ1v0sKirKVq1aZVmyZLHq1au7p2/evNk6d+5sGTJksIULF/qwwoSPHt6euXPnWrZs2SxTpkxWsWJF900R9+7da0WKFLEsWbLYY489Zg0bNrQUKVLY9u3bfVxxwuA81ea7776zjRs32i+//GJmZpGRkVaoUCErU6aMHThwwM6cOWMRERFWrVo1e/rpp31VMh5QixcvNpfLZdmzZ7cNGza4p1++fNkGDx5siRIlsmnTppnZlSGuY08x5nQxenc79u7d6/731KlTbcOGDXbp0iUrUqSINWjQwFKlSmUffPCBx/I1atSwBQsW+KBaIGEhJN2kHTt2ePx8+fJlW7lypWXJksVq1Kjhnr5x40br0aOH7du3716XmODRw9sT+4f+8OHDljt3bvvoo49s/vz51rNnT8uWLZuNHj3avezw4cOtd+/e9tJLL9mePXt8VXKC4ryOrU+fPpYhQwZLly6dlS5d2t59910zM9u/f78VLVrUMmXKZIUKFbISJUpYoUKF3PdC4lo43CnHjh2zF1980fz9/d3XYMbuxF++fNmGDh1qLpfLFi9e7H4M298V9O7W/Pzzz5YoUSKbMWOGvfTSS5YyZUp3aFq6dKmlT5/e6tevb2ZXvrg8d+6c1a9f36pXr851l4ARkm7KkSNHLEOGDNa8eXOP6ZGRkbZo0SJLnDixx7zw8PB7XWKCRw+9s3HjRuvfv791797dvVPw119/2aBBgyxLliz21ltv+bjChMm5g/Tzzz9bwYIFbcuWLbZy5Up76aWXLGvWrB4hc/LkyTZ27FibOHEio9jBa9c6gnHu3Dlr3769BQYG2qpVq8zsf9vq5cuX7eOPP37otzt6572jR4/aG2+8YUmSJLGQkBA7cuSImV3p09mzZ2306NHm5+dntWvXtkaNGlmVKlXs0UcfdX85RFDCw46QdBPOnz9vH3/8sWXPnt2eeeYZj3lnz561kiVLmsvlskaNGpnZw/3N1bXQw1sX24P9+/fbiy++aKlSpfI44mb2v6AUGhpqQ4cOjfPYh5mzB1OnTrWnn37a+vTp457mDJnOIead2EnA7XLu5H/88cfWt29f69Spk82ZM8cuX75skZGR1qFDBwsMDHSPznb1+/Zh3dmnd3fOhx9+aC6Xy5ImTWqffPKJx7zw8HDbuHGjdejQwXr06GGjR4/myyHAgZD0/67+1urqn8+fP28zZsywTJkyeezkR0dH23PPPWfz58+3/fv335NaEyp6eOd9+eWX1r59e1u4cKF17drVgoKC7NNPP/VY5u+//7Y+ffpYwYIF7d9///VRpQmLc9uLHWI+TZo09tRTT3ks99dff9ngwYMte/bsNnjw4HtcJR4G/fr1s/Tp09tLL71kTz31lOXKlcu6detmZmYnT560jh07WrJkyWzJkiU+rjThoXe3znlUzezKbTR+/PFHe+ONNyxFihTu64+u90UaXw4BVxCSHA4dOmTjx493/3ytnfyMGTNakyZN7JtvvrGePXtagQIF7OjRo/e63ASJHnov9o/XwYMHLV++fPbRRx+ZmdmePXusc+fOlj9/fvvss888HnP48GE7ceLEPa81oXvppZesa9eutnLlSuvSpYslT548zrepf//9t/Xs2dOaNGnCETjcUcuXL7dcuXLZpk2bzMxs/vz5FhQU5LENnj9/3p544gmrVq2ar8pMkOjdrXP+vT1//rxH2Dl48KC9+uqrliJFCvvwww/d04cPH24//PCDmXEGAnA1QtL/u3z5sr300kv2yCOP2Ntvv+2efvVO/sWLF23p0qWWL18+y5Url+XLl4+br/0/enjnrFy50kaOHGnt27e38+fPu6fv2LHDunTpYvnz57dZs2b5sMKEyflH/ocffrCCBQu6d7L27Nnj7t3VIfP48ePux7KjgNt19bbzySefWIUKFczsysiUKVKksPfff9/Mrlxbs2bNGve/H+YR2MzonbecPRg7dqzVrl3batasaV27dnVP//PPP+21116zwMBA69Gjh9WsWdMeeeQRjhwB18ANVP6fv7+/evToofDwcM2fP18xMTF66aWX3Degi72/QpIkSVSnTh3t3LlTJ06cUGBgoFKnTu3j6hMGenjnLF68WOPHj1eOHDl05swZJUuWTJJUuHBh9ejRQ4kSJVLPnj2VKFEiNWvWzMfVJhyx90MZPXq0Dh48qMqVK6tMmTKSpHz58qlHjx6SpOHDh8vlcqlVq1aSpHTp0kmSxz1VgFsVu+2cPHlSadOmVaJEiZQ1a1Z9/fXXat++vd555x0999xzkqRVq1Zp7dq1KlCggNKnTy8p/nt5PSzonXdiX/uAAQM0ffp0vfjii0qZMqUGDhyoI0eOaN68ecqWLZu6d++uzJkz67PPPlNoaKiWLFnivgn5w9w/IF6+TmkJzdGjR6179+5WtmxZjxHDYr+liYiIsKFDh3ocroYnenhnxA5nO3bsWI+jSWZm27dvt169ejFM+jV07drVXC6XlShRwk6ePOkxb+fOnfb8889bqlSpbNmyZT6qEA+qsWPHWs+ePc3syj1nkiZNai6Xy6ZOnepe5uLFi1a3bl175plnOHLpQO+889VXX1nBggXd95D66quvLFmyZJY0aVKrVKmSe9Q6syt/h2P7xyANQPwISfFw7uSPGDHCPf3ixYv2/PPPW0BAgP36668+rDDho4c3L/YP1V9//WV79+61nTt3uuf17NnTAgIC7OOPP7aLFy96PC4iIuKe1plQXetUm0GDBpnL5bIJEybYhQsXPOZt27bN3n77bU4zwR333nvvWWBgoO3atcvMruyoJk+e3Hr06GErVqyw5cuXW61atezRRx9175yys38FvfPO3Llzbfjw4WZm9vXXX1vq1Klt0qRJ9u2331pAQIA9/vjjcf5u0D/g2ghJ13D10ZDo6Gjr1auXJUuWzLZu3err8u4L9PDGYv9Affnll1aiRAnLmTOnlStXzh5//HH3Mn369LHEiRPbtGnT4uzsP+ycAWnLli22bt06W7t2rXtaz549LXHixPGGzFgEJdwu5w5m7L9Pnjxp9evXt0GDBtnly5ctPDzcFixYYKGhoZY1a1YrVaqUNWnS5KG/Fw29uzsOHTpkYWFhVq5cOXvjjTfM7Mp9CvPly2cul8s6d+7s4wqB+wch6Tpid/IrVKhghQsXtiRJkrBzf4vo4Y2tWLHCkiRJYu+//779+eefNnXqVHO5XB6jOPXt29dcLpfNmDHDh5UmLM6drJdfftkKFy5sOXLksNKlS1udOnXc8/r06WOBgYE2depUQibuitgAHrtNDhgwwPLkyeNxU+xTp07Z/v377e+//+Y0Jwd6d/ucXxI5Pw/37NljoaGh7gGRjh49am3atLHNmzcTLIFbQEi6gaNHj1r79u0tb968tn37dl+Xc1+ih9cWExNj/fv3t5dfftnMrgxHnSNHDve9QJxeeeUVTlGMx6hRoyxNmjS2ceNGCw8PtyFDhpjL5XLfZNLMrHfv3uZyuWzx4sU+rBQPis2bN7v/PW3aNGvcuLFt377dY8f9kUcesV69el1zHQ/riGz0zjsrV650HyEyi78Xp0+ftixZslizZs3su+++s5o1a1qNGjXcyxKUgJvDUCY3kDFjRo0cOVLff/+9ihYt6uty7kv08MqoafFxuVzasWOHAgMDdeLECZUrV0516tTRhAkTJEkzZ87UJ598IunKiGwFChS4ZzXfDyIjI7V9+3a98847KleunJYtW6YxY8Zo8uTJeuyxx3Tu3DlJV0a7Gz9+vOrUqePjinG/++CDD9S4cWPt3r1bknTq1Cm5XC6VL19e3bt318yZMyVJHTp00B9//KG///473vU8jCOJ0TvvREREaM6cOZozZ47eeecdSXKPHhvLzJQyZUpNnDhRP/74o5599llFRETom2++cS/r7+/vq5cA3Fcezk+aW5QuXTplyJDB12Xc1x72HrpcLp08eVL//vuvJGnhwoWaO3euJOmxxx7Tb7/9phIlSqhevXqaPHmyJOnSpUtau3atDhw4oMjISJ/VnpBcHTb9/Py0e/du+fv7a9myZWrdurXeeustderUSZcvX9bkyZP1xRdfSJJ76PTLly/7onQ8AKZMmaJu3bpp0qRJ7i8sevfurYULF2ratGm6dOmSXnjhBbVv316RkZFavny5li9f7uOqEwZ6573AwEANHjxY1atX1/z58zVy5EhJnkEpdij1unXr6pdfftGKFSu0Zs0aBQQE6PLlyw9twARuB+8W4C5btmyZjh07pvz582v8+PGaOnWqnnjiCffOeuXKlbVmzRolTZpUffr0kXTlCMmwYcO0ZMkStW7dWokTJ/blS0gQfvnlF4WFhUmSXnnlFS1cuFCJEiVStWrVNHPmTDVv3lzvvPOOunbtKkk6ceKE1qxZozNnznisJ1Eibg+HWzd58mR169ZNc+fO1eOPP+6evnHjRklSixYtNHHiRK1cuVKHDx/W5s2bFR4erq+++krStY8mPwzo3Z2TOXNmvfzyyypdurQWLFgQb1A6duyYOnbsqGXLlik0NNQ9j88+4Bb59GQ/4AHXq1cvCwwMNLMrw7MGBASYy+WySZMmmdn/LrZdvny5BQcHW7Vq1axy5cr2+OOPW7p06dwX3j7Mpk2bZr/++qu5XC4bMmSIde3a1ZInT+4eKn39+vWWNm1aq1Chgu3du9fMrlwHV79+fatQoQLn38NrCxYsMJfLZYsWLfKY3rhxY2vXrl2c+5hdvHjRfvrpJ3v77bcf+gEG6N3dca3bbBw5csQqVqxoefPmpX+Al/haAbhLfv75Zy1atEgrV66UJGXJkkWpUqXSiRMndPLkSfed5c1MtWrV0ooVK7RhwwZt27ZNJUuW1MiRI5U3b14fvwrfqly5sk6dOqVdu3Zp7ty5euqpp+Tn56cVK1aoUKFCiomJUYUKFfTZZ5/p6aef1lNPPaULFy4oVapUioiI0MaNG+Xv76/o6GjOw8dtiYiI0LJly5QrVy4dOHDAPf0///mP9u7dqyVLlihZsmTu6TExMUqSJImKFy+u4sWLS5IuX778UH6LT+/unowZM+rVV1/V8OHDtXDhQvn7+6tDhw5q3bq1Tp8+rV27dilRokR89gFe4JMHuEsyZMigsLAwbdu2TZcuXVKnTp00e/ZsnThxQi1atFB4eLj69OmjNGnSSJLKlCmjMmXK+LjqhGPZsmU6evSotm7dKkn6888/lTFjRv39999avXq1ChQo4A6ZderU0apVq7Rr1y7t3btXhQoVUtOmTeXv789OFrwSGBioQYMGKTAwUJ9//rnMTOvWrdPevXu1ePFihYaGyszc14LEd83Hw7r90bu7KzYovfnmm5o/f76GDx+uzJkz6+eff3Zfg0T/gNvnMuNkX+BOi4mJUWRkpKZNm6bBgwfr5MmTmj9/vvt8/E8//VTPPPOMXn31Vb344otKmzat3nnnHeXOnVtPPPGEj6tPGPbs2aMyZcpo5MiROnr0qL788kt9//33WrFihVq2bKkBAwaod+/e7pAZH75FxZ1y7NgxDR8+XF9//bXOnj2rX375RVmyZFFUVJQCAgIkSQ0aNFDRokX15ptv+rjahIXe3V3Hjh3TSy+9pBMnTuirr74iIAF3CO8g4C7w8/NTUFCQgoODdfLkSYWEhOiff/5xz2/btq0k6dlnn9Uff/whl8uluXPnatOmTb4qOUGJiYlRzpw59cYbb2jAgAEKDw/X3r17lSpVKjVv3lzh4eF65pln5O/vrx49eihdunR66qmn1K5dO9WtW9e9HgIS7pSMGTPqtddek5+fn9avX6/PP/9cffv2VUBAgKKjo9W4cWPt27dPCxcu9HWpCQ69u7syZsyocePGKSQkRH5+fgQk4A7hXQTcATExMR6nisR+Q3r27Fl99NFHOnr0qIYNG6ZLly6pV69ekq4EpaRJk+qzzz6TmWnz5s169NFHffUSEhQ/Pz8FBgbq+PHjunjxolKlSqXFixe7R65r27atXC6XOnTooB07dujw4cM6ffq0Pv30Ux9XjgdZhgwZNGDAAA0fPtw9hH/fvn31xBNPaP/+/dq5cyff4l8Dvbu7UqVKJUmMYgfcQZxuB9whe/bs0YwZM9S5c2dly5bNIzQdOnRIH330kaZNm6Y+ffq4g5IkXbx4Uf7+/goMDPRF2QlKbNiM3VH66KOP9Mgjj+j777/X+++/H6d3X331lZYtW6aAgACNHj3afR8kdhJwNx07dkxvvvmmtm7dqn379illypTs5N8kegfgfkFIAu6AqKgoVaxYUVu2bFGePHnUpEkTlS5dWs2bN3cvc+DAAU2dOlVTp07VSy+9pBdeeMGHFSc8X3zxhZYvX/5/7d1/TFX1H8fx1xGkG11uiBY37pbOXZIBV374q1EjIPMSrdCpBBnbZYmMHMUfmdEkV9ZNsHCS9kc/vZutrXKjW4nOiLsJrpE1YS6HzWr9usO46hw1oyHfPxx3l6GGevHi1+djuxv3cz7nnPc5/1xe55zP5+i5557THXfcobi4uOCyn3/+We++++4FQ2bouAb+ycK1wjiQK8e5A3A9ICQBYbJlyxZFR0crPT1dnZ2dam5u1kMPPaS7775b1dXVmjJlivr7+9Xc3KzGxkY1NTXpySefjHTZk8KZM2eUnZ2tM2fOyGq1auHChbr33nvlcrmCfUZC5vvvv69169bp6aefjlzBgKRTp04xDuQKce4ATHaEJCBMfD6fiouL1dbWpvnz58vv9+utt95SY2Oj0tPTtXr1ahUWFiouLk5vvvmmVq5cecO/B2nE0NCQ6uvrNXPmTC1YsEBfffWVXnnlFRUVFSk1NVXPPvuspk6dqlOnTqmpqUlut1sffPCBSktLI106MGZMIsaPcwdgsiIkAWG0bt06+f1+vfPOOzKZTCotLVV3d7cWLVqkH3/8UV9//bW2bdum1atXBx8Rw3mtra169NFH1dHRoblz5+rs2bNyu916+eWXlZGRobKyMi1dulTx8fHyer2qqKhg9joAADAhCElAGH3yySdqampSR0eH1qxZo88//1xtbW1KS0tTb2+v9u7dq8WLFystLS3SpU5Ka9eulSTt2LFDkpSWlqa77rpLdrtd3d3d+vLLL7Vr1y499thjkngPEgAAmBiEJCDM7rvvPnV0dMhqtWrPnj3KyMiIdEnXjZHJGT777DPdf//9io2N1Z49e2SxWPT777/rwIEDWrFiBeMXAADAhOJBYCBMRq43rF+/Xna7XTt27FBGRoa4DjF+TzzxhAYHBzV9+nRZLBZ5vV5ZLBZJks1mU2lpaXCabwAAgIlCSALCxDAMSdK8efN07tw5ffvtt6PacWkjYfKpp55SWlqaXn/9dSUkJFwwZHInCQAATCRCEhBmiYmJ2rhxo7Zu3aqurq5Il