diff --git a/notebooks/5_validate_outputs.ipynb b/notebooks/5_validate_outputs.ipynb
new file mode 100644
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--- /dev/null
+++ b/notebooks/5_validate_outputs.ipynb
@@ -0,0 +1,2007 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import acbm\n",
+    "from acbm.validating.plots import plot_comparison, plot_activity_sequence_comparison, plot_intrazonal_trips\n",
+    "from acbm.validating.utils import calculate_od_distances, process_sequences\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create folder to save plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Define the path to the folder you want to create\n",
+    "validation_plots_path = acbm.root_path / \"data/processed/plots/validation\"\n",
+    "# Create the folder if it does not exist\n",
+    "os.makedirs(validation_plots_path, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Read in the data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# NTS data \n",
+    "legs_nts = pd.read_parquet(\n",
+    "    acbm.root_path / \"data/external/nts/filtered/nts_trips.parquet\"\n",
+    ")\n",
+    "\n",
+    "legs_nts = legs_nts[legs_nts[\"TravDay\"] == 3]\n",
+    "\n",
+    "# Model outputs \n",
+    "legs_acbm = pd.read_csv(\n",
+    "    acbm.root_path / \"data/processed/activities_pam/legs.csv\"\n",
+    ")\n",
+    "legs_acbm_geo = pd.read_parquet(\n",
+    "    acbm.root_path / \"data/processed/activities_pam/legs_with_locations.parquet\"\n",
+    ")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "\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>Unnamed: 0</th>\n",
+       "      <th>pid</th>\n",
+       "      <th>hid</th>\n",
+       "      <th>freq</th>\n",
+       "      <th>ozone</th>\n",
+       "      <th>dzone</th>\n",
+       "      <th>purp</th>\n",
+       "      <th>origin activity</th>\n",
+       "      <th>destination activity</th>\n",
+       "      <th>mode</th>\n",
+       "      <th>seq</th>\n",
+       "      <th>tst</th>\n",
+       "      <th>tet</th>\n",
+       "      <th>duration</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
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+       "      <td>1900-01-01 13:00:00</td>\n",
+       "      <td>0:30:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
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+       "      <td>89</td>\n",
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+       "      <td>home</td>\n",
+       "      <td>car</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1900-01-01 16:30:00</td>\n",
+       "      <td>1900-01-01 17:00:00</td>\n",
+       "      <td>0:30:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>312</td>\n",
+       "      <td>139</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00059045</td>\n",
+       "      <td>E00058294</td>\n",
+       "      <td>escort</td>\n",
+       "      <td>home</td>\n",
+       "      <td>escort</td>\n",
+       "      <td>car</td>\n",
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+       "      <td>1900-01-01 06:35:00</td>\n",
+       "      <td>1900-01-01 06:54:00</td>\n",
+       "      <td>0:19:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
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+       "      <td>312</td>\n",
+       "      <td>139</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00058294</td>\n",
+       "      <td>E00169797</td>\n",
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+       "      <td>escort</td>\n",
+       "      <td>work</td>\n",
+       "      <td>car</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1900-01-01 06:55:00</td>\n",
+       "      <td>1900-01-01 07:22:00</td>\n",
+       "      <td>0:27:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>312</td>\n",
+       "      <td>139</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00059045</td>\n",
+       "      <td>E00059045</td>\n",
+       "      <td>home</td>\n",
+       "      <td>work</td>\n",
+       "      <td>home</td>\n",
+       "      <td>car</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1900-01-01 18:00:00</td>\n",
+       "      <td>1900-01-01 18:35:00</td>\n",
+       "      <td>0:35:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15098</th>\n",
+       "      <td>15098</td>\n",
+       "      <td>794027</td>\n",
+       "      <td>334502</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00187122</td>\n",
+       "      <td>E00057826</td>\n",
+       "      <td>home</td>\n",
+       "      <td>visit</td>\n",
+       "      <td>home</td>\n",
+       "      <td>car</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1900-01-01 22:30:00</td>\n",
+       "      <td>1900-01-01 22:50:00</td>\n",
+       "      <td>0:20:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15099</th>\n",
+       "      <td>15099</td>\n",
+       "      <td>794272</td>\n",
+       "      <td>334647</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00170040</td>\n",
+       "      <td>E00169786</td>\n",
+       "      <td>shop</td>\n",
+       "      <td>home</td>\n",
+       "      <td>shop</td>\n",
+       "      <td>car</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1900-01-01 08:50:00</td>\n",
+       "      <td>1900-01-01 09:00:00</td>\n",
+       "      <td>0:10:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15100</th>\n",
+       "      <td>15100</td>\n",
+       "      <td>794272</td>\n",
+       "      <td>334647</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00169786</td>\n",
+       "      <td>E00170040</td>\n",
+       "      <td>home</td>\n",
+       "      <td>shop</td>\n",
+       "      <td>home</td>\n",
+       "      <td>car</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1900-01-01 10:20:00</td>\n",
+       "      <td>1900-01-01 10:30:00</td>\n",
+       "      <td>0:10:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15101</th>\n",
+       "      <td>15101</td>\n",
+       "      <td>794273</td>\n",
+       "      <td>334647</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00170040</td>\n",
+       "      <td>E00058885</td>\n",
+       "      <td>shop</td>\n",
+       "      <td>home</td>\n",
+       "      <td>shop</td>\n",
+       "      <td>car</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1900-01-01 08:50:00</td>\n",
+       "      <td>1900-01-01 09:00:00</td>\n",
+       "      <td>0:10:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15102</th>\n",
+       "      <td>15102</td>\n",
+       "      <td>794273</td>\n",
+       "      <td>334647</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00058885</td>\n",
+       "      <td>E00170040</td>\n",
+       "      <td>home</td>\n",
+       "      <td>shop</td>\n",
+       "      <td>home</td>\n",
+       "      <td>car</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1900-01-01 10:20:00</td>\n",
+       "      <td>1900-01-01 10:30:00</td>\n",
+       "      <td>0:10:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>15103 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Unnamed: 0     pid     hid  freq      ozone      dzone    purp  \\\n",
+       "0               0     200      89   NaN  E00059031  E00058877    work   \n",
+       "1               1     200      89   NaN  E00059031  E00059031    home   \n",
+       "2               2     312     139   NaN  E00059045  E00058294  escort   \n",
+       "3               3     312     139   NaN  E00058294  E00169797    work   \n",
+       "4               4     312     139   NaN  E00059045  E00059045    home   \n",
+       "...           ...     ...     ...   ...        ...        ...     ...   \n",
+       "15098       15098  794027  334502   NaN  E00187122  E00057826    home   \n",
+       "15099       15099  794272  334647   NaN  E00170040  E00169786    shop   \n",
+       "15100       15100  794272  334647   NaN  E00169786  E00170040    home   \n",
+       "15101       15101  794273  334647   NaN  E00170040  E00058885    shop   \n",
+       "15102       15102  794273  334647   NaN  E00058885  E00170040    home   \n",
+       "\n",
+       "      origin activity destination activity mode  seq                  tst  \\\n",
+       "0                home                 work  car  1.0  1900-01-01 12:30:00   \n",
+       "1                work                 home  car  2.0  1900-01-01 16:30:00   \n",
+       "2                home               escort  car  1.0  1900-01-01 06:35:00   \n",
+       "3              escort                 work  car  2.0  1900-01-01 06:55:00   \n",
+       "4                work                 home  car  3.0  1900-01-01 18:00:00   \n",
+       "...               ...                  ...  ...  ...                  ...   \n",
+       "15098           visit                 home  car  4.0  1900-01-01 22:30:00   \n",
+       "15099            home                 shop  car  1.0  1900-01-01 08:50:00   \n",
+       "15100            shop                 home  car  2.0  1900-01-01 10:20:00   \n",
+       "15101            home                 shop  car  1.0  1900-01-01 08:50:00   \n",
+       "15102            shop                 home  car  2.0  1900-01-01 10:20:00   \n",
+       "\n",
+       "                       tet duration  \n",
+       "0      1900-01-01 13:00:00  0:30:00  \n",
+       "1      1900-01-01 17:00:00  0:30:00  \n",
+       "2      1900-01-01 06:54:00  0:19:00  \n",
+       "3      1900-01-01 07:22:00  0:27:00  \n",
+       "4      1900-01-01 18:35:00  0:35:00  \n",
+       "...                    ...      ...  \n",
+       "15098  1900-01-01 22:50:00  0:20:00  \n",
+       "15099  1900-01-01 09:00:00  0:10:00  \n",
+       "15100  1900-01-01 10:30:00  0:10:00  \n",
+       "15101  1900-01-01 09:00:00  0:10:00  \n",
+       "15102  1900-01-01 10:30:00  0:10:00  \n",
+       "\n",
+       "[15103 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "legs_acbm\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Preprocess: Rename columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# rename origin activity and destination activity columns\n",
+    "\n",
+    "legs_acbm = legs_acbm.rename(columns={\"origin activity\": \"oact\", \"destination activity\": \"dact\"})\n",
+    "legs_acbm_geo = legs_acbm_geo.rename(columns={\"origin activity\": \"oact\", \"destination activity\": \"dact\"})\n",
+    "\n",
+    "\n",
+    "# rename distance column in NTS\n",
+    "legs_nts = legs_nts.rename(columns={\"TripDisIncSW\": \"distance\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Preprocess: Edit distance column\n",
+    "\n",
+    "The NTS distance is in miles, but the distance we are using for acbm is kms. Convert the NTS distance to kms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert legs_nts[\"distance\"] from miles to km\n",
+    "\n",
+    "legs_nts[\"distance\"] = legs_nts[\"distance\"] * 1.60934"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Preprocess: Add columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create hour column \n",
+    "\n",
+    "# acbm - tst is in datetime format\n",
+    "# Convert tst to datetime format and extract the hour component in one step\n",
+    "legs_acbm['tst_hour'] = legs_acbm['tst'].apply(lambda x: pd.to_datetime(x).hour)\n",
+    "legs_acbm['tet_hour'] = legs_acbm['tet'].apply(lambda x: pd.to_datetime(x).hour)\n",
+    "\n",
+    "# nts - tst is in minutes\n",
+    "# Convert legs_nts[\"tst\"] from minutes to hours\n",
+    "legs_nts['tst_hour'] = legs_nts['tst'] // 60\n",
+    "legs_nts['tet_hour'] = legs_nts['tet'] // 60"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  <thead>\n",
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+       "      <th></th>\n",
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+       "      <th>ozone</th>\n",
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+       "      <th>dact</th>\n",
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+       "      <th>tet_hour</th>\n",
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+       "    <tr>\n",
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+       "      <td>3</td>\n",
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+       "      <td>car</td>\n",
+       "      <td>795.0</td>\n",
+       "      <td>810.0</td>\n",
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+       "      <th>4659597</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>home</td>\n",
+       "      <td>medical</td>\n",
+       "      <td>car</td>\n",
+       "      <td>505.0</td>\n",
+       "      <td>525.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4659609</th>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>home</td>\n",
+       "      <td>work</td>\n",
+       "      <td>pt</td>\n",
+       "      <td>445.0</td>\n",
+       "      <td>556.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4659616</th>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>home</td>\n",
+       "      <td>other</td>\n",
+       "      <td>car</td>\n",
+       "      <td>720.0</td>\n",
+       "      <td>750.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4659617</th>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>visit</td>\n",
+       "      <td>home</td>\n",
+       "      <td>car</td>\n",
+       "      <td>985.0</td>\n",
+       "      <td>1041.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>17.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5150053</th>\n",
+       "      <td>6</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>escort</td>\n",
+       "      <td>home</td>\n",
+       "      <td>car</td>\n",
+       "      <td>1020.0</td>\n",
+       "      <td>1035.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5150079</th>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>home</td>\n",
+       "      <td>education</td>\n",
+       "      <td>car</td>\n",
+       "      <td>480.0</td>\n",
+       "      <td>525.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5150080</th>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>education</td>\n",
+       "      <td>home</td>\n",
+       "      <td>car</td>\n",
+       "      <td>965.0</td>\n",
+       "      <td>1035.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>17.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5150098</th>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>home</td>\n",
+       "      <td>shop</td>\n",
+       "      <td>car</td>\n",
+       "      <td>645.0</td>\n",
+       "      <td>660.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5150099</th>\n",
+       "      <td>2</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>shop</td>\n",
+       "      <td>home</td>\n",
+       "      <td>car</td>\n",
+       "      <td>900.0</td>\n",
+       "      <td>915.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>59359 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         seq  ozone  dzone       oact       dact mode     tst     tet  \\\n",
+       "4659590    3      8    8.0       shop       home  car   795.0   810.0   \n",
+       "4659597    1      2    2.0       home    medical  car   505.0   525.0   \n",
+       "4659609    1      8    7.0       home       work   pt   445.0   556.0   \n",
+       "4659616    1      8    8.0       home      other  car   720.0   750.0   \n",
+       "4659617    3      8    8.0      visit       home  car   985.0  1041.0   \n",
+       "...      ...    ...    ...        ...        ...  ...     ...     ...   \n",
+       "5150053    6      9    9.0     escort       home  car  1020.0  1035.0   \n",
+       "5150079    1      9    8.0       home  education  car   480.0   525.0   \n",
+       "5150080    2      8    9.0  education       home  car   965.0  1035.0   \n",
+       "5150098    1      9    9.0       home       shop  car   645.0   660.0   \n",
+       "5150099    2      9    9.0       shop       home  car   900.0   915.0   \n",
+       "\n",
+       "         tst_hour  tet_hour  \n",
+       "4659590      13.0      13.0  \n",
+       "4659597       8.0       8.0  \n",
+       "4659609       7.0       9.0  \n",
+       "4659616      12.0      12.0  \n",
+       "4659617      16.0      17.0  \n",
+       "...           ...       ...  \n",
+       "5150053      17.0      17.0  \n",
+       "5150079       8.0       8.0  \n",
+       "5150080      16.0      17.0  \n",
+       "5150098      10.0      11.0  \n",
+       "5150099      15.0      15.0  \n",
+       "\n",
+       "[59359 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "legs_nts[\n",
+    "    [\n",
+    "        \"seq\",\n",
+    "        \"ozone\",\n",
+    "        \"dzone\",\n",
+    "        \"oact\",\n",
+    "        \"dact\",\n",
+    "        \"mode\",\n",
+    "        \"tst\",\n",
+    "        \"tet\",\n",
+    "        \"tst_hour\",\n",
+    "        \"tet_hour\",\n",
+    "    ]\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     oact    dact  trip_type\n",
+      "0    home    work    primary\n",
+      "1    work    home    primary\n",
+      "2    home  escort  secondary\n",
+      "3  escort    work  secondary\n",
+      "4    work    home    primary\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # add a column in legs_acbm to identify whether the trip is a primary or secondary trip\n",
+    "\n",
+    "# # Define the conditions for primary trips\n",
+    "# conditions_primary = (\n",
+    "#     ((legs_acbm['oact'] == 'home') & (legs_acbm['dact'].isin(['work', 'education']))) |\n",
+    "#     ((legs_acbm['oact'].isin(['work', 'education'])) & (legs_acbm['dact'] == 'home'))\n",
+    "# )\n",
+    "\n",
+    "# # Add the trip_type column\n",
+    "# legs_acbm['trip_type'] = np.where(conditions_primary, 'primary', 'secondary')\n",
+    "\n",
+    "# # Print the resulting DataFrame to verify\n",
+    "# print(legs_acbm[['oact', 'dact', 'trip_type']].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Preprocess: Abbreviate trip purpose columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Mapping dictionary\n",
+    "activity_mapping = {\n",
+    "    \"home\": \"h\",\n",
+    "    \"other\": \"o\",\n",
+    "    \"escort\": \"e\",\n",
+    "    \"work\": \"w\",\n",
+    "    \"shop\": \"sh\",\n",
+    "    \"visit\": \"v\",\n",
+    "    \"education\": \"edu\",\n",
+    "    \"medical\": \"m\",\n",
+    "}\n",
+    "\n",
+    "legs_acbm[\"oact_abr\"] = legs_acbm[\"oact\"].replace(activity_mapping)\n",
+    "legs_acbm[\"dact_abr\"] = legs_acbm[\"dact\"].replace(activity_mapping)\n",
+    "\n",
+    "legs_nts[\"oact_abr\"] = legs_nts[\"oact\"].replace(activity_mapping)\n",
+    "legs_nts[\"dact_abr\"] = legs_nts[\"dact\"].replace(activity_mapping)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Compare distributions between NTS and output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Matching "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matching: Trip Purpose "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get number of trips by mode for legs_nts, and legs_acbm, and plot a comparative bar plot\n",
+    "\n",
+    "# NTS\n",
+    "purpose_nts = legs_nts.groupby(\"dact\").size().reset_index(name=\"count\")\n",
+    "purpose_nts[\"source\"] = \"nts\"\n",
+    "\n",
+    "# ACBM\n",
+    "purpose_acbm = legs_acbm.groupby(\"dact\").size().reset_index(name=\"count\")\n",
+    "purpose_acbm[\"source\"] = \"acbm\"\n",
+    "\n",
+    "# Combine the data\n",
+    "purpose_compare = pd.concat([purpose_nts, purpose_acbm])\n",
+    "\n",
+    "# Calculate the percentage of trips for each mode within each source\n",
+    "purpose_compare[\"percentage\"] = purpose_compare.groupby(\"source\")[\"count\"].transform(lambda x: (x / x.sum()) * 100)\n",
+    "\n",
+    "\n",
+    "sns.barplot(data=purpose_compare, x=\"dact\", y=\"percentage\", hue=\"source\")\n",
+    "plt.xlabel('Trip purpose')\n",
+    "plt.ylabel('Percentage of total trips')\n",
+    "plt.title('Percentage of Trips by Purpose for NTS and ACBM')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matching: Trip Mode \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get number of trips by mode for legs_nts, and legs_acbm, and plot a comparative bar plot\n",
+    "\n",
+    "# NTS\n",
+    "modeshare_nts = legs_nts.groupby(\"mode\").size().reset_index(name=\"count\")\n",
+    "modeshare_nts[\"source\"] = \"nts\"\n",
+    "\n",
+    "# ACBM\n",
+    "modeshare_acbm = legs_acbm.groupby(\"mode\").size().reset_index(name=\"count\")\n",
+    "modeshare_acbm[\"source\"] = \"acbm\"\n",
+    "\n",
+    "# Combine the data\n",
+    "modeshare_compare = pd.concat([modeshare_nts, modeshare_acbm])\n",
+    "\n",
+    "# Calculate the percentage of trips for each mode within each source\n",
+    "modeshare_compare[\"percentage\"] = modeshare_compare.groupby(\"source\")[\"count\"].transform(lambda x: (x / x.sum()) * 100)\n",
+    "\n",
+    "\n",
+    "sns.barplot(data=modeshare_compare, x=\"mode\", y=\"percentage\", hue=\"source\")\n",
+    "plt.ylabel('Percentage of total trips')\n",
+    "plt.title('Percentage of Trips by Mode for NTS and ACBM')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matching: Time of Day "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot aggregate\n",
+    "plot_comparison(\n",
+    "    legs_acbm,\n",
+    "    legs_nts,\n",
+    "    value_column=\"tst_hour\",\n",
+    "    max_y_value=15,\n",
+    "    plot_type=\"time\",\n",
+    "    figsize=(10, 5),\n",
+    "    plot_mode=\"aggregate\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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69Yy3t7d59NFHzfLly83GjRvNp59+atauXWu1zyy07tWrl9XX/fffbxYtWmTWr19vNmzYYL755hvz7LPPmvr167sNrbds2WIFXt7e3mb06NFm1apV5tdffzXvvfeeqVy5stX3E0884XZbnLU1atTI+Pn5mcqVK5u3337brF+/3qxbt85MmjTJ+Pn5GUkmMDDQnDx50m0/Wbl69app2bKly5fan3/+udm4caP55ptvTPfu3a15VapUcQnhzpw5Y7Zv3+7yGhg2bJg1bfv27ebIkSM5qsvpWkLrevXqGUmmTZs2Zt68eWbjxo1mxYoV5oMPPrDaZze4rF+/vvH29jbly5c3//3vf80vv/xi1qxZY5588knj6+trPbZpvyzPrtQBw4gRI0xiYuI1LX/ixAmzfft2061bNyOlHBCSer87f1Lr37+/ufnmm83YsWPNZ599ZtatW2c2bNhgFi5caB555BHrOVu6dGlz4sSJTPdPrVq1jKenp4mIiLCelz/99JOZMmWKuXz5stm+fbuZNWuWy2s2bW1nzpzJ0b5zyq/QukGDBsbHx8fUqlXLzJo1y2zYsMH8+OOP5v7777eW79+/v/niiy+MJNOiRQvzySefmI0bN5qlS5eaO++802r35JNPul1XXFycqVixopFSDoAYOXKk+fzzz82GDRvMqlWrzPjx460DVW6++WZz9uzZa9o3+/fvN9u3bzcvvPCCVcuyZctc9n/qwDEvxxzn67Br165m0aJFZtOmTWbJkiXXfHBNTkJrY4zLwUbOgwucMgqtjxw5kuVz9syZM2b37t1m+/btZtiwYVa7tM/ry5cvW/3+5z//cdkv06dPNytWrDAbN240n3zyict775tvvpnr/Tp8+HCr/a233mpmzZplVq9ebX799Vfz/vvvm9q1a1vzv/7663TrS71/brnlFuPh4WEGDhxofXb48ssvXWpOexBZTt6XsqNVq1ambt265plnnjFffvml+eWXX8z69evNZ599Zvr162cdqFCjRg1z8eJFt338+OOP1kE2np6eZtCgQebLL780mzZtMj///LN5//33Tc+ePY23t3eutiej0DopKcn6PHXbbbdlur2jR4+26jx+/LjLvIxCa+dr3rmObt26patz//79Vnvn41ijRo1Maxk1apQVzqatJScyG9PPnj1rihUrZiSZhx56KNN+GjRoYL0u0nL236RJE2sbZ86caTZs2GBWrFhhHn74Yes5ExgYaGJiYtyuI7evp8aNG1ufFYYPH24WL15sNmzYYH755RfzxRdfmHHjxpmqVasSWgMAAOQjQmsAAFDkfPzxx9aXVs8880yu+/u///s/68u0OnXquA12vv/+e6tNs2bN0s1Pe9Zy2jN1jDEmNjbWOtuzdOnSxuFwmC+//DJdu23btlnrcp6VnVrasyrvvPNOt2eOPvfcc1abcePGpZv/559/ZhpiJSYmmttuu836svbq1avp2qQOpTw8PMyyZcsy7M+YjEPrixcvWiFNRmdSO506dSrdNGd47+np6baG06dPW8GKh4eH24MBUof0jRs3NvHx8enapH7uvf7665nWmZG3337b6uOBBx4wycnJ6do8/fTTWYbszvkTJ07MUR0ZuZbQOrNtcMpucOl8nqUNvYxxDT+aNm2ao+1auHChS91hYWHmoYceMnPnzjW///57ptuQWkYBhjt//PFHpv3+3//9nylRooSRZCZMmOC2Ter9U7du3Uxfs6nfh1Kf1ZtX8iu0dgZ258+fT9emd+/e1ms7JCTE9OrVK9170dWrV02LFi2MlHLmurv3w/vuu8963FIHSalt3rzZ+Pv7G0nm6aefznL73MkopE0tP8acjJ4/1yKnoXXqgwvSnkWZ1f7I7nM2q+eSMSlnszrHkYkTJ7p97SUlJVn1lihRwpw+fTrDerLarz/88IPVLvWBOqldvHjRtG/f3nrupX1upr16xkcffZSuj0uXLpk6depk+vy+lvel7NizZ0+m85cvX249P91t+8WLF60wt3jx4pk+tu4CzGvZnoxCa2OMGTNmTIZhtFPqcLtTp07XXEtmB+OlNnPmTOtx/vnnn922uXz5snVmerdu3TLtL7uyGtOdV0sJCgoyFy5ccNvH5s2brT6mTp2abn7q53BGV7z48MMPrTZ9+vRJNz+3r6d9+/ZZy2cWSicnJ6d73QMAACDvcE9rAABQ5Jw6dcr6f5kyZXLd3/Tp05WcnCxJ+uCDDxQcHJyuze23364hQ4ZIkn799Vdt2LAhw/6aN2+uxx9/PN30smXLqkePHpJS7r3ct29fde/ePV27evXqqXXr1pKk6OjoTGv39fXV+++/Ly8vr3TznnnmGdWpU0eSNHPmzHT3SC5VqpTbbXXy8fHRK6+8IinlvpJbt27NtJZBgwapU6dOmbbJyOnTp3XlyhVJ0q233ppp25CQEJffUz8eDz74oNsaSpYsqRkzZkhKuZf0O++8k+k6Zs2apcDAwHTT77vvPpUvX15S1o9NRqZNmyZJKl26tN5++2239xudPHmydZ/2999/X4mJiTlaV34LDg7OcBty4rXXXlPZsmXTTW/Xrp0efPBBSSn3vM7snrQZ6dWrlyZOnGjVeuLECc2YMUMDBw5UZGSkQkJC1K1bN82bN896LuZWlSpVMt03devW1T/+8Q9J0ldffZVlf9OmTcv0NVtYORwOffDBBypevHi6ec57pyYlJenSpUuaMWOGPD09Xdp4enrqoYcekpQyPuzcudNl/sGDB/XZZ59Jkt5++21VrlzZbR0NGzbUiBEjJClf7/+d12NO9erVrXvJ2iE0NNT6/5kzZ2yr47XXXtOVK1fUpEkTl9d6ah4eHvrvf/8rX19f/fXXX1q4cGGG/WW1X1966SVJKe8tQ4cOddvGz89Pb7/9tqSUcXTVqlUZ9tezZ0/df//96ab7+vpq5MiRktw/v/NDtWrVMp3fsWNHde3aVZL7964PP/xQx44dkyT9+9//Tne/6dQqVqyY4zqz0r9/f0kp7x/z589322bVqlVWrc72+eGee+5RQECAJGn27Nlu2yxevFhxcXGSZL3+85tzDIqPj9eiRYvctnHW6+Pj4/Y5mtqMGTNUokSJdNMHDBigO+64Q5L05Zdf6vjx4y7zc/t6St1fZp8jHQ6HSpYsmek2AAAAIOcIrQEAQJFz7tw56//+/v657m/FihWSpNq1a6t58+YZtnOGZqmXcadfv34Zzqtfv/41tdu/f3+GbSSpU6dOVoialoeHhwYOHCgpJRTevHlzpn0lJiYqJiZGO3fu1I4dO7Rjxw4ZY6z527Zty3T53HzZGxoaKh8fH0nSRx99pKtXr2Z72dSPRUZfdEpSq1atFBkZmW6ZtOrWrat69eq5nedwONSwYUNJWT827hw7dky///67JKlv377WF9hpeXl5afDgwZJSQqCsHju7dOnSJcNtuFYlS5ZUt27dMpyf+gv8zB6/zEyaNEm//PKLevfuLT8/P5d5Z8+e1TfffKP+/furdu3aOQrGs3LmzBnt27dPv/32m/UacwaWO3fuzDQsr1ixotq0aZPnNV0P6tWrZ70200r9nnnbbbelO2jFXbu0r83vvvtOSUlJKl68uBWaZMQZdhw7dkwxMTHZqv9a5fWYc88996QL8gtS6oAq9fhc0BYvXiwpJfTK7GCR4OBg1a1bV5K0bt26DNtltl8TEhK0evVqSVLv3r0zrSsyMlKlSpXKcn2ZjaGNGze2/p+TsSe3/vzzT+3du9d639qxY4dKly4tyf1ng2+//VZSyme01M/jgtaoUSPrALB58+a5beOcXqxYMevAwvzg7+9vfe5bsGCBLly4kK6NMxwuW7as7rzzznyrJbVbb71V1atXd1l/apcvX7b2UZcuXaznsjt169Z1ea6m5RzHr169ar1+pLx5PZUrV876f34edAQAAIDMEVoDAIAiJ3VIdv78+Vz1lZiYqL1790pSpuGBlHIWnre3tyRpx44dGbZzfvnnTuoz6rLTLqsAoGnTppnOb9asmfX/7du3p5t//vx5TZkyRfXr15e/v7/Cw8NVu3Zt1a1bV3Xr1rUCWknW2T8ZySjozQ5fX1/dc889kqSFCxeqatWqeuKJJ7RkyRKdPXs202Wdj4WPj48aNGiQaVvnY7x37950Z547Ob/gzogzNMtJOJP6eZPV8y31/Myeb3bKzWOeVsOGDd1eMcCpQYMG1oEN7p7L2dW0aVN9/vnnOn36tH788UdNmTJFvXv3tgIYKeX5ERUVlSf7ffv27RoyZIjKlSunkJAQVa1aVXXq1LFeY84zOZOTkzM9SzUv9/X1Ji/fM6X0r03nAQgXLlyQl5eXHA5Hhj933323tVzaMwHzQn6MOXY/N1Lvb3dXqCgIhw4d0p9//ilJGj9+fKaPscPhsJ4TmT3Gme3XLVu2WGfL33vvvVmuzzl+Zra+zMae1AdrFNSBAT///LPuuecehYaGqkyZMqpevbr1vlW3bl29//77ktx/NtiyZYuklLDd3RUUCpLzYIBff/1Vf/zxh8u8xMRE6+zirl275tlBWBlxntWckJCgL774wmXe8ePHtXTpUkkpZyVnNh7mNecBfz/++KMOHTrkMu+bb76xrnCU1dnfOf1Mmhevp8qVK1sHdk2dOlW1a9fWs88+qx9//NHtAQIAAADIH4TWAACgyEl9KdITJ07kqq/UIVFWlxr39va21n369OkM22X2Ba2Hh8c1tXN+iZeRrGoOCwuz/p+25oMHD6pu3bp6+umn9X//939KSkrKtK+LFy9mOj+3l1t8++231aVLF0kpAcQrr7yiu+66S6GhoWratKleeeUVxcfHp1vOuV0hISFZfsnrvPS0MSbDgDCrL9idj01W+8ud1I9BVo9d6stkZ/Z8s1NeXmIzq/3h5eVlBTd5sT+KFSumdu3a6amnntLnn3+u2NhYffnll9alas+fP69Ro0blah0zZ85Uo0aNNHv27GwFoJm9xm7ky5nm5XumlP61efLkyRzVlR9BR36MOXY/N1KHlhmdCZ/f8uMxzmy/5sf6cvr8zg+TJk1S69attWDBgizfb929bzmfE6nPfrXLfffdZ/3/k08+cZn33XffWQfG5eelwZ2aNWtmneWf9qzmDz/80LrKTEFdGtxp4MCB8vb2ljFGc+fOdZk3a9YsSdJNN92kzp07Z9pPTj+T5tXr6dNPP1XLli0lpVy95Pnnn1eHDh0UHBysW2+9Ve+++64uXbqUo3UBAAAgewru0EsAAIDrROrLwOblZZPz6r68BSk3NQ8YMEAHDhyQw+HQ4MGD1a9fP0VGRqp06dLy8fGRw+FQcnKydXnU1JcKdye3l6cNDAzUN998o19//VULFizQ6tWrtXXrViUlJWnjxo3auHGjXn31VX311VfWl5KpFbbHr7DV605eXpLY7v3h6emp7t27q0qVKmrSpIkuX76sH3/8UadPn85RELdr1y498sgjunr1qsqUKaNx48apffv2ioiIUEBAgHUG7axZs6yz3DJ7jdl5+efCzhnylSpVKtN7CqeV0b2v80pePeftfm44z6qVMj8bPj+lDnKfffZZ9enTJ1vLZXaLkcz2a+r1vffee7rllluytT67DzDIjpUrV2ry5MmSpJtvvln//Oc/1bp1a1WqVEn+/v7WwWHPPvusnn/+eTtLzZabb75ZLVu21Lp16zRv3jxNnDjRmue87HVoaKhuv/32AqnnH//4hx5//HGtXr1aBw8eVEREhKS/Q+yWLVtmecWXvBYWFqa7775bX375pebMmaN//etfcjgcOnbsmH744QdJ0gMPPJDle01O39Py6vV00003ae3atVq5cqUWLVqkNWvWWLfeiI6OVnR0tF599VUtWbLEtvcqAACAGx2hNQAAKHJq166tUqVKKS4uTtHR0UpISMjxJUlTf+GV1VnbV69etS6RaNfZZGllVXPq+alr3rVrl3766SdJ0tNPP60XXnjB7fJ2nOHbrFkz6xKS586d0+rVqzVnzhwtWrRIJ0+eVK9evbRv3z4VK1ZM0t/bderUKV29ejXTs62dZ7s6HA5bwoPUj0FWj13qM3Ovl+dbfsrO6y/1WfX5pW7dumrevLmio6NljNG+fftytL45c+bo6tWr8vT01Jo1azIMIa7Xs+hvJM6zlc+dO6fIyEhbQ97CPuakdfjwYe3evVuSVKNGDZfL7Bek1Fdg8fb2Vp06dQpsfcWLF8/39RUk52W/S5YsqfXr12f4mGb23lWqVCkdOXJEsbGx+VLjterfv7/WrVunPXv2aOPGjWrSpIkSEhL03XffSZL69OljHUiU3+6//3498cQTSkxM1Jw5czRp0iStX79eu3btklTwZ1k7/eMf/9CXX36pAwcOaM2aNYqKitKHH35oBcqDBw/Oso+cfibN69dThw4d1KFDB0kpnw1XrFihGTNm6Mcff9S+fft0zz33uBxsAwAAgLzD5cEBAECR43A4NHDgQEkpl/D94IMPctyXr6+vqlWrJkn65ZdfMm27ZcsWXblyRZKumy+oN2zYkO35qWv+7bffrP877yXtjvO+n3YJCAhQly5d9MUXX+ixxx6TJMXGxlqBu/T3dl2+fFlbt27NtL9ff/1VklStWjXr/sgFKfVjkNXzzVlr2uVuVFu3brUujerOtm3brPuQ5/f+KF++vPX/tGeOZfdMMudrrH79+pmeNZeXrzG7z1a/XjVs2FBSyv1r7X5PK+xjTlpvvfWWdRuLHj162FbHzTffrKCgIEkp92LObw0aNLBebwWxvqzk5Wvf+d7Vrl27TA9CyOy11KhRI6tNTi6zn9fvZX379rUOaHOeXf3FF19Yl4rOzaXBr7XWkJAQ9ezZU5I0d+5cGWOsS3D7+/tn+pksP91+++2qUKGCpL/P+nb+26ZNG+t9KzM5/Uyan6+n0NBQ3XPPPVq5cqW6du0qKeXzxt69e/N0PQAAAEhBaA0AAIqk0aNHW/d/fPbZZ60zVLKSnJyc7p6GHTt2lJTyRW3qoDCt1OG4cxm7/fDDDxmeyZScnGzdm7BkyZLWl8iSXMLB8+fPZ9j/u+++m0eV5p7zrBnJ9R6qqR8L5xe/7qxbt047d+5Mt0xBKl++vCIjIyVJCxYs0F9//eW2XVJSkubMmSMp/WN3ozp9+rQWL16c4fzUj21OHr+sLm+fup3ztgMOh8O6dKuTn5+fpJQANDPO11hmr6/Y2Fh988032aorO5y1SVnXV5R06dLFCkTeeOMNe4tR4R5zUvvpp5+s/enn55fre8Dnhqenp+68805JKePi77//nq/rK126tFq0aCEpJQT9888/83V9Wcnu+1J2ZOe9a8uWLZkedNGlSxdJKfccnjFjxjXXkJfbI6U8Xp06dZIkzZ8/X8nJyVZ4HR4erlatWuW475zU+o9//EOSdPDgQX333Xf67LPPJEm9e/dWQEBAjmvJDQ8PD+ts6oULF2rp0qXas2ePpOyf/b19+/ZMz2B2juOenp6KioqyphfU6ymjz5EAAADIO4TWAACgSLrpppv09ttvS0r5YrVt27Zas2ZNpsvs3LlTt99+u1555RWX6cOGDZOHR8rHqoceekgJCQnplv3hhx80c+ZMSSmXr27atGlebEauJSYm6uGHH3a5H6DTSy+9pO3bt0tK+cLR19fXmpf6jBlnOJrW9OnT9fXXX+dtwRnYv39/lo+f876Kkuu9Zps1a6YmTZpISrms6cqVK9MtGx8fr4cfflhSyhezw4YNy4uyc2TEiBGSpD///NM6ezytyZMnWwH7gw8+6PLY3cjGjBnj9vKia9assYKPxo0b5+j1d+utt+qTTz6xztbOyOTJk60zsFq1aqVSpUq5zC9Xrpwk6eTJkzp37lyG/ThfY3v37tXatWvTzb9w4YLuu+8+Xbx48Zq2IzPO2iRp3759edZvYVejRg3rHsfz58/X66+/nmn7AwcO6NNPP823egrzmCOlHBA1Z84cde7c2Qo433zzTYWFhdla1/jx4+Xp6ank5GT17t1bR44cybBtUlKSPvnkk0zbZGXChAmSpISEBPXu3Vtnz57NsG1iYqKmTZtmndmb17L7vpQdzveun376SX/88Ue6+X/++acGDBiQaR/333+/brrpJknSM888k+n47u4xyMvtcXKeTR0bG6t58+ZZ97e/7777cnVmt7PWa3nPbdeunapUqSIpZYx3vgfYdWlwpyFDhsjhcOjChQtWgB0QEJDte8RLKe9p7g54mDdvnpYsWSJJ6t69u8t4JeX+9bR169ZMr7ZjjNGKFSskuT8gDQAAAHmDe1oDAIAia/DgwTpy5IieffZZnTx5UlFRUerUqZO6deumyMhIBQcH6/Tp09qzZ4++++47LV26VElJSapfv75LP3Xr1tXYsWP1yiuvaNu2bWrUqJGefPJJNWzYUOfPn9fixYv11ltvKSkpST4+Pnrvvfds2uL0mjRposWLF6tVq1YaPXq0qlWrppMnT2ru3LmaP3++JKlChQr617/+5bJcw4YNVadOHe3YsUPvvfeezpw5owEDBqhcuXI6cuSIPv74Yy1cuFCtWrUqkEufxsTEqF27dqpVq5Z69OihJk2aWF94Hz58WJ999pkWLFggKeUyks2bN3dZ/v3331fz5s11+fJl3XnnnXr00UfVpUsX+fv7a8uWLXrppZe0f/9+SdI///lPWy+1+8gjj+iTTz7RunXrNHv2bB06dEjDhw9X5cqVFRsbq1mzZmnRokWSpCpVqqR77G5U9evX186dO9W4cWONHz9ezZo1U2JiopYsWaKpU6da9yufNm1ajvr//fffdf/99+uxxx5T9+7d1bp1a1WpUkVBQUH666+/tH37dn388cfW893Hx0evvvpqun5uueUWSSnB3SOPPKJHH33UJdiuWrWqJGnAgAH673//q+TkZN11110aN26cWrduLT8/P23atElTp07V3r178/Q1VqlSJVWoUEFHjhzRq6++qgoVKqhGjRrWPZzDwsJsO4vPbtOnT9fGjRu1f/9+jR07Vl9//bUeeOAB1a5dW76+vjp16pS2bdumpUuX6scff1SPHj1077335ksthWHM2bNnj3UliOTkZMXHx+v48eP65Zdf9OWXX1rvpx4eHpo4caIeeuihAq8xrbp16+rVV1/V6NGjtXPnTtWpU0cPPfSQ2rdvr7CwMF26dEkHDx7UunXrtHDhQsXGxmr79u3WJZGv1Z133qnHH39cb775pv73v/8pMjJSjzzyiFq3bq3Q0FCdP39ef/zxh6Kjo7Vo0SKdOXPGurVJXsvu+1J2PPDAA1q8eLF1QOBTTz2lxo0bS5LWrl2r119/XcePH1fLli21bt06t334+fnpo48+UqdOnXThwgV17NhRAwYMUPfu3VWhQgUlJiZq165dWrJkib755pt0Zynn5fY4devWTf7+/jp//rweffRR62C/3Fwa3FnrqlWrtGHDBr300ku644475O/vL0kqVqyY9VkmNYfDoSFDhuiZZ57R8ePHJaVs06233pqrWnIrIiJCHTt21PLly626+vbta21PVpo0aWLdM/zJJ59U3bp1FR8fr4ULF1rvYwEBAW7H1ty+nrZu3arBgweradOm6tKlixo1aqSyZcvqypUrOnDggGbPnq3ly5dLkrp27ZouNAcAAEAeMQAAAEXcF198YSIiIoykLH9q165tli1blq6PpKQkM3z48EyXDQoKcrusMcasWrXKardq1aoMa509e7bV7sCBAxm2mzhxotUurQMHDljzZs+ebQYNGpRhzeXKlTO//fab23Vs2bLFlCxZMsNl69ata44dO2b9PnHixGuq053w8HAjyQwcONBleur9l9lPzZo1zf79+932vWzZMhMYGJjp8iNGjDBJSUnXVFtaAwcONJJMeHh4trbZnVOnTplWrVplWmtkZKQ5ePBghn1k9rjkRurn6OzZs9PNT/v8y0pWz5HU+/399983Xl5ebveHj4+P+fTTT3O8XfXr18/Wc8z5ulm6dKnbfpKSkky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Go3MPCADwunWtO2hvVENF7d+Tp2O+lFXf9kU11LrWHQIPyjIaAOAT3ji+xI2oyTu3uJMMSo4ahyoo4EbratWqacqUKbrrrrs0Y8YM3/adO3dq586d+T6GRmugeOrUj/WNQpKkxpvXBvS4xo6jzTkeBwAAKo781n0OdrYVhYRImX9tGS7p9QByuGnECL2R4/dCR+yWcPQ3o6zyl/M+MJjR6AAAeJ2FVNOMYRM16bk7lCn/e5xMSY6kl4dNLHA61jxKOiMNAABeRY1DFRVwo7WZyf6cVnDz5s2llhCAv/ClLgAAlV9+6z4HM9tK9rrPOUfsxm/6SSokTrb4MffovEBmbXFhfexSGWVVyTAaHQBQWX3bKUGPjZuq0bOnKXr/Ht/2fVEN9fKwifq2U0JggVy4JwEAVBzr1q0r18eXKWocqrCgpgdv3ry523kAyAdf6gIAUHXkXPc5mNlWstd9zjlid0t0Y+199z9F3kts6dJPYUWNanJpfWzXR1lVQnRcBFAqKvMUk5X5tVVC33ZK0IqOvdRuw2pFJiXqQESM1rXuEHjtd+meBADgfSlJeyU5GjFihCvx0tOOuhKn1FDjUMUF1WgNoGzwpS4AAFWTW42Wrt5LLFrkPzVZnifLWh9bixZJCQmFhnJtlFUlRcdFAK6rzFNMVubXVolZSLXgl7hw8Z4EAOBt6UcOSjJ1vPxWNWjRNug4O39cqjVzntexY8fcS640UONQxRWr0dpxnNLKA0AB+FIXAICqx81GS9fuJXbudPW4Eo+yqsTouAjAVV6dYtKN0dFefW0oXS7fkwAAvC+sYZzqx7UJ+vHJO7e4l0xposahiitWo/WUKVM0ZcqUgI93HMf7PVfgDUzlVSi+1AUAoGpxu9HSlXuJxo3dPU4lHGVVydFxEYArvDrFpBujo7362lD6SuGeBAAAT6DGoYorVqN1eHi4IiMjSykVVFlM5RUQvtQFAKBqcbvRsrj3Elu3bvXf0KSJmjdqpGq7d8vJp4HAHEfHGjXStiZNpI0b84+BYqHjIoAS8+IUk26Njvbia8tGx/zS1atX1vdGO3bk32nBcbL29+pV9rkBAFAS1DhUccVqtL7xxht19913l1YuqMQSExOVlJSUZ3vdefMUe/31kplyTj5vf35Y3f3UUzo8YIBvO198AgCAqqQ8Gi2PHk6WnBD17ds3z76LJL0lyZR39LfMdOnOnXq3Td4p29LTmX0pWK51XKQBBaiavDbFpJujo0vhtTmZGSWvuXTMLxXr1q3z+z1ywgS1uPlmSfL/PunP/24eP14HVq/OEycmJkZxcXGllCUAAMXnRo0rtL7xWRAVSLEarYFgJCYmqmWrVjqYnOy3PUTSFuX90lOSHDNlSjp63XU6QX9+EZoDX3wCAICqoqxnW0lPPSJZprpf+6jqN/H/0HtU0oM/LdfVH81Qg+S9vu17I6L1/LmjdbR9V52X4/jEjT9q+fT7lZGRUTbJI380oKCcbdu2TYmJiSWOQ2NTELw2xaSbo6Ndfm1dVi7MM7vJ3qiGmlGc2U1YY9t1KUl7JTkaMWJEnn0XSXpCUrMc27ZLmmimd//8sj+32rXraP36dVxLAADlzs0aV2B947MgKhgarVHqkpKSdDA5WQk3Pqm60Y1820/e9JOavXRvgY8LkRQn6ZYx9+jHlu0l8cVncbgxKj0iIkIxMTEuZAMAQBVUwXsz16kfq7AGx+XZ/mPCcRp/5uB8R6KF5Tr28N5dZZMsCkYDCsrZtm3b1LZtO6WkHClxLBqbguC1KSbdHB3t4mvrsnKhJj13R57tUfv3aNJzd+ixcVOLbrgujTW2K/i9hBvSjxyUZOp4+a1q0KKt375Dkv6emamTtv+i+ocOaF9YpNY0O0GZISHqn0+s5J1btPyle5WYmMh1BABQ7tyqcQXWNz4LogKi0Rplpm50I78vPhtvXhvQ4xo7jjb/+Ti++CxaYVN6Fle98HBt2riRhmsAAIqrkvdmLuvR3whSaTSgAMWUmJiolJQj6jrmHoU3jg86Do1NQapWLav2XHxx1r/5nNcD588JJ6dNK7trgJujo116bU5mhkbPnpb1/7n2hShr5rdRs6dpRcdehU8V7vYa25X8XqK4whrGqX5c3iVIJOm3+HbKfpciyywjAADcUSo1js+CqKBotEa5ORARWENooMchS2FTekpSSGam2m9Zp6iD+7W/XpR+im+nzJDcE7RndRBY+Ph4JSUl0WgNAEBx0JsZXuF2AwpQAuGN4wv8Mg7uyXcq9vh4RT78sI575BGF7vlr6uujDRvqt5tu0oH4eGnlSt/23Osqusrtkd9DhmTV1fwad6dNC6jettuw2m9K8NxCJMXs36N2G1YX3mHLzVHk3EuUD0a2AwAqCz4LooIKuNE6MzP3qsJAyaxr3UF7oxoqav+ePGtaS1m9mfdFNdS61h3KOrVKIb8pPYNZo4tpxgEAKAZ6M6Oc5bx3C1u1SrEBPGb3qlU61Oyv1dIKvXfjC33As4qaij1EUi9JjSXtlLRo925lFrDurySlpx11P8nSGPk9ZEhWXQ3y2hSZFNh660Ue59Yocu4lygcj2wEAlYmbnemyFfOzYL6dKYMQExPDTEtVCCOtUW4spJpmDJuoSc/doUzJr+E6U1nTcr08bGLh028hYMVdo4tpxgEACAK9mVFO8rt3O0vSwgAee+mNN+rLG2/0/V7gvRtf6AOeFuhU7HslhUoq6JPezh+Xas2c53Xs2LFSyFKujI7Oo1q1oOuqa7PAuTWKnHuJssfIdgBAZePmkixSsT8LFtWZsjhq166j9evX0XBdRdBojXL1bacEPTZuap7Rv/uiGurlQkb/oniCWaOrqGnGA1XkNONujtZh5A8AoLyVRm9mIAD53buFZGbqj0evU3Ty3gJnNtobEa3wyU/rvD+Xiynw3o0v9IEKo6RTsSfv3OJeMgUp4ehoN7k2C5xbo8i5lyhbjGwH4GV814lgubkkSxCfBYvqTBmSmamTtv+i+ocOaF9YpNY0OyHfJUyTd27R8pfuVWJiIo3WVQSN1ih333ZK0IqOvdRuw2pFJiXqQESM1rXuwAhrF5Vkja78phl3jZujdRj5AwBVm1c+zLvdmxkoptz3bjMvm1zozEYzh09WndgiPvx74Av9xMREJSUllTgOy9YAHlLM0dFuTDGZ33rdrs4C58Yocu4lyhYj2wF4Fd91ophy3+dETpigFn8uBZNzIFv2p7rN48frwOrVfo9JS0tTzZo1/9qQkaGTrrlGNczyDIaTmUxS+rXXak2zZn6fBbNzya8zZTBLmKLqoNEanmAh1fI0lsI9rq3RlYOTmVGyjgZujtZh5A8AVG1e+jDvZm9mwAXBzmyUc23sWsuWqWkAX+jveOMNpXbr5tvsVgNxYmKiWrZqpYPJySWOxbI1QMXk5hSTUt71ul2dBa6ko8i5lyhbjGwH4EV814liSEnaK8nRiBEj8uy7SNITkprl2LZd0kQzvftng7afXLPFFLXklCMpdPduTTr9dH2Zz/7c91zFXcI0W34dD4uLtbErhoAarZOTk1WrVi2FhoaWdj4ASoFra3T9qcS9odwcreOBkT8AgHJUGh/mSzJq262pQQEXFWdmo/zWxh4u6fUAnuemESP0Ro7f3WogTkpK0sHkZCXc+KTqRjcKOk6Ry9YA8KxA1+suSmHrdbs6C1wJ1tjmXqKMlcbIdq/MAASgYuK7ThRT+pGDkkwdL79VDVq09dt3SNLfC5iKu3+uONn3STnjJPy0THrvuSJzOHvwOIW2/6sDc373XMEsYVpYg3xxsTZ2xRBQo3VUVJSmTJmiu+66S5I0ZswYDR48WBdccEGpJgfAHa6t0aXge0OV2mid0pjKiw+YAFAxlMaHeTdGbbsxNSjgskBnNspvbez4TT9JL91b5GPjx9yj81q2lxRgA3Ex77nqRjcqvWVrAFQIpb1et2dmgeNeouy4PbLdSzMAAaiYWLYAQQprGFfgfdJv8e2UfVZFFvD47PuknHGOpRwO6LmPtTzZ77nzu+cKZgnTwhrki4O1sSuOgBqtHcdRZmam7/cZM2YoPj6eRmuggnBrja5gekOV+mgdt6fy4gMmAHhW7jVtXZ+y2M1R2yWdGhQoZznXxt4S3Vh73/1PkR0gt3Tpp7BARyRyzwVUGW5M5+hGDK8qcK3u+HjpnXcU9v33qpGYqPSYGB069dSse4mVK/McXpnfo1Ll5sh2pvMF4AaWLYCHuDkYriRLmBbWII/KJaBG6yZNmujXX38t7VwAlCI31ugKqjdUaY/WcXMqLz5gAoBn5bemraudoEpj1HZJpgYFPMStDpA+Lt9zOZkZ7kznC8BVbk7nmC33uojlraQNxTt37tTFF1+i1NQUlzLy3ntUIbgxsp3pfAEEKXfnpbCDB3VCAI/75eBBHfqzExMdl1Ba3Pws6PYSphKfBSujgBqte/furVdffVWJiYlq/GfDz3vvvactW7YU+jjHcfTiiy+WOEkA7ijpGl0l6Q1VaqN13JrKiw+YAOBp+a1p62onqCCnYMs9+jsYOZfQALwq2A6Qec7vjAw1v+46VTPLM3OPzGSOo2PXX69tJ53ku+cq7N9Il5UL8+S0N6qhZgTYKRNA6XFrOkep8LWoy4PbDfKnjbxD9eNalyiG194jL8u3caeYI9tjYmL8pxdlOl8AQdi2bZvatm2nlJQjvm0hkrZIaioV+L3pb5LajRunzFz76LiE0uDGYDjJ3VHbEp8FK6uAGq0ffvhh7d69W5999pkyMzPlOI5WrVqlVatWFfo4Gq0B7ynJGl1u9YZydbROkFN5lfoUs5J7a2OzxjYA+ORc09bVTlBBTMGW3+jvkkhP50tmeFtxOkDmt0SMJJ0laWEhz+GYqcbOnRrbpo2+zLUv97+RLisXatJzd+SJEbV/jyY9d4ceGzeVLysAD3BjOsei1qIua241yGc3NNeOblrp3iMvcrOzQc2atfT222/5BvdELVmiFgE8bvOSJdofHu77PU/jd058FwBUeomJiUpJOaKuY+5ReON43/aX1q/Q3W8/XeD3ptOHXqe+bTv7ttNxCaWtpIPhJHfbBPgsWHkF1GgdGxuruXPnKj09XTt37lR8fLwmTpyoCRMmlHZ+ADzEzd5Qro3WkaQOHVT3qacUc//9qr5rl29zeqNG2vuPf+hwhw7Sxo1/Pce+ferTt68OHTzo2+bqFLOSe+s0ur3eIx96AVQiJfnAk7ue1MrMVNMAnnNHZqZS/6wpW7duzTP6OxiJG3/U8un3KyMjI+gYQFkJtANkfkvESNKZq7+W/vdkkY8/95LxCuvQU1L+/0aczAyNnj0t6/9zPTZEWdeAUbOnaUXHXkwPB6DUlLRBnobmsuVWZ4M/NqzWqtlPaNCgQb5tRXXKynbVnXfqyzvv9P1eu3YdrV+/Lm/DtdvfBQDwtPDG8X71ZF1cGz0W07TA703XdUpQ/RyPp56gLJRkMFw2N0Zt81mwcguo0TpbjRo1FBcXp7POOksdO3ZU8+bNSysvVAQ0flU5bq9n6MZonZxCJPWS1FjSTkmLdu5U5nXXFXh8zxseV0RsVhOFq1PMurVOo9trbPOhF0AlVNwPPAXVk0CnYGsxYkSeKdhCw2N8o7+DcXjvrqIPAiqonEvESFJqs0BW6Ms6Lvtx+f0babdhtd+/+dxCJMXs36N2G1aX+IsVAEDl4k5nA//G75DMTP3x1E2KPri/wHvJxPD6qnXdI+ofEuKLs/yle5WYmOjfaO32dwEAKiQ3RrYCXlTSc5vPgpVbsRqtsy1YsMDtPFDR0PhVZbm1hkW2ko7Wyc9BSWGSzilgf/ZonZoRMa5MMes3Wi+IdRqlfKYZd3uNbbc/9NJpBYBbXLieFOcDT2H1ZOZPy3XH6/8qsGPWrMsm65z2XX3bGSENFJ9bM/dEJiUG9HyBHieJ+xt4F+cm4Em5G79nXn5zoZ38Z112kyLj2xUe1O3vAgBUaG6MbAW8qCTndql8FoRnBNVone3w4cN67733tGrVKiUnJys8PFwdO3bU4MGDVbduXbdyhNfQ47PKK8+efrlH6wQjv9E6wYwiz2+0XrDrNOaZZnzRIv9OIXkSzlpjW4sWSQkJhTyjSqcBnE4rANzg4vWkuB948qsnPyYcp8fCowvsmPVjpwSF5TieEdJA8bk1c8+BiJhC9xf3OO5v4Fmcm0CF4Uonfze/CwAAoBIqyWfBdevW5X9wRobCvv9eNRITlR4To0Onnlrg9+QxMTF5l/WAa4JutH777bd19dVX68CBA7IcDSGO4ygyMlIvvPCChvABqsJLTExUUlLSXxuCGEWa7xrEqPAqY0+/4n7AzG+0XjDrNGZPM/7DDz/4ll0IW7VKsQHkvHvVKh1q1sz3e54R25K7H3oZsQ3ALR7tBMcUbEDpc+NLfbdGbEvy7PUI4NwEKp5g7iVzfoEetWSJWgTwPJuXLNH+8HDf74V+gc7nbqDs8e8OKDXBfBZMSdorydGIESPyHH+RpCckNcuxbbukCZLezSd+7dp1tH79OhquS0lQjdZLlizR8OHDVa1aNf3tb39T79691bhxY+3atUsLFizQyy+/rOHDh+vLL79U9+7d3c4ZZSQxMVEtW7XSweRk37ZgR5FKUnr6sVLIEnBXMB8wc47WC2adxmBGbGe79MYb9eWNN/p+zzNiW8q6QQ5EUcdVlRHbfLAAAlOSfysen/awMnbMArympB1E3Bqx7fXrEaowzk2gwgr0XjK/L9AD/S7gqjvv1Jd33un7vcAv0N3+3M3nZaBoXv2+C6gkgvksmH7koCRTx8tvVYMWbX3be6xfobvffjrPczSV9Lak+4Zep8VtO/u2J+/couUv3avExEQarUtJUI3WU6dOVc2aNbV48WJ16ODfc/3SSy/VtddeqzPOOENTp07VBx984EqicFGAN5hJSUk6mJyshBufVN3oRpKCG0XKeo+oaErSWBFMT6/8RmyHZGbqj0evU3Ty3gLj7I2IVvjkp3VeSNYR2SO2k5KS/ButGzcOLPmijqsKI7bd/GDBh/ngLFokDRzIe+V1Jf23wrSHAFTyDiLBjtjOORNUrWXL1DSA69GON95Qarduvs35zm4D5FTSe0FqJVDp5fcFekhmpv546iZFH9xf4HcBieH1Veu6R9T/z+8CCvwC3e3P3XxeRlVQ0nOTWVKAMhHsZ8GwhnGqH9dGkuRkZuj6Z27N+v9cx4Uoq+Ze98X/tL7fcGbfK0NBNVovXbpUl156aZ4G62ynnHKKhg0bpvfff79EyXlGSb9Ad+tGzI04Qdxg1o1uVKJRpKz3iKqkJKN+cq+vOvOyyYXGmTl8surE5u3RlWdK/iZN1LxRI1XbvVtOPiM1zHF0rFEjbWvSRNq40bc9z5exlX3EtpsfLPgwH7xBg7zR87+yxnEjlhv/Vty6ngCo8oozYju/2W2GS3o9gOe5acQIvZHj93xnt6loPNJRzMnMcGdJBi/V3CDuBbdt26bExETf78FMEVzgGn0APC3nF+iSNPPymwv9LmDWZTcpMr5d4UFL43O3Fz8vo2LzUu2Win1u5q7dysjQSddcoxoFLaspKf3aa7WmWTNfftRuIHglnb2r3YbVfg3euYVIitm/R+02rA68s7WXvr8rjZzKQFCN1keOHFFsbOGrrcbGxurIkSNBJeW2p59+Wo888oh27dqlDh066N///rdOP/30wAOU5At0NxtTShrHhRtMV9eOAyopN9ZpDCZOfl/EZrtI0luSTHk/9MpMl+7cqXfbtPF7TJ4vYyvziG03P9DzYb7kyrvnf2WN40asIP+tJCYmKikpyfd7rcxMNQ0g3R2ZmUr9szNNng45APCnQEds5ze7Tfymn6SX7i3ysfFj7tF5LdtLKmR2m4qmpB3FXNBl5cI897p7oxpqRjHumSV5q+YGcS+4bds2tW3bTikpf32HEuwUwZKUnnY0sFwBeJIr3ym4+bnbq5+XUbF5qXZnxynGuRlM7XYkhe7erUmnn553WU1qNxCUkszeFZmUWPRBxTjOU9/flUZOZSSoRuv4+Hh99tlnmjp1aoHHzJ8/X/Hx8cHm5Zo333xTkyZN0rPPPquuXbtq2rRpGjBggH7++Wc1bNgw8EDBNja4cSPmRhyXbjBdWzsOqORK2tMrmDj5fRGb7aikB39arqs/mqEGyXt92/dGROv5c0fraPuuOi/H8dlfxv7www9q3rx51sbKPGLbrQ/0fJh3R3n2/K+scdyKFcS/lcTERLVs1UoHk5N9h4VI2qKsNYIK6gT3m6QWI0Zkda7JIT39WOE5AkARcs5usyW6sfa++58iO+Vu6dJPYZXxM0453ld0WblQk567I8/2qP17NOm5O/TYuKmBNcx4qeaWoHNXSsoRdR1zj8Ibx0sKborgnT8u1Zo5z+vYMWolUNEF851CzhGbwczWkC0mJsZ/mnEvfl5Gxeal2i0FdW7mV7sTflomvfdckU939uBxCm2ftewMtRsoPwciAuuAHNBxXvv+zu2cylBQjdbDhg3T/fffr1GjRunBBx9UkyZNfPt27typ22+/Xd99953uuusu1xIN1mOPPaa///3vuuqqqyRJzz77rD766CO99NJLuu222wIPVNybJ7duxNwa0RTEWm0FjWhyaxQpUNmVdJ3GYOPknmY8248Jx2n8mYPz/dAbluvYgkZtBzNiu25YmBZ88YXq168vKbgRllI+jd9u9hx3qyGdD/PuKY+e/5U1Tgli5b6fCFu1SoXPtZNl96pVOtSsmaSs+4mDyclKuPFJ1Y1u5Dtm5k/Ldcfr/ypk2sPJOqd9179y2fijlk+/XxkZGQFkAACBqfKdcsvpvsLJzNDo2dOy/j/Xvuz140bNnqYVHXsV/t6Xc83NPS1o2IoVOiGAe8FfXnxRhzp39m3ObmgKbxxfoimCk3duKfi5AVQ4gX4XkJK0V5KjESNG+LaVZLaG2rXraP36dX81XHvt8zIqNg98XnajfudXu4+lHC44Rg7HWp7sewy1Gyg/rs0q7OL1KJhlBqR8OpxV4O+Wg2q0vvXWWzV37lzNmjVLb775po4//njFxsZq9+7d+vXXX3X06FGdfvrpuvXWW93Ot1iOHj2q7777TrfffrtvW0hIiPr166elS5fmOT4tLU1paWm+37O/pPWNC/qzQP3+0ktK7dKl0Oeu9e23ahJAsSsqVjBx9u/fr/MvuFCHDx30HTZU0kuFZpxlwogRejvXtr3bN2aN4Mzh0wZN9Pk1/1S7bb8o8tB+HQiL0rq4E5QZEiJt/8Xv2IN7svJP3rVV1VSyL5rdilVZ43gxJ6/F8WJOZR1ncZ0wqc6fzdQ7NuZ7zL5tv0iWqZMvnqDwmL8amfZIum3TGo1b/IEaHP5r1OSeuhF6vscg7Wl5knr45bRdP7zzH78lGRxJayQ1UcEjLHdIOnnECOUsq3XDwvTBnDmKioqSJIUtW6ZA5svYs2yZDkVGFnpMrUOH1KTQI7L8fuiQUletKnC/WzkFW0MiIyN9nQPyk/znSFfL74aljBVZc/8U0N+vHGtuRYgTbKz87id6SPq40GfKMvTGG7X4xhv9tqUeOazQun/dT3zV6mSlXny9rpr3qmIO7vdtTwyP0vSzr9A3rU6Wctx/HDuadb545ZrrZqzKGseLOXktjhdz8lqcssjp0wZNdKCw61GDJn6fcY7s250V7+BBX23NrULV3OxGgrlzs9Y3c9mhQ4ckSfu2/qxjaSmSpPbbflGN/Xt0sJDHhe7fo5gFb+unuBOy8t21TZL03Xff+WKGff+9jg+gvv361FM6dOqpvs0hISHKzPxrLo9g4uzevVsjR16ptLRU32GBfu6+Y9y4PJ+7JemPX9f43iNJ+qhepBIv+Lv+/sX/FHPowF/H1YvSf3tfrOX1IqUNq3zbk3dmdTxP2rFBNarn+ZorYF6L48WcvBbHizl5LY4Xc3Irzt6NaySZWiZcoojYrA7shyxTv37womJSDhX4ufuP2mE6dP5Yner8dcSRfbv182e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0BtysCo1LVMzpK9oat2hTrSZDQMnJVV6v+FWIEvdCARC+GIFcOU7MbuLILTnCfSYZBxryrvwSBgAAAABcwvHbRVaXQ9/jcy6I+o6mNeBm1WhcomI1eUVbtaWne6/I2rIl8Jf6Ho/38fT0CkPVyJWI3IMUgAswAjk0qn3Vdk3MJBOCupSZmans7BK1s6BAXa++Wg3NSs1uIjOZpMPXXKPV7dr58ivrdiWu+xIGAAAAgOMCnldUUnVvgVhXOTGjmGMc/B6fc0HUZzStATdzsHGJOiYy0juF6PDh3t9z8d+/59evwadMCeoL+epciRjooDf+44/V9qGHFLX9t4OmvKQkbb7pJu0+7bRS6ycmJjLFOIAawQjk0KnsVdvF60nzzz9XhyCe8+Pnn2tXbKzv5zLrSQjujZ2Zmam0tM7KyTnot/wUSfPLeZ5HUtS2bZpwwglaUOKxw7l5pdZ31ZcwAAAAABxV1nlFVQU6pwh3Tswo5giHv8fnXBD1FU1rwCXKGlUXP26cOtx0kyT5XbFTVPp+vP567V61yre8vo6sC0tDh3qnEA30RfyUKUF/EV+VKxHLuu/3eZJmBYjRYPt2pd50k4ZLerPEY40bN9G6dWtpXAOoEYxAdrdA9aSixm6Ry26/XQtuv933c8B64vS9sYO8Yjs7O1s5OQd14uV3KjY51bc845vF0lvTKnyZM84do6hj+0j67XYj+fn5Add1zZcwAAAAABxV1nlFZVV0ToFa4OAFSEU4F0R9RNMacIGKRtWdJ2mqpHbFlm2SNN5Mb/7a0C6pPo6sC0tDh3rv6VmNKU+rciVioPt+RxQWato/bpRn365SU55G/Brrn7EJOjj2IRVGeF9lb9ZGLXnmbmVnZ9O0BlBjGIHsXmXVkx3/uFEt9u0qczBVdmyCGo19SAPKqydO3xu7Cldsxyan+t1qJD/nQMWvIyn/yN/5nlejtxsBAAAA4Holzysqi3MKl3DoAiSgPqNpDbhARaPq9ku6srBQXTd9p4T9u7UzJl6r2x2jwogIDSixLiPrwlBkZPD39CxDVa9ELH7f7y7frlDLfbvKfI0ISUl7d+qk3BxGAQKodYxAdrfi9USSnrv4pnIHUz1/0Y2KT+1cflAn743t0BXb3GcdAAAAAOoxBy5AAuozmtaAi1Q0qm5zamcVfTUbX8Y6jKxDWap7JWL8ntLT11dnPQBA/eXItO5ZWcG9WIn1St2SpaBAXa++Wg3NSs0kIjOZpMPXXKPV7dr5vmgo63Ys3GcdAAAAAOo5By5AAuormtYA4BLVvR95MM+vzpWIu+MSHV0PAFC/VWUwVfFaF7Nvn44J4nW+27dP+1eskCRlZWVp+PDzdehQju/xiu6x7ZEUtW2bJpxwghaUeCzQ7Vi4zzoAAAAAAEDl0bQGgBDL2fOLJI9GjBjhSLyaup85U54CAJwW7GCqQLUyQtJGSW2kMuvSZkmdx4xRYYnHeo28TQkpHSVJGd8slt6aVmEOZ5w7RlHH9pFU8e1YuM86AAAAAABA5dC0BoAQO3xwnyRT94tvUcsOaVWOU9P3M6+RKU8LCrjHCwCgQmXVymfWLdMdbzxRZl2aMWysTk873re8qFY2btHGd0uW/JwDQeWQf+TvfM8J5nYsrrvPOjUXAAAAAOofzgVRh9C0BgCXiElKKfee5hWpjfuZOzrl6ezZ0rhx0ubNvy1r21aaOlUaOtS5pAEAYaNkrVyb0kmPJLYpsy6t7ZmhhGLPD1Qr68VMItRcAAAAAKh/OBdEHUPTGqhJjGJCGHJkytPZs6XhwyUz/+VbtniXz5rFgRMAICjVrUs1MpOIm1BzAQAAAKD+4VwQdVCgiwkAOGH2bCk1VTr1VOnii73/T031LgfquKIpTz8/4Qyt6dSz8lOCjxtX+oBJ+m3Z+PHe9QAACEK16pJ+m0lkV/Mkv+U7myfpkTH3VW4mETeh5gIAAABA/cO5IOoorrQGagKjmICyLVzoPyVNSWbSpk3e9TIyai0tAED95shMIm5DzQXgZsxMBgAAUDM4F0QdRdMaqKbMzExlZ2f/tqCgQF2vvloNzeQpubKZTNLha67R6nbtfCfka9eura10gdDLynJ2PQAAHFJ0xXbYoOYCcCvurwgAAOq46n6nX6M9Ac4FUUfRtAaqITMzU2lpnZWTc9C37BRJ88t5jkdS1LZtmnDCCVpQ4rHDuXnOJwm4TXKys+sBAIDAqLkA3IiZyQAA4Y7ZRMJazp5fJHk0YsQIR+LVSE+Ac0HUUTStgWrIzs5WTs5BnXj5nYpNTpUkZXyzWHprWoXPPePcMYo6to8kKevrRVo951/Kz8+vyXQBd0hP915FsWVL4PuqeDzex9PTaz83ALWPk3mg5lBzAbhNRfdX9Hi891c85xyOBwAAdROziYS9wwf3STJ1v/gWteyQVuU4NdoT4FwQdRRNa8ABscmpSkjpJEnKzzkQ1HPyj/yd7zl7szbWVGqA+0RGeg/Uhw/3HiAVP3Dy/Dqp/pQpfEkF1AeczAM1i5oLwG24vyIAIJwxm0i9EpOU4vt+vypqtCfAuSDqqIhQJwCEm7Udu+mX5kkqLOPxQknZzZO0tmO32kwLCKm1a9dqxYoVv/2XmqofHnxQeS1b+q2Xl5SkHx58UCtSU/3XX7FCmZmZIcoeQI0oOpkv+cV10cn87NmhyQsIN0OHer8ca9PGf3nbtnxpBqD2cX9FAEC4qmg2Eck7m0hBQa2mhXqMc0HUQVxpDTjMIiI184LxmjDtNhXKf2RIobz3tH72gvGyCEYxIfxVdI+XCEnpkpIlZUlauG2bCm+6KeC6jRs30bp1a5WSklJD2QKoNUwNCtSYtWvXll6YmirNnq2YL79Uw+xsHU5M1P4ePbx/XytWlFo9MTGRegugZnB/RQBAuGI2EbjR0KHe71a4LRvqCJrWQA1Y2jNDj4y5T6Nem6IWu7b7lu9snqRnLxivpT0zQpccUIuCvcfLL5KiJJ1exuN7szZqyTN3Kzs7my/RgTooMzNT2dnZvp9jli3TMUGczH83fbr2H3+8pDIacQB8KhooVhkMFAPgJL8a3rSpuiYlqeH27fIEWNckHW7VSqubNi01qIYBNQAAV2M2EbhVZKQzAyUKCmh+o8bRtAZqyNKeGVrWPV2d169S/J5s7Y5L1NqO3bjCGvVSde/xAqDuyszMVFpaZ+XkHPQtu1DSy0E8984xY/RKiWWHc/OcTA8IG8EOFKsIA8UAOKWswTTnSZolb4O65MxkknThtm1684QTSsVjQA0AwNWYTQThbPZs74x5xS9AaNvWe99sphmHg2haAzXIIiK1plPPUKcBhD9G+gGulZ2drZycgzrx8jsVm5wqSWrz0zrphckVPrfNiFs0oL23+Zb19SKtnvMv5efn12S6QJ3HQDEAblHWYJr9ku5dt0zXfPiSWu7b5VueHZugpwZcpP1px2tAiVgMqAEAuF56ureJt2VL4FtheTzex9PTaz83oDpmz5aGDy+9XW/Z4l3O/bHhIJrWQCA0wIC6g5F+QJ0Qm5zqa6RtaXu0fvnPDDXftd3vCqsihfLeUmNLv8FK+HWGkr1ZG2stVwDl4DgZQCUFGkyzNqWTru9/YcCZyRJClCcAANUSGen9Lmr4cG+DuniDz/PrTTGmTOHYGXVLQYH3e9dAAzHMvNv2+PHe+2azbcMBNK2BkmiAAXUHI/2AOskiIjXzgvGaMO02Far01KAeSc9eMJ5bagBuw3EyAAdVZWYyv/tjF1dQoJgvv1TD7GwdTkzU/h49yvzitFbujc0AHwAIe5mZmcrOzvZfmJqq+AcfVNuHHlLU9u2+xXlJSdp8443anZoqrVjhW15mXQPcYuFC//O/ksykTZu86zlx32zUezStgeJogAF1ByP9gNCq5pexS3tm6JEx92nUa1PUYtdvJ/M7myfp2QvGa2nPjBpIGkCVcZwMIITKuj+25L1H9lRJ7Yot2yRpnKQ3A8Sq8XtjM8AHAMJeZmam0tI6KyfnYMDHIySlS0qWlCVp4bZtKrzppjLjHc7Nq4k0gerLynJ2PaACNK2BIjTAgLqFkX5A6Dj0ZezSnhla1j094NSgAFyE42QAIVbW/bH7rVumO954otT6bSS9IemeYWP1WdrxvuU1fm9sBvgAQL2QnZ2tnJyDOvHyOxWbnFrmer9IipJ0ehmPZ329SKvn/Ev5+fk1kCXggORkZ9cDKkDTGuGlOld90QAD6paaGunHVH5A+Rz+MrYqU4MCqGUcJwNwieL3x/YUFujaJ2/x/rvEehHy3nJk7Meva13/C2tnQFxNDPBx8tzEqVicLwGo4wJO610kyNtNFE3rHZuc6qtLVbE3a2OVnwvUivR070UKW7YEPsbxeLyPp6dXLi7HEygDTWuEj+pe9cVUF0DdUhMj/Zycyo8vhRCOuNoSqJ8YKAaEFn8rAXVev8rvFiMlRUhK3LVdndevCn6AnJsGwjt5buJULDdOfc7fB4BKKG9a78rebkJiWm/UA5GR3jo/fLj3O5/i3wd5fh02OGVK5WqvG79/hWvQtEZ4cOKqL6a6AFyvaCSrJKlpU3VNSlLD7dtLXVkhSSbpcKtWWt20qbRihW95YmJi4KkAnbx6NJy/FELd56YvYwG4VvGaG7Nvn44J4jnf7dun/cHUXIkaBwSLv5Uyxe8p4yq5Kq7nqoHwTp+bOBHL6anPnfiS2em/D774BsJeWdN6V/Z2E0zrjXAVcCaC1FTFP/ig2j70kKK2/zZgMC8pSZtvvFG7U1P9vnstEvB8sArHE2XNjhD/8ceBc7rpJu0+7bTg8ikSzhcfuTGnctC0DsbChdKgQaGfgskpbvwDrESsUjupggJ1vfpqNTQr3bgy8zaurrlGq9u184uZm5ur6Ojo39atQgPMr4EGoMbk7PlFkkcjRozwW36epFny/n1GFFte+Ov/L9y2TW+ecILfcxo3bqJ169b6H6Q4efWoW78UqiuqW3OdEq61u5Jf7JWsuc0//1wdgniZHz//XLt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+      "text/plain": [
+       "<Figure size 2000x1000 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot facet\n",
+    "plot_comparison(\n",
+    "    legs_acbm,\n",
+    "    legs_nts,\n",
+    "    value_column=\"tst_hour\",\n",
+    "    max_y_value=70,\n",
+    "    plot_type=\"time\",\n",
+    "    plot_mode=\"facet\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matching: Activity Sequences"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create chains from data and count number of observations of each chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "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>activity_sequence</th>\n",
+       "      <th>count_nts</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>e - e - e - h - e - e - v - v - h - e - h</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>e - e - e - h - o - h</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>e - e - h</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>e - h</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>e - h - e</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1818</th>\n",
+       "      <td>w - w - w - h - o - h - o - h</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1819</th>\n",
+       "      <td>w - w - w - h - w - w - h - w - h</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1820</th>\n",
+       "      <td>w - w - w - sh - w</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1821</th>\n",
+       "      <td>w - w - w - w - h - h - h - w</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1822</th>\n",
+       "      <td>w - w - w - w - w - h</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1823 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              activity_sequence  count_nts\n",
+       "0     e - e - e - h - e - e - v - v - h - e - h          1\n",
+       "1                         e - e - e - h - o - h          1\n",
+       "2                                     e - e - h          1\n",
+       "3                                         e - h         10\n",
+       "4                                     e - h - e          3\n",
+       "...                                         ...        ...\n",
+       "1818              w - w - w - h - o - h - o - h          1\n",
+       "1819          w - w - w - h - w - w - h - w - h          1\n",
+       "1820                         w - w - w - sh - w          1\n",
+       "1821              w - w - w - w - h - h - h - w          1\n",
+       "1822                      w - w - w - w - w - h          1\n",
+       "\n",
+       "[1823 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sequence_nts = process_sequences(\n",
+    "    df=legs_nts,\n",
+    "    pid_col=\"IndividualID\",\n",
+    "    seq_col=\"seq\",\n",
+    "    origin_activity_col=\"oact_abr\",\n",
+    "    destination_activity_col=\"dact_abr\",\n",
+    "    suffix=\"nts\",\n",
+    ")\n",
+    "\n",
+    "sequence_nts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "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>activity_sequence</th>\n",
+       "      <th>count_acbm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>h - e</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>h - e - e - e</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>h - e - e - e - h</td>\n",
+       "      <td>96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>h - e - e - e - o - v - h</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>h - e - e - e - sh - h - v - h</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>486</th>\n",
+       "      <td>h - w - w - w - w - h</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>487</th>\n",
+       "      <td>h - w - w - w - w - h - o - h</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>488</th>\n",
+       "      <td>h - w - w - w - w - h - w - w - w - w - h</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>489</th>\n",
+       "      <td>h - w - w - w - w - w - h</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>490</th>\n",
+       "      <td>h - w - w - w - w - w - w - h - m - h</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>491 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             activity_sequence  count_acbm\n",
+       "0                                        h - e           5\n",
+       "1                                h - e - e - e           1\n",
+       "2                            h - e - e - e - h          96\n",
+       "3                    h - e - e - e - o - v - h           1\n",
+       "4               h - e - e - e - sh - h - v - h           1\n",
+       "..                                         ...         ...\n",
+       "486                      h - w - w - w - w - h           3\n",
+       "487              h - w - w - w - w - h - o - h           1\n",
+       "488  h - w - w - w - w - h - w - w - w - w - h           1\n",
+       "489                  h - w - w - w - w - w - h           1\n",
+       "490      h - w - w - w - w - w - w - h - m - h           1\n",
+       "\n",
+       "[491 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sequence_acbm = process_sequences(\n",
+    "    df=legs_acbm,\n",
+    "    pid_col=\"pid\",\n",
+    "    seq_col=\"seq\",\n",
+    "    origin_activity_col=\"oact_abr\",\n",
+    "    destination_activity_col=\"dact_abr\",\n",
+    "    suffix=\"acbm\",\n",
+    ")\n",
+    "\n",
+    "sequence_acbm\n",
