diff --git a/.DS_Store b/.DS_Store
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diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..fce99e90624450e05cd60b7550e3ef48d1673a7e
--- /dev/null
+++ b/README.md
@@ -0,0 +1 @@
+Weather forecasting using machine learning
diff --git a/training/.DS_Store b/training/.DS_Store
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index 0000000000000000000000000000000000000000..c8bd0e4ab5306c9285117392c99c9c68852f2bec
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diff --git a/training/.dockerignore b/training/.dockerignore
new file mode 100644
index 0000000000000000000000000000000000000000..6b8710a711f3b689885aa5c26c6c06bde348e82b
--- /dev/null
+++ b/training/.dockerignore
@@ -0,0 +1 @@
+.git
diff --git a/training/.gitignore b/training/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..cdb93cd5ebbb0b464b7cec99bbc254d23aac6815
--- /dev/null
+++ b/training/.gitignore
@@ -0,0 +1 @@
+.python-version
diff --git a/training/data/weather-data.tar.gz b/training/data/weather-data.tar.gz
deleted file mode 100644
index b0118bee517bf49485360db0862365ce9150be5c..0000000000000000000000000000000000000000
--- a/training/data/weather-data.tar.gz
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8b8096982477f4058aee79f5b4b41f8dacf9e5bcda5d97434ccbf2a573189e14
-size 38169226
diff --git a/training/src/.DS_Store b/training/src/.DS_Store
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diff --git a/training/src/.ipynb_checkpoints/MSML-602-Final-Project-checkpoint.ipynb b/training/src/.ipynb_checkpoints/MSML-602-Final-Project-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c02243cef9540eda141019040222c726ea8815f7
--- /dev/null
+++ b/training/src/.ipynb_checkpoints/MSML-602-Final-Project-checkpoint.ipynb
@@ -0,0 +1,1085 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "fb7daf67-de7e-4626-a194-417aa210c959",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "!pip install keras tensorflow seaborn requests-cache"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c005096b-eaca-4244-998d-a92338d22902",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sqlite3\n",
+    "import IPython\n",
+    "import IPython.display\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import tensorflow as tf\n",
+    "import requests\n",
+    "import requests_cache\n",
+    "from requests_cache.backends.sqlite import SQLiteCache\n",
+    "import sqlite3\n",
+    "import datetime\n",
+    "from datetime import date, timedelta, timezone\n",
+    "import time\n",
+    "import pytz\n",
+    "\n",
+    "local_tz = pytz.timezone('America/New_York')\n",
+    "\n",
+    "\n",
+    "mpl.rcParams['figure.figsize'] = (8, 6)\n",
+    "mpl.rcParams['axes.grid'] = False\n",
+    "\n",
+    "# initialize cache\n",
+    "requests_cache.install_cache('./data/weather_api_cache')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "2e054aef-ce49-47a8-ba20-dc2e6928600e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting Date: 2022-12-07\n",
+      "--------------------------------\n",
+      "Already up to date!\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>temp</th>\n",
+       "      <th>obs_timestamp</th>\n",
+       "      <th>pressure</th>\n",
+       "      <th>wspd</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>50.0</td>\n",
+       "      <td>2000-01-02 00:51:00</td>\n",
+       "      <td>30.22</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>45.0</td>\n",
+       "      <td>2000-01-02 01:51:00</td>\n",
+       "      <td>30.21</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>2000-01-02 02:51:00</td>\n",
+       "      <td>30.21</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>46.0</td>\n",
+       "      <td>2000-01-02 03:51:00</td>\n",
+       "      <td>30.21</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>2000-01-02 04:51:00</td>\n",
+       "      <td>30.20</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   temp       obs_timestamp  pressure  wspd\n",
+       "0  50.0 2000-01-02 00:51:00     30.22   NaN\n",
+       "1  45.0 2000-01-02 01:51:00     30.21   5.0\n",
+       "2  47.0 2000-01-02 02:51:00     30.21   5.0\n",
+       "3  46.0 2000-01-02 03:51:00     30.21   NaN\n",
+       "4  47.0 2000-01-02 04:51:00     30.20   5.0"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "# API_KEY = os.env.get(\"API_KEY\")\n",
+    "DATE_FORMAT = \"%Y-%m-%d %H:%M:%S\"\n",
+    "API_KEY = \"e1f10a1e78da46f5b10a1e78da96f525\"\n",
+    "BASE_URL = \"https://api.weather.com/v1/location/KDCA:9:US/observations/historical.json?apiKey={api_key}&units=e&startDate={start_date}&endDate={end_date}\"\n",
+    "\n",
+    "# Field descriptions here\n",
+    "# https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/511371/1/LapamonpinyoEtAl_engrXiv_2021.pdf    \n",
+    "# Sample\n",
+    "# {\n",
+    "#     'key': 'KDCA', 'class': 'observation', 'expire_time_gmt': 946709460, 'obs_id': 'KDCA', 'obs_name': 'Washington/Natl', \n",
+    "#     'valid_time_gmt': 946702260, 'day_ind': 'N', 'temp': 41, 'wx_icon': 33, 'icon_extd': 3300, 'wx_phrase': 'Fair', 'pressure_tend': None,\n",
+    "#     'pressure_desc': None, 'dewPt': 34, 'heat_index': 41, 'rh': 76, 'pressure': 30.19, 'vis': 5, 'wc': 41, 'wdir': None, \n",
+    "#     'wdir_cardinal': 'CALM', 'gust': None, 'wspd': None, 'max_temp': 57, 'min_temp': 41, 'precip_total': None, \n",
+    "#     'precip_hrly': None, 'snow_hrly': None, 'uv_desc': 'Low', 'feels_like': 41, 'uv_index': 0, 'qualifier': None, 'qualifier_svrty': None,\n",
+    "#     'blunt_phrase': None, 'terse_phrase': None, 'clds': 'CLR', 'water_temp': None, 'primary_wave_period': None, 'primary_wave_height': None, \n",
+    "#     'primary_swell_period': None, 'primary_swell_height': None, 'primary_swell_direction': None, 'secondary_swell_period': None, \n",
+    "#     'secondary_swell_height': None, 'secondary_swell_direction': None\n",
+    "# }\n",
+    "\n",
+    "conn = sqlite3.connect(\"./data/weather-raw.db\")\n",
+    "cur = conn.cursor()\n",
+    "\n",
+    "\n",
+    "def create_weather_table(cur, table_name):\n",
+    "    cur.execute(\"\"\"\n",
+    "    CREATE TABLE IF NOT EXISTS {table_name}(\n",
+    "        key, class, expire_time_gmt, obs_id, obs_name, valid_time_gmt INTEGER NOT NULL PRIMARY KEY, day_ind, temp, wx_icon, icon_extd, \n",
+    "        wx_phrase, pressure_tend, pressure_desc, dewPt, heat_index, rh, pressure, vis, wc, wdir, wdir_cardinal, \n",
+    "        gust, wspd, max_temp, min_temp, precip_total, precip_hourly, snow_hrly, uv_desc, feels_like, uv_index,\n",
+    "        qualifier, qualifier_svrty, blunt_phrase, terse_phrase, clds, water_temp, primary_wave_period, \n",
+    "        primary_wave_height, primary_swell_period, primary_swell_height, primary_swell_direction, \n",
+    "        secondary_swell_period, secondary_swell_height, secondary_swell_direction, obs_timestamp)\n",
+    "    \"\"\".format(table_name=table_name))\n",
+    "    cur.execute(\"\"\"\n",
+    "    CREATE INDEX idx_obs_timestamp ON weather_raw(obs_timestamp);\n",
+    "    \"\"\")\n",
+    "    cur.execute(\"\"\"\n",
+    "    CREATE INDEX idx_obs_timestamp_month ON weather_raw(STRFTIME('%M', obs_timestamp));\n",
+    "    \"\"\")\n",
+    "    cur.execute(\"\"\"\n",
+    "    CREATE INDEX idx_obs_timestamp_date ON weather_raw(STRFTIME('%Y-%m-%d', wr.obs_timestamp));\n",
+    "    \"\"\")\n",
+    "    \n",
+    "# Create tables for raw & cleaned data respectively (if they don't exist already)\n",
+    "create_weather_table(cur, \"weather_raw\")\n",
+    "\n",
+    "\n",
+    "# Get the latest date that data has been downloaded for and start downloading new data from that timestamp\n",
+    "cur.execute(\"SELECT MAX(obs_timestamp) FROM weather_raw\")\n",
+    "max_date = cur.fetchone()[0]\n",
+    "target_date = datetime.datetime.strptime(max_date, DATE_FORMAT).date() if max_date else date(2000, 1, 1)\n",
+    "\n",
+    "print(f\"Starting Date: {target_date}\")  \n",
+    "print(\"--------------------------------\")\n",
+    "defaults = {\n",
+    "    'key': None,'class': None,'expire_time_gmt': None,'obs_id': None,'obs_name': None,'valid_time_gmt': None,\n",
+    "    'day_ind': None,'temp': None,'wx_icon': None,'icon_extd': None,'wx_phrase': None,'pressure_tend': None,\n",
+    "    'pressure_desc': None,'dewPt': None,'heat_index': None,'rh': None,'pressure': None,'vis': None,'wc': None,\n",
+    "    'wdir': None,'wdir_cardinal': None,'gust': None,'wspd': None,'max_temp': None,'min_temp': None,'precip_total': None,\n",
+    "    'precip_hrly': None,'snow_hrly': None,'uv_desc': None,'feels_like': None,'uv_index': None,'qualifier': None,\n",
+    "    'qualifier_svrty': None,'blunt_phrase': None,'terse_phrase': None,'clds': None,'water_temp': None,\n",
+    "    'primary_wave_period': None,'primary_wave_height': None,'primary_swell_period': None,'primary_swell_height': None,\n",
+    "    'primary_swell_direction': None,'secondary_swell_period': None,'secondary_swell_height': None,'secondary_swell_direction': None\n",
+    "}\n",
+    "\n",
+    "\n",
+    "INSERT_SQL = \"\"\"\n",
+    "INSERT OR IGNORE INTO weather_raw VALUES (\n",
+    "    :key, :class, :expire_time_gmt, :obs_id, :obs_name, :valid_time_gmt, :day_ind, :temp, :wx_icon, :icon_extd, :wx_phrase,\n",
+    "    :pressure_tend, :pressure_desc, :dewPt, :heat_index, :rh, :pressure, :vis, :wc, :wdir, :wdir_cardinal,\n",
+    "    :gust, :wspd, :max_temp, :min_temp, :precip_total, :precip_hrly, :snow_hrly, :uv_desc, :feels_like, :uv_index,\n",
+    "    :qualifier, :qualifier_svrty, :blunt_phrase, :terse_phrase, :clds, :water_temp, :primary_wave_period,\n",
+    "    :primary_wave_height, :primary_swell_period, :primary_swell_height, :primary_swell_direction,\n",
+    "    :secondary_swell_period, :secondary_swell_height, :secondary_swell_direction, :obs_timestamp\n",
+    ")\n",
+    "\"\"\"\n",
+    "\n",
+    "def augment_data(rec):\n",
+    "    rec[\"obs_timestamp\"] = datetime.datetime.fromtimestamp(rec[\"valid_time_gmt\"]).strftime(DATE_FORMAT)\n",
+    "    return rec\n",
+    "\n",
+    "today = datetime.date.today()\n",
+    "if target_date == today:\n",
+    "    print(\"Already up to date!\")\n",
+    "    \n",
+    "while target_date != today:\n",
+    "    end_date = target_date + timedelta(days=1) \n",
+    "    start_date_str = target_date.strftime(\"%Y%m%d\")\n",
+    "    end_date_str = end_date.strftime(\"%Y%m%d\")\n",
+    "    target_url = BASE_URL.format(api_key=API_KEY, start_date=start_date_str, end_date=start_date_str)\n",
+    "    res = requests.get(target_url)\n",
+    "    target_date = end_date\n",
+    "\n",
+    "    data = res.json()\n",
+    "    if not \"observations\" in data:\n",
+    "        print(f\"Date: {target_date} NF\", end=\"\\r\")\n",
+    "        continue\n",
+    "    params = ({k: d.get(k, defaults[k])  for k in defaults} for d in data[\"observations\"])\n",
+    "    params = list(map(augment_data, params))\n",
+    "\n",
+    "    cur.executemany(INSERT_SQL, params)\n",
+    "    conn.commit()\n",
+    "    # time.sleep(0.05)\n",
+    "    # was_cached = \"Cache HIT\" if res.from_cache else \"Cache MISS\"\n",
+    "    print(f\"Date: {target_date} OK\", end=\"\\r\")\n",
+    "    target_date = end_date\n",
+    "\n",
+    "SQL_CLEANED_DATA = \"\"\"\n",
+    "    WITH RECURSIVE generate_series(x) AS (\n",
+    "        SELECT 0\n",
+    "        UNION ALL\n",
+    "        SELECT x+1 FROM generate_series LIMIT 24\n",
+    "    ), distinct_dates AS (\n",
+    "        SELECT DISTINCT DATE(obs_timestamp) AS obs_date\n",
+    "        FROM weather_raw wr \n",
+    "        WHERE obs_timestamp >= '2000-01-01'\n",
+    "    ), hours AS (\n",
+    "        SELECT x AS hrs FROM generate_series\n",
+    "    )\n",
+    "    SELECT w.temp, w.obs_timestamp, w.pressure, w.wspd\n",
+    "    FROM weather_raw w \n",
+    "    JOIN (\n",
+    "        SELECT d.obs_date, COUNT(*)\n",
+    "        FROM distinct_dates d \n",
+    "        CROSS JOIN hours h \n",
+    "        INNER JOIN (SELECT * FROM weather_raw WHERE STRFTIME('%M', obs_timestamp) in ('51', '52')) wr \n",
+    "            ON DATE(STRFTIME('%Y-%m-%d', wr.obs_timestamp)) = d.obs_date \n",
+    "            AND CAST(STRFTIME('%H', obs_timestamp) AS INTEGER) = h.hrs\n",
+    "        GROUP BY d.obs_date\n",
+    "        HAVING COUNT(*) = 24\n",
+    "    ) d ON d.obs_date = STRFTIME('%Y-%m-%d', w.obs_timestamp) \n",
+    "    WHERE STRFTIME('%M', w.obs_timestamp) in ('51', '52');\n",
+    "\"\"\"\n",
+    "df = pd.read_sql(SQL_CLEANED_DATA, conn, parse_dates=[\"obs_timestamp\"])   \n",
+    "df.head() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "7233dd6b-241b-46c6-a206-184b33d43792",
+   "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>temp</th>\n",
+       "      <th>pressure</th>\n",
+       "      <th>wspd</th>\n",
+       "      <th>day_sin</th>\n",
+       "      <th>day_cos</th>\n",
+       "      <th>year_sin</th>\n",
+       "      <th>year_cos</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>45.0</td>\n",
+       "      <td>30.21</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.465615</td>\n",
+       "      <td>0.884988</td>\n",
+       "      <td>0.013798</td>\n",
+       "      <td>0.999905</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>30.21</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.678801</td>\n",
+       "      <td>0.734323</td>\n",
+       "      <td>0.014514</td>\n",
+       "      <td>0.999895</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>30.20</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.955020</td>\n",
+       "      <td>0.296542</td>\n",
+       "      <td>0.015948</td>\n",
+       "      <td>0.999873</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>44.0</td>\n",
+       "      <td>30.19</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.999229</td>\n",
+       "      <td>0.039260</td>\n",
+       "      <td>0.016664</td>\n",
+       "      <td>0.999861</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>48.0</td>\n",
+       "      <td>30.18</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0.975342</td>\n",
+       "      <td>-0.220697</td>\n",
+       "      <td>0.017381</td>\n",
+       "      <td>0.999849</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   temp  pressure  wspd   day_sin   day_cos  year_sin  year_cos\n",
+       "1  45.0     30.21   5.0  0.465615  0.884988  0.013798  0.999905\n",
+       "2  47.0     30.21   5.0  0.678801  0.734323  0.014514  0.999895\n",
+       "4  47.0     30.20   5.0  0.955020  0.296542  0.015948  0.999873\n",
+       "5  44.0     30.19   3.0  0.999229  0.039260  0.016664  0.999861\n",
+       "6  48.0     30.18  12.0  0.975342 -0.220697  0.017381  0.999849"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def prepare_dataframe(_df):\n",
+    "  _df = _df.astype(\n",
+    "      {\n",
+    "          'temp': 'float',\n",
+    "          'pressure': 'float',\n",
+    "          'wspd': 'float'\n",
+    "      },\n",
+    "  )\n",
+    "  _df = _df.dropna()\n",
+    "  _df = _df.sort_values(by=['obs_timestamp'])\n",
+    "  date_time = _df.pop('obs_timestamp')\n",
+    "  timestamp_s = date_time.map(pd.Timestamp.timestamp)\n",
+    "  day = 24*60*60\n",
+    "  year = (365.2425)*day\n",
+    "  \n",
+    "  _df['day_sin'] = np.sin(timestamp_s * (2 * np.pi / day))\n",
+    "  _df['day_cos'] = np.cos(timestamp_s * (2 * np.pi / day))\n",
+    "  _df['year_sin'] = np.sin(timestamp_s * (2 * np.pi / year))\n",
+    "  _df['year_cos'] = np.cos(timestamp_s * (2 * np.pi / year))\n",
+    "\n",
+    "  \n",
+    "  return _df, date_time\n",
+    "\n",
+    "# Field descriptions here\n",
+    "# https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/511371/1/LapamonpinyoEtAl_engrXiv_2021.pdf  \n",
+    "df, date_time = prepare_dataframe(df)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "049cf91d-30f9-4c42-b705-fdfb48f0d1e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_64/4039997715.py:7: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
+      "  plot_features.index = date_time[-480:]\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>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>temp</th>\n",
+       "      <td>179231.0</td>\n",
+       "      <td>59.138720</td>\n",
+       "      <td>17.768787</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>45.000000</td>\n",
+       "      <td>60.000000</td>\n",
+       "      <td>74.000000</td>\n",
+       "      <td>118.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pressure</th>\n",
+       "      <td>179231.0</td>\n",
+       "      <td>30.034228</td>\n",
+       "      <td>0.216623</td>\n",
+       "      <td>28.610000</td>\n",
+       "      <td>29.900000</td>\n",
+       "      <td>30.030000</td>\n",
+       "      <td>30.170000</td>\n",
+       "      <td>30.860000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wspd</th>\n",
+       "      <td>179231.0</td>\n",
+       "      <td>8.984768</td>\n",
+       "      <td>4.745124</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>12.000000</td>\n",
+       "      <td>45.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>day_sin</th>\n",
+       "      <td>179231.0</td>\n",
+       "      <td>-0.015076</td>\n",
+       "      <td>0.706559</td>\n",
+       "      <td>-0.999391</td>\n",
+       "      <td>-0.731354</td>\n",
+       "      <td>-0.034899</td>\n",
+       "      <td>0.681998</td>\n",
+       "      <td>0.999391</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>day_cos</th>\n",
+       "      <td>179231.0</td>\n",
+       "      <td>0.007189</td>\n",
+       "      <td>0.707461</td>\n",
+       "      <td>-0.999391</td>\n",
+       "      <td>-0.681998</td>\n",
+       "      <td>0.034899</td>\n",
+       "      <td>0.731354</td>\n",
+       "      <td>0.999391</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year_sin</th>\n",
+       "      <td>179231.0</td>\n",
+       "      <td>0.004124</td>\n",
+       "      <td>0.709370</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.705328</td>\n",
+       "      <td>0.007787</td>\n",
+       "      <td>0.717489</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>year_cos</th>\n",
+       "      <td>179231.0</td>\n",
+       "      <td>-0.003987</td>\n",
+       "      <td>0.704817</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.708857</td>\n",
+       "      <td>-0.004902</td>\n",
+       "      <td>0.696913</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             count       mean        std        min        25%        50%  \\\n",
+       "temp      179231.0  59.138720  17.768787   6.000000  45.000000  60.000000   \n",
+       "pressure  179231.0  30.034228   0.216623  28.610000  29.900000  30.030000   \n",
+       "wspd      179231.0   8.984768   4.745124   0.000000   6.000000   8.000000   \n",
+       "day_sin   179231.0  -0.015076   0.706559  -0.999391  -0.731354  -0.034899   \n",
+       "day_cos   179231.0   0.007189   0.707461  -0.999391  -0.681998   0.034899   \n",
+       "year_sin  179231.0   0.004124   0.709370  -1.000000  -0.705328   0.007787   \n",
+       "year_cos  179231.0  -0.003987   0.704817  -1.000000  -0.708857  -0.004902   \n",
+       "\n",
+       "                75%         max  \n",
+       "temp      74.000000  118.000000  \n",
+       "pressure  30.170000   30.860000  \n",
+       "wspd      12.000000   45.000000  \n",
+       "day_sin    0.681998    0.999391  \n",
+       "day_cos    0.731354    0.999391  \n",
+       "year_sin   0.717489    1.000000  \n",
+       "year_cos   0.696913    1.000000  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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TMXnyZK2793rOXr2NzBwr7mblorwJMXPiQSfXCujR0L6dmYPzN9IBCF7WxNQM2eB6ljIxOblWzNl9Ac2rlEBokUIYMH0PAN+LC3NeQt93/haTdu0P9a+fru/y3vC5cSheNBBtqxnzSNxMd31onbiShhNX0pCWkY3p/Zsaaj9blKxjN0YB5BmjLBF7Cuzczswx5D2xk6vioac38940g9QkJfx/DzjKVj0xZWve7w8UL4KGBmL+TlxxNBS/WXMS7WvmT9jOX083bJBKGaNi9p2/aeg7qCHherohg1TKGL1+OxMlGRonF2+mO/x99todfLDoEACg8edrmYzjSsYooH8iKMdXq07gZnoWFg5uhcAA/VGNCdfvYNPJq3ioQnG8Nmufy/u74m+gaWVj3nbnJEUxexNu4vlfd2D1W20N7cNM+CguewFGZ016ybznQbwh42FkxbW0TBy9nOIkbq3vOzsvw8tmetpsuJudi1xGxsQbc/bjw8WH0WHiJpxQeAh4M1IOktVHkvD7lngHD7Ze3vnngOTrxxP5yp/FnriK67cz8cumM7pF1NUYnjfvsLlXpDxVbRnEru4/fxO9VMTgnbyiLwmwIAmrn7qShmF/75d9/7TOYwQI49Kvm+NdXhfHbWfLqBWoZZcK2S2jyZ4rDivHWzIQUXFh5ZEkzLsXb8iCrpP4xMsDwpjwy6Yz3NoHhMIQUvvYfOoaDl9KxdM/uyYYa6HtV7H4aMkRPD5ZOnP/GYPtW1VkwOkdkzyF75UvUsBeoSg9PR1FihTxcG+E5dw7mTkoVSyIaRWkyymuMyPeJrgtJwvZuTYsPngFL7QwHmf436F83dk+v+/K+z05LQNlQgrrbvfyrbuYsf0ccnJtCPC3YMn+y/i9X2PEnriKvi0rMfGY2Tl5JQ3z9150ef3VmXsBCDFQQ9tVZbY/MYcvGzdID0gkVojpPGkTrt3OwoK9l7DqrTaa21czMXz5zz1YMFg6oVELUvI71xkYu7wSQuyYZZAu2HeJa/tHL6fi02VH3G7z78HLeKaJPoWUIX/tU7xef9ty1mEJXytqjIQEFykubScwI5tfxbwbd7IQ85X0JMy+QnH0ciqmbzuHhUNaGvL0soxvdmb43Dhs5FiIAgAGztjj9npSutY8zYJ9rs8dX6fAGaT+/v4IDw/Pq9seHBxsSjnMO5nZyMqxwZaT/wC8eC0lz9uYk1WYaaKLeD92Mu9mwKYjgSArMyuvvRupt13btgnf6+aNa1h39jbWnrvBxCCVY9DMvVikIwZz2+lrOHw5BX/tPI+E647LSY99L8xSL9+6iy961mXSzws30h2SmaTgOaglSUxKtDJ8bpzb96/dFq4F56VStahZqNibwDY72ig5uVb8vOksWjxYkvvSLKBsoCyJu4TuDR4wtI/ktAx8ufK4oTacsVpt+GnTGTSpVAJNKpVQpTAhjv/Uwo07WaqKBuxnFC7jjmtpfFe/AMBfxzPr7NXbeOSbjYrbTb+nY9xr6jbNy+o/bTyDqRtOY9covgnIWoxRvYuh3mBw/rElHgNa63uWHkv03VVFOQqcQQrk66HajVIzkIrlEO/9dqA/SjAySO9m5+L6bddB8fY1fxQPDtTscbmVnp0nnZF21R93XTLpbcjOtWHd2dtYeOwObBC0z4a0q6rb2yglQWNH70Pl+d+UE1pYGj9qpIx4eq2NJs0d47zkD7CP77JzJTUD07aewwvNKuRl6bOqJPb37gv4apWQyW1GPPOU9afdvv/mnDjDBumrf+5V3CYlPVtTEtui/ZcwYaU5x0kubIUln/57VNfnePg7/HQ02oXjErqd8SuESU2Nj1Zy28c5jYk/NrOCoznw6bKjeKRGGVTSoQFsQs606RRIg9RisaBcuXIoU6YMsrPNyTR8eWGs4jbr3o7R3G5WTi6mbTuHZpVL4GhiKsqFFcHny+QHzrc6VMPjGjNZP/33SN6MtHKpooh3GhCsNuBmhhUZOfk3/tTYM0jLyMFnPepo2pedbKtnypAeT0pDcmoGyoTqDwnQwlEGy+pyBBgMNOv6Hf8HGC8GzdyLuAu3sOzgZQx7pCoyc6zMasCf1ukN1suhS7e4tHsrPQuT15/Gkw3LS2peOtPgs9WIH6fesDyjs3LbF8uPYtRjtTR9ZsdZ5cx0o/yx1TU+VQojBqjazPcNJ5LROrqU6nYv3brLJVHQE1y/oy1e3UPpIsw4fyNdl0GakaNOgnHZwct4vJ4xdQuzKJAGqR1/f39TpKRupWfhUpryxVG4sHYj6M9NZ/Dt+nMAzqnafntCGp5qpm0/KdmWvP4XDbaq+i4AVD3kvJGmY9cZ9uioFV2/yrEUoBmyYkbhJdBsv/Yu3ryL9xcccrutkZjkjxbLVzJjhRo74sKNdM16rR8vOYKlBy7j9y3qDC2zHuy/bo7XbJB6k9GRaSBRcezyY6q2sydEvhbzIB4IV86F6PfHLsVtzObZn7fjk+61NaseaD3XXnRp6EJvoZNZO86r2u712ft9xiAtsFn2ZqImSB0AZu9UdwGJib+WrryRCD2BzmKzRkvpukOX9Isuj/tP3cDMi+Fz9mP3Of21kdVWB9GbZa/Gk+ILBqmRhzcrmn6xTvfkyYxa72oUDB6esEFVVq2YI5f5iqKbiWsYkedYtN8xOUzLXahlgjZzRwJem6kcagEAp7ywzPPO+BuawwjSs3Ic5OHU4ClFHTku3bqLSv9brnp7q8b+rzqShDFL3ScP+ipkkDJAbcKHXaNNC3qWhxI1JruIZ2i86tfvOHsdI+bF5Un8/L2LnfyIHhbHXcbTP2mT3cjJteKDRYew7OBlHLx4i0/H7pGh4gGs1yC9kpqB1l/yq/zkjfT4YSs2cc7a5Y3WqlB6kjm1LI2LH6M/xvKR6LlwIx1vKSTeOcO7ZKwektMyMGJuHJI1as4amfSral9lJSebzYbP3ISKsWLK+tOaEyj1mKMrDycpbwTtz1IAaKWxqt5tCf1kdwyauTcvMU0t7krfehNkkOrkhw2n8fW9xIdUDRUxun63mYk2pTvUVJURs55z+UMAeO6XHVi47xI+W65uUFM7UJrJwn2XMHvnebw+ez/emssuySIn14r35h/Aov353m01s2a99ZDfnX/QraAyK/Rql/LiJS9a1ly0/yLem39AU6EJWX1gGfRMV577ZYfyRhKwzuC38+rMvS4eSSXkynK6w4jmpRq7v+kX67BQ4/dQ27YRuk2R1sh0ZvvZ66pDP4yQcEPbiiAAyQRfJV6bpc7zrHb10whTY90nNbIgLcPcql16KdAxpLzIzMnNy8J9oXkFTTO0Y4mpqPbhCsSPe5SbHFWvqdtQPyocD0WFY8wTtbnsQy8XVA443aZs8bqKTedc9AfZsHD/JczbcxHz9lxEwvV0pGflok/ziqo+OzX2NIbEaNM5NcNT+MXyo7glUQXKFzByX8ZduIUGUeGK29knNFrix7R6gngbM2aslJ7SkWC2X6OSxsGLtzD2Pz4GtVG8ZTU6xYR7+fct8ViuULdeitFLj2DFmw9z6JG2TPa9CTfx00btE5tz19N1xYhrIVdjuI+nIA+pDsSDRFaOVVcMy37OCUEHLtzC9G3ncNLkjGEpxPF7u8/d9Nn4lx91DDZqEM/wJ609hV82ncWf28+p+uyElSew7bQ6bce1R69gwPTderqoiYs30/Hr5nj8I1EwQA6z6267w0giWo8ftipvJGKZhgdwnAkam4Aw4fYW9BjVWgXbxaVN1eKsRCKHzWbTFaolxper2GlBb0iAcwlTKeIu3ELfP3Zpfh5qkd968sdtulettIaPacVDojaaIYOUAXpmsQtUPqyNOjkyTVhyUML5Ia02/iUlPRsv/bELi3UsdbEm12rj5q2Q0tHTosWqRn8VECoiGQnPUPtg7DjRfbEAKZ77ZQf2GEgyY4keL40ZzN1zAd+vO6Vq20/+PaK7bOC83Z6N7xYjVX1LDfZwKl4MFi35uuvjiStpupJZxbygcH+zKr3rDiPedm9QY+nxw1ZsPHlVsxqBn0mJo0mpGZrCd7QyZPZezUmRnoAMUoO8N/8gPlqiXRomTWMgs158uU5232m7sOnkVcVqQmagd+YLKC93SRm6exhXL9p+xriOY+dJyoZmwvU7ujKijyWm4inOXoKCwMQ1JxW3eX/+QUzbek73Pm7c8R5vtV5tzSkb+MblHU9Kw2fLjmKEwtikV9JHjFIs9ler+RjfO85ex1M/bsPxpFRDk3F3RVBYkJaRgyVx6pwWlzXmV7Qavx6V/rccowx6udXQhaMu9IUbdzFgxm70+X2nqoRZT0EGqUF2xt/QZVwuPcBGxFsJd0sO567dQXeNS4xm4g0zazt3s/VPIOp/utrt+yxkS5LT3A+0vX/Vl6yiFVbVknyZrQohFB/rmMBqYe4eYx7OPQnqPNUXVCyVFmR+3xKPhfsv4ew1aU/0krhL6DV1G/d+3NCR1KOG537ZgT0JNzFgmrEwHzMqKb05J071tq/+uUdz+3/tPM89jtadws2125noNXUr5hm4t2NPXMXmU9fw9y5jHnuekEHqQdTIkxj1cLor5vPOPwe8op6vGm6l81+WcofepUM1sNAQ9JYYIW9JwvAkSkusf27nr21qBLUqHd4a2mDHXuaSN9m50he9FiPJCCuPqJMwkmPQzD1uJ8XXbmeprgokhZoxwYgmtFZW61ztcqd8su6Y/hU0NXy96gT2nb+F93QoSDhzR2OMtZmQQepB1EilGF3an7/HMVb13LU76DBxI+bvvehViSRG4Z1FaHRi4E52g0XJS18OzdCKtwlhFzR4aRGLWWsgBEYtajKe9ZY/9QZWHk5C2682GG5n1ZErikla83ZrL7hiR2ls3nr6GvekHha4W/0ZOEO711ULLEP8Dl/iV87aKGSQehA1sRxG7azftsQ7xNe8OWc/Tiffxjv/HPApI0YuuPzTf4/ise83c1+GMCrR1e7rjYrL6r5A6y/XY6cb8XQzrqkbJiRx8ODzZUfxKIM4sYIQFvGyjmVT1uTkWtH+m43GG/LQ/Oi1WXuRcJ1N2IS7r5CVa8V2DQUTnMlRWL5RWlFQS3qWtNHGagKrtTAFS1iGPeR68YSeDFIPwnMZWMybc+IwaKbwADggEpw3a/8skIuF/WNrPI5cTsWHOmuOq5UaMXqkrt3ORNMv1hlsRR4LzPEcXrx512086i+bzhpqX+k7nE5OQ/Nx/I6jWjkfPfy2JR5HE417Jzaf4q8j682JD1pwdz3tivcOVQdvQIu8kVZyZEIazCKJUeWukQsPIUljUpRWpK7X7Fwr/jtkLCzDVyCD1JOoGANYGRmrjvBfIuMJL/UNtdVHvN2bPOzv/egySagCxlvew968fT9Wqw1Wqw02mw3z9uhf2gOAyiP/cyuT03/6btmYPTUoLRV/uJh/Nq1RcnJt3M/xKoNxiSxgMfY1+WIdfpZZus9hdQw9MDawnnzy/AqvztyLLadcE/3sYwYr5BwsLKvSTVzDV06s8edrcdipXKyWcr6+DhmkHkRNUgDLYYeHB01JO43V8mKnbzdxK7mqRgjc273JO+Nv4MSVNMzYdg71P12N3zYb81QqcfhSCup/shpD/tqLKh/8hyof/IdNEg8dPcgJiX++7Cgu3DD2cHn5zz1ur9mDF7yvZK0zr87ci0afr8FlifrUvhhfa5/QONOfQRGHa7czMc6k5CazyM61oifj7H3eE+4Xf3dcls/JtaLzpE3oZzCDX4z4O4jv8W8YymJlci77ff1OFkbMi+O6j9gTngs9UIIM0vsIHs+qumNWu9Vo0yLw7o6LN+9i33lHbU5Wxm71D1cqioF7u4fUzhf/HUNaRg4+X36M637e+ecA0jJzHJaS+nKuFf8bo1ra9T9ZLavtqLXKj6e4mZ6Nb1a7apJeYyQBZEaG+Jxd52G12tBh4kZ0nrTJxSiNPcE/NIEJJswB7OPTW3PjED1qBXNJPJ5L9lIcupSCU8m3sZFDGeMbd7Lw0KdrMHzOfgBsn3sHL/KfsDrPzVg7Q4ysMPGGDNL7COfL8ASDsqJ3s3Pxl5slVqWAdiMcT2RXUu+9BflyGjabzUXM2aSCHUzhWfnDaJKXGngJat/JysVcL6pGpJcF+y4iVaTecDcrl2nyg7O3NddqYxpb+r+Fh5CYmoGz1+7gVPJt01U/WJUw3iWSLDrFqVSzfXxaxKlqXWZOrsP59kVPu31IWrD3ItIyc7D4nnoJqxhSgG+MuR3ncc9XnCEsIIPUC0nLyM7zFrDU+vPEIPP8r2wyKO1k5Vi5J1y8OnMvan68EseTxAkovjcqtJ/IIINYBt5HY+G+i6j58Ur8uf0cl/bNvhdS3ch+GWHDvczflYeTUPPjlbrKtspReeR/DlrJHb/diHpjVjOdKHQQZbmbPTqd5WBc/BirLDXljXSYuMlBusgH7dE8nA04VkoEZnHp1l2HMAMjYvi+BhmkHsY5a+908m3UHbMaA2awi62x48NjDABhkGw6di1qfbwSmTm5XDwqP288k1cm9NdNbJaIxZhZT5jnQMwiW9wdI+YdAAB8vISNF8uZYwy961I4S1NtO80vMSE1Ixtv3SthyfqeaDZ2XV7lqbNX7yAr14qjieyWLcVlZsXC4+2+jmW2D0BdERIWLOTkwTSD9R6UNWKBUSP6r5ebsekIAyavzy99y0Kn2lcgg1QlyakZeV6VzGx2S6ErDzt6QGftEKq48Iif8uVZLyBosd1Kz4bVBly4kY6tZ9gk0YiRS4C4cIONcfcS5zhLZ3h4As0OX7BabUhmbFAsP5R/36VmZDOvXtLwszVO1Wf43HwHL6YIXkuOqwbOOpG8wjXsk7WM7FzmS6Ni1YDrtzN9Ust1G4fxTg5ffFQMnrUXgP5JWcWSwSy7Y5iCIr2mBTJIVbDqSBKajl2X54WQq12shzH/qtPBZMGU9adM2xcA/LKJ8fKVaJS0WCxuS7npwbmaktjwYuX52OJU55xXnKSdCavYy5SYET8qZvjcODQdy0d7NCM7F/XGrEbt0as0Ge/nxj+muI0RpYOHo0up2k6tbJkUHWqW1fU5f07n3y7YzUNn1nZvEnvu2h00+nwtujIoUMCK97pUR5mQIMXtWIc/yWGz2bCWcylMHmw4cRXJqRkO3kUt+Kucaf+xJd6UkJ9W49dz34e3QQapCibfM+TsQdLnGXnLpBBf6Ky8cna+13mj6mXsf2zlVsTVQng8EuuOWe3wt/i5e0xhibpttdKqjQgxTb9Yq/kzWuAR02bEQ1qldFHNn1l6gN+SlbhuOzNdyntk6FxJ6d+qEmYO5Lt8GF2mGH7r21jXZ3llZNsF1G+lsw/FGb30CB6esAE9pm4FwL88qpbQnOBC/pjzanOOvdHG16tPYNDMvVzavngznWstdSNZ++XCiiCsSCHF7T5ddhTfrePv3LmuoyLdY/XKceiJeZBBqgLn1Z0NJsXaPDzBeJ1iX+DVNlXwTOPyituJBdMtFgv3EAQtchtvdojWbERcSc3wOpmhaf2aKG5jRDZk/dsxuj/LGpvNmDD3u52ru31f/HDUspvyxYWlwwfCi+jqlxb+17WG4jaBAY6PCV4OctYrHlLwMHalqPnxStXbFgn0R5XSxTj2Rj3Hk1LxwwY+iVnx1+6g9ZcbUHv0Km7n2nm5XsmR4MzsV9SN4ZPW6jNI3+1cXdXqih2t41OJ4ECtXfIqyCBVgfiiyMm15nlK1dKiSknV2x5L4pts4Y188GhNTHiqPmpEhLjdTjxjtIB/gpANNhy4cEtVvNlDUeGa2s7IzkUzTsvQzmgZ1NrVKMOxJwLnxj+maVDmxbSt5xz+TriuLW6xT4uKDHuTT0jhAABAzXKhXNoX81rbBxW3ea5JlMM1pHZpM6Z6aU19yTUx4U8tvR56ANP7K0/SnNEioN7zIWEyvnhoK837YU2XSfpDGWYObOr2fXHJ23fnH3SzpSs/vdgIbauVxtONlB0XYtSGZpQNFUImakeGoVnlEpr2oQWtq2j/aKx8N6JjNVXbmZlcqwUySFUgXsoT61Wq5ZPutVVv6+v1lQ9fSsF1GdFxJdTeTIDgpeEdyjhvz0V0/2Er3vnngIr+aOuMnDA7D2a50YktqIzvVVdxm0+XOcZvrzumbuXjzfbRAPjFUrarLkwKvuhZh0v7Wvlze4KDWLefxeJSpMKZl1pUxDgV50BMrtWGo5fVe7TeuHceeFImtDBiqmubpGkxrM+NfyzPA90gKhyf9fCOc66FDjXLYtbAZng4ujS+e66B7HbiefHZq9omf13qRGDGgKb46un6+O0l+VCTtAx9K07d6kXm/f7yw1V0taGGIoX8NW2v1d4oXjQQs19pphgfPpqRBi9ryCBVgTjeaOE+7ckt1cq69/x5M4/WjUB7FV6zjOxc/LzxDB6fvAWNPtcXF6nVqDMruUaL7MafA9x7Cewc0fDgNcpf95Qb1PLTiw059cQ81MSCOXPg4i3Fbc6MfRRv3Zs4Bfirv/60+CPsDsiyoYXxqYbJrBa0ZhSLKwMtP3gZvRRKVz5SowzKhWkLOTiWlIZHv1fn0epWP9IhoEZP/LYST9SPxOuPVAUA/PC8+nvihw3mxuor8UB4EXStE8Gt/d/6Nkbre8c/3IQl4w615I0tvbGdb3TIn9yoed4B+hRMok2wBVo+WEoxPnymxmeCWZBByhmtXgJvY+oLjfB7vyaY3Psht9sN+3u/4ZrRWpJlLLDg0k1jdc150Kaa8jLl6eQ0bkkDUqhNQrF7N7rUKYcutfk9wOy0fFB9KItavnm6Pl5oVgGddfR/o0aptaAAbd4OPbzUohKXdsdqHJee/DHfAFWTHNlWxX3gzJcaxo/vnm3g8PcLzdiHT3zf+yEUCxLCJ7Qki0xc41rSVS2ss7drRIRgweCW+PHFRnivi/uYZxa0iS6FkkWljVJvrv4UWjh/Auun8kGkdkXFTqliykoK9ztkkHLipxcbok/zippiXrac0qczt2BwC8S+E6Prs2p5tG45FHLjEbKLyRtB6wNerCXpjt2jOujpDjcGTN+jvJETxz/rkvd7CZkBXw4/lXd59wYP5P3+WoxybKFRit572LPiq6fq4clG5fFFz7qqHypZovhg1ln2YrzpWfz3K81RJqQw133oWb24dEv9BNPPz4LI8PzvUFDKK7KO7fv22QaICBOOk5yhyBKLxYJdMuOtnqxxb2bdveTmA6LVA3cEmCzgXMnLdFXVQAYpJ7rUKYfPetRBgL/yIT5/PR17zt3Ai7/r05lrVLEEKpXSLqejBX8/C356sRHXfTxQXP0Sn9oHUM1yoSgdEsRV9PjRuo7eOHcxToA+2bDCotij2pHakl3UaIAvG9ba4e9iQey9fz/34Xv9PKUx4QFw9Mopxf591qOO6oQeO3vO3cChi9oqGxUK4Dsss6x3L0VEaL6hOPfV5niJU/LXkw3LY2Dryor3my9hNx5ZIZ4I8ZgUNawQ7vKa3D2iVx/UW7E/g7r/sFXV9oUL8b2vnWOqtcY+ewNkkHoBbb7agL0J7pME5Fg7og3j3uTToabjBf0I5wzsyhyM6lyrYI3NfbUF+rWshHVvt2W+j6kvOBpa7mKcWKD1waIkfdKldgTqPBDm8BprrcmxPeu6LKOXKsbWY6PHK7dBtEyv5CHt01y7YfXUT9vRbcoW1UbgZ91rOywf8iC8iONxd7fyoQexpmazKiXxaXc+iToB/n746PFaXO631W/xG1fd0akW/1AZlshd1VNf8P04dCWMaNmyLlN6aEwnl6TgdxRk6bwRMki9BD2T13PjH0PVMvyCpL95uoHD3xaLBcWD2T4s5zoJQh/5pDPT9uuVDwcgeB7GPFEbD3qJ3p8RWGv4VZDwHmv1BOqhI2fDXUmGxkzUnrI+nGJG7TwcXQq1nDzszTXI0qmB92pNkIQHmfXV6qlEVD8/C0qrqNqkh0gOurZ1nSaydh6ty06gPTiQb6y23tWzXfHaVj8aiKQBW1UthfrlpY+dVlpUKYkQiUlsMcYhUWZABqmXoNXGYO1dkiJMwvhkndnezOlhqDauUE033nikKj56vJaebt1XvCiREMLaQyrlIWzHeUnp4ejSmP9aC9XbhxbWPoCr9aSpub0/fKym5v1rRap4w7dOCULezsExnVxe05rR781EaQhdUiJcNIazViIYHPMg3uuiXFjBKMvfeJhr+0ZW/rpN2aJqu2GPVMUnTisFD1Uornu/Yvq3qqTrc96YZEYGqZeg1evVp3klPh1RoFVV9vIqelAThzmiU3Vd8j9akFNR6N4gUvJ1FrAeR6Q8pGqTgtQiVUbTYrFwF3/XEgaix4Ok1pOmZvDv36qy5v2zwNeyf6WSH+sy8ja5owpnz68dVpPBr56q53BNs3Qm/DmgKd7vUoO7F25avyaS97DzypoRlCquseBtiWcRq/2asZplFswN0jFjxsBisTj8RETkx8XYbDaMGTMGkZGRKFKkCGJiYnDkiHeKtLLkR8YxNSpypQxRVGaZRK1YN48a6mKe/1VfAhhrQmS8anKv6+EfDV4+VrAe42rKVOFilXkqF0pSRMNy33GdVdLqPKBsVKuZcPJ+sMjd02ZgRhnURhXZeJzkmP2KOfXmWV0HTzeOYtKOFKxXUOSQqxznvLJmhOBAzyxtFw0KUF2q1B16nQde6CDl4yGtXbs2EhMT834OHTqU996ECRMwceJETJkyBbt370ZERAQ6duyItLSCUzJTaibtnDTiTHqWtgoTvCt/yQ2KahMuvlyprCnYTmNpQVY00Fjm0x1m3NRNKjmWsrPBplq8WS+sqxC1lPGss/LEVpcxeM142DzVUDm7f7pTmVJPMLC1vAd274d8pdGUhLpZ8AtnFYeIsMJMY+jlSiXztPVY9b+gyGx5mpYPGl9xFKta+DpcDNKAgABERETk/ZQuLRgeNpsNkyZNwqhRo9CrVy/UqVMHM2bMQHp6OmbPns2jK6bzWY86mDPIdSatdAP/sEGbR1HK41KlNLslpSA3Jc4eZLSfSc+6F9vnhZ7a1HJ4YpJps/H3prFespeDlTi+moIEvKioYin3gEbpJx4MaVdV9r2SnJftzdDAZPUd1rtR4mDlGfysRx3JeF6Ab8GF5W88zEQthQxS70FvspoXOkj5GKSnTp1CZGQkKleujOeeew5nz54FAMTHxyMpKQmdOuUHpQcFBaFt27bYtk2+FF1mZiZSU1MdfryVPs0rSopOs04Gqn8ve1zMwsEtmbUvlclqh5UeqVTSlBmwLG9XVSZr38I87zcfG/gvmbGMDfvYTWLZ800rMNsPTya5SfypWMK4xq0ZlbEKa6yjzZJiDENYeBJSOABV3ChxPNeUzTJ4n+YVZbPpeU42I8OL4Pe+jV0k/bRi1pI9bwqCd9GdvqmvVYpkbpA2a9YMf/75J1atWoVff/0VSUlJaNmyJa5fv46kpCQAQNmyjnIvZcuWzXtPinHjxiEsLCzvJyqKX2wML1gm1wxqUwUxEsvdLA2ti27KckpJTHgbT6pYRmWBs4SOHdZSOg7YIHn+WVK4kD/+ea0F5r7aHOUMinWHu5l4uHtPC7zlvFq48eSWLGrcM6elKIQvEhwYgHmDjMdC/805jvONR6Ldvj+iI/8EGDkpJVZYLBZNpVCliDRB1eBlNyEmrJgjkxxlhkefFe486uFu7I7jSd7n2GNukHbt2hVPPvkk6tatiw4dOmD58uUAgBkzZuRt4+wttNlsbj2II0eOREpKSt7PhQsXWHebOyw9TgNbV2bucdWCL0yO2yoYbLyzQx+tG8Ekpq1zbWmtzmcaRzHxVEeVkH+wNKlUAs2qlESHmsb0Qt1VQGI1uXEXRvIr50o+YcGF0ILjBGTb/x7h1raZNK1cAj0fekB5Qze4mxiwoGVV9+2bkdHcQKL6EWtyco0t2PLSShVjhrB7cRknDkuHwp8DPKeHLBdbD0B3MR6ecJd9Klq0KOrWrYtTp07lZds7e0OTk5NdvKZigoKCEBoa6vBzP1PGhGWGqmXkPU68h2SttdqlUJLZYV2dxhmLxYJOBpdhX25dGd/3zo+ztdcmfqxeOfj5WdCljvFlXndL0XaMCvEr6e2x0Ed0l+THQoBf6RC8/oh8fKbR9nkImnsKPVqvdswwgmpH8pePUoLFyKQ0vimVyfU0TzcqrxhisuX9dob3I1dBrbybibpWlOLbeZa1dhd+kno3m9t+9cLdIM3MzMSxY8dQrlw5VK5cGREREVizZk3e+1lZWdi4cSNatmQX/+itHP+si6e7oJo20fI3EW/vrJ6a5M4oDbie9DCrpUOtsg7LMUuGtsafA5riRR0lLOUoUkjZQDDy6CodEuR2cgOw8Tqxrl6lFaP6vJ7uv1l0NVDBh7emsLfAYmwaHON+glTOyyc5n/VQlhcsX5yfIfdoHTaVprrVV9ajXjK0FZN9FQSYG6TvvPMONm7ciPj4eOzcuRNPPfUUUlNT0bdvX1gsFgwfPhxjx47FokWLcPjwYfTr1w/BwcF4/vnnWXfFdJSyF81IKpjcm03mursHJG9bjkUFCUWD1PAegO0j+S6lOqsmhAUXQptqpU0XQjYS+K9Gf5eFxJTVVXffp+BdRtUslATLGxqoTtOVwYqAL1CdQdnSN9u7j4U1WrZSzrPICk8m4AFA/ahwLBrS0pRiCCxzP7SQnJbpkf26g7lBevHiRfTu3RvVq1dHr169EBgYiB07dqBiRcGr895772H48OEYMmQIGjdujEuXLmH16tUICfFM7WCWqC17aYSFQ9x7ko0moNhxZxSq1SJ1x9NuvKAsVpOUbNqXH65ieB+8yxVKqTWwRk0yjRGR/8ZOGqpSsJCY8vWkICUPa0ODcYWVVCwLspDgKqlQ0tjIZIqFXJEvEBFWGCO76ivJOb1/E+z8oL3icTZqBPFc8Wdd4tQd7p4TD1UojrUj5CXA1NDNYPIYTzIlqud5GuYW1Jw5c9y+b7FYMGbMGIwZM4b1rj2OGbVhlTwMrLyX7paNAt1IQqlh6eut3JaMZLF82bSye0NoUJsqqsT7CzKb3m3HdRlUbQJLjYgQrDl6Rdc+ShYNxKIhrbgv57JSA9DL7Fea4+9d5/HJv0d1fT6murIx90e/Jvh9Szy+WnVC1z4AZUPFyNyjSillJYU6D4Ti8CXvyx62s+nddm5leuxUc5OM4o7mVUqa4l3kGWJiRhEFO0pOJKOTZW9e+eDt5dYD1bJXQd8W7GL2+MPGIn22CR9prV0ftEe98uEo5Kb2KYuxrpLCUoufn8WrY3qPfcq/b1I17KXQe0Wp9XQYSVb54NGaqr+HXqb3b8L1If/JE7UVtylcyN9QrXs1upGFC/mjnsGlXKUSoUbiI9XoFs9/rSXWvNVG9z54U6FksKqkVL06n1quUyO11NWM0a88rO961VIY4MDoTsobucGoc8UdlUoGc89VGNGxmu7PcpUm1AkZpCpQuzzhffMN7USEFlb0YBohVIUny6wED0/HKblDSw123sjVk1ZC7VhsZBn3SQYJcEqo8S4a4SUTJrzPN1NXgMCIBubuUR1MCVtyR+FC/ohmEIPpaepx1iIFgCExD+r+bFEV45MZ4vnenOj2hIqEJqMYUa0o64VFAcggVYFa13bfFpX4dsQE/hzYFPUkqkA5UzZUn1dLzSBl1CBtXFF/4gRrPKlBx4qKJfUF9rdSWaeZRVITL153U26TFby9KF8/XV9R6cCO3tjCVx6ubIosk1p4HtLHTYgLLF40UDFBzChGrrsANytchIAZbhU1zjIWqjVmQVeVCgL8lA/Tx4/XUoxbNAMjA3Hn2mVRTaV3YfkbD+vahxr9z+ebGvMYzWVQEUaJ51SGNHiyxrqnUauXyyKpiQffPdcAb3dSvyTWq6F20XeeGoQA0KFmWVMeSB88WpP7PrQQoOOaqqEybpN3JSU7zTQuqarxWpqKd97WmjGjrK9e1DhveBbtYA0ZpAwoWTQQA1SWOdObPamWWgaW2rUsf8hVuFBCzay8VmQo4j7uqKt9QNsS8PAO7uVRnDn5eVdsfq+dphrBPI2OZxt7ZxldLRI9ZmRP61k+K1+8iCYv0oeP1dK8DxaKFe4oHWKOpIy36fqqcSKImTmwKZYNa61qW2/VlN/7kf4xUwtq79fnmqgLE/F2XtMZ2mBG5JmaErAdZar9edcdK0AGqQqUsud3ftBedVuD2j6ItSP4Bd0XLuSvO9DbouES5a2FaZY2m9bA7sAAP0SV0BasznNJevyTdfGQCaUGtfJ4PfUGYKli+pZ6tXguvnuugWLCjTNajSw9Fcb4e4f5P3ZYyEWxJkBjJbaQwoVUL0N7Y3YyYF5MvNqVwMqliuLQGGNJR6z56ql6mj/TICocg3UYpTyvk4YVwnH4k86qpAdDCxfC0U87u7zujVcxGaQqUDpxWuNpqpYJwe5RHTT3Q42WIKA/0NvLnBzoUJO/58wMkXmeRofFYkExHYkkn3ZXzuw2gtY4YLUhEGKGd1Tv3bZYLJr1VM24Hcb1VO9p91a8sQylOxUPKbRI9j3VqDz8/Swo7mEpME+h5b4I4bwCoIWRXWvg6cZRiH0nBn4Wdd5FO1rPtcUCvNCMX7Kiv5+2cT840HVbb0wII4NUBTxc72q06MQE+Fnw1dP12XdEBG+DtHdTbUbHry/x16MzwyDVsrxvFmqTXPSidRl+/JPaPRc1IrSFp4zrVVdVDLMdPVnC7go+SFErko+ahR0zJplaJx/1o8L5dETEFI0V67R8gzIhhXHs0y7YZ9ISubfxtJeGCSlh9yBXKlUUxz/rquka8dcYAnLis66I5FieVc/Y5Cwv520OKIAMUlXwcL1rXQ48/lkXNFFR9cYIDbg/KLR9Zz1xaVoNAj3JD1rRelzHmuA1411q09PyP1I8VKG4Jn1XPQM270mjFooFBZiiEqDVQ7pYodocC1pwDiMIDPDzurhZPQRp1OF8p1M1XaEp3oDYC671/Gl9TmjVN9XqGNFz6TmXojZJXVETZJD6CFrDAsReObVLlU/U15YlrHWZ1QzjL6qEtgSi2pH8M2a1fmu1mpF29AwsWge0af2aqN5W60POTpNK6uW69C6XarmPzNBR1Eq/lpUAqCsRfHB0J11emo8e15acpXXFXo8hp1UAXOs+vPHhDHifZM/rj2hLAtXKBB0rJWpJy8jR/VmtMclamTlQmzygnrFJS46IpyCDVAXeOli5o3fTCogf9yjixz2KLe89orj93FebaxZjH/9kPdWZqYCxsoFq0Tpw+PtZuHuRvNGTovWablejjCoR5roPhOHE51119UlL3B/vY9qoYnHdxSH+eY2f7NiYJ2ojftyj2PY/5Xtab+zywNaV8dOLjVRvH2iCJuUb7fkaQt6Z4iFoyL7VQX81HpbwLoMZP+5RPMOpQiAAbDp1VfdntVSP0kPLB0tpTrosiJBBqgIe2YtmmCgWi0V4cKvYmRlGU2ETdPL6NNceSP5O5+q6M73VwPvI6gkp0VN8QM01MqhtFc3t2nmuqXrPsJYkFK3MHNgUCwa31B1fzKvKmZ28+5ojWmKAx3phjLRWcnWGsAxqo/96V4vWfAMtaLmLPtboObejVoqQ9zVtRI/z8XrlUOcBvvd1Fw1SeXrGJmdnjTeuAJFBqoJ3Oumv+SuHmReDmotXb3fUGjbVyhbD4LbapTO0JAR1qFlGd1bnH/34JVBpObZPNtS+RCeVQamEHnNOTVZmeBH98WVFNEz8jFSKmfiM+zhPo4luarJfA/39MFlj4o2ZBAb44dQX6jzdehLkfu/LP2HRDEY+WhMLFWJi7bJYepM0tRgqvOjRIFJzOJSd0d30GbJqUZssayTJqHAhfywbpq4YzMsqNcmdeb1dVdXFGVpVVVcFT4xzDkrNct5XYtf7Mg+8EB51xc2sVa7mAan3EazWUbX6rba62u/dtAJCCgfg9dn7Fbc1Ij+jplzqquH69GO1zPzrldce0zq6Wy1cvnUXRy6nqv6Mnnjen/s0QtfvNrvdJteA51KL1zbGQAUspez8ZpX562quf6ctyhfnVzCBxRK3VukkLbSvyXf5VytGPO4NyoejdmSo7P03+xVjJUA9nSB4bvxjhj7P2/NZpZS6CRGLNZXiwYVwMz1b9v3PutdGH50lxIsXDcTK4W0QeyIZ/abtdrutnntTPNGuVraYV4aSkYeUMITWbEI9dFYpgJ7DWQ8xmrNUEgA8qyOGqnzxYCx/42H0Vrnk3b5GGc0FAQB1S9F6QgHsqDUKGlUsjve66K94ptRHM6TAWOxDrjBA/fJhmpOA5Fgw2H08bIgXqinooZCBcczPz4I/B2hLStECz3AiM9Cy8qGHfedvmtaPkgrnIpSBtmdMdeVwGaOjx9scVn1ZQAYpAUD/kr3aJQYjqJ0NeqPQrxZOfN7FULyympCIZcNa4/d+TbgZXVYDk4Igld99weCWKB2i/yFtxGhmhdbSllL81KeRpEdd79KqFI0qlpD1kDWuWBx/v2rM+6eGF5vzLUH5WL1yeMig5J3c+TRDS9kQJtwKdR4IRTcdpXvVorZylBYhfDnmDXI/QdNSoc4IRodvLxgCJSGDVCe1yoXi4ehS+LmP+mxU70bfFW6W2//zHnVk3/vh+YZ4OLqU7qB7tfD+qkYzOUOLKHureHv/jLTfJlr/MrwWvKGykBGDWozU0Tbr280f3BJ1HuAvm1bX4D6UPGM/PN/Q8DgmN7/gnZlulPLF3cdVqk1IcofFYsHk3g/h3PjHDC//S6E2b4BFCIo7DdaPHq/FbHxtpmBkG63+F2xiyKAWyCDVye/9GmPmwGaql5O9Hd7LKkZ5sXlFvC2zDNmpdlnMHNgMZUKVtRm9hSOfdGY+KKjRmeNtkKqN55LC38+CE5+rF67XC28JFyX0xiKrxgSLdMnQVvx3co9eOhL9xDSsGM6mI27wdMayXsmgX/s2Rkz10rITJDOOnVGeqB+Jx+qWwxc95Z0WZqBGFk8tSrkEelcDRz1aE70aPoDWOpKizIAMUp2UCzOuGfbts8pVXZpUKs69ck/fFhW9MuPOmWESiRqvtqnCNfmCF0WDAvDXy83YNqrimcj7wWkkqQkwx1isWS5E9TKfXtxJ9VRnGOYi5dnjUVlOTP9WlUwp/wkA1cuGGL6/zVieNCPu2B1RJfQ9jx4sXQzT+zfVVJTC2wgM8MMPLzTkWjteiR4NItHjIW2FZdwhV6p0XK+66NXwATxWV1/4wSttqmDiMw0Me1h54XtP8gJEz4eUZ/7/vNZSc+UerXzSvQ7XpXeeuowsRaNrK9QVZ32MWJdqFXdPzuDiPQ55w3K4EhaLRTEWzCisluSVkLokeQtsszLwvn66Pp5VqIvu9TGY9zDbQeocnmS0Co+c4gMPY/7Dx2qyb1SBvi3YG6tdakegR4NITOvfBJOee4ipY+SBcOnVvt5NK2DiMw0Myd55MwXzW/kQv5gUg6pHMJ4VPMdqlg8Cnl4OKY8ZawNX3JpcPK1RD+nioa3Qq6G8J4CFiLeSTihPlCYlamlZxfwlsZkDm+KZxuVNqGrEhqcalceXT9VDGzcSXhVKGk/QqliyqPJGBjF7yf7F5hUR93HHvL8rlTL2HeWuGR7TS5ZJd8789lJjPNckCktfb4VeIo+lu2tMK3+/0hxPNSqP8U/WxaTnHkI7FVnxWpEqElKJwb3g7RQMzQ4fppObGFSWdYw/fFyYlT5Sswz6K2icsYbnWM3yQcDTS7zmrbZ4eMIGbu0Djv2XizEyerwaRIWjQVQDPN+0Ap76abvL+yy0NXs1LI8R8w4YbkcPnzxRm0k7LAwpNYjP5sPRpfGwSYlhLHm8XjlsOqm/rKMc/7zWAgv3XcL/utbA37vOM29fjNkGqcUChAcHOnxHIxQLCkCNiBAcT0pzeJ1HWVjnIzWoTRXUYjQR7FCrLDrcSySb+GwDtK1eGkcup2qqPKZEiwdLosWDfLWKpbytS4aqL9Ptq5BB6oV8+WRdHEtMY7ocHRTgj8/uZaqP61UXq44kIfYE+4eAFDyX7M2I3YqpXtpw8lpUiWC83bEavllzUvL9DgyEwguJSsPJFV4oGsQmRrNxJdeQgNVvcU7WMYGIMDaJcQNbV8b121noULMMnv9tZ97rBaFeNeuyrbzu4CaVSrhUp+GF2SF59t2x/I7OpSUBIDyYvZSes/E+8lF+S/jdGzyA7g3YxXZ6iueaRCGMw7nwNsgg1YFctjcL1r/dFlVK8xVg7920AhpWKM7MIC0TEoTktEzZ9z/iKMfE9Dkg8aDt3TQK43rVY9I8b69ZUIA/JjxZD5k5uZJi2q2qllQUdtZCpZLBOHc9Pe/vamW9PzFOCVbVkwoX8sfHEiUT/zZYtccZT1RbqWxwedgZM76DnwXgGd5s9nngsT+zvLxeWCDI62F9z3krZJCqJLRwAFIzcgAAr8Vor8muFt7GqJ3qESEY3a0WIhhIJa1482H8tiUezzSOQruvYx3eq1kulKtgPf9sQXbtd6sXiYTr6WhUUSqjlc3T8hk3lZ7+epmtMfTXK80xcPpul2U+Qh7WkxKznu0hhQOQdm/8e4FxPLoZ36Fd9TJYdzzZhD2ZA49jJmXk8jCEyCDVTr9WlTzdBVOgpCaVjO2VL73kizJDUvRvVRlddcpHiClZLAjvd6khOXj5/tjDzq3i52fBG+2j0cpLNeC08kB4EUx8poGnu+Ez9GQoC2NHenLDHrH3jPX4Z1a8bUGCx0TcucX3u9RANw6Vh7yxhrq342ntZLMoGJaVCRQrIDWbzcaXxh4p09NbS6xpZeGQllzarRUZivG96jKv5f16u6pM25PCObZZSYxaL6vfaoP/da2Bcb3Y6wkP71AN73epgbUj+Mbv2rOUyzGKsRVjVpynFNV9JMxk7Yg23LVCnWXoBsc8aIrhS0jz3xsPo0TRQCwYzGfs9kbIylKJUok1Qhpf9waaZZDy2M+0/k3yFBUe5BgKIiVRYpR3OldHgL8Fk9aeYt62nRkDmmDx/ktoUqkE9ibc5CasXa1sCLf42iKB/hjMMYTIzhc966DeA2FM6oF7EzMHsptI/dynEQbN3MusPTFVy4RgbM+66PjtJi7tA0DflpUwfds5bu3boWepOmpFhmLfRx2VNyxAkEGqkqplQjC590Moy6E8ZdtqpbHx5FW0qMJXSsJsPu1eG88oCF97E1JGIe+qN3aaVWHvJaoZke8BZJ0ZbQbiLg9qWwU/bzzLtP0yIYXxahvBmHuogu9WqjGD0MKF8EqbKqbtr+4DYRjegb+eKstyw51rR2Ba/yb4eMlhXLhxl1m7dqLL8nsGAY4qHTypWsY3vNKE+ZBBqoFu9dnH0wDA9889hGWHLusuB+atvNSiEtf2eVfbAfgvJ256tx22n71muF63FL4ULiGF2IQe2bUm6pcPLxCySYQy/w7zTc3FdtXL4J1O1fHmnDgu7fN6BgH666ProWigP+5k5Zq2P8I3IIPUCwgLLuTROrwsebB0UZy5egeD2vL1phQPLsS8HrnYG7rp3XbYf+Eml6B+MRVKBqNCST6lYcX2qA86SF06/WgBm7AR0vRuyn5VpW310lh3PBlBAX7IzLEyb1+MfcyoXz6c635YE1K44OtcEt4NGaQEU+YNaoGtZ66jc23jQu9m07VOORy+lIoKJYLvGYo+nv0rskitPmiRSonvEwWfJ+qzVyN4oVlFlCwahEYVi6P5uHXM2xfj52cpEGLsPKFMe0IKMkgJppQsFoQnOC4r8eTVNlXwYOli3LNZzSJU5PEo6oMqEW2qlca0fk1QtYw52ryEd8BDWtjfz1LgErJ4EslBTUHMc02i8NuWeDRjvMpF+Da+95QiCLBUB82nkL8futQxViLUmyhcyB//vfEwbLChcCHf1LFrx7AGNeEb8K5K4+9nQS7Psk0+TKOKxbE34SZ6c1DOEPNelxpoWbWkRyW/CO+DDFLCp2hXvTQ2nLjKPWGqoFArMlR5I4LwEj7tXptp5rsUJYoG4qqbUsf3MzMGNEXc+VtozkH1Q0xggB8eqeF7YV0EX8ggJXyKH19shAMXbplWoYYgCPN4nHMSIUDC7O4oFhSA1tG+rR1N+C5UqYnwKQoX8kezKiURUEDKtxIEAez8oD3Wv90WJYoGct/Xq/f0VH0x8ZIgCjLkISUIgiA8Ci+xdykGtq6MFg+W5FY9iyAIfZBBShAEQdw3WCwW1I4M83Q3CIJwgtY9CYIgCIIgCI9CBilBEARBEAThUXxyyd52r+pMamqqh3tCEARBEARBSGG302wqqgX6pEGalpYGAIiKYl/zmCAIgiAIgmBHWloawsLcx25bbGrMVi/DarXi8uXLCAkJMaUmbmpqKqKionDhwgWEhpLQuDNNmjTB7t27TdkXnQvvQM15MPO6uF/x1fuhIF4bvnouvAkW1wWdB+/Afh7Onz8Pi8WCyMhI+Pm5jxL1SQ+pn58fypcvb/p+Q0ND6QKXwN/f3/TjQufCO3B3HjxxXdyv+Nr9UJCvDV87F94Ey+uCzoN3EBYWpvo8UFITYZihQ4d6uguEF0LXBSEHXRuEFHRd3N/45JK92aSmpiIsLAwpKSk04/IwdC68AzoP3gGdB++BzoV3QOfBO9BzHshDqoKgoCCMHj0aQUFBnu7KfQ+dC++AzoN3QOfBe6Bz4R3QefAO9JwH8pASBEEQBEEQHoU8pARBEARBEIRHIYOUIAiCIAiC8ChkkBIEQRAEQRAehQxSgiAIgiAIwqOQQUoQBEEQBEF4FDJICYIgCIIgCI9CBilBEARBEAThUcggJQiCIAiCIDwKGaQEQRAEQRCERyGDlCAIgiAIgvAoZJASBEEQBEEQHoUMUoIgCIIgCMKjkEFKEARBEARBeBQySAmCIAiCIAiPQgYpQRAEQRAE4VHIICUIgiAIgiA8SoCnO6AHq9WKy5cvIyQkBBaLxdPdIQiCIAiCIJyw2WxIS0tDZGQk/Pzc+0B90iC9fPkyoqKiPN0NgiAIgiAIQoELFy6gfPnybrfxSYM0JCQEgPAFQ0NDPdwbgiAIgiAIwpnU1FRERUXl2W3u8EmD1L5MHxoaSgYpQRAEQRCEF6MmvJKSmgiCIAiCIAiPQgYpQRAEcX+x8Stg1lNAbrane0IQxD3IICUIKVIvA8f/A6xWT/fEe8m8DRxdAmTd8XRPCEIbGz4HTq8Bji31dE8IgrgHGaQEIcWkusCc3sChfzzdE+9l8WvAvJeAxYM93ROC0Ed2hqd74L2cXgskHfJ0L4j7CDJICSL2S2BKUyD9Rv5r1hzh/zPrPdMnX+DYv8L/R5d4th8EQbDl2ilg1pPAT6093RPiPoIMUoKIHQtcOwHsmOr6no2W7AmCkGH/LOCbGkDiQf77slqBaY8Cfz3Df1/XT/PfBwDYbOzbu3YamFgL2PUr27YJ7pBBSng/1lxz9pObJfEiwwHz1gVg+w9AZhq7NuXIzgB2/AhcP8N/XwThsxi8v5cMBdISgUWvsemOO27GAwlbgVOr8kMNzBobWWO1Aqs/BCbWBO5cY9PmnWuCITqlEZB6CfjvHTbtEqZBBinh3az+EPiyEnDrPP992Wfru39zfY0Fv7QFVn0ArPgfuzbl2PQVsPJ/wOSGfNpPPsanXeL+IkdqEmgirO5ve4gPT5wVAa4cAcaVBzZ9zX/fLLFagZ8fBrZNFoz5bZPZtLvjRyDtstO+fNRgv08hg1QtN+KBDWOBO9fZt52dQdnccmybDGSmApu/MWFn9x5Oy9/m03z6vWvn7AY+7Ys5v931tewMdgP01OZs2pEjfhOw9Tv2S3qE93BoPvB5aWDbFA92gtH1de0Em3bkOLESmNrM8bUV7wPZ6cD6z9jvL/su+zbtpJwHrhzm176YA3PM2Q8Psu/mj39Z6Z7ti0mQQaqW39oDG78Elgxh2276DeCLssD0x9i2W9AwwzCR3AeP/VqEpfQ1HwO3kzm0f28fYjLTgHEPAL/EcNofY2Z0E47Pif883RPfI/O2b3iGFgwU/l89yoOdUK4eoxqeRsM/fZ1e4DQeXtwDjAkD5vd3fD0jxTcnh8lHzQmRYk3KReCLCODv54A1o4Gx5YD4zZ7uFXfIIFWL3buVsI1tu/YH7nmG7e6bCWyXSNAhvIdf2wkewIWv8mnfuUxbwjZhWTHJhOQLlswfCJxa6+lesGPao8DE2sbbuXVB8JA5xwjfTr438WhrfB8Fjc0TgYPOMm4Mjaz9M4V7mgtO97OzB5NF6EPiQcHx4szZjcD4CvxWjngZ1wCwfYoQ1sBt4s+J/bOE/0+uBLZOEn5f/aHHumMWZJBqhuGMWo6cLOFhc2qN9s/abMDS14FVI4VZllmkJnL2ypjhIbUCx5Y5vcZhvxaL4HEAgIu72bfvSQ7NB9aOYXfccu4Cfz3Jpi1Pk5MlJKWkXpQwjDQypzew8ydg+uOOr59cKfxfUPQjrbnAyg+A48uNtZN4EFj3CbDwZTb9kmLFe4JXP/k4+7YtTo/qLd86/v19A+P7kFIZAfJDAvb8bnwfUuRk8mlXjP2+MMKda9K6tTlZ5hi8RhVfErYDy0YAGals+sMBMki1knnPkLh7S8iwjN+kvY3LccCiwUDKJcfXz8YK/+/5Q3jY/PWU9rbFhoBZFXTObQEm1gBm9uS3j7QkYSnSzuGFwPJ32BrBNhsw9wV27ckimtRk3ZbfzNAuTJg4SbFgoPCwPLNO2+d2/was4xALJ0f6DUfdWTMQP1AWvmxswmg3OJ2TOMyYMBslN0e9t+3gPGDHD8Cc543tM10mk9vZsGPBXQ7Xlc1pnHOWZUp1epao5dYF4VmUeNDNJJLBNZV2RWQIObW38yfj7QN8x7zUROCrB4FJdVzf+7EF8HW0EO7AdUwxOMmf1kWYVPCIOWYEGaR62P8XsO5Twa0+o5v2z//SFjgwG1jwsqOR9Wd3YVBY+b7+vjnMokx6ONmz0uM38tvHqdXAV1Xz/57fH9j9K/BzG4Y7MSuG1Aw8bJholXJZ/jaw+WsgyYRkh9xsYEJl4Ueulnl2hiAOzhSna+m0RqNdjttX839n9VDOSAFunnN87WZCvmffCAf+dlSyAIB9fwLL3nJN7nQxuBlz4yx7I8JmA+7eFIw9VuQ4eeZsVv1GqJj5/YVn0c8Py2/j7J3VSvoN4JtqwPioey/44Jhqdxbduer6nn1y8Ft7YUwx6vHNTJPWgb1ylE3ys1kaszogg1QPS4YAVxlkVV497mp8JsZJb7vlW+C/d1UshYren/2MsNTFPYPfJOMnRyLz88phY99PHPAudWxZaeSJYX24bDYg8UB+UsX5HXwnB1LkOsne6F1eyjRhOUlsVN29Jb3Nbx2AKY3ZGY0Av6SQ/X8Cfz9/T4qL0cU1oQrwXX3BKM1IEZLhvqsnSLCJuXJU2LeWEIE7EsubS4cJK0POS6tGlyltNuDyfvdLqpL6w0b2aRWO06Q6/JZybVbBmDaKQ3iBzPVpNO5c/EzbNhmYP8BYe3q4flq4Ts/vNN6W1SpcU3KGp5TRqoYrR4Q+jisvXbLalgus/Vhf2z4CGaRqkPKiJGzJ/31mL6E6BAv+6CL9+toxwK5flAd+8UPvZryw1CUXG2QnYZvwHfSKqEvJGGXeFm5+b5ezEuubSj38zokyG3OzhTgcT2snOnN4geApntZV+PuPzmzbz80B/ukP7PzZTR/mO/7t1Rm5YqNNpp9X7t1nB+cy3C+nY7LuU+DEcuDPHuw8pHZdzYRtwIZxwgMYcL1HZnQT9v17Jw2Nu+nj3ZvC/9fPCMawvTytHnKzgU/CBWN60SB9/dFDwtb833klEd6IZ9OO+HqRM/6zDaoHiJ8Bqz8ELu011p4etn4nXKd/aLhOk48BqRIe+h1ThWvqn37Sn5NbdVHij65CH93BQrP1zHoPS63JQwapGg7Nd//+mXXAvD46GpZ4QDkvzTijNDhIDSpSMTp3rgkhAocXCIbMmXXAXD3fAfkPESB/8Jn+mHDz75uhr00tGPKiiAdkhXjUVaOEOJxlww3sT4L4zcYM97jZwv+JccLSJ2uOLgaOLBSSNjaMk/ZwJB3KN1oA6Da+lOKe7fs3crw0GW0MjRXn61SP8WizCTHbUtxOgkN/Hc6HBpzjst0tm9tjM7UYLe6WgC/uEq6ByQ0FD62R5fQVMqFPLLSkL+wW4gqlOLJY9AeD60fKwAkuCYRFub6uGVH/eCSlWq1AfCz7dnmTclHQWp5YU5iQi1ectt8z5uQk6eyhPqmJwnWilkwG4TBq8ajUmjxkkKpBzTKingQFPV4kxWxBiTatOcDJ1Y5LlUuHCXExYuOCRVb+AZFxBAjxYrxZ94mgnWc0c1lpQN51z0MY95ex/ThXnZrxeP5x04Off/7vS4fpb0cO8fW/cbwwiXFm+xRHjVMtkwSx0fHXU/nxWs7kZObv/9hS9e27Q09SohJpScDvnYWEHDHO97ue+//QfPWaxXp1C087yWyxNlTcGeJ7pzuW4azaQf9+5LLCbyWo748UF/cCv3cQEjmVYOGxlkp8vLDDWNuLBgsZ1+ImeFSb2vO7Oq/e7avCdWdkZYWl51Ucy75pguNzTGlsK15R+H9iDeE6ubSPXb8KOGSQMkPP4KDj5lPKCpW6WdISgdlPCx5RQPCOSs7uGCwpHlnk+LfFX3o7IzjLVmz7Xvhfj6SLeFBPS1L/OVZLZnaOLtH/Wb8A5W3EGnaJB/TvSy1aYqydNTPt16mYY8uAz8vk/72SUfnVBQPlDWBA+0P/7EZg9rOCwbDwlfzXE7Ybq05jswEnViiH34gnS3qTUcTeTotfvgYzMxSOqXiyEfYA433DuIF9Touhz8AglUsk01tOOeWiMAHe87tj+JHepWZ3SMVCSjGlETDrSddJnBbOrNf/WXfsne74t1av/fkdzLpS0CGDVBUqBhVvUVtxN8O0L+HJVethEvdnETwIeX8yvsROrZWXcDHc9ir127LQ/RNj5DipMUivncz/XS5OWYrTa4Vge61s1xCjpObB6izHZX9IX9rn3qBUg9wSuFaSjwF/PuGamLh/lhDqYY/x1cOxpULVlssK3haxscTk3rO4lqFlFS/vKexC43loHLwVPYmicdSIF/P2VeDHVsCWSfrbkEKcxGUVGaFqPKRTmmocD1R+f/v9zEIvlAlunoVWjYa7pyT4xPAsBcsQMkiZoScejH0vFBvNSAFSZORIstKASXWFuJeD84SgbT1G6m+P5P/ux/gS++tJ4PuHjLdz/QzwXQNgr4oY17VjjO9PCSOeZD+Fzzp7PuwesPVfAD80k/fAXDsleC2cJXq8gex0YHJjoeLVn92lkw/k4BVDKvegXjJUw/5k0LP8rtcgFSsmSB2rKY30testHF8m/brNJoREKAnbKy3ZOsjqWIR2Zz8n/KgZT625gsrD11UFr/reacqf0YK4D2IjVI1Beu0E3yx5PfkA+/8SQrY8wW6JsBB7Sdw8GBuk1lzgwBxtKgusE105QQYpK/TMgtQEMY8J0xYYrXRDK+kI3jovGH0LXxGW3zd9pX7fUlj8BINozx/6s/h5sHyEoEKw80flbfWIZzs/eJQMCrHhbrNpmwgoGbNSElDzXhJio64elx5UAXP06ozEcF4X6YRqMUidObxQuM+cl+bscFMM4KxEoNcg3fFD/u/isANWaBkr92gwxjLTgF2/agu9ETOliWBMTG3mfjs5qTA7zobd7WTg5ArhJ0Phs4CwxKungpvaMVbuGaE2hjRLZQLb3ZtC6IoWxCs5alkyRPtntHD7ivx7y0e4vuashMPaQ7rvT0E1QotjxowwLQaQQWoWV44Isjm7ftX+2d81BPYrPTzV6AWKM53jZJJtMm8D238QxLIdO+D459lYYbtlbwmZs1rgVbnnznXjy7zuWDJUWNIXH8cZj8tuDiDfqLTmCl6/WRrKZSp5SI9LxAuLY1blHlBXOZRAdEZPYQkptGSUO98jN+49xP9903XbA7MFL7JesWt39+Ot88K9kSmRtOLMbh3jht4Hod7sfHek3xCkZrQai1rE3/97F/jvHR2hETbhvhNPcNwRN0t90xY/xxAj/0AV3dEZ46p2jBXf74WC839XGxvrzkATs0BHTH/yUeE6UaP/fGmf/GTaKEwnoSruQ7FSjRJGpNC8HBXBZ4S6ZQSFi+7Hlky6oozCjaSmBJ+aZZxVHwiSTs4eVKkbee1o5X1KsflrfZ9TYn5/Pu3a2X/vgTW3D9B4AFBTwRgF8r1Z106KNB9t6owKJU+YUg1quc87i92zhqVGrXNVIbn97fzRsRiC3HZirp0QPLnRHbX36+hi+fc2f3Ov/VNAt0na21aCd+za9h+UtwEED7T9nov7C2igswxoRgpQ2M3S7IkVwv9aBeO/jgb8CqnfXpMOsc1x7M/OAAKLKnyG43m7cdZRP1OPxmiuysmZs1qDWlaPEoyugQox/b+209e+MzlZQICKiQJPFmsI69FaltmHIA+pGtQMcN4QuAywX14UB8Afmi/ErgD5GY3OMzupKixi9s1k1zcpsu8qHwOzqhidWSck46g5J1JeTtXn0uC1J2WQ2mzAhs+NtauEs5i+EfwVDIrsDODQPGEiFTvO/bbiGOg8dB7j9SqOodL1ON85Jk0lWgynrHRtY8e108KxFCP1+YwUxwlg8lH1+3BGSaPWyBisNlHl/E7pinFyHF7o+LeSzjRvfmpj7ByYhdalfr2sGgV8XlqoOMYScSa+NUc5zEFO05QFl+P4tc0YMkhVoWagNtkgzUgRfq6eANaMzr8BWBuk9gE0+64QX7VokGCEyiVGKYUELH2djd6pHF9ECNVZWJfsk/peSp42O2rqfyceALZPFcSY7Sh55m02Ib715Ap1/ZBDyiBV+92MkHyMXVsVWsi/l5kGjHtAoVqPCKklazWrJFL3Hos4XL2G+6qR6uS34jcDY8tpS3yQWvGRSv6TMiL1huLYrMKKzCkZzxtrRQ9nDs3XVukHcI2JtPfRZnOVrzODLMb3dUaqcN6dx0eeldpys4G1n+j/fEaKELa17K18NZDYsY7byD3ftOzDzu5fhfvrgETVt5vngDUfg1s8+dElrrJ6XgwZpKrwEu+nnZwsYHwF4eeHpoKMSV78G2uD9J6XRTyzVwrqV+Lb2sBYJ31Bm41t3XiXTEeDSEmvqH2wfllReZtrJwUDwgElT+8m4WGgJf5ICskHuRmlPxnuw50xcm6rcdFvLXGD3oSSbimQH998QUOdb6llW7GcUm62/HWpdsnXmRMrBI/zX3Lx1ZzHaT1jivN1aZdom/0sMD7KccJw55r6MB1vYe1oYVL8U2vH1+e+yG+fu38HtkzU//nxFYCvqghJYHacpcxWvKe/fQAOY5t9hXXRq66bTXtUKGuqlx9bu89LEReZ8AHIIGVF+jU+wsJSSMXL2cWkDZXRlMBuiIpnvOIbWS/O1UdWvA989aDrEpdeLuxi0447jAidq8Hd9ZR5W58+qBRSS3i8a9FvGKtPvUCJlIuOfU86xKbUq6pCCHqPGWcDxH5Msu4Ay9/hU53KmV9igC8rASkakpKUcLeyYrM5Jg+d2cBuv0ZwSRS6d43YNY/tKgLHlwvj3/K3jd97az5mN44qIbUcfPeWvLQWC/TGprrjKsPVGkD9OdSStCfFlUNCIp+cWD/vcZwxZJCy5Fep2DMOSMU7Fb1XxYb5BXivPbGha6+MxIrM2/llOfUmQPFG0gPH2ZBwJ2Y87gEJj6pOpEqhspjYyGWPX9wLbPzSePtibDbBc/JtbSEuDBA8dD+1FiqVsWifFzc4yqHtnS4ck3/6CklUu391r2zAQs/xxtn8ydqJ5cbbs3PXTYWci3sc/57Zg91+WSJXdti+2rLnd+Peua3f8U/ctCM1TigVbzDK6TV822eBmvGChWPHjp7kNC+EDFKWJB00Zz9SXgd7MhFrD6kdXu0CwITK+b/fOg/M7Gnci8M6eUBSWskmeHb1SHmpQaqGtVmolXZxhz2L3BnmpSgBwAas/kj41a6j+WUlds3nZgkPmeRj8uoDRoxWNdJPRji6RP58sGTrd476iEbLdIrZ92f+78nHHZO23BmrrGARoyrn5Q0Iyv/dF5KO7DhXJbNa2cWGr/k4//fkY8A//bWVJNbCA43Ztuec8CfFsrfY7S9Xi/LDPYyG3nGADFJfZPbTbt7k5MnhMQP7+3ng6knXm+nMejb6lM7JKbk5+mNqDvzt+tr5HcDOn4QlEzssS7TZ4x5tNuG72LUweS/D5GY7JlfpRXaJlUP/bTY2BoNcRmpgsDDxmNo8P5bQahW0EK8cEZbMjMSZGo1x9RbERgSg70GphqnNhOxoltJhYqQUClhcX7t/Az6PcH1da310j+JmZWhmd3XGmBq2fpdvgP7RBTiyUMiZ4EGxsvm/swi901KGmgUZKcIzwmYTVihSLgqOHXeKEGboTGuEdEhZM7MX0PotoGxt4FYCEMmgzKVaeJVPy8kCvqvPvt0Ty4UYGF78EgP0ngNUvyeUfXSxtGGpFykB6y8kHjZG2fUrsOJdILoT8MI/bL1OdsaEAQ1fAp6YzDbDfut3wNmNgsf6gUZAp884edsZJYPIZaReP5ufSGHXFt38NbDhC+P7LMjs/Ilv+3v/AJq8DObhM3F/AY2dlr1ZZfFLGQkp59m0LcfRpWxK2CrBOj7ZrtKgpsKVEbLvCA6G8k2BQ//w3RcghC2xZP4AwRBtN0r9mMRz1VMn5CFVg5bklTPrhKzVCZUFg+i8hsxVb2XeS/zavsV5IP77OcGLEr+Zr/5obk6+Nisr7J5Q+0P91Grhf17eNPGSKAuuHhM8ZmfWAQlbhdjjzNv8PLxig3T2s2zbzkxxjUVlaYzKPRx4hYOc2aC+BKQ3s/xtQWPW7aqRDpwNoNwcPt7ewwvYt+lM+g1gXh8gk5XMlA1I2O76Mmsjy74vMzgbK8iebZ+iTqbPCFeOyGgdG8Ceya9lTGItjcgAMkjVkLBV/2dZBvV7CqM6l55mzUfCJIG1wSVm33RhiYQlNiuwbbJj0sve6fwTFlipB0hpt1r8wOUhk3jQ8UFyciX7ffAk5WK+d+lmgiBVBTiGg7BkZg9g8WBz1Ch4I5ab4sHx/4DPSvJp+04y8G1dPm1npAInV2kv2ayGaV2A1MuOr/F4Ttjjws1i92/5ZZx5sO5TE6s2KsBCI5kxtGTPm63fAVHNPN2L+xu7+DFPdv7CoVEbsPpDx5ek6q2zJDuDXX15KU78x0dn0VnY2tf4+WHh/37LgemPCb+/yrmi2NHF7kub+gpKlbf0cHCeEHoFAHN6s29fDK/l+tnPAue38WkbACbWdPzbuYw0CxK2SgvK8+JWAnsJKDFmJBaqpVoXT/fABTJI1VCiivb6yGLU1I8nfBsj14ccvOPvpPiirPI2RlgwEAgK5bsPX+acaDXGE+efEPClTHc5eBqjZiIlKM8TlnJM3kx4lKd74AIt2auhcLine0B4O2prYWuhoA6MzGLZPEjcbD7tiuW2WCbgEdr5/iF+MbwE4Wl4l9rVgff1yBvxwhNHEIQHWTyYT7u+VDayoHPjLL8YXoLwNDxjZXVClpYayCAlCIIgCKKg4IV2jff1yBvxwhNHEEQBZPdvnu4BQRD3A15o1zDv0Y8//oh69eohNDQUoaGhaNGiBVasyJeDsNlsGDNmDCIjI1GkSBHExMTgyJEjrLvBFi88cQRBEARBELqQLIftWZhbWuXLl8f48eOxZ88e7NmzB4888gi6d++eZ3ROmDABEydOxJQpU7B7925ERESgY8eOSEtjWB2GNWSQEgRBEARRUPBCu4Z5j7p164ZHH30U1apVQ7Vq1fDFF1+gWLFi2LFjB2w2GyZNmoRRo0ahV69eqFOnDmbMmIH09HTMns0pa5UFlGhAEARBEERBwQvtGq4mcm5uLubMmYM7d+6gRYsWiI+PR1JSEjp16pS3TVBQENq2bYtt2+Q10zIzM5GamurwYypeOJMgCIIgCIIoKHCxtA4dOoRixYohKCgIr732GhYtWoRatWohKSkJAFC2rKP4dtmyZfPek2LcuHEICwvL+4mKMlnQlQxSgiAIgiAIbnCxtKpXr464uDjs2LEDgwcPRt++fXH0aH7lC4uTq9hms7m8JmbkyJFISUnJ+7lw4QKPbstDBilBEARBEAQ3uJQODQwMRNWqVQEAjRs3xu7du/Hdd9/h/fffBwAkJSWhXLlyedsnJye7eE3FBAUFISgoiEdX1eGF2WgEQRAEQRAFBVNcfzabDZmZmahcuTIiIiKwZs2avPeysrKwceNGtGzZ0oyu6MT7gn8JgiAIgiAKCsw9pB988AG6du2KqKgopKWlYc6cOYiNjcXKlSthsVgwfPhwjB07FtHR0YiOjsbYsWMRHByM559/nnVX2EFL9gRBEARBENxgbpBeuXIFffr0QWJiIsLCwlCvXj2sXLkSHTt2BAC89957uHv3LoYMGYKbN2+iWbNmWL16NUJCQlh3hR1kkBIEQRAEQXDDYrPZbJ7uhFZSU1MRFhaGlJQUhIaG8t/hnBeA48v474cgCIIgCII3Y1JM2Y0We41cf2rwQgFZgiAIogBSvqmne0AQHoEMUjV4+5J91Y6e7gHBGwspPRDEfUGTl4ESVTzdC4IwHS+3tLwEbzdIn57m6R4QvClby9M9IAjCDOo+7ekeEIRH8HJLy1swuGQ/6gqbbshiAUpVk36r2/ec902YAu9JUekafNsnCJ4El2TTjtw4aiZ+9FgmDNLgRU/3QBd05avBqDFQqDBQrr7ja8UrGWtTjMUCvLQU6DBG4j0Gp7j7D8bbUMPoW0DMB2zbrNyGbXuewo9LDYt8ekzl2z5B8OTtk2zaCSjMph2j+F6uMeFN9PgBKFvH073QDBmkatCa1NRqOBD5kONrYoOiZDTgV8hwt/Kw+AGh5YDWb7m+Z8s13v5DJs22eCSPPfw2+zY9wROTgaKlgUe/5tO+vwcroRHeiX+gp3ugHn/OEzZevLwOqNha4g0fNUi7fgWEV/R0L7yXoqWBBi+Ys68+i8zZD0PIIFWDVi9jkXAgvILja+KZd8z/wHbAcWPIGR0cut/znPX1UdkrM5OBHv8WqPcsn7bL1gbeOQU0fYVP+/6iCRJvb6wZSK0WENoIfcDTPTCfoqWkXy9ZldMOLcCzM/P/rNaF036cGH6YT7vNXgUGbRQMU5bUfMLx765fSTtgvJmgUGEMF69G8QypK1aGX9ucIINUDYWCtX/Gecmletf83+s8aaw/zih5FpUG0z6L5d976N5srvLDQPVHNXVLFS1eZ9+mGDMluxoP0HetqIXndykcDtTsBlTrCnx0DWj7P7btPz3DNN07AECxCPP2VVBhFbc88hKbdsTwmvgVKSE9Hg7by2d/sAHBJYD3E4TJf69f7r3MyUP6+CTgxQVAeBSf9gGgSHGgyUC2bXYem//70zMEw7dRf7b74E2F5q5jeAiNU2LIIFVDO41xjTab64Un9pDqNSxkH7Ki9l6NBVq+4fh2xZbu232wnb7+GKVcfaDzF8CLC4HB2++9yHogFh2bSg8D/7vAuP17sIph8xQWP+DZWcDzc4Tr8+G3gacYqjfU7sGuLSWqxAgGS7UuQGAx8/bri7ibQLEySIOKAU9PZ9OWnQYv8NHrtOWaOx7aDc8i4cLkv3CY/Q0++2vcH6jagV17IZEy9xjjybPFArx5UBijanW/96IPhDV0+TL/91OrXd+3Wc3riw9ABqka1Li+O36a/7vNKnicxEQ5DZ56ZsAthylvE/kQ0Okz8Y6070cWxoNM4gHh/6rt2coa1X8+/3fxQ/W5v4DCnCp7hZTl0y4A9PjR8e83D7qPD+r4mfx7Ytp9mP+7ywQqEKjTS107Wuj0hfD/g+3Zt23nxYVCpvLzc9kbQgBQvgkQ3Zm9F7bnz8L/9XuzbdcdxdxctyyVHWr3BF5aAvT9V38bYkPKYgEXg8T0pCaZ72BGUlPtnvo/22cxUK6BMIGV6iuP1ZziFYVVHHvbrI9RhRZs2wOAZoPcv+/8HaoYmAw99o3gdHnkQ+VtvRQySFnRVHzh2YD2HwvB6j3vLcGUqw8MWGUsdkcc2xdaXvASNH1VMB7kuB+zNeUGQzP0ZHkMxPWec/y7eEXgwUfkt2/1hvx7Yqp1Fv0h0293mZrtdAx8LV8Xlu5bD3d8XTKxQyXOkj/i8xzAIVmrfm/ghXmOk1AWVOsiHJueP7FtV0yr4cALC4Bn/wKimgO9/5bfVsv9ElZBCPdw10aVGGOqF9GdlbfRgpTSid371mW863sRddnuHzBnfC5UVPi/MaNl9PAKghd50EbhuRZaznUbU0KlGBy7j64L990jHwL1njHenjPi4yA5AbAJIQ52qhm4xhu8APRbBrR5V38bHoYMUh7YIATH918O1BfFOlVoLordMXoz2YTg6EcZB4+LCQpz/FvvIMMzrlJpf+Kb3ZQCBxwGYjldwndOy3+m9Qht+5A7t2FuYs0qNNO2Dzn6LhMMPL2ElXf8W/xdWCe19ftPFLvG2Jjwc9PX4YeMt99uFNDxEyC6A1DzcWDgKqBMTfntjd4vTV6WDmUR35Ms0ZpVHCyRwGRPKmo+2PW9/iuE88+Skg9Kv87SUO32neCZljKy9eB8/p6bLUw0WB6b4pWESZMdqePB4hj5BwgrKW3eBRr20/8d+i0HAoq430bqfrJZHZOz9Ex66j0LDN0NFJLYf5na2tvzIGSQssLhgc5p1muxANUfE35vMVTdZ/TG0D3/DzD8gL7POlPJgPdLD2KvmPh3uQdsYSfDu8nLwgDz4kJj/XhutrHPK1GstPx7HUYD78W7vi53DOQM0scnau+XVio/DAQW1f95d8vyzt+3ZLT+/QBApVb8hMvFxrNzVrGU8aSV6E4a+6Ple0rEzVdpJ32NthqurR95/VGY7IlXDVRVO9K41BwUIpx/I9Ts5vi3XFY/y2eIfyHBYHS3kqYFZ09f6eqCwWv02Igp8SB/DWnnkBs/P/3fwd0zrvS9SZ9UIp6LUW1xDdFSosePQGmZgg4DV5snM8UAMkj14i47XOvMre7TQK9fgUGb3G/34CPCw3fQZqD5EPfbdhkvGFZRTaX7U6SE8H9giPTnq3Vi6MnQ4DVkMeu1xz4WrwzHgV2mHw80dvy76SBhgKmqMs5RbimxxmPqPu8OcTysVoJLuL7mXKDBjpzxERqpf/9ukTgXRXXIlHx41X3db2evo9I9pgWlZEGtiPvqnH3rbChVelhb291/ACIbSL9X43Hh/6BQoKLogSzlJZSj1Zuu3nQ5467Vm8IYphVxfF2Z2tJL7k/9AdR5StDtVSJIZuxzhlW8c/FKQlKOHXda1EyX8uXa0rmaYyS8RgsOYxIHJ48a5QTnCYQeXlkvJO1KLsdLTYo0mmXuVlaCivGVI2QMGaR6iPkA6PS5mw1U3DziAefJ34T4FTljYeQlIZGlVLQwyy1XT9lb0HywEOQst13/FcLA/eoG5b4apekrQqJNqzf5tO+8zPFAI+CN/cDgbY7HWW3IgZbQhN5z+XpCgxhnics+6EyUx5KjSLi27VsOU/b6iAf317YAgQzDR8IrCEvpEfXYtCf2kFpznN90/FNrprS7kphPzxCW/P53HmjYN/91LQ+xJi+7qpFcPyO9rcUijGFaKVVVkEd66yhQtKQw6a73rKNGcp0ngad+l16+dMa5eIkc7oqL1HlKeak2rx0tRhVDAyyqub7PNXtN+nUz4kMtFsf9SC7ZG8xQVzO2iicQegkMlk/adf5efv7sQ8sCgvLlxLwcMkj1ULq6600pnu2ylnIIKiYksrCkTA1h4C6lYQlTrzh0dEch0UZNEoyewS6sgutrJaoIA4F4KVg2nlDH4N90EBAzEqjexbFKTMOXhP/1PgRcusYx6cEh1tLNcS8tF2fI+sGksT01A7d4G6n4LCOZxoBglLKSlvJzY5A6n59aTkv6RvAPEJb8nDPX1T4YH/9W+KzzhOIKB/H1IuFA2D3B/qKlhAdtZRlvsd3gfGKKTGMWwcOuRAmnOE/76li374QxtERl5TYAuI4zbu5te2GHxgPyPdha6fadEMMrlXSkhjbvSb9umrazgf3Yx2EjSD1XpLArM+g5Ljar4/m1+PukoD0ryCDVg/OFV76pY1yZc5UmSWQGo6f+AB5+R3fXVKG3FGjb94RZc7//9C2v8hrI3E0AQiIEqaHHJsqXF9Rj9DUZeK/ilhORDYRqHP2Wa29TEtYGqag9m0rj47XNwMA1jPvBAhXXk5JR9chH0q+3fV9DPxidI/H94RKG4PRdS1QB3pXxQAJCYYNRV7T3QXwvqb1fGw+Qfl2thE2UiuS46jrCXwauFRL/GvYR7kmpcxoQKGhpyn2+6atCPLaYTp8DI44Djfpp75Na6j8n7OOxicAzMwXDUqu3LqCIMTk62fOvcRxn4WWUusfcjdtqQjbksJ/Xx75Rt709Vv+BRjp2ZnMMPfHzAyq3VbYB7MoiPKSqPAgZpCywu/77LBIExY3oCNZ5Emgv85BkRROd5ScDiwJdvxQCv986Aoy8KJ04I4caw89dPKBsu6IlNamBqOXrjpVD2n3o5C3VYVC4M3SKldFfW9s/SIjls6N0zNQ8zMU4tCc2SN3EIfkXuheP62WIz4FdccA5HthdfJU7iojib+3lc+WWeO1GbZOX9e1LimaDHctIShkHsgkxECZGepb+1IS4RHcSvG8t3xBqscuhpC08cK3QxtMzlPvlrOOsBv+A/KSqYmW0Ky5ENRFUTJyTHi0WfV5H+7Ht8Inwv1LySmg5YV9+foJhWbObEAvMCi0OAvGKj1bHgppEwuYSSbpK+1EbA6yVxycJz7VqKpMA7WFAT/7u+Hr/FcqfdR7fLX7C91ayAUpVA94/x9Dx4R2QQcoC6z2D6MFHBP1RvQ9BMygcJi31Yh8k1RIQKAwIUokzcqh5QNZ5SpCn0YI4Ps5dooCdtu8C/0sQfb66tv0B4BZz+VEyMGS76AUFg1TN93VA7CEVecOUrtlipYW6yz0ZxSJJPWy0PujE2xcrDXxwWcgqddhG570ovlZDIoS25YyvSq2EydmjXwsP38Lh7gXnpShdw/HvQoXzl22FDmlrz2bTaZCqCDdqMVTwInX6DCjfWHqb6o/Kx8TbiWoitKHGuJOTR9KE88P/3jG1T2Lbf8xgH+52f2//rYcL14se3Uup60r2OGucaPee41rlz84z4kmDymvxmT+FmvNlaihvK6WyEVBY8B4WLSPobjsTWk4wHu0TRne0eVd4tthxNxmwWNQ/18QGobM3Wk3SY+gDcDieWsarIsW12RpK96MXQAYpC+I3av9M96kALPmVa7giGpjeOS0tFq5ruUEj/gGOEj1Sout+fkJogAMyA2Dff4H2ox1lLapLiHNLERQieGjajxZK9snhYBSIu6RyUJYaSOWwe9rEuppKunRGwiBsKj2kdhr1ddTVdYdzpTJnHmgsyK6oWXJyKDohxum7BxZ1HaDFRpmW0Azn+FqptsUEhQjbDd0pLA/7a5DYeeuokIDnjJ6EvPwPO31G7ecZhR8oVajRQqfP9cdRiiktM/GM7iCENzz8tr52VastiI6tXu8e04QX5/unmLq4crV9qNVdqDkPKJ8/l33dS2p6fS8w4qj8ilPj/sL43Xuu+/Yf+dCxSp+7JD+1NOyrX9KwzyKg6wSgYgvH7+5ujKnaUd++7IiPkTeueIEMUn2wSDSp1Ar46KqwnCzFm/c0QMs3Mb4vMXJZyZUfzjfsHv+W7T7FiJNI3CWD2DUTI+oJS8ZSPNAYeHiE8KCJqAtUaOm6vOaOqCbC552NMfH5lYsHVjsoD1wlb9Q6I9bdG7QZ6DzWMetZiq4THAsYKCVTOazYi/5gqa0ZUAQYts/9NoUKC2Efapa15OTH1Bhp4ipOeg1SLfj5C/eY877K1HZNjrETGqnC06HVQ2rl5yF1dxyrPyokP1ZgJIlVqrqgpsAi/rxWD8e/xVnkhQyUDO3wicqqXQy+gxaPmGL4k4b7QXzO3YWKyPHsLMe67s6UkQnv8A+QH//FVO+iXJaWdZEMIzz4iOOkrXJboGxd15USQHgGfXQNeHF+/mt6JOdCywmhCB0+8dqlfp2Bbvch/VcA01R639Ti7kYrXgn4+KaJGY0QKj89MYWf8LczLtI2Inr9AhyYI8TUrvwfcHiB/LZ+/sCrm1ylQvQQ2dBJTUDOS6ByP2HlhUoca8e43y6inrBvO+XqqZPGKVtLiCW6exM4NM9VELzmE8CxpaIXZJbs9RJWAUg57/iaPe5NCRevh5tj+tIS4TsufBXIzVLe3k6x0kLFl0KFtV3Xej2r+R9y/FPOkGj9lptrSYWHtPEAYM8frr/bJITq1WB0sv3cbKENrWNIkeLC+eWJxSKsTK35SCj8wSqbOaiYIGm3RmHJn8VEX2xUNXwJOLdFmOQnHnDcrsdPOuJubXC4pwqHC5Pk3BwhbKXPYiAzVZ82scUi7eWM+UA4D/aSrUbwUzBnxPegWskvrft7YT6waJC6MAI7Foswvrm7b+y2whv7gVNr9asIBJdwLdvsRZCHVC1SMxL7DPt1FQK7evDzM9cgte/TLNwZpEWKC1qqxcoIcUJ2wqKEQbJaV0dNSSPHSvy5l9eqOwYsl84qPSwItutNhPLzE3QZmw929V50d5K8ERscRkXvLRahBKUZVIkRHrzi4672fNd8XLtup9GkN4fjXN54fXrZSloiI0cuYU3ybxmMTlLUTkaceW2LcJ+LPdpCh4z1x5mHXhDUCcpzCFGyy6PJrVLIhQxoQbxi88RkYSWikET8ZQM1ibUK94/FAry0FOj/n/D7g+3YGI5iWr8lLLubom0quqeN5nmEV5RWbYjuKFxf1bu4vue2byrvmxJVhDAIIx59L4Y8pEbo+qXwQ+jDneC0GHHsDyzKSzOa0RNrx3gA5TUgFw4Tlj2vnXB9L+wB4IUF2gXpxbS/J4mzQ4NHwBDiGDeOkycHUW49DYg+NOKIa5uq+qD1oakxJlipDTMJKy8YJhsn8N8Xr3ut3zLg1GohNGCsKFGrVg8gO12lHKACxSveu2fvhbGw/i7O7fE2FMUhZPWeAw7O4bcvvc6Wym1d80SGH5Tf3mwnUgGCPKSEcdyVUXWHVaVBaiZqB2RfGnQcvF5OBkd0B/lMaTUUKixoJopxrp2sNhi/zpPSr8sG/es8B3KZxI47Ff1u0ENqR0t8MyB41Kp1Va8bbLMK8ZYVW7uWKVTrEZJbsheHk5hptPIsDMGaoqWABs+7VgN7Zgbwwj/sxozoDo4eXhYrQ3bMKgsqxeMT83/nIemkd5L27ExB0sm5NK5p+NCzxiBkkOrChwZJM+j8hSDErZay9zLHneMdPYVb2RlG4tBinv1L/2d1ISOGzwpnJQHnsrpqVxHUxDbpWbJ3pmhp5W3UlJ7Uuo+evwixa+Lz7+58WCzA83OUtSftpSurdhSOff/l+TFnzV4TROUdDEodKJVxZAaNrR6lanvgxQXAcA5VtpQILAr0/FlQfOnMQX1G7zJ94TCg7lOCBx9gVyqYcIGW7PVAF6QrzsbBIx8B6z+T3rb/cuDibqByDO9eqaNIcSGGM6CweiOH53Ixa9zGFhrAnvxVtKQg5p+ZKvxdqDCQczd/O7XGnX8hIY5w2XAh2/TqcddtHI47Y89B7Z6CFNmFXUKs3IJ7xRT0GGBP/QEsHeYoYVaqKvBqLJOuOjD8EJB8RFhadEZrSJFsDKlJXpqQCCAt0Zx9FUSqdnBU63BH/CbHv+3jn9Z4a9WouIbqP+e64qIWORWLvN0bjBtt+aag5cla+YbIgwxSLbx1FLhzlZFIs4nU7A7snyVfIo8FVTsAsePUbVs4TP+gx+u5KCcaLGeg6o27rNwGLkYh72XJJgOBVR8IBsttHeUknXnzIJCZJhgPdsrVB85tzv9bnIGqlP0qpmFfQSMwoi4wXmKJzMFDqr5ZR2SOd89f5GXRtFK6miD5pbcvWihWGigWY7wdQN4gNStE5cnfgWVviWL2yGOqiRfdqJE4Ix4L6j1nrqFV43H9Gp5yhJQFXtsqv9wf3QnYNCG/9rxW/AM4GusEQAapNsIeEH58jeiOgmdGaQZphPKNBW21M+uFv3k9wMIr8mlXC2/E6V/SlaqsxPuaajZYKDFatjbwS4zx9opLnAPn8x0UImTQ5mZrk9fx8xM0eh0bz/81PAq4e8P1dS2Iy4L6EuEVgFvn+XrnnSdHFVsDCVuARv2FVQ1hI377L/kg0HcpMOZevC0LaTJPYPEXkja9YbySo3A4kHFL+L3Xz+bu+zlOYUsREsVW7EQ1EfSdw8rLb+ON+FK+gkF8aN2R0I3FIsSuOWSrc8Ch8hLjm6j/SkFT06h8jmYkvkcJA1UuLBbHeuu1eggC+Dzx8xMmDIWK8PPGSmUQdxjDPhas61f5v+sdqOs/B9TvrVxLnCf246VFRufFhUI9cx7L/nL0WShIMjV43rx9FgRe3SCcKy0eS73oEaoH8ouPFFSk5LDK1dNW7powFfKQEr5BxRbCT0EgtJxQpSgoRHvmtWE4GaQdPxMEtFXpHxqgmChZqKhOYXP/QvkTm9TL+a/LeR55nKOhu4RYScVqOiJKRQsVb3jibOQHBLmWrzVzFd2XsuzFlKvP/1zZqfmEUBShvFYh/AKOUa1Rr+H+8ZCSQUowxEjt7QJOjceB48uAFkOFvz21bMTrAR9cwqRlP9F1VaeX8eZCI4UEvMCiroUJnpgC3Dijo+KNCgoV0WaMehWmWqQm7stH8fPXWQWqoB9begb5GmSQEoQ7WBnWz8wUEuJCyrJp735CTlCcVSxlm3ekX2/Yh037hH581UNKuGK2k4KcIj5HgTVIbTYbcnJykJvrheLrBZWStYFi9zKjLUXyf8/IcPsxf39/BAQEwOKVA4ioTy1edxV9V4ufn5cYoz70gH9xIZCwDaj3TP5r4gxaozIuhDZKVQOunRQS5Ajfp3QNc/dX43FBQUEcQ88Tr3yeEO4okAZpVlYWEhMTkZ6e7umu3F8UqQ+0/02Q1cjJBFp9I7weH6/40eDgYJQrVw6BgYxkd3jAQ6zZbB77BvizOxAz0tM9UaZqe+FHTNFSQg1v/yB2Ek2EOgZvB3KzXCsRccWHJlC+RsthwjjtXNWLF8XKAB9czi/kwBtf0op2x31kWBc4g9RqtSI+Ph7+/v6IjIxEYGCgl3reCjh3rgF37kkclZHPSrfZbMjKysLVq1cRHx+P6Oho+OmtOcyDgnbtVIkBRl0RxOt9lYYveboH9yf+Aa5xtrwhe5QfAUHAI6PM3WegROY7LwqKQXofUeAM0qysLFitVkRFRSE42MyZPOFAYASQfVPIUC7s3vgpUqQIChUqhISEBGRlZaGwwvam8mB7oHilglWdy5eNUYIz3jYBI4uU0Iu3XcsaeWwisGa0ULDjPoH5FGLcuHFo0qQJQkJCUKZMGfTo0QMnTpxw2ObKlSvo168fIiMjERwcjC5duuDUqVNM++FVXrb7Eb8AQZdULiHFeXNvPV+FCgPD9gPP/OnpnhCECXiZAUhJTYRefN1D2mQg8L/zgqD/fQLzM7Zx40YMHToUO3bswJo1a5CTk4NOnTrhzp07AIQl2h49euDs2bNYsmQJ9u/fj4oVK6JDhw552xAFhIKy3O3nV3C+C0H4EgFBnu4B4asUhDHbWx01nGC+ZL9y5UqHv6dNm4YyZcpg7969aNOmDU6dOoUdO3bg8OHDqF27NgBg6tSpKFOmDP7++2+8/PLLrLtEEARBqMJLHuJPTwfWfSr8TxB68HUP6X0I9zOWkpICAChRQijXlZmZCQAOcYL+/v4IDAzEli1bJNvIzMxEamqqww9BEARRQKndE3hjv1DqkSD0QAapz8H1jNlsNowYMQKtW7dGnTpCnfMaNWqgYsWKGDlyJG7evImsrCyMHz8eSUlJSExMlGxn3LhxCAsLy/uJiori2W2fISsry9NdcMEb+0QQBEHcZxSEJfv7DK4G6euvv46DBw/i77//znutUKFCWLBgAU6ePIkSJUogODgYsbGx6Nq1K/z9pYWuR44ciZSUlLyfCxcu8Oy2x4iJicHrr7+O119/HeHh4ShZsiQ+/PBD2O4F9leqVAmff/45+vXrh7CwMLzyyisAgG3btqFNmzYoUqQIoqKi8MYbbzjE406dOhXR0dEoXLgwypYti6eeeirvvfnz56Nu3booUqQISpYs6RDLGxMTg+HDhzv0sUePHujXr1/e33r7RBAEQRDcCCnn6R4QGuFmkA4bNgxLly7Fhg0bUL68Y93uRo0aIS4uDrdu3UJiYiJWrlyJ69evo3Jlab3KoKAghIaGOvxowmYDsu545kdjluiMGTMQEBCAnTt34vvvv8e3336L3377Le/9r776CnXq1MHevXvx0Ucf4dChQ+jcuTN69eqFgwcPYu7cudiyZQtef/11AMCePXvwxhtv4NNPP8WJEyewcuVKtGnTBgCQmJiI3r17Y8CAATh27BhiY2PRq1evPANYLVr7RBCEl0JeJaKg0PNnQXf5xYWe7gmhEuZJTTabDcOGDcOiRYsQGxsra2QCQFhYGADg1KlT2LNnDz777DPW3RHITgfGRvJpW4kPLmsSA46KisK3334Li8WC6tWr49ChQ/j222/zPI+PPPII3nknv/b2Sy+9hOeffz7PkxkdHY3vv/8ebdu2xY8//ojz58+jaNGiePzxxxESEoKKFSvioYceAiAYpDk5OejVqxcqVqwIAKhbt67mr6i1T16lM0oQBEEUPEpUBl5a4uleEBpgbpAOHToUs2fPxpIlSxASEoKkpCQAgvFZpIhQMuyff/5B6dKlUaFCBRw6dAhvvvkmevTogU6dOrHujs/RvHlzh8pSLVq0wDfffIPc3FwAQOPGjnWA9+7di9OnT+Ovv/7Ke81ms+VVrOrYsSMqVqyIKlWqoEuXLujSpQt69uyJ4OBg1K9fH+3bt0fdunXRuXNndOrUCU899RSKFy+uqc9a+1SzZk1N7RMEQRAEUbBhbpD++OOPAIT4QzHTpk3Liz1MTEzEiBEjcOXKFZQrVw4vvfQSPvroI9ZdyadQsOCp9ASF2FaLKlrU0dtqtVoxaNAgvPHGGy7bVqhQAYGBgdi3bx9iY2OxevVqfPzxxxgzZgx2796N8PBwrFmzBtu2bcPq1asxefJkjBo1Cjt37kTlypXh5+fnsnyfnZ1tuE8EQRAEQRBiuCzZK/HGG29IGivcsFjMraFrgB07drj8HR0dLZvw1bBhQxw5cgRVq1aVbTMgIAAdOnRAhw4dMHr0aISHh2P9+vXo1asXLBYLWrVqhVatWuHjjz9GxYoVsWjRIowYMQKlS5d2UD7Izc3F4cOH0a5dO7ffQU2fCIIgCIIg7JBQl5dx4cIFjBgxAidOnMDff/+NyZMn480335Td/v3338f27dsxdOhQxMXF4dSpU1i6dCmGDRsGAFi2bBm+//57xMXFISEhAX/++SesViuqV6+OnTt3YuzYsdizZw/Onz+PhQsX4urVq3lL6o888giWL1+O5cuX4/jx4xgyZAhu3bql+B2U+kQQBEEQBCGGuYeUMMZLL72Eu3fvomnTpvD398ewYcPw6quvym5fr149bNy4EaNGjcLDDz8Mm82GBx98EM8++ywAIDw8HAsXLsSYMWOQkZGB6Oho/P3336hduzaOHTuGTZs2YdKkSUhNTUXFihXxzTffoGvXrgCAAQMG4MCBA3jppZcQEBCAt956S9E7qqZPBEEQBEEQYiw2rRo/XkBqairCwsKQkpLiIgGVkZGB+Ph4VK5c2eeyuWNiYtCgQQNMmjTJ010xHV8+bwRRYNj5M7DiPeH3MSme7QtBED6PO3vNGVqyJwiCIAiCIDwKGaQEQRDEPUgYnyAIz0AxpF5EbGysp7tAEARBEARhOuQhJQiCIAiCIDwKGaQEQRAEQRCERymwBqkPigfc19D5IggvwEIxpARBeIYCZ5AWKlQIAJCenu7hnhBasJ8v+/kjCMID0MSQIAgPUeCSmvz9/REeHo7k5GQAQHBwMCw06/dabDYb0tPTkZycjPDwcNkSqQRBEARBFFwKnEEKABEREQCQZ5QS3k94eHjeeSMIwkPQ5J0gCA9RIA1Si8WCcuXKoUyZMsjOzvZ0dwgFChUqRJ5RgvAGKrcV/vcrkI8GgiC8mAI96vj7+5OhQxAEoZbS1YDX9wJFS3m6JwRB3GcUaIOUIAiC0Eipqp7uAUEQ9yEFLsueIAiCIAiC8C3IICUIgiAIgiA8ik8u2dtF1FNTUz3cE4IgCIIgCEIKu52mpviNTxqkaWlpAICoqCgP94QgCIIgCIJwR1paGsLCwtxuY7H5YM1Gq9WKy5cvIyQkxBTR+9TUVERFReHChQsIDQ3lvj9fo0mTJti9e7cp+6Jz4R2oOQ9mXhf3K756PxTEa8NXz4U3weK6oPPgHdjPw/nz52GxWBAZGQk/P/dRoj7pIfXz80P58uVN329oaChd4BL4+/ubflzoXHgH7s6DJ66L+xVfux8K8rXha+fCm2B5XdB58A7CwsJUnwdKaiIMM3ToUE93gfBC6Log5KBrg5CCrov7G59csjeb1NRUhIWFISUlhWZcHobOhXdA58E7oPPgPdC58A7oPHgHes4DeUhVEBQUhNGjRyMoKMjTXbnvoXPhHdB58A7oPHgPdC68AzoP3oGe80AeUoIgCIIgCMKjkIeUIAiCIAiC8ChkkBIEQRAEQRAehQxSgiAIgiAIwqOQQUoQBEEQBEF4FDJICYIgCIIgCI9CBilBEARBEAThUcggJQiCIAiCIDwKGaQEQRAEQRCERyGDlCAIgiAIgvAoZJASBEEQBEEQHoUMUoIgCIIgCMKjkEFKEARBEARBeBQySAmCIAiCIAiPQgYpQRAEQRAE4VHIICUIgiAIgiA8ChmkBEEQBEEQhEcJ8HQH9GC1WnH58mWEhITAYrF4ujsEQRAEQRCEEzabDWlpaYiMjISfn3sfqE8apJcvX0ZUVJSnu0EQBEEQBEEocOHCBZQvX97tNj5pkIaEhAAQvmBoaKiHe0MQBEEQBEE4k5qaiqioqDy7zR0+aZDal+lDQ0PJICUIgiAIgvBi1IRXUlITQRAEQRAE4VHIICW8irs5d/Hyqpcx6+gsT3eFIAiCIAiTIIOU8CrmnZiHnUk78eXuLz3dFYIgCIIgTMInY0jVYLPZkJOTg9zcXE93pUDg7++PgIAA7jJb6dnpXNsnCIIg1HH42mGkZ6ejabmmnu4KcR9QIA3SrKwsJCYmIj2djBuWBAcHo1y5cggMDPR0VwiCIAjO9F7eGwCw4ZkNKFWklId7QxR0CpxBarVaER8fD39/f0RGRiIwMJDE8w1is9mQlZWFq1evIj4+HtHR0YoCt7r3BRuXdgmCIAh9XL97nQxSgjsFziDNysqC1WpFVFQUgoODPd2dAkORIkVQqFAhJCQkICsrC4ULF/Z0lwiCIAiCKCAU2KQmXh68+xk6pgRBEPcftHJFmAFZGARBEARBEIRHIYOUAABMnz4d4eHhnu4GQRAEQRD3IdwN0nHjxsFisWD48OF5r9lsNowZMwaRkZEoUqQIYmJicOTIEd5dIQiCIAhCIzYbLdkT/OFqkO7evRu//PIL6tWr5/D6hAkTMHHiREyZMgW7d+9GREQEOnbsiLS0NJ7dIXwAilUiCIIgiPsPbgbp7du38cILL+DXX39F8eLF81632WyYNGkSRo0ahV69eqFOnTqYMWMG0tPTMXv2bF7d8Xr+/fdfhIeHw2q1AgDi4uJgsVjw7rvv5m0zaNAg9O7dGwkJCejWrRuKFy+OokWLonbt2vjvv/8AALGxsbBYLFi+fDnq16+PwoULo1mzZjh06JDD/qZPn44KFSogODgYPXv2xPXr1837sgRBEARBECK4GaRDhw7FY489hg4dOji8Hh8fj6SkJHTq1CnvtaCgILRt2xbbtm2TbCszMxOpqakOP1qw2WxIz073yI/apY42bdogLS0N+/fvBwBs3LgRpUqVwsaNG/O2iY2NRdu2bTF06FBkZmZi06ZNOHToEL788ksUK1bMob13330XX3/9NXbv3o0yZcrgiSeeQHZ2NgBg586dGDBgAIYMGYK4uDi0a9cOn3/+uaZjygtaGiIIgiCI+w8uOqRz5szBvn37sHv3bpf3kpKSAABly5Z1eL1s2bJISEiQbG/cuHH45JNPdPfnbs5dNJvdTPfnjbDz+Z0ILqSshxoWFoYGDRogNjYWjRo1QmxsLN566y188sknSEtLw507d3Dy5EnExMRg6tSpePLJJ1G3bl0AQJUqVVzaGz16NDp27AgAmDFjBsqXL49FixbhmWeewXfffYfOnTvjf//7HwCgWrVq2LZtG1auXMnwmxMEQRAEQaiDuYf0woULePPNNzFr1iy34unO1ZNsNptsRaWRI0ciJSUl7+fChQtM++wtxMTEIDY2FjabDZs3b0b37t1Rp04dbNmyBRs2bEDZsmVRo0YNvPHGG/j888/RqlUrjB49GgcPHnRpq0WLFnm/lyhRAtWrV8exY8cAAMeOHXN433l7gtCKzWZDXHIc7mTf8XRXCKLAkW3Nxr4r+5Cdm+2R/VNsP2EGzD2ke/fuRXJyMho1apT3Wm5uLjZt2oQpU6bgxIkTAARPably5fK2SU5OdvGa2gkKCkJQUJDuPhUJKIKdz+/U/XkjFAkoonrbmJgY/P777zhw4AD8/PxQq1YttG3bFhs3bsTNmzfRtm1bAMDLL7+Mzp07Y/ny5Vi9ejXGjRuHb775BsOGDXPbvt3gp2VxgjVLzizBR1s/QtXwqljUfZGnu0MQBYpxO8fhn5P/4MnoJzGm5RhPd4cguMDcQ9q+fXscOnQIcXFxeT+NGzfGCy+8gLi4OFSpUgURERFYs2ZN3meysrKwceNGtGzZknV3AAiGWHChYI/8yHl9pbDHkU6aNAlt27aFxWJB27ZtERsbmxc/aicqKgqvvfYaFi5ciLfffhu//vqrQ1s7duzI+/3mzZs4efIkatSoAQCoVauWw/vO2xOEVv498y8A4PSt0x7uCUEUPP45+Q8AYMGpBR7uCUHwg7mHNCQkBHXq1HF4rWjRoihZsmTe68OHD8fYsWMRHR2N6OhojB07FsHBwXj++edZd8ensMeRzpo1C9999x0AwUh9+umnkZ2djZiYGADC8evatSuqVauGmzdvYv369ahZs6ZDW59++ilKliyJsmXLYtSoUShVqhR69OgBAHjjjTfQsmVLTJgwAT169MCqVavw34r/zPyqBEF4IZm5mRi0ZhCal2uO1+q/5unuFAg+2f4JUjJT8E3bbzQ5KLwJWrJ3T3xKPN7f9D5erfcqOlTsoPwBQhKPVGp67733MHz4cAwZMgSNGzfGpUuXsHr1aoSEhHiiO15Fu3btkJubm2d8Fi9eHLVq1ULp0qXzjM7c3FwMHToUNWvWRJcuXVC9enVMnTrVoZ3x48fjzTffRKNGjZCYmIilS5ciMDAQANC8eXP89ttvmDx5Mho0aICVq1bi1RGvesVSPg18vgmdt4LB0jNLsffKXvwQ94Onu1IgsNlsmH9yPtYkrEFCqnTSLuH7fLD5Axy7cQxvxb7l6a74NFyy7J2JjY11+NtisWDMmDEYM2aMGbv3Kb7++mt8/fXXDq/FxcU5/D158mTFdlq3bo3Dhw/Lvj9gwAAMGDAAAHA76zYSUhPQb0g/zf0lCEIdmbmZ+O/sf2j1QCuUCS7j6e5IkpWb5ekuFFisNqunu6AbC3zTs2sWt7Nve7oLBQJTDFKCIIj7nalxU/HH4T9QonAJbHx2o/IHPIA3rJIQ3oH4WqAVEMIMPLJkTxBayLXmeroLhAJkyCiz6eImAMCNjBse7ok8ZHiwI9eaS8eTIDRABmkBIyYmBjabDeHh4Z7uChPWn1+PJn81wcpzJNrvzdCDt2BAEws2ZOVm4bFFj2HI2iGe7gpB+AxkkBJezZsb3kS2NRvvbnzX010hiAIPTSzYsOfKHly6fQlbL2/1dFfYQJcFYQJkkBJehSc9NCmZKfh277c4fZO0NLVCnjVlfEHyp6Ccxy2XtuCXg78UmO+jl6Q7SZi4dyISbyd6uit5rDu/DtMPT/d0N/LIzs3G5P2TEZcc5+mu3PcU2KSm+30g4kFBP6Zf7PwCK+JX4I/Df+BQ30Oe7g5BmE5B8ZAOXjsYABAdHo12Fdp5uDeeY/DawTh96zQ2nN+Af3v+6+nuAACGbxgOAHio7EOoX7q+ZzsDYNaxWfjl4C/45eAvNO57mALnIS1UqBAAID093cM9KXjYj6n9GBc0jl4/6ukuEAUYX5DOKSgGqZ2k9CRPdyEPTxxbe+W0c6nnTN+3EtfuXvN0FwAIovaEd1DgPKT+/v4IDw9HcnIyACA4WFv5zvuRzKxMWLMFjbyMjAyX9202G9LT05GcnIzw8HD4+/ub3UWC8Hl8wdgr6KsgBEF4LwXOIAWAiIgIAMgzSgn3ZOZm4vrd6wCAgFvyl0R4eHjesSUIMb5gbBHKFLTz6E0Gti94yMVwvxa859QQXkKBNEgtFgvKlSuHMmXKIDs729Pd8TpmHZ2FHFsO+tXuh7kn5mLLpS1ISBHK2i3tuVTyM4UKFSrwnlGzHhg2mw3jd41H6eDSeL7G8/h428foUKEDulTu4rBdenY6Ptr6ETpV6oTOlTrnvb4naQ9mHp2J/zX9H8oVK2dKnwnj+JpBIsfSM0sxassofNXmK5drlhDgfa77ruiLfcn7sOnZTSheuLiuNtKy0jB622g8VvkxtK/Y3u22BW2i4kskpCbgmz3f4JW6r+BW5i0sPLUQH7X4CCUKl/B015hTIA1SO/7+/gXeiNLK7azb+O7QdwCAp2s9ja/jHMuUFi5c2BPduq84desUZh+fDQDIseZg1blVWHVulcvD/Y/Df2B1wmqsTljtYJD2X9UfAJCWnYY/Ov9hXsfd4E2eKIIvo7aMAgC8u+ldMkg9xL7kfQCAoeuGYvZjs3W18dOBn7AmYQ3WJKzxSDIPGbnqGL5huJCYdmFD3mvBhYLxResvPNgrPhS4pCaeJN1JwpFrR7jv58DVA7iafhXZudnYkbgDGTmucZ1asNls2HdlH25k3ECuLb/qkfh3T5CenY6diTuRY83xaD8A4PTN00hITeDWfmZuJnYk7kBWbhbu5tzNe/3kzZOyn7mecd1tm3qkXKw2K3Yl7kJaVprmz7ojy+paA/1sylmcvXXW4bVj14/hYtpFpvvmQVZuFnYk7kBmbqbuNrKtwv1rP9/uzrW34CsTC5vNhr1X9ipWvfLV/IH9yftlk36O3zie9/uFtAu693H17lXV2/K4Lq6kX2HeJg9sNhv2JO3BrYxbqrZPTk/GwasHme1farxMTi+Y4YhkkGqg4/yOeG75c1yz8g5dPYQX/3sRj/zzCL7e8zVeWf0KRm4eaajNzZc2o+/KvuiyoItXPXDeWP8GXl79Mn468JNH+5GenY6eS3tynbF/tPUjvLL6FYzbNc7hHKxJWMNtn1LMOT4HA1cPxEsrXmLarrNCQXZuNrov7o7uS7rnGWRJd5LwzLJn0HVhV6b75sEXO7/AK6tfwZhtY3S3MXnfZLyy+hWMiB3hMw8QpXtg2+VtJvXEPRsvbkS/lf3QZYF7D603jXdq2Z20Gy+teAnt5knLVT3979NsduThQzN+13jPdkAlqxNWo/+q/nhi8ROqtm//T3u88N8LpNqiAzJIdXDs+jFube+5sifvd/uy7trzaw21aa+hLfbMAZ6PaduZtBMAMP/k/LzXPLGMo+SJZMGK+BUAHL+rJ1gevxxAvhwML9Jz8mXXUjNTAcDFW+rNLDy1EACw7Owy3W3MOTEHgCDSfi7lHItucUfJgNt6yTsqD8mNaQWB7Ze3e7oLhAj7UvnNzJuaPrc/eT+T/fuql18PZJCqZMHJBXm/+3Lsiy/3/YnFTzgsV+llbcJadF/cHSdunJDdZsr+Kei9rDeXB57Z5+Dbvd/ixf9eRFZulse9InpJz05H72W9MWX/FGZtrj+/Hk8sfiJvgvnlri/Rd0VfZFvlEyGXnV2GHot7aDYw9Zxzm82GwWsH48MtH2r+rDPX7l5Dr6W9MPuY+3hDT4wPt7Nu45l/n8EvB38xZX/TD0/HU0ufQkpmCrd9SBkRao+teLvD1w6r9sxpRcu5nnVsFp5c+qRiiIRWZh+bjV5Le6nSJJ1xZAaeXPokbmZoMwzt3Mm+g2eXPeuyIsfL4Pvt0G9c2gV8+znuDjJIVTJm+xhPd4EJ4gvZG2de7ry28SnxeVU+jPBW7Fs4m3IW72x8R3abnw/+jMPXD2PpaWnVASOwWkZUOyj9cfgPHLh6AKvOrWKyX62wGDwXnV6Ew9cP4+eDPzPokcCbG950uKZmHZuFfcn73HoBR24eiTMpZzB622jF9o2uQJy+dRpbLm3BkjNLDLUDAJP3T8apm6cwbtc4t9spnSseD8K/j/+NYzeOYfL+yczbluKbvd/gxM0T+OOwdyQEumPw2sHcQsS0nMv/4v/DyZsn8fMBdvcfAIzbNQ6nbp5Sde6/3vM1Tt48qdvQm3diHo5eP4of4n7Q9Dm997G3CP/7EmSQEqrJteZi/sn5OJviuPR6LuUc/jn5T15y0u2s25hzfE7eDbkuYR32JOWHIlxNvyobm6c0SMp5LI9cP4J/z2grjZfXlptdXsu4hjnH5zh4U2w2G5acXuLgrd13ZZ/qeFCzZrenb57OW3oG2Cax2du22qwu793Oum3IQxSXHIfV51bnXW/Hrh/DrKOzJLe12qxYeGohztw6o+0LiHBOrsi1Kh8nrZ5zqe++5dIWt8av1LF1x7mUc/j5wM/469hfSMtKw6Grh/Df2f8AQH1ipJtTlGPNcVgpYoVUQtzV9KuYc3wO7mTfYb6/vP3muu6XFUYmneLPKn1/I2OJUh+l3p99fDa2XdqG2AuxqvezJ2kP1iWsc7uNPXkw8XYi5h6f6/Z6VXPelp1d5pKArPd8mxnalp6djrnH5yLpTn6FMZ7XqbdRoGWfeOHL7nIjA+X8k/Px+c7PAcBBJqTb4m4AgMycTLxY60WM2T4Gq86twtwTczGl/RQMjx3u8Jm+K/s6ZIeyOJ7PLXsOAFA2uCyalmtquD079uWd9efX45dOwpLi5kub8eFWYRlV/J0A4N8e/6JSWCVm+3eH0kDZc2lPl+1ZXbv2tgP9A/F4lccd3hu9bTRGt1T2IMrRZ0UfAEDvGr3x9/G/3W679MzSPG+lXukaZ0PdecKlF3crELezbufVW9/9wm4UDjAut2a/DwFg75W9eROk8iHldS0XOzP3xFyH2GCe2MeIw9cO4/PWn5uyT954OmafFYPWDgIAbHhmA0oVKaW4vV2mbkWvFSgfUt7ttk8ufRJp2WlISEvAe03e09W/vVf25iUC+1pt+ol7J2Luibn4+eDPWP/MegDSjoSCci05Qx5SA2Tnshfd5yGBJDZC3cXHKXHg6gG378ddjQMAbDgvBIGfvnXaJbvYZrMZkipR4kyKfk+ZO7Yn5icauIs9TbyjLMUkNynwxYzgo9ePutwH6y+sd/jbPng6GzvZ1my3XsDlZ5cr7v/wtcNqu6qaS7cvKW6jxsATPzScz63Y8yUnLaXWoy01Dom99WoMbKvNihxrjtvvFZccp6o/LLCPEfbkJV7wGMONtukQVqXC8EjP1jdJEO9Hz3NBrQySHXcyU/b7Iy1bkKPbfnm7bJ+UQs2kVkuyc7Mdv6/MOZIag80MbdtyaQsAZUkul7GUw3XsCcgg1YHNZsO4nePQcFZDppnD6dnp+H7/98zak6Lj/I55v5sxy3K+wYeuG6ppe6P7c7stB0+3mjblBtpXVr/CujsOfLj1Q+bZ3ney76D1nNaOL9qc/3Q9Jpm5mWg/rz36/NeHaX9YsOAUm2Xp29m3Zd9T0rzNtebi2WXPKu5j9bnVaDirIRadWiS7zUdbP1JMZnt22bPoNL+TR5YHncch8fIu63t0xpEZeb/POjYLDWc1dDvB1MqE3RPQcFZDSRULtWoeV9PV64OmZKag2exmWH1uterPSBEzN8arFAtO3zqN1n+3ZqKXvP78ejSc1RDTDk/Le63hrIZYGb/SZdt3N71reH9ms/fKXjSc1RC/HvzV010xDBmkOrFLMv16iN1FsCNxB7O2xJg1w7Mbg+72t/nSZlP6ogWtDz2326toSs4Ysctg8cTugWDF5oubXZZx1RzPg1cP4mbmTRy8Ji8graYdX/EqO38XpYQatcbL2xvfBgB8vO1jt9vl2NyvvBy/cRxX71516001K1TJHuLDY59f7/na5TWWyVQzj84EAPwY96PLe0vPqEuQ/Pdsfiy82rHbfh3oJTUrlZlMkR6kznN6Tjo2XzT+vLAnLTqPU1LGp6cSP43w6fZPAYC7M8sMyCA1CZvNhp8P/Gzogl8Rv0LXLMhbHtpyXhxNXk2lQHyOD826M+q69USx4MtdX6qWo7HBhjvZdzBh9wSmlUHcse3yNkzcOzHvbykvu9w50Hodutt+d9JufL37a1WVlK6mX8X4XeOZxYY6k5aVhgm7J+SFD1htVvRa2svtZ7Zezk9m0jJhXHx6sYOXTy2qj73TZmdTzqLBnw3w4wFXAwsQlgon7pnoMpn+4/AfbjVc552YhznH57i87vzdUjJTMGbbGOy9stfhdZarOxsvblQVpqHEqZun8n5nPfHjgfM18e3eb7HxwkZM2jtJVWKfVnYl7nLTGfVZ6e7O/bbL2zBp76T8ZlVc90rXkthzPPPozLxnwB+H/0DdGXVx+iYfTecTN07gy11fSr4nJ31ltVnx/b7vuYe68IKSmlTgbklHrQG0P3k/psQJGoriuuRaeG+TEOTdIrIF6pSqo6sNnkgdC/FrSl4cNWgVJ2bNx9s+xrCHhsm+b9QgnnVsFtKy0lQnckzePxl/HfsLM4/ONCWAf9CaQYrb2Gw2JgaDu2M5YNUA1e28t+k97LmyBwtPLcSuF9w8FDUgftBN2jsJ807OyzsHq86tcjBOhA8w2a2w/A7gkQqPICokik2jIpyPeffF3QEAU+OmIiYqxmX7eSfnYdqRaZh2ZFre9Xfq5il8u/dbAHBJeAOE0KTPdnwGAOha2bFql5QHc8GpBVhwaoGu61vt/Th47WAs7WFM4k1pEqLHMWBm8srxG8fx+vrXAQAVQyviiQfd658qed2dmRI3BYPqy48fo7aM0tSeFM7jk7j+u17E8dgTdk8AAPSM7pl3jfdc2hOVQisZ3g/geI089e9TstvJlSFedW5V3qqtryV0AeQhVYWzEaTH6FCzBKd28LmVeUvz/iX3x2kp39cyADUv2bt5sLDw0KqtomSBxZDcERMkTrXt3j/3H1O+Rlh59o9cF+RfeMXIOZ8vsWSLGvTcLyxi67Qi5TWTSlBUGp/EcapZuVleMV7wLAdtx9vUWdz1R801rFWWTAkepTavpl/lkyvgJauOgON51Dr2eBtkkBrEExemNwzgavGmG5cV7gY4Nd/XE+LjvNByLTKN1fUwLL+LripOXnxslDCz7740VkrhqeIlaiaVZpxHqe/vDQVdeH13b/hunoQMUhU4D2p6jCyWhpnWQZbVzaO2HSM3lS8/aAHf779Z+PrAy/s8K445HrjMeHxnX78OfBnDzySGlwOv+4lbu17qaPH15w8ZpCpwXuqzi6IDbC8A1YOzys2mHZ6GkZtHalpaGbx2sO6A6Ct3rmDAqgEOx+uvY38pfs5+DI9eP6qqnN/vh37HqC2jJAeF8bvGY+U5VzkPlmg9587xhGonFOLv566ij526M+oazpTdlbgLr619DRfTLrq851z5BJC/ZrVOmnYn7cZra1/DhVRRwQSZQd9oPfvs3GyMiB3h8FrdGXVlt2dRY93dNWOK/JrKa1a8Ha8EPpe+cPz66TnpGLZ+mOYqblqw2qz4YPMHXNrmKcXk7pr48cCPmktsOvPrwV/x4ZYPlTcEsPLcSslQj58O/IQx28ZwMwD1Sn45H7tzqed098E+Hs09PldfX7zUONYDGaQq0JPVyhO1D7CJeydi2dll2JWkPpFjy6Utilqhchy8dhC7k3Y7vLb2/FrVn39pxUuqtpu0bxKWnlmKPVf2SL7/7kaVWnL37mPeN/Sra1413MZra19zeU3qgaL2GMoxcPVAbL20Ff/b/D+X93ov722obTHO1/CAVQOw9dJWvLPpnbzX5CZSWuvZO+9ryZklqsu8AmxlgVjBciIsd/07JyGqNmq96AE5/ch0xF6IxQdb+BiMALAzcaeDVJOvoHQ+9daMt/P9/u+x5MwSQ22cTTmLBacW4MTNfMOR5QSu38p+uj7H8hpfdnYZ1iSswec7P/eqe8cTkEGqAndB+iviV0gmluRac7H54mZZCR+rzYqtl7Zi2+VtmHV0Fq7cuaK+3rQC2dZsBy+n3Cw7M0dZMkeOxNvKFYnUYh8Y1Uj4iMnIyXD5rnr2yxKpAUWtnImYzNxMVRp8UpONGxk3NO/PGbs4t/06vZFxQ/J4sQ6iv3Inv668VI1zd2y/vF3y+DvrDzpLCOnBJYveAGoqIDkfe5vN5jL5c/t5p+MSlxyH86nnVX8eAC6lGZdG0rNfO3rkddRKqGndVoya1QsAuH5XSGxNz07HjCMzsO/KPl37k8O+unAx7SLztlmh1+CSezacvHkSS04vwYkbJyTvB3vlIzncFa9wh70qoV7E5yg1K9VQW2KUju+JGyfw44EfPZIUqQaSfVKB0lJ6jyU9XCQWZh+fjQm7JyAqJAr/9frP5TNLzyzNk3ABgC93S+uN2dFy0U6Nm6pqdmvEa9BpQSePy0pYLBb8dOAn3Uuqug1St7r4ym0qSVfZK4El3012u52cdmLPJT2x8dmNiv1Qg/06DQ8K1/Q5qQFPPFjK3VNGJgmvrnkVP7T/AW3Kt8l7zdn4ycrNcquPqYXjN46jRokahtsZsm4IlnRfgirhVfJeUxpzDl47iPG7xqveh/i4JqQmoM8KoUKW8z3s7oEmtSypxVt1IfVC3n7tLD2zVLXmZc+lPXHwpYOwWCxcYk9fWvESlvTQ5tE7dfMUZhxVt4Jm154dsXFEnhG77ul1KBNcRltHZXh00aM41PcQui4UpLQWPKFQccwDzrhVCfp0uKXGjhxrDp5c+qTbz228yGYcdEaL7JwU9nM0v9t8Ft1RjV1KaunppVjx5ApT960G8pCqQM8SgV0AX65ue+yFWE3t2WfXgPIsaPHpxaralFvylsPbAsQtsGDJaWNLQnowehzUeBWNlK9k4SG1Y79OtUqNia9XO2YE3G+/vN3h70PXHA0uvXW/pbAL4TsjWSxA4RqX0xWUQ6sHTHzsnb27ZiVCnLrl6lWOvRCr2RPOCz2FE/RIFYk9qpdvX9b8ebUcu36MW9uAvuuGReUlO54oc8sa5+vHrCS/i7ddcwS8ATJIVaD1YeGMc3WmWxm3sO78OpmtpXlisXuRYjHODz89Bt/WS1vx2MLH8Pfxv2W32ZG4A10WdNHctjOpWal4dOGjmj/31e6vcCX9iuR7agwPm82GkzdPotvibpr3Lcfmi5vRbVG3vOQsqXrJShOcYzeMP0i6LuiquM2w9cNkrw37wKg3Xuubvd/k/Z6enY5eS3s5VFCRg3cMVZeFxq9XJaQe1EPWDXH7GaWwjrUJa3Hoar5xbcSIdDbaxcdca2nfWcdm5f1ed0Zd1J1RFwNXD5TcVqrP+5P3O9QYV8vCUwvzfu+2qFveJOzHAz+6TVDzNC8sf0H2vSWnl6i6b90h/u5jd451eO+tDW85/O1LGdnOnnWeLDip3xngjLPRHJ8S73KOxfeflophTyx+Aq+ve91h5cKXzqkUZJCawDsb33H4e9oR7QOwAyZMol5b+xrOp513GNScjYVXVr/CpOQeIO9JdseZFHlReKmKL87YYMPIzSM179fdTT/v5DycSz2XV8VDql6yGaiZAcdeiFUMBdE7YxfHaS4+vRinbp5yOF9m6UM6n6s72XdM2a9Wph6Y6vb9tze+nVdFBzBmuM87Oc/hb196iNn7mm3NznvtXOq5vJLKU+PcH0dPc/CafInfD7d+yNRz5Rw7vfb8WodYTKPn3VPyh7zHjjHbxzBry7lS1KfbP2V2juNT4rmFJHgKMkhNQnzz51i1lVxzxp0cy9ZLW1VVhdLC2oS1eGzhY1wlSKRQCkh3xz8n/1GVJKY1kQowXiXHyHI8a+QMTlYTDQCSmbZ2g90Zo6VhM3MzMfPoTCSkJhhqRy96k53UjAniUAwt6hWAIC3jCVbEr3DxyPJAbKDK4W61Ry1WmxXP/PsM5p0QjHqjS6x+FnMfwWJjzqhxaH+mpWSmYMaRGXmJkGo+403wNOqcj7FUeArPY7InaQ+WnhFK4rJMnuIFGaQ+yMpzK7EjcYfke1LyQEYN1Ldi38L5tPNM6gJrYfDawYY+r+R10jsgq9FW9RWUvA0svBFSMa37kvNjIFlm6v9z8h9M2D0Bjy8SaqibLaPSa2kvXceMt2FiT6gxm/c2vZcve+ZhW8R5CVsPH2z5AMduHMNnOz5j0CPfryQFCMfk6z1fq5K380aDVI8Kilpcvq/E1+d5TPqv6o9RW0bhyPUj6LG4B7f9sIIMUk64G2hYDEIJKZ7xAPkSzvI+zh5eG2xcDRazPcp6uJtz162X2Iwge56JHSxRez71eN0zczOZyb5p3a8vXKd2eB+j1KzUvDEhMzfTRQHgv7OOiil3s33n2AH5MY13c+7qlrmyYz8Xdtm907dO5+1DziPPIhHJDCPezHuC5TNI7vgm3k7E1bvKHmxPQwYpI7RcVGplQgh2LD69GE3/aor5J/NlNmyw4XyaPk1EO+60IJv+1dRQ22bQ/p/2aDyrseySpxmDf9+Vfbm0q6VCmRI/H/hZ9fmcEqe9klSONQdN/mrCVCFBDY1nNUbLv1uasi8WnqBms5tJetRtNhuTAiat/m6F4RuGIz07Hc3/ao5nlz3ruB/Rd1h8ejE+3/m5of2ZXTq1xd8tcODqATT9q6nbeFY1DFw90CWTPz07HS1mt0DDWQ0lP6OlIIUnafpXU3yy/RN8tt2YJ/zgVcdjLHUPsNQE/fHAj8za8gRkkDIi16ZOS48VVANaGbExZdd8/WT7J0z3MXHPRKbteQq5ZXNfXlJkOdDrMTL1oFV9437EHhMnxgabqkRGNay/sB77k/cjx5bjUCHIGbGOtF48cX8NXmMsFErMr4d+dfj78LXDpsl48V7+n39yvksCoFbEKhRyaJWA1IOvjOPMDdJx48ahSZMmCAkJQZkyZdCjRw+cOOF4U9tsNowZMwaRkZEoUqQIYmJicOSIa51sXyQrNwvNZzd3qeTA+kHz2Y7P8Nn2z5CSmYJFpxbhxzjfnhn5KoevHzZNZuZuzl18seMLU/YFCCUzV5zzPvFktdzJvsMkblAOe310lskCn27/FF/t/grrEgqeYcrKgJBajWJtnIhj8b/a/ZVq8X6tmO0RZ42zx9MKdqsSctidMUZzDMxmzvE5LrrIn27/1K1aDCuGxw7nvg8WMDdIN27ciKFDh2LHjh1Ys2YNcnJy0KlTJ9y5ky+3MmHCBEycOBFTpkzB7t27ERERgY4dOyItzTvLWWlh9rHZktIyK+LZP9jnnZyHCbsn4ONtH2PqganchZB9DSWha1+rG/z7od8x58QcLm1LzaD1VsDyFn6I+0F3aUA12BOz7LFzrPjz6J+Gl4ILMlLGJ897+c+jf2LlOVc9YRawSo7yFszw9tnZn7zftH0ZJceagy92ujoTcmzGFHeMwGuSZQTmpUNXrnS8cadNm4YyZcpg7969aNOmDWw2GyZNmoRRo0ahV69eAIAZM2agbNmymD17NgYNGsS6S6Zw5tYZVA6rjE2X9NVV14s4yN7XZ9usUVqy1ZN84kkOXD3Are39yftROKAwt/Y9gR5tW62wrGlPqIOFfJNWTt08xVQNwk7inUScuXXG1Ovobi6/hB2eGet2Tt867VPOhMu3LyPIP8j0/RpNWvMEFhvnM3v69GlER0fj0KFDqFOnDs6ePYsHH3wQ+/btw0MPPZS3Xffu3REeHo4ZM1wD0zMzM5GZmW88pKamIioqCikpKQgNDeXZfQDw6sofhHvstbrpHN5/NCzT0EFeivAcf3T+A9czruPdjXwKRVQJq6Kr9CfBlrCgMFMMoWEPDcPk/ZO576cgE9cnDv5+/tz3k5qairCwMFX2GtekJpvNhhEjRqB169aoU6cOACApSZhlli1b1mHbsmXL5r3nzLhx4xAWFpb3ExUVxbPbBEEUAMwWHSfkWRm/kqs4Pxmj3oFZXrmfD/xsyn4KMt6oCct1xH799ddx8OBB/P236xKLc5a4zWaTzRwfOXIkUlJS8n4uXOC/FEcQhG9DBilBFExIZaZgwjyG1M6wYcOwdOlSbNq0CeXLl897PSIiAoDgKS1Xrlze68nJyS5eUztBQUEICjI/BoMgCN/FV6RO7he80SNDEIT3wNyFYLPZ8Prrr2PhwoVYv349Kleu7PB+5cqVERERgTVr8uUisrKysHHjRrRsaY5AM3H/MOPIDK7JQIT3Ql4U72HeyXmYGue+lC9BqMXXElIJdTD3kA4dOhSzZ8/GkiVLEBISkhcXGhYWhiJFisBisWD48OEYO3YsoqOjER0djbFjxyI4OBjPP/886+4Q9zmsxLIJ32NH4g5Pd4EQcen2JU93gSCIe3jjigVzg/THHwWB9piYGIfXp02bhn79+gEA3nvvPdy9exdDhgzBzZs30axZM6xevRohISGsu0MQBEEQBEF4Odxln3igRUaABSQZRBAEQRBEQWFfn30o5FeI+368RvaJIAiCIAiC8DK80BVJBilBEARBEAThUcggJQiCIAiCIDwKGaQEQRAEQRD3Ed6YZU8GKUEQBEEQBOFRyCAlCIIgCIIgPAoZpARBEARBEIRHIYOUIAiCIAjiPoJiSAmCIAiCIAjCCTJICYIgCIIgCI9CBilBEARBEMR9hDdWjSeDlCAIgiAIgvAoZJASBEEQBEEQHoUMUoIgCIIgCMKjkEFKEARBEARxH0GyTwRBEARBEAThBBmkBEEQBEEQ9xE51hxPd8EFMkgJgiAIgiDuI/Yk7fF0F1wgg5QgCIIgCOI+4tLtS57uggv/b+/ew6Kq8zCAv8MdARUVUAQJQRBNJQQvmGJuaVhbprZaCpK3UPKWaeu23l0znh7LLS+Pl57MdVNSezTTkn0qNV0FCYXE1LyhibESQiFym+/+YXNiZLiKc84M7+d5eJj5nTNzvue8M3N+c27DDikRERFRE5Jfkq92CVWwQ0pERETUhOigU7uEKtghJSIiImpCdDp2SImIiIiIjLBDSkRERNSE2Giw+6e9ioiIiIjowdHeHnt2SImIiIiaEp7URERERESq4m/ZWyg3Bze1SyAiIiJqFDyG1EL9Wvqr2iUQERERNQpe9omIiIiIVMVjSImIiIiI7sEOKREREVETwl32RERERET3YIeUiIiIiFTFDmkdrP7TarVLICIiImoUNjrtdf/s1C7AEgzwGYCM2AwIBCICnU4HHXTKhWWru12ZDjroRV/jdHQ6nfL8AJTbhv960Ru9iAz3RUxf4NbwWIFUGe/eaRhUnmZ1z2kYr/I0qhteW3tt864XPXTQVanR1DzcOy/3jl9dHbUtC1PPZare6paHYR6qm+fKNdw7XVPLq/K0TS0LgRhNr6Yaa8q6pnorT8PUPN77vPfWVNfp1NRe3XgLji7Angt7AACnYk9VW1tNx1DVZ/nU9jo09Z4y9byGZVTTsJraTOVa3Wulpnky9dozfIYo4/z+eVa5vbb5M1VLdUK3hNY4nIjujxavQ8oOaR0ZOqGV1wlGK4Nqbldmq7Otw4RM3P79/73faJT7Na3nTT1fdcNNTLPRnre29mqe0+S3uPrWXNf6qnve6trqchu1fBOtzzRMjVOXmmt73voc217NuHXKqT7qmqWJ8fp691U6pDY6m4bV1pDlU1smDc2qPmr4/Kj3PNUyTpXPs/q8f7R3PgVRk8KTmu6xZs0a+Pv7w8nJCT179sThw4fVLIeIrMBQ/6F4Z+A7ODDigNqlEBFpkhZ32atW0fbt2zFz5ky88cYbSE9PR//+/REdHY3s7Gy1SiIiK2Cjs8Hjfo+jnWs7tUshItIkdkgrWblyJSZMmICJEyciJCQE7777Lnx9fbF27Vq1SiIiIiKyeuyQ/q60tBRpaWkYPHiwUfvgwYNx9OjRKuOXlJSgsLDQ6I+IiIiI6k+LJzWpUtHNmzdRUVEBLy8vo3YvLy/cuHGjyvhvvvkmWrRoofz5+vqaq1QiIiIiqxLpHal2CVWo2kU2dUkdU2d+zZs3DwUFBcrf1atXzVUiERGZWXpMOpztnBv8eDcHt3qNb9h96evmizbObWocN9wrvMbhLR1bAgBc7V2VNk9nT5PjPtz6YeX2Q80fQpfWXRDkHoQon6i6lI3ng55HW5e2Joc52TrV6Tlq0rlVZ/Rp1weezp7o376/0TA7Xd0v0tPKqRUAwMXepV7Try2Lul5KrmOLjtUOq++u64AWAQCA9q7tAdR/nurD0dYRQN1ezw42DlXaWju1RvRD0fB28TZqf7P/m/Btrr0Ne6pc9qlNmzawtbWtsjU0Nze3ylZTAHB0dISjo6O5yiMiIhXZ2dghZUyK2mUQkRmpsoXUwcEBPXv2RHJyslF7cnIyIiO1txmZiIiIiB4c1S6M/+qrryImJgbh4eHo27cv1q9fj+zsbMTHx6tVEhERERGpQLUO6ahRo5CXl4clS5YgJycHDz/8MPbt2wc/P79aH2v42TmebU9ERESkTYZ+Wl1+olondf0haw25du0az7QnIiIisgBXr16Fj49PjeNYZIdUr9fj+vXrcHNzM8vvsRYWFsLX1xdXr15F8+bNH/j0LE1ERARSU1PNMi1moQ11ycGcr4umylLfD9b42rDULLSkMV4XzEEbDDlkZ2dDp9PB29sbNjY1n7ak2i77+2FjY1NrT/tBaN68OV/gJtja2pp9uTALbagpBzVeF02Vpb0frPm1YWlZaEljvi6Ygza0aNGizjlo71L9ZHESEhLULoE0iK8Lqg5fG2QKXxdNm0Xusje3wsJCtGjRAgUFBfzGpTJmoQ3MQRuYg3YwC21gDtrQkBy4hbQOHB0dsXDhQl6cXwOYhTYwB21gDtrBLLSBOWhDQ3LgFlIiIiIiUhW3kBIRERGRqtghJSIiIiJVsUNKRERERKpih5SIiIiIVMUOKer2G6tERObGzyYiY3xPWK8m3yHNzc3Fr7/+qtzni109P/74I5KTk9Uuo8m7evUq0tLScP36dbVLadIKCgpQUVGh3Odnk3rOnTuH+Ph4HD58WO1SmjSur7XhQa2rm2yHtLy8HBMmTECvXr3w+OOPY8yYMbh58yZ0Op3apTVJGRkZCAoKwgsvvIArV66oXU6TVFZWhpdffhlhYWEYP348evTogSNHjqhdVpNTVlaGhIQEDB06FEOHDsXSpUtRUVHBzyYV6PV6zJo1C6GhoSgqKjLqDJH5cH2tHQ9yXd0kO6Tl5eWIi4tDVlYWNm/ejBdeeAEZGRkYPnw4zpw5o3Z5TVJpaSmGDBkCe3t7JCYmql1Ok/Pbb79h5MiROH/+PA4cOICkpCSEhYVh/vz5ALglwlySk5PRpUsXnD59GnPmzIGvry+2bt2KRYsWAWAO5rZ//36kpqZi//792LJlC4YOHaoMYxbmwfW1tjzIdXWT7JDm5OQgJSUFCQkJiIqKwqxZs5CcnIyLFy9i7dq1+Pnnn9Uuscn57rvv4O7ujq1bt2L9+vVISUlRu6QmJSsrC2fOnMH8+fPxyCOPIDg4GM8//zzc3Nyg1+u5JcIMCgsLkZSUhCFDhiA5ORnDhg3D2rVrMXr0aKSmpuL27dvMwcw2btyI0NBQREVF4eDBg5g/fz4+/PBDZGdnMwsz4fpaWx7kurpJdkjz8vJw7do19OnTBwBQUlKCtm3bYt68eThw4AAOHTqkcoVNg16vV247OjrCz88PgwYNQkREBBYvXgzg7kqaHrzS0lL8+OOPys+83bx5E6tXr4a3tzc++OADFBcXq1yh9RMRPProo5g4cSLs7e0hInBwcMCdO3dQXFyMZs2acaucGf3666+4efMm/vSnP2HZsmUYPXo0MjMzsWDBAgwaNAifffaZ2iU2CVxfq6/y586DXFdbfYd0+fLlWLhwIbZt26a0hYSEwNPTE//6178AADY2dxdDQkIC3NzcsH//fpSUlKhSrzW7NwvDcgfufuv67bffAABbt27FF198gejoaAwZMgQ//PCDKvVaK1PviUcffRRRUVF46aWXEB0dDS8vL7Rt2xYODg6YN28exo0bh8zMTBWrtj779u0D8McXsxYtWmDcuHEIDQ01ai8oKEDHjh0BgFvlHhBDFpVXvG5ubigrK8PGjRtx7tw57Nq1Czt27MCVK1cQEBCADz74gJ9NjWz9+vXYsGGDUSezU6dOaNu2LdfXZmTI4eDBgwDufu4YPo8e6LparNTx48elQ4cOEhYWJtHR0eLm5iYjRoyQCxcuiIjIa6+9JkFBQfLzzz+LiEhxcbGIiGzevFlatmyp3Kf7ZyqLkSNHyvnz55VxRo8eLf/5z39ERGTDhg3i7Ows9vb2smPHDrXKtjrV5fDDDz+IiEhhYaGcP39eIiMj5e2331Yel56eLh07dpSkpCS1Srcqe/fulfbt24tOp5MjR46IiEhFRUWV8fR6vYiI9O7dWzZu3GjURo3DVBZ6vV5Zzps2bRKdTidBQUGSm5urPO7QoUPSrl07OXr0qCp1W5t///vf4unpKX379pXQ0FDx8PCQf/zjHyIiUlBQIHPnzuX62gxM5bB8+XIRESkpKRGRB7uuttoO6auvvipPPfWUiNz9sM/MzBQ/Pz+Jj4+XW7duybFjxyQsLEymTp0qIn980H/99dfi6ekpp06dUq12a1NdFlOmTJFr166JiMjYsWMlJiZGIiIixMPDQ5YuXSru7u5GHSO6PzXlcP36dRERSU1NleDgYMnNzVXeE+Xl5cyikRw+fFiefPJJeeWVVyQ6OlrCw8NrHP/SpUvi4eGhfGkQEeVLtalOLNVdXbLIysqSgQMHSpcuXSQnJ0dpLy4uFldXV/nkk0/MWbJV2rp1q/To0UPWrVsnIiI//fSTvP/+++Li4iIFBQUiIpKcnCwRERFcXz9ANeVQWFiojDdu3LgHtq62ul32IoKCggKkpKQgJCREaX/44Yfx+uuv4/jx49i2bRt69+6NmJgYfPjhh/j0009RVlYGADhy5Ai6dOmCbt26qTULVqO2LFJSUvDpp58CAIqKivD555+jV69eSE9Px9///ne8/vrrmDNnDi5fvqzSHFiHuuSQlJQE4O5uynPnzuHq1avK7uHPPvsM/v7+GDRokCr1WwP5fVewl5cXBg8ejFdffRVLly5FVlYWNm3aBMD4mGqDL774Ar6+vggODkZ6ejp69+6NPn36oLy83OiQF6q7umRhuP5rUFAQZs6ciQsXLmDdunX46aefAAB79uxBt27dMGDAAHVmwgoYcigrK0Pv3r0RGxsLAPD29kZoaCjat2+PrKwsAHcPKXrxxRexefNmrq8bWV1yMFzNoLi4GIWFhdi3b9+DWVffd5dWA9LS0uTWrVtGbeHh4fLyyy+LiMidO3dERKS0tFSGDx8uzzzzjPz0009SWloqc+bMETc3N4mKipLnn39enJ2dZfXq1SLC3WMN0ZAs8vPzJSMjQzIzM40ed+fOHUlMTOSWoAaobw7Dhg2TK1euSFFRkYwaNUqaNWsm8fHxEhsbK25ubrJgwQK+HxrAVA7l5eUiIlJWViazZ88WDw8PJQ8Dw7KeNm2ajBw5UmbNmiU2NjYyYcKEKuNS3dQ3i8qfO//85z/F29tbgoOD5bnnnhMXFxdllzLVT1pamuTn5yv3b926peRgcPLkSWnbtq388ssvSlthYaHMnTuX6+tG0tAcUlJS5PTp00bjNda62qI7pDt27BAfHx8JCAiQDh06yIIFC5RdwKtWrRJXV1cpKioSkT+Of9i5c6f4+PgoxwuJiHzyySeycOFCiY+PlzNnzph/RqxAQ7No3749j8NqRPfznjDkUFRUJHPnzpW4uDiJjY2Vs2fPqjMzFsxUDoZdvpWPUbx48aL4+vrK7NmzlWEGFRUV4ufnJzqdTgYOHFhlJUB109As7l25Hjt2TNasWSPz5s3je6IB7s1h/vz5cuPGDWV45eW9cuVK6devn4j88TllwPX1/WloDub4ImyxHdLU1FTp3LmzvPvuu3Lq1ClZs2aNeHh4yJQpU+TWrVty5coVCQgIULYIlZaWKo9t3bq1bNq0Sa3SrQ6z0Ib7zcFw4oxBWVmZWeu3FjXlkJeXJyJ/bJnT6/WyZs0asbOzk4sXL4rI3RVwUVGRFBcXy/Lly+XLL79UbV4sXWNkUfn4OWqYuuRQUVGhfOY899xzkpCQoGbJVknrOVhch9TwbXbt2rXi4+OjHPQsIvL+++9Lr1695M033xQRkdWrV4utra0cPHhQGefChQsSEBAgO3fuNG/hVohZaANz0IbacujTp48sXbq0yuPy8vIkMjJSnn32WUlLS5MnnnhCtmzZYra6rVFjZTF48GDZsmULdwc3UH1zqKioEL1eLwEBAbJ3714RETl79qyMHj1asrOzzVu8FbGUHCzuqHjDiRaXLl1CUFAQ7OzslGFxcXGIiIjA7t27ce7cOUyZMgWjR4/GqFGjsGTJEpw8eRKJiYlo1qyZcpFdajhmoQ3MQRtqy6Fnz57Yv38/Tp8+DeCPE2datWqFSZMmYc+ePYiIiICjoyOGDx9u/hmwIo2VhYODA0aMGMHrvzZQfXOwsbFBamoqmjVrhrCwMMycORPdu3dHXl4ePD09VZkHa2AxOTywrm4jOXDggEybNk3effddOX78uNK+e/ducXJyUi6BYtjtcuDAAYmMjJSVK1cq406bNk1CQ0MlMDBQwsLCJCMjw7wzYSWYhTYwB21oSA79+vUzyqGkpERWr14tNjY2EhUVJd9//715Z8JKMAttaIwcFi5cKDqdTtzc3KRLly5y4sQJ886EFbDUHDTbIb1+/bo8/fTT4unpKWPGjJFu3bpJixYtlIVbXFwsnTt3lsmTJ4uI8YG4/fv3lylTpij3KyoqpKioyOhaflR3zEIbmIM23G8OhmspiojcuHFDZsyYIZs3bzbvTFgJZqENjZnDsmXLxMPDg4cQNYCl56DJDmlRUZGMGzdORo0apRxcLiISEREhcXFxInK3Z//RRx+JjY2N0RnzIiJjxoyRxx57TLnP438ajlloA3PQhsbOgRqOWWhDY+QwcOBA5X7lX8SiurOGHDR5DGmzZs3g6OiIuLg4+Pv7o7y8HADw9NNPKxdotbW1xV/+8hc8++yzmDhxIg4ePAgRwY0bN3D+/HmMGTNGeT4e/9NwzEIbmIM2NHYO1HDMQhsaI4exY8cqz+fh4aHKfFg6a8hBJ/L7Zfo1pqysDPb29gDu/pKATqdDTEwMnJ2dsX79eqXtzp07iI6ORlZWFkJDQ/H999+jQ4cOSEpKgq+vr8pzYR2YhTYwB21gDtrBLLSBOWiDpeeg2Q6pKQMGDMD48eMRFxcHEYFer4etrS1+/vlnZGRkIDU1FQ899BBefPFFtUu1esxCG5iDNjAH7WAW2sActMGScrCYDunFixcRGRmJzz//HD179gQAlJaWwsHBQeXKmh5moQ3MQRuYg3YwC21gDtpgaTlo8hjSygz95W+//Raurq7KQl28eDFmzJiB3NxcNctrUpiFNjAHbWAO2sEstIE5aIOl5mBX+yjqMpx8kZKSghEjRiA5ORmTJ0/G7du3sWXLFl4s14yYhTYwB21gDtrBLLSBOWiDxebwwM7fb0TFxcUSGBgoOp1OHB0dZcWKFWqX1GQxC21gDtrAHLSDWWgDc9AGS8zBYo4hfeKJJ9CpUyesXLkSTk5OapfTpDELbWAO2sActINZaANz0AZLy8FiOqQVFRWwtbVVuwwCs9AK5qANzEE7mIU2MAdtsLQcLKZDSkRERETWSfNn2RMRERGRdWOHlIiIiIhUxQ4pEREREamKHVIiIiIiUhU7pERERESkKnZIiYiIiEhV7JASkdX65ptvoNPpcOvWLbNNc+DAgZg5c6bZpkdEZA3YISUiaoDqOru7du3C0qVL1Snqd+wUE5GlsVO7ACIia9KqVSu1SyAisjjcQkpEFq2kpATTp0+Hp6cnnJyc8OijjyI1NdVonCNHjqBHjx5wcnJC7969kZmZqQy7cuUK/vznP8Pd3R0uLi7o2rUr9u3bV+M0L1++jMceewwA4O7uDp1Oh7i4OABVt04+9NBDWLZsGWJjY+Hq6go/Pz/s3r0b//vf//Dss8/C1dUV3bp1w4kTJ4ymcfToUQwYMADOzs7w9fXF9OnTUVRUpAxfs2YNOnXqBCcnJ3h5eWHkyJEAgLi4OBw8eBCrVq2CTqeDTqfD5cuXUVFRgQkTJsDf3x/Ozs4IDg7GqlWrjKYZFxeHYcOGYfny5fDy8kLLli2xePFilJeXY86cOWjVqhV8fHzwwQcfGC0LnU6Hbdu2ITIyEk5OTujatSu++eabmoMjIqqEHVIismhz587Fzp07sXnzZnz33XcIDAzEkCFD8MsvvyjjzJkzB2+//TZSU1Ph6emJZ555BmVlZQCAhIQElJSU4NChQ8jMzMRbb70FV1fXGqfp6+uLnTt3AgDOnj2LnJycKp27yt555x3069cP6enpeOqppxATE4PY2FiMHTtWqTk2NhaGX3LOzMzEkCFDMHz4cGRkZGD79u349ttv8corrwAATpw4genTp2PJkiU4e/YsvvjiCwwYMAAAsGrVKvTt2xeTJk1CTk4OcnJy4OvrC71eDx8fHyQlJSErKwsLFizA3/72NyQlJRnV+tVXX+H69es4dOgQVq5ciUWLFuHpp5+Gu7s7jh8/jvj4eMTHx+Pq1atGj5szZw5mz56N9PR0REZG4plnnkFeXl5dIiQiAoSIyEL99ttvYm9vL1u3blXaSktLxdvbWxITE+Xrr78WALJt2zZleF5enjg7O8v27dtFRKRbt26yaNGiek/b8Nz5+flG7VFRUTJjxgzlvp+fn4wdO1a5n5OTIwBk/vz5Stt///tfASA5OTkiIhITEyOTJ082et7Dhw+LjY2NFBcXy86dO6V58+ZSWFhosrZ7a6jO1KlTZcSIEcr9cePGiZ+fn1RUVChtwcHB0r9/f+V+eXm5uLi4yMcffywiIpcuXRIAsmLFCmWcsrIy8fHxkbfeeqvWGoiIRES4hZSILNaFCxdQVlaGfv36KW329vbo1asXzpw5o7T17dtXud2qVSsEBwcrw6dPn45ly5ahX79+WLhwITIyMhq9zu7duyu3vby8AADdunWr0pabmwsASEtLw4cffghXV1flb8iQIdDr9bh06RKeeOIJ+Pn5oWPHjoiJicHWrVtx+/btWutYt24dwsPD4eHhAVdXV2zYsAHZ2dlG43Tt2hU2Nn+sGry8vIxqtbW1RevWrZVaDSovYzs7O4SHhxtlQERUE3ZIichiye+7uHU6XZX2e9vuZRg+ceJEXLx4ETExMcjMzER4eDjee++9Rq3T3t6+ynRNten1euX/yy+/jJMnTyp/p06dwvnz5xEQEAA3Nzd89913+Pjjj9GuXTssWLAAPXr0qPHyVklJSZg1axbGjx+PAwcO4OTJk3jppZdQWlpaba2G2ky1GWqtSW0ZEBEZsENKRBYrMDAQDg4O+Pbbb5W2srIynDhxAiEhIUrbsWPHlNv5+fk4d+4cOnfurLT5+voiPj4eu3btwuzZs7Fhw4Zap+3g4AAAqKioaIxZMRIWFobTp08jMDCwyp9hunZ2dnj88ceRmJiIjIwMXL58GV999ZVS2711HT58GJGRkZg6dSoeeeQRBAYG4sKFC41Wc+VlXF5ejrS0NKNlTERUE172iYgslouLC6ZMmaKcAd6hQwckJibi9u3bmDBhAk6dOgUAWLJkCVq3bg0vLy+88cYbaNOmDYYNGwYAmDlzJqKjoxEUFIT8/Hx89dVXRp3Z6vj5+UGn02Hv3r0YOnQonJ2daz0Zqq5ef/119OnTBwkJCZg0aRJcXFxw5swZJCcn47333sPevXtx8eJFDBgwAO7u7ti3bx/0ej2Cg4MB3D2z//jx47h8+TJcXV3RqlUrBAYG4qOPPsKXX34Jf39/bNmyBampqfD392+UmlevXo1OnTohJCQE77zzDvLz8zF+/PhGeW4isn7cQkpEFm3FihUYMWIEYmJiEBYWhh9//BFffvkl3N3djcaZMWMGevbsiZycHOzZs8doC2dCQgJCQkLw5JNPIjg4GGvWrKl1uu3bt8fixYvx17/+FV5eXsoZ8I2he/fuOHjwIM6fP4/+/fvjkUcewfz589GuXTsAQMuWLbFr1y4MGjQIISEhWLduHT7++GN07doVAPDaa6/B1tYWXbp0gYeHB7KzsxEfH4/hw4dj1KhR6N27N/Ly8jB16tRGq3nFihV466230KNHDxw+fBi7d+9GmzZtGu35ici66cRwEBYREVE9Xb58Gf7+/khPT0doaKja5RCRheIWUiIiIiJSFTukREQmxMfHG112qfJffHy82uUREVkV7rInIjIhNzcXhYWFJoc1b94cnp6eZq6IiMh6sUNKRERERKriLnsiIiIiUhU7pERERESkKnZIiYiIiEhV7JASERERkarYISUiIiIiVbFDSkRERESqYoeUiIiIiFTFDikRERERqer/QhcdTU7p4fAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 800x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_cols = ['temp', 'pressure', 'wspd']\n",
+    "plot_features = df[plot_cols]\n",
+    "plot_features.index = date_time\n",
+    "_ = plot_features.plot(subplots=True)\n",
+    "\n",
+    "plot_features = df[plot_cols][-480:]\n",
+    "plot_features.index = date_time[-480:]\n",
+    "_ = plot_features.plot(subplots=True)\n",
+    "df.describe().transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "f8fd563c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Year signal')"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(2, 1)\n",
+    "fig.tight_layout(h_pad=5)\n",
+    "axes[0].plot(np.array(df['day_sin'])[:150])\n",
+    "axes[0].plot(np.array(df['day_cos'])[:150])\n",
+    "axes[0].set_xlabel('Time [h]')\n",
+    "axes[0].set_title('Time of day signal')\n",
+    "\n",
+    "axes[1].plot(np.array(df['year_sin'])[:15000])\n",
+    "axes[1].plot(np.array(df['year_cos'])[:15000])\n",
+    "axes[1].set_xlabel('Time [h]')\n",
+    "axes[1].set_title('Year signal')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "a56d8d86",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split data\n",
+    "column_indices = {name: i for i, name in enumerate(df.columns)}\n",
+    "\n",
+    "n = len(df)\n",
+    "train_df = df[0:int(n*0.7)]\n",
+    "val_df = df[int(n*0.7):int(n*0.9)]\n",
+    "test_df = df[int(n*0.9):]\n",
+    "\n",
+    "num_features = df.shape[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "d710186c-3f8c-4d35-842f-361508b2c781",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Normalization\n",
+    "\n",
+    "train_mean = train_df.mean()\n",
+    "train_std = train_df.std()\n",
+    "\n",
+    "train_df = (train_df - train_mean) / train_std\n",
+    "val_df = (val_df - train_mean) / train_std\n",
+    "test_df = (test_df - train_mean) / train_std"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "1613147d-946e-4e5c-949c-1e30bd46adc6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class WindowGenerator():\n",
+    "  def __init__(self, input_width, label_width, shift,\n",
+    "               train_df=train_df, val_df=val_df, test_df=test_df,\n",
+    "               label_columns=\"temp\"):\n",
+    "    # Store the raw data.\n",
+    "    self.train_df = train_df\n",
+    "    self.val_df = val_df\n",
+    "    self.test_df = test_df\n",
+    "\n",
+    "    # Work out the label column indices.\n",
+    "    self.label_columns = label_columns\n",
+    "    if label_columns is not None:\n",
+    "      self.label_columns_indices = {name: i for i, name in\n",
+    "                                    enumerate(label_columns)}\n",
+    "    self.column_indices = {name: i for i, name in\n",
+    "                           enumerate(train_df.columns)}\n",
+    "\n",
+    "    # Work out the window parameters.\n",
+    "    self.input_width = input_width\n",
+    "    self.label_width = label_width\n",
+    "    self.shift = shift\n",
+    "\n",
+    "    self.total_window_size = input_width + shift\n",
+    "\n",
+    "    self.input_slice = slice(0, input_width)\n",
+    "    self.input_indices = np.arange(self.total_window_size)[self.input_slice]\n",
+    "\n",
+    "    self.label_start = self.total_window_size - self.label_width\n",
+    "    self.labels_slice = slice(self.label_start, None)\n",
+    "    self.label_indices = np.arange(self.total_window_size)[self.labels_slice]\n",
+    "\n",
+    "  def split_window(self, features):\n",
+    "    inputs = features[:, self.input_slice, :]\n",
+    "    labels = features[:, self.labels_slice, :]\n",
+    "    if self.label_columns is not None:\n",
+    "      labels = tf.stack(\n",
+    "          [labels[:, :, self.column_indices[name]] for name in self.label_columns],\n",
+    "          axis=-1)\n",
+    "\n",
+    "    # Slicing doesn't preserve static shape information, so set the shapes\n",
+    "    # manually. This way the `tf.data.Datasets` are easier to inspect.\n",
+    "    inputs.set_shape([None, self.input_width, None])\n",
+    "    labels.set_shape([None, self.label_width, None])\n",
+    "    return inputs, labels\n",
+    "\n",
+    "  def plot(self, model=None, plot_col='temp', max_subplots=3):\n",
+    "    inputs, labels = self.example\n",
+    "    plt.figure(figsize=(12, 8))\n",
+    "    plot_col_index = self.column_indices[plot_col]\n",
+    "    max_n = min(max_subplots, len(inputs))\n",
+    "    for n in range(max_n):\n",
+    "      plt.subplot(max_n, 1, n+1)\n",
+    "      plt.ylabel(f'{plot_col} [normed]')\n",
+    "      plt.plot(self.input_indices, inputs[n, :, plot_col_index],\n",
+    "              label='Inputs', marker='.', zorder=-10)\n",
+    "\n",
+    "      if self.label_columns:\n",
+    "        label_col_index = self.label_columns_indices.get(plot_col, None)\n",
+    "      else:\n",
+    "        label_col_index = plot_col_index\n",
+    "\n",
+    "      if label_col_index is None:\n",
+    "        continue\n",
+    "\n",
+    "      plt.scatter(self.label_indices, labels[n, :, label_col_index],\n",
+    "                  edgecolors='k', label='Labels', c='#2ca02c', s=64)\n",
+    "      if model is not None:\n",
+    "        predictions = model(inputs)\n",
+    "        plt.scatter(self.label_indices, predictions[n, :, label_col_index],\n",
+    "                    marker='X', edgecolors='k', label='Predictions',\n",
+    "                    c='#ff7f0e', s=64)\n",
+    "\n",
+    "      if n == 0:\n",
+    "        plt.legend()\n",
+    "\n",
+    "    plt.xlabel('Time [h]')\n",
+    "\n",
+    "  def make_dataset(self, data):\n",
+    "    data = np.array(data, dtype=np.float32)\n",
+    "    ds = tf.keras.utils.timeseries_dataset_from_array(\n",
+    "        data=data,\n",
+    "        targets=None,\n",
+    "        sequence_length=self.total_window_size,\n",
+    "        sequence_stride=1,\n",
+    "        shuffle=False,\n",
+    "        batch_size=32,)\n",
+    "\n",
+    "    ds = ds.map(self.split_window)\n",
+    "\n",
+    "    return ds\n",
+    "\n",
+    "  @property\n",
+    "  def train(self):\n",
+    "    return self.make_dataset(self.train_df)\n",
+    "\n",
+    "  @property\n",
+    "  def val(self):\n",
+    "    return self.make_dataset(self.val_df)\n",
+    "\n",
+    "  @property\n",
+    "  def test(self):\n",
+    "    return self.make_dataset(self.test_df)\n",
+    "\n",
+    "  @property\n",
+    "  def example(self):\n",
+    "    \"\"\"Get and cache an example batch of `inputs, labels` for plotting.\"\"\"\n",
+    "    result = getattr(self, '_example', None)\n",
+    "    if result is None:\n",
+    "      # No example batch was found, so get one from the `.train` dataset\n",
+    "      result = next(iter(self.train))\n",
+    "      # And cache it for next time\n",
+    "      self._example = result\n",
+    "    return result\n",
+    "\n",
+    "  def __repr__(self):\n",
+    "    return '\\n'.join([\n",
+    "        f'Total window size: {self.total_window_size}',\n",
+    "        f'Input indices: {self.input_indices}',\n",
+    "        f'Label indices: {self.label_indices}',\n",
+    "        f'Label column name(s): {self.label_columns}'])\n",
+    "\n",
+    "MAX_EPOCHS = 30\n",
+    "\n",
+    "def compile_and_fit(model, window, patience=2, epochs=MAX_EPOCHS):\n",
+    "  early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss',\n",
+    "                                                    patience=patience,\n",
+    "                                                    mode='min')\n",
+    "\n",
+    "  model.compile(\n",
+    "      loss=tf.keras.losses.MeanSquaredError(),\n",
+    "      optimizer=tf.keras.optimizers.Adam(),\n",
+    "      metrics=[tf.keras.metrics.MeanAbsoluteError()]\n",
+    "  )\n",
+    "  \n",
+    "  history = model.fit(window.train, epochs=epochs,\n",
+    "                      validation_data=window.val,\n",
+    "                      callbacks=[early_stopping])\n",
+    "  return history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "82b6ec85-5684-49b1-9a86-348f8d465f4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Total window size: 192\n",
+       "Input indices: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
+       "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
+       "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
+       "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
+       "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
+       "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
+       " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
+       " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
+       " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
+       " 162 163 164 165 166 167]\n",
+       "Label indices: [168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185\n",
+       " 186 187 188 189 190 191]\n",
+       "Label column name(s): ['temp']"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x800 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "OUT_STEPS = 24\n",
+    "multi_window = WindowGenerator(\n",
+    "    input_width=168, label_width=OUT_STEPS, shift=OUT_STEPS,\n",
+    "    label_columns=['temp']\n",
+    ")\n",
+    "multi_window.plot()\n",
+    "multi_window"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "6279e34f-b48e-420d-8d44-48d11353ebf7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1115/1115 [==============================] - 4s 4ms/step - loss: 2.1419 - mean_absolute_error: 1.1699\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x800 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "class MultiStepLastBaseline(tf.keras.Model):\n",
+    "  def call(self, inputs):\n",
+    "    return tf.tile(inputs[:, -1:, :], [1, OUT_STEPS, 1])\n",
+    "\n",
+    "last_baseline = MultiStepLastBaseline()\n",
+    "last_baseline.compile(loss=tf.keras.losses.MeanSquaredError(),\n",
+    "                      metrics=[tf.keras.metrics.MeanAbsoluteError()])\n",
+    "\n",
+    "multi_val_performance = {}\n",
+    "multi_performance = {}\n",
+    "\n",
+    "multi_val_performance['Last'] = last_baseline.evaluate(multi_window.val)\n",
+    "multi_performance['Last'] = last_baseline.evaluate(multi_window.test, verbose=0)\n",
+    "multi_window.plot(last_baseline)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "e8f4aa02-1978-4fa3-b23a-d731594d8cd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1119/1119 [==============================] - 6s 5ms/step - loss: 2.1284 - mean_absolute_error: 1.1630\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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soW31vl9ERA1ZRaX2nl77HjHaFCFbpQgOCtSbte+l/RraWlRpv8ZsKdKrflH940g/VVvnzp3V7js7O5cpMeLn51fmfnVH+isyaNAguLu7o1WrVhg/fjx+++03FBRwKi5RXfr1xD0AgG9Le2yc6osjcwYazGZ3oT3dcHTOi2jvVDKtMq9IXsUziIhIl/1Tak99c7vgiEKERkohUwiqUnuyYjkiIiK0HXK1lPbrAz8TtdJ/wRGFGLO1UNWvD3ub6FW/qP5xpL+emZuIcXVBzX6BS80uwsvf/qm2vtZIBOyf+QKcbKq/M7y5ibhGr/ssExMTtfsikQhKpbKCs9XPAwAjo5LfmJ5eZlBcXFzl862srHD27FkcOnQIe/fuxaeffor58+fj9OnTsLW1rUEPiKg6CmUKRMQnAwCm9m8Fv9aNtRyR5jnbmGPaix545/ez2HgqGdNf9IDEmL+DExHpo+XLl+Pa1SsI2BSP2LFQldqbO3culi1dgtBIqarUXm9fHyxfvlzbIVfLkiVL8Gv4egz5rQB7Xv+n9F+TEU2wc/djjNlaWFL677cCSEzEWLJkibZDJh3Fbzj1TCQSwUJiXKNbqyaWWBzcCeK/k2exSITFwZ3Qqolljdqpj5JaJ06cKHO/XbuSjbOaNGkCAEhJSVE9fv78ebXzJRIJFApFmXaNjY3x8ssvY9myZbh48SISExNx4MABDUdPRAAQc+EBcorkaG5njgFtm2o7nDrj39ERjtamSM+TYvfllKqfQEREOql0x32vLj3Qb22Bajf7hQsXYltUNHbdUqLf2gJ4delRZak9XbJnzx7IihWAsxn6rS1A7G05XKe5w3GUI1ynuWPnLTn6rS0AnMwgK1YgLu75lhCT4eJIv54I7emG/p5NkJhegBYOFjq7/nTLli3o0aMH+vbti99++w2nTp3C6tWrAQBt2rSBq6sr5s+fj0WLFuHmzZv45ptv1J7fokUL5OXl4Y8//kCXLl1gYWGBAwcO4M6dO+jfvz/s7Oywa9cuKJVKtG3bVhtdJDJogiBgw/GSqf3jfd0hfnbXOwNiIjbCa73c8d3+Gwg/fg+vdG2m7ZCIiKiWNFVqT5dER0fD0tMSru+5IuX3FNj0soFVp5L4rbpawXWGO7JPZcP5NWckf5eMqKgojBs3TstRky5i0q9HnG3MdTbZL/X5559j06ZN+Pe//w0nJyf89ttv6NChA4CS5QEbN27E//3f/6FLly7o2bMnFi1ahDFjxqie37t3b7z99tsIDQ3FkydP8Nlnn+Hll1/Gtm3bMH/+fBQVFcHDwwMbN25Ex44dtdVNIoN1NikLVx7mwNTYCCE9XLUdTp17tZcrfjxwE/H3MnHlYTY6uthoOyQiIqql0lJ7zxo8eLBebnCXkZkBsa0YYnMxmk9pXuZxq05Wqh8BjGyNkJGZUd8hkp5g0k8VWrdunerfhw4dKvN4dHR0mWMuLi7Yu3dvhW326dMHFy9eVDv2bCnBVatWYdWqVWrHynt9ItK88OOJAIARXVxg10ii3WDqQVNrMwzxcsLOiykIP34PS0Z1rvpJRERE9cDezh6KB2WXvZZHmaWEfXP7Oo6I9BXX9BMREQDgca4UsZdK1rZP8HPXcjT1Z4JfCwBA9PkHyC6oenNRIiKi+hAYGIi8G3mQpkorPU+aIkXejTwEBQXVU2Skb5j0ExERAGDz6SQUKwR0cbVF5+a22g6n3vRsYYd2TlYoKlZiy5lkbYdDREQEABgzZgzsGtvhUcQjCEqh3HMEpYBHWx7BrrEdRo8eXc8Rkr5g0k8aIwgCAgMDtR0GEdWCXKHEbyeTAAATfBvOKD9QUlVl/N8zG349cQ/KCr5YERER1SczMzOsX7seeefzkLwiucyIvzRFiuQVycg7n4f1a9fDzKz6pbypYWHST0RE2J+QhpTsItg3kiCgs7O2w6l3gV2bwcrMGIlPCnD4Vrq2wyEiIgIAjBgxAlFRURDfE+PmnJtI/DIRSSuTkPhlIm5+dBPie2JER0djxIgR2g6VdBiTfiIiQviJRABAaE9XmJmItRuMFjQyNcZo75KdkUs3MyQiItIFI0eOxMP7DxEeHg5/L390b9Qd/l7+CA8Px8P7D5nwU5W4ez8RUQN3Ky0XR289gZEIeN3HTdvhaM14X3esPZqIP66lITmjAK72FtoOiYiICEDJVP9x48Zh3Lhx2g6F9JDejfSvXLkSLVu2hJmZGby9vXH48OEKz922bRsGDRqEJk2awNraGn5+foiLi1M7Z926dRCJRGVuRUVFdd0VIiKdEH78HgDgxXaOaG7XcBPdVk0s0c/DAYIA/HrynrbDISIiItIIvUr6N2/ejLCwMHzyySc4d+4c+vXrh6FDhyIpKanc8//66y8MGjQIu3btwpkzZzBw4ECMGDEC586dUzvP2toaKSkpajduhEFEDUGeVI7Isw8ANKwyfRUZ//cmhhGnk1FUXL3ayEREZHiKiooQHh6OUaNGYeCLAzFq1CiEh4fr/cCgofaLKicSBEFvtin28fFB9+7dsWrVKtWx9u3bIzAwEIsXL65WGx07dkRoaCg+/fRTACUj/WFhYcjKyqp1XDk5ObCxsUF2djasra3VHisqKsLdu3dVsxOoYeDfnfRF+Il7mBd9GS0dGuGPmS/AyEik7ZC0SqEU0H/ZQTzIKsTXY7qo1vkTEVHDERMTg4mTJyLzSSYsPS0hthVDkaVA3o082DW2w/q16/VyHb2h9qshqywPfZrejPTLZDKcOXMG/v7+asf9/f1x7NixarWhVCqRm5sLe3t7teN5eXlwd3dH8+bNMXz48DIzAajm1q1bB1tb2+duRyQSITo6+rnbIaKyBEFQbVo33te9wSf8ACA2EuF135J9DbihHxFRwxMTE4OgoCAo3BXwWOKBFh+3gOu/XdHi4xbwWOIBhbsCgYGBiImJ0XaoNWKo/aLq0ZukPz09HQqFAo6OjmrHHR0dkZqaWq02vvnmG+Tn5yMkJER1rF27dli3bh1iYmKwceNGmJmZoU+fPrh582aF7UilUuTk5KjdDNHEiRMRGBio7TCIqI6cvJuBG4/yYG4ixiiOaKuE9nCFRGyEC/ezcT45S9vhEBFRPSkqKsLEyRNh2dUSrtNcYepkqva4qZMpXKe5wrKrJSZOnqg3U+INtV9UfXqT9JcSidRHogRBKHOsPBs3bsT8+fOxefNmNG3aVHXc19cX48aNQ5cuXdCvXz9ERETA09MTP/74Y4VtLV68GDY2Nqqbq6tr7TtUDVx7Q0R1oXQDv8BuzWBjbqLlaHRHY0tTDO/sDADYwNF+IqIGY8uWLch8kgnHEEeIKpj9JjISwXGMIzKfZGLr1q31HGHtGGq/qPr0Jul3cHCAWCwuM6qflpZWZvT/WZs3b8aUKVMQERGBl19+udJzjYyM0LNnz0pH+j/66CNkZ2erbsnJydXvSA3FxMTApbkLJkyYgL2X9+Jc/jnsvbwXEyZMgEtzF+zYsaPOXrsy3377LTp16oRGjRrB1dUV//73v5GXl1fmvOjoaHh6esLMzAyDBg0q817t2LED3t7eMDMzQ6tWrfD5559DLpeX+5oymQzTpk2Ds7MzzMzM0KJFi2rv5UBE6lKzi7DnSsnnKTfwK2v83+/JzospyMiXaTkaIiKqD9HR0bD0tCwzEv4sU2dTWHpaIioqqp4iez6G2i+qPr1J+iUSCby9vbFv3z614/v27UPv3r0rfN7GjRsxceJE/P777wgICKjydQRBwPnz5+Hs7FzhOaamprC2tla71QVdXntjZGSEH374AZcvX8b69etx4MABzJo1S+2cgoICfPHFF1i/fj2OHj2KnJwcjB07VvV4XFwcxo0bh3fffRdXr17FTz/9hHXr1uGLL74o9zV/+OEHxMTEICIiAtevX8evv/6KFi1a1GU3iQzW76eSoFAK6NnCDu2d6+YzTJ91dbVFp2Y2kMmV2Hy67n7YJSIi3ZGRmQGxrbha5xrZGiEjM6OOI9IMQ+0XVZ+xtgOoiZkzZ2L8+PHo0aMH/Pz88PPPPyMpKQlvv/02gJIR+AcPHmDDhg0AShL+CRMm4Pvvv4evr69qloC5uTlsbGwAAJ9//jl8fX3h4eGBnJwc/PDDDzh//jz+85//aKeTf3t27c2zU3FK194kr0jGxMkT8fD+w3rdJT4sLEz175YtW2LhwoX4v//7P6xcuVJ1vLi4GCtWrICPjw8AYP369Wjfvj1OnTqFXr164YsvvsCcOXPwxhtvAABatWqFhQsXYtasWfjss8/KvGZSUhI8PDzQt29fiEQiuLtzdJKoNmRyJTaeKil1Ot6vhXaD0VEikQgT/Nzx4daL+PXEPbzVvxXE3Oiw3hUVFWHLli2Ijo5GRmYG7O3sERgYiDFjxtTo/3maaoeIDJu9nT0UD6pXrlWZpYR9c/uqT9QBddEvfj7rl2qN9F+8eLHGt4qmaD+P0NBQLF++HAsWLEDXrl3x119/YdeuXarkLyUlBUlJSarzf/rpJ8jlcrzzzjtwdnZW3WbMmKE6JysrC2+99Rbat28Pf39/PHjwAH/99Rd69eql8fhrQtfX3hw8eBCDBg1Cs2bNYGVlhQkTJuDJkyfIz89XnWNsbIwePXqo7rdr1w62trZISEgAAJw5cwYLFiyApaWl6jZ16lSkpKSgoKCgzGtOnDgR58+fR9u2bfHuu+9i7969dd9RIgMUdyUVj3OlaGJliiEdnbQdjs4a0cUFthYmeJBViAPX0rQdToOjqeVturpMjoh0T2BgIPJu5EGaKq30PGmKFHk38hAUFFRPkT0fTfeLn8/6RyQIglDVSUZGRhCJRKjGqarzb9y4gVatWj13gPqgsvqIta3XPmrUKOy9vBctPm5R5bmJXybC38sfkZGRNQ29UhMnTkRWVlaZknn37t1Du3bt8PbbbyM0NBT29vY4cuQIpkyZgszMTNja2mLdunWYOnUqpFIpjIz++W3Jzs4O33//PSZMmABzc3N8/vnnCA4OLvParVq1Ul13UVFRqioCOTk52L17N/bv348tW7bg5Zdf1snNRmr7dyeqDyH/PY5TiRl49yUPzBzkqe1wdNriXQn46a876OfhgPApPtoOp8EoXd5m2dUSjiGOautQpalSPIp4hLzzeYiKisLIkSPrvB0iahiKiorg0twFCndFuTNtAUBQCkhekQzxPXG9z7StLU32i5/PuqWyPPRp1V7Tf/LkSdy9e7fK2507d/Ti4td1urz2Jj4+HnK5HN988w18fX3h6emJhw8fljlPLpcjPj5edf/69evIyspCu3btAADdu3fH9evX0aZNmzK3p38oeJq1tTVCQ0Pxyy+/YPPmzYiMjERGBtcdEVVXQkoOTiVmQGwkwmu93LQdjs4b5+sOkQg4fDMddx6X3ayUNE9TpaVYooqIasrMzAzr165H3vk8JK9ILjMyLk2RInlFMvLO52H92vV6k/Noql/8fNZf1VrT/8ILL6BNmzawtbWtVqP9+/eHubn588TV4OnKmqLs7GycP39e7ViTJk0gl8vx448/YsSIETh69Cj++9//lnmuiYkJpk+fjh9++AEmJiaYNm0afH19VUsnPv30UwwfPhyurq4YM2YMjIyMcPHiRVy6dAmLFi0q0953330HZ2dndO3aFUZGRtiyZQucnJyqfV0SEbDh7zJ9gzs6wslGP76saJOrvQUGtm2KA9fS8OuJJHw6ooO2QzJ4pcvbPD70qHJ5282PbmLr1q0YN25cnbVDRA3LiBEjEBUVhYmTJ+LmnJuw9LSEka0RlFlK5N3Ig11jO0RHR2PEiBHaDrVGNNEvfj7rr2qN9B88eLBGidWuXbsq3f2eqqYra4oOHTqEbt26qd3WrFmDb7/9FkuXLoWXlxd+++23ckvnWVhYYPbs2Xjttdfg5+cHc3NzbNq0SfX44MGDsXPnTuzbtw89e/aEr68vvv322wo36LO0tMTSpUvRo0cP9OzZE4mJidi1a1eFswKISF12YTGizz0AAIz3baHdYPRIaUnDLWeSUSDT/H41pE5TpaVYooqIamvkyJF4eP8hwsPD4e/lj+6NusPfyx/h4eF4eP+h3iX8pZ63X/x81l/VWtNPlauLNf2GuqaooeCaftJFa47cxYKdV+HpaIm4sP4QibgbfXUolQJe/OYQEp8UYHFwJ7zKZRF1auCLA3Eu/xxc/+1a5blJK5PQvVF3HDxwsM7aISKiEvx81j3VXdNfren9M2fOrPYLf/vtt9U+lypWuvYmMDAQySuSy25wkSLFoy0lG1xER0czsSSiSimVAn49UTK1f7xfCyb8NWBkJMI4X3csik3A+mOJGNvTle9fHdLU8jZdLlFFRKSPdPnzWRNyc3MRFhaGkJAQDB48WHU8Li4OERERWL58OaysrOolFk2rVtJ/7tw5tftnzpyBQqFA27ZtAQA3btyAWCyGt7e35iNswAx1TRER1b+jt9NxJz0flqbGCOrWTNvh6J0x3q74eu91XEvNRfy9TPRsoR+1mfVRYGAgtm3bBmmqtNKpn6rlbfPKX96mqXZKxcTEYOLkich8kglLT0uIbcVQPFBg27ZtmPHeDKxfu57/PyYig6arn8+akJubiyH+g3DsxEn8Gr4B26KiERAQgNjYWAQHBUJWLMe1q1ewZ+8+vUz8azy9/9tvv8WhQ4ewfv162NnZAQAyMzMxadIk9OvXD++//36dBKrL6mJ6/7NtbN26FVFRUaqRhaCgIIwePZojCzqK0/tJ10zdEI99Vx/hDT93fP6Kl7bD0Uuzt17E5vhkjOjigh9f7abtcAyWppa36WKJKiIifaaLn8+aUJrwX74Qj9ixpvj6uBy7bysxa/YcLFu6BMPaGOF9X2MEbJLCq0sPnUr8qzu9v8ZJf7NmzbB371507NhR7fjly5fh7+9fbuk2Q1fXST/pH/7dSZfczyxA/2UHoRSA/TP7o01T3fgflb65/CAbw388AhOxCEfnvIimVvxvuyop2YW4m56Plg6N4GxT/ao+O3bsQGBgYPlJ9jPL2yobXddEO7r25ZSISJt06fNZU6ZMmYI1a9bg8CQL9HUzhkwhIGSrFNuvyRDYXoLNo0whEYtwJEmOfmsLMHnyZKxevbpOY6ouja7pf7bhR48elUn609LSkJubW/NIiYioTv12MglKAejdujET/ufg1cwG3u52OHMvE5tOJePdlzy0HZJO23w6CR9tuwSlABiJgMXBnRDas3qbIGpqeZsulagiIjIEuvT5rCkhISH4NXwDvjkhR69mYkjEIkSMNkXsDTECPI0hEYsgUwj4+rgcEhNjhISE1HlMmlbjWmdBQUGYNGkStm7divv37+P+/fvYunUrpkyZguDg4LqIkYiIaqmoWIHNp5MBABP8Wmg3GANQWr7vt5P3UKxQajka3SQIAnZdfIjZkSUJPwAoBeDjbZeRkl1Y7XY0VTJLV0pUEREZCl35fNaUwYMHY1tUNHbdUiI0UgqZQoBELEJQexNVwh+yVYrdt5XYFhWttsmfvqjx9P6CggJ88MEHWLNmDYqLiwEAxsbGmDJlCr766is0atSoTgLVZZzeT8/i3510xbaz9zEz4gJcbMzw16yBMBbX+LdeeopUrkCfJQeQnifDyte7Y1gnZ22HpDMy8mXYdvY+fj+VhDuP88s9Z+NUX/i1blzPkT0flpYiImoY5s2bh0WLFmFbiDmC2puojkclFCM4ohBz587FwoULtRhhWdWd3l/jb38WFhZYuXIlnjx5gnPnzuHs2bPIyMjAypUrG2TCT0SkyzYcLynT95qPGxN+DTA1FmPs31PUNxxP1G4wOkAQBBy//QTvbjwH3y//wKLYBNx5nA8zk7LXmlgkQgsHCy1E+Xzs7eyhyKpBaSk7VnYgqqnc3FxMmTIFcXFxasfj4uIwZcqUGi0hLioqQnh4OEaNGoWBLw7EqFGjEB4ejqKiIk2HrXc09T5r8u+lCZqIJzY2FsuWLkFgewkCPNVXwAd4GuOVdhIsW7oEsbGxGo29vtT6G2BKSgpSUlLg6emJRo0aoYYTBoiIqI5dvJ+F88lZMBGLqr2Wmqr2mo8bjETAiTsZuPGoYe5l8yRPip//uo0Xv/kTr/5yAjEXHkKmUKJTMxt8EeSF05+8jKWjOkEsKlkDLxaJ8GWwV40289MVgYGByLuRB2mqtNLzVKWlguq+tBSRISndOX3NmjUYOWK4KqmKjY3FyBHDsWbNGgzxH1StxC0mJgYuzV0wYcIE7L28F+fyz2Hv5b2YMGECXJq7YMeOHXXdHZ2lqfdZk38vTdBEPHFxcQgOCsSwNkaqTftkCgFRCcWqqf4Ro00xtLURgoMCy/y4oA9qnPQ/efIEL730Ejw9PTFs2DCkpKQAAN58880GWa6Pnt/8+fPRtWtX1f2JEyciMDDwudrURBtE+q50lH9YJ2c0sap8PTJVn4utOfw7OAEAwv9+jxsCpVLA0VvpeOf3s/Bd/Ae+3HUNd9Pz0Ugixms+btgxrS92TO+L133cYWVmgtCebjgyZyA2TvXFkTkD9faHpzFjxsCusR0eRTyCoCx/gENQCni05RHsGtth9OjR9Rwhkf56ulTa4UkWqqRq3rx5qiTs8CQLXL4QX2XiVlpaU+GugMcSD7T4uAVc/+2KFh+3gMcSDyjcFQgMDERMTEw99lA3aOp91uTfS5f6FRERAVmxHO/7Gqut4Q+OKFRb4/+BnzFkxXJERETUab/qQo2T/vfeew8mJiZISkqChcU/0/RCQ0OxZ88ejQZH2p0+M3HiRIhEIohEIpiYmKBVq1b44IMPkJ9f/lpNTfn++++xbt26ap2bmJgIkUiE8+fP17oNIkN0LSUH2889APDP5nOkOaXv6dYzyfgj4VGNNqgrT0p2IY7dTn/udjTl6Xge50qx6tBtDPzmEF7/30nEXkxBsUJAl+Y2WBLcCac+eRlfBnVCp+Y2ZdpxtjGHX+vGejnCX8rMzAzr165H3vk8JK9ILjPiL02RInlFMvLO52H92vXcx4WoBsLCwnDsxEnEjjVFXzdj1WjqokWLVKOufd2METvWFMdOnERYWFi57RQVFWHi5Imw7GoJ12muZTbeNHUyhes0V1h2tcTEyRMb3FR/Tb3PmmpH1/q1fPly9Pb1QcAmKY4kyVWb9s2dO1e1ud+RJDkCNknR29cHy5cvr9N+1YUal+zbu3cv4uLi0Lx5c7XjHh4euHev4Yx41IfSX6+OnTiJX8M3YFtUNAICAhAbG4vgoEDIiuW4dvUK9uzdByuruinDNWTIEKxduxbFxcU4fPgw3nzzTeTn52PVqlVq5xUXF8PExKSCVmrGxqbsF0dttEGkrzafTsKcyEsoHZO8+SgP3u5cZ6xJfq0bo6mVKdJypZiyPr7GJeme9jyl7erC0/EAJTGV/tvS1BiB3VwwtqcbvJo1nM9ZXSotRWRINFUqjaU1K6ep91nXSttpKh4rKyvs2bsPQ/wHod/ak5CYGKvyLl9fXwQHBSI6oQC9fX3qNO+qSzUe6c/Pz1cb4S+Vnp4OU1NOH9UUXZk+Y2pqCicnJ7i6uuK1117D66+/jujoaNWU/DVr1qBVq1YwNTWFIAjIzs7GW2+9haZNm8La2hovvvgiLly4oNbmkiVL4OjoCCsrK0yZMqXMr63PTs1XKpVYunQp2rRpA1NTU7i5ueGLL74AALRs2RIA0K1bN4hEIgwYMKDcNqRSKd599100bdoUZmZm6Nu3L06fPq16/NChQxCJRPjjjz/Qo0cPWFhYoHfv3rh+/brqnAsXLmDgwIGwsrKCtbU1vL29ER8fr4m3mUhjUrIL8dG2fxJ+APgkqmal0qhqqTlFeJz7z4ivUgBmR15Cu7m70X7enmrf2s3d/dyl7TSp9Pp5eha7UgA6ulhj2ejOOPXJS1gU2KlBJfyldKW0FJEh0VSpNJbWrJym3mddK22nyXhKE//JkycjZsdOBAQEAAACAgIQs2MnJk+erLcJP1CLpL9///7YsGGD6r5IJIJSqcRXX32FgQMHajS4hkzXps+UMjc3V5VqvHXrFiIiIhAZGamaXh8QEIDU1FTs2rULZ86cQffu3fHSSy8hIyMDQMmamc8++wxffPEF4uPj4ezsjJUrV1b6mh999BGWLl2KefPm4erVq/j999/h6OgIADh16hQAYP/+/UhJScG2bdvKbWPWrFmIjIzE+vXrcfbsWbRp0waDBw9WxVXqk08+wTfffIP4+HgYGxtj8uTJqsdef/11NG/eHKdPn8aZM2cwZ84cjc1uINKUu+n5eHbZsUIQkJheoJ2ADNTd9HyUt7q7SK5EYbGi2rciubJMG9r8e5V3/QDA3IAOCOnhCgtJjScIGhQzMzOMGzcOkZGROHjgICIjIzFu3DhO6Sd6DgEBAZg1ew6iE2SIvSFXeyz2hhzbr8kwa/YcVRJWnozMDIhtxdV6PSNbI2RkZlR9ooHRxPusyXY0RZPxWFlZYfXq1WV+HBg8eDBWr16ttwk/UIvp/V999RUGDBiA+Ph4yGQyzJo1C1euXEFGRgaOHj1aFzE2SLo2fQYoSbB///13vPTSSwAAmUyG8PBwNGnSBABw4MABXLp0CWlpaapZH19//TWio6OxdetWvPXWW1i+fDkmT56MN998EwCwaNEi7N+/v8K1Vbm5ufj++++xYsUKvPHGGwCA1q1bo2/fvgCgeu3GjRvDycmp3DZKlyOsW7cOQ4cOBQD88ssv2LdvH1avXo0PP/xQde4XX3yBF154AQAwZ07JB0RRURHMzMyQlJSEDz/8EO3atQNQsqSFSNe42pVdO62vpdJ0WUuHRmrT3oGSafBb3vZDU6vqJ4BpuUUY89/jau2IRdDa38uxnNh5/RBRZYqKirBlyxZER0cjIzMD9nb2CAwMxJgxY6r1g1h1S6X5+vpWmLjZ29lD8aAGpTWbN7wlb5p4nzXZjqboWjy6qsYj/R06dMDFixfRq1cvDBo0CPn5+QgODsa5c+fQunXruoixQdKV6TM7d+6EpaUlzMzM4Ofnh/79++PHH38EALi7u6uSbgA4c+YM8vLy0LhxY1haWqpud+/exe3btwEACQkJ8PPzU3uNZ+8/LSEhAVKpVPVDQ23cvn0bxcXF6NOnj+qYiYkJevXqhYSEBLVzO3furPq3s7MzACAtLQ0AMHPmTLz55pt4+eWXsWTJElWfiHTJybuZavf1uVSaLnO2McfiYPWSdIuDO8Hb3R6u9hbVvnm722NxcCc8vQT11V5uWvt77Ut4pHaf1w8RVeZ5S+RpqlQaS2tWTlPvs66VttO1eHRZrebpOTk54fPPP9d0LPSM0ukqixYtQuwNMYLa/zOVvHS6yty5c+v0V6uBAwdi1apVMDExgYuLi9p09kaNGqmdq1Qq4ezsjEOHDpVpx9bWtlavb27+/F80BaFkCE0kEpU5/uyxp/tX+phSWTL9dv78+XjttdcQGxuL3bt347PPPsOmTZsa3P84SHfJFUr8eOAmAOD/BrRGf48maOFgwYStjoT2dEN/zyZITC94rve5tJ1le64h6txDxN/LhFIpwKiCzajqSr5Ujp//ugMAmBfQHh1cbHj9EFGFSkvkWXa1hMeHHmrr6aWpUjyKeITAwEBERUVh5MiR5bbxT6k0C7VBre3XZAhsL1Elch/4GWP7tQJERESUO9A1ZswYzHhvBh5FPILrNNdyN/NryKU1NfU+a6odXetXQ1DjkX6gZBrPqVOnsHPnTsTExKjd6trKlSvRsmVLmJmZwdvbG4cPH670/D///BPe3t4wMzNDq1at8N///rfMOZGRkejQoQNMTU3RoUMHndnco7rTVWJjY+sshkaNGqFNmzZwd3evcv169+7dkZqaCmNjY7Rp00bt5uDgAABo3749Tpw4ofa8Z+8/zcPDA+bm5vjjjz/KfVwikQAAFIqKp3S1adMGEokER44cUR0rLi5GfHw82rdvX2mfnuXp6Yn33nsPe/fuRXBwMNauXVuj5xPVpahzD3DvSQHsG0kwbWAbvS+Vpg80VZLO2cYcn43oCCtTY1xLzcWeK6kairD61h9PREa+DC0dGuGN3i14/RBRhTRVIk9TpdJYWrNymnqfda20na7Fo9OEGtq9e7fQpEkTQSQSlbkZGRnVtLka2bRpk2BiYiL88ssvwtWrV4UZM2YIjRo1Eu7du1fu+Xfu3BEsLCyEGTNmCFevXhV++eUXwcTERNi6davqnGPHjglisVj48ssvhYSEBOHLL78UjI2NhRMnTlQ7ruzsbAGAkJ2dXeaxwsJC4erVq0JhYWGN+rpnzx5BYmIsBLaXCNK5VoLwmbUgnWslbAsxV7v/SjuJIDExFvbs2VOj9qvjjTfeEF555ZVyH/vss8+ELl26qB1TKpVC3759hS5dugh79uwR7t69Kxw9elT45JNPhNOnTwuCUPI3NDU1FVavXi1cv35d+PTTTwUrKyu1tp593fnz5wt2dnbC+vXrhVu3bgnHjx8X/ve//wmCIAjFxcWCubm5sGjRIiE1NVXIysoqt40ZM2YILi4uwu7du4UrV64Ib7zxhmBnZydkZGQIgiAIBw8eFAAImZmZquecO3dOACDcvXtXKCgoEN555x3h4MGDQmJionDkyBGhdevWwqxZs8p9f2r7dyeqrWK5Qui/7IDgPnun8N9Dt7QdDtXSN3HXBPfZOwX/b/8UFAplvb1ublGx0PXzOMF99k4h8kxyvb0uEemnDRs2CAAEjyUegtc6rwpvHos9BABCeHh4hW3l5OQIvX19BACCxMRY2LlzpyAIgrBz505BYmIsABB6+/oIOTk5Vca1fft2wa6xnQBAsPS0FKx7WQuWnpYCAMGusZ0QExOjsfdA32jqfdbk30sTdC2e+lZZHvq0Gif9rVu3Fv79738LqamptQ6utnr16iW8/fbbasfatWsnzJkzp9zzZ82aJbRr107t2L/+9S/B19dXdT8kJEQYMmSI2jmDBw8Wxo4dW+246iLpnzx5sgBAODzJQi3BB6D2Q8DhSRYCAGHy5Mk1ar86apr0C0LJf3jTp08XXFxcBBMTE8HV1VV4/fXXhaSkJNU5X3zxheDg4CBYWloKb7zxhjBr1qxKk36FQiEsWrRIcHd3F0xMTAQ3Nzfhyy+/VD3+yy+/CK6uroKRkZHwwgsvlNtGYWGhMH36dMHBwUEwNTUV+vTpI5w6dUr1eFVJv1QqFcaOHSu4uroKEolEcHFxEaZNm1bh35VJP9W3zaeTBPfZO4XuC/YK+dJibYdDtZSVLxO8PtsjuM/eKey88LDeXnfFgZuC++ydwsCvDgrFckW9vS4R6afg4GDB0tOy0oS/9GbpaSkEBwdX2l5OTo4wefLkMoNYe/bsESZPnlyjhK2wsFAIDw8XgoODhQEDBwjBwcFCeHg4v5MJmnufNfn30gRdi6c+VTfpFwmCUF7VoQpZW1trZdM+mUwGCwsLbNmyRW0N9YwZM3D+/Hn8+eefZZ7Tv39/dOvWDd9//73qWFRUFEJCQlBQUAATExO4ubnhvffew3vvvac657vvvsPy5ctx7969asWWk5MDGxsbZGdnw9raWu2xoqIi3L17V7Ukobpyc3MxxH8QLl+IR+xYU3x9XI7dt5WYNXsOli1dgmFtjPC+rzECNknh1aWHXteNNES1/bsT1UaxQokXvzmE5IxCfDysHd7qz01V9dl3+27g+z9uwtPREntm9K/ztf15Ujn6Lj2ArIJifBfaBUHdmtfp6xGR/hv44kCcyz8H13+7Vnlu0sokdG/UHQcPHKyHyIgalsry0KfVeE3/6NGjy92ora6lp6dDoVCo6rOXcnR0RGpq+WsfU1NTyz1fLpcjPT290nMqahMApFIpcnJy1G6aZmVlhT1798GrSw/0W1ug2qV/4cKFql39+60tYMJPRIg6+wDJGYVwsJRgnK+7tsOh5zS5b0tYmRnjxqM87LqcUuevt/5YIrIKitHKoRFGdmlW569HRPrP3s4eiqwalMizq7pEXlFREcLDwzFq1CgMfHEgRo0ahfDw8Ar3AyCi6qvx7v0rVqzAmDFjcPjwYXTq1KnM5m7vvvuuxoIrT3V2YK/q/GeP17TNxYsX10v1gtLEPywsDCEhIardJgMCAhCzYyciIiKwfPlyJvxEDVixQokfD5bs2P+v/q1hIalVURbSITbmJpjStyWW77+J7/ffxFAvZ4jraLQ/t6hYtWP/uy951NnrEJFhCQwMxLZt2yBNlZbZxO9pqhJ58yqvdBQTE4OJkyci80kmLD0tIbYVQ/FAgW3btmHGezOwfu16jBgxQtPdIGowavzt8Pfff0dcXBzMzc1x6NChMslzXSX9Dg4OEIvFZUbg09LSyozUl3Jycir3fGNjYzRu3LjScypqEwA++ugjzJw5U3U/JycHrq5VT2+qDSsrK6xevbrM8cGDBzfYkhNE9I/IM/c5ym+AJvVpidVH7uJmWh5iL6VgZBeXOnmd9ccSkV1YjFZNGmFEHb0GERkeTZbI00TpPyKqXI2n98+dOxcLFixAdnY2EhMTcffuXdXtzp07dREjgJLSbN7e3ti3b5/a8X379qF3797lPsfPz6/M+Xv37kWPHj1UMxQqOqeiNgHA1NQU1tbWajciovomkyux4uAtAMDbL7SGuUSs5YhIU2zMTfBm31YAgB/+uAmFskbb71RLTlExfjl8FwAwg6P8RFQDmiqRp6nSf0RUuRon/TKZDKGhoTAyqvFTn9vMmTPxv//9D2vWrEFCQgLee+89JCUl4e233wZQMgI/YcIE1flvv/027t27h5kzZyIhIQFr1qzB6tWr8cEHH6jOmTFjBvbu3YulS5fi2rVrWLp0Kfbv34+wsLD67h4RUY1Enr2P+5mFcLA0xes+HOU3NJP6toC1mTFupeVh58WHGm9//dGSUf42TS0xvDNH+YmoZkaMGIGoqCiI74lxc85NJH6ZiKSVSUj8MhE3P7oJ8T0xoqOjK52Wv2XLFmQ+yYRjiGO5swUAQGQkguMYR2Q+ycTWrVvrqjtEBq3Gmfsbb7yBzZs310UsVQoNDcXy5cuxYMECdO3aFX/99Rd27doFd/eSL7spKSlISkpSnd+yZUvs2rULhw4dQteuXbFw4UL88MMPGDVqlOqc3r17Y9OmTVi7di06d+6MdevWYfPmzfDx8dFo7DUskkB6jn9vqmsyuRIrDpSO8rfiKL8BsjYzwZv96ma0v2SUn2v5iej5jBw5Eg/vP0R4eDj8vfzRvVF3+Hv5Izw8HA/vP6xyHX50dDQsPS0r3RcAAEydTWHpaYmoqChNhk/UYNS4ZN+7776LDRs2oEuXLujcuXOZjfy+/fZbjQaoDyorlaBQKHDjxg00bdpUtY8AGb4nT54gLS0Nnp6eEIuZjJHm/X4yCR9HXUITK1McnjUQZia8zgxRTlEx+i09iOzCYnw/tite6aqZ3fW/338T3+2/gTZNLREX1p9JPxFpBUv/ET2f6pbsq/FGfpcuXUK3bt0AAJcvX1Z7rLId7xsqsVgMW1tbpKWlAQAsLCz4PhkwQRBQUFCAtLQ02NraMuGnOiGTK/Gfv9fy/98LrZnwGzBrMxO82bclvtl3A9//cRPDO7s8d4KeXViM1UdKRvm5lp+ItMnezh6KBzUo/de86tJ/RFRWjZJ+hUKB+fPno1OnTrC353901eXk5AQAqsSfDJ+tra3q706kaVvOJONBViGaWpniNR83bYdDdWxinxZYffQu7jzOx44LDxHY7flG+9cevYucIjk8mlpiWCdnDUVJRFRzmi79R0Tlq/H0fjMzMyQkJKBly5Z1FZPeqe60CoVCgeLi4nqMjLTBxMSEI/xUZ6RyBQZ+dQgPs4vw2YgOmNSHn8UNwX8O3sJXcdfRyqER9r7XH8bi2m2mm11YjL5LDyC3SI4Vr3XjBn5EpFVFRUVwae4Chbui0tJ/ySuSIb4nxsP7DyusBEDUENXZ9P5OnTrhzp07TPprQSwWMxkkoueyJf4+HmYXoamVKV7txVH+hmKCnzt+OXwHd9LzsePiQwR1a16rdtYcuYvcIjk8HS0xzIuj/ESkXaWl/wIDA5G8IhmOIY5qI/7SFCkebXmEvPN5iI6OZsJPVEs1Hir44osv8MEHH2Dnzp1ISUlBTk6O2o2IiOqGVK5QreX/9wCu5W9IrMxMMFW1k/8tyBXKGreRXVCMNUfuAgBmvOQJI67lJyIdoInSf0RUuRqP9A8ZMgRASYmOpzekEwQBIpEICkX1NuMgIqKaiTidjJTsIjham2IsR/kbnDd6t8D/Dt/B3fR8bD//EKO8azbav/roXeRK5WjraIWhXtxzhIh0R2npv61btyIqKgoZmRmwb26PoHlBGD16NEf4iZ5TjZP+gwdZJoOIqL6VjPLfBgD8e0AbjvI3QJamxpjavxWW7bmOHw/cxCtdXaq9tj+7oBhrS0f5X/bgKD8R6RwzMzOMGzcO48aN03YoRAanxkn/Cy+8UBdxEBFRJTafTkZqThGcrM0Q2rPqesZkmN7wa4Ff/rqDxCcFiD7/EKOrOdq/+sgd5ErlaOdkhSEdOcpPRETUkNQ46QeArKwsrF69GgkJCRCJROjQoQMmT54MGxsbTcdHRNTgFRU/tZZ/INfyN2SNTI3xVv/WWLrnGn48cBOB1RjtzyqQYc3RRABAGEf5iehvRUVF2LJlC6Kjo0um09vZIzAwEGPGjOF0eiIDU+ON/OLj49G6dWt89913yMjIQHp6Or799lu0bt0aZ8+erYsYiYgatM2nk/EoRwpnG47yU8lO/vaNJLj3pABR5x5Uef7qI3eR9/cov38HjvITERATEwOX5i6YMGEC9l7ei3P557D38l5MmDABLs1dsGPHDm2HSEQaVOOk/7333sPIkSORmJiIbdu2ISoqCnfv3sXw4cMRFhZWByESETVcRcUKrDxUOsrfBqbGHOVv6BqZGuNf/Ut28v/xwC0UV7KTf1aBDGtVo/zcsZ+IShL+oKAgKNwV8FjigRYft4Drv13R4uMW8FjiAYW7AoGBgYiJidF2qESkIbUa6Z89ezaMjf9ZGWBsbIxZs2YhPj5eo8ERETV0m04l4VGOFC42ZgjpUbva7GR4xvu5o3EjCZIyKh/t/+XwHeRJ5WjvbA3/Do71GCER6aKioiJMnDwRll0t4TrNFaZOpmqPmzqZwnWaKyy7WmLi5IkoKirSUqREpEk1Tvqtra2RlJRU5nhycjKsrKw0EhQREZWO8v+9Yz9H+ekpFhJj/OuF0tH+m+WO9mfmy7COa/mJ6ClbtmxB5pNMOIY4QlTBZ4LISATHMY7IfJKJrVu31nOERFQXapz0h4aGYsqUKdi8eTOSk5Nx//59bNq0CW+++SZeffXVuoiRiKhB+v1kEtJyS0b5x3CUn54xztcdDpYSJGcUYtvZ+2Ue/+XwHeTLFOjAUX4i+lt0dDQsPS3LjPA/y9TZFJaeloiKiqqnyIioLtV49/6vv/4aIpEIEyZMgFwuBwCYmJjg//7v/7BkyRKNB0hE1BAVFSuw6s+SUf53XuQoP5VlITHGv/q3xhe7EvDjgVsI7t4cJn/v5J+RL8P6Y4kASkb5RSKO8hMRkJGZAbFt9f5/YmRrhIzMjDqOiIjqQ41H+iUSCb7//ntkZmbi/PnzOHfuHDIyMvDdd9/B1LTyXw2JiKh6fjuZhMe5UjSzNccYb+7YT+UrHe2/n1mIyDP/jPaXjvJ3dLHGII7y65yioiKEh4dj1KhRGPjiQIwaNQrh4eFcP011zt7OHoosRbXOVWYpYW9nX+k5ubm5mDJlCuLi4tSOx8XFYcqUKcjNza11rGT4eP3Unxon/aUsLCzQqVMndO7cGRYWFpqMiYioQSuUKbDq77X87wxsA4lxrT+qycCZS8R4+4XWAEp28pfJlXiSJ31qlN+To/w6hqXSSJsCAwORdyMP0lRppedJU6TIu5GHoKCgCs/Jzc3FEP9BWLNmDUaOGI7Y2FgAQGxsLEaOGI41a9ZgiP8gJm5ULl4/9avG3yTz8/Mxb9489O7dG23atEGrVq3UbkRE9Hx+O3kP6Xklo/yjvbmWnyr3uo87HCxN8SCrEJFn7+OXw3dRIFOgUzMbvNy+qbbDo6ewVBpp25gxY2DX2A6PIh5BUArlniMoBTza8gh2je0wevTocs8pTdguX4jH4UkWGNraCMFBgZg3bx6CgwIxrI0RDk+ywOUL8UzcqAxeP/VPJAhC+f/FV+DVV1/Fn3/+ifHjx8PZ2bnMCMKMGTM0GqA+yMnJgY2NDbKzs2Ftba3tcIhIj919nI9X/nMUOUXFWBzcCa/2ctN2SKQH/nf4DhbFJqCplSmyC4shlSux+o0eeKk9p/briqKiIrg0d4HCXQHXaa7l7pwuKAUkr0iG+J4YD+8/hJmZmRYiJUO3Y8cOBAYGwrKrJRxDHNU29ZOmSPFoyyPknc9DdHQ0RowYUW4bU6ZMwZo1a3B4kgX6uhlDphAQslWK7ddkCGwvweZRppCIRTiSJEe/tQWYPHkyVq9eXV9dJB3H60dzqpuH1ngjv927dyM2NhZ9+vR5rgCJiEjd5tNJmBN5CaW/xNbwN1lqwMb5umP5/ptIy/1nyu7j3Mqn71L9Ki2V5vGhR5Wl0m5+dBNbt27FuHHj6jlKaghGjBiBqKgoTJw8ETfn3ISlpyWMbI2gzFIi70Ye7BrbVZrwA0BISAh+Dd+Ab07I0auZGBKxCBGjTRF7Q4wAT2NIxCLIFAK+Pi6HxMQYISEh9dhD0nW8fupfjaf329nZwd6+8k09iIioZlKyCzFn2z8JPwDMi76ClOxCrcVE+iOzQIZ8qVzt2CdRl3n96BCWSiNdMnLkSDy8/xDh4eHw9/JH90bd4e/lj/DwcDy8/7DShB8ABg8ejG1R0dh1S4nQSClkCgESsQhB7U1UCVvIVil231ZiW1Q0Bg8eXE89I33A66f+1TjpX7hwIT799FMUFBTURTxERA2OIAhYefA2nh3YVwgCEtP5WUtVu5uej2fnhfD60S0slUa6xszMDOPGjUNkZCQOHjiIyMhIjBs3rtrLSgICAjBr9hxEJ8gQe0P9R8fYG3JsvybDrNlzEBAQUBfhk57j9VO/apz0f/PNN4iLi4OjoyM6deqE7t27q93qSmZmJsaPHw8bGxvY2Nhg/PjxyMrKqvD84uJizJ49G506dUKjRo3g4lKyU+7Dhw/VzhswYABEIpHabezYsXXWDyKipxXKFHhv83mEn7hX5jGxSIQWDqyOQlVr6dAIz84Y5/WjWzRdKg3QXOk/lhCsnK69P7oST2xsLJYtXYLA9hIEeKqvGA7wNMYr7SRYtnSJald2oqfx+qlfNd7I7/PPP6/08c8+++y5AqrI0KFDcf/+ffz8888AgLfeegstWrSosLRNdnY2Ro8ejalTp6JLly7IzMxEWFgY5HI54uPjVecNGDAAnp6eWLBggeqYubk5bGxsqh0bN/IjotpIzijAv8LP4GpKDsRGIgzzcsKuSylQCCUJ25fBXgjtyY38qHo2n07Cx9suQyEIvH50UHh4OCZMmACPJR6VTvGXpkhx86ObCA8Pr3RNf0xMDCZOnojMJ5mw9LSE2FYMRZZCtSZ7/dr1VU7R1mQ7hkrX3h9diScuLg4jRwzHsDZGqk3XZAoBsTfkamuyS6dox+zYySnapMLrR3Oqm4fWOOnXhoSEBHTo0AEnTpyAj48PAODEiRPw8/PDtWvX0LZt22q1c/r0afTq1Qv37t2Dm1vJF6EBAwaga9euWL58ea3jY9JPRDV15GY6pm88i8yCYjRuJMGK17rDr3VjpGQXIjG9AC0cLOBsY67tMEnP8PrRXZrcvb+09F+5u6+nSvEoomT39aioKIwcObLCmDTVjqHStfdHl+Lh7uv0PHj9aI5BJf1r1qzBzJkzy0znt7W1xXfffYdJkyZVq539+/fD398fWVlZqjdlwIABuHLlCgRBgKOjI4YOHYrPPvsMVlZWFbYjlUohlf6zK3JOTg5cXV2Z9BNRlQRBwC+H72DJ7mtQCkDn5jb47zhvuNgyQSMydJoolaapHw9YQrByuvb+6Fo8T9dZjx1riq+Py7H7thKzZs/BsqVLMKyNEd73NUbAJim8uvTAnr37Kv1uTQ0Lrx/NqW7SX601/fb29khPT6/2i7u5ueHevbLrU2srNTUVTZs2LXO8adOmSE1NrVYbRUVFmDNnDl577TW1N+T111/Hxo0bcejQIcybNw+RkZEIDg6utK3Fixer9hawsbGBq6trzTpERA1SgUyO6RvP4ctdJQn/aO/miPiXHxN+ogaitFSa+J4YN+fcROKXiUhamYTELxNx86ObEN8TV1kqrbT0n2OIY5Wl/zKfZGLr1q112o6h0rX3R9fisbKywp69++DVpQf6rS1Q7bK+cOFC1a7s/dYWMGGjcvH6qX/GVZ8CZGVlYffu3dVe5/7kyRMoFFVvVjN//vwq9wg4ffo0AEAkKucXTUEo9/iziouLMXbsWCiVSqxcuVLtsalTp6r+7eXlBQ8PD/To0QNnz56tcGPCjz76CDNnzlTdLx3pJyKqyL0n+fhX+BlcS82FsZEIn43ogHG+7tX6DCMiw1FaKm3r1q2IiopCRmYG7JvbI2heEEaPHl3l6GxtSv+VtzeAptoxVLr2/uhaPMA/iVtYWBhCQkJUa64DAgIQs2MnIiIisHz5ciZsVC5eP/WrWkk/ALzxxhsaf/Fp06ZVuVN+ixYtcPHiRTx69KjMY48fP4ajo2Olzy8uLkZISAju3r2LAwcOVDn9vnv37jAxMcHNmzcrTPpNTU1halr5hy4RUalD19Pw7sZzyCmSw8HSFKvGdUfPFlXvzE1Ehqm0VFptkjJNlf5jCcHK6dr7o2vxlLKysip3rfXgwYO58RpViddP/alW0q9UKuvkxR0cHODg4FDleX5+fsjOzsapU6fQq1cvAMDJkyeRnZ2N3r17V/i80oT/5s2bOHjwIBo3blzla125cgXFxcVwdnaufkeIiMohCAJWHrqNr/dehyAAXV1t8d9x3nCyaTjrYolIs+zt7KF4UIPSf83L/4FRU+08raioCFu2bEF0dHTJDAY7ewQGBmLMmDE1Wl+uC+3o2vuj6Xhyc3PLjLACJbuqc4SV9JEufG7osmqt6de29u3bY8iQIZg6dSpOnDiBEydOYOrUqRg+fLjazv3t2rVDVFQUAEAul2P06NGIj4/Hb7/9BoVCgdTUVKSmpkImkwEAbt++jQULFiA+Ph6JiYnYtWsXxowZg27duqFPnz5a6SsRGYY8qRz//u0svoorSfhf7eWKzf/yZcJPRM8lMDAQeTfyIE2VVnqeNEWKvBt5CAoKqtN2SsXExMCluQsmTJiAvZf34lz+Oey9vBcTJkyAS3OXCkss62o7uvb+aDKe0k3U1qxZg5EjhqvqoMfGxmLkiOFYs2YNhvgPQm5ubqWvRaQrdOVzQ5fpxe79AJCRkYF3330XMTExAErWxK1YsQK2traqc0QiEdauXYuJEyciMTERLVu2LLetgwcPYsCAAUhOTsa4ceNw+fJl5OXlwdXVFQEBAfjss89gb1/9qbcs2UdET7ubno+3NsTjZloeTMQifD7SC6/5sF46ET0/Xdy9X9dKCGqiHV17fzQVD3dNJ0OjS58b2mBQJft0HZN+IkrJLsTd9Hw8yCzEgp1XkVskR1MrU6wa5w1vdztth0dEBkQTpf801Y6u/QihyWRdl94fTcXD+uhkSHTxc6O+abRkHxERVWzz6ST0WXIAr/1yEh9uvYjcIjl6uNth5/S+TPiJSOM0UfpPU+3oWglBTZa206X3R1PxhISEQGJijG9OyCFTCJCIRYgYbYptIeaqhF+mEPD1cTkkJsYICQmpsC0ibdPFzw1dxaSfiOg5pGQX4qNtl6B8as6UCMC3IV3Q1Fo3fgUmIsNTWvovPDwc/l7+6N6oO/y9/BEeHo6H9x9WmfBrqp3alJLTh3ZK6cr7o6l4Bg8erKqDHhopVSX+Qe1NVAl/yFapqm46d1AnXaarnxu6qNol+56mUCgQFRWFhIQEiEQitGvXDoGBgTA2rlVzRER66256vlrCDwACgAdZRXBr3EgrMRFRw/A8pf801Y6ulRCsi9J2uvD+aCoeoKQO+qzZc7Bo0SLE3hAjqL2J6rHYG3JsvybD3LlzERAQUKv2ieqLLn9u6JoaZ+mXL1/GK6+8gtTUVNXO+Tdu3ECTJk0QExODTp06aTxIIiJdZWtuUuaYWCRCCwcLLURDRFS/dK2EYF2U2nseuhYPULJL/7KlSxDYXoIAT/VUIMDTGK+0k2DZ0iXw9fVl4k86zVA/N+pCjaf3v/nmm+jYsSPu37+Ps2fP4uzZs0hOTkbnzp3x1ltv1UWMREQ663+H76rdF4tE+DLYC8425lqKiIio/uhaCUFNl9p7XroWT1xcHIKDAjGsjZHaGv6ohGK1Nf5DWxshOCgQcXFxdRoP0fMw1M+NulDj3fvNzc0RHx+Pjh07qh2/fPkyevbsicLCQo0GqA+4ez9Rw3TkZjrGrT4JkQj4ZUIPNJIYo4WDBRN+ImowdG33bF3bhVvX4uHu/WRIDPVzoybqbPf+tm3b4tGjR2WOp6WloU2bNjVtjohILxUVK/BJ9CUAwARfd7zc3hF+rRsz4SeiBsXMzAzr165H3vk8JK9ILjNSJk2RInlFMvLO52H92vUVflHWtXY0RdfiWb58OXr7+iBgkxRHkuSqTfvmzp2r2tzvSJIcAZuk6O3rg+XLl9dpPETPw1A/N+qEUEOxsbFCx44dhS1btgjJyclCcnKysGXLFqFTp05CbGyskJ2drbo1FNnZ2QKABtVnooZu2Z4EwX32TsHni/1CTqFM2+EQEWnV9u3bBbvGdgIAwdLTUrDuZS1YeloKAAS7xnZCTEyMXrajKboUT05OjtDb10cAIEhMjIWdO3cKgiAIO3fuFCQmxgIAobevj5CTk1NvMRE9D0P93KiO6uahNZ7eb2T0z+QAkUhU+sNBmfsikQgKRfU2RNB3nN5P1LDceJSLYd8fhlwp4L/jvDHEy0nbIRERaV1RURG2bt2KqKgoZGRmwN7OHkFBQRg9enSNRsZ0rR1N0aV4cnNzERYWhpCQELWyfHFxcYiIiMDy5cthZWVVrzERPQ9D/dyoSnXz0Bon/X/++We1z33hhRdq0rTeYtJP1HAolQLG/HQcZ+5l4uX2jvhlgrfqB08iIiIiovpS3Ty0xiX7GkoiT0RUno2nk3DmXiYaScRY8EpHJvxEREREpNNqnPQDJdMeLl68iLS0NCiVSrXHRo4cqZHAiIh0TVpOEZbsvgYAeN+/LVxsuWkfEREREem2Gif9e/bswYQJE5Cenl7msYa0jp+IGp4FO68it0iOTs1s8EbvFtoOh4iIiIioSjUu2Tdt2jSMGTMGKSkpUCqVajcm/ERkqA5eT8POiykwEgGLgztBXE4NVyIiIiIiXVPjpD8tLQ0zZ86Eo6NjXcRDRKRzCmRyzI26DACY3KclvJrZaDkiIiIiIqLqqXHSP3r0aBw6dKgOQiEi0k3L99/Eg6xCNLM1x3uDPLUdDhERERFRtdV4Tf+KFSswZswYHD58GJ06dYKJiYna4++++67GgiMi0rYrD7Ox+shdAMDCwI5oZFqr/U+JiIiIiLSixt9ef//9d8TFxcHc3ByHDh1SK1clEomY9BORwVAoBXy87RIUSgEBnZzxYjsuayIiIiIi/VLjpH/u3LlYsGAB5syZAyOjGq8OICLSG+HHE3HhfjasTI3x6YgO2g6HiIiIiKjGapy1y2QyhIaGMuEnIoOWkl2Ir+KuAwBmDW0HR2szLUdERERERFRzNc7c33jjDWzevLkuYiEi0hnzY64gX6ZAdzdbvN7LTdvhEBERERHVSo2TfoVCgWXLluGFF17A9OnTMXPmTLVbXcnMzMT48eNhY2MDGxsbjB8/HllZWZU+Z+LEiRCJRGo3X19ftXOkUimmT58OBwcHNGrUCCNHjsT9+/frrB9EpPv2XklF3JVHMDYSYXFwZxgZiap+EhERERGRDqrxmv5Lly6hW7duAIDLly+rPfb0pn6a9tprr+H+/fvYs2cPAOCtt97C+PHjsWPHjkqfN2TIEKxdu1Z1XyKRqD0eFhaGHTt2YNOmTWjcuDHef/99DB8+HGfOnIFYLNZ8R4hIp+VJ5fgs5goA4K3+rdDWyUrLERERERER1V6Nk/6DBw/WRRyVSkhIwJ49e3DixAn4+PgAAH755Rf4+fnh+vXraNu2bYXPNTU1hZOTU7mPZWdnY/Xq1QgPD8fLL78MAPj111/h6uqK/fv3Y/DgwZrvDBHptK/jriMluwhu9hZ49yUPbYdDRERERPRcar0b361btxAXF4fCwkIAgCAIGgvqWcePH4eNjY0q4QcAX19f2NjY4NixY5U+99ChQ2jatCk8PT0xdepUpKWlqR47c+YMiouL4e/vrzrm4uICLy+vStuVSqXIyclRuxGR/ruQnIX1xxMBAF8EecHMhLN9iIiIiEi/1Tjpf/LkCV566SV4enpi2LBhSElJAQC8+eabeP/99zUeIACkpqaiadOmZY43bdoUqampFT5v6NCh+O2333DgwAF88803OH36NF588UVIpVJVuxKJBHZ2dmrPc3R0rLTdxYsXq/YWsLGxgauray17RkS6Qq5Q4qNtlyAIQGBXF/TzaKLtkIiIGpzc3FxMmTIFcXFxasfj4uIwZcoU5Obm6mU7mqJr8RCRfqhx0v/ee+/BxMQESUlJsLCwUB0PDQ1Vrbevrvnz55fZaO/ZW3x8PIDy9wsQBKHSfQRCQ0MREBAALy8vjBgxArt378aNGzcQGxtbaVxVtfvRRx8hOztbdUtOTq5mj4lIV609moirKTmwMTfB3OEdtB0OEVGDk5ubiyH+g7BmzRqMHDFc9X0tNjYWI0cMx5o1azDEf1CVia2utaMpuhYPEemPGif9e/fuxdKlS9G8eXO14x4eHrh3716N2po2bRoSEhIqvXl5ecHJyQmPHj0q8/zHjx/D0dGx2q/n7OwMd3d33Lx5EwDg5OQEmUyGzMxMtfPS0tIqbdfU1BTW1tZqNyLSX8kZBfh23w0AwMfD2sHB0lTLERERNSylCe3lC/E4PMkCQ1sbITgoEPPmzUNwUCCGtTHC4UkWuHwhvtLEVtfa0RRdi4eI9EuNk/78/Hy1Ef5S6enpMDWt2RdlBwcHtGvXrtKbmZkZ/Pz8kJ2djVOnTqmee/LkSWRnZ6N3797Vfr0nT54gOTkZzs7OAABvb2+YmJhg3759qnNSUlJw+fLlGrVLpMtSsgtx7HY6UrILDaodTXmYVYDpG8+isFiBXi3tEdKDy3WIiOpbWFgYjp04idixpujrZoyI0aYY2toIixYtwrA2Rtg8quR47FhTHDtxEmFhYXrRjqboWjxEpF9qvHt///79sWHDBixcuBBAybR7pVKJr776CgMHDtR4gADQvn17DBkyBFOnTsVPP/0EoKRk3/Dhw9V27m/Xrh0WL16MoKAg5OXlYf78+Rg1ahScnZ2RmJiIjz/+GA4ODggKCgIA2NjYYMqUKXj//ffRuHFj2Nvb44MPPkCnTp1Uu/kT6bPNp5Pw0bZLUAqASAS8+6IHBncsv5pFZeKupOKHAzchaLAdIxGwOLgTQnu61bgdTdl8OglzIi+hdBvSfm0c6rT0KBERlS8kJAS/hm/ANyfk6NVMDIlYhIjRpoi9IUaApzEkYhFkCgFfH5dDYmKMkJAQvWhHU3QtHiLSLyKhhtvuX716FQMGDIC3tzcOHDiAkSNH4sqVK8jIyMDRo0fRunXrOgk0IyMD7777LmJiYgAAI0eOxIoVK2Bra6s6RyQSYe3atZg4cSIKCwsRGBiIc+fOISsrC87Ozhg4cCAWLlyotvFeUVERPvzwQ/z+++8oLCzESy+9hJUrV9Zoc76cnBzY2NggOzubU/1JJwiCgL1XUvGvX89qO5RKiUUiHJkzEM425vX+2inZhei95ACe/gTUZjxERA1dbGysaqr65lGmkIj/+RFWphAQslWK3beV2BYVjYCAAL1pR1N0LR4i0r7q5qE1TvqBkl3vV61ahTNnzkCpVKJ79+545513VNPmGxom/aQrsgpkiDz7ABtPJeFWWl6559iam0BiXP2VPTK5ElmFxXXWzq9TeqFvPe+ULwgC5kZfxm8nk8o8tnGqL/xaN67XeIiIqMS8efOwaNEibAsxR1B7E9XxqIRiBEcUYu7cuarZpvrUjqboWjxEpF11lvQnJSXB1dW13CmwSUlJcHPT3lRdbWHST9okCAJO3c3AxlNJ2HU5FTK5EgBgZmyEor//Xao2I9kp2YXos+QAlM85Il5eOwDQs4Udfh7fA3aNJNVu63kUyOSYHXkJOy48LPMYR/qJSF/k5uYiLCwMISEhGDx4sOp4XFwcIiIisHz5clhZWelVO7o2Qq9rI+u6Fg8RaV9189Aab+TXsmVLPH78uMzxJ0+eoGXLljVtjohqKSNfhv8dvoOXvv0ToT+fQPT5h5DJlejgbI1FgV44PfdlLB3VCeK/f6ATi0T4Mtirxgmts405Fgdrvh0jESARi3A6MRMjVhzB5QfZNWqvNpKeFCB45THsuPAQxkYivNLVBaXfmWrbLyKi+qZrJek00U5cXFyZhFamEBCVUAyZQlCtYS/dtf7ZOvW62o6m6Fo8RKRnhBoSiURCWlpameOJiYmChYVFTZszCNnZ2QIAITs7W9uhkIFTKpXC0VuPhWm/nxU8Pt4luM/eKbjP3im0n7dbmBN5QbiQnCkolUq15zzMKhCO3UoXHmYVPNdr10U711JyhP7LDgjus3cKnp/sEqLO3n+utitz6Hqa0Hl+nOA+e6fgvXCvcPLOkzLxEBHpupycHKG3r49gbS4WDk+yEF5pJxEkJsbC3LlzBYmJsRDYXiIcnmQhWJuLhd6+PkJOTo5etDN58mQBgHB4koUgfGYtSOdaCa+0kwgAhMD2EkE610oQPrMWDk+yEAAIkydP1ot2NEXX4iEi3VDdPLTa0/tnzpwJAPj+++8xdepUtbJ9CoUCJ0+ehFgsxtGjRzX1e4Te4PR+w5CSXYi76flo6dDouUZ766IdE7ERIs/cx6bTybibnq86p1MzG7zayw0ju7rA0rTGxTh0QnZBMWZsPodD10tmEE3u0xIfDWsHE3GNJyKVSxAErPrzNr6Kuw5BALq62uK/47zhZGOmkfaJiOrTlClTsGbNGhyeZIG+bsaqad3br8kQ2F6iGgU+kiRHv7UFmDx5MlavXq3z7Txdhz52rCm+Pi7H7ttKzJo9B8uWLsGwNkZ439cYAZuk8OrSA3v27it3uYCutaMpuhYPEekGja/pLy3H9+eff8LPzw8SyT/rbyUSCVq0aIEPPvgAHh4ezxm6/mHSr/+eLm1nJAIWvuKFMbWo174lPhnztl/WaDsAIBYBir//3UgixivdmuHVnm7o1Nymxm3rIoVSwPL9N/DjgVsAAJ+W9vjP693hYGn6XO3mS+X4cOsF7LqUCgAY29MVn7/SEabG4ueOmYhIG+Li4jByxPAy07xjb8jVSreVru+O2bFTbY29rrYD/JPYHjtxEhITY9Xa9NK17LJiOXr7+lSZ0OpaO5qia/EQkfbV2UZ+kyZNwvfff8/k9ilM+vVbRRvM6Zr2TlZ4o3cLjOjigkZ6OqpflT2XU/F+xHnkyxRwtjHDT+O90bm5ba3aupuej3+Fx+PGozyYiEX4fKQXXvNpeBuNEpHh0bWN6jS5wZwubSyoyXY0RdfiISLtqtOSfaSOSb9+O3Y7Ha/9clLbYVSpoZSSu5WWi7c2nMGd9HxIjI3wRWDNZ0scuPYIMzadR26RHE2tTLFqnDe83e3qKGIiovqnayXpWEqOiKj+MemvR0z69dul+1kYsUJ9LwojEbAnrD8crau/7vtRThGGLP9LbcaAptppaKXkcoqKMXPzeexPSAMATPBzx9yADpAYV77OX6kUsOLgLXy3/wYEAfB2t8Oq17ujaQ3efyIyLIY44qtrI/QsJVc1Xbp+iMhwMOmvR0z69ds7v51F7KUU1f3S0m2hPWs+FXzz6SR8vO0yFIKgE+3oM6VSwI8HShJ4AOjZwg7/eb07mlqVn8DnFhVjZsQF7Lv6CAAwztcNnw7vWOUPBURkuAxxbbeurcXX5Jp+Q6VL1w8RGZbq5qH8NkwN2oFrjxB7KQViIxHWT+qJjVN9cWTOwFon2KE93XBkzkCdaUefGRmJMONlD/xvQg9YmRrjdGImRvx4BGeTMsuceystD6/85yj2XX0EidgIy0Z1xqLATkz4iRqwp3c7PzzJQlW/fN68eapR6cOTLHD5QnylNeQ11Y6mREREQFYsx/u+6gl1cEQhQiOlqprtH/gZQ1YsR0REhF60Y6h07fohogaq7qoGNhzVrY9oKB5mFQhHbz3WSL12TbRTW/nSYqH34j8E99k7hS9ir2olBqqe22m5wkvfHBLcZ+8UPD7eJfx+8p7q+tl08p7Q8dM9gvvsnYLPF/uFc0mZ2g6XiHSAodZrz8nJEXr7+gjW5mLh8CQL4ZV2EkFiYizMnTtXkJgYC4HtJcLhSRaCtblY6O3rI+Tk5OhFO4ZK164fIjIs1c1DOb1fAxrS9P5nS9stDu5U6+nrmmjneXwRexW/HL6LZrbm2DezPywkhrkjvqHIk8rxQcQF7LmSWu7jvVra4z+vdUcTq+cr80dEhsGQp6/r2rIFTl+vmC5eP0RkOLimvx41lKS/otJ2LrZmMDaq/jRquVKJh1lFasfqe6O6yw+y8cp/jkKhFLB2Yk8MbNe0Xl6Xno8gCFi65xr+++cdteMiAH/OGgA3+0baCYyIdJIhb1SnaxsUcqO6iuni9UNEhoFJfz1qKEl/zPkHeHfT+Tprv75K0imUAoJWHsXF+9kI6OyM/7zWvc5fkzSnohKLDaWkIRHVDEvSkS7g9UNEdaG6eSjnM1O1HLudjk+3Xylz3EgE/HecNxpbVn9K9ZM8Kf716xk8/XOTCIB74/oZ5d9wPBEX72fDyswYnw3vUC+vSZrT0qERjEQoU9KwhYOF9oIiIp0UGxuLZUuXILC9BAGe6l95AjyN8Uo7CZYtXQJfX98qR/o10Q41TLx+iEjbuLU1VUoQBPzv8B2MX30KWYXFcLExg9Hfs9LEIhEWB3eCf0cneLvbVfvm39EJS4I7QSz6Z3qbACD2YvlrtTXpYVYhvo67DgCYPaQd67frIWcbcyx+6vopLWlYX0tDiEg/xMXFlZlSLVMIiEooVu0oHzHaVLWbelxcXJ22Qw0Trx8i0gUc6acKFcoUmB15ETEXHgIAgrs3w5dBnZBZIENiegFaOFjUOtEK7emG/p5NkJhegFN3n+C7/Tfx5e4EtHRohJc7OGqyG2o+i7mCfJkC3u52eK1XwyuDZyievn6e5zokIsP1Tyk5C7XN0rZfkyGwvUSVgH3gZ4zt1woQERFR7gZqmmqHGiZeP0SkC7imXwMMcU1/ckYB3go/g4SUHIiNRJgX0B5v9G4B0VOj85oiCAI+ib6M308moZFEjK3/1xvtnTX/Pu65nIq3fz0DYyMRYt/th7ZODXNDISKihuDp+uixY03x9XE5dt9WYtbsOVi2dAmGtTHC+77GCNgkhVeXHhXuLK+pdqhh4vVDRHWJG/nVI0NL+g/ffIzpG88hq6AYDpYSrHitO3xb1e0GacUKJd5YcwrHbj9BM1tzRL/TR6Ol13KLijHo27+QmlOEdwa2xoeD22msbSIi0k0sSUe6gNcPEdUVJv31yFCSfkEQ8NNfd7BszzUoBaBLcxv8d7x3vU2dziqQIWjlMdxNz0d3N1v8PtUXZiZijbQ9P+YK1h1LhHtjC8SF9ddYu0REpNtYko50Aa8fIqoLTPrrkSEk/flSOWZtvYjYSykAgJAezbHgFa96T47vPM5D4H+OIqdIjqBuzfBtSJfnXlJwPjkLQSuPQhCAX6f4oK+Hg4aiJSIiIiIi0o7q5qF6s3t/ZmYmxo8fDxsbG9jY2GD8+PHIysqq9Dkikajc21dffaU6Z8CAAWUeHzt2bB33RrckpucjeOUxxF5KgYlYhEWBXlg6qrNWRsNbNbHEqnHeEBuJEHXuAVYeuv1c7RUrlPho2yUIAhDUrRkTfiIiIiIialD0Jul/7bXXcP78eezZswd79uzB+fPnMX78+Eqfk5KSonZbs2YNRCIRRo0apXbe1KlT1c776aef6rIrOuXg9TSMXHEE1x/loomVKTa95Ytxvu51smFfdfVp44DPR3YEAHwVdx17LqfUuq21R+8iISUHthYmmBvQXlMhEhFRHcvNzcWUKVPKlDCLi4vDlClTkJubq6XIiIiI9ItelOxLSEjAnj17cOLECfj4+AAAfvnlF/j5+eH69eto27Ztuc9zcnJSu799+3YMHDgQrVq1UjtuYWFR5lxDp1QKWHnoFr7ZdwOCAHR3s8Wqcd5w1JG69eN83XErLQ/rjiXivc0X0NzOAl7NbGrURnJGAb7bdxMA8PHQ9mhsqbmNAYmIqO48vfHZr+Ebyt347NrVK9z4jIiIqBr0YqT/+PHjsLGxUSX8AODr6wsbGxscO3asWm08evQIsbGxmDJlSpnHfvvtNzg4OKBjx4744IMPqhw9kEqlyMnJUbvpkzypHP/32xl8vbck4X/dxw2b3vLTmYS/1NyA9ujv2QSFxQq8uT4eaTlF1X6uIAiYt/0yCosV8GlpjzE9mtdhpEREpClPlzg7PMkCQ1sbITgoEPPmzUNwUCCGtTHC4UkWuHwhHkP8B3HEn4iIqAp6kfSnpqaiadOmZY43bdoUqamp1Wpj/fr1sLKyQnBwsNrx119/HRs3bsShQ4cwb948REZGljnnWYsXL1btLWBjYwNXV9fqd0aLUrILsfVMMob/cBhxVx5BIjbCkuBO+CKoEyTGuncpGIuNsOK1bmjT1BKpOUWYuiEeRcWKaj1358UUHLr+GBKxEb4M7qTV5QpERFR9YWFhOHbiJGLHmqKvmzEiRptiaGsjLFq0CMPaGGHzqJLjsWNNcezESYSFhWk7ZCIiIp2m1Uxv/vz5FW62V3qLj48HgHKTNkEQqp3MrVmzBq+//jrMzNRHs6dOnYqXX34ZXl5eGDt2LLZu3Yr9+/fj7NmzFbb10UcfITs7W3VLTk6uQa+1Y/PpJPRecgAfbLmIxCcFsDYzxuZ/+WJsLzdth1YpazMTrH6jB+wsTHDhfjbe33IBVRWcyC4sxuc7rgIA/j2wNVo3sayPUImISANCQkIgMTHGNyfkkCkESMQiRIw2xbYQc2weZQqJWASZQsDXx+WQmBgjJCRE2yETERHpNK2u6Z82bVqVO+W3aNECFy9exKNHj8o89vjxYzg6Olb5OocPH8b169exefPmKs/t3r07TExMcPPmTXTv3r3cc0xNTWFqqj/rw1OyC1U72JfKk8rhZKNb0/kr4t64Ef47zhvjVp9E7MUUeDS1RNjLnhWev3TPNaTnSdGqSSP834DW9RgpERE9r8GDB2NbVDSCgwIRGilVJfpB7U0AADKFgJCtUuy+rcS2qGi1mudERERUllaTfgcHBzg4VF1Czc/PD9nZ2Th16hR69eoFADh58iSys7PRu3fvKp+/evVqeHt7o0uXLlWee+XKFRQXF8PZ2bnqDuiJu+n5UD4zOK4UgMT0AjjbmGsnqBryadUYXwR2wqzIi1i+/yZaN7HEiC4uZc6LT8zA7yeTAABfBnWCqXH9lx0kIqLnExAQgFmz52DRokWIvSFWJfwAEHtDju3XZJg7dy4CAgK0GCUREZF+0L2F3OVo3749hgwZgqlTp+LEiRM4ceIEpk6diuHDh6vt3N+uXTtERUWpPTcnJwdbtmzBm2++Wabd27dvY8GCBYiPj0diYiJ27dqFMWPGoFu3bujTp0+d96u+tHRoBKNnVkGIRSK0cLDQTkC1FNLTFW/1L6m88MGWCzifnKX2uEyuxEfbLpWc26M5fFs1ru8QiYhIA2JjY7Fs6RIEtpcgwFN9fCLA0xivtJNg2dIliI2N1VKEtcdShEREVN/0IukHSnbY79SpE/z9/eHv74/OnTsjPDxc7Zzr168jOztb7dimTZsgCAJeffXVMm1KJBL88ccfGDx4MNq2bYt3330X/v7+2L9/P8RiwxkhdrYxx+LgThD/vf+BWCTCl8FeejPK/7TZQ9rhpXZNIZUrMXVDPB5mFaoe++XwHdxMy0PjRhJ8PKy9FqMkIqLaiouLU+3S//Qa/qiEYrU1/qW7+j+bPOuy0soEa9aswcgRw1U/WsTGxmLkiOFYs2YNKxIQEZHGiYSqdkWjKuXk5MDGxgbZ2dmwtrbWdjgVSskuRGJ6AVo4WOhlwl8qTyrH6FXHcC01Fx2crbH1//yQliOF//K/IJMr8V1oFwR1Y4k+IiJ9NGXKFKxZswaHJ1mgr5uxag3/9msyBLaXqH4IOJIkR7+1BZg8eTJWr16t7bCr9HQpwtixpvj6uBy7bysxa/YcLFu6BMPaGOF9X2MEbJLCq0sP7Nm7D1ZWVtoOm4iIdFh181Am/RqgL0m/IbmfWYDA/xxFep4ML3g64EmeDJcf5qCfhwM2TO7FEn1ERHrKUJNjQ/0xg4iItKe6eajeTO8nelpzOwv8NN4bYpEIf95Ix+WHOQAAv1aNmfATEekxKysr7Nm7D15deqDf2gLVLv0LFy7Etqho7LqlRL+1BXqV8AMsRUhERNrDpJ/0loutOZTPTFT5Zu8NpGQXVvAMIiLSB6WJ/+TJkxGzY6dql/6AgADE7NiJyZMn61XCD/xTinDXLSVCI6WqxD+ovYkq4WcpQiIiqgtM+klv3U3Px7NrUxSCgMT0Aq3EQ0REmmNlZYXVq1eXSX4HDx6M1atX61XCX6q0FGF0ggyxN+Rqj5WWIpw1ew5LERIRkUYx6Se9ZSilCImIqGEw5FKERESku5j0k94ypFKERERk2Ay5FCEREek246pPIdJdoT3d0N+ziUGUIiQiIsMVEREBWbEc7/taqK3hf3b3/g/8jLH9WgEiIiK4rp+IiDSCI/2k95xtzOHXujETfiIi0lnLly9Hb18fBGyS4kiSXLVp39y5c1Wb+x1JkiNgkxS9fX2wfPlybYdMREQGgiP9RERERHWstCLBEP9B6Lf2JCQmxtgWFY2AgAD4+voiOCgQ0QkF6O3ro3eVCYiISLdxpJ+IiIioHhhiKUIiItJ9IkEQnq16RjWUnZ0NW1tbJCcnw9raWtvhEBERERERkYHLycmBq6srsrKyYGNjU+F5nN6vAbm5uQAAV1dXLUdCREREREREDUlubm6lST9H+jVAqVTi4cOHsLKygkgkqvoJWlL6SxBnJJAh4PVMhoTXMxkSXs9kSHg9ky4TBAG5ublwcXGBkVHFK/c50q8BRkZGaN68ubbDqDZra2t+aJHB4PVMhoTXMxkSXs9kSHg9k66qbIS/FDfyIyIiIiIiIjJQTPqJiIiIiIiIDBST/gbE1NQUn332GUxNTbUdCtFz4/VMhoTXMxkSXs9kSHg9kyHgRn5EREREREREBooj/UREREREREQGikk/ERERERERkYFi0k9ERERERERkoJj0ExERERERERkoJv0NyMqVK9GyZUuYmZnB29sbhw8f1nZIRFX666+/MGLECLi4uEAkEiE6OlrtcUEQMH/+fLi4uMDc3BwDBgzAlStXtBMsUSUWL16Mnj17wsrKCk2bNkVgYCCuX7+udg6vZ9IXq1atQufOnWFtbQ1ra2v4+flh9+7dqsd5LZM+W7x4MUQiEcLCwlTHeE2TPmPS30Bs3rwZYWFh+OSTT3Du3Dn069cPQ4cORVJSkrZDI6pUfn4+unTpghUrVpT7+LJly/Dtt99ixYoVOH36NJycnDBo0CDk5ubWc6RElfvzzz/xzjvv4MSJE9i3bx/kcjn8/f2Rn5+vOofXM+mL5s2bY8mSJYiPj0d8fDxefPFFvPLKK6okiNcy6avTp0/j559/RufOndWO85omvSZQg9CrVy/h7bffVjvWrl07Yc6cOVqKiKjmAAhRUVGq+0qlUnBychKWLFmiOlZUVCTY2NgI//3vf7UQIVH1paWlCQCEP//8UxAEXs+k/+zs7IT//e9/vJZJb+Xm5goeHh7Cvn37hBdeeEGYMWOGIAj8fCb9x5H+BkAmk+HMmTPw9/dXO+7v749jx45pKSqi53f37l2kpqaqXdumpqZ44YUXeG2TzsvOzgYA2NvbA+D1TPpLoVBg06ZNyM/Ph5+fH69l0lvvvPMOAgIC8PLLL6sd5zVN+s5Y2wFQ3UtPT4dCoYCjo6PacUdHR6SmpmopKqLnV3r9lndt37t3TxshEVWLIAiYOXMm+vbtCy8vLwC8nkn/XLp0CX5+figqKoKlpSWioqLQoUMHVRLEa5n0yaZNm3D27FmcPn26zGP8fCZ9x6S/ARGJRGr3BUEoc4xIH/HaJn0zbdo0XLx4EUeOHCnzGK9n0hdt27bF+fPnkZWVhcjISLzxxhv4888/VY/zWiZ9kZycjBkzZmDv3r0wMzOr8Dxe06SvOL2/AXBwcIBYLC4zqp+WllbmF0sifeLk5AQAvLZJr0yfPh0xMTE4ePAgmjdvrjrO65n0jUQiQZs2bdCjRw8sXrwYXbp0wffff89rmfTOmTNnkJaWBm9vbxgbG8PY2Bh//vknfvjhBxgbG6uuW17TpK+Y9DcAEokE3t7e2Ldvn9rxffv2oXfv3lqKiuj5tWzZEk5OTmrXtkwmw59//slrm3SOIAiYNm0atm3bhgMHDqBly5Zqj/N6Jn0nCAKkUimvZdI7L730Ei5duoTz58+rbj169MDrr7+O8+fPo1WrVrymSa9xen8DMXPmTIwfPx49evSAn58ffv75ZyQlJeHtt9/WdmhElcrLy8OtW7dU9+/evYvz58/D3t4ebm5uCAsLw5dffgkPDw94eHjgyy+/hIWFBV577TUtRk1U1jvvvIPff/8d27dvh5WVlWrEyMbGBubm5qqa0LyeSR98/PHHGDp0KFxdXZGbm4tNmzbh0KFD2LNnD69l0jtWVlaq/VVKNWrUCI0bN1Yd5zVN+oxJfwMRGhqKJ0+eYMGCBUhJSYGXlxd27doFd3d3bYdGVKn4+HgMHDhQdX/mzJkAgDfeeAPr1q3DrFmzUFhYiH//+9/IzMyEj48P9u7dCysrK22FTFSuVatWAQAGDBigdnzt2rWYOHEiAPB6Jr3x6NEjjB8/HikpKbCxsUHnzp2xZ88eDBo0CACvZTI8vKZJn4kEQRC0HQQRERERERERaR7X9BMREREREREZKCb9RERERERERAaKST8RERERERGRgWLST0RERERERGSgmPQTERERERERGSgm/UREREREREQGikk/ERERERERkYFi0k9ERERERERkoJj0ExERERERERkoJv1EREREREREBopJPxEREREREZGBYtJPREREREREZKD0LulfuXIlWrZsCTMzM3h7e+Pw4cMVnnvo0CGIRKIyt2vXrqmdFxkZiQ4dOsDU1BQdOnRAVFRUXXeDiIiIiIiIqM4ZazuAmti8eTPCwsKwcuVK9OnTBz/99BOGDh2Kq1evws3NrcLnXb9+HdbW1qr7TZo0Uf37+PHjCA0NxcKFCxEUFISoqCiEhITgyJEj8PHxqVZcSqUSDx8+hJWVFUQiUe07SERERERERFQNgiAgNzcXLi4uMDKqeDxfJAiCUI9xPRcfHx90794dq1atUh1r3749AgMDsXjx4jLnHzp0CAMHDkRmZiZsbW3LbTM0NBQ5OTnYvXu36tiQIUNgZ2eHjRs3Viuu+/fvw9XVtWadISIiIiIiInpOycnJaN68eYWP681Iv0wmw5kzZzBnzhy14/7+/jh27Filz+3WrRuKiorQoUMHzJ07FwMHDlQ9dvz4cbz33ntq5w8ePBjLly+vdmxWVlYASt7sp2cUEBEREREREdWFnJwcuLq6qvLRiuhN0p+eng6FQgFHR0e1446OjkhNTS33Oc7Ozvj555/h7e0NqVSK8PBwvPTSSzh06BD69+8PAEhNTa1RmwAglUohlUpV93NzcwEA1tbWTPqJiIiIiLQgNzcXYWFhCAkJweDBg1XH4+LiEBERgeXLl1eZHJW2M336dDRp0gR37txBRmYG7O3s0apVKzx+/Bg//vhjtdohqi9VLTHXm6S/1LMdEgShwk62bdsWbdu2Vd338/NDcnIyvv76a1XSX9M2AWDx4sX4/PPPaxM+ERERERFpWG5uLob4D8KxEyfxa/gGbIuKRkBAAGJjYxEcFAhZsRzXrl7Bnr37Kk3Yc3Nz4dOzBxKu34CRCJA4m8G0uSmk16WQRRVBKQCnTh7HyVPxTPxJb+jN7v0ODg4Qi8VlRuDT0tLKjNRXxtfXFzdv3lTdd3JyqnGbH330EbKzs1W35OTkar8+ERERERFpTmnCf/lCPA5PssDQ1kYIDgrEvHnzEBwUiGFtjHB4kgUuX4jHEP9Bqlm65bXj07MHku7cwOFJFgjwNIYsrQgSRwlkaUUY3tYYhydZIOn2Dfj07FFhO0S6Rm+SfolEAm9vb+zbt0/t+L59+9C7d+9qt3Pu3Dk4Ozur7vv5+ZVpc+/evZW2aWpqqprKzyn9RGTIUrILcex2OlKyC3WiHSIiomeFhYXh2ImTiB1rir5uxogYbYqhrY2waNEiDGtjhM2jSo7HjjXFsRMnERYWVm4706dPR8L1G9gzzgJ93YyxdYw5Alob4/GOxxjexhhbRpujr5sx9oyzQML1G5g+fXr9dpSolvRqev/MmTMxfvx49OjRA35+fvj555+RlJSEt99+G0DJCPyDBw+wYcMGAMDy5cvRokULdOzYETKZDL/++isiIyMRGRmpanPGjBno378/li5dildeeQXbt2/H/v37ceTIEa30kYhIV2w+nYSPtl2CUgCMRMDi4E4I7VlxedS6boeIiKg8ISEh+DV8A745IUevZmJIxCJEjDZF7A0xAjyNIRGLIFMI+Pq4HBITY4SEhJTbTpMmTWAkAr46LlO1s3WMOWJvyNXaWXZMBiMR0LRp03ruKVHt6FXSHxoaiidPnmDBggVISUmBl5cXdu3aBXd3dwBASkoKkpKSVOfLZDJ88MEHePDgAczNzdGxY0fExsZi2LBhqnN69+6NTZs2Ye7cuZg3bx5at26NzZs3w8fHp977R0SkK1KyC1WJOgAoBWB25CXsvpwKcxNxtdspLFbg0PXHqvtKAfh422X092wCZxtzTYdNREQN0ODBg7EtKhrBQYEIjZRi8yhTSMQiBLU3AQDIFAJCtkqx+7YS26Ki1Tb5e9qdO3cgcTbDzltFGLO1EFtGm5dpZ/SWQsTelkPibIbbt2/XWx+JnodIEARB20Hou5ycHNjY2CA7O5tT/YlIrxUVK7D7cgp+OnQH1x7V3VrFdwa2xvQXPWBWgx8QiIiIKjNv3jwsWrQI20LMVYk6AEQlFCM4ohBz587FwoULK3z+wBcH4lz+OUgcJXi843GF7TQZ0QTSR1J0b9QdBw8crNM+EVWmunmoXo30ExFR3bjxKBcbTyVh29kHyC4sLvcckQh4378tbMxNyn28PNmFxfgm7jqe/XX5Pwdv47eTSQju1hyv9nKFhyN3QCYiotqLjY3FsqVLENheggBP9RQnwNMYr7STYNnSJfD19UVAQEC5bdjb2UN6XYrcs9kY2c643HZGtDVG7O7HkDQ1g72vfZ31h0iTmPQTETVQhTIFdl1KwcZTSYi/l6k63szWHK/2coWpsRhLdl+DQhAgFonwZbBXrdbiN7GU4ONtl6EQBBiJgJfaO+Lqwxw8yCrEmqN3seboXfRsYYdXe7lhWCdnjv4TETUwubm5CAsLQ0hIiNrU+7i4OERERGD58uWVlseLi4tT7dJfOrVfphDU1uJHjDZFyFYpgoMCEbNjZ7lT/Fu1agVZVMku/aVT+59tZ+sY85Ip/jeK0Lp16zrtF5GmMOknImpgrqXmYOPJJGw79wC5RXIAgNhIhEHtHfGqjxv6tnGA2EgEABjexRmJ6QVo4WBR6zX4oT3d0N+ziVo7CqWAv24+xsaTSfjjWhpOJ2bidGIm5sdcQXD35ni1lxvaOvGLEBGRoSstt3fsxEn8Gr4B26KiERAQgNjYWAQHBUJWLMe1q1ewZ+++ChPkiIgIyIrleN/XQpWoh2yVYvs1GQLbS1Q/BHzgZ4zt1woQERFRbtL/+PFjKAXgQz+Jqp3RWwqx47ocI9v980PArN4S7LguR1paWp32i0hTuKZfA7imn4h0XYFMjp0XS0b1zyVlqY672ptjbE83jPFujqbWZlqJ7VFOEbbEJ2PT6WTcz/ynpF93N1u82ssNwzu7wFzC0X8iIkNTmhhfvhCP2LGm+Pq4HLtvKzFr9hwsW7oEw9oY4X1fYwRsksKrS48KE2RNtuPTqweSbpeU7Vt2TIbY23I0HtoET3aXlO370E+CIb8WwK21J06eiq/TeIiqUt08lEm/BjDpJyJdk5JdiLvp+SiWK7E/IQ3R5x4gV1oyqm9sJIJ/R0e82ssNfVo7wOjvUX1tUyoFHLmVjo2nkrDv6iPI/y4dYGVmjKBuzTC2pxvsGpngbno+Wjo04u7/RER6bsqUKVizZg0OT7JAXzfjCkfojyTJ0W9tASZPnozVq1eX29bTI+sSE+NyR9Z7+/pUmWDn5ubCp2cPJFy/ASMRIHE2g6S5BLL7MshSiqAUgPbtKk74Nd0vosow6a9HTPqJSJdsPp2EOdsu4dlPd/fGFhjb0w2jvZujiZWpdoKrprTcImw9cx+bTiUjKaOgzONGImBxcKda7TFARES6IS4uDiNHDK90Lf7T5fYqWotfSlNr6HNzczF9+nQ0bdoUt2/fRkZmBuzt7NG6dWukpaXhxx9/rHKPAU32i6giTPrrEZN+ItIVKdmF6LPkAJRPfbKLAPzwalcEdHLRmVH96lIqBRy7/QRrjt7FgWvqayfFIhGOzBnIEX8iIj1WOhL/dIJc6unEuHTkXl8Yar9It1Q3DzWqx5iIiKiO3U3PV0v4AUAA4GBppncJPwAYGYnQ18MBb/ZrWeYxhSAgMb3sLAAiItIfAQEBmDV7DqITZIi9IVd7LPaGHNuvyTBr9hy9S4wNtV+kn5j0ExEZEJdyRr3FIhFaOFhoIRrNaenQCM/+ZmEkgt73i4iooYuNjcWypUsQ2F6CAE/1wmIBnsZ4pZ0Ey5YuQWxsrJYirB1D7RfpJyb9REQG5GxSptp9sUiEL4O99H4KvLONORYHd1JL/EN6uOp9v4iIGrK4uLgyU+BlCgFRCcWQKQRIxCJEjDbF0NZGCA4KRFxcnLZDrpbSfg1tLaq0X0NaifSqX6S/mPQTERmQDcfvAQD+1b8lNk71xZE5Aw1ms7vQnm44OudFjO7eHABwKy1PyxEREdHziIiIgKxYjvd91Te3C44oRGikVJUgf+BnDFmxHBEREdoOuVpK+/WBn4mqX6O3FCI4ohBjthaq+vVhbxO96hfpL27kpwHcyI+IdMHF+1kYueIoTMQiHJvzks7v0F9baTlF6L3kAORKAbHv9kVHFxtth0RERLVgqPXsHz9+jObNnGEiUmDP6xZYdkyG2NtyNB7aBE92P8bwNsb40E+CIb8VoFgQ4/6DFDRp0kTbYZMe4kZ+REQNTOko/7BOzgab8ANAU2szDPFyAgCE/91nIiLSP1ZWVtizdx+8uvRAv7UFqt3sFy5ciG1R0dh1S4l+awv0KuEHgD179kBWrACczdBvbQFib8vhOs0djqMc4TrNHTtvydFvbQHgZAZZsYLT+6nOMeknIjIAmfky7LjwEAAwwa+FdoOpB2/0bgEAiD7/ANkFxdoNhoiIaq008Z88eTJiduxU7WYfEBCAmB07MXnyZL1K+AEgOjoalp6WcJvTErb9bOE6wx1WXUvit+pqBdcZ7rDtZwu3j1rC0tMSUVFRWo6YDJ1x1acQEZGui4hPhlSuREcXa3R3s9V2OHWuh7sd2jlZ4VpqLracScab/VppOyQiIqolKysrrF69uszxwYMHY/DgwVqI6PlkZGZAbCuG2FyM5lOal3ncqpMVrDqV/AhgZGuEjMyM+g6RGhiO9BMR6TmFUsCvJ0umuU/wc4dIJKriGfpPJBKpZjT8euIelEpuT0NERLrB3s4eiixFtc5VZilhb2dfxxFRQ8ekn4hIz/15Iw3JGYWwMTfByC7NtB1OvQns5gIrM2MkPinAXzcfazscIiIiAEBgYCDybuRBmiqt9DxpihR5N/IQFBRUT5FRQ8Wkn4hIz60/VjLKP8a7OcwlYi1HU38sJMYY7V0ybZIb+hERka4YM2YM7Brb4VHEIwgVzEQTlAIebXkEu8Z2GD16dD1HSA0Nk34iIj2WmJ6PP2+UjHKP83XXcjT1b/zffT5wPQ3JGQVajoaIiAgwMzPD+rXrkXc+D8krksuM+EtTpEhekYy883lYv3Y9zMzMtBQpNRRM+omI9NivJ0pGuAe0bYIWDo20HE39a9XEEv08HCAIUO1rQEREpG0jRoxAVFQUxPfEuDnnJhK/TETSyiQkfpmImx/dhPieGNHR0RgxYoS2Q6UGgEk/EZGeKpQpEBGfDKBkA7+GqnRDv4jTySgqrt7GSURERHVt5MiReHj/IcLDw+Hv5Y/ujbrD38sf4eHheHj/IRN+qjcs2UdEpKdiLjxATpEcrvbmeMGzqbbD0ZoX2zVFM1tzPMgqxM6LKap1/kRERNpmZmaGcePGYdy4cdoOhRowvRvpX7lyJVq2bAkzMzN4e3vj8OHDFZ67bds2DBo0CE2aNIG1tTX8/PwQFxends66desgEonK3IqKiuq6K0REtSYIAjb8vXndOB93iI0Mv0xfRcRGIrzu6wYA2HA8UbvBEBGRVhUVFSE8PByjRo3CwBcHYtSoUQgPD9f77/aG2i+qH3qV9G/evBlhYWH45JNPcO7cOfTr1w9Dhw5FUlJSuef/9ddfGDRoEHbt2oUzZ85g4MCBGDFiBM6dO6d2nrW1NVJSUtRu3FCDiHTZ2aQsXHmYA1NjI4T0cNV2OFoX2sMVErERLt7PxvnkLG2HQ0REWhATEwOX5i6YMGEC9l7ei3P557D38l5MmDABLs1dsGPHDm2HWCuG2i+qPyJBEMqvI6GDfHx80L17d6xatUp1rH379ggMDMTixYur1UbHjh0RGhqKTz/9FEDJSH9YWBiysrJqHVdOTg5sbGyQnZ0Na2vrWrdDRFRdMzadw/bzDzHauzm+HtNF2+HohJmbz2PbuQcI7t4M34Z01XY4RERUj2JiYhAUFATLrpZwDHGEqZOp6jFpqhSPIh4h73weoqKiMHLkSC1GWjOG2i/SjOrmoXoz0i+TyXDmzBn4+/urHff398exY8eq1YZSqURubi7s7e3Vjufl5cHd3R3NmzfH8OHDy8wEeJZUKkVOTo7ajYiovjzOlWLXpRQAwBt/b2JHwITeLQAAOy+mICNfpt1giIio3hQVFWHi5Imw7GoJ12muaokxAJg6mcJ1missu1pi4uSJejMl3lD7RfVPb5L+9PR0KBQKODo6qh13dHREampqtdr45ptvkJ+fj5CQENWxdu3aYd26dYiJicHGjRthZmaGPn364ObNmxW2s3jxYtjY2Khurq6cWktE9Wfz6SQUKwR0dbVFp+Y22g5HZ3R1tUXn5jaQyZXYfDpZ2+EQEVE92bJlCzKfZMIxxBGiCva4ERmJ4DjGEZlPMrF169Z6jrB2DLVfVP/0Jun/f/buPCzKcv0D+HdmYEBkF0VRBBdwA/cENE1NUUOMRbFFzSQ7/c6xXFrUjlYnO26nxU4dO6cSUyoFERBBRc0lzd3cUBQ3BGUT2QUGmHl/fxCTI9uMDMzC93Ndc13xzjPP3O/wNnK/z3LXEIlUL3hBEGodq8uWLVvw0UcfISIiAh06/LnLtbe3N2bMmIEBAwZg5MiRiIyMhLu7O7766qt6+1q6dCkKCwuVj/R0/nFJRC2jSq7ATyer9zFpzWX66jPTu/oz+fHEHcgVBrN6jYiImiA2NhaW7pa1RsIfZ9bJDJbuloiJiWmhyJrGWM+LWp7BJP0ODg6QSCS1RvVzcnJqjf4/LiIiAqGhoYiMjMS4ceMabCsWi/HUU081ONJvZmYGa2trlQcRUUvYn5yDzMJy2LeV4jnPTroOR+/4D3CCrYUp7hWU4cDVHF2HQ0RELSAvPw8SW4labcW2YuTl5zVzRNphrOdFLc9gkn6pVIohQ4Zg3759Ksf37duH4cOH1/u6LVu2YPbs2fj555/h5+fX6PsIgoDz58+jUyf+MU1E+if8RCoAYPpTzjA3Ve8PgdbE3FSC6X9UM2D5PsPHElVEpA57O3vIC+RqtVUUKGBvZ994Qz3QHOelre9VY/x+Li4uRmhoaK0S74mJiQgNDUVxcbGOIms6g0n6AWDRokX4/vvvERYWhuTkZCxcuBBpaWl44403AFRPu581a5ay/ZYtWzBr1ix89tln8Pb2RlZWFrKyslBYWKhs849//AOJiYm4desWzp8/j9DQUJw/f17ZJxGRvriRU4zfbjyAWAS87NVV1+HorRneLhCJgCPXc3Hrfomuw6EnxBJVRKSugIAAlKSUQJYla7CdLFOGkpQSBAYGtlBkTaPt89LW96oxfj8XFxdjou94hIWFYYr/ZCQkJAAAEhISMMV/MsLCwjDRd7zBJv5qley7ePGixh337dsXJiYmTxRUQ9avX4+1a9ciMzMTHh4e+OKLLzBq1CgAwOzZs5GamopDhw4BAEaPHo3Dhw/X6uOVV17BDz/8AABYuHAhoqOjkZWVBRsbGwwaNAgfffQRfHx81I6JJfuIqCV8uCMJm47fwfi+jvhu1lBdh6PXQn84jV+u5mDOiG74wL+vrsMhDbFEFRFpory8HE5dnCB3kcN5nnOdm94JCgHpX6dDckeCjLsZMDc310GkmtHmeWnre9UYv59rEv6kC2eQ8IIZPj1ehd03FXhv8RKsXbMaz/UU421vE/htlcFjwFDs2bsPVlZWug4bgPp5qFpJv1gshkgkghpNle1TUlLQvXt39SM2YEz6iai5lciq4L3yF5TIqhAeOgwj3drrOiS9duhaDmZvPA0rcxOcfP9ZWEi1fxOaGpdZWIbbuQ/RzaEtOtm0Ues1xvrHOxE1r507dyIgIKDuZDRThuxt1clobGws/P39dRipZrRxXtr6XjXW7+fQ0FCEhYXhyKsWeLqrCSrkAkKiZNhxtQIBfaSICDaDVCLC0bQqjNxYijlz5mDDhg26DhuA+nmo2tP7T548idu3bzf6uHXrlkH8comIDEnMuXsokVWhu0NbjOjhoOtw9N4ot/ZwbWeB4vIqxJ7L0HU4rVLE6TSMWH0AL313EiNWH0DE6TS1XscSVUT0JPz9/RETEwPJHQmuL7mO1JWpSFufhtSVqbi+9DokdyQGl/AD2jkvbX2vGuv3c0hICKSmJvjsRBUq5AKkEhEip5ohOqSNMuGvkAv49HgVpKYmKuXfDYVaSf8zzzyDnj17wsXFpdGHq6srRo0ahTZt1LujT0REDRMEAeF/bEo3w9sF4nr+oaU/icUizPijfN/m46lqz1Qj7biZU4Il2y+hpmqiQgDej05CZmFZo69liSoielJTpkxBxt0MhIeHw9fDF4PbDoavhy/Cw8ORcTfD4BL+Gk09L219rxrr9/OECRMQHROLXTcUmL5dpkz8A/uYKhP+kCgZdt9UIDomFhMmTNB1yBpTa77jwYMHNep0165dTxQMERHVdvJ2HlKyS9DGVILgIV10HY7BmDbEGZ/uvYarWcU4cycfT7kaxm7NhizpXiF+PpWG6LN38fhtFrkgIDW3tNFp/ixRRURNYW5ujhkzZmDGjBm6DkWrmnJe2vpeNebvZz8/P7y3eAk++eQTJKRIENjHVPlcQkoVdlytwLJly9SqBqePDGr3fiKi1qim9FzAoM6waWPacGNSsrEwxfMDOgMANh+/o+NojFeJrAo/n0yD/1dHMfmro/j5ZBrKqxS12klEIrg6WDTanz6XqCIiMkTa+l7V19KI2ii1l5CQgLVrViOgjxR+7qrj4n7uJni+txRr16xW7upvaNQa6V+0aJHaHX7++edPHAwREanKKixH4uVsAMAsHxcdR2N4Zvq4IOJMOnZfykSOXx90sOaeM9ogCAIu3SvEllNp2HE+A6UV1X8ESiViTPDoiBeHOSPtQSn+HpMEuSBAIhJhZZCHWpv5BQQEIDo6GrIsWYNTSJUlqpY3XqJq9pzZyH+QD0t3S0hsJZDfkyM6OhrzF87Hpo2bDHbKLxGROrT1vart72dtqNl5/9iJk/gxfDOiY2Lh5+eHhIQEBAUGoKKyClevXG5wx/3ExEQEBQbguZ5ilTX8CSlV8HM3Ua7xD4mSISgwAHE74w1uir9au/ePGTNG5eezZ89CLpejV69eAICUlBRIJBIMGTIEBw4caJ5I9Rh37yei5vL5vhT8+5frGOZqj8g31C8lSn8K/uYYzt7Jx8Jx7pg/zk3X4Ri04vJK7DifgS2n0nA5o0h5vLtDW7w4rCuCBndGO8s//xDMLCxDam4pXB0sdLJ7vzGWliIi0pSx7t6vrVJ7rWH3frWS/kd9/vnnOHToEDZt2gQ7OzsAQH5+Pl599VWMHDkSb7/9dtMiN0BM+omoOVRUKTBizQHcL5bhqxcHwX+Ak65DMkg7zt/D/K3n4WhthqOLx8JUwpVtjXm01F5Ha3OcTy/AllNp2HkhE2WVf4zqm4jxnEdHvDisK4Z1s4dIpL0NJvWpRBURkTHQVklDfSqNqK1kXVs3D3Sh2ZL+zp07Y+/evejXr5/K8aSkJPj6+iIjo/WVRmLST0TNYeeFDLy55RzaW5nht8VjITVhsvokZFVyjFh9ALklFVj/8mA859lJ1yHptYjTaVgaXb3zvgiAo7UZsopkyud7drCsHtUf1Bl2baXNFsfj0/LFtmIoChQoSSmBXTu7Rqflh4eHY9asWXBb7dboNNTrS68jPDzc6Db+IiJ6VFO/V7XdT1MlJiZiiv/kBqflP7rzfkPT8h9dJiA1NalzmcBwby+9SviBZkz6rayssGPHDowdO1bl+IEDB/D888+rtVGCsWHST0TNIeS/x3EqNQ9vPeuGRePddR2OQfs08Rq+PngD3t3tsfV1LpOoT0ZBKUasOYjH/zKQSkSY3N8JL3p1xVAXO62O6jekvLwcUVFRiImJQV5+Huzt7BEYGIipU6c2OiofHByMvUl74fq+a6Pvk7oyFb4evti+fbuWIici0k9N+V5tjn6aqiYpfzTxr/F4qb3Gdt4vLi7GggULEBISonJzIDExEZGRkVi3bp1eJfxAMyb9s2bNwuHDh/HZZ5/B29sbAHDixAm8++67GDVqFDZt2tS0yA0Qk34i0rbkzCJM+vIIJGIRfls8Fh1tOO24KTIKyvD0mgNQCMDehaPg7qhf/2jrWmFpJWLO3cWGo7eRnl9W6/nvZw3FuL6OOojsyY0ZOwbnHp6D81+dG22btj4Ng9sOxsEDmpUoJiIi3Vu+fDk++eQTRIe0USm1F5NciaDIMixbtgwrVqzQYYTNR908VOO5ov/973/h5+eHGTNmwMXFBS4uLnj55ZcxadIkrF+/vklBExFRtZoScxP7dWTCrwVOtm0w/o+ktaYEYmsnCALOpOZhUeR5DFu5Hx/tvFJnwi8RidCvs+Hd0NbX0lJExkQbpdJqsLRm/bT1OWvz96UNLLXXcjQe6a/x8OFD3Lx5E4IgoGfPnmjbtq22YzMYHOknIm0qLKuE98pfUFYpx9bXveHdvZ2uQzIKx27k4qXvT6KtVIIT7z8LK3PTxl9khApKK7D993vYcioNN3JKlMd7d7TCS15dIVcI+CQ+WaXU3vSnuuow4ifDNf1EzUuba6DrLK1ZIG/xNeL6SFufs76tWddGPNpc02+omm2kv0ZmZiYyMzPh7u6Otm3b4gnvHRAR0WO2n72Lsko53B0t4dWNo4/a4tOjHXp2sMTDCjmif7+n63BalCAIOHnrARZsPYdhK3/BivgruJFTgjamEoQM7YKYvw7H7vkjMcvHFa+O6IajS8Zgy1xvHF0yxiATfgCYNm0a7NrZITsyG4Ki7r9RBIWA7G3ZsGtnh6lTp7ZwhESG69Hdzo+8aoFJPcQICgzA8uXLleurj7xqgaQLZzDRd3yDI7Y1pTXlLnK4rXaD6/uucP6rM1zfd4XbajfIXeQICAhAXFxcC56hftDW56zN35c+nVdkZCQqKqvwtrdqgh8UWYbp22WokAuQSkR4x8cEFZVViIyMbNbz0mcaj/Q/ePAAISEhOHjwIEQiEa5fv47u3bsjNDQUtra2+Oyzz5orVr3FkX4i0pZ7+aWY+s1xZBaVY0WAB2Z6u+g6JKOy6VgqPoy7DNd2FlgZ6Ilu7duqXT++Lo+WtmtKP9ryeDx5DysQ/ftdbDmVhpv3Hyrb9e1kjRe9uuL5gU6wNuIZD/pUWorImGirVBpLazZMW5+zvtWhZ6k97VE3DzWp95l6LFy4EKampkhLS0OfPn2Ux6dPn46FCxe2yqSfiEgbIk6nYUn0JeXO6Yp6RifpyQUN7ox/JlxB6oNSvPT9SYhFwKogzycazX60tF1T+tEWlVJ7IqB/ZxskZxajQq4AAFhIJXh+oBNeHNYVnp1tWmwHfl3y9/dHTEwMZs+ZjetLrtdZWooJP5HmQkJC8GP4Znx2ogrDOksglYgQOdUMCSkSlWnVnx6vgtTUBCEhIXX2s23bNuQ/yIfbu251JvwAIBKL4DjNEdeXXkdUVFSrWoajrc9ZW/3o23lZWVlhz959mOg7HiM3qi4T8Pb2RlBgAGKTS/Wy1F5L03ikv2PHjkhMTMSAAQNgZWWFCxcuoHv37rh9+zY8PT1RUlLSeCdGhiP9RNRUmYVlGLG6enf5GhKRCEeXjNGLEWRjkVlYBp9VB1SOiQC86NUVlmbq3wcvkVVhy8k0PPoPqC5/X3VdPzU8O9vgxWFdMWWgk0bnaEz0pbQUkTHRRqk0ltZsnLZK0mmztJ02tPZSe9rSbCP9Dx8+hIWFRa3jubm5MDOrf6McIiKq3+3ch7USNrkgIDW3lEm/Ft3OfVjrmADg55NpTe5bl7+vuq4fAFgZ4IGXuEQE5ubmmDFjRqsaISRqbn5+fnhv8RJ88sknSEiRqJRKS0ipwo6rFVi2bFmDCVtefh4kthK13k9sK0Zefl6T4zY02victdmPtmgzHisrqzqn/0+YMMHoNu57Uhon/aNGjcLmzZuVtQ5FIhEUCgX+9a9/YcyYMVoPkIioNejmULsCikQkgqtD7Zus9OS6ObSFWASVBFkE4KUnGOn/uY6Rfl39vuq7fsb06aCDaIioNVC3VJq3t3e9iZu9nT3k9zQordml9W1uq43PWZv9aIu+xWPsNE76//Wvf2H06NE4c+YMKioq8N577+Hy5cvIy8vDb7/91hwxEhEZvYeyKpWfa0qlcZRfuzrZtMGqIE+8H53U5JJ0/bvYKNfQA8AiX3ed/b7uPChV+ZnXDxE1pry8HNu2bUNsbKxy2UtAQACmTZvW6LKXxMTEWlOzHy+VFjnVrHon9cCAekulBQQEIDo6GrIsWaOlNUtSShC4PLDJ521ItPU5a6sffTsvUp/GJfv69u2LixcvYtiwYRg/fjwePnyIoKAgnDt3Dj169GiOGImIjN6Xv9wAAIxyczD4Umn6bvpTXbVSkm76U13x25KxGOhsAwC4eV93e9qs258CAAgc5MTrh4gaFRcXB6cuTpg1axb2Ju3FuYfnsDdpL2bNmgWnLk7YuXNng6/XVqk0ltZsmLY+Z30rbadv8bQGGm/kR7VxIz8iaoqU7GJMWPcrBAFIeOtp9HOy0XVIpIEL6QV4/j+/QSwC9i96Bt3bW7bo+x+/+QAvfncCUokYh94dDSdbju4TUf3i4uIQGBhYdynLLBmyI6tLWcbExGDKlCl19qHNUmksrVk/bX3O+lbaTt/iMWRq56HCEygrKxNOnjwp7Ny5U9ixY4fKo7n95z//EVxdXQUzMzNh8ODBwq+//tpg+0OHDgmDBw8WzMzMhG7dugnffPNNrTZRUVFCnz59BKlUKvTp00eIjo7WKKbCwkIBgFBYWKjR64iIBEEQ/vbTWcFlcbzw+ubTug6FntCcjacEl8XxwsKt51r8vUP+e0xwWRwvLIu51OLvTUSGpaysTLBrZydYD7YW+oX1Ezx+8Kj16BfWT7AebC3YtbMTysrK6u2rqKhIGO7tJQAQpKYmQnx8vCAIghAfHy9ITU0EAMJwby+hqKio0bh27Ngh2LWzEwAIlu6WgvUwa8HS3VIAINi1sxPi4uK09hkYGm19ztr8fWmDvsVjqNTNQzVO+nfv3i20b99eEIlEtR5isfiJA1bH1q1bBVNTU+G7774Trly5IsyfP19o27atcOfOnTrb37p1S7CwsBDmz58vXLlyRfjuu+8EU1NTISoqStnm2LFjgkQiEVauXCkkJycLK1euFExMTIQTJ06oHReTfiJ6UteyigTXJfGCy+J44UoGv0MM1YX0fMFlcbzQbUm8cCOnuMXe97cb9wWXxfGC2/u7hIyC0hZ7XyIyTJs3bxYACG6r3epM+GsebqvcBABCeHh4g/0VFRUJc+bMEfbs2aNyfM+ePcKcOXM0StjKysqE8PBwISgoSBg9ZrQQFBQkhIeHN3jjobXQ1ueszd+XNuhbPIZI3TxU4+n9PXv2xIQJE/DBBx/A0dFR0xkITeLl5YXBgwfjm2++UR7r06cPAgICsGrVqlrtFy9ejLi4OCQnJyuPvfHGG7hw4QKOHz8OAJg+fTqKioqwe/duZZuJEyfCzs4OW7ZsUSsuTu8noif1t59+R8KlTEzy6IhvZgzRdTjUBK9tOo39yTkIGOiEdS8Mavb3EwQB0789gVO38zDLxwUfP+/R7O9JRIYtODgYe5P2wvV910bbpq5Mha+HL7Zv3978gRHRE1E3D9V4I7+cnBwsWrSoxRP+iooKnD17Fr6+virHfX19cezYsTpfc/z48VrtJ0yYgDNnzqCysrLBNvX1CQAymQxFRUUqDyIiTV3LKkbCpUwAwFvPuuk4Gmqq+c+6AwDiLmTgRk7zb+p3/OYDnLqdB6lEjL+O7tns70dEhi8vPw8SW4labcW2YuTl5zVzRETUEjRO+qdOnYpDhw41QygNy83NhVwur3WzwdHREVlZWXW+Jisrq872VVVVyM3NbbBNfX0CwKpVq2BjY6N8ODs7P8kpEVEr9+Uv1TuuP+fZEX06cZaQofPsYoNxfRyhEICvDlxv1vcSBAFf/LFj/4vDnNHRpuHyWkREAGBvZw95gVyttooCBezt7BttV15ejvDwcAQHB2PM2DEIDg5GeHg4ysvLmxouEWmJiaYv+PrrrzFt2jQcOXIEnp6eMDU1VXn+rbfe0lpwdRGJRCo/C4JQ61hj7R8/rmmfS5cuxaJFi5Q/FxUVMfEnIo1czSrCrkvVNxdrRojJ8C0Y54b9ydmIu5CBN8e6oWeH5tnJ/9jNBzidmg+piRj/x1F+IlJTQEAAoqOjIcuSqeyS/zhZpgwlKSUIXB7YYH9xcXGYPWc28h/kw9LdEhJbCeT35IiOjsb8hfOxaeOmVrfjPpE+0jjp//nnn5GYmIg2bdrg0KFDtZLn5kr6HRwcIJFIao3A5+Tk1LvUoGPHjnW2NzExQbt27Rps09DyBTMzM5iZ1f9FSUTUmC/3V48E+3l2Qq+OLENjLDw622B8X0fsu5KNf/9yHf9+Uftr+wVBwBf7qkf5XxrWlaP8RKS2adOmYf7C+ciOzIbzPGeIxLUHuQSFgOxt2bBrZ4epU6fW29ejpf/c3nWrs/RfQEBAg6X/iKhlaDy9f9myZfj4449RWFiI1NRU3L59W/m4detWc8QIAJBKpRgyZAj27duncnzfvn0YPnx4na/x8fGp1X7v3r0YOnSocoZCfW3q65OIqKmuZBRhd1IWRCKu5TdG8//4ne68mIHr2cVa7/+3Gw9w5k4+zEzE+L/RPbTePxEZL3Nzc2zauAkl50uQ/nU6ZFkyledlmTKkf52OkvMl2LRxE8zN676pWF5ejtlzZsNyoCWc5znXmjVg1tEMzvOcYTnQErPnzOZUfyId0zjpr6iowPTp0yEWa/zSJlu0aBG+//57hIWFITk5GQsXLkRaWhreeOMNANXT7mfNmqVs/8Ybb+DOnTtYtGgRkpOTERYWhg0bNuCdd95Rtpk/fz727t2LNWvW4OrVq1izZg3279+PBQsWtPTpEVEr8e9fqkf5n+Mov1Hy6GwD376OEATg3wduaLXvR9fyv+TVFY7WHOUnIs34+/sjJiYGkjsSXF9yHakrU5G2Pg2pK1Nxfel1SO5IEBsb2+C0/G3btiH/QT4cQxzrnC0AACKxCI7THJH/IB9RUVHNdTpEpAaNM/dXXnkFERERzRFLo6ZPn45169bh448/xsCBA/Hrr79i165dcHFxAQBkZmYiLS1N2b5bt27YtWsXDh06hIEDB2LFihX497//jeDgYGWb4cOHY+vWrdi4cSP69++PH374AREREfDy8mrx8yMi43c5oxB7LleP8i/gKL/RWjCuep+G+IsZSNHiaP+R67k4WzPK/wxH+YnoyUyZMgUZdzMQHh4OXw9fDG47GL4evggPD0fG3YxG1+HHxsbC0t2ywX0BAMCskxks3S0RExOjzfCJSEMioWZnOzW99dZb2Lx5MwYMGID+/fvX2sjv888/12qAhkDd+ohERK9vPoO9V7LhP8AJXzXDem/SH2+En8Wey1nw698J/3lpcJP7EwQBwd8cw+9pBZgzohs+8O+rhSiJiDQ3ZuwYnHt4Ds5/bXwj67T1aRjcdjAOHjjYApERtS7q5qEab+R36dIlDBpU/YdqUlKSynMN7XhPRNTaJd0rxN4r2dVr+cdyx3VjN3+cG/ZczsKuS5m4llXc5KUcv17Pxe9pBTAzEeONZ7prKUoiIs3Z29lDfk+D0n9dGi/9R0TNR6OkXy6X46OPPoKnpyfs7fk/LxGRJr78Yy2/f38nuDlyLb+x69PJGpM8OmJ3Uhb+/ct1/OflJx/tFwQB6/5Yyz/D2wUduJafiHRI26X/iKh5abSmXyKRYMKECSgsLGyueIiIjFLSvULsqxnlf5aj/K3F/HHV+zYkXMrE1ayiJ+7ncMp9nEsrgLmpGH/hKD8R6di0adNg184O2ZHZEBR1rxRWt/QfETU/jTfy8/T0bNbSfERExmjd/upR/ikDnNCzA0f5W4veHa3xnGdHAH9WbdBU9Y791a+d4eWCDlYc5Sci3dJW6T8iahkaJ/3//Oc/8c477yA+Ph6ZmZkoKipSeRARkapLdwuxPzkbYhHw5lju2N/azH+2eif/XZeykJyp+b+Th1Lu40J6zSg/d+wnIv2gjdJ/RNQyNN7Ib+LEiQCqS308unGfIAgQiUSQy9Xb1IOIqLX48pfqtdjVo/yWOo6GWlqvjlbw8+yEhEuZ+Pcv1/HNjCFqv1YQBKzbV339zPR2QXurhstjERG1pJrSf1FRUYiJiUFefh7su9gjcHkgpk6dyhF+Ij2hcdJ/8CDLbRARqevi3QLsT86BWAS89SxH+Vur+ePcsCspE7uTsnAlowh9ndQr73ro2n1cuFuINqYSjvITkV4yNzfHjBkzMGPGDF2HQkT10Djpf+aZZ5ojDiIio1Szlj9gYGd0b89R/tbK3bF6tD/+Yia+/CUF/5s5tNHXVK/lrx7ln+XjAgdLjvITEVBeXo5t27YhNja2emTdzh4BAQGYNm0aR9aJqE4aJ/0AUFBQgA0bNiA5ORkikQh9+/bFnDlzYGNjo+34iIgM1oX0Ahy4Wj3KP28sd+xv7eY/64aES5lIvJyNyxmF6OfU8L+ZB67m4OIfo/xzR3HHfiIC4uLiMHvObOQ/yIeluyUkthLI78kRHR2N+QvnY9PGTVxDT0S1aLyR35kzZ9CjRw988cUXyMvLQ25uLj7//HP06NEDv//+e3PESERkkGrqqgcM4ig/AW6OVpjc3wkA8OX+hnfyFwRBOUtk1nCO8hNRdcIfGBgIuYscbqvd4Pq+K5z/6gzX913httoNchc5AgICEBcXp+tQiUjPiARBqLu4Zj1GjhyJnj174rvvvoOJSfVEgaqqKrz22mu4desWfv3112YJVJ8VFRXBxsYGhYWFsLZWb50mERm3c2n5CFx/DBKxCPsXPYNuDm11HRLpgevZxfBd9ysEAYh/82l4dK57tH//lWy8tvkMLKQSHHlvDNox6Sdq1crLy+HUxQlyFzmc5zlDJBbVaiMoBKR/nQ7JHQky7mZwqj9RK6BuHvpEI/2LFy9WJvwAYGJigvfeew9nzpx5smiJiIzMl7/8uZafCT/VcHO0gn/NaP8vdY/2C4KAdb/UrOV3ZcJPRNi2bRvyH+TDMcSxzoQfAERiERynOSL/QT6ioqJaOEIi0mcaJ/3W1tZIS0urdTw9PR1WVlZaCYqIyJD9npaPQ9fuQyIW4U2u5afHvPWsG0QiYN+VbCTdK6z1/P7kHCTdK4KFVILXuZafiADExsbC0t0SZh0bvglo1skMlu6WiImJaaHIiMgQaJz0T58+HaGhoYiIiEB6ejru3r2LrVu34rXXXsOLL77YHDESERmUmvXagYM6w5Wj/PSYnh0sMWVA9Wj/usfW9lev5a8e5X9luCvs20pbPD4i0j95+XmQ2ErUaiu2FSMvP6+ZIyIiQ6Jx0v/pp58iKCgIs2bNgqurK1xcXDB79mxMnToVa9asaY4YiYgMxtk7+TicwlF+atibY90gFgH7k7Nx6e6fo/37rmTjckYR2koleH0kR/n1TXl5OcLDwxEcHIwxY8cgODgY4eHhKC8v13VoZOTs7ewhL5Cr1VZRoIC9nX2DbYqLixEaGorExESV44mJiQgNDUVxcfETx0rGj9eP4dE46ZdKpfjyyy+Rn5+P8+fP49y5c8jLy8MXX3wBMzOuOySi1q1mlDZoUGe4tOMoP9VNdbS/+pp5dMf+2SNcYcdRfr0SFxcHpy5OmDVrFvYm7cW5h+ewN2kvZs2aBacuTti5c6euQyQjFhAQgJKUEsiyZA22k2XKUJJSgsDAwHrbFBcXY6LveISFhWGK/2QkJCQAABISEjDFfzLCwsIw0Xc8EzeqE68fw6Rx0l/DwsICnp6e6N+/PywsLLQZExGRQTp7Jw9HrufCRCzCm2PddB0O6bm3nq0e7f/lag4u3i3A3ivZuJJZBEszE7z2NEf59QlLpZGuTZs2DXbt7JAdmQ1BUXfhLUEhIHtbNuza2WHq1Kl1tqlJ2JIunMGRVy0wqYcYQYEBWL58OYICA/BcTzGOvGqBpAtnmLhRLbx+DJfGJfsePnyI1atX45dffkFOTg4UCoXK87du3dJqgIaAJfuIKLOwDK9vPoNL94owfagz1kztr+uQyAAsijiP6HP34NPdHvcKypGWV4p5Y3rinQm9dB0a/YGl0khf7Ny5EwEBAbAcaAnHEEeVTf1kmTJkb8tGyfkSxMbGwt/fv84+QkNDERYWhiOvWuDpriaokAsIiZJhx9UKBPSRIiLYDFKJCEfTqjByYynmzJmDDRs2tNQpkp7j9aN/1M1DTep9ph6vvfYaDh8+jJkzZ6JTp04QieouG0JE1FpEnE7DkuhLqLmF6urA2U+knnljeyLm3D0cv/XnplvtLDmtX5/UlEpze9et0VJp15deR1RUFGbMmNHCUVJr4O/vj5iYGMyeMxvXl1yHpbslxLZiKAoUKEkpgV07uwYTfgAICQnBj+Gb8dmJKgzrLIFUIkLkVDMkpEjg524CqUSECrmAT49XQWpqgpCQkBY8Q9J3vH4Ml8Yj/ba2tkhISMCIESOaKyaDw5F+otYrs7AMI1YfwKOzLSUiEY4uGYNONm10FxgZhMzCMvisOqByjNePfgkODsbepL1wfd+10bapK1Ph6+GL7du3N39g1GqVl5cjKioKMTExyMvPg72dPQIDAzF16lS1ZpkkJCQop2LXjMzWqBm53X1TgeiYWPj5+TXnqZAB4vWjX9TNQzVe029nZwd7+4Z3BCUiai3O3snH48sr5YKA1NxS3QREBuV27sNax3j96BeWSiN9Y25ujhkzZmD79u04eOAgtm/fjhkzZqi9rMTPzw/vLV6C2OQKJKRUqTyXkFKFHVcr8N7iJUzYqE68fgyTxkn/ihUr8MEHH6C0tGX/IMnPz8fMmTNhY2MDGxsbzJw5EwUFBfW2r6ysxOLFi+Hp6Ym2bdvCyal6x92MjAyVdqNHj4ZIJFJ5vPDCC818NkRkDE7eeoDlsUm1jktEIk7xJ7V0c2iLx2eM8/rRL9oulQZor/QfSwg2TN8+H32JJyEhAWvXrEZAHyn83FVX+vq5m+D53lKsXbNauSs70aN4/Rgmjaf3Dxo0CDdv3oQgCHB1dYWpqanK87///rtWA6wxadIk3L17F99++y0A4PXXX4erq2u9JXIKCwsxdepUzJ07FwMGDEB+fj4WLFiAqqoqnDlzRtlu9OjRcHd3x8cff6w81qZNG9jY2KgdG6f3E7UugiBg8/E7WBF/BVUKAR2tzZBTLINCqE7YVgZ5YPpTXXUdJhmIiNNpeD86CXJB4PWjh8LDwzFr1iy4rXZT2TjtcbJMGa4vvY7w8PAG1/THxcVh9pzZyH+QD0t3S0hsJZAXyJVrsjdt3NTgmmxt92Os9O3z0Zd4EhMTMcV/ssrU7Aq5gISUKpU12TVTtON2xmPChAnNHhcZBl4/+kfdPFTjpP8f//hHg89/+OGHmnSnluTkZPTt2xcnTpyAl5cXAODEiRPw8fHB1atX0auXerscnz59GsOGDcOdO3fQtWv1H1SjR4/GwIEDsW7duieOj0k/UetRXinH+zGXEP37PQDAlAFOWB3sicKySqTmlsLVwYJrsUljmYVlvH70lDZ3768p/Vfn7utZMmRHVu++HhMTgylTptQbk7b6MVb69vnoUzzcfZ2agteP/mm2pF8XwsLCsGjRolrT+W1tbfHFF1/g1VdfVauf/fv3w9fXFwUFBcoPZfTo0bh8+TIEQYCjoyMmTZqEDz/8EFZWVvX2I5PJIJPJlD8XFRXB2dmZST+RkbtXUIa/hJ9B0r0iiEXA+8/1QejT3VjFhMjIaaNUmrZuHrCEYMP07fPRt3gerbOe8IIZPj1ehd03FXhv8RKsXbMaz/UU421vE/htlcFjwFDs2buvwb+JqXXh9aN/mm0jP13IyspChw4dah3v0KEDsrKy1OqjvLwcS5YswUsvvaTygbz88svYsmULDh06hOXLl2P79u0ICgpqsK9Vq1Yp9xawsbGBs7OzZidERAbn2M1c+H91FEn3imBnYYofQ73w2sjuTPiJWoGaUmmSOxJcX3IdqStTkbY+DakrU3F96XVI7kgaLZVWU/rPMcSx0dJ/+Q/yERUV1az9GCt9+3z0LR4rKyvs2bsPHgOGYuTGUuUu6ytWrEB0TCx23VBg5MZSJmxUJ14/hkutpN/e3h65ublqd9q1a1fcuXOn0XYfffRRrU30Hn/UrL+v6w9rQRDU+oO7srISL7zwAhQKBdavX6/y3Ny5czFu3Dh4eHjghRdeQFRUFPbv39/g3gRLly5FYWGh8pGent5oDERkmARBwPdHbmHmhlPIe1iBfk7W2Pnm0xje00HXoRFRC5oyZQoy7mYgPDwcvh6+GNx2MHw9fBEeHo6MuxmNrseOjY2Fpbtlg/sCAIBZJzNYulsiJiamWfsxVvr2+ehbPMCfiducOXMQtzNeucu6n58f4nbGY86cOUzYqF68fgyTSeNNgIKCAuzevVvtze0ePHgAubzxnW7nzZvX6E75rq6uuHjxIrKzs2s9d//+fTg6Ojb4+srKSoSEhOD27ds4cOBAo9PvBw8eDFNTU1y/fh2DBw+us42ZmRnMzBr+8iYiw1dWIceS6IvYcb666kfQoM5YGeQJc1P1yncRkXGpKZXW0EZ99dFW6T+WEGyYvn0++hZPDSsrqzrXWk+YMIEbr1GjeP0YHrWSfgB45ZVXtP7mDg4OcHBofLTMx8cHhYWFOHXqFIYNGwYAOHnyJAoLCzF8+PB6X1eT8F+/fh0HDx5Eu3btGn2vy5cvo7KyEp06dVL/RIjI6KTnleL18LNIziyCRCzCcr8+eGW4K6fzE9ETsbezh/yeBqX/utRd+k9b/Rgrfft89C0eImqd1Jrer1AoNH50795da0H26dMHEydOxNy5c3HixAmcOHECc+fOxeTJk1V27u/du7dyWlRVVRWmTp2KM2fO4KeffoJcLkdWVhaysrJQUVEBALh58yY+/vhjnDlzBqmpqdi1axemTZuGQYMGYcSIEVqLn4gMy5Hr9+H/9VEkZxahXVspfnrNC7NHcMM+InpyAQEBKEkpgSxL1mA7WaYMJSklCAwMbNZ+HqWt+vH60I++fT7ajqe4uBihoaFITExUOZ6YmIjQ0FAUFxc3GhORPtGH743WwCB27weAvLw8vPXWW4iLiwNQvbbu66+/hq2trbKNSCTCxo0bMXv2bKSmpqJbt2519nXw4EGMHj0a6enpmDFjBpKSklBSUgJnZ2f4+fnhww8/hL29+ndaWbKPyDgIgoD//XoLa/dchUIABnSxwTczhsDJliXUiKhp9HX3fm3Vj9eXfvTt89FmPDU7px87cRJSUxNEx8TCz88PCQkJCAoMQEVlFYZ7e3E9NRkMffneMGRGVbJP3zHpJzJ8D2VVeG/7RSRczAQATBvSBSsCPLh+n4i0Rhul/7TZj7bqx+tbP/r2+WgjHpZKI2Ojb98bhopJfwti0k9kuDILy3Di5gN8deAGbuU+hKlEhA/8+2GGV1dO5ycirXt8REpsK4aiQNHkkS1N+9G3mQfNPUKvq89HW/GEhoYiLCwMR161wNNdTVAhFxASJcOOqxUI6CNFRLAZpBIRjqZVYeTGUsyZM6fOjdaI9IG+fm8YInXzULXW9BMRGaOI02kYvvoAFkZewK3ch7A0M8GWud6Y6e3ChJ+ImkVTS/9pqx9t1Y/Xt35q6Mvno614QkJCIDU1wWcnqlAhFyCViBA51QzRIW2UCX+FXMCnx6sgNTVBSEhIg/0R6ZK+fm8YM470awFH+okMT2ZhGUasPgDFI9+AYhHw25Kx6GTDNfxEZNyCg4OxN2kvXN93bbRt6spU+Hr4Yvv27Xrfj7boWzwAlGv3n+spVib6NWpG/nffVCjX+hPpK2P93tAFdfNQtUv2PUoulyMmJgbJyckQiUTo3bs3AgICYGLyRN0REbW42/cfqiT8AKAQgNTcUib9RGT0tFU/Xt/60RZ9iwcA/Pz88N7iJfjkk0+QkCJBYB9T5XMJKVXYcbUCy5YtY8JPes9Yvzf0mcZZelJSEp5//nlkZWUpy+WlpKSgffv2iIuLg6enp9aDJCLStqtZRbWOSUQiuDpY6CAaIqKWpa368frWj7boWzxA9Uj/2jWrEdBHCj931T/h/dxN8HxvKdauWQ1vb28m/qTX9Pl7o7y8HNu2bUNsbCzy8vNgb2ePgIAATJs2zaD3A9B4Tf9rr72Gfv364e7du/j999/x+++/Iz09Hf3798frr7/eHDESEWlV3sMKfH3wJgCgZnKkRCTCyiAPjvITUaugrfrx+taPtuhbPImJibWm9lfIBcQkV6qs8Z/UQ4ygwAAkJiY2azxETaGv3xtxcXFw6uKEWbNmYW/SXpx7eA57k/Zi1qxZcOrihJ07dzZ8YnpM4zX9bdq0wZkzZ9CvXz+V40lJSXjqqadQVlam1QANAdf0ExmWd7ZdQNTZu+jlaIXvXhmCe/nlcHWwYMJPRK2Gvu2erW+7cOtbPNy9n4yJPn5vGGrpv2bbvb9Xr17Izs6udTwnJwc9e/bUtDsiohZ17GYuos7ehUgErAzyRFf7tvDp0Y4JPxG1Kubm5ti0cRNKzpcg/ev0WiNlskwZ0r9OR8n5EmzauKneP5T1rR9t0bd41q1bh+HeXvDbKsPRtCrlpn3Lli3DrhsKTN9efdxvqwzDvb2wbt26Zo2HqCn07XujvLwcs+fMhuVASzjPc1ZJ+AHArKMZnOc5w3KgJWbPmY3y8vImnL2OCBpKSEgQ+vXrJ2zbtk1IT08X0tPThW3btgmenp5CQkKCUFhYqHy0FoWFhQKAVnXORIaorKJKGPOvg4LL4njh7zEXdR0OEZHO7dixQ7BrZycAECzdLQXrYdaCpbulAECwa2cnxMXFGWQ/2qJP8RQVFQnDvb0EAILU1ESIj48XBEEQ4uPjBampiQBAGO7tJRQVFbVYTERNoS/fG5s3bxYACG6r3QSPHzzqfbitchMACOHh4do4fa1QNw/VeHq/WPzn5ICaOtY1XTz6s0gkglyu3sYKho7T+4kMw+f7UvDvX66jvZUZ9i96BjZtTBt/ERGRkSsvL0dUVBRiYmKUG1cFBgZi6tSpGo1g61s/2qJP8RQXF2PBggUICQnBhAkTlMcTExMRGRmJdevWwcrKqkVjImoKffjeMOTSf+rmoRon/YcPH1a77TPPPKNJ1waLST+R/ruRU4xJXx5BpVzAf14aDL/+nXQdEhERERHp2JixY3Du4Tk4/9W50bZp69MwuO1gHDxwsAUia5y6eajGJftaSyJPRMZDoRDwfnQSKuUCxvbugOc8O+o6JCIiIiLSA/pYolPbNE76gerpExcvXkROTg4UCoXKc/q0myEREQBsO5uOU6l5aGMqwcfP91MuRSIiIiKi1i0gIADR0dGQZclqbeL3KGXpv+XNW6KzOWg8vX/Pnj2YNWsWcnNza3fWitbxP4rT+4n0V26JDM9+dhiFZZX4+3N9MHdUd12HRERERER6Qt9KdGqi2Ur2zZs3D9OmTUNmZiYUCoXKozUm/ESk31bEX0FhWSX6OVnj1RGuug6HiIiIiPSIvpXobA4aT+/PycnBokWL4Ojo2BzxEBFpza8p97HjfAbEImBVkCdMJBrf5yQiIiIiI+fv74+YmBjMnjMb15dch6W7JcS2YigKFChJKYFdOzvExsbC399f16E+EY2T/qlTp+LQoUPo0aNHc8RDRKQVZRVyLItNAgC8MtwV/bvY6jYgIiIiItJbU6ZMQcbdDNXSf13sEbhcdyVDtUXjNf2lpaWYNm0a2rdvD09PT5iaqta5fuutt7QaoCHgmn4i/bNmz1V8c+gmOtmYY9+iZ2Bp9kT7lhIRERER6aVmK9n3888/IzExEW3atMGhQ4dUdsEWiUStMuknIv1yNasI3/16CwDwjyn9mPATERERUaul8V/Cy5Ytw8cff4wlS5ZALOb6WCLSLwqFgKXRl1ClEDChnyN8+3XUdUhERERERDqjcdZeUVGB6dOnM+EnIr3006k0nEsrgKWZCf4xxUPX4RARERER6ZTGmfsrr7yCiIiI5oilQfn5+Zg5cyZsbGxgY2ODmTNnoqCgoMHXzJ49GyKRSOXh7e2t0kYmk+HNN9+Eg4MD2rZtiylTpuDu3bvNeCZE1Fyyi8qxdvdVAMA7vu7oaGO4G64QEREREWmDxtP75XI51q5di8TERPTv37/WRn6ff/651oJ71EsvvYS7d+9iz549AIDXX38dM2fOxM6dOxt83cSJE7Fx40blz1KpVOX5BQsWYOfOndi6dSvatWuHt99+G5MnT8bZs2chkUi0fyJE1Gw+3nkFxbIqDHC2xUwfV12HQ0RERESkcxon/ZcuXcKgQYMAAElJSSrPPbqpnzYlJydjz549OHHiBLy8vAAA3333HXx8fHDt2jX06tWr3teamZmhY8e61/QWFhZiw4YNCA8Px7hx4wAAP/74I5ydnbF//35MmDBB+ydDRM3iwNVsJFzKhEQswqpAT0jEzfN9RERERERkSDRO+g8ePNgccTTo+PHjsLGxUSb8AODt7Q0bGxscO3aswaT/0KFD6NChA2xtbfHMM8/gn//8Jzp06AAAOHv2LCorK+Hr66ts7+TkBA8PDxw7doxJP5GBeCirwvLYywCA157uhr5OLJ1JRERERAQ8wZr+Gjdu3EBiYiLKysoAAIIgaC2ox2VlZSkT9Ud16NABWVlZ9b5u0qRJ+Omnn3DgwAF89tlnOH36NMaOHQuZTKbsVyqVws7OTuV1jo6ODfYrk8lQVFSk8iAi3fliXwruFZShs20bzB/nputwiIhaneLiYoSGhiIxMVHleGJiIkJDQ1FcXGyQ/WiLvsVDRK2MoKHc3Fxh7NixgkgkEsRisXDz5k1BEARhzpw5wqJFizTq68MPPxQANPg4ffq08M9//lNwd3ev9fqePXsKq1atUvv9MjIyBFNTU2H79u2CIAjCTz/9JEil0lrtxo0bJ/zlL3/ROO7CwkK1YyEi7bh0t0DotiRecFkcLxy4mq3rcIiIWp2ioiJhuLeXAECQmpoI8fHxgiAIQnx8vCA1NREACMO9vYSioiKD6kdb9C0eIjIehYWFauWhGo/0L1y4EKampkhLS4OFhYXy+PTp05Wb7Klr3rx5SE5ObvDh4eGBjh07Ijs7u9br79+/D0dHR7Xfr1OnTnBxccH169cBAB07dkRFRQXy8/NV2uXk5DTY79KlS1FYWKh8pKenqx0DEWmPXCHg/ZhLUAjA5P6dMKZX7RlBRETUfIqLizHRdzySLpzBkVctMKmHGEGBAVi+fDmCAgPwXE8xjrxqgaQLZzDRd3y9I9r61o+26Fs8RNRKaXo3wdHRUTh//rwgCIJgaWmpHOm/deuW0LZt2ye4P9G4K1euCACEkydPKo+dOHFCACBcvXpV7X5yc3MFMzMzYdOmTYIgCEJBQYFgamoqREREKNtkZGQIYrFY2LNnj9r9qnuHhUgTGQWlwm837gsZBaVG1Y+2ZBSUCstiLgkui+MFjw/3CNlFZboOiYio1ZkzZ44AQDjyqoUgfGgtyJZZCc/3lgoAhIA+UkG2zEoQPrQWjrxqIQAQ5syZYxD9aIu+xUNExqXZRvofPnyoMsJfIzc3F2ZmZk9046Exffr0wcSJEzF37lycOHECJ06cwNy5czF58mSVTfx69+6NmJgYAEBJSQneeecdHD9+HKmpqTh06BD8/f3h4OCAwMBAAICNjQ1CQ0Px9ttv45dffsG5c+cwY8YMeHp6KnfzJ9KFiNNpGLH6AF767iRGrD6AiNNpRtGPttTEE37iDgBgXJ8O6GBlrtOYiIhao5CQEEhNTfDZiSpUyAVIJSJETjVDdEgbRASbQSoRoUIu4NPjVZCamiAkJMQg+tEWfYuHiFonkSBotgOfn58fBg8ejBUrVsDKygoXL16Ei4sLXnjhBSgUCkRFRTVLoHl5eXjrrbcQFxcHAJgyZQq+/vpr2NraKtuIRCJs3LgRs2fPRllZGQICAnDu3DkUFBSgU6dOGDNmDFasWAFnZ2fla8rLy/Huu+/i559/RllZGZ599lmsX79epU1jioqKYGNjg8LCQlhbc9dwapqMglKMWHMQj/+f6eFkDamJ+vfpKqoUSMqovcmkNvqRiEQ4umQMOtm0UbsfbcksLMOI1QegeOTzkYiAo0vG6iQeIqLWLiEhQTlVvSaRrVEhFxASJcPumwpEx8TCz8/PYPrRFn2Lh4iMh7p5qMZJ/5UrVzB69GgMGTIEBw4cwJQpU3D58mXk5eXht99+Q48ePZocvKFh0k/aUFhWidhz9/D9kVtIzy/TdTiN2jLXGz492rX4++68kIE3t5zTm3iIiAhYvnw5PvnkE0SHtEFgH1Pl8ZjkSgRFlmHZsmVYsWKFwfWjLfoWDxEZh2ZL+oHqUnfffPMNzp49C4VCgcGDB+Nvf/sbOnXq1KSgDRWTfnpSgiDg97R8/HwyHQmXMlBeqaiznVgErAz0hF1bqdp95z+swNLoS3j0f3Bt9QMA/3i+H17xcVW7H204fvMB/u/Hsygoq1Q5rsuZB0REmiguLsaCBQsQEhKCCRMmKI8nJiYiMjIS69atg5WVlUH1o28j9Po2sq5v8RCR8Wi2pD8tLQ3Ozs4QiUR1Pte1a1fNozVwTPpJU4WllYg+dxdbTqUhJbtEebyXoxVeHOYMAcAn8cmQCwIkIhFWBnlg+lOa/78VcToN70cnabWfR80d2Q2LJ/aGiUTj7UE0IggCNv6Win/uSoZcIaCTjTmyi8qhENCk8yIiakk1O7kfO3ESUlMTZZJXkxRWVFZhuLcX9uzd12CirU/9JCYmYor/ZJWEtkIuICGlCn7uJsqfaxLbuJ3xKjcX9LUfbdG3eIjIuKidh2q6Q6BYLBays2vXws7NzRXEYrGm3RkF7t5P6lAoFMKp2w+EhVvPCe5/3yW4LK6uLd9r2S7h7cjzwpnUPEGhUCjbZxSUCsdu5Gpl131t9pOe91BYvTtZGf+L3x4XHpTImtR3Q0plVcKCreeU77dg6zmhVFaltfMiImoJNbXardtIhCOvWgjP95YKUlMTYdmyZYLU1EQI6CMVjrxqIVi3kTRYs13f+tG3Xff1bbd8fYuHiIyLunmoxkm/SCQScnJyah1PTU0VLCwsNO3OKDDp1y19K0n3eD95JTLh+yO3hGc/O6RMXF0WxwsTvjgsbDp2WygorWjS++lKwsUMoc/y3YLL4nhh+KpfhEt3C7T+HmkPHgrPffmr4LI4Xui+NEEIO3pL5cYIEZGh0LekVlv96NtNCG31oy36Fg8RGRd181C1p/cvWrQIAPDll19i7ty5KmX75HI5Tp48CYlEgt9++02tqQjGhNP7dSfidBqWRl+CQqher74qyPOJp69rux+RCBjQxQZXMotRUVW9Vr+NqQRTBjjhRa+uGNDFps5lMobkWlYx/hJ+BqkPSmFmIsaqIE8EDe6ilb6PXs/Fm1t+R35pJdq1leLrlwZzoz4iMlj6Nn1dm9PO9Wm5gTb70RZ9i4eIjIfW1/SPGTMGAHD48GH4+PhAKv1zIzCpVApXV1e88847cHNza2LohodJv27UVbpNBGBy/06wkJqo3U9pRRV2XsxUOaatfmr07WSNl7y64vmBTrAyN62zjaEqLKvEgq3ncPDafQDA7OGu+LtfH5g+4Tp/QRDw3ZFbWL37KhQC0L+LDf47YwicbLlJHxEZNn3bqE6bG8zp08aC2uxHW/QtHiIyDs22kd+rr76KL7/8ksntI5j068axm7l46buTug6jUf8M8MBLXl0NflS/IQqFgHX7U/DvAzcAAMO62WP9y4PhYGmmUT+lFVV4L+oi4v+4eTJtSBesCPCAualE6zETEemCvpWkYyk5IiLD1awl+0gVk37duJdfihFrDqocEwH4yzPdNRpNLy6vxP8O31IpSaetflpbKbnEy1l4O/ICSmRV6GRjjv/OGIIBzrZqvfbOg4f4S/hZXM0qholYhA/9+2KGt4tR3ywhotZF30boWUqOiMiwMelvQUz6dWPLqer18zX0rbRday0ldyOnBK+Hn8Gt+w8hNRHjkwAPhAx1bvA1h67l4K0t51BUXgUHSzN8M2MwnnK1b6GIiUgfGds0b31bi89ScurRl+uHiKguzVayj2rj7v0tL7uoTPD8cI/gsjhe+Czxql6WtmvNpeSKyiqE1zadVlYqWBZzSZBVymu1UygUwtcHrguuS6rbBfznqJBZUKaDiIlIn9TseA5AkJqaCPHx8YIgCEJ8fLwgNTURAKi107m2+tEGfdt1n6XkGqdP1w8RUV2arWQf1cakv+W9+fPvgsvieMHv378KlVW1k0nSPblcIXy5P0WZ0Aev/03ILvozoS8urxTeCD+jvDGwZPtFobyySocRE5E+YOm21lnaTt/w8yEiQ8CkvwUZStKvb/Xsn9TBq9mCy+J4oduSeOFiuvZrw5N2/ZKcJXj8MStj2D/3CYmXM4XtZ9OFZ9YeEFwWxwtu7+8Sfj55R9dhEpGeMOaRbH2bwcCR7Prp4/VDRPQ4dfNQrunXAkNY0x9xOg1Ltl+CgOpN6p7z7ISBam6w9qjz6QXYdSkTAppWz/5JlVXI4bvuMNLzyjBnRDd84N+3xd6bntzt3Id4ffMZXM8pUTlubW6CH+YMw+CudjqKjIj0jbGvWde3vQq4Zr1u+nr9EBE9ihv5tSB9T/rrqmevLS29O/2q3cn43+FbcLIxx75Fz6CtmUmLvC813Y2cEoz7/LDKMbEI+G3J2FZT3YCI1MPd6Ukf8PohIn2nbh7KjKkVuJ37sM6Ef2RPB7S3Ur+O+v1iGY7cyFU5JhcEpOaWtkjSlpxZhO+P3AYAfPy8BxN+A5NTXF7rmEJAi10/RGQ4/Pz88N7iJfjkk0+QkCJRqR+fkFKFHVcrsGzZskYTLW31Q60Trx8iMhbMmlqBbg5tIRZBJfGXiERYO62/RslWfTMG5M0xheAxcoWApdGXIFcImNivI8b1dWz29yTtqu86dHWw0F1QRKSXEhISsHbNagT0kcLPXfVPFT93EzzfW4q1a1bD29u70ZF+bfRDrROvHyIyFmJdB0DNr5NNG6wK8oREVD0traZ+vKajq4/3U2Px9ovILZFpLd66/HTyDs6nF8DSzAQfTenXrO9FzUNb1yERGbfExMRaU6or5AJikitRIRcglYgQOdUMk3qIERQYgMTExGbth1onXj9EZEw40t9KTH+qK0a5t0dqbilcHSyeONF6tB87C1O88eNZpD4oxV/Cz+LnuV4wM5FoOXIgq7Aca/dcAwC8N7EXOtqYa/09qGVo6zokIuMVGRmJisoqvO1tobJZ2o6rFQjoI1UmYO/4mGDH1VJERkbWuYGatvqh1onXDxEZE27kpwX6vpFfc7qRU4LA9b+huLwKQYM747NpAyB6bCZAU/3fj2exOykLA51tsf3/hkMi1m7/RESkP4qLizHRdzySLpxBwgtm+PR4FXbfVOC9xUuwds1qPNdTjLe9TeC3VQaPAUOxZ+++OneX11Y/1Drx+iEiQ8Dd+1tQa076AeDI9fuYvfE05AoB703shb+O7qm1vvdfycZrm89AIhYh/s2n0adT6/t8iYham5qE69iJk5Camih3R6/ZTb2isgrDvb0aTbS01Q+1Trx+iEjfMelvQa096QeA8OOpWL7jMgDgvzOGYKJHxyb3+VBWhfGfH0ZGYTn+8kx3LJ3Up8l9EhGRYWAdetIHvH6ISJ8ZXdKfn5+Pt956C3FxcQCAKVOm4KuvvoKtrW29r6lvmvnatWvx7rvvAgBGjx6Nw4dVa4dPnz4dW7duVTs2Jv3VPtyRhE3H76CNqQTb3vCBR2ebJvX38c4rCPvtNpzt22DvgmfQRqr9/QKIiIiIiIgMkbp5qMHs3v/SSy/h/Pnz2LNnD/bs2YPz589j5syZDb4mMzNT5REWFgaRSITg4GCVdnPnzlVp97///a85T8VoLZ/cFyPdHFBWKcfczWeQU1S7Lru6Lt0txA/HbgMAVjzvwYSfiIiIiIjoCRjE7v3JycnYs2cPTpw4AS8vLwDAd999Bx8fH1y7dg29evWq83UdO6pOMd+xYwfGjBmD7t27qxy3sLCo1ZY0ZyIR4+uXBiNo/W+4ef8h5oafRcTr3jA31Sxhr5IrsDTmIhQC4D/ACaN7dWimiImIiIiIiIybQYz0Hz9+HDY2NsqEHwC8vb1hY2ODY8eOqdVHdnY2EhISEBoaWuu5n376CQ4ODujXrx/eeecdFBcXay321samjSk2vPIUbC1McSG9AO9GXYSmK0h+OJaKpHtFsDY3wQeT+zZTpERERERERMbPIEb6s7Ky0KFD7dHeDh06ICsrS60+Nm3aBCsrKwQFBakcf/nll9GtWzd07NgRSUlJWLp0KS5cuIB9+/bV25dMJoNMJlP+XFRUpOaZtA6uDm3xzctDMHPDSey8kIGe7S0xf5ybWq+9V1CGz/elAACWPtcH7a3MmjNUIiIiIiIio6bTkf6PPvoIIpGowceZM2cA1L0pnyAIateEDwsLw8svvwxzc3OV43PnzsW4cePg4eGBF154AVFRUdi/fz9+//33evtatWoVbGxslA9nZ2cNzrp18OnRDv8M9AAAfLE/BfEXMxp9jSAI+CA2CaUVcjzlaofpQ/m5EhERERERNYVOR/rnzZuHF154ocE2rq6uuHjxIrKzs2s9d//+fTg6Ojb6PkeOHMG1a9cQERHRaNvBgwfD1NQU169fx+DBg+tss3TpUixatEj5c1FRERP/Okx/qiuuZ5fg+6O38XbkBTjbWWCAs2297fckZeGXqzkwlYiwMtATYrF6N3SIiIiIiIiobjpN+h0cHODg4NBoOx8fHxQWFuLUqVMYNmwYAODkyZMoLCzE8OHDG339hg0bMGTIEAwYMKDRtpcvX0ZlZSU6depUbxszMzOYmXHauTqWPtcHt3If4sDVHMzdfAY75o1AJ5s2tdoVlVfiw7jLAIA3nukBN0fWvCUiIiIiImoqg9jIr0+fPpg4cSLmzp2LEydO4MSJE5g7dy4mT56ssnN/7969ERMTo/LaoqIibNu2Da+99lqtfm/evImPP/4YZ86cQWpqKnbt2oVp06Zh0KBBGDFiRLOfV2sgEYvw5QsD0cvRCjnFMszdfAalFVW12v1rzzXkFMvQzaEt/jampw4iJSIiql9xcTFCQ0ORmJiocjwxMRGhoaHcBJiIiPSWQST9QPUO+56envD19YWvry/69++P8PBwlTbXrl1DYWGhyrGtW7dCEAS8+OKLtfqUSqX45ZdfMGHCBPTq1QtvvfUWfH19sX//fkgkrAuvLVbmpvj+laFo11aKpHtFWBRxAQrFnzv6/56Wjx9P3gEA/DPAQ+MSf0RERM2puLgYE33HIywsDFP8JyMhIQEAkJCQgCn+kxEWFoaJvuOZ+BMRkV4SCZrWU6NaioqKYGNjg8LCQlhbW+s6HL11JjUPL313EhVyBeaN6Yl3JvRCpVwB/6+O4mpWMYIGd8bnIQN1HSYREZFSTcKfdOEMEl4ww6fHq7D7pgLvLV6CtWtW47meYrztbQK/rTJ4DBiKPXv3wcqKS9SIiKj5qZuHGsxIPxm+oa72WB3sCQD4+uAN/PDbbSyPTcLVrGLYWZhimV9fHUdIRESkasGCBTh24iQSXjDD011NEDnVDJN6iPHJJ5/guZ5iRARXH094wQzHTpzEggULdB0yERGRCib91KKCBnfBX0f3AAB8tPMKtp5OBwCM6+sI+7ZSXYZGRERUS0hICKSmJvjsRBUq5AKkEhEip5ohOqQNIoLNIJWIUCEX8OnxKkhNTRASEqLrkImIiFQw6acW97JX11rHos/eRWZhmQ6iISIiqt+ECRMQHROLXTcUmL5dpkz8A/uYKhP+kCgZdt9UIDomFhMmTNB1yERERCqY9FOLu5NXWuuYXABSc2sfJyIi0jU/Pz+8t3gJYpMrkJCiWoEmIaUKO65W4L3FS+Dn56ejCImIiOrHpJ9aXDeHthCLVI9JRCK4OljoJiAiIqIGJCQkYO2a1QjoI4Wfu4nKc37uJni+txRr16xW7upPRESkT5j0U4vrZNMGq4I8IRFVZ/4SkQgrgzzQyaaNjiMjIiJSlZiYiKDAAOWmfTVT+mOSK1XW+E/qIUZQYAASExN1HTIREZEKk8abEGnf9Ke6YpR7e6TmlsLVwYIJPxER6aXIyEhUVFbhbW8LlTX8O65WIKCPVHkj4B0fE+y4WorIyEiu6yciIr3CkX7SmU42beDTox0TfiIi0lvr1q3DcG8v+G2V4WhalXLTvmXLlik39zuaVgW/rTIM9/bCunXrdB0yERGRCo70ExEREdXDysoKe/buw0Tf8Ri58SSkpiaIjomFn58fvL29ERQYgNjkUgz39sKevftgZWWl65CJiIhUcKSfiIiIqAE1if+cOXMQtzNeuUu/n58f4nbGY86cOUz4iYhIb4kEQRB0HYShKywshK2tLdLT02Ftba3rcIiIiIiIiMjIFRUVwdnZGQUFBbCxsam3Haf3a0FxcTEAwNnZWceREBERERERUWtSXFzcYNLPkX4tUCgUyMjIgJWVFUQiUeMv0JGaO0GckUDGgNczGRNez2RMeD2TMeH1TPpMEAQUFxfDyckJYnH9K/c50q8FYrEYXbp00XUYarO2tuaXFhkNXs9kTHg9kzHh9UzGhNcz6auGRvhrcCM/IiIiIiIiIiPFpJ+IiIiIiIjISDHpb0XMzMzw4YcfwszMTNehEDUZr2cyJryeyZjweiZjwuuZjAE38iMiIiIiIiIyUhzpJyIiIiIiIjJSTPqJiIiIiIiIjBSTfiIiIiIiIiIjxaSfiIiIiIiIyEgx6W9F1q9fj27dusHc3BxDhgzBkSNHdB0SUaN+/fVX+Pv7w8nJCSKRCLGxsSrPC4KAjz76CE5OTmjTpg1Gjx6Ny5cv6yZYogasWrUKTz31FKysrNChQwcEBATg2rVrKm14PZOh+Oabb9C/f39YW1vD2toaPj4+2L17t/J5XstkyFatWgWRSIQFCxYoj/GaJkPGpL+ViIiIwIIFC/D3v/8d586dw8iRIzFp0iSkpaXpOjSiBj18+BADBgzA119/Xefza9euxeeff46vv/4ap0+fRseOHTF+/HgUFxe3cKREDTt8+DD+9re/4cSJE9i3bx+qqqrg6+uLhw8fKtvweiZD0aVLF6xevRpnzpzBmTNnMHbsWDz//PPKJIjXMhmq06dP49tvv0X//v1VjvOaJoMmUKswbNgw4Y033lA51rt3b2HJkiU6iohIcwCEmJgY5c8KhULo2LGjsHr1auWx8vJywcbGRvjvf/+rgwiJ1JeTkyMAEA4fPiwIAq9nMnx2dnbC999/z2uZDFZxcbHg5uYm7Nu3T3jmmWeE+fPnC4LA72cyfBzpbwUqKipw9uxZ+Pr6qhz39fXFsWPHdBQVUdPdvn0bWVlZKte2mZkZnnnmGV7bpPcKCwsBAPb29gB4PZPhksvl2Lp1Kx4+fAgfHx9ey2Sw/va3v8HPzw/jxo1TOc5rmgydia4DoOaXm5sLuVwOR0dHleOOjo7IysrSUVRETVdz/dZ1bd+5c0cXIRGpRRAELFq0CE8//TQ8PDwA8Homw3Pp0iX4+PigvLwclpaWiImJQd++fZVJEK9lMiRbt27F77//jtOnT9d6jt/PZOiY9LciIpFI5WdBEGodIzJEvLbJ0MybNw8XL17E0aNHaz3H65kMRa9evXD+/HkUFBRg+/bteOWVV3D48GHl87yWyVCkp6dj/vz52Lt3L8zNzettx2uaDBWn97cCDg4OkEgktUb1c3Jyat2xJDIkHTt2BABe22RQ3nzzTcTFxeHgwYPo0qWL8jivZzI0UqkUPXv2xNChQ7Fq1SoMGDAAX375Ja9lMjhnz55FTk4OhgwZAhMTE5iYmODw4cP497//DRMTE+V1y2uaDBWT/lZAKpViyJAh2Ldvn8rxffv2Yfjw4TqKiqjpunXrho4dO6pc2xUVFTh8+DCvbdI7giBg3rx5iI6OxoEDB9CtWzeV53k9k6ETBAEymYzXMhmcZ599FpcuXcL58+eVj6FDh+Lll1/G+fPn0b17d17TZNA4vb+VWLRoEWbOnImhQ4fCx8cH3377LdLS0vDGG2/oOjSiBpWUlODGjRvKn2/fvo3z58/D3t4eXbt2xYIFC7By5Uq4ubnBzc0NK1euhIWFBV566SUdRk1U29/+9jf8/PPP2LFjB6ysrJQjRjY2NmjTpo2yJjSvZzIE77//PiZNmgRnZ2cUFxdj69atOHToEPbs2cNrmQyOlZWVcn+VGm3btkW7du2Ux3lNkyFj0t9KTJ8+HQ8ePMDHH3+MzMxMeHh4YNeuXXBxcdF1aEQNOnPmDMaMGaP8edGiRQCAV155BT/88APee+89lJWV4a9//Svy8/Ph5eWFvXv3wsrKSlchE9Xpm2++AQCMHj1a5fjGjRsxe/ZsAOD1TAYjOzsbM2fORGZmJmxsbNC/f3/s2bMH48ePB8BrmYwPr2kyZCJBEARdB0FERERERERE2sc1/URERERERERGikk/ERERERERkZFi0k9ERERERERkpJj0ExERERERERkpJv1ERERERERERopJPxEREREREZGRYtJPREREREREZKSY9BMREREREREZKSb9REREREREREaKST8RERERERGRkWLST0RERERERGSkmPQTERERERERGSkm/URERERERERGyuCS/vXr16Nbt24wNzfHkCFDcOTIkXrbHjp0CCKRqNbj6tWrKu22b9+Ovn37wszMDH379kVMTExznwYRERERERFRszPRdQCaiIiIwIIFC7B+/XqMGDEC//vf/zBp0iRcuXIFXbt2rfd1165dg7W1tfLn9u3bK//7+PHjmD59OlasWIHAwEDExMQgJCQER48ehZeXl1pxKRQKZGRkwMrKCiKR6MlPkIiIiIiIiEgNgiCguLgYTk5OEIvrH88XCYIgtGBcTeLl5YXBgwfjm2++UR7r06cPAgICsGrVqlrtDx06hDFjxiA/Px+2trZ19jl9+nQUFRVh9+7dymMTJ06EnZ0dtmzZolZcd+/ehbOzs2YnQ0RERERERNRE6enp6NKlS73PG8xIf0VFBc6ePYslS5aoHPf19cWxY8cafO2gQYNQXl6Ovn37YtmyZRgzZozyuePHj2PhwoUq7SdMmIB169bV259MJoNMJlP+XHPfJD09XWVGAREREREREVFzKCoqgrOzM6ysrBpsZzBJf25uLuRyORwdHVWOOzo6Iisrq87XdOrUCd9++y2GDBkCmUyG8PBwPPvsszh06BBGjRoFAMjKytKoTwBYtWoV/vGPf9Q6bm1tzaSfiIiIiEgDxcXFWLBgAUJCQjBhwgTl8cTERERGRmLdunWNJjU1/bz55pto3749bt26hbz8PNjb2aN79+64f/8+vvrqK7X6ITI0jS0xN5ikv8bjJyQIQr0n2atXL/Tq1Uv5s4+PD9LT0/Hpp58qk35N+wSApUuXYtGiRcqfa+6wEBERERGR+oqLizHRdzyOnTiJH8M3IzomFn5+fkhISEBQYAAqKqtw9cpl7Nm7r8GEvbi4GF5PDUXytRSIRYC0kznMuphBdk2GiphyKATg1MnjOHnqDBN/anUMZvd+BwcHSCSSWiPwOTk5tUbqG+Lt7Y3r168rf+7YsaPGfZqZmSlH9Tm6T0RERESkuZqEP+nCGRx51QKTeogRFBiA5cuXIygwAM/1FOPIqxZIunAGE33Ho7i4uN5+vJ4airRbKTjyqgX83E1QkVMOqaMUFTnlmNzLBEdetUDazRR4PTW03n6IjJXBJP1SqRRDhgzBvn37VI7v27cPw4cPV7ufc+fOoVOnTsqffXx8avW5d+9ejfokItI3mYVlOHYzF5mFZXrRDxER0eMWLFiAYydOIuEFMzzd1QSRU80wqYcYn3zyCZ7rKUZEcPXxhBfMcOzESSxYsKDOft58800kX0vBnhkWeLqrCaKmtYFfDxPc33kfk3uaYNvUNni6qwn2zLBA8rUUvPnmmy17okQ6ZlDT+xctWoSZM2di6NCh8PHxwbfffou0tDS88cYbAKqn3d+7dw+bN28GAKxbtw6urq7o168fKioq8OOPP2L79u3Yvn27ss/58+dj1KhRWLNmDZ5//nns2LED+/fvx9GjR3VyjkRETRVxOg1Loy9BIQAiEfDa090wulcHjfs5dC0H3x+9DUEAxCJgVZAnpj9Vf3lUIiIiTYSEhODH8M347EQVhnWWQCoRIXKqGRJSJPBzN4FUIkKFXMCnx6sgNTVBSEhInf20b98eYhHwr+MVyn6iprVBQkqVSj9rj1VALAI6dND830QiQ2ZQSf/06dPx4MEDfPzxx8jMzISHhwd27doFFxcXAEBmZibS0tKU7SsqKvDOO+/g3r17aNOmDfr164eEhAQ899xzyjbDhw/H1q1bsWzZMixfvhw9evRAREQEvLy8Wvz8iIiaQlYlx9bT6fhwx2XlMUEAvjtyG98dud2kvhUCsCT6Ery7t4NLu7ZNDZWIiAgTJkxAdEwsggIDMH27DBHBZpBKRAjsYwoAqJALCImSYfdNBaJjYlU2+XvUrVu3IO1kjvgb5ZgWVYZtU9vU6mfqtjIk3KyCtJM5bt682WLnSKQPREJNvTl6YkVFRbCxsUFhYSHX9xNRi7t5vwRbT6Uh6uxd5JdW1tnG2a4NLKTq3+ctrahCen7tKf2WZhK88FRXvDCsK3p2sHzimImIiGosX74cn3zyCaJD2igTdQCISa5EUGQZli1bhhUrVtT7+jFjx+Dcw3OQOkpxf+f9evtp798esmwZBrcdjIMHDjbrORG1BHXzUIMa6SciomrllXIkXs7CzyfTcPJ2nvJ4e0sz5JbI8OjdXIlIhMg3fNDJpo3a/WcWlmHE6gNQPHZbuEQmx/dHb+P7o7cxrJs9XhzmjEkenWBuKmniGRERkaHRRqm9hIQErF2zGgF9pPBzV01N/NxN8HxvKdauWQ1vb2/4+fnV2Ye9nT1k12Qo/r0QU3qb1NmPfy8TJOy+D2kHc9h72zf7eRHpE4PZyI+IiIDr2cX4eOcVeK/6BfO3nsfJ23kQi4Bne3fA97OG4vjSsVgd7AnJH2VHJSIRVgZ5aJTwA0AnmzZYFfRYP4Ee2PDKUIzr0wFiEXDqdh4WRlyA18pf8I+dl5GSzd2QiYhai5qd98PCwjDFfzISEhIAVCfxU/wnIywsrMEd94HqJLpml/6aqf0VcgExyZWokAvKNf41u/onJibW2U/37t1RkVmu3LSvrn5qNveryCxHjx49mvW8iPQNR/qJiPRceaUcuy5lYsupNJxOzVced7IxR8hTzggZ6gwn2z+T+ulPdcUo9/ZIzS2Fq4OFxgl/Y/0828cRmYVl2HbmLiJOp+NeQRk2/paKjb+lYqiLHV4c1hV+/Tn6T0RkrB4vtffp8SoEBQbgvcVLsHbNajzXU4y3vS3gt7W61N6evfvqHBmPjIxERWUV3va2UCbqIVEy7LhagYA+UuWNgHd8TLDjaikiIyPrXNd///59KATgXR+psp+p28qw81oVpvT+80bAe8Ol2HmtCjk5Oc16XkT6hmv6tYBr+olIWzILy3A79yG6ObRFUVkVtpxKQ/Tvd1FUXgUAkIhFGNu7A14aVp2QS8QincYrVwj49fp9bDmZhl+u5kD+x3oAa3MTBA3ugheGOaN3R2uV83rSmxBERKQfQkNDERYWhiOvVpfIqy9ZP5pWhZEbSzFnzhxs2LChVj+PJtkJL5jh0+NV2H1T8ViSbQK/rTJ4DBhab5JdXFwMr2FDkXazumzf2mMVSLhZhXaT2uPB7uqyfe/6SDHxx1J07eGOk6fO1NmPts6LqKWom4cy6dcCJv1EpA2Pltp7XGfbNnjhKWdMG+qMjjbmLR+cGrKLyrHtTDq2nKoe/a/R1b4N0vPKIICl/4iIjEFiYiKm+E+uNS3/8RJ5NTvvx+2Mr3fn/ZrE/9iJk5CamiA6JhZ+fn5ISEhAUGAAKiqrMNzbq9FR9eLiYng9NRTJ11IgFgHSTuaQdpGi4m4FKjLLoRCAPr3rT/i1fV5ELYFJfwti0k9ETVXfxnnPuDvg1RHdMNJN96P66lIoBBy5kYstJ9Ow70oW5I+dk0QkwtElYzjiT0RkwGqS8kcT5BqPl9qrbwO+GtraOK+4uBhvvvkmOnTogJs3byIvPw/2dvbo0aMHcnJy8NVXX6m1saC2zououTHpb0FM+omoqY7dzMVL352sdXzLXG/49Ging4i0Y9elDPz1p3O1jhv6eRERUdNL7ekrYz0vMj7q5qHcvZ+ISA90c2hb65hEJIKrg4UOotGeQV3t8PgEBWM4LyKi1k7dUns1u98bCmM9L2rdmPQTEemBgtJKlZ+ftNSevqkp/fdo4v+3sT0M/ryIiFozbZXa0zc15zWph6jB85rYXWRQ50XEpJ+ISA9sPn4HADC2dwdsmeuNo0vGGM1md9Of6orfloxVTufPKZLpOCIiImqKP0vtqW5uFxRZhunbZcoE+R0fE1RUViEyMlLXIaul5rze8TFVKf0XFFmGaVFlyvN6d7ipQZ0XEZN+IiIdKyyrROy5ewCA10d1h0+PdkY3Et7Jpg0WjXcHAMSev4fCx2Y2EBGR4Vi3bh2Ge3vBb6sMR9OqlJvbLVu2DLtuKDB9e/Vxv60yDPf2wrp163QdslpWr14NqakEE38qxdG0KkzdVoaEm1Vo798e8TeqMC2qDEfTqjDxp1JITSVYvXq1rkMmUguTfiIiHYs6exdllXK4O1rCq5u9rsNpNkNd7NC7oxXKKxXYdjZd1+EQEdETsrKywp69++AxYChGbixV7ma/YsUKRMfEYtcNBUZuLIXHgKGNltrTJ3v27EFFpRzoZI6RG0uRcLMKzvNc4BjsCOd5Loi/UYWRG0uBjuaoqJRzej8ZDCb9REQ6pFAI+PFE9dT+mT6uEIkMoyzfkxCJRJjl4woACD9xB4rH6xMSEZHBqEn858yZg7id8crydX5+fojbGY85c+YYVMIPALGxsbB0t0TXJd1gO9IWzvNdYDWwOn6rgVZwnu8C25G26Lq0GyzdLRETE6PjiInUY9J4EyIiai5Hb+Tidu5DWJmZIGhQZ12H0+wCBjlh1e5k3HlQil+v38foXh10HRIRET0hKysrbNiwodbxCRMmYMKECTqIqGny8vMgsZVA0kaCLqFdaj1v5WkFK8/qmwBiWzHy8vNaOkSiJ8KRfiIiHarZwC94SBe0NTP++7AWUhNMG+IMAAj/49yJiIj0gb2dPeQFcrXaKgoUsLcz3iV5ZFyY9BMR6Uh6XikOXM0GAMzwdtFxNC1npk/1uR64loP0vFIdR0NERFQtICAAJSklkGU1XGVGlilDSUoJAgMDWygyoqZh0k9EpCM/nUyDQgBG9GyHnh0sdR1Oi+nm0BYj3RwgCFDuZ0BERKRr06ZNg107O2RHZkOoZ98ZQSEge1s27NrZYerUqS0cIdGTYdJPRKQD5ZVyRJxOAwDM9HbVbTA6ULOhX8SZdJRXqjeVkoiIqDmZm5tj08ZNKDlfgvSv02uN+MsyZUj/Oh0l50uwaeMmmJub6yhSIs0Y/wJSIiI9lHAxE/mllXCyMce4Pq1vM7uxvTugs20b3Csow84LGZg21FnXIREREcHf3x8xMTGYPWc2ri+5Dkt3S4htxVAUKFCSUgK7dnaIjY2Fv7+/rkMlUhtH+omIdGDzH9PaX/Z2gYmk9X0VS8QivOzdFUD1ZoaCwPJ9RESkH6ZMmYKMuxkIDw+Hr4cvBrcdDF8PX4SHhyPjbgYTfjI4HOknImphF+8W4EJ6AaQSMaY/1XpHuKcPdca6/ddx6V4hzqcXYFBXO12HREREBKB6qv+MGTMwY8YMXYdC1GQGN7y0fv16dOvWDebm5hgyZAiOHDlSb9vo6GiMHz8e7du3h7W1NXx8fJCYmKjS5ocffoBIJKr1KC8vb+5TIaJWqqZM33OeHeFgaabjaHSnnaUZJvfvBIDl+4iIWrvy8nKEh4cjODgYY8aOQXBwMMLDww3+b3JjPS8yLAaV9EdERGDBggX4+9//jnPnzmHkyJGYNGkS0tLS6mz/66+/Yvz48di1axfOnj2LMWPGwN/fH+fOnVNpZ21tjczMTJUHN+YgouaQ97ACcRcyAAAz/9jMrjWr2dAv/mImHpQ0XCKJiIiMU1xcHJy6OGHWrFnYm7QX5x6ew96kvZg1axacujhh586dug7xiRjreZHhEQkGtJDSy8sLgwcPxjfffKM81qdPHwQEBGDVqlVq9dGvXz9Mnz4dH3zwAYDqkf4FCxagoKDgieMqKiqCjY0NCgsLYW1t/cT9EJHx++/hm1i9+yr6OVkj/s2nIRKJdB2Szk35+igu3i3EexN74a+je+o6HCIiakFxcXEIDAyE5UBLOIY4wqzjnzPgZFkyZEdmo+R8CWJiYjBlyhQdRqoZYz0v0i/q5qEGM9JfUVGBs2fPwtfXV+W4r68vjh07plYfCoUCxcXFsLe3VzleUlICFxcXdOnSBZMnT641E+BxMpkMRUVFKg8iosbIFYKyLv0sHxcm/H+Y6e0CAPjpRBrk9dRFJiIi41NeXo7Zc2bDcqAlnOc5qyTGAGDW0QzO85xhOdASs+fMNpgp8cZ6XmS4DCbpz83NhVwuh6Ojo8pxR0dHZGVlqdXHZ599hocPHyIkJER5rHfv3vjhhx8QFxeHLVu2wNzcHCNGjMD169fr7WfVqlWwsbFRPpydW+9GXESkvkPXcnA3vww2bUwxZUBnXYejN/wHOMHWwhT3Cspw4GqOrsMhIqIWsm3bNuQ/yIdjiCNE4rpvhIvEIjhOc0T+g3xERUW1cIRPxljPiwyXwST9NR4fGRMEQa3Rsi1btuCjjz5CREQEOnT4sya2t7c3ZsyYgQEDBmDkyJGIjIyEu7s7vvrqq3r7Wrp0KQoLC5WP9PT0Jz8hImo1ajbwCxnaBW2kEh1Hoz/MTSWYPrT65unm46m6DYaIiFpMbGwsLN0ta42EP86skxks3S0RExPTQpE1jbGeFxkug0n6HRwcIJFIao3q5+Tk1Br9f1xERARCQ0MRGRmJcePGNdhWLBbjqaeeanCk38zMDNbW1ioPIqKGpOY+xOGU+xCJgBl/TGenP83wdoFIBBy5notb90t0HQ4REbWAvPw8SGzVuwkuthUjLz+vmSPSDmM9LzJcBpP0S6VSDBkyBPv27VM5vm/fPgwfPrze123ZsgWzZ8/Gzz//DD8/v0bfRxAEnD9/Hp06dWpyzERENcL/WMv/jHt7uLRrq+No9I+zvQXG9qqehVXzWRERkXGzt7OHvECuVltFgQL2dvaNN9QDxnpeZLgMJukHgEWLFuH7779HWFgYkpOTsXDhQqSlpeGNN94AUD3tftasWcr2W7ZswaxZs/DZZ5/B29sbWVlZyMrKQmFhobLNP/7xDyQmJuLWrVs4f/48QkNDcf78eWWfRERNVVYhx7Yz1cuAZvlwlL8+M//4bKLO3kVpRZWOoyEiouYWEBCAkpQSyLIaLtkqy5ShJKUEgYGBLRRZ0zTHeZWXlyM8PBzBwcEYM3YMgoODER4ervEmgNrqR58UFxcjNDQUiYmJKscTExMRGhqK4uLiFu1HH6lVsu/ixYsad9y3b1+YmJg8UVANWb9+PdauXYvMzEx4eHjgiy++wKhRowAAs2fPRmpqKg4dOgQAGD16NA4fPlyrj1deeQU//PADAGDhwoWIjo5GVlYWbGxsMGjQIHz00Ufw8fFROyaW7COihmw9lYYl0ZfQ1d4CB98ZDUk9m/q0dgqFgLGfHULqg1KsDPTES15ddR0SERE1o/Lycjh1cYLcRQ7nec51bnonKASkf50OyR0JMu5mwNzcXAeRakbb5xUXF4fZc2Yj/0E+LN0tIbGVQF4gR0lKCeza2WHTxk3w9/dvNC5t9aNPiouLMdF3PI6dOAmpqQmiY2Lh5+eHhIQEBAUGoKKyCsO9vbBn7z5YWVk1ez8tTd08VK2kXywWQyQSQY2myvYpKSno3r27+hEbMCb9RFQfQRDg9++juJJZhPef643XR/XQdUh67fsjt/BJQjJ6d7TC7vkjWdaQiMjI7dy5EwEBAXXXs8+UIXtbdT372NhYg0pItXVecXFxCAwMrLufLBmyI6v7iYmJwZQpU5q9H31Sk6gnXTiDhBfM8OnxKuy+qcB7i5dg7ZrVeK6nGG97m8BvqwweA4bWm7Brqx9d0HrSf+rUKbRv377RNxYEAR4eHrh48SKTfiJq9c7eyUPwN8dhZiLGyfefha2FVNch6bXC0kp4rdqP8koFIv/ig2HduM5RFzILy3A79yG6ObRFJ5s2Ou+HiIzb4yPQYlsxFAUKgx6BBpp+XtqaMWCsMypCQ0MRFhaGI69a4OmuJqiQCwiJkmHH1QoE9JEiItgMUokIR9OqMHJjKebMmYMNGzY0Wz+6oG4eqtb8+2eeeQY9e/aEra2tWm8+atQotGnDf9yJiGrK9E0Z4MSEXw02FqYIGNgZW0+nY/PxVCb9OhB+/A4+2JEEAYAIwCTPjhjobKtxP+fTC7D7UhYEAGIRsCrIE9Of4pINIqptypQpyLibgaioKMTExCAvPw/2XewRuDwQU6dONYgEtC5NPa9t27Yh/0E+3N51qzNRBwCRWATHaY64vvQ6oqKiMGPGjGbrR9+EhITgx/DN+OxEFYZ1lkAqESFyqhkSUiTwczeBVCJChVzAp8erIDU1QUhISLP2o8/UGumnhnGkn4jqcr9YhuGrf0GlXMDOeU/Ds4uNrkMyCJczCuH376MwEYtwbMlYdLA2zD/2DM3VrCJ8/+stRP1+r1n6l4hEOLpkDEf8iYjUFBwcjL1Je+H6vmujbVNXpsLXwxfbt29vtn70Uc2a++d6ipUj8jVqRux331Qo1+g3dz8tTasj/UREpLmtp9JQKRcw0NmWCb8G+jnZYIiLHc7eyceWU+mYP85N1yEZrdKKKsRfzMSWU2k4l1ZQb7uRbg5ob2VW7/OPu18sw5HruSrH5IKA1NxSJv1ERGrKy8+DxFaiVluxrRh5+XnN2o8+8vPzw3uLl+CTTz5BQooEgX1Mlc8lpFRhx9UKLFu2rNFEXVv96Cu1kv5Fixap3eHnn3/+xMEQERmLKrkCP59KAwC8Mpxl+jQ1y8cFZ+/k4+dTd/DXMT1gKjGoCrN673JGIbaeSkfsuXsollWXRzQRi/C0mwMOp9zHo3MAJSIR1k7tr1GynllYhhGrD0DxWD+uDhYaxVleXo5t27YhNja2elqsnT0CAgIwbdo0g53uS0SkLns7e8jvydVqqyhQwL5L3UvitNWPthUXF2PBggUICQnBhAkTlMcTExMRGRmJdevWNbphXkJCAtauWY2APlL4uaumtn7uJni+txRr16yGt7d3oyP92uhHX6mV9J87d07l57Nnz0Iul6NXr14AgJSUFEgkEgwZMkT7ERIRGaD9ydnILCxHu7ZSPOfZSdfhGJyJHh3hYClFdpEMey9nw68/P8Omeiirws4LGdhyKg0X7hYqj7u0s8ALT3XF1CFd0N7KDBGn0/B+dBLkggCJSISVQR4aj853smmDVUGeTeqnztJS9+SIjo7G/IXzDXZjLyIidQUEBCA6OhqyLJnKbvuPk2XKUJJSgsDlgc3ajzY9WiLvx/DNdZbIu3rlcoM75ScmJtaakl8hF5CQUqVcix851QwhUTIEBQYgbme8ys0FbfejzzRe0//555/j0KFD2LRpE+zs7AAA+fn5ePXVVzFy5Ei8/fbbzRKoPuOafiJ63EvfncCxmw/w19E98N7E3roOxyB9mngNXx+8Aa9u9oj4i4+uwzFYSfcK8fOpNOw4dw8PK6pHekwlIvj264iXhnWFT/d2ED+2sVNmYRlSc0vh6mDR5N37n6QfYywtRUSkKWPdvV9bJfK4e7+WS/Y9qnPnzti7dy/69euncjwpKQm+vr7IyMh4sogNGJN+InrUjZxijPv8V4hFwJHFY9HZlmuYn0RGQRlGrj0IuUJA4oJR6NVRP2ri6rOaEnkdrMxx6nYetpxKw6V7f47qu7azwIvDuiJ4SBc4WKq/Rr8l6dsfp0REurRz504EBATUfRM0U4bsbdU3QWNjYxuc/aStfrRBW0m2tm4eaKsfXWi2jfyKioqQnZ1dK+nPyclBcXGx5pESERmZmjJ9z/ZxZMLfBE62bTC+jyP2XM5C+IlUfBLgqeuQ9NrWU2lYGnMJj9/Kl0rEmOjRES8O6wrv7vYQieou16QvjLW0FBHRk/D390dMTAxmz5mN60uuw9LdEmJbMRQFCpSklMCunZ1aibq2+tEGbZXIs7Kywp69+zDRdzxGbjwJqamJcpmAt7c3ggIDEJtciuHeXg0m6trqR59pPNI/a9YsHD58GJ999hm8vb0BACdOnMC7776LUaNGYdOmTc0SqD7jSD8R1SiRVcF75S8okVUhPHQYRrq113VIBu3YjVy89P1JWEglOPH+s7A2N238Ra1McXklNh+/g38lXqv13Ftje2L2iG6wbyvVQWRPxphLSxERPany8nJERUUhJiZGubFpYGAgpk6dqtFsJ23101TaLJGnjQ0BtdlPS2q26f2lpaV45513EBYWhsrKSgCAiYkJQkND8a9//Qtt27ZtWuQGiEk/EdUIP56K5Tsuo3v7tti/8Jlaa6VJM4IgYPwXv+JGTgk+8u+L2SO66TokvSAIAi7cLcSWk2mIu5CBssq6d2XeMtcbPj3atXB0TTNm7Bice3gOzn91brRt2vo0DG47GAcPHGyByIiISJuWL1+OTz75BNEhbVRK5MUkVyIosgzLli3DihUrdBih/lM3D9W4BpKFhQXWr1+PBw8e4Ny5c/j999+Rl5eH9evXt8qEn4iohiAIyqn9M71dmPBrgUgkwkzv6pKH4SfuQMP71EansKwSm4+nYtKXRxDwn98QcSYdZZVyuLazwONX25OUyNMH9nb2kBdoUFrKrmVKSxEZk+LiYoSGhiIxMVHleGJiIkJDQzVaslteXo7w8HAEBwdjzNgxCA4ORnh4OMrLy7UdtsHR1ueszd+XNmgjHnVL5CUkJGg19tbqiQsfZ2ZmIjMzE+7u7mjbtm2r/0OMiOjErTxczymBhVSC4CFddB2O0Qga3BltpRLcvP8Qx24+0HU4LU4QBJy9k493tl2A18r9+GDHZVzNKoaZiRhBgzsj6g0fHHxnNFYHe0Lyx3r9Jy21pw8CAgJQklICWZaswXbK0lKBzV9aisiY1GxaFhYWhin+k5VJVUJCAqb4T0ZYWBgm+o5XK3GLi4uDUxcnzJo1C3uT9uLcw3PYm7QXs2bNglMXJ+zcubO5T0dvaetz1ubvSxu0EU99JfJikitRIReUa/wn9RAjKDCg1s0F0pzGSf+DBw/w7LPPwt3dHc899xwyMzMBAK+99lqrLNdHRFQj/EQqACBgUGeuPdciK3NTBA2uvomy+XiqboNpQYWllfjht9uYuO4Igr85hqizd1FeqUAvRyt85N8Xp94fh89DBmKoa/XmfNOf6oqjS8Zgy1xvHF0yBtOf6qrrU3gi06ZNg107O2RHZkNQ1D2gICgEZG/Lhl07O0ydOrWFIyQyXI/uUn7kVQtlUrV8+XJlEnbkVQskXTjTaOJWU1pT7iKH22o3uL7vCue/OsP1fVe4rXaD3EWOgIAAxMXFteAZ6gdtfc7a/H3p03lFRkaiorIKb3v/uWlfSJQMQZFlmL5dpkz83/ExQUVlFSIjI5v1vFqDJ9rILycnB99//z369OmDCxcuoHv37ti7dy8WLlyIy5cvN1eseotr+onoQno+Atcfg0IA9iwYid4d+V2gTSnZxfD94leIAHz54iA85WrX5Prxt3MfoptDW70YDa+Jx7WdBTIKyvHzqTQkXMyErEoBADA3FWNyfye8OKwrBne11fsd+JtKn0pLERkTbZVKY2nNhhlr/Xh9K7VHzViyb+/evUhMTESXLqpTV93c3HDnzh3NIyUiMnARp9OwZPsl1NxBvZBewKRfy9wdrdDdoS1u5T7EW1vOQSwC3vHthcn9nTTuK/5iBj7dew0KARCLgFVBnjodFY84nYal0ZdQ16B2745WeMmrK54f2Bk2bVrP7BF9Ki1FZEy0VSqNpTUbpq3PWVv96Nt5tYYSefpG45F+Kysr/P7773Bzc4OVlZVypP/06dOYOHEiHjxofestOdJP1HplFpZhxOoDKgmbRCTC0SVj9GIE2VhkFpZh+KoDaI7dY3T5+6rr+gEA//6dEDqyOwZ0sTH6Uf2G6EtpKSJjoo1SaSyt2ThtlaTTZmk7bdDHUnutWbPt3j9q1Chs3rxZ+bNIJIJCocC//vUvjBkz5smiJSIyULdzH9ZK2OSCgNTcUt0EZKRu5z6sM+E3NxGjrVSi9sPcpPY/e7r8fdV1/QDAS14uGOhs/NP4G2Nubo4ZM2Zg+/btOHjgILZv344ZM2Yw4SdqAj8/P7y3eAlikyuQkFKl8lxCShV2XK3Ae4uXNJiw5eXnQWIrUev9xLZi5OXnNSlmQ6SNz1mb/WiLNuOxsrLChg0bVBJ+AJgwYQI2bNjAhF+LNJ7e/69//QujR4/GmTNnUFFRgffeew+XL19GXl4efvvtt+aIkYhIbznVMTpsqKXS9Fk3h7YQi1BrRsXBd0drNEJf18i6WASd/b7sLKS1jvH6IaKGlJeXY9u2bYiNjVXOgAkICMC0adPUuiGmbqk0b2/vehM3ezt7yO9pUFqzS+srramNz1mb/WiLvsVD6tF4pL9v3764ePEihg0bhvHjx+Phw4cICgrCuXPn0KNHj+aIkYhIbx24mqPysyGXStNnnWzaYFVQ00vS1fTz6BJUP89OOvt9xV3IUPmZ1w8RNaSpJfK0VSqNpTUbpq3PWd9K2+lbPKQ+jUf6AaBjx474xz/+oe1YiIgMSnmlHN8cvgkAWDyxNwY628LVwYIJWzOZ/lRXjHJvj9Tc0iZ9zjX9bDhyG98fvY0Tt/NQXimHual6U1W15UGJDJuOpQIA1gT3R1d7C14/RFSvmhJ5lgMt4faum2pViywZsiOzERAQgJiYGEyZMqXOPv4slWahUirt8d3X3/ExwY6rpYiMjKw19RqoLq05f+F8ZEdmN7h7f2stramtz1lb/ejbeVHL03ikH6ieVnTq1CnEx8cjLi5O5dHc1q9fj27dusHc3BxDhgzBkSNHGmx/+PBhDBkyBObm5ujevTv++9//1mqzTFjV8gAARNxJREFUfft29O3bF2ZmZujbty9iYmKaK3wiMiI/nUzD/WIZOtu2QejT3eDTox0TtmbWyaaNVj7nTjZt8N7E3uhs2wb3i2X46WSaliJU33dHbqO0Qg7PzjYIGdqF1w8R1au8vByz58yG5UBLOM9zVkn4AcCsoxmc5znDcqAlZs+ZjfLy8jr7WbduHYZ7e8FvqwxH06qUm64tW7YMu24oMH179XG/rTIM9/bCunXr6uzH3NwcmzZuQsn5EqR/nV5rxF+WKUP61+koOV+CTRs3tbp9OLT1OWurH307L9IBQUO7d+8W2rdvL4hEoloPsVisaXca2bp1q2Bqaip89913wpUrV4T58+cLbdu2Fe7cuVNn+1u3bgkWFhbC/PnzhStXrgjfffedYGpqKkRFRSnbHDt2TJBIJMLKlSuF5ORkYeXKlYKJiYlw4sQJteMqLCwUAAiFhYVNPkciMgylsiphyIp9gsvieOHnk3V/B5H++/nkHcFlcbwwZMU+oVRW1WLvm1tcLvRZvltwWRwv7L+S1WLvS0SGafPmzQIAwW21m+Dxg0e9D7dVbgIAITw8vN6+ioqKhOHeXgIAQWpqIsTHxwuCIAjx8fGC1NREACAM9/YSioqKGo1rx44dgl07OwGAYOluKVgPsxYs3S0FAIJdOzshLi5Oa5+BodHW56zN35c26Fs8rZ26eajGJft69uyJCRMm4IMPPoCjo6P27j6owcvLC4MHD8Y333yjPNanTx8EBARg1apVtdovXrwYcXFxSE5OVh574403cOHCBRw/fhwAMH36dBQVFWH37t3KNhMnToSdnR22bNmiVlws2UfU+nx/5BY+SUhGF7s2OPD2aEjr2BWe9F+lXIExnx7C3fwyLPPrg9dGdm+R9121Kxn/+/UW+nexwY6/jWj1u/QTUcO0XSJPm6XSWFqzftr6nPWttJ2+xdOaqZuHapz0W1tb62TTvoqKClhYWGDbtm0qm4HMnz8f58+fx+HDh2u9ZtSoURg0aBC+/PJL5bGYmBiEhISgtLQUpqam6Nq1KxYuXIiFCxcq23zxxRdYt24d7ty5o1ZsTPqJWpeyCjlGrj2A3JIKrA7yxAvDuuo6JGqCiNNpWLz9EhwszXDkvTFoI23etf25JTKMXHMQZZVyhM0eirG9W/YGOhEZnjFjx+Dcw3Nw/qtzo23T1qdhcNvBOHjgYAtERkS6pG4eqvHQ1NSpU3Ho0KGmxPZEcnNzIZfLa80ucHR0RFZWVp2vycrKqrN9VVUVcnNzG2xTX58AIJPJUFRUpPIgotbjxxN3kFtSAWf7Ngge0kXX4VATBQ3uAmf7NsgtkeHHE+rd7G2Kb3+9hbJKOQZ0scGYXh2a/f2IyPDZ29lDXqBBiTy7xkvklZeXIzw8HMHBwRgzdgyCg4MRHh5e734ARGS4NN69/+uvv8a0adNw5MgReHp6wtTUVOX5t956S2vB1eXxKZCCIDQ4LbKu9o8f17TPVatWsXoBUStVWlGF//6xY/+bY9xgKuG0fkNnKhHjzTFueG/7Rfzv15t42bsrLKRPVNymUfeLZdh8PBUAsGCcO6f1E5FaAgICEB0dDVmWrNYmfo9Slshb3nCJvLi4OMyeMxv5D/Jh6W4Jia0E8ntyREdHY/7C+di0cRP8/f21fRpEpCMa/1Xz888/IzExEW3atMGhQ4dqJc/NlfQ7ODhAIpHUGoHPycmpd2+Bjh071tnexMQE7dq1a7BNQ/sVLF26FIsWLVL+XFRUBGfnxqdbEZHh+/HEHTx4WIGu9hYIHNxZ1+GQlgQO7oyvD95AWl4pfjxxB6+Pap4lbN/+ehPllQoMcLbF6F7tm+U9iMj4aLNEnjZK/xGRYdF4iGrZsmX4+OOPUVhYiNTUVNy+fVv5uHXrVnPECACQSqUYMmQI9u3bp3J83759GD58eJ2v8fHxqdV+7969GDp0qHKGQn1t6usTAMzMzGBtba3yICLjV1pRhf8drv6emze2J0f5jYipRIx5Y3sCAP53+BZKK6q0/h45xeUI/2P5wIJxbhzlJyK1aatEnrZK/xGRYdH4L9aKigpMnz4dYnHL/7G7aNEifP/99wgLC0NycjIWLlyItLQ0vPHGGwCqR+BnzZqlbP/GG2/gzp07WLRoEZKTkxEWFoYNGzbgnXfeUbaZP38+9u7dizVr1uDq1atYs2YN9u/fjwULFrT06RGRngs//sgo/yCO8huboEGd4dLOAg8eViD8uPbX9n97+BbKKxUY6GyL0e4c5Scizfj7+yMmJgaSOxJcX3IdqStTkbY+DakrU3F96XVI7kgQGxvb4LT8bdu2If9BPhxDHOucLQAAIrEIjtMckf8gH1FRUc11OkTUgjTO3F955RVEREQ0RyyNmj59OtatW4ePP/4YAwcOxK+//opdu3bBxcUFAJCZmYm0tDRl+27dumHXrl04dOgQBg4ciBUrVuDf//43goODlW2GDx+OrVu3YuPGjejfvz9++OEHREREwMvLq8XPj4j010NZFf73a/Uo/5sc5TdKJhIx5o35Y7T/11t4KNPeaH9OcTl+PMlRfiJqmilTpiDjbgbCw8Ph6+GLwW0Hw9fDF+Hh4ci4m9HoOvzY2FhYuls2uC8AAJh1MoOluyViYmK0GT4R6YjGJfveeustbN68GQMGDED//v1rbeT3+eefazVAQ8CSfUTG75tDN7Fmz1W4tLPAL4uegQmTfqNUJVfg2c8P486DUiyZ1BtvPKOdtf0f77yCsN9uY1BXW0T/33Am/USkEyz9R2Rc1M1DNd7I79KlSxg0aBAAICkpSeU5/hFDRMbooawK3/76x479Y92Y8BsxE4kYb451wzvbLuDbX29hprcL2po1bSf/nKJy/KQc5eeO/USkO/Z29pDf06D0X5fGS/8Rkf7T6C8ZuVyOjz76CJ6enrC355cAEbUOm46nIr+0Eq7tLBAw0EnX4VAzCxjohP8cvIHbuQ+x+fgd/N/opo32f3P4JmRVCgzuaotRbg5aipKISHPaLv1HRIZBo+EqiUSCCRMmoLCwsLniISLSKyWyKnynXMvPUf7WoHq0v3pt/7e/3kRJE9b25xSV4+eT1XvNLBzPUX4i0q1p06bBrp0dsiOzISjqXuGrbuk/IjIcGv/16unp2ayl+YiI9MmmY9Wj/N0c2uJ5jvK3GlMGOKGbQ1vkl1Zi07HUJ+5n/aHqUf4hLnZ4uidH+YlIt7RV+o+IDIvGSf8///lPvPPOO4iPj0dmZiaKiopUHkRExqK4vBLfHam+yfnWsz05yt+KmEjEeOvZ6tH+747ceqLR/qzCcvx86o9Rfq7lJyI9oY3Sf0RkWDTenWjixIkAqkuGPPoHjCAIEIlEkMvV2xyEiEjfbT5+BwWlleju0Bb+/TnK39r493fCV7/cwK3ch9h0LBV/+6Ocn7r+e/gmKqoUGOpihxE92zVTlEREmqsp/RcVFYWYmBjk5efBvos9ApcHYurUqRzhJzIyGif9Bw+ybAcRGb/i8kp8+2vNKD/X8rdG1aP9blgQcR7fHbmFWT4usDI3bfyFeGyUn2v5iUgPmZubY8aMGZgxY4auQyGiZqZx0v/MM880RxxERHrlh99SUVhWie7t28J/AEf5Wyv/AU7494HruHW/erR/3lg3tV63/tANVFQpMMzVHsN7cJSfiIiIdOeJig8XFBRgw4YNSE5OhkgkQt++fTFnzhzY2NhoOz4iohZXVF6J74/eBgDMf9YNEjFHaVsriViE+c+6Yf7W8/juyG28Mty10dH+zMIybD2VDgBYMM6No/xEBAAoLy/Htm3bEBsbWz2d3s4eAQEBmDZtGqfTE1Gz0ni+6pkzZ9CjRw988cUXyMvLQ25uLj7//HP06NEDv//+e3PESETUompG+Xu0b4vJXMvf6k3u74Qe7duisKwSP/yW2mj79QdvokKuwLBu9vDhKD8RAYiLi4NTFyfMmjULe5P24tzDc9ibtBezZs2CUxcn7Ny5U9chEpEREwmCUHeRznqMHDkSPXv2xHfffQcTk+qJAlVVVXjttddw69Yt/Prrr80SqD4rKiqCjY0NCgsLYW1tretwiKgJisor8fTqAygqr8KXLwzE8wM76zok0gM7zt/D/K3nYW1ugqNLxsK6ntH+jIIyjP7XIVTIFfh5rheG92CZPqLWLi4uDoGBgbAcaAnHEEeYdTRTPifLkiE7Mhsl50sQExODKVOm6DBSIjI06uahTzTSv3jxYmXCDwAmJiZ47733cObMmSeLlohIT2w8moqi8ir07GDJUX5SmtzfCT07WKKovKrB0f71h26gQq6AVzd7JvxEhPLycsyeMxuWAy3hPM9ZJeEHALOOZnCe5wzLgZaYPWc2ysvLdRQpERkzjZN+a2trpKWl1Tqenp4OKysrrQRFRKQLhWWV+P7onzv2cy0/1ahZ2w8A3x+5hcKyylpt7hWUIeJ0zVp+9xaNj4j007Zt25D/IB+OIY4Q1fNvikgsguM0R+Q/yEdUVFQLR0hErYHGSf/06dMRGhqKiIgIpKen4+7du9i6dStee+01vPjii80RIxFRi9j4220Ul1fBrYMl/Dw76Toc0jPPeXaCWwOj/esP3kClXIB3d67lJ6JqsbGxsHS3rDXC/zizTmawdLdETExMC0VGRK2Jxrv3f/rppxCJRJg1axaqqqoAAKampvi///s/rF69WusBEhG1hMKySmyo2bF/HEf5qTaJWIT549ww7+dz+P7oLcwe4QqbNtVr++/mlyLyDEf5iUhVXn4eJLYStdqKbcXIy89r5oiIqDXSeKRfKpXiyy+/RH5+Ps6fP49z584hLy8PX3zxBczMGr6LSUSkr8KOVo/yuzta4jkPjvJT3Z7z6AR3R0sUl1ch7I+bRADwn4M3USkX4NO9Hby7c5Rf35SXlyM8PBzBwcEYM3YMgoODER4ezvXT1Ozs7ewhL5Cr1VZRoIC9nX2DbYqLixEaGorExESV44mJiQgNDUVxcfETx0rGj9dP66Vx0l/DwsICnp6e6N+/PywsLLQZExFRiyosrVQmcPOfdYeYo/xUD7FYhPnPVo/kh/12G4VllbibX4ptf4zyLxzPUX59w1JppEsBAQEoSSmBLEvWYDtZpgwlKSUIDAyst01xcTEm+o5HWFgYpvhPRkJCAgAgISEBU/wnIywsDBN9xzNxozrx+mndNE76Hz58iOXLl2P48OHo2bMnunfvrvIgIjI0G47eQrGsCr0crTDJo6OuwyE9N8mjI3o5WqG4vAobjt7Gfw7eQJVCwIie7TCsW8OjdNSyakqlyV3kcFvtBtf3XeH8V2e4vu8Kt9VukLvIERAQgLi4OF2HSkZq2rRpsGtnh+zIbAiKuqtkCwoB2duyYdfODlOnTq2zTU3ClnThDI68aoFJPcQICgzA8uXLERQYgOd6inHkVQskXTjDxI1q4fVDIkEQ6v4GqseLL76Iw4cPY+bMmejUqRNEItURsfnz52s1QEOgbn1EItI/17KKEPCf31BWqcD6lwfjOW7gR2rYdSkTf/3pd1hIJSivlEMhANve8MFTrkz69UV5eTmcujhB7iKH8zznOndOFxQC0r9Oh+SOBBl3M2Bubq6DSMnY7dy5EwEBAbAcaAnHEEeVTf1kmTJkb8tGyfkSxMbGwt/fv84+QkNDERYWhiOvWuDpriaokAsIiZJhx9UKBPSRIiLYDFKJCEfTqjByYynmzJmDDRs2tNQpkp7j9WO81M1DNd7Ib/fu3UhISMCIESOaFCARka5FnE7Dku2XUHPns64ybER1mdivIzpamyGr6M8pu7fulzDp1yM1pdLc3nVrtFTa9aXXERUVhRkzZrRwlNQa+Pv7IyYmBrPnzMb1Jddh6W4Jsa0YigIFSlJKYNfOrsGEHwBCQkLwY/hmfHaiCsM6SyCViBA51QwJKRL4uZtAKhGhQi7g0+NVkJqaICQkpAXPkPQdrx/SeHq/nZ0d7O35Rw0RGbbMwjKVhB8AlsUkIbOwTGcxkeHILi5HdpHqGt33o3n96BOWSiN9MmXKFGTczUB4eDh8PXwxuO1g+Hr4Ijw8HBl3MxpM+AFgwoQJiI6Jxa4bCkzfLkOFXIBUIkJgH1NlwhYSJcPumwpEx8RiwoQJLXRmZAh4/ZDGSf+KFSvwwQcfoLS0tDniqVd+fj5mzpwJGxsb2NjYYObMmSgoKKi3fWVlJRYvXgxPT0+0bdsWTk7Vm/hkZGSotBs9ejREIpHK44UXXmjmsyEiXZJVybEsJgmPr22SCwJSc1v2u40M0+3ch7x+9BxLpZG+MTc3x4wZM7B9+3YcPHAQ27dvx/+3d+dRTZ3pH8C/IRAWgSCggBWqVVkUcMEFtIvWigsu4ILWVotYOs7oWEadVh0dre1P1Jl2aMexta1Yy9gqLrjRilqXumFFSl3qXhFQEBcIKHtyf384pEYQEknITfh+zsk55S7vfZLzntQn773P8/rrr2v9WElYWBjeeXcutp2vRMqlao19KZeqsf1CJd55dy7CwsIMET6ZOM6f5k3n2/s//PBDXL16FW5ubmjXrh2srKw09mdkZOgtuEdNnDgRubm52L17NwDgrbfewqRJk55Ydbe0tBQZGRlYuHAhunbtisLCQsTGxmLkyJFIT0/XODYmJgZLlixR/21ra2uQ90BExneruBx//O8pZGQX1donlUjQzpXdSKhh7V1bwEICPFqXi/NHXJxbOkN5Q4dWaW0bvouxvLwcmzZtwrZt23Cv8B6cWzojPDwc48aN06kegL7GMVdi+3zEEk9KSgpWLF+GcD8Zwrw1/wkf5m2JUb4yrFi+DMHBwUzcqBbOn+ZN50J+7733Xr37Fy1a1KiA6nL+/Hl07twZaWlp6NOnDwAgLS0NISEhuHDhAnx8fLQa5+TJk+jduzeuX78OLy8vAA9X+rt164b4+Pinjo+F/IhMQ3rWPfxxfQZul1TAwcYSY3u0xdfHr0MpCJBKJFg62h/je3kZO0wyERtPZmP+1rOcPyKVmJiIyZMno9OyTvXe4l+RV4HL8y4jMTGx3mf6d+zYgajoKBTeLYS9tz2kTlIoi5TqZ7LXrV3X4C3a+hzHXInt8xFLPKmpqRg5YjiGdbRQF12rVApIuVSt8Ux2zS3aO3bu4i3apMb5Y760zUN1TvqNISEhAbNmzap1O7+TkxP+9a9/YcqUKVqNs2/fPoSGhqKoqEj9ofTv3x/nzp2DIAhwc3PD0KFDsWjRIjg4OGgdH5N+InETBAHrT2TjvZ3nUKUU4OPmgNWTgtDOtQXyFGXIulOKdq528JDzLh/SDeePeOmzen9N6786q6/nV+BW0sPq68nJyRg5cuQTY9LXOOZKbJ+PmOJh9XVqDM4f82VWSf/SpUvx1Vdf4dKlSxrbvb29MWXKFMybN6/BMcrLy/H888/D19cX//3vf9Xbv/jiC7Rv3x7u7u44e/Ys5s2bh44dO2Lv3r1PHKuiogIVFb8XcCouLoanpyeTfiIRKq9S4u/bzyIpPRcAEBbggRVjA9HCWuenm4jIxOijVZq+fjxgC8H6ie3zEVs8j/ZZT5lgjX8er8b3V1V45925WLF8GYZ1tMDsYEuEbaiAf9ee2L1nr04LWGTeOH/Ml7ZJv1aF/JydnXHnzh2tL+7l5YXr1683eNzixYtrFdF7/FXz/L1EUseXrSDUuf1xVVVVmDBhAlQqFVatWqWxLyYmBq+88gr8/f0xYcIEbN68Gfv27au3NkFcXJy6oKBcLoenp2eDMRBR08tTlGH86uNISs+FhQSYO9QXKyd2Z8JP1EzUtEqTXpfi8tzLyFqahexV2chamoXL8y5Del3aYKu0mtZ/bpFuDbb+K7xbiM2bNxt0HHMlts9HbPE4ODhg95698O/aEy+sLVVXWX///ffVVdlfWFvKhI3qxPlDWv3Lt6ioCN9//z3kcrlWg969exdKZcPFc2bMmNFgpfx27drh9OnTuHXrVq19t2/fhpubW73nV1VVITIyEteuXcP+/fsbXInv0aMHrKyscPnyZfTo0aPOY+bNm4dZs2ap/65Z6Sci8Tjx211M/yYDd+5XQm5rhX+/2h0vercydlhE1MRqWqVt3rwZycnJDwuxtXVGxMIIjB07tsHV2adp/VdXbQB9jWOuxPb5iC0e4PfELTY2FpGRkepnrsPCwrBj5y4kJSUhPj6eCRvVifOnedN6ueuNN97Q+8VdXV3h6ura4HEhISFQKBT46aef0Lt3bwDAiRMnoFAo0Ldv3yeeV5PwX758GQcOHICLi0uD1zp37hyqqqrg4eHxxGOsra1hbV3//wSIyDgEQcC6Y1n4IOU8qlUC/Dwcsfr1IHi5sKo6UXNV0yrtaZIyfbX+YwvB+ont8xFbPDUcHBzqfNZ68ODBLLxGDeL8ab60SvpVKpWh46iXn58fhgwZgpiYGKxevRrAw5Z9w4cP16jc7+vri7i4OERERKC6uhpjx45FRkYGdu3aBaVSifz8fAAPH1eQyWS4evUq1q9fj2HDhsHV1RW//vorZs+eje7du6Nfv35Gea9E9PTKq5SYn3wGWzNuAABGdm2D5WMCYSvT7h9uRESP01frv+bQQrAx44jt89F3PCUlJbVWWIGHVdW5wkqmSAzfG6Q9rZ7pF4P169cjICAAoaGhCA0NRWBgIBITEzWOuXjxIhQKBQAgNzcXO3bsQG5uLrp16wYPDw/169ixYwAAmUyGH374AYMHD4aPjw9mzpyJ0NBQ7Nu3D1IpkwQiU5JbWIqxnx3D1owbkFpIsCDMDx9P6MaEn4gaJTw8HPcv3UdFfkW9x1XkVeD+pfuIiIgw6Dg1duzYgTZt22Dy5MnYc3YPfn7wM/ac3YPJkyejTds22LlzZ/1vTGTjiO3z0Wc8NUXUEhISMHLEcKSkpAB42Dd95IjhSEhIwJDQQSgpKan3WkRiIZbvDdKeSVTvFzu27CMyrmNX7mDGtz/j3oNKOLeQYeWr3dG3Y8OPDhERNUSM1fvF1kJQH+OI7fPRVzysmk7mRkzfG2RmLfvEjkk/UdPLU5Th2u0HSPvtLlYeuAKVAPg/44jPXg9C25Z8fp+I9Ecfrf/0NY7YfoTQZ7Iups9HX/GwPzqZEzF+bzR3em3ZR0QkJhtPZqPfsv2Y+OUJfLL/YcI/uscz2DytLxN+ItI7fbT+09c4YmshqM/WdmL6fPQVT2RkJGRWlvgwrRqVSgEyqQRJY62xNdJWnfBXKgX883g1ZFaWiIyMfOJYRMYmxu8N0g6TfiIyKXmKMszbegaqR+5RkkiAOaHesLHi8/tEZBg1rf8SExMR6h+KHi16INQ/FImJibiZe7PBhF9f4zxNKzlTGKeGWD4ffcUzePBgdR/08Vsq1Il/hJ+VOuGP3Fyh7pvOCuokZmL93qCGad2y71FKpRLJyck4f/48JBIJfH19ER4eDkvLpxqOiEhr1+480Ej4AUAQgOt3y9DGiav8RGQ4jWn9p69xxNZC0BCt7cTw+egrHuBhH/R33p2LDz74ACmXpIjws1LvS7lUje0XKrFgwQKEhYU91fhETUXM3xtUP52z9LNnz2LUqFHIz89Xt8u7dOkSWrVqhR07diAgIEDvQRIR1ZBJa9+gJJVI0M6VCT8RmT+xtRA0RKu9xhBbPMDDKv0rli9DuJ8MYd6a//QO87bEKF8ZVixfhuDgYCb+JGpi/t5g67/66Xx7/5tvvokuXbogNzcXGRkZyMjIQE5ODgIDA/HWW28ZIkYiIgCAIAj4+IfLGtukEgmWjvaHh9zWSFERETUdsbUQ1HervcYSWzypqakYHRGOYR0tNJ7hTz5fpfGM/9AOFhgdEY7U1FSDxkPUGGL93mDrv4bpXL3f1tYW6enp6NKli8b2s2fPolevXigrK9NrgKaA1fuJmsb2zBt4e0MmZJYWSIzuDZUAtHO1Y8JPRM2G2Kpni60Kt9jiYfV+Midi/N5o7q3/DFa938fHB7du3aq1vaCgAB07dtR1OCIirRSVVuL9Xb8CAGYM6Ig+z7kgpIMLE34ialZsbGywbu063M+8j5yVObVWyiryKpCzMgf3M+9j3dp1T/yHstjG0RexxRMfH4++wX0QtqECR7Kr1UX7FixYoC7udyS7GmEbKtA3uA/i4+MNGg9RY4jte6O8vBxR0VGw72YPzxmetQoDWrtbw3OGJ+y72SMqOgrl5eWNePcmTtBRSkqK0KVLF2HTpk1CTk6OkJOTI2zatEkICAgQUlJSBIVCoX41FwqFQgDQrN4zUVN7d/MvwrPv7hIGfnhQqKhSGjscIiKj2r59u9DSpaUAQLD3thccezsK9t72AgChpUtLYceOHSY5jr6IKZ7i4mKhb3AfAYAgs7IUdu3aJQiCIOzatUuQWVkKAIS+wX2E4uLiJouJqDHE8r3x9ddfCwCETss6Cf5f+T/x1SmukwBASExM1MfbFxVt81Cdb++3sPj95gCJRFLzw0GtvyUSCZRK7Qo0mDre3k9kWD9du4fI1ccBAJumhaBXO8MXXiIiErvy8nJs3rwZycnJ6sJVERERGDt2rE4r2GIbR1/EFE9JSQliY2MRGRmp0ZYvNTUVSUlJiI+Ph4ODQ5PGRNQYYvjeGDNmDPac3YN289s1eJ2spVkI9Q/Fli1btI7NFGibh+qc9B86dEjrY1966SVdhjZZTPqJDKeiWolhHx/G1dsP8GpvT8SNDjR2SERERERkZANeHoCfH/wMzz95Nnhs9qps9GjRAwf2H2iCyJqOtnmozi37mksiT0TisPrQb7h6+wFc7WWYO8TP2OEQERERkQiw9Z/2dE76gYcfxunTp1FQUACVSqWxzxyrIhKRcfx2+z5WHrgCAFg4vDPkdlZGjoiIiIiIxCA8PBxbt25FRX5FrSJ+j1K3/lvYcOu/qOgoFN4thL23PaROUihvKLF161a8/Ze3sW7tOowYMULfb6NJ6Hx7/+7duzF58mTcuXOn9mDN6Dn+R/H2fiL9EwQBE784geO/3cWL3q2wbkovdd0QIiIiImre2PrPgC37ZsyYgXHjxiEvLw8qlUrj1RwTfiIyjC0ZN3D8t7uwsbLA/4X7M+EnIiIiIjW2/tOezkl/QUEBZs2aBTc3N0PEQ0SEew8q8X8pvwIAYl/xhqeznZEjIiIiIiKxGTFiBJKTkyG9LsXluZeRtTQL2auykbU0C5fnXYb0uhTbtm2r97b8TZs2ofBuIdwi3eq8WwAAJBYSuI1zQ+HdQmzevNlQb8dgdE76x44di4MHDxogFCKihz5I+RWFpVXwdXfA1OfbGzscIiIiIhKpkSNH4mbuTSQmJiLUPxQ9WvRAqH8oEhMTcTP3ZoPP4W/btg323vb11gUAAGsPa9h72yM5OVmf4TcJnQv5rVy5EuPGjcPhw4cREBAAKyvNwlozZ87UW3BE1PwcvXIHWzNuQCIB4kYHwEqq82+TRERERNSM2NjY4PXXX8frr7+u87n3Cu9B6iTV6lgLJwvcK7yn8zWMTeek/5tvvkFqaipsbW1x8OBBjedsJRIJk34iemrlVUr8LfkMAGBS8LPo7tXSyBERERERkTkzROs/sdF5CW3BggVYsmQJFAoFsrKycO3aNfXrt99+M0SMRNRM/OfAFWTdLYWbozXmDPYxdjhEREREZObCw8Nx/9L9WoUAH6du/RdRf+s/MdI56a+srMT48eNhYcFbbolIfy7fKsFnh64CABaP6AJHG6sGziAiIiIiapxx48ahpUtL3Eq6BUFVdzd7QSXg1qZbaOnSEmPHjm3iCBtP58z9jTfewMaNGw0RCxE1UyqVgHlbz6BKKeAVv9YY4u9u7JCIiIiIqBnQV+s/MdP5mX6lUokVK1YgNTUVgYGBtQr5ffTRR3oL7lGFhYWYOXMmduzYAeBhlcZ///vfcHJyeuI5UVFRWLdunca2Pn36IC0tTf13RUUF5syZg2+//RZlZWUYOHAgVq1ahbZt2xrkfRBRbRvTc5B+vRB2MineG+WvUSuEiIiIiMiQalr/RUVH4fLcy7D3toeFkwVURSrcv3QfLV1aNtj6T8x0TvrPnDmD7t27AwDOnj2rsc+Q/1CfOHEicnNzsXv3bgDAW2+9hUmTJmHnzp31njdkyBCsXbtW/bdMJtPYHxsbi507d2LDhg1wcXHB7NmzMXz4cJw6dQpSqXZVHIno6RWUlCPuu/MAgNmhPnjGydbIERERERFRc1PT+m/z5s1ITk7GvcJ7cG7rjIiFERg7dqxJrvDXkAiCUPeDCyJy/vx5dO7cGWlpaejTpw8AIC0tDSEhIbhw4QJ8fOou+BUVFYWioiJs27atzv0KhQKtWrVCYmIixo8fDwC4efMmPD098d1332Hw4MFaxVdcXAy5XA6FQgFHR0fd3yBRM/bnb3/Gzl9uIuAZObZN7wepBVf5iYiIiIgaom0e+tTV+K5cuYLU1FSUlZUBAAz528Hx48chl8vVCT8ABAcHQy6X49ixY/Wee/DgQbRu3Rre3t6IiYlBQUGBet+pU6dQVVWF0NBQ9bY2bdrA39+/3nErKipQXFys8SIi3R28WICdv9yEhQSIGx3AhJ+IiIiISM90Tvrv3r2LgQMHwtvbG8OGDUNeXh4A4M0338Ts2bP1HiAA5Ofno3Xr1rW2t27dGvn5+U88b+jQoVi/fj3279+PDz/8ECdPnsTLL7+MiooK9bgymQwtW2r2Andzc6t33Li4OMjlcvXL09PzKd8ZUfNVVqnEgm0PHxGa0q89/J+RGzkiIqLmp6SkBFOnTkVqaqrG9tTUVEydOhUlJSUmOY6+iC0eIqKnoXPS/5e//AVWVlbIzs6GnZ2devv48ePVz9tra/HixZBIJPW+0tPTAdRdL0AQhHrrCIwfPx5hYWHw9/fHiBEj8P333+PSpUtISUmpN66Gxp03bx4UCoX6lZOTo+U7JqIa8T9cQm5hGZ5xssWsQd7GDoeIqNkpKSnBkNBBSEhIwMgRw9X/PkpJScHIEcORkJCAIaGDGkxsxTaOvogtHiKip6Vz0r9nzx4sX768VnX7Tp064fr16zqNNWPGDJw/f77el7+/P9zd3XHr1q1a59++fRtubm5aX8/DwwPPPvssLl++DABwd3dHZWUlCgsLNY4rKCiod1xra2s4OjpqvIhIe7/eLMaXh68BAJaM6oIW1jrXFCUiokaoSWjP/pKOw1PsMLSDBUZHhGPhwoUYHRGOYR0tcHiKHc7+kl5vYiu2cfRFbPEQETWGzkn/gwcPNFb4a9y5cwfW1tY6jeXq6gpfX996XzY2NggJCYFCocBPP/2kPvfEiRNQKBTo27ev1te7e/cucnJy4OHhAQAICgqClZUV9u7dqz4mLy8PZ8+e1WlcokflKcpw7Ood5CnKzGocfcktLMWfv82AUiVgqL87Bvpp/8MdERHpR2xsLI6lnUDKBGs872WJpLHWGNrBAh988AGGdbTAxjEPt6dMsMaxtBOIjY01iXH0RWzxEBE1hs7Lay+++CK+/vprvP/++wAe3navUqnwj3/8AwMGDNB7gADg5+eHIUOGICYmBqtXrwbwsGXf8OHDNSr3+/r6Ii4uDhEREbh//z4WL16MMWPGwMPDA1lZWZg/fz5cXV0REREBAJDL5Zg6dSpmz54NFxcXODs7Y86cOQgICMArr7xikPdC5m3jyWzM3XoGggBIAET2aovg51x0Hiftt7tIOpkLAfobp6ZY3vheXjqPoy8bT2Zj7pYzqCn72cOrZb3HExGRYURGRuK/iV/jw7Rq9H5GCplUgqSx1ki5JEWYtyVkUgkqlQL+ebwaMitLREZGmsQ4+iK2eIiIGkPnln2//vor+vfvj6CgIOzfvx8jR47EuXPncO/ePRw9ehQdOnQwSKD37t3DzJkzsWPHDgAP+yiuXLkSTk5O6mMkEgnWrl2LqKgolJWVITw8HD///DOKiorg4eGBAQMG4P3339covFdeXo6//vWv+Oabb1BWVoaBAwdi1apVOhXnY8s+ul9RjcTj17F89wVjh1IvCwlwdO7L8JDbNvm1bxaVot+yA3j0C0cqkeDI3AFGiYeIqLlLSUlR36q+cYw1ZNLf6xlVKgVEbq7A91dV2Jq8DWFhYSYzjr6ILR4iosdpm4fqnPQDD6vef/rppzh16hRUKhV69OiB6dOnq2+bb26Y9DdPgiDgdK4C3/6UjR2/3ERppbLO4wKecYSTnUzrcYtKK3HmRu02kPoa54vJQRjU2V3rcfSholqJaYmncODi7Vr7vo0JRkgH3e9iICKixlu4cCE++OADbI20RYSflXp78vkqjE4qw4IFC9R3d5rSOPoitniIiB5lsKQ/Ozsbnp6edVa3z87OhpeX8W4dNhYm/c1LcXkVtmfexLcnsvFr3u9J9bPOtsi+V9bolew8RRn6LdsP1SMD6WscAPBwssGayb3QuU3TzNU8RRn++N8MZOYU1drHlX4iMhUlJSWIjY1FZGQkBg8erN6empqKpKQkxMfHw8HBwaTGEdsKvdhW1sUWDxHR47TNQ3Uu5Ne+fXvcvl17te7u3bto3769rsMRmQRBEPBzdiHe2fwL+vzfD1i47Sx+zSuGzNICEd2fQdIfQnDwrwOwbEwApP/7QUwqkWDpaH+dE1oPuS3iRut/HAsJ4NxChryicoz+9Ci2Z97Qabyn8dO1exjx7yPIzCmC3NYKU59v3+j3RUTU1MTWkk4f46SmptZKaCuVApLPV6FSKaifYa+pWv94n3qxjqMvYouHiKhRBB1JJBKhoKCg1vasrCzBzs5O1+HMgkKhEAAICoXC2KGQnhWVVgrrjl0TBv/rkPDsu7vUr1c+PCisOfybUPigotY5N4tKhWNX7gg3i0obdW1DjFP4oEKYtOaE+n28v/OcUFWtbNT4dVGpVMJXR68JHealCM++u0sY/K9DwvU7D2rFQ0QkdsXFxULf4D6Co61UODzFThjlKxNkVpbCggULBJmVpRDuJxMOT7ETHG2lQt/gPkJxcbFJjBMdHS0AEA5PsROERY5CxQIHYZSvTAAghPvJhIoFDoKwyFE4PMVOACBER0ebxDj6IrZ4iIjqom0eqvXt/bNmzQIAfPzxx4iJidFo26dUKnHixAlIpVIcPXpUX79HmAze3v908hRluHbnAdq7tmjUaq++x2nnYoc8RTm+OZGDlDM3UV6lAgBYW1ogLNADE3t7IejZlnU+4mIKlCoBH+29iP8cuAoA6NvBBf9+tTtc7HVrufkk5VVK/C35LLZk5AIARnZtg2VjAmAn07lZCBGR0U2dOhUJCQk4PMUOz3tZqm/r3n6hEuF+MvUq8JHsarywthTR0dFYs2aN6Md5tA99ygRr/PN4Nb6/qsI7787FiuXLMKyjBWYHWyJsQwX8u/bE7j1763xcQGzj6IvY4iEiqoven+mvacd36NAhhISEQCb7vaCYTCZDu3btMGfOHHTq1KmRoZseJv2623gyG/O2noFKACQSYMaAjgh9iuJye37Nx8oDVx62yNPTOI/zcXPAq709EdG9LeR2VrUPMFHfn8nD7E2/oLRSiWecbLF6UhD8n5E3aswbRWWYlngKZ24oYCEB5g/zw9Tn25vsDyRERKmpqRg5Ynit27xTLlVrtG6reb57x85dGs/Yi3Uc4PfE9ljaCcisLNXPptc8y15ZVY2+wX0aTGjFNo6+iC0eIqLHGayQ35QpU/Dxxx8zuX0Ek37dPKnAnNiEBbhj6gvPobunk9kmrZduleAPiadw7c4DWFtaYGlEAMYEtX2qsY5dvYMZ3/yMew8q0dLOCv+Z2AN9O7rqOWIioqYntkJ1+iwwJ6bCgvocR1/EFg8R0aMM2rKPNDHp182xq3cw8YsTtba7tLCCtaVU63EqqpW4+6DKYOM0l1ZyirIq/GVjJvZfKAAARPVth7+F+cFKql2dT0EQsObINcR9fwFKlYAubRyxelIQ2ra0a/hkIiITIbaWdGwlR0RETPqbEJN+3aT9dhcTPk/T2Ca21nbNrZWcSiUg/ofL+OSHywCA3u2d8Z+JPdDKof7n/MsqlZi79TS2Z94EAIzu/gyWjg6AjZX2P7oQEdVFTCusYluhZyu5holp/hARGYrBWvYRNYYgCIjfd0ljm9ha2zXHVnIWFhLMGuSNzycFwd7aUqPV3pPk3CvF6E+PYXvmTUgtJFg8ojM+jOzKhJ+IGk1fre30QWwt6dhKrmFimj9ERGLApJ+a1KZTuUj77R5sraTY8scQfBsTjCNzB2B8L6+nGm98Ly8cmTtANOOYutAu7tg2vR86tGqB/OJyRH52HBtPZtc67vDl2xix8gjO5xXDpYUM69/sg6h+LNhHRI33aNX0w1Ps1MnrwoUL1cnu4Sl2OPtLepMkbklJSaisqsbsYM0ieaOTyjB+S4U60Z4TYonKqmokJSWZxDjmSmzzh4hIDHh7vx6Yyu39+mpt97Tu3q/AwI8Ooai0CvOH+eKtFzs0eQyknZLyKsxO+gV7fr0FAHitjxf+8OJzyC0sw5Erd/DZoatQCUDXtnJ8+noQ2jg1n7siiMiw9NWSTl/E1pKOreTqJ7b5Q0RkSHymvwmZQtL/aIs8CwkQNzqgyVezZ23MxNafb8DPwxE7ZvTTulAcGYdKJeA/B67go32X6mxlOC6oLd4P9+ft/ESkV/psSacvYmtJx1ZyTybG+UNEZChM+puQ2JN+MRSqO3L5Dl5fcwISCZD8p37o5unUJNelxtt8KgdzNp3W2CaRAEffHYA2TqzQT0T6J8ZCdWJrScdCdU8mxvlDRGQITPqbkNiT/ie1yGuqlnTlVUoMjv8R1++WIqpvOywe2cXg1yT9Mfb8IaLmiS3pqDE4f4ioOWD1flJr79oCFnXUV2vjZNMk1//3/su4frcU7o42mB3q3STXJP2pa/5IJRK0c+UqPxEZRkpKClYsX4ZwPxnCvC019oV5W2KUrwwrli9TV2UnehTnDxGRJib9zcDjLelqfPLDFRj6Ro+L+SVYfeg3AMDikV3gYGPVwBkkNmxpSERNiS3pqDE4f4iIarNs+BAyB+N7eeFF71bIulOK/OJyzE7KxJaMXHRys8e0lwxTRV+lEjA/+QyqVQIGdXbDEH93g1yHDO/R+dPO1Y4JPxEZzO8t6ew0iq49Xn19Togltl8oRVJSEguxkRrnDxFRbVzpb0Y85LYI6eCCiO7PYNGIh8/VL999AXvO5Rvket+ezMap64VoIZPiPT7Hb/Jq5g8TfiIypPj4ePQN7oOwDRU4kl2tLrq2YMECfHdFhfFbHm4P21CBvsF9EB8fb+yQSUQ4f4iIamMhPz0QeyG/ugiCgIXbz+K/admwk0mxaVoIurSR6238guJyDPzoEErKq7FoRGdM6ddeb2MTEZF5Y0s6agzOHyJqLli9vwmZYtIPAFVKFaasPYkjV+6gjdwG22b0Q2sH/RT3m/5NBlJO5yGwrRzJf+oHaV2VBImIiJ6ALemoMTh/iKg5YNLfhEw16QcARWkVIlYdxW93HqCbpxM2vBUMGytpo8Y8cKEAU746CamFBNun94P/M/q7g4CIiIiIiIjMsGVfYWEhJk2aBLlcDrlcjkmTJqGoqKjecyQSSZ2vf/zjH+pj+vfvX2v/hAkTDPxuxENuZ4U1Ub0gt7VCZk4R3tl8ulEV/Usrq7Fg21kAQHS/dkz4iYiIiIiIjMhkkv6JEyciMzMTu3fvxu7du5GZmYlJkybVe05eXp7GKyEhARKJBGPGjNE4LiYmRuO41atXG/KtiE571xb49PUesLSQYMcvN7Fy/5WnHit+32XcKCrDM062+Msgbz1GSURERERERLoyiZZ958+fx+7du5GWloY+ffoAAL744guEhITg4sWL8PHxqfM8d3fNFnHbt2/HgAED8Nxzz2lst7Ozq3Vsc9O3gyuWjPLH/OQz+HDvJTzXyh5hgR46jXHupgJrjlwDALwf3gV2MpOYXkRERERERGbLJFb6jx8/Drlcrk74ASA4OBhyuRzHjh3Taoxbt24hJSUFU6dOrbVv/fr1cHV1RZcuXTBnzhyUlJTUO1ZFRQWKi4s1XuZgYh8vRP+vyv7sTZk4nVuk9blKlYB5W89AqRIQFuCBl33dDBQlERERERERacskkv78/Hy0bt261vbWrVsjP1+7HvPr1q2Dg4MDRo8erbH9tddew7fffouDBw9i4cKF2LJlS61jHhcXF6euLSCXy+Hp6an9mxG5+cN80d+nFcqrVIj5Oh35inKtzks8noXTuQo42Fhi0YjOBo6SiIiIiIiItGHUpH/x4sVPLLZX80pPTwfwsCjf4wRBqHN7XRISEvDaa6/BxkazJV1MTAxeeeUV+Pv7Y8KECdi8eTP27duHjIyMJ441b948KBQK9SsnJ0eHdy1ullILfPJqd3RqbY9bxRWI+TodZZXKes/JU5ThH6kXAQDvDvFFa0f9tP0jIiIiIiKixjHqQ9czZsxosFJ+u3btcPr0ady6davWvtu3b8PNreHbyA8fPoyLFy9i48aNDR7bo0cPWFlZ4fLly+jRo0edx1hbW8Pa2rrBsUyVo40V1rzRC+GrjuLMDQVmb8rEyld7wMKi7h9YFm0/hweVSvTwcsLE3l5NHC0RERERERE9iVFX+l1dXeHr61vvy8bGBiEhIVAoFPjpp5/U5544cQIKhQJ9+/Zt8Dpr1qxBUFAQunbt2uCx586dQ1VVFTw8dCtiZ268XOzw2etBsJJK8N2ZfMTvu1Tncann8rHn11uwtJAgbnTgE38YICIiMoaSkhJMnToVqampGttTU1MxderUBuv4EBERmTqTeKbfz88PQ4YMQUxMDNLS0pCWloaYmBgMHz5co3K/r68vkpOTNc4tLi7Gpk2b8Oabb9Ya9+rVq1iyZAnS09ORlZWF7777DuPGjUP37t3Rr18/g78vsevd3hlLIwIAAJ/sv4LtmTc09t+vqMai7ecAAG+9+Bx83B2aPEYiIqInKSkpwZDQQUhISMDIEcORkpICAEhJScHIEcORkJCAIaGDmPgTEZFZM4mkH3hYYT8gIAChoaEIDQ1FYGAgEhMTNY65ePEiFAqFxrYNGzZAEAS8+uqrtcaUyWT44YcfMHjwYPj4+GDmzJkIDQ3Fvn37IJVKDfp+TMW4np74w0sPWxz+dfNpZGQXqvf9M/Ui8ovL4eVsh5kDOxkrRCIiolpqEv6zv6Tj8BQ7DO1ggdER4Vi4cCFGR4RjWEcLHJ5ih7O/pDPxJyIisyYRBEEwdhCmrri4GHK5HAqFAo6OjsYOR++UKgF/SDyFfedvwdXeGl9MDsLZGwos/N8qf+LU3nihUysjR0lERPS7qVOnIiEhAYen2OF5L0tUKgVEbq7A9guVCPeTYeMYa8ikEhzJrsYLa0sRHR2NNWvWGDtsIiIirWmbh5rMSj8Zj9RCgo8ndIOfhyPu3K9AxKpj6oS/m6ecCT8REYlOZGQkZFaW+DCtGpVKATKpBEljrbE10lad8FcqBfzzeDVkVpaIjIw0dshEREQGwaSftNLC2hJLI/xrbT+dq0CeoswIERERET3Z4MGDsTV5G767osL4LRXqxD/Cz0qd8EdursD3V1XYmrwNgwcPNnbIREREBsGkn7RWVqWstU0lAFl3So0QDRERUf3CwsLwzrtzse18JVIuVWvsS7lUje0XKvHOu3MRFhZmpAiJiIgMj0k/aa29aws83pFPKpGgnaudcQIiIiKqR0pKClYsX4ZwPxnCvC019oV5W2KUrwwrli9TV/UnIiIyR0z6SWseclvEjQ6AVPIw85dKJFg62h8eclsjR0ZERKQpNTVVXaX/0Wf4k89XaTzjX1PVPzU11dghExERGYRlw4cQ/W58Ly+86N0KWXdK0c7Vjgk/ERGJUlJSEiqrqjE72E7jGf7Hq/fPCbHE9gulSEpK4nP9RERklrjSTzrzkNsipIMLE34iIhKt+Ph49A3ug7ANFTiSXa0u2rdgwQJ1cb8j2dUI21CBvsF9EB8fb+yQiYiIDIIr/URERGR2HBwcsHvPXgwJHYQX1p6AzMoSW5O3ISwsDMHBwRgdEY5t50vRN7gPdu/ZCwcHB2OHTEREZBBc6SciIiKzVJP4R0dHY8fOXeoq/WFhYdixcxeio6OZ8BMRkdmTCIIgGDsIU1dcXAy5XA6FQgFHR0djh0NERERERERmTts8lLf360HN7ybFxcVGjoSIiIiIiIiag5r8s6F1fCb9elBSUgIA8PT0NHIkRERERERE1JyUlJRALpc/cT9v79cDlUqFmzdvwsHBAZL/9bAXo+LiYnh6eiInJ4ePIZDJ43wmc8L5TOaE85nMCecziZkgCCgpKUGbNm1gYfHkcn1c6dcDCwsLtG3b1thhaM3R0ZFfWmQ2OJ/JnHA+kznhfCZzwvlMYlXfCn8NVu8nIiIiIiIiMlNM+omIiIiIiIjMFJP+ZsTa2hqLFi2CtbW1sUMhajTOZzInnM9kTjifyZxwPpM5YCE/IiIiIiIiIjPFlX4iIiIiIiIiM8Wkn4iIiIiIiMhMMeknIiIiIiIiMlNM+omIiIiIiIjMFJP+ZmTVqlVo3749bGxsEBQUhMOHDxs7JKIG/fjjjxgxYgTatGkDiUSCbdu2aewXBAGLFy9GmzZtYGtri/79++PcuXPGCZaoHnFxcejVqxccHBzQunVrhIeH4+LFixrHcD6Tqfj0008RGBgIR0dHODo6IiQkBN9//716P+cymbK4uDhIJBLExsaqt3FOkylj0t9MbNy4EbGxsfjb3/6Gn3/+GS+88AKGDh2K7OxsY4dGVK8HDx6ga9euWLlyZZ37V6xYgY8++ggrV67EyZMn4e7ujkGDBqGkpKSJIyWq36FDhzB9+nSkpaVh7969qK6uRmhoKB48eKA+hvOZTEXbtm2xbNkypKenIz09HS+//DJGjRqlToI4l8lUnTx5Ep9//jkCAwM1tnNOk0kTqFno3bu3MG3aNI1tvr6+wty5c40UEZHuAAjJycnqv1UqleDu7i4sW7ZMva28vFyQy+XCZ599ZoQIibRXUFAgABAOHTokCALnM5m+li1bCl9++SXnMpmskpISoVOnTsLevXuFl156SXj77bcFQeD3M5k+rvQ3A5WVlTh16hRCQ0M1toeGhuLYsWNGioqo8a5du4b8/HyNuW1tbY2XXnqJc5tET6FQAACcnZ0BcD6T6VIqldiwYQMePHiAkJAQzmUyWdOnT0dYWBheeeUVje2c02TqLI0dABnenTt3oFQq4ebmprHdzc0N+fn5RoqKqPFq5m9dc/v69evGCIlIK4IgYNasWXj++efh7+8PgPOZTM+ZM2cQEhKC8vJy2NvbIzk5GZ07d1YnQZzLZEo2bNiAjIwMnDx5stY+fj+TqWPS34xIJBKNvwVBqLWNyBRxbpOpmTFjBk6fPo0jR47U2sf5TKbCx8cHmZmZKCoqwpYtW/DGG2/g0KFD6v2cy2QqcnJy8Pbbb2PPnj2wsbF54nGc02SqeHt/M+Dq6gqpVFprVb+goKDWL5ZEpsTd3R0AOLfJpPz5z3/Gjh07cODAAbRt21a9nfOZTI1MJkPHjh3Rs2dPxMXFoWvXrvj44485l8nknDp1CgUFBQgKCoKlpSUsLS1x6NAhfPLJJ7C0tFTPW85pMlVM+psBmUyGoKAg7N27V2P73r170bdvXyNFRdR47du3h7u7u8bcrqysxKFDhzi3SXQEQcCMGTOwdetW7N+/H+3bt9fYz/lMpk4QBFRUVHAuk8kZOHAgzpw5g8zMTPWrZ8+eeO2115CZmYnnnnuOc5pMGm/vbyZmzZqFSZMmoWfPnggJCcHnn3+O7OxsTJs2zdihEdXr/v37uHLlivrva9euITMzE87OzvDy8kJsbCyWLl2KTp06oVOnTli6dCns7OwwceJEI0ZNVNv06dPxzTffYPv27XBwcFCvGMnlctja2qp7QnM+kymYP38+hg4dCk9PT5SUlGDDhg04ePAgdu/ezblMJsfBwUFdX6VGixYt4OLiot7OOU2mjEl/MzF+/HjcvXsXS5YsQV5eHvz9/fHdd9/h2WefNXZoRPVKT0/HgAED1H/PmjULAPDGG2/gq6++wjvvvIOysjL86U9/QmFhIfr06YM9e/bAwcHBWCET1enTTz8FAPTv319j+9q1axEVFQUAnM9kMm7duoVJkyYhLy8PcrkcgYGB2L17NwYNGgSAc5nMD+c0mTKJIAiCsYMgIiIiIiIiIv3jM/1EREREREREZopJPxEREREREZGZYtJPREREREREZKaY9BMRERERERGZKSb9RERERERERGaKST8RERERERGRmWLST0RERERERGSmmPQTERGRThYvXoxu3bo1+XUPHjwIiUQCiUSC8PBw9fb+/fsjNja23nPbtWunPreoqMigcRIREYkJk34iIiJSq0mMn/SKiorCnDlz8MMPPxgtxosXL+Krr77S6ZyTJ09iy5YthgmIiIhIxCyNHQARERGJR15envq/N27ciL///e+4ePGieputrS3s7e1hb29vjPAAAK1bt4aTk5NO57Rq1QrOzs6GCYiIiEjEuNJPREREau7u7uqXXC6HRCKpte3x2/ujoqIQHh6OpUuXws3NDU5OTnjvvfdQXV2Nv/71r3B2dkbbtm2RkJCgca0bN25g/PjxaNmyJVxcXDBq1ChkZWU9VdwqlQrvvPMOnJ2d4e7ujsWLFz/9h0BERGRGmPQTERFRo+3fvx83b97Ejz/+iI8++giLFy/G8OHD0bJlS5w4cQLTpk3DtGnTkJOTAwAoLS3FgAEDYG9vjx9//BFHjhyBvb09hgwZgsrKSp2vv27dOrRo0QInTpzAihUrsGTJEuzdu1ffb5OIiMjkMOknIiKiRnN2dsYnn3wCHx8fREdHw8fHB6WlpZg/fz46deqEefPmQSaT4ejRowCADRs2wMLCAl9++SUCAgLg5+eHtWvXIjs7GwcPHtT5+oGBgVi0aBE6deqEyZMno2fPnkatO0BERCQWfKafiIiIGq1Lly6wsPh9LcHNzQ3+/v7qv6VSKVxcXFBQUAAAOHXqFK5cuQIHBweNccrLy3H16lWdrx8YGKjxt4eHh/paREREzRmTfiIiImo0Kysrjb8lEkmd21QqFYCHz+AHBQVh/fr1tcZq1aqVXq5fcy0iIqLmjEk/ERERNbkePXpg48aNaN26NRwdHY0dDhERkdniM/1ERETU5F577TW4urpi1KhROHz4MK5du4ZDhw7h7bffRm5urrHDIyIiMhtM+omIiKjJ2dnZ4ccff4SXlxdGjx4NPz8/REdHo6ysjCv/REREeiQRBEEwdhBEREREDTl48CAGDBiAwsJCODk5Nfn5REREpogr/URERGRS2rZti1dffVWnc7p06YKhQ4caKCIiIiLx4ko/ERERmYSysjLcuHEDAGBvbw93d3etz71+/TqqqqoAAM8995xGe0EiIiJzxqSfiIiIiIiIyEzxZ24iIiIiIiIiM8Wkn4iIiIiIiMhMMeknIiIiIiIiMlNM+omIiIiIiIjMFJN+IiIiIiIiIjPFpJ+IiIiIiIjITDHpJyIiIiIiIjJTTPqJiIiIiIiIzBSTfiIiIiIiIiIz9f/K0Qe5vJ3l8wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1200x800 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "repeat_window = WindowGenerator(\n",
+    "    input_width=OUT_STEPS, label_width=OUT_STEPS, shift=OUT_STEPS,\n",
+    "    label_columns=['temp']\n",
+    ")\n",
+    "\n",
+    "class RepeatBaseline(tf.keras.Model):\n",
+    "  def call(self, inputs):\n",
+    "    return inputs\n",
+    "\n",
+    "repeat_baseline = RepeatBaseline()\n",
+    "repeat_baseline.compile(loss=tf.keras.losses.MeanSquaredError(),\n",
+    "                        metrics=[tf.keras.metrics.MeanAbsoluteError()])\n",
+    "\n",
+    "multi_val_performance['Repeat'] = repeat_baseline.evaluate(repeat_window.val)\n",
+    "multi_performance['Repeat'] = repeat_baseline.evaluate(repeat_window.test, verbose=0)\n",
+    "repeat_window.plot(repeat_baseline)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9279cea8-0f0f-44a9-a255-2d4cb1e56fd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/30\n",
+      "3915/3915 [==============================] - 333s 84ms/step - loss: 0.1327 - mean_absolute_error: 0.2702 - val_loss: 0.0802 - val_mean_absolute_error: 0.2103\n",
+      "Epoch 2/30\n",
+      "3915/3915 [==============================] - 348s 89ms/step - loss: 0.0916 - mean_absolute_error: 0.2263 - val_loss: 0.0747 - val_mean_absolute_error: 0.2001\n",
+      "Epoch 3/30\n",
+      "3915/3915 [==============================] - 325s 83ms/step - loss: 0.0883 - mean_absolute_error: 0.2206 - val_loss: 0.0724 - val_mean_absolute_error: 0.1961\n",
+      "Epoch 4/30\n",
+      "3915/3915 [==============================] - 340s 87ms/step - loss: 0.0865 - mean_absolute_error: 0.2175 - val_loss: 0.0715 - val_mean_absolute_error: 0.1940\n",
+      "Epoch 5/30\n",
+      "3915/3915 [==============================] - 350s 89ms/step - loss: 0.0853 - mean_absolute_error: 0.2154 - val_loss: 0.0713 - val_mean_absolute_error: 0.1936\n",
+      "Epoch 6/30\n",
+      "3915/3915 [==============================] - 343s 88ms/step - loss: 0.0844 - mean_absolute_error: 0.2139 - val_loss: 0.0708 - val_mean_absolute_error: 0.1925\n",
+      "Epoch 7/30\n",
+      "3707/3915 [===========================>..] - ETA: 16s - loss: 0.0839 - mean_absolute_error: 0.2131"
+     ]
+    }
+   ],
+   "source": [
+    "multi_lstm_model = tf.keras.Sequential([\n",
+    "    # Shape [batch, time, features] => [batch, lstm_units].\n",
+    "    # Adding more `lstm_units` just overfits more quickly.\n",
+    "    tf.keras.layers.LSTM(32, return_sequences=False),\n",
+    "    # Shape => [batch, out_steps*features].\n",
+    "    tf.keras.layers.Dense(OUT_STEPS,\n",
+    "                          kernel_initializer=tf.initializers.zeros()),\n",
+    "    tf.keras.layers.Reshape([OUT_STEPS, 1])\n",
+    "])\n",
+    "\n",
+    "history = compile_and_fit(multi_lstm_model, multi_window)\n",
+    "\n",
+    "IPython.display.clear_output()\n",
+    "\n",
+    "multi_val_performance['LSTM'] = multi_lstm_model.evaluate(multi_window.val)\n",
+    "multi_performance['LSTM'] = multi_lstm_model.evaluate(multi_window.test, verbose=0)\n",
+    "multi_window.plot(multi_lstm_model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4388d821-d3d2-4dc6-9906-b755897522ef",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd23a70d-a219-4953-8754-63a209566f40",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10d24123-9822-4d13-a49f-40e1d0a4b572",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Comparing the forecasts with the actual values\n",
+    "yhat = [x[0] for x in model.predict(Xval)]\n",
+    "y = [y[0] for y in Yval]\n",
+    "# Creating the frame to store both predictions\n",
+    "days = df['obs_timestamp'].values[-len(y):]\n",
+    "frame = pd.concat([\n",
+    " pd.DataFrame({'day': days, 'temp': y, 'type': 'original'}),\n",
+    " pd.DataFrame({'day': days, 'temp': yhat, 'type': 'forecast'})\n",
+    "])\n",
+    "# Creating the unscaled values column\n",
+    "frame['temp_absolute'] = [(x * train_std['temp']) + train_mean['temp'] for x in frame['temp']]\n",
+    "# Pivoting\n",
+    "pivoted = frame.pivot_table(index='day', columns='type')\n",
+    "pivoted.columns = ['_'.join(x).strip() for x in pivoted.columns.values]\n",
+    "pivoted['res'] = pivoted['temp_absolute_original'] - pivoted['temp_absolute_forecast']\n",
+    "pivoted['res_abs'] = [abs(x) for x in pivoted['res']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6bc48d05-c104-463e-a702-42cd545b55d5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "015e5ba16a4bb6c2d4406ff94ae7629b76540da55dd7d929cadb2b51e4d9f31f"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/training/src/.ipynb_checkpoints/MSML-602-HW1-Q4-checkpoint.ipynb b/training/src/.ipynb_checkpoints/MSML-602-HW1-Q4-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..8d9fb3593cc439e1399390c03279943df06f24d0
--- /dev/null
+++ b/training/src/.ipynb_checkpoints/MSML-602-HW1-Q4-checkpoint.ipynb
@@ -0,0 +1,283 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "d10fb797-f9d4-40c7-ba48-08e4ece334f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Data for sum\n",
+      "     x      P(X)  x * P(X)\n",
+      "0   10  0.125000  1.250000\n",
+      "1   11  0.125000  1.375000\n",
+      "2    9  0.115741  1.041667\n",
+      "3   12  0.115741  1.388889\n",
+      "4    8  0.097222  0.777778\n",
+      "5   13  0.097222  1.263889\n",
+      "6    7  0.069444  0.486111\n",
+      "7   14  0.069444  0.972222\n",
+      "8    6  0.046296  0.277778\n",
+      "9   15  0.046296  0.694444\n",
+      "10   5  0.027778  0.138889\n",
+      "11  16  0.027778  0.444444\n",
+      "12   4  0.013889  0.055556\n",
+      "13  17  0.013889  0.236111\n",
+      "14   3  0.004630  0.013889\n",
+      "15  18  0.004630  0.083333\n",
+      "\n",
+      "Expectation for sum: 10.5\n",
+      "\n",
+      "Data for product\n",
+      "      x      P(X)  x * P(X)\n",
+      "0    12  0.069444  0.833333\n",
+      "1    24  0.069444  1.666667\n",
+      "2    30  0.055556  1.666667\n",
+      "3    60  0.055556  3.333333\n",
+      "4    36  0.055556  2.000000\n",
+      "5    18  0.041667  0.750000\n",
+      "6    72  0.041667  3.000000\n",
+      "7     6  0.041667  0.250000\n",
+      "8    48  0.041667  2.000000\n",
+      "9    20  0.041667  0.833333\n",
+      "10    8  0.032407  0.259259\n",
+      "11   90  0.027778  2.500000\n",
+      "12   40  0.027778  1.111111\n",
+      "13   16  0.027778  0.444444\n",
+      "14   10  0.027778  0.277778\n",
+      "15    4  0.027778  0.111111\n",
+      "16   15  0.027778  0.416667\n",
+      "17  120  0.027778  3.333333\n",
+      "18   32  0.013889  0.444444\n",
+      "19   25  0.013889  0.347222\n",
+      "20  108  0.013889  1.500000\n",
+      "21  100  0.013889  1.388889\n",
+      "22   96  0.013889  1.333333\n",
+      "23    3  0.013889  0.041667\n",
+      "24   80  0.013889  1.111111\n",
+      "25   75  0.013889  1.041667\n",
+      "26  150  0.013889  2.083333\n",
+      "27  144  0.013889  2.000000\n",
+      "28    5  0.013889  0.069444\n",
+      "29  180  0.013889  2.500000\n",
+      "30   50  0.013889  0.694444\n",
+      "31    9  0.013889  0.125000\n",
+      "32   45  0.013889  0.625000\n",
+      "33    2  0.013889  0.027778\n",
+      "34   54  0.013889  0.750000\n",
+      "35    1  0.004630  0.004630\n",
+      "36  125  0.004630  0.578704\n",
+      "37   64  0.004630  0.296296\n",
+      "38   27  0.004630  0.125000\n",
+      "39  216  0.004630  1.000000\n",
+      "Expectation for product 42.875\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "    \n",
+    "dice1 = [1, 2, 3, 4, 5, 6]\n",
+    "dice2 = [1, 2, 3, 4, 5, 6]\n",
+    "dice3 = [1, 2, 3, 4, 5, 6]\n",
+    "\n",
+    "\n",
+    "data = [[d1, d2, d3, d1 + d2 + d3, d1 * d2 * d3] for d1 in dice1 for d2 in dice2 for d3 in dice3]\n",
+    "\n",
+    "df = pd.DataFrame(data, columns=[\"dice1\", \"dice2\", \"dice3\", \"sum_of_dots\", \"product_of_dots\"])\n",
+    "total_count = len(dice1) * len(dice2) * len(dice3)\n",
+    "\n",
+    "# Expectation of sum of number of dots on the three rolls\n",
+    "sum_pmf = [(x[0], count_x / (total_count * 1.0)) for x, count_x in df.value_counts([\"sum_of_dots\"]).items()]\n",
+    "sum_pmf_df = pd.DataFrame(sum_pmf, columns=[\"x\", \"P(X)\"])\n",
+    "sum_pmf_df[\"x * P(X)\"] = sum_pmf_df[\"x\"] *  sum_pmf_df[\"P(X)\"] \n",
+    "expectation_sum = sum_pmf_df[\"x * P(X)\"].sum()\n",
+    "\n",
+    "# Expectation of product of number of dots on the three rolls\n",
+    "product_pmf = [(x[0], count_x / (total_count * 1.0)) for x, count_x in df.value_counts([\"product_of_dots\"]).items()]\n",
+    "product_pmf_df = pd.DataFrame(product_pmf, columns=[\"x\", \"P(X)\"])\n",
+    "product_pmf_df[\"x * P(X)\"] = product_pmf_df[\"x\"] *  product_pmf_df[\"P(X)\"] \n",
+    "expectation_product = product_pmf_df[\"x * P(X)\"].sum()\n",
+    "\n",
+    "print(\"\\nData for sum\")\n",
+    "print(sum_pmf_df)\n",
+    "print(\"\\nExpectation for sum: {}\".format(expectation_sum))\n",
+    "\n",
+    "print(\"\\nData for product\")\n",
+    "print(product_pmf_df)\n",
+    "print(\"Expectation for product {}\".format(expectation_product))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "301ba7da-d523-48eb-8a6d-25e8d6fa5ffe",
+   "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>x</th>\n",
+       "      <th>P(X)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.125000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>11</td>\n",
+       "      <td>0.125000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9</td>\n",
+       "      <td>0.115741</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>12</td>\n",
+       "      <td>0.115741</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8</td>\n",
+       "      <td>0.097222</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    x      P(X)\n",
+       "0  10  0.125000\n",
+       "1  11  0.125000\n",
+       "2   9  0.115741\n",
+       "3  12  0.115741\n",
+       "4   8  0.097222"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "42954464-bab8-44fc-9087-8c4f07bac679",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<zip at 0x7f9ae912ac40>"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "4de2391b-e130-405b-bc26-b04584b2ed9c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "56b9fd02-79ec-4c7d-9eca-17c04a7df44d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "09e2316e-e242-48ab-9803-ef9e9212ea94",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "45a28a91-dc74-469b-8a1c-caa43ed0b907",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2268\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(expectation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4b63417-d541-44d1-98ad-2027865e1818",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/training/src/.ipynb_checkpoints/MSML-602-HW1-checkpoint.ipynb b/training/src/.ipynb_checkpoints/MSML-602-HW1-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c9a90596744e018c8ebbbfbeca060a6dcbdd781b
--- /dev/null
+++ b/training/src/.ipynb_checkpoints/MSML-602-HW1-checkpoint.ipynb
@@ -0,0 +1,1114 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "90b406fa-994c-4ce8-9d8d-2cf7e9358915",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Data for sum\n",
+      "     x      P(X)  x * P(X)\n",
+      "0   10  0.125000  1.250000\n",
+      "1   11  0.125000  1.375000\n",
+      "2    9  0.115741  1.041667\n",
+      "3   12  0.115741  1.388889\n",
+      "4    8  0.097222  0.777778\n",
+      "5   13  0.097222  1.263889\n",
+      "6    7  0.069444  0.486111\n",
+      "7   14  0.069444  0.972222\n",
+      "8    6  0.046296  0.277778\n",
+      "9   15  0.046296  0.694444\n",
+      "10   5  0.027778  0.138889\n",
+      "11  16  0.027778  0.444444\n",
+      "12   4  0.013889  0.055556\n",
+      "13  17  0.013889  0.236111\n",
+      "14   3  0.004630  0.013889\n",
+      "15  18  0.004630  0.083333\n",
+      "\n",
+      "Expectation for sum: 10.5\n",
+      "\n",
+      "Data for product\n",
+      "      x      P(X)  x * P(X)\n",
+      "0    12  0.069444  0.833333\n",
+      "1    24  0.069444  1.666667\n",
+      "2    30  0.055556  1.666667\n",
+      "3    60  0.055556  3.333333\n",
+      "4    36  0.055556  2.000000\n",
+      "5    18  0.041667  0.750000\n",
+      "6    72  0.041667  3.000000\n",
+      "7     6  0.041667  0.250000\n",
+      "8    48  0.041667  2.000000\n",
+      "9    20  0.041667  0.833333\n",
+      "10    8  0.032407  0.259259\n",
+      "11   90  0.027778  2.500000\n",
+      "12   40  0.027778  1.111111\n",
+      "13   16  0.027778  0.444444\n",
+      "14   10  0.027778  0.277778\n",
+      "15    4  0.027778  0.111111\n",
+      "16   15  0.027778  0.416667\n",
+      "17  120  0.027778  3.333333\n",
+      "18   32  0.013889  0.444444\n",
+      "19   25  0.013889  0.347222\n",
+      "20  108  0.013889  1.500000\n",
+      "21  100  0.013889  1.388889\n",
+      "22   96  0.013889  1.333333\n",
+      "23    3  0.013889  0.041667\n",
+      "24   80  0.013889  1.111111\n",
+      "25   75  0.013889  1.041667\n",
+      "26  150  0.013889  2.083333\n",
+      "27  144  0.013889  2.000000\n",
+      "28    5  0.013889  0.069444\n",
+      "29  180  0.013889  2.500000\n",
+      "30   50  0.013889  0.694444\n",
+      "31    9  0.013889  0.125000\n",
+      "32   45  0.013889  0.625000\n",
+      "33    2  0.013889  0.027778\n",
+      "34   54  0.013889  0.750000\n",
+      "35    1  0.004630  0.004630\n",
+      "36  125  0.004630  0.578704\n",
+      "37   64  0.004630  0.296296\n",
+      "38   27  0.004630  0.125000\n",
+      "39  216  0.004630  1.000000\n",
+      "\n",
+      " Expectation for product 42.875\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Problem 4\n",
+    "\n",
+    "import pandas as pd\n",
+    "    \n",
+    "dice1 = [1, 2, 3, 4, 5, 6]\n",
+    "dice2 = [1, 2, 3, 4, 5, 6]\n",
+    "dice3 = [1, 2, 3, 4, 5, 6]\n",
+    "\n",
+    "\n",
+    "data = [[d1, d2, d3, d1 + d2 + d3, d1 * d2 * d3] for d1 in dice1 for d2 in dice2 for d3 in dice3]\n",
+    "\n",
+    "df = pd.DataFrame(data, columns=[\"dice1\", \"dice2\", \"dice3\", \"sum_of_dots\", \"product_of_dots\"])\n",
+    "total_count = len(dice1) * len(dice2) * len(dice3)\n",
+    "\n",
+    "# Expectation of sum of number of dots on the three rolls\n",
+    "sum_pmf = [(x[0], count_x / (total_count * 1.0)) for x, count_x in df.value_counts([\"sum_of_dots\"]).items()]\n",
+    "sum_pmf_df = pd.DataFrame(sum_pmf, columns=[\"x\", \"P(X)\"])\n",
+    "sum_pmf_df[\"x * P(X)\"] = sum_pmf_df[\"x\"] *  sum_pmf_df[\"P(X)\"] \n",
+    "expectation_sum = sum_pmf_df[\"x * P(X)\"].sum()\n",
+    "\n",
+    "# Expectation of product of number of dots on the three rolls\n",
+    "product_pmf = [(x[0], count_x / (total_count * 1.0)) for x, count_x in df.value_counts([\"product_of_dots\"]).items()]\n",
+    "product_pmf_df = pd.DataFrame(product_pmf, columns=[\"x\", \"P(X)\"])\n",
+    "product_pmf_df[\"x * P(X)\"] = product_pmf_df[\"x\"] *  product_pmf_df[\"P(X)\"] \n",
+    "expectation_product = product_pmf_df[\"x * P(X)\"].sum()\n",
+    "\n",
+    "print(\"\\nData for sum\")\n",
+    "print(sum_pmf_df)\n",
+    "print(\"\\nExpectation for sum: {}\".format(expectation_sum))\n",
+    "\n",
+    "print(\"\\nData for product\")\n",
+    "print(product_pmf_df)\n",
+    "print(\"\\n Expectation for product {}\".format(expectation_product))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "74441428-8bb6-4f30-8f48-52feafa735d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Welcome to the CBC MILP Solver \n",
+      "Version: 2.10.3 \n",
+      "Build Date: Dec 15 2019 \n",
+      "\n",
+      "command line - /opt/conda/lib/python3.9/site-packages/pulp/apis/../solverdir/cbc/linux/64/cbc /tmp/b6c9e0371f914493bd8e01c99d488dc1-pulp.mps max timeMode elapsed branch printingOptions all solution /tmp/b6c9e0371f914493bd8e01c99d488dc1-pulp.sol (default strategy 1)\n",
+      "At line 2 NAME          MODEL\n",
+      "At line 3 ROWS\n",
+      "At line 9 COLUMNS\n",
+      "At line 20 RHS\n",
+      "At line 25 BOUNDS\n",
+      "At line 29 ENDATA\n",
+      "Problem MODEL has 4 rows, 3 columns and 7 elements\n",
+      "Coin0008I MODEL read with 0 errors\n",
+      "Option for timeMode changed from cpu to elapsed\n",
+      "Presolve 0 (-4) rows, 0 (-3) columns and 0 (-7) elements\n",
+      "Empty problem - 0 rows, 0 columns and 0 elements\n",
+      "Optimal - objective value 148.4\n",
+      "After Postsolve, objective 148.4, infeasibilities - dual 0 (0), primal 0 (0)\n",
+      "Optimal objective 148.4 - 0 iterations time 0.002, Presolve 0.00\n",
+      "Option for printingOptions changed from normal to all\n",
+      "Total time (CPU seconds):       0.00   (Wallclock seconds):       0.00\n",
+      "\n",
+      "Solution:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table><tr><td>Variable</td><td>Value</td></tr><tr><td>X</td><td>8.6</td></tr><tr><td>Y</td><td>8.4</td></tr><tr><td>Z</td><td>2.6</td></tr><tr><td>Objective</td><td>148.4</td></tr></table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Problem 5\n",
+    "from pulp import *\n",
+    "from IPython.display import HTML, display\n",
+    "\n",
+    "def display_table(table):\n",
+    "    display(HTML(\n",
+    "       '<table><tr>{}</tr></table>'.format(\n",
+    "           '</tr><tr>'.join(\n",
+    "               '<td>{}</td>'.format('</td><td>'.join(str(_) for _ in row)) for row in table)\n",
+    "           )\n",
+    "    ))\n",
+    "    \n",
+    "problem = LpProblem('MSML_602_PCS2_HW1_Q5', LpMaximize)\n",
+    "\n",
+    "X = LpVariable('X', cat='Continuous')\n",
+    "Y = LpVariable('Y', cat='Continuous')\n",
+    "Z = LpVariable('Z', cat='Continuous')\n",
+    "\n",
+    "problem += 15 * X + 2 * Y + Z, \"Objective Function\"\n",
+    "problem += X <= 10, \"Constraint X\"\n",
+    "problem += X + Y <= 17, \"Constraint X, Y\"\n",
+    "problem += 2 * X + 3 * Z <= 25, \"Constraint X, Z\"\n",
+    "problem += Y + Z >= 11, \"Constraint Y, Z\"\n",
+    "\n",
+    "problem.solve()\n",
+    "print(\"Solution:\")\n",
+    "\n",
+    "data = [[\"Variable\", \"Value\"]] + [[v.name, v.varValue] for v in problem.variables()]\n",
+    "data += [[\"Objective\", problem.objective.value()]]\n",
+    "\n",
+    "display_table(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "96979ae9-7c40-41e9-bef1-ec1ef9e89e9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Welcome to the CBC MILP Solver \n",
+      "Version: 2.10.3 \n",
+      "Build Date: Dec 15 2019 \n",
+      "\n",
+      "command line - /opt/conda/lib/python3.9/site-packages/pulp/apis/../solverdir/cbc/linux/64/cbc /tmp/7efaabba2ec74d7a9fa959ed5c88312d-pulp.mps timeMode elapsed branch printingOptions all solution /tmp/7efaabba2ec74d7a9fa959ed5c88312d-pulp.sol (default strategy 1)\n",
+      "At line 2 NAME          MODEL\n",
+      "At line 3 ROWS\n",
+      "At line 10 COLUMNS\n",
+      "At line 65 RHS\n",
+      "At line 71 BOUNDS\n",
+      "At line 84 ENDATA\n",
+      "Problem MODEL has 5 rows, 12 columns and 18 elements\n",
+      "Coin0008I MODEL read with 0 errors\n",
+      "Option for timeMode changed from cpu to elapsed\n",
+      "Continuous objective value is 19 - 0.00 seconds\n",
+      "Cgl0004I processed model has 5 rows, 11 columns (11 integer (11 of which binary)) and 18 elements\n",
+      "Cutoff increment increased from 1e-05 to 0.9999\n",
+      "Cbc0038I Initial state - 0 integers unsatisfied sum - 0\n",
+      "Cbc0038I Solution found of 19\n",
+      "Cbc0038I Before mini branch and bound, 11 integers at bound fixed and 0 continuous\n",
+      "Cbc0038I Mini branch and bound did not improve solution (0.00 seconds)\n",
+      "Cbc0038I After 0.00 seconds - Feasibility pump exiting with objective of 19 - took 0.00 seconds\n",
+      "Cbc0012I Integer solution of 19 found by feasibility pump after 0 iterations and 0 nodes (0.00 seconds)\n",
+      "Cbc0001I Search completed - best objective 19, took 0 iterations and 0 nodes (0.00 seconds)\n",
+      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
+      "Cuts at root node changed objective from 19 to 19\n",
+      "Probing was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "Gomory was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "Knapsack was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "Clique was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "MixedIntegerRounding2 was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "FlowCover was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "TwoMirCuts was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "ZeroHalf was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "\n",
+      "Result - Optimal solution found\n",
+      "\n",
+      "Objective value:                19.00000000\n",
+      "Enumerated nodes:               0\n",
+      "Total iterations:               0\n",
+      "Time (CPU seconds):             0.00\n",
+      "Time (Wallclock seconds):       0.00\n",
+      "\n",
+      "Option for printingOptions changed from normal to all\n",
+      "Total time (CPU seconds):       0.00   (Wallclock seconds):       0.00\n",
+      "\n",
+      "Shortest distance from v1 to v5 =  19.0\n",
+      "['v1->v3', 'v3->v4', 'v4->v5']\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.9/site-packages/pulp/pulp.py:1352: UserWarning: Spaces are not permitted in the name. Converted to '_'\n",
+      "  warnings.warn(\"Spaces are not permitted in the name. Converted to '_'\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "# HW1 Problem 6\n",
+    "\n",
+    "from pulp import *\n",
+    "import networkx as nx\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "\n",
+    "# I assumed the following arbitrary graph\n",
+    "G = nx.Graph()\n",
+    "G.add_edge(\"v1\", \"v2\", weight=12)\n",
+    "G.add_edge(\"v1\", \"v3\", weight=5)\n",
+    "G.add_edge(\"v1\", \"v5\", weight=25)\n",
+    "G.add_edge(\"v2\", \"v5\", weight=10)\n",
+    "G.add_edge(\"v3\", \"v4\", weight=6)\n",
+    "G.add_edge(\"v4\", \"v5\", weight=8)\n",
+    "\n",
+    "# I am finding the shortest path from vertex 1 to vertex 5\n",
+    "source = \"v1\"\n",
+    "target = \"v5\"\n",
+    "\n",
+    "elarge = [(u, v) for (u, v, d) in G.edges(data=True) if d[\"weight\"] > 0.5]\n",
+    "esmall = [(u, v) for (u, v, d) in G.edges(data=True) if d[\"weight\"] <= 0.5]\n",
+    "\n",
+    "pos = nx.spring_layout(G, seed=7) \n",
+    "\n",
+    "# nodes\n",
+    "nx.draw_networkx_nodes(G, pos, node_size=700)\n",
+    "\n",
+    "# edges\n",
+    "nx.draw_networkx_edges(G, pos, edgelist=elarge, width=6)\n",
+    "nx.draw_networkx_edges(\n",
+    "    G, pos, edgelist=esmall, width=6, alpha=0.5, edge_color=\"b\", style=\"dashed\"\n",
+    ")\n",
+    "\n",
+    "# node labels\n",
+    "nx.draw_networkx_labels(G, pos, font_size=20, font_family=\"sans-serif\")\n",
+    "# edge weight labels\n",
+    "edge_labels = nx.get_edge_attributes(G, \"weight\")\n",
+    "nx.draw_networkx_edge_labels(G, pos, edge_labels)\n",
+    "\n",
+    "ax = plt.gca()\n",
+    "ax.margins(0.08)\n",
+    "plt.axis(\"off\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "prob = pulp.LpProblem(\"Shortest Path Problem\", LpMinimize)\n",
+    "cost = nx.get_edge_attributes(G, \"weight\")\n",
+    "target_vars = {}\n",
+    "\n",
+    "for i, j in G.edges:\n",
+    "    x = LpVariable(\"x_{0}_{1}\".format(i,j), cat=\"Binary\")\n",
+    "    y = LpVariable(\"x_{0}_{1}\".format(j, i), cat=\"Binary\")\n",
+    "    target_vars[i, j] = x\n",
+    "    target_vars[j, i] = y\n",
+    "\n",
+    "prob += lpSum([cost[i, j] * target_vars[i, j] for i, j in G.edges] + [cost[i, j] * target_vars[j, i] for i, j in G.edges]), \"Objective function\"\n",
+    "\n",
+    "for node in G.nodes:\n",
+    "    if node == source:\n",
+    "         prob += pulp.lpSum([target_vars[i, j] for i, j in target_vars if i == node]) == 1\n",
+    "    elif node == target:\n",
+    "         prob += pulp.lpSum([target_vars[i, j] for i, j in target_vars if j == node]) == 1\n",
+    "    else:\n",
+    "        prob += pulp.lpSum([target_vars[i, j] for i, j in target_vars if i == node]) - pulp.lpSum([target_vars[i, j] for i, j in target_vars if j == node]) == 0\n",
+    "\n",
+    "prob.solve()\n",
+    "print(\"Shortest distance from {0} to {1} = \".format(source, target), value(prob.objective))\n",
+    "\n",
+    "chosen_vars = list(filter(lambda v: v.varValue > 0, prob.variables()))\n",
+    "routes = list(map(lambda x: x.name.replace(\"x_\", \"\").replace(\"_\", \"->\"), chosen_vars))\n",
+    "print(routes)\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "dc74b8c2-e352-4284-b9f6-c936d1a7604d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('job1', 'cpu1'), ('job1', 'cpu2'), ('job1', 'cpu3'), ('job1', 'cpu4'), ('job2', 'cpu1'), ('job2', 'cpu2'), ('job2', 'cpu3'), ('job2', 'cpu4'), ('job3', 'cpu1'), ('job3', 'cpu2'), ('job3', 'cpu3'), ('job3', 'cpu4'), ('job4', 'cpu1'), ('job4', 'cpu2'), ('job4', 'cpu3'), ('job4', 'cpu4')]\n",
+      "Welcome to the CBC MILP Solver \n",
+      "Version: 2.10.3 \n",
+      "Build Date: Dec 15 2019 \n",
+      "\n",
+      "command line - /opt/conda/lib/python3.9/site-packages/pulp/apis/../solverdir/cbc/linux/64/cbc /tmp/c85182008d6145a5a1478dcbce27ffa7-pulp.mps timeMode elapsed branch printingOptions all solution /tmp/c85182008d6145a5a1478dcbce27ffa7-pulp.sol (default strategy 1)\n",
+      "At line 2 NAME          MODEL\n",
+      "At line 3 ROWS\n",
+      "At line 13 COLUMNS\n",
+      "At line 94 RHS\n",
+      "At line 103 BOUNDS\n",
+      "At line 120 ENDATA\n",
+      "Problem MODEL has 8 rows, 16 columns and 32 elements\n",
+      "Coin0008I MODEL read with 0 errors\n",
+      "Option for timeMode changed from cpu to elapsed\n",
+      "Continuous objective value is 10 - 0.00 seconds\n",
