home_work.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sqlite3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the dummy CDR into a database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2279095"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the dumm CDR\n",
    "cdr = pd.read_csv(\"dummy_data/cdr.csv\")\n",
    "\n",
    "# create in-memory database\n",
    "db = sqlite3.connect(\":memory:\")\n",
    "\n",
    "# drop the cdr table if already exists\n",
    "db.execute(\"DROP TABLE IF EXISTS cdr\")\n",
    "\n",
    "# load dummy CDR to the in-memory database\n",
    "cdr.to_sql(\"cdr\", db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Executing the queries\n",
    "\n",
    "Please note that this notebook uses sqlite, but in the study PostgreSQL was used. The PostgreSQL dialects of these queries are also included in this repository.\n",
    "\n",
    "## Home\n",
    "\n",
    "```sql\n",
    "SELECT\n",
    "    device_id, cell_id, count(*) AS count\n",
    "FROM\n",
    "    cdr\n",
    "WHERE\n",
    "(\n",
    "        -- weekends, 0 is Sunday, 6 is Saturday\n",
    "        mod(cast(strftime(\"%w\", cdr.timestamp) AS INT), 6) = 0\n",
    "    OR\n",
    "        -- Good Friday\n",
    "        date(cdr.timestamp) = '2017-04-14'\n",
    "    OR\n",
    "        -- Easter Monday\n",
    "        date(cdr.timestamp) = '2017-04-17'\n",
    "    )\n",
    "OR\n",
    "    (\n",
    "        -- weekdays, 0 is Sunday, 6 is Saturday\n",
    "        mod(cast(strftime(\"%w\", cdr.timestamp) AS INT), 6) > 0\n",
    "    AND\n",
    "        -- Good Friday\n",
    "        date(cdr.timestamp) != '2017-04-14'\n",
    "    AND\n",
    "        -- Easter Monday\n",
    "        date(cdr.timestamp) != '2017-04-17'\n",
    "    AND\n",
    "        (\n",
    "            time(cdr.timestamp) >= '22:00:00'\n",
    "        OR\n",
    "            time(cdr.timestamp) <= '06:00:00'\n",
    "        )\n",
    "    )\n",
    "GROUP BY\n",
    "    device_id, cell_id\n",
    "ORDER BY\n",
    "    device_id, count DESC;\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"home_and_work_detection/home.sqlite.sql\") as fp:\n",
    "    home_sql = fp.read()\n",
    "\n",
    "most_frequent_weekend_and_night_cells = pd.read_sql(home_sql, db)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Work\n",
    "\n",
    "```sql\n",
    "SELECT\n",
    "    device_id, cell_id, count(*) AS count\n",
    "FROM\n",
    "    cdr\n",
    "WHERE\n",
    "    -- weekdays, 0 is Sunday, 6 is Saturday\n",
    "    mod(cast(strftime(\"%w\", cdr.timestamp) AS INT), 6) > 0\n",
    "AND\n",
    "    -- Good Friday\n",
    "    date(cdr.timestamp) != '2017-04-14'\n",
    "AND\n",
    "    -- Easter Monday\n",
    "    date(cdr.timestamp) != '2017-04-17'\n",
    "AND\n",
    "    time(cdr.timestamp) >= '09:00:00'\n",
    "AND\n",
    "    time(cdr.timestamp) <= '16:00:00'\n",
    "GROUP BY\n",
    "    device_id, cell_id\n",
    "ORDER BY\n",
    "    device_id, count DESC;\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "with open(\"home_and_work_detection/work.sqlite.sql\") as fp:\n",
    "    work_sql = fp.read()\n",
    "\n",
    "most_frequent_weekday_workhour_cells = pd.read_sql(work_sql, db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selecting the most frequent home and work candidate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "home = most_frequent_weekend_and_night_cells.drop_duplicates(subset=\"device_id\", keep=\"first\").reset_index(drop=True).copy()\n",
    "\n",
    "home[\"total\"] = most_frequent_weekend_and_night_cells.groupby(\"device_id\")[\"count\"].sum().tolist()\n",
    "home[\"certainty\"] = home[\"count\"] / home[\"total\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "work = most_frequent_weekday_workhour_cells.drop_duplicates(subset=\"device_id\", keep=\"first\").reset_index(drop=True).copy()\n",
    "\n",
    "work[\"total\"] = most_frequent_weekday_workhour_cells.groupby(\"device_id\")[\"count\"].sum().tolist()\n",
    "work[\"certainty\"] = work[\"count\"] / work[\"total\"]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Printing the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>device_id</th>\n",
       "      <th>cell_id_work</th>\n",
       "      <th>count_work</th>\n",
       "      <th>total_work</th>\n",
       "      <th>certainty_work</th>\n",
       "      <th>cell_id_home</th>\n",
       "      <th>count_home</th>\n",
       "      <th>total_home</th>\n",
       "      <th>certainty_home</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2749618</td>\n",
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       "      <td>87</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>2082758.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.030303</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>667986</td>\n",
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       "      <td>0.043478</td>\n",
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       "      <td>34.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "      <td>105</td>\n",
       "      <td>0.019048</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.024691</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>738684</td>\n",
       "      <td>6</td>\n",
       "      <td>246</td>\n",
       "      <td>0.024390</td>\n",
       "      <td>51256.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>0.025641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2749618</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>2747090.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.031250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>9982</th>\n",
       "      <td>9996</td>\n",
       "      <td>2749618</td>\n",
       "      <td>5</td>\n",
       "      <td>215</td>\n",
       "      <td>0.023256</td>\n",
       "      <td>10314.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>0.027027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9983</th>\n",
       "      <td>9997</td>\n",
       "      <td>2702588</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>2738127.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.200000</td>\n",
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       "    <tr>\n",
       "      <th>9984</th>\n",
       "      <td>9998</td>\n",
       "      <td>2697628</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>731888.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.095238</td>\n",
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       "    <tr>\n",
       "      <th>9985</th>\n",
       "      <td>9999</td>\n",
       "      <td>2749618</td>\n",
       "      <td>3</td>\n",
       "      <td>90</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>731888.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.029412</td>\n",
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       "    <tr>\n",
       "      <th>9986</th>\n",
       "      <td>10000</td>\n",
       "      <td>2749618</td>\n",
       "      <td>3</td>\n",
       "      <td>104</td>\n",
       "      <td>0.028846</td>\n",
       "      <td>2738118.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>0.033708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9987 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      device_id  cell_id_work  count_work  total_work  certainty_work  \\\n",
       "0             1       2749618           3          87        0.034483   \n",
       "1             2        667986           2          46        0.043478   \n",
       "2             3       2749618           2         105        0.019048   \n",
       "3             4        738684           6         246        0.024390   \n",
       "4             5       2749618           2          44        0.045455   \n",
       "...         ...           ...         ...         ...             ...   \n",
       "9982       9996       2749618           5         215        0.023256   \n",
       "9983       9997       2702588           1           5        0.200000   \n",
       "9984       9998       2697628           1          24        0.041667   \n",
       "9985       9999       2749618           3          90        0.033333   \n",
       "9986      10000       2749618           3         104        0.028846   \n",
       "\n",
       "      cell_id_home  count_home  total_home  certainty_home  \n",
       "0        2082758.0         2.0        66.0        0.030303  \n",
       "1        2743054.0         1.0        34.0        0.029412  \n",
       "2        2738118.0         2.0        81.0        0.024691  \n",
       "3          51256.0         5.0       195.0        0.025641  \n",
       "4        2747090.0         1.0        32.0        0.031250  \n",
       "...            ...         ...         ...             ...  \n",
       "9982       10314.0         4.0       148.0        0.027027  \n",
       "9983     2738127.0         1.0         5.0        0.200000  \n",
       "9984      731888.0         2.0        21.0        0.095238  \n",
       "9985      731888.0         2.0        68.0        0.029412  \n",
       "9986     2738118.0         3.0        89.0        0.033708  \n",
       "\n",
       "[9987 rows x 9 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge\n",
    "hw = home.merge(work, on=\"device_id\", how=\"outer\", suffixes=[\"_work\", \"_home\"])\n",
    "hw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mapping cells to districts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.10/site-packages/geopandas/_compat.py:112: UserWarning: The Shapely GEOS version (3.11.0-CAPI-1.17.0) is incompatible with the GEOS version PyGEOS was compiled with (3.9.1-CAPI-1.14.2). Conversions between both will be slow.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "from shapely import wkt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load shapefile, downloaded from OSM, enriched with information as district group and agglomeration sector\n",
    "bp = gpd.read_file(\"input/budapest_border.geojson\", crs=4326)\n",
    "cities = gpd.read_file(\"input/cities_with_labels.geojson\", crs=4326)\n",
    "\n",
    "cells = pd.read_csv(\"dummy_data/cells.csv\")\n",
    "cells[\"geometry\"] = cells[\"geometry\"].apply(wkt.loads)\n",
    "cells = gpd.GeoDataFrame(cells, geometry=\"geometry\", crs=4326)\n",
    "cells.rename({\"cid\": \"cell_id\"}, axis=1, inplace=True)\n",
    "\n",
    "# map cells to districts or settlements\n",
    "cells = cells.sjoin(cities, how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>radio</th>\n",
       "      <th>mcc</th>\n",
       "      <th>mnc</th>\n",
       "      <th>lac</th>\n",
       "      <th>cell_id</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>range</th>\n",
       "      <th>samples</th>\n",
       "      <th>created</th>\n",
       "      <th>updated</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "      <th>osm_id</th>\n",
       "      <th>name</th>\n",
       "      <th>city</th>\n",
       "      <th>sector</th>\n",
       "      <th>district_group</th>\n",
       "      <th>city_part</th>\n",
       "      <th>did</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GSM</td>\n",
       "      <td>216</td>\n",
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       "      <td>19.284950</td>\n",
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       "      <td>1515062886</td>\n",
       "      <td>POINT (19.28495 47.29704)</td>\n",
       "      <td>86.0</td>\n",
       "      <td>214784</td>\n",
       "      <td>Ócsa</td>\n",
       "      <td>ocsa</td>\n",
       "      <td>south_eastern</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GSM</td>\n",
       "      <td>216</td>\n",
       "      <td>70</td>\n",
       "      <td>141</td>\n",
       "      <td>12151</td>\n",
       "      <td>19.269680</td>\n",
       "      <td>47.343904</td>\n",
       "      <td>25773</td>\n",
       "      <td>383</td>\n",
       "      <td>1317853422</td>\n",
       "      <td>1515063475</td>\n",
       "      <td>POINT (19.26968 47.34390)</td>\n",
       "      <td>63.0</td>\n",
       "      <td>214782</td>\n",
       "      <td>Felsőpakony</td>\n",
       "      <td>felsopakony</td>\n",
       "      <td>south_eastern</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GSM</td>\n",
       "      <td>216</td>\n",
       "      <td>70</td>\n",
       "      <td>141</td>\n",
       "      <td>14765</td>\n",
       "      <td>19.277820</td>\n",
       "      <td>47.285802</td>\n",
       "      <td>22136</td>\n",
       "      <td>1188</td>\n",
       "      <td>1317853422</td>\n",
       "      <td>1618193051</td>\n",
       "      <td>POINT (19.27782 47.28580)</td>\n",
       "      <td>86.0</td>\n",
       "      <td>214784</td>\n",
       "      <td>Ócsa</td>\n",
       "      <td>ocsa</td>\n",
       "      <td>south_eastern</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GSM</td>\n",
       "      <td>216</td>\n",
       "      <td>70</td>\n",
       "      <td>141</td>\n",
       "      <td>12152</td>\n",
       "      <td>19.291631</td>\n",
       "      <td>47.297692</td>\n",
       "      <td>11950</td>\n",
       "      <td>1204</td>\n",
       "      <td>1317854261</td>\n",
       "      <td>1515062696</td>\n",
       "      <td>POINT (19.29163 47.29769)</td>\n",
       "      <td>86.0</td>\n",
       "      <td>214784</td>\n",
       "      <td>Ócsa</td>\n",
       "      <td>ocsa</td>\n",
       "      <td>south_eastern</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GSM</td>\n",
       "      <td>216</td>\n",
       "      <td>70</td>\n",
       "      <td>141</td>\n",
       "      <td>14764</td>\n",
       "      <td>19.224429</td>\n",
       "      <td>47.341574</td>\n",
       "      <td>22578</td>\n",
       "      <td>1462</td>\n",
       "      <td>1317854261</td>\n",
       "      <td>1571281991</td>\n",
       "      <td>POINT (19.22443 47.34157)</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1385739</td>\n",
       "      <td>Gyál</td>\n",
       "      <td>gyal</td>\n",
       "      <td>south_eastern</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3987</th>\n",
       "      <td>UMTS</td>\n",
       "      <td>216</td>\n",
       "      <td>70</td>\n",
       "      <td>235</td>\n",
       "      <td>2698548</td>\n",
       "      <td>19.155275</td>\n",
       "      <td>47.605460</td>\n",
       "      <td>1969</td>\n",
       "      <td>390</td>\n",
       "      <td>1613229242</td>\n",
       "      <td>1640668373</td>\n",
       "      <td>POINT (19.15527 47.60546)</td>\n",
       "      <td>22.0</td>\n",
       "      <td>214931</td>\n",
       "      <td>Fót</td>\n",
       "      <td>fot</td>\n",
       "      <td>northern</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3988</th>\n",
       "      <td>UMTS</td>\n",
       "      <td>216</td>\n",
       "      <td>70</td>\n",
       "      <td>3111</td>\n",
       "      <td>267276</td>\n",
       "      <td>18.934753</td>\n",
       "      <td>47.459028</td>\n",
       "      <td>3809</td>\n",
       "      <td>327</td>\n",
       "      <td>1621498140</td>\n",
       "      <td>1648376182</td>\n",
       "      <td>POINT (18.93475 47.45903)</td>\n",
       "      <td>39.0</td>\n",
       "      <td>113463</td>\n",
       "      <td>Budaörs</td>\n",
       "      <td>budaors</td>\n",
       "      <td>western</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3989</th>\n",
       "      <td>UMTS</td>\n",
       "      <td>216</td>\n",
       "      <td>70</td>\n",
       "      <td>3111</td>\n",
       "      <td>447500</td>\n",
       "      <td>18.932141</td>\n",
       "      <td>47.457515</td>\n",
       "      <td>1276</td>\n",
       "      <td>297</td>\n",
       "      <td>1621498243</td>\n",
       "      <td>1647764986</td>\n",
       "      <td>POINT (18.93214 47.45752)</td>\n",
       "      <td>39.0</td>\n",
       "      <td>113463</td>\n",
       "      <td>Budaörs</td>\n",
       "      <td>budaors</td>\n",
       "      <td>western</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3990</th>\n",
       "      <td>UMTS</td>\n",
       "      <td>216</td>\n",
       "      <td>70</td>\n",
       "      <td>3115</td>\n",
       "      <td>454155</td>\n",
       "      <td>19.042941</td>\n",
       "      <td>47.466129</td>\n",
       "      <td>944</td>\n",
       "      <td>390</td>\n",
       "      <td>1632393634</td>\n",
       "      <td>1653712405</td>\n",
       "      <td>POINT (19.04294 47.46613)</td>\n",
       "      <td>71.0</td>\n",
       "      <td>221998</td>\n",
       "      <td>11. kerület</td>\n",
       "      <td>bp_11</td>\n",
       "      <td>None</td>\n",
       "      <td>south-buda</td>\n",
       "      <td>inner</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3991</th>\n",
       "      <td>UMTS</td>\n",
       "      <td>216</td>\n",
       "      <td>70</td>\n",
       "      <td>3111</td>\n",
       "      <td>438284</td>\n",
       "      <td>18.928071</td>\n",
       "      <td>47.460284</td>\n",
       "      <td>1123</td>\n",
       "      <td>771</td>\n",
       "      <td>1640407792</td>\n",
       "      <td>1646475994</td>\n",
       "      <td>POINT (18.92807 47.46028)</td>\n",
       "      <td>39.0</td>\n",
       "      <td>113463</td>\n",
       "      <td>Budaörs</td>\n",
       "      <td>budaors</td>\n",
       "      <td>western</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3992 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     radio  mcc  mnc   lac  cell_id        lon        lat  range  samples  \\\n",
       "0      GSM  216   70   141    12142  19.284950  47.297036  39621     1999   \n",
       "1      GSM  216   70   141    12151  19.269680  47.343904  25773      383   \n",
       "2      GSM  216   70   141    14765  19.277820  47.285802  22136     1188   \n",
       "3      GSM  216   70   141    12152  19.291631  47.297692  11950     1204   \n",
       "4      GSM  216   70   141    14764  19.224429  47.341574  22578     1462   \n",
       "...    ...  ...  ...   ...      ...        ...        ...    ...      ...   \n",
       "3987  UMTS  216   70   235  2698548  19.155275  47.605460   1969      390   \n",
       "3988  UMTS  216   70  3111   267276  18.934753  47.459028   3809      327   \n",
       "3989  UMTS  216   70  3111   447500  18.932141  47.457515   1276      297   \n",
       "3990  UMTS  216   70  3115   454155  19.042941  47.466129    944      390   \n",
       "3991  UMTS  216   70  3111   438284  18.928071  47.460284   1123      771   \n",
       "\n",
       "         created     updated                   geometry  index_right   osm_id  \\\n",
       "0     1317853422  1515062886  POINT (19.28495 47.29704)         86.0   214784   \n",
       "1     1317853422  1515063475  POINT (19.26968 47.34390)         63.0   214782   \n",
       "2     1317853422  1618193051  POINT (19.27782 47.28580)         86.0   214784   \n",
       "3     1317854261  1515062696  POINT (19.29163 47.29769)         86.0   214784   \n",
       "4     1317854261  1571281991  POINT (19.22443 47.34157)         20.0  1385739   \n",
       "...          ...         ...                        ...          ...      ...   \n",
       "3987  1613229242  1640668373  POINT (19.15527 47.60546)         22.0   214931   \n",
       "3988  1621498140  1648376182  POINT (18.93475 47.45903)         39.0   113463   \n",
       "3989  1621498243  1647764986  POINT (18.93214 47.45752)         39.0   113463   \n",
       "3990  1632393634  1653712405  POINT (19.04294 47.46613)         71.0   221998   \n",
       "3991  1640407792  1646475994  POINT (18.92807 47.46028)         39.0   113463   \n",
       "\n",
       "             name         city         sector district_group city_part   did  \n",
       "0            Ócsa         ocsa  south_eastern           None      None  None  \n",
       "1     Felsőpakony  felsopakony  south_eastern           None      None  None  \n",
       "2            Ócsa         ocsa  south_eastern           None      None  None  \n",
       "3            Ócsa         ocsa  south_eastern           None      None  None  \n",
       "4            Gyál         gyal  south_eastern           None      None  None  \n",
       "...           ...          ...            ...            ...       ...   ...  \n",
       "3987          Fót          fot       northern           None      None  None  \n",
       "3988      Budaörs      budaors        western           None      None  None  \n",
       "3989      Budaörs      budaors        western           None      None  None  \n",
       "3990  11. kerület        bp_11           None     south-buda     inner    11  \n",
       "3991      Budaörs      budaors        western           None      None  None  \n",
       "\n",
       "[3992 rows x 20 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging settlement information to the detected home-work location DataFrame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cells_for_home = cells[[\"cell_id\", \"lon\", \"lat\", \"geometry\", \"city\", \"sector\", \"district_group\"]].copy()\n",
    "cells_for_home.columns = [i+\"_home\" for i in cells_for_home.columns]\n",
    "cells_for_work = cells[[\"cell_id\", \"geometry\", \"lon\", \"lat\", \"city\", \"sector\", \"district_group\"]].copy()\n",
    "cells_for_work.columns = [i+\"_work\" for i in cells_for_work.columns]\n",
    "\n",
    "merged = hw.merge(cells_for_home, on=\"cell_id_home\", how=\"left\")\\\n",
    "           .merge(cells_for_work, on=\"cell_id_work\", how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>device_id</th>\n",
       "      <th>cell_id_work</th>\n",
       "      <th>count_work</th>\n",
       "      <th>total_work</th>\n",
       "      <th>certainty_work</th>\n",
       "      <th>cell_id_home</th>\n",
       "      <th>count_home</th>\n",
       "      <th>total_home</th>\n",
       "      <th>certainty_home</th>\n",
       "      <th>lon_home</th>\n",
       "      <th>...</th>\n",
       "      <th>geometry_home</th>\n",
       "      <th>city_home</th>\n",
       "      <th>sector_home</th>\n",
       "      <th>district_group_home</th>\n",
       "      <th>geometry_work</th>\n",
       "      <th>lon_work</th>\n",
       "      <th>lat_work</th>\n",
       "      <th>city_work</th>\n",
       "      <th>sector_work</th>\n",
