Flight_Data_Analysis.ipynb

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   "cell_type": "markdown",
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     "slide_type": "-"
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   "source": [
    "# Flight Data Analysis\n",
    "We have dataset contains information about all flights that departed from NYC in 2013, Contains around 336,776 total flights.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This dataset is composed by the following variables:\n",
    "\n",
    "1.\t**year**            2013\n",
    "2.\t**month**           1-12\n",
    "3.\t**day**\t\t\t\tDay of month(1-31)\n",
    "4.\t**dep_time**\t\tDeparture times, local timezone\n",
    "5.\t**sched_dep_time**\tScheduled departure time\n",
    "6.\t**dep_delay**\t\tDeparture delay, in minutes, Negative times represent early departures\n",
    "7.\t**arr_time**\t\tArrival times, local timezone\n",
    "8.\t**sched_arr_time**\tScheduled arrival time\n",
    "9.\t**arr_delay**\t\tArrival delay, in minutes, Negative times represent early arrivals\n",
    "10.\t**carrier**\t\t\tTwo letter carrier abbreviation\n",
    "11.\t**flight**\t\t\tFlight number\n",
    "12.\t**tailnum**\t\t\tPlane tail number\n",
    "13.\t**origin**\t\t\tAirport codes for origin\n",
    "14.\t**dest**\t\t\tAirport codes for destination\n",
    "15.\t**air_time**\t\tAmount of time spent in the air, in minutes\n",
    "16.\t**distance**\t\tDistance flown, in miles\n",
    "17.\t**hour**\t\t\tTime of departure broken in to hour\n",
    "18.\t**minute**\t\t\tTime of departure broken in to minutes\n",
    "19.\t**time_hour**\t\tTimestamp\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
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   "outputs": [],
   "source": [
    "#Load dependencies\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_profiling as pdp\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# This line tells the notebook to show plots inside of the notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Loading the data\n",
    "\n",
    "The first step is to load the data we're working with into a pandas DatFrame.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>sched_dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>sched_arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>carrier</th>\n",
       "      <th>flight</th>\n",
       "      <th>tailnum</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "      <th>air_time</th>\n",
       "      <th>distance</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>time_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>517.0</td>\n",
       "      <td>515</td>\n",
       "      <td>2.0</td>\n",
       "      <td>830.0</td>\n",
       "      <td>819</td>\n",
       "      <td>11.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>1545</td>\n",
       "      <td>N14228</td>\n",
       "      <td>EWR</td>\n",
       "      <td>IAH</td>\n",
       "      <td>227.0</td>\n",
       "      <td>1400</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>01-01-2013 05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>533.0</td>\n",
       "      <td>529</td>\n",
       "      <td>4.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>830</td>\n",
       "      <td>20.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>1714</td>\n",
       "      <td>N24211</td>\n",
       "      <td>LGA</td>\n",
       "      <td>IAH</td>\n",
       "      <td>227.0</td>\n",
       "      <td>1416</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>01-01-2013 05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>542.0</td>\n",
       "      <td>540</td>\n",
       "      <td>2.0</td>\n",
       "      <td>923.0</td>\n",
       "      <td>850</td>\n",
       "      <td>33.0</td>\n",
       "      <td>AA</td>\n",
       "      <td>1141</td>\n",
       "      <td>N619AA</td>\n",
       "      <td>JFK</td>\n",
       "      <td>MIA</td>\n",
       "      <td>160.0</td>\n",
       "      <td>1089</td>\n",
       "      <td>5</td>\n",
       "      <td>40</td>\n",
       "      <td>01-01-2013 05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>544.0</td>\n",
       "      <td>545</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1004.0</td>\n",
       "      <td>1022</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>B6</td>\n",
       "      <td>725</td>\n",
       "      <td>N804JB</td>\n",
       "      <td>JFK</td>\n",
       "      <td>BQN</td>\n",
       "      <td>183.0</td>\n",
       "      <td>1576</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "      <td>01-01-2013 05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>554.0</td>\n",
       "      <td>600</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>812.0</td>\n",
       "      <td>837</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>DL</td>\n",
       "      <td>461</td>\n",
       "      <td>N668DN</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>116.0</td>\n",
       "      <td>762</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>01-01-2013 06:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  dep_time  sched_dep_time  dep_delay  arr_time  \\\n",
       "0  2013      1    1     517.0             515        2.0     830.0   \n",
       "1  2013      1    1     533.0             529        4.0     850.0   \n",
       "2  2013      1    1     542.0             540        2.0     923.0   \n",
       "3  2013      1    1     544.0             545       -1.0    1004.0   \n",
       "4  2013      1    1     554.0             600       -6.0     812.0   \n",
       "\n",
       "   sched_arr_time  arr_delay carrier  flight tailnum origin dest  air_time  \\\n",
       "0             819       11.0      UA    1545  N14228    EWR  IAH     227.0   \n",
       "1             830       20.0      UA    1714  N24211    LGA  IAH     227.0   \n",
       "2             850       33.0      AA    1141  N619AA    JFK  MIA     160.0   \n",
       "3            1022      -18.0      B6     725  N804JB    JFK  BQN     183.0   \n",
       "4             837      -25.0      DL     461  N668DN    LGA  ATL     116.0   \n",
       "\n",
       "   distance  hour  minute         time_hour  \n",
       "0      1400     5      15  01-01-2013 05:00  \n",
       "1      1416     5      29  01-01-2013 05:00  \n",
       "2      1089     5      40  01-01-2013 05:00  \n",
       "3      1576     5      45  01-01-2013 05:00  \n",
       "4       762     6       0  01-01-2013 06:00  "
      ]
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     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "#loading and showing head data\n",
    "df = pd.read_csv('flight_data.csv', na_values=['NA'])\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 336776 entries, 0 to 336775\n",
      "Data columns (total 19 columns):\n",
      "year              336776 non-null int64\n",
      "month             336776 non-null int64\n",
      "day               336776 non-null int64\n",
      "dep_time          328521 non-null float64\n",
      "sched_dep_time    336776 non-null int64\n",
      "dep_delay         328521 non-null float64\n",
      "arr_time          328063 non-null float64\n",
      "sched_arr_time    336776 non-null int64\n",
      "arr_delay         327346 non-null float64\n",
      "carrier           336776 non-null object\n",
      "flight            336776 non-null int64\n",
      "tailnum           334264 non-null object\n",
      "origin            336776 non-null object\n",
      "dest              336776 non-null object\n",
      "air_time          327346 non-null float64\n",
      "distance          336776 non-null int64\n",
      "hour              336776 non-null int64\n",
      "minute            336776 non-null int64\n",
      "time_hour         336776 non-null object\n",
      "dtypes: float64(5), int64(9), object(5)\n",
      "memory usage: 48.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Understanding and cleaning the data\n",
    "\n",
    "The flight data contains some missing and wrong-formatted data which has to be cleaned up first.<br><br>Generally, we're looking to answer the following questions:<br>\n",
    "**Is there anything wrong with the data?**<br>\n",
    "**Are there any quirks with the data?**<br>\n",
    "**Do we need to fix or remove any of the data?**<br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Cleaning and formatting time columns\n",
    "df['dep_time'] = df.dep_time[~df.dep_time.isna()].astype(np.int64).apply('{:0>4}'.format)\n",
    "df['dep_time'] = pd.to_timedelta(df.dep_time.str[:2]+':'+df.dep_time.str[2:]+':00')\n",
    "\n",
    "df['sched_dep_time'] = df.sched_dep_time[~df.sched_dep_time.isna()].astype(np.int64).apply('{:0>4}'.format)\n",
    "df['sched_dep_time'] = pd.to_timedelta(df.sched_dep_time.str[:2]+':'+df.sched_dep_time.str[2:]+':00')\n",
    "\n",
    "df['arr_time'] = df.arr_time[~df.arr_time.isna()].astype(np.int64).apply('{:0>4}'.format)\n",
    "df['arr_time'] = pd.to_timedelta(df.arr_time.str[:2]+':'+df.arr_time.str[2:]+':00')\n",
    "\n",
    "df['sched_arr_time'] = df.sched_arr_time[~df.sched_arr_time.isna()].astype(np.int64).apply('{:0>4}'.format)\n",
    "df['sched_arr_time'] = pd.to_timedelta(df.sched_arr_time.str[:2]+':'+df.sched_arr_time.str[2:]+':00')\n",
    "\n",
    "df[['dep_time','sched_dep_time', 'arr_time', 'sched_arr_time']] = df[['dep_time','sched_dep_time', 'arr_time', 'sched_arr_time']].apply(lambda x: pd.to_timedelta(x))\n",
    "\n",
    "#We have 458 flights where flight dep_time is present but arr_time, arr_delay and air_time is missing, \n",
    "#So we can replace arr_time and arr_delay \n",
    "\n",
    "#Add minutes\n",
    "def addMinutes(time, mins):\n",
    "    return (pd.to_timedelta(time) + pd.Timedelta(minutes=mins))\n",
    "\n",
    "#Replace arr_time with sched_arr_time + dep_delay\n",
    "mask = (~df.dep_time.isna() & df.arr_time.isna())\n",
    "df.loc[mask, \"arr_time\"] = df[mask].apply(lambda row: addMinutes(row['sched_arr_time'], row['dep_delay']), axis=1)\n",
    "\n",
    "#Using proper data types\n",
    "#df[['dep_time','sched_dep_time', 'arr_time', 'sched_arr_time']] = df[['dep_time','sched_dep_time', 'arr_time', 'sched_arr_time']].apply(lambda x: pd.to_timedelta(x))\n",
    "\n",
    "#Replace arr_delay with arr_time - sched_arr_time\n",
    "arr_delay_mask = (df.arr_time.notna() & df.arr_delay.isna())\n",
    "df.loc[arr_delay_mask, \"arr_delay\"] = ((df.loc[arr_delay_mask, \"arr_time\"] - df.loc[arr_delay_mask, \"sched_arr_time\"]).astype('timedelta64[s]')//60)\n",
    "\n",
    "#Arrival delay threshold is -100 mins\n",
    "arr_delay_thresh_mask = (df.arr_delay.notna() & (df.arr_delay < -100))\n",
    "df.loc[arr_delay_thresh_mask, \"arr_delay\"] = (1440 + df.loc[arr_delay_thresh_mask, \"arr_delay\"])\n",
    "\n",
    "#Replace add missing air_time\n",
    "air_time_mask = (df.dep_time.notna() & df.arr_time.notna() & df.air_time.isna())\n",
    "df.loc[air_time_mask, \"air_time\"] = abs((df.loc[air_time_mask, \"arr_time\"] - df.loc[air_time_mask, \"dep_time\"]).astype('timedelta64[s]')//60)\n",
    "\n",
    "\n",
    "# Create two columns with Ontime, Late and Cancelled status for both departure and arrival\n",
    "df.loc[df.dep_delay.isna(), \"dep_status\"]=\"Canceled\"\n",
    "df.loc[df.dep_delay <= 0, \"dep_status\"]=\"OnTime\"\n",
    "df.loc[df.dep_delay > 0, \"dep_status\"]=\"Late\"\n",
    "df.loc[df.arr_delay.isna(), \"arr_status\"]=\"Canceled\"\n",
    "df.loc[df.arr_delay <= 0, \"arr_status\"]=\"OnTime\"\n",
    "df.loc[df.arr_delay > 0, \"arr_status\"]=\"Late\"\n",
    "\n",
    "#Dropping unwanted columns\n",
    "df.drop(\"year\", 1, inplace=True)\n",
    "df.drop(\"hour\", 1, inplace=True)\n",
    "df.drop(\"minute\", 1, inplace=True)\n",
    "df.drop(\"tailnum\", 1, inplace=True)\n",
    "df.drop(\"time_hour\", 1, inplace=True)\n",
    "\n",
    "#Using proper data types \n",
    "df[['month','day', 'carrier', 'origin', 'dest', 'dep_status', 'arr_status']] = df[['month','day', 'carrier', 'origin', 'dest', 'dep_status', 'arr_status']].apply(lambda x: x.astype('category'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 336776 entries, 0 to 336775\n",
      "Data columns (total 16 columns):\n",
      "month             336776 non-null category\n",
      "day               336776 non-null category\n",
      "dep_time          328521 non-null timedelta64[ns]\n",
      "sched_dep_time    336776 non-null timedelta64[ns]\n",
      "dep_delay         328521 non-null float64\n",
      "arr_time          328521 non-null timedelta64[ns]\n",
      "sched_arr_time    336776 non-null timedelta64[ns]\n",
      "arr_delay         328521 non-null float64\n",
      "carrier           336776 non-null category\n",
      "flight            336776 non-null int64\n",
      "origin            336776 non-null category\n",
      "dest              336776 non-null category\n",
      "air_time          328521 non-null float64\n",
      "distance          336776 non-null int64\n",
      "dep_status        336776 non-null category\n",
      "arr_status        336776 non-null category\n",
      "dtypes: category(7), float64(3), int64(2), timedelta64[ns](4)\n",
      "memory usage: 25.4 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
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       "      <th>month</th>\n",
       "      <th>day</th>\n",
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       "      <th>sched_arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>carrier</th>\n",
       "      <th>flight</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "      <th>air_time</th>\n",
       "      <th>distance</th>\n",
       "      <th>dep_status</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>05:17:00</td>\n",
       "      <td>05:15:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>08:30:00</td>\n",
       "      <td>08:19:00</td>\n",
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       "      <td>UA</td>\n",
       "      <td>1545</td>\n",
       "      <td>EWR</td>\n",
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       "      <td>227.0</td>\n",
       "      <td>1400</td>\n",
       "      <td>Late</td>\n",
       "      <td>Late</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>05:33:00</td>\n",
       "      <td>05:29:00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>08:50:00</td>\n",
       "      <td>08:30:00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>1714</td>\n",
       "      <td>LGA</td>\n",
       "      <td>IAH</td>\n",
       "      <td>227.0</td>\n",
       "      <td>1416</td>\n",
       "      <td>Late</td>\n",
       "      <td>Late</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>05:42:00</td>\n",
       "      <td>05:40:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>09:23:00</td>\n",
       "      <td>08:50:00</td>\n",
       "      <td>33.0</td>\n",
       "      <td>AA</td>\n",
       "      <td>1141</td>\n",
       "      <td>JFK</td>\n",
       "      <td>MIA</td>\n",
       "      <td>160.0</td>\n",
       "      <td>1089</td>\n",
       "      <td>Late</td>\n",
       "      <td>Late</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>05:44:00</td>\n",
       "      <td>05:45:00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>10:04:00</td>\n",
       "      <td>10:22:00</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>B6</td>\n",
       "      <td>725</td>\n",
       "      <td>JFK</td>\n",
       "      <td>BQN</td>\n",
       "      <td>183.0</td>\n",
       "      <td>1576</td>\n",
       "      <td>OnTime</td>\n",
       "      <td>OnTime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>05:54:00</td>\n",
       "      <td>06:00:00</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>08:12:00</td>\n",
       "      <td>08:37:00</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>DL</td>\n",
       "      <td>461</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>116.0</td>\n",
       "      <td>762</td>\n",
       "      <td>OnTime</td>\n",
       "      <td>OnTime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>05:54:00</td>\n",
       "      <td>05:58:00</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>07:40:00</td>\n",
       "      <td>07:28:00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>1696</td>\n",
       "      <td>EWR</td>\n",
       "      <td>ORD</td>\n",
       "      <td>150.0</td>\n",
       "      <td>719</td>\n",
       "      <td>OnTime</td>\n",
       "      <td>Late</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>05:55:00</td>\n",
       "      <td>06:00:00</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>09:13:00</td>\n",
       "      <td>08:54:00</td>\n",
       "      <td>19.0</td>\n",
       "      <td>B6</td>\n",
       "      <td>507</td>\n",
       "      <td>EWR</td>\n",
       "      <td>FLL</td>\n",
       "      <td>158.0</td>\n",
       "      <td>1065</td>\n",
       "      <td>OnTime</td>\n",
       "      <td>Late</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>05:57:00</td>\n",
       "      <td>06:00:00</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>07:09:00</td>\n",
       "      <td>07:23:00</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>EV</td>\n",
