employee_scheduler.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ortools.sat.python import cp_model\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_nurses_data(filepath):\n",
    "    nurse_df = pd.read_csv(filepath)\n",
    "    return nurse_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nurse</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>A</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>B</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
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       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
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       "      <th>6</th>\n",
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       "      <td>7</td>\n",
       "      <td>D</td>\n",
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       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>D</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Nurse Team\n",
       "0      0    A\n",
       "1      1    A\n",
       "2      2    B\n",
       "3      3    B\n",
       "4      4    B\n",
       "5      5    C\n",
       "6      6    C\n",
       "7      7    D\n",
       "8      8    D\n",
       "9      9    E"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nurse_filepath = 'data/Nurses.csv'\n",
    "nurse_df = read_nurses_data(nurse_filepath)\n",
    "nurse_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create the data\n",
    "def create_nurse_variables(df, n_days):\n",
    "    \"\"\"Creates variables for downstream scheduling functions\"\"\"\n",
    "    num_days = n_days\n",
    "    num_nurses = len(df['Nurse'])\n",
    "    num_shifts = 3\n",
    "    all_nurses = range(num_nurses)\n",
    "    all_shifts = range(num_shifts)\n",
    "    all_days = range(num_days)\n",
    "\n",
    "    return num_nurses, num_shifts, num_days, all_nurses, all_shifts, all_days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Nurses: range(0, 10)\n",
      "All Shifts: range(0, 3)\n",
      "All days: range(0, 56)\n"
     ]
    }
   ],
   "source": [
    "n_day = 56\n",
    "num_nurses, num_shifts, num_days, all_nurses, all_shifts, all_days = create_nurse_variables(nurse_df, n_day)\n",
    "print(f\"All Nurses: {all_nurses}\")\n",
    "print(f\"All Shifts: {all_shifts}\")\n",
    "print(f\"All days: {all_days}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_team_info():\n",
    "    teams = nurse_df['Team'].unique()\n",
    "    print(f\"Number of teams: {len(teams)}\")\n",
    "    for team in teams:\n",
    "        team_size = len(nurse_df[nurse_df['Team'] == team])\n",
    "        print(f\"Team {team}: {team_size} nurses\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of teams: 5\n",
      "Team A: 2 nurses\n",
      "Team B: 3 nurses\n",
      "Team C: 2 nurses\n",
      "Team D: 2 nurses\n",
      "Team E: 1 nurses\n"
     ]
    }
   ],
   "source": [
    "print_team_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_problem(all_nurses, all_shifts, all_days):\n",
    "    \"\"\"Initializes the scheduling problem.\"\"\"\n",
    "    model = cp_model.CpModel()\n",
    "    shifts = {}\n",
    "    for d in all_days:\n",
    "        for n in all_nurses:\n",
    "            for s in all_shifts:\n",
    "                shifts[(n, d, s)] = model.NewBoolVar(f'shift_n{n}d{d}s{s}')\n",
    "    return model, shifts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "model, shifts = initialize_problem(all_nurses, all_shifts, all_days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_basic_constraints():\n",
    "    \"\"\"Adds basic constraints to the model.\"\"\"\n",
    "    assign_one_nurse_per_shift()\n",
    "    limit_one_shift_per_day()\n",
    "    distribute_shifts_evenly()\n",
    "\n",
    "def assign_one_nurse_per_shift():\n",
    "    \"\"\"Ensures each shift is assigned to exactly one nurse.\"\"\"\n",
    "    for d in all_days:\n",
    "        for s in all_shifts:\n",
    "            model.Add(sum(shifts[(n, d, s)] for n in all_nurses) == 1)\n",
    "\n",
    "def limit_one_shift_per_day():\n",
    "    \"\"\"Limits each nurse to at most one shift per day.\"\"\"\n",
    "    for d in all_days:\n",
    "        for n in all_nurses:\n",
    "            model.Add(sum(shifts[(n, d, s)] for s in all_shifts) <= 1)\n",
    "\n",
    "def distribute_shifts_evenly():\n",
    "    \"\"\"Distributes shifts evenly among nurses.\"\"\"\n",
    "    min_shifts_per_nurse = (num_shifts * num_days) // num_nurses\n",
    "    max_shifts_per_nurse = min_shifts_per_nurse + 1\n",
    "    for n in all_nurses:\n",
    "        num_shifts_worked = sum(shifts[(n, d, s)] for d in all_days for s in all_shifts)\n",
    "        model.Add(num_shifts_worked >= min_shifts_per_nurse)\n",
    "        model.Add(num_shifts_worked <= max_shifts_per_nurse)\n",
    "\n",
    "def distribute_weekend_shifts_evenly():\n",
    "    \"\"\"Distributes weekend shifts evenly among nurses.\"\"\"\n",
    "    weekend_days = [d for d in all_days if d % 7 in [5, 6]]\n",
    "    min_weekend_shifts = (len(weekend_days) * num_shifts) // num_nurses\n",
    "    max_weekend_shifts = min_weekend_shifts + 1\n",
    "    for n in all_nurses:\n",
    "        weekend_shifts = sum(shifts[(n, d, s)] for d in weekend_days for s in all_shifts)\n",
    "        model.Add(weekend_shifts >= min_weekend_shifts)\n",
    "        model.Add(weekend_shifts <= max_weekend_shifts)\n",
    "\n",
    "def prevent_consecutive_shifts(shift_set):\n",
    "    \"\"\"Prevents nurses from working consecutive days for specified shifts.\"\"\"\n",
    "    for n in all_nurses:\n",
    "        for d in range(num_days - 1):\n",
    "            model.Add(sum(shifts[(n, d, s)] for s in shift_set) + \n",
    "                    sum(shifts[(n, d + 1, s)] for s in shift_set) <= 1)\n",
    "\n",
    "def enforce_consecutive_days_for_shift(shift, days):\n",
    "    \"\"\"Enforces consecutive days for a specific shift.\"\"\"\n",
    "    for week in range(num_days // 7):\n",
    "        start_day = week * 7\n",
    "        for n in all_nurses:\n",
    "            for d in range(len(days) - 1):\n",
    "                model.Add(shifts[(n, start_day + days[d], shift)] == \n",
    "                        shifts[(n, start_day + days[d+1], shift)])\n",
    "\n",
    "def limit_shifts_per_week(max_shifts):\n",
    "    \"\"\"Limits the number of shifts a nurse can work per week.\"\"\"\n",
    "    for week in range(num_days // 7):\n",
    "        for n in all_nurses:\n",
    "            week_shifts = sum(shifts[(n, d, s)] for d in range(week * 7, (week + 1) * 7) \n",
    "                            for s in all_shifts)\n",
    "            model.Add(week_shifts <= max_shifts)\n",
    "\n",
    "def restrict_shift_for_team(team, shift):\n",
    "    \"\"\"Prevents nurses from a specific team from working a particular shift.\"\"\"\n",
    "    team_nurses = nurse_df[nurse_df['Team'] == team]['Nurse'].tolist()\n",
    "    for n in team_nurses:\n",
    "        for d in all_days:\n",
    "            model.Add(shifts[(n, d, shift)] == 0)\n",
    "\n",
    "def limit_nurses_per_team_per_day(max_nurses):\n",
    "    \"\"\"Limits the number of nurses from each team working on any given day.\"\"\"\n",
    "    teams = nurse_df['Team'].unique()\n",
    "    for team in teams:\n",
    "        team_nurses = nurse_df[nurse_df['Team'] == team]['Nurse'].tolist()\n",
    "        for d in all_days:\n",
    "            model.Add(sum(shifts[(n, d, s)] for n in team_nurses \n",
    "                        for s in all_shifts) <= max_nurses)\n",
    "\n",
    "def balance_shift_types():\n",
    "    \"\"\"Create variables to measure the imbalance in shift types.\"\"\"\n",
    "    target_shifts_per_type = num_days // num_shifts\n",
    "    imbalance = {}\n",
    "    for n in all_nurses:\n",
    "        for s in all_shifts:\n",
    "            num_shifts_of_type = sum(shifts[(n, d, s)] for d in all_days)\n",
    "            imbalance[(n, s)] = model.NewIntVar(0, num_days, f'imbalance_n{n}_s{s}')\n",