       "      <th>district_group_work</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2749618</td>\n",
       "      <td>3</td>\n",
       "      <td>87</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>2082758.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>19.155262</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (19.15526 47.71823)</td>\n",
       "      <td>szodliget</td>\n",
       "      <td>northern</td>\n",
       "      <td>None</td>\n",
       "      <td>POINT (19.11704 47.53163)</td>\n",
       "      <td>19.117037</td>\n",
       "      <td>47.531626</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>667986</td>\n",
       "      <td>2</td>\n",
       "      <td>46</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>2743054.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>18.968748</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (18.96875 47.77106)</td>\n",
       "      <td>visegrad</td>\n",
       "      <td>north_western</td>\n",
       "      <td>None</td>\n",
       "      <td>POINT (19.10315 47.48817)</td>\n",
       "      <td>19.103152</td>\n",
       "      <td>47.488165</td>\n",
       "      <td>bp_8</td>\n",
       "      <td>None</td>\n",
       "      <td>inner-pest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2749618</td>\n",
       "      <td>2</td>\n",
       "      <td>105</td>\n",
       "      <td>0.019048</td>\n",
       "      <td>2738118.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.024691</td>\n",
       "      <td>19.154384</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (19.15438 47.71111)</td>\n",
       "      <td>god</td>\n",
       "      <td>northern</td>\n",
       "      <td>None</td>\n",
       "      <td>POINT (19.11704 47.53163)</td>\n",
       "      <td>19.117037</td>\n",
       "      <td>47.531626</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>738684</td>\n",
       "      <td>6</td>\n",
       "      <td>246</td>\n",
       "      <td>0.024390</td>\n",
       "      <td>51256.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>19.337478</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (19.33748 47.59498)</td>\n",
       "      <td>godollo</td>\n",
       "      <td>eastern</td>\n",
       "      <td>None</td>\n",
       "      <td>POINT (19.00867 47.49683)</td>\n",
       "      <td>19.008669</td>\n",
       "      <td>47.496834</td>\n",
       "      <td>bp_12</td>\n",
       "      <td>None</td>\n",
       "      <td>north-buda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>738684</td>\n",
       "      <td>6</td>\n",
       "      <td>246</td>\n",
       "      <td>0.024390</td>\n",
       "      <td>51256.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>19.344040</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (19.34404 47.58780)</td>\n",
       "      <td>godollo</td>\n",
       "      <td>eastern</td>\n",
       "      <td>None</td>\n",
       "      <td>POINT (19.00867 47.49683)</td>\n",
       "      <td>19.008669</td>\n",
       "      <td>47.496834</td>\n",
       "      <td>bp_12</td>\n",
       "      <td>None</td>\n",
       "      <td>north-buda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11683</th>\n",
       "      <td>9996</td>\n",
       "      <td>2749618</td>\n",
       "      <td>5</td>\n",
       "      <td>215</td>\n",
       "      <td>0.023256</td>\n",
       "      <td>10314.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>19.105231</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (19.10523 47.51570)</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "      <td>POINT (19.11704 47.53163)</td>\n",
       "      <td>19.117037</td>\n",
       "      <td>47.531626</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11684</th>\n",
       "      <td>9997</td>\n",
       "      <td>2702588</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>2738127.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>19.165791</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (19.16579 47.66910)</td>\n",
       "      <td>god</td>\n",
       "      <td>northern</td>\n",
       "      <td>None</td>\n",
       "      <td>POINT (19.11602 47.54371)</td>\n",
       "      <td>19.116023</td>\n",
       "      <td>47.543709</td>\n",
       "      <td>bp_15</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_outer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11685</th>\n",
       "      <td>9998</td>\n",
       "      <td>2697628</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>731888.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>19.087592</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (19.08759 47.51948)</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "      <td>POINT (19.14061 47.54559)</td>\n",
       "      <td>19.140612</td>\n",
       "      <td>47.545592</td>\n",
       "      <td>bp_15</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_outer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11686</th>\n",
       "      <td>9999</td>\n",
       "      <td>2749618</td>\n",
       "      <td>3</td>\n",
       "      <td>90</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>731888.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>19.087592</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (19.08759 47.51948)</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "      <td>POINT (19.11704 47.53163)</td>\n",
       "      <td>19.117037</td>\n",
       "      <td>47.531626</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11687</th>\n",
       "      <td>10000</td>\n",
       "      <td>2749618</td>\n",
       "      <td>3</td>\n",
       "      <td>104</td>\n",
       "      <td>0.028846</td>\n",
       "      <td>2738118.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>0.033708</td>\n",
       "      <td>19.154384</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (19.15438 47.71111)</td>\n",
       "      <td>god</td>\n",
       "      <td>northern</td>\n",
       "      <td>None</td>\n",
       "      <td>POINT (19.11704 47.53163)</td>\n",
       "      <td>19.117037</td>\n",
       "      <td>47.531626</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11688 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       device_id  cell_id_work  count_work  total_work  certainty_work  \\\n",
       "0              1       2749618           3          87        0.034483   \n",
       "1              2        667986           2          46        0.043478   \n",
       "2              3       2749618           2         105        0.019048   \n",
       "3              4        738684           6         246        0.024390   \n",
       "4              4        738684           6         246        0.024390   \n",
       "...          ...           ...         ...         ...             ...   \n",
       "11683       9996       2749618           5         215        0.023256   \n",
       "11684       9997       2702588           1           5        0.200000   \n",
       "11685       9998       2697628           1          24        0.041667   \n",
       "11686       9999       2749618           3          90        0.033333   \n",
       "11687      10000       2749618           3         104        0.028846   \n",
       "\n",
       "       cell_id_home  count_home  total_home  certainty_home   lon_home  ...  \\\n",
       "0         2082758.0         2.0        66.0        0.030303  19.155262  ...   \n",
       "1         2743054.0         1.0        34.0        0.029412  18.968748  ...   \n",
       "2         2738118.0         2.0        81.0        0.024691  19.154384  ...   \n",
       "3           51256.0         5.0       195.0        0.025641  19.337478  ...   \n",
       "4           51256.0         5.0       195.0        0.025641  19.344040  ...   \n",
       "...             ...         ...         ...             ...        ...  ...   \n",
       "11683       10314.0         4.0       148.0        0.027027  19.105231  ...   \n",
       "11684     2738127.0         1.0         5.0        0.200000  19.165791  ...   \n",
       "11685      731888.0         2.0        21.0        0.095238  19.087592  ...   \n",
       "11686      731888.0         2.0        68.0        0.029412  19.087592  ...   \n",
       "11687     2738118.0         3.0        89.0        0.033708  19.154384  ...   \n",
       "\n",
       "                   geometry_home  city_home    sector_home  \\\n",
       "0      POINT (19.15526 47.71823)  szodliget       northern   \n",
       "1      POINT (18.96875 47.77106)   visegrad  north_western   \n",
       "2      POINT (19.15438 47.71111)        god       northern   \n",
       "3      POINT (19.33748 47.59498)    godollo        eastern   \n",
       "4      POINT (19.34404 47.58780)    godollo        eastern   \n",
       "...                          ...        ...            ...   \n",
       "11683  POINT (19.10523 47.51570)      bp_14           None   \n",
       "11684  POINT (19.16579 47.66910)        god       northern   \n",
       "11685  POINT (19.08759 47.51948)      bp_14           None   \n",
       "11686  POINT (19.08759 47.51948)      bp_14           None   \n",
       "11687  POINT (19.15438 47.71111)        god       northern   \n",
       "\n",
       "      district_group_home              geometry_work   lon_work   lat_work  \\\n",
       "0                    None  POINT (19.11704 47.53163)  19.117037  47.531626   \n",
       "1                    None  POINT (19.10315 47.48817)  19.103152  47.488165   \n",
       "2                    None  POINT (19.11704 47.53163)  19.117037  47.531626   \n",
       "3                    None  POINT (19.00867 47.49683)  19.008669  47.496834   \n",
       "4                    None  POINT (19.00867 47.49683)  19.008669  47.496834   \n",
       "...                   ...                        ...        ...        ...   \n",
       "11683  eastern-pest_inner  POINT (19.11704 47.53163)  19.117037  47.531626   \n",
       "11684                None  POINT (19.11602 47.54371)  19.116023  47.543709   \n",
       "11685  eastern-pest_inner  POINT (19.14061 47.54559)  19.140612  47.545592   \n",
       "11686  eastern-pest_inner  POINT (19.11704 47.53163)  19.117037  47.531626   \n",
       "11687                None  POINT (19.11704 47.53163)  19.117037  47.531626   \n",
       "\n",
       "       city_work sector_work district_group_work  \n",
       "0          bp_14        None  eastern-pest_inner  \n",
       "1           bp_8        None          inner-pest  \n",
       "2          bp_14        None  eastern-pest_inner  \n",
       "3          bp_12        None          north-buda  \n",
       "4          bp_12        None          north-buda  \n",
       "...          ...         ...                 ...  \n",
       "11683      bp_14        None  eastern-pest_inner  \n",
       "11684      bp_15        None  eastern-pest_outer  \n",
       "11685      bp_15        None  eastern-pest_outer  \n",
       "11686      bp_14        None  eastern-pest_inner  \n",
       "11687      bp_14        None  eastern-pest_inner  \n",
       "\n",
       "[11688 rows x 21 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating the detected population by settlements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "population = merged.groupby(\"city_home\")[\"device_id\"].count().reset_index()\n",
    "population.columns = [\"city\", \"count\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>city</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alsonemedi</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>biatorbagy</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bp_1</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bp_10</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bp_11</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>vacratot</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>vecses</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>veresegyhaz</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>visegrad</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>zsambek</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           city  count\n",
       "0    alsonemedi      3\n",
       "1    biatorbagy     17\n",
       "2          bp_1     56\n",
       "3         bp_10    141\n",
       "4         bp_11    144\n",
       "..          ...    ...\n",
       "81     vacratot     14\n",
       "82       vecses     17\n",
       "83  veresegyhaz     27\n",
       "84     visegrad     69\n",
       "85      zsambek      2\n",
       "\n",
       "[86 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "cities = cities.merge(population, on=\"city\", how=\"left\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating Figure 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "\n",
    "\n",
    "def thousand_format(x, p):\n",
    "    return format(int(x), \",\") if int(x) >= 10000 else int(x)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the `vmax` is adjusted to the dummy data. Also, the code only demonstrates the figure plotting, so the result does not mean anything."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18.680802993, 19.469559507, 47.201914304000006, 47.856988496)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap_br = ListedColormap(sns.color_palette(\"BrBG_r\", 256)[128:], N=128)\n",
    "\n",
    "legend_kwds = {\"orientation\": \"horizontal\", \"pad\": 0.025,\n",
    "               \"format\": FuncFormatter(thousand_format)}\n",
    "missing = {\n",
    "    \"color\": \"#e3e3e3\",\n",
    "    \"edgecolor\": \"#2d2d2dA0\",\n",
    "    \"hatch\": \"///\",\n",
    "    \"label\": \"Missing values\"}\n",
    "fig, ax = plt.subplots(figsize=(6, 8))\n",
    "cities.plot(column=\"count\", cmap=cmap_br, edgecolor=\"#2d2d2d\", ax=ax,\n",
    "            linewidth=.5, missing_kwds=missing, legend=True, vmax=1500,\n",
    "            legend_kwds=legend_kwds)\n",
    "\n",
    "bp.plot(ax=ax, color=\"none\", edgecolor=\"#2d2d2d\", linewidth=1, zorder=10)\n",
    "ax.margins(.01)\n",
    "ax.axis(\"off\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating Figure 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_fig9(df, city, cmap, bp, without_locals=False, ax=None):\n",
    "    if ax is None:\n",
    "        _, ax = plt.subplots(figsize=(6, 6))\n",
    "    query = \"city_home == @city\"\n",
    "    title = f\"workers of {city}\"\n",
    "    if without_locals:\n",
    "        title += \", without locals\"\n",
    "        query += \" & city_home != city_work\"\n",
    "    sns.kdeplot(data=df.query(query), x=\"lon_work\", y=\"lat_work\",\n",
    "                levels=6, fill=True, cmap=cmap, ax=ax)\n",
    "    bp.geometry.boundary.plot(color=None, linewidth=1, ax=ax,\n",
    "                              edgecolor=\"#2d2d2d\")\n",
    "    cities.query(\"city == @city\").geometry.boundary\\\n",
    "          .plot(color=None, linewidth=1, ax=ax, edgecolor=\"#2d2d2d\")\n",
    "    ax.axis(\"off\")\n",
    "    ax.margins(0)\n",
    "    ax.set_title(title)\n",
    "    if not ax:\n",
    "        return ax.get_figure(), ax\n",
    "\n",
    "\n",
    "c256 = sns.color_palette(\"BrBG_r\", 256)\n",
    "cmap_br_fig_9 = ListedColormap(c256[152:])\n",
    "cmap_bg_fig_9 = ListedColormap(c256[::-1][152:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>device_id</th>\n",
       "      <th>city_home</th>\n",
       "      <th>city_work</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>bp_14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>bp_14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>bp_14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>bp_14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>13</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>bp_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11681</th>\n",
       "      <td>9995</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>bp_14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11682</th>\n",
       "      <td>9996</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>bp_14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11683</th>\n",
       "      <td>9996</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>bp_14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11685</th>\n",
       "      <td>9998</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>bp_15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11686</th>\n",
       "      <td>9999</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>bp_14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3850 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       device_id city_home city_work\n",
       "6              6     bp_14     bp_14\n",
       "7              6     bp_14     bp_14\n",
       "8              7     bp_14     bp_14\n",
       "13            12     bp_14     bp_14\n",
       "14            13     bp_14     bp_12\n",
       "...          ...       ...       ...\n",
       "11681       9995     bp_14     bp_14\n",
       "11682       9996     bp_14     bp_14\n",
       "11683       9996     bp_14     bp_14\n",
       "11685       9998     bp_14     bp_15\n",
       "11686       9999     bp_14     bp_14\n",
       "\n",
       "[3850 rows x 3 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.query(\"city_home == 'bp_14'\")[[\"device_id\", \"city_home\", \"city_work\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>device_id</th>\n",
       "      <th>cell_id_work</th>\n",
       "      <th>count_work</th>\n",
       "      <th>total_work</th>\n",
       "      <th>certainty_work</th>\n",
       "      <th>cell_id_home</th>\n",
       "      <th>count_home</th>\n",
       "      <th>total_home</th>\n",
       "      <th>certainty_home</th>\n",
       "      <th>lon_home</th>\n",
       "      <th>...</th>\n",
       "      <th>geometry_home</th>\n",
       "      <th>city_home</th>\n",
       "      <th>sector_home</th>\n",
       "      <th>district_group_home</th>\n",
       "      <th>geometry_work</th>\n",
       "      <th>lon_work</th>\n",
       "      <th>lat_work</th>\n",
       "      <th>city_work</th>\n",
       "      <th>sector_work</th>\n",
       "      <th>district_group_work</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>2749618</td>\n",
       "      <td>5</td>\n",
       "      <td>43</td>\n",
       "      <td>0.116279</td>\n",
       "      <td>10314.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>19.102747</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (19.10275 47.51460)</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "      <td>POINT (19.11704 47.53163)</td>\n",
       "      <td>19.117037</td>\n",
       "      <td>47.531626</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>2749618</td>\n",
       "      <td>5</td>\n",
       "      <td>43</td>\n",
       "      <td>0.116279</td>\n",
       "      <td>10314.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>19.105231</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (19.10523 47.51570)</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "      <td>POINT (19.11704 47.53163)</td>\n",
       "      <td>19.117037</td>\n",
       "      <td>47.531626</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>2749618</td>\n",
       "      <td>7</td>\n",
       "      <td>275</td>\n",
       "      <td>0.025455</td>\n",
       "      <td>731888.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>0.014563</td>\n",
       "      <td>19.087592</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (19.08759 47.51948)</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "      <td>POINT (19.11704 47.53163)</td>\n",
       "      <td>19.117037</td>\n",
       "      <td>47.531626</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12</td>\n",
       "      <td>2749618</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>2701514.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>19.107611</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (19.10761 47.51728)</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "      <td>POINT (19.11704 47.53163)</td>\n",
       "      <td>19.117037</td>\n",
       "      <td>47.531626</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>18</td>\n",
       "      <td>2749618</td>\n",
       "      <td>4</td>\n",
       "      <td>77</td>\n",
       "      <td>0.051948</td>\n",
       "      <td>731888.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.053571</td>\n",
       "      <td>19.087592</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (19.08759 47.51948)</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "      <td>POINT (19.11704 47.53163)</td>\n",
       "      <td>19.117037</td>\n",
       "      <td>47.531626</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>11680</th>\n",
       "      <td>9994</td>\n",
       "      <td>2699429</td>\n",
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       "      <td>9</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>2749619.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>19.104343</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (19.10434 47.53542)</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "      <td>POINT (19.11547 47.52978)</td>\n",
       "      <td>19.115468</td>\n",
       "      <td>47.529778</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11681</th>\n",
       "      <td>9995</td>\n",
       "      <td>2749618</td>\n",
       "      <td>6</td>\n",
       "      <td>190</td>\n",
       "      <td>0.031579</td>\n",
       "      <td>731888.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0.022222</td>\n",
       "      <td>19.087592</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (19.08759 47.51948)</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "      <td>POINT (19.11704 47.53163)</td>\n",
       "      <td>19.117037</td>\n",
       "      <td>47.531626</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11682</th>\n",
       "      <td>9996</td>\n",
       "      <td>2749618</td>\n",
       "      <td>5</td>\n",
       "      <td>215</td>\n",
       "      <td>0.023256</td>\n",
       "      <td>10314.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>19.102747</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (19.10275 47.51460)</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "      <td>POINT (19.11704 47.53163)</td>\n",
       "      <td>19.117037</td>\n",