       "      <td>5708</td>\n",
       "      <td>LGA</td>\n",
       "      <td>IAD</td>\n",
       "      <td>53.0</td>\n",
       "      <td>229</td>\n",
       "      <td>OnTime</td>\n",
       "      <td>OnTime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>05:57:00</td>\n",
       "      <td>06:00:00</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>08:38:00</td>\n",
       "      <td>08:46:00</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>B6</td>\n",
       "      <td>79</td>\n",
       "      <td>JFK</td>\n",
       "      <td>MCO</td>\n",
       "      <td>140.0</td>\n",
       "      <td>944</td>\n",
       "      <td>OnTime</td>\n",
       "      <td>OnTime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>05:58:00</td>\n",
       "      <td>06:00:00</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>07:53:00</td>\n",
       "      <td>07:45:00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AA</td>\n",
       "      <td>301</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ORD</td>\n",
       "      <td>138.0</td>\n",
       "      <td>733</td>\n",
       "      <td>OnTime</td>\n",
       "      <td>Late</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  month day dep_time sched_dep_time  dep_delay arr_time sched_arr_time  \\\n",
       "0     1   1 05:17:00       05:15:00        2.0 08:30:00       08:19:00   \n",
       "1     1   1 05:33:00       05:29:00        4.0 08:50:00       08:30:00   \n",
       "2     1   1 05:42:00       05:40:00        2.0 09:23:00       08:50:00   \n",
       "3     1   1 05:44:00       05:45:00       -1.0 10:04:00       10:22:00   \n",
       "4     1   1 05:54:00       06:00:00       -6.0 08:12:00       08:37:00   \n",
       "5     1   1 05:54:00       05:58:00       -4.0 07:40:00       07:28:00   \n",
       "6     1   1 05:55:00       06:00:00       -5.0 09:13:00       08:54:00   \n",
       "7     1   1 05:57:00       06:00:00       -3.0 07:09:00       07:23:00   \n",
       "8     1   1 05:57:00       06:00:00       -3.0 08:38:00       08:46:00   \n",
       "9     1   1 05:58:00       06:00:00       -2.0 07:53:00       07:45:00   \n",
       "\n",
       "   arr_delay carrier  flight origin dest  air_time  distance dep_status  \\\n",
       "0       11.0      UA    1545    EWR  IAH     227.0      1400       Late   \n",
       "1       20.0      UA    1714    LGA  IAH     227.0      1416       Late   \n",
       "2       33.0      AA    1141    JFK  MIA     160.0      1089       Late   \n",
       "3      -18.0      B6     725    JFK  BQN     183.0      1576     OnTime   \n",
       "4      -25.0      DL     461    LGA  ATL     116.0       762     OnTime   \n",
       "5       12.0      UA    1696    EWR  ORD     150.0       719     OnTime   \n",
       "6       19.0      B6     507    EWR  FLL     158.0      1065     OnTime   \n",
       "7      -14.0      EV    5708    LGA  IAD      53.0       229     OnTime   \n",
       "8       -8.0      B6      79    JFK  MCO     140.0       944     OnTime   \n",
       "9        8.0      AA     301    LGA  ORD     138.0       733     OnTime   \n",
       "\n",
       "  arr_status  \n",
       "0       Late  \n",
       "1       Late  \n",
       "2       Late  \n",
       "3     OnTime  \n",
       "4     OnTime  \n",
       "5       Late  \n",
       "6       Late  \n",
       "7     OnTime  \n",
       "8     OnTime  \n",
       "9       Late  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Pandas Profiling\n",
    "\n",
    "Generates profile reports from a pandas DataFrame.<br><br>For each column the following statistics - if relevant for the column type - are presented in an interactive HTML report:<br>\n",
    "**Essentials:** type, unique values, missing values <br>\n",
    "**Quantile statistics:** like minimum value, Q1, median, Q3, maximum, range, interquartile range <br>\n",
    "**Descriptive statistics:** like mean, mode, standard deviation, sum, median absolute deviation, coefficient of variation, kurtosis, skewness <br>\n",
    "**Correlations:** highlighting of highly correlated variables, Spearman and Pearson matrixes <br>\n",
    "**Most frequent values** <br>\n",
    "**Histogram**<br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generate profile reports\n",
    "profileReport = pdp.ProfileReport(df)\n",
    "#Import profile report\n",
    "profileReport.to_file(outputfile=\"profile.html\")\n",
    "#profileReport"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Distribution of data\n",
    "\n",
    "it's always a good idea to look at the distribution of our data — especially the outliers.<br>\n",
    "Let's start by printing out some summary statistics about the data set.<br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "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>dep_time</th>\n",
       "      <th>sched_dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>sched_arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>flight</th>\n",
       "      <th>air_time</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>328521</td>\n",
       "      <td>336776</td>\n",
       "      <td>328521.000000</td>\n",
       "      <td>328521</td>\n",
       "      <td>336776</td>\n",
       "      <td>328521.000000</td>\n",
       "      <td>336776.000000</td>\n",
       "      <td>328521.000000</td>\n",
       "      <td>336776.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0 days 13:42:10.090131</td>\n",
       "      <td>0 days 13:37:02.696629</td>\n",
       "      <td>12.639070</td>\n",
       "      <td>0 days 15:13:12.569607</td>\n",
       "      <td>0 days 15:33:26.591621</td>\n",
       "      <td>7.329635</td>\n",
       "      <td>1971.923620</td>\n",
       "      <td>151.225736</td>\n",
       "      <td>1039.912604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0 days 04:52:43.859391</td>\n",
       "      <td>0 days 04:41:08.833305</td>\n",
       "      <td>40.210061</td>\n",
       "      <td>0 days 05:20:10.560649</td>\n",
       "      <td>0 days 04:58:58.522489</td>\n",
       "      <td>45.591673</td>\n",
       "      <td>1632.471938</td>\n",
       "      <td>95.297581</td>\n",
       "      <td>733.233033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0 days 00:01:00</td>\n",
       "      <td>0 days 01:06:00</td>\n",
       "      <td>-43.000000</td>\n",
       "      <td>0 days 00:01:00</td>\n",
       "      <td>0 days 00:01:00</td>\n",
       "      <td>-86.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0 days 09:07:00</td>\n",
       "      <td>0 days 09:06:00</td>\n",
       "      <td>-5.000000</td>\n",
       "      <td>0 days 11:05:00</td>\n",
       "      <td>0 days 11:24:00</td>\n",
       "      <td>-17.000000</td>\n",
       "      <td>553.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>502.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0 days 14:01:00</td>\n",
       "      <td>0 days 13:59:00</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>0 days 15:36:00</td>\n",
       "      <td>0 days 15:56:00</td>\n",
       "      <td>-5.000000</td>\n",
       "      <td>1496.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>872.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0 days 17:44:00</td>\n",
       "      <td>0 days 17:29:00</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0 days 19:41:00</td>\n",
       "      <td>0 days 19:45:00</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3465.000000</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>1389.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1 days 00:00:00</td>\n",
       "      <td>0 days 23:59:00</td>\n",
       "      <td>1301.000000</td>\n",
       "      <td>1 days 02:29:00</td>\n",
       "      <td>0 days 23:59:00</td>\n",
       "      <td>1272.000000</td>\n",
       "      <td>8500.000000</td>\n",
       "      <td>1289.000000</td>\n",
       "      <td>4983.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     dep_time          sched_dep_time      dep_delay  \\\n",
       "count                  328521                  336776  328521.000000   \n",
       "mean   0 days 13:42:10.090131  0 days 13:37:02.696629      12.639070   \n",
       "std    0 days 04:52:43.859391  0 days 04:41:08.833305      40.210061   \n",
       "min           0 days 00:01:00         0 days 01:06:00     -43.000000   \n",
       "25%           0 days 09:07:00         0 days 09:06:00      -5.000000   \n",
       "50%           0 days 14:01:00         0 days 13:59:00      -2.000000   \n",
       "75%           0 days 17:44:00         0 days 17:29:00      11.000000   \n",
       "max           1 days 00:00:00         0 days 23:59:00    1301.000000   \n",
       "\n",
       "                     arr_time          sched_arr_time      arr_delay  \\\n",
       "count                  328521                  336776  328521.000000   \n",
       "mean   0 days 15:13:12.569607  0 days 15:33:26.591621       7.329635   \n",
       "std    0 days 05:20:10.560649  0 days 04:58:58.522489      45.591673   \n",
       "min           0 days 00:01:00         0 days 00:01:00     -86.000000   \n",
       "25%           0 days 11:05:00         0 days 11:24:00     -17.000000   \n",
       "50%           0 days 15:36:00         0 days 15:56:00      -5.000000   \n",
       "75%           0 days 19:41:00         0 days 19:45:00      14.000000   \n",
       "max           1 days 02:29:00         0 days 23:59:00    1272.000000   \n",
       "\n",
       "              flight       air_time       distance  \n",
       "count  336776.000000  328521.000000  336776.000000  \n",
       "mean     1971.923620     151.225736    1039.912604  \n",
       "std      1632.471938      95.297581     733.233033  \n",
       "min         1.000000      20.000000      17.000000  \n",
       "25%       553.000000      82.000000     502.000000  \n",
       "50%      1496.000000     130.000000     872.000000  \n",
       "75%      3465.000000     192.000000    1389.000000  \n",
       "max      8500.000000    1289.000000    4983.000000  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us generate some correlation plots of the features to see how related one feature is to the next. To do so, we will use the Seaborn plotting package which allows us to plot heatmaps very conveniently as follows\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BrVvFqMoZGSJIuXgRmDZNBEYrV4ogKXse9Q1SZWcn/up6+pfTzXheZL2hy3qznRsZGaqni0rKfc3NYFz69jWn86AvTX5cS7rs2QNMmSIChaxkMnF9NGqkXoXuTRl6TJXVA7XJz+snL9c1kPOTfWPKj2vk1i3R1e/x45pp3dxEaeT+/aK001QK6jMyaZIo1ZozR+x/WpoIInbuFC9LS9HN708/aa9OSERFBj/BVDjZ2qre//WX4UXd/v6i2Dw1Vdyo9O4tbuKqVxfVVVxcRNG6vsBBn9Klgd9+E/34X74MHDsmXhcuiPWfPSueut27J26ost5UxcerP93NTvnDnPVGqyB5eIiAKihIlHQYYtIk4OBBMdL0rFni6aJyX3Nzc1iQ+6q8lkqXzl3bhdzau1fUhZck8eS1Tx/RL361auIaK1FCjLWRn4GDIcc0Lk78Ncb1k/26zknWG05jXdv6ZP2+WbZMVLcxxOPHoupiTIwIyHr0AJo2FddB9eqqNhVlyhRc4JCUlPdlbW3FeenfX4xhkRdt24pXbKwIHk6cEO2LHj4U38Nz54qA4rff8p5PIjI5Bg5UOJUtq3qvr4GvJKkPoASIhq6pqaJKlL+/qAqTXX42UDQzE4FJo0ZikKbISFHasX69qMaxc6eoQqFsWAmIdhzZB2vK6u5d8TfrMgXtvfeAX34BwsJEANShg/5l0tOBP/8UjZtfvhRtPgBVvh88EDcMup5wp6aqSjgKYl+V11JEhBigLr9uVidPFteel5cIGrNW9VLK70awXl6iYwHltaFLSIiqfYIxrp+yZcVnUJL05y3rfGNe2znx9FS9z8v3zaxZIiCQy0U1pcaNtS8XHZ23/JmZiapOObWNyOsAhoA4f7du5W3fsytWTATRygbmly+LAfGCg0WHF7/+KkogiKhIYq9KVDjVrq2qa7t3r+508fHiSbKXF/DVV6rpFy6Ivx06aA8aAPWn6oa0BQDEj2Hz5uLJ+u3bmvOdndXrSSt7F2rWTNVAcscO3esPCwPOnxfvmzY1LG9v4tNPVT/qEybkrhHnokUiaADEk1pllSVl4/TkZFEaocv+/arG2AWxr8p8ZGQABw7oTrd5s2pE66wNX7WJihJ11wERbGkLGoCcrzF9N2DaKPfl4UMRQOiS9doyxvXj6KjqCGDnzpzTKudXrlx46r07O6uq6u3bp7sNgkKhKkEYNEg1Xfl9U7eu9qBBmUZZKmDo940y2NUViIaHi9HZ80p5XV25ovosazNihChd8/VVlWjNni1KVnQ9BPH1BcaNE+9TUkzXoxQR5QsGDlQ4mZsDQ4eK93/9JZ5oazN1qvjRfPxYvZcZ5c3rvXvaf6SfPFH1wAQY3ouQp6doGB0TI3qE0ibrjZ0yeHFxAXr1Eu9//110UZidQiFu4NPSxM2lMbswLFtWdVxu3xa9IWXtpjW7nTtVAZunpyhlUereXdTtBkRVJm1PRKOiVNtzcRFVPPJbjx6ilypA5DUiQjNNZKTowjQhQQRt+npxytqe484d7Wlu3BB1upWyX2NZ63rn9vr78ENVPfXRo7VXT3n0SDwBB8QNna7ek/LbJ5+Iv//+q7s6yoIFqt6Xhg83Tr5y6+OPxd+7d3X3kPTbb2J+aKiokqakvB6Cg7Wfk5gY8ZlW0na+ldeDtnnK749Dh7T3dPXdd2/W4Fq57+npIp/KhuJZ+fmJanfR0SLgUz7YsbAQ3xUXLqgCqOyU34X29uJBDxEVXaYeSIL+gwwdOVqXyEhJ8vRUjcb6xReq0Vj9/dVHZ27eXIz8qvTee6p5/fuLkWsjI8XI1L/8ohpdWfkaMUJ927oGU8pqyBDV8kOGiMHcwsPFSKtr1khS6dJinqenGAxN6dEj1QBdDg6S9OuvYlpkpBgUrF071Xq/+EJ9mzmNKJyVcuC1vIyim5YmSd27q7ZTurQYROriRdVoy4cPS9K776rSFC+ufTC7nTtVaXx8JGnLFjFQ3/Pn4r2Pj2r+/v3qy2YduErbwG6SpD6I2rFjuvdp2zZVOi8vSVq3Toxu++yZJO3aJUnVqqnmr1ihvqyuAeB8fVXLjBsnri3lqNFTp6oGalO+Zs9WXz7rSN3ffScGW3v1SszL7cjR9eqJAbjCw8XgWytWiFGslQMMZh8gMDfH9NgxVZpTp3Qf0+yyjxw9erQ4FtHR4u/o0aqRl5s00dx+fg0Al9eRo5OTxUjcyvUMGya+Z6KiJOnGDUn6/HNV/itWVB9EbtIk1XLt20vShQvifAYESNLSparvROWrUyfN7SvPW9eu4trMOmDi99+rlm3dWnwWIyLEdpQD9jk66h8ATtc5lyQx0rcyXYsWYkTxiAgxqv2iRar1W1uLUaeVoqJU36fOziLtvXti2X//laQxY1TrnTw5L2eGiAoRBg6U//IrcJAk8QNUsaL6j272V8OG4sYpq6Ag1Y27rtc770hS48bifdu26svnJnB4+VK1vK5XqVLixzM7Pz/9+Zs4UfOH3hiBgySJEYUnTRKjU+eUR0CSatVSv5HIbuVKcROra3k7O0natElzufwMHCRJkpYtE6OQ68qHTCa2mZ2uwOHyZUmyt8/52AwdqhqRetgw9eXT0lSBcfabXn3nefp01U2stpeLi/bjUZCBgySJka31fSY6dND8vEqS6QMHSRIBbcOGOee/YkVxM51VdLQkVa2a83KNG4vvHECSypfX3Hb//urpvbxU82Jj1YOa7K/evVXnNq+BQ2qqJH30Uc774OAgAtXsTpzQ/1no3l19ZHgiKpJYVYkKt8qVRZWPRYvEKLxOTqJIv2RJoE0b0dXphQuadcy9vYFr10QXgD4+ot6+lZWoTtOzp+hG88AB1QBe58+LKk+GKF5c9Jr0++8iL87OIm+OjqJe74wZoi93bQN1NWwo5s2aJd4XLy5GAa5YUVRNunRJVJcwVdeFFhaikfT9+6IaRIsWYgwECwtRVcbLS4xCu327qBetHA1Ym+HDRfWOsWNFF6W2tqIBZe3aoqrZ3bvAwIEFv08jRoiqa+PGiWomdnbiuvDyEvXV/fyA6dNzv74GDcSgYUOHiuvK3Fycw/LlRe80p04Ba9aI3r0A0c4jJUW1vLm5uAbbtBFVOOzsVPXG9Zk2TVzfw4aJ7Vlbi8+Er6+oHnXnjmq7xlSqlGgfsm6dGMHcxUUc47JlxWdt1y5R9VBXmxBTK11afJ+sWwd07iyueXNzcb02aSJ6Brp+XXynZOXoKLpg/vpr0VbCykrst5ub6BFu3TrxXaG8zh890myjsmSJuA6dnMTyMplqVGYHB5Gvn38W7SiUn6FmzYC1a8VxfdPvCgsLYNUqMWDgwIHic2FlJa7p6tXFSON37qhGFs+qbVtRXWnCBFE9zs5OrM/dXVQV3LFDtB1ho2iiIk8mSZJk6kwQEREREVHhxhIHIiIiIiLSi4EDERERERHpxcCBiIiIiIj0YuBARERERER6MXAgIiIiIiK9GDgQEREREZFeDByIiIiIiEgvBg5ERERERKQXAwciIiIiItKLgQMREREREenFwIGIiIiIiPRi4EBERERERHoxcCAiIiIiIr0YOBARERERkV4MHIiIiIiISC8GDkREREREpBcDByIiIiIi0ouBAxERERER6cXAgYiIiIiI9GLgQEREREREejFwICIiIiIivRg4EBERERGRXgwciIiIiIhILwYORERERESkFwMHIiIiIiLSi4EDERERERHpxcCBiIiIiIj0YuBARERERER6MXAgIiIiIiK9GDgQEREREZFeDByIiIiIiEgvBg5ERERERKQXAwciIiIiItKLgQMREREREenFwIGIiIiIyMSio6PRoUMH+Pn56Uzz999/o3v37qhTpw66dOmCU6dOqc1fuXIlWrZsiTp16mDQoEF49OhRvuaRgQMRERERkQlduXIF/fr1w+PHj3WmCQ4OxtixY/HZZ5/B398fY8eOxfjx4/HixQsAwO7du7FhwwasXr0afn5+qF69OsaNGwdJkvItnwwciIiIiIhMZPfu3fjiiy8wYcIEvekaNGiA9u3bw9zcHF27doWvry+2bdsGANi+fTsGDhyIihUrwsrKChMnTkRoaGiOJRiGYuBARERERJRH4eHhuH37ttorPDw818s3b94cx44dQ9euXXNM9+DBA1SqVEltWoUKFXDv3j2t8y0sLODt7Z05Pz+Y59uaiIiIiIhMYKTM22Tbrr5wIhYvXqw2bcyYMRg7dmyulndxcclVuoSEBNjY2KhNs7a2RmJiYq7m54dCGziY8gIg41kmBWOFYxVTZ4OM4JOYezhyP/dPYKjo6lTZFasuhZg6G2QEwxt6Ybkfz/XbYEQjL1NnodDq168f2rZtqzYtt8GAIWxsbJCcnKw2LTk5GXZ2drmanx8KbeBARERERFTYubq6wtXVtcC3U6lSJdy+fVtt2oMHD1CjRg0AQMWKFREYGIg2bdoAANLS0hAcHKxRvelNsI0DERERERVpcpnpXsbSo0cPXLp0CYcOHUJ6ejoOHTqES5cuoWfPngCAvn37YuPGjbh37x5SUlIwd+5cODs7o0GDBvmWBwYORERERESFUN26dbFv3z4AgI+PD5YsWYLly5fD19cXS5cuxaJFi1CuXDkAwLvvvoshQ4bg008/RePGjXHnzh0sX74cFhYW+ZYfVlUiIiIioiJNLjPio/8CdP/+fbX/r127pvZ/ixYt0KJFC63LymQyDBs2DMOGDSuw/LHEgYiIiIiI9GLgQEREREREerGqEhEREREVacZspPw2Y4kDERERERHpxRIHIiIiIirS/iuNows7ljgQEREREZFeDByIiIiIiEgvVlUiIiIioiKNjaONgyUORERERESkF0sciIiIiKhIY+No42CJAxERERER6cUSByIiIiIq0tjGwThY4kBERERERHoxcCAiIiIiIr1YVYmIiIiIijQ2jjYOljgQEREREZFeLHEgIiIioiKNT8KNg8eZiIiIiIj0YuBARERERER6saoSERERERVpbBxtHCxxICIiIiIivVjiQERERERFGkeONg6WOBARERERkV4scSAiIiKiIo1tHIyDJQ5ERERERKQXAwciIiIiItKLVZWIiIiIqEhj42jjYIkDERERERHpxRIHIiIiIirS2DjaOFjiQEREREREejFwICIiIiIivVhViYiIiIiKNDaONg6WOBARERERkV4scSAiIiKiIo2No42DJQ5ERERERKQXSxyIiIiIqEhjGwfjYIkDERERERHpxcCBiIiIiIj0YlUlIiIiIirSWFXJOFjiQEREREREerHEgYiIiIiKNHbHahwscSAiIiIiIr0YOBARERERkV6sqkRERERERRobRxsHSxyIiIiIiEgvljgQERERUZHGxtHGwRIHIiIiIiLSiyUORERERFSksY2DcbDEgYiIiIiI9GLgQEREREREerGqUiFh71wSk//ZhY3Dv0LA3xdNnR16Q9bOJdFywQy4NW8IKT0Dgdv34eK3v0DKyNBIW2lAb9QZ/zFs3Uoh5m4g/L6fg7AL/ibINWkT9zIGW5f8gge3/oWZmRwNWndEr2GjIZdrfn3e9v8H+9YtQ1RYKBxdSqHn0FGo4dsMAKBQKDC5f2dAkoAsjfhmrd8LK2sbXD17AuvnzoS5pWXmvFqNW+DDz78t+J0kNY/+vYS/t63Cq/DncHB2Rev+H8OnbmOtaRWKDJzZthq3zx1HWmoKvKrVQYeh42BfwkktXWLsS2z6fjw6DZ+AslVrZ06/+88pXNizEfHRUbAr4YgGnfuiTrtuBbp/pPLo+iWcfX2uizm5omX/j1E+h3N9dttq3D0vznXZqnXQLsu5jnj8EH9vWYEXwQ8gNzeHV436aD1wBGwcigMAAi6fhd/eTXgVHgZrewdUb9ERjXu+D5kZn+HmBzaONg4GDoWAT9P6GLxuLlwreJs6K5RP2q+Zj4TnL7CxakvYujqj05alqDl6MG4sWqOWzqtLGzSfNx3Hh3yGJ8fOwKtrO3TZvgK7WvfFqwdBJso9ZbX212koUdIZM//YjdiYaKz84Suc3rsd7foMVEsXHvoEa36aisFfTEN136a4/s8ZrP15Gr5dvgUlnFwQ9iQYGenp+HXbEZhbWGhs53HgPfhjkgVVAAAgAElEQVS26Yj3P/vaWLtGWsSEPcPehTPQbfQU+NRtjAD/c9i3eBaG/7oWDiWdNdL/s2czgm9exaAZi2Fla4ejqxfgyKr56PvFD5lpngbcxuHlv+JleKjashFPgnBk1Tz8b8ovcK9QFc8CbmPb7MlwLuOFMpVrFvi+vu1iwp5h/8IZeGf0FJSv0xiB/udwYMksDP1F+7n227sZIbeu4v3vF8PS1g7H1yzAsdXz0XviD0hLTcGuOd+gZuuu6D3xB6QmJeGvFb/gyMo56PX5TLwICsBfy39Bt0+/QbnaDRH9/Cl2z/0GFtY2aNDlXRPsPVHeMMw1scYf9sWwzb9h7zdzTJ0VyifFypWFe4tG8Js2BxlJyYgLeYqrv/6O6h+/r5G2wrvd8HDHATw+chqSQoHgA8fw/B9/VP6gjwlyTtlFhD7Fg5vX0HPIaFhaWcO5tDs69RuMMwd3aaS9dPIvlK9WG7Uat4Rcbo56zduiQo06uHBkHwDgceBdeHj7aA0aACAk8C48K1Qp0P0h/W6dPQqPyjVQsUEzmMnlqNKoFTyr1MT1U4e0pr/592E07PY/FHNyhZWNHdoOGo1HNy7jZfjzzPUdXDobLd4borFsTNgzKBQKSAoFpNclUTIzM8gtLDXSUv67c+4oylSugQr1xbmu3KgVylSpiZundZ9r33f+B4fX57r1B6MR9Ppcx0WFw6VseTTu9T7k5hawcSiGWm3ewdP7NwEAryJfoFabd1C+bmPIzMzg5FEWFeo3w7N7N425y/9pcpnpXm8Tg0sc2rZtiz59+qB3797w