    "            model.Add(imbalance[(n, s)] >= num_shifts_of_type - target_shifts_per_type)\n",
    "            model.Add(imbalance[(n, s)] >= target_shifts_per_type - num_shifts_of_type)\n",
    "    \n",
    "    total_imbalance = model.NewIntVar(0, num_nurses * num_shifts * num_days, 'total_imbalance')\n",
    "    model.Add(total_imbalance == sum(imbalance.values()))\n",
    "    return total_imbalance\n",
    "\n",
    "def ensure_minimum_rest_period():\n",
    "    \"\"\"Count violations of minimum rest period between shifts, especially after night shifts.\"\"\"\n",
    "    night_shift = 2  # Assuming shift 2 is the night shift\n",
    "    min_rest_days = 2\n",
    "    rest_violations = []\n",
    "    for n in all_nurses:\n",
    "        for d in range(num_days - min_rest_days):\n",
    "            # Check if nurse works a night shift followed by any shift within min_rest_days\n",
    "            violation = model.NewBoolVar(f'rest_violation_n{n}_d{d}')\n",
    "            night_shift_worked = shifts[(n, d, night_shift)]\n",
    "            subsequent_shifts = [shifts[(n, d+i, s)] for i in range(1, min_rest_days+1) for s in all_shifts]\n",
    "            model.Add(sum([night_shift_worked] + subsequent_shifts) <= 1).OnlyEnforceIf(violation.Not())\n",
    "            model.Add(sum([night_shift_worked] + subsequent_shifts) > 1).OnlyEnforceIf(violation)\n",
    "            rest_violations.append(violation)\n",
    "    \n",
    "    total_violations = model.NewIntVar(0, len(rest_violations), 'total_rest_violations')\n",
    "    model.Add(total_violations == sum(rest_violations))\n",
    "    return total_violations\n",
    "\n",
    "def limit_night_shifts():\n",
    "    \"\"\"Encourage limiting the number of night shifts a nurse can work in a given period.\"\"\"\n",
    "    night_shift = 2  # Assuming shift 2 is the night shift\n",
    "    target_night_shifts = num_days // (2 * num_nurses)  # This was previously the maximum\n",
    "    night_shift_violations = []\n",
    "    for n in all_nurses:\n",
    "        nurse_night_shifts = sum(shifts[(n, d, night_shift)] for d in all_days)\n",
    "        violation = model.NewIntVar(0, num_days, f'night_shift_violation_n{n}')\n",
    "        model.Add(violation >= nurse_night_shifts - target_night_shifts)\n",
    "        night_shift_violations.append(violation)\n",
    "    \n",
    "    total_violations = model.NewIntVar(0, num_nurses * num_days, 'total_night_shift_violations')\n",
    "    model.Add(total_violations == sum(night_shift_violations))\n",
    "    return total_violations\n",
    "\n",
    "def ensure_skill_mix():\n",
    "    \"\"\"Encourage a mix of nurses from different teams across shifts using soft constraints.\"\"\"\n",
    "    teams = nurse_df['Team'].unique()\n",
    "    \n",
    "    # Create a variable to count the number of shifts with good team mix\n",
    "    good_mix_count = model.NewIntVar(0, num_days * num_shifts, 'good_mix_count')\n",
    "    \n",
    "    mix_indicators = []\n",
    "    for d in all_days:\n",
    "        for s in all_shifts:\n",
    "            team_represented = []\n",
    "            for team in teams:\n",
    "                team_nurses = nurse_df[nurse_df['Team'] == team]['Nurse'].tolist()\n",
    "                team_working = model.NewBoolVar(f'team_{team}_working_d{d}_s{s}')\n",
    "                model.Add(sum(shifts[(n, d, s)] for n in team_nurses) >= 1).OnlyEnforceIf(team_working)\n",
    "                model.Add(sum(shifts[(n, d, s)] for n in team_nurses) == 0).OnlyEnforceIf(team_working.Not())\n",
    "                team_represented.append(team_working)\n",
    "            \n",
    "            # Indicator for good mix (at least 2 teams represented)\n",
    "            good_mix = model.NewBoolVar(f'good_mix_d{d}_s{s}')\n",
    "            model.Add(sum(team_represented) >= 2).OnlyEnforceIf(good_mix)\n",
    "            model.Add(sum(team_represented) < 2).OnlyEnforceIf(good_mix.Not())\n",
    "            mix_indicators.append(good_mix)\n",
    "    \n",
    "    # Set the good_mix_count to the number of shifts with good mix\n",
    "    model.Add(good_mix_count == sum(mix_indicators))\n",
    "    \n",
    "    # Maximize the number of shifts with good mix\n",
    "    model.Maximize(good_mix_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "def solve_model():\n",
    "    \"\"\"Solves the constraint satisfaction problem.\"\"\"\n",
    "    solver = cp_model.CpSolver()\n",
    "    status = solver.Solve(model)\n",
    "    return solver, status\n",
    "\n",
    "def get_solution(solver):\n",
    "    \"\"\"Retrieves the solution if one exists.\"\"\"\n",
    "    if solver.StatusName() == 'OPTIMAL':\n",
    "        rota_dict = {(n, d, s) for (n, d, s) in shifts if solver.Value(shifts[(n, d, s)]) == 1}\n",
    "        solution = pd.DataFrame(rota_dict, columns=['Nurse', 'Day', 'Shift']).sort_values(by=['Day'])\n",
    "        rota = solution.pivot(index='Day', columns='Shift', values='Nurse')\n",
    "        return rota\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "\n",
    "def summarize_shifts(solver):\n",
    "    \"\"\"Summarizes the total, weekend, and type-specific shifts for each nurse.\"\"\"\n",
    "    total_shifts = {n: 0 for n in all_nurses}\n",
    "    weekend_shifts = {n: 0 for n in all_nurses}\n",
    "    shift_types = {n: {s: 0 for s in all_shifts} for n in all_nurses}\n",
    "    \n",
    "    for n in all_nurses:\n",
    "        for d in all_days:\n",
    "            for s in all_shifts:\n",
    "                if solver.Value(shifts[(n, d, s)]) == 1:\n",
    "                    total_shifts[n] += 1\n",
    "                    shift_types[n][s] += 1\n",
    "                    if d % 7 in [5, 6]:\n",
    "                        weekend_shifts[n] += 1\n",
    "    \n",
    "    summary = pd.DataFrame({\n",
    "        'Nurse': all_nurses,\n",
    "        'Total Shifts': [total_shifts[n] for n in all_nurses],\n",
    "        'Weekend Shifts': [weekend_shifts[n] for n in all_nurses],\n",
    "        'ED Day 1 Shifts': [shift_types[n][0] for n in all_nurses],\n",
    "        'ED Day 2 Shifts': [shift_types[n][1] for n in all_nurses],\n",
    "        'ED Night Shifts': [shift_types[n][2] for n in all_nurses]\n",
    "    })\n",
    "    return summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_shift_summary(summary):\n",
    "    \"\"\"Plot a bar chart of total and weekend shifts for each nurse.\"\"\"\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    summary.plot(x='Nurse', y=['Total Shifts', 'Weekend Shifts'], kind='bar')\n",
    "    plt.title('Shift Distribution Among Nurses')\n",
    "    plt.xlabel('Nurse')\n",
    "    plt.ylabel('Number of Shifts')\n",
    "    plt.legend(title='Shift Type')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "def plot_schedule_heatmap(rota):\n",
    "    \"\"\"Plot a heatmap of the nurse schedule.\"\"\"\n",
    "    plt.figure(figsize=(20, 10))\n",
    "    sns.heatmap(rota, cmap='YlGnBu', cbar_kws={'label': 'Nurse'})\n",
    "    plt.title('Nurse Schedule Heatmap')\n",
    "    plt.xlabel('Shift')\n",
    "    plt.ylabel('Day')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def generate_fairness_report(summary):\n",
    "    \"\"\"Generate a report on the fairness of shift distribution.\"\"\"\n",
    "    fairness_report = {\n",
    "        'Total Shifts': summary['Total Shifts'].agg(['mean', 'std', 'min', 'max']).to_dict(),\n",
    "        'Weekend Shifts': summary['Weekend Shifts'].agg(['mean', 'std', 'min', 'max']).to_dict(),\n",
    "        'ED Day 1 Shifts': summary['ED Day 1 Shifts'].agg(['mean', 'std', 'min', 'max']).to_dict(),\n",
    "        'ED Day 2 Shifts': summary['ED Day 2 Shifts'].agg(['mean', 'std', 'min', 'max']).to_dict(),\n",
    "        'ED Night Shifts': summary['ED Night Shifts'].agg(['mean', 'std', 'min', 'max']).to_dict(),\n",
    "    }\n",
    "    return pd.DataFrame(fairness_report)\n",
    "\n",