+    "                                  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YExMTY7y9vW0KKWOM6dKli3Fzc0vxB3zSj8YFCxYkW4dJ7iy6k9b37t27bcYrU6aMzTq7s+g2JvViIWnb+umnn2zaK1SoYDPvlFy5csWUKlXKSDLBwcGmW7duZtq0aebUqVPJxq1Xr54pX768uXbtmrUtMTHR1KhRw5QoUcLa9tprrxlJ1kLQmFvr0s/P776L7g8++MBky5bN/P333zbjDRo0yGTJksVER0cbY/7vx32ePHnM+fPnreN99913RpL54YcfrG21a9c22bNnT/ZPiduLo549e5r8+fObs2fP2ozz7LPPGj8/P+vn7+mnnzZly5ZN9j7uJWk9FyhQwMTExFjbv/nmGyPJTJw40ZqpRIkSpmHDhjb5rly5YooUKWIaNGhgbRszZkyy5WyMMb/99puRZJYtW2aMMeaPP/4wkkzbtm1N1apVreO1aNHCPProo9bnaV32v/zyi5FkZs+ebTPe8uXLk7Un7SNvL/BOnz5tvLy8zBtvvHHP5ZZUdBtjTN26dU1gYKB1XTxI0b1nzx6TNWtWI8lUqlTJ9OvXzyxZssTExcXdM5MxyffHxhgzd+7cZO81aX/Vo0cPm3FbtWpl8uTJY32e9L3z8ssv24zXsWPHNBfd8fHxJk+ePOadd96xmb5ixYrJ5i3J5p+md5P0Hu585MyZ0yxfvtxm3NuL7j179hhJ5pdffjHGGDN58mTj6+tr4uLiKLoBJ+H0cgB2ExMTI0lp7jhr2bJlkpTs2sE33nhDkpJd+12mTBlVr17d+rxq1aqSpCeffFKFCxdO1v7PP/8km2ffvn2tfyedHh4fH69Vq1ZZ27NmzWr9+8KFC7p06ZJq1aqV7FRwSQoLC1OZMmXu8U5vXRe9devWVHuu3rZtm06fPq2XX37Z5rrjpk2bqnTp0ileB//iiy/aPK9Vq1aK7/l2q1atUnx8vF577TW5uf3fV0Lv3r2VI0eOB7refvbs2QoNDbV2upY9e3Y1bdrU5hTzxMRELVmyRM2bN1doaGiy17BYLOmeb+vWreXu7q758+db2/bs2aN9+/bddw/q9evXV1BQkE32PXv26I8//kjxutXbZc2aVVu3brWekhwREaGePXsqf/78euWVV6yXTpw/f16rV69Wu3btdPnyZZ09e1Znz57VuXPn1LBhQ0VFRVkvLVi2bJmqVaumxx9/3Doff39/derU6b7en3TrUoZatWopV65c1nmfPXtW9evXV0JCgtavX28zfvv27ZUrVy7r81q1akn6v8/ZmTNntH79evXo0cPm8yj933o1xmjRokVq3ry5jDE2823YsKEuXbpk/ZzlzJlTx44dS/EU9rTo0qWLzb7omWeeUf78+a37nZ07dyoqKkodO3bUuXPnrDni4uJUr149rV+/Ptmp3nd69NFH5evra11Wv/zyiwoWLKguXbpo+/btunLliowx2rBhg3V5SWlf9gsWLJCfn58aNGhgM16VKlXk6+urNWvW2OQpU6aMzXz8/f1VqlSpe+4X7jR06FCdPHlSU6ZMSdd0KSlbtqx27typzp076/Dhw5o4caJatmypfPny6Ysvvrjn9Lfvj69du6azZ8+qWrVqkpTiPjml/eK5c+es309J6//VV1+1GS89nSn+9NNPOnfunDp06GBt69Chg3bt2qW9e/da29L7nZhk0aJF+vnnn7Vy5UrNmDFDJUuWVJs2bbRp06YUxy9btqwqVKiguXPnSrrVQeTTTz+damd4AOyPohuA3SRdq3r58uU0jX/kyBG5ubkl6xk7MDBQOXPm1JEjR2za7/wh7+fnJ0kqVKhQiu13XvPo5uamokWL2rSVLFlSkmyuif3xxx9VrVo1eXt7K3fu3PL399dnn32mS5cuJXsPRYoUudfblHTrWvc9e/aoUKFCevzxxzV06FCbH8JJ77VUqVLJpi1dunSyZeHt7S1/f3+btly5ciV7z3dKbT6enp4qWrRosvmk1cWLF7Vs2TKFhYXpwIED1scTTzyhbdu2WW+Lc+bMGcXExKhcuXL3NZ+U5M2bV/Xq1dM333xjbZs/f77c3d3VunXr+3pNNzc3derUSUuWLLFeDzp79mx5e3tbrze/Gz8/P40ePVqHDx/W4cOHNW3aNJUqVUqTJk3SBx98IEk6cOCAjDF677335O/vb/NIup4+qaOpI0eOqESJEsnmk9L2klZRUVFavnx5snnXr1/fZt5J7vz8JRXgSdtc0vZ8t3V75swZXbx4UVOnTk023+7du9vM96233pKvr68ef/xxlShRQn369LG51vpe7lxeFotFxYsXt37Wo6KiJEldu3ZNluXLL7/U9evXU/zM3y5LliyqXr26fvnlF0m3iu5atWqpZs2aSkhI0JYtW7Rv3z6dP3/ephhO67KPiorSpUuXFBAQkGzc2NjYe64jKW37hTvVrl1bdevWzbBru0uWLKmvvvpKZ8+e1R9//KGPPvpI7u7uev75523+4ZmS8+fPq1+/fsqXL5+yZs0qf39/6343pfVzr+006XunWLFiNuOl57P09ddfq0iRIvLy8rLu64oVKyYfHx+bf9Sl9zsxSe3atVW/fn01aNBA3bp1U2RkpLJnz65XXnkl1Wk6duyoBQsW6MCBA9q0aVOyThwBOBa9lwOwmxw5cigoKEh79uxJ13RpPbqZJUuWdLWbOzpIS4tffvlFLVq0UO3atfXpp58qf/788vDw0IwZM1LswOn2ozB3065dO9WqVUvffvutVq5cqTFjxmjUqFFavHixGjdunO6cqb1nZ1mwYIGuX7+usWPHpnif39mzZ9v11kDPPvusunfvrp07d6pSpUr65ptvVK9ePZsegNOrS5cuGjNmjJYsWaIOHTpozpw5atasmfWfOmkVHBysHj16qFWrVipatKhmz56t4cOHW4+iDhgwQA0bNkxx2rTeqi0t7uxkLzExUQ0aNNCbb76Z4vhJ/5BKkhGfs6T33Llz51R7ta9QoYIk6ZFHHtH+/fv1448/avny5Vq0aJE+/fRTvf/++xmyLSVlGTNmTLIeoZOk5f7GNWvW1Icffqhr167pl19+0TvvvKOcOXOqXLly+uWXX5QvXz5Jsim607rsExMTFRAQkGqHhHf+4y0j94VDhgxRnTp19Pnnn2fYbdKyZMmi8uXLq3z58qpevbrq1q2r2bNnW//ZkJJ27dpp06ZNGjhwoCpVqmS9lVajRo1SPBMhI5dBSmJiYvTDDz/o2rVrKf4jbM6cOfrwww9lsVhUunRpSdLu3btt1n96+fr6qmrVqvruu+8UFxenbNmyJRunQ4cOGjx4sHr37q08efLoqaeeuu/5AXhwFN0A7KpZs2aaOnWqNm/ebHMqeEqCg4OVmJioqKgoPfLII9b2U6dO6eLFiyn2dvsgEhMT9c8//9gUE0lHYJPuBb1o0SJ5e3trxYoV8vLyso43Y8aMB55//vz59fLLL+vll1/W6dOnVblyZX344Ydq3Lix9b3u379fTz75pM10+/fvz7Blcft8bj/qHx8fr0OHDt31x+/dzJ49W+XKlbMeob3d559/rjlz5ig8PFz+/v7KkSPHPf8xk97TzFu2bKkXXnjBeor533//rcGDB99zurvNp1y5cnr00Uc1e/ZsFSxYUNHR0frf//6Xrly3y5Url4oVK2Z970nL38PD457LPTg42Hpk9nb79+9PcT6390Qv3Vq/J06csGkrVqyYYmNj73ud3ynp/dxt3Sb1Fp2QkJCm+WbLlk3t27dX+/btFR8fr9atW+vDDz/U4MGD73n7tzuXlzFGBw4csBb1SUc6c+TIcc8sd9tOatWqpfj4eM2dO1f//vuvtbiqXbu2teguWbKktfhOmndaln2xYsW0atUqPfHEE2n+B19GCQsLU506dTRq1Ci9//77Gf76SZeX3Lld3u7ChQuKjIxUeHi4TYaUPgtplfS9c/DgQZuj2yl9llKyePFiXbt2TZ999lmyf+rt379f7777rjZu3KiaNWuqefPmGjFihL7++usHKrqlW3f6kGS9u8KdChcurCeeeEJr167VSy+9lGJP5wAch9PLAdjVm2++qWzZsqlXr146depUsuEHDx7UxIkTJd26v6gkTZgwwWaccePGSbp1PXNGS7p9lHTrR/ikSZPk4eGhevXqSbp1lMRisdgcFTx8+LCWLFly3/NMSEhIdhpkQECAgoKCrNf3hoaGKiAgQFOmTLG5XdpPP/2kP//8M8OWRf369eXp6alPPvnE5sjPtGnTdOnSpfuaz9GjR7V+/Xq1a9dOzzzzTLJH9+7ddeDAAW3dulVubm5q2bKlfvjhB23bti3ZayVlSvpReWfxmJqcOXOqYcOG+uabbzRv3jx5enqqZcuW95wuW7Zsdz2F+LnnntPKlSs1YcIE5cmTJ01nJezatUtnz55N1n7kyBHt27fP+kM/ICDAeiQxpcLjzJkz1r+bNGmiLVu26Ndff7UZntIR0GLFiiW7Hnvq1KnJjnS3a9dOmzdv1ooVK5K9xsWLF60/8tPK399ftWvX1vTp0xUdHW0zLGm9ZsmSRW3atNGiRYtSLM5vf8933hLK09NTZcqUkTFGN27cuGeeWbNm2ZzWu3DhQp04ccK6DqtUqaJixYrp448/Vmxs7F2z3G17rFq1qjw8PDRq1Cjlzp1bZcuWlXSrGN+yZYvWrVuXrOBK67Jv166dEhISrJck3O7mzZtp/nzcr6Rru6dOnXrfr/HLL7+kuL6Srq2+22ndSUet7zxKfed3Rnokrf9PPvnkvl7z66+/VtGiRfXiiy8m29cNGDBAvr6+1s9l9erV1ahRI3355ZcpfofEx8drwIAB95zn+fPntWnTJgUGBt71NmvDhw/XkCFD7noaOgDH4N9eAOyqWLFimjNnjtq3b69HHnlEXbp0Ubly5RQfH69NmzZpwYIF1nsFV6xYUV27dtXUqVN18eJFhYWF6ddff9XMmTPVsmVL1a1bN0OzeXt7a/ny5eratauqVq2qn376SUuXLtXbb79tPU2zadOmGjdunBo1aqSOHTvq9OnTmjx5sooXL64//vjjvuZ7+fJlFSxYUM8884wqVqwoX19frVq1Sr/99pv1VOykH+3du3dXWFiYOnTooFOnTmnixIkKCQnR66+/niHLwN/fX4MHD1Z4eLgaNWqkFi1aaP/+/fr000/12GOP3bOTsJTMmTNHxhi1aNEixeFNmjSRu7u7Zs+erapVq+qjjz7SypUrFRYWpueff16PPPKITpw4oQULFmjDhg3KmTOnKlWqpCxZsmjUqFG6dOmSvLy89OSTT971B2f79u3VuXNnffrpp2rYsGGaTomtUqWK5s+fr/79++uxxx6Tr6+vmjdvbh3esWNHvfnmm/r222/10ksvycPD456v+fPPP2vIkCFq0aKFqlWrZr0//PTp03X9+nWbe2hPnjxZNWvWVPny5dW7d28VLVpUp06d0ubNm3Xs2DHt2rVL0q1/Zn311Vdq1KiR+vXrp2zZsmnq1KkKDg5Otl326tVLL774otq0aaMGDRpo165dWrFiRbKjcgMHDtT333+vZs2aqVu3bqpSpYri4uK0e/duLVy4UIcPH0736fmffPKJatasqcqVK+v5559XkSJFdPjwYS1dulQ7d+6UJI0cOVJr1qxR1apV1bt3b5UpU0bnz5/X9u3btWrVKp0/f16S9NRTTykwMFBPPPGE8uXLpz///FOTJk1S06ZN09QxVe7cuVWzZk11795dp06d0oQJE1S8eHH17t1b0q3r9r/88ks1btxYZcuWVffu3VWgQAH9+++/WrNmjXLkyKEffvhB0q3tRJLeeecdPfvss/Lw8FDz5s2VLVs2+fj4qEqVKtqyZYv1Ht3SrSPdcXFxiouLS1Z0p3XZh4WF6YUXXtCIESO0c+dOPfXUU/Lw8FBUVJQWLFigiRMn6plnnknXOkqPsLAwhYWFad26dff9GqNGjdLvv/+u1q1bW88y2L59u2bNmqXcuXPftQOzHDlyqHbt2ho9erRu3LihAgUKaOXKlTp06NB956lUqZI6dOigTz/9VJcuXVKNGjUUGRmpAwcO3HPa48ePa82aNck6YUvi5eWlhg0basGCBfrkk0/k4eGhWbNm6amnnlLr1q3VvHlz1atXT9myZVNUVJTmzZunEydOJLtX98KFC+Xr6ytjjI4fP65p06bpwoULmjJlyl3PukhaXwBcgMP7Swfwn/T333+b3r17m5CQEOPp6WmyZ89unnjiCfO///3P5vZIN27cMOHh4aZIkSLGw8PDFCpUyAwePNhmHGNu3Q6nadOmyeaj225zk+T2W6kkSbptysGDB81TTz1lfHx8TL58+cyQIUNsbp1ljDHTpk0zJUqUMF5eXqZ06dJmxowZ1lu53Gvetw9LuvXM9evXzcCBA03FihVN9uzZTbZs2UzFihVTvKf2/PnzzaOPPmq8vLxM7ty5TadOncyxY8dsxkntFjApZUzNpEmTTOnSpY2Hh4fJly+feemll5Ld1iattwwrX768KVy48F3HqVOnjgkICDA3btwwxhhz5MgR06VLF+Pv72+8vLxM0aJFTZ8+fcz169et03zxxRemaNGiJkuWLDa3+rrzlmFJYmJirLcmuv3+50lSumVYbGys6dixo8mZM6f1Fl93atKkSbLb1d3NP//8Y95//31TrVo1ExAQYNzd3Y2/v79p2rSpzW3wkhw8eNB06dLFBAYGGg8PD1OgQAHTrFkzs3DhQpvx/vjjDxMWFma8vb1NgQIFzAcffGCmTZuW7FZWCQkJ5q233jJ58+Y1Pj4+pmHDhubAgQPJbhlmjDGXL182gwcPNsWLFzeenp4mb968pkaNGubjjz+23gM6pc9Tktu38yR79uwxrVq1Mjlz5jTe3t6mVKlS5r333rMZ59SpU6ZPnz6mUKFCxsPDwwQGBpp69eqZqVOnWsf5/PPPTe3atU2ePHmMl5eXKVasmBk4cKD1XtepSVrPc+fONYMHDzYBAQEma9aspmnTpineX33Hjh2mdevW1vkEBwebdu3aJbuv9AcffGAKFChg3Nzcki3zgQMHGklm1KhRNtMUL17cSDIHDx5MNt+0LPskU6dONVWqVDFZs2Y12bNnN+XLlzdvvvmmOX78uHWc1PaRqX1e7pTa/ixpeeo+bxm2ceNG06dPH1OuXDnj5+dnPDw8TOHChU23bt1SXC53OnbsmHV78vPzM23btjXHjx9Ptu2ltr9Kut3Z7evr6tWr5tVXXzV58uQx2bJlM82bNzdHjx695y3Dxo4dayTd9Z7jERERRpL57rvvrG1XrlwxH3/8sXnssceMr6+v8fT0NCVKlDCvvPKKOXDgQLL3cPsjW7Zspnr16uabb76xmc/dPpe345ZhgHNYjMmgniQAIBPp1q2bFi5cmOJppMDdtGrVSrt3707TkTBHi4iIUPfu3XXo0CFrvwQAAMC5uKYbAIA0OnHihJYuXarnnnvO2VEAAEAmwTXdAADcw6FDh7Rx40Z9+eWX8vDw0AsvvODsSAAAIJPgSDcAAPewbt06Pffcczp06JBmzpypwMBAZ0cCAACZBNd0AwAAAABgJ0490j1ixAg99thjyp49uwICAtSyZUvt37/fZpxr166pT58+ypMnj3x9fdWmTZsU7/V7O2OM3n//feXPn19Zs2ZV/fr1FRUVZc+3AgAAAABAMk4tutetW6c+ffpoy5Yt+vnnn3Xjxg099dRTiouLs47z+uuv64cfftCCBQu0bt06HT9+XK1bt77r644ePVqffPKJpkyZoq1btypbtmxq2LChrl27Zu+3BAAAAACAlUudXn7mzBkFBARo3bp1ql27ti5duiR/f3/NmTNHzzzzjCTpr7/+0iOPPKLNmzerWrVqyV7DGKOgoCC98cYbGjBggCTp0qVLypcvnyIiIvTss8/eM0diYqKOHz+u7Nmzy2KxZOybBAAAAABkesYYXb58WUFBQXJzS/14tkv1Xn7p0iVJUu7cuSVJv//+u27cuKH69etbxyldurQKFy6catF96NAhnTx50mYaPz8/Va1aVZs3b05T0X38+HEVKlToQd8OAAAAAOAhd/ToURUsWDDV4S5TdCcmJuq1117TE088oXLlykmSTp48KU9PT+XMmdNm3Hz58unkyZMpvk5Se758+dI8zfXr13X9+nXr86SD/0ePHlWOHDnu6/0AAAAAAB5eMTExKlSokLJnz37X8Vym6O7Tp4/27NmjDRs2OHzeI0aMUHh4eLL2HDlyUHQDAAAAAFJ1r0uSXeI+3X379tWPP/6oNWvW2ByWDwwMVHx8vC5evGgz/qlTp1K9R2pS+509nN9tmsGDB+vSpUvWx9GjRx/g3QAAAAAAcItTi25jjPr27atvv/1Wq1evVpEiRWyGV6lSRR4eHoqMjLS27d+/X9HR0apevXqKr1mkSBEFBgbaTBMTE6OtW7emOo2Xl5f1qDZHtwEAAAAAGcWpRXefPn309ddfa86cOcqePbtOnjypkydP6urVq5JudYDWs2dP9e/fX2vWrNHvv/+u7t27q3r16jadqJUuXVrffvutpFuH9l977TUNHz5c33//vXbv3q0uXbooKChILVu2dMbbBAAAAAD8Rzn1mu7PPvtMklSnTh2b9hkzZqhbt26SpPHjx8vNzU1t2rTR9evX1bBhQ3366ac24+/fv9/a87kkvfnmm4qLi9Pzzz+vixcvqmbNmlq+fLm8vb3t+n4AAAAAwJESEhJ048YNZ8d4KHl4eChLliwP/DoudZ9uVxETEyM/Pz9dunSJU80BAAAAuBxjjE6ePJms/ytkrJw5cyowMDDFztLSWje6TO/lAAAAAIC0SSq4AwIC5OPjc88etJE+xhhduXJFp0+fliTlz5//vl+LohsAAAAAMpGEhARrwZ0nTx5nx3loZc2aVZJ0+vRpBQQE3Pep5i5xyzAAAAAAQNokXcPt4+Pj5CQPv6Rl/CDXzVN0AwAAAEAmxCnl9pcRy5iiGwAAAAAAO6HoBgAAAADATuhIDQAAAAAeEiGDljp0fodHNk3X+N26ddPMmTM1YsQIDRo0yNq+ZMkStWrVSl27dtXMmTNTnT44OFiHDx/WoUOH9M4772jt2rU6f/688ubNqypVqmjUqFEqXbr0fb8fe+BINwAAAADAYby9vTVq1ChduHAh2bCJEyfqxIkT1ockzZgxw/r8t99+040bN9SgQQNdunRJixcv1v79+zV//nyVL1/eJe9bzpFuAAAAAIDD1K9fXwcOHNCIESM0evRom2F+fn7y8/OzacuZM6cCAwOtz3fu3KmDBw8qMjJSwcHBkm4dAX/iiSfsH/4+cKQbAAAAAOAwWbJk0UcffaT//e9/OnbsWLqn9/f3l5ubmxYuXKiEhAQ7JMxYFN0AAAAAAIdq1aqVKlWqpCFDhqR72gIFCuiTTz7R+++/r1y5cunJJ5/UBx98oH/++ccOSR8cRTcAAAAAwOFGjRqlmTNn6s8//0z3tH369NHJkyc1e/ZsVa9eXQsWLFDZsmX1888/2yHpg+Ga7geQlp4B09ubHwAAAAD8F9SuXVsNGzbU4MGD1a1bt3RPnz17djVv3lzNmzfX8OHD1bBhQw0fPlwNGjTI+LAPgCPdAAAAAACnGDlypH744Qdt3rz5gV7HYrGodOnSiouLy6BkGYeiGwAAAADgFOXLl1enTp30ySefpHmanTt36umnn9bChQu1b98+HThwQNOmTdP06dP19NNP2zHt/eH0cgAAAACA0wwbNkzz589P8/gFCxZUSEiIwsPDdfjwYVksFuvz119/3Y5J7w9FNwAAAAA8JFy9T6mIiIhkbSEhIbp+/XqK4xtjkrXlzZtXEydOzOhodsPp5QAAAAAA2AlFNwAAAAAAdkLRDQAAAACAnXBNt70N9bvH8EuOyQEAAAAAcDiOdAMAAAAAYCcU3QAAAAAA2Amnlz8kQgYtvec4rn77AAAAAAB42HCkGwAAAAAAO6HoBgAAAADATii6AQAAAACwE67pBgAAAICHxb1uWZzh87u/WyBv3rxZNWvWVKNGjbR0qW3/VPHx8ZowYYJmz56tqKgo+fj4qFSpUurVq5c6d+4sDw8PdevWTTNnzrROkzt3bj322GMaPXq0KlSoYG23WCzW+VWrVs3afv36dQUFBen8+fNas2aN6tSpc1/vIy040g0AAAAAcKhp06bplVde0fr163X8+HFre3x8vBo2bKiRI0fq+eef16ZNm/Trr7+qT58++t///qe9e/dax23UqJFOnDihEydOKDIyUu7u7mrWrFmyeRUqVEgzZsywafv222/l6+trvzd4G4puAAAAAIDDxMbGav78+XrppZfUtGlTRUREWIdNmDBB69evV2RkpPr06aNKlSqpaNGi6tixo7Zu3aoSJUpYx/Xy8lJgYKACAwNVqVIlDRo0SEePHtWZM2ds5te1a1fNmzdPV69etbZNnz5dXbt2tft7lSi6AQAAAAAO9M0336h06dIqVaqUOnfurOnTp8sYI0maPXu26tevr0cffTTZdB4eHsqWLVuKrxkbG6uvv/5axYsXV548eWyGValSRSEhIVq0aJEkKTo6WuvXr9dzzz2Xwe8sZRTdAAAAAACHmTZtmjp37izp1inily5d0rp16yRJUVFRKl26dJpe58cff5Svr698fX2VPXt2ff/995o/f77c3JKXuT169ND06dMlSREREWrSpIn8/f0z6B3dHUU3AAAAAMAh9u/fr19//VUdOnSQJLm7u6t9+/aaNm2aJFmPeKdF3bp1tXPnTu3cuVO//vqrGjZsqMaNG+vIkSPJxu3cubM2b96sf/75RxEREerRo0fGvKE0oPdyAAAAAIBDTJs2TTdv3lRQUJC1zRgjLy8vTZo0SSVLltRff/2VptfKli2bihcvbn3+5Zdfys/PT1988YWGDx9uM26ePHnUrFkz9ezZU9euXVPjxo11+fLljHlT98CRbgAAAACA3d28eVOzZs3S2LFjrUeod+7cqV27dikoKEhz585Vx44dtWrVKu3YsSPZ9Ddu3FBcXFyqr2+xWOTm5mbTYdrtevToobVr16pLly7KkiVLhr2ve+FINwAAAADA7n788UdduHBBPXv2lJ+f7f3E27Rpo2nTpmnDhg1aunSp6tWrpw8++EA1a9ZU9uzZtW3bNo0aNUrTpk1TpUqVJN261/bJkyclSRcuXNCkSZMUGxur5s2bpzj/Ro0a6cyZM8qRI4dd3+edKLoBAAAAAHY3bdo01a9fP1nBLd0qukePHq39+/fr559/1vjx4/X5559rwIAB8vHx0SOPPKJXX31V5cqVs06zfPly5c+fX5KUPXt2lS5dWgsWLFCdOnVSnL/FYlHevHnt8t7uxmLSc6X6f0RMTIz8/Px06dKlu/4XJGTQ0nu+1mHvjncfYeil9Ma7/ywjm2bIvAAAAAA4z7Vr13To0CEVKVJE3t7ezo7zULvbsk5r3cg13QAAAAAA2IlTi+7169erefPmCgoKksVi0ZIlS2yGWyyWFB9jxoxJ9TWHDh2abPy03ucNAAAAAICM5NSiOy4uThUrVtTkyZNTHH7ixAmbx/Tp02WxWNSmTZu7vm7ZsmVtptuwYYM94gMAAAAAcFdO7UitcePGaty4carDAwMDbZ5/9913qlu3rooWLXrX13V3d082LQAAAAAAjpZpruk+deqUli5dqp49e95z3KioKAUFBalo0aLq1KmToqOjHZAQAAAAAABbmeaWYTNnzlT27NnVunXru45XtWpVRUREqFSpUjpx4oTCw8NVq1Yt7dmzR9mzZ09xmuvXr+v69evW5zExMRmaHQAAAAAyWmJiorMjPPQyYhlnmqJ7+vTp6tSp0z27xL/9dPUKFSqoatWqCg4O1jfffJPqUfIRI0YoPDw8Q/MCAAAAgD14enrKzc1Nx48fl7+/vzw9PWWxWJwd66FijFF8fLzOnDkjNzc3eXp63vdrZYqi+5dfftH+/fs1f/78dE+bM2dOlSxZUgcOHEh1nMGDB6t///7W5zExMSpUqNB9ZQUAAAAAe3Jzc1ORIkV04sQJHT9+3NlxHmo+Pj4qXLiw3Nzu/8rsTFF0T5s2TVWqVFHFihXTPW1sbKwOHjyo5557LtVxvLy85OXl9SARAQAAAMBhPD09VbhwYd28eVMJCQnOjvNQypIli9zd3R/4LAKnFt2xsbE2R6APHTqknTt3Knfu3CpcuLCkW0edFyxYoLFjx6b4GvXq1VOrVq3Ut29fSdKAAQPUvHlzBQcH6/jx4xoyZIiyZMmiDh062P8NAQAAAICDWCwWeXh4yMPDw9lRcBdOLbq3bdumunXrWp8nneLdtWtXRURESJLmzZsnY0yqRfPBgwd19uxZ6/Njx46pQ4cOOnfunPz9/VWzZk1t2bJF/v7+9nsjAAAAAACkwKlFd506dWSMues4zz//vJ5//vlUhx8+fNjm+bx58zIiGgAAAAAADyzT3KcbAAAAAIDMhqIbAAAAAAA7oegGAAAAAMBOKLoBAAAAALATim4AAAAAAOyEohsAAAAAADuh6AYAAAAAwE4ougEAAAAAsBOKbgAAAAAA7ISiGwAAAAAAO6HoBgAAAADATii6AQAAAACwE4puAAAAAADshKIbAAAAAAA7oegGAAAAAMBOKLoBAAAAALATim4AAAAAAOyEohsAAAAAADuh6AYAAAAAwE4ougEAAAAAsBOKbgAAAAAA7ISiGwAAAAAAO6HoBgAAAADATii6AQAAAACwE4puAAAAAADshKIbAAAAAAA7oegGAAAAAMBOKLoBAAAAALATim4AAAAAAOyEohsAAAAAADuh6AYAAAAAwE4ougEAAAAAsBOKbgAAAAAA7ISiGwAAAAAAO6HoBgAAAADATii6AQAAAACwE4puAAAAAADsxN3ZAeBAQ/3uMfySY3IAAAAAwH8ER7oBAAAAALATim4AAAAAAOzEqUX3+vXr1bx5cwUFBclisWjJkiU2w7t16yaLxWLzaNSo0T1fd/LkyQoJCZG3t7eqVq2qX3/91U7vAAAAAACA1Dm16I6Li1PFihU1efLkVMdp1KiRTpw4YX3MnTv3rq85f/589e/fX0OGDNH27dtVsWJFNWzYUKdPn87o+AAAAAAA3JVTO1Jr3LixGjdufNdxvLy8FBgYmObXHDdunHr37q3u3btLkqZMmaKlS5dq+vTpGjRo0APlBQAAAAAgPVz+mu61a9cqICBApUqV0ksvvaRz586lOm58fLx+//131a9f39rm5uam+vXra/PmzalOd/36dcXExNg8AAAAAAB4UC5ddDdq1EizZs1SZGSkRo0apXXr1qlx48ZKSEhIcfyzZ88qISFB+fLls2nPly+fTp48mep8RowYIT8/P+ujUKFCGfo+AAAAAAD/TS59n+5nn33W+nf58uVVoUIFFStWTGvXrlW9evUybD6DBw9W//79rc9jYmIovAEAAAAAD8ylj3TfqWjRosqbN68OHDiQ4vC8efMqS5YsOnXqlE37qVOn7npduJeXl3LkyGHzAAAAAADgQWWqovvYsWM6d+6c8ufPn+JwT09PValSRZGRkda2xMRERUZGqnr16o6KCQAAAACAJCcX3bGxsdq5c6d27twpSTp06JB27typ6OhoxcbGauDAgdqyZYsOHz6syMhIPf300ypevLgaNmxofY169epp0qRJ1uf9+/fXF198oZkzZ+rPP//USy+9pLi4OGtv5gAAAAAAOIpTr+netm2b6tata32edF11165d9dlnn+mPP/7QzJkzdfHiRQUFBempp57SBx98IC8vL+s0Bw8e1NmzZ63P27dvrzNnzuj999/XyZMnValSJS1fvjxZ52oAAAAAANibU4vuOnXqyBiT6vAVK1bc8zUOHz6crK1v377q27fvg0QDAAAAAOCBZaprugEAAAAAyEwougEAAAAAsBOKbgAAAAAA7ISiGwAAAAAAO6HoBgAAAADATii6AQAAAACwE4puAAAAAADshKIbAAAAAAA7oegGAAAAAMBOKLoBAAAAALCTBy66Y2JitGTJEv35558ZkQcAAAAAgIdGuovudu3aadKkSZKkq1evKjQ0VO3atVOFChW0aNGiDA8IAAAAAEBmle6ie/369apVq5Yk6dtvv5UxRhcvXtQnn3yi4cOHZ3hAAAAAAAAyq3QX3ZcuXVLu3LklScuXL1ebNm3k4+Ojpk2bKioqKsMDAgAAAACQWaW76C5UqJA2b96suLg4LV++XE899ZQk6cKFC/L29s7wgAAAAAAAZFbu6Z3gtddeU6dOneTr66vg4GDVqVNH0q3TzsuXL5/R+QAAAAAAyLTSXXS//PLLevzxx3X06FE1aNBAbm63DpYXLVqUa7oBAAAAALhNuotuSQoNDVVoaKiMMTLGyGKxqGnTphmdDQAAAACATO2+7tM9bdo0lStXTt7e3vL29la5cuX05ZdfZnQ2AAAAAAAytXQf6X7//fc1btw4vfLKK6pevbokafPmzXr99dcVHR2tYcOGZXhIAAAAAAAyo3QX3Z999pm++OILdejQwdrWokULVahQQa+88gpFNwAAAAAA/1+6Ty+/ceOGQkNDk7VXqVJFN2/ezJBQAAAAAAA8DNJddD/33HP67LPPkrVPnTpVnTp1ypBQAAAAAAA8DO6r9/Jp06Zp5cqVqlatmiRp69atio6OVpcuXdS/f3/reOPGjcuYlAAAAAAAZELpLrr37NmjypUrS5IOHjwoScqbN6/y5s2rPXv2WMezWCwZFBEAAAAAgMwp3UX3mjVr7JEDAAAAAICHzn3dpxsAAAAAANxbmo50t27dWhEREcqRI4dat25913EXL16cIcEAAAAAAMjs0lR0+/n5Wa/R9vPzs2sgAAAAAAAeFmkqumfMmCFJMsYoPDxc/v7+ypo1q12DAQAAAACQ2aXrmm5jjIoXL65jx47ZKw8AAAAAAA+NdBXdbm5uKlGihM6dO2evPAAAAAAAPDTS3Xv5yJEjNXDgQJt7cgMAAAAAgOTSfZ/uLl266MqVK6pYsaI8PT2TXdt9/vz5DAsHAAAAAEBmlu6ie/z48daezAEAAAAAQOrSXXR369bNDjEAAAAAAHj4pPua7ixZsuj06dPJ2s+dO6csWbJkSCgAAAAAAB4G6S66jTEptl+/fl2enp4PHAgAAAAAgIdFmk8v/+STTyRJFotFX375pXx9fa3DEhIStH79epUuXTrjEwIAAAAAkEmluegeP368pFtHuqdMmWJzKrmnp6dCQkI0ZcqUdM18/fr1GjNmjH7//XedOHFC3377rVq2bClJunHjht59910tW7ZM//zzj/z8/FS/fn2NHDlSQUFBqb7m0KFDFR4ebtNWqlQp/fXXX+nKBgAAAADAg0pz0X3o0CFJUt26dbV48WLlypXrgWceFxenihUrqkePHmrdurXNsCtXrmj79u167733VLFiRV24cEH9+vVTixYttG3btru+btmyZbVq1Srrc3f3dPcXBwAAAADAA0t3NbpmzZoMm3njxo3VuHHjFIf5+fnp559/tmmbNGmSHn/8cUVHR6tw4cKpvq67u7sCAwMzLCcAAAAAAPcj3R2pOdOlS5dksViUM2fOu44XFRWloKAgFS1aVJ06dVJ0dPRdx79+/bpiYmJsHgAAAAAAPKhMU3Rfu3ZNb731ljp06KAcOXKkOl7VqlUVERGh5cuX67PPPtOhQ4dUq1YtXb58OdVpRowYIT8/P+ujUKFC9ngLAAAAAID/mExRdN+4cUPt2rWTMUafffbZXcdt3Lix2rZtqwoVKqhhw4ZatmyZLl68qG+++SbVaQYPHqxLly5ZH0ePHs3otwAAAAAA+A9y+R7GkgruI0eOaPXq1Xc9yp2SnDlzqmTJkjpw4ECq43h5ecnLy+tBowIAAAAAYCNNRfcff/yR5hesUKHCfYe5U1LBHRUVpTVr1ihPnjzpfo3Y2FgdPHhQzz33XIblAgAAAAAgLdJUdFeqVEkWi0XGmBSHJw2zWCxKSEhI88xjY2NtjkAfOnRIO3fuVO7cuZU/f34988wz2r59u3788UclJCTo5MmTkqTcuXPL09NTklSvXj21atVKffv2lSQNGDBAzZs3V3BwsI4fP64hQ4YoS5Ys6tChQ5pzAQAAAACQEdJUdCfdozujbdu2TXXr1rU+79+/vySpa9euGjp0qL7//ntJt4r+261Zs0Z16tSRJB08eFBnz561Djt27Jg6dOigc+fOyd/fXzVr1tSWLVvk7+9vl/cAAAAAAEBq0lR0BwcH22XmderUSfXouaS7Dkty+PBhm+fz5s170FgAAAAAAGSI++5Ibd++fYqOjlZ8fLxNe4sWLR44FAAAAAAAD4N0F93//POPWrVqpd27d9tc522xWCQpXdd0AwAAAADwMEv3fbr79eunIkWK6PTp0/Lx8dHevXu1fv16hYaGau3atXaICAAAAABA5pTuI92bN2/W6tWrlTdvXrm5ucnNzU01a9bUiBEj9Oqrr2rHjh32yAkAAAAAQKaT7iPdCQkJyp49uyQpb968On78uKRbna3t378/Y9MBAAAAAJCJpftId7ly5bRr1y4VKVJEVatW1ejRo+Xp6ampU6eqaNGi9sgIAAAAAECmlO6i+91331VcXJwkadiwYWrWrJlq1aqlPHnycLsuAAAAAABuk+6iu2HDhta/ixcvrr/++kvnz59Xrly5rD2YAwAAAACA+7imu0ePHrp8+bJNW+7cuXXlyhX16NEjw4IBAAAAAJDZpbvonjlzpq5evZqs/erVq5o1a1aGhAIAAAAA4GGQ5tPLY2JiZIyRMUaXL1+Wt7e3dVhCQoKWLVumgIAAu4QEAAAAACAzSnPRnTNnTlksFlksFpUsWTLZcIvFovDw8AwNBwAAAABAZpbmonvNmjUyxujJJ5/UokWLlDt3buswT09PBQcHKygoyC4hAQAAAADIjNJcdIeFhUmSDh06pEKFCsnNLd2XgwMAAAAA8J+S7luGBQcH6+LFi5o2bZr+/PNPSVLZsmXVo0cP+fn5ZXhAZD4hg5bec5zDI5s6IAkAAAAAOFe6D1dv27ZNxYoV0/jx43X+/HmdP39e48aNU7FixbR9+3Z7ZAQAAAAAIFNK95Hu119/XS1atNAXX3whd/dbk9+8eVO9evXSa6+9pvXr12d4SAAAAAAAMqN0F93btm2zKbglyd3dXW+++aZCQ0MzNBwAAAAAAJlZuk8vz5Ejh6Kjo5O1Hz16VNmzZ8+QUAAAAAAAPAzSXXS3b99ePXv21Pz583X06FEdPXpU8+bNU69evdShQwd7ZAQAAAAAIFNK9+nlH3/8sSwWi7p06aKbN29Kkjw8PPTSSy9p5MiRGR4QAAAAAIDMKt1Ft6enpyZOnKgRI0bo4MGDkqRixYrJx8cnw8MBAAAAAJCZpfv08h49eujy5cvy8fFR+fLlVb58efn4+CguLk49evSwR0YAAAAAADKldBfdM2fO1NWrV5O1X716VbNmzcqQUAAAAAAAPAzSfHp5TEyMjDEyxujy5cvy9va2DktISNCyZcsUEBBgl5AAAAAAAGRGaS66c+bMKYvFIovFopIlSyYbbrFYFB4enqHhAAAAAADIzNJcdK9Zs0bGGD355JNatGiRcufObR3m6emp4OBgBQUF2SUkAAAAAACZUZqL7rCwMEnSoUOHVLhwYVksFruFAgAAAADgYZDuW4YFBwfbIwcAAAAAAA+ddPdeDgAAAAAA0ibdR7qBDDHU7x7DLzkmBwAAAADYUZqOdH///fe6ceOGvbMAAAAAAPBQSVPR3apVK128eFGSlCVLFp0+fdqemQAAAAAAeCikqej29/fXli1bJEnGGHouBwAAAAAgDdJ0TfeLL76op59+WhaLRRaLRYGBgamOm5CQkGHhAAAAAADIzNJUdA8dOlTPPvusDhw4oBYtWmjGjBnKmTOnnaMBAAAAAJC5pbn38tKlS6t06dIaMmSI2rZtKx8fH3vmAgAAAAAg00v3LcOGDBkiSTpz5oz2798vSSpVqpT8/f0zNhkAAAAAAJlcmjpSu92VK1fUo0cPBQUFqXbt2qpdu7aCgoLUs2dPXblyxR4ZAQAAAADIlNJddL/++utat26dvv/+e128eFEXL17Ud999p3Xr1umNN95I12utX79ezZs3V1BQkCwWi5YsWWIz3Bij999/X/nz51fWrFlVv359RUVF3fN1J0+erJCQEHl7e6tq1ar69ddf05ULAAAAAICMkO6ie9GiRZo2bZoaN26sHDlyKEeOHGrSpIm++OILLVy4MF2vFRcXp4oVK2ry5MkpDh89erQ++eQTTZkyRVu3blW2bNnUsGFDXbt2LdXXnD9/vvr3768hQ4Zo+/btqlixoho2bMi9xQEAAAAADndfp5fny5cvWXtAQEC6Ty9v3Lixhg8frlatWiUbZozRhAkT9O677+rpp59WhQoVNGvWLB0/fjzZEfHbjRs3Tr1791b37t1VpkwZTZkyRT4+Ppo+fXq6sgEAAAAA8KDSXXRXr15dQ4YMsTnafPXqVYWHh6t69eoZFuzQoUM6efKk6tevb23z8/NT1apVtXnz5hSniY+P1++//24zjZubm+rXr5/qNJJ0/fp1xcTE2DwAAAAAAHhQ6e69fOLEiWrYsKEKFiyoihUrSpJ27dolb29vrVixIsOCnTx5UpKSHVXPly+fddidzp49q4SEhBSn+euvv1Kd14gRIxQeHv6AiQEAAAAAsJXuortcuXKKiorS7NmzrYVshw4d1KlTJ2XNmjXDAzrC4MGD1b9/f+vzmJgYFSpUyImJAAAAAAAPg3QX3ZLk4+Oj3r17Z3QWG4GBgZKkU6dOKX/+/Nb2U6dOqVKlSilOkzdvXmXJkkWnTp2yaT916pT19VLi5eUlLy+vBw8NAAAAAMBt0n1Nt6MUKVJEgYGBioyMtLbFxMRo69atqV477unpqSpVqthMk5iYqMjIyAy93hwAAAAAgLS4ryPdGSU2NlYHDhywPj906JB27typ3Llzq3Dhwnrttdc0fPhwlShRQkWKFNF7772noKAgtWzZ0jpNvXr11KpVK/Xt21eS1L9/f3Xt2lWhoaF6/PHHNWHCBMXFxal79+6OfnsAAAAAgP84pxbd27ZtU926da3Pk66r7tq1qyIiIvTmm28qLi5Ozz//vC5evKiaNWtq+fLl8vb2tk5z8OBBnT171vq8ffv2OnPmjN5//32dPHlSlSpV0vLly1O8zRkAAAAAAPbk1KK7Tp06MsakOtxisWjYsGEaNmxYquMcPnw4WVvfvn2tR74BAAAAAHCW+7qm++LFi/ryyy81ePBgnT9/XpK0fft2/fvvvxkaDgAAAACAzCzdR7r/+OMP1a9fX35+fjp8+LB69+6t3Llza/HixYqOjtasWbPskRMAAAAAgEwn3Ue6+/fvr27duikqKsrm2uomTZpo/fr1GRoOAAAAAIDMLN1F92+//aYXXnghWXuBAgV08uTJDAkFAAAAAMDDIN1Ft5eXl2JiYpK1//333/L398+QUAAAAAAAPAzSXXS3aNFCw4YN040bNyTd6mE8Ojpab731ltq0aZPhAQEAAAAAyKzSXXSPHTtWsbGxCggI0NWrVxUWFqbixYsre/bs+vDDD+2REQAAAACATCndvZf7+fnp559/1oYNG/THH38oNjZWlStXVv369e2RDwAAAACATCvdRXeSmjVrqmbNmhmZBQAAAACAh0q6i+5PPvkkxXaLxSJvb28VL15ctWvXVpYsWR44HAAAAAAAmVm6i+7x48frzJkzunLlinLlyiVJunDhgnx8fOTr66vTp0+raNGiWrNmjQoVKpThgQEAAAAAyCzS3ZHaRx99pMcee0xRUVE6d+6czp07p7///ltVq1bVxIkTFR0drcDAQL3++uv2yAsAAAAAQKaR7iPd7777rhYtWqRixYpZ24oXL66PP/5Ybdq00T///KPRo0dz+zAAAAAAwH9euo90nzhxQjdv3kzWfvPmTZ08eVKSFBQUpMuXLz94OgAAAAAAMrF0F91169bVCy+8oB07dljbduzYoZdeeklPPvmkJGn37t0qUqRIxqUEAAAAACATSnfRPW3aNOXOnVtVqlSRl5eXvLy8FBoaqty5c2vatGmSJF9fX40dOzbDwwIAAAAAkJmk+5ruwMBA/fzzz/rrr7/0999/S5JKlSqlUqVKWcepW7duxiUEAAAAACCTSnfRnaR06dIqXbp0RmYBAAAAAOChcl9F97Fjx/T9998rOjpa8fHxNsPGjRuXIcEAAAAAAMjs0l10R0ZGqkWLFipatKj++usvlStXTocPH5YxRpUrV7ZHRgAAAAAAMqV0d6Q2ePBgDRgwQLt375a3t7cWLVqko0ePKiwsTG3btrVHRgAAAAAAMqV0F91//vmnunTpIklyd3fX1atX5evrq2HDhmnUqFEZHhAAAAAAgMwq3UV3tmzZrNdx58+fXwcPHrQOO3v2bMYlAwAAAAAgk0v3Nd3VqlXThg0b9Mgjj6hJkyZ64403tHv3bi1evFjVqlWzR0YAAAAAADKldBfd48aNU2xsrCQpPDxcsbGxmj9/vkqUKEHP5QAAAAAA3CbdRXfRokWtf2fLlk1TpkzJ0EAAAAAAADws0n1Nd9GiRXXu3Llk7RcvXrQpyAEAAAAA+K9Ld9F9+PBhJSQkJGu/fv26/v333wwJBQAAAADAwyDNp5d///331r9XrFghPz8/6/OEhARFRkYqJCQkQ8MBAAAAAJCZpbnobtmypSTJYrGoa9euNsM8PDwUEhKisWPHZmg4AAAAAAAyszQX3YmJiZKkIkWK6LffflPevHntFgoAAAAAgIdBunsvP3TokD1yAAAAAADw0El30S1JkZGRioyM1OnTp61HwJNMnz49Q4IBAAAAAJDZpbvoDg8P17BhwxQaGqr8+fPLYrHYIxcAAAAAAJleuovuKVOmKCIiQs8995w98gAAAAAA8NBI93264+PjVaNGDXtkAQAAAADgoZLuortXr16aM2eOPbIAAAAAAPBQSffp5deuXdPUqVO1atUqVahQQR4eHjbDx40bl2HhAAAAAADIzNJddP/xxx+qVKmSJGnPnj02w+hUDQAAAACA/5PuonvNmjX2yJGqkJAQHTlyJFn7yy+/rMmTJydrj4iIUPfu3W3avLy8dO3aNbtlBAAAAAAgJfd1n25JOnDggA4ePKjatWsra9asMsbY5Uj3b7/9poSEBOvzPXv2qEGDBmrbtm2q0+TIkUP79++3PucIPAAAAADAGdJddJ87d07t2rXTmjVrZLFYFBUVpaJFi6pnz57KlSuXxo4dm6EB/f39bZ6PHDlSxYoVU1hYWKrTWCwWBQYGZmgOAAAAAADSK929l7/++uvy8PBQdHS0fHx8rO3t27fX8uXLMzTcneLj4/X111+rR48edz16HRsbq+DgYBUqVEhPP/209u7da9dcAAAAAACkJN1HuleuXKkVK1aoYMGCNu0lSpRI8drrjLRkyRJdvHhR3bp1S3WcUqVKafr06apQoYIuXbqkjz/+WDVq1NDevXuTZU5y/fp1Xb9+3fo8JiYmo6MDAAAAAP6D0n2kOy4uzuYId5Lz58/Ly8srQ0KlZtq0aWrcuLGCgoJSHad69erq0qWLKlWqpLCwMC1evFj+/v76/PPPU51mxIgR8vPzsz4KFSpkj/gAAAAAgP+YdBfdtWrV0qxZs6zPLRaLEhMTNXr0aNWtWzdDw93uyJEjWrVqlXr16pWu6Tw8PPToo4/qwIEDqY4zePBgXbp0yfo4evTog8YFAAAAACD9p5ePHj1a9erV07Zt2xQfH68333xTe/fu1fnz57Vx40Z7ZJQkzZgxQwEBAWratGm6pktISNDu3bvVpEmTVMfx8vKy+1F6AAAAAMB/T7qPdJcrV05///23atasqaefflpxcXFq3bq1duzYoWLFitkjoxITEzVjxgx17dpV7u62/yfo0qWLBg8ebH0+bNgwrVy5Uv/884+2b9+uzp0768iRI+k+Qg4AAAAAwIO6r/t0+/n56Z133snoLKlatWqVoqOj1aNHj2TDoqOj5eb2f/87uHDhgnr37q2TJ08qV65cqlKlijZt2qQyZco4LC8AAAAAANJ9FN0zZsyQr6+v2rZta9O+YMECXblyRV27ds2wcEmeeuopGWNSHLZ27Vqb5+PHj9f48eMzPAMAAAAAAOmV7tPLR4wYobx58yZrDwgI0EcffZQhoQAAAAAAeBiku+iOjo5WkSJFkrUHBwcrOjo6Q0IBAAAAAPAwSHfRHRAQoD/++CNZ+65du5QnT54MCQUAAAAAwMMg3UV3hw4d9Oqrr2rNmjVKSEhQQkKCVq9erX79+unZZ5+1R0YAAAAAADKldHek9sEHH+jw4cOqV6+e9fZdiYmJ6tKlC9d0AwAAAABwm3QV3cYYnTx5UhERERo+fLh27typrFmzqnz58goODrZXRgAAAAAAMqV0F93FixfX3r17VaJECZUoUcJeuQAAAAAAyPTSdU23m5ubSpQooXPnztkrDwAAAAAAD410d6Q2cuRIDRw4UHv27LFHHgAAAAAAHhrp7kitS5cuunLliipWrChPT09lzZrVZvj58+czLBzwoEIGLb3nOIdHNnVAEgAAAAD/RekuuidMmGCHGAAAAAAAPHzSXXR37drVHjkAAAAAAHjopPuabkk6ePCg3n33XXXo0EGnT5+WJP3000/au3dvhoYDAAAAACAzS3fRvW7dOpUvX15bt27V4sWLFRsbK0natWuXhgwZkuEBAQAAAADIrNJddA8aNEjDhw/Xzz//LE9PT2v7k08+qS1btmRoOAAAAAAAMrN0F927d+9Wq1atkrUHBATo7NmzGRIKAAAAAICHQbqL7pw5c+rEiRPJ2nfs2KECBQpkSCgAAAAAAB4G6S66n332Wb311ls6efKkLBaLEhMTtXHjRg0YMEBdunSxR0YAAAAAADKldBfdH330kUqXLq1ChQopNjZWZcqUUe3atVWjRg29++679sgIAAAAAECmlO77dHt6euqLL77Q+++/r927dys2NlaPPvqoSpQoYY98AAAAAABkWmkuuhMTEzVmzBh9//33io+PV7169TRkyBBlzZrVnvkAAAAAAMi00nx6+Ycffqi3335bvr6+KlCggCZOnKg+ffrYMxsAAAAAAJlamovuWbNm6dNPP9WKFSu0ZMkS/fDDD5o9e7YSExPtmQ8AAAAAgEwrzUV3dHS0mjRpYn1ev359WSwWHT9+3C7BAAAAAADI7NJcdN+8eVPe3t42bR4eHrpx40aGhwIAAAAA4GGQ5o7UjDHq1q2bvLy8rG3Xrl3Tiy++qGzZslnbFi9enLEJAQAAAADIpNJcdHft2jVZW+fOnTM0DAAAAAAAD5M0F90zZsywZw4AAAAAAB46ab6mGwAAAAAApA9FNwAAAAAAdkLRDQAAAACAnVB0AwAAAABgJxTdAAAAAADYCUU3AAAAAAB2QtENAAAAAICdUHQDAAAAAGAnFN0AAAAAANgJRTcAAAAAAHbi7uwAgNMN9bvH8EuOyQEAAADgoePSR7qHDh0qi8Vi8yhduvRdp1mwYIFKly4tb29vlS9fXsuWLXNQWgAAAAAAbLl00S1JZcuW1YkTJ6yPDRs2pDrupk2b1KFDB/Xs2VM7duxQy5Yt1bJlS+3Zs8eBiQEAAAAAuMXli253d3cFBgZaH3nz5k113IkTJ6pRo0YaOHCgHnnkEX3wwQeqXLmyJk2a5MDEAAAAAADc4vJFd1RUlIKCglS0aFF16tRJ0dHRqY67efNm1a9f36atYcOG2rx5s71jAgAAAACQjEt3pFa1alVFRESoVKlSOnHihMLDw1WrVi3t2bNH2bNnTzb+yZMnlS9fPpu2fPny6eTJk3edz/Xr13X9+nXr85iYmIx5AwAAAACA/zSXLrobN25s/btChQqqWrWqgoOD9c0336hnz54ZNp8RI0YoPDw8w14PAAAAAAApE5xefrucOXOqZMmSOnDgQIrDAwMDderUKZu2U6dOKTAw8K6vO3jwYF26dMn6OHr0aIZlBgAAAAD8d2Wqojs2NlYHDx5U/vz5UxxevXp1RUZG2rT9/PPPql69+l1f18vLSzly5LB5AAAAAADwoFz69PIBAwaoefPmCg4O1vHjxzVkyBBlyZJFHTp0kCR16dJFBQoU0IgRIyRJ/fr1U1hYmMaOHaumTZtq3rx52rZtm6ZOnerMtwFIkkIGLb3nOIdHNnVAEgAAAACO4tJF97Fjx9ShQwedO3dO/v7+qlmzprZs2SJ/f39JUnR0tNzc/u9gfY0aNTRnzhy9++67evvtt1WiRAktWbJE5cqVc9ZbAAAAAAD8h7l00T1v3ry7Dl+7dm2ytrZt26pt27Z2SgQAAAAAQNplqmu6AQAAAADITCi6AQAAAACwE4puAAAAAADshKIbAAAAAAA7cemO1ID/nKF+9xh+yTE5AAAAAGQIjnQDAAAAAGAnFN0AAAAAANgJRTcAAAAAAHZC0Q0AAAAAgJ1QdAMAAAAAYCcU3QAAAAAA2AlFNwAAAAAAdkLRDQAAAACAnVB0AwAAAABgJxTdAAAAAADYCUU3AAAAAAB2QtENAAAAAICdUHQDAAAAAGAnFN0AAAAAANgJRTcAAAAAAHZC0Q0AAAAAgJ1QdAMAAAAAYCcU3QAAAAAA2AlFNwAAAAAAdkLRDQAAAACAnVB0AwAAAABgJxTdAAAAAADYCUU3AAAAAAB2QtENAAAAAICdUHQDAAAAAGAnFN0AAAAAANgJRTcAAAAAAHZC0Q0AAAAAgJ24OzsAAMcLGbT0nuMcHtnUAUkAAACAhxtHugEAAAAAsBOKbgAAAAAA7ISiGwAAAAAAO6HoBgAAAADATii6AQAAAACwE4puAAAAAADsxKWL7hEjRuixxx5T9uzZFRAQoJYtW2r//v13nSYiIkIWi8Xm4e3t7aDEAAAAAAD8H5cuutetW6c+ffpoy5Yt+vnnn3Xjxg099dRTiouLu+t0OXLk0IkTJ6yPI0eOOCgxAAAAAAD/x93ZAe5m+fLlNs8jIiIUEBCg33//XbVr1051OovFosDAQHvHAwAAAADgrlz6SPedLl26JEnKnTv3XceLjY1VcHCwChUqpKefflp79+696/jXr19XTEyMzQMAAAAAgAeVaYruxMREvfbaa3riiSdUrly5VMcrVaqUpk+fru+++05ff/21EhMTVaNGDR07dizVaUaMGCE/Pz/ro1ChQvZ4CwAAAACA/5hMU3T36dNHe/bs0bx58+46XvXq1dWlSxdVqlRJYWFhWrx4sfz9/fX555+nOs3gwYN16dIl6+Po0aMZHR8AAAAA8B/k0td0J+nbt69+/PFHrV+/XgULFkzXtB4eHnr00Ud14MCBVMfx8vKSl5fXg8YEAAAAAMCGSx/pNsaob9+++vbbb7V69WoVKVIk3a+RkJCg3bt3K3/+/HZICAAAAABA6lz6SHefPn00Z84cfffdd8qePbtOnjwpSfLz81PWrFklSV26dFGBAgU0YsQISdKwYcNUrVo1FS9eXBcvXtSYMWN05MgR9erVy2nvA8iUhvrdY/glx+QAAAAAMjGXLro/++wzSVKdOnVs2mfMmKFu3bpJkqKjo+Xm9n8H7C9cuKDevXvr5MmTypUrl6pUqaJNmzapTJkyjooNIB1CBi295ziHRzZ1QBIAAAAg47l00W2Muec4a9eutXk+fvx4jR8/3k6JAAAAAABIO5e+phsAAAAAgMyMohsAAAAAADuh6AYAAAAAwE4ougEAAAAAsBOKbgAAAAAA7ISiGwAAAAAAO6HoBgAAAADATii6AQAAAACwE3dnBwAAVxEyaOk9xzk8sqkDkgAAAOBhQdENwPUN9bvH8EuOyQEAAACkE0U3ALggjroDAAA8HLimGwAAAAAAO6HoBgAAAADATii6AQAAAACwE67pBgDcFdeXAwAA3D+KbgBID3pST5mDlgv/AAAAAJkNRTcA4OHCP0YAAIAL4ZpuAAAAAADshCPdAADcB051BwAAacGRbgAAAAAA7ISiGwAAAAAAO6HoBgAAAADATrimGwAyK3rpBgAAcHkU3QAAZHJ06vYA+OcVAMDOKLoBALAXVyroXCkLAAD/IVzTDQAAAACAnVB0AwAAAABgJ5xeDgAAMkyari/37nj3ETjVHQDwEKHoBgAAD6W0/QPAAUEAAP/nP9jHCEU3AACAndHDPAD8d1F0AwAA/IfwDwBkOFc6culKWYD/j6IbAAAAtihcgIcLn2mnougGAABwBfwoBoCHEkU3AAAAnCLTnerOP0aAh4uDPtMU3QAAAPjPc6Xe7l3p1nuZb7k4IIhYR0gfN2cHAAAAAADgYcWRbgAAALguTukGMg1XOuruSlk40g0AAAAAgJ1QdAMAAAAAYCeZouiePHmyQkJC5O3trapVq+rXX3+96/gLFixQ6dKl5e3trfLly2vZsmUOSgoAAAAAwP9x+aJ7/vz56t+/v4YMGaLt27erYsWKatiwoU6fPp3i+Js2bVKHDh3Us2dP7dixQy1btlTLli21Z88eBycHAAAAAPzXuXzRPW7cOPXu3Vvdu3dXmTJlNGXKFPn4+Gj69Okpjj9x4kQ1atRIAwcO1COPPKIPPvhAlStX1qRJkxycHAAAAADwX+fSvZfHx8fr999/1+DBg61tbm5uql+/vjZv3pziNJs3b1b//v1t2ho2bKglS5akOp/r16/r+vXr1ueXLt3qBTMmJuau+RKvX7nXW1CMxdxjhLvPI63IQhaykIUsZCELWchCFrKQhSyOy5JULxpzj9cxLuzff/81ksymTZts2gcOHGgef/zxFKfx8PAwc+bMsWmbPHmyCQgISHU+Q4YMMZJ48ODBgwcPHjx48ODBgwePdD2OHj1617rWpY90O8rgwYNtjo4nJibq/PnzypMnjywWy32/bkxMjAoVKqSjR48qR44cGRGVLGQhC1nIQhaykIUsZCELWcjiAlmMMbp8+bKCgoLuOp5LF9158+ZVlixZdOrUKZv2U6dOKTAwMMVpAgMD0zW+JHl5ecnLy8umLWfOnPcXOgU5cuRw+kaVhCwpI0vKyJIysqSMLCkjS8rIkjKypIwsKSNLysiSMrKk7EGz+Pn53XMcl+5IzdPTU1WqVFFkZKS1LTExUZGRkapevXqK01SvXt1mfEn6+eefUx0fAAAAAAB7cekj3ZLUv39/de3aVaGhoXr88cc1YcIExcXFqXv37pKkLl26qECBAhoxYoQkqV+/fgoLC9PYsWPVtGlTzZs3T9u2bdPUqVOd+TYAAAAAAP9BLl90t2/fXmfOnNH777+vkydPqlKlSlq+fLny5csnSYqOjpab2/8dsK9Ro4bmzJmjd999V2+//bZKlCihJUuWqFy5cg7P7uXlpSFDhiQ7dd0ZyEIWspCFLGQhC1nIQhaykIUsjs9iMeZe/ZsDAAAAAID74dLXdAMAAAAAkJlRdAMAAAAAYCcU3QAAAAAA2AlFNwAAAAAAduLyvZfjwcTHx+v06dNKTEy0aS9cuLCTEsGVsb2kjOUCPDwSExN14MCBFD/TtWvXdliOa9euydvb22HzuxdXWS6u5uLFi/r1119TXC5dunRxUirX4Crfja607brK9uJq+5ckSf13WywWp+ZwxrZL7+UZ6NSpUxowYIAiIyN1+vRp3bloExISHJYlKipKPXr00KZNm2zajTGyWCwOzZKUZ82aNSlu4O+//77DciQkJCgiIsK6ju7Msnr1aodliYuL08iRI1PN8s8//zgsi6ttL5GRkakul+nTpzssB8slda7ymXal/S5ZUucq2+6WLVvUsWNHHTlyJNkycfRnOkeOHGrdurU6deqkevXq2dz+1NFcabm40nfjDz/8oE6dOik2NlY5cuSwKRQsFovOnz/vsCyS63yOXOm70ZW2XVfaXry9vfX4448rLCxMderUUY0aNZQ1a1aHzf9Os2bN0pgxYxQVFSVJKlmypAYOHKjnnnvOoTmcue1ypDsDdevWTdHR0XrvvfeUP39+p/4Xp1u3bnJ3d9ePP/7o9CxffPGFXnrpJeXNm1eBgYHJdkKO/IHer18/RUREqGnTpipXrpxTl0uvXr20bt06Pffcc05fR660vYSHh2vYsGEKDQ11ehaWS8pc6TPtavtdsiTnStvuiy++qNDQUC1dutTpWWbOnKk5c+bo6aeflp+fn9q3b6/OnTsrNDTU4Vlcabm40nfjG2+8oR49euijjz6Sj4+P03JIrvU5cqXvRlfadl1pe1m1apXWr1+vtWvXavz48bp586ZCQ0OtRXiDBg0clmXcuHF677331LdvXz3xxBOSpA0bNujFF1/U2bNn9frrrzssi1O3XYMM4+vra3bs2OHsGMYYY3x8fMyff/7p7BjGGGMKFy5sRo4c6ewYxhhj8uTJY5YuXersGMYYY/z8/MyGDRucHcMY41rbS2BgoJk1a5azYxhjWC6pcaXPtCvtd8mSMlfadn18fExUVJSzY9iIiYkx06dPNw0aNDBZsmQxJUqUMOHh4Q7N4ErLxdW+Gw8ePOjsGMYY1/scucp3oyttu660vdzuxo0bZtOmTaZr167G3d3duLm5OXT+ISEhZubMmcnaIyIiTEhIiEOzOHPbpSO1DFSoUKFkp7Y4S5kyZXT27Flnx5AkXbhwQW3btnV2DEmSp6enihcv7uwYkqRcuXIpd+7czo4hybW2l/j4eNWoUcPZMSSxXFLjSp9pV9rvkiVlrrTtVq1aVQcOHHB2DBvZs2dX9+7dtXLlSv3xxx/Kli2bwsPDHZrBlZaLK303NmzYUNu2bXN2DEmu9Tlype9GV9p2XWl7kaS///5bU6dOVZcuXdSmTRv98MMPatasmcaNG+fQHCdOnEhx261Ro4ZOnDjh0CzO3Ha5pjsDrVy5UmPHjtXnn3+ukJAQh88/JibG+ve2bdv07rvv6qOPPlL58uXl4eFhM26OHDkclqtnz5567LHH9OKLLzpsnqkZO3as/vnnH02aNMnpnTh8/fXX+u677zRz5kynnIbkqtvLW2+9JV9fX7333nsOm+ftWC735kqfaWfvd8lyb87edv/44w/r3wcPHtS7776rgQMHpviZrlChgqPj6dq1a/r+++81Z84cLV++XPny5VOHDh00cuRIu87XVZeLs78bv//+e+vfZ86c0bBhw9S9e/cUl0uLFi0clsvZnyNX+m50pW3XVbeXAgUK6OrVq6pTp47q1KmjsLAwVahQwSm/fcuVK6eOHTvq7bfftmkfPny45s+fr927d9t1/q6y7VJ0P6BcuXLZbMBxcXG6efOmfHx8kq1Ie3eg4ObmZpPF/P9OAW5nHNTJxSeffGL9Oy4uTuPGjVPTpk1T3MBfffVVu2Zp3bq1zfPVq1crd+7cKlu2bLIsixcvtmuWRx991GadHDhwQMYYhYSEJMuyfft2u2Zxpe2lf//+1r8TExM1c+ZMVahQQRUqVEi2XOz9H1qWS8pc6TPtSvtdsqTMlbbdpM90aj93koY5utOlFStWaM6cOVqyZInc3d31zDPPqFOnTg7rbdmVlourfTemxX/tO8CVvhtdadt1pe3ldpUqVdJff/2lypUrWwvvmjVrOuUfWYsWLVL79u1Vv3596zXdGzduVGRkpL755hu1atXKrvN3lW2XjtQe0IQJE5wdwWrNmjXOjmA1fvx4m+e+vr5at26d1q1bZ9NusVjs/gPdz8/P5rm9P9x307JlS6fN+06utL3s2LHD5nmlSpUkSXv27LFpd8R/aFkuKXOlz7Qr7XfJkjJX2nYPHTpk93ncj1atWqlZs2aaNWuWmjRpkqyIsjdXWi6u9N14Z6/gzuRKnyNX+m50pW3XlbaX2+3cuVMXL17U+vXrtW7dOr399tvat2+fKlWqpLp16+rDDz90WJY2bdpo69atGj9+vJYsWSJJeuSRR/Trr7/q0Ucftfv8XWXb5Ug3AACAg12+fFnZs2d3dgwAD7lz585p7dq1+u677zR37lwlJiY6/LaRoOgGAAAAgIfG4sWLtXbtWq1du1b79u1T7ty5VbNmTev13RUrVnR2xP8cim4AAAAAeEgEBASodu3a1iK7fPnyzo70n0fRDQAAAACAnXCfbgAAAAAA7ISi207mzp2ruLg4Z8eQJI0cOVIXL150dgyXc+zYMZfpdXLjxo26fv26s2NIYntJDcvF9bnSfpcsru/ll1/W2bNnnR1DkmutI1daLq703di0aVOdOHHC2TFcjit9N7rStsv2kjJX2tc5etvl9HI7yZEjh3bu3KmiRYs6O4pLZSlfvryWLVumQoUKOTuKSy0XspAls2bhM00WspDlv5Ale/bs2rVrl0tkcaXlQpaUudL24kpcaR05OgtHuu3Elf6X4UpZDh8+rBs3bjg7hiTXWi5kSRlZUuZKWfhMp4wsKSNLysiSMlfK4kpcabmQBenhSuvI0VkougEAAIC7CA4OloeHh7NjIJNwpe3FlU7p/i9zd3aAh9VPP/2kAgUKODuGJGnfvn0KCgpydgxJUq1atZQ1a1Znx5Akvf3228qdO7ezY0iSPv/8c+XLl8/ZMSS51vbSuXNn5ciRw9kxJLFcUuNKn2lX2u+SJWWutO1evnzZ2RGsXGkdudJycaXvxj179jg7gpUrfY5c6bvRlbZdV9peXnjhBVWtWtUlTul2pX2do7ddrukGAAAAgIcQ15e7Bk4vBwAAAADATii6AQAAAOAh5EqndP+XcXo5AAAAAAB2wpHuDHTt2jVnR0iRMcaluujHLa64vcTHx2v//v26efOms6MAgF0dO3ZMx44dc3YMl8Ny+T/Dhw/XoUOHnB3DpbnKb8wDBw5oxYoVunr1qiTXujUVXMc///zjtHlzpDsDeXt76/HHH1dYWJjq1KmjGjVqOLVX31mzZmnMmDGKioqSJJUsWVIDBw7Uc88955Q88fHxOn36tBITE23aCxcu7LAM69evv+vw2rVrOyiJlCNHDrVu3VqdOnVSvXr15ObmvP+BXblyRa+88opmzpwpSfr7779VtGhRvfLKKypQoIAGDRrk0DwJCQlasmSJ/vzzT0lS2bJl1aJFC2XJksWhOYwxWrhwodasWZPitrt48WKH5jl48KBmzJihgwcPauLEiQoICNBPP/2kwoULq2zZsg7NIkm///67dR2VKVNGlStXdngGSYqMjFRkZGSK62j69OkOzbJu3Tp9/PHHNstl4MCBqlWrlkNzSK61XBITE3XgwIEUszhyv5uYmKjhw4dr7Nixio2NlXSrk6E33nhD77zzjsP3w66yjlxtuURFRaW6333//fcdlqNixYras2ePqlatqs6dO6tdu3bKmzevw+Z/J1f6DnCV35jnzp1T+/bttXr1alksFkVFRalo0aLq0aOHcuXKpbFjxzosy6lTpzRgwADrZ/rOEishIcFhWVyNq+zr3NzcVLBgQWutFhYWpuLFiztk3twyLAOtWrVK69ev19q1azV+/HjdvHlToaGh1hXboEEDh2UZN26c3nvvPfXt21dPPPGEJGnDhg168cUXdfbsWb3++usOyxIVFaUePXpo06ZNNu3GGFksFofuhOrUqZOszWKxWP92ZJaZM2dqzpw5evrpp+Xn56f27durc+fOCg0NdViGJIMHD9auXbu0du1aNWrUyNpev359DR061KFF94EDB9S0aVMdO3ZMpUqVkiSNGDFChQoV0tKlS1WsWDGHZXnttdf0+eefq27dusqXL5/NtuJo69atU+PGjfXEE09o/fr1+vDDDxUQEKBdu3Zp2rRpWrhwocOynD59Ws8++6zWrl2rnDlzSpIuXryounXrat68efL393dYlvDwcA0bNkyhoaHKnz+/U9fR119/re7du6t169Z69dVXJUkbN25UvXr1FBERoY4dOzosiystly1btqhjx446cuRIsh+hjv4OeOeddzRt2jSNHDnS5rtx6NChunbtmj788EOHZXGldeRKy+WLL77QSy+9pLx58yowMNBmuVgsFocW3bt27dLevXs1e/Zsffzxx3rttdfUoEEDderUSS1btpSPj4/DsrjSd4Ar/cZ8/fXX5e7urujoaD3yyCPW9vbt26t///4OLbq7deum6Ohovffee07/TLsSV9rXHT16VGvXrtW6des0evRo9e7dW0FBQQoLC1PdunXVq1cv+83cwC5u3LhhNm3aZLp27Wrc3d2Nm5ubQ+cfEhJiZs6cmaw9IiLChISEODRLjRo1TO3atc2yZcvMjh07zM6dO20ejnTx4kWbx5kzZ8zKlStN1apVzapVqxyaJUlMTIyZPn26adCggcmSJYspUaKECQ8Pd2iGwoULm82bNxtjjPH19TUHDx40xhgTFRVlsmfP7tAsjRs3No0aNTLnzp2ztp09e9Y0atTINGnSxKFZcuXKZZYuXerQeaamWrVqZuzYscYY23W0detWU6BAAYdmadeunQkNDTX79u2ztu3du9eEhoaaZ5991qFZAgMDzaxZsxw6z9SULl3ajBs3Lln72LFjTenSpR2axZWWS8WKFU3btm3Nvn37zIULF5Lthx0pf/785rvvvkvWvmTJEhMUFOTQLK60jlxpuRQuXNiMHDnSofNMqw0bNpiXX37Z+Pv7O/y70ZW+A1zpN2a+fPmsvyVvXy4HDx402bJlc2gWX19fs2PHDofOMzNwpX3dnf7++2+H1Woc6c5gf//9t9auXWt9XL9+Xc2aNUvxCKs9nThxQjVq1EjWXqNGDZ04ccKhWXbu3Knff/9dpUuXduh8U+Ln55esrUGDBvL09FT//v31+++/OzxT9uzZ1b17d3Xv3l379u1Tp06dFB4e7tD/5p85c0YBAQHJ2uPi4hz+H8l169Zpy5Ytyp07t7UtT548NkdgHMXPz89l7mu5e/duzZkzJ1l7QECAzp4969Asy5cv16pVq2yOKpQpU0aTJ0/WU0895dAs8fHxKe7rnOGff/5R8+bNk7W3aNFCb7/9tkOzuNJyiYqK0sKFCx12Ct/dnD9/PsXvotKlS+v8+fMOzeJK68iVlsuFCxfUtm1bh84zrbJly6asWbPK09NTly9fdui8Xek7wJV+Y8bFxaV4xsH58+fl5eXl0CyFChVyqWvJXeWUblfa1125ckUbNmyw1mk7duxQ6dKl1bdvX7vXanSkloEKFCigatWqafny5apWrZp++uknnT17Vt9++6369evn0CzFixfXN998k6x9/vz5KlGihEOzlClTxuFfCOmVL18+7d+/3ynzvnbtmr755hu1bNlSlStX1vnz5zVw4ECHZggNDdXSpUutz5MK7S+//FLVq1d3aBYvL68Uf8zExsbK09PToVmGDh2q8PBwa8cszpQzZ84Uf8zs2LHD4bcCSUxMlIeHR7J2Dw+PZF/s9tarV68Uf4g6Q6FChRQZGZmsfdWqVSpUqJBDs7jScqlataoOHDjg7BiSbl2jO2nSpGTtkyZNUsWKFR2axZXWkSstl7Zt22rlypUOnefdHDp0SB9++KHKli2r0NBQ7dixQ+Hh4Tp58qRDc7jSd4Ar/casVauWZs2aZX1usViUmJio0aNHq27dug7NMmHCBA0aNEiHDx926HxTEh4erqeeekqRkZE6e/asLly4YPNwJFfa1+XMmVPPPfecrl27pkGDBun48ePasWOHxo8fr6efftqu8+ZIdwby9/fXX3/9pZMnT+rkyZM6deqUrl696tBrfpKEh4erffv2Wr9+vfXo4MaNGxUZGZnijjKjxcTEWP8eNWqU3nzzTX300UcqX758sh/rOXLksHueJH/88YfNc2OMTpw4oZEjR6pSpUoOyyFJK1as0Jw5c7RkyRK5u7vrmWee0cqVKx3aqVCSjz76SI0bN9a+fft08+ZNTZw4Ufv27dOmTZu0bt06h2Zp1qyZnn/+eU2bNk2PP/64JGnr1q168cUX1aJFC4dmadeunebOnauAgACFhIQk23a3b9/usCzPPvus3nrrLS1YsMD6o2Ljxo0aMGCAunTp4rAckvTkk0+qX79+mjt3roKCgiRJ//77r15//XXVq1fP7vPv37+/9e/ExERNnTpVq1atUoUKFZKto3Hjxtk9T5I33nhDr776qnbu3Gn9r/7GjRsVERGhiRMn2n3+rrRcbt/XvvLKK3rjjTd08uTJFL8DKlSoYNcstxs9erSaNm2qVatWWf+huHnzZh09elTLli2z+/xdaR3dztnL5ZNPPrH+Xbx4cb333nvasmVLittLUn8JjlCtWjX99ttvqlChgrp3764OHTo47X7HrvQd4OzfmLcbPXq06tWrp23btik+Pl5vvvmm9u7dq/Pnz2vjxo12n3+uXLlszgiMi4tTsWLF5OPjk2zbdeRZI1OmTFFERITTOk921X1dkyZNtGHDBs2bN89ar9WpU0clS5a0+7zpvTyDXbx4UevXr9e6deu0bt067du3T5UqVVLdunUd2hGJdKtn4fHjx1t70X3kkUf0xhtv6NFHH7X7vN3c3Gx2Qub/d5p2O+OEjtSSct252VerVk3Tp0936CnwPj4+atasmTp16qQmTZqkeOTQkQ4ePKiRI0dq165dio2NVeXKlfXWW2+pfPnyDs1x8eJFde3aVT/88IN1mdy8eVMtWrRQREREipcI2Eu7du20Zs0aPfPMMyl2pDZkyBCHZYmPj1efPn0UERGhhIQEubu7KyEhQR07dlRERIRDe3Y/evSoWrRoob1791qP4B49elTlypXT999/r4IFC9p1/mk9emGxWLR69Wq7ZrnTt99+q7Fjx9rsdwcOHGj3/6BLrrVcUtvX3p7BGd8BknT8+HFNnjxZf/31l6Rb6+jll1+2/gPJnlxpHd3JmculSJEiaRrPYrE49JY/77zzjjp16qQyZco4bJ6pcaXvAMm5vzHvdOnSJU2aNMnm90ufPn2UP39+u8876a4vadG1a1c7JrGVJ08e/frrrw7tfPZ2rryvk279YzipVvvll1/k7u6uOnXqaPbs2XabJ0W3nZw7d05r167Vd999p7lz5yoxMfE/dauA9BwdDQsLs2MSW0eOHLF57ubmJn9/f3l7ezssQ5LLly8re/bsDp9vZnHgwAGbL3NnXA+aLVs2rVixQjVr1nT4vFNz9OhR7d69W7GxsXr00UcdfipfEmOMVq1aZfMDvX79+k7JAtdz5772boKDg+2YBHi4uMp3AFzbW2+9JV9fX7333nvOjuKSjDHasWOH1qxZozVr1mjFihUyxujmzZt2mydFdwZavHix9cL8ffv2KXfu3KpZs6b1PnCOvi4KwIMpXbq0vvnmG4ee/grYQ0xMjFavXq3SpUu7RKeWQFolJCRo9+7dCg4OVq5cuZwdB0jV9u3b5eHhYT1D8LvvvtOMGTNUpkwZDR061O790tx5SvfMmTNVoUIFp5/S7UrGjRuntWvXasOGDbp8+bIqVqyo2rVrq06dOqpVq5Zd9zEU3RkoICDAuuLCwsIcflquq1q+fLl8fX2tRwsnT56sL774wtrbsb2/RG+/VuxeHHmtGFzf0qVL9b///U9TpkxRSEiIs+MAadauXTvVrl1bffv21dWrV1WxYkUdPnxYxhjNmzdPbdq0cViWmTNnKm/evGratKkk6c0339TUqVNVpkwZzZ07lyPdsPHaa6+pfPny6tmzpxISElS7dm1t3rxZPj4++vHHHx1+NxggrR577DENGjRIbdq00T///KMyZcqodevW+u2339S0aVNNmDDBrvN39VO6XcFjjz2msLAwa5HtyEsWKbphd+XLl9eoUaPUpEkT7d69W6GhoXrjjTe0Zs0alS5dWjNmzLDr/O+8VuzMmTO6cuWKcubMKenWNcQ+Pj4KCAhw6LVicH25cuXSlStXdPPmTad3igKkR2BgoFasWKGKFStqzpw5GjJkiHbt2qWZM2dq6tSp2rFjh8OylCpVSp999pmefPJJbd68WfXq1dOECRP0448/yt3dXYsXL3ZYFri+ggULasmSJQoNDdWSJUvUp08frVmzRl999ZVWr17tkM6xgPvh5+en7du3q1ixYho1apRWr16tFStWaOPGjXr22Wd19OhRZ0eEE9F7Oezu0KFD1k5IFi1apObNm+ujjz7S9u3b1aRJE4fMP8mcOXP06aefatq0aSpVqpQkaf/+/erdu7deeOEFu2dB5mLv/0oD9nLp0iXrve6XL1+uNm3ayMfHR02bNnX4LQmPHj1q7ZNhyZIleuaZZ/T888/riSee4Kglkjl79qwCAwMlScuWLVPbtm1VsmRJ9ejRwyF3AQDulzHGetvMVatWqVmzZpJu3U7S1W+dC/vjPt3/AXPnzlVcXJzT5u/p6akrV65IurUTeuqppyRJuXPntrm1mCO89957+t///mctuKVbR2HGjx+vd99916FZbufsdXQ7V8ribF27dr3rA0jNxo0bdf36dafNv1ChQtq8ebPi4uK0fPly6373woULDu840tfXV+fOnZMkrVy5Ug0aNJAkeXt76+rVqw7NcruRI0fq4sWLTpu/q3L2csmXL5/27dunhIQELV++3Lq9XLlyxeE9dN+ufPnyHKlMgSv9ZnD2thsaGqrhw4frq6++0rp166yX1Bw6dEj58uVzWi64Boru/4AXXnhBp06dctr8a9asqf79++uDDz7Qr7/+at0J/f3333a/tdCdTpw4kWLPhAkJCU5dRs5eR7dzpSz8yEmZKy2Xpk2b6sSJE86O4XIaN26sf//912nzf+2119SpUycVLFhQQUFB1iPK69evd3h/Iw0aNFCvXr3Uq1cv/f3339YznPbu3evUvhI++ugjLhFJgbOXS/fu3dWuXTuVK1dOFovFeleErVu3OrUTwMOHD+vGjRtOm//tXOk7wJV+Mzh7250wYYK2b9+uvn376p133rGe4bNw4ULVqFHDabngGji9/D/A2ZftT5o0SS+//LIWLlyozz77TAUKFJAk/fTTT2rUqJFDs9SrV08vvPCCvvzyS1WuXFnSrXtNvvTSS0693ZGz19HtXCmLK/3IcSWutFzWr1/v1KOVrsrZn6OXX35ZVatWVXR0tBo0aCA3t1v/Yy9atKiGDx/u0CyTJ0/Wu+++q6NHj2rRokXKkyePpFv73g4dOjg0y+2cvY5clbOXy9ChQ1WuXDkdPXpUbdu2lZeXlyQpS5YsGjRokFOzuQpX+g5w9vZyO2dnqVChgnbv3p2sfcyYMU49SwOugaLbTubOnasWLVooW7Zszo7idIULF9aPP/6YrH38+PEOzzJ9+nR17dpVoaGh1k6xbt68qYYNG+rLL790eB4AsJcqVaqoSpUqNm1JZxo5Us6cOTVp0qRk7eHh4Q7PgszhmWeeSdbm7Et6atWqpaxZszo1AzInR1/Sg7QZOXKkXnzxRWvHyvZG0W0nL7zwgqpWraqiRYs6O4p++ukn69Hl/zp/f38tW7ZMf//9t/766y9Jt+7FXLJkSafmcqV15EpZ+JGTMldaLsHBwcl6dYf0+eefcw2fi9u3b5+CgoKcHcPlsFxStmzZMmdHsHKl7wBX+s3Atov0+Oijj9SuXTuHFd3cMsxOsmfPrl27drlE0Y3k4uPjdejQIRUrVkzu7vzvCffG2SsAAAAPB0fXanSkhv+UK1euqGfPnvLx8VHZsmUVHR0tSXrllVc0cuRIJ6eDK3OlzmIAAACQeVB024krnW6D/zN48GDt2rVLa9eutbnGpn79+po/f74Tk8HVcVIQAABIC2ffNhL3tm/fPgUHBztsfhTddlKzZk1rj5v4P86+n+OSJUs0adIk1axZUxaLxdpetmxZHTx40Gm5AMBeXOn2Qs6+jy4yF25JiMzK2beNxL0VKlTIob3KU3TDoZx9iu6ZM2cUEBCQrD0uLs6mCAfuxNkryKxc6fZCzr6P7u2MMZzB4uJc5ZaE8fHxOnbsmKKjo20e/1XXrl1zdgSrf/75x9kRUsS+BXeiB6mHWGRkpCIjI3X69GklJibaDJs+fbpTMjl7JxQaGqqlS5fqlVdekSRrof3ll1+qevXqDs/jSuvIlbJIt37kpJSlcOHCDs8i3Tp7xRW42nKBraioKK1ZsybFdfT+++87KZXrcPZ3gCTNmjVLY8aMUVRUlCSpZMmSGjhwoJ577jknJ3OehIQERUREpPodsHr1aiclc66oqCj16NFDmzZtsmk3xshisSghIcHhmVzhOyBnzpx6/PHHFRYWpjp16qhGjRpO6029ePHiKliwoDVLWFiYihcv7pQswN1QdD+kwsPDNWzYMIWGhip//vwcxf3/PvroIzVu3Fj79u3TzZs3NXHiRO3bt0+bNm3SunXrHJrFldaRK2VxxR85rsCVlsupU6c0YMAA6w/0Owup/+o6+uKLL/TSSy8pb968CgwMtPkcWSwWpxXdrnR7IWcbN26c3nvvPfXt21dPPPGEJGnDhg168cUXdfbsWb3++utOTugc/fr1U0REhJo2bapy5cq5zG8GZ9+SsFu3bnJ3d9ePP/7Id+NtVq1apfXr12vt2rUaP368bt68qdDQUGvh26BBA4dlOXr0qNauXat169Zp9OjR6t27t4KCghQWFqa6deuqV69eDstyO24biTtxy7CHVP78+TV69GiX+8/9hg0b9Nhjjzn1eveDBw9q5MiR2rVrl2JjY1W5cmW99dZbKl++vENzuNI6cqUsTzzxhNzd3TVo0KAUf+RUrFjRScmcy5WWS+PGjRUdHa2+ffummOXpp592WBZXEhwcrJdffllvvfWWs6O4rKNHjyooKMih19HdrkiRIgoPD1eXLl1s2mfOnKmhQ4fq0KFDTsnlbHnz5tWsWbPUpEkTZ0dxKdmyZdPvv/+u0qVLOzuKS30H3O7mzZv67bff9Pnnn2v27NlKTEx06j9eo6Ki9OGHH7pEFrguZ9w6mCPdGcxVTtGNj49XjRo1HDa/tHKFU3SLFSumL774wtkxXGoduVKWnTt3usyPHFfiSstlw4YN+uWXX1SpUiVnR3EpFy5cUNu2bZ0dw6UVKlTIqfM/ceJEivu6GjVq/Kc77PL09OSU3BSUKVNGZ8+edXYMSa71HSBJf//9t9auXWt9XL9+Xc2aNVOdOnUcmuPKlSvasGGDNceOHTtUunRp9e3b1+FZ4PquXLmiV155RTNnzpR0azsuWrSoXnnlFRUoUECDBg2y27zpSC0DhYeH66mnnlJkZKTOnj2rCxcu2DwcqVevXpozZ45D54n0caV15EpZXOlHjitxpeVSqFAhl7g219W0bdtWK1eudHYM3EXx4sX1zTffJGufP3++SpQo4YREruGNN97QxIkT+VxLiomJsT5GjRqlN998U2vXrtW5c+dshsXExDg0lyt9BxQoUEDVqlXT8uXLVa1aNf300086e/asvv32W/Xr18+hWXLmzKnnnntO165d06BBg3T8+HHt2LFD48eP/8+edYXUOfPWwZxenoGcfYpu//79rX8nJiZq5syZqlChgipUqJDsmqhx48Y5Oh7kWuvIlbLc/uNl27Ztevfdd/XRRx+pfPnyybLkyJHDrlnu5MyzV1x1uaxcuVJjx47V559/rpCQEIfN1xV98skn1r/j4uI0btw4NW3aNMV19Oqrrzo6Hu6waNEitW/fXvXr17de071x40ZFRkbqm2++UatWrZyc0HFat25t83z16tXKnTu3ypYtm2zbXbx4sSOjOZWbm5vNqdtJ10zfzlHXUbvqd0ClSpX0119/qXLlyqpTp47q1KmjmjVrysfHx2EZkrRs2VIbNmyQp6enNUudOnVUsmRJh2eB6wsODtb8+fNVrVo1Zc+eXbt27VLRokV14MABVa5c2a7/TKPo/n/t3XlcVNX/P/DXIPu+CIpYgGAKgpLihmwuueSGS26VSJqYhpqWpKWC+1JpWi5Yimkaamp9zS0XBNLUVDAEDQ0tTQFXVFQEzu8Pf3M/jGyDznLV1/Px8FFz587c99y53Hvec885bw1ycHDAkSNH4OHhoZftt23bVq31FArFCzsTqb7J6TuSUyxyauSUVtUEc1u2bNHq9uW0X+zs7FS2fffuXRQVFcHc3LxM408uJaF0wd3dXa31FAqFbEvbvGiOHTuGBQsWIDMzEwDg5eWF8ePH49VXX9VzZLoVERGh9rqrVq3SYiTyUp1JVUNCQrQYibyuAY+7efMmkpKScODAARw4cAAZGRnw8/ND27ZtMXPmTJ3GAgAnT56UYklOToahoSFCQ0Px/fff6zwWki9zc3Okp6ejXr16Kkl3WloagoODcevWLa1tm0m3BkVHR8PS0hKTJ0/WdyhEzxQ5NXJK03fvFTntF+X4J3WEh4drMRKqDjmUFypN2eSQy+zYRHImp2tARa5du4bExET89NNPWL9+vd4mLxNC4MSJE9i/fz/279+PXbt2QQiBoqIincbBspHyFhwcjDfeeANRUVGwsrLCyZMn4e7ujqioKGRlZWHnzp1a2zaT7qckpy66ciaXCeZKW79+PXr06AELCwu9bJ/kT9+9V4ielJzKCwGsjU3qY0lC+du8ebM0cVlGRgbs7e0RGBgo1cnW5UzqX3zxBRITE5GSkoLbt2+jSZMmCA4ORmhoKIKCgmBnZ6ezWKoqG3n8+HGdxULlS0lJQZcuXfDWW28hPj4ekZGRKqWDmzVrprVtM+l+SnLqoitX+u6iWxFra2ukpqaiXr16etk+yR97r9CzSk7lhSqqjf31119jxowZL2xtbCofSxLKn5OTk5TYhoSE6LzkamnNmzeX6oMHBQXBxsZGb7GwbOSzQV+lg5l0k9bpu4tuRUqP5SBSYu8Veh7Iqb4wa2NTdVhZWbEkIT2TeDOHKsM63aR1cqoBTVSVEydOqDxWNvzS09NVlnNMKsmZnMoLsTY2VQdLEtKzSlk2csSIEfoOhWSId7pJ6+TaRTclJQXNmzeHiYmJvkMhInpqci0v5OPjg0GDBmHSpEkqy2fMmIGEhAT8+eefOoulNM7rUb6LFy+iTp06MDAw0Mv25VqScM6cORgxYgRsbW31HQrJCMtGkrqYdJNWsIsuaQIbOeWT035h4lI+X19fbN++HS+99JLOtinX8kJyrY3NrqDl08d+eRZKEsrpeJHTNeBFx7KRpC4m3aQVnGCONEFOjRw5kdN+kVMscqKPOSPkXF5IjrWxOa9H+fSxX56FkoRyOl543iV69nBMN2nF/v379R0CPQf4m2D55LRf5BTLi05fdXrV0axZM6xdu1bfYZBM6SuRflbJ6bwrp95O7AFA1aHrY1c/A3aIiIieY0FBQTAzM9N3GFSJHTt2wMXFRd9hyM6kSZNgb2+v7zBkJyMjA66urvoOQ3YiIyORk5Oj7zAAALNmzdLb8AN69uj62GX3ciKSrX///Rd16tRBjRo19B2KrMhpv3BCQiIi3ZLTNUBO3e7lFAvJn66PF3YvpxfO3r17sXfvXuTm5qKkpETluZUrV+opKiqPLiehepbIab8EBgbqOwQioheKnK4BRKQeJt30QomNjcW0adPg7+8PZ2dn1lomIiIiekJyGqaRkZGBOnXq6DsMekbo+thl93J6oTg7O2PevHl4++239R0KEZFOyGlyITlNukTyx+OFnlX6KBtJ8sakm14oDg4OOHLkCDw8PPQdChGRTsipvJCcYiH5k9Pxomwus4ccqYPjy+lx7F5OL5Rhw4Zh3bp1mDx5sr5DITWwkVNWYWEhsrOz4eHhAUND/ZzC79+/D1NTU71s+1lQWFhY7pwRL7/8sl7ikdNv63KIhfN6lJWUlFTp88HBwTqKRJUcjpfvvvsO8+fPR1ZWFgDglVdewUcffaTXHnO8NhI9e5h003Nv3Lhx0v+XlJQgLi4Oe/bsQePGjWFkZKSy7hdffKHr8Kgccmzk6FtBQQGioqKwevVqAMBff/2FevXqISoqCi4uLvj44491FoutrS1atGiBkJAQhIaGIiAggOWxAGRlZeGdd97BwYMHVZYLIaBQKFBcXKynyEiJ83qULzQ0tMyy0vvmRT12v/jiC0yePBnvv/8+2rRpA+BRxYYRI0bg6tWr+OCDD3QaD6+Nzw6WjaTHMemm596JEydUHvv5+QEA0tPTVZaz8SUPcmvkyMXEiRORlpaGxMREdO7cWVreoUMHxMTE6DTp3rNnD5KSkpCYmIgFCxagqKgI/v7+UhL+2muv6SwWORkyZAgMDQ2xbds2WSV0cppcSN+TLi1btgzx8fFMUh5z48YNlccPHz7EiRMnMHnyZMycOVNPUen/eFm8eDGWLl2KwYMHS8t69OiBRo0aISYmRqfXI14bKyeHXmClbd++Xd8hkMxwTDcRyYq7uztiY2NVGjkAsHr1asTExCA7O1tPkemXq6srEhIS0KpVK5WxYmfPnkXTpk2Rn5+vl7iKiopw9OhRLF++HN9//z1KSkpe2LtiFhYWOHbsGBo2bKjvUKgCnNejeg4cOIBx48bh2LFj+g5FL0xNTZGeng5PT0+V5VlZWfD19cX9+/d1Fovcro1yGaYhp15g9GzQ17FroLV3JiJ6ApcvX0ZAQECZ5QEBAbh8+bIeIpKHvLw8ODk5lVl+9+5dvdxR/euvvxAXF4fBgwejT58++L//+z9069bthR6i4e3tjatXr+o7DKqEcl4PUk+tWrVw5swZfYehN56entiwYUOZ5QkJCahfv75OY5HTtTE2NhYdO3bE3r17cfXqVdy4cUPlny6V7gVWeq6RDh06ICEhQaexkPzp89jVf/8LIqJSlI2cSZMmqSzXRyNHTvz9/fHLL78gKioKwP+GQ3zzzTdo3bq1TmNxcXHBvXv3EBoaitDQUERHR6Nx48ay6U6tS6V7GMydOxcTJkzArFmz4OvrW2bOCGtra12HR+C8Huo4efKkymMhBC5fvow5c+ZIQ7JeRLGxsejfvz+SkpKkLt2//fYb9u7dW24yrk1yujbKaZjG1q1bpV5gpa9BjRo1wrlz5/QYGcmRPo9dJt1EJCtyauTIyaxZs9ClSxdkZGSgqKgIX375JTIyMnDw4EEcOHBAp7E4Ojri9OnTuHLlCq5cuYKcnBzcu3cP5ubmOo1DDmxtbVUaekIItG/fXmUdTqSmX5zXo2p+fn5QKBRlZgtv1arVCzujOwD06dMHhw8fxoIFC7B161YAgJeXF44cOYJXX31Vp7HI6dpYWFhY7l13fZBbLzCSN30euxzTTUSyc+zYMSxYsACZmZkAHjVyxo8fr/NGjtycO3cOc+bMQVpaGu7cuYOmTZsiOjoavr6+Oo/l5s2bSEpKwoEDB3DgwAFkZGTAz88Pbdu21evES7pWnR88QkJCtBhJxVheiKpy4cIFlccGBgZwdHTUS2lAliSsmFyujdHR0bC0tJRF+dXg4GC88cYbiIqKgpWVFU6ePAl3d3dERUUhKysLO3fu1HlMcisbSf+jz2OXSTcRET2xa9euITExET/99BPWr1//Qk+kJjdyKi8kl0mXSP5MTU1ZklCGHh+msXr1ajRu3FjvwzRSUlLQpUsXvPXWW4iPj0dkZKRKL7BmzZrpLBaWjZQnuRy77F5ORETVsnnzZiQmJiIxMREZGRmwt7dHYGAgPv/8c73dzZWDnTt3wtLSEoGBgQCAr7/+GitWrIC3tze+/vpr2NnZ6SwWOZUXYm1seVq0aJHa644ePVqLkahiSUJ5kuswjcDAQKSmpmLOnDnw9fXF7t270bRpUxw6dEjnvcDkWjbyRSeXY5d3uomIqFqcnJwQHByM0NBQhISE6KV7uxz5+vpi7ty5eP311/Hnn3/C398f48ePx/79+9GwYUOsWrVKZ7HIqbyQs7Mz5s2bJ4tJl+h/3N3dVR7n5eWhoKAAtra2AB4NITE3N4eTkxP+/vtvPUTIkoT0bGHZSKoM73QTEVG15Obm6jsEWcrOzoa3tzcA4Mcff0T37t0xa9YsHD9+HK+//rpOY5FTeSE5TbpE/1P6h5d169ZhyZIl+Pbbb9GgQQMAwJkzZ/Duu+8iMjJS57H99ddfUm+axMREPHjwAN26dUNoaKjOYyFSF8tGUmVYp5uIiEgDjI2NUVBQAOBRF9mOHTsCAOzt7VVKi+mCnOoLsza2/E2ePBmLFy+WEm4AaNCgARYsWIBPP/1Up7G4uLigVatW2LlzJ1q1aoUdO3bg6tWr2LJlC8aMGaPTWEpbv3497t69q7ftkzzl5+dL/5RlIxMTE3Ht2jWV53R9DSD54Z1uIpKt9evXo0ePHrCwsNB3KLLC/SJPgYGBGDduHNq0aYMjR44gISEBwKO7dnXr1tVpLPouL8Ta2M+Wy5cvo6ioqMzy4uJi5OTk6DQWuZYkjIyMRMuWLVGvXj29xgHwGiAnLBtJ6uKYbiKSLWtra6SmpsqikSMn3C/y9M8//2DkyJH4999/MXr0aAwdOhQA8MEHH6C4uLhaE1dpgj7LC7Vt21at9RQKBfbt26flaKgq3bt3x6VLl/DNN9+gadOmAB4dP8OHD4eLiwt+/vlnncYjx5KEVlZWSEtLk8V5l9cA+XgWykaSPDDpJiLZklMjR064X4hIk/Ly8hAeHo6dO3dKPRGKiorQqVMnxMfHw8nJSS9xyakkoZzOu3KKRU7YA4DkjGO6iYjoiXGcI9Gzz9HREdu3b8fp06exceNGbNy4EZmZmdi+fbvOE+7Nmzdj9OjRaNy4MWrVqoX33nsPd+7cweeff47jx4/rNJbSduzYARcXF71tn6oWGRmp8+EQpe3cuRMpKSnS46+//hp+fn4YNGgQbty4obe4SB54p5uIZCslJQXNmzeHiYmJvkORFTntF3ZzJHp+FBYWIjs7Gx4eHjA01M+0PyxJWDU5XQPkRN89AORUNpLkh0k3ERE9MX03cojo6RUUFCAqKgqrV68G8Gjyv3r16iEqKgouLi74+OOP9RwhUdX0fT2ytLREeno63NzcEBMTg/T0dGzatEkqG3nlyhW9xEXywO7lRERERC+wiRMnIi0tDYmJiTA1NZWWd+jQQZqFn0ju9D0EQE5lI0l+mHQTEdET03cjR67kNNZdTrGQPG3duhVfffUVAgMDVcofNWrUCOfOndNjZETqCwwM1GuXe2XZyOnTp+PIkSPo2rUrAP2UjST5YdJNRERPTN+NHLnS94Q+pckpFpKnvLy8cidMu3v3rkoSTkQV++qrr2BoaIhNmzZh6dKl0g/SO3bsQOfOnfUcHembfmbJICIieo7JaboUOcVC8uTv749ffvkFUVFRACAl2t988w1at26tz9D0bu/evdi7dy9yc3NRUlKi8tzKlSv1FBXJ0csvv4xt27aVWb5gwQI9RENyw6SbiGSHjZzycb8QkTbMmjULXbp0QUZGBoqKivDll18iIyMDBw8exIEDB/QWl77rLsfGxmLatGnw9/eHs7Oz3u/68xpA9Oxi0k1EsiK3Ro5ccL88W+Q01l1OsZA8BQYGIjU1FXPmzIGvry92796Npk2b4tChQ3ot2RUZGYmWLVvqbTbqZcuWIT4+Hm+//bZetl8arwFEzzaWDCMiWXF2dsa8efNk0ciRE+4XInrR6LsElIODA44cOQIPDw+9bL80XgMqxh4A9CzgnW4ikpXCwkIEBAToOwzZkdt+YSOHiJ53w4YNw7p16zB58mR9hyK7a4BcsAcAPSt4p5uIZCU6OhqWlpayaOTIiZz2S1WNnC1btugpMiJ6nqSkpKB58+Y6rZAwbtw46f9LSkqwevVqNG7cGI0bN4aRkZHKul988YXO4pLTNUBO5NoDQN/zEZD8MOkmIr2TayNH3+S6X+TayCEielpt27ZVaz2FQoF9+/ZpNRa5XgPkRE5DAEqztrZGamqq3oZGkPww6SYivZNTI0dO5Lpf5NrIISJ6nsj1GiAncu0BoO/5CEh+mHQTEVG1yLWRIwdyGusup1jo2cFusSR3z0IPACbd9DhOpEZERFV6vJETFxeHPXv2yKqRo29ymtBHTrHQs0XfZbqIqnLixAmVx35+fgCA9PR0leX6PO+xVCM9jne6iYioSuzmWDU5jXWXUyz0bOEdOiIizeOdbiIiqtL+/fv1HYLsyamkj5xiIaouDo0goueNgb4DICIieh4oa/rKgZxioWeLvrvFxsbGomPHjti7dy+uXr2KGzduqPwjInoWsXs5ERHRE5LThD5yioXoSXFoBBE9j9i9nIiI6AnJaUIfOcVC9KQ4NIKInke8001EREREssCShPQs43wEVBHe6SYiIiIivWFJQnoesFQjVYZ3uomIiIhIb1iSkJ4HnI+AKsOkm4iIiOgFx26xRE/HwcEBR44cgYeHh75DIRliyTAiIiKiFxjLdBE9PZZqpMrwTjcRERHRC4zdYomeDEs1kro4kRoRERHRC4xluoieDEs1krp4p5uIiIjoBcYyXURE2sWkm4iIiOgFw26xRES6w6SbiIiI6AXDMl1ERLrDpJuIiIiIiIhIS1gyjIiIiIiIiEhLmHQTERERERERaQmTbiIiIiIiIiItYdJNREREREREpCVMuomIiIiIiIi0hEk3ERGRTCgUCmzdulXt9d3c3LBw4UKtxUNERERPjyXDiIiIdCwmJgZbt25FamqqyvIrV67Azs4OJiYmar1PXl4eLCwsYG5uDuBR0r5lyxaEhYVpOGIiIiJ6Uob6DoCIiIgeqV27drXWd3R01FIkREREpCnsXk5ERPQEdu7cicDAQNja2sLBwQHdunXDuXPnpOcvXryIgQMHwt7eHhYWFvD398fhw4cRHx+P2NhYpKWlQaFQQKFQID4+HoBq9/KAgABER0erbDMvLw9GRkZISkoCoNq93M3NDQDQq1cvKBQKuLm54fz58zAwMMAff/yh8j4LFy6Eq6srSkpKKv2MN27cwJtvvglHR0eYmZmhfv36WLVqlfT8v//+i379+sHW1hb29vbo2bMnzp8/Lz1fXFyMcePGSftowoQJCA8PV7kTX14XeT8/P8TExEiPb968iWHDhsHR0RHW1tZo164d0tLSpOdjYmLg5+eHNWvWwM3NDTY2NhgwYABu374trVNSUoJ58+bB09MTJiYmePnllzFz5ky1PwsREdGTYtJNRET0BO7evYtx48bhjz/+wN69e2FgYIBevXqhpKQEd+7cQUhICC5duoSff/4ZaWlpmDBhAkpKStC/f3+MHz8ejRo1wuXLl3H58mX079+/zPu/+eab+OGHH1B6FFhCQgLq1KmDoKCgMusfPXoUALBq1SpcvnwZR48ehZubGzp06KCSKCvXGTJkCAwMKm8GTJ48GRkZGdixYwcyMzOxdOlS1KxZEwDw8OFDdOrUCVZWVkhOTsZvv/0GS0tLdO7cGYWFhQCAzz//HPHx8Vi5ciVSUlJw/fp1bNmypXo7GsAbb7yB3Nxc7NixA8eOHUPTpk3Rvn17XL9+XVrn3Llz2Lp1K7Zt24Zt27bhwIEDmDNnjvT8xIkTMWfOHOkzrVu3DrVq1VL7sxARET0xQURERE8tLy9PABB//vmnWL58ubCyshLXrl0rd92pU6eKJk2alFkOQGzZskUIIURubq4wNDQUSUlJ0vOtW7cW0dHR0mNXV1exYMGCcl+vlJCQIOzs7MT9+/eFEEIcO3ZMKBQKkZ2dXeVn6t69u4iIiCj3uTVr1ogGDRqIkpISadmDBw+EmZmZ2LVrlxBCCGdnZzFv3jzp+YcPH4q6deuKnj17VvgZhBCiSZMmYurUqUIIIZKTk4W1tbUUv5KHh4dYvny5EOLR/jQ3Nxf5+fnS8x999JFo2bKlEEKI/Px8YWJiIlasWPHEn4WIiOhJ8U43ERHRE8jKysLAgQNRr149WFtbS927//nnH6SmpuLVV1+Fvb39E7+/o6MjOnbsiO+//x4AkJ2djUOHDuHNN9+s1vuEhYWhRo0a0h3m+Ph4tG3bVoq3Mu+99x5++OEH+Pn5YcKECTh48KD0XFpaGs6ePQsrKytYWlrC0tIS9vb2uH//Ps6dO4dbt27h8uXLaNmypfQaQ0ND+Pv7Vyv+tLQ03LlzBw4ODtJ2LC0tkZ2drdKd383NDVZWVtJjZ2dn5ObmAgAyMzPx4MEDtG/fvsJtVPZZiIiIngYnUiMiInoC3bt3h6urK1asWIE6deqgpKQEPj4+KCwshJmZmUa28eabb2L06NFYvHgx1q1bB19fX/j6+lbrPYyNjTF48GCsWrUKvXv3xrp16/Dll1+q9douXbrgwoUL2L59O3799Ve0b98eo0aNwmeffYY7d+6gWbNm0o8CpVVngjcDAwOVLvTAo+7eSnfu3IGzszMSExPLvNbW1lb6fyMjI5XnFAqFNGa9qu9DU5+FiIioPLzTTUREVE3Xrl3DmTNn8Omnn6J9+/bw8vLCjRs3pOcbN26M1NRUlTHHpRkbG6O4uLjK7fTs2RP379/Hzp07sW7duirvchsZGZX7vsOGDcOePXuwZMkSFBUVoXfv3lVuW8nR0RHh4eFYu3YtFi5ciLi4OABA06ZNkZWVBScnJ3h6eqr8s7GxgY2NDZydnXH48GHpvYqKinDs2LEy73/58mXpcX5+PrKzs6XHTZs2xZUrV2BoaFhmO8rx5VWpX78+zMzMsHfv3nKfr+qzEBERPQ0m3URERNVkZ2cHBwcHxMXF4ezZs9i3bx/GjRsnPT9w4EDUrl0bYWFh+O233/D333/jxx9/xKFDhwA86gqdnZ2N1NRUXL16FQ8ePCh3OxYWFggLC8PkyZORmZmJgQMHVhqXm5sb9u7diytXrqj8CODl5YVWrVohOjoaAwcOVPtO/JQpU/DTTz/h7NmzOHXqFLZt2wYvLy8Aj+7C16xZEz179kRycjKys7ORmJiI0aNH4+LFiwCAMWPGYM6cOdi6dStOnz6NkSNH4ubNmyrbaNeuHdasWYPk5GT8+eefCA8PR40aNaTnO3TogNatWyMsLAy7d+/G+fPncfDgQXzyySdlZmWviKmpKaKjozFhwgR89913OHfuHH7//Xd8++23an8WIiKiJ8Wkm4iIqJoMDAzwww8/4NixY/Dx8cEHH3yA+fPnS88bGxtj9+7dcHJywuuvvw5fX1/MmTNHSib79OmDzp07o23btnB0dMT69esr3Nabb76JtLQ0BAUF4eWXX640rs8//xy//vorXnrpJbz66qsqzw0dOhSFhYV455131P6cxsbGmDhxIho3bozg4GDUqFEDP/zwAwDA3NwcSUlJePnll9G7d294eXlh6NChuH//PqytrQEA48ePx9tvv43w8HC0bt0aVlZW6NWrl8o2Jk6ciJCQEHTr1g1du3ZFWFgYPDw8pOcVCgW2b9+O4OBgRERE4JVXXsGAAQNw4cIFafZxdUyePBnjx4/HlClT4OXlhf79+0tjvtX5LERERE9KIR4fSEVERETPnenTp2Pjxo04efKkXuMYMmQIbt68KdUjJyIiet7xTjcREdFz7M6dO0hPT8dXX32FqKgofYdDRET0wmHSTURE9Bx7//330axZM4SGhpbpWj5ixAiVMlyl/40YMUJPERMRET1f2L2ciIjoBZWbm4v8/Pxyn7O2toaTk5OOIyIiInr+MOkmIiIiIiIi0hJ2LyciIiIiIiLSEibdRERERERERFrCpJuIiIiIiIhIS5h0ExEREREREWkJk24iIiIiIiIiLWHSTURERERERKQlTLqJiIiIiIiItIRJNxEREREREZGWMOkmIiIiIiIi0hIm3URERERERERawqSbiIiIiIiISEuYdBMRERERERFpCZNuIiIiIiIiIi1h0k1ERERERESkJUy6iYiIiIiIiLSESTcRERERERGRljDpJiIiIiIiItISJt1EREREREREWsKkm4iIiIiIiEhLmHQTERERERERaQmTbiIiIiIiIiItYdJNREREREREpCVMuomIiIiIiIi0hEk3ERERERERkZYw6SYiIiIiIiLSEibdRERERERERFrCpJuIiIiIiIhIS5h0ExEREREREWkJk24iIiIiIiIiLWHSTURERERERKQlTLqJiIiIiIiItIRJNxEREREREZGWMOkmIiIiIiIi0hIm3URERERERERawqSbiIiIiIiISEuYdBMRERERERFpCZNuIiIiIiIiIi1h0k1ERERERESkJUy6iYiIiIiIiLSESTcRERERERGRljDpJiIiIiIiItISJt1EREREREREWsKkm4iIiIiIiEhLmHQTERERERERaQmTbiIiIiIiIiItYdJNREREREREpCVMuomIiIiIiIi0hEk3ERERERERkZYw6SYiIiIiIiLSEibdRERERERERFrCpJuIiIiIiIhIS5h0ExEREREREWkJk24iIiIiIiIiLWHSTURERERERKQlTLqJiIiIiIiItIRJNxEREREREZGWMOkmIiIiIiIi0hIm3URERERERERawqSbiIiIiIiISEuYdBMRERERERFpCZNuIiIiIiIiIi1h0k1ERERERESkJUy6iYiIiIiIiLSESTcRERERERGRljDpJiIiIiIiItISJt1EREREREREWsKkm4iIiIiIiEhLmHQTERERERERaQmTbiIiIiIiIiItYdJNREREREREpCVMuomIiIiIiIi0hEk3ERERERERkZYw6SYiIiIiIiLSEibdRERERERERFrCpJuIiIiIiIhIS5h0ExEREREREWkJk24iIiIiIiIiLWHSTURERERERKQlTLqJiIiIiIiItIRJNxEREREREZGWMOkmIiIiIiIi0hIm3URERERERERawqSbiIiIiIiISEuYdBMRERERERFpCZNuIiIiIiIiIi1h0k1ERERERESkJUy6iYiIiIiIiLSESTcRERERERGRljDpJiIiIiIiItISJt1EREREREREWsKkm4iIiIiIiEhLmHQTERERERERaQmTbiIiIiIiIiItYdJNREREREREpCVMuomIiIiIiIi0hEk3ERERERERkZYw6SYiIiIiIiLSEibdRERERERERFrCpJuIiIiIiIhIS5h0ExEREREREWkJk24iIiIiIiIiLWHSTURERERERKQlTLqJiIiIiIiItIRJNxEREREREZGWMOkmIiIiIiIi0hIm3URERERERERawqSbiIiIiIiISEuYdBMRERERERFpCZNuIiIiIiIiIi1h0k1ERERERESkJUy6iYiIiIiIiLSESTcRERERERGRljDpJiIiIiIiItISJt1EREREREREWsKkm4iIiIiIiEhLmHQTERERERERaQmTbiIiIiIiIiItYdJNREREREREpCVMuomIiIiIiIi0hEn3U4iJiYFCocDVq1f1HYosKRQKvP/++0/8+iFDhiA0NFRzAVUiNDQUPj4+OtnWsyA0NBRDhgzRdxj0mPj4eCgUCn2HUann9bzo5uaGmJgYjb1Xt27dNPJez6IhQ4bA0tLyiV+fmJgIhUKB8+fPaywmhUJR7e93yJAhcHNz01gM+hQTE6Ozz/Ik+1qXzp8/D4VCgfj4eH2HolfK640m/850eZw9rdDQUJU2qLaPC+V5LTExUSvvT/rHpJteGP/99x9iYmKQmpqq71BIjzIyMhATE6PRhgQRUUFBAWJiYthofkasW7cOCxcu1HcYRPSCMNR3AES68t9//yE2NhZubm7w8/PTdzikJxkZGYiNjUVoaOgz84s7EWnXvXv3YGhYvSbRihUrUFJSIj0uKChAbGwsAOislxY9uXXr1iE9PR1jx45VWe7q6op79+7ByMhIP4GRLPG4oKfFO91EOlJSUoL79+/rO4wX1v3791UayKRZd+/e1XcIRE/M1NS02km3kZERTExMtBQR6YtCoYCpqSlq1Kih71BIRnhc0NNi0q0BN2/exJAhQ2BrawsbGxtERESgoKBAZZ2ioiJMnz4dHh4eMDExgZubGyZNmoQHDx6orKcc65eYmAh/f3+YmZnB19dX6q62efNm+Pr6wtTUFM2aNcOJEyfKxHP69Gn07dsX9vb2MDU1hb+/P37++Wetff6qbN26FT4+PjAxMUGjRo2wc+dOjW/j77//xhtvvAF7e3uYm5ujVatW+OWXX6TnExMT0bx5cwBAREQEFApFuWNzMjIy0LZtW5ibm8PFxQXz5s0rs60HDx5g6tSp8PT0hImJCV566SVMmDChzHepHNP+/fffo1GjRjAxMdHKZ6/Ixo0b0axZM5iZmaFmzZp46623cOnSJY1v59KlS3jnnXdQq1Yt6TteuXJlmfUWL16MRo0awdzcHHZ2dvD398e6devKvNfQoUNRp04dmJiYwN3dHe+99x4KCwuldar6roH/jY364Ycf8Omnn8LFxQXm5uZYtGgR3njjDQBA27ZtpeNAl91B1d1fT6Np06bo3bu3yjJfX18oFAqcPHlSWpaQkACFQoHMzExp2YkTJ9ClSxdYW1vD0tIS7du3x++//67yXsqxfgcOHMDIkSPh5OSEunXrVhjPhQsX4OnpCR8fH+Tk5GjoU1bs5s2bqFGjBhYtWiQtu3r1KgwMDODg4AAhhLT8vffeQ+3atTW27StXriAiIgJ169aFiYkJnJ2d0bNnz3KHM6SkpKBFixYwNTVFvXr18N1335VZpzrHe0JCAiZNmoTatWvDwsICPXr0wL///quxz6aOhw8fIjY2FvXr14epqSkcHBwQGBiIX3/9tcy6ly5dQlhYGCwtLeHo6IgPP/wQxcXFGovFx8cHbdu2LbO8pKQELi4u6Nu3r7Ts8XHGt2/fxtixY+Hm5gYTExM4OTnhtddew/Hjx6V1So/pPn/+PBwdHQEAsbGx0rlFF2OXc3JyYGhoKN1lL+3MmTNQKBT46quvNLY9da+BDx48wAcffABHR0dYWVmhR48euHjxYpn3q2hsvHJ+iMetXbsWLVq0kK4lwcHB2L17t/T8Tz/9hK5du0rXEQ8PD0yfPl3l2AoNDcUvv/yCCxcuSN9V6e+yvPbBvn37EBQUBAsLC9ja2qJnz54q587SMZ89e7bKdqEuqNsePHXqFNq1awczMzPUrVsXM2bMKPdH6oqOaTc3N43OA6M8p23YsAGxsbFwcXGBlZUV+vbti1u3buHBgwcYO3YsnJycYGlpiYiIiDLHH/DoWFG2g+zt7TFgwIByz4lxcXHw8PCAmZkZWrRogeTk5DLrVHRcnD59Gv369YOjoyPMzMzQoEEDfPLJJ9LzFy5cwMiRI9GgQQOYmZnBwcEBb7zxhiyGuCmv5SkpKRg9ejQcHR1ha2uLyMhIFBYW4ubNmxg8eDDs7OxgZ2eHCRMmqFw/qXrYvVwD+vXrB3d3d8yePRvHjx/HN998AycnJ8ydO1daZ9iwYVi9ejX69u2L8ePH4/Dhw5g9ezYyMzOxZcsWlfc7e/YsBg0ahMjISLz11lv47LPP0L17dyxbtgyTJk3CyJEjAQCzZ89Gv379cObMGRgYPPr95NSpU2jTpg1cXFzw8ccfw8LCAhs2bEBYWBh+/PFH9OrVq9LPcuPGDbUaPebm5jA3N69yvZSUFGzevBkjR46ElZUVFi1ahD59+uCff/6Bg4NDla9XR05ODgICAlBQUIDRo0fDwcEBq1evRo8ePbBp0yb06tULXl5emDZtGqZMmYLhw4cjKCgIABAQECC9z40bN9C5c2f07t0b/fr1w6ZNmxAdHQ1fX1906dIFwKPGWo8ePZCSkoLhw4fDy8sLf/75JxYsWIC//voLW7duVYlt37592LBhA95//33UrFmz0u7Md+7cUetOuJGREWxsbCpdJz4+HhEREWjevDlmz56NnJwcfPnll/jtt99w4sQJ2NraVrkddeTk5KBVq1bSDwyOjo7YsWMHhg4divz8fKnb3ooVKzB69Gj07dsXY8aMwf3793Hy5EkcPnwYgwYNAvCo+3+LFi1w8+ZNDB8+HA0bNsSlS5ewadMmFBQUwNjYWK3vurTp06fD2NgYH374IR48eICOHTti9OjRWLRoESZNmgQvLy8AkP5bngcPHuD27dtq7Y+aNWtqZH89raCgIKxfv156fP36dZw6dQoGBgZITk5G48aNAQDJyclwdHSUPv+pU6cQFBQEa2trTJgwAUZGRli+fDlCQ0Nx4MABtGzZUmU7I0eOhKOjI6ZMmVLhne5z586hXbt2sLe3x6+//lrpPnr48CFu3bql1me0t7eXznuPs7W1hY+PD5KSkjB69GgAj85FCoUC169fR0ZGBho1aiTtA+X5QBP69OmDU6dOISoqCm5ubsjNzcWvv/6Kf/75R+Xv/+zZs+jbty+GDh2K8PBwrFy5EkOGDEGzZs2k2Kp7vM+cORMKhQLR0dHIzc3FwoUL0aFDB6SmpsLMzKzCmDW134FHScfs2bMxbNgwtGjRAvn5+fjjjz9w/PhxvPbaa9J6xcXF6NSpE1q2bInPPvsMe/bsweeffw4PDw+89957asVSlf79+yMmJgZXrlxR+WElJSUF//33HwYMGFDha0eMGIFNmzbh/fffh7e3N65du4aUlBRkZmaiadOmZdZ3dHTE0qVL8d5776FXr17Sj17Kv7XylJSU4Pr162p9Fhsbmwq7tdaqVQshISHYsGEDpk6dqvJcQkICatSoIf3Y+LSqcw0cNmwY1q5di0GDBiEgIAD79u1D165dn2r7sbGxiImJQUBAAKZNmwZjY2McPnwY+/btQ8eOHQE8uv5ZWlpi3LhxsLS0xL59+zBlyhTk5+dj/vz5AIBPPvkEt27dwsWLF7FgwQIAqHRyvz179qBLly6oV68eYmJicO/ePSxevBht2rTB8ePHy1zb1WkXlqegoECt5LxGjRqws7OrdB1124NXrlxB27ZtUVRUJK0XFxdX6TlDV2bPng0zMzN8/PHHOHv2LBYvXgwjIyMYGBjgxo0biImJwe+//474+Hi4u7tjypQp0mtnzpyJyZMno1+/fhg2bBjy8vKwePFiBAcHq7SDvv32W0RGRiIgIABjx47F33//jR49esDe3h4vvfRSpfGdPHkSQUFBMDIywvDhw+Hm5oZz587h//7v/zBz5kwAwNGjR3Hw4EEMGDAAdevWxfnz57F06VKEhoYiIyNDrbZ0aZo8XytFRUWhdu3aiI2Nxe+//464uDjY2tri4MGDePnllzFr1ixs374d8+fPh4+PDwYPHlytmOn/E/TEpk6dKgCId955R2V5r169hIODg/Q4NTVVABDDhg1TWe/DDz8UAMS+ffukZa6urgKAOHjwoLRs165dAoAwMzMTFy5ckJYvX75cABD79++XlrVv3174+vqK+/fvS8tKSkpEQECAqF+/fpWfSbn9qv5NnTq1yvcCIIyNjcXZs2elZWlpaQKAWLx4cZWvDw8PFyEhIVWuN3bsWAFAJCcnS8tu374t3N3dhZubmyguLhZCCHH06FEBQKxatarMe4SEhAgA4rvvvpOWPXjwQNSuXVv06dNHWrZmzRphYGCgsi0hhFi2bJkAIH777TeVz29gYCBOnTpV5WdQfl519n1V+6SwsFA4OTkJHx8fce/ePWn5tm3bBAAxZcqUKmMJCQkR4eHhVa43dOhQ4ezsLK5evaqyfMCAAcLGxkYUFBQIIYTo2bOnaNSoUaXvNXjwYGFgYCCOHj1a5rmSkhIhhPrf9f79+wUAUa9ePSkGpY0bN5b5u6nMqlWr1Ppe1Dmdqru/qoqlKsrPmJGRIYQQ4ueffxYmJiaiR48eon///tJ6jRs3Fr169ZIeh4WFCWNjY3Hu3Dlp2X///SesrKxEcHBwmTgCAwNFUVGRyraV58W8vDyRmZkp6tSpI5o3by6uX79eZdzK702df9nZ2ZW+16hRo0StWrWkx+PGjRPBwcHCyclJLF26VAghxLVr14RCoRBffvlllbG5urpWed67ceOGACDmz59f5XsBEElJSdKy3NxcYWJiIsaPHy8tq+7x7uLiIvLz86V1N2zYIABU+fk0ud+bNGkiunbtWuk6ynPdtGnTVJa/+uqrolmzZpW+tnS8VcVy5syZcq83I0eOFJaWlip/b49f12xsbMSoUaOq/Byurq7S47y8PLWvj0IIkZ2drfZ+r+p8pWwP/PnnnyrLvb29Rbt27aqMZerUqSqfpSLqXgOV7Z6RI0eqrDdo0KAy++jx/Vg6ptLnu6ysLGFgYCB69eolHftKymuEEKLc82hkZKQwNzdXaRt17dq13O0qv5fSbQU/Pz/h5OQkrl27Ji1LS0sTBgYGYvDgwWVirqpdWBHl66v6p853pW57UHmeOXz4sLQsNzdX2NjYlPk7q+j4dnV1VavNoO5xpvwb9/HxEYWFhdLygQMHCoVCIbp06aKyfuvWrVXe9/z586JGjRpi5syZKuv9+eefwtDQUFqubC/5+fmJBw8eSOvFxcWVaW+Vd1wEBwcLKysrlba5EFUfj4cOHSrT5lR+5qr+1jV5vlZeyzt16qQSc+vWrYVCoRAjRoyQlhUVFYm6deuq1S6n8vFOtwaMGDFC5XFQUBC2bNmC/Px8WFtbY/v27QCAcePGqaw3fvx4fPbZZ/jll19UusB5e3ujdevW0mPl3aV27drh5ZdfLrP877//RmhoKK5fv459+/Zh2rRpuH37tsrduU6dOmHq1Km4dOkSXFxcKvws33//Pe7du1flZ65Xr16V6wBAhw4d4OHhIT1u3LgxrK2t8ffff6v1enVs374dLVq0QGBgoLTM0tISw4cPx8SJE5GRkaFWOTBLS0u89dZb0mNjY2O0aNFCJdaNGzfCy8sLDRs2VCmJ1K5dOwDA/v37Ve6eh4SEwNvbW63PMWHCBJXtV6SqX7f/+OMP5ObmIiYmBqamptLyrl27omHDhvjll1/K7YZYXUII/Pjjj+jXrx+EECr7o1OnTvjhhx9w/PhxtGnTBra2trh48SKOHj0qdfMvraSkBFu3bkX37t3h7+9f5nllF8Pqftfh4eFP/Wt9p06dyu0aW13V2V9PS3nnNikpCV5eXkhOTkbz5s3x2muvYfbs2QAedcFOT0+XugQWFxdj9+7dCAsLU/n7dnZ2xqBBg7BixQrpnKb07rvvVji+LT09Hf3794enpyd27Nih8rqKNGnSRO19XVWX8KCgIHz99dc4c+YMGjRogOTkZHTq1AmOjo5ITk7GiBEjkJKSAiGExu50m5mZwdjYGImJiRg6dGilf6ve3t4q23V0dESDBg1UzjfVPd4HDx4MKysr6XHfvn3h7OyM7du3S3f8y6PJ/W5ra4tTp04hKysL9evXr3Td8q6da9asUSsOdbzyyivw8/NDQkKCVL6yuLgYmzZtQvfu3Ss9N9ja2uLw4cP477//UKdOHY3FVFrt2rXV3u9NmjSp9PnevXtj1KhRSEhIkI6J9PR0ZGRkYMyYMU8dq5K610Blu+fx427s2LFlhhWpa+vWrSgpKcGUKVPK3L0r3Q299Pd6+/ZtPHjwAEFBQVi+fDlOnz5d5b583OXLl5GamooJEybA3t5eWt64cWO89tpr0mctrap2YUUGDx6s8vdekaqua9VpD27fvh2tWrVCixYtpHUcHR3x5ptvYsmSJVXGok2DBw9W6eHRsmVLrF+/Hu+8847Kei1btsSiRYtQVFQEQ0NDbN68GSUlJejXr5/KcVq7dm3Ur18f+/fvx6RJk6T2krLXhNKQIUPw0UcfVRpbXl4ekpKSMGbMGJW2OVDx8fjw4UPk5+fD09MTtra2OH78ON5+++1q7RNNnq+Vhg4dqhJzy5YtcejQIQwdOlRaVqNGDfj7++PYsWPVipf+h0m3Bjz+x6ZsaN24cQPW1ta4cOECDAwM4OnpqbJe7dq1YWtriwsXLlT6fsquxI93c1Euv3HjBoBH3RWFEJg8eTImT55cbqy5ubmVJt2aaPCX9vhnAR7tH2XMmnDhwoUy3V6B/3UZvnDhglpJd926dcuMH7Ozs1MZA5uVlYXMzExp7N7jcnNzVR67u7tXuV0lb29vtRP0yiiPpwYNGpR5rmHDhkhJSXnqbQCPLjg3b95EXFwc4uLiyl1HuT+io6OxZ88etGjRAp6enujYsSMGDRokHW95eXnIz8+v8nuq7nddnf1fEWdnZzg7Oz/1+1Rnfz2tWrVqoX79+khOTkZkZCSSk5PRtm1bBAcHIyoqCn///TcyMzNRUlIiJX55eXkoKCgo97jx8vJCSUkJ/v33X6nrM1D5/u3evTtq1aqFXbt2qV2T2c7ODh06dKjmpy2f8nMlJyejbt26OHHiBGbMmAFHR0d89tln0nPW1tbVboRXxMTEBHPnzsX48eNRq1YttGrVCt26dcPgwYPLNH7UOTdW93h/PMlVKBTw9PSscuygJvf7tGnT0LNnT7zyyivw8fFB586d8fbbb5fpZm1qalrmPKrpawPwqIv5pEmTpAQjMTERubm56N+/f6WvmzdvHsLDw/HSSy+hWbNmeP311zF48GC1f3BWh6mpqcb2e82aNdG+fXts2LAB06dPB/Coa7mhoWGZ+R2ehrrXQGW7p/SP7kD51yV1nTt3DgYGBlVeJ0+dOoVPP/0U+/btQ35+vspz6nbLLa2ya6qXlxd27dqFu3fvwsLCQlpeVbuwIvXq1dPIMVad9mBF55mn+a40pTrt4ZKSEty6dQsODg7IysqCEKLCH/6Uibzyu318PSMjoyq/B+UPpFW1W+7du4fZs2dj1apVuHTpksqY6Cc5HjV5vlaqzn7W9Dn6RcKkWwMqutNT+g8LQLkTglTn/arajnLSiw8//BCdOnUqd93HE//H5eXlqTWm29LSUq2GtLr7Rg7UibWkpAS+vr744osvyl338RNUde6y3rp1S61eBsbGxiq/tuuL8nh76623EB4eXu46yoa2l5cXzpw5g23btmHnzp348ccfsWTJEkyZMkUjd90rookxaffu3VP7wljZr8rV2V+aEBgYiL179+LevXs4duwYpkyZAh8fH9ja2iI5ORmZmZmwtLTEq6+++sTbqGz/9unTB6tXr8b333+PyMhItd6vsLBQ7TGujo6Olc4iW6dOHbi7uyMpKQlubm4QQqB169ZwdHTEmDFjcOHCBSQnJyMgIECtMW/qGjt2LLp3746tW7di165dmDx5MmbPno19+/ap7Gs5nRs1ud+Dg4Nx7tw5/PTTT9i9eze++eYbLFiwAMuWLcOwYcOk9XQ1A3D//v0xceJEbNy4EWPHjsWGDRtgY2ODzp07V/q6fv36SXcnd+/ejfnz52Pu3LnYvHmzNMfH0youLkZeXp5a69rb26vciSvPgAEDEBERgdTUVPj5+WHDhg1o3759lXNNVEd1r4HqqKht9CST6t28eRMhISGwtrbGtGnT4OHhAVNTUxw/fhzR0dE6q2DxpH/fd+7cwZ07d9R6/4p++AA00x6sDk1OgFja07SHFQoFduzYUe666v4QrAlRUVFYtWoVxo4di9atW8PGxgYKhQIDBgx4ouNRk+drpersZzm2358VTLp1wNXVFSUlJcjKylKZsCknJwc3b96Eq6urRraj/FXOyMjoiX8Fa968eZk77+WZOnWqTmZlVYerqyvOnDlTZvnp06el5wH1f/SojIeHB9LS0tC+fXuNvF9pY8aMwerVq6tcLyQkpNLZtpWf98yZM1KXP6UzZ85o7HhTzkhbXFys1vFmYWGB/v37o3///igsLETv3r0xc+ZMTJw4EY6OjrC2tkZ6enql76Hud12Z6n5vCQkJiIiIUGvdyi5G1d1fTysoKAirVq3CDz/8gOLiYim5DAwMlJLugIAA6aLq6OgIc3PzCvevgYFBtRrU8+fPh6GhoTSJonLCvMocPHiw3Nmmy5OdnV1lnfWgoCAkJSXB3d0dfn5+sLKyQpMmTWBjY4OdO3fi+PHjWvnRx8PDA+PHj8f48eORlZUFPz8/fP7551i7dm213qe6x3tWVpbKYyEEzp49W+WPOZre7/b29oiIiEBERATu3LmD4OBgxMTEqCTduuLu7o4WLVpIXcw3b96MsLAwtUp9OTs7Y+TIkRg5ciRyc3PRtGlTzJw5s8Kku7rnln///Vft3jj79++vsvZ3WFgYIiMjkZCQAAD466+/MHHixGrFVBV1r4HKds+5c+dU7piWdzzb2dnh5s2bZZY/3hbx8PBASUkJMjIy4OfnV+52ExMTce3aNWzevBnBwcHS8uzs7DLrqvt9lb6mPu706dOoWbOmyl3up/HZZ5+pdU5ydXWttAdLddqDrq6uZc4dgPrfVWFhIS5fvlxlzLrk4eEBIQTc3d3xyiuvVLie8rvNyspSaS89fPgQ2dnZlfaCUu7jqtotmzZtQnh4OD7//HNp2f3798s95tWh6fM16Q6Tbh14/fXXMWnSJCxcuBDLly+Xlit/KX7a2TyVnJycEBoaiuXLlyMqKqpMl9i8vLxKfxkFND+mWxdef/11LFy4EIcOHZLGwt+9exdxcXFwc3OTuqIpL4pPeqIDHt392L59O1asWIHhw4erPHfv3j2UlJQ88cVXU2O6/f394eTkhGXLluGdd96RGpc7duxAZmamyuyeT6NGjRro06cP1q1bh/T09DJdrEofb9euXVOZrd7Y2Bje3t7YsWMHHj58CFNTU4SFhWHt2rX4448/yozrFkJAoVCo/V1XprrHgabGdFdnf2mCsnv13Llz0bhxY6m7WFBQEJYuXYr//vtPpdthjRo10LFjR/z00084f/68dKHOycnBunXrEBgYqNa4bCWFQoG4uDjcvn0b4eHhsLS0RI8ePSp9jabHqgUFBeG7775DQkKClCgZGBggICAAX3zxBR4+fKjRmcsLCgpgYGCgMpeCh4cHrKysyi1nU5XqHu/fffcdJk6cKI3r3rRpEy5fvozo6OhKt6PJ/f7437qlpSU8PT11XrqstP79+2P8+PFYuXIlrl69WmXX8uLiYty5c0elSoSTkxPq1KlT6feonIVY3XOLJsd0A4/GoXfq1AkbNmyAEALGxsYICwtT6/3Vpe41sEuXLpg0aRIWLVqEr7/+Wlpn4cKFZd7Tw8MDt27dwsmTJ6UfiC5fvlymsktYWBiio6Mxbdo0bNq0SaWHivIaofwRsfQPoIWFheWOTbawsFCrF5OzszP8/PywevVqTJw4UZr1Oj09Hbt371bruq0uTY3prk57UHmeOXLkiDSuOy8vD99//32Z9/Xw8EBSUpLKsri4OK3d6X5SvXv3xsSJExEbG4u1a9eq/MAihMD169fh4OAAf39/ODo6YtmyZYiIiJB6k8THx1f5d+zo6Ijg4GCsXLkS48aNU+mirTwegUfX1sd/kF+8ePET7zNtjOkm3WDSrQNNmjRBeHg44uLipK5PR44cwerVqxEWFqb2L1bq+PrrrxEYGAhfX1+8++67qFevHnJycnDo0CFcvHgRaWlplb5e02O6deHjjz/G+vXr0aVLF4wePRr29vZYvXo1srOz8eOPP0oXZg8PD9ja2mLZsmWwsrKChYUFWrZsWa1xv2+//TY2bNiAESNGYP/+/WjTpg2Ki4tx+vRpbNiwAbt27Sp3IjB1aGpMt5GREebOnYuIiAiEhIRg4MCBUskwNzc3fPDBB0+9DaU5c+Zg//79aNmyJd599114e3vj+vXrOH78OPbs2SN1gerYsSNq166NNm3aoFatWsjMzMRXX32Frl27SgnCrFmzsHv3boSEhEilaC5fvoyNGzciJSUFtra2an/XlfHz80ONGjUwd+5c3Lp1CyYmJmjXrh2cnJzKXV9TY7qrs780wdPTE7Vr18aZM2cQFRUlLQ8ODpaSsMcTzhkzZuDXX39FYGAgRo4cCUNDQyxfvhwPHjwot2Z9VQwMDLB27VqEhYVJjfXHe1+UpumxasrPd+bMGcyaNUtaHhwcjB07dsDExKTcif2e1F9//YX27dujX79+8Pb2hqGhIbZs2YKcnJxKy1NVpLrHu729PQIDAxEREYGcnBwsXLgQnp6eePfddyvdjib3u7e3N0JDQ9GsWTPY29vjjz/+kEpv6Uu/fv3w4Ycf4sMPP4S9vX2Vn/X27duoW7cu+vbtiyZNmsDS0hJ79uzB0aNHVe5WPc7MzAze3t5ISEjAK6+8Ant7e/j4+FQ45lOTY7qV+vfvj7feegtLlixBp06dNFYeUknda6Cfnx8GDhyIJUuW4NatWwgICMDevXtx9uzZMu85YMAAREdHo1evXhg9ejQKCgqwdOlSvPLKKyp10T09PfHJJ59g+vTpCAoKQu/evWFiYoKjR4+iTp06mD17NgICAmBnZ4fw8HCMHj0aCoUCa9asKbcXUrNmzZCQkIBx48ahefPmsLS0RPfu3cv93PPnz0eXLl3QunVrDB06VCoZZmNjo9Fef5oa0w2o3x6cMGEC1qxZg86dO2PMmDFSyTBXV1eVOW2AR2XgRowYgT59+uC1115DWloadu3apdEhDJrg4eGBGTNmYOLEiTh//jzCwsJgZWWF7OxsbNmyBcOHD8eHH34IIyMjzJgxA5GRkWjXrh369++P7OxsrFq1Sq3vYdGiRQgMDETTpk0xfPhwuLu74/z58/jll1+QmpoKAOjWrRvWrFkDGxsbeHt749ChQ9izZ88Tl83Vxphu0hGdzJH+nCpdGqc05RT8pafqf/jwoYiNjRXu7u7CyMhIvPTSS2LixIkqpRyEeFR2obxyKwDKlC9Rli94vDzNuXPnxODBg0Xt2rWFkZGRcHFxEd26dRObNm16yk9cPeXFLIT6pSXULRkmxKPP3LdvX2FraytMTU1FixYtxLZt28qs99NPPwlvb29haGioUvohJCSk3JJW5ZUyKSwsFHPnzhWNGjUSJiYmws7OTjRr1kzExsaKW7duSetV9Pl1JSEhQbz66qvCxMRE2NvbizfffFNcvHhRrdeqWzJMCCFycnLEqFGjxEsvvSSMjIxE7dq1Rfv27UVcXJy0zvLly0VwcLBwcHAQJiYmwsPDQ3z00Ucq+0sIIS5cuCAGDx4sHB0dhYmJiahXr54YNWqUSikPdb5rZUmNjRs3lhvzihUrRL169USNGjXUKtGhSersr4qoWzJM6Y033hAAREJCgrSssLBQmJubC2NjY5WSckrHjx8XnTp1EpaWlsLc3Fy0bdtWpYRh6TjKK+9W3nmxoKBAhISECEtLS/H777+rHb8mODk5CQAiJydHWpaSkiIAiKCgILXfR52SYVevXhWjRo0SDRs2FBYWFsLGxka0bNlSbNiwocx7lXeeDwkJKXPOq87xvn79ejFx4kTh5OQkzMzMRNeuXcuUstG2GTNmiBYtWghbW1thZmYmGjZsKGbOnKlS9ic8PFxYWFiUee3jJaIqom7JsNLatGkjgLKlO5VQqhTSgwcPxEcffSSaNGkirKyshIWFhWjSpIlYsmSJymvKuz4cPHhQNGvWTBgbG1erfJim5OfnCzMzMwFArF27Vu3XqVvKSQj1r4H37t0To0ePFg4ODsLCwkJ0795d/Pvvv+Xul927dwsfHx9hbGwsGjRoINauXVvh8bBy5Urp2mZnZydCQkLEr7/+Kj3/22+/iVatWgkzMzNRp04dMWHCBKn0aulz/Z07d8SgQYOEra2tShmu8kpDCSHEnj17RJs2bYSZmZmwtrYW3bt3l8oylt6P6rYLdUHd9uDJkydFSEiIMDU1FS4uLmL69Oni22+/LRNzcXGxiI6OFjVr1hTm5uaiU6dO4uzZs1orGfb4Nbyia09F+/3HH38UgYGBwsLCQlhYWIiGDRuKUaNGiTNnzqist2TJEuHu7i5MTEyEv7+/SEpKKnM+rui4SE9PF7169ZLO0Q0aNBCTJ0+Wnr9x44aIiIgQNWvWFJaWlqJTp07i9OnTZfaZuiXDNKm6+7OiczepRyEER8STPA0ZMgTnz5+vdPwyaUdoaCjc3NwQHx+v71ColPj4eERERHAiEz1wc3PDkCFDZDOXRWmJiYlo27YtNm7ciL59++o7HK1Tfl6OV9ScmJgYxMfHVznTPdHT4HFGLzLNTdlKRERERERERCqYdBMRERERERFpCZNuIiIiIiIiIi3hmG4iIiIiIiIiLeGdbiIiIiIiIiItYdJNREREREREpCVMuomIiIiIiIi0hEk3ERERERERkZYw6SYiIiIiIiLSEibdRERERERERFrCpJuIiIiIiIhIS5h0ExEREREREWkJk24iIiIiIiIiLfl/PQLv1nT2NEkAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_activity_sequence_comparison(\n",
+    "    sequence_nts=sequence_nts,\n",
+    "    sequence_acbm=sequence_acbm,\n",
+    "    activity_mapping=activity_mapping,\n",
+    "    perc_cutoff=0.35,\n",
+    "    #save_path=validation_plots_path / \"4_matching_activity_sequences.png\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assigning: Trip distances"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Mean crowfly distance per trip purpose"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate travel distance for acbm activities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "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>pid</th>\n",
+       "      <th>hid</th>\n",
+       "      <th>ozone</th>\n",
+       "      <th>dzone</th>\n",
+       "      <th>purp</th>\n",
+       "      <th>oact</th>\n",
+       "      <th>dact</th>\n",
+       "      <th>mode</th>\n",
+       "      <th>seq</th>\n",
+       "      <th>tst</th>\n",
+       "      <th>tet</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>start_location_id</th>\n",
+       "      <th>end_location_id</th>\n",
+       "      <th>start_location_geometry_wkt</th>\n",
+       "      <th>end_location_geometry_wkt</th>\n",
+       "      <th>start_geometry</th>\n",
+       "      <th>end_geometry</th>\n",
+       "      <th>distance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>200</td>\n",
+       "      <td>89</td>\n",
+       "      <td>E00059031</td>\n",
+       "      <td>E00058877</td>\n",
+       "      <td>work</td>\n",
+       "      <td>home</td>\n",
+       "      <td>work</td>\n",
+       "      <td>car</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1900-01-01 12:30:00</td>\n",
+       "      <td>1900-01-01 13:00:00</td>\n",
+       "      <td>0:30:00</td>\n",
+       "      <td>1735597748</td>\n",
+       "      <td>284127794</td>\n",
+       "      <td>POINT (-1.399263822377326 53.934588245650026)</td>\n",
+       "      <td>POINT (-1.5223977194364153 53.80563634318617)</td>\n",
+       "      <td>POINT (-155765.336 7157777.811)</td>\n",
+       "      <td>POINT (-1.5223977194364153 53.80563634318617)</td>\n",
+       "      <td>27.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>200</td>\n",
+       "      <td>89</td>\n",
+       "      <td>E00059031</td>\n",
+       "      <td>E00059031</td>\n",
+       "      <td>home</td>\n",
+       "      <td>work</td>\n",
+       "      <td>home</td>\n",
+       "      <td>car</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1900-01-01 16:30:00</td>\n",
+       "      <td>1900-01-01 17:00:00</td>\n",
+       "      <td>0:30:00</td>\n",
+       "      <td>284127794</td>\n",
+       "      <td>1735597748</td>\n",
+       "      <td>POINT (-1.5223977194364153 53.80563634318617)</td>\n",
+       "      <td>POINT (-1.399263822377326 53.934588245650026)</td>\n",
+       "      <td>POINT (-169472.539 7133431.742)</td>\n",
+       "      <td>POINT (-1.399263822377326 53.934588245650026)</td>\n",
+       "      <td>27.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>312</td>\n",
+       "      <td>139</td>\n",
+       "      <td>E00059045</td>\n",
+       "      <td>E00058294</td>\n",
+       "      <td>escort</td>\n",
+       "      <td>home</td>\n",
+       "      <td>escort</td>\n",
+       "      <td>car</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1900-01-01 06:35:00</td>\n",
+       "      <td>1900-01-01 06:54:00</td>\n",
+       "      <td>0:19:00</td>\n",
+       "      <td>1750466246</td>\n",
+       "      <td>1325685728</td>\n",
+       "      <td>POINT (-1.4001307419061457 53.927941746853776)</td>\n",
+       "      <td>POINT (-1.5100280118435312 53.89948871840075)</td>\n",
+       "      <td>POINT (-155861.841 7156521.118)</td>\n",
+       "      <td>POINT (-1.5100280118435312 53.89948871840075)</td>\n",
+       "      <td>13.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>312</td>\n",
+       "      <td>139</td>\n",
+       "      <td>E00058294</td>\n",
+       "      <td>E00169797</td>\n",
+       "      <td>work</td>\n",
+       "      <td>escort</td>\n",
+       "      <td>work</td>\n",
+       "      <td>car</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1900-01-01 06:55:00</td>\n",
+       "      <td>1900-01-01 07:22:00</td>\n",
+       "      <td>0:27:00</td>\n",
+       "      <td>1325685728</td>\n",
+       "      <td>443167284</td>\n",
+       "      <td>POINT (-1.5100280118435312 53.89948871840075)</td>\n",
+       "      <td>POINT (-1.5484560286132372 53.797016858417656)</td>\n",
+       "      <td>POINT (-168095.549 7151143.595)</td>\n",
+       "      <td>POINT (-1.5484560286132372 53.797016858417656)</td>\n",
+       "      <td>19.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>312</td>\n",
+       "      <td>139</td>\n",
+       "      <td>E00059045</td>\n",
+       "      <td>E00059045</td>\n",
+       "      <td>home</td>\n",
+       "      <td>work</td>\n",
+       "      <td>home</td>\n",
+       "      <td>car</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1900-01-01 18:00:00</td>\n",
+       "      <td>1900-01-01 18:35:00</td>\n",
+       "      <td>0:35:00</td>\n",
+       "      <td>443167284</td>\n",
+       "      <td>1750466246</td>\n",
+       "      <td>POINT (-1.5484560286132372 53.797016858417656)</td>\n",
+       "      <td>POINT (-1.4001307419061457 53.927941746853776)</td>\n",
+       "      <td>POINT (-172373.337 7131807.059)</td>\n",
+       "      <td>POINT (-1.4001307419061457 53.927941746853776)</td>\n",
+       "      <td>29.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>312</td>\n",
+       "      <td>139</td>\n",
+       "      <td>E00059045</td>\n",
+       "      <td>E00059120</td>\n",
+       "      <td>shop</td>\n",
+       "      <td>home</td>\n",
+       "      <td>shop</td>\n",
+       "      <td>car</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1900-01-01 19:00:00</td>\n",
+       "      <td>1900-01-01 19:10:00</td>\n",
+       "      <td>0:10:00</td>\n",
+       "      <td>1750466246</td>\n",
+       "      <td>1065505008</td>\n",
+       "      <td>POINT (-1.4001307419061457 53.927941746853776)</td>\n",
+       "      <td>POINT (-1.5891715167646434 53.78010086020202)</td>\n",
+       "      <td>POINT (-155861.841 7156521.118)</td>\n",
+       "      <td>POINT (-1.5891715167646434 53.78010086020202)</td>\n",
+       "      <td>34.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>312</td>\n",
+       "      <td>139</td>\n",
+       "      <td>E00059120</td>\n",
+       "      <td>E00059045</td>\n",
+       "      <td>home</td>\n",
+       "      <td>shop</td>\n",
+       "      <td>home</td>\n",
+       "      <td>car</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1900-01-01 19:40:00</td>\n",
+       "      <td>1900-01-01 19:45:00</td>\n",
+       "      <td>0:05:00</td>\n",
+       "      <td>1065505008</td>\n",
+       "      <td>1750466246</td>\n",
+       "      <td>POINT (-1.5891715167646434 53.78010086020202)</td>\n",
+       "      <td>POINT (-1.4001307419061457 53.927941746853776)</td>\n",
+       "      <td>POINT (-176905.764 7128619.540)</td>\n",
+       "      <td>POINT (-1.4001307419061457 53.927941746853776)</td>\n",
+       "      <td>34.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>313</td>\n",
+       "      <td>139</td>\n",
+       "      <td>E00059045</td>\n",
+       "      <td>E00057830</td>\n",
+       "      <td>work</td>\n",
+       "      <td>home</td>\n",
+       "      <td>work</td>\n",
+       "      <td>car</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1900-01-01 06:35:00</td>\n",
+       "      <td>1900-01-01 06:54:00</td>\n",
+       "      <td>0:19:00</td>\n",
+       "      <td>1750466246</td>\n",
+       "      <td>446256422</td>\n",
+       "      <td>POINT (-1.4001307419061457 53.927941746853776)</td>\n",
+       "      <td>POINT (-1.4996387249709981 53.76805382233579)</td>\n",
+       "      <td>POINT (-155861.841 7156521.118)</td>\n",
+       "      <td>POINT (-1.4996387249709981 53.76805382233579)</td>\n",
+       "      <td>32.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>313</td>\n",
+       "      <td>139</td>\n",
+       "      <td>E00059045</td>\n",
+       "      <td>E00059045</td>\n",
+       "      <td>home</td>\n",
+       "      <td>work</td>\n",
+       "      <td>home</td>\n",
+       "      <td>pt</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1900-01-01 18:00:00</td>\n",
+       "      <td>1900-01-01 18:20:00</td>\n",
+       "      <td>0:20:00</td>\n",
+       "      <td>446256422</td>\n",
+       "      <td>1750466246</td>\n",
+       "      <td>POINT (-1.4996387249709981 53.76805382233579)</td>\n",
+       "      <td>POINT (-1.4001307419061457 53.927941746853776)</td>\n",
+       "      <td>POINT (-166939.019 7126350.273)</td>\n",
+       "      <td>POINT (-1.4001307419061457 53.927941746853776)</td>\n",
+       "      <td>32.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>313</td>\n",
+       "      <td>139</td>\n",
+       "      <td>E00059045</td>\n",
+       "      <td>E00169811</td>\n",
+       "      <td>shop</td>\n",
+       "      <td>home</td>\n",
+       "      <td>shop</td>\n",
+       "      <td>car</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1900-01-01 19:00:00</td>\n",
+       "      <td>1900-01-01 19:10:00</td>\n",
+       "      <td>0:10:00</td>\n",
+       "      <td>1750466246</td>\n",
+       "      <td>443167282</td>\n",
+       "      <td>POINT (-1.4001307419061457 53.927941746853776)</td>\n",
+       "      <td>POINT (-1.5489307938428294 53.796367947390756)</td>\n",
+       "      <td>POINT (-155861.841 7156521.118)</td>\n",
+       "      <td>POINT (-1.5489307938428294 53.796367947390756)</td>\n",
+       "      <td>29.9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   pid  hid      ozone      dzone    purp    oact    dact mode  seq  \\\n",
+       "0  200   89  E00059031  E00058877    work    home    work  car  1.0   \n",
+       "1  200   89  E00059031  E00059031    home    work    home  car  2.0   \n",
+       "2  312  139  E00059045  E00058294  escort    home  escort  car  1.0   \n",
+       "3  312  139  E00058294  E00169797    work  escort    work  car  2.0   \n",
+       "4  312  139  E00059045  E00059045    home    work    home  car  3.0   \n",
+       "5  312  139  E00059045  E00059120    shop    home    shop  car  4.0   \n",
+       "6  312  139  E00059120  E00059045    home    shop    home  car  5.0   \n",
+       "7  313  139  E00059045  E00057830    work    home    work  car  1.0   \n",
+       "8  313  139  E00059045  E00059045    home    work    home   pt  2.0   \n",
+       "9  313  139  E00059045  E00169811    shop    home    shop  car  3.0   \n",
+       "\n",
+       "                   tst                  tet duration start_location_id  \\\n",
+       "0  1900-01-01 12:30:00  1900-01-01 13:00:00  0:30:00        1735597748   \n",
+       "1  1900-01-01 16:30:00  1900-01-01 17:00:00  0:30:00         284127794   \n",
+       "2  1900-01-01 06:35:00  1900-01-01 06:54:00  0:19:00        1750466246   \n",
+       "3  1900-01-01 06:55:00  1900-01-01 07:22:00  0:27:00        1325685728   \n",
+       "4  1900-01-01 18:00:00  1900-01-01 18:35:00  0:35:00         443167284   \n",
+       "5  1900-01-01 19:00:00  1900-01-01 19:10:00  0:10:00        1750466246   \n",
+       "6  1900-01-01 19:40:00  1900-01-01 19:45:00  0:05:00        1065505008   \n",
+       "7  1900-01-01 06:35:00  1900-01-01 06:54:00  0:19:00        1750466246   \n",
+       "8  1900-01-01 18:00:00  1900-01-01 18:20:00  0:20:00         446256422   \n",
+       "9  1900-01-01 19:00:00  1900-01-01 19:10:00  0:10:00        1750466246   \n",
+       "\n",
+       "  end_location_id                     start_location_geometry_wkt  \\\n",
+       "0       284127794   POINT (-1.399263822377326 53.934588245650026)   \n",
+       "1      1735597748   POINT (-1.5223977194364153 53.80563634318617)   \n",
+       "2      1325685728  POINT (-1.4001307419061457 53.927941746853776)   \n",
+       "3       443167284   POINT (-1.5100280118435312 53.89948871840075)   \n",
+       "4      1750466246  POINT (-1.5484560286132372 53.797016858417656)   \n",
+       "5      1065505008  POINT (-1.4001307419061457 53.927941746853776)   \n",
+       "6      1750466246   POINT (-1.5891715167646434 53.78010086020202)   \n",
+       "7       446256422  POINT (-1.4001307419061457 53.927941746853776)   \n",
+       "8      1750466246   POINT (-1.4996387249709981 53.76805382233579)   \n",
+       "9       443167282  POINT (-1.4001307419061457 53.927941746853776)   \n",
+       "\n",
+       "                        end_location_geometry_wkt  \\\n",
+       "0   POINT (-1.5223977194364153 53.80563634318617)   \n",
+       "1   POINT (-1.399263822377326 53.934588245650026)   \n",
+       "2   POINT (-1.5100280118435312 53.89948871840075)   \n",
+       "3  POINT (-1.5484560286132372 53.797016858417656)   \n",
+       "4  POINT (-1.4001307419061457 53.927941746853776)   \n",
+       "5   POINT (-1.5891715167646434 53.78010086020202)   \n",
+       "6  POINT (-1.4001307419061457 53.927941746853776)   \n",
+       "7   POINT (-1.4996387249709981 53.76805382233579)   \n",
+       "8  POINT (-1.4001307419061457 53.927941746853776)   \n",
+       "9  POINT (-1.5489307938428294 53.796367947390756)   \n",
+       "\n",
+       "                    start_geometry  \\\n",
+       "0  POINT (-155765.336 7157777.811)   \n",
+       "1  POINT (-169472.539 7133431.742)   \n",
+       "2  POINT (-155861.841 7156521.118)   \n",
+       "3  POINT (-168095.549 7151143.595)   \n",
+       "4  POINT (-172373.337 7131807.059)   \n",
+       "5  POINT (-155861.841 7156521.118)   \n",
+       "6  POINT (-176905.764 7128619.540)   \n",
+       "7  POINT (-155861.841 7156521.118)   \n",
+       "8  POINT (-166939.019 7126350.273)   \n",
+       "9  POINT (-155861.841 7156521.118)   \n",
+       "\n",
+       "                                     end_geometry  distance  \n",
+       "0   POINT (-1.5223977194364153 53.80563634318617)      27.9  \n",
+       "1   POINT (-1.399263822377326 53.934588245650026)      27.9  \n",
+       "2   POINT (-1.5100280118435312 53.89948871840075)      13.4  \n",
+       "3  POINT (-1.5484560286132372 53.797016858417656)      19.8  \n",
+       "4  POINT (-1.4001307419061457 53.927941746853776)      29.7  \n",
+       "5   POINT (-1.5891715167646434 53.78010086020202)      34.9  \n",
+       "6  POINT (-1.4001307419061457 53.927941746853776)      34.9  \n",
+       "7   POINT (-1.4996387249709981 53.76805382233579)      32.1  \n",
+       "8  POINT (-1.4001307419061457 53.927941746853776)      32.1  \n",
+       "9  POINT (-1.5489307938428294 53.796367947390756)      29.9  "
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Apply the function to legs_acbm_geo\n",
+    "legs_acbm_geo = calculate_od_distances(\n",
+    "    df=legs_acbm_geo,\n",
+    "    start_wkt_col=\"start_location_geometry_wkt\",\n",
+    "    end_wkt_col=\"end_location_geometry_wkt\",\n",
+    ")\n",
+    "\n",
+    "legs_acbm_geo.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot: Aggregate\n",
+    "plot_comparison(\n",
+    "    legs_acbm_geo,\n",
+    "    legs_nts,\n",
+    "    value_column=\"distance\",\n",
+    "    bin_size=2,\n",
+    "    value_threshold=50,\n",
+    "    max_y_value=30,\n",
+    "    figsize=(10, 5),\n",
+    "    plot_type=\"distance\",\n",
+    "    plot_mode=\"aggregate\",\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot: Facet by activity_type\n",
+    "plot_comparison(\n",
+    "    legs_acbm_geo,\n",
+    "    legs_nts,\n",
+    "    value_column=\"distance\",\n",
+    "    bin_size=2,\n",
+    "    value_threshold=50,\n",
+    "    max_y_value=30,\n",
+    "    plot_type=\"distance\",\n",
+    "    plot_mode=\"facet\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test number of intrazonal trips\n",
+    "\n",
+    "To ensure that our intrazonal logic is working. It appears we are underestimating the number of trips that are 2-4km and 4-6km. \n",
+    "\n",
+    "This could also be a result of travel times not correlating directly with distance, and that effect being more pronounced in shorter trips.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Plot intrazonal trips by trip purpose"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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1Z88effjhh4qNjX2i+EuWLKnRo0cnWO/r62t3n47yuNfOHm5ubvFWh46JiVGdOnUUFhamDz/8UAEBAcqQIYNCQkLUtm3beK/r2bNn1apVK9WpU0effPLJU8WTmJ+HmTNnqm3btmratKn69u0rLy8vOTs7a8SIETYL+iW1J31PPE7z5s1VtWpVLVy4UL///ru++OILjRw5UgsWLLDOX7fH37+seBbv+WrVqllXL09MTHFiYmISjCmpX+PHGTVqlNq2batFixbp999/V/fu3a1z4/PmzWv3awcAKRlJNwA8IwUKFJAxRvnz51fhwoWfuI/t27frwYMHj73SU6BAAa1atUpVqlRJsg/Qj/vgvm7dOoWGhmrBggWqVq2atfyvq7VLUr58+SQ9WrStZs2a1vKHDx/q3LlzNguLFShQQPv371etWrXsvs9w3H6OHz9uHY4e5/jx49b6Z+lJ7pV88OBBnThxQtOnT1fr1q2t5X/88Ue8tvfu3dOrr76qLFmy6Oeff46XwP/1NfnrFdzo6GidPXv2ie5R/ssvv8jf318LFiywOb5BgwbZ3ZejnDx5Ml7ZiRMnlD59epsvnry9vfXee+/pvffe07Vr11S2bFkNHz48UUn3yZMnba4Mnzp1SrGxsdbRG0nxnn9aWbNmjbdyuPToqnRir+jHOXnypM37NyIiQpcvX1aDBg0k2f+zVrJkSZUsWVIDBgzQli1bVKVKFX377bf65JNPUsRrBwBJhTndAPCMvPrqq3J2dtaQIUPiXUU2xig0NPRf+3jttdd048YNjR8/Pl5dXJ/NmzdXTEyMhg0bFq/Nw4cPE/wA/m/i7hv9923jrkD99Xiio6P1zTff2LQrV66csmfPrkmTJunhw4fW8lmzZsUbOt28eXOFhIRo0qRJ8eK4d++e7t69+9g4y5UrJy8vL3377bc2txdbvny5jh49qoYNG/7LkSa9uLm19rzuCb2uxhiNGTMmXtt33nlHJ06c0MKFC+PNj5cerTPg6uqqsWPH2vQ3efJk3b59+4lek4Ti2759u7Zu3Wp3X46ydetWm/nlFy9e1KJFi1S3bl05OzsrJiYm3jB9Ly8v+fj4JPrWdBMmTLB5Pm7cOEmyJuxJ8Z5/WgUKFNC2bdsUHR1tLVuyZMkT3Xbr+++/t5lKMHHiRD18+NB6vIn9WQsPD7f5PSA9SsCdnJysr31KeO0AIKlwpRsAnpECBQrok08+Uf/+/a23y8qYMaPOnj2rhQsXqlOnTurTp88/9tG6dWvNmDFDvXr10o4dO1S1alXdvXtXq1at0nvvvacmTZqoevXq6ty5s0aMGKF9+/apbt26SpcunU6ePKl58+ZpzJgxev311+2KvUyZMnJ2dtbIkSN1+/Ztubm56cUXX1TlypWVNWtWtWnTRt27d5fFYtGPP/4Y70Oyq6urBg8erG7duunFF19U8+bNde7cOU2bNk0FChSwuVr69ttva+7cuXrnnXe0du1aValSRTExMTp27Jjmzp2rlStXPnYIf7p06TRy5Ei1a9dO1atXV8uWLa23DPPz81PPnj3tOu6k4OHhoWLFimnOnDkqXLiwsmXLphIlSlhvW5WQgIAAFShQQH369FFISIgyZcqk+fPnx/uCYunSpZoxY4Zee+01HThwwGbetaenp5o2baqcOXOqf//+GjJkiF566SU1btxYx48f1zfffKPnn3/eZtG0xHr55Ze1YMECvfLKK2rYsKHOnj2rb7/9VsWKFVNERITd/TlCiRIlVK9ePZtbhkmP7pcuPVr0L2/evHr99ddVunRpeXp6atWqVdq5c6dGjRqVqH2cPXtWjRs31ksvvaStW7dq5syZevPNN1W6dGlJSfOef1odO3bUL7/8opdeeknNmzfX6dOnNXPmzCea+x0dHa1atWqpefPm1p+hF154QY0bN5akRP+srVmzRl27dlWzZs1UuHBhPXz4UD/++KOcnZ2t6xGkhNcOAJLMM1wpHQDSLHtuPzV//nzzwgsvmAwZMpgMGTKYgIAA06VLF3P8+HFrm+rVq5vixYsnuH1kZKT56KOPTP78+U26dOlM7ty5zeuvv25Onz5t0+777783QUFBxsPDw2TMmNGULFnSfPDBB+bPP/+0tsmXL59p2LBhvH38/XZCxhgzadIk4+/vb5ydnW1u6bR582ZTsWJF4+HhYXx8fMwHH3xgVq5cmeAtxsaOHWvy5ctn3NzcTPny5c3mzZtNUFCQeemll2zaRUdHm5EjR5rixYsbNzc3kzVrVhMUFGSGDBlibt++/W8vsZkzZ44JDAw0bm5uJlu2bKZVq1bm0qVLNm2S4pZhGTJkiNc2oVtYbdmyxQQFBRlXV1ebW2g9rg9jjDly5IipXbu28fT0NDly5DDBwcFm//79RpKZOnWqTVwJPfLly2fT3/jx401AQIBJly6dyZUrl3n33XfNzZs3bdo87ueuTZs2Nv3FxsaaTz/91HouAwMDzZIlS+K1MyZpbxl2/fr1eO0fd8uwLl26mJkzZ5pChQpZY/xrn1FRUaZv376mdOnSJmPGjCZDhgymdOnS5ptvvvnXGOP2eeTIEfP666+bjBkzmqxZs5quXbva3KouztO+5/8phoRek78bNWqUyZMnj3FzczNVqlQxu3bteuwtwxK6jVfcz9n69etNp06dTNasWY2np6dp1aqVCQ0Njdf+337Wzpw5Y9q3b28KFChg3N3dTbZs2UzNmjXNqlWr4vWVmNcOAFI6izGJWCkHAAAHiI2NVc6cOfXqq68mOJwceBIWi0VdunRJcBpGUhg8eLCGDBmi69ev/+MiZmnFtGnT1K5dO+3cufOxo0wAAI/HnG4AwDNx//79eMPOZ8yYobCwMNWoUSN5ggIAAHAw5nQDAJ6Jbdu2qWfPnmrWrJmyZ8+uPXv2aPLkySpRooSaNWuW3OEBAAA4BEk3AOCZ8PPzk6+vr8aOHauwsDBly5ZNrVu31meffSZXV9fkDg8AAMAhmNMNAAAAAICDMKfbQUJDQ+Xl5aVz584ldyjxVKxYUfPnz0/uMAAAAAAgzSPpdpDhw4erSZMm8vPzs5ZNmzZNpUqVkru7u7y8vNSlS5cEtz116pQyZsyoLFmy2JQ/ePBAQ4cOVYECBeTu7q7SpUtrxYoVNm1iYmL08ccfK3/+/PLw8FCBAgU0bNgwm8WLBgwYoH79+ik2NjbJjhcAAAAAEF+qTrqNMQoPD4+3Gm5yi4yM1OTJk9WhQwdr2ejRo/XRRx+pX79+Onz4sFatWqV69erF2/bBgwdq2bKlqlatGq9uwIAB+u677zRu3DgdOXJE77zzjl555RXt3bvX2mbkyJGaOHGixo8fr6NHj2rkyJH6/PPPNW7cOGub+vXr686dO1q+fHkSHzkAAAAA4K9S9Zzu8PBwZc6cWbdv31amTJmSOxyrX375Re+9956uXbsmSbp586by5MmjxYsXq1atWv+47Ycffqg///xTtWrV0vvvv69bt25Z63x8fPTRRx/ZXCF/7bXX5OHhoZkzZ0qSXn75ZeXKlUuTJ09+bBtJat++vR48eKAff/wxKQ4ZAAAAAJCAVH2lO6XauHGjgoKCrM//+OMPxcbGKiQkREWLFlXevHnVvHlzXbx40Wa7NWvWaN68eZowYUKC/UZFRcnd3d2mzMPDQ5s2bbI+r1y5slavXq0TJ05Ikvbv369Nmzapfv36NtuVL19eGzdufKrjBAAAAAD8M5JuBzh//rx8fHysz8+cOaPY2Fh9+umn+vrrr/XLL78oLCxMderUUXR0tKRHC6+1bdtW06ZNe+xV+3r16mn06NE6efKkYmNj9ccff2jBggW6fPmytU2/fv3UokULBQQEKF26dAoMDNT777+vVq1a2fTl4+OjixcvMq8bAAAAAByIpNsB7t27Z3NFOjY2Vg8ePNDYsWNVr149VaxYUT///LNOnjyptWvXSpKCg4P15ptvqlq1ao/td8yYMSpUqJACAgLk6uqqrl27ql27dnJy+r/TOHfuXM2aNUs//fST9uzZo+nTp+vLL7/U9OnTbfry8PBQbGysoqKikvjoAQAAAABxSLodIEeOHLp586b1ube3tySpWLFi1rKcOXMqR44cunDhgqRHQ8u//PJLubi4yMXFRR06dNDt27fl4uKiKVOmWLf59ddfdffuXZ0/f17Hjh2Tp6en/P39rf327dvXerW7ZMmSevvtt9WzZ0+NGDHCJsawsDBlyJBBHh4eDnsdAAAAAOC/ziW5A0iLAgMDbRYtq1KliiTp+PHjyps3r6RHSe+NGzeUL18+SdLWrVsVExNj3WbRokUaOXKktmzZojx58tj07+7urjx58ujBgweaP3++mjdvbq2LjIy0ufItSc7OzvGGkR86dEiBgYFJcLQAAAAAgMch6XaAevXqqX///rp586ayZs2qwoULq0mTJurRo4e+//57ZcqUSf3791dAQIBq1qwpSSpatKhNH7t27ZKTk5NKlChhLdu+fbtCQkJUpkwZhYSEaPDgwYqNjdUHH3xgbdOoUSMNHz5czz33nIoXL669e/dq9OjRat++vU3/GzduVN26dR34KgAAAAAAGF7uACVLllTZsmU1d+5ca9mMGTNUoUIFNWzYUNWrV1e6dOm0YsUKpUuXLtH93r9/XwMGDFCxYsX0yiuvKE+ePNq0aZOyZMlibTNu3Di9/vrreu+991S0aFH16dNHnTt31rBhw6xtQkJCtGXLFrVr1y5JjhcAAAAAkDDu0+0gS5cuVd++fXXo0KF4w72T24cffqibN2/q+++/T+5QAAAAACBNY3i5gzRs2FAnT55USEiIfH19kzscG15eXurVq1dyhwEAAAAAaR5XugEAAAAAcJCUNe4ZAAAAAIA0hKQbAAAAAAAHIekGAAAAAMBBSLoBAAAAAHAQkm4AAAAAAByEpBsAAAAAAAch6QYAAAAAwEFIugEAAAAAcBCSbgAAAAAAHISkGwAAAAAAByHpBgAAAADAQVySO4BnrWFQj+QO4T9n6e4xyR0CAAAAACQLrnQDAAAAAOAgJN0AAAAAADgISTcAAAAAAA6S7El3SEiI3nrrLWXPnl0eHh4qWbKkdu3aldxhAQAAAADw1JJ1IbWbN2+qSpUqqlmzppYvX66cOXPq5MmTypo1a3KGBQAAAABAkkjWpHvkyJHy9fXV1KlTrWX58+dPxogAAAAAAEg6yTq8/LffflO5cuXUrFkzeXl5KTAwUJMmTXps+6ioKIWHh9s8AAAAAABIqZI16T5z5owmTpyoQoUKaeXKlXr33XfVvXt3TZ8+PcH2I0aMUObMma0PX1/fZxwxAAAAAACJZzHGmOTauaurq8qVK6ctW7ZYy7p3766dO3dq69at8dpHRUUpKirK+jw8PFy+vr66ffu2MmXKlKh9Ngzq8fSBwy5Ld49J7hAAAAAAIFkk65Vub29vFStWzKasaNGiunDhQoLt3dzclClTJpsHAAAAAAApVbIm3VWqVNHx48dtyk6cOKF8+fIlU0QAAAAAACSdZE26e/bsqW3btunTTz/VqVOn9NNPP+n7779Xly5dkjMsAAAAAACSRLIm3c8//7wWLlyon3/+WSVKlNCwYcP09ddfq1WrVskZFgAAAAAASSJZ79MtSS+//LJefvnl5A4DAAAAAIAkl6xXugEAAAAASMtIugEAAAAAcBCSbgAAAAAAHISkGwAAAAAAByHpBgAAAADAQUi6AQAAAABwEJJuAAAAAAAchKQbAAAAAAAHIekGAAAAAMBBSLoBAAAAAHAQkm4AAAAAAByEpBsAAAAAAAch6QYAAAAAwEFIugEAAAAAcBCSbgAAAAAAHISkGwAAAAAAByHpBgAAAADAQUi6AQAAAABwEJJuAAAAAAAchKQbAAAAAAAHIekGAAAAAMBBSLoBAAAAAHAQkm4AAAAAAByEpBsAAAAAAAch6QYAAAAAwEFIugEAAAAAcBCSbgAAAAAAHISkGwAAAAAAByHpBgAAAADAQUi6AQAAAABwEJJuAAAAAAAchKQbAAAAAAAHIekGAAAAAMBBSLoBAAAAAHAQkm4AAAAAAByEpBsAAAAAAAch6QYAAAAAwEFIugEAAAAAcBCSbgAAAAAAHISkGwAAAAAAByHpBgAAAADAQUi6AQAAAABwEJJuAAAAAAAchKQbAAAAAAAHIekGAAAAAMBBSLoBAAAAAHAQkm4AAAAAAByEpBsAAAAAAAch6QYAAAAAwEFIugEAAAAAcBCSbgAAAAAAHISkGwAAAAAAByHpBgAAAADAQUi6AQAAAABwEJJuAAAAAAAchKQbAAAAAAAHIekGAAAAAMBBSLoBAAAAAHAQkm4AAAAAAByEpBsAAAAAAAdJ1qR78ODBslgsNo+AgIDkDAkAAAAAgCTjkphGBw4cSHSHpUqVsiuA4sWLa9WqVf8XkEuiQgIAAAAAIMVLVIZbpkwZWSwWGWMSrI+rs1gsiomJsS8AFxflzp3brm0AAAAAAEgNEpV0nz171mEBnDx5Uj4+PnJ3d1elSpU0YsQIPffccw7bHwAAAAAAz0qiku58+fI5ZOcVKlTQtGnTVKRIEV2+fFlDhgxR1apVdejQIWXMmDFe+6ioKEVFRVmfh4eHOyQuAAAAAACSwhNPoD5y5IguXLig6Ohom/LGjRsnuo/69etb/1+qVClVqFBB+fLl09y5c9WhQ4d47UeMGKEhQ4Y8acgAAAAAADxTdifdZ86c0SuvvKKDBw/azPO2WCySZPec7r/KkiWLChcurFOnTiVY379/f/Xq1cv6PDw8XL6+vk