+      "Cgl0004I processed model has 8 rows, 16 columns (16 integer (16 of which binary)) and 32 elements\n",
+      "Cutoff increment increased from 1e-05 to 0.9999\n",
+      "Cbc0038I Initial state - 0 integers unsatisfied sum - 0\n",
+      "Cbc0038I Solution found of 10\n",
+      "Cbc0038I Before mini branch and bound, 16 integers at bound fixed and 0 continuous\n",
+      "Cbc0038I Mini branch and bound did not improve solution (0.00 seconds)\n",
+      "Cbc0038I After 0.00 seconds - Feasibility pump exiting with objective of 10 - took 0.00 seconds\n",
+      "Cbc0012I Integer solution of 10 found by feasibility pump after 0 iterations and 0 nodes (0.00 seconds)\n",
+      "Cbc0001I Search completed - best objective 10, took 0 iterations and 0 nodes (0.00 seconds)\n",
+      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
+      "Cuts at root node changed objective from 10 to 10\n",
+      "Probing was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "Gomory was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "Knapsack was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "Clique was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "MixedIntegerRounding2 was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "FlowCover was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "TwoMirCuts was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "ZeroHalf was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "\n",
+      "Result - Optimal solution found\n",
+      "\n",
+      "Objective value:                10.00000000\n",
+      "Enumerated nodes:               0\n",
+      "Total iterations:               0\n",
+      "Time (CPU seconds):             0.00\n",
+      "Time (Wallclock seconds):       0.00\n",
+      "\n",
+      "Option for printingOptions changed from normal to all\n",
+      "Total time (CPU seconds):       0.00   (Wallclock seconds):       0.00\n",
+      "\n",
+      "############ TIME COST MATRIX\n",
+      "{'job1': {'cpu1': 2, 'cpu2': 6, 'cpu3': 5, 'cpu4': 4}, 'job2': {'cpu1': 4, 'cpu2': 6, 'cpu3': 7, 'cpu4': 9}, 'job3': {'cpu1': 8, 'cpu2': 3, 'cpu3': 4, 'cpu4': 1}, 'job4': {'cpu1': 2, 'cpu2': 3, 'cpu3': 1, 'cpu4': 1}}\n",
+      "################## VARIABLES\n",
+      "{'job1': {'cpu1': time_job1_cpu1, 'cpu2': time_job1_cpu2, 'cpu3': time_job1_cpu3, 'cpu4': time_job1_cpu4}, 'job2': {'cpu1': time_job2_cpu1, 'cpu2': time_job2_cpu2, 'cpu3': time_job2_cpu3, 'cpu4': time_job2_cpu4}, 'job3': {'cpu1': time_job3_cpu1, 'cpu2': time_job3_cpu2, 'cpu3': time_job3_cpu3, 'cpu4': time_job3_cpu4}, 'job4': {'cpu1': time_job4_cpu1, 'cpu2': time_job4_cpu2, 'cpu3': time_job4_cpu3, 'cpu4': time_job4_cpu4}}\n",
+      "########### VALUES ##########\n",
+      "time_job1_cpu1 = 1.0\n",
+      "time_job1_cpu2 = 0.0\n",
+      "time_job1_cpu3 = 0.0\n",
+      "time_job1_cpu4 = 0.0\n",
+      "time_job2_cpu1 = 0.0\n",
+      "time_job2_cpu2 = 1.0\n",
+      "time_job2_cpu3 = 0.0\n",
+      "time_job2_cpu4 = 0.0\n",
+      "time_job3_cpu1 = 0.0\n",
+      "time_job3_cpu2 = 0.0\n",
+      "time_job3_cpu3 = 0.0\n",
+      "time_job3_cpu4 = 1.0\n",
+      "time_job4_cpu1 = 0.0\n",
+      "time_job4_cpu2 = 0.0\n",
+      "time_job4_cpu3 = 1.0\n",
+      "time_job4_cpu4 = 0.0\n",
+      "\n",
+      "####### JOB ASSIGNMENTS ######\n",
+      "\n",
+      "job1 is assigned to ['cpu1']\n",
+      "job2 is assigned to ['cpu2']\n",
+      "job3 is assigned to ['cpu4']\n",
+      "job4 is assigned to ['cpu3']\n",
+      "\n",
+      "Value of Objective Function =  10.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "## Problem 8a\n",
+    "\n",
+    "from pulp import *\n",
+    "import random\n",
+    "\n",
+    "cpus=[\"cpu1\", \"cpu2\", \"cpu3\", \"cpu4\"]\n",
+    "jobs=[\"job1\", \"job2\", \"job3\", \"job4\"]\n",
+    "\n",
+    "\n",
+    "\n",
+    "prob = LpProblem(\"CPU Assignment\", LpMinimize) \n",
+    "time_values = {\n",
+    "    'job1': {'cpu1': 2, 'cpu2': 6, 'cpu3': 5, 'cpu4': 4}, \n",
+    "    'job2': {'cpu1': 4, 'cpu2': 6, 'cpu3': 7, 'cpu4': 9}, \n",
+    "    'job3': {'cpu1': 8, 'cpu2': 3, 'cpu3': 4, 'cpu4': 1}, \n",
+    "    'job4': {'cpu1': 2, 'cpu2': 3, 'cpu3': 1, 'cpu4': 1}\n",
+    "}\n",
+    "time_vars = {}\n",
+    "for j in jobs:\n",
+    "    time_vars[j] = {}\n",
+    "    for c in cpus:\n",
+    "        time_vars[j][c] = LpVariable(\"time_{0}_{1}\".format(j,c), 0, None, LpInteger)\n",
+    "\n",
+    "        \n",
+    "job_cpu_combinations = [(j, c) for j in jobs for c in cpus]\n",
+    "print(job_cpu_combinations)\n",
+    "\n",
+    "prob += (\n",
+    "    lpSum([time_vars[j][c] * time_values[j][c] for (j, c) in job_cpu_combinations]),\n",
+    "    \"Sum_of_Assignment_Costs\",\n",
+    ")\n",
+    "\n",
+    "\n",
+    "for j in jobs:\n",
+    "    prob+= lpSum(time_vars[j][c] for c in cpus) == 1\n",
+    "\n",
+    "for c in cpus:\n",
+    "    prob+= lpSum(time_vars[j][c] for j in jobs) == 1\n",
+    "    \n",
+    "prob.solve()\n",
+    "\n",
+    "print(\"############ TIME COST MATRIX\")\n",
+    "print(time_values)\n",
+    "print(\"################## VARIABLES\")\n",
+    "print(time_vars)\n",
+    "print(\"########### VALUES ##########\")\n",
+    "for v in prob.variables():\n",
+    "    print(v.name, \"=\", v.varValue)\n",
+    "\n",
+    "\n",
+    "print(\"\\n####### JOB ASSIGNMENTS ######\\n\")\n",
+    "assignments = { }\n",
+    "for job in time_vars:\n",
+    "    cpus = time_vars[job]\n",
+    "    assigned = []\n",
+    "    for cpu in cpus:\n",
+    "        if cpus[cpu].varValue == 1:\n",
+    "            assigned.append(cpu)\n",
+    "    assignments[job] = assigned\n",
+    "    print(\"{0} is assigned to {1}\".format(job, assigned))\n",
+    "\n",
+    "print(\"\\nValue of Objective Function = \", value(prob.objective))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "fb2eebbd-59a0-4301-8057-e725ff3cf2ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "############ TIME COST MATRIX\n",
+      "{'cpu1': {'job1': 2, 'job2': 7}, 'cpu2': {'job1': 1, 'job2': 3}}\n",
+      "################## VARIABLES\n",
+      "{'cpu1': {'job1': time_job1_cpu1, 'job2': time_job2_cpu1}, 'cpu2': {'job1': time_job1_cpu2, 'job2': time_job2_cpu2}}\n",
+      "#############################\n",
+      "[('cpu1', 'job1'), ('cpu1', 'job2'), ('cpu2', 'job1'), ('cpu2', 'job2')]\n",
+      "Welcome to the CBC MILP Solver \n",
+      "Version: 2.10.3 \n",
+      "Build Date: Dec 15 2019 \n",
+      "\n",
+      "command line - /opt/conda/lib/python3.9/site-packages/pulp/apis/../solverdir/cbc/linux/64/cbc /tmp/06652fcfafa94d56aa62c1066888fd9d-pulp.mps timeMode elapsed branch printingOptions all solution /tmp/06652fcfafa94d56aa62c1066888fd9d-pulp.sol (default strategy 1)\n",
+      "At line 2 NAME          MODEL\n",
+      "At line 3 ROWS\n",
+      "At line 9 COLUMNS\n",
+      "At line 30 RHS\n",
+      "At line 35 BOUNDS\n",
+      "At line 40 ENDATA\n",
+      "Problem MODEL has 4 rows, 4 columns and 8 elements\n",
+      "Coin0008I MODEL read with 0 errors\n",
+      "Option for timeMode changed from cpu to elapsed\n",
+      "Continuous objective value is 5 - 0.00 seconds\n",
+      "Cgl0004I processed model has 0 rows, 0 columns (0 integer (0 of which binary)) and 0 elements\n",
+      "Cbc3007W No integer variables - nothing to do\n",
+      "Cuts at root node changed objective from 5 to -1.79769e+308\n",
+      "Probing was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "Gomory was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "Knapsack was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "Clique was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "MixedIntegerRounding2 was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "FlowCover was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "TwoMirCuts was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "ZeroHalf was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "\n",
+      "Result - Optimal solution found\n",
+      "\n",
+      "Objective value:                5.00000000\n",
+      "Enumerated nodes:               0\n",
+      "Total iterations:               0\n",
+      "Time (CPU seconds):             0.00\n",
+      "Time (Wallclock seconds):       0.00\n",
+      "\n",
+      "Option for printingOptions changed from normal to all\n",
+      "Total time (CPU seconds):       0.00   (Wallclock seconds):       0.00\n",
+      "\n",
+      "time_job1_cpu1 = 1.0\n",
+      "time_job1_cpu2 = 0.0\n",
+      "time_job2_cpu1 = 0.0\n",
+      "time_job2_cpu2 = 1.0\n",
+      "Value of Objective Function =  5.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Problem 8b\n",
+    "from pulp import *\n",
+    "import random\n",
+    "\n",
+    "cpus=[\"cpu1\", \"cpu2\"]\n",
+    "jobs=[\"job1\", \"job2\"]\n",
+    "\n",
+    "time_values = {\n",
+    "    \"cpu1\": {\"job1\": 2, \"job2\": 7 },\n",
+    "    \"cpu2\": {\"job1\": 1, \"job2\": 3 }\n",
+    "}\n",
+    "\n",
+    "prob = LpProblem(\"CPU Assignment\", LpMinimize) \n",
+    "time_vars = {}\n",
+    "for c in cpus:\n",
+    "    time_vars[c] = {}\n",
+    "    for j in jobs:\n",
+    "        time_vars[c][j] = LpVariable(\"time_{0}_{1}\".format(j,c), 0, cat=\"Integer\")\n",
+    "\n",
+    "print(\"############ TIME COST MATRIX\")\n",
+    "print(time_values)\n",
+    "print(\"################## VARIABLES\")\n",
+    "print(time_vars)\n",
+    "print(\"#############################\")\n",
+    "cpu_job_combinations = [(c, j) for c in cpus for j in jobs]\n",
+    "print(cpu_job_combinations)\n",
+    "\n",
+    "prob += (\n",
+    "    lpSum([time_vars[c][j] * time_values[c][j] for (c, j) in cpu_job_combinations]),\n",
+    "    \"Sum_of_Assignment_Costs\",\n",
+    ")\n",
+    "\n",
+    "\n",
+    "for j in jobs:\n",
+    "    prob+= lpSum(time_vars[c][j] for c in cpus) == 1\n",
+    "\n",
+    "for c in cpus:\n",
+    "    prob+= lpSum(time_vars[c][j] for j in jobs) == 1\n",
+    "prob.solve()\n",
+    "\n",
+    "for v in prob.variables():\n",
+    "    print(v.name, \"=\", v.varValue)\n",
+    "    \n",
+    "print(\"Value of Objective Function = \", value(prob.objective))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae8280c4-63a0-43d6-b879-89aa85d542ba",
+   "metadata": {},
+   "source": [
+    "### Integer LP VS LP Relaxation \n",
+    "\n",
+    "I have assumed the following costs for each job, cpu combination:\n",
+    "\n",
+    "| | cpu1 | cpu2 |\n",
+    "|-|------|------|\n",
+    "|job1|2|1|\n",
+    "|job2|7|3|\n",
+    "\n",
+    "I have set up the following variables in PulPfor ILP & LP Relaxation respectively:\n",
+    "\n",
+    "**For ILP**\n",
+    "\n",
+    "The variable category is set as Integer \n",
+    "\n",
+    "| | cpu1 | cpu2 |\n",
+    "|-|------|------|\n",
+    "|job1|x-j1c1-integer|x-j1c2-integer|\n",
+    "|job2|x-j2c1-integer|x-j2c2-integer|\n",
+    "\n",
+    "**For LP Relaxation**\n",
+    "\n",
+    "The variable category is set as Continuous\n",
+    "\n",
+    "| | cpu1 | cpu2 |\n",
+    "|-|------|------|\n",
+    "|job1|x-j1c1-continuous|x-j1c2-continuous|\n",
+    "|job2|x-j2c1-continuous|x-j2c2-continuous|\n",
+    "\n",
+    "I defined two different problems:\n",
+    "- `prob_integer` for ILP\n",
+    "- `prob_relaxed` for LP Relaxation \n",
+    "\n",
+    "\n",
+    "### Findings:\n",
+    "I get the same solution for bot ILP and LP Relaxation. I could not find an optimal solution for LP Relaxation that is lower than the ILP solution.\n",
+    "\n",
+    "Here were my results:\n",
+    "\n",
+    "**For ILP**\n",
+    "\n",
+    "| | cpu1 | cpu2 |\n",
+    "|-|------|------|\n",
+    "|job1|1|0|\n",
+    "|job2|0|1|\n",
+    "\n",
+    "Value of Objective Function: 5.0\n",
+    "\n",
+    "**For LP Relaxation**\n",
+    "\n",
+    "| | cpu1 | cpu2 |\n",
+    "|-|------|------|\n",
+    "|job1|1|0|\n",
+    "|job2|0|1|\n",
+    "\n",
+    "Value of Objective Function: 5.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0fc0adbe-7980-4ae3-b5f0-9b73e64b902b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU_Assignment_Relaxed:\n",
+      "MINIMIZE\n",
+      "2.0*job_1_cpu_1_cont + 1.0*job_1_cpu_2_cont + 7.0*job_2_cpu_1_cont + 3.0*job_2_cpu_2_cont + 0.0\n",
+      "SUBJECT TO\n",
+      "_C1: job_1_cpu_1_cont >= 0\n",
+      "\n",
+      "_C2: job_1_cpu_1_cont <= 1\n",
+      "\n",
+      "_C3: job_2_cpu_1_cont >= 0\n",
+      "\n",
+      "_C4: job_2_cpu_1_cont <= 1\n",
+      "\n",
+      "_C5: job_1_cpu_2_cont >= 0\n",
+      "\n",
+      "_C6: job_1_cpu_2_cont <= 1\n",
+      "\n",
+      "_C7: job_2_cpu_2_cont >= 0\n",
+      "\n",
+      "_C8: job_2_cpu_2_cont <= 1\n",
+      "\n",
+      "_C9: job_1_cpu_1_cont + job_1_cpu_2_cont = 1\n",
+      "\n",
+      "_C10: job_2_cpu_1_cont + job_2_cpu_2_cont = 1\n",
+      "\n",
+      "_C11: job_1_cpu_1_cont + job_2_cpu_1_cont = 1\n",
+      "\n",
+      "_C12: job_1_cpu_2_cont + job_2_cpu_2_cont = 1\n",
+      "\n",
+      "VARIABLES\n",
+      "job_1_cpu_1_cont free Continuous\n",
+      "job_1_cpu_2_cont free Continuous\n",
+      "job_2_cpu_1_cont free Continuous\n",
+      "job_2_cpu_2_cont free Continuous\n",
+      "\n",
+      "Welcome to the CBC MILP Solver \n",
+      "Version: 2.10.3 \n",
+      "Build Date: Dec 15 2019 \n",
+      "\n",
+      "command line - /opt/conda/lib/python3.9/site-packages/pulp/apis/../solverdir/cbc/linux/64/cbc /tmp/1ae75808cf8a4b5f8ae6348d6e1229dc-pulp.mps timeMode elapsed branch printingOptions all solution /tmp/1ae75808cf8a4b5f8ae6348d6e1229dc-pulp.sol (default strategy 1)\n",
+      "At line 2 NAME          MODEL\n",
+      "At line 3 ROWS\n",
+      "At line 9 COLUMNS\n",
+      "At line 30 RHS\n",
+      "At line 35 BOUNDS\n",
+      "At line 40 ENDATA\n",
+      "Problem MODEL has 4 rows, 4 columns and 8 elements\n",
+      "Coin0008I MODEL read with 0 errors\n",
+      "Option for timeMode changed from cpu to elapsed\n",
+      "Problem is unbounded - 0.00 seconds\n",
+      "Option for printingOptions changed from normal to all\n",
+      "Total time (CPU seconds):       0.00   (Wallclock seconds):       0.00\n",
+      "\n",
+      "Welcome to the CBC MILP Solver \n",
+      "Version: 2.10.3 \n",
+      "Build Date: Dec 15 2019 \n",
+      "\n",
+      "command line - /opt/conda/lib/python3.9/site-packages/pulp/apis/../solverdir/cbc/linux/64/cbc /tmp/57fe04c5ac14421b9bed79f136159511-pulp.mps timeMode elapsed branch printingOptions all solution /tmp/57fe04c5ac14421b9bed79f136159511-pulp.sol (default strategy 1)\n",
+      "At line 2 NAME          MODEL\n",
+      "At line 3 ROWS\n",
+      "At line 17 COLUMNS\n",
+      "At line 38 RHS\n",
+      "At line 51 BOUNDS\n",
+      "At line 56 ENDATA\n",
+      "Problem MODEL has 12 rows, 4 columns and 16 elements\n",
+      "Coin0008I MODEL read with 0 errors\n",
+      "Option for timeMode changed from cpu to elapsed\n",
+      "Presolve 0 (-12) rows, 0 (-4) columns and 0 (-16) elements\n",
+      "Empty problem - 0 rows, 0 columns and 0 elements\n",
+      "Optimal - objective value 5\n",
+      "After Postsolve, objective 5, infeasibilities - dual 0 (0), primal 0 (0)\n",
+      "Optimal objective 5 - 0 iterations time 0.002, Presolve 0.00\n",
+      "Option for printingOptions changed from normal to all\n",
+      "Total time (CPU seconds):       0.00   (Wallclock seconds):       0.00\n",
+      "\n",
+      "\n",
+      "############### Integer LP #################### \n",
+      "\n",
+      "job_1_cpu_1_int = 1.0\n",
+      "job_1_cpu_2_int = 0.0\n",
+      "job_2_cpu_1_int = 0.0\n",
+      "job_2_cpu_2_int = 1.0\n",
+      "Value of Objective Function (Integer LP) =  5.0\n",
+      "\n",
+      "############### LP Relaxation #################### \n",
+      "\n",
+      "job_1_cpu_1_cont = 1.0\n",
+      "job_1_cpu_2_cont = 0.0\n",
+      "job_2_cpu_1_cont = 0.0\n",
+      "job_2_cpu_2_cont = 1.0\n",
+      "Value of Objective Function (Relaxed LP) =  5.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "## LP Relaxed\n",
+    "from pulp import *\n",
+    "import random\n",
+    "\n",
+    "cpus=[\"cpu1\", \"cpu2\"]\n",
+    "jobs=[\"job1\", \"job2\"]\n",
+    "\n",
+    "combinations = [\n",
+    "    (1,1),\n",
+    "    (1,2),\n",
+    "    (2,1),\n",
+    "    (2,2),\n",
+    "]\n",
+    "\n",
+    "# Cost (job, cpu)\n",
+    "costs = {\n",
+    "    (1,1): 2.0, \n",
+    "    (1,2): 1.0,\n",
+    "    (2,1): 7.0,\n",
+    "    (2,2): 3.0\n",
+    "}\n",
+    "variables_integer = {\n",
+    "    (1,1): LpVariable(\"x-j1c1-integer\", cat=\"Integer\"),\n",
+    "    (1,2): LpVariable(\"x-j1c2-integer\", cat=\"Integer\"),\n",
+    "    (2,1): LpVariable(\"x-j1c2-integer\", cat=\"Integer\"),\n",
+    "    (2,2): LpVariable(\"x-j1c2-integer\", cat=\"Integer\")\n",
+    "}\n",
+    "   \n",
+    "variables_relaxed = {\n",
+    "    (1,1): LpVariable(\"x-j1c1-continuous\", cat=\"Continuous\"),\n",
+    "    (1,2): LpVariable(\"x-j1c2-continuous\", cat=\"Continuous\"),\n",
+    "    (2,1): LpVariable(\"x-j1c2-continuous\", cat=\"Continuous\"),\n",
+    "    (2,2): LpVariable(\"x-j1c2-continuous\", cat=\"Continuous\")\n",
+    "}\n",
+    "    \n",
+    "prob_integer = LpProblem(\"CPU Assignment Integer\", LpMinimize) \n",
+    "prob_relaxed = LpProblem(\"CPU Assignment Relaxed\", LpMinimize)\n",
+    "\n",
+    "prob_integer += (\n",
+    "    lpSum([variables_integer[(j, c)] * costs[(j, c)] for (j, c) in combinations]),\n",
+    "    \"Sum_of_Assignment_Costs (Integer LP)\",\n",
+    ")\n",
+    "prob_relaxed += (\n",
+    "    lpSum([variables_relaxed[(j, c)] * costs[(j, c)] for (j, c) in combinations]),\n",
+    "    \"Sum_of_Assignment_Costs (LP Relaxed)\",\n",
+    ")\n",
+    "\n",
+    "prob_integer += lpSum([variables_integer[(1,1)], variables_integer[(1,2)]]) == 1 \n",
+    "prob_integer += lpSum([variables_integer[(2,1)], variables_integer[(2,2)]]) == 1 \n",
+    "prob_integer += lpSum([variables_integer[(1,1)], variables_integer[(2,1)]]) == 1 \n",
+    "prob_integer += lpSum([variables_integer[(1,2)], variables_integer[(2,2)]]) == 1\n",
+    "\n",
+    "prob_relaxed += variables_relaxed[(1,1)] >= 0\n",
+    "prob_relaxed += variables_relaxed[(1,1)] <= 1\n",
+    "prob_relaxed += variables_relaxed[(2,1)] >= 0\n",
+    "prob_relaxed += variables_relaxed[(2,1)] <= 1\n",
+    "prob_relaxed += variables_relaxed[(1,2)] >= 0\n",
+    "prob_relaxed += variables_relaxed[(1,2)] <= 1\n",
+    "prob_relaxed += variables_relaxed[(2,2)] >= 0\n",
+    "prob_relaxed += variables_relaxed[(2,2)] <= 1\n",
+    "prob_relaxed += lpSum([variables_relaxed[(1,1)], variables_relaxed[(1,2)]]) == 1 \n",
+    "prob_relaxed += lpSum([variables_relaxed[(2,1)], variables_relaxed[(2,2)]]) == 1 \n",
+    "prob_relaxed += lpSum([variables_relaxed[(1,1)], variables_relaxed[(2,1)]]) == 1 \n",
+    "prob_relaxed += lpSum([variables_relaxed[(1,2)], variables_relaxed[(2,2)]]) == 1\n",
+    "print(prob_relaxed)\n",
+    "\n",
+    "prob_integer.solve()\n",
+    "prob_relaxed.solve()\n",
+    "\n",
+    "print(\"\\n############### Integer LP #################### \\n\")\n",
+    "for v in prob_integer.variables():\n",
+    "    print(v.name, \"=\", v.varValue)\n",
+    "    \n",
+    "print(\"Value of Objective Function (Integer LP) = \", value(prob_integer.objective))\n",
+    "\n",
+    "print(\"\\n############### LP Relaxation #################### \\n\")\n",
+    "for v in prob_relaxed.variables():\n",
+    "    print(v.name, \"=\", v.varValue)\n",
+    "    \n",
+    "print(\"Value of Objective Function (Relaxed LP) = \", value(prob_relaxed.objective))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ce857f3-4a5b-42b9-b454-7c4a14820320",
+   "metadata": {},
+   "source": [
+    "### Problem 9\n",
+    "\n",
+    "I set up an arbitrary graph using Networkx and implemented the constraints that I wrote in the homework sheet. The different colors of nodes in the graph represent the two groups the graph is bisected to. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "54768339-c33b-4b7f-bedf-d87d2167bfce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{('v1', 'v2'): edge_v1_v2, ('v1', 'v3'): edge_v1_v3, ('v1', 'v5'): edge_v1_v5, ('v2', 'v5'): edge_v2_v5, ('v3', 'v6'): edge_v3_v6, ('v5', 'v4'): edge_v5_v4, ('v5', 'v6'): edge_v5_v6, ('v4', 'v6'): edge_v4_v6}\n",
+      "Bisection_problem:\n",
+      "MINIMIZE\n",
+      "1*edge_v1_v2 + 1*edge_v1_v3 + 1*edge_v1_v5 + 1*edge_v2_v5 + 1*edge_v3_v6 + 1*edge_v4_v6 + 1*edge_v5_v4 + 1*edge_v5_v6 + 0\n",
+      "SUBJECT TO\n",
+      "_C1: partition_v1 + partition_v2 + partition_v3 + partition_v4 + partition_v5\n",
+      " + partition_v6 = 3\n",
+      "\n",
+      "_C2: - edge_v1_v2 + partition_v1 - partition_v2 <= 0\n",
+      "\n",
+      "_C3: - edge_v1_v2 - partition_v1 + partition_v2 <= 0\n",
+      "\n",
+      "_C4: - edge_v1_v3 + partition_v1 - partition_v3 <= 0\n",
+      "\n",
+      "_C5: - edge_v1_v3 - partition_v1 + partition_v3 <= 0\n",
+      "\n",
+      "_C6: - edge_v1_v5 + partition_v1 - partition_v5 <= 0\n",
+      "\n",
+      "_C7: - edge_v1_v5 - partition_v1 + partition_v5 <= 0\n",
+      "\n",
+      "_C8: - edge_v2_v5 + partition_v2 - partition_v5 <= 0\n",
+      "\n",
+      "_C9: - edge_v2_v5 - partition_v2 + partition_v5 <= 0\n",
+      "\n",
+      "_C10: - edge_v3_v6 + partition_v3 - partition_v6 <= 0\n",
+      "\n",
+      "_C11: - edge_v3_v6 - partition_v3 + partition_v6 <= 0\n",
+      "\n",
+      "_C12: - edge_v5_v4 - partition_v4 + partition_v5 <= 0\n",
+      "\n",
+      "_C13: - edge_v5_v4 + partition_v4 - partition_v5 <= 0\n",
+      "\n",
+      "_C14: - edge_v5_v6 + partition_v5 - partition_v6 <= 0\n",
+      "\n",
+      "_C15: - edge_v5_v6 - partition_v5 + partition_v6 <= 0\n",
+      "\n",
+      "_C16: - edge_v4_v6 + partition_v4 - partition_v6 <= 0\n",
+      "\n",
+      "_C17: - edge_v4_v6 - partition_v4 + partition_v6 <= 0\n",
+      "\n",
+      "VARIABLES\n",
+      "edge_v1_v2 free Continuous\n",
+      "edge_v1_v3 free Continuous\n",
+      "edge_v1_v5 free Continuous\n",
+      "edge_v2_v5 free Continuous\n",
+      "edge_v3_v6 free Continuous\n",
+      "edge_v4_v6 free Continuous\n",
+      "edge_v5_v4 free Continuous\n",
+      "edge_v5_v6 free Continuous\n",
+      "partition_v1 free Integer\n",
+      "partition_v2 free Integer\n",
+      "partition_v3 free Integer\n",
+      "partition_v4 free Integer\n",
+      "partition_v5 free Integer\n",
+      "partition_v6 free Integer\n",
+      "\n",
+      "Welcome to the CBC MILP Solver \n",
+      "Version: 2.10.3 \n",
+      "Build Date: Dec 15 2019 \n",
+      "\n",
+      "command line - /opt/conda/lib/python3.9/site-packages/pulp/apis/../solverdir/cbc/linux/64/cbc /tmp/96a3ea460af84356a66114e3afc70b73-pulp.mps timeMode elapsed branch printingOptions all solution /tmp/96a3ea460af84356a66114e3afc70b73-pulp.sol (default strategy 1)\n",
+      "At line 2 NAME          MODEL\n",
+      "At line 3 ROWS\n",
+      "At line 22 COLUMNS\n",
+      "At line 97 RHS\n",
+      "At line 115 BOUNDS\n",
+      "At line 130 ENDATA\n",
+      "Problem MODEL has 17 rows, 14 columns and 54 elements\n",
+      "Coin0008I MODEL read with 0 errors\n",
+      "Option for timeMode changed from cpu to elapsed\n",
+      "Continuous objective value is 0 - 0.00 seconds\n",
+      "Cgl0003I 0 fixed, 12 tightened bounds, 0 strengthened rows, 0 substitutions\n",
+      "Cgl0004I processed model has 17 rows, 14 columns (6 integer (0 of which binary)) and 54 elements\n",
+      "Cbc0012I Integer solution of 3 found by DiveCoefficient after 0 iterations and 0 nodes (0.00 seconds)\n",
+      "Cbc0031I 10 added rows had average density of 13.7\n",
+      "Cbc0013I At root node, 10 cuts changed objective from 0 to 2.8785087 in 93 passes\n",
+      "Cbc0014I Cut generator 0 (Probing) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.003 seconds - new frequency is -100\n",
+      "Cbc0014I Cut generator 1 (Gomory) - 182 row cuts average 13.4 elements, 0 column cuts (0 active)  in 0.007 seconds - new frequency is 1\n",
+      "Cbc0014I Cut generator 2 (Knapsack) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.001 seconds - new frequency is -100\n",
+      "Cbc0014I Cut generator 3 (Clique) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is -100\n",
+      "Cbc0014I Cut generator 4 (MixedIntegerRounding2) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.002 seconds - new frequency is -100\n",
+      "Cbc0014I Cut generator 5 (FlowCover) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.001 seconds - new frequency is -100\n",
+      "Cbc0014I Cut generator 6 (TwoMirCuts) - 25 row cuts average 11.4 elements, 0 column cuts (0 active)  in 0.002 seconds - new frequency is 1\n",
+      "Cbc0010I After 0 nodes, 1 on tree, 3 best solution, best possible 2.8785087 (0.05 seconds)\n",
+      "Cbc0001I Search completed - best objective 3, took 711 iterations and 2 nodes (0.05 seconds)\n",
+      "Cbc0032I Strong branching done 20 times (158 iterations), fathomed 2 nodes and fixed 0 variables\n",
+      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
+      "Cuts at root node changed objective from 0 to 2.87851\n",
+      "Probing was tried 93 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.003 seconds)\n",
+      "Gomory was tried 103 times and created 200 cuts of which 0 were active after adding rounds of cuts (0.008 seconds)\n",
+      "Knapsack was tried 93 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.001 seconds)\n",
+      "Clique was tried 93 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "MixedIntegerRounding2 was tried 93 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.002 seconds)\n",
+      "FlowCover was tried 93 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.001 seconds)\n",
+      "TwoMirCuts was tried 103 times and created 46 cuts of which 0 were active after adding rounds of cuts (0.003 seconds)\n",
+      "ZeroHalf was tried 1 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+      "\n",
+      "Result - Optimal solution found\n",
+      "\n",
+      "Objective value:                3.00000000\n",
+      "Enumerated nodes:               2\n",
+      "Total iterations:               711\n",
+      "Time (CPU seconds):             0.05\n",
+      "Time (Wallclock seconds):       0.05\n",
+      "\n",
+      "Option for printingOptions changed from normal to all\n",
+      "Total time (CPU seconds):       0.05   (Wallclock seconds):       0.06\n",
+      "\n",
+      "edge_v1_v2 = 0.0\n",
+      "edge_v1_v3 = 1.0\n",
+      "edge_v1_v5 = 0.0\n",
+      "edge_v2_v5 = 0.0\n",
+      "edge_v3_v6 = 0.0\n",
+      "edge_v4_v6 = 0.0\n",
+      "edge_v5_v4 = 1.0\n",
+      "edge_v5_v6 = 1.0\n",
+      "partition_v1 = 1.0\n",
+      "partition_v2 = 1.0\n",
+      "partition_v3 = 0.0\n",
+      "partition_v4 = 0.0\n",
+      "partition_v5 = 1.0\n",
+      "partition_v6 = 0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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UBZ2/LyUXLsSjdCmHvparMJtVbt26z+XL97h8OYxr1+5z82YUN29Gc/du8pzA8HBDJkLg90BcBqtpDTTJ4HPsZTWwJ4PPCQEGZ/iVHg+B+fN7EhzsRaFCvhQp4k+RIvkebhYdGOhDdu/de3/JEm5+9LFd7rU9NobrRiPVPPWU9/DATVH48OYN/nlwAs0TPXYAioLW358ya1bLSlkhRI6S53rsADwrV6bEb7O40rcf5oQEh/XcJakK0SYd66u/xKcS6mym0SgULhxA4cIBNGpUzmJbs1nlzp1ILl26x6VL9x4uDEk9JzCR+/eTiIszYzBktJqc1WNnXeZ63oxGCAszERZm4uJFAxCdbludjkfmBHoQFOT5YE6g+xNzAn18fOx+goNqNnN34iS73a+Jdyb+m6kqpshI7i/5i6B+fe1WixBCZFWe7LFLkXjuHFeHDsN45YrdV8qaVThv8Oet609xS/HjyJH+VKwoK+myk6qqREZGWt0jMOWxxMREoDdgOVw6zmzgUgafUw3oav9SbHIAWJ7qEb1eb3FOYHDwf3MCAwN90Gish8CYrVu5OmAg8WYzTc+fJ04109nXj++srFQ9Gh9PzyuXAfgwJISXAwLTbWu1x+4BXeHClFm/DkWjsVq3EEI4Q57ssUvhUa4cpf9dyr2JEwmb8atd9sBKUhUUYHJYFaaHVSIJDaDy2msL2L17hF3qFpmjKAr58uUjX758VKhgeQGHqqpERUVx7txdLl4M4/r1CK5fj3owJzC5JzB5ODiJ6GgzSUmOqDj3HzEXHx9PaGgooaHpLVrxBd4FwM0teYuYlJ7AlOHg5H0Ck88OLl48kMCZs0GrRQ+09vVhWVQUG2KiiTOb8bIQsFL2qdQCHe20T6Xxxg1id+zEp2l2DdcLIURqeTrYAWg8PAgZMQLfNm24M+4H4vbuTX0UkY2SVAUNKrvjCvDD3eqcTkx9FuyePdFMm7aZAQNa2LF64SiKouDv70/duv7UrVvWYluzWeXevShCQ5P3Cbx2LYLr1yNThcCICCP37xszGAKzdhav82Vtw+qkJJWICJWICAOhoWkPB3trjOwpu5OUjr3Ofn4si4oiXlXZGBNNZ7+057uZVJVVD3rgGnl7E+Rmp7c+rZaoFSsk2Akhcow8H+xS6GvUoMSc2SRevEjEwoVELvkLc+yDX6xubjzx2/iRxxQfHxbdKcrsW2W4akz/F+v772+lR4/6ciqBi9FoFEJC/AkJ8adBA8ttzWaVsLBoQkPvPlgYEvFgs+gY7tyJfWSfQCOxsSoWjh9Oh2vPCazsEcGjo7WNvbwJ0moJM5lYERWVbrDbExfHvQd/rHW256kyJhPxhw/b735CCJFFEuwe41G6NAU//JAC772H4dIlEk6cIOHECQzXrqPGx4OioOg9cS9WHM8qVfCsUhn3EiUoPXsHV/ttsHjviAgzgwbNZ8GCN5z01YicRqNRCA72IzjYj/r1y1hsq6qfEh0dbfOcwPj4eHLaUKx1Gau3imc4JhW0D8KdVlHo4OvHvPsR7IiNJSIpiYA0euOWR0UCoFcUWvv6ZqLO9BkuX8YcF4fGS/5gE0JkPwl26VC0WjzKlMGjTBn8n3nGavu+fZsyY8Z+du6MtNhu0aJrDB58mmbNKtqrVOGiFEXBz88PPz8/ypWzvIBDVVViYmI4d+4uoaFhD88OvnkzOlVP4P37SURF5aQ5gRnrYazsGYGKAo/sPdfFLznYJQFroqPpGZB6GkSi2cz6mOTaWvn44m3vhQ6qSsLpM3jVrmXf+wohRCZIsLOj337rRbVqU0lMTH+FrdkMr7/+N6dPv4dWKyvphH0oioKvry+1a/tSu3Zpi23NZpXw8BguXUoeDr5+/T7Xr0c+3CcweWFI8hYx0dFm0tun90mOH4oNcYvHTUn981Vdr6eETsdlo5Hl0VFPBLtNsTHEPFgU1dnPjsOwj0iycHKFEEI4kwQ7OypXriDDhlVm7NgTFtudP5/Il1/+yxdfPOecwoR4hEajkD+/L/nz+1K3ruUQCBAeHvNgTuA9rl1LDoG3b8dw+3Ys9+7FP1gdnEhcnBvx8RndKDBjPXaeStqLmp7282NyWBiH4uO5bjSk2p5k+YNFEwFaLY0ddNaumpDhyZBCCOEQEuzs7JtvurJ48VkuXbLczfH990fo2/cpSpYMdlJlQmROYKAPgYE+1KljbZPtL4iJibHp3ODbt28TGxtLRnvsktS0e7k7+/kzOSwMFVgRFc2AoCAAIk0mtj1YBNXB1xedg47MUHTyViqEyBnk3cjOdDotv/zShY4d/7K453F8vMrAgbNYs8a1zo8VeZuPjw8+Pj6UKWN5YQgkn3t7/vwdLl4Me3h28KNzAsPDU+YEmh4OB8ea3VBVnjjSrKS7O9U8PTmWkMDyqMiHwW5tdDTGBz+IjhqGBVA8PR12byGEyAgJdg7Qvn11nn12F//8czOdFkZgM2vX7mbLlgY0b97cmeUJkSN4e3tTo0YpatSwftxeREQsly7dJW5yNOqO1ShpbCTe2c+PYwkJnDcYOJOQQAVPT1Y8GIYtotNR01Nv968hhXsZy3sdCiGEs8jsfQeZPr03fn5pDfucBSYBOwEzgwYNwpDxA0yFyFMCArypVaskVZ9thSad02E6+fqhffDx8ugobhmN7I+PA5L3rrP3mbUpok06itWZyfDhf3DjRoRDXkMIIWwlwc5B8uf35csvGz/ySBTwB7AA+G9LlNOnT/P99987uTohcifPKpXTvRbk5kYjr+TFESuiolgRFUVKBHzaQcOwZhWOJwRw966ZCRNOUaLET7Ru/RMrVx5xyOsJIYQ1iqpamgkmssJsVqlb93sOHdoAbAbS7pnz9PTk+PHjNs1LEiIvU81mzjVrjule2tuL/BsZyfu3kqdA+Gk0RJnNVPLwYEnJ9Id7Y81m1j44RzbFkshIDsbHAzAyOJgArfbhtYoenlR6MKfOpMJP96oxPfzJwFmihDuvvVaFd95pK6fNCCGcRoKdg4WGXqJKlcoPTgVIX4cOHVi5cqXDhouEcBV3J0/m3sRJyZtCPibWbKbZ+XPEP/K2Nio4mL6BQene77rRQNuLF21+/SFBQQzNn7yaPUlVaHWhC/dM6c/f8/RU6NixEKNHt6JhQ5mLJ4RwLBmKdbBSpUry2WefWW23evVq/vzzTydUJETulq9793SveWs0tPL578gwDclz7xwhSVVYG13UYqgDSEhQ+fvvGzRq9DtVqnzH7NnLSXLM0R9CCCE9ds5gNBqpVasWJ05Y3ri4UKFCnDp1Cn//tA8yF0Iku/7uCKLWrAFT2hsWO8vLV1pxMD4je1EmAeMoXDiQAQMG0L9/fwoXLuyo8oQQeZD02DmBTqfjl19+sdru5s2bfPzxx06oSIjcLWTUSBR3d7Lrr1KTqrAssngGQx3AcSCBGzdu8Pnnn1O8eHG6d+/Ohg0bkL+xhRD2IMHOSZo0acLrr79utd2kSZPYv3+/EyoSIvfSFSrEnqqdyI4ZqaqikKDzZEJsnUw8e1+qz0wmE0uWLKFNmzZUrFiR8ePHExEhW6YIITJPgp0TjRkzhvz581tso6oqAwcOlDk4Qljw88/r6TtPy47YAiSpzo13iqpS4adxnL/9Od9/35AKFWw9deLGg39pO3v2LO+88w5FihTh9ddflz/whBCZInPsnGz27Nm89tprVttNmDCBt956y/EFCZHLzJ27g75912EygZ/GwNziGyjlHo2b4py3spCRIwh6441Uj23deprvv9/EunV3SExMr46lwOEMvVa9evXo128YvXp1ky1ThBA2kWDnZKqq0rJlS7Zs2WKhVWE8Pbtw6NCnVKwoE6uFSLF8+SG6dl368OxYgCBtAjOLbXJKuAt++y3yDx6c7vXw8Bh++GEds2ef5Nq1R4okARhH8uKJjOqCXl+HDh1kyxQhhHUS7LLB6dOnqV69OsZHfzsB4AG0AuoBCg0a+LF797vOL1CIHGj79jO0a7eAtLaE9NUY+LHwDhp738Gsgsaeo7NaLYpGQ4GPPiKgZw+bnmI2qyxbdogff9zG9u33MZl2AWsy8eIewAhA9/CRypX1DBxYm0GDWuLuLsd9CyFSk2CXTT755BO++uqrRx6pAnQAfFK1mzatBf37t3BiZULkPEePXqFp01lERVl6u1J5wf8i74ccwkOrolHTPlM2ozyrVaXwmO/wKJ3+6RWWXLlyjzlzfmfWrJ+5mIGNkJM1IPl94Ul+fhqef744H3zQngoVCmWqNiGE65Fgl03i4+OpWrUqFy9GAJ2AtIdXAgI0hIaOlPk1Is+6ePEODRpM5d492/as+2RQYQa4HSd6w4bkB9I4ocIijQbMZrQBAQQNGEDgK31QHjlSLLPMZjPr1q1j8uTJLF++HLNNdb0JWF5wpShQr54fQ4c24KWXGqOxa3elECK3kWCXjZYs2UD37psAy8MpvXoVY/5861ulCOFqbt26T926E7l+3ba5aa+8UpLZs18DwHjrFvcX/UnEggWYUrYQcXODx1ecazTJ6ejBZsf6OnUI7PMyvq1bo+h0OMKVK1eYNm0aM2bM4Pbt2+m0Kgm8mqH7hoS40atXWUaPbk/hwgFZLVMIkQtJsMtmTz31Izt3Rlpso9HApk09adasopOqEiL7RUbGUbfuBM6fT7SpfefOBVi6dNATPVZqUhKJFy+ScOIkCSdOkHj6NKbYWEhKQvHwwC0kBM+qVdBXqYJnlSq4WdmSyJ4MBgN///03U6ZMSWNB1QtA5Uzd180NmjcPYuTI5nToUD3LdQohcg8Jdtns3LlbVKs21cIWCcnKlvXg9On30Gpl60Hh+oxGI02bfsyePZbPYU3RtGk+Nm16K1f/fJw4cYJffvmFOXPmEBVlBt7BHluNliyp49VXq/LOO21lSocQeYAEuxxg1Kg/GTvW8jmyAJ9+WpMvvnjO8QUJkY3MZjMvv/wyCxb8ATwPVLXYvlYtH3bufBtPT8cMmzpbTEwMkyf/w88/X35sy5Ss0esVOnQoyHvvtaZBA9kyRQhXJcEuBzAaTZQrN4bLlw0W2+n1CqdPv0nx4s4bKhLCmVRVZejQoUyePPnBIwrJi4vqptm+fHlP9u17Gz8/23r2chOzWeXffw8yfvwOtm+PwGSy31t15cp6Bg2qw8CBLWTLFCFcjAS7HGLNmqN07PgX1v5vtGwZyMaNciKFcE2ffvop//vf/9K40hJoluqRYsV07Ns3lAIF/J1SW3a6fPke3323lj/+uEBYmG2rg23h56fQtWtJPvigHeXLy5YpQrgCCXY5yHPP/cLSpbestluwoAM9ezZ0QkVCOM/48eN55513LLRoCLQHIDhYy549AylVKsQpteUUJpOZ2bO3M2nSXg4dirH6h6CtFAXq1/dl6NBG9O7dSLZMESIXk2CXg9y7F02ZMj8+mDidvgIFtISGvode7+6kyoRwLFvPUIaa+Ps/y9at/ahevbijy8rRjh27yrffrmPp0qvExtrvbTwkRMuIEQH07/8SAQGyZYoQuY0Euxxm/Pi1vPPOTqvt+vcvw7RpfZxQkRCOtXTpUrp164bJZH2IMSgoiI0bt1G9eiUnVJY7xMcbmDRpIzNmHObMmQQ73PEGMB29Xk+vXr0YMmQIderUscN9hRDOIMEuhzGbVWrXHsuRI7EW27m5we7dr1KnTuaOORIiJ9i0aRMdO3YkMdH6XnU+Pj5s2rSJunXTXkghYOvW03z//SbWrr2NwfJaLAuWAodTPVKvXj2GDBlCjx490Otdb6GKEK5Egl0OdPjwZerVm/XEBvmPq17di0OHRsl8GJEr7d+/n5YtWxITE2O1rYeHB6tWraJly5ZOqCz3Cw+PYezYtcyZc9LmUzuSJQDjgLSfExAQwGuvvcagQYMoX768PUoVQtiZBLscatCg35k69bzVduPGNeLdd9s7oSIh7OfUqVM0bdqUsLAwq221Wi1Llizh2WefdUJlriX1linhWB/t3g2ssenebdq0YciQIXTp0gU3N9kyRYicQoJdDpWQYKR06W+5edPyO7Gvr8K5c8PzxJYPwjUcO3aFNm0GcefOKpva//bbb7z6asbOTBVPStkyZeHC84SHp7dAayJgPWw/qkiRIvToMYKBA3vKlilC5AAS7HKwJUv20b37CqvtunQpyL//DnJCRUJkTWjoHRo0mMrduyZgC7DZYvsff/yR4cOHO6GyvMNkMvPbb9uZPPnxLVNCgTmZvOubKEp+6tf3Y9iwhvTqJVumCJFdJNjlcG3b/sz69db/gl658nk6dqzhhIqEyJzbtyOpV28iV68+ekzWXiDtnruPP/44nc2Khb2k3jLlD+BUJu5SEkjdo1qggJZevcozenR7ChXKl/VChRA2k2CXw129GkbFihOJi7P8v6lYMR0XLryPTqd1UmVC2C4qKp66dcdz7lxaq1+PkrwS87/hwSFDhjBx4kQURXp9nCE+3sA///zNL79MZuvWrRl89gtA5TSvuLlBixb5GTGiGR06VM9ynUII6yTY5QJffvkvn3120Gq7d96pxA8/9HBCRULYLiHBSOPGEzh0yNLq17PAn0ASvXr14vfff0ej0TipQvGoEydOMGXKFObMmUN0dLSV1j7AO4D1/1clS7rTt29Vhg9v65Jn+wqRU0iwywXMZpXKlcdY3XzU3R2OHBlAxYqFnVSZEJaZTGZatvyJbdvu29D6Mu3bR7Bs2Z/odDpHlyasiImJYf78+UyePJkjR46k06oZyef42k6vh44dC/Pee62pX79MlusUQqQmwS6X2LHjLM2azcds+bQxGjTwY/fud51TlBAWmM0qzz33C8uW3bapfdmyHuzf/zb+/l4OrkxkhKqq7N69m8mTJ7No0SIMD3c+VoDhgF+m712lip6BA+swcGAL3N1lyxQh7EGCXS7y8sszmTfvitV206a1oH//Fo4vSAgLXnvtN2bPvmRT2yJF3Ni/fygFC+ZzaE0ia+7evcusWbP45ZdfCA31AOwz9cPPT6Fbt5K8/3472TJFiCySYJeLREXFU7r0WMLCLO9tFxCgITR0pPR8iGzz7rt/8OOPtq2wzJ9fy549AyldOsTBVQl7MZvNzJu3hfHjDzy2ZUrWaDRQr55smSJEVkiwy2VmztzK669vtNquV69izJ//uhMqEiK1r79ezscf77eprZ+fhm3bXqN69eIOrko4ytGjV/j223X8++81YmPt9+skZcuU995rLz25QmSABLtcqHHjH9m1K9JCCzMazR6OHv2BKlWqOK0uIaZM2cibb261qQdHr1dYu7YnTZpUcHxhwuHi4w1MnLiBGTOOcPas5YVeGZG8ZUoQI0Y0ly1ThLCBBLtc6OzZm1SvPo3ExLT+110DlgO3adq0KZs3b5ZtI4RTLFy4m5dfXm3DeaSg08Fffz1L5861HF+YcLotW07x/febWLfuDg/XWtiBbJkihHUS7HKpkSMXMW7cyUceSQDWAwdStZs5cyZ9+/Z1ZmkiD1qz5gjPPPO3Tb/EtVqYNastffo85fjCRLYKD49h7Ng1zJlziuvXk+x2X70+kUGDDAwePIhy5crZ7b5CuAIJdrmU0WiiXLkxXL5sIHnn/rVA7BPtgoKCOH36NPnz53d2iSKP2LXrHG3azLd6OgqAosCECU0YNqyNEyoTOYXZrPLvvwf58cft7NgRYVOvrmW7gTUAtG3blsGDB9OlSxfc3GTLFCEk2OVia9cepUOHrqjqBYvt+vbty8yZM51UlchLjh+/StOmM7l/37a3kU8/rckXXzzn2KJEjnbp0l2++24tf/xxgfBwKxtzpmsikPoM7SJFijBgwAD69+9PoUKyZYrIuyTY5XLDhw9nwoQJVttt2bKFZs2aOaEikVdcunSXBg1+4c4d27pfhg6twM8/93JwVSK3MJnMzJq1jSlT9mVwy5RQYE66V93c3HjuuecYMmQILVq0kPOGRZ4jwS6Xi4qKolKlSty4ccNiu0qVKnH48GHc3d2dVJlwZXfvRlGnzs9cvWq0qb1svyMsydiWKYsA2/ZIrFixIoMHD+aVV14hX758WS1TiFxBgp0LWLx4MS+88ILVdl9//TUffvihEyoSriwqKp769SdYPbs4RYcOwaxYMUQ2mxVWWd8yJRoYD2RsCNfLy4s2bd5nyJBnad9etkwRrk2CnQtQVZXOnTuzcuVKi+08PT05ceIEpUuXdlJlwtUYDEk0ajSegwdjbGrfuLE/mze/hU6ndXBlwtWkvWXKZmBLJu7mA7wDaGTLFOHyJNi5iNDQUKpUqUJ8fLzFdh06dGDlypUy70RkmMlkpk2biWzeHG5T++rVvdi9ezh6vQz/i8y7dy+aH35Yy5w5J7h+fSzJvXYZ1QxomeoRvR46dSrM+++3oW5d+WNXuA4Jdi7k22+/5YMPPrDabtGiRTYN3QqRwmxW6dp1KkuX3rKpfZkyHuzb9xYBAd4OrkzkFaqqsmvXLqZMmcKiRYsw2LzzsQIMB/zSbVGlip5Bg+owYEAL3N1lyxSRu0mwcyEGg4FatWpx8uRJi+0KFKjEsWO7CQ5O/41OiEf16zebWbNCbWpbpIgbe/e+SeHCAQ6uSuRVd+/eZdasWfzyyy+Ehlr7vqwI9LDpvv7+Grp2LcEHH7SnXLmCWa5TiOwgwc7FbNu2zcK2JlrgKaApXboU4d9/BzuxMpFbjRq1iLFjLf+xkCIoSMOePQMpU6aAg6sSAsxmM2vWrGHKlCksX76ctH+dvQyUydB9NRqoX9+fYcMa0rNnQ1n4I3IVCXYu6PXXX09jQ+KSwNNA8gkUigIrVjxPx441nFydyE3mzZvHyy+PIvmXo5fFtr6+Clu3vkbNmiWcUpsQj7p8+TLTpk1jxowZ3Llz58GjgcCwLN23QAEtvXtXYPTodhQsmC+rZQrhcBLsXFBYWBgVKlQgLCyM5F/G7YAnA1zx4jrOn39fViyKNC1fvpznnnsOk8lE8h8EfUhvnpJeD6tW9aB580rOLFGIJxgMBv766y8mT57Mtm16oJFd7qvTQYsW+RkxoplsmSJyNAl2LmrWrN/o1+8noC2Q/pL+d9+tzLhxLzqtLpE7bN26lfbt25OQ8OheYv4kh7ugVG11Oli8+Bmeeaa2M0sUwqrkLVM2s27dbWxea2GDUqU8eO21KrJlisiRJNi5KLNZpVKlMels8vkfd3c4enQAFSoUdlJlIqc7dOgQLVq0ICoqKo2rXiSHu+SJ5RoNzJzZhldfbeLMEoXIkP+2TDnF9etJdruvXq/QqVMh3nuvDfXqyZYpImeQYOfCduw4S7Nm8zFb2aS9YUM/du161zlFiRzt7NmzNGnShLt371po5QH0QlFKMG5cI955p72zyhMiS8xmlaVLD/Djj9vZufM+JtuOObZJ1apeDBhQm4EDZcsUkb0k2Lm43r1/ZcGCq1bbTZ/ekjfeaO6EikROde3aNZ566imuXLliQ2s3vv56Hh9+KMP4Ine6dOkuY8asZdGiC4SHZ+yIMktkyxSR3STYubioqHhKlx5LWJjlP00DAzVcvDgSf3/LKx+Fa7p37x5Nmzbl9OnTNrX/7rvvGDVqlIOrEsLxTCYzs2ZtY/LkfRw6ZNtRebbQaC7RrVscgwcPpkWLFnLaj3AaCXZ5wMyZW3n99Y1W2/XqVYz58193QkUiJ4mOjqZVq1bs37/fpvbvvfce3377rYOrEsL5jh69wrffruPff68RG5vVX42LgFMAVKpUiUGDBvHKK6+QL1++rJYphEUS7PKIxo1/ZNeuSIttNBrYsqUXTZpUcFJVIrslJCTQqVMnNm3aZFP7/v37M3XqVOl9EC4tLs7AxIkbmDHjMOfOJWbiDtHAeCD1EK+Xlxe9e/dm8ODB1K4tq8iFY0iwyyPOnr1J9erTSEy0/L+7XDkPTp9+X3ZazwMMhiS6du3DihULbWr/wgsvsGDBArRa2fdQ5B1btpziu+82sX79nQxsmbIF2GyxRYMGDRg8eDAvvvgier1smSLsR5PdBQjnKF++EEOHVrba7ty5RP73v3+dUJHITiaTmQ4dJrNiRWFSTiOxpF27dsydO1dCnchzmjevxIoVQ7h+fQTvv1+NIkWsrXg1Awes3nfPnj289tprFC1alJEjR3L+/Hm71CuE9NjlIUajiXLlxnD5suU/O728FE6depPixa3/whe5j9ms0r37NP7+++aDR+KB34EbabZv1KgR69atw9vb21klCpFjmc0qf/+9nwkTdqSzZcopkufXZZRCjRpf0L9/Y/r3by5bpohMk2CXx6xefZROnf7C2v/1Vq0C2bDhLecUJZyqf/85zJhx8bFHDcBCIDTVo1WrVmXLli0EBgY6qzwhco3kLVPWsGhRKOHhKQlvDo//HNmmItADkC1TRNZIsMuDnn32F/7995bVdn/80ZEXX2zghIqEs7z33mK+++54OldNwGIgecuT0qVLs337dgoVKuSs8oTIlYxGE7/9to3p07eyb9/nQGZ+rfYBUp9eodFA/fp+DBvWiJ49G8rcZ2ETCXZ50N27UZQtO56oKMubchYooCU09D30encnVSYcacyYlbz//l4rrVTgXwoWvMWOHTsoXVqOSRIiIy5dusS0adOYMWOGlRNcHhUIDLPYokABN3r3Lsfo0e0pWDBfVssULkyCXR71449reffdnVbb9e9fhmnT+jihIuFI06dvZtCgzVaPlwPw9IQtW7pTv35VxxcmhItKTEzkr7/+YsqUKWzbts1K63ZAI5vuq9NBixb5GTGiGe3bV89yncL1SLDLo8xmlVq1vufo0TiL7dzcYO/e16hVq6RzChN2t2TJPnr2XEGSDWef63SwaFEXnnuujuMLEyKPOH78OFOmTGHu3LlER0c/dtUNeBfI+JYnpUq507dvNYYPb4uvr6c9ShUuQIJdHnb48GXq1Ztl9Rd+9epeHDo0SuZ35EIbNpzg6af/JNGGPVY1GpgxozV9+zZ1fGFC5EHR0dHMmzePKVOmcPTo0QeP1gCey9J99XqFTp0K8f77bahbV6ZP5HUS7PK4AQPmMn36BavtfvihEe+8094JFQl72bfvAq1a/U5MjG0/4mPHNmTEiA4OrkoIoaoqO3fuZMqUKSxc6IXJVNhu965a1YtBg+rIlil5mAS7PC4+3kDp0t9x65blbjs/Pw3nzw8nONjPSZWJrDh9+gaNG88gIsKGSXXAhx9W5+uvuzq4KiHE41K2TPnjj4s2/7zawt9fQ7duJfngg/aULVvAbvcVOZ8EO8Gff+7lxRdXWm33zDMFWbp0kBMqEllx9WoY9etPsRrWUwwcWJZffnnZwVUJISxJ2TJlypT9HDoUY7f7pmyZ8tZbjenRo4FMqckDJNgJANq0+ZkNG8IstlEUWLmyKx06yEqsnCosLJq6dX/m0iXbDrXs3r0If/zxhrzZC5GDHDlyhW+/XceyZdeIjbXfr+gCBbS89FIFRo9uT4EC/na7r8hZJNgJAK5cuUelSpOIi7P87VC8uI7z599Hp5MzQ3Oa2NhE6tcfz8mT8Ta1b9MmiDVrhkqoEyKHio1NZOLEDfz661HOnUuw2311OmjZMpiRI5vRtm01u91X5Aya7C5A5AzFi+dn1KiaVttduWLkgw+WOL4gkSEGQxKtWk20OdQ1aODLypVDJNQJkYN5e3vw3nudOHv2fTZt6kGnTiG4u2f9Z9ZohLVr79Ku3Vs0bNiQOXPmkJBgv+Aospf02ImHzGaVSpXGcPas5R9wd3c4dWoApUvbbyWXyDyzWaVdu4lWh9JTVK6sZ+/e4Xh7ezi4MiGEvd27F83YsWuZO/cUN27YNo82bWZgPJC8r15gYCD9+vVj0KBBlClTxg6ViuwiwU6ksn37GZo3X4jZnN63RRiwnBdeqMeiRYucWZpIg9ms0qPHDBYvvm5T+1Kl3Nm//y0CA30cXJkQwpHMZpW//97PhAk72bkzApMpo3c4BaT9Ht6+fXsGDx7M008/jZubc7dMuR9n4Pj1KI5dj+RKeCyJRjMq4OGmoVigF1WL+FOtiD+B3nLUZXok2Ikn9O79KwsWXH3s0SRgG7CD5MPiYeXKlXTs2NHJ1YlHDRr0O1OnnrepbcGCbuzdO5hixYIcXJUQwpkyt2XKHCDUYouiRYsycOBA3njjDQoWLJjlOtMTEWvgzwNXmbfnCpfDkk9D0iigURRSIoqiKKgqmB58XjSfnl4NivNi3WIE+8row6Mk2IknREbGUbr0WMLDU94gLgIrgPBU7UqVKsXx48fx8vJydokC+PDDJfzf/x2zqW1AgIZdu/pToUIhB1clhMguyVumbGfy5H0cPmxpy5QwYKLN93Vzc6Nr164MHjyYZs2a221u7tXwOMavP8vSwzcwqSoZTSMaJTnwdapakOFty1MmWEYiQIKdSMevv27ljTf+BdYAx9Nt9+GHH/L11187rS6RbOzY1Ywatdumtj4+Cps29ZGjhoTIQ5K3TFnLsmU3iI19vBdvDWDb+0dqgRQoMDzLW6aYzSrz9lzmqxWnSDKrmNKd+mMbrUZBo8Do9hXp16QU2jy+KEyCnUhX9+69WLJkocU2Op2Ow4cPU7lyZSdVJWbO3Er//hsx2zDi4uEBK1e+SKtW8v9HiLwoNjaRn39ez6+/HuX8+USSp9WMAzKzCrYd0AhI2TIlPyNGNKddO9u3TLkTlcCwBYfYExpuvXEm1CyWj4m9a1E0IO+OJEmwE+m6ceMGFStWJDo62mK7Zs2asXnzZhQlb/+V5Ax//72fF19cTpINi+Hc3GDhwqfp1q2e4wsTQuR4mzefYvLkv1i16ltiYjJ6uoUb8C6gf+JK6dLu9O1bjeHD2+Lj45nuHa6Gx9Fj2i5uRyVmuZcuPVqNQoCXjoUDGlI2xNchr5HTSbATFv3888+89dZbVtvNmjWL1157zfEF5WGbNp2kU6c/SUiw/iOr0cC0aa14/fVmTqhMCJGbREdHM2/ePCZPnsyxY7bN04UawHMWW3h5KXTqVJj3329DnTqlUl27cT+e5yfv4F6MIdOhLvHmORKvHiPx5jmMYVcxx0ViSohG0erQ+gTiUbgCPlXb4F2qBv6eOv4a0piS+b0z9Vq5mQQ7YZHJZKJBgwYcOHDAYrugoCBOnz5N/vz5nVRZ3nLhwgXq1RtFRIRtx7l9910DRo2SFctCiPSpqsrOnTuZPHkyixcvxmCwdBThG0ARm+9drZoXAwfWoX//5pgVhU4/beNyWFyWeupu/T6axOsnrbbzqtCEkGdGUDDAl7XvNMPXU5fp18yNJNgJqw4cOED9+vUxW5nU1a9fP3799VcnVZV33LhxgyZNmhAaGgo0A1pabP/++9X4v//r5pTahBCu4c6dO8ycOZOpU6dy6dKlx64WAgZk6r758ilU71OFK156sho2bi/8ENVsxqNIJXRBxdB6B6DR+2KOi8RwN5SYQ6tIirwNgFfFphR47j161ivGN13z1vnmEuyETd566y1+/vlnq+22bt1K06ZNnVBR3hAeHk6zZs04ceLEI4/WAzql2b5//zJMm9bHKbUJIVyPyWRi9erVTJkyhZUrVz7YR+4ZoFam7ude2JuCL1eyyxxs1WxC0aR/TrnZmMidhR+ReOM0AIX6TcQ9uCS/v96AJuXyzmiSBDthk6ioKCpWrMjNmzcttqtcuTKHDh3C3V12Bc+qmJgY2rZty+7daW1LUBV4nkePe+7atTB//tlfzn8VQthFaGgoU6dOZ+JEDbGxmTuBovAbVXEL8ERx0vtS7Kmt3Pv3OwAC2w7Cv05nCvh5snV0S3RajZVnu4a88VWKLPPz82PChAlW25086cnw4QucUJFrS0xMpGvXrumEOkjeW3AhyVsXQKtWgRLqhBB2VapUKb799htu3/6E//u/epQtm7ETHjxL+qEL0qOaErny4wtcHtOZe8vGWn1e4o0zXB7TmctjOhN1YFmGXlPR/bcqV00yYFbhZmQC60/eztB9cjMJdsJm3bt3t3CEWAjQD+jC9OmhHDp0yXmFuRiTycTLL7/MunXrrLQ8B8ylQQNPVq0aIqFOCOEQ3t4evP/+05w79wGbNvWgU6cQ3N2tv9/41g5BNalodJ54lWsIQNy53ZgNlvfQiz25JfkDRYN3xYxN7Yk9teXhx25BxQDQKvDbzksZuk9uJsFO2ExRFCZOnIin56P7FOmAtsBAIPmHKCkJXnttEWYH7VPkylRVZdCgQSxevNim9q1alWXz5uG4uzv3oG4hRN7UokUlVqwYwvXr7zJ6dFUKF077vUfrq0NfNh+KNjkAelduAYBqTCD+fPqnXqhmE3GntwHgWbIWWu98FutRVTOm2AjiLx/hzl9fEfcgFLoFFkVfqjYAJhX2hIZz/k5G9+7LnSTYiQwpXbo0n3766YPPygNvAo15/Fvp6NE4JkxY6+Tqcr8PPviAGTNm2NS2Xr16/PPPP48FbSGEcLz8+X0ZM6Y7V69+xOLFT9OkST40j/wa8Czhl6q9Z8maaLzyAY/0yKUh4fJRTLERAHhXaZFuu2tT+nF5TGeufPcM1yb24c7Cj4g/lxwYtX4hBD/3QaqFFooC287dzeBXmTtJsBMZNmLECIoXfwPoBaR/VuDnn+/h7t0op9WV240ZM4YxY8bY1LZSpUqsWrUKX9+8ubO6ECJn0GgUunWrx7Ztwzl/fggDB5YlIECDe0FveGTURtFo8a6UPKwaH3oQU1xkmveLPbk5ub3O4+Hwre3FaPFv8hKF+03EPbhE6kuKwrHrab+mq5FVsSJTFi3aQ48eq6y2e+aZgixdOsgJFeVu06dPZ8AA2/aJKlGiBNu3b6do0aIOrkoIITLOaDTR7H9ruWlIvfdp4o0z3Jo7AoDAdkPwrZV62yY1ycDVn19GNcThVak5wc+MSv81wq+jmoygqpjjo0m4fpKYQysxxUfhXbEpge2GoHFPffxZyfzebB7Zwj5fZA4mPXYiU158sQGtWgVabbds2S1Wrz7qhIpyrx9/XM2AAf8C1icjh4SEsG7dOgl1QogcS6fTEp7GfvYehSvgFlAYgNgTm5+4Hnd+L6ohDrA8DAugCyyCe3BJ3ENK4VmiOvka96TQ65NxDylF7IlN3Pp9FObEuFTPuRwWiyHJ8kb7rkCCnci0WbN64+VlOYyoKgwatByj0eSkqnKX337bxsiRu4E6QDcg/c03/fz8WLNmDeXKlXNWeUIIkWFJJjOJ6QQo78rNAUi8furhKREpUoZhNXo/9CUzviGy1tOH/J3eBcB49xKRu/9MdV1VId7g+r+LJNiJTCtePD+jRtW02u7yZQMffLDE8QXlMv/8c4ABAzbw30ltVUiet/jkuYaenp4sX76cmjVrOq9AIYTIhCQLOyKkrI4FNdUiClNCDPEX9wPJx4Ep2syt9NflL/awVzDuzI4nrhtM0mMnhEWffvoM5cpZ37Ty559Pcvas5VMr8pItW07Ru/cyjMbHr5QBXgH+W+nq5ubG4sWL5ag2IUSuYOmEB11gEdwLlQf+66EDiDu9HUzJG67/F/4yR+uVvKgvKerOE9c8dK4fe1z/KxQOpdEozJzZNdUy97QYDPDaawudU1QOd+jQJbp0WUR8fHotigJ9AR8URWH27Nk8/fTTzitQCCGyQKtR8HZPf1pJSnAz3ruC4U4o8N/Gwlr/AngUqZil10+KDgNAo0u9eEKrUfDSpV+Xq5BgJ7KsSZMK9OhRzGq7XbsimTlzqxMqyrnOnbtF27ZziY62thg9+SSPH36YRO/evZ1RmhBC2E3VIulvheVdqRkoyfEj9uRmkqLukXj1RPK1yi1QlMyfopN48yymBz11use2PClfwAe3PHBerOt/hcIppkzpRWCg9W+nUaM2ExWVbleVS7txI4IWLX4lLMy2ybv9+tVm+PDBDq5KCCHsr0axfLilc8yh1jsfniVrAhB7ciuxpzaDmjz3LWVxxeMSb5wh8dZ5i6+ZFH2PsBU/Pvzcu0qrhx+7aRRqFgvIwFeQe8k5RMIu/P29GDOmOf37b7LYLjzczJAhC/j9935OqixniIiIpWnTKdy48cSkujQ991whpk9/xcFVCSGEY1Qr4m95EUWVliSEHsQUfZeoXcmrV90LlME9f/E02xvDrhK2cjweRSqhL1sf95DSaB7MpTNFh5Fw5Sgxx9ajJsYC4FmiJj7V2zx8vsmsUs1CL6IrkWAn7OaNN5rz668H2L3b8mkTCxZcYfDgszz1VHknVZa94uMNNG8+mYsXDTa1b9kykMWL+6NJ569dIYTI6ZqVC8Zdq0l3FapXuYaE6zxQjYmYH4Sx9HrrHpV4/RSJ109ZbONdtQ2B7QahKP+NIikKtKoYkoGvIPeSoVhhV7Nm9cTd3XIbsxn69fsLs4W/5lyF0WiidetJHDsWa1P7OnV8WL16CNo8MA9ECOG6/L10PFerMNp0/kDVuOvRl33kyDBFg1elZunez6tiU0Je/BK/Bt3xKFoFt3wFUdz1oHVDo/fDo3BF/Op3pVDfn8n/9HA0uv92FtBqFNpVKUhB/7xxrrYcKSbs7t13/+DHHy3/RQXwxRe1+fTTZ5xQUfYwm1U6dZrMmjW2HTxdsaIn+/YNx8cnb7z5CCFc27FrkXSZuD27ywBgfv8GNC6TP7vLcArpFhB2N2ZMd4oXf3KT3SfbHeLq1TAnVJQ9Xn55ps2hrnhxHVu3DpFQJ4RwGdWK+lO3REDykQ/ZRKtRqFzIl0alg7KtBmeTYCfsTqfT8ssvnbG2Yj0uTqVv3/nOKcrJhg6dz4IFV21qW6CAli1bBhAc7OfgqoQQwnkSEoxcW3IQ1ZS9A4M/9KiZpS1UchsJdsIhOnasQefOBay227AhjD//3OuEipzn00//YdKksza1zZdPYcOGvpQsGezgqoQQwnliYhJo3HgCezbcI2LztWypQQGGty5HxYJ5649mCXbCYaZP742vr/W/kt56aw3x8batGM3pfvppHV99ddimtt7eCqtWvUSVKkUdW5QQQjhReHgM9etP4NChGACiD9wm/lIUqhMXzGk1CtWL+jOoRRmnvWZOIcFOOEyBAv58/nlDq+1u3TLxzjuLnFCRY82du4N3391h03QSd3dYsuR5GjYs6/jChBDCSW7ciKBevZ84dSr1RvR3/z6H4U6cU8KdVlEold+b2f3qWzy31lXJqljhUGazSq1a33P0aJzFdm5usG9fX2rWLGGxXU61bNkhunVbitGG/Yfd3OD33zvQo4f10CuEELlFaOgdmjWbzrVrab8RKu5aQl4sh0chHxQH7dOpUaBiQV9+f6Mhgd5W9t5yUXkvygqn0mgUfvvtRdysbIWdlASvvvpHrtzbbuvW0/To8a9NoU5R4Oefm0moE0K4lFOnrtOo0dR0Qx2AajBxZ+FZYg4nn+Vqz2iXkhO71S7KHwMb5dlQBxLshBPUqlWSvn2tz3M4ejSOn35a54SK7OfIkSt06bKI+HjbAulXX9Vl0KBW1hsKIUQuceBAKE899Su3b1s/B1tNMpOw4yrv1gymoL8n9ui40ygQ6O3OrL71+P6FGvh6Wt9uy5XJUKxwivh4A6VKjbH6g+/np+H8+eG5YuuPCxdu07DhNO7ds/5mBvDuu5UZN+5FB1clhBDOs337GTp1Wkh0tG1Rws9Pw4oVPWjSpAKxiUnM3X2Z2TsvcTMyAa1GwWTjqI1WAZMKwT4evNKoBK80Lom/Pm8HuhQS7ITTLFq0hx49VllpFcPrr3sxY8aXTqkps27duk/dupO4ft2G8Vfg1VdL8ttvrzm2KCGEcKLduw/RuvU/xMXZ1u0WEKBhw4ZXqFWrZKrHTWaVrWfvsnDfFfZdiiA8NnmXBI3Cw/3nVFUlJfPl0+uoUyKAF+sVo3XFENzy4AIJSyTYCadq3fonNm4MT+OKCuwDNqLRGNm3bx+1a9d2cnW2iYyMo27dCZw/n2hT+2eeKcjffw9E46DJwkII4Ww7duygU6dOREWVAp6z2r5AAS2bN79OxYqFrba9HZXAsWuRXA6PIzHJhKqCp05LsQA91Yr6U9DPM09tOJxREuyEU125co+KFSc9NiftFrAMuPHwkbp167J79260Wq2zS7TIYDDQqNE3HDxoW/tmzQLYuHEYWvmLUgjhItatW8dzzz1HXFzKbgf1gY7pti9aVMfWrf0pVSrEKfXldfLbRjhV8eL5GTWqxoPPDMBqYBqPhjqA/fv3M2XKFCdXZ5nJZOLVV1/l4MEfAOtnwNau7cOaNUMk1AkhXMbSpUvp3LnzI6EOYC+wIc32pUu7s2/fmxLqnEh67ITTmUxmatV6h2PHZgHR6bbz9fXl9OnTFC5sveve0VRV5c0333wkbOqBl4AiabYvX96DffuG4+end1aJQgjhUPPmzePVV1/FZEpvwVhroMnDzypX1rNt25sEBvo4pT6RTLoShNNptRoWLBiAm1u8xXbR0dG88847TqrKsk8++eSxHsR4YDZw8Ym2xYrp2LZtiIQ6IYTLmDp1Kn369LEQ6iC51y757O/atX3Ys+dtCXXZQIKdyBZVqlRh1KhRVtstWrSI1atXO6Gi9P3www98/fXXaVwxAvOBUw8fCQ7WsGVLf0JC/J1VnhBCONTYsWMZNGgQtg3wraJdOwM7dryNj4+nw2sTT5KhWJFt4uLiqFq1KqGhoRbblS5dmuPHj6PXO78HbNasWfTr189KKwXogr9/HbZv70vVqsWcUZoQQjiUqqp8/vnnfPml7dtPvfHGG/zyyy85buFbXiI9diLbeHl5MXHiRKvtLl68mE6PmWP9888/vPHGGza0VAkK2smGDT0k1AkhXIKqqowYMSJDoe6dd95h2rRpEuqymfTYiWz3wgsvsHjxYottdDodR44coVKlSk6paePGjXTs2BGDwWC1ra+vL5s2baJOnTpOqEwIIRzLaDQxcOCbzJo11ebnfPbZZ3z22Weyv1wOIMFOZLvr169TqVIloqPTXyEL0Lx5czZt2uTwN459+/bRqlUrYmJirLb18PBg9erVtGjRwqE1CSGEMyQkGGnTZiI7dhwFfgeSrD5n7NixjBgxwuG1CdvIUKzIdkWKFOGrr76y2m7Lli1MnbrAobWcOnWKjh072hTqtFotf/zxh4Q6IYRLiIlJ4KmnJrBjRyRQAugBpD+sqigKU6dOlVCXw0iPncgRTCYT9evX52C6Rzr4Ax3x8qrI6dNDKVYsyO41HD16hXbtunH79n6b2s+ePZtXXnnF7nUIIYSzhYfH0LTpJE6efHwbqpPAYpKPffyPVqtl9uzZvPTSS84qUdhIeuxEjqDVapk6dWoaw6waoDHwJlCBuDiVfv3m2/31L168Q5s2s7l9ux1Q1mr78ePHS6gTQriEW7fuU6/eT2mEOoDKwLOpHnF3d2fJkiUS6nIoCXYix6hbty5vvvnmI48UBQYAbQHdw0fXrw9j8eK9dnvd27cjad58Onfvmh68Ti+gSrrtP/30U95++227vb4QQmSX0NA71Ks3iYsXLS0Uq0HKWbBeXl4sX76cZ5991kJ7kZ1kKFbkKJGRkVSsWItbt6oA6a8yLVRIy/lzo9DeuEr88RMknEj+Z4qIQDUaUXQ6NP7+6KtUxrNKFTyrVMGjXDmUx5bhR0bGUa/eBM6dS3zsFVRgBXAg1aNDhw7lp59+kpVfQohc79Sp67RsOZPbty2dJvEfL699rF37IU899ZSDKxNZIcFO5Djjxq1m5Mjd6V731yTS1T+UgcUu4ZcYmfygmxskpbF665HH3UJCCOjdm3zdu+GWPz8JCUYaNRrP4cOxFqrZAGwHoHfv3sydOxeNRjq6hRC526FDl2jdeg4REWab2nt5Kfz11/O0b1/dwZWJrJJgJ3Ics1mlVq3vOXo0LtXjvhoDw/MfpXu+i2gfTOTVZLTjTKMBRcG3c2f6b/dn9c7He+rSspNOndz555+/0el01psLIUQOtn37GTp1Wkh0tG2//n19FVau7EmTJhUcXJmwBwl2Ikc6cCCUhg1nP+yEa+p9k68L7iVAm4hWyfq3rAmFaJOOT2/VY31MUYtty5XzYP/+t/Hz88ry6wohRHZas+YoXbv+TVycbe+jAQEaNmx4hVq1Sjq2MGE3EuxEjvXGG3P47dfzfFTgID3zXcCkgtaOU9vManKP37LI4nx8uz5G9cn9mooW1bF//1AKFPC33wsLIUQ2WLJkHy+9tJLERNt+7RcooGXTpn5UqlTEwZUJe5JgJ3Ks2MhY5td6lsa66xkfcs0Aswp74kJ483pTElS3h48HB2vZvXsgpUuHOO7FhRDCCWbP3k7//usxGm1rX7Sojq1b+1OqlLz/5TYS7ESOpJpMXB/+DlHr16Nk8lt04r27TA4Ls6ntzKLFSaIUb15rihEtfn4K27b1pXr14pl6bSGEyCkmTdrA229vw2Tb4ldKl3Znx44hFCyYz6F1CceQ5X0iR7o3aRLRWQh1GaVRVBp73WZkyBG8vBRWrOgpoU4Iket9++0Khg2zPdRVqqRn3763JNTlYm7WmwjhXPEnTnDvl6lgx1C3tGQpi9eL6HRoFOgTcI7WH/WS1V9CiFzvo4/+4ptvjtrcvnZtH7ZsGYqPj6cDqxKOJsFO5CiqwcCN0e+BnTcALufhYdvrKwol/pmB+Y1n0XjJKlghRO40bNgCJk48Y3P7p57yZ/36oXh6ypZOuZ0MxYocJWLhQgwXL2LzuIGdKaqK8dYtwn77LVteXwghssJsVnn11d8yFOrats3Ppk1vSahzERLsRI6hms2Ez5lLvNlM3bNnqXzmNKNv3LD6vKPx8VQ+c5rKZ07ze0R41gsxm4mYNx/V1uVjQgiRA5jNKt27T2POnEs2P+f55wuxevWb6HRPbvckcicJdiLHiN21C+O1a+gVhda+PgBsiIkmzmz5yJsV0VEAaIGOvn52qcUUFkb0xk12uZcQQjia0WiiQ4dJ/P33TZuf06dPCRYvHoDGkftJCaeTYCdyjPsLF4I2+a/Gzn7JAS1eVdkYE53uc0yqyqqo5GDXyNubILe0p42+fvUKjc6dpfqZ0zQ5f45Xr1xmelgYkekN+Wq1RCyYn4WvRgghnCMhwUirVj+xbt09m5/z5pvlmTOnr4Q6FyTBTuQIqqoSu3vPw7l1jb28CXoQ8lY8CG5p2RMXx70Hz+lsobduV1wckWYzSUC4ycS++Hh+vHeX9hcvsCE6jeBoMhF/8BBqyplmQgiRA8XEJPDUUxPYvj3S5ud88EE1Jk7s7cCqRHaSVbEiR0i6eRPzIwFLqyh08PVj3v0IdsTGEpGUREAavXHLo5LfzJKHb32fuF7e3YPWvj5U89QT4uaGUVW5ZDCwPCqKHXGxRJnNDL9xnUlFitLMxyfVc1WDgcSLF/EsX97OX60QQmRdeHgMTZtO4uTJeJvaKwp8/XU9PvjgaQdXJrKT9NiJHCH+xIknHuvyYDg2CViTRq9aotnM+pgYAFr5+OKtSf3t/EpAIP+UKsWw/MG08PGhsqcnNfR6nvX3Z3qxYnxWoAAAJuDT27dISGMuX8KJk1n8yoQQwv7u37/P00934uTJ0za112phwoQmEuryAAl2IkcwXLj4cH5diup6PSV0ycvvl0c/ORy7KTaGmAdhLGVO3qP8tJZXefXIF0B3f38A7iQlse7xuXxubhgunLf5axBCCGe4e/cuLVu2ZPfubcB84LrF9jod/PprG4YNa+OU+kT2kmAncgRzfHyamxI//SCwHYqP57rRkOra8gdz7wK0Whp7e2fqdV/Ml+/hx/vj4tKoKyFT9xVCCEe4fv06zZo14/Dhww8eMQDzgDtptvfwUFiw4GlefbWJkyoU2U2CncgZzGmvTu3sl9yjpgIrov7rUYs0mdgWGwtAB19fdJk8qaKM+38nUtxOY6GEapLFE0KInOHixYs0bdqU06cfH36NB+YAqffx9PJS+Oef5+nWrZ6zShQ5gAQ7kSMo7mkf+VXS3Z1qnsnnFqYslABYGx2N8cFZsmkNw9rK2mm0Gg85M1EIkf1OnTpF06ZNCQ0NTadFLMnhLvl90tdXYfXqnnToUN1ZJYocQoKdyBHcQkLSPUYsJbidNxg4k5A8NJqyBUoRnY6anvpMv+6FxMSHH4c8vurWbMYtJDjT9xZCCHs4ePAgzZo144bVk3gigTkEBSWwefOrNG1awRnliRxGgp3IETwrVwY17f6zTr5+pCyDWB4dxS2jkf3xyfPhOvv6oWRyGBZgUeT9hx/X1Xulvmg241mlSqbvLYQQWbVjxw5atmzJvXu2bT5coUIwhw4NpXbtko4tTORYEuxEjuBRofwTq2JTBLm50cgreXHEiqgoVkRFkbIxydPpDMOeTUzgssGQ5rUUf9yPYElk8rBFfq2WNmnsg9fro+3s2nXOxq9CCCHsZ/369bRr144oC5u0P6pGjRps3bqVYsWKOrgykZPJBsUiR9C4u+NRriyJZ86m2XPX2c+P7XGx3EpKYnp4GACVPDwo65H23LwTCQl8eusW9b28aOrtQ3kPD/JptSSpKqEGA8uiItn5YBWsFvi8YEG8HtsH75rBm2UbY1jWeB516/ry0UcteO65Ovb9woUQIg1Lly7lxRdfxGDlD9QUDRs2ZOXKlQQEBDi4MpHTSbATOYb/s89x57vv0rzW2tcX/e1bxKsqURb2rnuUieSjxHalsY1JinxaLf8rUJBWPql760wqLI0q+fDz/fujef75ZZQtu5aRIxvyxhvN0Wqlw1sIYX9jxqziww//h9lsW6hr1aoVS5cuxeex03NE3qSoajoTm4RwMtP9+5xr2gzVaEzz+qgbN1jxYKNiDbChdBkKPNjA+HFhSUlsjo3hSHw8pxISCTMlcd9kQgX8tVoqeHjQxNub5/388UljCNikQuuLXbiT5PXkzYGCBd0YPLg6o0Z1QK93z9TXK4QQj/voo7/55psjJK/ZXwxYPv2mS5cuLFq0CE9PWcEvkkmwEznKjQ8/JHLpv+mukHWGJFVhU0xh3r5hfUNPPz+FV14pz6efPk1wcOa3XRFCiGHDFjBx4plHHjEDC4C0T8Dp0aMHc+fORZfOH7gib5JgJ3KUxIuhXHzmGUhjs2BnManQ43JbTiYG2vwcDw8YOhRGjhxEwYIFHVidEMLVmM0qffvOZs6cS2lcTQJ+By6nevT1119n6tSpaK0cnSjyHpkkJHIUj9KlCBn+dra9vlmFGeGVMhTqABITQxk37gtKlCjBgAEDOHv2rIMqFEK4ErNZ5YUXpqcT6iB5KnxvoMjDR4YPH8706dMl1Ik0SY+dyHHUpCQu9ehJwunTTh2SVRUN11Vfnj7bFqOa0TfM34ELDz9TFIXnn3+e0aNH06BBA7vWKYRwDUajic6df2Ht2rs2tE4AZvHJJwP54osvsrR/p3BtEuxEjmS4dInQF3tgjokBs9n6E7JKo0Hx8KDkgvlsv5bEF1+sY8eOyPT2TH7MbeCXdK82b96c0aNH07FjR3kzFkIAkJBgpG3bSWzfft/m5wwdWoaff+7juKKES5BgJ3Ks+OMnuPzKK6iJiY7tudNoUNzcKD7zV7zq1n348JEjl/nkk5WsWnXbypS/v4BjVl+matWqjBo1il69eslkZyHysJiYBJo3n8jBgzE2P+eDD6rxzTfdHFiVcBUS7ESOlnDqFJdf65vcc+eIcKfVovH0pNiM6XjVqpVmkytX7vHpp8v588/LxMU9/uNyH/gZsL1XsVixYrzzzju8/vob+Pk9edqFEMJ1hYfH0LTpJE6ejLepvaLAV1/V5cMPOzu4MuEqJNiJHM94+zY3P/6Y2G3bk9/l7Pgt61W3LoW+/T/ci1o/gicyMo6vvlrBr7+eIiIiJcitAvZm4pUV9Pq3ef75avzvf50pXTokE/cQQuQmt27dp0mTKVy4kGhTe60WfvyxCcOGtXFwZcKVSLATuYKqqkT+/Q+3vvoKNT4+a+FOo0HR6Qh5bzQBPXuiaDK2ONxgSGLChHVMnLiLK1e+BdLeUNmyKkB3IPnNu23bYL78siP16pXOxL2EEDndpUt3adZsGlev2vZ+4eYG06a1pm/fpg6uTLgaCXYiV0mKiCByyRLCf/+dpFu3wU0LSdaHaFWNFsVsQps/P4Ev9SZf9+64BQdnqRZVVVm/fj3fffcd69evz+CzBwCFnni0QQM/PvmkFU8/XTNLtQkhco7Tp2/QosVMbt+2bX9ODw+FefM60a1bPQdXJlyRBDuRK6kmE7HbtxO9cRN3d+6FK5fQKU9+KyepChcT/SjWph5lezyDb8uWKG72PyL54MGDfP/99yxatAiz1VW8pQHLK9sqVPBk1KjG9O3bFI1GVtIKkVsdPnyZ1q1nEx5u2zxcLy+FJUuep0OH6g6uTLgqCXYi19u79wJNGvxGaY8ofDVG3BUzRlVDjFnHeYMfRlXLmjXdaNeumsNruXjxIj/88AMzZ84kPj69ydF9SA531hUu7Mabb9bk3Xfb4+kpK2mFyE127DhLp04LiYqyLdT5+mpYvvxFmjWr6ODKhCuTYCdyvWPHrlK9+q8W2yxd+gzPPFPbSRXB3bt3mThxIhMnTiQ8PPyRKwWBgRm+X758Gl57rQKffPI0gYE+dqtTCOEYa9ce4/nn/0pjJX3aAgI0rFvXhzp1Sjm4MuHq5Egxket5e3tYbRMXZ3BCJf8JDg7miy++4MqVK/z000+UKFHiwZWnMnW/+/fNjB9/iqJFx9KnzyxCQ+/Yr1ghhF399dd+nnnG9lAXEuLG9u2vS6gTdiHBTuR6tgxRJibaNmnZ3ry9vRk2bBjnzp1j7tz5FC1aI0v3i4+H33+/TPnyk3n66ckcOBBqp0qFEPYwd+4OevZcTmKibaGuSBE3du8eQOXKRaw3FsIGEuxEruflZb3HLiEhM1uS2I9Op+Pll3tx+fLHzJzZikqV9Fm6X1ISrFx5h3r1ZtO48Y+sXn3UTpUKITJr8uSN9O27DqONbzelS7uzd++blCol+1gK+5FgJ3I9Ly93q22yq8fucRqNQt++zTh58j1Wrnyehg39yMrxsaoKu3ZF0rHjX1SpMoZZs7ZhNsu0WSGcbcyYlQwdutXmA3IqVdKzd+9bFC4c4NjCRJ4jwU7kejqdm9VwFB+fvT12aenYsQa7dr3L3r2v0KlTCFndheXkyXj69dtAiRLfMGbMSgyGnBFmhXB1H3/8N++/v9fmfdNr1fJhz563CQqShVDC/iTYiVxPUZJPb7Akp/TYpaVu3dKsWDGEs2eH8PLLxdFnbZSWa9eMvP/+XgoW/IYRIxYRERFrn0KFEE94660FfP31EZvbN27sz86db+Pr6+nAqkReJsFOuASt1nKXXU4OdilKlQph7tx+XLs2kuHDK5EvX9Y2Jo6IMPPDDycpWnQsr776G1ev3rNTpUIIVVX54INv+fnnEzY/p3XrIDZvfkv2pBQOJcFOuARrw5gJCTk/2KUIDPThxx97cPPmh3z9dV0KF87aGG1cnMqcORcoX74KQ4cOJTRUVtIKkRWqqjJq1Ci+/fYDYCFgfWLds88WZM2aN9HprAwvCJFFEuyES3Bzs9y7ZTDYOKM5B/H01PHhh525evUjpk9vSYUKWRm6OUpCwh0mTZpE2bJl6dWrF4cOHbJbrULkFSaTiUGDBjFu3LgHj1wAlgDpT7B7+eUS/PXXQLRa+ZUrHE++y4RLSD/YmQEDRqNzNyi2J41G4Y03mnP69PssW/YsDRr4ZuIuOx9+ZDabWbhwIbVr16Zdu3asX78eOYBGCOuMRiOvvPIK06ZNe+zKKWBpms8ZMqQ8c+f2lTOfhdPIkWLCJVSoUJ+zZ0+TPCRiApJ4dHjks88+4/PPP8+e4hxg794LfPbZatatu2vD9gpnSB4uSl+tWrUYPXo03bt3xy2ry3OFcEEJCQn07NmTpUvTDnDJ6gGdHn723nvV+Pbbbg6vTYhHSY+dcAleXkYgGogDEnl8zktiYmI2VOU49euXYdWqNzl7dgi9ehXD09NSb8AOq/c7dOgQvXr1onz58kyaNIm4uDj7FStELhcbG0uXLl2shDqAfcB6FAW+/rquhDqRLSTYCZfg7m55k2JXC3YpSpcOYf7817l69V3eeqsi/v6P/0hfffDPNqGhoQwdOpQSJUowfPjPXL0aZtd6hcht7t+//3DKgi10ur38+ms9Pvyws4MrEyJtEuyES/DwsHysmMGQe+fY2SJ/fl8mTOjJzZvv87//1aFQoZSVd9szdb9796KZMOEmZcr8zDPPTOHo0Sv2K1aIXOLu3bu0atWKnTt3Wm8M6PV6li1bRt++Tzu4MiHSJ8FOuARrwc5Ve+wep9e78/HHXbh27WOmT29O06YFM3mneoA7RiMsW3abmjVn0rTpeNavP27PcoXIsa5fv07z5s1tXj3u5+fH2rVrad++vYMrE8IyCXbCJVgbinX1HrvHJa+kbcnWrVvYtWsXzz//PIrNh9JqgQapHlFV2L79Pm3bLqZate/4/fedciatcFmHD1/mqac6cOrUKZvaBwUFsXHjRpo0aeLgyoSwToKdcAnSY5e+hg0b8tdff3Hq1Cn69+9vNQRDDSD9MyyPH4+jT5+1lCr1DT/8sAajMfftEShEenbsOEvz5rO5fLkpYP18v4IFC7Jlyxbq1Knj+OKEsIEEO+ES8uriiYyoUKEC06ZN49KlS7z//vv4+/un0UoBGtt0vytXjIwYsYtChb7hvfcWExUVb9d6hXC2tWuP0a7dAqKizEAI8DKQ/ntLiRIl2LZtG1WqVHFWiUJYJcFOuIS8vngiIwoVKsT//d//ceXKFb7//nsKFy78yNUKQFCG7hcWZuK7745TuPB3vPHGHK5fD7drvUI4wz//HOCZZ5YQF/foFIPCQG/gyb0dy5cvz7Zt2yhbtqyzShTCJhLshEuQodiM8/PzY+TIkYSGhjJr1iwqVqwEZH6OUGysyq+/XqRUqZ94/vmpHDtm+zYrQmSnuXN38OKLy0n7baIE0JPkuafJatSowbZt2yhWrJiTKhTCdhLshEuQxROZ5+7uzmuvvcbx48eZPLkXZctaDsnWGI3wzz83qVHjV5o3n8CmTSftVKkQ9jd58gb69l2H0WhpMVAZoBug0KBBAzZt2kRISIiTKhQiYyTYCZcgPXZZp9VqGDy4FefOfcCSJZ2pUyf9BRS2UFXYujWCVq0WUaPG9yxcuFtW0oocZcyYlQwdus2GY/kAKlGr1pusW7eOgIAAR5cmRKZJsBMuQXrs7Ktr17rs3z+SHTteok2b/Giy+E5x9GgsvXqtpkyZbxg/fq2spBXZ7pNP/ub99/di62npNWt6s2XLd/j6+jq2MCGySFFVW7+thci53n57Aj/9tJLkSc7aB//++7ho0QCuXp2YnSXmamfP3uSTT1awdOl1EhOz/paRP7+W/v0r88EHT+Pr62mHCoWw3dtvL+Snn07b3L5RIz82bBiKXm9tqyAhsp8EO+ESBgyYy/TpF9K9HhSk5d69T5xYkWu6fTuSL79cwdy554iOzvpbh7e3Qq9epfnii84ULizDW8KxzGaV11+fw2+/hdr8nNatg1i1agg6ndZ6YyFyABmKFS7Bw+PJ7QgelZQkf7/YQ4EC/kya1JubN9/n009rUqBA1n7ZxcaqzJhxgZIlJ/DGG19y9aqspBWOYTar9OgxI0Oh7tlnC7JmzZsS6kSuIsFOuAQJds7l7e3BF188x/XrHzFxYlNKl87aEJXRGMevv/6P0qVL8+qrr3L8uJxJK+zHaDTRqdNkFi++bvNzXn65OH/9NRCtVn5NitxFvmOFS/DwsPwXdVKSkwrJY7RaDW++2ZoLFz5k8eKnqV07sytp9wBJJCUlMWfOHKpVq0bnzp3ZunUrMltEZEVCgpFWrX5mzZq7Nj9n8OByzJ3bD43G1vOVhcg5JNgJl2Ctx85kknDgaN261ePAgZFs29aLVq0CM7CS1gjse+LRFStW0Lx5cxo1asRff/2FybY9KYR4KDY2kSZNJrB9+30bn6Hy3ntVmTz5JUeWJYRDSbATLsH6UCyyh5qTNGlSgQ0b3uL48f50714EKzvRAAeB9M+Z3bNnD926daNy5cpMnz6dhIQEe5YrXFRERCwNGkzgwIEYm9orCnz1VV2+/ba7gysTwrEk2AmX4Omps9omKUl6fJypUqUi/Plnfy5fHs7AgWXx8UlrWMsM7LLpfmfPnmXAgAGUKlWKb7/9lvv379uzXOFCbt26T/36P3HiRJxN7TUamDChCR991MXBlQnheBLshEvw9LTcYwfJwzLC+QoWzMcvv7zMzZvv8fHHNQgOfnQ+5AkgMkP3u3XrFh988AGFCg2ie/dpnD59w671itzt0qW71K8/ifPnbft5d3ODGTNaM2xYGwdXJoRzSLATLsGWHrv4eDl9Ijv5+Hjyv/89z40bH/LTT09RqpQO2JHJuwWSkFCBJUtuUKXKNFq1+ont28/Ys1yRC509e5OGDX/h6lWjTe3d3WH+/E707dvUwZUJ4TwS7IRLkGCXe7i5aRk2rC0XLnzIli2LePrppzNxl0ZA8tCu2QybNoXTtOkCatcey5Il+2w+Jkq4jsOHL9O48Qxu37ZtyoVeD//88zwvvFDfwZUJ4VwS7IRL0OutB7u4OAl2OYmiKDRr1ozly5dz7NgxXn31VdzcrA+pgzdQM80rhw7F0L37CsqV+z8mTdqAyWS2Z8kih9q58xzNm88mLMy2UOfrq7BqVQ86dqzh4MqEcD4JdsIlSI9d7la1alV+++03Ll68yLvvvouPj6X98BqQfA5w+i5cSGTo0G0ULvw1n376j8yvdGHr1h2nXbsFREXZFuIDAjRs3NiH5s0rObgyIbKHBDvhEiTYuYZixYoxbtw4rly5wtdff01ISMhjLdyBejbf784dE//732EKFfqWIUPmcft2xhZqiJxt1ap9dOmymNhY20JdcLCWbdv6UbduaQdXJkT2kWAnXIKXl/UjrRIS5PiJ3CIgIIAPP/yQy5cvM3XqVMqWLfvgSm3AM8P3i45WmTLlHCVKjKdHjxmcOXPTrvUK59uwYQPdu7ciMfGkTe2LFHFj9+6BVKlS1MGVCZG9JNgJl6DXWw920mOX+3h6ejJgwABOnz7N4sWLKV26ZZbul5iosmjRNSpXnkqbNj+zY8dZO1UqnGnZsmU8/fTTxMXFAEuAcxbblyrlzp49Qyhd+vEeYCFcjwQ74RJsCXYJCbZtgSByHq1WS7du3Th37msWLOhA9epeWbqf2QwbNoTRpMl86tYdx99/77dTpcLRFi5cSNeuXUlMTJk3aQYWAZfSbF+xoid79w6jSJFAJ1UoRPaSYCdcggS7vEGjUejZsyFHjoxm48YXad48IANn0qbtwIFounZdTrly/8eUKRtlJW0ONmPGDHr37k1S0uPTKpKABcD1VI/WrOnNnj1vkz+/r7NKFCLbSbATLsGWOXbx8RLsXEnLlpXZvPltjhx5neeeK4TO+voZi86fT2TIkK0UKfI1n3++VLbHyWHGjx9P//79UdPdpNAA/A7cBqBRI3927nwbPz+9s0oUIkeQYCdcgpeXh9U2iYmyeMIVVa1ajL//Hkho6Fu8/nppvL3TOpPWdrdvm/jii0MUKvQtQ4fO5+7dKDtVKjJDVVW+/PJL3nnnHRtaJwBz6dDBmy1b3rKpJ18IVyPBTrgEnU6LYuX3uQzFurYiRQKZMeMVbtwYzXvvVSMoSGv9SRZERZmZNOksxYr9SM+eMzh37padKhW2UlWV0aNH89lnn9n8nLfeep0VK0ag02Xt/78QuZUEO+EytFbexxMTbduVXuRufn56vv22Gzdvfsi4cY0oXjxrY7SJiSp//HGNKlVa079/f86ckTNpncFsNjN48GDGjh1r83M++ugjxo8fjyarEy+FyMXku1+4DDc3y112iYnSY5eX6HRa3n23PaGhH/L77+2oVi0rK2nvYDSeZMaMGVSqVImuXbuye/duu9UqUjMYkujT5xWmTp1q83PGjBnDV199hWKt614IFyfBTrgM6bETadFoFF56qTFHj45m/foXaNIkn9Vh+yftePiRqqr8/fffNGrUiGbNmrFixQrMZllJay+xsYk89dQE5s+/YvNzJk+ezOjRox1YlRC5hwQ74TJ0Oms9drJ4Iq9r3boK27YN5/DhfjzzTEEbV9JGAsfTvLJt2zY6d+5M9erVmTNnDgaDrKTNioiIWBo0GM/+/dFAK5LPBU6fRqNhzpw5DB482Cn1CZEbSLATLsNaj50cKSZSVK9enKVLB3HhwjD69i2Fl5elPwp2k7wJbvpOnDjBq6++SpkyZfjhhx+Ijo62a715we3bkdSv/xMnTsQ/8mgHoGaa7XU6HX/++Sd9+