From 9459ef54faba13c8036bcf95546ee0922a0cd373 Mon Sep 17 00:00:00 2001
From: BIAOU Olagnika Bruno <88730002+bbTKbj@users.noreply.github.com>
Date: Mon, 17 Jul 2023 19:03:10 +0100
Subject: [PATCH] Update 01 - Flights Challenge.ipynb

---
 challenges/01 - Flights Challenge.ipynb | 1400 ++++++++++++++++++++++-
 1 file changed, 1392 insertions(+), 8 deletions(-)

diff --git a/challenges/01 - Flights Challenge.ipynb b/challenges/01 - Flights Challenge.ipynb
index 6f216da..7543edd 100644
--- a/challenges/01 - Flights Challenge.ipynb	
+++ b/challenges/01 - Flights Challenge.ipynb	
@@ -6,16 +6,214 @@
    "source": [
     "# Flights Data Exploration Challenge\n",
     "\n",
-    "In this challenge, you'll explore a real-world dataset containing flights data from the US Department of Transportation.\n",
+    "In this challge, you'll explore a real-world dataset containing flights data from the US Department of Transportation.\n",
     "\n",
     "Let's start by loading and viewing the data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "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>Year</th>\n",
+       "      <th>Month</th>\n",
+       "      <th>DayofMonth</th>\n",
+       "      <th>DayOfWeek</th>\n",
+       "      <th>Carrier</th>\n",
+       "      <th>OriginAirportID</th>\n",
+       "      <th>OriginAirportName</th>\n",
+       "      <th>OriginCity</th>\n",
+       "      <th>OriginState</th>\n",
+       "      <th>DestAirportID</th>\n",
+       "      <th>DestAirportName</th>\n",
+       "      <th>DestCity</th>\n",
+       "      <th>DestState</th>\n",
+       "      <th>CRSDepTime</th>\n",
+       "      <th>DepDelay</th>\n",
+       "      <th>DepDel15</th>\n",
+       "      <th>CRSArrTime</th>\n",
+       "      <th>ArrDelay</th>\n",
+       "      <th>ArrDel15</th>\n",
+       "      <th>Cancelled</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2013</td>\n",
+       "      <td>9</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1</td>\n",
+       "      <td>DL</td>\n",
+       "      <td>15304</td>\n",
+       "      <td>Tampa International</td>\n",
+       "      <td>Tampa</td>\n",
+       "      <td>FL</td>\n",
+       "      <td>12478</td>\n",
+       "      <td>John F. Kennedy International</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>NY</td>\n",
+       "      <td>1539</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1824</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2013</td>\n",
+       "      <td>9</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1</td>\n",
+       "      <td>WN</td>\n",
+       "      <td>14122</td>\n",
+       "      <td>Pittsburgh International</td>\n",
+       "      <td>Pittsburgh</td>\n",
+       "      <td>PA</td>\n",
+       "      <td>13232</td>\n",
+       "      <td>Chicago Midway International</td>\n",
+       "      <td>Chicago</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>710</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>740</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2013</td>\n",
+       "      <td>9</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS</td>\n",
+       "      <td>14747</td>\n",
+       "      <td>Seattle/Tacoma International</td>\n",
+       "      <td>Seattle</td>\n",
+       "      <td>WA</td>\n",
+       "      <td>11278</td>\n",
+       "      <td>Ronald Reagan Washington National</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>DC</td>\n",
+       "      <td>810</td>\n",
+       "      <td>-3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1614</td>\n",
+       "      <td>-7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2013</td>\n",
+       "      <td>7</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1</td>\n",
+       "      <td>OO</td>\n",
+       "      <td>13930</td>\n",
+       "      <td>Chicago O'Hare International</td>\n",
+       "      <td>Chicago</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>11042</td>\n",
+       "      <td>Cleveland-Hopkins International</td>\n",
+       "      <td>Cleveland</td>\n",
+       "      <td>OH</td>\n",
+       "      <td>804</td>\n",
+       "      <td>35</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1027</td>\n",
+       "      <td>33</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2013</td>\n",
+       "      <td>5</td>\n",
+       "      <td>16</td>\n",
+       "      <td>4</td>\n",
+       "      <td>DL</td>\n",
+       "      <td>13931</td>\n",
+       "      <td>Norfolk International</td>\n",
+       "      <td>Norfolk</td>\n",
+       "      <td>VA</td>\n",
+       "      <td>10397</td>\n",
+       "      <td>Hartsfield-Jackson Atlanta International</td>\n",
+       "      <td>Atlanta</td>\n",
+       "      <td>GA</td>\n",
+       "      <td>545</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>728</td>\n",
+       "      <td>-9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Year  Month  DayofMonth  DayOfWeek Carrier  OriginAirportID  \\\n",
+       "0  2013      9          16          1      DL            15304   \n",
+       "1  2013      9          23          1      WN            14122   \n",
+       "2  2013      9           7          6      AS            14747   \n",
+       "3  2013      7          22          1      OO            13930   \n",
+       "4  2013      5          16          4      DL            13931   \n",
+       "\n",
+       "              OriginAirportName  OriginCity OriginState  DestAirportID  \\\n",
+       "0           Tampa International       Tampa          FL          12478   \n",
+       "1      Pittsburgh International  Pittsburgh          PA          13232   \n",
+       "2  Seattle/Tacoma International     Seattle          WA          11278   \n",
+       "3  Chicago O'Hare International     Chicago          IL          11042   \n",
+       "4         Norfolk International     Norfolk          VA          10397   \n",
+       "\n",
+       "                            DestAirportName    DestCity DestState  CRSDepTime  \\\n",
+       "0             John F. Kennedy International    New York        NY        1539   \n",
+       "1              Chicago Midway International     Chicago        IL         710   \n",
+       "2         Ronald Reagan Washington National  Washington        DC         810   \n",
+       "3           Cleveland-Hopkins International   Cleveland        OH         804   \n",
+       "4  Hartsfield-Jackson Atlanta International     Atlanta        GA         545   \n",
+       "\n",
+       "   DepDelay  DepDel15  CRSArrTime  ArrDelay  ArrDel15  Cancelled  \n",
+       "0         4       0.0        1824        13         0          0  \n",
+       "1         3       0.0         740        22         1          0  \n",
+       "2        -3       0.0        1614        -7         0          0  \n",
+       "3        35       1.0        1027        33         1          0  \n",
+       "4        -1       0.0         728        -9         0          0  "
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "\n",
@@ -61,24 +259,1210 @@
     "    - Use statistics, aggregate functions, and visualizations to answer the following questions:\n",
     "        - *What are the average (mean) departure and arrival delays?*\n",
     "        - *How do the carriers compare in terms of arrival delay performance?*\n",
-    "        - *Is there a noticable difference in arrival delays for different days of the week?*\n",
+    "        - *Are some days of the week more prone to arrival days than others?*\n",
     "        - *Which departure airport has the highest average departure delay?*\n",
     "        - *Do **late** departures tend to result in longer arrival delays than on-time departures?*\n",
     "        - *Which route (from origin airport to destination airport) has the most **late** arrivals?*\n",
     "        - *Which route has the highest average arrival delay?*\n",
     "        \n",
-    "Add markdown and code cells as required to create your solution.\n",
+    "Add markdown and code cells as requried to create your solution.\n",
     "\n",
     "> **Note**: There is no single \"correct\" solution. A sample solution is provided in [01 - Flights Challenge.ipynb](01%20-%20Flights%20Solution.ipynb)."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Clean missing values\n",
+    "\n",
+    "Find how many null values there are for each column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year                    0\n",
+       "Month                   0\n",
+       "DayofMonth              0\n",
+       "DayOfWeek               0\n",
+       "Carrier                 0\n",
+       "OriginAirportID         0\n",
+       "OriginAirportName       0\n",
+       "OriginCity              0\n",
+       "OriginState             0\n",
+       "DestAirportID           0\n",
+       "DestAirportName         0\n",
+       "DestCity                0\n",
+       "DestState               0\n",
+       "CRSDepTime              0\n",
+       "DepDelay                0\n",
+       "DepDel15             2761\n",
+       "CRSArrTime              0\n",
+       "ArrDelay                0\n",
+       "ArrDel15                0\n",
+       "Cancelled               0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_flights.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hmm, looks like there are some null \"late departure\" indicators. Departures are considered late if the delay is 15 minutes or more, so let's see the delays for the ones with a null late indicator:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>DepDelay</th>\n",
+       "      <th>DepDel15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>359</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>429</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>545</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>554</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>271410</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>271607</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>271634</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>271671</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>271885</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2761 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        DepDelay  DepDel15\n",
+       "171            0       NaN\n",
+       "359            0       NaN\n",
+       "429            0       NaN\n",
+       "545            0       NaN\n",
+       "554            0       NaN\n",
+       "...          ...       ...\n",
+       "271410         0       NaN\n",
+       "271607         0       NaN\n",
+       "271634         0       NaN\n",
+       "271671         0       NaN\n",
+       "271885         0       NaN\n",
+       "\n",
+       "[2761 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_flights[df_flights.isnull().any(axis=1)][['DepDelay','DepDel15']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can't see them all in this display, but it looks like they may all have delay of 0. Let's check by looking at the summary statistics for these records:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    2761.0\n",
+       "mean        0.0\n",
+       "std         0.0\n",
+       "min         0.0\n",
+       "25%         0.0\n",
+       "50%         0.0\n",
+       "75%         0.0\n",
+       "max         0.0\n",
+       "Name: DepDelay, dtype: float64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_flights[df_flights.isnull().any(axis=1)].DepDelay.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The min, max, and mean are all 0; so it seems that none of these were actually *late* departures. Let's replace the missing **DepDel15** indicator with a 0 and confirm there are no more missing values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year                 0\n",
+       "Month                0\n",
+       "DayofMonth           0\n",
+       "DayOfWeek            0\n",
+       "Carrier              0\n",
+       "OriginAirportID      0\n",
+       "OriginAirportName    0\n",
+       "OriginCity           0\n",
+       "OriginState          0\n",
+       "DestAirportID        0\n",
+       "DestAirportName      0\n",
+       "DestCity             0\n",
+       "DestState            0\n",
+       "CRSDepTime           0\n",
+       "DepDelay             0\n",
+       "DepDel15             0\n",
+       "CRSArrTime           0\n",
+       "ArrDelay             0\n",
+       "ArrDel15             0\n",
+       "Cancelled            0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_flights.DepDel15 = df_flights.DepDel15.fillna(0)\n",
+    "df_flights.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Clean outliers\n",
+    "\n",
+    "View the distribution and summary statistics for the **DepDelay** and **ArrDelay** columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DepDelay \n",
+      "Minimum:-63.00\n",
+      "Mean:10.35\n",
+      "Median:-1.00\n",
+      "Mode:-3.00\n",
+      "Maximum:1425.00\n",
+      "\n",
+      "ArrDelay \n",
+      "Minimum:-75.00\n",
+      "Mean:6.50\n",
+      "Median:-3.00\n",
+      "Mode:0.00\n",
+      "Maximum:1440.00\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Function to show summary stats and distribution for a column\n",
+    "def show_distribution(var_data):\n",
+    "    from matplotlib import pyplot as plt\n",
+    "\n",
+    "    # Get statistics\n",
+    "    min_val = var_data.min()\n",
+    "    max_val = var_data.max()\n",
+    "    mean_val = var_data.mean()\n",
+    "    med_val = var_data.median()\n",
+    "    mod_val = var_data.mode()[0]\n",
+    "\n",
+    "    print(var_data.name,'\\nMinimum:{:.2f}\\nMean:{:.2f}\\nMedian:{:.2f}\\nMode:{:.2f}\\nMaximum:{:.2f}\\n'.format(min_val,\n",
+    "                                                                                            mean_val,\n",
+    "                                                                                            med_val,\n",
+    "                                                                                            mod_val,\n",
+    "                                                                                            max_val))\n",
+    "\n",
+    "    # Create a figure for 2 subplots (2 rows, 1 column)\n",
+    "    fig, ax = plt.subplots(2, 1, figsize = (10,4))\n",
+    "\n",
+    "    # Plot the histogram   \n",
+    "    ax[0].hist(var_data)\n",
+    "    ax[0].set_ylabel('Frequency')\n",
+    "\n",
+    "    # Add lines for the mean, median, and mode\n",
+    "    ax[0].axvline(x=min_val, color = 'gray', linestyle='dashed', linewidth = 2)\n",
+    "    ax[0].axvline(x=mean_val, color = 'cyan', linestyle='dashed', linewidth = 2)\n",
+    "    ax[0].axvline(x=med_val, color = 'red', linestyle='dashed', linewidth = 2)\n",
+    "    ax[0].axvline(x=mod_val, color = 'yellow', linestyle='dashed', linewidth = 2)\n",
+    "    ax[0].axvline(x=max_val, color = 'gray', linestyle='dashed', linewidth = 2)\n",
+    "\n",
+    "    # Plot the boxplot   \n",
+    "    ax[1].boxplot(var_data, vert=False)\n",
+    "    ax[1].set_xlabel('Value')\n",
+    "\n",
+    "    # Add a title to the Figure\n",
+    "    fig.suptitle(var_data.name)\n",
+    "\n",
+    "    # Show the figure\n",
+    "    fig.show()\n",
+    "\n",
+    "# Call the function for each delay field\n",
+    "delayFields = ['DepDelay','ArrDelay']\n",
+    "for col in delayFields:\n",
+    "    show_distribution(df_flights[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are a outliers at the lower and upper ends of both variables - particularly at the upper end.\n",
+    "\n",
+    "Let's trim the data so that we include only rows where the values for these fields are within the 1st and 90th percentile."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DepDelay \n",
+      "Minimum:-11.00\n",
+      "Mean:-0.92\n",
+      "Median:-2.00\n",
+      "Mode:-3.00\n",
+      "Maximum:16.00\n",
+      "\n",
+      "ArrDelay \n",
+      "Minimum:-32.00\n",
+      "Mean:-5.03\n",
+      "Median:-6.00\n",
+      "Mode:0.00\n",