    "def generate_work_pattern_report(rota):\n",
    "    \"\"\"Generate a report on work patterns.\"\"\"\n",
    "    consecutive_shifts = []\n",
    "    for nurse in rota.columns:\n",
    "        shift_count = 0\n",
    "        for day in rota.index:\n",
    "            if pd.notna(rota.loc[day, nurse]) and rota.loc[day, nurse] != -1:\n",
    "                shift_count += 1\n",
    "            else:\n",
    "                if shift_count > 1:\n",
    "                    consecutive_shifts.append(shift_count)\n",
    "                shift_count = 0\n",
    "        if shift_count > 1:\n",
    "            consecutive_shifts.append(shift_count)\n",
    "    \n",
    "    work_pattern_report = {\n",
    "        'Metric': ['Mean Consecutive Shifts', 'Max Consecutive Shifts', 'Min Consecutive Shifts',\n",
    "                'No more than 4 shifts/week', 'Mon-Wed night shift pattern', \n",
    "                'Minimum rest period', 'Maximum night shifts'],\n",
    "        'Value': [f\"{np.mean(consecutive_shifts):.2f}\" if consecutive_shifts else \"N/A\", \n",
    "                f\"{max(consecutive_shifts)}\" if consecutive_shifts else \"N/A\", \n",
    "                f\"{min(consecutive_shifts)}\" if consecutive_shifts else \"N/A\",\n",
    "                'Enforced by constraint', \n",
    "                'Enforced by constraint',\n",
    "                'Enforced by constraint', \n",
    "                'Enforced by constraint']\n",
    "    }\n",
    "    return pd.DataFrame(work_pattern_report)\n",
    "\n",
    "def generate_team_depletion_report(summary, nurse_df):\n",
    "    \"\"\"Generate a report on team depletion avoidance and shift distribution among teams.\"\"\"\n",
    "    # Merge on 'Nurse' column\n",
    "    team_summary = summary.merge(nurse_df, on='Nurse')\n",
    "    \n",
    "    # Calculate total shifts per team\n",
    "    team_shifts = team_summary.groupby('Team').agg({\n",
    "        'Total Shifts': ['sum', 'mean', 'std', 'min', 'max'],\n",
    "        'ED Day 1 Shifts': ['sum', 'mean'],\n",
    "        'ED Day 2 Shifts': ['sum', 'mean'],\n",
    "        'ED Night Shifts': ['sum', 'mean']\n",
    "    }).reset_index()\n",
    "    \n",
    "    # Flatten column names\n",
    "    team_shifts.columns = ['_'.join(col).strip() for col in team_shifts.columns.values]\n",
    "    team_shifts.rename(columns={'Team_': 'Team'}, inplace=True)\n",
    "\n",
    "    team_shifts = team_shifts.replace({np.nan: 0.00})\n",
    "    \n",
    "    team_shifts['Compliance'] = 'Max 1 nurse per team per day (enforced by constraint)'\n",
    "    \n",
    "    # Calculate average number of nurses working per team per day\n",
    "    total_days = summary['Total Shifts'].sum() // len(summary)  # Assuming all nurses work every day\n",
    "    avg_nurses_per_team = team_summary.groupby('Team')['Total Shifts'].sum() / total_days\n",
    "    \n",
    "    skill_mix_report = pd.DataFrame({\n",
    "        'Metric': ['Average number of nurses working per team per day'],\n",
    "        'Value': [f\"{avg_nurses_per_team.mean():.2f}\"]\n",
    "    })\n",
    "    \n",
    "    return team_shifts, skill_mix_report\n",
    "\n",
    "def calculate_avg_teams_per_shift(summary, nurse_df, num_shifts):\n",
    "    team_summary = summary.merge(nurse_df[['Nurse', 'Team']], left_index=True, right_on='Nurse')\n",
    "    total_teams_represented = 0\n",
    "    for shift_type in ['ED Day 1 Shifts', 'ED Day 2 Shifts', 'ED Night Shifts']:\n",
    "        teams_represented = (team_summary[shift_type] > 0).groupby(team_summary['Team']).any().sum()\n",
    "        total_teams_represented += teams_represented\n",
    "    avg_teams_per_shift = total_teams_represented / (3 * num_shifts)  # 3 shift types\n",
    "    return avg_teams_per_shift\n",
    "\n",
    "def plot_shift_type_distribution(summary):\n",
    "    \"\"\"Plot a stacked bar chart of shift type distribution for each nurse.\"\"\"\n",
    "    shift_types = ['ED Day 1 Shifts', 'ED Day 2 Shifts', 'ED Night Shifts']\n",
    "    plt.figure(figsize=(15, 8))\n",
    "    summary[shift_types].plot(kind='bar', stacked=True)\n",
    "    plt.title('Distribution of Shift Types Among Nurses')\n",
    "    plt.xlabel('Nurse')\n",
    "    plt.ylabel('Number of Shifts')\n",
    "    plt.legend(title='Shift Type')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding basic constraints...\n",
      "Solving model...\n",
      "Distributing shifts evenly...\n",
      "Solving model...\n",
      "Distributing weekend shifts evenly...\n",
      "Solving model...\n",
      "Preventing consecutive shifts...\n",
      "Solving model...\n",
      "Enforcing consecutive days for night shift...\n",
      "Solving model...\n",
      "Limiting shifts per week...\n",
      "Solving model...\n",
      "Restricting shift for team A...\n",
      "Solving model...\n",
      "Limiting nurses per team per day...\n",
      "Solving model...\n",
      "Ensuring skill mix...\n",
      "Solving model...\n",
      "Adding optimization objectives...\n",
      "Solving model with optimization objectives...\n",
      "\n",
      "Final Rota:\n"
     ]
    },
    {
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       "    <tr>\n",
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>52</th>\n",
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       "      <td>2</td>\n",
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       "      <th>53</th>\n",
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       "      <th>55</th>\n",
       "      <td>2</td>\n",
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       "      <td>6</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "Shift  0  1  2\n",
       "Day           \n",
       "0      2  6  1\n",
       "1      4  9  1\n",
       "2      3  5  1\n",
       "3      8  9  4\n",
       "4      5  7  3\n",
       "5      8  6  2\n",
       "6      5  7  4\n",
       "7      8  6  1\n",
       "8      3  5  1\n",
       "9      6  2  1\n",
       "10     3  5  8\n",
       "11     7  2  9\n",
       "12     6  1  4\n",
       "13     3  0  8\n",
       "14     9  7  1\n",
       "15     4  6  1\n",
       "16     8  2  1\n",
       "17     7  9  3\n",
       "18     2  8  5\n",
       "19     7  0  4\n",
       "20     6  3  9\n",
       "21     5  8  1\n",
       "22     3  6  1\n",
       "23     2  5  1\n",
       "24     6  4  7\n",
       "25     9  3  8\n",
       "26     6  0  2\n",
       "27     9  5  4\n",
       "28     3  8  0\n",
       "29     2  9  0\n",
       "30     6  7  0\n",
       "31     9  5  4\n",
       "32     3  8  6\n",
       "33     7  0  2\n",
       "34     9  1  5\n",
       "35     7  4  0\n",
       "36     9  2  0\n",
       "37     8  4  0\n",
       "38     6  3  9\n",
       "39     5  7  4\n",
       "40     8  0  2\n",
       "41     9  7  3\n",
       "42     6  4  0\n",
       "43     5  7  0\n",
       "44     6  4  0\n",
       "45     5  7  3\n",
       "46     2  6  8\n",
       "47     9  5  4\n",
       "48     7  3  1\n",
       "49     2  5  0\n",
       "50     7  3  0\n",
       "51     9  4  0\n",
       "52     8  1  2\n",
       "53     9  4  7\n",
       "54     8  5  3\n",
       "55     2  1  6"
      ]
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     "metadata": {},
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    {
     "name": "stdout",
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     "text": [
      "\n",
      "Final Shift Summary:\n"
     ]
    },
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       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Nurse  Total Shifts  Weekend Shifts  ED Day 1 Shifts  ED Day 2 Shifts  \\\n",
       "0      0            17               5                0                5   \n",
       "1      1            17               4                0                4   \n",
       "2      2            16               5                7                4   \n",
