From ccec0df1310d5eab6f41f7dbebaa7c66c879b1eb Mon Sep 17 00:00:00 2001
From: Hussein Mahfouz <45176416+Hussein-Mahfouz@users.noreply.github.com>
Date: Thu, 19 Sep 2024 16:20:03 +0100
Subject: [PATCH 01/11] save cleaned nts for validation

---
 scripts/2_match_households_and_individuals.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/scripts/2_match_households_and_individuals.py b/scripts/2_match_households_and_individuals.py
index 84361fd..9183b97 100644
--- a/scripts/2_match_households_and_individuals.py
+++ b/scripts/2_match_households_and_individuals.py
@@ -1116,3 +1116,13 @@ 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")

From 5f0d653ae93f40edd646850d9347c3c51eb6b27c Mon Sep 17 00:00:00 2001
From: Hussein Mahfouz <45176416+Hussein-Mahfouz@users.noreply.github.com>
Date: Thu, 19 Sep 2024 16:20:19 +0100
Subject: [PATCH 02/11] validation notebook

---
 notebooks/5_validate_outputs.ipynb | 1382 ++++++++++++++++++++++++++++
 1 file changed, 1382 insertions(+)
 create mode 100644 notebooks/5_validate_outputs.ipynb

diff --git a/notebooks/5_validate_outputs.ipynb b/notebooks/5_validate_outputs.ipynb
new file mode 100644
index 0000000..0e887b3
--- /dev/null
+++ b/notebooks/5_validate_outputs.ipynb
@@ -0,0 +1,1382 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import acbm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Read in the data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "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": 41,
+   "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>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",
+       "      <td>199</td>\n",
+       "      <td>89</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00059031</td>\n",
+       "      <td>E00058720</td>\n",
+       "      <td>other</td>\n",
+       "      <td>home</td>\n",
+       "      <td>other</td>\n",
+       "      <td>walk</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1900-01-01 10:00:00</td>\n",
+       "      <td>1900-01-01 10:45:00</td>\n",
+       "      <td>0:45:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>199</td>\n",
+       "      <td>89</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00058720</td>\n",
+       "      <td>E00059031</td>\n",
+       "      <td>home</td>\n",
+       "      <td>other</td>\n",
+       "      <td>home</td>\n",
+       "      <td>walk</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1900-01-01 10:45:00</td>\n",
+       "      <td>1900-01-01 11:30:00</td>\n",
+       "      <td>0:45:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>199</td>\n",
+       "      <td>89</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00059031</td>\n",
+       "      <td>E00057312</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 12:00:00</td>\n",
+       "      <td>1900-01-01 12:10:00</td>\n",
+       "      <td>0:10:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>199</td>\n",
+       "      <td>89</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00057312</td>\n",
+       "      <td>E00059031</td>\n",
+       "      <td>home</td>\n",
+       "      <td>shop</td>\n",
+       "      <td>home</td>\n",
+       "      <td>car</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1900-01-01 12:45:00</td>\n",
+       "      <td>1900-01-01 12:55:00</td>\n",
+       "      <td>0:10:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>199</td>\n",
+       "      <td>89</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00059031</td>\n",
+       "      <td>E00058564</td>\n",
+       "      <td>work</td>\n",
+       "      <td>home</td>\n",
+       "      <td>work</td>\n",
+       "      <td>car</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1900-01-01 13:30:00</td>\n",
+       "      <td>1900-01-01 13:50:00</td>\n",
+       "      <td>0:20:00</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>14970</th>\n",
+       "      <td>14970</td>\n",
+       "      <td>794026</td>\n",
+       "      <td>334502</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00056891</td>\n",
+       "      <td>E00057826</td>\n",
+       "      <td>home</td>\n",
+       "      <td>escort</td>\n",
+       "      <td>home</td>\n",
+       "      <td>car</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1900-01-01 20:45:00</td>\n",
+       "      <td>1900-01-01 21:05:00</td>\n",
+       "      <td>0:20:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14971</th>\n",
+       "      <td>14971</td>\n",
+       "      <td>794027</td>\n",
+       "      <td>334502</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00057826</td>\n",
+       "      <td>E00057536</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 07:40:00</td>\n",
+       "      <td>1900-01-01 07:55:00</td>\n",
+       "      <td>0:15:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14972</th>\n",
+       "      <td>14972</td>\n",
+       "      <td>794027</td>\n",
+       "      <td>334502</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00057536</td>\n",
+       "      <td>E00057826</td>\n",
+       "      <td>home</td>\n",
+       "      <td>escort</td>\n",
+       "      <td>home</td>\n",
+       "      <td>pt</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1900-01-01 15:00:00</td>\n",
+       "      <td>1900-01-01 15:55:00</td>\n",
+       "      <td>0:55:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14973</th>\n",
+       "      <td>14973</td>\n",
+       "      <td>794027</td>\n",
+       "      <td>334502</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00057826</td>\n",
+       "      <td>E00056882</td>\n",
+       "      <td>other</td>\n",
+       "      <td>home</td>\n",
+       "      <td>other</td>\n",
+       "      <td>pt</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1900-01-01 18:35:00</td>\n",
+       "      <td>1900-01-01 19:35:00</td>\n",
+       "      <td>1:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14974</th>\n",
+       "      <td>14974</td>\n",
+       "      <td>794027</td>\n",
+       "      <td>334502</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00056882</td>\n",
+       "      <td>E00057826</td>\n",
+       "      <td>home</td>\n",
+       "      <td>other</td>\n",
+       "      <td>home</td>\n",
+       "      <td>car</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1900-01-01 20:45:00</td>\n",
+       "      <td>1900-01-01 21:05:00</td>\n",
+       "      <td>0:20:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>14975 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Unnamed: 0     pid     hid  freq      ozone      dzone    purp  \\\n",
+       "0               0     199      89   NaN  E00059031  E00058720   other   \n",
+       "1               1     199      89   NaN  E00058720  E00059031    home   \n",
+       "2               2     199      89   NaN  E00059031  E00057312    shop   \n",
+       "3               3     199      89   NaN  E00057312  E00059031    home   \n",
+       "4               4     199      89   NaN  E00059031  E00058564    work   \n",
+       "...           ...     ...     ...   ...        ...        ...     ...   \n",
+       "14970       14970  794026  334502   NaN  E00056891  E00057826    home   \n",
+       "14971       14971  794027  334502   NaN  E00057826  E00057536  escort   \n",
+       "14972       14972  794027  334502   NaN  E00057536  E00057826    home   \n",
+       "14973       14973  794027  334502   NaN  E00057826  E00056882   other   \n",
+       "14974       14974  794027  334502   NaN  E00056882  E00057826    home   \n",
+       "\n",
+       "      origin activity destination activity  mode  seq                  tst  \\\n",
+       "0                home                other  walk  1.0  1900-01-01 10:00:00   \n",
+       "1               other                 home  walk  2.0  1900-01-01 10:45:00   \n",
+       "2                home                 shop   car  3.0  1900-01-01 12:00:00   \n",
+       "3                shop                 home   car  4.0  1900-01-01 12:45:00   \n",
+       "4                home                 work   car  5.0  1900-01-01 13:30:00   \n",
+       "...               ...                  ...   ...  ...                  ...   \n",
+       "14970          escort                 home   car  4.0  1900-01-01 20:45:00   \n",
+       "14971            home               escort   car  1.0  1900-01-01 07:40:00   \n",
+       "14972          escort                 home    pt  2.0  1900-01-01 15:00:00   \n",
+       "14973            home                other    pt  3.0  1900-01-01 18:35:00   \n",
+       "14974           other                 home   car  4.0  1900-01-01 20:45:00   \n",
+       "\n",
+       "                       tet duration  \n",
+       "0      1900-01-01 10:45:00  0:45:00  \n",
+       "1      1900-01-01 11:30:00  0:45:00  \n",
+       "2      1900-01-01 12:10:00  0:10:00  \n",
+       "3      1900-01-01 12:55:00  0:10:00  \n",
+       "4      1900-01-01 13:50:00  0:20:00  \n",
+       "...                    ...      ...  \n",
+       "14970  1900-01-01 21:05:00  0:20:00  \n",
+       "14971  1900-01-01 07:55:00  0:15:00  \n",
+       "14972  1900-01-01 15:55:00  0:55:00  \n",
+       "14973  1900-01-01 19:35:00  1:00:00  \n",
+       "14974  1900-01-01 21:05:00  0:20:00  \n",
+       "\n",
+       "[14975 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "legs_acbm\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "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",
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+       "\n",
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+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>TripID</th>\n",
+       "      <th>DayID</th>\n",
+       "      <th>IndividualID</th>\n",
+       "      <th>HouseholdID</th>\n",
+       "      <th>PSUID</th>\n",
+       "      <th>PersNo</th>\n",
+       "      <th>TravDay</th>\n",
+       "      <th>seq</th>\n",
+       "      <th>ShortWalkTrip_B01ID</th>\n",
+       "      <th>NumStages</th>\n",
+       "      <th>...</th>\n",
+       "      <th>tst</th>\n",
+       "      <th>tet</th>\n",
+       "      <th>TripDisIncSW</th>\n",
+       "      <th>TripDisExSW</th>\n",
+       "      <th>TripTotalTime</th>\n",
+       "      <th>TripTravTime</th>\n",
+       "      <th>ozone</th>\n",
+       "      <th>dzone</th>\n",
+       "      <th>W5</th>\n",
+       "      <th>W5xHH</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
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+       "      <td>2019009707</td>\n",
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+       "      <td>7</td>\n",
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+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>810.0</td>\n",
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+       "      <td>30.0</td>\n",
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+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
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+       "      <td>1060.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.004688</td>\n",
+       "      <td>1.328628</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4659585</th>\n",
+       "      <td>2019009711</td>\n",
+       "      <td>2019004512</td>\n",
+       "      <td>2019000703</td>\n",
+       "      <td>2019000291</td>\n",
+       "      <td>2019000032</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1200.0</td>\n",
+       "      <td>1230.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.767622</td>\n",
+       "      <td>1.015126</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4659586</th>\n",
+       "      <td>2019010362</td>\n",
+       "      <td>2019004797</td>\n",
+       "      <td>2019000744</td>\n",
+       "      <td>2019000306</td>\n",
+       "      <td>2019000033</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>990.0</td>\n",
+       "      <td>995.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.048438</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4659587</th>\n",
+       "      <td>2019010377</td>\n",
+       "      <td>2019004802</td>\n",
+       "      <td>2019000744</td>\n",
+       "      <td>2019000306</td>\n",
+       "      <td>2019000033</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>925.0</td>\n",
+       "      <td>958.0</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>33</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.123451</td>\n",
+       "      <td>1.071548</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",
+       "      <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>5150103</th>\n",
+       "      <td>2022111203</td>\n",
+       "      <td>2022059534</td>\n",
+       "      <td>2022009864</td>\n",
+       "      <td>2022004368</td>\n",
+       "      <td>2022000756</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>965.0</td>\n",
+       "      <td>982.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>17</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>1.406813</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5150104</th>\n",
+       "      <td>2022111204</td>\n",
+       "      <td>2022059535</td>\n",
+       "      <td>2022009864</td>\n",
+       "      <td>2022004368</td>\n",
+       "      <td>2022000756</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>800.0</td>\n",
+       "      <td>804.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>1.613264</td>\n",
+       "      <td>1.146751</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5150105</th>\n",
+       "      <td>2022111205</td>\n",
+       "      <td>2022059535</td>\n",
+       "      <td>2022009864</td>\n",
+       "      <td>2022004368</td>\n",
+       "      <td>2022000756</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>810.0</td>\n",
+       "      <td>828.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>1.613264</td>\n",
+       "      <td>1.146751</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5150106</th>\n",
+       "      <td>2022111206</td>\n",
+       "      <td>2022059535</td>\n",
+       "      <td>2022009864</td>\n",
+       "      <td>2022004368</td>\n",
+       "      <td>2022000756</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>960.0</td>\n",
+       "      <td>985.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>1.512388</td>\n",
+       "      <td>1.075046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5150107</th>\n",
+       "      <td>2022111207</td>\n",
+       "      <td>2022059535</td>\n",
+       "      <td>2022009864</td>\n",
+       "      <td>2022004368</td>\n",
+       "      <td>2022000756</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1110.0</td>\n",
+       "      <td>1115.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>1.512388</td>\n",
+       "      <td>1.075046</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>420180 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             TripID       DayID  IndividualID  HouseholdID       PSUID  \\\n",
+       "4659583  2019009707  2019004508    2019000702   2019000291  2019000032   \n",
+       "4659584  2019009709  2019004508    2019000702   2019000291  2019000032   \n",
+       "4659585  2019009711  2019004512    2019000703   2019000291  2019000032   \n",
+       "4659586  2019010362  2019004797    2019000744   2019000306  2019000033   \n",
+       "4659587  2019010377  2019004802    2019000744   2019000306  2019000033   \n",
+       "...             ...         ...           ...          ...         ...   \n",
+       "5150103  2022111203  2022059534    2022009864   2022004368  2022000756   \n",
+       "5150104  2022111204  2022059535    2022009864   2022004368  2022000756   \n",
+       "5150105  2022111205  2022059535    2022009864   2022004368  2022000756   \n",
+       "5150106  2022111206  2022059535    2022009864   2022004368  2022000756   \n",
+       "5150107  2022111207  2022059535    2022009864   2022004368  2022000756   \n",
+       "\n",
+       "         PersNo  TravDay  seq  ShortWalkTrip_B01ID  NumStages  ...     tst  \\\n",
+       "4659583       2        7    1                    2          1  ...   810.0   \n",
+       "4659584       2        7    3                    2          1  ...  1050.0   \n",
+       "4659585       3        4    2                    2          1  ...  1200.0   \n",
+       "4659586       2        2    3                    2          1  ...   990.0   \n",
+       "4659587       2        7    2                    2          1  ...   925.0   \n",
+       "...         ...      ...  ...                  ...        ...  ...     ...   \n",
+       "5150103       1        6    2                    2          1  ...   965.0   \n",
+       "5150104       1        7    1                    2          1  ...   800.0   \n",
+       "5150105       1        7    2                    2          1  ...   810.0   \n",
+       "5150106       1        7    3                    2          1  ...   960.0   \n",
+       "5150107       1        7    4                    2          1  ...  1110.0   \n",
+       "\n",
+       "            tet TripDisIncSW TripDisExSW  TripTotalTime  TripTravTime  ozone  \\\n",
+       "4659583   840.0          4.0         4.0             30          30.0      7   \n",
+       "4659584  1060.0          1.0         1.0             10          10.0      7   \n",
+       "4659585  1230.0          3.0         3.0             30          30.0      7   \n",
+       "4659586   995.0          2.0         2.0              5           5.0      2   \n",
+       "4659587   958.0          6.9         6.9             33          33.0      2   \n",
+       "...         ...          ...         ...            ...           ...    ...   \n",
+       "5150103   982.0          6.0         6.0             17          17.0      9   \n",
+       "5150104   804.0          0.5         0.5              4           4.0      9   \n",
+       "5150105   828.0          3.0         3.0             18          18.0      9   \n",
+       "5150106   985.0          3.0         3.0             25          25.0      9   \n",
+       "5150107  1115.0          0.5         0.5              5           5.0      9   \n",
+       "\n",
+       "         dzone        W5     W5xHH  \n",
+       "4659583    7.0  0.756184  1.000000  \n",
+       "4659584    7.0  1.004688  1.328628  \n",
+       "4659585    7.0  0.767622  1.015126  \n",
+       "4659586    2.0  1.048438  1.000000  \n",
+       "4659587    2.0  1.123451  1.071548  \n",
+       "...        ...       ...       ...  \n",
+       "5150103    9.0  1.406813  1.000000  \n",
+       "5150104    9.0  1.613264  1.146751  \n",
+       "5150105    9.0  1.613264  1.146751  \n",
+       "5150106    9.0  1.512388  1.075046  \n",
+       "5150107    9.0  1.512388  1.075046  \n",
+       "\n",
+       "[420180 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "legs_nts"
+   ]
+  },
+  {
+   "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": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matching: Trip Mode "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matching: Time of Day "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matching: Activity Sequences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "def process_sequences(df, \n",
+    "                      pid_col, \n",
+    "                      seq_col, \n",
+    "                      origin_activity_col, \n",
+    "                      destination_activity_col,\n",
+    "                      suffix):\n",
+    "    # Step 1: Sort the DataFrame by 'pid' and 'seq'\n",
+    "    sorted_df = df.sort_values(by=[pid_col, seq_col])\n",
+    "    \n",
+    "    # Step 2: Group by 'pid' and concatenate 'origin activity' values followed by the last 'destination activity' value\n",
+    "    activity_sequence_df = sorted_df.groupby(pid_col).apply(\n",
+    "        lambda x: ' - '.join(x[origin_activity_col].tolist() + [x[destination_activity_col].iloc[-1]])\n",
+    "    ).reset_index()\n",
+    "    \n",
+    "    # Rename the columns for clarity\n",
+    "    activity_sequence_df.columns = [pid_col, 'activity_sequence']\n",
+    "    \n",
+    "    # Step 3: Group by the resulting 'activity_sequence' column and count the number of values in each group\n",
+    "    result = activity_sequence_df.groupby('activity_sequence').size().reset_index(name=f'count_{suffix}')    \n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_652845/537327160.py:13: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "  activity_sequence_df = sorted_df.groupby(pid_col).apply(\n"
+     ]
+    },
+    {
+     "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>education - education - education</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>education - education - education - home</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>education - education - education - home - oth...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>education - escort</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>education - escort - home</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1818</th>\n",
+       "      <td>work - work - work - home - other - home - oth...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1819</th>\n",
+       "      <td>work - work - work - home - work - work - home...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1820</th>\n",
+       "      <td>work - work - work - shop - work</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1821</th>\n",
+       "      <td>work - work - work - work - home - home - home...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1822</th>\n",
+       "      <td>work - work - work - work - work - home</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                     education - education - education          4\n",
+       "1              education - education - education - home          1\n",
+       "2     education - education - education - home - oth...          1\n",
+       "3                                    education - escort          1\n",
+       "4                             education - escort - home          2\n",
+       "...                                                 ...        ...\n",
+       "1818  work - work - work - home - other - home - oth...          1\n",
+       "1819  work - work - work - home - work - work - home...          1\n",
+       "1820                   work - work - work - shop - work          1\n",
+       "1821  work - work - work - work - home - home - home...          1\n",
+       "1822            work - work - work - work - work - home          1\n",
+       "\n",
+       "[1823 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sequence_nts = process_sequences(df = legs_nts, \n",
+    "                                 pid_col = 'IndividualID', \n",
+    "                                 seq_col = 'seq', \n",
+    "                                 origin_activity_col = 'oact', \n",
+    "                                 destination_activity_col = 'dact',\n",
+    "                                 suffix = 'nts')\n",
+    "\n",
+    "sequence_nts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "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>seq</th>\n",
+       "      <th>origin activity</th>\n",
+       "      <th>destination activity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>home</td>\n",
+       "      <td>work</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>work</td>\n",
+       "      <td>gym</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>gym</td>\n",
+       "      <td>home</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>home</td>\n",
+       "      <td>school</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>school</td>\n",
+       "      <td>home</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>work</td>\n",
+       "      <td>gym</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   pid  seq origin activity destination activity\n",
+       "0    1    1            home                 work\n",
+       "1    1    2            work                  gym\n",
+       "2    1    3             gym                 home\n",
+       "3    2    1            home               school\n",
+       "4    2    2          school                 home\n",
+       "5    3    1            work                  gym"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "legs_acbm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_652845/537327160.py:13: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "  activity_sequence_df = sorted_df.groupby(pid_col).apply(\n"
+     ]
+    },
+    {
+     "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>home - education</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>home - education - escort - home</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>home - education - escort - shop - home</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>home - education - home</td>\n",
+       "      <td>289</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>home - education - home - education - home</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>452</th>\n",
+       "      <td>home - work - work - work - home - other - home</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>453</th>\n",
+       "      <td>home - work - work - work - home - other - hom...</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>454</th>\n",
+       "      <td>home - work - work - work - home - other - oth...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>455</th>\n",
+       "      <td>home - work - work - work - work - home</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>456</th>\n",
+       "      <td>home - work - work - work - work - home - work...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>457 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     activity_sequence  count_acbm\n",
+       "0                                     home - education          10\n",
+       "1                     home - education - escort - home           9\n",
+       "2              home - education - escort - shop - home           1\n",
+       "3                              home - education - home         289\n",
+       "4           home - education - home - education - home           2\n",
+       "..                                                 ...         ...\n",
+       "452    home - work - work - work - home - other - home           1\n",
+       "453  home - work - work - work - home - other - hom...           2\n",
+       "454  home - work - work - work - home - other - oth...           1\n",
+       "455            home - work - work - work - work - home           1\n",
+       "456  home - work - work - work - work - home - work...           1\n",
+       "\n",
+       "[457 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sequence_acbm = process_sequences(df = legs_acbm,\n",
+    "                                  pid_col = 'pid', \n",
+    "                                  seq_col = 'seq', \n",
+    "                                  origin_activity_col = 'origin activity', \n",
+    "                                  destination_activity_col = 'destination activity', \n",
+    "                                  suffix = 'acbm')\n",
+    "\n",
+    "sequence_acbm\n",
+    "                                  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "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",
+       "      <th>count_acbm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>home - education</td>\n",
+       "      <td>45</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>home - education - escort - home</td>\n",
+       "      <td>21</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>home - education - escort - shop - home</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>home - education - home</td>\n",
+       "      <td>1241</td>\n",
+       "      <td>289</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>home - education - home - education - home</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>433</th>\n",
+       "      <td>home - work - work - work - home - other - home</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
+       "      <td>home - work - work - work - home - other - hom...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>435</th>\n",
+       "      <td>home - work - work - work - home - other - oth...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>436</th>\n",
+       "      <td>home - work - work - work - work - home</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>home - work - work - work - work - home - work...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>438 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     activity_sequence  count_nts  count_acbm\n",
+       "0                                     home - education         45          10\n",
+       "1                     home - education - escort - home         21           9\n",
+       "2              home - education - escort - shop - home          1           1\n",
+       "3                              home - education - home       1241         289\n",
+       "4           home - education - home - education - home          7           2\n",
+       "..                                                 ...        ...         ...\n",
+       "433    home - work - work - work - home - other - home          4           1\n",
+       "434  home - work - work - work - home - other - hom...          1           2\n",
+       "435  home - work - work - work - home - other - oth...          1           1\n",
+       "436            home - work - work - work - work - home          4           1\n",
+       "437  home - work - work - work - work - home - work...          1           1\n",
+       "\n",
+       "[438 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# join the two dataframes by 'activity_sequence'\n",
+    "\n",
+    "# sequence_nts['activity_sequence'] = sequence_nts['activity_sequence'].str.replace(' - ', '-')\n",
+    "\n",
+    "# sequence_acbm['activity_sequence'] = sequence_acbm['activity_sequence'].str.replace(' - ', '-')\n",
+    "\n",
+    "sequence_nts_acbm = sequence_nts.merge(sequence_acbm, on='activity_sequence', how='inner')\n",
+    "sequence_nts_acbm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='activity_sequence'>"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# bar chart of the counts \n",
+    "\n",
+    "#sequence_nts_acbm.plot(kind='bar', x='activity_sequence', y=['count_nts', 'count_acbm'])\n",
+    "\n",
+    "# sort the values by 'count_nts' and plot the top 10 values\n",
+    "\n",
+    "#sequence_nts_acbm.sort_values(by='count_nts', ascending=False).head(10).plot(kind='bar', x='activity_sequence', y=['count_nts', 'count_acbm'])\n",
+    "\n",
+    "# plot % instead of counts\n",
+    "\n",
+    "sequence_nts_acbm['count_nts'] = sequence_nts_acbm['count_nts'] / sequence_nts_acbm['count_nts'].sum() * 100\n",
+    "sequence_nts_acbm['count_acbm'] = sequence_nts_acbm['count_acbm'] / sequence_nts_acbm['count_acbm'].sum() * 100\n",
+    "\n",
+    "# instead of top 10, keep all rows where count_nts or count_acbm > 10\n",
+    "\n",
+    "sequence_nts_acbm_filtered = sequence_nts_acbm[(sequence_nts_acbm['count_nts'] > 1) | (sequence_nts_acbm['count_acbm'] > 1)]\n",
+    "\n",
+    "sequence_nts_acbm_filtered.sort_values(by='count_nts', ascending=False).plot(kind='bar', x='activity_sequence', y=['count_nts', 'count_acbm'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assigning: Trip distances"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Mean crowfly distance per trip purpose"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Crowfly distance cumulative chart (KM distance vs. % of trips)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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
+}

From 4c930a6fc8715959019ad2b4f9bf28639e5d986f Mon Sep 17 00:00:00 2001
From: Hussein Mahfouz <45176416+Hussein-Mahfouz@users.noreply.github.com>
Date: Thu, 19 Sep 2024 18:02:39 +0100
Subject: [PATCH 03/11] validation notebook: activity sequences

---
 notebooks/5_validate_outputs.ipynb | 497 +++++++++++------------------
 1 file changed, 180 insertions(+), 317 deletions(-)

diff --git a/notebooks/5_validate_outputs.ipynb b/notebooks/5_validate_outputs.ipynb
index 0e887b3..b6723f4 100644
--- a/notebooks/5_validate_outputs.ipynb
+++ b/notebooks/5_validate_outputs.ipynb
@@ -753,20 +753,95 @@
     "## Matching: Activity Sequences"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Abbreviate values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Abbreviate the column values \n",
+    "\n",
+    "# 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",
+    "# Replace values in 'origin activity' and 'destination activity' columns\n",
+    "legs_acbm['origin activity'] = legs_acbm['origin activity'].replace(activity_mapping)\n",
+    "legs_acbm['destination activity'] = legs_acbm['destination activity'].replace(activity_mapping)\n",
+    "\n",
+    "# Replace values in 'oact' and 'dact' columns\n",
+    "\n",
+    "legs_nts['oact'] = legs_nts['oact'].replace(activity_mapping)\n",
+    "legs_nts['dact'] = legs_nts['dact'].replace(activity_mapping)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create chains from data and count number of observations of each chain"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
+    "from pandas import DataFrame\n",
     "\n",
-    "def process_sequences(df, \n",
-    "                      pid_col, \n",
-    "                      seq_col, \n",
-    "                      origin_activity_col, \n",
-    "                      destination_activity_col,\n",
-    "                      suffix):\n",
+    "def process_sequences(df: DataFrame, \n",
+    "                      pid_col: str, \n",
+    "                      seq_col: str, \n",
+    "                      origin_activity_col: str, \n",
+    "                      destination_activity_col: str,\n",
+    "                      suffix: str) -> DataFrame:\n",
+    "    \"\"\"\n",
+    "    Processes a DataFrame to generate activity sequences and counts the number of occurrences of each sequence.\n",
+    "\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    df: pd.DataFrame\n",
+    "        The input DataFrame containing the data.\n",
+    "    pid_col: str\n",
+    "        The name of the column representing the unique identifier for each group.\n",
+    "    seq_col: str\n",
+    "        The name of the column representing the sequence order within each group.\n",
+    "    origin_activity_col: str\n",
+    "        The name of the column representing the origin activity.\n",
+    "    destination_activity_col: str\n",
+    "        The name of the column representing the destination activity.\n",
+    "    suffix: str\n",
+    "        The suffix to be added to the count column name.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    pd.DataFrame\n",
+    "        A DataFrame with the activity sequences and their counts.\n",
+    "        \n",
+    "        activity_sequence           count_{suffix}\n",
+    "        ----------------------      --------------\n",
+    "        home - work - visit - home              5\n",
+    "        home - school - home                    3\n",
+    "        home - work - home                     20\n",
+    "    \"\"\"\n",
     "    # Step 1: Sort the DataFrame by 'pid' and 'seq'\n",
     "    sorted_df = df.sort_values(by=[pid_col, seq_col])\n",
     "    \n",
@@ -785,14 +860,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_652845/537327160.py:13: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "/tmp/ipykernel_652845/491085427.py:44: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
       "  activity_sequence_df = sorted_df.groupby(pid_col).apply(\n"
      ]
     },
@@ -824,28 +899,28 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>education - education - education</td>\n",
-       "      <td>4</td>\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>education - education - education - home</td>\n",
+       "      <td>e - e - e - h - o - h</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>education - education - education - home - oth...</td>\n",
+       "      <td>e - e - h</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>education - escort</td>\n",
-       "      <td>1</td>\n",
+       "      <td>e - h</td>\n",
+       "      <td>10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>education - escort - home</td>\n",
-       "      <td>2</td>\n",
+       "      <td>e - h - e</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -854,27 +929,27 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1818</th>\n",
-       "      <td>work - work - work - home - other - home - oth...</td>\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>work - work - work - home - work - work - home...</td>\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>work - work - work - shop - work</td>\n",
+       "      <td>w - w - w - s - w</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1821</th>\n",
-       "      <td>work - work - work - work - home - home - home...</td>\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>work - work - work - work - work - home</td>\n",
+       "      <td>w - w - w - w - w - h</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -883,23 +958,23 @@
        "</div>"
       ],
       "text/plain": [
-       "                                      activity_sequence  count_nts\n",
-       "0                     education - education - education          4\n",
-       "1              education - education - education - home          1\n",
-       "2     education - education - education - home - oth...          1\n",
-       "3                                    education - escort          1\n",
-       "4                             education - escort - home          2\n",
-       "...                                                 ...        ...\n",
-       "1818  work - work - work - home - other - home - oth...          1\n",
-       "1819  work - work - work - home - work - work - home...          1\n",
-       "1820                   work - work - work - shop - work          1\n",
-       "1821  work - work - work - work - home - home - home...          1\n",
-       "1822            work - work - work - work - work - home          1\n",
+       "                              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 - s - 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": 60,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -917,112 +992,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "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>seq</th>\n",
-       "      <th>origin activity</th>\n",
-       "      <th>destination activity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>home</td>\n",
-       "      <td>work</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>work</td>\n",
-       "      <td>gym</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>gym</td>\n",
-       "      <td>home</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>home</td>\n",
-       "      <td>school</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>school</td>\n",
-       "      <td>home</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>work</td>\n",
-       "      <td>gym</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   pid  seq origin activity destination activity\n",
-       "0    1    1            home                 work\n",
-       "1    1    2            work                  gym\n",
-       "2    1    3             gym                 home\n",
-       "3    2    1            home               school\n",
-       "4    2    2          school                 home\n",
-       "5    3    1            work                  gym"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "legs_acbm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_652845/537327160.py:13: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "/tmp/ipykernel_652845/491085427.py:44: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
       "  activity_sequence_df = sorted_df.groupby(pid_col).apply(\n"
      ]
     },
@@ -1054,28 +1031,28 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>home - education</td>\n",
-       "      <td>10</td>\n",
+       "      <td>h - e</td>\n",
+       "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>home - education - escort - home</td>\n",
-       "      <td>9</td>\n",
+       "      <td>h - e - e - e - h</td>\n",
+       "      <td>91</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>home - education - escort - shop - home</td>\n",
-       "      <td>1</td>\n",
+       "      <td>h - e - e - e - h - o - h</td>\n",
+       "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>home - education - home</td>\n",
-       "      <td>289</td>\n",
+       "      <td>h - e - e - e - h - s - h</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>home - education - home - education - home</td>\n",
-       "      <td>2</td>\n",
+       "      <td>h - e - e - h</td>\n",
+       "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1084,27 +1061,27 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>452</th>\n",
-       "      <td>home - work - work - work - home - other - home</td>\n",
+       "      <td>h - w - w - w - h - o - h</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>453</th>\n",
-       "      <td>home - work - work - work - home - other - hom...</td>\n",
+       "      <td>h - w - w - w - h - o - h - o - h</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>454</th>\n",
-       "      <td>home - work - work - work - home - other - oth...</td>\n",
+       "      <td>h - w - w - w - h - o - o - o - h</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>455</th>\n",
-       "      <td>home - work - work - work - work - home</td>\n",
+       "      <td>h - w - w - w - w - h</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>456</th>\n",
-       "      <td>home - work - work - work - work - home - work...</td>\n",
+       "      <td>h - w - w - w - w - h - w - h</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1113,23 +1090,23 @@
        "</div>"
       ],
       "text/plain": [
-       "                                     activity_sequence  count_acbm\n",
-       "0                                     home - education          10\n",
-       "1                     home - education - escort - home           9\n",
-       "2              home - education - escort - shop - home           1\n",
-       "3                              home - education - home         289\n",
-       "4           home - education - home - education - home           2\n",
-       "..                                                 ...         ...\n",
-       "452    home - work - work - work - home - other - home           1\n",
-       "453  home - work - work - work - home - other - hom...           2\n",
-       "454  home - work - work - work - home - other - oth...           1\n",
-       "455            home - work - work - work - work - home           1\n",
-       "456  home - work - work - work - work - home - work...           1\n",
+       "                     activity_sequence  count_acbm\n",
+       "0                                h - e           5\n",
+       "1                    h - e - e - e - h          91\n",
+       "2            h - e - e - e - h - o - h           6\n",
+       "3            h - e - e - e - h - s - h           1\n",
+       "4                        h - e - e - h           8\n",
+       "..                                 ...         ...\n",
+       "452          h - w - w - w - h - o - h           1\n",
+       "453  h - w - w - w - h - o - h - o - h           2\n",
+       "454  h - w - w - w - h - o - o - o - h           1\n",
+       "455              h - w - w - w - w - h           1\n",
+       "456      h - w - w - w - w - h - w - h           1\n",
        "\n",
        "[457 rows x 2 columns]"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1147,161 +1124,22 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 62,
+   "cell_type": "markdown",
    "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",
-       "      <th>count_acbm</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>home - education</td>\n",
-       "      <td>45</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>home - education - escort - home</td>\n",
-       "      <td>21</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>home - education - escort - shop - home</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>home - education - home</td>\n",
-       "      <td>1241</td>\n",
-       "      <td>289</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>home - education - home - education - home</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>433</th>\n",
-       "      <td>home - work - work - work - home - other - home</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>434</th>\n",
-       "      <td>home - work - work - work - home - other - hom...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>435</th>\n",
-       "      <td>home - work - work - work - home - other - oth...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>436</th>\n",
-       "      <td>home - work - work - work - work - home</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>437</th>\n",
-       "      <td>home - work - work - work - work - home - work...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>438 rows × 3 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                     activity_sequence  count_nts  count_acbm\n",
-       "0                                     home - education         45          10\n",
-       "1                     home - education - escort - home         21           9\n",
-       "2              home - education - escort - shop - home          1           1\n",
-       "3                              home - education - home       1241         289\n",
-       "4           home - education - home - education - home          7           2\n",
-       "..                                                 ...        ...         ...\n",
-       "433    home - work - work - work - home - other - home          4           1\n",
-       "434  home - work - work - work - home - other - hom...          1           2\n",
-       "435  home - work - work - work - home - other - oth...          1           1\n",
-       "436            home - work - work - work - work - home          4           1\n",
-       "437  home - work - work - work - work - home - work...          1           1\n",
-       "\n",
-       "[438 rows x 3 columns]"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "# join the two dataframes by 'activity_sequence'\n",
-    "\n",
-    "# sequence_nts['activity_sequence'] = sequence_nts['activity_sequence'].str.replace(' - ', '-')\n",
-    "\n",
-    "# sequence_acbm['activity_sequence'] = sequence_acbm['activity_sequence'].str.replace(' - ', '-')\n",
-    "\n",
-    "sequence_nts_acbm = sequence_nts.merge(sequence_acbm, on='activity_sequence', how='inner')\n",
-    "sequence_nts_acbm"
+    "Plot the comparison"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<Axes: xlabel='activity_sequence'>"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1309,24 +1147,49 @@
     }
    ],
    "source": [
-    "# bar chart of the counts \n",
+    "# join the two dataframes by 'activity_sequence'\n",
+    "sequence_nts_acbm = sequence_nts.merge(sequence_acbm, on='activity_sequence', how='inner').sort_values(by='count_nts', ascending=False)\n",
+    "sequence_nts_acbm\n",
     "\n",
-    "#sequence_nts_acbm.plot(kind='bar', x='activity_sequence', y=['count_nts', 'count_acbm'])\n",
+    "# Get % contribution of each unique activity sequence\n",
+    "sequence_nts_acbm['count_nts'] = sequence_nts_acbm['count_nts'] / sequence_nts_acbm['count_nts'].sum() * 100\n",
+    "sequence_nts_acbm['count_acbm'] = sequence_nts_acbm['count_acbm'] / sequence_nts_acbm['count_acbm'].sum() * 100\n",
     "\n",
-    "# sort the values by 'count_nts' and plot the top 10 values\n",
+    "# Filter rows where both count columns are bigger than x %\n",
+    "x = 0.35\n",
     "\n",
-    "#sequence_nts_acbm.sort_values(by='count_nts', ascending=False).head(10).plot(kind='bar', x='activity_sequence', y=['count_nts', 'count_acbm'])\n",
+    "sequence_nts_acbm_filtered = sequence_nts_acbm[\n",
+    "    (sequence_nts_acbm['count_nts'] > x) & (sequence_nts_acbm['count_acbm'] > x)\n",
+    "]\n",
     "\n",
-    "# plot % instead of counts\n",
+    "# Plot the results as a bar chart\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
-    "sequence_nts_acbm['count_nts'] = sequence_nts_acbm['count_nts'] / sequence_nts_acbm['count_nts'].sum() * 100\n",
-    "sequence_nts_acbm['count_acbm'] = sequence_nts_acbm['count_acbm'] / sequence_nts_acbm['count_acbm'].sum() * 100\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "\n",
+    "sequence_nts_acbm_filtered.plot(\n",
+    "    x='activity_sequence', \n",
+    "    y=['count_nts', 'count_acbm'], \n",
+    "    kind='bar', \n",
+    "    ax=ax\n",
+    ")\n",
+    "\n",
+    "plt.ylabel('Percentage of total trips')\n",
+    "\n",
+    "plt.title('Comparison of Activity Sequences between NTS and ACBM')\n",
+    "\n",
+    "\n",
+    "# Generate custom legend\n",
+    "legend_labels = [f'{abbr} = {full}' for abbr, full in activity_mapping.items()]\n",
+    "custom_legend = ' | '.join(legend_labels)\n",
     "\n",
-    "# instead of top 10, keep all rows where count_nts or count_acbm > 10\n",
+    "# Add the custom legend to the plot\n",
+    "plt.legend(['NTS', 'ACBM'], loc='upper right')\n",
     "\n",
-    "sequence_nts_acbm_filtered = sequence_nts_acbm[(sequence_nts_acbm['count_nts'] > 1) | (sequence_nts_acbm['count_acbm'] > 1)]\n",
+    "# Add the custom legend below the chart\n",
+    "plt.figtext(0.5, -0.2, custom_legend, wrap=True, horizontalalignment='center', fontsize=12)\n",
     "\n",
-    "sequence_nts_acbm_filtered.sort_values(by='count_nts', ascending=False).plot(kind='bar', x='activity_sequence', y=['count_nts', 'count_acbm'])"
+    "plt.show()\n"
    ]
   },
   {

From 10344f6267915342da39d824b0054109d083da41 Mon Sep 17 00:00:00 2001
From: Hussein Mahfouz <45176416+Hussein-Mahfouz@users.noreply.github.com>
Date: Tue, 24 Sep 2024 17:40:40 +0100
Subject: [PATCH 04/11] validate time of day

---
 notebooks/5_validate_outputs.ipynb | 1039 ++++++++++++++++++++++------
 1 file changed, 829 insertions(+), 210 deletions(-)

diff --git a/notebooks/5_validate_outputs.ipynb b/notebooks/5_validate_outputs.ipynb
index b6723f4..ced7f75 100644
--- a/notebooks/5_validate_outputs.ipynb
+++ b/notebooks/5_validate_outputs.ipynb
@@ -2,12 +2,15 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
-    "import acbm"
+    "import acbm\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
@@ -19,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -318,7 +321,7 @@
        "[14975 rows x 14 columns]"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -329,7 +332,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# rename origin activity and destination activity columns\n",
+    "\n",
+    "legs_acbm = legs_acbm.rename(columns={\"origin activity\": \"oact\", \"destination activity\": \"dact\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -378,124 +392,124 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>4659583</th>\n",
-       "      <td>2019009707</td>\n",
-       "      <td>2019004508</td>\n",
-       "      <td>2019000702</td>\n",
-       "      <td>2019000291</td>\n",
-       "      <td>2019000032</td>\n",
-       "      <td>2</td>\n",
-       "      <td>7</td>\n",
+       "      <th>4659590</th>\n",
+       "      <td>2019010628</td>\n",
+       "      <td>2019004945</td>\n",
+       "      <td>2019000771</td>\n",
+       "      <td>2019000319</td>\n",
+       "      <td>2019000035</td>\n",
        "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
        "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>...</td>\n",
+       "      <td>795.0</td>\n",
        "      <td>810.0</td>\n",
-       "      <td>840.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>30</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>7</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.756184</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>15</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.997967</td>\n",
+       "      <td>1.227954</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4659584</th>\n",
-       "      <td>2019009709</td>\n",
-       "      <td>2019004508</td>\n",
-       "      <td>2019000702</td>\n",
-       "      <td>2019000291</td>\n",
-       "      <td>2019000032</td>\n",
+       "      <th>4659597</th>\n",
+       "      <td>2019010396</td>\n",
+       "      <td>2019004812</td>\n",
+       "      <td>2019000748</td>\n",
+       "      <td>2019000308</td>\n",
+       "      <td>2019000033</td>\n",
        "      <td>2</td>\n",
-       "      <td>7</td>\n",
        "      <td>3</td>\n",
+       "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>...</td>\n",
-       "      <td>1050.0</td>\n",
-       "      <td>1060.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>10</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>7</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.004688</td>\n",
-       "      <td>1.328628</td>\n",
+       "      <td>505.0</td>\n",
+       "      <td>525.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>20</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.872082</td>\n",
+       "      <td>1.067045</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4659585</th>\n",
-       "      <td>2019009711</td>\n",
-       "      <td>2019004512</td>\n",
-       "      <td>2019000703</td>\n",
-       "      <td>2019000291</td>\n",
-       "      <td>2019000032</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
+       "      <th>4659609</th>\n",
+       "      <td>2019010979</td>\n",
+       "      <td>2019005120</td>\n",
+       "      <td>2019000798</td>\n",
+       "      <td>2019000329</td>\n",
+       "      <td>2019000036</td>\n",
        "      <td>2</td>\n",
+       "      <td>3</td>\n",
        "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
        "      <td>...</td>\n",
-       "      <td>1200.0</td>\n",
-       "      <td>1230.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>30</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>7</td>\n",
+       "      <td>445.0</td>\n",
+       "      <td>556.0</td>\n",
+       "      <td>52.3</td>\n",
+       "      <td>52.3</td>\n",
+       "      <td>111</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>8</td>\n",
        "      <td>7.0</td>\n",
-       "      <td>0.767622</td>\n",
-       "      <td>1.015126</td>\n",
+       "      <td>1.097494</td>\n",
+       "      <td>1.007174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4659586</th>\n",
-       "      <td>2019010362</td>\n",
-       "      <td>2019004797</td>\n",
-       "      <td>2019000744</td>\n",
-       "      <td>2019000306</td>\n",
-       "      <td>2019000033</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
+       "      <th>4659616</th>\n",
+       "      <td>2019010994</td>\n",
+       "      <td>2019005127</td>\n",
+       "      <td>2019000799</td>\n",
+       "      <td>2019000330</td>\n",
+       "      <td>2019000036</td>\n",
+       "      <td>1</td>\n",
        "      <td>3</td>\n",
+       "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>...</td>\n",
-       "      <td>990.0</td>\n",
-       "      <td>995.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.048438</td>\n",
+       "      <td>720.0</td>\n",
+       "      <td>750.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.784036</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4659587</th>\n",
-       "      <td>2019010377</td>\n",
-       "      <td>2019004802</td>\n",
-       "      <td>2019000744</td>\n",
-       "      <td>2019000306</td>\n",
-       "      <td>2019000033</td>\n",
-       "      <td>2</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2</td>\n",
+       "      <th>4659617</th>\n",
+       "      <td>2019010996</td>\n",
+       "      <td>2019005127</td>\n",
+       "      <td>2019000799</td>\n",
+       "      <td>2019000330</td>\n",
+       "      <td>2019000036</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
        "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>...</td>\n",
-       "      <td>925.0</td>\n",
-       "      <td>958.0</td>\n",
-       "      <td>6.9</td>\n",
-       "      <td>6.9</td>\n",
-       "      <td>33</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.123451</td>\n",
-       "      <td>1.071548</td>\n",
+       "      <td>985.0</td>\n",
+       "      <td>1041.0</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>56</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.784036</td>\n",
+       "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -522,187 +536,187 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5150103</th>\n",
-       "      <td>2022111203</td>\n",
-       "      <td>2022059534</td>\n",
-       "      <td>2022009864</td>\n",
-       "      <td>2022004368</td>\n",
+       "      <th>5150053</th>\n",
+       "      <td>2022111153</td>\n",
+       "      <td>2022059517</td>\n",
+       "      <td>2022009859</td>\n",
+       "      <td>2022004366</td>\n",
        "      <td>2022000756</td>\n",
-       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
        "      <td>6</td>\n",
        "      <td>2</td>\n",
-       "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>...</td>\n",
-       "      <td>965.0</td>\n",
-       "      <td>982.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>17</td>\n",
-       "      <td>17.0</td>\n",
+       "      <td>1020.0</td>\n",
+       "      <td>1035.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>15.0</td>\n",
        "      <td>9</td>\n",
        "      <td>9.0</td>\n",
-       "      <td>1.406813</td>\n",
+       "      <td>0.912527</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5150104</th>\n",
-       "      <td>2022111204</td>\n",
-       "      <td>2022059535</td>\n",
-       "      <td>2022009864</td>\n",
-       "      <td>2022004368</td>\n",
+       "      <th>5150079</th>\n",
+       "      <td>2022111179</td>\n",
+       "      <td>2022059524</td>\n",
+       "      <td>2022009860</td>\n",
+       "      <td>2022004366</td>\n",
        "      <td>2022000756</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>...</td>\n",
-       "      <td>800.0</td>\n",
-       "      <td>804.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4.0</td>\n",
+       "      <td>480.0</td>\n",
+       "      <td>525.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>45.0</td>\n",
        "      <td>9</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.613264</td>\n",
-       "      <td>1.146751</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.912527</td>\n",
+       "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5150105</th>\n",
-       "      <td>2022111205</td>\n",
-       "      <td>2022059535</td>\n",
-       "      <td>2022009864</td>\n",
-       "      <td>2022004368</td>\n",
+       "      <th>5150080</th>\n",
+       "      <td>2022111180</td>\n",
+       "      <td>2022059524</td>\n",
+       "      <td>2022009860</td>\n",
+       "      <td>2022004366</td>\n",
        "      <td>2022000756</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
-       "      <td>1</td>\n",
        "      <td>...</td>\n",
-       "      <td>810.0</td>\n",
-       "      <td>828.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>18</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>9</td>\n",
+       "      <td>965.0</td>\n",
+       "      <td>1035.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>8</td>\n",
        "      <td>9.0</td>\n",
-       "      <td>1.613264</td>\n",
-       "      <td>1.146751</td>\n",
+       "      <td>0.912527</td>\n",
+       "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5150106</th>\n",
-       "      <td>2022111206</td>\n",
-       "      <td>2022059535</td>\n",
+       "      <th>5150098</th>\n",
+       "      <td>2022111198</td>\n",
+       "      <td>2022059531</td>\n",
        "      <td>2022009864</td>\n",
        "      <td>2022004368</td>\n",
        "      <td>2022000756</td>\n",
        "      <td>1</td>\n",
-       "      <td>7</td>\n",
        "      <td>3</td>\n",
+       "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>...</td>\n",
-       "      <td>960.0</td>\n",
-       "      <td>985.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>25</td>\n",
-       "      <td>25.0</td>\n",
+       "      <td>645.0</td>\n",
+       "      <td>660.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>15.0</td>\n",
        "      <td>9</td>\n",
        "      <td>9.0</td>\n",
-       "      <td>1.512388</td>\n",
-       "      <td>1.075046</td>\n",
+       "      <td>1.621441</td>\n",
+       "      <td>1.152563</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5150107</th>\n",
-       "      <td>2022111207</td>\n",
-       "      <td>2022059535</td>\n",
+       "      <th>5150099</th>\n",
+       "      <td>2022111199</td>\n",
+       "      <td>2022059531</td>\n",
        "      <td>2022009864</td>\n",
        "      <td>2022004368</td>\n",
        "      <td>2022000756</td>\n",
        "      <td>1</td>\n",
-       "      <td>7</td>\n",
-       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>...</td>\n",
-       "      <td>1110.0</td>\n",
-       "      <td>1115.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5.0</td>\n",
+       "      <td>900.0</td>\n",
+       "      <td>915.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>15.0</td>\n",
        "      <td>9</td>\n",
        "      <td>9.0</td>\n",
-       "      <td>1.512388</td>\n",
-       "      <td>1.075046</td>\n",
+       "      <td>1.621441</td>\n",
+       "      <td>1.152563</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>420180 rows × 25 columns</p>\n",
+       "<p>59359 rows × 25 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "             TripID       DayID  IndividualID  HouseholdID       PSUID  \\\n",
-       "4659583  2019009707  2019004508    2019000702   2019000291  2019000032   \n",
-       "4659584  2019009709  2019004508    2019000702   2019000291  2019000032   \n",
-       "4659585  2019009711  2019004512    2019000703   2019000291  2019000032   \n",
-       "4659586  2019010362  2019004797    2019000744   2019000306  2019000033   \n",
-       "4659587  2019010377  2019004802    2019000744   2019000306  2019000033   \n",
+       "4659590  2019010628  2019004945    2019000771   2019000319  2019000035   \n",
+       "4659597  2019010396  2019004812    2019000748   2019000308  2019000033   \n",
+       "4659609  2019010979  2019005120    2019000798   2019000329  2019000036   \n",
+       "4659616  2019010994  2019005127    2019000799   2019000330  2019000036   \n",
+       "4659617  2019010996  2019005127    2019000799   2019000330  2019000036   \n",
        "...             ...         ...           ...          ...         ...   \n",
-       "5150103  2022111203  2022059534    2022009864   2022004368  2022000756   \n",
-       "5150104  2022111204  2022059535    2022009864   2022004368  2022000756   \n",
-       "5150105  2022111205  2022059535    2022009864   2022004368  2022000756   \n",
-       "5150106  2022111206  2022059535    2022009864   2022004368  2022000756   \n",
-       "5150107  2022111207  2022059535    2022009864   2022004368  2022000756   \n",
+       "5150053  2022111153  2022059517    2022009859   2022004366  2022000756   \n",
+       "5150079  2022111179  2022059524    2022009860   2022004366  2022000756   \n",
+       "5150080  2022111180  2022059524    2022009860   2022004366  2022000756   \n",
+       "5150098  2022111198  2022059531    2022009864   2022004368  2022000756   \n",
+       "5150099  2022111199  2022059531    2022009864   2022004368  2022000756   \n",
        "\n",
        "         PersNo  TravDay  seq  ShortWalkTrip_B01ID  NumStages  ...     tst  \\\n",
-       "4659583       2        7    1                    2          1  ...   810.0   \n",
-       "4659584       2        7    3                    2          1  ...  1050.0   \n",
-       "4659585       3        4    2                    2          1  ...  1200.0   \n",
-       "4659586       2        2    3                    2          1  ...   990.0   \n",
-       "4659587       2        7    2                    2          1  ...   925.0   \n",
+       "4659590       1        3    3                    2          1  ...   795.0   \n",
+       "4659597       2        3    1                    2          1  ...   505.0   \n",
+       "4659609       2        3    1                    2          3  ...   445.0   \n",
+       "4659616       1        3    1                    2          1  ...   720.0   \n",
+       "4659617       1        3    3                    2          1  ...   985.0   \n",
        "...         ...      ...  ...                  ...        ...  ...     ...   \n",
-       "5150103       1        6    2                    2          1  ...   965.0   \n",
-       "5150104       1        7    1                    2          1  ...   800.0   \n",
-       "5150105       1        7    2                    2          1  ...   810.0   \n",
-       "5150106       1        7    3                    2          1  ...   960.0   \n",
-       "5150107       1        7    4                    2          1  ...  1110.0   \n",
+       "5150053       3        3    6                    2          1  ...  1020.0   \n",
+       "5150079       4        3    1                    2          1  ...   480.0   \n",
+       "5150080       4        3    2                    2          2  ...   965.0   \n",
+       "5150098       1        3    1                    2          1  ...   645.0   \n",
+       "5150099       1        3    2                    2          1  ...   900.0   \n",
        "\n",
        "            tet TripDisIncSW TripDisExSW  TripTotalTime  TripTravTime  ozone  \\\n",
-       "4659583   840.0          4.0         4.0             30          30.0      7   \n",
-       "4659584  1060.0          1.0         1.0             10          10.0      7   \n",
-       "4659585  1230.0          3.0         3.0             30          30.0      7   \n",
-       "4659586   995.0          2.0         2.0              5           5.0      2   \n",
-       "4659587   958.0          6.9         6.9             33          33.0      2   \n",
+       "4659590   810.0          6.1         6.1             15          15.0      8   \n",
+       "4659597   525.0          3.5         3.5             20          20.0      2   \n",
+       "4659609   556.0         52.3        52.3            111         101.0      8   \n",
+       "4659616   750.0         18.0        18.0             30          30.0      8   \n",
+       "4659617  1041.0         17.1        17.1             56          56.0      8   \n",
        "...         ...          ...         ...            ...           ...    ...   \n",
-       "5150103   982.0          6.0         6.0             17          17.0      9   \n",
-       "5150104   804.0          0.5         0.5              4           4.0      9   \n",
-       "5150105   828.0          3.0         3.0             18          18.0      9   \n",
-       "5150106   985.0          3.0         3.0             25          25.0      9   \n",
-       "5150107  1115.0          0.5         0.5              5           5.0      9   \n",
+       "5150053  1035.0          5.0         5.0             15          15.0      9   \n",
+       "5150079   525.0         12.0        12.0             45          45.0      9   \n",
+       "5150080  1035.0         19.0        19.0             70          60.0      8   \n",
+       "5150098   660.0          7.0         7.0             15          15.0      9   \n",
+       "5150099   915.0          7.0         7.0             15          15.0      9   \n",
        "\n",
        "         dzone        W5     W5xHH  \n",
-       "4659583    7.0  0.756184  1.000000  \n",
-       "4659584    7.0  1.004688  1.328628  \n",
-       "4659585    7.0  0.767622  1.015126  \n",
-       "4659586    2.0  1.048438  1.000000  \n",
-       "4659587    2.0  1.123451  1.071548  \n",
+       "4659590    8.0  0.997967  1.227954  \n",
+       "4659597    2.0  0.872082  1.067045  \n",
+       "4659609    7.0  1.097494  1.007174  \n",
+       "4659616    8.0  0.784036  1.000000  \n",
+       "4659617    8.0  0.784036  1.000000  \n",
        "...        ...       ...       ...  \n",
-       "5150103    9.0  1.406813  1.000000  \n",
-       "5150104    9.0  1.613264  1.146751  \n",
-       "5150105    9.0  1.613264  1.146751  \n",
-       "5150106    9.0  1.512388  1.075046  \n",
-       "5150107    9.0  1.512388  1.075046  \n",
+       "5150053    9.0  0.912527  1.000000  \n",
+       "5150079    8.0  0.912527  1.000000  \n",
+       "5150080    9.0  0.912527  1.000000  \n",
+       "5150098    9.0  1.621441  1.152563  \n",
+       "5150099    9.0  1.621441  1.152563  \n",
        "\n",
-       "[420180 rows x 25 columns]"
+       "[59359 rows x 25 columns]"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -711,6 +725,207 @@
     "legs_nts"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "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>seq</th>\n",
+       "      <th>ozone</th>\n",
+       "      <th>dzone</th>\n",
+       "      <th>oact</th>\n",
+       "      <th>dact</th>\n",
+       "      <th>mode</th>\n",
+       "      <th>tst</th>\n",
+       "      <th>tet</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4659590</th>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>shop</td>\n",
+       "      <td>home</td>\n",
+       "      <td>car</td>\n",
+       "      <td>795.0</td>\n",
+       "      <td>810.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>59359 rows × 8 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",
+       "[59359 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 31,
+     "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",
+    "    ]\n",
+    "]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -732,11 +947,271 @@
     "## Matching: Trip Purpose "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "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": "code",
+   "execution_count": 85,
+   "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>dact</th>\n",
+       "      <th>count</th>\n",
+       "      <th>source</th>\n",
+       "      <th>percentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>education</td>\n",
+       "      <td>1823</td>\n",
+       "      <td>nts</td>\n",
+       "      <td>3.071143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>escort</td>\n",
+       "      <td>5180</td>\n",
+       "      <td>nts</td>\n",
+       "      <td>8.726562</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>home</td>\n",
+       "      <td>26434</td>\n",
+       "      <td>nts</td>\n",
+       "      <td>44.532421</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>medical</td>\n",
+       "      <td>859</td>\n",
+       "      <td>nts</td>\n",
+       "      <td>1.447127</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>other</td>\n",
+       "      <td>9628</td>\n",
+       "      <td>nts</td>\n",
+       "      <td>16.219950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>shop</td>\n",
+       "      <td>5621</td>\n",
+       "      <td>nts</td>\n",
+       "      <td>9.469499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>visit</td>\n",
+       "      <td>3313</td>\n",
+       "      <td>nts</td>\n",
+       "      <td>5.581293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>work</td>\n",
+       "      <td>6501</td>\n",
+       "      <td>nts</td>\n",
+       "      <td>10.952004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>education</td>\n",
+       "      <td>389</td>\n",
+       "      <td>acbm</td>\n",
+       "      <td>2.597663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>escort</td>\n",
+       "      <td>1708</td>\n",
+       "      <td>acbm</td>\n",
+       "      <td>11.405676</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>home</td>\n",
+       "      <td>6473</td>\n",
+       "      <td>acbm</td>\n",
+       "      <td>43.225376</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>medical</td>\n",
+       "      <td>181</td>\n",
+       "      <td>acbm</td>\n",
+       "      <td>1.208681</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>other</td>\n",
+       "      <td>2487</td>\n",
+       "      <td>acbm</td>\n",
+       "      <td>16.607679</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>shop</td>\n",
+       "      <td>1235</td>\n",
+       "      <td>acbm</td>\n",
+       "      <td>8.247078</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>visit</td>\n",
+       "      <td>829</td>\n",
+       "      <td>acbm</td>\n",
+       "      <td>5.535893</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>work</td>\n",
+       "      <td>1673</td>\n",
+       "      <td>acbm</td>\n",
+       "      <td>11.171953</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        dact  count source  percentage\n",
+       "0  education   1823    nts    3.071143\n",
+       "1     escort   5180    nts    8.726562\n",
+       "2       home  26434    nts   44.532421\n",
+       "3    medical    859    nts    1.447127\n",
+       "4      other   9628    nts   16.219950\n",
+       "5       shop   5621    nts    9.469499\n",
+       "6      visit   3313    nts    5.581293\n",
+       "7       work   6501    nts   10.952004\n",
+       "0  education    389   acbm    2.597663\n",
+       "1     escort   1708   acbm   11.405676\n",
+       "2       home   6473   acbm   43.225376\n",
+       "3    medical    181   acbm    1.208681\n",
+       "4      other   2487   acbm   16.607679\n",
+       "5       shop   1235   acbm    8.247078\n",
+       "6      visit    829   acbm    5.535893\n",
+       "7       work   1673   acbm   11.171953"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "purpose_compare"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Matching: Trip Mode "
+    "## Matching: Trip Mode \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "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()"
    ]
   },
   {
@@ -746,6 +1221,150 @@
     "## Matching: Time of Day "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Create a new DataFrame\n",
+    "legs_acbm_clean = legs_acbm.copy()\n",
+    "\n",
+    "# Convert tst to datetime format\n",
+    "legs_acbm_clean['tst'] = pd.to_datetime(legs_acbm_clean['tst'])\n",
+    "\n",
+    "# Extract the hour component\n",
+    "legs_acbm_clean['hour'] = legs_acbm_clean['tst'].dt.hour #+ 1\n",
+    "\n",
+    "# Calculate the percentage of each hour in legs_acbm_clean['hour']\n",
+    "acbm_hour_counts = legs_acbm_clean['hour'].value_counts(normalize=True).sort_index() * 100\n",
+    "\n",
+    "# Convert legs_nts[\"tst\"] from minutes to hours\n",
+    "legs_nts['hour'] = legs_nts['tst'] // 60\n",
+    "\n",
+    "# Calculate the percentage of each hour in legs_nts['hour']\n",
+    "nts_hour_counts = legs_nts['hour'].value_counts(normalize=True).sort_index() * 100\n",
+    "\n",
+    "# Plot the histogram using seaborn\n",
+    "sns.histplot(legs_acbm_clean['hour'], bins=24, kde=False, discrete=True, stat='percent')\n",
+    "plt.xlabel('Hour of Day')\n",
+    "plt.ylabel('Percentage')\n",
+    "plt.title('Histogram of TST by Hour of Day in legs_acbm')\n",
+    "plt.xticks(range(0, 24))  # Set x-ticks to represent each hour of the day\n",
+    "\n",
+    "# Add dots to represent the percentage values of legs_nts[\"tst\"]\n",
+    "for hour, percent in nts_hour_counts.items():\n",
+    "    plt.plot(hour, percent, 'ro')  # 'ro' means red dot\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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o1fFJhtYDEGQpHq7k0FY9IyaqkFQHAAAGYk9xRCb2PwIAIOIxMw0AgOArz8jRwYRkJRyuanbmhV3SoYRklWfkBDo0AL7Iy5NSU52TSpq7r2YyOY/n5bXcxqmJKo3PPzVRZdkyqaCg9ThKS5339k6f7JKaKs2Z0/a5AICwVV5eblhbSUlJSktLM6Qts9ksq9WY+0tGxgXvkBRHZPJm/6P8/ICFBQAAjMPMNAAAgs8RFa1FowtVtGC67HJfktAu5xYni0cXyhHFB9KBsBAd7Uw8jxrlTICfntg2fb9GU0mJ2yQTt0SBzaaLHnpIZzgcTVd0cjjkkHTif/5H2/v1c7XRJDlgRFIdABBW6msOSjJp7NixhrUZG3umduwo9zsBbTablZWVrfr6YyEVF7xHUhyRycj9jwAAQEhiZhoAAKFhY26+Zk94VuOXlqjn4SpX+aGEZC0eXaiNufnBCw6A9woKnInn5mZql5S4JaQbJwqulbSmlaZNkjofOKCiK67QB9+XuSUHWP0RADqkE8eOSHJowF1T1at/lt/t1VoqtGHhTFmtVr+Tz1arVfX1xzTo/hmKS0kPmbjgPZLiiExG7X8EILTYbM4VHiwW5+9vXh6DYKADY2YaAAChY2NuvjYNyFP2rq3qUWNVdXySyjNy6IeBcFVQ4Ew8tzEGb5woyP98vbR8QZvN33DbBHW+8MqmyQFWf0Qk4n4W4LFuyWlKTMts/wv58HsZl5IemNjQbkiKIzIZsf8RgNDCfmIAmsHMNAAAQocjKlpfZOYGOwwARomO9jjxfCpRcLL+qEf1T55zcfOJBVZ/RKThfhYQevi97LBIiiMy+bD/EYAQxn5iAFrBzDQAAAAgNPi6xVF5ebkkqduRIzrfg+t8eeSI6srKXM+b7EsOhALuZwGhpx1+L012G/ekwgRJcUQMs9ksq9X6Q0F6unq88IJSZ81S56ofZo4dT07WvkcfVXV6uvT9m+dTb7wBhCD2EwPgAWamAQAAAMHn7RZH9TUHJZk0duxY6fv6FZLOanTu6W3sk5Q9YYLsp5W77UsOhALuZwGhpx1+LweWrWmyeuHBhGQtYvXCkERSHBHBbDYrKytb9fXHmhyLkpQnKUWSRdLaAwdknzKl2XZONBxvzzAB+MLo/cTYxwkAAAAAgHbjzRZHJ44dkeTQgLumqlf/LEnSwh2b9MRf5raYVH/t9om6PutyV3mTfcmBUGD0/SwA/jP493Jg2RoVLZjepDzhcJWKFkzX7AnPkhgPMSTFERGsVqvq649p0P0zFJeS3mydg5I6S7q+mWOWbeu0fcXLOnnyZDtGCcAnRu4nxn4xAIzAh2sAAACAVnm7xVG35DTXHuPlaZmanXRWi0n18tx8JQbkVQB+MPJ+FgBjGPh7abLbNH5pifP7Rsei5Pwg17ilJdo0II+l1EMISXGEPi9uPMelpLveQHuj1lLhZ5AA2k1KijH1jNovhmQY0LFF+odr+BsHAAAAg/izxZG3SXUg5Bh1PwuAcQz8vczetdXtg1uNRUlKOlyl7F1b2e4vhJAUR2iL9BvPANqWl+f8vd+/v/n9Xkwm5/G8vJbbMGq/GP4mAR2bUR+uCSCz2Syr1epR3R7/+pdSZ81S56ofBnXHk5O1b8oUVV93nUdtJCUlsWQlAAAADOFPUh0IOiPuZwHwW3l5+Q9PunbVRcnJOqOqqsnsbklySDrRu7e2d+0qlZU138b3etR4eK/Fw3ouXk5W8Oa+T1s6wj0dkuIIXWF44xlAO4iOdiadR41yDhhO/5tg+v7tS0lJ68lsI/aL4W8SEPGaG+S42Gy66KGHdIbD0XTg5HA4B07/8z/a3q9fmzOrAzXIMJvNysrKVn39sTbr/ljSsmbKO1VVKX3KFI2S9I4H14yNPVM7dpRH/CAKAAAAAFplxP0sAD6rrzkoyaSxY8e6lZ+6/+GQczb3Kfbvv9554IDeueKKZts80XDc9X11fJJHcXhaT5LXE7K8ue/jiY5wT4ekOEKTUbM6AUSGggJn0rm5NwUlJW0no/3dL4a/SUBEa2mgdLprJa1ppQ2TpM4HDqjoiiv0QRvXC9Qgw2q1qr7+mAbdP0NxKekt1ouy27XgpUdlOnK4xX2w5scl6tjEWbJHRTXTglOtpUIbFs6U1WqN6AEUAAAAAHjE3/tZAHx24tgRSQ4NuGuqevXPcpXXSXp6xyb9zz+XqNeRw65ya1yi5v3XGNVlXa7/atSWZds6bV/xsk6ePOkqK8/I0cGEZCUcrlJzd0rskg4lJKs8I8ezgH2YkOXJfZ8ou10X7f1SiXXVOtSth7b3O7/ZezsBv6cTpO37SIojNBkxqxNAZCkocCadfeks/d0vhr9JQERraaB0uvzP10vLF7TZ1g23TVDnC69s8XgwEsdxKelKTMts8fgFO8vcBoKNRUlKrj2kIQ31Hi1f2eqM+9PZbOr26ac6w2rViaQk1V16qVcDIK9n3AdywMXe7EB44ncXQAfm8Xs4g84DOgx/7mcB8Fu35LQm90TK0zL182F3KnvXVvWosao6PknlGTlyREUrsZk2ai0VTcocUdFaNLpQRQumy66ms85NkhaPLpQjyoPfdT8nZLV032dg2RqNX1ritvf5wYRkLRpdqI25+W3H1V6CuEUpSfH2xIDad/7O6gQQmaKjPU46u+2n4sN+MW7JFv4moT3xfiFkNDdQOuVk/VGP2jh5zsWtJqB90s4/I0btg+XJjPtTfixpjqR+p5XtlTRZni3TLnk54z6QA65AD+4C/TeEv1kIZf78fAbxxoyh/P0d5Xcc6HC8eQ/XmtOXlAW8Eqp9j5FxeXE/C0BgOKKiPfrgf2s25uZr9oRnmySeDyUka7E3ied2mJA1sGyNihZMb1KecLhKRQuma/aEZ71PjBvxdzHIW5SSFG8vDKj9O9/fWZ0Awp4/nza3WCwaNeoOffddvavM2/1i3JIt/E1Ce4mU9wsdgOHLYnkqAD8jRu2D5cmMe0m6ascmPfGXuU3Kz5L0F0lP3T5R/866vNVreTXjPpADrkAP7gL9NyTSE/4Ib/78fAb5xoxh/P0d5X0J0CF5+h6uJc0tKQt4LFT7nlCNC0DI2Zibr00D8pqdde4xgydkmew2jV9a4vy+0bFT2+SNW1qiTQPyPI/TiL+LIbBFKUnx9hBCA2qT3eb7L2MwB9R5ec66+/c3/wtiMjmP5+V59loAhA2jPqUuSZfdM12JaRmSvNsvpkmyhb9JHUugkjAh9H4BbTN0WSx59sGfHv/6l/pPmSLJfRDj2LdPuv127Zk1S9XXXefXNSTjE/6tzbg32W2a9Mepzu8bHTs1MJv4r7e1Y9id3g0gWxLIAVegB3fBSMD7eT23VVza0ONf/1LqrFnqXPXDp+2PJydr35Qprf7cn86rJfZJwIc3f34+Q+DGTJN4fPlZ9Pd3lPclQIfX2nu41jS3pCzgkVDte0I1LgAhy+9Z5wZPyMretdVt5npjUZKSDlcpe9dWz+I26u9iCGxRGhZJ8blz52rWrFmqrKxUTk6O/vCHP+iK02bzNfb222/r8ccfV0VFhTIyMvT888/rpptuCkywITSg9mu/gGAPqKOjncnzUaOc/2ant2P6/hZqSQk3qoAI5O+n1KUfPqke2/Mst0G9t/vFnJ5Q6jF5cvPJqe+/7vn5z1W9daur3Ou9buHibb9vqEB9GjyE3i/Ac0Ysi+XpB3+iJFWo6eoWkvNvkF1SpylTNFA/rHjRkraWsjQ64d8awwdmbQnkgCuQ1wr03xADrmc2m5WVla36+mNtXu7U6i6NdaqqUvqUKRolz5bZ93iJfWYCGSJo/be/P58hcGPGxdefRX//DXhfAgARgXvoERwXgMhm8IQso7bJk2Ts38UQ2KI05JPib731loqKijR//nwNGjRIJSUlGj58uHbu3Knk5OQm9T/++GONGTNGxcXFuuWWW7RkyRLddtttKisr00UXXdT+AYfIgNqv/QJCZUBdUOBMnjd3U6CkhBtUQITz9VPqUuufVPfkk3stJa1a2gO30OHQO98nzE/xaq9buHjb7xsqkJ8GD5H3C/Cev8tiefrBn0u+3qF+rz/f4vEoSWmSHhk7VZ+d3Xw73ixladg+WG0wdGDmiUAOuAy8Vlszqrtt2qTzPfgb8uWrr6ru8taXovfoQ1wG/M2yWq2qrz+mQffPUFxKeotNRdntWvDSozIdOdziagLz4xJ1bOIs2aOaW9vAyeMl9pkJZIig9t/+/nyGwI0ZSf79LPr7b8D7EgAIe9xDN0ioxgUgshk8SdSobfIkGft3MQS2KA35pPjs2bP14IMP6r777pMkzZ8/X3//+9+1cOFC/epXv2pSf86cObrxxhs15fvkxNNPP61Vq1bppZde0vz589s/4BAYUPu9X0AoDagLCpzJc5YyBBBALSWt6iQ9aLfror1fKrGuWoe69dD2fufLHhXlWnpd8nKvW7jxtt83jIGfevRkeeCEjz9Wfw/C2vPxxzocF9fsMU+Xxobx/F4WS21/8Cet8muP2kmLidW+FtrxdilLQ/bBaoOhAzO1/XvQ7cgRne9BO18eOaK6srIWj3uUODZocOfJjOo7Jb3hwaVmTJigN9uoExPTRX/5yzKltBKXkX+z4lLSW/3Zv2Bnmds2J41FSUquPaQhDfX+rybATCDDBK3/lvwfg4fAjRm/fxb9/TcIgfsYAAD/cA/dIKEaF4DIZ+AkUUO3yTPy72IIbFEa0knx48ePa/PmzZo2bZqrLCoqSsOGDdO6deuaPWfdunUqKipyKxs+fLiWL1/enqH+IAQG1H4vSxmkAXWrSYS4OOdDkk5bnvgUEgMA2kNLSat96dk69dakR0Ajimy+9PuGMegDXZ4uD3ytpDUehHXfr3+tD37961brtLU0NsKT0cljTxmR8G+NUQMzb5ehP0tNl6E/db19krInTGh1GXqPVv8waHDnyYzqs77eIbWykoCr3tip+q8WVhKQpG93bdWWpXN0yy23tNpOIP9mBXQ1AWYCGSKo/bfk/xg8BG7M+P2z6O+/QQjcxwAA+I576AYK1bgAdAwGTRI1dJs8I/8uhsC2ySGdFLdarbLZbOrdu7dbee/evbVjx45mz6msrGy2fmVlZYvXaWhoUENDg+t5TU2NJKm2ttb7oHNypL59pW++abnOWWc56/nSfjPq6uokSYe+3qmTDfU6f+dmedLyyZ2bVRUVpdpKsyRp8+bNqqurU7eqKp3nwflfVVWp7sMPJTnfaNntzluJvpx/4MAB3XPPvWpo+M6DM1v27VfbdbKh3uvzai3O2Vg1+3fpjE6N59e3//mhEAOvgX+DUIkhIl7D939X6+rqfOtLTm/r+/Mdzd2kjTDe9vuG9t+7d3teL7flhGFFRYXq648p87/u0pmJvVusV+ew66v/e1VJ9XUtJuq+je2mulsf0KWm5pcHPlRRrq83rNTBr8tlks2z+P1gxO9mKF4r0Nfz9FpWSf/p1kOJddUt/owc7J6gDyXZd23x61pG8fR6v80bqV+teKXJfumnBma/yxupA7u3tXqtg7u3S3LonPw7FN87tdW6v923S0/9+2/NXk+SfnfVLcpJzWjx/GOHDmjnqiV69913lZnZ+rYe8RMmKP3xxyW5r9jkkCSHQxU/+5lq/v3vVtvYuXOnJOnk8YYW39duSe7n0c/HluR+srfy3rjhSLU8+XcM5N+sXUeqPRrL7DpSraoWfvYlD/tig/72N3v9769ZW1ur7t27y2Rq/9/BYPFl3B5yY/DiYumee5o/5nBIzz4rHT3qfWye8vdn0d9/Ay/Pb3wPwhdBf7/OmIXXECIxBPv8UIjBkNfQwcfg3EM3UAjEZUQ/e4qRY8JQbSuUY6Ot4LUVyrE1zsc1KyrK+bdGklq5h3Dq/kFzfy/+3r2HrD96UA/+620l1VW7yr/tnqD/HTpKG7r3kE4bU7fYlxr9d3HYMOlPf5KmTnVvs29f6bnnnMd9+Pvqcf/tCGH79+93SHJ8/PHHbuVTpkxxXHHFFc2ec8YZZziWLFniVjZ37lxHcnJyi9eZMWOGQ857VTx48ODBg0dIPvbu3et/xxrivO336b958ODBg0c4PGpqagLVlQaFL+N2+nAePHjw4BHqj3Aag3MPnQcPHjx48HA+2uq/Q3qmeFJSkqKjo3XgwAG38gMHDqhPnz7NntOnTx+v6kvStGnT3JaLsdvtOnTokHr27On3J/pra2vVr18/7d27V3Et7K/XnueHQgy8htCIIdjnh0IMvAb+DUIpBk85HA4dOXJEffv2bdfrhAJv+/1Q7r9D+XqReq1AX4/XFn7XCvT1IvVagb5euF7rVP/dvXt3de/e3aAIQ5Mv4/ZQ7sODfX4oxBDs80MhBl5DaMQQ7PNDIQZeQ+Df54TjGDxY99Crq6t19tlny2w2Kz4+3o9XYOz/c0doK5Rjo63gtRXKsdFW8NoK5djaYwzeVv8d0knxzp0767LLLtPq1at12223SXIOllevXq1JkyY1e87gwYO1evVqFRYWuspWrVqlwYMHt3idmJgYxcTEuJX16NHD3/DdxMXF+fWf6u/5oRADryE0Ygj2+aEQA6+Bf4NQisET/g4uw4W3/X449N+hfL1IvVagr8drC79rBfp6kXqtQF8vHK9F/93yuD0c+vBgnx8KMQT7/FCIgdcQGjEE+/xQiIHXENj3AuHWhwfzHrrk/Pcy6v/GyP/njtCW0e3RVmS0ZXR7tBUZbRndXii25Un/HdJJcUkqKirSuHHjdPnll+uKK65QSUmJjh49qvvuu0+SdO+99+qss85ScXGxJGny5Mm69tpr9bvf/U4333yz3nzzTW3atEkvv/xyMF8GAADwQFv9PgAACD303wAABBf30AEAaFvIJ8V/8pOf6Ntvv9UTTzyhyspKDRgwQCtXrlTv3r0lSWazWVFRUa76Q4YM0ZIlS/TYY49p+vTpysjI0PLly3XRRRcF6yUAAAAPtdXvAwCA0EP/DQBAcHEPHQCAtoV8UlySJk2a1OJSL2vWrGlSdscdd+iOO+5o56g8ExMToxkzZjS7tEwgzg+FGHgNoRFDsM8PhRh4DfwbhFIMaFlr/X6gBPr/OJDXi9RrBfp6vLbwu1agrxep1wr09SL1WpEoFPpvKfjv8yLhvWqwzw+FGHgNoRFDsM8PhRh4DfTP3gj0PXQj/29oK7jt0VZktGV0e7QVGW0Z3V6otuUpk8PhcATsagAAAAAAAAAAAAAABFBU21UAAAAAAAAAAAAAAAhPJMUBAAAAAAAAAAAAABGLpDgAAAAAAAAAAAAAIGKRFG9nc+fOVXp6urp06aJBgwbpk08+8fjcDz/8ULfeeqv69u0rk8mk5cuXe3xucXGxBg4cqO7duys5OVm33Xabdu7c6VXs8+bN0yWXXKK4uDjFxcVp8ODB+sc//uFVG6d77rnnZDKZVFhY6FH9J598UiaTye2RlZXl9XX379+vsWPHqmfPnoqNjdXFF1+sTZs2eXRuenp6kxhMJpMmTpzo0fk2m02PP/64+vfvr9jYWJ177rl6+umn5XA4PI7/yJEjKiws1Nlnn63Y2FgNGTJEGzdubLF+Wz83DodDTzzxhFJSUhQbG6thw4Zp165dHp9fWlqqG264QT179pTJZNKWLVs8vv6JEyc0depUXXzxxeratav69u2re++9V998841Xr+HJJ59UVlaWunbtqoSEBA0bNkwbNmzw+PzT/fd//7dMJpNKSko8Pn/8+PFNfiZuvPFGr16DJJWXl+tHP/qR4uPj1bVrVw0cOFBms9mj85v7uTSZTJo1a5ZH59fV1WnSpElKTU1VbGysLrjgAs2fP9/j+A8cOKDx48erb9++OvPMM3XjjTe6/Rx58jfou+++08SJE9WzZ09169ZNt99+uw4cOOBVGy+//LLy8/MVFxcnk8mk6upqj88/dOiQHn74YWVmZio2NlZpaWn6+c9/rpqaGo+vP2HCBJ177rmKjY1Vr169NHLkSO3YsaPxfzXCiFF9j6f86aO85W+f5g0j+j9veNtXesPfftXIa7XVBxt5PU/7bCOuJbXdtxt5rdM19z7A6Ot58r7BqGtJrb+/MPJabb0XMfp6bb13MfJabb3PQegK1vhb8n8MHuzxt2TM+6Bgjr+lwI/BjXifEOwxuBF9dLDH4MEef3vSRqiPwYM9/vY0BsbgwWfEPefTGd3/neJLP3g6o+8NGDX2N3Jcb/S43Z9xuZHjbiPH1UaOmY0cExs55jV6PGvkmNXIMamR400jx5NG3EP3pq3W+nJv2vKkX/cmrkD27yTF29Fbb72loqIizZgxQ2VlZcrJydHw4cNVVVXl0flHjx5VTk6O5s6d6/W1P/jgA02cOFHr16/XqlWrdOLECd1www06evSox22kpqbqueee0+bNm7Vp0yZdd911GjlypD7//HOv49m4caMWLFigSy65xKvzLrzwQlksFtfjo48+8ur8w4cP66qrrtIZZ5yhf/zjH/riiy/0u9/9TgkJCR7Hffr1V61aJUm64447PDr/+eef17x58/TSSy+pvLxczz//vF544QX94Q9/8Pg1/PSnP9WqVav0//7f/9O2bdt0ww03aNiwYdq/f3+z9dv6uXnhhRf0+9//XvPnz9eGDRvUtWtXDR8+XN99951H5x89elRXX321nn/+ea+vf+zYMZWVlenxxx9XWVmZSktLtXPnTv3oRz/y6jWcf/75eumll7Rt2zZ99NFHSk9P1w033KBvv/3Wo/NPeeedd7R+/Xr17dvXq+tL0o033uj2s/HGG2941cbu3bt19dVXKysrS2vWrNFnn32mxx9/XF26dPHo/NOvbbFYtHDhQplMJt1+++0enV9UVKSVK1fq9ddfV3l5uQoLCzVp0iStWLGizfMdDoduu+02/ec//9Ff//pXffrppzr77LM1bNgw198YT/4GPfLII/q///s/vf322/rggw/0zTffqKCgwHXckzaOHTumG2+8UdOnT28SZ1vnf/PNN/rmm2/029/+Vtu3b9eiRYu0cuVKPfDAAx5f/7LLLtNrr72m8vJyvfvuu3I4HLrhhhtks9ma/XdHePC37/GUv32Ut/zt07xhRP/nDW/7Sm/4268aea22+mAjr+dpn23EtaS2+3Yjr3VKS+8D2uN6bb1vMOpabb2/MPJabb0XMfp6bb13MepanrzPQWgK5vhb8n8MHgrjb8m/90HBHn9LgR+DG/E+IdhjcCP66GCPwYM9/vakjVAfgwd7/O1pDIzBg8+Ie86nM7L/O8WffvB0Rt0bMHLsb+S43uhxuz/jciPH3UaOq40cMxs5JjZyzGv0eNbIMauRY1Ijx5tGjieNuIfuTVut9eXetOVJv+5NXAHt3x1oN1dccYVj4sSJruc2m83Rt29fR3FxsddtSXK88847PsdSVVXlkOT44IMPfG7D4XA4EhISHP/7v//r1TlHjhxxZGRkOFatWuW49tprHZMnT/bovBkzZjhycnK8D/I0U6dOdVx99dV+tXG6yZMnO84991yH3W73qP7NN9/suP/++93KCgoKHHfffbdH5x87dswRHR3t+Nvf/uZWnpub6/j1r3/d5vmNf27sdrujT58+jlmzZrnKqqurHTExMY433nijzfNPt2fPHockx6effurx9ZvzySefOCQ5vv76a5/bqKmpcUhyvPfeex6fv2/fPsdZZ53l2L59u+Pss892vPjiix5ff9y4cY6RI0e2GlNbbfzkJz9xjB071ufzGxs5cqTjuuuu8/j8Cy+80PHUU0+5lbX0c9X4/J07dzokObZv3+4qs9lsjl69ejleeeWVZmNo/DeourraccYZZzjefvttV53y8nKHJMe6des8auN077//vkOS4/Dhw82e29b5pyxdutTRuXNnx4kTJ3w6f+vWrQ5Jjq+++qrFOghtRvQ9njK6j/KWt32aN/zt/7zhb1/pDX/7VX+udTpP+mAjr3dKW322kddqrW834lqevg8w4nrevm/w51revL/w91qNtfZexIjrefPexZ9r+fI+B6EhlMbfDocxY/BAjr8dDv/fBwV7/O1wBHcMbsT7hGCPwY3oo4M9Bg/2+LulNsJtDB7s8benbTAGDz6j7jmfzpf+7xR/+sHTGXlvoD3H/v6M640ctxs5Ljdy3G3kuNrIMbORY2Ijx7xGj2eNHLMaOSY1crxp9HjSiHvoLbV1Ok/6ck/bOqWtft2bttqzf2emeDs5fvy4Nm/erGHDhrnKoqKiNGzYMK1bty7g8ZxatiAxMdGn8202m958800dPXpUgwcP9urciRMn6uabb3b7t/DUrl271LdvX51zzjm6++67vV76ccWKFbr88st1xx13KDk5WZdeeqleeeUVr+OQnP+nr7/+uu6//36ZTCaPzhkyZIhWr16tL7/8UpK0detWffTRRxoxYoRH5588eVI2m63JJ6ViY2N9+nTinj17VFlZ6fZ/ER8fr0GDBgXl51Jy/myaTCb16NHDp/OPHz+ul19+WfHx8crJyfHoHLvdrnvuuUdTpkzRhRde6NN116xZo+TkZGVmZuqhhx7SwYMHPT7Xbrfr73//u84//3wNHz5cycnJGjRokNdLNJ5y4MAB/f3vf2/2k1gtGTJkiFasWKH9+/fL4XDo/fff15dffqkbbrihzXMbGhokye3nMioqSjExMS3+XDb+G7R582adOHHC7WcxKytLaWlpLf4s+vt3zJPza2pqFBcXp06dOnl9/tGjR/Xaa6+pf//+6tevn08xIjT42/d4ysg+ylu+9Gne8Lf/84bRfaU3QrFfbU/+9tme8qVv94YR7wO85c/7Bk8Z/f7CG768F/GWP+9dvOHL+xwEX6iNvyX/3rsGa/wt+fc+KNjjbym0xuCh+j7Bn/7c1z46mGPwUBh/S+E3Bg/2+NuTNhiDhwZ/f1ZO50//d4q//eDpjLo30F5jf3/H9UaO29tzXB6q/WlzjBoz+zsmNnrMa9R4tj3HrP6OSY0ab/o7njTiHnpLbfnDiH7d07bau38nKd5OrFarbDabevfu7Vbeu3dvVVZWBjQWu92uwsJCXXXVVbrooou8Onfbtm3q1q2bYmJi9N///d965513dMEFF3h8/ptvvqmysjIVFxd7G7YGDRrkWnZh3rx52rNnj/Ly8nTkyBGP2/jPf/6jefPmKSMjQ++++64eeugh/fznP9fixYu9jmf58uWqrq7W+PHjPT7nV7/6le68805lZWXpjDPO0KWXXqrCwkLdfffdHp3fvXt3DR48WE8//bS++eYb2Ww2vf7661q3bp0sFovXr+HUz14o/FxKzv0wpk6dqjFjxvz/7d17cFTl4cbxJ4Qst3AxGJoE2AgFQ4nBchGrTEEaGoiMEBSChCIRKm25CFiwOoViS2VkLBAKKMXSaKq0oK2KMG2IaYkzOJoKZgpCuUq5BRkUGmkopOT9/ZFf0gSS7Dm7b7LZ8P3MZEZ29z3vWTns8z57NnvUoUMHV2O3bdumyMhItW7dWqtWrVJeXp5uvfVWR2OXL1+uli1b6vHHH/dntzVq1Cjl5OQoPz9fy5cvV0FBgVJTUx1/nce5c+d06dIlPffccxo1apR27NihcePG6cEHH1RBQYHr/XnllVfUvn37Wr82pS5r1qxR37591a1bN3k8Ho0aNUrr1q3T0KFDfY6tDN6nn35aFy5c0NWrV7V8+XKdOnWq1uOyttegs2fPyuPx3LBIrOtYDOR1zOn48+fPa+nSpZoxY4ar8S+88IIiIyMVGRmpP/3pT8rLy5PH43G9j2gabGSPUzYzyi1/Ms2NQPPPDdtZ6UZTy9WGFEhmOxVItrsR6DrArUDXDU7ZXl+44c9axK1A1i5uuF3noGloSv1b8n/tGsz+LQW+Dgp2/5aaVgdviusEf/M80IwOZgdvCv1bCq0OHuz+7WsbdPCmI9BjpVKg+Vcp0ByszuZ7Aw3V/QPt9TZ7e0P28qaYp7Wx0ZltdWKbnddmn23IzhpoJ7XVNwPpkzbeQ69vW/6yketOttVY+V7/KXs0C7NmzdK+ffv8+lRWQkKCioqK9K9//UtvvPGGpk6dqoKCAkcLk5MnT2ru3LnKy8vz6zqG1T+V1q9fP919992Kj4/Xli1bHH/ip7y8XIMGDdKyZcskSf3799e+ffu0fv16TZ061dX+bNy4Uampqa6uO7llyxa99tpr2rRpkxITE1VUVKR58+YpLi7O8fy//e1vNW3aNHXt2lXh4eEaMGCAJk2apN27d7va/6amrKxM6enpMsboxRdfdD1++PDhKioq0vnz5/XSSy8pPT1dH374obp06VLvuN27d2v16tXas2eP378d+fDDD1f9d1JSkvr166evfvWr2rlzp5KTk32OLy8vlySNHTtW8+fPlyR9/etf1/vvv6/169dr2LBhrvbnN7/5jSZPnuzq39maNWv0wQcfaOvWrYqPj9d7772nWbNmKS4uzuenaSMiIvTHP/5R06dPV1RUlMLDwzVixAilpqbKGHPD4wN5DbK1DV/jS0pKNHr0aPXt21fPPPOMq/GTJ0/Wt7/9bRUXF+sXv/iF0tPTtWvXLr9e9xB8NrLHKZsZ5ZY/meaGjfxzo7lmZVMRaGY75W+2u2FjHeBWoOsGp2yvL9zwZy3iViBrFzfcrnOA2vi7dg1m/5YCXwcFu39LdPD6BJLngWR0sDt4U+jfUmh18GD3b1/boIM3HTbe75ECy79KNnKwOpvvDTRU9w+019vu7c01P52w1ZltdGLbnddmn23IzhpoJ7XVNwPpk7ZeUxt7W05y3cm2Gi3frX8hO4wxxly5csWEh4ffcG2CRx55xIwZM8b19uTnNc1mzZplunXrZo4dO+Z6bG2Sk5PNjBkzHD32zTffNJJMeHh41Y8kExYWZsLDw81///tf1/MPGjTIPPXUU44f7/V6zfTp02vc9sILL5i4uDhX8x4/fty0aNHCvPXWW67GdevWzaxdu7bGbUuXLjUJCQmutmOMMZcuXTJnzpwxxhiTnp5u7r//fp9jrj9ujh49Wuu1UoYOHWoef/xxn+OrC+R6ZlevXjVpaWmmX79+5vz5866eQ1169eplli1b5nP8qlWrqo7B6sdlixYtTHx8vN/z33rrrWb9+vWOnsOVK1dMy5YtzdKlS2s87sknnzT33nuvq3147733jCRTVFRU575dP760tNRERETccJ2f6dOnm5EjR7qa/+LFi+bcuXPGmIrrOM6cObPG/XW9BuXn59d63RKv12tWrlzpaBvV1XcdFF/jS0pKzD333GOSk5PN5cuXXY+v7sqVK6Zt27Zm06ZNPh+L0OE2e5yylVFu+ZtpbtjMPzf8yUo3As3VQOaqrjGvKe4mswOd63p1ZXsgc7ldBwQ6X13qWzf4O5fb9UUgc1XnZC0S6Hxu1y6BzFWdr3UOmo6m0r+NsdvBg92/jXG3Dgp2/zYmuB3cxjoh2B3cRkYHu4MHu3/Xto1Q6uDB7t9O96ESHTx4bL/nXJ2b/KvUUDlYnb/vDTRE97fR6xuqtwfay232bpu92mZnttmJbXZe233WZme12Ult9k1bfdLGe+i+tlWd02uK28h1N/tVqSHzna9PbyAej0cDBw5Ufn5+1W3l5eXKz8/3+5oobhhjNHv2bL355pv6y1/+oh49eljZbnl5edV1EXxJTk7W3r17VVRUVPUzaNAgTZ48WUVFRQoPD3c196VLl3T06FHFxsY6HjNkyBAdPHiwxm2HDh1SfHy8q7mzs7PVpUsXjR492tW40tJStWhR859ZeHh41aei3GjXrp1iY2N14cIF5ebmauzYsa630aNHD8XExNQ4LktKSvThhx82ynEp/e+Tc4cPH9a7776rzp07W9mu02NzypQp+vvf/17juIyLi9PChQuVm5vr19ynTp3S559/7vjY9Hg8uuuuu6wcmxs3btTAgQNdXV+mrKxMZWVlVo7Njh07Kjo6WocPH9ZHH31UdVz6eg0aOHCgIiIiahyLBw8e1IkTJ6qOxUBfx5yMLykpUUpKijwej7Zu3Vrjk2f+zG+MkTHG8eskmj5/sscpWxnllr+Z5obN/HPDRla60RRytSE1VGY75Wbd6VRDrAPccrtucMrm+sINf9Yibtlcu7hR1zoHTU+w+7fUMB08mP1bcr8OCnb/lppWB28q64SGyHM3x2awO3iw+7cUGh082P3b6TZqm5cO3rga6j3n6vzpAQ2Rg9UF8t5AQ3R/G72+oXq77V7eVPK0Ng3dmf35t9DQnTeQPttQnTXQTtpQfdNJn7TxHrrTbblhI9cD2a+GzHe+Pr0BPfHEE5o6daoGDRqkwYMHKysrS//+97/16KOPOhp/6dIlHTlypOrPn376qYqKihQVFSWv11vv2FmzZmnTpk16++231b59+6rrC3Ts2FFt2rRxNP/TTz+t1NRUeb1effnll9q0aZN27tzp+MWzffv2N1wXoF27durcubOj6xgsWLBADzzwgOLj43XmzBktWbJE4eHhmjRpkqP5JWn+/Pm69957tWzZMqWnp6uwsFAbNmzQhg0bHG+jvLxc2dnZmjp1qlq2dPdP5oEHHtCzzz4rr9erxMREffzxx1q5cqWmTZvmeBu5ubkyxighIUFHjhzRwoUL1adPnzqPI1/Hzbx58/Tzn/9cvXv3Vo8ePbR48WLFxcUpLS3N0fgvvvhCJ06c0JkzZySpKsRiYmIUExNT7/jY2FiNHz9ee/bs0bZt23Tt2rWqYzMqKqrqGhH1baNz58569tlnNWbMGMXGxur8+fNat26dTp8+rQkTJjh6DtcvUCIiIhQTE6OEhASf46OiovTTn/5UDz30kGJiYnT06FE9+eST6tWrl0aOHOn472HhwoWaOHGihg4dquHDh+vPf/6z3nnnHe3cudPReKkieF5//XWtWLHC9XEwbNgwLVy4UG3atFF8fLwKCgqUk5OjlStXOhr/+uuvKzo6Wl6vV3v37tXcuXOVlpamlJQUSb5fgzp27Kjp06friSeeUFRUlDp06KA5c+bonnvu0Te+8Q1H25Aqrqty9uzZqn3du3ev2rdvL6/Xq0WLFtU7vjK4S0tL9eqrr6qkpEQlJSWSpOjoaM2ZM6fe8ceOHdPmzZuVkpKi6OhonTp1Ss8995zatGmj+++//4a/E4QGG9njlI2MciuQTHPDRv654TYr3Qg0V23O5SuDbc7nNLNtzOUk223N5WQdYHM+p+sGW8/N1/rC5lxS/WsR2/P5WrvYnMvXOgdNUzD7txR4Bw92/5YCXwcFu39Ljd/BbawTgt3BbWR0sDt4sPu3k2009Q4+c+bMoPbv8PBwn8+BDt402HjPubpA86+SjRyszuZ7A7a7v61eb7u3B9LLbfZum73aZme22Yltdl7bfdZmZ7XZSW32TZt90sZ76E63JdWf5VFRUY635STXnW6r0fPd+u+eo4Y1a9YYr9drPB6PGTx4sPnggw8cj638CoPrf6ZOnepzbG3jJJns7GzH80+bNs3Ex8cbj8djoqOjTXJystmxY4fj8bUZNmyYmTt3rqPHTpw40cTGxhqPx2O6du1qJk6caI4cOeJ6znfeecfccccdplWrVqZPnz5mw4YNrsbn5uYaSebgwYOu5y4pKTFz5841Xq/XtG7d2vTs2dP8+Mc/NleuXHG8jc2bN5uePXsaj8djYmJizKxZs8zFixfrfLyv46a8vNwsXrzYfOUrXzGtWrUyycnJNZ6br/HZ2dm13r9kyRKf4yu/lqa2n7/+9a+O9uHy5ctm3LhxJi4uzng8HhMbG2vGjBljCgsLHT+H68XHx5tVq1Y5Gl9aWmpSUlJMdHS0iYiIMPHx8eaxxx4zZ8+edfX3YIwxGzduNL169TKtW7c2d955Z42vPHIy/le/+pVp06ZNrceDr/HFxcUmMzPTxMXFmdatW5uEhASzYsUKU15e7mj86tWrTbdu3UxERITxer1m0aJFNY5rJ69Bly9fNjNnzjS33HKLadu2rRk3bpwpLi52tY0lS5bU+Rhf4+t6jpLqPVYrx58+fdqkpqaaLl26mIiICNOtWzeTkZFh/vGPf9R6nCE02MoepwLNKLcCyTQ3bOSfG26z0o1Ac9XmXL4y2OZ8TjPbxlxOst3WXLW5fh1gcz6n6wabz62+9YXtuepbi9iez9faxeZcvtY5aLqC1b+NCbyDB7t/G2NnHRTM/m1M43dwG+uEYHdwGxkd7A4e7P7tZBtNvYM7Gd+Q/dvJPtDBm4ZA8+56DZF/ldzmYHW23xuw2f1t9XrbvT2QXm6zd9vs1TY7s81ObLPz2u6zNjurzU5qs2/a7JOB5rfbbdWX5W625STXnW6rsfM97P93CgAAAAAAAAAAAACAZodrigMAAAAAAAAAAAAAmi1OigMAAAAAAAAAAAAAmi1OigMAAAAAAAAAAAAAmi1OigMAAAAAAAAAAAAAmi1OigMAAAAAAAAAAAAAmi1OigMAAAAAAAAAAAAAmi1OigMAAAAAAAAAAAAAmi1OigMAAAAAAAAAAAAAmi1OigMIig0bNqh79+5q0aKFsrKygr07AADAAfIbAIDQRIYDABB6yG/ALk6KAyEsMzNTaWlpN9y+c+dOhYWF6eLFi42+T06UlJRo9uzZ+tGPfqTTp09rxowZtT4uLCys6qddu3bq3bu3MjMztXv37kbeYwAA7CG/AQAITWQ4AAChh/wGUImT4gD8VlZW5te4EydOqKysTKNHj1ZsbKzatm1b52Ozs7NVXFysTz75ROvWrdOlS5d09913Kycnx9/dBgDgpkZ+AwAQmshwAABCD/kNNB2cFAduEn/4wx+UmJioVq1a6bbbbtOKFStq3B8WFqa33nqrxm2dOnXSyy+/LEk6fvy4wsLCtHnzZg0bNkytW7fWa6+9VutcJ06c0NixYxUZGakOHTooPT1dn332mSTp5ZdfVlJSkiSpZ8+eCgsL0/Hjx+vc706dOikmJka33XabUlJS9MYbb2jy5MmaPXu2Lly4IEn6/PPPNWnSJHXt2lVt27ZVUlKSfve731VtIycnR507d9aVK1dqbDstLU1Tpkzx+f8OAIBgIb/JbwBAaCLDyXAAQOghv8lvNG+cFAduArt371Z6eroefvhh7d27V88884wWL15cFdZuPPXUU5o7d64OHDigkSNH3nB/eXm5xo4dqy+++EIFBQXKy8vTsWPHNHHiREnSxIkT9e6770qSCgsLVVxcrO7du7vah/nz5+vLL79UXl6eJOk///mPBg4cqO3bt2vfvn2aMWOGpkyZosLCQknShAkTdO3aNW3durVqG+fOndP27ds1bdo01/8PAABoDOQ3+Q0ACE1kOBkOAAg95Df5jeavZbB3AEBgtm3bpsjIyBq3Xbt2rcafV65cqeTkZC1evFiSdPvtt2v//v16/vnnlZmZ6Wq+efPm6cEHH6zz/vz8fO3du1effvppVVDn5OQoMTFRf/vb33TXXXepc+fOkqTo6GjFxMS4ml+S+vTpI0lVn47r2rWrFixYUHX/nDlzlJubqy1btmjw4MFq06aNMjIylJ2drQkTJkiSXn31VXm9Xt13332u5wcAIFDkN/kNAAhNZDgZDgAIPeQ3+Q1I/KY4EPKGDx+uoqKiGj+//vWvazzmwIEDGjJkSI3bhgwZosOHD98Q/r4MGjSo3vsPHDig7t271/jkWt++fdWpUycdOHDA1Vx1McZIqvi6GqliAbN06VIlJSUpKipKkZGRys3N1YkTJ6rGPPbYY9qxY4dOnz4tqeIraDIzM6u2AQBAYyK/yW8AQGgiw8lwAEDoIb/Jb0DiN8WBkNeuXTv16tWrxm2nTp1yvZ2wsLCqoKxUVlZW63zBVrkw6NGjhyTp+eef1+rVq5WVlaWkpCS1a9dO8+bN09WrV6vG9O/fX3feeadycnKUkpKiTz75RNu3bw/K/gMAQH6T3wCA0ESGk+EAgNBDfpPfgMRJceCm8LWvfU27du2qcduuXbt0++23Kzw8XFLF17AUFxdX3X/48GGVlpb6NdfJkyd18uTJqk+67d+/XxcvXlTfvn0DeBb/k5WVpQ4dOmjEiBGSKp7L2LFj9Z3vfEdSxTVZDh06dMN83/3ud5WVlaXTp09rxIgRrq/DAgBAYyK/K5DfAIBQQ4ZXIMMBAKGE/K5AfqM54+vTgZvAD3/4Q+Xn52vp0qU6dOiQXnnlFa1du7bGNUS+9a1vae3atfr444/10Ucf6fvf/74iIiJczzVixAglJSVp8uTJ2rNnjwoLC/XII49o2LBhPr82pjYXL17U2bNn9c9//lN5eXkaP368Nm3apBdffFGdOnWSJPXu3Vt5eXl6//33deDAAX3ve9/TZ599dsO2MjIydOrUKb300kuaNm2a630BAKAxkd8VyG8AQKghwyuQ4QCAUEJ+VyC/0ZxxUhy4CQwYMEBbtmzR73//e91xxx36yU9+op/97GfKzMysesyKFSvUvXt3ffOb31RGRoYWLFigtm3bup4rLCxMb7/9tm655RYNHTpUI0aMUM+ePbV582a/9v3RRx9VbGys+vTpox/84AeKjIxUYWGhMjIyqh6zaNEiDRgwQCNHjtR9992nmJgYpaWl3bCtjh076qGHHlJkZGSt9wMA0JSQ3xXIbwBAqCHDK5DhAIBQQn5XIL/RnIWZ6y+AAADNWHJyshITE/XLX/4y2LsCAAAcIr8BAAhNZDgAAKGH/EZzxUlxADeFCxcuaOfOnRo/frz279+vhISEYO8SAADwgfwGACA0keEAAIQe8hvNXctg7wAANIb+/fvrwoULWr58OWEOAECIIL8BAAhNZDgAAKGH/EZzx2+KAwAAAAAAAAAAAACarRbB3gEAAAAAAAAAAAAAABoKJ8UBAAAAAAAAAAAAAM0WJ8UBAAAAAAAAAAAAAM0WJ8UBAAAAAAAAAAAAAM0WJ8UBAAAAAAAAAAAAAM0WJ8UBAAAAAAAAAAAAAM0WJ8UBAAAAAAAAAAAAAM0WJ8UBAAAAAAAAAAAAAM0WJ8UBAAAAAAAAAAAAAM3W/wE5zNEzHWJv1QAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 2000x1000 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Create a new DataFrame\n",
+    "legs_acbm_clean = legs_acbm.copy()\n",
+    "\n",
+    "# Convert tst to datetime format\n",
+    "legs_acbm_clean['tst'] = pd.to_datetime(legs_acbm_clean['tst'])\n",
+    "\n",
+    "# Extract the hour component\n",
+    "legs_acbm_clean['hour'] = legs_acbm_clean['tst'].dt.hour\n",
+    "\n",
+    "# Convert legs_nts[\"tst\"] from minutes to hours\n",
+    "legs_nts['hour'] = legs_nts['tst'] // 60\n",
+    "\n",
+    "# Get unique dact values\n",
+    "unique_dact_values = legs_acbm_clean['dact'].unique()\n",
+    "\n",
+    "# Create a figure with subplots\n",
+    "num_plots = len(unique_dact_values)\n",
+    "num_cols = 4\n",
+    "num_rows = (num_plots + num_cols - 1) // num_cols\n",
+    "fig, axes = plt.subplots(num_rows, num_cols, figsize=(20, num_rows * 5), sharey=False)\n",
+    "axes = axes.flatten()\n",
+    "\n",
+    "# Iterate over unique dact values and create plots\n",
+    "for i, dact_value in enumerate(unique_dact_values):\n",
+    "    ax = axes[i]\n",
+    "    acbm_data = legs_acbm_clean[legs_acbm_clean['dact'] == dact_value]\n",
+    "    nts_data = legs_nts[legs_nts['dact'] == dact_value]\n",
+    "\n",
+    "    # Plot histogram for acbm_data\n",
+    "    sns.histplot(acbm_data['hour'], bins=24, kde=False, discrete=True, stat='percent', ax=ax)\n",
+    "    ax.set_xlabel('Hour of Day')\n",
+    "    ax.set_ylabel('Percentage')\n",
+    "    ax.set_xticks(range(0, 24))  # Set x-ticks to represent each hour of the day\n",
+    "    ax.set_title(f'dact: {dact_value}')\n",
+    "    \n",
+    "    # Add dots to represent the percentage values of legs_nts[\"hour\"]\n",
+    "    nts_hour_counts = nts_data['hour'].value_counts(normalize=True).sort_index() * 100\n",
+    "    for hour, percent in nts_hour_counts.items():\n",
+    "        ax.plot(hour, percent, 'ro')  # 'ro' means red dot\n",
+    "\n",
+    "# Remove any empty subplots\n",
+    "for j in range(i + 1, len(axes)):\n",
+    "    fig.delaxes(axes[j])\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('O')"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# get the column type of tst\n",
+    "\n",
+    "legs_acbm['tst'].dtype"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

From 3a3f9541c34ed0fc05f25e4d422cbfd196d217f1 Mon Sep 17 00:00:00 2001
From: Hussein Mahfouz <45176416+Hussein-Mahfouz@users.noreply.github.com>
Date: Wed, 25 Sep 2024 17:57:25 +0100
Subject: [PATCH 05/11] validate distances