+      "Maximum:37.00\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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88ce3HYLUivnHnbPRvuee+90hRjKY+eduPF5JGs+oHesnLOaaK0Q/D7wIeAh4IfDeqrpygO0+Bdyy3uO+FgLHAWc1X7/a135ikkvpXezwYFPwXQGc2XfRw38GTquqNUkeSnIwvenbtwHnDvJDT6VRuYGgNGxj3Sx4rVG6WfBa3ixY0qYatWP9hMVcVVWSr1XVPvQuPhjUy4E/BG5Mcl3T9h56RdxlSd4B3AG8uen7GvB6YAXwc3q3QqEp2t4PXNus95dVtaZ5/yfAhcDTgK83L0mSpGlj0GnW5UleVlXXTrxqT1X9E7Cx+769eoz1C3jXRvZ1AXDBGO1Lgb0HjWkYFi1aBMAb3vCGliPZPHxAvQZ15uW9gfH3zDtpg76fHrkPAM/58o1DjWk8Zx7ZxPvlDeOVpPGM2rF+0AsgDgKuTvLD5oa+Nya5YSoD66rly5ezfPnytsOQhu7Y66/g2OuvGLPvkYN255GDdh9yROM79qArOPagseOVpPGM2rF+3JG5JLtX1Y/p3etNkiRJI2aiadavAPtX1R1JvlhVvzeMoCRJkjSYiaZZ+895e/5UBiJJkqTJm6iYq428lyRJ0giYaJr1JUkeojdC97TmPc1yVdV2UxqdJEmSxjVuMVdVM4YVyJZil112aTsEqRU37vwbG+3beuWDQ4xkMDeu3Hi8kjSeUTvWp3d7t+lj7ty5tXTp0rbD6AzvMyepq24/67C2Q5CetCTLqmrueOsMep85SZIkjSCLOUmSpA6zmNvMFixYwIIFC9oOQxq62z80n9s/NH/MvjvOOow7RmzK6/az5nP7WWPHK0njGbVjvcWcJElSh1nMSZIkdZjFnCRJUodZzEmSJHWYxZwkSVKHWcxJkiR12ETPZtUkzZ/vrQ40PZ126Ikb7dvxSzcMMZLBnPaljccrSeMZtWO9j/OaAj4CS5La5+O8tCUY5HFejsxJkrZIW9J/rC1MNR7PmdvMli1bxm/OuK/tMKShO+a6yznmusvH7Hv4wN14+MDdhhzR+I458HKOOXDseCVpPMuWLWPZsmVth/FrjsxtZosXL+blW8O//WJm26FIQ/XBKz4OwCX7zdugb81R+wLwrCV3DjWm8XzwqCbeJRvGK0njWbx4MQAHHHBAy5H0ODInSZLUYRZzkiRJHeY0qyRJI25LuZjDCzmmRudH5pLMS3JrkhVJTm07HkmSpGHq9MhckhnAXwOvBVYC1yZZWFU3txuZJEla35Yywvj2p7Udwbq6PjJ3ILCiqv69qh4DLgWOaDkmSZKkoen0EyCSvAmYV1X/pVn+Q+CgqjpxvfVOAE5oFl8I3DrUQCdnJ+AnbQcx4szRYMzTxMzRYMzTxMzRYMzTxNbP0fOqatz7nXV6mnVQVXUecF7bcQwiydKJHtsx3ZmjwZiniZmjwZiniZmjwZiniW1Kjro+zXoX0H9b+dlNmyRJ0rTQ9WLuWmDPJHsk2Ro4GljYckySJElD0+lp1qp6PMmJwBXADOCCqvp+y2E9WZ2YDm6ZORqMeZqYORqMeZqYORqMeZrYpHPU6QsgJEmSpruuT7NKkiRNaxZzkiRJHWYxNwKSvD/JDUmuS/KNJLs27UnyseZRZTck2b/tWNuU5MNJftDk4stJtu/rO63J061JDm0zzjYl+f0k30/yqyRz1+szR318FODYklyQ5N4kN/W17ZjkyiS3NV93aDPGtiXZLclVSW5u/t5ObtrNUyPJtkmWJLm+ydGCpn2PJNc0f3efby5enNaSzEjyvSSLm+VJ58hibjR8uKr2rar9gMXAe5v21wF7Nq8TgE+0FN+ouBLYu6r2Bf4NOA0gyV70rmR+MTAP+JvmUW/T0U3AUcB3+hvN0br6HgX4OmAv4JgmR4IL6f2O9DsV+FZV7Ql8q1mezh4H/rSq9gIOBt7V/P6Ypyc8Cryqql4C7AfMS3Iw8CHg7Kp6AXA/8I4WYxwVJwO39C1POkcWcyOgqh7qW3wGsPaqlCOAT1fP1cD2SXYZeoAjoqq+UVWPN4tX07uvIPTydGlVPVpVPwJW0HvU27RTVbdU1VhPODFH6/JRgBtRVd8B1qzXfARwUfP+IuCNQw1qxFTV6qpa3rx/mN6BeBbm6dea49YjzeJTm1cBrwK+0LRP6xwBJJkNHAZ8slkOm5Aji7kRkeQDSe4E3sITI3OzgDv7VlvZtAn+GPh68948Tcwcrct8TM7OVbW6eX83sHObwYySJHOAlwLXYJ7W0UwfXgfcS29m5YfAA33/KffvDs4B/hz4VbP8HDYhRxZzQ5Lkm0luGuN1BEBVnV5VuwEXAyeOv7ct10R5atY5nd40x8XtRdqeQXIkTZXq3c/Ke1oBSZ4JfBE4Zb0ZFvMEVNUvm9OHZtMbDX9RyyGNlCTzgXuratmT3VenbxrcJVX1mgFXvRj4GnAG0/BxZRPlKckfAfOBV9cTN0mcVnmaxO9Sv2mVowGYj8m5J8kuVbW6OdXj3rYDaluSp9Ir5C6uqi81zeZpDFX1QJKrgN+md7rQVs3I03T/u3s5cHiS1wPbAtsBH2UTcuTI3AhIsmff4hHAD5r3C4G3NVe1Hgw82DeEP+0kmUdvOPrwqvp5X9dC4Ogk2yTZg94FI0vaiHGEmaN1+SjAyVkIHNe8Pw74aouxtK45r+lTwC1V9ZG+LvPUSDJz7R0HkjwNeC29cwuvAt7UrDatc1RVp1XV7KqaQ+8z6NtV9RY2IUc+AWIEJPki8EJ6c+Z3AO+sqruaD4yP07uy7OfA26tqaXuRtivJCmAb4KdN09VV9c6m73R659E9Tm/K4+tj72XLluRI4FxgJvAAcF1VHdr0maM+zf+Gz+GJRwF+oOWQRkKSS4BDgJ2Ae+jNEnwFuAzYnd5n1Jurav2LJKaNJK8AvgvcyBPnOr2H3nlz5glIsi+9k/dn0Bs4uqyq/jLJ8+ldcLQj8D3grVX1aHuRjoYkhwB/VlXzNyVHFnOSJEkd5jSrJElSh1nMSZIkdZjFnCRJUodNu1uT7LTTTjVnzpy2w5AkSZrQsmXLflJVM8dbZ9oVc3PmzGHp0ml7QagkSeqQJHdMtI7TrJIkSR1mMSdJktRhFnOSJEkdZjEnSZLUYRZzkiRJHWYxJ0mS1GEWc5IkSR1mMSdJktRhFnOSJEkdZjEnSZLUYRZzkiRJHWYxJ0mS1GEWc5IkSR1mMSdJktRhFnOSJEkdZjEnSZLUYRZzkiRJHWYxJ0mS1GEWc5IkSR1mMSdJktRhFnOSJEkdZjEnSZLUYRZzkiRJHWYxJ0mS1GEWc5IkSR1mMSdJktRhFnOSJEkdZjEnSZLUYRZzkiRJHWYxJ0mS1GEWc5IkSR1mMSdJktRhFnOSJEkdZjEnSZLUYRZzkiRJHWYxJ0mS1GEWc5IkSR1mMSdJktRhFnOSJEkdZjEnSZLUYVu1HYA03e24447cf//9bYexWdQZ25EFD7UdxlDssMMOrFmzpu0wJMliTmrb/fffT1W1Hcbm8b5nbzk/ywSStB2CJAFOs0qSJHWaxZwkSVKHdXqaNckFwHzg3qrau+141koybaaaJEntGet4M9YpAGvXGa/vyWzbtf1OZUxt6PrI3IXAvLaDkCRpFGzsXM4k4/Y9mW27tt+pjKktnR6Zq6rvJJnTdhySJI2S8UagBh2dmuy2XdvvVMY0bJ0u5gaV5ATgBIDdd999WN9zKN9HUnv8O5c0CqZFMVdV5wHnAcydO3coE9ttz5+rOywIusu/c7XNzw/BNCnmJEmaTsYr8iYqADd1267tdypjGrauXwAhSZIaGxstrqpx+57Mtl3b71TG1JZOj8wluQQ4BNgpyUrgjKr6VLtRSZLUnvEKi4mKjk3dtmv7ncqY2tDpYq6qjmk7hrGM4j+0JGnL4/FG4DSrJElSp1nMSZIkdVinp1mlLcWoXRm1qeqM7baYn2UiO+ywQ9shSBJgMSe1bks756Xe13YEkjS9OM0qSZLUYRZzkiRJHWYxJ0mS1GEWc5IkSR1mMSdJktRhFnOSJEkdZjEnSZLUYRZzkiRJHWYxJ0mS1GEWc5IkSR1mMSdJktRhFnOSJEkdZjEnSZLUYRZzkiRJHWYxJ0mS1GEWc5IkSR1mMSdJktRhFnOSJEkdZjEnSZLUYRZzkiRJHWYxJ0mS1GEWc5IkSR1mMSdJktRhFnOSJEkdZjEnSZLUYRZzkiRJHWYxJ0mS1GEWc5IkSR1mMSdJktRhFnOSJEkdZjEnSZLUYRZzkiRJHWYxJ0mS1GEWc5IkSR1mMSdJktRhFnOSJEkdZjEnSZLUYRZzkiRJHWYxJ0mS1GEWc5IkSR2Wqmo7hqFKch9wR9txjGMn4CdtBzHizNFgzNPEzNFgzNPEzNFgzNPE1s/R86pq5ngbTLtibtQlWVpVc9uOY5SZo8GYp4mZo8GYp4mZo8GYp4ltSo6cZpUkSeowizlJkqQOs5gbPee1HUAHmKPBmKeJmaPBmKeJmaPBmKeJTTpHnjMnSZLUYY7MSZIkdZjF3AhI8v4kNyS5Lsk3kuzatCfJx5KsaPr3bzvWNiX5cJIfNLn4cpLt+/pOa/J0a5JD24yzTUl+P8n3k/wqydz1+sxRnyTzmlysSHJq2/GMiiQXJLk3yU19bTsmuTLJbc3XHdqMsW1JdktyVZKbm7+3k5t289RIsm2SJUmub3K0oGnfI8k1zd/d55Ns3XasbUsyI8n3kixuliedI4u50fDhqtq3qvYDFgPvbdpfB+zZvE4APtFSfKPiSmDvqtoX+DfgNIAkewFHAy8G5gF/k2RGa1G26ybgKOA7/Y3maF3Nz/7X9P7G9gKOaXIkuJDe70i/U4FvVdWewLea5ensceBPq2ov4GDgXc3vj3l6wqPAq6rqJcB+wLwkBwMfAs6uqhcA9wPvaDHGUXEycEvf8qRzZDE3Aqrqob7FZwBrT2Q8Avh09VwNbJ9kl6EHOCKq6htV9XizeDUwu3l/BHBpVT1aVT8CVgAHthFj26rqlqq6dYwuc7SuA4EVVfXvVfUYcCm9HE17VfUdYM16zUcAFzXvLwLeONSgRkxVra6q5c37h+kdiGdhnn6tOW490iw+tXkV8CrgC037tM4RQJLZwGHAJ5vlsAk5spgbEUk+kORO4C08MTI3C7izb7WVTZvgj4GvN+/N08TM0brMx+TsXFWrm/d3Azu3GcwoSTIHeClwDeZpHc304XXAvfRmVn4IPND3n3L/7uAc4M+BXzXLz2ETcmQxNyRJvpnkpjFeRwBU1elVtRtwMXBiu9G2Z6I8NeucTm+a4+L2Im3PIDmSpkr1boHgbRCAJM8Evgicst4Mi3kCquqXzelDs+mNhr+o5ZBGSpL5wL1VtezJ7murzRCPBlBVrxlw1YuBrwFnAHcBu/X1zW7atlgT5SnJHwHzgVfXE/fVmVZ5msTvUr9plaMBmI/JuSfJLlW1ujnV4962A2pbkqfSK+QurqovNc3maQxV9UCSq4Dfpne60FbNyNN0/7t7OXB4ktcD2wLbAR9lE3LkyNwISLJn3+IRwA+a9wuBtzVXtR4MPNg3hD/tJJlHbzj68Kr6eV/XQuDoJNsk2YPeBSNL2ohxhJmjdV0L7NlcNbY1vYtDFrYc0yhbCBzXvD8O+GqLsbSuOa/pU8AtVfWRvi7z1Egyc+0dB5I8DXgtvXMLrwLe1Kw2rXNUVadV1eyqmkPvM+jbVfUWNiFH3jR4BCT5IvBCenPmdwDvrKq7mg+Mj9O7suznwNuraml7kbYryQpgG+CnTdPVVfXOpu90eufRPU5vyuPrY+9ly5bkSOBcYCbwAHBdVR3a9JmjPs3/hs8BZgAXVNUHWg5pJCS5BDgE2Am4h94swVeAy4Dd6X1Gvbmq1r9IYtpI8grgu8CNPHGu03vonTdnnoAk+9I7eX8GvYGjy6rqL5M8n94FRzsC3wPeWlWPthfpaEhyCPBnVTV/U3JkMSdJktRhTrNKkiR1mMWcJElSh1nMSZIkdZjFnCRJUodZzEmSJHWYxZwk9UlyVZJD12s7JcknNrL+PySZO5zoJGlDFnOStK5L6N3As9/RTbskjRyLOUla1xeAw5onQ6x9kPquwDFJlib5fpIFY22Y5JG+929KcmHzfmaSLya5tnm9fKp/CEnTh8WcJPVp7onYQIoAAAEwSURBVNi/BHhd03Q0vbv6n15Vc4F9gd9p7nA/qI8CZ1fVy4DfAz65GUOWNM1t1XYAkjSC1k61frX5+g7gzUlOoPe5uQuwF3DDgPt7DbBX7wl9AGyX5JlV9cg420jSQCzmJGlDXwXOTrI/8HRgDfBnwMuq6v5m+nTbMbbrfz5if/9TgIOr6v9OUbySpjGnWSVpPc2I2VXABfRG6bYDfgY8mGRnnpiCXd89SX4ryVOAI/vavwGctHYhyX5TErikacliTpLGdgnwEuCSqroe+B7wA+BzwD9vZJtTgcXAvwCr+9r/GzA3yQ1JbgbeOWVRS5p2UlUTryVJkqSR5MicJElSh1nMSZIkdZjFnCRJUodZzEmSJHWYxZwkSVKHWcxJkiR1mMWcJElSh1nMSZIkddj/B9ZK4Z+tHFiTAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 720x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Trim outliers for ArrDelay based on 1% and 90% percentiles\n",
+    "ArrDelay_01pcntile = df_flights.ArrDelay.quantile(0.01)\n",
+    "ArrDelay_90pcntile = df_flights.ArrDelay.quantile(0.90)\n",
+    "df_flights = df_flights[df_flights.ArrDelay < ArrDelay_90pcntile]\n",
+    "df_flights = df_flights[df_flights.ArrDelay > ArrDelay_01pcntile]\n",
+    "\n",
+    "# Trim outliers for DepDelay based on 1% and 90% percentiles\n",
+    "DepDelay_01pcntile = df_flights.DepDelay.quantile(0.01)\n",
+    "DepDelay_90pcntile = df_flights.DepDelay.quantile(0.90)\n",
+    "df_flights = df_flights[df_flights.DepDelay < DepDelay_90pcntile]\n",
+    "df_flights = df_flights[df_flights.DepDelay > DepDelay_01pcntile]\n",
+    "\n",
+    "# View the revised distributions\n",
+    "for col in delayFields:\n",
+    "    show_distribution(df_flights[col])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That looks a bit better.\n",
+    "\n",
+    "### Explore the data\n",
+    "\n",
+    "Let's start with an overall view of the summary statistics for the numeric columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>Year</th>\n",
+       "      <th>Month</th>\n",
+       "      <th>DayofMonth</th>\n",
+       "      <th>DayOfWeek</th>\n",
+       "      <th>OriginAirportID</th>\n",
+       "      <th>DestAirportID</th>\n",
+       "      <th>CRSDepTime</th>\n",
+       "      <th>DepDelay</th>\n",
+       "      <th>DepDel15</th>\n",
+       "      <th>CRSArrTime</th>\n",
+       "      <th>ArrDelay</th>\n",
+       "      <th>ArrDel15</th>\n",
+       "      <th>Cancelled</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>214397.0</td>\n",
+       "      <td>214397.000000</td>\n",
+       "      <td>214397.000000</td>\n",
+       "      <td>214397.000000</td>\n",
+       "      <td>214397.000000</td>\n",
+       "      <td>214397.000000</td>\n",
+       "      <td>214397.000000</td>\n",
+       "      <td>214397.000000</td>\n",
+       "      <td>214397.000000</td>\n",
+       "      <td>214397.000000</td>\n",
+       "      <td>214397.000000</td>\n",
+       "      <td>214397.000000</td>\n",
+       "      <td>214397.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2013.0</td>\n",
+       "      <td>7.018368</td>\n",
+       "      <td>15.794703</td>\n",
+       "      <td>3.902737</td>\n",
+       "      <td>12757.827661</td>\n",
+       "      <td>12726.276147</td>\n",
+       "      <td>1278.223879</td>\n",
+       "      <td>-0.921692</td>\n",
+       "      <td>0.018116</td>\n",
+       "      <td>1461.406596</td>\n",
+       "      <td>-5.030276</td>\n",
+       "      <td>0.068602</td>\n",
+       "      <td>0.013228</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.006398</td>\n",
+       "      <td>8.859118</td>\n",
+       "      <td>1.997744</td>\n",
+       "      <td>1510.058629</td>\n",
+       "      <td>1506.251757</td>\n",
+       "      <td>469.440262</td>\n",
+       "      <td>5.708594</td>\n",
+       "      <td>0.133371</td>\n",
+       "      <td>485.676457</td>\n",
+       "      <td>11.424280</td>\n",
+       "      <td>0.252776</td>\n",
+       "      <td>0.114249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>2013.0</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>10140.000000</td>\n",
+       "      <td>10140.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-11.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-32.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2013.0</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>11292.000000</td>\n",
+       "      <td>11292.000000</td>\n",
+       "      <td>850.000000</td>\n",
+       "      <td>-5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1054.000000</td>\n",
+       "      <td>-13.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2013.0</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>16.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>12892.000000</td>\n",
+       "      <td>12892.000000</td>\n",
+       "      <td>1235.000000</td>\n",
+       "      <td>-2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1445.000000</td>\n",
+       "      <td>-6.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2013.0</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>14100.000000</td>\n",
+       "      <td>14057.000000</td>\n",
+       "      <td>1655.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1845.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2013.0</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>15376.000000</td>\n",
+       "      <td>15376.000000</td>\n",
+       "      <td>2359.000000</td>\n",
+       "      <td>16.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2359.000000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Year          Month     DayofMonth      DayOfWeek  OriginAirportID  \\\n",
+       "count  214397.0  214397.000000  214397.000000  214397.000000    214397.000000   \n",
+       "mean     2013.0       7.018368      15.794703       3.902737     12757.827661   \n",
+       "std         0.0       2.006398       8.859118       1.997744      1510.058629   \n",
+       "min      2013.0       4.000000       1.000000       1.000000     10140.000000   \n",
+       "25%      2013.0       5.000000       8.000000       2.000000     11292.000000   \n",
+       "50%      2013.0       7.000000      16.000000       4.000000     12892.000000   \n",
+       "75%      2013.0       9.000000      23.000000       6.000000     14100.000000   \n",
+       "max      2013.0      10.000000      31.000000       7.000000     15376.000000   \n",
+       "\n",
+       "       DestAirportID     CRSDepTime       DepDelay       DepDel15  \\\n",
+       "count  214397.000000  214397.000000  214397.000000  214397.000000   \n",
+       "mean    12726.276147    1278.223879      -0.921692       0.018116   \n",
+       "std      1506.251757     469.440262       5.708594       0.133371   \n",
+       "min     10140.000000       1.000000     -11.000000       0.000000   \n",
+       "25%     11292.000000     850.000000      -5.000000       0.000000   \n",
+       "50%     12892.000000    1235.000000      -2.000000       0.000000   \n",
+       "75%     14057.000000    1655.000000       1.000000       0.000000   \n",
+       "max     15376.000000    2359.000000      16.000000       1.000000   \n",
+       "\n",
+       "          CRSArrTime       ArrDelay       ArrDel15      Cancelled  \n",
+       "count  214397.000000  214397.000000  214397.000000  214397.000000  \n",
+       "mean     1461.406596      -5.030276       0.068602       0.013228  \n",
+       "std       485.676457      11.424280       0.252776       0.114249  \n",
+       "min         1.000000     -32.000000       0.000000       0.000000  \n",
+       "25%      1054.000000     -13.000000       0.000000       0.000000  \n",
+       "50%      1445.000000      -6.000000       0.000000       0.000000  \n",
+       "75%      1845.000000       2.000000       0.000000       0.000000  \n",
+       "max      2359.000000      37.000000       1.000000       1.000000  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_flights.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### What are the mean departure and arrival delays?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DepDelay   -0.921692\n",
+       "ArrDelay   -5.030276\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_flights[delayFields].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### How do the carriers compare in terms of arrival delay performance?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for col in delayFields:\n",
+    "    df_flights.boxplot(column=col, by='Carrier', figsize=(8,8))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Are some days of the week more prone to arrival days than others?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for col in delayFields:\n",
+    "    df_flights.boxplot(column=col, by='DayOfWeek', figsize=(8,8))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Which departure airport has the highest average departure delay?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>DepDelay</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OriginAirportName</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Chicago Midway International</th>\n",
+       "      <td>2.365960</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Dallas Love Field</th>\n",
+       "      <td>2.148798</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>William P Hobby</th>\n",
+       "      <td>1.561927</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metropolitan Oakland International</th>\n",
+       "      <td>0.964853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Denver International</th>\n",
+       "      <td>0.807272</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Memphis International</th>\n",
+       "      <td>-2.962737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tucson International</th>\n",
+       "      <td>-2.989154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ted Stevens Anchorage International</th>\n",
+       "      <td>-3.149758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Richmond International</th>\n",
+       "      <td>-3.198073</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Long Beach Airport</th>\n",
+       "      <td>-3.447844</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>70 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     DepDelay\n",
+       "OriginAirportName                            \n",
+       "Chicago Midway International         2.365960\n",
+       "Dallas Love Field                    2.148798\n",
+       "William P Hobby                      1.561927\n",
+       "Metropolitan Oakland International   0.964853\n",
+       "Denver International                 0.807272\n",
+       "...                                       ...\n",
+       "Memphis International               -2.962737\n",
+       "Tucson International                -2.989154\n",
+       "Ted Stevens Anchorage International -3.149758\n",
+       "Richmond International              -3.198073\n",
+       "Long Beach Airport                  -3.447844\n",
+       "\n",
+       "[70 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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8fwn5AgDALrHpJdJqrd8vpTwjyV9kWCLtTbXWz226ZgAAsIssZZ3gWuuHknxoGXkBAMCu5o5xAAB0RxAMAEB3BMEAAHRHEAwAQHcEwQAAdEcQDABAdwTBAAB0RxAMAEB3BMEAAHRHEAwAQHcEwQAAdEcQDABAdwTBAAB0RxAMAEB3BMEAAHRHEAwAQHcEwQAAdEcQDABAdwTBAAB0RxAMAEB3BMEAAHRHEAwAQHcEwQAAdEcQDABAdwTBAAB0RxAMAEB39tnqCrB8F5983FZXAQBgj+ZMMAAA3REEAwDQHUEwAADdEQQDANAdQTAAAN0RBAMA0B1BMAAA3REEAwDQHTfL6JibagAAvXImGACA7giCAQDojiAYAIDumBPMXOYOAwA3Ns4EAwDQHUEwAADdEQQDANAdQTAAAN0RBAMA0B1BMAAA3bFEGkthGTUA4IbEmWAAALojCAYAoDumQ7BbmTYBAOwJnAkGAKA7gmAAALpjOgR7HFMmAIBdzZlgAAC6IwgGAKA7gmAAALojCAYAoDuCYAAAuiMIBgCgO4JgAAC6IwgGAKA7bpbBDZabagAAGyUI5kZNoAwAzGI6BAAA3REEAwDQHUEwAADdMScYYu4wAPRGEAwLaAmWBdQAsOczHQIAgO4IggEA6I4gGACA7giCAQDojiAYAIDuCIIBAOiOJdJgi1hKDQC2jjPBAAB0RxAMAEB3BMEAAHRHEAwAQHcEwQAAdEcQDABAdwTBAAB0RxAMAEB3BMEAAHRHEAwAQHcEwQAAdGefra4AsLaLTz5uq6sAADdKzgQDANAdQTAAAN0RBAMA0B1zguFGwNxhAFiMM8EAAHRHEAwAQHc2FQSXUh5dSvlcKeW6UspRy6oUAADsSpudE/zZJI9M8rol1AXYhcwbBoAdNhUE11ovSJJSynJqAwAAu4E5wQAAdGfumeBSyl8mud2Ml15Qa31fa0GllJOSnJQkd7jDHZorCAAAyzY3CK61/uQyCqq1npLklCQ56qij6jLyBACAjTAdAgCA7mx2ibQTSimXJrl/kg+WUv5iOdUCAIBdZ7OrQ7wnyXuWVBcAANgtTIcAAKA7m71ZBnAj03JTDTfeAOCGzplgAAC640wwsMs4YwzAnsqZYAAAuuNMMLDlnDEGYHdzJhgAgO4IggEA6I4gGACA7pgTDNwgmDcMwDI5EwwAQHcEwQAAdEcQDABAd8wJBm5UzB0GoIUzwQAAdEcQDABAdwTBAAB0x5xgoDut84bNLwa48RIEA2yCQBnghsl0CAAAuiMIBgCgO4JgAAC6Y04wwG5g7jDAnsWZYAAAuiMIBgCgO4JgAAC6IwgGAKA7gmAAALojCAYAoDuCYAAAuiMIBgCgO4JgAAC6IwgGAKA7gmAAALojCAYAoDuCYAAAuiMIBgCgO4JgAAC6IwgGAKA7gmAAALojCAYAoDuCYAAAuiMIBgCgO4JgAAC6s89WVwCAHS4++bitrgJAF5wJBgCgO4JgAAC6IwgGAKA75gQD3MCYNwywec4EAwDQHUEwAADdEQQDANAdQTAAAN1xYRzAjZQL6ADW5kwwAADdEQQDANAdQTAAAN0xJxigY+YNA70SBAMwl2AZuLExHQIAgO4IggEA6I4gGACA7giCAQDojgvjAFgKF88BNySCYAB2K8EysCcwHQIAgO4IggEA6I7pEADskVqmTbROrTAFA5jmTDAAAN1xJhgA4mwx9MaZYAAAuiMIBgCgO4JgAAC6IwgGAKA7LowDgAW4gA5uHJwJBgCgO4JgAAC6IwgGAKA7gmAAALojCAYAoDuCYAAAumOJNABYMsuowZ5PEAwAW0SwDFvHdAgAALojCAYAoDuCYAAAumNOMADswcwbhl3DmWAAALojCAYAoDuCYAAAuiMIBgCgO4JgAAC6IwgGAKA7mwqCSykvK6V8oZTy6VLKe0opt1pWxQAAYFfZ7JngjyT5kVrrPZP8Q5Lnb75KAACwa20qCK61nlZr/f74598nOWTzVQIAgF1rmXOCT0zy50vMDwAAdom5t00upfxlktvNeOkFtdb3jWlekOT7Sd6+Tj4nJTkpSe5whztsqLIAALAMc4PgWutPrvd6KeUXk/xskofUWus6+ZyS5JQkOeqoo9ZMBwAAu9rcIHg9pZSHJfmvSR5ca/235VQJAAB2rU0FwUleneSmST5SSkmSv6+1Pm3TtQIAFnLxycdtdRXgBmVTQXCt9U7LqggAsOsJlmHgjnEAAHRns9MhAIAbGWeL6YEzwQAAdEcQDABAdwTBAAB0RxAMAEB3XBgHAGyIC+i4IRMEAwC7jECZPZXpEAAAdEcQDABAdwTBAAB0RxAMAEB3BMEAAHRHEAwAQHcEwQAAdEcQDABAd9wsAwDYcm6qwe4mCAYAbhAEyiyT6RAAAHRHEAwAQHcEwQAAdEcQDABAdwTBAAB0RxAMAEB3BMEAAHRHEAwAQHcEwQAAdEcQDABAdwTBAAB0RxAMAEB3BMEAAHRHEAwAQHcEwQAAdEcQDABAd/bZ6goAACzTxScft9VV4AbAmWAAALrjTDAA0CVnjPsmCAYAWINA+cbLdAgAALojCAYAoDuCYAAAuiMIBgCgO4JgAAC6IwgGAKA7gmAAALojCAYAoDuCYAAAuiMIBgCgO4JgAAC6IwgGAKA7gmAAALojCAYAoDuCYAAAuiMIBgCgO4JgAAC6IwgGAKA7+2x1BQAAbuguPvm4ra4CC3ImGACA7giCAQDojiAYAIDuCIIBAOiOIBgAgO4IggEA6I4gGACA7lgnGABgN7CW8J7FmWAAALojCAYAoDuCYAAAuiMIBgCgO4JgAAC6IwgGAKA7lkgDANiDWEpt9xAEAwDcwAiUN08QDABwIyVYXps5wQAAdEcQDABAdwTBAAB0RxAMAEB3BMEAAHRHEAwAQHcEwQAAdEcQDABAdwTBAAB0RxAMAEB3BMEAAHRHEAwAQHcEwQAAdEcQDABAdwTBAAB0Z5+trgAAAFvr4pOP2+oq7HbOBAMA0B1BMAAA3REEAwDQnU3NCS6lvDTJI5Jcl+SyJL9Ya/3nZVQMAIA9x41t3vBmzwS/rNZ6z1rrvZN8IMlvLKFOAACwS20qCK61XjXx5y2S1M1VBwAAdr1NL5FWSvkfSZ6Y5Mokx2y6RgAA3GDdUKZNzD0TXEr5y1LKZ2c8HpEktdYX1Fpvn+TtSZ6xTj4nlVLOKaWcc/nlly9vCwAAYEFzzwTXWn+yMa+3J/lQkhetkc8pSU5JkqOOOsq0CQAAtsym5gSXUu488ecjknxhc9UBAIBdb7Nzgk8updw1wxJplyR52uarBAAAu9amguBa688vqyIAALC7uGMcAADdEQQDANAdQTAAAN0RBAMA0B1BMAAA3REEAwDQHUEwAADdEQQDANAdQTAAAN0RBAMA0J1N3TYZAAAWdfHJx211FZwJBgCgP4JgAAC6IwgGAKA7gmAAALrjwjgAAPZIu/ICOmeCAQDojiAYAIDuCIIBAOiOIBgAgO4IggEA6I4gGACA7giCAQDojiAYAIDuCIIBAOiOIBgAgO4IggEA6I4gGACA7giCAQDojiAYAIDuCIIBAOiOIBgAgO4IggEA6I4gGACA7giCAQDojiAYAIDuCIIBAOiOIBgAgO4IggEA6I4gGACA7giCAQDozj5bXQEAANioi08+bkPvcyYYAIDuCIIBAOiOIBgAgO4IggEA6I4gGACA7giCAQDojiAYAIDuCIIBAOiOIBgAgO4IggEA6I4gGACA7giCAQDojiAYAIDuCIIBAOiOIBgAgO4IggEA6I4gGACA7giCAQDojiAYAIDuCIIBAOiOIBgAgO4IggEA6I4gGACA7giCAQDojiAYAIDuCIIBAOiOIBgAgO4IggEA6E6pte7+Qku5PMklU0/fNsk35ry1Jc1W5LUVZe6peW1FmT3ktRVl7ql5bUWZe2peW1FmD3ltRZl7al5bUeaemtdWlLmn5rUVZW4mrzvWWg9albLWukc8kpyzjDRbkdcNvf7aYs/P64Zef22hLW5Ied3Q668ttIW2mJ+u1mo6BAAA/REEAwDQnT0pCD5lSWm2Iq+tKHNPzWsryuwhr60oc0/NayvK3FPz2ooye8hrK8rcU/PaijL31Ly2osw9Na+tKHPZ9d+aC+MAAGAr7UlnggEAYLcQBAMA0B1BMOxmpZQHtDwHrK2UsvdW1wFuDEopj255bk+1mc/U3T4nuJTyyPVer7W+u5RydZI1K1ZrPWAqz99N8qZa6+dmlPecOeW9YsF0fzanbseXUl41J81/HvP6zBrpypCs3nOdtlhJM90WD0jy4iR3TLLPRLrDWuo+5tFa/7npSilHrvX6mOaTC+Q1t73GvOaW2dIPx7zm9otSym3mpPmXnSpbyidrrUeu99x6fXp8vXVfNvXrMW1J8rgkh9VaX1JKuUOS29Vazxpfn9sXF9jfre3fsi9b+2vLsdt0vC2r77fWfSyzZbxoHceWmVfLMdK6v5uO8THtRUn+NMmptdbPr3pDe79YZv038/k23ceaj90x/QOT3LnWemop5aAk+9Vavzy+1jRGLbktWvJqbYuWdp27jQseby1lLu0zdZG6zbNoXvM+kxrHstY+1jL2LP0zdS37zEuwCzx8nddqknfXWvdPklLKS5N8NckfZWigxyX5wRnvuyDJKaWUfZKcmuSPa61Xjq/t31iv1nQvb0hzTmNePzsvwUpbLOCNSZ6d5Nwk10691lL3pL3+Lel+d53XapKfWCCvue21QJlz++H4/5b2P3d8T1kjr8OSpJRy/yQ/nuSgqQ+IA5JMn9Var08n7ftykf7zP5Ncl6F9XpLk6gxBxn2S5r7Yur9b278lv9b+OrfNFjjeltX3W+uetPX/1vovktddM/SB949/PzzJWQuW2bq/W4/xJLlXkl9I8oZSyl5J3pTkHbXWq8bXW9u2ZTtb69/8+bZAveYqpbwoyVEZtuHUJPsmeVuSlbNhTWNUY5lLGzsXaIuWMlu2cZHjraXMRT9TH5DkHkn+ZPz70Uk+P5VmIaWUHxnz3DajvHnv/ekkP5Pkh0spfzDx0gFJvj/xd8tY1trHWo7xXfGZOtMevTpEKeX8Wuu95j038dpdkzw5yWOS/G2S19daT9/1Nd19SikHZ6Kz11r/z9Trn6i1/thurxhzlVIenOToJE9L8tqJl65O8me11gtnvGe39emVb86llPNqrUeMz613vK3bF28MetjGeUopZyY5rtZ69fj3/kk+WGt90NbWbIfx2PpfSW6V5F1JXlpr/dKCeWzJdi6jj5VSPpXkiCSfnDh2Pz155vyG4MZ+vJVS/j7JA2ut3x//3jfJR2ut99tgfi/K8JlyjyQfSvLTST5Wa31U4/vvleTeGU56/MbES1cnOb3W+q8bqdfuspHP1GlbcSb4eqWU45L8++zc6V8ykeSbpZTHJXlHhuj/MUm+uUZeeye52/j4RpLzkzynlPLUWusvlFK2JfmlGeWdOJXPuunWOZW/km7y57qDkvy3TH1Lq7X+xOR7Sin3S/KqJHdPcpMM32C+OTnVoZRyfIZvYz+U5LIM0x0uGOs56fRSyssyfEv9zkSZn5zI685JfmtGvQ6byGeR+remW/WNtdb61kXzammvBcuc1w/n9ouJdLdOcuepNGeO//5NKeVjSe5Za/3N6brOqPu6fXpM07ovW+r/vbHMOr7noAxnhqfr1dQXW9p+TDe3/VvyW6Afzm2zBbZxKX2/NZ8xbct40dpfW46lH0jy3Ym/vzs+N12v1jJbjreWbdw7yXEZviRuz7C/3p7k/8kQENxlTNfatq3b2dpf1023QB9radfv1lprKWXl2L3FdH0m8ltzjFqwzKWNnQt8vrWWue42LnK8tZTZOg4nuXWGs5QrP+fvNz43WdYidXtUhl9Ezqu1PrmU8gMZfgFoyqvWen4p5bNJjq21vmXWtk9r/Ext6WNNn+PL/EydqTbeX3nZjwxR+1uT/FOSFyX5TJI3TqXZnuR9GQKAy5O8N8n2GXn9XpIvJXldkvtOvfbF8d93Jnlpkn9M8qQkpyV55Yy81k2X4eBc8zGV12kZDv4Lkjw4w891vz2jzHOS3CnJeRk6wpOT/NZUmvOTHJihs0JtUEMAACAASURBVCfJMdPtNT5/+ozHX0+l+ViShyT59FjvFyd5yYy8Wus/N924j09P8vUMP9d9Lcm7NpjX3PZqLbOlH7b2nyRPGd//r2O535pu+zHd3zUcH7+X5MKs06cX3Jct9X9chp+Cv5LkfyT5YpJHz8hrbl9cYH+3tn/Lvmztr3PbrGUbl9n3W+u+wHjROt615PWCsT1ePD4+leT5G+xjrfu7pV4XZZj+9eMz3v8HG+gXc7dzgfq3fL619rGWdv1/M4wVFyX55SR/l+Q/z8irdYxa5r5syau1LVrade42tvaJBcpsHYefnOSSJG9O8pYkX07ypE3U7azx33MzBNclyRc20Pc/muQms8rYwFjW2sdajvGlfaauuU0bfeNmH0k+PfXvfhl+FthIXk9Ocos1Xrvl+O95U+Xtm+TvZ6RvStdYr3Mn8xr/f/aszjAj3XlrpDk/yV4r/99kvT4z/dwG6z833diR91qpc4YzLB/ZYF5z26u1zNZ+2NIvxvK2JfnU+PfdMswBnM7rNRmCzSckeeTKY9E+veC+bO3/d0vyK+Pj7muUP7cvLrC/W9u/ZV8u2l/XbLOWbVxm32+te2v/X2B/tx5LRyZ55vg4Yo16tRwjrfu7ZRsfOON9D9jIPmrdzgXqPzfdAn2sdV/+VJKXZZin+lNrbF/rGLXMfdmSV2tbtLTr3G1csE+0lNk0Do/P3y7JI8bH7TbZX/9nhilAT8tw0uS8DBeKLjomvjXJ2UlemOQ5K481+s+8say1j7Uc40v7TF3rsZXTIb41/vtvpZQfSnJFxoveNnCV5JuTnFCGq2Nrhjkx7xnTrlxM9L3x3/87ns7/WpKDZ2TflK7xVP5KXl8df0755ySzrnr8t1LKTZJ8qpTyOxkuBpxevu7/llL2S3JmkreXUi7LjKkhpZRbZvi2tjKP7W8yfCOdvKjqO+OFJBeWUp6R4czffjPq1Vr/lnTfqrVeV0r5finlgAw/ed1+g3m1tFdrmWv2wzXqtV6/+Hat9dullJRSblpr/cI4p3fatrGcyZ+3anZcUJIkj6+1njr5plLKX9VaH7LJfTmv/988Q1+uSW424/WVPOb1xdb93dr+Lfm19teWNms63hrLXGbdk7b+37q/18yr7HyF9sXjIyuv1akrtBvLbN3fLdv4BxmC1kmvmvHcum274HY299eGdIv2sTXbtZTy27XW/5bkIzOem9Q6Ri1zX7bk1doWLWW2bOMix1tLmeuOKWX16gr/NP77Q6WUH6oT0xUXqVut9enjf19bSvlwkgNqrZ/eQF7/OD72yvoXNLaMZa19rOUYX+Zn6mwtkfKueGT4xnGrJD+f4aD4aoaLGZLhJ5M1H2t8Gzotw9mzJyf5cJI/nErzlAxzbx6c4Sejy5I8bUZerelaTuX/bJJbJvmRDKfyz01y/Iy87jjuxAMyBLCvSHKnqTS3GMvZZ2yH/5zkwBl5/WmS38xw9eRhY37T34Lvk+EAPSTDTxrvTnK/GXm11n9uusz5xrpgXnPbq7XM9frhov0iyXvGvF6cYTB/X5IPLXhcbMswSJ0/lneb8bE9Ez9zbWBfttT/NzJ8837x2IfOT/LrM/Ka2xcX2N+t7d+yL1v769w2a9nGZfb91rovMF60jmNr5pXhp9qLxn9X/r/y90Ub7GOt+3u9et0/yX/JEEg8Z+Lx4sw+e7hu287Yzsm/L9pg/eemW6CPtbTrJ2e879Mznmsao5a8L1vyam2Llnadu43z+sQGylx3TMnsaYorj+mpGi1jyt3Gf4+c9djIdo7p98uwtN5ar7eMZa19rGUc2/Rn6rzHHrE6RCnlpkm21Z3PcE2+fvNa67+t8/4vZPjpto5/75Xkc7XWu++SCg9lnFNrPapMXIFbJq6q3yqllE/VWu8977mtVErZntXfWLe8zHn9cMHyHpxh8PlwrfW7U68dkuGM1cryRR9N8sxa66WllGcmeVaGC0S+kh1LxFyVYWWIV2+2buvU+YtJ7lVr/fb4980y/Aw165v3IvluT8P+bm3/reg/y3JDq3sppSS5fd0FV+lv9HgrS7gifBkW6K9LG1dm5P2fkjw9wwmPf5x4af8kf1trffw6711zjNpAPXbZNm6mzGVuY2uZu0Mp5fW11l8upZw+4+Va17jAb538fiTDUrQrZ4m/keSJdY116sf3bM/8z9Rl9rENfabOzXcrg+BSyo9nOMN1/bSMuvMV0/fPcOHDfrXWO5RhOY+n1h0/Aayk+0CSX6m1XjL+fcckr661PnwizU0zfJObLm/6qtLWdGcm+ckkb8iOb4e/WCeWkyrDlZm/PCOv6Stsp29wsZJu8mr1Ryb57Qw/I5XxUevqKyn/Lslza60fm8j75bXW+0+kuUuS584ob9aqDy31b033wzPKnL5idG5eLe21YJnr9sMxTWu/2DvDPKnJNNPL2H0kw3JOfzQ+9fgkj6u1/tREml+ttb5qentmbF/rvpxb/3FAPaHW+n/Hv2+V4VeE6bxa++Lcth/TzW3/lvwW6Idz22yBbVxK32/NZ0zbMl609teWvD5Taz18uh4z6tVaZsvx1lKvO66M+XPq1bqPVtaiP7TW+tIydbOYRerfkm6BPrZmu5Zh+tutM6xM8LyJt11dV09XWcmvZYxa5r5sGXua2mKBMtfdxkWOt5YyFxiHb57hV4s71FpPKsOqEnettX5go3VbzwJ9/+NJXlDH5TdLKUcn+f9qrT8+I8+Wz9SWPtb0Ob6sz9S1bNmc4FLKHyX5dxmuwF25qUPNMEF7xe8nOTbj4uV1WM7jQRN5rNytZf8kF5RSVgar+2bnxdyT4TT6lRl+DvhO1taa7gkZ5q88I8PNKW6f4UCfzuujSf4yq29cMWm9G1ys+J0kD6+1XrBOPslwduSt4+CYDFdVPmkqzTsznEF5/Zx6tdZ/brpSym8n+Y8ZFgaf3N/TQVFLmS3t1VRmYz9cqde6/aKU8qsZftb5enYsLVaTTK/VeVDdeb7vm0spz5pMUGt9VeOH7SL7cl6/vjLJ58YBpWa40OasMi6iXnfMxZ/bF1v3d2v7N+bX2l9b2qz1eFtW32+te9LW/1vHsZa8PllKuU+t9ew59Wo5RlqPt5Z63bSUckpWHyPTZ8Fa23byZjEvzdTNYhapf2O6RfrYzHYdz0hemWH50JQd6+zuV0rZb0aw0DpGLXNftvTFprZoKbNxG5uPt8btbB2HT83QDivB5VfG935gIk3LmNJ0176WvEa3qBPrz9dazygzltlr/Ext7WNzj/FlfqauqS5xbsUijwxLdpQ5aT4x/nvexHPnT/z/wes9pvL6bGO9mtI15vWpxnSfaEjzt415HTr+e0CGnyquf24izcyrVjdR/7npMiy3ddMl5TW3vVrLbOmHrf0iwzJ9q+axzUj3Vxm+qe49Ph6f5K+m0vxRko9n+GB+1fj4gxl5te7Llvo/ab3HIn1xgf3d2v4t+7K1v85tswWOt6X0/da6j2lbxovW8a4lry9kuHvUP2ZYAuozmT3ftKWPte7vlnqdn+Q/ZTjp8aMrj030i0+O/878vFmw/i2fb619rKVdH55hjuY3M8xlvi7DlMDpdK1j1DL3ZUterW3R0q5zt3HB462lzNZxeGVFhPX6WMuYcur4+GCGE11/Oj7+JckHFt3ODHNvX5jhC+X2JL+e5D0z0rWMZa19rOUYX9pn6lqPrVwd4rMZlgr56jpp/mk8G1bLcGeVZ2bokEmGhZJX/l9KuV2GwbBmWALka1N5fbyUcnit9TNz6tWUrvFU/gdKKT9Ta/3QnDLn3uAiyTmllD/JsFbyZJrpqx//NMPE+KsmnntXhg+IFX9WSnl6ho4/mdf0z2et9W9Jd1GGpXHWOyvVmldLe7WW2dIPk7Z+8U8ZznjMc2KGoPb3xr//NsOFlZOOSnKPOh7h62jdl3PrX2t9Sxmu1r3L+NQXa63fm5G0pS+27u/W9m/Jr7W/trRZ6/G2rL7fWvekrf+3jncteR3bUKfWMlv3d0u9vl9rfU1DvVrbtuVmMa31b0nX2sda2vW/J7lfkr+stR5RSjkmQyAwrXWMWua+bMmrtS2a4obM38ZFjreWMlvH4e+W4VqLlT7277J6XJhbt1rrk8f3n5bhc+Kr498/mGG1rOa8RidmuBh6pb0/Oj43rWUsa+1jLcf4Mj9TZ9qyOcFlmH947wzTFiYb4PiJNLdN8soMc29LhhUgnllrvWIqr6dkuLL9r8d0D86wLNibJtJ8PsNqDl8ey1uZc3TPqbxa030hM07lT9atlHJ1hqtev5sdS5XUunrO1+lZrdad5yieukaaE8fX75bhjja/k2Fu0ooDMswRvv7OO6WUL6+R1/RcnNb6z01XSvnTDHe2+avsvL93Wu6uMa+57dVaZks/HNPN7RellDcmuWuGb+eTeb1iRn3XVUp5Z4bF7tf9gFlgX7bU/+gMC7hfPL5++wxngKenMKzbF8c0rfu7tf1b9mVrf53bZi3b2FrmMus+pm0ZL1rHsdZj6V4Z7sSWDGuknj+jXi19rHV/t2zjizOsNLBu4LFAv3hchp96j8xwHDwqw+oo79xg/ed9vrX2sZZ2XblQ+/wM6xtfV2bc8rx1jFryvmzJq7UtWtp17jZu4HibV2brOPxTGc6y3iNDPPOADNcSnbHBul1QJxYAKFOLAiyS15j+lkmuq+Otw2e83jKWtfaxlmN8aZ+pa9nKIPjBs56vE2d3F8jrixnuGnTF+PeBST5eJ65qL8PFcrPK2+nCigXSfaLW+mOL1nVXKaU8IsnPJTk+4xzq0dVJ3lFr/fiWVGxUSnnSrOdr460ad1WZrf2wpV+U4T7us9LsdDvHUsphGb7c3S/DGYG/S/LsWutFE2maPmBaNdb/3CSPrbV+cfz7Lkn+uNb6o7PeO6e8pv29QPvv9v6zLFvU95vGsca8npnh4pqVs0QnJDmlTl242djHljnuNwUeC+Z5twx3/ioZfk69YOr11v66zO1sade/zDD+/1aS22b4cnCfOnVh0wJj1NL25ZL74twyW7dxmWUumN+BGcb+kuGmId/YSD5jXq/OcEvhPx6f+o9JvlRr/dUF87lPhrvJrawRfGWSE2ut506la/lMXVr7L/Mzdc0ytioITpIy3Od65aKDs2qtl43P/9da6++UNW6aMeNs0seTHF3HZTPK8JPuGTMGgLlnM+alKzsWvf4PGeaerPuTfBnuib5yMd8ZdeIq0Ik0c29wURqXACml3L/W+neztmsizb4Z5tJdX68kr6szfvpuqf8C29nyU/vcvFraa5Ey1+qHM9K19p/9kqTWes0ar/99kj/MjoHrF5L86uSXqgU+YBbZl+vWv0ws9zfnuda+2Lq/W9u/ZV+29MO5bda6jQuUuZS6j+ma+n/j2duWsefTSe5fa/3m+PctMtymdPrilNYy5+7vRY7xFuu1bSnlgFrrVWXnm2Zcr64+s9zaX9dNt2Afm3fs3iLDTR32yrDCxS2TvG267hPp1x2jWsps2cYF6r9IW7SWOW8cbjreWsqcN6aUUu5Whxs9TN80I2MdF44bJtKeMJH2zDreKGyRvMZj/FdqrR8d/35gkv+5xjHeOq7Pa/9FPsc3/Zm6pto4OXzZjwxB5CUZfnZ6a4afSh41vvaz479PmvWYyGNlkfS3Zli0+cVjo34yyZunyntmhrk9Lxkfnxkbabpe66bLYoten5zhZ4MTx8dHMnVDjTFdyw0uPpJhjss+4+MXM/s2tL+TYQrEvmPZl2e4+9hkmjeM7f4T4+PUJG+YkVdr/eemy7Cu5yUZOvqZ4/5+0AbzmtterWVmnX64aP/JsCD5eWN+l2SYKvPvZ+Q166Kijd4Cu3VfttT/TWN+R4+P1yd504y85vbFBfZ3a/u37MvW/jq3zVq2cZl9v7XuC4wXreNdS16fybA26srf2zJxe9gF+1jr/m6p1xNnPRbdRxkvJErDTUEWqP/cdAv0sZZ2/e0Z75v1XOsYtcx92ZJXa1u0tOvcbZzXJzZQ5rpjSoZfTpLlxg17Z8YNlDaY16xbpc+6AcvRmT+WtfaxlmN8l3+mzk2wqx4Zruw9eOLvg7LjftRvnnj+Sevk8aL1HtONlGEZkJW/b7FGwzWla9zGT2e8D/pEp51V5qorOKefa0kz+VyGnyzfmOGMwPTVp7PuqjTzPu2N9Z+bbuy8d534+y6ZcUVtY16tbTG3zPX64aL9IsNqDsdM/H10hmk503n9doY1PbdnuLDyv2b4GfM2SW4zprk6ww0yrkry7Qzzzq9q3G8brf9NM3ypfPf4eHZmXAnc2F9b93dr+7fsy9b+OrfNFuhjS+n7rXVfoP1bx7uWvJ4z7qcXj49PJXnWBvtY6/5uqderJh6vzxC4vmvRfZThLPeqdl6j7VvrPzfdgn1sXru23jGudYxa5r5syau1LVrade42Lni8tZS57piS5NHjv4c19LFF6va+DGsObyqvDMvRvm5sqwdnWJXoFVl9B7qWsay1j7Uc40v7TF3rsZWrQ+xVd/5J4YrsuG/05Cn4Z2b4hrVKXT0vZL1T5iU7r0V37fjchtKN8+ROzRCsvD5DZ3lerfW0qaS3yrBsSTIEpLN8q5TywLrzDS6+NZXmilLK47PjdP9jMrTZtH3Hf49L8s5a65WlrKr+taWUf1dr/cexvMOy9hqCLfVvSbdvHeeaJkmt9R/Gn5A2kldLe7WWuV4/nNTSL5rWWsxwZiFJnjr1/C9kmP5zWK31+vu3l2EHPiLDfKdprftybv1rrd/JMPDNu+igpS+27u/W9m/Nr6W/trRZ6/HWUuYy65609f/W8W7NvEop987wQf6KUsoZSR44vufJtdbzZuTVUmbr/p67jXVq3mMZbu7yjhl5Jeu37R9mGL9TSvm7OnFjoRla69+SrrWPrdmuZeKOceNP2ivp98twhfy01jFqmfuyJa/Wtmgps3UbW4+3ljLnjSnPz7Ae8Lsy9rU5Wut26wxru5+VYXm8JKuuHWnJa+UCyhdNPX9Ehs+klYvVWsay1vZvGceW9pk64z1JtvBmGUk+XEr5i+w8obtluZJVytQt/0ops275d2qST5RSVubL/FyGM6XTWtOdWGt9ZSnl2CQHZrh5xh9luOJzxW8lOa8MFzmVDHNfnrcqp2Eu0VvGOTIlQ4f9xenysmMJkJrhG9KTZ+T1Z2VYueJbSf5TGZb5+fZUmudmWJ7korG8O66RV2v9W9KdU0p5Q5K3jX8/Lsk5G8yrpb1ay2zthy394qJSyguz811rVk3Mr7UeOiP/NdXhq+57y3CRwHRbtO7LNetfSvlMZsy9nyh/el5YS19s3d+t7d+SX2t/bWmz1uNtWX2/te5JW/9vHcfWy+sNGYKrczNs/99mmAs888rxxjJb93frMT7pm0lmHVvz2nYyINs2p4zW+reka+1j67Xr/0ry52m/Y1zTGDWnzEW2sTWv1rZoKbNlGxc53lrKnDemXFGG5cwOK6W8f+q900HrInV74RrPN+dVhhUlXlNr/d9z8kraxrLWPtZyjM/Na6z/42uts770zbXVF8b9fCYmwtdxQncp5bIM3+hLhg6307f7OvvCuLm3/CvDpPSVsxkfXeNsRlO6Ml4wVEp5ZYbJ5u8ppZxXaz1iKt0PZucJ9dPrF0+mPWDcvqvWStOiDBd4XFlrvbYMt2k8YLrcMtzKcmX1jC+OZwFn5dVU/3npxvJ+JRPtmmHi/apyFyhz3fZqLXOtfjgjv3X7RSnl1hnmOE2W9+Ja67/OyKvllqor9sqwbvCDZ52lWmBfzqx/2XH1dsmwFM3PTL6vbuwq7kX299z2X2BftvadpjZrsay+v8hYMaaf1/+bxrv18hrHj/tmuMPVj4/1+1qGmxs8fUY+LWNn0/E2bxvLjjuGJsPPvHdP8r9rrasChvXatgzLih2d4Tj76/H/1wfG08HkAuNF83bOs86xe/Mk36s7LsC6a4bj95K6eo3dRceope3LRfriPPPKbN3GBT+bW8eomWNKGS4mOzJDMPeU6ffW1Rc8LzQWrKclrzIusdeQ19yxbJE+NqZf7xhv3ZerYq9WWxoEr6WssQzHirp6maVZ6yGeX2u9V2m88rc13UT+pyb54QxnHu6VYRA+o9b6o6XxStBSyuNrrW8rpTxnjXSvKIuvlPHENfJ6aynlJ2qtf13WuOXiyqC5QP0XuuJ1PS15tbRXa3kL1GuhftGY58zbcNad11o8deIt38+wdu/r644VVFr35aL9+pO11pn7YNG+uLss0F/ntlnrNi6r7y+ST+N40TreLXQsleEnyPtlCASemOEn4sPG15Z2jCxSr7LzCirfzxD4XTrxemu/uDjDTTFmTRepdRNLrs2yQB+b266llDOT/FKt9cJSyp0yLKn49gzr0J496wvBnLotc1+21H+3jinL/Mwa82sah8e0e2dYMWJVELzRupVS7pfhDPrdk9wkQxzyzST3XSSvUsrJSb6R5E+y87SKhT/f5tkVn+OllJdnWBbt3XXBoHa3T4copXys1vrAMiziPFnZkmHAOWA6yG2w3inz/5XkZzNM6F5VXnbMFWlNl1JKyXBzjoMyXD38b2VY+2/l54/nJDkpye/OqOvk/JqVuS37r5Eu2XGHvFk/Jc9yn4n/b8uw5uUnM1zV+uAMZzoevkZ5Kwdsa/3npiul/O9a638oa/zkXnf81N5SZkt7paXMln44/j23X5RSfr/W+qypM1OT5U2v7Tv3bnB1vCPQOlr3ZXO/bjC3L7bu79b2b8yvtb+2tFnr8basvt9a96St/7fu77l5lVIem+Hs770zLAN5dpJPJHng1NmklmOk9XhrOsYzvOlvys5LV104lb6pbWut22e8vpMF+mtLutY+1rIvb11rXdnuJ2VY1/tXxzOP52b86XuBMWqZ+7Kl/k1t0VJm4zY2H2+N29k6DqcOv8z++xnpViwyFqx4dYZ5r+/M8LnyxAwXqy2a138c//2VqXQrX3RbPlNb+1jL2LPoZ+pTM2zz90sp387qvrimPfJM8KLKgqffl1TmZ2qth89Js63W+u2G5x5Qp+azTD9XSnl0nbhz0VrPzajDrTLcLONhE88dWmv98lS6Wc+11n/N50opP1hr/WppvwnJ3PzntdeiZW5WKeVHa63nlva1fefeDa60r8XbtC/n1H/ybMHbkzw2O/8cPH3WYM2+uOy2XyS/Bfrm3DZrPd6W1fdb6z4+P3e8aLVeXuOH/xeTvDbD+qP/sGj+G9U4Jv6HJC/LsCZrybAO7XNrre+ael9z2+5OGx3Tp9Jfv453KeVvk7ys1vre8e/rfyFddIza3ZbUFs3buOw+scBn6msy/IL8zux8xnXyjPEiY8HKnQIn+8H1UwOWtZ0tY9kGPgfXG3t2X3+tc5br2FWPJH/U8twSy/urJT/3lgx35FmvzFnL1mzoudb3zUizb4b5SfPymrV81TLr37qO5dLaoqXM1n7Y0i8yBKnTaWY9d3qSf03yFxnu7vf+JO+fStO6bmbrvlyz/llg7etdsL9b279lX26mv04v87Nb+/4ix3djma3j2Jp5Zfhp9cgkz8hwVu/cJB9I8oIkP7FIH9vA/m7ZxtYlujY0dq7R9q31n/vcAv1pvWP3bUlenmE5w68nufn4/K3WaIvWMWqZ+7Ilr9a2aGnXudu44PG20X05axw+dcbjTZuo25kZpkG8NcM9Ap6dnZdmWzevleM4ySNnPWa8t2Usa+1jLcf4unkludv475GzHrPabPqxlatD7PSzQCllnyQL3Z51rVPlK2qtx5dStiW5eZLbjmeMV85wHZDhG9lKXk3pJvxYkseVUi7J8I1u5fT7PUsptxvfc7NSyhFTed18osz7Z/i58aCy8/yYAzJ8AKWU8tMZLnT44VLKH0yl+f6cNrn+YpHxtbtlaPdblp3nMB2QiauiF6h/U7rRTyX5b1PP/fTKcy15tbTXImWO1u2HC/aLJ2W4deOkX5zx3Itn1HXaQbXWUyf+fnMp5VkT9Wrdl3PrX2s9pqE+i/bFlrZP2seBNfNboL/ObbPWbVxW318kn8bxonW8m5tXrfXaDFOpPpnk1WWYevDoJM/KcNODhcoczTveFjnG1126asF91Kq1v66ZboE+1tKuv5xhGdHtSR5aa/238fl7ZAiOp607Ri15X87Na9HPt3llztvGDfaJ9fZl0zi8oq4zzW2DdXtChuPiGRkC4Nsn+fkF8npwGqdzjFrG9Xl9bJFjfN5n6kamkOxkK+YEPz/Jr2XYOStXA5Yk301yylTauyR5TZIfqLX+SCnlnkmOr7X+9zHJyyfe//rMuOoyw1yRZyX5oQxnMlY6w1UZ5tMsmm7Fsets5rEZdtQh2XnN1aszbPuKm2RYz3Gf7Dw/5qokjxr//88Z5ksdP9ZrMq9nzyh7cuCbvljkrhnmaN0qO3f6qzMMpovWf266Mnsdy2TY3smfQlrKbGmvpjIX6Idz+0Up5TEZphAcWnZe/mb/7Fif8Xq17aeceetmtu7LRfv1eub2xdb93dr+jfm19teWNms93pbV91vrnrT1/9b9PTevcbz98YnHTTIsXfWq7Hzsthwjrcdb0zE+mrV01Z9PvN7UtmWNC7dW1OECrtb+2pKutY/Nbdda67cy3BVsus4fz7CvVurVOkYtc1+29MWmtmgps3Ebm4+3xu1sGodL2wWAi4wFK+9bmVL1rQxTQlfKe1JLXrXWF43/rgrOxy+9K/9v+Uxt7WMtY09TXrXWk8Z/V53IKWvfh2BnLaeLd8Uja9yicCrN32RYnue8iec+u0baVbf9m3p91S1DN5NuIv3BSe6w8ph67ecb87hjQ5p9F6jTD2Q4MH82Ez8XTrzedIekBeq/ZroMi3Nvz/BBdceJx8y7uLSUOa+9FimzpR/O6xdj3kdnuDr1wROPI5PsMyP93LvBjXm+P8Ntry9L8t7p/rXgvlyoX2+0L25gf6/b/gvuKslk7wAAIABJREFUy9b+OrfNWo+3ZfX91rqv9I1l7e/18spwBvgPMnwBW/OuVIuUucDxNncbx3SPzI4bvJyw6D4aX/9ydtwm+doMV8lfMf7/yxusf8vnW2sf2/Sxu4Exapn7siWv1rZYs8xFtnHB461lX647piR5+Pjvk2Y9NlG3lb6702MjeY3pb5XklzLcavmfJ56fO5ZtoI/dcVn9deJ9JcNiAG9M8vWWbd7qdYJvneTOmfjZoNZ65sTrZ9da71N2nuj9qVrrvWfktebSThNpfiTDz0ST5b11I+lKKcdnOAX/QxmClDsmuaDWOv3TyXEZfi6ZzOslU2kOynCbv+l0k1eq3jnDwtfT9drp6v7ScLHI+DPVL80o78QZbTG3/gumO3gqzf9ZNK+W9lqkzHn9cCJdU/9ZRCk77gZXF1zOaHz/IvtyKfVv7Ytj2pb93dT+Lfk1Hm9z22zBbVxK318gn6b+3ziOLXQszdNY5tz9vV69yrAU2A/U1RfVPDDJV+t4166p11r6xeuTvKfW+qHx759O8nO11qdOpWsdL+Z9vi3Sx5Y+9syzrH3ZkteCbdE8Xqyn9XhrKXORcXgq320ZAuTpiwJbx4IDJ/7clmG60m1qrb/Rmlcp5WYZPoMem+EOcftnuKHJmbXW69ao99xxfZ5ljj1lWCrusRnqfZsMq1y8v7YsjrDIt4RlPjJMXfhMhguETs9wOv+vp9L8eYb1VFcu1HhUkj+feP02E4/zM9xC8PrnpvJ60VjO1zNMRv9aZt9nvjXd+RnuFHfe+PcxSd44lea1GSas/9OY72em04zpTstwAF2Q4dvOm7J6svnHMnzD+XSGgPvFSV6yRr3m3ef8nUlemuQfM3wTPS3JK2fk1Vr/ueky/FR0YYb501/OsC7n5zaY19z2ai2zpR+29osM66ieneSaDD+ZrTrDu87xsNKPXpbkqTNef2qSk2c837ovW/v1ypm1383aZ9bm9sUF9ndr+7fsy9b+OrfNWrZxmX2/te4LjBet+7vpWGrswy3HSOv+XrNeGS7OO3zGew5P8mcb2Udjus/Me26B+rd8vrX2saZ92biPmsao/5+99473bLoa/99rBjMYoycRbRAleo8aJUEiHhG9M0FIEUEQIgyP6DUSoj+IOjrRGaO3YWYYJZgRvSRPMNGH9ftj7XPv/uzPPues87mfcfP9PdbrdV73nnPWZ+119l577bZKl9vSQ8tbF556rf1Gr0w0KNOlhwPuQMwO+sJQJ2lduHkroT/GSwtzeH0Z2zldL/A2qYK2R5d5ZcyjxyppAUcGfu4I7TR7Ff/Zb+qkI3XjCo0xGBgb7hfDAh3HOAsCtwMfAK+GjjJ/9H4SvcdY6TUxU94AwoQQMxnIedt78R4Nf8dhDhrQPtkcn/wdgmWbyQptgRf+f6QE54n0Wcp/cj8g8+zxhK9pgQcztLz81+LhWDQ0oFVbX94yPXLolQvMtu0bwOOYMhlO5iiNVg/czTGbvgeKbwM7ocm0Y5spUIO29PB/GqaYhofrZuBPFfJaKosN2ttb/5629MprbZ15vrGbsu/l3Sv/nvZu0pc8l1PGvO1dylcVf+Qnsl65uAU4GDv2HYZFwbilQ/4945tbpzvqdRHML+ZWzNHpTvITUq+O6mZbemg1qYu6eq39Rq9MNCjTo1PWAs7AJp1XYouBGTqV1/AujoawIrAHrdEhKmlhCZvGA78G5gnPJubKCu88uswrYx49VkkLO4W/FxtHB9Xxn7v6MzrER2qxNBGRQWrZTRZNcP6uqt8Vy1Y0QJOc9aq6QIPyPlTVz0VkiliavrcwT8pO8d4RkSFYiJKLxFI9v5/gfBj+fiAiX8fszObK0Po0/H09HF28hu1mx/CxWI7s50TkF9iiYEiGlifPeVHeO+GY6g3MtjkFL/8evE9V9Z8iMkBEBqjqKBE5uVNa4W9VfXnL9MghOOVCVZ8XkYFqnvXnicjjwIEJWuxAUWSD+2G4H6ShJyd0Pw+mE23fGP7WtqWD/3WBbxbli8j5wIQMLY8setvbW/8eek37W1Wdeftbt2Tfy3vMf5X8e/WYty8BEOpkiOZTNXvKdLd3BV+zlPEHTJ/jK/ytq9ttsN2yqzHnpbvDs0749+C5ZcxRryOxXb+z6M1CmQWnjupmW3poeevCVabjG5v0N0+ZlTpFRF4BXsIc/X+tqpNFZJL2RvOIoQlvcVSEYizZ0ktLVZcVi3CxDXC7iPwDmElEvqqqb2bKc+l1p4y5dE8NrbmwHextgJNFZBTmyDiNquaii7RBf06CXxFL5HANcJuI/AtIA+lPEpGbsVR+d/axvEdDeWdhu23/xoyuO8X7IebUtDewHWY4ntrs3BBoHYc5mShwdobWESIyM7Av5nk9lPbID3thoU1+iR27rIsdu7SAqu4nrXnOz9T2POdnitk4/Q5zvhqCZcBLwcu/B8+zaPDS8tSXt0yPHIJPLj4Qy9Q0VkSOBV4nCtlUgFZng/tQRBbW3ixQQI/N3IcZfG9bevh/HnPwLL5/3vAsBY8setvbW/8eel559dSZq785y+wm7+CTf68eq6UlIhdjO0yfYUeTQ0XkFFU9roMyve1dxdejIrKbqp6V8LkrrREGCqitW7GUtqeq6naZ33fCvwfPK2Oeep2iqqfX8A5OHeUss5u601sXnjI939ikv3nKrNMpV2D2qlsBn4nItZSHd3XzpvXhLWtpqeoz2OLvUBFZAZtQPiIir6jqagk9jy7zyphHj1XSChPjm7HNv0FYMIDpgVdF5A5V3bamfv4zMsaJZQWZGbhZVT+Jns+AfdTW2Hb/DVj2s3v7WN4wYKiqju8GnrPMQcBgVX23r7T6A7z8l+GJ7eYX6QyLRcNFqvrPdirNyqz4faMyy+QwgzeMjFyIZdN5EwsBs3egdZqqPh/e7wbcparPiYhgdlibYQp1Z1V9TMwZ51TgCHoH9BWxle+vNDjt9AVS/qU3tvTMWArahwPqysDDqrp2B2V00t6l9d9BW37h/a1bst9t3vuqxyQ4I4vIdpge/g12lLl0xW9qy/T2t8zvvort1n5Cax+ZDrNjf6Pit6V1KyL3YskDXLw00BcdfWcJrWHkdc8IbIf1aizFNWDh3RK8Sh3VpMwEp0+6sxOomDc0+sYm/a0vbRl0/trYJHPDQGcX4EZV/XcT3sQyig4r5kJi8XaLnfOLc9/a8DsFWFPbHVZrdVknMlbBR0e0xE4cNlGH82h/R4cYiNkH9exIa4mXYVhlnQJsp6q5xAie8ubGjO7j8nKerKV4UpJHvPirSa5qEVkNsy+LaeU8tHfL4MXe6osA+2X4WreErxb+Yr5CZ9gsU17O+7SW/yZ4Hqij5amvhuW55NArPxXlPAksp6qfisi22Ap4fcwj91BVXTPgLYm19ZLhp08Cx6vqExmaTdoyy7+UpKaMcNJ0l5Wy2BSa6AEHLU9/q62zJt/YLdlv0Ndc8u+RV6fumQAsiznR/FFVR0uUkreDMmvb28nXOvT2kQmqWnpa6JSLC7DkQtfRmtL2xATPqy8q8RrKWGW9isikzGerZqIreKFbbenkv0lddEVfNOm3ZWWKyI4VRaiqXlhCb1osLvA2wAaqOkcT3kTkEmzyeUO4fxaLXTwDlkVtOy+t/oJuj+OdQr+ZQ4jIntgW/JuYhyHYJG7pBG8t7Ajhe5iR9JbRu8HYMd036PV6zNqBiMgxgc5T9NpMFXZfTfDuAL6GZVK5tKrziciFWHSLsQmtVACvBe7BnADL7Lkqbb5UtSfotEQh5UrgWuBdbBfl4zIkL/8ePLFsOsdgdlJC+aLBU6anvlxlNpDDWvkRkdUxr+ZUkRcD0RRVLeygNgIuCCvo28WOegr8J8kfBebA25al/MeT3LDLtlK4fVhbM3IVUGt/2KC9vfXvacsm/a2uzlw2lt2S/Qa8F/xXyr9X33loYfXwIuYUc3fYnWmzCXb2EVd7e/hS1VGYt34lNKjbF8I1gNYg/jEtr7x68LwyVluv6vSPcegod5nd1J3466K2TM83NulvNWWulOIH2BjL2JadBIdx4AbMXKHFjt3J26LFBDjAB6p6Qvj9PZ18pwecuswlY/j0mJdW56ANvOi6eWG2hrPX4LyIHe9sA8yYeX8Zljt9d8xeJxuSJOA+S/AerCmzFg/bkh+OeROPxjKp5ALgPw3tnv4ZvLEOnDZP2QrcbJ7x6H024Ugf+K/FC+39zS7Rqq0vb5keOWwgF89gKSS/gnnQzh7Txmyy5sI8jd8Eloi/29u+Hbalh/8tMdOM8zElOQnYvBNZbNDe3vr3tKVXXmvrzNvfuiX7Xt4DrkdfePVdJS1sQrhl8kzIB7/3yJi3vV19vFttlOAPwZz/+iqvdeObV8Y89ToDFtnizHC/MLBRBq9SR02ltvTQ8taFp15rv7Fhf/N+pwDbYxtylwFLTy15BZ5K7uOkFU83odWQN48u88qYR4+5aPXl6k/HuJex3ZgqWFrzXsgFLK6qSwGIyDn02jLmYCIWtqR0t8yLp2ZTc56Y9/zWWFalwbSmJwQ7xv4aZsxdBTeIyIZabe95vYj8jBqbLyfcLyJLaeZ4PQEv/x68N1X1aQdvHlqe+vKW6ZFD8MnPu6p6U8X7Q7DTjIFYIO8J0HPaMdHBQw68benh/7fAShp2f8Nx1e2YU0cMHln0tre3/j30vPLqqTNvf+uW7Ht5B5/8e/VdJS01r/79gcujZ4p5ondSpre9vX3cA666DWZIFxI81MU85Xcs+mkAL/8ePK+Meer1POxko3BkehXbXb0hwavTUU3K7Kbu9NaFp0zPNzbpb5Vlisg0WIriXwMPYhsHzzro9oW3ySKyiKr+DXrrSSzSQxxFq5KWiGyvqn8RsyluA01MgfDpMq+Mefq4l1bHZh/9ZhMcJq2LAn+lVehPjHDmwZyEikgH9wB7qeor4X1Llrj0PinvSmAZzJwhLu+XTfFCZW+DZWO7F7hMVe8hAbFwHctik/OY1sYJ3mRgxoDzKfkjhkqbr3BMUcDxWIeMEa+KaD2FmZBMCmUW5aXHWF7+a/FE5BSsM16T4FxFBE5atfXlLdMjhwHPIxdHYxPcqxKcxyKcaYCZNMpkI+ZsIFriHFEFDdrSw/8TxaIy3BexPZdKaFXKYsDxtre3/j1t6ZXX2jrz2lh2S/a9vAdcj77w6jsPraOxVMKX0WormzpdeWTM297ePj4/sLCq3h6OlafRJJRmA7m4H/itmpkFIrI2cKRGHvIN+PeMb14Z89Tro6q6orRmV22z2/boqAZldlN3euvCU68ePdykv5WWKSI/xyJb3IElengx8x0xrdoNC6dO+R62+fZ77IQRYAXgIGyOdJOHlojsrqpniMihOV5U9bCEN48u88qYV/d4aGXNPlJ9l4P+3Al+KVzThSsH52HOGFuE++3Ds/XC/TIiUuwUCxYf7j3yCvO6cNVBJZ6IvAi8A1wK/ISwIyIiy0Nb44yoKyxMNL6nSQrQDM5vVPWyClJx7NnRyb1iQoSICGZHnQtlk8IIB44XbyiW9GT9HF9eWp76alimRw7BJz/fCn9XTMrrce5Qs1lvSeWoqm2hw8QcRU7H0sMuKSJLAxur6hERTpO29PBfG2PaKYvgb29v/XvojajhyVVnDb7RVSZd4j3izSP/te3dgNZW4e/Po2eKJTNqVCaO9vbyJRZt5SfYzu1CwDyYXel3EtQRNTwVMGMxAQZQ1bvCArUR/x68hjLmqddPwiJAA/2FyO+81uqoBmV2RXc2rAtPmZ5vHOEoy1PmqVhUjjWA1aU3lHt2MwI4Tcwx938wx7bcDnMtb6p6c9j42h8LKwcW031TNZ8SFy1VPSP8PawKLwKPLqut/wa6xyuvK2KWAc13dbWLthXeC5vZX+TAa7MZyT1zljeqG3jAXZgzxigsdvGo+D6h9YyTv8cdOI/WvF8Nv41TW1alkrqo5d+DF3CO72KZnvqqLbOBHHrkYiCwt6f+nW00GgtR9nj0LJcxztuWlfxHuEXa5BMpT5tcJ4tN2ttb/5629PY3T51VfqO3zG7zHvAr5b9he9f2JScdbx+pbW8vX9iuz3RJH0mzYzaRi6uxOK/DwnUwcHVT/hvgeWWsti2xjaHRwNvARZg/zdoZWrU6qptt2YB/b11Ulun5xoYyUVkm5rBVepX8ZmHgKMy+9mJgvU54c/Lu/c7zsLTFLVeGnkeXucZBpx7z0hoJzNVJPeUCGE91UAtwPL9YEOQq+KeIbC8iA8O1PZbxpJPyPhcLzNwnPFVdW1XXCde60f/raBTOJdB6VkTmc7B4h4hsJpLNCFbA7SLyaxGZV0RmK67o/Q7AGBG5VER2FpGvVdB6TETKvFob8e/BCzirl71vWiaO+vKU6ZVDp1x8Rnt2qb7ADKqa2rjn7DC9bVkr/wH3KlXdB8vJfk0JWqUsNmxvb/172tLb32rrjPr+1jXZb8g71Mh/k/auowUgIjOIyMEicma4X1hENmpaZgO97+IL+Fhb48NOA61hIhvW7Y+BObFdrSuBOcKzRvw3+E6vjHnGrtuwBezO2EnOiqp6V4ZWrY7qZls2kEVvXVSW6fnGJjJRV6aq/r3qKvnNc9gC6wAslfIfROQZEdm0qS4QkTlF5DgRuVFE7iyupt+J2Y7/NVx3YDu+LeZ5DXSZdxz06LFKWiJyvYhch/XVp0TkFhG5rrg8TPSnTXBtTEYxe69TgVUx5XY/sKeqvtxBeddi8VhvS8pLbeRceM4y7w60Hk5oldkEf4ZlBWtsExzhLYZ5U26ARbEYhWVUuS8IFSLyDLYafTHwVWZH6uW/Fk9ETsdCxoxMcFIbUQ+t2vrylumRw4BXKxcichLmAJLaTj4W3mft1VO8gHsT8AtgpKouLyKbA7uo6vcTvrxtWcq/iKwCHA38L5at6UJMqQzAHINuTmh5bIK97e2tf09beuW1ts4a9LeuyL6X94Dr0RdefeehdRnmdLWjmmnODMD9qrpsQsvTR7zt7eHrWMw0bUdgTyxKz1Oq+tuEVmXdisiFqrqDiOylqqdQAQ3494xvXhnz1GuRvGBBVT08THy+li6k63RUwzK7qTu9deGp19pvbNjfXN/pATGztuHAD7D6OEctSdLXgQdUdf6GvN0avvPXmJnXTsDbqnpA0+9M6A4A7tUkY5xTl3llzNPH68bUtaq+Q5MY9znoT5vg2piMYSWVDmBtTl9OuIp2e8S+4Hngdx4kjWL8VuC44kCqpUB8BjhJzEZsHcym+kR67Wo28NDCyb8TbzC2ix/b8uRsRGtpeeqrQZm1chjAIxfFpODwpLyi/BMoh9TO6edY8PPFRORVzIlr+8zvvG1Zxf8fMYeKmTETn++r6oNhQXUJtojqZdQni9729ta/h55XXmvrzNvfnGV2k3ev/Lv0mJPWQqq6lYhsE37zQcnujadMV3s7+foNlnHrCSxM5o3k08vW1e0KYRLy4zDhafk2bXUA9MqrZ3zzypinXk/DYtiui+mfydhudnriUaejmpTZNd3ZoC48ZXq+0d3fnGV64VRMRg9S1Q97mFN9TUQO7oC32VX1nLCAGw2MFpFHovdNaMWwMBaWLAWPLnPJmLOPV9IqJrkisgDwuqp+FO6nx5Kb1IN2wfakLxd27NsE/6WKd0MxJ4nZyMftnR4LMl1XhgvPye/8wHeLb8UiA6Q4RXzB34X7eYGV03qiIg5k/N25K1PmGsDw8P+cwAKd8t8Erxt15qmvqSGH3ZSLBnzNWFeXDdoyyz+RnT1JvGIydlt1sji16r8bsuOtsybf2C3Zb8C7S/498urUPfcHWo+F+4WwRCod95G69u52H6+qW8yx6GnMkWwituAsrol9kdcqvIYyVlmvUdvE9tHjOq2vbralk/9GOqUb+qJpv+2ijvL0S68ueDD8vQXbXV4OeKEpLWzR9F7092/AZt343opv7Fofx0KPThfdTwc84vrt1PzIGqZXxTLIvBTul8FyQtf97uXMs92BN7DjzazywqIlPAtMCvfLYrFaU1qVeMDyVVdCazfgkUIoQ8e+I1Pm6cCfCBMQYNa0AbHjgP0JzlFBmOPJyyTaFXhZXRwKXA/8Ldx/HTOXSPny8l+LByyC2RoV/C8NHNwhrdr68pbplUOP/GArz3OAm8L94pgJQ06Ol8SSU+xYXMn7fTLXLsCyHbZlKf9EyVVIEq2k9x5ZbNje3vr3tKVXXmvrzPON3ZR9L+8N9IVX33lo1TpdNegj3vb28LU6dqT8N3p1X9uktYFcnJ6r7w75r8VrIGOeen0IcyQqJsNzkl/AunRUl9vSQ8tbF556rf1Gr0w0KNMri566aMLbRtgJ3pKY6eMY4L86oeW58Okyr4x5+riXVk5WXIvAjiqiGxfWaecl4/1O+Y7m7MArGVrPAXPUlDcmCEudt30lHr2RIB7AYts9Gn7zKWbT09Iw1Hgvh2e1q3iC92wVToO6H4utwmJa40vwPPx7vLS90Q48tFy7Hp4yq+SwqfwAN2ET23HhfpqS+jo0yNCbmFfuG8AVCc7FmEI9IVzPYnZYjwD7d9CWpfxjNlnFLsCU8H9x/2mGVq0sNmhvb/172rKJvFbWmecbuyn7Xt698u+R14Z9aXZsp2kjSnStp8wG7e35Rndmqqq6BYaGv7WnaA34r8VrIGOeet0Os1l9BYsd+yywRYaWV0d1sy09tLx14anX2m+sk4kOyvTKYq4uOuathN9fNZB996ZewPfoMq+Mefq4l9ZtWAjR4v6HOCf7/WkTjKq+nJiWFUGOx2B2Hzm7s08yz17AYtdVwaeq+m5S3udN8VR1HQARuQoTkifC/ZK0x+T7WFU/KWhJxnu5KFNEBhbvxDJ1pbx540AiIrNiK77BEd9xnvZPVFVFpKCVxsJsyr8HbwZVfTip11y0Aw8tT325y6yQw5YyHfIzh6peLiIHBrpTRCRHa3NsN+FxVR0uIl/F0n/HMA8mX/8GEAtm/lfg21j/ODbgeduylH9VHVjymzLwyKK3vb3176HnlVdPnXn7W7dk38s7+OTfre/qaEUOnUXWqfnEvP3/rhb3ulGZ3v7m+EZvNqm6ur0Ym9znxh0liYfs5N+D55WxynoNTkyTsJ3U7wT+N9F8Zi+vjupqWzpoucc3R5meb2zS3zxlemUxVxdpuY14y8A+wMlOWk38VMCny9wy5ujjXlp7ABeJyB8x+X8ZO2GthX5NmyyWeU1FZFos68rT0MhIvoADsVSoD1GeHWmCiGwLDBSRhTE7sPsztLx4i2qU+UVVnxSRbyY4o0XkICyJx3qY9/L1GVp/wGJUfkVEfo9NklKD9hGYg9K8InIRdvwyPCUkIrtidTkPtgpcBdu1joX5chE5A5hFLOD8j8k7lHj59+D9Iyi2QuA3J5/K0UPLU1/eMkvlMAGPXLwvIrNH5a1CPt3mh2rpaKeIyFAs2Pq8Cc5XaB0EPsUSZ3woIvFzb1t65doDI6iXRW97e+vfQ88rr54683yjt8xu8g4++fe2t4fWadjO0HhsgFkSC8w/s4j8VFVvbVCmt709fI0SkeOoySZFTd2q6kbhr2fc8fLvwRuBT8Yq6zXokj+pZYp7poZ/r47qZlt6aI3AVxeeMj3f2KS/ecr0yqKnLprwloN4hlon++s0oAs+XeaVMU8fd9FS1ReAVURkSLj3Z1/1bBdPjQsLwXQRdiT8FrYT1ubA5aT1MBb9YDgWImQnYKcEZwbsmOiRcB0BDMrQ8uJdgg2ca4frLOCSBGcAZpMzErgC2K3iGxbDIgL8AvhmCY7nSPIJbAd4bET3qgzeesBxWIrl9Upoufj34GG7KbdjO/avYumm5+9DmZ76qi3TK4ceucAmCvdhnfQ+zJxhmQyt04BZsNXrc8DjwHkJzu+wdJiHhutR4BDMUe6iBNfTli65btDnKmWxQXt769/Tlk36m6fOPP2tK7LfhHeP/Ddpbwetq4AlovvFA48L0uqX4Okjbr3v4GtU5rozg1dZtzTz8/DKqxfPI2Oeej0e2AyqEybh11Fda0uvLDrrorZMzzfWyUQHZXplMa6LR8P/g/uiCzJlvNSUFpFvCiV+KgHPo8tcMubs401o/QA7DTmkuFz11aRyu3kBq3ueOWl5sgvl7KP68mwwsDe2krk6/J8K816Z3+WeXVj3jLwjR+7ZI+HvWIKiASYkOMdkfpd75uW/9hnB+54o2kHxrANatfXlLdMrhx65AAZhpytLYLtl01Iz0cQyUy1d8m4lbNdhLyz4fQ7H25YuufZcHlls0N7e+ve0pVc2a+usQX/riux7eQ/PPfrCq8c8tHJ2noU9+dgmZTZobw9fA6vk1Fu3NPPz8PJf+6yBjHnqdTJ2lPwJkZd/5ncuHdXltvTQ8taFp15rv7Fhf+vaXKUb8hq193uZazIwpel3YqHbiusszLnvigyeR5d5ZczTx720/gxcgJlBHIptBp7jqu+p1ZCOhs55nbc9c9I6EsshPxflDg2u8prwhRmcL1U0jpNWzmM39cgfiAV9B5tszwaMw7wni+8bRiYlIjYhnwU7XrobuBa40cFXzpmqI/5zeCU4Y7pBK66vpmV2Uy4ays7cWKrrbxdXBmcgFrlgvuJy8uVty0b9rYks9rG9vc88bemV1/FNv7FTee0L7yVy1yb/fZDhHK3LME/utcJ1GnA5NkA90pB+N/maiO3mZ0+COpCLq4ClovslaXdY7bO+6JKMdTpWfuH67ouuiz7guPpbCb2ZsdPoR8N1AjBz9P56zHExe3XKW4ft7dk0nAW42UmvK2NqSR/30hqf/B0C3OOpoy/cJlhEVsUG/zlFZJ/o1VCsEmLchbBoEB+LyNpYOI4LVPWdhGyRWu/A6JkCC4rI94ENgblF5A9JeT0G3V68CH9t4HwsXJBgtkw7qerdYkHltwUWkNbUfTNhWbkKGgdiSQqmF5H36LXl+QRLlAAW/u1X2GRoTITzHpbkoAVU9Ufh3xGHPAZVAAAgAElEQVQiMgrrnDeF8n6K2QQtKCLjE77ui/jy8l+LJ5ZwYQnMfnDTCGcokeOek5anvlxleuXQIxdiKarnDnwtF/E1FDv+agEROQbYCgu7Uxj5K7ZoKXD2xFa0bwYcCThLh/fetmwk1zVQK4sN2ttb/5629Mqrp85c/a1bsu/lPeDWyn8DfefqSwF2xurtV+H+Pixh0afAOs4+4m3vJnwtA2wNnCPmHHYucKmqvhdoues2QKmfRwP+PXheGfPU60Bgeu11oF0F25wBm+xMDs9dOqrLbemRRW9d1Jbp+caG/c09V8Fk70kskgHADljkn6LfH08NdCCvU5PW+0CPjbxTl3llzKPHGo2pWMY5gA/EEt/8E9sUrYX+cIybDpulT0Nr9pX3MMPoGK4EVhSRb2CVcy3mybthjKTVDg2vYSuzjbFOVsBkzIShKV4BJwDrq+qzACKyCGYnvAJm6P46ZksUe19OxpxLCr6PAo4SkaNUNZ7Ax992CnCKiOypqqdWfCeBjwtVdYfw2yKbyoVYp7wYmxAfhWVb6uFLW7Miufh34i2K2XjNgsVIjHF2a0LLU18NyvTKoUcuNsAmCvNguwExzkEZ/jbBBtys93OAvQLOP0vee9uyqVyXglMWve3trX8PPa+81tZZg/7WLdn38u6Vf1d7N+hLqDljngrcii3EnlXVT8Prf4uIp0xXezfkazJ2dHuWWPrUi7FMmVdg6b/ddRtgvIicTW+klu0iPK+81uI1kDFPvR6D2akW0WIuwSZjgzGfggPCc6+O6lpbemg1qAtPmZ5vbCITTeYqC6nqZtH9YSIytrjRKH2viEyH2cIWfamIeNVUXqugES0RuZ7eqBEDMLv/yyMUjy5zyZizjzcdU28QkVmwk6HHwrfkHMTbQMLW8RcOIjK/WlrkKpzHVHV5EdkP+EhVTxWRx9W8YFPcJbGGi8OCXRC9nzZS3FVlevHGq+rSdc+8ICJzY5ldehYm2hrWDDEP1WEJzgUJzmOqunx0PxCLq7d4gjcQC0Qd03qpE949ICKrquoDXaTnqa/aMj1yGPBq5UJENlPVKx20bsJs4ko9WMV28dfT1hBUZbi1bemVay/UyaK3vRvUf7flx1Nntf3NWVZXeQ80K+W/SXs7aK1NcuqFOR6n/c3TR1zt7eRrIOYMMxxrpwsxB6Y1gSNVdRFPORG9wcBPMfMksJOZ0zWkYm3CfwM8j04vrVcReRxYqdATxfgoIoIdB6+R4Ht1VNfassGY6qkLz7zB9Y1ecJb5ALCfqt4b7lcHjlfVVRO8H2D2qy9gfWkBYHf1hVebahAWkQVMwcIfvpLB84yp7vp39PHGbSkigzD/rFxEijbozxBpg0TkTNqFPg7l9WnY1t+J3tXHtCkhsRiqa2OT4BuxoNX3YobSBawsIiPorXCx4rQlBmQDvEczuwaPJnxtiq3UvxLoFLSGJnhHY8d6VcfjF2LpSscmOBeE9+kRA6G8tmNEEfkFZjP8Jr1x+XqO2jvg34P3vFiolmG0tvePm9Ly1FeDMj1yCD65uEEs/E1KK857DuZZO1ZE7qA8pN9E4C4R+WuCE6+K3W3p5N8FdbIYwNXe+Ou/ll4Dea2tM+c3dk32vbwHXI/8u9rbSavq1CsGT5mu9nby9Rzm0HacqsZhpq4QkWIi667bMNk9KVxl4JXXWjyvjFFdrwOShfIBoRyVEC4qAa+O6lpbemg1qAtPmbXf2KS/OcvcA7hALH42wL+weUsKJwDrqOrzgY+FsPjvPZPghrxVQgPZH535eQ48et0lY84+7qU1A7Av5jezm4jMJyJrquoNdR/UnzvB47AV0RiiwNOqOibCWRwTrgdU9RIRWQDYUlWPSWg9QW/ygWUkJB9Q1fUinGewI5i0vH8mtLx4g7DQHsVK+x4sleLHEc7zWArDXOzEmNazWISA0uNxEXkaWFxrGkwcx4iBr2+l31SC5+G/Fk9E7sfqKK3XKxM8D63a+vKW6ZHDgFcrFyJyMxbKJcVpCUguIjnliKqeH+EcWoJzWELL25YuufaARxYbtLe3/j1t2UReK+usQX/riux7eQ+4Hn3h1WMeWq5TL2cf8ba3h68h6ogH2kAuVscWR+nOVDxZ8/LvGd+8MlZar4HGyhpsf6PfzAw8pKqLJc+9Oqqbbemh5a0LT73WfmPD/uYpcwFVnSQW9x1Vfa94ltB6RFVXiu4FeDh55ubNwbtX9r0bCB5d5pUxTx/30ros4OyoqkuGSfH9qrps1XcXxPrlIuMp3gdaDxc0McNpod1b/SEnLRdewK2LDnGfk85NwJAanJHAXE56lZEHsN2TaRx0vPzX4pHJ7d0HWrX15S3TK4ceuSATSqpGdpYsk50GdLxt6ZZrB61aWWzQ3t7697SlV15r68zb37ol+17eA65HX3j1nYfWubTGRD8bOLeTMhu0t4evwdhGxGmBx3NL+PLKRW3q2wb81+I1kLHSesWyg91IFDkGm8TfCPw6g+/SUV1uSw8tb1146rX2Gxv2N0+Z3mg4p4e22RnbKb4hyO+mwKZNeevWdwLPUxNlJeB5dJlXxjx93EvLlXY7d/WnOcT1IvIzLKRXfNwbe3MvjDmxpLa+OdOEWTAniTHAv7GYjzF4M7q48KQiOkTC12XANQmtq5IyPcfjcwBPicjDCc7GCV+eIwbXUXsD/j14N4jIhqp6I9XgoeWpL2+ZtXIYwCMX94vIUhp5mOfAIztiKST3xzxyY9lPjxq9bemVfw94ZNHb3t7699Dzyqunzlz9zVlmN3kHn/x729tD66fYZLN4djc2cKfgKdPb3h6+LsQmrhsAh2MmabkdL2/delLfevn34HllrLReVfVEEfkAuFd603//GzhaVU/P8O/SUVVlNvxGLy1vXXjK9Hxjk/5WWqY4o+FEMBgzwypscN8GpsfMPRWroya81YGX1pvq23n26DKvjHn6uJeWO+12Cv1pDjEp81i19ejpXixM1EmYkAzHbKAOqaA7DBiqquOT56NKyktt0bx4Y4BtNbGTU9UVIpzzSmildrCe4/G1SnBabHmcRwzeo3Yv/7V4IjIZC7D9MRZaqey4xUOrtr68ZXrkMODVyoWIPAV8A5gUyizKS4+NPbJzKxaf9deYSdBOwNuqekBCy9uWLrn2gEcWG7S3t/49bemV19o6a9DfuiL7Xt4DrkdfePWYqy8lv1kfcwJaL3nu6SPe9vZ8Y+EENl5VlxZLaXuPqq6S0PLKxdFY6KvSyVoD/j3jm1fGvG05U3gxOYNf4Hh1VDfb0kPLWxeeeq39xob9rbRMEfkhFu1nYyzmbwGTsXB9jVPTN+Gtr7SiiftawNeomSw7dZlXxjx93EtrPeBgbMP0Vizt9s6qeleujJbf9tck2AMiMkZVVxCRJ1R1qfhZ+H8xVX1GRJbP/V472+Xy8tbV6BDdAnFEHvgSpg6IyPy555p4FntkJ5L9nueS2JN9CV/C1AQRWRezhfw6Njgeg8U+FeD36QD5RYOIPKyqK4vI3Vgc4zcw07jGzp6BXtcWiv+p4NVR/y9Df3yj+KPhLAicAqyC7Vo+AOytqhOnFm81/OQmyQV0OvHuWv03oSUis2P1KsCDqvoPTxn9kSxj06r3iWL9WCwI+nNiHt2vYnH7CtgXi1HXYiRdkALWldYg17nyTgx8ufAiKI0OIRZTs3R1oWG7X8yhrwpv6bDyyuGUeYyWHjFIayzAXHkbN+S/Fq9sgRLhPNaAVm19BVq1ZXrl0CMXIjJbuC3dgUmgNrIItsoGeF0srM5rWCYlAl/etmwq16XgkcUG7e2tf09beuW1ts68/a1bsu/lPZTp0RdefefpSydgmTgfwGxlHwB+o6otSXqcfcTb3q4+HuBMEZkV+B22Czck/F/w5a7bcL9OGW4D/mvxGshYN/uuS0d1uS09tLx14anX2m9s2N+azFW2FYtkFcO7mK3qtdGzi4E/AUVSq62xSCvfaiqvVeClparDnfQ8uswrYx495h5TRWQa4DM1R9HxwLewMI7/mZNgWgMtp1DYxBSwF5Yd5JdY8PN1icKOqOpu4W+p8qI1yHUVePEKSO3k7qHXTi6d0JTBRnUIqtqUryIVYw5qs9YE8PLvwcstUApQrE29tGrrq0GZXjn01P+Y8BspoZXuTFXJTgFHiHl474vlcx9Ka3ILb1s2lZ9ScMqit7299e+h55XX2jpr0N+6Jfte3sEn/17+PbQ0Ok68RkReTSfADcr0tnctXyJyMpYM4CZV/RcwmvY+Bs3qtqD9A9rt8A/Hz38tXgMZ61rfxa+jutmWtbQa1IWnTM83NpGJJnOVQVgCjJHhfjPsCH8ZEVlHVYtsizOo6oXR7/4ilgeBhrzVQSNaYjGyd6Fd9oudYI8u88qYR/e4aInIbtgJ1b9F5L+B/bBkGcuJyLmaRBLLwX+0OUQdNFyp/Z8BMQPx+TTYnH4J/2+BWBKAX6pqVbzSL+FLmKogIhMxm/QCjsMGGaD/9Gs4FVwtXGAT4vuxdM7jVPXzst/W0P0ztumyDhYBY3PMvGKXPjM9lUFEtgBuVtXJInIwsDxwxNQ0CfwSekFEHgRWV9XPwv002ObGGkTJqkTkGCyG8KXYZG4rYFasb6HtToVfCIjISMzJdFsiJ1NV3as/+PGCiEzA6ngmzCl2flX9h1iItEdUdYlaGv+Jk+AGR71dt2fxQsmW/rvYCuwI7SD+ajdARP4L2/GaTlUXEJFlgcO13cv2S/iCQUQuV9Uty46DEpvgh1V15S+UwS/hS4igP/WrF0Tk6/ROiH8IzJkxEfPSKhzsir9DsN3mNbvI8lSBiOc1gCOwSdUhqvqtfmbt/wSIOaSvrCFLWTjFe1hVF5Uoy63knewK0E7t2fsK4nQy/U+DpG7HqeoyuXdV0J8h0qrAddTrtWeZSnATFoLs4nC/NbaL8AbwP1QfpUxNGAGsDNwFoKpjgzH+l9D/UKyqPcdB94nIH7EIEe8XD7/c2fkSvijoZ/1aCSIiWIz21TBP8MWxDHKN01pH8GH4+0GYXP8TmKsvfH6BUITD/AFwpqr+VUSO6E+G/o/BsZgvzl3YEf63gSPFwtbdXiCp6gL9w14tFD4o74jIktg85iv9yI8XpheR5YABwHTh/yLZRy5EXRv8R+4Ee6GbjgOB3gBgc1W93IH7mKoun3smUTSLLxpE5EFVXSVZIZVGrQjfPERV38u9/yKhEzOA4BgzryYh8f6TQUSO0fZQZy3PpKGneti1Qr+gqCBiXrsLq+rtwfxmGq0IzfSfCFV1FgavD1X1c7EQdothu4KfJnizd+PUp1t0InoDi6PZ/7+BiNyG2ciPBR7EPMG7kV3rd5j9/Xcw5yUFzlbV31X+sPPyvq9JXGIR2UNV/9wBrRswx/H1MFOID7GdyGUqf/gldA1EZC5sAwrsKP616N3+qnps+H8LVR0ZvTtSVQ+K7ruqCzwgIrsCV2ILy/8hOJmq6hlTudw+mW6WjJM9UOMvZjT6axIsFiv1XODi4NwQv/NGAcjG+4zwDms6URaRR1V1xRr2EUuluJuqPhzuV8IU5jLR0cKcWPSKYZTn2Uba03Xmcquvginob2LZxgYC76dHfyJyDnAH8BvMOP+XWEayPSKci7HYs58Bj2ADyimqelxCaxEsw81X1VIRLg1srKpHJHgLAHtmvnPjBG9jbIUMMFpVrycBjxlAWG1vHMoaA7yFZcZpa+u6MqU95/jCwKKa5BwXS8V9JPB1Vf2+WErvVVX1nARvGaA4Pr1HVcdleMotoDoKryciS2G7X7NhcvM2sJOqPpnBLXP6aVrmbljUgNlUdaFQZ39W1e9EOKtj2YXeF5HtsYH5FG0PF+eyZRSRq4BzsIlo1ubT4dxR4NXWWdBPa2L2evdh/eQTVd0uofUcNhk7L/Cm0Tt3CMcqOpnv9OiLidigdp6qPpWh0SQKgwtEZK/A/2TMpnY5LJrErRHOtJhjaE+fxGTn0xq+euKDisgZwNLYRO9BLGrFA5qERJKG3vYiMkhDfHURGYTJ0EeaxFwXkdVo13VtO9B1eGIpaA9W1TvD/f7AOqr6/XDvHruCHvseZn/6XJiQLRXXfcA7AcuqN6GKtqctA96StCezuiDBycb1j3VPg/FtILbbPYzWek3H8YHAVxOcl6L3rrEt4Hp11Ny0p9y+O7zr0fmp/s/cu3VBHdTpRBGZV1VfLvntRplx0KvXK+s/4JSabkpvdIgsaDdtp7VL6fmaXlgA5N9j6fouxbL+FJPy+auuhuUcGq6LseOyE8L1N+AvGfyjMWeQebFBcjZssE/xVgSewDxAJwHjgZWwQNJbBpz7Mc/FLbEJ6WbAZhlannSdj4Y6exxTEMOBozK0Zgj1+kj4ze+BwQnO2PB3u1AX0wLjM7RGYyvbOBVhWxpDYBw22V4HC7q9FrBWgnMUNjn/cbhuA47M0DoJ+CM2AVm+uBKcx8PfXYHDwv85/mvLxMwN9i++K9RfW2pIzPxlS0IqRqxzP5Hg7AU8iTkWHB7kY8/o/U/Ds/eDvBTXpFgWsVTK54f2ezT8v1SJfN+PDZzF/dpYzvQU78/YxO9lrD88AZzTYd8diw1UsVykdTEem7gsE2T259giJKU1PvxdAzPh+QGZNKvAd4GLgBewPrpoBmckFkXmBSyKzK2Ygm5cZ4Q0qNjibv+43yR4gu2+XYLpsiOBRcK7M8PfUZnrTi+dDvXFTNgC/H5sovgTLIlQ8b7Qp8eGa6lwHY1lG+tELoq+sQHmOb8ESTpZbEJ1PuZNvi420J+d4atW92OL9/WxBcHN2IL4/Oj9TlVXhv9c6tuU/wtDnZ6GTdpOBf6Q+V0tHpYl7UFM1/0eW7RMF72vHbuKNiUaq6get3bFFnUPYRshM/ehLQ/FZPnN0I5vAFdkaO0bXb/FFi3nJjje8e3GwM9hUf0cmuDsiYXHmoDpuSdIxgecY5tXR2Hj/IvAX4Hrw3Vd9P7x3P8l925d4OiTlToR0yXDMr8bDryQqwtq9Lqn/gPeGGBmMuMINiZODH8/C/T+Gf6f1EldlNZRN4l12EgDsF29V4GXgnC3dd6S3xaD06nAH9Irwb0bmCm6nwm4O0NzUuaamOAMxAJcExqxTJHU5tkOeJ7c6kVu7PHRs8c99DO0JmAT35GEySqZPNvYkU7agXMTAQ//47Fsf3Ed5jqGZ8LwBGardyuwUlovTcqM6rUy57inLkJ5M0b3MybtNTO2g3EJrYP7bBHOD7EB78fYbtfS4f/ngB9m+Mrxmns2Pvk7BNup7kR+HorrAlsQpPVaTCIPAXaJnyV4BY2jsCx6lXId6nAPbDJ/P6asp01oFd84LXZU3rjOMAW/KjZJWaKQu5p6WQfTY+9gg+yqHdZvJR0c/S3BXyvQex+bgH4jrf+Sttu06qqQsVOAH+Xoe+XV+V2DsInJAcC12GSsso1K6HwNWAHzLl+O3sX32sAzCe7ThM2aGppevK9geuO8Mnwqxi7ghvA3njRkx62E5qLYgufv2CR7neS9py2fwMbvYsL8VeA2Z7vdlTxzjW9k9HwG53mSRWEGxzW2xThU6CjgWWBQRXmP5f7P3Sfv+qRTqNGJwIbYomrh6NmBoW3nKfsOKvS6p/4D3oOZNkjHkbOADaP77wNnNKmDuqtfHePCEcRwrCGuxHZ61gDuBJZ1HJEUdmCemHhfBT6J7j8Jz1pAHYbrqvqZWGDskzR4g5aAJ882+HKrfyAi02HG98cCr2MKqAXCMc+vaT8uim1Jz8BWreOAu4N9Z84m+B9iObg10N48lJvCKcE05dYK/gFmAYpjjJkzdFCHDQ+2y3oLZgLxiJjj33MluHVlenOOvy+WkabAWwWLBhKD0OugQvi/J85hkJV3gW0Cja9gR1RDRGSI2nHR4cB6qvpiRGe8iNyJDfRx4HWAicGWsYg9uT02GKbQTaef0SJyEOaUsB6WrSs1bZksIgcGfr4tZns+bYbWq+F4ez3gmHAM3SbXYLZygd4O2CS10Bc7YRMWr3OHp872wgaDq1V1QpCxUTU8vYntglwHLIstMhfwHOF66ETF1uqLqMzhodwTQn2tie2mLdKLKqur6n3hZjV6679JnFSAMWLpvhcADhRL45uarnwmIgup6guhvAWJ+oy0Jk8o+o7Saw4xVEROwhziFsbk4AHspGMnVX0nZVTMblDT55FO3ADYGZgHq6ei3MnAQcnPnsQmzTk96MKLvlHC3+mwuKebi4hqe3SL0rFLVTcKf90OV0E2FgvXP7BxYB8R2V1Vtw5onrYsbOaniMhQzCxtXgcLM2B1HYNrfANuEpH1NTHLSOBl2nVzCt6xDXw6aiKm33JjB1i84PewNp8+/E+4b3HgaqgL6qBSJ6rqjSLyMVavm2AnBSsD39bETDWAR6976h9ggohsCwwUM6n7JbaxEcMqGvJBBH5vCvLRPejmjLrhCmUMdlS9LckKCgsoDjVHJMCvsAabxlHeb7HOPiJcY4GDMngzYDmoi6PMhYGNMnilx/aY8nwv/P0cm4AU9+9laI3KXOnu5/xYZxmKHQGdCCyUoTUOO3ZfGdvdWAFYwVE/bXWIKebbsSx0rwL3kjFHwVbIr2Ar1TL+t8F2Hf4H242aBGydoTVz+LbCFOAESnbaHd+UK3OrBGe9wPfb2CThRWDtDK3lsWPEd8PfvwFLJzj7ZGTsVxla/4VN2t8PPH0OTAjvJlR8z1OZZ7NiJx+PhesUYNYM3u+wBcFmmCJ8HfjvDut1AHbUPhK4IvwvCc7XQn2sGe7nA3Ys6W+bEnYisIn5+hm8q4GnsInpXMm7Yhdp11Afa2GD0lvA7jV1NgY4OVdnzrr4W6jb3K7JAeGv5wi3lk5079EXEzEb6tUy9P4Q/b9CkNkXsb4ylsT8qKFcLA/MEu5nz/SR72Anfndh/e5Fkl1IRzm/DHwPdOKvEF2rY/rl2Axem6la9O56bCIyCovzegu9iYmuy+C78Jz8145dWAaymaP7WYBNMrROwnTPGVhIr/jdsw3b8rRQzh6B5uOYDXpa5hP0mn5NwPrlLxKc3Pj2jQytH2F6s21MxfTNPkHu78V0RfFsn4RObmwbVlL/tToK28R7PtRr9jS6QXu7dYGDVk4n7pHBWxNbEF1HYj6Z4NXqdU/9R/VaZ7p5CzYfGxau3wK3ZGgtn7kWwjE37E/HuAW1Jl+2BCc1iZyGpDXqwfHYjsBiWEe7jxA4XTOG0yKyArZzBHac9HgG5zJsYNxRzWB+hkBv2QSvbVeICu/9voKI7KWqpziejVHVFUpobK+qfylzuNDWnamBwDGq+msxT/kBWuL9LyLPA4ur6ie59xHeXJjdNJjn8hsZnCuxXZTzw6MdgGVUddMIp4lTg6dMV85xsQDoiwa8ZzWJFBBwlqdXxu4pkbFxmD3k7WoOlOsA26vqLuHdf2m7E8H8wPXagcNSpvxBmLLxrNanOojFNl1YVc8TcyYdoqqTEpy2ExWJHJmmAk+eExVEZEtNoslIu/d3rdOjhC3AbvAe6A3RBpFCxOKaEstEmZ4oQNudkb5dgnd3gjcI60dg/SjbholczIGZBEzK4TYFiRxwg4POeA3OPWKOXJthi4K9VHWSiKxVRU9VRyf0s/iqOlqcqcUTepVjl4iMzYxRbXFSRWQ4cLmqvk8CQQbmUqczZ/LbYZh9cluUnqC7CpgCvKmqU6L3A4ELNHE6LSlnEmYy9kTaX8ThKJ+hVzm2JTxWOdrtVFLm+bnnNWX9RFXPTJ4draq/aUrLUVZ8KjEI2zkuTjBVO4i5XdYOufp30JoNWxQVuuVuzA/ofxO8B7GJb2GzvCS24JoZ+KlWnBz0a4g0qfFWF5G7MYeYs+ndvdpZk7Av4RhlRWxCvGq43tGQpSXC83gsFhPv0iDMDb7vDo085suehed1dZGLKJBTciOw1d7VtB6V/m847jrDK6QSwq05vvMa4Ceq+lYFjqsuSpR5yzMRGY1lrjojaqMnVXXJ8L/bKz/gxxEk7tLEIzbg1HoIS96jdXI6WY5kbBywnNqR4ji1yCKbYI5KR2KLMTDZ/g22C3BNoOFKKBOV6fKqrgJpEFFA8qZM/1bVFpOUIIsrYo5ui4iZaoxU1dUTvNKQhNH97NhO2eqBz3uw3e5/Jr+7DdhCw9G5WJi9S1V1gwhnHHbEPobouF5VxyS0PHwdA9yRU8RN2rHhItYb2WVmWgeZ0ZiH9rtNJxXhWwoYjJ1GjVHVdUVkXVW9U0qyfGqSfc4rFx5I+uUAbEf4D6q6aHg/Hjt2/UBENsJ2IbfB7IO3SORiAeB1Vf0o3E+P1fGLDfjJbaAUoOlCK/ymbhLWttCSTKjOOj0sImepRckp3eTx6tcSfRjj9UxkROReYF2t30i5Gzup6zQrYOOwqiKyJ9ZH3qTXJETrFradgojcCFykqheF+z9hmxbuzIWdfGcNvXtVdQ1pNVeCismy1ITtLNF9RcKxM4o+5uTvKiyk24RwvzhmWrg/ZlmwbNlv+80mWEpSVCZoO2BK6xfA3pi90WYZctNjxygzh+s1bGc4Li8W5J6VDuZ4FIPLRlQqwr6IhSWZEZgjDLCFjdlQYO4MrdK6ELM93hZYQESui342E722rjEUK9L9omcKLKgh5l+DFdnjocyRtCZsSG0BZwGeEZFHaJ14bxzqYgacdQF8KCJrqOq9AGIhWT5McGZQ1YdFWtKKT4n+3wfzhs/lO1dsF5ZA/2hsp/ii8GgvEVlNo7iNAX6nqiPFdqe+g4V2OR2IMzI9hsnov8J3zgK8ISJvYuH0ignUO0FB3A1cJCJvEepXVa8Jux37YrZgYGYAW2pruLUiocym2BHVX8L9NpiMp3A98BHWLzoaQPAl+Sjgj1gCmZHYZGZHeu1QY/gRNtl4DEBVXxOzPwRARL6GyUkRFD2WnxkSWpdidVroiO2w6B/fTfDm0Mh2VFX/JWafHcMUVT297ONE5PuYL8PcIvKH6MHc7/YAACAASURBVNVQWmURzLnuajH7uU9pHTiatOOM4e9M1MNZhIUiVth4sdCI6WnJudjJy5bhfgfMSWvTpjs3qtpiQywi82KmJmDHsXeStzNW2u2LK+WiIYyhd7drCmaCFE8oVFU/CP9vikVNGYPZxf4soTWS3nTNYGPJSHpPm4DSReD7qjpUfX4PMS3P2PWoiJyIxTcG89ofE9Fw6WEN9pc1PHr1a1zvObw4idNELEHQdbSONelkbSJwl4jcROtYEy8AqyZXs9LeP+tgL2wx1ha7VxpkAW0AmwHXicjnWNi7d5pMgAMUfWVRTDaLucN/0T7PqgVVXSP8re2DYrbHF2IRShCRf2An62lYvonAnJijOFgK6cnYOHEWsIM4T+Sw6BkTovdPhcXaxGSe0Ab96Ri3mvamqDxMLH5hS+BwVf272PFo2VHGmdju6WQs5Mv9wImaN+guFeQERmDhduYVkYuwXaWdM3jxcdJgbIJQOOrtjtkrfx1TBEUrvIdNDlKoqov7sR3wOWhVOpOxrf8WUIeDhFg61FyHTdOgDsYcqGKByw1YVTtGTevip8D5YYdKsIl+etRU6dSgqj8JE46DNTj8VMCGwLLFzoKInI/ZtqWT4Dgj01maz8h0GxYi6JZAa31MoZ2H2c8VE+YfYhPSvbGJ2szYqrXgfxw2aSwFDcevInKCtsa1vl5Eco6i8/R150KTWJAO/OelN2nDeSLyOGYnFsMnqqoiUrTljMn72GkpHhBzTktzqep/R/dHiMhWGdY+F5H5ip00sePatD9cHyZAbScq4d/XsEF1Y6LJRuBr74TWidjpVNsRbpN2VDvFGYjZQNYllKlbKBawkKrGGwuHicjYwNP+qnqslMTb1STObgZewSaBqOqh4e/wmt8UUCcXhOe1cbkdOlHCovQDbIF7WvQuzTo1TbxbqaqfiJ1EplC7CJT2mMl3YTtgqZmVZ+zaE7MjvSzc34ZNhAuI9XB8Etaih6Vkp74AVb1KVX8S/q+czHvGogheCNcAqhd5k8I1XbhyUDW5GqqqOzTgC6odvZpkAa