3PdGAmT+fn5CgQC2r9//6h2AACAa4WQBEyA/Px8LViwQElJSZEu5bpjs9lUV1en1157Td9//32kywEAADcgnlkBJoDNZlNra6tMJlOkS7kuFRUV6dChQ0pJSYl0KQAA4AbE7HYAJqXh4WEZhsE03wAA4JojJAEAAABACMYkAQAAAEAIQhIAAAAAhCAkAQAAAEAIQhIAAAAAhCAkAQAAAEAIQhIAICIMw1BLS8tFl/t8PhmGodOnTwfbWlpaZLfbFRUVpdra2gmvEQBwYyIkAQDC7s8//1R1dbXuvPNO3XTTTbJarXI6ners7Bz3NnJycuT3+3XrrbcG26qqqrRixQr9+uuv2rRpk1wul5YuXfqf23K5XDIMQ5s3bx7V3tLSIsMwxl0TAODGEB3pAgAA/3+WL1+uwcFBeTwezZ49W319fWpra1MgEBj3NmJiYmS1WoPfBwYGdOLECTmdTiUlJV12TSaTSQ0NDaqqqtK0adMue30AwI2DO0kAgLA6ffq0Dhw4oIaGBuXn52vmzJlauHCh6urq9Mgjj4zq29/fr2XLlik2NlbJycnyer3BZaGP2/l8PsXFxUmSCgoKZBiG8vLy5PF49Omnn8owDBmGIZ/Pd9G6Fi9eLKvVqldfffWifQKBgMrKymSz2RQbGyuHw6EPP/xwVJ+8vDzV1NSotrZW06ZNU2Jiot5++2399ddfqqioUFxcnOx2u1pbW0etd+TIET344IMym81KTExUeXm5+vv7x3taAQDXECEJABBWZrNZZrNZLS0t+ueffy7Z98UXX1RJSYl6enpUVFSkVatW6eTJk2P65eTkqLe3V5K0e/du+f1+eb1elZSUqLCwUH6/X36/Xzk5ORfdV1RUlNxut9544w399ttvF+xz9uxZzZs3T1988YWOHDmiNWvWqLy8XF1dXaP6eTwezZgxQ11dXaqpqVF1dbVWrlypnJwcfffdd1qyZInKy8v1999/SzofHAsKCpSVlaVDhw5p79696uvrU0lJySXPDwAgMghJAICwio6O1s6dO+XxeBQfH6977rlHzz//vHp6esb0dblcKisrk91ul9vt1sDAwJhAIp1/9O7222+XJCUkJMhqtcpisejmm28OjnmyWq2KiYm5ZG3Lli1TZmamNm7ceMHlNptNzzzzjDIzMzV79mzV1NSosLBQH3300ah+GRkZ2rBhg5KTk1VXVyeTyaQZM2aosrJSycnJeuGFFxQIBILHvH37dmVlZcntdislJUVZWVl677331N7ermPHjo3rvAIArh1CEgAg7JYvX64//vhDXq9XhYWF8vl8ys7O1s6dO0f1mzt3bvDvW265RRaLRSdOnJjQ2hoaGuTxeHT06NExy4aGhrRp0yY5HA4lJCTIbDZr3759+uWXXy5ad1RUlKZPny6HwxFsS0xMlKTgsXR3d6u9vT14l81sNislJUWSdPz48bAfIwDg6hCSAAATwmQy6YEHHlB9fb0OHjwol8s15g7O1KlTR303DEPnzp2b0Lpyc3PldDpVV1c3ZtmWLVu0bds2rV+/Xu3t7Tp8+LCcTqcGBwf/s+7QtpEZ80aOZWBgQA8//LAOHz486vPDDz8oNzc33IcIALhKzG4HALgmUlNTL/lepCsRExOjoaGhy15v8+bNyszM1Jw5c0a1d3Z2qri4WI8//rik8yHn2LFjSk1Nvao6s7OztXv3bs2aNUvR0fz0AsBkx50kAEBYBQIBFRQUaNeuXerp6dFPP/2kjz/+WI2NjSouLg7rvmbNmqWenh719vaqv79f//7777jWczgcWrVqlZqbm0e1Jycna//+/Tp48KCOHj2qqqoq9fX1XXWda9eu1cmTJ1VWVqZvvvlGx48f1759+1RRUXFFIQ8AMLEISQCAsDKbzVq0aJG2bt2q3Nxcpaenq76+XpWVldq+fXtY91VZWak5c+Zo/vz5uu222y7rZbUvvfTSmEf7NmzYoOzsbDmdTuXl5clqtY7rZbX/JSkpSZ2dnRoaGtKSJUvkcDhUW1ur+Ph4TZnCTzEATDbG8PDwcKSLAAAAAIDJgstXAAAAABCCkAQAAAAAIQhJAAAAABCCkAQAAAAAIQhJAAAAABCCkAQAAAAAIQhJAAAAABCCkAQAAAAAIQhJAAAAABCCkAQAAAAAIQhJAAAAABCCkAQAAAAAIf4HwYv8my0A2nAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Your data\n",
    "ns_output = pd.DataFrame()\n",
    "ns_output['Shift No.'] = ns.ft.get_values().keys()\n",
    "ns_output['Min Required'] = [x+y for x, y in zip(z_ft, z_pt)]\n",
    "ft = list(ns.ft.get_values().values())\n",
    "pt1 = list(ns.pt1.get_values().values())\n",
    "pt2 = list(ns.pt2.get_values().values())\n",
    "pt3 = list(ns.pt3.get_values().values())\n",
    "\n",
    "# Calculate the number of people currently working a shift\n",
    "ns_output['FT Working'] = [sum(ft[max(0, z-3):z+1]) if z <= 7 else sum(ft[max(0, z-4):z+1]) for z in range(len(ft))]\n",
    "ns_output['PT1 Working'] = [sum(pt1[max(0, z-0):z+1]) for z in range(len(pt1))]\n",
    "ns_output['PT2 Working'] = [sum(pt2[max(0, z-1):z+1]) for z in range(len(pt2))]\n",
    "ns_output['PT3 Working'] = [sum(pt3[max(0, z-2):z+1]) for z in range(len(pt3))]\n",
    "ns_output['total'] = ns_output['FT Working'] + ns_output['PT1 Working'] + ns_output['PT2 Working'] + ns_output['PT3 Working']\n",
    "# shift names\n",
    "df_ratio = pd.read_excel('Papa Johns Demand.xlsx', sheet_name='Ratios')\n",
    "df_ratio = df_ratio.drop(df_ratio.index[10:], axis=0)\n",
    "df_shifts = df_ratio\n",
    "df_shifts = df_shifts.iloc[:,:1]\n",
    "ns_output['Shift Name'] = list(df_shifts.loc[1:,'Shift Name'])\n",
    "\n",
    "grace_variable = ns_output['total']\n",
    "\n",
    "# Create a bar plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(ns_output['Shift Name'], ns_output['total'], label='Total Working', color=['brown'])\n",