       "3      3            17               5                7                5   \n",
       "4      4            17               5                2                7   \n",
       "5      5            17               5                6                9   \n",
       "6      6            17               5                9                6   \n",
       "7      7            17               5                7                8   \n",
       "8      8            16               4                8                4   \n",
       "9      9            17               5               10                4   \n",
       "\n",
       "   ED Night Shifts  \n",
       "0               12  \n",
       "1               13  \n",
       "2                5  \n",
       "3                5  \n",
       "4                8  \n",
       "5                2  \n",
       "6                2  \n",
       "7                2  \n",
       "8                4  \n",
       "9                3  "
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Shift type imbalance: 372\n",
      "Rest period violations: 23\n",
      "Night shift violations: 36\n",
      "\n",
      "Fairness Report:\n"
     ]
    },
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       "      <th>Total Shifts</th>\n",
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       "      <th>mean</th>\n",
       "      <td>16.800000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>5.600000</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.421637</td>\n",
       "      <td>0.421637</td>\n",
       "      <td>3.627059</td>\n",
       "      <td>1.837873</td>\n",
       "      <td>4.087923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
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       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>13.000000</td>\n",
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       "      Total Shifts  Weekend Shifts  ED Day 1 Shifts  ED Day 2 Shifts  \\\n",
       "mean     16.800000        4.800000         5.600000         5.600000   \n",
       "std       0.421637        0.421637         3.627059         1.837873   \n",
       "min      16.000000        4.000000         0.000000         4.000000   \n",
       "max      17.000000        5.000000        10.000000         9.000000   \n",
       "\n",
       "      ED Night Shifts  \n",
       "mean         5.600000  \n",
       "std          4.087923  \n",
       "min          2.000000  \n",
       "max         13.000000  "
      ]
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     "text": [
      "\n",
      "Work Pattern Report:\n"
     ]
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       "      <td>Mean Consecutive Shifts</td>\n",
       "      <td>56.00</td>\n",
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       "      <th>1</th>\n",
       "      <td>Max Consecutive Shifts</td>\n",
       "      <td>56</td>\n",
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       "                        Metric                   Value\n",
       "0      Mean Consecutive Shifts                   56.00\n",
       "1       Max Consecutive Shifts                      56\n",
       "2       Min Consecutive Shifts                      56\n",
       "3   No more than 4 shifts/week  Enforced by constraint\n",
       "4  Mon-Wed night shift pattern  Enforced by constraint\n",
       "5          Minimum rest period  Enforced by constraint\n",
       "6         Maximum night shifts  Enforced by constraint"
      ]
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     "text": [
      "\n",
      "Team Depletion Report:\n",
      "Team Shifts:\n"
     ]
    },
    {
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       "      <th>Team</th>\n",
       "      <th>Total Shifts_sum</th>\n",
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       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>34</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
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       "      <td>0.577350</td>\n",
       "      <td>16</td>\n",
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       "      <td>16</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>16</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>18</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
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       "      <td>15</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>33</td>\n",
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       "      <td>0.707107</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
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       "      <td>12</td>\n",
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       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>17</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
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      ],
      "text/plain": [
       "  Team  Total Shifts_sum  Total Shifts_mean  Total Shifts_std  \\\n",
       "0    A                34          17.000000          0.000000   \n",
       "1    B                50          16.666667          0.577350   \n",
       "2    C                34          17.000000          0.000000   \n",
       "3    D                33          16.500000          0.707107   \n",
       "4    E                17          17.000000          0.000000   \n",
       "\n",
       "   Total Shifts_min  Total Shifts_max  ED Day 1 Shifts_sum  \\\n",
       "0                17                17                    0   \n",
       "1                16                17                   16   \n",
       "2                17                17                   15   \n",
       "3                16                17                   15   \n",
       "4                17                17                   10   \n",
       "\n",
       "   ED Day 1 Shifts_mean  ED Day 2 Shifts_sum  ED Day 2 Shifts_mean  \\\n",
       "0              0.000000                    9              4.500000   \n",
       "1              5.333333                   16              5.333333   \n",
       "2              7.500000                   15              7.500000   \n",
       "3              7.500000                   12              6.000000   \n",
       "4             10.000000                    4              4.000000   \n",
       "\n",
       "   ED Night Shifts_sum  ED Night Shifts_mean  \\\n",
       "0                   25                  12.5   \n",
       "1                   18                   6.0   \n",
       "2                    4                   2.0   \n",
       "3                    6                   3.0   \n",
       "4                    3                   3.0   \n",
       "\n",
       "                                          Compliance  \n",
       "0  Max 1 nurse per team per day (enforced by cons...  \n",
       "1  Max 1 nurse per team per day (enforced by cons...  \n",
       "2  Max 1 nurse per team per day (enforced by cons...  \n",
       "3  Max 1 nurse per team per day (enforced by cons...  \n",
       "4  Max 1 nurse per team per day (enforced by cons...  "
      ]
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     "text": [
      "\n",
      "Skill Mix Report:\n"
     ]
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       "                                              Metric Value\n",
       "0  Average number of nurses working per team per day  2.10"
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      "\n",
      "Average number of teams represented per shift: 1.56\n"
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "\n",
    "    constraints = [\n",
    "        (\"Adding basic constraints...\", add_basic_constraints),\n",
    "        (\"Distributing shifts evenly...\", distribute_shifts_evenly),\n",
    "        (\"Distributing weekend shifts evenly...\", distribute_weekend_shifts_evenly),\n",
    "        (\"Preventing consecutive shifts...\", lambda: prevent_consecutive_shifts([0, 1])),\n",
    "        (\"Enforcing consecutive days for night shift...\", lambda: enforce_consecutive_days_for_shift(2, [0, 1, 2])),\n",
    "        (\"Limiting shifts per week...\", lambda: limit_shifts_per_week(4)),\n",
    "        (\"Restricting shift for team A...\", lambda: restrict_shift_for_team('A', 0)),\n",
    "        (\"Limiting nurses per team per day...\", lambda: limit_nurses_per_team_per_day(1)),\n",
    "        (\"Ensuring skill mix...\", ensure_skill_mix),\n",
    "    ]\n",
    "\n",
    "    for i, (message, constraint_func) in enumerate(constraints):\n",
    "        print(message)\n",
    "        constraint_func()\n",
    "        \n",
    "        print(\"Solving model...\")\n",
    "        solver = cp_model.CpSolver()\n",
    "        solver.parameters.max_time_in_seconds = 180.0  # 3 minutes\n",
    "        status = solver.Solve(model)\n",
    "        \n",
    "\n",
    "    if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE:\n",
    "        print(\"Adding optimization objectives...\")\n",
    "        shift_imbalance = balance_shift_types()\n",
    "        rest_violations = ensure_minimum_rest_period()\n",
    "        night_shift_violations = limit_night_shifts()\n",
    "\n",
    "        # Combine objectives with weights\n",
    "        model.Minimize(shift_imbalance + 10 * rest_violations + 5 * night_shift_violations)\n",
    "\n",
    "        print(\"Solving model with optimization objectives...\")\n",
    "        solver = cp_model.CpSolver()\n",
    "        solver.parameters.max_time_in_seconds = 600.0  # 10 minutes for final optimization\n",
    "        status = solver.Solve(model)\n",
    "\n",
    "        if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE:\n",
    "            rota = get_solution(solver)\n",
    "            summary = summarize_shifts(solver)\n",
    "            print(\"\\nFinal Rota:\")\n",
    "            display(rota)\n",
    "            print(\"\\nFinal Shift Summary:\")\n",
    "            display(summary)\n",
    "            print(f\"\\nShift type imbalance: {solver.Value(shift_imbalance)}\")\n",
    "            print(f\"Rest period violations: {solver.Value(rest_violations)}\")\n",
    "            print(f\"Night shift violations: {solver.Value(night_shift_violations)}\")\n",
    "\n",
    "            # Generate reports\n",
    "            print(\"\\nFairness Report:\")\n",
    "            fairness_report = generate_fairness_report(summary)\n",
    "            display(fairness_report)\n",
    "\n",
    "            print(\"\\nWork Pattern Report:\")\n",
    "            work_pattern_report = generate_work_pattern_report(rota)\n",
    "            display(work_pattern_report)\n",
    "\n",
    "            print(\"\\nTeam Depletion Report:\")\n",
    "            team_shifts, skill_mix_report = generate_team_depletion_report(summary, nurse_df)\n",
    "            print(\"Team Shifts:\")\n",
    "            display(team_shifts)\n",
    "            print(\"\\nSkill Mix Report:\")\n",
    "            display(skill_mix_report)\n",
    "\n",
    "            # Calculate and display average teams per shift\n",
    "            avg_teams_per_shift = calculate_avg_teams_per_shift(summary, nurse_df, num_shifts)\n",
    "            print(f\"\\nAverage number of teams represented per shift: {avg_teams_per_shift:.2f}\")\n",
    "\n",
    "\n",
    "            # Generate plots\n",
    "            plot_shift_summary(summary)\n",
    "            plot_shift_type_distribution(summary)\n",
    "            plot_schedule_heatmap(rota)\n",
    "\n",
    "        else:\n",
    "            print(\"Failed to optimize objectives.\")\n",
    "            print(f'Solver status: {solver.StatusName()}')\n",
    "    else:\n",
    "        print(\"Failed to find a feasible solution.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h2 style='color: white;'>Nurse 9 Schedule - Team E</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_d1dfa th {\n",
       "  text-align: center;\n",
       "  color: white;\n",
       "  background-color: black;\n",
       "}\n",
       "#T_d1dfa td {\n",
       "  background-color: #000000;\n",
       "}\n",
       "#T_d1dfa_row0_col0, #T_d1dfa_row0_col1, #T_d1dfa_row0_col2, #T_d1dfa_row0_col3, #T_d1dfa_row2_col0, #T_d1dfa_row2_col1, #T_d1dfa_row2_col2, #T_d1dfa_row2_col3, #T_d1dfa_row4_col0, #T_d1dfa_row4_col1, #T_d1dfa_row4_col2, #T_d1dfa_row4_col3, #T_d1dfa_row5_col0, #T_d1dfa_row5_col1, #T_d1dfa_row5_col2, #T_d1dfa_row5_col3, #T_d1dfa_row6_col0, #T_d1dfa_row6_col1, #T_d1dfa_row6_col2, #T_d1dfa_row6_col3, #T_d1dfa_row7_col0, #T_d1dfa_row7_col1, #T_d1dfa_row7_col2, #T_d1dfa_row7_col3, #T_d1dfa_row8_col0, #T_d1dfa_row8_col1, #T_d1dfa_row8_col2, #T_d1dfa_row8_col3, #T_d1dfa_row9_col0, #T_d1dfa_row9_col1, #T_d1dfa_row9_col2, #T_d1dfa_row9_col3, #T_d1dfa_row10_col0, #T_d1dfa_row10_col1, #T_d1dfa_row10_col2, #T_d1dfa_row10_col3, #T_d1dfa_row12_col0, #T_d1dfa_row12_col1, #T_d1dfa_row12_col2, #T_d1dfa_row12_col3, #T_d1dfa_row13_col0, #T_d1dfa_row13_col1, #T_d1dfa_row13_col2, #T_d1dfa_row13_col3, #T_d1dfa_row15_col0, #T_d1dfa_row15_col1, #T_d1dfa_row15_col2, #T_d1dfa_row15_col3, #T_d1dfa_row16_col0, #T_d1dfa_row16_col1, #T_d1dfa_row16_col2, #T_d1dfa_row16_col3, #T_d1dfa_row18_col0, #T_d1dfa_row18_col1, #T_d1dfa_row18_col2, #T_d1dfa_row18_col3, #T_d1dfa_row19_col0, #T_d1dfa_row19_col1, #T_d1dfa_row19_col2, #T_d1dfa_row19_col3, #T_d1dfa_row21_col0, #T_d1dfa_row21_col1, #T_d1dfa_row21_col2, #T_d1dfa_row21_col3, #T_d1dfa_row22_col0, #T_d1dfa_row22_col1, #T_d1dfa_row22_col2, #T_d1dfa_row22_col3, #T_d1dfa_row23_col0, #T_d1dfa_row23_col1, #T_d1dfa_row23_col2, #T_d1dfa_row23_col3, #T_d1dfa_row24_col0, #T_d1dfa_row24_col1, #T_d1dfa_row24_col2, #T_d1dfa_row24_col3, #T_d1dfa_row26_col0, #T_d1dfa_row26_col1, #T_d1dfa_row26_col2, #T_d1dfa_row26_col3, #T_d1dfa_row28_col0, #T_d1dfa_row28_col1, #T_d1dfa_row28_col2, #T_d1dfa_row28_col3, #T_d1dfa_row30_col0, #T_d1dfa_row30_col1, #T_d1dfa_row30_col2, #T_d1dfa_row30_col3, #T_d1dfa_row32_col0, #T_d1dfa_row32_col1, #T_d1dfa_row32_col2, #T_d1dfa_row32_col3, #T_d1dfa_row33_col0, #T_d1dfa_row33_col1, #T_d1dfa_row33_col2, #T_d1dfa_row33_col3, #T_d1dfa_row35_col0, #T_d1dfa_row35_col1, #T_d1dfa_row35_col2, #T_d1dfa_row35_col3, #T_d1dfa_row37_col0, #T_d1dfa_row37_col1, #T_d1dfa_row37_col2, #T_d1dfa_row37_col3, #T_d1dfa_row39_col0, #T_d1dfa_row39_col1, #T_d1dfa_row39_col2, #T_d1dfa_row39_col3, #T_d1dfa_row40_col0, #T_d1dfa_row40_col1, #T_d1dfa_row40_col2, #T_d1dfa_row40_col3, #T_d1dfa_row42_col0, #T_d1dfa_row42_col1, #T_d1dfa_row42_col2, #T_d1dfa_row42_col3, #T_d1dfa_row43_col0, #T_d1dfa_row43_col1, #T_d1dfa_row43_col2, #T_d1dfa_row43_col3, #T_d1dfa_row44_col0, #T_d1dfa_row44_col1, #T_d1dfa_row44_col2, #T_d1dfa_row44_col3, #T_d1dfa_row45_col0, #T_d1dfa_row45_col1, #T_d1dfa_row45_col2, #T_d1dfa_row45_col3, #T_d1dfa_row46_col0, #T_d1dfa_row46_col1, #T_d1dfa_row46_col2, #T_d1dfa_row46_col3, #T_d1dfa_row48_col0, #T_d1dfa_row48_col1, #T_d1dfa_row48_col2, #T_d1dfa_row48_col3, #T_d1dfa_row49_col0, #T_d1dfa_row49_col1, #T_d1dfa_row49_col2, #T_d1dfa_row49_col3, #T_d1dfa_row50_col0, #T_d1dfa_row50_col1, #T_d1dfa_row50_col2, #T_d1dfa_row50_col3, #T_d1dfa_row52_col0, #T_d1dfa_row52_col1, #T_d1dfa_row52_col2, #T_d1dfa_row52_col3, #T_d1dfa_row54_col0, #T_d1dfa_row54_col1, #T_d1dfa_row54_col2, #T_d1dfa_row54_col3, #T_d1dfa_row55_col0, #T_d1dfa_row55_col1, #T_d1dfa_row55_col2, #T_d1dfa_row55_col3 {\n",
       "  color: grey;\n",
       "  text-align: center;\n",
       "}\n",
       "#T_d1dfa_row1_col0, #T_d1dfa_row1_col1, #T_d1dfa_row1_col2, #T_d1dfa_row1_col3, #T_d1dfa_row3_col0, #T_d1dfa_row3_col1, #T_d1dfa_row3_col2, #T_d1dfa_row3_col3, #T_d1dfa_row17_col0, #T_d1dfa_row17_col1, #T_d1dfa_row17_col2, #T_d1dfa_row17_col3, #T_d1dfa_row29_col0, #T_d1dfa_row29_col1, #T_d1dfa_row29_col2, #T_d1dfa_row29_col3 {\n",
       "  color: blue;\n",
       "  text-align: center;\n",
       "}\n",
       "#T_d1dfa_row11_col0, #T_d1dfa_row11_col1, #T_d1dfa_row11_col2, #T_d1dfa_row11_col3, #T_d1dfa_row20_col0, #T_d1dfa_row20_col1, #T_d1dfa_row20_col2, #T_d1dfa_row20_col3, #T_d1dfa_row38_col0, #T_d1dfa_row38_col1, #T_d1dfa_row38_col2, #T_d1dfa_row38_col3 {\n",