2EZOd8V+AazMTzMBGZTRvExi3mS2HnfHkN5h5iJ8V/7SOfdSfqZ2I2wKMC/trYpDZePILNe+LF4/Ui8oiqriQixYR2JHYidzatTucpTBCR07GNELA2f0rM9KrNbDGG/pwEl3qri2nuQ7Ed4AHh0RTgVG0N7j8fZsfyHGbc/goWSiQHLo9FVb1VLFB+YSO6l2ZsRFW1ZRUsZp98S3h3iljK24OSQbkMim3/troIE4+/Y4NoKUiz48b4uH8wtuvyGgl4Byw1O7ev0mp7+1Z4dwoWPWJPVT3VQWss5kk7NNznolb8HOtoi4nIq5hS3D6h83log9rc4TiiVuDzEE49WW8VkeNVdfeAXzyPF1C1NmMicoMGL/AMzCiRfb3YcW1uwuDxqq7jIz0K63lF+5GY19v78lCvs4gl4fgxpjCBHpu680VkM1W9sobFW0Vka6Cw0d2c0CcT+C1wr5hZjWCTqJ8kOKUnKoGvcSLyJLCB1tv9vYzFIK2yPXO1o0aRaWrK9Hq/V528NIm+QzJZHoB5s6emR4OwheEwWgfRNGlLpVwEWnthTpmFbvuLiJyZ0zNSndThZMzZ7D3gaVV9NPxmOdrr7G0R2VhVrws4P8QcitrAsQg8HfOsLybdO4RnuyakaseuoFNKbUYb6OFGEUHEkThEHLbiuU2ukt948KomV/+U1pOblH5uYVfsPv+VZPdZVV8P/zfaICiBeOe8+PuDcKU7515wRcXygpSYhtB6KjFjMQEGUNW7ShaxQ5LNiPmw0J0Fn1BzIhfBzliUol+F+/uwHeRPsRP2UujPSfANIjILcBymKJVeJbc3tgO7kgZHCLFQOqeLyN7FLoiqfi9MmJfAVhn7AkuKyP9iGYTiHcpSQY6ZEjtKuRjz5G1zHqiAlrAvYbDaFAtUXQfXV9RFwdemWEDur0BPbux44uE+bkwnEyJyCebNSfLcm4Fry8D7XYGvU0VkP1W9IvrNqU6FOTvWydYAVMxW7PB4FyRMFL4r9U4Nd4jIZphNUNkE5CjM7GNU4P3b5AeTLbGjqeNV9R0xh7v9EpzXReQAWlejb4aVc48JgqMtU8iZjRSwNybXEwOd+bFdnxSqjuRdoI6jsAh2wI6AK02ZVPV4sTBr72FHd4eo6m3Fewl2sMAwydi5Jf13N0wJFiYFA7DQdrsTfauq3ixmz1jYu/8qXeh6drFCH59XRKbTalvG2iNc/O0Idmz8R2zXLz46jiectQvFAFUnLwuJyMqYfaLnCDmeLE8BLtH2k5hrsQndGMrDSdXKRYBdgG8VelrM9voBzAyhB8Tsi9fGJsE3YrFG78WSx6Cq54rILVh/jHeS38AiEsWwB2bC9Eesvl4mf2rjWQSupK2+JneK2aKnUDt2Sc2xcbFJgi3m2zZKik0S78ZHoHkh5oE/lt5dOiXUawSxnuyxFafVLG1OzH4zHWtSR1QPXtXk6t+0ntx44KVwle4+d6DT26DhzrkXLgAeFpGrw/0mWLSkTsFzKuEN27kvthnxAlZfCwA/C+N6sbFQdyJX3H9IbzKZFCodhPvVMa6HicRbPayY10sHptABbtXEGSy8mwebOK+GHU3MrqqzRO+9zmBrYZOXH2ChOy7FApJ/lODFNkADseOXw1U1Plo6HlPKpZOwMClZRVXvz9VFhPc8FjXg6QyZGG8Bbfesb3uWvF8U+KuqfiN5PhLLKLMtdly/HbZTsleCNw5rr7fC/ZxY5INlIpyswkxX3mJOS3fTO5HZDnOE+G5uEhRDZkEzGdtNm4LttmcVkzgiSAS8yqNXMQ/2ngk8tho9HBv051PV5wOeqy0juuemC4/k/SAsQgpYcP+cDXupV7UXRGSoqr4nJQ4vqWLqBkhDZ04nTW/qT09K2AuwI/9SW8YGuqe2HQOeOzKNY6FY4LWdvEjD6DvhN9MFfMUiP3ySvO9xYO0rBB28kvY6qQ3GkiCkzmBPEDJcqTmffhXLuLZeBe0Rqjqi4n2d08/8mIPytNgCZ2bgtEIHBJzHMMe7OGbyFdruaFkrP1LjyCkih6nqoWKmcBlSbRsbpQ6TEc7TWFSgRvpEgq24RmY4YjGJL8Mm8ntgC7G3VfWA5Le1eGL2+mdg5hU9kytsk2Y3VT2ZLkNTnV5D6+fYwjN23N1GVU+r/mUpveXpHbeyUbEa0BqFjfWli+LA72FE0UywiA5tZqqJzns2M8/KzVtUoxTxAS9NJV8g1u6e92eItHuxjnUPlvRgcvSuVFFKaxSAX2JKejVsd+v+6HpCO/QgDbQHYivV3YDvZSZOlWFfAk4xCfsMO2Ism4S1RXnI8HOfOjyjJe+t3hI2TdqDtb8BHKjtO8SPq4XwGq9mszwtNvlbJcFr8UIOE/txyTOXwsy1fUG/bDAooJMJUaBfmvM9wkmPXn+ExZLOHb3OqBWnCJ62FGdIvPDcs8PeJ6/qQOMGVd2oSjGJM4KENDOtaMLj0rTXRXqEOx6bFC2N2WufgzkerhXhZHcPVXXzhFZXJudioRj3weJw7yYWPH5RzUQqqaHTdKHomex4o+9sSPvkY3dVvSnCORMza2txXM58R+3OWvjWnbBdIgi7XOkkR0I4NDEzt3Uw+9CnVXUxSiCnR6N3lZF8vCAi38HkL979H67RUXIDWqWhMTsB8YWqHAn8UoNJQAPagsVDXzx6NkZVV5DWcKiPaKtZQy1eGHs2x04c2iZXInKyqv5KSqKyaGs0lia4rvHZA+IMd1dDo6sbFpFeWQI7mak8UY9+NxAzj8iZNbo2Gpz8PYMtNtNFYJ0PWL+aQ+yArU42A44Ty1pyj6ruTasNSwrxu2GY4fTedR1RnMctAXd6zKxgK2yXqM3mT81pLzYQ/7qItBiIq//42HNs/6hYDONraBW+qwLPi4Vvm1laj7uGkjh3NODLm4HrZrHjxNgRIc2S5820VGrX2ckkN6xKF6a1ze+O3h8T+J1Aq41TyyQYx9FrmIyejR29zSe2c7y7qqYe5pVtGWAnLPFFDDunz8R/JOk5kq8E9WWncjmIeGVQLDPa8xoim0TPdwcW0Ch2poici01s07ZMHTmnqKqK2XP+SVXPEZHU+3pzencPhxe7h5nvKBxQZtDeCAPpN3h0z3mYAi9s/1/FdFt2ElwxEWsaQaE0OkSEUxt9J8CJWOKL4sRjIWywjB2e1wB2Dgupj+md3KZOm8dSsbMWJjsPYrt7xY7T8JJdrkfFzM3Owur431jfrYKsS7n4ohoVu89lEQrGq2ox+V2YmpjJTvmpPDYOk5h3VfWchPYuWPzldHe0ymGymBTOhDkfPZyUmYZnrLUVp3eseT3I9muE6AJN8NR8QfZXi92dMy0pjuiPz7xrjBuNtR6d7oWBIr1xw8M8o8wJsAwuxnRxYWfcwzKd2RcXesVjGnIxtkv/GXaaPlRETlHV4xK8SjOlgON1Hn03Xmw3gf6OEzwXZsu6JqZUXlKz8/2M1ugLPT/BTAVy6VfryvIet1yO2SzdHPBH53bPxBk7UHwxaGuP7aXmGCsM6Jtg3upxKLXJWAzUlnSETr52xTLhFDtmQzDbvD9HOILZQq9Ea5KIqxNaozDll1WY0ro7XeyeQ2982bguvDFQd8VsmObBJomrYLbisS3as1gmpMqkC+I4ehWRh7BB8TrNxC+O8ErbUnpD4q1J60R8JuBzbY/x6d1h76Y5wXXYgufasomfg4YrhmjYvVsx/b4wCRof162IPJXuTpaUPRrr38OxPvAW7ScXrt1DEVkV20keoqrZhY9H90iD+ORlEzFtHkapbNdprKouK+3Rdx7Eksnkou+07dwF3fBw8mz+3G+13RTFc1riOUGbE9thfV7Nln8YJUkdkt8NKNH7xalY8XcIcJOqrpngHYvpsIvDo62xNitO3WaRit3mhJZHfiqPjYMcr5JOHsR2+R/NjFsPAPtpq8Pk8aq6qjRPHBJH95kCvKiJrbhYfOZ7MN+BU7FF12EaHBCb4ImFvfwH7TbzcVziTTETwNpkOyKygrbHB99IVW8o0eVRkeVmbBXlHYfJbLHw3x14WVX3bUhHgHk1yYXQDRAzn1LNmFlF+mM7bBPxN1i88LY41tSYKYnI2ZhJUXwi8Zmq7prQOhqbK1xFSSr5UtAO0vp148KOzB7CJinLY3ZrU7O8MeHv+OjZIxm8DXCk48RSJM5eg3M0lhr6x+G6jSjtc8AZAKzexe9c1YGT4+vIPpT5hANnrdyV4AhmO1tHazS2UHk8evZkji9s52RsuF+MkJI7wrkJm8DUlVmbEhl4KPyN+RqX4AzEBpOycubHVscPJHW1PPnU1iNJ0gjXfMcQz/c62vI0LGrJFdgkLE13WaQOj6+XsZ2qBTFHrYn0hjyKr4lV7Rq9m5Dcn4MtCOr496T+9KaEfQgbkEtlEYfuwUy4pgceC/cLYRPIHP/jk79DsIUnRClbc1eG1gPAGtH96thCEWyh8CjmSPMTYCloTY8d8DYN1+nYjs7O2ETtBswONsVfBnOY/AV2xJ6jdQo2idkmerZpgns8dpLYxlN4vyu2wHkAm3xuXIJ3qrfO6O3jD2LhxgZhE+yU5mNlz7C+cSsWyei69Mr8zjV21cj8uIp3bfob27CI02k/TpI2OepLG2Mnp19rwtPUuqjRKQHnvPBdF2I7pqXpdbFd6yWj+20KOZhK/A/Adj+vCNfuOFOEe9q2j7ytiI2rL4ZrHLBCgjMBm7iOJIzxOfkj6Ddst3ooNv4/Uye3Jc9GZa47Pd/Un+YQf8B2DovMPKNF5G4NTgJTAVzHLap6i4isFnYMSm0s8YVcK4tB2xMmR52hvMQZqQF4XkQOot0uMsarjI0rzbPNPCYiK6nqIxW/GR12gRZW1dvFbCAHJjgq5gG9VJZIL3hjoH6kqh+JCGIpdp8RcwKMj+g+wDy476B1BdnisKeqJ4rIXVQfvb4sZhKh4RhnL3rDTBV0Pgu7KllQM7N5JfA+ugwvgjnwHUl6A5jXQuBrtLTazZ+LKbICTsZCFl6MKbetsYndY8C56veE/lBEFlbV55LvWZj2JCoXAA+IyBtUHLWrOT6eGN2/RGI+or07uX8WkZup2D1U1ZcTWUzjWXp0z6H44pNDRXhJej3fV8eOGYu4sVtgSVdSKI0Oof7oO3FEmjexRRLA29jEvgekPqxZTOsDYP3oXmk1bdkdW8xMEZHcCdqvgCVU9W0xp7OLaD0lK8AVAi5ALqrR2Rm8gSKysqoWSY9WolffvQAcgvXHnDd7Cq6xS6rtKweIyFdV9c3kN9lwWeoIVRlO2g7BohIVUYEOV9Vzw/sy/4C2fin5sGXvYrvU10Z4c2LyM4yS8c2jW9RMnKbFjuC3Af4kIrdpssMYYHPgChEpTuh2pFUui53/I7C+eTN2erq3WmSbRhDG5dPD1VeoHZsbwrnAz1T1HgCx5FHn0Roi7Qx6J8h3h3E/ZxPsMVP6TEQW0lbn0bZ4wdowAU0M/TYJ1t64hUOwY8kR2LH1wKrf9QGOCIp+X3qPUdKg9rU2ltEE0RVyDV8MWo9N8IVYpIYNiCI1ZPCuxY6Lbqc6uHQVX7Fd4e70HsuUwbeA7UTk79jxU07J7YbtJs2G1e/cmDdzmkLa02m9MVBfCZ3sGuA2EfkXtvqH3oFvDPmBseA7HmxeDFfxLnWA2wPbwZobs+m8ldaA9QWMlYpMfGGi/LmIzKxJlJAMjKh5X4A3gLkLpN5ufmNtPco/MxyTHRAWaQWduhiih2Axjo+gNY30gfTGhCzgHOy4LJsVTxqk/gyTv+2wTIuHi8h88aQmgtqFDw7do6q3iUULqIxPHqB0IqYhZrGI/BTb4Z0S7v+M6YUWqJvsBH30pIi8g01K3sV2zlYmJMnRBmG1qLGtb0JL6+3KP1HVtwPuRIlidSd0amN1R7hFyMsrReQGMpF8AuwKnBvGN8EmAbuIRes4ErOVHO1c6NbKj9TbVx4H/FVE9qXXHneF8LzN5lUcoSqx0GfLFc/Cb+7HJkrQLIHEYOykbmS43wzbwV1GRNZR1aKv145vIpJNNJRuZKnqp2I+Eoot1jahPUZzITtbY+PIS8D6aiG5YlhfVfcXkR9hY8SmtEY5coOYaUvbPEAdkQ4yUDs2N4TPiglw4OlesRwOMZ/FKQoAIvISSazeoF+PUouAUbXRsB8wSlpDR7bpCHE4+JZBf0aHOAHrYEOwjnMvdqSXiyf3RfJVaWMpDSIUiNl3Ho1tzffEoFXVy+LfiM8m2Bupoc3GL/MNLr7icmvozZ97rpGdn5hTxcrYMVJh85jLbf8M8A1sslo2oV4Qm9SthqUongRsr6ovVvC4FjbZv0nbjeqrvq1QSMVWXyEXQu8i8jca8rw7aZ6XeazxboaIXIudDtxG60S5LktXWZlt9qW5Z05atXbzYjaFJ2HHeWC7Kfuo6iqxjIo52RTQE0NUW+22l8SUYWH/+yRmUpKmRn9AVSuTyjT4xtOxifS6qvpNMQfLW7XdW30ObOHzXewY8xZsAlvrlZwpszZKSeY3ZSEVn8VMowrb6lkxe95FE7wXsKP9ezB9MiF61yj6jliCjz1p36WLvei9Yc3Ox+oxDhN1QtJH7tB2G/meZ2LpyC+NXm8d36d9KewyHkD7bmosizNgk9H51BHFIwzO5AbjLsurx77y+5h95pKYHpsAHK0ZhyKpCFUZ4dwfnn0S7qfD/EvaFtZSkkwpev8gZhb4WbifBpPJNTBZWzw894xvccSewdhGy2MaRXYJdbEVtnC4C3PEvlWjCE/SvpP9FWwR+DFAMiY9qeafcjYW5u7mPujX2RP+twBmU9VDOqBVOzY3pHcytmC4BKubrbA5y18C3cdE5Ejg2KTv7quqBye02sb/kjIHUe88WhvNpAz60xziAayi3qzF7ANIScrPAjKTirooBoNUNU3Xmpa5CRZL8xKxI/Si8x+gmRi0jh0N8EdquEFENlTVNDpDXF4bX9jgm0V38HaEJqkoxXbU42cfq6UYLd5PU0J7g7rC1JksQ0QuLPjS3vS0LXyFQewo2ge+IjNY5dFaGDifxALou9LLOne7rqI9qkGu/FWw3aFvYt66A4H3tT3EmDeAuQfOweJWVp00bIdNDk/D6uRBYHuxHeRfFEiq2pLcRUIM0fiZqj5Je+rsHDwu5pl8PRUe2qHOJmhvKtGZsIXvQxHat1R1ebGY5ajqvySTHldtt3a7HDPSIO2wOKKUSEVKW7HINPF3Hk17EpgRmZ8uju0WrYlF6VkUsz39EQ2i7wS4BpON68nsxAc4D3hIWoP3n5vBW7oYRKGn/ovF82DMyWyOMMAWC9ShtCaWSZPZ1CVJuAhb1P2AyAEtw39tFI90Z0rMGTPdmao8EWo4dn2oZlo3RWxX/y3MVj3Gv4nWSB1V4ElB/jzWltcGPn8IjJdwWqrhVFQcyZSAWbENsaJ+ZsQmfp+JRY4qwDO+7Rnfi52aXJqg7Yi19e65SVWAJjvZN4QNnA+Bn4Zx4aOa32Qhs4A+WcyxsfEkWM28rja1eAMoJvXpZuBymAysC3w/niOFvrshcHDym9pTXzGTuw3oXVh/N+i69MS9NJpJHXzhk2CxwM1gA/DcYfejB9TjzdcMmth7Qb2N5fcItrMVsD1mY/QBtmtyHzYpziZhgJ7VUmkoL+w4eVYsP/x1hEgNGVJ7AQeJyCf0Tpw1nRSFQS32qH0YcxDqBJZIvmUgdtQWw2ixY/DpxTJB/QwbLFugWKGKyFdIQrtF9PdJ7iFkoVI73m3C13lYhz4JO7IZTj69bxbU7A0LcxFvetna6Baqen6YdC0SHj1bsoP9R2yHayRmJrBj9JsYfowFML8KU1b3hGeNQR1282GhUpaCtS07YQSvYBP6TmB6rM9W2ZGC1X3slf9+5tmnQV4Kk5s5yZtYLIhN9lcJuA9gk8aJNEs7vAm2o1jlrX4FZqZVyHhsiNzynap6nthR77fCo+wCHDtS/jT8/RybPBUpzyt9AzLwkdpRaCmoz7YezIZ1Vg2RKMTMkgpZ2x0zhfk6raG23sP6Q1GW28whwOxq4fL20l6793SAXkhVtxI7TUNVPxBpNQgP4Ak9Nxiz545DncXt2GTs6iQMXBV4UpC/EK4CCtvddFPnt9juf0syJXpPicBC4o0NslEs2o4MGx23S2v0oIPCxNib+fJ9oGUzQ1W3qcAvcOKTzOWJkiCl8xRV/Y2YXfC7YeL+PrYoaAzRHAlsLFqRDudq0iC1uAfUZ3s7UMwH5+PAw/SYA2kKHlON67HFRNbELYKq9O+V8IWbQ0hvtqPBWOOOwz5+acwIvivHQ5lyc5nU2lYhUhL+JdpJHIcdoWTjSGprGJZh9B4nropNMh9R1Q2TMmtDeU1tEJGXVXXe8H98DPQNbMUPiZCKyIHYgmB6zImlwPkESyTR4wAoFtJqF2yCIphCPVsTARQL3XYCNsC9he1QP62qS0Q4F2OyU0yiNwLG07tzNbABX0Xw9Z6jGely4PkUwq7Qfli8w2woNTGb3fMx+zLBdnV20vYkHkVorTh4fKPA6h3wX5v9T8zkI7f7mWamysUQfVFVcyl+uwKSDwvWU3/hfjta7Z03Bw5W1ZHJ7x4E/kRvjOytgT1V9Vs0gDBh3UJLMpAFnE0C/W9gE45LNMpAlsGvW1gTFupPYI6Ct2d2oZp8w7ahvFspCVMk0elMzbMdsT5c1PcWWASbOIbonp0O5iX8P6hmrnMLZtP4Gna0vVCEcz92vH5fOClYCGuHlRNapaHnOuBri4zc9TwLk/B5VPXlcD8MRxi4mjILE71i4jGA3t3quklnSqs2mVJ4PhdmDgU2Tr6WvB+AmfikqbjT8uLkFgOxRfXl2hpT3J3mWEQOweSvmEhuAozU9pCc3Ur8MCq6nYKZ+52gqs92QGs8VmeFDf6M2NyiI5vgsNDakXaTp1j3H4BtgBRmf8OxqCfHJrQ8ZpTjPbyG3e4L6PVv+hc2Xtb2gf60Cb4KOFSDXV8QoBGaZGTqYnljsN22V8P9Wvx/7J15/G1T/f+f73svrumiUOapovhSyqwMpRIVMnQRkUqJi6JBhXyLVN/IVGROlJAyXGSeh3vNQoY0CamkzLx/f7zW/px19ll7n7X355zP5/p9v+/H4zw+n7PPOmuvc87ea73X+/16v15wZPlGDK/1sBh4J3X6PEqBpZxg9145vxXoyDmvCTxe3k0Fp3M1hNl7a3jPt9x9i3LUM3HCHrEDy+AATrznD+6+ZPT5685Z5vQ8OHYsR2Nhk7EhWpDfZmYbILzvJ6I2VwEfKBwGU/HJ+ShKP8M7+LHKcZnZmu5+Q1jU1kVRicvQb3uIl7CTGePekaCrHg79FtEr9UyCFvhUrZsTtmuBDNfrtsXEZ4oen152zsN38R5UGPVXBOP5uPfify9BTlaM0zrD3fvCTxLj78tNbCr0LGwyUtj7i/fiMGOYQ5JDtMG4shhUwtxzBZ3q688ikYfNSu1WQA6PAZd6QrghNUlbwAKaUt2V5t1Y2bNQqrGWpSS0nRtFmbYBXgvs573crFkbaxO/+LrI+XgBZa6ucvdL68aeMjM7GEU8H6SbOz3G1HZx45qi7SOYz1J/b6ETJb3M3e8pvT43KhBb0t0/ZS1V9qL+cjhoN0Jp3bcgZ38ddL9dUeqrkmc3arN4OE/BFnM1wkH/qdRXSgW0/D1m4SsHYVahoFaY9zLTfAcFuWIxpTu8l6M/Z9OWU6MSB7JeAh5JfKfZMscmfP0q3sGxz4loN5eP2iQLE9v4M2a2rJdqoywRxMvsKwuD36C/6xC0rSsy66WsiwlzXeD1L3H3chahaFcL1TDBxC5194szxzdS4Gtme3qGRPZ4YoKX96iwxd3vMrO2adAc2xX4pZl9EEV3DkZUYV1m/VkM7sm4Cb+CIr8LAfehi+ZI4FOexlFWUnlRrwDVMxGZSKNX8kd+QwAAIABJREFUQ/g2gGlmto67f7lm8jK0mKrT5qD58ywwJZjZ9uj7Pby0o9sUOIhO4U/VzvtFd3/SzCaYCOsvN4HxY1uYyFFAabHXufuzFuHHwmeuKjY6OoxzGsIX7hHGtyEV+FPrVggs+vpDcOT2RHRNM8NnWxVhLN3dTy11lcNuMVu883f3+03FkGX7GIp2fA45BEugyuqyLei9GMsUpjzH+qr/ea8E9+kkYBAu2MdC4f8y/jJ+fw4FUS6Dyq4o0vdV9Btciu55rJsN5HE6Czdm9hrvFJoV7S40sy8hzGFRKFLgFddCVIqnIz7hZPYoWMETm2PPIfjPv9C1nYINTaOzsd6g2FiXG7nop84Nr2+MruN9KVGbZdpWiE2jR/HToqyRmf2LznfxAipyLbcvosP3JI4VdgJK/ReFWFX43HXKG6vUsch5fopSNXvUJpfFY1fgFAuFcYTIVKnNiYhCcKvwfPtwbKMwxo3RGrWYdVOITaGXEjKLCstUoPYtYFF33zhsNNbykpJcaFvllOaorY2Yu+8TNsWFs3+s94opJTdtdENFIINJyUXH+Xq0sXO6IRuFPZbjAAf7C/oOCozvHOhaiy1LYTLTfkE3NKs41iY7mcLg9/zWDWyyZ8CkPAN/bnlQjRuAc0xZgL7wF+9mt9mbUn1J1WDH5YEWhh+j3dP6CMt0+pDPuRZKm98ELFTR5jZUYBST398Z/X9rxnnuRZPzASj9P1+f9ucgyrIDUCHMucAFpTY9ghoVx+4gEh5BDlJBqr9e3SN6z8MIs51FCB7OaYRJANGCXVlq8wByXJLE9lG73yC88xHhGjkc4anjNl9Dzub+4XELwkfPDZwWtTsEwQkuQNCJXxPI6EmQ2fcZ1+5IhehutAu+M/pebwCWTrxnaeSElI8vGz7nM2gyvQZYqtTmhMT9ccIorv0ZREIkyHlq+h38Gjlql6NF/SJqSP5L712eSFQgXC8HhO/076G/J5AiYfK+DH83R5P4fATSdALRPeHejH6X2VLff59xFtf+w9H/xfOHatrFj4eie+/9CE5xK3LiVxzFb7ghchhvQ47IO2ra3lx8b6iYF0riIuHYWejevAhhN9ejJHzSYHy/BBbu0+bgzL5mlp5PRAGI+Ngt8e8e/k8R6VcKV5SOLYQc9WPD/XdCfM8RNu/h/yWQ4/O2Pp9jCoImQK+4zm1V13n4fxXkOD8S/haPLYAFSu+7F0GTHkTz8cj8VGp3IcIp3x59ppRYxi6hj3+g+/1ZEuIDaLO0fNtrOuqnr7BROP40ikC+gDaBTwP/Soz9D0jkpYCU7Vxq01eMpXRd/zn0dyKqWzibSEyFDOGHjO9gBRTAeDAeE+IL77l3G/S7Kgry7NHves3oay/kuC6CAoWvQQWM5d+oEEh6LlyX/0r0dQcwd/R87vI1i+bTvn5DxVj/mNNuPCPBOyGi9mnh+VUMhhy6yxLRz7nQTv94U5Xhh0pv6cdicHi/c7r7CiFStDZyYL4UUva3I4fuxFL7zcO/B5jwQPOhaFdsR9C7O0wdgwoOYM/jpMTzhQwKe8ndPaRWj3QVl5QlXP+IlLSSu/fIPoxunL1QJG8+FNWLx3eQiVewiADt6u5FEUlcqb851cVGy9alqxPXxbTQVwozOcUT9Gzu/vsiPdP7knexW5jopWL7DNpMFOnwq1H0GhhJc1V+l96Lo9oPuMaERzaUgvpU1fsrLDsCZN2FLI6gGnH6cy8UGVrNQ5rPVGR2jJnt5e7fL3VZzFWbIDzeU9apR7oJ3QdZDCpWQ7jv7suY9ZcbzblHXFmf6cB0E83PVMQtfqC7Hxm3DenyA+jNlMTwqt+gheMaFI3awSJOVO+GTtRxZMf2LeR41DF95Nr8wL2mYrKkcIvXZ2eaRoxfCKlpD+9dLj6vSdJ6bWAh64aVTSHNR1/JQRsyhN8G/m1mByFM/0zgbWZ2grt/O/WFeH1k6smQOSuyDVNRoVzx3tvN7C7gfV5R5GdmS4brNBfWtKC7/zx8z7j7S2aW+u37ZhJCVvW7KGi0jJm9FTFglOEQOfjbumzoiHkek1I//mLQNdBPjKWwc8KjsCsSbQZRmLg8qm+Zn+6i4qfRfJVtVs9vP5LRamEvIKaP/eisP44CO3oS/UZhLv0wiuz3DJPu++xlerNluX5DyrLeM55iGc+hivzyYjdoa5S6oQ+LgbuflNNJuMjOC87a2xFG99OoIv9EAJOK0IIe8TS6UjkfQKppM1pM5AfTS430pUS7vmbC4xYFaXd7EFpI2NNhUt0eeFdIXZRT9/sCFwQnrFJcxLvFJ+qqu2ei3fmkMNZiMYjtoTCOlBP8BHlqTYXVKQTWVaGmXjsLWLX0WbvSXe7+vElJ8FIU+bjPu9PMTeh7cPFWrkpnMtrTq8UYqvooikPnpkPJ9CYUwbiw1LbfYvUxYKN4DC5S+u0R1rI8L+RQEBUMKl+lw6DytcS5awn3w4auVrnQzDZ098usgrbMOzRXcyDHfSpyun9A94Ja2PFoYzAjNaZgTYQkcjbWIMfyeDM73QMTwyhs/34NTHCtjyKYQyxGdFUY98HAwZZXZ7A/9Sp7s6NrYBLdsLJ/oShu2ebyEk41sj0RPG5eBLFZyt3/ZqoZuRk5ef2svMDvjAIZ30ffwXWUfmMX08ASZja7J2AmaJOzqosK6yzvpolK2X+CU1hsHNYkPa/lOKUHIMjBFWGstyU28yDmh37426xNm/UX1wFtJGLazKeJNhehfZN76WSrYOoxs83MbGFvoDBZc54CmrSWu4+G2QM0jxRBCOg4hEVQoo3wBogj+w2ptcPMJnnEswyaSxEMdX96/ZAT6Q/VKETJLiThN1iv8NHIcMiEdI0HRdrP3X3rqkhWIoI1KsuNfkb2JVRccydyWi8gLYtZaabCtLXRpLwiSqFfiy6g66Km3ya9sN2NLpANqZ/IeyY8z+Qm7jP+xdCO+Dk63JpbmUDqm3soLoxsG2Bb4BPu/lczWxLtFmP7JtodTw6fqXzOuou5K2pgZrujBfAxOrtHp1u6Eeolkf/d8NqoUwh8s6kKNzX2kckmRFNWBOYrOU9TKOE6TfKoP0SpMUORlk8XG6aw6E1EBYQ5tDWg7+rxcK63mDIhtWIMFXYV8M7gcF6MnIBtKPHlWn2B5mypidRFOdeDffZ6CqKFow1icT8dFf7OnRh/nbNTWD+M5XqokDJFA+fA2WZ2ChImuAAVWN1Vc76nPCFa0NVpJuVXuC7udvcVwvvqrvNt0Hd2s5ndguadi9tEXjLvp7rsDGa2grvfC5xp3VRRxTlmRv/X4nO9Q3N2kneoFxcA/lnx+eo4aF8Im4R/mNkDxXlcFGkp5zRlXecMYypnnFL2MHCtKXMV8wn/D92OdY5jszfaIC5nZtciCEhqQ5DjlL7o3RkZSM/hffG3DTZtMffziLgOsGE0ByT5i+NOLLMoMbRdnxJTj5ntGObOKjrU1swcwAMhELc0FdLQ/cybZ3Nz7QE6jEtluwlYtbS2FRRvPZzJLrrEK+n8Bim6xAJiNjsJvyEzM1Br40GRtoi7P2oDVjLJOG+uqECRLsVrCnX6nOtsws2A2AqSk6QFloCK18qUTUt5d6HZEsBH3f07pfdtjrBbT4Xn8yNVn182GP85wLnlqLcp9foRd2/Mf2glCrA+bWsrgE2VvWt4Hzon62YeGLGws+9Rm+rTVzLK5e4HVl3LUZtiAf4w2u1+iO4iqKcRU8PIBilEPTf1QIFlSvWeXzg2UbtLEZatVh7SBkjDZ6EyPWxG5nQJQnSpI1lvgeZUhFP9StxHXf+J40kKIjN7FEGpqhhbuuA0Jgnm6yqcnaLNvago6Pc0lBs1s4+4+1lm9goRrVTchN6N3SFoTjqbCnqxJhYcgN29BtJRaj8BZReOQZulE1Fxa3batLSRnR1lYf5T+py1VHBmdpxLiS2VdXJ33zDlHJcazQx9fR1RY91rishfiCj4XkLMK79JjH9ulPLt4lgP18NUtKj/BG36i9T+T9z9zYnvoKt7dK9MCuOqGX6XSEW/uWfkXqm7p6K+lkFZreXDmO4D3lqz2cM6apvT47XMzI5HmaovoYDMHmhzu2vp/YejQtpfUhKxMbMprkr+OH0ff8ba6y+sg4e5+0eqvqeorwOj912CihJj8aDtPFLYi9r2ZeoJ32uRta2kQ80xEwPD1ZSyQl4qNu7Tx+c8QK7MbEWPlCBHY8E3WBHhxLsCS9G6cCKde+AlNIcel/KnrKLYfBBjzbVxo0gbawtRjh5RAQ8pN9N2dn9UZV+IJbwMHBEvotZcga5uTA+4+xtyXwvO+VZoMl4UOMfdv1Bqk+KnzKGV+ZS7Hxv+v88rKMJSr1VM/E+hgrXPu9Lch6KoZV+qk36TeVggN/JS6iXRbjLiVAUVZT0XvXYBAvVfgSIO1/Trb1BmGemu8gYpXJ83lTdNlimvbDU0fC3GfyuCCX0fRf/vtl4u0DvQ4vpKeD4RFTAVXMYvx+ONu0eFWV3RYKuhIMpZ/Et9Fc5OJeH+aDbpFtENNhhTpdPXpJ+ov6vQdXET3ddFT+TRJNayE2IiuAhtXNYFPlaeSxqcfwQL6N38rNlUcDV9v4IYSoqob7z5GfnOzOxuYCV3dzP7FHJc343S2id7idu3zzmroGDFSXOzMZjZ5xOH50YZyNe6+zwV75snnOvf0bHiPirSvzEvetc1HdqXqULfBRzlaarQWgfFBAXZj27u94PieTa066qB6XTlO5vZee6+qXXL04/89RLlaGKMhrIePRR74fUJwDzejc2uWieTPM6WpkFMHetLh5pjVeNo2EejzVGDfusCS39CfOPlYISHNl3QR6vI6Hp38O9NwBfojYoPTENhPOAQZYepfNFnk3A3NXd/wMwmuopATgyLeYE7yy3UaapAV2e/MbNvIhL+AqNlSNnrsvB8XlQhui2avM8GlnH3xSv6TKmd5fzO8YWbVEwLE0oKh3wYqpj9aejnowg/NxMVI6yPCr2+YM2UfqqsDpqAqZjxWwhz90g41xJhMt7P3V909w8EJ3l9lKL9rpn9gVDIVN6Nhg3IvvRy0La5GXPSXbcER/3n6P7YCqWstwhtiwKOs8mQVyaz8CTTpqH75pzgAC+LIgNlSxZohvGnrqM6q6MgqqMe6zHPS6EtSx4ePmWNxhPG1MSJWsKDMEJ07PXeDXtKYaFTfc0A/omweF/yDkzhRlOxXisL81kKC5iightZD6xGGjr0ezZK6W+J8OFnoOswFVl+oZhXUeHY6WHu/22YI3rMKiA8bZyZms8wUosQ5vdpaBNyBok6BVMG5FS0acfM/gbs4O53t7iPcqlCYwcllvEecVDc/RnkBO9Xd0Kvwd+6+6bhb1b63tLiOjNLbX6KPufLCKo1xcwO9+6saW1RYsluMbMf05lvtiP4AdacDjXH+kpDN7TG81GVBWd3TsQ0VBbvmIigm7nnqys2L+xMBAv8MdW1EqOy8YBD/BKlRs5GKeAxCX1bH1GB4BB3FeqE4wshjNzAFbhMBUY/RrimQgp1FXSD7eLu/zazZ1E056so8uVm9lDVDtnMTkCLWoGJ3A1RmHw8apNSzxs5ZmbfRxfznt6tNPN95EyVo4xdqfBw7LYQcex5rWLc8eL3XbT7G7HI6atKD7qHiH0Y/7yIR7YQOZkS+n3W3acl3l+ktDZGtFavjyNFZnYxotT5AppgdwSe8P7Y0tR5+qa7KqIn8WfdObSrjHaH17/l7l8xpbF2QgU+GyLqo9m8Rboux0yysocg53ikQNPdf9ayv5vcffXgtG2AICS/9cDE4g2rna2CA9XSePi3oyhbCg+f6rtNJLgJf+tLaHH4RHBEaqM9ZrYg8GTkEMav9RDztzVLYwHX8xoVUCvBukKUt1IaOt4ohs3XR1HE+RGU2bgtev0GRJf1GHJQ3h7Ncfd6L7QoBeG5xQckBFQ612uQM78dwpse7hWFiWG+2K/YiJkwqt9y97VT7TPOvRbwI3SNb+LpNHUl5Myai2VU4m9N9SOVlghGxJHIpLhOtPZshxz9LyFYYhe8MIypuDavBfZI+SMmKM1udKS+rwaOdhUv34si8b9G8McbvQ80rZ9ZNyznBVoEjMzsIVSDNAEVJsZY6q71tOHYRhhBXEw6I4wgTSPOlpHRtSGrt8I4OMEAJgLxLdAENhk5F2c0XcgannMpNBnOjqK+86ELucBcVmJWU68F5/iL9GIUY3Wkae5+eOl9qWPlqNND0Wt7ou9pbrRr/RlSYKlygudGUaD3oInqEuCbHjERpC7W+GIzFSYdjCqti/Tvkmiy/oqXMM4mdaTv09GC3xLY2yVBOpLaqXI8wmtZTl/FZ56Mqo8LGdHfIaiLl9pNRNyNbywdfx2dQsKb3P1xK1VjW0deOZYnrsR015n1SXeFce7hvTRhcZtktBthOffzTvVy6rdOYvwajD8rKm6SQY2/10YFmqW+jkbUWR9Fk/u/EbVXdpV31FclPtoy8fBWTVFn6Nqbo+GYLqTz260Sft9bPZ2mvhVRMe2C8LUPWoA8mWofDkER+INQBHFBtBju4O7TQx+NlSgzPkN8D8dYwMdL7SphXdZcGnrF0P5jwL7u/vPotTXQnLUQwo0eFI5/AEE9ppb6qoXwDMpMCmpbIGaOo7xGKju0TwUZsoILUfuy4/oWFAj6ByQd10oHxTqKbFuggFYRIZ2KiuD2KrWvxN9G91HXZgf9Zgt7ItJtfWp2TDCYt4ZzHuliXMqS321j1qFDLWAQlXSoY2WjWU/79DsDBVGu8I7a6V3uvpJlwC5D+2LuWRFh03syutbBiO+BirnPKbUZnK/ooyS5Hs0DTczbImzX3kM+12RUqb0SCTJ4aoQDUq+hqvhPILqc9VDa/9sZ7+sR20Dp+Pmi5/MDm5XaLIucgDvRDv6LaLGN20wELq/5HI3IuFH067/CY66afpdFO+G/IeqxX6NFbE5g3dAmi3y9we85EaXxTkWR/V9Er91f8777S8+3Rk7kyUh7/GFgy8T7bgh/L0KUV28DHiy1WQdtOu4nIbAQtftvJPtc9/lu6vP691EWYd7o2BS0sB4eHbsdWICI2JwKkvMG333ttY8gHh9E0Yd9UMHVpLa/deL8SwMrj+L9lcT8iP6o6n33Rf8vVfdoMaZC3CIWfugRUwjHZ0bX2z3huy6O3YIwmluFe23N6DPGfe9f9xjUbxWdb16UPbko3BffA/5U035utDaci3iR14teK+bCG9HGe0tUdDbaMd4R3xPhHukRnBjAeV5B818sKpAUfgjtz0GBjaXD46to49DknOvVPRLtjw/f+5dRxHpvSms0Qawk41itKEjp+NKoOPN3qLCzOG5kiusgx+nPqHbAwj15danNoWi+nA3h058Ati+1uTNcE8lH4ryTgDXQnPcA8HLL68PQRuFr4fkSwOqDvg5bjq1YB+O5pBAnylpPyJh7yBAjGthnGqcvcm2UirgN4WfeOcRzTQoX/N9QenNmuOAPRengot3LdE9I8cT0YqLfGfEFEP4vFrKpyBH8J5GiFnL+Lk30lZokKpXpkCP/TSIFrui1S6lQqENpwxMR9unE6PEDYO2W3+9E4LsZ7bIUgTL6WQ+l8v6I+Hb/SslBR1XIOyTeuz0lZTPkJC4cPV+ItOrUpih6ulL4HYsCk7jNvQhOsTCSoX4tKnQp91WoHj1LterR94t7A6X0VkV8oMXrv4NeFZ3we/wuev48vZNJpYOe+RvUXfuLodTzFeEzHAZcGY4t2uZ8od91COpC4Xf8H1o4m6Wx9qipxd9d6T0TUvfboB7h+3otHWd2TUqqi1HbeAFaBKVnnyk+U/Tab6veN+CxH0FQz0o9Qptnw3XwzuK6rbv+wnW8Cdrk3oYEI4rXCsjEgSgrsDcVjlqi3/NqXpuKNsQnoU3xw8A2iXYrI4aXWqWxAX6/C4Tvciaadw6jpBgX2i0FvCf8PyfRBrnFOftujtAmeNno+TLlay4cvzTcsxPDY3tK6yDKEJ4U+tyFaG0Or++NAgzLRMeWRZuqvUptlyk9N+CNpWOVCpSl73Mp5JjfTWKjG66DQ8I9+Pfw9xC01iZVaTO++2MQnPG30e9/8zCvsQZjOx5tTu8Iv9kRwA/He1yjeYxHYdzvkXN4BlKseikcXxXaUwLV2HdQFGIZ78WHfpegWOfNCwwKCp1HTZyufyEULiBs0KMoDRkXOjxNia8wWKNiNhffaFVBwr+BO0MKqostwAdLxl30+7KZrdu/5egLs0zVp39Ak8QXXEprD3vARUa2G+Jp3ZkOrvMdBFxnqe0E707XPkni9/AOx+1TCJOasr5cr6GvnMKsAi4R03s5SkWFbjQrlfp+2czi4/f44PHsddf+N4Fj3L1Ls93M9kAQmx1Lx3PUpEC/+SpmtgpyfH6MIvfrtRh/HQfqeWZ2HGk8/KAKVVKWy98KUSGTi25yAzrqia9E7cpCLSPXhZn9oG4w3oCtgbxi4S8j2MLRwOlmlsSGm9mGod3qSMzkcO+oQRb2DTqfJcmkUGOLVb3gGRzrppqLlZFTFBeMtcJY1lmAee2Ksmp3IqadFyvafhKtp69BRcmLo4Kid5faZVGFekQnVmN7oQLlh+hEXD+daFcpCmIq+tsPpcYPRTj3VAFUE3Gds4iUVN3dzewMIjEiOmJOKQXK4n0xJenznmaG+TjCE+9LDR1qQ1vDRTV2axjHP0xiHbOC7Y5+r+cRPPMiBLtqbMFH2crd/xmeL4Bgse+L2uwGnFZqM9Xdj0712WociXV0qBYmmeKkTm/hw8CoL8L5GuFDG/S7Kdr1LYFu8CmIEP9XUf9ZQgaWUczWYFw7po67qjo/ibA8vzPd8ccjeMQjqEiwLSfpMWhxOZNuxzsuZht1YZaZHYZ4du9CeK9zke59FT56QzpY63vc/dJEm++gRa2oEt4GRTi/GF4vf2cnoOjPI8COHpF7Wx+uV8vkN80xU4Hp2e5+Sun49sDWHjB+uTitJlZ37Vui4Ch6X4pe7wH6q0mNYJtNHKt/dklzj5r6p4yPtoZ4+NGamW3k7peE/yfR4W+9H9FN9RRemmoqDkBRVVCE9RthIa+jzBqhnquaJwrzTGGOis/UQ+UVvVYUs01FkaT9UWr//vD6K3SkoZ0S7rqhc54a2wlewkNaEOiouj/j+9LM7vEKOq5BW9govIjutY1REdieFW1vQxuHG72D1eyiLQzHaqlCo3a5uP85UFYPtJ4mRVBqPuPLKKt3Pmn1xj1Cu741O9YRIyoXgk0B9nH3FaP3HILWkmfR9zY/yhKsUXGOgdGM9TMzuxFtam8Oc17r4vwQcHq+37HxMMugc81pM+pxjLUTPNZmZve7+5uavjagc+cKGcTFbKC0z397t6xuk/MmKUxMOvRvc/cXzWxbFFF7L8K37u/u70z0FVMGXenuv060OTExDC8vNlH7Lsej9NoM5Gj+1BPV0sERXR8toh8I/XwCuCC16OaYmX2EqHLZ3c+JXsv+zqwP12vF613tzGx7d/+JVRQueYcGrmAxeJZEtNs7PKBfAS70XiWeoVjdBJV6zcyudfe+VFwmZaHpaCP1LlQscXt5kc/op0tNrabdnHRYNx703mxD0e7t7j6jdGxT71bHqzvP/Side350bALaoC7i7u9PvOcstBEsHNWPAat4C87nqM9Kx7VBHzGVlyHY2Q5eQdQf2k9FkIM3hGNDc85D/wsAS3ik6GVmx7r7p/rdv6Ht8cD33P2e0Ywjc6wjTmzYIN1U5YiZ2Y3uvoZ1CiQnIWhNmcv2Fnd/h3UX+Kbuyyw2HKsQsAmv9eXUz/2965zQaIPcRIzIEMygUKCcG/EJPxa1ic93Gh2BlGJsg85aF+fdDgVjVkX3+JaIRvXMFn2lCqMbO/QmtcJK8wT/eEafM9Ba9YfwfCm0IV41anMnqv/w8HwiClKtmOqzjY05HGIc7B4z26EiYnZv207D7uyT1HO9VkIT4r6Cs1vW1W47rhEKE2AZiyhMgJe8k07bFDjFRYHzG5OYRbmvg9EuuaAM2sMEpfhKafy1Ffplx8NHIeMa/l4OXB6idu9DC+nRCH7S2FzUZFVqPNnfmfeJ+vd7PVgh85uCTIwsKMHJXcO6o90XeG+0+0FgmglGcDtSzbo4tcHoZ5andFWWhB55O4rIlO2WEPHqUZMqtcuR5u5rYcG7z8yW9Bp6Rnd/FqWg+9lxYX65C8BEDbcnkOUEo+v3QhMbyTmmFPgvEFY8JckMsJy7x5LpB4ZIYGMrO65mVuu49rFjESb38tD3+ojFIknl5QlY12id3JSZso8fQvP0DODxsPnaO5zzU6Hpxt5LMdglZ44gONeb2V/R9ZqtJNjCRqAP7v6SldL1JbsybHjnNLONkJhNT8ACeMaUWr8tzF+PkobjvdaVbZnmHfnpLlU5qxCwQd8RdMNkDkRR/y5r8HuvYmb/Shw3ggPuHbjfu7wkB2+9nNfHl9ZqQ9nFGD4SQxn/iuoQRoZOB5o2ELPA/+3upwUH8d1hXJshiEuTvl6PsrNzmtnb6DjvU4C5WgxvLRSxPx0VpA6Ce3g/4JoQ4DCU2fpUqc104Gdm9qPw/NOk5bRb2/+GSHBWxKxFvzlcr5XQhPB69u4q3MQHINzVJDqTbxcUwOopTGYiDFSBgdywWOzM7LcepD+jvrIog0yqLscArwvnWRkVjf131GaoMq5mNmdwXBqZ9cGktvjONqE3hdgl29vWzGw1r5E3zXj/2xAH8nsRbOM3KBp/U+b7+ypdWT01T8+GqaK9e0sKnxyzBmpqGX0ti5zWbdEkvgOSu87mCjXxqF6EoCXbozToXjXtr0fp3WvC83VQcWolH29NXwPjoLUBUHk1PF8t33l0rIiO7oKiwPtbWvGrb9TMBN/ZG22QRvDXnqEk2NSsW1Uxhrf04OZDZHMXutXbflwED6J2S9FLFXqUuz9YaneDi+LyIlSU9xfEwLNc1Ob1jTn6AAAgAElEQVROOgI2q1gQsPG09PDAYVlVlvk7HoTmrM+asgPnIzq/2vlrmGbiHH6/u/++dHxndI9mO8LB9/g48nVupuO0Pg2clAgy9OtvIrARCjitjL6v01tuluN+F0RFwCDmibJOwwTk+Babk0vQdT044QyfBarzxuKBHMPdw+PdA+gvSfGSaDc7HWq2csXrE6jidx+U4l2PCtoa8pkH6ihMNkXUMX9FN3zx+nrA+Ym+siiDECZx9dI57yq1uQrdgJcSMWZUfGcro0KH+9AEvAaCIWR95w1/xweAN9e8nv2doUKUU9COeX+0UB4/yvG9BRUePECCfmgU/U5BePBjW75/XkTV9DBhEzHo36Z0vi0QI8ZT1NBJ9eljUvTb9TxGMbY3Iaqy6TSk6qLD/LEpKso8gwQbSOk9RVT/92hjdiuCQ7QZe4oJpedYZl+jpvJqeL4UBeWMxLE7EYvGxUgRFLqZTV6PiqZ+izZHxfe/PsK5xn1dP6zPM4rvYWJ5nA3f/7PEsRw2nJuK7zzMJ1Y1jtRvNYDP/anS87XQOvFHuhlDDqi4zg9Fc/bNiAM855zJ+ZJMesw+fX8gvP+N0bEvhet38ZbfUdbnatjnHMjBfgL43Cj7Wgxlit5VPAY93n6PcYVDWAbedFDm7pcRpIgHZH2lDUNU5WS0WBXSvTt6J1Xzejq7q22p311lMQ8Ad5uwqxPN7I2IM/E6EMNBiATM693p8FtQurlsBwO3mrByI6pfiXZzuftNpXRdmWR9XGVca+wxrynKavidre3uK4co04Fm9j0EP2hkZrY0uiamopToUsA7vBQhaNjnXGiBWMKV/n0d8Lx3UsG5/ZSVrlb1FtCKqL9KNalS00PJKKDrYzeh8V5pZke4++414/oJ2txd7e49sCnrFct4DXJGbjQzPD89Hqdc70C/S3EsmXJ199tRenhKeJ5KE+faQ2b2NbqFDNoqyO2M0t5no7FfHY4N1KxTAFWG3kwhysBE9g0UGb3G3W8O0fvfRa+/Dy3qi9Od8n4a8RHHdqtJlvfX1MN3xsw8E+JTYz0ZBM9jw7nFxLJyHHKE/w0MhHUo03ZFEJzCZkdsIZPohpP9i8C0UrpebkTr0k2Am9kWGb/jOyqOH48i612Z4Sbm7heY2fMIHrUZiuyvjhzDtnPs4mGeeBr9TquidfXiph2ZiiA3QevS0ihAdU7de/r09220hpaZVq4ys5+7+9aJeVaNBgg/Gjc4hPXiTaeiNGB50pklzbqlDQvslnt3imoGsK2HArUAGzjdEzKA4QKbinCOB7r7kaXXa5kHonZzIazNe8Ohi1CVebIa1EJRSM3n7Kv6ZVK7+hyimlnVzLZE2M2NozbLAI96wNyZCo9eV3bszGx1L6XnrabQKKRL5mnrCJjZ4Wgz0g+TmtNXUZxyA4pcPomw0G8otTPkRC7r7t8w4VtfHzYS16PF/AxUzPE7Ew3cMm0+X3TOn6EJegcXZGUupGhUqVyX6KOR0lVmn5VqUqV2WQV0fc41kpJNpUxLbTdA8IZ3IjzercBVHtQew8ao0nwI6fFobHOgKP7SdNcjNIbdhFTwgXRLwh7QZNE14Wbn9ZKCl5ktjKL1z0XHamFdlldMlV0A1cTM7CMewdkq2ow5fCfHRgPxsUjm28z2dfdDq34Hr2DnCBv3Kd5dcPh01MdcdDOVlNfKYjO8Lp0NVGozHJ8zCbEws6Wq7r+K36+wvr+jmU33dLHqjV7BLNHUzOydyLm8DjH9PNfnLXV93e6CqrwPbRq+CpxaN/dV9HMKygpcgO6xu9qOKerzPlT01uObmNkiLvrH5Dw7yPl1PJ3gLLzpq9kqcGddxxK7q18BJ3gJq2wZlcuh3VZeqiJNHYteq6u6TR1/CnjEIznNEFk5FqU1/oFSQdt5N8/iLShS+kJ4PjtwrZdkh00Y3J5Co3iCCZGYXdGO+2bkNB7u7o0LpQa5qIWI2hEIv3QUmsyPc/evl9odg3a+G7r7m4MjcrG7r2aiPlsVXQc/dffrzOwhr6CBazC2oio8dgSbSq++gjYKL9G9QDbWto/6TFHgpI6NerMSX+v9nODQZiLaAG6ArrdnPWKVsEymiUGbmU1H92G5HuF7lW8a7niORdjys0vHNwfe6+6fiY7dSyJi5io2LbCMleZRIZX14Ttv49DZEDH9wzTrSBl3mYci5Iq5HHTvnufui4R2H3T3X1f9Di6qzYFRPY4MInMzXHrP4rGTbGaHufue1isRXYyrMeY/13KDVH36KDYNhiAHL6J7ZDTz6x2u7OThqE7onKrNQ59+XqGzuRrU3H8h4gnODqaEoNdUdz+tb+PcPsfZCV7fQ6GTKc16xavJCbZuOMcV5Wilif/3FTra6tsBEwsHa0i7q0aUKFU72/DaDcghuwNd7Cuh1MV8wGe8lFIx0cxM8CBKUnot5dikCmn6FhoVfZmoZFZFEI0Zs9K1EzY3kz1RIGUdSp+kQ2rigd2CDpfq/EgxK6uArWI81yHn/Npw7uVQVmL1tn32OV8Bv1jS3T9pguYsn7hHLkVFjwVP81RgJ3cvk/yPerNiZs8gbLWh6O4DxUuUKvzDuOZG6d2rUSr9cUpmDQs+B2FWw5naoI+ko1BYE4fBzGZ4IrsVXrvbu/lZRx0xy3Vucxy6Ur8/RBHLDZAYy5Yo+/WJnAh16w80IDMVpsVZu8ej11JBlBHzPOaanL56AjOZfeZuhiuzIBboCvttCEI/J6NIcyzC8L14Pmlyj+QGqcbawry5GLAMqiWYiHyV5P06lmaielwF1QnFG4c9TBCO3dDYf4Xw1p9Da8rt7v7hQY1jPDHBuXjTgZjlK1Pl9ncImnCKHck0M1vHu0nHP4N+yGKCvBpReRW2PdpdTUP0YyPdl8cWHKP9iTDUBIL88PrGCFi/mHWrQU2hhM+NI8OFA1wRLf4LgjUUbAhvQfi6fdGO9+Iw4fzDlQbbBHiXqYL6GO9OczxhZh/yjpjIh5GUdZe5VIA+iiJ+f0CRpDLrw2wmerTNgCNdHL6tdnOWwWwRtV0AOaVxlOgqM1sN+KMHqIiZ7UAQITGzA7yX0eLFEEX00H4huivNnyJIWptSylsD3zdh/pZo8znRtTMd4dJPQxjcj7fsK8dORNG+Am/4Z0TQX4a1pNSkesblfWj4Mu3N/ZuM2B2oWGolFHX9p5ldn7gWF0A4/FEzTTSw68zsv9w9h8Ktyr4b/hrCCu4yir7qKJfK9FuXm2A1/WBdCwFfpJeDdkNUwAZ9lOo81JiUnd0aq8P056jijZuZ2dYISncF+k2PMLN93P0XkO/k5jh+TRzmBvakibY03gw/mWh3Lp0sSDmNfreZ7UlHYe94jzKWJVu5cIABXKps5ehocY9sgbJQRTBrKmLYGLEhfSeDsE8g9dGH3P0ZM3stQa1vFrCiQD624to7FWWVr0dz01fQdb2Zu7eig6yycaVIswy86QDPlaVM1aC/SjiHmX3OA6bXzFb0UdKIhH5qCfJNPLBvRU5qnH5/GrjcI4xfbrQ4FXGyDt3abUgucmWUurkfFSVMRw7WBHffLnrfcmjDsCi6mP+IYA8PhNfLAPiF0WT3PHQD4U0SvF9EFfKbIEWvn3hC7KOfmTgK9wF+5CVKuVK7XdBmZXHgNkTrcr1L4GIm8B53/7uZvQvheXdHv8eb3X3LUl+tyNCtBuuW+VlfG8ZtJOhoBmmWCb8IG8drM45NRhN6OVU9VCymmc2LnPIvINz2HKXX+0adhjCme9BC/zAD4Kq1UdJXFfdQOVMRNoffc/d3RcdyYV1ZYg2hbVLoo2m02zIx/bOimdntSFb48fB8IaRY2oieLrqek46fu+9lZhu6+2WW5gNvW0+xFNoMr0VnM7xHOcNSlwWxXoW9R9x9WkXb21Em+h/h+WtQcX6P+E4xl2Ucm+WgNGbV9SfjOa6UmdkSwEfd/TvWLRQzEfFZL+mjwEdX2bhFgq2DKyowPYua0uldeNMBWi0LQEubHyiifPNFx3cGisK2U4l0zEdhtQT5rorx283sp16tL58dLQ52twm/ekZ4vg0SHynwShu4+1uCg/JnRJP1sonY+o64IxcP5ZpVCxai5Mkyd/8Bqkwt7BFTIVMby2G2ADnAqyHncQNThfq3wmsTo2jvNohG5yzgLEuIGHiCDD3n2mzjAFsvfu/R8HfJEFkeiuoR8IKp+LGIdi9Hb+QGtPCVx5g6diqiCXwf2uhtRyciOHAzs88hOM7bEbvLCWhx7bIhO7tVMKaNE8dGY6ONhOwD/NzMTqKbi30HJNHbOVF+xCxHrKGf0Md3aWbnmdgOvoOoKx3BIuJz1kWox9MmeDdc50nSIhi15h0M8fdKTt6vTXUdIErBy0iLuTiK8jc97yOo0LGf1WVB3hI5TsejIsEq+x4SPTkTzcFbAt+saDu3mS3r7g+FvpehI2pEOJaE0mR8nmHb0YT6EzRvPo2EoVare9NYWbiftkKbrEXpsE3EQjEvm9mfhuEAw/jCIY6mAm9qZj140wFYrjJVruXCOQahrALwrJmt690E+SlxiNXN7ADS1dd/QWm9D9FZrEA3Roqc/+NIeajQq78WRWZeRDf7Vajz58zsEQ8E1u7uZtbjiMc75cLpjHbKTwAvFg68mS2PHPZHyr+RVUBDUOS4qf0tOGiFs7YlHUcxtufC58SkvX5vGCOIjm5S2Ly9m27Vm557LOzGnyFSdLL29Eb9rK5YKknBNSA7gF74xUgazszWQoWUC1m3RPQUhFsr2xvcfSsz+7CrOOenJJzSAdpkRJc1o25TbmZrIqf9zYiiaSLwH8+EWZnZxe7+3vD/l9394OK1CgcYRu+0FpGvwiaaoD6xJGxSlCY5GG0iV0fQr4+Hw3cDa3gaR50TMSvmj0dD+78gZze2WoU678aAzo74nAHuSwUKXIqHoM3reaQx/aehCPUmRBHqcl/jYNNNwhYFnGAbVGvS1iodP3ffP/wddVrdMrHW1skUTgJ2MrOH6M2CZCvsufspIRBRbMq28Gop7L2AK8I5Da2tny61GQg95hBsDQ/1JzAC+5h9PAcUsmtboLqfN6FN0zLuvnjULFYINKR89y9GCWNN2Xg6wX3xpgM+3xTkeLw3OtZq1wrg7qeb5DiLHdUXvQPnmN9UGT0BmFJOG7V0vD8DnBwcQEMR6I8n2lXyFbr77WZ2Fyqy6ouTc+Efv0fakfq3mS0cHBgDiv8JzxeKG2fslKejdPfvzOwNCAt0GrCpSSktxlqfgKAhW4fnH0MY1GR6ro/thhbTFczszwRmi0S7P4Uo0S+BS8ysUJADLTxXmtnf0Mbk6vCZ30DaMT+fThXwZFS0cB8d+eOBWRF5M7PJ5Z209UrCDvK8F4dFpoBfTPNu+EVfTs+SFQvcP0ME8K8IMjMUc/fvmtm6hGsrRCzm8ZIaGcr4fBThnYvo55vIt/g+2QptrvvZIK6fGVEfoMhnYQ40YiMJzu7+/do1iJj9d5jrPo82GVPo3ajPXTjAYQxXhGxi+ZzrU8/XHrddm6joysT5fErUpG+EejzM3fcxs4/Q4ds+1t17OFzN7FLvLTrtOUaG42eDoerLxVrnZAobOU7ufnfIHkyG6kCEu083FfYWLDD3ei+tVxGQesbMFkWR+EVyPljZbLD1S7X1J+Nkj6N7/quo4NiDvzRi7p4KhAzFxpMdohZv6g34S8fSzGyFEAVMRmncfaaNkouwz/lrCfIto/razK5Gqnkv9Gn3RrQol1N/Badn7aLn7gdGfRVULcXfeYALPeB4rRsDdBBSqtst7FpneITVSl0fo71mwuI5AW2UPuo1FCwm3Nx8YfxF5HpNgiqVu/8nHHsTcpxqIQfhWvqsu+8SHcsu2Mv8fI1YQ0ZruYutZeKcTZjss4D/Ak5CDvTX3P1Hde+r6KuvBHm4tt+BGC3eFBa2M73EVWwd7PMI9aE1wNhaQ9q2ij56rp9Z1frNA6HNRIQH/X6fvs5BzntMq/V2d9+81C6Lr93MTkWsIbfRCSC4R8wPliEnPCta2PDOhdTf1qez+ZmC6O16aP6Ck1vp+Nk4UPVZdwajsKdTkf0+/XwIBXcWRU7ZUsBvPWIyKbXv2hwBXZsjS9Nj/tjds0SiSucaWP2Staw/GaaZihc/ijILp6PMyiU+ShrQtjaekeB+eNOBmg2usGZvlO5O3ehFevlYhB0d2A7DzKahaOfTwHFh4Uspv+RUXz8MXGtmv6K7oj1WSyKcb39Uub8BSmdPiNofSL712ynH39WGCJeHu79g4ijs6svyoCGVZt0ULOcCvwnPP48gOqeV2p/q7h8LYypwc6eiSCHufkP5HO5+f85YwsapvHE5jlCwF9rcYYIANHKCzez16DPOaap+jhe/uqr+VhYttgtad4p9ShhH2Y60XmaPp1CE6EfAQu7+R3cvsJlXEaKUZpaNIy9ZjrrT5kh8YCaAu//FlMYr2zNho3abmR2KoDRNsJjLhvvQov9HzDNYJiqun8ZmYjI5YLT99LG+ETMXBnAqmnfqLFehbrbCAQ79329ilynbOxCutG7ezolQj5lZtyBFj0XRw08jWNuidEf9/0WnfqVsb6fj+K2SiIov7hX0mk3N8rHWM4ElEHOAobqcv5rZY8An3X0GeXYQylL9xt3fZqop2b5ibMnNETDyXXgelCbXBla/5C3rT4Zp7n4YcJiJDrVgglrUzL6IZNaz1s1B2Xg6wR+nHm86aBtIYY131NU29ur08g7AUWZ2P0rzT/fRM1/s7O6Hm5RfXoucr1PphY0Ui2Fc1FDGfj4YHhPoTkWXbU53v9TMLETrDgg31Ndr3lNlqaKT46LX7zCz76ICuzcQPld4T9l2BU4JixFoQqwl2U9YTMHySaSyZ8DmnqZg6YoQhGhVK65F68bATkC79L+UmuUW7PWzWBL2e3Sc0n/RKwk7CIsX2xml86UW24cQJCDGMj6NIAXHAauZ2fu9V1lwJ5ROSyoJ9rEcCfIXQpquSCP2pNmDfQz9hp9DztASKEWcazHfZVYhV+b108Y+hCLkrczyhHr6zQOFXWtmR6IoUbxRnxn6nQic7XmFdreY2Y/p5mtPpeHvQowIPTUBYW7fFc1NiyH6rXGnxXL3eWEke/YomtcKRoBFonaHA4eb2e7ufkS/fnMcPwZD1VdYLtb6EhR5vyiM873ofjsR1RnlbgZfdPcnzWyCmU1w98vN7LCKtn03R5ZgyjCzp4A7PYGL72MDq18K18VVwElFhnJWMRfe/FvAt0wQt6kIxz6mbCzjSpE2llakKKM03GzA1e6+Zsv++qaXTQwCGyNHZD6UipqOBAsa6YvbgJRfSn3O5e7P1Lx+HZKx/AWqBv4zcIi7L1/1nszz9ghJmJgEpqGJ+wQX20WRhlrO3U9N9DMCDTGzPcMOM3cMWRQsZvZl5CzOiaAShVP3AsLdxVjl3HPHMJKXEFbxLO+Wl+0rRd3wnPu6+6GlY8t4L8Z1IGZme7hYPOJjcyRSqjd7r2rgzS71vLtRNPwwYBN3/114/cuoqGJjr5FVrRlbX3UnM/sC4oTeCEGCdkYKfkeU+no3kp9ulIkYjeVcPy37He180lSop05Qpi+VmknQZIt+Ebdwnt3oloY+OnEtXo5oDW+i+7r4kDWg3xoPszT9YCzAU1svUXawzOy3VDh+1l2k9ka0kR0VVZ8FwRXrhhWl5oaReTs6VqyN2ZA4M/sN4pk/BAWVHgdWc/e1E23PRPCcVMF00eZ8RO9WXLfroyDAMojPv2f9qulrkCqmOyGWm7VQcOFqJP9+btO+/n+18cQE1+JNh3C+m9x9dZPG+mdRYc1NTc8XpZd/ghbiON37Q6+QUA1O3gZoAl3LSxyDGeetVX4pRYd6zCOog6ky/3iEV13SxDH8aXf/bOmcq6Fo+fwofTQF+I4nUv81464UkgBSQhKtzMz+4O5LNmhf3rDU4jHN7OA2Dm9bs7QU9fbliGiD/lIOSqXS12gt1yEKi+37PBSkmJgzLnLJSRcb13cjaMRmiDh9deQU/4MWluNghXYboUJaC2O6JNHXyWiB+TthgUHFHq3GNp4WImKNi2asQ724NYrmFTYFOVKrD2MeMKn1vQ1FB+NocUoOeXbE4PEKYofoqYewGs7n0qZ5Elo7hoKnb2MhYHEUghc6iqrtVjh1FY5VYT0OVp3jZ+L0reusDZVjFtbaxB99Kd0wyo2A9wM35/4mJkXL59C9vT26Vk9LXYd1m6OozUWInu+x8Px1KGo+FTmdWQqPISDzbXf/Qk77XAt+y9Yo276AhwzC/9n4wiFq8aZDsGNNGMWvIpWSeYDGoHW608sxhvZpSullE/XWn0LEYQ0U5v+aR0o1Dayf8kuTi/ow9Dl+BSOsEe8qN3L3our532SqzJjZee4eYzV/BLwnvPYutPMuhCSOJc0E0MaaUtE1pWDZz6RotIy7H2Qi9l7EW5COW54q00PAe6xGijrzXCsgKMd8pWjQFKLN56DMmmOQPw9cY2YPhrbLAJ8Nn/tkABckZyekhnUdsOFoop6emcYOTm+P41tqsyOACd+6JXJEFmUIc6uV8MKJsTRWqbPBFGDmUC82mgeCE/EtYFF339jEHrSWux8fNTubXnafVORyE+CHCAJmwDJm9mmPIDHB+fhRVRCDBvRb42TbAoeHB8A14RjQis5sQVSjk3L8HqMDDemnzJZruVjrbZHf8Mvw/NpwbCIdtqBKszSGuvgxvx7mof3c/dLo9QMyxr9E4QAHezwc+7sl6EKrzIWHX6d/yzwzwYDegn6zq9F9Nixu+FeljWckuEh/xDvsYUamelK/qWMN+vuISxChrs1tCE+0NMK6nAus6O4faHG+b7j716PnE4FTPFJla9BXoYzUT83rEmAr79ZXP8Pd31fTd1dKtZSSOwqpPh0Qng+MBaRpJLhF/8cQSMdDlHIBxATRmHTcBGlJyXD+EkYiT4OgH8IkT70ZclBiJ+pp9Fte13T8fc63I9okvoNu3OXTCJfWg2uz7ir0+7wbFlIsWoaUCV9EGMVR8UVaBVetmT1M9QbFE5Gp7VG68b+QDPg1CGZ1fZtx9RnzE0hp8XTgRkobP28h3GGZiokZ/UwETnX3bStebzQPmOBAJyKHZJUQfb3Vu1liprmwrvQ5di+wqXfUKZcDzi87vCGyvLsnaLLM7GU60WajGx7V+jocD6u69ktt6qLiA4OGmNm33f2LlsCTj7WFa3glFBFuev0fjZRLi8/wESQEtg9wXu7GO/R1DAoknEl3hqMNJvgctCm/B/HpXxUCLLOUhfV0CXe/o2/jAdt4RoKfN7MJiBf2cwhvOs8Qz3cWvSpUv6BlcZO7n5UxmbwSogabA0e4+xEWSKtb2BIWyPSD0/BzoG1ffzRhbd2EjZ5GukhwQe/VV+/HzVoeU1MhiRSf7YLu/reKXTx0FqVh2iBJx9fxhBqTu8eRj3Pp0A+llNayzIX9OtfM1hqGY5Y438mIz7rvJjGyyir0YaTtrJ6rtgxTmkAnjZi63w5DEcYfInny37cc05vQglnQtgE91fGvR6nfqSj6dT6i+hqNLPtACjBDBGsJM5vd09SLjeYBNPf83IT/LqKv5TqKHelEPgv7eOLY04UDHOwhtCkr2wKItegmup2PD/kY8pa2MTNbHEVQiyji1Yib+0+ldlk8zX02VE2U2frZB8zsS8CX6TiQPWZmh7n7nlVZtDZZkEQfLyPV1TLuP0cQZzfk+Bbf/ykIp+80L/SfjFhT4nu/rRLf5uEzvBllfy83s4neLUwxLmbSWfgQuv9nAI+b2bXuXgvtHLSNpxM8Dd2MeyC86QY0r/Dva8NKB2dOJi+aqH52pCMxmaLmybGdgdPCorAB4tbsRyFUZbuihWIxlMq8CN3EZXvFIgJxExasNnXgveD9pkISN5vZJz3gjk0E8AcDbxqGQ9TABkk63leGkwHSDwW71cx2Y/QUgbl2qZn9DyVVPy8VMVleFfqgrVLdyd2fDOOagJgf9glj28QTilLuvqCZrYg+5zdNtQ73eaDTa2BnIkf6OCpo28IiPR2pg82BnOErzOxAd6+iuepnuYqJOVZHvdh0HviPCfJVjGvNol2YU7dFsIY4uzEvHRn72G4xswtQ4MCRKMnNxXoQRdjawONmFTsR+Cn6bCCc64lo0xRbrbJZn0BDEfEeJDRkOqp5mMc68LTy+aDDBd1UCruxeS/3eF9BnODs/iI8Rnv+USvxFWaikXwnmp/mRwXuw1TabGLzuYrad0FZ7f3N7H9PJNhb4E1b2vJIbWZ+urXOn0bUWG0tRyZxJ+RwftPdHw7OTnaVKIB1i3IcjrB116IFZVXvI8SQMpdyVw6MYj+E17wSTUrvpDuCk3Oub5qquAshiWKCnYAwgWXbFjgh7BIXRZW7w5L2bWI/QLrmC5vZNwmk4y372pNeNaby9zpI+iEYEEVgAzuePFW/HG7WQVslV23IjOyM8IjXIF7NB5K9qP0UlAZdCkWz56Pd5ugldz+mX6Pg/G6CHOCl6VyXbS2lmJjkS82wSurFFvPA3gi+s5yZXYto9Arc8HXIUV+Qbr72pxHHd9kmI7hRkeJ/AmWOPkgUYQup/qWAN7r7b0zFU7N0BDiyhdw9Ln47ySRKULZanubMQMMgJW2/6lK7O9fdP1zVyAP/b/iN5kRMPvdVtR+0ufsDIYL6MlKQvBVFr4HsaHGW2WCFkt6PnN7D3X0QNIqDtElmtghaI/Ybt1G4+7g8UMHJ/NHzBVAF9rDOt9aA+7sx/L0BOWtzAA8MYdyX1zwuS7SfAzmSX0F8vl8Hvl5qsyzwa7QYPI5S78tWnH9BtInYFKUox+La2AwtaH8B3jCW12Wfca2AnIbPAW9u2UeRXp8DsXysAsyRaHcPomG7Dy3sdwJ3jGLst4a/d4S/syFBl2F9V7dlHjsTFRiO5e/4NbQp/ghiiXkURalBOL7fo43KFuVHoq87ED/ptih63zkPoAoAACAASURBVHZMByDWmkWA1xSPUptTUFHLfwMrDfg7mRuYd0B9zYOYZ0bbzySUuVgJCV6k2iwFvCf8P+doPgMKitwMPBievxG4dCyvzVGM/VK0eZkYHtunxl537Y/TuGeGv6dmtv9gmBMfDs/fCvxqyGO8Cjm2pwCHog3y7aU2t6BCwVvD978TcHDL812JGHBujY7dNd7X2BC+162K+TM8XxZBSMZ0HONZGNfDSZk6NoDz7OvuhwacTwpL1EOnk9lvX5lEG2MauHDOvlKWZnZDGHMhUPBRVBCyRni9rzT0kIZfYMyWQ5PIm1D0+wh3P2pY58y1AId4Hd2YzZ4imox+bvE+FHlWQUPkLeiHQn8DoQhscL7rgX28W9Xvu+6+VqldX/qhYZqVuGrN7CTqC+OS8BHrw7mdMY5Uga57t5TzK3RgBvEYG0fgrAGlYoM+V0IZh0La9m+INqoVZtn6S9V+EmVQXuPuy4X59ofeK82dpRZqKmReHQU4iiLBHl7aWdHCfHEEoutzFC3fo25+Kl/742FmdhdiATkIQY+6zHv5i2egzOAVY/Ubhe/2cRQ42Atle472KENko5RPL52v4EmPC9dbFZEHyM+3gYXRPPGqK+Qcto0nJrgx3rSlFSnflEJQa/M8mcSB0cCZ2beAQ72bqeHz7l5OyedgSefybvLun5hZPAHlSEPHY8sp6sm1O4FdXLuzh01ysI0X5EGbme2OfsvHiNgJgMbE8MBvTGIMZTWsGM+4B6Ie6sGhtrSCIvBrdCgC2yj/5dpnUIHcfOi7+jsqWirbAUMcQ5dZDVetSTL47+6eGmNdnyOc20Al53Y/c/dlMtoMkkJyGPj6Y4G93f1yADNbH2GcewQI+pnlYcV3IzitAO7+O0sX7uZCgZ53ybQXY5jEcNakgVvYHFduHHOu/bEZaY/tin6PMlwR0sVgL7r7UyUc8lB/oyjw8CyS6U7ZaOXTYxskTv9Q4IM+zlLJKTPV1XyS3o3usOpU0uMYx0jw+9Gk2YU39SCH+GqwjEjFwGjgKiLnKfGBY1HktAdLamZFhOaLqBihIFbfBhFotxKDMLPbUVFPOfqcq+M+y5uZPYAYIp4cQF85Ub9d0KZpEtpMnT6eEZu2ZpGq3ywwlpkodf53E1ftGXS4at/s7o05q83sRoRV/ZWPgmIsvK92PpnVzfqoljXsq1KxLGrTRfUYnNaZXlIss0y10OC8/BMVPu2OMib3uPv44RUzrR+OdBjX/iDNzD7h3RzQVe2OR9CPLyEnfg8Eldl1CGMqlPGSFl9nIYj3GIJNJKPFDc47MKEkE9vCwHiHB2kmgZer6fUbclmFBmLjWRg3PaTbi4loT1fB1kDNMoQJWvabE6kYJA3cRItkZ03FAXNE44mlLHcyFV2VpSxn0OFcBfh01L8TAf2jfnMW5qyinjozs5+7+9aJiae1FOeA7Y+kq9gbW2bU78fAj81seeQM32EqEDquiLQ1McsTHxi1VaXZi8hNkWY3s2vcfV3rrUYfZrpuYhTx2gbJXp+Fsjm3te3U3f9Yikw1kkSHcWPJyIYKZNpDJphYkWXaHtGRtbG7ECVcXQTsSjP7CirM2gg5rb9OtCvYDP4ZIBt/Renhsn0JfRd3ornxAnc/ruX4x9qOI/A9A7j7HWb2U4QfhyFd+wO0F0N0ussSa83uqIjqeQTnuwhBKYZhm/ZvInP3R8KavIi7V0WLc/satVCSdZiwbjHxOv+SbrhZY7q1Idhc7v7F8R7EmDvB1os3LSoWlwzwiEHjTYdFqZJT1V6mgduQ9jRwpyHaqaICeCeColawvjdsjvMVW4OF+ddm9llUpR7faE1SbAXZevbEM8b2EGJ0OJ/uz9gGOzkXgpws6e6fMmEZl3f380rtJqJivBUQvvJ2YG+T2tVHG572JIL4QHh+P4JjDNQJJjPN7u7rhr9jSXvXlKs2x3I5t/vZeLBkwGBZQ3ZG6eKz0TxxdTjWxuoUywrrcVoRXWXZcqFAu7uENkYcX0uIb8yi1o/veRjX/iAtFh2ajMY4k9Ja48Ld78cYsAlEMIgi0luwhsxJ6Tszsw8iX2N2RN33VlRw2EbFsUsoKQogNBFKiqElzyD598JacQ4Pwc4zsw+4+wXjOYgxh0OY2bFh4U9Fs7wljjT33LPT4fe7z92z5QwTfVVqqw/LzGxjNDkAXJKCjlgk1RwweSsjDr5/Rm0mIpqlpemO8P5Pqa++KcnQrm96/9VuZrZ/6nibXX/Ymc9ARUMrBaf4Oo8KH8zs+2hDcBnCBt8UvXafuy/f8JwDK7bIONdEdG9k8VjbgAoOM86zH/ABtKFYEljV3d3EVXtynDa0bk7xwp4C7nT3x6N2C6LizfegKPbFSKSgEWymyXwSIkTPuvsrIQ2+AuINbzyf5UIF+vQxmW4Z3RNGM7eGPtdLHfeSiIMJV4i7PzGa84W+UvCygRdrD8NMCnufA850ifpsCXzC3TcOr2df+7OCmdn8SNHy/eH5UDK6mWPpW4BpAyzYs4zi9gZ9rePu1/Y7Nh4WsoBzIxakFxinor0x3wG6+6fC36YqKqOy4BCejOiPDCmw7ejuV7XssjJSMawb1qV1X+YiLttZwDvC5HYsoj/7KZoAC/s18BxasOo4TXNSko0jzHVms2g162hTXCVbzt23MZH+4+7PmPUwzt+BODT/0/t2Vm9xzkrxgUGbSz1sKioIrTXrLjgsrsW2BYf9xtWEq/YTqNK+2KyvjxalZUwS5qeGPnM5t5MWzRXz0j/yWdhVwDtDdPNiROu1Tctx5EIF6uxkumV034wo5lqb13D2hntlf+T0TQjHXka1ED3Rsn5QIGsuvjErWi3fc8Nrf1aw/wDxujJ0kYwayynAHGTB3iCFko6gVyk3dWzMbYyzgJU2rmkQG9tCkO8B7/VAsB0iKKfTUjaZ+qr2gd+wlk/GXUg1b0G1VPPiXoOxzV2YzWxDd7+sImrWFnc0S1Wz2nDkOl8IKbXCIV2OkjSyu59oZguY1MhirOZV3q5Ark58YBh2rZkdSS8DRhnuNA1BQUZdcJhjHpQIS8fuTzSdhAqGHoMRR+oUYA3gKpPwTc1pPBen2GausLBx+gQqvjnU2uM6B8EaMkgZXUI/I9E3BMlaDBXfvhsVHq0DrObuD4f2ywLHmNleiQzESdRDgZqKb8xy5hk40gbX/phbaX6dgGhFf168Xs4AjLHlsIbcbWbbItjJGxEE8rqW5xu1UJKJtWZtYCHrrtOYwiwiABM2s9sBy7j7QWa2BMJUj3r+aGLj5gTb2BeCzOaRwoy73x9Sf40tpG9/5O4rpF4f0g3bV7oxWCHVvAPVUs0Xmtl73f3iinPlLszroXR9mdoG2uOOHptVHOBgw5Dr3B/JhS5hZqehBf3jcQMTO8Q0YHF0j6wJXE9L9Tx3nxlSzMuj6Pqo4EAZVsAs4shcD70eAyw4HLAtUTjAwR4Px/5uZi8SOfaRzY0iyK8ls1gnniuCo11gI2+KYRcls7DIbRfOBy0XNlcBJoilpy18aZAyuoXVRd8+BmzkUSG1uz9kZtujyHjZCV7Q3X9ukpwvxhinmR8BHkGR/1el2WBwpONp8fz6EvCIu/+p3MjGgXufvALMcsHedBoW7FlecXuuzY42tJPortP4F8MNfjSxo1H2b0P0Xf0b6ResVvemQdt4RoLHuhDkFjP7MfCT8Hw7WnIHh3TvfRbxHMdmDahVGp63VroxWI5U8w3AOSbmihfphRz8GVHtlLFE6xJBI9y9wMl+o4jIRG3bQiRmqWpWj+Q6B9jnJSbKojXRdz/Ne5lRpqHJ4AZ338DMVkAp3UZmET9oWPzfzhjwg/aDO0XRiYEVHA7YrjDxf58Znn8kHJsb+Kd3i8/Mi36vnRD1VBvs3tbAd4Ar0DVxhJnt4+6/SDTfE93357j73SEK2ogxxAYrljFIGd3C6qJvsyXuF9z9iYrARhYUyGZRKFamnUsHR/p8n7aznJU2gwsiOeeUDYx7v4H1LcD0UsGeidXnSMSDm2sDKwoP3+eVZnaStxRYGgNbw4VfvxXA3f9hqtsaUxtPJzgLbzpA+wyKLhQKcVejnUhbWwClQG6iO937IYbDcJBFxu0SV9gjev4wmthj+x8U9bizYhNyGAm6NDTJHkZv5PcsejFGv6Ad1GQKs1A1q5nVpkPbbGhMPJ2gdCvAW8wM78anP+fuz5kZJmq8e8PE2tR+hIq2ivMeQocf9FiGGBUws03opd4qIlNFdOIP4TF7eMwqthtyfIuCoVOQpKejxRcT7/beaEN9Mio2+kfL8+2H0vuPh74XAn6D7qMuKxa46PlDRPd8phXf//Jos1VgYT9IQziDuw8jvVoXfXuh5n2p13KhQLMUFKuhDRJHOmYWNiSHIOz1QShgsyAwwcx2cPfppbfM6e6XmpkF5+4AU1HaMIV/NkPF5T10eSY+5u8Ci6LAzVHI+V2Dhpvhwlk1s1Pd/WOl85yKMiBN7SQzS8H4hkZA0MBeDFn1YnO6EPU1SkOx8aBIa1MIMmpzsSUciYi2X0Hp4LrJtJ99reqFIe28PoZSnp9DmLgl0CLdZSamhtRFH6eL/oi0yKui1a9L4ZHc/U4zWzo61wrIyZnPunHBU4gcnybm7ju1ed8Q7RX0ff4ULcLPDqDPWJ1vMkr7FtXFhf3JVCH9S+ASM/sHStk2tXHhBzWzHyJ6wA1Q1GRLIufKE4WGITMxj88Cwhrh3vgFCScUwMy+A2yBNhL/5e7/HuUpJ5TgD09S2uTaAPHpxfdvktFetcCQmtkBwPnNhz9wq4u+xZHn2IzEvNMACjSrQbGa2KhxpONkRwJfQeISlwEbu/sNYW0pYAWxDZJ7P9c+CHw/3Cs/A6a7