    "plt.xlabel('Shift Name')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Cumulative Working Counts of Employees on ' + day)\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45, ha=\"right\")  # Rotate x-axis labels for better readability\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### No of Staff working in Shift i vs Demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Your data\n",
    "# Assuming ns_output, z_ft, z_pt, ft, pt1, pt2, pt3, df_ratio, df_shifts, day, and rec_shift_req are defined\n",
    "\n",
    "ns_output['Min Required'] = [x + y for x, y in zip(z_ft, z_pt)]\n",
    "ns_output['FT Working'] = [sum(ft[max(0, z-3):z+1]) if z <= 7 else sum(ft[max(0, z-4):z+1]) for z in range(len(ft))]\n",
    "ns_output['PT1 Working'] = [sum(pt1[max(0, z-0):z+1]) for z in range(len(pt1))]\n",
    "ns_output['PT2 Working'] = [sum(pt2[max(0, z-1):z+1]) for z in range(len(pt2))]\n",
    "ns_output['PT3 Working'] = [sum(pt3[max(0, z-2):z+1]) for z in range(len(pt3))]\n",
    "ns_output['total'] = ns_output['FT Working'] + ns_output['PT1 Working'] + ns_output['PT2 Working'] + ns_output['PT3 Working']\n",
    "ns_output['total_req'] = rec_shift_req[day]\n",
    "ns_output['Shift Name'] = list(df_shifts.loc[1:, 'Shift Name'])\n",
    "\n",
    "# Create numerical representation for x-axis ticks\n",
    "shift_values = np.arange(len(ns_output['Shift Name']))\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "width = 0.35\n",
    "\n",
    "# Determine the colors and labels based on the conditions\n",
    "colors = ['green' if total_val > total_req_val else ('orange' if total_val == total_req_val else 'red')\n",
    "          for total_val, total_req_val in zip(ns_output['total'], ns_output['total_req'])]\n",
    "\n",
    "colors1 = ['red' if total_val < total_req_val else '#ADD8E6'\n",
    "          for total_val, total_req_val in zip(ns_output['total'], ns_output['total_req'])]\n",
    "\n",
    "legend_labels = {'red': 'Did not meet demand', 'orange': 'Meets demand', 'green': 'Exceeds demand'}\n",
    "\n",
    "# Plot the bars with the determined colors\n",
    "bar1 = plt.bar(shift_values - width/2, ns_output['total'], width, label='Total Working', color=colors, edgecolor='grey')\n",
    "bar2 = plt.bar(shift_values + width/2, ns_output['total_req'], width, label='Total Required', color=colors1, edgecolor='grey')\n",
    "\n",
    "# Create a legend for the color map\n",
    "legend_handles = [plt.Line2D([0], [0], color=color, marker='o', linestyle='', markersize=10, label=label)\n",
    "                  for color, label in legend_labels.items()]\n",
    "\n",
    "plt.legend(handles=legend_handles, title='Demand Status', loc='upper left')\n",
    "\n",
    "plt.xlabel('Shift Name')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Total Staff Working and Required for each shift')\n",
    "plt.xticks(shift_values, ns_output['Shift Name'], rotation=45, ha=\"right\")  # Rotate x-axis labels for better readability\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### No of Staff starting Shift i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Your data\n",
    "# Assuming ns_output, z_ft, z_pt, ft, pt1, pt2, pt3, df_ratio, df_shifts, and day are defined\n",
    "\n",
    "ns_output['Min Required'] = [x + y for x, y in zip(z_ft, z_pt)]\n",
    "ns_output['FT Starting'] = [int(ft[z]) for z in range(len(ft))]\n",
    "ns_output['PT1 Starting'] = [int(pt1[z]) for z in range(len(pt1))]\n",
    "ns_output['PT2 Starting'] = [int(pt2[z]) for z in range(len(pt2))]\n",
    "ns_output['PT3 Starting'] = [int(pt3[z]) for z in range(len(pt3))]\n",