8PAoiDy9Ve4cOYNLm/ZAkZGBj7ctNnV2KB84VqmA5OiXSAwLz5wWc/8hHDw9YFnMAamxcZnTZWZypCUmqa9AoUCJiuWNlV3KwfPHQbB1KIbiTqqnj6U9vRET8QKJ8XGwtXfInB72OAjuXurnrbSnN54FPQAgShTSUlMw5/OPERX+HKXLeKP74BEoX7UmFAoFnj4KgJW1DU7s2gyFQoHq9Rujx5BRatugghf5LAQunuXUpjl5eCHi8UONtCmJCYiLjlRLb1fcEdZ29oh4/AglXN3gXbMBqjVtBzO5HPuX/Ki2vHfN+nDzqYLNMydAZmYGSaFA6wGfwK185YLZOVIT+SwEzmWynWt33ec6PjoSztnPta09Ip88QoX6zdDnC/XzG3D5LEp5VwQAVPJtgUq+LTLnpaWmIOj6JVRp2jY/d4mowBlc4jB58mTcunULnTp1wpAhQ7B//36kpKQURN7eCrEvIqDQUu+dii4LBzukJyaqTUt/HRxY2NuqTQ/afxSV+veEW1NfyORyeHVpC/eWTWBubW20/JJuKUmJsLJSPxcWr/9PSVYP+JKTEmGZ7bxZWllnprOwtIJXpWoY/s2P+H71TtRo1Ay/T5+IqLBQxL96iTLlKqF209b4ZulGTPjld4Q/f4r182YU4N6RNqnJSZnnWMnC0gqpKcla0orPufb04rzblygJM7lc67Yy0tNQ3KU03vvyJ0xYfQB9Js7E+V3rEXSTbZyMIS0pCebZzp25pRXSknN/rs2trJCa7btAkiSc37EWj65dROsPRmuuKykR+xZMh7mlJep3YukyFS0Glzh07twZnTt3RlRUFPbt24f169dj5syZ6Nq1K/r27YuaNQ2rlxkeHo6IiAi1adWrVzc0W0SFRnpCEsxtbNSmmduK/1PjE9SmP9x1CNZOJdHit5mwKlEMT46dwcOdB2Fuw8ChMLC0tkFqtgcjaa9vIK1t1INASysbpGVLm5qSnJmu90dj1Oa16z0AfscP4bb/P2jZrS8++0lV4ljSxRo9h4zCvC9GIDkxEda26tui/HNx3xZc3Lcl8383nyoa5zEtNQWW1jbZF828iUzLFlSI9PrP2fldG2BuaQnvGvUAAD51GqFqkza4fvIQytVsYPC+UM789m3Bpf2qc13apwrSU9XPdXpqCixsdJ/r9FT1c52eon6uU5IScGTlXIQHB+J/38zVKL2Kfv4E+xfOgG1xR7w35VdY2vCznV/YONo48tw42snJCT179oS1tTW2b9+OnTt34ujRoyhdujRmzZqFqlWr5mo927Ztw+LF6lV07t+/n9dsEZlc9N1AWDs5wsbFCUkRUQAAx8o+iH/2HGmx8WppbVyd8eTEWdxeuTFzWs+jWxG0/6hR80zauXmVQ0LcK8TGRKOYY0kAQNiTYJRwdoWNnb1aWnevcnjyMEBtWtiTYJR93W7hwIYVqN20NTx9KmXOT09Lg4WlFZ4FPcCVM8fR/cMRkL3+8UtPS4NMZga5BfuwKEiNewxA4x4DMv8/++davAgOVEsT9SwEpctVyr4orO0cYO/ojKgs1ZviX0YjOT4OzmW89W47Lioc1nbqVdHM5HLIzXnOC0KjHgPQKMu5PvfnWoSHZDvXoSEo5Z3DuX6qqt6U8DIayQmqc/3yRSh2z50KBycXvP/94szelJQeXb+EQ0t/RM3WXdHifx/pLIkiKswMrqqUmpqKw4cPY+TIkWjZsiW2b9+O3r1748yZMzh37hxat26NMWPG6F/Ra/369cOuXbvUXkRFWeyjEDz/xx9NZn8NC3s7OJT1QL1Jo3B/w06NtG7NfNF9/3rYe7pDbmWJGiM/RImK5RCwZY8Jck7Zubp7ony1Wti1aiGSExMRFRaKI9vWoXH7dzTS+rbphAe3ruHquZPIyEjH1XMn8eDWNfi26QgACA0Jwq6VCxEbE4W0tFQc3roWyUkJqNWkJWwdiuHMwV04sWszMjLSER3xAnvXLkXDdp1hwYayRlWtWTs8uXsD9/z+hiIjA/f8/saTuzdQrVl7relrtuyIf/Zuxsvw50hNSsSpjb/Ds0otOJZy17stn7qNcc/vbwTd8IckSXhy9wbunD+Jaqz3bhTKc33/9bm+r+dcV2/RERf3bcarCHGuT2/6HWWq1EKJUu5ITojDnz9NhnvFaug7abZG0BD64C72//Y9Wg8ciVYDPmHQUADMZDKTvd4mMkmSJEMWaNCgAeRyObp164Y+ffpoVCt68OABPvnkE5w8efKNMjZS5v1GyxdFy6RgzGvd/60ax2GZFIwVjv+9nmRsXJzQ7Jdv4d6iESSFAgFb9+LS9DmQFAoMfXIFZz+fhgd/HgAA1Jv8KaoO6QcLO1tE3riDf76ejaibd028B/nvk5h7OHI/XH/CQiY2Jho7ls9H4M1rkJnJ0LBNZ/QYPBJmcjm++F9H9Bv9BXxbi+Dg7lU/7Fu3DJFhz1DSpTR6DBmF6g2aAAAS4mKxZ81i3PG/iJSUZHhVrIo+w8fCo1wFAEDgrWvYv34Fnoc8goWlJeq1aIeeQ0bBwtLKZPueV50qu2LVpRBTZyPPgm744+9tq/Ay/DmKO7miVf+PUb5OQwDAnfMncHTtbxi/SvSWlZGejnM71+HO+RNITU5E2aq10XHYeNgVd9RY76+DOqLf17+qjeNw9egeXD22Dwkvo1HMyQWNuvdHtWbtjLOj+WB4Qy8s9yu65zr4hj/ObhfnupiTK1r0/xjla4tzfffCCRxf+xvGrlSd6ws71+HuBXGuPavWRodh42FbzBFXDu/A31tWwNzSKrPUUGnsyn3YM/87PPrXT+Pz7FG5hkaj6sJqRCMvU2chRwfcTdeFcbfQt6d3LIMDh0OHDqF9+/awtCzYp2BvY+DwNvqvBg6kqagGDmS4oh44UO4V9cCBcq+wBw4HPWuZbNvvPLlhsm0bm8EVKbt27YonT57gxYsXUMYcaWlpCAgIwJAhQ/I7f0REREREVAgYHDgsX74c8+fPzyyKkyQJMpkMVatWZeBARERERPQfZXDgsHnzZixcuBCWlpY4efIkPv/8c8ycORNubm4FkT8iIiIiohyZvW1DOJuIwb0qxcbGomPHjqhSpQpu3bqFEiVK4JtvvsGhQ9qHaCciIiIioqLP4BIHV1dXxMfHo1SpUnj69CkkSULJkiXx6tWrgsgfEREREVGOZHKDn4VTHhgcOPj6+mLcuHFYsGABqlWrhnnz5sHKygqlSpUqiPwREREREVEhYHB49tVXX8HLywvp6en4+uuvceLECWzbtg1ff/11QeSPiIiIiIgKAYNLHOzt7TFt2jQAQMmSJdm2gYiIiIhMSsbG0UaR68Bh8eLFetOMGTPmjTJDRERERESFU64DBz8/vxznZx9inYiIiIjIGNgdq3HkOnDYsGFDQeaDiIiIiIgKsTz1XfXw4UP88MMPGDNmDGJiYrBx48b8zhcRERERERUiBgcO58+fx3vvvYeYmBhcuHABycnJWLJkCVasWFEQ+SMiIiIiypHMzMxkr7eJwXs7b948zJ8/H3PnzoVcLoebmxtWrFiBbdu2FUT+iIiIiIioEDC4O9aQkBC0bNkSgKpBdM2aNTlyNBERERGZBBtHG4fBJQ7u7u64evWq2rSbN2/Czc0t3zJFRERERESFi8ElDiNGjMCoUaMwYMAApKWlYeXKldiwYQM+//zzgsgfEREREVGOOACccRgcOLzzzjuwt7fHpk2b4O7ujosXL+Kbb75Bp06dCiJ/RERERERUCBgcOABAq1at0KpVq/zOCxERERERFVK5DhymTJmiN83s2bPfKDNERERERIaSyd+ublFNxeCjHBMTg3379iEuLg4lSpRASkoKDhw4gNTU1ILIHxERERERFQK5LnFQliaMHDkSCxcuRLt27TLnnTt3DsuWLcv/3BERERER6cHuWI3D4BIHPz8/tGnTRm1akyZNcPv27XzLFBERERERFS4GBw4eHh44fPiw2rRdu3bBy8sr3zJFRERERESFi8G9Kk2YMAGfffYZNm3aBDc3Nzx9+hQBAQGsqkREREREJiEzY1UlYzC4xKFdu3bYt28fmjZtCjs7O7Rq1Qr79u1Do0aNCiJ/RERERERUCORpHIfy5ctjzJgxOufXq1cPV69ezXOmiIiIiIhyy4zdsRpFgRxlSZIKYrVERERERGQieSpx0EcmYz0zIiIiIjIOGbtjNQqW6xARERERkV4MHIiIiIiISK8CqapERERERGQsrKpkHCxxICIiIiIivQqkxIG9KhERERGRsRTl7lijoqLw7bff4tKlS5DL5ejRowe+/PJLmJur36YPHz4cV65cUZuWmJiIfv36YcaMGYiMjESzZs1ga2ubOd/R0REnT57Mt7zmKXAICgrCwYMHERERAXd3d3Tr1g0eHh6Z80+cOJFvGSQiIiIi+q8aP348SpUqhbNnzyIyMhKjRo3CH3/8geHDh6ulW7Vqldr/O3bswOLFizPHVrt58yY8PDzyNVDIzuDw7Pjx4+jevTvOnTuHuLg4nDhxAt26dYO/v39mmpIlS+ZrJomIiIiI/mtCQkJw6dIlTJo0CTY2NvD09MTo0aOxadOmHJd79OgRZs6ciTlz5sDV1RWACBxq1KhRoPk1uMRh/vz5+OGHH9CrV6/MaTt27MDs2bOxc+fOfM0cEREREZE+RbVxdGBgIEqUKIFSpUplTvPx8UFoaChiY2NRrFgxrct9//336NWrFxo0aJA57ebNm3j16hW6deuGyMhI1KxZE19++SUqVKiQb/k1uMQhNDQUPXr0UJvWu3dvBAcH51eeiIiIiIiKhPDwcNy+fVvtFR4enqtlExISYGNjozZN+X9iYqLWZfz9/XH9+vXMKkpKxYoVQ/369bF+/XocP34c3t7eGDp0KOLi4vKwV9oZXOJQq1YtHD16FJ07d86cdunSJdSpUyffMkVERERElFtmZqYrcdi2bRsWL16sNm3MmDEYO3as3mVtbW2RlJSkNk35v52dnc7tdenSBS4uLmrT586dq/b/lClTsHPnTvj7+6NNmzZ685IbBgcOZcqUwcSJE7F//354eXnhxYsXOH78OBo0aIApU6Zkpps9e3a+ZJCIiIiIqLDq168f2rZtqzYt+029LhUrVsTLly8RGRkJZ2dnAMDDhw9RunRpODg4aKRPT0/HiRMnsGTJErXp8fHxWLJkCT744IPMDosyMjKQnp4Oa2vrvOyWVgYHDgqFIrOqUkxMDCwtLdG1a9d8yxARERERkSFkJuyO1dXVNbOBsqG8vb1Rv359/Pjjj5gxYwZiYmKwdOlSvPvuu1rT379/HykpKahXr57adHt7e1y4cAHPnj3DrFmzYGZmhp9//hllypRRawfxpgwOHFiSQERERESUPxYuXIgZM2agXbt2MDMzQ69evTB69GgAQN26dfH9999nPrR/8uQJihcvDisrK431LF26FLNnz0b79u2RlpaGRo0aYeXKlbCwsMi3vOZpHIfz589j48aNePHiBZYvX441a9Zg4sSJGgNVEBERERGRbs7Ozli4cKHWedeuXVP7v3PnzmrtjLPy8PDQaGuR3wwu19m/fz8mTZqESpUqISQkBABw8uRJzJs3L98zR0RERESkj5lcZrLX28TgwGHFihVYunQpJkyYADMzM7i4uGD58uU4cOBAQeSPiIiIiIgKAYPrFoWFhaF27doAAJlMRFleXl46+5olIiIiIipIRXUAuKLG4BIHb29vnDhxQm3ahQsX4OXllW+ZIiIiIiKiwsXgEocJEyZg9OjRaNeuHZKTkzF9+nTs37+fbRyIiIiIiP7DDA4cmjZtiq1bt2Lbtm1o3LgxFAoF1q5di1q1ahVE/oiIiIiIcmTKcRzeJrkOHAYNGpTZpkFJkiQEBwdjzpw5AID169fnb+6IiIiIiKhQyHXg0KhRIwDA06dPcfz4cfTt2xdly5ZFWFgYtm/frrNPWSIiIiKigvS2dYtqKrkOHMaMGQMAGDhwIFasWKE21HWnTp3w7bff5n/uiIiIiIioUDC4jcPdu3czu2NVqly5MoKDg/MrT0REREREuSYzY4mDMRjcksTHxwd//PGH2rRly5ahSpUq+ZUnIiIiIiIqZAwucfj6668xcuRIbNiwAaVLl0ZoaCgUCgVWr15dEPkjIiIiIqJCwODAoV69ejh69ChOnz6NFy9eoHTp0mjbti0cHBwKIn9ERERERDkyY3esRmFw4AAAJUqUQK9evfI7L0REREREVEjJJEmSTJ0JIiIiIqK8uju0h8m2XXXtPpNt29jyVOJgDCsc2dj6bfBJzD2MlHmbOhtkBMukYPz9MNLU2SAjaOXjjHVXnpg6G2QEg+t74qdTgabOBhnBV20qmjoLVAiwQhgREREREelVaEsciIiIiIhyQ8bG0UbBo0xERERERHqxxIGIiIiIijSZGZ+FGwOPMhERERER6cUSByIiIiIq0jgAnHHwKBMRERERkV4MHIiIiIiISC9WVSIiIiKiIo3dsRoHjzIREREREenFEgciIiIiKtJY4mAcPMpERERERKQXAwciIiIiItKLVZWIiIiIqEjjyNHGwaNMRERERER6scSBiIiIiIo0mVxu6iy8FVjiQEREREREerHEgYiIiIiKNHbHahw8ykREREREpBcDByIiIiIi0otVlYiIiIioSDNjd6xGwaNMRERERER6scSBiIiIiIo0No42Dh5lIiIiIiLSi4EDERERERHpxapKRERERFSksaqScfAoExERERGRXixxICIiIqIiTcbuWI2CR5mIiIiIiPRi4EBERERERHqxqhIRERERFWlsHG0cPMpERERERKQXSxyIiIiIqEhjiYNx8CgTEREREZFeLHEgIiIioiLNjCUORsGjTEREREREejFwICIiIiIivVhViYiIiIiKNI4cbRw8ykREREREpBdLHIiIiIioSGN3rMbBo0xERERERHoxcCAiIiIiIr1YVYmIiIiIijRWVTIOHmUiIiIiItKLJQ5EREREVKSxO1bj4FEmIiIiIiK9WOJAREREREWamVxu6iy8FVjiQEREREREejFwICIiIiIivVhViYiIiIiKNHbHahw8ykREREREpBdLHIiIiIioSGOJg3HwKBMRERERkV4MHIiIiIiISC9WVTIia+eSaLlgBtyaN4SUnoHA7ftw8dtfIGVkaKStNKA36oz/GLZupRBzNxB+389B2AV/E+Sa8pu9c0lM/mcXNg7/CgF/XzR1dkiP2Jcx2LDwZwTcvAYzuRyN23TEu8PHQC7X/Pq8efkCdq75HZFhoSjpWgrvDvsUtRo1AwCM7dNeLa1CUiAtJQXDJ09Hw9Yd8ORRIHasWoyQB/dhbmGBanUb4r2Px8KheAmj7Ofb7ME1P5zauhIvw8NQzMkVbQd+gor1GmtNq1Bk4NSWVbh59hjSU1PgVb0OugwbD3tHJwBAwqsYHF41HyF3r8PMTI4azduj3fsjYCaX4/DqBbh17rja+tJTU+Fdoy4GTPkZ6WmpOLNjHW6fP4G05GSUrVYbHQd/imJOrgV+DN5WT25ehv/uPxAfGQa7ki7w7TMMnrUaak2rUGTgyu51eHDxJNJTU+BWuRaavv8pbIuXBAA88j+DM2vmQG5hmbmMV50maDl0IgAg8MJx3DyyAwkvo+DoXhYN+gxF6Yo1Cn4n3xIcOdo4eJSNqP2a+UhLSMTGqi2xu9178GjdBDVHD9ZI59WlDZrPm46L3/2Cdd6+uL5oNbpsX4HiFcqZINeUn3ya1sfkf3bBtYK3qbNCubTip29hbWODXzbsxdfzV+Luv/44vnubRroXz55g2axv0HPQx/htxxH0eP8jLP/pW8RERgAAFu06rvaq36wNqtdvhPot2iA1JQULv5sIn2o1MWfTfkz/fSPi415h3fwfjb27b53o50+xa8H3aPnuUExctRct3v0QuxfORFx0pNb053dvQtDNKxg2aynGLt4KcwsrHFw5N3P+7kU/wMLaBuOWbMOQmYsRdOsqLh3aAQDo8tF4TFp7IPPVd8J0WNvZof2gUQCA01tX4/6ls+j/1U/4bNmfKFnaA5t//BIZ6WkFfyDeQq9ePMOpFbNRr8cHeH/+dtTt9j5OrfwZCTHaz/31Q9vw7M5VdJ8yH/1+WgdzS0uc37Awc35kcCB8GrXFoN92ZL6UQcPj6374Z/MS+PYdhvfnb0WNjn1xbNE0vAp7apR9JcovDByMpFi5snBv0Qh+0+YgIykZcSFPcfXX31H94/c10lZ4txse7jiAx0dOQ1IoEHzgGJ7/44/KH/QxQc4pvzT+sC+Gbf4Ne7+ZY+qsUC6Fhz5FwI1r6DtsNKysreHi5oF3+g/Bqf07NdL+c/wwKlSvjbpNW0IuN0eDlu1QqUZdnP1rr0baC8cO4s61y/ho0jTI5eaIjghDmXIV0W3AUJhbWMC+WHG07NITAbf+NcZuvtVunD0Kzyo1Udm3GczkclRr3Bplq9bCtZMHtab/99RhNOneD8WcXGFla4cOgz/Fw+uXEfMiFNFhz/D4znW0HfgxLKys4VjKHc17vw//Y5rXQGLsK+xdMhsdPhwDlzLeAIDbF06iee8P4FLGG3JzC7Tu/xHioiMQfOtaQR6Ct9aDiydRqkJ1eNVpAjO5HOUatEDpSjUQcO6I1vQB54+iZqd3YV/SBZY2tmj0v0/w9PYVxEWEAQAiQwLh7FVB67KPLp9G+Yat4FmrIczM5PCu2xSlKtZAwIVjBbZ/bxuZ3Mxkr7cJqyoZiWOVCkiOfonEsPDMaTH3H8LB0wOWxRyQGhuXOV1mJkdaYpL6ChQKlKhY3ljZpQJw58gZXNq0B4qMDHy8bbGps0O5EBoSBDuHYijh5JI5za1sOURHvEBifBxs7R1UaR8HoYy3j9ry7mW98eTRA7VpiQnx+HPVYgwcPRH2xYoDAEqX8cJnM+eqpbt67jS8KlTO712ibCKfhsDFU70019nDC+EhDzXSJifGIy46Qi29fXFH2NjZI/xxEGQywMbeAQ6Ozmrrio0MR3JCPKzt7DOnn9q6Em7lK6FG83aZ0xQKBSysrTP/l0EGmUyGqNDH8KmjvfoM5d3L0BA4enipTSvh5onop0EaaVOTEpAYEwlHD+/MaTbFHGFla4/oZ0Gwd3JF1OOHMLe0ws2jOyEpFChTowEa9B4KKzt7SAoFzC2t1dYpk8lY4kBFjsFh0qBBg7Bnzx4kJSXpT0yZLBzskJ6YqDYt/XVwYGFvqzY9aP9RVOrfE25NfSGTy+HVpS3cWzaBubX6lw4VLbEvIqDQ0p6FCq/kpERYWtuoTbO0tgIApCQnaUmr/hm1tLLSSHdy759wKuWGBi3bQRtJkrBn3QrcuHQe/UaMf9NdID1SkxJhYaV+3iwsrZCarPkbl/r6dy97enNLa6SlJCE1OUlzXa//z7q+l+HPcfPscbTp95Fa2ioNW+D8ns2IeRGK9NRU/P3nH0hLTUFaamred5B0SktJ0riZN7e0QlqK5rlPe33+LLKll1taIT0lGcnxr+DkWR7e9Zqhz/Tf8c7kXxEbHooza0UJs1fdpnhw8STCAm5CkZGBkH8v4vm968hI47nNLyxxMA6DSxxat26N1atXY+bMmejcuTP69u2LevXq5TkD4eHhiIiIUJtWvXr1PK+vsEpPSIK5jfoNiLmt+D81PkFt+sNdh2DtVBItfpsJqxLF8OTYGTzceRDmNgwciIzJytoaqSnJatNSk1PEPBtbLWlT1NOmpMA6SzpJknDuyH70GDQcMplMY3tJiQn4Y94sPH5wH1/8vARlyvlopKE3c37PZlzYuznzf/cKVZGeqn7e0lJTYJnt/AKqICAtW/r01GRYWttAkiSkZbsG0l5fP5ZZvv+vn/4LZSpXRylv9Wot7d4fgVNbVmHDjAkwk8tRp3VXuHqWUyupoLy7fng7bvy1PfN/F+9KGuc+PTUFFlY22RfNDDDSU9W/DzJSU2BuZQObYo7o+sXPmdPtS1qjQZ+hOPDzRKQlJ6K8byskx8fi/MZFSEmMR5kaDVDOt5XG9okKO4MDh48++ggfffQRbt++jd27d2PcuHGwt7dH37590bNnT7i6Gtb7w7Zt27B4sXq1jfv37xuarUIv+m4grJ0cYePihKSIKACAY2UfxD97jrTYeLW0NqATyYQAACAASURBVK7OeHLiLG6v3Jg5refRrQjaf9SoeSZ627l7lUdC7CvExkSjmKPoOeX54yA4OrvCNtvNnLtXeTx+EKA2LfRxMLwrqqobBQfcRdyrGNRv3lZjW+HPn2LRd1+gpEspfP3bavamVECa9RqIZr0GZv5/etsahAUHqqWJfBYCt3KVNJa1sXeAQ0lnRD4Nhuvr6krxL6ORFB8HF89ykBQKJMXHIv5VDOyLO2auy6GkC6xtVdfLvctn0fid9zTWHxcThWa930enoWMBAEnxcbiwdzPcymvmhQxXu8v/ULvL/zL/v7JnPaKeqFdJe/n8idZ2ClZ29rAt4YSY548zqyslvopBSkIcHD28EP00CI8u/436vQZnPhRQpKdBJpPBTG6BxFcx8KhWD9XadM9c54GfJ8KrbtMC2FOigpPn8pXq1atjypQpmDVrFmxtbTF37lx06NAB48aNQ2hoaK7X069fP+zatUvt9V8U+ygEz//xR5PZX8PC3g4OZT1Qb9Io3N+g2cjSrZkvuu9fD3tPd8itLFFj5IcoUbEcArbsMUHOid5epTw8UaF6LWxb8RuSExMQGRaKg1v/QLOO3TTSNm7bGQE3r8L/zAlkZKTD/8wJBNy8isZtO2emeXD7BspWqAKrbFWaEuJiMe+rcfCpWhOf/TCfQYMR1WjeHo/vXMedi6ehyMjAnYun8fjOddRo0UFr+lotO+H8nk14Gf4cKUmJOLZ+KcpWrQXHUu4o6VYGZSrXwPH1S5GSlIiX4c9xbvcm1G6tugYS414h6tljeFappbHuS4d24MCyX5CanISk+DgcWbsQpctVhLtPlQLb/7eZT+M2CAu4iSD/s1BkZCDI/yzCAm7Cp5FmYA8AFZu2x/VD2xAXGYa05ERc+nMFSlesgWIubrCyc8Dd0wdw6+hOKDIyEB8djss716BCk/aQW1ggLPAm/po3BfFR4UhPS8XtE3vx6sVTVGisvcoiGU5mZmay19tEJkmSZOhCN27cwL59+3Do0CEAQPfu3dGnTx+UKlUKc+fOxfXr17Fv3743ytgKx//eF6WNixOa/fIt3Fs0gqRQIGDrXlyaPgeSQoGhT67g7OfT8ODPAwCAepM/RdUh/WBhZ4vIG3fwz9ezEXXzron3IP99EnMPI2Xeps6G0S2TgjGvdf+3ahyHZVIw/n6ovZvDwiw2Jhqbf5+H+zeuwkwmQ+N2XdB36CiYyeUY26c9Phg7CY3adAIA3L7ih51rlyLi+TM4uZZG32GjUdNX9URx89K5iH/1Ep9Mmam2jWO7tuLPVYtgaWWtUYVp0S71fv+LglY+zlh35Ymps5Frj65fxsmtq/DyRSiKOZdC2wEfo0LdRgCAW+dO4PDq+Zi0Vnw3Z6Sn48yfa3Hr/AmkJiXCq1oddBk+AXavSxjiX8Xg6NpFCLnzL2RmZqjZogPaDBgOMzM5AOD5o/tYO/VTTPrjICwsrdTykZKYgMNrFiDohhizp3wtX3QY/ClsHYob61AYbHB9T/x0KlB/wkLq2e0r8N/9B2IjwmDv5IIGvYfCs6YvAOCh3ylc2LwEg34T3ekqMtJxdd9GPPQ7hbTkpNfjOIyBTTER6IcF3IT/nnV4GRoCuYUlyjVoiQZ9hsL89bgO1w5sxv2zfyE9JQlOnj5o+N7HcCpbdKojftWmoqmzkKOELT+YbNt2A6aabNvGZnDg0LlzZzx9+hTNmzdHnz590LZtW5ibq2o8BQYGYsCAAfD3f7PByv6LgQNpelsDh7dRUQ0cyHBFLXCgvCvqgQPlXmEPHBK3zTbZtm37TTHZto3N4DYOffr0Qe/eveHi4qJ1vpeXF06fPv2m+SIiIiIiokLE4MDhk08+QVJSEsLCwqBQKAAAaWlpCAgIQIcOHWBpaQlLS0s9ayEiIiIioqLE4MBh586dmDlzJlKydTnn5OSEDh20NyYjIiIiIiowr9sRUcEyOHBYtmwZxo8fDzs7O1y+fBmDBw/Gr7/+imbNmhVE/oiIiIiIqBAwuA+piIgIDB48GE2aNMHjx49RvXp1/Pjjj/jzzz8LIn9ERERERDkzMzPd6y1i8N46OTkhLS0Nbm5uCAoKAgC4u7sjKioq3zNHRERERESFg8FVlWrVqoXvvvsO3377Lby9vbFlyxZYW1ujRAkOWERERERExieTs42DMRgcOEyZMgVTp05FQkICJk2ahJEjRyI5ORmzZ5uu/1wiIiIiIipYBgcOrq6uWLFiReb7ixcvIi0tDTY2NvmeOSIiIiIiKhxyHTjs2bNHb5pevf7P3p2Hx3T9cRz/TPbNnsQaQmyl1L7TSiha1NpUKap0iVhapWhVUVRLlaJqaS2lUkrVVi1qraKoovY1FZLYZV8mvz/8mt9vmjCZSGYy9X49zzyPOffcO99zb56Yb77n3NvhgYIBAAAALMbtWK0iy4nD9OnT77vdYDCQOAAAAAD/UllOHLZs2ZKbcQAAAADZQ8XBKrJ189nr169rwYIFmjBhgmJiYvTzzz/ndFwAAAAA8hCLE4ejR4+qdevW+uGHH7R8+XLduHFDgwYN0rfffpsb8QEAAADIAyxOHCZOnKjhw4dr2bJlcnJykp+fn2bOnKn58+fnRnwAAADAfRkcHGz2elDXrl1TSEiI6tSpo/r162v8+PFKSUnJtG/fvn1VrVo11axZM/21fft2SVJqaqomTZqkRo0aqWbNmnrttdcUFRX1wPH9P4tHe/LkST3zzDOS7i6IlqSmTZsqMjIyRwMDAAAA/u0GDx4sDw8P7dixQytWrNDu3bu1YMGCTPseOXJE8+fP18GDB9NfzZo1kyR99tln2rVrl7799lvt2LFDbm5ueuedd3I0VosTh8KFC+vs2bMmbWfPnpW3t3eOBQUAAABkmYOj7V4P4MKFC9q7d6+GDh0qd3d3+fn5KSQkREuWLMnQNzw8XLdu3VKVKlUyPdby5cvVr18/FS9eXF5eXnr77be1fft2hYeHP1CM/8/ixOH555/XK6+8om+++UYpKSlav369Bg0apODg4BwLCgAAAPi3O3XqlAoWLKiiRYumtwUEBCgiIkK3b9826Xv48GF5enrq9ddfV4MGDdS2bVutWLFCknTnzh1duXJFFStWTO/v7e2tAgUK6MSJEzkWr8VPju7Zs6ccHR21cOFCGY1GTZs2TcHBwerdu3eOBQUAAABkmQ1vxxoVFaXo6GiTNh8fH/n6+prdNzY2Vu7u7iZtf7+Pi4tT/vz509uTkpJUo0YNvf7666pQoYL27NmjAQMGyNPTUzVr1pQkeXh4mBzLzc1NsbGx2RpXZixOHCSpe/fu6t69e44FAQAAANijsLAwzZgxw6QtNDRUAwYMMLuvh4eH4uPjTdr+fu/p6WnS3qFDB5OHLTdp0kQdOnTQhg0b1KhRI5N9/5aQkJDhOA8iy4nDP09IZkJDQx8oGAAAAMCeBAcHKzAw0KTNx8cnS/tWqFBBN2/e1NWrV9PXC585c0bFihVTvnz5TPquWLFCnp6eatOmTXpbUlKSXF1dVaBAARUtWlSnT59On64UHR2tmzdvmkxfelBZThz27Nkj6W7mcvjwYVWpUkWlSpVSZGSkDh06pMaNG+dYUAAAAEBW5cRtUbPL19c3S9OSMuPv76/atWtrwoQJGjt2rG7cuKFZs2apS5cuGfrGxMTo448/VpkyZVS5cmVt375da9euTX8kQqdOnfTZZ5+pWrVqKlSokCZMmKB69eqpdOnSDzS+/5flxGHx4sWSpOHDh6tTp07q1q1b+rZVq1bpxx9/zLGgAAAAgIfB9OnTNXbsWAUFBcnBwUEdOnRQSEiIJKlmzZoaM2aM2rdvr169eikuLk6hoaG6du2a/Pz8NGnSJNWpU0eS1L9/f6WkpKh79+6KjY1V/fr19cknn+RorIa0tLQ0S3aoVauWfvvtNzn8X2aXmpqqOnXq6ODBgzkW2JxClXPsWMi7Xr5xXK8a/G0dBqxgdtp5bTtz1dZhwAoeD/DWwv05d/s/5F29avvpg59P2ToMWMHw5hVsHcJ9JW5ZZLPPdg3sabPPtrZsPcdh3759Jm07d+7MdokGAAAAQN5n8V2VXnnlFfXr10+tWrVSiRIlFB4erk2bNmnSpEm5ER8AAACAPMDixKFr164qVaqUvv/+ex05ckTFihXTggULVKtWrdyIDwAAALg/Gz7H4WGSrec4NGzYUA0bNrzn9nbt2mnNmjXZDgoAAABA3pKtxMGcv/76KzcOCwAAAGRgcKTiYA25ctNbg8GQG4cFAAAAYCO5UnEAAAAArMaGD4B7mHCWAQAAAJhF4gAAAADALKYqAQAAwL5xO1arsLjiEBcXZ7ZPWlpatoIBAAAAkDdZnDi0bdtWMTEx9+2zaNGibAcEAAAAWMLg4Giz18MkW2sc4uPj77u9WrVq2QoGAAAAQN5k8RqH+vXrq2vXrmrWrJl8fX1NtoWGhuZYYAAAAADyDosTh7/++kt+fn46d+6czp07l97OQ98AAABgEzzHwSosThyee+45tWjRQq6urrkRDwAAAIA8yOL0bMyYMXIgqwMAAEAeweJo67A4A6hWrZrWr1+fG7EAAAAAyKMsnqp08+ZNvfXWWxo1apS8vb1N1jZs3rw5R4MDAAAAkDdYnDj06NEj0/aUlJQHDgYAAACw2EM2ZchWLE4catWqpVmzZikyMlJGo1GSlJycrHPnzqlr1645HiAAAAAA27N4jcM777yjS5cuKV++fEpNTVXFihV16tSpe1YiAAAAgFzl4GC710PE4tEePnxYM2fOVEhIiLy8vPTOO+/o448/1u7du3MjPgAAAAB5gMVTlTw8PFSgQAE5OTnp5MmTkqRmzZrprbfeyvHgAAAAAHMMjqxxsAaLKw6lS5fWtm3b5OnpKaPRqPDwcEVGRrI4GgAAAPgXs7ji8PLLL2vgwIFau3atgoOD9dxzz8nR0VFBQUG5ER8AAACAPMDixCEwMFA//vijihQpopCQEPn7+ysmJkYdOnTIjfgAAACA++N2rFZhceIgSUWLFk3/91NPPZVjwQAAAADIm7KVOAAAAAB5BhUHq3i4bj4LAAAAIFtIHAAAAACYxVQlAAAA2DXDQ/YEZ1vhLAMAAAAwy5CWlpZm6yAAAACA7DKe/tVmn+1QvoHNPtva8uxUpY0nomwdAqygVSVfbTtz1dZhwAoeD/DWqwZ/W4cBK5iddl7u9fvbOgxYQfyemXKp2cfWYcAKkg5+YesQkAfk2cQBAAAAyBIDs++tgbMMAAAAwCwSBwAAAABmMVUJAAAA9o2pSlbBWQYAAABgFhUHAAAA2LU0Kg5WwVkGAAAAYBaJAwAAAACzmKoEAAAA+8ZUJavgLAMAAAAwi4oDAAAA7JvBYOsIHgpUHAAAAACYRcUBAAAA9s2Bv4VbA2cZAAAAgFkkDgAAAADMYqoSAAAA7BpPjrYOzjIAAAAAs6g4AAAAwL5RcbAKzjIAAAAAs0gcAAAAAJjFVCUAAADYN6YqWQVnGQAAAIBZVBwAAABg36g4WAVnGQAAAIBZVBwAAABg13gAnHVwlgEAAACYReIAAAAAwCymKgEAAMC+MVXJKjjLAAAAAMyi4gAAAAD7ZjDYOoKHAhUHAAAAAGaROAAAAAAwi6lKAAAAsG8sjrYKzjIAAAAAs6g4AAAAwK7x5Gjr4CwDAAAAMIuKAwAAAOybA38LtwbOMgAAAACzSBwAAAAAmMVUJQAAANg3FkdbBWcZAAAAgFlUHAAAAGDfqDhYBWcZAAAAgFkkDgAAAADMYqoSAAAA7BtTlayCswwAAADALCoOAAAAsGtpVBysgrMMAAAAwCwqDgAAALBvVBysgrMMAAAAwCwSBwAAAABmMVUJAAAA9s1gsHUEDwUqDgAAAADMouIAAAAA+8biaKsgcchBd27e0LKZH+r0kd/l4OCoOk88qQ59QuTomPE0H/1tt75fOFvXrkSokE9RPfPia3q0bmNJktFo1LDnWktpaSalt/GLVsvVzV0HdmzWoinj5OTikr6teoOm6vnGqNwfJCRJt2/e0OLpk3Ty8EE5ODqqQfMn1aVvaKbX+vC+X/TtF5/p6pUIFfYtqi59+qt6/bvXekCnFiZ9jWlGJScmqu+w91TviZYKP3tKK+bN0IXTJ+Tk7KwqNeupa78BylegoFXGiQfn5V1Yw3av1Fd9h+vktl9tHQ6yoVWjqnq//zMqW9Jb4VduaOSnq7Rh1xGz+41+pa26ta6nyh3fTW8rXaywJr/RRU1rVVCq0agNO49o8EffKDY+MTeHgCxq3aSaJgzsqrKlfBR++ZqGf7Jc63ccMrvfmJCO6vZ0Q1V8elh6W2D9KpowsIvKly6qG3diteC7HRo/Z01uhg/kOhKHHPTlR6NVsLC3xi1Ypds3rmvu+8O1dfU3Cur0vEm/qIhwffHBO+r15mhVrdtIh3Zv15eTRmvU51+rYBEfXQk/r9SUFH0UtlFOzs4ZPufiqeOq2/xJdR800lpDwz/M+WCUChXx0YeLV+v2jWuaOfYtbVoVplZdupv0i7wUrtnj31bfYWNUvX4jHdy1TZ9/MErvzw1TIW8ffbpyk0n/LyaP051bN1S7aXMlJSZq+rtD1LR1ew0YM1kJ8XH6Yso4LZw6QaHvfWjN4SKbAhrVVq+FU+Rb3t/WoSCbAvx89PXEvuo16kut33VEHZ6ooa8mvKRqXd5TRPSte+73RJ1KeuOFlrr8f32cnRy1Znqotuw9rnJPj5Snu6u++fBlvd//Gb0++RtrDAf3Ub60r8I+6q8XRnyudTsOqWNgbS2d9KqqPDNCEdE377lf83qPaEjvNiZ9Chfw1LdTB6jXyDn6futBVS5bXJvmvaXzl65qybrd1hgOkCuo6+SQ6Ii/dPrwQT3TO0Qurm7yLlZCrYJ7afu6lRn67t3yg8pVeUzVGzSTo6OTajUJVPlHa+iXjd9Lki6eOqaS/gGZJg2SdOHUMfmVr5yr48G9RUX8pZN/HFTnPiFydXOTT/GSevq53vp5zbcZ+u7etEHlqz6mmo3uXus6zYJU8dGa2vHD6gx9f/lpnf48uE8vDR0tR0cnXY++olJlK6httxfl5Owsr/wF1KzNMzp55HdrDBMPqEHPzuqzdJpWvz3Z1qHgAfR4qr52HTqjNdv/UGqqUd9uPqAdB06pT4cm99zHt3A+zRr5vGaGbTVpf7ppNbm5OGvIx8sVn5isqzdj9PyIeZoR9nMujwJZ8ULbxtp58KS+33pQqalGrfhpn7YfOKm+nR+/5z6+hfPrs1G99enSn0zaSxcvIk93Vzk4/G/WQFpamuISknIt/oddmsHBZq+HicUVhw0bNqhNmzYZ2sPCwhQcHJwjQdmjyxfPySNffhUo4p3eVszPXzeiIxUXc0ceXvnS269cPKcSZcqZ7F/Mz1+Xzp2WdLeikJyUqMlv9NO1qMsqVspf7Xq9onKPVJPRaNRfZ0/K1c1dm1culdFoVNXaDdS+92smn4HcE3HhnDzz5VfBIj7pbcVLl9X1TK51xMVzKuUfYLJ/idL+Cj972qQtLjZGy+fN0PMhQ+SVv4AkqVipMho0bopJvwM7t6pM+Uo5PSTkgj83btfeJd/JmJqqfmEzbB0OsqlKueI6ejrCpO34uSuqXqFkpv0NBoO+HNNbUxb/pMSkFHUKrJm+rU6VMjp06i+NfqWdurWuK0lateWgRs9m+kpeUCWgpI6cvmTSduxshKpX9Mu0v8Fg0MIJL2vygvVKTEpR55b/2/b78Yta8eM+fTMlVCkpqXJyctSMrzdp1eb9uTkEINdlKU2Kj49XRESEIiIiNHLkSF2+fDn9fUREhE6cOKEPPvggt2PN0xLj4+Tq6mbS5vzf94kJ8SbtCfFxcnEz7evi6pbez9nFVWUqVlHftydozPxv9Wj9xvrsvSG6diVCMbduqlTZinqs0RN6e9ZXev3DzxR1+S8t+nhsLo4O/+/u9XM3aXNxc5WU1WvtmqHfltXLVaRocdVpFpTpZ6alpem7hXP0x95dCn5l8IMOAVZwOzJaxtRUW4eBB+Tl4abYBNP1B3GJSfJ0d820/1svttLt2HjNX7Uzw7ZC+T3VulFVxScmqVrXMXrytU/UrHZFTRzQMVdih2W8PN0U94+1JvEJSfL0cMu0/4i+bXU7Jk7zvt2WYZuri5Ou3YrRc0NnKn/DV9Ws93g926qeendomiuxQ3cXR9vq9RDJUsUhJiZGTz/9tBISEiRJgYGB6dvS0tJkMBjUokWLe+1+X1FRUYqOjjZpq1q1araOZUsubu5KSjT9hZOcePd8ubl7mPZ1dVfyP/omJSak9+v4UqjJtqCO3bRn03od/W23mrXtrEEf/O+vl4V93PRM79f08ZuvKCEuTm4epp+FnOfq5qak/17bvyX994uF6z+u9d2+/7zWiSY/E2lpadq5cY3av9BXhkzuQx0fF6sFH4/XxdMn9OakmSpVNiBDHwA5Y2ivVhrWu1X6+31Hz8vDzcWkj4eri2LiEv65qxrXKK+ebRuqUa/M/5CWlJyiyGu39cEXP0iSzkdc00cLNmrq0GDWONjAW32e1lsvPZ3+fu/hs3L/x7V2d3NRTGzGa92kVkX1bN9EDZ4fk+mxXwsOkn8Jb63cdLfC8OuhM5qxdJNe7dpcC77bkYOjwL/BtWvXNGrUKO3du1eOjo5q37693nrrLTk5Zfya/vXXX2vBggWKioqSr6+vevbsqe7d766vNBqNql27dvp387/t2rVLHjn0/TBLiYOPj482bdqk+Ph4tWvXTmvXrjXZ7urqKm9v73vsfX9hYWGaMcO0jH/ixIlsHcuWipcpq9g7t3T7xnXlL1RYknQl/LwKevvK3dPLpG+JMmUVfuakSduV8PMq/d91C2sXz9FjjZ6QX0DF9O0pyclydnHVpXOntX/7JrXr+Ur6D0VKcrIMBgc5OrPW3RpKlCmn2Num1/ryxXMq5O0rjwzXupwunja91hEXz8u/wv+mG50/eezugugmgfqnqMt/6dN331Rhn6IaOW0+d1MCctlHCzfqo4Ub09+/92o71ahkOlWlctliOnD8YoZ9u7WpK59C+XRs1d0KsIuzk9xcnHR500fqPGS2jp27rI5BNWUwGJSWliZJcnR04LlVNjLpi3Wa9MW69Pdj+3dSjUfKmPR5pFwJHfjzfIZ9n3+qoXwL59PJdXdvVOHi5Cg3V2dFbZ+hDgOnya9YYbm6mP6fnJySoqSUlJwfCOze4MGDVbRoUe3YsUNXr17Va6+9pgULFqhv374m/TZt2qSPP/5Yc+fO1WOPPabff/9dL7/8sry9vdWqVSudPn1aycnJOnDggFxcXO7xaQ8my/WVIkWKqFSpUtq/f79Klixp8spu0iBJwcHBWrlypcnLHvmW8FO5KtW1ct50JcTF6dqVCG0MW6gGLZ7O0Ldu81Y6feSgDuzcotTUFB3YuUWnjxxU3eZPSro7h37l3Om6feOakpOTtGHZl0qIj1X1hs3kkS+/tq9bqc0rlyo1NUXXoyO1+stZqhfUWs7OufNDAlNFS/qpfNXqCpszTQlxsbp6JULrli1Q4yfbZujbILC1Th4+oN+2b1Zqaop+275ZJw8fUIPA1ul9Th/9Q6XLV5brP6Y0xd65rY+HD1TAI9U06P2pJA2ADSzdsFfNalVQ56BacnR0UOegWmpWq4KWrt+boW/oxK/l0/wNFW8xVMVbDNWgD8MUfuWGircYql8OndG3mw7I0cFBk1/vIhdnJ5UuXlhDe7XS1xsyHgvWt2TdL3q8diV1aVlXjo4O6tKyrh6vXUlL1v6SoW/I+wtVuHGIfJuFyrdZqAZM/EoXr1yXb7NQ/fL7Ka3bfkhNalbUC+0aSZKqV/RT/24ttHQdt2TOLWkGg81eD+LChQvau3evhg4dKnd3d/n5+SkkJERLlizJ0DcyMlL9+vVTjRo1ZDAYVLNmTdWvX1/79u2TJB0+fFiVKlXKtaRBysbi6KtXr2rmzJkKDw9Xyj8y50WLFlkcgK+vr3x9fS3eLy/q89Y4rfh8qsb0e1YGB4PqNW+t1sG9JElvPvukgkPeVN0nnlTRUmXUd+QEfb9wtr7+9AMV9immPsPfl2/J0pKk7oNG6LsvZmjSwBeVmJigMhUeUf+xU+WZL7888+XXK+9O0ppFc7Txm0VydnFRraZBeqb3a7Yc+kPn1ZHjtfSzjzWiT1c5GAxqENRGbbv1lnT32Qw9BgxV/eatVNyvjEJGfaBvv5ylhdMmqohvMb369ngVLVU6/VjRVy6pUJGMyfcvP63X9ehI/bZji/bvNL3ryj9v4wogd5y8EKlnh83R+6Ed9Nnb3XXxynV1GzFPp8OjJEnPtaqrT4d3k0/zN8we69qtWAX2m6IPX++i02velyQt+2GfRs36PlfHgKw5cf6KurzxqSYM6qrPR7+oi5evKnjoTJ26GClJ6tamgWa+01OFG4eYPdaWPX+q19tz9NZLbTV1WHdFXr+tTxZv1OxvtuT2MGADmU279/HxydL321OnTqlgwYIqWrRoeltAQIAiIiJ0+/Zt5c+fP7397ylJf7t27Zr27dunESNGSLqbOCQmJqpz5866dOmSAgICNGTIENWqVetBhmfCkPZ3vTSLXnzxRd26dUtNmzaV8z9uFxoaGnqPvSy38URUjh0LeVerSr7aduaqrcOAFTwe4K1XDf62DgNWMDvtvNzr97d1GLCC+D0z5VKzj63DgBUkHfzC1iHcV1x8xrUo1jJ/3twM0+5DQ0M1YMAAs/uuXr1aU6dO1datW9PbLl68qJYtW2rbtm0qVqxYpvtFR0frlVdeUaFChfT555/LyclJH3zwgW7fvq1BgwapQIECWrJkiWbMmKHvv/9efn6Z3x3MUhZXHH7//Xdt375d+fJx608AAAA83IKDg01uHCTdrThkhYeHh+LjTe+0+Pd7T0/PTPf5/fffNWjQINWpU0cTJ05MX0Q9f9mraAAAIABJREFUfPhwk34vvfSSVq5cqW3btqlHjx5ZisccixOH4sWLy8Hh4br1FAAAAPIuo2UTaHLUg0y7r1Chgm7evKmrV6+mrxk+c+aMihUrlukf6VesWKH3339fAwcOVJ8+ptW+qVOnqlWrVqpSpUp6W1JSklxdM799dHZkOQP4+5kN7du314gRI3Ts2DGTZzlERESYPwgAAAAASZK/v79q166tCRMmKCYmRuHh4Zo1a5a6dOmSoe/GjRv13nvv6dNPP82QNEjSyZMnNX78eEVHRyspKUkzZsxQTEyMWrZsmaFvdmW54hAYGGhyC7kff/wx/Xagf98v9tixYzkWGAAAAPBvN336dI0dO1ZBQUFycHBQhw4dFBJydxF+zZo1NWbMGLVv314zZsxQamqqBg4caLJ/u3btNHbsWE2cOFGTJk3SM888o/j4eFWrVk1ffvmlChbMubsyZjlx2Lx5c459KAAAAJBTbDdR6cF5e3tr+vTpmW47ePBg+r/XrFlz3+MULFhQEydOzNHY/inLiUPJkiUl6Z5TkpydnZWUlJSr944FAAAAYBsWL45u2bKljEajJGV4pLWDg4MaNWqkSZMmqXDhwjkXJQAAAHAPRnsuOdgRi2+PNGLECDVq1Ehr167VH3/8oXXr1unxxx9X//79tWrVKnl5eeV6mQQAAACAdVmcOCxcuFBTpkxRQECAXFxcVK5cOU2aNEnfffedKlasqHHjxmn79u25ESsAAAAAG7F4qtKNGzfk6Oho0mYwGHTt2jVJkru7e/pUJgAAACC3pdnwOQ4PE4srDk2bNtWQIUN04cIFJScn68KFCxo5cqSaNGmipKQkTZ8+XVWrVs2NWAEAAADYiMWJw+jRo5WamqpWrVqpevXqat26tVJTUzVmzBj99ttv2rp1q0aNGpUbsQIAAAAZGNNs93qYWDxVqWDBgpo/f74iIyN15coVlShRQj4+PpKkRo0aafXq1TkeJAAAAADbynLisH//ftWuXVv79u0zaT9//rzOnz8vSapbt26OBgcAAACY85D94d9mspw49OvXTwcOHNALL7yQ6XaDwaBjx47lWGAAAAAA8o4sJw4HDhyQJB0/fjzXggEAAACQN2U5cYiIiDDbp0SJEg8UDAAAAGCph22Rsq1kOXEIDAyUwWCQlPm9cpmqBAAAAPx7ZTlx8PLy0urVq9WyZUtt3ryZB20AAAAgT+B7qXVkOXFwcXHRggUL5OjoqFWrVmV6gUJDQ3M0OAAAAAB5Q5YTh1GjRmn58uUyGo369ddfM2z/exoTAAAAgH+fLCcObdq0UZs2bdS1a1ctXrw4N2MCAAAAssxo6wAeEg6W7rB8+fLciAMAAABAHpbligMAAACQF7E22josrjgAAAAAePhQcQAAAIBd4wFw1kHFAQAAAIBZJA4AAAAAzGKqEgAAAOwaT462DioOAAAAAMyi4gAAAAC7xgPgrIOKAwAAAACzSBwAAAAAmMVUJQAAANg11kZbBxUHAAAAAGZRcQAAAIBdM1JysAoqDgAAAADMouIAAAAAu0a9wTqoOAAAAAAwi8QBAAAAgFlMVQIAAIBdMzJXySqoOAAAAAAwi4oDAAAA7Bp3Y7UOKg4AAAAAzCJxAAAAAGAWU5UAAABg14w8ycEqqDgAAAAAMIuKAwAAAOwai6Otg4oDAAAAALOoOAAAAMCu8QA466DiAAAAAMAsEgcAAAAAZjFVCQAAAHaNxdHWQcUBAAAAgFlUHAAAAGDXeACcdVBxAAAAAGAWiQMAAAAAswxpaSwnAQAAgP36I+KWzT67eokCNvtsa8uzaxzm7b1g6xBgBX3rldHC/eG2DgNW0Ku2n9zr97d1GLCC+D0z9arB39ZhwApmp53Xm87lbB0GrGBy8llbh4A8IM8mDgAAAEBWGJlAYxWscQAAAABgFhUHAAAA2LVUo60jeDhQcQAAAABgFokDAAAAALOYqgQAAAC7xuJo66DiAAAAAMAsKg4AAACwa6lUHKyCigMAAAAAs0gcAAAAAJjFVCUAAADYNRZHWwcVBwAAAABmUXEAAACAXePJ0dZBxQEAAACAWSQOAAAAAMxiqhIAAADsGoujrYOKAwAAAACzqDgAAADArvHkaOug4gAAAADALCoOAAAAsGtGCg5WQcUBAAAAgFkkDgAAAADMYqoSAAAA7Foqc5WsgooDAAAAALOoOAAAAMCu8QA466DiAAAAAMAsEgcAAAAAZjFVCQAAAHYtlZlKVkHFAQAAAIBZVBwAAABg11gcbR1UHAAAAACYRcUBAAAAdo0HwFkHFQcAAAAAZpE4AAAAADCLqUoAAACwayyOtg4qDgAAAADMouIAAAAAu8YD4KyDigMAAAAAs0gcAAAAAJjFVCUAAADYNRZHWwcVBwAAAMBGrl27ppCQENWpU0f169fX+PHjlZKSkmnfbdu2qV27dqpRo4batGmjn3/+2WT73Llz1axZM9WoUUMvvPCCzp49m6OxkjgAAADArhmNaTZ7PajBgwfLw8NDO3bs0IoVK7R7924tWLAgQ7/z589rwIABGjRokH777TcNGDBAgwcPVmRkpCRp1apVWrx4sebPn689e/aoatWqGjhwoNJysBpD4gAAAADYwIULF7R3714NHTpU7u7u8vPzU0hIiJYsWZKh76pVq1SnTh21aNFCTk5Oeuqpp1S3bl2FhYVJkr755hs9//zzqlChglxdXTVkyBBFRERoz549ORYvaxwAAABg12x5O9aoqChFR0ebtPn4+MjX19fsvqdOnVLBggVVtGjR9LaAgABFRETo9u3byp8/f3r76dOnVbFiRZP9y5cvr+PHj6dv79evX/o2Z2dn+fv76/jx42rQoEG2xvZPJA4AAABANoWFhWnGjBkmbaGhoRowYIDZfWNjY+Xu7m7S9vf7uLg4k8Qhs75ubm6Ki4vL0vacQOIAAAAAZFNwcLACAwNN2nx8fLK0r4eHh+Lj403a/n7v6elp0u7u7q6EhASTtoSEhPR+5rbnBBIHAAAA2DVb3o7V19c3S9OSMlOhQgXdvHlTV69elbe3tyTpzJkzKlasmPLly2fSt2LFijp69KhJ2+nTp/Xoo4+mH+vUqVNq3ry5JCk5OVnnz5/PML3pQbA4GgAAALABf39/1a5dWxMmTFBMTIzCw8M1a9YsdenSJUPf9u3ba+/evVq/fr1SUlK0fv167d27V88884wkqXPnzvrqq690/PhxJSYmasqUKfL29ladOnVyLF4SBwAAANi11LQ0m70e1PTp05WSkqKgoCA9++yzatq0qUJCQiRJNWvW1Pfffy/p7qLpmTNn6vPPP1fdunU1a9YsffrppypbtqwkqUuXLurdu7f69++vBg0a6M8//9Tnn38uZ2fnB47xb0xVAgAAAGzE29tb06dPz3TbwYMHTd43bdpUTZs2zbSvwWBQnz591KdPnxyP8W9UHAAAAACYRcUBAAAAdi0nnuAM86g4AAAAADCLigMAAADsmi2fHP0wIXHIYWd/36ttYfN0K+qy8nn76onn+imgZuaP+TYaU7U9bL6O7tyk5KRElalSQy1fHCivgkVM+sXdvqklYwarVd/XVfqRx9Lbj+3+Wb9895Virl+TZ8FCqtO6s2oEtc3V8T3sTh/co5+XzdXNqCvKX8RXgc+/rAq17n19f/56ng7v+EkpSYkqU7WG2vQZLK9Cd69v7K0b2jBvqi4cOyQHB0c92qSFgrq/IgdHR22Y/4mO7NxkcryUpCT5P1pT3UZMUkpykravWKijuzYrOSFBpas8pid79Vf+Itm7jzQs16pRVb3f/xmVLemt8Cs3NPLTVdqw64jZ/Ua/0lbdWtdT5Y7vpreVLlZYk9/ooqa1KijVaNSGnUc0+KNvFBufmJtDQA7z8i6sYbtX6qu+w3Vy26+2DgcW8vIpoi6fjVfA4w1kTEnR/qWrtXbYBBlTUzP0rdOzswKHvqr8JYvqytGTWj9iks7u3CdJGn/jsElfg4ODXDzc9VWPQfo9bI1VxgLkFqYq5aAbVy5p9fSxatK5lwbO+U6NO/XU9zPG6871q5n23/3dUp0/fEAvjJ2h16YvlZOzizbOm2rS56+TR7VkzGDdjIowaY8OP6eN8z5Wm35vatDc79Sm35va8tVn+uuE6S8s5Jzrl//Syk/GqFmXFzVk3mo17dJTq6aPu+f13bVqic4d3q8+42dpwIxlcnJ21bq5U9K3r/r0fTm7uWvgzDD1HjdD544c0N71KyRJbV4arKFfrk1/dX79Pbl5eqrFC69JkrYum68Te3foueEfaNDs5SpcrKSWTnhLqSnJuX8ioAA/H309sa/Gfr5WRYPe1Ptz1+mrCS+phE+B++73RJ1KeuOFliZtzk6OWjM9VJeibqrc0yNV49lxCijlo/f7P5ObQ0AOC2hUW8N2r5RveX9bh4Js6rF0uhJj4zS2dANNa9RRFQMbqdmgjHenqdI2SJ1nvq81b03UKO8a2jplrvqu/VI+Fe/eEvPtQtVMXn+s3KDjG7fpjxXrrT2kh4oxLc1mr4cJiUMOOrLjR5Ws9Kgq1GksB0dHVa7/uPwqV9OhnzP/ZXF42wbVa/us8hfxlau7pwJfCNHZP/bpZtTl9OOtmzVRTbv2zrDvjSuXZDQalWY0Ki0tTTIYZHBwkKOzS24O8aH2x44f5Ve5mirVvXt9qzR4QqUfqa6DW9Zl2v/3nzeoYbvgu9fXw1Mte/XXmUP7dCMyQtevXNLFPw8p8Pl+cnZ1U6GiJdSkY3f99tPqDMeJu31Lq2dOVMueofIp5S9JOvrLFjXp2EM+pfzl6OSsJ557SXeuR+v8kYMZ9kfO6/FUfe06dEZrtv+h1FSjvt18QDsOnFKfDk3uuY9v4XyaNfJ5zQzbatL+dNNqcnNx1pCPlys+MVlXb8bo+RHzNCPs51weBXJKg56d1WfpNK1+e7KtQ0E2FQkoo/JPNNS64R8oOT5B18+F66cJM9Q4pGeGvjWfa6+Dy77XsfVblGY06sh3G3V2x17V6/1shr51enZWxaAmWtrz9UwrF4C9YapSDrp66YJ8/MqatBUpWUbRF89k6JsYF6s716+a9PcsUEhunl6KvnhWBX2Ly79aHVVpFCQHR0etmTnBZH//arVVPKCylo57XQYHB6UZjXqi28sqXq5S7gwOuvpXxuvrXbKMoi5kvL4JcTG6cz3apL9XgUJy9/RS1MVzMhgkd698ylfI2+RYt69GKSE2Rm6eXuntPy+bq+LlKurRJkHpbUajUc5ubunvDTLIYDDoWsRFBdSolyPjxb1VKVdcR0+bVgGPn7ui6hVKZtrfYDDoyzG9NWXxT0pMSlGnwJrp2+pUKaNDp/7S6FfaqVvrupKkVVsOavRspjTYiz83btfeJd/JmJqqfmEzbB0OsqFYlQqKvXZDty9HpbdFHjutQmVKyq1APiXcupPe7uDoqKTYOJP904xp8qlUzqTNLX8+tftwpFYOeFdx12/m7gAAK8lWxWHXrl169dVX1alTJ0VHR2vSpElKSUnJ6djsTlJCvJxd3UzanF1clZSYkEnfu790Mu8fL0nyKlhYDo6OmX5WakqyCvgUU9e3PtDr89eq05Bx2rVykc4d/i0nhoJMJMXHZX69EuIz6Xu37Z/9nVzclJwYn/nPyn/f///xbkZd1uEdm9Q8+CWTvpXrNdWu75bqRmSEUpKStG35AiUnJSo5KSn7A0SWeXm4KTbBdP1BXGKSPN1dM+3/1outdDs2XvNX7cywrVB+T7VuVFXxiUmq1nWMnnztEzWrXVETB3TMldiR825HRvPXZDvnms9TSbGmv8uT4+6+d/XyNGk/vOoH1e7RSeWa1pODo6Oqtmuh8oGN5Oxu+ju9SWgv3bjwlw4tz7wqjZxlz0+OticWVxzWrFmjiRMnqmvXrtq37+5CoC1btshgMGjYsGEWBxAVFaXo6GiTtqpVq1p8HFv49fuv9ev3X6e/Lx5QWcmJpl8mkpMS5eLmnmHfv78kJv8jqbjb38PsZ+9auVhOLi7yf7SWJCmgRn090rC5Dm1Zr7LV6lg8FmS067ul+mX10vT3Jco/opSkTK6ve8brlX59/9E/JSlBLm7uSktLy/iz8t+fBRf3//28HNr6g0pVqqqi/uVN+gZ1f0U/fz1Pi8e+LgdHR9V44in5+pU1qVQg5wzt1UrDerdKf7/v6Hl5uJlOC/RwdVFMXMY/EjSuUV492zZUo14fZHrspOQURV67rQ+++EGSdD7imj5asFFThwbr9cnf5OAoANxLUmy8XDz+8cccj7u/ixPvxJq0//7NWnn6FFbX2RPlXii/jv+wVb+HrcmQONTvE6yNY0zXLQL2zuLEYc6cOZo1a5Zq1KihpUuXysfHR59//rl69uyZrcQhLCxMM2aYlnZPnDhh8XFsoUH7bmrQvlv6+x3Lv1Tk+VMmfa5duqBiZStm2NfNM5+8Cnnr2v9Nb4q5eV0JMXfk/d957Pdz51qU3DzzmbQ5ODrK0YnZZzmlcYfn1bjD8+nvt4Z9oSv/uL5XL11Q8Uyur7tXPuUr7K2rf52X7/9d3/iYO/LxK6s0o1HxMbcVc+uGvAoUSj9WvsI+cvP435f/4/t2qMHTXTMc/86Na2rcsbtavThAkhQfc0e/rF6q4uUyxoIH99HCjfpo4cb09++92k41KvmZ9KlctpgOHL+YYd9uberKp1A+HVs1VpLk4uwkNxcnXd70kToPma1j5y6rY1BNGQyGu+uVJDk6OshgyMUBATBx5ehJeXoXlpevt2Ki7t7wougj5XUzPEIJt++Y9M1X1FsnNm7XrpmL0tsG7PxWh1f+kP7er251efkW0SEWRFtNKg+AswqLpypduXJFjz1295aghv/+z1amTBnFxcXdb7d7Cg4O1sqVK01e9qpK4yCFH/tDx/dskzE1Vcf3bFP4sT9UpXGLTPtXa/akdq9eqptRl5UUH6efv/pMfpWrq1DREmY/K6BmAx3fs03n/vhNaWlpCj/2h/7ctUVVGgXm9LDwX482aaGLfx7Sn79ulTE1VX/+ulUX/zykR5u2zLR/9WattOu7JboZdVmJ8XH6adEslX7k7vUtXLyUSlV6VJsWzVJifJxuRl3WzlVL9NgTrdP3j7tzS9cuXZRf5eoZjr13/Qqtnf2hkhLiFR9zRxu/nK5iZSuoREDlXBs//mfphr1qVquCOgfVkqOjgzoH1VKzWhW0dP3eDH1DJ34tn+ZvqHiLoSreYqgGfRim8Cs3VLzFUP1y6Iy+3XRAjg4Omvx6F7k4O6l08cIa2quVvt6Q8VgAcsfV0+d1duc+PTNllFy9PFXYv5RajgzV3i+XZ+hbrll9vbZpqQqVLiEnVxc1HfiifCqW02+L//f9pWzjuvrrwBElx2esQgL2zOI/T/v7+2vz5s1q0eJ/X4Z/+eUXlSlTJlsB+Pr6ytf333Hv+SIlSqvD4Pe0LWyefpj3sQoU8dUzA99V4eKlJEl/7tqsH7+cpsHzvpckNezQQ6mpqfr6/SFKSohT6UceU7vQt7P0WdWfaKOUpERtXjxLsTevK38RH7XsPeCez4zAg/MuWVpd3hijLcvmaf2cKcrvXVSdBo9Wkf9e3yM7N2vD/Kka+uVaSVKTTi/ImJqixWNfV1J8nMpUqaGOA0elH6/T4NH68ctPNWtQDxkcHFStaUs16dQjffut6CuSpHyFvfVPgd36acMXn2jmwLsVkXLV66rLkLG5NnaYOnkhUs8Om6P3Qzvos7e76+KV6+o2Yp5Oh99dWPlcq7r6dHg3+TR/w+yxrt2KVWC/Kfrw9S46veZ9SdKyH/Zp1Kzvc3UMAEwtCu6vjtPe08hT25RmNOq3r1bpp/GfSrr7bIYVIe/o4NerdWj5OvlWClDojm/l6uWhSwePavaT3RUTfS39WEXK+unWpSu2GgqQawxpaZat6vjll18UEhKioKAgbdq0SR07dtTatWs1ZcoUPf744zkW2Ly9F3LsWMi7+tYro4X7w20dBqygV20/udfvb+swYAXxe2bqVYO/rcOAFcxOO683ncuZ7wi7Nzn5rK1DuK+Pd2S8w6G1vNE0wGafbW0WT1Vq1KiRli1bpvz586t+/foyGo364osvcjRpAAAAAJC3ZGslbeXKlTV69OicjgUAAACwGIujrcPixOHw4cOaMmWKLl26++Ti/7d58+YcCwwAAABA3mFx4jBixAhVqFBB7dq1k4NDtp4fBwAAAOQYKg7WYXHicOnSJa1atUrOzs65EQ8AAACAPMjikkHdunV17Nix3IgFAAAAQB5lccVh8ODB6tmzp+rXr6/8+fObbJs4cWKOBQYAAABkBVOVrMPiisP48eNVpEgReXp65kY8AAAAAPIgiysOR48e1a5du0gcAAAAkCdQcbAOiysOZcqUUWxsbG7EAgAAACCPsrji0LFjR/Xp00edO3dWwYIFZTAY0rd16NAhR4MDAAAAkDdYnDgsWrRIkrR48WKTdoPBQOIAAAAAq2OqknVYnDhs2bIlN+IAAAAAkIdlOXHYv3+/ateurX379mW63WAwqE6dOjkWGAAAAJAVVBysI8uJQ79+/XTgwAG98MILmW43GAw8GA4AAAD4l8py4nDgwAFJ0vHjx3MtGAAAAMBSVBysw+Lbsd5rAXRgYOADBwMAAAAgb8pSxeHixYv67LPPJEmnT5/WiBEjTLbHxMQoISEh56MDAAAAkCdkKXEoXbq0ChUqpBs3bmS6vXDhwpo6dWqOBgYAAABkBVOVrCPLaxyGDRsmSfLz81NISMh9+65du1Zt27Z9sMgAAAAA5BkWr3EwlzRI0rvvvputYAAAAABLpRjTbPZ6mFicOGRFWtrDdRIBAACAf7tcSRwMBkNuHBYAAACAjWR5jQMAAACQF7E42jpypeIAAAAA4N+FigMAAADsGhUH62BxNAAAAACzLE4c5s2bp7i4uPv2ady4cbYDAgAAAJD3WJw4zJkzR66urvftM2PGjGwHBAAAAFgiNS3NZq+HicWJQ9OmTTV37lxFRUXlRjwAAAAA8iCLF0fv379f69at07Rp0zJsO3bsWI4EBQAAAGQVi6Otw+LE4cMPP8yNOAAAAADkYRYnDvXq1cuNOAAAAIBsoeJgHVlOHNq1a6c1a9YoMDBQBoMh0z6bN2/OscAAAAAA5B1ZThxefvllSdKAAQMy3Z6SkpIzEQEAAADIcyyqOEhSrVq1NGvWLEVGRspoNEqSkpOTde7cOXXt2jV3ogQAAADugalK1mHx7VjfeecdXbp0Sfny5VNqaqoqVqyoU6dOqUePHrkRHwAAAIA8wOLE4fDhw5o5c6ZCQkLk5eWld955Rx9//LF2796dG/EBAAAA95VqNNrs9TCxOHHw8PBQgQIFVLp0aZ08eVKS1KxZM509ezbHgwMAAACQN1icOJQuXVrbtm2Tp6enjEajwsPDFRkZyeJoAAAA4F/M4uc4vPzyyxo4cKDWrl2r4OBgPffcc3J0dFRQUFBuxAcAAADcF4ujrcPixCEwMFA//vijihQpopCQEPn7+ysmJkYdOnTIjfgAAAAA5AEWJw6SVLRo0fR/P/XUUzkWDAAAAGApKg7WYfEaBwAAAAAPn2xVHAAAAIC8IoWKg1VQcQAAAABgFokDAAAAALOYqgQAAAC7xuJo66DiAAAAAMAsKg4AAACwa1QcrIOKAwAAAACzSBwAAAAAmMVUJQAAANg1pipZBxUHAAAAAGZRcQAAAIBdo+JgHVQcAAAAAJhFxQEAAAB2jYqDdRjS0tI40wAAALBbLWbstNlnbwptYrPPtrY8W3H4fM8FW4cAK3ilfhl98PMpW4cBKxjevIJcavaxdRiwgqSDX+hN53K2DgNWMDn5rF41+Ns6DFjB7LTztg4BeUCeTRwAAACArEhjqpJVsDgaAAAAgFlUHAAAAGDXjFQcrIKKAwAAAACzSBwAAAAAmMVUJQAAANg1ni5gHVQcAAAAAJhFxQEAAAB2jduxWgcVBwAAAABmUXEAAACAXeN2rNZBxQEAAACAWSQOAAAAAMxiqhIAAADsWprR1hE8HKg4AAAAADCLigMAAADsGg+Asw4qDgAAAADMInEAAAAAYBZTlQAAAGDXeI6DdVBxAAAAAGAWFQcAAADYtTQqDlZB4gAAAADkQXFxcRo3bpy2bNmilJQUBQUFafTo0fL09My0/8aNGzVr1iyFh4erYMGC6tSpk0JCQuTgcHeSUZs2bRQREZH+XpJWrFihgICALMVD4gAAAAC79m+tOIwbN06XL1/Wxo0blZqaqsGDB2vy5MkaPXp0hr5HjhzRsGHD9Mknn+jxxx/XuXPn1K9fP3l4eKhPnz6KiYnRuXPntHnzZpUsWTJb8bDGAQAAAMhj4uPjtWbNGg0cOFAFCxZUkSJF9Oabb2rlypWKj4/P0P/SpUt67rnn1Lx5czk4OCggIEAtW7bUvn37JN1NLAoWLJjtpEGi4gAAAADYREJCgiIjIzPdFh8fr+TkZFWsWDG9LSAgQAkJCTp//rweeeQRk/6tWrVSq1atTI69detWtWvXTpJ0+PBhubu7q0ePHjp16pRKliypAQMGqHnz5lmOl8QBAAAAds1owydHR0VFKTo62qTNx8dHvr6+Zvc9dOiQevbsmem2QYMGSZI8PDzS29zd3SVJsbGx9z1uTEyMBg0aJDc3N/Xu3VuSZDAYVK1aNb3xxhsqUaKEfvjhBw0YMEBfffWVatSoYTZWicQBAAAAyLawsDDNmDHDpC00NFQDBgwwu2/9+vV14sSJTLf9+eefmjZtmuLj49MXQ/89RcnLy+uexzx79qwGDhyoIkWKaNGiRel9+/bta9Kvffv2Wrt2rTZu3EjiAAAAgIeDLRdHBwcHKzAw0KTNx8fngY9btmxZOTs76/Tp03rsscckSWfOnJGzs7P8/f0z3Wfbtm1644039Oyzz2rIkCFycvrfV/358+erSpUqati0phNjAAAgAElEQVSwYXpbUlKSXF1dsxwTiQMAAACQTb6+vlmalmQpd3d3tWnTRpMnT9a0adMkSZMnT1bbtm3l5uaWof/vv/+u/v3767333lOXLl0ybL98+bKWL1+uuXPnqnjx4vruu+908OBBjRkzJssxcVclAAAAIA8aPXq0/P391a5dO7Vu3VqlSpXSu+++m7796aef1uzZsyVJs2fPVkpKisaPH6+aNWumv/6eojRs2DA1a9ZMzz//vOrUqaNly5Zpzpw5KlOmTJbjoeIAAAAAu/ZvfY6Dl5eXxo0bp3HjxmW6fd26den//juBuBcXFxeNHDlSI0eOzHY8VBwAAAAAmEXFAQAAAHbN+C+tOOQ1VBwAAAAAmEXFAQAAAHYtzYYPgHuYUHEAAAAAYBaJAwAAAACzmKoEAAAAu5ZmtHUEDwcqDgAAAADMouIAAAAAu8btWK2DigMAAAAAs0gcAAAAAJjFVCUAAADYtTSmKlkFFQcAAAAAZlFxAAAAgF2j4mAdVBwAAAAAmEXiAAAAAMAspioBAADArhnTmKpkDVQcAAAAAJhFxQEAAAB2jcXR1kHFAQAAAIBZVBwAAABg16g4WAcVBwAAAABmkTgAAAAAMIupSgAAALBrRqYqWQUVBwAAAABmUXHIYWcP7dWOsHm6FXVZ+Yv4qtlz/VSuZoNM+xqNqdoRNl/Hdm1SclKiSj9SQ0EvDpRXwSKSpOiLZ7Tt6zmKPH9ajk5OKvNobT3x/Ctyz1dAknRy3w7tWb1Et6KuyM0rn6o2fVINnukugwP5oLWEH96n31YtUMzVK/Is7KO6nfrIr3q9TPsajanav2qhTv+6RSlJiSpeqboade8vjwKFJUlnf9uu7V9MlqOzS/o+ZWo0VLMXh0iSTv2ySYc3rlDszWsqVKK06nR6UcUqPJr7g0SmWjeppgkDu6psKR+FX76m4Z8s1/odh8zuNyako7o93VAVnx6W3hZYv4omDOyi8qWL6sadWC34bofGz1mTm+EjC7x8iqjLZ+MV8HgDGVNStH/paq0dNkHG1NQMfev07KzAoa8qf8miunL0pNaPmKSzO/dJksbfOGzS1+DgIBcPd33VY5B+D+M62yMv78Iatnulvuo7XCe3/WrrcCApjQfAWQXfMHPQjSuXtGb6WDXu3Euhn3+nhp16au3M8bpz/Wqm/fesXqoLRw6o+5gZennaUjm5uOin+VMlSclJiVo5+W2VqFBVr366TL0mzFVCzG1tnDtZkhR57qR++PxDNe7cW/1nr1THIeN1dMeP2r9xpdXG+7C7FXlJP8+ZqFrte6j71G9Us213/Tx3kmJvZH69D60P06U/D6jdiKkK/mChnFxctGvx9PTtV8+fUkD9QL0wbUX66++k4eKhPdq9dKbqdu6j7lOX6dEnO+unT0fr1pW/rDJWmCpf2ldhH/XXe7NWybtpf42dvVpLJ72qEj4F77tf83qPaEjvNiZthQt46tupAzRh7hp5N+2vdv2n6rXgIHV/umFuDgFZ0GPpdCXGxmls6Qaa1qijKgY2UrNBfTL0q9I2SJ1nvq81b03UKO8a2jplrvqu/VI+FctKkt4uVM3k9cfKDTq+cZv+WLHe2kNCDghoVFvDdq+Ub3l/W4cCWB2JQw76c+ePKlXpUZWv3VgOjo6qVP9xlapcTYe3Zv6fw+FtG1T36f+0d+dhUZV9H8C/w47gBoqjKeKGBjzI6hoKgysoIpgrLvVY+iblo/W8Gllauaf2mJpkWpaaCy4FYppYZMSDu6ZG4kYCyo7IsM0Mc79/+Do5ggyjDGB8P9fFdTHnvs99fjP3nDnnd+6zjEVTWzuYW1rBN+w13PztFO5m30FRXjZa23dGn+BJMDYxhWXTZnD1C0T6lftHrgpzs+DqF4jO7n0gMTKC7XP26OrZHxl/XKxyWVT7riX9iDZdndHRrS+MjI3RycsHUkcXpCQcqbJ+yq8/4B9Dx8DapjXMLJug99hXkX75DIpyMgEAuX9eRauOXauc98apeHTuNRAdXHvByMgYDu790KabC1ISjxrs/dHjTR7RHwnnUhAdfw4VFWrsPXoKx8+mYHrowMfOY2fTDBvfnYZ132j3mX1bW1hZmsPISKKZJoRASZnCYPGTbrZdOqKrb1/Ezl8OZWkZ8m+m4ejS9ej/2pRKdd3HB+HcrmgkH/oRQq3GpW+P4MYvJ9Fr2thKdb2mhMLR/wV8M2VOlSMX1LD1mRKKl79Zi+/eWVXfoRDVi6c6VSk/Px82Nja1FcszLzfjT7Rq30lrmm27jsi5db1S3fKSYsjzc9Gqw1/1rZq3hEUTa+Sm3UBXz/4IeWup1jwpp35BG4duAABHbx84evtoypSKcty8cBI9+slq8y1RNe7e/hMtn+uoNa1F2w7IT79Zqa6itBglBblo+ZyDZppls5Ywb2KN/IybsLa1Q96t6zAxM8fFH/ZBqNVo7+IFr9EvwdzKGkKthomZhVabEomEIw71xKnLc7h0LUNrWvKN23B17FBlfYlEgq+WvopVWw+hXKFC6OC/ys7/cQt7fziFPavDoVJVwMTEGOt3xuHAsTOGfAukg9SpG4rzCnDvTrZmWlbyNbTs+BwsmjdFWWGRZrqRsTEUxSVa8wu1QOvunbWmWTRripErI7D/9fdQkn/XsG+ADOL3I8dxcse3UFdU4JXd6+s7HHoIn+NQN/QecVCpVPj444/h6ekJmUyGtLQ0hIaGIjs7W/fMVcjOzsbly5e1/p5VytJSmJhr79yZmJlDWVZWqa6i7P5GxvTR+ubmUJSVak0TQuDXvV/ixrkk+Ia9Vrmt0hJE/2cRTMzM4Dk05GnfBtWQsry00s68iZk5lOWllev+f5+aPlLf2MwcqvIylMkLYduhMxw8+iNk0UYE/u9HuJd9G8e/vH9Uq6N7P1xL+hGZKRehrqjAn+eTcOePC6hQ8qh0fbC2skBJabnWtNIyBayaWFRZ/+3pI3BPXoLN+36uVGZuZoK8QjnG/3sDmvWdiQHTlmDs0F6YFuxTRUtUV8ybWkFRrL0uK0vuvza3ttKafvHAYXiGhaCzTy8YGRvDeeQgdJX1g6ml9vfhhfCpKPgzHReiYg0bPBnMvawcjhRRo6b3iMO6deuQlJSEtWvXYs6cObC1tYVUKsWSJUuwdu1avQPYvXs31q/XztqvXLmidzv14UT0TpyM2al5Le3SAyqF9s6ESlEOU0vLSvM+SBhUCu2kQlVeDjOLJprX5aXFOPL5amSnXsXYd1ajdQftEY38O2mI+eQDNGneEi++/RHMLJuADOPC93vw2+E9mtetHRyr7m/zyv39IMF4tL8rFOUwMbeEZbOWCHhrhWa6tY0FvEJewsEVb0JZVoLO3gNRJr+HX7evQ3mJHO1dvNDJe2Cl5ZNhzHs5EPP+Gah5ffLiDVhamGnVsbQwg7y48kGCFzwcMSXoBfSZ+H6Vbf/POH84tGuF/XH3RxiSLlzH+m/iMPNFP2z99pdafBekD0VxKcweSQRNm9xft8uLirWmn99zEFatbfBi5DJYtmyGPw7H4/zumEqJQ++Xx+HI+x8bNnCiRoq3Y60beicOMTEx2LlzJ9q0aQOJRIImTZpg2bJlGDx4sO6ZqzBu3DjIZM/m6TW9gyagd9AEzeuEqC+R/edVrTp5t/9EGwfHSvNaWDWFdctWyEv/6/Sm4rv5KCsuQqv2DgCAu1m3cWD1AjS1bY1J76/X3E3pgRsXTuLQp0vxD98A+Iz9J4yMjWv5HdLDeg4fi57D/zpn+cy3XyMvTfs0tLt30qq8TsHcyhpNWtii4M4tzelKJYUFKC8uQsvnOiI//SZunPoZnsFTIZHcP9ddrVJCIpHAyNgUJYUFeM7JA05+IzVtHlzxJjq69zPAO6VHrfgiFiu++Oso8QezQuD2vPZpas93boezv6dWmndiQF/Y2TRFSuxKAICZiTEszE2RfXw9gt9Yiw5SG5ibaf8UK1UqKFSq2n8jVGOZl1Ng1coG1natIM++f8ODNs93xd202yi7V6RVt2mbVrhy5Dh+3fC1ZtrrCftwcf9hzesO3q6wtrPFBV4QTUTPML1PVSopKdFc1/Dg1lcWFhYwesJbgNrZ2cHZ2Vnr71nl1N8facm/4cqJn6GuqMCVEz8jLfk3OPUfVGV9Z58hSIr+BoU5d6AoLUH8jo1o38MVLdq0Q1lxEaKW/y/adXNC6L+XVUoabl9LRsza9+E7cSYGTniVSUM96NLHD5kpF3Hz9C9QV1Tg5ulfkJlyEV16V50Id+s3CBcO7UZRbiaUZSU4GbUJ0m4uaNa6LcytmiI5/iAu/bAP6ooKyPOzcWrfF+jadxCMTU2RefUiDq95G/K8bKiUClw+9h0Ks9LRtY9/Hb9rAoAdsYkY6NkdYwZ7w9jYCGMGe2OgZ3fsOJhYqe5ri7+CTf/XYDcgHHYDwvH6su24lZkPuwHhSDx/FbHHL+AFd0dMHnk/CXR17IBZEwbhm1je4rE+5V5LxY2EUxi1+l2YW1vBxqE9BkeE4+SXUZXqdh7QG/8T9w1a2reDibkZfN54Ca0dO+P0tr/uctepvzfSz16CsrTyqBQRPT2hrqi3v8ZE7xEHNzc3rF+/HnPmzNEcGd22bRv+8Y9/1HpwzxqbdvYYNXsRftmzGT9sWYNmtnYY+cZ7aNm2PQAgOfEY4r5ci9c/jwYA9AkOg7qiArsXvwlFWQk6PN8TI8LfAQBcPn4ERXnZuHLiZ6ScPK61nNc/j8bJmJ2oqFDhp+2f4qftn2rKnuvuUumiajKMFtIO8J/5Dk4f2IqEbZ/A2rY1/F59G83bPAcAuH7iJyR+swGT1+4FALgFToC6ogKHVs2DsqwUbbu7wveV+QAAq5atMHjWQpz+9itc+H43jE3N0MlrALxCXgIAdPYagMLMdBxc+RZU5aWw7dAFw/61FJbNqr/9JxnGldRMjJm7Dktnv4jPFr6EW3dyMe7fG3D1VhYAYMLwPtiwYAps+le+JulRP574HVPf2YR5/xyBj/93ErLy7+E/244gcs+Phn4bpMPX42Zh9NpFiLj6M4RajdPbD+DoknUA7j+bYe9rC3Bu53e4EBULu+5dEP7LPphbN0HGucuIHDIJ8pw8TVu2nTqgMCOzvt4KEVGtkAg9n5iRlpaGqVOnQqVSIS8vDx07dkRxcTG+/PJLdO7cWXcDNfTZiT9rrS1quGb07ojlP13VXZGeefP9usHMvfI98OnvR3HuC7xlWnvbA2q4VilvYKbEob7DoDoQKVLrO4RqOUzfXW/LTt08rt6WXdf0HnHo0KEDYmNjER8fj4yMDEilUvj6+sLa2toQ8RERERERVauxnTJUX/S+MEGhUCAyMhIuLi6YPn068vLysHnzZqjVakPER0REREREDYDeicOyZctw/PhxGP//xbjOzs5ISEjAqlV8iiIRERER1T1eHF039E4cfvjhB2zZsgXt2rUDAHh5eSEyMhLR0dG1HhwRERERETUMeicO5eXlaNJE+yFj1tbWUPGe40REREREf1t6Jw5eXl5YtmwZFAoFgPuJxMqVK+Hh4VHrwRERERER6SIqKurtrzHR+65K77zzDqZPnw4PDw+0bNkSBQUF6NSpEyIjIw0RHxERERERNQBPdDvWQ4cO4cyZM8jNzYVUKoWrqytMTPRuioiIiIjoqTW2i5TryxPt7VdUVMDe3h7t299/InJ2djYAaC6YJiIiIiKivxe9E4fvv/8e7733HuRyuWaaEAISiQTJycm1GhwRERERkS4ccagbeicO69atw6RJkzB69GienkRERERE1Ejoved/584dhIeHM2kgIiIiImpE9N77d3Z2xrVr19CjRw9DxENEREREpBeeqlQ39E4cPDw8MG3aNAwbNgytWrXSKgsPD6+1wIiIiIiIqOHQO3E4d+4cunXrhuvXr+P69eua6RKJpFYDIyIiIiKqCY441A29E4dt27YZIg4iIiIiImrAnugK56SkJGRlZUEIAQBQKpW4cuUKFixYUKvBERERERFRw6B34rB48WLs2rULVlZWAO4/DK64uBg+Pj61HhwRERERkS48ValuPNED4LZv347S0lJER0dj6dKlWLFiBUpKSgwRHxERERERNQB6Jw6lpaVwc3NDTk4OLl++DIlEgvDwcAQEBBgiPiIiIiKiaqk54lAnjPSdQSqVIi8vD61bt0ZmZiaUSiUsLCwgl8sNER8RERERETUAeo84DBw4ENOmTcNXX30Fb29vREREwNzcHA4ODgYIj4iIiIioerzGoW7oPeIwd+5cjBo1CqampnjvvfdQUFCAa9euYfHixYaIj4iIiIiIGgC9Rxzi4uIwffp0AEDTpk2xefNmAMDu3bvh5ORUu9EREREREVGDUKPEobS0FAUFBQCAiIgIuLm5aZ7hAABFRUVYvnw5xo0bZ5goiYiIiIgeg6cq1Y0aJQ5yuRyBgYEoKysDAMhkMk3ZgwRi8ODBBgiPiIiIiIgagholDq1bt0ZcXBxKS0sxcuRIHDx4UKvc3NwcrVq1MkiARERERETVERUccagLNb7GwdbWFgBw5swZGBn9dU319evXYWKi96USRERERET0DNH7rkrnz59HcHAwAGDXrl0IDAyEv78/4uLiaj04IiIiIiJqGPQeKli9ejV8fX0hhMBnn32G5cuXo0WLFli9ejUGDRpkiBiJiIiIiB6LF0fXDb1HHG7cuIHZs2fjxo0byM3NRUBAAHx9fZGenm6I+IiIiIiIqAHQe8TB2NgYxcXFOH78ONzc3GBmZoaMjAxYW1sbIj4iIiIiompxxKFu6J04DBo0CGFhYcjIyMCCBQtw7do1zJo1CyNGjDBEfERERERE1ADonTi8++67+Pbbb2FpaYmAgACkpqZi/PjxmDJliiHiIyIiIiKqFkcc6sYTnaoUGhqqee3g4ICXXnqpVoMiIiIiIqKGpcaJw8iRIxETEwOZTAaJRFJlnWPHjtVaYERERERE1HDUOHF49dVXAQCvv/56leWPSyaIiIiIiAxJqNX1HUKjUOPEYc+ePYiKiqq2zoMHwxERERER0d9LjROH3r17AwDS09MRFxeH0NBQ2NvbIzMzE3v27MGwYcMMFiQRERER0ePw4ui6UePEITw8HAAwceJEbNq0CR4eHpqyoUOH4t1336396IiIiIiIqEHQ+8nRycnJ6Nmzp9a07t27IzU1tbZiIiIiIiKiBkbvxKFLly7YunWr1rTIyEj06NGjtmIiIiIiIqoxoa6ot7/GRO/nOERERGDmzJnYtm0bpFIpbt++DbVajS1bthgiPiIiIiIiagD0Thw8PDzwww8/ID4+HllZWZBKpZDJZGjatKkh4iMiIiIiqpa6kR35ry96Jw4A0KJFC956lYiIiIioEXmixIGIiIiIqKEQFRxxqAt6XxxNRERERESNDxMHIiIiIiLSiacqEREREdEzrbHdFrW+cMSBiIiIiIh04ogDERERET3TOOJQNzjiQEREREREOjFxICIiIiIinXiqEhERERE903iqUt3giAMREREREenEEQciIiIieqZxxKFuSIQQor6DICIiIiJ6UmbuL9fbshXnvqi3Zdc1Jg4NRHZ2Nnbv3o1x48bBzs6uvsMhA2JfNx7s68aDfd14sK+pMeM1Dg1ETk4O1q9fj5ycnPoOhQyMfd14sK8bD/Z148G+psaMiQMREREREenExIGIiIiIiHRi4kBERERERDoZL1q0aFF9B0H3WVlZoVevXrCysqrvUMjA2NeNB/u68WBfNx7sa2qseFclIiIiIiLSiacqERERERGRTkwciIiIiIhIJyYORERERESkExMHIiIiIiLSiYkDERERERHpxMSBiIiIiIh0YuJAREREREQ6MXEgImogsrOzUVJSUt9h0BP6888/6zsEqkVcH4kqY+Kgw7p16zB58uQ6X+78+fMxf/78GtWdPHky1q1bZ+CIqDbp02cymQz79+83cESky6FDh9C3b194enqie/fuSE9PBwC4u7vj9OnTOudPT0/Xmu9Rubm5GDp0KPLz82s1bqqZ6OhoBAYGPvH8K1aswMaNGzWva/q9oLrxYFte037m+khUNZP6DoCI6FkQFRWFwMBATJs2Df7+/prp586dq5X2y8rKeHSzHgUFBSEoKOiJ5y8oKNB6XVvfC6pdNe1nro9EVeOIwyPOnj2L0NBQuLm5Yfz48VpHBxMTEzFmzBh4eXkhMDAQ0dHRmrL58+cjIiICU6ZMgZubG4YPH464uLgaL/fYsWMIDAyEm5sbZsyYUWkjFBsbi5EjR8LT0xMhISFISEiosh25XI4FCxZgyJAhcHNzg4+PDyIjIzVteHp6ory8XFP/8OHD8PPzgxCixrH+Hf34448YP348+vbti549eyIsLAypqanYv38/QkJC8PLLL8PLywsxMTGYPHky5s+fDz8/P/j6+kIul+tsPyoqCv7+/nB3d8e8efNQWlqqKRNC4Ouvv8bQoUPh5eWFiRMn4tKlS1W2k5WVhX/961+QyWTo2bMn/P39sXfvXgDApk2bMHToUK36W7ZswaRJk57ikyEAGDNmDJKSkrBr1y4MHjxYq6x79+44ceIEgPs7j3PmzIGnpyf8/f2xbds2ODk5af2OxMTEYPjw4XBzc8O0adOQlZWFiooKjBgxAgAwYsQIHDp0qO7eXCNT3bouk8kAACdOnMDAgQPx5ptvwsvLC5s2baq2zQ0bNiAmJgYxMTGandKHvxcymQxffvklgoKC0LNnT0yYMAGXL1/GK6+8And3dwQEBOC3337TtFfdtoZq5nHb8of7WaVSYdGiRejfvz969+6NiRMn4syZM1WujwqFAitWrMDw4cPh7u6Ovn374sMPP9RsOydPnozVq1dj0qRJcHd3x/Dhw7XW47S0NMycOROenp7o27cvFi1aBIVCAQC4desWZs6cid69e8PPzw8ff/yxpoyowRGkkZ+fL7y8vMRnn30mFAqFOH36tPDw8BBhYWEiOTlZuLq6iiNHjgiVSiXOnDkjevfuLY4fPy6EEGLevHmiR48eIjY2ViiVSnHgwAHh7Owsrl27pnO5169fF87OzuK7774TSqVSHD16VDz//PNi3rx5Qggh4uPjhaenpzh58qRQqVTixx9/FG5ubiIlJUUIIURYWJj45JNPhBBCLFy4UEydOlUUFhYKtVotDh8+LBwdHUVqaqooLy8X3t7eIjY2VrPsGTNmiLVr19b2R/lMuXPnjnBxcRHHjh0TQtz/HkycOFG89dZbYt++fcLR0VHs379flJeXi9LSUhEWFiZ8fHxEZmamKCws1Nl+YmKicHFxEYmJiUKpVIodO3YIR0dHTZ9t375d+Pr6iuTkZKFQKERUVJTw8vISOTk5Qggh/Pz8xL59+4QQQkyfPl289dZboqSkRKhUKvHFF18IV1dXIZfLRVZWlnj++efF+fPnNcseMWKE2Lt3b21/ZI3Sg/UsLS1NODo6irS0NCGEEI6OjiIpKUkIIcTLL78s/vnPf4qCggKRl5cnXnrpJU3dB/PNmjVL3Lt3T9y9e1cEBweLd999VwghKrVLtU/Xuu7n5yeEECIpKUk4OjqK9evXC4VCIYqKinS2PW/ePM1vthDa3ws/Pz8xfPhwcefOHVFUVCSGDBki3N3dxdmzZ0V5ebmYPXu2mDx5shBC6NzWkG7Vbcsf7ue9e/eKoKAgUVhYKFQqlVizZo0YOXKkEKLy+rhp0yYRGBgosrKyhBBCnD17Vjg5OYnExEQhxP3fh169eonLly+L8vJysWbNGuHp6SnKysqEUqkUgwcPFgsWLBByuVzk5uaKUaNGiVWrVoni4mLh5+cnVq1aJcrKysTt27fFmDFjxKpVq+rhkyPSjSMOD4mPj4elpSVeeeUVmJqawtPTE6GhoQCAXbt2wd/fH0OGDIGxsTE8PDwwduxY7NixQzO/r68vAgICYGJiguDgYLi4uNToyOGhQ4fg4uKCoKAgmJiYYNCgQfDz89OUb9++HRMmTIC3tzeMjY3h5+cHmUyGXbt2VWrr9ddfx3/+8x9YW1sjMzMT5ubmAO5f5GVmZoYRI0bgu+++AwDk5eUhISEBo0ePfqrP7VlnY2OD2NhYyGQyyOVyZGZmomXLlsjKygIAmJqaYtSoUTAzM4OFhQUAYMCAAWjTpg2aNWums/3o6GgMGTIEffv2hYmJCSZOnAgnJydN+Y4dOzBjxgz06NEDpqamGDNmDLp06VLlUcbFixdj4cKFMDU1xe3bt2FlZYWysjIUFhbCzs4OPj4+mv69fPky0tPTMWzYsNr4mEiHrKwsJCQkICIiAi1atICNjQ0iIiIq1Zs5cyaaNm2K5s2bw8fHB7du3aqHaBsnXev6o8aMGQNTU1NYW1s/9bJDQ0MhlUphbW0NV1dX9O7dG+7u7jAzM8MLL7yAjIwMADXb1lD1qtuWP8zCwgLp6enYu3cvbt68idmzZz92dGfs2LHYunUrWrdujezsbJSVlcHKykrruzN06FA4OTnBzMwMo0ePRlFREfLy8nD27FlkZGQgIiICVlZWsLW1xfr16/Hiiy8iPj4eCoUCc+fOhbm5Odq2bYvZs2ezv6nB4jUOD8nKykLbtm0hkUg00+zt7ZGcnIyMjAwkJSXBy8tLU1ZRUQF7e3vNawcHB6322rZti5ycnBott127dlrT7O3tNacrZWRk4OTJk9i5c6fWsvv06VOprby8PCxZsgS///472rdvDxcXFwCAWq0GAISEhGDcuHHIy8tDdHQ0PDw80KFDB50x/p2Zmpri4MGD2LVrFyQSCRwdHSGXy2Ficn/1aN26NYyMtHNsOzu7GreflZUFZ2dnrWkPf+YZGRlYsWIFVq1apZmmUqk0ffewtLQ0rFy5EqmpqXBwcEDHjh0BaPfvwoUL8fbbb+PAgQMYNmwYrKysahwrPbk7d+4AANq3b6+ZVtW61aJFC83/pqamqKioMHxwBMhcZG8AAAbCSURBVED3uv4ofdZzXR7ud2NjYzRv3lzz2sjISHPKS022NVS96rblDwsMDIRSqURUVBTWrFkDW1tbzJw5ExMmTKjUZmlpKT744AOcOnUKUqkUTk5OEEJofnuB+9uKBx58p9RqNXJyctCyZUtYWlpqyh/8Thw5cgT5+fnw9vbWlAkhoFQqkZeXB1tb26f8NIhqFxOHh0ilUmRkZECtVmt2FDMzMzVlo0ePxgcffKCpn52drXVtwKNHrdLT0zXnUupabnx8vNa0h0cLpFIpgoOD8eqrr2rKb9++rTn6/bDZs2dDJpNhy5YtMDExQUFBAfbs2aMpd3FxQdeuXXHkyBHExsbWyx2jGprvv/8e27dvx86dOzU74h9++CFSUlIAQGvj80BV0x5HKpUiLS1Na1pmZia6deumKX/jjTe07vRx69YtrR0NAFAqlZgxYwbmzp2LiRMnQiKR4NKlS1pHyGQyGRYuXIhff/0V33//PdauXVvjOOnpPEj+MzIy0KlTJ83/1HDoWtcfpc96rktN26rJtoaqV922/GE3b96Es7MzgoODUVZWhsOHD2PevHnw8vLS2skHgAULFqB58+ZISEiAubk51Gq11s6+rngKCgpQWlqqaff06dO4dOkSpFIp7O3tcfjwYU19uVyOvLw82NjYPOlHQGQwPFXpITKZDEIIrFu3DgqFApcuXUJUVBSA+0PWBw8eREJCAtRqNVJTUxEWFoYvvvhCM//Ro0eRmJgIlUqFvXv3IiUlRXOBVXWCgoKQkpKCPXv2QKVSISEhAUePHtWUjx07Fl9//bXm4rmLFy8iJCQEBw8erNRWUVERLCwsYGxsjPz8fCxevBjA/Z3OB0JCQrBnzx6kpqZiyJAhT/Zh/Y0UFRXByMgIFhYWEELg+PHj+Pbbb7U+s6cRGhqKuLg4/PTTT1CpVDhw4AAuXLigKR87diw2btyI69evAwB++eUXBAYG4tSpU1rtKJVKlJWVwcLCAhKJBLdv38ZHH32kKQPuH1ENCgrC2rVrYW1trXXUkgzLzs4Ofn5++Oijj1BYWIjCwkKsXLmyxvM/OFBQk4vt6ckYcl03MzNDUVHRU7dTk20NVa+6bfnDfvrpJ4SHhyM9PR0WFhZo0aIFTExM0LRp00rro1wuh7m5OYyMjCCXy7Fy5UrI5fIafXdcXV3h4OCAFStWoLS0FLm5uVi2bBny8/Ph5+eH4uJibN68GQqFAvfu3cO8efMwZ86cWk1ciWoLE4eHNGvWDFu2bMF///tf9OrVC++8847mLjU9e/bEmjVrsGbNGnh7eyMsLAwymQxvvvmmZn4vLy98/vnn6NWrF7755hts2rSpRqcBdejQAZGRkdixYwc8PT3x6aefat25ZdiwYZg7dy4iIiLg4eGB2bNnY9q0aVWOFixbtgyHDh2Ch4cHQkJC0KZNGzg5OWkdURs5ciSuXbuGgICASkdVGqPRo0ejX79+CAwMRJ8+fbBx40ZMnToVN2/erJUdCk9PT6xcuRLLly+Hl5cXjhw5gv79+2vKp02bhuDgYLz22mtwd3fHkiVL8N5772nd8hMAmjRpgqVLl2LDhg1wd3fHlClT0L9/f7Rq1Uqrf0NCQvD7778jJCTkqWMn/SxZsgQSiQS+vr4YPXq05loWU1NTnfO2atUKgwcPxrhx47ROS6TaY8h1PSAgAGfPnoWvr+9TtVOTbQ1Vr7pt+cOmTJkCX19fjB8/Hm5ubvjoo4/w8ccfQyqVVlofFyxYgD/++AO9evXCsGHDIJfL4ePj89jRqoeZmpoiMjISWVlZ8PX1xahRo+Dt7Y033ngD1tbW2Lp1K06cOIEBAwZg0KBBMDIy0nomCFFDIhEc/6wVDx7Wtnz58nqORLeKigq88MILiIyMRM+ePes7HKpld+/ehY+PD+Li4tCmTZv6DqdR+fXXX+Hp6ak5jfDKlSsIDg7G+fPnNUcwiYiInlUccWhkrl69ig0bNkAqlTJp+JtRKBS4evUqVq9ejYEDBzJpqAcPnh6sUqkgl8uxceNG9OvXj0kDERH9LfDiaAPLy8vDoEGDqq1Tl08YnTFjBgDgk08+qbNl/p399ttvmDp16mPL27Vrh9jY2DqJRaFQYPz48Wjbtq3moX9Ut1avXo3FixejT58+MDIygo+Pj17XOVDDNGvWLCQmJj62/P3333+qp04TET0reKoSERERERHpxFOViIiIiIhIJyYORERERESkExMHIiIiIiLSiYkDERERERHpxMSBiIiIiIh0YuJAREREREQ6MXEgIiIiIiKdmDgQEREREZFOTByIiIiIiEin/wNHsQVrPpLudAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x144304ec2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.title('Pearson Correlation of Features', y=1.05, size=20, color='R')\n",
    "sns.heatmap(df.corr(),linewidths=0.1,vmax=1.0, \n",
    "            square=True, cmap=plt.cm.RdBu_r, linecolor='white', annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df\n",
    "temp = df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "## 5. Exploratory data analysis\n",
    "\n",
    "<br>Data cleaning step is completed now we can start analyzing the data.<br>Exploratory analysis is the step where we start delving deeper into the data set beyond the outliers and errors. This is the stage where we plot all the data in as many ways as possible.<br><br>\n",
    "# 5.1 Flights Frequency\n",
    "## 5.1.1 Busiest airport in terms of flights departure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "origin\n",
       "EWR    120835\n",
       "JFK    111279\n",
       "LGA    104662\n",
       "Name: origin, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buzAir = df.groupby(['origin'])['origin'].count()\n",
    "buzAir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1442d5e7c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[20, 20])\n",
    "ax = fig.add_subplot(221)\n",
    "ax.pie(buzAir, labeldistance=1.1, labels=buzAir.index, startangle=95, autopct='%1.1f%%', shadow=True)\n",
    "ax.set_title(\"Flights frequency %\", fontsize=20);\n",
    "plt.gcf().gca().add_artist(plt.Circle( (0,0), 0.2, color='white'))\n",
    "\n",
    "ax2 = fig.add_subplot(222)\n",
    "ax2.set_title(\"Flights frequency\", fontsize=20);\n",
    "ax2.set_facecolor('white')\n",
    "buzAir.plot(kind='bar', ax=ax2,  legend=False, fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EWR is the busiest airport with 35.9% of total flights departed from this airport, followed by JFK(33%) and LGA(31.1%)<br><br> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1.2 Flights frequency across months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "1     27004\n",
       "2     24951\n",
       "3     28834\n",
       "4     28330\n",
       "5     28796\n",
       "6     28243\n",
       "7     29425\n",
       "8     29327\n",
       "9     27574\n",
       "10    28889\n",
       "11    27268\n",
       "12    28135\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buzMonth = df.groupby(['month'])['month'].count()\n",
    "buzMonth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1441b0027f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[18, 10])\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_facecolor('white')\n",
    "ax.set_title(\"Flights frequency across months\", fontsize=20);\n",
    "buzMonth.plot(ax=ax,  legend=False, fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Flights frequency is lowest in February which is below 25000.<br><br> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.2 Departure and Arrival status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dep_status\n",
      "Canceled      8255\n",
      "Late        128432\n",
      "OnTime      200089\n",
      "Name: dep_status, dtype: int64\n",
      "\n",
      "arr_status\n",
      "Canceled      8255\n",
      "Late        134032\n",
      "OnTime      194489\n",
      "Name: arr_status, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "depStatus = df.groupby('dep_status')['dep_status'].count()\n",
    "arrStatus = df.groupby('arr_status')['arr_status'].count()\n",
    "print(depStatus)\n",
    "print()\n",
    "print(arrStatus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1441af8ca20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[20, 20])\n",
    "ax = fig.add_subplot(221)\n",
    "ax.pie(depStatus, labeldistance=1.1, labels=depStatus.index, startangle=95, autopct='%1.1f%%', shadow=True, colors=['red','yellow','green'])\n",
    "ax.set_title(\"Departure Status\", fontsize=20);\n",
    "plt.gcf().gca().add_artist(plt.Circle( (0,0), 0.2, color='white'))\n",
    "\n",
    "ax2 = fig.add_subplot(222)\n",
    "ax2.pie(arrStatus, labeldistance=1.1, labels=arrStatus.index, startangle=95, autopct='%1.1f%%', shadow=True, colors=['red','yellow','green'])\n",
    "ax2.set_title(\"Arrival Status\", fontsize=20);\n",
    "plt.gcf().gca().add_artist(plt.Circle( (0,0), 0.2, color='white'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Out of 336776 flights,  200089(59.1%) flights in 2013 were departed on time, 128432(38.1%) were delayed and just 8255(2.5%) of flights were canceled.<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.3 Delayed Flights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3.1 Best and worst airport in terms of departure and arrival delay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>dep_status</th>\n",
       "      <th>Late</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EWR</th>\n",
       "      <td>52711.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JFK</th>\n",
       "      <td>42031.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGA</th>\n",
       "      <td>33690.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "dep_status     Late\n",
       "origin             \n",
       "EWR         52711.0\n",
       "JFK         42031.0\n",
       "LGA         33690.0"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depAir = df.loc[df['dep_status'] == 'Late'].groupby(['origin','dep_status'])['dep_status'].count().unstack('dep_status')\n",
    "depAir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>arr_status</th>\n",
       "      <th>Late</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EWR</th>\n",
       "      <td>50531.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JFK</th>\n",
       "      <td>43182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGA</th>\n",
       "      <td>40319.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "arr_status     Late\n",
       "origin             \n",
       "EWR         50531.0\n",
       "JFK         43182.0\n",
       "LGA         40319.0"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrAir = df.loc[df['arr_status'] == 'Late'].groupby(['origin','arr_status'])['arr_status'].count().unstack('arr_status')\n",
    "arrAir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x144374a58d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[16, 12])\n",
    "ax = fig.add_subplot(221)\n",
    "depAir.plot(kind='bar', ax=ax, title =\"Departure delay w.r.t Airports\",  legend=False, fontsize=12)\n",
    "ax2 = fig.add_subplot(222)\n",
    "ax.set_facecolor('white')\n",
    "ax2.set_facecolor('white')\n",