qOZAdHrHIKjaxgi6djKwnbs/1/J8K8ZPgqPYtnbpC9H/k5HP8FJF27G2HyBa1YXN7JtojSgr4A7dxoMiLUl9U9iwAPAhKvVD4EE0ES6D5E37sS3U9dlTvexRQYLlF7MNzELar7DJwFaI2uXrUZuTEP7vQhIpaDP7nbu/saL/B9z9DeH/D6Nd8ofoRJJAEc4z3L1xYYD1UT4aDwsT8lQ0Gd6DHOKLo4lwtP0vARzm7h8x8UveHm9Qwj0zH5p8G23czOwu4K0BCnEvUmW8qnjNWyibZZ63oNwq/s6DaLzeWWr3UwTfeRmxHEwBDnf37wxjXLnWLzVuZq+ge+clGL3YR3CqV0YLPwj/f4e77xu1ebu7z6iaQ9vMnWZ2H7Cyd0R45gjnbZN1mGXNMoqwzexwlJ2cJaBYORbu71fQ53ojghe1xZGOuVlE02hmv3X3N0evpVRSV0M81vOjyPF8wLfd/cYhj3M25ORuA6yLKEp3sRLNpJk95C3xySbM+leAOVE2FPQ7voCCF60UXRPnucnd2zAMDdzC2vpu9DkvHY9N6HjAIbLwpkOw7wEbeJAxNFXkn09/yrGkWX31cmG5xWx156nDFz+PnPqD3f12AO+tsj8skS56ODyqUtC3mNkny+kfU7HWiBSyu58LnGtma7n79Q0+Vp31Uz4ac3P3exEObX8z2walxr+NMJyDsD+hjRIo2rVs+M2uA64FrvcWqkHBTkep5b+hKPbVACYKvWEWpBUR82fMbFEU2Vwk0e4t7v4vM9sO3YtfQtfYuDrB9EmNu/tAcYjuvk9wvAv4xQ/d/ZelNgPHp6Nr+SYzOyc83wyxKYyLhY314u5+VHh+I4IvAHzR3c+sfHN1n7lF2LMUFCvTFqNThPpqtDj9Xc6ypbIdN4d//42KxyaiNXaoTrC7v2jiYnbkpG4G7AJMNrO3wYgK6/Pxc28g/uXuB5vZt4EfezvFxh4LkK3CJqCI8nyD6Hu0ZmY/QMGyo8ZzHOPhBDfFmw7KnvZuHe+H6GAy21gOd2BuMVud1eGLJwEroUWrIOiOsbkTkPPd9TunUtEl2xMVzm1Hx+l9B3KYN0+039zM7kaT2HQU0drL3X+SaNvP+ikfjbmZ2WJoot0cabnvhdI4bfs7gm46oLcidSTc/R0hq7A6orjZAzjVzP4KXOvun21yLh8/ftDzApzjO+izOWk1r9lClGUz4Miw2Iy1alrKxiQ1bt2y0fFF/ykzew5tcvdz90uj9+RAnnLObcgRvBAoIvQ7uXuZUnEsbV90rxU2B8Isz42Koho7wWQWYc+CUKwce3hI8LuxsgLaEhdUQgnaYmZT0Jq7GMo6XhKefx7R2J02rAGaRKq2QVzhV6B5bOvw8qOoxqawv0bPU2w4teYSwBkkO8KMMA5D6+jDdBhlxttmAF811bqcgxziVmQFo7HxcIKz8KZDsFvM7ALEPegIJnBzgWVtkfLK4Q7MKmars4wJ7sGS4xuD8V9C4iBbx28wAdD3pbdoacPw9zFgbTPbADnZAOe7+2UVY3ivu+9rZpuH822BCP3bOMF/C1H6Aiy/JWNXPNljZnYlwq//HFUiF5H22c3sNd6OXSG+0V8CTo8zI65K4yvM7Ga0yVoHZRFaFb74OPCDeocn9ywTy8JkT/Mb/whdM7cj/t2lEI3PeNuYsJR4DWF8iHKthBb4GLbyjuj/EchTi3O7mV3g4vgdtFx9W5vd3f8YPb8mZLeeNDFztLHaIuzSprTH3L1p0eFY2sJWw/Th48+yUmueX1R5KgpAXI8isF9Bjt3m7j602oZgOyAs8KcL2FBhPhzRr5lmtloU9W5tPkAhq0Gbu58MnByi1R8Bvm1i3EpCMYdl44EJzsKbDuG8J9a87E3TD8Gp/Se6QXZH1cv3uPt+UZulkBLW7Ch6OB8it3+gt8dG5z7P3VszUJjZxeim/gLCY+4IPOHuX2zZ393uvqKJgu4X7j7dzG5391Va9LUsKjZaG016D6Mig3GJdpjZ7+kskCns50D5KU2E62uj6PDzCCd7I4JE/HWQ5xq25eAwK943yQeEt25rFfNF43liQGP5tLv/qE+bGe7euHjGzE5GEfhRL7iDsLo1wMwedPflWvR5ObqfqkR/dqx7f1isZ0kzs0dRDUWSnDkj6/eqMIskiMPm8FFgSW9ffNb0/HH9z5zApFFA1Pqd614kQf4IYp5qje8OWbbPAAUj0RVI42CYHPGNzMxWR5H2DwO/dfdhoQHS5x8HJ/h04LIKvOlG7r7NmA6opZkqVD+B8GMGXFT+TKHdwLTtoz6Tevamgpl/BBzt1ujCfxA53s9H7Wa4+9stFC2FYze7e6s0jJkdgtLZz6I0/vzAee6+Rpv+Qp+VykevdqvBeRsigb8V4cuvGnbEdlhWhcNMRdWsnkrt/6xkFZCnz7TcdN6LCqp+zygX3EGYSTzmisT68GlgfXef2qLPgRUSzmpmZjPdfdwlcIdt5c85lp/bovofd1/OJNjxQ3d/d5+3tj3fUqnjbQJBITA1G2KsALFMvezuu7Qf4WAsBBI3Rz7KGcAv3f2fYz6OcXCCX4fwHy+QwJsOK9plZosjpoai8ORqJFLQo0qT2d80dz88dSxg7WJt+wKPk9S2b3HuE8oRKTM7CmFxJwP3IdqY6ejzTnD37aK2N7j7mmZ2EaIp+QuK4DaOskR9vgZ4yiUkMhcwpc1vaWbzoe+u2LleicQ4ZkVVsVYWbn7oiJgUv80x6HpZAEWD10a0To+iNOD1NZCUWcrM7Ldk4DCtgkrN3ccVtzbo+WKQFiKbhRU4v++22TANcsEdhJnqKgoISgHReDvCBm/m3Sp+/+utKiDy/5uZFP4KPn6jw6AwdEETE5Xk6sCNxXcdR6aHdM5V6OD0r/ZQ+N6in56MbNss7aAtbGzP8oTwzZiOY6yd4JETd+NN7x724m5mlyBqq8Lx2B6l2Tdq2V/PTrSYkAJGa2NER9WlbY9orsqynqM2M7vH3d9iZpMRA8fCwSE1RHn0X1HbTdGivgRa6KcAB7h7WQoy99w7pI7npL4TfZ2FMHzxznUVd9+i+l2vLkstXFWRjbBp3AoVKy7TAEM3rmZmZwJ7uHstntsyqdTG2gY9XwzbzGxPdz+s5XvXRaneE0Pmah4vKUCOtZnZhnT4Uke1PtirWwmu1kZRl/B/lmlmdqO7rxGt75OAmWG+qo1GewN2iOh805DSXFF/sDmiSDuiRV8zga3c/cHwfFkU8Br37EHIpm8LLOvu3zCzJYHXu3sjsZ5Rj2O8nOCxNivx+VUdy+hnKvrh1iXQTQWbF3jF3d9tYoDo0rYP710IVehn79xrUudAR7EsdqLapI5GuYjGN+dkRBM3090bq5EN6nealS1EFnbzUAwXsLNHu/tbTbzIa0eP2RFV2vWIHWLMq2ebmHWL4VTiMKP2xQJzAyqofBI5PUOpDci1V9t1aGZ/cPclW7xvf5SJW97d32SiszvT3dfp89ZXjZnZA7x6leD+z8bZrKb+J8rKTEb30e1ok7UycIu7r9XifHcAa7n7f8LzuVEWsA0m+N2IVeWhMK6lEANMI5n1YZiZHYMo8jZ09//X3n2Hu1qV6R//3gdQioIoYgOkqIOoFEGl6Q8dcURHFBURkFHBNjIK9lHHsY4FRbGOHZURHBgEK4gioICAHKRbUMDeQJr0cv/+WCuc7Jxdkuwkb05yf65rXyd5k7x59j57JytrPet5Hippbcr4aJDVMRbUZNvkUbtS0vNYVox+T+buTz6f0ynL0+swsxLDdZRSLdB7b/v5tDbA7V//7VxCb2ntEhYzdwyLZXU25/NqSom6ntmeUWpLpTTWV/o5F3CjpB1tn1rPtQOD6dI2TvYDPl9TP0TZANhKb/kCcCqlbNV/2P5NIxH27wM93n+2UmrL5dY3YFCvF6My68aoLuxGKa/YKtH3B0lzVqxYQXVV7k5j2KgnxsKc3Qtdq0NI+iql8+IF9frDgbf1+Xxi2T4K6uW+/r5dWkw/mJJWB6Vb4s3zPWaEHmP7kXXSENtXqVTTGqlpmgl+IGXpfzvKG+3plOXagQ8y5pt57Tehf6El9DqjMycvsEtY0m9tr99rXHOcaxVKW+aeu07VXKgvUSppiNJT/gX95kQtlqQ3UPItb5/ltsVW6VgLYMLynR/EPM1w2pblDqT8DZ7jWglCpVvZXKXURmqUrxeDsIiZ4LNsP7r1WrKYWadxpS47wamUQ3wdZfd8K/dzaF0VY3KoVkha6FiX53o1pWLTjAY2vazUqtQa/q3rvpyasvgsSsWJt41DCo1KI5ztgR/X156eV8oHYSpmglVKqry7cyl2keecL8+sp9723T+lduhYQr+z5vBCg9wu9P1pqG0JnBrTZvRX1J462N1CpTg6tpuuGbs+sFTS/p0DO0reVs8k/WfHdWBiKiJ02wxnvXp905rycxploNlzq+1BG8brxSBoZmONGTdRNgr140hJnwLuobILfl/GYyZ+kLrtBDd2jXqiOd2mIlbnq1RiaNXG35tlK8M9sf3B+oGslZLUTwObTwFPBJD0OOC9lFSOLSklSHtOVRyCj1AG+utK+i9KTG8ZdRDTNBN8KiX35JYBnW+keWaStgY+z7IZ0quAfXtJvF/oTdR2Xx+KNLME0W3Ar91/1Y27Uj6xbsjM+rKNDRDr5oePUXrWt/KYWnH1s/HhNW1XV6WkvPzUDdSgHTTNU2pPs+yorstf21BmBLarX1fb3mzowc5j0K8X40zSzsws9fjdhkNqhEpb3H+j5EQ/UqVRz362d2k4tGiA5qic0uK2CioqG9Lb6/H+APhvj6iOcSe1VYBQqRz1V9tvq9fHZm+DpE0pe4gEnNhE3v40DYK/BDyU0nKxVWql7446kk5rYvPIirCEXpe+97S9/4J3Xv6xx1NmDZfSlhdl++A5HzQCknYCjqbkhd3ZPMO1y94iz31XyuBjp8Weq2nqsRlO/X3ejjLrsR2lxvQFbriF7aBfL2L0JL3e9kGaoyOcO2pWa/ZGPc+zffkIwo0xM09q1w7An1qpXQN8vs4W6q3LK1M6KXY9SSXpQmBL27ep1AJ/ie0ftG4bhxQfSYfZ3mehY8M2FekQ1a/q1xLKznVYRAoAI2qrqjlaYrYtoY/Fm7KkrShVM3anvHn0+3NYz3Zf7YGHQaVu6cHAxpSZwWHkJq9OSQ9of96HUPITH8jMGfFFD7qH7GxJL/bszXCWtl3/NKUE1nWUjninAx+0fdUog53HoF8vxso8q0IAeALKh1FWbmBmm/I52b4UeKImuFFP9GSu1K5r621P6zFlYl7uaKGuUi5yf8pmvGNmfdDcjgBOkXQFZWP5D+s5H0SZZBoHnTnUK1Fqgo/UNA2CL7Y9I09V0u6LOF+3eWaL1dNObZW6su8G7m97F0mbUcqtfG7AcbUGanvWrysorZjlxfVTP13SI1q7bMfAmcB7gH/xgJZNOl44V6JU7+hM9ziK0jXuM8zcKTzuDgSOkbQ3szTDabvfBpQGCJdQ6lr/jlKGaFwM+vVirLTecCW9k1Lt5jDK7NPewP0aDG1gXOuee4G2xyvKREOM3H1mex+yfYGkDevVvjdGz0WlWs6BlJJshwOPst1TZRrb/yXpRMrf8glt711LKLnBjZH0RuBNwGp171QrCf8WykrMaOOZonSI2ZpbTFzLyZrXdijwZttbqBT2/klnLuaAnusOyifM/Wz/sh671PbGfZyrNTBcmdLG9VLKDHvTbVzv7QG2vK7nbM81u41Swum2jvsstT3yT8WDoi6a4aiMMh7GsprID6dUA/mR7XmrnQzbFL1ejG1HqcWS9PX5bm9tfNQiK+vEZOo1tWsAz7cO8BpgD8r+n4+Oc9rjYkl6j+3ZZtpHauJngiXtAjwFeICkj7TdtCZ97PydK7+spTPPbFDUfQ3LdWwfWT9tUXOChjWT+EzgucBJNZf3K9B3vdKBf6IehEEPgOs5f1032+1I+V06Fejc/fsNSS+nLIO1p9s0XtqmGy7F2OctyF5nJy6UdDVlie4ayu/Boymts0du0K8XK4Dr66z9Vyi/i3vSlgO9gtsO+C1lafhM5nhtyiA35tBVPyLRhwAAH+5JREFUalc9ti2lpOJDKateKwHX95hW9Gvgr5RJrBuA/dorlUzKikSdBLq6NQCuEybPAC4HPj7qzcgTPwgG/kDJCduVmb+41wGv6uN8TXXs+gy1hiWA7fMlHQ50DoKvl3Qv6kC9/nEO5dOk7WOBY2sO3dMpSzjrqnSCOcb2CT2c7s/Ay4AHUTaffa5zdnRSqJRI251lqTNfkHRUxwea59d/X9d2zJTc5BWepFeybAb4VpaVR/s85f+/KYN+vRh3ewEfrl+mlKrbq9GIBue+wM6Ugf1ewLeAI2xf1H6njg87yxnWxEaMvW5Tu6BUD3ouJY1tG0oqw0N6fL6D2i5PWsOadkdSfn7XSNqS8jN7D6V82yeAF40ymGlKh1iFMujfwPbPm46nV63SU2prmjFbqZM6w/hRytLyhZR802fb7qtmYR9xrk0Z4O1h+x97eNz/UgZDPwR2oZRZO2A4UTZL0s+BLVrlcyStBpzrPpqLrKgkfZBaG9j2H5uOp5OkVWzfWi+vDaw/qr+hGLxagWVPSmfCt9v+WNttz5/zgSycUxyTrcvUrrNtbyPp/FbqnmZpcLXA8+xJyd8d586Ui9bxM/oAcIft10taQnkfHGnq4zTMBLc8mdLS9S7ARvUTyDs8ZgXx53GFpE1YNsP7bMqGlhlsn6NSt/cfKMt/P2+9mY9C3d3/aXpPcN+slbcs6XPAWYOObTEGXK3hD5T6wK0aknelbA7rfM7tWb5e8pf6eL6xY3vWzUhj5LuSdqX87JcCf5F0uu2Jmg2WdCizlw9b4WtWw52D36dSBsAbsqxA/50yyI35dJPaBdygUvP8XEkHUd6blyzwmE4bAEfVCbsTgeOAswa1IXuMtKclPYFagcP2HVK/2ZT9m6ZB8NsouYYnA9g+V9JGTQbUo/0pA8tNJf2eWsOy804qnezaPUTSNZTaq38Zfph9u3OgXvOYm4xlNouu1tCWT34NcJGk79brO9Mx6Jd0GLAJcG7b85nSUjqGby3b19b8vy/ZfqukSZwJ/mbb5VUpy5R/aCiWgVKp9fxw4NuU2d8L57jfIbYP1MzOl3dagSZKojn7UAa9/0ZJm1qf0vSpa7bfB7xP0t0p3d72BT4p6afA8ZRa8n8eaNTN+L6kIykfFNYGvg8g6X6UChEjNU3pEGfY3rYjneD8UU+9L5YWqGEp6VuUDSGtT647UWayNqLMfB82ijh7VTfvtTbktNrA3gAz2lE3ZhDVGnpZdq0vfJtN4CzACqFWK3kS8EVKpZUfr4ivF72qS5Kn2t6+6VgWq1avab2mtP8dzXhNkbS17aWa2fnyTrZPGW6kEXNTKXO6C/Ak2//UdDyLVasC7UEp33ak7d/X41sB69r+zijjmaaZ4Isk7QWsJOnBwCspG3H6IunewItZfrl6oMuIkp5n+3/UUctSc9ewXBl4aOsTo0rd4C8Bj6G0chzLQbDtlZqOYQGLrtbQ47LrhZSNPWOXLzsl3gF8hzIg/LFKN7FLGo5pFB4MrNt0EINgu6vlaNutTU/3Ar5l++b57h/RSaWL3NtYPl2u643Mkja1/bO6r6fTSW64a+qg1Imdr8xyvLNC0khM0yD4FcCbKQOYIyhvcO9cxPm+RtnE9T2G28xg9fpvt7tF1+9YMvlLPfY3SSPLDZ5Ai67WoLm7C91ZC7ltSfbuwMWSzmLmoDtLsyPg0ijjqLbrl9Lj8uaKQMs6x7XatP4JeEOjQTXnacCHJP2A0vjn+EmtUBMD9zlKGsRS+h8PvBp4CaVDaSdT8mdjwKYmHWLQZqvMMKTneZ/tN0ja3R0drOa4/yeoCfb10LMo3bheB3zTi+vmFougmU0yllPrB8+6JNt2nyzNjkDdL/AKll/pyYeQCVY3Je1CWa7dEfiu7ZGWbIoVj6QzbT9mQOdatVU5aL5jMRgTPwhWl12D+jjvuyjlnb7dV2DdP88FwObAUnfZrUrSsygv4FDKUB2d3NLFk/RwYDPKBiJgMNUaJO0I7Gl7/47j9wEeVa+eNeYbGyeKpPMoszsXAHe0jk/ih5BaAu7BzPy9/kFzETWrDoSfDLwQeJztdRoOKcacpPdSGmR8lZkrd+f0ca6p6FY5LqYhHaKrrkF9OAB4k6RbWFbZYBgbuI4HrgLuptJnu2W5DWOSVqLUMdwUOHrAcUw1ldaqO1EGwd+mzBadSp/VGuomgL0oNZUvY1njjNbtz6HUND2Z8n/9UUmvs/1//X0H0aObbM/bRGES1OoXBwDrUSqRbAv8iClcelXpFrgH5e/8ZOCzwHMaDClWHK1Z4G3ajvWUwiDpvsADgNXq+0NrrLImy9IiV3jzpAUCMOrNx9MwE7wSy7oGbc4cXYPGnaSv2X56N/cDXmH7NyMIa2rUP9wtgJ/Y3qLO0v6P7Z17OMdDKL+HewJXUPIOX2t7uTSJOhO5c2v2t27E/J7tLRb/3cRC6ibaBwMnsMiZnXFWf68fBZxhe0tJmwLvtt1ZanHiSTqC8jd5XDbHxajV6kEvoAyk2zvTXgd8wfZXZ3vciqYtLbC18tnarL83gO1/H2k8kz4Ibqd5ugb1eb5dgcfVqyfb/uZ891/kc60B3FgLSj8E2JTyYn1rx/1+AGxFqTvbKg+UXMZFknSW7UdLWgo8nvLC9NM6697tOe6gbKbcz/Yv67FLZ9tBLOkC1+Yh9foS4Lz2YzE8kt5Dqf35K5alQ9j9NUcZW1rWifJc4DG2b5Z0ke2HNR3bqNXX2Jts3z7fa2xEJ0lrAW9l2XjgFEpJ0mv6ONezbE/8Sq5m6ajXRNrHNKRDdNU1qI9zvpcyg/LleugASTvYfuNizjuPHwCPrfl7JwA/pizd7d1xv7cM6fmn3dmS7kFplrEU+Dtl2bgXz6T0lz9J0vGUMjFzpeccL+k7lDQe6uOO6znq6NfuwMa2R168fcR+V3+vj6V0ybsK+HXDMTWl29fYiE6fp5S1bKXP7AMcSnnN74ntoyU9FXgYM/P03zGAOMeJ6pjptHple3rvsrf4ICZ9JlgzuwZ9xXN0DerjvOcDW9q+o15fibJUPpR8ltYnJEmvAFazfdBcFSqyoWq4JG0IrGm7rw5idcbp6ZQPZU+g5BUfY/uEjvs9E9ihXv2h7WP7jTl6I+lY4CXT9LdTK5OsxZTOfvbyGhvRbrbfk35/dyR9kpID/HhKXvqzKe/j+w0k2DEhaWvKh4e16qGrgX1HnXI2DTPBz6OkBRwAvFLL2vEOohPZPYBWs4S15rvjAEjSdpRZidYfw3INJrKhangkbU5bySxJD+onT8v29cDhwOF11ml3Sm3WE9rqtsLMWeKXSLqJsjz/Ztsn9v2NRDfuAfxM0o+ZkjrNrcoXkn5DKbM4bbp6jY2YxY2SdrR9KtzZPOPGPs+1fa0Zf77tt0s6mAlcBaxNaraoqST0kzoyCBM/CHaXXYP68B7gJ5JOogxWHgcMKxUCyiD+jZQZw4tUOlidNMv93gw8qnNDFZBB8CJI+jxlY+VFtOWI0lHVoVe2rwI+Xb+wPWdTlLra8HBKCs7DF/O8saC3Nh1AgwZVQWdFcyDdvcZGdPpX4It1QCfK5Njz53/InFqD5xsk3R+4ktJieKLUFet3A/e3vYtKe+jtbH9ulHFM/CB4WGwfIelklqUdvMH2n4b4lPdpn4WyfamkH85yvyUdS7hX0kCezQTa1vZmTQZg+3bgPEkfbTKOSVc/bHyql02PE2ayc+TmUGfCT2m7finwyuYiihWF7XMps5qtleXrKfs4+kmZ+2bN038/cA7l7/GzAwl0vHyBkjf95nr9F5TqLCMdBE98TvCwSDrR9j8udGyAz9dVAW1J76fMWLY2VO0BXGD79cOIa1pI+hxwsO2Lm44lhm/SSw1KevVcN1HSbe45yniaJOkQ2wdqWcvyGSY5BSYWpw5696fU9/0aZdV1f+A1wPndlDVd4Px3BVZtKlVgGCStbPu2tso0d1aJaCIHPzPBPZK0KiVpfZ2az9le0PoBQ3i+XYCnAA+Q1F68f01gub72tl9XN1S1OsZ92vaiKmEEUDav/UjSnyg5oq2c8pEW9o6RWRu4SNKklhqcM+0G+PDIohgPrTqlH2g0ilgRHUZpZvUj4MWUWU0Bu9XZ4b7USgkbsmz/yUC6k46Js4BHAtdLuhf1g6ekbYGRD/YzCO7dSym5Y/enLFW0XAssqu7wHP5AKZy9K6U0V8t1wKvmeMxS4Frb35O0uqS7275uCLFNk89Ryt7MaKMbE2uiSw3afnvTMYyLukFnIltix9Bt3KrdLumzwB+BDWzf1O8JJR0GbELp4Hh7PWz67E46hloTh68Gvg5sIuk04N6UShijDSbpEP2R9ArbI8nNrDmKh9neq4v7vhh4CXBP25tIejDwyWGlaUwLST+yvV3TcUQzJO0I7Gl7/wXvHCskSZcxezrEcs1sImD5lMRBNHuQ9FNgM0/o4EzS74AP1qtLgLtSBsY3A7fb/uBcjx2GzAT37/c17aDdNZT824HWFq0djNaXdJcuivfvDzwaOLM+9hJJ6w4ynin1E0mHA99gZsmsiWhlGcuTtBWwF6WE3WXAxHdxmnLbtF1elfL/PjW50dGXLSRdWy8LWK1eX0wJ1guB+1JmlSfRSsDdWL4KzeoNxJJB8CLsB2zHshI6O1HSEDaS9A7bh831wD5dBpwm6evMzFHs/NR0s+1bWvWQJa3MlO72HrDVKIPfJ7UdW3SJtBgvKu1y96xfV1B2K8v24xsNbEgkbWT7soWOTQPbV3YcOkSlTfp/NhFPjD/bw6gjvQ5wcd2PMIk1yv84Tt3vMgju3yrAQ23/Ge6sefcl4DGU9puDHgT/qn4tYf5NLadIehPlE+nOwMsps5fRp5qOcqXt1zYdSwzdz4AfAv9s+5cAkubKvZ8ER1M2qbT7P2DrBmJplKT2n8MSysxw3iNj1N7WdABDNlZ1yPMH3r/1WgPg6i/A+rb/JmngLUd72Mjy75RZ6gsom/i+zWTWGByZmo6yw8L3jAnwTEp9z5MkHQ98hTF70R4ESZsCDwPW6kjrWpOSCjCNDm67fBtwOfCcZkKJaTUFGzTHan9SNsb1SdInKK1Fj6qHngX8Dngd8M1BL5/Wzm+vp7xx3fkmZfsJg3yemJ2k/6aUwDuKmekoSYeYQJLWAJ5OSYt4AmWV5xjbJzQa2IBIejrwDErVma+33XQd8BXbpzcSWMSUqx9K3wesS/kAvpj84lhABsF9Ukm6fRbQmiE8DTh6WDs6JZ1AyU98LfAySkvGv9p+Q7396ZTZ6Y/X62dSSo5A6WZ31PJnjW5JOnSWw7a978iDiZGq9cB3B/aYpCorNc3nDbbf3XQs46C2vH0r8Lh66BTgHZPUqCDGn6RfAk+z/dOmY5kGGQSvICQttb21pPNbDRpaHVfq5dOA59r+bb1+LmXZYQ3g0El6846IwZB0lu1HNx3HOJB0NGVn/hfroX2ALWx3VgGKGBpJp9lO+t2IJCe4Tw0sWbTyjP8o6amUJhrt5Xvu0hoAV6fW3c5X1qXdWARJ6wEfZdnM/w+BA2z/rrmoIhbtNEkfo6wytaf5nDP3QybWJraf1Xb97XUyIWKUzpb0v8CxpBzn0GUQ3L+DGO2Sxbvqct1rKIOxNZnZMW7t9jvb/re2q/cmFutQ4HDKsjjA8+qxnRuLKGLxtqz/tpcsMiUPetrcKGlH26cC1M2wNzYcU0yfNYEbSDnOkUg6RJ/GbclC0peBk21/puP4S4GdbO/ZTGSTQdK5trdc6FhMhrp6cqPtO2rt4E2B42wPvPJLjAdJW1A2QK5VD10FPN/2+c1FFRHDlEFwnyR9mNLVZahLFpI+yjzNLmy/st5v3bZYWkuZW1NaEj6jo5xb9EjSiZSZ3yPqoT2BFybXejLVJgmPpaywnAb8GLjF9t6NBjZg2Qy2PElrAti+VtKBtg9pOqaYfJJeb/ugud7zW+/1MVhJh+jfqJYszm67/HbKG9Zyaqvm7SU9gVJGDeBbtr8/4Him1b6UNJQPUf6fTwde2GhEMUyyfYOk/YBP1DenScwP/TxlM1irHu4+lA97U7sZzPa1bVdfDWQQHKPQSq08e957xUBlJngFIukntrdqOo6ISSfpJ5Ruix8C9rN9kaQLbD+i4dAGKmk+85P0W9vrNx1HRAxHZoJ71PCSRT6xjJik/5znZtt+58iCiVE6EHgjpUHGRZI2Bk5qOKZhyGaw+eU1N0ZK0neB3W1fXa+vTWlg80/NRjaZMgjuXZYspsv1sxxbg9Ka+l5ABsETqLYuPaXt+qXAJObkvQz4Us0NhroZrMF4Rk7Sdcw+2BWw2ojDibh3awAMYPuquucnhiDpEGOu4wV6dUoeMqSV4shJujtwAGUAfCRwcM3Fjgkh6RDbB0r6BrOv9OzaQFgDJ2kD279pu37nZrDmooqIuil3t9bfp6QHUlakHtlsZJMpM8F9qmWTXgtsSNvP0fZA62vavvsgzxe9k3RPygaZvSndpB5p+6pmo4ohOaz++4FGoxi+Y4FHQumU1tEkIiKa82bgVEmnUCa7Hgu8pNmQJldmgvsk6Tzgk8BS4PbWcdtLGwsqBk7S+yk75T8NfNz23xsOKWLR2jfZZsNtxHiRtA6wbb16hu0rmoxnkmUQ3CdJS21v3XQcMVyS7qDUXr6NmcvjSUeZYJIuY/Z0iI0bCGfgJJ3TWl5tvxwRzZP0AOCBzFxl/kFzEU2upEP0qC6NA3xD0suBY5jZLONvjQQWQ2F7SdMxRCO2abu8KqVd9j3nuO+KaAtJ11I3f9XLkA93EY2S9D5gD+Ai4I562EAGwUOQmeAetc0QaZabPSkzRRExU1Z/ImLYJP0c2Nz2zQveORYtM8E9sr1R0zFExHBJak8PWEKZGc7rZUQM26XAKrStMMfw5EW9T5J2B463fZ2k/6DstH6n7Z80HFpELN7BbZdvAy5nWWvhiIhhuQE4V9KJzEy1nMQ65Y1LOkSfJJ1ve3NJOwLvAt4P/KftxzQcWkRERKyAJM3arMb2F0cdyzTIILhPrbJCkt4DXGD78JQaipgMtYPaW4HH1UOnAO+wfU1zUUVExCBlENwnSd8Efg/sTEmFuBE4y/YWjQYWEYsm6WjgQkpzFIB9gC1sP7O5qCJiUkk60vZzJF3A7OUZN28grImXQXCfJK0OPJkyC3yJpPsBj7B9QsOhRcQiSTrX9pYLHYuIGARJ97P9x9omeTm2fz3qmKZBaqD2yfYNtr8KXCNpA8puzp81HFZEDMaNNd8fAEk7UFZ7IiKGYQ1JO9j+dfsXsB4pYjA0GQT3SdKuki4BLqPkC14GHNdsVBExIC8DPi7pckmXAx8DXtpsSBExwQ4Brp3l+LX1thiCDIL7905Kb+9f1NrBTwTOaDakiBgE2+fV/P7NKYXrtwKe0HBYETG57mP7gs6D9diGow9nOmQQ3L9bbV8JLJG0xPZJzGy1GhErONvX2m7Nzry60WAiYpLdY57bVhtZFFMmg+D+XS3pbpR+3l+W9GHg+oZjiojhma1VekTEIJwt6cWdByW9CFjaQDxTIdUheiRpbdtXSVqDslFmCbA3sBbw5To7HBETRtJvbG/QdBwRMXkk3Qc4BriFZYPebYC7ALvZ/lNTsU2yDIJ7JOkvwBXAacDpwGm2f9FsVBExCJKuY5YanZRZ4NVsZ5d2RAyNpMcDD69XL7L9/SbjmXQZBPdB0kOA7du+7k3ZFHea7YOajC0iIiIiFpZB8CJJ2gR4CnAA8ADbSWCPiIiIGHMZBPdIUmv2dztgfeBSyizwGcA5tm9pMLyIiIiI6EIGwT2SdAdwDvAh4BjbNzQcUkRERET0KIPgHkm6L8tygR9NaWd4DvAj4Ee2L20wvIiIiIjoQgbBiyRpdWBf4EBgI9srNRxSRERERCwg5X56JGktSj5wazZ4K+AS4BuUsmkRERERMeYyE9wjSX+lpj5QBr0/tn1js1FFRERERC8yE9y7DwHH2f5J04FERERERH8yCO7dr4ADJG0BnAccB5xg+6pmw4qIiIiIbiUdYhEkbQU8GXgSsBLwPeB422c1GlhEREREzCuD4AGRtCawM/BPtl/SdDwRERERMbclTQewopK0uqS3SPp0PXQf4OYMgCMiIiLGXwbB/TsUuJlSJg3g98C7mgsnIiIiIrqVQXD/NrF9EHArQG2frGZDioiIiIhuZBDcv1skrQYYQNImlJnhiIiIiBhzKZHWv7cCxwPrS/oysAPwgkYjioiIiIiupDpEHyQtAZ4NnAhsS0mDOMP2FY0GFhERERFdySC4T5LOtr1N03FERERERO8yCO6TpPcCVwD/C1zfOm77b40FFRERERFdySC4T5Ium+WwbW888mAiIiIioicZBEdERETE1El1iD5JWgX4V+Bx9dDJwKds39pYUBERERHRlcwE90nSZ4FVgC/WQ/sAt9t+UXNRRUREREQ3Mgjuk6TzbG+x0LGIiIiIGD/pGNe/22uXOAAkbQzc3mA8EREREdGl5AT373XASZIupTTLeCDwwmZDioiIiIhuJB1iESTdFfiHevXntm9uMp6IiIiI6E4GwX2StBLwVGBD2mbUbX+wqZgiIiIiojtJh+jfN4CbgAuAOxqOJSIiIiJ6kEFw/9azvXnTQURERERE71Idon/HSXpS00FERERERO8yE9y/M4BjJC0BbqVUiLDtNZsNKyIiIiIWko1xfZJ0GfB04ALnhxgRERGxQkk6RP9+C1yYAXBERETEiifpEP27FDhZ0nHAnfWBUyItIiIiYvxlENy/y+rXXepXRERERKwgkhMcEREREVMnM8E9knSI7QMlfQNY7hOE7V0bCCsiIiIiepBBcO8Oq/9+oNEoIiIiIqJvGQT3yPbSenFL2x9uv03SAcApo48qIiIiInqREmn9e/4sx14w6iAiIiIioneZCe6RpD2BvYCNJH297aa7A39rJqqIiIiI6EUGwb07HfgjsA5wcNvx64DzG4koIiIiInqSEmkRERERMXUyE9wjSdcxS2k0QIBtrznikCIiIiKiR5kJjoiIiIipk5ngRZK0LrBq67rt3zQYTkRERER0ISXS+iRpV0mXAJdRagNfDhzXaFARERER0ZUMgvv3TmBb4Be2NwL+ETij2ZAiIiIiohsZBPfvVttXAkskLbF9ErBN00FFRERExMKSE9y/qyXdDfgB8GVJfwGubzimiIiIiOhCqkP0SdIawI2U2fS9gbWAL9fZ4YiIiIgYYxkED4CkdYArnR9mRERExAohOcE9krStpJMlfVXSVpIuBC4E/izpyU3HFxERERELy0xwjySdDbyJkv7waWAX22dI2hQ4wvZWjQYYEREREQvKTHDvVrZ9gu2jgD/ZPgPA9s8ajisiIiIiupRBcO/uaLt8Y8dtmVaPiIiIWAEkHaJHkm6nlEITsBpwQ+smYFXbqzQVW0RERER0J4PgiIiIiJg6SYeIiIiIiKmTQXBERERETJ0MgiMiIiJi6mQQHBFTR9J6kr4m6RJJv5L0YUl3meV+95f0f12c79uS7tHF/VaW9FdJ7+04/llJm/X2XXRH0k6Stm+7/jZJN0hat+3Y34fx3BER4yyD4IiYKpIEfBU41vaDgYcAdwP+q+N+K9v+g+1nL3RO20+xfXUXT78z8Atg9xpH6/Evsn3xLLGu1MU55yRpZWAnYPuOm64AXrOYc0dErOgyCI6IafME4CbbhwLYvh14FbCvpJdL+rqk7wMnStqwtkZH0uqSjpR0saRjJJ0paZt62+WS1qn3/6mkz0i6SNIJklZre+49gQ8DvwG2ax2srdhb5/q7pIMlnQdsV899kKQLJJ0l6UH1fhtK+r6k8yWdKGmDevwLkj4p6UzgSOBlwKsknSvpsfUpPw/sIemenT8cScdKWlrjf0nb8b9Len89/j1Jj65xXypp13qflep9flzjeuli/7MiIoYlg+CImDYPA5a2H7B9LWVgujLwSODZtv9fx+NeDlxlezPgLcDWc5z/wcDHbT8MuBp4FoCkVYEnAt8AjqAMiGezBnCm7S1sn1qPXWP7EcDHgEPqsY8CX7S9OfBl4CNt51gP2N72M4FPAh+yvaXtH9bb/04ZCB8wy/Pva3trYBvglZLu1RbX9+v3dR3wLsrM9m7AO+p99quxPgp4FPBiSRvN8X1GRDQqg+CIiJm+a/tvsxzfEfgKgO0LgfPnePxlts+tl5cCG9bL/wycZPtG4GjgGXOkO9xeb293RNu/rRnk7YDD6+XDanwtR9UZ7vl8BHi+pLt3HH9lnYU+A1ifMqgHuAU4vl6+ADjF9q31cut7fBLwL5LOBc4E7tX2+IiIsbJy0wFERIzYxcCMPF9JawIbALdROkIuxs1tl2+ndJaEMvO7o6TL6/V7UVIzvtvx+JtmGcB6jstzWfB7sH21pMOB/VvHJO1Ema3ezvYNkk4GVq033+pl3ZXuoH6ftu+oucdQOme+wvZ3uogxIqJRmQmOiGlzIrC6pH+BOzefHQx8gWVt0GdzGvCc+pjNgEd0+4R1kP1YYAPbG9rekDL4nCslotMebf/+qF4+HXhuvbw38MPOB1XXAZ2zvS0fBF7KsgmRtSgpHzdI2hTYtsv4Wr4D/KukVQAkPUTSGj2eIyJiJDIIjoipUmczd6NUaLiEUq3hJuBNCzz0E8C9JV1MyYe9CLimy6fdjZJP2z5L/DXgaZLu2sXj15Z0PiWH91X12CuAF9bj+zB7fi+UHOTdOjbGAWD7CuAYoBXD8cDKkn4KvJeSEtGLz1Jm2s+pGwo/RVYcI2JMadnqVkREzKXOGK9i+yZJmwDfA/7B9i1Dft7LgW3qgDUiIgYkn9AjIrqzOnBSXeoX8PJhD4AjImJ4MhMcEREREVMnOcERERERMXUyCI6IiIiIqZNBcERERERMnQyCIyIiImLqZBAcEREREVMng+CIiIiImDr/Hx48n5yrMpQkAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 864x864 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "departure_airport_group = df_flights.groupby(df_flights.OriginAirportName)\n",
+    "\n",
+    "mean_departure_delays = pd.DataFrame(departure_airport_group['DepDelay'].mean()).sort_values('DepDelay', ascending=False)\n",
+    "mean_departure_delays.plot(kind = \"bar\", figsize=(12,12))\n",