       "  color: green;\n",
       "  text-align: center;\n",
       "}\n",
       "#T_d1dfa_row14_col0, #T_d1dfa_row14_col1, #T_d1dfa_row14_col2, #T_d1dfa_row14_col3, #T_d1dfa_row25_col0, #T_d1dfa_row25_col1, #T_d1dfa_row25_col2, #T_d1dfa_row25_col3, #T_d1dfa_row27_col0, #T_d1dfa_row27_col1, #T_d1dfa_row27_col2, #T_d1dfa_row27_col3, #T_d1dfa_row31_col0, #T_d1dfa_row31_col1, #T_d1dfa_row31_col2, #T_d1dfa_row31_col3, #T_d1dfa_row34_col0, #T_d1dfa_row34_col1, #T_d1dfa_row34_col2, #T_d1dfa_row34_col3, #T_d1dfa_row36_col0, #T_d1dfa_row36_col1, #T_d1dfa_row36_col2, #T_d1dfa_row36_col3, #T_d1dfa_row41_col0, #T_d1dfa_row41_col1, #T_d1dfa_row41_col2, #T_d1dfa_row41_col3, #T_d1dfa_row47_col0, #T_d1dfa_row47_col1, #T_d1dfa_row47_col2, #T_d1dfa_row47_col3, #T_d1dfa_row51_col0, #T_d1dfa_row51_col1, #T_d1dfa_row51_col2, #T_d1dfa_row51_col3, #T_d1dfa_row53_col0, #T_d1dfa_row53_col1, #T_d1dfa_row53_col2, #T_d1dfa_row53_col3 {\n",
       "  color: red;\n",
       "  text-align: center;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_d1dfa\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d1dfa_level0_col0\" class=\"col_heading level0 col0\" >Day</th>\n",
       "      <th id=\"T_d1dfa_level0_col1\" class=\"col_heading level0 col1\" >DOW</th>\n",
       "      <th id=\"T_d1dfa_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
       "      <th id=\"T_d1dfa_level0_col3\" class=\"col_heading level0 col3\" >Hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_d1dfa_row0_col0\" class=\"data row0 col0\" >0</td>\n",
       "      <td id=\"T_d1dfa_row0_col1\" class=\"data row0 col1\" >Mon</td>\n",
       "      <td id=\"T_d1dfa_row0_col2\" class=\"data row0 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row0_col3\" class=\"data row0 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_d1dfa_row1_col0\" class=\"data row1 col0\" >1</td>\n",
       "      <td id=\"T_d1dfa_row1_col1\" class=\"data row1 col1\" >Tue</td>\n",
       "      <td id=\"T_d1dfa_row1_col2\" class=\"data row1 col2\" >ED Day 2</td>\n",
       "      <td id=\"T_d1dfa_row1_col3\" class=\"data row1 col3\" >10am-8pm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_d1dfa_row2_col0\" class=\"data row2 col0\" >2</td>\n",
       "      <td id=\"T_d1dfa_row2_col1\" class=\"data row2 col1\" >Wed</td>\n",
       "      <td id=\"T_d1dfa_row2_col2\" class=\"data row2 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row2_col3\" class=\"data row2 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_d1dfa_row3_col0\" class=\"data row3 col0\" >3</td>\n",
       "      <td id=\"T_d1dfa_row3_col1\" class=\"data row3 col1\" >Thu</td>\n",
       "      <td id=\"T_d1dfa_row3_col2\" class=\"data row3 col2\" >ED Day 2</td>\n",
       "      <td id=\"T_d1dfa_row3_col3\" class=\"data row3 col3\" >10am-8pm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_d1dfa_row4_col0\" class=\"data row4 col0\" >4</td>\n",
       "      <td id=\"T_d1dfa_row4_col1\" class=\"data row4 col1\" >Fri</td>\n",
       "      <td id=\"T_d1dfa_row4_col2\" class=\"data row4 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row4_col3\" class=\"data row4 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_d1dfa_row5_col0\" class=\"data row5 col0\" >5</td>\n",
       "      <td id=\"T_d1dfa_row5_col1\" class=\"data row5 col1\" >Sat</td>\n",
       "      <td id=\"T_d1dfa_row5_col2\" class=\"data row5 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row5_col3\" class=\"data row5 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_d1dfa_row6_col0\" class=\"data row6 col0\" >6</td>\n",
       "      <td id=\"T_d1dfa_row6_col1\" class=\"data row6 col1\" >Sun</td>\n",
       "      <td id=\"T_d1dfa_row6_col2\" class=\"data row6 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row6_col3\" class=\"data row6 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_d1dfa_row7_col0\" class=\"data row7 col0\" >7</td>\n",
       "      <td id=\"T_d1dfa_row7_col1\" class=\"data row7 col1\" >Mon</td>\n",
       "      <td id=\"T_d1dfa_row7_col2\" class=\"data row7 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row7_col3\" class=\"data row7 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_d1dfa_row8_col0\" class=\"data row8 col0\" >8</td>\n",
       "      <td id=\"T_d1dfa_row8_col1\" class=\"data row8 col1\" >Tue</td>\n",
       "      <td id=\"T_d1dfa_row8_col2\" class=\"data row8 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row8_col3\" class=\"data row8 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_d1dfa_row9_col0\" class=\"data row9 col0\" >9</td>\n",
       "      <td id=\"T_d1dfa_row9_col1\" class=\"data row9 col1\" >Wed</td>\n",
       "      <td id=\"T_d1dfa_row9_col2\" class=\"data row9 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row9_col3\" class=\"data row9 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_d1dfa_row10_col0\" class=\"data row10 col0\" >10</td>\n",
       "      <td id=\"T_d1dfa_row10_col1\" class=\"data row10 col1\" >Thu</td>\n",
       "      <td id=\"T_d1dfa_row10_col2\" class=\"data row10 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row10_col3\" class=\"data row10 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_d1dfa_row11_col0\" class=\"data row11 col0\" >11</td>\n",
       "      <td id=\"T_d1dfa_row11_col1\" class=\"data row11 col1\" >Fri</td>\n",
       "      <td id=\"T_d1dfa_row11_col2\" class=\"data row11 col2\" >ED Nights</td>\n",
       "      <td id=\"T_d1dfa_row11_col3\" class=\"data row11 col3\" >8pm-9am</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_d1dfa_row12_col0\" class=\"data row12 col0\" >12</td>\n",
       "      <td id=\"T_d1dfa_row12_col1\" class=\"data row12 col1\" >Sat</td>\n",
       "      <td id=\"T_d1dfa_row12_col2\" class=\"data row12 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row12_col3\" class=\"data row12 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_d1dfa_row13_col0\" class=\"data row13 col0\" >13</td>\n",
       "      <td id=\"T_d1dfa_row13_col1\" class=\"data row13 col1\" >Sun</td>\n",
       "      <td id=\"T_d1dfa_row13_col2\" class=\"data row13 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row13_col3\" class=\"data row13 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_d1dfa_row14_col0\" class=\"data row14 col0\" >14</td>\n",
       "      <td id=\"T_d1dfa_row14_col1\" class=\"data row14 col1\" >Mon</td>\n",
       "      <td id=\"T_d1dfa_row14_col2\" class=\"data row14 col2\" >ED Day 1</td>\n",
       "      <td id=\"T_d1dfa_row14_col3\" class=\"data row14 col3\" >8am-9pm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_d1dfa_row15_col0\" class=\"data row15 col0\" >15</td>\n",
       "      <td id=\"T_d1dfa_row15_col1\" class=\"data row15 col1\" >Tue</td>\n",
       "      <td id=\"T_d1dfa_row15_col2\" class=\"data row15 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row15_col3\" class=\"data row15 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_d1dfa_row16_col0\" class=\"data row16 col0\" >16</td>\n",
       "      <td id=\"T_d1dfa_row16_col1\" class=\"data row16 col1\" >Wed</td>\n",
       "      <td id=\"T_d1dfa_row16_col2\" class=\"data row16 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row16_col3\" class=\"data row16 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_d1dfa_row17_col0\" class=\"data row17 col0\" >17</td>\n",
       "      <td id=\"T_d1dfa_row17_col1\" class=\"data row17 col1\" >Thu</td>\n",
       "      <td id=\"T_d1dfa_row17_col2\" class=\"data row17 col2\" >ED Day 2</td>\n",
       "      <td id=\"T_d1dfa_row17_col3\" class=\"data row17 col3\" >10am-8pm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_d1dfa_row18_col0\" class=\"data row18 col0\" >18</td>\n",
       "      <td id=\"T_d1dfa_row18_col1\" class=\"data row18 col1\" >Fri</td>\n",
       "      <td id=\"T_d1dfa_row18_col2\" class=\"data row18 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row18_col3\" class=\"data row18 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_d1dfa_row19_col0\" class=\"data row19 col0\" >19</td>\n",