---
 notebooks/5_validate_outputs.ipynb | 1586 ++++++++++++++++++----------
 1 file changed, 1028 insertions(+), 558 deletions(-)

diff --git a/notebooks/5_validate_outputs.ipynb b/notebooks/5_validate_outputs.ipynb
index ced7f75..e2ce020 100644
--- a/notebooks/5_validate_outputs.ipynb
+++ b/notebooks/5_validate_outputs.ipynb
@@ -2,15 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 163,
    "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import acbm\n",
-    "\n",
     "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "import geopandas as gpd\n",
+    "from shapely import wkt\n",
+    "\n",
+    "from typing import Optional, Tuple\n"
    ]
   },
   {
@@ -22,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 164,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 165,
    "metadata": {},
    "outputs": [
     {
@@ -321,7 +324,7 @@
        "[14975 rows x 14 columns]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 165,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -330,404 +333,58 @@
     "legs_acbm\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Preprocess: Rename columns"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 166,
    "metadata": {},
    "outputs": [],
    "source": [
     "# rename origin activity and destination activity columns\n",
     "\n",
-    "legs_acbm = legs_acbm.rename(columns={\"origin activity\": \"oact\", \"destination activity\": \"dact\"})"
+    "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: Add columns"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 167,
    "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>TripID</th>\n",
-       "      <th>DayID</th>\n",
-       "      <th>IndividualID</th>\n",
-       "      <th>HouseholdID</th>\n",
-       "      <th>PSUID</th>\n",
-       "      <th>PersNo</th>\n",
-       "      <th>TravDay</th>\n",
-       "      <th>seq</th>\n",
-       "      <th>ShortWalkTrip_B01ID</th>\n",
-       "      <th>NumStages</th>\n",
-       "      <th>...</th>\n",
-       "      <th>tst</th>\n",
-       "      <th>tet</th>\n",
-       "      <th>TripDisIncSW</th>\n",
-       "      <th>TripDisExSW</th>\n",
-       "      <th>TripTotalTime</th>\n",
-       "      <th>TripTravTime</th>\n",
-       "      <th>ozone</th>\n",
-       "      <th>dzone</th>\n",
-       "      <th>W5</th>\n",
-       "      <th>W5xHH</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4659590</th>\n",
-       "      <td>2019010628</td>\n",
-       "      <td>2019004945</td>\n",
-       "      <td>2019000771</td>\n",
-       "      <td>2019000319</td>\n",
-       "      <td>2019000035</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>795.0</td>\n",
-       "      <td>810.0</td>\n",
-       "      <td>6.1</td>\n",
-       "      <td>6.1</td>\n",
-       "      <td>15</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.997967</td>\n",
-       "      <td>1.227954</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659597</th>\n",
-       "      <td>2019010396</td>\n",
-       "      <td>2019004812</td>\n",
-       "      <td>2019000748</td>\n",
-       "      <td>2019000308</td>\n",
-       "      <td>2019000033</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>505.0</td>\n",
-       "      <td>525.0</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>20</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.872082</td>\n",
-       "      <td>1.067045</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659609</th>\n",
-       "      <td>2019010979</td>\n",
-       "      <td>2019005120</td>\n",
-       "      <td>2019000798</td>\n",
-       "      <td>2019000329</td>\n",
-       "      <td>2019000036</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>...</td>\n",
-       "      <td>445.0</td>\n",
-       "      <td>556.0</td>\n",
-       "      <td>52.3</td>\n",
-       "      <td>52.3</td>\n",
-       "      <td>111</td>\n",
-       "      <td>101.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.097494</td>\n",
-       "      <td>1.007174</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659616</th>\n",
-       "      <td>2019010994</td>\n",
-       "      <td>2019005127</td>\n",
-       "      <td>2019000799</td>\n",
-       "      <td>2019000330</td>\n",
-       "      <td>2019000036</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>720.0</td>\n",
-       "      <td>750.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>30</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.784036</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4659617</th>\n",
-       "      <td>2019010996</td>\n",
-       "      <td>2019005127</td>\n",
-       "      <td>2019000799</td>\n",
-       "      <td>2019000330</td>\n",
-       "      <td>2019000036</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>985.0</td>\n",
-       "      <td>1041.0</td>\n",
-       "      <td>17.1</td>\n",
-       "      <td>17.1</td>\n",
-       "      <td>56</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.784036</td>\n",
-       "      <td>1.000000</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",
-       "      <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>2022111153</td>\n",
-       "      <td>2022059517</td>\n",
-       "      <td>2022009859</td>\n",
-       "      <td>2022004366</td>\n",
-       "      <td>2022000756</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1020.0</td>\n",
-       "      <td>1035.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>15</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>9</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>0.912527</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5150079</th>\n",
-       "      <td>2022111179</td>\n",
-       "      <td>2022059524</td>\n",
-       "      <td>2022009860</td>\n",
-       "      <td>2022004366</td>\n",
-       "      <td>2022000756</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>480.0</td>\n",
-       "      <td>525.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>45</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>9</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.912527</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5150080</th>\n",
-       "      <td>2022111180</td>\n",
-       "      <td>2022059524</td>\n",
-       "      <td>2022009860</td>\n",
-       "      <td>2022004366</td>\n",
-       "      <td>2022000756</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>965.0</td>\n",
-       "      <td>1035.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>70</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>0.912527</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5150098</th>\n",
-       "      <td>2022111198</td>\n",
-       "      <td>2022059531</td>\n",
-       "      <td>2022009864</td>\n",
-       "      <td>2022004368</td>\n",
-       "      <td>2022000756</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>645.0</td>\n",
-       "      <td>660.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>15</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>9</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.621441</td>\n",
-       "      <td>1.152563</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5150099</th>\n",
-       "      <td>2022111199</td>\n",
-       "      <td>2022059531</td>\n",
-       "      <td>2022009864</td>\n",
-       "      <td>2022004368</td>\n",
-       "      <td>2022000756</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>900.0</td>\n",
-       "      <td>915.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>15</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>9</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>1.621441</td>\n",
-       "      <td>1.152563</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>59359 rows × 25 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             TripID       DayID  IndividualID  HouseholdID       PSUID  \\\n",
-       "4659590  2019010628  2019004945    2019000771   2019000319  2019000035   \n",
-       "4659597  2019010396  2019004812    2019000748   2019000308  2019000033   \n",
-       "4659609  2019010979  2019005120    2019000798   2019000329  2019000036   \n",
-       "4659616  2019010994  2019005127    2019000799   2019000330  2019000036   \n",
-       "4659617  2019010996  2019005127    2019000799   2019000330  2019000036   \n",
-       "...             ...         ...           ...          ...         ...   \n",
-       "5150053  2022111153  2022059517    2022009859   2022004366  2022000756   \n",
-       "5150079  2022111179  2022059524    2022009860   2022004366  2022000756   \n",
-       "5150080  2022111180  2022059524    2022009860   2022004366  2022000756   \n",
-       "5150098  2022111198  2022059531    2022009864   2022004368  2022000756   \n",
-       "5150099  2022111199  2022059531    2022009864   2022004368  2022000756   \n",
-       "\n",
-       "         PersNo  TravDay  seq  ShortWalkTrip_B01ID  NumStages  ...     tst  \\\n",
-       "4659590       1        3    3                    2          1  ...   795.0   \n",
-       "4659597       2        3    1                    2          1  ...   505.0   \n",
-       "4659609       2        3    1                    2          3  ...   445.0   \n",
-       "4659616       1        3    1                    2          1  ...   720.0   \n",
-       "4659617       1        3    3                    2          1  ...   985.0   \n",
-       "...         ...      ...  ...                  ...        ...  ...     ...   \n",
-       "5150053       3        3    6                    2          1  ...  1020.0   \n",
-       "5150079       4        3    1                    2          1  ...   480.0   \n",
-       "5150080       4        3    2                    2          2  ...   965.0   \n",
-       "5150098       1        3    1                    2          1  ...   645.0   \n",
-       "5150099       1        3    2                    2          1  ...   900.0   \n",
-       "\n",
-       "            tet TripDisIncSW TripDisExSW  TripTotalTime  TripTravTime  ozone  \\\n",
-       "4659590   810.0          6.1         6.1             15          15.0      8   \n",
-       "4659597   525.0          3.5         3.5             20          20.0      2   \n",
-       "4659609   556.0         52.3        52.3            111         101.0      8   \n",
-       "4659616   750.0         18.0        18.0             30          30.0      8   \n",
-       "4659617  1041.0         17.1        17.1             56          56.0      8   \n",
-       "...         ...          ...         ...            ...           ...    ...   \n",
-       "5150053  1035.0          5.0         5.0             15          15.0      9   \n",
-       "5150079   525.0         12.0        12.0             45          45.0      9   \n",
-       "5150080  1035.0         19.0        19.0             70          60.0      8   \n",
-       "5150098   660.0          7.0         7.0             15          15.0      9   \n",
-       "5150099   915.0          7.0         7.0             15          15.0      9   \n",
-       "\n",
-       "         dzone        W5     W5xHH  \n",
-       "4659590    8.0  0.997967  1.227954  \n",
-       "4659597    2.0  0.872082  1.067045  \n",
-       "4659609    7.0  1.097494  1.007174  \n",
-       "4659616    8.0  0.784036  1.000000  \n",
-       "4659617    8.0  0.784036  1.000000  \n",
-       "...        ...       ...       ...  \n",
-       "5150053    9.0  0.912527  1.000000  \n",
-       "5150079    8.0  0.912527  1.000000  \n",
-       "5150080    9.0  0.912527  1.000000  \n",
-       "5150098    9.0  1.621441  1.152563  \n",
-       "5150099    9.0  1.621441  1.152563  \n",
-       "\n",
-       "[59359 rows x 25 columns]"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "legs_nts"
+    "# 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": 31,
+   "execution_count": 168,
    "metadata": {},
    "outputs": [
     {
@@ -759,6 +416,8 @@
        "      <th>mode</th>\n",
        "      <th>tst</th>\n",
        "      <th>tet</th>\n",
+       "      <th>tst_hour</th>\n",
+       "      <th>tet_hour</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -772,6 +431,8 @@
        "      <td>car</td>\n",
        "      <td>795.0</td>\n",
        "      <td>810.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>13.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4659597</th>\n",
@@ -783,6 +444,8 @@
        "      <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",
@@ -794,6 +457,8 @@
        "      <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",
@@ -805,6 +470,8 @@
        "      <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",
@@ -816,6 +483,8 @@
        "      <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",
@@ -827,6 +496,8 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5150053</th>\n",
@@ -838,6 +509,8 @@
        "      <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",
@@ -849,6 +522,8 @@
        "      <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",
@@ -860,6 +535,8 @@
        "      <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",
@@ -871,6 +548,8 @@
        "      <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",
@@ -882,30 +561,45 @@
        "      <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 × 8 columns</p>\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",
+       "         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 8 columns]"
+       "[59359 rows x 10 columns]"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 168,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -922,6 +616,8 @@
     "        \"mode\",\n",
     "        \"tst\",\n",
     "        \"tet\",\n",
+    "        \"tst_hour\",\n",
+    "        \"tet_hour\",\n",
     "    ]\n",
     "]"
    ]
@@ -949,7 +645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 169,
    "metadata": {},
    "outputs": [
     {
@@ -990,7 +686,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 170,
    "metadata": {},
    "outputs": [
     {
@@ -1157,7 +853,7 @@
        "7       work   1673   acbm   11.171953"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 170,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1176,7 +872,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 171,
    "metadata": {},
    "outputs": [
     {
@@ -1223,12 +919,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 172,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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nr01wcDCAf4+jJ06cwKVLlxAeHo6UlBTt5/jgwQN07doVe/bsMep15ufx/cLR0VF7nFIqlUhJSUGVKlXQpEkTnW26pPdXS2EiYwEuLi4A/t0Ii3Ljxg3Y2dnpjYipWbMm3NzccOPGDZ3yvMkKALi6ugIA6tSpk295amqqTrmdnZ3OAQxQd8gEoDPM99dff8VTTz2lHX5avXp1fPnll0hLS9N7DfXq1SvqZQIA5s+fjzNnzqBOnTp48sknMWfOHJ2kQ/NamzRpovdYf39/vfeiUqVKegd7d3d3vdf8uIKex8HBAfXr19d7HkPdu3cP//vf/9CpUydcvnxZe+vQoQOOHDmCixcvAgDu3LmD9PR0NG/e3KTnyY+npye6du2KDRs2aMvWr1+PChUqoF+/fibVaWdnhyFDhuDnn39GZmYmAHWiVqlSJbz44ovFjjkjIwOAbtK/cuVKPPHEE6hUqRKqVauG6tWrY8uWLflud/m5e/cuJkyYgBo1asDJyQnVq1fXbp9F1VG1atUi91vNck3Mhr7vly5dwm+//Ybq1avr3EJDQwH826lcw9B9SsPT0xOhoaF6tzZt2uisd+fOHWRmZua7jwUEBEClUiE+Pt6o5y4s5kGDBqFDhw545ZVXUKNGDQwePBgbNmwo8sve2OOiuTx+fHV3dwfw73H00qVLAIARI0bofZZff/01srOzDd5WC/L4fqFSqfDpp5+iUaNGcHR0hKenJ6pXr45Tp07pPFdJ76+WwkTGAlxcXFCrVi2cOXPGqMcZ+ivc3t7eqHIRMSoOANi7dy9eeOEFVKpUCV988QX+97//YceOHQgPD8+3PkNHJgwcOBBXr17F559/jlq1amHBggVo1qwZtm7danSMQMGv2VI2btyI7OxsfPLJJ2jUqJH2pvlVmfeXUkkYPHgwLl68iBMnTgAANmzYgK5du8LT09PkOocPH46MjAz8/PPPEBGsXbsWvXr10ibKxXHmzBnY29trvwBXr16tnbPnm2++wW+//YYdO3agS5cuBv/KHThwIL766iuMHTsWMTEx2L59O3777TcAKLKOgIAApKWlFTqXx6lTpwBApwXSkPddpVLh2WefzbfVZMeOHejfv7/O85TGaJ+iFHRMUiqV+ZbnF7OTkxP27NmD33//HcOGDcOpU6cwaNAgPPvsswXWY0gMJaWo46hmG1qwYEGBn6WmRcVUZ86cgZeXl/ZH8bx58xAZGYmOHTti9erV2LZtG3bs2IFmzZrpbdMlub9aCodfW0ivXr2wfPlyHDhwQOc0UH7q1q0LlUqFS5cuISAgQFt++/Zt3Lt3D3Xr1jVrbCqVClevXtW2wgDQthRo5ir58ccfUalSJWzbtg2Ojo7a9VasWFHs5/f29sbrr7+O119/HUlJSQgMDMT777+P5557TvtaL1y4gC5duug87sKFC2Z7L/I+T97WqZycHFy7dk37K9lYa9asQfPmzTF79my9ZcuWLcPatWsRFRWF6tWrw8XFpchk19iDeJ8+fTBmzBjtaY6LFy9i2rRpRT6usOdp3rw5WrdujTVr1sDHxwdxcXH4/PPPjYorP3Fxcfjzzz/x9NNPa395btq0CfXr10dMTIxOTI+/nwXFm5qaip07dyIqKgrvvPOOtlzzK7oovXr1wrp167Bq1SrMnDlTb3l6ejp++eUX+Pv767QUGPK+N2jQABkZGSZvW+ZSvXp1VK5cGRcuXNBbdv78edjZ2WlbdzWtEffu3dOZfsDY1hA7Ozt07doVXbt2xcKFCzFv3jzMmDEDu3btKvD9KKnjYnETowYNGgBQ/2Atic/ywIEDuHLlis6poE2bNqFz58745ptvdNa9d++e3o+UktpfLYktMhYyZcoUODs745VXXsHt27f1ll+5cgWLFi0CAO0cGo/PNrlw4UIA6v4h5rZ48WLt/yKCxYsXo2LFiujatSsA9a8ShUKh84vp+vXr+Pnnn01+TqVSqdfk6uXlhVq1aiE7OxsA0LZtW3h5eWHp0qXaMgDYunUrYmNjzfZehIaGwsHBAZ999plOC9M333yDtLQ0k54nPj4ee/bswcCBAzFgwAC926hRo3D58mUcPHgQdnZ26NOnD/773//iyJEjenVpYnJ2dgagPmAZws3NDWFhYdiwYQN++OEHODg4oE+fPkU+ztnZudDm8GHDhmH79u2Ijo5GtWrV8NxzzxkUT0Hu3r2Ll156CUqlEjNmzNCWa34N5/1MDh48iAMHDug8vnLlygD035f8Hg/o71sFGTBgAJo2bYoPP/xQ73NRqVQYN24cUlNT9RIrQ973gQMH4sCBA9i2bZve8967dw+5ubkGxVhc9vb26NatG3755RedU8m3b9/G2rVr8cwzz2hbAjRf2nn77zx48AArV640+Pnu3r2rV6a5hEDeffxxJXVcLGpbL0qbNm3QoEEDfPzxx9pTQHnduXPH5Lpv3LiBkSNHwsHBAZMnT9aW29vb623TGzduREJCQr71mHt/tTS2yFhIgwYNsHbtWgwaNAgBAQE6M/vu378fGzduxMiRIwEALVu2xIgRI7B8+XLcu3cPnTp1wqFDh7By5Ur06dMHnTt3NmtslSpVwm+//YYRI0agXbt22Lp1K7Zs2YLp06dr+5v07NkTCxcuRPfu3REeHo6kpCQsWbIEDRs21DatG+v+/fvw8fHBgAED0LJlS1SpUgW///47Dh8+jE8++QQAULFiRXz00UcYNWoUOnXqhJdeegm3b9/GokWL4Ofnh4kTJ5rlPahevTqmTZuGqKgodO/eHS+88AIuXLiAL774AkFBQSZ1jFu7di1EBC+88EK+y3v06IEKFSpgzZo1aNeuHebNm4ft27ejU6dOeO211xAQEIDExERs3LgR+/btg5ubG1q1agV7e3t89NFHSEtLg6OjI7p06QIvL68C4xg0aBCGDh2KL774AmFhYQZN5NemTRusX78ekZGRCAoKQpUqVfD8889rl4eHh2PKlCn46aefMG7cOFSsWNHg9+XixYtYvXo1RATp6ek4efIkNm7ciIyMDO02ptGrVy/ExMSgb9++6NmzJ65du4alS5eiadOmOl8aTk5OaNq0KdavX4/GjRvDw8MDzZs3R/PmzdGxY0fMnz8fjx49Qu3atbF9+3Zcu3bNoFgdHBywadMmdO3aFc8884zOzL5r167FsWPH8NZbb+U762pR7/vkyZOxefNm9OrVCyNHjkSbNm3w4MEDnD59Gps2bcL169eLdQrQGO+99x527NiBZ555Bq+//joqVKiAZcuWITs7G/Pnz9eu161bN/j6+uLll1/G5MmTYW9vj2+//RbVq1c3eCr9uXPnYs+ePejZsyfq1q2LpKQkfPHFF/Dx8cEzzzxT4ONK6rhY1LZeFDs7O3z99dd47rnn0KxZM4waNQq1a9dGQkICdu3aBRcXF/z3v/8tsp5jx45h9erVUKlUuHfvHg4fPowff/wRCoUC33//PZ544gntur169cLcuXMxatQotG/fHqdPn8aaNWv0+jpqFGd/tUoWGStFWhcvXpRXX31V/Pz8xMHBQapWrSodOnSQzz//XGdSp0ePHklUVJTUq1dPKlasKHXq1Cl0QrzHIZ9hrvkN88tvQrwaNWrI7Nmz9Yb3ffPNN9KoUSNxdHQUf39/WbFiRb7DMfN77rzLNENks7OzZfLkydKyZUvtJFwtW7bMd86X9evXS+vWrcXR0VE8PDwKnRDvcQUNGc3P4sWLxd/fXypWrCg1atSQcePG6cxVk7e+ooaPtmjRQnx9fQtdJyQkRLy8vOTRo0ciop4Ubvjw4VK9enVxdHSU+vXry/jx43WGf3711VdSv359sbe31xk2/fjwa4309HRxcnISADrz82jkN/w6IyNDwsPDxc3NTWeSsLx69OihN/S/KMgz/NfOzk7c3NykdevWMmHChHyH66tUKpk3b57UrVtXHB0dpXXr1vLrr7/KiBEj9GLav3+/tGnTRhwcHHS2s5s3b0rfvn3Fzc1NXF1d5cUXX5R//vmnwOHa+UlKSpLIyEhp2LChODo6ipubm4SGhmqHXOenqPddRD1p2bRp06Rhw4bi4OAgnp6e0r59e/n444+1cxcZMjz3caZOiBcWFiZVqlSRypUrS+fOnfP9bI8ePSrt2rUTBwcH8fX1lYULFxY6Id7jdu7cKb1795ZatWqJg4OD1KpVS1566SW9Yej5MfS4aMzw64K29YLeJ83n8fgke8ePH5d+/fpJtWrVxNHRUerWrSsDBw6UnTt3Fvr8j0/oWKFCBfHw8JB27drJtGnT8p3r5eHDh/LWW2+Jt7e3ODk5SYcOHeTAgQMFHgNETNtfrZVCxISenlRmjRw5Eps2bcq3SZSoMH379sXp06dx+fJlS4dCREUoS/sr+8gQUbElJiZiy5YtGDZsmKVDIaIilLX9lX1kiMhk165dw19//YWvv/4aFStWxJgxYywdEhEVoKzur2yRISKT/fnnnxg2bBiuXbuGlStXombNmpYOiYgKUFb3V/aRISIiIpvFFhkiIiKyWUxkiIiIyGaV+c6+KpUK//zzD6pWrVrq1+QgIiIi04gI7t+/j1q1ammv7p2fMp/I/PPPP3pXfSYiIiLbEB8fDx8fnwKXl/lERnOxufj4eO31QYiIiMi6paeno06dOtrv8YKU+URGczrJxcWFiQwREZGNKapbCDv7EhERkc1iIkNEREQ2i4kMERER2SwmMkRERGSzmMgQERGRzWIiQ0RERDbLoonMnj178Pzzz6NWrVpQKBT4+eefdZaLCN555x14e3vDyckJoaGhuHTpkmWCJSIiIqtj0UTmwYMHaNmyJZYsWZLv8vnz5+Ozzz7D0qVLcfDgQTg7OyMsLAwPHz4s5UiJiIjIGll0QrznnnsOzz33XL7LRATR0dGYOXMmevfuDQBYtWoVatSogZ9//hmDBw8uzVCJiIjIClltH5lr167h1q1bCA0N1Za5urqiXbt2OHDgQIGPy87ORnp6us6NiKjMUiqB3buBdevUf5VKS0dEVKqsNpG5desWAKBGjRo65TVq1NAuy88HH3wAV1dX7Y0XjCSiMismBvDzAzp3BsLD1X/9/NTlROWE1SYyppo2bRrS0tK0t/j4eEuHRERkfjExwIABwM2buuUJCepyJjNUTlhtIlOzZk0AwO3bt3XKb9++rV2WH0dHR+0FInmhSCIqk5RKYMIEQER/maYsIoKnmahcsNpEpl69eqhZsyZ27typLUtPT8fBgwfx9NNPWzAyIiIL27tXvyUmLxEgPl69HlEZZ9FRSxkZGbh8+bL2/rVr13DixAl4eHjA19cXEREReO+999CoUSPUq1cPs2bNQq1atdCnTx/LBU1EZGmJieZdj8iGWTSROXLkCDp37qy9HxkZCQAYMWIEvvvuO0yZMgUPHjzAa6+9hnv37uGZZ57Bb7/9hkqVKlkqZCLLUyrVv7QTEwFvbyA4GLC3t3RUVJq8vc27HpENU4jkd5K17EhPT4erqyvS0tLYX4ZsX0yMum9E3tMKPj7AokVAv36Wi4tKl1KpHp2UkJB/PxmFQr1dXLvGJJdslqHf31bbR4aIHsNRKqRhb69OXgF10pKX5n50NJMYKheYyBDZAo5Socf16wds2gTUrq1b7uOjLmcLHZUTFu0jQ0QGMmaUSkhIqYVFFtavH9C7N/tMUbnGRIbICsXFxSE5OVl7333/ftQz4HHX9u9H6mPnkj09PeHr62vmCMlq2NszeaVyjZ19iaxMXFwc/P0DkJWVqS3rBGC3AY8NAfDnY2VOTpVx/nwskxkisimGfn+zRYbIyiQnJyMrKxPtRs+Gi7cfAMBOpcKdxZNQ7X5qvh3bVACSXTxQafwCPGv37xrpiddx8NsoJCcnM5EhojKJiQyRlXLx9oOHbxPt/VXhkxG5bDpU0O2lrwKgAPD9S5Pg5hdQylESEVkWRy0R2YjDgSFYOGYeUt29dMrvunth4Zh5OBwYYpnAiIgsiC0yRDbkcGAIjrQKRsClk3BLS8Y9V0/ENmoJseMoFSIqn5jIENkYsbPHuSaBlg6DiMgq8NQSERER2SwmMkRERGSzmMgQERGRzWIiQ0RERDaLiQwRERHZLCYyREREZLOYyBAREZHNYiJDRERENouJDBEREdksJjJERERks5jIEBERkc1iIkNEREQ2i4kMERER2SwmMkRERGSzmMgQERGRzWIiQ0RERDaLiQwRERHZLCYyREREZLOYyBAREZHNYiJDRERENouJDBEREdksJjJERERks5jIEBERkc1iIkNEREQ2i4kMERER2SwmMkRERGSzmMgQERGRzapg6QCIiKhgcXFxSE5ONktdnp6e8PX1NUtdRNaCiQwRkZWKi4uDv38AsrIyzVKfk1NlnD8fy2SGyhQmMkREVio5ORlZWZloN3o2XLz9ilVXeuJ1HPw2CsnJyUxkqExhIkNEZOVcvP3g4dvE0mHo4CkvshZMZIiIyCg85UXWhIkMEVE5Ehsba5Y6eMqLrAUTGSKiciArLQWAAkOHDjVbnU4etazulBeVP0xkiIjKgUeZ9wEIWoW/jer1/ItVV+LpAzizeTlyc3PNExxRMTCRISIqR6p4+Ra7FSU98bp5giEyA87sS0RERDaLiQwRERHZLCYyREREZLOYyBAREZHNYiJDRERENouJDBEREdksJjJERERksziPDBERQaFSIuDSSbilJeOeqydiG7WE2NlbOiyiIjGRISIq54KO7cbIDdGolpqkLUtx98J3AyNwODDEcoERGYCnloiIyrGgY7sRuWw6PPIkMQDgnpqEyGXTEXRst2UCIzIQExkionJKoVJi5IZo9f+PLbMDIABGbIiGQqUs5ciIDMdEhoionAq4dBLVUpP0khgNOwCeqUkIuHSyNMMiMgoTGSKicsotLdms6xFZAhMZIqJy6p6rp1nXI7IEJjJEROVUbKOWSHH3gqqA5SoAye5eiG3UsjTDIjKKVScySqUSs2bNQr169eDk5IQGDRrg3XffhYhYOjQiIpsndvb4bmAEFIBeMqOCugPwyoERnE+GrJpVJzIfffQRvvzySyxevBixsbH46KOPMH/+fHz++eeWDo2IqEw4HBiChWPmIdXdS6f8rrsXFo6Zx3lkyOpZ9YR4+/fvR+/evdGzZ08AgJ+fH9atW4dDhw4V+Jjs7GxkZ2dr76enp5d4nERknLi4OCQnm6cDqaenJ3x9fc1SV3l1ODAER1oFc2ZfsklWnci0b98ey5cvx8WLF9G4cWOcPHkS+/btw8KFCwt8zAcffICoqKhSjJKIjBEXFwd//wBkZWWapT4np8o4fz6WyUwxiZ09zjUJtHQYREaz6kRm6tSpSE9Ph7+/P+zt7aFUKvH+++9jyJAhBT5m2rRpiIyM1N5PT09HnTp1SiNcIjJAcnIysrIy0W70bLh4+xWrrvTE6zj4bRSSk5OZyBCVU1adyGzYsAFr1qzB2rVr0axZM5w4cQIRERGoVasWRowYke9jHB0d4ejoWMqREpGxXLz94OHbJN9lvIAhERnKqhOZyZMnY+rUqRg8eDAAoEWLFrhx4wY++OCDAhMZIrJtvIAhERnDqkctZWZmws5ON0R7e3uoVAXNekBEtowXMCQiY1l1IvP888/j/fffx5YtW3D9+nX89NNPWLhwIfr27Wvp0IjIzHgBQyIyhVWfWvr8888xa9YsvP7660hKSkKtWrUwZswYvPPOO5YOjYjMTHMBw4LkvYAhR9cQkYZVJzJVq1ZFdHQ0oqOjLR0KEZUwXsCQiExh1aeWiKj84AUMicgUTGSIyCrwAoZEZAomMkRkFXgBw7JDoVKi6YVjaH9oO5peOMYO2lSirLqPDBGVL5oLGD4+j8xddy+s5DwyNoHzAFFpYyJDRFaFFzC0XZp5gB6nmQeIV9OmksBEhoisDi9gaHuKmgdIBfU8QEdaBTMpJbNiHxkiIio2zTxAjycxGnnnASIyJyYyRERUbJwHiCyFp5aIyDyUSmDvXiAxEfD2BoKDAXueQigvOA8QWQpbZIio+GJiAD8/oHNnIDxc/dfPT11O5QLnASJLYSJDRMUTEwMMGADcvKlbnpCgLmcyUy5wHiCyFCYyRGQ6pRKYMAEQ0V+mKYuIUK9HZZ5mHqBUdy+d8rvuXhx6TSWGfWSIyHR79+q3xOQlAsTHq9cLCSm1sMhyOA8QlTYmMkRkusRE865HZQLnAaLSxESGiEzn7W3e9cqIuLg4JCcXf5hxbGysGaIhKtuYyBCR6YKDAR8fdcfe/PrJKBTq5cHBpR+bhcTFxcHfPwBZWZlmq/NRdo7Z6iIqa5jIEJHp7O2BRYvUo5MUCt1kRvH/c7xGR5er+WSSk5ORlZWJdqNnw8Xbr1h1JZ4+gDOblyM3N9c8wRGVQUxkiKh4+vUDNm1Sj17K2/HXx0edxPTrZ7HQLMnF2w8evk2KVUd64nXzBENUhjGRIaLi69cP6N2bM/sSUaljIkNE5mFvzyHWRFTqOCEeERER2Sy2yBARlTKFSskJ44jMhIkMEVEpCjq2GyM3RKNaapK2LMXdC98NjOAU/kQm4KklIqJSEnRsNyKXTYdHniQGANxTkxC5bDqCju22TGBENoyJDBGVXUolsHs3sG6d+q8FL16pUCkxckO0+v/HltkBEAAjNkRDoeIFNomMwUSGiMqmmBjAzw/o3BkID1f/9fNTl1tAwKWTqJaapJfEaNgB8ExNQsClk6UZFpHNYyJDRGVPTIx6tuHHr8ydkKAut0Ay45Zm2LWXDF2PiNTY2ZeIbJ7OxRWVSjQfNw4VRfRbP0QgAB69/jrO1KmjN2Gfp6cnfH19SyTGe66eZl2PiNSYyBCRzcpKSwGgwNChQ7VlnQDsLuQxCgAOt28j8skn8edjy5ycKuP8+dgSSWZiG7VEirsX3FOT8m0KVwG46+6F2EYtzf7cRGUZExkislmPMu8DELQKfxvV6/kDAELO/g38vKzIx3brMwYOzZ7S3k9PvI6D30YhOTm5RBIZsbPHdwMjELlsOlTQPa+vgjrBWjkwgvPJEBmJiQwRGSQuLg7JycXvv6FzGshMqnj5ai/QmJv1wKDH5NZvUeyLOhrrcGAIFo6ZpzePzF13L6zkPDJEJmEiQ0RFiouLg79/ALKyMs1W56PsHLPVlZe1n8I5HBiCI62CObMvkZkwkSGiIiUnJyMrKxPtRs+Gi7dfsepKPH0AZzYvR25urnmCe4wtnMIRO3ucaxJosecnKkuYyBCRwVy8/Yp9OiY98bp5gikET+EQlR9MZIioTOIpHKLygYkMEZVZPIVDVPZxZl8iIiKyWUxkiIiIyGYxkSEiIiKbxUSGiIiIbBYTGSIiIrJZTGSIiIjIZjGRISIiIpvFRIaIiIhsFifEIyIyhFIJ7N0LJCYC3t5AcDBgz1mCiSyNiQwRUVFiYoAJE4CbN/8t8/EBFi0C+vWzXFxExFNLRESFiokBBgzQTWIAICFBXR4TY5m4iAgAExkiooIpleqWGBH9ZZqyiAj1ekRkEUxkiIgKsnevfktMXiJAfLx6PSKyCCYyREQFSUw073pEZHZMZIiICuLtbd71iMjsmMgQERUkOFg9OkmhyH+5QgHUqaNej4gsgokMEVFB7O3VQ6wB/WRGcz86mvPJEFkQExkiosL06wds2gTUrq1b7uOjLuc8MkQWxQnxiIiK0q8f0Ls3Z/YlskJMZIiIDGFvD4SEWDoKInoMTy0RERGRzTI5kbl37x6+/vprTJs2DXfv3gUAHDt2DAkJCWYLjoiIiKgwJp1aOnXqFEJDQ+Hq6orr16/j1VdfhYeHB2JiYhAXF4dVq1aZO04iIiIiPSa1yERGRmLkyJG4dOkSKlWqpC3v0aMH9uzZY7bgiKiEKZXA7t3AunXqv7xmEBHZGJMSmcOHD2PMmDF65bVr18atW7eKHVReCQkJGDp0KKpVqwYnJye0aNECR44cMetzEJVLMTGAnx/QuTMQHq7+6+fHqzkTkU0x6dSSo6Mj0tPT9covXryI6tWrFzsojdTUVHTo0AGdO3fG1q1bUb16dVy6dAnu7u5mew6icikmBhgwQP+qzgkJ6nLOj0JENsKkROaFF17A3LlzsWHDBgCAQqFAXFwc3n77bfTv399swX300UeoU6cOVqxYoS2rV6+e2eonKpeUSmDCBP0kBlCXKRRARIR63hTOk0JEVs6kU0uffPIJMjIy4OXlhaysLHTq1AkNGzZE1apV8f7775stuM2bN6Nt27Z48cUX4eXlhdatW+Orr74q9DHZ2dlIT0/XuRFRHnv3AjdvFrxcBIiPV69HRGTlTGqRcXV1xY4dO7Bv3z6cOnUKGRkZCAwMRGhoqFmDu3r1Kr788ktERkZi+vTpOHz4MN588004ODhgxIgR+T7mgw8+QFRUlFnjICpTEhPNux4RkQUVa2bfZ555Bs8884y5YtGjUqnQtm1bzJs3DwDQunVrnDlzBkuXLi0wkZk2bRoiIyO199PT01GnTp0Si5HI5nh7m3e9MiY2Ntaijyci45iUyHz22Wf5lisUClSqVAkNGzZEx44dYV/M8+ve3t5o2rSpTllAQAB+/PHHAh/j6OgIR0fHYj0vUZkWHKy+4GFCQv79ZBQK9fLg4NKPzYKy0lIAKDB06FCz1PcoO8cs9RBR4UxKZD799FPcuXMHmZmZ2hFEqampqFy5MqpUqYKkpCTUr18fu3btKlZrSIcOHXDhwgWdsosXL6Ju3bom10lU7tnbA4sWqUcnKRS6yYxCof4bHV3uOvo+yrwPQNAq/G1Ur+dvcj2Jpw/gzOblyM3NNV9wRFQgkzr7zps3D0FBQbh06RJSUlKQkpKCixcvol27dli0aBHi4uJQs2ZNTJw4sVjBTZw4EX///TfmzZuHy5cvY+3atVi+fDnGjx9frHqJyr1+/dRDrGvX1i338Sn3Q6+rePnCw7eJyTdnz/J5So7IUkxqkZk5cyZ+/PFHNGjQQFvWsGFDfPzxx+jfvz+uXr2K+fPnF3sodlBQEH766SdMmzYNc+fORb169RAdHY0hQ4YUq14igjpZ6d1bPTopMVHdJyY4uNy1xBCRbTMpkUlMTMy32TQ3N1c7s2+tWrVw//794kUHoFevXujVq1ex6yGifNjbAyEhlo6CiMhkJp1a6ty5M8aMGYPjx49ry44fP45x48ahS5cuAIDTp09z8joiIiIqUSYlMt988w08PDzQpk0b7Sihtm3bwsPDA9988w0AoEqVKvjkk0/MGiwRERFRXiadWqpZsyZ27NiB8+fP4+LFiwCAJk2aoEmTJtp1OnfubJ4IiYiIiApQrAnx/P394e9v+jBFIiIiouIwOZG5efMmNm/ejLi4OOTk6E78tHDhwmIHRkRE5UeBMyIrlahy/DgqJifjkacnMlq3LnBknaenJ3x9fUswSrJGJiUyO3fuxAsvvID69evj/PnzaN68Oa5fvw4RQWBgoLljJCKiMqqwGZX7AlgEIO+0qvEAJgD4KZ+6nJwq4/z5WCYz5YxJicy0adMwadIkREVFoWrVqvjxxx/h5eWFIUOGoHv37uaOkYiIyqiCZlTucP4I3vlxid76tQH8CGBu//H4y7+ttjw98ToOfhuF5ORkJjLljEmJTGxsLNatW6euoEIFZGVloUqVKpg7dy569+6NcePGmTVIIiIq2zQzKgOAQqXEG1+8rf7/sfXsAKgAjP9jI86HDobYcQLH8s6kRMbZ2VnbL8bb2xtXrlxBs2bNAADJycnmi46IzIJXdCZbEnDpJKqlJhW43A6AZ2oSAi6dxLkm7M5Q3pmUyDz11FPYt28fAgIC0KNHD7z11ls4ffo0YmJi8NRTT5k7RiIyEa/oTLbILc2wH8SGrkdlm0mJzMKFC5GRkQEAiIqKQkZGBtavX49GjRpxxBKRFeEVnckW3XP1NOt6VLaZlMjUr19f+7+zszOWLl1qtoCIyPzy9j8wRXridfMFQ1SE2EYtkeLuBffUpHynn1cBuOvuhdhGLUs7NLJCJl2ioH79+khJSdErv3fvnk6SQ0REZCyxs8d3AyOggDppyUsFdQfglQMj2NGXAJiYyFy/fh1KpVKvPDs7GwkJCcUOioiIyrfDgSFYOGYeUt29dMrvunth4Zh5OBwYYpnAyOoYdWpp8+bN2v+3bdsGV1dX7X2lUomdO3fCz8/PbMEREVH5dTgwBEdaBSPg0km4pSXjnqsnYhu1ZEsM6TAqkenTpw8AQKFQYMSIETrLKlasCD8/P17xmoiIzEbs7DnEmgplVCKjUqnPVtarVw+HDx+Gpyd7jBMZRKkE9u4FEhMBb28gOLjA68UQEZHhTBq1dO3aNXPHQVR2xcQAEyYAN2/+W+bjAyxaBPTrZ7m4iIjKAJOvfr1z507s3LkTSUlJ2pYajW+//bbYgRGVCTExwIABgIhueUKCunzTJiYzRETFYNKopaioKHTr1g07d+5EcnIyUlNTdW5EBPXppAkT9JMY4N+yiAj1ekREZBKTWmSWLl2K7777DsOGDTN3PERlx969uqeTHicCxMer1wsJKbWwiIjKEpNaZHJyctC+fXtzx0JUtiQmmnc9IiLSY1Ii88orr2Dt2rXmjoWobPH2Nu96RESkx6RTSw8fPsTy5cvx+++/44knnkDFihV1lvPCkURQD7H28VF37M2vn4xCoV4eHFz6sRERlREmJTKnTp1Cq1atAABnzpzRWaZQKIodFFGZYG+vHmI9YIA6acmbzGj2k+hozidDRFQMJiUyu3btMnccRGVTv37qIdb5zSMTHc2h10RExWTyPDIAcPnyZVy5cgUdO3aEk5MTRIQtMlRuxcXFITk5WX+Bnx8QE4Mqx4+jYnIyHnl6IqN1a3VLzLFjeqvHxsaWfLBERGWESYlMSkoKBg4ciF27dkGhUODSpUuoX78+Xn75Zbi7u/N6S1TuxMXFwd8/AFlZmWar81F2jtnqIiIqq0xKZCZOnIiKFSsiLi4OAQEB2vJBgwYhMjKSiQyVO8nJycjKykS70bPh4u1XrLoSTx/Amc3LkZuba57giIjKMJMSme3bt2Pbtm3w8fHRKW/UqBFu3LhhlsCIbJGLtx88fJsUq470xOvmCYaIqBwwaR6ZBw8eoHLlynrld+/ehaOjY7GDIiIiIjKESYlMcHAwVq1apb2vUCigUqkwf/58dO7c2WzBERERERXGpFNL8+fPR9euXXHkyBHk5ORgypQpOHv2LO7evYu//vrL3DESERER5cukFpnmzZvj4sWLeOaZZ9C7d288ePAA/fr1w/Hjx9GgQQNzx0hERESUL5PnkXF1dcWMGTPMGQsRERGRUUxqkVmxYgU2btyoV75x40asXLmy2EERERERGcKkROaDDz6Ap6enXrmXlxfmzZtX7KCIiIiIDGFSIhMXF4d69erpldetWxdxcXHFDoqIiIjIECYlMl5eXjh16pRe+cmTJ1GtWrViB0VERERkCJMSmZdeeglvvvkmdu3aBaVSCaVSiT/++AMTJkzA4MGDzR0jERERUb5MGrX07rvv4vr16+jatSsqVFBXoVKpMHz4cPaRISIiolJjdCIjIrh16xa+++47vPfeezhx4gScnJzQokUL1K1btyRiJCIiIsqXSYlMw4YNcfbsWTRq1AiNGjUqibiIiIiIimR0Hxk7Ozs0atQIKSkpJREPERERkcFM6uz74YcfYvLkyThz5oy54yEiIiIymEmdfYcPH47MzEy0bNkSDg4OcHJy0ll+9+5dswRHREREVBiTEpno6Ggzh0FERERkPJMSmREjRpg7DiIiIiKjmdRHBgCuXLmCmTNn4qWXXkJSUhIAYOvWrTh79qzZgiMiIiIqjEmJzJ9//okWLVrg4MGDiImJQUZGBgD1JQpmz55t1gCJiIiICmJSIjN16lS899572LFjBxwcHLTlXbp0wd9//2224IiIiIgKY1Iic/r0afTt21ev3MvLC8nJycUOioiIiMgQJiUybm5uSExM1Cs/fvw4ateuXeygiIiIiAxhUiIzePBgvP3227h16xYUCgVUKhX++usvTJo0CcOHDzd3jERERET5MimRmTdvHgICAuDr64uMjAw0bdoUHTt2RPv27TFz5kxzx0hERESUL6PmkVGpVFiwYAE2b96MnJwcDBs2DP3790dGRgZat27NC0gSERFRqTIqkXn//fcxZ84chIaGwsnJCWvXroWI4Ntvvy2p+IiIiIgKZNSppVWrVuGLL77Atm3b8PPPP+O///0v1qxZA5VKVVLxERERERXIqEQmLi4OPXr00N4PDQ2FQqHAP//8Y/bAiIiIiIpiVCKTm5uLSpUq6ZRVrFgRjx49MmtQRERERIYwqo+MiGDkyJFwdHTUlj18+BBjx46Fs7OztiwmJsZ8Eebx4YcfYtq0aZgwYQKvwE1ERETGJTL5XfV66NChZgumMIcPH8ayZcvwxBNPlMrzERERkfUzKpFZsWJFScVRqIyMDAwZMgRfffUV3nvvPYvEQERERNbHpAnxStv48ePRs2dPhIaGFrludnY20tPTdW5ERERUNhnVImMJP/zwA44dO4bDhw8btP4HH3yAqKioEo6KiIiIrIFVt8jEx8djwoQJWLNmjd5oqYJMmzYNaWlp2lt8fHwJR0lERESWYtUtMkePHkVSUhICAwO1ZUqlEnv27MHixYuRnZ0Ne3t7ncc4OjrqjKoiIiKissuqE5muXbvi9OnTOmWjRo2Cv78/3n77bb0khoiIiMoXq05kqlatiubNm+uUOTs7o1q1anrlREREVP5YdR8ZIiIiosJYdYtMfnbv3m3pEIiIiMhKsEWGiIiIbJbNtcgQERGZRKkE9u4FEhMBb28gOBjgoBGbx0SGiIjKvpgYYMIE4ObNf8t8fIBFi4B+/SwXFxUbTy0REVHZFhMDDBigm8QAQEKCujwmxjJxkVkwkSEiorJLqVS3xIjoL9OURUSo1yObxESGiIjKrr179Vti8hIB4uPV65FNYiJDRERlV2Kiedcjq8NEhoiIyi5vb/OuR1aHiQwREZVdwcHq0UkKRf7LFQqgTh31emSTmMgQEVHZZW+vHmIN6CczmvvR0ZxPxoYxkSEiorKtXz9g0yagdm3dch8fdTnnkbFpnBCPiIjKvn79gN69ObNvGcREhoiIygd7eyAkxNJRkJnx1BIRERHZLCYyREREZLOYyBAREZHNYiJDRERENouJDBEREdksjloiIqIyIzY21iz1eHp6wtfX1yx1UcliIkNERDYvKy0FgAJDhw41S31OTpVx/nwskxkbwESGiIhs3qPM+wAErcLfRvV6/sWqKz3xOg5+G4Xk5GQmMjaAiQwREZUZVbx84eHbxNJhUCliZ18iIiKyWUxkiIiIyGYxkSEiIiKbxUSGiIiIbBYTGSIiIrJZTGSIiIjIZjGRISIiIpvFRIaIiIhsFhMZIiIisllMZIiIiMhmMZEhIiIim8VEhoiIiGwWExkiIiKyWUxkiIiIyGYxkSEiIiKbxUSGiIiIbBYTGSIiIrJZTGSIiIjIZjGRISIiIpvFRIaIiIhsFhMZIiIisllMZIiIiMhmMZEhIiIim8VEhoiIiGwWExkiIiKyWRUsHQAREZHNUSqBvXuBxETA2xsIDgbs7S0dVbnERIaIiMgYMTHAhAnAzZv/lvn4AIsWAf36WS6ucoqnloiIiAwVEwMMGKCbxABAQoK6PCbGMnGVY0xkiIiIDKFUqltiRPSXacoiItTrUalhIkNERGSIvXv1W2LyEgHi49XrUalhHxkiIqJ8xMbG6tx3378f9Qx43LX9+5Hq4qK97+npCV9fXzNHRxpMZIiIiPLISksBoMDQoUN1yjsB2G3A40fNmIE/Z8zQ3ndyqozz52OZzJQQJjJERER5PMq8D0DQKvxtVK/nry23U6lwZ/EkVLufmm+/DBWAZBcPVBq/AM/aqddIT7yOg99GITk5mYlMCWEiQ0RElI8qXr7w8G2iU7YqfDIil02HCrqdTFUAFAC+f2kS3PwCSjFKYmdfIiIiAx0ODMHCMfOQ6u6lU37X3QsLx8zD4cAQywRWjrFFhoiIyAiHA0NwpFUwAi6dhFtaMu65eiK2UUuIHWf2tQQmMkREREYSO3ucaxJo6TAIPLVERERENsyqE5kPPvgAQUFBqFq1Kry8vNCnTx9cuHDB0mFReaJUArt3A+vWqf9yxk4iIqti1YnMn3/+ifHjx+Pvv//Gjh078OjRI3Tr1g0PHjywdGhUHsTEAH5+QOfOQHi4+q+fH6+lQkRkRay6j8xvv/2mc/+7776Dl5cXjh49io4dO1ooKipL4uLikJycrFfu9scfqDd5MgD1kEoNuXkT6N8f1xYswL0uXbTlj88ASkREpcOqE5nHpaWlAQA8PDwKXCc7OxvZ2dna++np6SUeF9mmuLg4+PsHICsrU6fcDsB1AAL9JksF1PNFVJg8GUH//39ej7JzSiZYIiLKl80kMiqVChEREejQoQOaN29e4HoffPABoqKiSjEyslXJycnIyspEu9Gz4eLtpy1/4sZ51Fn9UYGPswPgC2Di0Ldxqq561s/E0wdwZvNy5ObmlmzQRESkw2YSmfHjx+PMmTPYt29foetNmzYNkZGR2vvp6emoU6dOSYdHNszF209n9k7fWzcMepyvoxNu/v/j0hOvl0RoRERUBJtIZN544w38+uuv2LNnD3x8fApd19HREY6OjqUUGZVF91w9zboeERGVHKsetSQieOONN/DTTz/hjz/+QL16hlxAnah4Yhu1RIq7l17/Fw0VgGR3L8Q2almaYRERUT6sOpEZP348Vq9ejbVr16Jq1aq4desWbt26haysLEuHRmWY2Nnju4ER2o69eWkuDLdyYASnIycisgJWnch8+eWXSEtLQ0hICLy9vbW39evXWzo0KuN4YTgiIttg1X1kRMTSIVA5xgvDERFZP6tOZIgsjReGIyKybkxkiIiIbERBs5GbwtPTE76+vmapy5KYyBAREdmAgmYjN5WTU2WcPx9r88kMExkiIiIbUNBs5KZIT7yOg99GITk5mYkMERERlZ7HZyMv76x6+DURERFRYZjIEBERkc1iIkNEREQ2i4kMERER2Sx29iUiIrIUpRLYuxdITAS8vYHgYMCes4cbg4kMERGRJcTEABMmADdv/lvm4wMsWgT062e5uGwMTy0RERGVtpgYYMAA3SQGABIS1OUxMZaJywYxkSEiIipNSqW6JSa/CyNryiIi1OtRkZjIEBERlaa9e/VbYvISAeLj1etRkZjIEBERlabERPOuV86xsy8REVEJi42N1f5f5f59NDbgMRfv30fGsWP51kH/YiJDRERUQrLSUgAoMHToUG2ZHYDrAGoj/9MiKgA3AQSMGQNVPssfZeeYP1AbxkSGiIiohDzKvA9A0Cr8bVSv568t//b8Ebzz4xKooJvMqAAoAKzoPx5d/dvq1JV4+gDObF6O3NzcUojcdjCRISIiKmFVvHx1rlgd69sECz1rY+SGaFRLTdKW33X3wsqBEYgNDIHHY3WkJ14vnWBtDBMZIiIiCzgcGIIjrYIRcOkk3NKScc/VE7GNWkLsOLOvMZjIEBERWYjY2eNck0BLh2HTOPyaiIiIbBYTGSIiIrJZPLVERERUBihUynLZ34aJDBERkY0LOrZbbwRUirsXvhsYgcOBIZYLrBTw1BIREZENCzq2G5HLpsMjTxIDAO6pSYhcNh1Bx3ZbJrBSwkSGiIjIRilUSozcEK3+/7FldgAEwIgN0VCoyu6VtJnIEBER2aiASydRLTVJL4nRsAPgmZqEgEsnSzOsUsVEhoiIyEa5pSWbdT1bxESGiIjIRt1z9TTreraIiQwREZGNim3UEinuXvleJRtQX4Qy2d0LsY1almZYpYqJDBERkY0SO3t8NzACCkAvmdFcSXvlwIgyPZ8MExkiIiIbdjgwBAvHzEOqu5dO+V13LywcM6/MzyPDCfGo7FEqgb17gcREwNsbCA4G7MvurxEiovJ8JW0mMmRz4uLikJycfw98tz/+gM+CBXBI+ndiqBwvL9ycPBn3unTRWTc2NrZE4yQiKk3l9UraTGTIpsTFxcHfPwBZWZl6y/oC2JTPYyokJcFv8mQMAPBTPssfZeeYOUoiIiotTGTIpiQnJyMrKxPtRs+Gi7efttxOpcKyxZOguJ+a7+yWKgBLXTyQOX4BVHbqrmGJpw/gzOblyM3NLa3wiYjIzJjIkE1y8faDh28T7f2mF46h+v3UAte3A+CVfhfts7O0Ta/piddLOEoiIippTGSoTODslkREZmRDgyaYyFCZwNktiYjMJCYGmDABuHnz3zIfH2DRIqBfP8vFVQDOI0NlAme3JCIyg5gYYMAA3SQGABIS1OUxMZaJqxBskSmDChuebCxPT0/4+vqapa6SpJndMnLZdKigm6GXl9ktiYiKRalUt8SI6C8TARQKICIC6N3bqk4zMZEpYwobnmwKJ6fKOH8+1iaSGc3sliM3RKNa6r/zyNx198LKgRFlfnZLIqJi2btXvyUmLxEgPl69XkhIqYVVFCYyZUxBw5NNkZ54HQe/jUJycrJNJDJA+Z7dkoioWBITzbteKWEiU0Y9Pjy5PCmvs1sSERWLt7d51ysl7OxLRERE6iHWPj7qvjD5USiAOnXU61kRJjJERESk7sC7aJH6/8eTGc396Gir6ugL8NQSERFRuaV38Vw/P7jNn5//xXcnTcI9Pz/g2DGdh1h6dCsTGSIionImKy0FgAJDhw7Nd7kdgGAA3gASAey9fRuqyZPzXdfSo1uZyBAREZUzjzLvAxC0Cn8b1ev5F7heCgAHAF0LWG4No1uZyBAREZVTVbx8bX6EKzv7EhERkc1iiwxZBxu60ioREVkPJjJkeTZ2pVUiIrIePLVUkpRKYPduYN069V+l0tIRWR8bvNIqERFZDyYyJSUmBvDzAzp3BsLD1X/9/PjFnFdRV1oF1FdaZQJIREQFYCJTEtjKYBhjrrRKRESUD/aRMbeiWhkUCnUrQ+/exnVmtcbOsMWNyUavtEpERNaDiYy5GdPKEBJiWJ1m7AyrUCkRcOkk3NKScc/VE7GNWkLsTEiITIgpLi4OycnJ2vtV7t9HYwOe6uL9+8j4/ymx9abTJiKico2JjLmZu5VBc5rq8RYezWmqTZsMTmaCju3GyA3RqJb67/UzUty98N3ACBwODDEsHhNjiouLg79/ALKyMrVldgCuA6iN/M9xqgDcBBAwZgxUjy17lJ1jeLxERFRm2UQfmSVLlsDPzw+VKlVCu3btcOjQIUuHVDBvb/OtZ8bOsEHHdiNy2XR45EliAMA9NQmRy6Yj6NjuouMpRkzJycnIyspEu9Gz8eyMFXh2xgp0nbEC3/YfDwWgl6ioACgArOg/Hl3/f/1nZ6xA8xdeAwDk5uYaFi8REZVpVp/IrF+/HpGRkZg9ezaOHTuGli1bIiwsDElJSUU/2BKCg9WnWB6/BLqGQgHUqaNeryhm6gyrUCkxckO0+v/HltkBEAAjNkRDoTJgdFAxY3Lx9oOHbxPtLbbbECwcMw+p7l46691198LCMfMQ222IzvrOngYmikREVC5Y/amlhQsX4tVXX8WoUaMAAEuXLsWWLVvw7bffYurUqRaOLh/29up+IgMGqJOWvC0XmuQmOtqwTrFmOk0VcOmkzumkx9kB8ExNQsClkzjXJLBUYsrrcGAIjrQKNk/fHSIiKlesOpHJycnB0aNHMW3aNG2ZnZ0dQkNDceDAgXwfk52djezsbO39tLQ0AEB6errZ47t16xZu3bqlv8DTE65z56L2okVwyNO5NcfTEwlvvok0T09gzx6dh9jZ2UGl0j3BUiUpCQ0NiONyUhIy/r++CxcuAADu3riA3OwsAEDjC0dhyKvPvXAUSXb/NtKl34oDABw9ehQZGRkmx1RQXI+7bWcHaFpmrpzOd530xBsAgLSES6hYoYBWLwOxLtuOyVrrssaYykNd1hhTeahL8z2RkZFh9u9ZTX2SX1eGvMSKJSQkCADZv3+/TvnkyZPlySefzPcxs2fPFqjPlvDGG2+88cYbbzZ+i4+PLzRXsOoWGVNMmzYNkZGR2vsqlQp3795FtWrVoCio30oJSk9PR506dRAfHw8XFxerqMsaYyoPdVljTOWhLmuMyVrrssaYykNd1hiTuesyhYjg/v37qFWrVqHrWXUi4+npCXt7e9y+fVun/Pbt26hZs2a+j3F0dISjo6NOmZubW0mFaDAXFxezbQjmqssaYyoPdVljTOWhLmuMyVrrssaYykNd1hiTuesylqura5HrWPWoJQcHB7Rp0wY7d+7UlqlUKuzcuRNPP/20BSMjIiIia2DVLTIAEBkZiREjRqBt27Z48sknER0djQcPHmhHMREREVH5ZfWJzKBBg3Dnzh288847uHXrFlq1aoXffvsNNWrUsHRoBnF0dMTs2bP1TndZsi5rjKk81GWNMZWHuqwxJmutyxpjKg91WWNM5q6rJClEihrXRERERGSdrLqPDBEREVFhmMgQERGRzWIiQ0RERDaLiQwRERHZLCYyREREZLOYyBAREZHNYiJTAlQqFZRKpaXDKJA1jbhPTEzEuXPnzFaf5n0v7mvMzMxETk6OOULCzZs3cfz4cbPUZS4qlUrvautE5vbgwYMSqdeajmEa1hgTALPu59b6GpnImNm5c+cwfPhwhIWFYdy4cdi/f3+x6jNXQvTgwQPcv38f6enpxb545t27d3H+/HlcunSpWF/2CQkJaNGiBWbOnIkjR44UKyYAOHHiBPr06YPMzMxivcYzZ85g4MCB+Pvvv5GdnV2smM6ePYv27dtj9erVAIp3ULl58yY2bNiAmJgYnD592uR6zp07h5EjRyI0NBSvvfYafvjhB5PrMoS1HvxMISJm2yfv3r2LO3fumKWuy5cv4/Dhw2ar66effip2In/hwgWMHTsWN2/eLHZMmZmZSE1NxcOHDwHA7BcANmUbTUxMxKFDh7Bt2zYolcpixaTZpsyRdKSkpOD8+fP4+++/AQB2dnYm15uQkIAdO3Zg5cqVyM3NhUKhsM79udBrY5NRzp8/L66urjJ48GCZOnWqtGzZUtq2bSuLFi0yqb4LFy7Ixx9/LP/880+x4jp79qx069ZNWrduLbVq1ZLVq1eLiIhKpTK6rtOnT0vr1q2lRYsW4ujoKO+++67k5uaaFNeuXbukQoUK0qVLFxk+fLgcPXpUu8zY2E6cOCFOTk7y9ttv65QbW8+ZM2fEzc1NxowZI3FxcUY9Nr+YKleuLPXq1ZOaNWvK7du3Ta7r1KlTUrduXWnbtq3UqFFDnn/+ebl8+bLR9cTGxoq7u7u8/PLL8sknn0hYWJg0bNhQ3njjDZNj07hw4YJMmTJFRo4cKdHR0XLx4kXtMmM/h9u3b0tqamqxY7p69aosXLhQIiMj5YcffihWXRcuXJAJEyZIz549JSoqSpKTk02u68qVK1K/fn2ZNWuWJCQkFCuu48ePi4uLiyxfvrxY9YiInDx5UqpXry6vvvpqseLS7I8KhUJWrFhRrJjOnDkjvXr1koCAAOnTp4/8+uuvJtd1/vx5mTp1qgwdOlQWLFggx48f1y4zZhs9efKk1K1bVxo3biyurq7i7+8va9eulZSUFKNjOn36tISEhGiPN0ql0ug6NE6dOiVPPvmkNGnSRLy8vCQsLEy7zNh98NSpU9K4cWMJDAwUZ2dnCQwMlJycHJNjK0lMZMxEpVLJ9OnTZeDAgdqy9PR0ee+996RVq1by0UcfGVXfpUuXxMPDQxQKhUybNk3u3LljUlxnz56VatWqycSJE2XNmjUSGRkpFStW1NmBja1r0qRJcvbsWfn4449FoVCY/IWfkpIiL7zwgixbtkwCAwNlyJAhcubMGRExbmc+efKkODs7y+TJk3XKs7OzjYonIyNDunXrJuPGjdOWxcbGyvHjx+XGjRtG1aU5kE+fPl3u3LkjzZo1k/fee09UKpXRB5Tr169L7dq1ZerUqZKRkSH/+9//pGbNmnLw4EGj6nn48KEMGTJE3nzzTW1ZVlaWtG7dWhQKhbz00ktG1ZfX2bNnxdXVVbp37y79+/cXV1dXCQ0Nla+++kq7jqGv+9y5c+Lg4CADBgyQtLQ0k2M6deqU+Pj4SNeuXaV9+/ZiZ2cn8+fPN7kuLy8vGTBggIwZM0YcHBxkzpw5Jse2dOlSUSgU0rp1a3n//fclMTFRu8yYbUSTLEdGRpoci8aNGzfE19dXbz/Ky5C4NNv+lClTZNKkSRIcHKzz+oxx9uxZcXd3l/Hjx8vSpUulQ4cOEh4ebnRMmrrc3NzkxRdflLFjx0qdOnUkMDBQvvzyS6PqSkpKEn9/f5k+fbpcuXJFEhISZNCgQRIQECCzZ8+WpKQkg1/ftWvXpGHDhqJQKKRRo0YSHx8vIqYlM+fPnxdPT0+ZOnWqHDhwQLZt2yb169eXadOmGV1XbGyseHp6ysyZM+XGjRty9epV8fT0LFYSWZKYyJjRyJEjpWPHjjpl6enp8vHHH0vbtm21LSFFycjIkNGjR8vIkSNlyZIlolAoZPLkyUYnMykpKdKtWzedLy4RkZCQEPnPf/4jIoYfBO7cuSMdO3aUCRMmaMtUKpV0795d9u/fL8ePHzcqocnNzZWkpCRp3Lix3Lx5U2JiYiQoKEheffVVad++vfTv39+gehITE6VmzZraXx65ubkSEREhPXv2FH9/f/n0008lNjbWoLoePnwozzzzjBw7dkxyc3MlLCxMgoKCpGrVqvLUU0/J119/bVA9J0+eFEdHR5k+fbqIqA9KAwYMkKCgIO06xiQzy5Ytk5CQEJ3H9OjRQ5YtWyYrV66UP/74w+C6unbtqv0CzsrKEhGRKVOmSP/+/SUwMFAWLFhgcF0a2dnZMnToUHn11Ve1ZZcuXZJBgwbJU089ZVSL5K1bt6R9+/bSpUsX8fT0lBdffNGkZOb69evSsGFDmTJlivZL4ZtvvpEaNWrotBQZ4urVq+Ln56fzhTBnzhx5/fXX9X6hGvq5njx5UkaMGCHvvfee1KpVS959912jW6AuXrwojo6OMmPGDBERycnJkc2bN8vy5cvll19+kYyMDKPq++9//ys9evTQ1jVjxgzp06ePvPLKK7Jy5UrteoW9xiNHjoiLi4t221+3bp24urrKvn37RMS4L+jMzEzp06ePzjHnl19+kb59+8rt27fl/v37BsUkInL//n0JCwuTKVOmaMtu3rwp1apVkxo1asj7779vcFxnz54VPz8/OXLkiE7522+/LS1atJD58+fLgwcPiqwnKytLZs6cKX379pWdO3dKx44dpW7duiYlM/fv35eBAwfK66+/ri1TKpXyn//8R1544QWD6xERuXfvnvTo0UMiIiJ0ysPCwuSrr76ShQsXyrlz5yQzM9OoeksSExkz0OxEn332mXTo0EHOnz+vs/zu3bvaL2hDNvDMzExZsmSJtil8/fr1JiUzt27dkieffFL27NkjIv/uGKNGjZIhQ4YYXI+ISHJyssybN0/nS2Du3LmiUCikVatW4uPjI2FhYbJ3716D6tO8Z0OGDJHffvtNRES2bNkinp6eUrVqVYOboxMTE6Vv377Stm1b+fnnn6V79+7StWtXeeutt2T8+PFSr149efnllw1qUbl165ZUr15dtm/fLhMnTpSwsDA5efKkbN26VSZPniw1a9aUjRs3FlnPoUOHZNasWSLy73uuOe34xRdfGPS68lq6dKnUr19fjh07JiIi7733nigUCgkNDZWgoCDx8vIq8v1SqVTy4MEDCQ4OlmHDhsmjR49ERH0wr1u3rnz77bcydOhQ6dy5s9HxiYg8++yz8tprr2mfS0T9C3/kyJESHBwsmzdvNqierVu3Snh4uBw+fFgOHjwoHh4eRiczSqVSPvzwQ+nevbvcu3dPW65poXl8/yxMbm6uLFiwQMaNG6cTwyuvvCJPP/20BAUFydixYw1+fRonTpyQRo0aiUqlkqioKKlTp45ER0dL3759tUlAYR49eiRvvvmmVKtWTbtN9ujRQ5544gnx8/MTOzs7efHFF7XbjCGioqLkqaeeEhGR0NBQCQkJkQkTJsizzz4rrVq1KvKXfUZGhjg7O8vEiRN1yrt27SpdunTRbnOGUiqVEhwcLFFRUdqySZMmiZ+fn9SuXVu6dOkiU6dONaiuBw8eSFBQkKxdu1Z7X0TkxRdf1LbY/e9//zOorhMnToiPj4/2uJr3C/3NN9+UevXqycmTJ0Wk6ATrhx9+kPXr14uIOvkODg42KZm5f/++vPzyy3qnF9evXy8tWrSQ7Oxso04LLV26VPbv36+9/+6770qFChWkc+fOEhAQIDVq1NDGbUoXBXNjImNGly9fFk9PTxk9erT214LmQ46LixOFQiFbt241qK7Hf0398MMPolAoZNKkSdpz80qlUq5evVpoPXkTD82GPHPmTBk2bJjOenl/3RQkPT1d+/+6detEoVDI+vXrJSUlRf78808JCgoyurl9+PDh2oPRyy+/LO7u7tK0aVMZPXq0wadO/vnnHxk+fLg4OTnJs88+q9N3Yc2aNeLm5mbQQUqlUsngwYPljTfekF69emkTLBGR+Ph4GTp0qIwdO1Zyc3ON2nlVKpXcu3dP+vTpIwMHDjT68VevXpX27dtLw4YNpX///qJQKOTnn38WlUolt2/fljfffFNCQkIkOTm5yHr37dsndnZ20rFjRxk2bJg4OzvLK6+8IiLqc/VVq1aV8+fPGxxfbm6u5OTkyKhRo2TAgAHy8OFDUalU2gPwlStX5Omnn5ZBgwYZVF9SUpLs2rVLe//AgQPaZCZvUlJUfH/++afel5xSqRQ/Pz+d+g0RHx8vBw4c0N5/9913xd7eXmbMmCGfffaZBAUFSZcuXYw+fdKtWze5du2aiIjMnz9fnJ2dxdXVVbZt22bQ4y9evCivvfaaPPXUU1KnTh3p0aOHxMbGSmZmphw5ckRq164tw4cPNzieHTt2SJcuXeTrr7+WZ599Vm7evCki6l/omiTn7NmzhdaheT0iou0799VXX0njxo21feAM+XJWKpWSlpYmYWFh0rdvX1myZIlMmzZNnJycZMWKFbJ161aJioqSwMBA+eWXXwqtS7Of1KpVS6fFMT4+Xpo2bSorV66UJ554QrsfGCIoKEgn6X/48KH2/7Zt28rgwYMNritvnFeuXNG2zGje/4cPH8qxY8cK/RGsVCrl+vXrOnWJ/JvI5FVYPfntV3v27JEGDRrI5s2btY994YUXpG3btoa/uBLGRMbM/vjjD3F0dJTx48frtJ4kJiZKy5YtdbJcQ+T90tMkD5MnT5aEhASZOHGi9OvXz6BWnrwHjxkzZuh0Aps3b5588sknRv1iun79uk7nXBGRnj17yvPPP2/Q4zWv6bvvvpPZs2fLuHHjxNvbW65evSoxMTHSoEEDGTt2rPb0R1ESEhJk2rRpsnPnTp36RUQaNmxY6Hn/vA4fPizOzs6iUCj0fmW/9dZb0rFjR5N/gfz444+iUCi0zezGuHr1qqxfv15mz54tAwYM0Fn24YcfSsuWLQ1+rw4dOiRDhw6VV155RZYsWaIt/+WXXyQgIEAnYSjI4x28d+/eLfb29jqnkTTr7N69W+zs7LT9n4qqS0Ozzf799986LTM5OTnyxRdfyPbt2w2qR/N5KZVKqVevns7jfv/993z7NBRUV3JyskREROj8IDl37lyhP1IKqiskJER7yubll18WFxcXqVmzpsyfP7/AjraP13X58mUZNmyY9OzZU6+lafPmzaJQKOTChQsG1RUbGyu1atWSpk2bSmhoqM6yuLg4qVy5srZFo6B68ts37t+/L3Xq1JHx48fnG0dhMf3999/SvXt3CQ8PlyZNmsg333yjXXbr1i3x9fWVDz74wKC6Fi9eLAqFQkaPHi0zZ86UKlWqaE+Hbty4Ufz8/CQ5OVkv0crIyJD09HSdFrljx46Jl5eXTr8yzfEzMjKywONgfnWJ6B6fL1++rE1mrl69KuPHj5e2bdvqnXo0pK6NGzdKs2bNtPcjIyOlV69eeu9NQXWJqFttr1y5ovMaFyxYIO3atbOazr9MZErA5s2bxdHRUfr16yc//PCDnDt3TqZOnSre3t7aJkNj5P2F+8MPP0jFihWlSZMmUqFCBaM67WoOMjNmzJDnnntORERmzZolCoVCTpw4YXRcGkqlUrKysmTQoEFGnWsWUf9yVigUUrNmTZ1zzj/99FORrU2PS0tL0+ngq1KpJDk5WZ5++mlZs2aNwfXs2bNHFAqF9OrVS+fL980335RXXnnF5J03OztbunXrJkOGDDH5/PJXX30lPXv21HmdEydOlN69exvVJyK/L5xJkyZJSEhIkadxChpN9/HHH4udnZ1OB18RkaNHj0pAQIDOr/Wi6nqc5jTTwIEDZdSoUVKxYkWdUVv51ZP3NT569EgyMjKkYcOG8vfff4uIyLRp00ShUOglDUXFpPnhoNkvT506JYGBgXLq1CmDXp9m+3n77bfl+++/l//85z9Sq1YtuXr1qsybN08qV64sn3zyid6XTUFx3bhxQ7Zu3aqtV/O6N23aJP7+/vn2vSmorl9//VUqVKggXl5eOj+6srOzpUuXLjqtlIa8V5rXsGTJEmnQoIFevxJD6srIyJDc3Fx5+umntaczRNTv47PPPqtNxvN+3vnVpVQq5bvvvpOgoCDp3r27zgCMzz//XFq3bq23XxQ04jMrK0vWrVsnnp6eMmDAAMnJydEeo4cOHSqDBw+WR48e6dRnzOjRK1euSEhIiCgUCnF2dpZDhw4ZFNfjdW3ZskWaNGkiIqJt0crbumhsXBovv/yyjB492ujThSWFiUwJOXr0qHTq1Enq1q0rDRo0kMaNGxt1vvpxeUcydOnSRTw8PPI9cBZGs6PNnj1bXnvtNVmwYIE4OjrqtayYYtasWeLr62t0R8qcnBz55ptvDD6nbKx33nlHGjVqpNPsaog///xTatWqJU8++aS8/PLLMmzYMHF1dZXTp08XK54PPvhAXFxcijWKw9XVVebPny+rVq2SKVOmiJubm9HbQl6nTp2S119/XVxcXIpMaAsbTffgwQOJiooShUIhM2fOlGPHjklKSopMnTpVGjZsqNfyYezIvH379olCoRAPDw+dbdaQejTJtubLdO7cufl+QRRWl2bbfHwbnT59urRr187o1/ftt9+KQqEQb29vOXz4sLb8o48+0tuPiqqroMQ0LCxMLzEtqq5169aJnZ2dhIWFybp16+TSpUsydepUqVWrlk6HfmM+P82prrwtgIbGpFQqJSMjQ9q1ayezZs2S1NRUuX//vsyaNUvbimvM68vKytI5FSQi8sYbb8iAAQMkKytL+14WNOJTcxx/8OCBbN68WXx8fMTf31976tjZ2VnvOGHs6NHs7GwZPHiweHh46J3OM6auX375RZ566imZPn26ODg46B3rjY0rJydHZs6cKZ6engYPoigNTGRKUFpamly7dk1OnTpl8vDpvHJzc2XixImiUCi0X/ym0HQWdXV11TmAmmLDhg0yfvx4qVatmsmJWnHmTSjIunXr5LXXXhN3d3eT4zp//rzMnDlTQkNDZdy4ccVKYjQHx7t370qbNm3ybZ0w1B9//CENGjSQRo0aSUhISLG2hYcPH0pMTIwMHjy4yHoKGk2X9wtcqVTKypUrpWbNmlK7dm3x9/eXWrVq6R1AjR2Zl52dLWPHjpWqVavqHNiNrad169YSFBQkDg4Oetu+sXWdPXtWZs6cKS4uLnrvnSF1XbhwQWbOnKn90ihoPzCkrryJzJkzZ2TGjBni4uKil+Aa+hp///13efrpp6VGjRri7++v90PMlJGVI0aMkCZNmkhOTo5OvIbWpRn00LhxY2nXrp3UrVtXb9829r2KjY2ViIgIqVq1qs57ZciIT4309HSZMmWKvPLKK/LGG2/oJR7Gjh5VKpXy+eefi729vd7rM7YuzXvm7u6u1xpmbF2///679O/fX3x8fIr1o7wkMJGxIbm5ufL111+bNAdMXocPHxaFQlFkxz1DnDlzRgYOHCjnzp0rdl3mdPLkSenZs2eB/TKMoVQqzZZsqVQqo4fF5iclJUVu3bpllknjHj58aFBMhY2me7w14tq1a/Lnn3/K1q1btZ0WDa0rvy/DQ4cOSbNmzfRaUAytJzc3V1JSUsTV1VXs7e3zbcEyJqYbN25I3759JSAgIN9WLEPrytu/raDWSGPiunbtmnTv3l3q16+f73HCmLqSk5Pl4sWLcvz4cb1lxtSjeV1///13vqeLjalr37598t5778nSpUvz/TFgTF3p6eny2WefSadOnfTeK0NGfOY95a+R33HClNGjmzdvzrd129i6Ll++LMHBwflu78bUpemIPGfOHKNG/ZUWJjI2xlynXszxZaphLR2+HmfshHhUtMJG02m+KB49emTQcHdDR+ZpTmfcvXvX5HoePXokd+7ckd9++63Q5NaQunJzc+X27dsSHx9faJ+3wurSJH6GjDw0Jq6kpCS5du1aoe+/oe9XUa2Ghn5+mo6iptal2a5ycnIMatk25jN89OhRgduVoSM+8566K+j4bGhdeUeGFsTYkaiFHesNrUtTR0m0npsDExkiMpoho+kyMjIMSryLqqtPnz4GdY4uqp6+ffsaNMLP0JgMHSVmrpGHlojLkM/Q0Ndnjm2hb9++pb5dmXPEZ2nW9fHHHxt8+Rhzj2otbUxkiMgk5hpNV1RdxpyPL6gee3t7i8VUVF1lIa7Sen2W3K5EzDPiszzUVdqYyBCRycwxms7cdVljTOWhLmuMyVx1mXPEZ3moq7QxkSGiYjHXaDpz1mWNMZWHuqwxJnPWZc4Rn+WhrtJiByKiYmrWrBmOHTuGJ554wmrqssaYykNd1hiTueoKCwsDAOzfvx9t27YtVjzloa7SohARsXQQRGTbRAQKhcKq6rLGmMpDXdYYkznrevDgAZydnc0QUfmoqzQwkSEiIiKbxVNLREREZLOYyBAREZHNYiJDRERENouJDBEREdksJjJERERks5jIEBERkc1iIkNEZdLy5ctRp04d2NnZITo62tLhEFEJYSJDRAUaOXIk+vTpo1e+e/duKBQK3Lt3r9RjMkR6ejreeOMNvP3220hISMBrr72W73oKhUJ7c3Z2RqNGjTBy5EgcPXq0lCMmIlMxkSEiq/Xo0SOTHhcXF4dHjx6hZ8+e8Pb2RuXKlQtcd8WKFUhMTMTZs2exZMkSZGRkoF27dli1apWpYRNRKWIiQ0Rm8eOPP6JZs2ZwdHSEn58fPvnkE53lCoUCP//8s06Zm5sbvvvuOwDA9evXoVAosH79enTq1AmVKlXCmjVr8n2uuLg49O7dG1WqVIGLiwsGDhyI27dvAwC+++47tGjRAgBQv359KBQKXL9+vcC43dzcULNmTfj5+aFbt27YtGkThgwZgjfeeAOpqakAgJSUFLz00kuoXbs2KleujBYtWmDdunXaOlatWoVq1aohOztbp+4+ffpg2LBhRb53RGQ6JjJEVGxHjx7FwIEDMXjwYJw+fRpz5szBrFmztEmKMaZOnYoJEyYgNjZWewG7vFQqFXr37o27d+/izz//xI4dO3D16lUMGjQIADBo0CD8/vvvAIBDhw4hMTERderUMSqGiRMn4v79+9ixYwcA4OHDh2jTpg22bNmCM2fO4LXXXsOwYcNw6NAhAMCLL74IpVKJzZs3a+tISkrCli1bMHr0aKPfAyIyXAVLB0BE1u3XX39FlSpVdMqUSqXO/YULF6Jr166YNWsWAKBx48Y4d+4cFixYgJEjRxr1fBEREejXr1+By3fu3InTp0/j2rVr2gRl1apVaNasGQ4fPoygoCBUq1YNAFC9enXUrFnTqOcHAH9/fwDQtuTUrl0bkyZN0i7/z3/+g23btmHDhg148skn4eTkhPDwcKxYsQIvvvgiAGD16tXw9fVFSEiI0c9PRIZjiwwRFapz5844ceKEzu3rr7/WWSc2NhYdOnTQKevQoQMuXbqkl/QUpW3btoUuj42NRZ06dXRaWZo2bQo3NzfExsYa9VwF0VxLV3O1ZKVSiXfffRctWrSAh4cHqlSpgm3btiEuLk77mFdffRXbt29HQkICAPUprpEjR5rt6s1ElD+2yBBRoZydndGwYUOdsps3bxpdj0Kh0CYIGvl15nV2dja6bnPTJET16tUDACxYsACLFi1CdHQ0WrRoAWdnZ0RERCAnJ0f7mNatW6Nly5ZYtWoVunXrhrNnz2LLli0WiZ+oPGEiQ0TFFhAQgL/++kun7K+//kLjxo1hb28PQH2aJzExUbv80qVLyMzMNOm54uPjER8fr22VOXfuHO7du4emTZsW41X8Kzo6Gi4uLggNDQWgfi29e/fG0KFDAaj76Vy8eFHv+V555RVER0cjISEBoaGhRvfNISLj8dQSERXbW2+9hZ07d+Ldd9/FxYsXsXLlSixevFinX0mXLl2wePFiHD9+HEeOHMHYsWNRsWJFo58rNDQULVq0wJAhQ3Ds2DEcOnQIw4cPR6dOnYo8LZWfe/fu4datW7hx4wZ27NiBAQMGYO3atfjyyy/h5uYGAGjUqBF27NiB/fv3IzY2FmPGjNGOksorPDwcN2/exFdffcVOvkSlhIkMERVbYGAgNmzYgB9++AHNmzfHO++8g7lz5+p09P3kk09Qp04dBAcHIzw8HJMmTSp0fpeCKBQK/PLLL3B3d0fHjh0RGhqK+vXrY/369SbFPmrUKHh7e8Pf3x/jxo1DlSpVcOjQIYSHh2vXmTlzJgIDAxEWFoaQkBDUrFkz34kCXV1d0b9/f1SpUiXf5URkfgp5/KQ1ERGZrGvXrmjWrBk+++wzS4dCVC4wkSEiMoPU1FTs3r0bAwYMwLlz59CkSRNLh0RULrCzLxGRGbRu3Rqpqan46KOPmMQQlSK2yBAREZHNYmdfIiIisllMZIiIiMhmMZEhIiIim8VEhoiIiGwWExkiIiKyWUxkiIiIyGYxkSEiIiKbxUSGiIiIbNb/ASFFxLtx5uFVAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1238,34 +934,21 @@
     }
    ],
    "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "\n",
-    "# Create a new DataFrame\n",
-    "legs_acbm_clean = legs_acbm.copy()\n",
-    "\n",
-    "# Convert tst to datetime format\n",
-    "legs_acbm_clean['tst'] = pd.to_datetime(legs_acbm_clean['tst'])\n",
-    "\n",
-    "# Extract the hour component\n",
-    "legs_acbm_clean['hour'] = legs_acbm_clean['tst'].dt.hour #+ 1\n",
-    "\n",
     "# Calculate the percentage of each hour in legs_acbm_clean['hour']\n",
-    "acbm_hour_counts = legs_acbm_clean['hour'].value_counts(normalize=True).sort_index() * 100\n",
+    "acbm_hour_counts = legs_acbm['tst_hour'].value_counts(normalize=True).sort_index() * 100\n",
     "\n",
     "# Convert legs_nts[\"tst\"] from minutes to hours\n",
-    "legs_nts['hour'] = legs_nts['tst'] // 60\n",
+    "legs_nts['tst_hour'] = legs_nts['tst'] // 60\n",
     "\n",
     "# Calculate the percentage of each hour in legs_nts['hour']\n",
-    "nts_hour_counts = legs_nts['hour'].value_counts(normalize=True).sort_index() * 100\n",
+    "nts_hour_counts = legs_nts['tst_hour'].value_counts(normalize=True).sort_index() * 100\n",
     "\n",
     "# Plot the histogram using seaborn\n",
-    "sns.histplot(legs_acbm_clean['hour'], bins=24, kde=False, discrete=True, stat='percent')\n",
+    "sns.histplot(legs_acbm['tst_hour'], bins=24, kde=False, discrete=True, stat='percent')\n",
     "plt.xlabel('Hour of Day')\n",
     "plt.ylabel('Percentage')\n",
-    "plt.title('Histogram of TST by Hour of Day in legs_acbm')\n",
-    "plt.xticks(range(0, 24))  # Set x-ticks to represent each hour of the day\n",
+    "plt.title('Comparison of Activity Data Over Hours of the Day')\n",
+    "plt.xticks(range(0, 24), rotation=45)  # Set x-ticks to represent each hour of the day\n",
     "\n",
     "# Add dots to represent the percentage values of legs_nts[\"tst\"]\n",
     "for hour, percent in nts_hour_counts.items():\n",
@@ -1276,12 +959,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 173,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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tk3QiPFrhJ4/bLAVSwizLDChZbZNqOzQAAAA4wS+L4itWrLB5vnDhQkVHR2vz5s3q06ePh6KCLwmJbVXtq8MqWoMKAAAAAABvQrEPqJoREKiFI9OU/vpUmSWb/ytmSSZJb49MYwYUAAAAL2dvzON3CgoKJEkRERF29xcVFamwsNDmAQAAAAAA4M9Kin0mWYp7pVHsA361sWuKZk98TifDo222/xwerdkTn9PGrimeCQwAUCNM5mJ12L1Fvb77TB12b5HJXOzpkAC4gV/eKV6a2WxWWlqaevfurU6dOtk9JiMjQzNmzKjlyAAAAAAAADyrpNg3bkmmmp48bt3+c3i03h6ZRrEP+MXGrina1DlZ7bO3KawgT/mhkcpqm8RFIwDgZ7pvWV3ue9GJ8Ggt5HsR4PP8viiempqqHTt26Ouvv67wmClTpig9Pd36vLCwUC1atKiN8AAAAAAAADyKYh/gGCMgUDsTu3o6DABADem+ZbXSX59abnv4yeNKf30qs4MAPs6vi+KTJk3Sxx9/rLVr16p58+YVHhcUFKSgoKBajAwAAAAA4CiTuZhiHVDDKPYBAIC6zGQu1rglmZa/l9kXIMvSMmOXZGpT52TGIoCP8suiuGEYeuCBB7Rs2TKtXr1aCQkJng4JAAAAAFANTF8IAAAAoKa1z95mM+YoK0BS5Mnjap+9jQsJAR8V4OkAakJqaqreffddLVq0SE2aNNHRo0d19OhRnTt3ztOhAQCACmRkZKh79+5q0qSJoqOjNXToUO3evdvTYQEAPKhk+sKIMienSqYv7L5ltWcCAwAAAOBXwgry3HocAO/jl0XxuXPnqqCgQCkpKYqNjbU+Fi9e7OnQAABABdasWaPU1FStX79eK1eu1MWLFzVgwACdOXPG06EBADygqukLDVmmLzSZi2s5MgAAAAD+Jj800q3HAfA+fjt9OgAA8C0rVqyweb5w4UJFR0dr8+bN6tOnj4eiAgB4CtMXAgAAAKgtWW2TdCI8WuEnj9u9m9Qs6efwaGW1Tart0KqvuFj66ispN1eKjZWSk6VA1kNH3eWXRXEAAOD7CgoKJEkREREejgQA4AlMXwgAAACgthgBgVo4Mk3pr0+VWbbTLJtlmb3q7ZFpMgLKF5WzsrKq/bqRkZGKj4+vdvsKLV0qTZ4sHTr067bmzaWXXpKGDXP/6wE+gKI4AMA7cOUiSjGbzUpLS1Pv3r3VqVMnu8cUFRWpqKjI+rywsLC2wgMA1AKmLwQAAABQmzZ2TdHsic9p3JJMm1mrfg6P1tsj07Sxa4rN8ecKTkgyacyYMdV+zeDgy7RrV5b9wnh1z5cuXSqNGCGVnVX58GHL9g8+oDCOOomiOADA87hyEWWkpqZqx44d+vrrrys8JiMjQzNmzKjFqAAAtckvpy/0chkZGVq6dKl27dql4OBg9erVS7NmzVJiYmKFbRYuXKh77rnHZltQUJDOnz9f0+ECAAAAbrexa4o2dU5W++xtCivIU35opLLaJtm9Q/zi2VOSDHUe/ZiiEto5/VqFufu1Yf4M5eXllS+KV/d8aXGxpZ29ZYYNQzKZpLQ0acgQbkhCnUNRHADgWVy5iDImTZqkjz/+WGvXrlXz5s0rPG7KlClKT0+3Pi8sLFSLFi1qI0QAQC1wZfpCVM+aNWuUmpqq7t2769KlS5o6daoGDBignTt3qlGjRhW2CwkJ0e7du63PTSZTbYQLAAAA1AgjIFA7E7s6fHzj6HhFxFd8IanTXDlf+tVXtoX0sgxDOnjQclxKittCBnwBRXEAgOdw5SJKMQxDDzzwgJYtW6bVq1crISGh0uODgoIUFBRUS9EBADzB2ekL4ZoVK1bYPF+4cKGio6O1efNm9enTp8J2JpNJMTExNR0eAAAA4P9cPV+am+vY6zh6HOBHKIoDADyHKxdRSmpqqhYtWqQPP/xQTZo00dGjRyVJoaGhCg4O9nB0AABPcWb6QrhXQUGBJCkiIqLS406fPq2WLVvKbDara9eueu6559SxY8cKjy8qKlJRUZH1eWFhoXsCBgAAAHydq+dLY2Mdex1HjwP8iL2l2QAAqB1cuYhS5s6dq4KCAqWkpCg2Ntb6WLx4sadDAwB4WMn0hd9eO0A7E7tSEK8FZrNZaWlp6t27tzp16lThcYmJiZo/f74+/PBDvfvuuzKbzerVq5cOVXIiLyMjQ6GhodYHy58AAAAAv3D1fGlysmXt8YqWNDKZpBYtLMcBdQx3igMAPIcrF1GKYW9aKAAA4BGpqanasWOHvv7660qP69mzp3r27Gl93qtXL7Vv316vv/66nnnmGbttpkyZovT0dOvzwsJCCuMAAADwKyZzcfVmu3L1fGlgoPTSS5a1x00m22nYSwrlmZksVYk6iaI4/E5OTo7y8vKq1TYrK8vN0QCoVMmVi4cP218nx2Sy7OfKRQAAgFozadIkffzxx1q7dq2aN2/uVNv69eurS5cu2rNnT4XHBAUFKSgoyNUwAQAAAK/UfctqjVuSqaYnj1u3nQiP1sKRadrYNaXyxu44XzpsmPTBB5a1yUvP4NS8uaUgPmyYU+8H8BcUxeFXcnJy1K5de507d9alfi4WXXBTRAAqxZWLAAAAXsMwDD3wwANatmyZVq9erYSEBKf7KC4u1vbt2/Wb3/ymBiIEAAAAvFv3LauV/vrUctvDTx5X+utTNXvic5UXxt11vnTYMGnIEMva47m5ljvLk5M5z4o6jaI4/EpeXp7OnTurHuOnKSS2ldPtc7ev046P3tClS5fcHxwA+7hyEQAAwCukpqZq0aJF+vDDD9WkSRMdPXpUkhQaGqrg4GBJ0t13363LL79cGRkZkqSZM2fquuuu0xVXXKH8/Hw9//zzOnDggO677z6PvQ8AAADAE0zmYo1bkmn5e5l9AZLMksYuydSmzsmVT6XurvOlgYFSSorD8QP+jqI4/FJIbCtFxCc63a4wd7/7gwFQNa5cBAAA8Li5c+dKklLKnDhbsGCBxo0bJ8kyO1dAQIB138mTJ/W73/1OR48eVXh4uLp166Zvv/1WHTp0qK2wAcBnVXu9WQCAV2qfvc1myvSyAiRFnjyu9tnbtDOxa+Wdcb4UcDuK4gAA78CViwAAAB5l2FuzsIzVq1fbPH/xxRf14osv1lBEAOC/XFpvFgDglcIK8tx6HOdLAfcKqPoQAAAAAAAAAIA7lKw3G1HmbsKS9Wa7b1ntmcAAAC7JD41063EA3IuiOOoMk7lYHXZvUa/vPlOH3VtkMhd7OiQAAAAAAADUIVWtN2vIst4s560AwPdktU3SifBomSvYb5aUFx6trLZJtRmWa4qLpdWrpX/8w/JnMfkJvovp01EnMCUVAAAAAAAAPM2t680CALyKERCohSPTlP76VJlle1eqWZaLod4emSYjwHZd8KysLJdeNzIyUvHx8S71YdfSpdLkydKhQ79ua95ceukly5rngI+hKA6/VzIlVVklU1LNnvgchXEAAAAAAADUOLevNwsA8Cobu6Zo9sTnyt2k93N4tN4uc5PeuYITkkwaM2aMS68ZHHyZdu3Kcm9hfOlSacQIyTBstx8+bNn+wQcUxuFzKIrDr1U1JZVZlimpNnVOLnd1FgAAAAAAAOBOrDcLAP5vY9cUbeqcrPbZ2xRWkKf80EhltU0qV4O4ePaUJEOdRz+mqIR21Xqtwtz92jB/hvLy8txXFC8uttwhXrYgLlm2mUxSWpo0ZIgUSF0FvoOiOPwaU1IB3smVKYFqbDogAAAAAABqWMl6s+Enj9tMq1vCLMvdhD613iwAoBwjINDhmkPj6HhFxCfWcERO+Oor2ynTyzIM6eBBy3EpKbUWFuAqiuLwa0xJBXgXd0wJVCPTAQEAAP9WXGw5YZObK8XGSsnJ3NEAAPCI6q43CwBAZdy6LnlurmONHD0O8BIUxeHXmJIK8C6uTglUI9MBAahbKIwBdc/SpZap/0rf6dC8ufTSS6yBBwDwCGfWmwUAoDI1si55bKxjjRw9DvASFMXh15iSCvBOXjclEIC6gcIYUPcsXSqNGFF+LbzDhy3bP/iA//8AAI9wdL3Zsly9E7A0licDAN9XI+uSJydbzpccPmx/XXGTybI/Odm14IFaRlEcfo0pqQAAgCQKY0BdVFxsuRDG3kkcw7CcyElLk4YMYcYIAIBHOLPerLvuBCyN5ckAwH+49SakwEDLDQQjRljGTaXHVCaT5c/MTMZR8DkUxeH3mJIKAIA6jsIYUDd99ZXtzBBlGYZ08KDluJSUWgsLAIDqcMedgKWxPBkAoFLDhlluILA3415mJjcWwCdRFEedUN0pqQAAgB+gMAbUTbm57j0OAAAvwHJkAIBaM2yY5QaCr76yjJtiYy1Tptu5oSAnJ0d5eXkuvRzLeqCmURRHneHMlFQAAMCPUBgD6qbYWPceBwAAAAB1TWBglTcQ5OTkqF279jp37qxLL8WyHqhpFMUBAADg3yiMAXVTcrJlar/Dh+0vn2AyWfYnJ9d+bAAAAADgJ/Ly8nTu3Fn1GD9NIbGtyu0PMJvV6eAPijidr58bh2lHiytlDgiwOabSZT2Kix26Wx2oCkVxAAAA+J28vDwVFBRYnsTFqWVMjAKPHZPJTmHMMJl0KSZGOXFx0t69OnDgQC1HC6BGBAZKL70kjRhhKYCX/v9vMln+zMzkZAoAAAAAuEFIbKtyS3x037Ja45ZkqunJ49ZtJ8KjtXBkmjZ2Tam606VL7a9r/tJLrGsOp1EUBwAAgF/Jy8tT6zZtdKqw0LrtNkkfSDIklb4W2SxJhqE7cnO1LNF24Hbx4qWaDxZAzRo2TPrgA/snUTIzOYkCAAAAAL/Iyspya7vuW1Yr/fWp5baHnzyu9NenavbE5yovjC9darnIuewNDocPW7Z/8AFjOjiFojgAAAD8SkFBgU4VFirloZfVqGmMJOmCpIzvN2jCJwsVVXjCeuyJ0KZ64zfjdKFjD938y7a8vdu1YcEzKi4urv3gAbis3GwPSUnSqlVquHGj6v30ky5FRel89+6WO8T37rU5NDQ0VJGRkbUYLQAAAAB41rmCE5JMGjNmjEv9XCy6YP27yVyscUsyLX8vc1yALDcpjF2SqU2dk2UE2Jm9q7jYcnGzvaWwDMMy+1damjRkiGOzfzEFO0RRHAAAAH6qUdMYNY5qbn2+PaW5HuwzVO2ztymsIE/5oZHKapskIyBQjUu1O3PiaO0HC8BlF84USqYA9evXr9p9NAkJ0Y9791IYBwAAAFBnXDx7SpKhzqMfU1RCO6fb525fpx0fvaFLl36dca999jabKdPLCpAUefK42mdv087EruUP+Oor29m+yjIM6eBBy3EpKZUHyBTs+AVFcQAAANQZRkCg/cEWAJ938fxZyTCr5/1/UURcvNPtz5w4qtUvPqiCggKK4gAAAADqnMbR8eXWBHdEYe7+ctvCCvIcalvhcbm5jr14VccxBTtKoSgOAAAAAPAbl0U0s5klAgAAAPAJTO8MP5If6tiFxhUeFxvr2AtVdpy7p2CHzwvwdAAAAAAAAAAAANeYzMXqsHuLen33mTrs3iKTudjTIQFw1NKlUqtW0g03SKNHW/5s1cqyHfBBWW2TdCI8WuYK9psl5YVHK6ttkv0DkpMtU5ybyq5I/guTSWrRwnJcRZyZgt0RxcXS6tXSP/5h+bOYPOtruFMcAAAAAAAAAHxY9y2rNW5Jps36rSfCo7VwZJo2dk3xXGAAqsb0zvBDRkCgFo5MU/rrU2WW7R26ZkkmSW+PTJMRUMEd2oGBljW/R4ywFMBL//8oKZRnZlZ+h7e7pmCXWJfcT3CnOAAAAAAAgK/hThUAv+i+ZbXSX5+qiFIFcUkKP3lc6a9PVfctqz0TGICqVTW9s2SZ3pk8Dx+0sWuKZk98TifDo222/xwerdkTn6v6oq1hwywXhVx+ue325s0du1jEHVOwS79euFL2rvOSC1eY0cFncKc4AAAAAACAL+FOFQC/MJmLNW5JpuXvZfYFyHI33tglmdrUObniu/EAeI4z0zunpNRaWIC7bOyaok2dk9U+e5vCCvKUHxqprLZJjuekYcMsa35/9ZXlju7YWMuU6Y6sAV4yBfvhw/YvPDGZLPsrm4Kddcn9CkVxAIDPycrKsr+juFiN//tf1c/L08XISJ3u0sXul5HIyEjFx8fXcJQAakRxcfUGQgAA+AumWAVQSvvsbTZTppcVICny5HG1z96mnYlday8wAI5x5/TOgJcyAgJdy0GBgdW7KMQdU7Bz4YpfoSgOAPAZ5wpOSDJpzJgx5fbdJuklSS1KbTsoabKkZWWODQ6+TLt2ZVEYB3wNd8UBAOo67lQBUEZYQZ5bjwNQs3JycpSX9+v/x8anTulKB9r9cOqUTm/ZUvGNIgDsK5mC3d75pMzMqs8nceGKX6EoDgDwGRfPnpJkqPPoxxSV0M66vfeuTXr6n3PKHX+5pH9Kmjk8Vd+0u0aSVJi7Xxvmz1BeXh5FccCXcFccAADcqQKgnPzQSLceB6Dm5OTkqF279jp37qx1W4Ck/bKcwwqw08Ys6ZCk9hMnylxq+8WiCzUYKeBnXJmC3V3rksMrUBQHAPicxtHxiohPlGRZP23Sa49Z/l7muJL101K/eF+7+t/J+mmAr+KuOAAALLhTBUAZWW2TdCI8WuEnj1dYUPs5PFpZbZNqOzQAZeTl5encubPqMX6aQmJbWbfP/+VmD7NsC+NmWc51LRieqn6/3OyRu32ddnz0hi5dulSLkQN+oLpTsLtjXXJ4DYri8Cplp4+xcnCdYKaPAeoe1k8D6gDuigMAwII7VQCUYQQEauHINKW/PrXCgtrbI9O4SBzwIiGxraw3e0hSVnyiZkdernFLMm3Ocf0cHq23R6Ypq2uKIn7ZVpi7v3aDBWqZqzWeyMhI984O6o51yUsUF1fvbnW4DUVxeA1708dIzq0TXILpY4C6g/XTgDqAu+IAALDgThUAdmzsmqLZE5+rsKC2sWuK54ID4JCNXVO0qXOy2mdvU1hBnvJDI5XVNokLWlBnnCs4IcmkMWPGuNRPcPBl2rUry72FcVfXJZcsywLaa//SSywHWIsoisNr2Js+xpl1giWmjwHqItZPA+oA7ooDAMDCnXeqAPArFNQA32cEBDLLIeqsi2dPSTLUefRjikpoV60+CnP3a8P8GcrLy3NvUVxybV3ypUst39/LXtR6+LBl+wcfUBivJRTF4XVKpo+pzjrBTB8D1D2sn4Zax1RHtY+74gAA/sBd3yHccacKAL9EQQ0A4OsaR8fbLC/gVaqzLnlxseV7u73zWYZhOaeVlmYpuHN+scbZqx8AXqFkneCyBfESpdcJBlB3layfZpKlAF4a66fB7ZYulVq1km64QRo92vJnq1aW7ag5JXfFSb/eBVeCu+IAAL7A3d8hhg2T9u+XvvxSWrTI8ue+fRTEAQAAAG/y1Ve2F7KWZRjSwYOW4xxRXCytXi394x+WP4uLnY/JHX34KO4Uh9dinWAAjmL9NNQKpjpyj+reJcddcQAAX1VT3yGqc6cKAAAA4OeysrKq3TYyMtK9U6/n5rrvOHesS17H1zanKA6vxTrBAJzB+mmoUUx15B6ufvF2Zf0mAAA8ge8QAADUDSy1BnjcuYITkkwaM2ZMtfsICmqof/7zA8XGxla7D5vCuqP9VHWcOy605YYfiuLwXqwTDMBZrJ+GGuPMVEdV3bFVVwfKTn7xzsvLU0FBgf2+WrSwPCTL1LFlHDhwwE1BAwDgInd+hwCA2lZXxy6As+r4nZeAt7h49pQkQ51HP6aohHZOt/8pe5u2LnlJt9xyi0txBAdfpl27siyF8eRky++Dw4ftXyhrMln2JydX3KE7LrR158W6Pvz9gKI4akc1/pOUrBOc/vpUmSWbwjjrBAOoET6c0FHD3DXVkTsGyp7+nFbn9Z384p2Xl6fWbdroVGGhS6FevHjJpfYAALjMndMlAkBtosgHOIY7LwGv0zg6XhHxiU63K8zdL1eK6iV9bJg/Q3l5eZaieGCgJXeOGGE5/1X6d4XJZPkzM7Pyc2vuuNDWXRfr+vj3A4riqHku/CdhnWAAtcZdCd3TBUvUDHdMdeSuaY48WVSv7us7+cW7oKBApwoLlfLQy2rUNMax2ErJ27tdGxY8o+LiYqfbAgDgVu6aLhEAaoi9dVfDvvhCCY88IslyU0oJ49Ahafhw7Xv+eeXfeGO5dlWuw8p4Gd6qup9NlkkB/FJ1i+oVGjbMct7P3jm1zMyqz+m540Jbd/ThBxcBURSvSd7wRc/TMbjhPwnrBAOoce5K6D5+pRwq4epUR+4YKHu6qO7K61fzi3ejpjFqHNXcsbalnDlx1Ok2AAA/4O7xrzv6c8d0iQBQAypadzVA0n5JhlRuOUOTLLM31nvkEXX/5e+l2UwXW1ZNjZc9fe4TrnPHv6EnLv6WWCYFgOOGDbOc96vO7yp3XGjrah/uvgjIQ/nb3lLNfmHOnDlq1aqVGjZsqB49eui7776r3QCWLpVatZJuuEEaPdryZ6tWlu11JYaq/pNIlv8kDtzFVbJO8LfXDtDOxK4UxAG4j7t+V5UUDMsOhkoKhrX5+9/HeTyH21My1ZH069RGJRyZ6siZgbI97vicuvIZdfX1uUsOAHyKs7n4/fffV7t27dSwYUNdddVV+ve//11LkZbi7vGvu/pz4TtETk6OtmzZ4paHvTtBAdRtpddd/X9PLLA+HhrzmFqo4pPGAZLiJT00xrZdj/HTdO7cWeXl5ZVvVFPjZU+f+/QDHh9/u+Pf0JU+XP1sskwKAGcEBloukBk1yvKno0Xgkgtty44nSphMUosWlV9o62ofrp7bLM2D+dsv7xRfvHix0tPTNW/ePPXo0UOZmZkaOHCgdu/erejo6JoPwBumEPCGGLhSDoAvcMfvKqbLchuP5/DKuDLVkasDZVc/p65+Rl19fe6SAwCf4Wwu/vbbbzVq1ChlZGTolltu0aJFizR06FBt2bJFnTp1qp2g3T3+dXd/1fgOkZOTo3bt2uvcubOOv44DLhZdcGt/AHxf2Sli448ecKhdfFCwDjkytWxNjZe94dynj/P4+Ntds6E50UdOTs6vF24UF6vTH/6g+oahciUiw5Ah6eL992tHixY2n82ioiIFBQVJkhqfOqUrHXirP5w6pdNcpAbUKa7+f7dZkqQa65Lb/L77RdjkyfaXR/nlz30PPqj8bdvsx+Cui4A8nL/9sig+e/Zs/e53v9M999wjSZo3b54++eQTzZ8/X48//njNvrg3FEa8IQaJK+UA+AZ3/K7iIiC38WgOd0R1pzpy9U5pDxTV8/LyVFBQIElqvHWrmjnw8se2btXpFi0kSZcuXVK9er9+1Ww0ZYqaTZokmUwylfqOYvzy5f3Y44/rzP79kqQDBxw7EQcAcD9nc/FLL72km266SY/8cnLlmWee0cqVK/Xqq69q3rx5NR9wTUzjVxPjaSe/Q+Tl5encubPqMX6aQmJbOf46Fcjdvk47PnpDly5dcrkvAP4tPzTSrcfVyHjZW859+jifP4fuZB9lLzjrK2l1JSGaJDU4dkzp116rNTY7fi1IlSw3cLnsz65glnRIUvuJE22WG+AiNcB/VbQ8ibOCghrqn//8QLEl5wpbtVLYn/+s5s8/rwbHj1uPuxAdrUMPP6z8Vq2kLVskSbm5uRox4nadP3+uXL+3SXpJUotS2w5KSjMMLftlTFfCZlkUd8wC6QX52++K4hcuXNDmzZs1ZcoU67aAgAD1799f69ats9umqKhIRUVF1uclJ4ALCwudD8DRL3orVtTc3VDeEIMkhYQ4flxhoU6fPi1J+vnAbl0qKv+f1RGFuZaT6AWHs1W/XgXTQNRge2/pwxticEcf3hCDO/rwhhjc0YffxHA0R5J0+vRpy+95J39X2bV3r2N97N0rde3q2LG/KMlFhr0vC37G2Rzu1vxtx88//6z8/Hz7O8PCLA9J2r696s5CQhQfHa3A48fLX4EuyxWZl5o108GQEGnr1nL7G54+rTg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cAKeYv321U/FYANkU9XcoIMydPHpIkqlF7/tVrV4jSdJhSSPXLdUdX07z+b0ipVIVvdrleh1udK66ZImx46fvtGrmv5WWliZJWtuguf6oXF2V9+326fT2yFDmZSnWNmied3IBnnjk70QBj/Z+Xs+eipX1yiW99W2jc73L8j1RoKhK2LEWndbFpTAH6m4TyoPsYJzZCAAePXtm7kwUsD3ymdalEFPUSNk6Z2jTUJqVsJ3jYMl+EJa8epH04bh8H3fJVQMV1bStpHwOXIrwvgY6bZa/UdBSkKYYD8aU6cGcdj0Y+7/B2ubprEJJ1b17YNt8OF5CJRwH4wBwrdz21QrDc8YaEDJu2Qcuah4B/g4FlEax1ZO8g+slaW1SQw3q/H9+LyVRJdtjD+7Y7PO3RURq0nVDNHTcg8pQzrO1HUmTrxuS/2UpCnniUdYTBSSpzfJ5Gvr+yzkeXvXQPj36/st6buCT+r5Vco77/SpKe1QCj7XotC4ugR6oB5mTkV6068SE+iA7GGc2AiiV8hwhXqlS5k2SVq70u8qOHTvUq9e1On78mHdZYaao8emcoU1DSVbKdo6DzXMQlnbsSIHWT6t/ts+Bjl9FfF8DnTbL3yjooEwxHoxLJwTz8gvB2v8NxjZPxzdKuoJu8268hEpRvzduG4wDIOz521cLVPYz1oCQcMuArWDlERnJbHpAgIpyKYnvWyXruYFPqv97Y1R135+zwe2tXF2TrxtSsE7iIJx45GSkq/97YzL/n+0+z+8l/d4bo6UtOubfZ1eU9siNx1oFUCo6rV9++WU988wz2rlzp5o3b64XX3xR5/mZ/97jP//5jx555BFt3rxZDRo00OjRo3XZZZcF/sQhOqBss3xeji/mH5Wra1JBv5huOMju2DHzy7d9u/8vleNk3t+xY/6vB0CpEMzR5ZLUuu+DqpLUQFLgU9Tk6JyhTQtIoHUbxagU7hwXl6JMVeUzGCfAa2P7G8hT2FyyjoIu7BTjPoJx6YRgXX4hGPu/hdjmfWb2+J/4OXNU7957Jfke4Nq2bdI112jTM8/4TL1erNOuo9QJWg0uaDvvtkuoFPV747bBOABKlexnrAUi+xlrKLqQ/R5dUrllwJZb8gBQKN+3StbSFh0Lf0JnEE48arxhpU/fXHYRkhL27VbjDSvz7qAvanvktmOtAgr7Tut3331XQ4cO1Wuvvabzzz9fY8aMUdeuXbV+/XpVr149x/oLFy7U9ddfr1GjRql79+6aNm2arrrqKi1fvlzNmjUL7MlDcEDZZvk8DR33YI7llfft1tBxD+Y/7YBbDrIjIzN/GOjVK3P9rLGc//18N2YMB+kAvIIxulz6c4R5haqnF3qKGu9jsnQWxQ8e7L8T4n//bho0SPuznf2da0dEGAu0bqMYldKd4+JSmKmq/A3GKey1sbNO9RiMabMCnWLcr2BcOqGQMXw6iwMcCOCRo40OcJvfunWrGjVqrGPHjnpXiZC0WTln9pAyP5cMSWXuvVdt9OdsH8U27TpKnaDX4IK08266hEowvjduGowDAAiZkP4eXRK5ZcCWW/IAUCRFOVs7GCceBeX3kmC0R2461gpA2HdaP/fcc7r11lt10003SZJee+01ffrpp5owYYIeeOCBHOuPHTtW3bp1073/61gYOXKkZs+erZdeekmvvfZa4AmcwgPKoEw74KaD7J49M38Y8DfSfcwYfmgD4FdRRpdLeY8wL+hOT17Xfx0rqXaWZb9JGmKmD/5Xd7Ly2xER5gKt2ygmQThjtPLChapXgKfatHCh9lWqlPfU/mEi0Kmq/A3GCfTa2LlN9VjUabMCnWLcI+vnHHvokM4qQIyfDx3S4eXLvX/7dBYXYhR09s7iwg4EKFeuvN5/f7oS/xe7MNv8sWNHdf7Nj6lSYl1J0jlb1qn21NG5PjZCUpKkv/e5Xz/WaVR8066jVCq2GpzXjyBuuYRKsL43IRyMAwBwj5D/Hl3SuGXAllvyABA6QTiZsrC/l/gIRnvklmOtAIV1p3VqaqqWLVum4cOHe5dFRESoc+fO+u677/w+5rvvvtPQoUN9lnXt2lUffvih3/VPnDihEydOeP8+cOCAJOlg9hU3bpRaFXJ0hx+HDx+WJO3dsl5pJzKvu9p0688qu2+3DuXxuKh9u5Uw932tTjpLB3dulSQtW7bMGy9+zhzVLcDzb54zR/sj/jz/IyIiQhkZGUGL4ZWQIL35pmJ//FFl/vhDaVWr6vA552Q2Cl9/7bPq+vXrJfm+J4E6uGOLJOnA9g0qWybHuTYlKoabciGGe3MhRvHE+WPjKkmm+snXKq5GLe/yzZKutgydvWe7qh47oj8qxOinaqcrw4lQy2wxju7dpfWzp2nz5s2Kj4/PmefBzEpj/n5cLaEKU7dzrcMHc1RiBKKgO8ezZkkdO+q3337Tuee28bkWfHtJnxXgqfo+9JC+fegh7997flkV0joerDi5xfi0Yrw+v+lRNd72iyofOaB9MXFaW+tMZURESBtW+I2RfvKE9z3ZEVUh536mHzuiKijtxDGln0zN9bUUNBd/ryVF0q+x8apyeH+uU4z/UbGyvpaUsWGFUjb+JEk+g3kcSask1VTOs4o9MbZLOnvgQGVt6cqVK68pU95UjRo1JDM1SUhQ2ZSUnGdJK/OM5ZPVqmmNmXffcf369Tp27Kgadumt6Co1dO6WdTq46HN/b6OPc9teqoN1MgcPHPj9V/264CN1797de39ht/kTRw57P9/ofbsL9PlG79uttNPqKC01s/07fPjwn+1egN9fFE641eGg1uDsK1aqJOVWl5s3l2rWlH7/PffkTj89c73irO3B+t5UqlSw58vrPQkwhr/fBgrDLfvjxHBvLsRwby5hFeN/v1X67NtkX8flNTikv0eX1OPgjRsLvl4Qf193Sx7BqOWu+Q67JIabciGGe3Px1z8mSUpIUNyIETp97FhFZTk5IzUhQdsHDdKBhASf3xck3+9voL+X+K19wWiPiuFY65TUYAtj27dvN0m2cOFCn+X33nuvnXfeeX4fU7ZsWZs2bZrPspdfftmqV6/ud/3HHnvMlPl7GDdu3Lhx43bKbxs3bgxO0XSBwtRt6jA3bty4cQvlLVzqMDWYGzdu3LiVtJtbazC/R3Pjxo0bt3C/FWcNDuszrU+F4cOH+4yE279/v+rUqaOtW7cqLi6u0HEPHjyo2rVr67ffflOlgo60Jkaxx3BTLsRwby7EcG8u4RTjwIEDSkpKUpUquV1Ru3Qojjrsls/YLTHclAsx3JsLMdybCzGKJw51mBpcknIhhntzIYZ7cyGGe3OhBru3BgcrDjGCH8NNuRDDvbkQw725uCXGqajBYd1pnZCQoMjISO3atctn+a5du3Taaaf5fcxpp50W0PrlypVTuXLlciyPi4sr0pfBo1KlSkWOQ4zgx3BTLsRwby7EcG8u4RQjIsLfRDMlU2HqdnHWYbd8xm6J4aZciOHeXIjh3lyIUTxxwqUOU4OLL4abciGGe3MhhntzIYZ7c3FrDS7pv0eH23ZCDPfmQgz35kIM9+bilhjFWYPdWd2DJCoqSq1bt9ZXX33lXZaRkaGvvvpKF1xwgd/HXHDBBT7rS9Ls2bNzXR8AAARHYeo2AAAoOmowAADBwe/RAAAUXlifaS1JQ4cOVb9+/XTuuefqvPPO05gxY3TkyBHddNNNkqQbb7xRp59+ukaNGiVJGjx4sC688EL961//0uWXX6533nlHS5cu1b///e9QvgwAAEqF/Oo2AAAoHtRgAACCg9+jAQAonLDvtP7rX/+qPXv26NFHH9XOnTvVokULzZo1SzVq1JAkbd261edU9nbt2mnatGl6+OGH9eCDD6pBgwb68MMP1axZswI9X7ly5fTYY4/5naIlEMGIQ4zgx3BTLsRwby7EcG8uxHC//Op2ftzy3oZTDDflQgz35kIM9+ZCDHfn4ibU4ODGcFMuxHBvLsRwby7EcG8uJaEGl8Tfo9lOwjuGm3IhhntzIYZ7cwmnGPlxzMyKLToAAAAAAAAAAAAAAHkI62taAwAAAAAAAAAAAADcjU5rAAAAAAAAAAAAAEDI0GkNAAAAAAAAAAAAAAgZOq0BAAAAAAAAAAAAACFDpzUAAAAAAAAAAAAAIGTotEaxysjICHUKkiQzC3UKAACcUm6pwRJ1GABQulCDAQAIDWowAJRsdFoHWSiLkZsK4fLlyyVJERERhd5ZOHHiRJHz+OOPPyRJjuMUKc7s2bP1+++/Fzkfj2B8VsGK4YkTyp26rHkUNY6//wcjXiikpqYWOcbixYt16NChIGSTqbDvSVG3NTduq0XJI9y2VTcJ1Xvhps8gGDVYck8dpgYXv2DU4XBr19xYg6XCvS/hUoODlUu4batuQg2mBufHTd85t7Rt1GD/3FiHQ1GDgxUjGKjB7sbv0eFXg6Xg1uFg1s9gxHBbu1aUGP7+H4x4oUINDm6MYChtNZhO6yI6dOiQdu/erePHj0vKLEZF+cCK8ljHcXTs2LEixdi/f7+2bdumDRs2FDrGBx98oHPPPVf9+/eXVLidhddff13333+/jhw5Uug8Zs6cqbvuuksvvvhioWNI0nvvvaeuXbvqjjvuKHSMDRs2aMGCBXrrrbd07NixQm0nJ0+e1OHDh71/O45T5MYy685TRERmc1CY7SctLa3IeRR1R07KzP3o0aPemJ5lgVi2bJlWrlzpEyNQU6dO1dixYwv1WI9Zs2bpySef1LJlywr9nX733Xd14YUX6o033ih0HikpKdq4caM2btyo1NTUQrdxWT/jwmxrbtxWi5KHW7bVcOCWOhxONVhyTx2mBuevqO2aJ5eitiNuadfCqQZLwanD4VKDPbkUtQ67ZVsNB+FUg6Wi12FqcE5urcGeOB5uOb4oLLe0a8GowZJ76rAbarAnhodbtlVqcOi5pQZ7njvUx8LhVoOlotfhYNRgKbyPhcOpBkvhdSxMDc6ZR6mqwYZCe++99+zCCy+06tWrW4sWLaxfv372+++/Bxxn7ty59tlnn9nJkycLncsXX3xhffv2tXr16lmnTp3sH//4R8AxPvzwQ7v44outSpUqVrNmTbvuuusCjnH8+HHr37+/OY5jSUlJ1q9fP+996enpBYoxadIkcxzH7rzzTjt48GDAOZiZTZkyxSpUqGB//etf7eOPPy5UDDOz8ePHm+M4VrFiRatRo4Z99913Acd45513rEmTJhYTE2OO49g555wT8Ov68MMP7YorrrB69erZxRdfbPfdd58dPXo04Fyy+vjjj+3666+3Nm3a2A033GDTpk2zQ4cOmVnBP6us70dht99vvvnGHnvsMbvkkktsyJAhtmzZMktLSws4zhdffGF9+vSxOnXq2MUXX2zjx4+348ePBxTjP//5jzmOY/3797effvop4BzMzCZOnGiO49jw4cML/Rm9+eabFh8fb8nJyTZ37twi5eE4jl166aWFijFjxgxr166dxcbGWmJiot1+++12+PDhgOPMnj3bBg0aZJdeeqn9/e9/t7lz53rfm4Jsa2yrOQVjWw0HbqnD4VSDzdxTh6nBeQtGu2YWnLbNLe1aONVgs+DU4XCpwWbhta2Gg3CqwWZFr8PU4JzcWoPN3NO2hVO7FowabOaeOuyGGmzGtpodNTiTW2qwmTuOhcOtBpsVvQ4HowabhfexcLi1a+F0LEwN9hVu22pB0GldSNOnT7eyZcvaFVdcYQ8//LBdffXVVrVqVatdu7ZNmzbNDhw4UKA477//vjmOYy1btrT//ve/heoAmTJlilWpUsXatGljvXr1skaNGllsbKwNHjw4oBixsbF2+eWX24gRI6x3794WFRVld911V8D5vPLKKxYdHW09evSw+Ph4GzBggPe+/L7QnoZt2LBhtnXr1oCf2yyzMahWrZoNGTLE1q9f73edjIyMfON4cnn00Uftk08+sXLlytk999wTUC7Tp0+38uXLW9++fe3NN9+0f/3rX1apUiUbNGhQgWO89dZbFhUVZRdccIHdfPPN1rJlS+8Ox+zZs+3IkSMB5WRmNm3aNIuKirL27dvbVVddZfXr17dKlSrZRRddZLt27TKz/N8jT0N12WWXeZcF2vhOmTLFqlWrZmeddZadddZZFh0dbY0aNbJPP/00oDieotq+fXvr06ePNWrUyKpWrWpffvllQHEGDRpkjuNYhQoVrG/fvrZ69eqAHu/ZZoYOHWrbtm3zLg9kJ3n27NkWGxtrf//73+3HH3/0u05+n40nj8GDB9sDDzxgjuPYhAkTCpyDmdnbb79tsbGx1r17dxsxYoR16dLFYmNjbdasWQHFefPNN61ChQrWuHFja9WqlcXHx1uFChXsxhtvtH379pmZ5dnulZRttSBtipl7ttVw4JY6HE412Mw9dZgaXPztmllw6rBb2rVwqsFmwanD4VKDzYJTh92yrYaDcKrBnjjBqMPU4D+5tQabuadtowbn5JY67IYabFZytlVq8Knllhps5q5j4XCpwVlzKWwdDkYNNgvvY+FwqsFm4XUsTA32VVprMJ3WAcrIyLCDBw9a586d7eqrr7bt27ebWeaorm+++cYuvPBCq1ixoj3//PPeL0FufvzxRzvnnHOsTp06VqNGDWvatKnNnj07oB2F+fPnW40aNWzQoEG2YcMGMzPbuXOndenSxRITE23p0qX5xvj8888tISHB7rrrLtu4caOZmR05csSaNGli3bp1K3AuHkuXLrX27dvbkiVLrE+fPhYbG2sDBgzwfoly+zJNmjTJIiIicjSwu3fvtvXr19vevXsL9Pzjxo2zBg0a2LJly3xijxkzxp555hnbuXNnvl9oz4i2oUOH2vbt2+348eN23XXXWcWKFW3hwoUFymP79u3WsmVLu+mmm2zz5s1mltkwdejQwW6//XafdXPL59dff7X69evbzTffbL/++quZme3du9fuu+8+cxzHGjdubFOmTPGO8imI33//3Ro2bGj9+/f3vs9Hjx61Bx54wGrUqGFJSUn2888/m1nuxW3JkiVWt25dq1q1qjmOY927d/feV9DGd9asWValShW7++67bdWqVXbs2DH79ttvrW7duta5c+cCv56ZM2d6d748ee/Zs8ccx7H777/fZ93c3mfP8lmzZln79u3t3nvvNcdxrHfv3gVugN98803vDu6mTZu8y9PS0vJtC7LmMHjwYOvYsaOtW7fOe9+3335rX3zxha1YsSLfUWGekaFDhw61vXv32po1ayw+Pt4uvfRSO3DgQIGK2Zo1a6xu3br2t7/9zee1xMbG2tixY/3m7c+yZcssISHBBg4caD///LOlp6fbxo0brXXr1uY4jp177rmWkpKS6+thW80pGNtqSeemOhxONdjMPXWYGlz87ZpZcNo2t7Rr4VSDzYJTh8OlBpuF17Za0oVbDTYLbh2mBmdyaw02c0/bFk7tWlFrcNY83FCH3VCDzdhWs6MGu6sGm7nvWDgcarBZ0etwMGqwWXgfC4dbuxZOx8LUYF/htq0Ggk7rQjh+/LidccYZdsstt5iZ7we6Z88e69Gjh0VHR9u///1vO3bsmN8Yqamp9tBDD5njODZx4kT74YcfLCkpyZo1a1bgHYXDhw/bwIED7ZxzzrEVK1b43LdixQqLioqyl19+Oc8Ye/futWuvvdY6depka9euNbPML9vx48ftsssus86dO9u3335rCxYssNTU1HxzMjM7duyYJSYm2rhx4+zAgQP217/+1SpVquQzys2zM+Lx66+/WkREhCUmJto333zjXT5w4ECrX7++OY5jderUsRtuuCHfPAYMGGBNmjTx/t29e3eLioqy8uXLm+M4VqtWLXv99ddz3en4/PPPLSIiwgYPHuwzvc57771njuPYY489Zmb5j8hZt26dRUdH2/jx433Wv/HGG23gwIH2wAMP2IgRI2z79u25Ngrff/+9RUVF2QcffGBmfzaEhw8ftrZt25rjOFa3bl2bOXOmmRVsdM2mTZssJibGO9LJ01Cmpqbam2++aXXr1rVatWp5G+Xsje+hQ4fs7rvvNsdx7NVXX7VHH33UHMexHj16eNfJr/FNSUmxK6+80i655BLvducxZswYcxzH5s2bl+9r2bx5s3Xu3Nl69uzp3Uk2M9uyZYs1btzYhg0bZtu2bbM9e/Z4t5u8Rplt3LjRYmJibNasWTZu3DhzHMduuOGGfBvgb7/91lv4sn7nH3zwQfvLX/5iiYmJds0119hbb72V7xQt559/vvXp08f79zXXXOPddsuXL28dO3bM9bPx5HzPPfd4D2DMzEaMGGGRkZHe9zS/7eSTTz6xypUr2/z5883M7MSJE3bw4EHr1KmTTZw40aZMmWJff/11vlOiTJ482apUqWKLFi3yWf77779bgwYNzHEca9KkiXdnIXtebKvB31bDhRvqcDjVYDP31GFqcPG3a2bBadvc0q6FWw02C04dDocabBZe22q4CJcabBb8OkwNzuTWGmzmjrYtnNq1YNZgM3fUYTfUYDO21dxQg0Nfg83ceSxc0muwWXDqcDBqsFn4HguHW7sWbsfC1OA/hdu2Gig6rQvh0KFD1qxZM7vmmmvMLPPDzPqB7t+/37p27WrVq1f3jizz94F//vnndscdd3j/XrBggSUlJRV4hNvhw4ctOTnZHnroIe+yjIwMy8jIsJ07d1piYqLdfffdecZITU21l19+2aZOneqz/NVXX/UWoK5du1pcXJw1a9bMFi9enGc8T8433XST3XzzzWZmtmPHDu/OQv/+/e2KK66wK6+80qdInzhxwsaOHWsxMTHWv39/2759u1166aUWGxtr//d//2dPP/20XXLJJVamTBk799xz82xo77vvPmvUqJGlp6fb3XffbVWqVLFp06bZTz/9ZDNnzrSLLrrIYmJi7KWXXvK7w7FhwwZ75ZVXbMeOHTnuu+qqqywhIcG785BXY7ty5UpzHMdeeOEF73vz+uuvm+M41qhRI6tfv77FxMRY7dq17cMPP/Qb6+uvvzbHcbw7Aenp6d6cBw0aZG3btrWmTZtaw4YNve9nfgVg+/btFhUVZaNHj/Yu83xu6enp9vbbb1tiYqKdc8453hFZWWMeOHDAhgwZYsOGDTMzs3379tn9998fUOP7888/m+M49s9//jPHfYsXL7aIiAh755138nwdZpmN5XXXXefdifLwbL+1atWyzp07W506dWzAgAF5vkee96BHjx7ea/A89dRT3gbYM7WPvylSFi9ebJdccolFRUV5R35dfvnlVq5cOWvfvr316NHDTjvtNCtfvrzdc889eU6h065dO+9OQr9+/axq1ar27LPP2ty5c+3OO++0hIQEq127tm3ZsiXHaxk/frzdcccdPjsIZpk7mzVq1LAuXboUaISoZ5Rp1mlCPNtu5cqVrUyZMlahQgXr1KlTnkX+6aeftujoaG9xzcjIsPT0dDt69Ki1adPGGjRoYFFRUdazZ0+/7wnbavC31XDhhjocTjXYzD11mBpc/O2aWXDaNre0a+FWg82CU4fDoQabhde2Gi7CpQabBbcOU4P/5NYabOaOti2c2rVg1mAzd9RhN9RgM7ZVarB/bqjBZu47Fg6HGmwWnDocjBpsFr7HwuHWroXbsTA1+E/htq0Gik7rQnr22WfNcRyfDz3rB7pu3Tpr2LChtW3bNs84ng/c89hvv/02oB2FX3/91dtYZN8Rad26tfXs2dPnefzJftH1jz/+2BzH8V7Y/dixY/b2229bkyZNrGHDhgW6Psr48eOtWrVq3qlIPCPoypUrZ5GRkfbuu+/6vG6zzB2WF154wcqWLWvVqlWzM844wz777DM7fPiwmWV+4Z9//nkrX768XXHFFbk+9xdffGGOk3nNhEGDBtl9993n8/pTUlKsW7duVqNGDfvtt9/8xsjt/fJMuTF8+PB8f2Q7fvy4XX311eY4jnXu3NkuvfRS79QLmzdvtrS0NJs3b55dcMEFlpSU5M0l63uSkpJiiYmJdtFFF+W4wH2XLl2sb9++3pEtBflhxjOdUKdOnaxFixa2cuXKHK85LS3Nnn/+eatUqZLdf//9fl+nZ4oajz179nivVZG18T1x4kSuucyYMcNbzLJuu7/99ptVrVrVRo0aZWb5/5iZ/Vozr7zyijmOY3fddZfNmjXLtmzZYn369LGKFSvaNddck++B8uDBg61x48benbFnnnnGHMex//u//7M2bdpY06ZNbc+ePTke98MPP1j37t3NcRxr2LChnX766fbBBx94n+/QoUN28cUXW/ny5e3VV1/NUQA878Gjjz5qiYmJ9v7779v5559vL730ks+IrilTplhiYqK1bt3a7zQ8WXegsz7HLbfcYuXLl7dvv/3WzPJuExYtWmSVKlWyli1b2rBhw2zw4MHebXfJkiV26NAhu++++6xKlSqWnJyc63RAH330kTmOY08//bTPiL+0tDRLTk62559/3q666iorW7asffrpp96DHM/7sX///oC31ezv62+//Rbwtpo1D7PM6wEVZlvN3iZnndrGrGDbavbXk/Xvwm6r4cINdTicarCZe+owNbj4a7BZcOowNdhXMGqwWXDqcEmtwZ7nz6owdTh7LacGB1e41GCz4NdharA7a7AnvlvqMDXYl5vqcChqcNb3wPP/knwsTA0uXm6owWbuPBYu6TXYrOh1OBg12JNruB4Lh1MNNguvY2FqMDXYg07rQtqxY4e1bNnSGjdubAsWLPAu93yIJ06csNGjR1v58uV9phcpiKw7CllHlixevNjvaKvsPDm0a9fO50tgZt4dirwe9+6773qnU/E4cuSIvfbaa+Y4To5rCPizdOlSq127ti1fvty7rFu3bhYVFWUxMTE+07NkdfLkSXvllVesevXq9sgjj3i/3J5G4dChQ9a7d2+LjY31ucZCVgcPHvSOXouIiLAHHnggxzoLFiywMmXK2OOPP57va8kqLS3NWrdubU2bNvW+P3mNJNu2bZvdfffddvbZZ9s555xjTZs2tV9++cV7//Hjx23mzJnmOI79/e9/9xvDM7rt4osvtpkzZ9qCBQvsjjvusDJlynhHr7Rt29bOP//8PHPJet8777xjjuPYoEGDbOfOnT6vzyyzIezcubM1adLEp4H1N0LTEze3xjc9Pd3WrFnjLZb57fgePHjQateuneO6CikpKT4NefZcPHk8+uijNmzYMJ/rdpw8edKuvfZai4mJ8Sk2Wd8Tz//nzJljZ511ls9UPE8++aQ5jmPR0dH21FNP+Txv1tezfPlyu+KKKywpKclee+01b76ef48fP25169a1Cy+8MNfP6quvvrJy5cpZ586dLS4uzltAPTE8UzlFRkb6XM8mt+k7PMs3bdpkNWrUsKuvvtrvetnfj6lTp1q9evUsISHBqlSpYh06dLDt27d74x0/ftzuuuuuHCPgsufyf//3fxYTE2PPP/+8rV692rZv3+7d6di9e7cdPXrU4uPjbeDAgWaWuZOTPY9At9WjR4/65JD9YKwg2+rRo0d9Plt/221e22puuaSnpwe8rWZ/Tzw7BoFuq+HGrXW4JNdgM3fXYWpw0Wtw1ufJTUHqMDU4+DU4+3tS2DocDjXYE6eodZgaXHzCrQZnfWxR6zA1OJObanD2XEv6sXC41mCzkn8sXJQabOaeOkwNdje31uCsOYTqWDhca7Anj4LW4WDUYLPwOxYOpxqc/fWU9GNhajA1ODs6rYtgxYoVVr16devQoYPPl9XzIf7222/mOE6OqU4KwrOj0KxZM/vqq6/syy+/tDPOOMOSk5PzvYaGZ2Pu0qWLz0XZ16xZY23btrVu3brl+/xZR2h4/r9r1y5zHMdnaoS8NGvWzB544AFLTU21K664wipXrmyvv/66XX/99XmOxDp+/LhNnz7dp6BmzWPWrFnmOI7Nnj071+detWqV1ahRwzvSY//+/Wb2ZwN26NAhq1q1qt1zzz0Fei1mf76v06dPN8dxbOTIkQV6XHp6uh04cMDmz5/v85o9jf7BgwetatWqPg1ldvPnz7eqVaua4zgWFRVlsbGx9uabb3obxeeff97i4+Nt69atPo3S3Llz7bPPPvO+d1kbuuHDh1tERISNHDnSZwSMZxTS559/7p1SJmsMf8XNX+PrGX347bffWrNmzeyyyy4r0HVoDhw4YHXq1PF5r1avXm39+/e3m2++2T799NN8R/dn7dz2vB7PdT4ef/xxn9eT3d69ey0uLs5ee+01M8tspK+++mqrUKGCOY5jffr0sUmTJvnEyPqeL1q0yIYNG5Zj1KQnj7///e9Wrlw5mzBhgs9ryRrjH//4hzmO4x0Z5uH5vHfs2OEdaZbXa8nq8OHD1rdvXytfvrx98cUX3uV5bSMbN260TZs22ejRo+3hhx/OkcemTZvMcRx78cUXc8TxbBMrV6606667zhzHsfj4eKtWrZpFR0fb5MmTvetdcskl1qpVK7vhhhusXr161qlTJxsxYoT3+e67774CbavPPfec9e3b1xvDM11J1vc3v221SZMmdtZZZ/mNkX1HIa9ttV+/frnmkvXzymtbffjhh3ONYVawbXXNmjUWztxYh0t6DTZzZx2mBhe+Brdv396uuOKKAtVPs9zbtq5du9qNN96Yby2nBueUWw02C04dHjRoUFjU4Pbt21v37t1zjVPQOtylSxdr0aIFNbiYhWsNNit6HaYGm/f5QlWDzdxTh4NxLBxONXjVqlU2Z86cXOOUtGPhotbgLl262CeffOJTc0rysTA1+NRwYw02c8excLjVYLPC1eFg1GCzkn8sHE41eM2aNTne15J8LEwNpgbnhU7rIlq0aJFVrlzZ2rRpY7NmzfK577PPPrMqVarY559/XqjYixcvtqSkJGvQoIHVr1/f4uPjbdmyZQV+/FVXXWVt27a11NRUW716tXXv3t3i4uJsyZIlBY6RteGaOHGixcbGeq9nkdvIHM9jbrnlFrvsssusR48eFh8f751n/48//rABAwbkOjItawzPc2R9rscff9xiYmJy7ERkt2TJEouKijLHcWzUqFE+U0J8/fXXVrNmTXv++efzfC3+bN682Zo1a2Znn322bdy4scCPW7RokUVERPhsJxkZGfbFF19Y1apV7cknn8wzl23bttmMGTNs4sSJOUYo3nbbbdagQQOfAvr++++b4zjWsmVL++9//+szEsgsc0fptttus8jISHvwwQdzvJZXXnnFYmJi/Mbwx5P3rl277KGHHjLHcaxjx4525plnmiRr3LhxvjHMMhvRpk2bekdA/vjjj3b55ZdbmTJl8s0ltylOzDJH/ZQvXz7PGOnp6ZaWlmaXX365dweye/fuFh8fbzNmzLDRo0ebJJNkLVq08Pu+mpl3CiF/efXt29e7A5s9D8+/Bw8etPvuu88cx7EKFSrY9OnTfeK9//77+b4Wf5YvX26RkZHeopbbNpI91tSpU61t27beKZY8r3fKlCkWFRXlLba55XLkyBGbMmWKDR061IYNG5ZjaqFGjRpZ2bJlrU2bNtarVy9r1KiRxcbGevM8duxYvttqdHS0xcfH54gxePDgHO9Dbttq/fr1TZI1a9Ys3xhmuW+rkZGRFhcXl2cuBdlW84tx8uTJPLdVx3Hstttuy3eK4JLOrXW4JNfgrHHcVoepwf7l1q6dd955Vq5cuYDqhb+2rVWrVvnWcmpw3rLXYE+sotbh/PaPSkoN9myr+dW+rPxtq82bN8+3llODgyeca7BZ4HWYGuzfqa7BZu6pw8E4Fg6nGly/fn3vmUuhrsNuqMHJycnWtGlTq1KlSsjrcDCOhanBp5Zba7BZaI6Fw70GmxWuDhe1BpuV3GPhcKrBjuNYly5d8nxfzUrOsTA1mBqcHzqtg2Dp0qV25plnWmxsrP3jH/+w7777zmbOnGmXX365JSUl2bZt2wKKl3UDeuGFF8xxMi82n/3LlZ8bbrjBmjdvbitWrLDu3btbTEyMrVixosCPz7rh/vDDD3bxxRdbixYtfKZEyMtnn33mHdEyffp0n2k9CtKg+ctj+fLl1r59e/vLX/7iM41Bbn744QerVauWOY5jvXv3tkmTJtm4ceMsOTnZatSokWNe/4KaPHmyOY7jHaFTEPv27bOzzz7bLrjgAu+ovNmzZ9ull15qNWvWLHQuy5cvt7Zt21qfPn3s+PHjlpGRYT/++KOdc845VqdOHatRo0au16T5448/vNNjXHnllfbhhx+aWeZ0NR07drTy5ctb7dq184yRlee+P/74w/r06WOSLDIy0mrWrFngGGZm5513nl1zzTW2YcMGu/TSSy06OtoaNGiQ7+vJKut2s2zZMmvZsqXFxMQU6PU89dRTlpSU5J0S5b333rNjx47Zjz/+aDVr1sz19fibEiX7qLf69etbXFycJSUl5ZlH1gIWFxdnY8aMsS1bttjMmTOtffv2FhUVZbVq1Qr4/fBcS2TKlCl5biNZ26GPPvrIIiIibMSIEfbrr7+amdnChQutW7duVrt2bWvcuHFAn01Wr776qkVGRtp5551nGzZsMDOznTt3WpcuXey0006zpUuXmlnmSLTcttW2bdtamTJl7NZbb80RIzEx0Rsj62vyt6162omCxjDLua1WqFDBqlataoMGDSpwnOzb6tlnn21ly5a12267Lc8YZrlvq2ZmL774oq1evbpAn0NJ58Y6HC41OHsubqjD1GD/srdrjuNYpUqV7Kyzzgq4jc7atnXo0MEiIiIsMTGRGhykGrxw4cJ8t5OC1OHC5JKVW2qw4zhWsWLFgOunme+2ev7555vjOHb99ddTg0+hcKzBZkWrw9RgX6eyBpuZa+pwMI6Fw6kGN2nSxC655BJr1qxZvp9NSTgWLmoNXrRokdWqVctiYmL81r6SdixMDQ4NN9Zgs9AeC4dzDTYLvA4XVw02c/excDjVYLPMs9PzOq4vScfC1GBqcEHQaR0kv/zyi/Xt29eioqKsbNmyVqVKFTvjjDN8rlkQqNmzZ1ubNm2sUqVKAX3YWRuD008/3Tp06GAxMTH2ww8/FCqPKVO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       "text/plain": [
        "<Figure size 2000x1000 with 8 Axes>"
       ]
@@ -1291,78 +974,86 @@
     }
    ],
    "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
+    "def plot_time_of_day_comparison(legs_acbm: pd.DataFrame, \n",
+    "                                legs_nts: pd.DataFrame, \n",
+    "                                activity_column: str = 'dact', \n",
+    "                                hour_column: str = 'tst_hour', \n",
+    "                                num_cols: int = 4, \n",
+    "                                figsize: Optional[Tuple[int, int]] = (20, 5)) -> None:\n",
+    "    \"\"\"\n",
+    "    Plots a comparison of activity data over the hours of the day for different activity types.\n",
     "\n",
-    "# Create a new DataFrame\n",
-    "legs_acbm_clean = legs_acbm.copy()\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    legs_acbm : pd.DataFrame\n",
+    "        DataFrame containing the ACBM data.\n",
+    "    legs_nts : pd.DataFrame\n",
+    "        DataFrame containing the NTS data.\n",
+    "    activity_column : str, optional\n",
+    "        The column name for the activity types. Default is 'dact'.\n",
+    "    hour_column : str, optional\n",
+    "        The column name for the hour values. Default is 'tst_hour'.\n",
+    "    num_cols : int, optional\n",
+    "        The number of columns for the subplots. Default is 4.\n",
+    "    figsize : tuple of int, optional\n",
+    "        The size of the figure. Default is (20, 5).\n",
     "\n",
-    "# Convert tst to datetime format\n",
-    "legs_acbm_clean['tst'] = pd.to_datetime(legs_acbm_clean['tst'])\n",
+    "    Returns\n",
+    "    -------\n",
+    "    None\n",
+    "        This function generates and displays a plot but does not return any value.\n",
+    "    \"\"\"\n",
+    "    # Get unique dact values\n",
+    "    unique_dact_values = legs_acbm[activity_column].unique()\n",
     "\n",
-    "# Extract the hour component\n",
-    "legs_acbm_clean['hour'] = legs_acbm_clean['tst'].dt.hour\n",
+    "    # Create a figure with subplots\n",
+    "    num_plots = len(unique_dact_values)\n",
+    "    num_rows = (num_plots + num_cols - 1) // num_cols\n",
+    "    fig, axes = plt.subplots(num_rows, num_cols, figsize=(figsize[0], num_rows * figsize[1]), sharey=False)\n",
+    "    axes = axes.flatten()\n",
     "\n",
-    "# Convert legs_nts[\"tst\"] from minutes to hours\n",
-    "legs_nts['hour'] = legs_nts['tst'] // 60\n",
+    "    # Iterate over unique dact values and create plots\n",
+    "    for i, dact_value in enumerate(unique_dact_values):\n",
+    "        ax = axes[i]\n",
+    "        acbm_data = legs_acbm[legs_acbm[activity_column] == dact_value]\n",
+    "        nts_data = legs_nts[legs_nts[activity_column] == dact_value]\n",
     "\n",
-    "# Get unique dact values\n",
-    "unique_dact_values = legs_acbm_clean['dact'].unique()\n",
+    "        # Plot histogram for acbm_data\n",
+    "        hist = sns.histplot(acbm_data[hour_column], bins=24, kde=False, discrete=True, stat='percent', ax=ax, label='ACBM')\n",
+    "        ax.set_xticks(range(0, 25))  # Set x-ticks to represent each hour of the day, including 24\n",
+    "        ax.set_xlim(0, 24)  # Ensure the x-axis covers the full range from 0 to 24\n",
+    "        ax.set_title(f'dact: {dact_value}', fontsize=16)\n",
+    "        ax.tick_params(axis='x', rotation=45, labelsize=12)  # Rotate x-axis labels by 45 degrees\n",
     "\n",
-    "# Create a figure with subplots\n",
-    "num_plots = len(unique_dact_values)\n",
-    "num_cols = 4\n",
-    "num_rows = (num_plots + num_cols - 1) // num_cols\n",
-    "fig, axes = plt.subplots(num_rows, num_cols, figsize=(20, num_rows * 5), sharey=False)\n",
-    "axes = axes.flatten()\n",
+    "        # Remove individual subplot labels\n",
+    "        ax.set_xlabel('')\n",
+    "        ax.set_ylabel('')\n",
     "\n",
-    "# Iterate over unique dact values and create plots\n",
-    "for i, dact_value in enumerate(unique_dact_values):\n",
-    "    ax = axes[i]\n",
-    "    acbm_data = legs_acbm_clean[legs_acbm_clean['dact'] == dact_value]\n",
-    "    nts_data = legs_nts[legs_nts['dact'] == dact_value]\n",
+    "        # Add dots to represent the percentage values of legs_nts[\"hour\"]\n",
+    "        nts_hour_counts = nts_data[hour_column].value_counts(normalize=True).sort_index() * 100\n",
+    "        line, = ax.plot(nts_hour_counts.index, nts_hour_counts.values, 'ro', label='NTS')  # 'ro' means red dot\n",
     "\n",
-    "    # Plot histogram for acbm_data\n",
-    "    sns.histplot(acbm_data['hour'], bins=24, kde=False, discrete=True, stat='percent', ax=ax)\n",
-    "    ax.set_xlabel('Hour of Day')\n",
-    "    ax.set_ylabel('Percentage')\n",
-    "    ax.set_xticks(range(0, 24))  # Set x-ticks to represent each hour of the day\n",
-    "    ax.set_title(f'dact: {dact_value}')\n",
-    "    \n",
-    "    # Add dots to represent the percentage values of legs_nts[\"hour\"]\n",
-    "    nts_hour_counts = nts_data['hour'].value_counts(normalize=True).sort_index() * 100\n",
-    "    for hour, percent in nts_hour_counts.items():\n",
-    "        ax.plot(hour, percent, 'ro')  # 'ro' means red dot\n",
+    "    # Remove any empty subplots\n",
+    "    for j in range(i + 1, len(axes)):\n",
+    "        fig.delaxes(axes[j])\n",
     "\n",
-    "# Remove any empty subplots\n",
-    "for j in range(i + 1, len(axes)):\n",
-    "    fig.delaxes(axes[j])\n",
+    "    # Add a main legend at the bottom\n",
+    "    handles, labels = ax.get_legend_handles_labels()\n",
+    "    fig.legend(handles, labels, loc='lower center', ncol=2, bbox_to_anchor=(0.5, -0.05))\n",
     "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dtype('O')"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# get the column type of tst\n",
+    "    # Add a main title to the figure\n",
+    "    fig.suptitle('Comparison of Activity Data Over Hours of the Day', fontsize=20)\n",
+    "\n",
+    "    # Add a single centered x-label and y-label\n",
+    "    fig.text(0.5, 0.02, 'Hour of Day', ha='center', fontsize=14)\n",
+    "    fig.text(0.02, 0.5, 'Percentage of Trips', va='center', rotation='vertical', fontsize=14)\n",
     "\n",
-    "legs_acbm['tst'].dtype"
+    "    # Adjust layout to make room for the main title and labels\n",
+    "    plt.tight_layout(rect=[0.03, 0.05, 1, 0.95])\n",
+    "    plt.show()\n",
+    "\n",
+    "# Example usage\n",
+    "plot_time_of_day_comparison(legs_acbm, legs_nts)"
    ]
   },
   {
@@ -1381,7 +1072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 174,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1400,13 +1091,13 @@
     "}\n",
     "\n",
     "# Replace values in 'origin activity' and 'destination activity' columns\n",
-    "legs_acbm['origin activity'] = legs_acbm['origin activity'].replace(activity_mapping)\n",
-    "legs_acbm['destination activity'] = legs_acbm['destination activity'].replace(activity_mapping)\n",
+    "legs_acbm['oact_abr'] = legs_acbm['oact'].replace(activity_mapping)\n",
+    "legs_acbm['dact_abr'] = legs_acbm['dact'].replace(activity_mapping)\n",
     "\n",
     "# Replace values in 'oact' and 'dact' columns\n",
     "\n",
-    "legs_nts['oact'] = legs_nts['oact'].replace(activity_mapping)\n",
-    "legs_nts['dact'] = legs_nts['dact'].replace(activity_mapping)\n"
+    "legs_nts['oact_abr'] = legs_nts['oact'].replace(activity_mapping)\n",
+    "legs_nts['dact_abr'] = legs_nts['dact'].replace(activity_mapping)\n"
    ]
   },
   {
@@ -1418,7 +1109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 175,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1479,14 +1170,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 176,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_652845/491085427.py:44: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "/tmp/ipykernel_358065/491085427.py:44: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
       "  activity_sequence_df = sorted_df.groupby(pid_col).apply(\n"
      ]
     },
@@ -1518,28 +1209,28 @@
        "  <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",
+       "      <td>education - education - education</td>\n",
+       "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>e - e - e - h - o - h</td>\n",
+       "      <td>education - education - education - home</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>e - e - h</td>\n",
+       "      <td>education - education - education - home - oth...</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>e - h</td>\n",
-       "      <td>10</td>\n",
+       "      <td>education - escort</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>e - h - e</td>\n",
-       "      <td>3</td>\n",
+       "      <td>education - escort - home</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1548,27 +1239,27 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1818</th>\n",
-       "      <td>w - w - w - h - o - h - o - h</td>\n",
+       "      <td>work - work - work - home - other - home - oth...</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>work - work - work - home - work - work - home...</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1820</th>\n",
-       "      <td>w - w - w - s - w</td>\n",
+       "      <td>work - work - work - shop - work</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>work - work - work - work - home - home - home...</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1822</th>\n",
-       "      <td>w - w - w - w - w - h</td>\n",
+       "      <td>work - work - work - work - work - home</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1577,23 +1268,23 @@
        "</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 - s - w          1\n",
-       "1821              w - w - w - w - h - h - h - w          1\n",
-       "1822                      w - w - w - w - w - h          1\n",
+       "                                      activity_sequence  count_nts\n",
+       "0                     education - education - education          4\n",
+       "1              education - education - education - home          1\n",
+       "2     education - education - education - home - oth...          1\n",
+       "3                                    education - escort          1\n",
+       "4                             education - escort - home          2\n",
+       "...                                                 ...        ...\n",
+       "1818  work - work - work - home - other - home - oth...          1\n",
+       "1819  work - work - work - home - work - work - home...          1\n",
+       "1820                   work - work - work - shop - work          1\n",
+       "1821  work - work - work - work - home - home - home...          1\n",
+       "1822            work - work - work - work - work - home          1\n",
        "\n",
        "[1823 rows x 2 columns]"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 176,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1611,14 +1302,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 177,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_652845/491085427.py:44: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "/tmp/ipykernel_358065/491085427.py:44: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
       "  activity_sequence_df = sorted_df.groupby(pid_col).apply(\n"
      ]
     },
@@ -1650,28 +1341,28 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>h - e</td>\n",
-       "      <td>5</td>\n",
+       "      <td>home - education</td>\n",
+       "      <td>10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>h - e - e - e - h</td>\n",
-       "      <td>91</td>\n",
+       "      <td>home - education - escort - home</td>\n",
+       "      <td>9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>h - e - e - e - h - o - h</td>\n",
-       "      <td>6</td>\n",
+       "      <td>home - education - escort - shop - home</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>h - e - e - e - h - s - h</td>\n",
-       "      <td>1</td>\n",
+       "      <td>home - education - home</td>\n",
+       "      <td>289</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>h - e - e - h</td>\n",
-       "      <td>8</td>\n",
+       "      <td>home - education - home - education - home</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1680,27 +1371,27 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>452</th>\n",
-       "      <td>h - w - w - w - h - o - h</td>\n",
+       "      <td>home - work - work - work - home - other - home</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>453</th>\n",
-       "      <td>h - w - w - w - h - o - h - o - h</td>\n",
+       "      <td>home - work - work - work - home - other - hom...</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>454</th>\n",
-       "      <td>h - w - w - w - h - o - o - o - h</td>\n",
+       "      <td>home - work - work - work - home - other - oth...</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>455</th>\n",
-       "      <td>h - w - w - w - w - h</td>\n",
+       "      <td>home - work - work - work - work - home</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>456</th>\n",
-       "      <td>h - w - w - w - w - h - w - h</td>\n",
+       "      <td>home - work - work - work - work - home - work...</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1709,23 +1400,23 @@
        "</div>"
       ],
       "text/plain": [
-       "                     activity_sequence  count_acbm\n",
-       "0                                h - e           5\n",
-       "1                    h - e - e - e - h          91\n",
-       "2            h - e - e - e - h - o - h           6\n",
-       "3            h - e - e - e - h - s - h           1\n",
-       "4                        h - e - e - h           8\n",
-       "..                                 ...         ...\n",
-       "452          h - w - w - w - h - o - h           1\n",
-       "453  h - w - w - w - h - o - h - o - h           2\n",
-       "454  h - w - w - w - h - o - o - o - h           1\n",
-       "455              h - w - w - w - w - h           1\n",
-       "456      h - w - w - w - w - h - w - h           1\n",
+       "                                     activity_sequence  count_acbm\n",
+       "0                                     home - education          10\n",
+       "1                     home - education - escort - home           9\n",
+       "2              home - education - escort - shop - home           1\n",
+       "3                              home - education - home         289\n",
+       "4           home - education - home - education - home           2\n",
+       "..                                                 ...         ...\n",
+       "452    home - work - work - work - home - other - home           1\n",
+       "453  home - work - work - work - home - other - hom...           2\n",
+       "454  home - work - work - work - home - other - oth...           1\n",
+       "455            home - work - work - work - work - home           1\n",
+       "456  home - work - work - work - work - home - work...           1\n",
        "\n",
        "[457 rows x 2 columns]"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 177,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1734,8 +1425,8 @@
     "sequence_acbm = process_sequences(df = legs_acbm,\n",
     "                                  pid_col = 'pid', \n",
     "                                  seq_col = 'seq', \n",
-    "                                  origin_activity_col = 'origin activity', \n",
-    "                                  destination_activity_col = 'destination activity', \n",
+    "                                  origin_activity_col = 'oact', \n",
+    "                                  destination_activity_col = 'dact', \n",
     "                                  suffix = 'acbm')\n",
     "\n",
     "sequence_acbm\n",
@@ -1751,12 +1442,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 178,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -1825,6 +1516,785 @@
     "### Mean crowfly distance per trip purpose"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate travel distance for acbm activities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: add crs to config, and check other scripts\n",
+    "\n",
+    "def calculate_distances(df: pd.DataFrame, \n",
+    "                        start_wkt_col: str, \n",
+    "                        end_wkt_col: str, \n",
+    "                        crs_epsg: int = 4326, \n",
+    "                        projected_epsg: int = 3857) -> pd.DataFrame:\n",
+    "    \"\"\"\n",
+    "    Calculate distances between start and end geometries in a DataFrame.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "\n",
+    "    df: pd.DataFrame\n",
+    "        DataFrame containing WKT geometry columns.\n",
+    "    start_wkt_col: str \n",
+    "        Column name for start location WKT geometries.\n",
+    "    end_wkt_col: str\n",
+    "        Column name for end location WKT geometries.\n",
+    "    crs_epsg: int\n",
+    "        EPSG code for the original CRS (default is 4326 for WGS84).\n",
+    "    projected_epsg: int\n",
+    "        EPSG code for the projected CRS (default is 3857).\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    pd.DataFrame\n",
+    "        DataFrame with an additional 'distance' column containing distances in meters.\n",
+    "    \"\"\"\n",
+    "        # Convert WKT strings to shapely geometries\n",
+    "    df['start_geometry'] = df[start_wkt_col].apply(wkt.loads)\n",
+    "    df['end_geometry'] = df[end_wkt_col].apply(wkt.loads)\n",
+    "\n",
+    "    # Create GeoDataFrame\n",
+    "    gdf = gpd.GeoDataFrame(df, geometry='start_geometry')\n",
+    "\n",
+    "    # Set the original CRS\n",
+    "    gdf.set_crs(epsg=crs_epsg, inplace=True)\n",
+    "\n",
+    "    # Create a separate GeoDataFrame for the end geometries\n",
+    "    end_gdf = gdf.set_geometry('end_geometry')\n",
+    "\n",
+    "    # Set the original CRS for the end_gdf\n",
+    "    end_gdf.set_crs(epsg=crs_epsg, inplace=True)\n",
+    "\n",
+    "    # Transform both GeoDataFrames to a projected CRS\n",
+    "    gdf = gdf.to_crs(epsg=projected_epsg)\n",
+    "    end_gdf = end_gdf.to_crs(epsg=projected_epsg)\n",
+    "\n",
+    "    # Calculate the distance between start and end geometries (in km)\n",
+    "    gdf['distance'] = round(gdf.geometry.distance(end_gdf.geometry) / 1000 , 1)\n",
+    "\n",
+    "    return gdf\n",
+    "\n",
+    "# Apply the function to legs_acbm_geo\n",
+    "legs_acbm_geo = calculate_distances(legs_acbm_geo, 'start_location_geometry_wkt', 'end_location_geometry_wkt')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "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>199</td>\n",
+       "      <td>89</td>\n",
+       "      <td>E00059031</td>\n",
+       "      <td>E00058720</td>\n",
+       "      <td>other</td>\n",
+       "      <td>home</td>\n",
+       "      <td>other</td>\n",
+       "      <td>walk</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1900-01-01 10:00:00</td>\n",
+       "      <td>1900-01-01 10:45:00</td>\n",
+       "      <td>0:45:00</td>\n",
+       "      <td>1735597798</td>\n",
+       "      <td>967294624</td>\n",
+       "      <td>POINT (-1.3986079763910049 53.933681557220275)</td>\n",
+       "      <td>POINT (-1.5127441120162681 53.8180210228714)</td>\n",
+       "      <td>POINT (-155692.328 7157606.366)</td>\n",
+       "      <td>POINT (-1.5127441120162681 53.8180210228714)</td>\n",
+       "      <td>25.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>199</td>\n",
+       "      <td>89</td>\n",
+       "      <td>E00058720</td>\n",
+       "      <td>E00059031</td>\n",
+       "      <td>home</td>\n",
+       "      <td>other</td>\n",
+       "      <td>home</td>\n",
+       "      <td>walk</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1900-01-01 10:45:00</td>\n",
+       "      <td>1900-01-01 11:30:00</td>\n",
+       "      <td>0:45:00</td>\n",
+       "      <td>967294624</td>\n",
+       "      <td>1735597798</td>\n",
+       "      <td>POINT (-1.5127441120162681 53.8180210228714)</td>\n",
+       "      <td>POINT (-1.3986079763910049 53.933681557220275)</td>\n",
+       "      <td>POINT (-168397.904 7135766.710)</td>\n",
+       "      <td>POINT (-1.3986079763910049 53.933681557220275)</td>\n",
+       "      <td>25.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>199</td>\n",
+       "      <td>89</td>\n",
+       "      <td>E00059031</td>\n",
+       "      <td>E00057312</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 12:00:00</td>\n",
+       "      <td>1900-01-01 12:10:00</td>\n",
+       "      <td>0:10:00</td>\n",
+       "      <td>1735597798</td>\n",
+       "      <td>157001772</td>\n",
+       "      <td>POINT (-1.3986079763910049 53.933681557220275)</td>\n",
+       "      <td>POINT (-1.5448043854850726 53.780926914380444)</td>\n",
+       "      <td>POINT (-155692.328 7157606.366)</td>\n",
+       "      <td>POINT (-1.5448043854850726 53.780926914380444)</td>\n",
+       "      <td>33.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>199</td>\n",
+       "      <td>89</td>\n",
+       "      <td>E00057312</td>\n",
+       "      <td>E00059031</td>\n",
+       "      <td>home</td>\n",
+       "      <td>shop</td>\n",
+       "      <td>home</td>\n",
+       "      <td>car</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1900-01-01 12:45:00</td>\n",
+       "      <td>1900-01-01 12:55:00</td>\n",
+       "      <td>0:10:00</td>\n",
+       "      <td>157001772</td>\n",
+       "      <td>1735597798</td>\n",
+       "      <td>POINT (-1.5448043854850726 53.780926914380444)</td>\n",
+       "      <td>POINT (-1.3986079763910049 53.933681557220275)</td>\n",
+       "      <td>POINT (-171966.838 7128775.165)</td>\n",
+       "      <td>POINT (-1.3986079763910049 53.933681557220275)</td>\n",
+       "      <td>33.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>199</td>\n",
+       "      <td>89</td>\n",
+       "      <td>E00059031</td>\n",
+       "      <td>E00058564</td>\n",
+       "      <td>work</td>\n",
+       "      <td>home</td>\n",
+       "      <td>work</td>\n",
+       "      <td>car</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1900-01-01 13:30:00</td>\n",
+       "      <td>1900-01-01 13:50:00</td>\n",
+       "      <td>0:20:00</td>\n",
+       "      <td>1735597798</td>\n",
+       "      <td>1428773232</td>\n",
+       "      <td>POINT (-1.3986079763910049 53.933681557220275)</td>\n",
+       "      <td>POINT (-1.5029553091989152 53.789477653046596)</td>\n",
+       "      <td>POINT (-155692.328 7157606.366)</td>\n",
+       "      <td>POINT (-1.5029553091989152 53.789477653046596)</td>\n",
+       "      <td>29.6</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",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14970</th>\n",
+       "      <td>794026</td>\n",
+       "      <td>334502</td>\n",
+       "      <td>E00056891</td>\n",
+       "      <td>E00057826</td>\n",
+       "      <td>home</td>\n",
+       "      <td>escort</td>\n",
+       "      <td>home</td>\n",
+       "      <td>car</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1900-01-01 20:45:00</td>\n",
+       "      <td>1900-01-01 21:05:00</td>\n",
+       "      <td>0:20:00</td>\n",
+       "      <td>6495819</td>\n",
+       "      <td>1238900496</td>\n",
+       "      <td>POINT (-1.584746581534615 53.793797905031)</td>\n",
+       "      <td>POINT (-1.5320470710998348 53.78289959964661)</td>\n",
+       "      <td>POINT (-176413.182 7131200.405)</td>\n",
+       "      <td>POINT (-1.5320470710998348 53.78289959964661)</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14971</th>\n",
+       "      <td>794027</td>\n",
+       "      <td>334502</td>\n",
+       "      <td>E00057826</td>\n",
+       "      <td>E00057536</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 07:40:00</td>\n",
+       "      <td>1900-01-01 07:55:00</td>\n",
+       "      <td>0:15:00</td>\n",
+       "      <td>1238900496</td>\n",
+       "      <td>1362793606</td>\n",
+       "      <td>POINT (-1.5320470710998348 53.78289959964661)</td>\n",
+       "      <td>POINT (-1.4689248218157287 53.796667804818235)</td>\n",
+       "      <td>POINT (-170546.700 7129146.824)</td>\n",
+       "      <td>POINT (-1.4689248218157287 53.796667804818235)</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14972</th>\n",
+       "      <td>794027</td>\n",
+       "      <td>334502</td>\n",
+       "      <td>E00057536</td>\n",
+       "      <td>E00057826</td>\n",
+       "      <td>home</td>\n",
+       "      <td>escort</td>\n",
+       "      <td>home</td>\n",
+       "      <td>pt</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1900-01-01 15:00:00</td>\n",
+       "      <td>1900-01-01 15:55:00</td>\n",
+       "      <td>0:55:00</td>\n",
+       "      <td>1362793606</td>\n",
+       "      <td>1238900496</td>\n",
+       "      <td>POINT (-1.4689248218157287 53.796667804818235)</td>\n",
+       "      <td>POINT (-1.5320470710998348 53.78289959964661)</td>\n",
+       "      <td>POINT (-163519.963 7131741.273)</td>\n",
+       "      <td>POINT (-1.5320470710998348 53.78289959964661)</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14973</th>\n",
+       "      <td>794027</td>\n",
+       "      <td>334502</td>\n",
+       "      <td>E00057826</td>\n",
+       "      <td>E00056882</td>\n",
+       "      <td>other</td>\n",
+       "      <td>home</td>\n",
+       "      <td>other</td>\n",
+       "      <td>pt</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1900-01-01 18:35:00</td>\n",
+       "      <td>1900-01-01 19:35:00</td>\n",
+       "      <td>1:00:00</td>\n",
+       "      <td>1238900496</td>\n",
+       "      <td>1033390048</td>\n",
+       "      <td>POINT (-1.5320470710998348 53.78289959964661)</td>\n",
+       "      <td>POINT (-1.5911288565275319 53.79690447266708)</td>\n",
+       "      <td>POINT (-170546.700 7129146.824)</td>\n",
+       "      <td>POINT (-1.5911288565275319 53.79690447266708)</td>\n",
+       "      <td>7.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14974</th>\n",
+       "      <td>794027</td>\n",
+       "      <td>334502</td>\n",
+       "      <td>E00056882</td>\n",
+       "      <td>E00057826</td>\n",
+       "      <td>home</td>\n",
+       "      <td>other</td>\n",
+       "      <td>home</td>\n",
+       "      <td>car</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1900-01-01 20:45:00</td>\n",
+       "      <td>1900-01-01 21:05:00</td>\n",
+       "      <td>0:20:00</td>\n",
+       "      <td>1033390048</td>\n",
+       "      <td>1238900496</td>\n",
+       "      <td>POINT (-1.5911288565275319 53.79690447266708)</td>\n",
+       "      <td>POINT (-1.5320470710998348 53.78289959964661)</td>\n",
+       "      <td>POINT (-177123.654 7131785.878)</td>\n",
+       "      <td>POINT (-1.5320470710998348 53.78289959964661)</td>\n",
+       "      <td>7.1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>14975 rows × 19 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          pid     hid      ozone      dzone    purp    oact    dact  mode  \\\n",
+       "0         199      89  E00059031  E00058720   other    home   other  walk   \n",
+       "1         199      89  E00058720  E00059031    home   other    home  walk   \n",
+       "2         199      89  E00059031  E00057312    shop    home    shop   car   \n",
+       "3         199      89  E00057312  E00059031    home    shop    home   car   \n",
+       "4         199      89  E00059031  E00058564    work    home    work   car   \n",
+       "...       ...     ...        ...        ...     ...     ...     ...   ...   \n",
+       "14970  794026  334502  E00056891  E00057826    home  escort    home   car   \n",
+       "14971  794027  334502  E00057826  E00057536  escort    home  escort   car   \n",
+       "14972  794027  334502  E00057536  E00057826    home  escort    home    pt   \n",
+       "14973  794027  334502  E00057826  E00056882   other    home   other    pt   \n",
+       "14974  794027  334502  E00056882  E00057826    home   other    home   car   \n",
+       "\n",
+       "       seq                  tst                  tet duration  \\\n",
+       "0      1.0  1900-01-01 10:00:00  1900-01-01 10:45:00  0:45:00   \n",
+       "1      2.0  1900-01-01 10:45:00  1900-01-01 11:30:00  0:45:00   \n",
+       "2      3.0  1900-01-01 12:00:00  1900-01-01 12:10:00  0:10:00   \n",
+       "3      4.0  1900-01-01 12:45:00  1900-01-01 12:55:00  0:10:00   \n",
+       "4      5.0  1900-01-01 13:30:00  1900-01-01 13:50:00  0:20:00   \n",
+       "...    ...                  ...                  ...      ...   \n",
+       "14970  4.0  1900-01-01 20:45:00  1900-01-01 21:05:00  0:20:00   \n",
+       "14971  1.0  1900-01-01 07:40:00  1900-01-01 07:55:00  0:15:00   \n",
+       "14972  2.0  1900-01-01 15:00:00  1900-01-01 15:55:00  0:55:00   \n",
+       "14973  3.0  1900-01-01 18:35:00  1900-01-01 19:35:00  1:00:00   \n",
+       "14974  4.0  1900-01-01 20:45:00  1900-01-01 21:05:00  0:20:00   \n",
+       "\n",
+       "      start_location_id end_location_id  \\\n",
+       "0            1735597798       967294624   \n",
+       "1             967294624      1735597798   \n",
+       "2            1735597798       157001772   \n",
+       "3             157001772      1735597798   \n",
+       "4            1735597798      1428773232   \n",
+       "...                 ...             ...   \n",
+       "14970           6495819      1238900496   \n",
+       "14971        1238900496      1362793606   \n",
+       "14972        1362793606      1238900496   \n",
+       "14973        1238900496      1033390048   \n",
+       "14974        1033390048      1238900496   \n",
+       "\n",
+       "                          start_location_geometry_wkt  \\\n",
+       "0      POINT (-1.3986079763910049 53.933681557220275)   \n",
+       "1        POINT (-1.5127441120162681 53.8180210228714)   \n",
+       "2      POINT (-1.3986079763910049 53.933681557220275)   \n",
+       "3      POINT (-1.5448043854850726 53.780926914380444)   \n",
+       "4      POINT (-1.3986079763910049 53.933681557220275)   \n",
+       "...                                               ...   \n",
+       "14970      POINT (-1.584746581534615 53.793797905031)   \n",
+       "14971   POINT (-1.5320470710998348 53.78289959964661)   \n",
+       "14972  POINT (-1.4689248218157287 53.796667804818235)   \n",
+       "14973   POINT (-1.5320470710998348 53.78289959964661)   \n",
+       "14974   POINT (-1.5911288565275319 53.79690447266708)   \n",
+       "\n",
+       "                            end_location_geometry_wkt  \\\n",
+       "0        POINT (-1.5127441120162681 53.8180210228714)   \n",
+       "1      POINT (-1.3986079763910049 53.933681557220275)   \n",
+       "2      POINT (-1.5448043854850726 53.780926914380444)   \n",
+       "3      POINT (-1.3986079763910049 53.933681557220275)   \n",
+       "4      POINT (-1.5029553091989152 53.789477653046596)   \n",
+       "...                                               ...   \n",
+       "14970   POINT (-1.5320470710998348 53.78289959964661)   \n",
+       "14971  POINT (-1.4689248218157287 53.796667804818235)   \n",
+       "14972   POINT (-1.5320470710998348 53.78289959964661)   \n",
+       "14973   POINT (-1.5911288565275319 53.79690447266708)   \n",
+       "14974   POINT (-1.5320470710998348 53.78289959964661)   \n",
+       "\n",
+       "                        start_geometry  \\\n",
+       "0      POINT (-155692.328 7157606.366)   \n",
+       "1      POINT (-168397.904 7135766.710)   \n",
+       "2      POINT (-155692.328 7157606.366)   \n",
+       "3      POINT (-171966.838 7128775.165)   \n",
+       "4      POINT (-155692.328 7157606.366)   \n",
+       "...                                ...   \n",
+       "14970  POINT (-176413.182 7131200.405)   \n",
+       "14971  POINT (-170546.700 7129146.824)   \n",
+       "14972  POINT (-163519.963 7131741.273)   \n",
+       "14973  POINT (-170546.700 7129146.824)   \n",
+       "14974  POINT (-177123.654 7131785.878)   \n",
+       "\n",
+       "                                         end_geometry  distance  \n",
+       "0        POINT (-1.5127441120162681 53.8180210228714)      25.3  \n",
+       "1      POINT (-1.3986079763910049 53.933681557220275)      25.3  \n",
+       "2      POINT (-1.5448043854850726 53.780926914380444)      33.1  \n",
+       "3      POINT (-1.3986079763910049 53.933681557220275)      33.1  \n",
+       "4      POINT (-1.5029553091989152 53.789477653046596)      29.6  \n",
+       "...                                               ...       ...  \n",
+       "14970   POINT (-1.5320470710998348 53.78289959964661)       6.2  \n",
+       "14971  POINT (-1.4689248218157287 53.796667804818235)       7.5  \n",
+       "14972   POINT (-1.5320470710998348 53.78289959964661)       7.5  \n",
+       "14973   POINT (-1.5911288565275319 53.79690447266708)       7.1  \n",
+       "14974   POINT (-1.5320470710998348 53.78289959964661)       7.1  \n",
+       "\n",
+       "[14975 rows x 19 columns]"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "legs_acbm_geo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the distance comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Parameters\n",
+    "distance_column = 'distance'\n",
+    "bin_size = 2\n",
+    "distance_threshold = 50  # Set this to None if you don't want to use a threshold\n",
+    "\n",
+    "# Create distance_binned column\n",
+    "legs_acbm_geo['distance_binned'] = (legs_acbm_geo[distance_column] / bin_size).round() * bin_size\n",
+    "legs_nts['distance_binned'] = (legs_nts[distance_column] / bin_size).round() * bin_size\n",
+    "\n",
+    "# Define the bins\n",
+    "max_distance_data = max(legs_acbm_geo['distance_binned'].max(), legs_nts['distance_binned'].max())\n",
+    "if distance_threshold is not None:\n",
+    "    max_distance = min(distance_threshold, max_distance_data)\n",
+    "else:\n",
+    "    max_distance = max_distance_data\n",
+    "bins = range(0, int(max_distance) + bin_size, bin_size)\n",
+    "\n",
+    "# Create a figure\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "\n",
+    "# Plot histogram for legs_acbm_geo data\n",
+    "sns.histplot(legs_acbm_geo['distance_binned'], bins=bins, kde=False, discrete=True, stat='percent', ax=ax, label='ACBM', shrink=0.8)\n",
+    "\n",
+    "# Add dots to represent the percentage values of legs_nts[\"distance_binned\"]\n",
+    "nts_distance_counts = legs_nts['distance_binned'].value_counts(normalize=True).sort_index() * 100\n",
+    "if not nts_distance_counts.empty:\n",
+    "    bin_centers = nts_distance_counts.index\n",
+    "    ax.plot(bin_centers, nts_distance_counts.values, 'ro', label='NTS')  # 'ro' means red dot\n",
+    "\n",
+    "# Set x-axis limits based on max_distance\n",
+    "ax.set_xlim(0, max_distance)\n",
+    "# Add x-axis ticks and labels for each bar\n",
+    "ax.set_xticks(bins)\n",
+    "ax.set_xticklabels([str(bin) for bin in bins], rotation=45, ha='right')\n",
+    "\n",
+    "\n",
+    "# Add labels and title\n",
+    "ax.set_xlabel('Distance (km)', fontsize=14)\n",
+    "ax.set_ylabel('Percentage of Trips', fontsize=14)\n",
+    "ax.set_title('Overall Comparison of Trip Distances', fontsize=20)\n",
+    "ax.legend()\n",
+    "\n",
+    "# Adjust layout to make room for the main title and labels\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_distance_comparison(legs_acbm: pd.DataFrame, \n",
+    "                             legs_nts: pd.DataFrame, \n",
+    "                             activity_column: str = 'dact', \n",
+    "                             distance_column: str = 'distance', \n",
+    "                             bin_size: int = 2, \n",
+    "                             num_cols: int = 4, \n",
+    "                             figsize: Optional[Tuple[int, int]] = (20, 5),\n",
+    "                             distance_threshold: Optional[int] = None) -> None:\n",
+    "    \"\"\"\n",
+    "    Plots a comparison of distance data for different activity types.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    legs_acbm : pd.DataFrame\n",
+    "        DataFrame containing the ACBM data.\n",
+    "    legs_nts : pd.DataFrame\n",
+    "        DataFrame containing the NTS data.\n",
+    "    activity_column : str, optional\n",
+    "        The column name for the activity types. Default is 'dact'.\n",
+    "    distance_column : str, optional\n",
+    "        The column name for the distance values. Default is 'distance'.\n",
+    "    bin_size : int, optional\n",
+    "        The size of the bins for rounding distances. Default is 2 km.\n",
+    "    num_cols : int, optional\n",
+    "        The number of columns for the subplots. Default is 4.\n",
+    "    figsize : tuple of int, optional\n",
+    "        The size of the figure. Default is (20, 5).\n",
+    "    distance_threshold : int, optional\n",
+    "        The maximum value for the x-axis. Default is None.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    None\n",
+    "        This function generates and displays a plot but does not return any value.\n",
+    "    \"\"\"\n",
+    "    # Create distance_binned column\n",
+    "    legs_acbm['distance_binned'] = (legs_acbm[distance_column] / bin_size).round() * bin_size\n",
+    "    legs_nts['distance_binned'] = (legs_nts[distance_column] / bin_size).round() * bin_size\n",
+    "\n",
+    "    # Define the bins\n",
+    "    max_distance_data = max(legs_acbm['distance_binned'].max(), legs_nts['distance_binned'].max())\n",
+    "    if distance_threshold is not None:\n",
+    "        max_distance = min(distance_threshold, max_distance_data)\n",
+    "    else:\n",
+    "        max_distance = max_distance_data\n",
+    "    bins = range(0, int(max_distance) + bin_size, bin_size)\n",
+    "\n",
+    "    # Get unique dact values\n",
+    "    unique_dact_values = legs_acbm[activity_column].unique()\n",
+    "\n",
+    "    # Create a figure with subplots\n",
+    "    num_plots = len(unique_dact_values)\n",
+    "    num_rows = (num_plots + num_cols - 1) // num_cols\n",
+    "    fig, axes = plt.subplots(num_rows, num_cols, figsize=(figsize[0], num_rows * figsize[1]), sharey=False)\n",
+    "    axes = axes.flatten()\n",
+    "\n",
+    "    # Iterate over unique dact values and create plots\n",
+    "    for i, dact_value in enumerate(unique_dact_values):\n",
+    "        ax = axes[i]\n",
+    "        acbm_data = legs_acbm[legs_acbm[activity_column] == dact_value]\n",
+    "        nts_data = legs_nts[legs_nts[activity_column] == dact_value]\n",
+    "\n",
+    "        # Plot histogram for acbm_data\n",
+    "        hist = sns.histplot(acbm_data['distance_binned'], bins=bins, kde=False, discrete=True, stat='percent', ax=ax, label='ACBM')\n",
+    "        ax.set_title(f'dact: {dact_value}', fontsize=16)\n",
+    "        ax.tick_params(axis='x', rotation=45, labelsize=12)  # Rotate x-axis labels by 45 degrees\n",
+    "\n",
+    "        # Set x-axis limits based on max_distance\n",
+    "        ax.set_xlim(0, max_distance)\n",
+    "        # Add x-axis ticks and labels for each bar\n",
+    "        # ax.set_xticks(bins)\n",
+    "        # ax.set_xticklabels([str(bin) for bin in bins], rotation=45, ha='right')\n",
+    "\n",
+    "        # Remove individual subplot labels\n",
+    "        ax.set_xlabel('')\n",
+    "        ax.set_ylabel('')\n",
+    "\n",
+    "        # Add dots to represent the percentage values of legs_nts[\"distance_binned\"]\n",
+    "        nts_distance_counts = nts_data['distance_binned'].value_counts(normalize=True).sort_index() * 100\n",
+    "        if not nts_distance_counts.empty:\n",
+    "            bin_centers = nts_distance_counts.index\n",
+    "            ax.plot(bin_centers, nts_distance_counts.values, 'ro', label='NTS')  # 'ro' means red dot\n",
+    "\n",
+    "    # Remove any empty subplots\n",
+    "    for j in range(i + 1, len(axes)):\n",
+    "        fig.delaxes(axes[j])\n",
+    "\n",
+    "    # Add a main legend at the bottom\n",
+    "    handles, labels = ax.get_legend_handles_labels()\n",
+    "    fig.legend(handles, labels, loc='lower center', ncol=2, bbox_to_anchor=(0.5, -0.05))\n",
+    "\n",
+    "    # Add a main title to the figure\n",
+    "    fig.suptitle('Comparison of Distance Data for Different Activity Types', fontsize=20)\n",
+    "\n",
+    "    # Add a single centered x-label and y-label\n",
+    "    fig.text(0.5, 0.02, 'Distance (km)', ha='center', fontsize=14)\n",
+    "    fig.text(0.02, 0.5, 'Percentage of Trips', va='center', rotation='vertical', fontsize=14)\n",
+    "\n",
+    "    # Adjust layout to make room for the main title and labels\n",
+    "    plt.tight_layout(rect=[0.03, 0.05, 1, 0.95])\n",
+    "    plt.show()\n",
+    "\n",
+    "# Example usage\n",
+    "plot_distance_comparison(legs_acbm_geo, legs_nts2, distance_threshold=50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "legs_a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "legs_acbm_test = legs_acbm_geo.copy()\n",
+    "legs_nts_test = legs_nts2.copy()\n",
+    "\n",
+    "legs_acbm_test['distance_binned'] = (legs_acbm_test['distance'] / 2).round() * 2\n",
+    "legs_nts_test['distance_binned'] = (legs_nts_test['distance'] / 2).round() * 2\n",
+    "\n",
+    "legs_acbm_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "legs_nts_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_distance = max(legs_acbm_test['distance_binned'].max(), legs_nts_test['distance_binned'].max())\n",
+    "bins = range(0, int(max_distance) + 2, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_distance, bins"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unique_dact_values = legs_acbm_test['dact'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_plots = len(unique_dact_values)\n",
+    "num_cols = 4\n",
+    "figsize = (20, 5)\n",
+    "num_rows = (num_plots + num_cols - 1) // num_cols\n",
+    "fig, axes = plt.subplots(num_rows, num_cols, figsize=(figsize[0], num_rows * figsize[1]), sharey=False)\n",
+    "axes = axes.flatten()\n",
+    "\n",
+    "for i, dact_value in enumerate(unique_dact_values):\n",
+    "    ax = axes[i]\n",
+    "    acbm_data = legs_acbm_test[legs_acbm_test['dact'] == dact_value]\n",
+    "    nts_data = legs_nts_test[legs_nts_test['dact'] == dact_value]\n",
+    "\n",
+    "    # Plot histogram for acbm_data\n",
+    "    hist = sns.histplot(acbm_data['distance_binned'], bins=bins, kde=False, discrete=True, stat='percent', ax=ax, label='ACBM')\n",
+    "    ax.set_title(f'dact: {dact_value}', fontsize=16)\n",
+    "    ax.tick_params(axis='x', rotation=45, labelsize=12)  # Rotate x-axis labels by 45 degrees\n",
+    "\n",
+    "    # Remove individual subplot labels\n",
+    "    ax.set_xlabel('')\n",
+    "    ax.set_ylabel('')\n",
+    "\n",
+    "    # Add dots to represent the percentage values of legs_nts[\"distance_binned\"]\n",
+    "    nts_distance_counts = nts_data['distance_binned'].value_counts(normalize=True).sort_index() * 100\n",
+    "    if not nts_distance_counts.empty:\n",
+    "        bin_centers = nts_distance_counts.index\n",
+    "        ax.plot(bin_centers, nts_distance_counts.values, 'ro', label='NTS')  # 'ro' means red dot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nts_data = legs_nts_test[legs_nts_test['dact'] == \"education\"]\n",
+    "nts_data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "legs_nts_test['dact']"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