    "\n",
    "# Create a bar chart\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "bar_width = 0.2  # Adjust the width of the bars\n",
    "\n",
    "# Plotting the bars\n",
    "plt.bar(shift_values - 1.5 * bar_width, ns_output['FT Starting'], width=bar_width, label='8 hours (10 hrs in shift 5)')\n",
    "plt.bar(shift_values - 0.5 * bar_width, ns_output['PT1 Starting'], width=bar_width, label='2 hours')\n",
    "plt.bar(shift_values + 0.5 * bar_width, ns_output['PT2 Starting'], width=bar_width, label='4 hours')\n",
    "plt.bar(shift_values + 1.5 * bar_width, ns_output['PT3 Starting'], width=bar_width, label='6 hours')\n",
    "\n",
    "plt.xlabel('Shift Name')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Starting Employee Counts for each shift on ' + day)\n",
    "plt.legend()\n",
    "plt.xticks(shift_values, shift_names, rotation=45, ha=\"right\")  # Rotate x-axis labels for better readability\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 06. Ratio Split - Insiders/Driver"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ratio Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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>Tuesday</th>\n",
       "      <th>Tuesday_1</th>\n",
       "      <th>Tuesday_1_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Drivers</td>\n",
       "      <td>Insiders</td>\n",
       "      <td>Sum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5.8</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.75</td>\n",
       "      <td>3.866667</td>\n",
       "      <td>6.616667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.75</td>\n",
       "      <td>1.533333</td>\n",
       "      <td>4.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.5</td>\n",
       "      <td>1.933333</td>\n",
       "      <td>6.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.25</td>\n",
       "      <td>1.066667</td>\n",
       "      <td>4.316667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Tuesday Tuesday_1 Tuesday_1_2\n",
       "0  Drivers  Insiders         Sum\n",
       "1        2       5.8         7.8\n",
       "2      0.5  0.466667    0.966667\n",
       "3      0.5  0.466667    0.966667\n",
       "4        3         6           9\n",
       "5     2.75  3.866667    6.616667\n",
       "6     2.75  1.533333    4.283333\n",
       "7      4.5  1.933333    6.433333\n",
       "8     3.25  1.066667    4.316667\n",
       "9      NaN       NaN         NaN"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#RATIO work\n",
    "df_ratio = pd.read_excel('Papa Johns Demand.xlsx', sheet_name='Ratios')\n",
    "df_ratio = df_ratio.drop(df_ratio.index[10:], axis=0)\n",
    "df_shifts = df_ratio\n",
    "df_shifts = df_shifts.iloc[:,:2]\n",
    "#df_ratio\n",
    "\n",
    "# Iterate through the columns and merge headers\n",
    "columns = df_ratio.columns\n",
    "merged_columns = []\n",
    "current_day = None\n",
    "counter = 1\n",
    "\n",
    "for column in columns:\n",
    "    if 'Unnamed' in column:\n",
    "        if current_day is None:\n",
    "            current_day = column.split(': ')[0]\n",
    "        else:\n",
    "            current_day += f\"_{counter}\"\n",
    "            counter += 1\n",
    "    else:\n",
    "        current_day = column\n",
    "        counter = 1\n",
    "    merged_columns.append(current_day)\n",
    "\n",
    "# Update the DataFrame with the merged column headers\n",
    "df_ratio.columns = merged_columns\n",
    "\n",
    "# Update table to the day we are interested in\n",
    "column_index = df_ratio.columns.get_loc(day)\n",
    "df_ratio = df_ratio.iloc[:,column_index:column_index+3]\n",