vRxRnlC5BoS7ITL0OksfzsbDDIUK1IrViyImTNf5caNUYwaVZXAwMe/hxKAgzbf79q1a4wYMYLixYvz9tvfcPHiHbvW66quXLlHvXoTOX8+MY2rzwBVUj2i1+tZtmwZXbt2dUp9QuQmEuyEy7C2eEKCnUiPv78X333XnZs3P+T77xtSrFjKGO1ekje+zZj79+/z00+XKF9+Mh06TGLv3gt2rdeVnD17kwYNfuHq1fQWNylAV6AcAL6+vqxevZr27ds7q0QhchUJdsJlWJtjZzDIBHdhmbu7GyNHduDSpQ+ZM6ctrVr5ZPJOJYHCmEywZs1dGjSYS4MGP7B8+SE7Vpv7HTlyhcaNZ3DrlrVpEhrgRfz9a7Jx40aaNWvmjPKEyJUk2AmXYW0oVhZPCFtpNAp9+jzFhg3/cvDgQXr16pXBvdGeeuKRvXuj6NJlKRUrfsuMGVswm9M7Gitv2LXrHM2azSIszLaedF9fHUuXzqdu3boOrkyI3E2CnXAZMhQrHKFWrVrMnz+f8+fPM3ToUPR6a2ePFgDKpnv1zJkE+vffRLFi3/D118vz5Iko69cfp23b+URF2RZu8+XTsGFDH5o3r+TgyoTI/RQ1/ROVhchV2rR5lQ0btgGmB/+SUn3cu/cLzJv3a3aWKFzAvXv3mDhxIhMnTiQsLCyNFl2BajbfL18+hVdfrcCnn3YmMDCzQ7+5xz//HKBXr+UkJNj2qyc4WMumTX2pUqWogysTwjVIj51wGX5+0UAocAW4DtwG7gERQDRJSTHZWJ1wFfnz5+fzzz/n8uXL/Pzzz5QsWfKRq/48voLTmvv3VSZMOE3RomN5+eWZhIa67kraefN28uKLy2wOdYULu7F790AJdUJkgAQ74TI8PDwsXpfNY4U9eXt7M3ToUM6dO8f8+fOpWbMm0IjMvq3Gx8O8eVcoX34ynTpNYv/+i/YsN9tNnbqJV19di9HGkedSpTzYu3cIpUuHOLYwIVyMBDvhMtzd3S1eT0xMa48sIbLGzc2NXr16cfDgQWbP/ojKla3NwbMsKQlWrbpL/fpzaNToB1atOmKnSrPP2LGrGTJkCyYbp7lWqODJ3r1DKVIk0LGFCeGCJNgJlyE9diI7KYrCK6804cSJ91i1qiuNG/tn4kza/6gq7N4dRadOf1Op0rfMmrXN6StpVVUlMt7I7agEbtyPJywmkSRTxrYN+vTTfxg1aje2Hqdbo4Y3e/e+Tf78vpmoWAghiyeEyxg2bBgTJ05M93qTJk3Ytm2bEysSed2BA6F8+ukq1q69Q5IddtspUsSNN9+sxTvvtMPT06aDbjPEZFbZcf4e+y6Fc+RaJEev3ud+fOqxU3ethkqFfKlZLB/Vi+ajbZUC+KVTy/DhC5kw4bTNr9+woR8bNw5Fr7fc+y6ESJ8EO+EyRowYwQ8//JDu9Xr16rF3714nViREstDQO3z22QoWL75MfLz19tYEBGh47bUKfPJJZwICvLN8v/BYA4v2X2X2zkvcjExAq1Ewm1Us/XJw0ygkmVU83DR0q1OUPg1LUKmQHwBms8obb8xh1qxQm2to1SqQ1avfRKezcuizEMIiCXbCZXz44Yf83//9X7rXa9SoweHDh51XkBCPCQ+P4X//W8Fvv53h/v2sn4Si1yt0716cL7/sTMmSwRl+fpLJzPRtofyw7gwms0pmR3q1GgWTWaVdlQL875mqDOs/lz//vG7z87t0KcDffw9Eq5XZQUJklfwUCZchiydEThcY6MOPP/bg5s0P+OabehQp4pal+8XHq8yde5ly5SbRo8c3XLxo+0rac7ejeW7yDsasPo3RlPlQB8lDuAAbTt2h0VdrWXHc9m7J3r2L888/gyTUCWEn8pMkXIYsnhC5haenjg8+eJorVz7i119bUbGiZ5bul5QEixaNp1y5cvTs2ZODBw9abL/6+E06/bSNUzejs/S6jzOZVUyKhuBnyhDYvgRYWTwyaFBZ5s3rh0aThVUmQohUJNgJl2Et2EmPnchpNBqFfv2acerU+yxf/hwNGvhl8k5ngbuYzWb++OMP6tSpQ9u2bVm3bh2Pz7b5+9A1Bs87SJJJfdjTZk/Kg5DmUyOY/F1KpxvuRo+uypQpL9v99YXI62SOnXAZEydOZNiwYelez58/P3fv3nViRUJk3P79Fx+spL1r875vMIvkE1eeVKtWLUaPHk337t3ZeOYeg34/kKVh18fFXzpM7IlNJF47iSk2HBQtWu98uAeXxKNEDRSqE77u1sP2igJfflmHjz/uYr8ihBAPSbATLmP69OkMGDAg3et+fn5ERkY6sSIhMu/ixTt8+ulyliy5auUIrmuA9TOQS1SuhdszX2BSNRZXu9rKlBBD2MrxxJ/bbbFdodd+Imo/xB4PQ6OBceMaM3x4OztUIIRIS9Zm7gqRgxgMPkAdQPvIP7eHH8fHy95YIvcoXTqE33/vx/jx0Xz11Qpmzz7D/ftpRbIdNt0vvlo3PJPMKHZYpGBOjOXOHx9juHUeAH2ZenhVaoYuXyFU1Ywp6g6JN88Rd2YHqqoS2LYkxmvRTBnXhH79mmX59YUQ6ZMeO+Eyvv9+FaNH77HYxmT6TCZqi1wpIcHIuHFrmDTpMDdvpux2HAZMAit9cD7V2xHU8S271XJv+ThiT2wCjZb8nUfgXSntsKaqKqhmQEMFf3fWfNgWJSvHcQghrJLFE8JleHhY74A2GOyw/b8Q2cDTU8dHH3Xm2rWPmDatBRUqeJLcW2flb3ONlnzNXkFVs75vHkDCtRPJoQ7wb9Qj3VAHycesKRotikbhbLSRvaHhdqlBCJE+CXbCZdhyxFJ8vGx5InI3jUahf/8WnD79Prt2TaFbt24We8G8yjVE650PNcnAlR9f4PKYztxbNtbq6yTeOMPlMZ25PKYzUQeWPXw8+sByABR3PX71n7e5bq1GYe7uyza3F0JkjgQ74TL0euvBLi5OtjwRrqNhwwYsXryY06dPM2DAgDS3/PGt0wXVbEKj88SrXEMA4s7txmxIsHjv2JNbkj9QNHhXbAqAajISfz55uoO+VB007vrkx80mkiLvkBR1B9VkTPN+JrPKquO3uBNt+XWFEFkjwU64DFuGYqXHTrii8uXLM3XqVC5dusQHH3yAv78/AFq/YDyLVUXRJJ+/6l25BQCqMYH48+mvZlXNJuJObwPAs2QttN75ADDcCUVNSv4Z8ihSEVNMBPdWjOfq+B5c/6Uf16f04+r4Htz+8zMSrp168r6qyr+Hb9jryxZCpEGCnXAZer31Va9xcRLshOsqWLAg33zzDVevXmXcuHEUqtIo1XXPkjXReOUDHumRS0PC5aOYYiMA8K7S4uHjxntXH36sJhm4MfNNYo+vRzUmpHo84eIBbs9/j6h9S1PdVwEOX72fuS9OCGET2e5EuAzpsRMima+vL++++y5x5doxc8clzA+Of1A0WrwrNSX6wDLiQw9iiotE6+X/xPNjT25Obq/zeDh8C2BO+O8Isvs7FoDJiL5MPfybvIR7/hKYE2OJO7uTiM2/oRriiNg4A11gEfRl6gJgUuHglQgHfuVCCOmxEy7Dy8t6j11CQtrzf4RwRcdvRD8MdSlShmMxm4g78+QeeGqSgbizuwDQl234cB4dgPmRnjlMRvRl6xPc7RM8CpZFcdOh9c6Hb61OhLzwGSgaQCVi86xUx5rduJ9AZJz8HArhKBLshMuwbVWs/EIReceV8LgnHvMoXAG3gMIAxJ7Y/MT1uPN7UQ3Jz3t0GBZA0ab+4ymgRT8U5clfI55Fq+BVPnkY2HjvMsZ7qVfD3oiMt/lrEEJkjAQ74TJsmWMnQ7EiL0lISnvvOu/KzQFIvH6KpMjbqa6lDMNq9H7oS9ZKdU3j8V/vnZt/AXRBRdN9bc9StR9+bLh5NnVdRpsPwRVCZJAEO+EybBmKTUyUDYpF3pHe7nYPh2NRUy2iMCXEEH9xPwBeFZuiaFPPW3Xzzf/wY+0jH6fl0bamuNRnNMvpE0I4jgQ74TKkx06I1Dx12jQf1wUWwb1QeeC/HjqAuNPbwZT8x89/4e+R5+Uv8d8n1k6yePS6kroOT5386hHCUeSnS7gML68nN2d9XEKC9NiJvKOAXgvpHAeeEtyM965guBMKQOyp5N47rX8BPIpUfOI5bv4haP2Ck593/6bF1zbev/Xf83yDHn6sAMUCvGz+GoQQGSPBTrgMW3rsZFWscHUGQxJTpmykdu2xbFpyFtWcTrCr1OzBytXkXrukqHskXj2RfK1yi3SHS73KNwbAHHs/zU2IU8Sd3fnwY4+iVR5+XDzIC28btiYSQmSOBDvhMnQ6Ldam7sgcO+GqTpy4xmuv/UZIyDcMGbKVQ4diSLwVi6JN+21e650Pz5I1AYg9uZXYU5sfDp+mLK5Ii1+9Z1Hckv+IitgwNc2jyWJObCLxyjEA9GXq4eaXPN9Oq1GoXTwgs1+iEMIG8meTcClubmC00CknQ7HClRgMScyYsYXp0w9y5EjsE6OuhhuxqKqabu+bd5WWJIQexBR9l6hdfwLgXqAM7vmLp/uabn4h+Dd5ifubZ2G4dZ5bc97Fr0E3dMHJGxTHn91J9KFVACjuXgS0euPhc81mldolJNgJ4UgS7IRL0WoVjMa0h55AeuyEazh+/CrffbeepUuvEBWV/ve7KdZI/MVI9KX8UDRP9tx5lWtIuM4D1ZiIOTEWsNxbl8K/QTfMCdFE7V6CMewKYSt/fKKNxisfIV0/QhdY5OFjOq2GZ2oUtuVLFEJkkgQ74VLcrHxHS7ATuZXBkMT06f/1ztkq+uAdvMrkS/Oaxl2PvmxD4h4smkDR4FWpmU33DWj+Gl5lGxB9aCUJ105iiglHcXNHF1gYfdkG+NXpgsbD+2F7rUbh+dpF8Ndb30hcCJF5EuyES7Ee7GRjVJG7HD16he++W8+yZdeIirKyxUgaEi5GkhSZiNbXHUXz5JBs8DOj4JlRmarNo0glPIpUsqmtyazSp2EJ6w2FEFkiwU64FDc3DZB+eJMeO5EbJCQYH86dO3r0yWPBMip841VCni9rh8oyR6sodKlRiKpF/LOtBiHyCgl2wqVY67EzGKTHTuRcKb1z//57lejo9OfOZVTCuQg8bkdhKOCH/e5qG0UBfy8dnz9TxXpjIUSWSbATLkWnkx47kbskJBgfzp07dizrvXOPCgjQ0K1bSUaPbkdQ4UBajdtMVLyRdLa2cwhVhTHdqpPPhiP/hBBZJ8FOuBSdzvJGdgZDxucoCeEIhw9f5rvv1rN8+TW79s4pCtSr58ugQfV55ZWn0D6yj930V+ry0vQ9GM3m9A6ksLuhLcvStnIB57yYEEKCnXAtyXPs0idDsSI7JSQYmTp1EzNmHOb4cfv2zgUGaujWrRSjRrWlXLmCabapVzKQGa/W5Y3Z+0kymx3ec/d6k1KMaFfesS8ihEhFgp1wKdZ77CTYCee7ePEiM2bM4NdfF3DnzsuA1i731Wj+653r0yd171x6mpUP5vc3GvD67H3EGUyY7JzuNAqYVXi3bXmGtSqb7ubIQgjHkGAnXEryHLv0yVCscBaDwcC///7LtGnTWLdu3SNXTgLVsnTvoCAt3bsn986VKZPxYc76pQLZMKI5H/x1jA2n7qCAXRZVaBQo5K/nhxdr0KB0kB3uKITIKAl2wqW4u1sOdkajBDvhWBcuXGD69OnMmjWLO3fupNHiAJkJdhoN1K/vy+DBDXjppcY29c5ZEuLryYxX6vLvkRt8/u8JIuKMD3vbMlybAoqi8EqjEoxqXwEvd/nVIkR2kZ8+4VKs99jJUKywP4PBwNKlS5k2bRrr16+30voyEAbY1qMVFKThhRdKM2pUO0qXDslqqakoisKzNYvQoWpBVh+/xeydlzh45T4KySdFJKWT8lL2OTarEOLrwSuNSvBivWKE+HratT4hRMZJsBMuxd3d8twl6bET9nT27Hl+/TW5d+7u3bsZeOYBoF26VzUalfr1/RkypAG9ezfKcu+cNR5uWp6tWYRnaxbh3O1o9l2K4Nj1SA5fjeDi3VgMSWZUQKdVyO/jQa3i+ahWJB81ivpTv1Qgbg6uTwhhOwl2wqVYD3bO3p5VuJr4eAOTJ29k5swjnDy5DViRibscBlrz+CKKoCANL75YhpEj21C6dPZsEVKugC/lCvimekx9sDeKLIQQIueTYCdcSoECd4GVJG9SbAKSUn3s41M1G6sTudnevRcYO3YjK1feIDY25Q+E6sA6wJDBu8WTsohCo4EGDfx4882G9OzZ0OG9c5khgU6I3EOCnXApwcExwD4LLfI7qxThAmJjE5kyZRO//nqY06cT0mjhDlQFDmb43iVL3qFTp3KMHNmWUqXsO3dOCJF3SbATLsXd3fKxRYmJiU6qRORme/acZ+zYTaxceYO4OGvD93WwNdjpdDq6devGgAEDaNGihfSECSHsToKdcCkeHh4Wr0uwE+mJjU18OHcu7d659BQGCgK30m1Rrlw5BgwYwKuvvkpwcHBWSxVCiHRJsBMuxVqwMxgyOhdKuLrdu88zduxGVq26aUPvXHrq8PgiCnd394e9c82bN5feOSGEU0iwEy5FhmKFLWJiEpg4cQO//XaMM2cy0juXnurAWsBIhQoVGDBgAK+88gr588ucTiGEc0mwEy5FeuyEJbt2nWPs2E2sXp2V3rknabU6nn56CO+++zzNmjWT3jkhRLaRYCdcivTYicel9M7NmnWMs2ft0Tv3nwIFtPToUZYRI9pSvLj0zgkhsp8EO+FSpMdOpNix4yzjxm1i9epbxMfbs3cOGjfOx9ChjejevT4ajfTOCSFyDgl2wqXY0mOnqqoMlbmo6Oh4fv45ee7cuXP27Z0tUEBLz57J+84VLWrbOa9CCOFsEuyES7HWYwdgNBqtBkCRu5w8eZKff57N7Nl64uPtd1+tFp56Kh9vvfUUzz9fV3rnhBA5ngQ74VJsCXYGg0GCnQuIj49n8eLFTJ06lR07djx4dCDJe8plTcGCbvTsWZaRI9tRpEhglu8nhBDOIsFOuBRbAltiYiI+Pj5OqEY4wokTJ5g2bRpz5szh/v37j109ADydqfu6uUGTJoEMHdpIeueEELmWBDvhUhTFEygBaB/555bq45gYA0EyRSpXiY+P588//2Tq1Kns3LnTQstjQDtAZ/O9CxXS0rNneUaMaCu9c0KIXE+CnXApcXHewGsW20RGGp1Si8i648ePM23aNObOnZtG71xaEoHjQC2LrdzcVJo2DWLYsMY8+2wd6Z0TQrgMCXbCpej11ntq4uJky5OcLDIyjgkT1vPXXws5cmRBJu5wgPSCXaFCbvTundw7V6hQQJbqFEKInEiCnXApnp7Wg11CgvTY5USbN59i3LjNrFt3m+R9pPNl8k7XgdtAASB57lzTpoG8/fZTdOlSW3rnhBAuTYKdcCl6vfXFE/Hx0mOXU9y/H8uECeuZM+cEFy8+/v8lBCgKXMvEnQ9QuPAz9O5dgREj2lKwYL4s1yqEELmBBDvhUmwJdtJjl/02bjzBDz9sYf36O1g+5a0OGQl2er2enj178sYbA2jYsIH0zgkh8hwJdsKl2DLHLj5egl12iIiIZfz4dcyde5LQUFt7TasCawDLZ7xWr16dgQMH8tJLL+Hv75/VUoUQIteSYCdcipeX9Q2KpcfOudavP86PP25lw4a7JCZm9MxWN6A6sPeJK15eXvTs2ZOBAwdSr149OSZOCCGQYCdcjAzF5gzh4TEP585dupTV/951eDTY1ahRg4EDB9K7d2/pnRNCiMdIsBMuxbZgl+SESvKmdeuOPeydM9htjUoIen15XnqpGQMGDKBu3brSOyeEEOmQYCdcik6nRVFAtTDil5gowc6ewsNj+PHHdfz++0k79M6lVrSoGy+/XIl3332X4GA/u95bCCFckQQ74XLc3MBoIV/IUKx9rFlzlPHjt7Fxoz1750Cng5Yt8zN8eFM6dKguvXNCCJEBEuyEy3FzUzAa0++yMxhMTqzGtdy7F8348euZO/ckV67YNyAXK5bSO9eO/Pl97XpvIYTIKyTYCZej1Vq+LnPsMm779kN89dUONm0Kw2DI6MrW9Lm7K6l654QQQmSNBDvhctzcFCD98CFz7GwTHR3N/PnzmTZtGgcPHgNGANa3k7FF8eI6+vSpzPDhbaR3Tggh7EiCnXA5ycEufTIUmz5VVTlw4ADTpk1j/vz5xMbGPnL1KFAv0/d2d4dWrYIZPrwJ7dvXyHKtQgghniTBTrgcnZXDJ2Qo9klRUVEPe+cOHTqUTqsDZCbYlSiR3Dv3zjttCQz0yVKdQgghLJNgJ1yOm5sGSL9Xzmg0O6+YHExVVfbv38/UqVNZsGABcXFxVp5xm+RzW4tavbe7O7RuHczbbzelfXuZOyeEEM4iwU64HJ3O8lBsXp9jd+dOJL//vpi5cydy+PDhDD77AJaCXcmS7g/nzknvnBBCOJ8EO+FydDqNxet5cY6d2ayyYsUhfvppO1u2hGM0ngAOZ+JOJ4AOPLqIwsNDoXXrYN55pxlt2lS1T8FCCCEyRYKdcDnWF0/knaHY27cj+eGHdcybd5rr1x/tqawIeAHWhl8fZyRlEUWpUim9c20JCPC2V8lCCCGyQIKdcDnu7pY3snP1HjuzWWX58kNMmLCdrVvDSUpz5FkL1AR2Zujefn5+vPBCZXr1eoHWratkvVghhBB2JcFOuBxrc+xcdfHErVv3GTduHQsWnHmsdy49dbA12DVs2JABAwbw4osv4u0tvXNCCJFTSbATLsfd3docO9cJdmazyrJlB/nppx0WeufSEwiUBC6ledXf358+ffrQv39/qleXla1CCJEbSLATLkenszwUm5SU+4PdzZsRjBu3jvnzz3DzZlaGluvweLBr1KgRAwcO5IUXXsDLyysrZQohhHAyCXbC5VifY5c7g53ZrLJ06QF+/nkn27aFkZRkecjZNpUAL/z9dbzyyiv079+fatWq2eG+QgghsoMEO+FyrA3FGo32O8TeGW7ciGDs2LUsXHj2kd45e4Q6KFNGz/Dhs+jXr7P0zgkhhAuQYCdcjrUeu6SknB/szGaVf/7Zz08/7WDHjvsZnDtnmaenQrt2Ibz7bguaN69kvxsLIYTIdhLshMuxFuxy8qrY69fDH/TOnePWLftuy1K2rAevvVaVYcPa4Oent+u9hRBC5AwS7ITLyW09dmazyl9/7ePnn3eyY8d9THbMc3o9tG9fiBEjWtCkSQX73VgIIUSOJMFOuBxPT8vf1jlljl1YWBhz585lypQ/OHu2vV3vXa6cB6+9Vo1hw1rj6yu9c0IIkVdIsBMuRVVV9EFJ6Mvdxb1gfnRB+dDodKBRUI0GTLHRuMeEc+xaJOUL+uDhZrl3zxH1bdu2jWnTprF48WISExMfXKlA8p5ymafXK3ToUJARI1ry1FPls1qqEEKIXEhRVTVndF8IkQVhMYn8sf8qc3dd5mZkAgCqKQkUDYomeZWsqqpgNoFGi6Io6LQKHasW4tXGJahdPABFsc9K0zTrCwtjzpw5TJs2jdOnT6fRoirQLVP3Ll/ek759qzF0aGt8fDyzVKcQQojcTYKdyNVuRSbw3erT/HvkBiZVJaPfzVqNgsmsUr6AD++2rUCHqgXtVpuqqmzduvVh75zBYLBUCTACsG3YVK+Hjh0LM3JkSxo1KmePcoUQQrgACXYiV1JVlcUHrvHZvydITDJjMmft21hRQFXh6WqF+N9zVQn0ds/0ve7du8fs2bOZPn06Z86cycAz2wGNLLaoUMGTvn2r8+abraR3TgghxBMk2IlcJzLeyFsLDrHl7F0UwJ7fwFpFwcfTjZ961aJ5+WCbn2c2qyxatIdJk3axe/dSkpK2ZOLV8wNvPvGol5dCx46FGDmyFQ0bls3EfYUQQuQVEuxErnI3OpGXZuzmwp1YTA761lVI7sEb37MWz9QobLHtlSv3+O67tSxadIG7d1P2KYkEJpC5yPkaUAKAihU96devBkOGtMLb2yMT9xJCCJHXyKpYkWtExhnpNX03offsH+qSYsK5MWMwamIsAB7FqvI23+KuVehQtVCqtmazysKFu5k8eTe7d0emse+cP1AGOJ/hOvLlO0ObNo0ZObIlDRpI75wQQoiMkWAncgWzWWXg7/sJveuYnrqIdb88DHUPqTB0/iH+edOLqkX8CQ29w9ix6/jzz4uP9M6lpw4ZCXatWrViwIABPPfcc3h4SO+cEEKIzJFgJ3KFeXsus/tiuEPuHXd+D3Fnd6Lxyoc57v7Dx9UH//pN24nbhtPs2RWJ2ebTyMoDvkB0ui3y589P3759eeONNyhfXvadE0IIkXWa7C5ACGuuhsfx1YpTDrm32RBP+NpfAAho2e+J6yazyu0EEycV7wyEOkj+0aqZ5pXWrVvzxx9/cO3aNb777jsJdUIIIexGgp3I8T779wRJWdzOJD33t87BFH0Xj+LV8anaKs02iqLg36gwbvkyOkRam+SlGBAcHMzo0aM5d+4c69ev58UXX5QhVyGEEHYnQ7EiR7sSFsem03cwGRO4NrEPqiEe78otyN9lpMXnJd44w625IwAIaDMQvzpd0mwTfXAFaN0IajfEai0+NYO5v/laBqrPR5MmPXjrra48++yzuLtnfm88IYQQwhYS7ESONm/vZTSKgqrzxKtcQ2JPbCLu3G7MhgQ07ulv0Bt78sE+cooG74pNn7iumk2ErZkIqhn/Bi+iCypqsQ5Fo+BbI5jI7ddRkyz3Hnp7K3TpUoSRI1tTp04p61+kEEIIYScS7ESOZUgys2DPlYerYL0rtyD2xCZUYwLx53fjXblFms9TzSbiTm8DwLNkLbTe+Z5oE7X3L4x3QnHLVwj/Ri/aVI/G0w2vCoHEnghL83qVKnpef70mgwa1RK+X3jkhhBDOJ8FO5Fgnb0YRlZD08HPPkjUfrlyNPbkl3WCXcPkoptgIALyrPNnGeP8WkTsWAhDYbjCKm20hTDWZ8SzplyrY+fgodOlSlFGjWlOrVknbvjAhhBDCQSTYiRzr2PXIVEeGKRot3pWaEn1gGfGhBzHFRaL18n/iebEnNye313ngVa7hE9fDV09ETUrEq2JT9KVq21yPotXgUdgHgKpVvejfvxYDBrTA01OX0S9NCCGEcAgJdiLHOn4tEq1GSbUi1rtyC6IPLAOzibgzO/Ct1SnVc9QkA3FndwGgL9sQjbs+1fWY4xtIuHwYxd2LgNb9M1yTLsCDXftepWFdmTsnhBAi55HtTkSOdfJm1BPbnHgUroBbQPL5rbEnNj/xnLjze1ENccCTw7CmuEgiNv4KQL5mfXDzCcx4UYqCV8GAjD9PCCGEcAIJdiLHik4wpvm4d+XmACReP0VS5O1U11KGYTV6P/Qla6W6FrFxBub4KNwLlsO39tOZrismMcl6IyGEECIbyFCsyLHS25TYu3ILInf8f3v3EyJnecBx/PfO7O7sv2Q3us0ax2QTjCbGNS5aMNhWGkUoVNEWpFCheBAv3nppL57aQoqXXgs99JZDURB76KHUWBVpsT2UmvgHY9MISv4gGmdN1p2ZHhYTJU5DTGZ39uHzuc7wPs/CwH553vd53oNJumkdfvH8rtb22U/y6dHXkiTju7+Tqn7h57185nRar7+QJBmd25vFIy/937E7ix+dPzJlaPq6NK7fdeFa7f4clgwAV0rYMbAaQ1+9oDx8TTMjW27O0vtvpXX40PmwW3zj5aS9spp20Y7ZzoVVto//9swlx/7s9PGcev7plWvN3/elsOs1LwBYa/5DMbC+Mdn7lVufh9tnp/6bpRPvJklaR1ZW2OpTs2k0d/dtXtdOOqMOgMFkxY6BtXfrdF479uFX3pKduOWefPiX3yXdTlqHD6U2uiHnjr++8tme76aqqi99f2hqNnM/++Mlxzz26weSJI2t87nuxwcu+rwxVMuOmcmv8+cAQN8JOwbWfHOq53N29YnpjG5fyNl3/5nW4b+mNrYh6XaSXNhc0Q97tmxMvVZd+osAsAbcimVg7W1efPjwF03cuj9J0j5zMh+/+ockycjsjRmZ2daX+QzVqixsm+7LtQHgahB2DKy5a8eza3YyvdbHxm/al2p45Tm8zrlWkv6u1i13uvn+bVv6dn0AuFLCjoFVVVUe+9aO9DpcpDYylrGdX3hlWFXL+C339GcuSW7aPJk75xxODMDgqrrdrkO5GFiLS8v55i//nMWl9lpPJb/6wXwevWturacBAD1ZsWOgjY8M5fFv7+h5O3Y11KpkZnIkDy8013AWAHBpwo6B9+S9O7N9ZiL1Naq7Tjd5+pHbM9GwiRyAwSbsGHiNoXp+86OFns/a9VOtSh6584bs37V5DUYHgMsj7FgXbt86nZ/ef/OqjlmvVdm6aTxPPbhnVccFgK9L2LFuPLl/Zx67e/uqjFWvVZmZHMnBJ/Zl4+jwqowJAFfKrljWlW63mwN/eiO/ffFoqqQvt2frVZXmprEcfGJfmtNjfRgBAPpD2LEuPfOP9/LUc//OueVO2j1eO3a5Pg/FB/ZuyS8ems+miZGrcl0AWC3CjnXrg4/O5ufP/iuH3jyZeq26osCrVcnGseEc+OFt+d68t0sAsD4JO9a1brebF948kd+/8p+89Pap1C4j8OpV0u4msxsa+cnd2/PoXdsyPW6VDoD1S9hRjGOnWzn49+N59Z1TOfL+mSy1Oz2/e/30aO7YtikPLTRz7+7NqdfW8ghkALg6hB1FWm538s7JVt4+cSafLrXT7nQzOlzP5g2N3NqcytSYna4AlEfYAQAUwjl2AACFEHYAAIUQdgAAhRB2AACFEHYAAIUQdgAAhRB2AACFEHYAAIUQdgAAhRB2AACFEHYAAIUQdgAAhRB2AACFEHYAAIUQdgAAhRB2AACFEHYAAIUQdgAAhRB2AACFEHYAAIUQdgAAhRB2AACFEHYAAIUQdgAAhRB2AACFEHYAAIUQdgAAhRB2AACFEHYAAIUQdgAAhRB2AACFEHYAAIUQdgAAhRB2AACFEHYAAIUQdgAAhRB2AACFEHYAAIX4Hwpj8n7t8I6oAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# HW1 Problem 9\n",
+    "\n",
+    "from pulp import *\n",
+    "import networkx as nx\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "G = nx.Graph()\n",
+    "G.add_edge(\"v1\", \"v2\")\n",
+    "G.add_edge(\"v1\", \"v3\")\n",
+    "G.add_edge(\"v1\", \"v5\")\n",
+    "G.add_edge(\"v2\", \"v5\")\n",
+    "G.add_edge(\"v4\", \"v5\")\n",
+    "G.add_edge(\"v3\", \"v6\")\n",
+    "G.add_edge(\"v4\", \"v6\")\n",
+    "G.add_edge(\"v5\", \"v6\")\n",
+    "\n",
+    "partitions = {}\n",
+    "for node in G.nodes:\n",
+    "   partitions[node] = LpVariable(\"partition_{0}\".format(node), cat=\"Integer\")\n",
+    "\n",
+    "\n",
+    "prob = pulp.LpProblem(\"Bisection problem\", LpMinimize)\n",
+    "\n",
+    "edge_vars = {}\n",
+    "\n",
+    "for edge in G.edges:\n",
+    "    edge_vars[edge] = LpVariable(\"edge_{0}_{1}\".format(edge[0],edge[1]))\n",
+    "    \n",
+    "print(edge_vars)\n",
+    "\n",
+    "prob+= lpSum([edge_vars[edge] for edge in G.edges]), \"Our objective statement\"\n",
+    "prob+= lpSum([partitions[node] for node in G.nodes]) == 3\n",
+    "    \n",
+    "for x, y in G.edges:\n",
+    "    prob += partitions[x] - partitions[y] <= edge_vars[(x,y)]\n",
+    "    prob += partitions[y] - partitions[x] <= edge_vars[(x,y)]\n",
+    "    \n",
+    "print(prob)    \n",
+    "\n",
+    "prob.solve()\n",
+    "for v in prob.variables():\n",
+    "    print(v.name, \"=\", v.varValue)\n",
+    "    \n",
+    "pos = nx.spring_layout(G, seed=7)  # positions for all nodes - seed for reproducibility\n",
+    "\n",
+    "# nodes\n",
+    "\n",
+    "colors = [\"tab:red\" if partitions[node].varValue > 0 else \"tab:blue\" for node in G.nodes()]\n",
+    "nx.draw_networkx_nodes(G, pos, node_size=700, node_color=colors)\n",
+    "\n",
+    "# edges\n",
+    "nx.draw_networkx_edges(G, pos, width=6)\n",
+    "nx.draw_networkx_edges(\n",
+    "    G, pos, width=6, alpha=0.5, edge_color=\"b\", style=\"dashed\"\n",
+    ")\n",
+    "\n",
+    "# node labels\n",
+    "nx.draw_networkx_labels(G, pos, font_size=20, font_family=\"sans-serif\")\n",
+    "\n",
+    "ax = plt.gca()\n",
+    "ax.margins(0.08)\n",
+    "plt.axis(\"off\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/training/src/.ipynb_checkpoints/MSML602_Midterm_Karki-checkpoint.ipynb b/training/src/.ipynb_checkpoints/MSML602_Midterm_Karki-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..38f4f92cc158bb0f543acd1e1645d1074a3553c1
--- /dev/null
+++ b/training/src/.ipynb_checkpoints/MSML602_Midterm_Karki-checkpoint.ipynb
@@ -0,0 +1,831 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 82,
+      "metadata": {
+        "cellView": "form",
+        "id": "AXI2uCSkxx7m"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Setup\n",
+        "\n",
+        "%%capture\n",
+        "!pip install networkx pulp numpy pandas\n",
+        "\n",
+        "!rm -rf ./data/\n",
+        "!mkdir -p ./data/\n",
+        "!wget -c -O ./data/lastfm_asia.zip \"https://snap.stanford.edu/data/lastfm_asia.zip\"\n",
+        "!unzip -q ./data/lastfm_asia.zip -d ./data/"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Problem 3: Linear Programming\n",
+        "\n",
+        "\n",
+        "from pulp import *\n",
+        "from IPython.display import HTML, display\n",
+        "\n",
+        "def display_table(table):\n",
+        "    display(HTML(\n",
+        "       '<table><tr>{}</tr></table>'.format(\n",
+        "           '</tr><tr>'.join(\n",
+        "               '<td>{}</td>'.format('</td><td>'.join(str(_) for _ in row)) for row in table)\n",
+        "           )\n",
+        "    ))\n",
+        "\n",
+        "problem = LpProblem(\"MSML_602_Midterm_Q3\", LpMaximize)\n",
+        "\n",
+        "X = LpVariable(\"X\", cat=\"Integer\")\n",
+        "Y = LpVariable(\"Y\", cat=\"Integer\")\n",
+        "\n",
+        "problem += (5 * X) + (3 * Y), \"Objective\"\n",
+        "problem += X + (2 * Y) <= 14, \"Constraint 1\"\n",
+        "problem += (3* X) - Y >= 0, \"Constraint 2\"\n",
+        "problem += X - Y <= 2, \"Constraint 3\"\n",
+        "\n",
+        "problem.solve()\n",
+        "print(\"Solution:\\n\")\n",
+        "\n",
+        "data = [[\"Variable\", \"Value\"]] + [[v.name, v.varValue] for v in problem.variables()]\n",
+        "data += [[\"Max value for objective function: \", problem.objective.value()]]\n",
+        "display_table(data)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 140
+        },
+        "cellView": "form",
+        "id": "ALmlZnbcx-9e",
+        "outputId": "45e2c507-3265-4b22-e21a-2d6dbd72f05f"
+      },
+      "execution_count": 83,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Solution:\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "<table><tr><td>Variable</td><td>Value</td></tr><tr><td>X</td><td>6.0</td></tr><tr><td>Y</td><td>4.0</td></tr><tr><td>Max value for objective function: </td><td>42.0</td></tr></table>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Problem 5: Graph Metrics\n",
+        "\n",
+        "import pandas as pd \n",
+        "import networkx as nx\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "df = pd.read_csv(\"/content/data/lasftm_asia/lastfm_asia_edges.csv\")\n",
+        "G = nx.from_pandas_edgelist(df, source=\"node_1\", target=\"node_2\")\n",
+        "shortest_path = nx.shortest_path_length(G, 0)\n",
+        "del shortest_path[0]\n",
+        "num = len(shortest_path)\n",
+        "total_length = sum([shortest_path[k] for k in shortest_path])\n",
+        "avg_shortest_path = total_length / num\n",
+        "print(f\"The average shortest path length from node 0 to all other nodes is: {avg_shortest_path}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "cellView": "form",
+        "id": "87mMC-B1yJoq",
+        "outputId": "83ff28ec-7d51-4f6a-ced6-358538a58f83"
+      },
+      "execution_count": 84,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The average shortest path length from node 0 to all other nodes is: 5.651974288337925\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Problem 6: Extracting Webpage Data"
+      ],
+      "metadata": {
+        "id": "mAiJRhb5iW5O"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Scraping result\n",
+        "\n",
+        "import requests\n",
+        "from bs4 import BeautifulSoup\n",
+        "import pandas as pd \n",
+        "import numpy as np\n",
+        "\n",
+        "page = requests.get(\"https://www.worldometers.info/coronavirus/#countries\")\n",
+        "html = page.content\n",
+        "\n",
+        "soup = BeautifulSoup(html, 'html.parser')\n",
+        "table = soup.find(\"table\", {\"id\": \"main_table_countries_today\"})\n",
+        "\n",
+        "cols = [\n",
+        "    '#', 'Country', 'TotalCases', 'NewCases', 'TotalDeaths', 'NewDeaths', 'TotalRecovered',\n",
+        "    'NewRecovered','ActiveCases','Serious,Critical','TotalCases/1M pop','Deaths/1M pop', \n",
+        "    'TotalTests', 'Tests/1M pop', 'Population', 'Continent', '1 Case every X ppl', '1 Death every X ppl',\n",
+        "    '1 Test every X ppl', 'New Cases/1M pop', 'New Deaths/1M pop', 'Active Cases/1M pop'\n",
+        "]\n",
+        "\n",
+        "tbody = table.find(\"tbody\")\n",
+        "rows = tbody.find_all(\"tr\")\n",
+        "\n",
+        "data = []\n",
+        "for row in rows:\n",
+        "  cells = row.find_all(\"td\")\n",
+        "  values = [c.text for c in cells]\n",
+        "  data.append(values)\n",
+        "\n",
+        "def sanitize_country_number(row):\n",
+        "  val = row[\"#\"]\n",
+        "  if not val.strip():\n",
+        "    return np.NaN\n",
+        "  else:\n",
+        "    return val\n",
+        "\n",
+        "def fill_active_cases(row):\n",
+        "  val = row[\"ActiveCases\"]\n",
+        "  if not np.isnan(val):\n",
+        "    return val\n",
+        "  active_per_1_mil = row[\"Active Cases/1M pop\"]\n",
+        "  if np.isnan(active_per_1_mil):\n",
+        "    return np.nan\n",
+        "  population = row[\"Population\"]\n",
+        "  return (active_per_1_mil/1000000) * population\n",
+        "\n",
+        "def to_float(col):\n",
+        "  def mapper(row):\n",
+        "    if row[col] == \"N/A\":\n",
+        "      return np.NaN\n",
+        "    val = row[col]\n",
+        "    val = val.replace(\",\", \"\").strip()\n",
+        "    if not val:\n",
+        "      return np.NaN\n",
+        "    return float(val)\n",
+        "  return mapper \n",
+        "\n",
+        "df = pd.DataFrame(data, columns=cols)\n",
+        "df.replace(r\"\\n\", \"\", regex=True, inplace=True)\n",
+        "\n",
+        "df.head()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 386
+        },
+        "cellView": "form",
+        "id": "Ay-ceRkkzcVg",
+        "outputId": "3b1e8535-f211-45ba-9b90-85c279e522ec"
+      },
+      "execution_count": 85,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "  #        Country   TotalCases  NewCases TotalDeaths NewDeaths  \\\n",
+              "0    North America  118,308,960   +16,354   1,557,219       +76   \n",
+              "1             Asia  195,343,819  +168,618   1,491,630      +230   \n",
+              "2           Europe  235,496,414   +41,389   1,948,669      +165   \n",
+              "3    South America   64,557,158   +10,126   1,333,737       +79   \n",
+              "4          Oceania   12,691,699    +3,057      21,779        +9   \n",
+              "\n",
+              "  TotalRecovered NewRecovered ActiveCases Serious,Critical  ... TotalTests  \\\n",
+              "0    113,762,872      +16,362   2,988,869            7,881  ...              \n",
+              "1    188,186,652      +78,736   5,665,537            9,159  ...              \n",
+              "2    229,427,346     +175,758   4,120,399            7,685  ...              \n",
+              "3     62,884,992       +7,699     338,429           10,119  ...              \n",
+              "4     12,512,305                  157,615               97  ...              \n",
+              "\n",
+              "  Tests/1M pop Population          Continent 1 Case every X ppl  \\\n",
+              "0                              North America                      \n",
+              "1                                       Asia                      \n",
+              "2                                     Europe                      \n",
+              "3                              South America                      \n",
+              "4                          Australia/Oceania                      \n",
+              "\n",
+              "  1 Death every X ppl 1 Test every X ppl New Cases/1M pop New Deaths/1M pop  \\\n",
+              "0                                                                             \n",
+              "1                                                                             \n",
+              "2                                                                             \n",
+              "3                                                                             \n",
+              "4                                                                             \n",
+              "\n",
+              "  Active Cases/1M pop  \n",
+              "0                      \n",
+              "1                      \n",
+              "2                      \n",
+              "3                      \n",
+              "4                      \n",
+              "\n",
+              "[5 rows x 22 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-ba4843e7-ab30-4f7d-ab50-7ddf8afb1bbb\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>#</th>\n",
+              "      <th>Country</th>\n",
+              "      <th>TotalCases</th>\n",
+              "      <th>NewCases</th>\n",
+              "      <th>TotalDeaths</th>\n",
+              "      <th>NewDeaths</th>\n",
+              "      <th>TotalRecovered</th>\n",
+              "      <th>NewRecovered</th>\n",
+              "      <th>ActiveCases</th>\n",
+              "      <th>Serious,Critical</th>\n",
+              "      <th>...</th>\n",
+              "      <th>TotalTests</th>\n",
+              "      <th>Tests/1M pop</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Continent</th>\n",
+              "      <th>1 Case every X ppl</th>\n",
+              "      <th>1 Death every X ppl</th>\n",
+              "      <th>1 Test every X ppl</th>\n",
+              "      <th>New Cases/1M pop</th>\n",
+              "      <th>New Deaths/1M pop</th>\n",
+              "      <th>Active Cases/1M pop</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td></td>\n",
+              "      <td>North America</td>\n",
+              "      <td>118,308,960</td>\n",
+              "      <td>+16,354</td>\n",
+              "      <td>1,557,219</td>\n",
+              "      <td>+76</td>\n",
+              "      <td>113,762,872</td>\n",
+              "      <td>+16,362</td>\n",
+              "      <td>2,988,869</td>\n",
+              "      <td>7,881</td>\n",
+              "      <td>...</td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td>North America</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>1</th>\n",
+              "      <td></td>\n",
+              "      <td>Asia</td>\n",
+              "      <td>195,343,819</td>\n",
+              "      <td>+168,618</td>\n",
+              "      <td>1,491,630</td>\n",
+              "      <td>+230</td>\n",
+              "      <td>188,186,652</td>\n",
+              "      <td>+78,736</td>\n",
+              "      <td>5,665,537</td>\n",
+              "      <td>9,159</td>\n",
+              "      <td>...</td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td>Asia</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>2</th>\n",
+              "      <td></td>\n",
+              "      <td>Europe</td>\n",
+              "      <td>235,496,414</td>\n",
+              "      <td>+41,389</td>\n",
+              "      <td>1,948,669</td>\n",
+              "      <td>+165</td>\n",
+              "      <td>229,427,346</td>\n",
+              "      <td>+175,758</td>\n",
+              "      <td>4,120,399</td>\n",
+              "      <td>7,685</td>\n",
+              "      <td>...</td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td>Europe</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>3</th>\n",
+              "      <td></td>\n",
+              "      <td>South America</td>\n",
+              "      <td>64,557,158</td>\n",
+              "      <td>+10,126</td>\n",
+              "      <td>1,333,737</td>\n",
+              "      <td>+79</td>\n",
+              "      <td>62,884,992</td>\n",
+              "      <td>+7,699</td>\n",
+              "      <td>338,429</td>\n",
+              "      <td>10,119</td>\n",
+              "      <td>...</td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td>South America</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>4</th>\n",
+              "      <td></td>\n",
+              "      <td>Oceania</td>\n",
+              "      <td>12,691,699</td>\n",
+              "      <td>+3,057</td>\n",
+              "      <td>21,779</td>\n",
+              "      <td>+9</td>\n",
+              "      <td>12,512,305</td>\n",
+              "      <td></td>\n",
+              "      <td>157,615</td>\n",
+              "      <td>97</td>\n",
+              "      <td>...</td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td>Australia/Oceania</td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 22 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ba4843e7-ab30-4f7d-ab50-7ddf8afb1bbb')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-ba4843e7-ab30-4f7d-ab50-7ddf8afb1bbb button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-ba4843e7-ab30-4f7d-ab50-7ddf8afb1bbb');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 85
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Data sanitization / generation\n",
+        "\n",
+        "#@markdown Some of the countries (actually ships, in this case) did not have any population data, so I excluded those records from the dataset.\n",
+        "\n",
+        "#@markdown Some countries didn't have data for exact active cases, but had data for **active cases per 1 million population**. \n",
+        "#@markdown For these countries, I calculated their active cases by using the active cases per 1 million population data as follows: \n",
+        "\n",
+        "#@markdown ```Active Cases = (Active cases per 1 million population / 1,000,000) * Population```\n",
+        "\n",
+        "df[\"country_number\"] = df.apply(sanitize_country_number, axis=1)\n",
+        "\n",
+        "data_by_country = df[df[\"country_number\"].notna()].copy()\n",
+        "data_by_country[\"ActiveCases\"] = data_by_country.apply(to_float(\"ActiveCases\"), axis=1)\n",
+        "data_by_country[\"Active Cases/1M pop\"] = data_by_country.apply(to_float(\"Active Cases/1M pop\"), axis=1)\n",
+        "data_by_country[\"Population\"] = data_by_country.apply(to_float(\"Population\"), axis=1)\n",
+        "data_by_country[\"ActiveCases\"] = data_by_country.apply(fill_active_cases, axis=1)\n",
+        "data_by_country[data_by_country[\"ActiveCases\"] == \"N/A\"].head(20)\n",
+        "aggregated = data_by_country.groupby(\"Country\").agg({'ActiveCases':'mean', 'Population':'sum'}, as_index=False)\n",
+        "aggregated.reset_index(inplace=True)\n",
+        "dropped_countries = aggregated[aggregated[\"Population\"] == 0 ]\n",
+        "aggregated = aggregated[aggregated[\"Population\"] != 0 ]\n",
+        "aggregated[\"PercentageInfected\"] = aggregated.apply(lambda x: x[\"ActiveCases\"]/x[\"Population\"], axis=1)\n",
+        "aggregated.sort_values([\"PercentageInfected\"], ascending=False, inplace=True)\n",
+        "\n",
+        "print(\"These were the countries(ships) that didn't have population data:\\n\")\n",
+        "print(dropped_countries)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "cellView": "form",
+        "id": "Roitzj22-VO5",
+        "outputId": "5dc61fa2-31c9-4fa4-828c-c9f88fb449e2"
+      },
+      "execution_count": 86,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "These were the countries(ships) that didn't have population data:\n",
+            "\n",
+            "              Country  ActiveCases  Population\n",
+            "56   Diamond Princess          0.0         0.0\n",
+            "120        MS Zaandam          0.0         0.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Average active cases & the proportion of the total population affected\n",
+        "\n",
+        "from IPython.display import HTML, display\n",
+        "\n",
+        "def display_table(table):\n",
+        "    display(HTML(\n",
+        "       '<table><tr>{}</tr></table>'.format(\n",
+        "           '</tr><tr>'.join(\n",
+        "               '<td>{}</td>'.format('</td><td>'.join(str(_) for _ in row)) for row in table)\n",
+        "           )\n",
+        "    ))\n",
+        "\n",
+        "avg_active_cases = aggregated[\"ActiveCases\"].mean()\n",
+        "\n",
+        "aggr = aggregated.agg({\"ActiveCases\": \"sum\",  \"Population\": \"sum\"}, as_index=False)\n",
+        "final_df = aggr.to_frame().T\n",
+        "final_df[\"PercentageInfected\"] = final_df.apply(lambda x: (x[\"ActiveCases\"]/x[\"Population\"]) * 100, axis=1)\n",
+        "percentage_infected = final_df[\"PercentageInfected\"].to_numpy()[0]\n",
+        "\n",
+        "display(HTML(\n",
+        "    \"\"\"\n",
+        "      <h3>Result:</h3>\n",
+        "      <br>\n",
+        "      <table border=\"1\">\n",
+        "        <tr>\n",
+        "          <th align=\"left\">Average active cases:</th>\n",
+        "          <td>{0:.2f}</td>\n",
+        "        </tr>\n",
+        "        <tr>\n",
+        "          <th align=\"left\">Proportion of total <br>population currently infected:</th>\n",
+        "          <td>{1:.2f}%</td>\n",
+        "        </tr>\n",
+        "      </table>\n",
+        "      <br>\n",
+        "    \"\"\".format(avg_active_cases, percentage_infected))\n",
+        ")\n",
+        "\n",
+        "\n",
+        "print(\"\"\"\n",
+        "I was unsure whether the problem wanted the percentage of the population\n",
+        "affected for each country, so I have included the percentage for each country \n",
+        "as well, just in case:\n",
+        "\"\"\")\n",
+        "aggregated.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 439
+        },
+        "cellView": "form",
+        "id": "lRtwSfqSAPAY",
+        "outputId": "a7037d5d-fbd6-48b3-e47b-32090720dfd1"
+      },
+      "execution_count": 87,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "      <h3>Result:</h3>\n",
+              "      <br>\n",
+              "      <table border=\"1\">\n",
+              "        <tr>\n",
+              "          <th align=\"left\">Average active cases:</th>\n",
+              "          <td>60038.20</td>\n",
+              "        </tr>\n",
+              "        <tr>\n",
+              "          <th align=\"left\">Proportion of total <br>population currently infected:</th>\n",
+              "          <td>0.17%</td>\n",
+              "        </tr>\n",
+              "      </table>\n",
+              "      <br>\n",
+              "    "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "I was unsure whether the problem wanted the percentage of the population\n",
+            "affected for each country, so I have included the percentage for each country \n",
+            "as well, just in case:\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "            Country    ActiveCases  Population  PercentageInfected\n",
+              "129      Martinique  222576.901869    374087.0            0.594987\n",
+              "68   Faeroe Islands   26936.998989     49233.0            0.547133\n",
+              "195       St. Barth    4854.999825      9945.0            0.488185\n",
+              "84       Guadeloupe  193026.939904    399794.0            0.482816\n",
+              "93          Iceland  130899.111498    345393.0            0.378986"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-5b3415ae-443b-4212-be42-19efc6bff309\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>Country</th>\n",
+              "      <th>ActiveCases</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>PercentageInfected</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>129</th>\n",
+              "      <td>Martinique</td>\n",
+              "      <td>222576.901869</td>\n",
+              "      <td>374087.0</td>\n",
+              "      <td>0.594987</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>68</th>\n",
+              "      <td>Faeroe Islands</td>\n",
+              "      <td>26936.998989</td>\n",
+              "      <td>49233.0</td>\n",
+              "      <td>0.547133</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>195</th>\n",
+              "      <td>St. Barth</td>\n",
+              "      <td>4854.999825</td>\n",
+              "      <td>9945.0</td>\n",
+              "      <td>0.488185</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>84</th>\n",
+              "      <td>Guadeloupe</td>\n",
+              "      <td>193026.939904</td>\n",
+              "      <td>399794.0</td>\n",
+              "      <td>0.482816</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>93</th>\n",
+              "      <td>Iceland</td>\n",
+              "      <td>130899.111498</td>\n",
+              "      <td>345393.0</td>\n",
+              "      <td>0.378986</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5b3415ae-443b-4212-be42-19efc6bff309')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-5b3415ae-443b-4212-be42-19efc6bff309 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-5b3415ae-443b-4212-be42-19efc6bff309');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 87
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/training/MSML-602-Final-Project-Final-Version.ipynb b/training/src/MSML-602-Final-Project-Final-Version.ipynb
similarity index 100%
rename from training/MSML-602-Final-Project-Final-Version.ipynb
rename to training/src/MSML-602-Final-Project-Final-Version.ipynb
diff --git a/training/MSML-602-Final-Project.ipynb b/training/src/MSML-602-Final-Project.ipynb
similarity index 100%
rename from training/MSML-602-Final-Project.ipynb
rename to training/src/MSML-602-Final-Project.ipynb
diff --git a/training/downloader.py b/training/src/downloader.py
similarity index 100%
rename from training/downloader.py
rename to training/src/downloader.py
diff --git a/web/.DS_Store b/web/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..118b7cef83a8c160b967ea3632dd5706d0c96202
Binary files /dev/null and b/web/.DS_Store differ
diff --git a/web/.dockerignore b/web/.dockerignore
new file mode 100644
index 0000000000000000000000000000000000000000..85dcc16df69a9860507592c89f31f438c8fe7b41
--- /dev/null
+++ b/web/.dockerignore
@@ -0,0 +1,2 @@
+.git
+node_modules
diff --git a/web/.gitignore b/web/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..cdb93cd5ebbb0b464b7cec99bbc254d23aac6815
--- /dev/null
+++ b/web/.gitignore
@@ -0,0 +1 @@
+.python-version
diff --git a/web/Dockerfile.backend b/web/Dockerfile.backend
new file mode 100644
index 0000000000000000000000000000000000000000..56d7c3abb0d3191c96928f570bdfb087bac9706c
--- /dev/null
+++ b/web/Dockerfile.backend
@@ -0,0 +1,11 @@
+FROM tensorflow/tensorflow:2.11.0
+
+WORKDIR /app
+
+COPY ./requirements.txt /app/requirements.txt
+
+RUN pip install -r requirements.txt
+
+COPY ./src /app
+
+CMD ["python3", "-m", "flask", "run", "--host=0.0.0.0"]
diff --git a/web/Dockerfile.frontend b/web/Dockerfile.frontend
new file mode 100644
index 0000000000000000000000000000000000000000..3cf56759b56f7255cad33f93dc2184cdbf6ebee1
--- /dev/null
+++ b/web/Dockerfile.frontend
@@ -0,0 +1,13 @@
+FROM node:18-alpine3.15
+
+WORKDIR /app
+
+COPY package.json ./
+
+COPY package-lock.json ./
+
+RUN npm update && npm install
+
+COPY . .
+
+CMD ["npm", "start"]
diff --git a/web/Makefile b/web/Makefile
new file mode 100644
index 0000000000000000000000000000000000000000..11e44fa440eb4dda3a019b2331a822715c045732
--- /dev/null
+++ b/web/Makefile
@@ -0,0 +1,19 @@
+.PHONY: build-backend
+build-backend: ## Build backend services
+	docker build -f Dockerfile.backend -t pred-backend ./backend
+
+.PHONY: build-frontend
+build-frontend: ## Build frontend services
+	docker build -f Dockerfile.frontend -t pred-frontend ./frontend
+
+.PHONY: up-prod
+up-prod: ## Build backend services
+	docker build -f Dockerfile.backend -t pred-backend ./backend
+	docker build -f Dockerfile.frontend -t pred-frontend ./frontend
+	docker-compose -f docker-compose.yml -f docker-compose.prod.yml up -d 
+
+.PHONY: up-dev
+up-dev: ## Build backend services
+	docker build -f Dockerfile.backend -t pred-backend ./backend
+	docker build -f Dockerfile.frontend -t pred-frontend ./frontend
+	docker-compose up -d 
diff --git a/web/README.md b/web/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..101b6c4e2510bc9a88cc7aeb0522db4d81347118
--- /dev/null
+++ b/web/README.md
@@ -0,0 +1 @@
+MSML 602 Assignments/Projects
diff --git a/web/backend/.DS_Store b/web/backend/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..43b0834167ba7ae34323804f8857283a145c62e0
Binary files /dev/null and b/web/backend/.DS_Store differ
diff --git a/web/backend/.dockerignore b/web/backend/.dockerignore
new file mode 100644
index 0000000000000000000000000000000000000000..6b8710a711f3b689885aa5c26c6c06bde348e82b
--- /dev/null
+++ b/web/backend/.dockerignore
@@ -0,0 +1 @@
+.git
diff --git a/web/backend/.gitignore b/web/backend/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..d35ef6f723159707e9228f1fd58cf9bc7f91595d
--- /dev/null
+++ b/web/backend/.gitignore
@@ -0,0 +1,2 @@
+.python-version
+__pycache__
diff --git a/web/backend/README.md b/web/backend/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..101b6c4e2510bc9a88cc7aeb0522db4d81347118
--- /dev/null
+++ b/web/backend/README.md
@@ -0,0 +1 @@
+MSML 602 Assignments/Projects
diff --git a/web/backend/requirements.txt b/web/backend/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..76d1d6414d1e33a91ca898a596b3a7f51ff49915
--- /dev/null
+++ b/web/backend/requirements.txt
@@ -0,0 +1,7 @@
+flask==2.2.2
+flask-cors==3.0.10
+pandas==1.5.0
+numpy==1.23.3
+scipy==1.9.1
+scikit-learn==1.1.2
+requests-cache==0.9.7
diff --git a/web/backend/src/.DS_Store b/web/backend/src/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..1d8f5849d5dca4c4e7d71dc165035cefeec86a14
Binary files /dev/null and b/web/backend/src/.DS_Store differ
diff --git a/web/backend/src/app.py b/web/backend/src/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..9db5907ccf7313e144fe23c24ac998a3e463b915
--- /dev/null
+++ b/web/backend/src/app.py
@@ -0,0 +1,244 @@
+from flask import Flask, request
+import tensorflow as tf
+from datetime import datetime, timedelta
+import logging 
+import requests
+import requests_cache
+import pandas as pd
+import json
+import numpy as np
+import pickle
+import math
+import pytz
+
+from flask_cors import CORS, cross_origin
+
+
+session = requests_cache.CachedSession('requests-cache')
+
+app = Flask(__name__)
+cors = CORS(app)
+app.config['CORS_HEADERS'] = 'Content-Type'
+
+app.logger.setLevel(logging.INFO)
+DATE_FORMAT = "%Y-%m-%d %H:%M:%S"
+API_KEY = "e1f10a1e78da46f5b10a1e78da96f525"
+BASE_URL = "https://api.weather.com/v1/location/KDCA:9:US/observations/historical.json?apiKey={api_key}&units=e&startDate={start_date}&endDate={end_date}"
+model = tf.keras.models.load_model('/app/model', compile=False)
+
+scaler = pickle.load(open('./model/scaler.pkl','rb'))
+cols_to_scale = ["pressure", "wspd","heat_index","dewPt", "rh",	"vis", "wc", "wdir_degree", "clds_ordinal",
+             "day_sin", "day_cos", "year_sin", "year_cos", "wdir_sin", "wdir_cos"]
+
+def get_NaN_counts(df):    
+    nan_counts = df.isna().sum()
+    return pd.concat([nan_counts, ((nan_counts/len(df))*100).round(2)], 
+                     axis=1, 
+                     keys=["NaN count", "Percentage"])
+
+def clds_to_ordinal(row):
+  mapping = {
+      "SKC": 0,
+      "CLR": 0,
+      "FEW": 1,
+      "SCT": 2,
+      "BKN": 3,
+      "OVC": 4,
+      "VV": 5
+  }
+  clds = row["clds"]
+  if pd.isnull(clds):
+      return np.NaN 
+  return mapping[clds]
+
+def clean_wspd(row):
+  if row["wdir_cardinal"] == "CALM":
+    return 0
+  return row["wspd"]
+
+def restrict_wspd(row):
+  if row["wspd"] < 0:
+    return 0
+  return row["wspd"]
+
+def restrict_rh(row):
+  if row["rh"] < 0:
+    return 0
+  if row["rh"] > 100:
+    return 100
+  return row["rh"]
+
+def clean_wdir(row):
+  if row["wdir_cardinal"] == "CALM":
+    return 0
+  return row["wdir"]
+
+def wdir_cardinal_to_deg(row):
+    wdir = row["wdir"]
+    if not pd.isnull(wdir):
+      return wdir 
+    cardinal_directions = {
+        'N': 0,
+        'NNE': 22.5,
+        'NE': 45,
+        'ENE': 67.5,
+        'E': 90,
+        'ESE': 112.5,
+        'SE': 135,
+        'SSE': 157.5,
+        'S': 180,
+        'SSW': 202.5,
+        'SW': 225,
+        'WSW': 247.5,
+        'W': 270,
+        'WNW': 292.5,
+        'NW': 315,
+        'NNW': 337.5,
+        'CALM': 0,
+        'VAR': -1
+    }
+    wdir_cardinal = row["wdir_cardinal"]
+   
+    return cardinal_directions[wdir_cardinal] if wdir_cardinal in cardinal_directions else np.NaN
+
+def prepare_dataframe(_df, start_timestamp, end_timestamp):
+  dates_df = pd.DataFrame()
+  dates_df["obs_timestamp"] = pd.date_range(start_timestamp, end_timestamp, freq="H")
+
+  _df = dates_df.merge(_df, how='left', on='obs_timestamp')
+  _df = _df.astype(
+      {
+          'temp': 'float',
+          'pressure': 'float',
+          'wspd': 'float',
+          'heat_index': 'float'
+      },
+  )
+
+  _df["wdir_cardinal"].fillna(method="bfill", inplace=True)
+  _df["wdir_degree"] = _df.apply(wdir_cardinal_to_deg, axis=1)
+  _df["clds_ordinal"] = _df.apply(clds_to_ordinal, axis=1)
+  _df["temp"].interpolate("polynomial", order=2, inplace=True)
+  _df["pressure"].interpolate("polynomial", order=2, inplace=True)
+  _df["heat_index"].interpolate("polynomial", order=2, inplace=True)
+  _df["wdir"].fillna(method="bfill", inplace=True)
+  _df["wdir"] = _df.apply(clean_wdir, axis=1)
+  _df["wspd"] = _df.apply(clean_wspd, axis=1)
+  _df["wspd"].interpolate("polynomial", order=2, inplace=True)
+  _df["wspd"] = _df.apply(restrict_wspd, axis=1)
+  _df["clds"].fillna(method="bfill", inplace=True)
+  _df["clds_ordinal"].interpolate("linear", inplace=True)
+  _df["dewPt"].interpolate("polynomial", order=2, inplace=True)
+  _df["rh"].interpolate("polynomial", order=2, inplace=True)
+  _df["rh"] = _df.apply(restrict_rh, axis=1)
+  _df["wc"].interpolate("polynomial", order=2, inplace=True)
+  _df["vis"].fillna(method="bfill", inplace=True)
+  _df.drop(["wdir", "wdir_cardinal", "clds"], axis=1, inplace=True)
+
+  _df = _df.dropna()
+
+  _df = _df.sort_values(by=['obs_timestamp'])
+  date_time = _df.pop('obs_timestamp')
+  timestamp_s = date_time.map(pd.Timestamp.timestamp)
+  day = 24*60*60
+  year = (365.2425)*day
+  
+  _df['day_sin'] = np.sin(timestamp_s * (2 * np.pi / day))
+  _df['day_cos'] = np.cos(timestamp_s * (2 * np.pi / day))
+  _df['year_sin'] = np.sin(timestamp_s * (2 * np.pi / year))
+  _df['year_cos'] = np.cos(timestamp_s * (2 * np.pi / year))
+  _df['wdir_sin'] = np.sin(_df["wdir_degree"])
+  _df['wdir_cos'] = np.cos(_df["wdir_degree"])
+
+  return _df, date_time
+
+
+def map_data_to_dataframe(data, target_date):
+    end_timestamp = target_date - timedelta(minutes=8)
+    start_timestamp = end_timestamp - timedelta(days=8) + timedelta(hours=1)
+    
+    df = pd.read_json(json.dumps(data))
+    df["obs_timestamp"] = df.apply(lambda x: datetime.fromtimestamp(x["valid_time_gmt"]).strftime(DATE_FORMAT), axis=1)
+    df = df.astype({'obs_timestamp': 'datetime64[ns]'})
+    initial_cols = ["temp", "obs_timestamp", "pressure", "wspd", "heat_index", "dewPt", "rh", "vis", "wc", "wdir", "wdir_cardinal", "clds" ]
+    df = df[initial_cols]
+    
+    df, _ = prepare_dataframe(df, start_timestamp.strftime(DATE_FORMAT), end_timestamp.strftime(DATE_FORMAT))
+    return df 
+
+
+def map_to_timestamp(predictions, target_date):
+  start = target_date + timedelta(hours=1)
+  end = start + timedelta(hours=23)
+  target_hours = [x.to_pydatetime().strftime(DATE_FORMAT) for x in pd.date_range(start, end, freq="H")]
+  return { h: predictions[idx] for idx, h in enumerate(target_hours)}
+
+def predict(df):
+    predict_df = df[-168:]
+    predict_df_features = predict_df[cols_to_scale]
+    predict_df_features = scaler.transform(predict_df_features.values)
+    predict_df[cols_to_scale] = predict_df_features
+    predictions = model(predict_df.to_numpy().reshape(1, 168, 16))
+    return predictions
+
+def predict_for_date(target_date):
+    date_format = "%Y%m%d"
+    start_date = target_date - timedelta(days=9)
+    res = session.get(BASE_URL.format(api_key=API_KEY, start_date=start_date.strftime(date_format), end_date=target_date.strftime(date_format)))
+    data = res.json()
+    df = map_data_to_dataframe(data["observations"], target_date)
+    predictions = predict(df)
+    flattened = list(map(lambda x: math.floor(x), predictions.numpy().flatten().tolist()))
+    return map_to_timestamp(flattened, target_date)
+
+def get_actual_temperatures(target_date):
+  date_format = "%Y%m%d"
+  start_date = target_date - timedelta(days=1)  #Because api uses utc
+  end_date = target_date + timedelta(days=1)
+  start_date_str = (start_date - timedelta(days=1)).strftime(date_format)
+  end_date_str = end_date.strftime(date_format)
+  today = datetime.today().astimezone(pytz.timezone("America/New_York")).date()
+  req_url = BASE_URL.format(api_key=API_KEY, start_date=start_date_str, end_date=end_date_str)
+  if target_date.date() < today:
+    res = session.get(req_url)
+  else:
+    res = requests.get(req_url)
+  start_timestamp = target_date + timedelta(minutes=52)
+  end_timestamp = end_date + timedelta(days=1) - timedelta(minutes=8)
+  
+
+  data = res.json()
+  df = pd.read_json(json.dumps(data["observations"]))
+  df["obs_timestamp"] = df.apply(lambda x: datetime.fromtimestamp(x["valid_time_gmt"]).astimezone(pytz.timezone("America/New_York")).strftime(DATE_FORMAT), axis=1)
+  df = df.astype({'obs_timestamp': 'datetime64[ns]'})
+  initial_cols = ["temp", "obs_timestamp"]
+  df = df[initial_cols]
+  dates_df = pd.DataFrame()
+  dates_df["obs_timestamp"] = pd.date_range(start_timestamp, end_timestamp, freq="H")
+  df = dates_df.merge(df, how='left', on='obs_timestamp')
+ 
+  df["obs_timestamp"] = df.apply(lambda x: (x["obs_timestamp"] + timedelta(minutes=8)).strftime(DATE_FORMAT), axis=1)
+  dicts =  df.to_dict("records")
+  reduced = { k["obs_timestamp"]: k["temp"] for k in dicts}
+  for k in reduced:
+    if np.isnan(reduced[k]): 
+      reduced[k] = None
+  return reduced
+
+@app.route("/predictions")
+@cross_origin()
+def get_predictions():
+    today = datetime.today().astimezone(pytz.timezone("America/New_York")).date()
+    target_date = datetime.strptime(request.args["target_date"], "%Y-%m-%d")
+    app.logger.info(today)
+    app.logger.info(target_date)
+    # target_dates = list(filter(lambda x: x < today, [x.to_pydatetime() for x in pd.date_range(start_date, end_date, freq="D").to_list()]))
+    predictions = predict_for_date(target_date)
+    actual_temp = get_actual_temperatures(target_date) if target_date.date() <= today else None
+
+    merged = { k: {"predicted": predictions[k], "actual": actual_temp[k] if actual_temp else None} for k in predictions}
+    response = app.response_class(response=json.dumps(merged),
+                                  status=200,
+                                  mimetype='application/json')
+    return response
+
diff --git a/web/backend/src/model/keras_metadata.pb b/web/backend/src/model/keras_metadata.pb
new file mode 100644
index 0000000000000000000000000000000000000000..3c430558dd2991b086a220a96537f6cc10450c6c
--- /dev/null
+++ b/web/backend/src/model/keras_metadata.pb
@@ -0,0 +1,8 @@
+
+�%root"_tf_keras_sequential*�%{"name": "sequential", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 168, 16]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "lstm_input"}}, {"class_name": "LSTM", "config": {"name": "lstm", "trainable": true, "dtype": "float32", "return_sequences": false, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 12, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}, "shared_object_id": 2}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 3}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 24, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "Zeros", "config": {}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Reshape", "config": {"name": "reshape", "trainable": true, "dtype": "float32", "target_shape": {"class_name": "__tuple__", "items": [24, 1]}}}]}, "shared_object_id": 10, "build_input_shape": {"class_name": "TensorShape", "items": [null, 168, 16]}, "is_graph_network": true, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 168, 16]}, "float32", "lstm_input"]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 168, 16]}, "float32", "lstm_input"]}, "keras_version": "2.9.0", "backend": "tensorflow", "model_config": {"class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 168, 16]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "lstm_input"}, "shared_object_id": 0}, {"class_name": "LSTM", "config": {"name": "lstm", "trainable": true, "dtype": "float32", "return_sequences": false, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 12, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}, "shared_object_id": 2}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 3}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}, "shared_object_id": 5}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 24, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 6}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 7}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 8}, {"class_name": "Reshape", "config": {"name": "reshape", "trainable": true, "dtype": "float32", "target_shape": {"class_name": "__tuple__", "items": [24, 1]}}, "shared_object_id": 9}]}}, "training_config": {"loss": {"class_name": "MeanSquaredError", "config": {"reduction": "auto", "name": "mean_squared_error"}, "shared_object_id": 11}, "metrics": null, "weighted_metrics": null, "loss_weights": null, "optimizer_config": {"class_name": "Adam", "config": {"name": "Adam", "learning_rate": 0.0010000000474974513, "decay": 0.0, "beta_1": 0.8999999761581421, "beta_2": 0.9990000128746033, "epsilon": 1e-07, "amsgrad": false}}}}2

+�
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+�root.layer-2"_tf_keras_layer*�{"name": "reshape", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Reshape", "config": {"name": "reshape", "trainable": true, "dtype": "float32", "target_shape": {"class_name": "__tuple__", "items": [24, 1]}}, "shared_object_id": 9}2

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+�
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diff --git a/web/backend/src/model/saved_model.pb b/web/backend/src/model/saved_model.pb
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index 0000000000000000000000000000000000000000..61914fa8cd611145798d1b1f4baa5b9577abb0de
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diff --git a/web/backend/src/model/scaler.pkl b/web/backend/src/model/scaler.pkl
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index 0000000000000000000000000000000000000000..c3e37aeb139bc6625373d9d05e0985c23fa86fa9
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diff --git a/web/backend/src/model/variables/variables.data-00000-of-00001 b/web/backend/src/model/variables/variables.data-00000-of-00001
new file mode 100644
index 0000000000000000000000000000000000000000..14393b7ed4f3ddf02717455e8dc8c3c844bc8c54
Binary files /dev/null and b/web/backend/src/model/variables/variables.data-00000-of-00001 differ
diff --git a/web/backend/src/model/variables/variables.index b/web/backend/src/model/variables/variables.index
new file mode 100644
index 0000000000000000000000000000000000000000..9e2b3a5eadf5bc1f8b227ec9c791f21c9113cc6b
Binary files /dev/null and b/web/backend/src/model/variables/variables.index differ
diff --git a/web/docker-compose.prod.yml b/web/docker-compose.prod.yml
new file mode 100644
index 0000000000000000000000000000000000000000..4e621ccbe6ce8d213015a942036aea9242904e48
--- /dev/null
+++ b/web/docker-compose.prod.yml
@@ -0,0 +1,10 @@
+version: "3.5"
+services:
+  pred-backend:
+    ports:
+      - "81:5000"
+    
+  pred-frontend:
+    command: npm start
+    ports:
+      - "80:4200"
diff --git a/web/docker-compose.yml b/web/docker-compose.yml
new file mode 100644
index 0000000000000000000000000000000000000000..199afb5c286940b6c7835a3397b530db20fc518d
--- /dev/null
+++ b/web/docker-compose.yml
@@ -0,0 +1,34 @@
+version: "3.5"
+services:
+  pred-backend:
+    image: pred-backend:latest
+    ports:
+      - "3001:5000"
+    volumes:
+      - ./backend/src:/app
+    networks:
+      - prediction-project
+    logging:
+      driver: "json-file"
+      options:
+        max-size: "10m"
+  
+  pred-frontend:
+    image: pred-frontend:latest
+    command: npm run start-dev
+    ports:
+      - "4200:4200"
+    volumes:
+      - ./frontend/src:/app/src
+    networks:
+      - prediction-project
+    logging:
+      driver: "json-file"
+      options:
+        max-size: "10m"
+
+volumes:
+  db-volume:
+
+networks:
+  prediction-project:
diff --git a/web/frontend/.browserslistrc b/web/frontend/.browserslistrc
new file mode 100644
index 0000000000000000000000000000000000000000..4f9ac26980c156a3d525267010d5f78144b43519
--- /dev/null
+++ b/web/frontend/.browserslistrc
@@ -0,0 +1,16 @@
+# This file is used by the build system to adjust CSS and JS output to support the specified browsers below.
+# For additional information regarding the format and rule options, please see:
+# https://github.com/browserslist/browserslist#queries
+
+# For the full list of supported browsers by the Angular framework, please see:
+# https://angular.io/guide/browser-support
+
+# You can see what browsers were selected by your queries by running:
+#   npx browserslist
+
+last 1 Chrome version
+last 1 Firefox version
+last 2 Edge major versions
+last 2 Safari major versions
+last 2 iOS major versions
+Firefox ESR
diff --git a/web/frontend/.dockerignore b/web/frontend/.dockerignore
new file mode 100644
index 0000000000000000000000000000000000000000..70d5723693800f5cd32dd0db6d4d9d9ebfd4cba3
--- /dev/null
+++ b/web/frontend/.dockerignore
@@ -0,0 +1,3 @@
+node_modules
+.github
+.angular
diff --git a/web/frontend/.editorconfig b/web/frontend/.editorconfig
new file mode 100644
index 0000000000000000000000000000000000000000..59d9a3a3e73ffc640517ef488f6f89d6270195d1
--- /dev/null
+++ b/web/frontend/.editorconfig
@@ -0,0 +1,16 @@
+# Editor configuration, see https://editorconfig.org
+root = true
+
+[*]
+charset = utf-8
+indent_style = space
+indent_size = 2
+insert_final_newline = true
+trim_trailing_whitespace = true
+
+[*.ts]
+quote_type = single
+
+[*.md]
+max_line_length = off
+trim_trailing_whitespace = false
diff --git a/web/frontend/.github/CODE_OF_CONDUCT.md b/web/frontend/.github/CODE_OF_CONDUCT.md
new file mode 100644
index 0000000000000000000000000000000000000000..64da87772ba970899e7b8ad3ee10be7bb7851b11
--- /dev/null
+++ b/web/frontend/.github/CODE_OF_CONDUCT.md
@@ -0,0 +1,46 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at . The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at [http://contributor-covenant.org/version/1/4][version]
+
+[homepage]: http://contributor-covenant.org
+[version]: http://contributor-covenant.org/version/1/4/
\ No newline at end of file
diff --git a/web/frontend/.github/COMMIT_CONVENTION.md b/web/frontend/.github/COMMIT_CONVENTION.md
new file mode 100644
index 0000000000000000000000000000000000000000..261c7ca20e4595661f530350ce622794137f9a33
--- /dev/null
+++ b/web/frontend/.github/COMMIT_CONVENTION.md
@@ -0,0 +1,83 @@
+## Git Commit Message Convention
+
+> This is adapted from [Angular's commit convention](https://github.com/conventional-changelog/conventional-changelog/blob/master/packages/conventional-changelog-angular/convention.md).
+
+#### Examples
+
+Appears under "Features" header, `compiler` subheader:
+
+```
+feat(compiler): add 'comments' option
+```
+
+Appears under "Bug Fixes" header, `sidebar` subheader, with a link to issue #28:
+
+```
+fix(sidebar): handle events on blur
+
+close #28
+```
+
+Appears under "Performance Improvements" header, and under "Breaking Changes" with the breaking change explanation:
+
+```
+perf(core): improve vdom diffing by removing 'foo' option
+
+BREAKING CHANGE: The 'foo' option has been removed.
+```
+
+The following commit and commit `667ecc1` do not appear in the changelog if they are under the same release. If not, the revert commit appears under the "Reverts" header.
+
+```
+revert: feat(compiler): add 'comments' option
+
+This reverts commit 667ecc1654a317a13331b17617d973392f415f02.
+```
+
+### Full Message Format
+
+A commit message consists of a **header**, **body** and **footer**.  The header has a **type**, **scope** and **subject**:
+
+```
+<type>(<scope>): <subject>
+<BLANK LINE>
+<body>
+<BLANK LINE>
+<footer>
+```
+
+The **header** is mandatory and the **scope** of the header is optional.
+
+### Revert
+
+If the commit reverts a previous commit, it should begin with `revert: `, followed by the header of the reverted commit. In the body it should say: `This reverts commit <hash>.`, where the hash is the SHA of the commit being reverted.
+
+### Type
+
+If the prefix is `feat`, `fix` or `perf`, it will appear in the changelog. However if there is any [BREAKING CHANGE](#footer), the commit will always appear in the changelog.
+
+Other prefixes are up to your discretion. Suggested prefixes are `docs`, `chore`, `style`, `refactor`, and `test` for non-changelog related tasks.
+
+### Scope
+
+The scope could be anything specifying place of the commit change. For example `core`, `compiler`, `ssr`, `v-model`, `transition` etc...
+
+### Subject
+
+The subject contains succinct description of the change:
+
+* use the imperative, present tense: "change" not "changed" nor "changes"
+* don't capitalize first letter
+* no dot (.) at the end
+
+### Body
+
+Just as in the **subject**, use the imperative, present tense: "change" not "changed" nor "changes".
+The body should include the motivation for the change and contrast this with previous behavior.
+
+### Footer
+
+The footer should contain any information about **Breaking Changes** and is also the place to
+reference GitHub issues that this commit **Closes**.
+
+**Breaking Changes** should start with the word `BREAKING CHANGE:` with a space or two newlines. The rest of the commit message is then used for this.
\ No newline at end of file
diff --git a/web/frontend/.github/CONTRIBUTING.md b/web/frontend/.github/CONTRIBUTING.md
new file mode 100644
index 0000000000000000000000000000000000000000..6658bcf678ee7410dae97c0818938fe14941897d
--- /dev/null
+++ b/web/frontend/.github/CONTRIBUTING.md
@@ -0,0 +1,178 @@
+# Contributing to CoreUI
+
+Looking to contribute something to CoreUI? **Here's how you can help.**
+
+Please take a moment to review this document in order to make the contribution process easy and effective for everyone involved.
+
+Following these guidelines helps to communicate that you respect the time of the developers managing and developing this open source project. In return, they should reciprocate that respect in addressing your issue or assessing
+patches and features.
+
+## Using the issue tracker
+
+The [issue tracker](https://github.com/coreui/coreui-free-angular-admin-template/issues) is
+the preferred channel for [bug reports](#bug-reports), [features requests](#feature-requests)
+and [submitting pull requests](#pull-requests), but please respect the following
+restrictions:
+
+* Please **do not** use the issue tracker for personal support requests.
+
+* Please **do not** post comments consisting solely of "+1" or ":thumbsup:".
+  Use [GitHub's "reactions" feature](https://github.blog/2016-03-10-add-reactions-to-pull-requests-issues-and-comments/)
+  instead.
+
+## Bug reports
+
+A bug is a _demonstrable problem_ that is caused by the code in the repository.
+Good bug reports are extremely helpful, so thanks!
+
+Guidelines for bug reports:
+
+0. **Validate and lint your code** &mdash; to ensure your problem isn't caused by a simple error in your own code.
+
+1. **Use the GitHub issue search** &mdash; check if the issue has already been reported.
+
+2. **Check if the issue has been fixed** &mdash; try to reproduce it using the latest `master` or development branch in the repository.
+
+3. **Isolate the problem** &mdash; ideally create a [reduced test case](https://css-tricks.com/reduced-test-cases/) and a live example. [This JS Bin](http://jsbin.com/lefey/1/edit?html,output) is a helpful template.
+
+
+A good bug report shouldn't leave others needing to chase you up for more
+information. Please try to be as detailed as possible in your report. What is
+your environment? What steps will reproduce the issue? What browser(s) and OS
+experience the problem? Do other browsers show the bug differently? What
+would you expect to be the outcome? All these details will help people to fix
+any potential bugs.
+
+Example:
+
+> Short and descriptive example bug report title
+>
+> A summary of the issue and the browser/OS environment in which it occurs. If
+> suitable, include the steps required to reproduce the bug.
+>
+> 1. This is the first step
+> 2. This is the second step
+> 3. Further steps, etc.
+>
+> `<url>` - a link to the reduced test case
+>
+> Any other information you want to share that is relevant to the issue being
+> reported. This might include the lines of code that you have identified as
+> causing the bug, and potential solutions (and your opinions on their
+> merits).
+
+## Feature requests
+
+Feature requests are welcome. Before opening a feature request, please take a
+moment to find out whether your idea fits with the scope and aims of the
+project. It's up to *you* to make a strong case to convince the project's
+developers of the merits of this feature. Please provide as much detail
+and context as possible.
+
+
+## Pull requests
+
+Good pull requests—patches, improvements, new features—are a fantastic
+help. They should remain focused in scope and avoid containing unrelated
+commits.
+
+**Please ask first** before embarking on any significant pull request (e.g.
+implementing features, refactoring code, porting to a different language),
+otherwise you risk spending a lot of time working on something that the
+project's developers might not want to merge into the project.
+
+Adhering to the following process is the best way to get your work
+included in the project:
+
+1. [Fork](https://help.github.com/fork-a-repo/) the project, clone your fork,
+   and configure the remotes:
+
+   ```bash
+   # Clone your fork of the repo into the current directory
+   git clone https://github.com/<your-username>/coreui-vue.git
+   # Navigate to the newly cloned directory
+   cd coreui
+   # Assign the original repo to a remote called "upstream"
+   git remote add upstream https://github.com/coreui/coreui-vue.git
+   ```
+
+2. If you cloned a while ago, get the latest changes from upstream:
+
+   ```bash
+   git checkout master
+   git pull upstream master
+   ```
+
+3. Create a new topic branch (off the main project development branch) to
+   contain your feature, change, or fix:
+
+   ```bash
+   git checkout -b <topic-branch-name>
+   ```
+
+4. Commit your changes in logical chunks. Please adhere to these [git commit
+   message guidelines](http://tbaggery.com/2008/04/19/a-note-about-git-commit-messages.html)
+   or your code is unlikely to be merged into the main project. Use Git's
+   [interactive rebase](https://help.github.com/articles/interactive-rebase)
+   feature to tidy up your commits before making them public.
+
+5. Locally merge (or rebase) the upstream development branch into your topic branch:
+
+   ```bash
+   git pull [--rebase] upstream master
+   ```
+
+6. Push your topic branch up to your fork:
+
+   ```bash
+   git push origin <topic-branch-name>
+   ```
+
+7. [Open a Pull Request](https://help.github.com/articles/using-pull-requests/)
+    with a clear title and description against the `master` branch.
+
+**IMPORTANT**: By submitting a patch, you agree to allow the project owners to
+license your work under the terms of the [MIT License](LICENSE).
+
+### Semantic Git commit messages
+
+Inspired by Sparkbox's awesome article on
+[semantic commit messages](http://seesparkbox.com/foundry/semantic_commit_messages).
+Please use following commit message format.
+
+* chore (updating npm tasks etc; no production code change) -> ```git test -m 'chore: commit-message-here'```
+* docs (changes to documentation) -> ```git commit -m 'docs: commit-message-here'```
+* feat (new feature) -> ```git commit -m 'feat: commit-message-here'```
+* fix (bug fix) -> ```git commit -m 'fix: commit-message-here'```
+* refactor (refactoring production code) -> ```git commit -m 'refactor: commit-message-here'```
+* style (formatting, missing semi colons, etc; no code change) -> ```git commit -m 'style: commit-message-here'```
+* test (adding missing tests, refactoring tests; no production code change) -> ```git test -m 'refactor: commit-message-here'```
+
+## Code guidelines
+
+### HTML
+
+[Adhere to the Code Guide.](http://codeguide.co/#html)
+
+- Use tags and elements appropriate for an HTML5 doctype (e.g., self-closing tags).
+- Use CDNs and HTTPS for third-party JS when possible. We don't use protocol-relative URLs in this case because they break when viewing the page locally via `file://`.
+- Use [WAI-ARIA](https://developer.mozilla.org/en-US/docs/Web/Accessibility/ARIA) attributes in documentation examples to promote accessibility.
+
+### CSS
+
+[Adhere to the Code Guide.](http://codeguide.co/#css)
+
+- When feasible, default color palettes should comply with [WCAG color contrast guidelines](http://www.w3.org/TR/WCAG20/#visual-audio-contrast).
+- Except in rare cases, don't remove default `:focus` styles (via e.g. `outline: none;`) without providing alternative styles. See [this A11Y Project post](http://a11yproject.com/posts/never-remove-css-outlines) for more details.
+
+### JS
+
+- No semicolons (in client-side JS)
+- 2 spaces (no tabs)
+- strict mode
+- "Attractive"
+- Don't use [jQuery event alias convenience methods](https://github.com/jquery/jquery/blob/master/src/event/alias.js) (such as `$().focus()`). Instead, use [`$().trigger(eventType, ...)`](http://api.jquery.com/trigger/) or [`$().on(eventType, ...)`](http://api.jquery.com/on/), depending on whether you're firing an event or listening for an event. (For example, `$().trigger('focus')` or `$().on('focus', function (event) { /* handle focus event */ })`) We do this to be compatible with custom builds of jQuery where the event aliases module has been excluded.
+
+## License
+
+By contributing your code, you agree to license your contribution under the [MIT License](LICENSE).
diff --git a/web/frontend/.github/FUNDING.yml b/web/frontend/.github/FUNDING.yml
new file mode 100644
index 0000000000000000000000000000000000000000..efc571339e284d5aa4389d53754a68ef9953f48d
--- /dev/null
+++ b/web/frontend/.github/FUNDING.yml
@@ -0,0 +1,4 @@
+# These are supported funding model platforms
+
+custom: "https://coreui.io/pricing?support=angular"
+open_collective: coreui
diff --git a/web/frontend/.github/ISSUE_TEMPLATE/bug_report.md b/web/frontend/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 0000000000000000000000000000000000000000..71407283f3172df48f060debfdb08f7d2bdfad85
--- /dev/null
+++ b/web/frontend/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,20 @@
+---
+name: Bug report
+about: Tell us about a bug you may have identified in CoreUI Free Angular Admin Template.
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+Before opening:
+
+- [Search for duplicate or closed issues](https://github.com/coreui/coreui-free-angular-admin-template/issues?utf8=%E2%9C%93&q=is%3Aissue)
+- [Validate](https://html5.validator.nu/) any HTML to avoid common problems
+- Read the [contributing guidelines](https://github.com/coreui/coreui-free-angular-admin-template/blob/v4-dev/.github/CONTRIBUTING.md)
+
+Bug reports must include:
+
+- Operating system and version (Windows, macOS, Android, iOS)
+- Browser and version (Chrome, Firefox, Safari, Microsoft Edge, Opera, Android Browser)
+- A [reduced test case](https://css-tricks.com/reduced-test-cases/) or suggested fix using [CodePen](https://codepen.io/) or [JS Bin](https://jsbin.com/)
diff --git a/web/frontend/.github/ISSUE_TEMPLATE/feature_request.md b/web/frontend/.github/ISSUE_TEMPLATE/feature_request.md
new file mode 100644
index 0000000000000000000000000000000000000000..9f43db8e7a579ffd170894fd860087cde63638a3
--- /dev/null
+++ b/web/frontend/.github/ISSUE_TEMPLATE/feature_request.md
@@ -0,0 +1,18 @@
+---
+name: Feature request
+about: Suggest an idea for a new feature in CoreUI Free Angular Admin Template.
+title: ''
+labels: feature
+assignees: ''
+
+---
+
+Before opening:
+
+- [Search for duplicate or closed issues](https://github.com/coreui/coreui-free-angular-admin-template/issues?utf8=%E2%9C%93&q=is%3Aissue)
+- Read the [contributing guidelines](https://github.com/coreui/coreui-free-angular-admin-template/blob/main/.github/CONTRIBUTING.md)
+
+Feature requests must include:
+
+- As much detail as possible for what we should add and why it's important to Bootstrap
+- Relevant links to prior art, screenshots, or live demos whenever possible
diff --git a/web/frontend/.github/SUPPORT.md b/web/frontend/.github/SUPPORT.md
new file mode 100644
index 0000000000000000000000000000000000000000..c182511c232ad6176ad94c4b55215597d7599a65
--- /dev/null
+++ b/web/frontend/.github/SUPPORT.md
@@ -0,0 +1,9 @@
+### Bug reports
+
+See the [contributing guidelines](CONTRIBUTING.md) for sharing bug reports.
+
+### How-to
+
+For general troubleshooting or help getting started:
+
+- Go to [Discussions](https://github.com/coreui/coreui-free-angular-admin-template/discussions).
diff --git a/web/frontend/.github/workflows/build-check.yml b/web/frontend/.github/workflows/build-check.yml
new file mode 100644
index 0000000000000000000000000000000000000000..b76e76667171f5dcc3b07139ce3ddb36e7e63c83
--- /dev/null
+++ b/web/frontend/.github/workflows/build-check.yml
@@ -0,0 +1,39 @@
+name: Build Check
+
+on:
+  push:
+    branches-ignore:
+      - "dependabot/**"
+  pull_request:
+    branches:
+      - main
+      - v4
+  schedule:
+    - # build runs every weekday at 4:15AM UTC
+    - cron: '15 4 * * *'
+
+env:
+  FORCE_COLOR: 2
+  NODE: 16
+
+jobs:
+  build:
+    strategy:
+      matrix:
+        platform: [ubuntu-latest, windows-latest, macOS-latest]
+        node-version: [16.x, 18.x]
+    runs-on: ${{ matrix.platform }}
+    steps:
+      - name: Clone repository
+        uses: actions/checkout@v3
+
+      - name: Set up Node.js
+        uses: actions/setup-node@v3
+        with:
+          node-version: ${{ env.node-version }}
+
+      - name: Install npm dependencies
+        run: npm install
+
+      - name: Run build
+        run: npm run build
diff --git a/web/frontend/.github/workflows/stale.yml b/web/frontend/.github/workflows/stale.yml
new file mode 100644
index 0000000000000000000000000000000000000000..163fd323271b9dd5f19ba4a923a74fcb2dbcccf2
--- /dev/null
+++ b/web/frontend/.github/workflows/stale.yml
@@ -0,0 +1,27 @@
+# This workflow warns and then closes issues and PRs that have had no activity for a specified amount of time.
+#
+# You can adjust the behavior by modifying this file.
+# For more information, see:
+# https://github.com/actions/stale
+name: Mark stale issues and pull requests
+
+on:
+  schedule:
+  - cron: '34 23 * * 0'
+
+jobs:
+  stale:
+
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+      pull-requests: write
+
+    steps:
+    - uses: actions/stale@v5
+      with:
+        repo-token: ${{ secrets.GITHUB_TOKEN }}
+        stale-issue-message: 'This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions'
+        stale-pr-message: 'This PR has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions'
+        stale-issue-label: 'no-issue-activity'
+        stale-pr-label: 'no-pr-activity'
diff --git a/web/frontend/.gitignore b/web/frontend/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..9314c255e389a723aab0f6f3292d2097b0316a3e
--- /dev/null
+++ b/web/frontend/.gitignore
@@ -0,0 +1,45 @@
+# See http://help.github.com/ignore-files/ for more about ignoring files.
+
+# Compiled output
+/dist
+/tmp
+/out-tsc
+/bazel-out
+
+# Node
+/node_modules
+npm-debug.log
+yarn-error.log
+
+# profiling files
+chrome-profiler-events*.json
+
+# IDEs and editors
+.idea/
+.project
+.classpath
+.c9/
+*.launch
+.settings/
+*.sublime-workspace
+
+# Visual Studio Code
+.vscode/*
+!.vscode/settings.json
+!.vscode/tasks.json
+!.vscode/launch.json
+!.vscode/extensions.json
+.history/*
+
+# Miscellaneous
+/.angular/cache
+.sass-cache/
+/connect.lock
+/coverage
+/libpeerconnection.log
+testem.log
+/typings
+
+# System files
+.DS_Store
+Thumbs.db
diff --git a/web/frontend/README.md b/web/frontend/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..4f2b80ce4804e1f4ff7a1d53d15ac2027ab96681
--- /dev/null
+++ b/web/frontend/README.md
@@ -0,0 +1,219 @@
+[![@coreui angular](https://img.shields.io/badge/@coreui%20-angular-lightgrey.svg?style=flat-square)](https://github.com/coreui/angular)
+[![npm-coreui-angular-next][npm-coreui-angular-badge-next]][npm-coreui-angular]
+[![NPM downloads][npm-coreui-angular-download]][npm-coreui-angular]  
+[![@coreui coreui](https://img.shields.io/badge/@coreui%20-coreui-lightgrey.svg?style=flat-square)](https://github.com/coreui/coreui)
+[![npm package][npm-coreui-badge]][npm-coreui]
+[![NPM downloads][npm-coreui-download]][npm-coreui]  
+![angular](https://img.shields.io/badge/angular-^14.2.0-lightgrey.svg?style=flat-square&logo=angular)
+
+[npm-coreui-angular]: https://www.npmjs.com/package/@coreui/angular
+[npm-coreui-angular-badge]: https://img.shields.io/npm/v/@coreui/angular.png?style=flat-square
+[npm-coreui-angular-badge-next]: https://img.shields.io/npm/v/@coreui/angular/next?style=flat-square&color=red
+[npm-coreui-angular-download]: https://img.shields.io/npm/dm/@coreui/angular.svg?style=flat-square
+[npm-coreui]: https://www.npmjs.com/package/@coreui/coreui
+[npm-coreui-badge]: https://img.shields.io/npm/v/@coreui/coreui.png?style=flat-square
+[npm-coreui-download]: https://img.shields.io/npm/dm/@coreui/coreui.svg?style=flat-square
+
+# CoreUI 4 Angular 14 Free Admin Template
+
+CoreUI is meant to be the UX game changer. Pure & transparent code is devoid of redundant components, so the app is light enough to offer ultimate user experience. This means mobile devices also, where the navigation is just as easy and intuitive as on a desktop or laptop. The CoreUI Layout API lets you customize your project for almost any device – be it Mobile, Web or WebApp – CoreUI covers them all!
+
+[CoreUI Angular Admin Template](https://coreui.io/angular)  
+[Demo](https://coreui.io/angular/demo/4.0/free/)
+
+## Table of Contents
+
+* [Versions](#versions)
+* [CoreUI Pro](#coreui-pro)
+* [Quick Start](#quick-start)
+* [Installation](#installation)
+* [Basic usage](#basic-usage)
+* [What's included](#whats-included)
+* [Documentation](#documentation)
+* [Versioning](#versioning)
+* [Creators](#creators)
+* [Community](#community)
+* [Copyright and License](#copyright-and-license)
+
+## Versions
+
+* [CoreUI Free Bootstrap Admin Template](https://github.com/coreui/coreui-free-bootstrap-admin-template)
+* [CoreUI Free Angular Admin Template](https://github.com/coreui/coreui-free-angular-admin-template)
+* [CoreUI Free React.js Admin Template](https://github.com/coreui/coreui-free-react-admin-template)
+* [CoreUI Free Vue.js Admin Template](https://github.com/coreui/coreui-free-vue-admin-template)
+
+## CoreUI Pro
+
+**Only customers with [Enterpise Membership Plan](https://coreui.io/pro/#buy) have access to private github CoreUI Pro repository.**
+
+* 💪  [CoreUI Pro Angular Admin Template](https://coreui.io/product/angular-dashboard-template/)
+* 💪  [CoreUI Pro Bootstrap Admin Template](https://coreui.io/product/bootstrap-dashboard-template/)
+* 💪  [CoreUI Pro React Admin Template](https://coreui.io/product/react-dashboard-template/)
+* 💪  [CoreUI Pro Vue Admin Template](https://coreui.io/product/vue-dashboard-template/)
+
+## Quick Start
+
+- [Download the latest release](https://github.com/coreui/coreui-free-angular-admin-template/)
+- Clone the repo: `git clone https://github.com/coreui/coreui-free-angular-admin-template.git`
+
+#### <i>Prerequisites</i>
+Before you begin, make sure your development environment includes `Node.js®` and an `npm` package manager.
+
+###### Node.js
+Angular 14 requires `Node.js` version `^14.15` or `^16.10`.
+
+- To check your version, run `node -v` in a terminal/console window.
+- To get `Node.js`, go to [nodejs.org](https://nodejs.org/).
+
+###### Angular CLI
+Install the Angular CLI globally using a terminal/console window.
+```bash
+npm install -g @angular/cli
+```
+
+### Installation
+
+``` bash
+$ npm install
+```
+
+### Basic usage
+
+``` bash
+# dev server with hot reload at http://localhost:4200
+$ npm start
+```
+
+Navigate to [http://localhost:4200](http://localhost:4200). The app will automatically reload if you change any of the source files.
+
+#### Build
+
+Run `build` to build the project. The build artifacts will be stored in the `dist/` directory.
+
+```bash
+# build for production with minification
+$ npm run build
+```
+## What's included
+
+Within the download you'll find the following directories and files, logically grouping common assets and providing both compiled and minified variations. You'll see something like this:
+
+```
+coreui-free-angular-admin-template
+├── src/                         # project root
+│   ├── app/                     # main app directory
+|   │   ├── containers/          # layout containers
+|   |   │   └── default-layout/  # layout containers
+|   |   |       └── _nav.js      # sidebar navigation config
+|   │   ├── icons/               # icons set for the app
+|   │   └── views/               # application views
+│   ├── assets/                  # images, icons, etc.
+│   ├── components/              # components for demo only
+│   ├── scss/                    # scss styles
+│   └── index.html               # html template
+│
+├── angular.json
+├── README.md
+└── package.json
+```
+
+## Documentation
+
+The documentation for the CoreUI Admin Template is hosted at our website [CoreUI for Angular](https://coreui.io/angular/)
+
+---
+
+This project was generated with [Angular CLI](https://github.com/angular/angular-cli) version 14.0.0.
+
+## Versioning
+
+For transparency into our release cycle and in striving to maintain backward compatibility, CoreUI Free Admin Template is maintained under [the Semantic Versioning guidelines](http://semver.org/).
+
+See [the Releases section of our project](https://github.com/coreui/coreui-free-angular-admin-template/releases) for changelogs for each release version.
+
+## Development server
+
+Run `ng serve` for a dev server. Navigate to `http://localhost:4200/`. The app will automatically reload if you change any of the source files.
+
+## Code scaffolding
+
+Run `ng generate component component-name` to generate a new component. You can also use `ng generate directive|pipe|service|class|guard|interface|enum|module`.
+
+## Build
+
+Run `ng build` to build the project. The build artifacts will be stored in the `dist/` directory.
+
+## Running unit tests
+
+Run `ng test` to execute the unit tests via [Karma](https://karma-runner.github.io).
+
+## Running end-to-end tests
+
+Run `ng e2e` to execute the end-to-end tests via a platform of your choice. To use this command, you need to first add a package that implements end-to-end testing capabilities.
+
+## Further help
+
+To get more help on the Angular CLI use `ng help` or go check out the [Angular CLI Overview and Command Reference](https://angular.io/cli) page.
+
+## Creators
+
+**Łukasz Holeczek**
+* <https://twitter.com/lukaszholeczek>
+* <https://github.com/mrholek>
+* <https://github.com/coreui>
+
+**CoreUI team**
+* https://github.com/orgs/coreui/people
+
+## Community
+
+Get updates on CoreUI's development and chat with the project maintainers and community members.
+
+- Follow [@core_ui on Twitter](https://twitter.com/core_ui).
+- Read and subscribe to [CoreUI Blog](https://coreui.io/blog/).
+
+## Support CoreUI Development
+
+CoreUI is an MIT-licensed open source project and is completely free to use. However, the amount of effort needed to maintain and develop new features for the project is not sustainable without proper financial backing. You can support development by buying the [CoreUI PRO](https://coreui.io/pricing/) or by becoming a sponsor via [Open Collective](https://opencollective.com/coreui/).
+
+<!--- StartOpenCollectiveBackers -->
+
+### Platinum Sponsors
+
+Support this project by [becoming a Platinum Sponsor](https://opencollective.com/coreui/contribute/platinum-sponsor-40959/). A large company logo will be added here with a link to your website.
+
+<a href="https://opencollective.com/coreui/contribute/platinum-sponsor-40959/checkout"><img src="https://opencollective.com/coreui/tiers/platinum-sponsor/0/avatar.svg?avatarHeight=100"></a>
+
+### Gold Sponsors
+
+Support this project by [becoming a Gold Sponsor](https://opencollective.com/coreui/contribute/gold-sponsor-40960/). A big company logo will be added here with a link to your website.
+
+<a href="https://opencollective.com/coreui/contribute/gold-sponsor-40960/checkout"><img src="https://opencollective.com/coreui/tiers/gold-sponsor/0/avatar.svg?avatarHeight=100"></a> 
+
+### Silver Sponsors
+
+Support this project by [becoming a Silver Sponsor](https://opencollective.com/coreui/contribute/silver-sponsor-40967/). A medium company logo will be added here with a link to your website.
+
+<a href="https://opencollective.com/coreui/contribute/silver-sponsor-40967/checkout"><img src="https://opencollective.com/coreui/tiers/gold-sponsor/0/avatar.svg?avatarHeight=100"></a>
+
+### Bronze Sponsors
+
+Support this project by [becoming a Bronze Sponsor](https://opencollective.com/coreui/contribute/bronze-sponsor-40966/). The company avatar will show up here with a link to your OpenCollective Profile.
+
+<a href="https://opencollective.com/coreui/contribute/bronze-sponsor-40966/checkout"><img src="https://opencollective.com/coreui/tiers/bronze-sponsor/0/avatar.svg?avatarHeight=100"></a> 
+
+### Backers
+
+Thanks to all the backers and sponsors! Support this project by [becoming a backer](https://opencollective.com/coreui/contribute/backer-40965/).
+
+<a href="https://opencollective.com/coreui/contribute/backer-40965/checkout" target="_blank" rel="noopener"><img src="https://opencollective.com/coreui/backers.svg?width=890"></a>
+
+<!--- EndOpenCollectiveBackers -->
+
+## Copyright and License
+
+copyright 2022 creativeLabs Łukasz Holeczek.   
+
+ 
+Code released under [the MIT license](https://github.com/coreui/coreui-free-react-admin-template/blob/master/LICENSE).
+There is only one limitation you can't re-distribute the CoreUI as stock. You can’t do this if you modify the CoreUI. In past we faced some problems with persons who tried to sell CoreUI based templates.
diff --git a/web/frontend/angular.json b/web/frontend/angular.json
new file mode 100644
index 0000000000000000000000000000000000000000..aac3bd42a80372c8c38c5a6431bcc07a763b5802
--- /dev/null
+++ b/web/frontend/angular.json
@@ -0,0 +1,118 @@
+{
+  "$schema": "./node_modules/@angular/cli/lib/config/schema.json",
+  "cli": {
+    "analytics": false
+  },
+  "version": 1,
+  "newProjectRoot": "projects",
+  "projects": {
+    "weather-forecaster": {
+      "projectType": "application",
+      "schematics": {
+        "@schematics/angular:component": {
+          "style": "scss"
+        },
+        "@schematics/angular:application": {
+          "strict": true
+        }
+      },
+      "root": "",
+      "sourceRoot": "src",
+      "prefix": "app",
+      "architect": {
+        "build": {
+          "builder": "@angular-devkit/build-angular:browser",
+          "options": {
+            "outputPath": "dist/weather-forecaster",
+            "index": "src/index.html",
+            "main": "src/main.ts",
+            "polyfills": "src/polyfills.ts",
+            "tsConfig": "tsconfig.app.json",
+            "inlineStyleLanguage": "scss",
+            "preserveSymlinks": true,
+            "assets": [
+              "src/favicon.ico",
+              "src/assets"
+            ],
+            "styles": [
+              "src/scss/styles.scss"
+            ],
+            "scripts": [],
+            "allowedCommonJsDependencies": [
+              "chart.js",
+              "@coreui/chartjs/dist/js/coreui-chartjs.js"
+            ]
+          },
+          "configurations": {
+            "production": {
+              "budgets": [
+                {
+                  "type": "initial",
+                  "maximumWarning": "1500kb",
+                  "maximumError": "6mb"
+                },
+                {
+                  "type": "anyComponentStyle",
+                  "maximumWarning": "2kb",
+                  "maximumError": "4kb"
+                }
+              ],
+              "fileReplacements": [
+                {
+                  "replace": "src/environments/environment.ts",
+                  "with": "src/environments/environment.prod.ts"
+                }
+              ],
+              "outputHashing": "all"
+            },
+            "development": {
+              "buildOptimizer": false,
+              "optimization": false,
+              "vendorChunk": true,
+              "extractLicenses": false,
+              "sourceMap": true,
+              "namedChunks": true
+            }
+          },
+          "defaultConfiguration": "production"
+        },
+        "serve": {
+          "builder": "@angular-devkit/build-angular:dev-server",
+          "configurations": {
+            "production": {
+              "browserTarget": "weather-forecaster:build:production"
+            },
+            "development": {
+              "browserTarget": "weather-forecaster:build:development"
+            }
+          },
+          "defaultConfiguration": "development"
+        },
+        "extract-i18n": {
+          "builder": "@angular-devkit/build-angular:extract-i18n",
+          "options": {
+            "browserTarget": "weather-forecaster:build"
+          }
+        },
+        "test": {
+          "builder": "@angular-devkit/build-angular:karma",
+          "options": {
+            "main": "src/test.ts",
+            "polyfills": "src/polyfills.ts",
+            "tsConfig": "tsconfig.spec.json",
+            "karmaConfig": "karma.conf.js",
+            "inlineStyleLanguage": "scss",
+            "assets": [
+              "src/favicon.ico",
+              "src/assets"
+            ],
+            "styles": [
+              "src/scss/styles.scss"
+            ],
+            "scripts": []
+          }
+        }
+      }
+    }
+  }
+}
diff --git a/web/frontend/karma.conf.js b/web/frontend/karma.conf.js
new file mode 100644
index 0000000000000000000000000000000000000000..b9754549134ba70de04be9c4534311d8489ed7d8
--- /dev/null
+++ b/web/frontend/karma.conf.js
@@ -0,0 +1,44 @@
+// Karma configuration file, see link for more information
+// https://karma-runner.github.io/1.0/config/configuration-file.html
+
+module.exports = function (config) {
+  config.set({
+    basePath: '',
+    frameworks: ['jasmine', '@angular-devkit/build-angular'],
+    plugins: [
+      require('karma-jasmine'),
+      require('karma-chrome-launcher'),
+      require('karma-jasmine-html-reporter'),
+      require('karma-coverage'),
+      require('@angular-devkit/build-angular/plugins/karma')
+    ],
+    client: {
+      jasmine: {
+        // you can add configuration options for Jasmine here
+        // the possible options are listed at https://jasmine.github.io/api/edge/Configuration.html
+        // for example, you can disable the random execution with `random: false`
+        // or set a specific seed with `seed: 4321`
+      },
+      clearContext: false // leave Jasmine Spec Runner output visible in browser
+    },
+    jasmineHtmlReporter: {
+      suppressAll: true // removes the duplicated traces
+    },
+    coverageReporter: {
+      dir: require('path').join(__dirname, './coverage/coreui-free-angular-admin-template'),
+      subdir: '.',
+      reporters: [
+        { type: 'html' },
+        { type: 'text-summary' }
+      ]
+    },
+    reporters: ['progress', 'kjhtml'],
+    port: 9876,
+    colors: true,
+    logLevel: config.LOG_INFO,
+    autoWatch: true,
+    browsers: ['Chrome'],
+    singleRun: false,
+    restartOnFileChange: true
+  });
+};
diff --git a/web/frontend/package-lock.json b/web/frontend/package-lock.json
new file mode 100644
index 0000000000000000000000000000000000000000..6153baa8dabe3cdfa96be77db704c614ca811cd4
--- /dev/null
+++ b/web/frontend/package-lock.json
@@ -0,0 +1,20994 @@
+{
+  "name": "coreui-free-angular-admin-template",
+  "version": "4.2.18",
+  "lockfileVersion": 2,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "coreui-free-angular-admin-template",
+      "version": "4.2.18",
+      "dependencies": {
+        "@angular/animations": "^14.2.12",
+        "@angular/cdk": "^14.2.7",
+        "@angular/common": "^14.2.12",
+        "@angular/compiler": "^14.2.12",
+        "@angular/core": "^14.2.12",
+        "@angular/forms": "^14.2.12",
+        "@angular/language-service": "^14.2.12",
+        "@angular/material": "^14.2.7",
+        "@angular/platform-browser": "^14.2.12",
+        "@angular/platform-browser-dynamic": "^14.2.12",
+        "@angular/router": "^14.2.12",
+        "@coreui/angular": "~4.2.18",
+        "@coreui/angular-chartjs": "~4.2.15",
+        "@coreui/chartjs": "^3.0.0",
+        "@coreui/coreui": "~4.2.4",
+        "@coreui/icons": "^2.1.0",
+        "@coreui/icons-angular": "~4.2.15",
+        "@coreui/utils": "^1.3.1",
+        "@fullcalendar/angular": "^5.11.3",
+        "@fullcalendar/daygrid": "^5.11.3",
+        "chart.js": "^3.9.1",
+        "moment": "^2.29.4",
+        "ngx-perfect-scrollbar": "^10.1.1",
+        "rxjs": "~7.5.0",
+        "tslib": "^2.4.1",
+        "zone.js": "~0.11.8"
+      },
+      "devDependencies": {
+        "@angular-devkit/build-angular": "^14.2.10",
+        "@angular/cli": "^14.2.10",
+        "@angular/compiler-cli": "^14.2.12",
+        "@angular/localize": "^14.2.12",
+        "@types/jasmine": "^4.3.1",
+        "@types/node": "^16.18.6",
+        "jasmine-core": "~4.5.0",
+        "karma": "~6.4.1",
+        "karma-chrome-launcher": "~3.1.1",
+        "karma-coverage": "~2.2.0",
+        "karma-jasmine": "~5.1.0",
+        "karma-jasmine-html-reporter": "~2.0.0",
+        "typescript": "~4.7.4"
+      },
+      "engines": {
+        "node": "^14.15.0 || >=16.10.0",
+        "npm": ">= 6"
+      }
+    },
+    "node_modules/@adobe/css-tools": {
+      "version": "4.0.1",
+      "resolved": "https://registry.npmjs.org/@adobe/css-tools/-/css-tools-4.0.1.tgz",
+      "integrity": "sha512-+u76oB43nOHrF4DDWRLWDCtci7f3QJoEBigemIdIeTi1ODqjx6Tad9NCVnPRwewWlKkVab5PlK8DCtPTyX7S8g==",
+      "dev": true
+    },
+    "node_modules/@ampproject/remapping": {
+      "version": "2.2.0",
+      "resolved": "https://registry.npmjs.org/@ampproject/remapping/-/remapping-2.2.0.tgz",
+      "integrity": "sha512-qRmjj8nj9qmLTQXXmaR1cck3UXSRMPrbsLJAasZpF+t3riI71BXed5ebIOYwQntykeZuhjsdweEc9BxH5Jc26w==",
+      "dev": true,
+      "dependencies": {
+        "@jridgewell/gen-mapping": "^0.1.0",
+        "@jridgewell/trace-mapping": "^0.3.9"
+      },
+      "engines": {
+        "node": ">=6.0.0"
+      }
+    },
+    "node_modules/@angular-devkit/architect": {
+      "version": "0.1402.10",
+      "resolved": "https://registry.npmjs.org/@angular-devkit/architect/-/architect-0.1402.10.tgz",
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+      "dev": true,
+      "dependencies": {
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+        "rxjs": "6.6.7"
+      },
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+      }
+    },
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+      "resolved": "https://registry.npmjs.org/rxjs/-/rxjs-6.6.7.tgz",
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+      "dev": true,
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+      },
+      "engines": {
+        "npm": ">=2.0.0"
+      }
+    },
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+      "dev": true
+    },
+    "node_modules/@angular-devkit/build-angular": {
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+        "@babel/helper-annotate-as-pure": "7.18.6",
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+        "@babel/plugin-transform-async-to-generator": "7.18.6",
+        "@babel/plugin-transform-runtime": "7.18.10",
+        "@babel/preset-env": "7.18.10",
+        "@babel/runtime": "7.18.9",
+        "@babel/template": "7.18.10",
+        "@discoveryjs/json-ext": "0.5.7",
+        "@ngtools/webpack": "14.2.10",
+        "ansi-colors": "4.1.3",
+        "babel-loader": "8.2.5",
+        "babel-plugin-istanbul": "6.1.1",
+        "browserslist": "^4.9.1",
+        "cacache": "16.1.2",
+        "copy-webpack-plugin": "11.0.0",
+        "critters": "0.0.16",
+        "css-loader": "6.7.1",
+        "esbuild-wasm": "0.15.5",
+        "glob": "8.0.3",
+        "https-proxy-agent": "5.0.1",
+        "inquirer": "8.2.4",
+        "jsonc-parser": "3.1.0",
+        "karma-source-map-support": "1.4.0",
+        "less": "4.1.3",
+        "less-loader": "11.0.0",
+        "license-webpack-plugin": "4.0.2",
+        "loader-utils": "3.2.1",
+        "mini-css-extract-plugin": "2.6.1",
+        "minimatch": "5.1.0",
+        "open": "8.4.0",
+        "ora": "5.4.1",
+        "parse5-html-rewriting-stream": "6.0.1",
+        "piscina": "3.2.0",
+        "postcss": "8.4.16",
+        "postcss-import": "15.0.0",
+        "postcss-loader": "7.0.1",
+        "postcss-preset-env": "7.8.0",
+        "regenerator-runtime": "0.13.9",
+        "resolve-url-loader": "5.0.0",
+        "rxjs": "6.6.7",
+        "sass": "1.54.4",
+        "sass-loader": "13.0.2",
+        "semver": "7.3.7",
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+        "stylus-loader": "7.0.0",
+        "terser": "5.14.2",
+        "text-table": "0.2.0",
+        "tree-kill": "1.2.2",
+        "tslib": "2.4.0",
+        "webpack": "5.74.0",
+        "webpack-dev-middleware": "5.3.3",
+        "webpack-dev-server": "4.11.0",
+        "webpack-merge": "5.8.0",
+        "webpack-subresource-integrity": "5.1.0"
+      },
+      "engines": {
+        "node": "^14.15.0 || >=16.10.0",
+        "npm": "^6.11.0 || ^7.5.6 || >=8.0.0",
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+      },
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+      },
+      "peerDependencies": {
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+        "@angular/service-worker": "^14.0.0",
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+        "protractor": "^7.0.0",
+        "tailwindcss": "^2.0.0 || ^3.0.0",
+        "typescript": ">=4.6.2 <4.9"
+      },
+      "peerDependenciesMeta": {
+        "@angular/localize": {
+          "optional": true
+        },
+        "@angular/service-worker": {
+          "optional": true
+        },
+        "karma": {
+          "optional": true
+        },
+        "ng-packagr": {
+          "optional": true
+        },
+        "protractor": {
+          "optional": true
+        },
+        "tailwindcss": {
+          "optional": true
+        }
+      }
+    },
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+      },
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+      }
+    },
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+      }
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+      "resolved": "https://registry.npmjs.org/yallist/-/yallist-4.0.0.tgz",
+      "integrity": "sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==",
+      "dev": true
+    },
+    "yaml": {
+      "version": "1.10.2",
+      "resolved": "https://registry.npmjs.org/yaml/-/yaml-1.10.2.tgz",
+      "integrity": "sha512-r3vXyErRCYJ7wg28yvBY5VSoAF8ZvlcW9/BwUzEtUsjvX/DKs24dIkuwjtuprwJJHsbyUbLApepYTR1BN4uHrg==",
+      "dev": true
+    },
+    "yargs": {
+      "version": "17.5.1",
+      "resolved": "https://registry.npmjs.org/yargs/-/yargs-17.5.1.tgz",
+      "integrity": "sha512-t6YAJcxDkNX7NFYiVtKvWUz8l+PaKTLiL63mJYWR2GnHq2gjEWISzsLp9wg3aY36dY1j+gfIEL3pIF+XlJJfbA==",
+      "dev": true,
+      "requires": {
+        "cliui": "^7.0.2",
+        "escalade": "^3.1.1",
+        "get-caller-file": "^2.0.5",
+        "require-directory": "^2.1.1",
+        "string-width": "^4.2.3",
+        "y18n": "^5.0.5",
+        "yargs-parser": "^21.0.0"
+      }
+    },
+    "yargs-parser": {
+      "version": "21.1.1",
+      "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-21.1.1.tgz",
+      "integrity": "sha512-tVpsJW7DdjecAiFpbIB1e3qxIQsE6NoPc5/eTdrbbIC4h0LVsWhnoa3g+m2HclBIujHzsxZ4VJVA+GUuc2/LBw==",
+      "dev": true
+    },
+    "zone.js": {
+      "version": "0.11.8",
+      "resolved": "https://registry.npmjs.org/zone.js/-/zone.js-0.11.8.tgz",
+      "integrity": "sha512-82bctBg2hKcEJ21humWIkXRlLBBmrc3nN7DFh5LGGhcyycO2S7FN8NmdvlcKaGFDNVL4/9kFLmwmInTavdJERA==",
+      "requires": {
+        "tslib": "^2.3.0"
+      }
+    }
+  }
+}
diff --git a/web/frontend/package.json b/web/frontend/package.json
new file mode 100644
index 0000000000000000000000000000000000000000..216d80a0eb76deb193a7b628cc276823aaccd511
--- /dev/null
+++ b/web/frontend/package.json
@@ -0,0 +1,64 @@
+{
+  "name": "weather-forecaster",
+  "version": "4.2.18",
+  "config": {
+    "coreui_library_short_version": "4.2",
+    "coreui_library_docs_url": "https://coreui.io/angular/docs/"
+  },
+  "scripts": {
+    "ng": "ng",
+    "start-dev": "ng serve --host=0.0.0.0",
+    "start": "ng serve --host=0.0.0.0 --configuration=production",
+    "build": "ng build",
+    "watch": "ng build --watch --configuration development",
+    "test": "ng test"
+  },
+  "private": true,
+  "dependencies": {
+    "@angular/animations": "^14.2.12",
+    "@angular/cdk": "^14.2.7",
+    "@angular/common": "^14.2.12",
+    "@angular/compiler": "^14.2.12",
+    "@angular/core": "^14.2.12",
+    "@angular/forms": "^14.2.12",
+    "@angular/language-service": "^14.2.12",
+    "@angular/material": "^14.2.7",
+    "@angular/platform-browser": "^14.2.12",
+    "@angular/platform-browser-dynamic": "^14.2.12",
+    "@angular/router": "^14.2.12",
+    "@coreui/angular": "~4.2.18",
+    "@coreui/angular-chartjs": "~4.2.15",
+    "@coreui/chartjs": "^3.0.0",
+    "@coreui/coreui": "~4.2.4",
+    "@coreui/icons": "^2.1.0",
+    "@coreui/icons-angular": "~4.2.15",
+    "@coreui/utils": "^1.3.1",
+    "@fullcalendar/angular": "^5.11.3",
+    "@fullcalendar/daygrid": "^5.11.3",
+    "chart.js": "^3.9.1",
+    "moment": "^2.29.4",
+    "ngx-perfect-scrollbar": "^10.1.1",
+    "rxjs": "~7.5.0",
+    "tslib": "^2.4.1",
+    "zone.js": "~0.11.8"
+  },
+  "devDependencies": {
+    "@angular-devkit/build-angular": "^14.2.10",
+    "@angular/cli": "^14.2.10",
+    "@angular/compiler-cli": "^14.2.12",
+    "@angular/localize": "^14.2.12",
+    "@types/jasmine": "^4.3.1",
+    "@types/node": "^16.18.6",
+    "jasmine-core": "~4.5.0",
+    "karma": "~6.4.1",
+    "karma-chrome-launcher": "~3.1.1",
+    "karma-coverage": "~2.2.0",
+    "karma-jasmine": "~5.1.0",
+    "karma-jasmine-html-reporter": "~2.0.0",
+    "typescript": "~4.7.4"
+  },
+  "engines": {
+    "node": "^14.15.0 || >=16.10.0",
+    "npm": ">= 6"
+  }
+}
diff --git a/web/frontend/src/app/app-routing.module.ts b/web/frontend/src/app/app-routing.module.ts
new file mode 100644
index 0000000000000000000000000000000000000000..86b10be0a8e04a800601b17a52e80a147f9fa97f
--- /dev/null
+++ b/web/frontend/src/app/app-routing.module.ts
@@ -0,0 +1,47 @@
+import { NgModule } from '@angular/core';
+import { RouterModule, Routes } from '@angular/router';
+
+import { DefaultLayoutComponent } from './containers';
+
+const routes: Routes = [
+  {
+    path: '',
+    redirectTo: 'dashboard',
+    pathMatch: 'full'
+  },
+  {
+    path: '',
+    component: DefaultLayoutComponent,
+    data: {
+      title: 'Home'
+    },
+    children: [
+      {
+        path: 'dashboard',
+        loadChildren: () =>
+          import('./views/dashboard/dashboard.module').then((m) => m.DashboardModule)
+      },
+      {
+        path: 'about',
+        loadChildren: () =>
+          import('./views/about/about.module').then((m) => m.AboutModule)
+      },
+    ]
+  },
+
+  {path: '**', redirectTo: 'dashboard'}
+];
+
+@NgModule({
+  imports: [
+    RouterModule.forRoot(routes, {
+      scrollPositionRestoration: 'top',
+      anchorScrolling: 'enabled',
+      initialNavigation: 'enabledBlocking'
+      // relativeLinkResolution: 'legacy'
+    })
+  ],
+  exports: [RouterModule]
+})
+export class AppRoutingModule {
+}
diff --git a/web/frontend/src/app/app.component.spec.ts b/web/frontend/src/app/app.component.spec.ts
new file mode 100644
index 0000000000000000000000000000000000000000..35cd3ceb1e1ab919f10416f88042724355cdfcfb
--- /dev/null
+++ b/web/frontend/src/app/app.component.spec.ts
@@ -0,0 +1,28 @@
+import { TestBed } from '@angular/core/testing';
+import { RouterTestingModule } from '@angular/router/testing';
+import { AppComponent } from './app.component';
+
+describe('AppComponent', () => {
+  beforeEach(async () => {
+    await TestBed.configureTestingModule({
+      imports: [
+        RouterTestingModule
+      ],
+      declarations: [
+        AppComponent
+      ],
+    }).compileComponents();
+  });
+
+  it('should create the app', () => {
+    const fixture = TestBed.createComponent(AppComponent);
+    const app = fixture.componentInstance;
+    expect(app).toBeTruthy();
+  });
+
+  it(`should have as title 'CoreUI Free Angular Admin Template'`, () => {
+    const fixture = TestBed.createComponent(AppComponent);
+    const app = fixture.componentInstance;
+    expect(app.title).toEqual('CoreUI Free Angular Admin Template');
+  });
+});
diff --git a/web/frontend/src/app/app.component.ts b/web/frontend/src/app/app.component.ts
new file mode 100644
index 0000000000000000000000000000000000000000..6de02a1b502efd1b5f857af9812be82b5f0db2b6
--- /dev/null
+++ b/web/frontend/src/app/app.component.ts
@@ -0,0 +1,33 @@
+import { Component, OnInit } from '@angular/core';
+import { Router, NavigationEnd } from '@angular/router';
+
+import { IconSetService } from '@coreui/icons-angular';
+import { iconSubset } from './icons/icon-subset';
+import { Title } from '@angular/platform-browser';
+
+@Component({
+  // tslint:disable-next-line:component-selector
+  selector: 'body',
+  template: '<router-outlet></router-outlet>',
+})
+export class AppComponent implements OnInit {
+  title = 'CoreUI Free Angular Admin Template';
+
+  constructor(
+    private router: Router,
+    private titleService: Title,
+    private iconSetService: IconSetService
+  ) {
+    titleService.setTitle(this.title);
+    // iconSet singleton
+    iconSetService.icons = { ...iconSubset };
+  }
+
+  ngOnInit(): void {
+    this.router.events.subscribe((evt) => {
+      if (!(evt instanceof NavigationEnd)) {
+        return;
+      }
+    });
+  }
+}
diff --git a/web/frontend/src/app/app.module.ts b/web/frontend/src/app/app.module.ts
new file mode 100644
index 0000000000000000000000000000000000000000..9274e95eb3f6101e1ec34b7d95ff7c5be1e89965
--- /dev/null
+++ b/web/frontend/src/app/app.module.ts
@@ -0,0 +1,112 @@
+import { NgModule } from '@angular/core';
+import { FullCalendarModule } from '@fullcalendar/angular';
+import { HashLocationStrategy, LocationStrategy, PathLocationStrategy } from '@angular/common';
+import { BrowserModule, Title } from '@angular/platform-browser';
+import { BrowserAnimationsModule } from '@angular/platform-browser/animations';
+import { ReactiveFormsModule } from '@angular/forms';
+import { HttpClientModule } from '@angular/common/http';
+import { 
+  MatDatepickerModule,
+} from '@angular/material/datepicker';
+
+
+import {
+  PERFECT_SCROLLBAR_CONFIG,
+  PerfectScrollbarConfigInterface,
+  PerfectScrollbarModule,
+} from 'ngx-perfect-scrollbar';
+
+// Import routing module
+import { AppRoutingModule } from './app-routing.module';
+
+// Import app component
+import { AppComponent } from './app.component';
+
+// Import containers
+import {
+  DefaultFooterComponent,
+  DefaultHeaderComponent,
+  DefaultLayoutComponent,
+} from './containers';
+
+import {
+  AvatarModule,
+  BadgeModule,
+  BreadcrumbModule,
+  ButtonGroupModule,
+  ButtonModule,
+  CardModule,
+  DropdownModule,
+  FooterModule,
+  FormModule,
+  GridModule,
+  HeaderModule,
+  ListGroupModule,
+  NavModule,
+  ProgressModule,
+  SharedModule,
+  SidebarModule,
+  TabsModule,
+  UtilitiesModule,
+} from '@coreui/angular';
+
+import { IconModule, IconSetService } from '@coreui/icons-angular';
+
+const DEFAULT_PERFECT_SCROLLBAR_CONFIG: PerfectScrollbarConfigInterface = {
+  suppressScrollX: true,
+};
+
+const APP_CONTAINERS = [
+  DefaultFooterComponent,
+  DefaultHeaderComponent,
+  DefaultLayoutComponent,
+];
+
+@NgModule({
+  declarations: [AppComponent, ...APP_CONTAINERS],
+  imports: [
+    BrowserModule,
+    BrowserAnimationsModule,
+    AppRoutingModule,
+    MatDatepickerModule,
+    AvatarModule,
+    BreadcrumbModule,
+    FooterModule,
+    DropdownModule,
+    GridModule,
+    HeaderModule,
+    SidebarModule,
+    IconModule,
+    PerfectScrollbarModule,
+    NavModule,
+    ButtonModule,
+    FormModule,
+    UtilitiesModule,
+    ButtonGroupModule,
+    ReactiveFormsModule,
+    SidebarModule,
+    SharedModule,
+    TabsModule,
+    ListGroupModule,
+    ProgressModule,
+    BadgeModule,
+    ListGroupModule,
+    CardModule,
+    HttpClientModule,
+  ],
+  providers: [
+    {
+      provide: LocationStrategy,
+      useClass: HashLocationStrategy,
+    },
+    {
+      provide: PERFECT_SCROLLBAR_CONFIG,
+      useValue: DEFAULT_PERFECT_SCROLLBAR_CONFIG,
+    },
+    IconSetService,
+    Title
+  ],
+  bootstrap: [AppComponent],
+})
+export class AppModule {
+}
diff --git a/web/frontend/src/app/containers/default-layout/_nav.ts b/web/frontend/src/app/containers/default-layout/_nav.ts
new file mode 100644
index 0000000000000000000000000000000000000000..e81dd63c2b1e8a06a34287e64a3945fb2ea69c08
--- /dev/null
+++ b/web/frontend/src/app/containers/default-layout/_nav.ts
@@ -0,0 +1,245 @@
+import { INavData } from '@coreui/angular';
+
+export const navItems: INavData[] = [
+  {
+    name: 'Dashboard',
+    url: '/dashboard',
+    iconComponent: { name: 'cil-speedometer' },
+  },
+  {
+    name: 'About',
+    url: '/about',
+    iconComponent: { name: 'cil-notes' },
+  },
+  // {
+  //   title: true,
+  //   name: 'Theme'
+  // },
+  // {
+  //   name: 'Colors',
+  //   url: '/theme/colors',
+  //   iconComponent: { name: 'cil-drop' }
+  // },
+  // {
+  //   name: 'Typography',
+  //   url: '/theme/typography',
+  //   linkProps: { fragment: 'someAnchor' },
+  //   iconComponent: { name: 'cil-pencil' }
+  // },
+  // {
+  //   name: 'Components',
+  //   title: true
+  // },
+  // {
+  //   name: 'Base',
+  //   url: '/base',
+  //   iconComponent: { name: 'cil-puzzle' },
+  //   children: [
+  //     {
+  //       name: 'Accordion',
+  //       url: '/base/accordion'
+  //     },
+  //     {
+  //       name: 'Breadcrumbs',
+  //       url: '/base/breadcrumbs'
+  //     },
+  //     {
+  //       name: 'Cards',
+  //       url: '/base/cards'
+  //     },
+  //     {
+  //       name: 'Carousel',
+  //       url: '/base/carousel'
+  //     },
+  //     {
+  //       name: 'Collapse',
+  //       url: '/base/collapse'
+  //     },
+  //     {
+  //       name: 'List Group',
+  //       url: '/base/list-group'
+  //     },
+  //     {
+  //       name: 'Navs & Tabs',
+  //       url: '/base/navs'
+  //     },
+  //     {
+  //       name: 'Pagination',
+  //       url: '/base/pagination'
+  //     },
+  //     {
+  //       name: 'Placeholder',
+  //       url: '/base/placeholder'
+  //     },
+  //     {
+  //       name: 'Popovers',
+  //       url: '/base/popovers'
+  //     },
+  //     {
+  //       name: 'Progress',
+  //       url: '/base/progress'
+  //     },
+  //     {
+  //       name: 'Spinners',
+  //       url: '/base/spinners'
+  //     },
+  //     {
+  //       name: 'Tables',
+  //       url: '/base/tables'
+  //     },
+  //     {
+  //       name: 'Tabs',
+  //       url: '/base/tabs'
+  //     },
+  //     {
+  //       name: 'Tooltips',
+  //       url: '/base/tooltips'
+  //     }
+  //   ]
+  // },
+  // {
+  //   name: 'Buttons',
+  //   url: '/buttons',
+  //   iconComponent: { name: 'cil-cursor' },
+  //   children: [
+  //     {
+  //       name: 'Buttons',
+  //       url: '/buttons/buttons'
+  //     },
+  //     {
+  //       name: 'Button groups',
+  //       url: '/buttons/button-groups'
+  //     },
+  //     {
+  //       name: 'Dropdowns',
+  //       url: '/buttons/dropdowns'
+  //     },
+  //   ]
+  // },
+  // {
+  //   name: 'Forms',
+  //   url: '/forms',
+  //   iconComponent: { name: 'cil-notes' },
+  //   children: [
+  //     {
+  //       name: 'Form Control',
+  //       url: '/forms/form-control'
+  //     },
+  //     {
+  //       name: 'Select',
+  //       url: '/forms/select'
+  //     },
+  //     {
+  //       name: 'Checks & Radios',
+  //       url: '/forms/checks-radios'
+  //     },
+  //     {
+  //       name: 'Range',
+  //       url: '/forms/range'
+  //     },
+  //     {
+  //       name: 'Input Group',
+  //       url: '/forms/input-group'
+  //     },
+  //     {
+  //       name: 'Floating Labels',
+  //       url: '/forms/floating-labels'
+  //     },
+  //     {
+  //       name: 'Layout',
+  //       url: '/forms/layout'
+  //     },
+  //     {
+  //       name: 'Validation',
+  //       url: '/forms/validation'
+  //     }
+  //   ]
+  // },
+  // {
+  //   name: 'Charts',
+  //   url: '/charts',
+  //   iconComponent: { name: 'cil-chart-pie' }
+  // },
+  // {
+  //   name: 'Icons',
+  //   iconComponent: { name: 'cil-star' },
+  //   url: '/icons',
+  //   children: [
+  //     {
+  //       name: 'CoreUI Free',
+  //       url: '/icons/coreui-icons',
+  //       badge: {
+  //         color: 'success',
+  //         text: 'FREE'
+  //       }
+  //     },
+  //     {
+  //       name: 'CoreUI Flags',
+  //       url: '/icons/flags'
+  //     },
+  //     {
+  //       name: 'CoreUI Brands',
+  //       url: '/icons/brands'
+  //     }
+  //   ]
+  // },
+  // {
+  //   name: 'Notifications',
+  //   url: '/notifications',
+  //   iconComponent: { name: 'cil-bell' },
+  //   children: [
+  //     {
+  //       name: 'Alerts',
+  //       url: '/notifications/alerts'
+  //     },
+  //     {
+  //       name: 'Badges',
+  //       url: '/notifications/badges'
+  //     },
+  //     {
+  //       name: 'Modal',
+  //       url: '/notifications/modal'
+  //     },
+  //     {
+  //       name: 'Toast',
+  //       url: '/notifications/toasts'
+  //     }
+  //   ]
+  // },
+  // {
+  //   name: 'Widgets',
+  //   url: '/widgets',
+  //   iconComponent: { name: 'cil-calculator' },
+  //   badge: {
+  //     color: 'info',
+  //     text: 'NEW'
+  //   }
+  // },
+  // {
+  //   title: true,
+  //   name: 'Extras'
+  // },
+  // {
+  //   name: 'Pages',
+  //   url: '/login',
+  //   iconComponent: { name: 'cil-star' },
+  //   children: [
+  //     {
+  //       name: 'Login',
+  //       url: '/login'
+  //     },
+  //     {
+  //       name: 'Register',
+  //       url: '/register'
+  //     },
+  //     {
+  //       name: 'Error 404',
+  //       url: '/404'
+  //     },
+  //     {
+  //       name: 'Error 500',
+  //       url: '/500'
+  //     }
+  //   ]
+  // },
+];
diff --git a/web/frontend/src/app/containers/default-layout/default-footer/default-footer.component.html b/web/frontend/src/app/containers/default-layout/default-footer/default-footer.component.html
new file mode 100644
index 0000000000000000000000000000000000000000..c3f7c09aeb7237aa6a1eb42685cb027da4a87ba2
--- /dev/null
+++ b/web/frontend/src/app/containers/default-layout/default-footer/default-footer.component.html
@@ -0,0 +1,7 @@
+<!--<c-footer>-->
+  <div class="ms-auto">
+    <span> &copy; 2022 Sushant Karki </span>
+    <span style="padding-left: 0.4em;"><a href="https://github.com/00ber" target="_blank"><i class="fa fa-github" style="font-size:24px"></i></a></span>
+    <span style="padding-left: 0.4em;"><a href="https://www.linkedin.com/in/sushant-karki-b6b29b61/" target="_blank"><i class="fa fa-linkedin-square" style="font-size:24px"></i></a></span>
+  </div>
+<!--</c-footer>-->
diff --git a/web/frontend/src/app/containers/default-layout/default-footer/default-footer.component.scss b/web/frontend/src/app/containers/default-layout/default-footer/default-footer.component.scss
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/web/frontend/src/app/containers/default-layout/default-footer/default-footer.component.spec.ts b/web/frontend/src/app/containers/default-layout/default-footer/default-footer.component.spec.ts
new file mode 100644
index 0000000000000000000000000000000000000000..c287eb6a42a04d3f69b6b7f7c310e714d85c53c6
--- /dev/null
+++ b/web/frontend/src/app/containers/default-layout/default-footer/default-footer.component.spec.ts
@@ -0,0 +1,25 @@
+import { ComponentFixture, TestBed } from '@angular/core/testing';
+
+import { DefaultFooterComponent } from './default-footer.component';
+
+describe('DefaultFooterComponent', () => {
+  let component: DefaultFooterComponent;
+  let fixture: ComponentFixture<DefaultFooterComponent>;
+
+  beforeEach(async () => {
+    await TestBed.configureTestingModule({
+      declarations: [ DefaultFooterComponent ]
+    })
+    .compileComponents();
+  });
+
+  beforeEach(() => {
+    fixture = TestBed.createComponent(DefaultFooterComponent);
+    component = fixture.componentInstance;
+    fixture.detectChanges();
+  });
+
+  it('should create', () => {
+    expect(component).toBeTruthy();
+  });
+});
diff --git a/web/frontend/src/app/containers/default-layout/default-footer/default-footer.component.ts b/web/frontend/src/app/containers/default-layout/default-footer/default-footer.component.ts
new file mode 100644
index 0000000000000000000000000000000000000000..2002f42e4fc7d8dd09d5484b01ed4534ae5076ff
--- /dev/null
+++ b/web/frontend/src/app/containers/default-layout/default-footer/default-footer.component.ts
@@ -0,0 +1,13 @@
+import { Component } from '@angular/core';
+import { FooterComponent } from '@coreui/angular';
+
+@Component({
+  selector: 'app-default-footer',
+  templateUrl: './default-footer.component.html',
+  styleUrls: ['./default-footer.component.scss'],
+})
+export class DefaultFooterComponent extends FooterComponent {
+  constructor() {
+    super();
+  }
+}
diff --git a/web/frontend/src/app/containers/default-layout/default-header/default-header.component.html b/web/frontend/src/app/containers/default-layout/default-header/default-header.component.html
new file mode 100644
index 0000000000000000000000000000000000000000..e96f7e37c521ca0339765c2cb1eee73e98be037d
--- /dev/null
+++ b/web/frontend/src/app/containers/default-layout/default-header/default-header.component.html
@@ -0,0 +1,28 @@
+<!--<c-header class="mb-4 d-print-none" position="sticky">-->
+<ng-container>
+  <c-container [fluid]="true">
+    <button
+      toggle="visible"
+      cHeaderToggler
+      [cSidebarToggle]="sidebarId"
+      class="ps-1"
+    >
+      <svg cIcon
+        name="cilMenu"
+        size="lg"
+      ></svg>
+    </button>
+    <c-header-nav class="d-none d-lg-flex me-auto">
+      <c-nav-item>
+        <a cNavLink routerLink="/dashboard" routerLinkActive="active">
+          Dashboard
+        </a>
+      </c-nav-item>
+    </c-header-nav>
+  </c-container>
+  <!-- <c-header-divider></c-header-divider> -->
+  <!-- <c-container [fluid]="true">
+    <c-breadcrumb-router class="ms-2"></c-breadcrumb-router>
+  </c-container> -->
+</ng-container>
+<!--</c-header>-->
diff --git a/web/frontend/src/app/containers/default-layout/default-header/default-header.component.scss b/web/frontend/src/app/containers/default-layout/default-header/default-header.component.scss
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/web/frontend/src/app/containers/default-layout/default-header/default-header.component.spec.ts b/web/frontend/src/app/containers/default-layout/default-header/default-header.component.spec.ts
new file mode 100644
index 0000000000000000000000000000000000000000..ad6662e0b69ee60320e47a7f1bcae2761ab1f317
--- /dev/null
+++ b/web/frontend/src/app/containers/default-layout/default-header/default-header.component.spec.ts
@@ -0,0 +1,43 @@
+import { ComponentFixture, TestBed } from '@angular/core/testing';
+
+import {
+  AvatarModule,
+  BadgeModule,
+  BreadcrumbModule,
+  DropdownModule,
+  GridModule,
+  HeaderModule,
+  NavModule, SidebarModule
+} from '@coreui/angular';
+import { IconSetService } from '@coreui/icons-angular';
+import { iconSubset } from '../../../icons/icon-subset';
+import { DefaultHeaderComponent } from './default-header.component';
+import { RouterTestingModule } from '@angular/router/testing';
+
+describe('DefaultHeaderComponent', () => {
+  let component: DefaultHeaderComponent;
+  let fixture: ComponentFixture<DefaultHeaderComponent>;
+  let iconSetService: IconSetService;
+
+  beforeEach(async () => {
+    await TestBed.configureTestingModule({
+      declarations: [DefaultHeaderComponent],
+      imports: [GridModule, HeaderModule, NavModule, BadgeModule, AvatarModule, DropdownModule, BreadcrumbModule, RouterTestingModule, SidebarModule],
+      providers: [IconSetService]
+    })
+      .compileComponents();
+  });
+
+  beforeEach(() => {
+    iconSetService = TestBed.inject(IconSetService);
+    iconSetService.icons = { ...iconSubset };
+
+    fixture = TestBed.createComponent(DefaultHeaderComponent);
+    component = fixture.componentInstance;
+    fixture.detectChanges();
+  });
+
+  it('should create', () => {
+    expect(component).toBeTruthy();
+  });
+});
diff --git a/web/frontend/src/app/containers/default-layout/default-header/default-header.component.ts b/web/frontend/src/app/containers/default-layout/default-header/default-header.component.ts
new file mode 100644
index 0000000000000000000000000000000000000000..5e9e6defcf37daf9ae4aa9ff5fc20716f8cb4d38
--- /dev/null
+++ b/web/frontend/src/app/containers/default-layout/default-header/default-header.component.ts
@@ -0,0 +1,21 @@
+import { Component, Input } from '@angular/core';
+import { FormControl, FormGroup } from '@angular/forms';
+
+import { ClassToggleService, HeaderComponent } from '@coreui/angular';
+
+@Component({
+  selector: 'app-default-header',
+  templateUrl: './default-header.component.html',
+})
+export class DefaultHeaderComponent extends HeaderComponent {
+
+  @Input() sidebarId: string = "sidebar";
+
+  public newMessages = new Array(4)
+  public newTasks = new Array(5)
+  public newNotifications = new Array(5)
+
+  constructor(private classToggler: ClassToggleService) {
+    super();
+  }
+}
diff --git a/web/frontend/src/app/containers/default-layout/default-layout.component.html b/web/frontend/src/app/containers/default-layout/default-layout.component.html
new file mode 100644
index 0000000000000000000000000000000000000000..a87cece508a4a159f3dd225cafdef867c5761b22
--- /dev/null
+++ b/web/frontend/src/app/containers/default-layout/default-layout.component.html
@@ -0,0 +1,51 @@
+<!--sidebar-->
+<c-sidebar
+  #sidebar="cSidebar"
+  class="d-print-none sidebar sidebar-fixed"
+  id="sidebar"
+  visible
+>
+  <c-sidebar-brand
+    [brandFull]="{
+      src: 'assets/img/logo.png',
+      width: 200,
+      height: 46,
+      alt: 'CoreUI Logo'
+    }"
+    [brandNarrow]="{
+      src: 'assets/img/brand/coreui-signet-white.svg',
+      width: 46,
+      height: 46,
+      alt: 'CoreUI Logo'
+    }"
+    routerLink="./"
+  >
+  </c-sidebar-brand>
+    <perfect-scrollbar [config]="perfectScrollbarConfig">
+  <!--  [perfectScrollbar]="perfectScrollbarConfig"-->
+  <c-sidebar-nav
+    [navItems]="navItems"
+    dropdownMode="close"
+  >
+  </c-sidebar-nav>
+    </perfect-scrollbar>
+  <c-sidebar-toggler
+    *ngIf="!sidebar.narrow"
+    toggle="unfoldable"
+    cSidebarToggle="sidebar"
+  ></c-sidebar-toggler>
+</c-sidebar>
+
+<!--main-->
+<div class="wrapper d-flex flex-column min-vh-100 bg-light dark:bg-transparent">
+  <!--app-header-->
+  <app-default-header class="mb-4 d-print-none header header-sticky" position="sticky" sidebarId="sidebar"></app-default-header>
+  <!--app-body-->
+  <div class="body flex-grow-1 px-3">
+    <c-container breakpoint="lg" class="h-auto">
+      <router-outlet></router-outlet>
+    </c-container>
+  </div>
+  <!--app footer-->
+  <app-default-footer></app-default-footer>
+</div>
diff --git a/web/frontend/src/app/containers/default-layout/default-layout.component.ts b/web/frontend/src/app/containers/default-layout/default-layout.component.ts
new file mode 100644
index 0000000000000000000000000000000000000000..ece4b874db465c68a9a149b2984c746884bec8af
--- /dev/null
+++ b/web/frontend/src/app/containers/default-layout/default-layout.component.ts
@@ -0,0 +1,18 @@
+import { Component } from '@angular/core';
+
+import { navItems } from './_nav';
+
+@Component({
+  selector: 'app-dashboard',
+  templateUrl: './default-layout.component.html',
+})
+export class DefaultLayoutComponent {
+
+  public navItems = navItems;
+
+  public perfectScrollbarConfig = {
+    suppressScrollX: true,
+  };
+
+  constructor() {}
+}
diff --git a/web/frontend/src/app/containers/default-layout/index.ts b/web/frontend/src/app/containers/default-layout/index.ts
new file mode 100644
index 0000000000000000000000000000000000000000..158fc8790d1f99e6565ab630f5682c7d82208244
--- /dev/null
+++ b/web/frontend/src/app/containers/default-layout/index.ts
@@ -0,0 +1,3 @@
+export * from './default-footer/default-footer.component';
+export * from './default-header/default-header.component';
+export * from './default-layout.component';
diff --git a/web/frontend/src/app/containers/index.ts b/web/frontend/src/app/containers/index.ts
new file mode 100644
index 0000000000000000000000000000000000000000..473078d6e3290d0dd5bc907ae86bc2886a03b2f7
--- /dev/null
+++ b/web/frontend/src/app/containers/index.ts
@@ -0,0 +1 @@
+export * from './default-layout';
diff --git a/web/frontend/src/app/icons/icon-subset.ts b/web/frontend/src/app/icons/icon-subset.ts
new file mode 100644
index 0000000000000000000000000000000000000000..06dc54443d93dc8cc552e14e5355de6fe927fb67
--- /dev/null
+++ b/web/frontend/src/app/icons/icon-subset.ts
@@ -0,0 +1,275 @@
+import {
+  cibCcAmex,
+  cibCcApplePay,
+  cibCcMastercard,
+  cibCcPaypal,
+  cibCcStripe,
+  cibCcVisa,
+  cibFacebook,
+  cibGoogle,
+  cibLinkedin,
+  cibSkype,
+  cibTwitter,
+  cifBr,
+  cifEs,
+  cifFr,
+  cifIn,
+  cifPl,
+  cifUs,
+  cilAlignCenter,
+  cilAlignLeft,
+  cilAlignRight,
+  cilApplicationsSettings,
+  cilArrowBottom,
+  cilArrowRight,
+  cilArrowTop,
+  cilBasket,
+  cilBell,
+  cilBold,
+  cilBookmark,
+  cilCalculator,
+  cilCalendar,
+  cilChart,
+  cilChartPie,
+  cilCheck,
+  cilChevronLeft,
+  cilChevronRight,
+  cilCloudDownload,
+  cilCode,
+  cilCommentSquare,
+  cilCreditCard,
+  cilCursor,
+  cilDollar,
+  cilDrop,
+  cilEnvelopeClosed,
+  cilEnvelopeOpen,
+  cilFile,
+  cilHome,
+  cilInbox,
+  cilIndentDecrease,
+  cilIndentIncrease,
+  cilItalic,
+  cilJustifyCenter,
+  cilLayers,
+  cilList,
+  cilListNumbered,
+  cilLocationPin,
+  cilLockLocked,
+  cilMagnifyingGlass,
+  cilMap,
+  cilMediaPlay,
+  cilMenu,
+  cilMoon,
+  cilNotes,
+  cilOptions,
+  cilPaperclip,
+  cilPaperPlane,
+  cilPen,
+  cilPencil,
+  cilPeople,
+  cilPrint,
+  cilPuzzle,
+  cilReportSlash,
+  cilSave,
+  cilSettings,
+  cilShare,
+  cilShareAll,
+  cilShareBoxed,
+  cilSpeech,
+  cilSpeedometer,
+  cilSpreadsheet,
+  cilStar,
+  cilSun,
+  cilTags,
+  cilTask,
+  cilTrash,
+  cilUnderline,
+  cilUser,
+  cilUserFemale,
+  cilUserFollow,
+  cilUserUnfollow,
+} from '@coreui/icons';
+
+export const iconSubset = {
+  cibCcAmex,
+  cibCcApplePay,
+  cibCcMastercard,
+  cibCcPaypal,
+  cibCcStripe,
+  cibCcVisa,
+  cibFacebook,
+  cibGoogle,
+  cibLinkedin,
+  cibSkype,
+  cibTwitter,
+  cifBr,
+  cifEs,
+  cifFr,
+  cifIn,
+  cifPl,
+  cifUs,
+  cilAlignCenter,
+  cilAlignLeft,
+  cilAlignRight,
+  cilApplicationsSettings,
+  cilArrowBottom,
+  cilArrowRight,
+  cilArrowTop,
+  cilBasket,
+  cilBell,
+  cilBold,
+  cilBookmark,
+  cilCalculator,
+  cilCalendar,
+  cilChart,
+  cilChartPie,
+  cilCheck,
+  cilChevronLeft,
+  cilChevronRight,
+  cilCloudDownload,
+  cilCode,
+  cilCommentSquare,
+  cilCreditCard,
+  cilCursor,
+  cilDollar,
+  cilDrop,
+  cilEnvelopeClosed,
+  cilEnvelopeOpen,
+  cilFile,
+  cilHome,
+  cilInbox,
+  cilIndentDecrease,
+  cilIndentIncrease,
+  cilItalic,
+  cilJustifyCenter,
+  cilLayers,
+  cilList,
+  cilListNumbered,
+  cilLocationPin,
+  cilLockLocked,
+  cilMagnifyingGlass,
+  cilMap,
+  cilMediaPlay,
+  cilMenu,
+  cilMoon,
+  cilNotes,
+  cilOptions,
+  cilPaperclip,
+  cilPaperPlane,
+  cilPen,
+  cilPencil,
+  cilPeople,
+  cilPrint,
+  cilPuzzle,
+  cilReportSlash,
+  cilSave,
+  cilSettings,
+  cilShare,
+  cilShareAll,
+  cilShareBoxed,
+  cilSpeech,
+  cilSpeedometer,
+  cilSpreadsheet,
+  cilStar,
+  cilSun,
+  cilTags,
+  cilTask,
+  cilTrash,
+  cilUnderline,
+  cilUser,
+  cilUserFemale,
+  cilUserFollow,
+  cilUserUnfollow,
+};
+
+export enum IconSubset {
+  cibCcAmex = 'cibCcAmex',
+  cibCcApplePay = 'cibCcApplePay',
+  cibCcMastercard = 'cibCcMastercard',
+  cibCcPaypal = 'cibCcPaypal',
+  cibCcStripe = 'cibCcStripe',
+  cibCcVisa = 'cibCcVisa',
+  cibFacebook = 'cibFacebook',
+  cibGoogle = 'cibGoogle',
+  cibLinkedin = 'cibLinkedin',
+  cibSkype = 'cibSkype',
+  cibTwitter = 'cibTwitter',
+  cifBr = 'cifBr',
+  cifEs = 'cifEs',
+  cifFr = 'cifFr',
+  cifIn = 'cifIn',
+  cifPl = 'cifPl',
+  cifUs = 'cifUs',
+  cilAlignCenter = 'cilAlignCenter',
+  cilAlignLeft = 'cilAlignLeft',
+  cilAlignRight = 'cilAlignRight',
+  cilApplicationsSettings = 'cilApplicationsSettings',
+  cilArrowBottom = 'cilArrowBottom',
+  cilArrowRight = 'cilArrowRight',
+  cilArrowTop = 'cilArrowTop',
+  cilBasket = 'cilBasket',
+  cilBell = 'cilBell',
+  cilBold = 'cilBold',
+  cilBookmark = 'cilBookmark',
+  cilCalculator = 'cilCalculator',
+  cilCalendar = 'cilCalendar',
+  cilChart = 'cilChart',
+  cilChartPie = 'cilChartPie',
+  cilCheck='cilCheck',
+  cilChevronLeft = 'cilChevronLeft',
+  cilChevronRight = 'cilChevronRight',
+  cilCloudDownload = 'cilCloudDownload',
+  cilCode = 'cilCode',
+  cilCommentSquare = 'cilCommentSquare',
+  cilCreditCard = 'cilCreditCard',
+  cilCursor = 'cilCursor',
+  cilDollar = 'cilDollar',
+  cilDrop = 'cilDrop',
+  cilEnvelopeClosed = 'cilEnvelopeClosed',
+  cilEnvelopeOpen = 'cilEnvelopeOpen',
+  cilFile = 'cilFile',
+  cilHome = 'cilHome',
+  cilInbox = 'cilInbox',
+  cilIndentDecrease = 'cilIndentDecrease',
+  cilIndentIncrease = 'cilIndentIncrease',
+  cilItalic = 'cilItalic',
+  cilJustifyCenter = 'cilJustifyCenter',
+  cilLayers = 'cilLayers',
+  cilList = 'cilList',
+  cilListNumbered = 'cilListNumbered',
+  cilLocationPin = 'cilLocationPin',
+  cilLockLocked = 'cilLockLocked',
+  cilMagnifyingGlass = 'cilMagnifyingGlass',
+  cilMap = 'cilMap',
+  cilMediaPlay = 'cilMediaPlay',
+  cilMenu = 'cilMenu',
+  cilMoon = 'cilMoon',
+  cilNotes = 'cilNotes',
+  cilOptions = 'cilOptions',
+  cilPaperclip = 'cilPaperclip',
+  cilPaperPlane = 'cilPaperPlane',
+  cilPen = 'cilPen',
+  cilPencil = 'cilPencil',
+  cilPeople = 'cilPeople',
+  cilPrint = 'cilPrint',
+  cilPuzzle = 'cilPuzzle',
+  cilReportSlash = 'cilReportSlash',
+  cilSave = 'cilSave',
+  cilSettings = 'cilSettings',
+  cilShare = 'cilShare',
+  cilShareAll = 'cilShareAll',
+  cilShareBoxed = 'cilShareBoxed',
+  cilSpeech = 'cilSpeech',
+  cilSpeedometer = 'cilSpeedometer',
+  cilSpreadsheet = 'cilSpreadsheet',
+  cilStar = 'cilStar',
+  cilSun = 'cilSun',
+  cilTags = 'cilTags',
+  cilTask = 'cilTask',
+  cilTrash = 'cilTrash',
+  cilUnderline = 'cilUnderline',
+  cilUser = 'cilUser',
+  cilUserFemale = 'cilUserFemale',
+  cilUserFollow = 'cilUserFollow',
+  cilUserUnfollow = 'cilUserUnfollow',
+}
diff --git a/web/frontend/src/app/predictions.service.ts b/web/frontend/src/app/predictions.service.ts
new file mode 100644
index 0000000000000000000000000000000000000000..6f7bb52d94f321e6c97a726118469fbde0008cfe
--- /dev/null
+++ b/web/frontend/src/app/predictions.service.ts
@@ -0,0 +1,27 @@
+import { Injectable } from '@angular/core';
+import { HttpClient } from '@angular/common/http';
+import { BehaviorSubject, Observable } from 'rxjs';
+import { environment } from './../environments/environment';
+
+
+export interface Predictions {
+  [k: string]: {
+    predicted: number;
+    actual: number | null
+  }
+}
+
+@Injectable({
+  providedIn: 'root'
+})
+export class PredictionsService {
+
+  public $predictions = new BehaviorSubject<Predictions>({})
+  constructor(private http: HttpClient) { }
+
+  public getPredictions(targetDate: string): void {
+    this.http.get<Predictions>(`${environment.apiUrl}/predictions`, { params: { target_date: targetDate }}).subscribe(data => {
+      this.$predictions.next(data)
+    })
+  }
+}
diff --git a/web/frontend/src/app/views/about/about-routing.module.ts b/web/frontend/src/app/views/about/about-routing.module.ts
new file mode 100644
index 0000000000000000000000000000000000000000..bffcf383b002097d68c759b8c5c499c5760db37f
--- /dev/null
+++ b/web/frontend/src/app/views/about/about-routing.module.ts
@@ -0,0 +1,21 @@
+import { NgModule } from '@angular/core';
+import { RouterModule, Routes } from '@angular/router';
+
+import { AboutComponent } from './about.component';
+
+const routes: Routes = [
+  {
+    path: '',
+    component: AboutComponent,
+    data: {
+      title: "About"
+    }
+  }
+];
+
+@NgModule({
+  imports: [RouterModule.forChild(routes)],
+  exports: [RouterModule]
+})
+export class AboutRoutingModule {
+}
diff --git a/web/frontend/src/app/views/about/about.component.html b/web/frontend/src/app/views/about/about.component.html
new file mode 100644
index 0000000000000000000000000000000000000000..6e855fc76baf84dd58c0f6d0b5870659422be1f2
--- /dev/null
+++ b/web/frontend/src/app/views/about/about.component.html
@@ -0,0 +1,17 @@
+<c-card class="mb-4">
+    <c-card-header>
+      <h2 class="card-title mb-0" id="about">About this site</h2>
+    </c-card-header>
+    <c-card-body>
+        <p cCardText>
+            This site makes temperature predictions for each hour of a particular day in Washington D.C. based on the hourly weather data for the previous 7 days. 
+            For each day, predictions are made for the next 24 hours starting at 00:00 am of the target date by using data of the last 168 hours (last 7 days). The graph shows the comparision between the temperatures predicted by the model vs the actual hourly temperatures for that particular day.
+        </p>
+        <p cCardText>
+                The model was trained on 20 years of historical hourly data of the Ronald Reagan National Airport acquired from the <strong>api.weather.com</strong> API (the same one used by <a href="https://wunderground.com" target="_blank">Wunderground</a>.
+        </p>
+        <p cCardText>
+            Any feedback on the project is appreciated. Please feel free to reach out to me at <a href = "mailto: sushantkarkiii@gmail.com">sushantkarkiii@gmail.com</a>.
+        </p>
+    </c-card-body>
+</c-card>
diff --git a/web/frontend/src/app/views/about/about.component.ts b/web/frontend/src/app/views/about/about.component.ts
new file mode 100644
index 0000000000000000000000000000000000000000..75b5b7cc685d2689e71ac2714fa863c31464365b
--- /dev/null
+++ b/web/frontend/src/app/views/about/about.component.ts
@@ -0,0 +1,15 @@
+import { Component } from '@angular/core';
+
+
+export interface TableRow {
+  time: string;
+  predicted: number;
+  actual: number | null;
+  error: number | null;
+}
+
+@Component({
+    selector: 'about',
+    templateUrl: 'about.component.html'
+})
+export class AboutComponent {}
diff --git a/web/frontend/src/app/views/about/about.module.ts b/web/frontend/src/app/views/about/about.module.ts
new file mode 100644
index 0000000000000000000000000000000000000000..d35b5beb2cfc6513f3ac6718af26908d0dabdba4
--- /dev/null
+++ b/web/frontend/src/app/views/about/about.module.ts
@@ -0,0 +1,23 @@
+import { NgModule } from '@angular/core';
+import { CommonModule } from '@angular/common';
+import { ReactiveFormsModule } from '@angular/forms';
+import {MatNativeDateModule} from '@angular/material/core';
+import {
+  CardModule,
+  GridModule,
+  NavModule
+} from '@coreui/angular';
+import { MatDatepickerModule } from '@angular/material/datepicker';
+
+import { AboutComponent } from './about.component';
+import { AboutRoutingModule } from './about-routing.module'
+
+@NgModule({
+  imports: [
+    CardModule,
+    AboutRoutingModule
+  ],
+  declarations: [AboutComponent],
+})
+export class AboutModule {
+}
diff --git a/web/frontend/src/app/views/dashboard/dashboard-charts-data.ts b/web/frontend/src/app/views/dashboard/dashboard-charts-data.ts
new file mode 100644
index 0000000000000000000000000000000000000000..88bcc82d75220f8730d095ad975a54b39bba1786
--- /dev/null
+++ b/web/frontend/src/app/views/dashboard/dashboard-charts-data.ts
@@ -0,0 +1,133 @@
+import { Injectable } from '@angular/core';
+import { getStyle, hexToRgba } from '@coreui/utils/src';
+import { Predictions } from 'src/app/predictions.service';
+import { TableRow } from './dashboard.component';
+
+export interface IChartProps {
+  data?: any;
+  labels?: any;
+  options?: any;
+  colors?: any;
+  type?: any;
+  legend?: any;
+
+  [propName: string]: any;
+}
+
+@Injectable({
+  providedIn: 'any'
+})
+export class DashboardChartsData {
+  public mainChart: IChartProps = {};
+  public predicted: number[] = [];
+  public actual: number[] = [];
+
+
+  initMainChart(data: TableRow[]) {
+    const styleActual = '#20a8d8';
+    const stylePredicted = '#f86c6b';
+    const stylePredictedBg = hexToRgba(getStyle('--cui-info'), 10) ?? '#20a8d8';
+
+    this.predicted = data.map(d => d.predicted)
+    this.actual = data.map(d => d.actual).filter(d => !!d) as number[];
+    this.mainChart['elements'] = data.length;
+    this.mainChart['Predicted'] = this.predicted;
+    this.mainChart['Actual'] = this.actual;
+
+    let labels: string[] = data.map(d => d.time);
+
+    const colors = [
+      {
+        // stylePredicted
+        backgroundColor: stylePredictedBg,
+        borderColor: stylePredicted,
+        borderDash: [5, 5],
+        pointHoverBackgroundColor: stylePredicted,
+        fill: "+1",
+      },
+      {
+        // styleActual
+        backgroundColor: 'transparent',
+        borderColor: styleActual,
+        pointHoverBackgroundColor: '#fff',
+        fill: true
+      },
+    ];
+
+    const datasets = [
+      {
+        data: this.mainChart['Predicted'],
+        label: 'Predicted',
+        ...colors[0]
+      },
+      {
+        data: this.mainChart['Actual'],
+        label: 'Actual',
+        ...colors[1]
+      }
+    ];
+    // if(this.mainChart["Actual"].length > 0) {
+    //   datasets.push(
+        
+    //   )
+    // }
+
+    const plugins = {
+      legend: {
+        display: true,
+        position: "top"
+      },
+      tooltip: {
+        callbacks: {
+          labelColor: function(context: any) {
+            return {
+              backgroundColor: context.dataset.borderColor
+            };
+          }
+        }
+      }
+    };
+
+    const options = {
+      maintainAspectRatio: false,
+      plugins,
+      scales: {
+        x: {
+          grid: {
+            drawOnChartArea: true
+          }
+        },
+        y: {
+          beginAtZero: true,
+          max: 100,
+          ticks: {
+            callback: (val: string, _index: number, _ticks: unknown) => {
+              return `${val} °F`
+            },
+            maxTicksLimit: 20,
+            stepSize: 5
+          }
+        }
+      },
+      elements: {
+        line: {
+          tension: 0
+        },
+        point: {
+          radius: 0,
+          hitRadius: 10,
+          hoverRadius: 4,
+          hoverBorderWidth: 3
+        }
+      }
+    };
+
+    this.mainChart.type = 'line';
+    this.mainChart.options = options;
+    this.mainChart.data = {
+      datasets,
+      labels
+    };
+  }
+
+}
diff --git a/web/frontend/src/app/views/dashboard/dashboard-routing.module.ts b/web/frontend/src/app/views/dashboard/dashboard-routing.module.ts
new file mode 100644
index 0000000000000000000000000000000000000000..ee61ebf701a48b9fd075b183812a2232de5dbedb
--- /dev/null
+++ b/web/frontend/src/app/views/dashboard/dashboard-routing.module.ts
@@ -0,0 +1,21 @@
+import { NgModule } from '@angular/core';
+import { RouterModule, Routes } from '@angular/router';
+
+import { DashboardComponent } from './dashboard.component';
+
+const routes: Routes = [
+  {
+    path: '',
+    component: DashboardComponent,
+    data: {
+      title: $localize`Dashboard`
+    }
+  }
+];
+
+@NgModule({
+  imports: [RouterModule.forChild(routes)],
+  exports: [RouterModule]
+})
+export class DashboardRoutingModule {
+}
diff --git a/web/frontend/src/app/views/dashboard/dashboard.component.html b/web/frontend/src/app/views/dashboard/dashboard.component.html
new file mode 100644
index 0000000000000000000000000000000000000000..d3f89445af28b4cc6a663609bc29c2de63d345a1
--- /dev/null
+++ b/web/frontend/src/app/views/dashboard/dashboard.component.html
@@ -0,0 +1,47 @@
+<c-card class="mb-4">
+  <c-card-header>
+    <h2 class="card-title mb-0" id="forecasts">Hourly temperature forecasts for Washington D.C. for the last 30 days ({{ datesLabel }})</h2>
+  </c-card-header>
+  <c-card-body>
+    <c-row>
+    <c-col>
+        <horizontal-date-scroller [fromDate]="startDate" [toDate]="endDate" (dateChanged)="changeSelectedDate($event)">loading</horizontal-date-scroller>
+    </c-col>
+    </c-row>
+    <c-chart
+      [data]="mainChart.data"
+      [height]="300"
+      [ngStyle]="{ 'marginTop.px': 40 }"
+      [options]="mainChart.options"
+      [type]="mainChart.type"
+      [redraw]="true"
+    >
+    </c-chart>
+  </c-card-body>
+</c-card>
+
+<c-card>
+  <c-card-header>
+    <h2 class="card-title mb-0" id="table-title">Predicted vs Actual temperatures for {{ selectedDate }}</h2>
+  </c-card-header>
+  <c-card-body>
+        <table [striped]="true" cTable>
+          <thead>
+          <tr>
+            <th scope="col"><strong>Time</strong></th>
+            <th scope="col"><strong>Predicted (°F)</strong></th>
+            <th scope="col"><strong>Actual (°F)</strong></th>
+            <th scope="col"><strong>Error</strong></th>
+          </tr>
+          </thead>
+          <tbody>
+          <tr *ngFor="let row of tableRows$ | async">
+          <td>{{ row.time }}</td>
+          <td>{{ row.predicted }}</td>
+          <td>{{ row.actual }}</td>
+          <td>{{ row.error }}</td>
+          </tr>
+          </tbody>
+        </table>
+  </c-card-body>
+</c-card>
diff --git a/web/frontend/src/app/views/dashboard/dashboard.component.scss b/web/frontend/src/app/views/dashboard/dashboard.component.scss
new file mode 100644
index 0000000000000000000000000000000000000000..3e08d9395cd79f65400360c99edea62535ea6c8c
--- /dev/null
+++ b/web/frontend/src/app/views/dashboard/dashboard.component.scss
@@ -0,0 +1,7 @@
+:host {
+  .legend {
+    small {
+      font-size: x-small;
+    }
+  }
+}
diff --git a/web/frontend/src/app/views/dashboard/dashboard.component.ts b/web/frontend/src/app/views/dashboard/dashboard.component.ts
new file mode 100644
index 0000000000000000000000000000000000000000..ea92f603970a338d0b1a4c0d549a5fd4d8831aad
--- /dev/null
+++ b/web/frontend/src/app/views/dashboard/dashboard.component.ts
@@ -0,0 +1,104 @@
+import { Component, OnInit, ViewChild } from '@angular/core';
+import { UntypedFormControl, UntypedFormGroup } from '@angular/forms';
+
+import { DashboardChartsData, IChartProps } from './dashboard-charts-data';
+import { Predictions, PredictionsService } from "../../predictions.service"
+import * as moment from 'moment';
+import { Subject } from 'rxjs';
+import { ChartjsComponent } from '@coreui/angular-chartjs';
+
+export interface TableRow {
+  time: string;
+  predicted: number;
+  actual: number | null;
+  error: number | null;
+}
+
+@Component({
+  templateUrl: 'dashboard.component.html',
+  styleUrls: ['dashboard.component.scss']
+})
+export class DashboardComponent implements OnInit {
+  @ViewChild(ChartjsComponent, { static: true })
+  predChart!: ChartjsComponent;
+
+  constructor(private chartsData: DashboardChartsData, private predictionsApi: PredictionsService) {
+  }
+
+  public mainChart: IChartProps = {};
+  public chart: Array<IChartProps> = [];
+
+  public predicted: number[] = [];
+  public actual: number[] = [];
+
+  public tableRows$ = new Subject<TableRow[]>();
+  
+  public endDate: moment.Moment = moment(); 
+  public startDate: moment.Moment =  moment(this.endDate).subtract(30, "days");
+  public datesLabel: string = `${this.startDate.format("YYYY/MM/DD")} ~ ${this.endDate.format("YYYY/MM/DD")}`;
+  public selectedDate!: string;
+
+
+  ngOnInit(): void {
+    this.initCharts();
+
+    this.predictionsApi.$predictions.subscribe(data => {
+      this.predicted = Object.keys(data).map(k => data[k].predicted)
+      this.actual = Object.keys(data).map(k => data[k].actual).filter(i => !!i) as number[];
+      const tableRows = this.toTableRows(data);
+      this.tableRows$.next(tableRows)
+      this.chartsData.initMainChart(tableRows);
+    })
+  }
+
+  private getLabelText(label: string): string {
+    const hour = label.slice(0,2)
+    let hourValue = Number(hour);
+    const suffix = (hourValue >= 12) ? "pm" : "am";
+    if (hourValue > 12) {
+      hourValue = hourValue - 12;
+    }
+    let labelTxt = `${hourValue} ${suffix}`;
+    if (labelTxt == "0 am") {
+      labelTxt = "12 am";
+    }
+    return labelTxt
+  }
+
+  private toTableRows(predictions: Predictions): TableRow[] {
+    return Object.keys(predictions).map(datetime => {
+      const time = datetime.slice(-8)
+      const timeLabel = this.getLabelText(time);
+      let error = null;
+      const actual = predictions[datetime].actual;
+      const predicted = predictions[datetime].predicted;
+      if (!!actual) {
+        error = actual - predicted
+      } 
+      return {
+        time: timeLabel,
+        predicted,
+        actual,
+        error
+      }
+
+    });
+
+  }
+
+  initCharts(): void {
+    this.mainChart = this.chartsData.mainChart;
+  }
+
+  changeSelectedDate(selectedDate: string): void {
+    this.selectedDate = selectedDate;
+    this.tableRows$.next([])
+    if(this.predChart.chart){
+      this.mainChart.data.datasets.forEach((ds: any) => {
+        ds.data = [];
+      })
+      this.predChart.chart.update()
+    }
+    this.predictionsApi.getPredictions(selectedDate)
+  }
+}
diff --git a/web/frontend/src/app/views/dashboard/dashboard.module.ts b/web/frontend/src/app/views/dashboard/dashboard.module.ts
new file mode 100644
index 0000000000000000000000000000000000000000..c71465a1404ac9cf652343c7adc5995ade2e5e41
--- /dev/null
+++ b/web/frontend/src/app/views/dashboard/dashboard.module.ts
@@ -0,0 +1,58 @@
+import { NgModule } from '@angular/core';
+import { CommonModule } from '@angular/common';
+import { ReactiveFormsModule } from '@angular/forms';
+import {MatNativeDateModule} from '@angular/material/core';
+import {
+  AvatarModule,
+  ButtonGroupModule,
+  ButtonModule,
+  CardModule,
+  FormModule,
+  GridModule,
+  NavModule,
+  ProgressModule,
+  TableModule,
+  TabsModule
+} from '@coreui/angular';
+import { IconModule } from '@coreui/icons-angular';
+import { ChartjsModule } from '@coreui/angular-chartjs';
+import { MatDatepickerModule } from '@angular/material/datepicker';
+
+import { DashboardRoutingModule } from './dashboard-routing.module';
+import { DashboardComponent } from './dashboard.component';
+
+import { MatFormFieldModule } from '@angular/material/form-field';
+import { MatInputModule } from '@angular/material/input';
+import { MatCardModule } from '@angular/material/card';
+import { ScrollingModule } from '@angular/cdk/scrolling';
+import { HorizontalDateScroller } from "../virtual-scroll/scroller"
+
+@NgModule({
+  imports: [
+    DashboardRoutingModule,
+    CardModule,
+    NavModule,
+    MatDatepickerModule,
+    MatFormFieldModule,
+    MatInputModule,
+    MatNativeDateModule,
+    MatCardModule,
+    ScrollingModule,
+    IconModule,
+    TabsModule,
+    CommonModule,
+    GridModule,
+    ProgressModule,
+    ReactiveFormsModule,
+    ButtonModule,
+    FormModule,
+    ButtonModule,
+    ButtonGroupModule,
+    ChartjsModule,
+    AvatarModule,
+    TableModule,
+  ],
+  declarations: [DashboardComponent, HorizontalDateScroller],
+})
+export class DashboardModule {
+}
diff --git a/web/frontend/src/app/views/virtual-scroll/scroller.css b/web/frontend/src/app/views/virtual-scroll/scroller.css
new file mode 100644
index 0000000000000000000000000000000000000000..899eff2694b57e9625f6d6b79c325a643b38e85d
--- /dev/null
+++ b/web/frontend/src/app/views/virtual-scroll/scroller.css
@@ -0,0 +1,93 @@
+.mobileDatePicker{
+  display:flex;
+}
+
+.cdk-virtual-scroll-data-source-example {
+  display: flex;
+  height: 90px;
+  width: 100%;
+  max-width: 100%;
+}
+
+
+.cdk-virtual-scroll-data-source-example .example-viewport {
+  height: 100%;
+  width: 100%;
+  padding-left: 0.5em;
+  overflow-y:hidden;
+  box-shadow: 0px 0px 8px rgba(0, 0, 0, 0.25);
+}
+
+.cdk-virtual-scroll-data-source-example .example-viewport .cdk-virtual-scroll-content-wrapper {
+  /* padding-left: 1.5em; */
+  display: flex;
+  flex-direction: row;
+}
+
+.cdk-virtual-scroll-viewport .example-viewport .cdk-virtual-scrollable .cdk-virtual-scroll-orientation-horizontal {
+  padding-left: 1em;
+}
+
+.cdk-virtual-scroll-data-source-example .example-item {
+  height: 100%;
+  /* writing-mode: horizontal-tb; */
+  margin-right: .2rem;
+}
+
+.item{
+  margin:.5rem 0.2rem;
+  cursor:pointer;
+  display:flex;
+  flex-direction:column;
+  justify-content: center;
+  align-items:center;
+  font-family: Lato;
+  font-style: normal;
+  font-weight: bold;
+  font-size: 18px;
+  line-height: 21px;
+  width:60px;
+  height:60px;
+  /* border-radius:100%; */
+  border-radius:10px;
+  border: 1px solid rgba(204, 204, 204, 0.215);
+  color:#00000043;
+  transition: box-shadow .3s;
+}
+
+.item:hover {
+  box-shadow: 0 0 11px rgba(33,33,33,.2); 
+}
+
+.is-active{
+ background: #3c4b64; 
+ color: white !important;
+}
+
+.weekName {
+  font-size:1rem;
+  color:#3c4b64;
+}
+
+.is-active .weekName{
+  font-size:1rem;
+  color:#ffffff;
+}
+
+.month{
+  padding: 0 1.5rem;
+  background: #3c4b64;
+  border-radius: 5px 0px 0px 5px;
+  color:white;
+  display:flex;
+  flex-direction:column;
+  justify-content:center;
+  align-items:center;
+  width:30px !important;
+  box-shadow: 0px 0px 8px rgba(0, 0, 0, 0.25);
+}
+
+.monthIcon{
+  margin-top:.2rem;
+}
+
diff --git a/web/frontend/src/app/views/virtual-scroll/scroller.html b/web/frontend/src/app/views/virtual-scroll/scroller.html
new file mode 100644
index 0000000000000000000000000000000000000000..366e4812be40c6d2b416073356d25073a6e59577
--- /dev/null
+++ b/web/frontend/src/app/views/virtual-scroll/scroller.html
@@ -0,0 +1,17 @@
+<div class="mobileDatePicker">
+  <div class="month">
+    {{currentMonth | uppercase }}
+    <span class="material-icons monthIcon">calendar_today</span>
+  </div>
+  <div class="cdk-virtual-scroll-data-source-example">
+    <cdk-virtual-scroll-viewport orientation="horizontal" itemSize="60" minBufferPx="400" maxBufferPx="1000"  class="example-viewport" (scrolledIndexChange)="changeMonth($event)">
+      <div *cdkVirtualFor="let item of items" class="example-item">
+        <div class="item" (click)="select(item)" [ngClass]="{'is-active':item===selectedItem}">
+          <span class="weekName">{{returnWeekDay(item)}}</span>
+          <span class="weekName"> {{item | date:'d'}}</span>
+        </div>
+      </div>
+    </cdk-virtual-scroll-viewport>
+  </div>
+<div>
+
diff --git a/web/frontend/src/app/views/virtual-scroll/scroller.ts b/web/frontend/src/app/views/virtual-scroll/scroller.ts
new file mode 100644
index 0000000000000000000000000000000000000000..1ca7c3c98c199f369a2391afd7a502b7f5134b03
--- /dev/null
+++ b/web/frontend/src/app/views/virtual-scroll/scroller.ts
@@ -0,0 +1,83 @@
+import { CdkVirtualScrollViewport } from "@angular/cdk/scrolling";
+import {
+    ChangeDetectionStrategy,
+    Component,
+    EventEmitter,
+    Input,
+    Output,
+    ViewChild,
+    ViewEncapsulation
+  } from "@angular/core";
+  import * as moment from "moment";
+  
+  /** @title DatePicker by Horizontal virtual scroll */
+  @Component({
+    selector: "horizontal-date-scroller",
+    styleUrls: ["scroller.css"],
+    templateUrl: "scroller.html",
+    encapsulation: ViewEncapsulation.None,
+    changeDetection: ChangeDetectionStrategy.OnPush
+  })
+  
+  export class HorizontalDateScroller {
+    items: any[] = [];
+    currentDate = new Date();
+    currentMonth = "";
+    stopDate = new Date();
+    selectedItem = null;
+
+    @Input() fromDate!: moment.Moment;
+    @Input() toDate!: moment.Moment;
+
+    @Output() dateChanged: EventEmitter<string> = new EventEmitter();
+    
+    @ViewChild(CdkVirtualScrollViewport) viewPort!: CdkVirtualScrollViewport;
+ 
+    ngOnInit() {
+      // Creating an array with specified date range
+
+      this.items = this.getDates(this.fromDate, this.toDate);
+      const [lastItem] = this.items.slice(-1)
+      this.select(lastItem);
+      setTimeout (() => {
+        this.viewPort.scrollToIndex(this.items.indexOf(lastItem))
+     }, 1000);
+    }
+  
+
+    // Common method to create an array of dates
+    getDates(fromDate: moment.Moment, toDate: moment.Moment) {
+      let dateArray: string[] = [];
+      // let endDate = moment();
+      let currentDate = fromDate;
+      while (currentDate <= toDate) {
+        dateArray.push(currentDate.format("YYYY-MM-DD"));
+        currentDate = moment(currentDate).add(1, "days");
+      }
+      return dateArray;
+    }
+  
+    // Get the selected Date
+    select(item: any) {
+      this.selectedItem = item;
+      this.dateChanged.emit(moment(this.selectedItem).format("YYYY-MM-DD"))
+    }
+  
+    // Method for changing Month
+    changeMonth(e: any) {
+      const offset = this.viewPort.measureScrollOffset("end")    
+      const renderedRange = this.viewPort.getRenderedRange();
+      const renderedSize = this.viewPort.measureRangeSize(renderedRange)/(renderedRange.end - renderedRange.start)
+      const totalSize = this.items.length * renderedSize;
+      let indexNumer = Math.floor(((totalSize - offset)/totalSize) * this.items.length) - 1;
+      this.currentDate = this.items[indexNumer];
+      this.currentMonth = new Date(this.currentDate).toLocaleString("default", {month: "short"});
+    }
+  
+    // Method to get the current weekday of the date showon
+    returnWeekDay(item: any) {
+      return moment(item, "YYYY-MM-DD").format("dddd").substring(0,3);
+      // return new Date(item).toLocaleDateString("default", { weekday: "short" });
+    }
+  }
+  
\ No newline at end of file
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diff --git a/web/frontend/src/assets/img/brand/coreui-angular.svg b/web/frontend/src/assets/img/brand/coreui-angular.svg
new file mode 100644
index 0000000000000000000000000000000000000000..87dce30c0a8eb5259a0a35cf5e8450ecebb27de8
--- /dev/null
+++ b/web/frontend/src/assets/img/brand/coreui-angular.svg
@@ -0,0 +1,39 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 608 134">
+  <defs>
+    <style>
+      .cls-1 {
+        fill: #cf2f4c;
+      }
+
+      .cls-2 {
+        fill: #fff;
+      }
+    </style>
+  </defs>
+  <title>coreui angular</title>
+  <g id="Warstwa_1" data-name="Warstwa 1">
+    <g>
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+      <path class="cls-1" d="M246.9222,92.9944a12.1584,12.1584,0,0,0,7.1842-11.0771V78.2161A12.1494,12.1494,0,0,0,241.957,66.0667H225a1,1,0,0,0-1,1v44a1,1,0,0,0,1,1h6a1,1,0,0,0,1-1v-17h6.6215l7.9154,17.4138a1,1,0,0,0,.91.5862h6.5911a1,1,0,0,0,.91-1.4138Zm-.8158-11.0771a4.1538,4.1538,0,0,1-4.1489,4.1494h-9.8511v-12h9.8511a4.1538,4.1538,0,0,1,4.1489,4.1494Z"/>
+      <path class="cls-1" d="M289,66.0667H263a1,1,0,0,0-1,1v44a1,1,0,0,0,1,1h26a1,1,0,0,0,1-1v-6a1,1,0,0,0-1-1H270v-12h13a1,1,0,0,0,1-1v-6a1,1,0,0,0-1-1H270v-10h19a1,1,0,0,0,1-1v-6A1,1,0,0,0,289,66.0667Z"/>
+      <path class="cls-1" d="M327,66.0667h-6a1,1,0,0,0-1,1V89.7142a7.0066,7.0066,0,1,1-14,0V67.0667a1,1,0,0,0-1-1h-6a1,1,0,0,0-1,1V89.7142a15.0031,15.0031,0,1,0,30,0V67.0667A1,1,0,0,0,327,66.0667Z"/>
+      <rect class="cls-1" x="336" y="66.0667" width="8" height="38" rx="1"/>
+    </g>
+  </g>
+</svg>
diff --git a/web/frontend/src/assets/img/brand/coreui-signet-white.svg b/web/frontend/src/assets/img/brand/coreui-signet-white.svg
new file mode 100644
index 0000000000000000000000000000000000000000..ee9700dc778559fea80e0d653a0b209a005129f0
--- /dev/null
+++ b/web/frontend/src/assets/img/brand/coreui-signet-white.svg
@@ -0,0 +1,16 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 160 160">
+  <defs>
+    <style>
+      .cls-1 {
+        fill: #fff;
+      }
+    </style>
+  </defs>
+  <title>signet_white</title>
+  <g id="Warstwa_1" data-name="Warstwa 1">
+    <g>
+      <path class="cls-1" d="M125,47.091,86,24.5743a12,12,0,0,0-12,0L35,47.091a12.0336,12.0336,0,0,0-6,10.3923v45.0334a12.0335,12.0335,0,0,0,6,10.3923l39,22.5166a11.9993,11.9993,0,0,0,12,0l39-22.5166a12.0335,12.0335,0,0,0,6-10.3923V57.4833A12.0336,12.0336,0,0,0,125,47.091Zm-2,55.4257a4,4,0,0,1-2,3.464L82,128.4974a4,4,0,0,1-4,0L39,105.9807a4,4,0,0,1-2-3.464V57.4833a4,4,0,0,1,2-3.4641L78,31.5025a4,4,0,0,1,4,0l39,22.5167a4,4,0,0,1,2,3.4641Z"/>
+      <path class="cls-1" d="M103.0216,93.0379h-2.866a4,4,0,0,0-1.9246.4935L80.95,103.0167,61,91.4981V68.5206L80.95,57.002l17.2894,9.455a4,4,0,0,0,1.9192.4905h2.8632a2,2,0,0,0,2-2V62.2357a2,2,0,0,0-1.04-1.7547L84.793,49.9854a8.0391,8.0391,0,0,0-7.8428.09L57,61.5929A8.0243,8.0243,0,0,0,53,68.5216v22.976a8,8,0,0,0,4,6.9283l19.95,11.5185a8.0422,8.0422,0,0,0,7.8433.0879l19.19-10.5311a2,2,0,0,0,1.0378-1.7534v-2.71A2,2,0,0,0,103.0216,93.0379Z"/>
+    </g>
+  </g>
+</svg>
diff --git a/web/frontend/src/assets/img/brand/coreui-signet.svg b/web/frontend/src/assets/img/brand/coreui-signet.svg
new file mode 100644
index 0000000000000000000000000000000000000000..278b33c379866b41cd9345e09b6aa9fc645286eb
--- /dev/null
+++ b/web/frontend/src/assets/img/brand/coreui-signet.svg
@@ -0,0 +1,16 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 160 160">
+  <defs>
+    <style>
+      .cls-1 {
+        fill: #3c4b64;
+      }
+    </style>
+  </defs>
+  <title>signet</title>
+  <g id="Warstwa_1" data-name="Warstwa 1">
+    <g>
+      <path class="cls-1" d="M125,47.091,86,24.5743a12,12,0,0,0-12,0L35,47.091a12.0336,12.0336,0,0,0-6,10.3923v45.0334a12.0335,12.0335,0,0,0,6,10.3923l39,22.5166a11.9993,11.9993,0,0,0,12,0l39-22.5166a12.0335,12.0335,0,0,0,6-10.3923V57.4833A12.0336,12.0336,0,0,0,125,47.091Zm-2,55.4257a4,4,0,0,1-2,3.464L82,128.4974a4,4,0,0,1-4,0L39,105.9807a4,4,0,0,1-2-3.464V57.4833a4,4,0,0,1,2-3.4641L78,31.5025a4,4,0,0,1,4,0l39,22.5167a4,4,0,0,1,2,3.4641Z"/>
+      <path class="cls-1" d="M103.0216,93.0379h-2.866a4,4,0,0,0-1.9246.4935L80.95,103.0167,61,91.4981V68.5206L80.95,57.002l17.2894,9.455a4,4,0,0,0,1.9192.4905h2.8632a2,2,0,0,0,2-2V62.2357a2,2,0,0,0-1.04-1.7547L84.793,49.9854a8.0391,8.0391,0,0,0-7.8428.09L57,61.5929A8.0243,8.0243,0,0,0,53,68.5216v22.976a8,8,0,0,0,4,6.9283l19.95,11.5185a8.0422,8.0422,0,0,0,7.8433.0879l19.19-10.5311a2,2,0,0,0,1.0378-1.7534v-2.71A2,2,0,0,0,103.0216,93.0379Z"/>
+    </g>
+  </g>
+</svg>
diff --git a/web/frontend/src/assets/img/brand/forecaster.svg b/web/frontend/src/assets/img/brand/forecaster.svg
new file mode 100644
index 0000000000000000000000000000000000000000..4e2379d92ada34fe8d555e054591d516a10c6751
--- /dev/null
+++ b/web/frontend/src/assets/img/brand/forecaster.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xml:space="preserve" width="2048" height="2048" style="shape-rendering:geometricPrecision;text-rendering:geometricPrecision;image-rendering:optimizeQuality;fill-rule:evenodd;clip-rule:evenodd"><defs><style>.fil0{fill:none}.fil2{fill:#fffffe;fill-rule:nonzero}</style></defs><g id="Layer_x0020_1"><path class="fil0" d="M0 0h2048v2048H0z"/><path class="fil0" d="M0 0h2048v2048H0z"/><rect width="2048" height="2048" rx="256.001" ry="256.001" style="fill:#ffca28"/><path class="fil2" d="M1401.21 941.302c67.13 0 129.027 21.985 178.433 58.936 47.557 35.565 83.549 85.048 101.552 142.217 32.643 24.149 59.693 55.123 78.9 90.762 20.385 37.82 31.911 80.754 31.911 126.213 0 73.877-30.39 142.436-79.762 192.292-48.558 49.032-115.606 79.932-190.28 79.952-201.933.065-474.79-.97-660.158.246-10.406.07-20.94.14-30.391-.514-56.484-3.921-107.027-28.8-143.39-66.661-36.4-37.9-58.665-88.69-58.665-144.49 0-58.59 24.512-111.612 64.138-149.967 39.429-38.165 93.73-61.77 153.555-61.77 1.435 0 2.882.014 4.338.045 10.806-41.112 33.963-77.39 65.408-104.882 38.612-33.758 89.619-54.313 145.42-54.313 22.341 0 43.974 3.32 64.395 9.473a218.684 218.684 0 0 1 29.57 11.309c23.59-34.985 54.92-64.617 91.63-86.605 44.711-26.781 97.274-42.242 153.397-42.242z"/><path d="M640.232 642.231c104.934 0 189.999 85.065 189.999 189.999 0 42.112-13.708 81.017-36.895 112.514a260.084 260.084 0 0 0-32.528-2.041c-62.256 0-122.255 22.217-169.153 63.219a251.695 251.695 0 0 0-8.34 7.63c-77.127-24.186-133.083-96.218-133.083-181.322 0-104.934 85.066-190 190-190z" style="fill:#fffffe"/><path class="fil2" d="M1106.03 864.653c68.362 0 131.398 22.389 181.71 60.019a300.492 300.492 0 0 1 4.088 3.114 328.328 328.328 0 0 0-60.458 28.306c-32.966 19.746-62.242 45.203-86.22 75.113a251.662 251.662 0 0 0-9.303-3.005 255.177 255.177 0 0 0-73.63-10.833c-61.273 0-120.324 21.867-166.481 62.222-30.383 26.564-53.794 60.07-67.91 97.648-58.611 4.4-114.018 28.855-156.585 70.057-47.07 45.56-73.882 107.337-73.882 172.96 0 53.931 18.131 105.315 50.818 147.285-32.157.094-62.837.229-91.459.417-10.596.07-21.324.141-30.95-.524-57.52-3.993-108.991-29.329-146.022-67.885-37.07-38.597-59.743-90.319-59.743-147.143 0-59.668 24.962-113.663 65.315-152.724 40.154-38.865 95.452-62.904 156.376-62.904 1.462 0 2.935.015 4.419.046 11.004-41.867 34.586-78.81 66.61-106.808 39.32-34.378 91.264-55.31 148.09-55.31a227.03 227.03 0 0 1 65.578 9.646 222.814 222.814 0 0 1 30.112 11.517c24.025-35.627 55.93-65.804 93.314-88.195 45.532-27.273 99.06-43.018 156.213-43.018zM608.546 607.924V447.998h64.001v159.926zM416.791 863.238h-159.79v-64.002h159.79zM504.963 1012.11 391.975 1125.1l-45.25-45.25 112.988-112.991zM459.713 695.615 346.725 582.628l45.25-45.251 112.988 112.988zM943.334 863.238h-79.026v-64.002h159.86v33.115a335.15 335.15 0 0 0-80.834 30.887zM934.444 582.628 821.407 695.666l-45.251-45.25 113.038-113.039z"/></g></svg>
diff --git a/web/frontend/src/assets/img/logo.png b/web/frontend/src/assets/img/logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..3977056d7ab74e81517d79e30eb4cd24c2f0b786
Binary files /dev/null and b/web/frontend/src/assets/img/logo.png differ
diff --git a/web/frontend/src/declarations.d.ts b/web/frontend/src/declarations.d.ts
new file mode 100644
index 0000000000000000000000000000000000000000..336effa4d1d64e29e3abd587135a4be4d0537e9a
--- /dev/null
+++ b/web/frontend/src/declarations.d.ts
@@ -0,0 +1,9 @@
+declare module '@coreui/utils/src';
+
+declare module '@coreui/chartjs/dist/js/coreui-chartjs.js';
+
+declare module '*.json' {
+  const value: any;
+  export default value;
+}
+
diff --git a/web/frontend/src/environments/environment.prod.ts b/web/frontend/src/environments/environment.prod.ts
new file mode 100644
index 0000000000000000000000000000000000000000..eaac761a2e3bf4fbea00498f47b82b7de9f3b5f1
--- /dev/null
+++ b/web/frontend/src/environments/environment.prod.ts
@@ -0,0 +1,4 @@
+export const environment = {
+  production: true,
+  apiUrl: 'http://3.235.0.237:81'
+};
diff --git a/web/frontend/src/environments/environment.ts b/web/frontend/src/environments/environment.ts
new file mode 100644
index 0000000000000000000000000000000000000000..fab028f8e9e8a9798e835115b980137cd1c15687
--- /dev/null
+++ b/web/frontend/src/environments/environment.ts
@@ -0,0 +1,17 @@
+// This file can be replaced during build by using the `fileReplacements` array.