From 6146969ff8c689635aa1139453b8e8c9e6ab6efc Mon Sep 17 00:00:00 2001
From: Hussein Mahfouz <45176416+Hussein-Mahfouz@users.noreply.github.com>
Date: Fri, 27 Sep 2024 13:18:47 +0100
Subject: [PATCH 06/11] intrazonal trips

---
 notebooks/5_validate_outputs.ipynb | 630 +++++++++++++++++++++++------
 1 file changed, 503 insertions(+), 127 deletions(-)

diff --git a/notebooks/5_validate_outputs.ipynb b/notebooks/5_validate_outputs.ipynb
index e2ce020..a218bd4 100644
--- a/notebooks/5_validate_outputs.ipynb
+++ b/notebooks/5_validate_outputs.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -324,7 +324,7 @@
        "[14975 rows x 14 columns]"
       ]
      },
-     "execution_count": 165,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -342,7 +342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -356,6 +356,26 @@
     "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": 5,
+   "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": {},
@@ -365,7 +385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -384,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -599,7 +619,7 @@
        "[59359 rows x 10 columns]"
       ]
      },
-     "execution_count": 168,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -645,7 +665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -686,7 +706,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -853,7 +873,7 @@
        "7       work   1673   acbm   11.171953"
       ]
      },
-     "execution_count": 170,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -872,7 +892,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -919,7 +939,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -959,12 +979,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
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cAKeYv321U/FYANkU9XcoIMydPHpIkqlF7/tVrV4jSdJhSSPXLdUdX07z+b0ipVIVvdrleh1udK66ZImx46fvtGrmv5WWliZJWtuguf6oXF2V9+326fT2yFDmZSnWNmied3IBnnjk70QBj/Z+Xs+eipX1yiW99W2jc73L8j1RoKhK2LEWndbFpTAH6m4TyoPsYJzZCAAePXtm7kwUsD3ymdalEFPUSNk6Z2jTUJqVsJ3jYMl+EJa8epH04bh8H3fJVQMV1bStpHwOXIrwvgY6bZa/UdBSkKYYD8aU6cGcdj0Y+7/B2ubprEJJ1b17YNt8OF5CJRwH4wBwrdz21QrDc8YaEDJu2Qcuah4B/g4FlEax1ZO8g+slaW1SQw3q/H9+LyVRJdtjD+7Y7PO3RURq0nVDNHTcg8pQzrO1HUmTrxuS/2UpCnniUdYTBSSpzfJ5Gvr+yzkeXvXQPj36/st6buCT+r5Vco77/SpKe1QCj7XotC4ugR6oB5mTkV6068SE+iA7GGc2AiiV8hwhXqlS5k2SVq70u8qOHTvUq9e1On78mHdZYaao8emcoU1DSVbKdo6DzXMQlnbsSIHWT6t/ts+Bjl9FfF8DnTbL3yjooEwxHoxLJwTz8gvB2v8NxjZPxzdKuoJu8268hEpRvzduG4wDIOz521cLVPYz1oCQcMuArWDlERnJbHpAgIpyKYnvWyXruYFPqv97Y1R135+zwe2tXF2TrxtSsE7iIJx45GSkq/97YzL/n+0+z+8l/d4bo6UtOubfZ1eU9siNx1oFUCo6rV9++WU988wz2rlzp5o3b64XX3xR5/mZ/97jP//5jx555BFt3rxZDRo00OjRo3XZZZcF/sQhOqBss3xeji/mH5Wra1JBv5huOMju2DHzy7d9u/8vleNk3t+xY/6vB0CpEMzR5ZLUuu+DqpLUQFLgU9Tk6JyhTQtIoHUbxagU7hwXl6JMVeUzGCfAa2P7G8hT2FyyjoIu7BTjPoJx6YRgXX4hGPu/hdjmfWb2+J/4OXNU7957Jfke4Nq2bdI112jTM8/4TL1erNOuo9QJWg0uaDvvtkuoFPV747bBOABKlexnrAUi+xlrKLqQ/R5dUrllwJZb8gBQKN+3StbSFh0Lf0JnEE48arxhpU/fXHYRkhL27VbjDSvz7qAvanvktmOtAgr7Tut3331XQ4cO1Wuvvabzzz9fY8aMUdeuXbV+/XpVr149x/oLFy7U9ddfr1GjRql79+6aNm2arrrqKi1fvlzNmjUL7MlDcEDZZvk8DR33YI7llfft1tBxD+Y/7YBbDrIjIzN/GOjVK3P9rLGc//18N2YMB+kAvIIxulz6c4R5haqnF3qKGu9jsnQWxQ8e7L8T4n//bho0SPuznf2da0dEGAu0bqMYldKd4+JSmKmq/A3GKey1sbNO9RiMabMCnWLcr2BcOqGQMXw6iwMcCOCRo40OcJvfunWrGjVqrGPHjnpXiZC0WTln9pAyP5cMSWXuvVdt9OdsH8U27TpKnaDX4IK08266hEowvjduGowDAAiZkP4eXRK5ZcCWW/IAUCRFOVs7GCceBeX3kmC0R2461gpA2HdaP/fcc7r11lt10003SZJee+01ffrpp5owYYIeeOCBHOuPHTtW3bp1073/61gYOXKkZs+erZdeekmvvfZa4AmcwgPKoEw74KaD7J49M38Y8DfSfcwYfmgD4FdRRpdLeY8wL+hOT17Xfx0rqXaWZb9JGmKmD/5Xd7Ly2xER5gKt2ygmQThjtPLChapXgKfatHCh9lWqlPfU/mEi0Kmq/A3GCfTa2LlN9VjUabMCnWLcI+vnHHvokM4qQIyfDx3S4eXLvX/7dBYXYhR09s7iwg4EKFeuvN5/f7oS/xe7MNv8sWNHdf7Nj6lSYl1J0jlb1qn21NG5PjZCUpKkv/e5Xz/WaVR8066jVCq2GpzXjyBuuYRKsL43IRyMAwBwj5D/Hl3SuGXAllvyABA6QTiZsrC/l/gIRnvklmOtAIV1p3VqaqqWLVum4cOHe5dFRESoc+fO+u677/w+5rvvvtPQoUN9lnXt2lUffvih3/VPnDihEydOeP8+cOCAJOlg9hU3bpRaFXJ0hx+HDx+WJO3dsl5pJzKvu9p0688qu2+3DuXxuKh9u5Uw932tTjpLB3dulSQtW7bMGy9+zhzVLcDzb54zR/sj/jz/IyIiQhkZGUGL4ZWQIL35pmJ//FFl/vhDaVWr6vA552Q2Cl9/7bPq+vXrJfm+J4E6uGOLJOnA9g0qWybHuTYlKoabciGGe3MhRvHE+WPjKkmm+snXKq5GLe/yzZKutgydvWe7qh47oj8qxOinaqcrw4lQy2wxju7dpfWzp2nz5s2Kj4/PmefBzEpj/n5cLaEKU7dzrcMHc1RiBKKgO8ezZkkdO+q3337Tuee28bkWfHtJnxXgqfo+9JC+fegh7997flkV0joerDi5xfi0Yrw+v+lRNd72iyofOaB9MXFaW+tMZURESBtW+I2RfvKE9z3ZEVUh536mHzuiKijtxDGln0zN9bUUNBd/ryVF0q+x8apyeH+uU4z/UbGyvpaUsWGFUjb+JEk+g3kcSask1VTOs4o9MbZLOnvgQGVt6cqVK68pU95UjRo1JDM1SUhQ2ZSUnGdJK/OM5ZPVqmmNmXffcf369Tp27Kgadumt6Co1dO6WdTq46HN/b6OPc9teqoN1MgcPHPj9V/264CN1797de39ht/kTRw57P9/ofbsL9PlG79uttNPqKC01s/07fPjwn+1egN9fFE641eGg1uDsK1aqJOVWl5s3l2rWlH7/PffkTj89c73irO3B+t5UqlSw58vrPQkwhr/fBgrDLfvjxHBvLsRwby5hFeN/v1X67NtkX8flNTikv0eX1OPgjRsLvl4Qf193Sx7BqOWu+Q67JIabciGGe3Px1z8mSUpIUNyIETp97FhFZTk5IzUhQdsHDdKBhASf3xck3+9voL+X+K19wWiPiuFY65TUYAtj27dvN0m2cOFCn+X33nuvnXfeeX4fU7ZsWZs2bZrPspdfftmqV6/ud/3HHnvMlPl7GDdu3Lhx43bKbxs3bgxO0XSBwtRt6jA3bty4cQvlLVzqMDWYGzdu3LiVtJtbazC/R3Pjxo0bt3C/FWcNDuszrU+F4cOH+4yE279/v+rUqaOtW7cqLi6u0HEPHjyo2rVr67ffflOlgo60Jkaxx3BTLsRwby7EcG8u4RTjwIEDSkpKUpUquV1Ru3Qojjrsls/YLTHclAsx3JsLMdybCzGKJw51mBpcknIhhntzIYZ7cyGGe3OhBru3BgcrDjGCH8NNuRDDvbkQw725uCXGqajBYd1pnZCQoMjISO3atctn+a5du3Taaaf5fcxpp50W0PrlypVTuXLlciyPi4sr0pfBo1KlSkWOQ4zgx3BTLsRwby7EcG8u4RQjIsLfRDMlU2HqdnHWYbd8xm6J4aZciOHeXIjh3lyIUTxxwqUOU4OLL4abciGGe3MhhntzIYZ7c3FrDS7pv0eH23ZCDPfmQgz35kIM9+bilhjFWYPdWd2DJCoqSq1bt9ZXX33lXZaRkaGvvvpKF1xwgd/HXHDBBT7rS9Ls2bNzXR8AAARHYeo2AAAoOmowAADBwe/RAAAUXlifaS1JQ4cOVb9+/XTuuefqvPPO05gxY3TkyBHddNNNkqQbb7xRp59+ukaNGiVJGjx4sC688EL961//0uWXX6533nlHS5cu1b///e9QvgwAAEqF/Oo2AAAoHtRgAACCg9+jAQAonLDvtP7rX/+qPXv26NFHH9XOnTvVokULzZo1SzVq1JAkbd261edU9nbt2mnatGl6+OGH9eCDD6pBgwb68MMP1axZswI9X7ly5fTYY4/5naIlEMGIQ4zgx3BTLsRwby7EcG8uxHC//Op2ftzy3oZTDDflQgz35kIM9+ZCDHfn4ibU4ODGcFMuxHBvLsRwby7EcG8uJaEGl8Tfo9lOwjuGm3IhhntzIYZ7cwmnGPlxzMyKLToAAAAAAAAAAAAAAHkI62taAwAAAAAAAAAAAADcjU5rAAAAAAAAAAAAAEDI0GkNAAAAAAAAAAAAAAgZOq0BAAAAAAAAAAAAACFDpzUAAAAAAAAAAAAAIGTotEaxysjICHUKkiQzC3UKAACcUm6pwRJ1GABQulCDAQAIDWowAJRsdFoHWSiLkZsK4fLlyyVJERERhd5ZOHHiRJHz+OOPPyRJjuMUKc7s2bP1+++/Fzkfj2B8VsGK4YkTyp26rHkUNY6//wcjXiikpqYWOcbixYt16NChIGSTqbDvSVG3NTduq0XJI9y2VTcJ1Xvhps8gGDVYck8dpgYXv2DU4XBr19xYg6XCvS/hUoODlUu4batuQg2mBufHTd85t7Rt1GD/3FiHQ1GDgxUjGKjB7sbv0eFXg6Xg1uFg1s9gxHBbu1aUGP7+H4x4oUINDm6MYChtNZhO6yI6dOiQdu/erePHj0vKLEZF+cCK8ljHcXTs2LEixdi/f7+2bdumDRs2FDrGBx98oHPPPVf9+/eXVLidhddff13333+/jhw5Uug8Zs6cqbvuuksvvvhioWNI0nvvvaeuXbvqjjvuKHSMDRs2aMGCBXrrrbd07NixQm0nJ0+e1OHDh71/O45T5MYy685TRERmc1CY7SctLa3IeRR1R07KzP3o0aPemJ5lgVi2bJlWrlzpEyNQU6dO1dixYwv1WI9Zs2bpySef1LJlywr9nX733Xd14YUX6o033ih0HikpKdq4caM2btyo1NTUQrdxWT/jwmxrbtxWi5KHW7bVcOCWOhxONVhyTx2mBuevqO2aJ5eitiNuadfCqQZLwanD4VKDPbkUtQ67ZVsNB+FUg6Wi12FqcE5urcGeOB5uOb4oLLe0a8GowZJ76rAbarAnhodbtlVqcOi5pQZ7njvUx8LhVoOlotfhYNRgKbyPhcOpBkvhdSxMDc6ZR6mqwYZCe++99+zCCy+06tWrW4sWLaxfv372+++/Bxxn7ty59tlnn9nJkycLncsXX3xhffv2tXr16lmnTp3sH//4R8AxPvzwQ7v44outSpUqVrNmTbvuuusCjnH8+HHr37+/OY5jSUlJ1q9fP+996enpBYoxadIkcxzH7rzzTjt48GDAOZiZTZkyxSpUqGB//etf7eOPPy5UDDOz8ePHm+M4VrFiRatRo4Z99913Acd45513rEmTJhYTE2OO49g555wT8Ov68MMP7YorrrB69erZxRdfbPfdd58dPXo04Fyy+vjjj+3666+3Nm3a2A033GDTpk2zQ4cOmVnBP6us70dht99vvvnGHnvsMbvkkktsyJAhtmzZMktLSws4zhdffGF9+vSxOnXq2MUXX2zjx4+348ePBxTjP//5jzmOY/3797effvop4BzMzCZOnGiO49jw4cML/Rm9+eabFh8fb8nJyTZ37twi5eE4jl166aWFijFjxgxr166dxcbGWmJiot1+++12+PDhgOPMnj3bBg0aZJdeeqn9/e9/t7lz53rfm4Jsa2yrOQVjWw0HbqnD4VSDzdxTh6nBeQtGu2YWnLbNLe1aONVgs+DU4XCpwWbhta2Gg3CqwWZFr8PU4JzcWoPN3NO2hVO7FowabOaeOuyGGmzGtpodNTiTW2qwmTuOhcOtBpsVvQ4HowabhfexcLi1a+F0LEwN9hVu22pB0GldSNOnT7eyZcvaFVdcYQ8//LBdffXVVrVqVatdu7ZNmzbNDhw4UKA477//vjmOYy1btrT//ve/heoAmTJlilWpUsXatGljvXr1skaNGllsbKwNHjw4oBixsbF2+eWX24gRI6x3794WFRVld911V8D5vPLKKxYdHW09evSw+Ph4GzBggPe+/L7QnoZt2LBhtnXr1oCf2yyzMahWrZoNGTLE1q9f73edjIyMfON4cnn00Uftk08+sXLlytk999wTUC7Tp0+38uXLW9++fe3NN9+0f/3rX1apUiUbNGhQgWO89dZbFhUVZRdccIHdfPPN1rJlS+8Ox+zZs+3IkSMB5WRmNm3aNIuKirL27dvbVVddZfXr17dKlSrZRRddZLt27TKz/N8jT0N12WWXeZcF2vhOmTLFqlWrZmeddZadddZZFh0dbY0aNbJPP/00oDieotq+fXvr06ePNWrUyKpWrWpffvllQHEGDRpkjuNYhQoVrG/fvrZ69eqAHu/ZZoYOHWrbtm3zLg9kJ3n27NkWGxtrf//73+3HH3/0u05+n40nj8GDB9sDDzxgjuPYhAkTCpyDmdnbb79tsbGx1r17dxsxYoR16dLFYmNjbdasWQHFefPNN61ChQrWuHFja9WqlcXHx1uFChXsxhtvtH379pmZ5dnulZRttSBtipl7ttVw4JY6HE412Mw9dZgaXPztmllw6rBb2rVwqsFmwanD4VKDzYJTh92yrYaDcKrBnjjBqMPU4D+5tQabuadtowbn5JY67IYabFZytlVq8Knllhps5q5j4XCpwVlzKWwdDkYNNgvvY+FwqsFm4XUsTA32VVprMJ3WAcrIyLCDBw9a586d7eqrr7bt27ebWeaorm+++cYuvPBCq1ixoj3//PPeL0FufvzxRzvnnHOsTp06VqNGDWvatKnNnj07oB2F+fPnW40aNWzQoEG2YcMGMzPbuXOndenSxRITE23p0qX5xvj8888tISHB7rrrLtu4caOZmR05csSaNGli3bp1K3AuHkuXLrX27dvbkiVLrE+fPhYbG2sDBgzwfoly+zJNmjTJIiIicjSwu3fvtvXr19vevXsL9Pzjxo2zBg0a2LJly3xijxkzxp555hnbuXNnvl9oz4i2oUOH2vbt2+348eN23XXXWcWKFW3hwoUFymP79u3WsmVLu+mmm2zz5s1mltkwdejQwW6//XafdXPL59dff7X69evbzTffbL/++quZme3du9fuu+8+cxzHGjdubFOmTPGO8imI33//3Ro2bGj9+/f3vs9Hjx61Bx54wGrUqGFJSUn2888/m1nuxW3JkiVWt25dq1q1qjmOY927d/feV9DGd9asWValShW7++67bdWqVXbs2DH79ttvrW7duta5c+cCv56ZM2d6d748ee/Zs8ccx7H777/fZ93c3mfP8lmzZln79u3t3nvvNcdxrHfv3gVugN98803vDu6mTZu8y9PS0vJtC7LmMHjwYOvYsaOtW7fOe9+3335rX3zxha1YsSLfUWGekaFDhw61vXv32po1ayw+Pt4uvfRSO3DgQIGK2Zo1a6xu3br2t7/9zee1xMbG2tixY/3m7c+yZcssISHBBg4caD///LOlp6fbxo0brXXr1uY4jp177rmWkpKS6+thW80pGNtqSeemOhxONdjMPXWYGlz87ZpZcNo2t7Rr4VSDzYJTh8OlBpuF17Za0oVbDTYLbh2mBmdyaw02c0/bFk7tWlFrcNY83FCH3VCDzdhWs6MGu6sGm7nvWDgcarBZ0etwMGqwWXgfC4dbuxZOx8LUYF/htq0Ggk7rQjh+/LidccYZdsstt5iZ7we6Z88e69Gjh0VHR9u///1vO3bsmN8Yqamp9tBDD5njODZx4kT74YcfLCkpyZo1a1bgHYXDhw/bwIED7ZxzzrEVK1b43LdixQqLioqyl19+Oc8Ye/futWuvvdY6depka9euNbPML9vx48ftsssus86dO9u3335rCxYssNTU1HxzMjM7duyYJSYm2rhx4+zAgQP217/+1SpVquQzys2zM+Lx66+/WkREhCUmJto333zjXT5w4ECrX7++OY5jderUsRtuuCHfPAYMGGBNmjTx/t29e3eLioqy8uXLm+M4VqtWLXv99ddz3en4/PPPLSIiwgYPHuwzvc57771njuPYY489Zmb5j8hZt26dRUdH2/jx433Wv/HGG23gwIH2wAMP2IgRI2z79u25Ngrff/+9RUVF2QcffGBmfzaEhw8ftrZt25rjOFa3bl2bOXOmmRVsdM2mTZssJibGO9LJ01Cmpqbam2++aXXr1rVatWp5G+Xsje+hQ4fs7rvvNsdx7NVXX7VHH33UHMexHj16eNfJr/FNSUmxK6+80i655BLvducxZswYcxzH5s2bl+9r2bx5s3Xu3Nl69uzp3Uk2M9uyZYs1btzYhg0bZtu2bbM9e/Z4t5u8Rplt3LjRYmJibNasWTZu3DhzHMduuOGGfBvgb7/91lv4sn7nH3zwQfvLX/5iiYmJds0119hbb72V7xQt559/vvXp08f79zXXXOPddsuXL28dO3bM9bPx5HzPPfd4D2DMzEaMGGGRkZHe9zS/7eSTTz6xypUr2/z5883M7MSJE3bw4EHr1KmTTZw40aZMmWJff/11vlOiTJ482apUqWKLFi3yWf77779bgwYNzHEca9KkiXdnIXtebKvB31bDhRvqcDjVYDP31GFqcPG3a2bBadvc0q6FWw02C04dDocabBZe22q4CJcabBb8OkwNzuTWGmzmjrYtnNq1YNZgM3fUYTfUYDO21dxQg0Nfg83ceSxc0muwWXDqcDBqsFn4HguHW7sWbsfC1OA/hdu2Gig6rQvh0KFD1qxZM7vmmmvMLPPDzPqB7t+/37p27WrVq1f3jizz94F//vnndscdd3j/XrBggSUlJRV4hNvhw4ctOTnZHnroIe+yjIwMy8jIsJ07d1piYqLdfffdecZITU21l19+2aZOneqz/NVXX/UWoK5du1pcXJw1a9bMFi9enGc8T8433XST3XzzzWZmtmPHDu/OQv/+/e2KK66wK6+80qdInzhxwsaOHWsxMTHWv39/2759u1166aUWGxtr//d//2dPP/20XXLJJVamTBk799xz82xo77vvPmvUqJGlp6fb3XffbVWqVLFp06bZTz/9ZDNnzrSLLrrIYmJi7KWXXvK7w7FhwwZ75ZVXbMeOHTnuu+qqqywhIcG785BXY7ty5UpzHMdeeOEF73vz+uuvm+M41qhRI6tfv77FxMRY7dq17cMPP/Qb6+uvvzbHcbw7Aenp6d6cBw0aZG3btrWmTZtaw4YNve9nfgVg+/btFhUVZaNHj/Yu83xu6enp9vbbb1tiYqKdc8453hFZWWMeOHDAhgwZYsOGDTMzs3379tn9998fUOP7888/m+M49s9//jPHfYsXL7aIiAh755138nwdZpmN5XXXXefdifLwbL+1atWyzp07W506dWzAgAF5vkee96BHjx7ea/A89dRT3gbYM7WPvylSFi9ebJdccolFRUV5R35dfvnlVq5cOWvfvr316NHDTjvtNCtfvrzdc889eU6h065dO+9OQr9+/axq1ar27LPP2ty5c+3OO++0hIQEq127tm3ZsiXHaxk/frzdcccdPjsIZpk7mzVq1LAuXboUaISoZ5Rp1mlCPNtu5cqVrUyZMlahQgXr1KlTnkX+6aeftujoaG9xzcjIsPT0dDt69Ki1adPGGjRoYFFRUdazZ0+/7wnbavC31XDhhjocTjXYzD11mBpc/O2aWXDaNre0a+FWg82CU4fDoQabhde2Gi7CpQabBbcOU4P/5NYabOaOti2c2rVg1mAzd9RhN9RgM7ZVarB/bqjBZu47Fg6HGmwWnDocjBpsFr7HwuHWroXbsTA1+E/htq0Gik7rQnr22WfNcRyfDz3rB7pu3Tpr2LChtW3bNs84ng/c89hvv/02oB2FX3/91dtYZN8Rad26tfXs2dPnefzJftH1jz/+2BzH8V7Y/dixY/b2229bkyZNrGHDhgW6Psr48eOtWrVq3qlIPCPoypUrZ5GRkfbuu+/6vG6zzB2WF154wcqWLWvVqlWzM844wz777DM7fPiwmWV+4Z9//nkrX768XXHFFbk+9xdffGGOk3nNhEGDBtl9993n8/pTUlKsW7duVqNGDfvtt9/8xsjt/fJMuTF8+PB8f2Q7fvy4XX311eY4jnXu3NkuvfRS79QLmzdvtrS0NJs3b55dcMEFlpSU5M0l63uSkpJiiYmJdtFFF+W4wH2XLl2sb9++3pEtBflhxjOdUKdOnaxFixa2cuXKHK85LS3Nnn/+eatUqZLdf//9fl+nZ4oajz179nivVZG18T1x4kSuucyYMcNbzLJuu7/99ptVrVrVRo0aZWb5/5iZ/Vozr7zyijmOY3fddZfNmjXLtmzZYn369LGKFSvaNddck++B8uDBg61x48benbFnnnnGHMex//u//7M2bdpY06ZNbc+ePTke98MPP1j37t3NcRxr2LChnX766fbBBx94n+/QoUN28cUXW/ny5e3VV1/NUQA878Gjjz5qiYmJ9v7779v5559vL730ks+IrilTplhiYqK1bt3a7zQ8WXegsz7HLbfcYuXLl7dvv/3WzPJuExYtWmSVKlWyli1b2rBhw2zw4MHebXfJkiV26NAhu++++6xKlSqWnJyc63RAH330kTmOY08//bTPiL+0tDRLTk62559/3q666iorW7asffrpp96DHM/7sX///oC31ezv62+//Rbwtpo1D7PM6wEVZlvN3iZnndrGrGDbavbXk/Xvwm6r4cINdTicarCZe+owNbj4a7BZcOowNdhXMGqwWXDqcEmtwZ7nz6owdTh7LacGB1e41GCz4NdharA7a7AnvlvqMDXYl5vqcChqcNb3wPP/knwsTA0uXm6owWbuPBYu6TXYrOh1OBg12JNruB4Lh1MNNguvY2FqMDXYg07rQtqxY4e1bNnSGjdubAsWLPAu93yIJ06csNGjR1v58uV9phcpiKw7CllHlixevNjvaKvsPDm0a9fO50tgZt4dirwe9+6773qnU/E4cuSIvfbaa+Y4To5rCPizdOlSq127ti1fvty7rFu3bhYVFWUxMTE+07NkdfLkSXvllVesevXq9sgjj3i/3J5G4dChQ9a7d2+LjY31ucZCVgcPHvSOXouIiLAHHnggxzoLFiywMmXK2OOPP57va8kqLS3NWrdubU2bNvW+P3mNJNu2bZvdfffddvbZZ9s555xjTZs2tV9++cV7//Hjx23mzJnmOI79/e9/9xvDM7rt4osvtpkzZ9qCBQvsjjvusDJlynhHr7Rt29bOP//8PHPJet8777xjjuPYoEGDbOfOnT6vzyyzIezcubM1adLEp4H1N0LTEze3xjc9Pd3WrFnjLZb57fgePHjQateuneO6CikpKT4NefZcPHk8+uijNmzYMJ/rdpw8edKuvfZai4mJ8Sk2Wd8Tz//nzJljZ511ls9UPE8++aQ5jmPR0dH21FNP+Txv1tezfPlyu+KKKywpKclee+01b76ef48fP25169a1Cy+8MNfP6quvvrJy5cpZ586dLS4uzltAPTE8UzlFRkb6XM8mt+k7PMs3bdpkNWrUsKuvvtrvetnfj6lTp1q9evUsISHBqlSpYh06dLDt27d74x0/ftzuuuuuHCPgsufyf//3fxYTE2PPP/+8rV692rZv3+7d6di9e7cdPXrU4uPjbeDAgWaWuZOTPY9At9WjR4/65JD9YKwg2+rRo0d9Plt/221e22puuaSnpwe8rWZ/Tzw7BoFuq+HGrXW4JNdgM3fXYWpw0Wtw1ufJTUHqMDU4+DU4+3tS2DocDjXYE6eodZgaXHzCrQZnfWxR6zA1OJObanD2XEv6sXC41mCzkn8sXJQabOaeOkwNdje31uCsOYTqWDhca7Anj4LW4WDUYLPwOxYOpxqc/fWU9GNhajA1ODs6rYtgxYoVVr16devQoYPPl9XzIf7222/mOE6OqU4KwrOj0KxZM/vqq6/syy+/tDPOOMOSk5PzvYaGZ2Pu0qWLz0XZ16xZY23btrVu3brl+/xZR2h4/r9r1y5zHMdnaoS8NGvWzB544AFLTU21K664wipXrmyvv/66XX/99XmOxDp+/LhNnz7dp6BmzWPWrFnmOI7Nnj071+detWqV1ahRwzvSY//+/Wb2ZwN26NAhq1q1qt1zzz0Fei1mf76v06dPN8dxbOTIkQV6XHp6uh04cMDmz5/v85o9jf7BgwetatWqPg1ldvPnz7eqVaua4zgWFRVlsbGx9uabb3obxeeff97i4+Nt69atPo3S3Llz7bPPPvO+d1kbuuHDh1tERISNHDnSZwSMZxTS559/7p1SJmsMf8XNX+PrGX347bffWrNmzeyyyy4r0HVoDhw4YHXq1PF5r1avXm39+/e3m2++2T799NN8R/dn7dz2vB7PdT4ef/xxn9eT3d69ey0uLs5ee+01M8tspK+++mqrUKGCOY5jffr0sUmTJvnEyPqeL1q0yIYNG5Zj1KQnj7///e9Wrlw5mzBhgs9ryRrjH//4hzmO4x0Z5uH5vHfs2OEdaZbXa8nq8OHD1rdvXytfvrx98cUX3uV5bSMbN260TZs22ejRo+3hhx/OkcemTZvMcRx78cUXc8TxbBMrV6606667zhzHsfj4eKtWrZpFR0fb5MmTvetdcskl1qpVK7vhhhusXr161qlTJxsxYoT3+e67774CbavPPfec9e3b1xvDM11J1vc3v221SZMmdtZZZ/mNkX1HIa9ttV+/frnmkvXzymtbffjhh3ONYVawbXXNmjUWztxYh0t6DTZzZx2mBhe+Brdv396uuOKKAtVPs9zbtq5du9qNN96Yby2nBueUWw02C04dHjRoUFjU4Pbt21v37t1zjVPQOtylSxdr0aIFNbiYhWsNNit6HaYGm/f5QlWDzdxTh4NxLBxONXjVqlU2Z86cXOOUtGPhotbgLl262CeffOJTc0rysTA1+NRwYw02c8excLjVYLPC1eFg1GCzkn8sHE41eM2aNTne15J8LEwNpgbnhU7rIlq0aJFVrlzZ2rRpY7NmzfK577PPPrMqVarY559/XqjYixcvtqSkJGvQoIHVr1/f4uPjbdmyZQV+/FVXXWVt27a11NRUW716tXXv3t3i4uJsyZIlBY6RteGaOHGixcbGeq9nkdvIHM9jbrnlFrvsssusR48eFh8f751n/48//rABAwbkOjItawzPc2R9rscff9xiYmJy7ERkt2TJEouKijLHcWzUqFE+U0J8/fXXVrNmTXv++efzfC3+bN682Zo1a2Znn322bdy4scCPW7RokUVERPhsJxkZGfbFF19Y1apV7cknn8wzl23bttmMGTNs4sSJOUYo3nbbbdagQQOfAvr++++b4zjWsmVL++9//+szEsgsc0fptttus8jISHvwwQdzvJZXXnnFYmJi/Mbwx5P3rl277KGHHjLHcaxjx4525plnmiRr3LhxvjHMMhvRpk2bekdA/vjjj3b55ZdbmTJl8s0ltylOzDJH/ZQvXz7PGOnp6ZaWlmaXX365dweye/fuFh8fbzNmzLDRo0ebJJNkLVq08Pu+mpl3CiF/efXt29e7A5s9D8+/Bw8etPvuu88cx7EKFSrY9OnTfeK9//77+b4Wf5YvX26RkZHeopbbNpI91tSpU61t27beKZY8r3fKlCkWFRXlLba55XLkyBGbMmWKDR061IYNG5ZjaqFGjRpZ2bJlrU2bNtarVy9r1KiRxcbGevM8duxYvttqdHS0xcfH54gxePDgHO9Dbttq/fr1TZI1a9Ys3xhmuW+rkZGRFhcXl2cuBdlW84tx8uTJPLdVx3Hstttuy3eK4JLOrXW4JNfgrHHcVoepwf7l1q6dd955Vq5cuYDqhb+2rVWrVvnWcmpw3rLXYE+sotbh/PaPSkoN9myr+dW+rPxtq82bN8+3llODgyeca7BZ4HWYGuzfqa7BZu6pw8E4Fg6nGly/fn3vmUuhrsNuqMHJycnWtGlTq1KlSsjrcDCOhanBp5Zba7BZaI6Fw70GmxWuDhe1BpuV3GPhcKrBjuNYly5d8nxfzUrOsTA1mBqcHzqtg2Dp0qV25plnWmxsrP3jH/+w7777zmbOnGmXX365JSUl2bZt2wKKl3UDeuGFF8xxMi82n/3LlZ8bbrjBmjdvbitWrLDu3btbTEyMrVixosCPz7rh/vDDD3bxxRdbixYtfKZEyMtnn33mHdEyffp0n2k9CtKg+ctj+fLl1r59e/vLX/7iM41Bbn744QerVauWOY5jvXv3tkmTJtm4ceMsOTnZatSokWNe/4KaPHmyOY7jHaFTEPv27bOzzz7bLrjgAu+ovNmzZ9ull15qNWvWLHQuy5cvt7Zt21qfPn3s+PHjlpGRYT/++KOdc845VqdOHatRo0au16T5448/vNNjXHnllfbhhx+aWeZ0NR07drTy5ctb7dq184yRlee+P/74w/r06WOSLDIy0mrWrFngGGZm5513nl1zzTW2YcMGu/TSSy06OtoaNGiQ7+vJKut2s2zZMmvZsqXFxMQU6PU89dRTlpSU5J0S5b333rNjx47Zjz/+aDVr1sz19fibEiX7qLf69etbXFycJSUl5ZlH1gIWFxdnY8aMsS1bttjMmTOtffv2FhUVZbVq1Qr4/fBcS2TKlCl5biNZ26GPPvrIIiIibMSIEfbrr7+amdnChQutW7duVrt2bWvcuHFAn01Wr776qkVGRtp5551nGzZsMDOznTt3WpcuXey0006zpUuXmlnmSLTcttW2bdtamTJl7NZbb80RIzEx0Rsj62vyt6162omCxjDLua1WqFDBqlataoMGDSpwnOzb6tlnn21ly5a12267Lc8YZrlvq2ZmL774oq1evbpAn0NJ58Y6HC41OHsubqjD1GD/srdrjuNYpUqV7Kyzzgq4jc7atnXo0MEiIiIsMTGRGhykGrxw4cJ8t5OC1OHC5JKVW2qw4zhWsWLFgOunme+2ev7555vjOHb99ddTg0+hcKzBZkWrw9RgX6eyBpuZa+pwMI6Fw6kGN2nSxC655BJr1qxZvp9NSTgWLmoNXrRokdWqVctiYmL81r6SdixMDQ4NN9Zgs9AeC4dzDTYLvA4XVw02c/excDjVYLPMs9PzOq4vScfC1GBqcEHQaR0kv/zyi/Xt29eioqKsbNmyVqVKFTvjjDN8rlkQqNmzZ1ubNm2sUqVKAX3YWRuD008/3Tp06GAxMTH2ww8/FCqPKVO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       "text/plain": [
        "<Figure size 2000x1000 with 8 Axes>"
       ]
@@ -979,6 +999,7 @@
     "                                activity_column: str = 'dact', \n",
     "                                hour_column: str = 'tst_hour', \n",
     "                                num_cols: int = 4, \n",
+    "                                max_y_value: Optional[int] = None,\n",
     "                                figsize: Optional[Tuple[int, int]] = (20, 5)) -> None:\n",
     "    \"\"\"\n",
     "    Plots a comparison of activity data over the hours of the day for different activity types.\n",
@@ -995,6 +1016,8 @@
     "        The column name for the hour values. Default is 'tst_hour'.\n",
     "    num_cols : int, optional\n",
     "        The number of columns for the subplots. Default is 4.\n",
+    "    max_y_value : int, optional\n",
+    "        The maximum value for the y-axis. Default is 100.\n",
     "    figsize : tuple of int, optional\n",
     "        The size of the figure. Default is (20, 5).\n",
     "\n",
@@ -1022,6 +1045,8 @@
     "        hist = sns.histplot(acbm_data[hour_column], bins=24, kde=False, discrete=True, stat='percent', ax=ax, label='ACBM')\n",
     "        ax.set_xticks(range(0, 25))  # Set x-ticks to represent each hour of the day, including 24\n",
     "        ax.set_xlim(0, 24)  # Ensure the x-axis covers the full range from 0 to 24\n",
+    "        if max_y_value:\n",
+    "            ax.set_ylim(0, max_y_value) # So that all the plots have the same y axis\n",
     "        ax.set_title(f'dact: {dact_value}', fontsize=16)\n",
     "        ax.tick_params(axis='x', rotation=45, labelsize=12)  # Rotate x-axis labels by 45 degrees\n",
     "\n",
@@ -1053,7 +1078,7 @@
     "    plt.show()\n",
     "\n",
     "# Example usage\n",
-    "plot_time_of_day_comparison(legs_acbm, legs_nts)"
+    "plot_time_of_day_comparison(legs_acbm, legs_nts, max_y_value=70)"
    ]
   },
   {
@@ -1072,7 +1097,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1109,7 +1134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1170,14 +1195,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_358065/491085427.py:44: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "/tmp/ipykernel_540731/491085427.py:44: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
       "  activity_sequence_df = sorted_df.groupby(pid_col).apply(\n"
      ]
     },
@@ -1284,7 +1309,7 @@
        "[1823 rows x 2 columns]"
       ]
      },
-     "execution_count": 176,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1302,14 +1327,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_358065/491085427.py:44: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "/tmp/ipykernel_540731/491085427.py:44: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
       "  activity_sequence_df = sorted_df.groupby(pid_col).apply(\n"
      ]
     },
@@ -1416,7 +1441,7 @@
        "[457 rows x 2 columns]"
       ]
      },
-     "execution_count": 177,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1442,7 +1467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -1525,7 +1550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1589,7 +1614,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1977,7 +2002,7 @@
        "[14975 rows x 19 columns]"
       ]
      },
-     "execution_count": 180,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1995,12 +2020,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -2060,12 +2085,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2000x1000 with 8 Axes>"
       ]
@@ -2075,13 +2100,20 @@
     }
    ],
    "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "from typing import Optional, Tuple\n",
+    "\n",
     "def plot_distance_comparison(legs_acbm: pd.DataFrame, \n",
     "                             legs_nts: pd.DataFrame, \n",
     "                             activity_column: str = 'dact', \n",
     "                             distance_column: str = 'distance', \n",
     "                             bin_size: int = 2, \n",
     "                             num_cols: int = 4, \n",
-    "                             figsize: Optional[Tuple[int, int]] = (20, 5),\n",
+    "                             max_y_value: int = 100,\n",
+    "                             figsize: Tuple[int, int] = (20, 5),\n",
     "                             distance_threshold: Optional[int] = None) -> None:\n",
     "    \"\"\"\n",
     "    Plots a comparison of distance data for different activity types.\n",
@@ -2100,7 +2132,9 @@
     "        The size of the bins for rounding distances. Default is 2 km.\n",
     "    num_cols : int, optional\n",
     "        The number of columns for the subplots. Default is 4.\n",
-    "    figsize : tuple of int, optional\n",
+    "    max_y_value : int, optional\n",
+    "        The maximum value for the y-axis. Default is 100.\n",
+    "    figsize : tuple of int\n",
     "        The size of the figure. Default is (20, 5).\n",
     "    distance_threshold : int, optional\n",
     "        The maximum value for the x-axis. Default is None.\n",
@@ -2128,7 +2162,7 @@
     "    # Create a figure with subplots\n",
     "    num_plots = len(unique_dact_values)\n",
     "    num_rows = (num_plots + num_cols - 1) // num_cols\n",
-    "    fig, axes = plt.subplots(num_rows, num_cols, figsize=(figsize[0], num_rows * figsize[1]), sharey=False)\n",
+    "    fig, axes = plt.subplots(num_rows, num_cols, figsize=(figsize[0], num_rows * figsize[1]), sharey=True)\n",
     "    axes = axes.flatten()\n",
     "\n",
     "    # Iterate over unique dact values and create plots\n",
@@ -2144,9 +2178,12 @@
     "\n",
     "        # Set x-axis limits based on max_distance\n",
     "        ax.set_xlim(0, max_distance)\n",
+    "        # Set y-axis limits based on max_y_value\n",
+    "        ax.set_ylim(0, max_y_value)\n",
+    "\n",
     "        # Add x-axis ticks and labels for each bar\n",
-    "        # ax.set_xticks(bins)\n",
-    "        # ax.set_xticklabels([str(bin) for bin in bins], rotation=45, ha='right')\n",
+    "        ax.set_xticks(bins)\n",
+    "        ax.set_xticklabels([str(bin) for bin in bins], rotation=45, ha='right')\n",
     "\n",
     "        # Remove individual subplot labels\n",
     "        ax.set_xlabel('')\n",
@@ -2178,112 +2215,453 @@
     "    plt.show()\n",
     "\n",
     "# Example usage\n",
-    "plot_distance_comparison(legs_acbm_geo, legs_nts2, distance_threshold=50)"
+    "plot_distance_comparison(legs_acbm_geo, legs_nts, distance_threshold=50, max_y_value=30)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "legs_a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "legs_acbm_test = legs_acbm_geo.copy()\n",
-    "legs_nts_test = legs_nts2.copy()\n",
+    "### Test number of intrazonal trips\n",
     "\n",
-    "legs_acbm_test['distance_binned'] = (legs_acbm_test['distance'] / 2).round() * 2\n",
-    "legs_nts_test['distance_binned'] = (legs_nts_test['distance'] / 2).round() * 2\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",
-    "legs_acbm_test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "legs_nts_test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_distance = max(legs_acbm_test['distance_binned'].max(), legs_nts_test['distance_binned'].max())\n",
-    "bins = range(0, int(max_distance) + 2, 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_distance, bins"
+    "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": "code",
-   "execution_count": 120,
+   "execution_count": 41,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "unique_dact_values = legs_acbm_test['dact'].unique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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>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>tst_hour</th>\n",
+       "      <th>tet_hour</th>\n",
+       "      <th>oact_abr</th>\n",
+       "      <th>dact_abr</th>\n",
+       "      <th>intrazonal</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>199</td>\n",
+       "      <td>89</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00059031</td>\n",
+       "      <td>E00058720</td>\n",
+       "      <td>other</td>\n",
+       "      <td>home</td>\n",
+       "      <td>other</td>\n",
+       "      <td>walk</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1900-01-01 10:00:00</td>\n",
+       "      <td>1900-01-01 10:45:00</td>\n",
+       "      <td>0:45:00</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>h</td>\n",
+       "      <td>o</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>199</td>\n",
+       "      <td>89</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00058720</td>\n",
+       "      <td>E00059031</td>\n",
+       "      <td>home</td>\n",
+       "      <td>other</td>\n",
+       "      <td>home</td>\n",
+       "      <td>walk</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1900-01-01 10:45:00</td>\n",
+       "      <td>1900-01-01 11:30:00</td>\n",
+       "      <td>0:45:00</td>\n",
+       "      <td>10</td>\n",
+       "      <td>11</td>\n",
+       "      <td>o</td>\n",
+       "      <td>h</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>199</td>\n",
+       "      <td>89</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00059031</td>\n",
+       "      <td>E00057312</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 12:00:00</td>\n",
+       "      <td>1900-01-01 12:10:00</td>\n",
+       "      <td>0:10:00</td>\n",
+       "      <td>12</td>\n",
+       "      <td>12</td>\n",
+       "      <td>h</td>\n",
+       "      <td>sh</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>199</td>\n",
+       "      <td>89</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00057312</td>\n",
+       "      <td>E00059031</td>\n",
+       "      <td>home</td>\n",
+       "      <td>shop</td>\n",
+       "      <td>home</td>\n",
+       "      <td>car</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1900-01-01 12:45:00</td>\n",
+       "      <td>1900-01-01 12:55:00</td>\n",
+       "      <td>0:10:00</td>\n",
+       "      <td>12</td>\n",
+       "      <td>12</td>\n",
+       "      <td>sh</td>\n",
+       "      <td>h</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>199</td>\n",
+       "      <td>89</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00059031</td>\n",
+       "      <td>E00058564</td>\n",
+       "      <td>work</td>\n",
+       "      <td>home</td>\n",
+       "      <td>work</td>\n",
+       "      <td>car</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1900-01-01 13:30:00</td>\n",
+       "      <td>1900-01-01 13:50:00</td>\n",
+       "      <td>0:20:00</td>\n",
+       "      <td>13</td>\n",
+       "      <td>13</td>\n",
+       "      <td>h</td>\n",
+       "      <td>w</td>\n",
+       "      <td>False</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",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14970</th>\n",
+       "      <td>14970</td>\n",
+       "      <td>794026</td>\n",
+       "      <td>334502</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00056891</td>\n",
+       "      <td>E00057826</td>\n",
+       "      <td>home</td>\n",
+       "      <td>escort</td>\n",
+       "      <td>home</td>\n",
+       "      <td>car</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1900-01-01 20:45:00</td>\n",
+       "      <td>1900-01-01 21:05:00</td>\n",
+       "      <td>0:20:00</td>\n",
+       "      <td>20</td>\n",
+       "      <td>21</td>\n",
+       "      <td>e</td>\n",
+       "      <td>h</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14971</th>\n",
+       "      <td>14971</td>\n",
+       "      <td>794027</td>\n",
+       "      <td>334502</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00057826</td>\n",
+       "      <td>E00057536</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 07:40:00</td>\n",
+       "      <td>1900-01-01 07:55:00</td>\n",
+       "      <td>0:15:00</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
+       "      <td>h</td>\n",
+       "      <td>e</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14972</th>\n",
+       "      <td>14972</td>\n",
+       "      <td>794027</td>\n",
+       "      <td>334502</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00057536</td>\n",
+       "      <td>E00057826</td>\n",
+       "      <td>home</td>\n",
+       "      <td>escort</td>\n",
+       "      <td>home</td>\n",
+       "      <td>pt</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1900-01-01 15:00:00</td>\n",
+       "      <td>1900-01-01 15:55:00</td>\n",
+       "      <td>0:55:00</td>\n",
+       "      <td>15</td>\n",
+       "      <td>15</td>\n",
+       "      <td>e</td>\n",
+       "      <td>h</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14973</th>\n",
+       "      <td>14973</td>\n",
+       "      <td>794027</td>\n",
+       "      <td>334502</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00057826</td>\n",
+       "      <td>E00056882</td>\n",
+       "      <td>other</td>\n",
+       "      <td>home</td>\n",
+       "      <td>other</td>\n",
+       "      <td>pt</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1900-01-01 18:35:00</td>\n",
+       "      <td>1900-01-01 19:35:00</td>\n",
+       "      <td>1:00:00</td>\n",
+       "      <td>18</td>\n",
+       "      <td>19</td>\n",
+       "      <td>h</td>\n",
+       "      <td>o</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14974</th>\n",
+       "      <td>14974</td>\n",
+       "      <td>794027</td>\n",
+       "      <td>334502</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E00056882</td>\n",
+       "      <td>E00057826</td>\n",
+       "      <td>home</td>\n",
+       "      <td>other</td>\n",
+       "      <td>home</td>\n",
+       "      <td>car</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1900-01-01 20:45:00</td>\n",
+       "      <td>1900-01-01 21:05:00</td>\n",
+       "      <td>0:20:00</td>\n",
+       "      <td>20</td>\n",
+       "      <td>21</td>\n",
+       "      <td>o</td>\n",
+       "      <td>h</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>14975 rows × 19 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Unnamed: 0     pid     hid  freq      ozone      dzone    purp    oact  \\\n",
+       "0               0     199      89   NaN  E00059031  E00058720   other    home   \n",
+       "1               1     199      89   NaN  E00058720  E00059031    home   other   \n",
+       "2               2     199      89   NaN  E00059031  E00057312    shop    home   \n",
+       "3               3     199      89   NaN  E00057312  E00059031    home    shop   \n",
+       "4               4     199      89   NaN  E00059031  E00058564    work    home   \n",
+       "...           ...     ...     ...   ...        ...        ...     ...     ...   \n",
+       "14970       14970  794026  334502   NaN  E00056891  E00057826    home  escort   \n",
+       "14971       14971  794027  334502   NaN  E00057826  E00057536  escort    home   \n",
+       "14972       14972  794027  334502   NaN  E00057536  E00057826    home  escort   \n",
+       "14973       14973  794027  334502   NaN  E00057826  E00056882   other    home   \n",
+       "14974       14974  794027  334502   NaN  E00056882  E00057826    home   other   \n",
+       "\n",
+       "         dact  mode  seq                  tst                  tet duration  \\\n",
+       "0       other  walk  1.0  1900-01-01 10:00:00  1900-01-01 10:45:00  0:45:00   \n",
+       "1        home  walk  2.0  1900-01-01 10:45:00  1900-01-01 11:30:00  0:45:00   \n",
+       "2        shop   car  3.0  1900-01-01 12:00:00  1900-01-01 12:10:00  0:10:00   \n",
+       "3        home   car  4.0  1900-01-01 12:45:00  1900-01-01 12:55:00  0:10:00   \n",
+       "4        work   car  5.0  1900-01-01 13:30:00  1900-01-01 13:50:00  0:20:00   \n",
+       "...       ...   ...  ...                  ...                  ...      ...   \n",
+       "14970    home   car  4.0  1900-01-01 20:45:00  1900-01-01 21:05:00  0:20:00   \n",
+       "14971  escort   car  1.0  1900-01-01 07:40:00  1900-01-01 07:55:00  0:15:00   \n",
+       "14972    home    pt  2.0  1900-01-01 15:00:00  1900-01-01 15:55:00  0:55:00   \n",
+       "14973   other    pt  3.0  1900-01-01 18:35:00  1900-01-01 19:35:00  1:00:00   \n",
+       "14974    home   car  4.0  1900-01-01 20:45:00  1900-01-01 21:05:00  0:20:00   \n",
+       "\n",
+       "       tst_hour  tet_hour oact_abr dact_abr  intrazonal  \n",
+       "0            10        10        h        o       False  \n",
+       "1            10        11        o        h       False  \n",
+       "2            12        12        h       sh       False  \n",
+       "3            12        12       sh        h       False  \n",
+       "4            13        13        h        w       False  \n",
+       "...         ...       ...      ...      ...         ...  \n",
+       "14970        20        21        e        h       False  \n",
+       "14971         7         7        h        e       False  \n",
+       "14972        15        15        e        h       False  \n",
+       "14973        18        19        h        o       False  \n",
+       "14974        20        21        o        h       False  \n",
+       "\n",
+       "[14975 rows x 19 columns]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "num_plots = len(unique_dact_values)\n",
-    "num_cols = 4\n",
-    "figsize = (20, 5)\n",
-    "num_rows = (num_plots + num_cols - 1) // num_cols\n",
-    "fig, axes = plt.subplots(num_rows, num_cols, figsize=(figsize[0], num_rows * figsize[1]), sharey=False)\n",
-    "axes = axes.flatten()\n",
-    "\n",
-    "for i, dact_value in enumerate(unique_dact_values):\n",
-    "    ax = axes[i]\n",
-    "    acbm_data = legs_acbm_test[legs_acbm_test['dact'] == dact_value]\n",
-    "    nts_data = legs_nts_test[legs_nts_test['dact'] == dact_value]\n",
-    "\n",
-    "    # Plot histogram for acbm_data\n",
-    "    hist = sns.histplot(acbm_data['distance_binned'], bins=bins, kde=False, discrete=True, stat='percent', ax=ax, label='ACBM')\n",
-    "    ax.set_title(f'dact: {dact_value}', fontsize=16)\n",
-    "    ax.tick_params(axis='x', rotation=45, labelsize=12)  # Rotate x-axis labels by 45 degrees\n",
-    "\n",
-    "    # Remove individual subplot labels\n",
-    "    ax.set_xlabel('')\n",
-    "    ax.set_ylabel('')\n",
-    "\n",
-    "    # Add dots to represent the percentage values of legs_nts[\"distance_binned\"]\n",
-    "    nts_distance_counts = nts_data['distance_binned'].value_counts(normalize=True).sort_index() * 100\n",
-    "    if not nts_distance_counts.empty:\n",
-    "        bin_centers = nts_distance_counts.index\n",
-    "        ax.plot(bin_centers, nts_distance_counts.values, 'ro', label='NTS')  # 'ro' means red dot"
+    "# add binary column to identify intrazonal trips\n",
+    "legs_acbm['intrazonal'] = legs_acbm['ozone'] == legs_acbm['dzone']\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 64,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        purp  total_count  intrazonal_count  percentage\n",
+      "0  education          389                 3    0.771208\n",
+      "1     escort         1708                20    1.170960\n",
+      "2       home         6473               278    4.294763\n",
+      "3    medical          181                 2    1.104972\n",
+      "4      other         2487               119    4.784881\n",
+      "5       shop         1235                37    2.995951\n",
+      "6      visit          829                15    1.809409\n",
+      "7       work         1673                88    5.260012\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_540731/3001768473.py:22: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  barplot = sns.barplot(data=merged_counts, x='purp', y='percentage', palette='viridis')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "nts_data = legs_nts_test[legs_nts_test['dact'] == \"education\"]\n",
-    "nts_data\n"
+    "# Total number of trips per trip purpose\n",
+    "total_counts = legs_acbm.groupby('purp').size().reset_index(name='total_count')\n",
+    "\n",
+    "# Filter the DataFrame to include only rows where intrazonal_trips is TRUE\n",
+    "intrazonal_trips_true = legs_acbm[legs_acbm['intrazonal'] == True]\n",
+    "\n",
+    "# Total number of intrazonal trips per trip purpose\n",
+    "intrazonal_counts = intrazonal_trips_true.groupby('purp').size().reset_index(name='intrazonal_count')\n",
+    "\n",
+    "# Merge the two DataFrames and calculate intrazonal %\n",
+    "merged_counts = pd.merge(total_counts, intrazonal_counts, on='purp', how='left')\n",
+    "# Fill NaN values with 0 (in case there are purp groups with no intrazonal trips)\n",
+    "merged_counts['intrazonal_count'] = merged_counts['intrazonal_count'].fillna(0)\n",
+    "# Calculate the percentage of intrazonal trips\n",
+    "merged_counts['percentage'] = (merged_counts['intrazonal_count'] / merged_counts['total_count']) * 100\n",
+    "\n",
+    "# Print the resulting DataFrame\n",
+    "print(merged_counts)\n",
+    "\n",
+    "# Create the bar plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "barplot = sns.barplot(data=merged_counts, x='purp', y='percentage', palette='viridis')\n",
+    "plt.title('Percentage of Intrazonal Trips per Purpose')\n",
+    "plt.xlabel('Purpose')\n",
+    "plt.ylabel('Percentage of Trips that are Intrazonal')\n",
+    "plt.xticks(rotation=45)\n",
+    "\n",
+    "# Add text annotations above each bar\n",
+    "for index, row in merged_counts.iterrows():\n",
+    "    barplot.text(index, row['percentage'] + 0.2, f\"({int(row['total_count'])})\", color='black', ha=\"center\")\n",
+    "\n",
+    "# Remove the top and right spines (box frame) from the plot\n",
+    "barplot.spines['top'].set_visible(False)\n",
+    "barplot.spines['right'].set_visible(False)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Add footnote on the right below the plot\n",
+    "plt.figtext(0.95, -0.02, '(Total number of trips)', ha='right', fontsize=10)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
@@ -2291,9 +2669,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "legs_nts_test['dact']"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",