+    "mean_departure_delays"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Do *late* departures tend to result in longer arrival delays than on-time departures?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc8e0f30d30>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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VLR72dh+d5At7Ori1dlGSi5Jk5cqVzbgt5hNjDQH6cp1loZjx8JLW2jmttaNba8uTvCTJX7bWfjbJ1iQvHu52WpIrZtoWAADMRz3n6T4ryZlVdXMGY7w3d2wLAADG1mxOGZjW2mSSyeHrzyZ5+myeHwAA5iNPpAQAgM6EbgAA6EzoBgCAzoRuAADoTOgGAIDOhG4AAOhM6AYAgM6EbgAA6EzoBgCAzoRuAADoTOgGAIDOhG4AAOhM6AYAgM6EbgAA6EzoBgCAzoRuAADoTOgGAIDOFs91ATAfVdV3rGutzUElAAuT6ywLjZ5uGNGe/kOwr/UAjGbq9fRXf/VX97ge5huhG6aptZatW7fqeQHopLWW5z//+a6zLAhCNwAwdi655JJ9LsN8I3QDAGPn5S9/+T6XYb4RumGaqiqrVq0yxhCgk6rKX/zFX7jOsiAI3TCivY0tNOYQYHZMvZ7+zu/8zh7Xw3wjdMM0tNYedCOl/xAAzC7XWRYaoRsAADrzcByYBg9tAOhr0aJFD7quVlXuv//+OawIZkZPN4zIw3EA+toVuJcuXZq3vvWtWbp0aVprWbRIbGH+8u2FafJwHIA+dgXue+65J09+8pNzzz33PBC8Yb4SugGAsTM5ObnPZZhvhG4AYOxMTEzscxnmG6EbpsnDcQD6qKrs2LEjRxxxRG688cYcccQR2bFjh+st85rZS2BErTWzlwB0dP/992fRokXZsWNHXvOa1yQxewnzn55umAYPbQDo6/7773/QdVbgZr7T0w3ToKcbABiFnm4YkXm6AYBRCd0wTebpBgAOlOElAMBBcTD/RVCHCONGTzcAcFDsuvF8lJ/Hn/W+aR0H40ZPN0yTMdwAwIHS0w0j2lsPip4VAGBvhG6YBvN0AwCjELoBAKAzY7phGjwcBwAYhZ5uGJGH4wAAoxK6YZo8HAcAOFBCNwAAdCZ0AwBAZ26khGkyhhsAOFB6umFEHo4DAIxK6IZp8HAcAGAUhpfANJinGwAYhZ5uGJF5ugGAUQndME3m6QYADpTQDQAAnQndAADQmRspYZqM4QYADpSebhiReboBgFEJ3TAN5ukGAEYhdAMAQGfGdMM0eDgOADAKPd0wIg/HAQBGJXTDNHk4DgBwoIRuAADoTOgGAIDO3EgJ02QMNwBwoPR0w4g8HAcAGJXQDdPg4TgAwCgML4FpME83ADAKPd0wIvN0AwCjErphmszTDQAcKKEbAAA6E7oBAKAzN1LCNBnDDQAcKD3dMCLzdAMAoxK6YRrM0w0AjELoBgCAzmY8pruqlib5cJIlw/Nd3lp7fVU9Ick7k3xvkuuTvLS19q2ZtgfjwMNxAIBRzEZP984kP9Zae2qSpyV5XlU9I8nGJG9prT0xyb8mOX0W2oI55+E4AMCoZhy628D24eLhw5+W5MeSXD5cf2mSF820LRgnHo4DAByoWZkysKoOy2AIyROT/H6Sf05yd2vt3uEutyc5ai/HnpHkjCRZtmxZJicnZ6Mk6G5ycjLbt29/0HfW9xdg9rm2shDUbPbSVdWRSd6T5NeTXDIcWpKqOibJX7TWjt/X8StXrmwf//jHZ60e6GHXMJLWWiYnJzMxMfGgdQDMnuVnX5lbznvBXJcBB6Sqrm+trdzTtll9OE5r7e6q2prkmUmOrKrFw97uo5N8YTbbgrlmDDcAcKBmPKa7qh497OFOVR2R5LlJtiXZmuTFw91OS3LFTNuCceDhOADAqGZj9pLHJtlaVTck+bsk17TW3pfkrCRnVtXNGUwbuHkW2oKx4OE4AMAoZjy8pLV2Q5If2sP6zyZ5+kzPDwAA892sjumGQ4WH4wAAo/AYeBiRh+MAAKMSumGaPBwHADhQQjcAAHQmdAMAQGdupIRpMoYbADhQerphRB6OAwCMSuiGafBwHABgFIaXwDSYpxsAGIWebhiReboBgFEJ3TBN5ukGAA6U0A0AAJ0J3QAA0JkbKWGajOEGAA6Unm4YkXm6AYBRCd0wDebpBgBGIXQDAEBnxnTDNHg4DgAwCj3dMCIPxwEARiV0wzR5OA4AcKCEbgAA6EzoBgCAztxICdNkDDcAcKD0dMOIPBwHABiV0A3T4OE4AMAoDC+BaTBPNwAwCj3dMCLzdAMAoxK6YZrM0w0AHCihGwAAOhO6AQCgMzdSwjQZww0AHCg93TAi83QDAKMSumEazNMNAIxC6AYAgM6M6YZp8HAcAGAUerphRB6OAwCMSuiGafJwHADgQAndAADQmdANAACduZESpskYbgDgQOnphhF5OA4AMCqhG6bBw3EAgFEYXgLTYJ5uAGAUerphRObpBgBGJXTDNJmnGwA4UEI3AAB0JnQDAEBnbqSEaTKGGwA4UHq6YUTm6QYARiV0wzSYpxsAGIXQDQAAnRnTDdPg4TgAwCj0dMOIPBwHABiV0A3T5OE4AMCBEroBAKAzoRsAADpzIyVMkzHcAMCB0tMNI/JwHABgVEI3TIOH4wAAoxC6AQCgM6EbAAA6E7oBAKAzs5fA0MGcjcQYcAA4tOjphqFdN0SO8vP4s943reMAgEOL0A0AAJ0J3QAA0JnQDQAAnQndAADQmdANAACdCd0AANCZ0A0AAJ0J3QAA0JnQDQAAnQndAADQmdANAACdCd0AANDZjEN3VR1TVVur6qaqurGqfmm4/pFVdU1V/dPwf79n5uUCAMD8Mxs93fcm+W+tteOSPCPJL1bVcUnOTvKh1tqTknxouAwAAIecGYfu1tqXWmt/P3z99STbkhyV5NQklw53uzTJi2baFgAAzEeLZ/NkVbU8yQ8l+dsky1prXxpuuiPJsr0cc0aSM5Jk2bJlmZycnM2SoDvfWYC+XGdZCGYtdFfVw5L8WZJfbq39W1U9sK211qqq7em41tpFSS5KkpUrV7aJiYnZKgn6+8CV8Z0F6Mh1lgViVmYvqarDMwjcf9xa+/Ph6jur6rHD7Y9N8uXZaAsAAOab2Zi9pJJsTrKttfbmKZu2JDlt+Pq0JFfMtC0AAJiPZmN4ybOTvDTJp6rqE8N1r0tyXpJ3V9XpST6f5GdmoS0AAJh3Zhy6W2vXJqm9bD5ppucHAID5zhMpAQCgM6EbAAA6E7oBAKAzoRsAADoTugEAoDOhGwAAOhO6AQCgM6EbAAA6E7oBAKAzoRsAADoTugEAoDOhGwAAOhO6AQCgM6EbAAA6E7oBAKAzoRsAADoTugEAoDOhGwAAOhO6AQCgM6EbAAA6E7oBAKAzoRsAADoTugEAoDOhGwAAOhO6AQCgM6EbAAA6E7oBAKAzoRsAADoTugEAoLPFc10AADD/PPWNV+dr93z7oLS1/Owru7fxiCMOzydff3L3djh0Cd0AwMi+ds+3c8t5L+jezuTkZCYmJrq3czCCPYc2w0sAAKAzoRsAADoTugEAoDOhGwAAOhO6AQCgM6EbAAA6E7oBAKAzoRsAADoTugEAoDOhGwAAOhO6AQCgM6EbAAA6E7oBAKAzoRsAADoTugEAoDOhGwAAOhO6AQCgM6EbAAA6E7oBAKAzoRsAADoTugEAoDOhGwAAOhO6AQCgM6EbAAA6E7oBAKAzoRsAADoTugEAoDOhGwAAOhO6AQCgM6EbAAA6E7oBAKAzoRsAADoTugEAoDOhGwAAOhO6AQCgM6EbAAA6E7oBAKAzoRsAADoTugEAoDOhGwAAOhO6AQCgM6EbAAA6E7oBAKAzoRsAADoTugEAoLNZCd1V9baq+nJVfXrKukdW1TVV9U/D//2e2WgLAADmm9nq6b4kyfN2W3d2kg+11p6U5EPDZQAAOOTMSuhurX04yV27rT41yaXD15cmedFstAUAAPNNzzHdy1prXxq+viPJso5tAQDA2Fp8MBpprbWqanvaVlVnJDkjSZYtW5bJycmDURLMGt9Z4FD08BVn5ymXHqSRo5fuf5eZeviKZHLyof0b4pDVM3TfWVWPba19qaoem+TLe9qptXZRkouSZOXKlW1iYqJjSTDLPnBlfGeBQ9HXzz4vt5z3gu7tTE5OHpTr7PKzr8zEaf3b4dDVc3jJliSnDV+fluSKjm0BAMDYmq0pAy9L8jdJfqCqbq+q05Ocl+S5VfVPSZ4zXAYAgEPOrAwvaa2t2cumk2bj/AAAMJ95IiUAAHQmdAMAQGdCNwAAdCZ0AwBAZ0I3AAB0JnQDAEBnQjcAAHQmdAMAQGdCNwAAdCZ0AwBAZ0I3AAB0JnQDAEBnQjcAAHQmdAMAQGdCNwAAdCZ0AwBAZ0I3AAB0JnQDAEBnQjcAAHQmdAMAQGdCNwAAdCZ0AwBAZ0I3AAB0JnQDAEBnQjcAAHQmdAMAQGdCNwAAdCZ0AwBAZ0I3AAB0JnQDAEBnQjcAAHQmdAMAQGdCNwAAdCZ0AwBAZ0I3AAB0JnQDAEBnQjcAAHQmdAMAQGdCNwAAdCZ0AwBAZ0I3AAB0JnQDAEBnQjcAAHQmdAMAQGdCNwAAdCZ0AwBAZ0I3AAB0JnQDAEBnQjcAAHQmdAMAQGdCNwAAdCZ0AwBAZ0I3AAB0JnQDAEBnQjcAAHS2eK4LAADmp+VnX3lwGvpA/3YeccTh3dvg0CZ0AwAju+W8FxyUdpaffeVBawt6MrwEAAA6E7oBAKAzoRsAADoTugEAoDOhGwAAOhO6AQCgM6EbAAA6E7oBAKAzoRsAADoTugEAoDOhGwAAOhO6AQCgM6EbAAA6E7oBAKAzoRsAADpbPNcFQA9PfePV+do93z4obS0/+8rubTziiMPzydef3L0dAKAPoZsF6Wv3fDu3nPeC7u1MTk5mYmKiezsHI9gDAP0YXgIAAJ0J3QAA0JnQDQAAnXUP3VX1vKr6TFXdXFVn924PAADGTdfQXVWHJfn9JM9PclySNVV1XM82AQBg3PTu6X56kptba59trX0ryTuTnNq5TQAAGCu9pww8KsltU5ZvT/J/Td2hqs5IckaSLFu2LJOTk51L4lBxML5L27dvP2jfWf/fAA5Vrn8sBHM+T3dr7aIkFyXJypUr28GY85hDwAeuPCjzZx+seboP1vsBGDuufywQvYeXfCHJMVOWjx6uAwCAQ0bv0P13SZ5UVU+oqockeUmSLZ3bBACAsdJ1eElr7d6qek2Sq5IcluRtrbUbe7YJAADjpvuY7tba+5O8v3c7AAAwrjyREgAAOhO6AQCgM6EbAAA6E7oBAKAzoRsAADoTugEAoDOhGwAAOhO6AQCgM6EbAAA6E7oBAKAzoRsAADoTugEAoDOhGwAAOhO6AQCgM6EbAAA6E7oBAKAzoRsAADoTugEAoDOhGwAAOhO6AQCgM6EbAAA6E7oBAKAzoRsAADoTugEAoDOhGwAAOhO6AQCgM6EbAAA6E7oBAKAzoRsAADoTugEAoDOhGwAAOhO6AQCgM6EbAAA6E7oBAKAzoRsAADoTugEAoDOhGwAAOhO6AQCgM6EbAAA6E7oBAKAzoRsAADoTugEAoDOhGwAAOhO6AQCgM6EbAAA6WzzXBUAPD19xdp5y6dkHp7FL+zfx8BVJ8oL+DQEAXQjdLEhf33Zebjmvf0idnJzMxMRE93aWn31l9zYAgH4MLwEAgM6EbgAA6EzoBgCAzoRuAADoTOgGAIDOhG4AAOhM6AYAgM6EbgAA6EzoBgCAzoRuAADoTOgGAIDOhG4AAOhM6AYAgM6EbgAA6EzoBgCAzoRuAADoTOgGAIDOhG4AAOhM6AYAgM6EbgAA6EzoBgCAzoRuAADoTOgGAIDOhG4AAOhM6AYAgM6EbgAA6EzoBgCAzoRuAADoTOgGAIDOZhS6q+qnq+rGqrq/qlbutu2cqrq5qj5TVatnViYAAMxfi2d4/KeT/FSS/zV1ZVUdl+QlSZ6c5HFJPlhV399au2+G7QEAwLwzo57u1tq21tpn9rDp1CTvbK3tbK19LsnNSZ4+k7YAAGC+6jWm+6gkt01Zvn24DgAADjn7HV5SVR9M8pg9bFrfWrtipgVU1RlJzkiSZcuWZXJycqanhCQ5KN+l7du3H7TvrP9vAIcq1z8Wgv2G7tbac6Zx3i8kOWbK8tHDdXs6/0VJLkqSlStXtomJiWk0B7v5wJU5GN+lycnJg9LOwXo/AGPH9Y8Fotfwki1JXlJVS6rqCUmelORjndoCAICxNtMpAwyA5ucAABMySURBVH+yqm5P8swkV1bVVUnSWrsxybuT3JTkA0l+0cwlAAAcqmY0ZWBr7T1J3rOXbRuSbJjJ+QEAYCHwREoAAOhM6AYAgM6EbgAA6EzoBgCAzoRuAADoTOgGAIDOhG4AAOhM6AYAgM6EbgAA6EzoBgCAzoRuAADoTOgGAIDOhG4AAOhM6AYAgM6EbgAA6EzoBgCAzoRuAADoTOgGAIDOhG4AAOhM6AYAgM4Wz3UB0Mvys688OA19oH87jzji8O5tAAD9CN0sSLec94KD0s7ys688aG0BAPOX4SUAANCZ0A0AAJ0J3QAA0JnQDQAAnQndAADQmdANAACdCd0AANCZ0A0AAJ0J3QAA0JnQDQAAnQndAADQmdANAACdCd0AANCZ0A0AAJ0J3QAA0JnQDQAAnQndAADQmdANAACdCd0AANCZ0A0AAJ0J3QAA0JnQDQAAnQndAADQmdANAACdLZ7rAgCAQ0NVTe+4jaMf01qbVlvQi55uAOCgaK2N/LN169ZpHQfjRugGAIDOhG4AAOhM6AYAgM7cSAkAjJ2lS5dm586dDywvWbIkO3bsmMOKYGb0dAMAY2VX4F62bFn+8A//MMuWLcvOnTuzdOnSuS4Npk3oBgDGyq7Afccdd2T58uW54447HgjeMF8J3QDA2JmcnNznMsw3QjcAMHYmJib2uQzzjdANAIyVJUuW5M4778xjHvOY3HLLLXnMYx6TO++8M0uWLJnr0mDazF4CAIyVHTt2ZOnSpbnzzjvzile8IonZS5j/9HQDAGNnx44dD3oMvMDNfCd0AwBAZ0I3ADB21q1bl6VLl2bVqlVZunRp1q1bN9clwYwY0w0AjJV169Zl06ZN2bhxY4477rjcdNNNOeuss5Ik559//hxXB9OjpxsAGCsXX3xxNm7cmDPPPDNLly7NmWeemY0bN+biiy+e69Jg2oRuAGCs7Ny5M2vXrn3QurVr13oiJfOa0A0AjJUlS5Zk06ZND1q3adMm83QzrxnTDQCMlVe+8pUPjOE+7rjj8uY3vzlnnXXWd/R+w3widAMAY2XXzZKve93rsnPnzixZsiRr1651EyXzmuElAMDYOf/887Njx45s3bo1O3bsELiZ9/R0AwBj59hjj81tt932wPIxxxyTW2+9dQ4rgpnR0w0AjJVdgftZz3pW/vRP/zTPetazctttt+XYY4+d69Jg2oRuAGCs7ArcH/nIR/KoRz0qH/nIRx4I3jBfCd0AwNi5/PLL97kM843QDQCMnRe/+MX7XIb5RugGAMbKMccck+uuuy7Pfvaz89WvfjXPfvazc9111+WYY46Z69Jg2sxeAgCMlVtvvTXHHntsrrvuulx33XVJzF7C/KenGwAYO7feemtaa9m6dWtaawI3896MQndVvamq/qGqbqiq91TVkVO2nVNVN1fVZ6pq9cxLBQCA+WmmPd3XJDm+tXZCkn9Mck6SVNVxSV6S5MlJnpfkgqo6bIZtAQCHiGOPPTZVlVWrVqWqzNHNvDej0N1au7q1du9w8aNJjh6+PjXJO1trO1trn0tyc5Knz6QtAODQ4OE4LESzOab755P8xfD1UUmmzmB/+3AdAMA+eTgOC9F+Zy+pqg8mecweNq1vrV0x3Gd9knuT/PGoBVTVGUnOSJJly5ZlcnJy1FPAnPKdBZh9r33tazM5OZnt27dncnIyr33ta3Pddde55jJv7Td0t9aes6/tVfXyJD+e5KTWWhuu/kKSqZNpHj1ct6fzX5TkoiRZuXJlm5iY2G/RMDY+cGV8ZwFm31ve8pZ85CMfyeTkZCYmJvLsZz87SVxzmbdmOnvJ85L8apIXtta+OWXTliQvqaolVfWEJE9K8rGZtAUAHBo8HIeFaKYPx3lrkiVJrqmqJPloa21ta+3Gqnp3kpsyGHbyi621+2bYFgBwCPBwHBaiGYXu1toT97FtQ5INMzk/AHBo2hWwdw0vgfnOEykBgLFzwgknPGie7hNOOGGuS4IZEboBgLFywgkn5FOf+lRe+MIX5j3veU9e+MIX5lOf+pTgzbwmdAMAY2VX4L7iiity5JFH5oorrnggeMN8JXQDAGNn8+bN+1yG+UboBgDGzumnn77PZZhvhG4AYKw85SlPyZYtW3Lqqafm7rvvzqmnnpotW7bkKU95ylyXBtM203m6AQBm1Q033JATTjghW7ZsyZYtW5IMgvgNN9wwx5XB9OnpBgDGzg033JDWWrZu3ZrWmsDNvCd0AwBAZ0I3ADB2Lrvsshx//PE56aSTcvzxx+eyyy6b65JgRozpBgDGymWXXZb169dn8+bNue+++3LYYYc9MHvJmjVr5rg6mB493QDAWNmwYUM2b96cVatWZfHixVm1alU2b96cDRs2zHVpMG1CNwAwVrZt25YTTzzxQetOPPHEbNu2bY4qgpkTugGAsbJixYpce+21D1p37bXXZsWKFXNUEcyc0A0AjJX169fn9NNPz9atW3Pvvfdm69atOf3007N+/fq5Lg2mzY2UAMBY2XWz5Lp167Jt27asWLEiGzZscBMl85rQDQCMnTVr1mTNmjWZnJzMxMTEXJcDM2Z4CQAAdCZ0AwBAZ0I3AAB0JnQDAEBnQjcAAHQmdAMAQGdCNwAAdCZ0AwBAZ0I3AAB0JnQDAGNn9erVWbRoUVatWpVFixZl9erVc10SzIjQDQCMldWrV+fqq6/O2rVr8973vjdr167N1VdfLXgzry2e6wIAAKa65ppr8qpXvSoXXHBBJicnc8EFFyRJNm3aNMeVwfTp6QYAxkprLeeee+6D1p177rlprc1RRTBzQjcAMFaqKuecc86D1p1zzjmpqjmqCGbO8BIAYKw897nPzYUXXpgkOeWUU/LqV786F154YU4++eQ5rgymT+gGAMbKVVddldWrV2fTpk258MILU1U5+eSTc9VVV811aTBtQjcAMHZ2BezJyclMTEzMbTEwC4zpBgCAzoRuAADoTOgGAIDOhG4AAOhM6AYAgM6EbgAA6EzoBgCAzoRuAADoTOgGAIDOhG4AYOysXr06ixYtyqpVq7Jo0aKsXr16rkuCGRG6AYCxsnr16lx99dVZu3Zt3vve92bt2rW5+uqrBW/mtcVzXQAAwFTXXHNNXvWqV+WCCy7I5ORkLrjggiTJpk2b5rgymD493QDAWGmt5dxzz33QunPPPTettTmqCGZO6AYAxkpV5ZxzznnQunPOOSdVNUcVwcwZXgIAjJXnPve5ufDCC5Mkp5xySl796lfnwgsvzMknnzzHlcH0Cd0AwFi56qqrsnr16mzatCkXXnhhqionn3xyrrrqqrkuDaZN6AYAxs6ugD05OZmJiYm5LQZmgTHdAADQmdANAIyddevWZenSpVm1alWWLl2adevWzXVJMCOGlwAAY2XdunXZtGlTNm7cmOOOOy433XRTzjrrrCTJ+eefP8fVwfTo6QYAxsrFF1+cjRs35swzz8zSpUtz5plnZuPGjbn44ovnujSYNqEbABgrO3fuzNq1ax+0bu3atdm5c+ccVQQzJ3QDAGNlyZIl3/HI902bNmXJkiVzVBHMnDHdAMBYeeUrX/nAGO7jjjsub37zm3PWWWd9R+83zCdCNwAwVnbdLPm6170uO3fuzJIlS7J27Vo3UTKvGV4CAIyd888/Pzt27MjWrVuzY8cOgZt5T+gGAMaOebpZaAwvAQDGinm6WYj0dAMAY8U83SxEQjcAMFbM081CJHQDAGPFPN0sRMZ0AwBjxTzdLERCNwAwVszTzUJkeAkAMHbM081CI3QDAEBnQjcAMHYuu+yyHH/88TnppJNy/PHH57LLLpvrkmBGjOkGAMbKZZddlvXr12fz5s257777cthhh+X0009PkqxZs2aOq4Pp0dMNAIyVDRs2ZPPmzVm1alUWL16cVatWZfPmzdmwYcNclwbTJnQDAGNl27ZtOfHEEx+07sQTT8y2bdvmqCKYOaEbABgrK1asyLXXXvugdddee21WrFgxRxXBzAndAMBYWb9+fU4//fRs3bo19957b7Zu3ZrTTz8969evn+vSYNrcSAkAjJVdN0uuW7cu27Zty4oVK7JhwwY3UTKvCd0AwNhZs2ZN1qxZk8nJyUxMTMx1OTBjMxpeUlW/VVU3VNUnqurqqnrccH1V1e9V1c3D7T88O+UCAMD8M9Mx3W9qrZ3QWntakvcl+Y3h+ucnedLw54wkF86wHQAAmLdmFLpba/82ZfGhSdrw9alJ3t4GPprkyKp67EzaAgCA+WrGY7qrakOSlyX5WpJVw9VHJbltym63D9d9aQ/Hn5FBb3iWLVuWycnJmZYEB5XvLEA/27dvd51lQdhv6K6qDyZ5zB42rW+tXdFaW59kfVWdk+Q1SV4/SgGttYuSXJQkK1eubG6WYF75wJVu8AHoyI2ULBT7Dd2ttecc4Ln+OMn7MwjdX0hyzJRtRw/XAQDAIWems5c8acriqUn+Yfh6S5KXDWcxeUaSr7XWvmNoCQAAHApmOqb7vKr6gST3J/l8krXD9e9PckqSm5N8M8krZtgOAADMWzMK3a21/7yX9S3JL87k3AAAsFDMdJ5uAABgP4RuAADoTOgGAIDOhG4AAOhM6AYAgM6EbgAA6EzoBgCAzoRuAADoTOgGAIDOhG4AAOhM6AYAgM4Wz3UBMC6qanrHbRz9mNbatNoCAOYnPd0w1Fob+Wfr1q3TOg4AOLQI3QAA0JnQDQAAnQndAADQmdANAACdCd0AANCZ0A0AAJ0J3QAA0JnQDQAAnQndAADQmdANAACdCd0AANCZ0A0AAJ0J3QAA0JnQDQAAnQndAADQmdANAACdCd0AANCZ0A0AAJ0J3QAA0JnQDQAAnQndAADQmdANAACdCd0AANCZ0A0AAJ0J3QAA0JnQDQAAnQndAADQmdANAACdVWttrmt4QFV9Jcnn57oOGMGjknx1rosAWMBcZ5lPHt9ae/SeNoxV6Ib5pqo+3lpbOdd1ACxUrrMsFIaXAABAZ0I3AAB0JnTDzFw01wUALHCusywIxnQDAEBneroBAKAzoRsOQFU9r6o+U1U3V9XZe9i+pKreNdz+t1W1/OBXCTA/VdXbqurLVfXpvWyvqvq94TX2hqr64YNdI8yU0A37UVWHJfn9JM9PclySNVV13G67nZ7kX1trT0zyliQbD26VAPPaJUmet4/tz0/ypOHPGUkuPAg1wawSumH/np7k5tbaZ1tr30ryziSn7rbPqUkuHb6+PMlJVVUHsUaAeau19uEkd+1jl1OTvL0NfDTJkVX12INTHcwOoRv276gkt01Zvn24bo/7tNbuTfK1JN97UKoDWPgO5DoMY03oBgCAzoRu2L8vJDlmyvLRw3V73KeqFid5RJJ/OSjVASx8B3IdhrEmdMP+/V2SJ1XVE6rqIUlekmTLbvtsSXLa8PWLk/xlMwk+wGzZkuRlw1lMnpHka621L811UTCKxXNdAIy71tq9VfWaJFclOSzJ21prN1bVbyb5eGttS5LNSf6oqm7O4Gagl8xdxQDzS1VdlmQiyaOq6vYkr09yeJK01jYleX+SU5LcnOSbSV4xN5XC9HkiJQAAdGZ4CQAAdCZ0AwBAZ0I3AAB0JnQDAEBnQjcAAHQmdAOMkaq6r6o+UVU3VtUnq+q/VdW0r9VVdUtVfWr4c1NV/feqWnoAx22f8voDVXV3Vb1vt30uqarPDev9RFU9bbp1Aix05ukGGC/3tNaeliRV9X1J/iTJd2cwb/F0rWqtfbWqHpbkoiT/K//+MKcD8aYk35XkF/aw7Vdaa5fPoDaAQ4KeboAx1Vr7cpIzkrxm+CS+w6rqTVX1d1V1Q1X9QpJU1URVfbiqrqyqz1TVpj31jrfWtidZm+RFVfXI4bG/MuV8b9xLHR9K/v/27h/E5ziO4/jzpS7JWYiSxfkXJqW7STLdZJDFIoPLYLBdySzZWC5RyiAZWc9yAyXUMZgMLEoxKCzK9TZ8P3RdR3dX3/zOPR/L7/f7fj6fd5/v9vq9+3z78rW3G5WkNcDQLUkDrKre0r0JdRswQff661FgFDiXZKRNHQMuAAeB3cDJP9T7ArwD9iYZB/a2tYeAw0mOLnOLV1pgv55k/TLXStKaYeiWpNVjHDiT5BXwDNhCF5oBnlfV26qaA+4DR/5SJ/PqjQMvgVlg/7x6S3GprRkFNgMXl7FWktYUz3RL0gBLsguYAz7SheULVTW9YM4xoBYsXfj719xNwE7gTat3tapurWRvVfWhff2e5A4wuZI6krQW2OmWpAGVZCtwE5iqqgKmgfNJhtr4viQb2/SxJCPtLPcp4Mki9YaBG8DDqvrc6p1t10myoz28udT9bW+fAU4Ar1d4q5L037PTLUmDZUM7PjIE/ADuAtfa2G26LvVsC7qf6MIuwAtgCtgDzAAP5tWcafPXteuXAarqUZIDwNNumG/Aabqu+m9JHtMdIxlO8h6YaN32e+2PQYBXdA9pSpIWka55IklardrxksmqOv6v9yJJWpzHSyRJkqSe2emWJEmSemanW5IkSeqZoVuSJEnqmaFbkiRJ6pmhW5IkSeqZoVuSJEnqmaFbkiRJ6tlPkrCDQ9LnKrEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 864x864 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_flights.boxplot(column='ArrDelay', by='DepDel15', figsize=(12,12))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Which route (from origin airport to destination airport) has the most **late** arrivals?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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>ArrDel15</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Route</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>San Francisco International &gt; Los Angeles International</th>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Los Angeles International &gt; San Francisco International</th>\n",
+       "      <td>69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LaGuardia &gt; Hartsfield-Jackson Atlanta International</th>\n",
+       "      <td>68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Los Angeles International &gt; John F. Kennedy International</th>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LaGuardia &gt; Charlotte Douglas International</th>\n",
+       "      <td>51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Logan International &gt; Austin - Bergstrom International</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Logan International &gt; Memphis International</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Logan International &gt; Port Columbus International</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>San Diego International &gt; Cincinnati/Northern Kentucky International</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Louis Armstrong New Orleans International &gt; San Francisco International</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2479 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    ArrDel15\n",
+       "Route                                                       \n",
+       "San Francisco International > Los Angeles Inter...        90\n",
+       "Los Angeles International > San Francisco Inter...        69\n",
+       "LaGuardia > Hartsfield-Jackson Atlanta Internat...        68\n",
+       "Los Angeles International > John F. Kennedy Int...        52\n",
+       "LaGuardia > Charlotte Douglas International               51\n",
+       "...                                                      ...\n",
+       "Logan International > Austin - Bergstrom Intern...         0\n",
+       "Logan International > Memphis International                0\n",
+       "Logan International > Port Columbus International          0\n",
+       "San Diego International > Cincinnati/Northern K...         0\n",
+       "Louis Armstrong New Orleans International > San...         0\n",
+       "\n",
+       "[2479 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Add a routes column\n",
+    "routes  = pd.Series(df_flights['OriginAirportName'] + ' > ' + df_flights['DestAirportName'])\n",
+    "df_flights = pd.concat([df_flights, routes.rename(\"Route\")], axis=1)\n",
+    "\n",
+    "# Group by routes\n",
+    "route_group = df_flights.groupby(df_flights.Route)\n",
+    "pd.DataFrame(route_group['ArrDel15'].sum()).sort_values('ArrDel15', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Which route has the highest average arrival delay?"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "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>ArrDelay</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Route</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Louis Armstrong New Orleans International &gt; Ronald Reagan Washington National</th>\n",
+       "      <td>24.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cleveland-Hopkins International &gt; Palm Beach International</th>\n",
+       "      <td>18.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>John F. Kennedy International &gt; Louisville International-Standiford Field</th>\n",
+       "      <td>18.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cleveland-Hopkins International &gt; Philadelphia International</th>\n",
+       "      <td>12.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Memphis International &gt; Denver International</th>\n",
+       "      <td>9.758621</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lambert-St. Louis International &gt; Cleveland-Hopkins International</th>\n",
+       "      <td>-20.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Eppley Airfield &gt; LaGuardia</th>\n",
+       "      <td>-20.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Denver International &gt; Kahului Airport</th>\n",
+       "      <td>-22.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Jacksonville International &gt; Chicago Midway International</th>\n",
+       "      <td>-24.125000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Indianapolis International &gt; Logan International</th>\n",
+       "      <td>-26.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2479 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                     ArrDelay\n",
+       "Route                                                        \n",
+       "Louis Armstrong New Orleans International > Ron...  24.500000\n",
+       "Cleveland-Hopkins International > Palm Beach In...  18.000000\n",
+       "John F. Kennedy International > Louisville Inte...  18.000000\n",
+       "Cleveland-Hopkins International > Philadelphia ...  12.800000\n",
+       "Memphis International > Denver International         9.758621\n",
+       "...                                                       ...\n",
+       "Lambert-St. Louis International > Cleveland-Hop... -20.000000\n",
+       "Eppley Airfield > LaGuardia                        -20.750000\n",
+       "Denver International > Kahului Airport             -22.666667\n",
+       "Jacksonville International > Chicago Midway Int... -24.125000\n",
+       "Indianapolis International > Logan International   -26.000000\n",
+       "\n",
+       "[2479 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code to explore the data"
+    "pd.DataFrame(route_group['ArrDelay'].mean()).sort_values('ArrDelay', ascending=False)"
    ]
   }
  ],