+// `ng build` replaces `environment.ts` with `environment.prod.ts`.
+// The list of file replacements can be found in `angular.json`.
+
+export const environment = {
+  production: false,
+  apiUrl: "http://pred-backend:3001"
+};
+
+/*
+ * For easier debugging in development mode, you can import the following file
+ * to ignore zone related error stack frames such as `zone.run`, `zoneDelegate.invokeTask`.
+ *
+ * This import should be commented out in production mode because it will have a negative impact
+ * on performance if an error is thrown.
+ */
+// import 'zone.js/plugins/zone-error';  // Included with Angular CLI.
diff --git a/web/frontend/src/index.html b/web/frontend/src/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..d62ee7ec53ea8f7f16c29bb365b76cbd62678433
--- /dev/null
+++ b/web/frontend/src/index.html
@@ -0,0 +1,25 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="utf-8">
+  <base href="./">
+  <meta content="width=device-width, initial-scale=1, shrink-to-fit=no" name="viewport" />
+  <meta content="Temperature prediction for the next 24 hours for Ronald Reagan Washington National Airport" name="description" />
+  <meta content="Sushant Karki" name="author" />
+  <link href="assets/favicon.ico" rel="icon" type="image/x-icon">
+  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css">
+  
+  <title>Temperature prediction for the next 24 hours for Ronald Reagan Washington National Airport</title>
+  <link rel="preconnect" href="https://fonts.gstatic.com">
+  <link href="https://fonts.googleapis.com/css2?family=Roboto:wght@300;400;500&display=swap" rel="stylesheet">
+  <link href="https://fonts.googleapis.com/icon?family=Material+Icons" rel="stylesheet">
+</head>
+<body class="mat-typography">
+<noscript>You need to enable JavaScript to run this app.</noscript>
+
+<div class="fade show" style="text-align: center; padding-top: calc(100vh / 2); height: 100vh">
+  <i class="spinner-grow spinner-grow-sm"></i>
+  <span class="m-1">Loading...</span>
+</div>
+</body>
+</html>
diff --git a/web/frontend/src/main.ts b/web/frontend/src/main.ts
new file mode 100644
index 0000000000000000000000000000000000000000..c7b673cf44b388e9989fe908b78d7d73cd2e1409
--- /dev/null
+++ b/web/frontend/src/main.ts
@@ -0,0 +1,12 @@
+import { enableProdMode } from '@angular/core';
+import { platformBrowserDynamic } from '@angular/platform-browser-dynamic';
+
+import { AppModule } from './app/app.module';
+import { environment } from './environments/environment';
+
+if (environment.production) {
+  enableProdMode();
+}
+
+platformBrowserDynamic().bootstrapModule(AppModule)
+  .catch(err => console.error(err));
diff --git a/web/frontend/src/polyfills.ts b/web/frontend/src/polyfills.ts
new file mode 100644
index 0000000000000000000000000000000000000000..5813db6bd771271b8cb02e2d6373827d9d105ec1
--- /dev/null
+++ b/web/frontend/src/polyfills.ts
@@ -0,0 +1,58 @@
+/***************************************************************************************************
+ * Load `$localize` onto the global scope - used if i18n tags appear in Angular templates.
+ */
+import '@angular/localize/init';
+
+/**
+ * This file includes polyfills needed by Angular and is loaded before the app.
+ * You can add your own extra polyfills to this file.
+ *
+ * This file is divided into 2 sections:
+ *   1. Browser polyfills. These are applied before loading ZoneJS and are sorted by browsers.
+ *   2. Application imports. Files imported after ZoneJS that should be loaded before your main
+ *      file.
+ *
+ * The current setup is for so-called "evergreen" browsers; the last versions of browsers that
+ * automatically update themselves. This includes recent versions of Safari, Chrome (including
+ * Opera), Edge on the desktop, and iOS and Chrome on mobile.
+ *
+ * Learn more in https://angular.io/guide/browser-support
+ */
+
+/***************************************************************************************************
+ * BROWSER POLYFILLS
+ */
+
+/**
+ * By default, zone.js will patch all possible macroTask and DomEvents
+ * user can disable parts of macroTask/DomEvents patch by setting following flags
+ * because those flags need to be set before `zone.js` being loaded, and webpack
+ * will put import in the top of bundle, so user need to create a separate file
+ * in this directory (for example: zone-flags.ts), and put the following flags
+ * into that file, and then add the following code before importing zone.js.
+ * import './zone-flags';
+ *
+ * The flags allowed in zone-flags.ts are listed here.
+ *
+ * The following flags will work for all browsers.
+ *
+ * (window as any).__Zone_disable_requestAnimationFrame = true; // disable patch requestAnimationFrame
+ * (window as any).__Zone_disable_on_property = true; // disable patch onProperty such as onclick
+ * (window as any).__zone_symbol__UNPATCHED_EVENTS = ['scroll', 'mousemove']; // disable patch specified eventNames
+ *
+ *  in IE/Edge developer tools, the addEventListener will also be wrapped by zone.js
+ *  with the following flag, it will bypass `zone.js` patch for IE/Edge
+ *
+ *  (window as any).__Zone_enable_cross_context_check = true;
+ *
+ */
+
+/***************************************************************************************************
+ * Zone JS is required by default for Angular itself.
+ */
+import 'zone.js';  // Included with Angular CLI.
+
+
+/***************************************************************************************************
+ * APPLICATION IMPORTS
+ */
diff --git a/web/frontend/src/scss/_charts.scss b/web/frontend/src/scss/_charts.scss
new file mode 100644
index 0000000000000000000000000000000000000000..c55c2617e6d7bcaa3162456d06e7c23984745d98
--- /dev/null
+++ b/web/frontend/src/scss/_charts.scss
@@ -0,0 +1,7 @@
+// temp fix .chartjs-tooltip-body-item
+
+.chartjs-tooltip-body-item > td {
+  padding-bottom: 0 !important;
+  padding-top: 0 !important;
+  font-size: smaller;
+}
diff --git a/web/frontend/src/scss/_custom.scss b/web/frontend/src/scss/_custom.scss
new file mode 100644
index 0000000000000000000000000000000000000000..5a4593c0b3650da4b05a284e27d5807878bb935a
--- /dev/null
+++ b/web/frontend/src/scss/_custom.scss
@@ -0,0 +1,2 @@
+// Here you can add other styles
+
diff --git a/web/frontend/src/scss/_examples.scss b/web/frontend/src/scss/_examples.scss
new file mode 100644
index 0000000000000000000000000000000000000000..a7c86b11c89cb5b84876c203afbfefff7a99df8c
--- /dev/null
+++ b/web/frontend/src/scss/_examples.scss
@@ -0,0 +1,122 @@
+.callout-info {
+  background-color: $light-50 !important;
+
+  @at-root .dark-theme & {
+    background-color: rgba(255, 255, 255, .1) !important;
+  }
+}
+
+.example {
+  &:not(:first-child) {
+    margin-top: 1.5rem;
+  }
+
+  .tab-content {
+    background-color: $light-50 !important;
+
+    @at-root .dark-theme & {
+      background-color: rgba(255, 255, 255, .1) !important;
+    }
+  }
+
+  code[class*="language-"],
+  pre[class*="language-"] {
+    font-size: .875rem !important;
+  }
+
+  :not(pre) > code[class*="language-"],
+  pre[class*="language-"] {
+    background: transparent;
+  }
+
+  & + p,
+  & + h2,
+  & + h3,
+  & + h4,
+  & + h5,
+  & + h6 {
+    margin-top: 1.5rem;
+  }
+
+  // Components examples
+  .preview,
+  .preview .col {
+    + p {
+      margin-top: 2rem;
+    }
+
+    > .form-control {
+      + .form-control {
+        margin-top: .5rem;
+      }
+    }
+
+    > .nav + .nav,
+    > .alert + .alert,
+    > .navbar + .navbar,
+    > .progress + .progress {
+      margin-top: 1rem;
+    }
+
+    > .dropdown-menu {
+      position: static;
+      display: block;
+    }
+
+    > :last-child {
+      margin-bottom: 0;
+    }
+
+    // Images
+    > svg + svg,
+    > img + img {
+      margin-left: .5rem;
+    }
+
+    // Buttons
+    > .btn,
+    > .btn-group {
+      margin: .25rem .125rem;
+    }
+    > .btn-toolbar + .btn-toolbar {
+      margin-top: .5rem;
+    }
+
+    // List groups
+    > .list-group {
+      max-width: 400px;
+    }
+
+    > [class*="list-group-horizontal"] {
+      max-width: 100%;
+    }
+
+    // Navbars
+    .fixed-top,
+    .sticky-top {
+      position: static;
+      margin: -1rem -1rem 1rem;
+    }
+
+    .fixed-bottom {
+      position: static;
+      margin: 1rem -1rem -1rem;
+    }
+
+    @include media-breakpoint-up(sm) {
+      .fixed-top,
+      .sticky-top {
+        margin: -1.5rem -1.5rem 1rem;
+      }
+      .fixed-bottom {
+        margin: 1rem -1.5rem -1.5rem;
+      }
+    }
+
+    // Pagination
+    .pagination {
+      margin-top: .5rem;
+      margin-bottom: .5rem;
+    }
+  }
+}
diff --git a/web/frontend/src/scss/_fixes.scss b/web/frontend/src/scss/_fixes.scss
new file mode 100644
index 0000000000000000000000000000000000000000..7074618ed6377d76263ee283c519ee30d19e061f
--- /dev/null
+++ b/web/frontend/src/scss/_fixes.scss
@@ -0,0 +1,12 @@
+// Place for temp fixes
+
+.form-floating > .form-select {
+  padding-top: 1.625rem !important;
+  padding-bottom: 0.625rem !important;
+}
+
+// temp fix for scrollbar styling (wip)
+@import "scrollbar";
+
+// temp fix .chartjs-tooltip-body-item padding
+@import "charts";
diff --git a/web/frontend/src/scss/_layout.scss b/web/frontend/src/scss/_layout.scss
new file mode 100644
index 0000000000000000000000000000000000000000..dffcb950a7f29ccebeaae0f84cec2f5e0c726f02
--- /dev/null
+++ b/web/frontend/src/scss/_layout.scss
@@ -0,0 +1,7 @@
+.wrapper {
+  width: 100%;
+  @include ltr-rtl("padding-left", var(--cui-sidebar-occupy-start, 0));
+  @include ltr-rtl("padding-right", var(--cui-sidebar-occupy-end, 0));
+  will-change: auto;
+  @include transition(padding .15s);
+}
diff --git a/web/frontend/src/scss/_scrollbar.scss b/web/frontend/src/scss/_scrollbar.scss
new file mode 100644
index 0000000000000000000000000000000000000000..b094f1acf7b67b310acb3a6f52862bec2c6a17a3
--- /dev/null
+++ b/web/frontend/src/scss/_scrollbar.scss
@@ -0,0 +1,36 @@
+// scrollbar tinkering
+
+@supports (scrollbar-width: thin) {
+  * {
+    scrollbar-width: thin;
+    scrollbar-color: #444 transparent;
+  }
+}
+
+::-webkit-scrollbar {
+  width: .9em;
+}
+
+::-webkit-scrollbar-track {
+  background: var(--cui-body-bg, #fff);
+  border-radius: 100vw;
+  margin-block: .2em;
+}
+
+::-webkit-scrollbar-thumb {
+  background: #999;
+  border: .2em solid var(--cui-body-bg, #fff);
+  border-radius: 100vw;
+}
+
+::-webkit-scrollbar-thumb:hover {
+  background: #444;
+}
+
+.dark-theme::-webkit-scrollbar-thumb {
+  background: #444;
+}
+
+.dark-theme::-webkit-scrollbar-thumb:hover {
+  background: #999;
+}
diff --git a/web/frontend/src/scss/_variables.scss b/web/frontend/src/scss/_variables.scss
new file mode 100644
index 0000000000000000000000000000000000000000..65c15b996f53c3c4b717737c965b950257f5c2d2
--- /dev/null
+++ b/web/frontend/src/scss/_variables.scss
@@ -0,0 +1,1666 @@
+// Variable overrides
+// Variables
+//
+// Variables should follow the `$component-state-property-size` formula for
+// consistent naming. Ex: $nav-link-disabled-color and $modal-content-box-shadow-xs.
+
+// Color system
+
+// $white:     #fff;
+// $gray-base: #3c4b64;
+// $gray-100:  #ebedef;
+// $gray-200:  #d8dbe0;
+// $gray-300:  #c4c9d0;
+// $gray-400:  #b1b7c1;
+// $gray-500:  #9da5b1;
+// $gray-600:  #8a93a2;
+// $gray-700:  #768192;
+// $gray-800:  #636f83;
+// $gray-900:  #4f5d73;
+// $black:     #000015;
+
+// fusv-disable
+// $grays: (
+//   "100": $gray-100,
+//   "200": $gray-200,
+//   "300": $gray-300,
+//   "400": $gray-400,
+//   "500": $gray-500,
+//   "600": $gray-600,
+//   "700": $gray-700,
+//   "800": $gray-800,
+//   "900": $gray-900
+// );
+// fusv-enable
+
+// $blue:    #0d6efd;
+// $indigo:  #6610f2;
+// $purple:  #6f42c1;
+// $pink:    #d63384;
+// $red:     #dc3545;
+// $orange:  #fd7e14;
+// $yellow:  #ffc107;
+// $green:   #198754;
+// $teal:    #20c997;
+// $cyan:    #0dcaf0;
+
+// scss-docs-start colors-map
+// $colors: (
+//   "blue":       $blue,
+//   "indigo":     $indigo,
+//   "purple":     $purple,
+//   "pink":       $pink,
+//   "red":        $red,
+//   "orange":     $orange,
+//   "yellow":     $yellow,
+//   "green":      $green,
+//   "teal":       $teal,
+//   "cyan":       $cyan,
+//   "white":      $white,
+//   "gray":       $gray-600,
+//   "gray-dark":  $gray-800
+// );
+// scss-docs-end colors-map
+
+// $primary-dark:  #1f1498;
+// $primary:       #321fdb;
+// $primary-50:    #988fed;
+// $primary-25:    #ccc7f6;
+
+// $secondary-dark:  #212233;
+// $secondary:       #9da5b1;
+// $secondary-50:    #9da5b1;
+// $secondary-25:    #ced2d8;
+
+// $success-dark:  #1b9e3e;
+// $success:       #2eb85c;
+// $success-50:    #96dbad;
+// $success-25:    #cbedd6;
+
+// $info-dark:  #2982cc;
+// $info:       #39f;
+// $info-50:    #80c6ff;
+// $info-25:    #c0e6ff;
+
+// $warning-dark:  #f6960b;
+// $warning:       #f9b115;
+// $warning-50:    #fcd88a;
+// $warning-25:    #feecc5;
+
+// $danger-dark:  #d93737;
+// $danger:       #e55353;
+// $danger-50:    #f2a9a9;
+// $danger-25:    #f9d4d4;
+
+// $light-dark:     $gray-100;
+// $light:          $gray-100;
+// $light-50:       shift-color($light, -70);
+// $light-25:       shift-color($light, -80);
+
+// $dark-dark:      $gray-900;
+// $dark:           $gray-900;
+// $dark-50:        shift-color($dark, -70);
+// $dark-25:        shift-color($dark, -80);
+
+// scss-docs-start theme-colors-map
+// $theme-colors: (
+//   "primary":    $primary,
+//   "secondary":  $secondary,
+//   "success":    $success,
+//   "info":       $info,
+//   "warning":    $warning,
+//   "danger":     $danger,
+//   "light":      $light,
+//   "dark":       $dark
+// );
+// scss-docs-end theme-colors-map
+
+// Text colors
+// $high-emphasis-dark:    rgba(shift-color($gray-base, +26), .87);
+// $medium-emphasis-dark:  rgba(shift-color($gray-base, +26), .6);
+// $disabled-dark:         rgba(shift-color($gray-base, +26), .38);
+
+// $high-emphasis-light:    rgba($white, .87);
+// $medium-emphasis-light:  rgba($white, .6);
+// $disabled-light:         rgba($white, .38);
+
+// The contrast ratio to reach against white, to determine if color changes from "light" to "dark". Acceptable values for WCAG 2.0 are 3, 4.5 and 7.
+// See https://www.w3.org/TR/WCAG20/#visual-audio-contrast-contrast
+// $min-contrast-ratio:   4.5;
+
+// Customize the light and dark text colors for use in our color contrast function.
+// $color-contrast-dark:      $high-emphasis-dark;
+// $color-contrast-light:     $high-emphasis-light;
+
+// fusv-disable
+// $blue-100: tint-color($blue, 80%);
+// $blue-200: tint-color($blue, 60%);
+// $blue-300: tint-color($blue, 40%);
+// $blue-400: tint-color($blue, 20%);
+// $blue-500: $blue;
+// $blue-600: shade-color($blue, 20%);
+// $blue-700: shade-color($blue, 40%);
+// $blue-800: shade-color($blue, 60%);
+// $blue-900: shade-color($blue, 80%);
+
+// $indigo-100: tint-color($indigo, 80%);
+// $indigo-200: tint-color($indigo, 60%);
+// $indigo-300: tint-color($indigo, 40%);
+// $indigo-400: tint-color($indigo, 20%);
+// $indigo-500: $indigo;
+// $indigo-600: shade-color($indigo, 20%);
+// $indigo-700: shade-color($indigo, 40%);
+// $indigo-800: shade-color($indigo, 60%);
+// $indigo-900: shade-color($indigo, 80%);
+
+// $purple-100: tint-color($purple, 80%);
+// $purple-200: tint-color($purple, 60%);
+// $purple-300: tint-color($purple, 40%);
+// $purple-400: tint-color($purple, 20%);
+// $purple-500: $purple;
+// $purple-600: shade-color($purple, 20%);
+// $purple-700: shade-color($purple, 40%);
+// $purple-800: shade-color($purple, 60%);
+// $purple-900: shade-color($purple, 80%);
+
+// $pink-100: tint-color($pink, 80%);
+// $pink-200: tint-color($pink, 60%);
+// $pink-300: tint-color($pink, 40%);
+// $pink-400: tint-color($pink, 20%);
+// $pink-500: $pink;
+// $pink-600: shade-color($pink, 20%);
+// $pink-700: shade-color($pink, 40%);
+// $pink-800: shade-color($pink, 60%);
+// $pink-900: shade-color($pink, 80%);
+
+// $red-100: tint-color($red, 80%);
+// $red-200: tint-color($red, 60%);
+// $red-300: tint-color($red, 40%);
+// $red-400: tint-color($red, 20%);
+// $red-500: $red;
+// $red-600: shade-color($red, 20%);
+// $red-700: shade-color($red, 40%);
+// $red-800: shade-color($red, 60%);
+// $red-900: shade-color($red, 80%);
+
+// $orange-100: tint-color($orange, 80%);
+// $orange-200: tint-color($orange, 60%);
+// $orange-300: tint-color($orange, 40%);
+// $orange-400: tint-color($orange, 20%);
+// $orange-500: $orange;
+// $orange-600: shade-color($orange, 20%);
+// $orange-700: shade-color($orange, 40%);
+// $orange-800: shade-color($orange, 60%);
+// $orange-900: shade-color($orange, 80%);
+
+// $yellow-100: tint-color($yellow, 80%);
+// $yellow-200: tint-color($yellow, 60%);
+// $yellow-300: tint-color($yellow, 40%);
+// $yellow-400: tint-color($yellow, 20%);
+// $yellow-500: $yellow;
+// $yellow-600: shade-color($yellow, 20%);
+// $yellow-700: shade-color($yellow, 40%);
+// $yellow-800: shade-color($yellow, 60%);
+// $yellow-900: shade-color($yellow, 80%);
+
+// $green-100: tint-color($green, 80%);
+// $green-200: tint-color($green, 60%);
+// $green-300: tint-color($green, 40%);
+// $green-400: tint-color($green, 20%);
+// $green-500: $green;
+// $green-600: shade-color($green, 20%);
+// $green-700: shade-color($green, 40%);
+// $green-800: shade-color($green, 60%);
+// $green-900: shade-color($green, 80%);
+
+// $teal-100: tint-color($teal, 80%);
+// $teal-200: tint-color($teal, 60%);
+// $teal-300: tint-color($teal, 40%);
+// $teal-400: tint-color($teal, 20%);
+// $teal-500: $teal;
+// $teal-600: shade-color($teal, 20%);
+// $teal-700: shade-color($teal, 40%);
+// $teal-800: shade-color($teal, 60%);
+// $teal-900: shade-color($teal, 80%);
+
+// $cyan-100: tint-color($cyan, 80%);
+// $cyan-200: tint-color($cyan, 60%);
+// $cyan-300: tint-color($cyan, 40%);
+// $cyan-400: tint-color($cyan, 20%);
+// $cyan-500: $cyan;
+// $cyan-600: shade-color($cyan, 20%);
+// $cyan-700: shade-color($cyan, 40%);
+// $cyan-800: shade-color($cyan, 60%);
+// $cyan-900: shade-color($cyan, 80%);
+// fusv-enable
+
+// Characters which are escaped by the escape-svg function
+// $escaped-characters: (
+//   ("<", "%3c"),
+//   (">", "%3e"),
+//   ("#", "%23"),
+//   ("(", "%28"),
+//   (")", "%29"),
+// );
+
+// Options
+//
+// Quickly modify global styling by enabling or disabling optional features.
+
+// $enable-caret:                true;
+// $enable-rounded:              true;
+// $enable-shadows:              false;
+// $enable-gradients:            false;
+// $enable-transitions:          true;
+// $enable-reduced-motion:       true;
+// $enable-smooth-scroll:        true;
+// $enable-grid-classes:         true;
+// $enable-button-pointers:      true;
+// $enable-rfs:                  true;
+// $enable-validation-icons:     true;
+// $enable-negative-margins:     false;
+// $enable-deprecation-messages: true;
+// $enable-important-utilities:  true;
+
+// Set mobile breakpoint
+
+// $mobile-breakpoint:           lg;
+
+// Prefix for :root CSS variables
+
+// $variable-prefix:             cui-;
+
+// Gradient
+//
+// The gradient which is added to components if `$enable-gradients` is `true`
+// This gradient is also added to elements with `.bg-gradient`
+// $gradient: linear-gradient(180deg, rgba($white, .15), rgba($white, 0));
+
+// Spacing
+//
+// Control the default styling of most Bootstrap elements by modifying these
+// variables. Mostly focused on spacing.
+// You can add more entries to the $spacers map, should you need more variation.
+
+// $spacer: 1rem;
+// $spacers: (
+//   0: 0,
+//   1: $spacer / 4,
+//   2: $spacer / 2,
+//   3: $spacer,
+//   4: $spacer * 1.5,
+//   5: $spacer * 3,
+// );
+
+// $negative-spacers: if($enable-negative-margins, negativify-map($spacers), null);
+
+// Position
+//
+// Define the edge positioning anchors of the position utilities.
+
+// $position-values: (
+//   0: 0,
+//   50: 50%,
+//   100: 100%
+// );
+
+
+// Body
+//
+// Settings for the `<body>` element.
+
+// $body-bg:                   $white;
+// $body-color:                $high-emphasis-dark;
+// $body-text-align:           null;
+
+
+// Links
+//
+// Style anchor elements.
+
+// $link-color:                              $primary;
+// $link-decoration:                         underline;
+// $link-shade-percentage:                   20%;
+// $link-hover-color:                        shift-color($link-color, $link-shade-percentage);
+// $link-hover-decoration:                   null;
+
+// $stretched-link-pseudo-element:           after;
+// $stretched-link-z-index:                  1;
+
+// Paragraphs
+//
+// Style p element.
+
+// $paragraph-margin-bottom:   1rem;
+
+
+// Grid breakpoints
+//
+// Define the minimum dimensions at which your layout will change,
+// adapting to different screen sizes, for use in media queries.
+
+// scss-docs-start grid-breakpoints
+// $grid-breakpoints: (
+//   xs: 0,
+//   sm: 576px,
+//   md: 768px,
+//   lg: 992px,
+//   xl: 1200px,
+//   xxl: 1400px
+// );
+// scss-docs-end grid-breakpoints
+
+// @include _assert-ascending($grid-breakpoints, "$grid-breakpoints");
+// @include _assert-starts-at-zero($grid-breakpoints, "$grid-breakpoints");
+
+
+// Grid containers
+//
+// Define the maximum width of `.container` for different screen sizes.
+
+// scss-docs-start container-max-widths
+// $container-max-widths: (
+//   sm: 540px,
+//   md: 720px,
+//   lg: 960px,
+//   xl: 1140px,
+//   xxl: 1320px
+// );
+// scss-docs-end container-max-widths
+
+// @include _assert-ascending($container-max-widths, "$container-max-widths");
+
+
+// Grid columns
+//
+// Set the number of columns and specify the width of the gutters.
+
+// $grid-columns:                12;
+// $grid-gutter-width:           1.5rem;
+// $grid-row-columns:            6;
+
+// $gutters: $spacers;
+
+// Container padding
+
+// $container-padding-x: $grid-gutter-width / 2;
+
+
+// Components
+//
+// Define common padding and border radius sizes and more.
+
+// $border-width:                1px;
+// $border-widths: (
+//   0: 0,
+//   1: 1px,
+//   2: 2px,
+//   3: 3px,
+//   4: 4px,
+//   5: 5px
+// );
+
+// $border-color:                $gray-200;
+
+// $border-radius:               .25rem;
+// $border-radius-sm:            .2rem;
+// $border-radius-lg:            .3rem;
+// $border-radius-pill:          50rem;
+
+// $box-shadow:                  0 .5rem 1rem rgba($black, .15);
+// $box-shadow-sm:               0 .125rem .25rem rgba($black, .075);
+// $box-shadow-lg:               0 1rem 3rem rgba($black, .175);
+// $box-shadow-inset:            inset 0 1px 2px rgba($black, .075);
+
+// $component-active-color:      $high-emphasis-light;
+// $component-active-bg:         $primary;
+
+// $caret-width:                 .3em;
+// $caret-vertical-align:        $caret-width * .85;
+// $caret-spacing:               $caret-width * .85;
+
+// $transition-base:             all .2s ease-in-out;
+// $transition-fade:             opacity .15s linear;
+// $transition-collapse:         height .35s ease;
+
+// stylelint-disable function-disallowed-list
+// scss-docs-start aspect-ratios
+// $aspect-ratios: (
+//   "1x1": 100%,
+//   "4x3": calc(3 / 4 * 100%),
+//   "16x9": calc(9 / 16 * 100%),
+//   "21x9": calc(9 / 21 * 100%)
+// );
+// scss-docs-end aspect-ratios
+// stylelint-enable function-disallowed-list
+
+// Typography
+//
+// Font, line-height, and color for body text, headings, and more.
+
+// stylelint-disable value-keyword-case
+// $font-family-sans-serif:      system-ui, -apple-system, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", "Liberation Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";
+// $font-family-monospace:       SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
+// stylelint-enable value-keyword-case
+// $font-family-base:            var(--#{$variable-prefix}font-sans-serif);
+// $font-family-code:            var(--#{$variable-prefix}font-monospace);
+
+// $font-size-root effects the value of `rem`, which is used for as well font sizes, paddings and margins
+// $font-size-base effects the font size of the body text
+// $font-size-root:              null;
+// $font-size-base:              1rem; // Assumes the browser default, typically `16px`
+// $font-size-sm:                $font-size-base * .875;
+// $font-size-lg:                $font-size-base * 1.25;
+
+// $font-weight-lighter:         lighter;
+// $font-weight-light:           300;
+// $font-weight-normal:          400;
+// $font-weight-medium:          500;
+// $font-weight-semibold:        600;
+// $font-weight-bold:            700;
+// $font-weight-bolder:          bolder;
+
+// $font-weight-base:            $font-weight-normal;
+
+// $line-height-base:            1.5;
+// $line-height-sm:              1.25;
+// $line-height-lg:              2;
+
+// $h1-font-size:                $font-size-base * 2.5;
+// $h2-font-size:                $font-size-base * 2;
+// $h3-font-size:                $font-size-base * 1.75;
+// $h4-font-size:                $font-size-base * 1.5;
+// $h5-font-size:                $font-size-base * 1.25;
+// $h6-font-size:                $font-size-base;
+
+// scss-docs-start font-sizes
+// $font-sizes: (
+//   1: $h1-font-size,
+//   2: $h2-font-size,
+//   3: $h3-font-size,
+//   4: $h4-font-size,
+//   5: $h5-font-size,
+//   6: $h6-font-size
+// );
+// scss-docs-end font-sizes
+
+// $headings-margin-bottom:      $spacer / 2;
+// $headings-font-family:        null;
+// $headings-font-style:         null;
+// $headings-font-weight:        500;
+// $headings-line-height:        1.2;
+// $headings-color:              unset;
+
+// scss-docs-start display-headings
+// $display-font-sizes: (
+//   1: 5rem,
+//   2: 4.5rem,
+//   3: 4rem,
+//   4: 3.5rem,
+//   5: 3rem,
+//   6: 2.5rem
+// );
+
+// $display-font-weight: 300;
+// $display-line-height: $headings-line-height;
+// scss-docs-end display-headings
+
+// $lead-font-size:              $font-size-base * 1.25;
+// $lead-font-weight:            300;
+
+// $small-font-size:             .875em;
+
+// $sub-sup-font-size:           .75em;
+
+// $text-muted:                  $disabled-dark;
+
+// $initialism-font-size:        $small-font-size;
+
+// $blockquote-margin-y:         $spacer;
+// $blockquote-font-size:        $font-size-base * 1.25;
+// $blockquote-footer-color:     $gray-600;
+// $blockquote-footer-font-size: $small-font-size;
+
+// $hr-margin-y:                 $spacer;
+// $hr-color:                    inherit;
+// $hr-height:                   $border-width;
+// $hr-opacity:                  .25;
+
+// $vr-color:                    inherit;
+// $vr-width:                    $border-width;
+// $vr-opacity:                  .25;
+
+// $legend-margin-bottom:        .5rem;
+// $legend-font-size:            1.5rem;
+// $legend-font-weight:          null;
+
+// $mark-padding:                .2em;
+
+// $dt-font-weight:              $font-weight-bold;
+
+// $nested-kbd-font-weight:      $font-weight-bold;
+
+// $list-inline-padding:         .5rem;
+
+// $mark-bg:                     #fcf8e3;
+
+// Icons
+// $icon-size-base:  1rem;
+// $icon-size-sm:    $icon-size-base * .875;
+// $icon-size-lg:    $icon-size-base * 1.25;
+// $icon-size-xl:    $icon-size-base * 1.5;
+
+
+// Tables
+//
+// Customizes the `.table` component with basic values, each used across all table variations.
+
+// scss-docs-start table-variables
+// $table-cell-padding-y:        .5rem;
+// $table-cell-padding-x:        .5rem;
+// $table-cell-padding-y-sm:     .25rem;
+// $table-cell-padding-x-sm:     .25rem;
+
+// $table-cell-vertical-align:   top;
+
+// $table-color:                 $body-color;
+// $table-bg:                    transparent;
+
+// $table-th-font-weight:        600;
+
+// $table-striped-color:         $table-color;
+// $table-striped-bg-factor:     .05;
+// $table-striped-bg:            rgba($black, $table-striped-bg-factor);
+
+// $table-active-color:          $table-color;
+// $table-active-bg-factor:      .1;
+// $table-active-bg:             rgba($black, $table-active-bg-factor);
+
+// $table-hover-color:           $table-color;
+// $table-hover-bg-factor:       .075;
+// $table-hover-bg:              rgba($black, $table-hover-bg-factor);
+
+// $table-border-factor:         .1;
+// $table-border-width:          $border-width;
+// $table-border-color:          $border-color;
+
+// $table-striped-order:         odd;
+
+// $table-group-separator-color: currentColor;
+
+// $table-caption-color:         $text-muted;
+
+// $table-bg-scale:              -80%;
+
+// $table-variants: (
+//   "primary":    shift-color($primary, $table-bg-scale),
+//   "secondary":  shift-color($secondary, $table-bg-scale),
+//   "success":    shift-color($success, $table-bg-scale),
+//   "info":       shift-color($info, $table-bg-scale),
+//   "warning":    shift-color($warning, $table-bg-scale),
+//   "danger":     shift-color($danger, $table-bg-scale),
+//   "light":      $light,
+//   "dark":       $dark,
+// );
+// scss-docs-end table-variables
+
+
+// Buttons + Forms
+//
+// Shared variables that are reassigned to `$input-` and `$btn-` specific variables.
+
+// scss-docs-start button-form-variables
+// $input-btn-padding-y:         .375rem;
+// $input-btn-padding-x:         .75rem;
+// $input-btn-font-family:       null;
+// $input-btn-font-size:         $font-size-base;
+// $input-btn-line-height:       $line-height-base;
+
+// $input-btn-focus-width:         .25rem;
+// $input-btn-focus-color-opacity: .25;
+// $input-btn-focus-color:         rgba($component-active-bg, $input-btn-focus-color-opacity);
+// $input-btn-focus-box-shadow:    0 0 0 $input-btn-focus-width $input-btn-focus-color;
+
+// $input-btn-padding-y-sm:      .25rem;
+// $input-btn-padding-x-sm:      .5rem;
+// $input-btn-font-size-sm:      $font-size-sm;
+
+// $input-btn-padding-y-lg:      .5rem;
+// $input-btn-padding-x-lg:      1rem;
+// $input-btn-font-size-lg:      $font-size-lg;
+
+// $input-btn-border-width:      $border-width;
+// scss-docs-end button-form-variables
+
+// Buttons
+//
+// For each of Bootstrap's buttons, define text, background, and border color.
+
+// scss-docs-start button-variables
+// $btn-padding-y:               $input-btn-padding-y;
+// $btn-padding-x:               $input-btn-padding-x;
+// $btn-font-family:             $input-btn-font-family;
+// $btn-font-size:               $input-btn-font-size;
+// $btn-line-height:             $input-btn-line-height;
+// $btn-white-space:             null; // Set to `nowrap` to prevent text wrapping
+
+// $btn-padding-y-sm:            $input-btn-padding-y-sm;
+// $btn-padding-x-sm:            $input-btn-padding-x-sm;
+// $btn-font-size-sm:            $input-btn-font-size-sm;
+
+// $btn-padding-y-lg:            $input-btn-padding-y-lg;
+// $btn-padding-x-lg:            $input-btn-padding-x-lg;
+// $btn-font-size-lg:            $input-btn-font-size-lg;
+
+// $btn-border-width:            $input-btn-border-width;
+
+// $btn-font-weight:             $font-weight-normal;
+// $btn-box-shadow:              inset 0 1px 0 rgba($white, .15), 0 1px 1px rgba($black, .075);
+// $btn-focus-width:             $input-btn-focus-width;
+// $btn-focus-box-shadow:        $input-btn-focus-box-shadow;
+// $btn-disabled-opacity:        .65;
+// $btn-active-box-shadow:       inset 0 3px 5px rgba($black, .125);
+
+// $btn-link-color:              $link-color;
+// $btn-link-hover-color:        $link-hover-color;
+// $btn-link-disabled-color:     $gray-600;
+
+// Allows for customizing button radius independently from global border radius
+// $btn-border-radius:           $border-radius;
+// $btn-border-radius-sm:        $border-radius-sm;
+// $btn-border-radius-lg:        $border-radius-lg;
+
+// $btn-transition:              color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out;
+
+// $button-variants: (
+//   "primary": btn-color-map($primary, $primary),
+//   "secondary": btn-color-map($secondary, $secondary),
+//   "success": btn-color-map($success, $success),
+//   "danger": btn-color-map($danger, $danger),
+//   "warning": btn-color-map($warning, $warning),
+//   "info": btn-color-map($info, $info),
+//   "light": btn-color-map($light, $light),
+//   "dark": btn-color-map($dark, $dark)
+// );
+// scss-docs-end button-variables
+
+
+// Forms
+// scss-docs-start form-variables
+// scss-docs-start form-control-variables
+// $form-text-margin-top:                  .25rem;
+// $form-text-font-size:                   $small-font-size;
+// $form-text-font-style:                  null;
+// $form-text-font-weight:                 null;
+// $form-text-color:                       $text-muted;
+
+// $form-label-margin-bottom:              .5rem;
+// $form-label-font-size:                  null;
+// $form-label-font-style:                 null;
+// $form-label-font-weight:                null;
+// $form-label-color:                      inherit;
+
+// $input-padding-y:                       $input-btn-padding-y;
+// $input-padding-x:                       $input-btn-padding-x;
+// $input-font-family:                     $input-btn-font-family;
+// $input-font-size:                       $input-btn-font-size;
+// $input-font-weight:                     $font-weight-base;
+// $input-line-height:                     $input-btn-line-height;
+
+// $input-padding-y-sm:                    $input-btn-padding-y-sm;
+// $input-padding-x-sm:                    $input-btn-padding-x-sm;
+// $input-font-size-sm:                    $input-btn-font-size-sm;
+
+// $input-padding-y-lg:                    $input-btn-padding-y-lg;
+// $input-padding-x-lg:                    $input-btn-padding-x-lg;
+// $input-font-size-lg:                    $input-btn-font-size-lg;
+
+// $input-bg:                              $white;
+// $input-disabled-bg:                     $gray-200;
+// $input-disabled-border-color:           $gray-400;
+
+// $input-color:                           $body-color;
+// $input-border-color:                    $gray-400;
+// $input-border-width:                    $input-btn-border-width;
+// $input-box-shadow:                      $box-shadow-inset;
+
+// $input-border-radius:                   $border-radius;
+// $input-border-radius-sm:                $border-radius-sm;
+// $input-border-radius-lg:                $border-radius-lg;
+
+// $input-focus-bg:                        $input-bg;
+// $input-focus-border-color:              tint-color($component-active-bg, 50%);
+// $input-focus-color:                     $input-color;
+// $input-focus-width:                     $input-btn-focus-width;
+// $input-focus-box-shadow:                $input-btn-focus-box-shadow;
+
+// $input-placeholder-color:               $gray-600;
+// $input-plaintext-color:                 $body-color;
+
+// $input-height-border:                   $input-border-width * 2;
+
+// $input-height-inner:                    add($input-line-height * 1em, $input-padding-y * 2);
+// $input-height-inner-half:               add($input-line-height * .5em, $input-padding-y);
+// $input-height-inner-quarter:            add($input-line-height * .25em, $input-padding-y / 2);
+
+// $input-height:                          add($input-line-height * 1em, add($input-padding-y * 2, $input-height-border, false));
+// $input-height-sm:                       add($input-line-height * 1em, add($input-padding-y-sm * 2, $input-height-border, false));
+// $input-height-lg:                       add($input-line-height * 1em, add($input-padding-y-lg * 2, $input-height-border, false));
+
+// $input-transition:                      border-color .15s ease-in-out, box-shadow .15s ease-in-out;
+// scss-docs-end form-control-variables
+
+// scss-docs-start form-check-variables
+// $form-check-input-width:                  1em;
+// $form-check-min-height:                   $font-size-base * $line-height-base;
+// $form-check-padding-start:                .375em;
+// $form-check-margin-bottom:                .125rem;
+// $form-check-label-color:                  unset;
+// $form-check-label-cursor:                 null;
+// $form-check-transition:                   background-color .15s ease-in-out, background-position .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out;
+
+// $form-check-input-active-filter:          brightness(90%);
+
+// $form-check-input-bg:                     $body-bg;
+// $form-check-input-border:                 1px solid rgba(0, 0, 0, .25);
+// $form-check-input-border-radius:          .25em;
+// $form-check-radio-border-radius:          50%;
+// $form-check-input-focus-border:           $input-focus-border-color;
+// $form-check-input-focus-box-shadow:       $input-btn-focus-box-shadow;
+
+// $form-check-input-checked-color:          $component-active-color;
+// $form-check-input-checked-bg-color:       $component-active-bg;
+// $form-check-input-checked-border-color:   $form-check-input-checked-bg-color;
+// $form-check-input-checked-bg-image:       url("data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'><path fill='none' stroke='#{$form-check-input-checked-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M6 10l3 3l6-6'/></svg>");
+// $form-check-radio-checked-bg-image:       url("data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='2' fill='#{$form-check-input-checked-color}'/></svg>");
+
+// $form-check-input-indeterminate-color:          $component-active-color;
+// $form-check-input-indeterminate-bg-color:       $component-active-bg;
+// $form-check-input-indeterminate-border-color:   $form-check-input-indeterminate-bg-color;
+// $form-check-input-indeterminate-bg-image:       url("data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'><path fill='none' stroke='#{$form-check-input-indeterminate-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M6 10h8'/></svg>");
+
+// $form-check-input-disabled-opacity:        .5;
+// $form-check-label-disabled-opacity:        $form-check-input-disabled-opacity;
+// $form-check-btn-check-disabled-opacity:    $btn-disabled-opacity;
+// scss-docs-end form-check-variables
+
+// scss-docs-start form-switch-variables
+// $form-switch-color:               rgba(0, 0, 0, .25);
+// $form-switch-width:               1.5em;
+// $form-switch-padding-start:       .375em;
+// $form-switch-bg-image:            url("data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-color}'/></svg>");
+// $form-switch-border-radius:       $form-switch-width;
+
+// $form-switch-focus-color:         $input-focus-border-color;
+// $form-switch-focus-bg-image:      url("data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-focus-color}'/></svg>");
+
+// $form-switch-checked-color:       $component-active-color;
+// $form-switch-checked-bg-image:    url("data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-checked-color}'/></svg>");
+// $form-switch-checked-bg-position: right center;
+
+// $form-switch-widths: (
+//   lg: 2em,
+//   xl: 2.5em
+// );
+// scss-docs-end form-switch-variables
+
+// $form-check-inline-margin-end:    1rem;
+
+// scss-docs-start input-group-variables
+// $input-group-addon-padding-y:           $input-padding-y;
+// $input-group-addon-padding-x:           $input-padding-x;
+// $input-group-addon-font-weight:         $input-font-weight;
+// $input-group-addon-color:               $input-color;
+// $input-group-addon-bg:                  $gray-200;
+// $input-group-addon-border-color:        $input-border-color;
+// scss-docs-end input-group-variables
+
+// scss-docs-start form-select-variables
+// $form-select-padding-y:             $input-padding-y;
+// $form-select-padding-x:             $input-padding-x;
+// $form-select-font-family:           $input-font-family;
+// $form-select-font-size:             $input-font-size;
+// $form-select-indicator-padding:     1rem; // Extra padding to account for the presence of the background-image based indicator
+// $form-select-font-weight:           $input-font-weight;
+// $form-select-line-height:           $input-line-height;
+// $form-select-color:                 $input-color;
+// $form-select-disabled-color:        $gray-600;
+// $form-select-bg:                    $input-bg;
+// $form-select-disabled-bg:           $gray-200;
+// $form-select-disabled-border-color: $input-disabled-border-color;
+// $form-select-bg-position:           right $form-select-padding-x center;
+// $form-select-bg-size:               16px 12px; // In pixels because image dimensions
+// $form-select-indicator-color:       $gray-800;
+// $form-select-indicator:             url("data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'><path fill='none' stroke='#{$form-select-indicator-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='M2 5l6 6 6-6'/></svg>");
+
+// $form-select-feedback-icon-padding-end: add(1em * .75, (2 * $form-select-padding-y * .75) + $form-select-padding-x + $form-select-indicator-padding);
+// $form-select-feedback-icon-position:    center right ($form-select-padding-x + $form-select-indicator-padding);
+// $form-select-feedback-icon-size:        $input-height-inner-half $input-height-inner-half;
+
+// $form-select-border-width:        $input-border-width;
+// $form-select-border-color:        $input-border-color;
+// $form-select-border-radius:       $border-radius;
+// $form-select-box-shadow:          $box-shadow-inset;
+
+// $form-select-focus-border-color:  $input-focus-border-color;
+// $form-select-focus-width:         $input-focus-width;
+// $form-select-focus-box-shadow:    0 0 0 $form-select-focus-width $input-btn-focus-color;
+
+// $form-select-padding-y-sm:        $input-padding-y-sm;
+// $form-select-padding-x-sm:        $input-padding-x-sm;
+// $form-select-font-size-sm:        $input-font-size-sm;
+
+// $form-select-padding-y-lg:        $input-padding-y-lg;
+// $form-select-padding-x-lg:        $input-padding-x-lg;
+// $form-select-font-size-lg:        $input-font-size-lg;
+// scss-docs-end form-select-variables
+
+// scss-docs-start form-range-variables
+// $form-range-track-width:          100%;
+// $form-range-track-height:         .5rem;
+// $form-range-track-cursor:         pointer;
+// $form-range-track-bg:             $gray-300;
+// $form-range-track-border-radius:  1rem;
+// $form-range-track-box-shadow:     $box-shadow-inset;
+
+// $form-range-thumb-width:                   1rem;
+// $form-range-thumb-height:                  $form-range-thumb-width;
+// $form-range-thumb-bg:                      $component-active-bg;
+// $form-range-thumb-border:                  0;
+// $form-range-thumb-border-radius:           1rem;
+// $form-range-thumb-box-shadow:              0 .1rem .25rem rgba($black, .1);
+// $form-range-thumb-focus-box-shadow:        0 0 0 1px $body-bg, $input-focus-box-shadow;
+// $form-range-thumb-focus-box-shadow-width:  $input-focus-width; // For focus box shadow issue in Edge
+// $form-range-thumb-active-bg:               tint-color($component-active-bg, 70%);
+// $form-range-thumb-disabled-bg:             $gray-500;
+// $form-range-thumb-transition:              background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out;
+// scss-docs-end form-range-variables
+
+// scss-docs-start form-file-variables
+// $form-file-button-color:          $input-color;
+// $form-file-button-bg:             $input-group-addon-bg;
+// $form-file-button-hover-bg:       shade-color($form-file-button-bg, 5%);
+// scss-docs-end form-file-variables
+
+// scss-docs-start form-floating-variables
+// $form-floating-height:            add(3.5rem, $input-height-border);
+// $form-floating-padding-x:         $input-padding-x;
+// $form-floating-padding-y:         1rem;
+// $form-floating-input-padding-t:   1.625rem;
+// $form-floating-input-padding-b:   .625rem;
+// $form-floating-label-opacity:     .65;
+// $form-floating-label-transform:   scale(.85) translateY(-.5rem) translateX(.15rem);
+// $form-floating-transition:        opacity .1s ease-in-out, transform .1s ease-in-out;
+// scss-docs-end form-floating-variables
+
+// Form validation
+
+// $form-feedback-margin-top:          $form-text-margin-top;
+// $form-feedback-font-size:           $form-text-font-size;
+// $form-feedback-font-style:          $form-text-font-style;
+// $form-feedback-valid-color:         $success;
+// $form-feedback-invalid-color:       $danger;
+
+// $form-feedback-icon-valid-color:    $form-feedback-valid-color;
+// $form-feedback-icon-valid:          url("data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'><path fill='#{$form-feedback-icon-valid-color}' d='M2.3 6.73L.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/></svg>");
+// $form-feedback-icon-invalid-color:  $form-feedback-invalid-color;
+// $form-feedback-icon-invalid:        url("data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='#{$form-feedback-icon-invalid-color}'><circle cx='6' cy='6' r='4.5'/><path stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/><circle cx='6' cy='8.2' r='.6' fill='#{$form-feedback-icon-invalid-color}' stroke='none'/></svg>");
+
+// scss-docs-start form-validation-states
+// $form-validation-states: (
+//   "valid": (
+//     "color": $form-feedback-valid-color,
+//     "icon": $form-feedback-icon-valid
+//   ),
+//   "invalid": (
+//     "color": $form-feedback-invalid-color,
+//     "icon": $form-feedback-icon-invalid
+//   )
+// );
+// scss-docs-end form-validation-states
+// scss-docs-end form-variables
+// Z-index master list
+//
+// Warning: Avoid customizing these values. They're used for a bird's eye view
+// of components dependent on the z-axis and are designed to all work together.
+
+// scss-docs-start zindex-stack
+// $zindex-dropdown:                   1000;
+// $zindex-sticky:                     1020;
+// $zindex-fixed:                      1030;
+// $zindex-modal-backdrop:             1040;
+// $zindex-modal:                      1050;
+// $zindex-popover:                    1060;
+// $zindex-tooltip:                    1070;
+// scss-docs-end zindex-stack
+
+
+// Navs
+// scss-docs-start nav-variables
+// $nav-link-padding-y:                .5rem;
+// $nav-link-padding-x:                1rem;
+// $nav-link-font-size:                null;
+// $nav-link-font-weight:              null;
+// $nav-link-color:                    $link-color;
+// $nav-link-hover-color:              $link-hover-color;
+// $nav-link-transition:               color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out;
+// $nav-link-disabled-color:           $gray-600;
+
+// $nav-tabs-border-color:             $gray-300;
+// $nav-tabs-border-width:             $border-width;
+// $nav-tabs-border-radius:            $border-radius;
+// $nav-tabs-link-hover-border-color:  $gray-200 $gray-200 $nav-tabs-border-color;
+// $nav-tabs-link-active-color:        $gray-700;
+// $nav-tabs-link-active-bg:           $body-bg;
+// $nav-tabs-link-active-border-color: $gray-300 $gray-300 $nav-tabs-link-active-bg;
+
+// $nav-pills-border-radius:           $border-radius;
+// $nav-pills-link-active-color:       $component-active-color;
+// $nav-pills-link-active-bg:          $component-active-bg;
+// scss-docs-end nav-variables
+
+// Navbar
+// scss-docs-start navbar-variables
+// $navbar-padding-y:                  $spacer / 2;
+// $navbar-padding-x:                  null;
+
+// $navbar-nav-link-padding-x:         .5rem;
+
+// $navbar-brand-font-size:            $font-size-lg;
+// Compute the navbar-brand padding-y so the navbar-brand will have the same height as navbar-text and nav-link
+// $nav-link-height:                   $font-size-base * $line-height-base + $nav-link-padding-y * 2;
+// $navbar-brand-height:               $navbar-brand-font-size * $line-height-base;
+// $navbar-brand-padding-y:            ($nav-link-height - $navbar-brand-height) / 2;
+// $navbar-brand-margin-end:           1rem;
+
+// $navbar-toggler-padding-y:          .25rem;
+// $navbar-toggler-padding-x:          .75rem;
+// $navbar-toggler-font-size:          $font-size-lg;
+// $navbar-toggler-border-radius:      $btn-border-radius;
+// $navbar-toggler-focus-width:        $btn-focus-width;
+// $navbar-toggler-transition:         box-shadow .15s ease-in-out;
+
+// $navbar-dark-color:                 $medium-emphasis-light;
+// $navbar-dark-hover-color:           $high-emphasis-light;
+// $navbar-dark-active-color:          $high-emphasis-light;
+// $navbar-dark-disabled-color:        $disabled-light;
+// $navbar-dark-toggler-icon-bg:       url("data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'><path stroke='#{$navbar-dark-color}' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>");
+// $navbar-dark-toggler-border-color:  rgba($white, .1);
+
+// $navbar-light-color:                $medium-emphasis-dark;
+// $navbar-light-hover-color:          $high-emphasis-dark;
+// $navbar-light-active-color:         $high-emphasis-dark;
+// $navbar-light-disabled-color:       $disabled-dark;
+// $navbar-light-toggler-icon-bg:      url("data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'><path stroke='#{$navbar-light-color}' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>");
+// $navbar-light-toggler-border-color: rgba($black, .1);
+
+// $navbar-light-brand-color:                $navbar-light-active-color;
+// $navbar-light-brand-hover-color:          $navbar-light-active-color;
+// $navbar-dark-brand-color:                 $navbar-dark-active-color;
+// $navbar-dark-brand-hover-color:           $navbar-dark-active-color;
+// scss-docs-end navbar-variables
+
+// Dropdowns
+//
+// Dropdown menu container and contents.
+// scss-docs-start dropdown-variables
+// $dropdown-min-width:                10rem;
+// $dropdown-padding-x:                0;
+// $dropdown-padding-y:                .5rem;
+// $dropdown-spacer:                   .125rem;
+// $dropdown-font-size:                $font-size-base;
+// $dropdown-color:                    $body-color;
+// $dropdown-bg:                       $white;
+// $dropdown-border-color:             rgba($black, .15);
+// $dropdown-border-radius:            $border-radius;
+// $dropdown-border-width:             $border-width;
+// $dropdown-inner-border-radius:      subtract($dropdown-border-radius, $dropdown-border-width);
+// $dropdown-divider-bg:               $dropdown-border-color;
+// $dropdown-divider-margin-y:         $spacer / 2;
+// $dropdown-box-shadow:               $box-shadow;
+
+// $dropdown-link-color:               $gray-900;
+// $dropdown-link-hover-color:         shade-color($gray-900, 10%);
+// $dropdown-link-hover-bg:            $gray-100;
+
+// $dropdown-link-active-color:        $component-active-color;
+// $dropdown-link-active-bg:           $component-active-bg;
+
+// $dropdown-link-disabled-color:      $gray-600;
+
+// $dropdown-item-padding-y:           $spacer / 4;
+// $dropdown-item-padding-x:           $spacer;
+
+// $dropdown-header-color:             $gray-600;
+// $dropdown-header-padding:           $dropdown-padding-y $dropdown-item-padding-x;
+
+// $dropdown-dark-color:               $gray-300;
+// $dropdown-dark-bg:                  $gray-800;
+// $dropdown-dark-border-color:        $dropdown-border-color;
+// $dropdown-dark-divider-bg:          $dropdown-divider-bg;
+// $dropdown-dark-box-shadow:          null;
+// $dropdown-dark-link-color:          $dropdown-dark-color;
+// $dropdown-dark-link-hover-color:    $high-emphasis-light;
+// $dropdown-dark-link-hover-bg:       rgba($white, .15);
+// $dropdown-dark-link-active-color:   $dropdown-link-active-color;
+// $dropdown-dark-link-active-bg:      $dropdown-link-active-bg;
+// $dropdown-dark-link-disabled-color: $gray-500;
+// $dropdown-dark-header-color:        $gray-500;
+// scss-docs-end dropdown-variables
+
+
+// Pagination
+// scss-docs-start pagination-variables
+// $pagination-padding-y:              .375rem;
+// $pagination-padding-x:              .75rem;
+// $pagination-padding-y-sm:           .25rem;
+// $pagination-padding-x-sm:           .5rem;
+// $pagination-padding-y-lg:           .75rem;
+// $pagination-padding-x-lg:           1.5rem;
+
+// $pagination-color:                  $link-color;
+// $pagination-bg:                     $white;
+// $pagination-border-width:           $border-width;
+// $pagination-border-radius:          $border-radius;
+// $pagination-margin-start:           -$pagination-border-width;
+// $pagination-border-color:           $gray-300;
+
+// $pagination-focus-color:            $link-hover-color;
+// $pagination-focus-bg:               $gray-200;
+// $pagination-focus-box-shadow:       $input-btn-focus-box-shadow;
+// $pagination-focus-outline:          0;
+
+// $pagination-hover-color:            $link-hover-color;
+// $pagination-hover-bg:               $gray-200;
+// $pagination-hover-border-color:     $gray-300;
+
+// $pagination-active-color:           $component-active-color;
+// $pagination-active-bg:              $component-active-bg;
+// $pagination-active-border-color:    $pagination-active-bg;
+
+// $pagination-disabled-color:         $gray-600;
+// $pagination-disabled-bg:            $white;
+// $pagination-disabled-border-color:  $gray-300;
+
+// $pagination-transition:              color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out;
+// scss-docs-end pagination-variables
+
+// Cards
+// scss-docs-start card-variables
+// $card-spacer-y:                     $spacer;
+// $card-spacer-x:                     $spacer;
+// $card-title-spacer-y:               $spacer / 2;
+// $card-border-width:                 $border-width;
+// $card-border-radius:                $border-radius;
+// $card-border-color:                 rgba($black, .125);
+// $card-inner-border-radius:          subtract($card-border-radius, $card-border-width);
+// $card-cap-padding-y:                $card-spacer-y / 2;
+// $card-cap-padding-x:                $card-spacer-x;
+// $card-cap-bg:                       rgba($black, .03);
+// $card-cap-color:                    unset;
+// $card-height:                       null;
+// $card-color:                        unset;
+// $card-bg:                           $white;
+
+// $card-img-overlay-padding:          $spacer;
+
+// $card-group-margin:                 $grid-gutter-width / 2;
+// scss-docs-end card-variables
+
+// Accordion
+// scss-docs-start accordion-variables
+// $accordion-padding-y:                     1rem;
+// $accordion-padding-x:                     1.25rem;
+// $accordion-color:                         $body-color;
+// $accordion-bg:                            transparent;
+// $accordion-border-width:                  $border-width;
+// $accordion-border-color:                  rgba($black, .125);
+// $accordion-border-radius:                 $border-radius;
+
+// $accordion-body-padding-y:                $accordion-padding-y;
+// $accordion-body-padding-x:                $accordion-padding-x;
+
+// $accordion-button-padding-y:              $accordion-padding-y;
+// $accordion-button-padding-x:              $accordion-padding-x;
+// $accordion-button-color:                  $accordion-color;
+// $accordion-button-bg:                     $accordion-bg;
+// $accordion-transition:                    $btn-transition, border-radius .15s ease;
+// $accordion-button-active-bg:              tint-color($component-active-bg, 90%);
+// $accordion-button-active-color:           shade-color($primary, 10%);
+
+// $accordion-button-focus-border-color:     $input-focus-border-color;
+// $accordion-button-focus-box-shadow:       $btn-focus-box-shadow;
+
+// $accordion-icon-width:                    1.25rem;
+// $accordion-icon-color:                    $accordion-color;
+// $accordion-icon-active-color:             $accordion-button-active-color;
+// $accordion-icon-transition:               transform .2s ease-in-out;
+// $accordion-icon-transform:                rotate(180deg);
+
+// $accordion-button-icon:         url("data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$accordion-icon-color}'><path fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/></svg>");
+// $accordion-button-active-icon:  url("data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$accordion-icon-active-color}'><path fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/></svg>");
+// scss-docs-end accordion-variables
+
+// Tooltips
+
+// scss-docs-start tooltip-variables
+// $tooltip-font-size:                 $font-size-sm;
+// $tooltip-max-width:                 200px;
+// $tooltip-color:                     $high-emphasis-light;
+// $tooltip-bg:                        $black;
+// $tooltip-border-radius:             $border-radius;
+// $tooltip-opacity:                   .9;
+// $tooltip-padding-y:                 $spacer / 4;
+// $tooltip-padding-x:                 $spacer / 2;
+// $tooltip-margin:                    0;
+
+// $tooltip-arrow-width:               .8rem;
+// $tooltip-arrow-height:              .4rem;
+// $tooltip-arrow-color:               $tooltip-bg;
+// scss-docs-end tooltip-variables
+
+// Form tooltips must come after regular tooltips
+// $form-feedback-tooltip-padding-y:     $tooltip-padding-y;
+// $form-feedback-tooltip-padding-x:     $tooltip-padding-x;
+// $form-feedback-tooltip-font-size:     $tooltip-font-size;
+// $form-feedback-tooltip-line-height:   null;
+// $form-feedback-tooltip-opacity:       $tooltip-opacity;
+// $form-feedback-tooltip-border-radius: $tooltip-border-radius;
+
+
+// Popovers
+// scss-docs-start popover-variables
+// $popover-font-size:                 $font-size-sm;
+// $popover-bg:                        $white;
+// $popover-max-width:                 276px;
+// $popover-border-width:              $border-width;
+// $popover-border-color:              rgba($black, .2);
+// $popover-border-radius:             $border-radius-lg;
+// $popover-inner-border-radius:       subtract($popover-border-radius, $popover-border-width);
+// $popover-box-shadow:                $box-shadow;
+
+// $popover-header-bg:                 shade-color($popover-bg, 6%);
+// $popover-header-color:              $headings-color;
+// $popover-header-padding-y:          .5rem;
+// $popover-header-padding-x:          $spacer;
+
+// $popover-body-color:                $body-color;
+// $popover-body-padding-y:            $spacer;
+// $popover-body-padding-x:            $spacer;
+
+// $popover-arrow-width:               1rem;
+// $popover-arrow-height:              .5rem;
+// $popover-arrow-color:               $popover-bg;
+
+// $popover-arrow-outer-color:         fade-in($popover-border-color, .05);
+// scss-docs-end popover-variables
+
+
+// Toasts
+// scss-docs-start toast-variables
+// $toast-max-width:                   350px;
+// $toast-padding-x:                   .75rem;
+// $toast-padding-y:                   .5rem;
+// $toast-font-size:                   .875rem;
+// $toast-color:                       unset;
+// $toast-background-color:            rgba($white, .85);
+// $toast-border-width:                1px;
+// $toast-border-color:                rgba(0, 0, 0, .1);
+// $toast-border-radius:               $border-radius;
+// $toast-box-shadow:                  $box-shadow;
+// $toast-spacing:                     $container-padding-x;
+
+// $toast-header-color:                $gray-600;
+// $toast-header-background-color:     rgba($white, .85);
+// $toast-header-border-color:         rgba(0, 0, 0, .05);
+// scss-docs-end toast-variables
+
+
+// Badges
+// scss-docs-start badge-variables
+// $badge-font-size:                   .75em;
+// $badge-font-weight:                 $font-weight-bold;
+// $badge-color:                       $high-emphasis-light;
+// $badge-padding-y:                   .35em;
+// $badge-padding-x:                   .65em;
+// $badge-border-radius:               $border-radius;
+
+// $badge-font-size-sm:                .65em;
+// $badge-padding-y-sm:                .3em;
+// $badge-padding-x-sm:                .5em;
+// scss-docs-end badge-variables
+
+
+// Modals
+// scss-docs-start modal-variables
+// Padding applied to the modal body
+// $modal-inner-padding:               $spacer;
+
+// Margin between elements in footer, must be lower than or equal to 2 * $modal-inner-padding
+// $modal-footer-margin-between:       .5rem;
+
+// $modal-dialog-margin:               .5rem;
+// $modal-dialog-margin-y-sm-up:       1.75rem;
+
+// $modal-title-line-height:           $line-height-base;
+
+// $modal-content-color:               unset;
+// $modal-content-bg:                  $white;
+// $modal-content-border-color:        rgba($black, .2);
+// $modal-content-border-width:        $border-width;
+// $modal-content-border-radius:       $border-radius-lg;
+// $modal-content-inner-border-radius: subtract($modal-content-border-radius, $modal-content-border-width);
+// $modal-content-box-shadow-xs:       $box-shadow-sm;
+// $modal-content-box-shadow-sm-up:    $box-shadow;
+
+// $modal-backdrop-bg:                 $black;
+// $modal-backdrop-opacity:            .5;
+// $modal-header-border-color:         $border-color;
+// $modal-footer-border-color:         $modal-header-border-color;
+// $modal-header-border-width:         $modal-content-border-width;
+// $modal-footer-border-width:         $modal-header-border-width;
+// $modal-header-padding-y:            $modal-inner-padding;
+// $modal-header-padding-x:            $modal-inner-padding;
+// $modal-header-padding:              $modal-header-padding-y $modal-header-padding-x; // Keep this for backwards compatibility
+
+// $modal-sm:                          300px;
+// $modal-md:                          500px;
+// $modal-lg:                          800px;
+// $modal-xl:                          1140px;
+
+// $modal-fade-transform:              translate(0, -50px);
+// $modal-show-transform:              none;
+// $modal-transition:                  transform .3s ease-out;
+// $modal-scale-transform:             scale(1.02);
+// scss-docs-end modal-variables
+
+
+// Avatars
+// scss-docs-start avatar-variables
+// $avatar-width:  2rem;
+
+// $avatar-widths: (
+//   sm: 1.5rem,
+//   lg: 3rem,
+//   xl: 4rem
+// );
+
+// $avatar-transition: margin .15s;
+// scss-docs-end avatar-variables
+
+// Alerts
+//
+// Define alert colors, border radius, and padding.
+// scss-docs-start alert-variables
+// $alert-padding-y:                   $spacer;
+// $alert-padding-x:                   $spacer;
+// $alert-margin-bottom:               1rem;
+// $alert-border-radius:               $border-radius;
+// $alert-link-font-weight:            $font-weight-bold;
+// $alert-border-width:                $border-width;
+
+// $alert-bg-scale:                    -80%;
+// $alert-border-scale:                -70%;
+// $alert-color-scale:                 40%;
+
+// $alert-dismissible-padding-r:       $alert-padding-x * 3; // 3x covers width of x plus default padding on either side
+
+// $alert-variants: (
+//   "primary": alert-color-map($primary),
+//   "secondary": alert-color-map($secondary),
+//   "success": alert-color-map($success),
+//   "danger": alert-color-map($danger),
+//   "warning": alert-color-map($warning),
+//   "info": alert-color-map($info),
+//   "light": alert-color-map($light),
+//   "dark": alert-color-map($dark)
+// );
+// scss-docs-end alert-variables
+
+// Callouts
+// scss-docs-start callout-variables
+// $callout-padding-y:                 $spacer;
+// $callout-padding-x:                 $spacer;
+// $callout-margin-y:                  $spacer;
+// $callout-margin-x:                  0;
+// $callout-border-radius:             $border-radius;
+// $callout-border-width:              $border-width;
+// $callout-border-color:              $border-color;
+// $callout-border-left-width:         (4 * $callout-border-width);
+
+// $callout-variants: (
+//   "primary":    $primary,
+//   "secondary":  $secondary,
+//   "success":    $success,
+//   "danger":     $danger,
+//   "warning":    $warning,
+//   "info":       $info,
+//   "light":      $light,
+//   "dark":       $dark
+// );
+// scss-docs-end callout-variables
+
+
+// Progress bars
+
+// scss-docs-start progress-variables
+// $progress-height:                   1rem;
+// $progress-font-size:                $font-size-base * .75;
+// $progress-bg:                       $gray-200;
+// $progress-border-radius:            $border-radius;
+// $progress-box-shadow:               $box-shadow-inset;
+// $progress-bar-color:                $high-emphasis-light;
+// $progress-bar-bg:                   $primary;
+// $progress-bar-animation-timing:     1s linear infinite;
+// $progress-bar-transition:           width .6s ease;
+// scss-docs-end progress-variables
+
+// List group
+// scss-docs-start list-group-variables
+// $list-group-color:                  unset;
+// $list-group-bg:                     $white;
+// $list-group-border-color:           rgba($black, .125);
+// $list-group-border-width:           $border-width;
+// $list-group-border-radius:          $border-radius;
+
+// $list-group-item-padding-y:         $spacer / 2;
+// $list-group-item-padding-x:         $spacer;
+// $list-group-item-bg-scale:          -80%;
+// $list-group-item-color-scale:       40%;
+
+// $list-group-hover-bg:               $gray-100;
+// $list-group-active-color:           $component-active-color;
+// $list-group-active-bg:              $component-active-bg;
+// $list-group-active-border-color:    $list-group-active-bg;
+
+// $list-group-disabled-color:         $gray-600;
+// $list-group-disabled-bg:            $list-group-bg;
+
+// $list-group-action-color:           $gray-700;
+// $list-group-action-hover-color:     $list-group-action-color;
+
+// $list-group-action-active-color:    $body-color;
+// $list-group-action-active-bg:       $gray-200;
+
+// $list-group-variants: (
+//   "primary": list-group-color-map($primary),
+//   "secondary": list-group-color-map($secondary),
+//   "success": list-group-color-map($success),
+//   "danger": list-group-color-map($danger),
+//   "warning": list-group-color-map($warning),
+//   "info": list-group-color-map($info),
+//   "light": list-group-color-map($light),
+//   "dark": list-group-color-map($dark)
+// );
+// scss-docs-end list-group-variables
+
+
+// Header
+// scss-docs-start header-variables
+// $header-min-height:             4rem;
+// $header-padding-y:              $spacer / 2;
+// $header-padding-x:              $spacer / 2;
+// $header-brand-font-size:        $font-size-lg;
+// $header-color:                  $medium-emphasis-dark;
+// $header-bg:                     $white;
+// $header-border-color:           $border-color;
+// $header-border-width:           1px;
+// $header-hover-color:            $high-emphasis-dark;
+// $header-active-color:           $high-emphasis-dark;
+// $header-disabled-color:         $disabled-dark;
+
+// Compute the header-brand padding-y so the header-brand will have the same height as header-text and nav-link
+// $nav-link-height:               $font-size-base * $line-height-base + $nav-link-padding-y * 2;
+// $header-brand-height:           $header-brand-font-size * $line-height-base;
+// $header-brand-padding-y:        ($nav-link-height - $header-brand-height) / 2;
+// $header-brand-margin-end:       1rem;
+// $header-brand-font-size:        $font-size-lg;
+// $header-brand-color:            $gray-900;
+// $header-brand-hover-color:      shade-color($gray-900, 10%);
+
+// $header-toggler-padding-y:      .25rem;
+// $header-toggler-padding-x:      .75rem;
+// $header-toggler-font-size:      $font-size-lg;
+// $header-toggler-bg:             transparent;
+// $header-toggler-border:         0;
+// $header-toggler-border-radius:  $btn-border-radius;
+
+// $header-toggler-icon-bg:        str-replace(url("data:image/svg+xml;charset=utf8,%3Csvg viewBox='0 0 30 30' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath stroke='#{$header-color}' stroke-width='2.25' stroke-linecap='round' stroke-miterlimit='10' d='M4 7h22M4 15h22M4 23h22'/%3E%3C/svg%3E"), "#", "%23");
+// $header-toggler-hover-icon-bg:  str-replace(url("data:image/svg+xml;charset=utf8,%3Csvg viewBox='0 0 30 30' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath stroke='#{$header-hover-color}' stroke-width='2.25' stroke-linecap='round' stroke-miterlimit='10' d='M4 7h22M4 15h22M4 23h22'/%3E%3C/svg%3E"), "#", "%23");
+// $header-toggler-border-color:   rgba($black, .1);
+
+// $header-nav-link-padding-x:     .5rem;
+
+// $header-divider-border-width:   1px;
+// $header-divider-border-color:   $header-border-color;
+// scss-docs-end header-variables
+
+// Subheader
+// scss-docs-start subheader-variables
+// $subheader-min-height:           3rem;
+// $subheader-padding-y:            $spacer / 2;
+// $subheader-padding-x:            $spacer;
+// $subheader-border-color:         $border-color;
+// $subheader-border-width:         1px;
+
+// $subheader-nav-link-padding-x:  .5rem;
+// scss-docs-end subheader-variables
+
+// Default theme
+// scss-docs-start subheader-default-themes
+// $subheader-bg:                     $white;
+// $subheader-color:                  $medium-emphasis-dark;
+// $subheader-hover-color:            $high-emphasis-dark;
+// $subheader-active-color:           $high-emphasis-dark;
+// $subheader-disabled-color:         $disabled-dark;
+// scss-docs-end subheader-default-themes
+
+
+// Image thumbnails
+
+// $thumbnail-padding:                 .25rem;
+// $thumbnail-bg:                      $body-bg;
+// $thumbnail-border-width:            $border-width;
+// $thumbnail-border-color:            $gray-300;
+// $thumbnail-border-radius:           $border-radius;
+// $thumbnail-box-shadow:              $box-shadow-sm;
+
+
+// Figures
+
+// $figure-caption-font-size:          $small-font-size;
+// $figure-caption-color:              $gray-600;
+
+
+// Breadcrumbs
+// scss-docs-start breadcrumb-variables
+// $breadcrumb-font-size:              null;
+// $breadcrumb-padding-y:              0;
+// $breadcrumb-padding-x:              0;
+// $breadcrumb-item-padding-x:         .5rem;
+// $breadcrumb-margin-bottom:          1rem;
+// $breadcrumb-bg:                     unset;
+// $breadcrumb-divider-color:          $gray-600;
+// $breadcrumb-active-color:           $gray-600;
+// $breadcrumb-divider:                quote("/");
+// $breadcrumb-divider-flipped:        $breadcrumb-divider;
+// $breadcrumb-border-radius:          null;
+// scss-docs-end breadcrumb-variables
+
+// Carousel
+// scss-docs-start carousel-variables
+// $carousel-control-color:             $high-emphasis-light;
+// $carousel-control-width:             15%;
+// $carousel-control-opacity:           .5;
+// $carousel-control-hover-opacity:     .9;
+// $carousel-control-transition:        opacity .15s ease;
+
+// $carousel-indicator-width:           30px;
+// $carousel-indicator-height:          3px;
+// $carousel-indicator-hit-area-height: 10px;
+// $carousel-indicator-spacer:          3px;
+// $carousel-indicator-opacity:         .5;
+// $carousel-indicator-active-bg:       $white;
+// $carousel-indicator-active-opacity:  1;
+// $carousel-indicator-transition:      opacity .6s ease;
+
+// $carousel-caption-width:             70%;
+// $carousel-caption-color:             $high-emphasis-light;
+// $carousel-caption-padding-y:         1.25rem;
+// $carousel-caption-spacer:            1.25rem;
+
+// $carousel-control-icon-width:        2rem;
+
+// $carousel-control-prev-icon-bg:      url("data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$carousel-control-color}'><path d='M11.354 1.646a.5.5 0 0 1 0 .708L5.707 8l5.647 5.646a.5.5 0 0 1-.708.708l-6-6a.5.5 0 0 1 0-.708l6-6a.5.5 0 0 1 .708 0z'/></svg>");
+// $carousel-control-next-icon-bg:      url("data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$carousel-control-color}'><path d='M4.646 1.646a.5.5 0 0 1 .708 0l6 6a.5.5 0 0 1 0 .708l-6 6a.5.5 0 0 1-.708-.708L10.293 8 4.646 2.354a.5.5 0 0 1 0-.708z'/></svg>");
+
+// $carousel-transition-duration:       .6s;
+// $carousel-transition:                transform $carousel-transition-duration ease-in-out; // Define transform transition first if using multiple transitions (e.g., `transform 2s ease, opacity .5s ease-out`)
+
+// $carousel-dark-indicator-active-bg:  $black;
+// $carousel-dark-caption-color:        $high-emphasis-dark;
+// $carousel-dark-control-icon-filter:  invert(1) grayscale(100);
+// scss-docs-end carousel-variables
+
+// scss-docs-start sidebar-variables
+// $sidebar-width:                            16rem;
+// $sidebar-widths: (
+//   sm: 12rem,
+//   lg: 20rem,
+//   xl: 24rem
+// );
+// $sidebar-padding-y:                        0;
+// $sidebar-padding-x:                        0;
+// $sidebar-color:                            $high-emphasis-light;
+// $sidebar-bg:                               $gray-base;
+// $sidebar-border-width:                     0;
+// $sidebar-border-color:                     transparent;
+// $sidebar-transition:                       margin-left .15s, margin-right .15s, box-shadow .075s, transform .15s, width .15s, z-index 0s ease .15s;
+// $sidebar-reliant-transition:               width .15s;
+
+// $sidebar-brand-height:                     4rem;
+// $sidebar-brand-color:                      $high-emphasis-light;
+// $sidebar-brand-bg:                         rgba($black, .2);
+
+// $sidebar-header-height:                    4rem;
+// $sidebar-header-padding-y:                 .75rem;
+// $sidebar-header-padding-x:                 1rem;
+// $sidebar-header-bg:                        rgba($black, .2);
+// $sidebar-header-height-transition:         height .15s, padding .15s;
+
+// $sidebar-narrow-width:                     4rem;
+
+// $sidebar-backdrop-bg:                      $black;
+// $sidebar-backdrop-opacity:                 .5;
+// $sidebar-backdrop-transition:              opacity .15s linear;
+
+// $sidebar-nav-title-padding-y:              .75rem;
+// $sidebar-nav-title-padding-x:              1rem;
+// $sidebar-nav-title-margin-top:             1rem;
+// $sidebar-nav-title-color:                  $medium-emphasis-light;
+// $sidebar-nav-title-transition:             height .15s, margin .15s;
+
+// $sidebar-nav-link-padding-y:               .8445rem;
+// $sidebar-nav-link-padding-x:               1rem;
+// $sidebar-nav-link-color:                   $medium-emphasis-light;
+// $sidebar-nav-link-bg:                      transparent;
+// $sidebar-nav-link-transition:              background .15s ease, color .15s ease;
+// $sidebar-nav-link-icon-color:              $medium-emphasis-light;
+
+// $sidebar-nav-link-hover-color:             $high-emphasis-light;
+// $sidebar-nav-link-hover-bg:                rgba($white, .05);
+// $sidebar-nav-link-hover-icon-color:        $high-emphasis-light;
+
+// $sidebar-nav-link-active-color:            $high-emphasis-light;
+// $sidebar-nav-link-active-bg:               rgba($white, .05);
+// $sidebar-nav-link-active-icon-color:       $high-emphasis-light;
+
+// $sidebar-nav-link-disabled-color:          $disabled-light;
+// $sidebar-nav-link-disabled-icon-color:     $sidebar-nav-link-icon-color;
+
+// $sidebar-nav-icon-width:                   4rem;
+// $sidebar-nav-icon-height:                  1.25rem;
+// $sidebar-nav-icon-font-size:               $sidebar-nav-icon-height;
+
+// $sidebar-nav-group-bg:                     rgba(0, 0, 0, .2);
+// $sidebar-nav-group-transition:             background .15s ease-in-out;
+// $sidebar-nav-group-items-transition:       height .15s ease;
+// $sidebar-nav-group-toggle-show-color:      $sidebar-nav-link-color;
+
+// $sidebar-nav-group-indicator-color:        $medium-emphasis-light;
+// $sidebar-nav-group-indicator:              str-replace(url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 11 14'%3E%3Cpath fill='#{$sidebar-nav-group-indicator-color}' d='M9.148 2.352l-4.148 4.148 4.148 4.148q0.148 0.148 0.148 0.352t-0.148 0.352l-1.297 1.297q-0.148 0.148-0.352 0.148t-0.352-0.148l-5.797-5.797q-0.148-0.148-0.148-0.352t0.148-0.352l5.797-5.797q0.148-0.148 0.352-0.148t0.352 0.148l1.297 1.297q0.148 0.148 0.148 0.352t-0.148 0.352z'/%3E%3C/svg%3E"), "#", "%23");
+// $sidebar-nav-group-indicator-hover-color:  $sidebar-nav-link-hover-color;
+// $sidebar-nav-group-indicator-hover:        str-replace(url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 11 14'%3E%3Cpath fill='#{$sidebar-nav-group-indicator-hover-color}' d='M9.148 2.352l-4.148 4.148 4.148 4.148q0.148 0.148 0.148 0.352t-0.148 0.352l-1.297 1.297q-0.148 0.148-0.352 0.148t-0.352-0.148l-5.797-5.797q-0.148-0.148-0.148-0.352t0.148-0.352l5.797-5.797q0.148-0.148 0.352-0.148t0.352 0.148l1.297 1.297q0.148 0.148 0.148 0.352t-0.148 0.352z'/%3E%3C/svg%3E"), "#", "%23");
+// $sidebar-nav-group-indicator-transition:   transform .15s;
+
+// $sidebar-footer-height:                    auto;
+// $sidebar-footer-padding-y:                 .75rem;
+// $sidebar-footer-padding-x:                 1rem;
+// $sidebar-footer-bg:                        rgba($black, .2);
+// $sidebar-footer-height-transition:         height .15s, padding .15s;
+
+// $sidebar-toggler-height:                   3rem;
+// $sidebar-toggler-bg:                       rgba($black, .2);
+// $sidebar-toggler-transition:               transform .15s;
+
+// $sidebar-toggler-indicator-width:          4rem;
+// $sidebar-toggler-indicator-height:         3rem;
+// $sidebar-toggler-indicator-color:          $gray-600;
+// $sidebar-toggler-indicator:                str-replace(url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 11 14'%3E%3Cpath fill='#{$sidebar-toggler-indicator-color}' d='M9.148 2.352l-4.148 4.148 4.148 4.148q0.148 0.148 0.148 0.352t-0.148 0.352l-1.297 1.297q-0.148 0.148-0.352 0.148t-0.352-0.148l-5.797-5.797q-0.148-0.148-0.148-0.352t0.148-0.352l5.797-5.797q0.148-0.148 0.352-0.148t0.352 0.148l1.297 1.297q0.148 0.148 0.148 0.352t-0.148 0.352z'/%3E%3C/svg%3E"), "#", "%23");
+// $sidebar-toggler-hover-bg:                 rgba(0, 0, 0, .3);
+// $sidebar-toggler-hover-indicator-color:    $sidebar-nav-link-hover-color;
+// $sidebar-toggler-hover-indicator:          str-replace(url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 11 14'%3E%3Cpath fill='#{$sidebar-toggler-hover-indicator-color}' d='M9.148 2.352l-4.148 4.148 4.148 4.148q0.148 0.148 0.148 0.352t-0.148 0.352l-1.297 1.297q-0.148 0.148-0.352 0.148t-0.352-0.148l-5.797-5.797q-0.148-0.148-0.148-0.352t0.148-0.352l5.797-5.797q0.148-0.148 0.352-0.148t0.352 0.148l1.297 1.297q0.148 0.148 0.148 0.352t-0.148 0.352z'/%3E%3C/svg%3E"), "#", "%23");
+// scss-docs-end sidebar-variables
+
+// Footer
+// scss-docs-start footer-variables
+// $footer-min-height:    3rem;
+// $footer-padding-y:     $spacer / 2;
+// $footer-padding-x:     $spacer;
+// $footer-bg:            $gray-100;
+// $footer-color:         $body-color;
+// $footer-border-width:  1px;
+// $footer-border-color:  $border-color;
+// scss-docs-end footer-variables
+
+// Spinners
+// scss-docs-start spinner-variables
+// $spinner-width:           2rem;
+// $spinner-height:          $spinner-width;
+// $spinner-border-width:    .25em;
+// $spinner-animation-speed: .75s;
+
+// $spinner-width-sm:        1rem;
+// $spinner-height-sm:       $spinner-width-sm;
+// $spinner-border-width-sm: .2em;
+// scss-docs-end spinner-variables
+
+
+// Close
+// scss-docs-start close-variables
+// $btn-close-width:            1em;
+// $btn-close-height:           $btn-close-width;
+// $btn-close-padding-x:        .25em;
+// $btn-close-padding-y:        $btn-close-padding-x;
+// $btn-close-color:            $high-emphasis-dark;
+// $btn-close-bg:               url("data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$btn-close-color}'><path d='M.293.293a1 1 0 011.414 0L8 6.586 14.293.293a1 1 0 111.414 1.414L9.414 8l6.293 6.293a1 1 0 01-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 01-1.414-1.414L6.586 8 .293 1.707a1 1 0 010-1.414z'/></svg>");
+// $btn-close-focus-shadow:     $input-btn-focus-box-shadow;
+// $btn-close-opacity:          .5;
+// $btn-close-hover-opacity:    .75;
+// $btn-close-focus-opacity:    1;
+// $btn-close-disabled-opacity: .25;
+// $btn-close-white-filter:     invert(1) grayscale(100%) brightness(200%);
+// scss-docs-end close-variables
+
+// Code
+
+// $code-font-size:                    $small-font-size;
+// $code-color:                        $pink;
+
+// $kbd-padding-y:                     .2rem;
+// $kbd-padding-x:                     .4rem;
+// $kbd-font-size:                     $code-font-size;
+// $kbd-color:                         $high-emphasis-light;
+// $kbd-bg:                            $gray-900;
+
+// $pre-color:                         unset;
diff --git a/web/frontend/src/scss/styles.scss b/web/frontend/src/scss/styles.scss
new file mode 100644
index 0000000000000000000000000000000000000000..e7f0bde95058c664fb177379cc54504c48cbce01
--- /dev/null
+++ b/web/frontend/src/scss/styles.scss
@@ -0,0 +1,64 @@
+
+// Custom Theming for Angular Material
+// For more information: https://material.angular.io/guide/theming
+@use '@angular/material' as mat;
+// Plus imports for other components in your app.
+
+// Include the common styles for Angular Material. We include this here so that you only
+// have to load a single css file for Angular Material in your app.
+// Be sure that you only ever include this mixin once!
+@include mat.core();
+
+// Define the palettes for your theme using the Material Design palettes available in palette.scss
+// (imported above). For each palette, you can optionally specify a default, lighter, and darker
+// hue. Available color palettes: https://material.io/design/color/
+$coreui-free-angular-admin-template-primary: mat.define-palette(mat.$indigo-palette);
+$coreui-free-angular-admin-template-accent: mat.define-palette(mat.$pink-palette, A200, A100, A400);
+
+// The warn palette is optional (defaults to red).
+$coreui-free-angular-admin-template-warn: mat.define-palette(mat.$red-palette);
+
+// Create the theme object. A theme consists of configurations for individual
+// theming systems such as "color" or "typography".
+$coreui-free-angular-admin-template-theme: mat.define-light-theme((
+  color: (
+    primary: $coreui-free-angular-admin-template-primary,
+    accent: $coreui-free-angular-admin-template-accent,
+    warn: $coreui-free-angular-admin-template-warn,
+  )
+));
+
+// Include theme styles for core and each component used in your app.
+// Alternatively, you can import and @include the theme mixins for each component
+// that you are using.
+@include mat.all-component-themes($coreui-free-angular-admin-template-theme);
+
+/* You can add global styles to this file, and also import other style files */
+// If you want to override variables do it here
+@import "variables";
+
+$enable-ltr: true;
+$enable-rtl: true;
+
+// Import styles with default layout.
+@import "@coreui/coreui/scss/coreui";
+
+// Import Chart.js custom tooltips styles
+@import "@coreui/chartjs/scss/coreui-chartjs";
+
+// perfect scrollbar theming for use with [perfectScrollbar] directive
+//@import '~perfect-scrollbar/css/perfect-scrollbar.css';
+
+@import "layout";
+
+// Some temp fixes
+@import "fixes";
+
+// Prism.js
+@import "examples";
+
+// If you want to add something do it here
+@import "custom";
+
+html, body { height: 100%; }
+body { margin: 0; font-family: Roboto, "Helvetica Neue", sans-serif; }
diff --git a/web/frontend/src/test.ts b/web/frontend/src/test.ts
new file mode 100644
index 0000000000000000000000000000000000000000..2000d11def2870e5f65a70fef9675f8a247a8411
--- /dev/null
+++ b/web/frontend/src/test.ts
@@ -0,0 +1,27 @@
+// This file is required by karma.conf.js and loads recursively all the .spec and framework files
+
+import 'zone.js/testing';
+import { getTestBed } from '@angular/core/testing';
+import {
+  BrowserDynamicTestingModule,
+  platformBrowserDynamicTesting
+} from '@angular/platform-browser-dynamic/testing';
+
+declare const require: {
+  context(path: string, deep?: boolean, filter?: RegExp): {
+    <T>(id: string): T;
+    keys(): string[];
+  };
+};
+
+// First, initialize the Angular testing environment.
+getTestBed().initTestEnvironment(
+  BrowserDynamicTestingModule,
+  platformBrowserDynamicTesting(),
+  { teardown: { destroyAfterEach: true }},
+);
+
+// Then we find all the tests.
+const context = require.context('./', true, /\.spec\.ts$/);
+// And load the modules.
+context.keys().map(context);
diff --git a/web/frontend/tsconfig.app.json b/web/frontend/tsconfig.app.json
new file mode 100644
index 0000000000000000000000000000000000000000..dd63637a593fd54da5c192fd843efe102a0f6640
--- /dev/null
+++ b/web/frontend/tsconfig.app.json
@@ -0,0 +1,18 @@
+/* To learn more about this file see: https://angular.io/config/tsconfig. */
+{
+  "extends": "./tsconfig.json",
+  "compilerOptions": {
+    "outDir": "./out-tsc/app",
+    "types": [],
+    "paths": {
+      "@docs-components/*": ["./src/components/*"]
+    }
+  },
+  "files": [
+    "src/main.ts",
+    "src/polyfills.ts"
+  ],
+  "include": [
+    "src/**/*.d.ts"
+  ]
+}
diff --git a/web/frontend/tsconfig.json b/web/frontend/tsconfig.json
new file mode 100644
index 0000000000000000000000000000000000000000..91922226a6a71afb0a35ef38ad59552abe30a9e0
--- /dev/null
+++ b/web/frontend/tsconfig.json
@@ -0,0 +1,37 @@
+/* To learn more about this file see: https://angular.io/config/tsconfig. */
+{
+  "compileOnSave": false,
+  "compilerOptions": {
+    "baseUrl": "./",
+    "outDir": "./dist/out-tsc",
+    "forceConsistentCasingInFileNames": true,
+    "strict": true,
+    "noImplicitOverride": true,
+    "noPropertyAccessFromIndexSignature": true,
+    "noImplicitReturns": true,
+    "noFallthroughCasesInSwitch": true,
+    "sourceMap": true,
+    "declaration": false,
+    "downlevelIteration": true,
+    "experimentalDecorators": true,
+    "moduleResolution": "node",
+    "importHelpers": true,
+    "target": "es2020",
+    "module": "es2020",
+    "lib": [
+      "es2020",
+      "dom"
+    ]
+  },
+  "angularCompilerOptions": {
+    "enableI18nLegacyMessageIdFormat": false,
+    "strictInjectionParameters": true,
+    "strictInputAccessModifiers": true,
+    "strictTemplates": true
+  },
+  "files": [],
+  "references": [
+    { "path": "./tsconfig.app.json" },
+    { "path": "./tsconfig.spec.json" },
+  ]
+}
diff --git a/web/frontend/tsconfig.spec.json b/web/frontend/tsconfig.spec.json
new file mode 100644
index 0000000000000000000000000000000000000000..092345b02e807773164dba19f2b4d1f4ab869f67
--- /dev/null
+++ b/web/frontend/tsconfig.spec.json
@@ -0,0 +1,18 @@
+/* To learn more about this file see: https://angular.io/config/tsconfig. */
+{
+  "extends": "./tsconfig.json",
+  "compilerOptions": {
+    "outDir": "./out-tsc/spec",
+    "types": [
+      "jasmine"
+    ]
+  },
+  "files": [
+    "src/test.ts",
+    "src/polyfills.ts"
+  ],
+  "include": [
+    "src/**/*.spec.ts",
+    "src/**/*.d.ts"
+  ]
+}