+8PwAAAAAAHMnuW4b16NFD+fPn17Vr15Q+fXodPnxYGzZsULly5bRu3bqnCiYiIkKnT5+Wt7d3gvVubm7KlCmTzQMAAAAAgJTK7qR769atGjp0qHLkyCEnJyc5OTnphRde0IgRI9S9e3e7+urTp4/Wr1+vc+fOacuWLXrllVfk7Oysli1b2hsWAAAAAAApjt3Dy2NiYqyLnOXIkUN//vmnihQponz58un48eN29XXp0iW1bNlSoaGhypkzp1544QVt27ZNOXPmtDcsAAAAAABSHLuT7hIlSmj//v3Knz+/KlSooM8//1yurq76/vvv5e/vb1dfs2fPtnf3AAAAAACkGnYn3QMGDNDdu3clSUOHDtXLL7+sqlWrKnv27JozZ06SBwgAAAAAQGpld9Jdr1496/8LFiyoY8eOKSwsTFmzZrWuYA4AAAAAAJ7iPt1/lS1btqToBgAAAACANMXupPvu3bv67LPPtHr1al27dk2xsbE29WfOnEmy4AAAAAAASM3sTro7duyo9evX6+2335a3tzdDygEAAAAAeAy7k+7ly5dr6dKlqlKliiPiAQAAAAAgzXCyd4OsWbMyhxsAAAAAgESwO+keNmyYBg4cqMjISEfEAwAAAABAmmH38PJRo0bp9OnTypUrl/z8/JQuXTqb+j179iRZcAAAAAAApGZ2J91NmzZ1QBgAAAAAAKQ9difdgwYNckQcAAAAAACkOXYn3XF2796to0ePSpKKFy+uwMDAJAsKAAAAAIC0wO6k+9q1a2rRooXWrVunLFmySJJu3bqlmjVravbs2cqZM2dSxwgAAAAAQKpk9+rl3bp10507d3T48GGFhYUpLCxMhw4dUnh4uLp37+6IGAEAAAAASJXsvtK9YsUKrVq1SkWLFrWWFStWTBMmTFDdunWTNDgAAAAAAFIzu690x8bGxrtNmCSlS5dOsbGxSRIUkNKEhobKy8tL586dS+5QbBw5ckR58+bV3bt3kzsUAAAAAAmwO+l+8cUX1aNHD/3555/WspCQEPXs2VO1atVK0uCAlGL48OFq0qSJ/Pz8tH//frVs2VK+vr7y8PBQ0aJFNWbMmMduu3nzZrm4uKhMmTI25YMHD5bFYrF5BAQEWOvPnTsXrz7uMW/ePEmPRplUrFhRo0ePdshxAwAAAHg6dg8vHz9+vBo3biw/Pz/5+vpKki5evKgSJUpo5syZSR4gkNwiIyM1efJkrVy5UtKjlfu9vLw0c+ZM+fr6asuWLerUqZOcnZ3VtWtXm21v3bql1q1bq1atWrp69Wq8vosXL65Vq1ZZn7u4/N9b0tfXV5cvX7Zp//333+uLL75Q/fr1rWXt2rVTcHCw+vfvb7M9AAAAgORn9yd0X19f7dmzR6tWrdKxY8ckSUWLFlXt2rWTPDggJVi2bJnc3NxUsWJFSVL79u1t6v39/bV161YtWLAgXtL9zjvv6M0335Szs7N+/fXXeH27uLgod+7cCe7X2dk5Xt3ChQvVvHlzeXp6Wsvq1KmjsLAwrV+/ntEmAAAAQApj9/DyCxcuKDo6WnXq1FG3bt3UrVs31a5dW8YYXbhwwRExAslq48aNCgoK+sc2t2/fVrZs2WzKpk6dqjNnzmjQoEGP3e7kyZPy8fGRv7+/WrVq9Y/vod27d2vfvn3q0KGDTbmrq6vKlCmjjRs3JuJoAAAAADxLdifdfn5+Klu2rE6fPm1Tfu3aNeXPnz/JAgNSivPnz8vHx+ex9Vu2bNGcOXPUqVMna9nJkyfVr18/zZw587FDvitUqKBp06ZpxYoVmjhxos6ePauqVavqzp07CbafPHmyihYtqsqVK8er8/Hx0fnz5+08MgAAAACOZnfSLT0aTl6+fHmtXr3aptwYkyRBASnJvXv35O7unmDdoUOH1KRJEw0aNMh6y7yYmBi9+eabGjJkiAoXLvzYfuvXr69mzZqpVKlSqlevnpYtW6Zbt25p7ty5Ccbw008/xbvKHcfDw0ORkZFPcHQAAAAAHMnupNtiseibb77RgAED1LBhQ40dO9amDkhrcuTIoZs3b8YrP3LkiGrVqqVOnTppwIAB1vI7d+5o165d6tq1q1xcXOTi4qKhQ4dq//79cnFx0Zo1axLcT5YsWVS4cGGdOnUqXt0vv/yiyMhItW7dOsFtw8LClDNnzic8QgAAAACOYnfSHXc1u2fPnlq4cKEGDhyo4OBgRUdHJ3lwQEoQGBioI0eO2JQdPnxYNWvWVJs2bTR8+HCbukyZMungwYPat2+f9fHOO++oSJEi2rdvnypUqJDgfiIiInT69Gl5e3vHq5s8ebIaN2782MT60KFDCgwMfMIjBAAAAOAoT3V/ofr162vLli1q3LixduzYkVQxASlKvXr11L9/f928eVNZs2bVoUOH9OKLL6pevXrq1auXrly5IunRauM5c+aUk5OTSpQoYdOHl5eX3N3dbcr79OmjRo0aKV++fPrzzz81aNAgOTs7q2XLljbbnjp1Shs2bNCyZcsSjO/cuXMKCQnhDgIAAABACmT3le7q1avL1dXV+rxYsWLavn27smTJwpxupEklS5ZU2bJlrXOtf/nlF12/fl0zZ86Ut7e39fH888/b1e+lS5fUsmVLFSlSRM2bN1f27Nm1bdu2eFezp0yZorx581rnjP/dzz//rLp16ypfvnxPdoAAAAAAHMZiUnGmHB4ersyZM+v27dvKlClTorZpGNTDwVHh75buHpPcITy1pUuXqm/fvjp06JCcnJ5o/UGHiI6OVqFChfTTTz+pSpUqyR1OqhIaGqqiRYtqx44d8vPzS+5wrG7cuKFixYppz549yps3b3KHAwAAgKf0VNnD/fv3FR4ebvMA0qKGDRuqU6dOCgkJSe5QbFy4cEH/+9//SLifwPDhw9WkSRNrwt29e3cFBQXJzc1NZcqUidd+8ODBslgs8R4ZMmSwtlmwYIHKlSunLFmyKEOGDCpTpox+/PFHm34S6sNiseiLL76Q9GjhvtatW//j/d0BAACQeth9pfvu3bv68MMPNXfuXIWGhsarj4mJSbLg/g1XulOHtHClG2lLZGSkvL29tXLlSlWsWFHSo6S7SJEi2r59uw4cOKB9+/bZbBMREaGIiAibslq1aun555/XtGnTJEnr1q3TzZs3FRAQIFdXVy1ZskS9e/fW0qVLVa9ePUmyrgEQZ/ny5erQoYNOnTolf39/SY8W6gsKCtKff/6pbNmyOeAVAAAAwLNi90JqH3zwgdauXauJEyfq7bff1oQJExQSEqLvvvtOn332mSNiBIAktWzZMrm5uVkTbknW2x9ev35dBw4ciLeNp6enPD09rc/379+vI0eO6Ntvv7WW1ahRw2abHj16aPr06dq0aZM16c6dO7dNm0WLFqlmzZrWhFuSihcvLh8fHy1cuPCx92YHAABA6mD38PLFixfrm2++0WuvvSYXFxdVrVpVAwYM0KeffqpZs2Y5IkYASFIbN25UUFDQU/Xxww8/qHDhwqpatWqC9cYYrV69WsePH1e1atUSbHP16lUtXbo0wcS6fPny2rhx41PFCAAAgORnd9IdFhZmvSKTKVMmhYWFSZJeeOEFbdiwIWmjAwAHOH/+vHx8fJ54+/v372vWrFkJJsu3b9+Wp6enXF1d1bBhQ40bN0516tRJsJ/p06crY8aMevXVV+PV+fj46Pz5808cIwAAAFIGu5Nuf39/nT17VpIUEBBgvY3S4sWLlSVLliQNDgAc4d69e3J3d3/i7RcuXKg7d+6oTZs28eoyZsyoffv2aefOnRo+fLh69eqldevWJdjPlClT1KpVqwRj8fDwUGRk5BPHCAAAgJTB7jnd7dq10/79+1W9enX169dPjRo10vjx4/XgwQONHj3aETECQJLKkSOHbt68+cTb//DDD3r55ZeVK1eueHVOTk4qWLCgJKlMmTI6evSoRowYEW++98aNG3X8+HHNmTMnwX2EhYXFu2c7AAAAUh+7k+6ePXta/1+7dm0dO3ZMu3fvVsGCBVWqVKkkDQ4AHCEwMFAzZ858om3Pnj2rtWvX6rfffktU+9jYWEVFRcUrnzx5soKCglS6dOkEtzt06FC8RB0AAACpj13Dyx88eKBatWrp5MmT1rJ8+fLp1VdfJeEGkGrUq1dPhw8ftrnaferUKe3bt09XrlzRvXv3tG/fPu3bt0/R0dE2206ZMkXe3t6qX79+vH5HjBihP/74Q2fOnNHRo0c1atQo/fjjj3rrrbds2oWHh2vevHnq2LFjgvFFRkZq9+7dqlu3bhIcLQAAAJKTXVe606VLl+CtdAAgNSlZsqTKli2ruXPnqnPnzpKkjh07av369dY2gYGBkh5d2fbz85P06Kr1tGnT1LZtWzk7O8fr9+7du3rvvfd06dIleXh4KCAgQDNnztQbb7xh02727Nkyxqhly5YJxrdo0SI999xzj10ZHQAAAKmHxRhj7NmgZ8+ecnNzSxH35A4PD1fmzJl1+/ZtZcqUKVHbNAzq4eCo8HdLd49J7hCAeJYuXaq+ffvq0KFDcnKye01Jh6pYsaK6d++uN998M7lDAQAAwFOye073w4cPNWXKFK1atUpBQUHKkCGDTT2LqQFIDRo2bKiTJ08qJCREvr6+yR2O1Y0bN/Tqq68+9io4AAAAUhe7r3TXrFnzH+vXrl37VAHZgyvdqQNXugEAAAD8V9l9pftZJtXAv6n/ypDkDuE/Z/nCQckdAgAAAJBq2D2RsX379rpz50688rt376p9+/ZJEhQAAAAAAGmB3Un39OnTde/evXjl9+7d04wZM5IkKAAAAAAA0oJEDy8PDw+XMUbGGN25c0fu7u7WupiYGC1btkxeXl4OCRIAAAAAgNQo0Ul3lixZZLFYZLFYVLhw4Xj1FotFQ4YwvxYAAAAAgDiJTrrXrl0rY4xefPFFzZ8/X9myZbPWubq6Kl++fPLx8XFIkAAAAAAApEaJTrqrV68uSTp79qx8fX3l5GT3dHAAAAAAAP5T7L5lWL58+XTr1i3t2LFD165dU2xsrE1969atkyw4AAAAAABSM7uT7sWLF6tVq1aKiIhQpkyZZLFYrHUWi4WkGwAAAACA/8/uMeK9e/dW+/btFRERoVu3bunmzZvWR1hYmCNiBAAAAAAgVbI76Q4JCVH37t2VPn16R8QDAAAAAECaYffw8nr16mnXrl3y9/d3RDwA/uOqdh6W3CH852z87uPkDgEAACDNsjvpbtiwofr27asjR46oZMmSSpcunU1948aNkyw4AAAAAABSM7uT7uDgYEnS0KFD49VZLBbFxMQ8fVQAAAAAAKQBdifdf79FGAAAAAAASJjdC6kBAAAAAIDESfSV7rFjxyaqXffu3Z84GAAAAAAA0pJEJ91fffXVv7axWCwk3QAAAAAA/H+JTrrPnj3ryDgAAAAAAEhzmNMNAAAAAICDkHQDAAAAAOAgKSbp/uyzz2SxWPT+++8ndygAAAAAACSJFJF079y5U999951KlSqV3KEAAAAAAJBkkj3pjoiIUKtWrTRp0iRlzZo1ucMBAAAAACDJPFHSffr0aQ0YMEAtW7bUtWvXJEnLly/X4cOH7e6rS5cuatiwoWrXrv2vbaOiohQeHm7zAAAAAAAgpbI76V6/fr1Kliyp7du3a8GCBYqIiJAk7d+/X4MGDbKrr9mzZ2vPnj0aMWJEotqPGDFCmTNntj58fX3tDR8AAAAAgGfG7qS7X79++uSTT/THH3/I1dXVWv7iiy9q27Ztie7n4sWL6tGjh2bNmiV3d/dEbdO/f3/dvn3b+rh48aK94QMAAAAA8My42LvBwYMH9dNPP8Ur9/Ly0o0bNxLdz+7du3Xt2jWVLVvWWhYTE6MNGzZo/PjxioqKkrOzs802bm5ucnNzszdkAAAAAACShd1Jd5YsWXT58mXlz5/fpnzv3r3KkydPovupVauWDh48aFPWrl07BQQE6MMPP4yXcAMAAAAAkNrYnXS3aNFCH374oebNmyeLxaLY2Fht3rxZffr0UevWrRPdT8aMGVWiRAmbsgwZMih79uzxygEAAAAASI3sntP96aefKiAgQL6+voqIiFCxYsVUrVo1Va5cWQMGDHBEjAAAAAAApEp2Xek2xujKlSsaO3asBg4cqIMHDyoiIkKBgYEqVKjQUwezbt26p+4DAAAAAICUwu6ku2DBgjp8+LAKFSrELbsAAAAAAPgHdg0vd3JyUqFChRQaGuqoeAAAAAAASDPsntP92WefqW/fvjp06JAj4gEAAAAAIM2we/Xy1q1bKzIyUqVLl5arq6s8PDxs6sPCwpIsOAAAAAAAUjO7k+6vv/7aAWEAAAAAAJD22J10t2nTxhFxAAAAAACQ5tiddP/V/fv3FR0dbVOWKVOmpwoIAAAAAIC0wu6F1O7evauuXbvKy8tLGTJkUNasWW0eAAAAAADgEbuT7g8++EBr1qzRxIkT5ebmph9++EFDhgyRj4+PZsyY4YgYAQAAAABIleweXr548WLNmDFDNWrUULt27VS1alUVLFhQ+fLl06xZs9SqVStHxAkAAAAAQKpj95XusLAw+fv7S3o0fzvuFmEvvPCCNmzYkLTRAQAAAACQitmddPv7++vs2bOSpICAAM2dO1fSoyvgWbJkSdLgAAAAAABIzexOutu1a6f9+/dLkvr166cJEybI3d1dPXv2VN++fZM8QAAAAAAAUiu753T37NnT+v/atWvr2LFj2r17twoWLKhSpUolaXAAAAAAAKRmT3WfbknKly+f8uXLlxSxAAAAAACQptg9vBwAAAAAACQOSTcAAAAAAA5C0g0AAAAAgIOQdAMAAAAA4CB2J93Ozs66du1avPLQ0FA5OzsnSVAAAAAAAKQFdifdxpgEy6OiouTq6vrUAQEAAAAAkFYk+pZhY8eOlSRZLBb98MMP8vT0tNbFxMRow4YNCggISPoIAQAAAABIpRKddH/11VeSHl3p/vbbb22Gkru6usrPz0/ffvtt0kcIAAAAAEAqleik++zZs5KkmjVrasGCBcqaNavDggIAAAAAIC1IdNIdZ+3atY6IAwAAAACANMfupFuSLl26pN9++00XLlxQdHS0Td3o0aOTJDAAAAAAAFI7u5Pu1atXq3HjxvL399exY8dUokQJnTt3TsYYlS1b1hExAgAAAACQKtl9y7D+/furT58+OnjwoNzd3TV//nxdvHhR1atXV7NmzRwRIwAAAAAAqZLdSffRo0fVunVrSZKLi4vu3bsnT09PDR06VCNHjkzyAAEAAAAASK3sTrozZMhgncft7e2t06dPW+tu3LiRdJEBAAAAAJDK2T2nu2LFitq0aZOKFi2qBg0aqHfv3jp48KAWLFigihUrOiJGAAAAAABSJbuT7tGjRysiIkKSNGTIEEVERGjOnDkqVKgQK5cDAAAAAPAXdifd/v7+1v9nyJBB3377bZIGBAAAAABAWvFE9+mWpOjoaF27dk2xsbE25c8999xTBwUAAAAAQFpgd9J94sQJdejQQVu2bLEpN8bIYrEoJiYmyYIDAAAAACA1szvpbteunVxcXLRkyRJ5e3vLYrE4Ii4AAAAAAFI9u5Puffv2affu3QoICHBEPAAAAAAApBl236e7WLFi3I8bAAAAAIBESFTSHR4ebn2MHDlSH3zwgdatW6fQ0FCbuvDwcEfHCwAAAABAqpGo4eVZsmSxmbttjFGtWrVs2rCQGgAAAAAAthKVdK9du9bRcQAAAAAAkOYkKumuXr269f8XLlyQr69vvFXLjTG6ePFi0kYHAAAAAEAqZvdCavnz59f169fjlYeFhSl//vxJEhQAAAAAAGmB3Ul33Nztv4uIiJC7u3uSBAUAAAAAQFqQ6Pt09+rVS5JksVj08ccfK3369Na6mJgYbd++XWXKlEnyAAEAAAAASK0SnXTv3btX0qMr3QcPHpSrq6u1ztXVVaVLl1afPn2SPkIAAAAAAFKpRCfdcSuYt2vXTmPGjFGmTJkcFhQAAAAAAGlBopPuOFOnTnVEHAAAAAAApDl2L6QGAAAAAAASh6QbAAAAAAAHIekGAAAAAMBBSLoBAAAAAHAQu5Pu6dOna+nSpdbnH3zwgbJkyaLKlSvr/PnzSRocAAAAAACpmd1J96effioPDw9J0tatWzVhwgR9/vnnypEjh3r27JnkAQIAAAAAkFrZfcuwixcvqmDBgpKkX3/9Va+99po6deqkKlWqqEaNGkkdHwAAAAAAqZbdV7o9PT0VGhoqSfr9999Vp04dSZK7u7vu3buXtNEBAAAAAJCK2X2lu06dOurYsaMCAwN14sQJNWjQQJJ0+PBh+fn5JXV8AAAAAACkWnZf6Z4wYYIqVaqk69eva/78+cqePbskaffu3WrZsmWSBwgAAAAAQGpl95XuLFmyaPz48fHKhwwZkiQBAQAAAACQVtiddEvSzZs3NXnyZB09elSSVLRoUbVv317ZsmVL0uAAAAAAAEjN7B5evmHDBvn5+Wns2LG6efOmbt68qXHjxil//vzasGGDI2IEAAAAACBVsjvp7tKli9544w2dPXtWCxYs0IIFC3TmzBm1aNFCXbp0sauviRMnqlSpUsqUKZMyZcqkSpUqafny5faGBAAAAABAimR30n3q1Cn17t1bzs7O1jJnZ2f16tVLp06dsquvvHnz6rPPPtPu3bu1a9cuvfjii2rSpIkOHz5sb1gAAAAAAKQ4difdZcuWtc7l/qujR4+qdOnSdvXVqFEjNWjQQIUKFVLhwoU1fPhweXp6atu2bfaGBQAAAABAimP3Qmrdu3dXjx49dOrUKVWsWFGStG3bNk2YMEGfffaZDhw4YG1bqlSpRPcbExOjefPm6e7du6pUqVKCbaKiohQVFWV9Hh4ebm/4AAAAAAA8M3Yn3XH34v7ggw8SrLNYLDLGyGKxKCYm5l/7O3jwoCpVqqT79+/L09NTCxcuVLFixRJsO2LECG5NBgAAAABINexOus+ePZukARQpUkT79u3T7du39csvv6hNmzZav359gol3//791atXL+vz8PBw+fr6Jmk8AAAAAAAkFbuT7nz58iVpAK6uripYsKAkKSgoSDt37tSYMWP03XffxWvr5uYmNze3JN0/AAAAAACOkqik+7ffflP9+vWVLl06/fbbb//YtnHjxk8VUGxsrM28bQAAAAAAUqtEJd1NmzbVlStX5OXlpaZNmz62XWLnccfp37+/6tevr+eee0537tzRTz/9pHXr1mnlypWJ7gMAAAAAgJQqUUl3bGxsgv9/WteuXVPr1q11+fJlZc6cWaVKldLKlStVp06dJNsHAAAAAADJxa453Q8ePNBLL72kb7/9VoUKFXrqnU+ePPmp+wAAAAAAIKVysqdxunTpbO7DDQAAAAAAHs+upFuS3nrrLa5QAwAAAACQCHbfMuzhw4eaMmWKVq1apaCgIGXIkMGmfvTo0UkWHAAAAAAAqVmik25nZ2ddvnxZhw4dUtmyZSVJJ06csGljsViSNjoAAAAAAFKxRCfdxhhJ0tq1ax0WDAAAAAAAaYndc7oBAAAAAEDi2DWn+4cffpCnp+c/tunevftTBQQAAAAAQFphV9L97bffytnZ+bH1FouFpBsAAAAAgP/PrqR7165d8vLyclQsAAAAAACkKYme083K5AAAAAAA2CfRSXfc6uUAAAAAACBxEp10Dxo06F8XUQMAAAAAAP8n0XO6Bw0a5Mg4AAAAAABIc7hPNwAAAAAADkLSDQAAAACAgyQq6f7tt9/04MEDR8cCAAAAAECakqik+5VXXtGtW7ckSc7Ozrp27ZojYwIAAAAAIE1IVNKdM2dObdu2TdKjW4dxz24AAAAAAP5dolYvf+edd9SkSRNZLBZZLBblzp37sW1jYmKSLDgAAAAAAFKzRCXdgwcPVosWLXTq1Ck1btxYU6dOVZYsWRwcGgAAAAAAqVui79MdEBCggIAADRo0SM2aNVP69OkdGRcAAAAAAKleopPuOIMGDZIkXb9+XcePH5ckFSlSRDlz5kzayAAAAAAASOXsvk93ZGSk2rdvLx8fH1WrVk3VqlWTj4+POnTooMjISEfECAAAAABAqmR30t2zZ0+tX79ev/32m27duqVbt25p0aJFWr9+vXr37u2IGAEAAAAASJXsHl4+f/58/fLLL6pRo4a1rEGDBvLw8FDz5s01ceLEpIwPAAAAAIBU64mGl+fKlSteuZeXF8PLAQAAAAD4C7uT7kqVKmnQoEG6f/++tezevXsaMmSIKlWqlKTBAQAAAACQmtk9vHzMmDGqV6+e8ubNq9KlS0uS9u/fL3d3d61cuTLJAwQAAAAAILWyO+kuUaKETp48qVmzZunYsWOSpJYtW6pVq1by8PBI8gABAAAAAEit7E66JSl9+vQKDg5O6lgAAAAAAEhT7J7TDQAAAAAAEoekGwAAAAAAByHpBgAAAADAQUi6AQAAAABwkCdKum/duqUffvhB/fv3V1hYmCRpz549CgkJSdLgAAAAAABIzexevfzAgQOqXbu2MmfOrHPnzik4OFjZsmXTggULdOHCBc2YMcMRcQIAAAAAkOrYfaW7V69eatu2rU6ePCl3d3dreYMGDbRhw4YkDQ4AAAAAgNTM7qR7586d6ty5c7zyPHny6MqVK0kSFAAAAAAAaYHdSbebm5vCw8PjlZ84cUI5c+ZMkqAAAAAAAEgL7E66GzdurKFDh+rBgweSJIvFogsXLujDDz/Ua6+9luQBAgAAAACQWtmddI8aNUoRERHy8vLSvXv3VL16dRUsWFAZM2bU8OHDHREjAAAAAACpkt2rl2fOnFl//PGHNm3apAMHDigiIkJly5ZV7dq1HREfAAAAAACplt1Jd5wXXnhBL7zwQlLGAgAAAABAmmJ30j127NgEyy0Wi9zd3VWwYEFVq1ZNzs7OTx0cAAAAAACpmd1J91dffaXr168rMjJSWbNmlSTdvHlT6dOnl6enp65duyZ/f3+tXbtWvr6+SR4wAAAAAACphd0LqX366ad6/vnndfLkSYWGhio0NFQnTpxQhQoVNGbMGF24cEG5c+dWz549HREvAAAAAACpht1XugcMGKD58+erQIEC1rKCBQvqyy+/1GuvvaYzZ87o888/5/ZhAAAAAID/PLuvdF++fFkPHz6MV/7w4UNduXJFkuTj46M7d+48fXQAAAAAAKRidifdNWvWVOfOnbV3715r2d69e/Xuu+/qxRdflCQdPHhQ+fPnT7ooAQAAAABIhexOuidPnqxs2bIpKChIbm5ucnNzU7ly5ZQtWzZNnjxZkuTp6alRo0YlebAAACQkNDRUXl5eOnfuXHKHYuPGjRvy8vLSpUuXkjsUAACQTOxOunPnzq0//vhDR44c0bx58zRv3jwdOXJEv//+u3LlyiXp0dXwunXrJnmwAAAkZPjw4WrSpIn8/PwkSd27d7d+OVymTJl47Y8fP66aNWsqV65ccnd3l7+/vwYMGKAHDx5Y2yxYsEDlypVTlixZlCFDBpUpU0Y//vijTT8WiyXBxxdffCFJypEjh1q3bq1BgwY57NgBAEDKZvdCanECAgIUEBCQlLEAAGC3yMhITZ48WStXrrQpb9++vbZv364DBw7E2yZdunRq3bq1ypYtqyxZsmj//v0KDg5WbGysPv30U0lStmzZ9NFHHykgIECurq5asmSJ2rVrJy8vL9WrV0/So3VO/mr58uXq0KGDzWKi7dq1U1BQkL744gtly5YtqQ//PyM0NFRFixbVjh07rF+uPAv9+vXT3bt3NW7cuGe2TwBA2vJESfelS5f022+/6cKFC4qOjrapGz16dJIEBgBAYixbtkxubm6qWLGitWzs2LGSpOvXryeYdPv7+8vf39/6PF++fFq3bp02btxoLatRo4bNNj169ND06dO1adMma9KdO3dumzaLFi1SzZo1bfouXry4fHx8tHDhQnXo0OHJD/Q/7u+jGXbu3Kl+/fpp9+7dslgsKl++vD7//HOVLl1aknT//n2988472r17t44ePaqXX35Zv/76a7x+o6KiNHToUM2cOVNXrlyRt7e3Bg4cqPbt20uS+vTpI39/f/Xs2dPmvAIAkFh2Dy9fvXq1ihQpookTJ2rUqFFau3atpk6dqilTpmjfvn0OCBEAgMfbuHGjgoKCnqqPU6dOacWKFapevXqC9cYYrV69WsePH1e1atUSbHP16lUtXbo0wcS6fPnyNgk97BM3miHutY2IiNBLL72k5557Ttu3b9emTZuUMWNG1atXzzpFICYmRh4eHurevbtq16792L6bN2+u1atXa/LkyTp+/Lh+/vlnFSlSxFqfI0cO1atXTxMnTnTsQQIA0iy7k+7+/furT58+OnjwoNzd3TV//nxdvHhR1atXV7NmzRwRIwAAj3X+/Hn5+Pg80baVK1eWu7u7ChUqpKpVq2ro0KE29bdv35anp6dcXV3VsGFDjRs3TnXq1Emwr+nTpytjxox69dVX49X5+Pjo/PnzTxQj4o9mOHbsmMLCwjR06FAVKVJExYsX16BBg3T16lXr65whQwZNnDhRwcHB8UYkxFmxYoXWr1+vZcuWqXbt2vLz81OlSpVUpUoVm3aNGjXS7NmzHXuQAIA0y+6k++jRo2rdurUkycXFRffu3ZOnp6eGDh2qkSNHJnmAAAD8k3v37snd3f2Jtp0zZ4727Nmjn376SUuXLtWXX35pU58xY0bt27dPO3fu1PDhw9WrVy+tW7cuwb6mTJmiVq1aJRiLh4eHIiMjnyhGxB/NUKRIEWXPnl2TJ09WdHS07t27p8mTJ6to0aJ2zff+7bffVK5cOX3++efKkyePChcurD59+ujevXs27cqXL69Lly6luNXxAQCpg91zujNkyGCdx+3t7a3Tp0+rePHikh7dGgUAgGcpR44cunnz5hNt6+vrK0kqVqyYYmJi1KlTJ/Xu3VvOzs6SJCcnJxUsWFCSVKZMGR09elQjRoyIN99748aNOn78uObMmZPgfsLCwpQzZ84nihHxRzNkzJhR69atU9OmTTVs2DBJUqFChbRy5Uq5uCT+o82ZM2e0adMmubu7a+HChbpx44bee+89hYaGaurUqdZ2cfs+f/78M13EDQCQNth9pbtixYratGmTJKlBgwbq3bu3hg8frvbt29ssYgMAwLMQGBioI0eOPHU/sbGxevDggWJjY/+xTVRUVLzyyZMnKygoyLqI198dOnRIgYGBTx3jf9XfRzPcu3dPHTp0UJUqVbRt2zZt3rxZJUqUUMOGDeNdpf4nsbGxslgsmjVrlsqXL68GDRpo9OjRmj59uk0/Hh4eksRoBQDAE7H7Svfo0aMVEREhSRoyZIgiIiI0Z84cFSpUiJXLAQDPXL169dS/f3/dvHlTWbNmlfRoYbSIiAhduXJF9+7dsy70WaxYMbm6umrWrFlKly6dSpYsKTc3N+3atUv9+/fXG2+8oXTp0kmSRowYoXLlyqlAgQKKiorSsmXL9OOPP8ZbUCs8PFzz5s3TqFGjEowvMjJSu3fvtt6KDPb7+2iGn376SefOndPWrVvl5ORkLcuaNasWLVqkFi1aJKpfb29v5cmTR5kzZ7aWFS1aVMYYXbp0SYUKFZL0aKSCJEYrAACeiN1J919vl5EhQwZ9++23SRoQAAD2KFmypMqWLau5c+eqc+fOkqSOHTtq/fr11jZxV5nPnj0rPz8/ubi4aOTIkTpx4oSMMcqXL5+6du2qnj17Wre5e/eu3nvvPV26dEkeHh4KCAjQzJkz9cYbb9jsf/bs2TLGqGXLlgnGt2jRIj333HOqWrVqUh/6f0ZgYKBmzpxpfR4ZGSknJydZLBZrWdzzfxqp8HdVqlTRvHnzFBERIU9PT0nSiRMn5OTkpLx581rbHTp0SOnSpbNOpwMAwB52Dy/39/dXaGhovPJbt27Zff/KESNG6Pnnn1fGjBnl5eWlpk2b6vjx4/aGBAD4jxs4cKDGjBljTbjWrVsnY0y8R9x83DfeeEO7d+/WnTt3FBERocOHD6t///42Q5g/+eQTnTx5Uvfu3VNYWJi2bNkSL+GWpE6dOikyMtLmaulfjRkzRgMHDkz6g/4PqVevng4fPmy92l2nTh3dvHlTXbp00dGjR3X48GG1a9dOLi4uqlmzpnW7I0eOaN++fQoLC9Pt27e1b98+m9ubvvnmm8qePbvatWunI0eOaMOGDerbt6/at29vHVIuPZqzX7VqVZsyAAASy+6k+9y5c4qJiYlXHhUVpZCQELv6Wr9+vbp06aJt27bpjz/+0IMHD1S3bl3dvXvX3rAAAP9hDRs2VKdOnez+O+RoN27c0KuvvvrYq+BInL+OZpCkgIAALV68WAcOHFClSpVUtWpV/fnnn1qxYoW8vb2t2zVo0ECBgYFavHix1q1bp8DAQJu59Z6envrjjz9069YtlStXTq1atVKjRo00duxYm/3Pnj1bwcHBz+ZgAQBpTqKHl//222/W/69cudLmG/2YmBitXr3a7hU9V6xYYfN82rRp8vLy0u7du1WtWjW7+gIA/Le9//77yR1CPDly5NAHH3yQ3GGkCQMHDlTfvn0VHBwsJycn1alT57H3TI+TmFt8BQQE6I8//nhs/fLly+Xk5KTXX3/d3pABAJBkR9LdtGlTSZLFYlGbNm1s6tKlSyc/P7/HLiKTWLdv35YkZcuW7an6AQAAaUvDhg118uRJhYSEWG/19izcvXtXU6dOtetWZAAA/FWi/4LEzZPLnz+/du7cqRw5ciRpILGxsXr//fdVpUoVlShRIsE2UVFRNrdqCQ8PT9IYAABAypUcoxm4wg0AeFp2z+k+e/ZskifcktSlSxcdOnRIs2fPfmybESNGKHPmzNbHs/ymGwAAAAAAez3RWKnVq1dr9erVunbtWrxbc0yZMsXu/rp27aolS5Zow4YNNrfo+Lv+/furV69e1ufh4eEk3gAAAACAFMvupHvIkCEaOnSoypUrJ29vb5t7ZNrLGKNu3bpp4cKFWrdunfLnz/+P7d3c3OTm5vbE+wMAAAAA4FmyO+n+9ttvNW3aNL399ttPvfMuXbrop59+0qJFi5QxY0ZduXJFkpQ5c2buhQkAAAAASPXsntMdHR2typUrJ8nOJ06cqNu3b6tGjRry9va2PubMmZMk/QMAACDlCQ0NlZeXV6Ju65aU+vXrp27duj3TfQKA3Ul3x44d9dNPPyXJzo0xCT7atm2bJP0DAAAg5Rk+fLiaNGkiPz8/SVL37t0VFBQkNzc3lSlTJsFtVq5cqYoVKypjxozKmTOnXnvttXhJ+4QJE1S0aFF5eHioSJEimjFjhk19nz59NH36dJ05c8YBRwUACbN7ePn9+/f1/fffa9WqVSpVqpTSpUtnUz969OgkCw4AAABpS2RkpCZPnqyVK1falLdv317bt2/XgQMH4m1z9uxZNWnSRL169dKsWbN0+/Zt9ezZU6+++qr27Nkj6dEIyv79+2vSpEl6/vnntWPHDgUHBytr1qxq1KiRJClHjhyqV6+eJk6cqC+++MLxBwsAeoKk+8CBA9ZvIA8dOmRT9zSLqgEAACDtW7Zsmdzc3FSxYkVr2dixYyVJ169fTzDp3r17t2JiYvTJJ5/IyenRQM0+ffqoSZMmevDggdKlS6cff/xRnTt31htvvCFJ8vf3186dOzVy5Ehr0i1JjRo10kcffUTSDeCZsTvpXrt2rSPiAAAAwH/Axo0bFRQUZNc2QUFBcnJy0tSpU9W2bVtFREToxx9/VO3ata2jLqOiouTu7m6znYeHh3bs2GFNzCWpfPnyunTpks6dO2cd3g4AjvRE9+mWpFOnTun06dOqVq2aPDw8ZIzhSjcAwEa5/kOTO4T/nF0jBiZ3CMA/On/+vHx8fOzaJn/+/Pr999/VvHlzde7cWTExMapUqZKWLVtmbVOvXj398MMPatq0qcqWLavdu3frhx9+0IMHD3Tjxg15e3tLknXf58+fJ+kG8EzYvZBaaGioatWqpcKFC6tBgwa6fPmyJKlDhw7q3bt3kgcIAACAtOPevXvxrkj/mytXrig4OFht2rTRzp07tX79erm6uur111+XMUaS9PHHH6t+/fqqWLGi0qVLpyZNmqhNmzaSZB2SLsl6W9rIyMgkOiIA+Gd2X+nu2bOn0qVLpwsXLqho0aLW8jfeeEO9evXSqFGjkjRAAACQcpT+alByh/Cfs7/nkOQOIUnlyJFDN2/etGubCRMmKHPmzPr888+tZTNnzpSvr6+2b9+uihUrysPDQ1OmTNF3332nq1evytvbW99//711tfM4YWFhkmRTBgCOZHfS/fvvv2vlypXKmzevTXmhQoV0/vz5JAsMAAAAaU9gYKBmzpxp1zaRkZE2V6slydnZWZIUGxtrU54uXTrr59TZs2fr5Zdfttn20KFDSpcunYoXL/4k4QOA3eweXn737l2lT58+XnlYWJjc3NySJCgAAACkTfXq1dPhw4dtrnafOnVK+/bt05UrV3Tv3j3t27dP+/btU3R0tCSpYcOG2rlzp4YOHaqTJ09qz549ateunfLly6fAwEBJ0okTJzRz5kydPHlSO3bsUIsWLXTo0CF9+umnNvvfuHGjqlatah1mDgCOZnfSXbVqVc2YMcP63GKxKDY2Vp9//rlq1qyZpMEBAAAgbSlZsqTKli2ruXPnWss6duyowMBAfffddzpx4oQCAwMVGBioP//8U5L04osv6qefftKvv/6qwMBAvfTSS3Jzc9OKFSusyXNMTIxGjRql0qVLq06dOrp//762bNkSb7G02bNnKzg4+JkdLwDYPbz8888/V61atbRr1y5FR0frgw8+0OHDhxUWFqbNmzc7IkYAAACkIQMHDlTfvn0VHBwsJycnrVu37l+3adGihVq0aPHY+qJFi2rv3r3/2Mfy5cvl5OSk119/3d6QAeCJ2Z10lyhRQidOnND48eOVMWNGRURE6NVXX1WXLl2st2IAAAAAHqdhw4Y6efKkQkJC5Ovr+8z2e/fuXU2dOlUuLk9811wAsNsT/cbJnDmzPvroo6SOBQAAAP8R77///jPfJ1e4ASQHu+d0T506VfPmzYtXPm/ePE2fPj1JggIAAAAAIC2wO+keMWKEcuTIEa/cy8sr3uqQAAAAAAD8l9mddF+4cEH58+ePV54vXz5duHAhSYICAAAAACAtsDvp9vLy0oEDB+KV79+/X9mzZ0+SoAAAAAAASAvsTrpbtmyp7t27a+3atYqJiVFMTIzWrFmjHj16/ONtHAAAAAAA+K+xe/XyYcOG6dy5c6pVq5b1dguxsbFq3bo1c7oBAAAAAPgLu5JuY4yuXLmiadOm6ZNPPtG+ffvk4eGhkiVLKl++fI6KEQAAAACAVMnupLtgwYI6fPiwChUqpEKFCjkqLgAAAAAAUj275nQ7OTmpUKFCCg0NdVQ8AAAAAACkGXYvpPbZZ5+pb9++OnTokCPiAQAAAAAgzbB7IbXWrVsrMjJSpUuXlqurqzw8PGzqw8LCkiw4AAAAAABSM7uT7q+//toBYQAAAOBZqzu7f3KH8J/ze4sRyR0CgGfM7qS7TZs2jogDAAAAAIA0x+453ZJ0+vRpDRgwQC1bttS1a9ckScuXL9fhw4eTNDgAAAAAAFIzu5Pu9evXq2TJktq+fbsWLFigiIgISdL+/fs1aNCgJA8QAAAAAIDUyu6ku1+/fvrkk0/0xx9/yNXV1Vr+4osvatu2bUkaHAAAAAAAqZndSffBgwf1yiuvxCv38vLSjRs3kiQoAAAAAADSAruT7ixZsujy5cvxyvfu3as8efIkSVAAAAAAAKQFdifdLVq00IcffqgrV67IYrEoNjZWmzdvVp8+fdS6dWtHxAgAAAAAQKpkd9L96aefKiAgQL6+voqIiFCxYsVUrVo1Va5cWQMGDHBEjAAAAAAApEp236fb1dVVkyZN0sCBA3Xw4EFFREQoMDBQhQoVckR8AAAAAACkWolOumNjY/XFF1/ot99+U3R0tGrVqqVBgwbJw8PDkfEBAAAAAJBqJXp4+fDhw/W///1Pnp6eypMnj8aMGaMuXbo4MjYAAAAAAFK1RCfdM2bM0DfffKOVK1fq119/1eLFizVr1izFxsY6Mj4AAAAAAFKtRCfdFy5cUIMGDazPa9euLYvFoj///NMhgQEAAAAAkNolOul++PCh3N3dbcrSpUunBw8eJHlQAAAAAACkBYleSM0Yo7Zt28rNzc1adv/+fb3zzjvKkCGDtWzBggVJGyEAAAAAAKlUopPuNm3axCt76623kjQYAAAAAADSkkQn3VOnTnVkHAAAAAAApDmJntMNAAAAAADsQ9INAAAAAICDkHQDAAAAAOAgJN0AAAAAADgISTcAAAAAAA5C0g0AAAAAgIOQdAMAAAAA4CAk3QAAAAAAOAhJNwAAAAAADkLSDQAAAACAg5B0AwAAAADgICTdAAAAAAA4CEk3AAAAgEQLDQ2Vl5eXzp07l9yhxFOxYkXNnz8/ucMAbJB0AwAAAEi04cOHq0mTJvLz85Mkde/eXUFBQXJzc1OZMmUS3MYYoy+//FKFCxeWm5ub8uTJo+HDh9u0mTVrlkqXLq306dPL29tb7du3V2hoaIL9zZ49WxaLRU2bNrUpHzBggPr166fY2NinPUwgyZB0AwAAAEiUyMhITZ48WR06dLApb9++vd54443HbtejRw/98MMP+vLLL3Xs2DH99ttvKl++vLV+8+bNat26tTp06KDDhw9r3rx52rFjh4KDg+P1de7cOfXp00dVq1aNV1e/fn3duXNHy5cvf4qjBJKWS3IHAAAAACB1WLZsmdzc3FSxYkVr2dixYyVJ169f14EDB+Jtc/ToUU2cOFGHDh1SkSJFJEn58+e3abN161b5+fmpe/fu1vrOnTtr5MiRNu1iYmLUqlUrDRkyRBs3btStW7ds6p2dndWgQQPNnj1bDRs2fOrjBZICV7oBAAAAJMrGjRsVFBRk1zaLFy+Wv7+/lixZovz588vPz08dO3ZUWFiYtU2lSpV08eJFLVu2TMYYXb16Vb/88osaNGhg09fQoUPl5eUV70r7X5UvX14bN26078AAByLpBgAAAJAo58+fl4+Pj13bnDlzRufPn9e8efM0Y8YMTZs2Tbt379brr79ubVOlShXNmjVLb7zxhlxdXZU7d25lzpxZEyZMsLbZtGmTJk+erEmTJv3j/nx8fHTx4kXmdSPFIOkGAAAAkCj37t2Tu7u7XdvExsYqKipKM2bMUNWqVVWjRg1NnjxZa9eu1fHjxyVJR44cUY8ePTRw4EDt3r1bK1as0Llz5/TOO+9Iku7cuaO3335bkyZNUo4cOf5xfx4eHtZ9AikBc7oBAAAAJEqOHDl08+ZNu7bx9vaWi4uLChcubC0rWrSoJOnChQsqUqSIRowYoSpVqqhv376SpFKlSilDhgyqWrWqPvnkE129elXnzp1To0aNrH3EXcl2cXHR8ePHVaBAAUlSWFiYMmTIIA8Pj6c6ViCpJOuV7g0bNqhRo0by8fGRxWLRr7/+mpzhAAAAAPgHgYGBOnLkiF3bVKlSRQ8fPtTp06etZSdOnJAk5cuXT9KjVdGdnGxTE2dnZ0mPbjcWEBCggwcPat++fdZH48aNVbNmTe3bt0++vr7W7Q4dOqTAwMAnOj7AEZI16b57965Kly5tM1cDAAAAQMpUr149HT582OZq96lTp7Rv3z5duXJF9+7dsybF0dHRkqTatWurbNmyat++vfbu3avdu3erc+fOqlOnjvXqd6NGjbRgwQJNnDhRZ86c0ebNm9W9e3eVL19ePj4+cnd3V4kSJWweWbJkUcaMGVWiRAm5urpa49m4caPq1q37bF8Y4B8k6/Dy+vXrq379+skZAgAAAIBEKlmypMqWLau5c+eqc+fOkqSOHTtq/fr11jZxV5nPnj0rPz8/OTk5afHixerWrZuqVaumDBkyqH79+ho1apR1m7Zt2+rOnTsaP368evfurSxZsujFF1+Md8uwfxMSEqItW7Zo5syZSXC0QNJIVXO6o6KibBZECA8PT8ZoAAAAgP+egQMHqm/fvgoODpaTk5PWrVv3r9v4+Pho/vz5/9imW7du6tatW6LjmDZtWryysWPHqm3btsqbN2+i+wEcLVWtXj5ixAhlzpzZ+vjr3A0AAAAAjtewYUN16tRJISEhyR1KPF5eXho2bFhyhwHYSFVJd//+/XX79m3r4+LFi8kdEgAAAPCf8/7776fIC2C9e/dWrly5kjsMwEaqGl7u5uYmNze35A4DAAAAAIBESVVXugEAAAAASE2S9Up3RESETp06ZX1+9uxZ7du3T9myZdNzzz2XjJEBAAAAAPD0kjXp3rVrl2rWrGl93qtXL0lSmzZtElyNEAAAAACA1CRZk+4aNWrIGJOcIQAAAAAA4DDM6QYAAAAAwEFIugEAAAAAcBCSbgAAAAAAHISkGwAAAABgFRoaKi8vL507d+6Z7rdixYqaP3/+M93ns0DSDQAAAACwGj58uJo0aSI/Pz+FhobqpZdeko+Pj9zc3OTr66uuXbsqPDzcZptZs2apdOnSSp8+vby9vdW+fXuFhoZa66dNmyaLxWLzcHd3t+ljwIAB6tevn2JjY5/JcT4rybp6OQAAAICk02/dO8kdwn/OZzW+Te4QklRkZKQmT56slStXSpKcnJzUpEkTffLJJ8qZM6dOnTqlLl26KCwsTD/99JMkafPmzWrdurW++uorNWrUSCEhIXrnnXcUHBysBQsWWPvOlCmTjh8/bn1usVhs9l2/fn117NhRy5cvV8OGDZ/B0T4bJN0AAAAAAEnSsmXL5ObmpooVK0qSsmbNqnfffddany9fPr333nv64osvrGVbt26Vn5+funfvLknKnz+/OnfurJEjR9r0bbFYlDt37sfu29nZWQ0aNNDs2bPTVNLN8HIAAAAAgCRp48aNCgoKemz9n3/+qQULFqh69erWskqVKunixYtatmyZjDG6evWqfvnlFzVo0MBm24iICOXLl0++vr5q0qSJDh8+HK//8uXLa+PGjUl3QCkASTcAAAAAQJJ0/vx5+fj4xCtv2bKl0qdPrzx58ihTpkz64YcfrHVVqlTRrFmz9MYbb8jV1VW5c+dW5syZNWHCBGubIkWKaMqUKVq0aJFmzpyp2NhYVa5cWZcuXbLZj4+Pjy5evJim5nWTdAMAAAAAJEn37t2Lt8CZJH311Vfas2ePFi1apNOnT6tXr17WuiNHjqhHjx4aOHCgdu/erRUrVujcuXN6553/W2OgUqVKat26tcqUKaPq1atrwYIFypkzp7777jub/Xh4eCg2NlZRUVGOO8hnjDndAAAAAABJUo4cOXTz5s145blz51bu3LkVEBCgbNmyqWrVqvr444/l7e2tESNGqEqVKurbt68kqVSpUsqQIYOqVq2qTz75RN7e3vH6S5cunQIDA3Xq1Cmb8rCwMGXIkEEeHh6OOcBkwJVuAAAAAIAkKTAwUEeOHPnHNnFDv+OuRkdGRsrJyTa1dHZ2liQZYxLsIyYmRgcPHoyXkB86dEiBgYFPFHtKxZVuAAAAAIAkqV69eurfv79u3ryprFmzatmyZbp69aqef/55eXp66vDhw+rbt6+qVKkiPz8/SVKjRo0UHBysiRMnql69erp8+bLef/99lS9f3jo/fOjQoapYsaIKFiyoW7du6YsvvtD58+fVsWNHm/1v3LhRdevWfdaH7VBc6QYAAAAASJJKliypsmXLau7cuZIezbGeNGmSXnjhBRUtWlQ9e/ZU48aNtWTJEus2bdu21ejRozV+/HiVKFFCzZo1U5EiRWzu0X3z5k0FBweraNGiatCggcLDw7VlyxYVK1bM2iYkJERbtmxRu3btnt0BPwNc6QYAAAAAWA0cOFB9+/ZVcHCwatasqS1btvzrNt26dVO3bt0eW//VV1/pq6+++sc+xo4dq7Zt2ypv3rx2x5ySkXQDAAAAAKwaNmyokydPKiQkRL6+vs9sv15eXjaroqcVJN0AAAAAABvvv//+M99n7969n/k+nwXmdAMAAAAA4CAk3QAAAAAAOAhJNwAAAAAADkLSDQAAAACAg5B0AwAAAADgICTdAAAAAAA4CEk3AAAAAAAOQtINAAAAAICDkHQDAAAAAOAgJN0AAAAAADgISTcAAAAAAA7iktwBAAAAAADim7vtxeQO4T+necU1Sd4nV7oBAAAAAHAQkm4AAAAAAByEpBsAAAAAAAch6QYAAAAAwEFIugEAAAAAcBCSbgAAAAAAHISkGwAAAAAAByHpBgAAAADAQUi6AQAAAABwEJJuAAAAAAAchKQbAAAAAAAHIekGAAAAAMBBSLoBAAAAAHAQkm4AAAAAAByEpBsAAAAAAAch6QYAAAAAwEFIugEAAAAAcBCSbgAAAAAAHISkGwAAAAAAByHpBgAAAADAQUi6AQAAAABwEJJuAAAAAAAchKQbAAAAAAAHIekGAAAAAMBBSLoBAAAAAHAQkm4AAAAAAByEpBsAAAAAAAch6QYAAAAAwEFIugEAAAAAcBCSbgAAAAAAHISkGwAAAAAAByHpBgAAAADAQVJE0j1hwgT5+fnJ3d1dFSpU0I4dO5I7JAAAAAAAnlqyJ91z5sxRr169NGjQIO3Zs0elS5dWvXr1dO3ateQODQAAAACAp5LsSffo0aMVHBysdu3aqVixYvr222+VPn16TZkyJblDAwAAAADgqbgk586jo6O1e/du9e/f31rm5OSk2rVra+vWrfHaR0VFKSoqyvr89u3bkqTw8PBE7/NBTNS/N0KSsuf82Ovhg/sO6xsJc+T5lKSH0ZzTZ82R5zQmivP5rDn6PRpzn7+jz5pD/45Gcj6fNUe/R6PuRju0f8TnyHMaefehw/pGwp7kfGbMmFEWi+Wx9RZjjHmaoJ7Gn3/+qTx58mjLli2qVKmStfyDDz7Q+vXrtX37dpv2gwcP1pAhQ551mAAAAAAAJOj27dvKlCnTY+uT9Uq3vfr3769evXpZn8fGxiosLEzZs2f/x28WUrvw8HD5+vrq4sWL/3gykXpwTtMWzmfawzlNWzifaQ/nNG3hfKY9/7VzmjFjxn+sT9akO0eOHHJ2dtbVq1dtyq9evarcuXPHa+/m5iY3NzebsixZsjgyxBQlU6ZM/4kf2v8SzmnawvlMezinaQvnM+3hnKYtnM+0h3P6SLIupObq6qqgoCCtXr3aWhYbG6vVq1fbDDcHAAAAACA1Svbh5b169VKbNm1Urlw5lS9fXl9//bXu3r2rdu3aJXdoAAAAAAA8lWRPut944w1dv35dAwcO1JUrV1SmTBmtWLFCuXLlSu7QUgw3NzcNGjQo3tB6pF6c07SF85n2cE7TFs5n2sM5TVs4n2kP59RWsq5eDgAAAABAWpasc7oBAAAAAEjLSLoBAAAAAHAQkm4AAAAAAByEpBsAAAAAAAch6QYAAECaFxUVJUliDWEAzxpJN5AC8YEg7Yg7l3Ef9gAkn7/+bn348GEyRoJnberUqRoyZIhu3boli8XC39lULO69yzlEakLSnUbxiyh1s1gsmj59ur755pvkDgVPyWKx6Pfff1eHDh0UHR2d3OHgKcTGxiZ3CHgKxhhZLBZdv35dDx48kIuLi1avXq09e/Ykd2h4BrZu3aply5bpm2++0e3bt0m8U6FLly7JGCMXFxctWbJEI0eO5Bz+B6SVc0zSnQbE/TA+fPhQ9+/fl/Tog/5f65A6xJ2v06dPq0uXLrpz504yRwR7TZ48WadPn5b0f+dz6dKlyp49u1xdXZMzNDyF2NhYOTk9+pO5bt06rVmzRlu2bEnmqJBYcQn3tWvX1KpVKw0dOlSzZ89WnTp1dOXKleQOD8/A999/r9q1a2vBggUaN24ciXcqc+/ePdWvX18vvvii5s6dq8aNG6tAgQLWz7tIm+J+d2/cuFFDhgzR999/r0OHDlnrUhOLSW0Rw0bcD+Py5cutH/YrVaqkpk2bqm7duskdHp7Ajh07tG7dOl2/fl1ffPFFcocDO9y9e1dFixZV5syZtXjxYvn5+UmS2rZtq2zZsmn06NHJGyCeWu/evTVt2jR5enrqypUreuONN9SnTx+VKlUquUPDP7h48aJ8fX0VHh6uzz//XHPnztX58+c1ceJEtW/fXjExMXJ2dk7uMOEgfz2/vXr10oYNG9S0aVN169ZNmTNntn6WQspljNHx48dVrVo13bp1S5MmTVKbNm147/4HLFq0SC1btlTJkiV169YtZciQQSNHjlSdOnVS1XuXK92pnMVi0eLFi9WsWTMVKlRIgwcP1pYtW9SnTx+GzKVCoaGhGjFihIYMGaJLly5JevSHhu/GUocMGTJox44dSpcunZo2baqzZ89KejQKxcXFRdKjD39IPf763jt8+LAWLVqkZcuWaf369VqzZo3Wrl2rjz/+WGfOnEnGKPFPJk+erDfffFMRERHKlCmTXnrpJV2+fFne3t66ePGiHjx4IGdnZ96baZizs7P1vTx69GhVrVpVv/76K1e8U4nY2Fjr1JCwsDClT59e8+bNk/To3DL1J+26du2aduzYofHjx2v79u2aOnWqSpUqpY4dO+r3339PVe9dku5UzBijsLAwffnllxo8eLBGjBihl156SVevXtWLL76osmXLJneIsFP27NkVHBysGjVqaOnSpdq9e3eq+Qbvv84Yo9jYWOXOnVtLly5V+vTp9fLLL+vKlSuKiYmRl5eXJOnBgwfWDwjXrl1LzpCRCHHvv9GjR+vrr79W3bp1VaFCBeXLl09VqlTRypUrtXHjRk2cODGZI8XjFC5cWDNmzJCnp6fu3LmjEiVK6Ndff1WbNm20bNkyDRw4UA8fPiTxToPiPoxHR0fbLJz31Vdf6YUXXiDxTiWcnJy0YMECDR48WAsXLtT69eu1b98+vfTSS9b6uL+rJOBpx759+1S3bl398ccfKlOmjCSpcuXK6tWrl2rWrKng4GCtWrUq1bx3SbpTMYvFInd3d0VFRalZs2Y6f/68/P391ahRI3399deSpNWrV+vy5cvJGygeK+6XxP37963ztxs0aKCBAweqQoUK6tSpk/bu3ZtqfqH81zk5OWnx4sVasGCBfvnlFzk5Oalhw4Y6duyYBg0apCpVqqho0aIqWbKkypcvr1dffVWRkZHJHTb+xe3bt3XkyBHNmDHDekXbGKOoqCgVK1ZMI0aM0Jw5c3Tt2jXepylQ1apVlT9/fu3evVtVq1bVsWPHVKtWLfXs2VMvvvii1qxZo0GDBlkT76lTpzJSLA2IG3a6YsUKBQcHq1KlSho1apS2bdsmSfr666+tifc333yjmzdv8iV3ChP3+/TKlSsaPXq0Xn/9dTVq1EilS5fWnDlzdOjQITVo0EDGGDk5Oembb77RqFGjkjlqJJXr16/L29tbR44csfmsVKpUKfXq1Uu1a9fWK6+8orVr16aK965LcgeAJxcbG6sHDx4oLCxMc+fO1aRJk9SwYUPritcXL17U+PHj1bFjRzVs2DCZo8XfxX0gWLp0qb755htduHBBRYsWVevWrdWwYUN9/PHH+vLLL9WpUydNmjRJZcqUSVVzV/5rLBaLdu3apbZt2+qrr76Sj4+P/vjjD7Vo0UIbNmzQyJEjVbRoUd26dUvOzs5yc3NTiRIllD59+uQOHX/z9/dZ5syZ1bdvX2XMmFFjxozR3Llz1bx5c7m5uUmS3NzclCVLFrm7u/P+TMHCw8Pl5eWlHj166KuvvlLlypX14YcfSpLWrFmjkydPKl++fBo1apSOHTuWzNHiaVksFi1atEhvvvmm3nvvPeXNm1eLFi3SH3/8oT59+qh27dr6+uuv1bt3b/3www9Kly6devfuzXs4BbFYLFq5cqV+++03eXt7q2nTpta6ypUra86cOXrzzTdVokQJBQUF6eeff9bevXuTL2AkqTp16ihdunSKiopS586dNX36dJUrV07So8S7S5cucnd3V968eZM50kQySDViY2ONMcZERkbalI8aNcp4eHiYmjVr2pR/9NFHpkSJEubChQvPLEbYZ/HixSZ9+vTm448/NuvWrTNVqlQx+fPnNzt37jTGGLNq1SrTtGlTU6BAAbN///5kjhb/5MSJE2bkyJHmww8/NMYY8/DhQ2OMMZcvXzZBQUGmfPny5s8//0zOEJEIMTEx1v+HhYXZnLMrV66Y4OBg4+LiYmbMmGEuXbpkrly5YurWrWvq1Klj/R2NlCHufJw6dcr6d3PDhg3mlVdeMYGBgWbz5s3GGGNu3bplRo0aZRo2bGiqVKli9u3bl2wxI+kcOHDAFClSxHz//ffGGGMiIiJMtmzZTMGCBU3NmjXN6tWrrW379etnzpw5k1yh4h/MmTPHWCwW4+7ubnbt2hWv/syZM+att94ybdu2NQcOHEiGCJEU4n5fHz9+3OzatcusWbPGWrdp0ybTqFEjU7ZsWevn4zhRUVHPNM6nQdKdSsT9MC5dutQ0a9bMvPbaa2bFihXmzp075vr166Zjx44me/bsZtiwYearr74y77zzjsmYMaPZu3dv8gaOBMXGxprbt2+b2rVrm08//dQYY8zdu3dN3rx5Tbdu3Wzarly50rRo0YIPBClUbGysCQ0NNb6+vsbV1dW0bdvWWheXwF25csUEBQWZvHnzmnPnziVXqPgXf02ahwwZYp5//nnj4+NjqlWrZhYuXGiioqLM9evXTefOnY3FYjE5cuQwXbp0MdWqVTP37983xtgm7Ug+cefy119/NQULFjQ//PCD9RytXbvWNG3a1Cbxjo6ONjExMebOnTvJFjOS1v79+023bt1MZGSkOX/+vPH39zfvvvuuWbZsmcmVK5epWbOmWbJkSXKHiURYsmSJsVgsplOnTubq1avW8r/+zo57fyP1iTuP8+bNM3nz5jX+/v7G09PT1KhRw2zfvt0Y8+gL08aNG5sKFSqYrVu3Jme4T4ykOxXZtGmTSZ8+venatasJCgoyxYsXN0OGDDF37twxly9fNiNHjjQFChQwlSpVMm+88YY5ePBgcoeMfxAdHW2qVKlijh49ai5evGh8fHxMp06drPVLly41ISEhxphHCTlSnr/+wV+7dq0pWLCgKV26tNmyZYu1PC4J+/PPP03VqlXN6dOnn3mcsM/QoUNN9uzZzaRJk8zChQtNvXr1TJkyZcz48ePNw4cPzcWLF02fPn1MpkyZrFfRjEld37inVX/90mPRokUmQ4YMZsyYMfG+tNy4caNp3LixKV++vNmwYcOzDhMOEPf7+NatW9b34qVLl4wxxrRq1cq8/fbb1hEP9erVM97e3ua1114zERERjFJJIeLOw5UrV8zJkyfNzZs3refy559/NhaLxfTu3dtcv37dug1fdKYNW7ZsMRkzZjRTpkwxhw4dMkePHjVlypQxQUFBZvfu3caYR6M/a9asaWrUqGHu37+f6t63JN2pyI8//mgGDx5sfd6vXz8TGBhoBg4caMLCwowxxvotPd/4pWwxMTHm/v37plSpUuaDDz4wBQsWNJ06dTLR0dHGmEdDkps0aWJmz56dzJEiIXG/6OOGkMf90V+zZo3x8/Mzb775ps0ok7j6uPZImWJjY83Vq1dN2bJlzbRp02zqOnXqZIoVK2Y9rydOnDDdunUzmTJlMr/++msyRIu/Wrp0qc3zW7dumSpVqphhw4YZYx59IRIWFmZmzJhhdu3aZWJiYsyOHTtMzZo1TfXq1c29e/dS3Qc4/J+4c7d48WLz1ltvmSVLlpgHDx4YYx59HgoKCjKff/65MebRF97t2rUzX375JVN+UpC4c7hgwQJTsmRJkytXLlO2bFnTunVr62fcWbNmGYvFYvr27WtzxRupy/r1663nNM748eNN5cqVTVRUlPUz0927d03JkiVN3bp1re02bNhgLl68+EzjTSqsXp6Cmf+/auPevXu1fPlyHT9+XDly5LDWx90ibPHixRozZowuX74sT09PSZKrq2uyxIz4YmJirLewiIiIkPRocRA3Nzf17NlT3333nXLmzKnvvvtO6dKlkySNHz9eJ06cUMWKFZMtbiTM/P9FtlavXq0ePXrorbfe0meffaZr166pZs2a+v7777VlyxZ9+eWX2r9/v6RHq5pLj+4nipTLYrHIxcVFERER1nMWFRUlSfruu+9kjNF3330nSSpUqJB69eqldu3a6ZVXXtGSJUuSLe7/ut9//10DBw7UlStXrH83IyMjFRYWJn9/f/35558aOnSoXnnlFXXq1Ent27fX9OnT9fzzz2vw4MGaOXMmi+ClchaLRb/++quaNWumokWLqnDhwnJxebRW8P379+Xt7a29e/dq8eLFGjJkiDZs2KC33npL3t7eyRw54lgsFq1Zs0YtW7ZU27Zt9fPPP6t58+Y6ceKEXn75Zd26dUtvvvmm5syZoy+//FLjxo3j9mCp0K+//qr33nsv3u0ZL1++rNu3b8vV1VVOTk66d++e0qdPr6lTp2r79u3atWuXpEd3o0g1C6f9XfLm/Pg38+bNM56eniZv3rzGYrGYF154wYSGhtq0+fjjj03+/PnN8OHDGWaTgqxdu9Y6PNyYR9/AN2jQwFStWtVMmjTJhISEmLt375qePXuabNmyme7du5tPPvnEdOjQwWTKlIn5+CnYwoULjbu7u+nYsaOpU6eOKVeunMmXL585f/68McaY33//3RQqVMg0btyYhV1SsIR+Xz58+NAEBQWZ119/3VoWN7yxdevWpmPHjjbtT58+bfr27WuOHTvm2GDxWFeuXDFXrlwxxhib89C8eXOTNWtWkzVrVvPqq6+aiRMnmoiICFOjRg0THBycXOHCAS5evGhKlSplxo0bl2D9rFmzTOXKlU3evHlNwYIFrcNVkTLExsaamJgY07t3b9OmTRubut9//91UqFDBvPPOO9bRC7/++qs5fPhwMkSKpBA37ePcuXPmxo0bxhhjdu3aZTw8PMzXX39t03br1q2mQIEC5sSJE888zqRG0p0CxQ2xuX79umndurWZPHmyuXr1qhkxYoQJCgoywcHB5tq1azbbDBs2jIW2UpANGzYYPz8/8+GHH5rw8HCzb98+4+7ubj744APz8ssvm1KlSpng4GATEhJiwsPDzaRJk0zp0qVNjRo1TJs2bfhjkoJdu3bNlC5d2jpU0RhjDh48aOrWrWvy589vfW+uWLHClC5d2uaLF6QccR/ejDHm6NGj5vLly9YvNNeuXWs8PT1N9+7djTGPfifHxsaaChUqWFen/+tQ5L/2hWfrr+fh5MmTpkyZMtbzZsyjaVlz5swx9+7ds56ntm3bmvfff988fPiQIeWp0HfffWddAC/O2bNnTb58+Wzm5//93F6+fNmcOHHC+gUNUp7WrVubKlWqxCsfMGCAqVChQry79yB1iZtCacyjVcrz5MljPv/8c3Pjxg0TGxtrBg4caPLnz29Gjx5tjDEmPDzcDBw40AQEBKSJ6QQk3SnUzp07TbVq1UzdunVtVjsePXq0qVSpkunQoUO8xBspy4ABA0xQUJAZOHCgGTx4sPniiy+sdd98842pWLGiad++vTl79qwx5v9+Gf31lxJShrgPbw8ePDA3b940OXPmNL///ru1/uHDh2bfvn2mbNmyZty4cTbzkZCyjBgxwuaWI/369TMFCxY03t7epn379tZVUadNm2YyZMhgKlWqZF5//XXzwgsvmKJFi5Jgp1A7d+40H3zwgfnf//5nSpQoYfr16xevzfXr181HH31ksmTJYo4cOZIMUeJpxMTEmCtXrpjnn38+3oKU27ZtM66urtZbSv317+iePXvM0qVLWegwhYn7uxoSEmL9wnPChAmmXLlyZsuWLTZroCxcuNAUKFCAOfipUEIjyuJGBXbp0sUUKlTIjBkzxty9e9dcuXLFDB482Hh4eJj8+fOb0qVLm5w5c6aZkSnM6U6hjh49qoiICO3atUvp06e3lvfs2VPNmjXTyZMn1bVrV4WGhiZjlEjIgwcPJEnDhg1To0aNtHz5cs2aNUtubm7WNu+++65at26tI0eO6NNPP9WRI0es87nj5qEh5bBYLNq9e7fef/99PXjwQP7+/lq3bp213tnZWaVKlZKLi4uOHz9unQ/s4eGRTBEjIZs3b9bPP/+s4cOH69ixY1q7dq1mzpypcePG6f3339eVK1fUt29fbdmyRW3atNGOHTtUvHhxZc+eXZUrV9aBAwfk4uKihw8fJveh4C8ePnyocePG6ciRI+rbt6/eeOMNLVy4UB999JG1ze+//663335bc+bM0dq1a1W0aNFkjBhP4t69e8qVK5fWr18vf39/7dq1Sxs2bJAkVahQQTVr1lTHjh119epV699TSZo6daoWLVrE/N8UxPz/tVEWLVqkpk2bavny5YqOjlazZs0UGRmpwYMHa+vWrdb269atU65cuazrFiH1cHJy0pkzZ/T2229LejSnu0GDBgoJCdH48ePVsGFDjR49Wj/88IMyZsyoQYMGaffu3erevbs++OADbd++XWXLlk3mo0giyZ31I2EPHjwwc+bMMYULFza1atWyznmIM3z4cFOvXj2+9Ush/umbvJEjR5rs2bObpk2bxhse891335miRYuarl27coU7hfv6669NiRIlzM6dO03v3r3N888/b+bPn2/T5pVXXjEDBgywDkdGyjN79mxTq1Yt07x5c9O7d28zduxYa92qVatM06ZNTeXKlc2aNWsS3J4V6FOmI0eOmPTp05tffvnF3L9/3wwdOtQULVrUfPTRR8aYRytYT5s2jVv2pVKTJ082PXv2tI7wCw8PNwEBAaZ69erWIeWrV682L7zwgilevLhZt26d+e2330yfPn1MlixZWFsjBVqyZInx8PAwo0aNso74M+bRle+SJUuasmXLmhIlSphGjRqZzJkzs85NKhUbG2sWLVr0/9q787ia8v8P4K/TpmhRQrYpFFNUahJZRkI1vmNr5msZYydbaTGVZBlMM2bGkjUMQyMmS9JYhowwREKbFJNGDLIzJHLrfn5/+HZ+Gsxgum7xej4e9+Hecz7nnHf3uHXe9/P5vI8wNTUV7du3F5IkiXXr1pVr4+/vL8zNzcWCBQvK3Q7uTcOkuxIouzi/cOGCOH/+vFwIRqlUig0bNggXFxfRvXv3p8rr/7WgGqnX2bNnRd++fYUQQsTGxgpzc3Nx9uxZIYQQX3zxhbC1tRWTJ09+aj7Z999/X+4PDlUOZZ/LJ+eQdejQQfTs2VMoFArRp08f0bp1a+Hn5yc2btwofHx8hKGhocjJyVFXyPQ3nvxSa9OmTaJr167C1NRUfP311+Xa7d27V/Tp00d07NhR/Pzzz687THoBf/1Cq+xLTz8/P9GnTx9RXFwsCgoKxMyZM4Wtra0ICAhQR5hUgSZMmCDs7OzE9OnT5S+vMzMzhb29vfD09BTJyclCCCFSUlJEnz59RM2aNUWzZs1EmzZtmKxVMkqlUty7d0906dJFTJ06tdy6sikAN27cEOvWrRP+/v4iPDychSrfACEhIUKSJNG6dWt52YMHD+Tn/v7+wsrKSnz11VdP5TtvCibdalZ28RAbGyuaNWsmmjRpIoyMjMTYsWPlntKYmBjh4uIievbs+VSPN1UeSUlJokaNGqJNmzZCkiSxdu3acuunTZsmHBwcRGhoKAu5VBG7du0Sn376qdi9e7cQ4vHoBQsLC7FkyRLx4MEDERoaKtq2bSusrKxEx44deXFXST05EmXbtm3i+vXrIj4+Xrz33nvCzs7uqfOWmJgo3n//fTFmzJjXHCm9qP3794u1a9eWO7exsbHCxMREHDhwQAjxuMds0qRJok2bNqyB8gaYOnWqcHZ2FmFhYaKgoEAIIcSpU6eEjY2N8PDwkOsxCPG4OOLVq1ff2Iv3qu7evXuiWbNmco/nk5/jsqSc3gxleU5kZKTw8/MTlpaWolevXvL6Jzs2xo4dK1q2bPnGfm6ZdFcC+/fvF3p6eiIyMlLs27dPbNmyRZiamoo+ffqIixcvitLSUrF+/XphY2Mj+vbty9uCVWIzZ84UkiQJBwcHedmTxVumTZsmnJ2dxYQJE3gRWMkplUoxatQoIUmSMDExEdOnTxe///67CA8PF15eXiI3N1cI8fhi4dq1a6KwsFDNEdOzPNkrGhoaKszMzMSSJUuEEI97vF1dXUXv3r1Fenp6ue1OnDjB37WVVHFxsfD39xeSJAkvL69yRSpHjRolXFxcxN27d4UQQly9evWNHq74NigbpXL16lXx6aefihYtWohp06bJf0PLEu8PPvhA/sKFKpcni5EK8Xi6h7m5uQgODpbblE3dyc7OFmvWrBG3b99+7XGSaj169Ehs2rRJWFhYlEu8hRDyKME3+dpYEkIIdc8rf9uFhYUhPT0dO3bskJelp6ejS5cuGDx4MObPn4+SkhJs3boVTk5OsLCwUF+w9BTxv4IgALBx40ZkZGRg7dq1sLa2xu7duwEAxcXFciG1yZMn49dff0VcXBxq166ttrjpaU+eSwBISUlBREQEWrZsibi4ODg5OaGkpARJSUkYNWoUAgIC1BgtvYxZs2Zh4cKF2LlzJ5o1awYjIyMAj4u6REZGonr16pgxYwbs7OzKbadUKuXCeFS55OTkYNGiRdi3bx8AIDg4GPfu3cPevXvx2WefoWPHjmqOkCpKTEwMFi5cCGNjY5w8eRJ37tyBv78/xo0bBzMzM2RnZ+OTTz6Bvr4+vv32W7i4uKg7ZPqfsr+rBw8eRFZWFrp37w5zc3OEh4dj/fr1mDhxIoYPHy63nzhxIk6cOIH4+Hj59zRVLWXnPC0tDSdPnoQkSejYsSMsLCxw//597Nq1C0FBQWjZsiWio6Px7bffYvv27di7dy9MTEzUHb7KMOlWMyEERowYgUuXLmH37t1QKpUoKSmBjo4OoqOjMXHiRKSkpMDc3FzdodIzlP1iyczMxOXLl6FQKNCpUyekp6dj+PDhaNKkCRISEuT2aWlpcHBwwK1bt97oXyxVWWJiIn7//XeMHDkSSqUSfn5+KCoqQkREBKKjo5GamopVq1YBAI4cOYI2bdqoOWL6J7du3UK/fv0wdOhQDBw4EJcuXcJvv/2G9evXo2vXrjh37hwOHjyIe/fuYfXq1WjatKm6Q6YX9PDhQxQWFmLSpEn4448/cOrUKVy+fBm+vr5YsGCBusOjCnDy5Em4urpizpw56NmzJ2rVqoXx48fj4MGD6N27N3x9fVG7dm1kZmZi9OjR2LhxIxo1aqTusAn/f40UGxuLYcOGITg4GH369EGLFi2QnZ2NOXPm4PDhw+jWrRuaNm2KkydPYvPmzfj1119hb2+v7vDpFZSd8y1btsDPzw+1atVCjRo1cO7cOezcuROtWrVCUVER9u7dC19fXznviY+PR+vWrdUdvmqpp4P97aRUKuXhMzdv3pTv4btlyxZRrVo1sWfPHiHE/89tiYuLE9bW1iyYVslt2rRJmJiYiFatWglJkkSHDh1ERESEOHDggLC0tBTu7u7i3LlzIiwsTFhZWT1VwZwqj5KSEvHll18KSZLEoEGDxKFDh4RSqRSOjo5i5syZQggh/vzzT+Hj4yMaNGggDzGnyu3WrVuifv36IiwsTBw4cED069dPODs7CycnJ2FmZiZWrFghoqKixPjx4zmkvArLyMgQixcvFpaWlk9NF6Cqa8+ePaJBgwZPVZ4fM2aM0NfXFzNmzBCXLl0SQgjei7sSOnTokDAxMRGrVq16at3t27fFwoULhY2NjXB2dhY9evRgpfk3wP79+4WJiYlYsWKFEEKII0eOCEmShJGRkXy3AYVCIa5cuSLi4+PFhQsX1Bnua8Oe7tdg586daNCggfytXVxcHObOnYtr165hwIABcHFxwa5du7B7924sXLgQ3bp1AwCEhoZiz5492LNnD4yNjdX5I9BzpKWlwd3dHbNnz4aXlxeKi4sREhKCP/74A15eXnByckL//v2hoaGB4uJixMfHw8nJSd1h0z/IzMxEUFAQCgsL0bp1a3h6emLZsmUIDg5Gu3btAAB37txBzZo11RsovbBVq1YhKCgIpaWlGDNmDLp164auXbti4MCB0NPTw8qVK+W2HFJetYi/TAt5cjoPVV1l5zUxMRFDhw7F9u3bYWdnJ59fhUIBCwsLaGpqwtvbG6GhodDQ0Cj3f4HUp+z8ffXVV9i3bx8SEhJQVFSEgwcPYvXq1bh16xbGjRuH3r17AwBKSkpQWlrKz24VV1RUhPDwcOjo6GD69Om4dOkS2rVrBzc3N9y7dw8JCQnYu3fvm9+r/QxMulXs6tWrcHFxgaurK8LCwqBQKODi4oKJEyfixo0bOHToEKysrODs7Iw//vgDixcvhqOjI7S1tZGVlYXExEQ4ODio+8eg51i/fj3Cw8Nx5MgRGBgYQJIkXLlyBRMnTsSlS5eQmJiIBw8e4MiRI7CxsUH9+vXVHTK9oKtXryIhIQHz5s1Dbm4u6tSpg08++QRffPGFukOjV3ThwgUUFxfDysoKwOPk2t3dHc7Ozvjyyy/VHB1VlL8m4VR1POvcKZVK2Nvbo3bt2khISICWlhYA4NKlSxg3bhzMzc0xceJETsOrJP56DhctWoTo6Gj0798f+/btg1KphEKhQOPGjbFhwwYcPnwY1tbWaoyY/q2/nvOkpCRoamrCxsYG3bp1g4ODA5YtW4Z9+/ahS5cucpu3rfYCk+7XIDU1FaNHj0bbtm1Rt25dAMCUKVMAANu2bZOLg3z66acwMjLCzz//DBMTE/Tp00e+OKTKKSYmBlOmTMGhQ4dgZmaGkpISaGlpIT8/H02aNMHPP/8MDw8PdYdJ/4JCoUBISAgWL14MY2NjnD17FgYGBuoOi/6FwsJCpKen4+uvv8b58+eRmpoqX8gTkXqUXbgfOHAA27dvx82bN2Fvbw8/Pz+cOnUK3bt3R5MmTRAeHg4DAwNs3LgRhw4dwtatW1lwq5IoO4fJycnIyMjA/fv3oaOjg6SkJOTl5aFFixYYMmQIXF1dkZycDD8/P8TGxqJhw4bqDp1eUdk5P3LkCE6fPo1hw4bJ65KSkhAQEIDo6Gg0a9YM6enpCA8Ph5mZGcaPH493331XjZG/frzKeA0cHR2xfPlyjB07FlevXkX//v3ldT169AAAzJ8/H1FRUZg6dSpmz56trlDpJbVu3RoXL17EkiVLMGvWLPnCXZIktGjRgsOPqzghBLS1tTFv3jx0794dVlZWTLirOCEEjh8/jrlz50KhUODEiRPQ0tJCaWkpNDU11R0e0VtLkiTExcVh2LBh+PDDD9G4cWMEBAQgNTUV8+fPR0JCAgYMGIABAwagtLQUAFjhupIpK5o2YsQIeHp6Ij8/H0ZGRjA1NcW2bdvkjicA2L59OxQKBXR1ddUYMf0b4olCeWPHjkX//v3h5OQEW1tbAMC1a9dw/PhxaGtrAwA2b94MhUKBb775Bnp6euoMXT1e8xzyt1pGRoZo3LixaN++vcjKyiq3bseOHaJVq1Zi4MCB4v79++XuLUuVW3R0tNDR0RGTJk0Subm54urVqyIsLEw0atRILu5CVRc/i2+ehw8fitTUVLloWtm9Y4lIffLz80Xz5s3FokWLhBBC3Lt3T9SsWVP4+/vLbZRKpUhJSRHJycn8+1oJZWdni3feeUcsW7ZMCPH4Huq6uroiNDRUbnP48GHh6+sratasKdLS0tQUKVWU5ORkYWRkJFasWPFUIdJ79+4Jd3d3oampKdq1ayf09fXf6iKXHF7+mmVmZmLIkCFwdnbGhAkT0KJFC3ldQkICmjdvznlJVYwQAhs2bIC3tzeMjY2hq6uLoqIixMfHw9HRUd3hEdHfYNE0osrhzJkzGDRoEFJSUpCfn4/27dvjww8/xPLlywEAKSkpcHZ2VnOU9HcSEhIwadIkpKam4ty5c+jcuTM8PDzkc7hjxw7s2LED58+fx+zZs+UeUaq6Fi5ciISEBMTHx8uFDJ8cOXb9+nVER0dDoVCgd+/eaNasmZojVh8OL3/N7Ozs8P3332PkyJGIiIhAQEAAbGxsAADu7u5qjo5ehSRJ6N+/P1xcXHD69GmUlpbCzs6Oc5SIqgAm3ESVQ0lJCa5fv474+HgEBgbiww8/xJIlSwAA6enpCA0NxYIFC9CyZUs1R0rPI0kS6tWrh/z8fLz//vvo3r07li5dCuDx/N6TJ09i2LBhaNq0KUxMTNQcLb0q8UThtCtXruDixYtyoi2EkBPu48ePw8nJCQEBASxwCYBXG2rg4OCAlStXIjMzE7NmzcLp06fVHRJVAHNzc3h4eKB79+5MuImIiJ6jbJBlTk4ODh06hN9//x0tWrRAhw4d8Omnn8Le3h7Lly+X66Rs3LgRDx8+RO3atdUZNv0DKysr7N+/H02aNIGXlxeWL18uJ2AbNmxAYmIimjVrxoS7irt48aL8vEGDBrhx4wYyMjLkxFoIAYVCgaVLlyImJgYA3vqEG2DSrTYODg5YvHgxCgoKWASEiIiI3hqSJGHr1q1wdnbGsGHDYGNjg+joaHh4eMDa2hqlpaXYsWMHEhMTERgYiKVLlyIyMrJcIS6qfCwsLLB+/XpUr14denp6yM3NRVZWFoKCgrB27VrMmzeP17xVXG5uLpydnTFv3jwAwPjx42FmZoZhw4YhOTkZ9+7dw4MHD/D555/jl19+QZs2bdQcceXBOd1q9vDhQ1ZuJCIioreCUqnEnTt30LNnTwwePBhubm6IiYnBjBkzsGDBAvm2YT/99BMsLS1hZGSExYsXw97eXt2h0wsoLS3F2rVr4efnB0NDQxgYGEBHRwerV6+Gg4ODusOjf+n8+fOIiIhAXFwcAgIC4Ofnh6KiInTt2hWXL1+GJEl45513cPr0aezatYvn/AlMuomIiIhIpcqGnj58+BBCCHzxxRf47LPPYGxsDODxrVODg4MxZ84cDBgwAA8fPoS+vj40NTXZO1oFXbx4Efn5+dDX10fDhg1hamqq7pDoFTxrLvaFCxewbNkyREVFISQkBBMmTADweBpIQUEBDA0N4erqisaNG6sj5EqLhdSIiIiISKUkSUJ8fDwiIyPxxx9/QKlUol+/fnLSHRAQAEmSEBwcjGvXriEkJASGhoZqjppeVcOGDVnfpooqu6tH2b+HDh1CQUEB/vvf/wIA3nnnHYwePRoA8NVXX0FLSwvjxo1D37591Rl2pcc53URERESkUsePH8fgwYPRuHFjODs7Iy8vD99//z3Onz8vt/H398fMmTMRGRkJhUKhxmiJ3k6RkZGws7ODQqGAhoYG/vzzT3z//fcIDAzEli1b5Hbm5ubw9vZGq1atEBoaioULF6ox6qqBSTcRERERqUxeXh62bduG0NBQREZGYvXq1ViwYAFiY2OxbNmycol3SEgI8vLyUKtWLTVGTPR2cnBwQGFhIdzd3VFSUgIjIyOMHTsW3bt3R1hYGDZv3iy3tbCwgJ2dHYyNjbFq1SrcunULnLX8fBxeTkREREQqcffuXfTv3x/5+fnw9vaWl48dOxZKpRJfffUVNDU1MWLECHkOaM2aNdUULdHbrU2bNoiLi8Mnn3yCTp064cCBA2jdujU0NTVRWlqK6dOnQ5IkfPTRR/I2gYGBGDx4MD+3/4BJNxERERGphKGhIVasWIF+/frhwIEDyMrKQsuWLQE8vt2QpqYmAgICoKOjg8mTJ0NLS4v39CV6zYQQEEJAQ0MDWlpamDx5MoYMGYLevXtj69atcHR0xLhx46CpqYkxY8Zg8+bNkCQJCQkJSElJYcL9Aji8nIiIiIhUxsHBAZs3b8b9+/exaNEinDp1Sl43ZswYLF68GAMGDICWFvuCiNRBkiRoaGggNjYWnp6eOHr0KNq0aYP9+/ejc+fOKCkpgaOjI4KDgxEeHo7Lly9DqVQiMTERTZo0UXf4VQJvGUZEREREKpeWloaRI0fC0dERAQEBsLGxUXdIRPQ/Fy9eRJs2bTBx4kQEBgbiwYMHOHjwIEaPHo1GjRohMTFR/mLs0aNHEEKgWrVqao666mBPNxERERGpnIODA1auXInMzEzMmjULp0+fVndIRPQ/d+/ehRACnTt3BgDo6enBzc0NS5cuRUpKCvr27YtHjx4BAHR0dJhwvyQm3URERET0Wjg4OGDx4sUoKCiAkZGRusMhemv9dbBz48aNUb16dWzbtk1epqWlhXbt2sHa2hpbt25Fr169XneYbwwOLyciIiKi1+rhw4fQ1dVVdxhEbyUhBCRJQnJyMjIyMnD16lW4uLhg586dOHv2LAYOHIj+/fsDAEpLS+Ht7Y2ePXvCzs5OvssAvRwm3URERERERG+R2NhYjBgxAh988AHOnz8PPT09PHr0CCYmJrh58yY6d+6MLl26IC4uDvHx8Th8+DDq16+v7rCrLCbdREREREREb4mcnBx4enpi8uTJGD16NLKzs+Ho6Igvv/wSH374IaKiorBp0yYoFAro6OggJiYGDg4O6g67SmPSTURERERE9JZISEjApEmTkJqainPnzqFz585wd3fH8uXLIUkSTpw4gZYtW+LGjRuoUaMG78NdAVhIjYiIiIiI6C0hSRLq1auH/Px8vP/++/Dw8EBkZCQkSUJSUhJiYmJw48YNNGjQgAl3BWHSTURERERE9JawsrLC/v370aRJE3h5eWH58uXQ1NQEAGzcuBGZmZmoXr26mqN8s2ipOwAiIiIiIiJ6PSwsLLB+/XoMHDgQenp6yM3NRXFxMaKiovDDDz/g4MGDMDY2VneYbxTO6SYiIiIiInqLlJaWYu3atfDz84OhoSEMDAygo6OD1atXs2iaCjDpJiIiIiIiegtdvHgR+fn50NfXR8OGDWFqaqrukN5ITLqJiIiIiIiIVISF1IiIiIiIiIhUhEk3ERERERERkYow6SYiIiIiIiJSESbdRERERERERCrCpJuIiIiIiIhIRZh0ExEREREREakIk24iIiIiIiIiFWHSTURERERERKQiTLqJiIiIiIiIVIRJNxERUSU0dOhQSJIESZKgo6MDS0tLzJw5EyUlJeoOjYiIiF6ClroDICIiomfz9PTE6tWrUVxcjJ07d2L8+PHQ1tZGaGjoS+/r0aNH0NHRUUGURERE9HfY001ERFRJVatWDWZmZjA3N8fYsWPRtWtX/PTTT3B1dYW/v3+5tr1798bQoUPl1xYWFpg1axYGDx4MQ0NDeHt7Iz8/H5IkISYmBu3atYOuri5atmyJAwcOlNvXgQMH4OzsjGrVqqFevXqYNGlSuR72zZs3w9bWFnp6eqhVqxa6du2K+/fvy+tXrlwJa2tr6Orq4t1338XSpUtV8v4QERFVBUy6iYiIqgg9PT08evTohdvPmTMH9vb2SEtLw9SpU+XlQUFBmDhxItLS0uDi4oIePXrg5s2bAIBLly6he/fuaN26NTIyMhAZGYlVq1bhiy++AAAUFBRgwIABGD58OHJycrB//354eXlBCAEAWLduHaZNm4bw8HDk5OTgyy+/xNSpUxEVFVWB7wQREVHVweHlRERElZwQAnv37sXu3bvh6+uLY8eOvdB2bm5umDhxovw6Pz8fAODj44OPPvoIABAZGYldu3Zh1apVCA4OxtKlS9GoUSMsXrwYkiTh3XffxeXLlxESEoJp06ahoKAAJSUl8PLygrm5OQDA1tZWPsb06dMxd+5ceHl5AQAaN26M7OxsLF++HEOGDKmIt4OIiKhKYdJNRERUSW3fvh36+vpQKBRQKpX45JNP8Pnnn+M///nPC23v5OT0zOUuLi7ycy0tLTg5OSEnJwcAkJOTAxcXF0iSJLdp3749CgsLcfHiRdjb26NLly6wtbWFh4cH3N3d8fHHH8PY2Bj3799HXl4eRowYgVGjRsnbl5SUwMjI6FXeAiIioiqPSTcREVEl1blzZ0RGRkJHRwf169eHltbjP9saGhrycO4yCoXiqe1r1KhR4TFpampiz549OHz4MBISErBo0SKEhYXh6NGjqF69OgDgu+++Q5s2bZ7ajoiI6G3EOd1ERESVVI0aNWBpaYl33nlHTrgBoHbt2igoKJBfl5aWIisr64X3m5ycLD8vKSnBiRMnYG1tDQCwtrbGkSNHyiX1SUlJMDAwQMOGDQEAkiShffv2mDFjBtLS0qCjo4O4uDjUrVsX9evXx++//w5LS8tyj8aNG7/y+0BERFSVsaebiIioinFzc0NgYCB27NiBpk2bYt68ebhz584Lb79kyRJYWVnB2toa8+fPx+3btzF8+HAAwLhx4xAREQFfX1/4+PjgzJkzmD59OgIDA6GhoYGjR49i7969cHd3R506dXD06FFcv35dTtpnzJiBCRMmwMjICJ6eniguLsbx48dx+/ZtBAYGquLtICIiqtSYdBMREVUxw4cPR0ZGBgYPHgwtLS0EBASgc+fOL7z97NmzMXv2bKSnp8PS0hI//fQTTE1NAQANGjTAzp07ERQUBHt7e5iYmGDEiBGYMmUKAMDQ0BC//vorIiIicPfuXZibm2Pu3Ln44IMPAAAjR45E9erV8e233yIoKAg1atSAra3tU7c4IyIieltI4q+TwoiIiOiNlJ+fj8aNGyMtLQ2tWrVSdzhERERvBc7pJiIiIiIiIlIRJt1EREREREREKsLh5UREREREREQqwp5uIiIiIiIiIhVh0k1ERERERESkIky6iYiIiIiIiFSESTcRERERERGRijDpJiIiIiIiIlIRJt1EREREREREKsKkm4iIiIiIiEhFmHQTERERERERqQiTbiIiIiIiIiIVYdJNREREREREpCJMuomIiIiIiIhUhEk3ERERERERkYow6SYiIiIiIiJSESbdRERERERERCrCpJuIiIiIiIhIRZh0ExERERFRhbl58ybq1KmD/Px8tcXw+eefo1WrVmo7/t/Zv38/JEnCnTt31B2KLCkpCba2ttDW1kbv3r3/9f6GDh1aIfsp079/f8ydO7fC9ve6MekmIiIiIqIKEx4ejl69esHCwgKff/45JEn628ffqejkjZ4tMDAQrVq1wrlz57BmzZpntrGwsEBERMQL7W/BggXP3c+rmDJlCsLDw/Hnn39W2D5fJybdRERERERUIYqKirBq1SqMGDECAPDZZ5+hoKBAfjRs2BAzZ84st4wqxqNHj15527y8PLi5uaFhw4aoWbPmK++ntLQUSqUSRkZG/2o/f9WyZUs0bdoU0dHRFbbP14lJNxERERERVYidO3eiWrVqaNu2LQBAX18fZmZm8kNTUxMGBgby6+vXr8PNzQ16enqoVasWvL29UVhYCODxEPGoqCjEx8fLveL79+8HAISEhKBZs2aoXr06mjRpgqlTp0KhULxwnGVDvPfu3QsnJydUr14d7dq1w5kzZ+Q2z+pl9/f3h6urq/za1dUVvr6+8Pf3h7GxMerWrYvvvvsO9+/fx7Bhw2BgYABLS0v8/PPPT8WQlJQEOzs76Orqom3btsjKyiq3/tChQ+jYsSP09PTQqFEjTJgwAffv35fXW1hYYNasWRg8eDAMDQ3h7e39zJ+1uLgYEyZMQJ06daCrq4sOHTrg2LFjAID8/HxIkoSbN29i+PDhkCTpmT3Urq6uOH/+PAICAsqNUFizZg1q1qyJn376CTY2NqhWrRouXLjw1Hvn6uoKHx8f+Pj4wMjICKamppg6dSqEEHKbpUuXwsrKCrq6uqhbty4+/vjjcjH06NEDMTExz/wZKzsm3UREREREVCEOHjyI995774Xa3r9/Hx4eHjA2NsaxY8ewadMm/PLLL/Dx8QHwuJe8b9++8PT0lHvF27VrBwAwMDDAmjVrkJ2djQULFuC7777D/PnzXzresLAwzJ07F8ePH4eWlhaGDx/+0vuIioqCqakpUlJS4Ovri7Fjx+K///0v2rVrh9TUVLi7u2PQoEEoKioqt11QUBDmzp2LY8eOoXbt2ujRo4f8xUFeXh48PT3x0UcfITMzExs2bMChQ4fk96bMnDlzYG9vj7S0NEydOvWZ8QUHByM2NhZRUVFITU2FpaUlPDw8cOvWLTRq1AgFBQUwNDREREQECgoK0K9fv6f2sWXLlqdGKZQpKirC119/jZUrV+LUqVOoU6fOc98nLS0tpKSkYMGCBZg3bx5WrlwJADh+/DgmTJiAmTNn4syZM9i1axfef//9cts7OzsjJSUFxcXF/3BGKiFBRERERERUAXr16iWGDx/+3PXm5uZi/vz5QgghVqxYIYyNjUVhYaG8fseOHUJDQ0NcuXJFCCHEkCFDRK9evf7xuN9++61477335NfTp08X9vb2z22/b98+AUD88ssv5Y4NQDx48OC5x/bz8xOdOnWSX3fq1El06NBBfl1SUiJq1KghBg0aJC8rKCgQAMSRI0fKHTsmJkZuc/PmTaGnpyc2bNgghBBixIgRwtvbu9yxDx48KDQ0NOT4zM3NRe/evf/ubRGFhYVCW1tbrFu3Tl726NEjUb9+ffHNN9/Iy4yMjMTq1av/dl9Pnrsyq1evFgBEenp6ueV/fe86deokrK2thVKplJeFhIQIa2trIYQQsbGxwtDQUNy9e/e5x8/IyBAARH5+/t/GWRmxp5uIiIiIiCrEgwcPoKur+0Jtc3JyYG9vjxo1asjL2rdvD6VSWW6Y97Ns2LAB7du3h5mZGfT19TFlyhRcuHDhpeO1s7OTn9erVw8AcO3atVfeh6amJmrVqgVbW1t5Wd26dZ+5XxcXF/m5iYkJmjdvjpycHABARkYG1qxZA319ffnh4eEBpVKJc+fOyds5OTn9bWx5eXlQKBRo3769vExbWxvOzs7ysf4tHR2dcu/B87Rt27Zc4TwXFxfk5uaitLQU3bp1g7m5OZo0aYJBgwZh3bp1T40M0NPTA4CnllcFTLqJiIiIiKhCmJqa4vbt2yo9xpEjRzBw4EB0794d27dvR1paGsLCwl6pkJi2trb8vCwhVCqVAAANDY1yc44BPHPe+JP7KNvP3+33RRQWFmL06NFIT0+XHxkZGcjNzUXTpk3ldk9+YaEuenp6/1iF/p8YGBggNTUVP/74I+rVq4dp06bB3t6+3G3Vbt26BQCoXbv2vzqWOjDpJiIiIiKiCuHg4IDs7OwXamttbY2MjIxyxcGSkpKgoaGB5s2bA3jci1paWlpuu8OHD8Pc3BxhYWFwcnKClZUVzp8/X3E/xP/Url37qerq6enpFbb/5ORk+fnt27fx22+/wdraGgDg6OiI7OxsWFpaPvXQ0dF54WM0bdoUOjo6SEpKkpcpFAocO3YMNjY2LxXvs87Fyzh69Gi518nJybCysoKmpiYAQEtLC127dsU333yDzMxM5OfnIzExUW6flZWFhg0bwtTU9JVjUBcm3UREREREVCE8PDxw6tSpF+rtHjhwIHR1dTFkyBBkZWVh37598PX1xaBBg+Qh2RYWFsjMzMSZM2dw48YNKBQKWFlZ4cKFC4iJiUFeXh4WLlyIuLi4Cv9Z3NzccPz4cfzwww/Izc3F9OnTn6ow/m/MnDkTe/fuRVZWFoYOHQpTU1O54ndISAgOHz4MHx8fpKenIzc3F/Hx8U8VUvsnNWrUwNixYxEUFIRdu3YhOzsbo0aNQlFRkXxbtxdlYWGBX3/9FZcuXcKNGzdealsAuHDhAgIDA3HmzBn8+OOPWLRoEfz8/AAA27dvx8KFC5Geno7z58/jhx9+gFKplL98AR4X6XN3d3/p41YGTLqJiIiIiKhC2NrawtHRERs3bvzHttWrV8fu3btx69YttG7dGh9//DG6dOmCxYsXy21GjRqF5s2bw8nJCbVr10ZSUhJ69uyJgIAA+Pj4oFWrVjh8+PBzK3f/Gx4eHpg6dSqCg4PRunVr3Lt3D4MHD66w/c+ePRt+fn547733cOXKFWzbtk3uxbazs8OBAwfw22+/oWPHjnBwcMC0adNQv379VzrORx99hEGDBsHR0RFnz57F7t27YWxs/FL7mTlzJvLz89G0adNXGuI9ePBgPHjwAM7Ozhg/fjz8/Pzk25zVrFkTW7ZsgZubG6ytrbFs2TL8+OOPaNGiBQDg4cOH2Lp1K0aNGvXSx60MJPHXiQpERERERESvaMeOHQgKCkJWVhY0NNjHR4/v092qVStERES80vaRkZGIi4tDQkJCxQb2mmipOwAiIiIiInpz/Oc//0Fubi4uXbqERo0aqTscegNoa2tj0aJF6g7jlTHpJiIiIiKiCuXv76/uEOgNMnLkSHWH8K9weDkRERERERGRinCSBREREREREZGKMOkmIiIiIiIiUhEm3UREREREREQqwqSbiIiIiIiISEWYdBMRERERERGpCJNuIiIiIiIiIhVh0k1ERERERESkIky6iYiIiIiIiFSESTcRERERERGRivwfZSpg5BG1+qEAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate the percentage of intrazonal trips for each unique purpose\n",
+    "plot_intrazonal_trips(\n",
+    "    legs_acbm,\n",
+    "    validation_plots_path=validation_plots_path,\n",
+    "    plot_type=\"purp\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Plot intrazonal trips by OD pair"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate the percentage of intrazonal trips for each unique OD combination\n",
+    "# (e.g. home - work)\n",
+    "plot_intrazonal_trips(\n",
+    "    legs_acbm,\n",
+    "    validation_plots_path=validation_plots_path,\n",
+    "    plot_type=\"od\",\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "TODO: Remove later\n",
+    "\n",
+    "Check if total trips FROM and TO each activity are the same\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "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>activity</th>\n",
+       "      <th>total_trips_from</th>\n",
+       "      <th>total_trips_to</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>education</td>\n",
+       "      <td>373</td>\n",
+       "      <td>385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>escort</td>\n",
+       "      <td>1673</td>\n",
+       "      <td>1684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>home</td>\n",
+       "      <td>6752</td>\n",
+       "      <td>6498</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>medical</td>\n",
+       "      <td>183</td>\n",
+       "      <td>189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>other</td>\n",
+       "      <td>2389</td>\n",
+       "      <td>2457</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>shop</td>\n",
+       "      <td>1318</td>\n",
+       "      <td>1337</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>visit</td>\n",
+       "      <td>716</td>\n",
+       "      <td>816</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>work</td>\n",
+       "      <td>1699</td>\n",
+       "      <td>1737</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    activity  total_trips_from  total_trips_to\n",
+       "0  education               373             385\n",
+       "1     escort              1673            1684\n",
+       "2       home              6752            6498\n",
+       "3    medical               183             189\n",
+       "4      other              2389            2457\n",
+       "5       shop              1318            1337\n",
+       "6      visit               716             816\n",
+       "7       work              1699            1737"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ACBM: Check if total trips FROM and TO each activity are the same\n",
+    "\n",
+    "# Group by 'oact' and and calculate the total number of trips\n",
+    "total_trips_from = legs_acbm.groupby(['oact']).size().reset_index(name='total_trips_from')\n",
+    "# rename oact to activity\n",
+    "total_trips_from = total_trips_from.rename(columns={'oact': 'activity'})\n",
+    "total_trips_from\n",
+    "\n",
+    "# Group by 'dact' and and calculate the total number of trips\n",
+    "total_trips_to = legs_acbm.groupby(['dact']).size().reset_index(name='total_trips_to')\n",
+    "# rename dact to activity\n",
+    "total_trips_to = total_trips_to.rename(columns={'dact': 'activity'})\n",
+    "\n",
+    "# Merge the two DataFrames\n",
+    "total_trips = pd.merge(total_trips_from, total_trips_to, on='activity', how='outer')\n",
+    "\n",
+    "total_trips\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "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>activity</th>\n",
+       "      <th>total_trips_from</th>\n",
+       "      <th>total_trips_to</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>education</td>\n",
+       "      <td>1796</td>\n",
+       "      <td>1823</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>escort</td>\n",
+       "      <td>5158</td>\n",
+       "      <td>5180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>home</td>\n",
+       "      <td>26573</td>\n",
+       "      <td>26434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>medical</td>\n",
+       "      <td>850</td>\n",
+       "      <td>859</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>other</td>\n",
+       "      <td>9610</td>\n",
+       "      <td>9628</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>shop</td>\n",
+       "      <td>5633</td>\n",
+       "      <td>5621</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>visit</td>\n",
+       "      <td>3212</td>\n",
+       "      <td>3313</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>work</td>\n",
+       "      <td>6527</td>\n",
+       "      <td>6501</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    activity  total_trips_from  total_trips_to\n",
+       "0  education              1796            1823\n",
+       "1     escort              5158            5180\n",
+       "2       home             26573           26434\n",
+       "3    medical               850             859\n",
+       "4      other              9610            9628\n",
+       "5       shop              5633            5621\n",
+       "6      visit              3212            3313\n",
+       "7       work              6527            6501"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# NTS: Check if total trips FROM and TO each activity are the same\n",
+    "\n",
+    "# Group by 'oact' and and calculate the total number of trips\n",
+    "total_trips_from = legs_nts.groupby(['oact']).size().reset_index(name='total_trips_from')\n",
+    "# rename oact to activity\n",
+    "total_trips_from = total_trips_from.rename(columns={'oact': 'activity'})\n",
+    "total_trips_from\n",
+    "\n",
+    "# Group by 'dact' and and calculate the total number of trips\n",
+    "total_trips_to = legs_nts.groupby(['dact']).size().reset_index(name='total_trips_to')\n",
+    "# rename dact to activity\n",
+    "total_trips_to = total_trips_to.rename(columns={'dact': 'activity'})\n",
+    "\n",
+    "# Merge the two DataFrames\n",
+    "total_trips = pd.merge(total_trips_from, total_trips_to, on='activity', how='outer')\n",
+    "\n",
+    "total_trips"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Crowfly distance cumulative chart (KM distance vs. % of trips)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "acbm-7iKwKWLy-py3.10",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/2_match_households_and_individuals.py b/scripts/2_match_households_and_individuals.py
index ea74a62..5b217e4 100644
--- a/scripts/2_match_households_and_individuals.py
+++ b/scripts/2_match_households_and_individuals.py
@@ -1099,6 +1099,17 @@ def get_interim_path(
     # save the file as a parquet file
     spc_edited_copy.to_parquet(get_interim_path("spc_with_nts_trips.parquet"))
 
+    # save the nts data for later use in validation
+    nts_individuals.to_parquet(
+        acbm.root_path / "data/external/nts/filtered/nts_individuals.parquet"
+    )
+    nts_households.to_parquet(
+        acbm.root_path / "data/external/nts/filtered/nts_households.parquet"
+    )
+    nts_trips.to_parquet(
+        acbm.root_path / "data/external/nts/filtered/nts_trips.parquet"
+    )
+
 
 if __name__ == "__main__":
     main()
diff --git a/scripts/4_validation.py b/scripts/4_validation.py
new file mode 100644
index 0000000..8f6d270
--- /dev/null
+++ b/scripts/4_validation.py
@@ -0,0 +1,275 @@
+import os
+
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+
+import acbm
+from acbm.logger_config import validation_logger as logger
+from acbm.validating.plots import (
+    plot_activity_sequence_comparison,
+    plot_comparison,
+    plot_intrazonal_trips,
+)
+from acbm.validating.utils import calculate_od_distances, process_sequences
+
+# ----- Folder for validation plots
+
+logger.info("1. Creating folder for validation plots")
+
+validation_plots_path = acbm.root_path / "data/processed/plots/validation"
+os.makedirs(validation_plots_path, exist_ok=True)
+
+
+# ----- Reading in the data
+
+logger.info("2. Reading in the data")
+
+# NTS data
+legs_nts = pd.read_parquet(
+    acbm.root_path / "data/external/nts/filtered/nts_trips.parquet"
+)
+
+legs_nts = legs_nts[legs_nts["TravDay"] == 3]
+
+# Model outputs
+legs_acbm = pd.read_csv(acbm.root_path / "data/processed/activities_pam/legs.csv")
+legs_acbm_geo = pd.read_parquet(
+    acbm.root_path / "data/processed/activities_pam/legs_with_locations.parquet"
+)
+
+# ----- Preproccessing the data
+
+logger.info("3a. Preprocessing: renaming columns")
+
+# rename origin activity and destination activity columns
+legs_acbm = legs_acbm.rename(
+    columns={"origin activity": "oact", "destination activity": "dact"}
+)
+legs_acbm_geo = legs_acbm_geo.rename(
+    columns={"origin activity": "oact", "destination activity": "dact"}
+)
+
+# rename distance column in NTS
+legs_nts = legs_nts.rename(columns={"TripDisIncSW": "distance"})
+
+logger.info("3b. Preprocessing: Edit distance column in NTS")
+
+# convert legs_nts["distance"] from miles to km
+legs_nts["distance"] = legs_nts["distance"] * 1.60934
+
+logger.info("3c. Preprocessing: Adding hour of day column")
+
+# acbm - tst is in datetime format
+# Convert tst to datetime format and extract the hour component in one step
+legs_acbm["tst_hour"] = legs_acbm["tst"].apply(lambda x: pd.to_datetime(x).hour)
+legs_acbm["tet_hour"] = legs_acbm["tet"].apply(lambda x: pd.to_datetime(x).hour)
+
+# nts - tst is in minutes
+# Convert legs_nts["tst"] from minutes to hours
+legs_nts["tst_hour"] = legs_nts["tst"] // 60
+legs_nts["tet_hour"] = legs_nts["tet"] // 60
+
+
+logger.info("3d. Preprocessing: Abbreviating column values for trip purpose")
+
+# Mapping dictionary
+activity_mapping = {
+    "home": "h",
+    "other": "o",
+    "escort": "e",
+    "work": "w",
+    "shop": "sh",
+    "visit": "v",
+    "education": "edu",
+    "medical": "m",
+}
+
+legs_acbm["oact_abr"] = legs_acbm["oact"].replace(activity_mapping)
+legs_acbm["dact_abr"] = legs_acbm["dact"].replace(activity_mapping)
+
+legs_nts["oact_abr"] = legs_nts["oact"].replace(activity_mapping)
+legs_nts["dact_abr"] = legs_nts["dact"].replace(activity_mapping)
+
+
+# ----- Validation Plots
+
+logger.info("4. Validation plots")
+
+logger.info("4a.1 Validation (Matching) - Trip Purpose")
+
+# Get number of trips by mode for legs_nts, and legs_acbm, and plot a comparative bar plot
+# NTS
+purpose_nts = legs_nts.groupby("dact").size().reset_index(name="count")
+purpose_nts["source"] = "nts"
+
+# ACBM
+purpose_acbm = legs_acbm.groupby("dact").size().reset_index(name="count")
+purpose_acbm["source"] = "acbm"
+
+# Combine the data
+purpose_compare = pd.concat([purpose_nts, purpose_acbm])
+
+# Calculate the percentage of trips for each mode within each source
+purpose_compare["percentage"] = purpose_compare.groupby("source")["count"].transform(
+    lambda x: (x / x.sum()) * 100
+)
+
+
+sns.barplot(data=purpose_compare, x="dact", y="percentage", hue="source")
+plt.xlabel("Trip purpose")
+plt.ylabel("Percentage of total trips")
+plt.title("Percentage of Trips by Purpose for NTS and ACBM")
+# plt.show()
+
+# Save the plot
+plt.tight_layout()
+plt.savefig(validation_plots_path / "1_matching_trip_purpose.png")
+
+logger.info("4a.2 Validation (Matching) - Trip Mode")
+
+# Get number of trips by mode for legs_nts, and legs_acbm, and plot a comparative bar plot
+# NTS
+modeshare_nts = legs_nts.groupby("mode").size().reset_index(name="count")
+modeshare_nts["source"] = "nts"
+
+# ACBM