    "arrAir.plot(kind='bar', ax=ax2, title =\"Arrival delay w.r.t Airports\",  legend=False, fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LGA airport is best in terms of departure and arrival delay, where as EWR is worst with 5000+ delayed flights. <br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3.2 Best and worst airport w.r.t departure delay %"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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>total</th>\n",
       "      <th>late</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EWR</th>\n",
       "      <td>120835</td>\n",
       "      <td>52711.0</td>\n",
       "      <td>43.622295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JFK</th>\n",
       "      <td>111279</td>\n",
       "      <td>42031.0</td>\n",
       "      <td>37.770828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGA</th>\n",
       "      <td>104662</td>\n",
       "      <td>33690.0</td>\n",
       "      <td>32.189333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         total     late  percentage\n",
       "origin                             \n",
       "EWR     120835  52711.0   43.622295\n",
       "JFK     111279  42031.0   37.770828\n",
       "LGA     104662  33690.0   32.189333"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depAirPer = df.groupby('origin')['origin'].count().reset_index(name='total')\n",
    "delayedFlights = df.loc[df['dep_status'] == 'Late'].groupby(['origin','dep_status'])['dep_status'].count().unstack('dep_status')\n",
    "depAirPer['late'] = delayedFlights['Late'].values\n",
    "depAirPer['percentage'] = (depAirPer['late']/depAirPer['total'])*100\n",
    "depAirPer.set_index('origin', inplace=True)\n",
    "depAirPer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14436c41ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "depAirPer['percentage'].plot(kind='bar', figsize=[12,6], title =\"Departure delay % w.r.t Airports\",  legend=False, fontsize=12)\n",
    "plt.gca().set_facecolor('white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 43% of total flight departed from EWR were delayed, which is highest among all.<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3.3 Best and worst carrier in terms of departure delay %"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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>dep_status</th>\n",
       "      <th>Late</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carrier</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9E</th>\n",
       "      <td>7063.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <td>10162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B6</th>\n",
       "      <td>21445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL</th>\n",
       "      <td>15241.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EV</th>\n",
       "      <td>23139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F9</th>\n",
       "      <td>341.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>1654.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HA</th>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MQ</th>\n",
       "      <td>8031.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OO</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UA</th>\n",
       "      <td>27261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>4775.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VX</th>\n",
       "      <td>2225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WN</th>\n",
       "      <td>6558.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YV</th>\n",
       "      <td>233.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "dep_status     Late\n",
       "carrier            \n",
       "9E           7063.0\n",
       "AA          10162.0\n",
       "AS            226.0\n",
       "B6          21445.0\n",
       "DL          15241.0\n",
       "EV          23139.0\n",
       "F9            341.0\n",
       "FL           1654.0\n",
       "HA             69.0\n",
       "MQ           8031.0\n",
       "OO              9.0\n",
       "UA          27261.0\n",
       "US           4775.0\n",
       "VX           2225.0\n",
       "WN           6558.0\n",
       "YV            233.0"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depCarrier = df1.loc[df1['dep_status'] == 'Late'].groupby(['carrier','dep_status'])['dep_status'].count().unstack('dep_status')\n",
    "depCarrier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x144371c8668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "depCarrier.plot(kind='bar', figsize=[10,6], title =\"Departure delay % w.r.t Carriers\",  legend=False, fontsize=12)\n",
    "plt.gca().set_facecolor('white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "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>total</th>\n",
       "      <th>late</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carrier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9E</th>\n",
       "      <td>18460</td>\n",
       "      <td>7063.0</td>\n",
       "      <td>38.261105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <td>32729</td>\n",
       "      <td>10162.0</td>\n",
       "      <td>31.048917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>714</td>\n",
       "      <td>226.0</td>\n",
       "      <td>31.652661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B6</th>\n",
       "      <td>54635</td>\n",
       "      <td>21445.0</td>\n",
       "      <td>39.251396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL</th>\n",
       "      <td>48110</td>\n",
       "      <td>15241.0</td>\n",
       "      <td>31.679485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EV</th>\n",
       "      <td>54173</td>\n",
       "      <td>23139.0</td>\n",
       "      <td>42.713160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F9</th>\n",
       "      <td>685</td>\n",
       "      <td>341.0</td>\n",
       "      <td>49.781022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>3260</td>\n",
       "      <td>1654.0</td>\n",
       "      <td>50.736196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HA</th>\n",
       "      <td>342</td>\n",
       "      <td>69.0</td>\n",
       "      <td>20.175439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MQ</th>\n",
       "      <td>26397</td>\n",
       "      <td>8031.0</td>\n",
       "      <td>30.423912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OO</th>\n",
       "      <td>32</td>\n",
       "      <td>9.0</td>\n",
       "      <td>28.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UA</th>\n",
       "      <td>58665</td>\n",
       "      <td>27261.0</td>\n",
       "      <td>46.468934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>20536</td>\n",
       "      <td>4775.0</td>\n",
       "      <td>23.251850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VX</th>\n",
       "      <td>5162</td>\n",
       "      <td>2225.0</td>\n",
       "      <td>43.103448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WN</th>\n",
       "      <td>12275</td>\n",
       "      <td>6558.0</td>\n",
       "      <td>53.425662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YV</th>\n",
       "      <td>601</td>\n",
       "      <td>233.0</td>\n",
       "      <td>38.768719</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         total     late  percentage\n",
       "carrier                            \n",
       "9E       18460   7063.0   38.261105\n",
       "AA       32729  10162.0   31.048917\n",
       "AS         714    226.0   31.652661\n",
       "B6       54635  21445.0   39.251396\n",
       "DL       48110  15241.0   31.679485\n",
       "EV       54173  23139.0   42.713160\n",
       "F9         685    341.0   49.781022\n",
       "FL        3260   1654.0   50.736196\n",
       "HA         342     69.0   20.175439\n",
       "MQ       26397   8031.0   30.423912\n",
       "OO          32      9.0   28.125000\n",
       "UA       58665  27261.0   46.468934\n",
       "US       20536   4775.0   23.251850\n",
       "VX        5162   2225.0   43.103448\n",
       "WN       12275   6558.0   53.425662\n",
       "YV         601    233.0   38.768719"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depCarrierPer = df1.groupby('carrier')['carrier'].count().reset_index(name='total')\n",
    "delayedFlights = df1.loc[df1['dep_status'] == 'Late'].groupby(['carrier','dep_status'])['dep_status'].count().unstack('dep_status')\n",
    "depCarrierPer['late'] = delayedFlights['Late'].values\n",
    "depCarrierPer['percentage'] = (depCarrierPer['late']/depCarrierPer['total'])*100\n",
    "depCarrierPer.set_index('carrier', inplace=True)\n",
    "depCarrierPer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1441a9ad828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "depCarrierPer[['percentage']].plot(kind='bar', figsize=[10,6], title =\"Departure delay % w.r.t. Carriers\",  legend=False, fontsize=12, color='blue')\n",
    "plt.gca().set_facecolor('white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HA carrier is best in terms of departure delay with just 20% of total flight got delayed, On other hand WN performed worst with 53% of delayed flights followed by FL and F9.<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3.4 Departure delay across months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "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>dep_status</th>\n",
       "      <th>Late</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9662.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11209.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10543.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11291.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12655.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13909.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11713.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7815.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8722.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8239.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13550.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "dep_status     Late\n",
       "month              \n",
       "1            9662.0\n",
       "2            9124.0\n",
       "3           11209.0\n",
       "4           10543.0\n",
       "5           11291.0\n",
       "6           12655.0\n",
       "7           13909.0\n",
       "8           11713.0\n",
       "9            7815.0\n",
       "10           8722.0\n",
       "11           8239.0\n",
       "12          13550.0"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depMonth = df1.loc[df1['dep_status'] == 'Late'].groupby(['month','dep_status'])['dep_status'].count().unstack('dep_status')\n",
    "depMonth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1443515b7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "depMonth.plot(figsize=[10,6], title =\"Departure delay % w.r.t Months\",  legend=False, fontsize=12)\n",
    "plt.gca().set_facecolor('white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### As we can see from the above graph, flights delay is more in June, July and December months, where as September is best month to travel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.3.5 Departure delay count w.r.t origin and dest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "routeDelay = df.loc[df['dep_status'] == 'Late'].groupby(['origin','dest','dep_status'])['dep_status'].count().reset_index(name='total')\n",
    "routeDelay = routeDelay[routeDelay.groupby('origin')['total'].transform(max) == routeDelay['total']].sort_values('total')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1672acfcfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "routeDelay.plot(kind='bar', figsize=[10,5], title =\"Route with most number of delay from each origin\",  legend=False, fontsize=12)\n",
    "plt.gca().set_xticklabels(routeDelay['origin'].astype(str) + \" - \"+routeDelay['dest'].astype(str))\n",
    "plt.gca().set_facecolor('white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### JFK-LAX route had most number of flight delay in 2013."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.4 Canceled Flights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4.1 Best and worst airport in terms of number of flights canceled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "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>dep_status</th>\n",
       "      <th>Canceled</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EWR</th>\n",
       "      <td>3239.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JFK</th>\n",
       "      <td>1863.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGA</th>\n",
       "      <td>3153.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "dep_status  Canceled\n",
       "origin              \n",
       "EWR           3239.0\n",
       "JFK           1863.0\n",
       "LGA           3153.0"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canAir = df.loc[df['dep_status'] == 'Canceled'].groupby(['origin','dep_status'])['dep_status'].count().unstack('dep_status')\n",
    "canAir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14436938438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canAir.plot(kind='bar', figsize=[10,5], title =\"Canceled flights w.r.t Airports\",  legend=False, fontsize=12)\n",
    "plt.gca().set_facecolor('white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1863 flights got canceled from JFK airport in 2013, which is lowest among all.<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4.2 Best and worst airport w.r.t flight cancellation %"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "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>total</th>\n",
       "      <th>canceled</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EWR</th>\n",
       "      <td>120835</td>\n",
       "      <td>3239.0</td>\n",
       "      <td>2.680515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JFK</th>\n",
       "      <td>111279</td>\n",
       "      <td>1863.0</td>\n",
       "      <td>1.674170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGA</th>\n",
       "      <td>104662</td>\n",
       "      <td>3153.0</td>\n",
       "      <td>3.012555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         total  canceled  percentage\n",
       "origin                              \n",
       "EWR     120835    3239.0    2.680515\n",
       "JFK     111279    1863.0    1.674170\n",
       "LGA     104662    3153.0    3.012555"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canAirPer = df.groupby('origin')['origin'].count().reset_index(name='total')\n",
    "canceledFlights = df1.loc[df1['dep_status'] == 'Canceled'].groupby(['origin','dep_status'])['dep_status'].count().unstack('dep_status')\n",
    "canAirPer['canceled'] = canceledFlights['Canceled'].values\n",
    "canAirPer['percentage'] = (canAirPer['canceled']/canAirPer['total'])*100\n",
    "canAirPer.set_index('origin', inplace=True)\n",
    "canAirPer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1443611a828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canAirPer['percentage'].plot(kind='bar', figsize=[10,5], title =\"Flight cancellation % w.r.t Airports\",  legend=False, fontsize=12)\n",
    "plt.gca().set_facecolor('white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3% of total flight got canceled LGA airport, which is highest among all.<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4.3 Best and worst carrier w.r.t total flight canceled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "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>dep_status</th>\n",
       "      <th>Canceled</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carrier</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9E</th>\n",
       "      <td>1044.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <td>636.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B6</th>\n",
       "      <td>466.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL</th>\n",
       "      <td>349.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EV</th>\n",
       "      <td>2817.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F9</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MQ</th>\n",
       "      <td>1234.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OO</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UA</th>\n",
       "      <td>686.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>663.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VX</th>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WN</th>\n",
       "      <td>192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YV</th>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "dep_status  Canceled\n",
       "carrier             \n",
       "9E            1044.0\n",
       "AA             636.0\n",
       "AS               2.0\n",
       "B6             466.0\n",
       "DL             349.0\n",
       "EV            2817.0\n",
       "F9               3.0\n",
       "FL              73.0\n",
       "MQ            1234.0\n",
       "OO               3.0\n",
       "UA             686.0\n",
       "US             663.0\n",
       "VX              31.0\n",
       "WN             192.0\n",