    "df_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "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>Shift Name</th>\n",
       "      <th>Time</th>\n",
       "      <th>Tuesday Driver Ratio (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lunch AM</td>\n",
       "      <td>10:00 am - 11:59 am</td>\n",
       "      <td>25.641026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lunch PM</td>\n",
       "      <td>12:00 pm - 1:59 pm</td>\n",
       "      <td>51.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afternoon am</td>\n",
       "      <td>2:00 pm - 3:59 pm</td>\n",
       "      <td>51.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afternoon pm</td>\n",
       "      <td>4:00 pm - 5:59 pm</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Day</td>\n",
       "      <td>6:00 pm - 7:59 pm</td>\n",
       "      <td>41.561713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Evening AM</td>\n",
       "      <td>8:00 pm - 9:59 pm</td>\n",
       "      <td>64.202335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Evening pm</td>\n",
       "      <td>10:00 pm - 11:59 pm</td>\n",
       "      <td>69.948187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Late Night</td>\n",
       "      <td>12:00 am - 1:59 am</td>\n",
       "      <td>75.289575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>After</td>\n",
       "      <td>2:00 am - 3:59 am</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Shift Name                 Time Tuesday Driver Ratio (%)\n",
       "0      Lunch AM  10:00 am - 11:59 am                25.641026\n",
       "1      Lunch PM   12:00 pm - 1:59 pm                51.724138\n",
       "2  Afternoon am    2:00 pm - 3:59 pm                51.724138\n",
       "3  Afternoon pm    4:00 pm - 5:59 pm                33.333333\n",
       "4           Day    6:00 pm - 7:59 pm                41.561713\n",
       "5    Evening AM    8:00 pm - 9:59 pm                64.202335\n",
       "6    Evening pm  10:00 pm - 11:59 pm                69.948187\n",
       "7   Late Night    12:00 am - 1:59 am                75.289575\n",
       "8         After    2:00 am - 3:59 am                      NaN"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the ratio column, delete others\n",
    "df_ratio[f'{day} Driver Ratio (%)'] = (df_ratio[day].iloc[1:] / df_ratio[f'{day}_1_2'].iloc[1:])*100\n",
    "df_ratio = df_ratio.drop(df_ratio.columns[:3], axis=1)\n",
    "#df_ratio\n",
    "\n",
    "# Add back 'shift name' and 'time'\n",
    "df_ratio = pd.concat([df_shifts, df_ratio], axis=1)\n",
    "\n",
    "# Drop empty first row\n",
    "df_ratio = df_ratio.drop(0)\n",
    "\n",
    "# Reset index\n",
    "df_ratio = df_ratio.reset_index(drop=True)\n",
    "df_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "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>Shift Name</th>\n",
       "      <th>Time</th>\n",
       "      <th>Tuesday Driver Ratio (%)</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lunch AM</td>\n",
       "      <td>10:00 am - 11:59 am</td>\n",
       "      <td>25.641026</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lunch PM</td>\n",
       "      <td>12:00 pm - 1:59 pm</td>\n",
       "      <td>51.724138</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afternoon am</td>\n",
       "      <td>2:00 pm - 3:59 pm</td>\n",
       "      <td>51.724138</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afternoon pm</td>\n",
       "      <td>4:00 pm - 5:59 pm</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Day</td>\n",
       "      <td>6:00 pm - 7:59 pm</td>\n",
       "      <td>41.561713</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Evening AM</td>\n",