       "      <td>47.531626</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11683</th>\n",
       "      <td>9996</td>\n",
       "      <td>2749618</td>\n",
       "      <td>5</td>\n",
       "      <td>215</td>\n",
       "      <td>0.023256</td>\n",
       "      <td>10314.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>19.105231</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (19.10523 47.51570)</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "      <td>POINT (19.11704 47.53163)</td>\n",
       "      <td>19.117037</td>\n",
       "      <td>47.531626</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11686</th>\n",
       "      <td>9999</td>\n",
       "      <td>2749618</td>\n",
       "      <td>3</td>\n",
       "      <td>90</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>731888.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>19.087592</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (19.08759 47.51948)</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "      <td>POINT (19.11704 47.53163)</td>\n",
       "      <td>19.117037</td>\n",
       "      <td>47.531626</td>\n",
       "      <td>bp_14</td>\n",
       "      <td>None</td>\n",
       "      <td>eastern-pest_inner</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2532 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       device_id  cell_id_work  count_work  total_work  certainty_work  \\\n",
       "6              6       2749618           5          43        0.116279   \n",
       "7              6       2749618           5          43        0.116279   \n",
       "8              7       2749618           7         275        0.025455   \n",
       "13            12       2749618           2          31        0.064516   \n",
       "19            18       2749618           4          77        0.051948   \n",
       "...          ...           ...         ...         ...             ...   \n",
       "11680       9994       2699429           1           9        0.111111   \n",
       "11681       9995       2749618           6         190        0.031579   \n",
       "11682       9996       2749618           5         215        0.023256   \n",
       "11683       9996       2749618           5         215        0.023256   \n",
       "11686       9999       2749618           3          90        0.033333   \n",
       "\n",
       "       cell_id_home  count_home  total_home  certainty_home   lon_home  ...  \\\n",
       "6           10314.0         3.0        33.0        0.090909  19.102747  ...   \n",
       "7           10314.0         3.0        33.0        0.090909  19.105231  ...   \n",
       "8          731888.0         3.0       206.0        0.014563  19.087592  ...   \n",
       "13        2701514.0         1.0        23.0        0.043478  19.107611  ...   \n",
       "19         731888.0         3.0        56.0        0.053571  19.087592  ...   \n",
       "...             ...         ...         ...             ...        ...  ...   \n",
       "11680     2749619.0         1.0         9.0        0.111111  19.104343  ...   \n",
       "11681      731888.0         3.0       135.0        0.022222  19.087592  ...   \n",
       "11682       10314.0         4.0       148.0        0.027027  19.102747  ...   \n",
       "11683       10314.0         4.0       148.0        0.027027  19.105231  ...   \n",
       "11686      731888.0         2.0        68.0        0.029412  19.087592  ...   \n",
       "\n",
       "                   geometry_home city_home sector_home district_group_home  \\\n",
       "6      POINT (19.10275 47.51460)     bp_14        None  eastern-pest_inner   \n",
       "7      POINT (19.10523 47.51570)     bp_14        None  eastern-pest_inner   \n",
       "8      POINT (19.08759 47.51948)     bp_14        None  eastern-pest_inner   \n",
       "13     POINT (19.10761 47.51728)     bp_14        None  eastern-pest_inner   \n",
       "19     POINT (19.08759 47.51948)     bp_14        None  eastern-pest_inner   \n",
       "...                          ...       ...         ...                 ...   \n",
       "11680  POINT (19.10434 47.53542)     bp_14        None  eastern-pest_inner   \n",
       "11681  POINT (19.08759 47.51948)     bp_14        None  eastern-pest_inner   \n",
       "11682  POINT (19.10275 47.51460)     bp_14        None  eastern-pest_inner   \n",
       "11683  POINT (19.10523 47.51570)     bp_14        None  eastern-pest_inner   \n",
       "11686  POINT (19.08759 47.51948)     bp_14        None  eastern-pest_inner   \n",
       "\n",
       "                   geometry_work   lon_work   lat_work  city_work sector_work  \\\n",
       "6      POINT (19.11704 47.53163)  19.117037  47.531626      bp_14        None   \n",
       "7      POINT (19.11704 47.53163)  19.117037  47.531626      bp_14        None   \n",
       "8      POINT (19.11704 47.53163)  19.117037  47.531626      bp_14        None   \n",
       "13     POINT (19.11704 47.53163)  19.117037  47.531626      bp_14        None   \n",
       "19     POINT (19.11704 47.53163)  19.117037  47.531626      bp_14        None   \n",
       "...                          ...        ...        ...        ...         ...   \n",
       "11680  POINT (19.11547 47.52978)  19.115468  47.529778      bp_14        None   \n",
       "11681  POINT (19.11704 47.53163)  19.117037  47.531626      bp_14        None   \n",
       "11682  POINT (19.11704 47.53163)  19.117037  47.531626      bp_14        None   \n",
       "11683  POINT (19.11704 47.53163)  19.117037  47.531626      bp_14        None   \n",
       "11686  POINT (19.11704 47.53163)  19.117037  47.531626      bp_14        None   \n",
       "\n",
       "      district_group_work  \n",
       "6      eastern-pest_inner  \n",
       "7      eastern-pest_inner  \n",
       "8      eastern-pest_inner  \n",
       "13     eastern-pest_inner  \n",
       "19     eastern-pest_inner  \n",
       "...                   ...  \n",
       "11680  eastern-pest_inner  \n",
       "11681  eastern-pest_inner  \n",
       "11682  eastern-pest_inner  \n",
       "11683  eastern-pest_inner  \n",
       "11686  eastern-pest_inner  \n",
       "\n",
       "[2532 rows x 21 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.query(\"city_home==city_work\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig9, axs9 = plt.subplots(nrows=1, ncols=2, figsize=(10, 6))\n",
    "plot_fig9(merged, \"bp_5\", cmap_br_fig_9, bp, ax=axs9[0])\n",
    "plot_fig9(merged, \"bp_5\", cmap_bg_fig_9, bp, without_locals=True, ax=axs9[1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating Figure 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from mne_connectivity.viz import plot_connectivity_circle as chord\n",
    "from collections import Counter\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "c256 = sns.color_palette(\"BrBG_r\", 256)\n",
    "cmap_br = ListedColormap(c256[128:])\n",
    "cdg = sns.color_palette(\"BrBG\", 10)\n",
    "\n",
    "\n",
    "buda = [1, 2, 3, 12, 11, 22]\n",
    "pest = [4, 13, 5, 6, 7, 8, 10, 14, 15, 16, 17, 18, 19, 9, 20, 21, 23]\n",
    "gcolors = {\"eastern-pest_inner\": cdg[6],\n",
    "           \"eastern-pest_outer\": cdg[5],\n",
    "           \"inner-pest\": cdg[7],\n",
    "           \"north-buda\": cdg[3],\n",
    "           \"north-pest\": cdg[9],\n",
    "           \"south-buda\": cdg[2],\n",
    "           \"south-pest\": cdg[1]}\n",
    "node_colors = [gcolors[\"north-buda\"]]*4 + [gcolors[\"south-buda\"]]*2 +\\\n",
    "               [gcolors[\"north-pest\"]]*2 + [gcolors[\"inner-pest\"]]*4 +\\\n",
    "               [gcolors[\"eastern-pest_inner\"]]*2 +\\\n",
    "               [gcolors[\"eastern-pest_outer\"]]*5 + [gcolors[\"south-pest\"]]*4\n",
    "\n",
    "glabels = {\"inner-pest\": \"Inner Pest\",\n",
    "           \"south-pest\": \"South Pest\", \"north-buda\": \"North Buda\",\n",
    "           \"north-pest\": \"North Pest\", \"south-buda\": \"South Buda\",\n",
    "           \"eastern-pest_inner\": \"Eastern Pest (inner)\",\n",
    "           \"eastern-pest_outer\": \"Eastern Pest (outer)\"}\n",
    "\n",
    "artists = []\n",
    "labels = []\n",
    "for i in [\"inner-pest\", \"eastern-pest_outer\", \"north-buda\",\n",
    "          \"eastern-pest_inner\", \"north-pest\", \"south-buda\", \"south-pest\"]:\n",
    "    artists.append(plt.Circle((0, 1), (0, 0), facecolor=gcolors[i],\n",
    "                   edgecolor=\"#2d2d2d\"))\n",
    "    labels.append(glabels[i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3zeJdWruOznXrKTYWOOHkD/eJJB599FFOOeUU9tlnH37729/y5JNPsnDhwm1yfhm2LTJC2UJEUcSiRYu49NJLkf0UlmV4dyL02wGVRieWZZOkuxAGYVxDMWwnLqtsnlGiKCQIPEzTIgzC+HgKMFBhJ8Ju0IeP012bwuTJk7nxl7/ia5//AuvuvI+5v7yF8rqBG+UbFou88oubGX/4e9jh9JMY/N4DOfOcc7jn3ns3+bihQ4fy3HPP8alPfQrf93nppZe46667WLBgwYCdW4Y3BxmhbAGUUpimyYQJE7j77ru59tprN/uY5cuXc+UV36W1tfVNOMMMbxWiKNA1DQTgVBb3VFEuKi3CSqFQm+WTJNUlhABhomRUiU5UBFEJYTciZYRhmH2qyQghOOSQQ/jHbX/jg/sfyMs//BkrHn4ctZWbIyklL1zzC5omjWf0oQcAMGy3nZn8sdP46re/xTXX/rjXDdjMmTMZOXIkTU1NnH322XzkIx+hoaEhq6m8A5ERSh9RPf/h6quv5vjjj+eCCy7Y5GNefvllzjj9I7z8/BNc/JkLs9nb72J4pY36F5GLoxMHpSRphKLAcrRnV+iXYkv73glAk0kJpRSW5WoblzQ6sSDqBMBwmgEwzb5/lZVSSBlxztln8/tbfktuwTJevfZXdK5avUWvuRpzb/kzMgjZ4bQTa4itYexoZlz0cf72wP1c/H+fxff9bo91XZd9990X13W57bbbEELQ2tq6Sfv8DG9PZITSRyRfkiuuuIL29nYuvfRSgF53XQ899BDnn3cuF5xzKFddfiquUeKb3/ha5oH0LkQQlEH6gIFh6ZZg07KQUqUFeYSoEIoXiwA3EVFEYUAUBViWo2szSsXRiQDD1vUTwHT0vBTD6Jvjr1KKcrmMUgrHcZk4cSK/vekmzj35VOZc92uW3fOgdkjeAqx8/CnWv/wqM847E9N1u93uNNQz5eNn8OqaN/jY+efR2dnZ7T4zZszgtNNOI4oivv71r7Nw4ULOPPPMLTqPDG89MrfhPmDZsmUsXbqUZ599ljVr1nD55ZcDtVFLNf7wh1u5/qc/4ZufO57pO4wGoFjyueArt/D+o0/gnI9+9E09/wzbDkopSh1v6M4rUQeGiWlauLk85XIZGbQDJhg2C/77N/5+1cc55et/YcQO+2FZTo9Ow1JGlEsdGIaJVMRkom3whemilCBqexkVtOMOP4AoisjnC5tNeSml8LwyUkpc1+1WxF+0cB6Xfe1rLF2/kQmnfIjC8M27CncsW8EL1/yCqWd+mMEzpm76+aVk0V/uwF27gV9e/zNaWlp6vN+6detwXZf6+vrNPn+GtxeyCKUPkFLy1a9+lUcffXSTZKKU4pof/ZDf3PhLfvzN01IyASjkHb79hRP43W9/w3333femnn+GbQevtDEmExvL1XYmlm0DcZ0DIK6dWI6+fVMRil70iyTzUzSZVKITx61Dtwx3IKwGpJTd2oV7glbZe0gpcZzuZBKGIcNa6rnu6q9x5nEf5NVrf8nKR57cZG0lLJd5+Wc3MeaQ/TdLJvoyGEw44WiiCWM45bTTWLZsWY/3a2lpycjkHYqMUPqAcePG8dOf/pTBgwezcuVKoLvCWSnFlVd+l38/dDfXfut0Ro8Y3O04w4Y08s3PH8/Xv3Y5r7zyypty7hm2HcKgjAz14m9YdUgZxd1WOv2kZJI60p+VlFD8UtVfaxH45VgNb6XH055gCtPWj1eRD7KM4TahlNpsd5cmE58oirBtp9useKUUfrldH9PMc/LJp/D7W36L+errzP3FzfitbT0e98Vrf0X96JGMPaLvRpNCCMa+71Dq996VU08/PevkepchI5Q+YtKkSXz7299m0KDult9SSr71rW/yzH8f4+rLT6G5sdDrcaZsN5JLPn4kF3z6k6xatWpbnnKGbQglJX5VId5xXWQUYVo2QghdK1PJ7l5Th1PQLb5eqb3m7wnCMCAMfQzTIooiDMPQEYIKQRjYTl7b4sf1E8Nu0v/fRP1EKUUQBERRiGXZ2HH0VH2755Ug0sJJzDocN8/EiRP5/S23cOyBB/PSD37Gxvm1upC5t/6VoLOTKWeejNgMofWEkQfszfD3vYczzj6bOXPmbPHjM7w9kRHKFqCxsZFcrjbnLaXkq5dfxisvPMVVl51MQ/3mXVwP3Hsqxx05i09/6hMUi8VtdboZthGUUpRLG9B+XTaWkyeKNHkku39dmkw0KJo46ppGANC54Q19HyrlSyklvldCCAMZ6TZgmZAJ4Lj1GIahLU1CTUiJBqW3CEWTiU8YBliW1Y1MQJOYDMvazNLI4ebr0uNZlsWnP/kpvvftb7Pot39m+QOPoJTijf8+y9pnXmTGx8/Cyvc8bbIvGLb7Low69gjO/tj/46WXXur3cTK8fZARylYgiiK+dOkXWLzgJb73lZOpK3TvcOkNp3xwb7Yb28jnP/9Zom1khZFh28D3O2PNiQHCwbIdwsDHMMxKukvJWINS+YrlGlowLZvOjZpQEo1KxVpFxbU5U5ONUkCEMGwsW3+2lFQ6QjFyICyMeCZ9V1TIJIzJxOl2vygMCfxyrO4Hy2nqUW2///778+db/4A1bzFzf3Ezr//5dnb4yAnUjRi2dRcSGLrLTMadcDTnnn8ezzzzzFYfL8Nbi4xQ+gkpJZdd9iVWLpvLdy89iULe2fyDqiCE4OJzj6R9w0p+cPVV2+gsMww0ojAg8uKUlXCxbBelNBFYVRGA1O1ZNYV3YZgUmgbTuWENSborsaSXMiIRP1qWHae6tG7JyVUXqCUqKiHseqSUPaa7kpqJJhO7RzKRMtKpLqU0oRgOttt7dD1y5Eh+d/PNTBsxGtO0KAwAmSRo2XEaE049nk9ecAH//e9/B+y4Gd58ZITSDyil+OY3vs7iBa/y7S+cSM7tnkroC2zb5GuXHMeDD9zDn/70xwE+ywwDDaVkLGCUIFwwTGzbJQwCQNTs7rU+SdG1TlLXNITO1tUk0UgQeLFo0QAEtpMjCLy4/iIxrVx6XJ1GUyADhKEjlq7prqSbq7pm0r2BROKVS/rclA8obLd5s51ijuPw4x/+kM9/9rO8+pMbWP/KwNU+Bk3ZnkmnncCFF3+Gl19+ecCOm+HNRUYoWwilFN/9zrd59aWnueLSE8nntiwy6YrGhjzf/eIJ/OTaa7Ld2dsYSinKxbZ4ATZAWFoNj/bbsros3GnLcM1XTFDXPJTOjWtBCKSMYn8vASgcN6f/rVQcnYia6ERHQrquIkz9uTNNs+Z2TSYRtm3jON0jE6UUXixs1ORUBmFgOb03knTFiSecwE9//GOW/fmOtK4yEGjeYTvGnXA0H//EJ7Lur3coMkLZAiiluOr73+OZ2Y9xxZdPopDve81kUyh5AaZpsHFj3+y+M7z58P0iKtLtvoZdr5Xvtqtnk0BsBFlB2jJcs6ALGgYPp33dG6goikkn7gBz80RRGEc2Orqx3boaQpBSgozte4QmsOR2KSXlcjltDbbt7hsdTTjlSjuy0nNVLKdhi2azAMyaNYs/3Xor4rUFLPz9bcgtnH/SG1pmTmfk+w7lnI99jBUrVgzIMTO8ecgIZQvwq1/9kscefYDvf+VkGur6391SjaeefZ3/+8Yf+L/PXcphhx0+IMfMMLAI/BKR34lOQdUhpcSy9O4/CgMM0+wh9VTbMpxg8Kjt8MtFOjdURH1uroBSiigM0+hECBPLrv2M6ehEE4oSlQYAKSWeV0YprYDvqZsLIAx8ojDU7chKVorxbmO/rsuIESP43c03M94tMO+G3xN53X26+oNhe85i8P57cuZHz2H16v77i2V485ERSh9x55138odbf8uVXzqJxobNtwb3BXfc+yzf+ck/+eGPfsyRRx45IMfMMLAIAo/A69SLvGGBoXf+lu0SRWFs3lgbDfSkQdG/CgaP3gGAdcvmAAI3p1NNge9pN+KkTTjXPWroGqEYhkEUhZTLOnLK5XK9zkQJw4Ag8DEMAymlfi7pIcwchtH/kcD5fJ4f//CH7DZxO+ZcfyNB58C0wdeNH8Pates4/ayztri1Phsr8dYhI5Q+YPbs2Vx5xbf5zhdPYMjghq0+nlKKG//4CL+//WluuulmZs2aNQBnmWGgEYY+Qbm90m3lNhNFAaalF/MwCBBC1NQxgNqaQkoKuk6SEMr6mFCEMPDKZS2GlAEQYdp5TKt7lKGUiu8DwrDjyEQTkevmehU4RmGI75W1xkVKDNOMxxQrbLdpK65Qpa5z2Ze/xJH7H8Cr1/6K8oaNW3XMtS+9xks//TXjjjgEc9woLvjMZ/o8UvjVV1/l29/+Nm1tPav7M2xbZISyGSxYsIDPXvIZvnLRsWw3fvhWH08pxc9veYhHZi/m5lt+x/jx4wfgLDMMNMIw0HYkSg+1cnLNRPHO17ZzSCmRMsKyeuqiigveNekuXRcpDBqJW9fA+uXzAHTrbnLfGr+u7lBpsR4wbKIoxDRNcrlcr+LGKArxYsGkUjoycZwcKiyCMFM7l/4gaQIIwxDbtvncZ/+Ps08+hVev/RXF1Wv7dcyVjz/FnN/cynbHH82Y9xzA+OM/wOL2jVz+9c07dd92221cccUVPProo7zwwgv9ev4MW4eMUDYBz/P4xPkf52OnHshuO03c6uMppfjJjfcz+6WV/Pznv+rVbTXDW4soSsgkAiIsuw7TzhOGflV0ousFXYvx0Fv9RC+GQjgMHjme9SvmAwoV12NUpCMGJ9/Ya4FcKVWT8tKdXG6v94+iEK+ckIkmLTeXJwxKuhhv129xMb76XDyv0lFm2w5KKU4+8Tg+9bGzee2nv6Zz5RtbdMxF/7qPBX/7F9POOoXhe+4KgGGabHf6iTz27DP89Prre3/sokW8+uqr/OQnP+Haa6/ltttu69EmP8O2RUYom4BpmgwZMoSnnluE523dcCwpFT/65d28NG8t1177EwqFPL7vZ/NR3mYIw0B7bcV28YbpYOeatDYEsGMhYxgGmKbVo4+VnoNSPZGximCEyZAxk1mzZG5MJjZh6AERhul0q8ckULHrsEpmogizR8FigiiKYjJJble4cSQT+toLzM71rxhfscGPcJxKR5lX1rWmDx5zNJ/7zGeY87Ob6FjWt06tub//KysefoKZnziHwdOn1Nxmui6TP/oRfvfnP/H322/v8fHjx4/n0ksvpa6ujsGDBzN58uRsSupbgIxQNgHLsvjVDTfiNozkwst/x7oNHf06jlKKH/zibuYtaeXHP/gGjfUWSEkYBpTLpayI+DZBGCSRiQRChDBx84MBRRj4mKaNYZi6GwtqlPHVUCmZiDjKqYIQjJqyG16xnXVLXyWMong4l+iiiK89nu9rQkMGYNi9Ft8BZBThlYup87FSEsfRBfuku0sYtm4y2EJIGaWfWcdx0wjN98vpCGTDyvPBYz/I175yGXN/cTNti5Zu4niSl3/+G9a/ModdPnMejRPG9ng/p7GB7T/6Eb5z5RU9Ch+ribWpqYmVK1fy/PPPA2SbtjcRGaFsBq7rcsUV3+OgQ47kE5fexOuLtyyMV0rx41/fx4JlHfzohz+mvmEQyBLIDpASJSXlcokgCLIP/luEZMH2vQ5QEiEiUBKnMBhhmHpiImA7WncUhj5CGL0WwXV7L7EXV2WmfBKyjN5hbwCWv/IoRGVAYTn1PR4vivQCnvq9qTD28Oo5MkkWfCEEpmXHoksnJb8wDEBFGNaW1070BqgMgOvmUiPMIAiIfN2JZdgFXDeHEILDDzuM737zW8y74Xc9RioyDHnhhz+jtHoNsy75xGYHetWNGMb44z/Apy68kPXr1/d4H6UUpmny4Q9/mNtvv50NGzb0O62XYcuREUofIITgE5/8FBdc+Fku/urvefKZ+X16nFKK629+kFfmr+f6n/2ClqHDwawHI68V11FrnFrRRn6+72Wk8iYjEfuFfhGQCAOUDHFygzBNByUlYeCl0YmMIq1D6cHSJEEl4tTvpWHaVNdTGkdsT/2gISyf8yQgtY+WUyuSTfy4PC9ZwGtvF6L7VzeKQsqlIkKAHRtWmqZVI3KMAr3wm3bflfEp4fq67TiXy6edbVJK3VaNRJg5XDdfc10OOuggvn755cz91W9rCvVhucyzV/wYlGLniz+B29y3brMhO+9I/U7TuOizl/TY+ZWMDpg4cSLTp09nw4ZMLPxmIiOULcAHPvABfnTNtVx53V385c7N26TccOu/eeblVfz8F7+isbERwzDIF+rBrAOjHoggaks7d6IoolQqEYZhRixvAqSUlEudREEZkHoxijwspy61IvEDvaAnC37QizI+PWYUVaW5tNtwV52HMPKMnrwzy+c8jVK6qytZhHV9RmtLdJ3GJJfLkxKSMOMoqssslSBIC/C24+L7HsIwcOJoIT2/UKelTKtvwtxENJkYTbpVx0tnqagAhImbq+uRZA8/7DA+e+FFzP35b/A2bMRrbeOZ7/yI3JDBzPz0x7ALWxYtjX3foazoaOfqH/2wx9uFEOTzeY499lgmTZq0RcfOsHXICGULMWvWLG6+5bf84/5X+NEv7yGMeq5//OHvT/Lo7MX8/Be/oqmpsvvSH/YCGC6YcVE0atMDjqRECPB9D8/zstrKNkQUhpSLnbGuQ09IVFEJw3RTbYaUEVEYYFlOXItQRGGQDtHqChlFlMud1LoMi+4tvQJGT9mNjg2raVu7Kk0dJbqSpF7iurl0AU87x4SpU1bxMSvRQxnDMHHcXCySFN2iBd0l5iOM3ov5Ndcoqq2XVHuDKaUol0uoMI6gcg2bnBx5/Ic+xEdPP4NXf3ojz175YwZN2Z7p/+80TGfLjVWFaTLptBP42x13cPfdd/d6v3Hjxm3xsTNsHTJC6QfGjBnDzbf8jhXrJF/67p/pLHo1tz/4+Cv85V/Pcv3PfsHgwd1HAQshcNyc9mOyB4GwQRZBtqOiIFYzR3FtJesEG0jo2SNlvHIR0BoTy84RhcW4CD8oXTR9vzY6SXy77B6iE11fiMkEMK0kRSVja/rkBCTIMqOm7AnA8tceT9NbevHWXlzVaSWoSqMJA1SUpnZ8r6xTW5aF4+bwPW386Oby3Rb4KPQAibGZ6CSZpeJ5WnCZy+VrxgYnXV4q0mRsOYUehZhdcc7ZZ3PgXntjGAaTjj8aYfY+aXJzsOvr2P6Mk/jqN7/R62z6DG8+MkLpJxobG/nJddczduKOfOpLN7Nq9UYAXnx1KT/8xT38+NrrGDFiRK+PtywL07JQCOzcEDAKOnUQtSKDYlr0DYIgNf3LsHWQUUS5VIw1JBEgsZ06PRdeKdxCCyIujEdRgIxCbCeX6jjSIVo9OPz6sUBRCBOo8uGSAWHgx7YqUexWLBgybifcQj3LX32sJr2Vz+d7tJzX81UATECmFvRRFMZmkC6+V9J+Xrl8jwX+MK6fWE7Pwsnq1xMElXRbNTHpyKQcp/Z83b68BU7F3/7mNznkwIN4/ZY/obbyM90wfiwjDtmfCy+5mCDYurb+DAODjFC2ArZt8+WvXMbxJ32ET37pZu575CUu+/5f+fZ3rmDq1KmbfXwiSgujkHz9EAynBTBAdqCCVmQY4jgOoHeEuvc/S4NtKZRSBL5HuVzUDr8iVr+79ciohIx8nPyguHheKdQLIVJdSOLbVV3grkQIWpRomham5YBI0lwWIHVbsPJSS3qEg7AbGbX9jix/7SkdUaTprd7G+UoQIiYsKBXbtQ7EzWHZDoGvU6SO27ufV1I/McyetS4y7jhMHIu7iiaT23UXm+58c3vwHNsUhBB86+tfZ3S+jsV/vXOro++RB+1Lu23yvauv3qrjZBgYZISylRBCcPrpZ3DZ5V/nmhvu41OfvpD999+/z491HCdOMQTk8vW49SP1eFflQbQRv9yBaZrYtp3mtLNusL4jCkPKpc7YGDGxNwE314iMykRBEctpwKqyIInCACVlHJ2INDoRQqTRSRSFlIodRJEeUGWaNo5b2alLGel6B2alwUtYIDThYJhMmLkvG1YtZuPyOd38wLoiETYSE46BJJcvYJpWOlDLrtKFdIU2lvQRZs/K+qQRQCldu+kaJSX2+EopDKFARXr4Vx9SXV1h2zbX/vBHmG+sZcX9j2zx46shhGDChz/InXffxYMPPrhVx8qw9cgIZYBw8MEH88gjj3HCCSdu0eNM08KyLMIwIIoiTNOk0DAcy20BJERthF4bge+Ty+Xi+4aUSsWsvrIJSKlTQtorS2DZFjLSxepcvgkZlQn9Diy7DtutGH5qctcFbjOOWKRMWoX1zr5Sg0nqJTZOXABPiue+V46Jw9HzRgw3JhS9SAsh2GHv92OYJi88+NvNvpZU0xJHKI7jxClRXzcO9DIDJUEUar1LV/1JpajvxS3BuW7klmxkhNBRtY50jF6FmH1BfX09v7z+Z2z8zzOsfX7rJjTadQUmfeQEvnz5ZaxZs2arjpVh65ARytsAiYVGdeTh5OrJ1Y/SO1pZhKiNcrED4h2kaer6SqlUzCxcqpAskOVSZ1pfsCyD0C9iGBa5QhNRUCLw2jDtPHauqWYnHgb6PUiiE/03nZ83hKFrMLGwEbR1veNUuqn04i9T7yyEIAz8bqlKJSPqBo1lwsy9eO3Rv6bq+2pIKQl8r4a8Evt8JX2CwI8L8vYmyQRAhvHsk6pILGn80C3BVuxYXLskhGGYFuddN0fgdQAKJ9d/H7AEw4YN4yfXXMOSv9xB8Y2tI4LGieNo2Ws3vnz55dl34S1ERihvA+jUl5t21yQwTIt8wwgMuzEt2Id+R2qr4TgupmkShgmx/O+2GidEUip2pII+PbgqIvCLGKaNm2+MyaQV08rh5AbVLIpKSoLA07UQs9LKG0UhwjDw4qK3YZooFWFZTg3xaHt5XWi2HVdHI3Fkoe+j72cYejiWMgtM2/dwOje8wYJn7kFGEVEUEgYB5XIxTdVVVPYinSUf+u0Evodhmps0iEwgY1NJw7TTz5lOYWnRZE/HCMOgKnLJI6MQJbW/mWUPzLTSadOmceGnP8X8G39PGKvw+4vRhx3Ea4sWcMeddw7IuWXYcmSE8jaBaZqxUWBY09ElhCBXGIRbGEFSsCfqJEw7i8B1XEzTSvPgiXHf/wKU0rv4aiLJ5QvYjkPgdxAGJUzLxc01EAUl/PJGDMvFyQ/utoCmBpBOpa02iDUhKp4jYpgGMgqxbLcbmXjxoCsQuuuo2npF1HZKubk8llPHpF3eg5uv54UHfku5XMQrl7QvVpxiy+XrsJ247iIEmPrcwnI7hmF005r0ep2kHjesFHheOe3iyufz3Yr4CeEkyvhECxPE9iq92etvCZK6Yblc4pijj+bAvfZm4e//tlXRhWFZjD/pWL575RWsXds/+/wMW4eMUN5GSAqhPaWwTNsl1zAKYebjgn0rqIAoDOKds7YRN00rznmX446dd6fqPooiPK9Eqah38QmRuLm8TuUUW5FRiOPW4bh1RGEZv7wBw3Rx8y09tOVGhKFfI2JMit0QRxxKpq3Etu3WpLnK5WJM4nrh18XxhEz08cxYy6FipbvjuFj5oeywx8HMn30XMijj5vLk8gVy+Tocx43H9VbOUwgThI2S+r59Tjsp3SRQLVR0u6jo9blpMkkIJ7mPblQIEIaDsQljyr4gUd8HcTu1aVr838UX0+gHrHhg64r0DePG0LLnrnzlq199V37u3+7ICOVtBCFEPFdC9qg7MQyDfP0wrFwslozaIeqMF7qIwPeIQl9HO6aVzqzQnWE+Ukbv6C9ZYhtfLnXilYtEsR1IQiRCGPheB365HWEY5ArNWHYOGXr4pfUYpoNb6B6ZQK2IURf0i+nME+2LFdu1uwXsON2jSa1MudSJklJHITGZiBpTSJ3yssxacSCAsBuZtu/hhF6J1564LU2HdVO461/0/00XoZI6zuYRpXYwWtuUz9cKFaufx/f9tKZSnQbzPT1bpLqTbUtRHZVIKeNzUERRiGMbfO+bX2bNI0/SumBRv58DIDdyOE/+50n+8Ic/bNVxMmw5MkJ5m8E0TYQwNtnB5bgNcXtxbbSSrGFRGBCGPiiFaRiALgwnUYsml3dGrSUhEa9colTsSJXgtuOSL9TjxKNvZRRSLrUSBh6WnSOXb4qt5j280jqEYWvhYi+mikkaK4zbjJPrI4QgCBJ7kTpNWr5HqaRTi0kx3TT1AhyfdU0tDHRXlqgRCOrjG2aOUVP2omnoKF79958rNvW1V6EqfSYQZg4ZbH54VHrtSrHxpWnhum6P16AyMCtMB2alEVhcOxGG3a82YaiQbxKVGIZR8ayTAURFho8Yzre+/jUW/PYv/ZpNH5bLvPiTXzHv939l5H578ZPrr89GAb/JyAjlbQadCrFTk8DeYJoWhYZhmG4LYOpoRbZX5nAgUPHuT8lQd7AKvVvW5FJKW4/fbuQipUzNDhMSiSI9blenhOrSBU+LFkuUS62xSrwhNVuMQg+vuA5hWOR6IRMtTtStxWEQpoVu3SKsUCrSC6Dp4HleapRomkbcXqsFjakJo1LprPkk/QX6/aptAKi0HAu7kWn7HMaSlx9j46rXU4uX6utRscE3EFYdSA8Z9UQ+lcdoXzAfjMT1uGcPr54GZlXfz/f04m73IzpJ0luJKDdprU4/c9IDWUIYFvm6oRx88Hs45qijWHjrbTrq6yNWPvFf/vu172OYJrtdehETjzmSxhlT+iV4fCdH8W81/mcI5StfuZSLLrqAlStXvtWnslnotIfRpxkpbq4et364djBWIQTrMfDifL1e1Iw46pEyir+kStONkgS+T7lUpFQqvSXkopTSBBKG+F6ZUrGTcqkT39ckYsYkki/UpSrwSiFc4pXb0y6ufL5ZK9UBGfkxmRjkCkNSS5WuCHwvjRaEQCvPLSdVv+tCtkg1Qo7jks8XME2TKAx0l1VMJrqGoiMJJ+nyqkKNUDB+TtM0waxn6j6HgVK89thf8T0v3UwopbTNiT6CVstbWv8ReRt7vJ5Jc0biC2YZSXdZ9+giUb93HZiV3h5FmriE1es0yZ5Q6STTyvvq5gV9LiYGYTzsyyFXNySN4C656DM0R7Di4cc3+zx+WzvPXX0di26/i+1PPIbpHz8ztcIf875Duf/BB3juuec2e5zZs2dz9dVXs2rVqrfdBuudhP8JQnnooYd4ZvZTjB+mOOnEE/j1r294W3v/JLUUUN12qz3BNC1yhcEYzhAQDjLsIPLWYpt6qqCUCqkUwtA7b8uyY2+pJI2iUFLvzsulTorFTrxyhWAGYsemlEoXx6QdtVwuVgjE035WwhDYjksuVyBfqIs1N1a3nXUU+pSKG5FRgO0UtAVIvCDJKKBcXIcQBm4vZJIU0sNYn2GaNqblxMSWmDwKDNPGcXPk84V0qFQUafLr2mWV1L1s26G3K1ajV4n/LQyHQSN3YOT2O/LKw39CSS/10/I9LUgUcX1GE4oWYkZe7Yjb7iJF7QumYneArsX0JHpIrF96qqv4vk6t9dWvq5rQqr9j1USSy+URytceaoaNW6ht37Ztmx9edRWrHnh0k3Ppl9zzELO/9QPyQ4aw+5cvYeiuO9UcxyrkGXPMkXzp8ss3+X2/4YYb+PnPf05dXR233347r7/+ep9ea4bueNcTiud5XHnFd7jwo+/lrJMO5LrvnMFjD/+Tk048nqeffvqtPr1eYZpmag7ZlwXdMAxdnK4bAkYDKEVQXosMWnEdBzud2BcSRhGGaeLmCri5AnbcdpymhJTWXiQEUyp2UOzs0It/uYTvlQl8T/8Efu1PvKD5XhnPK6WkUSp2UCp2UC4XU4dc3flkYjtuHIXUk8sVsG0njqp6Ss9IfK8Tr9yOwCCXb8KuEhZGoUe5cw0CcOuGdJtFkiyi5VInMkoWGYNISi1AjJLahyCXr0+ddlORYxhUyCRXqKozRMiosnB3XcDSInzV/dP3zrTAamDng49h/Yr5zH/qDkAR+OW4y0zUkKJp58DME3kb0mMnWiTtw2XXiBQTbYyoilAS9btSqkd1vL5WkRZECjN1CdgUpNR1kp6sgRKjyVwuR+h3EgWdIGy9EerB9n706NFcfNFFLLr1b91MJItr1vLMd37IykeeYNpZpzDljJOw63omvCG77Ihfl+Om3/ym1/Our6/nQx/6EB//+Mdpb2+nUOh/48H/OoR6hyYMW1tbuf3229ljjz02acT485/9jBeefohvfO5D6d+UUjz8xKv85MYH2Huf/bj4ks/2aDP/ViNJR1iWHZtEbtnjiDr1uGEEdq4Z065PF59kN20YhnY+jqOANJKQkV4klURJlaaF+g4Rb6gFQhjxTlz/3xBG+ntfoc/bI/C0atyyXGy3dqBTGJTwS+sRhoVbaMEwrPS1hKFe8CuvI7EyMbAs3RKsoxV9PMt2q4rs8fF7IROllO70ir22nHjAVTXyeX3/xCEYIF/QavMw1LPsVeccfnPZx0AYnH7l/RiGm3aO6ZEGMjasNCivfQ4VtFI3+lCCIEBKiWEYaatxNUodq1FhJ4XmiYBuQvC8ZGZKd3V8Aq/cQRSUsJwGPW6h1/dG4nl+j9ony7JqajK+10HotYOwyNW1bHKGilKKs/7fR2kd3sLo9x6IlJKFt/2TVf+ZzYg9d2PC0YdjupsXWJbWrOXVH/+SO2//R4/f82eeeYbHH38c27b55z//yYc+9CH22muvPhm8ZqjFOzZCCUPdjfK9732P3/72tz3mPVeuXMnNN9/EJ856T83fhRAcvO90bvrRx3DFOj503LH8+c9/ftvlTg3DSJXwW8L7hmFQKNRh55rBbAJhEpQ34HW+gSDCjVM4ukVZt4qWSsV0qJeIW18dN5emngp1DeQL9VqQZzsIo1KjqfwYCMPEtGws245nmbvx/3WqzbLsXqOP3hCFAeVSK4HXqVMm+SZstw7dchrFnVmt+KX12i/LrMfzfIrFTkrFTrxymSj0U8fedECWEORydQihyUoYRuzfJdKILkGFTMxuZJJ0ngmj0qHXk74DqOn0ShZg07RAmAirkX0/eA7rl8/ntUf/imEaaVdV9WfTNG2E3QQyoNyxNq5/OJskh+QcwjCIyUT7dm3q/np+itFtPHH18ZKOt65kYts2+XyhpvU48ItVZNJzZFINIQTf+cY3eePfT7B69nM8/Y2rWP/Ka8w8/xy2O+HoPpEJQH7oEFpm7cS1P72ux9t33XVXDj74YJ555hm+8Y1vMHToUB544IFe59Zn6B1bp1B6CzF48GBOPvlkJk6cyCOPPNLjh/Oq71/Jh47ajZHDmns8RiHv8qmz3suRB+3ID37xG/5221/4ymVfZcqUKdv47PsO23aIolJKoFv6WMMw8coWyDJKFil3rMS0NdnYto1lWWlRPIr0T0Io1ake0F/waluSmmhGqqp/b7rukkYsm+EUpaROQSktGDRMF4WBVy38VApkWbdPCxvMuvT4xOemlNaB2E4uJmifKJTYTp4gKOspjKaFbecol4sx4VQ+T7VkUismjOLrZttOPL1TXwPtetBTDaGKUKIojQyFMFBWA5N33Zeh46fz5F9+wJR9j6XQMCSNaJLjBEGAcJr1H4IN5BuH94mgtVAz0q/D3bRdSxSFoGSP7sRSSm1K2SUVldT+eqrFBH6RoNwKwtSjAnppkuiKUaNG8anzz+fK73+Pse85gLFHvAejh+Nv9jiHHcQ/r/gxZ3zkNCZMmNDtdsMw2Hnnndlpp50wDIPXXnvtbZm1eLvjHRuhJB/yhx9+mKlTp3ZbwF555RWeeeZpTj52n80ea/uJI/jxN0/jiP0ncu7HPsqVV3yXzs7N9/m/GTCMSs9+f2CaJvlCHaZTH0crLlHQSbl9BX55I7rtVS8wekfpxNqLJGopxxFS9+hNxKmYJCXnui65nI5+CoU68vkCuVwe183F7aiapAxDty9L2fNPFEVEoYcMS7HC2wLDRWHGz2lWoh08UB6mXSBXN4R8voBlWSgZxS4