       "      <td id=\"T_d1dfa_row19_col1\" class=\"data row19 col1\" >Sat</td>\n",
       "      <td id=\"T_d1dfa_row19_col2\" class=\"data row19 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row19_col3\" class=\"data row19 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_d1dfa_row20_col0\" class=\"data row20 col0\" >20</td>\n",
       "      <td id=\"T_d1dfa_row20_col1\" class=\"data row20 col1\" >Sun</td>\n",
       "      <td id=\"T_d1dfa_row20_col2\" class=\"data row20 col2\" >ED Nights</td>\n",
       "      <td id=\"T_d1dfa_row20_col3\" class=\"data row20 col3\" >8pm-9am</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "      <td id=\"T_d1dfa_row21_col0\" class=\"data row21 col0\" >21</td>\n",
       "      <td id=\"T_d1dfa_row21_col1\" class=\"data row21 col1\" >Mon</td>\n",
       "      <td id=\"T_d1dfa_row21_col2\" class=\"data row21 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row21_col3\" class=\"data row21 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "      <td id=\"T_d1dfa_row22_col0\" class=\"data row22 col0\" >22</td>\n",
       "      <td id=\"T_d1dfa_row22_col1\" class=\"data row22 col1\" >Tue</td>\n",
       "      <td id=\"T_d1dfa_row22_col2\" class=\"data row22 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row22_col3\" class=\"data row22 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "      <td id=\"T_d1dfa_row23_col0\" class=\"data row23 col0\" >23</td>\n",
       "      <td id=\"T_d1dfa_row23_col1\" class=\"data row23 col1\" >Wed</td>\n",
       "      <td id=\"T_d1dfa_row23_col2\" class=\"data row23 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row23_col3\" class=\"data row23 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "      <td id=\"T_d1dfa_row24_col0\" class=\"data row24 col0\" >24</td>\n",
       "      <td id=\"T_d1dfa_row24_col1\" class=\"data row24 col1\" >Thu</td>\n",
       "      <td id=\"T_d1dfa_row24_col2\" class=\"data row24 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row24_col3\" class=\"data row24 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "      <td id=\"T_d1dfa_row25_col0\" class=\"data row25 col0\" >25</td>\n",
       "      <td id=\"T_d1dfa_row25_col1\" class=\"data row25 col1\" >Fri</td>\n",
       "      <td id=\"T_d1dfa_row25_col2\" class=\"data row25 col2\" >ED Day 1</td>\n",
       "      <td id=\"T_d1dfa_row25_col3\" class=\"data row25 col3\" >8am-9pm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "      <td id=\"T_d1dfa_row26_col0\" class=\"data row26 col0\" >26</td>\n",
       "      <td id=\"T_d1dfa_row26_col1\" class=\"data row26 col1\" >Sat</td>\n",
       "      <td id=\"T_d1dfa_row26_col2\" class=\"data row26 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row26_col3\" class=\"data row26 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "      <td id=\"T_d1dfa_row27_col0\" class=\"data row27 col0\" >27</td>\n",
       "      <td id=\"T_d1dfa_row27_col1\" class=\"data row27 col1\" >Sun</td>\n",
       "      <td id=\"T_d1dfa_row27_col2\" class=\"data row27 col2\" >ED Day 1</td>\n",
       "      <td id=\"T_d1dfa_row27_col3\" class=\"data row27 col3\" >8am-9pm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "      <td id=\"T_d1dfa_row28_col0\" class=\"data row28 col0\" >28</td>\n",
       "      <td id=\"T_d1dfa_row28_col1\" class=\"data row28 col1\" >Mon</td>\n",
       "      <td id=\"T_d1dfa_row28_col2\" class=\"data row28 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row28_col3\" class=\"data row28 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "      <td id=\"T_d1dfa_row29_col0\" class=\"data row29 col0\" >29</td>\n",
       "      <td id=\"T_d1dfa_row29_col1\" class=\"data row29 col1\" >Tue</td>\n",
       "      <td id=\"T_d1dfa_row29_col2\" class=\"data row29 col2\" >ED Day 2</td>\n",
       "      <td id=\"T_d1dfa_row29_col3\" class=\"data row29 col3\" >10am-8pm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "      <td id=\"T_d1dfa_row30_col0\" class=\"data row30 col0\" >30</td>\n",
       "      <td id=\"T_d1dfa_row30_col1\" class=\"data row30 col1\" >Wed</td>\n",
       "      <td id=\"T_d1dfa_row30_col2\" class=\"data row30 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row30_col3\" class=\"data row30 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "      <td id=\"T_d1dfa_row31_col0\" class=\"data row31 col0\" >31</td>\n",
       "      <td id=\"T_d1dfa_row31_col1\" class=\"data row31 col1\" >Thu</td>\n",
       "      <td id=\"T_d1dfa_row31_col2\" class=\"data row31 col2\" >ED Day 1</td>\n",
       "      <td id=\"T_d1dfa_row31_col3\" class=\"data row31 col3\" >8am-9pm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "      <td id=\"T_d1dfa_row32_col0\" class=\"data row32 col0\" >32</td>\n",
       "      <td id=\"T_d1dfa_row32_col1\" class=\"data row32 col1\" >Fri</td>\n",
       "      <td id=\"T_d1dfa_row32_col2\" class=\"data row32 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row32_col3\" class=\"data row32 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "      <td id=\"T_d1dfa_row33_col0\" class=\"data row33 col0\" >33</td>\n",
       "      <td id=\"T_d1dfa_row33_col1\" class=\"data row33 col1\" >Sat</td>\n",
       "      <td id=\"T_d1dfa_row33_col2\" class=\"data row33 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row33_col3\" class=\"data row33 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "      <td id=\"T_d1dfa_row34_col0\" class=\"data row34 col0\" >34</td>\n",
       "      <td id=\"T_d1dfa_row34_col1\" class=\"data row34 col1\" >Sun</td>\n",
       "      <td id=\"T_d1dfa_row34_col2\" class=\"data row34 col2\" >ED Day 1</td>\n",
       "      <td id=\"T_d1dfa_row34_col3\" class=\"data row34 col3\" >8am-9pm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "      <td id=\"T_d1dfa_row35_col0\" class=\"data row35 col0\" >35</td>\n",
       "      <td id=\"T_d1dfa_row35_col1\" class=\"data row35 col1\" >Mon</td>\n",
       "      <td id=\"T_d1dfa_row35_col2\" class=\"data row35 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row35_col3\" class=\"data row35 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "      <td id=\"T_d1dfa_row36_col0\" class=\"data row36 col0\" >36</td>\n",
       "      <td id=\"T_d1dfa_row36_col1\" class=\"data row36 col1\" >Tue</td>\n",
       "      <td id=\"T_d1dfa_row36_col2\" class=\"data row36 col2\" >ED Day 1</td>\n",
       "      <td id=\"T_d1dfa_row36_col3\" class=\"data row36 col3\" >8am-9pm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "      <td id=\"T_d1dfa_row37_col0\" class=\"data row37 col0\" >37</td>\n",
       "      <td id=\"T_d1dfa_row37_col1\" class=\"data row37 col1\" >Wed</td>\n",
       "      <td id=\"T_d1dfa_row37_col2\" class=\"data row37 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row37_col3\" class=\"data row37 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "      <td id=\"T_d1dfa_row38_col0\" class=\"data row38 col0\" >38</td>\n",
       "      <td id=\"T_d1dfa_row38_col1\" class=\"data row38 col1\" >Thu</td>\n",
       "      <td id=\"T_d1dfa_row38_col2\" class=\"data row38 col2\" >ED Nights</td>\n",
       "      <td id=\"T_d1dfa_row38_col3\" class=\"data row38 col3\" >8pm-9am</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "      <td id=\"T_d1dfa_row39_col0\" class=\"data row39 col0\" >39</td>\n",
       "      <td id=\"T_d1dfa_row39_col1\" class=\"data row39 col1\" >Fri</td>\n",
       "      <td id=\"T_d1dfa_row39_col2\" class=\"data row39 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row39_col3\" class=\"data row39 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "      <td id=\"T_d1dfa_row40_col0\" class=\"data row40 col0\" >40</td>\n",
       "      <td id=\"T_d1dfa_row40_col1\" class=\"data row40 col1\" >Sat</td>\n",
       "      <td id=\"T_d1dfa_row40_col2\" class=\"data row40 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row40_col3\" class=\"data row40 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "      <td id=\"T_d1dfa_row41_col0\" class=\"data row41 col0\" >41</td>\n",