+modeshare_acbm = legs_acbm.groupby("mode").size().reset_index(name="count")
+modeshare_acbm["source"] = "acbm"
+
+# Combine the data
+modeshare_compare = pd.concat([modeshare_nts, modeshare_acbm])
+# Calculate the percentage of trips for each mode within each source
+modeshare_compare["percentage"] = modeshare_compare.groupby("source")[
+    "count"
+].transform(lambda x: (x / x.sum()) * 100)
+
+# Plot
+sns.barplot(data=modeshare_compare, x="mode", y="percentage", hue="source")
+plt.ylabel("Percentage of total trips")
+plt.title("Percentage of Trips by Mode for NTS and ACBM")
+# plt.show()
+
+# Save the plot
+plt.tight_layout()
+plt.savefig(validation_plots_path / "2_matching_trip_mode.png")
+
+logger.info("4a.3 Validation (Matching) - time of day")
+
+# Plot aggregate
+plot_comparison(
+    legs_acbm,
+    legs_nts,
+    value_column="tst_hour",
+    max_y_value=20,
+    plot_type="time",
+    figsize=(10, 5),
+    plot_mode="aggregate",
+    save_path=validation_plots_path / "3a_matching_time_of_day_aggregate.png",
+)
+
+# Plot facet
+plot_comparison(
+    legs_acbm,
+    legs_nts,
+    value_column="tst_hour",
+    max_y_value=70,
+    plot_type="time",
+    plot_mode="facet",
+    save_path=validation_plots_path / "3b_matching_time_of_day_facet.png",
+)
+
+
+logger.info("4a.4 Validation (Matching) - Activity Sequences")
+
+# Process the sequences for ACBM and NTS data
+
+sequence_nts = process_sequences(
+    df=legs_nts,
+    pid_col="IndividualID",
+    seq_col="seq",
+    origin_activity_col="oact_abr",
+    destination_activity_col="dact_abr",
+    suffix="nts",
+)
+
+sequence_acbm = process_sequences(
+    df=legs_acbm,
+    pid_col="pid",
+    seq_col="seq",
+    origin_activity_col="oact_abr",
+    destination_activity_col="dact_abr",
+    suffix="acbm",
+)
+
+
+# Plot the comparison
+
+plot_activity_sequence_comparison(
+    sequence_nts=sequence_nts,
+    sequence_acbm=sequence_acbm,
+    activity_mapping=activity_mapping,
+    perc_cutoff=0.35,
+    save_path=validation_plots_path / "4_matching_activity_sequences.png",
+)
+
+logger.info("4b. Validation (Assigning) - Trip Distance")
+
+# Apply the function to legs_acbm_geo
+legs_acbm_geo = calculate_od_distances(
+    df=legs_acbm_geo,
+    start_wkt_col="start_location_geometry_wkt",
+    end_wkt_col="end_location_geometry_wkt",
+)
+
+
+# Plot: Aggregate
+plot_comparison(
+    legs_acbm_geo,
+    legs_nts,
+    value_column="distance",
+    bin_size=2,
+    value_threshold=50,
+    max_y_value=30,
+    figsize=(10, 5),
+    plot_type="distance",
+    plot_mode="aggregate",
+    save_path=validation_plots_path / "5a_assigning_distance_aggregate.png",
+)
+
+# Plot: Facet by activity_type
+plot_comparison(
+    legs_acbm_geo,
+    legs_nts,
+    value_column="distance",
+    bin_size=2,
+    value_threshold=50,
+    max_y_value=30,
+    plot_type="distance",
+    plot_mode="facet",
+    save_path=validation_plots_path / "5b_assigning_distance_facet.png",
+)
+
+
+logger.info("4c.1 Validation (Assigning) - Intrazonal Activities")
+
+# -- Plot: by trip purpose
+# Calculate the percentage of intrazonal trips for each unique purpose
+
+plot_intrazonal_trips(
+    legs_acbm,
+    validation_plots_path=validation_plots_path,
+    plot_type="purp",
+    plot_name="6a_assigning_intrazonal_purp.png",
+)
+
+# -- Plot: By OD pair
+# Calculate the percentage of intrazonal trips for each unique OD combination
+# (e.g. home - work)
+
+plot_intrazonal_trips(
+    legs_acbm,
+    validation_plots_path=validation_plots_path,
+    plot_type="od",
+    plot_name="6b_assigning_intrazonal_od.png",
+)
diff --git a/src/acbm/logger_config.py b/src/acbm/logger_config.py
index e86ec63..87797ea 100644
--- a/src/acbm/logger_config.py
+++ b/src/acbm/logger_config.py
@@ -54,3 +54,5 @@ def create_logger(name, log_file):
 assigning_facility_locations_logger = create_logger(
     "assigning_facility_locations", "assigning_facility_locations.log"
 )
+
+validation_logger = create_logger("validation", "validation.log")
diff --git a/src/acbm/validating/plots.py b/src/acbm/validating/plots.py
new file mode 100644
index 0000000..86eaaa5
--- /dev/null
+++ b/src/acbm/validating/plots.py
@@ -0,0 +1,574 @@
+from pathlib import Path
+from typing import Optional, Tuple
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+
+def plot_comparison(
+    legs_acbm: pd.DataFrame,
+    legs_nts: pd.DataFrame,
+    activity_column: str = "dact",
+    value_column: str = "distance",
+    bin_size: Optional[int] = None,
+    num_cols: int = 4,
+    max_y_value: Optional[int] = None,
+    figsize: Tuple[int, int] = (20, 5),
+    value_threshold: Optional[int] = None,
+    plot_type: str = "distance",
+    plot_mode: str = "facet",
+    save_path: Optional[str] = None,
+) -> None:
+    """
+    Plots a comparison of data for different activity types. It can plot either distance
+    or time data, and can plot the data in either facet or aggregate mode (facet mode
+    creates a separate plot for each activity type)
+
+    Parameters
+    ----------
+    legs_acbm : pd.DataFrame
+        DataFrame containing the ACBM data.
+    legs_nts : pd.DataFrame
+        DataFrame containing the NTS data.
+    activity_column : str, optional
+        The column name for the activity types. Default is 'dact'.
+    value_column : str, optional
+        The column name for the values to be plotted. Default is 'distance'.
+    bin_size : int, optional
+        The size of the bins for rounding values. Default is None.
+    num_cols : int, optional
+        The number of columns for the subplots. Default is 4.
+    max_y_value : int, optional
+        The maximum value for the y-axis. Default is None.
+    figsize : tuple of int, optional
+        The size of the figure. Default is (20, 5).
+    value_threshold : int, optional
+        The maximum value for the x-axis. Default is None.
+    plot_type : str, optional
+        The type of plot ('distance' or 'time'). Default is 'distance'.
+    plot_mode : str, optional
+        The mode of plot ('facet' or 'aggregate'). Default is 'facet'.
+    save_path : str, optional
+        The file path to save the plot. Default is None.
+
+    Returns
+    -------
+    None
+        This function generates and displays a plot but does not return any value.
+    """
+    if plot_type not in ["distance", "time"]:
+        msg = "plot_type must be either 'distance' or 'time'"
+        raise ValueError(msg)
+
+    if plot_mode not in ["facet", "aggregate"]:
+        msg = "plot_mode must be either 'facet' or 'aggregate'"
+        raise ValueError(msg)
+
+    if plot_type == "distance" and bin_size is None:
+        msg = "bin_size must be provided when plot_type is 'distance'"
+        raise ValueError(msg)
+
+    if plot_type == "distance":
+        # Create binned column for distance
+        legs_acbm["value_binned"] = (
+            legs_acbm[value_column] / bin_size
+        ).round() * bin_size
+        legs_nts["value_binned"] = (
+            legs_nts[value_column] / bin_size
+        ).round() * bin_size
+
+        # Define the bins
+        max_value_data = max(
+            legs_acbm["value_binned"].max(), legs_nts["value_binned"].max()
+        )
+        if value_threshold is not None:
+            max_value = min(value_threshold, max_value_data)
+        else:
+            max_value = max_value_data
+        bins = range(0, int(max_value) + bin_size, bin_size)
+    else:
+        legs_acbm["value_binned"] = legs_acbm[value_column]
+        legs_nts["value_binned"] = legs_nts[value_column]
+        bins = range(25)  # For time of day, we use 24 bins (0-24 hours)
+
+    if plot_mode == "facet":
+        # Get unique activity values
+        unique_activity_values = legs_acbm[activity_column].unique()
+
+        # Create a figure with subplots
+        num_plots = len(unique_activity_values)
+        num_rows = (num_plots + num_cols - 1) // num_cols
+        fig, axes = plt.subplots(
+            num_rows, num_cols, figsize=(figsize[0], num_rows * figsize[1]), sharey=True
+        )
+        axes = axes.flatten()
+
+        # Iterate over unique activity values and create plots
+        for i, activity_value in enumerate(unique_activity_values):
+            ax = axes[i]
+            acbm_data = legs_acbm[legs_acbm[activity_column] == activity_value]
+            nts_data = legs_nts[legs_nts[activity_column] == activity_value]
+
+            # Plot histogram for acbm_data
+            sns.histplot(
+                acbm_data["value_binned"],
+                bins=bins,
+                kde=False,
+                discrete=True,
+                stat="percent",
+                ax=ax,
+                label="ACBM",
+            )
+            ax.set_title(f"{activity_column}: {activity_value}", fontsize=16)
+            ax.tick_params(
+                axis="x", rotation=45, labelsize=12
+            )  # Rotate x-axis labels by 45 degrees
+
+            # Set x-axis limits based on max_value
+            if plot_type == "distance":
+                ax.set_xlim(0, max_value)
+            else:
+                ax.set_xlim(0, 24)  # For time of day, x-axis is 0-24
+
+            # Set y-axis limits based on max_y_value
+            if max_y_value:
+                ax.set_ylim(0, max_y_value)
+
+            # Add x-axis ticks and labels for each bar
+            if plot_type == "distance":
+                ax.set_xticks(bins)
+                ax.set_xticklabels([str(bin) for bin in bins], rotation=45, ha="right")
+            else:
+                ax.set_xticks(
+                    range(25)
+                )  # For time of day, set x-ticks to represent each hour of the day
+
+            # Remove individual subplot labels
+            ax.set_xlabel("")
+            ax.set_ylabel("")  # Remove y-axis label for individual plots
+
+            # Add dots to represent the percentage values of legs_nts["value_binned"]
+            nts_value_counts = (
+                nts_data["value_binned"].value_counts(normalize=True).sort_index() * 100
+            )
+            if not nts_value_counts.empty:
+                bin_centers = nts_value_counts.index
+                ax.plot(
+                    bin_centers, nts_value_counts.values, "ro", label="NTS"
+                )  # 'ro' means red dot
+
+        # Remove any empty subplots
+        for j in range(i + 1, len(axes)):
+            fig.delaxes(axes[j])
+
+        # Add a main legend at the bottom
+        handles, labels = ax.get_legend_handles_labels()
+        fig.legend(
+            handles, labels, loc="lower center", ncol=2, bbox_to_anchor=(0.5, -0.05)
+        )
+
+        # Add a main title to the figure
+        if plot_type == "distance":
+            fig.suptitle(
+                "Comparison of Trip Distance for Different Activity Types", fontsize=20
+            )
+            fig.text(0.5, 0.02, "Distance (km)", ha="center", fontsize=14)
+        else:
+            fig.suptitle(
+                "Comparison of Trip Start Time for Different activity Types",
+                fontsize=20,
+            )
+            fig.text(0.5, 0.02, "Hour of Day", ha="center", fontsize=14)
+
+        # Add a single centered y-label
+        fig.text(
+            0.02,
+            0.5,
+            "Percentage of Trips",
+            va="center",
+            rotation="vertical",
+            fontsize=14,
+        )
+
+        # Adjust layout to make room for the main title and labels
+        plt.tight_layout(rect=[0.03, 0.05, 1, 0.95])
+
+    elif plot_mode == "aggregate":
+        fig, ax = plt.subplots(figsize=figsize)
+
+        # Plot histogram for acbm_data
+        sns.histplot(
+            legs_acbm["value_binned"],
+            bins=bins,
+            kde=False,
+            discrete=True,
+            stat="percent",
+            ax=ax,
+            label="ACBM",
+        )
+        # ax.set_title('Aggregate Comparison', fontsize=16)
+        ax.tick_params(
+            axis="x", rotation=45, labelsize=12
+        )  # Rotate x-axis labels by 45 degrees
+
+        # Set x-axis limits based on max_value
+        if plot_type == "distance":
+            ax.set_xlim(0, max_value)
+            ax.set_xlabel("Distance (km)")
+        else:
+            ax.set_xlim(0, 24)  # For time of day, x-axis is 0-24
+            ax.set_xlabel("Hour of Day")
+
+        # Set y-axis limits based on max_y_value
+        if max_y_value:
+            ax.set_ylim(0, max_y_value)
+
+        # Add x-axis ticks and labels for each bar
+        if plot_type == "distance":
+            ax.set_xticks(bins)
+            ax.set_xticklabels([str(bin) for bin in bins], rotation=45, ha="right")
+        else:
+            ax.set_xticks(
+                range(25)
+            )  # For time of day, set x-ticks to represent each hour of the day
+
+        # Add dots to represent the percentage values of legs_nts["value_binned"]
+        nts_value_counts = (
+            legs_nts["value_binned"].value_counts(normalize=True).sort_index() * 100
+        )
+        if not nts_value_counts.empty:
+            bin_centers = nts_value_counts.index
+            ax.plot(
+                bin_centers, nts_value_counts.values, "ro", label="NTS"
+            )  # 'ro' means red dot
+
+        # Add a legend
+        handles, labels = ax.get_legend_handles_labels()
+        ax.legend(handles, labels, loc="upper right")
+
+        # Add a main title to the figure
+        if plot_type == "distance":
+            fig.suptitle("Comparison of Trip Distance", fontsize=20)
+        else:
+            fig.suptitle("Comparison of Trip Start Time", fontsize=20)
+
+        # Add a single centered y-label
+        ax.set_ylabel("Percentage of Trips")  # Set y-axis label for aggregate plot
+
+        # Adjust layout to make room for the main title and labels
+        plt.tight_layout(rect=[0.03, 0.05, 1, 0.95])
+
+    # Save the plot if save_path is provided
+    if save_path:
+        plt.savefig(save_path, bbox_inches="tight")
+
+    # Show the plot
+    # plt.show()
+
+
+# ----- Activity sequences
+
+
+def plot_activity_sequence_comparison(
+    sequence_nts: pd.DataFrame,
+    sequence_acbm: pd.DataFrame,
+    activity_mapping: dict,
+    perc_cutoff: float = 0.35,
+    save_path: Optional[str] = None,
+) -> None:
+    """
+    Plots the comparison of activity sequences between NTS and ACBM.
+
+    Parameters
+    ----------
+    sequence_nts : pd.DataFrame
+        DataFrame containing the NTS activity sequences and counts.
+    sequence_acbm : pd.DataFrame
+        DataFrame containing the ACBM activity sequences and counts.
+    activity_mapping : dict
+        Dictionary mapping activity abbreviations to full names.
+    validation_plots_path : Path
+        Path to save the validation plot.
+    perc_cutoff : float, optional
+        Percentage threshold for filtering sequences. Default is 0.35.
+    save_path : str, optional
+        The file path to save the plot. Default is None.
+    plot_name : str, optional
+        Name of the plot file to save. Default is "4_matching_activity_sequences.png".
+
+    Returns
+    -------
+    None
+    """
+    # Join the two dataframes by 'activity_sequence'
+    sequence_nts_acbm = sequence_nts.merge(
+        sequence_acbm, on="activity_sequence", how="inner"
+    ).sort_values(by="count_nts", ascending=False)
+
+    # Get % contribution of each unique activity sequence
+    sequence_nts_acbm["count_nts"] = (
+        sequence_nts_acbm["count_nts"] / sequence_nts_acbm["count_nts"].sum() * 100
+    )
+    sequence_nts_acbm["count_acbm"] = (
+        sequence_nts_acbm["count_acbm"] / sequence_nts_acbm["count_acbm"].sum() * 100
+    )
+
+    # Filter rows where both count columns are bigger than x %
+    sequence_nts_acbm_filtered = sequence_nts_acbm[
+        (sequence_nts_acbm["count_nts"] > perc_cutoff)
+        & (sequence_nts_acbm["count_acbm"] > perc_cutoff)
+    ]
+
+    fig, ax = plt.subplots(figsize=(10, 6))
+
+    sequence_nts_acbm_filtered.plot(
+        x="activity_sequence", y=["count_nts", "count_acbm"], kind="bar", ax=ax
+    )
+
+    plt.ylabel("Percentage of total trips")
+    plt.title("Comparison of Activity Sequences between NTS and ACBM")
+
+    # Add the color legend to the plot
+    plt.legend(["NTS", "ACBM"], loc="upper right")
+    # Generate custom legend
+    legend_labels = [f"{abbr} = {full}" for abbr, full in activity_mapping.items()]
+    custom_legend = " | ".join(legend_labels)
+    # Add the custom legend below the chart
+    plt.figtext(
+        0.5, -0.2, custom_legend, wrap=True, horizontalalignment="center", fontsize=12
+    )
+
+    # Ensure tight layout
+    plt.tight_layout()
+
+    # Save the plot
+    if save_path:
+        plt.savefig(save_path, bbox_inches="tight")
+
+    # Optionally, show the plot
+    # plt.show()
+
+
+# ----- Intrazonal trips
+
+
+def _calculate_intrazonal_counts(
+    legs_acbm: pd.DataFrame, group_by_columns: list
+) -> pd.DataFrame:
+    """
+    Calculate total and intrazonal counts and merge them.
+
+    Parameters
+    ----------
+    legs_acbm : pd.DataFrame
+        DataFrame containing the ACBM data.
+    group_by_columns : list
+        List of columns to group by.
+
+    Returns
+    -------
+    pd.DataFrame
+        DataFrame with total and intrazonal counts and percentages.
+    """
+    legs_acbm["intrazonal"] = legs_acbm["ozone"] == legs_acbm["dzone"]
+
+    # Total number of trips per group
+    total_counts = (
+        legs_acbm.groupby(group_by_columns).size().reset_index(name="total_count")
+    )
+
+    # Filter the DataFrame to include only rows where intrazonal_trips is TRUE
+    intrazonal_trips_true = legs_acbm[legs_acbm["intrazonal"]]
+
+    # Total number of intrazonal trips per group
+    intrazonal_counts = (
+        intrazonal_trips_true.groupby(group_by_columns)
+        .size()
+        .reset_index(name="intrazonal_count")
+    )
+
+    # Merge the two DataFrames and calculate intrazonal %
+    merged_counts = pd.merge(
+        total_counts, intrazonal_counts, on=group_by_columns, how="left"
+    )
+    # Fill NaN values with 0 (in case there are groups with no intrazonal trips)
+    merged_counts["intrazonal_count"] = merged_counts["intrazonal_count"].fillna(0)
+    # Calculate the percentage of intrazonal trips
+    merged_counts["percentage"] = (
+        merged_counts["intrazonal_count"] / merged_counts["total_count"]
+    ) * 100
+
+    return merged_counts
+
+
+def _plot_intrazonal_counts(
+    merged_counts: pd.DataFrame,
+    x_column: str,
+    hue_column: Optional[str],
+    title: str,
+    xlabel: str,
+    ylabel: str,
+    save_path: Optional[Path] = None,
+    plot_name: Optional[str] = None,
+) -> None:
+    """
+    Plot the counts and optionally save the plot.
+
+    Parameters
+    ----------
+    merged_counts : pd.DataFrame
+        DataFrame with merged counts and percentages.
+    x_column : str
+        Column name for the x-axis.
+    hue_column : Optional[str]
+        Column name for the hue (optional).
+    title : str
+        Title of the plot.
+    xlabel : str
+        Label for the x-axis.
+    ylabel : str
+        Label for the y-axis.
+    save_path : Optional[Path]
+        Path to save the validation plot (optional).
+    plot_name : str
+        Name of the plot file to save.
+
+    Returns
+    -------
+    None
+    """
+    # Create the bar plot
+    plt.figure(figsize=(10, 6))
+    if hue_column:
+        barplot = sns.barplot(
+            data=merged_counts,
+            x=x_column,
+            y="percentage",
+            hue=hue_column,
+            palette="viridis",
+        )
+    else:
+        barplot = sns.barplot(
+            data=merged_counts,
+            x=x_column,
+            y="percentage",
+            hue=x_column,
+            palette="viridis",
+            legend=False,
+        )
+
+    plt.title(title)
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.xticks(rotation=45 if x_column == "purp" else 60)
+
+    # Add text annotations above each bar
+    for bar, row in zip(barplot.patches, merged_counts.iterrows()):
+        barplot.text(
+            bar.get_x() + bar.get_width() / 2,
+            bar.get_height() + 0.2,
+            f"({int(row[1]['total_count'])})",
+            color="black",
+            ha="center",
+        )
+
+    # Remove the top and right spines (box frame) from the plot
+    barplot.spines["top"].set_visible(False)
+    barplot.spines["right"].set_visible(False)
+
+    # Add footnote on the right below the plot
+    plt.figtext(0.95, -0.02, "(Total number of trips)", ha="right", fontsize=10)
+
+    plt.tight_layout()
+    # plt.show()
+
+    # Save the plot if save_path is provided
+    if save_path:
+        plt.savefig(save_path / plot_name)
+
+
+def plot_intrazonal_trips(
+    legs_acbm: pd.DataFrame,
+    validation_plots_path: Optional[Path] = None,
+    plot_type: str = "od",
+    plot_name: Optional[str] = None,
+) -> None:
+    """
+    Plots the percentage of intrazonal trips per purpose or origin-destination pair.
+
+    Parameters
+    ----------
+    legs_acbm : pd.DataFrame
+        DataFrame containing the ACBM data.
+    validation_plots_path : Optional[Path]
+        Path to save the validation plot (optional).
+    plot_type : str, optional
+        The type of plot ('od' or 'purp'). Default is 'od'.
+
+    Returns
+    -------
+    None
+        This function generates and displays a plot but does not return any value.
+    """
+    if plot_type not in ["od", "purp"]:
+        msg = "plot_type must be either 'od' or 'purp'"
+        raise ValueError(msg)
+
+    # Set default plot name based on plot_type if not provided
+    if plot_name is None:
+        plot_name = f"assigning_intrazonal_activities_{plot_type}.png"
+
+    if plot_type == "od":
+        # Add the trip_type column
+        conditions_primary = (
+            (legs_acbm["oact"] == "home")
+            & (legs_acbm["dact"].isin(["work", "education"]))
+        ) | (
+            (legs_acbm["oact"].isin(["work", "education"]))
+            & (legs_acbm["dact"] == "home")
+        )
+        legs_acbm["trip_type"] = np.where(conditions_primary, "primary", "secondary")
+
+        # Create an od column to identify the origin-destination pairs
+        legs_acbm["od"] = legs_acbm["oact"] + " - " + legs_acbm["dact"]
+
+        # Calculate counts
+        merged_counts = _calculate_intrazonal_counts(legs_acbm, ["od", "trip_type"])
+        # Keep top 15 od pairs
+        merged_counts = merged_counts.sort_values(
+            by="total_count", ascending=False
+        ).head(15)
+        # Sort by percentage before plotting
+        merged_counts = merged_counts.sort_values(by="percentage", ascending=False)
+
+        # Plot counts
+        _plot_intrazonal_counts(
+            merged_counts,
+            "od",
+            "trip_type",
+            "Percentage of Intrazonal Trips per Purpose",
+            "Purpose",
+            "Percentage of Trips that are Intrazonal",
+            validation_plots_path,
+            plot_name,
+        )
+
+    elif plot_type == "purp":
+        # Calculate counts
+        merged_counts = _calculate_intrazonal_counts(legs_acbm, ["purp"])
+        # Sort by percentage before plotting
+        merged_counts = merged_counts.sort_values(by="percentage", ascending=False)
+
+        # Plot counts
+        _plot_intrazonal_counts(
+            merged_counts,
+            "purp",
+            None,
+            "Percentage of Intrazonal Trips per Purpose",
+            "Purpose",
+            "Percentage of Trips that are Intrazonal",
+            validation_plots_path,
+            plot_name,
+        )
diff --git a/src/acbm/validating/utils.py b/src/acbm/validating/utils.py
new file mode 100644
index 0000000..73a46fd
--- /dev/null
+++ b/src/acbm/validating/utils.py
@@ -0,0 +1,125 @@
+import geopandas as gpd
+import pandas as pd
+from shapely import wkt
+
+
+def process_sequences(
+    df: pd.DataFrame,
+    pid_col: str,
+    seq_col: str,
+    origin_activity_col: str,
+    destination_activity_col: str,
+    suffix: str,
+) -> pd.DataFrame:
+    """
+    Processes a DataFrame to generate activity sequences and counts the number of
+    occurrences of each sequence.
+
+
+    Parameters
+    ----------
+    df: pd.DataFrame
+        The input DataFrame containing the data.
+    pid_col: str
+        The name of the column representing the unique identifier for each group.
+    seq_col: str
+        The name of the column representing the sequence order within each group.
+    origin_activity_col: str
+        The name of the column representing the origin activity.
+    destination_activity_col: str
+        The name of the column representing the destination activity.
+    suffix: str
+        The suffix to be added to the count column name.
+
+    Returns
+    -------
+    pd.DataFrame
+        A DataFrame with the activity sequences and their counts.
+
+        activity_sequence           count_{suffix}
+        ----------------------      --------------
+        home - work - visit - home              5
+        home - school - home                    3
+        home - work - home                     20
+    """
+    # Step 1: Sort the DataFrame by 'pid' and 'seq'
+    sorted_df = df.sort_values(by=[pid_col, seq_col])
+
+    # Step 2: Group by 'pid' and concatenate 'origin activity' values followed by the
+    # last 'destination activity' value
+    activity_sequence_df = (
+        sorted_df.groupby(pid_col)
+        .apply(
+            lambda x: " - ".join(
+                [*x[origin_activity_col], x[destination_activity_col].iloc[-1]]
+            )
+        )
+        .reset_index()
+    )
+
+    # Rename the columns for clarity
+    activity_sequence_df.columns = [pid_col, "activity_sequence"]
+
+    # Step 3: Group by the resulting 'activity_sequence' column and count the number of
+    # values in each group
+    return (
+        activity_sequence_df.groupby("activity_sequence")
+        .size()
+        .reset_index(name=f"count_{suffix}")
+    )
+
+
+# TODO: add crs to config, and check other scripts
+def calculate_od_distances(
+    df: pd.DataFrame,
+    start_wkt_col: str,
+    end_wkt_col: str,
+    crs_epsg: int = 4326,
+    projected_epsg: int = 3857,
+) -> pd.DataFrame:
+    """
+    Calculate distances between start and end geometries in a DataFrame.
+
+    Parameters
+    ----------
+
+    df: pd.DataFrame
+        DataFrame containing WKT geometry columns.
+    start_wkt_col: str
+        Column name for start location WKT geometries.
+    end_wkt_col: str
+        Column name for end location WKT geometries.
+    crs_epsg: int
+        EPSG code for the original CRS (default is 4326 for WGS84).
+    projected_epsg: int
+        EPSG code for the projected CRS (default is 3857).
+
+    Returns
+    -------
+    pd.DataFrame
+        DataFrame with an additional 'distance' column containing distances in meters.
+    """
+    # Convert WKT strings to shapely geometries
+    df["start_geometry"] = df[start_wkt_col].apply(wkt.loads)
+    df["end_geometry"] = df[end_wkt_col].apply(wkt.loads)
+
+    # Create GeoDataFrame
+    gdf = gpd.GeoDataFrame(df, geometry="start_geometry")
+
+    # Set the original CRS
+    gdf.set_crs(epsg=crs_epsg, inplace=True)
+
+    # Create a separate GeoDataFrame for the end geometries
+    end_gdf = gdf.set_geometry("end_geometry")
+
+    # Set the original CRS for the end_gdf
+    end_gdf.set_crs(epsg=crs_epsg, inplace=True)
+
+    # Transform both GeoDataFrames to a projected CRS
+    gdf = gdf.to_crs(epsg=projected_epsg)
+    end_gdf = end_gdf.to_crs(epsg=projected_epsg)
+
+    # Calculate the distance between start and end geometries (in km)
+    gdf["distance"] = round(gdf.geometry.distance(end_gdf.geometry) / 1000, 1)
+
+    return gdf