BWqyYy9fpvytdmxCGSRh4sVOvi+MWCBNVfFW9IAx6JxMZRenoXSsWpCql4vpX7VcrJRRRHaFUrqlhWGA2gGGx3/Efp3X1El5+6A+EcVG8uvsqiiLt6WUWkN66Pkd7UWoCuXnvrzDW3ljx5MmEyEqlUtq5lcA0LXK5fHrtux+rlJKJnWvu8T6bwqmnnsr0HXfEaWzsF5mAnvA44uB9ufLqq3q8fdy4cRQKBb74xS/ygx/8gCFDhvTref7X8Y6todx5553Mnz+ftWvXctlll3XbvZ/38Y+x546DOe59u2/RcTe2dvKzWx5i9otL+Pznv8hhhx02kKfdLwRBQBD4cYqi/2NT9U7b0+3FsoSeuGdgu81YTq17rI5adK2lendtmmYqvtxat9mu0DPbPXxfT1A0LRfbKfQYfSol8YrrkZGH7TZi2nWxsaKfRiSVjrbKeWrRoiYOPYpZzxRRSlEqdsSmktqRNwyClDC6kklSNwHI5QuAoFTSeo1cbP/i+xVxo56qaKfPk7gV5wu6BVh3ufngr4FgHX+88nO0rV3OWVc9iJVrwjDtlIAMw0CGHlHnAlRpJe6Ig9PGhJ6iiXLHGpBFrMKobnWh3lDsWAcKDLsuJcpq9BbFdkUUlPFKGwAT023E3UQtZlNYsGABp55xOjtefH7aFryliPyAF6+4hh9fdTW77rprj/d58MEHmTVrFs3Nzf16jv91vKMiFCkld999Nx/72Mdoa2vjpJNO4stf/nI3Mpk9ezYLF8znA++dtcXP0dxUx+c/+X7OOnEfrvjutwfq1LcKyY6uv1FK5TjapkSYLpj1YDYCBkF5PeWOFYRBZw15OI4bd+bk02ucjCROBn0NlFdYFOk6ie91YggTN9+Em6vvmUxkRLlzLTLysNwmFLZ25vV1bcBxa8WPCbSNfKUTK5evT9N3qc1K3PywOTLR/mkqnoNi1DQ59EaAQGXRF9UanCB+bwXYgxDCYP8Tzqd93QpeuP93IP20KyyZ8W47OYTdDCiC4upYqFpKjR+jKIzn3JfSc7C7zIVPnj9KXI7j+kqx2AFKgjC72fUk3Vq6Btd9VHE1ZBTilTYCBsKu7zOZ9YRJkyZxyodPZuntd/f7GKZjM/KI93DFVd/v9TN7yCGHZGSyFXhHEUoURSxcuJDFixdjGAYjR47E7VKYU0rxox9ezVkn7Ydt9283r5Tivkde4ZyPfmwgTnuroWsXJmG49V5cyRAlx83pmoPRAIbu/vKLa/E634iHMVWe2zAMbNtJFxI93MmIZ2V4lEpFyuVy2m3UV+gisU+51IpXagOlcNx63Hxj72Nso4By5xqUDDGsesJQF5pNyyKXK5CL0y49W7HrxdW2XVy3kNZJEhGeEbdqh4GvycTsTiaA1udEUTo+IGlsAKrqS9WPEDXvm9YAxedVlVLTwkUL4Qxm9HbbMXbH/fnv7T/BL3cgCGoOapoWwm4Ew0GVV8XGoEZa30hV8vHzA5Rid4RisZNSqRi/b6VYya7vH0UhxKSbDPPS18xOtTibK6br1y/xSnFR2yz0aES5pTjv3HNRq9ey7uXX+n2MYbvvzMq1a3n88c2LJjNsOd5RhGLbNueddx6//vWvmTdvHnfe2X0m9aOPPsqGtas47MCZ/X6eBx9/hfai4qSTTtraUx4w6IVKDYgtfZKu0NGKrk9gNIJRh4wCvM438DpXV+k0uj/W7SL2U0qmyuhSqRQvTj0TYDIVsVzcqNtlpcR2CrGZY+/5/URjopQEo4BURmrx7rr5dDxv1+fy/XLFPsTJ1djOA2nx3Lbd2LZd26/0ZA0fhkE61CrRZujoIonsup+7ELV2HtpBWMaPjS3ic/lKR5fRhEKx3/HnUWxdy3N334ySIahKt58wTIRhYOSGofz1yNDHdXOxbsZO3yvbtiEmAEGlTVy3inc3otQXLXFN1huJpCOwr4SglMIrbdT6FzOPm8v3iYQ2B9d1+fpll7Psb/9C9jNaF4bBiCMO5qof/TCzWNkGeEcRSoLRo0fz5S9/mSOOOKJbKuJHV1/BOacehGn276UVSz4/velBLv3SV7a4eLgtkQjPtjbtVY1UTe3EegfD1YV7I08Ulil3rMAvrUuFcV2RRE5dU2NCEKdPyvFOuBQrvz28cielzg0EXmdcw6gnV2iuESd2hZQSr9SGV4xnXJj12G5Bm0P2oLuoeVy5M7ZR0YVv2+4e0YaBrw0mZZR6efVEJlJGqQ4lSd8k0U3lvj29hi4RSpfzdXOxkE7FjzdshNXEqAnj2W63I5h9x3X45aKufalK6ssyHYQzGFAEncuRUsZuwUGamrJtJx1lbBrEFjFJV1+ElFH6PurISQJSt/am7+eWRRaB164FkUYOy871Gm32B3vvvTfTd5jCqkf/0+9jDNlpBus7O3nggQcG7LwyaLwjCSVB1wX/8ccfxw88Dtp7Wr+PectfH2P3PfZkt91229rTG1DoyMDqdde/Nce1bR2tGKYVE0sezCaEkSf0Oyi1Lycob9ykuLGn1JjrJt5QktAvEngdcTpN6HZUM9+rx1aSuikVOyh3ricK2uOW0yHkCw2b3TFrm/v2eMHUH3PXzXe7XxRrfIRhEPh6WmNPZKIHaOlzd6puT1yFk89iT+eURCiVVueu5xqms2vSx7hDQEXsf+InCMqdPHjj5YCITSFjh2PLRVh1CKseWX5Dux9H2tLftm2iKNJ1rtiIMgzK6YYkiTITYkw+V0mEZZhOv6KKMCjpzjThIEx3s6OJ+4PPf/azrHzgUYJiafN37gHCMBhx5CFcfc01bzvt2Tsd72hCqYZSiut/ei2nfWifHtMOfcHa9e38497n+MzFnx3gsxsYJDu9nmalbC2SyXxaxCZAmKiEWEyXwGvVxOK19V01ryQyKqOiMiDjrq16LKcAmKliuzqfXy6XUn+vwCuhok5QHoaV17NgNkMkiXuw7xUxDG3ropTUaa4eWnl9XyvGEy2K08vQKS1elLhVYsBkp2+lRpw9E0r1caSM0smQyfnoqZACx9WRohAGUlkYdiMtw1vY+/hLeOXff2TOk/8EwCu1ImUUe3MJjNwwCNuQfpt+LVFIuawtUDSB6M+NaYo0TWmaZprek1LGqa18OselPzb1MgrwSxvTNu+BqJv0hEmTJvGeQw5mxX0P9/sYg6dPoSQU995778CdWIZ3D6HMnj2bdevW8J79ZvT7GL/582Mcc8wxDB06dADPbOBQvZBtC9RGK3GR2LBQogBWE8KwCcob4oiltUdi0WmjEuViK165HSUjbKdAvm4Qbq4e23Fjx958qqQ245qCjOeSKCV1akd2gtLFd8OqJ4rkJkWTUkaUSx168mKsLQnidFZPTrlJ7SSZptgTmYDuAIuiMPU8Sx6TFL0TI8/NIYoiyqXKrjqxZ9cNBTksy05bmAGEMxRUyB7vP4NRU/bk/l99no6NG1BKUu7cGHdwGeC06I6s4pKKiNN2qupcepOgZJjOJUns5Cs1EjtOh+lusy1N9yoZ4RU3xBGu3pgMRN2kK6IoolQqcv6557LmqWcpr+ufml0IwdCD9+Xnv74hq6UMIN41hHL9T3/Cacft3e/ayYpVG3jo8Vc57SMfSe3c327jcxNF9baeF1+JVpxKXh8LJeownGYMwyHwNlJqW45f3kgU+QR+kVJxI+XiRgK/2EN9pHamehjo0cW+p6+zACzb0Wp2Q2kyQSCsRiRWlUNxqUuHkpc6F5dLHdo5N1fAcXK6dqJUtyI8xCm12G9LCzN7JpMoDNMRw9XiQu0OEFXNZdF/73qMdC5KLKhMXIyr7yvi37UoMrlOglBZYOQwZCuHn3cNURhw9/UXodBpRBVqnzZhWJj5UShvDSoqx6p/O66LxDNZhImMvNS4sSuRVN4bCWy5xsgvt6FUBEYew7S2Sf1Rd6+VQSlaBjVw8okfZMU9D/X7eC07TmP12nV9srfP0De8KwjlhRdeYNnSxRx24I79Psavfv8gHzz6KJqbmkjs3L148eppxvtbhcQkcFufT7LLrUQrxGRmIMlj2M0IwyL0WvE6VhKUN8aLt+7WyhWa0o4tpVTFvbhc1EOz/DJKSt2llSvEuhEbGbQRBe2YVo58w3DyhYZUdZ/k/G3bTgWeURjo8bxRQOInlvh4haGeDhiGUdwU4Mci0YByuRhfT6tXryrtTFxCGEZN9JJEJ0ldK/lb7SJceX+0hqQyEljEnVX6PdTmkEkrr+/5VBf2lTUEoXyGjBzBQad/nSUvPMSL99+MYRUACYSgwKwbpe9fWh6PHYjiIr2naz/CAhXhOHavxXallC7Kb2FkEYVlorCMMPO6zuVsXonfVyQK/WKxUzs4V4lyTzv1w7S+No/iqtX9OrYwDIYesBe/uOGGATnXDO9gL69q/PaW3/Cho3bFsvqnO1m0dA1P/PdVjl5xN/car7HT4WczYvLuKClARYSBJAw8hGHGO79Nq4O3JQzDIIp6WsC23fO5bo4g8LX4T0WAREoFwgHTQRCgIg8VhihDgWkRRXr2SdJJlEAIbdViWjZGleOwjAK8uKPMdhu7KfeTTqQEelEvAVGsincRwkQpqVNjsQ29wqgV56lkBoyGlFGqcE+eJ+3KijuqlKJGIFhN5snfk7+VSsU4WtH/TgaaxUcHIXpMFVbsWnQ9J1Xz2w2owEF5q9jpvWew4Om7eOg3lzN6+v4MGTMZFZVACKQ0sAojCUsrEIWxlCtSIgzTQsq4aQDZ6+dGpR1kff8eKaXwS2362qOjuK1NdekNSBR7y0XJH7Vfm4pTck4DQ+qHcfZZZ/GXex5iuzP61+I/bM9deeabV7N48WLGjx+/Veed4R1svZJg7dq1HHvMB/j9dZ+gob57F09f8I0f/p2xY8YztX4drz36V4JyJ8Mn7cJOh5/F9nt/ANMuVFI/SZrCMOI0iLVVdihbiiQHrqfrbbv9gO5KiusaMqy49QJgaNFbOqRLYJmCKOyIC/CAcMHIIUwb0zAxqixbuiL0O/HLG7XKPT8Y0+pdUa0XG61iRymdJrNr01WJjYmbq6tpWdVNAOVUIKidj5OaRWJuqdM+SiZdT2aXWfCJS7CoSU9FcRRS0QvJLmRiYJhGaj2SDOlynBy+r/9vxW4EURjieaU4Gg0hKiK8peAMp6Mz4jefPZCmYeM463sPgdALOoAQimDtU4j8GMyGyVTb05eKrSh/PXa+Bdtt7PHahkEZv9yO6dTh9nHcr19u011dZp0etdyDELQv6JFE0hv1ZEfQmxgtfNXfuWKxyOHvex/bffRU6seM2uLnBZh7y5+Y3tzCj67+Qb8en6GCd3zK689//hMH7zu932SydMU6Zj+/kNPOPJ2DTv8iZ//oEQ48/XK8Uhv3Xn8RN124D0/+8bu0vjEfCFItgIpCQt+jXCpS7OzAK+uJg12tKgYa26owr320AgK/hFdqo1TcoIWHXgdhoM0UHbcON98Ud2kZ6cIMijCScfG+Wac+lAdRK4YqYVlmZUpkzXNKvNIG/PJGDNMlVzdsk2SiU1BFfK+EIQxyuXqcLvoVbSHiY1lOdzIpl+J6hY6M7JiUbdvGth0cx8V1K+r3JOXnurn0J3mqXC5X9Xc3bZtOU5LVZCKMqrShSO+rr5xKO7MSpALN5GWZ9Rh2M8pfTV3jIA7/+NW88fpzPPaH72KaDoZdiK+nQORGokrLMfBJpifqU7AAgyioRGPdrm8yIKyHUcE93z8g9Du1lY8wU2PRviI5v0SvlLghJxs1zd6edpMGDKuefF1zTaRaKBT4+Mc+xsq7H+rz8wLIMGTxXQ/wzNe/z8ZX5vLE44/T3t6+RcfI0B3v6AglCAKOPOIwrrj0BLabMLxfx/jOj+9gzHaz+H/nnB3nZnULp1I2S195jBfuuYlFzz8ISjFyyh5M3f94ttvzKHJ1zfERus4E0VGMXlxiNXM8I36gUlSlUjFNRfUHSimUjHTkEUcgqktayjCsWCNi6GUxtaXviciSAVCV+R6WZREF7YR+B6Aw7QK224RhJjPfwzjFFWI5DdhuwyZSMdqiRRe1tdrdsrovXtqwUT9fLl85XmI/L4SB7Tj4Xhnb6VkjkRBPTy3ESZtzQkDVz9s1bZZczyQCMgyTKIrSaYCJSaRl2Sh0LSgZuAXgeSWi1P4+rm2U5mtyKUzgnp9eyEsP/Y6TvnYHo3bYQy+8KkJJn2jj81j54ZhNO6bW+iAJSutA+uSbxvUYKZaLbcjIi2eVbDrqVkrhFdfpCMqox7SsPn0ee4tE9GfGTK+T7xVjIlEgbCynvleRped5HHr44Wx/7unUjdz0OuC3tfP6X++gfe4CGgY1c8CJx7Hjgftx+w9+whH77Mdpp5222deQoXe8ownlnnvu4eZfX8c13/hIvx6/YtUGPv6FG7nzzn9RyOs6ASrUkw6JdNpG2HSsX8acx//GnEdvY/3yeZiWw6Td38e0A09g7MwDSZxja9GVaEAPoBIx0eiOLT29sDLVsC9IWj7z+Z7TEkm6Kv2Rtb8n+fnkPJPBUJpIk2JxV3dZvfsWhpFOXNT1nIggGedrmggq7biW7WCZJmHQrgcroTDMHIbpEPpFMARubrAeadsLZBTh+UWUlBimheP0buPRU6orGUucmDzqrrKoZvFOn0tKyiXdoZbLF2puV0ql9ZIkrZMUjKvnnti2E3eP6VSgadnxom4TBEE6uRGgXIqnT9qa5NxcvmJWGYb4Xkl/dJRWrotgLfhvoNxxeD7c8rmDkVHAGVfcT+PQ0XjFVoQQhJ1LUKXl5IfvgzTqiKIIy7IIvTaIOnDrhus0bheUi63IyCdfP2Szn8XA7yQotyHMAkpYWsPSA0kl1653ErFqNlu+7+tNiIqbLIxc/H5umuB+ev31/OPZ/zLxwx/s8faN8xay5I676Vz5BpN2nsn+xx/LuOlT0+dd9NIr3Hv9r7jz9n+8ZfXRdwPe0YRy//33cdlXvsLuO0/imMN3ZtaOE7bow3DVz+5i+Nid+PQFFwJxR4nv6eKzLMbRiglGnS6myoA1i55nzmO3Mffxf1BqX0e+sYWp+5/A9INOZviknZEyIoqCeIfa26XtatNR+X96/qLq76K670fE6a44X68UinhEb2ozvqm3tJrk4gWgyjYkJY0q4ujJFr0aif1IGCbDuay4IK8LqLbjaHNLrzWNWEBguY3YbmOPC1EytyQMfRACx8lvshlCRxadWJaD4+Yr5xS3/DpuDiUl5XJRW8k73S1YyqUiSkly+bpupJVYmiRivYpDcKXzzjRNojBECC0MTESPYRjiOA6+75Or8rXy41kzuXwd5VInVpx2g4TcOuNrpeKGAwWl1wEwG6awccU8bv7iEdQ3D+eMK+7DsG0teBTgr/0vwrApjDqAIAh1p5kKIdygR/oWus/76CuhKKUod6zW4leRi6M9u+Z2KWX8XYhq0rPJuOmuEbt2IujUM3Bi6xeMXM312hQ2btzIEe97Hzt9/tM4jQ3pNVzx7ydY8+h/8Ds62f3I97LX0UfRPKy7zkwpxc8v+D++eumX2HvvvTf7fAnWrVvHDTfcwOLFiznvvPOYOnXq28qy6c3GO5pQANrb27nzzjv5w62/I/CLfOC9O3PkITvR1LDpomJbe4lTP3kdt//jTlpaWmpu0+2WZWRQivUQSpOK4cZ1g4goKLH0pYeZ8+jfWPDMvUSBx+DRk5lx8EeYftCHaRo2Lh5PGxBFiVNsLFgT6N9Vf2a194YeSEpo0ki0CIao/J5oH4i1LQORktNWH7p4n8xNCYMgzs0HcVpGYdo5lPTjTiyB5dRjOQ0Ypp4XIqMQ39f28Jbl9KgjqUbXVBeA75fjgVI2dtzGmsxz7xqdJHb0MopqooQEUkaUy+VU15EQiWmaqQ6lVNSfEyEEbq6QRm7J5EbXdfE8r6aZIknFubkCge+hUOTzdXE0VNaGkPoM9Y+wIOpAlBdj5kfi1I9lycuP8buvfIDhk3bi5K/+DYU22ozKa/DXv4BRGENu0FTCCGTkQdgBhOQbx3W7pqViK6oPhBIGJa2INwoYVkKCiiiS3br6ktRv0sXW03E1ebbFhXcBhosw7D47Gyf46je+wdOtaxlx0L4s/Ps/aXtlHrlCnv1P+CA7H3IgTm7TKbn//use2l6dz7U/+tEm7/fvf/+bkSNHMnny5HQu0yGHHMITTzzBlClTOPjgg/t8zu82vOMJJYFSiueff54//uH3PPTQw+y3xw4cc/guzJgypscP8R/+/iSL15h857tX9npMnXbohLCdNAVmFPQibFgoFYL08TrX8/pT/+K1R29jxZynABi744HMOPhkpux7HE6+oSbsr50PbsZfOtFryqDr6/R9HyX1bHht1UG86U/G6m46otiWSAqticeVaQhk2IGSAWAirDocVy/aUegR+u1EgR5UZZguSjgoZcRNAN0X955QneoyDDMlBzueZa+L3hFeL9FJEik4jps6CFe/nnK5VNNood16K+2xaXoK0ugm0bwkqa5cLk+5XKqpvyRRiO3ojUoQ+NhOLr12tS3OUrfzCgNVWoKI2nEHz8Qwc8x54nb+8t1TmTTrvXzg4l/oCYqFZkprniEsrsJs2hErN4hICl2XiDqw80Ox3fqa15oQSqFh004R5c61mjSMOgzTrPk8J+3dCYls7nMopaRc3BC3A5tayGma/bJtWbJkCcefdBIyChkzZQf2P/6DbDdrp76nkkslfvTRT/K3v/6V4cN7rsXceuutfOc73+Goo47iO9/5Dvfccw+LFi3i3HPP5ZlnnuFf//oXX/rSl7bovN9NeNcQSjU2btzI3//+d/70x1uxTcXRh+3M4QfNpK6QGOFJTrvgZ1z5vR+y0047bfJYid9T5LWCKgOmHk4ltJ2HaWkVdxSUQQW0rVnCnMf+xpzH/sbGVQuwnByTdjuCyXsfw3a7HUmuvrmKXKKarrC+7OaA1KOpUKgb0Os2kIiiSKunoyKJbgDDSc0Ytc+WExNLgF/eqDUVKBAmttOI5dZvlmSrU12W7erBV1LWtOEClMtFZA+1k6TGYtlaLd/tNcTKcuhOJJDYvcTW+LabDuiqEIoTT9vM4yWjhKtm+JSKHbGFvKHTe1VNHZZlp0SlU2c+YCKIoDgPw2nAadwBIQTP3n0D/7z2U8w46CQOO/d7uPlGDAM6lz8MpovRMDXWlwiIOsCwyNWPrHktpc5WlOyZUJLPbBh4yKAtHnmQdLeZmGal+aSvCMPY+wupNU3C1hY4WyGMPO2MMxi203QOOPG4fj3+H9dcz4Ezd+ass87q8XbP8wiCgC984Qv8+Mc/5tFHH2XZsmUce+yxAFxzzTUcd9xxTJkypV/P/07HuzLZ19zczJlnnskZZ5zBU089xR//8Ht+9ftrOXjf6Rxz2C6s29BBc/NgZs7c/MyUpJdf2g5eqRUVtkPUCkYdYai/FI6bw3GaiKKQphF59jjuInY/9pO88frzzHnsb7w++27mPvF3DNNm3MwDmbz3MUze6wPUDxqRFtATgtG5eV3k1Wkjq087vbcTtEngBpQMEKaLIkcoBZZBnA7SEYxX1mSTLChmrh5DRER+B4G3gcDbWJMO64rECFIIA8O00iK36+YxreqW4SiNWGq6tsIwdRhObO2T/L9Wm1emLPaUftFkUiJJM/Y0j6VaXS+qRI3J8yhIa03xq9LnHx8r8LXPmWU5MaFIMBxwhiH9VUh/A6Y7mFlHnEPnxtU8fMvXyTUM5qDTLyOXbyLXsiOlNc8g/NWI/Ghd2zNskB7lUntNA4MQ2hpSb3KI6yCythU+igWewsGJNwT9+WwGfpnAa9X/MHIgrHQUwtZ81s8791y+fdX32f+ED/brODMO2o/bb761V0KxbRvX1V50zz33HGPGjGHevHksXryYadOmMXLkSDZs2NDv83+n411JKAmEEOy1117stdderF27lttu+yuXXfVHNmzcyJe+9JUt+sAZhkG+bhCBXyAorU2NCzEKelEzzFi3UIdSBTzPY8TkvRix/a4cePplrHr9ORbMvpcFT9/NvddfyL0/u4iRk/dg8t5Hs8PexzBo5HbYttOtI6braFk9t+PNUclvKZRSBF47od8OsUjRsqsK5PEMDsuydfdXWGlcEMKKtSMmtlOfpsOSH8PKYTuNGFYlFRL42gHYtJy4LVjEaa/ahV+7+4qadJaUUY2tCujUldYSSXTaUC/mPZJJFFEulxACLMshCANq35Javy4dlekpl8lkRK3G7/oYVfNcwjBQkUShtOI90kV/aQ3GiFoJOpZgOE0IYbLfSZ+nY8MbPH3H9RSaWtjvhP/DrhtFWF5H0L6YXK6FyKxDhXGKKuzAKxsYpo66Ypd7TcxpZCji6ENHzH6xDYSDYZo13mZ9hd4EdMR6GCMmE2NAyARg3333RX7bZ+lrcxk3bcujhAk7Tufva9exYMECJk2a1O325Pz2228/7r//fj772c/y8ssv889//pNp06axbNkyPvCBD2zVa3gn412Z8toUoihi7ty57LDDDpttRewNUkq84trY9sKJu8CMuIYh4qFGZlxPCGLLEi2KVEqyftlcFjx9H6/Pvpu1i18CoGXcdLbb4/1M3utohk3cKS3cJjO/E2+maujoxUin7r2VJBOFPn55A0qGmHYex23qNutEL6ZlEksTnfbKxUaNPrqt2MS23fS9UTKKSaUDpSKEYWlbFkMrzJPup96GYiW6kup0lFK6PVgBrpMjim3ok6K6FmEKfN+LjSOdLsfU9ZikAC9l1K2DS3uG+eRyubSgX+3BZsROC2GoOwK1I7KN79e2D3ueRxTqTjXTsvC9on7NCBwzJGibg+m2YDdMQgiBjCL+9v2zePXRv/C+T17DrCM+Ckg6Vz6BDDpwhu6BlKbuYpSergkaji74A8gyhpXHsnMpiaQE7nUSeG1g1vWplbcrlJKUizqthrDitnwxYGSS4KabbuL+p//LcZd8ul+Pv+eG3zBl8DAuuvDCTd7vU5/6FNdccw0bNmzgF7/4BQsWLGD8+PFccskl5DbTAPBuxf8coQwUtIfRhnjwkx2Luxxk3LpbTSygU2N60YwN/aRewNpWL2Phsw/w+uy7WDHnP6AUjcPGM2mPo9huj/czespeGFVtlklNpysqSu1KMfTNIBilJIGn7TeEMHFyzd10JZXOLR1R6J2/rhVopXo8SjdehHskFqWIgiKh3667lSBdlEynvtcFqVwqas1OoS69fl5Z/00XlJMF3sS2KzY6XTUnCVLFfUwmugCvz7tai5HUUComkBUknwvf91LjyKQbrVTsqGkfDoIgFXS6uRxeOa4zYeK4LspbTVhcjl0/ASs/TD/GL3PrV49l2StPcNSnr2PaAScSBUWCtU+B4WA276TrKeGGeEOUEKYJRJhWDjfulquGV9xAFHoYThO53JY5U2gy0RuOpF6yLcgEoK2tjSOOPJJPXPcD6gc1b/HjV76+kNuuuJp77rq71660FStWcOqpp9LQ0MBFF13EbrvtRi6XS4Wr/6vICGUrEZTbCLwNcd98A3a8S07EfkIYqZU4VNIqSkqEkAii1Miw1LaeRc8/xOv/vYslLzyMjAIKTUOZuNuRjN/lvYyZcQBOXnfmJMXQ5POe5uSr3s6uxdKB/NImC7zvtYGSWHYddq67pkS7DJfTyYm2UxkJm6TBQC+yaTttvBj3RCwQK7rDIihNPggDy67DcupTJX7y3F65lKripZT4XilNaSWE1rXQXq05qX7exGNLCKNmTnolGsmjVGW8bjWSFuNyrMJPrFYcxyUIPARaTKkjJ90+nB47Fkqalk0UhoD2ErNsPZ8laJuLCjsQdduDoSM+r7ONv195Kstf+w/7nPg59jnxcxBuJFj3LMJpxmicgW1EhH4bTt0IZBQRBhUDTGHmsJ18jaai2P4GYOAWBm9RdKLnt6xH29vn0mhoW5BJgi9c+kXkiKHsc+z7t/ixSil++snP8P1vf5edd965x/s88sgjLF++nCOOOIJBgwZt7em+a5ARygAg8NoJyuvRHWANetG07LS42xOx6MJ0kBKAKRRK+bENOwTlMktfepTX/3sXC565h6DcgWFajNxhT8bOPJixOx3MsAk7dZtCWFHcJ2aH1QRj1NRi+hPFaM+vsp7cKEMM08F2mzC7DLBK5rNHsa289s3qbpeSWKxLqV1wHafipaWJJW4/jokljIJ4RrzAcQsIFRAGHalHlTC0TYdl1+F5HlJJXDcfdyglBCSwba0H6VEtHy/61d1YSWuwMIw4ajHS65HURKqRiB1d18X3g7iw76YWLdWF/qRtOV+oj6M0r6r9WJOVgLign/SIS8DUWiMVQul1BALqJmOa2jfN99q556cX8cq//8yOB5/C+y+4Dumtobz2OYQ7hFzLLgSlNzAMC7d+lDbdLLXHx05eiIVl5zEME7+4FmHmyNf1fQFVUlIudieTxDpoW0XRjz/+ON+++io+etW3+/X4B265lbFOgc/93/8N8Jm9u5ERygAh9DvxS2tJSMW0nLTYW00eeu561TyPuLMrqY9o+5KIKKwI25SC1QteYMkL/2bhs/exeuELAOQbWxi/83sYv/N7GLfTwRSahiHlloklk0inmmx6V6P7BGWtphaGGRNJd7+rMPDSBdayXWx707vQpBGh2sLFtvVM81piqegycrn6mq4qJSPCoEgUdKQRH8IGMw8kxWPdruz0IpjTAseKrU1XLzDDMHBzOqWha1thOosd9HuXuE/ror8XW/9X3vvkuiTHr359lu0CkjAI4o2CUXkvU01Kch11lOK4BQxDoMIifutrGE4TTuPk9Nil4kb+c9uPePwPVzJ2+r6ccOmtmGoj3sbXMAujcZq2Jyitxc4PxrQbKJfaQfpYTp12mJZB1fNHmHYdTq5337Wa6ykjnebqQiZCiH67EvcVYRhy6GHv5bRvXc6Q0VvuQrxywUJu++4PuOeuu96WDTBvV2SEMoAIgxJ+cTVggNmAMG1cN58ujJsiFj2fPEhTJbrgLpCRRxSUKwuLsCi2bWDVnKdY/PwDLHrufjo36gFDQyfsyMRd3sv4XQ5l9NS9MSwn9vBSKdH09e3W6vnYigVFFHQShSUQBrbbgGXX1XQwySgkCLzU2l3byjt9EmsmSJoYdLpLt2gmnUTlcrFKOS4wTCsmndqWat1FVCTy2yspMQwQDsLKkcs39rpA6KmQPo7jpM8bBtoqP5lPn3TgVZOIii3tq3VBugHBw7YdwjDcNMlXk4UQ2rsLMEwbIUi9uBINj2nZ6NECQU3bb1hcRdC5BKtuLHZhJKBNOMulVuY9dRf/uvZTNLSM4sOX/ZV8ziPqXILdMBGcwajIw20YjVf2QJaw7DxOrj4d6Rz5RTSJmamw1zAdLMvB6KF1WMYjgbuSCdBnO5UtRbIxkTIiDEOuuupq1lhwyKlbPitFKcW1H7+An/zwR0ybNm3Az/XdioxQBhhRWMbrXA0IMBtBmHHXTlVxuRdiEUKkxFLxidK7XoHUrbRBUpQFw3QwDZt1y+ez+Pn7WfjsfSx79QlkGGA5ecbteAATZr2XibPey+DRO9QQQMVAUltmVP87hZKxXUpSBI9nnMSDoHRXVGJ7HpshmsnwLO0XljznluzyklRhQq4i8SpD6UFaCIJQ29kIw0j1JTraC9MF2jBNTKGbBrTZYHzN7Hosp66G7BLH4OpUjJ/4uonE+4z0tVRHckmUklrSd0k19oSu0WCih3HcXNzAodNeoG1YHMfVDRC+l7ao+14nhmGSi+tqSin8tvlIfwNO01RMR8898b1OwqDM2sWv8dfvfoQo8Png525m2KgmVHkVdv1YpNmIablIswmkD0Tk61riemBA4LWD9DDsBp2WVbVedZpg7NRhOyi3k4wEpmpgVy6XG5D5Qck1TrzCtL2RBKkHwIHk5Zdf4Qtf/RafvP6afkUZ9/76FqYMHsqFF1yw1ef7v4KMULYBNKm8AVhgNoAQ3ZTbPROLU9PNVb1bT/y2ojDAti2UCogCj2TKnmE6ekEIQpa+8hiLnr2Phc/dz4YV8wBoHDpWp8Zm7M+YGfvTNGxcr+cvpSQMOlOHYK2KzpEuqumOunrXHZPMJr64tTUb0eWu3f8dRWGVyaauQ1XGChMP/qp1Tq5A7+RlHE0YpgGRh4xKsc0HsWeUizBddMaxmkyrbU82/brSKxATbfL+VXtaAXHRXqfVuhb8dQSmyBfqUksWy9aRUrlcSkWEpWIHIMgX6uLfJa5bwIwjKiUjvI0voZQkN2gGwtDapnJxo36e9lb+9I0TWLd8Lod+7Eqm7b4XqrQcMz8cnBYMqw5l5EF6OPlGTFM3EqioDNKn0DgivjyKMAgIk7SYqrxPNdctqfNgYNk2ZiJQFSJR6pAYoYr479XvpVJaWKkHvUXx4DNZ9f50Wb6U0ucii6ioxHGnfYZj/+9iRm3fXVOyOSybM4+7fvwz7vzHP7b4sW8FRjYaeFVfhz0PPJy77rrrTT2HjFC2EfxyO6G3Xu/qTZ0KqaSBalM0aedXFbH0FtGAnjSYuLsqGRKGSVosIRcb08phWS6ta5ay6DkdvSx54WG8olYnNw4dy5jp+zF2xv6MmbEfg0dNBiAKSwTlNpSKMEwXJ9eUqtSTHbJWbAOx/b7s5RPUU3SStFTr36F6QUj/nX4kuy4aZveFvea+vaGKEJTShKL82IwwSSXamjixY1GfjriqJ01Wv57q33XdRZHP59PX6Hle2tlmWVbaBQbEnl5OjUNvEM+8T6xhEudjN1eouX+p1ImS2hG5XNZ2P0KImvkvMizibXgFw67DaZoaR28BXqkNy84hw5DbrjidBc/ex67v/zj7H3cWqrQUwxmEyI9AWE0gwIg1P77vYYqQKCiSbxjRbbefRLdhGBEFnVpnQvVEz4EdBlfZOMSRoyCOTkp6uqUKUAgwC1xz/W9ZFgQcesapW/wsSil+ePZ53HLTbxg3rvcN2NsFg+sMjphWSS3OYydmz579pp5DRijbEOXiOmTQAUYB064nisIeSQU2TyzJcKdqXUN1y6tSqkIuYblqwJMmF9NyQcHaJS+z9OXHWPbK4yx75dG0/lJoGsqoKbszcofdGD1tH0Zuvwe2q4lQRywVIjFNK21ZrT7/ZGFJ6jVJG3PXj5hekCuGmDqFlCjTVdwdFmLE81WiKMCy3TQ1lyBJByapJt2iXKvRqXYb7nquYRgQhV68+64lF2HmcHONGH0wp6zWrFR3rQFpOjOJSgzDoFQqYsWeVQkSsaTj5rRDcVy7cdx8XIux4zSXfh+06l/EnWtRrF2paEPC8hqC9oVY+ZHY9WMB8MsdhKGHm29EILjn55/l6X/+nJYxUzjyY19l8GATzDxmYQxmfphW5Zs5ECaWIQm8dvINw3uti0Whj1dKWugrWqSk6UNHF1Gs/ekhsuhxUxBvBuKIRlXfTylNHmEHyKJOjQpbe+2Z9SAMXnnlVb5w+eV84rof9Cvt9fcf/ISj9t2fk07q38z6NxMtdQbvn1HZpLwiZ77phPKutl55q+HmB1OK9GyVKDAxnQJh3ALa1fFWCyHtdH5GEASxmaARGwzqWDZZtJI6SxiGcVeUjWHaOKYNbj0yConCMmHo6bZmrx3DsBk0ajuGjJvOru8/D6Uka5e8xJIXH2L5a/9hxZzZzH/qX/rc65oZPXVvRk3dixE77MHQCTNxcgVs2+0xB17ZtRt0lShUE00y9VFKFVuv1NyRZLFIyEUPAzPS+1YTalLoTp5bSb3QJV1WQErQOt0ouhXVhbDArMN0GlEyQAZFIECF7ZQ7tOWLZRcwrUI35X9XRFGkZ9ZDagpZHc0k0VnSUlyNCilqaxrTssAnbruuBGKGaUKoJzzajlaaG4ZFGPhYppN2vlm5ocigg7C0EmG6WPlh2G5BtwZ7neTyTRx5/g+ZtNth/OsnF/K7r32E3d53DrsfeijIBSgZYOSGoSI/NumMX4OMEGZ3QpEywiu3xo4Rlc+2dgTQ7yVCoJQJonvEIgxRVVdKdFXagy1Jcan4vRWGgKAD6W8kHcRlFlBmfSU1G5/vtOnTkUHA6iVLGT5+y6OM8TvvyONPPvmOIBQBGG9xQ1pGKNsQQghydcMot68E2YGM9EIRxIXenkbQ9kYsQGrEl8waqSaWKIpqCvyGaWGY9dhdycXv0AVWACLqBw9hx0NOYZcjz8W0crSuXsySlx5h6UuPsmLOkyx4WudgLbfA6Cl7MmbG/oydvh8jJu+Gk+ub27FeVE26NvYki7qUEWEQpC21wjD1THaVEI7o9piurgGmaaYz0aWMUqsSfW3CKp2IiIvaOrJLSBsUUoLlNundvwriNuQifmk9sB7DymFaBSy7llySc/ISP7G4qK/boKkhFH3uRjfho35frbTmk/xb14gS7UlyrFr1vRAWiAjfL+HmKt13dv14lPQJOhaBMLByQ7DdOp2ODUrYToEd9nw/IybvzsO/uZzZd/6S+bPv5ZCTP86Y7SOIyoj8GAQyTXtGkR/Pp6+NSgOvXaed4vEOCRKDycrnwIg/x4mtS3K/KBZX+rVRqGlhGba+3tJDehuI/DZAoYQD1iAw6xCmjZKqW0pUCMEhBx/Mq4//p1+EMmnnHfn5DTdrZ4Vt0Jk2oBDUfseiXu+57U4hS3lte4Shh9+pVcZmrgWUTs90LdT3BC20i2drk1iE2DUF3a7psoSUujrBykh360RhoojWEQVCGwQqhO5YjbUpluVQ7ljP8lefZNkrj7L05cdYvejFtLtqyLjpjJy8e/yzG0PGTe9TiqgaSkm8cqKkF5WSCLqry3EKGLHQsWt3Wo3mplvKpEvdRP+S3pbO8VAKw9DddZZlY8WNEdXXVsmAMOgkCiqty4aZw7QLmFaOslcRXybK76RrLGl91mku7QtWsWqpHTMcBj6+75HLFzAMM1XmEy/CruumY4qTLjZNLmDbJr5XwnZyqXNycn391jnIoB2ncXtMdzBeuZ0o9HHzjZimHRtddrD8tf9w788uYcPK15mx//vZ75gTyTUMxajfHsMdjAradceW2cVeREW6flFj46LTjXoUQ6VLLo08YgKR1ZNNhcA0LF23Mqy4m1AReRsJy+tQkRfXRuriHxfDsJGRrO3HiKFHAFg8//wLXPatb/L/fvDdHj+Dm8NPP3kxP7zye0yfPr1fj3+zMLTe4EM7V67/096OWcrr3QjLcomcZiJ/A5HXils3NN5d691xT7bnCRLTwrRtNAjwvKiGWKqjGi0Q1HoK8HWNQSjCoBMZ6khH25TUoTAI/CIy8ipiQMAQuhvHMA3qB41gyr4fZMq+HwSg3LGR5a89ycp5s1k5bzZzn7ydF+69UR/XyTNiu1mMmLxrSjRNwyf0mLvWivugypdML4ymZUFs5++4+U062ibk4vtB6jCQHIu4RlNZaVRKIKDSaAaIc/qCMIoIY3v2JBJITl2IPMLOIVSEkmVkVEaW1xMPGtALqVEgisy4xpCk5So1pGS3nhwzisJ4YxAX+I2KPY9tG5X2bCVrW7qFnr4powjDtOLbTIRhxg7M1JJzfjzIBfhtryPyoU4NIfBKbSQGjSAYPXUvPvLd+3jiz1fxzB3XseilpzjkpHOYuFMJVTcB4Q5DqCAdoJWk8wKvDYnQjQ0xLMvGtiwiGREEFQKpbTU2MC0rJZHqxgclQ8LSGsLyOlCRro3Yg9M2ZMvOVQxTu3y80hRwfD132mkn1q1cRWdrG3VNjb1+nnpC27r12Pkcd99999ueUETXCOWtOIcsQnlzoJSi1LFG7+SsZnL5BsqlIoahTQZ7WnSTnHy1DUjXdmLDMHEcu6aukSrPvc54aFUsNnTqsOw6pJKEgZ/WGQzTiu069JjYKPTSor4wTEzLxbT0brCnZoKNqxamBLNq3tO8seA5wtjQMN/QwsjJuzEijmKGTZqFU9dcNRZZP4dlaULUufhOTMvGdbsb7VXPKk/TRlWiQWGY3brhujsQR/Eslgp0bl5fwySHn7w+/X+oJgn9h0hrNpRfaUVOFlYjMUDcQs1FKmA1ury2Lq3L1RFZet/4nNLIs0o7g4TSQt18kJ+gaw5RWUc/lv78RaEmIzdXx+JXnuTe6y9k7ZJXmLz7gRxw3GnUD5mEURiNnRuMk4vntkch5eI6wNZzVoivk0gvWnxN9cZJF+grhNQVMvIIS+uIvA36OEY+ro24Om0XR19hTbu4RrXDQlec98lPMGbfPZmx/z69XHiNtnXrefbeB5j39LNsWLWaUkcHTUNaGDVsOL+75ZZNPvatxrAGgw/vWolQnuh88yOUtz2hLFu2jJdeeolZs2b1OpbznQIpQ8rtKwATM9eCaZj4fjk1L6y9r4znbfRsU9GVWJJdmUAr9lO7d2GCmUNJE0R10VvEEyd7/gJqjYcml3T3LwSm6cYE07sKPgoD1i55mZXzntY/c2ezbumraXqqYehYhk+axYjtd2XMtL0Zvt0uuPkGtK281lnk8vVp6ihRP1fbnFSbY4ax35ebK6S1pWphZGI8mVxDzysTxUPMbMdFxbUofVytl0gs7LsiSS8mHmOum0vrMKbQljkyqlj0IywMK4dUJojK+F/tREy3CCwKA6SMUr2NlJIoTGpuyWKanKuIIy3tHOA4eghXGHixsWPta1AywNv4mlbFN09FCQev3I5p2ji5hhoyjyJFFPo8e+dPefyPV4KAGfseyq6HHkvTyB1xB00BYcWfs7Aq0onJwzBrCGRzHVZRUCQsrUEG7YAAqx5l1FVIStjxOGW/22M3RSQJbrnlFh564Vne/8lza/7eE4GMmDCe7XefxcSddmTMlMl4xRI/Of8iHnvkkX6PvHgzMLzB4OTdK+vIY+19I5Tf/OY33H///dx00038+te/5qGHHmLkyJF84xvfwLZt7rzzTm699Vaampr47ne/S319fa/HetunvK76/pUsXTyPN9a0UldXYNasXdl1tz3YddddmTRpUr9aAd8qGIaFaTcQBW1EfgdWYRCGYeoRtJZFteFgUojvzUBPxEV9y9JfstAvEvmt6U7ZMB0suxGFoaMBUbXTRYAwEUbv0/YMw8JwLGynDqVkTC5+/P9y+hymqUmp+liGaTFk/EwGjZnGDgecAij8cgfrFr3IyvlPs/r1Z1m94Fnm/+f29Pmahk9kyLhpDB47leGTdmbIuJk0DBlTUxXRdZ3aWeXlkp5HbzsVR+JE7Z7MKdGuxnocr5IyJZPquSO24xKFAUGsUg/iWSjVNZWkCSCKorRekkAvanlstz6tu5SLrQhC3ToeX3up8ph2jmRQWleDSkMIPK+EGaeDlFKUQh+UqmiPYkdj181RLoU6K6Zk3JThahscv9yNrIRh4zZNwdv4Kl7rHNymqdhunZ5z4hdx3LqqyZAGpmmzxwcvZMq+H+SJP1/FS4/8iZcevZepex7IboefyOCJ+yPsOl2kjyOxnjZHm4IMSwTFN/Q1EibCHoSsUdcbcct42I1MeorOe8Nee+3Fr2++mfYNG3j23geYO/tZNqx6g1J7B8MnjGPy7rtqAtlhe2y3tgPTdhwampuYP3/+23q0rxBgb2Gbl+/7zJkzB4D169fz3//+l5tuuokbbriBBx98kEMOOYQ//vGP/PrXv+a+++7jT3/6E2effXavx3tbE4pSihdeeIEff+NURgxrZvHytbz46lKeevQOfvWLn9JZ8thl553Ydbc92XXXXZk+fXqNWOztCDvXRBR0gizhlXO4uTxeuUgY6DZQpSTlsp5j3tOkwATaP8uPO5ESOxbtWYWwkJjIRGVvaNt4K3YETgr4vu/FkYrdo/NuAr1rz6fTF2Wk9RtR5OuuMZ+4ZmGjMElmnQBpvt0ym3Cm7MWoqfvgxvYbHRveYOW82axe+AJvLHiBtUteZsHsu9JIxnbraBk3nWETZzJswo4MmzCToRN2xLYbNemWSzWana7Q89sTYtEGj0mEpsfsVj7+Qmj/scQlOoxJKAwDPQbAsuLWbZUW2qtbmKsT+UIIhOkgrLp00fPL2hFZpxSTdJuJLzwsu4BhxvPZq3RHZvyeJG3QCZFVR4ZJe3Ki+zEMA8fJUy536K6vLmlDYTo4zVPxE1Jpnoa0XMKgHDd0JItzPKpYQsOQ0Rx+3tXsffzFzL79Gl568A+8+uRDTN51H3Z/30cYOvlAhDssHqXcNzKRYZmgtBrpt4EwMdwWInKoqkmRCDOOSoKax+rX6GyRhcv222+PVy7xg3M+oQlkt1lMPO1kxkyZ3I1AesLY6VN55pln3taEAlveNvzXv/6VY445huuuu46XXnqJ3XffHYC9996bf/7zn0yaNInJkydjWRZ77703X/va1zZ5vLc1oaxatYowDBkxrBkhBBPGDGXCmKEcfdiuAKxZ18aLry3lxVcf547b/8TyFWuZPm0qs3bbnd12252dd96Zurq+tba+WTAMAzs/KB4jXERKB2HoNlJTSjyvnJJJT+G1jIKURLQyXmCYLhh2OixKQxOMToW4NcRUXcDXabPY7TYmlk2lDoQQmHGqTKd/dIpFyaAy+ArdyqrTaVa64OlWXpso0s0FhtvA6B0PZvSMg9C5fwMlQzaumMfaJa+wdvFLrF74EnMfv40X7rkhPXbTsPG0jJtOy9iptIydzugddsMZuX2vM931QqfwvUqdIwjDWPtg9nB/E9OMZ5sEOq0ok/Zky+pCvpvLGGvLGNMuECntdCzQzsg6qmzXRpaAYboYliaWMCR1q9YdaRFRmBTEk4hJYsWEopsOorTJw7IdwsAnssKUOFM9kDIwChOJOl/H2/gqyhkDhkUUeiRdf8m1UEqn01w3R2HCDN7/qZ+y13EX8PQd1/HC/bcy9+nHmDhzd/Y46jQmzDoGpYZvMmsgI5+wuJrI36jrN7khRKJAlBKzbkRIDCeryUSIyniDLYUQglm7zGLQTtOY9d5DtvjxY6ZN4b/PPM0pp5yyxY99syAEWFWXZs2aNSlBAJx77rmce24l5RcEAbNnz+bkk0/muuuuo729PU1n1dfX09bW1uPfNoW3NaE8//zzzJg6ttcP6NCWRt6z3wzes98MADo6y7w8ZxkvvjaHa35wLxg5bv3Dn97MU+4TbKeO0GtHyRKB52DaLlEYporrrmSirdlLeocrk6jDRhguSgndYSNV7FAci+LQX8YwDOL5HrV5Zh2ZWDXEkvyY8bzwrlb2yYKUFMQr4jwT09akZQgdOUWhjp4IKrvxSGlRIXE3j2kaWpMReigl4qmKBnVT9mT0lD1rnrdj3QpWL3qRNYteZNWCF1iz6EUWPn13VTRTYMi46QwZP4OWMTsweNQODB49meYRE5FKxdFJ3GQQj94tl6PN5N9FpeAtDAyhh2yVwjB2C6jsxnv6iFZ3WlVrUUzLwTZsQmljmgamoZChh4zKhF7lC1tqb8e0crqOoGQqyEzOVUqFaYo0SgmDANO04vtYQIBX1gaSyXtXc365sVBegvCXY9ZNIJIivo+F7TgEfhlhmGl3mmlq48chY6Zx4OnfYM9jzuOZu27kuXtu4Y9XXMTYqTewx1EfYdJux+DUj071Kvp1S921VVqrPw25FjAbCaPEzDNuJEBbFCV1ogS9NVhsCfbec08efO6ZfhHK+BnTuOV3f0rb8jeH6vtJKdm4cSODBw/u8+P7AwGYVcceOnToJmsod9xxB0cddVT674aGBt544w0AOjo6aGhooKGhgY4OnbLt7OyksXHTXXJvb0J57llmbN/3Qnx9XY69dt2evXbdnh2efJW7H1u2Dc9u6+DkB+N1rgRZJgqr3VgTu3upxYhBERkmbr8mwsihMHRqQAi9YzbtHi3EHcdJUwbaGr87sQCbFEomu9tktn1tUd/usmOONQZYOmEiLCCKO5UiUJ5uABJm3DVmanNBJWNBXs+RkRCChiGjaRgymnE7v0c77gpB4JXoXLuUtUtejsnmJV7/7528eN9N6WMN06Zp+ASaR27HoJHbM3T8NFrGTGHQqO2x8029Xpeaeklsk6KL5FFsiqiFmCL2q+q5taUiSKw+bvKa9PsMll0AuxDfLgmDMkFZD7oK/Y70GCoy8JS2lkdGhKGocmLWi36pGNAVidBTGDaG0O3Iqbt1rg6/dS5Rx+tY9RMIIgEqwLLq0qgIDILAxzByqTgxXzcIlGLfEy5m96M+yvP3/45n/vVr/nr152kY/H0m77of0w/4EKNnHIYwLIKO5SjpYzrNmLkh+GGAiqo742KFv2XXkEkiFB0IUeGuu+7KTb//Xb8eO2jEcMpemXXr1jFkyJAe76OUYs6cOXiex84774xSiiAI+P73v8/ixYv52c9+tk0JBWojlM1h0aJFzJkzhz/+8Y/Mnz+fl19+mZdeeolzzjmHJ598kp122onx48czf/58oijiySefZObMmZt+/q08/22K559/lo+dvOfm79gDXp6zgp132W2Az2jgYFqOboWUPii9mDiOSeh3dNGFGLFYzNRfOmHEC7nVp+4ZnSboiVisVC2eoOJ4bKZDv6T0a27XtYNKykUXSytq6Op2VsO0tG1JYnQpI6LI6xa9CMNCRh4CtclGgcTWPakp5AoNNE7elZGTd625X7ljA+uXz2P14ldYt2wOG1e+zoaV81ny/AO6YypGvqGFQaMn0zxiO5pGTGTQyO1pGTOFwaO2R6J363aXji89oEvPRgnDIJ0CGfhlZBTqyM5MNBp0SUNSEyUYhhHPSqksMkm9KgiiVNEfhZ6eSKkCZFjCj2swKhL4gaWJW1gkKU49PsCMo7+AINCRhuPk6ArDKuAOmo7XOo+wYwHYQ8FsoFxqw801oGTi9hulc1lAbzAwXFABTmEQexx9HrMOP405//kXc5/8F889+A+eue9vNA8byQ67HciUvY9gxNRDwGrCq9IeaYud5HqYNWTS9dpvLXbYYQfa1q2ns7WVuqamLXqsEIKRE8Yzd+7cXgnl+eef5+KLL6auro5//OMfacpu0aJFNMXPty3JRIgtq6F85jOfSX8/88wzOf/887nhhhs488wzGTFiBKeffjq2bXP88cdz1lln0djYyHe/u2lx6FvSNrxu3Tquu+466uvr2XPPPdlvv/263SeKIvbeey9u+9VF1BU2XzTrik9/+bd86qIvsPfeew/EKQ8okp1L6LWCLKIdbmv6mWLyqFok4lTW1s6S0M9dmeWu02RmGl1UW4KkHlk9GDxWI7XcjxcfIQSOm99krjsMA/xyu04VKYmqslkxLSftIBMxaSbz4ROLeCmj1Jm359fopSmgSEqEMHBsm7Y1S1i/fC7rV8xj/fLkZw6dG96ovG7DpHHYeAaPnszgUdvTNGx8/DOOpmHjcesqi5E+r3Lsz1XxG9NDsHTLczJ4q1RK2sBzlfZj38N2EudoiVQSFfud9fIGYhgCJYN4mmLYRatiYdk5XY8xdfed75eIwqDG5r77YSVe63xUsFF3WpmDNAnl6vE93fyAsFJ1f7lc0qQSJQ0hAmSAjkQjSm1rmf/fO3ntybtYNud5UIqWUeOZvPtBbL/7kbRM2B3DbdYRaw/RW39rJZvD/zv3XCa95wCm7bPlG9W7fv5r9pk8lbPOOqvH25Pv1EUXXcS1114LwAsvvMCCBQu47bbb+OUvf7lNm4bGNJt86qDKpuHPy6f9byjlC4UCRxxxBAsXLuQPf/gDM2fO7JabW7lyJc2N9f0ikyCImLtgOTvuuONAnfKAQLdzFnXbrapeCJI+fr3T1Cr1vvfwbwmSdmMhjDRtUz0PvTKPhbRzqLfjJCkvpSR+3EzQk7tvt+sgZTyf3a7oTWREFPlEUYCM25MDiLvHLD2vROjddxBPQuyNTHy/TBTqaCGhQT1PxGTQyEkMGjmJ7Tiy5nHljo2sWfoaa5e+xoYVr7NhxTw2rpzPspceJfA6a+7r1jWn5NIwdCx1g0YxeNQkmodPpL5lFIZbiOfea3ieJhJUiJSKUjGoIYzAryLx2CBRoZXwlu1gmTblWOsio1BPcrQLVZb4ETL08MudoAJCvx1I/NoEwrRBCbySj5Or7za2Wb+fBsoZDcJB+asxlI+0huGX27Hdel2DUtrxWgtQ4/M3bJBlLKcepXJEfieoiHxdjpkHH8/M95xKR+sG5v3nbuY8eQdP3v4bnrz9NxQamxk7ZWfGztiPsTsdStOIHcBwU3uabbWT32XnnXlt/uv9IpShE8bxStxi2xOS6C0IAlatWsWIESP4y1/+wiWXXMIjjzzCmjVrGDVqy8cRbwn+J80h8/k8e+65J3vuuSdz5syhtbW1G6HMnz+fCeOG9ev48xe9wZjRIzcpwNnWSDtqIp8oLOsOGlXt1hbbdSgfVAAiB8Igl68bUBO6JLrQ0YesIY+eUG3kVz1nXhONke6sk/RT7IWrc9252lbc3s7H97QXVbWRoTBMLEO3JuvziOK26CT9p0D5BOVSnG83464ms8uxS0RRpAnHMPFjjc+mdrtKKYSdo2X8TIZN2iW2uakIR8vt6yluWEHn+hW0rVlC6xuLaV29mPUr5rPouQd6JJzGoWNoGDKWhqFjaRwyhsahY2kYMoa6QSMoNA7Fspw0JZJ0UXVtgEiGaRlVdSozrjE4bmVejmnaGI6NEk5q3++6Ttx55yMjP55RovCLSZrRxjCd9AdhoQC7MBrhNhC0L0DI5eCMwC93YDsFPeI58vCjEOKmjjDUxwr9DpxcE6bjEnSuQWHGw+WgvnkYs474CLOO+Ajt61ax6KUnWfLiIyx55T/M+e/DwLdpGjqCEROnMmLidEZM2pkRk3cn3zgSw27YrMtzX6C1QZLtJk3ksb8+069jjJgwnnv/2fvAqiR12dLSwqJFixg+fDjjx48nl8vR3NzMfffdx4knnpjOzRlo6C6vt5ZR3hJCKRaL3HXXXcyePZuxY8cyduzYbveZP38+40cP6tfxFyxZzdSpb94c6GTxk2GAlIG2XO8yIjXViBgWRjxFTymJaThE/oaYVHS3l+H0XRjWFZo8ZBx5bCJ1UoWKh5JKCaW606v2viLttvH9cmqvoZROUyaTJXuD75eQMsJxC5tM3+nbXFQQgWHiuK4WVgZahZ5Y8usCv6MXtVAr6W3HTScdJhFZb5BS4nmebsGN58uoOO1kCIFtmRiNg8k3NNMyrvtnSilFuWM9bWuW0bF2Ge1rl9G2Zinta5eycdVClr74MIHXxebFtCg0D6OueQSF5mEUmofRNGQ0dYNGUB//1A0ajplr0G7DjosQOqXmOI5W08epyaT7CnRBO/A1cShlYDn1NecZBmUCrwMhQAiFDMtaE1U5MyKlU2Vm3Xii4jLwloE9nMBXWHY+rnFEoMy4gB7qKY9hB35pPSLYgLDyuHVjCQJPa6SEShszGlpGMfOgDzLzoA+iFKxdtoAlLz7CsteeYtmcF5nz1EP6VIRg0LBRDB07kcEjJ9A8YhKDR01m0Oip1DWP0FGaYceiyt7rbknrd9L8MXHiOFYtWtzr52FTGDpuLEsWLSEIgk2mrrbffns6Ojro7Ozktdde44ILLmDlypW8+uqrHHbYYduMULTb8P8goViWxfTp03n++ed7bUObP38OO05o6dfxFy1dy3bb96+Y3xuShTrZ8UkZaufZbsQBmjws0lqIoYviiSGiNrQT2E5O5/j9jaBChJEnCDysPrZHVtuSVNuE94SkHpJEGklqK/lJ0HXWSvUQr+Q5dYuqnvXh5PIIYcQFf/2Yahv9rm66UaiHZW3K9DE5D69cRClFLl/Qz+EH8eCrAiidIpOhTxiUgcTU0URFCi/0UVLhuLl0d6qURFFxLa5xuxWCMAjjY3W7ejotpyXp+h02dcu1NmK0aBoyLk15pK83DLWuKCjSvnYZG1YuoHXNMoqtq+ncsIrixtW0r13KqnmzKbWvq/G+SpCrH0zdoOHUNQ8n3zSUxiGjyTW0UGgaSq6hhbqmIdQPGkaurhm30Kg1K1FIEHipMDJ5/20nj4rfB9vNY1kOKt4M+X4xrcuEiRu11QzhRvBXgtVEKOtJJzEKExklRK1wC4Mob1yAQiGsZhSCSCowcwhkPCY4tv9RCggRRAwdO4mhYyex21FngmFRbN3AG4te1WLX159n5YKXmTv70ZprYrs5GluGU9fUTK6ugVyhHreugXx9E/n6Zpx8AyKeb687IwWGUAgBQbmT9o0bKHd2kttCjZqTc6lrrGPZsmVMnDix1/u1tbVx4403snbtWk477TR22mknnnvuOZ5//nlGjhy5zTq9BAJrS9q8tgHedEKRUuI4DlOnTuWYY47hscceo7Ozs5sAccH8uXzggIP79RyLl61nj/1HUuxs07tvw6haTOM3Mv7uSiWpuLlWfid1d00caiU9Eodh6UUME1QyV524mBw/lVRpKsIwTGyn1lreMB2kDNMm0yDwtU2IUjU/yXCqTRfJ9fTDZEpeNXH0BVqF7Kbtm9XEYhgGgR+LFC07baUFcF0XpZz0Mcl0yWrTR98vYRhWjb16T9CW9roQ7OZ0JBMGAUpF2HGnkkJgGA5YJpE0AO3iq5TW7CTwSyUqepKe5t4nnmAGIp0gmVyz7teu2ifM94N40er5+upIQJBraKG+eRjDJ+0Sz4fXmp3E6gWl3Y+9zg14besotq6hc+MbdGxYReuapZRa19K58Q02rHyd+RvfqOlUq3k+w8ApNOIWmnDrmsjXDyLf0EKuvjn9ceuaMZ08TqGBhsEjKTS04NY1EWHhuPVpuk/JUEfb0SCi4nIIW3XaLJ6ECOAXtT4hCkzKgQ3SQ5h5ZOThFdcBJqZbRxRG8bySAP19ivR3Jf6eQISKfJAhhYYGJs7ck4kzkw2hIPQ9WteupHX1YjauWkzbmqW0rl5GZ9taWtctotzRilds7+mSdPlcKUIJDe40Vi9eyrjpUzf7mK4YNWkSixYt6pFQhBAsXLiQ+fPns9NOOzFy5Mi0zXaXXXZhl1122aZzVXSX1/9YhLJo0SJuvPFGbNvG933e8573dAsBlVIsWbqSsaP6F6EsXLqaSeNHQFSMJ5LrxbdSweiqKN8UksUo3u0I7Vuk7UXQGoSqu6ZHlZHufRHJ4m7FWoBqzUZMXsIC5ek0htBjXcMg6FktV31maZ3DrNGDDASqicX3/dSAEcBx3dTGpev5JP5iNSp83yO5+o7rkkxsTK6DvoYV4tTaB60w9/1irPPQ9w/8Et2UFgISd10hLD2cCxWPho+0c3K1GzAmYGiBoZPfoutW8QmrNaAMwyB9P7qcGNXaHf2aZeyV5mJaNuVSEdN2yDcOJd84lOYxUzEtG8usuAzYjovv+ziOi19sZf2qhZTa1uMV21BBkXLHxvhnA52ta/E6Wyl3bqRj/Qr9986NyLC7RqXmtZkWpp3DdnOYdg7Lyeso2slhmgLDBMvOYboNWG69tqqxHR1xOg6moTBtF4Stv3PVjs2xHU+yD1JKxV+fKO40V3Fno0CgtTh+qYOg1I5f7iSo/vGKBOWi/r8Xt2tXXXYZE0cpUJQDKIeCcqAo+ZJQgrI6WLVocb8IZfDo0SxYsIBDDulZHDlx4sS0w6vHa7yN/eX/5yKU8ePHc/bZZ7NgwQI8z+Oggw7qdpE3bNiAaRo0Nmx5rrGz6NHa2sFgdzVBawfCdPVcbNNBmDmEkdM6inSxFpUfIXr4dy20i7ms+kftbheqFo44WomkJJKy1pOo+tgq+V2iF7vk4JVzNAyRkkZPO+f+oHuU092qXaeFgqrzUXqueRDo9y0Oq/QCQu1Ckv5eeZ7EzHFzqH6NEgUqSrvfRNw2HMaiOzcWg+oit08QBDEZVluORAR+dXNESBT4REEHhmnHbcp6jHJvAstqJMSSDMvS7gG1+p4KgSSvSX+uqrUphmGknV1OrqCnFkYBURikJpb6fZDpgQqNLdj5hpioBflCoeacPc9LSdkwTNyc/h6FfiklnWLbOjo2rMIvteN1tuOV2hFSp/xC3yP0S/p3r0zol/C9En6xk8hbp/8eBERBEN+/UksbMMQpYdvNYTkutuNguzkc16WucUhMdA6hNGgv+rS2FVm7ei3r1q5jw8ZW/KCyfbRMaKrPM3rMEEaNG8viznrWL1ver9MaNHoUCxYuGKhXOaDQG5r/sQjFNE0mTpy4yRzk0qVLGT2yf9HJspXrGTt6CIaQRN46LRzsCsNBGC7CdCq/Gw6Yblzoq/xeKfj19oXpHu1Uf7d6/Z7VPCz5RzLLovL3dFGSEMbTAqvvIrpMF0oW9p7PaWu/9Cr9v67ZVJ2FiE9MJOckMIRACp2i00V2UanzxF1CyThY0O21SkocN5fWWJJuJ8O0cXOFuLgcxLUaC7fK2l/GpK0bCrrWMyIiKTCsgk7VoXSLctyqHPqdJDGMYVhx95MeNLapLqPkuR3HjUkuTAdnVRNaAsNI7E2o+ltlxrxp6dHFylFEUUgYBPFx9edY1660fijAAxRRJLGqtujaTieMRwjr1nDLsrHdArZboKFFt64GgUfg65koQpjk893nz0BlLg9osaHyVhGVVqGEBfZQlFEfjzsICDtXQdgWRxwWyjAQxDNqVICSHkQBKB+kj1IBQkXpZi75HBmWow0zTQclLNo2trNi2XJWLlvKisWLWDZvPssXz6dcqqQ3LRPqXZPhzS4jRo1k7KTt2H7Gjozebntsx0mnbT7xzCJ++2D/SGHI6FE88/hT/Xrsm4G3ekzx21Ipv3TpUkYN71+H1/KV6xkzbgLuEG2KplSoR4dGnh4oFHkoqc0MVRRAWNJqcNVbO60RRzmVHyP9PaddZRNyEk7aWiuTNE1vXVZxfl0gUKL2bkY8WzxJl1XQRXXd89nGhCPS59H/q2Gh9Ib0v1XZGakqFu+GoY0GKxFRRQEexIsd6HbWrl5LYRgQecWaYVm6kSAkCELCMCIMo9h/KtJk4tQW7JPaU5LbD3wv9huztNFiGgVU5stX28pLKfF9Lx7xa9XoVyzDBDuuySipXZRjktFK/vhyCTOOYrTFTU+DxpJxs9pXK3EkSDqxIpSyKhFXl+YJ0zTTWS/VRXRdf9IpMRmn8fTMklJa49FW9kENgVa0RNr3y/e8buOgASzLSe1VRNVCpD+3ITLShp9eqSP9zggT3Y4cRkTeOpRcRrJXUSrEkJtOqxHrYvT3pi7e0GlbGCVsWjdsZMXSxaxYMpflCzVprFj8OsWOiseZY5sUbGjJGdQPKjByzGgmTJ7CmMnTGDp2ewqDhmr/O78DFekozrDrMd1mzNxgJu80iXW/fXIz59kzhowZxZIlS/v12G0NIajZWLwVeFsSypIlS/4/e98dJ1lV5f+9975U1XHyMDkwzAwgGUwYUNE1pzULrpjQxbD6W1fXdU27hlVZF9TVNSFm1ry6omsAVMSAAjIwMEzOoXs6Vr1ww++Pc+97r7qrOtTMwDD2+TgO013v1atXVffcc843YPHC6Vl1uti97zCWLFlBVqHGgDEDxiqAN3aw3lhZGKMBncHolIaHKoUx0v7M/lExdDoEqZMJEoWXVzic+zS053YR4h59mbhf/Jv7ABiSrAZPBFAK4H4AzkLLN4im1II5GkGLbwytVC7V0Yq/IQQazKycsKQT8SOyY83Ktxety4IQ6RVzCLv7Zpznjou5bpY9J2McaRLTbtv34fvhuOSlbUJyP1eKwAEAGlpgzYIxnjtTAm7GldlFNcv5RC6oivFh4LS8yixvQvU5Hovb4buE62ZFZTkXul7VdIdJcyyqJFziUrIgpGolkSaxlVwpACgySxH4DDIZRn1oEJ4AjOWkaJXCKCKQahnD6BR1KPq8O95Pk6BnZFTJi4DmikYBQoCLHkBU6TNtxUuZ8ACt8qqKvmsShw/sxq6tm7B7233YvX0rdm/fjD3bt6A2OpI/V7WjA7N6u3HS7AheV4pqyNDdEWHx6nVYuGo9Fqxej1kLl0MEnWBeBcYAMhlAWjsEGAXudyDoWg6vusAKVRI6bcnS2Rg41AeZZfCmyVzvmj0boyMjiOMYUTReyuaBDIaxM7z7P47LhLJzxzacvrK9CmXP/kGccd759A83E2mwSAWKxFKyjoWxc5YOFNt197jGOQDt4JSFDVPSIea7LJKSSmG0hJExjJZQOrMld+u2k2vOJfaKwDwkduGCTT6kdVX679LP88ewxp87A6TWWH0DaSXsAeQ8jqnMaMYOqQt9Kw2AIQib2xvngALG6JE2kTj3Q8/zCJgAwPN9JDHt0lu5W5ZbXbm0jczAGEcYhtP+opEaMsm/FM+jKLnYREMQY3o/k1o9b5G5asZVKyQCaRDH9vFGQqYGnBlbmUnopIY0NTCC25+RhIn7fZbW7SZGQ3JSFjCG2kwwEonR9vEORUVw6KTZi3OvkRMvCsy3SaAC4UUQfgWME7cnk8YmDg9RpcuqHzdWOzobQTK0GdAJjOgC/LnU7mIc/fv3YM/2e7Fvx33Ys+0e7Nx8F3ZtvRv1UsXR1TsXS1efikc88XmIPAU9tBO13X+GMAkYr2Pu0tVYsHI9Fqw+FXOXrYFfmQ3mdcEIUpdQ9YNIh3eT2yPj8Crz4XcsAg967IbNKlAEEYQXoCJ8zD9pIfr37sP8ZeM5cJN9Lrpn9aKvrw+LFy+e1rHHPBjgPcCOksdlQtm/fx8ed/5pbR27Z/8gnrxkSZFEgNJU1P1AjPmdBmlpuQTjBtBuV+N2ktxCQWF3pcrCeFWOLBobOQzVzhDoVAbMQpNhNJROkdUOQHgVaG2gVQJPcDI90jJfeKAzu5uUVDE1yLdMFqxEArOviRGM1hj3e0KMSXvNBXLKwW3doJkX/20fl/+3cRa19O84CSwJjYOheF5mPUm0UhBeAI+HYJxBKgOVSsiYFkQGhno2BGPI2IrJFJm0Cd7u9LPMtrSCEEmqIWVKhD9OOmhSjgUIuDmTqywaf1b8HPnzFLOo8nNLKEm7feJayDwJoAxBh3uvi0Fxq4W+ZQJghEwDFzAWqk4kWQ5jqBIggzPAGPv+cPK29/wI2tC/iati54MgfTEhCNGXxKPQWuWaX1mWQdtNBuccXtgcJMO8DowkPdhy5w3Ytfl27NmxA7u378Du7ZsR14qKo3vWPCxdfSoe/ZQXY+nqU7Fk1XrMnjMXfZtuxpbffx977/kO6lqh0tWLdec/EovWnoX5K09F0DEbPOgFC7phDH0HVToMObQDWW0fVSNeFUHPGvgdiwiEAw9g3CaRkGZhpSS4cOFCDB3qn3ZCAYDu2bNw6NCh4y6hMExvKL9p0ya8973vBeccy5Ytw3vf+15cc801J54F8OHDh9Hb3XxAOFns3X8Yq1atRhRFOdmvIP0VxLR88WcORorSgmIXASj6Yyw6R43lMZDOFOcczGPEY7AnKicagsnaXWezCsWQxzzjEQQPwLWG8H0Iw6GUJKSLFwAYj+xyO1j3B7YSMlrCmKzhd8ZVSXah00rCGA0GU9IA0vZYuwi6dqBNvMVcyDQ8plVMLPQyvWjOvihibPd+sm5++2FnSdyS/CAAQYs8JW16rwxjcHBZpxRNC7nI+UtgPjw/BJiAUpoQayKwCcPB1OlzQJ4uDFGlWvJE0TlyzollOtJmmiV2o+OcRgCpBHwhLGSXkqOzVA6jDsTxCJKkhgDVHApN8i5U+R3auwO7ttyNnVvuor83098NiaN3NhYvX4FHPuHJWLz6IVi88iFYvOpUzF24lDYs9cPY8vvvYOP//gv23ksyKF1zFmDdI/8Ky05/GOYuWw8WdIL53eBetQAxGAOdDiEdvA8qOQyAw6vOg9+5FDyYY++nAPdDeH5I4qItqux5c+di5PDhtt79zt5eHDp0qK1jj2XQDGXqFcqKFSvwpS99CQDwzne+Exs2bDgxLYAPDwyit2f6TovGGPQPDGH+/Pm5sGL5d2VmuVKqSXJx/1e+Lc7Pw/0BYEhLCszJk49fNmloKsC5b1s7vDT4NA3JRsk67QQZh0tiKivOmcVJvjgyi8hhOXzY/bdHbn9WjXfC9pbV4RIgC1mvhdDiVMMtTDJNkKY1eNZ8ipBU0lYQihggggNGQ2ZJ3toqKoQi0UlJelEMDEx4+YaAMW5JlgIGDKmVs+ecQylNXJjcobKAgrMGsEKxKWBluHgJSs7G/LuAlKPhXjnYsFPhHRv1eh2Ahu+JYh4zZnBtbMXHuYBmATy/Mu5chUaaaSDHEXTb3lOZ5aAD4XkIOUdcr+UOk1mW2qF+DWAM3Hq5lIEAUdSB2ugw9my/B7u3bsKe7fdi15a7sW/HpnGJo2fOAixdfSoe+/RLsWjlWixYsgqLV6xBV88cQI0A2QEwk0EbD4YN4+A91+O+33wPm//wc9SHB1DtnYszn/RSLD/zInQvWAIuPNK3szbWeSI2DDobQTq0GbK2H+C+rUaWgnkVUOXmbKrDKSlyz583H3sOD0z6uGbRMev4TChg05NeKcvHBEGA7du3n3gWwFprDA4Oo6dr+hXKaC2B73kIm3hEl6VHAB+F+yAZR+lcNLFob1CrKgRgrHZSySwKju1L7oj589i5AACLwBk/N2HMtpeskq4buofVWeDcQ70+ChggjEJkSQylaKEw0KWKR7Zss9FzOPZ3kXgMrMS2AZigYfnRkAgneXmFTKYQftQwN+E+AamUUrSgKQUwAxF15fyRcjiYsAjoXkZW2qWMDtNG581JEXXlsFs/9I6pUm3zmBiKzTmD1gyeH+WIsnq9BkoyXo4so/mahMoS1LPhRmQZ9whdJsQ4XxIAFh6cIkvTMd4ttuWnNCqVCjzPR70+CsfxkTJD3/5d2L11I/Zu34Q92+7B7m33YNeWu5GU+EI9cxZg2erTcNEzXpa3qpasWo+uXoL2a61oPpRXsRJGZTRWTIdQH9yH2358HbbdfjMMgEXrH4aHP/YSLDnt4WByEIAhCDILbYVRBZiAzurQWQ3p8HZkw4Ss8rtWIOheTW0tJsCEbyWMpve+z583D/fde/ekj9NaY/jwYfTv3ov+ffsxeOAgtt9zL7bOau6J8kDGWOmVySyAAeAXv/gFrrrqKqxYsQJSyhPPAnh4eBiVKITvT3+hGxiqobe3Z0qPLSoIketsaevnIaW0Q08No2hnTJ4khWSGlIkdxKegHSxVLcaYkj82cg8TR2Vxfhdaa0hjd6oW2hjX61Rh5NWUJntgbWhX1kTk0JH2cq2qhv9unXiMBBI5ms9Imv1BQ0JqXfm4XS8NwJvv1AkRJmziZvkA3veDvOVCg2sSUuScI4yKc41Fh6Vpks85SCDxgUgmk4d7XeUQQkBKA+GFlGgAez/q0CrLh+tKJg3IMqpEGLSUUAIWKcghBIeS9AGTWQq/BJv2fR/1eg17d9yHvdvvxfZNd2LHfXdi7/ZN2L3tnobE0TtnARatWIvHPO0lWLT8FCxeuRaLVq7F7HmLW2qv0XtWh8mGYZI+6KwfyIZBmzCNe3/3S/z5p1+HkhnWP+Y5WHfh09A5dxkABiYHYJhPRl2iAi/oBOce0lofjE4gR/ciHdoKozN41ZMQ9K4F9zoA0GzE9ytU1bQRc+fOxY7vbcT/XfMVDPX3Y/TwAJLRGmScIEsSZGmKNEmQJlRJd3R2oqunG72zZqHDC3DgwIG2nveYxpgKZTILYAC46KKLcNFFF+EDH/gAPM9DX18fgBPIAri/vx9CMPz81xvQ1VFBd1cFXR0Ruroq6KiEE5Z0A0M1zJrV29bzEqpH5Fawrj0mbZvAtSsouQgEYYf1p0iRpXUakEOCbqnI2yXOf92Fs9X1A9uSARCPDILxAMLzafdt+95ZWoxoszSxfIGCLV9ARL2iSV6KcnsLxkD4VsbdmDzhlHXM3Exlol13Q7LJd7pU3XlBlXba+e8bYb3ObTEoocLStND8UjKD0dr281ujw9xrK4dSEnGscjHL4yWxlK+3SI68yc8YPC9AqknWX2t679zcjXNqCWpFP0vqaX4uxoXdrAC7t23BwT07sXvr3cWMY+tGpHGheDxr7klYtOIUPOZpl2DJ6vVYufZMLF65Dh1dvbYKTBsADGlSg1IhPFslAYCWo5D1g8hGD0Cnh5HzuLxOsMoSpJnBTz/1tzi8ayOWnPF4nPfXb0f37PmA7AMzlCQNAsDrsvpgAiqrIctGoeI+JINbYOQoeDgLUe86iHA2KJFUrFzOkVXWURThwI5dmNPdi9lzZuPk9aeid/Zs9PT2oLu3Fz32T3dv7zh48E0//Rlu+017PJZjGQxsWigvkvKhTWpHRweUUrj11ltPLAvgRYsW4alPezp+fds+DA4OYmhoCENDwxgaHkFcj9HRUUF3Vwe6Oiv2T4iujhBdHQH6D49g1qz24MZjg1kmt4OhOjissUJ+qU0SXAgIvwOcAVLWLSHRwPMiGLCcsObC2eoWKiyE/mFeJ5EDrfNfEtdzoyplSXLGgHanE7TQnBCm0cZKdhu7iE+ttwy4qqcx2ThJ98Z/uwREkcWDYwbhLJ/pGG3snIBByRiMcfi+gNF2DpHU8yTsB+MNoMr3z7G2ORd5e7PQDstKEOJGCZRjEY2yKuOjWUIplJs1yjsB137UWltNNBLVJAJpYWillcRA3x7s2nIXdt53F3Zv3YhdW+/B3u2b7OCeYtbchVi8ah0e98xLcdLyU7DilLOwbM1pqHZ2I65TgvH8oIEICni5R4tWkvY8RkGmw8jSQZhsACY9DGi72eEhRGUBvMo8SFTBeACVJbjh48/H0P6teMyr/h3LznwMmMkA1U+4GD6bjlUDYOkeGB4BLISUCdLBzVDJYTCvimjuORCVhWBMwAur5ER5FLxRAFpnlixdin/52JXTPrazqwuDg4NH5TqOakxzhvLrX/8a1157LQBg2bJluOKKK3Do0KEjsgA+7hJKGIZ429v+senvpJQYHh7OE02RcOi/TTiAp1144VG/prLoIanmJvlOulE2nmx6qWoZhRCBbQEVMwDnIZ6Hoi+mNjxfKPNfWaMoP4ig6qPggiMMK3l7rmwZq42GVE1wTXYRI6kS3VDZTPR6mW3htQpiptehrBS6EKIh4eQDdl2aTxkJmU6AvbKaZmmcQDr0XKkacvwS14Ist7hc8i/L7zt/Ft93Ei/HMrE0lyRvVlEVCC2D8obSsd/pfafEcmD3Nuzacjd23LfBIqrusomjkByZPX8xFq9ci7VnPRJLV67FSctPxtLV61Dp6LAVTuk5OHnJcAso0ZrDmOI+Or05xjR0dhhCDUPFh6Az2ztnHljQC+YvA/N7AUHvvTIGTGvApLjvV9fi4Nbb8NhXX4llZz4W0BJM1wAWwPhzaPAODsheGLkfRtUga7uQjOwHmEDQewr8rtVgzAMTAYKoY1LjtulGd3c3RoYnVyhuFp3dXZPOEh6IoE3w1BOua3eV47LLLsNll13W8LOnP/3pePrTnz6lcx53CWWi8DwPs2bNOmpVyHSD2mIeokgUrSQYCNti0VrTvAM+wKylbS2FH3bA9yNwHuSkPNp5StsjF3YGY8PNBpREkicrBiUlUpZYkUgBVlqNtFZIk8RKobB8t2tAXiAya1xYHMrHCROO9UiZLHJvkyAq5OjHtN2kzCCTGNwLc80tBzsmQERWautRe4WqK2tYplq33rQG4gylhMMa/tsTHEYbKJ0hTSgJCc+D5wVH1at8slvVLKGUtccA2jgc2L0VuzbfhW2b7sDOzXdj7/Z7sXvbPchKm4w5C5Zg8cp1WHf2I7F45br8T8+suXmLLAxDJHE9rzycFH2akvMlY8y2Nq0sTJaino3QBkInUHE/jByETGlYrsEgwlkIetZA8Q5AdCKMOpCm1HLzhCCdMR3bqkWjf+cGBNVurDz/2eAmgazvBUQVxptboO70KIypw8BDPLQXKj4EEfYi6l4Izj2Y7CDg9cBwD0lco/mZN95jp93o7u5uOyl0dnVhaOg4rFCAhjXhgYgHVUI5XqKoWDykVtmVJDcicMFt+0VBKwGYFFkyCpll8C2qyml0eSKDMgp+NBtMhJBSUkJgZb6MHewCQEkY0f2cFuDCmdHzg6Z+67klcUNlY0jCY5IW2liFY2otxRDCb+ltImWGNIlztdvyrABMQGYKWabAeADPJ5/4IIjgWYCElGN7+YBLOp7nUScGrhpyr02WeDLjQ6X0hy6kGRCBoTwbKldH+bVPM8oJRSmFA7u2YOeWu7Dtnjuwa+vGpolj9oLFWLr6NJx+/kUNqKpqZ3eOggNI0UBKlS/ugKt6PBrOe5ZQKnyEkYd6nYF7HoIgRBzHUMkgTNYPkw7AZIPFHERUwKL5xAMJekgok3vgjNP7Go8AhsPzCc4L5a7dA3gFC065EJt+/R38/r//Bec8+cWA1wF4cy1vBzDJYRgoyPgwksN3w6gMwaxT4XeuoI++GgHUMFi2H8gOkLmX7IIUFQIheD48EeTt3Xaio6MDWZpO6r7YLCihtFfdHMtgjEE0AHdUy8ceq5hJKEcQjJF0OpHOEiSJlS8PQkSRWxhDyHQURqdIYwUwUpMVwoNKhgEweEEHHL/C+b8nSdle15QSjMn7yJQcyh8aZpOSzrkZjc6MomkPetotNKsOyy3iamwLrVUycc/lBB65IL2wJK7ZhcKzT8GsBDyzWlzF8Z5dECcKx4sZOwty1shu0J1L6EDmx0wUzaohozWgDWRWABao4tiOXVvuxq6tG7H93j9jz/Z7sXf7vQ1AizkLlmDp6lPxkAselyeORSvWggkCFgRNrKALRQJKpI7ASxwT+tu11NIsRRhG+XGcGaQje6H0ELL6QUDZthkPwMO5gN8LEc2F8CPILMk5Q0YrSBlTWWitBAAFmYDeG+a4I1Rpn/rYS7D/3ptx508+h4Nb/ohTL74cix/yOHAjATUCYxTSoR3IhreCeR2oLDwfIphFMjDcA0QHoOfB6BqgBgA5CIYBOr/ogsyqkKKDIMZCQHD6m/OptzUZY4RgGh7GrNmzp3SMi0q1inqtNvkD7+9gVg0jj5mE8qAMITxEFZHrYelYIQwr4IIgyZ7XgzQZhZJJvuAomQCyBuYRoodzky/8QngQXJAMRlTJZwLlVlgRxNimysf1xbWVPynCJReeo8SK1pZDuGFMuVy4RVq1AUVtPPplIwotX2RB7TfG+LiFv4DGKhKRDMIc6BCOUQ52FYpDgDmdMTeD8v2gpT4Xc20VBjSbA+UIPlkMu4UgBJzgLH/NZedOsg9urIZkluHA7q3YvfVe7N5Gf/Zs24R9O7c03Ju5C5dg0fI1OO3cR2LJyrVYvGodTlq+BlGlg9o4YxJVmqZkb6xF6bUUC6UQAkpalWELuxaigjiuN8z0ZJbCpANAdhgy7oNOB+j9YQLM74XoWIawcyEUAqu/RpbOSkmAeTmfSOsMKh0l9V4nH8N8FA6YAGC17AwQj47iYc97A+YsXYXb//ca/OI/X4NKzzysvuCpWHLaw1HtUIAahdexBOGs04jrxb0iUcKemncAguyejRoG9EieXAwYmOiEFp3QvErHg5I6ty1hwb0Jq5ggCCCz8aTkycKJoh5vMb5CmUjJ7djETEI5SuEY2kJ4SJI64riGMKzkvt5B2IGMMcgsRhBGyOIhGDAYhEiSOE8kzmrXzRucc6LvB0iTegMEGZbBTQRKnV8HJRen81RIz1CrrPgCkdNjkWjGfvGKpMPBuUFMW1JElU446fRyZaNLiDZjdAOnBHbGBGNytWAY8qenxbzwDknTBEqpHMLtWjphGOWJYKyZ1XTfq+ZDfJmz98vaT0pK7Nt5Xw7DdQKHe7bd2+BkOW/RcixZuQ5nPeKJWLxyHZasXIuTlp0MLwjBGPL3o4BtkyJCq31kLMtmZK4qsu+RJqfQJFYQVgKGQcOoBEKPQMYHoOM+KNvGYn4P/O7V0KIH8LpswieoOtPavo5CTYIxBpVREtEWOEJikhUEUTeE8OznMbOFHQMXjKD1ahicaZzy6Jfi5Asvxe47fopNN38bG352Le78v2vAPR9zlq7D/DUXYP7JA5i34kxUehfS+5+PDC1xi4EqE94LmB5KLroGqFEYNQqmqPVEfJYqDK9AiSoU93PEodtEGatbp7WiU3M+5vs0tXBin63sfA8cOIArrrgCW7ZswS233ALP8/CIRzwC69aRQ+S///u/o6dnany56QWbmaGcaMGFQBhVkSR1JEkdvgnzmYYfVKFkgjQZBHQKP5oNL+i0i6TMJeCpdVKQ9ozRObIMcN4jgUWNqXx+4nazbsbiflaG0NI5FZTSFhigGh5brmLKiS1NajBaIwyredus3EKTMoOSktqAYQSDoo2mtIIpfXFlluVKwvZE5BkPk6OM3IxKKZor+flOnKo+UjaW1jzKa3h903q/Sgk7juvYtXUjdm/ZaNnj92L31o3Ys/3eBvfEeYtWYOnqU3H2I5+Ek5adgoXL1mDVujNR6ehq+hxOZdg5JwJWEaBey18njLEzIYKlZ1kKzysInw2VUi4FBGgZQ6nEzkEGYLJhmonZKoQQWd1g3LOSp4wG6ABkkkBlnPKBXWTTet1WGhlS+x4LvxNcVJBJZQm+dtHKBUSt5TIL6L/1KCAqqHQtRhYfxtIzHoWT1qzHwM7fon/3Dgz21XBw+124+2fXYsNPPgcA8MIKuuYtQ/f85eiatwxd81ege/5ydM5dhqhzNtkKOPUb0UHVi2dgTALoOv1Rw2BqEMhcgiE4shYRNCdl4nJQpdFeW8h52DT7zPX09OCzn/0s3vSmN+U/W7NmDT7/+c+39VxTDsYmNIO7P2ImoRyD4Jwjskkls4nAcUo8L0QWHwLjPrygs2G37Fo9Skkr9VJY5lKVEyFNEpAnObeoMeTVh1IFL6ScDMqVSU6s9H1aAOC4MSqvZMYSMZ2cuueHTZnJrWYmQqCUJOn6GZg18Cr8PJTMoMYwWJTMoJWF/nKW7wpd1eSMuFx1IaW0BleTW/jKLMNeq021a8td2LnlbuzafBf2bN80JnEsx+KV63DGwy/G8pNPw9KTT8OSVesRVQqdOWc5HLVwO3Tv3dgWiUv+2sLPwRipMYO8VjKpAeY1sN7ze6MU4pH9MGkfdHwIUPYz4nWAVReDB3MRVGfbCpDarG7xpCrRJiOjwZSbw5FScjGro3kIcakklK0EtAbqDW9VUdUq6xPEjAR4J+LRAzCyhnR4F9LBTaj2noTZa54OL+qF50dQWYK+bXfg0PY7MbR/KwYPbMPA7vuw4/afw6jGVlRQ6UbUPQeV7jmIuuiP+++wowde2AEviOAFAXyfw/M9eP4w/MDPNz0Gglj5jJKL4KytCgWgjSPBu8cP9MMwHCf/tGXLFrzsZS/D2WefjTe+8Y1HBak2Nsa3vO7/mEkoxygYYwjDSj6ANsbAD0Ioi+cXfmfTFhMJ+QHpmDmJMdaASfC8tVQmygVBAGP8fPBcrlKcF71rZ9AiWFQvroIpw1nz5CJT2zdnkNZl0VUwwkJz03R8Mil7rNDvCqMwZri1siVGPEFalZURERbqWigMGG1I0BAYhz7zbLvMJRVKLGTypaS0ieOuhnbV3h2boEpIufmLV2LJqvU451FPscPxU7F45VoEYSU/p4M0C89vyTmZ6LMw1lSL3rfxdsDu8Zw3ujgaLSHjQ5C1/ZC1AzDaghX8bngdaxB2LYbwO5GmqZ2tWJg2I8SX8KsIbGVRq9lWmpEwOgYsc51cE0PAePAC4l0ZEBJQyjT/HAEmlyfKgQy5IjDdV6M0jBpFOrQV2fB2iMoCRHPOIqFPGSO1kjK9S9agd8mahtevlcRo/14MH9yBkb49iIf7UB/uQzzUh3j4EAb2bEJ96BaktalBd4UfwQ8r8IIQnv3bDyvI6nHbsxDBx28SJoof/OAH6O7uxvve9z7ccMMN4/gfRydISPWBjJmEcgzDzVUY58jSBLo2Qm54VgSvHMYYKEnKtTSHoB5yGNHO17W33Bc3SWIEQdCA2ionCGqJKbu7VHlfnOYV3A7vVWnBdDMVl2BoIZKG2hy+H8GJadJxCjIrIaMYs+RJqoyyNMl9zctWvYCtPiwbPElim2yDhqQmJXEnSL+L5/pn2lkBlBKukhIH9mzFnq33YNeWjdi19W7s3roR+3ZuzisOxhjmL1mFpavW47zHPK1IHCvWIpygunDuiwUTP83nPr4/ta9Pq+TDOc9BBmMf43kCSb2GdPgAVP0AZHyI3nvmwavMg/Fng4dzwLhF2nlUNZFOWJYLSPp+QJ+rNAa3nyVo2yZyMGEWAjwigUYAYHT/hUefr1RnYNxHEBZcokzWqYVkFMhPSEB4IZRtoRqTEpJrZAe8zqUIZ50B4ZPQqkMtmjEeNPl9ET665i1F17yJvUqUTBEP9yOtDUMmNYLnJzXItG7/XYOM6d9ZUqPfuZ8nNTuPaXO4bmcoUw03M7nooouwcePGY5NQGAOfqVBO7HCcFa1iqKQGJipUeqO0ky/JunDO4QchtbEsBNQlCt83uS+GVhJxrEtD5EbI5NhWmqtaqEVUbn0FNlEoW70oKwtD8FBmLXxpZyrgRG6zLEVmdbic2+JY3gjnAtz6aLhrckN3xhiyjLzMw7DRatjNjDjnhU+9oArt0K7NeaWxczOxx/du39Q4HD+JWlVnPeKJWLxqPZZYAmCl2lnSQpsagXPsvcwJqYr+lCuyVudr9ZhCgsXk90anQ5D1/chq+6HTQSgAzKsg6FwGr7qAfHMYR5omkFLC9/zCGrk0R3IJxW1q0ngUSa0PWjnnR0422SICDGzSlNCqcClNaoNgFkjAGUdST/JKqxnXR2U0oDfGUGUyuhtex1KEsx5CnxHLsWKkEUKq3HlOoQTTmkc0HtItvAgdsxahoynPmZX+bva+GHzhTy9rm+RqSuThyaJWqyEMQwghcNttt2HNmjWTH9RGMMZyrbUHKmYSyv0QStahkgGAeTAmABi1jbI0sUNop7cV5GirMuzUhYPQyoxaaJ4f0PwiTQGk41BiY48j6RidgwBc64tzke9mXWJQFtFlNBDH9ZydLwQpBjuhx3KbS0qZa0kxxqGNgc4K3TLuFhVQ3hGWUzKWpxLHMfbv2oqDu7fQjMMmkD3b7mm4L/NOWo4ldji+dNWpWLr6NCxauRZhVLVgAAlVAi1kpaTjrpFz3oTE2TrRkH6YgDFBQb4E3aNW4IBmbHn3/MZoSh4JtbOMJQmKsBeiczVYOBeVzrnj7hGzEFtjK4QsqVvzNANmMpLBl3XAkCU1jLRjfEEViYP8WgRXljSHmBqrvGBK90up0oLPiGiopCJSqs5Q69uNbHQf/I6lCGafBRF2ILRuj0aTQgL9cY6jY3yJWgaz6C9WPNTxclomjtahlG5bG8yhEJtFlmV43eteh3vuuQeXX3453vCGN+Bf/uVfUKlUsGTJErzuda9r6zknjZkK5cQPmdWQ1g5Sy6AyD0lMw800SegDIAR8Pxz34ZzoqyWEyFFABfS1vGvm+Y567OLo0Fu+7zccl9qFmqTQU0JrRdV87pJXMKmTSOHwSh9eSiYk8BiG1WIWYoma5DtTsiy2LbIDe7Zhz9Z7sdu2qXZu3oDdW+9p0Kqas3Aplq46FWc89PHUqrIkwEq1tRUpOIeABwRjvVgYhLUTyAmdYwbAjg+SJ5sxSgHuMX7O6s/y9pVr+5WrxrEJRasEsn4A2eg+qPgQlFHUMgpnQ3StBA/n5CKQWisk9UELNTY5F8ZFFjvl3lJSMGT6RgrYtn3KaTbChAdjYHeyDForcO7BDwJkaQatFaIK6c/FcS1vCRHE3QIHWJYjvHLEoaYWWP3g7ymZVOchnH0aIDzorI56VsfY1laTNw150nBJwhjbHh5z3KS5o8UDSt8HrTXENLSvytEKMgxQtfeZz3ym4WfXXXddW88z3ZgOyuuOO+7Ahz/8YXDOcdppp+Gtb30rvvCFL5x4FsAnSsh0BGm9D1wE8CtzrbyGgZO3D6NKS9E7Y3TLtgwXHpCltq3h53MPt2suqo/WVQswfphPlQntuA3IktYtjK4F5/ziXesKxoBxkn+h9lVhmlW+/n07t2Ln5jstDHcTdm+5e5wfx6x5i7B45Tpc9KyXY/ma07Hs5NOxdPWpqHZO7MEwWTjin0ssSqs8IQQl/gvZNBdqAWPJoe6elZMNMeU1/JB4NTS/yJBK4nUwq/5qkmHEo4dhkn4YaWU7mA8WzAELZhG0l3HyBZUZyMDYHqstGMFxhWxyy7LMJq8AaVwH9xiYTqEkJWPGA/hRN4RHBMU0Tag1yBg8n2RbwChRcO7BIMv1sggxiFx1WAgyaNM5sdUu/EZBZjGgUtQHNyId2gyvsgBB93JiuTvnRboiMIscozzn3CZhk4iTHColD8dHGRdNEkbTypLlm4ex0Qr2O1m4jcjR1IQ7GsEYt3OqqcWiRYvw2c9+1gryvg233nrriWkBfCJElgwhiw+DiRAQ3XbhVADj8LwoZ2m3/Ewa0+ILUlKrVQooGR+5RbJ51eLaXuOrFkIVMesaCHheBG10npQ452AgYAC1qUjOgxJXwY/RSmPPjs3Yu/1e7Nm2kQbkmzdg19aNDdaxs+aehCWrT8Xjn30Zlq4+FYtXrsOiFWsR2Ipo7LUlSWIJmGJas4+xQW27shdLCsaynOPiWQRXwYYnEITWqpCkURlgjNU/K641qTUqRRujYeQw8UNKcu/M64ToWAYRzQP3u6C0hjFE2gRzZmdF+yaO62CMj/PkAABtYmtlLQEzCp2SeZkXdEEZH2ACnl8ADnw/sPwfkxP8nNtlZtuoDg1I94bg2VlqrIipKS34NPxw3S9V70PSdwd40INw7lnU3kwPAPIweGURqHOmnReqrUBs9cRQOnc5LC/KevjISSG+45MP5SlH9m2UImo3oWh73LGA/h5RMEyL2Dh3buE6KYTApk2bTjwL4Ad7GGOQxQOQ6RDAAhhUCdkFBcY9RJUuwHIxsjSBEF7TD7Ux1GBoFo6A2AplUkZ7GeMIkK2rFmMIlmuMRliSCi+qltzRHgA97+FDe7H17tuIMW4JgLs23436aKHg2j1rHhavXIcLn/wiLF65FktXn4pV687OrWPL4VBdlBD9nBvjWmXFWsLy5FImX070fhTMdPfHgEMBjEzFUunaMUDz3XDx3IxbQysrh6I1nZtx8ow3ab/940ynGFUgwTKqRkRkVY9927ok5BgXzU3BCLU1HglGbqJ1IBtBmmkw5sHwKvyoG74f5uctt2byGZws3lMiiUoLatDQCkhlDCMzgJGMyvjbQPM0rQ2450PWDqJ+6PdgXgWVuQ8BD3phVAoWzAPSgzDxPsCfj2LBNyCdqebvGykLF5uiMgS+4TLYWDdMk0ObyxJDraI2OopqR0fL37eKLMvgTVNQ8v4IqlCKNvRULIAB4N5778XAwAC6urryz8oJYwH8YI3c87zeT9h+FgKiA5zTjkaIAEFUcE+CIEJcH0VWEvAbe76Jdk+M85z8OFE0DuQLmHBRtZAfudYKQVgd14IbOLQPO+67M29T7dp6N3Zv2YjaSMEB6Oqdi6WrT8Wjn/piLF19KhatWIcFS1ejs8eK7jkmvx3+qzE7vAL5xXPocAF/Rj6LKWY50i6IlCxoQ8/AWAFDLbS4JkgQzM4E7K7cDXbJtrnwY3GSJ80WpzQeRjqyDyzrh0r6QW2uAF5lAXg0F/BngZKwe68IHl7AmWm4nmUJPOGP055yHBuH2tJaQiZDkOkIvX7mwQt64QUdSOIalCQvFUpW49s6xr4fWhGJMUuyvBIDiooDIBQgGLecHQYHdSe7Bg6dpVBpDfHB3wMAKnPPABPVfCZlWAfgA8gOAul+IFhQqkwaKzxmh/uwpnSyBUhgbHjWYnu6yL0sIzBLpdoaMt4qRoaH0dXVXBXhAY0xFcpULIAHBwfx/ve/Hx/5yEdw1113Yf/+/QBOIAvgB1s4/kiajgKSvLSZ6CBJFRlbIcQQftgxDjLqmN4mCFBmd+ckuAkSCmccyjTnMLQ8hgsEgeOoSKQp8RMAg+HBAezfeasdjN+FXRaWOzJ0OD++s2c2Fq9ch4c+4dlWq2o9Fq9aj9nzTsorLaVUPuAntVyGNKnnCyWh2mgxdYKMBDfW8H0BJZO8imhwjLSVQLMKwuWMcjuFqrigNHsoxBebJYgy6VNpgrByQdXI2PurZQxZ24tsdK9NIgD3OxH0rIZXWQAR9o47JkmoPRWGIVVeUtHMxjghx0KOxlWg+XMbgyytQ6UxVEYET+FX4QXdSFIJ2OTn+YHl/7gkYmi+YTI74JcYa7hljIAQPpQmKLGrbvwgIF26eASMC4RRB5TSyNIYSmbgggM6RtJ/O3Q2jGju2eBeBWP5VRB2gJsdBNLdgDePoMr5O2bgiJytKpFyOLi7s4Fot+00NDSEzq6uto4fGR5GT8+RzfWOSUwT5SWlxNvf/na8+c1vxty5c3HaaafhG9/4xollAfxgCa2VXQQSQNcAkwLMQxDNhtIKWTpKPeiwE8Ib708CIIf9ZlnWoMzrZhKTVSjusdPpmw4dPpRzOLZv+jN2bb4Lu7fdg+GBvvwxHV29WLxyHc577DOwbM1pWLHmDEJVdfXYaoskQZysy9i2hOu9MwYkcR0wiryujQHjJPVB8OV6gxdLGjfOIVBKADxfQBr97N3vDYrBulJ0ftKoArQhNWfOOQRnTasNV62RRpNElmVIEseFCcBM1iSJdMHrWg3jz0G1a96EixNtGJSFXnt55UV2z3HpcaVWmkwAkwE6hoGEAiOHRK8DQtCMhDEimXoiA4wCdIakPgBXvRkAmaLnZ9wDhCjRPBiCiKrSOI7pM+2QcEIgiUdhjEFU6Wxc8I2BzurIRndBjuyA37USXjQHro0lvMDOXBQAD/C6AeYD2UGwbC+M6gT8OTb5aJvMSe2YtMEa7yNVq0USORoxNDSErkl2261ieGgYXV3HX0JhmJ6W109+8hNs2LABH/vYxwAAb3zjG3HuueeeWBbAx3NQNUIyFFprQKeUTKAhgi4wHiFNqR/v+RH8oDrhF8DttqTMGkyxtJk8oeSDeaPBm0i0jwwdpsRhrWN3bqGqY7D/QP6YSkcXlqxajwsueiYWr1qPRctPwcJlJ6N3zgI4mRHn+peWZOeF58FYljFnGoa5YTUAoqchkY299ywp5jBuOAo4GfASvyCX4y/mPFMJd5cb22QFs9/xb8qcmPIspgwHpvmGhywZQTq6C2l8AMioxcf9LoS9p8CrngQRdOWzisnCeX2XK8pcucDzc5FLlwyhE8BQMiYSYtWSYkkZQMm0xOEwiMv2HIbRTIbT+fwgyp1CkySGgspbhWmSIKoUsFzXak2TOozRCMIqjLUqkFlGz2ky6GwESd/t4OEsBD2rwL0qtKTWiFLk+0NgAQkYTi3gYBGMHADUIJDUAH8uiTy6obybrRgOLoo507EYfg8MDCCO6/jJD36AhYsWYfHSpZg1Z86UPm8jw8OTtn4ekGAMXEy9QnnKU56CpzzlKQ0/O/PMM/9yLIDjOEZ/f3/Dn76+PvT3H0J/3yH09fVh3br1ePNb/v6oPWe+67Usc/tTMBPD6DoY98C9bvoSqTq48BGEHVMmTAnPg0pkAwzRDdsnEjl0vxsZOoz9OzZjZ4kAuGvzXTh8aG/+2KjaabWqnoylq07FSSvWYMGSVZi3aDnC0PWQ3W7ZMqkNoDKJelZrgHLKNIZssX4yJixrHjDaSo+DBOs8ryBtuvuaJDEAjahkD+zmO05Uklt9r1bghdb3p2wo5pc4McWwv9x+cwN+mAwmPgBZ25dXIszrBOtYARbOhwi6GwQH3Vo3WevRvV/N4KvUKgQE54CqQdrWKTkgdtDiDE36XWUWObM7egOAC/sWUXLWBoCiuZBTcM6BHMYQMMBaLTiVBrpAO1+BAeMiJ6raiwesGVncdxvAOKLZDwFjHH7UhXS0VmpLMqpKjAQpI2t6PyoLoLIuID0Ilh2A4REgeom1z9znTMPoDOwYLk979uxBvVbHd79+HYaGBjE6MgptW5KB/RNGIaJKBZ2dXeidPQtz5s7FvAULsGfXLnQfjzMUC8t+IOO4TCjXXXcdNmz4M/r7DtnEcRiHDw9ASoVZvV3o7e3ErJ4qerur6O2OMKu7iiWrq/DXLcXHv/D9I04o5SRCePxCSoQhhcqGLbInsnwNCSF8eH4FXEzdNQ5APgRXFusP2DbWmLZMbWSo0Y9jy13Ycd+dOHywSBxhVMWSVetxxsOfgKWrT8XSVUQAnLNwST6PUA7hwxigUsSjMZoJFDZGY+XAOXEJpJIw2oALr8EgCwDSJKYEzDiUMlAqaWCSO2dJ0iMrUEg0dwlykUuqKqgt6Hb0Dvo8nfvsHk/P1cg9kWkNcnQ30nh/XonA64DoXAW/4yR4YU/+HmVZhjguvFqm8/z0nBrjTL+MAuQwkswOoplPhlEaoNaV28gIMBHC8yhBMwsmIBl8P4c9Ox017uTZLaKwHFpJSFCiKyqs8qCcAYbagE7OI42HCb47sh066Uc45yxwzzpCcqqI8hmN45QwYf92/i91ev3BIhg9DMjDYNk+gAeA10uDfGbshqNGySqI4HlHF1VVr9fxyMc+Bq//h7fmP0uSBIMDAxgaGMDgwAAGD9N/Dxw+jP6+Pmy+dxNu/e1vcWDffjzi4Q8/qtdzNIKBKtMHMo7LhHLlRz+Cv3nBo3DuKYswq2cNens6MKunA9VK81mEC6U0/vU/vt+WT3RDJaIKd0QhPHDBYVQMmQ7QDpEJgFdgmAfPD+H5UdsSDm4gSVLuIeqjw7jvrluxd8d92Ld9U96u6tu3Mz8miCpYvHIdTj330Vi8ci2Wr3kIlqxcizkLloCxkne8lSNPaofHP68lV3LukDWE5PEDkqiXmbTDV896l9vKQQPMuAE5QxCG43TEnGmV46yUTayk9UxxEvytiJ1j5fkduqssh+/66mMRY2Pf12b/1iqGqu2DrO2DTun+MK8TvHMVeLQAEBWqmAwg43r+XrlKiq6nYPM7fbLJIssK902tE0DVkIs0wqNqhAlQ8rAQZcap6nMzEamQyRicF0k1y4qND0rJSwiPfGpsBaascrIxaJDqt3fH/k0bCM8vKjFKJhpa1pEM3A0RzYdXXQgShvTIpoH5gGmsohwoQklJFYgTk4Qm8Eo0G0zXIONDMOkBMOaBB7OhUKWcZjTSpIY0oRYsVbpHzgE5ePAgZs1phK+HYYj5CxZg/oIFEx77uY9/AisXLT6i5z8mwTDjh9Is5s6dg4eesxorlsyb1nFCcMzq7UJfXx8WLlw46eMb21lOpsKij2z/XqbDyGpD9neUSJgI4PsRhBe2/cFO6jXs2krEv233/hk7N2/Anm334uDe7flj/CDE4hVrse6sh2PJypdj8cq1WLxiDeYuWEIqFWMWS2JIW64EE3ahp0XJMd0ZF9Zx0c5rXJuLeQijKjhnSOyA1vOD3MeF/DR8JNaPBXA8gPH3NE2pTRL4YV4VBE7wsuSDorVGYn1UcpK0XeyKnn7x3xTMPsbkyWWqYVQCkxyEToqZCEQHuG1nMavYa4onHvfaWkXua9L0GGPbiMqi1jJbdTgZGp9mDNwlk5Jci/2/8c9toHUjB0M6eC+d1OqZEUGW/nsi3azxry2zSgjklULHpoc3AEYhnH0a8itkdkNkbEvSZPR6wGC0sdIydkbmyIUgTa8stZ/ZYCE4MiAbgE4O0Jwt6IXhHdCaA8zY6jqFk3xx+nLtkBMPHjqEuUvbSwoD/Ycx94wz2zr22MZMy6tpzJs3F/2HR6adUABg7uxuHDx4sGVCIUc8BalkvlsDiiRCMiMSWTwILWvIE4kI4flVK+k99bZWEtexZ9vGkkIutasO7N6WLxKeH+CkZSfj5NPPxWOe9iIsWrEGi1ecgvmLlo9RD3XJgucKv1op+GHF+mc3H2DSwptQayoq4MtKSZtMGKKoCgMgrtfgvFucUrEbbjshyzISyZElc3a3438whnpcH3ctY6PsgZ6/ytJraHw9DEKUob6ASy7lBddVfYwxMJNC1vdB1fY3VCJe98l2sN6JYhF2m/vxCLDiOY0dnKvcr6T8O0q+HMz6zmudlfgw2lYjhIbygm54YTc4F7l6cKXSGsgR2/sZRZX8OpziAoBco43bz0ZcHyV4rzHQYxKS5wd2MWZ0XuPcQEn5OU1TcnFkJC0DALJ+ELK2B0HvqbbVJWzF4Rwcrc+8jkk7rLihDVG8356tWgj5p8ABMRtgXWB6GDo5DKAfjHmA1wXDIpt8NZRMoCTpfbkqyHGXpvLdPHToENacecakj2sWh/v6MG/e9NemYx5spuXVNObNm49D/cNtHTtnVicOHTqU/zsfxua+IGXjKVeJCFo0szrS+mgukwHmQfid8IOOlou1izSJsWfbPQ2y6ru23I39u7c2WPcuXLoKy9ecjkc88blYvOIULFm1FvMXrYDwiETmFrN8iO04FDaRlMMlBM69cU6KBRmQuCYEvQ3ypKAVLXj2hSJO6qVdOcvnFs1i7ALuflZWji12jixfpI3RuXiiQ7U56XwHSXXw3bI3ylSiaENJyLQGNaYS4X5nAzqr3XDX5746JONvmfZawUCjQFATGZBzDqPrMDoDwOFHs6xbZ/F+jpWybxaOWe/Y704A1KkMaKWQKIVczQRoIL9yW6lyIXKYupQWbkyPgFYSQRCgUqmgXhsCWRjQz9OBe8D9DkSz1kKrGA06XLB/88jC6FU+P2Fc5ImuYLebvI1H1ZaVttGShCj5bBrWO3vf7DDVpsy3FsBVgNPi6WymlWT5tRSQctZAenR/Dh44gNlzZrf1GTjc3485Y9plx0cwAmc8gHFcJpT5Cxai7/Ce9o6d24ldu3bm6rJEHisWSpqJCHDGqBJJR2Fk3NCCYCKEH/bA8xs9wN3Ac/e2jdhx3wbssklj19a7sX9XKXEIDwuWrMTS1evwsMc/E4tXnoLFK9dh4dLVVqHXEussIscYZr0iiIzGGKNhvwbtEmEl3+GY4PQvt+tM4rhYQca1iKxarEHJM2RcnwoOmcMFtctcC8olYEfELA/Qy39rrZHEdTjGuWO9lxdK0qViYwb4hS9LYWJFf5y+1lRE+IxKoGt7IUs8EeZ15Ogs5nUQB4N7kyKyWj6HcU6SGbT9UwY0cO6B8YDeN9cqUqPQsNBf0QmwkAiAY6ogN6tw+lrNQggPWUay75wXgABHfszfV9P4DjugiLIck3KFmiYWb8w8hEGY21ZzzqiaMqBhenwIOj2MzkWPhDFkuGWcZbEIiqrDJRQ1SvwTAEYrpInKr5U2SmXLAAHPG0Me1cr6s4RQshPkLlkH0zVADgAYAFhgUWIVarG5BAf7nQJVymMFJYwx2LFjB05avKTpfZ4s+o/TCoXNVCjNY968+dhx76a2jl22aDY2b7o3N39yH17GSPxQ6xRZLCmB5O0HAEyA8Q4wEcGAoV6vY+/GP+ey6vTnHuzfvTXf9XEhsGDxCixZsRYPvejpWLxiHRavXIuFy06G51MPOV84yj1xq9JV9Me1/X3BBShXUpOHGZcjMBaxU3r2psfbv+m1jZd00cogKxskMTT87Vpj7l47eREunCKAhNF6nHuji6IycRpOWS454pLZ2HZGc8Z6I0/EQYUL50qqPmkRE+MABQ13xWhb2RYJpLhgJ01CqsxBWMlRViobRZYMWbFNAYhO4pC4DYCF4hZtmqIyncgF0EGbqcojXxrS+SqO8f2Qql0QE1pmCbRy0inFJsBojSSp2ee1Jm6eB0/5RNaFpLaqVR1ODt8F7ncj6FqOtH6YqoPcm770uWLEmaEqJQP3KkSG9Ty7idOtLQNsknHfWc8LwPzQJnINpTrp/ZB1QNUBXQNTQ4AaggGzfvEuwQT2sypsJ9bkr39wcBAGBt29PS3vdauI4xhpkhyfPBRgZobSLBYtWoTf/Xpk8gc2iaWL5uBXt/4ZnAHaKItkcT1sp8xatHqUEti/Zyd2b92E3dvuocSx7V7s27ml0FviHPMXrcCSlWtx/kVPx5KV67F09XosWrEOQVQBQO5zBkUJTwtZ8d9AY6uoyci3+M+c+Fcu05H/d3loG9dH4flBA9NeyYwgl5wjtJIv5IKY5lLkjPF8oO35AQQXcI55bvGnHY8o0lHpdRR9+cb2l3EMdRtaKaSlLWKa1JEmpddiE1T+3/bf+SDfzrvSJAYYg8c1THIIstY6iZTDAQqcP0s5uaSpApDmaDPaldNnRqus1BIEGPcIzSd8K+TI7RxJQcUxVYDpcJ5IyP9mLpiIckteF5TEnE9LQbYEkM9nCtXh0r3UGgwOSEGVBecCngU/pGlK1betcBwkO03qtuqlkFlmiZEGsLa/rv0mPAGZUQIS3INUGWRtH0w2hGjhIyFTi3gTIYlT0oXlQ3fGGAyvACYF5Ag0fDBBBEsvjOAHIv8MNVgGWNsAqQpukHv/ynYBXhDBD6tFspcpjBylBGNiMD0AqAEYCLI15hFxXLiff8527NiBJUuXtVWl7t21C4sWL24LCHDsg0+L2Hgs4rhMKCtWrMCO3Ycmf2CTWL5kDrbt2AGtHHyR/miZ4MCe7di17T7s3nofdm+7L08crhXEGLMugOtx7qOfimWrT8PSk0/F4hXrEdhBaPFH28WX4LkTcTnKHIhyQihgqDonkAnhIYwqLc/VPIolvJxMIitG6cQXtSLkVtkUKij5l9ACRzavnh8U9ruTRJamyLIEQRBZFn3RI9dWH6u4GdZTBMy28AxJw49r1ZVenc5gkgPQyUHIHJ1VhehYCVFZQEmEMRjb1ilzVMYO+BuTi0SWptAyhs5Uw/Mz7sELqhB5AmlVxRhA1ZHJw4BR5H1TnQfhFU6WwvJBkiS1MyMJ3w8QRUGe5JxwJ7UG4/w4Jx6pdKP/OTl8Rg0tRSBt4DMBJJ4oM2GBAbALv9VKQ+OiSIoIo/QmGQGZEVFXDm8CD3rAK4uh1Si1x8IIcS7ZT+fyAzKKi+ujYF4nTDYA6FHLLeFIkhhRVGmYaYxdmPPvl260DRiP5nPS9gG4T9L/MCSzr7MRarnpOpgmxQbDPBgWwvAA27dswqKl7bW7du3YgRUrVrR17LEOxjCh/t/9EcdlQlm2bBn27usjjaJpOqrNnd2N0ZEafv2T69C/bwd2b9uE3Vs3Ye+OLQ0L27yTllnf8SdhsfUcX7RiDcIwKtBD9rFKpaiPNqOHF7tq7rSleCGfPVXynVLFQubaFVMPlq+DjcmkqEySuA6tNfwgyEUJPc/PIcEuwTjy4FiP94mCJO5Jht9Jepc5IZk1dArDgo+iNc0wnA/JWA4JLSgSsn4AcnQPVHwQgAHzOsA7VwLBXDBBDH8NQE8gfVKuhOjkFr6rG1tFjHtgTEAbSviGMSjNyQOqyczFGA2ZjiBLBgGjwXiAoDIH3Gve0uNcIIoi1Os1MMaQWbMy3/dLMjOE9nISLGN5IoxzCOHnUjJjkyXxmca3K30/QJIQPNcBJ7jwaM7geDlaIYmHYbRCEHYiS2swGtDpEFQygHDu+WCWxS68CrXtSvk/CCMLVqD/TpPYKg/XaDjPIwAsTyqtvhf5+8Wt42Z+v0uJxvGstBqXaBjjYEEvOJtN1ZysUQWj4zzBbN+2EYuWLWv6/JPF7h07sXLlypa/11rjk5/8JA4dOoTXv/719/PwfnrSK8ci7reEUh6EpmkKKSWq1WrTAWkYhpg/bw727D+MZYvnNjtdy+CcYd6sCv7jfW9DhcWYs2AxFq9Yh9POv8gmjrVYtPwURNXmPghNe9jMOpO4XW9pd03Vyfi9NbWLvByBxSdAiZVbRq2Ifq2CMUp9SskxyYTbQXktrzicF30QRPnir7Wm6sVKv4z1eJ8oypwTv9Ryc1Fmw7vKgBZLaeXDE5tY/EIxIDmMbHQ3stE9gM7ARIigeyX8ziUQQXfDc1MStBwJxuBZEqrjbRBsl2x0CdzQMKq27R5qLRo77+Kcww24jNHIUoUMqV3MaeFX2ShkMghjFLgIoRHBjzqs/PpE7xXLF37P8xpMvjzPJ2CG0UXFzDnZFXMOrS2rv7SAJklsyZ2EACQUmGrwQAEKu19Kv3QPfD9CmiYlORYFrTMApP5MbbsAtYN/BOMhvM5lORqMJO5L3xOj82QCkJ+JUgoqq4AJDaNGKKkwH0ZrxHEdYam6ahbF5kJbz5nGzoADf6AB+WiRYw1zQB/weul1awMDhS07DuIxT3nohO9Vq9izaxced+GjWv4+yzKcddZZ+Lu/+ztcfvnlAIDbb78dX/nKV5CmKS6//HKsW7eureeeNNj0eCgHDhzAFVdcgS1btuCWW26B53nHtwWw1hobN26ElBJnnHEGbrjhBvzXf/0XoijC+eefj1e/+tUtd8LLly/Dzt19004oALB2zTKseOKT8JJLX4ZKtcutusUu1U4Fck4HYw0VBQn5Ta26KH/w8w+8hT/qMbtMLohZL8a0UNyQX0wTKutei9EaSTwKcvajZKKUJSzCQHg+kRoZ8U0ct8XBTQHYRX96sjGEwlHErh+zODhzLqfDlV8tK8QXHaorqQ3CJPth4n3Ul2ccXnUhgs4lENFcNNM0cxWOZ4UVs8xqrWXK4hvKFQgldyECMJvk3exnXBtT0y54HCxaSUhZg6zX4djhPJhFCSXLLHhg8qrUVY1UmYWkWC1TZKkqPYYjjCpNF9xC5DK1rbK0JHhJj5dSNrQri+txr0nkn1ff95EmCWBIwYCJiFpGYFBpHaq2G37POlqotGtxWU8T976w8a3KIAgRKwVjOsGMhpFDYH4PDEieJa7XrAW2yCHfVHkVSaPV/WvWKis9gl5p6XsJl4A4teY2b96Gvzl5dYvjJ47dO3Zg+fLlLX8fhiEe+chH4qEPfSgWLlyI/v5+XHvttXjf+96HnTt3oqdn+kCAqQYDA5sGyqunpwef/exn8aY3vQkA0N/ff3xbAP/xj3/EW97yFsybNw/f/OY3sXz5cnzoQx9Cb28vrr32Wtx888141KOaZ/vlK1Zi+66DeOQFa6f9vKesXogDwxkqnbNReICzhr/bGcg1i+JL2zwx6jy5kJRJahd+P4hyWXuH6JqONpQLA20H6BxRxSYTKZHYQSxjAkpmEMLLEVZupuIQVEGThDDp81oINbMIrLG/IyQTa13x6Ay6vhdqZBdUYgmHfi9Exwr4HYvgB81bR2Ofh9BXqVV+tvBqAwAcTATwvAieP7FkT6tzAxa+mtXIgdNYFV1WBZgPrRm0pmqibJoFMDBeboM6iGyB5Mq1zgDAbmhcdWzsY5q9Jw7p5Tg91KItFmQAFgQgcxQZnadIrgBKgpGCEI8wCKJumkHAwAs6URu8HQCD320X3zE6ZFwEtvCT466XMYYwighKzjvBMASTDYKHcyxP0iCJ67keWXFc2aZ3/KzlSL+3g4ODGBwawoKTTpr2scaYKc1QNm7ciDCkiv2mm25CmqZ497vfjcc97nH4q7/6q3Yue2rBMC3plTAM8+sEgDvvvPP4tgA+++yz8X//9394y1veAoBmI64iGR0dnbC/eMra9bjpJ3e09bwnr1iA33z3dlRbtLXuz3CaVPACmIDMuLIsJvx/WocfWB/1CXddzYPkLmihcMlEZhnSNLaJgzgEvh/kiypVJSkAQ3OUKQ7ex0aW0vA+Csf3wzOrXhuGjcnEGA1ZP4BsZBdk7QAAbQmH6+B3LoZhAc0WlIaK63krrDxjIR4ISXCUYbxc+PCCENwjnk/OaZESSuu2pNC1rCONB0j5lvvwK7MhPAJMOLSYzMrX4MinhbJxK7CGlDqfuQnO87aUqzwccz4oKRuXwzk5GmNyuDVQGHlRlSpLnxHbIirt4GkzQ9a/XARgAGRah/BCaANkIzsgKvPBvao9Xts2YaHwkGYeIeOa+LNzLhBGVUoq6AL0IHTSD3g91AIDVYPcCm3eHz7t9913H1asWtmeXMv+/ahEFfT29jb9vbun9913HxYvXgxjDP785z/j5JNPxlOe8hR8//vfxxlnnIFFixYd4atoFY08lKlaALtoZvd7XFkAu755kiTo6+vDnDlz8i/Btm3bsGACEba1a9fiS18cbOt5V69YgE33bW46n3kggzFGEF3PJ+ZxWkOWjALg8LzxM4iJQsosJ6Uxq/+UpYmVQeFw4o3UVvBs1ZBY61+OMAzbFrR0i7Xn+WOkYdzvpDWroufV6QDSkV2Qo3vs4hwg6FoOv3MxeNDT8B5xHkFrlVu0AgkEByGdVOrKD7IT9isQnk+L4Zj3mdSAfStIWcxryA554taeknVk8QC0SsG4h6AyB8JvdNwsZkJ+TtgkmDihzITwIHxqLcksy6sHF+5cWkna5Y8TJWDQKkNcJ2HHscnVLb7uXrtw8wu6/2EjedG1qixvycAgs8N6LgKkCbW6NDzI+iEYOQKvdz3N39IERVKiSNOEEoNOyGd9zH11bURCNggiOspBQA5AhLOgDDHptVKQTFql6WMbmzZtwvJV7bW7tm3ejDVr1rT8vVtvDh8+jNNPPx2MMcydOxdnn3021qxZg56eHmzYsOGYJpRyp2QqFsDl6OrqOr4tgF0ZPHv2bGzZsgVz5syB53n44Ac/iL/+67+esEJZvXo1du4+iCxT8P3pLXy93VVEYYA9e/Zg8eLjTxWUSGQ+Qt6JuDYM8oqY+vEyS5GmdZsQOLShHa2yKryk5FvASsnuN7W72farEqAYxDPGxg3iXSuNMQbBJJKBHchGdkFLmzSrC+F3LoZXmdd0LmLPAmMUGCS1sYy2TGfafQkvsHpqk38m3MDfoaZoiE/aY07uvXwflIxtIknAmEBQmQ3hd054r9xOl9j+fg44kFmCMgBJeKQGYAxVEU6luXF+o3MBTfsK6H6UmOb0nJasC+SSQi7B5MZrmiT2pZPEh0AYVfK5GqHd7PMwsqKGysCEhZWP7ADAUZ29itSnYecnzLWeuCWl+wASm1S8XOXb3Qc3PPf8AFoJaPQCegQq6YcX9kIhgLNVSIweZ4NwtOOuu+/GyjbnJ1s3b8HaCQbqnHN85jOfwZe+9CVcfPHFeOxjH4u+vj50d3ejVqtheHj42DLsGct5Re3EcW8B7D4Yp5xyCg4ePAgAOZpg6dKluPfee7Fw4cKmWS+KIiw6aSG27zqIk1dOrhw8NtatWYw77rjjuEwoLgjiSf3tLI1zj4uJIssSZGkMzr28nUD4e1q98qRhF/uiKmGIovZl9l1ImTVlvBtjkMSjULW9QHoQo5Z0KMLZiHpWw+84CYyPHxjmaCyZQqnUsropuAiohGdkP6sBGGXAuAFjU68+qTLxLB9E59IublfNmYFMh6AlAQJIa2t6fuO0KGb5/aHn5blysVIaxqQ5QEFrAyFKMFnwfDxhEEMpjUqlaK85KSGAeDuQRYJxJMec6wRYeLYlSXIPxtjkZ/kaDvFFCYTn+nXGEMFVjuyE33ESuIjsPKxRdSEII9qkMLI/gE4hZdCAQmOMIwiKyormbhwy4wBGIZMBcK+SG9RpJVGvjTZAkI92/OlPf8Kbn/7Uto7dsXkL/uoJT5jwMY973OOwYMECbNu2DRs3bsTDHvYwXH311ajVarjssstw6qmntvXcU44JTPnGRpZleN3rXod77rkHl19+Od7whjc8OCyABwcHcf3116NSqWDr1q342te+hq1bt2LVqlV4yUte0rKMOuWUU7B5+4G2EsoZ6xbhD3/4HZ785Ccf6eUfkzDGEDyUMXheBJnVIWUCP2hOaiRORwKZJRDCRxBWLKSy3EqxHBILzT1aVYkLrcmoiaCqzqRKQ9YPIh3eCVW3cxGvA2HvWvgdi8H96rjzFEZfTo7cCUq2JhM6rS+C2ib5YjURHHtslImNNBivQcb9gCE/cy/ogR91T1A9jX0dJjewUpIUuxjj8H1SamZ5tVDI9jsuVOHpMv76ydeeNOgKx8pKgyQNbFtNKQkGqoBcheNcEx160CU4gnjb6sQqHhtDasQF4IBBJwMwqgZeOS2XiSndRfuayN0wjutUpZiMztPwWhqRWowxBGEEITwkCQMgoOUomJbwozkEAYdBmsRQypsWhH0q0d/fj0OHDmH5qlVtHb9t82ac8rrXTfiY1atXY/XqxgrowgsvxL59++4HQuT0KhTf9/GZz3ym4WdnnHHG8WsBzBjD1q1bsWXLFlxwwQUIggB//dd/jRe/+MWIomjS49etPw2bt/++rec+Y/0yfPjTP2vr2Psjyk6QfhBCK3JSbJZQaGcXQ8oUnhfADyJSlk0KeXjX4mKMNVQl0yEpThRlzonnB9DpELKRXchGd8PoFGA+eHURou7lEGHv+F66VlZyPC0kTRjLW1iEeJvI8rioMlw7JUmSBsn2qS4+ZE8wAJ0RK5x5HTAIIQ2HTlJ7vtYDYseBIV5PUY24FuPY4whJF8AYP/eAce1BAPm8qWhbNReKdIg8z/ORZWmODjSwytlW1oRUqOm1eT5Bk8EsPF27ZAIAHiUXXThCci5gFKHu/I4yEqoxOWRpAhYSdDvTAaBSwGQQXgVBEORmajQHS0vkTQ7heaiITqSJB5UJGDWMrLYffmUOMkltPiUz1KU8qtXKbbfdhvWnn9bW9yGu13Fg//4JSY2tIoqi+5Fdf4Iz5VeuXImrr766rWNPP/103PCzH7R17JpVC7F33z4MDAy0RGU8UFFeTFw7gHMPsolZO8lh1KFURu6QHrUVGi1dafheyHscvarEhVISKq2ByT7UB/ZargKHV50PBPNh/FmoVDoapECUa2PJNF948ypkmr4y+Stt4LGMTSzBhIuFMRpZMgiZEFLFC7rhh925t4xTC0iS5oKUzqTMtXUY5wj8EGmWgdvKZ7Jrd+ROgvyG+cJLTpaFCyUhsFRT61vOuVUeKORckiS2vBwPSrnrI/QUDe59BEGA2ugQYAzxZ7Sx7S/bemUWCDC0C8zrtJ4mBjDk8UKExtJQ3kLDUWp7KUVzIscRKldnbpPjkksYVSA9D2nsAWoIWf0QKTGLztw87GhWK3/84x+xbpIZQKu4Z8NdOGXd2mk7wd6/cWQzlKMRx6X0ioszzjgDmzbvRpJKhMH0LtUTHOvXLMWf/vQnXHTRRcfoCtuLLCvQSvlgmwFjd4E0l6hBa2l9tQP7BSuJFlqsfpqmR70qAQCjJbLRPUiGdpA2EwARzkI05yHwqydBaurFhkEANwtqhPSWqhAx+YxoqlFOLA7JRe6P3IoiFq/fGGNlUgYAoyH8DvhRryU4Fudzi2AZGQYAnm2Rufaik5lxVVEmlXs7p3jtVvadC4Shl/NI3PO6hOW4Ja3mXpwL8i2p1ylZWsABkRQFjCmUHzi3MiuGhB/DqANZmkBmtsrlPsKoQgt/fBBexxJ380AVQ3m25Vl2fQmOzAOawxiFJElyyHihnRaMe41kceAhqnYiTXzobJhM7WQMHnRBax9gDErKvFqZLvS7HDfedBMMo9d08VOfgtlzp06avuvPf8a5Z5/T1vPefzFWOeD+j+M6oVSrVaxatQIbN+3Gmae1Zqe66Ds8gts2bMOf7tyJP925A8OjdXR0PPBclHK4XS6AHHtPP1cNpCTS4KpBa4UgIHgs/dstEITaieuj9st99KoSYwxUfBDZyG5ktb20cPAIQc/JCDqXgvt0T5WSyNIaGDSyOC7aPzmkN5hQWPFoRBnJJSXNKOI4ziG9RifI4sMwWoKLEEGF2O2TnU8IgSxNrI+9vedWmmbsAs+abAYmCl4iOLoF0nFJnNqzlISQiuMYzkyrlZKB7/u5BphDdTllkryaYhwyszMUZiuHzJlrEShACI9IpjqDV1kIt0AFvg+ZaWg77yoDJygMyPI3AVQNBp3WXZIqG1d1jX2NbqaUZVY1OeqFlBUgG4JOhwjEIbpy35U0iXPOynQ3TMPDw9i/fz+e8by/xi2//BW+fs0X0dXdjYWLFuG8hz8MFz/1KeM85stx1x134BUv+5tpPecDEzMJZcI477wLcPtdO5omlIGhGm7fsB1/vHMHbtuwA339wzjnnLNxwUMvwqWvugBr1qw57mSm3RwCgDXboiCYZ6GvlcSjMEYjCKvgnKNeH22oaoTw8qF7Pow/wqpEZ6NIh3cgG90FoxKA+/Cqi2H8OfArcxGEkeVVxNZngxKjASGyvGlAeo92lBNLlmXWtbGPduvcQzBGAbhVaKWQlVjvpGjr2WqBFvjxkGPnB1OS98//NqXqxbHv6e+yfW85IREiS+QVJxEdaRbhUGGNag/ufLTgM+7n1+DEIpVSVnqeAyDlX7LfLSTstdZIRg4AAPyOBTCMIQiojQbjIy05UXq+D24tELQzh+MRybOYDIAHp9qgLIjA2QQ4jpDvN7bEpDSA4YA/C9AxjBwBdD+4V4VGBDBuXSnr004sf/jDH3Dq6afjkle9Epe86pVIkgT3bNiA22/9I26+8SZ87QvXFAnmEQ/HxU95cp5gpJS45667cfbZZ0/puR6wOELY8NGI4z6hnHPuefjKF38HABgejXH7hu340507cNuGndh7oB9nnXkGHvqwC/HCl12AtWvXHrVWz7EIKbMceVOG3SqZ2b62Z1VfR4lpHnXQLrVey8/hvN+TJM7RO25Y3U4YoyFr+5AOb4eK+wAweJX5JMZYmYc4jq10okZcG2hoZYF55E/hH1vuwHTCGAUjBwFVA8AB3gHDIxi07n27+Ylq8GcX5AVjme9lP5WyPbKDwwJAvfQ+TSXI8Gu8OvDYaxv7b5PDfht+YX9G2m5jiU0yrcFVJ/nvSsN4gD5nMu4DmAeNwHqv0OvVqng+z/dzmSDP9wsZmbxKqQOikxwTS9enlaaEDVh0o98yuYCFgO+T46WsAYjB/E4Y4xeJRccERmmhJlCOm3/zG5xxbtGyCsMQZ5xzDs445xxc8qpXIo5j3LPhLtzxxz/i5htuxNc+/wVKMIsXYeXJJ2PhggXHralWOcwD/D08/hPKOefgH/5hG179D9dg566DOOOM03HBQx+OZ77gb3Hqqace50OyIpx+E0DthwJ2a5ClxLTmjLwkACCMOmhXadtj1G6Jctiog2CmSb2tuURejYzshNEpmFchqG/nUjJPsokNinSeMmVbWUEVjHvI0gye7zcYez2QYYxGFg+S5hYYvLAbXtBtARBpPl9wLSY3XyDxwPLibNtRxkDLsa2dwuhsbDUCwH4WmV2vy3+P/5LHcZxzg8aGe3ySxNBaN8i9O6iyc7MsHk+eMsLz4fv0njgJl8bBu/2sWK8g7oXQRuT3QKWDDeoFOa8kh6frHEQw9t5Q56xCbo1QAOjzYUDXrFVJisaYEvmTrKeF8C1bvpi3aHBKLnoUJhsixj2vAizIXSvjWNnEFLTsSPz2t7/FG9/xj01/BxAS68xzz8GZ545PMD+//sc49VgpBB/VmKlQJo2enh589KNXolqt4iEPecj9Is9wtIOQWkWrK4xK1YmiloHnh0iSUZsoqmT8ZL/Evh+CC5HDTd2sxC1mDFPblRTVyA6o+BAABq86H0HncvBoLoxRkDKBimswprBG9v0IwidUFvX161YO5IF7L9xOXWsFmY5CZ0MADLVdRBVSc8h4LIfCtoBsKyoPC9Xl1sumIBti3N/lcIRDt7CTeq83JR6LEHycp8nYcP4mZQmhAglWzCKyzJnJMSilIYTOZxaUUApGfOlOAADCsBOZzCAzQouZdBB+F3mFVCrEIUrTFKqUCBwgAiXFZudPQs/BqUrxfKS5eZ3TK7NLjlUUpoqQODNaKWQpco6N7/vgYQgpQ2RZSOdUNUANAxDQpgKwCFwIaq0pVRxXSiz79+9Hf38/Vq05edL3xUU5wWy9dxOe9axnTfnYBzZmZiiTRitF4gdLyKxIDp5XlOeuOgFjJC/PBYKggtQmDgAIQgcHjschuFx7ZtK5QDaKdGQHsuFdMDoBE1SNeB1LACagZIKsdriA9gofRpP+UqXa2bA7Tp0oZHRs21xj/S/KsuYEd9VESFSjINFCHxAdua4XXRtrSA5OV8tZp5Hqc9g2z8HBdx3vx+3cafjsTzi/I0mcxmQxNsjfZDwfxYUb5susnvfPy/I3nud83xUA0dgG01SxpBlVoACDUTFxiryuBuiyUhJc+LZI0XCClK5SIzFKBQVQUuERVSk6AQRVYCaX0Bn3IhpJ+KRo2qDezCwogQe9kLKTpHzkKKBGANQpsfAonzs510pnWnbjjTfi7PPOa2uemmUZNtxxB87/8Ienfez9HzMVygkfZQtct4C5yNK6nYMwcM8H515OViSv8CBX7vWsFlTD4uPaLU0WJKpG9ttqhGRvvMoC2n16PdAqteQ3OgdBe0MIL6ABsMkQRtWG53Ne7I5IeCQx1oeknDDKA+5yuOTAmYJRI9SqYx68iFSAmy0YtOBJm9TpXjswhPOD0VqNv7fTCM4ZlALCMGrglUyUWFwVY4xGK+uDoorVaJXznGVzGHWAc2HRVXSsgxDTD0onsPMWbsUj7QG53wn3OvIKyCWnMAxRT524JCwgRDcY0nHOAS7snMOjpMI9gBUGc64iaXh/y/c9l4ZB/lxGa0hdcLS4qMCICoyKATlCiUXVoXUF4BWrFKFyO+if/N9P8LinPa35DZwk7tlwF5avWHFMfUyOWjDMwIZP5CCF34LNHlWKXriTUQFcRQBIi5pyC17xZW6O4Cp22sUXUmc1qkZGdsKoBExECHpOAa8sgDFAJlPbMmANSaRYvMi3xRuDoHFII0f4m849KCcNN7do5oyZJwwurG9NoycGjEYaH4bKRkGaW7PhBc3FG4lYKW1CptaSb1nm7vG+TdguATQTjJxaFArAYRhCaz+fdUkpm7ZhCri4QauNs+NxaN28ktFa5VI8bibnUG5hGNGMQlvJeVZco7YE2iCswDjEF+izAwBMEIfEQZedYCPjnDZANkmXyZ9lhQApM6Sxoc+ZGgVEt5VqGf85LrfMHNen+Gww96DyEYXcELNujCaj57GJRWmqWDw/QH//Ydx55wb8/fveO/nb2CRu+8Mf8PCHPaytY+//mKlQTugot66iSjXflcosRWYlxYUXlYiKtOi5L3LTqqQUprSTy0b3Ih3ZAVWnakRU5sOrLoIRXWRClaUA4/D8CMILm/JD3KzHkfwafp47OzZnLDf00ksVx/jEQY6Y1DLhDTOLCaVO0mFk8QAAAy/shh/2NJ1VFLIoVNkxzhEEUVMOB2OsQcoky7I8sUzHvZLl656BE2h053WJhdowRWIps+8nirKu11g5m9RKzwdhMdin10IoNJW5GVLjxsDNT5RGMSsDYKxAJBNkjIUGYqKBgQAgLdSYtVQG9jwfOoggUw2oETBdg+HV3E9+rBGXu2flKH+WCLLtXBxZY4JhAFhAycWkdsYyAqhRSBXixht+jrPOOxdRpblG3mTx5z/+CW96/evbOvb+Dysy+gDGTEI5RpFaUhwAq/HkbHdTpAkhuZgI8sc4iGpmrXqnwivRWQ1qdAdG+/fn1YjftQosmgetGRQMYCVbWiWRcjjTrDBq5GtkGcFpgyDI2xblSsMlj3K46sLpN5WTx3RCyTrS+mEYnYF7EYJodoOJkAtiimcFKo4LYsxPgVntZiGF+GQKzuU0eA6NfJDyeZsnFpFvFFoZcLlwz08VQYm3pCS16oKoIbG6oT2JblJ1UnZFDMMISa0GMJG3YvNjjW3Niohei6HWnBv+u8WKcwIoO2JmswiCkKpEI2F0DMY8GEZikmOTSrNw1Rkg8orYgTCUKlcyLE9wYBElFyOpfadj/OIXP8PDL27PJXFocBDbt2zBWWed1dbx93/MVCgnZMjSwkaGTvSFyNIEWWq5CswreCS+n39JJq9KrOvh8HZIV41Ec8G7ToYWnWSfpKmd5flhU/OpZkFD5SyXM6HncpIgmW2BOLG/IlziEMJHYbN85M57JOB4GCqrgbHWxMSx+lokizJ9JjUdK8B5kViSJCbr5GDie1gM/Zv/3iUWarFl1qKXqgulJk4o1PLjdnYF+zykPE2D9/FIO2oPuspDNMwryug9py7gQqajAA/g+VGuAl2+t+A+oOt0DusIOdF9DoIQsdYwmYJR5IkD7iOOY2ulML3ddIFwK+4DOWeqIsEwQeRIHmBocAC333E33vDP75nW87j47a9+hYc97GFTErI9bmJmhnJiBS26drjJBXy7U0vTuNSCKPyxGReQUk1alWhZt7yRHbYaCcEqS8DDeeRpAcDzwnEzkalEudXFuECapg3aVe4xDlXkfM0b2dpHJ4wxkMkQsmQQAOCHPfDC8ZLyDuzgKjziX7TmIUw13GskhBUt/vW6zIUXJ369E8uvUIstgO/7+ewGIM+aIPCbtvAASnSuDUrIK5lL8rRKsE2RXYCVXwEAkQ+tSYY+Bgx5p9A9JdVfLjicB7x9FTA6gwgi2x5s/Vkjnk0FdaOBbNCisroBLvL215F8flwV474zRYKh2dXPb7gRZ513Hjqshe104zc33oTnPvNZbV/f/R3z58/Hox/9mPzfc6ehVXa0YiahHMUgu1WHsuEII+rbJvFoiWFO5DLOOQj9qltWJc5rhKoRksTg4Wzw6grA77EJwIMfVCC86amxuvaB03Ry/ekCkVacKwjCIxLlm2qorI407ofREsKrwq/MahBwBIhHkllkFkAABt/zj5ropAu3+BOXI7UJIMvvxdjHAq0rlObnDi0SK4NSEvW6bKnF5hIK8SwEsiymqnAMOIJAIDFyFr1Fj7mWGwBolYFzH9pCfikJ20XZ6CKpWbSVVirf9TLGYJhnpVUonN3wRK+1UulA3ShADgF6FEAnDOd5++tofa7KCcb3A/zk/36Kv3rOs9o61+jICDbcfgf+46NXHpVruz/i+uuvf6AvYSahHK3QSll7VcLNR1HVkgBHSixjSiaMUTJxC8vYBaqoRnYSNJIH4NUlYMFcMEGzEM59SKkQRNW8RdUqHMqq3HvO2yCW5McYh2d3+JzzXMo913I6hqF1hqx+GErWwbiHsDofwm8colIbKrHSNVYZ+AigvlMNh9xqbIO1YmVPQ3IYyKugQnaEKqKxaDP3PMo6Nhqtx1UnOUdIazAogAkYNJIyOWfQSoP7Uf6RdO8tuUvqPHkIKwNkrIEYwMCFgBEBtMygsjrACSU32eeDMYZKtRv1UUlDcz0KoMMmlfiYcJr279+P+zZtwnltIrR+f/NvcPY556BzgurmwIEDmD9/fruXeELGTEI5CqGUym1YnQqw1gpJ3ZLuABTtBwYDjFs0jDGl2QhVIyzoBa8sBQt6LcQ3gueFYJyTHwrTTfkgYxNIWS/KIWpcyypLydOCUGgOOqyQZVmDM+OxCPInGYJMBgEw+FEvvKC7EQY9prV1fyWScpTbYC7RKlVv8h5O97yOiwLrb6JztBklmqLNJoQgrTFlmlYneQXjCagsARchjCk+WwDA8s8i2U6715b/bQxcO5ZamwHqtYKrpJQCNH2Gid9C/vP1eq3pxmjsPYyqsxCPwpIeXVIhiZmj7SX/ox/9CA9/zKPht6ms8Zsbb8TFj398y99nWYYvfelLuPbaa/GVr3wFp59+eruXekLFTEI5wiAxRwsBFh6CMIKSGdLEivHRb/Jkwu2QN+chqATZ8HakwztsNeKDVRaBh/PAvSqEH8Hzx3vBO9kOx1aeKIE4hVeXSFw4WHPYoBWlkSRJ3vI5Fgs3cUTqyOr9MEZB+FX4UWN7S2uFLH1gE8nYcHBqIbxxMGOK6WUUB2goBCkb0WauzeaIpArkEDm2OnFGY0IIK/jI4ABQAIpZmEWUyRIQwM1l6HTFQc2qL88LqMLNhEVSacAiyByXZSIyJ+ccUccsxDXYSmUEQCc0gDiuIwynP6hvFsYYfO/738cr39ge3Hd0ZAS33/pH/Nv7P9DyMb7v401vehN+//vfY+XKlThw4AA+9KEPYfny5bj44ouxfv36di//QR0zCeUIQmudKwEL4cEPQvIsUWUklLBtBIYgLGYRKh1GOrQV2cguABrM78mrEc+vUBJpAfN18w+AIUniKSeQcijZHNWVJAkAgzA8er3tcmiVIY37oWUMxn2E1bkQXoGicb71LpF4fnBELPZjEeWFP02THPnmZOmney5nB+1eo/OQL8OYKWjBL1cnZCmc2GTnI66N0owELOewuITlWPkNNEH7vMYYi5BSOUrPGKtabAAiFEpEURUZMxY0YQUnS1F2nySEYyOQgXOOSscs1EcZkRFtUilmKuM3T9ONW2+9FVJKnN4m3Pc3N96E884/v6W6sLtnN9xwA9avX4+Ojg50dHTgrW99K37wgx/giiuuwGte8xo873nPO64+t/dHzCSUNkNrNSaZBIjrwyiUay3JiPEGuW8VH0IyuMXKoXCwcC54tBAi7ILnRRB+OA7tM64CkRJuN0xcgMkTiDuP+zuxpDhhbVoJIkry+p7v2+czdudaKOcCaOtLYoyx9ruuvTULXtCVn4vaPUnuDHg8JpKxIYRAFFVyYy+lJNKUTcvkjBIKGhJK+fycO6tfZ/nM8uE8gJxwGoYRlKT/1nCfg7EIMAkhAihT/pkGt1UGYz60zkrXYd9vLmA0iTdKKeEFFUooRiIIOvNrKAdBmwuF57K1MWMuqaAhqcDOVMIwPKJW69e+/nX81bOe2fZn56af/gwveeELW/7evVc33XQTHv3oRwOgFtiCBQuwevVqPOlJT8LFF198XH92j1XMJJQ2QsoCzcWFABcccW0YRcvDfpm5hzAMwTmQjexEMrQFJhsBmA9eWQJeWQgv7CK+yBg0U0HiUjlTGnBQYw6jFYTt6xePlbkeFowzdppo12zydl3D68tSyKzJw0vBbAuP/lf+22of27/pWlPIZIDQW34HgmhW7k6ZK/baJ/Q8H34QtITQHm/hKgNHSKVZRkGKnAqpEoAlODYXgORcgEHbITtDmiZ5FUGEUyuVksW2AqFrapCYt58JxoUjygNAqXoBGXNp2TBXEcJDoeQAZGkMUekgiwOVgAE5n8Xz/LzaKkfZ98X5ybtKJR5lMGrEygF1AZwjSRL4vmnLmuLgwYO45ZZb8Iq/e+O0jwWA/kN9uO/ee/NE0Szce7Zr1y48zA79fd/H1q1b8cMf/hDPf/7zMWvWrLae/8Eef3EJRWuNW265Bb/65U143d9eMSGKo1mkaUzWqQCEoB1dZklgFMJWJQEEN8iGNiMd3gboFBAV8I5V8DoWwQ+q41paudGTlfK2Py0WaSAnQwJoUGQF7CJv5yqM80LW3lUZ9vUT61pAeJ5lGwBZmgKMIXBVgf2FsYxp+tuqhzX8Tb+HMdDGORQ6BJkGdB0wCYjU1gmF0Pbbi9kP3UthfeePPTz5WIRbZIIgRJqmdtEXDfOyiY6jSrP5Y9xGwfdDZFLZVhb5mbhzaJXZFhXxWahtJIt2FsqVc5noWHKM5D5gsoaE4Pu+Nbyi1pfjLHlBB7J6gjQZRlTthZR0jZEFpJT9WopguRqwS1ZhtQdJzGCyYZtUOgEuclHU6Vpaf/Ob38SjHndR29yTm372Mzz2sY+dkMy4ceNG/OQnP8HKlStzi/EDBw7gm9/8Jh772Mfi/PPPb+u5T4T4i0koo6Oj+N73voevfvmL4CaGLwwWLjwJl77sZVM63hjT4OnueQHJhjdUAALc8yGQIhu8C/HoHrj5iOhaA7+6AJ6VyigE8aiF5dpOzYKVZNgZY1BaEXTUDjFdtTDZF09rjaw+miPR3GJDdrBsSpIYU71XKqshjQcAoyH8TnCvM78GEjtsZIjTLrZQynWtEWbbeMeKSHn0gu4ltcGikr99fUJSpHutrTS9yOLA8U4CSBXnx7mFP47rYHBVBC8twqyU3C1SSxPfiHOWs8zd8zBuKwKdAaAFlVvfmyxL86RCcvYhVTQqhjEq57rQPfAs7NiZcblNj8nPCSCHSXMegfmMPG3ypOLZ8+mW+nFjI8syfP0b34Dn+1j2ne/iyc98xrQ/zzf930/x9295y4SP2bt3L373u9/h8OHD+P3vf4/zzz8f3/ve98A5x1Of+tRpPd+JFid8Qtm+fTu++pUv4Yc/+AFOW9WDlz9xFtYv78Lm3aP4+Je/iBe/5CWT4ujLsGCA2jiNyYTDGAbBRiEPb0OakJUuC+fB61wGvzIbDBzaaKRJkqvHjg0yIeIQ3AMXrRfQuk0K01X9dSZfZfth52s+2U56qqG1RFbvt5ySAGHHbAthJRmPgtnuwffDfPhrnJhkgzqxhim1WwB7j/IkI6gSY5Mn02Md+RAbRRtMCJGTIpWSCIKwJfqpVUJx1UkQVvLX3IB8syZapMLAAVYILZZviUMFG20gPJJdcXMZ91nkFhwhs1GEla78WGppNVYuWZrACzog4wEkdVelZMiyFGFYJCOnDOASS2GqRec1NkEBHiC6rULxEGCqACf+D8GKx88Wx8aPfvQjLF2+DA991KNw3bVfwlc/93k8/DGPxmWvvRzVKVQs9959N4aHhiatMC666CJcdNFFOHDgANI0xZ///Gfs2bMHl156aX4/H+jP4wMVJ2RC0Vrj17/+Nb7ypS9gw4a7cNE5c/CB16zF3J7Ci+TkJZ3oqQI///nP8cQnPrHlubIsJc4HLPlLy4ZFzhgGZIegazug5CjAPPDqUoiOJQCLYGwSaRaMCQivGFhOJWjx1bnE/VSDEodCEBTQzIJvIo6Yb0KKwCPI4sMAkHNK3HM74UmHhmtUnBUtRVLLyaWQN1cNfX3AesDzckVz/7bOaMEeLw7pSJFpmiCO603Z8BOZbcksgdNKA8jpkYoKllc9vueRU7NdcJMkto6e5TNZSDBzemsir3JclcJ9WnRVOgxgYem1kSxQbBWIHepLSXJoNCqG1tLOkgpGf/l44ux4OcS5EJxEvjGif3aTRIuukR4Zr0ADqNfrE0oTGWNwzRe/iJe+5lU496EPxdOe+xz84Te34Jtf+QouffZzsfbUU/HKN1yBlatXt3wPf/Sd7+IFz3/+lL+LjtS4ZMkSrFq1Km9//aUmE+AESyjDw8P43ne/i69+9VoEPMOTzuvFay8+HYHffLV6ygWz8MVrPts0obgdvVu4GCMNI1eVGJXBJPuh4z3UIhAViM6TgXBBPhhlcJDM4rzC+ke0OytQshhuTjWI05FQ4vIaIcJlGZB2Q6sUab0fWiWkCFyZDc6p904yKRrMQm2nm7io+hDjoKRl6Rhjk40cgyRwsyTBBbhwqsfH5steJqiWn6NMikzTNB/al4mA9Nqyceq9Lnm6Sg5AabjNSz9zr9tDYO0Pxop4GqMJrQXKCe66nPQLADCPFkSdjeboLxecc0RRRJpfpXkKFyG0zJDGI4iqvcgyeu5m7PcCSuzniaX8vhF/JUCacOKpmARQEm6u4hJlM8+a3/72t5BK4pwLLsiv94JHPgIXPPIR2L51K7779W/g/73mcsxfsBDPv/RSXPSkixuOHxwYwC2//BXe+ba3T/xGtwiXTP7Sg5lmvZcHWWzduhVf+fK1+NGP/hdnrO7FE8+bjbXLmhsvlUNrgzd9/C585N8/iTPPPLP0c40krtkdp7H9Z4uGkTXo+h6Y5CDcfIRVFoP5cwjxZb+0WukctUkLytFZ0JK4Dq0VokrHlM5ljEFcr8HAoGI9WdzcRGt9RHOTBigw4wiiWRB+h+37J/nw1fdDAgAc451bbuZVMmpyMvsunG6Ve6+O1jURuz1FpVKZsDVD0OI0r9YCy+Su12vw/aAB2ZSmdcgsRaXSBcY5QdXjOL9m5/ke1wZJqTroRBAEedWRw3mNAXTdioh6NjlUcpa7uy9KKYxu+yb87pPRufCCpigr53JpT2zPHROEuDoHAEeaJvA8P39tE4Xj2pRbfpRYGNLYGnSBAaKTQAMgAMfYTdBrLr8c5z/6Qlw8wQxjeGgI13//f/Ddr38DQgg85glPwCWvfiWCMMS3vvJVHNq1G+9///snveaZaB0P+oTynW9/G1d+9EN4/LlzcfF58zC7e3qtoJ/8bj/uOjgHn/7M5/OFsGGIaDSMVjByEKa+ByYbAM1H5oJXlkCEs8Bzz4piNjIWe380whiDem2Evqzh1CS1HSotDCt5deJY3qTTNX1oJgAoGSOt9zVAgcF4w/07HtjtAJCbNCmVAxpccE7JRdi/271Wt9BOJUETRyOz94kUCdI0gRAinz/Qez1MP4s6GjYBrrVEwopAffQwAIGo2t2ohGArIkLbkSac88tw7aM4rlvVBQ5jNEZ3/i+4342ORY9GFDU3pcrJtIZ4SkZLSlg8RNQxC6lVOJiKp48LcgRNxiUWo1OoZBCABjjNVRySkSD5Ahs3bsTlr30tPnvd16cktaKkxG9++Ut888tfwe6du3DamWdg17btuPKjH8VDHvKQKV3vTDSPB33La+FJJ6GnK8TzL1rU1mLwuHPm4X8+cTduvfVWrF+3FhYDS5BYLWGyfuj6LsDOR1hlKYKu5fDCzhwl43rBzhJ1cpnz9kKr6bW7yEvdsuFtMjnSuYkxGmn9MFQ2AsZIyJF7kZ2TOBdKn+DHR1kBuN0goy8OlI2alILSksh6WVpgpOz8hedtyam9hnLLayqPpWQukCRpvuMvw2ypDWVyvxNpkYDUKuO57Avn9vmaVFuc52Bz98z579xnwPN8EpW0VRz3u2GyoVxAtNnnOAjIKMu418sEwASMTiBliiAIEMcqT7BT+S64qslZTRP0mBIuC2bBZIN2rpIBvBCW9DwfV199NZ770hdPWbdLeB4uvOgiXHjRRbjvnnvwmauuRhgGM8nkKMTx8Y0/gnjYwx4GL+jCnzcPtnW853E8+8J5+PhVVyKvSJSEjvdBDd4OPXwPoCW8rjWoLnocqnNPB0QFSZLkw0XP8xFFFURRZdq4+elE2d1xsmiw8w3C4mdp+3MTldVQH94DlY3AC7oRdi6EYR7i+igyy7uIoioJ/R0nyaRZMMYgPA9BECGqdKBS7UQYVSzQgciJaRKjXhtFvTZq3TebI/OKc9Lf0yn46X5FDXbLjn8hLVGSCy9nzOTChAAANj5JREFUnXPO8yTnJFuUtRsQTY3UmsOUAZrPOIJkOUQ0GzobpspgHI+kOEdeveQEV3oNWTwMoHBsHOsKOfk9oVlNWHI5NcYAvBPgESUUNQhYeaPbbvsTNt57D/7q6U+f1vO4OHntWnhC4HWvfV1bx89EYxy/3/opBmMMl7zsMvzo94fbPsejz5qL3bu249Y//A463gM18EfokU3Eoeheh+qix8DrWolMSlvukz94GEaIospRg9xOFNQXl1P2Ok9TEn4MwjLfxFr8htP1TtFIan1IagfBGEfUuRBe0I00SSwUmSEMKwijypSS3fEWjmQXBCGiSjVPME76RWYpkriGuD5KQA05ng1eLN7TF4j0S9I8WZbZNpSEZ43S3IC9vAkQghw/aSDPm0Lfi7e4qFA8j14nULTpyp8FL5oDAFDJ4ZYJxV23a8/RlF+AfN0zJPVhW617LQiOk4fj8+QtRM4BUQVEFwBGci1yBJ/61KfxgksvRRCGk56zWWzauBH79+zFxRdfPPmDZ2LSeNAnFAB42tOehm17a9h1sN7W8Z7geM6j5uFTn7gSavg+gHHwrvWUSDqWIk0za4NLVq6VStXqDd1/0FRjWxBTaVNJmUFJ2WCFW8CGg2mJ7ykZI3ZVSdiNoLoAmdSILcnTt4vw/TF0v7/CJRi/lGAIbk3y9UlSR702giSuNzgpAtOrUMrh3ichBM0kABir26WUzOVKXFACse1ZJlpsaFzZ1PjaSBGYPkcN3jgARDibDkkHmsqojL3mYnDvqhQGLWvIsiRPyKmFjLcTrhWWy9tzn/gqLMStf/wDdu/Zjcc/5cltnRsAvvf16/CSl7ykLZmXmRgfJ0RCCcMQz3vBC3H9bw+1fY5HnTEXgwMDuPnPIwjnPQJex6J8uOh5nm1pRcdsPjJZyJwQOHEy0FojTeKc4Qy0NzehWUk/ktH9JMlSXQAmOpDEtTxZVaodx72A49EIxhg830cYVah6CSsQnk9IqiTOkwuMaUtx2D0HYAGFJILWgKgaC54gOLUlJE6xaqXj6CvvFtBxnwkegHlV6KQfANCKcOnCszB4qlI4taVgIJNhawTmLLCn1/oaG06IMwhCksznFXzy01/Giy67rG0DuAP79+NPf/g9nvvc5x7Rtc1EESdEQgGAF77wxbhlQz/6BpuTCCcLzhle9PhF+OSnv4jE7qh830elUm3JcL6/gvxDMgtznVhN2IlWlltd052bKJkgHtkLmQ7DC7rgV+Yjk8Rl4UIgqnQcMXflwRpu/hKGESrVDpq9eL4dahsomeYzpdyHZIrn5VxAawkYg8AP4ftBvrNvpq5gDFkYONJjs3PaR7qf5D8joIIHpVTD7twYAxHOgawfzNusk113/llgjFBkzAeMRFwfyoEqTsPrSMJVV5VKFTfe9EvEWYrHXPyEts/3/ev+G898xjPR1dU1+YNnYkpxwiSUOXPm4NnPeQ6+96sDbZ/j7FN60RWmuP76H9sB+/Gx+3aLyWQQX0cidBpf052bGGOQxgNIRvfRoladD/AqzUmMQRBGCMOjo/d1IkQ+ewlpuA+rPQbQcD2u11CvjdpB+8Q7fYDIirAeJMLz8wTCGM8FJ8sWBLAKxVM5d7Nwn6csSxuSiqgshFF1mGzQij5OXHW5pGL/AbAQAAd0giSu5VV9kiSTVjxTCSklrr76arz8ta9t+7N4uK8PP7/+x7jkkkuO+HpmoogTamW47BWvws0b+nFwoL0qhTGGFz1+IT71yY+jXm9vHnMswvE6xASlfdkwq5CyyCzTevK5iVYp4pF9kMkghN8BrzIPaabyc0aVjqYM5ZmgYFZDizGOqNKBqNIBPwjBGCX6em0USVxvMdAvzgEYCGtlIGWWD6d9P4BSCvV6zUKIbfXDeEsE2vgKpVEWpKgeqNJxIaon0fOP7gFQIM8mijKHBowRXwQGWtZyTgpAHJYjpb59/etfx6JlS3Hmeee2fY7//vJX8IxnPB0LFy6c/MEzMeU4oRLK7Nmz8bznPR/f+eX+ts9x8uJOrF1awec/99mjeGXtB7W7JMQEizm59tXBOM8hwuW5yUQ9Zsd2j0f2whgJP5oDw6rI0hScMURRtUFMciZaR1kB2M2wKLlU84U7SeqEFkvH79bLSaIgh/o5Eow4HcREd61NYWHFzXb+496zJu+hq0zK7SjuVcCDWVDxPgBUEcRxPClay/Faxra+smTUVslRTtBsN6kMDQ3hM5/9LF722svbOh4ADu4/gBt/8n945Ste2fY5ZqJ5nFAJBQBeftkr8bu7D2Nff9z2OV78hIX4+te+gm3bth29C2sz1BgBvbFRVhEOpzk30TpDMrofWTwA7lXghXOQSdLI8oMQYVR9UMKAH7hofp85FwjszCWwHB2Z0awlrtdypJiyRD7ib8icZFmch1tgiJ+3uRh3hNXm84nJKlNKVkHOlnchqidB1Q+Bo5C3T5LYtlBbt62CICgN6Uutr/qI/X2Ys+LbSSpXXXUVHv7oR2H5ypXTPtbFN774RTz3uc/FnDlz2j7HTDSPEy6h9PT04IUvfBG+dePets8xtyfEsx61AO97zz8fcXl+pOHgyq16xaSDpKY1NyFl4GHEw3uhVQovnAWNCqRUEMKjds1xMj96MIVL5hO1tIgEW83vMalRx6jXhq2Ao5OyNw2Ex/I5SCOL4GD0+WAtZx3Els9Zl02vy804yvL7XvUkAAY62Z8/xg3X6/V6TuptFjmIhXNAVOhadYykPpq/LqfhNZ3v1+23346f/fznuPQ1r57yMWNjz65d+M1Nv8Rll13W9jlmonU86BLKfffdN+ljXn7ZK/HnLaPYtm+07ef5qwsW4NC+7fjf//1h2+c40tCK1Ga9Fux7klZJIaYxNzFaIakdQFrvBxcBeDAbUjltJEtOnBm6txXTScDctiejSoc1O7NWwFrbYXtrT418IG+RWm5RbrD8za9p8vfSVSnl84toDsADpMO7weycxrXduHVUdNpizc5XzFMEERJBemJpXIcxGkJ4Y8y3Jo4sy/Ce974Xl/3t69B5BKisr37uc3jRC1+Inp6eCR938803Y8uWLW0/z19qPChWDmMMDh48iGc/+9l47Wtfi127dk34+M7OTrz6Na/D1362r+3nFILhFU9ZhA//2wcxNDTU9nmOJJxsRbN2l9EaSRLnZEtg8rmJknXUR/ZAywTC74ZGFVobeLbXP9HQfyYmj3bIjQ4pxrizbrbvgTE5M3/8rIUSDufEenfvP4l+Nu76Cz0vYCIWvxCN5EjP8+FVT0I2shueJ+A8aZzHi2tdxXE9V08e+7oKeRbP8lMUYFJIqy7MeaFJNll8+ctfRs/sWXjU4x836WNbxaaNG7Hh9jvw8pe/fMLHZVmGnTt34tnPfjbuvPPOtp/vLzGO+4Ry++23gzGGefPm4R3veAde9KIX4ZZbbpkUfvj8F7wAB4cY7mhT42u0LnHj7YchuJjSB/5oh/N+b4asym17jUFofSeM0S3nJgUc+AAY42BeL5TxwIWHyPJsZtpbRx7tsuVJrFKCCx+W1QjGhd3FZ+MSi7b+J45dX2bRZ1nWUDlQhdL4WWh17eUqRWsDr7qYBB9r1D521USZD5JfY1wfN7R3yYf+EeTSLNApjJZ2bsNyrbRWsXnzZnzik5/EU571rLY/p8YYfOGT/4m/fd3folqtTvg43/exZs0aPOEJT8Dpp58OYwxuvPHG3OFyJlrHcZtQtNa48sorcemll+JLX/oSAOC8887Dk570JNx2223YuXPnhMf7vo83vfnv8bWf7ZsWe9kYg99u6MPf/+fd6Fl6Ab77/R9g9uzZR/Ra2gnpqpMmfXTp5iZWDmSiuYnWEsnofshkEExUYFgXDBiCILLtrZmh+9GKIqFMj2uhneinlZDnQlgdtiiHa7vEksT13FCL86KidFWs5/l55eD8aFpgBcaFM34DCHFWmbUCTESo992Vo9TKGzknIe+SRpLE44btQngFx4VHVK2YFDDSWkPo/Nhmm0StNf7lX/8Va9evx7+957246gMfaovL8rtf34zRoWE861nPmvBx7j38/Oc/jxe96EUAgM9+9rP41re+hVe/+tX4yU9+Mu3n/kuK4zahcM7x/Oc/H9dddx1+97vfIcvoS7R48WKsXr0aN9xww6TneOITn4ioay5+/ee+KT1n32CCK6/bhm/9ZhQf/dgn8M/veu8DwqIle9QsN+UqBzndpbn7opO1cNLm5QShshriERq8M68LhlXAPRq6t5rLzET70W6F4hBaOvfSKVw1OeeEELPvmVIytzEoP0vhO2JyeLFTxC4LV052beUqhXMPfvfJVKFIQmk1m3kIQdJETgzSJTMXjfIsEQj5FZPFb+kz6LxZynHdddehVq/jfR+7Eh/6xNXYuGEDLnvu8/Dn226b8HWUQ0mJL37q03jLm988oT+Lq5K+973vobu7G+eddx4GBgawa9cuXHXVVbj00kvR1ze1teQvNY7bhAIACxcuxNq1a7FkyRJ8/etfz3/+ghe8ALt378YvfvEL/OIXv0CtVmt6PGMMb3v7O/G1n+1FLW7dttLa4Me/3Y+3/9c9OPuRz8C3vv19nHPOOUf99Uw1ygZV5XCIIMZYzg0hNVeZ+7DQ4wzS+uFcHRiiGwY+QYFnmO7HLNpJKCRvkoELL7cALlcJ+bk5z+X2XSpJkxhxvZZDy92gm+YXkV3gSwnFOZBOEGWocpqmiGatBcBR67srP38rEmUQhCUCY5LzTYo2rEV+8Qpdk44BLRvoMeXW2a5du/CJT3wCr3/bWyGEwOpTTsHV13weT3vuc/Cev38r/uXt75iSRtj13/8fnLRgAS688MIJH+ee9xe/+AVe8YpXwBiDm2++GQMDA7jrrrtw+PDhfPM2E83juF5Z3AJ5ySWX4IYbbshnGVEU4c4778SVV14JKeWEPdGzzjoLj37M43DdL5oP6HceqOHd19yHP2yPcM21X8HfXvH6KVmXHqsg18g0N3kq/9x9QR3fxEEvOS9UX6nFtQ8yHQLj1OLiwp+BAt9PUSY3TiWMIbVfzkSuJu3eI6VaLVwGjAv4QUjWAkkdcVyjhdnqb7lFnAb2RYUylfav+/wbYxBEXfA6lyId2pJzUiZCZjkRR9/3G5j9AErzFIf8ImtioxREqbKm1lmKf37Xu/DsF70QS1esKM7vefjrl74EV33+8xjo78fLn/PXuPmGG1teT/+hPnztC9fgbW9726Sf/V/84hd4zGMeg4MHD2L16tX43ve+h9tuuw3r16/HG97wBvT09ODiiy+e2ZBNEMe9BbDb4VxzzTXo7+/HGWecgVmzZmHTpk143vOeNyWL0YGBATzrGU/F/3vBcqxe3AkAyKTGd365Fz+9tR9XXPEGPO/5LzguPihZmiDLUoRRteG1uZ/7QZgT0eK4nqNpyLejhrRmFZdFB8CC48aG9y8l4pgY7K3sc8dGliXI0hjCC6GUQqVSBWMMcRzDGJ37xhePz5AlQxBeiDDqzJUUsqyYXZD8SzV/z9M0gUyGAOaBiXBKLor1es0mOAGd9GN4x/UI556LaNbahuucKMq2vpyL3O/eoRONSsmFEVS1CD+gaosxfPWrX8NPfvpTfPATV7dEH2qt8ZMf/BCf+/gncPLatXjnB/4V1c7Ohsf827vejbWrT8YbXv/6Ca/Vxd13343Pfe5zeOYzn4nbb78dj3rUo3DmmWfiS1/6Ei688EKsPAJC5V9CPPAr6BRjcHAQ3/72t3HPPffgrLPOwgtf+MIp+1X39vbi797y9/j8/+6B1gZ3bxvC2z59D/rUMnzzW9/FC174ouMimTiHO+dF74IWjBTCksvcEB5A3mJI6/1Iawct7r8bjEeIoqrVk5pJJvdXTLdCUZa4qpRu8NcRgjclSbo2mGtLOWn9qEIsfFjEX1wfzVth9FhGQ3BjWg7Ay+GqFKUUws6F4OFspIP35r93M82JglBekd0AqXxG4pj+3Ast8VEDug6VpRBCYMOGDbj22mvxlnf984RQds45/uoZT8envvplhFGIy573Avzou9/Pf/+H39yCrfduwqtf9apJr9XF+vXr8ZGPfATnnnsuRkZG8MUvfhHf+c53sHv3bhw40L7w7F9KHPcViovrr78e5513HubOndvW8cYYvPxlL8Xo4Z3oGwbe/o53HncubWmaQGYpoko1XzC0psWBcY4oquaPU0oRogsGSe0QjE4tiqYCz1YxM4nk/o80TSClnNIO3hiDem0IQvhQ2iAIwrzNS7pfccPPAKBeH4VRMYKwE54/3qWQPi81uFkJFwKeFyCtD9LPXKsJsCZxrRfsWo2IwZ7nIRvehtG9v0a08NEIOhdDaz2l11i+LucV70Q0yYFUQKsURo4CYBgelbjkb16Bl//t6/CIxzx6SucG6F7efMON+PiHP4wFC0/CW9/7HrzrzW/Be979bjz84Q+f8nnGxk9/+lNs2LABT37yk3HKKae0fZ6/lHjQJJSjETt37sT//u8P8aIXvRjd3d0P9OU0hNEa9fooWQvbdokxBrFtPVCSISKYkxtnJkNat6gT3gEmQgRhNGUTrZk4+uHen6kstuRdX4MQAdSYBdoYg3q9Bs/zG2Z6tdoIoBOabXjNZ32JtSn2fD+Hn0NnABTASQ3BwXs9z88FKFu9FgAIAh+HN30TPJyFjkUXkYLDmGubLAi9KPNzkv+Lyl0qVTqEf3jH+9A9dz5e83dvmvJ5yzE8NIRPf+w/cMtNv8QjL7wQH/v3f2/rPDPRXjzwfZ77MZYuXYrXvOby4y6ZAAUr3qkFO9FHYzTCiHS6HGSYcw6dDSOtH7Itrh54AUmmzySTBzamg/RyfBKl1Dg7aSeHP86ky+ltTUAucYq/nHNUqh0QTgHYHu/mbo6U2KoFVq6MpFQIe0+Bqu2FTAbAObeillNHPJVVk91rc+ASLnx887s/xp59B3DZ375uyuccG13d3Xj6c5+LSqWCf3jrW9s+z0y0F39RCeV4DaUK3xE3y5EZ2Q/7AbUlSKI+AWAAOQiVjRCmX3QjjDpmJOaPk5hOQtFagXHiZzTbCNAOvvB8L59zoveaWkrMQohpjsGFqyQ0krgGrdU4CZWxbHUnZknXqhH2rgHAkA7ek6PF0nRqWlxjry8Mo3weCAB/+MMf8LnPX4N/eN974R8ByjJNElz1wQ/hH//xH3HSSSe1fZ6ZaC9mEsoDHAQTjgHGCi8TWZAXiyF8TOxiOQCtEoB3gAdddgc6U5UcLzHVhGKMsUKQ9O9mABP3s2KhL/FIJkgoThpFa51XHsW8xcAYIInrSOK6lcQn1FeSxOOG7WUnR2U8hL1rIIe2QKWH86p5Mp+UVtcYBAHCMMKhQ4fwT//0TrzpHW/HwkWLpn2ucnztC1/EmpNX40lPetIRnWcm2ouZhPIAh5JZznJnjDWYZQUOwZUmMCoG5CCR00Q3/LDL8lFm3sLjKaacUOxC76C5zSoOV626Bbt8ysmq0bL6NOCSEzlCciGshLxEXB+FlBlVMVZFuCyfQuKVRbILZp8Bxn2kh/6UX9d0ZejLobXG2//xHfirZz8L5z70oW2dw8W9d9+Nn//oR/ind/zTTLX+AMXMavQAhjPC4lYI0BiDxPIYHHkxy1KodAhQwwBoXhJGXTNw4OM8JpstFLOR5u0uoFjMtVZNIMQTv/fuWMdsJ00v8qzXWjcoTMssRRzXIATP2fBlV8UGaXsIVOadBRUfgKrttq9FtzT4miw++MEPoqOnG8976UvaOt5FmiS46gMfxFvf+tYZ46wHMGYSygMYWUpcksAyiIshfMUiulLIuA/QdYCFYH4PomrnTIvrOA7G2JS4KOVhuxCtv4bCCkXmCaVUOUwWZQgy4MQk6RxOJ4w8cKrgjNHn0Wj4fiEw6WTmyzwt3rECIuhF0vcnGC2tYvD0q5Rrr70Wv/397/Gmf3z7EfPAPnv1x3HKmlPw5Cc/+YjOMxNHFjMJ5QEKpWRpEC+KIbwf2l1igqx2gOS+eRUi6EFU6TguCJgzMXFMPaEwMMvJaBWOj6SUKs45xcqUc2GH81adWLh5iG6YewghEEbVvA0msxS+nd05oUfnuWIvANH882BkDdngPfl1OaTiVOLHP/4xrvniF/Guj/wbOsaw26cbv/z5z3HHrX/Ee9797pmq/QGOmdXpKMfXvvoVvO89/zyhh4qDBDM7iG8Ywvs+SaiM7gegAd6FoNJrXf1mviwPhpgsoRhj8oQy2QbBIbYoAUyvAhg7nM8H80aNgyO7zyKRZxmyLIHg5KWSJESkLbPzWTAHQddypAN3Q0siQJJQ6eQD+ltvvRX/+v73450f+gDmL1gwrdc0Nvbs2oX/+thV+OhHPoLOI0xMM3HkMZNQjlJorfGhD74fX77mU7jnjl/hPe9+Z8tFJU1j63dRsQiuOhgjS9gsGSIJFXDA60HU0dPUsXEmjt9grLlsiotGR8XJ5YPKc5TphpvPSClt8qI5SkPFUwouBKJKNfePZ9B5+zWnsrjZyqwzAABp3+358WP9UMbG7bffjjf93d/hze98B1atWTPt11OOLE3x4Xe9B5e/5jU49dRTj+hcM3F0YiahHIVIkgRvefMbcdtvf4p3v3wN3vL8lbjrtt/g6qs+Nu6xUmaWxRyAc54P4YMwQhb3QyYD5Gzn96JS7Z4xwHoQhrPdbbWwNg7kJ/8KOpRVfr5p5BXOuR3OZzScFx4AbecozYEDzh7BKTZoJcEZg7LS+PnjvCqi2adBju6Eqh/Ir7FV62vv3r14wxWvRX14AMNHwVb7c5/4BJYsXpQbYc3EAx8zCeUIY2BgAK+87GWI++7B21+yCp0VD1Eo8NYXrcT//s+38NWvfjl/LPmZJGCcw/N8JHEdxmgEQYC0th8qGwV4BczrQaXSMdPiepCGm4lMnlAw4fzERaHr1h40t4AQS4gSwbG1PD6FEF6uvuBY7abEqOecQ3StAfc7kPT9MU9QzVpffX19eMXLL8EjlwzheWdm+Nj73ovvfuO6tl4PAPz8+utxx+9vxb+8719mvifHUcwklCOIXbt24aUveSFWzBrGFc9eDt8rbmd3h4+3v2QVPvPpT+CHP/xBQU4EiQBmaQKtFXzfQ1Y/CKMzgHeCeZ0N0uMz8eCLyayAjVMMbsE/aXY+zvm0rYVdOJSWlBmE0/8yakpQX1etBGFkn98izYwhT3juIZxzNnQ6CDm0OT+u3PoaHh7Gq17xMqzrOoALV2R4yEkaf3N+ii996hP43NUfn/br2Xzvvfj8J/4TH/vYxyaVUVJK4b//+7+xefPmCR83E0cnZhJKm7FhwwZc8tIX4QlnhnjxExbnbY5yzJ8V4e0vWY0Pf+hf8f3vfRdaKRrCK/oye4Ihqx+iL6roAkQ0Ja+KmTi+w71/rSqKYiA/9XZmI5O+neG8X8x1mKDBfIs5SqvjCWUokDP2Lb+FVxbBqy5E0n8HdEqtLGdNXavV8LrXvBIL2W48fnWSn3P1HIPLH5bix9/5b/zbu9415dcyNDiID/zTP+Mf3/52rJlkBuMcMPfs2YPnPOc56Ovrm3FbPMYxk1DaiBtvvBGvfc0r8fInLcCTLpgYpbJ0fhXveOlq/PuV/4brr/8xANLp4kxDJv30INENsGAmmZwgUXBRxi9ezqFxqvMTF2OlWaY7oHdsfCkzq+tFSWGc+OQEwTlHGFVyiSCA5GM4Fwjmng/GBeIDv4HRVPkMDQ3i8le9HMHIJjxlbW0c2nlRj8EVFya44+Yb8I9X/O2ki71SCh997/vwpCc+cUp8E4egGxoawlve8hbMmTNn5vt1jGMmoUwz/vu6b+Bd73wb/t8LVuD89bOndMyS+VW845KTcfV/fBTf+c63wSChswEQkqsbYF5OZpyJEyNaQYfLi+Z0ZHPyx+YL4vSrFEdYzOcoRk0Ib299niD35qGkosC9KsJ5D4VOB5D0/QnDw8O44rWvQTRyL5596iiaFPAAgDlV4PUXJjiw5U68/pJLJvSIv/ZT/wVugDe98Y1Tvt7//M//hO/7uPTSS7Ft2zZcddVVeN/73oe+vr5pvOqZmGrMrGBTDGMMPvbvV+Jz/3U13vU3a7Bmade0jl8yr4J3XHIy/vPj/4HvfOsbADzA6wEgEIThlN0nZ+LBEQ46PDaKqoVNa7fsqp7iPO1CiBmU1iD4sGoJH54sCF7c4a4GMAZeZSGC3vXo270Bl7/yUsxRW/HM9bWWycRFZwi87hEJ+MguXP6C52FkeHjcY/7nm9/Crb/5Da786EcbZPUnil/96le444478MY3vhH33XcffvrTn2LlypU4+eST8R//8R/IsqxtDbKZaB4zGh5TiCzL8E/veBu2bLwV73n5GnR3tMcLWTyvgn9+2Rr86xeuxWjM8JKXvhSe58/wTE7A4JxBKVPoaNko2O582u0XzjmUzskg074mqi48ZFkGJnwYlQDGQMqsQa9rOtcThBWkSR2uYhqUC/Dm916HVR39eOKadFybq1WEHvDKCxJ8/fYBvOb5z8OVn/8CFlj5+d/cdBO+/dWv4dovfhG9vb2TnusPf/gDvv3tb+PAgQN417vehUqlgj/96U/gnOMZz3gG7r77bhhjGpSUZ+LoxEyFMoX46U9/ipt/dRPe/pLVbScTFwvnRHj336zBd/77S/j0pz415d3WTDy4ohV02FUozUAckwW3zPVm551quM0L4/ZzZ2QuINne+TxwW/ns2LkTr3zFy7Gue3rJJD+XAF58doLT5tbwhkteivvuvRcb77wTn/jwR/Hxq6/GkiVLpnSe8847Lx/GL126FABw8OBBzJ07F/v27cOtt96KIAhmqpNjEH9RFsDtRpqmeO3lr0QvP4BXPHXpURnsDY1m+MBXtuCCRz4Bb3v7O2bmJydYOE/4sb7tSVyDUhk8vzIt+1yA+B1pUgdMiiDqhOeN95SfSjjfeyiCsYMTibHdtqsxBrfd9ie86Q1X4KJlg7hgWXvKw8X5gBu2eLhhs4ew2o0PvP/9eNSjHjXt8/zsZz/DggULoLXGKaecgne84x3o6uqCMQavetWrppygZmLqMbOKTSGCIMB/XPVJ7BiI8M0b9x6Vc3Z3+PinS1bjjt//HG958xtRr9ePynln4viIVtDhokJp96tnz9uGqZWLokrx4dBeTkCynfjtb3+L1//ta/HUkw8fcTIBCHdwziIJT3C8+EUvaiuZAMDjH/949Pb24l//9V/x/e9/H2vWrMGiRYvwrGc9ayaZHKOYqVCmEX19fXjpS16AJ5/biYvPn39UzplJjc/8YCcOjHbg6k98CvPnH53zzsQDG8YY1Os1eJ7XoNRbrw3DGIMw6ph2RaCUIqkek4ALH1FlYlLfRJEkCZTMrDWCB/AAlTYItT/4n//BB9//XrzwIcNYNefoLCWjKfDZP3TjhZe+Bi9/xSuP+Hy1Wg0//OEPsWbNGqxatWpSMuRMtB8zCWWasXPnTlx6yYtx6cXz8bDTjo6RjzEG3/vVPvzsT0O46uP/OSN0d4JEvV7L/dNd1EYHATBElc5pVynOowQ6ARhHtaO37WtrOJdRAK/AD4IpD+eVUrjyo/+G6//n23jJmcNY2HV0lpE4A77wp2487qkvwN+9+f8dlXPOxP0XMy2vacbSpUvxyf/8L1zz43347V39R+WcjDE861En4aWPn4tXv+qyGZmIEyQY4w0tr/LerZ05XH5MyXmx3XCikWCuSlJTHs4PDQ3hta9+BX77s2/j8guGjloyyRTwlTu6cd6FT8Kb/u4tR+WcM3H/xkxCaSPWr1+PT336s/jC9Xtxy4ajR5AKAwHP82Z8HU6Q4HwsW94tvNPjoLigY9yxpm3bXRc0S7Fe81pan5aJk9Rtt92G5z3nmfD6b8PLzhpCx/TRxk0jU8DX7ujCytMejn/65/fMMNofpDGTUNqM9evX49P/9Tlc8+N9+M2dR55U9hyq4z+/twNX/vtVWHCEpkMzcXzEWOiwcdXKESyWBDem47PsyBKKEAJcCJqhQFvmfOvh/I+vvx6vfc0r0dfX9//bu/P4qMp7j+OfOZOZZLIQIYECCVshgRAgEAJESsAEq9BLoSqgIost2nqxCOpti7jUWxUxUdJqXVEQBEEBkVa4ZRNQkEWBhEAIixIxCRFIApNtZs7MOfePLMieZE5IIL/3P7yyzDlPXi9mvufZfg/x4eXUonLMFbncsCg9iLBuA3kx+RXZ5Hsdk0DxQrdu3Xhn7jwWrMvnq4zTdb5OabmbVz7K5pFpjxMbG2tgC0VDqtprUvXUr1Pz8+Avp6KkS+UXusfrYocWi7UyUADdXVm49PwVZKqqMnvWc7z0/NP8tm8J/dvrvLPTD7cBdRYdKixIa0ZEn0SSX0mVzYbXOQkUL3Xt2pW5787ng/U/snnvqVq/XtN0Xlv5Pb8Y8ktGjxlTDy0UDeVcD6Xyk1c3JlCqeihGDHuZzWbMPj4VoaJ7QNdQVVd1r+rHH39k0oR7SftyFVMG2AkP1hnezUVzf513d9ZtH0yVssoJ+N4Db+f5WS9Jz+QGIIFigMjISOYvWMQnW8+wZsePtXrtko15mAPC+PNfnqin1omGcm4vSlUPpfonXlzz3G75ikCp+36UKhf2UirOoPewYcMGxtz1G8L1Q4yPKca/cr7ErMCEWCenS2F1Zt0+QoqdMO+bAAYmjeSZZ5+Tjb03CFk2XAsX1mW6UF5eHg/8bhIDu/ly55A2V30S/SLtNCu321n60fIa1SgS15/y8nJMJhN+fn643SouZxlmHyu+vrY6Xa9qB75JdwEmdJMFPwMqVbtcTtzOUsBDSZmHlOTZ7N21jbu6F9OhxaU/InLOmnh7u5V7eqtEt675+FdhGby/J4iRd93L1GmPyQT8DUQeC2rA4XDU6Pfatm3LwkVL2J0Ni9bnXnEJ5tGcEhZvOMFr/3xTwuQGdt5KLwOe3aqDw1R5gqNuYC9FsfD1N3u49+4x2L/dwsPx9suGCUB4sM6oaDcfp1soKqvZffLsJuZ+04zJD03nkemPS5jcYCRQrqKgoICnnnqKN998ky1btuB0Oq/4+6Ghoby/YDHZRQH8c+X3uNSLn9wK7S7mLDvGs3974aqnzonrW8XRvfoFDxd1f9udK2OvADqYFK/nUaBiN3lKSjLPPPkkIzqfYlRUGb41qFsa185D7zCdt3dcfZL+uwIT7+8J4omn/sa9942/4u+qqkp+fj6bN2+u+R8hGpwEyhWUl5czffp0+vfvT9euXTl48GCNngaDg4OZN/8DbCFRPP/Bt5wpOXdokEvVmLMsm3vunUhSUlJ9Nl80AlUT85qmVYeKtw/liqJUz8eYFSrnPOq+5OrLL79k1Ijh/LB3NVMHFtO1Ve16Ur/u7iLAqjP/68tvSsk4obB0fzAvp77GsKuctuhwOFi8eDErV65k1apVJCcn16o9ouFIoFyGqqqUl5czceJExo4dS1JSEkVFRaxZs6ZGr/fz8yPllVSG/PIOnn7vMMd/LEPXdeau/oFOETH8/g8P1fNfIBqDqiGqimEvY6YrTSYF9PNPbqzLsNeZM2f4y58e49mZj/HrLie5s3sp/nVYteujwP1xTk6cNbHu0PkfKZoOG476su77UN55dz7x8fFXvV5aWhoej4e7776b1NRUTp8+TW5ubu0bJq45OYzjEpxOJ7Nnz8ZkMjFo0CBUVcVisdC3b9/q+ZSsrCw6dOiAzXb5yVVFUfjj1Efo2KkTz7/4Ar27BJJfEsgHb86WseMm4qdVh01erO76KUVRwGTChBldd6MoVtxuFR8fnxr9v/J4PHyyYgWvvZpKz1blTI0vx+rlJ0GQH0zq5+LdnVY6tnAR2VLH6YYVmYFogR35aNnbhIaG1uhaaWlptGvXjhYtWrBu3Tp69uxJWFiYdw0U14Ss8rqArutMnz6d6OhoYmJiWLNmDRMnTqRz584cOHCAgwcPYrfbyc3N5ckna36OSXp6Ou+89TpPPfO/tKk8iU40DVUrvcxmE6rLgY/FF6vV7+ovvIyqSsaKyYPmUbH4BqGqKr6+flfdy/H1118z67m/guMUv+pSTFiwsW//Hd+b+c8hH34X52LV4SD63pzEM88+V6OzX7KysnC73QQGBvLyyy8TGhpKQEAAiYmJxMXFGdpOUT+kh3IBh8PByJEjGTp0KAC5ubmsXbuWKVOm4HK5SE1NpV+/fsyaNatWSzVjYmJ4/c136qvZohFTFFPlHEfF283bR7iqiXldVyq/rvi+2+2+bKAcPXqUf6SmsD99D7d3KaFna83ruZxLGdDeQ+ZJM3N3WXns8amMu29CrXrjTz/9NDNmzCAkJIQWLVrQq1cvCZPriPRQLsHpdGK1WtE0jb1797J+/XqeeOIJCgoKWLVqFXfeeacs9RU1pqouVFXFarHgcpVj9vE9r6R9XVSfZ6I7sVhtaLoZj8d90ZkmhYWFvPXGayxb/gnNbTrTEpxY6mlDuqbDlu98+fpEIE8/+1z1Q1ltHDhwgMOHD9OuXTvCw8Np3bp1PbRU1BcJlKsoLS1l2bJlNG/enE2bNpGcnFzro1tF0+Z2u3G5nPj4+OBWHV5tbDx3TRWXy4VJVzEpJizWQJxOBxaLFYvFQllZGQven8+iDxYQ01olpnU57+yw8t83O2kbbNAf9hNlLliRGYQS3Ik5//inVwVOr7aBWDReMuR1Bbqu43A4mDdvHh06dCAlJUXCRNTauaFR/bx/vLtmRTfDpJjRPK7KYTCF4uJiPvvs37w392063eTkoX4lhARUvCa+g8bH+/yYnlCzjbo1lXPWxNKMIG7/1W947H/+7HWBRwmT65cEyhWYTCZCQkKYOHEiw4YNk+63qJOqD8iq8vVGDAooivKTDY5QWHiaJR8u4aOlH9KxuYfxPUsumnBP6qKy67iJjBMKPdt4XypY02FbtoUvj/vzzF+f47bbb/f6muL6JkNeNeDxeKQSqvBKeXlFbRJdUzEpPthsAV5f0+VykpuTw4eL3uez1f8hurXGoPaltAq8/Ft62zEzXxyz8ESSd72UwjL45GAz/EM6MTtlDuHh4V5dT9wYJFCEuAacTkfF5kPdjclkxubv3amc6enpzH/vbXbu3ElsmJtB7R0E12Baxq1ByiZfBrRXSYqofS9F1+GbHDPrvg3ggQcfYuKk++VhS1STIS8hrgFFMeNxV9TcqusznKqqrF+/ngXvvc3pUycY0LaUPyW48avFlIWPAiOjVZbvszC4sxOfWtTKsDtgVVYgDmtr3l/4d6lDJy4igSLENVC1u71iQr52gXLo0CFWrljGmtWraRnoIb5tMVERGkod5667/0wjJAA+yTAzNubqJVuqeiXrvw1g9Jh7eHjqI3KyorgkCRQhroFzK70qJ+ivsjQ2Pz+fdWvX8umKjygqPE3v1uU80NdFqPdTL5hMMCraxdwdVoZ19dDsCltiCsrgX1nNIKAN8xa8TGRkpPcNEDcsmUMR4hopLy9H11RAx88WdFGlhbNnz7JhwwZW/2sFu9P2E2wzMbqnk5+H6HXujVzJoj2+lLt0Hox3XfQzjwbbj1vYcszG5Af/wMRJ9+PjI8+f4srkf4gQ14iiKHiqz9qqeI4rKChg27ZtrPn3J+xNzyCylUKP0BJ69dNZmmalc4heLyVSAH7VzUnqF77knoWwn2x2PHraxJqjzWgd3pkPP5pNhw4danxN2ZTYtEmgCHGNmM0KToeHAwf2s3PnLrZu2URO3gm6tDIT1aKY24do1YdaaXrF4FjGCYVebb3fM3IpLfzh5g4ay/b5Mj2h4oz4tUeDOOUK5E8zn+TWW2+tVTiUlZXx1VdfERISQp8+feqlzaJxk0ARoh5pmsaRI0fYtXMn27duJm1fBs39Fbo0L+eWVi7ad9UvudJKMUFcO42t2RZ6tb3yKaHeSOyisvO4lUV7fPne7sf9v53MxEn34+vrW+tr+fv7ExgYSHJyMgsXLpSJ+yZIAkUIA9ntdrKyssjMzGTPrq/YvTcdfyt0aq7SKbicITdrBNWwLmRsmJvt2VbcGrVa3ltTbg/syfXBbLbQrH0fPp01m5YtW3p1zfj4eFavXs3GjRsZNmyYQS0V1wsJFCG8tHXrlyxfupiDWVkUnbETFmKljb+DsEAnf4zXCK5jYeGfBek0s8H2YwoJnY0b9tJ0SM9V2JgdRNeoHixcPKPWq7d0XWf79u1kZWXRq1cv7HY7Xbp0YePGjdhsNgYOHGhYe8X1Q1Z5CeGlhx+ajCdvO/EdNEIDjF2RteVbM3vyLDxqQEFHXYdDJxU2ZAcRHBLO43+Z6dVZIytXrmT27Nm8+OKLZGRkUFhYiI+PDzExMWRnZ2O1Wpk8ebIMfTUhcqa8EF6Kiu6Nn8VMq0Djl/f2DvNQUKJTdvHK3hrzaJCep/D6rpvYcrIDjz/5Ih9+vMLrg6vuuOMOEhMTsdvtTJs2jeHDhxMbG0tRURE9e/bE5XLx+uuve3UPcX2RIS8hvNQtKortawMALz71LyPYD8Jugk1HzfxX96vvav8p1QN7chS25QTRqk07Zjw7jYSEBEOX9U6ZMoXNmzfzww8/sGPHDrp27cqIESMICQmhoKAAd2W5GdE0SKAI4aXIyEjyztTP0l6AfuEqn39rrXGglLpg13ELO3L8iO7Rk+S/TyU2NrZe2ta+fXtGjBjBvHnziIqKYvjw4UBF8cqTJ08yYsSIermvaJwkUITwUnh4OKVOD2Uq+NfDdEGP1hqf7tcoKKX6sKxLyS82sf0Hf/bnKyQlJvLeCw9ek1IpLVq0oLS0lDNnzrB7926OHTtGRkYGQ4cOpX379vV+f9F4yKS8EAa4Z/QoBrU4xM9D6ufttHC3L1bFzT19zu+leDQ4dEphV14QJ0t9uPve+xh79z2EhITUSzsuJycnh0WLFtGpUycAhgwZIgfSNUHSQxHCAN269+DE4cP1Fihx4Sqf7rcCFYGSX2xib54v6flW2rQNY8LDkxk2bFiDragKDw9nxowZ531PyrA0PRIoQhggKroXG/f9Byipl+tHttRwqBqrM80cLw2iRLUwctRveOyOu6p7BY2BpmkoiiJh0kRJoAhhgMjISBaXWg2/brkKB/IVMgubYVJcuFvGMXPG7+nfv3+jPCmxqoKyhEnTJIEihAEiIiI4UehA0/F6L4pDrZgX2X86iKMnPcT17cOk++5iyJAh+Pv7e91WTdPIy8vj2LFjJCQkeH09IapIoAhhgMDAQJrf1IyC0nJaBtZuHkXX4USxicOnzHxnD+J4gUpMz2jGPHgnSUlJNGvWzJA2Vg1HmUwmgoKCePXVV4mIiJDJc2EYCRQhDBIR0YUT9pM1ChS7A74rUPjurI3Dp8zY/ANJGDyER25JIi4uzpCeSJX8/Hz8/f2rg8lkMhEcHMzgwYP5/PPPGTdunGH3Ek2bBIoQBonq0Zsjm3bR6xI75oudFQGSfdaPY2eslDp1esf04pbbbuGZwYPrdb/G8uXLWbJkCRMmTKC0tJTExESysrLIzc0lMTGx3u4rmh4JFCEM0q1bFF/9nz/FDhe5doVcu8JJRyC5Z3TKXTq9Y3rwiztu4dEBA4iMjLzoCOD6MnToUNLS0hg7diyZmZmsWrWKzp07M27cOGw2Gxs2bODWW2+9Jm0RNzYJFCEM0r17dw7nq7xaGExkRGd6DOjDL3v0Iioqinbt2l2zALlQVFQUAQEBHD16lEGDBjFo0KDqny1dupSUlBR2797dIG0TNxbZKS+EgUpLS/H39290y2bz8/MpKioiKioKgB07drB161Y0TaN58+aMHz8em83WwK0U1zsJFCGaiJkzZ9KxY0fsdjuqqhIXF0dsbOw1L9Miblwy5CVEEzFgwADeeOMNUlNTCQsLIzg4uKGbJG4wcsCWEI1Q1dn0Rho1ahR9+/albdu2EiaiXkigCNHAnE7neV9rmobNZmPOnDnk5OQYeq8pU6bIkbyi3kigCNHA3nrrLWbMmMHx48fxeDwoioLFYiEiIoL09HRD7xUeHk5AwBUOVRHCCzKHIkQD0zSNs2fP8tlnn3Hy5EkGDBhASUkJp06dMnTHvBD1TXooQjSwhIQEAgICmDJlCuPHj2ffvn14PB7Gjh1LdHR0QzdPiBqTZcNCNDC3283HH3/MmDFjquc3VFXFYrHwwAMP8Oijj0qwiOuC9FCEaGA+Pj7YbDYOHToEwJYtW8jMzASgT58+pKWlNWDrhKg5CRQhGoHCwkK++OILALKzs0lJSWHdunUUFRU1yoO0hLgUGfISohE4cuQIL730EmPHjiUrK4tWrVoRGhqKpmncdtttDd08IWpEAkWIRiI9PZ0jR44AMHr0aAA5m11cVyRQhGgEqoLD4/HIEJe4bkmgCCGEMIRMygshhDCEBIoQQghDSKAIIYQwhASKEEIIQ0igCCGEMIQEihBCCENIoAghhDCEBIoQQghDSKAIIYQwhASKEEIIQ0igCCGEMIQEihBCCENIoAghhDCEBIoQQghDSKAIIYQwhASKEEIIQ0igCCGEMIQEihBCCENIoAghhDCEBIoQQghDSKAIIYQwhASKEEIIQ0igCCGEMIQEihBCCEP8P4yOlGbmITVIAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0e6c8029e0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd = merged[[\"city_home\", \"city_work\"]].query(\"city_home.str.startswith('bp_')\"\n",
    "                                              \" & city_work.str.startswith('bp_')\"\n",
    "                                              \" & city_home != 'bp_margitsziget'\"\n",
    "                                              \" & city_work != 'bp_margitsziget'\")\n",
    "dd[\"city_home\"] = dd[\"city_home\"].apply(lambda x: int(x[3:]))\n",
    "dd[\"city_work\"] = dd[\"city_work\"].apply(lambda x: int(x[3:]))\n",
    "\n",
    "con_mx = pd.pivot_table(dd, index=[\"city_work\"], columns=[\"city_home\"],\n",
    "                        aggfunc=len)\n",
    "\n",
    "con_mx = con_mx.reindex(buda+pest)[buda+pest]\n",
    "h2w = dd.apply(lambda x: f\"h{x['city_home']:02d}->w{x['city_work']:02d}\",\n",
    "               axis=1)\n",
    "Counter(sorted(h2w))\n",
    "\n",
    "a = con_mx.values\n",
    "b = con_mx.copy().values\n",
    "np.fill_diagonal(b, 0)\n",
    "b = np.transpose(b)\n",
    "\n",
    "c = a+b\n",
    "# c = np.where(c >= 250, c, np.nan)\n",
    "\n",
    "node_names = con_mx.index\n",
    "# %%\n",
    "# https://www.python-graph-gallery.com/406-chord-diagram_mne\n",
    "first_half = (np.linspace(45, 136, 6) + 90).astype(float)[::+1] % 360\n",
    "second_half = (np.linspace(15, 165, 17) - 90).astype(float)[::-1] % 360\n",
    "node_angles = np.array(list(first_half) + list(second_half))\n",
    "\n",
    "# %%\n",
    "fig, axes = chord(c, node_names, node_angles=node_angles,\n",
    "                  node_colors=node_colors, node_edgecolor=\"#2d2d2d\",\n",
    "                  node_linewidth=1, colorbar_pos=(-0.25, 0.5), padding=2,\n",
    "                  colormap=cmap_br, facecolor=\"white\", textcolor=\"#2d2d2d\")\n",
    "\n",
    "axes.legend(artists, labels, bbox_to_anchor=(1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating Figure 13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 576x576 with 1 Axes>, <AxesSubplot:>)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.patches import Patch\n",
    "import yaml\n",
    "\n",
    "with open(\"input/sector_settlements.yaml\") as fp:\n",
    "    sectors = yaml.safe_load(fp)\n",
    "\n",
    "\n",
    "def plot_sector_targets(data: pd.DataFrame, sector: list,\n",
    "                        axes=None,\n",
    "                        agglomeration: bool = True, legend_loc: str = \"best\"):\n",
    "    dest = data.query(\"city_home.isin(@sector)\")\\\n",
    "               .groupby(\"city_work\")[\"device_id\"].count().reset_index()\n",
    "    dest.columns = [\"city\", \"count\"]\n",
    "    dest = dest.query(\"city.str.startswith('bp_')\").copy()\n",
    "    dest[\"count\"] = np.round(dest[\"count\"] / dest[\"count\"].sum() * 100, 2)\n",
    "    cities = gpd.read_file(\"input/cities_with_labels.geojson\", crs=4326)\n",
    "    df = cities.query(\"city.str.startswith('bp_')\")\n",