       "      <td id=\"T_d1dfa_row41_col1\" class=\"data row41 col1\" >Sun</td>\n",
       "      <td id=\"T_d1dfa_row41_col2\" class=\"data row41 col2\" >ED Day 1</td>\n",
       "      <td id=\"T_d1dfa_row41_col3\" class=\"data row41 col3\" >8am-9pm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "      <td id=\"T_d1dfa_row42_col0\" class=\"data row42 col0\" >42</td>\n",
       "      <td id=\"T_d1dfa_row42_col1\" class=\"data row42 col1\" >Mon</td>\n",
       "      <td id=\"T_d1dfa_row42_col2\" class=\"data row42 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row42_col3\" class=\"data row42 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "      <td id=\"T_d1dfa_row43_col0\" class=\"data row43 col0\" >43</td>\n",
       "      <td id=\"T_d1dfa_row43_col1\" class=\"data row43 col1\" >Tue</td>\n",
       "      <td id=\"T_d1dfa_row43_col2\" class=\"data row43 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row43_col3\" class=\"data row43 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "      <td id=\"T_d1dfa_row44_col0\" class=\"data row44 col0\" >44</td>\n",
       "      <td id=\"T_d1dfa_row44_col1\" class=\"data row44 col1\" >Wed</td>\n",
       "      <td id=\"T_d1dfa_row44_col2\" class=\"data row44 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row44_col3\" class=\"data row44 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "      <td id=\"T_d1dfa_row45_col0\" class=\"data row45 col0\" >45</td>\n",
       "      <td id=\"T_d1dfa_row45_col1\" class=\"data row45 col1\" >Thu</td>\n",
       "      <td id=\"T_d1dfa_row45_col2\" class=\"data row45 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row45_col3\" class=\"data row45 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "      <td id=\"T_d1dfa_row46_col0\" class=\"data row46 col0\" >46</td>\n",
       "      <td id=\"T_d1dfa_row46_col1\" class=\"data row46 col1\" >Fri</td>\n",
       "      <td id=\"T_d1dfa_row46_col2\" class=\"data row46 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row46_col3\" class=\"data row46 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "      <td id=\"T_d1dfa_row47_col0\" class=\"data row47 col0\" >47</td>\n",
       "      <td id=\"T_d1dfa_row47_col1\" class=\"data row47 col1\" >Sat</td>\n",
       "      <td id=\"T_d1dfa_row47_col2\" class=\"data row47 col2\" >ED Day 1</td>\n",
       "      <td id=\"T_d1dfa_row47_col3\" class=\"data row47 col3\" >8am-9pm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "      <td id=\"T_d1dfa_row48_col0\" class=\"data row48 col0\" >48</td>\n",
       "      <td id=\"T_d1dfa_row48_col1\" class=\"data row48 col1\" >Sun</td>\n",
       "      <td id=\"T_d1dfa_row48_col2\" class=\"data row48 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row48_col3\" class=\"data row48 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "      <td id=\"T_d1dfa_row49_col0\" class=\"data row49 col0\" >49</td>\n",
       "      <td id=\"T_d1dfa_row49_col1\" class=\"data row49 col1\" >Mon</td>\n",
       "      <td id=\"T_d1dfa_row49_col2\" class=\"data row49 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row49_col3\" class=\"data row49 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
       "      <td id=\"T_d1dfa_row50_col0\" class=\"data row50 col0\" >50</td>\n",
       "      <td id=\"T_d1dfa_row50_col1\" class=\"data row50 col1\" >Tue</td>\n",
       "      <td id=\"T_d1dfa_row50_col2\" class=\"data row50 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row50_col3\" class=\"data row50 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
       "      <td id=\"T_d1dfa_row51_col0\" class=\"data row51 col0\" >51</td>\n",
       "      <td id=\"T_d1dfa_row51_col1\" class=\"data row51 col1\" >Wed</td>\n",
       "      <td id=\"T_d1dfa_row51_col2\" class=\"data row51 col2\" >ED Day 1</td>\n",
       "      <td id=\"T_d1dfa_row51_col3\" class=\"data row51 col3\" >8am-9pm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
       "      <td id=\"T_d1dfa_row52_col0\" class=\"data row52 col0\" >52</td>\n",
       "      <td id=\"T_d1dfa_row52_col1\" class=\"data row52 col1\" >Thu</td>\n",
       "      <td id=\"T_d1dfa_row52_col2\" class=\"data row52 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row52_col3\" class=\"data row52 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
       "      <td id=\"T_d1dfa_row53_col0\" class=\"data row53 col0\" >53</td>\n",
       "      <td id=\"T_d1dfa_row53_col1\" class=\"data row53 col1\" >Fri</td>\n",
       "      <td id=\"T_d1dfa_row53_col2\" class=\"data row53 col2\" >ED Day 1</td>\n",
       "      <td id=\"T_d1dfa_row53_col3\" class=\"data row53 col3\" >8am-9pm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
       "      <td id=\"T_d1dfa_row54_col0\" class=\"data row54 col0\" >54</td>\n",
       "      <td id=\"T_d1dfa_row54_col1\" class=\"data row54 col1\" >Sat</td>\n",
       "      <td id=\"T_d1dfa_row54_col2\" class=\"data row54 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row54_col3\" class=\"data row54 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d1dfa_level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
       "      <td id=\"T_d1dfa_row55_col0\" class=\"data row55 col0\" >55</td>\n",
       "      <td id=\"T_d1dfa_row55_col1\" class=\"data row55 col1\" >Sun</td>\n",
       "      <td id=\"T_d1dfa_row55_col2\" class=\"data row55 col2\" >Rest Day</td>\n",
       "      <td id=\"T_d1dfa_row55_col3\" class=\"data row55 col3\" ></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f874978e070>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "def create_nurse_schedule(rota, nurse_df, nurse_id):\n",
    "    # Creates schedule for 56 days\n",
    "    num_days = 56\n",
    "    schedule = pd.DataFrame({\n",
    "        'Day': range(num_days),\n",
    "        'DOW': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'] * 8,\n",
    "        'Description': [''] * num_days,\n",
    "        'Hours': [''] * num_days\n",
    "    })\n",
    "\n",
    "    # Fills in the schedule based on rota\n",
    "    shift_map = {0: 'ED Day 1', 1: 'ED Day 2', 2: 'ED Nights'}\n",
    "    hours_map = {'ED Day 1': '8am-9pm', 'ED Day 2': '10am-8pm', 'ED Nights': '8pm-9am'}\n",
    "    color_map = {'ED Day 1': 'red', 'ED Day 2': 'blue', 'ED Nights': 'green', 'Rest Day': 'grey'}\n",
    "\n",
    "    for day in range(num_days):\n",
    "        for shift in range(3):\n",
    "            if rota.loc[day, shift] == nurse_id:\n",
    "                schedule.loc[day, 'Description'] = shift_map[shift]\n",
    "                schedule.loc[day, 'Hours'] = hours_map[shift_map[shift]]\n",
    "\n",
    "    schedule.loc[schedule['Description'] == '', 'Description'] = 'Rest Day'\n",
    "\n",
    "    # Stylizes the schedule DataFrame\n",
    "    def style_row(row):\n",
    "        color = color_map[row['Description']]\n",
    "        return [f'color: {color}'] * len(row)\n",
    "\n",
    "    styled = schedule.style.apply(style_row, axis=1)\n",
    "    styled = styled.set_properties(**{'text-align': 'center'})\n",
    "    styled = styled.set_table_styles([\n",
    "        {'selector': 'th', 'props': [('text-align', 'center'), ('color', 'white'), ('background-color', 'black')]},\n",
    "        {'selector': 'td', 'props': [('background-color', '#000000')]},\n",
    "    ])\n",
    "\n",
    "    # Adds dynamic title that changes the nurse id and team\n",
    "    team = nurse_df.loc[nurse_df['Nurse'] == nurse_id, 'Team'].values[0]\n",
    "    title = f\"Nurse {nurse_id} Schedule - Team {team}\"\n",
    "    \n",
    "    return title, styled\n",
    "\n",
    "title, styled_schedule = create_nurse_schedule(rota, nurse_df, 9)\n",
    "\n",
    "# Display the schedule\n",
    "display(HTML(f\"<h2 style='color: white;'>{title}</h2>\"))\n",
    "display(styled_schedule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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