From 5719c54c964b19e96077d33124e79cc84aa616c5 Mon Sep 17 00:00:00 2001
From: Hussein Mahfouz <45176416+Hussein-Mahfouz@users.noreply.github.com>
Date: Mon, 30 Sep 2024 12:17:51 +0100
Subject: [PATCH 07/11] intrazonal by primary/secondary

---
 notebooks/5_validate_outputs.ipynb | 1692 ++++++++++++++--------------
 1 file changed, 860 insertions(+), 832 deletions(-)

diff --git a/notebooks/5_validate_outputs.ipynb b/notebooks/5_validate_outputs.ipynb
index a218bd4..3c23d31 100644
--- a/notebooks/5_validate_outputs.ipynb
+++ b/notebooks/5_validate_outputs.ipynb
@@ -7,6 +7,7 @@
    "outputs": [],
    "source": [
     "import pandas as pd\n",
+    "import numpy as np\n",
     "import acbm\n",
     "import seaborn as sns\n",
     "import matplotlib.pyplot as plt\n",
@@ -92,87 +93,87 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>0</td>\n",
-       "      <td>199</td>\n",
+       "      <td>200</td>\n",
        "      <td>89</td>\n",
        "      <td>NaN</td>\n",
        "      <td>E00059031</td>\n",
-       "      <td>E00058720</td>\n",
-       "      <td>other</td>\n",
+       "      <td>E00058877</td>\n",
+       "      <td>work</td>\n",
        "      <td>home</td>\n",
-       "      <td>other</td>\n",
-       "      <td>walk</td>\n",
+       "      <td>work</td>\n",
+       "      <td>car</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>1900-01-01 10:00:00</td>\n",
-       "      <td>1900-01-01 10:45:00</td>\n",
-       "      <td>0:45:00</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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1</td>\n",
-       "      <td>199</td>\n",
+       "      <td>200</td>\n",
        "      <td>89</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>E00058720</td>\n",
+       "      <td>E00059031</td>\n",
        "      <td>E00059031</td>\n",
        "      <td>home</td>\n",
-       "      <td>other</td>\n",
+       "      <td>work</td>\n",
        "      <td>home</td>\n",
-       "      <td>walk</td>\n",
+       "      <td>car</td>\n",
        "      <td>2.0</td>\n",
-       "      <td>1900-01-01 10:45:00</td>\n",
-       "      <td>1900-01-01 11:30:00</td>\n",
-       "      <td>0:45:00</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>199</td>\n",
-       "      <td>89</td>\n",
+       "      <td>312</td>\n",
+       "      <td>139</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>E00059031</td>\n",
-       "      <td>E00057312</td>\n",
-       "      <td>shop</td>\n",
+       "      <td>E00059045</td>\n",
+       "      <td>E00057153</td>\n",
+       "      <td>escort</td>\n",
        "      <td>home</td>\n",
-       "      <td>shop</td>\n",
+       "      <td>escort</td>\n",
        "      <td>car</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1900-01-01 12:00:00</td>\n",
-       "      <td>1900-01-01 12:10:00</td>\n",
-       "      <td>0:10:00</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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>3</td>\n",
-       "      <td>199</td>\n",
-       "      <td>89</td>\n",
+       "      <td>312</td>\n",
+       "      <td>139</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>E00057312</td>\n",
-       "      <td>E00059031</td>\n",
-       "      <td>home</td>\n",
-       "      <td>shop</td>\n",
-       "      <td>home</td>\n",
+       "      <td>E00057153</td>\n",
+       "      <td>E00169797</td>\n",
+       "      <td>work</td>\n",
+       "      <td>escort</td>\n",
+       "      <td>work</td>\n",
        "      <td>car</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1900-01-01 12:45:00</td>\n",
-       "      <td>1900-01-01 12:55:00</td>\n",
-       "      <td>0:10:00</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>199</td>\n",
-       "      <td>89</td>\n",
+       "      <td>312</td>\n",
+       "      <td>139</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>E00059031</td>\n",
-       "      <td>E00058564</td>\n",
-       "      <td>work</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>5.0</td>\n",
-       "      <td>1900-01-01 13:30:00</td>\n",
-       "      <td>1900-01-01 13:50:00</td>\n",
-       "      <td>0:20:00</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",
@@ -192,136 +193,136 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14970</th>\n",
-       "      <td>14970</td>\n",
-       "      <td>794026</td>\n",
+       "      <th>15098</th>\n",
+       "      <td>15098</td>\n",
+       "      <td>794027</td>\n",
        "      <td>334502</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>E00056891</td>\n",
+       "      <td>E00057268</td>\n",
        "      <td>E00057826</td>\n",
        "      <td>home</td>\n",
-       "      <td>escort</td>\n",
+       "      <td>visit</td>\n",
        "      <td>home</td>\n",
        "      <td>car</td>\n",
        "      <td>4.0</td>\n",
-       "      <td>1900-01-01 20:45:00</td>\n",
-       "      <td>1900-01-01 21:05:00</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>14971</th>\n",
-       "      <td>14971</td>\n",
-       "      <td>794027</td>\n",
-       "      <td>334502</td>\n",
+       "      <th>15099</th>\n",
+       "      <td>15099</td>\n",
+       "      <td>794272</td>\n",
+       "      <td>334647</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>E00057826</td>\n",
-       "      <td>E00057536</td>\n",
-       "      <td>escort</td>\n",
+       "      <td>E00170040</td>\n",
+       "      <td>E00058459</td>\n",
+       "      <td>shop</td>\n",
        "      <td>home</td>\n",
-       "      <td>escort</td>\n",
+       "      <td>shop</td>\n",
        "      <td>car</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>1900-01-01 07:40:00</td>\n",
-       "      <td>1900-01-01 07:55:00</td>\n",
-       "      <td>0:15:00</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>14972</th>\n",
-       "      <td>14972</td>\n",
-       "      <td>794027</td>\n",
-       "      <td>334502</td>\n",
+       "      <th>15100</th>\n",
+       "      <td>15100</td>\n",
+       "      <td>794272</td>\n",
+       "      <td>334647</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>E00057536</td>\n",
-       "      <td>E00057826</td>\n",
+       "      <td>E00058459</td>\n",
+       "      <td>E00170040</td>\n",
        "      <td>home</td>\n",
-       "      <td>escort</td>\n",
+       "      <td>shop</td>\n",
        "      <td>home</td>\n",
-       "      <td>pt</td>\n",
+       "      <td>car</td>\n",
        "      <td>2.0</td>\n",
-       "      <td>1900-01-01 15:00:00</td>\n",
-       "      <td>1900-01-01 15:55:00</td>\n",
-       "      <td>0:55:00</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>14973</th>\n",
-       "      <td>14973</td>\n",
-       "      <td>794027</td>\n",
-       "      <td>334502</td>\n",
+       "      <th>15101</th>\n",
+       "      <td>15101</td>\n",
+       "      <td>794273</td>\n",
+       "      <td>334647</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>E00057826</td>\n",
-       "      <td>E00056882</td>\n",
-       "      <td>other</td>\n",
+       "      <td>E00170040</td>\n",
+       "      <td>E00057187</td>\n",
+       "      <td>shop</td>\n",
        "      <td>home</td>\n",
-       "      <td>other</td>\n",
-       "      <td>pt</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1900-01-01 18:35:00</td>\n",
-       "      <td>1900-01-01 19:35:00</td>\n",
-       "      <td>1:00:00</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>14974</th>\n",
-       "      <td>14974</td>\n",
-       "      <td>794027</td>\n",
-       "      <td>334502</td>\n",
+       "      <th>15102</th>\n",
+       "      <td>15102</td>\n",
+       "      <td>794273</td>\n",
+       "      <td>334647</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>E00056882</td>\n",
-       "      <td>E00057826</td>\n",
+       "      <td>E00057187</td>\n",
+       "      <td>E00170040</td>\n",
        "      <td>home</td>\n",
-       "      <td>other</td>\n",
+       "      <td>shop</td>\n",
        "      <td>home</td>\n",
        "      <td>car</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1900-01-01 20:45:00</td>\n",
-       "      <td>1900-01-01 21:05:00</td>\n",
-       "      <td>0:20:00</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>14975 rows × 14 columns</p>\n",
+       "<p>15103 rows × 14 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "       Unnamed: 0     pid     hid  freq      ozone      dzone    purp  \\\n",
-       "0               0     199      89   NaN  E00059031  E00058720   other   \n",
-       "1               1     199      89   NaN  E00058720  E00059031    home   \n",
-       "2               2     199      89   NaN  E00059031  E00057312    shop   \n",
-       "3               3     199      89   NaN  E00057312  E00059031    home   \n",
-       "4               4     199      89   NaN  E00059031  E00058564    work   \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  E00057153  escort   \n",
+       "3               3     312     139   NaN  E00057153  E00169797    work   \n",
+       "4               4     312     139   NaN  E00059045  E00059045    home   \n",
        "...           ...     ...     ...   ...        ...        ...     ...   \n",
-       "14970       14970  794026  334502   NaN  E00056891  E00057826    home   \n",
-       "14971       14971  794027  334502   NaN  E00057826  E00057536  escort   \n",
-       "14972       14972  794027  334502   NaN  E00057536  E00057826    home   \n",
-       "14973       14973  794027  334502   NaN  E00057826  E00056882   other   \n",
-       "14974       14974  794027  334502   NaN  E00056882  E00057826    home   \n",
+       "15098       15098  794027  334502   NaN  E00057268  E00057826    home   \n",
+       "15099       15099  794272  334647   NaN  E00170040  E00058459    shop   \n",
+       "15100       15100  794272  334647   NaN  E00058459  E00170040    home   \n",
+       "15101       15101  794273  334647   NaN  E00170040  E00057187    shop   \n",
+       "15102       15102  794273  334647   NaN  E00057187  E00170040    home   \n",
        "\n",
-       "      origin activity destination activity  mode  seq                  tst  \\\n",
-       "0                home                other  walk  1.0  1900-01-01 10:00:00   \n",
-       "1               other                 home  walk  2.0  1900-01-01 10:45:00   \n",
-       "2                home                 shop   car  3.0  1900-01-01 12:00:00   \n",
-       "3                shop                 home   car  4.0  1900-01-01 12:45:00   \n",
-       "4                home                 work   car  5.0  1900-01-01 13:30:00   \n",
-       "...               ...                  ...   ...  ...                  ...   \n",
-       "14970          escort                 home   car  4.0  1900-01-01 20:45:00   \n",
-       "14971            home               escort   car  1.0  1900-01-01 07:40:00   \n",
-       "14972          escort                 home    pt  2.0  1900-01-01 15:00:00   \n",
-       "14973            home                other    pt  3.0  1900-01-01 18:35:00   \n",
-       "14974           other                 home   car  4.0  1900-01-01 20:45:00   \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 10:45:00  0:45:00  \n",
-       "1      1900-01-01 11:30:00  0:45:00  \n",
-       "2      1900-01-01 12:10:00  0:10:00  \n",
-       "3      1900-01-01 12:55:00  0:10:00  \n",
-       "4      1900-01-01 13:50:00  0:20:00  \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",
-       "14970  1900-01-01 21:05:00  0:20:00  \n",
-       "14971  1900-01-01 07:55:00  0:15:00  \n",
-       "14972  1900-01-01 15:55:00  0:55:00  \n",
-       "14973  1900-01-01 19:35:00  1:00:00  \n",
-       "14974  1900-01-01 21:05:00  0:20:00  \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",
-       "[14975 rows x 14 columns]"
+       "[15103 rows x 14 columns]"
       ]
      },
      "execution_count": 3,
@@ -642,6 +643,40 @@
     "]"
    ]
   },
+  {
+   "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": {},
@@ -665,12 +700,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -706,7 +741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -796,58 +831,58 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>education</td>\n",
-       "      <td>389</td>\n",
+       "      <td>385</td>\n",
        "      <td>acbm</td>\n",
-       "      <td>2.597663</td>\n",
+       "      <td>2.549162</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>escort</td>\n",
-       "      <td>1708</td>\n",
+       "      <td>1684</td>\n",
        "      <td>acbm</td>\n",
-       "      <td>11.405676</td>\n",
+       "      <td>11.150103</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>home</td>\n",
-       "      <td>6473</td>\n",
+       "      <td>6498</td>\n",
        "      <td>acbm</td>\n",
-       "      <td>43.225376</td>\n",
+       "      <td>43.024565</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>medical</td>\n",
-       "      <td>181</td>\n",
+       "      <td>189</td>\n",
        "      <td>acbm</td>\n",
-       "      <td>1.208681</td>\n",
+       "      <td>1.251407</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>other</td>\n",
-       "      <td>2487</td>\n",
+       "      <td>2457</td>\n",
        "      <td>acbm</td>\n",
-       "      <td>16.607679</td>\n",
+       "      <td>16.268291</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>shop</td>\n",
-       "      <td>1235</td>\n",
+       "      <td>1337</td>\n",
        "      <td>acbm</td>\n",
-       "      <td>8.247078</td>\n",
+       "      <td>8.852546</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>visit</td>\n",
-       "      <td>829</td>\n",
+       "      <td>816</td>\n",
        "      <td>acbm</td>\n",
-       "      <td>5.535893</td>\n",
+       "      <td>5.402900</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>work</td>\n",
-       "      <td>1673</td>\n",
+       "      <td>1737</td>\n",
        "      <td>acbm</td>\n",
-       "      <td>11.171953</td>\n",
+       "      <td>11.501026</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -863,17 +898,17 @@
        "5       shop   5621    nts    9.469499\n",
        "6      visit   3313    nts    5.581293\n",
        "7       work   6501    nts   10.952004\n",
-       "0  education    389   acbm    2.597663\n",
-       "1     escort   1708   acbm   11.405676\n",
-       "2       home   6473   acbm   43.225376\n",
-       "3    medical    181   acbm    1.208681\n",
-       "4      other   2487   acbm   16.607679\n",
-       "5       shop   1235   acbm    8.247078\n",
-       "6      visit    829   acbm    5.535893\n",
-       "7       work   1673   acbm   11.171953"
+       "0  education    385   acbm    2.549162\n",
+       "1     escort   1684   acbm   11.150103\n",
+       "2       home   6498   acbm   43.024565\n",
+       "3    medical    189   acbm    1.251407\n",
+       "4      other   2457   acbm   16.268291\n",
+       "5       shop   1337   acbm    8.852546\n",
+       "6      visit    816   acbm    5.402900\n",
+       "7       work   1737   acbm   11.501026"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -892,12 +927,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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pPK9atWqePhUqVHjvsQW5rly5gqlTp+LgwYPyL5NcuV98d+7cAQCFsy+AN39s3z0LKjY2FteuXUOlSpWUqv9tufOpUaNGnmGurq44duxY4QvzAfJbX4V5930gkUjg7OwsP8Xd0dERgYGBWLBgATZs2IDmzZuja9eu+OKLLxT+sBfE1tY2zynE1atXBwDcvn0bTZo0gZaWFvr164ewsDCkp6fDwMAAGzZsgEwmkx8XpazPP/8c/fv3R3x8PLZv3465c+fm20/ZdZTbL7/PS40aNXDu3Dn5c1Xf88pQ9b2ko6ODKlWqqDwfAJg6dSrWr1+P2bNnY/HixUWaxtv69u2Lvn37IjU1FTExMVizZg0iIyPRpUsXXL58WSGYvev48eMIDg5GdHR0ngCdkpKi8N5T5rvjzp07aNy4cZ5++b2u+UlOTsbu3bsREBCAmzdvytu9vb2xZcsW3LhxA9WrV4eJiQmANz+mVFGxYkWFM9U6deqEGjVq4NNPP8Xq1avz/dFYr149uLq6IjIyEmZmZrC2tpb/0CIGGY3XqFEjNGjQIN9hOTk5kEgk+Ouvv6CtrZ1nuJGRkcLzt38ZqSInJweWlpb5HgwHIE9AyK8W4M3pju+TnJwMHx8fmJiYYMaMGahWrRpkMhnOnTuHSZMmqXxAX2797u7uWLBgQb7D7ezsVJ5maSjq+irM/PnzMXDgQPz555/Yt28fRo8ejdDQUJw8ebLIfzTf5e/vj3nz5mH79u3o27cvIiMj0blzZ6XC0tu6du0KqVSKAQMGIDMzE7179y6W+pSh6nu+JEilUvmPFlU5OTnhiy++wMqVKzF58uRiq8nExARt27ZF27Ztoauri7Vr1yImJgY+Pj759o+Li0Pr1q3h6uqKBQsWwM7ODnp6eti9ezcWLlyY5/P8Id8dytq8eTMyMzMxf/58zJ8/P8/wDRs2ICQkBCYmJrC1tcXly5c/eJ6tW7cGABw9ejTfIAO8Ce5hYWEwNjbGZ599VuR1Xx4xyJRj1apVgxACjo6O8l/GRZlGTEwMXr16lWcLztt9Dhw4AG9v72L745q72+pdhw8fxpMnT7B161a0aNFC3v72WVoA5LsYbt68iZYtW8rbX79+jdu3b8PDw0Oh/gsXLqB169YFzrcgufO5fv16nl9I169fz3dXh7rExsYqPBdC4ObNmwqvBQC4u7vD3d0dU6dOxYkTJ+Dt7Y3ly5dj5syZhU7/wYMHePHihcJWmRs3bgCAwlYwNzc31KtXDxs2bECVKlUQHx9f6HVNCqKvr4/u3bvj119/RYcOHVCxYsV8+ym7jnL/ffd1yu33tpJ4z5f2e2nq1Kn49ddfMWfOnGKdbq4GDRpg7dq1he6+2rlzJzIzM7Fjxw6FrS1F2TWXy97eXql1WJANGzbAzc0NwcHBeYatWLECkZGRCAkJAQB07twZK1euRHR0NLy8vIpc8+vXrwG8ObOvIJ9//jmmTZuGhw8f5rmm1MeOka4c69GjB7S1tRESEpLnF4sQAk+ePHnvNHr27InHjx/j559/zjMsd5q9e/dGdnY2vv/++zx9Xr9+XaRLr+f+MXx33NxfZG8vT1ZWFpYtW6bQr0GDBrCwsMCqVavkXxLAmy+pd3dh9e7dG/fv38eqVavy1PHy5Uu8ePGiwDobNGgAS0tLLF++XOFU7b/++gvXrl1Dp06d3rOkpWfdunUKm8H/+OMPPHz4EB06dADwZp//268V8CbUaGlpKXUa+uvXr7FixQr586ysLKxYsQKVKlWCp6enQt/+/ftj3759WLRoESwsLOQ1qGr8+PEIDg7Gd999V2AfZdeRjY0N6tati7Vr1yocm7F//35cvXpVYZol8Z4v7fdStWrV8MUXX2DFihVISEgo0jTS09MRHR2d77C//voLQOG7dPL7PKekpCAiIqJI9QBAx44dcfLkSYWLyz169KjArWdvu3v3Lo4ePYrevXvj008/zfMYNGgQbt68iZiYGABvrvxraGiIoUOH5ntF9bi4OKV23e3cuRMAUKdOnQL7VKtWDYsWLUJoaKjC8T/ELTLlWrVq1TBz5kwEBQXh9u3b6N69O4yNjXHr1i1s27YNw4YNw/jx4wudhr+/P9atW4fAwECcOnUKzZs3x4sXL3DgwAF8/fXX6NatG3x8fDB8+HCEhobi/PnzaNeuHXR1dREbG4vNmzdj8eLF+PTTT1WqvW7dutDW1sacOXOQkpICqVSKVq1aoWnTpqhQoQIGDBiA0aNHQyKRYP369XmCmp6eHqZPn45Ro0ahVatW6N27N27fvo01a9agWrVqClte+vfvj99//x1fffUVDh06BG9vb2RnZ+Pff//F77//jr179xa4+05XVxdz5szBoEGD4OPjg759+yIxMRGLFy+Gg4MDxo4dq9JylyRzc3M0a9YMgwYNQmJiIhYtWgRnZ2d8+eWXAICDBw8iICAAvXr1QvXq1fH69WusX78e2tra6Nmz53unb2trizlz5uD27duoXr06fvvtN5w/fx4rV67MszXv888/x8SJE7Ft2zaMGDGiwK1971OnTp1Cv/wB1dZRaGgoOnXqhGbNmmHw4MF4+vQpfvrpJ9SuXVvh13JJvOfV8V769ttvsX79ely/fh21a9dWefz09HQ0bdoUTZo0Qfv27WFnZ4fk5GRs374df//9N7p374569eoVOH67du2gp6eHLl26YPjw4UhLS8OqVatgaWlZ5AORJ06ciPXr16N9+/b45ptvYGhoiJUrV8Le3h4XL14sdNzIyEgIIdC1a9d8h3fs2BE6OjrYsGEDGjdujGrVqiEyMhKfffYZatasqXBl3xMnTmDz5s15rhd1//59/PrrrwDehP0LFy5gxYoVBZ5U8TZlrknzUVLPyVL0oVQ5lXfLli2iWbNmwtDQUBgaGgpXV1cxcuRIcf36dXmfwk6/TE9PF99++61wdHQUurq6wtraWnz66aciLi5Ood/KlSuFp6en0NfXF8bGxsLd3V1MnDhR4YqXBV3Z8t1ThoUQYtWqVcLJyUloa2srnIp9/Phx0aRJE6Gvry9sbW3FxIkT5af1vnu69pIlS4S9vb2QSqWiUaNG4vjx48LT01O0b99eoV9WVpaYM2eOqF27tpBKpaJChQrC09NThISEiJSUlPe9xOK3334T9erVE1KpVJibm4t+/fqJe/fuKfQp7tOvC1pfBZ1+vXHjRhEUFCQsLS2Fvr6+6NSpk8J1W/777z8xePBgUa1aNSGTyYS5ublo2bKlOHDgwHtrzK3nzJkzwsvLS8hkMmFvb5/nWiBv69ixowCg9PV0hFDuVNP8riMjhHLrSIg3n5eaNWsKqVQqatWqJbZu3VrglX2Vec/np7D3gjJ1DhgwQBgaGhY6D2Xnl3sqd1Gv7Ltq1SrRvXt3+efMwMBA1KtXT8ybN09kZma+t7YdO3YIDw8PIZPJhIODg5gzZ478sg9vnyqtynfHxYsXhY+Pj5DJZKJy5cri+++/F+Hh4e89/drd3V1UrVq10Hp9fX2FpaWlwmUdbty4Ib788kvh4OAg9PT0hLGxsfD29hY//fST/JIQucuAt0671tLSEpaWlqJv377i5s2bCvMp6H38LmU+E+WdRIhiPEqKqIzLyclBpUqV0KNHj3x3JZVHhw8fRsuWLbF582aVtxKUpE8++QSXLl1SODOEiEhVPEaGyq2MjIw8u5zWrVuHp0+f5rlFAZWuhw8fYteuXejfv7+6SyEiDcdjZKjcOnnyJMaOHYtevXrBwsIC586dQ3h4ONzc3FS+ZgkVj1u3buH48eNYvXo1dHV1FS6gR0RUFAwyVG45ODjAzs4OS5YswdOnT2Fubg5/f3/Mnj1bpQvJUfE5cuQIBg0ahKpVq2Lt2rXym+URERUVj5EhIiIijcVjZIiIiEhjMcgQERGRxir3x8jk5OTgwYMHMDY2Vvny80RERKQeQgg8f/4ctra2hd5bqtwHmQcPHpTZm/4RERFR4e7evVvoTWvLfZAxNjYG8OaFyL3tOhEREZVtqampsLOzk/8dL0i5DzK5u5NMTEwYZIiIiDTM+w4L4cG+REREpLEYZIiIiEhjMcgQERGRxlLrMTLZ2dmYPn06fv31VyQkJMDW1hYDBw7E1KlT5fvEhBAIDg7GqlWrkJycDG9vb4SFhcHFxUWdpRMR0UckOzsbr169UncZ5Yquri60tbU/eDpqDTJz5sxBWFgY1q5di9q1a+PMmTMYNGgQTE1NMXr0aADA3LlzsWTJEqxduxaOjo747rvv4Ofnh6tXr0Imk6mzfCIiKueEEEhISEBycrK6SymXzMzMYG1t/UHXeVNrkDlx4gS6deuGTp06AXhzk7+NGzfi1KlTAN68gRYtWoSpU6eiW7duAIB169bBysoK27dvR58+fdRWOxERlX+5IcbS0hIGBga8sGoxEUIgPT0dSUlJAAAbG5siT0utQaZp06ZYuXIlbty4gerVq+PChQs4duwYFixYAAC4desWEhIS0KZNG/k4pqamaNy4MaKjo/MNMpmZmcjMzJQ/T01NLfkFISKicic7O1seYiwsLNRdTrmjr68PAEhKSoKlpWWRdzOpNchMnjwZqampcHV1hba2NrKzszFr1iz069cPwJskDABWVlYK41lZWcmHvSs0NBQhISElWzgREZV7ucfEGBgYqLmS8iv3tX316lWRg4xaz1r6/fffsWHDBkRGRuLcuXNYu3YtfvzxR6xdu7bI0wwKCkJKSor8cffu3WKsmIiIPjbcnVRyiuO1VesWmQkTJmDy5MnyXUTu7u64c+cOQkNDMWDAAFhbWwMAEhMTFfafJSYmom7duvlOUyqVQiqVlnjtREREpH5q3SKTnp6e546W2trayMnJAQA4OjrC2toaUVFR8uGpqamIiYmBl5dXqdZKREREZY9at8h06dIFs2bNQtWqVVG7dm38888/WLBgAQYPHgzgzSanMWPGYObMmXBxcZGffm1ra4vu3burs3QiIiIqA9QaZH766Sd89913+Prrr5GUlARbW1sMHz4c06ZNk/eZOHEiXrx4gWHDhiE5ORnNmjXDnj17eA0ZIiIiJWVnZ0MikeTZC1IeqHWJjI2NsWjRIty5cwcvX75EXFwcZs6cCT09PXkfiUSCGTNmICEhARkZGThw4ACqV6+uxqqJiIg+3B9//AF3d3fo6+vDwsICbdq0wYsXL5CTk4MZM2agSpUqkEqlqFu3Lvbs2SMf7/Dhw5BIJAoX6Tt//jwkEglu374NAFizZg3MzMywY8cO1KpVC1KpFPHx8cjMzMSkSZNgZ2cHqVQKZ2dnhIeHy6dz+fJldOjQAUZGRrCyskL//v3x+PHj0npJiqT8RTMiIqIy7uHDh+jbty8GDx6Ma9eu4fDhw+jRoweEEFi8eDHmz5+PH3/8ERcvXoSfnx+6du2K2NhYleaRnp6OOXPmYPXq1bhy5QosLS3h7++PjRs3YsmSJbh27RpWrFgBIyMjAEBycjJatWqFevXq4cyZM9izZw8SExPRu3fvkngJio1ady0RFcRzwjp1l1Cos/P81V0CEWmwhw8f4vXr1+jRowfs7e0BvDlzFwB+/PFHTJo0SX5G75w5c3Do0CEsWrQIS5cuVXoer169wrJly1CnTh0AwI0bN/D7779j//798gvNOjk5yfv//PPPqFevHn744Qd52y+//AI7Ozv5hWvLIgYZIiKiUlanTh20bt0a7u7u8PPzQ7t27fDpp59CW1sbDx48gLe3t0J/b29vXLhwQaV56OnpwcPDQ/78/Pnz0NbWho+PT779L1y4gEOHDsm30LwtLi6OQYaIiIje0NbWxv79+3HixAns27cPP/30E7799lvs37//vePmHrArhJC35Xdnbn19fYULzuXeEqAgaWlp6NKlC+bMmZNn2IfcC6mk8RgZIiIiNZBIJPD29kZISAj++ecf6OnpISoqCra2tjh+/LhC3+PHj6NWrVoAgEqVKgF4s3sq1/nz5987P3d3d+Tk5ODIkSP5Dq9fvz6uXLkCBwcHODs7KzwMDQ2LuJQlj0GGiIiolMXExOCHH37AmTNnEB8fj61bt+LRo0eoWbMmJkyYgDlz5uC3337D9evXMXnyZJw/fx7ffPMNAMDZ2Rl2dnaYPn06YmNjsWvXLsyfP/+983RwcMCAAQMwePBgbN++Hbdu3cLhw4fx+++/AwBGjhyJp0+fom/fvjh9+jTi4uKwd+9eDBo0CNnZ2SX6enwI7loiIiIqZSYmJjh69CgWLVqE1NRU2NvbY/78+ejQoQP8/PyQkpKCcePGISkpCbVq1cKOHTvg4uICANDV1cXGjRsxYsQIeHh4oGHDhpg5cyZ69er13vmGhYVhypQp+Prrr/HkyRNUrVoVU6ZMAQD5lqBJkyahXbt2yMzMhL29Pdq3b1+mrz8jEW/vZCuHUlNTYWpqipSUFJiYmKi7HFISz1oiInXLyMjArVu34OjoyIuwlpDCXmNl/36X3YhFRERE9B4MMkRERKSxGGSIiIhIYzHIEBERkcZikCEiIiKNxSBDREREGotBhoiIiDQWgwwRERFpLF7Zl6gI4me4q7uEAlWddkndJRARlRpukSEiIiKNxS0yREREKirt26gU921RJBIJtm3bhu7duxfrdNWBW2SIiIhIYzHIEBERlTO+vr4YPXo0Jk6cCHNzc1hbW2P69OkAAAcHBwDAJ598AolEIn9+4cIFtGzZEsbGxjAxMYGnpyfOnDmjngVQAXctERERlUNr165FYGAgYmJiEB0djYEDB8Lb2xunT5+GpaUlIiIi0L59e2hrawMA+vXrh3r16iEsLAza2to4f/48dHV11bwU78cgQ0REVA55eHggODgYAODi4oKff/4ZUVFRaNu2LQDAzMwM1tbW8v7x8fGYMGECXF1d5eNoAu5aIiIiKoc8PDwUntvY2CApKanA/oGBgRg6dCjatGmD2bNnIy4urqRLLBYMMkREROXQu7uFJBIJcnJyCuw/ffp0XLlyBZ06dcLBgwdRq1YtbNu2raTL/GAMMkRERB8ZXV1dZGdn52mvXr06xo4di3379qFHjx6IiIhQQ3WqYZAhIiL6yDg4OCAqKgoJCQl49uwZXr58iYCAABw+fBh37tzB8ePHcfr0adSsWVPdpb4XgwwREdFHZv78+di/fz/s7OxQr149aGtr48mTJ/D390f16tXRu3dvdOjQASEhIeou9b141hIREZGKivtKu8Xt8OHDedq2b98u/3+XLl3QpUsXheEbN24s4apKBrfIEBERkcZikCEiIiKNxSBDREREGotBhoiIiDQWgwwRERFpLLUGGQcHB0gkkjyPkSNHAgAyMjIwcuRIWFhYwMjICD179kRiYqI6SyYiIqIyRK1B5vTp03j48KH8sX//fgBAr169AABjx47Fzp07sXnzZhw5cgQPHjxAjx491FkyERERlSFqvY5MpUqVFJ7Pnj0b1apVg4+PD1JSUhAeHo7IyEi0atUKABAREYGaNWvi5MmTaNKkiTpKJiIiojKkzBwjk5WVhV9//RWDBw+GRCLB2bNn8erVK7Rp00bex9XVFVWrVkV0dHSB08nMzERqaqrCg4iIiMqnMhNktm/fjuTkZAwcOBAAkJCQAD09PZiZmSn0s7KyQkJCQoHTCQ0NhampqfxhZ2dXglUTERGVP9OnT0fdunXVXYZSyswtCsLDw9GhQwfY2tp+0HSCgoIQGBgof56amsowQ0RExSp+hnupzq/qtEulOj9NUiaCzJ07d3DgwAFs3bpV3mZtbY2srCwkJycrbJVJTEyEtbV1gdOSSqWQSqUlWS4RERGVEWVi11JERAQsLS3RqVMneZunpyd0dXURFRUlb7t+/Tri4+Ph5eWljjKJiIg0xp49e9CsWTOYmZnBwsICnTt3RlxcnHz4vXv30LdvX5ibm8PQ0BANGjRATEyMwjRWrFgBOzs7GBgYoHfv3khJSZEPGzhwILp3744ffvgBVlZWMDMzw4wZM/D69WtMmDAB5ubmqFKlCiIiIkp0OdUeZHJychAREYEBAwZAR+f/NhCZmppiyJAhCAwMxKFDh3D27FkMGjQIXl5ePGOJiIjoPV68eIHAwECcOXMGUVFR0NLSwieffIKcnBykpaXBx8cH9+/fx44dO3DhwgVMnDgROTk58vFv3ryJ33//HTt37sSePXvwzz//4Ouvv1aYx8GDB/HgwQMcPXoUCxYsQHBwMDp37owKFSogJiYGX331FYYPH4579+6V2HKqfdfSgQMHEB8fj8GDB+cZtnDhQmhpaaFnz57IzMyEn58fli1bpoYqiYiINEvPnj0Vnv/yyy+oVKkSrl69ihMnTuDRo0c4ffo0zM3NAQDOzs4K/TMyMrBu3TpUrlwZAPDTTz+hU6dOmD9/vvwQD3NzcyxZsgRaWlqoUaMG5s6di/T0dEyZMgXAm+NWZ8+ejWPHjqFPnz4lspxqDzLt2rWDECLfYTKZDEuXLsXSpUtLuSoiIiLNFhsbi2nTpiEmJgaPHz+Wb22Jj4/H+fPnUa9ePXmIyU/VqlXlIQYAvLy8kJOTg+vXr8uDTO3ataGl9X87d6ysrODm5iZ/rq2tDQsLCyQlJRX34smpPcgQERFR8evSpQvs7e2xatUq2NraIicnB25ubsjKyoK+vn6xzENXV1fhuUQiybft7V1WxU3tx8gQERFR8Xry5AmuX7+OqVOnonXr1qhZsyaePXsmH+7h4YHz58/j6dOnBU4jPj4eDx48kD8/efKkfBdSWcIgQ0REVM5UqFABFhYWWLlyJW7evImDBw8qXGOtb9++sLa2Rvfu3XH8+HH8999/2LJli8KV82UyGQYMGIALFy7g77//xujRo9G7d+9CL4GiDgwyRERE5YyWlhY2bdqEs2fPws3NDWPHjsW8efPkw/X09LBv3z5YWlqiY8eOcHd3x+zZs6GtrS3v4+zsjB49eqBjx45o164dPDw8yuQJNxJR0JG25URqaipMTU2RkpICExMTdZdDSvKcsE7dJRRqm/G893dSE14BlKh4ZGRk4NatW3B0dIRMJlN3OeVSYa+xsn+/uUWGiIiINBaDDBEREWksBhkiIiLSWAwyREREpLEYZIiIiApRzs+JUavieG0ZZIiIiPKRe4Xa9PR0NVdSfuW+tu9eDVgVvEUBERFRPrS1tWFmZia/T5CBgQEkEomaqyofhBBIT09HUlISzMzMFK5foyoGGSIiogLkXsW2JG96+DEzMzP74CsFM8gQEREVQCKRwMbGBpaWlnj16pW6yylXdHV1P2hLTC4GGSIiovfQ1tYulj+6VPx4sC8RERFpLAYZIiIi0lgMMkRERKSxGGSIiIhIYzHIEBERkcZikCEiIiKNxSBDREREGotBhoiIiDQWgwwRERFpLAYZIiIi0lgMMkRERKSxGGSIiIhIYzHIEBERkcZikCEiIiKNxSBDREREGotBhoiIiDQWgwwRERFpLAYZIiIi0lgMMkRERKSx1B5k7t+/jy+++AIWFhbQ19eHu7s7zpw5Ix8uhMC0adNgY2MDfX19tGnTBrGxsWqsmIiIiMoKtQaZZ8+ewdvbG7q6uvjrr79w9epVzJ8/HxUqVJD3mTt3LpYsWYLly5cjJiYGhoaG8PPzQ0ZGhhorJyIiorJAR50znzNnDuzs7BARESFvc3R0lP9fCIFFixZh6tSp6NatGwBg3bp1sLKywvbt29GnT59Sr5mIiIjKDrVukdmxYwcaNGiAXr16wdLSEvXq1cOqVavkw2/duoWEhAS0adNG3mZqaorGjRsjOjo632lmZmYiNTVV4UFERETlk1qDzH///YewsDC4uLhg7969GDFiBEaPHo21a9cCABISEgAAVlZWCuNZWVnJh70rNDQUpqam8oednV3JLgQRERGpjVqDTE5ODurXr48ffvgB9erVw7Bhw/Dll19i+fLlRZ5mUFAQUlJS5I+7d+8WY8VERERUlnxwkElNTcX27dtx7do1lce1sbFBrVq1FNpq1qyJ+Ph4AIC1tTUAIDExUaFPYmKifNi7pFIpTExMFB5ERERUPqkcZHr37o2ff/4ZAPDy5Us0aNAAvXv3hoeHB7Zs2aLStLy9vXH9+nWFths3bsDe3h7AmwN/ra2tERUVJR+empqKmJgYeHl5qVo6ERERlTMqB5mjR4+iefPmAIBt27ZBCIHk5GQsWbIEM2fOVGlaY8eOxcmTJ/HDDz/g5s2biIyMxMqVKzFy5EgAgEQiwZgxYzBz5kzs2LEDly5dgr+/P2xtbdG9e3dVSyciIqJyRuUgk5KSAnNzcwDAnj170LNnTxgYGKBTp04qX6iuYcOG2LZtGzZu3Ag3Nzd8//33WLRoEfr16yfvM3HiRIwaNQrDhg1Dw4YNkZaWhj179kAmk6laOhEREZUzKl9Hxs7ODtHR0TA3N8eePXuwadMmAG8ubleUcNG5c2d07ty5wOESiQQzZszAjBkzVJ42ERERlW8qB5kxY8agX79+MDIygr29PXx9fQG82eXk7u5e3PURERERFUjlIPP111+jUaNGuHv3Ltq2bQstrTd7p5ycnFQ+RoaIiIjoQxTpFgUNGjRAgwYNIISAEAISiQSdOnUq7tqIiIiIClWk68iEh4fDzc0NMpkMMpkMbm5uWL16dXHXRkRERFQolbfITJs2DQsWLMCoUaPk13KJjo7G2LFjER8fz4NyiYiIqNSoHGTCwsKwatUq9O3bV97WtWtXeHh4YNSoUQwyREREVGpU3rX06tUrNGjQIE+7p6cnXr9+XSxFERERESlD5SDTv39/hIWF5WlfuXKlwoXsiIiIiEpakc5aCg8Px759+9CkSRMAQExMDOLj4+Hv74/AwEB5vwULFhRPlURERET5UDnIXL58GfXr1wcAxMXFAQAqVqyIihUr4vLly/J+EomkmEokIiIiyp/KQebQoUMlUQcRERGRyop0HRkiIiKiskCpLTI9evTAmjVrYGJigh49ehTad+vWrcVSGBEREdH7KBVkTE1N5ce8mJqalmhBRERERMpSKshEREQAAIQQCAkJQaVKlaCvr1+ihRERERG9j0rHyAgh4OzsjHv37pVUPURERERKUynIaGlpwcXFBU+ePCmpeoiIiIiUpvJZS7Nnz8aECRMUrhlDREREpA4qX0fG398f6enpqFOnDvT09PIcK/P06dNiK46IiIioMCoHmYULF/KqvURERFQmqBxkBg4cWAJlEBEREalO5WNktLW1kZSUlKf9yZMn0NbWLpaiiIiIiJShcpARQuTbnpmZCT09vQ8uiIiIiEhZSu9aWrJkCYA3d7VevXo1jIyM5MOys7Nx9OhRuLq6Fn+FRERERAVQOsgsXLgQwJstMsuXL1fYjaSnpwcHBwcsX768+CskIiIiKoDSQebWrVsAgJYtW2Lr1q2oUKFCiRVFREREpAyVz1o6dOhQSdRBREREpDKVD/YlIiIiKisYZIiIiEhjMcgQERGRxmKQISIiIo2l1MG+Fy9eVHqCHh4eRS6GiIiISBVKBZm6detCIpEUeFXf3GESiQTZ2dnFWiARERFRQZQKMrnXkCEiIiIqS5Q6Rsbe3l7phyqmT58OiUSi8Hj7NgcZGRkYOXIkLCwsYGRkhJ49eyIxMVG1JSQiIqJyS+UL4uW6evUq4uPjkZWVpdDetWtXlaZTu3ZtHDhw4P8K0vm/ksaOHYtdu3Zh8+bNMDU1RUBAAHr06IHjx48XtWwiIiIqR1QOMv/99x8++eQTXLp0SeG4GYlEAgAqHyOjo6MDa2vrPO0pKSkIDw9HZGQkWrVqBQCIiIhAzZo1cfLkSTRp0kTV0omIiKicUfn062+++QaOjo5ISkqCgYEBrly5gqNHj6JBgwY4fPiwygXExsbC1tYWTk5O6NevH+Lj4wEAZ8+exatXr9CmTRt5X1dXV1StWhXR0dEFTi8zMxOpqakKDyIiIiqfVA4y0dHRmDFjBipWrAgtLS1oaWmhWbNmCA0NxejRo1WaVuPGjbFmzRrs2bMHYWFhuHXrFpo3b47nz58jISEBenp6MDMzUxjHysoKCQkJBU4zNDQUpqam8oednZ2qi0hEREQaQuVdS9nZ2TA2NgYAVKxYEQ8ePECNGjVgb2+P69evqzStDh06yP/v4eGBxo0bw97eHr///jv09fVVLQ0AEBQUhMDAQPnz1NRUhhkiIqJySuUg4+bmhgsXLsDR0RGNGzfG3Llzoaenh5UrV8LJyemDijEzM0P16tVx8+ZNtG3bFllZWUhOTlbYKpOYmJjvMTW5pFIppFLpB9VBREREmkHlXUtTp05FTk4OAGDGjBny3UG7d+/G4sWLP6iYtLQ0xMXFwcbGBp6entDV1UVUVJR8+PXr1xEfHw8vL68Pmg8RERGVDypvkfHz85P/39nZGf/++y+ePn2KChUqyM9cUtb48ePRpUsX2Nvb48GDBwgODoa2tjb69u0LU1NTDBkyBIGBgTA3N4eJiQlGjRoFLy8vnrFEREREAIqwRWbw4MF4/vy5Qpu5uTnS09MxePBglaZ179499O3bFzVq1EDv3r1hYWGBkydPolKlSgCAhQsXonPnzujZsydatGgBa2trbN26VdWSiYiIqJySiIJuoFQAbW1tPHz4EJaWlgrtjx8/hrW1NV6/fl2sBX6o1NRUmJqaIiUlBSYmJuouh5TkOWGdukso1DbjeeouoUBVp11SdwlERB9M2b/fSu9aSk1NhRACQgg8f/4cMplMPiw7Oxu7d+/OE26IiIiISpLSQcbMzEx+P6Tq1avnGS6RSBASElKsxREREREVRukgc+jQIQgh0KpVK2zZsgXm5ubyYXp6erC3t4etrW2JFElERESUH6WDjI+PDwDg1q1bsLOzg5aWyscJExERERUrlU+/tre3R3JyMsLDw3Ht2jUAb+5gPXjwYJiamhZ7gUREREQFUXmzypkzZ1CtWjUsXLgQT58+xdOnT7FgwQJUq1YN586dK4kaiYiIiPKl8haZsWPHomvXrli1ahV0dN6M/vr1awwdOhRjxozB0aNHi71IIiIiovyoHGTOnDmjEGIAQEdHBxMnTkSDBg2KtTgiIiKiwqi8a8nExATx8fF52u/evSu/KzYRERFRaVA5yHz22WcYMmQIfvvtN9y9exd3797Fpk2bMHToUPTt27ckaiQiIiLKl8q7ln788UdIJBL4+/vLb0egq6uLESNGYPbs2cVeIBEREVFBVA4yenp6WLx4MUJDQxEXFwcAqFatGgwMDIq9OCIiIqLCFPnu1wYGBnB3d4e7uzsMDAzw4sULle9+TURERPQhVA4ya9euxcuXL/O0v3z5EuvWle07FhMREVH5wrtfExERkcbi3a+JiIhIY/Hu10RERKSxinT366pVq0IikZRYUURERETKKNLdr4mIiIjKApXPWiIiIiIqKxhkiIiISGMpFWR27NiBV69elXQtRERERCpRKsh88sknSE5OBgBoa2sjKSmpJGsiIiIiUopSQaZSpUo4efIkAEAIwTOWiIiIqExQ6qylr776Ct26dZNfEM/a2rrAvtnZ2cVWHBEREVFhlAoy06dPR58+fXDz5k107doVERERMDMzK+HSiIiIiAqn9HVkXF1d4erqiuDgYPTq1QsGBgYlWRcRERHRe6l8Qbzg4GAAwKNHj3D9+nUAQI0aNVCpUqXirYyIiIjoPVS+jkx6ejoGDx4MW1tbtGjRAi1atICtrS2GDBmC9PT0kqiRiIiIKF8qB5mxY8fiyJEj2LFjB5KTk5GcnIw///wTR44cwbhx40qiRiIiIqJ8qbxracuWLfjjjz/g6+srb+vYsSP09fXRu3dvhIWFFWd9RERERAUq0q4lKyurPO2WlpbctURERESlSuUg4+XlheDgYGRkZMjbXr58iZCQEHh5eRVrcURERESFUXnX0uLFi+Hn54cqVaqgTp06AIALFy5AJpNh7969xV4gERERUUFU3iLj5uaG2NhYhIaGom7duqhbty5mz56N2NhY1K5du8iFzJ49GxKJBGPGjJG3ZWRkYOTIkbCwsICRkRF69uyJxMTEIs+DiIiIyheVt8gAgIGBAb788stiK+L06dNYsWIFPDw8FNrHjh2LXbt2YfPmzTA1NUVAQAB69OiB48ePF9u8iYiISHOpvEWmuKWlpaFfv35YtWoVKlSoIG9PSUlBeHg4FixYgFatWsHT0xMRERE4ceKE/AaWRERE9HFTe5AZOXIkOnXqhDZt2ii0nz17Fq9evVJod3V1RdWqVREdHV3g9DIzM5GamqrwICIiovKpSLuWisumTZtw7tw5nD59Os+whIQE6Onp5bk5pZWVFRISEgqcZmhoKEJCQoq7VCIiIiqD1LZF5u7du/jmm2+wYcMGyGSyYptuUFAQUlJS5I+7d+8W27SJiIiobClSkElOTsbq1asRFBSEp0+fAgDOnTuH+/fvKz2Ns2fPIikpCfXr14eOjg50dHRw5MgRLFmyBDo6OrCyskJWVhaSk5MVxktMTIS1tXWB05VKpTAxMVF4EBERUfmk8q6lixcvok2bNjA1NcXt27fx5ZdfwtzcHFu3bkV8fDzWrVun1HRat26NS5cuKbQNGjQIrq6umDRpEuzs7KCrq4uoqCj07NkTAHD9+nXEx8fzwntEREQEoAhBJjAwEAMHDsTcuXNhbGwsb+/YsSM+//xzpadjbGwMNzc3hTZDQ0NYWFjI24cMGYLAwECYm5vDxMQEo0aNgpeXF5o0aaJq2URERFQOqRxkcq/58q7KlSsXehBuUSxcuBBaWlro2bMnMjMz4efnh2XLlhXrPIiIiEhzqRxkpFJpvqc037hxA5UqVfqgYg4fPqzwXCaTYenSpVi6dOkHTZeIiIjKJ5UP9u3atStmzJiBV69eAQAkEgni4+MxadIk+bEsRERERKVB5SAzf/58pKWlwdLSEi9fvoSPjw+cnZ1hbGyMWbNmlUSNRERERPlSedeSqakp9u/fj2PHjuHixYtIS0tD/fr181yZl4iIiKikFfnKvs2aNUOzZs2KsxYiIiIilagcZJYsWZJvu0QigUwmg7OzM1q0aAFtbe0PLo6IiIioMCoHmYULF+LRo0dIT0+X36362bNnMDAwgJGREZKSkuDk5IRDhw7Bzs6u2AsmIiIiyqXywb4//PADGjZsiNjYWDx58gRPnjzBjRs30LhxYyxevBjx8fGwtrbG2LFjS6JeIiIiIjmVt8hMnToVW7ZsQbVq1eRtzs7O+PHHH9GzZ0/8999/mDt3Lk/FJiIiohKn8haZhw8f4vXr13naX79+Lb+yr62tLZ4/f/7h1REREREVQuUg07JlSwwfPhz//POPvO2ff/7BiBEj0KpVKwDApUuX4OjoWHxVEhEREeVD5SATHh4Oc3NzeHp6QiqVQiqVokGDBjA3N0d4eDgAwMjICPPnzy/2YomIiIjepvIxMtbW1ti/fz/+/fdf3LhxAwBQo0YN1KhRQ96nZcuWxVchERERUQGKfEE8V1dXuLq6FmctRERERCopUpC5d+8eduzYgfj4eGRlZSkMW7BgQbEURkRERPQ+KgeZqKgodO3aFU5OTvj333/h5uaG27dvQwiB+vXrl0SNRERERPlS+WDfoKAgjB8/HpcuXYJMJsOWLVtw9+5d+Pj4oFevXiVRIxEREVG+VA4y165dg7+/PwBAR0cHL1++hJGREWbMmIE5c+YUe4FEREREBVE5yBgaGsqPi7GxsUFcXJx82OPHj4uvMiIiIqL3UPkYmSZNmuDYsWOoWbMmOnbsiHHjxuHSpUvYunUrmjRpUhI1EhEREeVL5SCzYMECpKWlAQBCQkKQlpaG3377DS4uLjxjiYiIiEqVykHGyclJ/n9DQ0MsX768WAsiIiIiUpbKx8g4OTnhyZMnedqTk5MVQg4RERFRSVM5yNy+fRvZ2dl52jMzM3H//v1iKYqIiIhIGUrvWtqxY4f8/3v37oWpqan8eXZ2NqKiouDg4FCsxREREREVRukg0717dwCARCLBgAEDFIbp6urCwcGBd7wmIiKiUqV0kMnJyQEAODo64vTp06hYsWKJFUVERESkDJXPWrp161ZJ1EFERESksiLd/ToqKgpRUVFISkqSb6nJ9csvvxRLYURERETvo3KQCQkJwYwZM9CgQQPY2NhAIpGURF1ERERE76VykFm+fDnWrFmD/v37l0Q9REREREpT+ToyWVlZaNq0aUnUQkRERKQSlYPM0KFDERkZWRK1EBEREalE5V1LGRkZWLlyJQ4cOAAPDw/o6uoqDOeNI4mIiKi0qBxkLl68iLp16wIALl++rDCMB/4SERFRaVI5yBw6dKjYZh4WFoawsDDcvn0bAFC7dm1MmzYNHTp0APBm68+4ceOwadMmZGZmws/PD8uWLYOVlVWx1UBERESaS+VjZHLdvHkTe/fuxcuXLwEAQgiVp1GlShXMnj0bZ8+exZkzZ9CqVSt069YNV65cAQCMHTsWO3fuxObNm3HkyBE8ePAAPXr0KGrJREREVM6ovEXmyZMn6N27Nw4dOgSJRILY2Fg4OTlhyJAhqFChgkr3W+rSpYvC81mzZiEsLAwnT55ElSpVEB4ejsjISLRq1QoAEBERgZo1a+LkyZNo0qSJqqUTERFROaPyFpmxY8dCV1cX8fHxMDAwkLd/9tln2LNnT5ELyc7OxqZNm/DixQt4eXnh7NmzePXqFdq0aSPv4+rqiqpVqyI6OrrA6WRmZiI1NVXhQUREROWTyltk9u3bh71796JKlSoK7S4uLrhz547KBVy6dAleXl7IyMiAkZERtm3bhlq1auH8+fPQ09ODmZmZQn8rKyskJCQUOL3Q0FCEhISoXAcRERFpHpW3yLx48UJhS0yup0+fQiqVqlxAjRo1cP78ecTExGDEiBEYMGAArl69qvJ0cgUFBSElJUX+uHv3bpGnRURERGWbykGmefPmWLdunfy5RCJBTk4O5s6di5YtW6pcgJ6eHpydneHp6YnQ0FDUqVMHixcvhrW1NbKyspCcnKzQPzExEdbW1gVOTyqVwsTEROFBRERE5ZPKu5bmzp2L1q1b48yZM8jKysLEiRNx5coVPH36FMePH//ggnJycpCZmQlPT0/o6uoiKioKPXv2BABcv34d8fHx8PLy+uD5EBERkeZTOci4ubnhxo0b+Pnnn2FsbIy0tDT06NEDI0eOhI2NjUrTCgoKQocOHVC1alU8f/4ckZGROHz4MPbu3QtTU1MMGTIEgYGBMDc3h4mJCUaNGgUvLy+esUREREQAihBkAMDU1BTffvvtB888KSkJ/v7+ePjwIUxNTeHh4YG9e/eibdu2AICFCxdCS0sLPXv2VLggHhERERFQhCATEREBIyMj9OrVS6F98+bNSE9Px4ABA5SeVnh4eKHDZTIZli5diqVLl6paJhEREX0EVD7YNzQ0FBUrVszTbmlpiR9++KFYiiIiIiJShspBJj4+Ho6Ojnna7e3tER8fXyxFERERESlD5SBjaWmJixcv5mm/cOECLCwsiqUoIiIiImWoHGT69u2L0aNH49ChQ8jOzkZ2djYOHjyIb775Bn369CmJGomIiIjypfLBvt9//z1u376N1q1bQ0fnzeg5OTnw9/fnMTJERERUqlQKMkIIJCQkYM2aNZg5cybOnz8PfX19uLu7w97evqRqJCIiIsqXykHG2dkZV65cgYuLC1xcXEqqLiIiIqL3UukYGS0tLbi4uODJkyclVQ8RERGR0lQ+2Hf27NmYMGECLl++XBL1EBERESlN5YN9/f39kZ6ejjp16kBPTw/6+voKw58+fVpsxREREREVRuUgs2jRohIog4iIiEh1KgcZVe6lRERERFSSVD5GBgDi4uIwdepU9O3bF0lJSQCAv/76C1euXCnW4oiIiIgKo3KQOXLkCNzd3RETE4OtW7ciLS0NwJtbFAQHBxd7gUREREQFUTnITJ48GTNnzsT+/fuhp6cnb2/VqhVOnjxZrMURERERFUblIHPp0iV88sknedotLS3x+PHjYimKiIiISBkqBxkzMzM8fPgwT/s///yDypUrF0tRRERERMpQOcj06dMHkyZNQkJCAiQSCXJycnD8+HGMHz8e/v7+JVEjERERUb5UDjI//PADXF1dYWdnh7S0NNSqVQstWrRA06ZNMXXq1JKokYiIiChfKl9HRk9PD6tWrcK0adNw6dIlpKWloV69eryBJBEREZU6pYNMTk4O5s2bhx07diArKwutW7dGcHBwnlsUEBEREZUWpXctzZo1C1OmTIGRkREqV66MxYsXY+TIkSVZGxEREVGhlA4y69atw7Jly7B3715s374dO3fuxIYNG5CTk1OS9REREREVSOkgEx8fj44dO8qft2nTBhKJBA8ePCiRwoiIiIjeR+kg8/r1a8hkMoU2XV1dvHr1qtiLIiIiIlKG0gf7CiEwcOBASKVSeVtGRga++uorGBoaytu2bt1avBUSERERFUDpIDNgwIA8bV988UWxFkNERESkCqWDTEREREnWQURERKQyla/sS0RERFRWMMgQERGRxmKQISIiIo3FIENEREQai0GGiIiINBaDDBEREWkstQaZ0NBQNGzYEMbGxrC0tET37t1x/fp1hT4ZGRkYOXIkLCwsYGRkhJ49eyIxMVFNFRMREVFZotYgc+TIEYwcORInT57E/v378erVK7Rr1w4vXryQ9xk7dix27tyJzZs348iRI3jw4AF69OihxqqJiIiorFD6gnglYc+ePQrP16xZA0tLS5w9exYtWrRASkoKwsPDERkZiVatWgF4c2G+mjVr4uTJk2jSpIk6yiYiIqIyokwdI5OSkgIAMDc3BwCcPXsWr169Qps2beR9XF1dUbVqVURHR+c7jczMTKSmpio8iIiIqHwqM0EmJycHY8aMgbe3N9zc3AAACQkJ0NPTg5mZmUJfKysrJCQk5Dud0NBQmJqayh92dnYlXToRERGpSZkJMiNHjsTly5exadOmD5pOUFAQUlJS5I+7d+8WU4VERERU1qj1GJlcAQEB+N///oejR4+iSpUq8nZra2tkZWUhOTlZYatMYmIirK2t852WVCqFVCot6ZKJiIioDFDrFhkhBAICArBt2zYcPHgQjo6OCsM9PT2hq6uLqKgoedv169cRHx8PLy+v0i6XiIiIyhi1bpEZOXIkIiMj8eeff8LY2Fh+3IupqSn09fVhamqKIUOGIDAwEObm5jAxMcGoUaPg5eXFM5aIiIhIvUEmLCwMAODr66vQHhERgYEDBwIAFi5cCC0tLfTs2ROZmZnw8/PDsmXLSrlSIiIiKovUGmSEEO/tI5PJsHTpUixdurQUKiIiIiJNUmbOWiIiIiJSFYMMERERaSwGGSIiItJYDDJERESksRhkiIiISGMxyBAREZHGYpAhIiIijcUgQ0RERBqLQYaIiIg0FoMMERERaSwGGSIiItJYDDJERESksRhkiIiISGMxyBAREZHGYpAhIiIijcUgQ0RERBqLQYaIiIg0FoMMERERaSwGGSIiItJYDDJERESksRhkiIiISGMxyBAREZHGYpAhIiIijcUgQ0RERBqLQYaIiIg0FoMMERERaSwGGSIiItJYDDJERESksRhkiIiISGMxyBAREZHGYpAhIiIijaWj7gKIiKh8iJ/hru4SClV12iV1l0AlgEGGiIionPmYQqVady0dPXoUXbp0ga2tLSQSCbZv364wXAiBadOmwcbGBvr6+mjTpg1iY2PVUywRERGVOWoNMi9evECdOnWwdOnSfIfPnTsXS5YswfLlyxETEwNDQ0P4+fkhIyOjlCslIiKiskitu5Y6dOiADh065DtMCIFFixZh6tSp6NatGwBg3bp1sLKywvbt29GnT5/SLJWIiEiB54R16i6hQNuM1V1B6SmzZy3dunULCQkJaNOmjbzN1NQUjRs3RnR0tBorIyIiorKizB7sm5CQAACwsrJSaLeyspIPy09mZiYyMzPlz1NTU0umQCIiIlK7MrtFpqhCQ0Nhamoqf9jZ2am7JCIiIiohZTbIWFtbAwASExMV2hMTE+XD8hMUFISUlBT54+7duyVaJxEREalPmQ0yjo6OsLa2RlRUlLwtNTUVMTEx8PLyKnA8qVQKExMThQcRERGVT2o9RiYtLQ03b96UP7916xbOnz8Pc3NzVK1aFWPGjMHMmTPh4uICR0dHfPfdd7C1tUX37t3VVzQRERGVGWoNMmfOnEHLli3lzwMDAwEAAwYMwJo1azBx4kS8ePECw4YNQ3JyMpo1a4Y9e/ZAJpOpq2QiIiIqQ9QaZHx9fSGEKHC4RCLBjBkzMGPGjFKsioiIiDRFmT39moiIFJXlC7ABH9dF2KjsKLMH+xIRERG9D4MMERERaSwGGSIiItJYDDJERESksRhkiIiISGMxyBAREZHGYpAhIiIijcUgQ0RERBqLQYaIiIg0FoMMERERaSwGGSIiItJYDDJERESksRhkiIiISGMxyBAREZHGYpAhIiIijcUgQ0RERBqLQYaIiIg0FoMMERERaSwGGSIiItJYDDJERESksRhkiIiISGMxyBAREZHGYpAhIiIijcUgQ0RERBqLQYaIiIg0FoMMERERaSwGGSIiItJYDDJERESksXTUXUBZ4jlhnbpLKNQ243nqLqFAVaddUncJRET0EeIWGSIiItJYDDJERESksRhkiIiISGMxyBAREZHG0oggs3TpUjg4OEAmk6Fx48Y4deqUuksiIiKiMqDMB5nffvsNgYGBCA4Oxrlz51CnTh34+fkhKSlJ3aURERGRmpX5ILNgwQJ8+eWXGDRoEGrVqoXly5fDwMAAv/zyi7pLIyIiIjUr09eRycrKwtmzZxEUFCRv09LSQps2bRAdHa3Gyog+HmX9+kpn5/mruwQiUqMyHWQeP36M7OxsWFlZKbRbWVnh33//zXeczMxMZGZmyp+npKQAAFJTU987v+zMlx9Qbcl7rput7hIKpMzrqwqui6L72NZFcS9vWVbW10VZ/lwAH9dnozysi9w+QohC+5XpIFMUoaGhCAkJydNuZ2enhmqKl5u6CyhMqKm6KyhVXBdlh+lPX6m7BPr/yvTnAvioPhvlaV08f/4cpqYF9y/TQaZixYrQ1tZGYmKiQntiYiKsra3zHScoKAiBgYHy5zk5OXj69CksLCwgkUhKtN6SlJqaCjs7O9y9excmJibqLuejxnVRdnBdlB1cF2VHeVkXQgg8f/4ctra2hfYr00FGT08Pnp6eiIqKQvfu3QG8CSZRUVEICAjIdxypVAqpVKrQZmZmVsKVlh4TExONfmOWJ1wXZQfXRdnBdVF2lId1UdiWmFxlOsgAQGBgIAYMGIAGDRqgUaNGWLRoEV68eIFBgwapuzQiIiJSszIfZD777DM8evQI06ZNQ0JCAurWrYs9e/bkOQCYiIiIPj5lPsgAQEBAQIG7kj4WUqkUwcHBeXabUenjuig7uC7KDq6LsuNjWxcS8b7zmoiIiIjKqDJ/ZV8iIiKigjDIEBERkcZikCEiIiKNxSCjBr6+vhgzZkyBwx0cHLBo0SL5c4lEgu3bt5d4XURUsPd9bonKunffw+/+rdFUDDJl0OnTpzFs2DB1l0FU4hgOyqfy8geSNAODTBlUqVIlGBgYqLsM+kBZWVnqLuGjwdea6OPFIKMmr1+/RkBAAExNTVGxYkV899138jt8vu/XTHBwMGxsbHDx4kUAwLFjx9C8eXPo6+vDzs4Oo0ePxosXL0pjMcqdnJwczJ07F87OzpBKpahatSpmzZoFAJg0aRKqV68OAwMDODk54bvvvsOrV6/k406fPh1169bF6tWr4ejoCJlMpq7FKBG+vr4YNWoUxowZgwoVKsDKygqrVq2SX2nb2NgYzs7O+Ouvv+TjHDlyBI0aNYJUKoWNjQ0mT56M169fAwAGDhyII0eOYPHixZBIJJBIJLh9+/Z7x8utJSAgAGPGjEHFihXh5+cHALhy5Qo6d+4MExMTGBsbo3nz5oiLi8PRo0ehq6uLhIQEhWUaM2YMmjdvLn9+/Phx+Pr6wsDAABUqVICfnx+ePXuW7+uRmZmJ8ePHo3LlyjA0NETjxo1x+PDh4nipy7zc1z+/7zBfX1/cuXMHY8eOla9XKpr//e9/MDMzQ3b2mztZnz9/HhKJBJMnT5b3GTp0KL744gs8efIEffv2ReXKlWFgYAB3d3ds3LhRpfmtXr0aZmZmiIqKKtblKGkMMmqydu1a6Ojo4NSpU1i8eDEWLFiA1atXFzqOEAKjRo3CunXr8Pfff8PDwwNxcXFo3749evbsiYsXL+K3337DsWPHPvoLCBZVUFAQZs+eje+++w5Xr15FZGSk/CrSxsbGWLNmDa5evYrFixdj1apVWLhwocL4N2/exJYtW7B161acP39eDUtQstauXYuKFSvi1KlTGDVqFEaMGIFevXqhadOmOHfuHNq1a4f+/fsjPT0d9+/fR8eOHdGwYUNcuHABYWFhCA8Px8yZMwEAixcvhpeXF7788ks8fPgQDx8+hJ2d3XvHe7sWPT09HD9+HMuXL8f9+/fRokULSKVSHDx4EGfPnsXgwYPx+vVrtGjRAk5OTli/fr18/FevXmHDhg0YPHgwgDd/JFq3bo1atWohOjoax44dQ5cuXeR/RN4VEBCA6OhobNq0CRcvXkSvXr3Qvn17xMbGltCrX7YU9B22detWVKlSBTNmzJCvVyqa5s2b4/nz5/jnn38AvAn4FStWVAjMR44cga+vLzIyMuDp6Yldu3bh8uXLGDZsGPr3749Tp04pNa+5c+di8uTJ2LdvH1q3bl0Si1NyBJU6Hx8fUbNmTZGTkyNvmzRpkqhZs6YQQgh7e3uxcOFC+TAAYvPmzeLzzz8XNWvWFPfu3ZMPGzJkiBg2bJjC9P/++2+hpaUlXr58WbILUs6kpqYKqVQqVq1apVT/efPmCU9PT/nz4OBgoaurK5KSkkqqRLXy8fERzZo1kz9//fq1MDQ0FP3795e3PXz4UAAQ0dHRYsqUKaJGjRoK7/OlS5cKIyMjkZ2dLZ/mN998ozAfZcerV6+ewnhBQUHC0dFRZGVl5Vv/nDlz5J8xIYTYsmWLMDIyEmlpaUIIIfr27Su8vb0LXf7cWu/cuSO0tbXF/fv3Ffq0bt1aBAUFFTiN8kLV7zAquvr164t58+YJIYTo3r27mDVrltDT0xPPnz8X9+7dEwDEjRs38h23U6dOYty4cfLn737ectfTxIkThY2Njbh8+XKJLktJ4RYZNWnSpInCJlcvLy/ExsYW+Otv7NixiImJwdGjR1G5cmV5+4ULF7BmzRoYGRnJH35+fsjJycGtW7dKfDnKk2vXriEzM7PAXyO//fYbvL29YW1tDSMjI0ydOhXx8fEKfezt7VGpUqXSKFctPDw85P/X1taGhYUF3N3d5W25W6+SkpJw7do1eHl5KbzPvb29kZaWhnv37hU4D2XH8/T0VBjv/PnzaN68OXR1dfOd7sCBA3Hz5k2cPHkSALBmzRr07t0bhoaG8vGV/SV66dIlZGdno3r16gqfvSNHjiAuLk6paWg6Vb/DqGh8fHxw+PBhCCHw999/o0ePHqhZsyaOHTuGI0eOwNbWFi4uLsjOzsb3338Pd3d3mJubw8jICHv37s3zHfWu+fPnY9WqVTh27Bhq165dSktVvDTiXksEtG3bFhs3bsTevXvRr18/eXtaWhqGDx+O0aNH5xmnatWqpVmixtPX1y9wWHR0NPr164eQkBD4+fnB1NQUmzZtwvz58xX65f5RLK/eDQkSiUShLfcPW05OTonX8u5rXdj6AwBLS0t06dIFERERcHR0xF9//aWwif59478tLS0N2traOHv2LLS1tRWGGRkZKT0dovfx9fXFL7/8ggsXLkBXVxeurq7w9fXF4cOH8ezZM/j4+AAA5s2bh8WLF2PRokVwd3eHoaEhxowZ894D4Zs3b45du3bh999/Vzj2RpMwyKhJTEyMwvOTJ0/CxcUlz5dirq5du6JLly74/PPPoa2tjT59+gAA6tevj6tXr8LZ2bnEay7vXFxcoK+vj6ioKAwdOlRh2IkTJ2Bvb49vv/1W3nbnzp3SLlGj1KxZE1u2bIEQQh5wjh8/DmNjY1SpUgUAoKenl+cXvDLj5cfDwwNr167Fq1evCtwqM3ToUPTt2xdVqlRBtWrV4O3trTB+VFQUQkJC3rts9erVQ3Z2NpKSkhQOFv6YFPYdlt96paLJPU5m4cKF8tDi6+uL2bNn49mzZxg3bhyAN5+Rbt264YsvvgDw5sfEjRs3UKtWrUKn36hRIwQEBKB9+/bQ0dHB+PHjS3aBSgB3LalJfHw8AgMDcf36dWzcuBE//fQTvvnmm0LH+eSTT7B+/XoMGjQIf/zxB4A3Z9KcOHECAQEBOH/+PGJjY/Hnn3/yYN8ikMlkmDRpEiZOnIh169YhLi4OJ0+eRHh4OFxcXBAfH49NmzYhLi4OS5YswbZt29Rdcpn29ddf4+7duxg1ahT+/fdf/PnnnwgODkZgYCC0tN589Tg4OCAmJga3b9/G48ePkZOTo9R4+QkICEBqair69OmDM2fOIDY2FuvXr8f169flffz8/GBiYoKZM2di0KBBCuMHBQXh9OnT+Prrr3Hx4kX8+++/CAsLw+PHj/PMq3r16ujXrx/8/f2xdetW3Lp1C6dOnUJoaCh27dpVTK9g2VbYd5iDgwOOHj2K+/fv5/v6kfIqVKgADw8PbNiwAb6+vgCAFi1a4Ny5c7hx44Y83Li4uGD//v04ceIErl27huHDhyMxMVGpeTRt2hS7d+9GSEiIRl7/h0FGTfz9/fHy5Us0atQII0eOxDfffKPURfA+/fRTrF27Fv3798fWrVvh4eGBI0eO4MaNG2jevDnq1auHadOmwdbWthSWovz57rvvMG7cOEybNg01a9bEZ599hqSkJHTt2hVjx45FQEAA6tatixMnTuC7775Td7llWuXKlbF7926cOnUKderUwVdffYUhQ4Zg6tSp8j7jx4+HtrY2atWqhUqVKiE+Pl6p8fJjYWGBgwcPIi0tDT4+PvD09MSqVasUts5oaWlh4MCByM7Ohr+/v8L41atXx759+3DhwgU0atQIXl5e+PPPP6Gjk/+G64iICPj7+2PcuHGoUaMGunfvjtOnT380u3QL+w6bMWMGbt++jWrVqpXrY8ZKi4+PD7Kzs+VBxtzcHLVq1YK1tTVq1KgBAJg6dSrq168PPz8/+Pr6wtraGt27d1d6Hs2aNcOuXbswdepU/PTTTyWwFCVHIsT/v3gJEdFHYMiQIXj06BF27Nih7lI0lq+vL+rWrauRv96p/OExMkT0UUhJScGlS5cQGRnJEENUjjDIENFHoVu3bjh16hS++uortG3bVt3lEFEx4a4lIiIi0lg82JeIiIg0FoMMERERaSwGGSIiItJYDDJERESksRhkiKjc8vX1xZgxY9RdBhGVIAYZIiIi0lgMMkRERKSxGGSIqNT5+vpi1KhRGDNmDCpUqAArKyusWrUKL168wKBBg2BsbAxnZ2f89ddf8nGOHDmCRo0aQSqVwsbGBpMnT8br16/lw1+8eAF/f38YGRnBxsYG8+fPzzPfzMxMjB8/HpUrV4ahoSEaN26Mw4cPl8YiE1EJYZAhIrVYu3YtKlasiFOnTmHUqFEYMWIEevXqhaZNm+LcuXNo164d+vfvj/T0dNy/fx8dO3ZEw4YNceHCBYSFhSE8PBwzZ86UT2/ChAk4cuQI/vzzT+zbtw+HDx/GuXPnFOYZEBCA6OhobNq0CRcvXkSvXr3Qvn17xMbGlvbiE1Ex4ZV9iajU+fr6Ijs7G3///TcAIDs7G6ampujRowfWrVsHAEhISICNjQ2io6Oxc+dObNmyBdeuXYNEIgEALFu2DJMmTUJKSgrS09NhYWGBX3/9Fb169QIAPH36FFWqVMGwYcOwaNEixMfHw8nJCfHx8Qp3h2/Tpg0aNWqEH374oZRfBSIqDrzXEhGphYeHh/z/2trasLCwgLu7u7zNysoKAJCUlIRr167By8tLHmIAwNvbG2lpabh37x6ePXuGrKwsNG7cWD7c3NwcNWrUkD+/dOkSsrOzUb16dYU6MjMzYWFhUezLR0Slg0GGiNRCV1dX4blEIlFoyw0tOTk5xTK/tLQ0aGtr4+zZs9DW1lYYZmRkVCzzIKLSxyBDRGVezZo1sWXLFggh5AHn+PHjMDY2RpUqVWBubg5dXV3ExMSgatWqAIBnz57hxo0b8PHxAQDUq1cP2dnZSEpKQvPmzdW2LERUvHiwLxGVeV9//TXu3r2LUaNG4d9//8Wff/6J4OBgBAYGQktLC0ZGRhgyZAgmTJiAgwcP4vLlyxg4cCC0tP7vK6569ero168f/P39sXXrVty6dQunTp1CaGgodu3apcalI6IPwS0yRFTmVa5cGbt378aECRNQp04dmJubY8iQIZg6daq8z7x585CWloYuXbrA2NgY48aNQ0pKisJ0IiIiMHPmTIwbNw73799HxYoV0aRJE3Tu3Lm0F4mIignPWiIiIiKNxV1LREREpLEYZIiIiEhjMcgQERGRxmKQISIiIo3FIENEREQai0GGiIiINBaDDBEREWksBhkiIiLSWAwyREREpLEYZIiIiEhjMcgQERGRxmKQISIiIo31/wAdZLKpvVL/TgAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -939,12 +974,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -979,12 +1014,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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Mi16ZosU9B3JzF+Cwej2ntdMmT56snTt3Bn7Wr1/vdkhA1PDM/B+zZ0sZGdKJJ0oXXuj/NyPDv9yNdgAEkIdRF0qm1ujTp8+vP8cco/O3bJHJf/JZWrH8RevzN29Wn2OOCTxn5MiRkspPrxGxgQP9Fxl8ocrn8i/v0MG/HgA4hBwMAIA7yMGosdmzpREjyk8vtXGjfznXKGus65rlar09dKd2yV9US9m+RV3XLK/LsIAGIWrvtD7ooIMkSZs3b1ZaqTtRNm/erJ49e4Z8TpMmTdSkSZO6CA9oUJyc/8NXXFTz4cpLDubMgpeXHMzNmhVeL0Sn2gGiVE1ysEQeRt2obGqNO1ct1pXvvqA2u7f/un5Csh4ffIH2ZB4dNMVGZdNrRCQ21t8rfsQIf4G6dK4pKWRPmcLIHgBCIgcDAOAOcjDqVFGR/w7rstcmJf8yn0+aMEE66yzOHWsgaWdu1StVYz0A4YvaonWnTp100EEH6f333w8cGOzatUuLFi3SFVdc4W5wQAPi5PwfERW/nTqY46AQqBI5GPVBqKk1VqZ30TWDzg/ZOSq5zPOrml4jIsOG+TtAhRrmbcqUaneMiqjDF4B6hRwMAIA7yMGoU/Pnl7/DujQzaf16/3pZWXUWVrTYkZji6HoAwlevi9Z79uzRd999F/j9hx9+0LJly5ScnKz09HRNmDBBf/3rX9W5c2d16tRJf/7zn9WuXTudffbZ7gUNNCBOzv8RcfHbqYM5DgoBSeRgRC+LidU3XXq7HYa/MH3WWf58kpPjn8N64MBqd4hycrQTAN5ADgYAwB3kYHhGTo6z6yHIys499HOrVLXaviXk/LrFkra1StXKzj3qOjQg6tXrovXixYt14oknBn6fOHGiJOmiiy7SzJkzdcMNN2jv3r26/PLLtWPHDh1//PGaO3eumjZt6lbIQINSMv9HRUrP/1FZgcCR4rdTB3McFAKSyMFAnYiNjagDlJOjnUjyjzYSYREdQOTIwQAAuIMcDM8oNQS9I+tFqxqew1pMrGaeN0ETn7xZxVJQ4bpY/uvTT583gRHMgFpQr4vWWVlZslBD9P7C5/Ppjjvu0B133FGHUQEo4dT8H44Uv506mOOgEJBEDga8zsnRTiRJs2eHHq586tRqD1fe0GVnZys3N8SxT1GR4r/4Qo1zc3UgJUV7evWq8IJKSkqK0tPTazlSeBU5GAAAd5CD4RkDB/rPxzZuDD2Foc/nf3zgwLqPzSsiPIf9vHeWHhh7V7mRy7a1StXTbo5cRmdyRLl6XbQG4G1Ozf/hSPHbqYM5DgoBAPWAU6OdSPKf7I8YUT7vbdzoXz5rFoXrMGVnZyszs6vy8vYFLT9H0lRJHUotWy9pvKTXQrTTrFlzrVq1ksI1AAAA0BDFxvqLryNG+K9Flj5X8/3SbXnKlIZbzHToHPbz3lla3HOguq5ZrqSdudqRmKKVnXvU+h3WFXV0TvrgA7W//37Fbfn1XL8gNVUbrr9eO046qdz6dHZGfUTRGkCtcWr+D0eK304dzNXGQSE95AAgcuxLgzg12omKivy900N11DLz574JE/zzbzfgzztcubm5ysvbp2MvuU0JaRmSpAGrFuvWVx8tt+7Bkl6VdMfwcfok8+jA8l0567Ro+u3Kzc3lAgQAAADQUA0b5i++hrqbeMqUhtux2OFzWIuJrbqjt4Mq6+g8K8T6jbZsUcb112uEynd4prMz6iOK1oAXRcmFd6fm/3Cq+O3YwZyTB4UMtwoAYXOit/HKlStrPU4viKTDV+nPKH7xYh1eOkeVZSatX69vp03TnqN/LazSo7tyCWkZSk7vIl9xka567EZJFQ/jPu6Df2vVoPOZLw0AAABAsGHD/MXXKLiOHIlIzmHr7BpBmNf7Q3V0jiku1pOPXCff7u0Vnjc+kZCsfePuV3GM/+o5nZ1RX1G0BrwmyoqYTsz/4VTxW5JzB3NOtOPkcKtR0tEBACriZG9jSTqQX1ALUdZcJCfKoZ5bkw5feTt/luTTyJEjA8vOl/RiGDHcNnasXir1Oz26w+PoMO4AAAAAGp7YWCkry+0oXOHkOWytXiOowfX+ko7OktRt9VK12b29wuZjJKXu2qbj8vM4b0S9R9Ea8JIonTPSifk/nCh+B1TjYK6iu/oCEhL8P5K0fHmFq5W746yGQ9WEiof5TAA0BE71Ns75aoFWzPmnCgsL6zL8CoU6ya6p0ifZNenwdWDfbkmmnhfeqDadMiVJB/+4Snru3ipf++CRN2pwR/9zquzRTUerAMeGcQcAAACABsaJc9hav0bgwPV+zhvRkFC0BrwiyueMdGL+j+oWvyMd3iUnJ0cjRpyr/fvzImpHCnHH2fz5wb3ryvplqBrNnx8osIe6y5D5TAA0NJH2Nt6Vs64OogxfqJPs6qroJLumHb7iU9MDn/HG9ofp5//OqPKO7Y0DTldyOJ3RomxEmYAaFuIjGcYdAAAAABDZOWytXiNw6Ho/541oSChaA15RgyJmQxRO8dvJu9Ykqc+om5Wc3rnGzw95x1lOTnhPLrVe2bsMmc8EQEMXTb2NS59kV1dlJ9mRjnbi6BQdUTqiTCSF+JoM4w4AAAAACM3Rc9hIOXS9n/NGNCQUrQGvqEERE6E5cdea9Ouda81aH1zjQkKF0tJqvF7JXYbMZwKgoaO3cXgiHe3EkSk6onVEmQgL8Z66oAIAAAAAUcDRaSYj4dD1fs4b0ZBQtAa8IoIiJkKL5K41qZaHkB040H8X1saNoS/g+3z+xwcOrLCJWrnDkHlGAdQj9DauO5Hese34iDIu5Kvs7Gzl5pbKqUVF6n7FFWpsVm7EE5nJJB248kqt6NAhEFuoqUs8c0EFAAAAgCeVOxepgUinUaxvIj6HdYKD1/s5b0RDQdEa8AoHipioR2Jj/cOGjhjh/25Lf+e+Xy59T5lSa/OZhDpQTfrgA7W//37Fbfn1wKcgNVUbrr9eO046qdz6KSkpDDEOwFX0Nq5b1b1ju3SuafXpp+oUxnN++PRTbU9IkFRJnnFhXuzs7GxlZnZVXt6+wLITJH1UyXN8kuI2b9bEY47RvDKPHcgvCPrdExdUAAAAAHhOqHORSJQ9F4lmkY46FjGHr/dz3oiGgKI14JJQPeSSxo9Xp+uvl6SgO3ZKUtoP11yjHcuXB5Y3tB5yUWfYMP+woaEuvE+ZUivzYFY03/c5kmaFaKPRli3KuP56jZD0WpnHmjVrrlWrVlK4BuAqeht7T6hcU1WBt8TFt9yiebfcIqmCPOP0vNhh3rGdm5urvLx9OvaS25SQliFJyvp6ofT6k1W+xClnj1XcEf0k/Tr1SGFhYbn1XL+gAgAAAMBzQp2L1ERl5yKoJQ7ctFQW542IdhStARdU1kPuHElTJXUotWy9pAlmeu2XgnZZDamHXNQZNsw/f2cNhjityR2Goeb7jiku1pOPXCff7u3lhjeN+aWtJxKStW/c/SqO8b/Krpx1WjT9duXm5lK0BuA6eht7S0W5Zusj16n17u0VdrTKTUhW03H3a3BMTOg84/S82DW4YzshLSMw9Uhh3t6qX0NS4SFHBp5Tq1OPAAAAAIhapc9FaoJzEZdEeNMS0NBQtAZcUFkPuT2SLisuVvf13yp5zw5ti0/Sig6HqzgmRoPLtEMPuSgRGxve/J0h1PQOw9LzfXdbvVRtdm+v8DViJKXu2qbj8vPoyQfAs+ht7D2lc40kPXPh9ZV2tHr2guuUlNG14gadnBfbgTu2mVMdAAAAAFClCG5aAhoaitaAiyrrIbcho6tKLssmVfB8eshBivwOw6SduVWvVI31AAAIJeKh3HNywnuhMuuVm5KlqEjdr7hCjc3KjTAiM5mkA1deqRUdOgQuIoSakoU51QEAAAAAYYngpiWgIaFoDQB1JNI5yCt7fiR3GO5ITHF0PQAAKlLdjlalc1/87t06PIzX+Hb3bu1ZulSSlJOToxEjztX+/XmBx6uaX9snKW7zZk085hjNK/NY2SlZmFMdAAAAAADAGRStAaCW5e38WZJPI0eOdKQ9p+cwZ3hTAEBdCqejVajcGSNpnaSDpQrz1QZJXceOVXGZx/qMulnJ6Z0lSVlfL5Ref7LKOE85e6zijugnqfIpWZhTHQAAAAAAIHIUrQGglh3Yt1uSqeeFN6pNp8wat1Nbc5jXyvCmRUXM0wIAqLGKcuf0VYt166uPVpivZgwfp5Mzjw4sL8mdzVofHJiSpTBvb1gxFB5yZOA5VU3J4qk51cnBAAAAAICKcM4ID6NoDQB1JD41vcI5zMNRm3OYOzq86ezZ0vjx0oYNvy5r316aOlUaNsy5oAEAUa9s7lyZ3kUPpBxcYb5a2TtLyaWeHyp3RvUII+RgAAAAAEBFOGeEx1G0BpxELyXUY44Mbzp7tjRihGQWvHzjRv/yWbM4AAIARCTSfFUrI4x4ATkYAAAAAFARzhlRD4S6uQBATcyeLWVkSCeeKF14of/fjAz/cqCeKBne9NNjTtE3XXpXf0jw8ePLH/hIvy6bMMG/HgAAEYgoX+nXEUa2t0oNWr6tVaoeGHtX9UYY8QJyMAAAAACgIpwzop7gTmvACfRSAvyjDJQeWqYsM2n9ev96WVl1FhYAAKE4MsKIV5CDAUQDRi4DAACoHZwzop6gaA1UU3Z2tnJzc39dUFSk7ldcocZm8pVd2Uwm6cCVV2pFhw6BE+6VK1fWVbhA3cnJcXY9AABqWckd2/UeORhAfcf8igAAIApEet2/1uoGnDOinqBoDVRDdna2MjO7Ki9vX2DZCZI+quQ5Pklxmzdr4jHHaF6Zxw7kFzgfJOCWtDRn1wMAAOEhBwOozxi5DADQEDHCSFTJ2/mzJJ9GjhzpSHuO1w04Z0Q9QdEaqIbc3Fzl5e3TsZfcpoS0DElS1tcLpdefrPK5p5w9VnFH9JMk5Xy1QCvm/FOFhYW1GS5QtwYO9N8NsXFj6PlRfD7/4wMH1n1sALyNk3UgMuRgAPVVVfMr+nz++RXPOotjAwBA9GCEkahzYN9uSaaeF96oNp0ya9xOrdUNOGdEPUHRGqiBhLQMJad3kSQV5u0N6zmFhxwZeM6unHW1FRrgnthY/8H1iBH+A53SB0C+XwbPnzKFi00AgnGyDkSOHAygvmJ+RQBAQ8MII1EtPjU9UAOoiVqrG3DOiHoixu0AgPpuZece+rlVqooreLxYUm6rVK3s3KMuwwLqxMqVK7V06dJffzIy9P1996mgTZug9QpSU/X9ffdpaUZG8PpLlyo7O9ul6AG4ruRkvezF6pKT9dmz3YkLqI+GDfNf4Dr44ODl7dtz4QuAdzG/IgCgIalqhBHJP8JIUVGdhoUGgnNG1APcaQ1EyGJiNfO8CZr45M0qVnBPkGL557R++rwJshh6KSF6VDVPS4ykgZLSJOVImr95s4qvvz7kus2aNdeqVSuVnp5eS9EC8CSGAwUisnLlyvILMzKk2bMV/8UXapybqwMpKdrTq5f/b2jp0nKrp6SkkH8BuIv5FQEADQkjjMBtw4b5r7MwRRs8iqI14IDPe2fpgbF3afQrU9R6+5bA8m2tUvX0eRP0ee8s94IDakG487T8LClO0skVPL4rZ50WTb9dubm5XDQHolx2drZyc3MDv8cvXqzDwzhZ/3baNO05+ujQBTqgAaqq41h10HEMgBuCcnqLFuqemqrGW7bIF2Jdk3SgbVutaNEiqPMNnW4AAPUSI4zAC2JjnekUUVRE8RuOo2gNOOTz3lla3HOguq5ZrqSdudqRmKKVnXtwhzWiWqTztABoGLKzs5WZ2VV5efsCy86X9GIYz71t7Fi9VOr3A/kFTocH1CvhdhyrCh3HANS1ijrdnCNplvwF6rIjl0nS+Zs367Vjjgl6Dp1uAAD1EiOMIFrMnu0fPa/0zQjt2/vnzWaYcUSAojXgIIuJ1TddersdBhA96LEHRIXc3Fzl5e3TsZfcpoS0DEnSwT+ukp67t8rnHjzyRg3umKmcrxZoxZx/qrCwsJajBeoHOo4BqG8q6nSzR9KdqxbryndfUJvd2wPLcxOS9fjgC7Qn82gNLtUOnW4AAPXWwIH+wt7GjaGnyvL5/I8PHFj3sQHhmj1bGjGi/Da8caN/OfNjIwIUrQGJwhjgRfTYA6JOQlpGoMi2sf1h+vm/M9Rq+5agu6pKFMs/zcbGAacrOSZWu3LW1WWoADg+BlBLQnW6WZneRdcMOj/kyGXJLsUJAIDjYmP917VGjPAXqEsX/Xy/TJQxZQrH3fCuoiL/9dpQnS7M/NvxhAn+ebPZjlEDFK0BCmOA99BjD4h6FhOrmedN0MQnb1axyg8H6pP09HkTmGYDcAPHxwBcUN2Ry4Lmxi6tqEjxX3yhxrm5OpCSoj29elV40bTW58amAxAANGjZ2dnKzc0NXpiRoaT77lP7++9X3JYtgcUFqanacN112pGRIS1dGlheYb4D3DB/fvB5Yllm0vr1/vWcmDcbDQ5FazRsFMYA76HHHlB/RHgh9vPeWXpg7F0a/coUtd7+68n6tlapevq8Cfq8d1YtBA2gUhwfA/C4iubGlvzzY0+V1KHUsvWSxkt6LURbtTo3Nh2AAKBBy87OVmZmV+Xl7Qv5eIykgZLSJOVImr95s4qvv77C9g7kF9RGmED15OQ4ux5QBkVrNFwUxgBvosceUD84dCH2895ZWtxzYMjhQAHUMY6PAdQDFc2NPWDVYt366qPl1j9Y0quS7hg+Tp9kHh1YXqtzY9MBCAAavNzcXOXl7dOxl9ymhLSMCtf7WVKcpJMreDznqwVaMeefKiwsrIUogWpKS3N2PaAMitao/2p6lxeFMcCbnO6xx5B8gPMcvhBb3eFAAdQSjo8B1COl58b2FRfpqsdu9P+/zHox8k89Mu6Df2vVoPNrv2NcbXQAcuqcxmvtAICHhRzaWwp7GoqSYb0T0jIC+aomduWsq/FzAccNHOi/YWHjxtDHOj6f//GBA8Nvk+MKlELRGvVbJHd5MZQF4E1O9thzckg+LvAAftyJCUSv2jg+Ju8BtYO/rSBd1ywPmmqkrBhJKdu3qOua5eF3lPNKB3mnzmm81o5T+FsAUAsqGtq7utNQSAzrjSgTG+vP+SNG+K//lL425Pul6+CUKeHnYi9eu4WrKFqj/or0Li+GsgA8o6T3qSSpRQt1T01V4y1byt0lIUkm6UDbtlrRooW0dKkkKSUlpfyQfk7eCRqtF3jQcEVyIM+dmEBUKZ2D43fv1uFhPOfb3bu155ccLFWQhyXyHlBb+NsqJ2lniDvhIljPMx3knTqn8Vo7kjMXlrnQDaCWhBrau7rTUDCsN6JByBEHMjKUdN99an///Yrb8munwYLUVG247jrtyMgIXLMt4dS124pGQEj64IPQ8Vx/vXacdFLVsZSIxpuWvBRLGChah2P+fGnoUDZOF9sptzMqKlL3K65QY7PyRS0zf1Hryiu1okOHQJv5+flq0qTJr+vVoDAWVFgDELG8nT9L8mnkyJFBy8+RNEv+v8OYUsuLf/n3/M2b9doxxwSWN2vWXKtWrfz1gMPJO0G9eIGnoYkkDzsh2nJ5NS/ulc3BrT79VJ3CeJkfPv1U2xMSJJE/AS8KlYNjJK2T/8JbTIjnFEvaIKnr2LGBnCyFyMOS9wobTrYDRCLS7ZBjypB2JKY4t55XOsg7dU7jtXYkZ4rNXuykTJ4ByvPa30U14ykZ2rsm01AwrDfqu4pGHCgRI2mgpDRJOZLmb96s4uuvD7muE9duKxsBYVaI12y0ZYsyrr9eIxQ8EkLI81fJmzctOXHuUM86uzaIovWjjz6q+++/X5s2bVKPHj308MMP65hSxY4qnX66+xtnpLz2B1eNdkLtjE6Q9FElzfskxW3erInHHKN5gYW+cjvB6hbGSjCsC+CMA/t2SzL1vPBGtemUGVi+R9KdqxbryndfUJvd2wPLcxOS9fjgC7Qn82gN/mXZrpx1WjT9ds2fP19du3aVJMUvXqzDw7gT9Ntp07Tn6F97wZbraVfDCzNOdLQJGU89FHEOlmqeh50Qbbm8mhf3apKDS1x8yy2ad8stQcvIn4B3VJSDp/9yB0mxyh8f+yTNGD5OJ5e6g6QkD+fm5tZe5zEvncfUI47kYDgr0u2QKToqtLJzD/3cKlWttm+psNPNtlapWtm5R+UNOXH8X4MO8lL5jvaOndM4NUqOU+04cVeVk+dXTnZSbmB5BpUjD8t7fxcRxFMr01AAHhdqxIFQfpYUJ+nkCh4Pec5Yg+OKUPHEFBfryUeuk2/39go7lDyRkKx94+5XcUxM6Fgkb960FOk+tJ52do36ovXLL7+siRMn6oknntCxxx6rKVOmaMiQIVq9erVSU1PDb6g+31HntT+4arYTameU9fVC6fUnq3ypU84eq7gj+gWGY4mkMCYxrAtQW+JT05Wc3iVo2cr0Lrpm0Pnquma5knbmakdiilZ27iGLiVVyqfVC3Sl2vqQXw3jd28aO1Uulfm/SpKlefXWW0n6566EmF4pycnI0YsS52r8/L7BajTraqJKef/WEYzlYqt/50yux1OAibEUnBFsfuU6td2+v8KJwbkKymo67X4Nj/GuQPwHvKpuDV6Z30QMpB2v0K1OCLsxta5Wqp8+boJW9s4LycOB5pYcZr0H+rPVCglf253XE0RwMZzixHTJFR4UsJlYzz5ugiU/eXGGnm6fPmxC4+61CNfiMQ3Xyq1EH+TId7Wt6TlPuHMKp4cqdaMehu6pqen5V9nzPseJ3A8wzqBx5WN77u4gwHsenoQDqSCQj35U8t2TEASdjiWQUv9LxdFu9NKiuU1aMpNRd23Rcfl5Qh5Kgz8Wp4wEvjfxZjzu7Rn3R+oEHHtBll12miy++WJL0xBNP6L///a+mT5+um266KfyG3BoyKVJeGwYqgnZK74wK8/ZW/BqlFB5ypJLTuwSGY4mkMCYxrAtQ1ywmtsoeqqHuFDv4x1XSc/dW2f7BI2/U4I7+52xds1zLXpmq008/PfB4TS8USVKfUTcrOb2zpOp3tJEq6IVYzziWg6X6mz9djKXsHSk1KSJVdILyzIXXV3pR+NkLrlNSRtfAcvInUL983jtLi3sODHl8XJZTncdqbaoPL+3P65CjORiRcyiXM0VH5T7vnaUHxt5VYaebz3tnhXyeExdQy3byq2kH+UjPaUKeQzg1XLkT7Th0V1VNzq9Cne850rm4geYZVK7B52Gv/V04EI+j01AAdaCiKRlrItJR80LFUlUOLlHVKH7V7VBSk1jCvtnIqQ6mTuxD63Fn16guWhcUFGjJkiWaPHlyYFlMTIwGDRqkBQsWlFs/Pz9f+fn5gd937twpSdpVsqDki5w71z92fEXC3SCqascJTsXiYjt79uyRJG37cbUK8/13LuZK+j4+Scl7dlR4l9fPLVvpY0nFa5ZpV86PkqSdG9eocaNQA3RJm2NipFa/9HZc+1XIdcJpJxxeasdLsTjVjpdi8Vo7XorFqXZK2ig6kB/YRyxL7RDWPmJZagcV//Kc/N07JJkOyTpXiW3bS5KablmvXR+GmhUlWNMTR6hXagdJ0rZ1K/Xjorkq2L8vEE9OXLNfc0klcuKaBZ5TWODPR3v27NGuXeWfXbLMQh3AeEB1c7DkYB52gsu5fNOmTdq0aZMkKf6LL3RYGLF898gj2tOrlyRp8+bNGjXqD8rP3x9Ybbik6WG89s1jx+rVMsu2frcisG1K0n9bJin3zMt02Qf/VsqeHb+u17KV/nXiCC1qmSStWRZYzv6rdtvxUixOteOlWLzWTl3GEs7x8c9rVyjS/Llv22atfu8FvfPOO+rSxd9Bpib7Psmfa4qLf51xu6btHHTQQTrooIMqfJqX87CjOTjEMQhqoAbHFevXr9fRR/cNGr1ngKS3wni5Ubfcok/KXNwrm8urq77sv/7bMklvX3yrum74Tq327tT2Fola2f4wFcfEBB2bSFLuL/u10hctI/mM8/fuCfqM53U6QvMvuzN0LGW+i6IDBb/8G9k5Tck5xJIlSwLXUmSmbikpapybW/Fw5W3a6Bsz6eOPA8vL7k9r0k7ZNpI++EAZIZ5b1roPPtCOX0bsWb16deC9RXJ+Fep87+gfV2nXwrerbOfofqdq1y8dA0py1rp165SUlOT6eUND5OUcLDl4Pbo+52Cv/V04cD26uteiJW+dQzjVjpdicaodL8XiZDuhztOqq+Qa588/rpRPRY7GsseK9d0b05SSt6fCv6mtzeK154wx6uWLqTCeNbt3hHVMsGb3Dm1ZsyxkLI4cD0jS2rVhRPLLer0ruUnLiX2oU7GUUSc52KLYxo0bTZJ9+umnQcuvv/56O+aYY8qtf9ttt5n8x9n88MMPP/zwUy9+1q5dW1dptVqqm4PNyMP88MMPP/zUvx8v5mFyMD/88MMPPw3hx4s52Izr0fzwww8//ET/T23m4Ki+07q6Jk+erIkTJwZ+37Fjhzp27Kjs7GwlJibWuN1du3apQ4cOWr9+vRJ+GT7KrXa8FIvX2vFSLE6146VYvNaOl2Jxqh0vxeK1drwUi1Pt7Ny5U+np6UpODjWzaP1UG3nYS9+ZU+14KRavteOlWJxqx0uxeK0dL8XiVDteisVr7XgpFin68rCXz4W99t3znmq3HS/F4lQ7XorFa+14KRan2vFSLE6146VYJHJwuLz0vXkpFqfa8VIsTrXjpVi81o6XYnGqHS/F4rV2vBSLU+3Upxwc1UXrlJQUxcbGavPmzUHLN2/eHHKotyZNmqhJkybllicmJkb0RZZISEjwTDteisVr7XgpFqfa8VIsXmvHS7E41Y6XYvFaO16Kxal2YmJCDaLjvurmYKl287CXvjOn2vFSLF5rx0uxONWOl2LxWjteisWpdrwUi9fa8VIskjfzsNdysBSd3z3vqXbb8VIsTrXjpVi81o6XYnGqHS/F4lQ7XopF8mYOlrgeXV9icaodL8XiVDteisVr7XgpFqfa8VIsXmvHS7E41U59yMHezO4OiYuLU58+ffT+++8HlhUXF+v9999X//79XYwMAIDoRg4GAMAd5GAAANxDHgYAoOai+k5rSZo4caIuuugiHX300TrmmGM0ZcoU7d27VxdffLHboQEAENXIwQAAuIMcDACAe8jDAADUTNQXrX/3u99p69atuvXWW7Vp0yb17NlTc+fOVdu2bat8bpMmTXTbbbeFHKKlOrzUjpdi8Vo7XorFqXa8FIvX2vFSLE6146VYvNaOl2Jxqh2nYqlNkeRgyVufk5fa8VIsXmvHS7E41Y6XYvFaO16Kxal2vBSL19rxUixOtlNbvJCDnWrHS7E41Y6XYvFaO16Kxal2vBSL19rxUixOteOlWJxqx0uxONlObeJ6tHdjcaodL8XiVDteisVr7XgpFqfa8VIsXmvHS7E41Y6XYqmKz8ys1loHAAAAAAAAAAAAAKASUT2nNQAAAAAAAAAAAADA2yhaAwAAAAAAAAAAAABcQ9EaAAAAAAAAAAAAAOAaitYAAAAAAAAAAAAAANdQtAYAAAAAAAAAAAAAuIaiNaqtuLjY7RCCmJnbIQAAUCfIwQAAuIMcDACAe7yUh8nBAFB7KFpXwu0E5Pbrl7V06VJJUkxMTEQHCvn5+Y7E8/PPP0uSfD5fRO289957+umnn5wISZJz35sT7ZhZoB0vHNyVjieSNkL936k23VRQUBBxG4sWLdLu3bsdiOZXNf18nNj+vLQNOxVLNG/DTnPzfXntMyUHh8drf1Ne3YdF2k6o/zvRnpucyMGS83mYHPwrp95TqP9HGlc0Igf/ihwcHq/9TXlpH+bVHOxkO5Hwag6Wavb5kIMrbiPU/yONKxq5/b7cfv2yvJSHvZqDJW+dm3l5HxZJG6H+71Sbbom2HFzyvGjKw169Hl2b2y9F61J2796tLVu2aP/+/ZL8CcjJHVp1+Xw+5eXlRRzDjh07tGHDBq1Zs6bGbbz22ms6+uijNXr0aEk1P1B46qmndOONN2rv3r01jkWS5syZo6uuukoPP/xwRO288sorGjJkiK688soat7FmzRrNnz9fzz//vPLy8mq83Rw4cEB79uwJ/O7z+SLeKZY+iIqJ8f+513R7KiwsjCiWkngiPbAzM+3bty/QXsmy6lqyZImWL18e1E5NPPfcc5o6dWqNn19i7ty5uuuuu7RkyZIaf0cvv/yyTjjhBP3rX/+KKJbc3FytXbtWa9euVUFBQY236dLfd023Py9tw068n5LneGkb9hKn8zA5OBg5uGK1kYNL2ikRyX7DKzlYcmYfFo05WHImD5ODK48n0vdEDq4YOTg0cnDFGkoOlpw9j4iEl/Zh0ZiDJWfyMDk4NC9tv17jpRxc8vqR5mEncrDkrTzspRwsOZOHG0IOLomH69HlRWMOlryVh72Sg0ue45VrOVUymJnZK6+8YieccIKlpqZaz5497aKLLrKffvqpRm19+OGH9tZbb9mBAwdqHM8777xjo0aNsk6dOtmJJ55of/3rX2vUzuuvv24nn3yyJScnW7t27ey8886rdhv79++30aNHm8/ns/T0dLvooosCjxUVFYXdzsyZM83n89m4ceNs165d1Y6jxLPPPmvNmjWz3/3ud/bGG2/UuJ1p06aZz+ezli1bWtu2bW3BggXVbuOll16ybt26WYsWLczn89lRRx1Vo/f2+uuv25lnnmmdOnWyk08+2W644Qbbt29ftdsp7Y033rALLrjA+vbta7///e/thRdesN27d5tZ9b630p9LJNv0//73P7vtttvslFNOsQkTJtiSJUussLCwWm288847NnLkSOvYsaOdfPLJNm3aNNu/f3+1Y/n3v/9tPp/PRo8ebV999VW1n19ixowZ5vP5bPLkyRF9X88884wlJSVZVlaWffjhhxHF4vP57NRTT61xLLNnz7bjjjvO4uPjLS0tza644grbs2dPtdt577337JprrrFTTz3Vrr32Wvvwww8Dn1G425+XtmEntl8z723DXuJUHiYHh0YOrlht5GAzZ/ZhXsrBZs7sw6IxB5eOJ5I8TA6uWDQeR3oJOTg0cnDFGkIONuM8oiLRmIPNnMnD5ODQvLT9eo2XcrCZM3nYiRxs5q087KUcbOZMHm4IOdgsOvdhTuThaMzBZt7Jw17afs28dS0nHBStzWzWrFnWuHFjO/PMM+1Pf/qTnXPOOda6dWvr0KGDvfDCC7Zz586w23r11VfN5/NZr1697P/+7/9qtBE9++yzlpycbH379rURI0ZYZmamxcfH2/jx46vdTnx8vJ122ml2xx132IUXXmhxcXF21VVXVTumxx57zJo3b25nnHGGJSUl2ZgxYwKPhfPHWrIDmzRpkmVnZ1f79UssWLDA2rRpYxMmTLDVq1eHXKe4uDjseG699VZ78803rUmTJnbddddVK5ZZs2ZZ06ZNbdSoUfbMM8/YP/7xD0tISLBrrrmmWu08//zzFhcXZ/3797dLLrnEevXqFTjgeO+992zv3r3Vas/M7IUXXrC4uDgbMGCAnX322XbIIYdYQkKCnXTSSbZ582YzC+9zKtkZ/fa3vw0sq8mO9tlnn7U2bdrY4Ycfbocffrg1b97cMjMz7b///W/YbZQk0wEDBtjIkSMtMzPTWrdube+++26147nmmmvM5/NZs2bNbNSoUfb1119Xu42SbWjixIm2YcOGwPLqHICZ+ZNpfHy8XXvttfbll1+GXKeq76oklvHjx9tNN91kPp/Ppk+fXq04zMxefPFFi4+Pt9NPP93uuOMOGzx4sMXHx9vcuXOr1c4zzzxjzZo1s65du1rv3r0tKSnJmjVrZn/4wx9s+/btZmZV7hu9tA1Xtf2GE4eZ97ZhL3EqD5ODQyMHV6w2crCZM/swL+VgM+f2YdGWg0vHE0keJgdXzIk8TA6uGDm4cuTg8hpCDjarm/OIcHlpHxaNOdjMmTxMDg7NS9uv13gpB5s5k4edzMFm3sjDXsrBZs7k4YaQg824Hl2RaMzBZt7Jw17KwWbeupYTrgZdtC4uLrZdu3bZoEGD7JxzzrGNGzeamb8n1//+9z874YQTrGXLlvbggw8GNuzKfPnll3bUUUdZx44drW3btnbEEUfYe++9V62DhXnz5lnbtm3tmmuusTVr1piZ2aZNm2zw4MGWlpZmixcvDqudt99+21JSUuyqq66ytWvXmpnZ3r17rVu3bjZ06NCw4ymxePFiGzBggH322Wc2cuRIi4+PtzFjxgT+OCr7I5k5c6bFxMSU25lu2bLFVq9ebdu2bQs7jieffNI6d+5sS5YsCWp/ypQpdv/999umTZuq/IMt6dU2ceJE27hxo+3fv9/OO+88a9mypX366adhxbFx40br1auXXXzxxbZu3Toz8+98jj/+eLviiiuC1q0snu+//94OOeQQu+SSS+z77783M7Nt27bZDTfcYD6fz7p27WrPPvtsoDdPOH766Sfr0qWLjR49OvB579u3z2666SZr27atpaen27fffmtmlSe0zz77zDIyMqx169bm8/ns9NNPDzxWnR3t3LlzLTk52a6++mpbsWKF5eXl2SeffGIZGRk2aNCgsNqYM2dO4ACsJPatW7eaz+ezG2+8MWjdyj7vksfmzp1rAwYMsOuvv958Pp9deOGF1drRPvPMM4GD3h9++CGwvLCwMKx9RelYxo8fbwMHDrRVq1YFHvvkk0/snXfesWXLllXZG6ykx+jEiRNt27Zt9s0331hSUpKdeuqptnPnzrAT2DfffGMZGRn2xz/+Meg9xcfH29SpU0PGHsqSJUssJSXFxo4da99++60VFRXZ2rVrrU+fPubz+ezoo4+23NzcSt+Tl7ZhJ7ZfM+9tw17hZB4mB4dGDq7bHGzmzD7MSznYzJl9WDTmYDNn8jA5uGLReBzpFeTg8JCDgzWEHGzGeURFojEHmzmTh8nBoXlp+/USr+VgM2fysNM52MwbedgrOdjMmTzcEHKwWXTuwyLNw9Gag828k4e9tP2aeetaTnU06KK1mf+A4NBDD7VLL73UzIK/nK1bt9oZZ5xhzZs3t3/+85+Wl5dXYTsFBQV2yy23mM/nsxkzZtgXX3xh6enp1r1797APFvbs2WNjx461o446ypYtWxb02LJlyywuLs4effTRKtvZtm2bnXvuuXbiiSfaypUrzcz/h7R//3777W9/a4MGDbJPPvnE5s+fbwUFBVW2Z2aWl5dnaWlp9uSTT9rOnTvtd7/7nSUkJAT1cCs5ICnt+++/t5iYGEtLS7P//e9/geVjx461Qw45xHw+n3Xs2NF+//vfhxXLmDFjrFu3boHfTz/9dIuLi7OmTZuaz+ez9u3b21NPPVXhgcfbb79tMTExNn78+KDhdl555RXz+Xx22223mVnVvW5WrVplzZs3t2nTpgWt/4c//MHGjh1rN910k91xxx22cePGSneon3/+ucXFxdlrr71mZr/u8Pbs2WP9+vUzn89nGRkZNmfOHDMLrwfNDz/8YC1atAj0bCrZIRYUFNgzzzxjGRkZ1r59+8DON9ROdvfu3Xb11Vebz+ezxx9/3G699Vbz+Xx2xhlnBNYJZ0ebm5trZ511lp1yyimBbbHElClTzOfz2UcffVRpG+vWrbNBgwbZsGHDAgfOZmY//vijde3a1SZNmmQbNmywrVu3BrahqnqXrV271lq0aGFz5861J5980nw+n/3+978Pa0f7ySefBJJd6X3CzTffbL/5zW8sLS3Nhg8fbs8//3xYQ7Qce+yxNnLkyMDvw4cPD2zPTZs2tYEDB1b4XZXEft111wVOcszM7rjjDouNjQ18tuFsN2+++aa1atXK5s2bZ2Zm+fn5tmvXLjvxxBNtxowZ9uyzz9rHH39c5XAoTz/9tCUnJ9vChQuDlv/000/WuXNn8/l81q1bt8CBQqjYvLINO7H9mnlvG/YaJ/IwOZgcbOaNHGwW+T7MSznYzPl9WLTkYDPn8jA5OLRoPI70GnJw1cjBwaI9B5txHlGRaM3BZs7kYXJweV7afr3IKznYzJk8XBs52MwbedgrOdjMmTwc7TnYLDr3YU7m4WjLwWbeyMNe2n7NvHUtp7oafNF69+7d1r17dxs+fLiZ+b+Y0l/Ojh07bMiQIZaamhroVVbRl/f222/blVdeGfh9/vz5lp6eHnYvtz179lhWVpbdcsstgWXFxcVWXFxsmzZtsrS0NLv66qurfE8FBQX26KOP2nPPPRe0/PHHHw8kniFDhlhiYqJ1797dFi1aVGl7JXFffPHFdskll5iZWU5OTuBAYfTo0XbmmWfaWWedVS5B5+fn29SpU61FixY2evRo27hxo5166qkWHx9v559/vt133312yimnWKNGjezoo4+ucqd6ww03WGZmphUVFdnVV19tycnJ9sILL9hXX31lc+bMsZNOOslatGhhjzzySMiDjjVr1thjjz1mOTk55R47++yzLSUlJXAAUdmOdfny5ebz+eyhhx4KfEZPPfWU+Xw+y8zMtEMOOcRatGhhHTp0sNdff73Ctj7++GPz+XyBA4GioqJA3Ndcc43169fPjjjiCOvSpUvgs61qh79x40aLi4uze++9N7Cs5DssKiqyF1980dLS0uyoo44K9MIq2+bOnTttwoQJNmnSJDMz2759u914443V3tF+++235vP57G9/+1u5xxYtWmQxMTH20ksvVdrG2rVr7bzzzgscTJUo2Z7bt29vgwYNso4dO9qYMWOq/JxKPoszzjgjMDfPPffcE9jRlgz1U9HwKIsWLbJTTjnF4uLiAj2+TjvtNGvSpIkNGDDAzjjjDDvooIOsadOmdt1111U5pM5xxx0XOFC46KKLrHXr1vb3v//dPvzwQxs3bpylpKRYhw4d7Mcffyz3vqZNm2ZXXnll0AGCmf8AtG3btjZ48OCwe46W9EAtPTRIyTbdqlUra9SokTVr1sxOPPHEShP8fffdZ82bNw8k1eLiYisqKrJ9+/ZZ3759rXPnzhYXF2fDhg2r8LPxyjbsxPZr5r1t2GucysPkYHKwF3KwWeT7MC/lYDNn92HRlIPNnMvD5ODQovE40mvIweRgM3Iw5xHh7cOiNQebOZOHycHleWn79SKv5GAzZ/Kw0znYzDt52Cs52MyZPBztOdgsOvdhTubhaMvBZt7Iw17afs28dS2nuhp80drM7O9//7v5fL6gL7D0l7Nq1Srr0qWL9evXr8q2Sr7Akud/8skn1TpY+P777wM7hLIHI3369LFhw4YFvU5Fyk6k/sYbb5jP5wtM2J6Xl2cvvviidevWzbp06RLWPCnTpk2zNm3aBIYfKelF16RJE4uNjbWXX3456L2XKCgosIceesgaN25sbdq0sUMPPdTeeust27Nnj5n5/6AffPBBa9q0qZ155pmVxvDOO++Yz+efH+Gaa66xG264IeizyM3NtaFDh1rbtm1t/fr1Iduo6LMrGWJj8uTJVe489u/fb+ecc475fD4bNGiQnXrqqYFhFdatW2eFhYX20UcfWf/+/S09PT0QS9nPJjc319LS0uykk04qN4H94MGDbdSoUYGeK+FcqCkZYujEE0+0nj172vLly8u978LCQnvwwQctISHBbrzxxgrfa8lwNSW2bt0amJ+i9I42Pz+/0phmz54dSGKlt+n169db69at7e677zazynfYZeedeeyxx8zn89lVV11lc+fOtR9//NFGjhxpLVu2tOHDh4c198r48eOta9eugQOz+++/33w+n51//vnWt29fO+KII2zr1q0hn/vFF1/Y6aefbj6fz7p06WIHH3ywvfbaa4HX3b17t5188snWtGlTe/zxx0Pu7Es+i1tvvdXS0tLs1VdftWOPPdYeeeSRoJ5czz77rKWlpVmfPn1CDs1T+sC69Otceuml1rRpU/vkk0/MrOp9xsKFCy0hIcF69eplkyZNsvHjxwe26c8++8x2795tN9xwgyUnJ1tWVlaFwwT95z//MZ/PZ/fdd19Qz7/CwkLLysqyBx980M4++2xr3LhxYA6O0ttFUVGR7dixo0bbcNnPef369dXehktOzErMmjWrRttv2f136SFuzMLfhsu+p9K/R7INe41TeZgcTA52MweXvIYTedhLOdjM+TwcLTnYzJk8TA7+Vdl9b03ycNlcTg6uHDmYHEwOLs+JPOzVHGxW831YNOZgM2fyMDnYArGQg8PnlRxs5kwero0cbOZ+HvZKDjZzJg83hBxsxvXoUHk4WnOwmXt52Es5OFQ7NcnDXsjBFK3N30urV69e1rVrV5s/f35geckXkp+fb/fee681bdo0aEiRcJU+WCjda2TRokUhe1mVVRLHcccdF7Rxm1nggKKq57788suBoVRK7N2715544gnz+Xzl5ggIZfHixdahQwdbunRpYNnQoUMtLi7OWrRoETQ0S1kHDhywxx57zFJTU+3Pf/5z4I+35I9+9+7dduGFF1p8fHzQfApl7dq1K9CDLSYmxm666aZy68yfP98aNWpkf/nLX6p8T6UVFhZanz597Igjjgh8TpX1JNuwYYNdffXVduSRR9pRRx1lRxxxhH333XeBx/fv329z5swxn89n1157bYXtlPRwO/nkk23OnDk2f/58u/LKK61Ro0aB3in9+vWzY489Nqz5MczMXnrpJfP5fHbNNdfYpk2bgt6jmX8HNmjQIOvWrVu5g8pQPTdL2q5oR1tUVGTffPNNUHKuKint2rXLOnToUG7+hNzc3MCOu2wsJXHceuutNmnSpKD5Og4cOGDnnnuutWjRIiixlH5e6f9/8MEHdvjhhwcNzXPXXXeZz+ez5s2b2z333FMu5tLvaenSpXbmmWdaenq6PfHEE4GYS/7dv3+/ZWRk2AknnFDp9/b+++9bkyZNbNCgQZaYmBhInCXtlAz3FBsbGzTPTUUjPpQs/+GHH6xt27Z2zjnnVPjapT8PM7PnnnvOOnXqZCkpKZacnGzHH3+8bdy4MdDm/v377aqrrirXA65sPOeff761aNHCHnzwQfv6669t48aNgYOOLVu22L59+ywpKcnGjh0beM6+ffvKxVKTbXjfvn1BsZQ9eQtnG963b1/Qdx1qW65s+y2Jp2wsRUVFNdqGy342JQcGNdmGvao28zA5mBxcFzm4bLw1zcNeysGh4qnJPiwac3Coz6bs8nDyMDn41/hLb8NO5GFycPjIweRgcvCvnMjDXsrBpZ9X+v/V3YdFYw4u/XmY1TwPk4N/RQ6uPi/n4NJxVDcPO5mDzdzPw17KwWbO5OFozcGlX6siXI+OnhxcNh638rCXcnCoeGqSh93OwRStf7Fs2TJLTU21448/PuiPseQLWb9+vfl8vnLDnISr5GChe/fu9v7779u7775rhx56qGVlZVU5d0bJBjp48OCgida/+eYb69evnw0dOjSsGEr3vCj5/+bNm83n8wUNe1CZ7t2720033WQFBQV25plnWqtWreypp56yCy64oMoeWPv377dZs2YFJdLSscydO9d8Pp+99957lcawYsUKa9u2baAXx44dO8zs153U7t27rXXr1nbdddeF9Z7Mfv2MZ82aZT6fz+68886wnldUVGQ7d+60efPmBb33kp38rl27rHXr1kE7w1DmzZtnrVu3Np/PZ3FxcRYfH2/PPPNMYGfz4IMPWlJSkmVnZwftdD788EN76623Ap9h6Z3Z5MmTLSYmxu68886gHi4lvY3efvtt8/l89r///a9cO6GSWqgdbUlPxE8++cQGDBhgZ555pv33v/8Nq3fgzp07rWPHjkGf29dff21DhgyxP/zhD1X+XZQ+oS95TyXze8yaNavceypr27ZtlpiYaE888YSZ+XfG55xzjjVr1sx8Pp+NHDnSvvnmm3LtlP78Fy5caJMmTSrXk7IknmuvvdaaNGliK1assA8++KDCdv7617+az+cL9AgrUfL95+TkBHqZVfaeStuzZ4+NGjXKmjZtau+8807QY5VtN2vXrrUffvjB7r33XvvTn/5ULpYffvjBfD6fPfzwwxVuN8uXL7fzzjvPfD6fJSUlWZs2bax58+b29NNPB9Y75ZRTbPDgwfbmm2/aqFGjrFOnTnbiiSfaHXfcEXjNG264Iext+J133glqp2SoktKfdVXbcLdu3ezwww8P2UbZA4WKtt/Ro0fbRRddVGEspb+7qrbhyt6TWfjbcH1Rm3mYHEwOrkxNc7CZM3n4oYce8kwOHj16tF1yySVhtVPZPuwvf/lL1OXgGTNmVHlsUVpFeZgcXPE2fPrpp1fYTrh5ePDgwdazZ09ycDWRg6tGDg4WLTnYqXPh7t27229/+9uw5mmt7Rzs1LnwzJkzoy4HmzmTh6+55hpyMDnYMV7NwWbO5GEncrCZ+3nYSzm45HUjzcPRkoO5Hv2rsnl4+vTpQZ9LNORgL50LP/DAA57JwU5dj/7Tn/7kiRxM0bqUhQsXWqtWraxv3742d+7coMfeeustS05OtrfffrvG7S9atMjS09Otc+fOdsghh1hSUpItWbIk7OefffbZ1q9fPysoKLCvv/7aTj/9dEtMTLTPPvusWnGU3kHNmDHD4uPjA/NYVNQDp+Q5l156qf32t7+1M844w5KSkgLj5//88882ZsyYSnuHl26n5HVKv95f/vIXa9GiRbmDiFA+++wzi4uLM5/PZ3fffXfQsA8ff/yxtWvXzh588MFK31Mo69ats+7du9uRRx5pa9euDft5CxcutJiYmKDtpri42N555x1r3bq13XXXXVXGsmHDBps9e7bNmDGjXI/Fyy+/3Dp37hyUOF999VXz+XzWq1cv+7//+7+gHj9m/gOmyy+/3GJjY+3mm28u934ee+wxa9WqlT3yyCMh2wmlJP7NmzfbLbfcYj6fzwYOHGjHHHOMNWnSJOx2zPw7yyOOOCLQI/LLL7+03r17myTr2rVrhW1UNLSJmb9nT8uWLW3q1KmVxlJUVGSFhYV22mmnBQ4oTz/9dEtKSrLZs2fbvffeaz6fzwYPHlzpZ2xmgWGFQsU3atQoO+SQQwK9tMq2U/Lvrl277IYbbjCfz2fNmjWzWbNmBbX56quvWtOmTav1+Zr5e9/FxsYGJbKKtpuy7T333HPWr1+/wPBLJe/72Weftbi4uECirSievXv32rPPPmsTJ060SZMmlRtyKCsry4444ghLTk62vn372ogRIywzM9Pi4+MD8ebl5YW1Df/9738P2c748ePLfSYVbcOHHHKISbLu3btX2YZZ6O33tNNOs9jYWEtMTKw0lnC24XvuuafK93TgwIEqt+HLL7+8yiGTvKQ28zA5mBzsZA42cyYPt2jRwjM5+LTTTrNGjRpV2U5V+7Cq8lV9zMEtW7a0v/zlLxHnYXJw5dtwVfmztFDbcI8eParM5eTgipGDycENLQc7dS582GGHVXkOW1pt5mCnzoUlmSTr2bNn1OTgkvYizcNVfU/kYHJwTXg5B5s5k4drkoNLP88LedhrOdgs8jxc33Mw16NDxzdq1KhAJ4toysFeOhdu3ry5JSUleSIHO3U9umnTplW2UVc5mKJ1GYsXL7bDDjvM4uPj7a9//astWLDA5syZY6eddpqlp6fbhg0bqt1m6Q3ioYceMp/PP5F82T+aqvz+97+3Hj162LJly+z000+3Fi1a2LJly6rVRumN8YsvvrCTTz7ZevbsGTTcQWXeeuutQG+VWbNmBQ3lEc5Oq6JYli5dagMGDLDf/OY3QcMTVOaLL76w9u3bm8/nswsvvNBmzpxpTz75pGVlZVnbtm3Ljdkfrqefftp8Pl+gF044tm/fbkceeaT1798/0DPvvffes1NPPdXatWtX41jM/J9Nv379bOTIkbZ//34rLi62L7/80o466ijr2LGjtW3btsJ5an7++efAMBhnnXWWvf7662bmH7bmtNNOs8MPP9yOOOKIKtspreSxn3/+2UaOHGk+n88SEhLs8MMPr1Y7ZmbHHHOMDR8+3NasWWPHH3+8xcTEWFpaWthtlN6GlixZYscff3xgWJ1wYrnnnnssPT09MBzKK6+8EjjonDx5cqXvKdRwKGV7vXXr1s1OOeUU6969e5XxlE5ciYmJNmXKFPvxxx9tzpw5NmDAAIuLi7P27dtX+7MpmUvk008/rXK7Kb2v+s9//mMxMTF2xx132Pfff29mZp9++qkNHTrUOnToYF27dq329136s2nfvr21aNHCrrnmGluzZo2ZmW3atMkGDx5sBx10kC1evNjM/D3RKtuGDz30UGvTpk3IdtLS0gLtlH5vZbdhSYH9SLhtmAVvv6eeeqo1a9bMWrduXa1YQm3DPXr0sNTU1CrbMat8G3744Yft66+/Dus78RKn8zA5uOpYyMEVC5WDzcyRPDxw4EBr2rSpdejQwfUcfOqpp1rz5s2tc+fO1Wqn7D6sV69e1qJFi7DeU33JwUOHDrWDDz642nmvbB5+9tlnycEWehtu2bJltfOnWfA2fOyxx5rP57MLLriAHBwBcnDFyMGh1dcc7NS5sCSLjY21du3auZ6DnToX/vLLL61du3YVvqf6mIOdOheuyXl5aeRgcnBlvJqDzSLPw5HmYDPv5GEv5WCz2svD9SEHcz06dDwLFy60Qw45xBITEy09PT1qcrCXzoX79etnjRo1sssuu8z1HOzU9egjjzzSGjdubJdffrkncjBF6xC+++47GzVqlMXFxVnjxo0tOTnZDj300HLzElTXe++9Z3379rWEhIRqfXml/9gPPvhgO/74461Fixb2xRdf1DiWZ5991k4++WRLSkqq1gFLYWGhvfjii/baa68FzRcRiVdeecUGDRpkSUlJ1d6o165da5dddllgCIKkpCTr3r17YP6NmtiyZYu1b9/ejjrqKCsoKAi7d9yiRYssMTHRfD6ftWnTxlq3bl2j7ab0682cOdNOO+00a926ta1cudLMfp1XomSIji+++CIw3E9FO+qHH37Y2rZta7GxsYHhMdq2bWuXXXZZtdopbd68edazZ09LTEy0sWPHVrudoqIiO+aYY6x///42bNgwa9y4cY1jeffddwPb8+WXX15lOyX/fvTRR3bwwQdbx44d7eWXXw4c9NbkMy7tww8/tMGDB1urVq3sj3/8Y7XaeeqppwK94Xw+/3wQCQkJNY7lzTffNJ/PZxMnTrTJkydX2U7ppHXllVeaz+ezQw891I4//njr1KmTHXTQQWFtN2Xnzijx9ttv25AhQ6xZs2aWmZlZ7kRn2bJlFhcXZw8//HDQ8lDbcGpqqo0YMcKOOuqoCtt59NFHK/xs5s2bZ0ceeaTFxcVZly5dqtVG6e13xIgR1rx584hiKb0Nn3feeWG3U9E2XN/VRh4mB4dGDg5WVQ42cyYPp6SkBO6ydjsHl+zDLr300ojy8Iknnhi4yzpacnCLFi2sffv2ER0vvfnmmybJ+vXrRw4upfQ2XJ28V/p9lWzDZ599tjVq1MgOP/xwcrADyMGhkYMrVt9ysFPnwj169Ajc3eV2DnbqXDgac7BT58IZGRkWHx9PDjZycG3yUg42cz4P1zQHm3krD3spB5s5k4frWw7menRoH374oZ188slhnZeX5vUc7KVz4ZSUFGvWrFmN856TOdip69FZWVkWFxcX9nuqixxM0boChYWF9tVXX9kLL7xg7777btDk4jWxb98+u+CCC6xJkyY1PtgoGY4hISGhxm0UFBTYFVdcYZ06dbJevXrZihUrqt1GyVAWkSooKLAxY8ZYx44drWvXrjXq7WfmT6yrVq2y119/3ebPn2+bN2+OOLYHHnigRr1CVq5caRMnTrRzzz3X7r333oh6ln/wwQfWrVs3O+KII8p9Nm+//bZdeeWVgd/nz59v6enplfZUWrZsmc2aNctuuukm+9e//mXff/992O2UtWXLFhsyZIj5fD778ssva9zO0KFDzefzWXJysj3yyCPVbmP//v02aNAgy8jIsMzMzBrF8txzz9k777xT7qC3Ju8pLy/PzjjjDMvIyLBOnTpVK57S39WKFSvszTfftNtvv91efPFFe/rpp2v0+ZYYM2aMff3119WOZffu3fb3v//dunTpYscdd5xdeeWVtmbNmhp/31OnTrU+ffpYx44drW/fvnbLLbcEHisuLrbi4mLbtGmTpaWlBYZkqWwbXrFihWVlZYXVTllbtmyxoUOHmqSwYymr9Pb76aef1iiWstvwwoULq91ORdtwfedkHiYHh46FHFyxynKwmTN5eMaMGZ7JwV988UWN2im7D3vssceiLgf/+OOPNf6MS4wZM8aeeOIJcvAvSnKwz+erUd4rUdJGq1at7OijjyYHO4gcHBo5uGL1LQc7dS5c3bxXojZysFPnwtGYg506F542bRo5+Bfk4NrjtRxsFnkediIHm3krD3spB5s5l4frUw7mevSvyubhcI9P6lMO9sq58KOPPmr9+vXzRA526nr04YcfXu1r47Wdgyla16HvvvvOvv322xo/f8WKFda7d++IJzNfvXq1TZ8+vUZDnTvtyy+/tHvuuafcvBluqU5Pttq2f/9+W7RokW3cuDHk42V36J988km1Ekck7eTn59t//vOfoG2xJu387W9/syZNmgQOymrSxqOPPmrXX3990AFZddup6HuvSTx33323XXLJJUFz4TjxXdWkjZIeZaV7ltWknU2bNllRUVHQfBQ1aeezzz6zadOm2bp16+z7778P/N2XHdqmT58+NmzYsKDXqUhN29m3b19gG65pG3feeWfQ9lvTdh566KGgbTjcdqKlN3ldIQeXRw6uWFU52My9fXtt5eCatlM2D0djDq5pO2XzMDnYr3QOjqSd0nmYHOxt5ODyyMEVq6scXNN2yuZhL+XgmrQT6ruPxhxc03bK5mFysB85uP6INAebOZOHvZSDzbyVh8nB7p8LeykH1zSesnk4GnNwTdtxOg97KQdH0k7pPOy1HEzRup5xasPwUkIMNRcDaqb0jvrdd98NLF+0aFG1emfWdjsLFy4MHABt3bo14jYKCgpqHMtnn33m+GcTzt+pE59xbX9PCxYsCDpQrWq/Ec53Vdnfe0n7xx13nJ1xxhlBj61bty7s9xNuO5W9n8raKH1AWtX2G247pQ/AqtuOF06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       "text/plain": [
        "<Figure size 2000x1000 with 8 Axes>"
       ]
@@ -1097,7 +1132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1134,7 +1169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1195,14 +1230,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_540731/491085427.py:44: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "/tmp/ipykernel_753888/491085427.py:44: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
       "  activity_sequence_df = sorted_df.groupby(pid_col).apply(\n"
      ]
     },
@@ -1309,7 +1344,7 @@
        "[1823 rows x 2 columns]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1327,14 +1362,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_540731/491085427.py:44: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "/tmp/ipykernel_753888/491085427.py:44: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
       "  activity_sequence_df = sorted_df.groupby(pid_col).apply(\n"
      ]
     },
@@ -1367,27 +1402,27 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>home - education</td>\n",
-       "      <td>10</td>\n",
+       "      <td>12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>home - education - escort - home</td>\n",
-       "      <td>9</td>\n",
+       "      <td>home - education - education - home</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>home - education - escort - shop - home</td>\n",
-       "      <td>1</td>\n",
+       "      <td>home - education - escort - home</td>\n",
+       "      <td>7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>home - education - home</td>\n",
-       "      <td>289</td>\n",
+       "      <td>home - education - escort - shop - home</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>home - education - home - education - home</td>\n",
-       "      <td>2</td>\n",
+       "      <td>home - education - home</td>\n",
+       "      <td>281</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1395,53 +1430,53 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>452</th>\n",
-       "      <td>home - work - work - work - home - other - home</td>\n",
-       "      <td>1</td>\n",
+       "      <th>486</th>\n",
+       "      <td>home - work - work - work - work - home</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>453</th>\n",
-       "      <td>home - work - work - work - home - other - hom...</td>\n",
-       "      <td>2</td>\n",
+       "      <th>487</th>\n",
+       "      <td>home - work - work - work - work - home - othe...</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>454</th>\n",
-       "      <td>home - work - work - work - home - other - oth...</td>\n",
+       "      <th>488</th>\n",
+       "      <td>home - work - work - work - work - home - work...</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>455</th>\n",
-       "      <td>home - work - work - work - work - home</td>\n",
+       "      <th>489</th>\n",
+       "      <td>home - work - work - work - work - work - home</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>456</th>\n",
-       "      <td>home - work - work - work - work - home - work...</td>\n",
+       "      <th>490</th>\n",
+       "      <td>home - work - work - work - work - work - work...</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>457 rows × 2 columns</p>\n",
+       "<p>491 rows × 2 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "                                     activity_sequence  count_acbm\n",
-       "0                                     home - education          10\n",
-       "1                     home - education - escort - home           9\n",
-       "2              home - education - escort - shop - home           1\n",
-       "3                              home - education - home         289\n",
-       "4           home - education - home - education - home           2\n",
+       "0                                     home - education          12\n",
+       "1                  home - education - education - home           1\n",
+       "2                     home - education - escort - home           7\n",
+       "3              home - education - escort - shop - home           1\n",
+       "4                              home - education - home         281\n",
        "..                                                 ...         ...\n",
-       "452    home - work - work - work - home - other - home           1\n",
-       "453  home - work - work - work - home - other - hom...           2\n",
-       "454  home - work - work - work - home - other - oth...           1\n",
-       "455            home - work - work - work - work - home           1\n",
-       "456  home - work - work - work - work - home - work...           1\n",
+       "486            home - work - work - work - work - home           3\n",
+       "487  home - work - work - work - work - home - othe...           1\n",
+       "488  home - work - work - work - work - home - work...           1\n",
+       "489     home - work - work - work - work - work - home           1\n",
+       "490  home - work - work - work - work - work - work...           1\n",
        "\n",
-       "[457 rows x 2 columns]"
+       "[491 rows x 2 columns]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1467,12 +1502,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -1550,7 +1585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1614,7 +1649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1662,113 +1697,113 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>199</td>\n",
+       "      <td>200</td>\n",
        "      <td>89</td>\n",
        "      <td>E00059031</td>\n",
-       "      <td>E00058720</td>\n",
-       "      <td>other</td>\n",
+       "      <td>E00058877</td>\n",
+       "      <td>work</td>\n",
        "      <td>home</td>\n",
-       "      <td>other</td>\n",
-       "      <td>walk</td>\n",
+       "      <td>work</td>\n",
+       "      <td>car</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>1900-01-01 10:00:00</td>\n",
-       "      <td>1900-01-01 10:45:00</td>\n",
-       "      <td>0:45:00</td>\n",
-       "      <td>1735597798</td>\n",
-       "      <td>967294624</td>\n",
-       "      <td>POINT (-1.3986079763910049 53.933681557220275)</td>\n",
-       "      <td>POINT (-1.5127441120162681 53.8180210228714)</td>\n",
-       "      <td>POINT (-155692.328 7157606.366)</td>\n",
-       "      <td>POINT (-1.5127441120162681 53.8180210228714)</td>\n",
-       "      <td>25.3</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>199</td>\n",
+       "      <td>200</td>\n",
        "      <td>89</td>\n",
-       "      <td>E00058720</td>\n",
+       "      <td>E00059031</td>\n",
        "      <td>E00059031</td>\n",
        "      <td>home</td>\n",
-       "      <td>other</td>\n",
+       "      <td>work</td>\n",
        "      <td>home</td>\n",
-       "      <td>walk</td>\n",
+       "      <td>car</td>\n",
        "      <td>2.0</td>\n",
-       "      <td>1900-01-01 10:45:00</td>\n",
-       "      <td>1900-01-01 11:30:00</td>\n",
-       "      <td>0:45:00</td>\n",
-       "      <td>967294624</td>\n",
-       "      <td>1735597798</td>\n",
-       "      <td>POINT (-1.5127441120162681 53.8180210228714)</td>\n",
-       "      <td>POINT (-1.3986079763910049 53.933681557220275)</td>\n",
-       "      <td>POINT (-168397.904 7135766.710)</td>\n",
-       "      <td>POINT (-1.3986079763910049 53.933681557220275)</td>\n",
-       "      <td>25.3</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>199</td>\n",
-       "      <td>89</td>\n",
-       "      <td>E00059031</td>\n",
-       "      <td>E00057312</td>\n",
-       "      <td>shop</td>\n",
+       "      <td>312</td>\n",
+       "      <td>139</td>\n",
+       "      <td>E00059045</td>\n",
+       "      <td>E00057153</td>\n",
+       "      <td>escort</td>\n",
        "      <td>home</td>\n",
-       "      <td>shop</td>\n",
+       "      <td>escort</td>\n",
        "      <td>car</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1900-01-01 12:00:00</td>\n",
-       "      <td>1900-01-01 12:10:00</td>\n",
-       "      <td>0:10:00</td>\n",
-       "      <td>1735597798</td>\n",
-       "      <td>157001772</td>\n",
-       "      <td>POINT (-1.3986079763910049 53.933681557220275)</td>\n",
-       "      <td>POINT (-1.5448043854850726 53.780926914380444)</td>\n",
-       "      <td>POINT (-155692.328 7157606.366)</td>\n",
-       "      <td>POINT (-1.5448043854850726 53.780926914380444)</td>\n",
-       "      <td>33.1</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>1467798536</td>\n",
+       "      <td>POINT (-1.4001307419061457 53.927941746853776)</td>\n",
+       "      <td>POINT (-1.4563558108534633 53.80668344951966)</td>\n",
+       "      <td>POINT (-155861.841 7156521.118)</td>\n",
+       "      <td>POINT (-1.4563558108534633 53.80668344951966)</td>\n",
+       "      <td>23.7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>199</td>\n",
-       "      <td>89</td>\n",
-       "      <td>E00057312</td>\n",
-       "      <td>E00059031</td>\n",
-       "      <td>home</td>\n",
-       "      <td>shop</td>\n",
-       "      <td>home</td>\n",
+       "      <td>312</td>\n",
+       "      <td>139</td>\n",
+       "      <td>E00057153</td>\n",
+       "      <td>E00169797</td>\n",
+       "      <td>work</td>\n",
+       "      <td>escort</td>\n",
+       "      <td>work</td>\n",
        "      <td>car</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1900-01-01 12:45:00</td>\n",
-       "      <td>1900-01-01 12:55:00</td>\n",
-       "      <td>0:10:00</td>\n",
-       "      <td>157001772</td>\n",
-       "      <td>1735597798</td>\n",
-       "      <td>POINT (-1.5448043854850726 53.780926914380444)</td>\n",
-       "      <td>POINT (-1.3986079763910049 53.933681557220275)</td>\n",
-       "      <td>POINT (-171966.838 7128775.165)</td>\n",
-       "      <td>POINT (-1.3986079763910049 53.933681557220275)</td>\n",
-       "      <td>33.1</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>1467798536</td>\n",
+       "      <td>443167284</td>\n",
+       "      <td>POINT (-1.4563558108534633 53.80668344951966)</td>\n",
+       "      <td>POINT (-1.5484560286132372 53.797016858417656)</td>\n",
+       "      <td>POINT (-162120.787 7133629.133)</td>\n",
+       "      <td>POINT (-1.5484560286132372 53.797016858417656)</td>\n",
+       "      <td>10.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>199</td>\n",
-       "      <td>89</td>\n",
-       "      <td>E00059031</td>\n",
-       "      <td>E00058564</td>\n",
-       "      <td>work</td>\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>5.0</td>\n",
-       "      <td>1900-01-01 13:30:00</td>\n",
-       "      <td>1900-01-01 13:50:00</td>\n",
-       "      <td>0:20:00</td>\n",
-       "      <td>1735597798</td>\n",
-       "      <td>1428773232</td>\n",
-       "      <td>POINT (-1.3986079763910049 53.933681557220275)</td>\n",
-       "      <td>POINT (-1.5029553091989152 53.789477653046596)</td>\n",
-       "      <td>POINT (-155692.328 7157606.366)</td>\n",
-       "      <td>POINT (-1.5029553091989152 53.789477653046596)</td>\n",
-       "      <td>29.6</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>...</th>\n",
@@ -1793,216 +1828,203 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14970</th>\n",
-       "      <td>794026</td>\n",
+       "      <th>15098</th>\n",
+       "      <td>794027</td>\n",
        "      <td>334502</td>\n",
-       "      <td>E00056891</td>\n",
+       "      <td>E00057268</td>\n",
        "      <td>E00057826</td>\n",
        "      <td>home</td>\n",
-       "      <td>escort</td>\n",
+       "      <td>visit</td>\n",
        "      <td>home</td>\n",
        "      <td>car</td>\n",
        "      <td>4.0</td>\n",
-       "      <td>1900-01-01 20:45:00</td>\n",
-       "      <td>1900-01-01 21:05:00</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",
-       "      <td>6495819</td>\n",
-       "      <td>1238900496</td>\n",
-       "      <td>POINT (-1.584746581534615 53.793797905031)</td>\n",
-       "      <td>POINT (-1.5320470710998348 53.78289959964661)</td>\n",
-       "      <td>POINT (-176413.182 7131200.405)</td>\n",
-       "      <td>POINT (-1.5320470710998348 53.78289959964661)</td>\n",
-       "      <td>6.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14971</th>\n",
-       "      <td>794027</td>\n",
-       "      <td>334502</td>\n",
-       "      <td>E00057826</td>\n",
-       "      <td>E00057536</td>\n",
-       "      <td>escort</td>\n",
+       "      <td>411620812</td>\n",
+       "      <td>1238852478</td>\n",
+       "      <td>POINT (-1.58002323810948 53.774918897587455)</td>\n",
+       "      <td>POINT (-1.5332742693848147 53.78164817605564)</td>\n",
+       "      <td>POINT (-175887.382 7127643.348)</td>\n",
+       "      <td>POINT (-1.5332742693848147 53.78164817605564)</td>\n",
+       "      <td>5.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15099</th>\n",
+       "      <td>794272</td>\n",
+       "      <td>334647</td>\n",
+       "      <td>E00170040</td>\n",
+       "      <td>E00058459</td>\n",
+       "      <td>shop</td>\n",
        "      <td>home</td>\n",
-       "      <td>escort</td>\n",
+       "      <td>shop</td>\n",
        "      <td>car</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>1900-01-01 07:40:00</td>\n",
-       "      <td>1900-01-01 07:55:00</td>\n",
-       "      <td>0:15:00</td>\n",
-       "      <td>1238900496</td>\n",
-       "      <td>1362793606</td>\n",
-       "      <td>POINT (-1.5320470710998348 53.78289959964661)</td>\n",
-       "      <td>POINT (-1.4689248218157287 53.796667804818235)</td>\n",
-       "      <td>POINT (-170546.700 7129146.824)</td>\n",
-       "      <td>POINT (-1.4689248218157287 53.796667804818235)</td>\n",
-       "      <td>7.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14972</th>\n",
-       "      <td>794027</td>\n",
-       "      <td>334502</td>\n",
-       "      <td>E00057536</td>\n",
-       "      <td>E00057826</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",
+       "      <td>156862474</td>\n",
+       "      <td>13203295</td>\n",
+       "      <td>POINT (-1.5323959656166048 53.7918310553218)</td>\n",
+       "      <td>POINT (-1.6721547587985188 53.81393394394979)</td>\n",
+       "      <td>POINT (-170585.539 7130829.750)</td>\n",
+       "      <td>POINT (-1.6721547587985188 53.81393394394979)</td>\n",
+       "      <td>16.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15100</th>\n",
+       "      <td>794272</td>\n",
+       "      <td>334647</td>\n",
+       "      <td>E00058459</td>\n",
+       "      <td>E00170040</td>\n",
        "      <td>home</td>\n",
-       "      <td>escort</td>\n",
+       "      <td>shop</td>\n",
        "      <td>home</td>\n",
-       "      <td>pt</td>\n",
+       "      <td>car</td>\n",
        "      <td>2.0</td>\n",
-       "      <td>1900-01-01 15:00:00</td>\n",
-       "      <td>1900-01-01 15:55:00</td>\n",
-       "      <td>0:55:00</td>\n",
-       "      <td>1362793606</td>\n",
-       "      <td>1238900496</td>\n",
-       "      <td>POINT (-1.4689248218157287 53.796667804818235)</td>\n",
-       "      <td>POINT (-1.5320470710998348 53.78289959964661)</td>\n",
-       "      <td>POINT (-163519.963 7131741.273)</td>\n",
-       "      <td>POINT (-1.5320470710998348 53.78289959964661)</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",
+       "      <td>13203295</td>\n",
+       "      <td>156862474</td>\n",
+       "      <td>POINT (-1.6721547587985188 53.81393394394979)</td>\n",
+       "      <td>POINT (-1.5323959656166048 53.7918310553218)</td>\n",
+       "      <td>POINT (-186143.416 7134996.069)</td>\n",
+       "      <td>POINT (-1.5323959656166048 53.7918310553218)</td>\n",
+       "      <td>16.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15101</th>\n",
+       "      <td>794273</td>\n",
+       "      <td>334647</td>\n",
+       "      <td>E00170040</td>\n",
+       "      <td>E00057187</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",
+       "      <td>156862474</td>\n",
+       "      <td>892243500</td>\n",
+       "      <td>POINT (-1.5323959656166048 53.7918310553218)</td>\n",
+       "      <td>POINT (-1.4702034760278457 53.80747028988714)</td>\n",
+       "      <td>POINT (-170585.539 7130829.750)</td>\n",
+       "      <td>POINT (-1.4702034760278457 53.80747028988714)</td>\n",
        "      <td>7.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14973</th>\n",
-       "      <td>794027</td>\n",
-       "      <td>334502</td>\n",
-       "      <td>E00057826</td>\n",
-       "      <td>E00056882</td>\n",
-       "      <td>other</td>\n",
-       "      <td>home</td>\n",
-       "      <td>other</td>\n",
-       "      <td>pt</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1900-01-01 18:35:00</td>\n",
-       "      <td>1900-01-01 19:35:00</td>\n",
-       "      <td>1:00:00</td>\n",
-       "      <td>1238900496</td>\n",
-       "      <td>1033390048</td>\n",
-       "      <td>POINT (-1.5320470710998348 53.78289959964661)</td>\n",
-       "      <td>POINT (-1.5911288565275319 53.79690447266708)</td>\n",
-       "      <td>POINT (-170546.700 7129146.824)</td>\n",
-       "      <td>POINT (-1.5911288565275319 53.79690447266708)</td>\n",
-       "      <td>7.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14974</th>\n",
-       "      <td>794027</td>\n",
-       "      <td>334502</td>\n",
-       "      <td>E00056882</td>\n",
-       "      <td>E00057826</td>\n",
+       "      <th>15102</th>\n",
+       "      <td>794273</td>\n",
+       "      <td>334647</td>\n",
+       "      <td>E00057187</td>\n",
+       "      <td>E00170040</td>\n",
        "      <td>home</td>\n",
-       "      <td>other</td>\n",
+       "      <td>shop</td>\n",
        "      <td>home</td>\n",
        "      <td>car</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1900-01-01 20:45:00</td>\n",
-       "      <td>1900-01-01 21:05:00</td>\n",
-       "      <td>0:20:00</td>\n",
-       "      <td>1033390048</td>\n",
-       "      <td>1238900496</td>\n",
-       "      <td>POINT (-1.5911288565275319 53.79690447266708)</td>\n",
-       "      <td>POINT (-1.5320470710998348 53.78289959964661)</td>\n",
-       "      <td>POINT (-177123.654 7131785.878)</td>\n",
-       "      <td>POINT (-1.5320470710998348 53.78289959964661)</td>\n",
-       "      <td>7.1</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",
+       "      <td>892243500</td>\n",
+       "      <td>156862474</td>\n",
+       "      <td>POINT (-1.4702034760278457 53.80747028988714)</td>\n",
+       "      <td>POINT (-1.5323959656166048 53.7918310553218)</td>\n",
+       "      <td>POINT (-163662.302 7133777.465)</td>\n",
+       "      <td>POINT (-1.5323959656166048 53.7918310553218)</td>\n",
+       "      <td>7.5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>14975 rows × 19 columns</p>\n",
+       "<p>15103 rows × 19 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "          pid     hid      ozone      dzone    purp    oact    dact  mode  \\\n",
-       "0         199      89  E00059031  E00058720   other    home   other  walk   \n",
-       "1         199      89  E00058720  E00059031    home   other    home  walk   \n",
-       "2         199      89  E00059031  E00057312    shop    home    shop   car   \n",
-       "3         199      89  E00057312  E00059031    home    shop    home   car   \n",
-       "4         199      89  E00059031  E00058564    work    home    work   car   \n",
-       "...       ...     ...        ...        ...     ...     ...     ...   ...   \n",
-       "14970  794026  334502  E00056891  E00057826    home  escort    home   car   \n",
-       "14971  794027  334502  E00057826  E00057536  escort    home  escort   car   \n",
-       "14972  794027  334502  E00057536  E00057826    home  escort    home    pt   \n",
-       "14973  794027  334502  E00057826  E00056882   other    home   other    pt   \n",
-       "14974  794027  334502  E00056882  E00057826    home   other    home   car   \n",
-       "\n",
-       "       seq                  tst                  tet duration  \\\n",
-       "0      1.0  1900-01-01 10:00:00  1900-01-01 10:45:00  0:45:00   \n",
-       "1      2.0  1900-01-01 10:45:00  1900-01-01 11:30:00  0:45:00   \n",
-       "2      3.0  1900-01-01 12:00:00  1900-01-01 12:10:00  0:10:00   \n",
-       "3      4.0  1900-01-01 12:45:00  1900-01-01 12:55:00  0:10:00   \n",
-       "4      5.0  1900-01-01 13:30:00  1900-01-01 13:50:00  0:20:00   \n",
-       "...    ...                  ...                  ...      ...   \n",
-       "14970  4.0  1900-01-01 20:45:00  1900-01-01 21:05:00  0:20:00   \n",
-       "14971  1.0  1900-01-01 07:40:00  1900-01-01 07:55:00  0:15:00   \n",
-       "14972  2.0  1900-01-01 15:00:00  1900-01-01 15:55:00  0:55:00   \n",
-       "14973  3.0  1900-01-01 18:35:00  1900-01-01 19:35:00  1:00:00   \n",
-       "14974  4.0  1900-01-01 20:45:00  1900-01-01 21:05:00  0:20:00   \n",
+       "          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  E00057153  escort    home  escort  car  1.0   \n",
+       "3         312     139  E00057153  E00169797    work  escort    work  car  2.0   \n",
+       "4         312     139  E00059045  E00059045    home    work    home  car  3.0   \n",
+       "...       ...     ...        ...        ...     ...     ...     ...  ...  ...   \n",
+       "15098  794027  334502  E00057268  E00057826    home   visit    home  car  4.0   \n",
+       "15099  794272  334647  E00170040  E00058459    shop    home    shop  car  1.0   \n",
+       "15100  794272  334647  E00058459  E00170040    home    shop    home  car  2.0   \n",
+       "15101  794273  334647  E00170040  E00057187    shop    home    shop  car  1.0   \n",
+       "15102  794273  334647  E00057187  E00170040    home    shop    home  car  2.0   \n",
        "\n",
-       "      start_location_id end_location_id  \\\n",
-       "0            1735597798       967294624   \n",
-       "1             967294624      1735597798   \n",
-       "2            1735597798       157001772   \n",
-       "3             157001772      1735597798   \n",
-       "4            1735597798      1428773232   \n",
-       "...                 ...             ...   \n",
-       "14970           6495819      1238900496   \n",
-       "14971        1238900496      1362793606   \n",
-       "14972        1362793606      1238900496   \n",
-       "14973        1238900496      1033390048   \n",
-       "14974        1033390048      1238900496   \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        1467798536   \n",
+       "4      1900-01-01 18:00:00  1900-01-01 18:35:00  0:35:00         443167284   \n",
+       "...                    ...                  ...      ...               ...   \n",
+       "15098  1900-01-01 22:30:00  1900-01-01 22:50:00  0:20:00         411620812   \n",
+       "15099  1900-01-01 08:50:00  1900-01-01 09:00:00  0:10:00         156862474   \n",
+       "15100  1900-01-01 10:20:00  1900-01-01 10:30:00  0:10:00          13203295   \n",
+       "15101  1900-01-01 08:50:00  1900-01-01 09:00:00  0:10:00         156862474   \n",
+       "15102  1900-01-01 10:20:00  1900-01-01 10:30:00  0:10:00         892243500   \n",
        "\n",
-       "                          start_location_geometry_wkt  \\\n",
-       "0      POINT (-1.3986079763910049 53.933681557220275)   \n",
-       "1        POINT (-1.5127441120162681 53.8180210228714)   \n",
-       "2      POINT (-1.3986079763910049 53.933681557220275)   \n",
-       "3      POINT (-1.5448043854850726 53.780926914380444)   \n",
-       "4      POINT (-1.3986079763910049 53.933681557220275)   \n",
-       "...                                               ...   \n",
-       "14970      POINT (-1.584746581534615 53.793797905031)   \n",
-       "14971   POINT (-1.5320470710998348 53.78289959964661)   \n",
-       "14972  POINT (-1.4689248218157287 53.796667804818235)   \n",
-       "14973   POINT (-1.5320470710998348 53.78289959964661)   \n",
-       "14974   POINT (-1.5911288565275319 53.79690447266708)   \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          1467798536  POINT (-1.4001307419061457 53.927941746853776)   \n",
+       "3           443167284   POINT (-1.4563558108534633 53.80668344951966)   \n",
+       "4          1750466246  POINT (-1.5484560286132372 53.797016858417656)   \n",
+       "...               ...                                             ...   \n",
+       "15098      1238852478    POINT (-1.58002323810948 53.774918897587455)   \n",
+       "15099        13203295    POINT (-1.5323959656166048 53.7918310553218)   \n",
+       "15100       156862474   POINT (-1.6721547587985188 53.81393394394979)   \n",
+       "15101       892243500    POINT (-1.5323959656166048 53.7918310553218)   \n",
+       "15102       156862474   POINT (-1.4702034760278457 53.80747028988714)   \n",
        "\n",
        "                            end_location_geometry_wkt  \\\n",
-       "0        POINT (-1.5127441120162681 53.8180210228714)   \n",
-       "1      POINT (-1.3986079763910049 53.933681557220275)   \n",
-       "2      POINT (-1.5448043854850726 53.780926914380444)   \n",
-       "3      POINT (-1.3986079763910049 53.933681557220275)   \n",
-       "4      POINT (-1.5029553091989152 53.789477653046596)   \n",
+       "0       POINT (-1.5223977194364153 53.80563634318617)   \n",
+       "1       POINT (-1.399263822377326 53.934588245650026)   \n",
+       "2       POINT (-1.4563558108534633 53.80668344951966)   \n",
+       "3      POINT (-1.5484560286132372 53.797016858417656)   \n",
+       "4      POINT (-1.4001307419061457 53.927941746853776)   \n",
        "...                                               ...   \n",
-       "14970   POINT (-1.5320470710998348 53.78289959964661)   \n",
-       "14971  POINT (-1.4689248218157287 53.796667804818235)   \n",
-       "14972   POINT (-1.5320470710998348 53.78289959964661)   \n",
-       "14973   POINT (-1.5911288565275319 53.79690447266708)   \n",
-       "14974   POINT (-1.5320470710998348 53.78289959964661)   \n",
+       "15098   POINT (-1.5332742693848147 53.78164817605564)   \n",
+       "15099   POINT (-1.6721547587985188 53.81393394394979)   \n",
+       "15100    POINT (-1.5323959656166048 53.7918310553218)   \n",
+       "15101   POINT (-1.4702034760278457 53.80747028988714)   \n",
+       "15102    POINT (-1.5323959656166048 53.7918310553218)   \n",
        "\n",
        "                        start_geometry  \\\n",
-       "0      POINT (-155692.328 7157606.366)   \n",
-       "1      POINT (-168397.904 7135766.710)   \n",
-       "2      POINT (-155692.328 7157606.366)   \n",
-       "3      POINT (-171966.838 7128775.165)   \n",
-       "4      POINT (-155692.328 7157606.366)   \n",
+       "0      POINT (-155765.336 7157777.811)   \n",
+       "1      POINT (-169472.539 7133431.742)   \n",
+       "2      POINT (-155861.841 7156521.118)   \n",
+       "3      POINT (-162120.787 7133629.133)   \n",
+       "4      POINT (-172373.337 7131807.059)   \n",
        "...                                ...   \n",
-       "14970  POINT (-176413.182 7131200.405)   \n",
-       "14971  POINT (-170546.700 7129146.824)   \n",
-       "14972  POINT (-163519.963 7131741.273)   \n",
-       "14973  POINT (-170546.700 7129146.824)   \n",
-       "14974  POINT (-177123.654 7131785.878)   \n",
+       "15098  POINT (-175887.382 7127643.348)   \n",
+       "15099  POINT (-170585.539 7130829.750)   \n",
+       "15100  POINT (-186143.416 7134996.069)   \n",
+       "15101  POINT (-170585.539 7130829.750)   \n",
+       "15102  POINT (-163662.302 7133777.465)   \n",
        "\n",
        "                                         end_geometry  distance  \n",
-       "0        POINT (-1.5127441120162681 53.8180210228714)      25.3  \n",
-       "1      POINT (-1.3986079763910049 53.933681557220275)      25.3  \n",
-       "2      POINT (-1.5448043854850726 53.780926914380444)      33.1  \n",
-       "3      POINT (-1.3986079763910049 53.933681557220275)      33.1  \n",
-       "4      POINT (-1.5029553091989152 53.789477653046596)      29.6  \n",
+       "0       POINT (-1.5223977194364153 53.80563634318617)      27.9  \n",
+       "1       POINT (-1.399263822377326 53.934588245650026)      27.9  \n",
+       "2       POINT (-1.4563558108534633 53.80668344951966)      23.7  \n",
+       "3      POINT (-1.5484560286132372 53.797016858417656)      10.4  \n",
+       "4      POINT (-1.4001307419061457 53.927941746853776)      29.7  \n",
        "...                                               ...       ...  \n",
-       "14970   POINT (-1.5320470710998348 53.78289959964661)       6.2  \n",
-       "14971  POINT (-1.4689248218157287 53.796667804818235)       7.5  \n",
-       "14972   POINT (-1.5320470710998348 53.78289959964661)       7.5  \n",
-       "14973   POINT (-1.5911288565275319 53.79690447266708)       7.1  \n",
-       "14974   POINT (-1.5320470710998348 53.78289959964661)       7.1  \n",
+       "15098   POINT (-1.5332742693848147 53.78164817605564)       5.4  \n",
+       "15099   POINT (-1.6721547587985188 53.81393394394979)      16.1  \n",
+       "15100    POINT (-1.5323959656166048 53.7918310553218)      16.1  \n",
+       "15101   POINT (-1.4702034760278457 53.80747028988714)       7.5  \n",
+       "15102    POINT (-1.5323959656166048 53.7918310553218)       7.5  \n",
        "\n",
-       "[14975 rows x 19 columns]"
+       "[15103 rows x 19 columns]"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2020,12 +2042,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -2085,12 +2107,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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aq2qd+uvPfV9/q72q1qkNeWNYoe9L+3J+U9bfwzae1l9/q72q9H3p+/q+/vrj55aq/9UGf2xfVdqYvi99X3e4NWitqnrq1CkdNmyYVqhQQQ3DsP8EBATokCFD7KPrvpKRkaH169fXO++8U1Ud39jHjx/Xm266SUNDQ/W1114r8lL433//XceMGaMtWrSwz0Sw2bp1qwYFBenLL79corwuXryo8fHxOnv2bD1z5owOHjxYIyIiHGa97d6922mM0aNHa5MmTey/9+nTR4OCgjQkJEQNw9BatWrp66+/XuTfYOfOnRoaGqpvvvmmqqr9H9ztt9+uY8aM0YcfflifeuopPXTokNMPhD179mjlypXtV9xnZWWpquqlS5f0nXfe0bp162qtWrXsH8iFvWFPnDih/fr10549e+pPP/3k8NjMmTPVMAxduXKl0/bIb/fu3Vq5cmVdunSpzp49Ww3D0FtvvbVEs4bat2+vt912m/33AQMG2Ns3JCREk5KSnB7Xf//7X42KitJVq1apqmpmZqaePXtWu3btqnPmzNF58+bp6tWri10GxNM29tf2VTWnjXkPl34bq+bO4AoKCrJ/1mRmZtofW7lypXbu3FkjIyP1o48+KvJ43nzzTQ0ICCh0FuzgwYO1YcOGLi1rYvtMW79+vTZv3lz37Nmjn376qVarVk3bt2+v69evLzaGam7HMjg42D7TrXv37hoREaG9e/fWG2+8UcPCwjQqKqrYk6LrrrtO27Vr57Bt6tSpGhYWpnXq1NGYmBgdOHCgfvXVV0XG2LJli4aEhNjbz9bOGRkZ+vTTT2tMTIz9ZCRvG5QWT+tvadReVc/rr6e1V9U69ZfPrfJZG6zSvqrWbGN/eg+rWqv2qlqr/ppRe1WtVX/9te/rT7VX1Zq1QdUafV/al/Obsv4etvG0/vpr7VWl70vfl75vXtSGovlT+6rSxkUdkyp93+KU576v24PWNidOnNAvvvhC3333Xf3000/1yJEjnoY0xblz57RZs2Y6YMAAVc19U+d9Y58+fVp79eqlsbGx9llr+d/4v//+uyYnJ+ujjz5q32abpXDkyBGNj4/X++67z+WcbH/MUaNG6R133KGquZf5204eRo4cqX379tV+/fo5LfwPPfSQNmrUSLOzs/W+++7T6OhoXbBggf7www/6ySefaLdu3bRy5cr60ksv6aVLlwo8f9u2bWoYhv7rX/+y5/X666+rYRjaqFEjveKKK7Ry5cpau3Zt/eijj4p8kx06dEiDgoJ0+vTpBY4xOztbFy5cqPHx8dqiRQv7rML8sX7++Wc1DEP//ve/F4i/YcMGDQgI0Pfee6/ItsjP9vo33XST/f4v//jHP+wfvrZlbYpatsLmmmuusX/wjhgxQmNiYvT555/XFStW6L333qtVq1bV2rVr6759+wo9rrlz52pAQIB++eWX9m22No6KitIKFSpopUqVtGvXrnrixIlCY6h63sb+2r6q5rQx7+HSbWPbidjHH3+swcHBOnv2bIf2tVm2bJk2adJE27dvX+TSU2vWrLF3BG2vY4v17LPPalRUlMNSKfnlnw134MABjYiI0P/85z+qmnuiFBsbqx06dNDVq1drjx499NVXXy0Qx3ZMaWlpWqNGDZ08ebJ+9NFHWqNGDV22bJmeP39eVXPvVdOhQwcNCwvT//73v4XGycrK0iFDhmhERITOmTNHt27dqjNmzNCAgAAdOnSozpgxQx955BGNjY3VBg0aFHkCsX37djUMQ99+++0Cx5uVlaXTp0/XmJgY7du3b4mWb3OXp/XX7Nqrak799bT2qlqn/vK5VT5rg1XaV9V6bexP72Er1V5Va9VfM2uvqrXqr7/2ff2p9qpaszaoWqPvS/tyflPW38Nm1V9/q722OPR96fvS93VEbSicv7WvKm1M35e+rzs8HrS2sueff14Nw9APP/zQvi3vG3vnzp3asGFD7dChQ5ExfvvtN/s/rvwd+zZt2mhKSoqqFnxTOvPmm29qtWrV7MujnDp1Sm+55RYNDg7WwMBAff/99wvkmteyZcvUMAx966239P7779eHHnrI4fVPnDihvXv31ri4OD1w4ECB52dkZOjNN9+shmFo9+7d9frrr1fDMHTSpEm6d+9evXz5sq5cuVI7duyoCQkJ9hh587Etg9O1a1dt1aqVw/ILtlwuX76sL7zwgkZEROikSZPs/+DyW7JkiX0ZnbxtfODAAY2JidFp06apqhb5/MKMGzdOGzdubD95eu6559QwDB0yZIgmJiZq06ZN7UsW5GV7/ccff1zj4+P1gw8+0Pbt2+tLL73kMBNq3rx5Gh8fr23atCn0H+L69es1IiJCr776ap04caKOGzfO3sbffvutnjt3Th966CGNjo7W5ORke4y8bZydna2nT58ucRvnf98sXrzYrfbN/37PG7ck7Zv/mNxt3/zHtXbtWrfaOK/09HRT3sOLFi1yq43zH1Pe57r7Hrbx9D2smluw3WnjvEtIqeZ21K666ipt27atw4zAvMdrOymxLXmSnZ3tEOfSpUtF3gtqzpw5GhQUVODE49KlSw4x8p+4DBgwQCdMmKCquTORly5dqtWqVdNq1appxYoV7fc8ycnJcYiTnZ2tmZmZ2r17d23ZsqWOGzdOGzRoUODvsXTpUq1evbqmpKTY/23mb5tDhw5p7dq11TAMrVatmhqGoSNHjrTPTFPNvb9LeHi43nzzzfbldfIu3XL48GG98sor9YYbbnD4zLcd76VLl3TcuHEaHh5uPxkq7Rnnntbf0qi9qp7VX09rr6q16q8/1l5V9+qvGbVXteD7xpf118q1V9Va9Zfaa37tzZ+Pr+tv/mU13a29qtauv/7Y9/W32qvqn/WX2kvf15U2tnLf193aq2pO/f3uu+/sj/lL7aXvS9/XSvXXH2uvatmvv7YJGnl/d6d9rVx7Va1Vf61Ue1Xp+9L3LZpfD1qnpaXp1VdfrY0bN7bfLF31zwbLzMzU6dOna0hIiH7zzTcux7U9/5prrtGbbrrJ4THbiYYzGzdu1Nq1a+vmzZvt23r37q1BQUFauXJlh+VaCnP27Fn7rLaAgAB9+OGHC+yTmpqqFSpU0ClTphQa4+DBg3rfffdp8+bNtUWLFtq0aVP99ddf7Y9nZGToJ598ooZh6AMPPODw3LxvuPfee08Nw9D777/f4Sr7vP9Au3fvrk2aNCmwPn5xJ1xnz57V2rVr66RJkxy2nzhxwmGZh8IK3P/+9z+96qqr9PDhw/bHpk6dqoZhaGhoqP7jH/9w+tpff/21BgcHa/fu3bVKlSr2pSFsr3vp0iV99NFHNTAwUNeuXVtoLvPnz9d69epp1apVNTo6Wjt37qyHDh2yf2hlZGTo2LFjC8wounDhQoE4JW3jCxcuOLRvYW3trH3zxsmbi+3DuKTtm/cDTzX3A9Wd9s2fz9y5c0vUxunp6XrgwAGHIjNv3rwSt296erru37/foRAWdlJRXBvb8rHduyRvG5SkjQuL404bF9Y+c+bMKVEbL1myRA3D0BEjRjgc86pVqzQiIkIHDBigx44dK9DOqqpJSUmanJysqqoffPCBGoaht99+e6H7qv757+3DDz/UihUrOiyntXPnTv2///u/ArnkjfG3v/1N69evb//bnTt3Tjt06KCBgYHasGFDe10o6pi2bdumMTExahiG1qtXz+HkzOb+++/XyMhITU9PLzLOsWPHdPr06Tpt2jRNTk7WTz/9VFVz33u2YxwzZoxWqlRJDx8+rK+99pqOGzdOf//9d3uMF198UQ3D0H/+858Or5931luTJk3s7VvaSqP+elp7VT2rv2bUXlXf119/rL3583Gn/ppRe21xrFJ/rVJ7VQuvL76qv0eOHKH2lmLtnT17tr788suWqb+295mntVdVLV9//bHv6y+1N+8+zo61rNVfai9935K0sZX6vmbUXtWi61RJ6q8thj/VXvq+uej7+r7++mPtzZ9PWa+//lZ7Ven70vel72tG/XVr0Lpr167F/lx33XV6880368MPP+zyOu2lYevWrRobG6udO3d2+BCyNeyBAwfUMAz7rAZX2P4QPXr0cLjR/Y4dO7RDhw7au3fvYmM0a9ZMH374Yb106ZL27dtXo6Ki9PXXX9ehQ4eqYRjFLv+yfft2jYuLs8+AOX36tKr+OYvj3LlzGhMTo3/961+LjJGdna1nzpzRVatWObye7cPl7NmzGhMTo2PGjNEVK1bo559/bv+Hkfcf3+TJkzUgIECffvpph9ketnX+v/jiCzUMQ7/55psCcZw5c+aM1qlTxyG3H3/8UUeOHKl33HGHfvbZZ0XGOXXqlFapUsU+s+PChQt68803a6VKldQwDL3tttt0x44dBfLJOwPnmWeeUcPIvVf7s88+a99uKx5paWn22T9Ftc3u3bt1z549On36dP3b3/5WIMaePXvUMAx98cUXddmyZTp8+HCtV6+edu3aVZ966in7/g899JBLbTxjxgyHGLZlPvLnVVz7jhgxosg4JWlfZ8c0ZcoUl9p3zpw5BeI8+eST9v137drlUhvfddddet1112l0dLTWqFFDBw4caP9gnjRpksvv4Y8++sghzqBBg+z7558V66yNb7311iLjlKSN8+dzyy232I/rySefdLmN88cZMGCAPc7PP//sUhvPmDFDR44cqYZhaEJCgkOR/P333/WVV17R4OBgHTx4sO7fv1/z69mzpyYlJWlGRkaRcfK+j235rV692uEk6Mcff9Ru3brZ2yp/DNtn3Nq1a7Vu3bq6a9cuzcjI0L59+2pkZKQ+/PDDWqNGDW3UqJGuXbu2yFwyMjL0jTfe0Bo1atg/C/IvjXX//fdrnTp19OTJk0XGyevee+91mPVnc+edd2psbKz9BOHee+/Vs2fP2tvg8uXLOmLECA0JCdF33nnH4bm29/G///1vrVSpkv7www+lPttc1fz6a0btVfWs/ppRe237e7P+/utf//K72jtnzhynbeNq/b3nnns8rr3ffPNNgTrly/r7+uuvW6b2vvjiiwXqiy/rb7du3fSKK66g9uZhZu3t3r27Vq9eXQcNGmSJ+puQkKC33Xabx7X3+PHjOnfu3DJRf/2x71tWay993/JVe+n7lp2+73333edx7X3xxRed1k1X62+nTp38rvbS96Xvmx99X/q+hbVxjx49tFWrVn5Ve+n70vel72te/XVr0Nr2ZgwICLD/f96f/NsDAgKKvXq4NK1fv16joqI0MTFRly5d6vDY559/rtHR0frFF1+UOG7//v21Q4cOeunSJf3xxx+1T58+WqVKFf3222+LfI7tj3nnnXfqDTfcoDfddJNGRkba7ztw8uRJHT16tO7cubPY1//22281KChIDcPQadOmOdwMfvXq1VqjRg194YUXVNX55fjr16/XgIAAh7bJycnRZcuWaUxMjA4bNkwNw9Crr75aly9fXmCZg3Pnzundd9+tgYGB+sgjj+ju3bsd4r/yyisaFRWlL730UqFxipKVlaVNmza1v3e+//57vfHGG7VChQpO42RnZ+vly5f1xhtvtJ889enTRyMjI3XJkiU6ffp0NQxDe/ToUWgc23/Pnj2rDz30kBqGoZUqVdLFixc7vM4HH3ygISEhTmPYzJ8/Xzt06GBflseW57x58zQoKEgfeOABjY6O1sTERB04cKA2atRIw8LC7AXn4sWLxbZxaGioRkZGFogxbty4ErVvYGCgVqlSxWmcrKysYtu3a9euhR7T2LFjVTW3GBbXvuHh4frwww87jeNKGwcGBmqlSpX0xhtv1KeeekqHDRumQUFBes8999j/1q68h59//nkNCwsrECd/Lq60cWhoaJFxsrKyXHoPd+3atdB8bMd15swZl9vYWRzb50dx7+NPPvnE/l60fbbl/ew/evSovvDCCxoaGqrdu3d3uFfGjz/+qG3atNGRI0dqVlaWvvzyy0XGyf/va82aNWoYhq5cuVL37NmjN954o0ZEROjkyZOLjKGaO8OvevXqOn36dL3ttts0MjJSFy9erL///rt+8skneuWVV+qePXucHtPhw4f1xRdf1NjYWI2IiNAnnnjCfvKwZcsW7dixo/bq1UsvXLjgNE5mZqZmZ2dr+/bt9brrrtO0tDT7Y1u2bNE2bdpo06ZN1TAMnThxYqEnXz///LNef/31WqlSJX3ttdcK3J/q73//u8bGxjrMoixtpVF/3am9qubVX7Nqr6p36m/lypX9rvaGh4fbO6Ce1N/AwEANDw/3qPbaakNhdcoX9VdENDg4WNu2bevz2ms7v3FWX7xZf1u2bKkiotdeey219w+lUXs3b97sNB9V79bfmTNnelR7e/bsqa+++mqZqr/+2Pcta7WXvm/5qr30fYtvY6v0fUNCQpy2TUlqr62tPK2/L774ot/VXvq+9H0LQ9+Xvq+tjW39smbNmvlN7aXvS9/XCvXXn/q+bg1a22YFNG3aVBcuXKj79+/XzMxM3b9/vy5cuFCbN2+u/fr104MHD+pXX32lbdu21YCAAH3llVc8StYTGzdu1AYNGmhYWJg+88wzum7dOv3kk0/0xhtv1ISEBD148GCJY956663asmVL3bp1q/bp00crV67ssEyAM59//rkahmF/o5Zk+ZK8tmzZorVq1VLDMHTYsGE6d+5cnT17tiYnJ2tcXJzu2bOn2Bjp6enavHlz7dixo/0f81dffaXXX3+9VqtWTRs1aqR16tTRuLg4bdq0qX711VcFcjx58qT9vgP9+vXTjz76SFVzl4u58cYb9aqrrtKmTZsWGye/du3a6YABA/SXX37R66+/XkNDQ/XKK690Kc4//vEPTUhIsC9TsWjRIvsJ1uTJk/Wqq64qNs7Ro0f10UcfVcMwtEqVKjpz5kzdt2+ffvLJJ9qpUycNCgrSWrVqFRoj7wnbxx9/rAEBAfrUU0/pb7/9pqq5M2169+6tNWvW1GrVqun999+vv/zyi6qqHjlyRHv06KHVq1e334vg+PHjRbZxhw4dtEKFCnrXXXcViBEfH2+Pkf8kMn/7VqpUSWNiYgrNJW+c4tp33LhxRcbJe0xpaWlFtm/v3r21evXqRbZNfHy8w72fimrjNm3aaEBAgI4aNcp+QnD+/Hlt0qSJ9urVy/78c+fO6b333quGYWjfvn0LvIfr1aunMTExOnbs2AJx8s9yzfs+KqyNIyMjXYrzzDPPFNnGf/nLXzQqKqrQOHmP6/Tp0zpp0qQi2zguLk6jo6Ndyse2dEph7+MGDRrooUOHdOPGjdqpUyf99ttv9bbbbtOwsDC944477O+99PR0XbRokVavXl0jIyO1V69eOnr0aG3btq1GRUXZl70pLM7o0aPtcfLO5tqwYYMahqEzZ87UlJQUDQsL061btxYbQ1V1xIgRGhAQoNHR0bpo0SL7Z/Hly5ft90op7phOnjypH3zwgdapU0cNw9CmTZtqz5499aqrrtKoqCjdvn27S8dkex+HhYVply5ddNasWTpt2jTt3LmzfVmuCRMmONSrY8eO6c6dO+33Xdm/f7/2799fK1asqPfdd599FuCGDRu0V69e2rFjR01PT1dvMrv+elJ7Vc2pv2bUXtXSr79JSUkaEhKitWvX9pvaa6ubjRs3LjKOK/W3Xbt2GhgYqKNHj3a79t54441av359p3XKm/V31apVGh4errfddpvPa2/v3r01Pj6+yPri7frbtm1bNQxDBw8eTO0t5drrShxV79VfT2pvdHS0/v3vfy+T9dcf+75lpfbS9y1ftVeVvm9Z6fs6axt3aq9q8XXKlfrrj7XX1Xzo+xaPvi99X3/r+7Zv314Nw9ChQ4f6Te2l70vf1yr115/6vm4NWk+aNEnr1avnsKZ5Xr///rvWq1fPvpZ+enq6xsXFaWJiovuZmuDXX3/V4cOHa1BQkFasWFGjo6O1fv36um3bthLFyTtjrWbNmvaTqi1btrgc4/Lly7pw4UL98MMP9fz58yV6/fx2796td911l335hsjISG3WrJl+//33LsfYsGGDVqlSRQ0j96bsMTExesUVV+hdd91lX8Zhy5YtmpCQoM2aNSuyYL/44osaFxengYGBGhkZqdWqVdO4uLgSx1HNbed27dppx44ddeDAgRoaGqp33nlnsXFs/125cqXWrFlT69Spo++//779A8F2T4WS5PP666/bl8UxjNx7O0RERBQbI+8H3D333KOGYWj9+vW1c+fOWq9ePY2Li9NbbrlFW7RoUeDEc+vWrRoUFKQvvvii0zauWrWqVqpUSa+66qoiY7z88ssute/AgQOd5pI3TlHt+/vvv+uYMWNKdEz527dy5cpas2bNEuVTWBsnJCRoSEiIJiYm2otSdna2ZmRk6A033KDdu3fXb775Rr/55hv7DKWXXnpJY2NjNSAgwP4ejo2N1Z49e2rXrl2LjLNmzRpNTU0tcE+H/G3co0ePYuOsWrVKL126pOvWrdPatWtrQkKCQxufOnVKb7nlFqdx8h/XG2+8obGxsQXa2JXjWr16tT3O2LFjC7yP4+Pj7Z83Fy9e1Pj4eJ09e7aeOXNGBw8erBEREXrHHXfY2+X777/X48eP6z333KPt2rXTZs2aaUpKiv7444/2fYqKk3eW2IYNG1Q1955OISEhWqVKFYfPYldiPPnkkzpw4EBdsmSJw6zhvFw9plOnTumECRO0S5cu2rp1ax0+fLjDvWdcyWfZsmX65ptvanx8vP3zODExUQMCAjQ+Pt7hHlhjxozRK664Qg3D0Nq1a+uIESP00qVL9tmkFStW1MqVK2utWrW0Ro0aWq1atRLVBTOZUX/NqL2q5tVfM2qvaunV36pVq9pnmvtL7bW9n11pG2f1t06dOhoaGuq0bhZXe221oaR1qqg29rT+Wqn22s5viqsv3qq/ffv21cDAQKfnAtRedRqnJLXX1Tjeqr/u1t6kpCRdunRpma6//tj3tXrtpe9bvmqvKn3fstL3DQ0N1UqVKhV7LlCS2qtqTv31x9rraj70fYtG35e+rz/2ffv3768VKlRw+fvrslB76fvS97VS/fWnvq9bg9a1a9fWiRMnOt1n4sSJmpCQYP99xIgRGhYW5s7Lmery5cv6ww8/6IIFC/TLL7/06FJ12/IcERERJe78q/65nIgZcnJydOfOnfrRRx9pamqqHj16tMQxfvrpJ50wYYLecsstOn36dN2zZ49+8cUX9qUaVHNn/iQkJDid2bV161ZdvHixPvzww/rGG2/ob7/95nKc/Hr37q2GYWh0dLRu2bKlxHHmz5+vy5YtK3By5s5xbd++Xf/73//qk08+qQsXLtS33367RDHOnTunzz//vDZs2FCvueYaveeee3Tbtm2anJysjz76qD1OTk6O5uTk6JEjRzQ+Pt6+VEtRbfzyyy9rhw4dXIrhrH3Xrl3rci7O2vf3339365i2b9+un332mb19f/rppxLHydvGnTp10jFjxuiUKVMK3Dfo3//+txqGoXXr1tVevXppRESENm3aVNetW6eqqps2bXJ4D+/atUtffvnlYuNUqVJFmzVrZo+Tv42//fZbl+JERETo1VdfrevXr9eFCxfq559/7tDGly5dcjlO3uPaunWrQxv/+uuvLh+XLc6FCxd0+vTp9ja+55577LMRbe/5UaNG2YtqWlqavUiOGDFC+/btqzfeeKOeO3dOc3Jy9PLly3r+/Hn7vT5ciTNy5Ejt27ev9uvXTw8fPqwHDhzQoKAgDQwMtM/sLi7G7bffrn379tURI0bopk2bHGYc51WSYzp79qz9eZmZmQ73PnI1zsCBA3X//v164MABfe+993TLli16+PBhnTVrllauXFlHjhyphw4d0uuvv17DwsJ0yJAh+uyzz2rPnj01ICBAExMT9cKFC6qqunz5cp01a5bedddd+vzzz+uvv/5a6DF6i1n119Paq2pe/TWj9qqWXv2dM2eO39Xeffv2lThO/vp71113afv27T2qvW+88YZu3769xHWzsDY2o/5aqfbec889umPHDrfqVGn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       "text/plain": [
        "<Figure size 2000x1000 with 8 Axes>"
       ]
@@ -2231,355 +2253,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "<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",
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-       "      <th>freq</th>\n",
-       "      <th>ozone</th>\n",
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-       "      <th>duration</th>\n",
-       "      <th>tst_hour</th>\n",
-       "      <th>tet_hour</th>\n",
-       "      <th>oact_abr</th>\n",
-       "      <th>dact_abr</th>\n",
-       "      <th>intrazonal</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
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-       "      <td>1.0</td>\n",
-       "      <td>1900-01-01 10:00:00</td>\n",
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-       "      <td>10</td>\n",
-       "      <td>h</td>\n",
-       "      <td>o</td>\n",
-       "      <td>False</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
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-       "      <td>home</td>\n",
-       "      <td>walk</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1900-01-01 10:45:00</td>\n",
-       "      <td>1900-01-01 11:30:00</td>\n",
-       "      <td>0:45:00</td>\n",
-       "      <td>10</td>\n",
-       "      <td>11</td>\n",
-       "      <td>o</td>\n",
-       "      <td>h</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>199</td>\n",
-       "      <td>89</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>E00059031</td>\n",
-       "      <td>E00057312</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 12:00:00</td>\n",
-       "      <td>1900-01-01 12:10:00</td>\n",
-       "      <td>0:10:00</td>\n",
-       "      <td>12</td>\n",
-       "      <td>12</td>\n",
-       "      <td>h</td>\n",
-       "      <td>sh</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>199</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>E00057312</td>\n",
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-       "      <td>shop</td>\n",
-       "      <td>home</td>\n",
-       "      <td>car</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1900-01-01 12:45:00</td>\n",
-       "      <td>1900-01-01 12:55:00</td>\n",
-       "      <td>0:10:00</td>\n",
-       "      <td>12</td>\n",
-       "      <td>12</td>\n",
-       "      <td>sh</td>\n",
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-       "      <td>False</td>\n",
-       "    </tr>\n",
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-       "      <th>4</th>\n",
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-       "      <td>home</td>\n",
-       "      <td>work</td>\n",
-       "      <td>car</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1900-01-01 13:30:00</td>\n",
-       "      <td>1900-01-01 13:50:00</td>\n",
-       "      <td>0:20:00</td>\n",
-       "      <td>13</td>\n",
-       "      <td>13</td>\n",
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-       "      <td>w</td>\n",
-       "      <td>False</td>\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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14970</th>\n",
-       "      <td>14970</td>\n",
-       "      <td>794026</td>\n",
-       "      <td>334502</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>E00056891</td>\n",
-       "      <td>E00057826</td>\n",
-       "      <td>home</td>\n",
-       "      <td>escort</td>\n",
-       "      <td>home</td>\n",
-       "      <td>car</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1900-01-01 20:45:00</td>\n",
-       "      <td>1900-01-01 21:05:00</td>\n",
-       "      <td>0:20:00</td>\n",
-       "      <td>20</td>\n",
-       "      <td>21</td>\n",
-       "      <td>e</td>\n",
-       "      <td>h</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14971</th>\n",
-       "      <td>14971</td>\n",
-       "      <td>794027</td>\n",
-       "      <td>334502</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>E00057826</td>\n",
-       "      <td>E00057536</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 07:40:00</td>\n",
-       "      <td>1900-01-01 07:55:00</td>\n",
-       "      <td>0:15:00</td>\n",
-       "      <td>7</td>\n",
-       "      <td>7</td>\n",
-       "      <td>h</td>\n",
-       "      <td>e</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14972</th>\n",
-       "      <td>14972</td>\n",
-       "      <td>794027</td>\n",
-       "      <td>334502</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>E00057536</td>\n",
-       "      <td>E00057826</td>\n",
-       "      <td>home</td>\n",
-       "      <td>escort</td>\n",
-       "      <td>home</td>\n",
-       "      <td>pt</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1900-01-01 15:00:00</td>\n",
-       "      <td>1900-01-01 15:55:00</td>\n",
-       "      <td>0:55:00</td>\n",
-       "      <td>15</td>\n",
-       "      <td>15</td>\n",
-       "      <td>e</td>\n",
-       "      <td>h</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14973</th>\n",
-       "      <td>14973</td>\n",
-       "      <td>794027</td>\n",
-       "      <td>334502</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>E00057826</td>\n",
-       "      <td>E00056882</td>\n",
-       "      <td>other</td>\n",
-       "      <td>home</td>\n",
-       "      <td>other</td>\n",
-       "      <td>pt</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1900-01-01 18:35:00</td>\n",
-       "      <td>1900-01-01 19:35:00</td>\n",
-       "      <td>1:00:00</td>\n",
-       "      <td>18</td>\n",
-       "      <td>19</td>\n",
-       "      <td>h</td>\n",
-       "      <td>o</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14974</th>\n",
-       "      <td>14974</td>\n",
-       "      <td>794027</td>\n",
-       "      <td>334502</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>E00056882</td>\n",
-       "      <td>E00057826</td>\n",
-       "      <td>home</td>\n",
-       "      <td>other</td>\n",
-       "      <td>home</td>\n",
-       "      <td>car</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1900-01-01 20:45:00</td>\n",
-       "      <td>1900-01-01 21:05:00</td>\n",
-       "      <td>0:20:00</td>\n",
-       "      <td>20</td>\n",
-       "      <td>21</td>\n",
-       "      <td>o</td>\n",
-       "      <td>h</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>14975 rows × 19 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       Unnamed: 0     pid     hid  freq      ozone      dzone    purp    oact  \\\n",
-       "0               0     199      89   NaN  E00059031  E00058720   other    home   \n",
-       "1               1     199      89   NaN  E00058720  E00059031    home   other   \n",
-       "2               2     199      89   NaN  E00059031  E00057312    shop    home   \n",
-       "3               3     199      89   NaN  E00057312  E00059031    home    shop   \n",
-       "4               4     199      89   NaN  E00059031  E00058564    work    home   \n",
-       "...           ...     ...     ...   ...        ...        ...     ...     ...   \n",
-       "14970       14970  794026  334502   NaN  E00056891  E00057826    home  escort   \n",
-       "14971       14971  794027  334502   NaN  E00057826  E00057536  escort    home   \n",
-       "14972       14972  794027  334502   NaN  E00057536  E00057826    home  escort   \n",
-       "14973       14973  794027  334502   NaN  E00057826  E00056882   other    home   \n",
-       "14974       14974  794027  334502   NaN  E00056882  E00057826    home   other   \n",
-       "\n",
-       "         dact  mode  seq                  tst                  tet duration  \\\n",
-       "0       other  walk  1.0  1900-01-01 10:00:00  1900-01-01 10:45:00  0:45:00   \n",
-       "1        home  walk  2.0  1900-01-01 10:45:00  1900-01-01 11:30:00  0:45:00   \n",
-       "2        shop   car  3.0  1900-01-01 12:00:00  1900-01-01 12:10:00  0:10:00   \n",
-       "3        home   car  4.0  1900-01-01 12:45:00  1900-01-01 12:55:00  0:10:00   \n",
-       "4        work   car  5.0  1900-01-01 13:30:00  1900-01-01 13:50:00  0:20:00   \n",
-       "...       ...   ...  ...                  ...                  ...      ...   \n",
-       "14970    home   car  4.0  1900-01-01 20:45:00  1900-01-01 21:05:00  0:20:00   \n",
-       "14971  escort   car  1.0  1900-01-01 07:40:00  1900-01-01 07:55:00  0:15:00   \n",
-       "14972    home    pt  2.0  1900-01-01 15:00:00  1900-01-01 15:55:00  0:55:00   \n",
-       "14973   other    pt  3.0  1900-01-01 18:35:00  1900-01-01 19:35:00  1:00:00   \n",
-       "14974    home   car  4.0  1900-01-01 20:45:00  1900-01-01 21:05:00  0:20:00   \n",
-       "\n",
-       "       tst_hour  tet_hour oact_abr dact_abr  intrazonal  \n",
-       "0            10        10        h        o       False  \n",
-       "1            10        11        o        h       False  \n",
-       "2            12        12        h       sh       False  \n",
-       "3            12        12       sh        h       False  \n",
-       "4            13        13        h        w       False  \n",
-       "...         ...       ...      ...      ...         ...  \n",
-       "14970        20        21        e        h       False  \n",
-       "14971         7         7        h        e       False  \n",
-       "14972        15        15        e        h       False  \n",
-       "14973        18        19        h        o       False  \n",
-       "14974        20        21        o        h       False  \n",
-       "\n",
-       "[14975 rows x 19 columns]"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# add binary column to identify intrazonal trips\n",
-    "legs_acbm['intrazonal'] = legs_acbm['ozone'] == legs_acbm['dzone']\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add binary column to identify intrazonal trips\n",
+    "legs_acbm['intrazonal'] = legs_acbm['ozone'] == legs_acbm['dzone']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -2587,21 +2271,21 @@
      "output_type": "stream",
      "text": [
       "        purp  total_count  intrazonal_count  percentage\n",
-      "0  education          389                 3    0.771208\n",
-      "1     escort         1708                20    1.170960\n",
-      "2       home         6473               278    4.294763\n",
-      "3    medical          181                 2    1.104972\n",
-      "4      other         2487               119    4.784881\n",
-      "5       shop         1235                37    2.995951\n",
-      "6      visit          829                15    1.809409\n",
-      "7       work         1673                88    5.260012\n"
+      "0  education          385                 2    0.519481\n",
+      "1     escort         1684                16    0.950119\n",
+      "2       home         6498               399    6.140351\n",
+      "3    medical          189                 2    1.058201\n",
+      "4      other         2457               115    4.680505\n",
+      "5       shop         1337                24    1.795064\n",
+      "6      visit          816                15    1.838235\n",
+      "7       work         1737                77    4.432930\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_540731/3001768473.py:22: FutureWarning: \n",
+      "/tmp/ipykernel_753888/3001768473.py:22: FutureWarning: \n",
       "\n",
       "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
       "\n",
@@ -2610,7 +2294,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -2664,26 +2348,370 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at OD pairs. We can then distinguish between primary and secondary trips when considering intrazonal trips. This is useful because the logic for assigning trips to zones is different for primary and secondary trips. It is also different between home-work and home-education trips (both primary)"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                  od  trip_type  total_count  intrazonal_count  percentage\n",
+      "32     other - other  secondary          181              24.0   13.259669\n",
+      "53       work - home    primary         1399             152.0   10.864904\n",
+      "2   education - home    primary          339              35.0   10.324484\n",
+      "18      home - other  secondary         1961              79.0    4.028557\n",
+      "31      other - home  secondary         1866              74.0    3.965702\n",
+      "8      escort - home  secondary         1004              14.0    1.394422\n",
+      "20      home - visit  secondary          563               7.0    1.243339\n",
+      "21       home - work    primary         1451              18.0    1.240524\n",
+      "17    home - medical  secondary          167               2.0    1.197605\n",
+      "45      visit - home  secondary          530               6.0    1.132075\n",
+      "19       home - shop  secondary         1052              11.0    1.045627\n",
+      "7    escort - escort  secondary          292               3.0    1.027397\n",
+      "37       shop - home  secondary         1092              11.0    1.007326\n",
+      "15     home - escort  secondary         1067              10.0    0.937207\n",
+      "14  home - education    primary          373               2.0    0.536193\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Add the trip_type column\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",
+    "legs_acbm['trip_type'] = np.where(conditions_primary, 'primary', 'secondary')\n",
+    "\n",
+    "# Filter all trips where oact is home and dact is work or education\n",
+    "# legs_acbm_primary = legs_acbm[(legs_acbm['oact'] == 'home') & ((legs_acbm['dact'] == 'work') | (legs_acbm['dact'] == 'education'))]\n",
+    "legs_acbm_primary = legs_acbm.copy()\n",
+    "\n",
+    "# Create an od column to identify the origin-destination pairs\n",
+    "legs_acbm_primary['od'] = legs_acbm_primary['oact'] + ' - ' + legs_acbm_primary['dact']\n",
+    "\n",
+    "# Total number of trips per trip purpose\n",
+    "total_counts = legs_acbm_primary.groupby(['od', 'trip_type']).size().reset_index(name='total_count')\n",
+    "\n",
+    "# Filter the DataFrame to include only rows where intrazonal_trips is TRUE\n",
+    "intrazonal_trips_true = legs_acbm_primary[legs_acbm_primary['intrazonal'] == True]\n",
+    "\n",
+    "# Total number of intrazonal trips per trip purpose\n",
+    "intrazonal_counts = intrazonal_trips_true.groupby(['od', 'trip_type']).size().reset_index(name='intrazonal_count')\n",
+    "\n",
+    "# Merge the two DataFrames and calculate intrazonal %\n",
+    "merged_counts = pd.merge(total_counts, intrazonal_counts, on=['od', 'trip_type'], how='left')\n",
+    "# Fill NaN values with 0 (in case there are od groups with no intrazonal trips)\n",
+    "merged_counts['intrazonal_count'] = merged_counts['intrazonal_count'].fillna(0)\n",
+    "# Calculate the percentage of intrazonal trips\n",
+    "merged_counts['percentage'] = (merged_counts['intrazonal_count'] / merged_counts['total_count']) * 100\n",
+    "# Keep top 15 od pairs\n",
+    "merged_counts = merged_counts.sort_values(by='total_count', ascending=False).head(15)\n",
+    "\n",
+    "# Sort by percentage before plotting\n",
+    "merged_counts = merged_counts.sort_values(by='percentage', ascending=False)\n",
+    "\n",
+    "# Print the resulting DataFrame\n",
+    "print(merged_counts)\n",
+    "\n",
+    "# Create the bar plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "barplot = sns.barplot(data=merged_counts, x='od', y='percentage', hue='trip_type', palette='viridis')\n",
+    "plt.title('Percentage of Intrazonal Trips per Purpose')\n",
+    "plt.xlabel('Purpose')\n",
+    "plt.ylabel('Percentage of Trips that are Intrazonal')\n",
+    "plt.xticks(rotation=60)\n",
+    "\n",
+    "# Add text annotations above each bar\n",
+    "for bar, row in zip(barplot.patches, merged_counts.iterrows()):\n",
+    "    barplot.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.2, f\"({int(row[1]['total_count'])})\", color='black', ha=\"center\")\n",
+    "\n",
+    "# Remove the top and right spines (box frame) from the plot\n",
+    "barplot.spines['top'].set_visible(False)\n",
+    "barplot.spines['right'].set_visible(False)\n",
+    "\n",
+    "# Add footnote on the right below the plot\n",
+    "plt.figtext(0.95, -0.02, '(Total number of trips)', ha='right', fontsize=10)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Crowfly distance cumulative chart (KM distance vs. % of trips)"
+    "TODO: Remove later\n",
+    "\n",
+    "Check if total trips FROM and TO each activity are the same\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "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": 48,
+     "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": 57,
+   "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": 57,
+     "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": {