       "      <td>8:00 pm - 9:59 pm</td>\n",
       "      <td>64.202335</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Evening pm</td>\n",
       "      <td>10:00 pm - 11:59 pm</td>\n",
       "      <td>69.948187</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Late Night</td>\n",
       "      <td>12:00 am - 1:59 am</td>\n",
       "      <td>75.289575</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>After</td>\n",
       "      <td>2:00 am - 3:59 am</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Shift Name                 Time Tuesday Driver Ratio (%)  total\n",
       "0      Lunch AM  10:00 am - 11:59 am                25.641026    6.0\n",
       "1      Lunch PM   12:00 pm - 1:59 pm                51.724138    6.0\n",
       "2  Afternoon am    2:00 pm - 3:59 pm                51.724138    7.0\n",
       "3  Afternoon pm    4:00 pm - 5:59 pm                33.333333    6.0\n",
       "4           Day    6:00 pm - 7:59 pm                41.561713   10.0\n",
       "5    Evening AM    8:00 pm - 9:59 pm                64.202335   10.0\n",
       "6    Evening pm  10:00 pm - 11:59 pm                69.948187   10.0\n",
       "7   Late Night    12:00 am - 1:59 am                75.289575    8.0\n",
       "8         After    2:00 am - 3:59 am                      NaN    8.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "df_ratio = pd.concat([df_ratio,grace_variable], axis=1)\n",
    "df_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "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>Shift Name</th>\n",
       "      <th>Time</th>\n",
       "      <th>Tuesday Driver Ratio (%)</th>\n",
       "      <th>total</th>\n",
       "      <th>Drivers</th>\n",
       "      <th>Driver Count</th>\n",
       "      <th>Insider Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lunch AM</td>\n",
       "      <td>10:00 am - 11:59 am</td>\n",
       "      <td>25.641026</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.538462</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lunch PM</td>\n",
       "      <td>12:00 pm - 1:59 pm</td>\n",
       "      <td>51.724138</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.103448</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afternoon am</td>\n",
       "      <td>2:00 pm - 3:59 pm</td>\n",
       "      <td>51.724138</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.62069</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afternoon pm</td>\n",
       "      <td>4:00 pm - 5:59 pm</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Day</td>\n",
       "      <td>6:00 pm - 7:59 pm</td>\n",
       "      <td>41.561713</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.156171</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Evening AM</td>\n",
       "      <td>8:00 pm - 9:59 pm</td>\n",
       "      <td>64.202335</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.420233</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Evening pm</td>\n",
       "      <td>10:00 pm - 11:59 pm</td>\n",
       "      <td>69.948187</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.994819</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Late Night</td>\n",
       "      <td>12:00 am - 1:59 am</td>\n",
       "      <td>75.289575</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.023166</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>After</td>\n",
       "      <td>2:00 am - 3:59 am</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Shift Name                 Time Tuesday Driver Ratio (%)  total  \\\n",
       "0      Lunch AM  10:00 am - 11:59 am                25.641026    6.0   \n",