       "YV              56.0"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canCarrier = df.loc[df['dep_status'] == 'Canceled'].groupby(['carrier','dep_status'])['dep_status'].count().unstack('dep_status')\n",
    "canCarrier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x144373ae7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canCarrier.plot(kind='bar', figsize=[10,6], title =\"Flight cancellation w.r.t Carriers\",  legend=False, fontsize=12)\n",
    "plt.gca().set_facecolor('white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AS, F9 and OO carriers are best in terms of flight cancellation.<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4.4 Flight cancellation across months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "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>dep_status</th>\n",
       "      <th>Canceled</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>521.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>861.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>668.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>563.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1009.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>486.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>452.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>236.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>233.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1025.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "dep_status  Canceled\n",
       "month               \n",
       "1              521.0\n",
       "2             1261.0\n",
       "3              861.0\n",
       "4              668.0\n",
       "5              563.0\n",
       "6             1009.0\n",
       "7              940.0\n",
       "8              486.0\n",
       "9              452.0\n",
       "10             236.0\n",
       "11             233.0\n",
       "12            1025.0"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canMonth = df1.loc[df['dep_status'] == 'Canceled'].groupby(['month','dep_status'])['dep_status'].count().unstack('dep_status')\n",
    "canMonth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x144373c89e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canMonth.plot(figsize=[10,6], title =\"Flight cancellation % w.r.t Months\",  legend=False, fontsize=12)\n",
    "plt.gca().set_facecolor('white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Flight cancellation is lowest in October and November months"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.5 Flight speed\n",
    "## 5.5.1 Fastest and slowest flight in 2013"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month                           6\n",
      "day                             1\n",
      "dep_time          0 days 16:26:00\n",
      "sched_dep_time    0 days 16:29:00\n",
      "dep_delay                      -3\n",
      "arr_time          0 days 18:50:00\n",
      "sched_arr_time    0 days 18:53:00\n",
      "arr_delay                      -3\n",
      "carrier                        US\n",
      "flight                         17\n",
      "origin                        JFK\n",
      "dest                          PHX\n",
      "air_time                      144\n",
      "distance                     2153\n",
      "dep_status                 OnTime\n",
      "arr_status                 OnTime\n",
      "speed                     897.083\n",
      "Name: 222734, dtype: object\n",
      "month                           1\n",
      "day                            13\n",
      "dep_time          0 days 22:39:00\n",
      "sched_dep_time    0 days 21:59:00\n",
      "dep_delay                      40\n",
      "arr_time          0 days 01:10:00\n",
      "sched_arr_time    0 days 00:30:00\n",
      "arr_delay                      40\n",
      "carrier                        EV\n",
      "flight                       4519\n",
      "origin                        EWR\n",
      "dest                          BWI\n",
      "air_time                     1289\n",
      "distance                      169\n",
      "dep_status                   Late\n",
      "arr_status                   Late\n",
      "speed                     7.86656\n",
      "Name: 11245, dtype: object\n"
     ]
    }
   ],
   "source": [
    "df['speed'] = df['distance']/(df['air_time']/60)\n",
    "print(df.iloc[df['speed'].idxmax()])\n",
    "print(df.iloc[df['speed'].idxmin()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.5.2 Average flight speed across all carriers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "carrier\n",
       "9E    344.625846\n",
       "AA    416.857053\n",
       "AS    444.459187\n",
       "B6    399.678786\n",
       "DL    418.128015\n",
       "EV    362.338709\n",
       "F9    425.566706\n",
       "FL    393.730749\n",
       "HA    480.357719\n",
       "MQ    367.912746\n",
       "OO    366.320085\n",
       "UA    420.799192\n",
       "US    341.580788\n",
       "VX    446.195066\n",
       "WN    400.254407\n",
       "YV    331.664615\n",
       "Name: speed, dtype: float64"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flightSpeed = df.groupby(['carrier'])['speed'].mean()\n",
    "flightSpeed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9yvd/xbb58+envLw8N954YyZOnJh77703U6dOzQYbbJADDjggZ555ZtZbb72MHj06V1xxRe67777cf//9KSsrS7du3TJs2LBsueWWKx1/Tea7Jp/vFVZ1nE033XSloPzJObz11lur/d176623SpHxk+/RD37wg5SXl+fWW2/NRRddlBEjRmTbbbfNGWecUeU2awDgq0lkBADWmVtuuSVNmzbN8OHDV3rthBNOyNSpU7P77runXr166dOnT+6777706NEjL774YpUVXvXr10+XLl2qrAr7uDp16qx2DqecckpmzpyZa665Jh06dEidOnXy3nvv5eabby6NadKkSd55552V9v34tg033DDJ8lu3y8vLVxq7xRZbrHYOa6Nx48Z59913U1lZWSU0rmp+X5T99tsv++23XxYsWJDp06fnqquuypAhQ9KpU6fS6s4VwWuFFcGvQYMGpfdm6NCh6dKly0rHXxGh6tevn27dumXgwIErjVmxSnKTTTbJP/7xj5Ve/+T5P2nSpEm59tprM2zYsOy1116pX79+klS5dX7FPOfOnVvl6ckvv/xy5s6dm06dOq3y2BtttNEq57QiQK+4Tb5ly5a5+OKL89FHH+X555/PHXfckV/84hdp1qxZBg0aVPpexiFDhuTll1/Ob37zm1x++eU599xzc/XVV690/I/P95PvxQsvvJB27dqt0ee7iPr166d58+ZVboP/uNXdhp8sj95HHHFEjjjiiLzzzjv5/e9/nyuuuCKDBw/OY4899qm/twDAl5+nSwMA68Tbb7+dRx55JPvuu2/pydIf/2efffbJo48+Wnqy8IEHHpjnnnsuN954Yxo1apSdd965dKwuXbpk1qxZadGiRdq0aVP6584778zNN9+82u+0S5Y/HGWvvfbKzjvvXIoaDz/8cJL/v+Kvc+fOefbZZ0vRKFkekP74xz+W/t65c+cky1cafnwO8+bNy5gxYz4zgq2pLl26ZOnSpfntb39bZfuDDz641sf65GrIVTnxxBNTUVGRZHlQ2nvvvXPcccflo48+Kq1ATLLSfH71q1+lRo0a2XnnndOyZctsuummefXVV6u8N02aNMmoUaNKTwHv0qVLZsyYke233740Zscdd8y1116bBx54IMny27dfffXV0i2/yfLI9vGfxao888wzadWqVb797W+XAuMbb7yRl156qfRzXrHC75Pv5ejRo3Peeeet9tidO3fOnDlz8swzz1TZfuedd6Z27dpp27Zt7r///uy888556623UrNmzbRv3z7Dhg3LhhtumNdffz1z5sxJr169cv/99ydZHiR/+MMfplu3bnn99ddXed6WLVtmk002yW9+85sq2++666788Ic/zAcffLBGn+8iunTpkn/961/ZdNNNq/xsH3/88Vx99dWf+rvXr1+/nH/++UmWr5g8+OCDc8QRR2TBggVVnl4OAHw1WckIAKwTt912W5YuXbra24u/+c1vZsqUKbnpppty8sknp0ePHmnQoEGmTp2aAQMGVAlkAwYMyB133JEBAwbkqKOOyiabbJJ77703N910U04//fRPnUfbtm1z1113ZYcddkiTJk3y7LPP5sorr0yNGjVKt1l/73vfy4033pjvf//7padET5gwIUuWLCndSrrtttvmgAMOyFlnnZU5c+Zkxx13zKxZszJ69Og0a9YszZs3/wLeteVBq3v37qUnF2+xxRa55ZZb8uKLL37qba2rsmIl3AMPPJCePXtWeQr3CjvvvHPOOeecXHTRRenZs2fmz5+f8ePHp3nz5tluu+1K455//vmccsopOfDAA/O3v/0tl112WQ455JDSbb4nnXRSzj777NSsWTPf+MY3Mn/+/Fx++eV54403SrfaHnfccenXr1+OPvroHHbYYVlvvfUybdq0PPjgg6WH9xx44IG5/vrrU1FRkZNOOin16tXLxIkTPzOYtW3bNpdffnkmTZqUdu3a5R//+EeuvPLKLFmypPRz3m677dK3b99ccsklef/997PDDjtk+vTpeeCBB1b6XsaPO/jggzNlypRUVFTkhBNOyJZbbpnf/va3ufXWW1NRUZENN9wwHTp0SGVlZY4//vgMGjQo5eXlue+++7JgwYLsueeeadq0aZo0aZLzzz8/CxcuzFZbbZU///nP+f3vf5+jjz56leetWbNmBg8enJ/+9KcZNmxY9thjj7zyyisZM2ZMDjvssDRo0GCNPt9FHHzwwZk8eXIGDhyYY445Jptvvnkee+yxXHXVVfnud7+b2rVrr3bfzp075+c//3k222yztG/fPm+88UauueaadOnSZZW3xAMAXy0iIwCwTtx222352te+ViVUfVzbtm3TsmXL3HrrrRk8eHDq1KmTfffdN9ddd10OOOCAKmMbN26cqVOnZtSoURk2bFg++OCDNG/ePBdccEGV22FXZcSIETnvvPNKK9WaN2+ec889N3feeWfpQSAbbrhhrr/++lxwwQUZOnRoysvLc/jhh6du3bpVvvdw+PDhufLKKzN16tS8/vrr2XTTTbPPPvvkxBNP/NQVXWtr9OjRGTFiREaNGpWlS5emT58+Oeyww3L77bev1XG6du2abt26ZdSoUXn88cczadKklcb069cvH374YaZOnZopU6Zk/fXXzy677JIhQ4ZUCUj9+/fPG2+8kYqKimyyySY55phjqsSx73znOykvL8/VV1+dadOmpW7duunQoUMuueSSUojcbrvtcuONN2b06NEZOnRoli1blm233TYTJkxInz59kiy/9f26667LhRdemAsuuCA1atQoxcxPu2X86KOPzrvvvpvrr78+EyZMyOabb54DDzyw9PTvFQ8oufjiizN+/PjccMMNeffdd9OiRYuMGTNmpacuf9wGG2yQG264IaNGjcpll12WhQsXpmXLllU+f40aNcrVV1+dsWPH5swzz8x7772Xr33taxk3blxpVe748eNz6aWXZuzYsXn33Xez+eabp6KiosoTmT/piCOOSN26dfOzn/0st9xySxo3bpyjjjqqtM+afL6LqFu3bm688caMGjUqF198cRYsWFD6zs6jjjrqU/f90Y9+lDp16uTWW2/NhAkTUr9+/fTu3Tsnn3xy4XkBANWvxrJPfgM0AMD/uOeeey7z5s1Lr169StuWLl2a3XbbLfvuu+9nrpb8Is2ZMyd//OMf06dPn6y//vql7SeccEL++c9/5rbbbltnc1mhdevWqaioyODBg9f5uQEA+HKykhEA4BNee+21nHTSSTn++OPTpUuXvPfee5k6dWoWLFiQQw45ZJ3OpaysLKeddlr69OmTb3/726lZs2Yefvjh/PrXv17lA3QAAKA6iIwAAJ+w9957Z968eZkyZUp+9rOfpXbt2tlpp50yefLkVX6P4X/S5ptvnquuuioTJkzIiSeemKVLl2abbbbJJZdckv3222+dzgUAAFbH7dIAAAAAQCFlnz0EAAAAAGD1REYAAAAAoBCREQAAAAAoRGQEAAAAAApZ66dLv/fee+nQoUMqKyurbK9Tp07+9Kc/JUmef/75jBw5Mi+88ELq1q2bAw44ICeddFLq1KlTGv/mm29mxIgRefTRR/Phhx+mR48eOfPMM9O4ceOClwQAAAAArEtrHRn/9re/pbKyMpdeemmaNm1a2l5WtnxR5OzZszNw4MC0b98+Y8aMycyZMzN69OgsWLAg559/fpJk6dKl+eEPf5jFixdn2LBhWbp0aUaNGpWjjjoqt99+e2rXrv0FXR4AAAAA8J+21pHxr3/9a2rXrp0999xzlTHw6quvTnl5eS6//PLUqVMnvXr1yvrrr5/zzjsvxx57bJo2bZr7778/L774Yu6+++587WtfS5Jsv/322W+//XLvvffmwAMPLH5lAAAAAMA6sdbfyfjXv/41rVq1Wu1qw+nTp2e33Xarcmt03759U1lZmenTp5fGtGjRohQYk6RVq1bZZptt8vDDD6/tlAAAAACAarTWKxlffPHFlJWVZeDAgXn22WdTp06d9O3bN0OHDk2tWrUyZ86ctGjRoso+DRo0SL169fLKK68kSWbOnJnmzZuvdOytttoqs2bNWuO5vPnmm3nrrbeqbGvYsGEaNWq0tpcFAAAAAHxOaxUZKysr89JLL6WsrCynnHJKjjvuuPzpT3/K+PHjM2PGjIwePTpJUq9evZX2LS8vz8KFC5Mk8+fPz9Zbb73KMYsWLVrj+UybNi3jx4+vsq2ioiKDBw9em8sCAAAAAApYq8i4bNmyXHnlldlss82yzTbbJEk6d+6czTbbLEOGDMmTTz75qfvWqFFjpT9/csyKB8isiUMPPTS9e/eusq1hw4ZrvD8AAAAAUNxaRcaaNWuma9euK23fbbfdkiRz5sxJklWuRly8eHHq16+fJKlfv35pVePqxqyJRo0auTUaAAAAAKrZWj345Y033shNN92U119/vcr2999/P0my2WabpXHjxvnHP/5R5fW5c+dm4cKFadWqVZKkRYsWmT179krHnz17dmmFJAAAAADw1bBWkXHJkiU566yzMm3atCrb77333pSVlaVjx47p3r17HnrooSxZsqT0+v3335+aNWtm5513TpL06NEjM2fOzIwZM0pjZsyYkZkzZ6Z79+5FrgcAAAAAWMdqDhs2bNiaDt5oo43yz3/+M9OmTUuNGjWydOnS3HHHHbnsssty2GGH5aCDDkqLFi1yzTXX5A9/+EM23njjPPTQQ7n44otzyCGHZL/99kuStGzZMvfdd19uu+22bLbZZnnppZdy+umnZ4sttsiZZ565Vt/LCAAAAABUrxrLli1btjY7fPDBB7n66qtz55135rXXXkvjxo1zyCGH5Pvf/35q1qyZJHn66aczcuTI/PWvf80mm2ySAw88MD/60Y9Sq9b//wrIf/3rX7ngggvy6KOPpnbt2unevXtOP/1037EIAAAAAF8xax0ZAQAAAAA+zn3JAAAAAEAhIiMAAAAAUIjICAAAAAAUIjICAAAAAIWIjAAAAABAISIjAAAAAFCIyAgAAAAAFCIyAgAAAACFiIwAAAAAQCEiIwAAAABQiMgIAAAAABQiMgIAAAAAhYiMAAAAAEAhIiMAAAAAUIjICAAAAAAUIjICAAAAAIXUqu4JAABAEb/57TbVPYXV6tN7ZnVPAQBgnbCSEQAAAAAoRGQEAAAAAAoRGQEAAACAQkRGAAAAAKAQkREAAAAAKERkBAAAAAAKERkBAAAAgEJERgAAAACgEJERAAAAAChEZAQAAAAAChEZAQAAAIBCREYAAAAAoBCREQAAAAAoRGQEAAAAAAoRGQEAAACAQkRGAAAAAKAQkREAAAAAKERkBAAAAAAKERkBAAAAgEJERgAAAACgEJERAAAAAChEZAQAAAAAChEZAQAAAIBCREYAAAAAoBCREQAAAAAoRGQEAAAAAAoRGQEAAACAQkRGAAAAAKAQkREAAAAAKERkBAAAAAAKERkBAAAAgEJERgAAAACgEJERAAAAAChEZAQAAAAAChEZAQAAAIBCREYAAAAAoBCREQAAAAAoRGQEAAAAAAoRGQEAAACAQkRGAAAAAKAQkREAAAAAKKRWdU8AAAAAoIg217Wp7ims1p/6/6m6pwDrhJWMAAAAAEAhVjLCl4T/8wYAAAB8VVnJCAAAAAAUIjICAAAAAIWIjAAAAABAISIjAAAAAFCIyAgAAAAAFCIyAgAAAACFiIwAAAAAQCEiIwAAAABQiMgIAAAAABQiMgIAAAAAhYiMAAAAAEAhIiMAAAAAUIjICAAAAAAUIjICAAAAAIWIjAAAAABAISIjAAAAAFCIyAgAAAAAFCIyAgAAAACFiIwAAAAAQCEiIwAAAABQiMgIAAAAABQiMgIAAAAAhdSq7gkAAP+dXj3tkeqewmo1G7FrdU8BAAD+q1jJCAAAAAAUIjICAAAAAIWIjAAAAABAISIjAAAAAFCIyAgAAAAAFOLp0gAAAPxHND/tnuqewmq9MmLf6p4CwH8VKxkBAAAAgEJERgAAAACgELdLA/yPmXDMb6t7Cqt1/BW9q3sKAAAAfA5WMgIAAAAAhVjJyBdv2EbVPYPVG/bv6p4BAAAAwH8dKxkBAAAAgEJERgAAAACgEJERAAAAAChFj4XiAAAgAElEQVREZAQAAAAAChEZAQAAAIBCREYAAAAAoBCREQAAAAAoRGQEAAAAAAoRGQEAAACAQkRGAAAAAKAQkREAAAAAKERkBAAAAAAKERkBAAAAgEJERgAAAACgkEKRsaKiIr17966y7eWXX86gQYPSsWPHdO3aNWeccUbmz59fZczChQtz9tlnp3v37mnXrl0GDhyYGTNmFJkKAAAAAFBNPndkvOOOO/LAAw9U2TZ//vwMGDAgc+fOzciRI3PyySfngQceyIknnlhl3IrtJ598ckaOHJl33nkn/fv3z7x58z7vdAAAAACAalLr8+z0xhtv5IILLkiTJk2qbP/FL36R+fPn5/bbb0+DBg2SJI0bN86gQYPy9NNPp1OnTnn22Wfz0EMPZdKkSenVq1eSpFOnTunTp0+mTJmS4447ruAlAQAAAADr0udayfiTn/wk3bt3zy677FJl+/Tp09OxY8dSYEySXXfdNeXl5Xn44YdLY+rWrZvu3buXxjRo0CCdO3cujQEAAAAAvjrWOjLefPPNeeGFF3LWWWet9NrMmTPTokWLqicoK0uzZs3yyiuvlMY0a9YstWpVXUS51VZbZdasWWs7HQAAAACgmq3V7dJz5szJ8OHDM3z48CqrFVeYP39+ysvLV9peXl6ehQsXJkkWLFiQevXqrXLMokWL1mY6efPNN/PWW29V2dawYcM0atRorY4DAAAA8L/mr9ttX91TWK3tX/xrdU+BtbTGkXHZsmU544wz0qtXr+y1116rHVejRo1V7rtie2Vl5SrHJMtXPa6NadOmZfz48VW2VVRUZPDgwWt1HOCryb8QAQAA4MthjSPjjTfemL/97W+56667snTp0iTL42GSLF26NGVlZalXr15pxeLHLV68uPSQmPr16+edd95ZacyiRYtSv379tZr8oYcemt69e1fZ1rBhw7U6BgAAAABQzBpHxl/96ld5991306NHj5Ve22GHHVJRUZEWLVpk9uzZVV6rrKzMq6++mj333DNJ0qJFi0yfPj2VlZVVVi7Onj07LVu2XKvJN2rUyK3RAAAAAFDN1jgynnvuuSt9Z+KECRPy5z//ORMnTkyjRo1So0aN/OxnP8vcuXNL39n4yCOPZNGiRaWnSffo0SNXXHFFHnnkkfTq1StJMnfu3Dz11FM55phjvqjrAgAAAADWkTWOjKtaZbjxxhunTp06adOmTZLk8MMPz+TJkzNw4MBUVFRk3rx5ufjii9OzZ8+0b98+SdK5c+d06dIlQ4YMyZAhQ7Lxxhtn3LhxqV+/fvr16/cFXRYAAAAAsK6s1dOlP0uDBg1y/fXX58ILL8wpp5yS8vLy9O3bN0OHDq0ybvz48RkxYkRGjhyZysrKdOjQIWPGjMlGG230RU4HAAAAAFgHCkXGESNGrLRt2223zbXXXvup+2200UYZPnx4hg8fXuT0AAAAAMCXwBe6khEAgM9v2LBh1T2F1foyzw0AgOpX9tlDAAAAAABWT2QEAAAAAAoRGQEAAACAQkRGAAAAAKAQkREAAAAAKERkBAAAAAAKERkBAAAAgEJERgAAAACgEJERAAAAAChEZAQAAAAAChEZAQAAAIBCREYAAAAAoBCREQAAAAAoRGQEAAAAAAoRGQEAAACAQkRGAAAAAKAQkREAAAAAKERkBAAAAAAKERkBAAAAgEJERgAAAACgEJERAAAAAChEZAQAAAAAChEZAQAAAIBCREYAAAAAoBCREQAAAAAoRGQEAAAAAAoRGQEAAACAQkRGAAAAAKAQkREAAAAAKERkBAAAAAAKERkBAAAAgEJqVfcEAACAdavJ7/5Y3VNYrde/0a66pwAAfA5WMgIAAAAAhVjJCAAAsAaan3ZPdU9htV4ZsW91TwGA/3FWMgIAAAAAhYiMAAAAAEAhIiMAAAAAUIjICAAAAAAU4sEvAPAZRh26X3VPYbVOnnZ3dU8BAADASkYAAAAAoBiREQAAAAAoRGQEAAAAAAoRGQEAAACAQkRGAAAAAKAQkREAAAAAKERkBAAAAAAKERkBAAAAgEJERgAAAACgEJERAAAAAChEZAQAAAAAChEZAQAAAIBCREYAAAAAoBCREQAAAAAoRGQEAAAAAAoRGQEAAACAQkRGAAAAAKAQkREAAAAAKERkBAAAAAAKERkBAAAAgEJqVfcEvuyan3ZPdU9htV4ZsW91TwEAAAAArGQEAAAAAIoRGQEAAACAQkRGAAAAAKAQ38kIAAAAXybDNqruGazesH9X9wyALykrGQEAAACAQkRGAAAAAKAQkREAAAAAKERkBAAAAAAKERkBAAAAgEJERgAAAACgEJERAAAAAChEZAQAAAAAChEZAQAAAIBCREYAAAAAoBCREQAAAAAopFZ1TwAAAAAAvgomHPPb6p7Cah1/Re9qPb+VjAAAAABAISIjAAAAAFCIyAgAAAAAFCIyAgAAAACFiIwAAAAAQCEiIwAAAABQiMgIAAAAABQiMgIAAAAAhYiMAAAAAEAhIiMAAAAAUIjICAAAAAAUIjICAAAAAIWIjAAAAABAISIjAAAAAFCIyAgAAAAAFCIyAgAAAACFiIwAAAAAQCEiIwAAAABQiMgIAAAAABQiMgIAAAAAhYiMAAAAAEAhIiMAAAAAUIjICAAAAAAUIjICAAAAAIWIjAAAAABAISIjAAAAAFCIyAgAAAAAFCIyAgAAAACFiIwAAAAAQCEiIwAAAABQiMgIAAAAABQiMgIAAAAAhax1ZPzoo48yadKk7LHHHmnbtm0OOOCA3HHHHVXGvPzyyxk0aFA6duyYrl275owzzsj8+fOrjFm4cGHOPvvsdO/ePe3atcvAgQMzY8aMYlcDAAAAAKxztdZ2h0svvTTXXXddTjjhhLRp0ya///3vM3To0JSVlWX//ffP/PnzM2DAgDRq1CgjR47MO++8k4svvjivv/56fv7zn5eOc/LJJ+f555/PkCFDUq9evYwfPz79+/fPPffck4033vgLvUgAAAAA4D9nrSLjokWLMnny5PTv3z+DBg1Kkuyyyy554YUXMnny5Oy///75xS9+kfnz5+f2229PgwYNkiSNGzfOoEGD8vTTT6dTp0559tln89BDD2XSpEnp1atXkqRTp07p06dPpkyZkuOOO+4LvkwAAAAA4D9lrW6XXm+99TJt2rQMHDiwyvbatWtnyZIlSZLp06enY8eOpcCYJLvuumvKy8vz8MMPl8bUrVs33bt3L41p0KBBOnfuXBoDAAAAAHw1rFVkrFWrVrbbbrtsttlmWbZsWd56661ceeWVeeyxx3L44YcnSWbOnJkWLVpUPUlZWZo1a5ZXXnmlNKZZs2apVavqQsqtttoqs2bNKnA5AAAAAMC6ttbfybjCXXfdlSFDhiRJevXqlX322SdJMn/+/JSXl680vry8PAsXLkySLFiwIPXq1VvlmEWLFq3xHN5888289dZbVbY1bNgwjRo1WuNjAAAAAADFfO7IuNNOO2Xy5MmZNWtWLrvssvTr1y+33HJLkqRGjRorjV+2bFlpe2Vl5SrHJMtXPa6padOmZfz48VW2VVRUZPDgwWt8DAAAAACgmM8dGbfeeutsvfXW6dy5c7bccssMGDAgv/rVr1KvXr3SisWPW7x4cZo0aZIkqV+/ft55552VxixatCj169df4zkceuih6d27d5VtDRs2XMsrAQAAAACKWKvI+M477+Thhx9Oz549s+mmm5a2t2nTJkny+uuvp0WLFpk9e3aV/SorK/Pqq69mzz33TJK0aNEi06dPT2VlZZWVi7Nnz07Lli3XeD6NGjVyazQAAAAAVLO1evDL4sWLc9ppp+Xmm2+usv2RRx5JkrRu3Trdu3fPU089lblz51Z5fdGiRaWnSffo0SOLFi0q7Zckc+fOzVNPPZUePXp87osBAAAAANa9tVrJuOWWW+aggw7KhAkTUlZWljZt2uTPf/5zJk6cmB49eqRnz55p06ZNJk+enIEDB6aioiLz5s3LxRdfnJ49e6Z9+/ZJks6dO6dLly4ZMmRIhgwZko033jjjxo1L/fr1069fv//IhQIAAAAA/xlr/Z2M5513Xpo3b55bb70148aNS8OGDfO9730vxx13XGrUqJEGDRrk+uuvz4UXXphTTjkl5eXl6du3b4YOHVrlOOPHj8+IESMycuTIVFZWpkOHDhkzZkw22mijL+ziAAAAAID/vLWOjHXq1Mmxxx6bY489drVjtt1221x77bWfepyNNtoow4cPz/Dhw9d2CgAAAADAl8hafScjAAAAAMAniYwAAAAAQCEiIwAAAABQiMgIAAAAABQiMgIAAAAAhYiMAAAAAEAhIiMAAAAAUIjICAAAAAAUIjICAAAAAIWIjAAAAABAISIjAAAAAFCIyAgAAAAAFCIyAgAAAACFiIwAAAAAQCEiIwAAAABQiMgIAAAAABQiMgIAAAAAhYiMAAAAAEAhIiMAAAAAUIjICAAAAAAUIjICAAAAAIWIjAAAAABAISIjAAAAAFCIyAgAAAAAFCIyAgAAAACFiIwAAAAAQCEiIwAAAABQiMgIAAAAABQiMgIAAAAAhYiMAAAAAEAhIiMAAAAAUIjICAAAAAAUIjICAAAAAIWIjAAAAABAISIjAAAAAFCIyAgAAAAAFCIyAgAAAACFiIwAAAAAQCEiIwAAAABQiMgIAAAAABQiMgIAAAAAhYiMAAAAAEAhIiMAAAAAUIjICAAAAAAUIjICAAAAAIWIjAAAAABAISIjAAAAAFCIyAgAAAAAFCIyAgAAAACFiIwAAAAAQCEiIwAAAABQiMgIAAAAABQiMgIAAAAAhYiMAAAAAEAhIiMAAAAAUIjICAAAAAAUIjICAAAAAIWIjAAAAABAISIjAAAAAFCIyAgAAAAAFCIyAgAAAACFiIwAAAAAQCEiIwAAAABQiMgIAAAAABQiMgIAAAAAhYiMAAAAAEAhIiMAAAAAUIjICAAAAAAUIjICAAAAAIWIjAAAAABAISIjAAAAAFCIyAgAAAAAFCIyAgAAAACFiIwAAAAAQCEiIwAAAABQiMgIAAAAABQiMgIAAAAAhYiMAAAAAEAhIiMAAAAAUIjICAAAAAAUIjICAAAAAIWIjAAAAABAISIjAAAAAFCIyAgAAAAAFCIyAgAAAACFiIwAAAAAQCEiIwAAAABQiMgIAAAAABQiMgIAAAAAhYiMAAAAAEAhIiMAAAAAUIjICAAAAAAUIjICAAAAAIWIjAAAAABAISIjAAAAAFCIyAgAAAAAFCIyAgAAAACFiIwAAAAAQCEiIwAAAABQiMgIAAAAABQiMgIAAAAAhYiMAAAAAEAhIiMAAAAAUIjICAAAAAAUIjICAAAAAIWIjAAAAABAISIjAAAAAFCIyAgAAAAAFCIyAgAAAACFrHVkXLZsWaZNm5b9998/7du3T58+fXLBBRdk4cKFpTEvv/xyBg0alI4dO6Zr164544wzMn/+/CrHWbhwYc4+++x079497dq1y8CBAzNjxoziVwQAAPD/2rvzeK3n/H/8j5MsKZWlZkhZwkTGWNM6kb7zGUVDIlsGDTPGOrI0M0zpOyMjTXxk+ShDshTKUtYiSyZjm0/mN2E4DMkksrSSOv3+MM7XEdXpna4T9/vt5nbT6/061/W8XrezXNfj/VoAgDWqdnW/YPjw4RkyZEh69+6dNm3a5PXXX89ll12Wl19+Odddd13mzp2bY489No0bN87FF1+c2bNnZ9CgQZk5c2b+/Oc/Vz5Onz598vzzz+fss89OvXr1MnTo0Pz0pz/NPffck4YNG67WFwkAAAAAfH2qFTJWVFTkmmuuSc+ePdOnT58kSdu2bdOwYcOcccYZ+f/+v/8vf/nLXzJnzpzceeed2WSTTZIk3/nOd3LiiSfmmWeeyZ577pm//e1veeSRR3LNNdekY8eOSZI999wz++23X26++eb88pe/XM0vEwAAAAD4ulRrufS8efPSrVu3HHDAAVXat9lmmyTJ9OnTM3ny5Oyxxx6VAWOSdOjQIXXr1s1jjz2WJJk8eXI23HDDtGvXrrLPJptskr322quyDwAAAACwdqhWyFi/fv2cf/752WOPPaq0P/jgg0mS7bffPuXl5ZWhY+WT1KqVLbfcMv/617+SJOXl5dlyyy1Tu3bViZTNmjXLa6+9Vt3XAAAAAACUULX3ZPyi5557LsOGDUvnzp2z/fbbZ86cOalbt+4y/erWrVt5OMzcuXNTr169L+0zf/78lX7uWbNm5Z133qnS1qhRozRu3LiarwIAAAAAWFWFQsZnnnkmv/jFL9KsWbP84Q9/qGwvKytbpu/SpUsr2ysqKr60T/LprMeVNXr06AwdOrRK2ymnnJJTTz11pR8DAAAAAChmlUPGe+65J3379s0222yTa6+9tvJE6Hr16lXOWPy8BQsW5Lvf/W6SZKONNsrs2bOX6TN//vxstNFGK11Dz54906lTpyptjRo1qs7LAAAAAAAKWqWQcfjw4bnkkkuy11575corr6wSDG6zzTZ54403qvSvqKjIm2++mR/96EeVfSZPnpyKiooqMxffeOONbLvttitdR+PGjS2NBgAAAIASq9bBL0kyatSoDBo0KD/+8Y9z7bXXLjPzsF27dnn66afz3nvvVbY9/vjjmT9/fuVp0u3bt8/8+fPz+OOPV/Z577338vTTT6d9+/ar+loAAAAAgBKo1kzGd955JwMHDkyTJk1y9NFHZ9q0aVWuN2vWLEceeWRuvPHGHHfccTnllFPywQcfZNCgQfnhD3+Y3XbbLUmy1157pVWrVjn77LNz9tlnp2HDhrn88suz0UYb5fDDD199rw4AAAAA+NpVK2R89NFH89FHH2XGjBk56qijlrk+cODAdO/ePTfccEMuvPDCnHXWWalbt25+/OMf55xzzqnSd+jQobnoooty8cUXp6KiIrvvvnsuvfTSNGjQoNgrAgAAAADWqGqFjD169EiPHj1W2G+HHXbI9ddfv9w+DRo0yMCBAzNw4MDqlAAAAAAA1DDV3pMRAAAAAODzhIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKKRQyPjvf/87e+65Z/76179WaZ81a1bOPPPM7L333tl9991z2mmn5e23367SZ/HixfnTn/6Ujh07ZpdddknPnj3z7LPPFikHAAAAACiBVQ4ZZ8yYkeOOOy5z586t0r548eKccMIJ+fvf/57+/fvnggsuyPPPP5/jjz8+n3zySWW/P/zhD7nhhhtywgkn5NJLL816662Xn/3sZ3nttddW/dUAAAAAAGtctUPGioqKjBkzJt27d8/777+/zPX7778/L774Yq688srsv//+OfDAAzN8+PCUl5fn3nvvTfLpDMhbb701Z599do4++uh06tQp1157bRo2bJjhw4cXf1UAAAAAwBpT7ZDxpZdeSv/+/XPQQQfl4osvXub65MmTs80222T77bevbNtuu+3SvHnzPPbYY0mSKVOmZPHixfnRj35U2We99dbLPvvsk0cffXRVXgcAAAAAUCK1q/sFm2++eSZMmJDvfve7y+zFmCTl5eXZeuutl2lv1qxZ5VLo8vLybLjhhmnUqFGVPltttVXeeeedzJ8/P3Xr1q1uaQAAAABACVQ7ZGzYsOFyr8+ZMydbbbXVMu1169bN/PnzkyRz587NRhtt9KV9kmTevHkrFTLOmjUr77zzTpW2Ro0apXHjxiv8WgAAAABg9ah2yLgiS5cuTVlZ2Ze216r16ersioqKr+yTpLLfiowePTpDhw6t0nbKKafk1FNPrW7ZAAAAAMAqWu0h40YbbZR58+Yt075gwYLK2YvL6/PZ9ZXRs2fPdOrUqUrbF5dgAwAAAABfr9UeMm6zzTZ54YUXlml/4403sssuuyRJtt1228ybNy/vvfdeNtlkk8o+r7/+epo0aZINNthgpZ6rcePGlkYDAAAAQIlV+3TpFWnfvn3Ky8vzyiuvVLa98sorKS8vT7t27ZIkbdu2TZLcf//9lX0WLVqURx55JO3bt1/dJQEAAAAAX6PVPpOxS5cuufrqq3PCCSekT58+SZLBgwdnhx12yI9//OMkSZMmTXLwwQdn4MCB+fjjj7P11lvnuuuuy5w5c9K7d+/VXRIAAAAA8DVa7SHjeuutl+uuuy5/+MMfcv7552fddddNu3bt8utf/zq1a/+/pxswYEDq16+fYcOGZcGCBWnZsmWuu+66Lz2ZGgAAAACouQqFjHvvvXdeeumlZdo333zzZU59/qL11lsvv/nNb/Kb3/ymSAkAAAAAQImt9j0ZAQAAAIBvFyEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAoRMgIAAAAAhQgZAQAAAIBChIwAAAAAQCFCRgAAAACgECEjAAAAAFCIkBEAAAAAKETICAAAAAAUImQEAAAAAAopacj42GOPpXv37vnBD36QfffdN//zP/+TpUuXlrIkAAAAAKCaShYyPvfcc/nlL3+Z5s2b5/LLL0+3bt0yZMiQXH311aUqCQAAAABYBbVL9cRXXHFFWrRokUGDBiVJfvjDH2bx4sW55pprctxxx2WDDTYoVWkAAAAAQDWUZCbjokWL8te//jU/+tGPqrT/13/9VxYsWJBnnnmmFGUBAAAAAKugJCHj9OnT88knn2Trrbeu0r7VVlslSf71r3+t+aIAAAAAgFVSkuXSc+bMSZLUq1evSnvdunWTJPPmzVupx5k1a1beeeedKm2NGjVK48aNV0OVAAAAAMDKKFtaguOcn3322Rx55JG5/vrr06ZNm8r2xYsXp2XLlunTp09OPPHEFT7O5ZdfnqFDh1ZpO+WUU3Lqqaeu9pqLmuf7Y00AACAASURBVDVrVkaPHp2ePXsKQavBuFWfMVs1xq36jNmqMW7VZ8xWjXGrPmO2aoxb9RmzVWPcqs+YrRrjVn3GbNV808atJMul69evn2TZGYvz589PsuwMx6/Ss2fPjB07tsp/PXv2XL3FribvvPNOhg4duszMS5bPuFWfMVs1xq36jNmqMW7VZ8xWjXGrPmO2aoxb9RmzVWPcqs+YrRrjVn3GbNV808atJMulmzVrlnXWWSevv/56lfbP/r3ddtut1OM0btz4G5H0AgAAAMDarCQzGddff/3sueeemTBhQj6/WvuBBx5I/fr1s8suu5SiLAAAAABgFZQkZEySk046KVOnTs3pp5+eRx99NJdeemmuvfba/PznP88GG2xQqrIAAAAAgGpap3///v1L8cRNmzbN9773vTzwwAO56aabMmPGjJx00knp3bt3KcpZI+rWrZtWrVpVnqLNyjFu1WfMVo1xqz5jtmqMW/UZs1Vj3KrPmK0a41Z9xmzVGLfqM2arxrhVnzFbNd+kcSvJ6dIAAAAAwDdHyZZLAwAAAADfDEJGAAAAAKAQISMAAAAAUIiQEQAAAAAoRMgIAAAAABQiZAQAAAAAChEyAgAAAACFCBkBAAAAgEKEjKw13nvvvVKXAAAAAMCXEDJS4z3zzDPp06dP9tlnn1KXslZ45ZVXcv/996e8vLzUpQAAAKzVli5dWuoSYK0hZFyNHn300cybN2+F/WbMmJHf//73a6Citde8efMycuTIHHDAAenVq1fuv//+7L333qUuq0aZOHFiDjzwwIwcObKy7Y9//GMOPPDAnHHGGTnggAMyYMCAElYI327ekMI3g5UUUHMtXry41CWwlrrhhhtWqt+MGTNy9NFHf83V8E3Ws2fP3HbbbVmwYEGpS1kjypb6FLTa7Ljjjhk9enR22WWXJElFRUX233//XHHFFdluu+0q+02dOjWHH354XnjhhVKVWmM9//zzGTVqVO6777589NFH2XbbbdO9e/d069YtjRo1KnV5NcZf//rXHHvssdlpp51y1llnpU2bNnniiSfSu3fv7LXXXvntb3+bV199Needd15++9vf5pBDDil1yTXGokWL8ve//z177LFHkuSjjz7K3XffnRdeeCEbbLBB9txzz3Tq1CllZWUlrnTt88QTT2T8+PEZOHBgqUupEX74wx+me/fuOeSQQ9K0adNSl7NWmTJlSiZNmpQ333wzCxcuTJ06ddKgQYPsvPPO6dixY7bccstSl7hWeuCBB3LTTTet9Aerb7tnnnkmt9xySyZMmJDnn3++1OWsdebOnZuNNtqo1GXUCO+9915uuummPPTQQ5kxY0aWLl2aLbbYIp07d84RRxzhPe4qmDFjRm699daMGTMmkydPLnU5Nd6HH36YN954I1tvvbWfy//Ycccd06lTpwwcODD169f/0j7jxo3LgAED8vHHH/s78B933nlntfofdNBBX1Mla4+f/OQneemll7LhhhumS5cu6dGjR3bddddSl/W1ETKuRi1atMitt95aGTIuWbIkLVu2zJgxY9KyZcvKfkLGqhYuXJhx48Zl1KhReeGFF1K/fv107tw5Y8eOzQ033JC99tqr1CXWOL/4xS+yZMmS/M///E9q1fp0QvKpp56aiRMn5sEHH6wMNC677LL85S9/yejRo0tZbo0xffr09O7dOx999FEee+yx/Pvf/85Pf/rTvPHGG6lfv34++eSTLFy4MLvuumuGDx+eevXqlbrktcqIESNy0UUX+d32H7/85S/z2GOPpaKiIq1atcqhhx6a//N//k/WW2+9UpdWYy1evDhnnHFGJk6cmI033jhz5szJuuuum5133jlvvfVW/v3vf2edddZJ796986tf/arU5a51/Iyu2Lx583LHHXdk9OjRKS8vT61atdK2bdsMGzas1KXVKJ+FZhMnTsyMGTNSUVGRLbbYIj/84Q9z3HHHpVGjRjnttNPSrFmznHXWWaUut6SeeuqpnHHGGXnvvffSokWLNGvWLLVr18706dMzbdq0bLTRRhkyZEjatGlT6lJrvKVLl2bSpEkZNWpUnnjiiSxZsiTbbrtt7r333lKXVmM8//zzufLKK/PjH/+4MtwZOXJkLrnkkixatCjrr79+Tj311PTu3bvElZbeo48+mnPOOSd169bN4MGDs9tuu1VemzdvXi644IKMHz8+zZs3zyWXXJIWLVqUsNqaY2XG4fOTNbzn+NSLL76YO++8M+PHj8/s2bPTvHnz9OjRI926dcsmm2xS6vJWq9qlLoBvtwsuuCDjxo3LwoUL07p16wwePDidO3fORx99lDFjxpS6vBpr6tSpueCCCyoDxoqKikyZMiXbbbddlRlTrVq1yogRI0pVZo3zxz/+MbVq1cqVV16Z5NPvv0WLFmX06NH5wQ9+kOTTGVRnnXVWLr74YsvNKeTKK6/MBx98kHHjxuXOO+9Mnz590qBBg3Tr1i09evTI9773vVKXWONcddVVefHFF3PPPfekefPm+fDDD9OnT5/stttuOfnkkzN79uyMGDEi11xzTRo3bpyjjjqq1CXzDfFlKynOOussKym+xBdDs7Zt26ZWrVqZPn16RowYkVtvvTXHH398Hn744dx1112lLrekZs6cmVNPPTXNmzfPjTfemG233bbK9enTp+c3v/lNzjjjjNx111357ne/W6JKa7ZZs2bltttuy+23356ZM2emfv366dmzZw466KDKyR18GuYcffTR2WSTTdK9e/ckn/5uu/DCC7Pddtvl9NNPz6uvvpohQ4Zkq622SufOnUtccWl17Ngxd9xxR84444z06tUrp512Wk488cQ899xzOfvss/PWW2/l6KOPztlnn+0G8ec89NBDy70+ZcqU/PGPf8y8efMsM/+cFi1apG/fvjnnnHPy+OOP54477siQIUMyePDgdOrUKYcddljatWtX6jJXCyEjJXXLLbfke9/7Xvr165fdd9+9sv3jjz8uYVU139y5c6vc8XjppZcyb968ZfatrFWrVioqKtZ0eTXW008/nQEDBmTnnXdOkvzlL3/JH/7wh8qAMUnatGmTPn36ZNCgQUJGCmvYsGF69eqVXr165ZVXXskdd9yR8ePH58Ybb0zLli1z2GGHpUuXLmbN/sedd96Z008/Pc2bN0+SNGjQIL/5zW9yyCGHpHfv3tl0001z5plnpqysLDfffLOQkUK+bCVFly5dMnbs2PTv399Kii+xsqHZ5ZdfnuOPP77yZ/nb6vrrr0/Dhg0zfPjwbLjhhstcb9q0aYYPH57u3btnxIgROffcc0tQZc31xBNPZNSoUZk0aVKWLl2avffeOzNnzszQoUP9fH6Ja665JjvuuGOuv/761KlTJ0kq924fNGhQ5Qy0d999NyNHjvzWh4xJssUWW+Tmm2/OkCFDctlll+Xee+/NK6+8kk033TTXXntt2rZtW+oSa5wmTZp8afuCBQty8cUX59Zbb03Tpk1z1VVXZc8991zD1dV8tWrVSseOHdOxY8fMmzcv99xzT+6666787Gc/y+abb57u3bvnlFNOKXWZhTj4hZI66aSTMnfu3Bx11FHp2rVrhg8fnnfffbfUZdV4m222Wd56663Kf0+ZMiVlZWVp3bp1lX4vvPCCGRifs3jx4mywwQaV/65Tp0423XTTZfptttlm+eijj9ZkaXwLbLfddjn77LPzyCOPZNiwYdl6663zxz/+MR06dMivf/3rUpdXI7zzzjvZeOONq7RtttlmWbhwYWbMmFHZ1qZNmyq/A6G6LrjggnTo0CEXXHBBNt544wwePDiPP/54zj33XIc2LcfnQ7MvBozJp6HZ97///SxdutTf0SSTJk3KMccc86UB42fWX3/9HHPMMZk0adIarKxmGz58eH70ox+ld+/eeeWVV3LaaaflkUceyaWXXurnczmefvrp9OrVqzJgTJLJkyenadOmVZa4tm/fPtOmTStFiTVS7dq1c8QRR2S77bbLiy++mIqKipx99tkCxmqYMmVKDjzwwNx6663p1atX7r77bgHjSqhXr1569uyZm2++OTfccEPWW2+9XHHFFaUuqzAhIyV1+umn56GHHsqwYcOyww475L//+7+zzz775LTTTktZWZlZeF+hXbt2GTFiRObPn5+5c+dm9OjRqVevXjp06FDZ54MPPsgNN9zgVO7P2WuvvXL11Vdn0aJFSZKuXbvm5ptvrtLnk08+yZ///GfLb/jalJWVpX379rn44ovzpz/9Kd/5zneqvYn2N9VWW22VsWPHVmmbMGFCatWqlS222KKy7eGHH3b4C4XccsstadKkSUaOHJlrr702Xbp0yXrrrefQrxVYUWj27rvv5oYbbsiBBx7oMI58OvNz++23X2G/5s2bZ+bMmWugorXDJZdckjp16mTkyJG57777cuKJJ6ZRo0Z+Plfggw8+qLLkvry8PO+///4ynwXq1KlT+V6YZOzYsfnJT36St956K/369cvee++ds88+O/3797e6bgUWLFiQfv365fjjj8+6666bG2+8Mb/+9a+z/vrrl7q0tcLbb7+dP//5zzn44INzzDHHZPHixTn11FNLXVZhlkuvZtOmTav8ZbRkyZKUlZVl2rRpVY4rf/nll0tVXo302Qfu9u3bZ86cObn77rszduzYLF26NL/4xS+y3377pWvXrunQoUNq1/YtmyQnn3xy5b4NZWVlWbhwYfr161f5C33o0KEZM2ZMPvzww/z85z8vcbU1xznnnJMjjjgi+++/fw499NDsuuuuGTRoULp3754OHTpk0aJFmTBhQmbNmuX01c855phjVqqfD0gr53//938zbty43Hffffnggw+yxx575KSTTip1WTXCcccdl9/85jeZO3du9t1337z55pu56aab0r1799SpUyfPPfdcLr744kydOjWDBw8udbk1RqdOnVbqw/e8efPWQDVrh5NOOil33XVXjjrqqGy77bY5+OCDc9BBB/lgtAJvv/32ckOzioqKnH766dltt90yYcKENVhZzVSnTp3MmTNnhf0++OADJ/5+Trdu3TJhwoT87Gc/S+vWrXPQQQdlv/32K3VZNV7Dhg2rrAh78sknU1ZWtsyhQuXl5d+4gyZWxYcffpjzzz8/Dz74YHbfffcMGjQoTZo0yeGHH56rr746V1xxRZ555hmHvnyFKVOm5Le//W1mzpyZY489NmeccYa/oSth3rx5eeCBBzJu3Lg8/fTTqV27djp37pxzzjnnG3MAmNOlV6MWLVqkrKxsudP4P7teVlbmpKUVePHFF3P77bdn/Pjx+fDDD9OgQYM8+eSTpS6rxnj33XczevTozJ49O/vss09++MMfVl7r1KlTvvOd7+T888/PDjvsIJz9nJkzZ+ayyy7LxIkTM3fu3CrXatWqlT322CN9+vTJrrvuWqIKa55evXpVq/9n+//w//zrX//K3XffnfHjx2f69Olp3LhxDjrooBxyyCFp1qxZqcurUUaMGJErr7wyH374YWrXrp39998/AwYMSJ06dfLII49k1KhR6dWr1zdmc+zVobrL7QcOHPg1VbJ2Wbp0aZ544omMGTMmDz30UCoqKrLXXnvlySefzPXXX28lwJfo0KFD+vXrt8K93CZOnJj+/ft/62cznnDCCalfv/4Kb4r06dMnCxYsyFVXXbWGKqv55s+fn/Hjx2fs2LGZOnVqGjRokP322y933HFHRo4caSnmlzjzzDPz/vvv59prr01FRUUOO+ywvPrqq3n88ccrQ+xFixblkEMOyQ477PCtv1nXoUOHvP/++zn55JPz85//vPJAzc88++yzOeusszJ79uyceeaZOfbYY0tTaA0zf/78/PGPf8xtt92WbbbZJgMHDqyyvz3LWrx4cR599NHcfffdeeSRR/Lxxx9np512yiGHHJJu3bp9424yCRlXo6eeeqpa/Vu1avU1VfLN8tprr+Xkk0/Oa6+9JphdSRUVFXnrrbdy2223ZcyYMd/6N/lf5c0338x7772XTz75JBUVFSkrK0vz5s2X2ROOqioqKvLBBx8kSTbeeGPLl77C7Nmzc8899+Tuu+/OP/7xj6yzzjrZd99906NHj3To0GGZN7Mk48aNS4cOHVK/fv28++67qV+/fpV9VPlyn41bw4YNS13KWuvzKymmTZuWOnXqZL/99ssBBxyQ9u3bu1n3HyeddFLq1q2bSy65ZLn9+vTpk/nz5+fqq69eQ5XVTA8//HB++ctf5sILL6w87feLbrvttvTr1y/Dhg1z8+QrlJeX5/bbb8+4cePy7rvvpkmTJjnggAPStWvX7LDDDqUur8Z4+eWX07Nnz8q/BW+99VZOPvnkyuWXY8aMyU033ZR//vOfue2227LjjjuWstyS+6//+q8MHjy48kDILzNnzpz07ds3kyZN8jn0Pzp16pR///vflae8L+/k7bKyspx88slrsLqaae+9986cOXNSv379HHjggenRo8c3enaskPFr8Nprr+Wxxx7Lm2++mYULF6ZOnTpp0KBBWrZsmVatWqVu3bqlLrHGW7p0aSZNmpRRo0bliSeeyJIlS9K8efPcc889pS6tRvuycdt2221z7733lrq0GqO8vDxjx45NrVq1csghh2TrrbfOZZddlmHDhmXJkiVZZ5110qNHj5x//vlZZ511Sl1ujTJ+/PiMGjUqU6dOzeLFi5MkG2ywQXbfffccccQRTin8gpYtW2bJkiXZbrvtcsghh+QnP/mJ5UkrsOOOO2b06NFV9kS9+uqr06NHj2y22WYlrKxmM26r1+dXUnzwwQdp2LChlRT/8dhjj+XnP/95/vCHP6wwNLvmmmvSvn37NVxhzXPBBRfklltuSfv27dOpU6dsueWWWXfddfPmm2/mgQceyBNPPJFjjjnGAWArYcmSJZk0aVJuv/32TJ48OUuWLMn222+fu+++u9Sl1Rgvv/xyrrvuusqVTkcccUTltc+2nurfv386duxYwiprhh49eqRnz57p2rXrcg9nSj5dpVPdlT3fVNUJx6ze/NTxxx+fbt26Ve7//E0nZFzNLrrootxwww2VB5asv/762WSTTTJr1qwsWbIkDRo0SJ8+fXLYYYeVuNKaadasWbntttty++23Z+bMmalfv366du2agw46yEEcy2HcVs7TTz+d3r17p1atWll//fWzaNGinHTSSRkyZEjlHaW//e1vGT9+fM4888yceOKJpS65RqioqMhZZ52Ve++9N40bN06bNm0qg4u33347Tz31VN5555385Cc/yUUXXVTiamuO888/Pz169FjhEpKFCxfm1VdfTcuWLddQZTVXixYtcuutt1b+3lqyZEl23nnn3H777cZnOYzb1+OTTz7JxIkTM3bs2AwbNqzU5dQYv//973PjjTemQ4cO6dy5c5o0aZKysrJMnz49Dz74YKZMmSI0+4KbbropV111VZX98pKkcePGOeWUU3wuWAWzZ8/O2LFjc+edd5qEsJLefvvtNGrUyEqK//jJT36Sl156KRtuuGG6dOmSHj162C5pJYwaNSrt27d3AF81tGjRIjvssEMGDRqU733ve6Uu52tn7cdqdMMNN+Suu+7KVVddlVatWuX111/Peeedl549e6Z79+6ZNm1arr/++vTr1y/16tVLly5dSl1yjfHEE09k1KhRmTRpUpYuXZq99947M2fOzNChQ7PXXnuVurway7hVz9ChQ9OqVatcfvnlqVOnTgYPHpwhQ4bkpz/9afr27ZskOeqoo7LZZptl3LhxQsb/uPnmm3P//fenb9++OeaYY5Z5c1pRUZFbbrklF154YTp06JCuXbuWqNKaZeLEiVVmECRfPrvsn//8Zw4//HB3er+Ce6Grxrgt38oeaMWyzjvvvOywww656qqr8vjjj1e5tvnmm2fAgAE59NBDS1RdzXTUUUfliCOOyAsvvJDp06enoqIizZo1S8uWLW058iW+KqAuKyvLhhtumEaNGqV169Y54YQTcsIJJ6zh6mquFY3bZpttljZt2tg/7z/uuuuuvPjii7nzzjszfvz4jBkzJs2bN0+PHj3SrVs3q0++Qv/+/VNWVpZmzZqlffv2adeuXVq3br3C2aDfZsOGDUu/fv3So0ePnHrqqTnhhBO+0b/7hYyr0S233JIzzjijcvp5ixYtMmDAgBx33HE56KCD8v3vfz+DBw9O3bp1M3z4cCFjkuHDh+fWW2/NG2+8kW222SannXZaDj744Ky//vr2rFwO47Zqpk2blosuuih16tRJkvz0pz/NsGHDqhyakySdO3fOqFGjSlFijTR27Nj07NnzKze8rlWrVo466qi88sorufXWW4WM//H+++9XLitPPp1ddtlll6VDhw6WsEKJfVkI+/TTT2ennXZaZlubb/IHgVV12GGH5bDDDsu0adMyY8aMLF26NE2aNDFz9iuUl5dn2LBheeihhypPed9www3TuXPn9O7d276CX/DXv/71K68tWrQo77//fi699NJ07dp1hfuDfpuszLhddtllxu1zWrRokb59++acc87J448/njvuuCNDhgzJ4MGD06lTpxx22GH2Sv2CcePG5amnnsqzzz6bCRMm5Kabbkrt2rXzgx/8IO3atUv79u3z/e9/39/Oz+nQoUPGjx+fSy65JEOGDMmkSZNy8cUXp2nTpqUu7WshZFyNZsyYscw3SrNmzfLhhx/mjTfeSPPmzZMkXbt2zbhx40pRYo1zySWX5Hvf+15GjhxZZebdF0/9pSrjtmrmzp1b5a7kZxtj169fv0q/ddddNx9//PEara0m+9e//pXTTjtthf06dOiQiRMnroGK1l5ml0HNMHLkyCr/Xrx4cXbeeef83//7fwVly1GdJdBlZWW58MILv8Zq1g733ntv+vbtm3XWWSdt27ZNs2bNUrt27UyfPj0PP/xw7rvvvlx44YU54IADSl1qjfHwww8v9/qiRYsyceLEnHfeebnpppty1FFHraHKajbjtupq1aqVjh07pmPHjpk3b17uueee3HXXXfnZz36WzTffPN27d88pp5xS6jJrhO233z7bb7995ffPG2+8kaeeeirPPPNMxo4dm//+7/9OgwYN0rp167Rv397M9v/YcMMN87vf/S4HHHBA+vfvn27duuXcc89dZrJLkmyxxRYlqHD1ETKuRt/97nczceLEtG3btrLtySefTFlZWb7zne9Utj333HNp3LhxKUqscbp165YJEybkZz/7WVq3bp2DDjoo++23X6nLqvGM26r7/GEun91hc6dt+RYuXJgGDRqssN/GG2+c9957bw1UxLeNn9FVY9xWnrFaOXfccUfl+9oV7etmTD+dwfjrX/86++yzTwYMGLDMCfDz5s1Lv379ct5552XHHXesnJDA8q233nrp0qVLXnvttYwZM0ZYtpKM28qpV69eevbsmZ49e+bpp5/O+eefnyuuuELI+BWaNWuWZs2apUePHkk+zT9GjRqViRMn5sEHHxQyfsHuu++esWPH5rjjjssFF1zwpX3W9i2UhIyrUc+ePTNo0KAsWbIk++67b2bMmJHLL788++23X+rVq5eXXnop11xzTe677z6bYf/HxRdfnPnz52f8+PEZO3ZsfvWrX6VBgwbZb7/9UlZW5g3qVzBuq5fxWr6lS5eu1EnbtWrVqjz0ClbVySefvMzJe7/4xS+y7rrrVmkrKyszc/ZzjBtrwv77759HHnkkH3/8cfbff/907do1e+yxR6nLqrGuv/76bLfddhkyZMiX/h2tV69eBg0alCOPPDIjRozIgAEDSlDl2mv33XfPddddV+oy1jrGbfnefvvt3HPPPRk3blxefPHFNGnSJKeeemqpy6qxZs6cmccffzx/+ctf8tRTT+W9995L/fr107lz57Rv377U5dU4//jHPzJgwIBMnTo1Xbp0SYcOHUpd0monZFyNjj/++Lz99tu56aabMnr06CSf/hL/7A1DeXl5/vnPf6Z///5OkfucunXrVt4tKi8vz+23355x48Zl6dKlOffcc3PAAQeka9eu9qv5AuO2avr375969eol+X9LV88///wqe3B9tl8SsGYdfPDBpS5hrWTcWFOGDBmSjz76KA8//HDuvffeHHfccdl0003TtWvXdO3aNTvuuGOpS6xRpkyZkpNOOmm5N+pq1aqVww8/PEOHDl2DlX0zrLPOOlmyZEmpy1jrGLdlzZs3Lw888EDGjRuXp59+OrVr107nzp1zzjnnpE2bNqUur0ZZtGhRnn766Tz++OOZPHlyysvLs84662SXXXbJUUcdZU/Gr/Dxxx/n0ksvzciRI9OwYcMMHTo0nTt3LnVZX4uypTaIWu3ef//9/Otf/8omm2ySrbbaqtTlrJWWLFmSSZMm5fbbb8/kyZOzZMmSbL/99rn77rtLXVqNZtyWr1evXtXq/8U9u76tWrRokZ122qkynP0q8+bNywsvvLDWT/FfXVq0aJFbb701u+yyS5JPfz5btmyZsWPHZqeddqrsN3XqVKdLQ4l99vM5ZswYezJWw7x58zJhwoTce++9mTJlSrbccssccMAB6dKlS7bddttSl1dyu+yyS/785z9nzz33XG6/Z555Jr17987UqVPXUGXfDNddd11uv/323HPPPaUuZa1i3D61ePHiPProo7n77rsrZ2jvtNNOOeSQQ9KtW7dstNFGpS6xRtptt93y0UcfpWnTpmnbtm3at2+f1q1br/BzwrfZX/7yl/Tr1y/Tp09P165dc/755y+zfcY3iZmMX4ONN944G2+8canLWKuts8466dy5czp37pzZs2dn7NixufPOO0tdVo1n3JZPaLhqPjtcaEX3pOrWrbvCD1LfNiuzhHXRokVruizgK5h5UT316tXLwQcfnIMPPjgffPBBJkyYkPvuuy9XX311dthhh4wdO7bUJZZU/fr1M2vWrBX2e+edd6ocTMeK/f3vf88111xjdVg17Y83zwAAB3FJREFUGbf/p127dpkzZ07q16+fQw89ND169EiLFi1KXVaNt3DhwjRs2DCtW7dO69at06pVKwHjChx//PHZbLPNcsUVV3wrzlEwkxEAvgbV3Xt34MCBX1MlwBd16tRpmUBxxowZady4sX0sV9Fbb72VBx54IA888ECmTp2a+vXr569//Wupyyqp0047LQsWLMjw4cOX2+/EE0/MRhttlMGDB6+hymq2Y4455iuvLVq0KLNmzcq///3v7LTTTrnpppuywQYbrMHqai7jVj3HH398evTokc6dOy9zQ5iv9uKLL+axxx7L5MmT87e//S0VFRX5/ve/n/bt26d9+/b5wQ9+4IbdF5x99tk577zzVuogzW8CISMAAN8qffv2rdaHIDcBvtzMmTNz//3357777svzzz+fevXqZb/99sv++++fdu3apXbtb/eiqWeffTZHH310Tj755K88mXbIkCEZPnx4brnllsrtNb7tvmp7m7Kysmy44YbZbLPN0qpVq3Tp0uVb/z32ecaNNW3BggWZMmVKJk+enMmTJ2f69OmpX79+2rRpUxk6br755qUukzVMyAgAAKyUt99+O/fdd1/uv//+/O///m823HDD7LvvvpWnZJoRVNU111yTP/3pT9l2222z7777Zsstt0zt2rUzY8aMTJgwIa+++mrOPffcHHvssaUuFaCQ119/PU888USefPLJPPLII1m8eHGmTZtW6rJYw4SMAADACh1xxBGZOnVq1l9//XTs2DFdunRJx44ds/7665e6tBpt0qRJGTp0aP7xj39Uad91111zyimnpH379iWqDKC42bNn529/+1ueffbZPP/885k2bVo++eST7LrrrrnxxhtLXR5rmJARAABYoRYtWqRWrVpp2bJl6tSps9y+ZWVlGTFixBqqbO3w/vvvZ8aMGVm6dGmaNGnisBdgrVReXp7nnnuu8r833ngjSbLDDjukbdu2lQfCrOjvBN9MNmMAAABWaK+99qr8/xXNUzCPYVkbb7xxNt5441KXAbDKWrVqlblz51beLGnTpk1OP/30tG7d2o0TkpjJCAAAAMAKnHbaaWnXrl3atm2bpk2blrocaiAhIwAAAABQSK1SFwAAAAAArN2EjAAAAABAIUJGAAAAAKAQISMAACXXt2/f9OrVq9RlAACwihz8AgBAyc2dOzdLlixJw4YNS10KAACrQMgIAAAAABRSu9QFAABQsy1YsCB/+tOfcv/992f+/PnZaaedcu6552aXXXbJmDFjMnLkyLz66qupVatWvv/976dv375p2bJlkqRTp07p3LlzJk+enNmzZ+eyyy7LFVdckaZNm+bll1/Oa6+9lvPOOy9PPvlkZsyYkZEjRyZJysvLc9FFF+WZZ55J3bp1s/fee6dv375p1KhRkqRXr17LPMZBBx1UsjECAPi2sycjAADL9atf/SqTJk3KhRdemDvvvDPbbLNNevfunfvuuy/9+vXLsccem/vuuy8jRozIRx99lN/+9rdVvv6WW27Jeeedl+HDh2f33XdPkowdOzbHHHNMbrnllnTs2LFK/7fffjtHHnlkmjZtmttvvz1XX3115s2bl8MPPzwLFiyo7Le8xwAAYM0ykxEAgK/02muv5ZFHHsnw4cPToUOHJMnvfve71K1bN5tuuml+//vfV84gbNKkSQ499ND069evymN07Ngxbdu2rdK244475sADD/zS57zlllvSuHHj/O53v6tsu/TSS9O6devcf//96d69+wofAwCANUvICADAV3rppZeSJLvuumtl23rrrZdf//rXST5d1nzllVfm9ddfz2uvvZYXXnghFRUVVR5jq622WuZxv6ztM9OmTUt5eXl22223Ku0ff/xxysvLV+oxAABYs4SMAAB8pdq1P327WFZWtsy1e+65J+ecc04OOOCA7LLLLunRo0f++c9/ZsCAAVX6bbDBBst87Ze1faaioiKtW7deZkZkkmy00UYr9RgAAKxZQkYAAL5S8+bNkyR///vf06ZNmyTJ4sWL07lz56y//vrp0aNHLrjggsr+Dz30UJJk6dKlXxpMroztt98+9957bzbffPOst956SZIPPvgg5557bo477ri0bt26yEsCAOBr4OAXAAC+0jbbbJMf/ehHueCCCzJlypS89tpr+d3vfpdFixZlq622ynPPPZd//OMfeeONN3L99dfnxhtvTJIsWrRolZ/zyCOPzNy5c3PmmWfmhRdeyIsvvpg+ffrk+eefz/bbb7+6XhoAAKuRkBEAgOUaOHBgWrVqlV/96lfp3r173nrrrfz5z3/O+eefn8022yxHH310Dj300EyaNCkXX3xxkmTq1Kmr/HxNmzbNjTfemIULF+bII4/M0UcfnbKysowYMSKbbrrp6npZAACsRmVLly5dWuoiAAAAAIC1l5mMAAAAAEAhQkYAAAAAoBAhIwAAAABQiJARAAAAAChEyAgAAAAAFCJkBAAAAAAKETICAAAAAIUIGQEAAACAQoSMAAAAAEAhQkYAAAAAoBAhIwAAAABQiJARAAAAACjk/wd4VYAzD1cjSgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14435b6cfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flightSpeed.plot(kind='bar', figsize=[16,8], title =\"Average flight speed across carriers\",  legend=False, fontsize=12)\n",
    "plt.gca().set_facecolor('white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HA is best in terms of average flight speed with 480 miles/hr<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.6 Conclusion\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In 2013, around 39% of flight got delayed and most of them were in June, July and December months. From flight frequency graph we can see that air traffic is more in these 3 months, So this is on the reason flights got delayed in June, July and December months.<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parveen Dala<br>+91 904 141 1410<br>dParveen94@gmail.com"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}