From 81523f8331576976be8c6ee44a0e7d98db35bebd Mon Sep 17 00:00:00 2001
From: Hussein Mahfouz <45176416+Hussein-Mahfouz@users.noreply.github.com>
Date: Mon, 7 Oct 2024 11:37:21 +0100
Subject: [PATCH 08/11] add validation logger

---
 src/acbm/logger_config.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/acbm/logger_config.py b/src/acbm/logger_config.py
index b2fc82a..09431d3 100644
--- a/src/acbm/logger_config.py
+++ b/src/acbm/logger_config.py
@@ -53,3 +53,5 @@ 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")

From c63c7c1a10f3a66a1bd389de31e3849047974216 Mon Sep 17 00:00:00 2001
From: Hussein Mahfouz <45176416+Hussein-Mahfouz@users.noreply.github.com>
Date: Mon, 7 Oct 2024 15:04:26 +0100
Subject: [PATCH 09/11] validation script and src files

---
 scripts/4_validation.py      | 319 +++++++++++++++++++++++
 src/acbm/validating/plots.py | 491 +++++++++++++++++++++++++++++++++++
 src/acbm/validating/utils.py | 125 +++++++++
 3 files changed, 935 insertions(+)
 create mode 100644 scripts/4_validation.py
 create mode 100644 src/acbm/validating/plots.py
 create mode 100644 src/acbm/validating/utils.py

diff --git a/scripts/4_validation.py b/scripts/4_validation.py
new file mode 100644
index 0000000..4ee3307
--- /dev/null
+++ b/scripts/4_validation.py
@@ -0,0 +1,319 @@
+import os
+
+import matplotlib.pyplot as plt
+import numpy as np
+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_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: Adding primary / secondary trip column")
+
+# Define the conditions for primary trips
+conditions_primary = (
+    (legs_acbm["oact"] == "home") & (legs_acbm["dact"].isin(["work", "education"]))
+) | ((legs_acbm["oact"].isin(["work", "education"])) & (legs_acbm["dact"] == "home"))
+
+# Add the trip_type column
+legs_acbm["trip_type"] = np.where(conditions_primary, "primary", "secondary")
+
+logger.info("3e. 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
+
+# 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 %
+x = 0.35
+
+sequence_nts_acbm_filtered = sequence_nts_acbm[
+    (sequence_nts_acbm["count_nts"] > x) & (sequence_nts_acbm["count_acbm"] > x)
+]
+
+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
+)
+
+# plt.show()
+
+# Save the plot
+plt.tight_layout()
+plt.savefig(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/validating/plots.py b/src/acbm/validating/plots.py
new file mode 100644
index 0000000..4a7ad2e
--- /dev/null
+++ b/src/acbm/validating/plots.py
@@ -0,0 +1,491 @@
+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()
+
+
+# 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

From ff4230e79456c8ab4b531316d546155ffffef65b Mon Sep 17 00:00:00 2001
From: Hussein Mahfouz <45176416+Hussein-Mahfouz@users.noreply.github.com>
Date: Mon, 7 Oct 2024 15:39:18 +0100
Subject: [PATCH 10/11] activity sequence function

---
 scripts/4_validation.py      | 68 +++++------------------------
 src/acbm/validating/plots.py | 85 +++++++++++++++++++++++++++++++++++-
 2 files changed, 96 insertions(+), 57 deletions(-)

diff --git a/scripts/4_validation.py b/scripts/4_validation.py
index 4ee3307..8f6d270 100644
--- a/scripts/4_validation.py
+++ b/scripts/4_validation.py
@@ -1,13 +1,16 @@
 import os
 
 import matplotlib.pyplot as plt
-import numpy as np
 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_comparison, plot_intrazonal_trips
+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
@@ -67,17 +70,8 @@
 legs_nts["tst_hour"] = legs_nts["tst"] // 60
 legs_nts["tet_hour"] = legs_nts["tet"] // 60
 
-logger.info("3d. Preprocessing: Adding primary / secondary trip column")
-
-# Define the conditions for primary trips
-conditions_primary = (
-    (legs_acbm["oact"] == "home") & (legs_acbm["dact"].isin(["work", "education"]))
-) | ((legs_acbm["oact"].isin(["work", "education"])) & (legs_acbm["dact"] == "home"))
-
-# Add the trip_type column
-legs_acbm["trip_type"] = np.where(conditions_primary, "primary", "secondary")
 
-logger.info("3e. Preprocessing: Abbreviating column values for trip purpose")
+logger.info("3d. Preprocessing: Abbreviating column values for trip purpose")
 
 # Mapping dictionary
 activity_mapping = {
@@ -211,52 +205,14 @@
 
 # Plot the comparison
 
-# 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 %
-x = 0.35
-
-sequence_nts_acbm_filtered = sequence_nts_acbm[
-    (sequence_nts_acbm["count_nts"] > x) & (sequence_nts_acbm["count_acbm"] > x)
-]
-
-fig, ax = plt.subplots(figsize=(10, 6))
-
-sequence_nts_acbm_filtered.plot(
-    x="activity_sequence", y=["count_nts", "count_acbm"], kind="bar", ax=ax
+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",
 )
 
-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
-)
-
-# plt.show()
-
-# Save the plot
-plt.tight_layout()
-plt.savefig(validation_plots_path / "4_matching_activity_sequences.png")
-
-
 logger.info("4b. Validation (Assigning) - Trip Distance")
 
 # Apply the function to legs_acbm_geo
diff --git a/src/acbm/validating/plots.py b/src/acbm/validating/plots.py
index 4a7ad2e..86eaaa5 100644
--- a/src/acbm/validating/plots.py
+++ b/src/acbm/validating/plots.py
@@ -268,7 +268,90 @@ def plot_comparison(
     # plt.show()
 
 
-# Intrazonal trips
+# ----- 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(

From 785047077abfd717488f42dba251e3daa458b0c8 Mon Sep 17 00:00:00 2001
From: Hussein Mahfouz <45176416+Hussein-Mahfouz@users.noreply.github.com>
Date: Mon, 7 Oct 2024 15:40:14 +0100
Subject: [PATCH 11/11] notebook with funcitons

---
 notebooks/5_validate_outputs.ipynb | 1571 ++++++++--------------------
 1 file changed, 420 insertions(+), 1151 deletions(-)