       "1      Lunch PM   12:00 pm - 1:59 pm                51.724138    6.0   \n",
       "2  Afternoon am    2:00 pm - 3:59 pm                51.724138    7.0   \n",
       "3  Afternoon pm    4:00 pm - 5:59 pm                33.333333    6.0   \n",
       "4           Day    6:00 pm - 7:59 pm                41.561713   10.0   \n",
       "5    Evening AM    8:00 pm - 9:59 pm                64.202335   10.0   \n",
       "6    Evening pm  10:00 pm - 11:59 pm                69.948187   10.0   \n",
       "7   Late Night    12:00 am - 1:59 am                75.289575    8.0   \n",
       "8         After    2:00 am - 3:59 am                      NaN    8.0   \n",
       "\n",
       "    Drivers  Driver Count  Insider Count  \n",
       "0  1.538462           2.0            4.0  \n",
       "1  3.103448           4.0            2.0  \n",
       "2   3.62069           4.0            3.0  \n",
       "3       2.0           2.0            4.0  \n",
       "4  4.156171           5.0            5.0  \n",
       "5  6.420233           7.0            3.0  \n",
       "6  6.994819           7.0            3.0  \n",
       "7  6.023166           7.0            1.0  \n",
       "8       NaN           NaN            NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratio['Drivers'] = (df_ratio['total']/100) * df_ratio[f'{day} Driver Ratio (%)']\n",
    "\n",
    "# NaN's skipped\n",
    "df_ratio['Driver Count'] = df_ratio['Drivers'].apply(lambda x: math.ceil(x) if pd.notna(x) else x)\n",
    "\n",
    "# Insiders\n",
    "df_ratio['Insider Count'] = df_ratio['total'] - df_ratio['Driver Count']\n",
    "df_ratio['Insider Count'] = df_ratio['Insider Count'].apply(lambda x: math.ceil(x)if pd.notna(x) else x)\n",
    "\n",
    "df_ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ratio Visual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\arun9\\AppData\\Local\\Temp\\ipykernel_13084\\3837396144.py:22: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  ax.text(i, df_plot['Driver Count'][i] + insider / 2, str(insider), ha='center', va='center', color='white')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Selecting only the relevant columns for the bar graph\n",
    "df_plot = df_ratio[['Shift Name', 'Driver Count', 'Insider Count']]\n",
    "\n",
    "# Setting 'Shift Name' as the index for plotting\n",
    "df_plot.set_index('Shift Name', inplace=True)\n",
    "plt.figure(figsize=(10, 6))\n",
    "# Plotting\n",
    "ax = df_plot.plot(kind='bar', stacked=True, color=['red', 'brown'])\n",
    "\n",
    "# Plot 'Drivers' bars with counts\n",
    "ax.bar(9,df_plot['Driver Count'], width=0.2)\n",
    "for i, driver in enumerate(df_plot['Driver Count']):\n",
    "    ax.text(i, driver / 2, str(driver), ha='center', va='center', color='white')\n",
    "\n",
    "# Plot 'Insiders' bars with counts on top of 'Drivers'\n",
    "ax.bar(9,df_plot['Insider Count'], width=0.2, bottom=df_plot['Driver Count'])\n",
    "for i, insider in enumerate(df_plot['Insider Count']):\n",
    "    ax.text(i, df_plot['Driver Count'][i] + insider / 2, str(insider), ha='center', va='center', color='white')\n",
    "    \n",
    "# Setting labels and title\n",
    "plt.xlabel('Shift Name')\n",
    "plt.ylabel('Values')\n",
    "plt.title('Cumulative Working Counts of Employees on ' + day)\n",
    "\n",
    "# Display the legend\n",
    "plt.legend()\n",
    "# plt.grid(True)\n",
    "# Show the plot\n",
    "plt.xticks(rotation=45, ha=\"right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# THE END"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.6"
  }
 },
 "nbformat": 4,
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