    "\n",
    "    df = df.merge(dest, on=\"city\", how=\"left\")\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(8, 8))\n",
    "    if axes is not None:\n",
    "        ax = axes\n",
    "        fig = axes.get_figure()\n",
    "    bins = [0, 6, 12, 18, 24]\n",
    "    bins = [0, 5, 10, 15, 20, 25]\n",
    "    palette = sns.color_palette(\"BrBG_r\", 10)\n",
    "    cmap = ListedColormap(palette[5:])\n",
    "    df.plot(ax=ax, column=\"count\", cmap=cmap, scheme=\"user_defined\",\n",
    "            edgecolor=\"#2d2d2d\", linewidth=0.15, legend=False,\n",
    "            legend_kwds={\"loc\": legend_loc},\n",
    "            classification_kwds={\"bins\": bins},\n",
    "            missing_kwds={\"color\": \"#e7e7e7\", \"label\": \"Missing values\"}\n",
    "            )\n",
    "\n",
    "    if agglomeration:\n",
    "        cities.query(\"~city.str.startswith('bp_') & ~city.isin(@sector)\")\\\n",
    "              .plot(ax=ax, color=\"#fefefe\", edgecolor=\"#2d2d2d\", linewidth=0.1)\n",
    "    cities.query(\"city.isin(@sector)\")\\\n",
    "          .plot(ax=ax, color=palette[3], edgecolor=\"#2d2d2d\",\n",
    "                linewidth=0.15)\n",
    "\n",
    "    legend_elements = []\n",
    "    for i, e in enumerate(bins[:-1]):\n",
    "        patch = Patch(fc=palette[5+i], ec=\"#2d2d2d\", label=f\"{e}--{e+5}\")\n",
    "        legend_elements.append(patch)\n",
    "    legend_elements.append(Patch(fc=\"#e7e7e7\", ec=\"#2d2d2d\",\n",
    "                                 label=\"Unknown\"))\n",
    "    if legend_loc:\n",
    "        legend = ax.legend(handles=legend_elements, ncol=1)\n",
    "\n",
    "        legend.set_title(\"Percent\")\n",
    "    ax.axis(\"off\")\n",
    "    if axes is None:\n",
    "        return fig, ax\n",
    "\n",
    "\n",
    "plot_sector_targets(merged, sectors[\"northern\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating Figure 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.ticker import PercentFormatter, FuncFormatter\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "from matplotlib.patches import Rectangle\n",
    "from shapely.geometry import Polygon\n",
    "\n",
    "\n",
    "cities_gdf = gpd.read_file(\"input/cities_with_labels.geojson\", crs=4326)\n",
    "cities_gdf.to_crs(23700, inplace=True)\n",
    "duna = gpd.read_file(\"input/duna.geojson\", crs=23700)\n",
    "duna[\"is_polygon\"] = duna[\"geometry\"].apply(isinstance, args=(Polygon,))\n",
    "minX = 630000\n",
    "maxX = 680000\n",
    "minY = 215000\n",
    "maxY = 275000\n",
    "clip_area = Polygon([(minX, minY), (maxX, minY), (maxX, maxY),\n",
    "                     (minX, maxY), (minX, minY)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_to_plot(df, city, ref_table):\n",
    "    filtered = df[df[\"city_home\"] == city]\\\n",
    "              .groupby(\"district_group\")[\"device_id\"].count().reset_index()\n",
    "    filtered.columns = [\"district_group\", \"count\"]\n",
    "\n",
    "    count = filtered[\"count\"].sum()\n",
    "    filtered[\"ratio\"] = filtered[\"count\"] / filtered[\"count\"].sum()\n",
    "\n",
    "    counted = filtered.drop(\"count\", axis=1)\n",
    "    counted.columns = [\"district_group\", \"2017 (CDR)\"]\n",
    "\n",
    "    merged = ref_table.merge(counted, on=\"district_group\")\n",
    "\n",
    "    pearsonr = merged[\"2011\"].corr(merged[\"2017 (CDR)\"], method=\"pearson\")\n",
    "\n",
    "    melted = pd.melt(merged, id_vars=[\"district_group\"],\n",
    "                     value_vars=[\"1990\", \"2001\", \"2011\", \"2017 (CDR)\"])\n",
    "    melted[\"district_group\"] = melted[\"district_group\"].map(glabels)\n",
    "    return melted, pearsonr, count\n",
    "\n",
    "\n",
    "def plot_bar(df, figsize=(8, 5), legend_loc=\"upper left\"):\n",
    "    data = df.copy()\n",
    "    colors = sns.color_palette(\"BrBG\", 8)\n",
    "    fig, ax = plt.subplots(figsize=figsize)\n",
    "    sns.barplot(data=data, x=\"district_group\", y=\"value\", ax=ax,\n",
    "                hue=\"variable\", palette=colors[0:3][::-1]+[colors[7]])\n",
    "    ax.yaxis.set_major_formatter(PercentFormatter(1, decimals=0))\n",
    "    ax.tick_params(axis=\"x\", labelrotation=30)\n",
    "    ax.set_xlabel(\"\")\n",
    "    ax.set_ylabel(\"\")\n",
    "    ax.legend(*ax.get_legend_handles_labels(), loc=legend_loc)\n",
    "    ax.get_legend().set_title(\"\")\n",
    "    return fig, ax\n",
    "\n",
    "\n",
    "def plot_map(city: str, ax=None):\n",
    "    color = sns.color_palette(\"BrBG\", 8)\n",
    "    f, a = plt.subplots(figsize=(5, 6))\n",
    "    if ax:\n",
    "        a = ax\n",
    "        f = a.get_figure()\n",
    "    c = cities_gdf.query(\"city == @city\")\n",
    "    bp.to_crs(23700).boundary.plot(ax=a, color=\"#2d2d2d90\", linewidth=.9, zorder=1)\n",
    "    c.plot(ax=a, color=color[1], edgecolor=color[1], zorder=5)\n",
    "    a.margins(0)\n",
    "    a.axis(\"off\")\n",
    "    a.set_xlim(630000, 680000)\n",
    "    a.set_ylim(215000, 275000)\n",
    "    frame = Rectangle((630000, 215000), 50000, 60000, linewidth=2,\n",
    "                      fill=False, ec=\"#d6d6d6\")\n",
    "\n",
    "    a.add_patch(frame)\n",
    "    gpd.clip(duna.query(\"is_polygon\"), clip_area)\\\n",
    "       .plot(ax=a, zorder=0, color=color[6])\n",
    "    if not ax:\n",
    "        return f, a\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the reference values are set to constant only to demonstrate the plotting with the dummy mobile network data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census_dunakeszi = pd.DataFrame({\"district_group\": [\"north-buda\", \"south-buda\", \"north-pest\", \"inner-pest\", \"eastern-pest_inner\", \"eastern-pest_outer\", \"south-pest\"], \"1990\": [1/7]*7, \"2001\": [1/7]*7, \"2011\": [1/7]*7})\n",
    "\n",
    "city = \"dunakeszi\"\n",
    "melted, pearsonr, count = prepare_to_plot(merged.rename({\"district_group_work\": \"district_group\"}, axis=1), city, census_dunakeszi)\n",
    "fig, ax = plot_bar(melted)\n",
    "\n",
    "iax = inset_axes(ax, width=.9, height=.9,  # inch\n",
    "                 bbox_transform=ax.transAxes,  # relative axes coordinates\n",
    "                 bbox_to_anchor=(0.385, 1))\n",
    "\n",
    "plot_map(city, ax=iax)\n",
    "\n",
    "%matplotlib inline\n",
    "fig.set_facecolor(\"white\")\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating Figure 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def label_sectors(x: str, labels: dict) -> str:\n",
    "    if x.startswith(\"bp_\") or x == \"\":\n",
    "        return np.NaN\n",
    "    return labels[x]\n",
    "\n",
    "\n",
    "with open(\"input/sector_settlements.yaml\") as fp:\n",
    "    sector = yaml.safe_load(fp)\n",
    "\n",
    "sector_labels = {i: k for k, v in sectors.items() for i in v}\n",
    "xticklabels = [\"Southern\", \"Southeastern\", \"Eastern\",  \"Northern\",\n",
    "               \"Northwestern\", \"Western\", \"Summarized\"]\n",
    "browns = sns.color_palette(\"BrBG_r\", 12)[6:]\n",
    "\n",
    "devices = pd.read_csv(\"dummy_data/device.csv\")\n",
    "    \n",
    "fig11_data = merged.merge(devices, on=\"device_id\").dropna(subset=[\"city_work\"]).copy()\n",
    "fig11_data[\"city_home\"] = fig11_data[\"city_home\"].fillna(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig11_data.dropna(subset=[\"city_work\"], inplace=True)\n",
    "fig11_data[\"age\"] = pd.to_numeric(fig11_data[\"age\"], downcast=\"integer\")\n",
    "bins = list(range(20, 70, 10)) + [100]\n",
    "labels = [\"20-29\", \"30-39\", \"40-49\", \"50-59\", \"60+\"]\n",
    "fig11_data[\"age_group\"] = pd.cut(fig11_data[\"age\"], bins=bins, labels=labels)\n",
    "fig11_data[\"sector_home\"] = fig11_data[\"city_home\"].apply(label_sectors,\n",
    "                                                          labels=sector_labels)\n",
    "\n",
    "r2t1 = pd.pivot_table(fig11_data, index=\"age_group\", columns=\"sector_home\",\n",
    "                      values=\"device_id\", aggfunc=np.size)\n",
    "r2t1[\"all\"] = r2t1[\"southern\"] + r2t1[\"south_eastern\"] + r2t1[\"eastern\"]\\\n",
    "            + r2t1[\"northern\"] + r2t1[\"north_western\"] + r2t1[\"western\"]\n",
    "\n",
    "for i in r2t1.columns.tolist():\n",
    "    r2t1[i] = round(r2t1[i] / r2t1[i].sum() * 100, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "r2t1_melted = r2t1.reset_index().melt(\n",
    "    id_vars=[\"age_group\"],\n",
    "    value_vars=[\"southern\", \"south_eastern\", \"eastern\", \"northern\",\n",
    "                \"north_western\", \"western\", \"all\"],\n",
    "    var_name=\"sector\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0e579d5de0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NzS2SgkPa55xzzhdvvfXW4Ynua0ceeeTX58Wvvfbawu9+97tdpfT+fObm5u5t27bt3sGDB++SpAsuuGDrnXfe2a6yOjvppJO+Wr58+VIpsXPcUUaLO02Ulpbqwgsv7JSXl7f79ttv//oCliFDhnxx//33t5Kk+++/v9XQoUO/kKRJkyatWr58+dKyBDRp0qSmf/3rX9v961//+qhJkyZft9R37NiRsX379gxJev7555tmZmb6CSeckBZfDOvWrcsqu3p+586d9tprrzXt0aPH7kTr7IMPPji0bF3PPPNM86OPPvorKb3r7G9/+9vaDRs2vL927doPxo8f/8kpp5yyY8qUKZ8mUmcnnXTSV1u2bFm0du3aD9auXftB27Zt9y5YsGBZTk5OcTrXmSRt3749Y+vWrRllwzNnzmzaq1evrxLd11avXt2gbF1PPfVU827duqX9vpaTk1Pcrl27vYsWLTpUkqZNm9a0W7dulX4+6xNa3Gli+vTpjSdPntyqa9euX3Xv3j1fku644461d9xxx/rzzjvv6E6dOrU+4ogj9k6ePPnjipa/4YYbcvbu3ZsxePDgPEnq06fPzieeeOKzdevWZQ0ZMiQvIyPD27VrV/TEE0+kTecPa9asaTB69OjOJSUlcnc799xzt1x00UXbBg0atDOROrvxxhs7fvLJJw3NzDt27Lj34YcfXi0FPwjStc4qk+h+Vpl0r7PPP/8867zzzusiSSUlJXb++edvHjFixPYBAwbsSqTerrvuuo5Lly49TJI6duy499FHH60X+9q999772cUXX3zU3r17LScnZ8+TTz65qqSkRN9kX0sH3A5WA+rC7WBRVxu3g6WD2rodLMpq63awKKut28GSKcq3g5G4awC9gwFAtEQ5cXOOGwCACCFxAwAQISTumlFaWlpac48VAgAkTfh9Xe3nXNQVJO6asbiwsLAZyRsA6rbS0lIrLCxsJmlxbcdysLgdrAYUFxdfXlBQ8FBBQcEx4scQANRlpZIWFxcXX17bgRwsrioHACBCaB0CABAhkThU3rp1a8/Nza3tMAAASIn58+dvcvfsit6LROLOzc3VvHnzajsMAABSwsxWV/Yeh8oBAIgQEjcAABFC4gYAIEJI3AAARAiJGwCACCFxAwAQISRuAAAihMQNAECEkLgBAIgQEjcAABFC4gYAIEJI3AAARAiJGwCACIlE72CIrjPPGKSNhZvjztcmu5VenTEzBREBQLSRuJFUGws36+r+Fne+cW/ET+4AAA6VAwAQKSRuAAAihMQNAECEkLgBAIgQEjcAABFC4gYAIEJI3AAARAiJGwCACCFxAwAQISRuAAAihMQNAECEkLgBAIgQEjcAABGS1N7BzGyVpB2SSiQVu3tfM2sp6R+SciWtkvQDd9+azDgAAEgXqWhxD3L349y9bzh+s6QZ7t5V0oxwHAAAJKA2DpWfK2lCODxB0vdrIQYAACIp2YnbJU0zs/lmdkU4ra27r5ek8G+bJMcAAEDaSOo5bkn93X2dmbWRNN3Mlie6YJjor5CknJycZMUHAECkJLXF7e7rwr8bJT0v6SRJG8ysvSSFfzdWsuwD7t7X3ftmZ2cnM0wAACIjaYnbzA43syZlw5LOlrRY0lRJo8LZRkmakqwYAABIN8k8VN5W0vNmVradJ9z9ZTN7V9LTZvYTSZ9JGpnEGAAASCtJS9zu/omk3hVM3yzpjGRtFwCAdMaT0wAAiBASNwAAEULiBgAgQkjcAABECIkbAIAIIXEDABAhJG4AACKExA0AQISQuAEAiBASNwAAEULiBgAgQkjcAABECIkbAIAIIXEDABAhJG4AACKExA0AQISQuAEAiBASNwAAEULiBgAgQkjcAABECIkbAIAIIXEDABAhJG4AACKExA0AQISQuAEAiBASNwAAEULiBgAgQkjcAABECIkbAIAIyartAAAA6efMMwZpY+HmuPO1yW6lV2fMTEFE6YPEDQCocRsLN+vq/hZ3vnFvxE/u2B+HygEAiBASNwAAEULiBgAgQkjcAABECIkbAIAIIXEDABAhJG4AACKExA0AQISQuAEAiBASNwAAEULiBgAgQkjcAABECIkbAIAIIXEDABAhJG4AACKExA0AQISQuAEAiBASNwAAEULiBgAgQpKeuM0s08wWmtmL4XhLM5tuZivDvy2SHQMAAOkiFS3u6yQtixm/WdIMd+8qaUY4DgAAEpDUxG1mHSWdI+mhmMnnSpoQDk+Q9P1kxgAAQDpJdov7r5L+U1JpzLS27r5eksK/bSpa0MyuMLN5ZjavsLAwyWECABANSUvcZvZdSRvdff7BLO/uD7h7X3fvm52dXcPRAQAQTVlJXHd/ScPM7DuSGkpqamb/J2mDmbV39/Vm1l7SxiTGAABAWkla4nb3WyTdIklmdrqkG939EjMbK2mUpDvDv1OSFUNdcOYZg7SxcHPc+dpkt9KrM2amICIAQJQls8VdmTslPW1mP5H0maSRtRBDymws3Kyr+1vc+ca9ET+5AwCQksTt7q9Jei0c3izpjFRsFwCAdMOT0wAAiBASNwAAEULiBgAgQkjcAABECIkbAIAIIXEDABAhJG4AACIkofu4zSxDUm9JR0j6StISd9+QzMAAAMCBqkzcZna0pF9KOlPSSkmFCp47nmdmX0q6X9IEdy+tfC0AAKCmxGtx/17SOElXurvHvmFmbST9UNKPtK9/bQAAkERVJm53v6iK9zYq6G8bAADESGYHU9V6VrmZdZF0u6TDJN3l7m9Va2sAANQDyexgKt457obuvjtm0u8k/UaSS3pG0nHV3iIAADho8W4He8HMfhQzXiQpN3yVJCkmAABQiXiJe6ikZmb2spmdJulGSd+S9G1JFyc7OAAAsL94F6eVSPpfM5so6TZJ7SX92t0/TkVwAABgf/HOcZ8s6SZJeyX9QcHDV/6fmX0u6Xfuvi35IQIAgDLxrir/u6QRkhpLut/d+0u60MwGSnpa0pAkxwcAAGLES9wlCi5Ea6Sg1S1JcvdZkmYlLywAiLZk3seL+i1e4v6hpCsVJO1Lkx8OkF7q85d3omWX0rP8ybyPF/VbvMS90t1/UdUMZmblH4cKIFCfv7wTLbuUnuUHkiXe7WAzzexnZpYTO9HMDjGzwWY2QdKo5IUHAABixWtxD5X0Y0lPmllnSV8o6B0sU9I0SXe7+3vJDBAAAOwT7z7u3ZLuk3SfmTWQ1FrSV+7+RQpiAwAA5STcyYi7F0lan8RYAABAHPHOcQMAgDqExA0AQIQklLjN7HAzywiH88xsWHjOGwAApFCiLe7ZkhqaWQdJMyRdJml8soICAAAVSzRxm7t/KWm4pHvd/TxJ+ckLCwAAVCThxG1m/RT0wf3PcFrCV6QDAICakWjivk7SLZKed/clZnaUpPR6sDAAABEQt9VsZpmSvufuw8qmufsnksYkMzAAAHCguC1udy+RdEIKYgEAAHEkep56oZlNlfSMpF1lE939uaREBQAAKpRo4m4pabOkwTHTXBKJGwCAFEoocbv7ZckOBAAAxJfok9PyzGyGmS0Ox3uZ2a+SGxoAACgv0dvBHlRwO1iRJLn7+5IuTFZQAACgYokm7kbu/k65acU1HQwAAKhaool7k5kdreCCNJnZCNE3NwAAKZfoVeXXSHpAUnczWyvpUwWPPwUAACmUaOJ2dz/TzA6XlOHuO8ysczIDAwAAB0r0UPmzkuTuu9x9RzhtUnJCAgAAlamyxW1m3SX1lNTMzIbHvNVUUsNkBgYAAA4U71B5N0nfldRc0vdipu+Q9B9JigkAAFSiysTt7lMkTTGzfu7+VopiAgAAlUj04rQrzOyAFra7/7iG4wEAAFVINHG/GDPcUNJ5ktbVfDgAAKAqiXYy8mzsuJk9KenVqpYxs4aSZks6NNzOJHf/jZm1lPQPSbmSVkn6gbtvrXbkAADUQ4neDlZeV0k5cebZI2mwu/eWdJykoWZ2iqSbJc1w966SZoTjAAAgAQm1uM1sh4LHnVr4t0DSL6taxt1d0s5wtEH4cknnSjo9nD5B0mvx1gUAAAKJHipvcjArN7NMSfMldZH0N3efa2Zt3X19uN71ZtbmYNYNAEB9lOjFaTKzXgrOS3+9jLs/V9Uy7l4i6Tgzay7peTM7phrbu0LSFZKUkxPvqDwAAPVDoofKH5HUS9ISSaXhZJdUZeIu4+5fmNlrkoZK2mBm7cPWdntJGytZ5gEFHZuob9++nsh2AABId4m2uE9x9/zqrNjMsiUVhUn7MElnSvqTpKmSRkm6M/w7pTrrBQCgPks0cb9lZvnuvrQa624vaUJ4njtD0tPu/qKZvSXpaTP7iaTPJI2sXsgAANRfiSbuCQqSd4GC27xMwYXjvSpbwN3fl3R8BdM3SzrjIGIFAKDeSzRxPyLpR5I+0L5z3AAAIMUSTdyfufvUpEYCAADiSjRxLzezJyS9oOBQuaT4t4MBAICalWjiPkxBwj47ZlrCt4MBAICakeiT0y5LdiAAACC+RB/A0lnSz3Tgk9OGJScsAABQkUQPlU+W9LCCc9xcVQ4AQC1JNHHvdvd7khoJAACIK9HE/T9m9htJ07T/VeULkhIVAACoUKKJ+1gFD2AZrP07GRmcjKAAAEDFEk3c50k6yt33JjMYAABQtYwE51skqXkS4wAAAAlItMXdVsHT097V/ue4uR0MAIAUSjRx/yapUQAAgIQk+uS0WWbWVtKJ4aR33H1j8sICAAAVSegct5n9QNI7kkZK+oGkuWY2IpmBAQCAAyV6qPy/JJ1Y1so2s2xJr0qalKzAAADAgRK9qjyj3KHxzdVYFgAA1JBEW9wvm9krkp4Mxy+Q9FJyQgIAAJVJ9OK0m8xsuKQBkkzSA+7+fFIjAwAAB6hOt57/cvfnwvHDzCzX3VclMzgAALC/RM9TP6P9u/MsCacBAIAUSjRxZ8U+pzwcPiQ5IQEAgMokmrgLzezrx5ua2bmSNiUnJAAAUJlEryq/StLjZva/4fjnCrr5BAAAKZToVeUfSzrFzBpLMnffkdywAABARRJtcUuS3H1nsgIBAADx8fQzAAAipMrEbWYjw7+dUxMOAACoSrwW9y3h32eTHQgAAIgv3jnuzWY2U1JnM5ta/k13H1bBMgAAIEniJe5zJPWRNFHSX5IfDgAAqEqViTt8QtrbZnaquxeaWZNgMleXAwBQGxK9qrytmS2UtFjSUjObb2bHJDEuAABQgUQT9wOSbnD3Tu6eI+kX4TQAAJBCiSbuw919ZtmIu78m6fCkRAQAACqV6JPTPjGzXyu4SE2SLpH0aXJCAgAAlUm0xf1jSdmSngtfrSVdlqygAABAxRLtZGSrpDFJjgUAAMTBs8oBAIgQEjcAABFC4gYAIEISStxm1tHMnjezQjPbYGbPmlnHZAcHAAD2l2iL+1FJUyW1l9RB0gvhNAAAkEKJJu5sd3/U3YvD13gFt4cBAIAUSjRxbzKzS8wsM3xdImlzMgMDAAAHqs4DWH4gqUDSekkjwmkAACCFEn0Ay2eShiU5FgAAEEeVidvM/tPd/2xm90ry8u+7O09TA4B65KwzB2vDxk21HUa9Fq/FvSz8O6+6KzazIyU9JqmdpFJJD7j7/5hZS0n/kJQraZWkH4SPVAUA1HEbNm7SXf/RI+58Nz64LO48ODhVJm53fyEc/NLdn4l9z8xGxll3saRfuPsCM2siab6ZTZc0WtIMd7/TzG6WdLOkXx5U9AAA1DOJXpx2S4LTvubu6919QTi8Q0HrvYOkcyVNCGebIOn7CcYAAEC9F+8c97clfUdSBzO7J+atpgpa1Akxs1xJx0uaK6mtu6+XguRuZm2qGzQAAPVVvHPc6xSc3x4maX7M9B2Srk9kA2bWWNKzkn7u7tvNLKHAzOwKSVdIUk5OTkLLAACQ7uKd414kaZGZPeHuRdVduZk1UJC0H3f358LJG8ysfdjabi9pYyXbfkDSA5LUt2/fA65oBwCgPkr0HHeumU0ys6Vm9knZq6oFLGhaPyxpmbv/d8xbUyWNCodHSZpS7agBAKinqtPJyDgF57UHKbjNa2KcZfpL+pGkwWb2Xvj6jqQ7JZ1lZislnRWOAwCABCT05DRJh7n7DDMzd18t6XYzmyPpN5Ut4O6vS6rshPYZ1YwTAAAo8cS928wyJK00s2slrZXE1eAAAKRYoofKfy6pkaQxkk5QcAj80iTFBAAAKpFoJyPvhoM7JV1mZlmSLlBwXzYAAEiReA9gaSrpGgVPPJsqaXo4fqOkRZIeT3aAAFCX0MkGalu8FvdESVslvSXpckk3STpE0vfd/b3khgYAdQ+dbKC2xUvcR7n7sZJkZg9J2iQpJ3z2OAAASLF4F6d9/bQ0dy+R9ClJGwCA2hOvxd3bzLaHwybpsHDcJLm7N01qdAAAYD/xnlWemapAAABAfIk+gAVAHTNkyBCtX78+7nzt27fXK6+8koKIAKQCiRuIqPXr1+vN2S/Hne/Ubw1NQTQAUiXRJ6cBAIA6gMQNAECEkLgBAIgQznEDobPPPlMFBRvjzteuXRtNm/ZqCiICgAORuBFZNX1VdUHBRj15z+i48100ZnwC0QFIlvp+RwWJG5HFVdVA/VTfP/uc4wYAIEIi3eJO9HCJlL6HTIBUqwvdWvLZR30W6cSd6OESKX0PmQCpVhe6teSzj/os0okbSESDBlnq1atXbYcBIA3UhSNOJG6kvaKiYv176j1x5xs8bEwKokm9RH+4tG/fTq+8Mi0FEaVWfS8/alZdOOJE4gbSXH3/4VLfy1+fpeuPNhI3ACAtpeuPNhJ3OTw9C/FkZVpanjNvkJWRluUC4onavk/iLoenZyGe4hJP6ByXlNzzXDWtqLiUfR/1UqL7vlQ39v96k7hr+sridG11AQDqtnqTuGv6XEeira4otbgAAHUfjzwFACBCSNwAAERIvTlUDgBVidKVxYk+q73hoYdo9569Ca0zavcy12ckbgBQtK6qr063lolc2yNF717m+oxD5QAARAiJGwCACCFxAwAQISRuAAAihIvTsJ9En9V+SINM7S0qSUFEAIBYJG7spzrPaufJcQCQehwqBwAgQkjcAABECIkbAIAIIXEDABAhJG4AACKEq8rrgSFDztb69QW1HQYAoAaQuOuB9esL6GgAANIEh8oBAIgQEjcAABFC4gYAIEJI3AAARAiJGwCACEla4jazR8xso5ktjpnW0symm9nK8G+LZG0fAIB0lMwW93hJQ8tNu1nSDHfvKmlGOA4AABKUtMTt7rMlbSk3+VxJE8LhCZK+n6ztAwCQjlJ9jrutu6+XpPBvm8pmNLMrzGyemc0rLCxMWYAAANRldfbiNHd/wN37unvf7Ozs2g4HAIA6IdWJe4OZtZek8O/GFG8fAIBIS3XinippVDg8StKUFG8fAIBIS+btYE9KektSNzP73Mx+IulOSWeZ2UpJZ4XjAAAgQUnrHczdL6rkrTOStU0AANId3XpG2JAhQ7R+/fraDgMAkEIk7ghbv3693pz9ctz5Tv1W+efgAACiqs7eDgYAAA5E4gYAIEJI3AAARAiJGwCACCFxAwAQISRuAAAihMQNAECEkLgBAIgQEjcAABFC4gYAIEJ45CkAQA2yMtSrV6/aDgMJIHEDAFRUXKon7xkdd76LxoxPeiyoGofKAQCIEBI3AAARQuIGACBCSNwAAEQIiRsAgAghcQMAECEkbgAAIoTEDQBAhJC4AQCIEBI3AAARQuIGACBCSNwAAEQIiRsAgAghcQMAECEkbgAAIoTEDQBAhJC4AQCIEBI3AAARQuIGACBCSNwAAEQIiRsAgAghcQMAECEkbgAAIoTEDQBAhJC4AQCIEBI3AAARQuIGACBCSNwAAEQIiRsAgAghcQMAECEkbgAAIoTEDQBAhJC4AQCIEBI3AAARQuIGACBCaiVxm9lQM/vQzD4ys5trIwYAAKIo5YnbzDIl/U3StyXlS7rIzPJTHQcAAFFUGy3ukyR95O6fuPteSU9JOrcW4gAAIHJqI3F3kLQmZvzzcBoAAIjD3D21GzQbKWmIu18ejv9I0knu/rNy810h6YpwtJukD1Ma6P5aS9pUi9uvbfW5/PW57BLlp/z1t/y1XfZO7p5d0RtZqY5EQQv7yJjxjpLWlZ/J3R+Q9ECqgqqKmc1z9761HUdtqc/lr89llyg/5a+/5a/LZa+NQ+XvSupqZp3N7BBJF0qaWgtxAAAQOSlvcbt7sZldK+kVSZmSHnH3JamOAwCAKKqNQ+Vy939J+ldtbPsg1YlD9rWoPpe/PpddovyUv/6qs2VP+cVpAADg4PHIUwAAIiTtEreZ/ZeZLTGz983sPTM7+SDWcbqZnRozPt7MRtRspNWKJ3JlMrPRZnZEstafwPZLwroqe1X70brl66yuMzM3s7/EjN9oZrdXcx11Zt+Pcnlqa/83s7vN7Ocx46+Y2UMx438xsxuqsb468Rmoie/AZDOzf5lZ82+4jtPN7MV486VV4jazfpK+K6mPu/eSdKb2f9hLok6XVCM7qwUOup7rYpkSNFpStb64zKwmr7n4yt2Pi3ndeRDrOF3VrLMaLkN17ZE03MxaH8zCYeynq47s+6pj5amm0aqd/f9NheUN6761pJ4x758q6Y1qrO901fJnoAa/A5OibD939++4+xep2GZaJW5J7SVtcvc9kuTum9x9nZmdYWYLzewDM3vEzA6VJDNbVfalYGZ9zew1M8uVdJWk68NfdqeF6/6Wmb1pZp/E/mI3s5vM7N3wl+Ad4bRcM1tmZvdJWiDptHD8wfBX4zQzOyxKZQqnTzaz+WEZrginZYatmMVhLNeH6+or6fFwe4eZ2QlmNitc/hUzax8u/5qZ/cHMZkm6Lhz/k5m9Y2YrYmKtEWZ2W1i2xWb2gJlZOH2MmS0Ny/xURXVmZtlm9my4/Ltm1j9c9vZwXdMkPRaOPxKW5RMzG1OTZahCsYILaq6voNydzGxGWL4ZZpYTTh9vZv9tZjMl/aN8mcPFa2vfT1l5zOw+MxsWDj9vZo+Ewz8xs9+Hw5eE++V7ZnZ/uO/Xtf3/De1LtD0lLZa0w8xaWPAd0SPcbkWx1NXPQGXfgQd818XEMiHc11aZ2XAz+3P4/3nZzBqE860K6/4tM5tnZn3C+vjYzK4K52kc7l8LwuXPDaeX38+PLIvHzK6yfUf6Pg33RZnZ2eG2FpjZM2bWOJw+1MyWm9nrkoYnVCPunjYvSY0lvSdphaT7JA2U1FDBr7O8cJ7HJP08HF4lqXU43FfSa+Hw7ZJujFnveEnPKPihk6/gWeuSdLaCLxYL33tR0rck5UoqlXRKOF+ugi+h48LxpyVdEqUyhe+1DP8epuALoZWkEyRNj1lv8/Dva5L6hsMNFLQEssPxCxTcBlg2330xy78m6S/h8HckvXqQ+0JJWG9lrwtiyxAOT5T0vXB4naRDy5WhfJ09IWlAOJwjaVnMfPMlHRYz/qakQxW0eDZLapCC/X+npKbhPtBM0o2Sbg/fe0HSqHD4x5Imx+wHL0rKrIP7firLc6GkseHwO5LeDocflTREQcJ7oez/qOCzeKnq4P4f1leOpCsVJN7fhevqL+mtKmKpk58BVfAdGFPOyr7rXg/rvbekLyV9O3zveUnfj1n+6nD4bknvS2oiKVvSxnB6lqSm4XBrSR8p2OdzFbOfl48n5v8+R9L3wmVnSzo8fO+Xkm7Tvu/yruF6n5b0Yrw6qc3DejXO3Xea2QmSTpM0SMEv7j9K+tTdV4SzTZB0jaS/VnP1k929VNJSM2sbTjs7fC0Mxxsr+Ad8Jmm1u78ds/yn7v5eODxfwT8+SmWaLWmMmZ0XTj8ynP6hpKPM7F5J/5Q0rYLtdJN0jKTpFjRwMyWtj3n/H+Xmfy78m3A9VeArdz+ugumDzOw/JTWS1FLSEgVfyO8raCFNljS5knWeKSk/LIMkNTWzJuHwVHf/Kmbef3rQQthjZhsltVXw1MCkcvftZvaYpDGSYuPpp32/5idK+nPMe8+4e0kVq62VfT/F5Zkj6ecW9FS4VFKLsCXaL9z2KAVJ+t3w/3+YpI0K9p26tv+XtbpPlfTfCvqCOFXSNklrFfzfKoqlTn4GKvoOtPjXrLzk7kVm9oGCMr4cTv9A+9fp1Jjpjd19h4IjFLstOF+9S9IfzOxbChJ1h7Ac0oH7eXn/I+nf7v6CmX1XwQ/FN8K6O0TBj6juCj4fKyXJzP5P+x71Xam0StySFH5gX5P0WvhPG1XF7MXad7qgYZxV74kZtpi/f3T3+2NnDA8x7api+RIFH/yE1JEyna7gQ9vP3b8MD0s1dPetZtZbQavkGkk/UNAC2m9xSUvcvV8lcVRWVyWqwX3UzBoq+MXe193XWHChU1kdnaOgxThM0q/NrGcFq8hQUP7YLyeFH8R4/+9Uftb+quDw3aNVzBN7H2j52MurtX0/9FcluTzuvtbMWkgaquBHaksF+/JOd99hwT95grvfUn5ldXD/LzvPfayCI2NrJP1C0nZJ/5bUoZJY6uxnoJLvwKq+68oOq5eaWZGHzVwFyTer/Hzh9Nh4y+a7WEEL/ITwh8CqmG1Vup+Z2WhJnSRdWzZJwZGZi8rNd5z233cTklbnuM2sm5l1jZl0nKQNknLNrEs47UeSZoXDqxT8ipak82OW26HgkEk8r0j6ccy5ig5m1ubgoq9YHSpTM0lbw6TdXdIp4futJWW4+7OSfi2pTwXb+1BStgUXmcjMGlTypZBsZR+4TWH5ys5vZkg60t1nSvpPSc0VtCDL19k07fsgln3o6hx336LgkNtPYia/qeBwsBR8Gb1eyeJ1Zt8vk6LySEEL6OcKEvccBYfm54TvzZA0oqyMZtbSgvPsdXH/f0PBxVxb3L0krL/mCo4e/KOiWOryZ6CS78DVqvy7riY1U3DYvMjMBilIxlUKjw7cqOCUUGk4+W1J/cu+s82skZnlSVouqbOZHR3Od9EBK6xAWiVuBTvaBAsvsFBwaOJmSZdJeib8pVYq6e/h/HdI+h8zm6PgF2GZFySdZ/tf0HIAd5+m4JzPW+G6JynxL4molellSVlhDL9TsCNKwaGj18zsPQXnD8taJOMl/T2cnqkgSf7JzBYpOF+V7Ct9D7P9bwe704MrPh9UcFhssoLn5iuM7//C8i6UdHc4b/k6GyOprwUX7yxVcP6wrvqLgvNqZcZIuiz8//1I0nWVLFeX9v1YSS1PaI6kLHf/SEELv2U4Te6+VNKvJE0LtzldwUVTdXH//0BBXb1dbto2d99YSSx1+TNQ0Xfg7ar8u64mPa6gvPMU/EBcnsAy1yrYd2aG9faQuxcquNPgybAMb0vq7u67FRwa/6cFF6etTiQonpwGAECEpFuLGwCAtEbiBgAgQkjcAABECIkbAIAIIXEDABAhJG7Ua1YzPa/lmtkPkxFfFds8LYz7PYt59reZNTezn8aMn24J9DaUClbLvezFMrOfm1mjmPFv3LMTkCokbtRbVnO9DuVKqlbitm/eg9LFku7yoOez2KdYNZf004oXqT8sUNX3288VPPZWkuQp7NkJ+KZI3KjPKux1SAqefmQV96DUxcxeNbNFFvTyc7SkOxX0gvWeBb1DNTSzRy3oTWhh+MQlWdBH8zNm9oKCB3m0N7PZ4XKLK3pAiFXQC5yZXa7g0Zq3mdnj5Ra5U9LR4TrHhtMam9kkC3ogetzs6x7RKixjue1/z8zmhjG8auGzvS3oJWp6WAf3m9lq29dT06/DbU03syfN7MZEyhVOj9tjUzhfIj2THWlm48L1LImZb4yCLjdn2r6em2J7mroh/H8strBv65h1H0wvZ0DNOpjeWnjxSoeXKu91qKrenOZKOi8cbqig1Xa6Ynr0UfBc6EfD4e4KOt5oqODJSZ9rXy9rv5D0X+FwpqQm5eKrqhe48ZJGVFCmXEmLY8ZPV9C5REcFP9TfkjSgqjKWW18L7XtQ0+Xa13PV/0q6JRwequB5y60V9NL0noLnkTeRtFJh71JlMccp1yrF77EpoZ7JwnlbxtTva5J6xWwntienVWH8Jyh4ytjhCvaPJZKO1zfo5YwXr5p+pV0nI0CivPJeh+apgt6cLOgFqYO7Px8uv1v6uoOFWAMk3RvOs9zMVkvKC9+b7sGzo6XgkauPWNA/8GTf14NWmW6qmV7g3nH3z8NY31OQhL6oqIwVLNtRQb20V9Cj0acxZTwvLOPLZrY1ZvoUDw/fh0cXyotXrng9NlWnZ7IfWNB3fJaCIyz5Cn4MVGaApOfdfVcY/3MK9o+p+ga9nAE1icSNes0r7nVovirozcnMmia42gMyeYyvexRy99kWdBd4jqSJZjbW3R9LcD3VUVEvTfF6rCpzr6T/dvepFvQQd3uc2BKJOd488XpsSqhnMjPrrKCzhxM96MVuvOL3mFdVbN+0lzOgRnCOG/WWVd7rUIW9Obn7dkmfm9n3w+mHWnBlcvkelGYruHhMFvQAlBOus/z2Oyk4/PugpIe1r2epMstVeS9wlUm0N6xEe6xqpqAPZ2n/7mRfV3CeXWZ2toJD6mXTvxee52+s4EdJeQdTrliJ9kzWVEEi3xaem/92zHuV1dNsSd+3oPemwxUcVZhTwXxAraHFjfqssaR7w8OvxZI+knSFu++14Lale8ysmYLPyV8VnO/8kaT7zey3kookjVRw6LXYgt6Wxis4X/73sAVfLGm0u++p4JD66ZJuMrMiSTslXRr7prvvNrOyXuCyFBxa/3v5lZRbZrOZvWFmiyW9JOmflcxXVRlj3R5uf62CHo06h9PvUNDT0QUKku56STvc/V0zmyppkYIfQfMUnGP/RuUqt/w0M+uhoGcyKai7S1Suhyh3X2RmC8MyfaKgu8syD0h6yczWu/ugmGUWhC3zd8JJD7n7wrA1D9QJ9A4GoNrCq8BL3L04bLWPc/fjwvcah9cPNFLQgr3C3RfUYrhAWqHFDeBg5Eh62oJ7pfdK+o+Y9x4ws3wF55MnkLSBmkWLGwCACOHiNAAAIoTEDQBAhJC4AQCIEBI3AAARQuIGACBCSNwAAETI/wdUYpdQMncacAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "r2t1_fig, r2t1_ax = plt.subplots(figsize=(8, 6))\n",
    "sns.barplot(data=r2t1_melted, x=\"sector\", y=\"value\", ax=r2t1_ax,\n",
    "            hue=\"age_group\", palette=browns, edgecolor=\"#2d2d2d\")\n",
    "r2t1_ax.set_ylabel(\"Ratio of commuters (%)\")\n",
    "r2t1_ax.set_xlabel(\"Sectors of the agglomeration\")\n",
    "r2t1_ax.set_xticklabels(xticklabels)\n",
    "\n",
    "r2t1_ax.legend(title=\"Age category\",\n",
    "               loc=\"upper center\", bbox_to_anchor=(0.5, 1.125), ncol=5)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.5 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}