diff --git a/notebooks/5_validate_outputs.ipynb b/notebooks/5_validate_outputs.ipynb
index 3c23d31..e740135 100644
--- a/notebooks/5_validate_outputs.ipynb
+++ b/notebooks/5_validate_outputs.ipynb
@@ -2,19 +2,40 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "import acbm\n",
+    "import pandas as pd\n",
     "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "import geopandas as gpd\n",
-    "from shapely import wkt\n",
     "\n",
-    "from typing import Optional, Tuple\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)"
    ]
   },
   {
@@ -26,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
@@ -131,7 +152,7 @@
        "      <td>139</td>\n",
        "      <td>NaN</td>\n",
        "      <td>E00059045</td>\n",
-       "      <td>E00057153</td>\n",
+       "      <td>E00058294</td>\n",
        "      <td>escort</td>\n",
        "      <td>home</td>\n",
        "      <td>escort</td>\n",
@@ -147,7 +168,7 @@
        "      <td>312</td>\n",
        "      <td>139</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>E00057153</td>\n",
+       "      <td>E00058294</td>\n",
        "      <td>E00169797</td>\n",
        "      <td>work</td>\n",
        "      <td>escort</td>\n",
@@ -198,7 +219,7 @@
        "      <td>794027</td>\n",
        "      <td>334502</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>E00057268</td>\n",
+       "      <td>E00187122</td>\n",
        "      <td>E00057826</td>\n",
        "      <td>home</td>\n",
        "      <td>visit</td>\n",
@@ -216,7 +237,7 @@
        "      <td>334647</td>\n",
        "      <td>NaN</td>\n",
        "      <td>E00170040</td>\n",
-       "      <td>E00058459</td>\n",
+       "      <td>E00169786</td>\n",
        "      <td>shop</td>\n",
        "      <td>home</td>\n",
        "      <td>shop</td>\n",
@@ -232,7 +253,7 @@
        "      <td>794272</td>\n",
        "      <td>334647</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>E00058459</td>\n",
+       "      <td>E00169786</td>\n",
        "      <td>E00170040</td>\n",
        "      <td>home</td>\n",
        "      <td>shop</td>\n",
@@ -250,7 +271,7 @@
        "      <td>334647</td>\n",
        "      <td>NaN</td>\n",
        "      <td>E00170040</td>\n",
-       "      <td>E00057187</td>\n",
+       "      <td>E00058885</td>\n",
        "      <td>shop</td>\n",
        "      <td>home</td>\n",
        "      <td>shop</td>\n",
@@ -266,7 +287,7 @@
        "      <td>794273</td>\n",
        "      <td>334647</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>E00057187</td>\n",
+       "      <td>E00058885</td>\n",
        "      <td>E00170040</td>\n",
        "      <td>home</td>\n",
        "      <td>shop</td>\n",
@@ -286,15 +307,15 @@
        "       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  E00057153  escort   \n",
-       "3               3     312     139   NaN  E00057153  E00169797    work   \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  E00057268  E00057826    home   \n",
-       "15099       15099  794272  334647   NaN  E00170040  E00058459    shop   \n",
-       "15100       15100  794272  334647   NaN  E00058459  E00170040    home   \n",
-       "15101       15101  794273  334647   NaN  E00170040  E00057187    shop   \n",
-       "15102       15102  794273  334647   NaN  E00057187  E00170040    home   \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",
@@ -325,7 +346,7 @@
        "[15103 rows x 14 columns]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -343,7 +364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -368,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -386,7 +407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -405,7 +426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
@@ -620,7 +641,7 @@
        "[59359 rows x 10 columns]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -662,19 +683,51 @@
     }
    ],
    "source": [
-    "# add a column in legs_acbm to identify whether the trip is a primary or secondary trip\n",
+    "# # 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",
+    "# # 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",
+    "# # 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())"
+    "# # 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)"
    ]
   },
   {
@@ -700,7 +753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
@@ -739,184 +792,6 @@
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "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>dact</th>\n",
-       "      <th>count</th>\n",
-       "      <th>source</th>\n",
-       "      <th>percentage</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>education</td>\n",
-       "      <td>1823</td>\n",
-       "      <td>nts</td>\n",
-       "      <td>3.071143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>escort</td>\n",
-       "      <td>5180</td>\n",
-       "      <td>nts</td>\n",
-       "      <td>8.726562</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>home</td>\n",
-       "      <td>26434</td>\n",
-       "      <td>nts</td>\n",
-       "      <td>44.532421</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>medical</td>\n",
-       "      <td>859</td>\n",
-       "      <td>nts</td>\n",
-       "      <td>1.447127</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>other</td>\n",
-       "      <td>9628</td>\n",
-       "      <td>nts</td>\n",
-       "      <td>16.219950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>shop</td>\n",
-       "      <td>5621</td>\n",
-       "      <td>nts</td>\n",
-       "      <td>9.469499</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>visit</td>\n",
-       "      <td>3313</td>\n",
-       "      <td>nts</td>\n",
-       "      <td>5.581293</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>work</td>\n",
-       "      <td>6501</td>\n",
-       "      <td>nts</td>\n",
-       "      <td>10.952004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>education</td>\n",
-       "      <td>385</td>\n",
-       "      <td>acbm</td>\n",
-       "      <td>2.549162</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>escort</td>\n",
-       "      <td>1684</td>\n",
-       "      <td>acbm</td>\n",
-       "      <td>11.150103</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>home</td>\n",
-       "      <td>6498</td>\n",
-       "      <td>acbm</td>\n",
-       "      <td>43.024565</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>medical</td>\n",
-       "      <td>189</td>\n",
-       "      <td>acbm</td>\n",
-       "      <td>1.251407</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>other</td>\n",
-       "      <td>2457</td>\n",
-       "      <td>acbm</td>\n",
-       "      <td>16.268291</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>shop</td>\n",
-       "      <td>1337</td>\n",
-       "      <td>acbm</td>\n",
-       "      <td>8.852546</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>visit</td>\n",
-       "      <td>816</td>\n",
-       "      <td>acbm</td>\n",
-       "      <td>5.402900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>work</td>\n",
-       "      <td>1737</td>\n",
-       "      <td>acbm</td>\n",
-       "      <td>11.501026</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        dact  count source  percentage\n",
-       "0  education   1823    nts    3.071143\n",
-       "1     escort   5180    nts    8.726562\n",
-       "2       home  26434    nts   44.532421\n",
-       "3    medical    859    nts    1.447127\n",
-       "4      other   9628    nts   16.219950\n",
-       "5       shop   5621    nts    9.469499\n",
-       "6      visit   3313    nts    5.581293\n",
-       "7       work   6501    nts   10.952004\n",
-       "0  education    385   acbm    2.549162\n",
-       "1     escort   1684   acbm   11.150103\n",
-       "2       home   6498   acbm   43.024565\n",
-       "3    medical    189   acbm    1.251407\n",
-       "4      other   2457   acbm   16.268291\n",
-       "5       shop   1337   acbm    8.852546\n",
-       "6      visit    816   acbm    5.402900\n",
-       "7       work   1737   acbm   11.501026"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "purpose_compare"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -927,7 +802,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [
     {
@@ -974,14 +849,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 1000x500 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -989,37 +864,26 @@
     }
    ],
    "source": [
-    "# Calculate the percentage of each hour in legs_acbm_clean['hour']\n",
-    "acbm_hour_counts = legs_acbm['tst_hour'].value_counts(normalize=True).sort_index() * 100\n",
-    "\n",
-    "# Convert legs_nts[\"tst\"] from minutes to hours\n",
-    "legs_nts['tst_hour'] = legs_nts['tst'] // 60\n",
-    "\n",
-    "# Calculate the percentage of each hour in legs_nts['hour']\n",
-    "nts_hour_counts = legs_nts['tst_hour'].value_counts(normalize=True).sort_index() * 100\n",
-    "\n",
-    "# Plot the histogram using seaborn\n",
-    "sns.histplot(legs_acbm['tst_hour'], bins=24, kde=False, discrete=True, stat='percent')\n",
-    "plt.xlabel('Hour of Day')\n",
-    "plt.ylabel('Percentage')\n",
-    "plt.title('Comparison of Activity Data Over Hours of the Day')\n",
-    "plt.xticks(range(0, 24), rotation=45)  # Set x-ticks to represent each hour of the day\n",
-    "\n",
-    "# Add dots to represent the percentage values of legs_nts[\"tst\"]\n",
-    "for hour, percent in nts_hour_counts.items():\n",
-    "    plt.plot(hour, percent, 'ro')  # 'ro' means red dot\n",
-    "\n",
-    "plt.show()"
+    "# 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": 13,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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rr7+apUuXmpdfftnUqlXL5fnfs2dPutuTcpvSK1ovXrzYmvfpp5+mGefVq1dNaGiokWTuvffeVPPdFTauXLlitm/fbqZPn+7yvkgZ69mzZ63tcrZ79dVX04zlypUrpkSJEkaS6dChQ7r7Ny3JX0uZ9csvv6R7HO3du7e59dZbzciRI83nn39uNmzYYDZt2mTmz59vBg4caL3fSpQoYU6cOOGy7JEjR8z27dvNHXfcYSSZO+64I9V+2rNnj8syjRs3NjVq1DDPPfec+eqrr8zPP/9sNm7caD7//HPTs2dPq2BYuXJlc+nSpSztp3r16lnbfOzYseta9rHHHrOW/fbbb63pf/zxhzU9rRONnOrUqWMkmdtvvz3VvLVr11p5pmTJkmb8+PFm0aJFZsuWLWbRokXWCRmSTJcuXdz275xfo0YN69/p06ebX375xaxevdq8/fbb17XNOVG0zo5jdWYLj5ktWtesWdNIMh07djQLFiwwW7ZsMd9//711ctK2bdus11/58uXNW2+9ZVasWGG2bt1qfvrpJzN16lTz4IMPGj8/vywVrXNLTsjK+9a5zkaNGhkPDw9Tq1YtM336dLNp0ybz008/meHDh1v7Ljw83MTHx7td9xNPPGH1ddddd5np06ebVatWmV9++cVMnTrV3H777db8b7755rr3sTFZL1q//vrr1nKvvfZaqvllypQxDRs2NC+//LL59ttvzaZNm8y6devM7NmzTbt27TI8Pjuf+8qVK6cbx7Bhw6zifXR0dKbjBwAAQP5B0RoAACAXmj17tvUj4HPPPXfD/f3+++/Wj6rVq1e3ik7JLV682GrToEGDVPNTjlqePHlyqjbHjx83/v7+VqHH4XCYr776KlW75D+QO0dlJ5dyxGf79u3N1atXU7X7z3/+Y7UZNWpUqvmnTp1yu61O8fHxpnXr1kZKGl117dq1VG2SF609PDzM0qVL0+zPmLSL1pcuXbIKNmmNpHY6ffp0qmnO4r2np6fbGM6cOWON4vbw8HB7MkDyIn29evVMbGxsqjbJX3sTJ05MN860vPvuu1YfjzzyiElMTEzVJvkoyRst3GTW0KFDjZQ0Ii5l4cV5Ikb9+vXTXP6bb76xYoqMjEz3tRUVFZVqWma3J72idUbFaKfkxe05c+ZcVyzJ3+srV65MN9amTZsaSaZSpUpptlmwYIHVn7vjQWZkpWgdHx9vHWduvfXWVPP//PNPt69Np99//90ULVrUSDLPP/+82zbpFQ1T2rt3b7rzly1bZsX7v//9L8P+3HEeY8LCwq572Q8++CDN4nTdunXTLEY7JS9upzyJ4cqVK9ZVKtq1a2cuXLjgto+PPvrI6sPdVT+S54W7777bXL58+bq3M7nkRevx48enKmC6ezgLnmkVrbPjWJ3dRev0XsPGGPPCCy8YKekKDukVC8+dO5fm6PD05LaccD3v25SfRdwVpcePH2+1WbBgQar5P/zwgzU/rff2pUuXTMuWLa3PI+4+82Qkq0Xr5cuXW8v1798/1fyMjl3JT3Ravnx5qvnTpk2z5rsbyW1M0jHCeYWhTp06ZTp2AAAA5C/c0xoAACAXOn36tPX/0NDQG+7v/fffV2JioiTpf//7n4KCglK1adeunfr37y9J+uWXX7Rp06Y0+2vYsKGGDh2aanpYWJjuv/9+SUn38+zRo4c6d+6cql3NmjXVpEkTSdKaNWvSjd3X11dTp06Vl5dXqnnPPfecqlevLkmaNm1aqnsqFi9e3O22Ovn4+OiNN96QJB06dEi//fZburH07dtXbdq0SbdNWs6cOWPdK/Kuu+5Kt21wcLDL38mfj0cffdRtDMWKFdNHH30kKem+oSnvYZzS9OnTFRAQkGr6gw8+qNKlS0vK+LlJy3vvvSdJKlGihN59912Xe5E6vfTSS9Z92qdOnar4+PgsrSuzrl27ps8++0yS1LlzZxUtWtRl/sMPPywp6X6lu3fvdtvHa6+9JkkqUqSI5s+fn+5ry3k/5ezm5eWlBx54QJL0ww8/uBwrkvv0008lSUWLFlWnTp1yJBZJ+te//iVJ2rt3r9atW+e2zYwZMyQlHcvuu+++HIslJR8fH/n7+0tKuidrSs57NaelRo0a1vZ9/fXXNxxPxYoV053fqlUrdezYMcvri42NtY4xYWFh1718yZIlrf+nfF0576G+c+dObdu2ze3yztecw+HQgw8+6DJv7ty5OnjwoAoVKqSPP/5YRYoUcdvHo48+qgYNGkiSy/3cU/Lw8ND//vc/+fr6pr9R1+H5559XjRo1Mnxs3rw5zT5y4lidHSpVqpTu/bGjo6OtdslfBykFBgbKw+P6f0bKjTnhehUqVEgzZsyQj49Pqnn//ve/renu8qYzd3Tt2lUDBgxIs/93331XUtLnkZUrV2ZX6BkKCQmx/u/uWJnRsatfv36qXbu2JPfHrgceeMA6FjvzQUqLFi1STEyMJFmfRQEAAFDwULQGAADIhc6fP2/938/P74b7W758uSTp9ttvV8OGDdNs9+ijj6Zaxp2ePXumOa9WrVrX1e6vv/5Ks40ktWnTxiqipuTh4aE+ffpISioK//rrr+n2FR8fr6ioKO3atUs7duzQjh07ZIyx5qdVjHFyFm6yIiQkxPpR+5NPPtG1a9cyvWzy5yKtH7wlqXHjxqpatWqqZVKqUaOGatas6Xaew+FQnTp1JGX83Lhz7Ngx/fHHH5KkHj16WD9Up+Tl5aV+/fpJSvqRPKPn7kYtWbJEJ0+elCQ99NBDqeY/8MAD8vb2lpT0/KR0+vRpbdy40Wqb1mvyZnC+Dq9evap58+almn/p0iWrcNC5c+c0C4TZoXv37goMDJTkvhhx4sQJLV68WFLSiQHuTj7JSc6TE5IfU9Ny9uxZ7d+/Xzt37rSOD84TE3bt2mUVhLPLqVOntG/fPmtdO3bsUIkSJSRlfCxyJ/k2pjwpIzOSLxMXF+cyr2fPnlaxcs6cOW6Xd54U0rhxY0VERLjMW7hwoSSpWbNm1jamxXlSz4YNG9Js07hxY5UrVy7dfuyQ3cfq7PLAAw/I09MzzfmlSpWSlPQ6/+WXX7J13bk1J1yv1q1bp3kSob+/v1XYTZk34+LitGrVKklSt27d0l1H1apVVbx4cUnpv/6zW/L3fkbHSmOMoqOjtXfvXpdj1y233CLJ/bHLz8/P+jw4b948Xbx4MVUbZ/4ICwtT+/bts7wtAAAAyNsoWgMAAORCyX/UvXDhwg31FR8fr3379klSugVrSapTp45VuNuxY0ea7SpVqpTmvOSjTzPTLqMfSOvXr5/ufOeoPEnavn17qvkXLlzQhAkTVKtWLfn5+SkiIkK33367NWrOWaCVZI3ySUtahd7M8PX1tUbIzp8/X7fddptGjx6t77//XufOnUt3Wedz4ePjY41mSovzOd63b1+qkedOztFsaXGO9M5MoS+l5K+bjF5vyeen93rLDrNmzZKUNNq3devWqeYXL15cbdu2lSTNnj3b5WQGSfrtt9+saU2bNs3RWDPSsGFDVahQQdI/o1uTW7hwof7++29JN3aiRWYULlzYGlXrrhiR/AQNO0bPOV/D7q4qICUdM/r3769SpUopODhYt912m6pXr24dH5yjUxMTE92OQLxe69at0wMPPKCQkBCFhoaqUqVKLqN4p06dKinjY5E7yfOG8/m/HsmXSbm/SpcurRYtWkhKKk6nfH/8/PPP2r9/vyT3rznn6OSlS5fK4XCk+3jzzTcl/TP6150bORanZcaMGTJJt1BL99GsWbM0+8juY3V2yWh/9erVS97e3oqPj1fjxo3VoUMHffDBB6lO7MqK3JoTrldW8+bWrVutK9306tUrw9e/872f3us/uyWPOa1j5Xfffaf77rtPgYGBKlWqlCpXruxy7Pruu+8kpX3scl61Ii4uTl9++aXLvOjoaC1ZskSSPSc3AQAAIPegaA0AAJALJb9U44kTJ26or+SFlowuNe7t7W2t+8yZM2m2S2/kZvJLh2amnfPH3LRkFHPyS5mmjPngwYOqUaOGnn32Wf3+++9KSEhIt69Lly6lO79YsWLpzs/Iu+++qw4dOkhKuvznG2+8oXvvvVchISGqX7++3njjDcXGxqZazrldwcHBGf6Y67wssDEmzSJbRiNvnc9NRvvLneTPQUbPXfJLGKf3ertR586d06JFiyQljRhNax86LxEeFRVljYxzSv5DvHNUop2chcH169fr4MGDLvOchezQ0FC1atUqx2NxFiPOnz+v+fPnu8xzjp5r2LChqlWrluOxJBcfH28VY1Jecl9KuqVA3bp1NWPGjEwViDI6PmRk3LhxatKkiebNm5fh6z0r6woICLBe21kpeCXPNclzkJPzNXf48GH99NNPLvOcrzlvb29179491bLOqxxcj/T2wY0ei3NKdh+rs0tG+6tKlSr67LPPVKxYMV27dk3ffvutBg0apBo1aig0NFQPP/xwlm8XkRtzQlZkNW9m5bUvye1o5JySPL+lPFYaY/Svf/1L9913n7777rsMT2ZL633boEED1ahRQ1Lqq3J8/PHHtp7cBAAAgNyDojUAAEAulPwS29l5icz07t+aW91IzA8//LAOHDggh8Oh/v3764cfftDhw4d1+fJlJSYmyhjj8gNzRiPK0ru8amYEBARo4cKF+vnnnzVy5EjVq1dPnp6eSkxM1ObNmzV69GhVqlQpzcuC5rXnL7fE+/nnn1v3R33nnXfSHOHmHAkvJf2Inps5C4jGGOuyzFJSoWfp0qWSki4JfDNGrNWtW9e6YkHyYsTPP/+sXbt2SbKnELFt2zbrPV25cmWXebt379bAgQN17do1hYaG6o033tCWLVt0+vRpXblyxRpVO23aNGuZGxlxumLFCr300kuSpFtvvVVTpkzR77//rnPnzunq1avW+l544YUsr8PhcFgjaqOjo6+7cJ081yTPQU5du3ZVoUKFJLleIjwhIcG6TH27du3cFrydx9l77rlH27dvz/QjLTd6LM5pueXY55SZ/dW1a1cdOHBAH374obp06WJdxj0mJkazZ8/WXXfdpb59+2Z4oll6ctt+uRmSf8b48MMPM/3af+WVV25ajFu3brX+n/JYOX36dOs4WLt2bc2cOVN//PGH4uLidO3aNevY5TzpK73jpPMEp1WrVrmcbOXMG5GRkRmOaAcAAED+xjV3AAAAcqHbb79dxYsXV0xMjNasWaO4uLg0L9mYkeQjrDIatX3t2jWdPn1akvuRiXbIKObk85PHvHv3bq1du1aS9Oyzz2r8+PFul7djNFeDBg2sy5qfP39eq1at0syZM7VgwQKdPHlSXbt21f79+1W4cGFJ/2zX6dOnde3atXQLkc5ClcPhsGU0YvLnIKPnLnlRLSdfb1kpQH/55Zd67733rNF1zvuMStLx48ezLbasqlSpku644w5t3rxZc+bM0ZgxYyQlXXreeanhnL40eHL/+te/NHjwYK1evVoHDhxQ+fLlrUJEkSJF0r2/fU5ZtmyZ9f8mTZq4zJs5c6auXbsmT09PrV69Os1CSXYdH5yX/S5WrJg2btyY5n2db3R9d911l1V8XrhwoR577LFMLZeYmKjvv/9eUtKI0caNG6dqExAQoPvuu0/z58/X/Pnz9e6778rb21srVqyw3utpveZCQkJ07NgxXblyRdWrV8/KpuUJ2XWsdjgcMsZkWCC+0duHpBQYGKjHHnvMet388ccf+uabb/Tf//5Xx44d06xZs1SnTh0NHTo0033mxpxwMyU/iaNIkSK58vWf3rHSeey67bbbtH79eutzSUqZOXY99NBDGj16tOLj4zVz5kyNGzdOGzdu1O7duyUxyhoAAACMtAYAAMiVHA6H+vTpIynpR+n//e9/We7L19dXFStWlJQ08jE9W7du1dWrVyUp1/ywumnTpkzPTx7zzp07rf8nH0GbkvNeq3bx9/dXhw4d9OWXX+rf//63pKSiqLPgLv2zXVeuXNFvv/2Wbn+//PKLJKlixYry8fHJmaDTkfw5yOj15ow15XLZaf/+/Vq/fr2kpEuDf/bZZ+k+nKPbzp8/r6+++srqp06dOtYowZSXRraLs0C4Y8cO/f7775L+uUxzhQoVMrx/bFqyMhqyd+/eKly4sIwxmjlzpi5duqS5c+dKShrBmdWTbrLq8uXL+uCDDyQlbU+nTp1c5juPD7Vq1Up3ZF9Gx4fM7ivn+lq0aJFmwToz68tI3759rf+/9957mb7E/5dffqkjR45Ikjp37qygoCC37ZyvuTNnzmjx4sWS/nnN+fv7q2PHjm6Xc47E37x5c47fv9lO2XWsdt6fPL3Lhhtj9Oeff95AtBmrWrWqnnnmGW3cuFF+fn6SZI2qz6zclhOkmzviu3bt2tb61q1bd9PWm1k7duzQihUrJElly5bVHXfc4TLfeezq2LFjmgVrY0ymrgoUHBysLl26SJJmzZolY4ymT58uSfLz80v3sxoAAAAKBorWAAAAudTw4cOtUZ4vvviiNRIlI4mJiVYRwcl5X9udO3e6/CicUvLi+M24F25m/PDDD2mObE1MTNSsWbMkJY1grFu3rjXPeX9EKf3RaM7CVm5w9913W/9Pfo/J5M+F8wdedzZs2GBdjtmu56906dKqWrWqpKTixt9//+22XUJCgmbOnCkp9XOXnZKPsn7qqafUs2fPdB+jR4+2RsYlXzY4OFh33nmntV3Hjh277licl1Z2Xqr8RvXs2dO67O+nn36qI0eOWPedvZFR1s44pczHGhgYqG7duklKKkbMnz/fuj+7HaPnhg8f7lKEdb4mnZzHh/SODcePH9fChQvTXU9mn9PMrG/r1q0ZFvUyUqtWLbVt21aS9Pvvv+uNN97IcJmYmBiNGDFCUlIxb/To0Wm2bd++vTUq+NNPP9Xly5etkzvuv//+NItazmJ2bGxsqvvZ5ifZdawuX768pPRPYli8eLHOnTuXxUivT9myZVWpUiVJrrkpM3JbTpCy/1icnhIlSqhRo0aSki6rf+rUqRxfZ2ZdunRJjzzyiHVJ76eeeirV1QEyc+z65ptvMn0FEuclwg8ePKjvvvtOn3/+uSSpW7du1skaAAAAKLgoWgMAAORSt9xyi959911JST8WNmvWTKtXr053mV27dqldu3apChWDBg2Sh0fSR7/HHntMcXFxqZb94YcfrPsWNmjQQPXr18+Ozbhh8fHxevzxx92OGHzttdes+572799fvr6+1jzn6HJJ1g/hKb3//vv65ptvsjfgNPz1118ZPn8//PCD9X9n0UJKej6co5+mTp1qjYpKLjY2Vo8//rikpMv7Dho0KDvCzpLBgwdLkk6dOmWNHk/ppZdesoo2jz76qMtzl12MMZo9e7YkqVy5cqpXr16Gy3h5ealz586Sku5DnPyH+KefflqSdPHiRXXv3t0qyrrjLJgmV6pUKUlJo7+zQ1hYmFq2bClJ+uyzzzRnzhyr+HAjRWtnnNL1xeosRhw6dMgqfFaoUEHNmjXLcizXKyYmRg899JB1MkrJkiU1efLkVO2cx4d9+/ZZI/GTu3jxoh588EFdunQp3fU599Vff/2V7r1cnetbu3at29Gxp06dsu4Je6M++ugja6T0s88+q8mTJ6cZ26FDh9SqVSvr9Tpq1Kh0R+j7+PhYJycsWrRIc+bM0fnz5yWl/5rr06ePypYtKympMJbR1QrWrl2b4fEyN8quY7XzPfPzzz+7HZ0bHR2tJ598Mtvi/vrrr9MtgB8+fNg6cS55bsqs3JITnDL7vs0uzz//vCQpLi5O3bp1S3dfx8fH67333tPly5dzNKZdu3apSZMm1v2smzVr5va16Dx2LVq0yO0lwPfv3289v5nRokULVahQQVLS8+z8PMqlwQEAACBxT2sAAIBcrV+/fjpy5IhefPFFnTx5Us2bN1ebNm3UqVMnVa1aVUFBQTpz5oz27t2r7777TkuWLFFCQoJq1arl0k+NGjU0cuRIvfHGG9q2bZvq1q2rp59+WnXq1NGFCxe0aNEivfPOO0pISJCPj48+/PBDm7Y4tTvuuEOLFi1S48aNNXz4cFWsWFEnT57UrFmzrMsPlylTRi+88ILLcnXq1FH16tW1Y8cOffjhhzp79qwefvhhlSpVSkeOHNHs2bM1f/58NW7c+KZcsjMqKkotWrRQtWrVdP/99+uOO+7QLbfcIimpIPD5559bl12tXbt2qsLR1KlT1bBhQ125ckXt27fXk08+qQ4dOsjPz09bt27Va6+9pr/++ktSUlHIzsu7Dxw4UJ9++qk2bNigGTNm6NChQ3riiSdUvnx5HT9+XNOnT9eCBQskJRU1Uz532WXt2rXWPunatWuml+vataumTZumhIQEzZ49W6NGjZIkdejQQQMGDNC0adO0fv16VatWTUOGDFHjxo0VEBCgmJgYbd68WZ9//rlq1aqV6mSJO++8UwcOHNDChQv14YcfqnHjxtaIv4CAAIWGhl73Nvbu3VvLli3T4cOHNWHCBElJ7xnnqMisCA8PV5kyZXTkyBG9+eabKlOmjCpXrmyN6i5ZsqTbEXF33XWXKlWqpL1791r3pu3bt2+2Xor3woUL2rFjh/V3fHy8zp07p3379mnt2rVasGCBVWguXbq0vv76a4WHh6fq5+GHH9Z///tfJSYm6t5779WoUaPUpEkTFSpUSFu2bNGkSZO0b9++DI8Pd955p2bMmKGTJ09qxIgReuihhxQYGChJ8vb2VkREhCTpkUce0aJFi6wTkJ555hnrJIr169dr4sSJio6OVmRkpDZs2HBD+yg8PFxfffWVOnXqpLi4OA0fPlyffvqp+vTpo5o1a8rPz0/Hjh3TkiVLNGvWLGsE5QMPPKDx48dn2H/v3r01depUXbp0SSNHjpSU9JpIfqWIlHx9fTVv3jw1b95cf//9t1q2bKmePXuqc+fOKl++vBITE3X8+HFt2bJFX331lbZv367//ve/N/WEh+ySHcfqxx57TFOmTNG1a9fUoUMHvfjii2rSpImuXLmidevWaeLEibp69aoqVqyoffv23XDMkydPVu/evXXvvfeqZcuWqlq1qgIDA3X27Flt3rxZ//3vf6331cCBA6+7/9ySE5wy+77NLu3bt9fQoUP19ttv66efflLVqlU1cOBANWnSRCEhIbpw4YL+/PNPrVmzRgsWLNDZs2etW8Rk1cmTJ12OlRcuXNDZs2f1+++/a8WKFVq2bJlVsG/UqJHmz58vb2/vVP088sgjGjVqlI4dO6bIyEg9/fTTql69ui5fvqwff/xRkydPVnx8vOrWrZupS4Q7HA71799fzz33nJUnbrvtNt111103tL0AAADIJwwAAAByvS+//NKUK1fOSMrwcfvtt5ulS5em6iMhIcE88cQT6S4bGBjodlljjFm5cqXVbuXKlWnGOmPGDKvdgQMH0mw3duxYq11KBw4csObNmDHD9O3bN82YS5UqZXbu3Ol2HVu3bjXFihVLc9kaNWqYY8eOWX+PHTv2uuJ0JyIiwkgyffr0cZmefP+l96hSpYr566+/3Pa9dOlSExAQkO7ygwcPNgkJCdcVW0p9+vQxkkxERESmttmd06dPm8aNG6cba9WqVc3BgwfT7CO95yUz/vWvf1l9bNiwIdPLXblyxQQFBVmvkeSuXbtmhgwZYhwOR7rb5m4fb9261fj6+mbYPuXrPz1xcXGmcOHCLn1NmjQpw23MaN9OmTIlzW1LL6b/+7//s9p5eHiYw4cPZxhLZmTmveN8FCpUyPTv39/ExMSk2+dLL72Ubj8jR47M8Hh2/vx5c+utt7pdPuX7p1+/fmmuy9PT00yePPm6jzfp+f33303Dhg0z3F9FihQx//nPf0xiYmKm+k1MTDRly5Z16WPo0KGZWnbDhg2plk3rMWvWrFTL3+gxIaXkz29G7zWnZs2aZfgc3eix2hhjJk6cmOaywcHB5qeffrJiadasWarlM5uzU25TWg8PDw/z8ssvZ2ofuZMbcoLT9bxvM7vO9J4LY5LeNy+99JLx8vL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       "text/plain": [
        "<Figure size 2000x1000 with 8 Axes>"
       ]
@@ -1029,91 +893,15 @@
     }
    ],
    "source": [
-    "def plot_time_of_day_comparison(legs_acbm: pd.DataFrame, \n",
-    "                                legs_nts: pd.DataFrame, \n",
-    "                                activity_column: str = 'dact', \n",
-    "                                hour_column: str = 'tst_hour', \n",
-    "                                num_cols: int = 4, \n",
-    "                                max_y_value: Optional[int] = None,\n",
-    "                                figsize: Optional[Tuple[int, int]] = (20, 5)) -> None:\n",
-    "    \"\"\"\n",
-    "    Plots a comparison of activity data over the hours of the day for different activity types.\n",
-    "\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    legs_acbm : pd.DataFrame\n",
-    "        DataFrame containing the ACBM data.\n",
-    "    legs_nts : pd.DataFrame\n",
-    "        DataFrame containing the NTS data.\n",
-    "    activity_column : str, optional\n",
-    "        The column name for the activity types. Default is 'dact'.\n",
-    "    hour_column : str, optional\n",
-    "        The column name for the hour values. Default is 'tst_hour'.\n",
-    "    num_cols : int, optional\n",
-    "        The number of columns for the subplots. Default is 4.\n",
-    "    max_y_value : int, optional\n",
-    "        The maximum value for the y-axis. Default is 100.\n",
-    "    figsize : tuple of int, optional\n",
-    "        The size of the figure. Default is (20, 5).\n",
-    "\n",
-    "    Returns\n",
-    "    -------\n",
-    "    None\n",
-    "        This function generates and displays a plot but does not return any value.\n",
-    "    \"\"\"\n",
-    "    # Get unique dact values\n",
-    "    unique_dact_values = legs_acbm[activity_column].unique()\n",
-    "\n",
-    "    # Create a figure with subplots\n",
-    "    num_plots = len(unique_dact_values)\n",
-    "    num_rows = (num_plots + num_cols - 1) // num_cols\n",
-    "    fig, axes = plt.subplots(num_rows, num_cols, figsize=(figsize[0], num_rows * figsize[1]), sharey=False)\n",
-    "    axes = axes.flatten()\n",
-    "\n",
-    "    # Iterate over unique dact values and create plots\n",
-    "    for i, dact_value in enumerate(unique_dact_values):\n",
-    "        ax = axes[i]\n",
-    "        acbm_data = legs_acbm[legs_acbm[activity_column] == dact_value]\n",
-    "        nts_data = legs_nts[legs_nts[activity_column] == dact_value]\n",
-    "\n",
-    "        # Plot histogram for acbm_data\n",
-    "        hist = sns.histplot(acbm_data[hour_column], bins=24, kde=False, discrete=True, stat='percent', ax=ax, label='ACBM')\n",
-    "        ax.set_xticks(range(0, 25))  # Set x-ticks to represent each hour of the day, including 24\n",
-    "        ax.set_xlim(0, 24)  # Ensure the x-axis covers the full range from 0 to 24\n",
-    "        if max_y_value:\n",
-    "            ax.set_ylim(0, max_y_value) # So that all the plots have the same y axis\n",
-    "        ax.set_title(f'dact: {dact_value}', fontsize=16)\n",
-    "        ax.tick_params(axis='x', rotation=45, labelsize=12)  # Rotate x-axis labels by 45 degrees\n",
-    "\n",
-    "        # Remove individual subplot labels\n",
-    "        ax.set_xlabel('')\n",
-    "        ax.set_ylabel('')\n",
-    "\n",
-    "        # Add dots to represent the percentage values of legs_nts[\"hour\"]\n",
-    "        nts_hour_counts = nts_data[hour_column].value_counts(normalize=True).sort_index() * 100\n",
-    "        line, = ax.plot(nts_hour_counts.index, nts_hour_counts.values, 'ro', label='NTS')  # 'ro' means red dot\n",
-    "\n",
-    "    # Remove any empty subplots\n",
-    "    for j in range(i + 1, len(axes)):\n",
-    "        fig.delaxes(axes[j])\n",
-    "\n",
-    "    # Add a main legend at the bottom\n",
-    "    handles, labels = ax.get_legend_handles_labels()\n",
-    "    fig.legend(handles, labels, loc='lower center', ncol=2, bbox_to_anchor=(0.5, -0.05))\n",
-    "\n",
-    "    # Add a main title to the figure\n",
-    "    fig.suptitle('Comparison of Activity Data Over Hours of the Day', fontsize=20)\n",
-    "\n",
-    "    # Add a single centered x-label and y-label\n",
-    "    fig.text(0.5, 0.02, 'Hour of Day', ha='center', fontsize=14)\n",
-    "    fig.text(0.02, 0.5, 'Percentage of Trips', va='center', rotation='vertical', fontsize=14)\n",
-    "\n",
-    "    # Adjust layout to make room for the main title and labels\n",
-    "    plt.tight_layout(rect=[0.03, 0.05, 1, 0.95])\n",
-    "    plt.show()\n",
-    "\n",
-    "# Example usage\n",
-    "plot_time_of_day_comparison(legs_acbm, legs_nts, max_y_value=70)"
+    "# 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",
+    ")"
    ]
   },
   {
@@ -1123,43 +911,6 @@
     "## Matching: Activity Sequences"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Abbreviate values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Abbreviate the column values \n",
-    "\n",
-    "# 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",
-    "# Replace values in 'origin activity' and 'destination activity' columns\n",
-    "legs_acbm['oact_abr'] = legs_acbm['oact'].replace(activity_mapping)\n",
-    "legs_acbm['dact_abr'] = legs_acbm['dact'].replace(activity_mapping)\n",
-    "\n",
-    "# Replace values in 'oact' and 'dact' columns\n",
-    "\n",
-    "legs_nts['oact_abr'] = legs_nts['oact'].replace(activity_mapping)\n",
-    "legs_nts['dact_abr'] = legs_nts['dact'].replace(activity_mapping)\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1169,78 +920,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from pandas import DataFrame\n",
-    "\n",
-    "def process_sequences(df: DataFrame, \n",
-    "                      pid_col: str, \n",
-    "                      seq_col: str, \n",
-    "                      origin_activity_col: str, \n",
-    "                      destination_activity_col: str,\n",
-    "                      suffix: str) -> DataFrame:\n",
-    "    \"\"\"\n",
-    "    Processes a DataFrame to generate activity sequences and counts the number of occurrences of each sequence.\n",
-    "\n",
-    "\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    df: pd.DataFrame\n",
-    "        The input DataFrame containing the data.\n",
-    "    pid_col: str\n",
-    "        The name of the column representing the unique identifier for each group.\n",
-    "    seq_col: str\n",
-    "        The name of the column representing the sequence order within each group.\n",
-    "    origin_activity_col: str\n",
-    "        The name of the column representing the origin activity.\n",
-    "    destination_activity_col: str\n",
-    "        The name of the column representing the destination activity.\n",
-    "    suffix: str\n",
-    "        The suffix to be added to the count column name.\n",
-    "\n",
-    "    Returns\n",
-    "    -------\n",
-    "    pd.DataFrame\n",
-    "        A DataFrame with the activity sequences and their counts.\n",
-    "        \n",
-    "        activity_sequence           count_{suffix}\n",
-    "        ----------------------      --------------\n",
-    "        home - work - visit - home              5\n",
-    "        home - school - home                    3\n",
-    "        home - work - home                     20\n",
-    "    \"\"\"\n",
-    "    # Step 1: Sort the DataFrame by 'pid' and 'seq'\n",
-    "    sorted_df = df.sort_values(by=[pid_col, seq_col])\n",
-    "    \n",
-    "    # Step 2: Group by 'pid' and concatenate 'origin activity' values followed by the last 'destination activity' value\n",
-    "    activity_sequence_df = sorted_df.groupby(pid_col).apply(\n",
-    "        lambda x: ' - '.join(x[origin_activity_col].tolist() + [x[destination_activity_col].iloc[-1]])\n",
-    "    ).reset_index()\n",
-    "    \n",
-    "    # Rename the columns for clarity\n",
-    "    activity_sequence_df.columns = [pid_col, 'activity_sequence']\n",
-    "    \n",
-    "    # Step 3: Group by the resulting 'activity_sequence' column and count the number of values in each group\n",
-    "    result = activity_sequence_df.groupby('activity_sequence').size().reset_index(name=f'count_{suffix}')    \n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_753888/491085427.py:44: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
-      "  activity_sequence_df = sorted_df.groupby(pid_col).apply(\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -1269,28 +951,28 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>education - education - education</td>\n",
-       "      <td>4</td>\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>education - education - education - home</td>\n",
+       "      <td>e - e - e - h - o - h</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>education - education - education - home - oth...</td>\n",
+       "      <td>e - e - h</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>education - escort</td>\n",
-       "      <td>1</td>\n",
+       "      <td>e - h</td>\n",
+       "      <td>10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>education - escort - home</td>\n",
-       "      <td>2</td>\n",
+       "      <td>e - h - e</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1299,27 +981,27 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1818</th>\n",
-       "      <td>work - work - work - home - other - home - oth...</td>\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>work - work - work - home - work - work - home...</td>\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>work - work - work - shop - work</td>\n",
+       "      <td>w - w - w - sh - w</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1821</th>\n",
-       "      <td>work - work - work - work - home - home - home...</td>\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>work - work - work - work - work - home</td>\n",
+       "      <td>w - w - w - w - w - h</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1328,51 +1010,45 @@
        "</div>"
       ],
       "text/plain": [
-       "                                      activity_sequence  count_nts\n",
-       "0                     education - education - education          4\n",
-       "1              education - education - education - home          1\n",
-       "2     education - education - education - home - oth...          1\n",
-       "3                                    education - escort          1\n",
-       "4                             education - escort - home          2\n",
-       "...                                                 ...        ...\n",
-       "1818  work - work - work - home - other - home - oth...          1\n",
-       "1819  work - work - work - home - work - work - home...          1\n",
-       "1820                   work - work - work - shop - work          1\n",
-       "1821  work - work - work - work - home - home - home...          1\n",
-       "1822            work - work - work - work - work - home          1\n",
+       "                              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": 16,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "sequence_nts = process_sequences(df = legs_nts, \n",
-    "                                 pid_col = 'IndividualID', \n",
-    "                                 seq_col = 'seq', \n",
-    "                                 origin_activity_col = 'oact', \n",
-    "                                 destination_activity_col = 'dact',\n",
-    "                                 suffix = 'nts')\n",
+    "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": 17,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_753888/491085427.py:44: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
-      "  activity_sequence_df = sorted_df.groupby(pid_col).apply(\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -1401,28 +1077,28 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>home - education</td>\n",
-       "      <td>12</td>\n",
+       "      <td>h - e</td>\n",
+       "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>home - education - education - home</td>\n",
+       "      <td>h - e - e - e</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>home - education - escort - home</td>\n",
-       "      <td>7</td>\n",
+       "      <td>h - e - e - e - h</td>\n",
+       "      <td>96</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>home - education - escort - shop - home</td>\n",
+       "      <td>h - e - e - e - o - v - h</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>home - education - home</td>\n",
-       "      <td>281</td>\n",
+       "      <td>h - e - e - e - sh - h - v - h</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1431,27 +1107,27 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>486</th>\n",
-       "      <td>home - work - work - work - work - home</td>\n",
+       "      <td>h - w - w - w - w - h</td>\n",
        "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>487</th>\n",
-       "      <td>home - work - work - work - work - home - othe...</td>\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>home - work - work - work - work - home - work...</td>\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>home - work - work - work - work - work - home</td>\n",
+       "      <td>h - w - w - w - w - w - h</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>490</th>\n",
-       "      <td>home - work - work - work - work - work - work...</td>\n",
+       "      <td>h - w - w - w - w - w - w - h - m - h</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1460,34 +1136,36 @@
        "</div>"
       ],
       "text/plain": [
-       "                                     activity_sequence  count_acbm\n",
-       "0                                     home - education          12\n",
-       "1                  home - education - education - home           1\n",
-       "2                     home - education - escort - home           7\n",
-       "3              home - education - escort - shop - home           1\n",
-       "4                              home - education - home         281\n",
-       "..                                                 ...         ...\n",
-       "486            home - work - work - work - work - home           3\n",
-       "487  home - work - work - work - work - home - othe...           1\n",
-       "488  home - work - work - work - work - home - work...           1\n",
-       "489     home - work - work - work - work - work - home           1\n",
-       "490  home - work - work - work - work - work - work...           1\n",
+       "                             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": 17,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "sequence_acbm = process_sequences(df = legs_acbm,\n",
-    "                                  pid_col = 'pid', \n",
-    "                                  seq_col = 'seq', \n",
-    "                                  origin_activity_col = 'oact', \n",
-    "                                  destination_activity_col = 'dact', \n",
-    "                                  suffix = 'acbm')\n",
+    "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",
     "                                  "
@@ -1502,12 +1180,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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9evUyHh4eGf7YS/uBsWTJknSfYZp/Jmhpn/fu3bsd5qtatarDZ/bPBM2YzH9Ypu1b3377rUN5zZo1HWJn5MKFC6ZSpUpGkildurTp06ePmT17tjl+/Hi6eVu1amVq1KhhLl26ZC9LTU01DRs2NBUqVLCXvfDCC0aSPWkw5tpnGRISctMJ2htvvGECAgLMn3/+6TDfq6++ajw9PU1cXJwx5v9+CBYqVMicOXPGPt8XX3xhJJmvvvrKXta0aVMTFBSULoG9/od0//79TbFixcypU6cc5nnsscdMSEiI/fh78MEHTbVq1dK140bSPufixYubc+fO2cs//fRTI8lMmTLFXqcKFSqYyMhIh/pduHDBlClTxtx33332srfffjvddjbGmF9//dVIMt98840xxphdu3YZSeaRRx4x9evXt8/XsWNHU6dOHfv77G77n376yUgyCxcudJhv5cqV6crT+sjrk4ETJ04YHx8f8+KLL95wu6UlaMYY06JFCxMWFmb/LG4lQduzZ4/x8/Mzkkzt2rXN4MGDzfLly01SUtIN62RM+v7YGGM+/vjjdG1N66/69evnMG/nzp1NoUKF7O/TvneeffZZh/m6d++e7QQtOTnZFCpUyPzrX/9yWL5WrVrpYkty+ANbVtLa8M9X/vz5zcqVKx3mvT5B27Nnj5FkfvrpJ2OMMdOnTzeBgYEmKSmJBA23HS5xBJzs3LlzkpTtQSG++eYbSUp3r8OLL74oSenuVatataoaNGhgf1+/fn1JUsuWLVWqVKl05X/99Ve6mIMGDbL/P+0SxeTkZP3www/2cj8/P/v/z549q4SEBDVp0iTd5YiS1KxZM1WtWvUGLb12H9eWLVsyHcFv69atOnHihJ599lmH+6Tat2+vypUrZ3jf3tNPP+3wvkmTJhm2+Xo//PCDkpOT9cILL8jD4/+6wSeeeELBwcG3dH/gwoULVa9ePfuAIkFBQWrfvr3DZY6pqalavny5OnTooHr16qVbh81my3HcLl26KF++fFq8eLG9bM+ePfrf//530yNJtm7dWuHh4Q5137Nnj3bt2pXhfTbX8/Pz05YtW+yXxc2bN0/9+/dXsWLF9Nxzz9kv3z1z5ox+/PFHde3aVefPn9epU6d06tQpnT59WpGRkdq/f7/98tZvvvlG9957r+655x57nNDQUPXo0eOm2iddu5y2SZMmKlCggD32qVOn1Lp1a6WkpGj9+vUO8z/66KMqUKCA/X2TJk0k/d9xdvLkSa1fv179+vVzOB6l//tcjTH67LPP1KFDBxljHOJGRkYqISHBfpzlz59fhw8fzvAyyuzo1auXQ1/08MMPq1ixYvZ+Z8eOHdq/f7+6d++u06dP2+uRlJSkVq1aaf369ekuN/ynOnXqKDAw0L6tfvrpJ5UoUUK9evXStm3bdOHCBRljtGHDBvv2krK/7ZcsWaKQkBDdd999DvPVrVtXgYGBWrNmjUN9qlat6hAnNDRUlSpVumG/8E+jRo3SsWPHNHPmzBwtl5Fq1appx44d6tmzp2JjYzVlyhR16tRJRYsW1QcffHDD5a/vjy9duqRTp07p3nvvlaQM++SM+sXTp0/bv5/SPv/nn3/eYb6cDBT07bff6vTp0+rWrZu9rFu3btq5c6d+//13e1lOvxPTfPbZZ/r++++1atUqzZ07VxUrVtRDDz2kjRs3Zjh/tWrVVLNmTX388ceSrg1+9OCDD2Y60AvgzkjQACdLu7fm/Pnz2Zr/4MGD8vDwSDdCYFhYmPLnz6+DBw86lP/zR19ISIgkqWTJkhmW//MeDQ8PD5UtW9ahrGLFipLkcA/P119/rXvvvVe+vr4qWLCgQkNDNWPGDCUkJKRrQ5kyZW7UTEnX7s3bs2ePSpYsqXvuuUejRo1y+NGU1tZKlSqlW7Zy5crptoWvr69CQ0MdygoUKJCuzf+UWRxvb2+VLVs2XZzsio+P1zfffKNmzZopOjra/mrUqJG2bt1qH0r65MmTOnfunKpXr35TcTJSuHBhtWrVSp9++qm9bPHixcqXL5+6dOlyU+v08PBQjx49tHz5cvv9KwsXLpSvr6/9/rishISEaMKECYqNjVVsbKxmz56tSpUqadq0aXrjjTckSdHR0TLG6N///rdCQ0MdXmn3/6UNonDw4EFVqFAhXZyM9pfs2r9/v1auXJkuduvWrR1ip/nn8ZeWrKXtc2n7c1af7cmTJxUfH69Zs2ali9u3b1+HuK+88ooCAwN1zz33qEKFCho4cKDDvWE38s/tZbPZVL58efuxvn//fklS796909Xlww8/1OXLlzM85q/n6empBg0a6KeffpJ0LUFr0qSJGjdurJSUFG3evFn/+9//dObMGYfEKbvbfv/+/UpISFCRIkXSzZuYmHjDz0jKXr/wT02bNlWLFi2cdi9axYoV9d///lenTp3Srl279NZbbylfvnx68sknHf44lpEzZ85o8ODBKlq0qPz8/BQaGmrvdzP6fG60n6Z975QrV85hvpwcSx999JHKlCkjHx8fe19Xrlw5+fv7O/xRJ6ffiWmaNm2q1q1b67777lOfPn20evVqBQUF6bnnnst0me7du2vJkiWKjo7Wxo0b0w1QBNwuGMURcLLg4GCFh4drz549OVouu2dNPD09c1Ru/jH4R3b89NNP6tixo5o2bar33ntPxYoVk5eXl+bOnZvh4ATX/3U3K127dlWTJk30+eefa9WqVXr77bc1fvx4LVu2TG3bts1xPTNrs1WWLFmiy5cva+LEiRk+R2nhwoUuHU77scceU9++fbVjxw7Vrl1bn376qVq1auUwElpO9erVS2+//baWL1+ubt26adGiRXrggQfsfwDIrtKlS6tfv37q3LmzypYtq4ULF2rMmDH2szMvvfSSIiMjM1w2u483yI5/DiCTmpqq++67Ty+//HKG86f98SKNM46ztDb37Nkz09E9a9asKUmqUqWK9u3bp6+//lorV67UZ599pvfee08jRoxwyr6UVpe333473ch4abLz/KjGjRvrzTff1KVLl/TTTz/pX//6l/Lnz6/q1avrp59+UtGiRSXJIUHL7rZPTU1VkSJFMh1s559/pHFmXzhy5Eg1b95c77//vtMeLeDp6akaNWqoRo0aatCggVq0aKGFCxfaE9OMdO3aVRs3btSwYcNUu3Zt+/Dzbdq0yfAMpzO3QUbOnTunr776SpcuXcrwjyaLFi3Sm2++KZvNpsqVK0uSdu/e7fD551RgYKDq16+vL774QklJSQoICEg3T7du3TR8+HA98cQTKlSokO6///6bjgdYiQQNcIEHHnhAs2bN0qZNmxwuR8xI6dKllZqaqv3796tKlSr28uPHjys+Pj7DUb9uRWpqqv766y+HH55pZ3bSnrX12WefydfXV9999518fHzs882dO/eW4xcrVkzPPvusnn32WZ04cUJ33XWX3nzzTbVt29be1n379qlly5YOy+3bt89p2+L6ONefTUxOTtaBAwey/KGUlYULF6p69eoZPnj2/fff16JFixQVFaXQ0FAFBwffMInP6aWOnTp10lNPPWW/zPHPP//U8OHDb7hcVnGqV6+uOnXqaOHChSpRooTi4uL0n//8J0f1ul6BAgVUrlw5e9vTtr+Xl9cNt3vp0qXtZ3yut2/fvgzj/POhucnJyTp69KhDWbly5ZSYmHjTn/k/pbUnq882bdS8lJSUbMUNCAjQo48+qkcffVTJycnq0qWL3nzzTQ0fPvyGj0z45/Yyxig6OtqeAKadQQkODr5hXbLaT5o0aaLk5GR9/PHH+vvvv+0/xJs2bWpP0CpWrGhP1NJiZ2fblytXTj/88IMaNWqU7T8GOUuzZs3UvHlzjR8/XiNGjHD6+tMucf7nfnm9s2fPavXq1YqKinKoQ0bHQnalfe/ExMQ4nDXL6FjKyLJly3Tp0iXNmDEj3R+A9u3bp9dff10///yzGjdurA4dOmjs2LH66KOPbilBk66NeCzJPsrsP5UqVUqNGjXS2rVr9cwzz9z2z+FE3sUljoALvPzyywoICNCAAQN0/PjxdNNjYmI0ZcoUSdee3yJJ7777rsM8kyZNknTt/itnSxtyXbr2g23atGny8vJSq1atJF3766vNZnM42xAbG6vly5ffdMyUlJR0l+IUKVJE4eHh9vuR6tWrpyJFimjmzJkOjxj49ttv9ccffzhtW7Ru3Vre3t6aOnWqw1+UZ8+erYSEhJuKc+jQIa1fv15du3bVww8/nO7Vt29fRUdHa8uWLfLw8FCnTp301VdfaevWrenWlVantB8g/0w0MpM/f35FRkbq008/1SeffCJvb2916tTphssFBARkeRnb448/rlWrVundd99VoUKFsnW2c+fOnTp16lS68oMHD+p///uf/UdhkSJF7GcoMvqRevLkSfv/27Vrp82bN+uXX35xmJ7RmZVy5cqlu39s1qxZ6c6gde3aVZs2bdJ3332Xbh3x8fH2H4TZFRoaqqZNm2rOnDmKi4tzmJb2uXp6euqhhx7SZ599lmEid32b/zmMure3t6pWrSpjjK5cuXLD+ixYsMDh0rKlS5fq6NGj9s+wbt26KleunN555x0lJiZmWZes9sf69evLy8tL48ePV8GCBVWtWjVJ1xK3zZs3a926del+nGd323ft2lUpKSn2y2Kvd/Xq1WwfHzcr7V60WbNm3fQ6fvrppww/r7R7wbK6tDDtbNg/z3798zsjJ9I+/6lTp97UOj/66COVLVtWTz/9dLq+7qWXXlJgYKD9uGzQoIHatGmjDz/8MMPvkOTkZL300ks3jHnmzBlt3LhRYWFhWT6aYMyYMRo5cmSWl0IC7o4/LQAuUK5cOS1atEiPPvqoqlSpol69eql69epKTk7Wxo0btWTJEvuzmGrVqqXevXtr1qxZio+PV7NmzfTLL79o/vz56tSpk1q0aOHUuvn6+mrlypXq3bu36tevr2+//VYrVqzQa6+9Zr9UqH379po0aZLatGmj7t2768SJE5o+fbrKly+vXbt23VTc8+fPq0SJEnr44YdVq1YtBQYG6ocfftCvv/5qvxww7Qde37591axZM3Xr1k3Hjx/XlClTFBERoSFDhjhlG4SGhmr48OGKiopSmzZt1LFjR+3bt0/vvfee7r777hsOgJGRRYsWyRijjh07Zji9Xbt2ypcvnxYuXKj69evrrbfe0qpVq9SsWTM9+eSTqlKlio4ePaolS5Zow4YNyp8/v2rXri1PT0+NHz9eCQkJ8vHxUcuWLbP8cfLoo4+qZ8+eeu+99xQZGZmty7Lq1q2rxYsXa+jQobr77rsVGBioDh062Kd3795dL7/8sj7//HM988wz8vLyuuE6v//+e40cOVIdO3bUvffea3/+3pw5c3T58mWHZ5RNnz5djRs3Vo0aNfTEE0+obNmyOn78uDZt2qTDhw9r586dkq794eO///2v2rRpo8GDBysgIECzZs1S6dKl0+2XAwYM0NNPP62HHnpI9913n3bu3Knvvvsu3V/7hw0bpi+//FIPPPCA+vTpo7p16yopKUm7d+/W0qVLFRsbm+NLRKdOnarGjRvrrrvu0pNPPqkyZcooNjZWK1as0I4dOyRJ48aN05o1a1S/fn098cQTqlq1qs6cOaNt27bphx9+0JkzZyRJ999/v8LCwtSoUSMVLVpUf/zxh6ZNm6b27dtna9CFggULqnHjxurbt6+OHz+ud999V+XLl9cTTzwh6dp9hh9++KHatm2ratWqqW/fvipevLj+/vtvrVmzRsHBwfrqq68kXdtPJOlf//qXHnvsMXl5ealDhw4KCAiQv7+/6tatq82bN9ufgSZdO4OWlJSkpKSkdAladrd9s2bN9NRTT2ns2LHasWOH7r//fnl5eWn//v1asmSJpkyZoocffjhHn1FONGvWTM2aNdO6detueh3jx4/Xb7/9pi5dutjPXm7btk0LFixQwYIFsxycIzg4WE2bNtWECRN05coVFS9eXKtWrdKBAwduuj61a9dWt27d9N577ykhIUENGzbU6tWrFR0dfcNljxw5ojVr1qQbYCSNj4+PIiMjtWTJEk2dOlVeXl5asGCB7r//fnXp0kUdOnRQq1atFBAQoP379+uTTz7R0aNH0z0LbenSpQoMDJQxRkeOHNHs2bN19uxZzZw5M8uzuWmfF3Bby/VxI4E85M8//zRPPPGEiYiIMN7e3iYoKMg0atTI/Oc//3EYUvzKlSsmKirKlClTxnh5eZmSJUua4cOHO8xjzLUhpNu3b58ujq4bGjrN9cMPp0kbajgmJsbcf//9xt/f3xQtWtSMHDnSYbh5Y4yZPXu2qVChgvHx8TGVK1c2c+fOtQ9/fKPY109LG6758uXLZtiwYaZWrVomKCjIBAQEmFq1amX4zLLFixebOnXqGB8fH1OwYEHTo0cPc/jwYYd5Mhs2OaM6ZmbatGmmcuXKxsvLyxQtWtQ888wz6YaCzu4w+zVq1DClSpXKcp7mzZubIkWKmCtXrhhjjDl48KDp1auXCQ0NNT4+PqZs2bJm4MCB5vLly/ZlPvjgA1O2bFnj6enpMDz+P4fZT3Pu3Dn7cN7XP18uTUbD7CcmJpru3bub/Pnz24fF/6d27dqle8RDVv766y8zYsQIc++995oiRYqYfPnymdDQUNO+fXuHR0ekiYmJMb169TJhYWHGy8vLFC9e3DzwwANm6dKlDvPt2rXLNGvWzPj6+prixYubN954w8yePTvd8O8pKSnmlVdeMYULFzb+/v4mMjLSREdHpxtm3xhjzp8/b4YPH27Kly9vvL29TeHChU3Dhg3NO++8Y3/GVkbHU5rr9/M0e/bsMZ07dzb58+c3vr6+plKlSumeNXj8+HEzcOBAU7JkSePl5WXCwsJMq1atzKxZs+zzvP/++6Zp06amUKFCxsfHx5QrV84MGzbM/iyxzKR9zh9//LEZPny4KVKkiPHz8zPt27fP8Pl127dvN126dLHHKV26tOnatWu653a98cYbpnjx4sbDwyPdNh82bJiRZMaPH++wTPny5Y0kExMTky5udrZ9mlmzZpm6desaPz8/ExQUZGrUqGFefvllc+TIEfs8mfWRmR0v/5RZf5a2PXWTw+z//PPPZuDAgaZ69eomJCTEeHl5mVKlSpk+ffpkuF3+6fDhw/b9KSQkxDzyyCPmyJEj6fa9zPqrtEcEXP95Xbx40Tz//POmUKFCJiAgwHTo0MEcOnTohsPsT5w40UjK8plu8+bNM5LMF198YS+7cOGCeeedd8zdd99tAgMDjbe3t6lQoYJ57rnnTHR0dLo2XP8KCAgwDRo0MJ9++qlDnKyOy+sxzD5uNzZjnHTHKAC316dPHy1dujTDS5mArHTu3Fm7d+/O1l/Yc9u8efPUt29fHThwwH4fJQAAtyvuQQMAZOno0aNasWKFHn/8caurAgDAHY970AAAGTpw4IB+/vlnffjhh/Ly8tJTTz1ldZUAALjjcQYNAJChdevW6fHHH9eBAwc0f/58hYWFWV0lAADueNyDBgAAAABuwtIzaGPHjtXdd9+toKAgFSlSRJ06dUr3kMRLly5p4MCBKlSokAIDA/XQQw9l+Fyp6xljNGLECBUrVkx+fn5q3br1LT3QEQAAAAByg6UJ2rp16zRw4EBt3rxZ33//va5cuaL7779fSUlJ9nmGDBmir776SkuWLNG6det05MgRdenSJcv1TpgwQVOnTtXMmTO1ZcsWBQQEKDIyUpcuXXJ1kwAAAADgprnVJY4nT55UkSJFtG7dOjVt2lQJCQkKDQ3VokWL7A+h3Lt3r6pUqaJNmzbp3nvvTbcOY4zCw8P14osv2p9Mn5CQoKJFi2revHl67LHHbliP1NRUHTlyREFBQVk+DBEAAADAnc0Yo/Pnzys8PFweHq4/v+VWozgmJCRIkgoWLChJ+u2333TlyhW1bt3aPk/lypVVqlSpTBO0AwcO6NixYw7LhISEqH79+tq0aVO2ErQjR46oZMmSt9ocAAAAAHeIQ4cOqUSJEi6P4zYJWmpqql544QU1atRI1atXlyQdO3ZM3t7eyp8/v8O8RYsW1bFjxzJcT1p50aJFs73M5cuXdfnyZfv7tJOKhw4dUnBw8E21BwAAAMDt79y5cypZsqSCgoJyJZ7bJGgDBw7Unj17tGHDhlyPPXbsWEVFRaUrDw4OJkEDAAAAkGu3PrnFc9AGDRqkr7/+WmvWrHE4bRgWFqbk5GTFx8c7zH/8+PFMn8eTVv7PkR6zWmb48OFKSEiwvw4dOnQLrQEAAACAm2NpgmaM0aBBg/T555/rxx9/VJkyZRym161bV15eXlq9erW9bN++fYqLi1ODBg0yXGeZMmUUFhbmsMy5c+e0ZcuWTJfx8fGxny3jrBkAAAAAq1iaoA0cOFAfffSRFi1apKCgIB07dkzHjh3TxYsXJV0b3KN///4aOnSo1qxZo99++019+/ZVgwYNHAYIqVy5sj7//HNJ1049vvDCCxozZoy+/PJL7d69W7169VJ4eLg6depkRTMBAAAAIFssvQdtxowZkqTmzZs7lM+dO1d9+vSRJE2ePFkeHh566KGHdPnyZUVGRuq9995zmH/fvn32ESAl6eWXX1ZSUpKefPJJxcfHq3Hjxlq5cqV8fX1d2h4AAADAFVJSUnTlyhWrq3FH8vLykqenp9XVsHOr56C5i3PnzikkJEQJCQlc7ggAAADLGGN07NixdGMywLny58+vsLCwDAcCye3cwG1GcQQAAADgKC05K1KkiPz9/XNtJMG8whijCxcu6MSJE5KkYsWKWVwjEjQAAADALaWkpNiTs0KFClldnTuWn5+fJOnEiRMqUqSI5Zc7usUw+wAAAAAcpd1z5u/vb3FN7nxp29gd7vMjQQMAAADcGJc1up47bWMSNAAAAABwEyRoAAAAAOAmGCQEAAAAuM1EvLoiV+PFjmufo/n79Omj+fPna+zYsXr11Vft5cuXL1fnzp3Vu3dvzZ8/P9PlS5curdjYWB04cED/+te/tHbtWp05c0aFCxdW3bp1NX78eFWuXPmm2+POOIMGAAAAwOl8fX01fvx4nT17Nt20KVOm6OjRo/aXJM2dO9f+/tdff9WVK1d03333KSEhQcuWLdO+ffu0ePFi1ahR445+Lhxn0AAAAAA4XevWrRUdHa2xY8dqwoQJDtNCQkIUEhLiUJb2sOg0O3bsUExMjFavXq3SpUtLunZmrVGjRq6vvIU4gwYAAADA6Tw9PfXWW2/pP//5jw4fPpzj5UNDQ+Xh4aGlS5cqJSXFBTV0TyRoAAAAAFyic+fOql27tkaOHJnjZYsXL66pU6dqxIgRKlCggFq2bKk33nhDf/31lwtq6j5I0AAAAAC4zPjx4zV//nz98ccfOV524MCBOnbsmBYuXKgGDRpoyZIlqlatmr7//nsX1NQ9kKABAAAAcJmmTZsqMjJSw4cPv6nlg4KC1KFDB7355pvauXOnmjRpojFjxji5lu6DBA0AAACAS40bN05fffWVNm3adEvrsdlsqly5spKSkpxUM/fDKI434UbPncjpcyIAAACAO1mNGjXUo0cPTZ06NdvL7NixQyNHjtTjjz+uqlWrytvbW+vWrdOcOXP0yiuvuLC21iJBAwAAAOByo0eP1uLFi7M9f4kSJRQREaGoqCjFxsbKZrPZ3w8ZMsSFNbUWCRoAAABwm3H3K7bmzZuXriwiIkKXL1/OcH5jTLqywoULa8qUKc6umtvjHjQAAAAAcBMkaAAAAADgJkjQAAAAAMBNkKABAAAAgJsgQQMAAAAAN0GCBgAAAABuggQNAAAAANwEz0FzhVEhWUxLyL16AAAAALitcAYNAAAAANwECRoAAAAAuAkucQQAAABuN1ndUuOSeDd3m86mTZvUuHFjtWnTRitWrHCYlpycrHfffVcLFy7U/v375e/vr0qVKmnAgAHq2bOnvLy81KdPH82fP9++TMGCBXX33XdrwoQJqlmzpr3cZrPZ491777328suXLys8PFxnzpzRmjVr1Lx585tqR24iQbuNRLy6IsvpsePa51JNAAAAgBubPXu2nnvuOc2ePVtHjhxReHi4pGvJWWRkpHbu3Kk33nhDjRo1UnBwsDZv3qx33nlHderUUe3atSVJbdq00dy5cyVJx44d0+uvv64HHnhAcXFxDrFKliypuXPnOiRon3/+uQIDA3XmzJncabATcIkjAAAAAKdLTEzU4sWL9cwzz6h9+/aaN2+efdq7776r9evXa/Xq1Ro4cKBq166tsmXLqnv37tqyZYsqVKhgn9fHx0dhYWEKCwtT7dq19eqrr+rQoUM6efKkQ7zevXvrk08+0cWLF+1lc+bMUe/evV3eVmciQQMAAADgdJ9++qkqV66sSpUqqWfPnpozZ46MMZKkhQsXqnXr1qpTp0665by8vBQQEJDhOhMTE/XRRx+pfPnyKlSokMO0unXrKiIiQp999pkkKS4uTuvXr9fjjz/u5Ja5FgkaAAAAAKebPXu2evbsKenaZYoJCQlat26dJGn//v2qXLlyttbz9ddfKzAwUIGBgQoKCtKXX36pxYsXy8MjfSrTr18/zZkzR5I0b948tWvXTqGhoU5qUe4gQQMAAADgVPv27dMvv/yibt26SZLy5cunRx99VLNnz5Yk+5m07GjRooV27NihHTt26JdfflFkZKTatm2rgwcPppu3Z8+e2rRpk/766y/NmzdP/fr1c06DchGDhAAAAABwqtmzZ+vq1av2QUGka0mZj4+Ppk2bpooVK2rv3r3ZWldAQIDKly9vf//hhx8qJCREH3zwgcaMGeMwb6FChfTAAw+of//+unTpktq2bavz5887p1G5hDNoAAAAAJzm6tWrWrBggSZOnGg/87Vjxw7t3LlT4eHh+vjjj9W9e3f98MMP2r59e7rlr1y5oqSkpEzXb7PZ5OHh4TAYyPX69euntWvXqlevXvL09HRau3ILZ9AAAAAAOM3XX3+ts2fPqn///goJcXxe20MPPaTZs2drw4YNWrFihVq1aqU33nhDjRs3VlBQkLZu3arx48dr9uzZ9mH2L1++rGPHjkmSzp49q2nTpikxMVEdOnTIMH6bNm108uRJBQcHu7SdrkKCBgAAAMBpZs+erdatW6dLzqRrCdqECRO0b98+ff/995o8ebLef/99vfTSS/L391eVKlX0/PPPq3r16vZlVq5cqWLFikmSgoKCVLlyZS1ZsiTTh07bbDYVLlzYJW3LDTaTkzv08ohz584pJCRECQkJGWbeN3xgtG/3zCfe5FPYsxWXB1UDAADcMS5duqQDBw6oTJky8vX1tbo6d7SstvWNcgNn4x40AAAAAHATJGgAAAAA4CZI0AAAAADATZCgAQAAAICbIEEDAAAAADdBggYAAAC4sdTUVKurcMdzp23Mc9AAAAAAN+Tt7S0PDw8dOXJEoaGh8vb2ls1ms7padxRjjJKTk3Xy5El5eHjI29vb6iqRoAEAAADuyMPDQ2XKlNHRo0d15MgRq6tzR/P391epUqXk4WH9BYYkaAAAAICb8vb2VqlSpXT16lWlpKRYXZ07kqenp/Lly+c2ZyctTdDWr1+vt99+W7/99puOHj2qzz//XJ06dbJPz2wjTZgwQcOGDctw2qhRoxQVFeVQVqlSJe3du9dp9QYAAAByi81mk5eXl7y8vKyuCnKBpefwkpKSVKtWLU2fPj3D6UePHnV4zZkzRzabTQ899FCW661WrZrDchs2bHBF9QEAAADAqSw9g9a2bVu1bds20+lhYWEO77/44gu1aNFCZcuWzXK9+fLlS7csAAAAALg76++Cy6bjx49rxYoV6t+//w3n3b9/v8LDw1W2bFn16NFDcXFxWc5/+fJlnTt3zuEFAAAAALnttknQ5s+fr6CgIHXp0iXL+erXr6958+Zp5cqVmjFjhg4cOKAmTZro/PnzmS4zduxYhYSE2F8lS5Z0dvUBAAAA4IZumwRtzpw56tGjh3x9fbOcr23btnrkkUdUs2ZNRUZG6ptvvlF8fLw+/fTTTJcZPny4EhIS7K9Dhw45u/oAAAAAcEO3xTD7P/30k/bt26fFixfneNn8+fOrYsWKio6OznQeHx8f+fj43EoVAQAAAOCW3RZn0GbPnq26deuqVq1aOV42MTFRMTExKlasmAtqBgAAAADOY2mClpiYqB07dmjHjh2SpAMHDmjHjh0Og3qcO3dOS5Ys0YABAzJcR6tWrTRt2jT7+5deeknr1q1TbGysNm7cqM6dO8vT01PdunVzaVsAAAAA4FZZeonj1q1b1aJFC/v7oUOHSpJ69+6tefPmSZI++eQTGWMyTbBiYmJ06tQp+/vDhw+rW7duOn36tEJDQ9W4cWNt3rxZoaGhrmsIAAAAADiBpQla8+bNZYzJcp4nn3xSTz75ZKbTY2NjHd5/8sknzqgaAAAAAOS62+IeNAAAAADIC0jQAAAAAMBNkKABAAAAgJsgQQMAAAAAN0GCBgAAAABuggQNAAAAANwECRoAAAAAuAkSNAAAAABwEyRoAAAAAOAmSNAAAAAAwE2QoAEAAACAmyBBAwAAAAA3QYIGAAAAAG6CBA0AAAAA3AQJGgAAAAC4CRI0AAAAAHATJGgAAAAA4CZI0AAAAADATZCgAQAAAICbIEEDAAAAADdBggYAAAAAboIEDQAAAADcBAkaAAAAALgJEjQAAAAAcBMkaAAAAADgJkjQAAAAAMBNkKABAAAAgJsgQQMAAAAAN0GCBgAAAABuggQNAAAAANwECRoAAAAAuAkSNAAAAABwEyRoAAAAAOAmSNAAAAAAwE2QoAEAAACAmyBBAwAAAAA3QYIGAAAAAG6CBA0AAAAA3AQJGgAAAAC4CRI0AAAAAHATJGgAAAAA4CZI0AAAAADATeSzugJwolEhWUxLyL16AAAAALgpnEEDAAAAADdBggYAAAAAboIEDQAAAADchKUJ2vr169WhQweFh4fLZrNp+fLlDtP79Okjm83m8GrTps0N1zt9+nRFRETI19dX9evX1y+//OKiFgAAAACA81iaoCUlJalWrVqaPn16pvO0adNGR48etb8+/vjjLNe5ePFiDR06VCNHjtS2bdtUq1YtRUZG6sSJE86uPgAAAAA4laWjOLZt21Zt27bNch4fHx+FhYVle52TJk3SE088ob59+0qSZs6cqRUrVmjOnDl69dVXb6m+AAAAAOBKbn8P2tq1a1WkSBFVqlRJzzzzjE6fPp3pvMnJyfrtt9/UunVre5mHh4dat26tTZs25UZ1AQAAAOCmufVz0Nq0aaMuXbqoTJkyiomJ0Wuvvaa2bdtq06ZN8vT0TDf/qVOnlJKSoqJFizqUFy1aVHv37s00zuXLl3X58mX7+3PnzjmvEQAAAACQTW6doD322GP2/9eoUUM1a9ZUuXLltHbtWrVq1cppccaOHauoqCinrQ8AAAAAbobbX+J4vbJly6pw4cKKjo7OcHrhwoXl6emp48ePO5QfP348y/vYhg8froSEBPvr0KFDTq03AAAAAGTHbZWgHT58WKdPn1axYsUynO7t7a26detq9erV9rLU1FStXr1aDRo0yHS9Pj4+Cg4OdngBAAAAQG6zNEFLTEzUjh07tGPHDknSgQMHtGPHDsXFxSkxMVHDhg3T5s2bFRsbq9WrV+vBBx9U+fLlFRkZaV9Hq1atNG3aNPv7oUOH6oMPPtD8+fP1xx9/6JlnnlFSUpJ9VEcAAAAAcFeW3oO2detWtWjRwv5+6NChkqTevXtrxowZ2rVrl+bPn6/4+HiFh4fr/vvv1xtvvCEfHx/7MjExMTp16pT9/aOPPqqTJ09qxIgROnbsmGrXrq2VK1emGzgEAAAAANyNpQla8+bNZYzJdPp33313w3XExsamKxs0aJAGDRp0K1UDAAAAgFx3W92DBgAAAAB3MhI0AAAAAHATJGgAAAAA4CZI0AAAAADATZCgAQAAAICbIEEDAAAAADdBggYAAAAAboIEDQAAAADcBAkaAAAAALgJEjQAAAAAcBMkaAAAAADgJkjQAAAAAMBNkKABAAAAgJsgQQMAAAAAN0GCBgAAAABuggQNAAAAANwECRoAAAAAuAkSNAAAAABwEyRoAAAAAOAmSNAAAAAAwE2QoAEAAACAmyBBAwAAAAA3QYIGAAAAAG6CBA0AAAAA3AQJGgAAAAC4iVtO0M6dO6fly5frjz/+cEZ9AAAAACDPynGC1rVrV02bNk2SdPHiRdWrV09du3ZVzZo19dlnnzm9ggAAAACQV+Q4QVu/fr2aNGkiSfr8889ljFF8fLymTp2qMWPGOL2CAAAAAJBX5DhBS0hIUMGCBSVJK1eu1EMPPSR/f3+1b99e+/fvd3oFAQAAACCvyHGCVrJkSW3atElJSUlauXKl7r//fknS2bNn5evr6/QKAgAAAEBekS+nC7zwwgvq0aOHAgMDVbp0aTVv3lzStUsfa9So4ez6AQAAAECekeME7dlnn9U999yjQ4cO6b777pOHx7WTcGXLluUeNAAAAAC4BTlO0CSpXr16qlevnowxMsbIZrOpffv2zq4bAAAAAOQpN/UctNmzZ6t69ery9fWVr6+vqlevrg8//NDZdQMAAACAPCXHZ9BGjBihSZMm6bnnnlODBg0kSZs2bdKQIUMUFxen0aNHO72SAAAAAJAX5DhBmzFjhj744AN169bNXtaxY0fVrFlTzz33HAkaAAAAANykHF/ieOXKFdWrVy9ded26dXX16lWnVAoAAAAA8qIcJ2iPP/64ZsyYka581qxZ6tGjh1MqBQAAAAB50U2N4jh79mytWrVK9957ryRpy5YtiouLU69evTR06FD7fJMmTXJOLQEAAAAgD8hxgrZnzx7dddddkqSYmBhJUuHChVW4cGHt2bPHPp/NZnNSFQEAAAAgb8hxgrZmzRpX1AMAAAAA8rybeg4aAAAAAMD5snUGrUuXLpo3b56Cg4PVpUuXLOddtmyZUyoGAAAAAHlNthK0kJAQ+z1lISEhLq0QAAAAAORV2UrQ5s6dK0kyxigqKkqhoaHy8/NzacUAAAAAIK/J0T1oxhiVL19ehw8fdlV9AAAAACDPylGC5uHhoQoVKuj06dOuqg8AAAAA5Fk5HsVx3LhxGjZsmMMzz27W+vXr1aFDB4WHh8tms2n58uX2aVeuXNErr7yiGjVqKCAgQOHh4erVq5eOHDmS5TpHjRolm83m8KpcufIt1xUAAAAAXC3Hz0Hr1auXLly4oFq1asnb2zvdvWhnzpzJ9rqSkpJUq1Yt9evXL93okBcuXNC2bdv073//W7Vq1dLZs2c1ePBgdezYUVu3bs1yvdWqVdMPP/xgf58vX46bCQAAAAC5LseZy+TJk+0jOt6qtm3bqm3bthlOCwkJ0ffff+9QNm3aNN1zzz2Ki4tTqVKlMl1vvnz5FBYW5pQ6AgAAAEBuyXGC1qdPHxdUI3sSEhJks9mUP3/+LOfbv3+/wsPD5evrqwYNGmjs2LFZJnSXL1/W5cuX7e/PnTvnrCoDAAAAQLbl+B40T09PnThxIl356dOn5enp6ZRKZeTSpUt65ZVX1K1bNwUHB2c6X/369TVv3jytXLlSM2bM0IEDB9SkSROdP38+02XGjh2rkJAQ+6tkyZKuaAIAAAAAZCnHCZoxJsPyy5cvy9vb+5YrlJErV66oa9euMsZoxowZWc7btm1bPfLII6pZs6YiIyP1zTffKD4+Xp9++mmmywwfPlwJCQn216FDh5zdBAAAAAC4oWxf4jh16lRJks1m04cffqjAwED7tJSUFK1fv94loyWmJWcHDx7Ujz/+mOXZs4zkz59fFStWVHR0dKbz+Pj4yMfH51arCgAAAAC3JNsJ2uTJkyVdO4M2c+ZMh8sZvb29FRERoZkzZzq1cmnJ2f79+7VmzRoVKlQox+tITExUTEyMHn/8cafWDQAAAACcLdsJ2oEDByRJLVq00LJly1SgQIFbDp6YmOhwZuvAgQPasWOHChYsqGLFiunhhx/Wtm3b9PXXXyslJUXHjh2TJBUsWNB+OWWrVq3UuXNnDRo0SJL00ksvqUOHDipdurSOHDmikSNHytPTU926dbvl+gIAAACAK+V4FMc1a9Y4LfjWrVvVokUL+/uhQ4dKknr37q1Ro0bpyy+/lCTVrl07XR2aN28uSYqJidGpU6fs0w4fPqxu3brp9OnTCg0NVePGjbV582aFhoY6rd4AAAAA4AqWPsG5efPmmQ46ImU+IMn1YmNjHd5/8sknt1otAAAAALBEjkdxBAAAAAC4BgkaAAAAALgJEjQAAAAAcBPZugdt165d2V5hzZo1b7oyAAAAAJCXZStBq127tmw2W6aDdqRNs9lsSklJcWoFAQAAACCvyFaClvYMNAAAAACA62QrQStdurSr6wEAAAAAed5NPwftf//7n+Li4pScnOxQ3rFjx1uuFAAAAADkRTlO0P766y917txZu3fvdrgvzWazSRL3oAEAAADATcrxMPuDBw9WmTJldOLECfn7++v333/X+vXrVa9ePa1du9YFVQQAAACAvCHHZ9A2bdqkH3/8UYULF5aHh4c8PDzUuHFjjR07Vs8//7y2b9/uinoCAAAAwB0vx2fQUlJSFBQUJEkqXLiwjhw5IunaQCL79u1zbu0AAAAAIA/J8Rm06tWra+fOnSpTpozq16+vCRMmyNvbW7NmzVLZsmVdUUcAAAAAyBNynKC9/vrrSkpKkiSNHj1aDzzwgJo0aaJChQrpk08+cXoFAQAAACCvyHGCFhkZaf9/+fLltXfvXp05c0YFChSwj+QIAAAAAMi5HN+D1q9fP50/f96hrGDBgrpw4YL69evntIoBAAAAQF6T4wRt/vz5unjxYrryixcvasGCBU6pFAAAAADkRdm+xPHcuXMyxsgYo/Pnz8vX19c+LSUlRd98842KFCnikkrCWhGvrshyeuy49rlUEwAAAODOlu0ELX/+/LLZbLLZbKpYsWK66TabTVFRUU6tHAAAAADkJdlO0NasWSNjjFq2bKnPPvtMBQsWtE/z9vZW6dKlFR4e7pJKAgAAAEBekO0ErVmzZpKkAwcOqGTJkvLwyPHtawAAAACALOR4mP3SpUsrPj5es2fP1h9//CFJqlatmvr166eQkBCnVxAAAAAA8oocnwbbunWrypUrp8mTJ+vMmTM6c+aMJk2apHLlymnbtm2uqCMAAAAA5Ak5PoM2ZMgQdezYUR988IHy5bu2+NWrVzVgwAC98MILWr9+vdMrCQAAAAB5QY4TtK1btzokZ5KUL18+vfzyy6pXr55TKwcAAAAAeUmOL3EMDg5WXFxcuvJDhw4pKCjIKZUCAAAAgLwoxwnao48+qv79+2vx4sU6dOiQDh06pE8++UQDBgxQt27dXFFHAAAAAMgTcnyJ4zvvvCObzaZevXrp6tWrkiQvLy8988wzGjdunNMrCAAAAAB5RY4TNG9vb02ZMkVjx45VTEyMJKlcuXLy9/d3euUAAAAAIC/J8SWO/fr10/nz5+Xv768aNWqoRo0a8vf3V1JSkvr16+eKOgIAAABAnpDjBG3+/Pm6ePFiuvKLFy9qwYIFTqkUAAAAAORF2b7E8dy5czLGyBij8+fPy9fX1z4tJSVF33zzjYoUKeKSSgIAAABAXpDtBC1//vyy2Wyy2WyqWLFiuuk2m01RUVFOrRwAAAAA5CXZTtDWrFkjY4xatmypzz77TAULFrRP8/b2VunSpRUeHu6SSgIAAABAXpDtBK1Zs2aSpAMHDqhUqVKy2WwuqxQAAAAA5EU5Hma/dOnSrqgHAAAAAOR5OU7QgHRGhWQxLSH36gEAAADc5nI8zD4AAAAAwDWylaB9+eWXunLliqvrAgAAAAB5WrYStM6dOys+Pl6S5OnpqRMnTriyTgAAAACQJ2UrQQsNDdXmzZslScYYRnAEAAAAABfI1iAhTz/9tB588EH7g6rDwsIynTclJcVplQMAAACAvCRbCdqoUaP02GOPKTo6Wh07dtTcuXOVP39+F1cNAAAAAPKWbA+zX7lyZVWuXFkjR47UI488In9/f1fWCwAAAADynBw/B23kyJGSpJMnT2rfvn2SpEqVKik0NNS5NQMAAACAPCbHz0G7cOGC+vXrp/DwcDVt2lRNmzZVeHi4+vfvrwsXLriijgAAAACQJ+Q4QRsyZIjWrVunL7/8UvHx8YqPj9cXX3yhdevW6cUXX3RFHQEAAAAgT8hxgvbZZ59p9uzZatu2rYKDgxUcHKx27drpgw8+0NKlS3O0rvXr16tDhw4KDw+XzWbT8uXLHaYbYzRixAgVK1ZMfn5+at26tfbv33/D9U6fPl0RERHy9fVV/fr19csvv+SoXgAAAABghZu6xLFo0aLpyosUKZLjSxyTkpJUq1YtTZ8+PcPpEyZM0NSpUzVz5kxt2bJFAQEBioyM1KVLlzJd5+LFizV06FCNHDlS27ZtU61atRQZGcnDtQEAAAC4vRwnaA0aNNDIkSMdkqSLFy8qKipKDRo0yNG62rZtqzFjxqhz587pphlj9O677+r111/Xgw8+qJo1a2rBggU6cuRIujNt15s0aZKeeOIJ9e3bV1WrVtXMmTPl7++vOXPm5KhuAAAAAJDbcjyK45QpUxQZGakSJUqoVq1akqSdO3fK19dX3333ndMqduDAAR07dkytW7e2l4WEhKh+/fratGmTHnvssXTLJCcn67ffftPw4cPtZR4eHmrdurU2bdqUaazLly/r8uXL9vfnzp1zUisAAAAAIPtynKBVr15d+/fv18KFC7V3715JUrdu3dSjRw/5+fk5rWLHjh2TpHSXUxYtWtQ+7Z9OnTqllJSUDJdJq2tGxo4dq6ioqFusMQAAAADcmhwnaJLk7++vJ554wtl1sczw4cM1dOhQ+/tz586pZMmSFtYIAAAAQF6U43vQcktYWJgk6fjx4w7lx48ft0/7p8KFC8vT0zNHy0iSj4+PfUTKtBcAAAAA5Da3TdDKlCmjsLAwrV692l527tw5bdmyJdPBSLy9vVW3bl2HZVJTU7V69eocD2ACAAAAALntpi5xdJbExERFR0fb3x84cEA7duxQwYIFVapUKb3wwgsaM2aMKlSooDJlyujf//63wsPD1alTJ/syrVq1UufOnTVo0CBJ0tChQ9W7d2/Vq1dP99xzj959910lJSWpb9++ud08AAAAAMgRSxO0rVu3qkWLFvb3afeB9e7dW/PmzdPLL7+spKQkPfnkk4qPj1fjxo21cuVK+fr62peJiYnRqVOn7O8fffRRnTx5UiNGjNCxY8dUu3ZtrVy5MsNntwEAAACAO7mpBC0+Pl5Lly5VTEyMhg0bpoIFC2rbtm0qWrSoihcvnu31NG/eXMaYTKfbbDaNHj1ao0ePznSe2NjYdGWDBg2yn1EDAAAAgNtFjhO0Xbt2qXXr1goJCVFsbKyeeOIJFSxYUMuWLVNcXJwWLFjginoCAAAAwB0vx4OEDB06VH369NH+/fsdLjVs166d1q9f79TKAQAAAEBekuME7ddff9VTTz2Vrrx48eKZPkAaAAAAAHBjOU7QfHx8dO7cuXTlf/75p0JDQ51SKQAAAADIi3KcoHXs2FGjR4/WlStXJF0byCMuLk6vvPKKHnroIadXEAAAAADyihwnaBMnTlRiYqKKFCmiixcvqlmzZipfvryCgoL05ptvuqKOAAAAAJAn5HgUx5CQEH3//ffasGGDdu3apcTERN11111q3bq1K+oHAAAAAHnGTT+ounHjxmrcuLEz6wIAAAAAeVqOE7SpU6dmWG6z2eTr66vy5curadOm8vT0vOXKAQAAAEBekuMEbfLkyTp58qQuXLigAgUKSJLOnj0rf39/BQYG6sSJEypbtqzWrFmjkiVLOr3CAAAAAHCnyvEgIW+99Zbuvvtu7d+/X6dPn9bp06f1559/qn79+poyZYri4uIUFhamIUOGuKK+AAAAAHDHyvEZtNdff12fffaZypUrZy8rX7683nnnHT300EP666+/NGHCBIbcBwAAAIAcyvEZtKNHj+rq1avpyq9evapjx45JksLDw3X+/Plbrx0AAAAA5CE5TtBatGihp556Stu3b7eXbd++Xc8884xatmwpSdq9e7fKlCnjvFoCAAAAQB6Q4wRt9uzZKliwoOrWrSsfHx/5+PioXr16KliwoGbPni1JCgwM1MSJE51eWQAAAAC4k+X4HrSwsDB9//332rt3r/78809JUqVKlVSpUiX7PC1atHBeDQEAAAAgj7jpB1VXrlxZlStXdmZdAAAAACBPu6kE7fDhw/ryyy8VFxen5ORkh2mTJk1ySsUAAAAAIK/JcYK2evVqdezYUWXLltXevXtVvXp1xcbGyhiju+66yxV1BAAAAIA8IceDhAwfPlwvvfSSdu/eLV9fX3322Wc6dOiQmjVrpkceecQVdQQAAACAPCHHCdoff/yhXr16SZLy5cunixcvKjAwUKNHj9b48eOdXkEAAAAAyCtynKAFBATY7zsrVqyYYmJi7NNOnTrlvJoBAAAAQB6T43vQ7r33Xm3YsEFVqlRRu3bt9OKLL2r37t1atmyZ7r33XlfUEQAAAADyhBwnaJMmTVJiYqIkKSoqSomJiVq8eLEqVKjACI4AAAAAcAtynKCVLVvW/v+AgADNnDnTqRUCAAAAgLwqx/eglS1bVqdPn05XHh8f75C8AQAAAAByJscJWmxsrFJSUtKVX758WX///bdTKgUAAAAAeVG2L3H88ssv7f//7rvvFBISYn+fkpKi1atXKyIiwqmVAwAAAIC8JNsJWqdOnSRJNptNvXv3dpjm5eWliIgITZw40amVAwAAAIC8JNsJWmpqqiSpTJky+vXXX1W4cGGXVQoAAAAA8qIcj+J44MABV9QDAAAAAPK8HCdokrR69WqtXr1aJ06csJ9ZSzNnzhynVAwAAAAA8pocJ2hRUVEaPXq06tWrp2LFislms7miXgAAAACQ5+Q4QZs5c6bmzZunxx9/3BX1AQAAAIA8K8fPQUtOTlbDhg1dURcAAAAAyNNynKANGDBAixYtckVdAAAAACBPy/EljpcuXdKsWbP0ww8/qGbNmvLy8nKYPmnSJKdVDgAAAADykhwnaLt27VLt2rUlSXv27HGYxoAhAAAAAHDzcpygrVmzxhX1AAAAAIA8L8f3oKWJjo7Wd999p4sXL0qSjDFOqxQAAAAA5EU5TtBOnz6tVq1aqWLFimrXrp2OHj0qSerfv79efPFFp1cQAAAAAPKKHCdoQ4YMkZeXl+Li4uTv728vf/TRR7Vy5UqnVg4AAAAA8pIc34O2atUqfffddypRooRDeYUKFXTw4EGnVQwAAAAA8pocn0FLSkpyOHOW5syZM/Lx8XFKpQAAAAAgL8pxgtakSRMtWLDA/t5msyk1NVUTJkxQixYtnFo5AAAAAMhLcnyJ44QJE9SqVStt3bpVycnJevnll/X777/rzJkz+vnnn11RRwAAAADIE3J8Bq169er6888/1bhxYz344INKSkpSly5dtH37dpUrV87pFYyIiJDNZkv3GjhwYIbzz5s3L928vr6+Tq8XAAAAADhbjs+gSVJISIj+9a9/ObsuGfr111+VkpJif79nzx7dd999euSRRzJdJjg4WPv27bO/t9lsLq0jAAAAADhDjhO0uXPnKjAwMF2CtGTJEl24cEG9e/d2WuUkKTQ01OH9uHHjVK5cOTVr1izTZWw2m8LCwpxaDwAAAABwtRxf4jh27FgVLlw4XXmRIkX01ltvOaVSmUlOTtZHH32kfv36ZXlWLDExUaVLl1bJkiX14IMP6vfff3dpvQAAAADAGXKcoMXFxalMmTLpykuXLq24uDinVCozy5cvV3x8vPr06ZPpPJUqVdKcOXP0xRdf6KOPPlJqaqoaNmyow4cPZ7rM5cuXde7cOYcXAAAAAOS2HCdoRYoU0a5du9KV79y5U4UKFXJKpTIze/ZstW3bVuHh4ZnO06BBA/Xq1Uu1a9dWs2bNtGzZMoWGhur999/PdJmxY8cqJCTE/ipZsqQrqg8AAAAAWcpxgtatWzc9//zzWrNmjVJSUpSSkqIff/xRgwcP1mOPPeaKOkqSDh48qB9++EEDBgzI0XJeXl6qU6eOoqOjM51n+PDhSkhIsL8OHTp0q9UFAAAAgBzL8SAhb7zxhmJjY9WqVSvly3dt8dTUVPXq1cul96DNnTtXRYoUUfv27XO0XEpKinbv3q127dplOo+Pj498fHxutYoAAAAAcEtylKAZY3Ts2DHNmzdPY8aM0Y4dO+Tn56caNWqodOnSrqqjUlNTNXfuXPXu3dueFKbp1auXihcvrrFjx0qSRo8erXvvvVfly5dXfHy83n77bR08eDDHZ94AAAAAILflOEErX768fv/9d1WoUEEVKlRwVb0c/PDDD4qLi1O/fv3STYuLi5OHx/9dqXn27Fk98cQTOnbsmAoUKKC6detq48aNqlq1aq7UFQAAAABuVo4SNA8PD1WoUEGnT5/OteRMku6//34ZYzKctnbtWof3kydP1uTJk3OhVgAAAADgXDkeJGTcuHEaNmyY9uzZ44r6AAAAAECeleNBQnr16qULFy6oVq1a8vb2lp+fn8P0M2fOOK1yAAAAAJCX5DhBe/fdd11QDQAAAABAjhO03r17u6IeAAAAAJDn5fgeNEmKiYnR66+/rm7duunEiROSpG+//Va///67UysHAAAAAHlJjhO0devWqUaNGtqyZYuWLVumxMRESdLOnTs1cuRIp1cQAAAAAPKKHCdor776qsaMGaPvv/9e3t7e9vKWLVtq8+bNTq0cAAAAAOQlOU7Qdu/erc6dO6crL1KkiE6dOuWUSgEAAABAXpTjBC1//vw6evRouvLt27erePHiTqkUAAAAAORFOU7QHnvsMb3yyis6duyYbDabUlNT9fPPP+ull15Sr169XFFHAAAAAMgTcpygvfXWW6pcubJKliypxMREVa1aVU2bNlXDhg31+uuvu6KOAAAAAJAn5Pg5aN7e3vrggw80YsQI7d69W4mJiapTp44qVKjgivohD4t4dUWm02LHtc/FmgAAAAC5I9sJWmpqqt5++219+eWXSk5OVqtWrTRy5Ej5+fm5sn4AAAAAkGdk+xLHN998U6+99poCAwNVvHhxTZkyRQMHDnRl3QAAAAAgT8l2grZgwQK99957+u6777R8+XJ99dVXWrhwoVJTU11ZPwAAAADIM7KdoMXFxaldu3b2961bt5bNZtORI0dcUjEAAAAAyGuynaBdvXpVvr6+DmVeXl66cuWK0ysFAAAAAHlRtgcJMcaoT58+8vHxsZddunRJTz/9tAICAuxly5Ytc24NAQAAACCPyHaC1rt373RlPXv2dGplAAAAACAvy3aCNnfuXFfWAwAAAADyvGzfgwYAAAAAcC0SNAAAAABwEyRoAAAAAOAmSNAAAAAAwE2QoAEAAACAmyBBAwAAAAA3QYIGAAAAAG6CBA0AAAAA3AQJGgAAAAC4CRI0AAAAAHATJGgAAAAA4CZI0AAAAADATZCgAQAAAICbIEEDAAAAADdBggYAAAAAboIEDQAAAADcBAkaAAAAALgJEjQAAAAAcBMkaAAAAADgJkjQAAAAAMBNkKABAAAAgJsgQQMAAAAAN0GCBgAAAABuggQNAAAAANxEPqsrANyUUSE3mJ6QO/UAAAAAnIgzaAAAAADgJkjQAAAAAMBNuHWCNmrUKNlsNodX5cqVs1xmyZIlqly5snx9fVWjRg198803uVRbAAAAALg1bp2gSVK1atV09OhR+2vDhg2Zzrtx40Z169ZN/fv31/bt29WpUyd16tRJe/bsycUaAwAAAMDNcfsELV++fAoLC7O/ChcunOm8U6ZMUZs2bTRs2DBVqVJFb7zxhu666y5NmzYtF2sMAAAAADfH7RO0/fv3Kzw8XGXLllWPHj0UFxeX6bybNm1S69atHcoiIyO1adOmLGNcvnxZ586dc3gBAAAAQG5z6wStfv36mjdvnlauXKkZM2bowIEDatKkic6fP5/h/MeOHVPRokUdyooWLapjx45lGWfs2LEKCQmxv0qWLOm0NgAAAABAdrl1gta2bVs98sgjqlmzpiIjI/XNN98oPj5en376qVPjDB8+XAkJCfbXoUOHnLp+AAAAAMiO2+pB1fnz51fFihUVHR2d4fSwsDAdP37coez48eMKCwvLcr0+Pj7y8fFxWj0BAAAA4GbcVglaYmKiYmJi9Pjjj2c4vUGDBlq9erVeeOEFe9n333+vBg0a5FINcSeIeHVFptNix7XPxZoAAAAgr3HrSxxfeuklrVu3TrGxsdq4caM6d+4sT09PdevWTZLUq1cvDR8+3D7/4MGDtXLlSk2cOFF79+7VqFGjtHXrVg0aNMiqJgAAAABAtrn1GbTDhw+rW7duOn36tEJDQ9W4cWNt3rxZoaGhkqS4uDh5ePxfjtmwYUMtWrRIr7/+ul577TVVqFBBy5cvV/Xq1a1qAgAAAABkm1snaJ988kmW09euXZuu7JFHHtEjjzziohoBAAAAgOu49SWOAAAAAJCXkKABAAAAgJsgQQMAAAAAN0GCBgAAAABuwq0HCQHczqiQG0xPyJ16AAAA4I7EGTQAAAAAcBMkaAAAAADgJkjQAAAAAMBNkKABAAAAgJsgQQMAAAAAN0GCBgAAAABuggQNAAAAANwECRoAAAAAuAkSNAAAAABwEyRoAAAAAOAmSNAAAAAAwE2QoAEAAACAmyBBAwAAAAA3QYIGAAAAAG6CBA0AAAAA3AQJGgAAAAC4CRI0AAAAAHATJGgAAAAA4CZI0AAAAADATZCgAQAAAICbIEEDAAAAADdBggYAAAAAboIEDQAAAADcBAkaAAAAALgJEjQAAAAAcBMkaAAAAADgJkjQAAAAAMBNkKABAAAAgJsgQQMAAAAAN0GCBgAAAABuggQNAAAAANwECRoAAAAAuAkSNAAAAABwEyRoAAAAAOAm8lldAQDXRLy6ItNpsePa52JNAAAAYBXOoAEAAACAmyBBAwAAAAA3QYIGAAAAAG6CBA0AAAAA3AQJGgAAAAC4CRI0AAAAAHATbp2gjR07VnfffbeCgoJUpEgRderUSfv27ctymXnz5slmszm8fH19c6nGAAAAAHDz3DpBW7dunQYOHKjNmzfr+++/15UrV3T//fcrKSkpy+WCg4N19OhR++vgwYO5VGMAAAAAuHlu/aDqlStXOryfN2+eihQpot9++01NmzbNdDmbzaawsDBXVw8AAAAAnMqtz6D9U0JCgiSpYMGCWc6XmJio0qVLq2TJknrwwQf1+++/50b1AAAAAOCW3DYJWmpqql544QU1atRI1atXz3S+SpUqac6cOfriiy/00UcfKTU1VQ0bNtThw4czXeby5cs6d+6cwwsAAAAAcptbX+J4vYEDB2rPnj3asGFDlvM1aNBADRo0sL9v2LChqlSpovfff19vvPFGhsuMHTtWUVFRTq0vAAAAAOTUbXEGbdCgQfr666+1Zs0alShRIkfLenl5qU6dOoqOjs50nuHDhyshIcH+OnTo0K1WGQAAAAByzK3PoBlj9Nxzz+nzzz/X2rVrVaZMmRyvIyUlRbt371a7du0yncfHx0c+Pj63UlUAAAAAuGVunaANHDhQixYt0hdffKGgoCAdO3ZMkhQSEiI/Pz9JUq9evVS8eHGNHTtWkjR69Gjde++9Kl++vOLj4/X222/r4MGDGjBggGXtAAAAAIDscOsEbcaMGZKk5s2bO5TPnTtXffr0kSTFxcXJw+P/rtQ8e/asnnjiCR07dkwFChRQ3bp1tXHjRlWtWjW3qg0436iQLKYl5F49AAAA4FJunaAZY244z9q1ax3eT548WZMnT3ZRjQAAAADAddw6QQPgWhGvrshyeuy49rlUEwAAAEi3ySiOAAAAAJAXkKABAAAAgJsgQQMAAAAAN0GCBgAAAABuggQNAAAAANwECRoAAAAAuAkSNAAAAABwEyRoAAAAAOAmSNAAAAAAwE2QoAEAAACAm8hndQUA5D0Rr67IcnrsuPa5VBMAAAD3QoIGIHOjQrKYlpB79QAAAMgjSNAA5BlZnbnjrB0AAHAH3IMGAAAAAG6CBA0AAAAA3AQJGgAAAAC4Ce5BAwAX4943AACQXZxBAwAAAAA3wRk0AO4nLw3vn1VbpVtqL2fuAAC4/ZCgAUBelJeSYAAAbiNc4ggAAAAAboIzaAAAp8nqskqJSysBALgRzqABAAAAgJsgQQMAAAAAN0GCBgAAAABuggQNAAAAANwEg4QAgOTS55EBAABkFwkaAOC2x+iRAIA7BQkaAODOl5cezM3ZYAC4rZGgAQByT15KlAAAuAkMEgIAAAAAboIEDQAAAADcBJc4AgBwk7IanCTWt3vWC3NJJwAgAyRoAADcZrJODHOxIgBwMxjMKEskaAAAIFuyTAx5lAEAOAUJGgAAcFs84w5AXkOCBgAAbl88uuHOZdVlcHltn8pr7b0NkKABAIBbx488AHAKhtkHAAAAADfBGTQAAIB/sOreN6se3eCecW96tUDGbpPRI0nQAAAAkGfcMPl2UWJ447hZJMIuSoKvxb3pVcNFuMQRAAAAANwEZ9AAAAByikFRgCxZdfnqnXDZLGfQAAAAAMBNkKABAAAAgJsgQQMAAAAAN3FbJGjTp09XRESEfH19Vb9+ff3yyy9Zzr9kyRJVrlxZvr6+qlGjhr755ptcqikAAAAA3Dy3T9AWL16soUOHauTIkdq2bZtq1aqlyMhInThxIsP5N27cqG7duql///7avn27OnXqpE6dOmnPnj25XHMAAAAAyBm3T9AmTZqkJ554Qn379lXVqlU1c+ZM+fv7a86cORnOP2XKFLVp00bDhg1TlSpV9MYbb+iuu+7StGnTcrnmAAAAAJAzbj3MfnJysn777TcNHz7cXubh4aHWrVtr06ZNGS6zadMmDR061KEsMjJSy5cvzzTO5cuXdfnyZfv7hIRrw+OeO3cuw/lTL1/Ist7nbCaLiRmvMzuIm82YeS3uHfbZWhXXLT9bq+LeYZ+tVXHd8rO1Ku4d9tlaFdctP9vbMK47frZ5Le7ttk+l5QTG3GB5ZzFu7O+//zaSzMaNGx3Khw0bZu65554Ml/Hy8jKLFi1yKJs+fbopUqRIpnFGjhxpJPHixYsXL168ePHixYtXhq9Dhw7deoKTDW59Bi23DB8+3OGsW2pqqs6cOaNChQrJZrPlaF3nzp1TyZIldejQIQUHBzu7qsS1KCZx7+y4eamtxGWfIu7tGTcvtZW4d3bc27GtxhidP39e4eHhLqqdI7dO0AoXLixPT08dP37cofz48eMKCwvLcJmwsLAczS9JPj4+8vHxcSjLnz//zVX6/wsODs7VnS4vxs1LbSXunRuTuHd23LzUVuLeuTGJS9w7JeatxA0JCXFBbTLm1oOEeHt7q27dulq9erW9LDU1VatXr1aDBg0yXKZBgwYO80vS999/n+n8AAAAAOAu3PoMmiQNHTpUvXv3Vr169XTPPffo3XffVVJSkvr27StJ6tWrl4oXL66xY8dKkgYPHqxmzZpp4sSJat++vT755BNt3bpVs2bNsrIZAAAAAHBDbp+gPfroozp58qRGjBihY8eOqXbt2lq5cqWKFi0qSYqLi5OHx/+dCGzYsKEWLVqk119/Xa+99poqVKig5cuXq3r16rlSXx8fH40cOTLdJZPEvb1jEvfOjpuX2krcOzcmce/suHmprcS9s+PmpbbeLJsxuTVeJAAAAAAgK259DxoAAAAA5CUkaAAAAADgJkjQAAAAAMBNkKABAAAAgJsgQbsDXLp0KddjRkdH67vvvtPFixclXXvCem7I7bbGxMTo9ddfV7du3XTixAlJ0rfffqvff/89V+Lndnvj4+P14Ycfavjw4Tpz5owkadu2bfr7779ztR65wcq2WnH8XL16VT/88IPef/99nT9/XpJ05MgRJSYmujw2XMuKfiouLi7D/dYYo7i4OJfFlaw7dq2Km5ycrH379unq1asujXM9q9pqdT9lxe+p3I577ty5TKdFR0c7Pd6VK1dUrlw5/fHHH05fd3ZYvU/dNINbtn79etOjRw9z7733msOHDxtjjFmwYIH56aefXBYzJSXFjB492oSHhxtPT08TExNjjDHm9ddfNx9++KHL4p46dcq0atXK2Gw24+HhYY/bt29fM3ToUJfEtKqta9euNX5+fqZ169bG29vbHnfs2LHmoYcecllcq9q7c+dOExoaasqXL2/y5ctnj/uvf/3LPP744y6La8y146Vhw4amWLFiJjY21hhjzOTJk83y5ctdEs+qtlpx/BhjTGxsrKlcubLx9/d32Keef/5589RTT7ksrjHW9I9Wxc3t/dgY6/opDw8Pc/z48XTlp06dMh4eHi6La9Wxa0XcpKQk069fP+Pp6elw3A4aNMiMHTvWJTGNsW4bW9VPWfWda1Xcxo0bm0uXLqUr37t3rylevLhLYoaHh5v//e9/Lll3Vqz87rtVnEG7RZ999pkiIyPl5+en7du36/Lly5KkhIQEvfXWWy6LO2bMGM2bN08TJkyQt7e3vbx69er68MMPXRZ3yJAhypcvn+Li4uTv728vf/TRR7Vy5UqXxLSqra+++qrGjBmj77//3iFuy5YttXnzZpfFtaq9Q4cOVZ8+fbR//375+vray9u1a6f169e7LO6MGTM0dOhQtWvXTvHx8UpJSZEk5c+fX++++65LYlrVViuOH0kaPHiw6tWrp7Nnz8rPz89e3rlzZ61evdplca3qH62Ia8V+LFnXTxljZLPZ0pUnJiY6HFPOZtWxa0Xc4cOHa+fOnVq7dq1DzNatW2vx4sUuiSlZt42t6qes+s61Km5gYKA6d+7scEb2jz/+UPPmzfXQQw+5JObAgQM1fvz4XD0LLFm3TzmF1Rni7a527dpm/vz5xhhjAgMD7dn5tm3bTNGiRV0Wt1y5cuaHH35IF/ePP/4w+fPnd1ncokWLmh07dqSLGxMTYwICAlwS06q2BgQEmL/++itd3AMHDhgfHx+XxbWqvcHBwSY6Ojpd3NjYWJe2t0qVKubzzz9PF3f37t2mUKFCLolpVVutOH6MMaZgwYJm79696eIeOHDA+Pn5uSyuVf2jFXGt2I+Nyf1+asiQIWbIkCHGw8PDPPXUU/b3Q4YMMc8//7ypX7++adiwodPjprHq2LUibqlSpcymTZvSxdy/f78JCgpySUxjrNvGVvVTVn3nWhX3woULpmHDhqZr164mNTXV7N692xQpUsQMGTLEZTE7depkgoKCTLFixcz9999vOnfu7PByFav2KWfIZ3WCeLvbt2+fmjZtmq48JCRE8fHxLov7999/q3z58unKU1NTdeXKFZfFTUpKcvjLf5ozZ8647MnsVrU1f/78Onr0qMqUKeNQvn37dhUvXtxlca1qr4+PT4bXpv/5558KDQ11WdwDBw6oTp06GdYnKSnJJTGtaqsVx490bd9JO6NzvcOHDysoKMhlca3qH62Ia8V+LOV+P7V9+3ZJ186g7d692+Ev/97e3qpVq5Zeeuklp8dNY9Wxa0XckydPqkiRIunKk5KSMjx76SxWbWOr+imrvnOtiuvn56cVK1aoefPm6tq1q9avX69evXrp7bffdlnM/Pnzu+zsXFas2qecgUscb1FYWFiGN1Vu2LBBZcuWdVncqlWr6qeffkpXvnTp0gx/JDhLkyZNtGDBAvt7m82m1NRUTZgwQS1atHBJTKva+thjj+mVV17RsWPH7O38+eef9dJLL6lXr14ui2tVezt27KjRo0fbvxhsNpvi4uL0yiuvuLRjLVOmjHbs2JGufOXKlapSpYpLYlrVViuOH0m6//77HS6zs9lsSkxM1MiRI9WuXTuXxbWqf7QirhX7sZT7/dSaNWu0Zs0a9e7dW99++639/Zo1a/Tdd9/p/fffV4UKFZweN41Vx64VcevVq6cVK1bY36clZR9++KEaNGjgkpiSddvYqn7Kqu/c3Ix77tw5h5eHh4cWL16sLVu26KGHHtK///1v+zRXmDt3bpYvV7Fqn3IKq0/h3e7eeustU7VqVbN582YTFBRkfvrpJ/PRRx+Z0NBQM3XqVJfFXb58uQkJCTHjxo0z/v7+5u233zYDBgww3t7eZtWqVS6Lm3YqvE2bNsbb29s8/PDDpkqVKqZo0aL2SyKczaq2Xr582QwYMMDky5fP2Gw24+XlZTw8PEzPnj3N1atXXRbXqvbGx8eb1q1bm/z58xtPT09TsmRJ4+XlZZo2bWoSExNdFveDDz4wxYsXN5988okJCAgwH3/8sRkzZoz9/65gVVutOH6MMebQoUOmatWqpkqVKiZfvnzm3nvvNYUKFTKVKlXKcKAHZ7Gqf7QirhX7sTHW9VNWserYtSLuTz/9ZAIDA83TTz9tfH19zeDBg819991nAgICzNatW10S0xjrtrFV/ZRV37m5GTdtYKp/vmw2m31a2r+ucuXKFfP999+bmTNnmnPnzhljjPn777/N+fPnXRbTqn3KGWzG5NL46HcoY4zeeustjR07VhcuXJB07fKAl156SW+88YZLY//0008aPXq0du7cqcTERN11110aMWKE7r//fpfGTUhI0LRp0xziDhw4UMWKFXNZTKvaKl0bTnrPnj1KTExUnTp1XPrX4TRWtnfDhg3atWuXPW7r1q1dHnPhwoUaNWqUYmJiJEnh4eGKiopS//79XRrXirZacfxI14Ya/uSTTxza26NHD4cbp53Nqv7RqrhW7cdS7vRTXbp00bx58xQcHKwuXbpkOe+yZcucHv96Vhy7VsSNiYnRuHHjHPqLV155RTVq1HBpXMmabWxFPyVZ952bW3HXrVuX7XmbNWvm1NiSdPDgQbVp00ZxcXG6fPmy/vzzT5UtW1aDBw/W5cuXNXPmTKfHTGPVPnWrSNCcJDk5WdHR0UpMTFTVqlUVGBhodZWA286FCxeUmJiY4X0XuH1Z1T9aFfdO3Y/79u2rqVOnKigoSH379s1yXldetgTg5ly9elVvvfWW+vXrpxIlSuRa3E6dOikoKEizZ89WoUKFtHPnTpUtW1Zr167VE088of379+daXW4XJGh3gMTERKWmpjqUBQcHuyzepUuXtGvXLp04cSJd3I4dO7osrpS7bTXGaOnSpVqzZk2GbXX1X4il3P9sf/3110zbO2nSJJfFtYJVbbXq+Dly5Ig2bNiQYdznn3/eZXHhWu7QT+U2q45dq+KeOHEiw5g1a9Z0WUyr2mp1P5Xb37lWxA0KCtLu3bsVERHhkvVnpFChQtq4caMqVaqkoKAge4IWGxurqlWr2q90cAWr96mbxSiOt+jSpUv6z3/+k2lHtm3bNpfEPXDggAYNGqS1a9c6PIHe/P9n02Q0ao0zrFy5Ur169dKpU6fSTXNVXKva+sILL+j9999XixYtVLRoUZeOmnU9q9r71ltv6fXXX1elSpXStdeVbT99+rRGjBiR6TF05swZp8e0qq1WHD+SNG/ePD311FPy9vZWoUKF0rXXVV9SVvWPVsS1Yj+WrOunLl68KGOMfVTSgwcP6vPPP1fVqlVdelmYVceuFXF/++039e7dW3/88Yf++bf0O/G7wKp+yqrvXKvitmzZUuvWrcvVBM2q0RSt2qecgTNot6hHjx5atWqVHn744Qy/HEeOHOmSuI0aNZIxRoMHD84wriuuIZakChUq6P7779eIESNUtGhRl8T4J6vaWrBgQX300Ue5PtKPVe0tWrSoxo8frz59+rhk/Zlp166doqOj1b9//wzb27t3b6fHtKqtVhw/klSyZEk9/fTTGj58uDw8cm/wXqv6RyviWrEfS9b1U/fff7+6dOmip59+WvHx8apUqZK8vb116tQpTZo0Sc8884xL4lp17FoRt1atWipXrpxeeeWVDPep0qVLuySuVdvYqn7Kqu9cq+LOnDlTUVFR6tGjh+rWrauAgACH6a64kuPRRx9VSEiIZs2apaCgIO3atUuhoaF68MEHVapUKZddEm3VPuUUuTYcyR0qODjYbNiwIdfjBgQE2B++l5uCgoJcOtpcRqxqa0REhPnjjz9yPa5V7Q0LCzN//vlnrscNDAy0P7w5t1jVViuOH2OuPazTirhW9Y9WxLViPzbGun6qUKFCZs+ePcaYayNY1qxZ06SkpJhPP/3UVK5c2WVxrTp2rYgbGBho9u/fn6sxjbFuG1vVT1n1nWtV3LSRGzN6uWoUR6tGU7Rqn3KG2yyddD/Fixe35GF3d999tw4dOpTrcR9++GGtXbs2V2Na1dZRo0YpKipKFy9ezNW4VrV3yJAhmj59eq7HrVy5cq5vY6vaasXxI0n9+/fXkiVLcj2uVf2jFXGt2I8l6/qpCxcu2LfxqlWr1KVLF3l4eOjee+/VwYMHXRbXqmPXiritWrXSzp07czWmZN02tqqfsuo716q4qampmb5cdVlliRIltHPnTr322msaMmSI6tSpo3Hjxmn79u0uHUzJqn3KGbjE8RZ9++23mjp1qmbOnOmyyw0yEhMTo6efflo9e/ZU9erV5eXl5TDdVTcPX7hwQY888ohCQ0NVo0aNdHFdcT2vVW29ePGiOnfurJ9//lkRERHp4rrq/hmr2puamqr27dvrzz//VNWqVdPFddVgA7/++qteffVVjRgxIsP2uuJGaavaasXxI0kpKSl64IEHdPHixQzjuuqmf6v6RyviWrEfS9b1UzVr1tSAAQPUuXNnVa9eXStXrlSDBg3022+/qX379jp27JhL4lp17FoR99SpU+rdu7fuueeeDPcpVw0qZNU2tqqfsuo716q4eYlV+5QzMEjILapXr54uXbqksmXLyt/fP92H76obw0+ePKmYmBiHoY5tNpvLby79+OOPtWrVKvn6+mrt2rW5csOlVW3t3bu3fvvtN/Xs2TNXb763qr3PP/+81qxZoxYtWqS7mdaV8ufPr3Pnzqlly5YO5a5sr1VtteL4kaSxY8fqu+++U6VKleyxro/rKlb1j1bEtWI/lqzrp0aMGKHu3btryJAhatWqlRo0aCDp2tm0OnXquCyuVceuFXE3bdqkn3/+Wd9++226aXfid4FV/ZRV37lWxZWuPRftnXfe0R9//CFJqlq1qoYNG6YmTZq4LOb+/fszHURpxIgRLolp1T7lDJxBu0WtW7dWXFxcrt8YXrVqVVWpUkUvv/xyrt48HBYWpueff16vvvpqrt1waVVbAwIC9N1336lx48YuWX9mrGpvUFCQPvnkE7Vv394l68/MPffco3z58uXqjdJWtdWK40eSChQooMmTJ+f6Tf9W9Y9WxLViP5as66ck6dixYzp69Khq1apl359/+eUXBQcHq3Llyi6JadWxa0XciIgIPfDAA/r3v/+dq4MKWbWNreqnrPrOtSruRx99pL59+6pLly5q1KiRJOnnn3/W559/rnnz5ql79+5Oj/nBBx/omWeeUeHChRUWFpYuUXLVmX6r9imnsOjetzuGn5+fJTeG+/v7W3LzcIECBXL9hkur2lqpUiWzc+fOXI9rVXtLlSplyWADfn5+uX6jtFVtteL4McaYokWLWnLTv1X9oxVxrdiPjbGun7KKVceuFXEDAwMt6S+s2sZW9VNWfedaFbdy5cpm0qRJ6conTpzosgF+SpUqZcaNG+eSdWfFqn3KGRgk5BZZdWN4y5YtLbl5uHfv3lq8eHGuxrSqrRMnTtTLL7+s2NjYXI1rVXtHjRqlkSNHuvSBkRmpV69ert8obVVbrTh+JGnw4MH6z3/+k+txreofrYhrxX4s5W4/1aVLF507d87+/6xermLVsWtF3C5dumjNmjW5Fi+NVdvYqn7Kqu9cq+L+9ddf6tChQ7ryjh076sCBAy6JefbsWT3yyCMuWXdWrNqnnIF70G7RuHHj9OKLL+rNN9/M8AZEV90Y3qFDBw0ZMkS7d+/OMK6rbh5OSUnRhAkT9N1336lmzZq5csOlVW3t2bOnLly4oHLlyuXq/TNWtXfq1KmKiYlR0aJFc3Wwgeeee06DBw/WsGHDMmyvK26UtqqtVhw/0rXLzn788Ud9/fXXqlatWq7d9G9V/2hFXCv2Yyl3+6mQkBD7pUkhISFOW29OWHXsWhG3YsWKGj58uDZs2JCrgwpZtY2t6qes+s61Km7JkiW1evVqlS9f3qH8hx9+UMmSJV0S85FHHtGqVav09NNPu2T9mbFqn3IG7kG7RWnX3f/z2mHj4ps8s7p/xZVxW7RokWXcH3/80ekxrWrr/Pnzs5zuqvtnrGpvVFRUltNd9VDhjNrr6hulrWqrFcePJIeb0DPiqoeEWt0/5mZcK/Zjybp+yipWHbtWxC1Tpkym02w2m/766y+nx5Ss28ZW91MZuRN/x82YMUMvvPCC+vXrp4YNG0q6dg/avHnzNGXKFD311FNOiTN16lT7/5OSkjRp0iS1b98+V//YYNU+5QwkaLdo3bp1WU531Y3hwJ3iRs9Mys3h2eFcVvWPVsTNa/vxmDFj1KNHjyyTCADu6fPPP9fEiRPtozhWqVJFw4YN04MPPui0GNntG1z5x4bbGQkabtrhw4clXXsA4Z0qJSVFy5cvt3di1apVU8eOHeXp6WlxzVznt99+c2ivK4fMtpqVbbXi+Dl58qT27dsnSapUqZJCQ0NzLTZcx4p+qlatWtqzZ4/q16+vnj17qmvXripcuLDL4v2TVceuVXHTfqrl5tDgVrWVfgrOdlvuU9aMTXJnOXv2rHnnnXdM//79Tf/+/c2kSZNMfHy8y+OuXbvWPPDAA6ZcuXKmXLlypkOHDmb9+vUujZmSkmKioqJMcHCw8fDwMB4eHiYkJMSMHj3apKSkuCyuFW3dv3+/qVChgvH39zd16tQxderUMf7+/qZSpUouH1nLivYeP37ctGjRwthsNlOgQAFToEABY7PZTMuWLc2JEydcGjs6OtoMGjTItGrVyrRq1co899xzLt3GVrXVquMnMTHR9O3b13h6ehqbzWZsNpvJly+f6devn0lKSnJZXGOs6x+tiJvb+7Ex1vZTe/bsMcOHDzdlypQxXl5epl27dmbhwoUu3aesOnatijt//nxTvXp14+PjY3x8fEyNGjXMggULXBbPGOvaamU/ZcV3rlVx//3vf5sff/zRXLx40aVxrhcVFZXhZ3jhwgUTFRXlsrhW7lO3igTtFv3666+mYMGCpnjx4qZz586mc+fOpkSJEqZQoULmt99+c1nc//73vyZfvnyma9euZsqUKWbKlCmma9euxsvLyyxcuNBlcV999VUTGhpq3nvvPbNz506zc+dOM336dBMaGmpee+01l8S0qq1t27Y1bdq0MadPn7aXnTp1yrRp08a0a9fOZXGtam/Xrl1NvXr1zP/+9z972e+//27q1atnHnvsMZfFXblypfH29jb33HOPGTJkiBkyZIi55557jI+Pj1m1apVLYlrVViuOH2OMefLJJ03ZsmXNN998YxISEkxCQoJZsWKFKVeunHn66addFteq/tGKuFbsx8ZY10/904YNG8yzzz5rQkNDTVBQkMviWHXsWhF34sSJxt/f37z88svmiy++MF988YUZNmyY8ff3z3CYdGexahtb1U9Z9Z1rVdzWrVubgIAA4+PjYxo3bmz+9a9/me+//95cuHDBZTE9PDzM8ePH05WfOnXKeHh4uCyuVfuUM5Cg3aLGjRubPn36mCtXrtjLrly5Ynr37m2aNGnisrhWPMfCGGOKFStmvvjii3Tly5cvN+Hh4S6JaVVb/f39za5du9KV79ixwwQEBLgsrlXtDQ4ONr/88ku68i1btpiQkBCXxa1du7Z55ZVX0pW/8sorpk6dOi6JaVVbrTh+jDGmUKFCZs2aNenKf/zxR1O4cGGXxbWqf7QirhX7sTHW9VP/tH37dvPiiy+a4sWLG19fX5fFserYtSJuRESEmT9/frryefPmmYiICJfENMa6bWxVP2XVd65VcY251h9u2LDBvPXWWyYyMtIEBQUZb29v06hRI5fEs9lsGZ59Xb16tUs/W6v2KWcgQbtFvr6+GT7Q8ffffzd+fn4ui+vt7Z3hAw73799vfHx8XBbXx8fH7Nu3L1353r17XfalbFVbCxQoYH7++ed05Rs2bDAFChRwWVyr2hsYGGi2b9+ernzbtm0u/Yu4j49Phg+S3Ldvn8vaa2Vbc/v4MebaQ5Sv/2t4mj179hh/f3+XxbWqf7QirhX7sTHW9VPGGPPXX3+ZMWPGmKpVqxpPT0/TsmVL8+GHH7r0UlKrjl0r4vr4+GT4XfDnn3/ekd8FVvVTVn3nWhX3evv27TMzZ840Dz/8sMmXL58pVKiQU9efP39+U6BAAePh4WH/f9or7VL/Z5991qkxr2fVPuUMPKj6FgUHBysuLi5d+aFDhxQUFOSyuGnPsfgnVz7HQrp2Y/i0adPSlU+bNk21atVySUyr2vrAAw/oySef1JYtW2Su/TFDmzdv1tNPP+2y55NI1rW3ZcuWGjx4sI4cOWIv+/vvvzVkyBC1atXKZXFDQ0O1Y8eOdOU7duxQkSJFXBLTqrZacfxIUoMGDTRy5EhdunTJXnbx4kVFRUWpQYMGLotrVf9oRVwr9mPJun7q3nvvVfny5bV06VL17dtXBw8e1OrVq9W/f3+XPiPNqmPXirjly5fXp59+mq588eLFqlChgktiStZtY6v6Kau+c62KO2vWLHXv3l3FixdXw4YNtXLlSjVu3Fhbt27VyZMnnRrr3Xff1aRJk2SMUVRUlCZPnmx/zZw5Uxs2bND06dOdGvN6Vu1TTmFpengHeO6550yJEiXMJ598YuLi4kxcXJz5+OOPTYkSJczgwYNdFve9994z3t7e5umnnzYLFiwwCxYsME899ZTx8fExM2fOdFnctWvXmoCAAFOlShXTr18/069fP1OlShUTGBjoshtbrWrr2bNnTceOHY3NZjPe3t7G29vbeHh4mE6dOrn0L8RWtTcuLs7Url3beHl5mbJly5qyZcsaLy8vU6dOHXPo0CGXxY2KijL58+c348aNM+vXrzfr1683Y8eONfnz5zejR492SUyr2mrF8WOMMbt37zbh4eGmUKFCpmXLlqZly5amUKFCpnjx4mbPnj0ui2tV/2hFXCv2Y2Os66dee+018/vvv7ts/Zmx6ti1Iu7SpUuNp6eniYyMNKNHjzajR482kZGRJl++fGbZsmUuiWmMddvYqn7Kqu9cq+LabDZTpEgRM378eHP+/HmXxbne2rVrTXJycq7Eup5V+5QzMMz+LUpOTtawYcM0c+ZMXb16VZLk5eWlZ555RuPGjZOPj4/LYufGcywycuTIEU2fPl179+61x3322WcVHh7usphWtVWS9u/f79DW8uXLuzymVe01xuiHH35waG/r1q1dHvPdd9/VxIkT7X+xDQ8P17Bhw/T888+7bFhpK9oqWXP8SNKFCxe0cOFCh7g9evSQn5+fy2Ja1T9aEdeq/TiNFf2UVaw6dq2I+9tvv2ny5MkO3wUvvviiy4e8t2obW9FPSdZ951oRd/ny5Vq/fr3Wrl2rP/74Q3Xq1FHz5s3VvHlzNW7cWP7+/i6Ja9Vji6zap24VCZqTXLhwQTExMZKkcuXKuWwHB+5k58+flySXXv6G3GdV/2hVXPZjALeDhIQE/fTTT1qyZIk+/vhjeXh4OFwO6CzR0dFq166d/v77b1WqVEmStG/fPpUsWVIrVqxQuXLlnB7zdkeCdptLTk7WiRMnlJqa6lBeqlQpl8WMj4/XL7/8kmHcXr16uSxubrc1JSVF8+bN0+rVqzOM++OPP7okbhorPtvVq1dn2t45c+a4LK4VrGqrVcfP/v37tWbNmgzjjhgxwmVx4VpW91NWsOrYtSJuamqqoqOjM4zZtGlTl8SUrNvGVvZTVnznWhX39OnTWrdundauXau1a9fq999/V4ECBdSkSRN9/vnnTo/Xrl07GWO0cOFCFSxY0F6Hnj17ysPDQytWrHB6zDS363dfPqsrcLtLSkrSuHHjMu3I/vrrL5fE3b9/v/r166eNGzc6lBtjZLPZlJKS4pK4X331lXr06KHExEQFBwc7XLZjs9lc8gPTqrYOHjxY8+bNU/v27VW9enWXX6KUxqr2RkVFafTo0apXr56KFSuWa+09fvy4XnrpJfsx9M+/GbmivVa11YrjR5I++OADPfPMMypcuLDCwsLSxXXVl5RV/aMVca3YjyXr+imrWHXsWhF38+bN6t69uw4ePJhuf7oTvwus6qes+s61Km6NGjX0xx9/qECBAmratKmeeOIJNWvWTDVr1nRJPElat26dNm/ebE/OJKlQoUIaN26cGjVq5LK4Vu1TzsAZtFvUrVs3rVu3To8//niGHdngwYNdErdRo0bKly+fXn311QzjumpEuIoVK6pdu3Z66623cu1yIavaWrhwYS1YsEDt2rVzyfozY1V7ixUrpgkTJujxxx93yfoz07ZtW8XFxWnQoEEZttcV1+Jb1VYrjh9JKl26tJ599lm98soruRZTsq5/tCKuFfuxZF0/ZRWrjl0r4tauXVsVK1ZUVFRUhvuUq0bLtGobW9VPWfWda1Xc6dOnq1mzZqpevbpL1p+RggUL6uuvv1bDhg0dyn/++Wd16NBBZ86ccUlcq/Ypp8i98UjuTCEhIWbDhg25Htff3z/D5/zkRtyYmJhcj2lFW4sVK5bhM6tczar2FixY0ERHR+d63MyeueNKVrXViuPHGGOCgoIsiWtV/2hFXCv2Y2Os66euV716dRMXF5crsaw6dq2I6+/vn+FzslzNqm1sVT9l5e8pK+Ja4fHHHzfVqlUzmzdvNqmpqSY1NdVs2rTJVK9e3fTu3dtlca3ap5yB56DdogIFCjicss0tVatW1alTp3I9bmRkpLZu3ZqrMa1q64svvqgpU6aku7TE1axq74ABA7Ro0aJcj1uyZMlc38ZWtdWK40eSHnnkEa1atSrX41rVP1oR14r9WLKun7pebGysrly5kiuxrDp2rYhbv359RUdH52pMybptbFU/ZdV3rlVxrTB16lSVK1dODRo0kK+vr3x9fdWoUSOVL19eU6ZMcVlcq/YpZ+ASx1v00Ucf6YsvvtD8+fNdfsnSuXPn7P/funWrXn/9db311luqUaOGvLy8HOYNDg52Wtwvv/zS/v+TJ09q9OjR6tu3b4ZxnfVgVKva2qVLF4f3P/74owoWLKhq1aqli7ts2TKnxbWqvUOHDrX/PzU1VfPnz1fNmjVVs2bNdHEnTZrktLjXW7VqlSZOnKj3339fERERLokhWddWK44f6doXYpqkpCRNmjRJ7du3zzDu888/77S418vN/tHquLm1H0vW9VOZCQoK0s6dO1W2bFmXrN+qY9eKuLt27bL/PyYmRq+//rqGDRuW4XHrzHuGrNrGVvVTVn3nWhXXXURHRzs8UsAVjwNxh+8+ZyBBuwl16tRxuFY4OjpaxhhFRESk+/C3bdvmtLgeHh4Occ3/v5H0esYFN5d6eGTvRKsz41rV1r59+2Z73rlz5zotrlXtbdGiRbbms9lsTh0NrkCBAg7tS0pK0tWrV+Xv75/uGHLWtelWtdWK40eSypQpk+24zhw0w6r+0Yq4VuzHknX9VGbatWun2bNnq1ixYi5Zv1XHrhVx074LMvtpljbtTvkusKqfsvL3lBVx8xKr9ilnYxTHm9CpUydL4q5Zs8aSuP8cAS03WNXW3PgxkxGr2mtV3HfffTfXY+al40eSDhw4YElcq/pHK+JasR9L1vVTmfnmm29cuv681D9addxatY3zWnutipuXWLVPORtn0AAAAIA8pkaNGvrmm29UsmRJq6uCf2CQEAAAACCPyc0BfpAzJGgAAAAA4CZI0AAAAIA8pkmTJvLz88vVmDVq1NChQ4dyNebtiAQNyIbDhw9bNtgDAGRHbvdT8fHx+vDDDzV8+HD7KJXbtm3T33//nWt1AHDzvvnmG5eNvpoZLqvMHhI0J/r55591+fLlXI87btw4xcfH53rc9u3b6+jRo7ka06q2Vq1aVbGxsbke16r2BgcHWzL87Mcff6ykpKRcjWlVW604fiTr/nppVf9oRVwr9mMpd/upXbt2qWLFiho/frzeeecdez+1bNkyDR8+PFfqYNWxa0XcZ5991pKHGlu1ja3qp6z6zrUqbl5yu525I0FzorZt21ryl8O33nrLqc/Yya7169fr4sWLuRrTqrZaNdhpXmvvU089pePHj+dqTKvaasXxI1n310ur+kcr4lqxH0u5uy8PHTpUffr00f79++Xr62svb9eundavX58rdbDq2LUi7kcffeTwkOPcYtU2tqqfsuo716q4VrDiskrp9jtzx3PQnCgvfVlYJS+1VaK9uHPkpf4xL+zHv/76q95///105cWLF9exY8csqNGdLS/sU+4gL/VTVnH1cxPvFJxBw00rXbq0vLy8rK5GrnjttddUsGBBq6uRa3r27Kng4GCrq5ErrGqrVcePVX+9hOvlZj/l4+OT4RmdP//8U6GhoblSB6uOXfpH16OfgrPdbvsUD6p2okWLFunBBx9UQEBArsY9dOiQwsPD5enpmatxrZCX2irlvfZu2LBBd999t3x8fKyuCpzMqv7Rirh5YT8eMGCATp8+rU8//VQFCxbUrl275OnpqU6dOqlp06Z69913ra4ikGNWfefmdtz4+HgtXbpUMTExGjZsmAoWLKht27apaNGiKl68eK7UAVkjQQMAADmSkJCghx9+WFu3btX58+cVHh6uY8eOqUGDBvrmm29yPREHkD27du1S69atFRISotjYWO3bt09ly5bV66+/rri4OC1YsMDqKkIkaAAA4CZt2LBBu3btUmJiou666y61bt3a6ioByELr1q111113acKECQoKCtLOnTtVtmxZbdy4Ud27d7dkxGqkR4IGAAAA5AEhISHatm2bypUr55CgHTx4UJUqVdKlS5dcEpfLKnOGURyBTMTFxalkyZKy2WwO5cYYHTp0SKVKlbKoZs539epVLVq0SJGRkSpatKjV1bkjpe03RYoUcRiWHLhZV69e1VtvvaV+/fqpRIn/x955R0WRfG//GXKOAgoiCBgIKpgTQTDCV8SEARXFzApGTGsCFXNcXbMCJlRMuyYUJZoVBRVUwACYMKEiJuC+f/CbfhkGFNfpboT5nDPnaM3QT01P962uqhtqc65/9epVREdHIycnR6xA9sqVKyWuJ7VTVZfCwkKcP38ejRs3hpaWFt/dqdLwkeCntFvlyJEjoaOjg0OHDvHmVklEYs93lQlpFsdf5Ht1SdLT01nR/PbtG8zNzZGamsrK8StCTk4O4uPjER8fj5ycHN76wSZ169bFy5cvxdrfvHmDunXrsqYbFBSE/Px8sfZPnz4hKCiIFU05OTmMGTOGtZWzyoasrGyZ1+3r169ZC9ImIlhYWPBWKDM3Nxdbt27FjBkzmHo7iYmJrNUI+/btG3x8fPDw4UNWji+l+L5dtmwZCgoKONcODg5Gq1atsGPHDly7dg03btxgXjdv3mRFk287VVhYiIiICMyfPx/z589HREQEq+fex8cHHz58EGv/+PEjfHx8WNPlwz7Kysqic+fOePv2LSvH/x58jLmlISLOUu27u7sjKCiIqQkmEAiQmZmJadOmoXfv3qxo8lU3cdmyZWW2FxYWYuDAgazpSgSS8ku0b9+ePn/+LNZ+9+5dMjIyYk3X0NCQUlJSWDt+ebx//54GDRpEcnJyJBAISCAQkJycHHl5eVFubi4rmgUFBbR161YaMGAAubi4UIcOHURebCEQCCgnJ0es/dGjR6SiosKaroyMDL148UKs/dWrVyQjI8OarqOjIx05coS145dkzZo1FX6xgUAgKPMcP3nyhJSUlFjRJCKysrKiixcvsnb88khKSiI9PT2ysLAgOTk5ysjIICKiP//8kwYPHsyaroaGBj148IC143+Pu3fv0h9//EHOzs7k7OxMf/zxB929e5dVzaioKHJzcyMzMzMyMzMjNzc3OnPmDKua7u7uFBISwqpGWejr69OOHTs41+XSTpXk9u3bZGZmRioqKmRnZ0d2dnakqqpKpqamdOvWLVY0yxsLXr58SbKysqxoEvFnH5s1a0ZRUVGsHb88+BpziYi2bt1K1tbWpKCgQAoKCmRtbU1btmxhVTM3N5c6duxIWlpaJCsrS8bGxiQvL08ODg6Ul5fHiqaGhgalp6cTEZGamhozBj169IgUFRVZ0SQi0tPTo61bt4q0FRQUUJ8+fahhw4as6UoCqYvjL6KmpoaePXvin3/+gZxc8elMTU2Fs7MzPD09WdP9448/sGTJEmzdupXR5YIRI0bgxo0bOHbsGNq0aQMAuHjxIsaPH4/Ro0cjPDxc4prjx49HSEgI3NzcYGNjw/qW9KRJkwAUryrNnj0bKioqzHuFhYW4fPkybG1tWdOncrbdk5KSWK1x5Ovri0mTJiErKwvNmjUTy8LWuHFjiWmtWrWqQp8TCATw9/eXmO7atWuZ427duhVqamrMe4WFhYiLi0PDhg0lpleaxYsXIyAgABs2bICNjQ1rOqURrl4Kg8KFuLq6srqK6OHhgSNHjmDixImsaZTFwYMH0b9/fzRv3pyxU5cuXYKNjQ3Cw8NZWSX++++/MX78ePTp0wfjx49nNF1dXbFq1Sr88ccfEtcEgG7dumH69Om4detWmfetu7s7K7oyMjJo164dK8f+HlzaqZKMGDEC1tbWuHbtGrS1tQEAb9++xdChQzFq1ChcuHBBYlrv379ndlQ+fPggsuNQWFiIEydOQF9fX2J6Qvi2jwsWLMCUKVMwf/78Mn9btuqx8TXmzpkzBytXroSfn5/I89TEiRORmZnJ2u6dpqYmzpw5w2mCH77qJh4/fhydO3eGpqYm+vTpg4KCAnh6euLu3buIjo5mTVcSSJOE/CKfPn1Cx44dUbt2bYSHh+POnTtwcXGBl5cXKz74Qnr27ImzZ89CTU0NjRo1EjNkhw4dYkVXVVUVkZGRaN++vUh7fHw8unbtio8fP0pcs0aNGggLC4Orq6vEj10WHTp0AADExsaiTZs2UFBQYN5TUFCAqakppkyZgnr16klUV1tbGwKBAO/evYOGhobIgFFYWIi8vDyMGTMG69evl6iuEBkZcY9ngUDADF6FhYWs6HKJ0DX18ePHqF27toi7jvC3DQoKQqtWrVjR19bWRn5+PgoKCqCgoCBWNFPoeihp+AoKX7BgAVasWAEXF5cyH7gkOfkuibm5Oby8vMQecObOnYtdu3YhIyND4pq1a9fG9OnTMW7cOJH29evXIzg4mDVX0rLuWyFs3rdLly7F06dPOa93xpedUlZWxrVr12BtbS3Sfvv2bbRo0QKfPn2SmJaMjMx3FyIFAgECAwPx559/SkwT4N8+lvxtS35/tn5bvsdcPT09rF27FgMGDBBp37t3L/z8/PDq1StWdPmAz7qJ586dg4eHB3bt2oVt27YhPT0d586dq/RxrNIdtF9EWVkZx48fh5OTEzw9PREXF4chQ4aU6/cqKbS0tFjzFf4eurq60NTUFGvX1NRkVhUljYKCAiwsLFg5dlkIV1WGDRuGNWvWsLZqV5rVq1eDiODj44PAwECR8ywcHIWrbGxQHWKFhN+xQ4cOOHz4MOfB6HwV7+Vr9XLbtm3Q0tLC9evXcf36dZH3JL07WpJnz55hyJAhYu2DBg1izTbn5uaia9euYu2dO3fGtGnTWNEEIJacgyumTJkCNzc3mJubw8rKCvLy8iLvs7VIyJedql+/Pl68eCE2QcvJyZH4+BQdHQ0igrOzMw4ePCiyi6OgoAATExMYGhpKVBPg3z5yvaPB95j77ds3NG/eXKy9WbNmrMeVcp3gZ8WKFejTpw/09fXx6dMnODo6MnUTFy5cKHG9kjg7OyMsLAy9e/eGpaUlYmNjUaNGDVY1JYF0B+0/UNaDzrNnz9CpUyf873//w+LFi5l2rh7uuWLz5s04cOAAdu7ciZo1awIAnj9/Dm9vb/Tq1QujR4+WuOaKFSvw4MEDrFu3rlJn3JEEBQUF2L17N5ydnWFsbMx3dzgjOzsb//zzDzIzM/H161eR9yQ9WHz79g0NGzbEsWPHYGlpKdFjV1b4XL3kA1dXV/Tt2xfDhg0Tad+xYwfCw8MRGRkpcc2BAwfCzs4OAQEBIu3Lly/HtWvXWHH/Ls3nz585yxA6btw4bN26FR06dICBgYGYbd6xYwcn/eCKEydOYOrUqZg3bx5at24NoNiFNSgoCIsXLxbxKpHUuP/48WPUqVOH03GvutlHPsdcPz8/yMvLi41xU6ZMwadPn1jbuQsODsasWbPQoEEDsXtXIBDg3LlzrOgC3NRN7NWrV5ntly5dgoWFhcjkjK2FJEkgnaD9B8pzPxCeSq7cwgoKChATE4OMjAwMHDgQ6urqePr0KTQ0NER8xyWJnZ0d0tPT8eXLFybNfGZmJhQVFcVc/hITEyWi2bNnT0RHR0NHRwfW1tasrtT26tULISEh0NDQKPcmZ0O3JCoqKkhNTYWJiQkrx/8eO3fuxMaNG/Hw4UNcvHgRJiYmWL16NerWrYsePXqwonn27Fm4u7vDzMwMd+/ehY2NDR49egQiQtOmTVkZLIyMjBAVFcXLA0hGRgZ27NiBjIwMrFmzBvr6+jh58iTq1KkjtjovKd69e4c+ffrg2rVr+PDhAwwNDZnVyxMnToi5Hkqar1+/4uHDhzA3N+ckZnbjxo2YM2cOPD09RR6mDxw4gMDAQJHdB0nFaC1YsADLly9Hu3btROLezp8/j8mTJ4s8tEty57CwsBDBwcHYuHEjXrx4gfv378PMzAyzZ8+Gqakphg8fLjGtkqirqyM8PBxubm6sHP978GGnynK/KznmC///q+N+cnIybGxsICMjg+Tk5O9+lq14Oz7tY3x8PDZt2oQHDx7gwIEDMDIyws6dO1G3bl2x0ApJwdeY6+fnh7CwMBgbGzN26vLly8jMzMSQIUNEnnUkuVBpYGCAJUuWYOjQoRI7ZmWi9MLc96jMC0lSF8f/QGUILHz8+DG6du2KzMxMfPnyBZ06dYK6ujqWLFmCL1++YOPGjazoenh4sHLc76GlpYWePXtyoqWpqckMtmW5cnJBy5YtcePGDc4Hiw0bNmDOnDmYMGECFi5cyDxkaGlpYfXq1aw9+MyYMQNTpkxBYGAg1NXVcfDgQejr68PLy6tMlzFJwFeSndjYWHTr1g3t2rVDXFwcFi5cCH19fSQlJWHbtm2IiIhgRZePoHAAyM/Ph5+fH0JDQwGAmTz4+fnByMgI06dPZ0XX19cXQHHijr///rvM9wDJxmht27YN2traSElJQUpKCtOupaWFbdu2iWhKcoK2cOFChIaGYunSpRg5ciTTbmNjg9WrV7M2QdPR0YG5uTkrx/4efNkprsZ9W1tbPH/+HPr6+rC1tWUWfEvD5gIwX/bx4MGDGDx4MLy8vJCYmIgvX74AKF5gCg4OxokTJ1jR5WvMvX37Npo2bQoATFxsjRo1UKNGDdy+fZv5nKR3UPlK8MOVW6Vw0kX/V3tUT09PLN77t4DDjJFVjm/fvlFgYCBlZWVxrt2jRw8aNGgQffnyRSRlaXR0NFlYWHDeHymSY9++fWRmZkZ//fUXXbhwgZKSkkRebGFpaUmHDx8mItE0uLdu3SJdXV3WdNXU1Jj0u1paWnT79m0iIrp58yaZmJiwounh4UHq6upUq1Yt6ty5M/Xs2VPkxRatW7emFStWEJHoOb58+TKrZTn4wt/fn5o1a0bx8fGkqqrKfN8jR46Qra0tz72rGpibmzOpyUteU6mpqaSlpcWa7vbt28nT05M+fvzImkZZ8GWnuOLRo0dUVFTE/Pt7L7bgyz7a2tpSaGgoEYn+tomJiWRgYMCaLl9jLl8sWbKExo8fz6nmwoULSSAQUMOGDcnR0ZGcnJyYF1vlkgoLC0leXp7u37/PyvHZRrqD9gsIi4SWFYzONvHx8bhw4YJIhkEAMDU1ZS1bWEmuX7/OFMq2traGnZ0d65ovX77EvXv3AAANGjRgNbkBUJyhk4iYNPuPHz/G4cOHYWVlhc6dO7Om279/fwCiblBcuM0+fPiwzN9RUVGRleycQlRVVZm4s1q1aiEjI4Nx9WMrixVfSXZu3bqFPXv2iLXr6+uznrGL66BwADhy5Aj27duH1q1bi6wCW1tbs5JJsbJApVzf2OTJkydlJqkoKipiCtGywdq1a5GRkQEDAwOYmpqKuZ5LysW9NHzZKaA4Ecy2bdtExj4fHx+JeluU3MXhw80d4M8+3rt3Dw4ODmLtmpqayM3NZU2XrzG3JNnZ2QCKs8GyDR8JftasWYPt27dz6lYpIyODevXq4fXr1xLPus0F0gnaL+Ls7IzY2FiYmppyqltUVFSm0cjOzhapcyRpcnJy0L9/f8TExDAZnnJzc9GhQweEh4ezMmn6+PEj46stfLCUlZXFkCFD8Ndff4nUKZMkPXr0QK9evTBmzBjk5uaiZcuWUFBQwKtXr7By5UqMHTuWFV2+spTVrVsXN2/eFHsoOHXqFKuxCK1bt0ZCQgIsLS3h6uqKyZMn49atWzh06BDjly9p+PI719LSwrNnz5h01kJu3LgBIyMj1nR/FBTOFi9fviyzXtPHjx9Zn7zExsZi+fLlzMO0lZUVAgICYG9vz5pmWFgYli1bhrS0NADFmf8CAgIwePBg1jStrKwQHx8vdt9GRESwunDGh7s7wJ+dunbtGrp06QJlZWW0bNkSQPHCxsKFC3H69GnGVU2ShIaGokaNGkyc39SpU7F582ZYWVlh7969rE3g+LKPNWvWRHp6utjzVEJCAszMzFjT5WvMLSoqYkqR5OXlASiO7Zw8eTL+/PPP75bQ+BX8/f0RHR2NDh06QFdXl5OFJL7cKvmqPSoR+N3A+/3ZsGED1axZkyZPnkx79uyho0ePirzYwtPTk0aOHElExa4ADx48oA8fPpCzszMNHTqUVd3mzZtTSkoK03bnzh1q3rw59e/fnxXNUaNGkZmZGZ04cYLevXtH7969o+PHj5O5uTmNGTOGFU0iIl1dXcbdbsuWLdS4cWMqLCyk/fv3V/oK9P+FLVu2kJGREYWHh5Oqqirt3buXFixYwPybLTIyMhg3kry8PBo9ejQ1atSIevXqxaobDxFRTk4OxcfHU3x8POXk5LCqRUQ0efJkat++PT179ozU1dUpLS2NEhISyMzMjObNm8earr6+Pu3YsYO145eHvb09rV27loj+v50iIho3bhx16dKFNd2dO3eSnJwceXp60po1a2jNmjXk6elJ8vLytHv3blY0V6xYQSoqKjR16lTG/gcEBJCKigqtXLmSFU2iYndRTU1NWrx4MamoqNCyZctoxIgRpKCgQKdPn2ZNly/4slPt27enoUOH0rdv35i2b9++kbe3N9nb27OiWb9+fTp79iwREV24cIGUlZVp06ZN1L17d1ZdDYVwbR+Dg4PJysqKLl26ROrq6hQfH0+7du0iPT09xo5UJaZPn056enr0999/M+6U69evJz09PZo5cyZrumpqanTs2DHWjl8WfLhVEhWHTSgoKJCMjAwpKSmRtra2yKsyI52g/SICgaDcl4yMDGu6WVlZZGVlRZaWliQnJ0etW7cmXV1datCgAb148YI1XQ0NDbpy5YpY++XLl0lTU5MVTV1dXYqOjhZrP3fuHNWoUYMVTSIiZWVlevz4MRER9e3bl3mAzszMJGVlZdZ0iYjS09Np3Lhx5OLiQi4uLuTn58fEabHJrl27yMLCgrmGjYyMaOvWrazrck1eXh4NGzaMZGVlme8qJydHPj4+rMbUfPnyhUaMGEFycnIkEAhIXl6eZGRkaNCgQVRQUMCabs2aNXnxw4+Pjyc1NTUaM2YMKSkp0fjx46lTp06kqqpK165dY023YcOGZU6KVqxYwdriiqmpKRM/U5KQkBAyNTVlRVNIXFwcdezYkfT09EhZWZnatWtHkZGRrGoKuXbtGu3cuZN27txJiYmJnGjyYaeUlJQoNTVVrP3OnTusjQclx6CpU6fS4MGDiYjo9u3brI59fNnHoqIiZrIt1FVSUqJZs2axpimEjzG3Vq1aZS7kHzlyhAwNDVnTrVOnTpnXMpsUFhZS165dyczMjP73v/9xFtcYEhLy3VdlRjpB+4359u0b7dy5kwICAmjs2LG0ZcsWys/PZ1VTTU2Nbty4IdaemJhI6urqrGgqKyuL7NgJuX37NqmoqLCiSUTUqFEjWrNmDWVmZpKGhgZduHCBiIofSNgMWD516hQpKChQy5YtaeLEiTRx4kRq2bIlKSoqcrYi/vHjR1Yn+iW5cuUKXbp0Saz90qVLdPXqVVY0+dqVFfL48WM6fvw47du3j5OJE1+rl0TFDz4jRoygFi1akKWlJXl5eVFycjKrmgoKCpSWlibWnpaWRoqKiqxoKioqlql5//591jT55MWLF9ShQwcSCATMarRAICBnZ2dOdluIuLVT+vr6ZU56T506Rfr6+qxo6unpMZNeW1tbCgsLI6Lie0pVVZUVTSL+7eOXL1/ozp07dPnyZfrw4QPrenyNuYqKinTv3j2x9rt375KSkhJrunwk+Pnjjz9IUVGRunbtSt7e3jR06FCRlxRxpBM0KT+Fu7s7OTg40JMnT5i27OxscnR0JA8PD1Y0nZ2dqW/fvvTp0yemLT8/n/r27UsuLi6saBIRHThwgNnh6NSpE9MeHBxMXbt2ZU3X1taWpk2bJtY+bdo0srOzY02XL1q0aEEHDhwQaz948CC1bNmSFU2+dmX5gq/VS74wNzenjRs3irVv2LCBtSy31tbWtHDhQrH2+fPnk42NDSuaJfny5QtlZWXR48ePRV5swYe7O5/4+flR7dq1KTw8nDIzMykzM5P27t1LtWvXZm3xY+DAgdS0aVMaPnw4qaio0KtXr4iI6OjRo2Rtbc2KJlH1s498jbktW7YkPz8/sfZx48ZRq1atWNO1tbUldXV1UlNTIxsbG7KzsxN5sQEfbpVCCgoKKCIigubPn0/z58+nQ4cOseqxIimkSUIkAB/B6ACQlpZWbla2OXPmsKK5bt06uLu7w9TUFMbGxgCArKws2NjYYNeuXaxorlmzBl26dEHt2rXRpEkTAEBSUhKUlJQQGRnJiiYA9OnTB+3bt8ezZ88YXQBwcXFhtS5bamoq9u/fL9bu4+OD1atXs6b74sULTJkyBWfPnkVOTo5Y7R22MlmlpKSUGWBvZ2cnUk9KkuTn58PAwECsXV9fH/n5+axoAsXnMCQkhDnHpe9bNopyA/wEhQspKipCenp6md+3rIxtkmDy5Mnw9/fHzZs30bZtWwDA+fPnERISgjVr1rCiGRgYiH79+iEuLo4Jhj9//jzOnj1b5v0sKdLS0uDj44MLFy6ItBPLGehOnTolVszYysoK69evZzXLLV92avny5RAIBBgyZAgKCgoAAPLy8hg7diwWL17Miub69esxa9YsZGVl4eDBg9DV1QVQnEV5wIABrGgC/NnHjx8/YvHixeXaxwcPHrCiy9eYu3TpUri5uSEqKoopbn/x4kVkZWWxVvMN4CfBD191E9PT0+Hq6oonT56gQYMGAIBFixbB2NgYx48f56VPFUVApa2blJ9i165dGDZsGHr16iUyKB8+fBghISEYOHAgK7pbtmzB2LFjUaNGDdSsWVMsKxtbKY6B4oE/KioKd+/eBQBYWlpyUvB29+7dIppeXl6/Z/HBH2BsbIyVK1eib9++Iu379+/HlClTkJmZyYput27dkJmZiXHjxqFWrVpiD/FsFYDV1dXFsWPHmAFKyIULF+Dm5oa3b99KXNPFxQW6uroICwuDkpISgOKyCt7e3njz5g2ioqIkrgkA48aNQ0hICNzc3Mo8x6tWrWJFV11dHeHh4Uw2OK64dOkSBg4ciMePH4s9SLOdvvrw4cNYsWIFs3BmaWmJgIAA1q5joPjBedWqVSKakydPZjWbYrt27SAnJ4fp06eXeU2VXFySJOrq6oiPj4etra1I+40bN+Do6Ij379+zosuXnRKSn5/PlIgwNzdnLYswn/BlHwcMGIDY2FgMHjy4zN92/PjxrOjyNeYCwNOnT7F+/XqRZxtfX18YGhqypskHO3bswKlTp7Bjxw5O7xlXV1cQEXbv3g0dHR0AwOvXrzFo0CDIyMjg+PHjnPXlZ5FO0H4RS0tLjBo1ChMnThRpX7lyJbZs2cIM1JLGxMQEvr6+mDZtGivHr6706tULISEh0NDQQK9evb77WTZqhQBAUFAQVq1ahenTp4us/i9ZsgSTJk3C7NmzWdEt74GLbQYMGIBnz57h6NGjTD2h3NxceHh4QF9fn5Xdh9u3b6NLly748uVLmbuywjpskqZGjRoICwuDq6srK8cvDxMTE0RGRqJhw4ac6tra2qJ+/foIDAws84FLkvWjqiuqqqq4fv06579tjx49kJubi7179zIPk0+ePIGXlxe0tbVx+PBhVnT5slNckZycDBsbG8jIyCA5Ofm7n23cuDErfeDLPmppaeH48eOcp2Pna8zlGy7r2drZ2SEjIwNExGndRFVVVVy6dAmNGjUSaU9KSkK7du2Y8gaVEamL4y/y4MEDdO/eXazd3d0dM2fOZE337du3Yqs9XHH27NlyXRC2b98uEY1//vkH3bp1g7y8PP7555/vftbd3V0imkDxA6PwIZKvh8fZs2dDXV0dK1aswIwZMwAAhoaGmDdvnkghTUljbGwstsvBBcuXL4eDgwNMTEyYAeLmzZswMDDAzp07WdG0sbFBWlqayK7sgAEDWN+VVVBQKLOoMNvMmzcPc+fO5Xz1Mi0tDREREbx8ZwD4+vVrmXaqTp06rOjx4c5pZWXFepHzsuDD3R3gz05x5X5na2uL58+fQ19fH7a2tkzBZCFcFFDmyz5qa2szuxxcwteYCxQvRl65cqXMa2rIkCGsaPJRz5avuomKior48OGDWHteXh4UFBR46FHFke6g/SIWFhYICAjA6NGjRdo3btyIFStWMAVLJc3w4cPRokULjBkzhpXjl0dgYCCCgoLQvHnzMlfEJbVqKiMjwwxS3yvWyLabFN8IDQubxceFnD59GitWrMCmTZs4L7z+8eNH7N69G0lJSVBWVkbjxo0xYMAAsVW2350VK1bgwYMHWLduHadxYHytXjo7O2Pq1Kno2rUrK8cvDz7isrh05yzpPnjt2jXMmjULwcHBaNSokdhvq6GhITHd0vDh7s6XneLK/e7x48eoU6cOBAIBHj9+/N3PslWomi927dqFo0ePIjQ0lDfXUS7H3H///RdeXl7Iy8uDhoaGWKjKmzdvWNHt168fHjx4gLCwMCaGNCUlBd7e3rCwsMDevXtZ0eWDIUOGIDExEdu2bWMKzF++fBkjR45Es2bNEBISwm8Hv4N0gvaLbNiwARMmTICPj0+ZweilJ26/wtq1a5l/f/z4EStXroSbm1uZgzJbqz61atXC0qVLMXjwYFaOX5lYsGABvLy8ULduXU51Hz58iIKCAtSrV0+kPS0tDfLy8hJ9KNHW1hYZFD5+/IiCggKoqKiIXVNsDRZ8sGjRIhgYGMDHx0ekffv27Xj58qVEXYdLu8qeO3cOOjo6sLa2FjvHbLnNBgYGfvf9uXPnSkyrpFtWRkYGZs2ahYCAgDLtFFsuWnzEZXHpzikjIyNyfOHEsyRs77JwSWWwU3y4333+/JmJAeMSLu2jnZ2dyG+bnp7O+UISl2NuSerXrw9XV1cEBwdzOiHV1NREVFQUWrRoIdJ+5coVdO7cGbm5uaxpc+lWCRTvDHp7e+Pff/9lrqeCggK4u7sjJCSkUrvZSydoEoCrYPSKThQEAgFr2Y50dXVx5coVTjPfhIWFoV+/flBUVBRp//r1K8LDw1lzA2jSpAlu376NVq1aYdCgQfD09ESNGjVY0SqJo6MjfHx84O3tLdK+a9cubN26FTExMRLTCg0NrfBnS/fnV+DLhVWIqakp9uzZwyyqCLl8+TL69++Phw8fSkxr2LBhFf7sjh07JKbLF8LJQ3lDCxcuWnzEZamqqiIpKYkTd87Y2NgKf9bR0ZG1fnDh7g7wZ6dKUrduXZw4cUIkayXbaGhooGfPnhg0aBBcXFy+600iSbi0jz9aPCqJJBeSSsLlmFsSVVVV3Lp1C2ZmZqwcvzz4SPDDh1tlSdLS0kR2+vlyu/8ZpBM0KT/FtGnToKamxmnQrKysLJ49ewZ9fX2R9tevX0NfX5/VFeI7d+5g9+7dCA8PR3Z2Njp16gQvLy94eHiwtuKloaGBxMREMQOSnp6O5s2bs7q6xRV8u7AqKSkhNTVVbNHjwYMHsLKywufPnyWuWRngYvXyR25ZJWHLRatFixZYtWoV2rdvz8rxy4Ivd87MzEwYGxuXuYOWlZXFWrwdV+7ulQU+3O8OHz6MPXv24Pjx49DU1ES/fv0waNAgNG/enFXd6mYf+Rpze/Xqhf79+8PT05OV45cHHwl++HKrfPDgAecTYEkhTRLyi8yZMwcdOnRAmzZtOHVFCAoKwpQpU8QGik+fPmHZsmUSrYM2adIk5t9FRUXYvHkzoqKi0LhxYzEXhJUrV0pMV0hZ7jsAkJ2dzfr2tLW1NYKDgxEcHIzz589jz549mDBhAsaMGcNaGmmBQFBmUOu7d+9YnYxyOREuudpeeuWdC4yNjXH+/HmxB5Dz58+zmt7Y2dkZhw4dYlYQhbx//x4eHh6s1UHjcvWy5KQrLi4Obdu2hZyc6FBTUFCACxcuSHSCVvJ+XLJkCaZOncp6XFZJd04/Pz9MnjwZz58/59Sds27dumXet2/evEHdunVZsxkbN25ESEgI5+7uXNqpstzvDAwMOHO/69mzJ3r27IkPHz4gIiICe/fuRevWrWFmZoZBgwaxVu+UL/toZmaGq1evMvXehOTm5qJp06aseQZxOeaW9Bhxc3NDQEAAUlJSyrQZbHiPAPwk+OGrbqKFhQVq164NR0dHODk5wdHR8bfYPQOkO2i/TKdOnXDx4kUUFBSgRYsWzEXQrl07VrMdcTlIdejQoUKfEwgEEn3AFA6OSUlJsLa2FnnIKywsxMOHD9G1a1dWi8CW5ObNm9i1axfCw8Px+vVrfPr0iRWd7t27Q1lZGXv37oWsrCyA4u/br18/fPz4ESdPnmRFt+SuVkmePn0Kc3Nz1r4vHyxduhRLly7FsmXL4OzsDKDYXWvq1KmYPHkyk8lL0pR3jnNycmBkZIRv376xosvX6iWXdoqPuKzK4M4pIyODFy9eiE2yHz9+DCsrK3z8+JEVXT7c3QFu7VRlcL8rTUpKCry8vJCcnMzaNVXZ7OOLFy9gbGyMr1+/sqLL5ZhbUTdVtuNHuU7ww1fdxCdPniAmJgaxsbGIjY1FWloaDA0N4ejoiA4dOmDEiBGs6EoC6Q7aL3LmzBkUFBTg8uXLiIuLQ2xsLNauXYsvX76gRYsWSEhIYEW3vF2lpKQkiaepjY6OlujxKoowLevNmzfRpUsXqKmpMe8pKCjA1NQUvXv3ZrUPDx8+xJ49e7Bnzx7cu3cPjo6OCAwMRJ8+fVjTXLJkCRwcHNCgQQPY29sDAOLj4/H+/XtWdliEyWcEAgG2bt0qcp4LCwsRFxcn8ViekglvfgQbCW8CAgLw+vVr+Pr6MoO+kpISpk2bxsrDR8mdlpSUFDx//pz5f2FhIU6dOgUjIyOJ6wrha/WyPDv1+vVrqKqqSlSLDzslyVicn0Xo2SAQCDB79mwRb4rCwkJcvnyZ1VphI0aMwJ49ezhzd+fDTnE16foRnz9/xj///IM9e/bg1KlTMDAwQEBAAGt6XNvHkrtKkZGRIp4xhYWFOHv2LKvJurgcc/nwGCkLgUCATp06oVOnTpzoOTs7Y/z48WJulRMnToSLiwtrukZGRvDy8oKXlxeA4li0hQsXMqErlXmCJt1BkyD3799HdHQ0oqKicOTIEWhqakq8Po0wm9W7d+/E0rIWFhYiLy8PY8aMwfr16yWqWx5CA9awYUPWAvJDQ0PRr18/zrNZtW7dGlevXkXjxo3h5eWFAQMGsPoQXZKnT59i3bp1Imnnx40bx0qNGOHA9/jxY9SuXZtZQQT+/0Q4KCgIrVq1krimkJcvXyI/P1/E/U5FRQX6+vqsubUAxbVQUlNToaysjHr16oklopEUJXd3yjK5ysrK+Ouvv8SypkkKrlcvhZkrjx49iq5du4qc18LCQiQnJ6NBgwY4deqURHWrE0LPhtjYWLRp00akpo/wvp0yZYpYZrpfobS7e2hoKBo3bsyJuzsfdqokWVlZEAgEqF27NoDijHd79uyBlZUVRo0axYpmZGQk9uzZgyNHjkBOTg59+vSBl5cXazX1SsOlfQRQ5m60MIviihUr8L///Y8VfYDbMfd75ObmirnAswFXCX6EZGVlwd3dHXfu3BFzq/znn3+Y+0rS5OfnIyEhATExMYiJicGNGzfQsGFDODk5wcnJSeLJ/CSJdIL2i2zevJnZPv3y5Qvs7e2ZH75x48YSr3UUGhoKIoKPjw9Wr14tstIkHKTatGkjUc2SeHp6wsHBAePGjcOnT5/QpEkTPHr0CESE8PBw1ne0uOTPP/+El5cXrKys+O4KJ3To0AGHDh2CtrY2p7p79uzB33//jW3btqFBgwYAgHv37mHkyJEYPXo0s/LFJsKFhgYNGrCSpU1YG8vMzAxXrlwRcUdTUFCAvr6+yAOnpOE6KFyYuTI0NBSenp4i7t5COzVy5EjWsqKeOnUKampqTJKQ9evXY8uWLcyuIRvXeGhoKGrUqAE3NzcAwNSpU7F582ZYWVlh7969rCVEGTZsGNasWcNqvTMhfLm7l+4DH3bK3t4eo0aNwuDBg/H8+XPUr1+fKejs5+fHSjyYiooK/ve//8HLywuurq681YVk2z4KqVu3Lq5evcpJtuTKwJIlS2Bqaop+/foBAPr27YuDBw+iVq1aOHHiBCvlQAD+EvzwUTdRQUEB2tra8PLygpOTE+zt7Tm3Hf8ZkvJLCAQC0tfXpyVLltCHDx84042JiaGvX79ypifEwMCAbt68SUREu3fvJgsLC/r48SP9/fffZGtry4pmQUEBLVu2jFq0aEEGBgakra0t8qpqnDx5kuLj45n/r1u3jpo0aUIDBgygN2/ecNKHoqIiKioq4kTLzMyMEhMTxdqvXbtGpqamrGj27duX/vrrLyIiys/Pp3r16pG8vDzJyclRREQEK5p8kpmZSba2tiQvL09mZmZkZmZG8vLyZGdnR1lZWazpzps3j/Ly8lg7fnnY2NjQ8ePHiYgoOTmZFBQUaMaMGdS6dWsaOnQoK5r169ens2fPEhHRhQsXSFlZmTZt2kTdu3ennj17sqJZmqysLFZ/z8oGl3ZKS0uL7t69S0REa9asobZt2xIRUWRkJNWtW5cVzffv37Ny3B9R3ewjX2OuqakpnT9/noiITp8+TVpaWhQZGUnDhw+nTp06saZbs2ZNCgsLY+34lYkePXqQrq4u1apViwYMGECbNm2ie/fu8d2tCiGdoP0ihw8fpokTJ5KdnR0pKSlRmzZtaMaMGRQZGUkfP35kVbugoIAiIiJo/vz5NH/+fDp06BAVFBSwqqmkpESZmZlERDR48GCaNm0aERE9fvyYVFVVWdGcPXs21apVi5YvX05KSko0f/58Gj58OOnq6tKaNWtY0eQTPh4uhYSGhpKNjQ0pKiqSoqIiNWrUiHVDrqysTFeuXBFrv3z5MikrK7OiycdCg5D09HQaN24cubi4kIuLC/n5+VF6ejqrmkTFD7OnT5+mtWvX0tq1a+nMmTOsawrJycmh+Ph4io+Pp5ycHNb1VFVV6eHDh0RENHfuXOrduzcREV2/fp0MDAxY0VRWVqbHjx8TEdHUqVNp8ODBRER0+/ZtqlGjBiuaRESFhYUUGBhIGhoaJCMjQzIyMqSpqUlBQUFUWFjImi6f8GGnSl5T3bt3p8WLFxNR8dinpKTEqjbX8GkfY2Ji6H//+x+Zm5uTubk5de/eneLi4ljV5GvMLfk85e/vT6NGjSIionv37pGWlhZrujo6OpyMOaWJioqiGTNm0PDhw2nYsGEiL7ZJSkqitWvXUu/evUlfX58MDQ1p4MCBrOv+CtIJmgTJzc2lf//9l4YMGULy8vKkqKjImlZaWhrVq1ePVFRUyM7Ojuzs7EhFRYUaNGjA6o1Xr1492rdvH+Xl5ZGenh6zYnzz5k3S1dVlRdPMzIyOHTtGRERqamrM91uzZg0NGDCAFU0+4ePhkohoxYoVpKKiQlOnTqWjR4/S0aNHKSAggFRUVGjlypWs6f7vf/8jOzs7un79OtN27do1atq0KXXv3p0VTT4WGoiITp06RQoKCtSyZUuaOHEiTZw4kVq2bEmKiop0+vRp1nT54uPHjzRs2DCSlZUlgUBAAoGA5OTkyMfHh9UFLG1tbbpz5w4REbVr1442bdpEREQPHz5kbdKvp6fH7ATb2toyE4b09HRWr6np06eTnp4e/f3335SUlERJSUm0fv160tPTo5kzZ7Kmyxd82amWLVvStGnTKC4ujpSUlJgJzMWLF8nIyIg1XT7gyz7u3LmT5OTkyNPTk9asWUNr1qwhT09PkpeXp927d7Omy9eYW6tWLWYHrX79+rR//34iIrp79y6pq6uzpjt16lQKCgpi7fhlMW/ePJKRkaGWLVtSjx49yMPDQ+TFNkVFRXT9+nVavnw5ubm5kZycHMnKyrKu+ytIJ2gS4NWrV3Tw4EHy8/OjRo0akYyMDOnq6rJ60XXr1o26du1Kr1+/FulH165dydXVlTXd9evXk5ycHGlpaVGTJk2YFdq1a9eSk5MTK5oqKirMynTNmjWZB/mMjAzS0NBgRZNP+Hi4JCp2twgNDRVrDwkJYc3VkKh4d6Vbt24kEAhIQUGBFBQUSEZGhrp160YvXrxgRZOPhQai4gd34cNOSaZNm0Z2dnas6RLxs3o5atQoMjMzoxMnTtC7d+/o3bt3dPz4cTI3N6cxY8awptu9e3fq0qULBQUFkby8PGVnZxNRsTtavXr1WNEcOHAgNW3alIYPH04qKir06tUrIiI6evQoWVtbs6JJVPyQd/ToUbH2I0eOkKGhIWu6fMGXnYqOjiYtLS2SkZERuWdmzJjBmQsrV/BlHxs2bFjmJHvFihXUsGFD1nT5GnP/+OMPMjExoY4dO5Kuri4TJrN3716JjwfCBcGJEyfS+PHjSUtLixwcHGjcuHEi702cOFGiukL4cqtcsWIFde/enbS1tUlOTo6aNWtGEydOpKNHj3IWMvJfkabZ/0UaNWqE1NRUaGtrw8HBASNHjoSjoyNrRUmFxMbG4tKlSyIZhnR1dbF48WK0a9eONV1fX1+0atUKmZmZ6NSpE5N9yczMDAsWLGBFs3bt2nj27Bnq1KkDc3NznD59Gk2bNsXVq1dZyyrFJ+3bt8ekSZPQrl07XLlyBfv27QNQnCWUrUxHAPDs2TO0bdtWrL1t27Z49uwZa7p6eno4ceIE7t+/zwQPN2zYEPXr12dNc8KECfDy8oKamhrq1KkDJycnAMWFlRs1asSabmpqapl1+4RJf9jiR0HhbHHw4EFEREQw5xcAXF1doaysDE9PT2zYsIEV3XXr1sHX1xcRERHYsGEDk3315MmT6Nq1Kyua69evx6xZs5CVlYWDBw8yxXavX7+OAQMGsKIJFBekLiuDbsOGDfHmzRvWdPmCLzvl5OSEV69e4f379yJJBkaNGiVS4qAqwJd9fPDgAbp37y7W7u7ujpkzZ7Kmy9eYu2rVKpiamiIrKwtLly5lSkc8e/YMvr6+EtW6ceOGyP+FGX1v374t0s7W2PD169cy71u22bt3LxwdHTFq1CjY29uLJNar9PA9Q/zdWbduHd26dYtzXW1tbWZrvCQJCQlVLnHGtGnTaOHChUREFB4eTnJycmRhYUEKCgpl7kawgY2NDePywTaPHz8mNzc3aty4MW3dupVpnzBhAvn5+bGma21tzZznksyfP59sbGxY0xXy5csXunv3Ln379o11LSKiq1ev0qFDh0SS+xw7dowSEhJY06xduzbjxlKSffv2kbGxMWu6fK1eKisrU0pKilj77du3SUVFhfP+VEVatmxZpl0YN24ctWrViocesQvfdoov9uzZw2nCHT7so7m5OW3cuFGsfcOGDWRhYcGaLl9jbnWCD7fK3x1pmv3flCFDhiAxMRHbtm1Dy5YtAQCXL1/GyJEj0axZM4SEhPDbQRa5dOkSLly4gHr16pW52sYG6urqSEpKgpmZGSd6fHDw4EH069cPHTt2ZHZhz58/j7Nnz2L//v3o2bMnK7r5+fnw8/NDaGgogOJVSzMzM/j5+cHIyAjTp09nRRcoXtV7+PAhzM3NISfHvkNBUFAQVq1ahenTpzOriefPn8eSJUswadIk1or+6urq4sqVKzA3N2fl+OXh4uICXV1dhIWFMXUMP336BG9vb7x58wZRUVGc9qcqEhsbCzc3N9SpU4cpsXLx4kVkZWXhxIkTTOHdqgJfdopvNDQ0cPPmTU7HIK7t44YNGzBhwgT4+PiI2MeQkBCsWbMGo0ePZr0PUiQHn3UTqwLSCdpvSm5uLry9vfHvv/8yF3pBQQHc3d0REhLye23j/gZwPUHLyMjAjh07kJGRgTVr1kBfXx8nT55EnTp1YG1tzZru9evXsWrVKqSmpgIorlMyefJk2NnZsaY5fvx4nD9/HqtXr0bXrl2RnJwMMzMzHD16FPPmzRNzzZAEfE0KiQirV6/GihUr8PTpUwCAoaEhAgIC4O/vz5p7ybRp06CmpsbaBLA8bt++jS5duuDLly9MTZ+kpCQoKSkhMjKS1Wu5OvH06VOsX79epL6Qr68vU/OuqsGHneIbLscgPhfNDh8+jBUrVoj8tgEBAawXFOZrzK3KVIa6ib8z0gnab056erqIIbOwsOC5R5Jn0aJFMDAwgI+Pj0j79u3b8fLlS0ybNo31Pri6umLbtm2oVasW61qxsbHo1q0b2rVrh7i4OKSmpsLMzAyLFy/GtWvXEBERwXofuMTExAT79u1D69atRR5C0tPT0bRpU7x//17imnxMCkvz4cMHAMUPXmxQWVYv8/PzsXv3bpHJg5eXl0jxailSpHwfLidolcE+ckl1G3Ol/B5IJ2hSKj2mpqbYs2ePWIDp5cuX0b9/fzx8+JCnnrFDmzZt0LdvX0yaNElkUL5y5Qp69eqF7OxsvrsoUVRUVHD79m2YmZmJfN+kpCQ4ODjg3bt3EtfkY1LINdLVSylS2CE7OxuGhoZMkiwuSEhIQIsWLThJjFUd7GNJqtuYK+X3gDvrIqXK0ahRI2RlZbGu8/z58zJ3rvT09FjN2sUXt27dKjOOQl9fH69eveKhR+zSvHlzHD9+nPm/0M1v69atTEyNpHn58iX09fXF2j9+/MhZhkO2iY6OrtCrqk/O9u7di48fP3KquXjxYuTm5nKqKYU7rKys8OjRI04127dvz1nW4upgH0tSGcZcX1/fKjm+VyZ+N7ssnaBJEK4mLJWFR48e4du3b6zrGBsb4/z582Lt58+fr5IxFlpaWmVOPG/cuMGkC69KBAcHY+bMmRg7diwKCgqwZs0adO7cGTt27MDChQtZ0eRjUiiFH0aPHo0XL15wqhkcHFwlU9xLKaaqOx5VN/tYGcbcXbt2VbmdycrG72aXpXXQJAhXE5bqxsiRIzFhwgR8+/YNzs7OAICzZ89i6tSpmDx5Ms+9kzz9+/fHtGnTcODAAQgEAhQVFeH8+fOYMmUKhgwZwnf3JE779u1x8+ZNLF68GI0aNWLq3F28eJG1mjvBwcHo1q0bUlJSmElhSkoKLly4gNjYWFY0pfADHw/TVf0BXkrVprrZx8ow5kptBvv8budYuoMm5T9jb2/PSaB/QEAAhg8fDl9fX5iZmTHZpPz9/TFjxgzW9bkmODgYDRs2hLGxMfLy8mBlZQUHBwe0bdsWs2bN4qQPXLuFmZubY8uWLbhy5QpSUlKwa9cuVguiCieFBQUFzKRQX18fFy9eRLNmzVjTLUl2djaKioo40ZJSPfjdXHh+FT7cVwFg5syZ0NHR4VyXKyqDfTx//jy+fPnCiVZlGHOlSCmNNEmIBOEy019JGjVqhBMnTsDY2JhTXa7Jy8tDamoqlJWVUa9ePU788XNzcxEREYGMjAwEBARAR0cHiYmJMDAwYN31ISsrC7du3UJeXh7s7OxQr149VvVKwkfNnZycHOTk5IhNWho3bsxZH7iEj3PMJ76+vggKCkKNGjU41eUyuYKQrKwsGBoaQlZWljNNoPpdU9Xp+37+/JmpJVgd4OO35XPMlcI+fNnl/4rUxVGCnDhxghfd6uJaqaamhhYtWnCml5ycjI4dO0JTUxOPHj3CyJEjoaOjg0OHDiEzMxNhYWGs6hsbG/M26eZy3eb69evw9vZGamqqmK5AIEBhYSFnfeGS6rY2tmvXLkyZMoXzCVr79u051QNQLe7bykBV/75FRUVYuHAhNm7ciBcvXjD1yGbPng1TU1MMHz6c7y6yBh+/LZ9jrhT2+d1+W+kETUqlp0OHDt/NHMVWFrpJkyZh6NChWLp0qUitKldXVwwcOJAVzeqIj48P6tevj23btsHAwKBKZgmTUvUfpqVIkTQLFixAaGgoli5dipEjRzLtNjY2WL16dZWeoEmRUt2RTtCqAFzFgvGFra2tyP+/ffuGmzdv4vbt2/D29mZN9+rVq9i0aZNYu5GREZ4/f86abmXg5MmTnGWvevDgAQ4ePFgli6x/j6oexyKFe1JSUqpkZtvy4NJO8UFYWBg2b94MFxcXjBkzhmlv0qQJU/i9qrJp0yYYGBjw3Q0pUnhDOkGrAvDlWskVq1atKrN93rx5yMvLY01XUVGxzLS39+/fh56eHmu6lQEu3cJcXFyQlJRU7SZoVTHBzff48OED312o8vxuLjy/Ch/uq1zy5MmTMu1iUVFRlQ9rkHqpSKnuSLM4SvlpcnNzsXXrVsyYMYOpKZGYmIgnT55w2o9BgwZh+/btrB3f3d0dQUFBzEAoEAiQmZmJadOmoXfv3qzpVje2bt2K7du3IzAwEAcPHsQ///wj8mIbvjLBSeGGz58/s67x7ds3mJubIzU1lXUtKfwRHx+PQYMGoU2bNsx4t3PnTiQkJLCiZ2Vlhfj4eLH2iIgI2NnZsaJZGql9ZJ/09HRERkbi06dPAKTu4JIkKCgI+fn5Yu2fPn1CUFAQDz36CUjKL/P27VvasmULTZ8+nV6/fk1ERNevX6fs7GyeeyZ5kpKSSE9PjywsLEhOTo4yMjKIiOjPP/+kwYMHc9qXsLAwqlWrFmvHz83NpY4dO5KWlhbJysqSsbExycvLk4ODA+Xl5bGmWxIbGxvKzMzkRIsv/vnnH9LU1CSBQCD2kpGRYV1fXV2duY6lVA0KCwspKCiIDA0NSVZWlvl9Z82aRVu3bmVF09DQkFJSUlg5thT+iYiIIGVlZRoxYgQpKioy19Rff/1F3bp1Y0XzyJEjpKmpSYsXLyYVFRVatmwZjRgxghQUFOj06dOsaJamutlHLsfcV69ekYuLCzPWCc/zsGHDaNKkSZz0oaojIyNDL168EGt/9eoVJ88Xv4J0B+0XSU5ORv369bFkyRIsX76cqUFz6NChKunCJEyckZaWJpLy19XVFXFxcaxo9urVS+TVs2dPtG7dGsOGDcPo0aNZ0QQATU1NnDlzBv/++y/Wrl2LcePG4cSJE4iNjYWqqipruiWpDhk6/fz8MGjQIDx79gxFRUUiLy4yOJJ0tbLKsWDBAoSEhGDp0qVQUFBg2m1sbLB161ZWNP/44w8sWbIEBQUFrBxfCr8sWLAAGzduxJYtWyAvL8+0t2vXDomJiaxo9ujRA//++y+ioqKgqqqKOXPmIDU1Ff/++y86derEimZpqpt95HLMnThxIuTk5JCZmQkVFRWmvV+/fjh16hQnfajqEFGZiceSkpIqfQy4NAbtF6lumf74SJyhqakp8n8ZGRk0aNAAQUFB6Ny5MyuaJWnfvn2Vj3UoDZc1d16/fo2JEydKA8KlSAw+kitcvXoVZ8+exenTp9GoUSOxRZxDhw5JXPPbt29o2LAhjh07BktLS4kfv7LDpZ26d+8eHBwcxNo1NTVZLQ5ub2+PM2fOsHb8ysT79++hoaFR5nvp6elVLk759OnTiIyMRO3atUXa69Wrh8ePH/PUq6qBtrY2BAIBBAIB6tevLzJJKywsRF5ensjYUBmRTtB+ET4z/fFRRJmPxBk7duxg5bgV4erVq4iOji6zgPLKlStZ1+cyQydfNXd69eqF6OhomJubs3L8H8F1Jrj4+Hhs2rQJGRkZiIiIgJGREXbu3Im6detW2YWA9PR0ZGRkwMHBAcrKyuWuakoKPpIraGlpcR6bKi8vz0mMXWWCLztVs2ZNpKenw9TUVKQ9ISGBk2LKeXl5YmNQeZMZScKlfXRzc0NUVJRYUfl79+7BxcUF2dnZrPeByzH348ePIjtnQt68eSN2DqT8HKtXrwYRwcfHB4GBgSIL/QoKCjA1NUWbNm147OGPkU7QfhG+Mv3xVURZmDhj//79AKp24ozg4GDMmjULDRo0EKvPxVWtLi4zdPJVc6d+/fqYMWMGEhIS0KhRIxH3IQDw9/dnRVcIl5OigwcPYvDgwfDy8sKNGzfw5csXAMC7d+8QHBxc5TKyvn79Gv369cO5c+cgEAiQlpYGMzMzDB8+HNra2lixYgUrusLkCiYmJiLtbCZX4GshSehauXXrVsjJVf0hnS87NXLkSIwfPx7bt2+HQCDA06dPcfHiRUyZMgWzZ89mRfPhw4cYN24cYmJiRCbiwgUOLlzAubSPampq6NmzJ/755x/mWk5NTYWzszM8PT056QOXNtje3h5hYWGYP38+gOLniqKiIixduhQdOnTgrB9VEW9vbxQUFEAgEMDZ2fn3zHDLZwBcVWD48OHk4eFBX79+JTU1NXrw4AE9fvyY7OzsaPz48azpuri4UEBAABERqampMcGl58+fJxMTE9Z0uUqcoaWlRdra2hV6sYW+vj7t2LGDteNXNszNzSkqKoqIRK+p1NRU0tLSYk3X1NS03FfdunVZ0+UDW1tbCg0NJSLRc5yYmEgGBgZ8do0VBg8eTF26dKGsrCyR73vq1CmysrJiTZev5Arfvn2jM2fO0MaNG+n9+/dERPTkyRP68OEDa5oeHh6krq5OtWrVos6dO1PPnj1FXlUNvuxUUVERLViwgFRVVZkkRkpKSjRr1izWNNu2bUtt2rSh8PBwio6OppiYGJFXVSM/P5/atm1Lnp6eVFRURLdu3SJ9fX2aOHEi311jBeH369q1KykoKFCfPn3I0tKSDAwMKD09ne/uVQmUlZXp0aNHfHfjP1H1l9tYZsWKFejTpw/09fXx6dMnODo64vnz52jTpg0WLlzImi5frpXCxBkJCQlITk5GXl4emjZtio4dO0pUZ/Xq1cy/X79+jQULFqBLly7MlvTFixcRGRnJ2solUBzr1q5dO9aOX9ngq+bOw4cPWTt2ZYOvOBa+4CvGQphcISgoiEmu0LRpU1aTKzx+/Bhdu3ZFZmYmvnz5gk6dOkFdXR1LlizBly9fsHHjRlZ0+XCt5BO+7JRAIMCff/6JgIAApKenIy8vD1ZWVlBTU2NNMykpCdevX0eDBg1Y06hMKCsr4/jx43BycoKnpyfi4uIwZMgQLFu2jO+usYKNjQ3u37+PdevWQV1dHXl5eejVqxf++OMP1KpVi+/uVQlatmyJGzduiHlT/A5IJ2i/CFcTltLwXUSZ7cQZ3t7ezL979+6NoKAgjBs3jmnz9/fHunXrEBUVhYkTJ7LSh4kTJ2L9+vUik8WqDB9uYdUNvuNYuIbPGAuukyuMHz8ezZs3R1JSEnR1dZn2nj17irjiSRo+Y3T5gG87paCgACsrK9Z1AKBFixbIysqq0hO00s8xMjIy2LdvHzp16oTevXtj9uzZzGe4iLnjGk1NTfz55598d6PK4uvri8mTJyM7OxvNmjUTS97UuHFjnnr2Y6QTNAnBdaY/PmPBuE6cERkZiSVLloi1d+3aFdOnT5e4npApU6bAzc0N5ubmsLKyEouNYiMrG5/MmTMH3t7eePLkCYqKinDo0CHcu3cPYWFhOHbsGN/dYw0uM8HxEcfCJ5UhxoKr5Arx8fG4cOGCSFp/ADA1NWWKGrNFQUEBYmJikJGRgYEDB0JdXR1Pnz6FhoYGqzs8fMCXnfr8+TP++uuvcsc+NlLtb926FWPGjMGTJ09gY2MjNgZx8XDJtn3U0tIqM6abiLBx40Zs2rSJ05i7svrBZsz558+fkZycXOY15e7uzppudaF///4ARGPZBQIBr9dURZFO0CQAH5n++HKt5CNxhq6uLo4ePYrJkyeLtB89elRkpVrS+Pv7Izo6Gh06dICuri5niUEAfjJ08uEWxhd8ZYKbPn06ioqK4OLigvz8fDg4OEBRURFTpkyBn58fK5p8snTpUri4uODatWv4+vUrpk6dijt37uDNmzc4f/48a7p8JFcor25fdna2SAkWScOXayVf8GWnhg8fjtOnT6NPnz5o2bIlJ+PBy5cvkZGRgWHDhjFtXDxccmkfo6OjJXas/8qyZcsQEBAg1l5YWIhBgwZh7969rOieOnUKQ4YMwatXr8Teq+yTh9+F3zmEQkBUzaoQSpgfTVjOnTvHqj7XrpUGBgZYsmQJhg4dyqpOSUJCQjBixAh069YNrVq1AgBcvnwZp06dwpYtW1jri7q6OsLDw+Hm5sbK8cujdIbOe/fuwczMDLNmzWI1Q2d1IigoCKGhoQgKCsLIkSNx+/ZtmJmZYd++fVi9ejUuXrzIqv7Xr185i2Phm3fv3mHdunVISkpi7BTbMRbt2rUDEWH8+PFidhkAHB0dJa7Zr18/aGpqYvPmzVBXV0dycjL09PTQo0cP1KlThzVXRA8PD6irq2Pbtm3Q1dVFUlISzMzMEBMTg5EjRyItLY0V3eqGpqYmTpw4wWlcspWVFSwtLTF16tQyr2O24mr4sI8FBQUIDg6Gj4+PWMwq2+jr62PRokUiE8/CwkL0798ft2/fRmpqKiu69erVQ+fOnTFnzhxpHVAp4vCWnqSKUN0y/dWsWZPu37/Pue6lS5do4MCBZGdnR3Z2djRw4EC6dOkSq5p16tSh1NRUVjXKgq8MnSX58OEDvXv3TuRVleArE5wUblBVVaW7d+9yqpmVlUVWVlZkaWlJcnJy1Lp1a9LV1aUGDRrQixcvWNPV0dFhvmvJa/nhw4ekrKzMmm5lgEs7ZWlpSUlJSawdvyxUVFQoLS2NU00i/uyjmpoaPXz4kLXjl8eVK1dIS0uLDhw4QETF2Vh79uxJlpaW9OzZM9Z01dXVpdkaOSA9PZ3GjRtHLi4u5OLiQn5+fr/FeZe6OP4ifGb648O1kq/EGa1atcLu3bs51Zw3bx7mzp2LHTt2lJnogC34ytBZGWruNGrUCCdOnGC9ZglfmeD4iGPhGz5iLPhIrlC7dm0kJSUhPDyc8WoYPnw4vLy8WC18y5drJV/wZadWrFiBadOmYePGjZxlhHN2dkZSUlKZtopN+LKPzs7OiI2NFUuixDYtWrTAwYMH4eHhAQUFBWzbtg3p6emIjo5mdWerT58+iImJgbm5OWsa1Z3IyEi4u7vD1taWeVY/f/48rK2tK334hnSC9ovwNWHhq4gyX4kzMjIysGPHDjx48ACrV6+Gvr4+Tp48iTp16sDa2poVzbVr1yIjIwMGBgYwNTUV+65sPUzzlaFz0KBBICJs3769THcaLnj06BGrDwBC+MoEx0ccC5/wFWPBV3IFOTk5DBo0iJVjl0fnzp2xevVqbN68GUDxec3Ly8PcuXPh6urKaV+4gC871bx5c3z+/BlmZmZQUVERu6bevHkjcc3u3btj4sSJuHXrFho1aiSmydYCB1/2sVu3bpg+fTpu3bpVZsY9NpNmODs7IywsDL1794alpSViY2NRo0YN1vQAYN26dejbty/i4+PL/H1LJraQ8t+YPn06Jk6ciMWLF4u1T5s2rVJP0KQxaL9IUVER3NzccP/+fU4nLHzEggHAuHHjsHXrVnTo0KHMwZGNOIvY2Fh069YN7dq1Q1xcHFJTU2FmZobFixfj2rVriIiIkLgmAAQGBn73/blz57KiO2LECLx+/Rr79++Hjo4OkpOTISsrCw8PDzg4OLC2GKCmpsZ7zR11dXUmhoZNjh49Cm9vb8yYMQNBQUEIDAwUyQTHltHmI46FT/iKsbh06RIGDhyIR48eMW1cJFdIS0srd3d0zpw5rGhmZ2ejS5cuICKkpaWhefPmSEtLQ40aNRAXFwd9fX1WdPmCLzvVsWNHZGZmYvjw4WWOfSVLw0gKGRmZct9j8zrmyz5y+X179epVZvulS5dgYWEhMjlj6zlu27ZtGDNmDJSUlMQSkQkEAjx48IAV3eqEkpISbt26hXr16om0379/H40bNxbZha9sSHfQfhG+Mv3x5VoZGhqKgwcPcpo4Y/r06ViwYAEmTZok4rLj7OyMdevWsabL1gTsR/CVobMy1Nyxt7dn1R1MCF+Z4IyMjKqk21l5vHjxApMmTeI8AN7Hxwd2dnbYu3cvZ7ssW7ZswdixY1GjRg3UrFlT7GGLrQkaX66VfMGXnbpw4QIuXryIJk2acKZZepLPFXzZRy6/r6amZpntXbp04awPf/75JwIDAzF9+vTvTk6l/Hf09PRw8+ZNsQnazZs3K/3ilXQH7RfhK9Pf0qVL8fTpU85dK01MTBAZGYmGDRtypqmmpoZbt26hbt26Ijssjx49QsOGDVlfAbl+/TqTxcna2pqzos1cZ+jMyMjAmDFjMGjQIN5q7lR1Tp48ibVr13Iax8InPj4+aNeuHWtlC8pDVVWV89gdExMT+Pr6Ytq0aZxpVkf4slNNmzbF33//jdatW7NyfCn8QETIysqCnp4e5wsaOjo6uHr1qjQGjUWCgoKwatUqTJ8+HW3btgVQHIO2ZMkSTJo0qVLXH5VO0H4RPiYsAH+ulTt27MCpU6c4TZxRu3Zt7N+/H23bthWZoB0+fBhTpkxBRkYGK7o5OTno378/YmJioKWlBaC4PlmHDh0QHh7OajwYH/DlFsY3XBUyBorrGnl6eiIuLo6zOBY+yc/PR9++faGnp8dpjEX37t0xdOhQ9O7dm5Xjl4WGhgZu3rzJuntuWfDhWskXfNmp06dPIzAwEAsXLizzWmbLZsTGxmL58uXMIqGVlRUCAgJgb2/Pil5puLSPAPfft6ioCEpKSrhz547YLgvbTJw4EXp6epg5cyanutUJIsLq1auxYsUKPH36FABgaGiIgIAA+Pv7V+o4cOkE7RfhY8IC8BMLBgB2dnbIyMgAEXGWOGPKlCm4fPkyDhw4gPr16yMxMREvXrzAkCFDMGTIENZcEfv164cHDx4gLCwMlpaWAICUlBR4e3vDwsKCteKVAD8ZOvmqucMHfGWC4yOOhU/4irHYvHkzFixYAB8fH86SKwwfPhwtWrTAmDFjJH7s7/Ej18qqlhmULzsldEErrcemzdi1axeGDRuGXr16iWSgO3z4MEJCQjBw4ECJawL82Ue+vq+1tTW2bdvG+e6ov78/wsLC0KRJEzRu3FjMTrE11ldXPnz4AAC/TZiBdIL2i/AxYQH4c63kI3HG169f8ccffyAkJASFhYWQk5NDYWEhBg4ciJCQEMjKykpcEyj2UY+KikKLFi1E2q9cuYLOnTsjNzeXFV2+ip/z4RbGF3wUMgYAFRUVzuNY+KRmzZrw9/fnPMaCq2QDa9euZf798eNHrFy5Em5ubpzuFlY310q+7FRsbOx332fDZlhaWmLUqFGYOHGiSPvKlSuxZcsW1goo82Uf+fq+//77L5YuXYoNGzbAxsaGFY2y6NChQ7nvsTnWVycePnyIgoICsd3RtLQ0yMvLc17S4WeQTtB+Eb4y/fHlWsknWVlZuHXrFvLy8mBnZ8e6O4K6ujri4+Nha2sr0n7jxg04OjqWmQpfEvCVoZMPtzC+4CsTXHWLY6nqMRZ169at0OfY3C3k07WSD6qTnVJUVMSdO3fEJqPp6emwsbFhLf6aL/vI1/fV1tZGfn4+CgoKoKCgIBaLVtVcz6sTjo6O8PHxEfNO2bVrF7Zu3YqYmBh+OlYBpFkcfxG+Mv3xVURZCB+JM4yNjVkvYFwSZ2dnjB8/Hnv37oWhoSGA4gKeEydOhIuLC2u6fGXo5KvmDlAc2xcREYGMjAwEBARAR0cHiYmJMDAwgJGRkcT1+MoEt3jxYkyePJnzOBa+8Pb2xr59+6psjMXDhw/57gL69u2L06dPc+5ayRd826lt27aJjH0+Pj7lZgT8VYyNjXH27FmxCUtUVBSrYyFf9pGv78t1srWyyM7OBlAccy9Fcty4caPM56nWrVtj3LhxPPSo4kh30CQE1xMWvlwrq1PijKysLLi7u+POnTvM4JCVlQUbGxv8888/rBlSvjJ08lVzJzk5GR07doSmpiYePXqEe/fuwczMDLNmzUJmZibCwsIkrslXJjg+4lj4hM8YC66TDQQFBWHKlCliC2afPn3CsmXLJJqsozK4VvIFX3bq2rVr6NKlC5SVldGyZUsAxbHCnz59wunTp9G0aVOJa27YsAETJkyAj4+PSAa6kJAQrFmzBqNHj5a4JsCffeTr+/JFUVERFixYgBUrViAvLw9AsefO5MmT8eeff0pT70sATU1NxMTEiD2TX79+HU5OTkxcWmVEOkH7RfiasPDlWsln4gw+ICJERUXh7t27AIp95NlOd89Xhk6+6NixI5o2bYqlS5eKZOm8cOGCWLY2ScFXJjg+4lj4hK8YCz6SDcjKyuLZs2ditXVev34NfX19iV5TlcG1srphb28PCwsLbNmyBXJyxc5HBQUFGDFiBB48eIC4uDhWdA8fPowVK1YwCw2WlpYICAhAjx49WNED+M3oy8f3BYDCwkIcOXJEZKHd3d2dtRh3AJgxYwa2bduGwMBAxk4lJCRg3rx5GDlyJKt1T6sL3bt3h7KyMvbu3cv8loWFhejXrx8+fvyIkydP8tzD8pFO0H6R6jZh4StxRnWCrwydfKGpqYnExESYm5uLTNAeP36MBg0asBJ3UJ0yVlZH+Eg2ICMjgxcvXogtyp07dw79+vXDy5cvJa4phTuUlZVx48YNsbjvlJQUNG/eHPn5+Tz1TPJUN/uYnp4OV1dXPHnyhHHrvHfvHoyNjXH8+HHWYmgNDQ2xceNGMbfco0ePwtfXF0+ePGFFtzqRkpICBwcHaGlpMd4T8fHxeP/+Pc6dO8dpUpifRRqD9oucOnUKUVFRzOQMKDZu69evR+fOnVnX59q1sqioSGxHBwDk5eXF0sFXBc6ePYuzZ8+Wme5++/btrGiGhobi4MGDnGfoBPipuaOoqFhmwpX79++ztgP9+PFj/PPPP7xkrOQ6jqWywGWMxYMHD9C9e3exdnd3d4nHw2lra0MgEEAgEKB+/foiD7OFhYXIy8tjNT6MS9fKygIfdkpDQwOZmZliE7SsrCzW03Z//fq1zDGoTp06rOjxZR/nzJmDDh06oE2bNlBSUuJM19/fH+bm5rh06RJ0dHQAFO98Dxo0CP7+/jh+/Dgrum/evCkz0VvDhg2liUkkhJWVFZKTk7Fu3TokJSVBWVkZQ4YMwbhx45jfutJCUn4JNTU1unHjhlh7YmIiqaurs6b74sUL6tChAwkEAtLW1iZtbW0SCATk7OxMOTk5rOm6u7uTg4MDPXnyhGnLzs4mR0dH8vDwYE1XiI2NDWVmZrKuQ0Q0b948kpGRoZYtW1KPHj3Iw8ND5MUWderUodTUVNaOXx47d+4kOTk58vT0pDVr1tCaNWvI09OT5OXlaffu3azpDh8+nDw8POjr16+kpqZGDx48oMePH5OdnR2NHz+eFc3//e9/FBERwcqxv8fVq1dJR0eHjIyMqGfPntSzZ0+qXbs26erq0vXr1znvD9sUFhZSYGAgaWhokIyMDMnIyJCmpiYFBQVRYWEha7rm5ua0ceNGsfYNGzaQhYWFRLVCQkJox44dJBAIaM2aNRQSEsK89uzZQxcuXJCoXmlkZGToxYsXYu2vXr0iGRkZVrX5gC875efnR7Vr16bw8HDKzMykzMxM2rt3L9WuXZs1O3X//n1q3749c+8IXwKBgNXfli/72LFjR1JVVSVFRUVq3749/fnnn3TmzBnKz89nVVdFRYWSk5PF2m/evEmqqqqs6bZs2ZL8/PzE2seNG0etWrViTVfK74F0gvaL8DVh8fT0pObNm1NKSgrTdufOHWrevDn179+fNd3MzEyytbUleXl5MjMzIzMzM5KXlyc7OzvKyspiTVeImpoaZWRksK5DRFSzZk0KCwvjRKsk27dvJ09PT/r48SOnug0bNqSVK1eKta9YsYIaNmzImm5ubi517NiRtLS0SFZWloyNjUleXp4cHBwoLy+PFc1NmzaRsbExzZ07lyIiIujo0aMiL7Zo3749DR06lL59+8a0ffv2jby9vcne3p41Xb6YPn066enp0d9//01JSUmUlJRE69evJz09PZo5cyZrun///TcpKCjQmDFjKCwsjMLCwmj06NGkqKhY5sRNEsTExNDXr19ZOfb3EAgEZS7KnT17lmrUqMF5f9iGLzv15csX8vf3JwUFBWaipKioSBMmTKDPnz+zotm2bVtycHCgEydO0I0bN+jmzZsiL7bgyz4SFdvDhIQECg4Opi5dupC6ujopKChQu3btWNPU1tam8+fPi7UnJCSQtrY2a7oxMTGkqqpKlpaW5OPjQz4+PmRpaUlqamoUFxfHmm514uTJkxQfH8/8f926ddSkSRMaMGAAvXnzhsee/RhpDNovwlemPz5jwYiHxBlCSsYosY2uri6uXLnCeQ0nvjJ08lWDRkhCQgKSk5ORl5eHpk2bsnpN8ZUJrjrFsQD8xljwkWyAy0QDQtfKd+/eQUNDo1zXyvXr10tcm0/4tlP5+fnIyMgAAJibm7Na5kZVVRXXr1/nvN4pX/axJPfv30d0dDSioqJw5MgRaGpq4tWrV6xoDRkyBImJidi2bRuTofPy5csYOXIkmjVrhpCQEFZ0AeDp06dYv369yPOUr68vU9pHyq/RqFEjLFmyBK6urrh16xaaN2+OyZMnIzo6Gg0bNqzUMf3SGLRfxNjYGImJibxk+uMrFkwgEKBTp07o1KkTqzplYW9vL1ZEki1GjBiBPXv2YPbs2ZzoCfHw8OBUTwhfNWiEtG/fHu3bt2ddBwBv8ZJ8xrHwAZ8xFj179kTPnj1Z1ShJWYkGFi1axFqigdWrV4OI4OPjg8DAQJEYRgUFBZiamqJNmzYS1awM8G2nVFRU0KhRI9Z1gOL4GbYmJd+DL/u4efNmxMTEIDY2Fl++fIG9vT2cnJwwa9Ys1lL7A8VlK7y9vdGmTRvmuaqgoADu7u5Ys2YNa7pA8SKWNFsjezx8+BBWVlYAgIMHD6J79+4IDg5GYmIiXF1dee7d95HuoP2m9OjRA7m5uWJFlL28vKCtrY3Dhw+zps1H4gyumDRpEvPvoqIihIaGonHjxpzXcOIDPmvQXL16FdHR0WVeU1XpPPv7++Pw4cNYvny5yDkOCAhA7969K0XBVEnSqlUrtGrVSqRuFwD4+fnh6tWruHTpEqv6XCZXcHV1BRFh9+7dYokGZGRkWEs0EBsbi7Zt25a5YFcV4ctOffz4EYsXLy537JNUOYOSCZOuXbuGWbNmITg4uFoUtpeRkYGenh4mT54MX19fqKmpcaqflpYmstDORZKU3NxcXLlypcxrasiQIazrV3V0dHSQkJAAKysrtG/fHkOGDMGoUaPw6NEjWFlZVWqvFekETQLwMWHhy7UyMDAQQUFBaN68OWrVqiWWfpfNiSEXfK9uU0nYrOEkhOsMnQA/bmHBwcGYNWsWGjRoIJbSmc3zzEcmuK9fvyIgIAAbN25EQUEBgOJd77Fjx2Lx4sVQVFRkTZsPYmNj4ebmhjp16jC7ORcvXkRWVhZOnDjB2rlOS0uDj48PLly4INJOLNZxUlVVxaVLl8R2V5KSktCuXTumEC0b8FHDiU/4sFMDBgxAbGwsBg8eXObYN378eInoyMjIiBxbeM2WhM3rWAgf9vHIkSOIi4tDTEwMUlNTYWdnBycnJzg5OaF9+/asuZM+ePCAk7CJ0vz777/w8vJCXl6emJuyQCCQZnKUAO7u7vj69SvatWuH+fPn4+HDhzAyMsLp06cxbtw43L9/n+8ulot0gvaL8Dlh4SMWrFatWli6dCkGDx7Mqk51hq/i53xhYGCAJUuWYOjQoZxp8lHIuCRcxrHwDR8xFu3atYOcnBymT59epl1u0qSJxDV1dHRw7NgxZldHyPnz59G9e3fWHrb4quFU3dDS0sLx48cZe8EWPypmXxK2CtvzbR8B4N27d4iPj8eBAwewd+9eyMjIsBZfKCMjg9q1a8PR0RFOTk5wdHTkZPesfv36cHV1RXBwcJUeA/gkMzMTvr6+yMrKgr+/P4YPHw4AmDhxIgoLC8W8OyoV3OclqVrwlemPL3R0dCg9PZ3vblRp+MrQKeTLly+UlZVFjx8/FnmxRc2aNen+/fusHb8s+MoEJ4UbVFRUOC9VMXjwYLK2tqZLly5RUVERFRUV0cWLF8nGxoa8vb1Z0+3WrRt17dqVXr9+zbS9evWKunbtSq6urqzp8g3XdsrU1FTEJldl+LSPr169ooMHD5Kfnx81atSIZGRkSFdXl9Ws2NnZ2bRr1y4aOXIk1a9fnwQCARkZGdHAgQNpy5YtrOmqqKhwlpVayu+HdAftF+Er0x/Aj2vltGnToKamxnnijOoEXxk6+XALA4ClS5fi6dOnnMZf8ZUJjqs4lsoEHzEWLVq0wKpVqzhLOgMUf09vb2/8+++/YokGQkJCWCtEzqdrJR/wZad27dqFo0ePIjQ0tMrvdvBlHxs1aoTU1FRoa2vDwcGB2c1iM0FIWaSlpWHhwoXYvXs3ioqKWLumevXqhf79+8PT05OV40spJiMjAzt27EBGRgbWrFkDfX19nDx5EnXq1IG1tTXf3SsXaRbHX4SvTH8/cq2UJKUTZ2zevBlRUVGcJs7Izc1FREQEMjIyEBAQAB0dHSQmJsLAwABGRkasaPIFXxk6hw4dCjk5ORw7doz1a6okU6ZMgZubG8zNzWFlZSX23Q8dOiRxTb4ywY0YMeK7cSxVjR/FWEhyglYyucKSJUswdepUTpMraGlp4ejRo0hPTxeJjWLbVUpRUREfPnwQa8/Ly4OCggKr2nzApZ2ys7MTOX56ejoMDAw4LX/CB3zZxzFjxsDR0RE2NjasaZRFfn4+EhISEBMTg5iYGKYUyrhx4+Dk5CRRrX/++Yf5t5ubGwICApCSklKmnSpdnkTKzxMbG4tu3bqhXbt2iIuLw8KFC6Gvr4+kpCRs27YNERERfHexXKQ7aP+BypDpj8tYML4TZyQnJ6Njx47Q1NTEo0ePcO/ePZiZmWHWrFnIzMxEWFiYxDX5hK8MnXzV3Bk3bhy2bt2KDh06iCUJAcBKnRK+MsFxFcdSWeAyxqKyJFfgGj5rOPEBl3YqMDCwwp+dO3cuiz3hFj4z+vKBgoICtLW14eXlBScnJ9jb20NbW5sVre/VmCtJVbNTfNGmTRv07dsXkyZNEqmje+XKFfTq1QvZ2dl8d7FcpDto/4EbN26I/N/W1hYAcPv2bZF2Nlf2vn79KhaMzhbR0dGc6JTHpEmTMHToUCxdulSkVpSrqysnwcpcs27dOri7u8PU1FQsQ+euXbtY0+Wr5k5oaCgOHjwINzc3zjTHjh2LmjVrYsWKFdi/fz+A4t2Offv2sZoJTltbm0nBXh148uQJ/P39OXEJ49tO8QWfNZz4gEs7VZUmXT8DX/aRL1xdXZGQkIDw8HA8f/4cz58/h5OTE+rXry9xLb5qzFVXbt26hT179oi16+vr8/K88zNId9B+U6pTLJimpiYSExNhbm4usgLy+PFjNGjQgDV/eD4hjjJ0VoaaOyYmJoiMjOR8544PqlMcCyCNseASrl0ruaQy2Cm+Wbx4McaMGcNk9pUieZKTkxEbG4vY2FjEx8dDTk4OTk5O2L17N99dk/IfqV27Nvbv34+2bduKPD8ePnwYU6ZMYbIpV0akE7TfiMrgWskH+vr6iIyMhJ2dncgNdubMGfj4+CArK4vvLv62VAa3sB07duDUqVPYsWMH55MWLgoZlxXHQkRVNo6lZIzFy5cvERQUhGHDhkljLKT8ZyqDneIbDQ0N3Lx5k9N6XVwWeq8MEBFu3LiB6OhoREdHIzIyEkTE1KyU8vsxZcoUXL58GQcOHED9+vWRmJiIFy9eYMiQIRgyZEil3iWXTtB+I/iOBeOLESNG4PXr19i/fz90dHSQnJwMWVlZeHh4wMHBgdPsf1zBVYbOylBzx87ODhkZGZxOWrjMBFfd4likMRZSJE1lsFN8U3Jxkm34ypTJFytXrkRMTAwSEhLw4cMHNGnShMkiyWY8mhT2+fr1K/744w+EhISgsLAQcnJyKCwsxMCBAxESEgJZWVm+u1gu0gmalErPu3fv0KdPH1y7dg0fPnyAoaEhnj9/jjZt2uDEiRNQVVXlu4sShc/i53zwowkMG5MWPgoZS6k+NGrUCCdOnGA1452U6gWXE7TKYB+5vIdatGjBFKm2t7dnrSSGFP7IysrCrVu3kJeXBzs7O9SrV4/vLv0Q6QRNym9DQkICkpOTkZeXh6ZNm7ISk1UZ4DJDZ3WFr4yVUqoHXD5MS+Ge8+fPo3nz5lBUVORMMysrC4aGhpys+FcG+yi9h6RUd6RZHKX8NrRv357TwrN8wWWGzsrE9evXmSQH1tbWsLOzY02Lr4yVUrhHmlxBiqTp1q0b5/FgXO7GSu0j97i5uWHr1q2oVasW312RUkmQTtCk/BZcvXoV0dHRZcZkVaWEKAB/xc/5IicnB/3790dMTAzzEJ2bm4sOHTogPDwcenp6EtHhu5CxFH4IDg6Gp6cnpxM0e3t7KCsrc6YnROpayQ1V0fGostlHvu4hvoiLi8OnT5/47oaUSoR0gial0hMcHIxZs2ahQYMGYoWM2aw1xyWlM3Ru3rwZUVFRVT5DJwD4+fnhw4cPuHPnDiwtLQEAKSkp8Pb2hr+/P/bu3SsRHS0tLbFMcC4uLiKfqapB8NUZPh6mT5w4wbkmADx69Ajfvn3jRVvK701ls4983UNSpFQWpBM0KZWeNWvWYPv27Rg6dCjfXWGNylD8vCRcuoWdOnUKUVFRzOQMKHaxWb9+PTp37iwxncpWyJiPOBYpUqoSfLmvbtq0CQYGBpxqsk1ls4/VDRMTE7HFWCnVG2mSECmVnlq1aiEuLu63yLpTVeCy5o66ujri4+OZSamQGzduwNHRUcT1pirBR10jPuErxoLL5Ap84+rqim3btlWbOJaqfA99+/YNDRs2xLFjx0QWr6SwizRmteryu7mAV6xgjRQpPDJx4kSsX7+e725UK7hct3F2dsb48ePx9OlTpu3JkyeYOHGimItNVaK6rY3xFWNhbGxcLSZnQLFbWHWZnAFV+x6Sl5fH58+f+e5GtSM4OBhv3rzhuxtSWOB3cwGXujhKqfRMmTIFbm5uMDc3h5WVlZgbwKFDh3jqmRRJsG7dOri7u8PU1JRZ2crKyoKNjQ127drFc++kSJEihR/++OMPLFmyBFu3boWcnPRxjQuq8qRfyu+F9I6XUunx9/dHdHQ0OnToAF1d3SqTGKQyk5KSAkNDQ060jI2NkZiYiKioKNy9excAYGlpWWXr3AmpinEs36M6xFjk5uYiIiICGRkZCAgIgI6ODhITE2FgYAAjIyO+u1fl4NJO8cHVq1dx9uxZnD59Go0aNYKqqqrI+1VxcVJ6D0lhi98tM6g0Bk1KpUddXR3h4eFwc3PjuytSpEiRUibJycno2LEjNDU18ejRI9y7dw9mZmaYNWsWMjMzERYWxncXpfwC79+/Lze9fHp6OiwsLCSuOWzYsO++v2PHDolr8klluIeqU8yqlMqNdIImpdJjYmKCyMhINGzYkO+uSGGJs2fP4uzZs2XWudu+fTur2tKg8KoFX8kVOnbsiKZNm2Lp0qVQV1dHUlISzMzMcOHCBQwcOBCPHj3irC9SJI+9vT2ioqLEsq7eu3cPLi4uyM7O5qln7MKlfayu91BOTg7u3bsHAGjQoAH09fV57pGUyoA0SYiUSs+8efMwd+5c5Ofn890VKSwQGBiIzp074+zZs3j16hXevn0r8mIbaVB41YKv5ApXr17F6NGjxdqNjIzw/PlzVrVzc3OxdetWzJgxg7mWExMT8eTJE1Z1qxNqamro2bMnCgoKmLbU1FQ4OTmhd+/erOkWFBQgKioKmzZtwocPHwAAT58+RV5eHmuaJeHSPvJ5D/HBhw8fMHjwYBgZGcHR0RGOjo4wMjLCoEGD8O7dO767J4VnpDFoUio9a9euRUZGBgwMDGBqaioWx5KYmMhTz6RIgo0bNyIkJASDBw/mRV/qRFD14CO5gqKiYpklIe7fvw89PT3WdEu7hY0cORI6Ojo4dOiQ1LVSghw6dAgdO3aEl5cXwsPDcefOHbi4uMDLywsrV65kRfPx48fo2rUrMjMz8eXLF3Tq1Anq6upYsmQJvnz5go0bN7KiWxIu7SNf9xBfjBgxAjdu3MCxY8fQpk0bAMDFixcxfvx4jB49GuHh4Tz3UAqfSCdoUio9Hh4efHehysNnzZ2vX7+ibdu2nGryBR9xLNURPpIruLu7IygoCPv37wdQXFQ+MzMT06ZNY3WHZdKkSRg6dCjjFibE1dUVAwcOZE23uqGsrIzjx4/DyckJnp6eiIuLw5AhQ7Bs2TLWNMePH4/mzZsjKSkJurq6THvPnj0xcuRI1nT5gq97iC+OHTuGyMhItG/fnmnr0qULtmzZgq5du/LYMymVAekETUqlZ+7cuXx3ocrDZ82dESNGYM+ePZg9ezYv+lxmgnNzc6uWcSxcx1hoaWlx/kC3YsUK9OnTB/r6+vj06RMcHR3x/PlztGnTBgsXLmRN9+rVq9i0aZNYe1V1CyssLERISEi5Mavnzp2TmFbp3RwZGRns27cPnTp1Qu/evTF79mzmM+UtvPwK8fHxuHDhAhQUFETaTU1NOXNf5dI+8nUPBQUFYcqUKVBRURFp//TpE5YtW4Y5c+awoqurqwtNTU2xdk1NTWhra7OiWR35XTODSpOESPltuH79OlJTUwEA1tbWsLOz47lHVYvg4GDcv3+fE7ewSZMmMf8uKipCaGgoGjdujMaNG4u5sLLlPsQH3bp1g0AgwD///MOc49TUVDg7O8PT0xNr1qzhuYeS5cOHD/D19UV4eDgKCwsBALKysujXrx/Wr19f5sPJ705CQgKSk5ORl5eHpk2bsl4uQl9fH5GRkbCzsxNJrHDmzBn4+PggKyuLVX2uGTduHEJCQuDm5oZatWqJlV1ZtWqVxLRkZGTKLOsifGwSCAQgIggEAub6liTa2to4f/48rKysRH7bhIQE9O7dGy9evJC4ZmWA63tIVlYWz549E1s4ev36NfT19Vn5bQFg8+bNOHDgAHbu3ImaNWsCAJ4/fw5vb2/06tWrzHg8KT9HZcgM+l+RTtCkVHpycnLQv39/xMTEMJmkcnNz0aFDB4SHh1dJ33Q+6NmzJ86ePQs1NTXW3cI6dOhQoc8JBAKJrojzzadPn9CxY0fUrl2bszgWPunXrx9u3LiBv/76SyzGwtbWltUYi4KCAsTExCAjIwMDBw6Euro6nj59Cg0NDaipqbGmyzUjRozA69evsX//fujo6CA5ORmysrLw8PCAg4MDVq9ezXcXJUqNGjUQFhYGV1dX1rViY2Mr/FlHR0eJ6/fr1w+amprYvHkz1NXVkZycDD09PfTo0QN16tSpcmn2+UJGRgYvXrwQe5Y4d+4c+vXrh5cvX7Kia2dnh/T0dHz58gV16tQBAGRmZkJRURH16tUT+aw01v6/8TtnBpVO0KRUevr164cHDx4gLCyMiY9KSUmBt7c3LCwssHfvXp57WDWobjV3+CI3NxdOTk6oV68eJ3EsfKKqqioWYwEUu2517doVHz9+ZEW3dHKF+/fvw8zMDOPHj2c1ucLVq1cRHR1dpusdWxPwd+/eoU+fPrh27Ro+fPgAQ0NDxi3sxIkTYgstvzuGhoaIiYlB/fr1OdMsKChAcHAwfHx8ULt2bc50s7Oz0aVLFxAR0tLS0Lx5c6SlpaFGjRqIi4urkunYubyHtLW1IRAI8O7dO2hoaIjslhYWFiIvLw9jxozB+vXrJaorJDAwsMKflYZ6/Dc0NTWRmJgIc3NzkQna48eP0aBBA95COyoE8cDcuXMJAL18+ZIP+UoPAPrjjz/+8997e3uTo6Oj5Dr0HRwdHcna2ppVDQ0NDbpy5YpY++XLl0lTU5NV7Z/F0dGRvL29+e6GlFLs2LGDeDJ39O7dO7HX3bt3ydjYmMaOHcu0TZ8+vcrZRWNjYzI0NKS5c+eKtCclJZGRkdFPH8/ExITc3Nx++LkePXrQoEGD6MuXL6SmpkYZGRlERBQdHU0WFhY/rVsRFi5cSAKBgBo2bEiOjo7k5OTEvDp06CARDW9vb1JVVS3zvfj4eFq/fj0tWbKEzpw5U+ZnoqOjCQA9fPhQIv0hKh6vSv++P8Lb25tMTEz+k97y5cvJ19eXioqK/tPf/1fU1NTKPG9z5879z9+lInz79o127txJAQEBBIC6d+9O+fn5rOn9Cg8fPiQAtGPHjv/091zcQyUJCQmhHTt2kEAgoDVr1lBISAjz2rNnD124cOE/HVc43kjyPmP7OpMkjo6OIs+gv3pd/AihXYuOjhZ7T09PjxITE4mIRMaC06dPU+3atVnpj6SQJgmR8kOePn2KzZs3w8PDA7a2tpzrFxUVicUlAcWJLUqvsEn5Nb7nFpaZmYn9+/dj6NChMDU15burvwSXQeFaWlrlxrFs3LgRmzZtAhFVyXT/s2bNwvjx40VqNj1//hwBAQGsJoXhI7nCmjVrsH37dgwdOpSV4/+I9u3bi+1U/i7k5+dj6dKlcHJygpOT03c/m5CQgOjoaJw8eRLW1tZiYwMbGToBwNnZGbGxsZzbPjk5OQwaNAgAsGzZMjRt2hTKysqs6VUko++ePXuQk5ODCRMmSFSb63vI29sbBQUFEAgEcHZ2hrGxMSe6pZHG17PH75wZVDpBk/JDnj59isDAQJiamvIyQXN2dsb48eOxd+9eJpvUkydPMHHiRLi4uHDen6rKj2rudOzYEYGBgXBycvrtJ2iBgYEYM2aM2AQtPz8fgYGBEp2gRUdHV+hzO3bsQGhoqMR0KwMbNmzAly9fsGrVKhw5cgTA/4+xePnypUj2QUnGWBQVFZUZ2J+dnS2Sil6SyMjIoF27dqwc+0fw4Vop5NOnTz+dVGjLli0i/RTedwB+OEHT0tJCz549f7qfv0q3bt0wffp03Lp1C82aNWNcR+/du4dPnz6xppuWlsb8tkBxXFxQUBAAsJJdsCIZfffs2YPbt2+LTdBMTEzw6dOnMhdUKwIf95CcnBzGjh3LTJC4pLrE1//qdfEr8JUZVBJIJ2hSKj1r166Fu7s7TE1NmRWurKws2NjYYNeuXTz3rupQXs0dNzc3+Pr6sp5Ji0vo/zKvlSYpKQk6OjoS1RImDygvjuXjx49QVVWt8ETud8LDwwMPHz6Era1thRPDSILOnTtj9erV2Lx5M4DiVdO8vDzMnTuXteQSEydOxPr16zlPyhEcHIxZs2ahQYMGMDAwELmuy7rGJY2SktJP/82vPKjxFQvr6+sLQHzCy+bO95YtWzB27FjUqFGDyfKXmpqK3NxcCAQC1tK//9dC7wKB4D9dD0L4uodatmyJGzduwMTEhFNdPz8/fPjwAXfu3BGLr/f3968y8fW/el38Cpqamjhz5gznmUElAh9+lcIYtLS0NPL29iZNTU3S0NCgoUOH0sePH0U+++3bNwoKCiIzMzNSUFAgExMTmjFjBn3+/Fnkc8LYhOjoaGrWrBkpKSmRjY0N45N68OBBsrGxIUVFRWratCnjk1qS1NRU6t27N2lra5OioiI1a9aMjh49ytp5KA/8Xwza4cOHydramhQUFMjKyopOnjxZob//mRi0jIwM6tOnD2lra5OysjK1atWKjh07xrwv9O0t/RL6Egtj0O7cuUNOTk6krKxMhoaGtGTJEjGtz58/05w5c8jc3JwUFBSodu3aFBAQIPZbCr//rl27yMrKiuTk5OjQoUN0+vRpWrt2La1du7bcGAtJsH//fmratCkpKSmRrq4ueXl5UXZ2doX+9mdi0LKzs2nYsGGkr6/P/Mbbtm0T+9zatWvJysqKlJWVSUtLi5o1a0a7d+8WO5aPjw/VqlWLFBQUyNTUlMaMGUNfvnxhPvOj31pHR4dCQ0MJACkqKpKvry8ZGhqSQCAgeXn5Mq+Dsny+2aKi56sshDEBWlpapK2tTTIyMsy/hS8NDQ0CQHXr1hX5WxsbGwJASUlJTFt4eDgBoJSUFKYtMTGRunbtSurq6qSqqkrOzs508eJFkWMpKioSAIqJiaGxY8eSnp4eaWlpEVHZsbmPHj0ic3Nzsra2pufPn//0OftZ3r59SzIyMrRmzRqm7eXLlyQQCEhHR0ck7mfMmDFkYGDww2OamJhUKEbp2bNnNHToUDIyMiIFBQWqWbMmubu7i8RxCO18fHw8tWjRghQVFalu3boUGhoqcqysrCyysLAgdXV1AkAyMjIkJydHRkZG9OLFC+ZzQvsWHh5OM2bMIAMDA1JRUaHu3btTZmbmD/tcksLCQuratSuZmZnR//73P+rZs6fI60d8/fqV5s2bRxYWFqSoqEg6OjrUrl07On36NPMZYQxadnY29ejRg1RVVUkgEFCXLl2ooKDghxoVjUGztrYmJyenMr+joaEh9e7dm2lDqRi09+/f0/jx48nExIQUFBRIT0+POnbsSNevXxf5HsJ4GmF8SunXj66ZnJwcio+Pp/j4eMrJyfnhdy+L58+fk6ysLM2bN0/svbt37xIA+uuvv757jJ+JDaroGPj582eaMGECycjIkIKCAnXv3p2ysrLEzkt5sXxCW1KanTt3UosWLZixxN7eniIjI5n3jxw5Qq6urqSkpEQASFZWlszNzcnDw4O5jmvUqCH2W5X+LUvHGp09e5bat29PKioqpKmpSe7u7iK2s2Sf7e3tSU1NjeTk5EhOTo7q1KlD3bt3r9A99F/Zt28fmZmZ0V9//UUXLlygpKQkOnLkCHXs2JE0NDS++zx4+/Zt6tChAykpKZGRkRHNnz+ftm3bJnaflXVNa2hoUK1atcSeGcqKr6/odSa8x/ft20fz5s0jQ0NDUlNTo969e1Nubi59/vyZxo8fT3p6eqSqqkpDhw4Vu/6Iiq8V4XOQtrY29evXr0ybuGnTJjIzMyMlJSVq0aIFxcXFVTgGLTU1lfr27Us1atQgJSUlql+/Ps2cOZN5/9GjRzR27FiqX78+KSkpkY6ODvXp00fMfn0vBo0thM8U8fHx5OfnRzVq1CBNTU0aNWoUffnyhd6+fUuDBw8mLS0t0tLSooCAgJ+Om+V1B83T0xN169bFokWLkJiYiK1bt0JfXx9LlixhPjNixAiEhoaiT58+mDx5Mi5fvoxFixYhNTUVhw8fFjleeno6Bg4ciNGjR2PQoEFYvnw5unfvjo0bN2LmzJnMCtiiRYvg6emJe/fuQUZGBgBw584dtGvXDkZGRpg+fTpUVVWxf/9+eHh44ODBgz90qXj79m2FamWoqKiIuVWVRUJCAg4dOgRfX1+oq6tj7dq16N27NzIzM0V2N36FFy9eoG3btsjPz4e/vz90dXURGhoKd3d3REREoGfPnrC0tERQUBDmzJmDUaNGwd7eHgDQtm1b5jhv375F165d0atXL3h6eiIiIgLTpk1Do0aN0K1bNwDFLkfu7u5ISEjAqFGjYGlpiVu3bmHVqlW4f/8+4/4k5Ny5c9i/fz/GjRuHGjVqoG7durC1tUWnTp3EvkdeXl6FMvHIy8v/sO5SSEgIhg0bhhYtWmDRokV48eIF1qxZg/Pnz+PGjRuMG8Kv8uLFC7Ru3RoCgQDjxo2Dnp4eTp48ieHDh+P9+/eM68iWLVvg7++PPn36YPz48fj8+TOSk5Nx+fJlDBw4EECxC2rLli2Rm5uLUaNGoWHDhnjy5AkiIiKQn58PBQWFCv3WRUVFjNvRt2/fEBMTgylTpuDevXs4cOAABg0ahLVr12LmzJnMal95MQoA8OXLF3z48KFC56NGjRoSOV8/YvXq1SAi+Pj4IDAwUOR6UFBQwOHDhxETE8O0vXnzBnfu3IGMjAzi4+PRuHFjAMUxTnp6esz3v3PnDuzt7aGhoYGpU6dCXl4emzZtgpOTE2JjY9GqVSsAxefr5s2b8PX1hZ6eHubMmVNuJsOMjAw4OztDR0cHZ86c+e45+vbtG969e1ehc6Cjo8PYvdJoaWnBxsYGcXFx8Pf3B1BsiwQCAd68eYOUlBRYW1sz50BoD37E06dPmd3u8mIsevfujTt37sDPzw+mpqbIdP3YHwAATetJREFUycnBmTNnkJmZKeJSm56ejj59+mD48OHw9vZmYlaaNWvG9E1eXh4fPnzAt2/f0Lp1a8jIyCAzMxNPnz7F+fPnxez5woULIRAIMG3aNOTk5GD16tXo2LEjbt68+d14n5Lnfdq0aTh37hzat28PVVVVsd2roqKics87AMybNw+LFi3CiBEj0LJlS7x//x7Xrl1DYmKiiN0rLCxEly5d0KpVKyxfvhwTJkxAZGQkNm/ejLFjx5Z7/J+hX79+mDdvHp4/f87s3ADF18LTp0/Rv3//cv92zJgxiIiIwLhx42BlZYXXr18jISEBqampaNq0qdjn9fT0sGHDBowdOxY9e/ZEr169AIC510rz8eNHjBs3DmFhYYy9EtbWW7Rokdj4qqmpWe6OnYGBARwdHbF//36xLHn79u2DrKws+vbtW+53/Rl+ZgwcMWIEdu3aBTk5OcycORPJyclwc3P7Jf3AwEDMmzcPbdu2RVBQEBQUFHD58mWcO3cOnTt3BlA8/qmpqcHGxgZycnJ4/vw5MjIyoKSkhBYtWgAAmjZtipycHGRnZzM1575XtiIqKgrdunWDmZkZ5s2bh0+fPuGvv/5Cu3btkJiYKOYun5CQAH19fVhbW+PNmzdIS0uDhoYGmjdv/t3vl5+fj/z8/B+eB1lZWbEi0MLrWWjzqMSuqEAgwF9//VXm8+Dz58/RoUMHFBQUMM+NmzdvrnCMYHkx9JKIr1+0aBGUlZUxffp0pKen46+//oK8vDxkZGTw9u1bzJs3D5cuXUJISAjq1q0rshu7cOFCzJ49G56enhgxYgRevnyJv/76Cw4ODiLPQdu2bcPo0aPRtm1bTJgwAQ8ePIC7uzt0dHR+GM+XnJwMe3t7yMvLY9SoUTA1NUVGRgb+/fdfxvXw6tWruHDhAvr374/atWvj0aNH2LBhA5ycnJCSklKhZ2nhcaKjo/Hs2TOxZ8X58+eX+TffGydL4ufnh5o1ayIwMBCXLl3C5s2boaWlhQsXLqBOnToIDg7GiRMnsGzZMtjY2GDIkCEV6jMAfnfQfHx8RNp79uxJurq6zP9v3rxJAGjEiBEin5syZQoBoHPnzjFtJiYmBEAk605kZCQBIGVlZXr8+DHTvmnTJrHZtouLCzVq1EhkJaGoqIjatm1L9erV++F3Eur/6FWRlWQApKCgQOnp6UxbUlJShVbziCq+gzZhwgRmBUDIhw8fqG7dumRqakqFhYVERHT16tVyM/A4OjoSAAoLC2Pavnz5QjVr1hRZZd25cyfJyMiIaBERbdy4kQDQ+fPnRb6/jIwM3blzh2mLioqiGTNm0PDhw2nYsGEiL29v7wqd+x+dk69fv5K+vj7Z2NjQp0+fmPZjx44RAJozZ873TyhVfAdt+PDhVKtWLXr16pVIe//+/UlTU5PJ0tWjR48fZskcMmQIycjI0NWrV8XeE67YVOS39vT0JDc3NwJAAoGAUlJS6MOHD+Ts7ExDhw6lAwcO/NQqlXCFqSKvH1HR8/WjvhAV78qHhISUuRoo/I7C1d1//vmHFBUVyd3dnfr168d8rnHjxiIruh4eHqSgoMBkiCIievr0Kamrq5ODgwPTNmTIEAJARkZGtGvXLjp69Cjz6t+/P7ODlpqaSoaGhtSiRQt68+bND89PeTvdZb1+tHvyxx9/iOyMTZo0iRwcHEhfX582bNhARESvX79mMp99jxcvXjCr8cKdSoFAQM7OziK7Hm/fviUAtGzZsu8eT2hn4+LimLacnBxSVFSkyZMnM20VtW3C82ZkZETv379nPrt//34C8MPvJ8nz3qRJkx9mqBTauqCgIKZtyZIlpKenR82aNfvu35bs74/6cu/evTLHG19fX1JTUxO530qPa5qamj/MQlx65+fly5cVHh9HjRpFxsbGFT7vP7JXwueBW7duibRbWVmRs7OzSFtMTAz973//I3NzczI3N6fu3bvT0KFDK7SzUdExUPjc4+vrSz4+Psw9N3DgwP+8g5aWlkYyMjLUs2dP5toXUnJVvyw7Onr0aFJRURF5NnJzcytTt6ydEltbW9LX16fXr18zbUlJSSQjI0NDhgwR63OnTp1Ejln6ubA8hH//o1dZ/X706JHIq127dtSwYUO6d+8ePXr0iDlPpZ8HhXbm8uXLTFtOTg5pampWaAfN3d2dFBUVqW/fvkxbdnY2OTo6koeHh9j3+5kdNBsbG/r69SvTPmDAABIIBNStWzeRz7dp00bkuI8ePSJZWVlauHChyOdu3bpFcnJyTLvwecnW1lbEU2fz5s1iz1tlXRcODg6krq4u8mxO9OPr8eLFi2LPnN/bQSuZGbRJkyYSs9fCZ4ouXbqI9LlNmzYkEAhozJgxTFtBQQHVrl37p7Or87qDNmbMGJH/29vb4/Dhw3j//j00NDRw4sQJAMCkSZNEPjd58mQsX74cx48fF4lrsLKyYoqhAmBWrZ2dnZkigCXbHzx4ACcnJ7x58wbnzp1DUFAQPnz4ILLq36VLF8ydOxdPnjyBkZFRud9l9+7dFQoUNjMz++FngOLieubm5sz/GzduDA0NDTx48KBCf18RTpw4gZYtW4pk/lJTU8OoUaMwY8YMpKSkwMbG5ofHUVNTY7JMAcU7ES1bthTp64EDB2BpaYmGDRvi1atXTLuzszOA4kQKJXflHB0dYWVlBaB45S8oKAjNmzdHrVq1xFamFy5cKKJfHqVXzUpz7do15OTkYN68eSL+0m5ubmjYsCGOHz/+U3VLyoOIcPDgQXh6eoKIRM5Hly5dEB4ejsTERLRr1w5aWlrIzs7G1atXmRXMkhQVFeHIkSPo3r17mSuMwnNVkd96xYoVzPsCgQA+Pj5MzZ29e/ciLi7up75nly5dcObMmZ/6m7L4mfNVEb4XFC7cEYqLi4OlpSXi4+PRokULdOrUCYsWLQJQHMR9+/ZtJtNYYWEhTp8+DQ8PD5H7u1atWhg4cCC2bNnC2LSwsDAAxUluBg8eLPY9AeD27dvo168fLCwscPLkSWhoaPzwOzVp0qTC57rkjkhZ2NvbY/369bh37x4aNGiA+Ph4dOnSBXp6eoiPj8eYMWOQkJAAIvrhDpqfnx+Kiorg6+vL1BIqK8ZCWVkZCgoKiImJwfDhw797r1pZWYno6unpoUGDBiL25sSJE2jcuDFSUlIQExPDrEabm5sjKipKzLYNGTJEJHlInz59UKtWLZw4cYJZVS+Lkufdy8sLixYtEhlrSvKj866lpYU7d+4gLS1NrEhtaUqOnVOmTMHff/+NGzduoHv37hLJali/fn3Y2tpi3759GDduHIDi6zwiIgLdu3f/7g6BlpYWLl++jKdPnzJJnSTJwYMHsWfPHrHV7Zs3b2L+/Pk4ePCgSHuTJk2+e7xevXrhjz/+wL59+5hr4vbt20hJScH48eOZz+3atQvDhg1Dr169mGvi/PnzCAsLq1DcakXHwFmzZgEoPo9qamqYPXs2Ll26xOygX758GWvXrv2hXkmOHDmCoqIizJkzR+y8lRxPS/6ub9++xblz5/D161fk5+fj7t270NPTq5A9EvLs2TPcvHkTU6dOFTlHjRs3RqdOnZhnvJKMHDlS5P+lnwvLY8iQIRXKYlrWtVsy9uzNmze4cOECgoKCmD4Lf6/Sz4MnTpxA69at0bJlS+bv9fT04OXlhb///vuHfVm3bh3Mzc0RERHBPO9JKr5+yJAhIragVatW2Lt3L3x8fEQ+16pVK6xduxYFBQWQk5PDoUOHUFRUBE9PT5HrtGbNmqhXrx6io6Mxc+ZM5nlJuBsrZOjQoQgICPhu316+fIm4uDiMHz9ezF6Wdz1++/YN79+/h4WFBbS0tJCYmCg2hpZFycygb9++xfXr13/4N8LvWxGGDx8u0udWrVrh4sWLGD58ONMmKyuL5s2bV1hbCK8TtNI/jHBQfvv2LTQ0NPD48WPIyMjAwsJC5HM1a9aElpYWHj9+/N3jCd2XSm+1Ctvfvn0LoNhlhogwe/bsclM/5+TkfHeCJunMQ2UN8tra2kyfJcHjx4+ZyWpJhG5bjx8/rtAErXbt2mKTJm1tbSQnJzP/T0tLQ2pqarlZiYQZqoTUrVuX+ffGjRsREhLy3ZtROJn7FYTXU4MGDcTea9iwIRISEn5ZAyg2Trm5udi8eTOTxKA0wvMxbdo0REVFoWXLlrCwsEDnzp0xcOBA5np7+fIl3r9//8PfqaK/9bZt29CxY0d07doVJiYmGD58OLy8vP5TWudatWqhVq1aP/13pfmZ81VRygsKNzAwQL169RAfH4/Ro0cjPj4eHTp0gIODA/z8/PDgwQOkpqaiqKiImSS8fPkS+fn5ZV43lpaWKCoqQlZWFqytrbFjxw4MGzYMcXFxYpObefPmITAwEN27d4eBgQEiIyO/6zpUEm1tbYkFPQv7FR8fj9q1a+PGjRtYsGAB9PT0sHz5cuY9DQ2NHz78njp1Crq6uiL3vZWVFdavX8+4VQGAoqIilixZgsmTJ8PAwACtW7fG//73PwwZMkRsoKyIbXzw4AEKCwsxZ84c1KxZk7FPQnfE0rat9IRIIBDAwsICjx49+u73K3neFy9ejFOnTmHHjh0Vdr0pSVBQEHr06IH69evDxsYGXbt2xeDBg8Vc/ZSUlETOp7+/P7Kzs1FUVARdXV2JJQbp168fZs6cyTyMxsTEICcnB/369fvu3y1duhTe3t4wNjZGs2bN4OrqiiFDhlR4cfJH5Ofnw9jYWMy9ulatWpg7d+5P3wc1atSAi4sL9u/fz7g77du3D3Jycoy7JVC8ELh06VJMnDiRafP390eXLl1w7ty5H+pUdAwUuljv3r0bAoEAampqiI2NZRYZLl68iLt370IgEMDBwaFC3zEjIwMyMjI/HCfv3LmDWbNm4ezZs2Lu6e/evcP27dvx5cuXCmkC3x9TLS0tERkZySRJErJr1y64ubkx91Dp58LyMDMz+6VrLCMjA6tXr8alS5cq/DxY3rha1vctC2NjYxgaGqJ+/fro3r07gOLzIglb/jPPw0VFRXj37h10dXWRlpYGIip3kUg46RP+tqU/Jy8v/8PfQbiY9qPnlk+fPmHRokXYsWMHnjx5IuJ6WlGX/pKZQSU5Tgr5mfP8s8/vvE7QZGVly2wv+SMAFc9EVd7xfqQjNHxTpkxBly5dyvxs6UliaV6+fFmhGDQ1NbUKPXRV9NxUBirS16KiIjRq1KjclM+lL+aSE4KvX7+K7K6V5t27dxXavVRQUJB4hr7/gvB6GzRoELy9vcv8jPChzNLSEvfu3cOxY8dw6tQpHDx4EH///TfmzJkjkd280gh/y2HDhqFPnz6/dKxPnz5V2Ih+b7XqZ85XRfH19cXkyZORnZ0tkjIbKJ5AxMfH49OnT7h+/TrmzJkDGxsbaGlpIT4+HqmpqVBTU/ulWjXfm/D27t0boaGh2L17N0aPHl2h4339+hVv3ryp0Gf19PTKvWcBwNDQEHXr1kVcXBxMTU1BRGjTpg309PQwfvx4PH78GPHx8Wjbtu0PffR/JsZiwoQJ6N69O44cOYLIyEjMnj0bixYtwrlz50TOdUXsTUFBAezs7MRS9x89ehQeHh7f7fPPUPK8r1ixAo8ePYKenh6MjY3FdrJu3rz53fPu4OCAjIwMHD16FKdPn8bWrVuxatUqbNy4ESNGjGA+V/oYoaGh8PT0xN69exESEiKx79avXz/MmDEDBw4cwIQJE7B//35oamqia9eu3/07T09PZtfj9OnTWLZsGZYsWYJDhw4xMcm/Qps2bTBnzhwsW7aM8XT49OkTZsyYgaZNm+L58+cin9fR0RGrh1ea/v37Y9iwYbh58yZsbW2xf/9+uLi4iMR9PnjwgHmILkmDBg1w+vTpH/a7omOgl5cXtmzZgvT0dJEMiu/fv4empiYmTJiAefPmASi202VRkWeR0uTm5sLR0REaGhowMTFBjRo1MHXqVHh4eODr168oKipCz549MXLkyApPQP4LZ8+ehYGBAUxNTSEvL4/Xr18DAFxdXXHnzp1y/y4vL0+k3mJ5yMrKik2SIyMj4e7uDltbW1hYWODatWuoWbMmXr9+jfnz56NZs2Yin//R8+DPUFhYCENDQ/j5+UnsmMCvPQ8LBAKcPHmyzM9WdNFQEvj5+WHHjh2YMGEC2rRpA01NTQgEAvTv37/CMXolM4NKcpwU8jPn+Wef3yt1mn0TExMUFRUhLS1NZLXsxYsXyM3NlVhKVOFsX15e/j/Prlu0aCG2o1cWc+fOZYwr35iYmODevXti7Xfv3mXeBySTqtnc3BxJSUlwcXH56eONGDECe/bsKXc1a/z48RWqH+Xo6CiSAKI0wu977949xu1EyL179yR2venp6UFdXR2FhYUVut5UVVXRr18/9OvXD1+/fkWvXr2wcOFCzJgxg3E5uX379nePUdHfOjs7GwAQERGBlJQUkc8KEzBUlH379pX7AFGa7xmunz1fFaF0UDhQfJ0L+0FECA8PR2FhITMRad++PTNBa9u2LWOA9fT0oKKiUu75lZGREVuAmDhxIp49ewageEJY0iVk2bJlkJOTYxIECZPBfI8LFy5UOI39w4cPf1jHzt7eHnFxcUxyHnV1dTRp0gSampo4deoUEhMTK7RA4OzsjMjISJHV+O/VMDQ3N8fkyZMxefJkpKWlwdbWFitWrPhpdx+BQFDmAF76eheSlpYm8n8iQnp6+g8n/uWd97KuhaysrB+edx0dHQwbNgzDhg1DXl4eHBwcMG/ePJEJWll/w8bCU926ddGyZUvGzfHQoUPw8PCAoqLiD/+2Vq1a8PX1ha+vL3JyctC0aVMsXLiw3Anaz4wJa9asgYuLi4iXRWntkkRHR/+wtpqHhwdGjx6Nffv2AQDu37+PGTNmiHzG2NgYZ8+eFXs4f/DgQYVS0Vd0DBQ+92RkZGDfvn2YMmWKmH359OkTli1bBm1tbeTm5oodo/SziLm5OYqKipCSklJuLdOYmBi8fv0ahw4dQs+ePREREYEGDRqI9FVY6L1hw4Y//L7C7wKUfT/cvXsXNWrUEFkcA4oXz0q23bhxA5mZmWUmCCvJ8uXLK2STTExMxHbGp0+fjokTJ2Lx4sXIyclBeHg4hg0bhqKiIuzbtw/Tpk0r91ilbQdQ9vct67f6+vUrnj59isTERIwYMULMZm3fvv2H30fSmJubg4hQt25d1K9fv9zPCX/btLQ0keelb9++4eHDh9/1rhA+c//ouSUiIgLe3t5YsWIF0/b58+cyr/nymDJlCtzc3GBubg59fX1cunSpQn9XkXGSbSr1BM3V1RUzZ87E6tWrRQqaClegfjWrkRB9fX04OTlh06ZN8PPzEzPwL1++/GHBQEnHoHGBq6srVq9ejYsXLzKxex8/fsTmzZthamrKuEMIjeXP3BSl8fT0xIkTJ7BlyxaMGjVK5L1Pnz6hqKhIxCgnJCQwsYdFRUXYvHkzoqKi0LhxY7GV6alTp0okBq158+bQ19fHxo0b4ePjwzyInDx5EqmpqRKrOSMrK4vevXszxT5Lb/OXvN5ev34tkrVTQUEBVlZWOHnyJL59+wYlJSV4eHhg165duHbtmlgcGv1fva+K/NZbtmxhYlsuXbokMsgIBALGz76i14GkYtB+5nxVlIcPH5b73qNHj+Dk5IQlS5agcePGjMuCvb09NmzYgKdPn4osFsjKyqJz5844evQoHj16xBj1Fy9eYM+ePWjfvj3jmnPhwgUAxW5qJeNYXFxcmNV5gUCAzZs348OHD/D29oaamhrc3d2/+30kGYMm/K5hYWHYt28f81AtIyODtm3bYuXKlfj27VuFMjgKYyxWr17NZKkrK8YiPz8fMjIyIrGf5ubmUFdX/ymXKiFWVlZISkr6oW0TEhYWhhkzZjBxaBEREXj27Fm5D2ZCJHneS9/rampqsLCwQFZW1nf/bt68eYzrqaTp168fJk+ejO3bt+PVq1c/dG8sLCxEXl6eSHZUfX19GBoafvd3FLqzVcS22NjYIDU1FfPnz2fOTZ06deDi4lLm5PFHbrhAcbxXly5dsH//fhARFBQUxHZaJ0+eDH9/f9y8eZPx6Dh//jxOnTr1w+zAQMXHwG7dumHmzJlYu3YtNm7ciDFjxkBFRUWkNpiwsPfatWvx7t07JCcnM4sJz549E8tw7eHhgWnTpiEoKAgREREiO9/CMUK44ERETKH3r1+/4tu3b8xnhYXeVVVVK+QdUatWLdja2iI0NBQzZsxgsv/dvn0bp0+fLnPcnjp1qsjOZUhICI4ePfrDTL2/EoOWmpqK/fv3AxB9Hjx69KhYTbaS441wXL1y5QozPr58+RK7d+8W0zA3NxeL4/bw8EBRURGePXuGV69ecVK38Ef06tULM2bMQGBgIHbt2iXSJyLCmzdvoKuri+bNm0NPTw8bN27EsGHDmF3qkJCQH97Henp6cHBwwPbt2zFp0iQRN0Hh9QgUj62lF2//+uuvn9oh9vf3R3R0NDp06AAtLS0xN9ny4uUqGoPGJpV6gtakSRN4e3tj8+bNzPb7lStXEBoaCg8PD4kWPl2/fj3at2+PRo0aYeTIkTAzM8OLFy9w8eJFZGdnIykp6bt/L+kYNC6YPn069u7di27dusHf3x86OjoIDQ3Fw4cPcfDgQcaIm5ubQ0tLCxs3bmSMc6tWrcpdwSyLwYMHY//+/RgzZgyio6PRrl07FBYW4u7du9i/fz8iIyNFJhcvX77EjRs3mP8LV/1Kr7gIBAJYWVlJJAZNXl4eS5YswbBhw+Do6IgBAwYwafZNTU1FYg9+lcWLFyM6OhqtWrXCyJEjYWVlhTdv3iAxMRFRUVHMNnznzp1Rs2ZNtGvXDgYGBkhNTcW6devg5ubGPEwGBwfj9OnTcHR0ZNI3P3v2DAcOHEBCQgK0tLQq9FsvWLAAw4cPx5YtW7B8+XIxF8fnz59DVlYWS5Yswbt376CoqAhnZ2fo6+uX+R0lFYP2M+eronxvN9TExAQ1a9bEvXv3RNxOHBwcmAf20pOTBQsW4MyZM2jfvj18fX0hJyeHTZs24cuXL1i6dCnzuX///RdAcQpk4fXu7++PlStXIjg4mPmcjIwMdu3aBQ8PD+bBrvSubkkk7Vsv/H737t0T6ZeDgwNOnjwJRUXFMpPWlEYYY2Fvb888wJQVY3H//n24uLjA09MTVlZWkJOTw+HDh/HixYvvpnQvyZMnT5jkCe7u7khJSYGjoyMcHR2hrq6OK1eu4OnTpxg2bJiYa6aOjg7at2+PYcOG4cWLF1i9ejUsLCzEEhaUpqzzfv36dSYBTXklBcrCysoKTk5OaNasGXR0dHDt2jUmXf33WLt2LdLT0wEAjRo1ElvAKu3m+TN4enpiypQpmDJlCnR0dH54jX348AG1a9dGnz590KRJE6ipqSEqKgpXr14VWQUvjbKyMqysrLBv3z7Ur18fOjo6sLGxKTdGRUdHh0nxLin69euHQYMG4e+//0aXLl3ESqqMHTsWNWvWxIoVK5iHeUtLS/Tp06dCq/IVHQNtbW0xYMAAJsnEjh07cOXKFeY3BoCkpCTo6Oigf//+mDZtGnr27Al/f3/k5+djw4YNqF+/vsjvbmFhgT///BPz58+Hvb09evXqBUVFRVy9ehWGhoZYtGgR2rZtC21tbXh7ezPxxyUn1Z8+fcLy5cvh6urK/FaTJk1CixYtoKamVqb7J1DsEdCtWze0adMGw4cPZ9Lsa2pqlutNVPIeqohnEvBrMWh6enq4efMmE08lfB50dXWFgoICtmzZUubz4NSpU7Fz50507doV48ePZ9Lsm5iYiMTgA8WeQGPGjEHv3r3R6f+1d+9xNabr/8A/KyrROUWIVCSHyGHbhJTjlEOY2U4zTsPMbIdsx7bfdsrGxPdrBpvZg6HCkNMwM8YxJOdTymFySskQOUxoZoi6f394tb4tK8lYz7pbPZ/369Vrpmct677vp2dVV899XVenTkhOTsaePXtgY2ODkJAQg25Pfheenp6YPXs2pkyZgvT0dISGhsLGxgZpaWnYunUrPvnkE0ycOBHm5uaYPXs2Pv30UwQFBaFv375IS0tDVFRUib4OixcvRps2bdC0aVN88sknqF27NtLT0/HTTz8hKSkJANCtWzesWbMGdnZ2qF+/Po4dO4a4uLi3ajUVExODLVu2GOyGjlG9Vc1HAymqIasQ/1e2snB5y+fPn4uIiAhRu3ZtYW5uLtzc3IptVP0qAHolfwtKfr5a0jk1NVUMGjRIVK1aVZibm4vq1auLbt26ic2bN7/jit9OUXMW4uUaS1LC/c80qra3txcVKlQQf/nLX3SaFxf4/vvvtU2jAf1G1UXN4dWSsLm5uWLevHmiQYMGwtLSUjg4OIhmzZqJiIgI8ejRI+3zXrd+Y9mwYYPw8/PTNotVqlH13bt3xahRo4Sbm5swNzcXVatWFR06dBDLly/XPmfZsmWiXbt2wsnJSVhaWgpPT08xadIknfMlhBA3btwQgwYNEs7OzsLS0lJ4eHiIUaNGFdmo+nVfaxsbG/Htt98KAGLTpk1FznnFihXCw8NDlCtXrkQlrA2pJOfrdQqX2S9w7do1MXr0aNGhQwfRoUMHMWbMGG1riw8++EAAL5t9FsjNzRUVK1YUFhYWOm0YCiQmJoouXboIa2trUbFiRREYGKjT9kMIoT1vr7ZEuHr1qvaxwt8Xf//9dxEQECCsra3F8ePH33ySDMjFxUUA0GnsfPjwYQG8bCZbUiVpVH3//n0xatQoUa9ePVGpUiVhZ2cnWrZsKTZu3Kj3WkV9nw8ICBCWlpbC3d1d+1G9enVRsWJFYWZmJjQajbCwsBAuLi46jcgLyjOvX79eTJkyRbi4uAgrKysREhKiV/75Te7evSsCAwOFRqMptqXA68yePVv85S9/Efb29sLKykrUq1dPzJkzR6dUdkGj6sJmzpypbXcyc+ZMvY/CSlpmvzB/f38B6Le7KYBC5cOfPXsmJk2aJBo3bqxt2N64cWPx1Vdf6fybon4+HD16VDRr1kxYWFjolST//vvvteehcGuKoj7+rMePHwsrKysBQKxdu7bE/+5tGlWX9GegnZ2dtqk9AGFubq4t3W5hYSHMzMzEyJEjhRBC7NmzRzRs2FBYWFgIb29vsXbt2tc2ql61apX2Z5uDg4MICAgQe/fu1T5+5MgR8de//lVUqFBBlC9fXjg5OQkzMzMBQNja2gpvb29x9+5dkZOTIwYMGCDs7e11Ste/riFxXFyc8Pf3F1ZWVsLW1lZ07979tY2q/f39dd5DBefg9OnTJTrHf0ZERISwt7cXkZGRIiEhQSQkJIhJkyYJc3NzYW1tXezvg+fOnRMBAQFvbFSdl5cnwsPDReXKlUXFihVFly5dhJ2dnahevXqJfmd42zL7r/4ML/gZ+OrPntf9Pr5lyxbRpk0bUalSJVGpUiVRr149MWrUKHH58mWd53311Veidu3awtLSUjRv3vytGlVfuHBB9OrVS/s7ibe3t5g2bZr28V9//VUMHTpUVK5cWVhbW4suXbqIS5cu6f0uXFyZ/Zo1a4qUlJQ3nre39bbns6jv3W+iEaIUVp2gdzJkyBCkp6cXm29Fymjfvj3c3d1LzV/D3sbHH3+MFi1a6LW/KAsKGpAXfLsrnBRecPf7yJEjSE5Oxo8//vjGfIc/y8vLC5MmTdIr/vH1119jwYIFReYzmLqC8sze3t6lIseisPj4eAQGBmLTpk3vXBSnb9++uH79OlavXq3NmS5oKeDl5aVtKSBTwXpLQ37F2zAzM8OdO3fg4uJSbGEajUbzpwpklMT06dMRGBiIVq1a6WzFnTlzJqKjo99Y8fNtxMTEQAiBYcOGYeHChTpbKC0sLODu7q7TUkgJL168QGxsLM6dO4ecnBw0bdr0T1f0LSlZ7yEhBBYuXIgFCxbg9u3bAF4WS5o0aRLCwsIU23oYHh6ubaXwJkpcZ2oQFRX1TtV1ZSrVWxyJSFmF++l4eXlpe+4UtVWquH5QpqZwUvirx8PDwxUL0F6XxxIdHY1FixYpMqZMERERuHv3LsqVK2fQEvBvMmvWLG1xhcIKiisYKp+0sF27diEuLk6noFVRLQWU8me3VpqCwoF9Sau3GdqxY8fwxRdf4MWLF2jRogUCAgLQvn17nRwtQymoVlu7dm20bt1a73uxMZQvX75Eud2GJOs9pNFoMG7cOIwbN05b0KhwX0RDKtzX90359a+r+Eklt3jxYqSmpupUBi3sXbaAK40BGpGKvZrHUdBz5+DBgzrHNRpNmQrQCieFF1bwF2ulvC6PZcOGDejZs6di48ry9ddfo3LlyhgxYoRRq9dGRERoiysUVlBcQYkALT8/v8hfpItqKWBIWVlZ6NevH+Lj47V5U9nZ2QgMDERsbOxbF9Ap7VavXo2+ffvqFQTJzc1FbGwsBg0apMi4e/fuxYsXL3DixAkkJCTg4MGDWLx4MX777TfFAqiAgADk5eVhy5YtOsF3jx49SlQC/F1cvXoVBw4cQFZWlt71q8T7B5D3HkpLS8OLFy9Qp04dncDs6tWrMDc3N+jd5sK59UDx+fX07gzZVsXYGKARqVhx1QzLsleTwgskJSW9tuiJofTq1Qu9evVSdIzSIjc3V9EtUa8jClUCK6yguIISgoKCMHbsWKxfvx7VqlUDUHxLAUMZM2YMnjx5gosXL+ptCwsLCysVWysNaejQoejatave+/TJkycYOnSoYgEa8PKukr+/P5ydneHo6AgbGxts2bJFkbtoAHDt2jUEBwfj1q1b2t5jn3/+Odzc3PDTTz/B09NTkXFXrFiBv//976hcubJOo3fgZeCgVIAm6z00ZMgQDBs2TO/nwYkTJ/DNN98YNF3kwIEDBnsterMZM2bInsKf9w45ckRUhkRERIjffvtN7/jvv/8uIiIiJMxIOUUlhX/++efC3t5ezJo1S7Fxp02bJvbv319kkZGyaPLkyYqez1fZ29sLBwcHYWZmpv3/gg9bW1ud4gqGlpGRIZo0aSLMzc2Fh4eH8PDwEObm5sLPz0/cvHlTkTGFEMLW1lacPHlS7/iJEyeEnZ2dYuPKotFoiiy6kpSUJBwcHBQbd9myZaJ///6iWrVqwsnJSYSGhoqFCxeKpKQkkZ+fr8iY7733nujatat48OCB9tj9+/dF165dRXBwsCJjCvGysEJkZKRir/86st5DNjY24urVq3rHr169atT30KNHj8TWrVsVKWqhdqdPnxZr1qwRa9asEYmJibKnUyIsEkJEAF72HMnMzNT7y/SDBw/g4uKiWPK9DEJSUninTp1w7NgxvTwWf39/KXealPBqjkVMTAx8fX2NkmMhu7iCEAJxcXHahthFtRQwNBsbGxw6dEivAfHZs2cREBCAx48fKzq+sfj5+UGj0SA5ORkNGjTQaQ6dl5eHtLQ0dO3atcity4ZgZmYGZ2dnTJgwASNHjoS1tbUi4xRWqVIlbU5wYcnJyfD390dOTo4i49ra2iIpKUlK31YZ7yE7OzvEx8fr5W2eOXMG7du31+alGdrf/vY3tGvXDqNHj8Yff/yBxo0bIz09HUIIxMbGok+fPoqMqyamvAWcARoRAXj5C8jdu3f1vmHt378fffv2xb179yTNTFlKJ4W/6tU8lqNHj+LZs2do0aIFDh8+bJQ5KKmk/Sk1Gg3279+vyBwOHjworbiCsfXs2RPZ2dl628IGDhwIBwcHvabFpioiIkL73wkTJugESAXBd58+fbQNcw1t27ZtSEhIQHx8PFJSUuDn54f27dujffv2aNOmjSIV4hwdHbF9+3ZtQaECR44cQffu3d+6/2NJleWKvkXp3r07rKyssH79em1uX15eHvr27YvffvsNO3fuVGTcqlWrYvfu3WjcuDHWrVuHGTNmIDk5GTExMVi+fLlevhq9PVOorvs6DNCIVM7BwQEajQaPHj2Cra2tzt2jvLw85OTk4LPPPsPSpUslztKwCieFF6ZEUvjrXLlyBQcOHEBcXBy2bdsGOzs73L9/X/Fx1SIvLw/btm0zanGFffv2Yd++fUUWVlCqpcDNmzfRo0cPXLx4EW5ubtpjDRs2xA8//IAaNWooMq4sMTEx6Nu3r06pe2N79OgRDh06hE2bNmH9+vUwMzPD06dPDT7OoEGDkJiYiJUrV2obvZ84cQIjRoxAs2bNDNrOpXBF399++w1ffPEFQkJCjF7RV8Z76Oeff0a7du1gb2+Ptm3bAgAOHTqEx48fY//+/a9tmP6urKyscOXKFbi5uWHQoEGoVq0aIiMjkZGRgfr16yt2h1RN7OzsEBcXhxYtWugcP3nyJDp37ozs7Gw5EysBBmhEKid7W5gMAQEBGDZsmLacdYG1a9caPCm8sOXLlyM+Ph4HDx7Es2fP0LZtW+1f4X19fct85a6CX3jq1auHevXqKTZOUcUVLl++rGhxhYiICMyaNQvNmzeHq6ur3tdSyTtZMraFqdGDBw9w8OBBxMfHIz4+HhcvXoSDgwPatm2ryNc3OzsbgwcPxo8//qgNkl68eIEePXogOjpa53v1u6pdu3aJnqfRaHD9+nWDjVuYzPfQ7du3sWTJEiQnJ8PKygq+vr4YPXq0YkWFAKBu3bqYPXs2QkJCULt2bcTGxiIoKAjJycno0KED/2BnAKa8BZwBGhEBUNe2MFtbWyQmJsLLy0vn+LVr19C8eXPF/qomI49FJlk5FsHBwRBC4Ntvv9X+gvXgwQN8+OGHMDMzw08//WTwMV1dXTF//nx89NFHBn9t+j95eXn48ssvsXHjRmRkZCA3N1fncaW2/TVq1AgpKSlwcHBAu3bt0L59ewQEBMDX11eR8Qq7du2a9k6wj4+P3vetskJt76GvvvoKY8eOhbW1NWrVqoXExESYmZnhP//5D7777jtWfDQAU94CzgCNiLRkbAuTQVZSuIw8Fplk5VjIKK7g5OSEkydPKlb6vDgytoXJMn36dHzzzTeYMGECpk6din/9619IT0/Htm3bMH36dMW23y1duhQBAQGKbXcrTWQ0egfkvYd27doFa2trtGnTBsDLr/WKFSu0TbIdHBwUG/vMmTPIyMhAp06dtH+w++mnn2Bvbw9/f3/FxlULU94CzgCNiADI2RYmi6yk8MKMlccik6wcCxnFFcLDw2FtbY1p06YZ/LWLI3NbmAyenp5YvHgxQkJCYGNjg6SkJO2x48ePY926dbKnaPJkVfSV9R5q1KgR5s2bh+DgYJw/fx7NmzfHhAkTcODAAdSrVw9RUVFGnQ8ZlqluAWejaiIC8DLx29PTE8ePH9fbFhYWFqbItjBZ5s2bh3bt2sHb27vIpHAlFZfHUta4ubnh2LFjcHR0xK5duxAbGwsA+PXXXxUt8tCtWzd88sknesUVPvvsM/To0cNg47zaUmD58uWIi4szSkuBAl9//TWio6NVsy3szp072juj1tbWePToEYCXX3Nj/2JfVgkjNnovDe+htLQ01K9fHwCwZcsWdO/eHXPnzkViYiKCg4MVGZOMR6PRoFOnTujUqZPsqbwVBmhEBOBlDlrh4Ax4ueUkMjKyzG21qF+/Ps6dO6eTFD5o0CDFk8JfzWMZMWKE0fJYZPjHP/6BgQMHanMs2rdvDwBISEjQ235oSIsXL8bgwYPRqlUrveIKixYtMtg4r27RLEhEv3Dhgs5xJYu/5Obm6t0pLMtq1KiBzMxM1KxZE56entizZw+aNm2KU6dOwdLSUvb0TFpBRV+NRoO6deu+tqKvIZWG95CFhQV+//13AEBcXBwGDRoE4OWd+NJcRIJKxlS3gHOLIxEBkNdzR03UlMdSQGaOhRqKK8jaFibLP//5T9ja2uL//b//hw0bNuDDDz+Eu7s7MjIyMG7cOERGRsqeoslSY0VfAOjRowdyc3Ph7++Pf//730hLS0P16tWxZ88ejB49GleuXJE9RfqTTHkLOAM0IgJg3J47sslMCid6V69uC4uJiYGvr69Rt4WVFsePH8fRo0dRp04ddO/eXfZ0FNOoUSPs2LFDW+hASWqq6AsAGRkZGDlyJG7evImwsDB8/PHHAIBx48YhLy9Pp0ccmRZTrgzKAI2IABi3545sTAonUxYYGFii52k0GsVzKtXImMFSARsbGyQnJ8PDw8Mo46mlom9pIeOaUgOZ1XXfFQM0ItKhhm1h1tbWuHDhAtzd3TFz5kxcuHABmzdv1iaF37lzR/YUiagIn3/+OapUqYJhw4bpHF+1ahXu3buH8PBwxedg7GDJ2GOqqaJvgdTUVERFRSE1NRWLFi2Ci4sLdu7ciZo1a6JBgwaKjy/jmlIDU94CziIhRKTDy8urTAZlhTEpnMg0LVu2rMhS+g0aNEC/fv2MEqDJ0LZtW1hZWRllLDVV9AVebul877334O/vj4SEBMyZMwcuLi5ITk7GypUrsXnzZtlTpLdQGiqDGgIDNCJSnTZt2mD8+PHw9/fHyZMnsWHDBgDAlStXSnXjSiK1u3PnDlxdXfWOOzs7IzMz0yhzMGawVGDHjh1GG0tNFX2Bl4VnZs+ejfHjx8PGxkZ7PCgoCEuWLDHKHGRcU2VVaagMaggM0IhIdZYsWYKRI0di8+bN+O9//4vq1asDAHbu3ImuXbsaZQ5qyzmQtV61neeyzs3NDUeOHEHt2rV1jh85cgTVqlUzyhyMGSzJYGlpiSdPnugdz8nJgYWFhYQZKev8+fNF3pV1cXHB/fv3jTKHsn5NGdOBAwdkT8EgGKARkerUrFkT27dv1zv+5ZdfGm0O6enpeP78udHGk03WetV2nsu6ESNG4B//+AeeP3+OoKAgAC/7HE2ePBkTJkyQPLuywViN3ksLe3t7ZGZm6gX9Z8+e1f7xjsjYGKARkSrJTgonorc3adIkPHjwACNHjkRubi4AoEKFCggPD8eUKVMkz65sMFaj99KiIHdx06ZN0Gg0yM/Px5EjRzBx4kRtfjKRsbGKIxHpKevbwl5NCk9JSYGHhwciIyNx+vRpoySFBwcHY+XKlUXm05RFstartvOsFjk5OUhJSYGVlRXq1KkDS0tL2VMqc9RQ0RcAcnNzMWrUKERHRyMvLw/ly5dHXl4eBgwYgOjoaLYWICkYoBGRnrJe8rdVq1b44IMPtEnhBWs9efIkevfujV9++UX2FImIkJ2djc2bNyM1NRWTJk2Co6MjEhMTUaVKFW6/M7CbN2/i/PnzyMnJgZ+fH+rUqSN7SqRi3OJIRKpTGpLCiejtBQYGFlt9TcnG3MYOls6dO4eOHTvCzs4O6enpGDFiBBwdHfHdd98hIyMDq1evNviYaubm5mb0XSMMwOl1GKARkZ6yXvKXSeFEpqmgZHaB58+fIykpCRcuXMDgwYMVG1dGsDR+/HgMGTIE8+fP1yn/HhwcjAEDBhh8PDIuBuBUHAZoRKSnrJf8ZVI4kWl6XaXVmTNnIicnR7FxZQRLp06dwrJly/SOV69eHXfu3FFkTDIeBuBUHDPZEyAiMra5c+eiXr16cHNzQ05ODurXr4927dqhdevWmDp1quzpEdFb+vDDD7Fq1SrFXv/UqVP49NNP9Y4rGSxZWlri8ePHesevXLkCZ2dnRcYk45FxTZHpYIBGRKpjYWGBFStW4Pr169i+fTvWrl2LS5cuYc2aNYpX7MrOzsY333yDKVOm4OHDhwCAxMRE3Lp1S9FxZZG1XrWdZ7U7duwYKlSooNjrywiWevTogVmzZmn7+Gk0GmRkZCA8PBx9+vRRZMxXNWrUCDdv3jTKWGrDAJyKwyqORERG8mrOweXLl+Hh4YGpU6eWyZwDWetV23lWk969e+t8LoRAZmYmTp8+jWnTpmHGjBmKjDt8+HA8ePAAGzduhKOjI86dO4dy5cohNDQU7dq1w8KFCw0+5qNHj/D+++/j9OnTePLkCapVq4Y7d+6gVatW2LFjBypVqmTwMV9V1iv6vsqYLWZkXFNkOngHjYjISApyDq5evarz1/7g4GAkJCRInJkyZK1XbedZTezs7HQ+HB0d0b59e+zYsUOx4AwAFixYgJycHLi4uOCPP/5AQEAAvLy8YGNjgzlz5igypp2dHfbu3Ysff/wRixcvxujRo7Fjxw4cPHjQKMGZGqWnp2vvWCpNxjVFpoNFQohIiyV/laW2pH9Z61XbeVaTqKgoKeMWBEuHDx/GuXPnkJOTg6ZNm6Jjx46Kj92mTRu0adNG8XGKUtYr+sok85qi0o8BGhEBYMlfY1BbzoGs9artPJPxGDtYOnXqFA4cOICsrCzk5+frPPbFF18oPn5Zr+j7KhkBqcwAnEovBmhEBIAlf42hIOl/48aNAOQk/RuTrPWq7TyXdQ4ODsU2py6soCCMEowdLM2dOxdTp06Ft7c3qlSponMOSno+6O0YOyCVHYBT6cUiIUQE4OV2i8TERHh6euokht+4cQPe3t54+vSp7CkqwphJ4aUh6d+YZK1Xbee5rIuJidH+/4MHDzB79mx06dIFrVq1AvCyguPu3bsxbdo0jBs3TpE5vClY2r9/v8HHrFKlCubNm4chQ4YY/LVJPhnXFJkOBmhEBABwcXHB7t274efnpxOg7d27F8OGDSuzpZZlVClTW86BrPWq7TyrQZ8+fRAYGIjRo0frHF+yZAni4uKwbds2RcaVESy5uroiISEBderUMdqYZDwMwKk4DNCICIB6S/6qrYw0kSmztrZGUlISvLy8dI5fu3YNTZo0QU5OjiLjygiW5s+fj9u3b5fZ771qxwCcisMAjYgAqHdbWHBwMFauXAlXV1ejjKe2nANZ61XbeVaLWrVqISwsDBMmTNA5vmDBAixevBg3btxQZFwZwVJ+fj5CQkJw5coV1K9fH+bm5jqPf/fdd4qNzYq+ymMATsVhkRAiAqDekr/GTApXW9K/rPWq7TyrSUREBIYPH474+Hi0bNkSAHDixAns2rULK1asUGzciRMnIiQkBJ6enkYLlsLCwnDgwAEEBgbCycnJaNeuGiv6yghIZVxTZDp4B42IyEjUlnMga71qO89qc+LECSxevBgpKSkAAB8fH4SFhWkDNiWMHj0a33zzDQIDA/WCfkCZ/mw2NjaIjY1FSEiIwV+7OB07dkTTpk21FX0LtoAfPXoUAwYMQHp6ulHno7RXA9LLly/Dw8MDU6dOVTQglXFNkengHTQi0uK2MGWZmZnB399f9jSMRtZ61Xae1aZly5b49ttvjTpmTEwMtmzZYtRgydHREZ6enkYbr4DaGr3LajEj45oi02EmewJEVDrMnTsXLVu2RFRUFE6fPo2zZ89qP5KSkmRPr0wYN24cli5dKnsaRiNrvWo7z2qTmpqKqVOnYsCAAcjKygIA7Ny5ExcvXlRsTBnB0syZMzFjxgz8/vvvRh1XbY3eT506hU8//VTvuNIBqawAnEwDtzgSEQBuCzMGmUn/Mshar9rOs5ocPHgQ7733Hvz9/ZGQkICUlBR4eHggMjISp0+fxubNmxUZNyoqCrt27UJUVBQqVqyoyBiv8vPzQ2pqKoQQcHd317uOExMTFRlXbRV9ZbWYkXFNkengFkciAqC+bWEyksJlJf3LImu9ajvPavLPf/4Ts2fPxvjx43W2owUFBWHJkiWKjbt48WKkpqaiSpUqRguWQkNDDf6aJbFgwQK8//77cHFxwR9//IGAgABtRd85c+ZImZOSevTogVmzZmHjxo0AXhYSysjIQHh4OPr06aPYuDKuKTIdvINGRADUVfJXVlK4rKR/WWStV23nWU2sra1x/vx51K5dW+duR3p6OurVq4enT58qMm5ERESxj8+YMUORcWVSS0VfWS1m1HhNUcnxDhoRAVBXyV9ZSeFqyzmQtV61nWc1sbe3R2ZmJmrXrq1z/OzZs4r255L5y/KZM2e0FSsbNGgAPz8/o4zbpk0btGnTxihjySSrxQwDMCoO76AREQB1lfy1s7NDYmIiPD09df4Kf+PGDXh7eyv2V3i15RzIWq/azrOaTJw4ESdOnMCmTZtQt25dJCYm4u7duxg0aBAGDRqk+C+9xgyWsrKy0K9fP8THx8Pe3h7Ay63ZgYGBiI2NVbRgByv6Go+sAJxKNwZoRARAXdvCZCWFy0r6l0XWetV2ntUkNzcXo0aNQnR0NPLy8lC+fHnk5eVhwIABiI6ORrly5RQZV0aw1LdvX1y/fh2rV6+Gj48PAODnn3/G4MGD4eXlhfXr1xt8TODNjd7379+vyLgyyQhIZQbgVPpxiyMRAVDXtjBZSeGykv5lkbVetZ1nNbGwsMCKFSswffp0nD9/Hjk5OfDz80OdOnUUHXfMmDF48uQJLl68qBcshYWFKRIs7dq1C3FxcdrxAKB+/fpYunQpOnfubPDxCixatAirVq1STUXfNwWkSpFxTZHp4B00IgKgrm1hspLCicg02dnZIS4uDi1atNA5fvLkSXTu3BnZ2dkGH9PGxgaHDh1CkyZNdI6fPXsWAQEBRfYqMwRXV1ckJCQoHvSWFrJazMi4psh08A4aEQFQV8lfWUnhBdSWcyBrvWo7z6Sc/Px8ve+JAGBubq63Jc5QgoKCMHbsWKxfvx7VqlUDANy6dQvjxo1Dhw4dFBkT+L9G72qo6AvIazEj45oi08E7aEQEgCV/jUFtOQey1qu280zK69mzJ7Kzs/WCpYEDB8LBwQFbt241+Jg3b95Ejx49cPHiRbi5uWmPNWzYED/88ANq1Khh8DEB9TV6l9ViRsY1RaaDARoRqZKMpHBZSf+yyFqv2s4zKU9WsCSEQFxcHC5dugQA8PHxUfxOv5oq+gLyAlJZ1xSZBgZoRKRDDdvCZFUpU1vOgaz1qu08k3HICJZkUFNFX0BuQKqWa4reHnPQiAiAuraFyapSpracA1nrVdt5VqtGjRphx44d2rsPStNoNOjUqRM6depklPEAYN++fdi3b1+Rd/pXrVqlyJhqqugLADExMdiyZYuUgFTGNUWmgXfQiAiAuraFyapSpracA1nrVdt5VqvCPQyNwdjBUkREBGbNmoXmzZvD1dVV786OUtexmir6AkCtWrWwe/du1KtXz+hjywjAyTQwQCMiAOraFiYrKVxtOQey1qu286xWxgzQZARLrq6umD9/Pj766CODv3Zx1NboXVZAKisAJ9PALY5EBEBd28ImTpyIkJAQeHp6GjUp3M3NDYmJiarJOZC1XrWdZ7Vq27YtrKysjDLW119/jejoaKMGS7m5uWjdurXRxiugtkbvslrMyLimyHTwDhoRAVDXtjC1VSkjonfj5OSEkydPGjU3Kzw8HNbW1pg2bZrRxlQjWS1mZFxTZDoYoBERAHVtC5NZpUxtOQey1qu280zKMlawNH78eO3/5+fnIyYmBr6+vvD19dW7s6NUO5ACaqjoKxMDcCoOtzgSEQB1bQuTVaXsTTkHZY2s9artPJMyXg2Wli9fjri4OEWDpbNnz+p83qRJEwDAhQsXdI4reU2rqaJvYcYISGVcU2SaeAeNiFRHVlK4rKR/WWStV23nmZQRGBhYoucp2TtRBjVV9AWMG5Cq9Zqit8cAjYi01LItTFaVMrXlHMhar9rOM5EhqamiL6C+gJRMA7c4EhEAdW0Lk1WlbPjw4Vi3bp1qcg5krVdt51ltsrOzsXnzZqSmpmLSpElwdHREYmIiqlSpgurVq8uenslTU0VfANi1axfi4uK0wRkA1K9fH0uXLkXnzp0lzozUjHfQiAgAt4UppTQl/RuDrPWq7Tyr1blz59CxY0fY2dkhPT0dly9fhoeHB6ZOnYqMjAysXr1a9hRNnpoq+gIvi0YdOnRIm+9X4OzZswgICMDjx4/lTIxUjQEaEQFQ57YwYySFqy3nQNZ61Xae1apjx45o2rQp5s+fr9Oo+ujRoxgwYADS09NlT9HkqamiL6C+gJRMAwM0IgKgrpK/aq1SRmTq7OzskJiYCE9PT50A7caNG/D29sbTp09lT7FMEEKooqIvoL6AlEwDc9CIVEytJX/HjBmDJ0+e4OLFi3pJ4WFhYUwKJyqlLC0ti9xyduXKFf5hxYA0Gg06deqETp06yZ6K4tTUYoZMB++gEamYWreFqa1KGVFZMXz4cDx48AAbN26Eo6Mjzp07h3LlyiE0NBTt2rXDwoULZU+xTFBLRV+i0op30IhU7MCBA7KnIIXaqpQRlRULFizA+++/DxcXF/zxxx8ICAjAnTt30KpVK8yZM0f29MoENVX0LcCAlEob3kEjItVhUjiRaTt8+DDOnTuHnJwcNG3alNvRDEhtFX3fFJDy5wHJwACNiFSHSeFEREVTW0VftQWkZBoYoBGRKqmpShlRWXLq1CkcOHCgyO1oZamYkSxqqugLqC8gJdPAAI2IiIhMwty5czF16lR4e3ujSpUqOtvRyloxI2NSc6N3tQWkZBoYoBGRKjEpnMj0VKlSBfPmzcOQIUNkT6VMUVtFXzUHpGQaWMWRiFRHjVXKiMoCMzMz+Pv7y55GmaO2ir5nz57V+bxJkyYAgAsXLugc588GkoV30IhIdZgUTmSa5s+fj9u3b7PfGRGVaQzQiEh1mBROZJry8/MREhKCK1euoH79+nrb0b777jtJMyMiMhwz2RMgIjK24cOHY926dbKnQURvKSwsDAcOHEDdunXh5OQEOzs7nQ8iorKAd9CISBWYFE5k+mxsbBAbG4uQkBDZUyEiUgyLhBCRKjApnMj0OTo6cmsyEZV5vINGREREJiEqKgq7du1CVFQUKlasKHs6RESKYIBGREREJsHPzw+pqakQQsDd3V1ve3JiYqKkmRERGQ63OBIREZFJCA0NlT0FIiLF8Q4aERERERFRKcE7aERERGRSzpw5g5SUFABAgwYN4OfnJ3lGRESGwwCNiIiITEJWVhb69euH+Ph42NvbAwCys7MRGBiI2NhYODs7y50gEZEBsFE1ERERmYQxY8bgyZMnuHjxIh4+fIiHDx/iwoULePz4McLCwmRPj4jIIJiDRkRERCbBzs4OcXFxaNGihc7xkydPonPnzsjOzpYzMSIiA+IdNCIiIjIJ+fn5eqX1AcDc3Bz5+fkSZkREZHgM0IiIiMgkBAUFYezYsbh9+7b22K1btzBu3Dh06NBB4syIiAyHWxyJiIjIJNy8eRM9evTAxYsX4ebmpj3WsGFD/PDDD6hRo4bkGRIRvTsGaERERGQyhBCIi4vDpUuXAAA+Pj7o2LGj5FkRERkOAzQiIiIiIqJSgn3QiIiIyGTs27cP+/btQ1ZWll5hkFWrVkmaFRGR4TBAIyIiIpMQERGBWbNmoXnz5nB1dYVGo5E9JSIig+MWRyIiIjIJrq6umD9/Pj766CPZUyEiUgzL7BMREZFJyM3NRevWrWVPg4hIUQzQiIiIyCQMHz4c69atkz0NIiJFcYsjERERlVrjx4/X/n9+fj5iYmLg6+sLX19fmJub6zz3iy++MPb0iIgMjgEaERERlVqBgYElep5Go8H+/fsVng0RkfIYoBEREREREZUSzEEjIiIiIiIqJRigERERERERlRIM0IiIiIiIiEoJBmhERERERESlBAM0IiIiIiKiUoIBGhERSaXRaLBt27YSP9/d3R0LFy5UbD5EREQyscw+EREZxcyZM7Ft2zYkJSXpHL9z5w4cHBxgaWlZote5d+8eKlWqhIoVKwJ4GeBt3boVoaGhBp4xERGR8ZWXPQEiIlK3qlWrvtXznZ2dFZoJERGRfNziSEREJbZr1y60adMG9vb2cHJyQrdu3ZCamqp9/JdffkH//v3h6OiISpUqoXnz5jhx4gSio6MRERGB5ORkaDQaaDQaREdHA9Dd4ti6dWuEh4frjHnv3j2Ym5sjISEBgO4WR3d3dwBAr169oNFo4O7ujvT0dJiZmeH06dM6r7Nw4ULUqlUL+fn5xa7x119/xcCBA+Hs7AwrKyvUqVMHUVFR2sdv3ryJv/3tb7C3t4ejoyN69uyJ9PR07eN5eXkYP3689hxNnjwZgwcP1rnDV9Q2zSZNmmDmzJnaz7OzszF8+HA4OzvD1tYWQUFBSE5O1j4+c+ZMNGnSBGvWrIG7uzvs7OzQr18/PHnyRPuc/Px8zJ8/H15eXrC0tETNmjUxZ86cEq+FiIiMjwEaERGV2G+//Ybx48fj9OnT2LdvH8zMzNCrVy/k5+cjJycHAQEBuHXrFn744QckJydj8uTJyM/PR9++fTFhwgQ0aNAAmZmZyMzMRN++ffVef+DAgYiNjUXh3fcbNmxAtWrV0LZtW73nnzp1CgAQFRWFzMxMnDp1Cu7u7ujYsaNOUFXwnCFDhsDMrPgffdOmTcPPP/+MnTt3IiUlBf/9739RuXJlAMDz58/RpUsX2NjY4NChQzhy5Aisra3RtWtX5ObmAgAWLFiA6OhorFq1CocPH8bDhw+xdevWtzvRAD744ANkZWVh586dOHPmDJo2bYoOHTrg4cOH2uekpqZi27Zt2L59O7Zv346DBw8iMjJS+/iUKVMQGRmpXdO6detQpUqVEq+FiIgkEERERH/SvXv3BABx/vx5sWzZMmFjYyMePHhQ5HNnzJghGjdurHccgNi6dasQQoisrCxRvnx5kZCQoH28VatWIjw8XPt5rVq1xJdfflnkvy+wYcMG4eDgIJ4+fSqEEOLMmTNCo9GItLS0N66pe/fuYujQoUU+tmbNGuHt7S3y8/O1x549eyasrKzE7t27hRBCuLq6ivnz52sff/78uahRo4bo2bPna9cghBCNGzcWM2bMEEIIcejQIWFra6udfwFPT0+xbNkyIcTL81mxYkXx+PFj7eOTJk0SLVu2FEII8fjxY2FpaSlWrFjxp9dCRETGxztoRERUYlevXkX//v3h4eEBW1tb7RbDjIwMJCUlwc/PD46Ojn/69Z2dndG5c2d8++23AIC0tDQcO3YMAwcOfKvXCQ0NRbly5bR3rqKjoxEYGKidb3H+/ve/IzY2Fk2aNMHkyZNx9OhR7WPJycm4du0abGxsYG1tDWtrazg6OuLp06dITU3Fo0ePkJmZiZYtW2r/Tfny5dG8efO3mn9ycjJycnLg5OSkHcfa2hppaWk6W0rd3d1hY2Oj/dzV1RVZWVkAgJSUFDx79gwdOnR47RjFrYWIiORgkRAiIiqx7t27o1atWlixYgWqVauG/Px8NGzYELm5ubCysjLIGAMHDkRYWBj+85//YN26dWjUqBEaNWr0Vq9hYWGBQYMGISoqCr1798a6deuwaNGiEv3b9957Dzdu3MCOHTuwd+9edOjQAaNGjcL//u//IicnB82aNdMGkIW9TfESMzMznW2cwMsthwVycnLg6uqK+Ph4vX9rb2+v/X9zc3OdxzQajTbH7k1fD0OthYiIDIt30IiIqEQePHiAy5cvY+rUqejQoQN8fHzw66+/ah/39fVFUlKSTo5UYRYWFsjLy3vjOD179sTTp0+xa9curFu37o13z8zNzYt83eHDhyMuLg5fffUVXrx4gd69e79x7ALOzs4YPHgw1q5di4ULF2L58uUAgKZNm+Lq1atwcXGBl5eXzoednR3s7Ozg6uqKEydOaF/rxYsXOHPmjN7rZ2Zmaj9//Pgx0tLStJ83bdoUd+7cQfny5fXGKciHe5M6derAysoK+/btK/LxN62FiIjkYIBGREQl4uDgACcnJyxfvhzXrl3D/v37MX78eO3j/fv3R9WqVREaGoojR47g+vXr2LJlC44dOwbg5Xa8tLQ0JCUl4f79+3j27FmR41SqVAmhoaGYNm0aUlJS0L9//2Ln5e7ujn379uHOnTs6AaOPjw/++te/Ijw8HP379y/xHb7p06fj+++/x7Vr13Dx4kVs374dPj4+AF7e3atcuTJ69uyJQ4cOIS0tDfHx8QgLC8Mvv/wCABg7diwiIyOxbds2XLp0CSNHjkR2drbOGEFBQVizZg0OHTqE8+fPY/DgwShXrpz28Y4dO6JVq1YIDQ3Fnj17kJ6ejqNHj+Jf//qXXnXK16lQoQLCw8MxefJkrF69GqmpqTh+/DhWrlxZ4rUQEZHxMUAjIqISMTMzQ2xsLM6cOYOGDRti3Lhx+J//+R/t4xYWFtizZw9cXFwQHByMRo0aITIyUht49OnTB127dkVgYCCcnZ2xfv361441cOBAJCcno23btqhZs2ax81qwYAH27t0LNzc3+Pn56Tz28ccfIzc3F8OGDSvxOi0sLDBlyhT4+vqiXbt2KFeuHGJjYwEAFStWREJCAmrWrInevXvDx8cHH3/8MZ4+fQpbW1sAwIQJE/DRRx9h8ODBaNWqFWxsbNCrVy+dMaZMmYKAgAB069YNISEhCA0Nhaenp/ZxjUaDHTt2oF27dhg6dCjq1q2Lfv364caNG9oqjCUxbdo0TJgwAdOnT4ePjw/69u2rzVEryVqIiMj4NOLVTfBERERlxL///W9s2rQJ586dkzqPIUOGIDs7W9vvjYiI6HV4B42IiMqcnJwcXLhwAUuWLMGYMWNkT4eIiKjEGKAREVGZM3r0aDRr1gzt27fX29742Wef6ZSuL/zx2WefSZoxERHRS9ziSEREqpKVlYXHjx8X+ZitrS1cXFyMPCMiIqL/wwCNiIiIiIiolOAWRyIiIiIiolKCARoREREREVEpwQCNiIiIiIiolGCARkREREREVEowQCMiIiIiIiolGKARERERERGVEgzQiIiIiIiISgkGaERERERERKXE/wdbgY6G8LDtiAAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -1517,49 +1195,13 @@
     }
    ],
    "source": [
-    "# join the two dataframes by 'activity_sequence'\n",
-    "sequence_nts_acbm = sequence_nts.merge(sequence_acbm, on='activity_sequence', how='inner').sort_values(by='count_nts', ascending=False)\n",
-    "sequence_nts_acbm\n",
-    "\n",
-    "# Get % contribution of each unique activity sequence\n",
-    "sequence_nts_acbm['count_nts'] = sequence_nts_acbm['count_nts'] / sequence_nts_acbm['count_nts'].sum() * 100\n",
-    "sequence_nts_acbm['count_acbm'] = sequence_nts_acbm['count_acbm'] / sequence_nts_acbm['count_acbm'].sum() * 100\n",
-    "\n",
-    "# Filter rows where both count columns are bigger than x %\n",
-    "x = 0.35\n",
-    "\n",
-    "sequence_nts_acbm_filtered = sequence_nts_acbm[\n",
-    "    (sequence_nts_acbm['count_nts'] > x) & (sequence_nts_acbm['count_acbm'] > x)\n",
-    "]\n",
-    "\n",
-    "# Plot the results as a bar chart\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(10, 6))\n",
-    "\n",
-    "sequence_nts_acbm_filtered.plot(\n",
-    "    x='activity_sequence', \n",
-    "    y=['count_nts', 'count_acbm'], \n",
-    "    kind='bar', \n",
-    "    ax=ax\n",
-    ")\n",
-    "\n",
-    "plt.ylabel('Percentage of total trips')\n",
-    "\n",
-    "plt.title('Comparison of Activity Sequences between NTS and ACBM')\n",
-    "\n",
-    "\n",
-    "# Generate custom legend\n",
-    "legend_labels = [f'{abbr} = {full}' for abbr, full in activity_mapping.items()]\n",
-    "custom_legend = ' | '.join(legend_labels)\n",
-    "\n",
-    "# Add the custom legend to the plot\n",
-    "plt.legend(['NTS', 'ACBM'], loc='upper right')\n",
-    "\n",
-    "# Add the custom legend below the chart\n",
-    "plt.figtext(0.5, -0.2, custom_legend, wrap=True, horizontalalignment='center', fontsize=12)\n",
-    "\n",
-    "plt.show()\n"
+    "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",
+    ")"
    ]
   },
   {
@@ -1585,71 +1227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: add crs to config, and check other scripts\n",
-    "\n",
-    "def calculate_distances(df: pd.DataFrame, \n",
-    "                        start_wkt_col: str, \n",
-    "                        end_wkt_col: str, \n",
-    "                        crs_epsg: int = 4326, \n",
-    "                        projected_epsg: int = 3857) -> pd.DataFrame:\n",
-    "    \"\"\"\n",
-    "    Calculate distances between start and end geometries in a DataFrame.\n",
-    "\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "\n",
-    "    df: pd.DataFrame\n",
-    "        DataFrame containing WKT geometry columns.\n",
-    "    start_wkt_col: str \n",
-    "        Column name for start location WKT geometries.\n",
-    "    end_wkt_col: str\n",
-    "        Column name for end location WKT geometries.\n",
-    "    crs_epsg: int\n",
-    "        EPSG code for the original CRS (default is 4326 for WGS84).\n",
-    "    projected_epsg: int\n",
-    "        EPSG code for the projected CRS (default is 3857).\n",
-    "\n",
-    "    Returns\n",
-    "    -------\n",
-    "    pd.DataFrame\n",
-    "        DataFrame with an additional 'distance' column containing distances in meters.\n",
-    "    \"\"\"\n",
-    "        # Convert WKT strings to shapely geometries\n",
-    "    df['start_geometry'] = df[start_wkt_col].apply(wkt.loads)\n",
-    "    df['end_geometry'] = df[end_wkt_col].apply(wkt.loads)\n",
-    "\n",
-    "    # Create GeoDataFrame\n",
-    "    gdf = gpd.GeoDataFrame(df, geometry='start_geometry')\n",
-    "\n",
-    "    # Set the original CRS\n",
-    "    gdf.set_crs(epsg=crs_epsg, inplace=True)\n",
-    "\n",
-    "    # Create a separate GeoDataFrame for the end geometries\n",
-    "    end_gdf = gdf.set_geometry('end_geometry')\n",
-    "\n",
-    "    # Set the original CRS for the end_gdf\n",
-    "    end_gdf.set_crs(epsg=crs_epsg, inplace=True)\n",
-    "\n",
-    "    # Transform both GeoDataFrames to a projected CRS\n",
-    "    gdf = gdf.to_crs(epsg=projected_epsg)\n",
-    "    end_gdf = end_gdf.to_crs(epsg=projected_epsg)\n",
-    "\n",
-    "    # Calculate the distance between start and end geometries (in km)\n",
-    "    gdf['distance'] = round(gdf.geometry.distance(end_gdf.geometry) / 1000 , 1)\n",
-    "\n",
-    "    return gdf\n",
-    "\n",
-    "# Apply the function to legs_acbm_geo\n",
-    "legs_acbm_geo = calculate_distances(legs_acbm_geo, 'start_location_geometry_wkt', 'end_location_geometry_wkt')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [
     {
@@ -1744,7 +1322,7 @@
        "      <td>312</td>\n",
        "      <td>139</td>\n",
        "      <td>E00059045</td>\n",
-       "      <td>E00057153</td>\n",
+       "      <td>E00058294</td>\n",
        "      <td>escort</td>\n",
        "      <td>home</td>\n",
        "      <td>escort</td>\n",
@@ -1754,18 +1332,18 @@
        "      <td>1900-01-01 06:54:00</td>\n",
        "      <td>0:19:00</td>\n",
        "      <td>1750466246</td>\n",
-       "      <td>1467798536</td>\n",
+       "      <td>1325685728</td>\n",
        "      <td>POINT (-1.4001307419061457 53.927941746853776)</td>\n",
-       "      <td>POINT (-1.4563558108534633 53.80668344951966)</td>\n",
+       "      <td>POINT (-1.5100280118435312 53.89948871840075)</td>\n",
        "      <td>POINT (-155861.841 7156521.118)</td>\n",
-       "      <td>POINT (-1.4563558108534633 53.80668344951966)</td>\n",
-       "      <td>23.7</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>E00057153</td>\n",
+       "      <td>E00058294</td>\n",
        "      <td>E00169797</td>\n",
        "      <td>work</td>\n",
        "      <td>escort</td>\n",
@@ -1775,13 +1353,13 @@
        "      <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>1467798536</td>\n",
+       "      <td>1325685728</td>\n",
        "      <td>443167284</td>\n",
-       "      <td>POINT (-1.4563558108534633 53.80668344951966)</td>\n",
+       "      <td>POINT (-1.5100280118435312 53.89948871840075)</td>\n",
        "      <td>POINT (-1.5484560286132372 53.797016858417656)</td>\n",
-       "      <td>POINT (-162120.787 7133629.133)</td>\n",
+       "      <td>POINT (-168095.549 7151143.595)</td>\n",
        "      <td>POINT (-1.5484560286132372 53.797016858417656)</td>\n",
-       "      <td>10.4</td>\n",
+       "      <td>19.8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -1806,250 +1384,219 @@
        "      <td>29.7</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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15098</th>\n",
-       "      <td>794027</td>\n",
-       "      <td>334502</td>\n",
-       "      <td>E00057268</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",
-       "      <td>411620812</td>\n",
-       "      <td>1238852478</td>\n",
-       "      <td>POINT (-1.58002323810948 53.774918897587455)</td>\n",
-       "      <td>POINT (-1.5332742693848147 53.78164817605564)</td>\n",
-       "      <td>POINT (-175887.382 7127643.348)</td>\n",
-       "      <td>POINT (-1.5332742693848147 53.78164817605564)</td>\n",
-       "      <td>5.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15099</th>\n",
-       "      <td>794272</td>\n",
-       "      <td>334647</td>\n",
-       "      <td>E00170040</td>\n",
-       "      <td>E00058459</td>\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>1.0</td>\n",
-       "      <td>1900-01-01 08:50:00</td>\n",
-       "      <td>1900-01-01 09:00:00</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>156862474</td>\n",
-       "      <td>13203295</td>\n",
-       "      <td>POINT (-1.5323959656166048 53.7918310553218)</td>\n",
-       "      <td>POINT (-1.6721547587985188 53.81393394394979)</td>\n",
-       "      <td>POINT (-170585.539 7130829.750)</td>\n",
-       "      <td>POINT (-1.6721547587985188 53.81393394394979)</td>\n",
-       "      <td>16.1</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>15100</th>\n",
-       "      <td>794272</td>\n",
-       "      <td>334647</td>\n",
-       "      <td>E00058459</td>\n",
-       "      <td>E00170040</td>\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>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",
-       "      <td>13203295</td>\n",
-       "      <td>156862474</td>\n",
-       "      <td>POINT (-1.6721547587985188 53.81393394394979)</td>\n",
-       "      <td>POINT (-1.5323959656166048 53.7918310553218)</td>\n",
-       "      <td>POINT (-186143.416 7134996.069)</td>\n",
-       "      <td>POINT (-1.5323959656166048 53.7918310553218)</td>\n",
-       "      <td>16.1</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>15101</th>\n",
-       "      <td>794273</td>\n",
-       "      <td>334647</td>\n",
-       "      <td>E00170040</td>\n",
-       "      <td>E00057187</td>\n",
-       "      <td>shop</td>\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>shop</td>\n",
+       "      <td>work</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",
-       "      <td>156862474</td>\n",
-       "      <td>892243500</td>\n",
-       "      <td>POINT (-1.5323959656166048 53.7918310553218)</td>\n",
-       "      <td>POINT (-1.4702034760278457 53.80747028988714)</td>\n",
-       "      <td>POINT (-170585.539 7130829.750)</td>\n",
-       "      <td>POINT (-1.4702034760278457 53.80747028988714)</td>\n",
-       "      <td>7.5</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>15102</th>\n",
-       "      <td>794273</td>\n",
-       "      <td>334647</td>\n",
-       "      <td>E00057187</td>\n",
-       "      <td>E00170040</td>\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>2.0</td>\n",
-       "      <td>1900-01-01 10:20:00</td>\n",
-       "      <td>1900-01-01 10:30:00</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>892243500</td>\n",
-       "      <td>156862474</td>\n",
-       "      <td>POINT (-1.4702034760278457 53.80747028988714)</td>\n",
-       "      <td>POINT (-1.5323959656166048 53.7918310553218)</td>\n",
-       "      <td>POINT (-163662.302 7133777.465)</td>\n",
-       "      <td>POINT (-1.5323959656166048 53.7918310553218)</td>\n",
-       "      <td>7.5</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",
-       "<p>15103 rows × 19 columns</p>\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  E00057153  escort    home  escort  car  1.0   \n",
-       "3         312     139  E00057153  E00169797    work  escort    work  car  2.0   \n",
-       "4         312     139  E00059045  E00059045    home    work    home  car  3.0   \n",
-       "...       ...     ...        ...        ...     ...     ...     ...  ...  ...   \n",
-       "15098  794027  334502  E00057268  E00057826    home   visit    home  car  4.0   \n",
-       "15099  794272  334647  E00170040  E00058459    shop    home    shop  car  1.0   \n",
-       "15100  794272  334647  E00058459  E00170040    home    shop    home  car  2.0   \n",
-       "15101  794273  334647  E00170040  E00057187    shop    home    shop  car  1.0   \n",
-       "15102  794273  334647  E00057187  E00170040    home    shop    home  car  2.0   \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        1467798536   \n",
-       "4      1900-01-01 18:00:00  1900-01-01 18:35:00  0:35:00         443167284   \n",
-       "...                    ...                  ...      ...               ...   \n",
-       "15098  1900-01-01 22:30:00  1900-01-01 22:50:00  0:20:00         411620812   \n",
-       "15099  1900-01-01 08:50:00  1900-01-01 09:00:00  0:10:00         156862474   \n",
-       "15100  1900-01-01 10:20:00  1900-01-01 10:30:00  0:10:00          13203295   \n",
-       "15101  1900-01-01 08:50:00  1900-01-01 09:00:00  0:10:00         156862474   \n",
-       "15102  1900-01-01 10:20:00  1900-01-01 10:30:00  0:10:00         892243500   \n",
+       "   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",
-       "      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          1467798536  POINT (-1.4001307419061457 53.927941746853776)   \n",
-       "3           443167284   POINT (-1.4563558108534633 53.80668344951966)   \n",
-       "4          1750466246  POINT (-1.5484560286132372 53.797016858417656)   \n",
-       "...               ...                                             ...   \n",
-       "15098      1238852478    POINT (-1.58002323810948 53.774918897587455)   \n",
-       "15099        13203295    POINT (-1.5323959656166048 53.7918310553218)   \n",
-       "15100       156862474   POINT (-1.6721547587985188 53.81393394394979)   \n",
-       "15101       892243500    POINT (-1.5323959656166048 53.7918310553218)   \n",
-       "15102       156862474   POINT (-1.4702034760278457 53.80747028988714)   \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_geometry_wkt  \\\n",
-       "0       POINT (-1.5223977194364153 53.80563634318617)   \n",
-       "1       POINT (-1.399263822377326 53.934588245650026)   \n",
-       "2       POINT (-1.4563558108534633 53.80668344951966)   \n",
-       "3      POINT (-1.5484560286132372 53.797016858417656)   \n",
-       "4      POINT (-1.4001307419061457 53.927941746853776)   \n",
-       "...                                               ...   \n",
-       "15098   POINT (-1.5332742693848147 53.78164817605564)   \n",
-       "15099   POINT (-1.6721547587985188 53.81393394394979)   \n",
-       "15100    POINT (-1.5323959656166048 53.7918310553218)   \n",
-       "15101   POINT (-1.4702034760278457 53.80747028988714)   \n",
-       "15102    POINT (-1.5323959656166048 53.7918310553218)   \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",
-       "                        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 (-162120.787 7133629.133)   \n",
-       "4      POINT (-172373.337 7131807.059)   \n",
-       "...                                ...   \n",
-       "15098  POINT (-175887.382 7127643.348)   \n",
-       "15099  POINT (-170585.539 7130829.750)   \n",
-       "15100  POINT (-186143.416 7134996.069)   \n",
-       "15101  POINT (-170585.539 7130829.750)   \n",
-       "15102  POINT (-163662.302 7133777.465)   \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",
-       "                                         end_geometry  distance  \n",
-       "0       POINT (-1.5223977194364153 53.80563634318617)      27.9  \n",
-       "1       POINT (-1.399263822377326 53.934588245650026)      27.9  \n",
-       "2       POINT (-1.4563558108534633 53.80668344951966)      23.7  \n",
-       "3      POINT (-1.5484560286132372 53.797016858417656)      10.4  \n",
-       "4      POINT (-1.4001307419061457 53.927941746853776)      29.7  \n",
-       "...                                               ...       ...  \n",
-       "15098   POINT (-1.5332742693848147 53.78164817605564)       5.4  \n",
-       "15099   POINT (-1.6721547587985188 53.81393394394979)      16.1  \n",
-       "15100    POINT (-1.5323959656166048 53.7918310553218)      16.1  \n",
-       "15101   POINT (-1.4702034760278457 53.80747028988714)       7.5  \n",
-       "15102    POINT (-1.5323959656166048 53.7918310553218)       7.5  \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",
-       "[15103 rows x 19 columns]"
+       "                                     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": 20,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "legs_acbm_geo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the distance comparison"
+    "# 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": 21,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
+       "<Figure size 1000x500 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -2057,62 +1604,28 @@
     }
    ],
    "source": [
-    "\n",
-    "# Parameters\n",
-    "distance_column = 'distance'\n",
-    "bin_size = 2\n",
-    "distance_threshold = 50  # Set this to None if you don't want to use a threshold\n",
-    "\n",
-    "# Create distance_binned column\n",
-    "legs_acbm_geo['distance_binned'] = (legs_acbm_geo[distance_column] / bin_size).round() * bin_size\n",
-    "legs_nts['distance_binned'] = (legs_nts[distance_column] / bin_size).round() * bin_size\n",
-    "\n",
-    "# Define the bins\n",
-    "max_distance_data = max(legs_acbm_geo['distance_binned'].max(), legs_nts['distance_binned'].max())\n",
-    "if distance_threshold is not None:\n",
-    "    max_distance = min(distance_threshold, max_distance_data)\n",
-    "else:\n",
-    "    max_distance = max_distance_data\n",
-    "bins = range(0, int(max_distance) + bin_size, bin_size)\n",
-    "\n",
-    "# Create a figure\n",
-    "fig, ax = plt.subplots(figsize=(10, 6))\n",
-    "\n",
-    "# Plot histogram for legs_acbm_geo data\n",
-    "sns.histplot(legs_acbm_geo['distance_binned'], bins=bins, kde=False, discrete=True, stat='percent', ax=ax, label='ACBM', shrink=0.8)\n",
-    "\n",
-    "# Add dots to represent the percentage values of legs_nts[\"distance_binned\"]\n",
-    "nts_distance_counts = legs_nts['distance_binned'].value_counts(normalize=True).sort_index() * 100\n",
-    "if not nts_distance_counts.empty:\n",
-    "    bin_centers = nts_distance_counts.index\n",
-    "    ax.plot(bin_centers, nts_distance_counts.values, 'ro', label='NTS')  # 'ro' means red dot\n",
-    "\n",
-    "# Set x-axis limits based on max_distance\n",
-    "ax.set_xlim(0, max_distance)\n",
-    "# Add x-axis ticks and labels for each bar\n",
-    "ax.set_xticks(bins)\n",
-    "ax.set_xticklabels([str(bin) for bin in bins], rotation=45, ha='right')\n",
-    "\n",
-    "\n",
-    "# Add labels and title\n",
-    "ax.set_xlabel('Distance (km)', fontsize=14)\n",
-    "ax.set_ylabel('Percentage of Trips', fontsize=14)\n",
-    "ax.set_title('Overall Comparison of Trip Distances', fontsize=20)\n",
-    "ax.legend()\n",
-    "\n",
-    "# Adjust layout to make room for the main title and labels\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
+    "# 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": 22,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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ZkZwJs3nz5pn169ebzz77zPTt29eqU61aNY+J/S+//NKUKlXK9O3b17zxxhsmISHBbNy40Sxbtsy89NJLbhdpZ8yY4XG/pb9A2qBBA+Pv728efPBB8+WXX5r169ebefPmmW+//daq7y1p3atXL2tZgwYNMvHx8ea7774z69atM0uWLDFPPPGEadiwocek9aZNm0xAQICRZPz9/c2YMWPMqlWrzA8//GDefPNNEx0dbS173LhxHrfFFVuTJk1MUFCQiY6ONq+99pr57rvvzNq1a82TTz5pgoKCjCQTHBxsDh8+7HE52bl06ZJp1aqVFU/btm3Nhx9+aNavX2+WLFlievbsaU279tpr3ZIgJ06cMFu2bHE7B4YPH26VbdmyJduL0RnlJmltjHG7wSD9RW1jvCetf/zxRyuRGR0dbV566SWzcuVKs2nTJvP111+bt99+2wwYMMCUKlXKbdu3bNlili9fbi130qRJbtu9ZcsWc+jQIav+zp07TUBAgOnevbt59dVXzYoVK8zGjRvNihUrzPTp0831119vLeuJJ57wuI3pt+PGG280Pj4+ZsiQIdZ5vWjRIrdjmdUNHgcPHrTOa0mmffv2Zvbs2eb77783P/zwg/nggw/MfffdZ0JDQzMlGtLS0ky3bt2sebt3727ef/99s2bNGrN27VozdepUU61aNevzad26dTk+hllt6+Ukvx544AFrvow3c1y8eNH4+PiY9u3bmxdeeMEsW7bMbNiwwSQkJJgZM2a47bs777wz07LtOObeXO75l5efS3Xr1jW+vr6mevXq1ufSN998YyZPnnxZ25DbpPXzzz9vzfef//wn03RPyzxz5oy1X1zv/x49erjtr927d5tDhw5le6wzHueQkBDrs3j8+PFm0aJFZv369Wb58uVmxIgRVlK4RYsW5sKFC1e0X7/55hvrO0ClSpXMpEmTzNKlS82GDRvM0qVLzaBBg6y44+LiPO4/1/SWLVsaHx8f07BhQzNjxgyzbt068/XXX5sxY8ZYn4vVqlUzKSkp1rwXLlwwW7ZsMTNmzHBrkzOeCydOnMjx8UzvwoULVkL+rrvussrT0tJMZGSkkWR69erldRnvvfeeFVtQUJB58MEHzWeffWY2btxovv76a/Paa6+Zjh07mujoaGue/fv3my1btpgbbrjBSDI33HBDpm3avn2723qy+g5x4sQJExgYaCSZe+65x2ust99+u5FkypUr57afjck6ab19+3av5336cz81NdVEREQYSaZjx45eY3G19xUqVMgUS254S1pv377dmvbvf/87y2VcuHDBVKxY0Wpv0sur759X2q6dP3/e+kwpU6aMGTdunPn888/Nhg0bzNq1a83cuXPNyJEjTdWqVUlaAwAAFACS1gAAoFibM2eOdaHrn//85xUv76effrIuFterV8/jhd/PP//cqtO8efNM0zP2KJsyZUqmOsnJyaZMmTJGkqlYsaJxOBxm0aJFmeqlT+q5emWnl7HXVdeuXT32Gnz66aetOo888kim6UeOHPF6kTslJcXccsstRnL2drp06VKmOukvkPr4+Jjly5dnuTxjsr7gfO7cOSspkFVPapdjx45lKnNdPPX19fUYw/Hjx60kq4+Pj8ebAdIn6Zs2bWpOnTqVqU76997LL7/sNc6svPbaa26JurS0tEx1JkyYkG0yKzeJJ09ym7ROn6j5+uuv3aZ5S1r/61//MpKzB1VWvSCNMebkyZOZep9m7OXlzZkzZ0xSUlKW09PS0szQoUOtWE6ePJmpTsaete+//36mOufPnzf16tUzkkz58uU9nouuJIkk89xzz2UZ059//mmOHz/uVvbWW29ZSc/PP//c43zHjx+3ErKtW7fOcvne5DZpnb6H49NPP+02LS0tzezcudPr/E888YSRZBwOh9mxY0em6QV9zHMqJ+dfXn8u1a9fP9eJSZfcJq1XrFhhzZc+semS3TK93bDkktNjfeONNxpJpnHjxubIkSMe66Rvs996660s48luv164cMEaQaRz587m7NmzHuu5zlNJHkdkydhme0pQTpo0yaoTHx+faXr6Y+dpRIbcWrRokbXcr776ym3ao48+aiSZwMDATJ9NLklJSdbIJGFhYWbLli1Zrmvv3r2ZyrJKFHvi7X0UFxeXZTLa5eTJk1Zy+957773sWHJ67rjaOR8fH7Nv3z6PdQ4dOmR97xk1apTX5eWUt6S1McbExMQYSaZmzZpZLiM+Pt5aRsbvqHn1/fNK27WVK1day/eWlL548aLH73IAAADIWzzTGgAAFGvHjh2z/h8WFnbFy3v99detZ6q+8847Klu2bKY6nTt31l133SXJ+ezHdevWZbm8Fi1aaNSoUZnKK1eurNtvv12S87mPffv2Vc+ePTPVa9Cggdq0aSNJSkxM9Bp7YGCg3n77bfn5+WWa9s9//lP16tWTJL377ruZnpFcoUIFj9vqEhAQoBdeeEGS9Mcff2jz5s1eYxk6dKg6duzotU5Wjh8/bj0z86abbvJaNzQ01O11+uNxzz33eIyhXLlyeuuttyQ5nys8ffp0r+uYMWOGgoODM5UPGDBAVapUkZT9scnKtGnTJEkVK1bUa6+95vHZqU899ZT1nPa3335bKSkpuVpXfipfvrz1/xMnTuR4voMHD0qSatasqUqVKmVZLyQkRD4+uf/pU6pUKYWHh2c53eFw6KWXXpKvr6/Onj2rFStWeF1eXFycBg0alKk8MDBQI0eOlOT8bNq2bZvb9O3bt2vx4sWSpJ49e2rcuHFZrqN06dIqV66c9doYo+eee06S9I9//CPLZ5GWK1fOOlfXrFmjnTt3et2WvOTtfeBwOHTdddd5nf+JJ55QhQoVZIzRkiVLriiWvD7mVyI/PpemTZvm9TM7P+X2fM9riYmJ+vbbbyU5n49doUIFj/U6d+5sPZM5u2c9e9uv8+fP1++//66goCC99957KlmypMd699xzj5o3b57t+oKCgjRz5kwFBARkmvaPf/zDKs9t+5Ib7733niQpIiIi07OcBw8eLElKSUnRBx984HH+//73v/rrr78kSW+99Zb1vcOTyMjIPIjYs4EDB0pyvj8///xzj3UWLlxotaeu+vnhrrvuksPhUFpammbPnu2xzpw5c6zvPa7vl/nt//2//ydJ2rFjh9asWeOxzsyZMyU5v1/feuutWS4rt98/86Jdc32PkLx/Z/Tz8/P4XQ4AAAB5i6Q1AAAo1v7880/r/6VKlbri5bkSF9dff71atGiRZb177rkn0zye9OvXL8tpDRs2vKx6u3fvzrKOJHXs2NFKombk4+OjIUOGSHImhTdu3Oh1WSkpKdq7d6+2bdumrVu3auvWrTLGWNN//PFHr/NfyQXg8uXLWxfr33//fV26dCnH86Y/FnfffXeW9Vq3bq06depkmiej+vXrq0GDBh6nORwONW7cWFL2x8aTpKQk/fLLL5Kkvn37qkyZMh7r+fn5adiwYZKcF+CzO3Z2KF26tPX/9OdkdlxJxW3btumHH37I87iycvHiRe3fv1+//PKL9f5OSkqyknFX8v5u2rSp9f+M74tPP/3UOo/GjBlzWTFv27ZNu3btkiQrAZeV9Bfu165de1nruRKX8z5IS0tTUlKStm/fbh2DX375RREREZKyPwaX60qP+ZXI68+lyMhIxcTE5F2Alym353tec93YUKtWLdWvX99rXdc5sW7duizblOz2q2t9bdu2VcWKFXO0Pm/n3y233JLlzXZlypRRjRo1JOWufcmN48eP69NPP5XkvCkr401U119/vRo1aiTJ2TZ78sknn0iSrrnmGt122235F2w2unXrppCQEEnS3LlzPdZxlVerVi1fz6fq1aurQ4cOkrK+icGVHG7atGmW3znyWp8+fax95Fp/eocOHbIS/oMHD/aYkHbJ7ffPvGjX0t+c5Gk7AAAAULBIWgMAgGItfaLv7NmzV7SslJQUq/eGt4S1JDVu3Fj+/v6SpK1bt2ZZr2bNmllOS9+bKyf1sksONGvWzOt0V88vSdqyZUum6WfPntXkyZPVsGFDlSpVSlFRUbr++utVv3591a9f30rQStLRo0e9rutKLroGBgbqjjvukOTsCXXddddp3Lhx+uyzz3Ty5Emv87qORUBAgHVxPSuuY7xz585MPc9dXD2cs+Lq6Z2bxE36901277f007293+ySfvsvpydT//795e/vr5SUFLVu3Vrdu3fXG2+8kekmibxw8eJFTZs2TS1btlTp0qUVGRmpunXrWu/v+vXr6/Dhw5Kyf397e1+k7/2f8X2xadMmSZK/v79atmx5WfGvX7/e+n+rVq3kcDiy/EufVEzfCy2/Zfc+MMZozpw5ateunUqXLq2qVauqdu3absfANYpDdscgJ/LymF+JvP5cKqikVlZye77nNdc5sX37dq/ng8PhsEZAuHjxoo4fP+5xedntV9f6li9fnu36XnzxRUnez7/8bF9yY968edZ7ztNIEunLv/32WyvZ6HLx4kXrvd6mTRuPI4cUlMDAQCsJunTp0kz7MCkpSQkJCZKc7VB+x+rq1fzbb79l6jm/fv16a78VVC9rSSpRooQGDBggSVqwYIHVQ94l/U2D2cWV2++fedGutWnTRtdcc40kafTo0WrevLkmT56sNWvWZPkZCgAAgPxD0hoAABRr6YcpPXTo0BUtK/0wp9kNNe7v72+tO6sL4JKyHD5Ukttwxzmp5xq2PCvZxZx++OWMMf/++++qX7++JkyYoJ9++kmpqalel3Xu3Dmv09MPa5wbr732mrp37y7JORz5Cy+8oG7duql8+fJq1qyZXnjhBZ06dSrTfK7tCg0N9dorSHIO0S45E2lZDXHr7bhIfx+b7PaXJ+mPQXbHzhVrxvkKi/QJv4xDtntTu3ZtzZs3T+XKldOlS5f0ySefaPjw4apfv77CwsI0ePDgPBka9/jx42rVqpVGjhyp77//PtsL2dm9v3N6Xmd8X7j2U2hoqMchgb1xJVcvV8ZERH7y9j44f/68unXrpsGDByshISHbfZzd9Ozk9TG/0likvPtcutLP1yuV2/M9r+X1OZHdfs3N+ry9r/KzfckN19DgDRo0yLLn+oABA+Tr6+tW3+X48ePWzUbehuYvKK4RMc6dO6f4+Hi3afPnz7e+U+Xn0OAuPXv2tIavz9gbeMaMGZKcw8W7ksgFxZVM//PPP7Vw4UK3aa44W7Roobp163pdTm6/f+bFOezv76+lS5dao1SsW7dOEyZMUJs2bVS2bFl17txZc+fOLbDzCAAAoLjz/osXAADgKpd+iO28HDbZzh5CuXUlMQ8ePFh79uyRw+HQsGHD1K9fP9WpU0cVK1ZUQECA9TxG18Xq7HrBuurlVnBwsJYsWaIffvhBCxYsUEJCgjZv3qzU1FStX79e69ev14svvqjFixerVatWmeYvasevqMWbkasHseR91ABPevXqpQ4dOuiDDz7Q8uXLlZiYqCNHjujo0aOaM2eO5syZoyFDhmjGjBm5fq71qFGjtGHDBknO5MFdd92lBg0aKCwsTEFBQdb+r1atmvbt25fnvbzzQvoL7kuXLlX16tVzNF92yYS8lP59UKtWLbdpzz77rDXUbNu2bTVixAg1adJElStXVokSJaxje9NNNykxMfGKj0FhPOZ5dZ5f6efrlfJ2nAuS65xo2LCh5syZk+P5qlat6rE8u/3qWl+XLl30/PPP53h9RcH27dutRzT89NNPOXqvzpkzR08++WShbb/atm2rqlWr6sCBA5o7d641RLX099DgrhEX8ltAQIAGDx6sV155RR9++KH++9//qlSpUjp//rzmzZsnSbr99tsL/Dn1TZo0UePGjbVp0ybNnDlTd955pyTp+++/17Zt2yTlrPd3bt8DedWu1a1bV1u2bNHSpUu1dOlSff311/rtt9907tw5LV++XMuXL9fLL7+szz77rEDbRAAAgOKIpDUAACjWrr/+elWoUEFHjx5VYmKiTp8+nevhStP3ssqu1/alS5d07NgxSfb2NEsvu5jTT08f86+//qpvvvlGkjRhwgRNmjTJ4/x29PBt3ry5Nazkn3/+qYSEBM2aNUvx8fE6fPiwevXqpV27dqlEiRKS/t6uY8eO6dKlS157NbqGl3Q4HLb0XEx/DLI7dumHwiws7zeXffv2afv27ZKcCazsnvXqSUhIiO69917de++9kqRffvlFH3/8sf773/8qKSlJs2fPVuPGjTVq1KjLXvbp06f1wQcfSHL2qPOW3MqqZ2tecfW0O378uC5cuHBZva3TjypRtmxZ1atXL8/juxLGGLfnMLdp08Zt2jvvvCNJiomJ0VdffZXlDQh58TlTmI65VLQ+l3Liyy+/tP6f/jgXNNc5cebMmQI5H8qXL6+kpCRduHCh0J1/Vypjr+mc2L17t7755hvredChoaHy8fFRWlqakpOT8zrEy+bj46P+/fvrxRdf1MqVK3Xo0CFVqlRJO3bssG5oKYhe1i7/7//9P73yyis6c+aMPvzwQw0dOlSLFy+2HntSkEODZ4xrxIgRWr16tfbs2aPo6Girl3XJkiXVr1+/bJeR2++fedmu+fr6qmfPnurZs6ckKTk5WcuWLdO0adO0YcMGbdiwQffdd58WLVqU63UAAAAgewwPDgAAijWHw2H1njl79qyVGMmNwMBA1ahRQ5Kzl4k3mzZt0sWLFyWp0Fy8XrduXY6np4/5559/tv7vepa0J+mfPWiHMmXKqHv37vroo4/0j3/8Q5LzoqQr4S79vV0XLlywno2bFVevsho1alz2UM15If0xyO795oo143yFwauvvmoNs3r77bfnyTLr1Kmjxx57TN99951KlSolyfnMzfRy2rNr586d1rnq7f3966+/6syZM7mMOGeaNGkiyfns17Vr117WvOmfKb9mzZo8jSsvfPbZZ9q5c6ckqWXLlpmGtHclY/v06ZNlwvrMmTPWDRCeFMVjLhWtz6XsbN26VStXrpQkRUZG6oYbbrAtFtc5sXv37gJ5drtrfevXr7f9Wbl52bvZ9ax5yTk0+Lx587z+zZ07V0FBQZLck93+/v7Wez23oyXkda9tV1I6NTXVupHlf//7n7Wu/v375+n6vKlbt641MowrKewaGjwqKko333xzgcWS3sCBA1WiRAkZYzRr1iydO3dO8+fPl+QcCSUnN4Lm9vtnfrZr4eHhGjZsmNauXWu1vZ988km+PgoCAAAAJK0BAAA0ZswY69mQTzzxhH799dcczZeWlmZdvHTp0KGDJGciN32iMKP0yXHXPHb74osvsuzdlJaWptmzZ0ty9ih3XcCTnL3GXc6ePZvl8t944408ivTKpb+4m/75qumPhetisCdr1661hr606/hVqVLFegbjggULskyepaamatasWZIyHzu7ffPNN5oyZYok5/M4R48enafLj4yMtIYbT3+cXetzSUlJyXIZhen93a1bNysp49pvOdWkSRNFRERIkt566y2dP38+r8PLtaNHj+r++++3Xo8fP95tek6PwTvvvONWN6OieMylovW55M25c+d05513WsnIhx9+ONtndOen2267TZIz6Tp16tQCW9+pU6cyPZe4oOX0XMiJhIQE7d27V5J05513ql+/fl7/+vfvr06dOkmSPvzwQ7fPou7du0uS9uzZo48//viyY3Ft15Vuk0ujRo2s5zG7vu+5huOOiYlRtWrVcr3s3MTqeoZ0YmKiVq1aZd0AMnToUNuGWQ8JCVHv3r0lSbNnz9bChQt16tQpSTnv/Z3b758F0a75+/urbdu2kpxtg6tnOwAAAPIHSWsAAFDsVa1aVa+99pokZ4Kibdu2Wr16tdd5tm3bps6dO+uFF15wKx8+fLjVC/Dee+/V6dOnM837xRdf6N1335XkHL66WbNmebEZVywlJUX33Xef2zMCXf7zn/9oy5YtkpwXIQMDA61prt7lkqzkaEavv/56ri5A58bu3buzPX5ffPGF9f/o6Gjr/82bN7d6/r399tvWBeH0Tp06pfvuu0+Sc/jQ4cOH50XYuTJixAhJ0pEjR6ze4xk99dRTViLrnnvucTt2dklLS9OsWbPUqVMnK0E4depUVapU6bKWk35oVE/27dtn3YSS/jhLzmFFXT1Rd+3aleUyrrvuOisZMHv2bI+9/5YuXWp9huSnmjVrWr3RFy9enOnzJ72zZ8+6DV3t4+OjCRMmSHKeI3feeafXZMnp06cLZJu+/fZbNW/eXPv375ck9e/f30ruuVSsWNF6Vuu8efM8xr1u3Tr961//8rquonjMpaL3ueTJtm3b1KZNG+t51m3btrU9xo4dO1qPj3jhhRcyjcaQkeuZt7k1ZMgQRUZGSnIm7L/++muv9b/55pts27LcCg8Pt/7v7VzIifS9pXv16pWjeVxJzlOnTrl9Nxg5cqQ1OsZ9992nrVu3ZrkM12dGeq7t2r17d549Z97V2/qHH37QvHnzrBEhrnRocFesl7P/77jjDpUpU0bGGA0YMEBpaWlyOBwaNmzYFcVypVzJ9D/++EPjxo2TJF177bVWsjc7uf3+mRftWmJion777bcs57lw4YJ1HpYuXTpXjzABAABAzvFMawAAAEnDhg3T/v379cQTT+jw4cOKjY1Vx44d1aNHD9WpU0dly5bV8ePHtWPHDn366adatmyZUlNT1bBhQ7fl1K9fXw899JBeeOEF/fjjj2rSpIkeffRRNW7cWGfPntXSpUv16quvKjU1VQEBAXrzzTdt2uLMbrjhBi1dulStW7fWmDFjVKNGDR0+fFizZ8+2hnqMiIjIlBhq3Lix6tWrp61bt+rNN9/UiRMnNHjwYIWHh2v//v2aM2eOFi5cqNatWxfIsMR79+5Vu3btVLduXd1+++264YYbVLVqVUnOJOYHH3xgJScaNWqkFi1auM3/9ttvq0WLFrpw4YK6du2qBx98UN27d1epUqW0adMm/ec//9Hu3bslORMPdg63ff/99+t///uf1q5dq5kzZ+qPP/7QAw88oOjoaCUnJ2vGjBmKj4+X5LyAnF1SLy/t2LHD6v2dlpamU6dO6eDBg/r++++1aNEiax/6+Pho4sSJ1vOoL8eUKVM0cOBAdevWTe3bt1edOnUUEhKiEydOaP369frvf/9rDeWZvievJPn5+alZs2Zas2aNZsyYocaNG6tRo0by9/eX5HxuZmhoqMqXL6+uXbta533Hjh01fPhwRUVF6fDhw/roo480a9YsXXPNNTp58qSOHDlyJbstW9OnT9d3332npKQkjRs3TsuWLdPQoUNVu3ZtORwO/f7771q1apXmz5+vjz76SLGxsda8999/v7788kstWrRIH374oTZu3Kj77rtPzZs3V0hIiE6fPq1ff/1VCQkJWrJkiYKCgjRy5MgrivfAgQNuiac///xTx44d06ZNm7Rs2TJ9++231rRbb73VY09iHx8fDRw4UNOmTdNPP/2kNm3aaOzYsapRo4ZOnTqlzz77TNOnT1fp0qVVpUoV7dixw2MsRfWYS4X/c+nw4cNux9l108RPP/2klStX6ssvv7SSiC1bttTChQut/W6nuXPnqnnz5jp+/LjuuOMOzZkzR3fccYdq1KghX19fHT58WJs2bdLSpUv13Xff6aGHHrJ6A1+uwMBALViwQLGxsTpz5ozat2+vfv36qWfPnoqOjrae5bxhwwYtWrRIW7Zs0X//+98cJ/4uR7Vq1RQREaH9+/frxRdfVEREhGrVqiVfX19JUqVKlVSmTJlsl/PXX3/po48+kiQ1bdpU1atXz9H6u3fvroCAAF24cEHvvfeeNQx/5cqV9frrr+vOO+/U4cOH1bx5c91zzz3q0qWLKleurDNnzmjr1q1asmSJtm/fninhe+ONN2rmzJk6fPiwxo4dq0GDBikkJESSs8dsVFRUTneRZcCAAXr88cdljNEDDzwgSQoICFCfPn0ue1kZY92zZ4+WLFmiN998U61bt7Z6XwcHByssLCzTPKVKlVK/fv309ttvW0Pat2/fPlf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qQNWsK6RLYYUST2qDpVZv+YM/X90HAEUpbpWn1d66/3N2toz/6QcRcV7vPk7s4J18/mDGYHNxgwEnWyWalG3xGvz4o/x963cFtlfNzJC/b/1OZoyZKh+WwXt+AwDcY/YqY55+l2nmLa2sNEhsxduPAWWJS4PWzz77rBw9elS++uorycnJEcMwZOvWrbJ161anz/OXQetdV7aSigxYoxyyzNLVFr1fipVOiMxixpf4L3TrIeNXLBcRx5PgnIMHxRgwQGZ27S4P/O8r50Gys3MHvfMPWIvkbjMMkfHjRfr1K7+DtFyFDpjCUgOqZl0hbfIKJZ7WO0ut3iLWut83AHib0yujvDWAmZrqcOVvfrZ61zL9pHfyEXMGm10ZDBi3c7t3bjlisYkBg24bJYdPpDvdh6u+AaB0WWqVMRNvaVVc/86bg8RWvP0YUNa4NGgdFxcnS5culaysLElLS5O6devK+PHjZdy4caWdHwAfs8rS1WbfL8VTVpw15+kV0qZ8iZ+dLbeuW5ObT76HbCdnw9atKf7kLDXVcTA2P1WRAwe8tlyv5XAVOmAaSw2omnWFtIkrlJhR7yy1eovF7vcNAL7g81WeXKx3MU4Gts1k1mBzy/ST1hkMsNjEgMMn0qVWyiSn+xxcMt0ruQBAeWWpVcbMmrDtQv/Om4PElpoYAJRRLg1a21SsWFESEhLk2muvlVatWkmdOnVKKy8AFuLzLzX+YNb9UjxlxVlzHl8hbdaX+C6cdLpycvbiE0/JfcVn7fX7n5rGk2W9uQodMJWlBlTNukLapBVKzKx3llm9xaQvR4aNulsOHy/mirFqUbJgzmvuZOl73H4CQGlysd6dDHZxNRQPmTXY7PIguzf6MCZNDOAKaQDwH5ZaZcysCdsmfQ9pFktNDADKqBINWtusWLHC7DwAwCVWGEC32qw5U66QNmuGo6snXcXst/tStmtxvHj/U9N4uqx3aVyFbhucAMopywyomniFtBkrlJhd78xavcWjlUVM+nLk8PF0qd53gvN9PpnhWk42ZgwUmxGD2084x4A+4LmkJDkWHCJVMzOc1rttUTFeSceswWaXB9ld7MN4VO9MmhhQGldIe3JcDKIDgPsstcqYWRO2TfoeMi9P6pSlJgbk4enqnIA3uTVobXP+/Hn56KOPZOvWrXL27FmJiIiQVq1aSf/+/aVy5cpm5QgAlmKpWXNmXSFt1gxHV0+6itmvVE6krTAYYMay3mafkBc2OAGUQ5a4HYZJV0jbeLpCSWnUO08nn3m8sojJ9/v2lO2K7aS0Q3Lfjq0OE8iOhVSSF5u0kt0tmrl2xbYZg83cfsI5BvQBcwQGyszGzeXvW79zXu+8dI9tswabt0XFmNaH8bjeWWxigI2z43KlN2LZZcaZ0ASgDLDUKmNmTdg26XtIG2d1qtgLgsRiEwP+4OkxAd5W2L8dl3zwwQeSkJAgt99+u/zzn/+U119/Xf75z3/K7bffLgkJCbJkyRIz8wQAy7DUrLk/rpDOP2Bt4/IV0mZ9if/HSWdOEQ/niMjRkEq5nXgnbCfSxh/PyR/DdiLt8hcBS5aI1K0r0rWryLBhuf+tWzd3u6s8jVHcst4iuct6ZxdzlbmZJ+S2wQkGrAER+XNAdW27nrKjYWufzDy2XSGdHhXrsP1UVKzMGDPVtS+q8/DkmCxV7+SPlUVmPyLR+a7+tq0skrh5pQtBiq9TJ1y8mt3GyMmWJrs2yzXffilNdm0WI8fF1UIk94rtm2q1lqc2r5Nq+VY8qZpxUZ7avE7qf7+9+EBFfZ7bBptdqVVm1Skry84WWblSZOHC3P+W5FjMaGMAdqvjapha7zxhG2z2Rh9mVqNmxfZhuhw97Hm9+2NigLNc3h40XnKMonqS5kvcvNLpcXU5ethruZjKjL4mAHiJbZWxoupvanxN7yTyx4Rtj+uUSd9DihRfp5LSDhUbw/TvMz1UXB/alWMyy7BRd0tyn1uc/gwbdbfX8rHzpI+IUuHWldZr166VIUOGSGBgoNx5553StWtXiY+PlyNHjsiKFSvk7bffliFDhsiqVaukY8eOZucMAD5lqVlzZl0hbdYMRxeuEnR11qZpy/WacdWYGTHMWtY7KSn3qq5DhwofWDCM3MeLe/85G5wA4FOeXiFtFkvVO7NWFjH5anZPZ62bct/w4gabDSN3sLlfP+dfjpTm7Sc8vfLM18uem9XG+WNyaw6Uc1apd65ceTarUTOZakIfZnVxV49nZ8v4n34QEef17uPEDsXmYpsY4PRWISW4mt2TpUVdqXfjdm53Xu9MysVUZq9QYoXVwQD4PUusMibm3NLKrO8hTemX/cHM2495VO9c6EO7ekxmyHuLraKOq8S32PKUmStpWan+WikXN7g1aD116lQJDg6WNWvWSMuWLR0eGzx4sNxzzz1yzTXXyNSpU+XTT1lkAIB/sdRyOmZdIW3il/jFnXSW5OTM0xPpW0fcKS8sWiDVVAteja6aOxg//Hap6uxLZrO+qDZrWe/AwNyTp4EDc187b162GagzZxZ/MlLc4AQAn/J0GW0zWKre/bGySFFcXllETPpyRP6ciZ+fbdb66dbFT9415b7hLg42P31jP3ls6X+L3s8bt59w5wsAKyx7bvaAPrfmAOysUO9ETBhszheryD5McXFSUyXWyT22bfWuZfpJz3MpAU+X9W6ZftLzeudCLq5eoW+7N3aAam5umRlyMjhEtkXF2K/qK/be2GZPaDKr3lntNhZWmsCWNw5Qzlmp/npap8z4HtKUflkeZkwM8LjeudCHLskxiYgpn8WmLVduhVs45o1llcFvK+XiJrcGrdetWyeDBw8uMGBt06JFCxk0aJB8/PHHHiUHAFZl5qw5j5h1hbSY9yW+iLmzNj05ka728y/FD3JcOO+dwQAzl/VOSck9eSrsJGTmTNdOQrxxz3UAZZ5l6p1ZK4v8wdMvR8yaiW/KfcNdbBs9as6tQp5++115bOhQ5zuZ9QWAGXHMGFQwc0C/qGMC4HMeDTbn43YfxsXPmxgnA9um5fKH4iZpnWyVWGwMl/Mt5vi7HD0sE5YVncuMMVNdvjf2zXXbOx0MKPbe2GZOaLLK6mBi0oB+3pysMoGtqDgAfM6MAXRPv4c0pV+WjyfHlZR2SCZ8trjA9rz1rtgBXgtOSjZj4rcpuZg58cxKg99WyiWvEk4Yc2vQ+sKFCxIXF+d0n7i4OLlw4YI74U338ssvy3PPPSdHjhyRli1byosvvijt2rXzdVoAyjhLLKdj8jKnZi7RZ4VZm5YaDDBrWW+blJTckyd3Z7x56R60AMo+S9Q7s1YWycOTOmXWTHxT7htuVtu4OBFuRWCIPOYsTna2nBx+u0R7ssqJmLRaiog5gwpmTTzj1hyA5fm8D+Pi583JYNfrnSfMWtbb5XydHb+JS6ebMvht1mCAGV+cZ2fLieHDJVq1YA3/o2Yev/U2uf/mW+T9BW87TceUAX0Ra01gcxYHgN/wpIab0i8zi1m3xjLz4hkTPotNW4Ldi7dwLPYCJSsNflspl/yxSjhhrLDvI4pVt25d+eqrr5zu8/XXX0vdunXdCW+q999/XyZMmCBPPPGEbN68WVq2bCm9evWSY8eK/pIJAFxlOyFa266n7GjY2if30bJdIZ0eFeuw/VRUrMwYM9XlJdJsrHBMZrHUYIBtWW+RP5fxtinJst75YyYniwwdmvvfkjzXNoiePxcAKITPa8MfA6o5RTycIyInXFxZxAxmzcS33Tfc2XEdDankfEKTWW3zx0Q444/n5I9hnwhXXN1ITZWYC+cLDjT/wWGVEydsq6V4GseUQYXiaqZhiNSuXfzEM27NAaA4SUlyLDik2M/0bVExXknHtqy3s89i+yQtJ7ZFxXhe7/5YOt1pXXBl6fRiBr9VcgcDAooZ3JzylvPBX7vi+pMlmVzlJEbVCxeK/JLX9neK/XV3cdnmDujPfkSi803Osw3oJ25eWWyMYr84F8n94jw72/dxAEBM6peZ5Y9lvYurd8XeGsuFfqIrx3TriDvl2K23iRbxWZyjKieG317sZ7Ft4rdH5xRm1QWzLlAyo4aLmHNcVsrFxjb4XcI+sFuD1oMGDZJNmzbJiBEj5PDhww6PpaWlyciRI2XTpk0yePBgd8KbasaMGXLXXXfJqFGjpEmTJvLqq69KaGiovPXWW75ODQBM813rZBk79QN5asJL8q/RU+SpCS/JfVM/KPGAtb+x1GCAiAz7dKk8dnUHOZbvSoOjwSHy2NUdJPmthTJs1N3FxjGFs0F0ALAaswZUTWLWTHzbfcOdHddLTVo5n5RkYtuYMhHOpCvPTFuiz4wrDMyaeMatOQAUJzBQZjZubpl6Z9ay3q7Uu1mNmjn/HDVr6XSTBr//FxBszgCHGXXTrLYxaUDftC/OvRUHQLlnSr/MLGbdGsuFfqIrx2TWZGJT+ndm1QWzLlAyqe/79I39PD8us1aA8cbgdzHcWh580qRJsnTpUpk3b568//770qBBA4mLi5OjR4/Kr7/+KpcuXZJ27drJpEmT3AlvmkuXLsmmTZtk8uTJ9m0BAQHSvXt3WbduXYH9MzMzJTMz0/77mTNnREQkK+O8w36Xs7Lk7NmzTl/7claWZF08X+w+zuKYEaM8x7FSLmUxjpVysVocK+WSP862hIYi0jD3gTwdUiu1sTdzuXT5srza//9k7JynRKXw5dP/2bCZ/O2889d6vmEzeXTbd0XGmN3//+TSoW3F5rP/8DHJvP0puScnW67a/YNEnj0ppyNi5Of6zUUDAiVGRPZ//lKxcUzTvbvIO++ITJokZ/+YhFbozMlS5kr9tdJ72JtxrJRLWYxjpVzKYhwr5SIisqJqrEwd9bjc+uErEn36hH37yciqsuDme2Rz40S5vGeDV/LZHB4peyOrSuTpE0UupX0iOERiW7YUKaaN1zZOlAwnx7XChfpiZtusbZwo6x57p9A6JRfPF/+3iohw+hoO+zmJk1ahgrhUDYuJc+fst2RacIj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       "text/plain": [
        "<Figure size 2000x1000 with 8 Axes>"
       ]
@@ -2122,122 +1635,17 @@
     }
    ],
    "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import numpy as np\n",
-    "from typing import Optional, Tuple\n",
-    "\n",
-    "def plot_distance_comparison(legs_acbm: pd.DataFrame, \n",
-    "                             legs_nts: pd.DataFrame, \n",
-    "                             activity_column: str = 'dact', \n",
-    "                             distance_column: str = 'distance', \n",
-    "                             bin_size: int = 2, \n",
-    "                             num_cols: int = 4, \n",
-    "                             max_y_value: int = 100,\n",
-    "                             figsize: Tuple[int, int] = (20, 5),\n",
-    "                             distance_threshold: Optional[int] = None) -> None:\n",
-    "    \"\"\"\n",
-    "    Plots a comparison of distance data for different activity types.\n",
-    "\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    legs_acbm : pd.DataFrame\n",
-    "        DataFrame containing the ACBM data.\n",
-    "    legs_nts : pd.DataFrame\n",
-    "        DataFrame containing the NTS data.\n",
-    "    activity_column : str, optional\n",
-    "        The column name for the activity types. Default is 'dact'.\n",
-    "    distance_column : str, optional\n",
-    "        The column name for the distance values. Default is 'distance'.\n",
-    "    bin_size : int, optional\n",
-    "        The size of the bins for rounding distances. Default is 2 km.\n",
-    "    num_cols : int, optional\n",
-    "        The number of columns for the subplots. Default is 4.\n",
-    "    max_y_value : int, optional\n",
-    "        The maximum value for the y-axis. Default is 100.\n",
-    "    figsize : tuple of int\n",
-    "        The size of the figure. Default is (20, 5).\n",
-    "    distance_threshold : int, optional\n",
-    "        The maximum value for the x-axis. Default is None.\n",
-    "\n",
-    "    Returns\n",
-    "    -------\n",
-    "    None\n",
-    "        This function generates and displays a plot but does not return any value.\n",
-    "    \"\"\"\n",
-    "    # Create distance_binned column\n",
-    "    legs_acbm['distance_binned'] = (legs_acbm[distance_column] / bin_size).round() * bin_size\n",
-    "    legs_nts['distance_binned'] = (legs_nts[distance_column] / bin_size).round() * bin_size\n",
-    "\n",
-    "    # Define the bins\n",
-    "    max_distance_data = max(legs_acbm['distance_binned'].max(), legs_nts['distance_binned'].max())\n",
-    "    if distance_threshold is not None:\n",
-    "        max_distance = min(distance_threshold, max_distance_data)\n",
-    "    else:\n",
-    "        max_distance = max_distance_data\n",
-    "    bins = range(0, int(max_distance) + bin_size, bin_size)\n",
-    "\n",
-    "    # Get unique dact values\n",
-    "    unique_dact_values = legs_acbm[activity_column].unique()\n",
-    "\n",
-    "    # Create a figure with subplots\n",
-    "    num_plots = len(unique_dact_values)\n",
-    "    num_rows = (num_plots + num_cols - 1) // num_cols\n",
-    "    fig, axes = plt.subplots(num_rows, num_cols, figsize=(figsize[0], num_rows * figsize[1]), sharey=True)\n",
-    "    axes = axes.flatten()\n",
-    "\n",
-    "    # Iterate over unique dact values and create plots\n",
-    "    for i, dact_value in enumerate(unique_dact_values):\n",
-    "        ax = axes[i]\n",
-    "        acbm_data = legs_acbm[legs_acbm[activity_column] == dact_value]\n",
-    "        nts_data = legs_nts[legs_nts[activity_column] == dact_value]\n",
-    "\n",
-    "        # Plot histogram for acbm_data\n",
-    "        hist = sns.histplot(acbm_data['distance_binned'], bins=bins, kde=False, discrete=True, stat='percent', ax=ax, label='ACBM')\n",
-    "        ax.set_title(f'dact: {dact_value}', fontsize=16)\n",
-    "        ax.tick_params(axis='x', rotation=45, labelsize=12)  # Rotate x-axis labels by 45 degrees\n",
-    "\n",
-    "        # Set x-axis limits based on max_distance\n",
-    "        ax.set_xlim(0, max_distance)\n",
-    "        # Set y-axis limits based on max_y_value\n",
-    "        ax.set_ylim(0, max_y_value)\n",
-    "\n",
-    "        # Add x-axis ticks and labels for each bar\n",
-    "        ax.set_xticks(bins)\n",
-    "        ax.set_xticklabels([str(bin) for bin in bins], rotation=45, ha='right')\n",
-    "\n",
-    "        # Remove individual subplot labels\n",
-    "        ax.set_xlabel('')\n",
-    "        ax.set_ylabel('')\n",
-    "\n",
-    "        # Add dots to represent the percentage values of legs_nts[\"distance_binned\"]\n",
-    "        nts_distance_counts = nts_data['distance_binned'].value_counts(normalize=True).sort_index() * 100\n",
-    "        if not nts_distance_counts.empty:\n",
-    "            bin_centers = nts_distance_counts.index\n",
-    "            ax.plot(bin_centers, nts_distance_counts.values, 'ro', label='NTS')  # 'ro' means red dot\n",
-    "\n",
-    "    # Remove any empty subplots\n",
-    "    for j in range(i + 1, len(axes)):\n",
-    "        fig.delaxes(axes[j])\n",
-    "\n",
-    "    # Add a main legend at the bottom\n",
-    "    handles, labels = ax.get_legend_handles_labels()\n",
-    "    fig.legend(handles, labels, loc='lower center', ncol=2, bbox_to_anchor=(0.5, -0.05))\n",
-    "\n",
-    "    # Add a main title to the figure\n",
-    "    fig.suptitle('Comparison of Distance Data for Different Activity Types', fontsize=20)\n",
-    "\n",
-    "    # Add a single centered x-label and y-label\n",
-    "    fig.text(0.5, 0.02, 'Distance (km)', ha='center', fontsize=14)\n",
-    "    fig.text(0.02, 0.5, 'Percentage of Trips', va='center', rotation='vertical', fontsize=14)\n",
-    "\n",
-    "    # Adjust layout to make room for the main title and labels\n",
-    "    plt.tight_layout(rect=[0.03, 0.05, 1, 0.95])\n",
-    "    plt.show()\n",
-    "\n",
-    "# Example usage\n",
-    "plot_distance_comparison(legs_acbm_geo, legs_nts, distance_threshold=50, max_y_value=30)"
+    "# 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",
+    ")"
    ]
   },
   {
@@ -2252,49 +1660,20 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 23,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# add binary column to identify intrazonal trips\n",
-    "legs_acbm['intrazonal'] = legs_acbm['ozone'] == legs_acbm['dzone']\n"
+    "#### Plot intrazonal trips by trip purpose"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "        purp  total_count  intrazonal_count  percentage\n",
-      "0  education          385                 2    0.519481\n",
-      "1     escort         1684                16    0.950119\n",
-      "2       home         6498               399    6.140351\n",
-      "3    medical          189                 2    1.058201\n",
-      "4      other         2457               115    4.680505\n",
-      "5       shop         1337                24    1.795064\n",
-      "6      visit          816                15    1.838235\n",
-      "7       work         1737                77    4.432930\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_753888/3001768473.py:22: FutureWarning: \n",
-      "\n",
-      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
-      "\n",
-      "  barplot = sns.barplot(data=merged_counts, x='purp', y='percentage', palette='viridis')\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -2304,87 +1683,29 @@
     }
    ],
    "source": [
-    "# Total number of trips per trip purpose\n",
-    "total_counts = legs_acbm.groupby('purp').size().reset_index(name='total_count')\n",
-    "\n",
-    "# Filter the DataFrame to include only rows where intrazonal_trips is TRUE\n",
-    "intrazonal_trips_true = legs_acbm[legs_acbm['intrazonal'] == True]\n",
-    "\n",
-    "# Total number of intrazonal trips per trip purpose\n",
-    "intrazonal_counts = intrazonal_trips_true.groupby('purp').size().reset_index(name='intrazonal_count')\n",
-    "\n",
-    "# Merge the two DataFrames and calculate intrazonal %\n",
-    "merged_counts = pd.merge(total_counts, intrazonal_counts, on='purp', how='left')\n",
-    "# Fill NaN values with 0 (in case there are purp groups with no intrazonal trips)\n",
-    "merged_counts['intrazonal_count'] = merged_counts['intrazonal_count'].fillna(0)\n",
-    "# Calculate the percentage of intrazonal trips\n",
-    "merged_counts['percentage'] = (merged_counts['intrazonal_count'] / merged_counts['total_count']) * 100\n",
-    "\n",
-    "# Print the resulting DataFrame\n",
-    "print(merged_counts)\n",
-    "\n",
-    "# Create the bar plot\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "barplot = sns.barplot(data=merged_counts, x='purp', y='percentage', palette='viridis')\n",
-    "plt.title('Percentage of Intrazonal Trips per Purpose')\n",
-    "plt.xlabel('Purpose')\n",
-    "plt.ylabel('Percentage of Trips that are Intrazonal')\n",
-    "plt.xticks(rotation=45)\n",
-    "\n",
-    "# Add text annotations above each bar\n",
-    "for index, row in merged_counts.iterrows():\n",
-    "    barplot.text(index, row['percentage'] + 0.2, f\"({int(row['total_count'])})\", color='black', ha=\"center\")\n",
-    "\n",
-    "# Remove the top and right spines (box frame) from the plot\n",
-    "barplot.spines['top'].set_visible(False)\n",
-    "barplot.spines['right'].set_visible(False)\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Add footnote on the right below the plot\n",
-    "plt.figtext(0.95, -0.02, '(Total number of trips)', ha='right', fontsize=10)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
+    "# 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": [
-    "Let's look at OD pairs. We can then distinguish between primary and secondary trips when considering intrazonal trips. This is useful because the logic for assigning trips to zones is different for primary and secondary trips. It is also different between home-work and home-education trips (both primary)"
+    "#### Plot intrazonal trips by OD pair"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                  od  trip_type  total_count  intrazonal_count  percentage\n",
-      "32     other - other  secondary          181              24.0   13.259669\n",
-      "53       work - home    primary         1399             152.0   10.864904\n",
-      "2   education - home    primary          339              35.0   10.324484\n",
-      "18      home - other  secondary         1961              79.0    4.028557\n",
-      "31      other - home  secondary         1866              74.0    3.965702\n",
-      "8      escort - home  secondary         1004              14.0    1.394422\n",
-      "20      home - visit  secondary          563               7.0    1.243339\n",
-      "21       home - work    primary         1451              18.0    1.240524\n",
-      "17    home - medical  secondary          167               2.0    1.197605\n",
-      "45      visit - home  secondary          530               6.0    1.132075\n",
-      "19       home - shop  secondary         1052              11.0    1.045627\n",
-      "7    escort - escort  secondary          292               3.0    1.027397\n",
-      "37       shop - home  secondary         1092              11.0    1.007326\n",
-      "15     home - escort  secondary         1067              10.0    0.937207\n",
-      "14  home - education    primary          373               2.0    0.536193\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -2394,65 +1715,13 @@
     }
    ],
    "source": [
-    "# Add the trip_type column\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",
-    "legs_acbm['trip_type'] = np.where(conditions_primary, 'primary', 'secondary')\n",
-    "\n",
-    "# Filter all trips where oact is home and dact is work or education\n",
-    "# legs_acbm_primary = legs_acbm[(legs_acbm['oact'] == 'home') & ((legs_acbm['dact'] == 'work') | (legs_acbm['dact'] == 'education'))]\n",
-    "legs_acbm_primary = legs_acbm.copy()\n",
-    "\n",
-    "# Create an od column to identify the origin-destination pairs\n",
-    "legs_acbm_primary['od'] = legs_acbm_primary['oact'] + ' - ' + legs_acbm_primary['dact']\n",
-    "\n",
-    "# Total number of trips per trip purpose\n",
-    "total_counts = legs_acbm_primary.groupby(['od', 'trip_type']).size().reset_index(name='total_count')\n",
-    "\n",
-    "# Filter the DataFrame to include only rows where intrazonal_trips is TRUE\n",
-    "intrazonal_trips_true = legs_acbm_primary[legs_acbm_primary['intrazonal'] == True]\n",
-    "\n",
-    "# Total number of intrazonal trips per trip purpose\n",
-    "intrazonal_counts = intrazonal_trips_true.groupby(['od', 'trip_type']).size().reset_index(name='intrazonal_count')\n",
-    "\n",
-    "# Merge the two DataFrames and calculate intrazonal %\n",
-    "merged_counts = pd.merge(total_counts, intrazonal_counts, on=['od', 'trip_type'], how='left')\n",
-    "# Fill NaN values with 0 (in case there are od groups with no intrazonal trips)\n",
-    "merged_counts['intrazonal_count'] = merged_counts['intrazonal_count'].fillna(0)\n",
-    "# Calculate the percentage of intrazonal trips\n",
-    "merged_counts['percentage'] = (merged_counts['intrazonal_count'] / merged_counts['total_count']) * 100\n",
-    "# Keep top 15 od pairs\n",
-    "merged_counts = merged_counts.sort_values(by='total_count', ascending=False).head(15)\n",
-    "\n",
-    "# Sort by percentage before plotting\n",
-    "merged_counts = merged_counts.sort_values(by='percentage', ascending=False)\n",
-    "\n",
-    "# Print the resulting DataFrame\n",
-    "print(merged_counts)\n",
-    "\n",
-    "# Create the bar plot\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "barplot = sns.barplot(data=merged_counts, x='od', y='percentage', hue='trip_type', palette='viridis')\n",
-    "plt.title('Percentage of Intrazonal Trips per Purpose')\n",
-    "plt.xlabel('Purpose')\n",
-    "plt.ylabel('Percentage of Trips that are Intrazonal')\n",
-    "plt.xticks(rotation=60)\n",
-    "\n",
-    "# Add text annotations above each bar\n",
-    "for bar, row in zip(barplot.patches, merged_counts.iterrows()):\n",
-    "    barplot.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.2, f\"({int(row[1]['total_count'])})\", color='black', ha=\"center\")\n",
-    "\n",
-    "# Remove the top and right spines (box frame) from the plot\n",
-    "barplot.spines['top'].set_visible(False)\n",
-    "barplot.spines['right'].set_visible(False)\n",
-    "\n",
-    "# Add footnote on the right below the plot\n",
-    "plt.figtext(0.95, -0.02, '(Total number of trips)', ha='right', fontsize=10)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
+    "# 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"
    ]
   },
   {
@@ -2466,7 +1735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -2560,7 +1829,7 @@
        "7       work              1699            1737"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2587,7 +1856,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -2681,7 +1950,7 @@
        "7       work              6527            6501"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }