{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Network analysis in Python\n", "\n", "Finding a shortest path using a specific street network is a common GIS problem that has many practical\n", "applications. For example navigators are one of those \"every-day\" applications where **routing** using specific algorithms is used to find the optimal route between two (or multiple) points.\n", "\n", "It is also possible to perform network analysis such as tranposrtation routing in Python.\n", "[Networkx](https://networkx.github.io/documentation/stable/) is a Python module that provides tools for analyzing networks in various different ways. It also contains algorithms\n", "such as [Dijkstra's algorithm](https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.algorithms.shortest_paths.weighted.single_source_dijkstra.html#networkx.algorithms.shortest_paths.weighted.single_source_dijkstra) or\n", "[A*](https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.algorithms.shortest_paths.astar.astar_path.html#networkx.algorithms.shortest_paths.astar.astar_path) algoritm that are commonly used to find shortest paths along transportation network.\n", "\n", "To be able to conduct network analysis, it is, of course, necessary to have a network that is used for the analyses. [OSMnx](https://github.com/gboeing/osmnx) package that we just explored in previous tutorial, makes it really easy to retrieve routable networks from OpenStreetMap with different transport modes (walking, cycling and driving). OSMnx also combines some functionalities from `networkx` module to make it straightforward to conduct routing along OpenStreetMap data.\n", "\n", "Next we will test the routing functionalities of OSMnx by finding a shortest path between two points based on drivable roads. With tiny modifications, it is also possible to repeat the analysis for the walkable street network." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get the network\n", "\n", "Let's again start by importing the required modules" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import osmnx as ox\n", "import networkx as nx\n", "import geopandas as gpd\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "from pyproj import CRS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When fetching netowrk data from OpenStreetMap using OSMnx, it is possible to define the type of street network using the `network_type` parameter (options: `drive`, `walk` and `bike`).\n", "Let's download the OSM data from Kamppi but this only the drivable network. Alternatively, you can also fetch the walkable network (this will take a bit longer time). " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "place_name = \"Kamppi, Helsinki, Finland\"\n", "graph = ox.graph_from_place(place_name, network_type='drive')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the graph:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = ox.plot_graph(graph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okey so now we have retrieved only such streets where it is possible to drive with a car. Let's confirm\n", "this by taking a look at the attributes of the street network. Easiest way to do this is to convert the\n", "graph (nodes and edges) into GeoDataFrames.\n", "\n", "Converting graph into a GeoDataFrame can be done with function `graph_to_gdfs()` that we already used in previous tutorial. With parameters `nodes` and `edges`, it is possible to control whether to retrieve both nodes and edges from the graph. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Retrieve only edges from the graph\n", "edges = ox.graph_to_gdfs(graph, nodes=False, edges=True)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['osmid', 'oneway', 'lanes', 'name', 'highway', 'maxspeed', 'length',\n", " 'geometry', 'junction', 'bridge', 'access', 'u', 'v', 'key'],\n", " dtype='object')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check columns\n", "edges.columns" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "Name: WGS 84\n", "Axis Info [ellipsoidal]:\n", "- Lat[north]: Geodetic latitude (degree)\n", "- Lon[east]: Geodetic longitude (degree)\n", "Area of Use:\n", "- name: World\n", "- bounds: (-180.0, -90.0, 180.0, 90.0)\n", "Datum: World Geodetic System 1984\n", "- Ellipsoid: WGS 84\n", "- Prime Meridian: Greenwich" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check crs\n", "edges.crs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the CRS of the GeoDataFrame is be WGS84 (epsg: 4326)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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osmidonewaylanesnamehighwaymaxspeedlengthgeometryjunctionbridgeaccessuvkey
023856784True2Mechelininkatuprimary4040.885LINESTRING (24.92106 60.16479, 24.92095 60.164...NaNNaNNaN2521659413724257140
1[29977177, 30470347]True3Mechelininkatuprimary4016.601LINESTRING (24.92103 60.16366, 24.92104 60.163...NaNNaNNaN2523887413724257130
2[372440330, 8135861]True2NaNprimary4025.190LINESTRING (24.92129 60.16463, 24.92127 60.164...NaNNaNNaN25238944252165940
3[25514547, 677423564, 30288797, 30288799]True[2, 3]Mechelininkatuprimary40242.476LINESTRING (24.92129 60.16463, 24.92136 60.164...NaNNaNNaN252389443198962780
4[30568275, 36729015, 316590744, 316590745, 316...TrueNaNFredrikinkatutertiary30139.090LINESTRING (24.93702 60.16433, 24.93700 60.164...NaNNaNNaN25291537252915910
\n", "
" ], "text/plain": [ " osmid oneway lanes \\\n", "0 23856784 True 2 \n", "1 [29977177, 30470347] True 3 \n", "2 [372440330, 8135861] True 2 \n", "3 [25514547, 677423564, 30288797, 30288799] True [2, 3] \n", "4 [30568275, 36729015, 316590744, 316590745, 316... True NaN \n", "\n", " name highway maxspeed length \\\n", "0 Mechelininkatu primary 40 40.885 \n", "1 Mechelininkatu primary 40 16.601 \n", "2 NaN primary 40 25.190 \n", "3 Mechelininkatu primary 40 242.476 \n", "4 Fredrikinkatu tertiary 30 139.090 \n", "\n", " geometry junction bridge access \\\n", "0 LINESTRING (24.92106 60.16479, 24.92095 60.164... NaN NaN NaN \n", "1 LINESTRING (24.92103 60.16366, 24.92104 60.163... NaN NaN NaN \n", "2 LINESTRING (24.92129 60.16463, 24.92127 60.164... NaN NaN NaN \n", "3 LINESTRING (24.92129 60.16463, 24.92136 60.164... NaN NaN NaN \n", "4 LINESTRING (24.93702 60.16433, 24.93700 60.164... NaN NaN NaN \n", "\n", " u v key \n", "0 25216594 1372425714 0 \n", "1 25238874 1372425713 0 \n", "2 25238944 25216594 0 \n", "3 25238944 319896278 0 \n", "4 25291537 25291591 0 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "edges.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okey, so we have quite many columns in our GeoDataFrame. Most of the columns are fairly self-explanatory but the following table describes all of them.\n", "Most of the attributes come directly from the OpenStreetMap, however, columns `u` and `v` are Networkx specific ids. You can click on the links to get more information about each attribute:\n", "\n", "\n", "| Column | Description | Data type |\n", "|------------------------------------------------------------|-----------------------------|-------------------|\n", "| [bridge](http://wiki.openstreetmap.org/wiki/Key:bridge) | Bridge feature | boolean |\n", "| geometry | Geometry of the feature | Shapely.geometry |\n", "| [highway](http://wiki.openstreetmap.org/wiki/Key:highway) | Tag for roads (road type) | str / list |\n", "| [lanes](http://wiki.openstreetmap.org/wiki/Key:lanes) | Number of lanes | int (or nan) |\n", "| [lenght](http://wiki.openstreetmap.org/wiki/Key:length) | Length of feature (meters) | float |\n", "| [maxspeed](http://wiki.openstreetmap.org/wiki/Key:maxspeed)| maximum legal speed limit | int /list |\n", "| [name](http://wiki.openstreetmap.org/wiki/Key:name) | Name of the (street) element| str (or nan) |\n", "| [oneway](http://wiki.openstreetmap.org/wiki/Key:oneway) | One way road | boolean |\n", "| [osmid](http://wiki.openstreetmap.org/wiki/Node) | Unique ids for the element | list |\n", "| [u](http://ow.ly/bV8n30h7Ufm) | The first node of edge | int |\n", "| [v](http://ow.ly/bV8n30h7Ufm) | The last node of edge | int |\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look what kind of features we have in the `highway` column:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "residential 113\n", "tertiary 78\n", "primary 26\n", "secondary 17\n", "unclassified 10\n", "living_street 4\n", "primary_link 1\n", "Name: highway, dtype: int64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "edges['highway'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okey, now we can confirm that as a result our street network indeed only contains such streets where it is allowed to drive with a car as there are no e.g. cycleways or footways included in the data.\n", "\n", "As the data is in WGS84 format, we might want to reproject our data into a metric system before proceeding to the shortest path analysis.\n", "We can re-project the graph from latitudes and longitudes to an appropriate UTM zone using the [project_graph()](https://osmnx.readthedocs.io/en/stable/osmnx.html#osmnx.projection.project_graph) function from OSMnx. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Project the data\n", "graph_proj = ox.project_graph(graph) " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# Get Edges and Nodes\n", "nodes_proj, edges_proj = ox.graph_to_gdfs(graph_proj, nodes=True, edges=True)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coordinate system: +proj=utm +zone=35 +ellps=WGS84 +datum=WGS84 +units=m +no_defs +type=crs\n" ] } ], "source": [ "print(\"Coordinate system:\", edges_proj.crs)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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osmidonewaylanesnamehighwaymaxspeedlengthgeometryjunctionbridgeaccessuvkey
023856784True2Mechelininkatuprimary4040.885LINESTRING (384631.322 6671580.071, 384624.750...NaNNaNNaN2521659413724257140
1[29977177, 30470347]True3Mechelininkatuprimary4016.601LINESTRING (384625.787 6671454.380, 384626.281...NaNNaNNaN2523887413724257130
2[372440330, 8135861]True2NaNprimary4025.190LINESTRING (384643.473 6671561.534, 384643.045...NaNNaNNaN25238944252165940
3[25514547, 677423564, 30288797, 30288799]True[2, 3]Mechelininkatuprimary40242.476LINESTRING (384643.473 6671561.534, 384648.006...NaNNaNNaN252389443198962780
4[30568275, 36729015, 316590744, 316590745, 316...TrueNaNFredrikinkatutertiary30139.090LINESTRING (385515.553 6671500.134, 385514.557...NaNNaNNaN25291537252915910
\n", "
" ], "text/plain": [ " osmid oneway lanes \\\n", "0 23856784 True 2 \n", "1 [29977177, 30470347] True 3 \n", "2 [372440330, 8135861] True 2 \n", "3 [25514547, 677423564, 30288797, 30288799] True [2, 3] \n", "4 [30568275, 36729015, 316590744, 316590745, 316... True NaN \n", "\n", " name highway maxspeed length \\\n", "0 Mechelininkatu primary 40 40.885 \n", "1 Mechelininkatu primary 40 16.601 \n", "2 NaN primary 40 25.190 \n", "3 Mechelininkatu primary 40 242.476 \n", "4 Fredrikinkatu tertiary 30 139.090 \n", "\n", " geometry junction bridge access \\\n", "0 LINESTRING (384631.322 6671580.071, 384624.750... NaN NaN NaN \n", "1 LINESTRING (384625.787 6671454.380, 384626.281... NaN NaN NaN \n", "2 LINESTRING (384643.473 6671561.534, 384643.045... NaN NaN NaN \n", "3 LINESTRING (384643.473 6671561.534, 384648.006... NaN NaN NaN \n", "4 LINESTRING (385515.553 6671500.134, 385514.557... NaN NaN NaN \n", "\n", " u v key \n", "0 25216594 1372425714 0 \n", "1 25238874 1372425713 0 \n", "2 25238944 25216594 0 \n", "3 25238944 319896278 0 \n", "4 25291537 25291591 0 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "edges_proj.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okey, as we can see from the CRS the data is now in [UTM projection](https://en.wikipedia.org/wiki/Universal_Transverse_Mercator_coordinate_system) using zone 35 which is the one used for Finland, and indeed the orientation of the map and the geometry values also confirm this.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Furthermore, we can check the epsg code of this projection using pyproj CRS:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "32635" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "CRS(edges_proj.crs).to_epsg()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Indeed, the projection is now [WGS 84 / UTM zone 35N, EPSG:32635](https://epsg.io/32635)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyzing the network properties\n", "\n", "Now as we have seen some of the basic functionalities of OSMnx such as downloading the data and converting data from graph to GeoDataFrame, we can take a look some of the analytical features of omsnx. Osmnx includes many useful functionalities to extract information about the network.\n", "\n", "To calculate some of the basic street network measures we can use [basic_stats()](https://osmnx.readthedocs.io/en/stable/osmnx.html#osmnx.stats.basic_stats) function in OSMnx:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'n': 124,\n", " 'm': 249,\n", " 'k_avg': 4.016129032258065,\n", " 'intersection_count': 116,\n", " 'streets_per_node_avg': 3.217741935483871,\n", " 'streets_per_node_counts': {0: 0, 1: 8, 2: 1, 3: 71, 4: 44},\n", " 'streets_per_node_proportion': {0: 0.0,\n", " 1: 0.06451612903225806,\n", " 2: 0.008064516129032258,\n", " 3: 0.5725806451612904,\n", " 4: 0.3548387096774194},\n", " 'edge_length_total': 19986.84000000001,\n", " 'edge_length_avg': 80.2684337349398,\n", " 'street_length_total': 13671.708999999999,\n", " 'street_length_avg': 74.70879234972676,\n", " 'street_segments_count': 183,\n", " 'node_density_km': None,\n", " 'intersection_density_km': None,\n", " 'edge_density_km': None,\n", " 'street_density_km': None,\n", " 'circuity_avg': 1.0244573582451892,\n", " 'self_loop_proportion': 0.0,\n", " 'clean_intersection_count': None,\n", " 'clean_intersection_density_km': None}" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculate network statistics\n", "stats = ox.basic_stats(graph_proj, circuity_dist='euclidean')\n", "stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To be able to extract the more advanced statistics (and some of the missing ones above) from the street network, it is required to have information about the coverage area of the network. Let's calculate the area of the [convex hull](https://en.wikipedia.org/wiki/Convex_hull) of the street network and see what we can get.\n", "\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "" ], "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the Convex Hull of the network\n", "convex_hull = edges_proj.unary_union.convex_hull\n", "\n", "# Show output\n", "convex_hull" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can use the Convex Hull above to calculate [extended statistics for the network](https://osmnx.readthedocs.io/en/stable/osmnx.html#osmnx.stats.extended_stats). As some of the metrics are produced separately for each node, they produce a lot of output. Here, we combine the basic and extended statistics into one pandas Series to keep things in more compact form." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "n 124\n", "m 249\n", "k_avg 4.01613\n", "intersection_count 116\n", "streets_per_node_avg 3.21774\n", "streets_per_node_counts {0: 0, 1: 8, 2: 1, 3: 71, 4: 44}\n", "streets_per_node_proportion {0: 0.0, 1: 0.06451612903225806, 2: 0.00806451...\n", "edge_length_total 19986.8\n", "edge_length_avg 80.2684\n", "street_length_total 13671.7\n", "street_length_avg 74.7088\n", "street_segments_count 183\n", "node_density_km 148.885\n", "intersection_density_km 139.28\n", "edge_density_km 23997.9\n", "street_density_km 16415.4\n", "circuity_avg 1.27042e-05\n", "self_loop_proportion 0\n", "clean_intersection_count None\n", "clean_intersection_density_km None\n", "avg_neighbor_degree {25216594: 2.0, 25238874: 2.0, 25238944: 1.0, ...\n", "avg_neighbor_degree_avg 2.14315\n", "avg_weighted_neighbor_degree {25216594: 0.04891769597651951, 25238874: 0.12...\n", "avg_weighted_neighbor_degree_avg 0.0838883\n", "degree_centrality {25216594: 0.024390243902439025, 25238874: 0.0...\n", "degree_centrality_avg 0.0326515\n", "clustering_coefficient {25216594: 0, 25238874: 0, 25238944: 0, 252915...\n", "clustering_coefficient_avg 0.0954301\n", "clustering_coefficient_weighted {25216594: 0, 25238874: 0, 25238944: 0, 252915...\n", "clustering_coefficient_weighted_avg 0.0166188\n", "pagerank {25216594: 0.008700928735150002, 25238874: 0.0...\n", "pagerank_max_node 25416262\n", "pagerank_max 0.0239181\n", "pagerank_min_node 1008183915\n", "pagerank_min 0.00142892\n", "eccentricity {25216594: 1915.0230000000001, 25238874: 1788....\n", "diameter 2153.57\n", "radius 1007.07\n", "center [1372376937]\n", "periphery [319896278]\n", "closeness_centrality {25216594: 0.0009353926092551588, 25238874: 0....\n", "closeness_centrality_avg 0.00143689\n", "dtype: object" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculate the area\n", "area = convex_hull.area\n", "\n", "# Calculate statistics with density information\n", "stats = ox.basic_stats(graph_proj, area=area)\n", "extended_stats = ox.extended_stats(graph_proj, ecc=True, cc=True)\n", "\n", "# Add extened statistics to the basic statistics\n", "for key, value in extended_stats.items():\n", " stats[key] = value\n", " \n", "# Convert the dictionary to a Pandas series for a nicer output\n", "pd.Series(stats)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, now we have a **LOT** of information about our street network that can be used to understand its structure. We can for example see that the average node density in our network is `149 nodes/km` and that the total edge length of our network is almost 20 kilometers.\n", "\n", "Furthermore, we can see that the [degree centrality](https://en.wikipedia.org/wiki/Centrality) of our network is on average `0.0326515`. Degree is a simple centrality measure that counts how many neighbors a node has (here a fraction of nodes it is connected to). Another interesting measure is the [PageRank](https://en.wikipedia.org/wiki/PageRank) that measures the importance of specific node in the graph. Here we can see that the most important node in our graph seem to a node with osmid `25416262`. PageRank was the algorithm that Google first developed (Larry Page & Sergei Brin) to order the search engine results and became famous for.\n", "\n", "You can read the [Wikipedia article about different centrality measures](https://en.wikipedia.org/wiki/Centrality) if you are interested what the other centrality measures mean." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Shortest path analysis\n", "\n", "Let's now calculate the shortest path between two points using the [shortest path function in Networkx](https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.algorithms.shortest_paths.generic.shortest_path.html#shortest-path). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Origin and destination points \n", "\n", "First we need to specify the source and target locations for our route. If you are familiar with the Kamppi area, you can specify a custom placename as a source location. Or, you can choose from these options:\n", "- [Maria 01](https://nominatim.openstreetmap.org/ui/search.html?q=Maria+01) and old hospital area and current startup hub\n", "- [Tennispalatsi](https://nominatim.openstreetmap.org/ui/search.html?q=tennispalatsi) - a big movie theatre (*note! Routing in the drivable network will fail with this input*).\n", "\n", "We could figure out the coordinates for these locations manually, and create shapely points based on the coordinates.\n", "However, it is more handy to fetch the location of our source destination directly from OSM:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# Set place name\n", "place = \"Maria 01, Helsinki\"" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# Geocode the place name\n", "geocoded_place = ox.geocode_to_gdf(place)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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geometryplace_namebbox_northbbox_southbbox_eastbbox_west
0POLYGON ((24.92122 60.16644, 24.92126 60.16625...Maria 01, Baana, Hietalahti, Kamppi, Southern ...60.16752560.1662424.9231724.921221
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" ], "text/plain": [ " geometry \\\n", "0 POLYGON ((24.92122 60.16644, 24.92126 60.16625... \n", "\n", " place_name bbox_north bbox_south \\\n", "0 Maria 01, Baana, Hietalahti, Kamppi, Southern ... 60.167525 60.16624 \n", "\n", " bbox_east bbox_west \n", "0 24.92317 24.921221 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check the result\n", "geocoded_place" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As output, we received the building footprint. From here, we can get the centroid as the source location of our shortest path analysis. However, we first need to project the data into the correct crs:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# Re-project \n", "geocoded_place.to_crs(CRS(edges_proj.crs), inplace=True)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# Get centroid as shapely point\n", "origin = geocoded_place[\"geometry\"].centroid.values[0]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "POINT (384692.1788217347 6671817.486427824)\n" ] } ], "source": [ "print(origin)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great! Now we have defined the origin point of our analysis somewhere in the area of interest. \n", "\n", "Next, we still need the destination location. To make things simple, we can set the easternmost node in our road network as the destination. Let's have another look at our node data:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yxosmidlonlathighwaygeometry
252165946.671580e+06384631.3223722521659424.92105760.164794NaNPOINT (384631.322 6671580.071)
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252915646.671673e+06385779.2070152529156424.94167460.165948NaNPOINT (385779.207 6671672.709)
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" ], "text/plain": [ " y x osmid lon lat highway \\\n", "25216594 6.671580e+06 384631.322372 25216594 24.921057 60.164794 NaN \n", "25238874 6.671454e+06 384625.787221 25238874 24.921028 60.163665 NaN \n", "25238944 6.671562e+06 384643.473274 25238944 24.921286 60.164631 NaN \n", "25291537 6.671500e+06 385515.553244 25291537 24.937023 60.164325 NaN \n", "25291564 6.671673e+06 385779.207015 25291564 24.941674 60.165948 NaN \n", "\n", " geometry \n", "25216594 POINT (384631.322 6671580.071) \n", "25238874 POINT (384625.787 6671454.380) \n", "25238944 POINT (384643.473 6671561.534) \n", "25291537 POINT (385515.553 6671500.134) \n", "25291564 POINT (385779.207 6671672.709) " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nodes_proj.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can find the easternmost nodes based on the x coordinates:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "# Retrieve the maximum x value (i.e. the most eastern)\n", "maxx = nodes_proj['x'].max()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's find out the coresponding point geometries for these noodes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can do this by using the `.loc` function of Pandas that we have used already many times in earlier tutorials." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "POINT (385855.0300992894 6671721.810323974)\n" ] } ], "source": [ "# Easternmost point\n", "destination = nodes_proj.loc[nodes_proj['x']==maxx, 'geometry'].values[0]\n", "print(destination)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Nearest node\n", "\n", "Let's now find the nearest graph nodes (and their node IDs) to these points using OSMnx [get_nearest_node](https://osmnx.readthedocs.io/en/stable/osmnx.html#osmnx.utils.get_nearest_node). \n", "As a starting point, we have the two Shapely Point objects we just defined as the origin and destination locations. \n", "\n", "According to the documentation of this function, we need to parse Point coordinates as coordinate-tuples in this order: `latitude, longitude`(or `y, x`). As our data is now projected to UTM projection, we need to specify with `method` parameter that the function uses `'euclidean'` distances to calculate the distance from the point to the closest node (with decimal derees, use `'haversine'`, which determines the great-circle distances). The method parameter is important if you want to know the actual distance between the Point and the closest node which you can retrieve by specifying parameter `return_dist=True`.\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# Get origin x and y coordinates\n", "orig_xy = (origin.y, origin.x)\n", "\n", "# Get target x and y coordinates\n", "target_xy = (destination.y, destination.x)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "319896278" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Find the node in the graph that is closest to the origin point (here, we want to get the node id)\n", "orig_node_id = ox.get_nearest_node(graph_proj, orig_xy, method='euclidean')\n", "orig_node_id" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "317703609" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Find the node in the graph that is closest to the target point (here, we want to get the node id)\n", "target_node_id = ox.get_nearest_node(graph_proj, target_xy, method='euclidean')\n", "target_node_id" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have the IDs for the closest nodes that were found from the graph to the origin and target points that we specified. \n", "\n", "Let's retrieve the node information from the `nodes_proj` GeoDataFrame by passing the ids to the `loc` indexer" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "# Retrieve the rows from the nodes GeoDataFrame based on the node id (node id is the index label)\n", "orig_node = nodes_proj.loc[orig_node_id]\n", "target_node = nodes_proj.loc[target_node_id]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's also create a GeoDataFrame that contains these points" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yxosmidlonlathighwaygeometry
3198962786.671803e+06384645.07463331989627824.92117860.166794NaNPOINT (384645.075 6671802.525)
3177036096.671722e+06385855.03009931770360924.94301260.166410traffic_signalsPOINT (385855.030 6671721.810)
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" ], "text/plain": [ " y x osmid lon lat \\\n", "319896278 6.671803e+06 384645.074633 319896278 24.921178 60.166794 \n", "317703609 6.671722e+06 385855.030099 317703609 24.943012 60.166410 \n", "\n", " highway geometry \n", "319896278 NaN POINT (384645.075 6671802.525) \n", "317703609 traffic_signals POINT (385855.030 6671721.810) " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a GeoDataFrame from the origin and target points\n", "od_nodes = gpd.GeoDataFrame([orig_node, target_node], geometry='geometry', crs=nodes_proj.crs)\n", "od_nodes.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okay, as a result we got now the closest node IDs of our origin and target locations. As you can see, the `index` in this GeoDataFrame corresponds to the IDs that we found with `get_nearest_node()` function." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Routing\n", "\n", "Now we are ready to do the routing and find the shortest path between the origin and target locations\n", "by using the `shortest_path()` [function](https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.algorithms.shortest_paths.generic.shortest_path.html) of networkx.\n", "With `weight` -parameter we can specify that `'length'` attribute should be used as the cost impedance in the routing. If specifying the weight parameter, NetworkX will use by default Dijkstra's algorithm to find the optimal route. We need to specify the graph that is used for routing, and the origin `ID` (*source*) and the target `ID` in between the shortest path will be calculated:\n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[319896278, 1382320455, 25216594, 1372425714, 25238874, 1372425713, 529507771, 258188404, 1372318829, 159619609, 175832743, 1372425705, 1007980689, 149143065, 268177652, 60004721, 1372376937, 1372441170, 60170471, 1377211668, 1377211666, 25291565, 25291564, 317703609]\n" ] } ], "source": [ "# Calculate the shortest path\n", "route = nx.shortest_path(G=graph_proj, source=orig_node_id, target=target_node_id, weight='length')\n", "\n", "# Show what we have\n", "print(route)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a result we get a list of all the nodes that are along the shortest path. \n", "\n", "- We could extract the locations of those nodes from the `nodes_proj` GeoDataFrame and create a LineString presentation of the points, but luckily, OSMnx can do that for us and we can plot shortest path by using `plot_graph_route()` function:\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": 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COXPmoFAo9B6foHESEqagUenRoweDBg1i6dKlnD9/3tDhPFOcnJyYNm0aZmZmbNiwgVOnTtXp6z/++GNyc3NZunSpniKs4ubmxrhx48jIyGDbtm3Y2Ngwfvx4CgoKWL9+/SMT/NixYyktLWXbtm16jU/QeD0qYQq7ZAVGKSYmRtvdxMnJydDhPDNGjRrFzJkzuX37Nl988UWdkyXAmjVr8PX1xcHBQQ8RVm0Q69evH5MmTWL79u1s3LiRDh068NprrxEfH8+yZcseOxretm0bgYGBtGrVSi/xCZouYYQpMGqvvvoqLi4ufPPNN7WaEhQ8HXd3dyZNmoRMJmP9+vWcPn26XtebPn06UqmUOXPm6CjCKvb29owbN46ysjLWrl2LSCQiKioKc3NzVq9ezc2bN2t1ncDAQPr06cP8+fNRqVQ6jVHQ+AlTsoJG67333sPc3JyUlBRyc3OJj483dEhNhlQqZfz48bRr147z58+zfPlylEplva9ra2vLe++9x6pVqzh79qwOIoVOnToxYMAA9u3bR3x8vHajz7Fjx9i/fz9qtbpO13vxxRe5cuUK+/bt00l8gqZDSJiCRsvU1JTExERatGgBwKxZs/jjjz8MHFXjFxAQwJgxY1CpVCxfvpyLFy/q9Prjx4/H29ubzz//vF7Xsba2JjIyEgsLC9asWUNxcTHDhw/H1dWV1atXk5ub+9TXnTFjBr/++is3btyoV4yCpkVYwxQ0Wra2ttpkCTBkyBADRtP4mZqa8tprrzF+/HjOnDnD559/rvNkCbB27VpkMhkDBgx46muEhoYyY8YMLl++zE8//YS1tTUzZ86krKyM77///qmTJUBRURF79uwhMjISkeihr48CQQ1C4QKB0btx4wZpaWn4+voCIJPJ8PX1JS0tzcCRNT7dunVj8ODB3L17l/nz53Pt2jW93UupVLJ371769evHgQMH6nTMxNLSktGjR2Nra8uiRYvIz89nyJAhtG/fnvXr1+vsHOXx48cJCgqiW7duxMbG6uSagqZLmJIVNAq2traMHTuWrl278u233zJixAi2bdtW780pzwpra2umTZuGg4MDhw8ffmwBAl2r6zETf39/RowYQUJCAvv27aNFixaMGzeOq1evsnnzZsrKynQaX/PmzXn99df54YcfKCgo0Om1BY2TsIYpaBJ69OiBt7c3O3bs4IUXXkCj0dCnTx+Kiop49913uXLliqFDNDoDBgwgIiKC/Px8Fi9ezJ07dxr0/l5eXkydOpX//e9/5OfnP/Lz5HI5w4YNo02bNqxevZorV67Qu3dvunTpwtatWzlz5ozeYoyIiMDDw0OoMSsAhIQpaCLEYjGvv/46J06cICkpifPnz2vbgsXFxTF8+PA675Zsqqo7dlhYWLBjxw6DVk160jGTVq1aMW7cOLKysti6dSuWlpY8//zzlJeXs27dOoqKivQan1gs5s033yQ2NpbExES93ktg/IRNP4ImQa1Ws2bNGoYNG0Z4eDhmZmbaj7m6ujJjxgyhryYQFRXF9OnTKSgo4F//+pfBSwwuX74cBwcHAgICHvhY7969efHFF4mOjmb9+vW0bduWN954g9OnT7No0SK9J0uo+ne1fv16Bg0ahKWlpd7vJ2ichBGmoNFp0aIFR44cwc7OjtLSUqRSKSUlJbz44ouYmZnh7u7+zHY78fDwYOLEiUgkEtauXauzM5C68LBjJgMHDsTDw4Nly5ZRXFxMv379CA0NZeXKlWRnZzd4jAMGDMDe3p6VK1c2+L0FxkMYYQqajH79+mFnZweAubk5H374IZ6ensTFxXH48GHatGmDhYWFgaNsWFKplClTpvDyyy+TmZnJ7NmzjSpZQtUxE6lUyosvvoifnx89e/bEz8+PxYsXo1QqmTJlCq1bt2b+/PkGSZYA+/btw9nZmXbt2hnk/gLjJhwrETQ6aWlpqFQqJBIJGo0GiUQCVO2uXL9+PQ4ODkilUj788EMDR9owgoKCiIyMRKFQ8Ouvv2r7VBobpVLJ1atX+e6775BIJCQnJzNkyBCcnJwYN24cSUlJ7N6926Br0Eqlkg0bNvD888+TmZlJeXm5wWIRGB9hSlbQKM2bN49JkyYBUFZWxv79+/Hw8NCe1QRo3769Xs8ZGpq5uTlTpkzBxcWFhIQENm7caOiQnmj16tX069dP+/Znn32GlZUV69atIz093YCR1TRy5EhEIlGj+DsV6N6jpmSFEaag0TMzM3ug+k9paSklJSUGikj/evTowcCBAykqKmLevHnk5eUZOqRauXDhQo2EOWvWLIKDgykuLjZgVA/auXMnb7/9Nm3btiUzM9PQ4QiMhJAwBY2Ol5cXcrmcsrKyGrtkAS5fvsytW7dISkoyuhdhXbC1tWXq1KnY29uzf/9+9u7da+iQ6mT27NmMGjUKZ2dnoOphxxh/ThUVFWzatInRo0czb948odm0ABA2/QgaEbFYzPjx45k6dSoHDx7Ez8+PsLAwNm/eDFSNKjds2MCgQYPIzs4mMDDQwBHr1pAhQ3jvvfdQKpV8/fXXjS5ZAlRWVjJr1ixKS0u1R4SMVVpaGrm5ufTt29fQoQiMhLCGKWgU2rZty6RJkxCLxaxcuZLz58/X+Hjr1q0pKChg8ODBWFhYEBcXx5gxY9i5cyepqamNenq2ZcuWTJ48GXNzc7Zu3cqxY8cMHVK9mZqaEhERQc+ePfnqq6+M9udjYWHBrFmzWLJkiVBF6hkiVPoRNEpisZgJEybQvn17UlJSWLFixWN3UYpEIvr3709ERATPP/88NjY2ZGdn079//0bXwkksFjNmzBiCg4PJyspiyZIldSpg3hi89957lJeXM3/+fEOH8kjBwcFEREQwf/58oYrUM0I4hylodDw8PPj000/x9PRk0aJFLFu27IkvWBqNhl27dnH16lVsbGwAcHNzY9CgQQ0Qse54eXnx6aef0q5dO5YtW8avv/7a5JIlwKJFi3B2dqZjx46GDuWRkpKSKCwsJCIiwtChCAxM2PQjMDpisZiJEyfi5+fHuXPnWLVqVZ2f7E+cOIFarUYsFqPRaMjIyNBTtLoll8uZNGkSnp6eT/29Nya3bt0iOzubP//8E7FYzOzZs1myZImhw3rApk2bmD59OufOnXtsAXlB0yZMyQqMio+PDxMmTECtVrNs2bJ6bekfOnQoffr0wc7OjoSEBH788UcdRqp7Pj4+TJw4kYqKCv744w+DVbtpaMuXL2fw4MFAVeGANm3acPfuXQNH9aBu3boRGBjIggULeNzrpqDxE9YwBUbt/lHlmTNnWL16tc5GVra2trz77rvExsayY8cOnVxTV9599106duzIxYsXEYvFJCYmsnbtWkOH1WBMTU3ZvXs3/v7+QNVxjjZt2hhlhR2RSMTf/vY3kpKSOHr0qKHDEeiRULhAYLS8vLy0vS31UdqtoKCANWvWMG7cOC5evPjADltDGT9+PP/4xz+AqrXX119//ZlKlp07d2b48OHExMRQUlKCm5ubUTcE12g0rF+/ntdee43U1FQKCwsNHZKggQmbfgQGUz2qnDp1KhcuXOCLL77QWx3U06dPc/LkSSZNmmQ07Zu8vb21fxaJRA3e2NlQzM3Neeuttxg5ciSxsbF8/PHHDB48GH9/f86dO8e0adMMHeIj5efnExsby6hRowwdisAAhIQpMIj7d8D+/vvvTzwuogvr16/n1q1bvPXWW3q9T2306dMHU1NT7dm+S5cuERsba+Co9K9bt258/PHHmJub891337Fz584aH//9999xcXEhODjYMAHWwqFDh2jWrJlRxyjQD2ENU9Cgqqv1+Pv7k5yczMqVKxt0F6hcLufjjz/m8uXLLFq0qMHuez9PT09GjRrFL7/8QllZGXZ2dvTs2ROVStVki31bWloybdo0nJycOHToELt27Xrk544aNYqQkBBmz56NUqlswChrz9XVlZdeeom5c+ca5QYlQf0I5zAFBteqVSs++eQTvLy8WLRoEcuXL2/wIxOVlZX88ssveHp6PlCwvSGIRCKGDh3Kjh07KC4uRqlUcuPGDbZu3Yq3tzc+Pj4NHpO+9ejRg48++gi5XM5///vfxyZLgI0bN6JUKnnhhRcaKMK6y83N5dSpUwwbNszQoQgakJAwBQ2iTZs2vPbaa+Tk5DB79mwuXLhgsFiuXbvGqlWrCA8PJzQ0tEHv3apVKwCSk5NrvL+iooK1a9cyevToBwrKN1bW1ta8/fbbDBo0iP379/Pf//6XgoKCWn3t8uXL8fX1xd3dXb9B1sOePXtwdXWt0VJO0LQJCVOgd1KplClTppCWlsaiRYuM4iD+2bNn2b9/P1FRUbi5uTXYff38/EhJSXnoxzIzM0lOTmbo0KENFo++9OrViw8++ACxWMzXX3/Nvn376vT1mZmZpKena3ueGiOFQsHGjRsZOXIkJiYmhg5H0ACEhCnQu+eff57KykqWLVtm6FBq2Lt3L6mpqbzyyisNtnPWz8+P1NTUR348Ojoad3f3Rjs1a2NjwzvvvEO/fv3Ys2cP3333HUVFRU91rWXLliGXyxkxYoSOo9Sdixcvkp6ezsCBAw0diqABCAlToFdisRg/Pz+io6MNHcpDLVu2jNu3bzNz5kzEYv3+Otja2mJpaUlubu4jP6eyspL169c3yqnZ5557jvfffx+1Ws0333zDwYMH63U9pVLJxo0bCQsLw97eXjdB6sGOHTto166dUU8fC3RDSJgCnZBKpXz++efaF/tqffr0QalUcuLECQNG93jz589HIpHwxhtv6PU+3t7epKenP7GsWmZmJufOnWs0G0psbW1577336NOnD9u3b2fOnDlPPar8q8TERK5du8aUKVN0cj19KC8vZ8uWLURGRiKVCrVgmjIhYQp04tVXX2X69On07t2bBQsW4Onpyfjx45k1a5bRt9VSKpXMnz+fFi1aMHbsWL3dx8fHp9ZVhqKjo3Fzc6Ndu3Z6i0cXBg4cyHvvvUdFRQVffvmlXs6SLl68GFtbW3r06KHza+tKcnIy165do2/fvjg4OCASPfRUgqCREx6HBDrh6Oio/bNEIuHnn3+mQ4cOQNXI6o8//qC4uNhQ4T1RQUEBixcvZtq0aVy7do2YmBidXl8ikdC2bVvWrVtXq89XKBSsXbuWF154gUuXLlFaWqrTeOrLwcGBqVOnYmVlxebNm/Xa1LqkpIT9+/czcOBAEhISjLLOLFQVNNi/fz8ODg6cOHGCkSNH6uTnJhaL6dy5M35+fpSVlREbG0tOTo4OIhbUlTDCFOjE77//ri3tFh0dXeNFrXnz5rz++ut4eXkZKLrauXjxItu3b2fQoEE6j9Xd3Z0bN27U6QX08uXLJCUlGd2mlyFDhvD2229TUlLCv//9b70my2r79u2juLjYqKdmBw0ahIODAwAdO3bUSQ9WJycnXn/9dfz9/Tl27BhZWVm89NJLeHp61vvagroTRpgCnbh69SqrV6/mzz//5PTp0/Tt25dOnTohl8u5ePEiKpWKKVOmoFaruXr1KgkJCZw8edIojpjcLzY2FmdnZyZPnsz//vc/bt26pZPr1mU69n67d+9mxowZBAQEcPbsWZ3E8rScnJyYOnUqFhYWbNiwocHXpRcvXszMmTO1NWeNiUgkQi6X13hffZYiJBIJvXr1omvXruzatYuEhATtx27evMnYsWOZM2eO0Y62myphhCnQicDAQFQqlbbbxN69e1m8eDFQNbq6efMm//znP9mwYQNqtZrhw4fzr3/9i7///e/07dsXU1NTQ4Zfw7p167h69SpvvfXWAy+CT6t6w09dKZVK1q5dy/Dhwxu8aLyTkxNhYWGYmZkxfPhwZs6cSWFhIf/6178MsokrLy+PU6dOMWbMGL3vaK4tsVhMr169+PDDDxGJRMydO5c9e/awceNGJBLJU13TxcWFt956C1dXV77//vsayRKqZkJSU1MNUqnqWSfUkhXoxCuvvIJcLq/RpPny5ctYWVkBkJ6eTpcuXWp8Tdu2bQkPD6dt27aYmppSVFREWloahw8f1tnI7mmJxWI++ugjysvL+e677+p1rWbNmjF9+nS+/PLLp248PGDAABwdHRvsLGtgYCBbt27FysqKGzdusHHjRtauXUtiYmKD3P9RxGIxn3zyCRkZGaxYscKgsbi4uBAVFUVhYSE7duyoMaKs/pn/9ttv5OXl1ep6UqmUvn370qFDB7Zv305SUtIjP9fExIRZs2axfv16MjIy6vutCP5CqCUr0CtXV1fOnDlT430XL17U/vlhv9SZmZksW7aMzz//nG+//Za0tDR8fX159913+eyzz3jxxRfx8PDQe+wPo1armTdvHjY2Nrz00kv1upaPjw8XLlx46mQJVSN2e3v7BuuQERUVpX3YcXR0ZNeuXQZPllD1c/nzzz/x9/fH1dXVIDFIpVIGDhzI5MmTOXToEEuWLHlg+rWwsJDo6GjGjh1bq9Fw69atmTlzJnZ2dsybN++xyRKqSilu2LCByMhInc2CCJ5MSJiCenNwcEAulz+w+ePTTz8lOTmZn3/++YkttW7dusXGjRv56quv+Pzzzzly5AjNmzdn2rRp/Otf/+L111+nY8eODToVV1JSwoIFC/Dx8aF///5PfZ2nnY69n0qlYu3atQwdOlSbyPTF19e3Rq/OyspKg9b+/avz58+TmZlZ7weZp+Hu7l7rxHbixAlKSkro3bv3Iz9HLpczbNgwXnjhBaKjo1m5ciUlJSW1iuXChQtcvHhRJ5uLBLUjTMkK6m3kyJH4+fnx1Vdf1Xh/9bGDefPmPfW1xWIxoaGhdOrUCRcXF8RiMfn5+Zw5c4b4+PgGOW4RGhrKmDFjWLlyZZ033kgkEv75z3/y7bff6qQNVN++fXFxcWHp0qX1vtZfmZqaMnnyZFq3bs2ZM2cQiUSEhYVhYWHBvHnzDL7p6H5yuZxPP/2UuLg4duzY0SD3GzhwIO3bt2fLli0PFM9/FGtra2bMmMGiRYu4evVqjY95enoyevRosrKy2LZtG2VlZXWOy8zMjFmzZvHnn3/qrfn6s+hRU7LCLllBvVVPOf6Vu7v7E1s5PYlarebEiRPaTSYeHh50796d7t2707dvX0pLS8nMzOTo0aM1poB1KTExEWdnZ8aPH8/Nmze5du1arb/Wx8eH69ev66xn4oEDB3jzzTcJDQ3V6RRpz549GTBgAMXFxXz//ffa73HlypWMHj2aMWPGkJqaajT9KSsrK9m6dSsjRowgLi5Oe6RJH7y8vBg9ejQXL15k7ty5dUpsRUVF7NixgzFjxvDjjz+iVCoxMTFhyJAheHt7s2HDhnrNPpSVlbFp0yYiIyOZN28eCoXiqa8leDJhhCmoF6lUyhdffFHjRRaq1mRee+01PvnkE729yFpbWxMeHk67du2wt7dHo9GQl5fHuXPniIuL0/mW+2nTpuHu7s5///vfWpd+Gz9+PBcvXuT48eM6i6Nly5ZMmTKFefPm1Xr67lEcHByYPHkyNjY27N+//5FdRT755BNycnJYsmRJve6na2+//TYAc+bM0fm1zczMGDp0KG3btmXDhg31mpYeO3YsLVq0ICsrC39/f1JTU9m5cycVFRU6iXXcuHHcvXuXbdu26eR6z7pHjTCFhCmol7CwMIYMGcKnn35a4/2TJk3C0dGx3jtM6yIgIIBOnTrh5uaGiYkJd+/eJSsri/j4eDIzM3Vyj7fffhsrKyu+/vprKisrH/u5JiYmfPjhh/znP/95qum2xxk0aBDNmjXjzz//fOprREZG0qFDB65cucLixYsfO73t7u7O3/72N37//Xe9jeSfho2NDe+//z5btmzh6NGjOruum5sbL7zwAsnJyURHRz/xZ10bfn5+NG/enMzMTK5cuaKDKP+Pubk5s2bNIikpicLCQr2UKHyWCFOyAr0IDQ19YG0GqqZODx8+3KCxnD17VrvOZmNjQ3h4OH5+frz88suo1Wpu3LjBmTNnOHr06FOPPufPn88HH3zAzz//jImJCYmJifzvf/976Od6e3tz+fJlnSdLqNo1O2vWrKcqiODl5cWECROQSCSsXr1ae3b2cS5dukRKSgoTJ07k888/f9qwde7OnTvExMQwdOhQEhMTdZLYOnbsyMCBA1m7du1TFZt4lMe1dauv0tJSTE1NWb58OQArVqxg+vTpervfs0oYYQrq5YsvvmDLli01Dlc7OzszY8YMPvvsM528gNWXWCwmICCADh061Bh9Xrx4kaNHj9Z5s0RwcDB79+7V7tj99NNP+fHHHx84NvLSSy+RkpLywMFzXfH09CQyMpI5c+bU6u9ZLpfz0ksv0bZtW86dO8eqVavqVGlJLBbz2WefkZyczJo1a+oTus59/PHH3Lhxg99+++2pr2FnZ8fo0aMxNTVl3bp1XL9+XYcR6l9sbCx+fn5AVS1iJycnA0fUeAkjTIHOubq6IpVKOXnyZI339+rVi4KCAqNIllC1cej06dPakZStrS3dunXDz8+PV155BbVaTV5eHmfPnn1g9GllZYWzs7O2vF/1199/vKVv376IRCK2b99OZmYmb731Fu+88w7l5eUMHjxYb99XRkYGmZmZDBgwgK1btz72c7t168bgwYMpLS3lxx9/fGxPzkdRq9WsXr2aiRMnEhMTU6fNT/q2ZMkS3nrrracacYtEIsLDw+nduzeHDh3iyJEjRley8UkGDBhQ421jmjZvSoQRpuCpjR07Fnd3d/7zn//UeP+nn37K8ePHjbZp9P2qR58dO3akVatWmJiYUFJSwuXLl7l79y7/+c9/sLe35/Dhw0RFRaFUKpHJZKxdu5aePXtSUFBAr169sLGxYeDAgdjZ2fHKK69oE+rRo0f5f//v/3Hy5EltwtWl6rWrFStWcPny5Qc+bm9vz5QpU7Czs+PQoUP13rUM8Nprr2Fra0t2djbu7u6sWrVKZ2vE9TFhwgS8vb2ZPXt2rROeo6Oj9ue6fv16g1eYqisnJyemTJmCpaUlu3fvxt/fn8DAQIqLi/n444+N5qG1sRE2/Qh07uOPP+bs2bNs2bJF+z57e3veffddvvjiC6NrSVUbtra2dO/eHU9PT4YNG1ajss7AgQO1u11FIhEtW7ZkwIABFBYWsn79eqBqfTA+Pl6bMLdv386BAwdo164d2dnZbNq0iezsbJ3G3L59ewYNGsT3339f4wVyxIgRhIWFcf36dZYsWaKzps6mpqasWbOG7t27A1U1Xjt16lTvHbv1VZcp4/uLm+/evZuEhIR6VWIyhKioKEJDQ8nJyWHx4sU1ZkY+/vhjrl+/zsKFCw0YYeMlTMkKdMrU1BRLS8sHduP17t2boqKiRpksoaovZvX0ZlFRkTZhlpeX19jZqNFouHLlCsuXL+f111+nf//+7N69Gw8PD5YtW0a3bt3Iycnh73//O+Hh4UydOhWAN954gy5duuj03GBycjJdu3Zl586dlJWVsXz5cry9vZHJZKxbt07nJe3Ky8trJF8nJyecnZ0NXg1IrVZre4g+bsrY1dWVqKgoCgoK+P7773X2INFQ2rRpw6RJk5BKpaxateqhBSWWL1/Oa6+9Rtu2bY1i9N9UCAlT8FTCw8OpqKh4YArL19f3gZqyjdWiRYswNzenXbt2rFq16qFHASorK/n999+ZMmUKAQEBjBgxgsOHDxMeHq49f9q1a1ft5zs6OuLr66vTIxAAffr0ISQkBICQkBA++OAD/vzzT72dgV26dCn9+vVDJpNx9OhRo3lRPnfuHNnZ2UyePPmBylMymYx+/foREhLCtm3barU72JhIpVImTJiAn58faWlprFix4pE/38uXL5OWlsbEiROZPXt2A0fadAlTsnrSokULJk+ezI0bN1iyZEmj20TwJO+88w43btyo0T3D2tqajz76iK+++qrRPbXXl5OTE0lJSZiYmACwevVqXn/9daDqzOTSpUuRSqXcuHGDFStWsH37dk6dOoWbmxuFhYX1GnE6OzuzY8cOWrduDVQlcRcXF72smd7Pw8ODVq1aER8fr7MD+LpgamrKP//5T06ePMnx48e5cuUKbdu2ZfTo0eTk5LBt2zadVV5qKP7+/owdOxaVSsWyZctq9YAilUr59NNPSUpKYsOGDQ0QZdMhTMk2IJFIxObNm/Hy8gKqpoCa0lOeWCzG3t7+gV/CXr16cffu3WcuWULVbtrqZAkwevRo3n33Xf7xj3/w6quvIpFISEhIYNy4cVhbWzN06FC+/PJLwsLCKC0tZeLEiRw8eLDW93NwcKBTp06EhIRgaWnJ3r17GT16NFZWVmzZskXvyRKqdmIa427M8vJyUlJS+PHHHzE1NeXo0aMcPnyYzZs36/UspD5U1/d1c3Pj1KlTrF27ttZfW72Rady4ccTGxta6zZjg0YSEqQdWVlbaZAlVmy9OnDjBkSNHmkQy6dSpEyqV6oHzi+3btyctLc1AURlWRkYGV69epWXLlkDV9F/Hjh21o0yADh060LZtWwCuXLlCWFgYULXT9auvvmL+/PkUFhZSWlqKWq1GLBYjk8kwNTWlWbNmNGvWDFtbW8zMzBCJRJSWlpKamkp0dDQlJSW89957yOVyXn31VTp37qzTcnyNTY8ePbRNybt06cLf//73RvdvMywsjGHDhnH37l3mz5//VMd4Tp8+Tffu3Zk8eTLffPONHqJ8tggJUw+KioqIjo5m4MCBqNVqjhw5QmhoKD169KCsrIysrCzi4uKM8um8Njp27EhOTk6N95mbm2Ntbc2BAwcMFJXhTZgwgY0bN2Jra8u+ffuYPHlyjY+Xl5fj6+uLSqWipKSEwsJCmjVrBkB+fj6Ojo60bt0aqVSKSCRCo9GgVqtRqVTcvXuX4uJi0tPTSU1NfeDvv1plZSVr1qzhb3/7GxcvXmx0xyR0wdXVtcah/fLy8kZVhMDa2pqpU6fi6OjI4cOH6308a/HixXz88cc899xzj6wVLKgdIWHqyRdffKGt0ZmSkgKApaUl4eHhtG/fnmnTpqFWq7l27RpJSUkcO3bMaDpBPEnLli0fqGHas2dPysrKnskX6GpnzpzBx8eHL774gn/84x9IpVKcnZ3p3LkzJSUlTJw4sUa5wOzsbN58802uX7/ON998o7MSejdu3GD//v2MHTuWBQsWNLn180eRy+X079+foKAgvvnmG86cOUPHjh25ceNGo/ndeu6553juuee4desW//3vfykoKKj3NUtLS9m1axcDBgzg2LFjBj/+05gJm370ZPjw4RQVFT1yXUosFhMYGEinTp1wdXVFLpdz584d0tLSOHLkiNEmnnbt2vHCCy/w8ccf13j/+++/T05ODqtWrTJQZMZBLBbz+eef8//+3/9DpVIxbdo0zpw5o7fyeI8iEomYOnUqFy9erNPaaGPl4+PDyJEjyczMZPv27TWONb333nuUl5czf/58A0b4eA4ODkydOhVra2uio6OJiYnR+T3effddKisr+f7773V+7aZG2PTTwHx8fPjjjz8e+XG1Wk1SUpK2Y7uzs7O2VVWXLl2oqKjg8uXLHDt2TDtCNQZdu3Z9YPOAXC7H1ta2xo7ZZ5WzszO3b99GpVJhYmKCm5vbY/8d6ItGo2Ht2rXMmDGD9PT0hxbIbwosLS0ZOnQorVq1Yv369WRkZDzwOYsWLeKdd96hU6dODf7gUhvVBSauXLnC/Pnz9XaGefHixbzzzjt07NhR219WUDdCwtQDR0dHxGJxnXalXbt2jXXr1gFVCahbt24EBQXxwgsvAFVrXGfOnKnR5zE8PBxra2t2797dILsioart0d69e2u8r1u3blRUVBhVbVFD8fLy0h7g9/Ly4vLlywZr6ltUVMTWrVsZN24c8+fPbzTTkrXVoUMHBg4cyMmTJ1m/fv0j/55v3bpFbGwsI0aM4PTp00ZTLs7NzY0XX3wRExMT1q5dy6lTp/R6v1u3bhEfH8+IESM4c+aM0fw9NCZCwtQDX1/feu3Iq6ys5ODBg9qpND8/P8LCwujZsyf9+vWjpKSEtm3bMmrUKAC2bdvGiy++qIvQH8vZ2Rm5XE58fHyN9wcGBuq83Ftj5e3trf251fffgS6cPn0aPz8/Bg4c2GSaC9vb2zNq1ChMTU1ZvHhxrUbP27dvJygoiClTprBgwYIGiPLRxGIx48ePx9/fnwsXLvDHH3802MPM1q1bCQoKYtKkSULZvKcgJEw98PX15dChQzq7Xmpqqvb8mL29Pd27d2fChAnajw8ePJguXbpw/PhxvW7wqC42/tdfbicnJ51+v42ViYkJLi4uZGVlIRKJ8Pb2Nopdw5s3b2bmzJmkpaU9dMqysRCLxfTo0YOePXty4MAB4uLi6vTvffHixUyfPh1/f3/OnTunx0gfzdfXl+effx6o6rCiy36btbV06VLeeOONp+rs8qwTP/lTBHVhZmZGy5Yt9XZk5NatW2zevJlNmzZp35eTk8OQIUP48ssvef/99xkxYgQODg46v7eXlxfJyck13ufh4YFIJGp0Zcb0wcPDg+zsbBQKBS1btnxo6UBDKCsrY926dURFRWFmZmbocJ6Kq6srb731Fm3btuWHH354qhZc165dIzExkbFjxyKVNuxYQS6X88orr/DSSy9x/vx5Zs+ebbBklZOTw7lz5xg/fnyNNnWCJxNGmDrm7e1NVlaW3qdYZs+ejaenJzExMSxdupTi4mJcXV3p2rUr7du3p0uXLlRWVpKbm8vJkydJSkqq1+jT0tISCwuLB3bvdenShdu3b9f322kSvLy8SE9PB6o2fRl6OvZ+GRkZJCcnM2LEiAeOBBmz+4+KbN++XbtJ7mmtW7dOu9N76dKlugnyCTp27MiIESMoLy9/6l6kurZq1So+++wzxowZw+rVqw0dTqMhJEwd8/X1bZDyW56enuzevbvGOkRubq62dJZcLicsLIzAwEBGjx6t7c5QfWylrue7IiIiKC0tfaBSUZs2bfS+WaGx8Pb21u6I9fX1Zffu3QaOqKbo6GimT59OUFBQo5gR8Pb2ZtSoUWRmZjJnzhyd7R5dtmwZr776Kh4eHnotHmJubs60adNwdnYmPj7+iU2+G1J1M/BJkyZx+PBhYcNeLQkJU4fEYjHe3t7s3LlT7/fy8/N7bGKurKwkJiZGOyJ0c3Oja9euBAYGane11uXYSvv27R9o32RqaoqFhcUDLb6eRfb29kilUvLy8rCwsMDR0fGB0oGGplAoWL16NVOmTCErK8toyzRaWFgwbNiwxx4VqY+srCxSUlKYOHEiX3zxhV7W/SMiIujfvz937tzhu+++M4qp+b9KSUnh0qVLD+3sIng4IWHqUKtWrSgsLNT7C5FIJMLX17dOZa6ys7O1O1lNTU21ybP62MqNGzc4ffo0cXFxD2w3l0ql2Nrasnz58hrvr068uuzt2Fjdf5zE29ubjIyMBjvqUxdXrlwhLi6OMWPGsGjRIqNrmlx9VCQxMfGxR0Xqa8WKFXz22WdERUU9sdl0Xdja2jJt2jRsbW3Zu3evUWz6epylS5fyz3/+k4EDB9a7BN+zQOcJs5laTUeVCl+VChONhgKxmDMSCaclEpSihxZPaDKq+9TpW6tWrSguLn7qslnl5eUcOHBA+8tcXSyhV69eDBgwgKKiItLT07V9HT/44AM0Gg3//ve/tTVOoarlkHCcpIq3t7d2fc3Ydx8ePHgQX19funTp8sARIUOxt7dn5MiRmJmZ1fqoSH3cPyUZGxv70F6ndTVkyBDCw8O5fv06X331VaMoQVdeXs62bdsYPnw4R48eFR5+n0BnCVOs0eAhFjOkogKT+97vqFbTV63GX6XiT7mciiacNH19fVm/fr3e7/Ok6di6SklJ0U7LOjg40L17d3x8fOjZsycTJkxAIpEAcOzYMRwcHNi4cSO//fYb/v7+zJkzR2dxNFYSiYS2bduybt067bT89u3bDR3WI6nVatasWcPrr7/O+fPnDbpp6/6jIgcPHiQ2NrbBat+mpKSQlZXF5MmT+fLLL5/6Oi1btmTy5MmYm5uzceNGo6wm9DhHjx6la9euTJkyRfh9fgKd7Cnu0KoVF958k6OhoUxu25a2KhW2ajXi+6Z7WqjVPGegiicNwdbWFktLywbZAdeuXTu9bSzKz89n48aNfP311/z000/aZAlVTbElEglRUVHs2rWLqKgohgwZopc4GpPWrVuTn59PaWkpbm5u3Llzh+LiYkOH9Vg3b97kwIEDjBkzBpGBHmKrj4p4eHjwww8/EBMT0+CF4pcsWYKpqSnDhg2r89eKxWLGjh3LW2+9xc2bN5k9e3ajS5bVFi5ciIODA926dTN0KEZNJwnzk+7dsb15E5FCgZuDAz6WlkR6ezM9NJTh7u443Psl8FOpMDeyNRNdqZ6G0/eakJ2dHebm5g2SmM+fP88PP/yASqV65KaFV199FVdXV73HYsyM+TjJ48TGxiISiRr8RVIulzN06FBeeuklDh8+zKJFi3TSleNpVFZWsnnzZrp27Yq9vX2tv87Dw4NPPvmE9u3bs3z5cn799ddGXWquqKiIQ4cOMXjwYG0fUcGD6p0wRRoNksuX4d7ct0ajoW3bttjb22Mql+Pn5ISrtTVQNf/bsom2GmqoMmjV66QNtVnj008/pUWLFvj5+XHy5ElycnJqbMW/efMmb775JrNnz+aVV14hICCgQeIyJt7e3tqEaQzl8GqrukB7nz59aN68eYPc09vbm7fffhszMzPmzJlT73OVunDixAmuXr3KtGnTnvi5UqmUKVOm8PLLL3Px4kU+//xzo2qOUB+7du3i7t27vPTSS4YOxWjVew3TWaPh2OHD+HTujNndu+Tk5GBpaYmlpaX2c4rlcu2fm+IKpkwmo02bNg3S2qpdu3YcOXJE7/e5X/Vuz4SEBBYuXEh+fj6vvfYaCoWCBQsWUFlZSefOnQkNDeX5559n/Pjx5OXlPXLXbVNiaWmJnZ0dOTk5NGvWDGtr60c2dzZGt27dYt++fYwZM4ZffvlFbw9i+j4qUl+LFi16YpPloKAgIiMjtf/uL1261LBBNoAlS5Ywffp02rVr12QeBHSp3gnTTaVCoVRy9e5dSs+e5bZKhaKkhGYtWmAGXM3LI/PWLRCJUAFXm2ApJk9PT3Jzc6moqNDrfczMzHBxcTHIi01AQAAajUY7uvzPf/5T4+NxcXHExcUBVaOsrl27anfdFhYWkp6eTmxsbJ06uDQGXl5eXLx4EbVajY+PD+np6UZ3VONJ4uPjad++PT169KjR4FpXGuqoSH2UlpYSHR3NoEGDSEhIqHE0zNTUlKlTp+Lq6kpCQgIbN240YKT6de3aNc6cOcPYsWOZPXv2M9N8vLbqnTDtNRpMTU0psbCgUqXipljMNZUKSyA3IwNFQQHqe0nyvETC3Sa4S7ahqvtUl90zxAtOdef62khLS9NOSzo5OdGjRw+8vb3p1KkTCoWCa9eucerUKU6cONHoW079dTq2MVTQ+SuNRsO6det48803SUtLq/XP+Uka+qhIfcXExNC5c2fefPlljmzezLFLl+gYFsbgwYMpLi5m3rx5Te6B72HWrFmjLZu3Y8cOo9/A1pDqnTCb3UuYpeXlZIjFqKg6WG9lbc2N4mJtsswXidgrk9X3dkbJ19dXLx3S/8rPz89g0yRubm5PNRWcl5en7fMplUrp0KEDISEhDB48mBEjRlBUVMSFCxeIi4sz+hfUvxKJRHh5ebF7926kUqn2aEljVFBQwJ49exgzZgw///xzvUYWhjwqUh8ijYYbmzbx7ZAhvDdkCLdNTNjUrBm7Dx82ujKH+qRWqzlx4gQ//fQTP//8M4sWLeLdd981dFhGQScJ08zMjPLycgrFYtbLZHSytaW4shK1Ws0dkYgkqZRTEgmVTXB02bJlSxQKBTdv3tTrfQx5vs/a2hpTU9N6H3JXKpUcO3aMY8eOAVWjz27duuHt7U2HDh1QKpXk5uaSkJBAYmKiLkLXK2dnZ8rKyigoKMDLy4vr16/rrN6pIRw7dgx/f39tonsaLi4uREZGUlJSwg8//GCw3a915ahWM0ihYJy9PdWP9XYVFdxYvZrdZ88aNDZDGDBgABYWFgBMnTqVn376iczMTANHZXj1TpgWGg0mJiba9bvLEgm2vr6scXdn7cmT0AST5P0aaldkmzZtuHXrlkGmR7p160ZpaanOk0FeXp52PUgsFtOpUyc6duyoLRafl5dHYmIi8fHxRjl121h3xz7OunXrmD59OqmpqXWafpTL5fTr14/g4GCddBVpKBKNhnClkjClEisLixpHS1QqFTbp6bRQq7neBPdePIq5uTl2dnbatysrKyksLDRgRMajXglTrNEgoarSiUqlQgMoqFq7yM/Pb/LJEhquK4Wuq/vURbt27fS+I1CtVtcYffr4+BAeHk6/fv0YPHgwt27d4uzZsxw6dIjy8nK9xlJb3t7e2sbZvr6+D9TabYwKCwuJjo5m7Nix/Pjjj7WaStVXVxF9c1WpGKRQYKfR4OfnR8uWLcnOziYnJ0d7tlijVjO4spIlJiaon4HXs6CgIIYOHcr69evJy8sjJCSE3Nxc7t69a+jQjEK9EmZ1DRiRSIRarUZZ9QY2NjZN4mn7SSwtLXFwcGiQrhR+fn4sW7ZM7/d5mObNmzf4VPD58+e19VhdXV2JiIjQ1rstKioiJSWFw4cPG2zKTy6X4+LiQmZmJk5OTohEoibTIunEiRP4+/vTu3fvxxb4N/ajIo8i12jopVAQolJhY2NDUFAQYrGYs2fPcuPGDSoAk4QEQkNDCQ4OpujwYboqlcQ20T0YULXsMmrUKGxtbVm6dCm5ubnaMp8ffvghL730EgsWLDBwlIZXr4RZ3YtBo9EgEom0CbRZs2bPxBDex8enQbpSODo6IhaLuX79ul7v8zDt2rVDo9EYtJh4bm4uK1asAKpmLyIiIvD396dLly6UlZVx4cIFDh482KAJy8PDg9zcXBQKBe3bt29yZ9Y2bNjAjBkzSE1NfehmrMZwVORhPFUq+isUWAMBgYE4ODhw8+ZNzp49S5FGw265nAsSCUMqKxEnJREREYGfnx/q1FTSJRLym9jUrEgkolOnTvTv35/4+HiWL1/+wOvZ0qVLmTFjRpNZdqiPeiVMNaChajpNLBYjpmqnmY2NzTOTMBuquo+hpmM7depUNb1uJG7dusWGDRuAqrWWiIgIAgICmDFjBpWVlVy6dIkjR4480LtT1+4vh9euXTt27Nih1/s1tKKiInbs2MGYMWOYP3++dmrWzs6OUaNGNZqjItXMNRr6KhT4qVQ4OjpqHwQTExMpKCggSSLh4H3NIfbJZLSpqCA5OZmAgAByc3MZfPcuy+TyJjM1a29vT2RkJFKplF9//fWRx4mqz2Y+//zzz/zZzPpt+hGJUFK1OC6/V81HClhZWTX5hCmRSPDy8mLLli16v1e7du3Yu3ev3u/zMK1bt+bo0aMGufeTlJaWsnPnTnbu3IlcLqdr166EhIQwZcoU1Go1ubm5HDt2jFOnTun83j4+PixfvpxmzZpha2vbJKu+JCYmEhERwbp167hy5Qp79uwhKCioUR0VQaOhvUrFc0ollmIxwR06YGNjw7Vr10hJSaFAJGKnXE7OfU0GAMpFInbJZIy+cYM7d+4QHBxMcUwMnZVKjjbyqVmxWEz37t2JiIhg//79xMXFPbHYxurVq/nss8+IjIxk7dq1DRSp8an3LlnlvfXL6q4WMpEIqVRqlM1zdcnd3Z38/Hy997yzsLDA0dHRIFu6LS0tMTMz01bwMWaVlZUcOnSIQ4cOIRaLCQ0NpXPnzkRGRjJmzBiuX79OUlISzZs354svvqC4uJhXXnmFs09xZKB169YolUquXbtGly5dOH/+fONIHk9h1KhR+Pv7AxAWFsbAgQMN2g6sLqzVagYoFLRVq3FxccHHx4fKykqOHTtGUUkJx6VSYqXSR/bpvSCRkCqRIElKomfPnvj4+KA6f54LEgm3GunUrLOzM5GRkZSVldXp2I9arWbt2rW88MILxMbGNpqZBV2rf8Lk/6ZkoWqb9rPA19e3Qdb1fH19uXDhgkEeQLp160ZZWVmjaIR7v+qD1ydOnACqprS7devGc889x9/+9jfkcjnOzs58++23DBgwoM7XDw0N1Y5a27dvb7QjcF1o3bq19s9isbhRJEuRRkMHlYoeCgWWcjkhISFYWlpy+fJlMjIyuC4WE21iQl4tkt5emYzWFRWkpqbSvn37qqnZsjKWy+VoGtHUrFQqpU+fPnTu3JmdO3dy8uTJOl/j3LlzZGVlMWXKlHr1D23M6v2YVEnVlGz1CFOu0aBQKJA18mmLJ2mocnj67H35JO3bt28SU42pqaksXLiQzz77rMaRFEdHR8aPH4+5ufkTryGTyRgwYIB2zfTkyZOYmprSqlUrva+XGoKTkxP/+Mc/SEpKQq1Wo1KpGqQ5en01V6uZWFnJcwoFPm3a0KNHD8RiMbGxsaRlZHBQJuMPubxWyRKg9F6FsuvXr1NYWEhISAgt1Wo6NqIZNDc3N2bMmIGjoyPz5s17qmRZrbp/6MiRI3UXYCNS74SpEIlQKpVIpVWDVTlQXl6OiYlJfS9ttJo3b45cLtf7tIRUKsXDw8NgO1SbN2/eaBviPsrLL7/M1atXycjIYOLEiahUKvr27fvIz7ezs2PLli3k5OSwatUqNm7ciJmZGcXFxQQEBHDx4sUm141l+PDhzJw5k4KCAsaMGYOfnx+9evVCLBZrq78YG4lGQ7hCweSKCjxNTQkPD6dNmzZcuHCB+Ph40isrWWRiwjGptM4jw1SxmAsSCUlJScjlcry9vempUGBr5NPwcrmcYcOGMXHiRHbv3s3y5cvrXfiksrKSdevW0blzZ5ycnHQUaeOhlxFmRUVFk25C2lDTsW3atDFYuTUfHx9EIpHBRrf6snfvXoYMGcLatWtJT09n9+7dDB06lFdeeQVnZ+cHPn/mzJl0795du6kNoEePHowZM4ZJkyZx/PjxhgxfrxwcHPjoo4/o3Lkz69at4+eff6ayspL8/HySk5NJTExk+PDhhg7zAS5qNZMrKuiuVNLOx4euXbtSWVlJTEwMF7KziZbJWCWXU/C0644iEbtlMkpUKtLS0mjVqhXW5uYMUijASJegvLy8ePvttzE1NWXOnDmcO3dOZ9c+ffo02dnZTJ06VWfXbCzqvYZZIRKhUqkeGGE29YTZEBthfHx8DDa67Ny5s1EdJ9Gly5cvY2JiQlZWFlKpFJlMRlRUFLNmzSIqKgqxWEyLFi1o2bIlnTt3fuDr7ezstIe48/LymsR6zpAhQwgPDyc3N5c5c+Y8tJrSnj17mDlzJu3btyc5OdkAUdYk02iIUCoJVSppZm1NcHAwEomElJQUrl27xgWJhD2mphTrYK2xRCRin1SK6dWruLi4EBoaSumRI4SoVJyS1vtlVGfMzMwYMmQIHh4ebNiwQW/LBYsWLeLTTz9l6NChbNu2TS/3MEb1/kkrqCqqrd0le2+E2VSnZE1MTGjVqlWDVDXx8fFpkKbUD+Pu7t6kRk9/1a9fvwfWLp2dnXnxxRe1dVSTkpIwMzNDJpPh5OREcnIymzdvZu7cudqvGTt2LJs2bSI9Pb3RHN6/n4ODA9OmTcPS0pINGzZoN0o9jFKpZP369YwfP57MzEzKysoaMNKa2qhUDFAosAH8AwJwdHTk9u3bnD59mpJ7BQjOi8U6Lc95TiLBT6VCeuoUPXv2xNPTk8qMDC6KxRQZwa7Z9u3bM3z4cJKTk5kzZ45elwoqKyvZsGEDUVFRHDt2rMk+XP9VvRPmA1OyNO0RppeXF5cuXdL7upWdnR0mJiYGKbdmbm6Oubl5vbuTGCOpVErXrl1p1qzZAx8rLS1l1apVXLx4UVs784033mDBggWsW7dOe1bt5ZdfJiQkBICcnBzCwsKIiooiMTGRli1b8sYbb5Cbm8srr7xi1NvvBw0aRI8ePbhy5Qpz586tVY3eS5cukZyczNChQw1yHs9Mo6GPQoG/SoW9vT0BAQEAJCUlVdUblkjYL5dTro8drCIR0XI5LuXlnD9/Hl9fX65cucKgigpWy+UGq51taWnJiBEjcHJyYuXKlVy+fLlB7puYmEjXrl2ZNm0aX3/9dYPc09B0sunn/ilZE5p2wvTz82uQ6j4+Pj6kp6c/8UCxPnTt2pXy8vIaXeebAj8/P9555x3c3d158803OXPmTI2/39u3b9OlSxfeffdd3nzzTRYvXsy4ceO0CROqNlI4Ojpqv6ZNmzYcOHCAvXv34u7uzpdffkmrVq3o2rUrn332WYN/j7Vhb2/PBx98QHh4OBs3buTHH3+sU0H76Oho2rRpg7e3tx6j/AuNBj+VipcrKggCbZ3XmzdvcujQIS7evs2fcjk79JUs7ykWiTggk3HlyhWKi4sJDQ3FXa0m0EC7ZkNDQ5k1axY3b97k+++/b7BkWW3hwoVYWloycODABr2vodR/DZOqEWb1OUyZRtNkE6ZIJMLHx6dBqu74+PjUa/t3fQQEBDT4L54+mZubM3z4cFxdXdmwYQOtWrWiT58+/Pvf/9YWNlCr1Xz33XcsXboUsVhMmzZtmD59uvYavXv35pNPPqFjx464uLho31993OT27dsPnFEMCgrin//8JxcvXiQ2Npbs7OwG+54fZeDAgfTs2ZOrV68yb968p+r8Uj0dFxkZydy5c7Wt/fTFSqOhv0KBp0pFy5Yt8fHxQalUkpCQQGFRESekUmKkUhQNNMI7fW9qVnbqFD169MDDw4M+Fy+SJZHoZL20NmxsbBg1ahRWVlYsWrTIYDMZ5eXlbNmyhZEjR5KQkMCtW7cMEkdDqf8a5r1/INXTsnJosmuYrq6ulJSU6L1DhlQqxd3dndWrV+v1Po/i6OjIzp07DXJvXQsICGDYsGGcPn2a9evXM3LkSG7evKmtcrJ7925+/fVXiouLtbVh1Wo1Fy9eZO/evUyYMEF7rTFjxmjbPgGUlJTw4osv1ihcIJPJeP/997ly5QozZ86kTZs2tGvXjtdff11bHejkyZOcPHmyQXt82tvbM23aNJo1a8bmzZu1bdSeVkZGBhkZGQwcOJDNmzfrKMq/0GgIUamIUCiwlMkI6dABKysrcnNzOX/+PDfuFSC41tDrhyIRO2UyplVUcOHCBby9vbly5Qr9FQrWy2R6nZoViUSEhYXRr18/YmJiOHz4sMGrTB0/fpywsDCmTZvGf/7zH4PGom86WcOE+xKmSkVpebnRnteqj4aq1t+2bVuuXbtmkE0VAQEBBu9OoguWlpaMHDkSBwcHVq5cSbt27ZgwYQJbtmx5oBzeo0bys2bNIiQkBD8/PwDtrtoWLVoA8P333z9Q5WfJkiUsWbJE+3ZKSgrbt29HLBbToUMHOnTowNChQxk5ciQFBQWkpaURExOj14ew/v3706tXL65du8YPP/ygs2NK27dv5+233+bs2bM6L91op1YzSKHAVa3G3d2dtm3bUl5eTlxcHCVlZcRKpRyTSg1WCL1QLOaQTIYsJ4eWLVsSGhpKeVwc7cRiUvS0a7Z58+ZERkYiEon45ZdfjGqjzcKFC/n444/p379/g/QHNhSdHCsBtMUL5CoVt8vLa3Qubyp8fX3ZunWr3u9jyOMkYWFhBmkjpkshISEMHjyYhIQE4uPjiYyM5OrVq8ydO7dOjXCVSiV9+/blpZdeok+fPkycOBF7e3t+++03li1bVqcZALVaTUJCgrYQhKurK+Hh4QQEBNC1a1cqKirIysriyJEjXLx4sc7f88PY2try8ssvY2Njw5YtW3Rewq+8vJxNmzYxevRo5s2bp5NdwmKNhjClknClEkszM0JCQjA1NSUrK4usrCxy740qjaGW60mJBF+VClliIj169KBNmzb0vXSJyxIJd3WYyMViMT169KBnz57s27eP+Ph4g+xteJzS0lK2b9/O0KFDSUhIMFif2kexU6sJValwVasRATliMaeeoiawTo6VwP+NMGXQJAsXWFtbY2Nj0yDrUD4+Ptr+jw2tVatWBuuMUl/W1taMHj2aZs2asWzZMoKCghg3bhybN29+6nODZWVl/PLLL4hEIjw9Pbl16xb79u2r93R5bm6u9hqm9yrTBAYGMm3aNFQqFdnZ2cTExDz1jEbfvn3p06cP169f58svv9Rb8YvU1FSCgoLo379/vZuMt7g3qnRUq/Hy8sLNzY2SkhKOHDlCSWUlh2QyEiUSg+1GfYBIxA6ZjKn3Tc1eu3aNfgoFm+4rdFEf1cXSS0tL61Qs3RDi4uLo1KkT06ZN49tvvzV0OFoBSiUDFYoaO1wd1WpClUp2y2Qk1WFGQKdTslKpFHkT3fTj6+tLenq63tcL7O3tkcvlBjlO4uzsjFwub5THSTp16sSAAQOIj4/nyJEjjBs3jsuXLzNnzhydTG0fPnyYvn37smXLFp1PlZeXl7Nv3z727duHWCymU6dOdO7cmRdffBG1Wk1OTg5xcXG16qxiY2PDyy+/jK2tLdu3byc2NlansT7M1q1bmTVrFmfOnCEnJ6fOXy/VaOihVNJJqcTayorg4GBkMhlpaWlcuXKFi2Ixu3RUgEDXCsRijshkSHNycHFxISQkhPL4eHxUKs7/pWVYXUilUp577jk6der01MXSDWHhwoV89NFH9OnTh/379xs6HJzV6hrJUqzR0Fyj4YZYjAjor1CQLxZzpZYjTZ1t+qkuXtBUa8n6+vpy+vRpvd+n+jiJIYSHh3Pnzp0G3YxSX7a2towePRpTU1OWLl1Khw4diIqKYuPGjTqd1k5OTiY8PJx//OMf2NnZsXnzZr081KjVao4dO8axY8e0bcrCwsJ4/vnnef7557ly5Qrx8fEP7fH53HPP8dxzz5GXl8dXX33VYF1m7t69y9atW4mKiuL777+vU2ed1ioVAxUKbIF27dvTokUL7ty5Q1JSEsVqNXvlclJ1XIBA145LJPioVCQmJtK9e3fc3d3pf/ky2WIxZU8Rd9u2bRk1apR2GaExdQsqKSkhOjqaQYMGceLECYMfTeuoVGqTZStLS/q5uSFRKNh3/TqZxcWIgE5KJVdqOSOgk2MlcN+mnyY4wpRKpbRt27ZBDmr7+PgYrOC5p6dno+m8IRKJ6NKlC3379uXw4cNcv36dCRMmkJGRUetD+HXVrl07oqKiANi4cSNdunTR+T3u99c2ZUFBQXTr1o2oqCiioqLQaDS0bt2atLQ0KisrsbGxYefOncTExOg1roc5c+YMQUFB9OnThz179jzx8001GnorFASqVNjZ2REQEIBIJOL06dPcvHmTZImEfSYmT5VwGprm3tTs5IoKLl68iKenJ9euXaOvQsHWOkzNmpmZMWjQILy9vdm8eXOjreMcExNDx44defnll/nf//5n0FjaqtVINBrc1WoGuLlhZWUFwEALC349dQqlSIRHHR7wdDrClEql2mMlTSlhNtSu1erjJIYohycWi2nWrFmj6O1ob2+vrfm6ePFiwsLC6NKli15rZwI1Dup7e3tjYmKi9zOI9zt9+rR2liMyMpIFCxYgFosZMmQImzdvZvr06QYdjWzatImZM2dy7ty5x46+vVUq+isUWIlEBIWEYGdnR35+PmfPnqUQ2CWXk1mP6UxDuCkWEyuVIrl8mZYtWxISEkLF0aOkqlRk1OJ78ff3Z9iwYaSkpDBnzpwG/XelDwsXLuSDDz4gIiKCQ4cOGSwOB5UKX7UaE6iRk+RAK7WaLImkTtV7dNKtBO7b9NMER5gN1ZOybdu2XL16VS+joycJDAxEpVJx5cqVBr93bYlEIsLDw3njjTdITk7m4MGDTJo0CaVSyZw5c/Q+Ol6xYoV2DTsxMdGgL2qVlZXaYiEA58+fN/jUXXFxMTt37tQ+zPyVhUbDiMpKRlVW0tbRkYiICKysrDhx4gSnz5zhhETCQhOTRpcsqx2TSskTi0lMTMTCwoLWrVszQKHA5DE7Wq2trZk0aRL9+vVj5cqVbN68udEnS4CioiL27NlD//79sbS0bPD7m2s0DK+sxF2txtbcHHd3d4qKilCr1ag1GpLy88m+92/0Rh12yur+HCZQUV6OXC5HJBIZ3fbnp+Hq6oqDgwNlZWWkpaXVu6fco/j4+DTIOc+HCQkJ4caNGwa5d200b95cOw25aNEiwsPD6dq1K6tXr9b5GcBHWbFiBSdOnMDnXgspQx3/ee655wgPDycrK4s2bdqQnZ3NsmXLGjyOhzl58iRBQUH07NmTgwcPVr1To6GdSkVfpRJrqZTg0FCsra25cuUKaWlp3BSJiDYxqfXGC2OlvlfQ4MWKCjIzM/Hw8ODKlSv0USjY+ZepWZFIROfOnenXrx9Hjx5l5cqVdVr7bQwOHjyonZq9v2GBXt33b81KIsE+IACnkhKKi4vJysqiQiIhQyLhjkajXRdPrMMDWr0TpkYkQkFVwpTL5Yio2vWmUCiQyWRNornuwoUL8fHxwc/Pj8GDB3Pz5k3S0tJITU3VaUkqQx8nMcbdsZaWlnTv3p1OnTqxb98+ioqKeOmllzhz5gxz585t8A4h58+f5/z581hYWDB+/Hhmz57dYJVWrK2tefXVV7G1tWXLli189NFHtGzZkvz8fKP6PduwYQPTp08nOTmZ8hs3tGXtWrVqhZeXFxUVFRw9epTiu3eJl0qJl0pRNYK1ytrIE4s5KpUizsrCxcWF4OBglCdOkKZSkXXvhdnBwYHRo0cjFov59ddfjfpBtb5+//133n//fcLDw/W+Y9tKo2FAZSUeajUuLi74+PhQWVnJmYwMLMrKyBWLyfnLIO6CRMK5hkyYULWOqVQqMTMzA2p2LDGmX+SnVVZWRlJSEklJSdo6o35+fkyYMAGpVKpNnhcvXnzqHab29vbIZDKDdScxMzMzqoRpZ2dHz549CQoK4tSpU/z+++/06tWLli1bsmLFCoPXul29ejWffvopzz//PCtXrtT7/SIiIujfvz83b96ssQPWGKfQ79y5w57du5n30ktYx8eTfeECLVu2xNzcnEuXLnHx4kWui8XsNDGp03RYYxEnleKtUpGUlERYWBhubm6MLSzke6WSrr16ER4ebrQFCHTtzp077N+/n8GDB1e1XtPHssG9Eoq9FAos5XJCQkKwtLTk8uXLZGRkcF0k4qqJCW3Uamzv/X3fEYk4JZWSIJGgqcPDmk4S5gMtvu71xDQ1NTX4tmJdq64zevHiRbZt24aDgwN+fn706tWL8ePHc/HiRVJTU0lLS6vTPw5DHifp3LkzFRUVBl8Dg6o+nGFhYXh7e3P8+HG+++472rZty9SpU0lMTGTt2rVGcexFrVazYsUKpk6dSuvWrfWWwC0tLXnllVdwcHBg9+7d/zfNacSaqdWMLClhYGUldOhAWHAwJ06c4MiRI9ytqOCITMbxOr5QNSYqkYgdcjmTSkowMzPj+eefB6CPWMzKO3f4/vvvKSwsNHCUDWfv3r2EhIQwdepUvv/+e51e+1ElFGNjYykpL6/5b02jwere1xXDUx1V0knCrPhLi6+m3hPzfvn5+eTn53P48GHMzc21U7dDhgwhPz+f1NRUUlNTn1huzpDHSdq3b2/wkUpwcDD9+vVDqVRy4sQJNm3ahImJCaNHj8be3p4//vjjqQ7F69OFCxdIT0/nxRdf5IsvvtD59bt168aQIUMoKCjg66+/Nv6HT42GDveKpYfe129UIpFw6dIlMhUKdpiYcLsJjir/6ppYTIJUylutWyO698LsV1LC3sWLKXwGvv+/+v3333nvvffo1q0bcXFx9b6eWKOhk0pFd4UCq/tKKGZmZnLp0iVy7s1gFNz/dy0SUd/dJ7qZkuX/ChfA/7X4amrFC56ktLSUU6dOcerUKSQSiXbq9sUXX0QkEmmTZ2ZmZo0FfktLS9q0adMgU3sP06JFC7Zs2WKQe9vZ2TF69GjMzMxYu3Ytly5dQiKR0K1bNyIiIoiPj2fFihVGuyFi2bJlfPrpp4wePZoNGzbo5Jrm5uZMmzYNZ2dn9u/f3yhKFd7/pO/r64uZmZn2qFlRWRkrCwo4Ipc32VHlw1xu2xbFfTtEC+/cYVBlJYtNTJrMmm1tFRQUcOjQIYYMGUJSUlK9SjU63ave0+IhJRSLKys5KJORpKcSivqZkuXZGWE+ikql0rZA2rp1K05OTvj5+dG3b18cHR3JyMggNTWVvn378v7771NWVsbWrVsbfJTp5OSEVCp9aOUYfao+ItK7d28OHjxIbGwsarWaVq1aERkZSWFhIQsWLDCqjgwPo1QqWbt2LePHjyc2Npa8vLx6Xa9Tp06MGDGC4uJivvvuO6PvLyjSaOh870nfztqa4OBgJBIJCQkJHDp0CI27O9LRo8lydESTm2vocBuEXC5nwIABBAQE8OHu3bxubo69vT15eXnYazSEK5UclskMHWaD27VrF4GBgbz88stPNTUrufd3F6ZUYm1pSUhISIOXUNTppp/qKVkTml7xgvrKy8sjLy+PgwcPYmFhga+vL8HBwXzwwQeIRCIsLCyYPXs2gwYNatC4unTpQnFxcYOuCzo5OREZGYlSqeSnn37i1q1bNV5ktm/f3iBlCHXl7NmzhIeHM3XqVL766qunuoapqSlTp07F1dWVmJiYRtGP1EGtZvC9J/127drh7OzM7du3OX36NGVqNftlMs5cuEDw7t1ERkYyf/58g/du1DdPT09GjRrFpUuXtHWMiyorCdVoiIiIwNfXF3VaGmkSSZPc8PQk1VOzdd0166JWM6iyEnuNRvtvzRAlFHWzhknNEWZTLF6gS3fv3uXkyZOcPn2aTz/9FGtra6CqLuqXX37JjRs3OHv2LHFxcXorYiCRSPjkk08YNmxYg20kkUgk9OrVi65du7J7924SEhLQaDR4e3szatQoMjMzmTt3rt46a+jTkiVL+Oc//8mgQYPqnOyCg4OJiori7t27zJkzx+hH1WKNhq5KJd2USmybNSM4OBixWMyZM2fIz88nQyJh931P+klJSQQHB9OrVy+jKMitD6ampgwZMgQvLy82bNhQYwPfQZkMz4oKUlJS8Pf3Jycnh0FlZfzxjE1RQ9XUbPWu2VOnTj3xd12m0RChVBKqVGJrY0NQUBBisZizZ89y48YNUiUS9pqYUNpAf4+ix21rtrOzq9We574KBWFUFe8+dOgQ+2QybAYOBGgU6y+G1LVrV95//31u3rzJhx9+iJOTE2FhYbRu3RoTExNKSkpIT0/nyJEjOj1y8tprr/Hvf/8bqHrY6dq1KxkZGTq7/l+5uroSGRnJnTt32LhxI0VFRVhYWDB06FDc3NzYuHGjXu/fEMLCwhgxYgT//e9/a9WGSS6XM3nyZNzd3Tl69KjB1pHrwvnek74jVZvFnJycuHXrFmfOnOGuRsNeqZSUh6wfWVtbM2PGDH777bd6T1sbGz8/P0aOHElqaio7d+58aKUeT5WKyMpKOnbsiKmpKUeOHOGgTMYxPTWbNnbvv/++tmXZo7RWqRikUGBDVWN7BwcHbt68ydmzZynSaNglk9Wq7ODTuH379kMzsF7WMKtHmDY2Nrq4fJMWHx/PqFGjtG/funWLlJQUoGrE2aNHD/z8/AgNDaWyspLs7Gzi4+O1n/O0HB0dtX+WSCQ0b95cLwlLJpPRt29fQkND2bZtm3aqNTQ0lEGDBpGYmGiQAgT6cOzYMcLCwmrVD9Df359x48ZRUVHB/PnzDXL+ti6kGg3dlUo6K5XY2doSGBiISCQiKSmJW7dukSaRsFcuf2Tj5KKiIqKjoxkzZgw//fRTk5iaNTc3Z/jw4bi6uj6x4lSGREKqRII0KYmePXvi5eWF8sIFLojFz8Su4b9auHAh77zzDh07dtQ2F6hmcq8wf5BKhYODA+3bt0ej0ZCYmEhBQQFJEgkH5XIqDDA618kIs4tCQYRSSe/evTl06BDxYjElXbvi7u7OunXrdBbss0wul9OlSxeCg4NxcnIC4Pr16yQlJREfH1/nNciQkBA2bNhAs2bN2LZtG5MnT9b5i1jbtm0ZPXo0ubm5bN26lbt372JnZ8fIkSOxtLRk/fr1Bj/OomuWlpZ89NFH7N27lwMHDjzwcalUyksvvYSnpycnT55sFL8frvee9O35vyf96mLpJcBumYz0Wj7pT506lYyMDA4fPqzXmPUtMDCQoUOHcvr0aXbv3l2rBz5zjYZpFRV4Ojvj5+dHXFwcGRUVrHgGp2YBhg4dSpcuXZg9e7a2wI2nSsUAhYJmYjFBQUHY2tpy/fp1UlJSKAB2ymRkN0CtYf2OMO/9sO8vwF5RUfHMHSvRp8rKSg4fPqx9ofH39ycsLIy+ffsyePBg7ty5Q2pqKjExMfj7+zNz5kyuXr3Kxx9//EDtW7FYTLdu3Zg5cyaurq789NNPOo3VxMSEQYMG4efnx6ZNm0hNTUUsFtOzZ09t94IjR440iVHGX5WUlLB792769+9PQkJCjWIQPj4+TJgwAZVKxU8//WR050r/qnr9qINSib29PQEBATWe9M9JJOyTySivw4v9pk2bePPNNzl37hy3b9/WY/T6YWVlxciRI2nevDnLli2r08+wVCRir0yG+dWruLq6EhISQllcHKEqFSefwanZbdu2ERQUxMSJE/lz4UKeUyhop1LRokUL/Pz8UCqVHD9+nKLiYk5KpRyWSrXdsQxFZ1OycF+Lr8pKYdOPnp07d45z584B4OzsTI8ePQgICKBHjx5MmjQJ+b1izxUVFbz33ns1vtbDwwORSER2djYWFhY6jcvHx4dRo0Zx/vx5/ve//1FRUYGLiwuRkZGUlJTw448/NsoXyro4dOgQnTp14v333ychIYFjx44xcOBA/Pz8SEpKYvXq1YYO8Yncqxs7i0QEBgdrj0UkJyfXqwXX7du3OXToECNHjmTRokW6D1yPOnTowKBBgzh27NhTF0tPFYtpJ5EgO3WKHj160KZNGyKyssgQi5/JggYrli/n+7/9jWkjR3I1IwORSIS1tTW5ubmcP3+eWyIRO42oML/OjpXAgwlTGGE2jGvXrrFmzRoA3NzcmDZtmvZj969VVvP39+f06dP4+Phw6dIlncRw/waetWvXcvHiReRyOUOGDCE4OJgdO3Y0+FlPQzp27Bjr16/nrbfeori4mJUrV/Lrr7+SlZVl6NAey0Sjoc+9xs4ODg74+/trG1kXFhZySiLhoEymnVV6GkeOHCE4OJjQ0FASExN1GL1+2NjYMHr0aCwsLFi4cGH91ptFInbLZLQqL+fChQt4e3tz9epVBioUrJbL9X4swphYajQMu3uXblevIm7dGr/WrTl//jx79uyh+O5djkmlxBpZYX6dpO3qPWFKpRKZTIZcOFZiMNnZ2cyfPx+o2kA0Z86cGh8XiUT4+flx/vx5fH19OXv2bJ2u/7ARaWBgILNmzaKkpIS5c+dy8eJFvL29efvttzE3N2fOnDnPVLKEqh3j1eeSraysOHz4sNEnS0+VipcrKgjWaAgNDSUwMJC8vDwOHTrE5aIiVsrl7JbL65UsoaoO7/r16xk8eLBBeiXWlkgkokuXLkyfPp3MzEx+/PFHnWzOKhaJ2C+TkZOTw927dwkNDcVdrSbQSKtZ6ZxGQ4BSybSKCjpaWdXonZqXl0dmWRl/mJhwWCYzqmQJ+hphIhQuMKTPPvuMb7/9lrKysgc2A7m6ulJWVoaNjQ15eXm1LrhuamrK2rVrCQ8P58SJE4wePRoTExOGDBmCnZ2dttarpaUlkZGRtGrVivXr1zf6oyJPw8PDA29vbzQaDSKRiLKysjo/mDQkM42GvvfWj5ycnGjXrh0qlYqEhAQKi4o4IZUSo+P1oytXrpCQkMDIkSNZvny5zq6rK82bNycyMhKRSMQvv/yi87OxZyQS/FQqTE6donv37ri5udEnO5ssiUSvlWoMzfpeWbs2ajWenp64uLhQXFyMlZUVRXfvsvjyZbbK5aiN9O9Ap2uY92/6EUaYhvWoJtft2rUjOTmZdu3a1epoStu2bXnnnXdwcXEhPDwcgI4dO/L777+TnJzMkSNHtLVeO3bsyMCBAzlx4gTr169vEkdF6kIsFjNhwgTat2/P/v37+eOPP+jUqRMtWrRAZqSl0HxUKvorFFiJxYR07EizZs0arLHzvn37mD59OoGBgZw5c0Yv96grsVhM9+7diYiI0G8LLpGIaJkMl3vNpj09Pbl27Rr9FQrWy2RNb2pWoyH0XmF+G3NzQkNDkcvlpKamsm/fPhQtWrC6rIyc8nKj/t71kjBNqNrVKZVKEYvFTXI3ZGPVvn171qxZw6RJk/jtt9+e+PmrV6/Gw8PjgfffuXNHO4q1t7dn9OjRyOXy+q/xNFJeXl688MILQFX5r4sXLwJVXef9/PyIjIxk3rx5RvMQYa7R0F+hwEelwtnZGV9fX5RKJceOHaOopISjUilxel4/UiqVrFu3jhdffJGLFy9y9+5dvd2rNpycnIiKiqKioqJBNqcVisUcksmQZWXh7OxMSEgIiuPH8bt3ZrOpsL1XQtFVrcbHxwdXV1eKioo4evQoZffq6p68fbtRHK3Rz7GSe++vPlpSVlami9sI6ql58+baUX9FRQU3b9584ue7u7tr37569SqXLl3SHl1Zs2YN69atw83NjQMHDhAbG9vkG+L+lVgsZuLEifj5+XHu3DlWrVr1wANiamoqgYGBDBgwgG3bthko0ns0GvzUavoqFFhLJIR06oS1tTU5OTmkp6eTd68tUl4D7UrMyckhMTGR4cOHs2rVqga551/dX7Jx165dDdoA4aREgq9KxalTp+jWrRsuLi70vXqVy2Jxg5V705f7C/Pb3CuWLpVKSUlJ4dq1a1wWi4k2MeGOkeyArQ29jDDl9140q6dlhYRpHPz8/EhNTcXPz++x07HV5yj9/f3ZuHEjUVFRVFZW8s9//pNNmzbxwQcfMGTIEKBqejYiIsJgza8NqfpcpVqt5rfffntspZctW7Ywa9Yszp07p7OdyXVlodEwQKHAS6XCxcUFHx8fKisriY+Pp7i0lFiplGNSaYOvH+3Zs4eZM2fSvn17kpOTG/TeLi4uREVFcedeY+cG7zkqErFTJqNFWRnZ2dn4+PiQl5dHP4WCzfeOhjVG9xfm9/Pzo2XLltpi6aUqFQdkMk7rqQWXPukkYaoANVVTLCYmJkioKtAsFC8wLt7e3sTHx9OvXz82bdr00M/x9PQkMjKSCxcu8O2331JeXq49tnLjxg1GjRrFuHHjtJ9vYmLyTI4qJ02ahK+vL2fOnGH16tVPXHYoKytj8+bNREVFNfzUrEZDO5WKvkol1lIpIR06YGVlxeXLl8nIyOC6WMwOExPyDfSkXz01+8ILL5CVldUgxfelUil9+/alQ4cObN++naSkJL3f81Fui8UckcmQXriAk5MTwcHBKE+cIFWlqnUFJWMh1mjoplTS9V5h/qCgICQSCefOnSMvL++BwvyNjW7KS4hEVIpENerJmgAKhcJoNzs8a2QyGW5ubmzbtg1LS0uys7NrfNzExITBgwfj7e3Nhg0buHDhgvZjhw4dIioqiilTpmg7q1TbtGlTjc9t6u4fVdb1XGVKSgoBAQENOjVreW+t0kulolWrVnh5eVFeXk5cXBwlZWXEyGQcl0gMvn50+fJlzpw5w7Bhw/Re2KF169ZERUVx9epV5s6da/C1U4Dj903NdunSBScnJ/rfuEG2WFynSkqGVKMwv78/Tk5O2nZvdzUa9spkDy3M35jorB5TJdToiSnTaISEaUTc3d25evUqbm5uZGRk1BgVenl5MXr0aNLT05k7d+4D3RYOHDjA5MmTH0iWycnJTJ06tUHiNzSpVMqkSZPw9vYmKSmJtWvXPtVmtq1btzbM1KxGg79KxXNKJdYyGaGdOmFhYUFWVhaZmZlcvTeqvGVE60e7du1i5syZ2qUDXavuuerv78+WLVsafPr3cTT3pmZfunuXK1eu0K5dO/Lz8+mjULDDyKdmpRoNPZRKOimV2NvZERgYCFDrwvyNid4SphxhhGlM2rZtS2ZmJj4+Ptr1SxMTE4YOHYqnp+djz0xKJJIHiqSrVCrWrl2r97iNgZ+fH88//zwqlYpffvmFy5cvP/W1SktL2bRpk16nZq00GgZUVuKhVtO6dWs8PDwoLy8nNjaWkvJyYmQyEoxgVPlXCoWCDRs2MG7cOLKysnTaC9bT05PRo0eTlZXF3LlzjXJfxQ2xmHipFHFaGo6OjgQFBaE+dYpUlYosI52adVWpGKxQYH9fCcUbN25w7ty5qsL8cnmjm1Z+HJ0lTMVfpmSFhGlcPD092bt3L+Hh4WzYsAFvb29Gjx5NWloac+bM0XYL+CtXV1eioqI4e/YsM2bMoH379sTGxpKTk8PQoUOxsrJ65JnPxk4qlfLiiy/i5eXFqVOntOUH6yslJUU/u2Y1GgJVKvooFFibmBAaGoqZmRmZmZlcunSJK/dGlcbcTiozM5OUlBSGDh2qk04u1Y2dPT092bhxo9FvTouTSvFWqUhKSqJTp040b96cgbdusVAsrneFJV2S39fY2dHRkfbt26NSqbQlFM9KJOyvY2H+xkBnCbOCv4ww703JSp/BKvzGplWrVvj7+zNkyBBEIhEjRoygVatW2pqvD3P/poj7+1jez9HRkVGjRvHHH3/o+1tocO3ateP5559HoVDw888/P7DmW1+63jVrpdEwqLKSNmo1bdq0oW3btty9e7dqVFlRQYxMxgkjHFU+zM6dO3n77bfx9vauV4Jr164dI0aMICUl5bEPhcZELRKxUy5nUlER169fx9/fn1uHDhGhVLLHSAYfbaoL84vFBHfogI2NDdeuXSMlJYUikYhoudxoR8T1pdcRZnVtWYHhODg4MGLECEaNGqXtYHL79m2mTJnyyBcQNzc3oqKiuH79OvPmzXtk+bz9+/czY8YMAgICjLr0W13c368yMTFRb9POpaWlutk1q9EQrFLRS6GgmakpoaGhmJqacuHCBbKzs8m5d66ywIhHlX9VWVnJ+vXriYqKYs6cOQ+sqT+JhYUFw4YNw8XFhT///NPoa/j+1TWxmASpFHFKCj179iQwMBDN6dOkicXkGDARmd4rzB/wl2IXx48fp7i4mESplENSqVGNhHVN2PTThP3973/nn//8Z42fC1RV6XlYspTJZPTv35+goCC2bNmibR/2KCqVivXr1zNx4kQyMjKMcl2oLvz9/Rk7diwKhYIff/yR3Nxcvd4vOTm5XrtmrdVqBikUuKvVeHh44O7uTklJCUeOHOFuZSWHZDJONtJdiRkZGaSnpzN48GA2btxY668LCgpi6NChJCYmsm7dujo3VjcWMVIpXioVZ86cITQ0FFtbWwbducMisRilAX6e3vdKKDa7r9hFdQuugnujYkMm84ai04SpUqlqbPqprKwUEqYBvf/++0DVqKmkpARLS0vy8vL44YcfHvjcNm3aEBkZSU5ODnPnzq31Wbjs7GzOnTvH4MGDWb9+vU7jbyhSqZTJkyfj4eHByZMndbJ2VlvVU7Nnz56t/Wai++pyNjMzIzQ0FBMTE86fP09ubi7Z90aVjamCysNs376dt99+Gw8Pj0cuHVSztrZm5MiR2NnZsXTpUr0/7OibUiRih0zGCwUF5OfnExgYyJ3Dh+mhVHKgAV9TLe4V5vd9SLGLu6WlJNwrzG+IJG4IukuYIhHqe+1pRCKRNmHKjXxLdFPl4uJCSUkJdnZ2AFy/fp3Ro0dz7dq1Go1v5XI5AwcOpH379mzatOmptvPv2rWr1i9sxiYgIICxY8dSUVHBDz/88MBuYH2r3jU7ZsyYWk3N2twbVbqp1Xh5eeHm5kZxcTHHjx/nrkLBQZmMU410VPlXFRUVbNy4kcjISObOnfvIJYTqov9Hjx7VNgJoCnIlEhKlUkRnzxIREUH79u1RnzvHeYmEq/p+GLqv2EUzmYyQjh2xtLTUFrvIv9fY+VojfyirK52OMOH/RplyjYbKykpsbGx0dQtBLYjFYvr06UOXLl2YNWsWY8eOpVWrVnz22WcPPHV7eHgQGRlZ7632FRUVbN68mVGjRjF37txGMQ0ml8uZPHky7u7uJCQk1GnaT9eqd83279+f7du3P/yTNBo6VI8q/9Lt4erVq1y6N6osamIvYOfPn+fSpUsMGDCArVu31viYra0to0ePxszMjN9//53r168bKEr9OSSV4qlSce7cOYKCgsjOzmZQcTFLTEz0Vhjf6l6xC0+V6oFjSaXl5cRLpcQbWWPnhqLTTT9QlTDFYjFylYqiigphhNmAHB0dGTt2LCUlJcybNw93d3fS09OZP39+jYLS5ubmDBgwAB8fHzZs2KCTrfapqakEBwfz3HPPsWvXrnpfT5+CgoKIioqivLycH374gatXrxo6pBq7Zv86Nfu4bg93703RNca6nLW1detW3n77bXJzc7l9+zbZ2dl07dqVPn36EBMTQ0xMTJPtiFR5r6DBuJs3uX37NsHBwRQdPkxXpZIjup6a1WgIUqnoXX0sqXNnzM3NyczMJCsry+AlFI2BzkeYarW6KmEqlcKUbAMRiUT06NGDnj17smvXLk6fPs3w4cNxd3dn8eLF2mlGsVhM165d6d27N2fOnHmqHYiPU13F5syZM0bZ4ksulzNlyhRat27NsWPH2Lx5s6FD0vrrrlmlUolIo6GjSkXPR3R7yBKL2dmI63LWVllZGQUFBezevRuJRMLhw4eJjo7ml19+eWLHnabgkkTCWYkE8enTRERE4OvrizotjXSJhBs6Sl7N7k31t1aradu2LW3atOHu3btVG8gqKjhiJCUUDU13I8x7/6/uMi+jag1TKL6uX/b29owZMwa1Ws2PP/6ItbU1M2bMICsri++//1677uPt7c3QoUO5c+cOv/76Kzdu3NB5LCUlJURHRxMZGclPP/1kVE/9wcHBREZGUl5ezvz5840yod+/azZ+61YGKxS4qNX4+vri4uJSo9vDPpmMs014VPlXI0eO1B5Z6969O2PHjm0U5yp1Zb9MRtuKClJSUvD39yc3N5dBpaX8IZfXK4mJ7k3191QosDYzIyQkBFNTU9LT08nJySH33lS/MRe7aEg6S5im9/4vk8lQKpVUULW2JYww9UMkEtG5c2f69+/P/v37SUhIoH///gQGBrJ582ZtnUwbGxuGDx+Oo6Mj27dv10uNzvudOHGC4OBgwsPDiYmJ0eu9akMulzN16lTc3Nw4evQoW7ZsMXRIj7V182bWv/ce39nZcTsvj8LCQkQiEWfPnuXGjRtcFIvZ9QyMKquJRCLCw8OxsLDQvi8vL++ZSpYA5SIRu2QyRufl0apVK4KDgyk5coTOKhXHnrI4jP29UaXLXzaQxcTEcFeh4JBMRuIz9FBWGzpLmHYaDWKxGIlEQmVlJSVSqTAlqyeurq4MHz4cjUbDL7/8gqWlJTNmzKhxJEQkEtG1a1eee+45YmNjG3T34MaNG3njjTdISUnh1q1bDXLPhwkNDWXUqFGUlZUxb9488vLyDBZLbdir1Uw1MaFTQQEiW1scbW3JyMhg48aNlN7r9pD8DL2AOTg4EBUVhUql4oUXXuDll1+mTZs2XL58GalU2ig2l+nSBYmEVIkEaVISPXv2xMvLC+WFC1wQi+s0AhRrNHRWKumuVGJtYUFISAhyuZy0tDSuXLnSZDeQ6YLOEqaLWk2zZs20dUVviMXaBtKCp2dqakplZSXqe3+//fr1w9vbm+joaJKTkxk4cCDt2rWrcSTEycmJyMhIVCoVP//8c4Ov89y6dYv9+/czZswYFixY0OD9Mk1NTZkyZQqtWrUiPj7+gd2Vxkak0RB27wXM18oK0X0J8ebNm5wXidgtl1PyjCRKkUhE9+7d6dWrF3v27OHYsWNoNBo++eQTACZMmNAoNpfpw16ZjNYVFaSlpeHn50dubi6DKypYUcupWad7o0qn+zaQFRYWcvToUUpUKvY/Y1P9daWThCnVaHBUq7GxseHOnTsAXBGLEZWVYWZmpotbPJM+/PBD3n//fW7dusUPP/yAtbU1x48f57vvvqNVq1bMnDmTzMxM7ZEQiURCnz59CAsLY/fu3SQkJBisuXNcXBz+/v5069aN2NjYBrtvx44dGTlyJHfv3m0Uo8rqzvTOGg0BgYE4ODiQnZ2Nq6srpRoNv968ySaZ7Jl5AaseVSqVSn788Udu3779wOds2bKFmTNncvbsWaPY4dyQSkUi9spkmF+9iqurK6GhoZTFxtJBpeLEY6ZmJRoN4UolYUolzaysCAkJQSKRkJyczPXr17kgkbDbxOSZeSh7WjpJmPYaDWLA0tKSGzducEckolQkQiwkzKdmY2OjrdRjb2/PsGHDGDFiBABDhgzRNnquPhLSunVrIiMjyc/P5/vvv6eoqMhgsUPV5q9169bxxhtvkJaWpvepWVNTU6ZNm4aLiwuxsbGPPs9oJMQaDV2USroplTS3tSUoKAiAU6dOcfv2bdIlEq6FhhI2bBiSH35oMofxH0UsFtO9e3ciIiLYvXs3x48ff+TDXklJCTt37iQqKooffvjBqDaXNYRUsRg/iQTZqVP06NGDtm3bosjM5IJYTOFDplFd7jV2ttdoaNeuHc7OzhQUFHD69GmK1Wr2yuWkisXPzENZfegkYdrd+4dtYWHB3bt3uXnvh6ZWq1Eqlcjl8mdukb6+KioqKC4uxsrKCqg6O/jBBx9QWVnJ6dOntUdCTExMtNOyW7dufWL914ZUPTUbFRXFr7/+qrfRbqdOnRgxYgQlJSXMmTOH/Px8vdxHVxzvTYvdP6rMz8/n7Nmz3IX/ewE7dw6fDh3o1asX+/btM3TYeuPo6EhUVBQKhYIffviBgoKCJ35NYmIiQUFB9OzZk4MHD+o/SGMiErFHJsOtvJwLFy7g7e3NlStXGKRQ8Kdcrk18Mo2GnkolHZRKbG1sCAoKQiQScebMGfLz80mRSNhnYkKpkChrTScJs7lajUgkwszMjNLS0hoL0GVlZZibmwsJs47KysqYOHEiK1aswNLSEolEwiuvvEJgYKA2Ifj6+jJy5EjS09ONtiluXFwcAQEBdO3albi4OJ1e+/5R5ZEjR9ixY4dOr69rYo2GrtWjyvs60ycmJlJQUECaRMIemazGC9jGjRuZMWOGduqsKRGLxdrzw08aVT7Mxo0bmT59OsnJyUb/kKRrxSIR+2UyTHJycHFxITQ0lIr4eIJUKk5LpbipVAxSKLAF/AMCcHR05ObNm5w9e5YijYZdcjkZz0CxdF3T2ZTs/ZtTbt73gyi7Ny1bvbYpeDyJRELr1q3x8vLCy8uLW7duYWlpCVSdax05ciSHDh2iV69eWFlZsWbNGjIzMw0c9aNVT82+/vrrnD9/XmdTs2FhYQwbNozi4mK+++47g+7GrQ2ne2uVTveNKqs709+lqjP9+Ye8gBUVFREdHU1UVJTRnW2tD0dHR8aMGaOttlSbUeVf3blzh3379hEZGWmQzWWGdkYiwU+lwuTUKbp3746bmxu9s7NpqVYTqFJhd99DWfVU/2mJhANyORXCqPKp6CZhqtXa6VjggRGmsFP28SwsLPDx8cHX1xcvLy9u3rxJeno6W7duZcuWLXz99df07t0bKysrvvnmG2JjY/n00085ffp0o3gBvXnzJgcOHCAyMpLffvutXi9s5ubmTJs2DWdnZ23FF2Mm0WjoplTS5b5RpUaj0Y4qU++NKsse8wJ24sQJAgMDm8T0o1gspmfPnnTv3l07qqyP+Ph4AgMD6dKlC/Hx8TqKspEQiYiWyXCpqCAzM5OgoCBat26NT04ONjY22Nvbax/K7gA75XIuC6PKeql3whRpNNhqNFhYWGhbQt2875e/vLz8md/44+Pjw9/+9jfKy8v55ZdfuHr1Kp6ennh6euLh4YGNjQ0ZGRmkpaWxZcsWbcNmU1NTHBwc+Pbbbxk4cKD2ehUVFZw6dcpQ385TiY2Nxd/fv15Ts126dGHYsGEUFhY2ilGl873NFo5AYFAQzZs3176AlVA1qkyv5QvYhg0beOuttxr19KOTkxNRUVGUlZXxww8/6GTWSaPRsH79el577TVSU1OfuZmsQrGYQzIZToWF+Pj4IJFI0Gg0pKSkcOTIEQru3NE2dlYIo8p6q3fCtNVosLGywtvbm+zsbO6KRDWG+2XP+E5ZX19fDhw4oC0ROGHCBJYvX87ly5e5cOECGzZs4MqVK2g0Guzt7WnTpg1ubm60bt0aJycnbty4gUql4uTJk3To0AG1Wt0oz5/dPzWblpb20OMCj2Jubs7LL79MixYtOHTokNF//xKNhu73tvA3t7cnICAAjUbDyZMnuXPnDskSCfueMKr8qzt37rBnzx6ioqL45ZdfGtX0o1gsJiIigvDwcHbt2lWjEYAu5OfnExMTw6hRo1i8eLFOr90YnJRImOjpqS0dKBKJqqpCFRay08SEK0IBAp2pd8Js5+jI5JEjMTc3JyAggHOlpXDfQflnvXhBly5datTTtba2RiKRUFFRgbu7O76+vlhZWWmLPly9epWcnBx27txJTk6OtprJr7/+SpcuXejVqxeOjo6G+nbq5ebNmxw8eJCoqKhaT81269aNIUOGcOfOHf773/8+1VpXQ2qpVjO4spLmQFBwMPb29uTl5ZGcnEwx1GuzxfHjxwkKCiI8PJwjR47oNG59cXJyYsyYMZSWljJ//nwKCwv1cp/Dhw8TGBhIaGgoiYmJermHsQrt0AHZiBFUFBZiolRSoVCwMT+fVXpsAfasqnfC7OHlhbm5OVBVR7bLkCHIVq7UNsJ91o+UHD16tMZDQ0ZGBr/99htmZmYolUrKy8spKSmhsLDwsaW+1Go1cXFxnDhxgk8//ZRBgwaxc+fOhvo2dObIkSP4+/s/cc3J0tKSadOm4eTkxIEDB9izZ08DRll3Uo2GHkolnZRKHJo3x9/fv8ao8qxEwn6ZjPJ6vIBVTz++8cYbpKamGvWUtFgsplevXnTr1o3o6GhOnDih1/up1WrWrVvHlClTSE9P1y5rNGX37xLfffQo/z1yhH7e3sRevkzq9evCuUo9ED3uKd/Ozu6JQ4CZ1tb844UXkMlkqFQqlhUXk9WsGb/99hvl5eVMnjyZY8eO6b3otzHz9fVl3rx5HDlyhDlz5tT4ZTY3N6dXr15kZ2fX+gxlWFgYI0aM4D//+U+jXLNp3rw5r7/++iMruYSHhzN48GAKCgpYuHCh0Y8qXapHlSIRgYGB2Nvbc/36dVJSUigComUyMnW42SI8PJz27dvXewOVvrRo0YIxY8ZQUlLChg0b9DaqfJgBAwZgb2/PypUrG+yehtClSxeGDh1KSUkJCxcubLTr2sbq9u3bD33aqNfktlSjQZaXx759+zh79iwrV67k/y1ZUnWIdtAgAJydnfXSSqoxSUtLIzY2lkOHDtVIljKZjK1bt7J8+XIOHDjAsGHDanW9Y8eOkZ+fz5QpU/QVsl7dPzV7f91US0tLZs2axZAhQ9i/fz/ffvutUSdLqUbDcwoFL1RU4G1vT8+ePWnWrBknTpwgOTmZJLGYhSYmOk2WUHW2VSKREBYWptPr1pdYLOa5557j5ZdfJi4ujsWLFzdosgTYt28fzs7OtG/fvkHv21AsLS2ZOXMmw4cPJyYmhq+//lpIlg2oXlOyLdVqJFT9ohw/fpyMW7coMjUlOjqaDz74gFOnTlFZWWnUU0cNRaVSaaeuq7m7uxMSEgJUnb8cNmxYrQuFL1y4kA8//JBu3brpvCBAQzhy5AgdO3bkp59+Ii8vj+PHjxMeHs7t27cbxcjZVaVisEKBvUhEUEgIdnZ2XL9+neTkZIpEIqLlcrL0tIW/egPVa6+9xvnz543iocLZ2ZkxY8ZQVFRk0NKMSqWS9evXM378eDIzM42ymMfT6tWrF/369aOgoKBRrOc3RfVKmK3vVfhp1qwZZ8+eJfvebqzS0lKKi4vp1asXR48e1UmgjZ1KpdLuYoOqJ8Xhw4dTUlKiLUxQlzNpRUVFHD58mMGDB5OYmEh5ebnOY9YnjUZD9+7d6d27NwBXr15l+vTpHDhwwMCRPZ5MoyHiXrkxBwcH/P39UalUnDhxgsLCQk5JJByUyajU8/pRfn4+hw8fZvTo0SxcuFCv93ociURCr1696Nq1Kzt37uTkyZMGi6XapUuXSElJYfDgwaxfv97Q4dSbra0t06ZNw9bWlj179jT6s7iNWb0Sptu9llN3795FqVSSLZMBMGnSJN544w3u3r1b62nGpk6tViO9102gd+/e9O3bl4KCAgYOHEj//v25dOkSmzZtqtM1o6OjCQ0N5aWXXmLBggV6iFq/7p82MzU1NfpkWV1uzO6+UeW1a9dISUmhUCRq8IPhMTExBAQE0KlTJ50f1agNYxlVPkx0dDSzZs3C09OTjIwMQ4fz1AYMGEBERAR5eXl89dVXz8RmJmP21AlTqtHgrFZjZ2en3biRLZFga2vLd999h1QqxcbGhvfee48333xTZwHXVZ8+fejbt69Bq8L07duXt956C6VSSb9+/SgsLKzxpJiSkvLU116yZAnTp0/Hz8+v0Wyssra25pVXXuHixYvaIzLGPBMhvzeqDFUqcXR0pH379qhUKhISEigqKuKkgQ6Gq9Vq1q5dyyuvvEJ6enqDrRdKJBJ69+5Nly5d2LFjh1Ee46ioqGDjxo2MHj2auXPnNrqd+g4ODkydOhUrKyu2bdvWKJddmqKnTpgu99YvbW1tycrKokAkolgkwk4kqrGRQ2TArc3BwcGsXr0aiUTCq6++yvDhww3yD+9///ufdtq1X79+dOjQQWdPilevXuX06dOMGzeO2bNnG32pvF69etG/f3/y8/MZN24cLi4ueHh40KNHD1q1akVOTo6hQ6yhdXURa5GI4NBQbG1ttaPKOyIRO+RycgxYbiwvL4/4+HhGjRrFkiVL9H6/li1bMmbMGO7cuWN0o8q/Sk9P59KlS/Tv359t27YZOpxaGz58OF26dOHKlSvMmzev0S23NGVPvUu2tVqNWCzGysqKO3fuaNcvb9++zXvvvUdxcTFJSUl8+eWXOgu2rnx9fbXrhmKxmHbt2jV4DA4ODlhYWGjfvnbtms6nVdasWQPAuHHjdHpdXbK2tubvf/87/fr1Y9euXdrjNefPn2fHjh1kZGTw0ksvGTpMLRONhgGVlTxfWYmXgwMRERFYWlqSkJBAckoKJ6VSFpmYGDRZVjt48CDNmjXTbiDTB4lEQr9+/Zg6dSoxMTEsXbrUqJNltW3bthEYGIibm5uhQ3mili1b8vHHH9OpUyfWrVvHjz/+KCRLI/PUCbPVvfXLkpIS1Gq1NmFC1S/XlStX+O6777hy5YpOAn0a+/fvJzc3F6jaiLR3794Gvf/w4cN5++23WbNmDWfOnOHkyZNMnz5d5/dRq9X8+eefBAYG4uLiovPr11fPnj354IMPEIlEfPPNNxw6dOiBz1m6dCkmJibaJtmG1EalYlpFBR2A0NBQAgICyMvL4/Dhw1wqLmaFiQl7ZTKjqc2pUqlYu3YtQ4YM0fZP1SUXFxfeeustWrZsybx584xyCvZRSktL2bp1K5GRkTU23RmbqKgo3nrrLfLz8/niiy8a1d/xs+SpCxe8UV6Oj7MzzZo1IzU1lQUmJtwRi7XNggEUCgU9e/bk/Pnzuo+8lqytrWnfvj1Dhw4lPT2dpUuX6v2erq6uvPTSS5iamrJx48YG+8f/6quv4uDgYNBR/f0sLS155ZVXaN68ea129wUHBzN27FjmzZtHXl5ewwR5HxONhj4KBYEqVY21ylOnTlFUXMwJqZTDUilKI0mUf9W/f3+cnJxYtmyZTq4nkUh47rnn6Ny5M9u2bSMpKUkn1zWESZMmcf36daOrGOXu7s6LL76IVCplzZo1RtUA/lmm88IFYqp+oVQqFYD2RcTPz0/7OTKZzOBTIUVFRcTHx7Nw4UJ8fX1xd3fX273EYjHjxo3jzTff5MaNGw3+pLhkyRLMzMwYPHhwg93zUXr06MFHH32EWCzmm2++qdVW+KSkJHJzcw1SkMFDpeLligpCgA4dOhAQEMC1a9eqRpUlJawwMWG/TGa0yRKqDu03b96cgICAel/L2dmZ6dOn4+TkxLx58xp1sgTYtGkTYWFhODs7GzoUoOq14oUXXuBvf/sbOTk5zJ49W0iWjcBTb/oRUTWSMjU1JSMjAzVVLzTW1tYUFBRga2vLyZMnjaZIdGZmJunp6f+/vfsOiPJKFz/+nQ6IIiDSFLBQFcUSxYIm9gIaW2KiRkQ3e7e4mvVm199u1twkm73J3c1VUzbFaNDYRY29NwwooigiCCKgSFFBUJAyM8zM7w+Ba0GDMMMMeD5/Kcyc98GE95lz3nOeh1mzZvHRRx8ZfXxvb2/efPNNpFIpa9asMcuOVY1Gw65du5g4cSKnTp0yy8Hmmlmlk5MThw8f5ujRo8/1/lWrVvHee+8xevToJulKYlVdrae7ToezszMBAQFUVVVx5swZSkpLOSOX87MFzyofptPpiIqKYtasWWRmZtb2p30eEomEkJAQhgwZYrE7YBuitLSU/fv3M2XKFLM34u7SpQuzZs0CHnzINecKnPB8GrwkuzskhIHVmwyuX7/Oj25uVCkUbNmyBbVazX/913+xaNGi2iLslkAul/P+++8THx/Pzp07jTbmrFmz8PHxITk5mfXr15t9p+of/vAHlEol//rXv5r0ug/XgP3+++8bXK1n4MCBhIaGmrzij7dOx2itljZSKUFBQbRt25bc3FxSU1MprD5XmdcMWyONHTsWOzs7Nm7c+Fzva9u2La+99hrwYCOZpVdbaoh58+Zx5coVoqOjm/zaUqmUmTNn4u/vbzH3CqFuT1uSbfAM07tr19o/d+jQgf379nE5KwuDwYBSqaS4uNiikiU8KJu1Y8cOJk+ezMmTJxs9AwsMDGTatGlUVVXx7bffcu3aNeME2kirVq3i//2//8fLL7/cJFVBHu4scuTIEY4cOdKo8WJjY+nXrx9z587ls88+M1KU/8faYGCEVkuAToeLiwv+/v5UVVURFxdHyf37xMnlxMjlzbY10qFDh1iwYAEBAQH1OuMrkUgYMGAAw4cPJzo6mujoaIss6m4M27Zt43e/+x3JyclNWrLT19eXN998E71ez4oVK8jMzGyyawvG0+CEeT0jA6egIKB6uTMjA0P1DcZgMJj1/OWznD17loEDBxIREdHgm7GVlRVz5syhY8eOnD17lm3bthk5ysa5f/8+hw8fZuTIkZw5c4by8nKTXctU/SpXrVrFn//8Z4YMGWLU2YBP9ayytVRKr759sbOzIycnh7S0NAokEvaqVNxshrPKhz1cTzUrK+uZ9VTbt2/PlClT0Ov1fPPNNy2+kHdRURHHjh1jypQpTdLt5eEVqIsXL7Jp0yYxq2zGGpwwDx88SFVlJXK5nOPHj6OrLosHT9ZNtTSrVq3iL3/5C4MGDSImJua53tu/f3/CwsIoKysz227O+jh27Bh9+/YlIiKCL7/80ujj29jYMG/ePFxcXEzSr7KkpISjR48yevRozp492+ikb2MwMFKrxe+xWeXp06cpLSvjlFzOqWY8q3zctWvXuHTpEqGhoWzZsuWJ7z9crefgwYPEx8e32Fnl42JiYujRowf9+vUjLi7OZNcJCAhg+vTpVFVV8c0333D9+nWTXUtoGg1KmBKDAYnBwI0bNzAYDFTpdBiUytrv6/X62tqpz2qKbC7379/nxIkTjB07lnPnztXrcPDDy47mLLP3PFatWsWiRYuM3oU+ODiYsLAwo88qH3fkyBF69+7N3Llz+eKLLxo2iMGAv17PiKfMKm9JpexTqbjVzGeVdTlw4AALFizAx8eHK1eu1H69Y8eOTJ06lcLCQouv1mMKNd1efv3rX5Oammr0koJyuZzw8HC6dOlCQkJCnR9YhOapQQmzZu4olUrRarXo6niNRqNBpVJZZMKEBzeT+hYuHzZsGMOHD6eoqKhZtdW5c+cOcXFxTJo0iYsXLzb6v4WNjQ1z587F1dWVEydONMku1pqk35AC47YGA6O0Wrx1OlxdXfH390ej0dTOKmPkck7L5ehbyKzycRqNhm3btvHuu+9SVFTEyZMnAejVqxe7du0iKSnJzBGaz+3bt4mNjeXVV1816tns3r17M2nSJNRqNV999VVt4RShZWhUwpRIJBgMhjoTplqtRqVSNWhre1NZvXo18+fPx8/Pj9TU1Ce+7+joSEREBG3btuXAgQNm2VnXWDt27CAwMJC33nqLVatWNXicmqXo0tJSPvvssybbMHHnzh1iYmKYOHEiiYmJ9SuibTAQqNMxrKoKW5mMXi+9RJs2bbhx4wZXrlzhZvWs8nYLnFU+rnXr1kydOhWpVMqvf/1rli5dytKlSy3697KpHD9+nPnz59OzZ08SExMbNZatrS1z5szB1dWVuLg4duzYYaQoBUvS6BmmwWCo87mPRqNB+dAyrSWqKVw+ffr0JwqXh4aGMnDgQPLy8vjqq69MunHG1NasWcNvfvMbvL29SU9Pf673WllZMW/ePNzc3My2FL1nzx569uzJnDlzfnE1oI1ezxitlk56PW5ubvj5+dXOKkuqZ5VxLXhW+bjAwECk1R8MJBIJ+fn5IllWqzm3Onv2bK5evdrgf5eQkBDGjBnD3bt3m/TDpND0GpQwaz6XSySSB88r63hNzQzT0m3evJkPPviAr7/+msTERA4dOsRrr72GlZUV27Zt4+zZs+YOsdGys7O5dOkSb775Jh999FG9d+n169ePCRMmNPmssi4//PAD8+fPJzAwsO6lRIOB3jodQ7VabORyelUX0cjOziY9PZ08qZS9KhV3XoBZZQ0fHx98fHwoKirCwcGB+/fvW1xpOHPLycnh/PnzhIWFPfe5VVtbW+bNm0f79u2NcpxKsHwNS5jVu+mkUil6vf6pS7KWPsOEBxuUAgICeOWVV5g2bRrz5s3j66+/ZvXq1c2uh96zbNiwgffff5+pU6fWdjd5GisrK+bOnYu7uzsnT55k3759TRTl0+Xn55OQkMC0adO4fPnyI89j7fV6xmq1dHxsVnnq1CnulZdzUqHgrExWe+yppVOpVISGhtK1a1ciIyP517/+Rffu3enZsyfe3t5kZ2ebO0SLUnNu9Xl6ytbMKouLi01eYEOwHI1ekn1awqzZ9NMcdO/evfbPtra2rFixwozRmIZer2fTpk3MmjWLkydPkp+fX+fr+vbty6uvvsr9+/dZunSpRZ3Li4qKIiAggJkzZxIZGYnEYOAlnY4QrRarOmaV2dXPKu++YLPKyZMnk5qaytKlS2s/9MXExHDp0iXmz59PUlISt2/fNnOklkOr1bJt2zZef/11MjMzUavVT31tY0s/Cs2byTf9WDIrKyvCw8PJzc3FyckJ4Ll3YjYnKSkpZGVlMWfOHP7xj3888j0rKysiIiLo0KEDMTEx7Nmzx0xRPtuaNWv49a9/TV8PDwLS03HV63F3d8fX1xe1Wv2ghm55OccVCi7IZPCCzCqtrKwYP348Xbp0YcuWLWRkZDzxmnv37nH48GGmTJnCN99888Kcu6yPzMxMUlNTGTduHNu3b6/zNS+//DIjR46kqKiITz755IU7jiOYcNOPpS/JPlyA4I033qBr164EBgbi4eFB27ZtW+wSS2RkJEuWLHmkuHnv3r2ZPHkyZWVlFjerfFx2VhaOly6xY/x4tBUVZGVlodVquX79OlevXiVTKmW/lRWlL0iihEdnlcuWLXvmo4S4uDh69uxJcHAwp06dasIoLd/evXt555136NKlyyMfOOzt7YmIiMDBwaFebeqElssom37qYqlLsg8XIHj4+dytW7eIiYnhnXfeISIigv/93/81c6SmodFoyM/P55NPPuH111/n8uXL2NvbExsby+7du80d3jO5VD+rHKrXY61QYK1Q4Ofnx7fffktRZSVHFAqSX7BZZWhoKJ07d37qrPJxBoOBbdu28R//8R+kpKQY/dB+c6ZWq9m+fTvz5s2jpKSE5ORkdDodgwYN4vbt23z66adiVvmCa1DCrFmCNRgMSKVS6ro9WeIMsz4FCFauXMnixYsJDg7m9OnTZojS9BYsWEC7du3w9PTE39+fV1991WJL/AHIDQYGVVXRr6oKWxsbrK2ta79XWVlJokbDQZWKshckUcKDYt6TJk3i8uXLvzirfFxBQQExMTFMmjSJyMhI0wXZDOXn5zN9+nTs7OyAByUmP/30U86cOWPmyARL0KCEWbM/Ua/XI5FIkNfxLESj0WBra9uY2IzmeQoQlJSUcPLkSUJDQ0lISGhRO2XhQbUeBweH2r8XFhZadLJ01+sZp9HgYDDg6+tLhw4duHz5Ml5eXtg4OXHYwYHtFvbBzJQaMqusy4kTJ/j9739vlEP7LUnnzp1rkyVAbm6uSJZCrQZtH3w4YUql0jqzrqVs+pkwYQKLFi2ioqKCjz/+uF7Vevbt20dFRUVtk9eWom/fvvzlL38hOjoavV6PWq222KVnRXULrhlqNV6tWxMSEoKbmxspKSkcPXqURatXM3DdOgqcnOhV3Ze1pfP19WXhwoVotVqWLVvW4GQJDw7tb926ldDQUGxsbIwYZfPl6+vLlClTap/ha7VaUbFHeESDZpj6h9p4PSthmnNJ1t3dnfDw8AYXIPjxxx/5zW9+88QGgObIxsaGiIgI3NzciI2N5b333sPGxoZFixbRuXNnjh07Zu4QH+Gp0zFWq6UtENCtGy4uLhQXF5OYmMhdvZ4DSiUZMhlU18qdPHkyycnJLW41oIa1tTWhoaF4eXk1alb5uJycHC5cuEBoaOgvns1tyeRyOTNnzsTX15ekpCReeuklBgwYwLVr10hLSzN3eIIFMcqSbF2NvMy56WfSpEn069ePzMzMBhcgyM7O5vLly8yYMYMPP/zQBFE2jUGDBjFu3Dju3bv3SLWe8vJyfvjhB373u989tZZuU1MZDLyi1dJTp8Pe3p4ePXogkUhITEyksLCQCzIZx1Uq1A89q9yxYwfdu3dn9uzZLfL8rK+vL5MnTyYlJYXly5cb/UPBwYMHWbhw4RMdTV4UT2sC3xSNBYTmp9EJUyqV1vkM0xxLss7OzsydOxdra2vWr1/f6G4M69ev5/3332fixInNbmmmTZs2zJ07Fycnp6eW7crJySExMZE33niDDz74wKyNbbtWN3ZuI5HQIygIR0dHCgoKSEpKohjYp1SS/ZQeq5GRkfz+978nICCAlJSUpg3cRB6eVW7atInMzEyTXEer1bJ9+3amTJnySKGDlk6pVDJnzhw8PT1JSEggKirK3CEJzYDxnmE+ljSbuvj66NGjWbBgAcXFxXz00UdGaV1UVVXF9u3b6d+/P46OjkaIsmkMHz6cxYsXA/DJJ588s8bl5s2b0ev1vPnmm00V3iNaGQxM1GiYotHQ2cmJoUOHYmdnx7lz50i8eJF4mYxVKtVTkyU82JiRkJDA66+/jlze4J7oFsPPz4+FCxeiVqtZvny5yZJljatXr5KZmcno0aNNeh1L0bdvX5YsWUK7du344osvRLIU6q1hdxeJBB3/lzDhQTGDhyv+GAwGJE2wzV+pVPLb3/4WJycnfvrpJ6PvaEtISGDw4MHMmTOHf/3rX0Yd29gcHR2ZO3cudnZ27Nu3r7b/4bPo9XrWr19PREQEnTp1IisrqwkiBQwGuul0DK+qwlYqJahPH9q2bUt+fj4pKSnckUjYp1KRW8+ydlu2bMHf358ZM2YYtb9hU2qqWWVd9uzZw8KFC0lMTGyxtWYff5Zv6eeOBcvT4CKbVTy42cqqP/k/nnmfVdTAWGxsbPjzn/9Mq1atTHpWatWqVdjb2zN8+HCTjG8MYWFhLFq0iPLycj7++ON6Jcsa6enppKWlNdmu4DZ6PdM0GkK1Wrq6uTF06FBsbGyIi4vjUkoKp+RyfniOZFnjxx9/xM/Pjy5dupgoctPx9/dv0lnl48rLy9m9ezdTpkyp/Z1uSQYNGsRf//pXWrVqxdKlS0WyFBqkwetXOokEnU5Xu+z6+K9YTdk8U1qwYAGVlZUsXbr0ke4Vxnb//n3279/P2LFjOXfunEWVzXN1dSUiIgJra+tGtSNbu3YtS5YsYerUqSZbopJUt+AaotViq1TSq1cvbG1ta4ulN7axc1ZWFsnJycycOfO52piZk7W1NWFhYXh6ejb5rPJxFy9eJCgoiJdffrnFtKp6+Fn+sWPHRHszoVGMNsN8PGGaeoY5YcIEbGxsWL58uUmTZY2TJ09SUFDA3LlzTX6t+po2bRrz58+noKCADz/8sFG9O6uqqtiyZQu9e/fG1dXViFE+4KjXM1OjYYRWi7enJ4MHD0YmkxEbG8vl9HSOKxSsUSobnCxrrF+/HolEwuuvv26kyE2nZlZZWVlpllllXX766ScGDBhA+/btzR1Kow0bNozFixcjlUr5n//5H5EshUZrVMLU6XS1zzAf3ylb08nEFKRSKf3792ffvn1NuqtvxYoVODg4MGzYsCa7Zl06derEkiVLCAwMZO3atXz33XdG+XdISkoiOzubOXPmGCHKB6QGAwO1Wuao1XRWKhk4cCBdunQhKyuL2NhY0tRqVqlUxMnlRulXqdfr2bBhAz169MDDw8MIP4HxWVtb89prrxEaGsrGjRvZuXOnxexOLSkp4dChQ0yZMqVJ9iCYgr29Pe+++y7Dhw9n//79fPbZZxa1KiQ0Xw1OmNrqJdmaXYmPHyAxZcIMCQlBp9MRGxtrkvGfpmZpdsSIEbRp06ZJrw0PPijMnDmTt99+m+vXr/Phhx8a/RjFqlWrsLGxYcKECY0ey02vJ1ytJqSqCt+uXRk0aBB6vZ6ff/6Z1Kws9isUbFAqKTZyv8q0tDTS09OZPXu2Ucc1hodnlcuWLWu6TVbP4cyZM+j1egYMGGDuUJ7b6NGjeffdd1Gr1fz3f/93vSp7CUJ9NfhOVcGjR0dsHkuOWq3WZMdK+vfvb7YKHDVLs7/61a+a9Lq+vr68//77dOnShe+//57Vq1ebZClao9Gwbds2goODcXZ2btAYCoOBYVotM9VqvGxsGDx4MB4eHqSlpXH69GmSdTpWWlmRKJebrLPImjVrUCgUTJo0ySTjP6+6ZpVardbcYdXJYDCwdetWhg8f/khdVUvm6OjIn//8Z4YMGcLOnTv5/PPPuX//vrnDElqYBifMMokEjUaDQqEAHpyne1hlZaXJChfY29ubtZzbypUrcXR0pH///ia/llQqZfbs2YSHh5OamspHH31k8lJ9CQkJ3Lhxo0HPaz11OiLUal6qqsLfz4/g4GAqKyuJjo7mSm4uO5RKtikUJu9XWVVVxdatW3nppZdM8kz2edTMKisqKix2Vvm4wsJCfv75Z4v5wPEs48aNY9GiRZSVlfHxxx+32C5Dgvk1OGGWSyQ4OjrStWtX/Pz8eLx8c0VFxSNtmIzFy8sLg8FAXl6e0ceur5KSEmJiYggNDTXpQfmaWaWXlxfffvstGzZsaLKdnytXrsTa2rreS7NWBgNjNRqmazR4tmnDkCFDcHV15dKlS5w9e5bzBgPfq1SkNmG/ypozheHh4U1yvcfZ2Njw+uuvM378eDZu3MiuXbssdlZZlxMnTmBnZ0dQUJC5Q6mTk5MTixcvZuDAgfz00098+eWXlJeXmzssoQVrcMK0dXHhtddew9XVlYkTJzLisdlWzQzT2BsH2rVrZxE3nT179qDRaJgxY4bRx65rVllT47KpPM/SrK9Oxzy1mp56PYGBgfTt25fS0lJOnDjBldu32axUsleppMIMm0giIyNp1aoV48ePb9LrBgQEsHDhQsrKyli+fHmzmFU+Tq/XExUVxfjx4y2mVV+NCRMm8M4771BSUsLf//530YJLaBINnh45enrWLscCDA8O5riVFZmZmdy8eZNJkyYRFhaGjY0NixcvRq1WGyXgNm3aNMkxkvpYv3498+bNo2PHjty4ccMoY3p7ezNjxgwMBgPfffedWW+058+fp3///sydO5d//OMfT3zf1mBgpFaLj06Ho6MjgYGBAFy4cIHCO3c4J5cTLZejNeNuy8rKSnbu3Mmrr77K6dOna4vPm4qNjQ1hYWF07NiR9evXN/kHHWPLzc3l3LlzTJgwgfXr15s7nNpzx1ZWVkRFRZGQkGDukIQXSINnmPEpKRQWFgIPZiMJ0dG0b9+eN998k//5n//h22+/pUOHDsyePZv58+cbLeCaDimWICMjg8zMTKNUyJFKpbz11ltERESQlpbGRx99ZBGzklWrVj25NGsw0KOqirlqNX4GA7169SIoKIg7d+5w4sQJ0oqKWKdScUShMGuyrHHmzBny8/OJiIgw6XUen1U292RZ4/Dhw7i4uNC9e3ezxjF58uTac8cfffSRSJZCk2vwDPN2eTlr1qxh8ODBZGRkcPnGDbZXFzwPCQl5ZMNIcHBw4yOtVllZWXv20xKsXr2aJUuWMHLkyAYfjLakWeXjapZmp02bRlxcHOr8fMZotXjq9Tg7OxMQEIBOpyM+Pp7ikhJOyeWclsvRWUCifNjKlSv561//yvDhw41exabmGE6HDh1Yt24d169fN+r45lazgWrGjBlkZmY2+XNCDw8PZs+ejUKhYMOGDUZprCAIDdHghFkukaDVarl16xZVVVWPbPopKyt75LXGWo4FuHPnziNLweam0Wg4ePAgo0ePJiYm5rluJjXnKv39/UlKSmLjxo0WWc7t/PnzBPfrx79nzkR/5AjZGRl4enrSpk0bcnNzSU1NJV8qZa9KRaEFfZh5WHl5eW15w/j4eEpKSowybrdu3Zg4cSKJiYksX77cIp6vm8L169e5ePFikzablkqlvPbaa/Ts2ZP09HTWrFljMY9jhBdTgxNmGWDbujV9+vRBJpMhPXyYXu7u9O3fHzc3N/Lz82u38+/bt89Y8XLr1i2LmmECREdHM2DAAGbPns3XX39dr/c8PKtcsWKFRZRFe5r2ej1dT5zglVGjYMgQqgYOJCEhgdOnT3O3rIxohYJzMplRKvWY0smTJ3nppZeYO3cuS5cubdRYNbNKd3f3FjmrrMuBAwdYuHAhvr6+Jj8H3aVLF2bOnIlEIiEyMtJs564F4WENTpgGiYQRYWG4ubkBD+qapty/z8+nT5OSksJXX33F4sWLyc3NZe3atUYLuGZmYGtra1EHkyMjI1m4cCF+fn6kpqY+9XUPzyovXbrUpEdFnpfMYGBgVRXBVVX0bNeu9utyuZyMjAySKyrYr1Jxz8I+wDzL999/z+LFixk0aBAxMTENGuPhWeXnn3/eYmeVj9NqtbXL80uXLjXqylGNh38/kpOTWb9+vcX+fggvnkYdIlQ6ONT+WSqVsv3HH7lZPcsoLCxk27ZtODk5NS7COuirn59ZUsK8desWSUlJvP7663zwwQd1vqY5zSrd9XrGajQ4Ggx4e3tjY2NT2/+0HPixtJTTSmWTnak0lpKSEo4ePcq4ceM4f/78cy2hv4izysdlZGSQmprKuHHj2L59u1HH9vf3Z/r06ej1eov//RBeTI2aGmw/fBi1Wo1er+fo0aNYP/ZJ8O7duyYpraXVamn30IzHUmzcuBGZTPZEdRSpVMqsWbOIiIggPT2djz76yGJvBkqDgRE1Ze1sbQkJCaFjx47Ex8ezatUqvouJYZuLC7I+fZpdsqxx+PBh7t2791y7Zrt168bChQspLS3l888/fyGTZY19+/bh6+tL165djTKeUqlk3rx5vPXWW6SkpFj074fwYmvUDDMxPZ3vsrPp1asX58+fp9Vjm3Hu3btH27ZtG3OJOqnVaotMmHq9nu3btzNt2jSOHz9OcXExXbp0YdasWc1iVtlZp2O0Vosd0K17d5ydnSkuLiYxMZF7ej0HFQrSz51jlKNjbW/QyspKc4fdICtXrmTRokX07dv3mW3RxKzySWq1mu3btzN58mSWLVvWqE4rvXv3ZtKkSajVar766itycnKMGKkgGFejEmapREJlZSVKpRKpVEq7x+rJ3rlzB0dHR6N3LqmoqDBJIjaG8+fP8/LLL/PWW29RVFREQECAxT+rtDYYGK7V0u2xAgSJiYkUFhZyQSbjuEqFunpGefDgQfr06cPs2bP59ttvzRl6g925c4eYmBheffVVLl68WOdN/0V9VlkfaWlpZGVlMXr0aHbt2vXc77eysiIiIoIOHTpw+vRpdu7caYIoBcG4GrUke0sqRa/XU15ejq2tLa6PJYTKykoqKyuNntxKS0tp3bq1Ucc0pjVr1uDi4kLnzp1ZsWIF69ats8xkaTDgX13Wrgc8UYAg/c4dNiiVHFAqa5NljcjISLy8vPDz8zNP7EawZ88e1Go1YWFhj3zdxsaG6dOnM2bMGNatW8eePXtEsqzD7t27CQwMxNPT87ne17dvX9577z3s7OxYvny5SJZCs9GoGWZe9U303r172NnZ4VxSgsRgeOR4wc2bN3Fzc6O4uLhxkT6kpKTkuX9Jm9KdO3f43//9X+7cuWOZiRJobTAwSqulq06Hq6srfn5+tQUI7pWUcEYu52e5nKqnPKfMz8/n4sWLTJ8+nQ8//NBif85fkpqaSqdOnWr/HhgYSFhYmJhV1kNFRQU7duxg6tSpLF++/BfPSNrY2DBnzhzc3d2JiYlhz549TRSpIBhHo2aY9yQSyiUSSkpKsLOzQwW4Pbb0evnyZXr06NGYyzyhuLgYKysro45pbAUFBZaZRAwGelVVMbeyEn+plH79+hEQEEBeXh7R0dFcvX+fNSoVxxWKpybLGps2bQJg6tSpTRG5SXTr1o1hw4bRo0cPZsyYwciRI1m7dq2YVdZTcnIyeXl5jBw58pmvCwkJ4a9//Su2trYsXbpUJEuhWWrcATqJhBtSKYWFhTg6OiKTyQh87FNmYmIiPj4+Rm31VVRUZLLm1C2Zg17PmxoNo7RafL28CAkJQS6XExsbS3JaGifkclYrldys57lKvV7P5s2bCQoKMnvPyYYIDw/nD3/4A8HBwezdu5fy8nI+//xzsrOzzR1as7Jz50569+5Nhw4dnvhe27ZtWbRoEWPHjuXYsWN8+umnFBQUmCFKQWi8Rp84T5LJ0Gg0FBcX4+zsjL9Oh+1Ds8yKigouXbrEoEGDGnupWoWFhSbtQ9nSSAwG+ldVMUetxlupZODAgXTu3JnMzExiY2NJV6v5QaXitEKB/jmPiqSkpJCdnc3s2bNNFL3pPNwA3MbGhvT0dFF6rQHKysrYvXs3U6dORSaT1X599OjR/OlPf0Kn0/HJJ59w+PBhM0YpCI3X6ISZKZVyXyIhJycHLy8vrKRSRjy2lHXs2DEGDBhgtFnm7du3LaZjiaVrr9fzlkbDy1ot/t7eDBo0CJ1Ox88//8yVa9c4qFCwTqmkqBHVeiIjI2ndujXDhw83YuSm1bt3b+RyeW2CPH/+PFeuXDFzVM1XYmIiHh4eJCUl1c4kQ0JC2LlzJ8uWLTNa7V5BMKdGT9MMEglxcjm2RUWUlpbSqVMn9BkZ+Oh0XKn+tFlUVMTly5cZNGiQUT5l1pz9a9u2LXfv3m30eC3Rw2Xt7Fq3JigoCIVCQWpqKrm5uWRIpRywsqLUCB88Kisr2b9/P2PGjCEuLs6iKjA9zs7OjkmTJmFnZ8f777/PJ598gqenJ6dOnWrUecIXnUKh4I033kClUuHi4oJSqWT48OHN9pyuINTFKEVAz8lk3JRKSUtLw93dndatWzNSq0X10NLs0aNHjTrL1Ov1Jim71xK46fWEq9UM0ukI7NaNfv36UVZW9uCoSF4euxQKopRKoyTLGidPnuTu3buEh4cbbUxj69evH/Pnzyc7O5svv/ySvLw8MjIyOHr0KBUVFeYOr1l7fMXnxo0bIlkKLY5REqZBImGvQkGlRkN6ejr+/v60Bl55aGm2qKiI5ORkhg4daoxLotVqcXR0NMpYLYWiugDBTLUa77ZtGTJkCE5OTiQmJpKQkEAysFKlIkUuN0lZu1WrVuHm5kZQUJDRx24Me3t75s2bx0svvcR3333H0aNH0el05g6rRdFoNMyfP5/8/HwuXbrEkiVLzB2SIBid0XbOFEilnJbLGZifj4uLCx4eHhiuXydFpyO7emn28OHDLFiwgFOnTnHv3r1GXU+j0WBvb2+M0FsED52OsVot9kBgjx44OTlRUFBAUlISpcBBpZL0hzZkmMKdO3eIj49n8uTJXLp0yewbaCQSCQMGDGDYsGFER0fz888/W+ZRnxYiKiqKqKgoc4chCCZj1L5MsXI5dyQSLl++jKenJ9bW1ozVapFXL82WlJRw5swZRowY0ehrWXJ5vKakMhgYo9HwhkZDV0dHhg4dir29PQkJCVy8eJFEqZSVKpXJk2WN7du3U1VVxRtvvNEk13uadu3a8fbbb9OjRw+++eYboqOjRbIUBKFRjJowdRIJ+5RKKiorycrKIiAggLYGAyEPzTSOHz+On58f7du3b9S1ioqKLLIAe1Pq37Yt7yiV9DIY6NWrFz179qSwsJDo6Giu3b3LJqWSfUollU28o3jdunUEBASYpRqTRCIhJCSE3/zmNyQlJfHtt99SWFjY5HEIgtDyGP0wY65USoJcjuTGDVxcXHB3d+el3FwuV28MUqvVnDhxgrFjx7J69eoGX+fGjRsMHjzYiJE3L/81ahS/9/FBIpGQnZ1NXl5ebVm7BLmcE3I5WjMdvcnIyCA9PZ1Zs2bx97//vcmu2759e6ZOnYpWq+Wrr76iqKioya4tCELLZ9QZZo1ouZwSiYSUlBS6dOmClUrFWK0WafXS7KlTp3B2dn6khufzOnPmDNbW1i/kTll7YI6HB1KpFIlEgrOzM9HR0WSVlrJOpeKwQmG2ZFlj7dq1qFQqQkNDTX4tqVTKyy+/zNtvv83Zs2f5/vvvRbIUBMHoTJIwNRIJBxQKysrKyMnJebAEq9fTv3ppVqfTcfDgQcaOHdvga9y/f587d+480WmipevUrh3/HDyYKrW69muFRUWcksv5QaUitxEFCIxJo9Gwc+dOBg4caNLNWa6urvzud7+jU6dOfPHFF5w5c8aoreQEQRBqmOzumimTkSyTkZWVhbW1Ne3bt2dQVRUO1RsvEhMTkclkdO/evcHX2LVrF97e3i/EblmpVMqs3r1Z5uNDxeXLxMfHk52dTWJiIguOHCFaoUBnYdWP4uPjuX37NnPmzDH62DKZjBEjRjB37lxiY2P54YcfGr3zWhAE4VlMOh05olBQxoOOJb6+vlgpFIzVasFgwGAwsG/fPkaPHo20gbOitLQ0bt26ZZIbsiXxdHLiy5dfZnx5OVcSE+nYsSMajYYtW7aw+uBBjlvw8uOqVato164dwcHBRhvT3d2d+fPn4+bmxvLlyzl37pzRxhYEQXgakybMComEwwoF9+7d49atW3h7e9NBr6dX9aHxq1evUlRUREhISIOvUXNDNmZxd0shlUqZ+dJLLPPxoerCBUpKSvD39ycjI4Pk5GSu6/VsVSof6T9qaUpKSoiOjiY0NLTRHWbkcjljxowhPDycY8eOsWbNGkpLS40UqSAIwrOZ/IHXZamUqzIZGRkZ2Nvb4+DgwMtaLa2rnzP99NNPhISENHjzTklJCceOHWPcuHHY2NgYM3Sz8mjfns+HD2d8SQnp58/ToUMHWrVqRVxcHPm3b3O8umi6Mcvbmcr+/fupqKhoVEcTDw8P/vCHP+Do6Mjy5ctJTEw0YoSCIAi/zPQ7RCQSDioUlOt0XL58GX9/f6xlMsZqNGAwUFxczOHDh5k6dWqDO5AcOnSIkpKSFrE0K5VKmd6vH8t8fdGdPcvdu3fp1q0b2dnZXLx4keyqKiJVKuLkcoueWT5u9erVdO7cGV9f3+d6n0KhYPz48cycOZODBw+ybt06iy7uLghCy9UkWypLJRJOKBQUFRVRVFSEt7c3nfR6gqqXZuPi4qiqqmrUsuqqVatwd3end+/exgq7yXm5u7Nuxgze0Gq5Eh+Pu7s79vb2xMXFkZufz89yOT8qlRRayE7Y55GTk8OlS5eYPn16vZ9Zd+rUiQULFmBra8uyZcu4dOmSiaMUBEF4uia7856XybgulXLlyhUcHR1xcHDgFa0WO70eg8HA1q1beeWVVxpcvaegoIC4uDgmTZrU6GdlTU0qlTI9OJhDkycz2t6eQYMGMXToUPLy8jh//jy5Gg0/qlTENKDBsyXZsGEDUqmUyZMnP/N1SqWSiRMnMn36dPbs2cOmTZsoLy9voigFQRDq1nRTleqOJhXVS7MBAQFYy2SMq941W1RUxJEjR5gyZUqDl2Z37NiBWq1m1qxZRg7edDxcXFg+ejSvlZfjWP1zS6VS5HI5Obm5nJHLiVSpuNkMZ5WP0+v1REVF0bt3b5ydnet8TdeuXXnnnXdQKBQsW7aMy5cvN3GUgiAIdWvSu3CJVMqx6qXZwsJCfHx88NDr6VO9NHvq1CkABgwY0OBrREZG0rVrV/z8/IwSs6nIZDLeDA5mqY8PulOnuHnzJuqHihHkVlSwXqnkmAWer2yMpKQkcnNzn+ibqVKpmDx5MlOnTmX79u1ERUWJHpWCIFiUJp+2XJDJyJJKSU9Px8HBAUdHR4ZqtbStXpqNiopi+PDhODg4NGj8nJwcEhMTn+tZWVPzcHZm+YgRhBUXcyUuDnd3d9q1a8fGjRuJi4vjjMHAmd69yWmiDiNN7YcffsDZ2Zl///vfRERE0K1bN9555x30ej1Lly7lypUr5g5REAThCZJnlRFzcHAwSY2x1gYDcysrcbG3p1u3bpw+fZprej3rq88UhoSE4Ofnx/fff9+gMmdSqZQlS5aQnp7OunXrTPATNIxMJmP6Sy8xRaEgOykJpVKJp6cnmZmZ5OTkUCyRsE+hoKBVK/72t79x/PhxDh06ZO6wTeLChQt4eHgADzZ9/f73vycjI8PMUQmCIEBRUVGdy3pmmYKVSiQcUSgoLi7m9u3b+Pr60kGvp2/10uzPP/+MXC6nf//+DRpfr9ezbt06unfvbpYWU3XxcnHhixEjGF9UxJX4eDp06ICjoyNnzpzhRk4OZ6trwd6QyaisrGTv3r288sortGnTxtyhG521tXVtsgQwGAwiWQqCYPHMtmaZJJORIZVy9epV7OzscHJyYohWi8NDS7MjRoxocJ3Y9PR0rly5wltvvWXkyJ+PTCbjreBglnbtiiYujnv37hEYGEheXh4JCQnkqdWsU6k48liHkZiYGAoLC4mIiDBj9MZnb2/P7373OzIzM2u/dvToUTNGJAiCUD/me8gnkbBfqaTcYCA5ORk/Pz+sFQpCtVokBgMFBQWcPHnyF48gPMuPP/6IQqFg0qRJRgy8/jq7uvLlsGGMKSoi7exZOnbsSNu2bYmLiyMnN5e4X+gwsmrVKtq3b9/gmbalGTNmDO+++y4ajYZhw4YxefJkwsPDkUgkze4okCAILx6z7oq5L5FwSC7n3r173Lx5E19fX1z1evpVL82ePHkSKysr+vXr16Dxq6qq2Lp1Ky+99NJTjzGYgkwmIzw4mM86d0YdH09paSmBgYHk5ORw4cIFcjUa1qpUHFcoqHrGDti7d+8SHR1NWFgYVlZWTRa/sTk5ObF48WIGDx7Mjh07+PzzzykpKeH48ePs3LmTzMzMJumbKQiC0Bhm30aaIpORXl1rtnXr1rRv354QrZZ2ej16vZ4tW7YwatQo7OzsGjR+YmIiN27caLKyeV1dXfn3sGGMKiriSkICHh4etG7d+sGsMi+PU9XnKvPquYN3//79lJWVNaoOqzlNmDCBd955h5KSEv7+978TFxf3xGt2797dLI4CCYLwYjN7wqS62fT96qVZX19frJVKxlcvzd6+fZvY2NhGLav+8MMP2NraMmbMGCMG/iiZVEpE//78s1MnKuLjuX//Pt26deP69esPkrZWyxqVqkF9KyMjI/H09GxU79Cm5ubmxl//+ldeeukloqKi+Pe//01lZWWdr1Wr1WzZsoXJkydjbW3dxJEKgiDUj/kTJlAmkXBIoaCkpIS8vDz8/PxweWhp9vjx47Rp04ZevXo1aPyaXadDhgzB1tbWmKED0NXFha+HDWN4URHpFy7g6elZ21kk7+ZNfpbLWa1UcquB50Lz8/M5f/4806ZNs9izpQ+bNm0a8+fP5/bt23z00UckJCT84nuysrK4dOkS48ePb4IIBUEQnp/F3H1TZTJSZTKysrJo1aoV7du3Z3D1rtmakmrjxo1rcMKLjY3l7t27zJw502gxy6RSftW/P//q1Inys2cpLy8nICCArKysRzqLGKMG7JYtW9Dr9cyYMcNI0Rufl5cXS5YsITAwkHXr1rFixQo0Gk29379//366du1K165dTRilIAhCw1hMwgQ4VL00m5KSgq+vL1ZyOeOql2bz8vKIj49n4sSJDR5/7dq1eHp64u/vj1wub1SsPq6ufPvKK7xcVET6xYt4eXlhbW3N6dOnybt1ixPVnUUKjDgjXLt2LQEBAXh5eRltTGOQSqXMmDGDX//612RnZ/Phhx82qLOIRqPhp59+YtKkSSgUChNEKgiC0HAWlTDLJRIOKxTcu3eP27dv4+3tjbteT+/qpdmjR4/i7Ozc4Gd5eXl5ODs7c/LkSTIyMnj55Zefewy5TMav+/Xjn15elJ49S2VlJf7+/mRkZJCUlES2TkekSsVpE3QWycjIIC0tzaKKy3t7e7NkyRK8vb1ZtWoVkZGRVFVVNXi81NRUbty4wYgRI4wYpSAIQuNZVMIEuCyVclUm4+rVqzg4OODg4MDQ6jZgVVVVREVFMWHCBGxsbJ57bIlEwtixY5FKpbRu3Zq//OUvz/V+f1dXvhs6lCHFxaQnJdG5c2cUCsWDWeXt2xxXKEzer3Lt2rVmPVtaQy6XEx4eTkREBOnp6Xz44Yekp6cbZexdu3bRu3dv3NzcjDKeIAiCMVhcwkQi4aBCQblOR2pqKn5+fqikUsZUtwHLzs4mMTGRsLCw5x7azc3tkWdqRUVF9Xqfs15PeJs2/LNjR0oSElCr1fj6+nLlyhWSk5O5ptPxg0pFnFyOwcSdRcx1tvRhAQEBLFmyBA8PD7799lvWrVuHXq832vhlZWXs27ePKVOmNItNToIgvBgs8m5UKpFwVKHgzp073Lt3jy5duuCl19Ojemn24MGDdOzYsd7n9uRyOWPGjCE8PJz//M//5MiRI6SlpXHmzJlnvs/Xzo79b7zB/kmTmNOnDwW3btGlSxdkMhmnT58mv7CQIwoF65RKiprwxp6YmEhOTs4TLbJMTalU8vbbbzNr1iwuXrzIhx9+yLVr10xyrYSEBMrLyxk8eLBJxhcEQXheFpkwAS7KZFyTSrly5QouLi60adOGYVotrQ0GtFot27Zt49VXX/3FCjheXl4sWLAABwcHli9fzpYtW5g2bRqvv/46Tk5OdW6gkRkMDNFqWdm/P/2cnPD09MTPzw+5XM7FixdJSUkhS69nlUrF2SaYVdZl1apVtGnThpEjRzbJ9Xr37s3f/vY3nJyc+OKLL4iKijL5Nbdv387QoUNxdHQ0+bUEQRB+icUmTCQS9isUlGu1XLlyhYCAAKwkEkZVL81mZmaSmprKuHHj6ny7UqlkwoQJvPHGG+zfv5/169dz//792u9nZ2eTnp7+xDGNDjodEWo1A6qqaN+uXe3X5XI5ly9f5lppKfsUCtYrldw143JhU3U0sbKy4re//S1Tp04lPj6ejz/+mLy8PJNd72FFRUUcP37c7M9rBUEQwJITJnBPKuWEQsGtW7eoqKjAy8uLrjod/tXPy/bt24e3t/cT5/a6du3KO++8g1KpZNmyZSQnJ9c5/tq1a7G2tmbUqFFYGQyM0miYodHQpW1bhg4dSkFBARqNBoPBwM6kJDbJZKxQqbgol4MZZpWPM3VHk/79+/Pee+/Rpk0bli5dys6dO01ynWeJiYnBysqKPn36NPm1BUEQHmaWBtLPxWBgpkZDZ4WC/v37c+7cOQrKy1mpUlEukeDj48O8efPIz8/n7NmztG7dmp49e7J161auXLnyi8OHhITwx5EjkR86xPXLl3FwcKBdu3YUFhaSlJTEfYmEE7a2XHyOA/hNqW3btvzpT39i586dnD592ihj2traEhERgYuLC9HR0ezfv98o4zaUq6src+fOZdmyZY+sEgiCIJjC0xpIW37CBBz0euao1Xi6u+Pm5kZ8fDyXZTJ2KpV06NCBuLg4rK2tMRgMbNiwgT/96U+Ul5fXa+xJPXvy/ZAhSCQS9Ho9ly5d4vTp0xQXF3NeJuOEQoHaAmaTzzJmzBgGDx7M3//+96fWa62vIUOGMHr0aO7evcv3339PcXGxkaJsnNGjR+Pg4MCGDRvMHYogCC3c0xKmRS/J1iiSSomRy8nNzUWv1+Ph4YG/Toe3TkfPnj1rC3ZLJBImTJjwXAfn+3fogKQ6IUqlUq5fv07W3busUyo5qFRafLKE/+to0phds23btmXRokWMHj2aI0eO8M9//tNikiXAkSNHcHd3x9/f39yhCILwgmoWCRPgjFzOTamUlJQUXnnlFf7jP/6DFRMn0icgAK1WW/s6W1vb52oFFpWYSEVFBfCga8b169e5IJeTI5MZ/WcwpcjISDw8PBpUBWnkyJH86U9/QqfT8emnn3L06FETRNg4VVVVbNu2jYkTJ6JSqcwdjiAIL6BmsSRbo71ez8eenkx5aNdkVEYGB62t+fLLL1EqlZw5c+a523i9aWPDy23b0qVLF9LS0kjLzmZF9TPS5mTq1KkEBgbywQcf1KuQgKOjI3PnzsXOzo69e/cSExPTBFE2Tk0LsCtXrnDp0qXaDzuCIAjG8rQl2cZVIG9it6VSMlxdH/maTVoa59LS6B0by9/+9jcyMjIIDAwkKSmp3uPuKCuj/Z07SCQSOnfuzI0bNxis1XJQqTT2j2BSUVFRdOvWjRkzZvDjjz8+87VhYWEMGDCA3Nxcvvzyy3o/8zW3jIwM9u7di7W1NcnJyYwZM4aysjJzhyUIwgug2SzJ1vgsPp6z2dlUVlZy584drl27xhitFk1pKTdu3GDjxo1MnDiRtm3b1nvMMomEUwoFV69eRa/X061bN4J0OtobsdxbU6npaNKpU6c6v9+xY0fee+89+vXrx7Zt2/jqq6+aTbIEGD9+fO0z627duhEcHGzmiARBeFE0u4SpNRgI37aNZcuXs2/fPvz8/LADwpRK7ty5Q05ODtHR0UyfPv256pDGy2QUSSQkJyfTvn17WtnYMKK6SEJzkpGRQWpq6hN9P2tacP32t78lPz+fjz76iLNnz5opyoZLTU2t/XNlZSUZGRlmjEYQhBdJs1qSrZEnlRIvlyPJzcXZ2ZmRI0dibW3Nj9U3z5MnT+Lt7c2wYcM4fPhwvcbUV9evdSgspLS0lJ49e1J+6hTddDqSG9k7s6mtW7eOzZs3k5yczIULF/jqq68YPXo0BoOByMhI0tLSzB1ig23btg2VSsWMGTNYvXq1yWrZCoIgPK55ZYKHnJTL6aLT4eTkRFBQEAAL1WpWHjqEBti8eTPz588nIyODrKyseo2ZIZORIZWiSkxk8ODBdOjQgWG5uWTJZM1qA5CLiwtDhgxBKpXi6uqKm5sb33zzDVu2bDFqVxFz2bBhA0ql8qkVnARBEEyh2S3J1qiSSNijVNLOyan2a04qFcOqE1tpaSlbt27l9ddf/8UC7Q87rFBQVn28xMfHh9ZSKcMeOrbSHIwbN+6R5ehz586xadOmFpEsAWQyGe7u7ty6dcvcoQiC8AJptgkTIF8qZVdRETVHY65du0aPiorazTppaWmkpqYSGhpa7zHvSqX8XL0BSKvV0rNnT7rpdHhVtxazZL6+vrz//vt06NCBtWvXUlpayu3btzlw4IC5QzMaiUTCmDFjyM/P586dO+YORxCEF0izOodZF7nBwF/t7fFo3RoXFxdOnz7N9aoqflQqMUgkKJVKFi5cyI4dO+r97E5qMDBbo6FLq1b069ePCxcukFFUxCqVCq0FLs1aWVnx1ltv0alTJy5dusSGDRtqZ5MTJ06kb9++fPDBB89VAckS2dnZMXXqVJRKJZGRkeIMpiAIJtGsS+M9S5VEwto7d7hy5Qp5eXn4+PjgqtcTXJ0cNBoNUVFRTJ48GRsbm3qNqa9uLVZSPUMLDAzEHhhkgQln0KBBvPfee7Rr146vvvqKdevWPbL0umPHDjQazRNtzJqbPn361D6T/vbbb0WyFAShyTX7hAkPNutckcnIzMzE1tYWZ2dnBldV4VydODIzM0lKSmLixIn1HjNfKuWcXM6lS5cACAgI4KWHxjQ3R0dH/vM//5Px48dz8uRJ/vGPf5CTk1Pna9euXYufn99Tz2ZaMltbW9566y0GDx7MypUrOX78eIt5FisIQvPSIhImwAGFgvsGA8nJyfj6+mKtUhGq0SCrXnLev38/rq6u9OjRo95jnpTLuQckJyfj4uJCG1tbxmi1SMx8NvPVV19l0aJFaDQa/vGPf/ziM8qsrCzS0tKeOJtp6QIDA1mwYAE3b97kyy+/JD8/39whCYLwAmsxCbO8ehm1tLriT0BAAO0MBoZX73Ctqqpi8+bNhIWF0bp163qNqZFIOKhQUFBQwL179wgKCsJFr6evmTYAdenShSVLltCnTx+ioqL4/PPP690fcu3atSiVyueaZZuLtbU106dPZ+TIkaxevZqDBw+iawabrgRBaNlaTMIESJfJSJLJuHbtGjKZDC8vL3rpdPSqfvaYk5NDXFwcU6ZMqfeYGTIZqTIZFy5cQKlU0qVLF0K0Wto04bKgUqlk7ty5zJs3j2vXrvHBBx+QkJDwXGPUdPvo378/jo6OJoq0YZRKJa7VNYJ9fHxYuHAh9+/f54svvnjqMrMgCEJTa1EJEx6coywGkpKS6NChA05OTozQavGsnqEcPXoUW1tb+vXr91xj3tfpSE9Px8vLi1ZKJaOaqGxe//79WbJkCS4uLnz99desWbOmwbtdz58/T15eHhEREUaOsuE8PDw4e/YsycnJnDhxgsmTJ7N582Z27979SNs2QRAEc2txCVMjkbBNqaRUrebixYv4+/vTxtaWiVot9no9er2ezZs3M2rUKBwcHOo1ZplEwnG5nBs3blBRUUGvXr3ootfjb8JZpr29PX/84x+ZOHEisbGxfPzxx2RnZzd63MjISNq2bcvQoUONEGXjhYeH06FDB+DBM8vTp0+L+rCCIFikFpcwAQqkUnYpldwrKSEtLY2ePXvSRi7ndY2G1gYDt2/f5vjx47zxxhsoFIp6jZkok3FDKiUhIQFbW1vc3NwYodViZaRZpo+PD2+99Rbe3t5MmDCBd999F71ezyeffMLevXuNcg2A+/fvc/ToUUaNGlXvYzam0q5dO7y8vGr/XlVVJZZgBUGwWM2+cMGz9K+q4mWtls6dO9OuXTvOnTvHTb2e9SoVlRIJr732GiqVirVr1/Ksf4caDno9EWo1Ab6++Pj4UFBQwIGCAr7Jy2tUnL6+vhw+fJhWrVqh0WjYsmULa9asIT4+vlHjPsuf//xnSktL+fe//22yazyNVColJCSEIUOGcOzYMQYPHkz37t3ZvHkzO3fubPJ4BEEQHtYiGkg/rziZDEe9HjIzUSgUBAUFcf78eaZpNGxUKtm6dSvh4eGEhYXV60ZdJJUSK5fjfu8efn5+BAQEMKiqikt79/JzI7pmDBgwgFatWgEPNsCcO3fOpMkSHizNLliw4LmbbTeWs7Mz06ZNo6Kigi+//JLi4mJ+/vnnJru+IAhCQ7XIJdlaEgn7FArSZTLS0tJQq9V0794dd4OBSRoNhqoq1q5dS6dOnQgJCanXkHFyOXZdu9YWN5fL5YxxcWlUmKdPn65t4lxWVkZsbGyjxquPW7dukZCQwLRp056rb2hDyWQyRowYwa9+9StOnz7NypUrKS4uNvl1BUEQjKVlJ0zAIJGwU6EgWyolOTkZqVSKv78/nfR6QrVaNJWVREZG1i4L/hKdRMLGmzdRq9XAg+duFTduNCrG1NRURowYwR//+EdGjBjBlStXGjVefUVFRaHT6XjzzTdNeh13d3d+//vf4+7uzueff94sG1cLgiC06GeYD1MZDEzXaHADevfuzb1790hPT+e8TMZBhQI3d3ciIiJYvXo1N56VAA0Gxmq1DGndmhEjRnD58mUkvr786cKFZlmJxtvbm4iICL7++muj7MJ9mFwuZ8SIEfTp04fdu3eTmJho1PEFQRBMocUWX68vtUTCFqWSQoOBCxcu4OjoWFvYIKSqiry8PLZu3cqsWbOwt7evcwyZwcAYrZYeOh2urq4UFhaSnJzMzsuXmTNnDu3atWvin6rx0tPTuXr1KrNmzTLquJ6enixYsAAHBweWL18ukqUgCM3eC5Mw4UH5vE1KJcU6HQkJCbi5ueHu7s7Aqir6VlVx+fJljh8/Tnh4ONbW1o+819Zg4E2Nhp46Hf7+/ri5uZGSkkKJRMK2rCwOHDhAeHh4vY+pWJIff/wRKysrxo8f3+ixlEolYWFhzJgxgwMHDrB+/fp6l+8TBEGwZC9UwgQokUrZpFRyT6vl/PnzdOrUifbt2zNcq6VbVRWxsbGkp6czc+ZMZDIZAO56PeFqNR0lEoKDg3F1deXChQvk3L7NboUCnUTCuXPnuHHjBqNGjTLzT/j8NBoNO3fuZNCgQU+dXddHly5dWLBgAVZWVixdurS204sgCEJL8MIlTIA7UilRSiX3Kiq4cOECfn5+ODg4ME6rpatOx549e6isrGTy5Mn0l8t5S6/HxdaWkJAQFAoFMTExXC0q4keVihvVSRVg165dBAUF0bFjRzP+dA0THx/PrVu3mDNnznO/V6VSMWnSJKZNm8bOnTvZsmWL6FcpCEKL88Js+qmLl07HVI0Gx7Zt6dGjBxcuXKCopIRNSiUFKhVb580jWC7HYDBw7do1MjIyuHDhAhkSCbuUSiolTz4X7tGjB8OHD+fzzz9vdh022rRpw+LFi9m9e3e9j7b4+voyadIk0tLS2Lt3b+3uYUEQhObqhd/0U5drMhm7lUqK794lJSWFnj17YteqFVM1Gl4rK6OnVotUKkUmk+Hs7Mz58+c5JZMR9ZRkCXDx4kXu3LnDK6+80sQ/TeOVlJRw4sQJxo0bh5W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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the shortest path\n", "fig, ax = ox.plot_graph_route(graph_proj, route)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nice! Now we have the shortest path between our origin and target locations.\n", "Being able to analyze shortest paths between locations can be valuable information for many applications.\n", "Here, we only analyzed the shortest paths based on distance but quite often it is more useful to find the\n", "optimal routes between locations based on the travelled time. Here, for example we could calculate the time that it takes to cross each road segment by dividing the length of the road segment with the speed limit and calculate the optimal routes by taking into account the speed limits as well that might alter the result especially on longer trips than here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Saving shortest paths to disk\n", "\n", "Quite often you need to save the route e.g. as a Shapefile.\n", "Hence, let's continue still a bit and see how we can make a Shapefile of our route with some information associated with it.\n", "\n", "- First we need to get the nodes that belong to the shortest path:\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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13724411706.671610e+06385239.956998137244117024.93199960.165235NaNPOINT (385239.957 6671610.080)
601704716.671704e+06385382.6167386017047124.93451560.166117NaNPOINT (385382.617 6671703.996)
13772116686.671789e+06385514.573340137721166824.93684360.166917NaNPOINT (385514.573 6671789.024)
13772116666.671703e+06385570.886277137721166624.93790660.166160NaNPOINT (385570.886 6671702.892)
252915656.671586e+06385647.1242102529156524.93934460.165135traffic_signalsPOINT (385647.124 6671586.216)
252915646.671673e+06385779.2070152529156424.94167460.165948NaNPOINT (385779.207 6671672.709)
3177036096.671722e+06385855.03009931770360924.94301260.166410traffic_signalsPOINT (385855.030 6671721.810)
\n", "
" ], "text/plain": [ " y x osmid lon lat \\\n", "319896278 6.671803e+06 384645.074633 319896278 24.921178 60.166794 \n", "1382320455 6.671806e+06 384633.394361 1382320455 24.920966 60.166820 \n", "25216594 6.671580e+06 384631.322372 25216594 24.921057 60.164794 \n", "1372425714 6.671540e+06 384624.178763 1372425714 24.920951 60.164432 \n", "25238874 6.671454e+06 384625.787221 25238874 24.921028 60.163665 \n", "1372425713 6.671438e+06 384627.187049 1372425713 24.921063 60.163516 \n", "529507771 6.671292e+06 384711.598018 529507771 24.922665 60.162230 \n", "258188404 6.671276e+06 384721.946323 258188404 24.922860 60.162095 \n", "1372318829 6.671208e+06 384778.761107 1372318829 24.923922 60.161496 \n", "159619609 6.671215e+06 384787.503483 159619609 24.924076 60.161561 \n", "175832743 6.671239e+06 384806.144893 175832743 24.924398 60.161782 \n", "1372425705 6.671299e+06 384768.805480 1372425705 24.923691 60.162314 \n", "1007980689 6.671311e+06 384787.969869 1007980689 24.924029 60.162428 \n", "149143065 6.671362e+06 384867.310755 149143065 24.925429 60.162901 \n", "268177652 6.671387e+06 384899.509087 268177652 24.925995 60.163139 \n", "60004721 6.671440e+06 384980.836116 60004721 24.927429 60.163634 \n", "1372376937 6.671504e+06 385080.337968 1372376937 24.929185 60.164240 \n", "1372441170 6.671610e+06 385239.956998 1372441170 24.931999 60.165235 \n", "60170471 6.671704e+06 385382.616738 60170471 24.934515 60.166117 \n", "1377211668 6.671789e+06 385514.573340 1377211668 24.936843 60.166917 \n", "1377211666 6.671703e+06 385570.886277 1377211666 24.937906 60.166160 \n", "25291565 6.671586e+06 385647.124210 25291565 24.939344 60.165135 \n", "25291564 6.671673e+06 385779.207015 25291564 24.941674 60.165948 \n", "317703609 6.671722e+06 385855.030099 317703609 24.943012 60.166410 \n", "\n", " highway geometry \n", "319896278 NaN POINT (384645.075 6671802.525) \n", "1382320455 NaN POINT (384633.394 6671805.824) \n", "25216594 NaN POINT (384631.322 6671580.071) \n", "1372425714 NaN POINT (384624.179 6671539.986) \n", "25238874 NaN POINT (384625.787 6671454.380) \n", "1372425713 NaN POINT (384627.187 6671437.809) \n", "529507771 NaN POINT (384711.598 6671291.757) \n", "258188404 NaN POINT (384721.946 6671276.431) \n", "1372318829 NaN POINT (384778.761 6671207.956) \n", "159619609 NaN POINT (384787.503 6671214.847) \n", "175832743 NaN POINT (384806.145 6671238.924) \n", "1372425705 NaN POINT (384768.805 6671299.364) \n", "1007980689 NaN POINT (384787.970 6671311.466) \n", "149143065 NaN POINT (384867.311 6671361.707) \n", "268177652 crossing POINT (384899.509 6671387.230) \n", "60004721 NaN POINT (384980.836 6671439.807) \n", "1372376937 NaN POINT (385080.338 6671504.242) \n", "1372441170 NaN POINT (385239.957 6671610.080) \n", "60170471 NaN POINT (385382.617 6671703.996) \n", "1377211668 NaN POINT (385514.573 6671789.024) \n", "1377211666 NaN POINT (385570.886 6671702.892) \n", "25291565 traffic_signals POINT (385647.124 6671586.216) \n", "25291564 NaN POINT (385779.207 6671672.709) \n", "317703609 traffic_signals POINT (385855.030 6671721.810) " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the nodes along the shortest path\n", "route_nodes = nodes_proj.loc[route]\n", "route_nodes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, now we have all the nodes that were part of the shortest path as a GeoDataFrame.\n", "\n", "- Now we can create a LineString out of the Point geometries of the nodes:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "" ], "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from shapely.geometry import LineString, Point\n", "\n", "# Create a geometry for the shortest path\n", "route_line = LineString(list(route_nodes.geometry.values))\n", "route_line" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have the route as a LineString geometry. \n", "\n", "- Let's make a GeoDataFrame out of it having some useful information about our route such as a list of the osmids that are part of the route and the length of the route." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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geometryosmidslength_m
0LINESTRING (384645.075 6671802.525, 384633.394...[319896278, 1382320455, 25216594, 1372425714, ...2152.495705
\n", "
" ], "text/plain": [ " geometry \\\n", "0 LINESTRING (384645.075 6671802.525, 384633.394... \n", "\n", " osmids length_m \n", "0 [319896278, 1382320455, 25216594, 1372425714, ... 2152.495705 " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a GeoDataFrame\n", "route_geom = gpd.GeoDataFrame([[route_line]], geometry='geometry', crs=edges_proj.crs, columns=['geometry'])\n", "\n", "# Add a list of osmids associated with the route\n", "route_geom.loc[0, 'osmids'] = str(list(route_nodes['osmid'].values))\n", "\n", "# Calculate the route length\n", "route_geom['length_m'] = route_geom.length\n", "\n", "route_geom.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have a GeoDataFrame that we can save to disk. Let's still confirm that everything is ok by plotting our route on top of our street network and some buildings, and plot also the origin and target points on top of our map.\n", "\n", "- Get buildings:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "tags = {'building': True}\n", "buildings = ox.geometries_from_place(place_name, tags)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "re-project buildings" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", " and should_run_async(code)\n" ] } ], "source": [ "buildings_proj = buildings.to_crs(CRS(edges_proj.crs))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Let's now plot the route and the street network elements to verify that everything is as it should:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "image/png": 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ajxEfhqaXtXpNDDffMvaiWzXoVg8AnOz2C46dfJpRCEgFXBZqa2t54okn2LVrF5///OeZNm0a/fr141vf+haGYfDYY49x5MhhfOcYMsgpwbOLwHdPEvhnMBMj8OyDGDEVUDoLzz6A22rQVgideMkczPigDtnRzXWsXONbuPl9eE7n9Yt0sxtGrC+gCusyOI9VLvPQ+pjShzDgSSnxnENRvUlo8aiuVEALCjWkAjqFbDbL0qVLqa+vZ968eVRVVbV5vtm3bN8+xVD+7C2dz3SczGbiJXOJFcXQzXLiJVeRPfcCHWVVHSHfuAy98u6Im9RRbcZ3a/HdE1jJ0WGQlGhGObpe3unzBLCSo/DdWmSQwcluxEpdER1PN6tQ93hVjJfSxUwMCqf/N+C7JwAwYn2wkqMQwkRK/09aTn7SUAhIBVwUQRCwdu1atmzZwowZMxg0aFCHP/jAa2TWrBkAzJo1CyvZXbXyg0sXqBWXZyux1AQADKs7RnwwXn7XJV6pcLFBWBk4OLktePZhta3RBSsxAk0vxoj1v+wZMyEMYkUTyDcuj8ZOjFhNdB7xkpl4+X0gNIxYX2Rgk2t4EylthIhhpcZhWNUABEEOu+k94sVXIrQLm1x+mlBgahdwQRw4cIC33nqLoUOHMnnyZHS9/Z08CALc7FrszCasxHDiJdOj5zznJHb6vU4fL148PcwyIAg8Mmce75CHFEFYxFKTiKXaZ2NSSnznaFgnallaCS1OomTen8yYdrLbcfO7EMIkXnoVmta+je/kduHm1NinbnYnlhrXRjZFSkng1uI5B9tkWp90/KnT/gV8ylBfX8/ixYuxLIvPfe5zFBV1nIF4Ti35xjcJ/HMAOLmtGImhGKFKo2F1x7d6hSMUl0aLkL6BphnEi2eSa3i1w211qzeJ0jkdBoLAz+JkN3YoJSuDPE5uG7HU2E6d04VgJoaG9a8GnMwGYkXT2gUUw+yGl9+LmRyBYfVp97wQAmEUYxrtGeyfVhQCUgERHMeJBmDnzZtHz54XHzpVg7HnWj0SkG98g2T5Z6N6jpUcje+evoCsSFuoAdodUafNjPfDzbdVhxRagnjxjDaT+9HrpcSz9+PktsNFCsaefQDDqmkjb3u5EEIjVjSRXMNb+O5pAv8sutF2f5pRRqJsQcRZajnPgMCvRzcq2ikMSCmRMt9p5YFPGgoBqQCOHDnCokWL8DyPK6+8ktmzZ1/0jh14TeQa38B3T3bw3Fnc7EZiRapFLrQYVnIUdmZdp87Fze9VRERDFZvjJXNI1/0epIsRH0y8eCaa1v5rGwRZ7PQaAu9sp46j5G2v+pN0ijS9hFhqLEIraheMmnF+MFKPaW3Y3s2QgY2d3UjgnQ01uT99daVC278AXnvtNU6ePElRURFjxoy5YDAKggA7vY70mSc7DEbNUNKz6ehv3aqJakOdgZNZHw2yalqCZNmNJMtvIVl6VYfBCEL7ok5kYc3oSN72/cCI9fmTMq1meM5Jcg1v4jvHkUEeO7uh0xypTxIKAakA5s+fj67rdOnSsV6Pk9tN5txLZM8+g51ZDVyKP6NmzZohhMBKjgsVFS+NwG8M/dYUDKsrhnVx11ghDKzk5dldu7ldF9RiuhRk4OLmdmOn1+K7Z97XPkCRQ+3MBuz0e23cUHzn+GVxpD4pKASkAujTpw9f+9rX2LRpE8eOtVhOB0GOzLmXyDe+ie8cIfA7txwC8N0TOLmd0d+anmw3KX8xBH79BTOEIMh1WLDWzco2oyOdOApO5v1lIm5+F05uG55z5H0HDt9TIzMXMgNwspsjOZNPCwoB6VMMGbjk06sIvEbKy8u54YYbePTRR8lms+HzeXzn2CX2cmHkm95tM2tmxAagGRcnIgoRI5a6glhq0oWXju5phFaE5xxtJ9RvJoZelqi+7515XwHFTAzBSo7FSozASlyePK2UAU52G/nG5Rc0u1QbKk3ujnSYPqkoBKRPIVQ36rAqTDvHyaffQwZ5Ro0axYQJE/jpT3+K53noRjlmYsSfcKC2s2ZqSHU8F/raGVZvEqXzMGK9LlpUN2J90PQUgXe2TUBSpo07LzurcLNbCS5A4JTSxcluJ9e4VNkehbwoIUwMqxozMbhTlkzN8L1G8o1LO12/CrxzuK0yzU86CgHpUwbfO0e+aTl2Zh0yyCOEgQyy5NMrkdJj/vz5VFdX88tf/hJA8Wu0Zh6SwDxvgPRSOH/WTDNKMBNtl1VCSxIvnk6saMJldZas5Gg0o0Rdl1tLvnFJ+EO/3CWYaHGqDaFmz46Ra3gDN78r1ERqCQ5SSvLplQReQ6eOoLzc9mE3vR0Ftc7Cze/Cd+su6zUfVxQC0qcEzfNU+calUVZhWD1JlF2DECaBd04FKSn5/Oc/j+d5PPfcc2iaRrxkltKyLr+FWGriJV0+zke+cRlB0FIIN+ODo2WVGR9IonTuZXXh2l/XOvJN77yvektzVtZauC3ws9jpVdjp1e2Ggz1bDcg2i6/RiQAaBDns9LsRa1wI67LrVnZmLTK4vPf944hCQPqEQ8oAN7+PXMPiqHiq6aXEi6cRK5pE4DchDNVd853jONltaJrGl770Jfbt28fKlSsxY70p6nK76nRpycv2rJdBBrvpnehvIXSs1ATiJbMxEyO5/Iym1bKz4Y1oTu1yYMR6I7QEVnJkdD0qi9lLrvHNaBAWaJcR2pkNSOmh6cWXJDC69nHyDUvOU5XMoOkll3W+MsjhZDde1ms+jigEpE8wmpcx6s7sRj8CzShDN1UbXdOLiaUmKjkMBL53As8+jGVZPPDAA7z11lvs2dPSgtc0jXjpPC73q+Pmd+I5rTWEykPyY9CpAdzWCPxG7KZ3woyu89wjaGZ6T8NKjkPTiwiCdLTPfNMynOyWiOWt6WXES2YTL5kdCrApKEb5+daEbSEDFzu9Di+/vcNzlEFaqU1eBjznaKfHcD6uKASkTxiklOoHm9kQLmOaEFqMWNEVxEuuwkwMjabNQRVn1dzYLHS9FOmn8exDBH6GiooKbr/9dp599llqa2tbHwTE5Q6nyg5dN4TQO50tSOmrAnPDW/je5ddUjFgfEiVz0c1uCKERL74STS9T+kgNbxF44RiMMLCSo4iXzEI3yiMelW5Wh+dsoenl+L7P888/z49+9COOHDkSHcd3T5FrfBPPORy+/x3pZ0sQgstauWkJpUl+CY2pjzMK0/6fIEjp4WS3qKWZMBAIFTikT6J0PkIzkYGN5xzGiA1UrwnSCK0IN7cFzzneKltRP1jdrGTdunW88cYbPPTQQySTSYIgH45z2Bc8l7YwiKXGYSbHRzNulwvfPY2d2XjxNvkFILQ4Rnw0y97eztatW7Ftm7KyMm699VYqynXyjcuibXWzG1ZybBsR/mZDS88+hGH1wkqOpraugZdffpmmpibOnTtHLBbjrrtup6qivsVKOzoBK8y62rfvhV6MvAQ5U6KhaSlk0BSeY7Vyb/mYDuT+yb5sQogyIcSzQoidQogdQoipHWwzWwixUQixTQixLHxsSPhY83+NQohvhc99O9zfZiHE86GFdvO+/kEIsVcIsUsIseD9XPSnCTKw8eyjYT3lIKBkVeMlc4gXTcZMDANhKFPF9Gqc7FbspnfINy6JbKw9+wiG1QMrOTrca4CdXkngZ5gwYQLjxo3jZz/7GUEQoGlx4sVTOnVuutmNosrPEyua+L6CkQxs7PQ68k0r3lcwMmJ9OHCsJ999+DGOHDnCrbfeyt/93d8wYsQIHnnkERqbRJvlmKaXtnMEEUKoubWiqVipiaxes5FXXnmFnj17EovF0HUd27ZZtmx5ZGLQ9iIcJdN7/s1fmBfInlptoqUQQo+CESjSqe8cucirPr7o7DfkYeBVKeVQYAywo/WTYTD5MXCjlHIE8FkAKeUuKeVYKeVYYAKQBZ4PX7YYGCmlHA3sBv4h3Ndw4HZgBHAN8GNRkNS7IAK/iXzTO9iZNcggF3FidKMrmp5CM8ow4/2U1IUQ6KFttO/VEfiNSCSaliRWNAkAI9YvEjyT0lEjDYHD1VdfTbdu3XjkkUcAsBLD0YyLWAoJi3jJLFIVn4la85eDNkVr5/KL1kJLocUm8dQftvLyy6/yuc99ji984QtUV7nYjYuZPm0skydP5umnn8ZKjFCzcICb36NcQ0IvtmaZWTM+kJyd4sknn6S+vh4hBEVFRXzpS1/iC/fdQJculZSWlmOlxnV8PUG6DWFTN6tIlM5FExfq0ukIrUgF4airqUdBzcluIfAvr/b2ccAlA5IQogSYCfwSQErpSCnrz9vsTuA5KeXhcJvTtMdcYJ+U8lC4zeuyRVR4JdDsnncT8KSU0pZSHgD2ApMu66o+BVA/2CNhnUiJxZvxwSRK5xNLTcCI9e7wdUasH1ZqHGZcLdmUjKqNbnZFN3uEHmIS3eyujhM42GlFB7j99tvxPI9nn30WgETpXDr6ChmxPhR1ufOyGczNaPZyez9Fa1DBY9eBSr73/d+RSCT427/9W/r0rgyL1puR0sbJbGTWrFk0Njayd99hzGQzAVTxi3INb+I5xyKTx507d/LUU09RXV3NsWPHuPrqq5kyZRxOZjVlqf186Qvzqa+v51y97FAaBVQxXGhxrORozOQ4IEbgN9D6PZRSKt6XUEEsekwvBpSrr+oKajghTeOThM5kSP2BWuDXQogNQohHhBCp87YZDJQLIZYKIdYJIe7tYD+3A09c4BhfBF4J/78n0DofPRo+1gZCiAeFEGuFEGvbFFw/BQj8DPmmpeGskx1lNLpVjRBG2NLuuIMjhMCM9cVMjMSMDwk5RiqD0c0u6GaV6rwVXYFu9UJKFykzgETTNB588EH279/Pww8/zLHjTZitgo7QkiRKryFZdu371vPx3VpyjUvwvcv7TA2rBis5Bk+bxK8ffZclS5Zyzz33cNttn8Gzd7UtWhNmiN4RrrvuOl588UUMq0801iKDLJpRSqLkKly/lBdeeIEdO3ZQXFyMbdvcc889dCm3yTe00APc/E4WXD2TRYsWYcSHIc6/fqFjxgdhpaYBgnzjG/juYazUWBKlczHjA9DN7mh6kQpE0g9fqKvH/CYgAKER+A5S5vG9WrxWQ8ifBHQmIBnAeOAnUspxQAb4Lx1sMwG4DlgA/KMQIqLjCpUP3wg8c/7OhRD/DTU+/njzQx2cQ7vbgJTy51LKiVLKiV27Xr4b6ccVgZ8m3/S2cmgVOvGiK4iXzCNWdAXaZQjWq87R8HaKi2ZiJLpZhe+cUtmBEAR+E3b6Pdz8XgzDoEuXLtTX1/PMM89gJCYRL5lLvGQuqcq7IuPHjqB4Pvtx8wcuuI1mlCNE50cxIJSHLZrIyjXH+NGPf0vv3r3567/+a6q7Jcg1LAnZ1UF43bFIdcDNbmXo0H4kEgnefvttrORYQMNMDCNePJMDB0/x2GOPUV5eztmzZ5k8eTLz583Az69RriPSRf2ElLB/ceIgffv2Ze3aDcRaLd00vYREyRyMeD+8/FbVbZQ+TnYLQkuGrrqjlc3SecJyQouHtTNd1aH8RoRo+Tk4ue343jk+KehMQDoKHJVSrgr/fhYVoM7f5lUpZUZKWQcsR9WamrEQWC+lbDOiLYS4D7geuEu25J5HgZpWm/UCjnfmYj7pCIIs+aZ3kEEO3awO5756IoTAsC4+/9VZCKFhxPor6+sgR7xoOppeiu+eVh085yjz58+ntLSUnj178otf/JJf/XYJR08YF9Qqgmaez3Kc7Cac3FYC/0LjEzr6BbzUOoJmVNCU68H3v/99Nm7cyJe//GWuWTAPN7eJfNPyNnc3I9aHROm8SHVAzalt5rOf/SwrVqxAUkSi7Go0cyCvvfY669evp0uXLpw7d44777yTntWQa1gSKQ0IYRIvnoqZGKKu0TvLpAk92LZtG+lsHN2qQejFBH4W3z0ZZlStqhnSw821GBkILYZ1nrRu4KfR9NJoxKc9JE5mA8H5lkwfU1wyIEkpTwJHhBBDwofmAuezwv4IzBBCGEKIJDCZtoXvOzhvuSaEuAb4e1QhvPU7/SJwuxAiJoToBwwCVl/GNX1i4dmHw2DUnVjRpA7VCD8IqOxpdGRqGCueEhXL7cx6elSn+NrXvsapU6eora3l1KlTPPPMMx3WM6QMWvF8QvkS6V+QdSyEuOSwqhCGWprGR/HWOxl+8cjjjB8/noceeojSYodcY0u3UeIByl02lhqP0KxQdaAMUOz08lKHfv368fzzz+M4gscee4ySkhKampoYNGgQN964AOFtwMlsQOjq3Mz4AOKlV6GbVWoUxihHaMX49nbmz5vFokWLMOMjw4zHw8lujUZudKtHtMz27AN4dgvZ0bB6KFcUowIQaEYxvlffRiupzXuhFxH4adzc1g6f/7ihs122h4DHhRCbgbHA/yeE+IoQ4isAUsodwKvAZlTweERKuRUgDFDzgefO2+cPgWJgcUgJ+Gm4r23A06ig9yrwNSmjBfWnGr6j6hXvd+7rciCEprpPWgKkRDQTIcOJetM0I6faWCxGRUUFv/3tb88jCJ5RA6+tlkyaXqw4UcJUXT4p24mkmYnhHbbDlQjbaBKlV1PXNJQf/uSPHD16lG984xtMnzYRzzmmCr2tiIOxoqnEUiOjY3j2Edz8TgKvxTXWyW7m5puvY/fu3Xzve9+jvLycnTt3csMNNzBkYEmY2ZwKL78pXFIKpJ/Bze8n8GoJ/FzYmvfpUnaCyspKtm3fHdlnt7m+WN82YnJ2Zi2+21IzMxODMawaNfPmpzvMfIWwsFLjQQaAj2cfwvsTpGI+KujULVZKuRE4n8j00/O2+Tbw7Q5emwXaaXxKKQde5Hj/G/jfnTm3Twtk4EQ8HCe7GTe3A93shmZ2Q/qNqiBqVEQa0c1i8ULELqgbLaXEd4+hG1Xtpuw9+xB2ZgOty3dCS6IZZZF/muu60b+nT5/G8zyWLVvG3XffrcZWWs2vNSPwmwj8RmJFV4RDp6vw3VPKQSQsrguhiJT5phVtXiv0YnSrH8/+4Q/s3buXhQsXMmbMGDz7ILmGrR06wXr53VipK3Cy6/Hd2pDpnEFKiabFCQIXgYHw9lBUVER9fT27d+/mGw99Beltx860VAs0vVjxufwcTm4fwj6IEIk2HCFQJM4Z00bz+6eWMmDA7ehWD3xH7UczKhB6V0XBMLuHUsASzzmObnYNZw/3tAni50M3u2OlxoaNA7VkA3AyG9E6MA74OKEg8v8xgdAsEmXXEnhn8d2T+O4pPOcowjurZqvyexDCRDe7qREHYWCn31PLGy2FblWHXZyy6I4beHXY6TVYyVEYsQEIIcLC8852GjxGrA9WYiRCszh79iyPPvooyaQi+zmOQywWw7IsZs6cCShDRs2oaCO6L4SJmRyJbnbHzR/AzbUEETuzkXjxjDYusEasxXHEiPXhwOEYf3zx2/To0YO/+Zu/QdfssMB/YQlZzzmGbvXGTAwn3/AmUroILYFhdsezD6JpFjLI4TmHqO4Wp74eLMtC4zSO0xyMBGZiCGZ8MDLIkWtcgmaUqMDWOhgJI8xYAgJ3B3NmT+e1117j5psXknPPhKoK9cigATd/INIlby5qq5Gf9W26gW2hI/QizOQYhIjj5vfiZLdFz0rp4GTWd2jJ9HFBISB9xODZR3Htvehmd3SjO5pRGn25hNDC1nwXYGRYLK3Fd4+HVkNuuwFMKT2k30CQa8DN7URocbVvswo3r2yvnewW3Px+DKsnUtrtbIesxLCIzfzGG2+wevVqZsyYwYwZMzhy5AhLly6ltLSU3bt389JLL3HDDTdQU1NDLDWOXMNbQIBh9VQ21tLFbnob/7wgEnhn8OyDbbp0VmIk0s8g9f488cxiTp8+za233sqAAf1x87vJZ/bSGaUAJ7uZROl8rOQoAr9BMdfRCLy6UI0xAOkze0ZPGtM6p0+fJqA7sAWQ6EYlZnyoqm/pRViJ4TjZrWpJFeqL62YVVnKcMqfM70YIix5V9WwzTfbuPUK/3sOiupnd9A5SemGHrZRYaiyBdzbMKDvOioSWQkoH6TfgZNYDsg0rXHXj8vjuaTx7X8Qz+7ihMMv2EYKUAbmGN6KlmRAWCD3Merqhm11bajntXutF/vW+e7KdtGtnodjBipBnxgdhxgcitDh1dXU8/vjjGIbBPffcQ0lJCVIG+O5xNL0CTU/y85//nBMnTlBTU8MXv/hFQFlsB0E2DIAXX4oIYRAvnddmybFhwwZeffVVhgwZws0334z0z4V+cCoz0bRijHh/ZJDrUIVRM8qJJce3YYvLwEYGNk5+B55zGoEEfGWFXTyL3/z2cU6dOsX9d0+hOKmylVhqfBSUpZQhgdRFBh6x1Ch0q3eYYQbhPOF+ta0+miefeYu7774bnDXRULDQkljJMRiWIqD6Xj35xrc6eFMMNBHH95vaZD2aVkwQNCGEiZUci252Jde4JKyfaSRK5rwvhvxfAgXn2o8JPOewCkZCD2s/OoHfhGcfxLMPIkQMTS+JApTQi1tlTwaGVY1hVYeF4nP4zkl890TE5O4MgiCLEIYi8SWGArB48WLWrFnD7NmzmTZtWrSt9JsQWgrP3o+VHMnChQv54x//SG1tLZ7nYRiGWtp4PvnGty55Hmo4eDPxoskcPnyYp59+Gs/zuO+++6iu7oqT3dxqcFVTS1SrB0JLYsT64LvHI5E2IQzMxAiMWL82P+QgcMk1vBkNBgtUbcowe2AmhnDo0BGOHDmClJIXXtrJfXcMIPDTONktaGYVAgMnt6WFHZ8Y2iZQefYh/FajLlqwkyunT+aNN97g2oWzyDUsAQJ0szqSgJHSw821D6ZCSyGDPIFsX9hWQb5bWEtSDYBYajz5pneBADuzhnjJbD5uU1eFgPQRQbMeNIAZG4CZGI7vncNOr0CgqZaxsPC9WsVizm1FaAkVnIxu6FZVRAMQQqAbFaEZ4fCIB+O7J/G9MyRK5qglknMs5MW0ZMmJoqnY2Y0YVk1UKzIMg69+9QuUlnZvc86aUar+DSVua2pq+PrXv84TTzzB7373uyhLkn5jp4OiECbpdJqnnnqKbDZLr169qO5eQa7hzUiJoHkiX2gmTnYbMsggRHes5DjyTW+jm9VYqdHtSJ+BV4+d2YDQYki/JYPUtCL02GCefvpZDhw4wOzZs9m1axe1tbVosVsJsivUUjO9Ghlkoy6eEJYytjSqEZqBk9kYMcw1vQQZ5JDSoV9Nnq3b8hw5epYeVUNx8nvw3ZO4OaVYaWfWKF5W+PlpWgKEUmaQ0msTjISwVL1J2gRefXSNgZ/GybUwbQK/ETe3PXIB/rigsGT7iEAVKLcghEGiVAkcuPZB3Nw2rORYDKtXVC/yzpv0FnoxMshhmN3QrZ6h3k/H9xopgzZdt1zDG9HyR2hxEqXXIIRg2bJlrFixghkzZjBlYhVObgdmYmiUNV0MQRDw/e9/n6FDh3LNNdcolYGmFZ0aBzETQ3n51Z1UV1ezfft2PM/jgQcewM2uicY0rNQ4zFjf6FjIHJquppl871ybwr26ZhXsldeb+r6r1r3ESo3m4OE8zz33HNXV1dxxxx0YhnrvnnrqKXzf57ZbxkU3i+b3VTOriCXH4uR2EHhnCPwMoNgpmlFOLDUe3zurMisRx5YD+MNzb3HvvffgZlcg/Xq1HDcqopa/YfXBSo4AYeI5hxXvqdUSWje7hwxwEZJUj6ih6FAe5fwuo252I1Y05YJd1g8Lf7L8SAF/XkjpRim7ER+I0FQm1DxGEPiNCM3EiPWIpvXViMNwNL0SAhukh+ccw06vJlv/CnZ6NZ59DHkeg7f1l1NKiZWagBkfEt7R80i/kZUrV7J69Wq++tUHuGKswMltByRubkcbEl9H1+G7dQg8HnzwQTZt2sT69esV0TI1ls583Wzbp6mpiSlTpvDFL36RESNGsGrVKqzU6CgYONlt+H6aIMjiZFaRb1wSuYY0C6o1w3frOhT/161qzNRsnn52OX/4wx+46aabuOeee6JgBHDrrbdy+PBhjp2MtZKx1YgVT1c2TVoMKzGcIMgoyRKhK2G34lnh0roGMAiCNJbYzYQJY1m+/G1iqZCbJH18tza05J6IlRqHlD52Zk3UypcyBxhIKQBNib81vBHdlJzMhnCMpSUYKWnescSKpn7kgtGl8PE6208oXPsgEk+1xZuF06TbShWxhRfanCXoZhesxBBiqTHtJ+Klh+ecIJ9ZQ7Z+EfnG5bi53QReQxs2tVralWMlh5MonUuidAFSFLF06VLuv/9+YtqONoQ9UDbZF3JqdbKbyTe9jZ1eSSIR4wtf+AKvvfYaR44cUa3txLBLsrAPHc0xYEDLtPzUqVPZs2cPZ89mMRMjQgcUiZvZQL7xbVXAlx5udkvbt0C62JmNihbQSvy/2eHk8PEU//7dHwDw1998gAH9Studi2EY3HDDDTz77B/C6Xw1t+ZmVYAOvEby6XdB+gR+mlhqMmZ8IFLmcPN70TQ9yihlkGPoQE2x2+ucNooAutUT3eqFl99NrnFpNF7SvDyzUmPRdAvfPR4Gn5bPW0q3zdJUjcfMjSRn1DYfn1VQISB9yJCBg5fbhQx8jPhgpeooJbpRFQ636lgJVQdQREbFGG6WU70QO1foibB7JPG9Mzi5beQal5BreA07swHPOUlrAvzWrVv57vd+wjPPPEMQBOTzOTS9pAPVgEARM+2DUVbiu6fIN61UA78o80Unu5GuXbty88038/jjj9PY2IiZGEysaCodz0+rWa59+44zaNCg6DFN07juuut4+eWXEUZvReAMg3XzbJduVrXxj/OcE0o+5DzlRjM+EDM1iyeeep3nnnuOW265mdtuHoWbfRsns75dNgkwYsQIysvLefW1d7ASQwkCNWVvN60g16gUBDS9GCPWR1lzBwGefShcUp1At7pFnVHfOciC+ZN55ZVX0KwhERvdy+/DblqhMlFpI0QM3epFvGQutWcsvKDLRQeng6AJTS8jVjRZjceEx2sZ23m1Q6ffjyIKAelDhpvfq8h6QmDE1EyxnV6FnVmvtG8QOPl9BH4jgX8uKqg26xUZVs9wyVXUZr+G2RvDaq3aEt4tgxyefRA7/R7Z+pfJN72Hmz/Af/zHf5BOp9m1axeu6/LEE09y4EiSWPGC0B1kKJpeFkp0+DiZDeTqXyXXuJR808p23TzPPoyX382wYcOYMmUKv/jFL0LzybILcmQ0o4ra2lrOV2/o0qULgwcPZuXKlWoiPvzaCmESS00kVjQtqiEFvoOT3dLGOEDTS4iXzGLfIYPvfOd7mKbJ3/zN39C3l4+b36veF2njXGAe7M4772Tz5s3Up8vRDfU+N2evZmKoKjK7Z5Eyh/TrIpsnJ7OBfMOSaIZN04spLSsnFovxox/9lHNN1eGxnai+JrQksdQY0vl+PPb406xfv57HH38cl4EXmV3UMGJ9MMLZQ3V+Z6OxHRnkQx++90cF+Uui0GX7EKHaxAcBFWCa+Tex1Fic7FY8pxbN6I5uVCGDPJ5zIsyeyiKZVc0oxTJKUd20RjznBL57AjM+IMwQjqEZZcSLrsR3T2JnWhX/pR9134IgoF+/fnTv3p21a9dSXV3N4sWLef7556msrGTMmDFMmDADXdcRQhD4GXz3ZHj+HfOKnNx2hF7E7NmzOXXqFL/61a948MEHMRND8d1TCGG18HKExZn6BFVVVR2yjKdMmcJjjz3G4MGDKSseipvbHvKA8tH2nnNCkQ+jJYqGmRjCsZMmz/zsEYIg4POf/zz9+inypdQHhVQLFbw8+xCaUREVzJuRTCaZNWsWTzzxJF/7qzvJN70dfmbdMONDcXLbCIImQIaCazoIvdVArAjPI8bS5/5IfX09jY2NvLToPe6/+4qIiGrGB6LHhvDee6vZv38/C66eTlnROY7XjuC115Zx0/Xj2xklNBfQm8duFIVge0R6jT7qwMbOrA+L3B9dFnchQ/oQEfhnoy9t62xGaPHocTPWA8OqUAHK3h9OuocjCOfVBjS9BCsxhETJbIRm4kX1pu5hUbyGROkCNclvVtH88R89KUgmk9x7771s374dXXPpWuHw9a89yN/93d8xduxYNm3axLe//W1+8IMfsHjxYrI5pYyo1Cn7txckC2Fn1uF75/jsZz+L67o899xzigBZMgczOTLazoj358DBc22Wa60hhOC6665j0aJFaGZ/NKNScaCcg6Hu9mrs9EpVmJc2utWDROkctu7I8uijj5PL5ejRo0cUjNQ+TaxW5wDN0rDtZT6mT58OwMrVuzBiah+Bd5Z841J85yixosloeqnSK8+sw3cVoVIzytBi03j9zX2sX7+B2267njs/O5xRI3vh+z4+A9DNbsRL5nC2sSuPPvp7DEPnc58ZS5G1Gc85Qs/uDslkkj378+hGOBYqdKzkSOLFM6Ng5DmnQqfdlmCkPhcVgFpuIB9dFDKkDxG+czL8PxGR5ABk4EbWybrZnSDIhXdeVbQM/HPqThhOtpuJQW1er/ZhR3NkzfUmAE1PoukDMOMDwq7YaTZteYuBA9UyyvM8xo8uYdyYBNn6RWhGBeNG92TSpAcJgoA9e/awZs0afvSjH6HrOn379mXixIn06TMa6TeqL717so3UiJ1eSbxkNl/60pd4+OGHeffdd5k2bVqoTDkV3z2BYfZg//41TJw4sf11+Gl0s5KKigqGDx/OihXvcuX00cqFF8LCdUhdCAmRmXwFv/65CkSzZs1k2bLlTJ48ud1n0E4MTnp49r4O+Tt33nknv/jFLxg79qvoRpMaf5FaJOWrpv4bkEEG3SjFsGo4cESyfPmLXHnllQzoa+HmVlKU9Lhmbg3Havvw2utLuOGGG1j+9tscP36c6xZeSSq2v9V3A9zcTubMvpLHf/88vT9/I4a5R9mIhxrdMlDLVNc+ghbW/IQWJ5aagBBxPPdYRFtwclvQzS5t9L0/SigEpD8BKtVWWYZmVLZzq7gUmocrlY7ze+hmDwyrWyiNEaAbXRBaLLqrqXbu6GhEwU+vREoXQ/YFiFjdulndwljWEmh6+w4SqAzBsHpy+MhJ7r33avL5PK7rMm3KAIQWV8JsXh2+l8RrXIoM8vTtVc3A/gtAxKmta2T16nW88MIL5HI5KioqGDlyJBMmTCZZJPC9swRebeQsa1kWX/rSl/jJT35CWVkZw4cPx7C6Y1jdaWhoIBaLEY+fFyCEFUnLAlxxxRU89thjnB0+nNLUQNz83igY6WZ3jMQolix5l7Vr1zLpignMmNYVGdhkMhNZtmwZQ4YMabN73eyCYdW04Xa5+X0YVk2kmdSMyspKRo4cyRNPPMP9916Pn65DBjl854QyBwhraJpRiWaN5OVXlyOE4PbP34gud+FkW2bPZJChT0+Xkye78Nvf/paxY8cyfUoNTnYryPOF/wPwtjFnzhwWv7GCW265Re1DSnz3OE5mE1LaCAEIgbJ8M0L1BwNDGC3D0tLHzmwgXnzlR5ISUAhI7wOB1wBCw86sjx7T9CKEVqyWRlavdhnL+VByIi2F18A7iwzsUGirOciVqS9deLfUjYpWk/pnwmKpQDfUsdQPYy9ufi+JkrnEUhMIgoziM11gBi6bzeL7PlVVVbz11lt071YG0GoYNkAGWZzsJoLgHJ69H8/eT6J0AWVFR5k3M4Y+dw5oXdi89Rhbtmzl7bffxjRN+vTpw6RJk6ipGRWdd1lZGXfffTe/+93vePPNN5k3bx7Dhg1j27ZtDB/e3hRAvU60+XvhwoUsWrSIu+++A885DtLHSo3m+MmAZ37+M1KpFH/15c8R0/dE9Zl5c8bxgx/vYMOGDYwb19YZxEqOAqG1GiqW2NkNxItnRT/aZvPNBfPH8vAPnmT33rMM6K0CmZPdgtK7NrASIzhZa7J48QtMnjyJQf113Nx7oJ8vQ68aGtOmzGLqpD743ll0o4RYchiufaAj0Wb69unJSy+9xMMPP8xnPnMdXcvPRLIm4buDYdUgkXj5PTjZzehmN5zMxpYttBSB3xCyuEeef5APHYWAdJnw3brQckh1u4xYPzz7gMpIwqxE08suGZDOlxPxnFOtpCxUkdjN78V3T0fZjtZKmK01H0loKth4zXIWRjmaUYJmlJBvepds7mUMqyexoivanceaNWvo3l117LZu3crMaT3C/Ta7tGoIvQiEgREbgAzSSOkgtASBW4eUTjhIup/hAw1GDR+HYVRQd1awes1G/vCHP5DP56msrKRr1640NTUxZ84cKisrOXXqFC+++CLvvPMOp06dYtiwYQwYMIBk8uKZZpcuXSLt6onjp+AHJs8+9yIHDx5kwYK5DB+k4TlbkUHLr9rLb+Nzn72Fx3//NCNGjMCyVBYiAweJj5UcG07Lq/a4YfYAZKhPtDuUmg2QQYZbP3MTTz/zHH/zn7+BcE+Hri1VYIxgybJVnDt3jltumkvc2IObawqPo2RP2tiGCxM7u5HAqydROhNNK8H3zipukV6sMr8wyB09IXj2Zz/A8zwcx+Hdd5Zy/YK2MmPxkpnoRgVSBqF6wiFVqNeLkIGPJhLRkK6b34NuVv1FxP4uB4WAdJlobqMCGLH+WKE1taaXqeJqdqMiygU5fPsInnMIoRV3aKzYWk7ESo4k8NOhCeDJsPukt2mlO5lNePZRdLM7ntNSsAb1w2rWBYoeCxUAVA6vPmrfO4OmFeHmd6Obajxj2rRpOI5DOp2mfx+BECaa0fJll9LFd06GE++jlS2PECTKFpCrfy2y5Qn8BnznGL59kOIYzJ9dzjXzr0WKLmzesp+33nqLXC4XcYuef/55ampq2LVrF77vU1dXxxNPPIFlWcyePZuamtbS6m0xffp0HnnkEVasWEEul6Nv375866HPEjg78Bz1g5YywEqOxs1tIfDTdCk/w8CBA/nBD37A5z53G7169Q6JjQHxkjlYybE42Q2YieHoRjm+exY7uxHpNyCEhdDiBH4jPbs30atXL557/j+49ZYrQXocOurx1lvPMnnyRGZOq0L6Owj8HJpejJUch9BM8k3vRg1A3SghCLIYVjWBXqboB9KNiKi+n8a0eiKsoTz7h5c5dOgQ1103j8pyyTN/eJeTpzOY8ckRbQFhRKRTITRiqStwc9vwnKNoeimB335I186sI1Eyt50434eJj94i8iOOFpasFtkN6WY3hBbD988SNJMQ619V7WA/3U6cXUo3rDm0VRrU9CLM+CDiJTM4mxnD0nfrSGdTrfgngZJNzW2J9hl4Z/Gck238vYwwICnGr8q2dLMaGdjkG5eTrX8FN7+XXJO6m48aNYo1a9bQpUspmibQzao29QWVNQRRkFJ32P24ud1I6WDEBhAvnoaZGKbsu5vP1juHk92Bl32HEYPOcO+dVzB+bG9mzbqSmpoaHnroIUAV0mOxGBMmTODkyZPs37+fRYsWXfAz8DyPlStXcurUKRobG+natSu33jQU394cDiEbCL0L8ZI5GFZ3rOR4JAI3t4lErJGhA2y89AtI6WMlRmAlR6DcaZMhp6k4ZJ0vQyAxrN4kSucRL5mDEBaefZhbb5nNgQMH2HcgzaLXNrF582Y+d9tcBtScJHAPggwQWop4yRx0sxJNL8GI9Qn/Pw7CJFEyCzM+iMA7ie/WRcFIaAniRZPZd7iI7373J0gp+ebXbqB/z2OUJo/x9a/dRxAEbNqWQ2jhUlB6OJmtEdlV05NoZneEVoxrHwXaq2nKII+d3fCRYnIXMqTLRHNAMhOD0VsVW0ENRxLYBEEepBOyifNRxtIM3z2jipdsVYRGEW8lK9IF0HnssadwHIcdO8/yuc99jp7VCcUX8hrwvZYA5znH8P3GMEAFyqEivFNGRXOho5tdQXqY8SF49gGkdNi516esrAxN09i0aRNXTKhW1s1aW5Kl75xoZzHt2ftbislWdxAxrMRQzPgQde3uKVx7P4Fvg8wigxzFyRxXzShDiO3kmk5iWN2x7SbV/vZ9Fi9ejOd5WJZFfX09//zP/0wikSCRUJQC13WxbRspJRUVFcyfP5/169dTVVWFbvaIakCx1EQMq6WzqLhGNkiXaRMkjhPHsnTyTcsxzK4YrXhHgXcaO7MRGWQxYr3xnOOR3K6TeQcpHTS9FMtK0bdvX5566imuumoOV4xN4Dkbo9KPlDa6XoLvHMeI1SClh2Z0xYgNDOs+zQ60mxWXSksSBA5WYiC+6MfvHnuaM2fOcOutC+nZtZbAbZEn8ewD3H777fzmN79h+ND7EGwCCb57DDdfjBkfpLqNsRoCt3UpoDWUdZPvHMc3j1zQWPQvjUJAukxI6agli5YMpSFa3kIhRGSJA5BvWoEf5MO0ORXp5rRW+pOBQxA0EXh1imekJWjMj6Vv375ks1mSySTPPvssjuPQtWtXxo4dy6iRsxGyLlzanVFF6/DOGPgNZOtfCYOg+tLrRpXSxRE6ZmIYnnMIJGzacpxRo0YRBAH19fUMG9gr1JtuYfQqEbZTKsNqpRSglgHqi26nVyP9TCiT2xXd6IYR640R600+vY7ArUNoVkhkzCA0k8A9geOe4KoZlRw+fBLLsmhoaIjexx49erB//37KysqYMWMGmqaRSqWoqqpqMwA7ZcoUnnzySU6e9uhaFhaZM2sR+lykV6c0pvVSpe4oTHTdjQKcZ+/HjA3Bd+vCJZ6HnV4HQiNePA3NqFJW5enVBJ5a7liJ4dSny3n1xZc4d+4cUkpWr17DpAkL23xPzPhAzMRw3Nw2Ar+UfNN7IF1Fc/BORIYN0XdHi5Eomsz6DXt4440fMHz4cO6563qczAqCVhYXml6ClRxLdUowZMgQnvnD69x+2xh85yCgSgpIFys5Wp1HYiS+e6rN/FuzJredVuM+TnaL0pX6M7nYXA4+/DP4GEFKH6SPEAZOZj2aFr9o8br5S6B+5G70eDM7OVF2jXLe8LO4+d3IIItudGXv3r2MGzeOoUNbpD7y+Tzr1q1j7dq1vP7666RSKYYMGcLkSdNJxfNh2n8qPI6O55ykuVwQ+OewM5sxEwNDtcQ8CJ3Tp89xzz2TWb9+PRUVpQjhoxnlmK0kRgKvNpTGPQz4Su4C9QMSwkJKJ+QcGZGQnOr8dUEzu4Wvz6EbXbGS45BBE77XhJtTOkblFYPp0eMckyZNYvXqVXQtP0nfviNJlfRDCJg16+K1JCEE119/PU8//TR33/V5VWQmUHriSGKpKzBi/fCdYwRaDN87hwzC9rxehufsIV48i8BvQmhJzOQovPwehFYc/mAVwdGweigW9crNHDy4imsWzCNwd/LOu/vZtecUZxtKKU0Ut7ibOEcx40PDTqUHCKR0yTctV+etFys3WqFjJYaTtbvym1/8nnw+z/333091dXXI5C8n8M6pG2CQJfAb8d1jGFYvbr75Zv7t3/6Ng0cselfHo9qmKoyrOp9n70b9zNV30UqNw7B64+Z3EXgNEdP7oxCMoBCQLgvRXUboKiMRbYuBqg5wGqGZ6scadlSMWD90o2u4j4DAa0BoKTQtgaYlkLoT6S3rZjf271/CpEmT8L2zCDSEXko8Hmf69OlMnz6dIAjYt28fa9as4ee/+DW+7yOEYObMK7liwiCkfxbfPRp151T36ASevU/J4gJHj2skEgksy2LTpk0MHxrOxpk9VLDVi9GMKtVWb74+rx4ntwvdrMZMjMJMjCLwz+I7JxV506sNfxRSCcn5jaEMr6nY4kIg9BKEVoSbVfIaPhUEQcChQ4eYPm0iVWU7gTywg89cX4VhncNz4uhm5QV5M8XFxUydOpXFbyxlwdXjcNJrkX6jUnjUSwBf+Ze5tehWN3zbRkobGeQwY/3wvTPYTe8otYXESISwyDW8TnMzwEoM58RpkzfeeI5Ro0Zxx+fn4uY2Ic081y3oS02fkTzxxFN84+v3km9arhxNMPDcE5ix3upvoxTfyUTnHPgZDLMaKzma5W+v5t13n+aKK65g/vz50TZCCGLJcQRBE7rZPRSpy+JkNqMbVWiaxS233MJzzz3HX3/zLtzsmvBzUvrpVnIYRnwQnn0k6tpJv5F80zICvwkrObKNuYMMmiLW94eFQkC6HMgAEIiQrHe+lIZnH2wnngaqNd+srih9RXoUQg8F+DPhHJSC7Rah6zqWZZFv2oHvng4HJ3uHNtM6mqYxaNCgaMziX//1X8nn87z11jKWLXubsrIyhg0bxsQJ04iZjSpgNGdr4b/rNp6KSIK1tbV85oYeQB7NrCLw65FSZRmgIbRiQCKDHE5uByK3A01PhTKs1ZiJ4TjZTVjJMWh6kZqnc44gEeH1qrm2wDuneDLSVgPFWopDh+vo06cP+/btY8bU3vitBu5lkMXJ7UXTjqBkX7uFBgXd23WGhg8fzq5duzhwMEPv6m6htvhp3NxOPOdIqCopCLw0UvpoWgmx4ukYsT5qPtDsGrrzbgShZEZ0swKpj2DxkvdIp9N85pbriZsHcTKrVYaopQj8JsaN7sm6dTGWLF3PzGkD1aBxkCZwTyOtGpzsllYibDXoVi+QLmcbYjzxq1+g6zp/9Vd/RXl5+4l+zShFSNXYaLaGUoPAW7CSo+nft5zq6mpefGkl119Tozhy0sFzDmImhiCDHEJP4Xv1aMLCze9DN7qSKJnbMpAcZHEym/DdU8RLZrWrjf4lUQhIlwE3vwv1w8wSK53XbkhRub32AukgpRMS5qDN26zFMOODQOhq2DW9hmbin2aUs2f/YQYOHBiJnQERnwRhoBtd0a3uypNNS+D7PiUlJfTs2ZNZs2bRs2dPtmzZwsaNG1m1ahWaptG3b18mXTGBntVW9EM9crSO666/jRMnTmCaBpah0n0ntxVdL8GIDw59w06EbhdpJAGalkQIjcB38b3diPweEDGQbkh/KMFKlEBiCLnGZS1FXr8J128Kl5Sq82dY3dm/fz/9+vWjtrYWgvYSGVqruT7PORbJrehmT3SzHN3sGRXbFy5cyGOPPcbtt9+Cpp0FLBVA20jAquADEs/egxHrhecci6RTIECgYyZHs+9gjnfe+QPTpk1j0IAkbnY1gZ+ILKGCIE2+cRlubhd33n4jP/7p75gw4T8R146F53uEwK8Pl4MpFYT1IjSjildeeYXNmzczc+ZUpk6Z1CHLXwZ2SEjNEi+e1cYaynfPkGt4A6TH5z53E9/97o+pnTSeiuJakC6SACe7MSr0i/C7Z8VHYFh9oqzIs/eHsieqC+dk1obdxA8nNHRKwlYIUQY8AoxEVUq/KKV877xtZgPfA0ygTko5K7TffqrVZv2B/yGl/J4Q4rPAPwHDgElSyrXhfszwWONRv+TfSSn/z8XO788hYSul5LV9r7H37F4GVgxkwYAFoanhCYQwSJbfcMnX+84xlbYnhqB3kAoHfhY3t12NWPhZrOQQ/mPRdq666iqKU1ns9Go0o0IFBudoO11qKzmKYycN9uzZw7x5s5GB3U6GpLa2lpUrV7J3717y+TxVVVVUVlawZctW7r//ftavX4/jnOPauSqDa65VGLF+KsAYXdTjQm+jzS2DXGgIkAChhZmQDLuF1ZFdtJLdbX5NXjHGw3pavPhKfvvofzBy5EgsU2NQn7bZpRIxM8IbQVsIEYsClaaXols9MMweHDpyhrVr13LT9RNxcxsv+hmp/VgIYRE0y8RaPbD9/ixevIx4PM5Vc6ag+TtaieWZJErnIISpTAecI9GNYuW6HFu3buMrD94WZpetjqMliCXHcfyUw9NPP01xcTGfv+1KDLEPXS8jVnxlG0E1KdXSN1e/GAiwkqMw4wMJvCbymZXIwMaM9Yv4ZNt2myxdupSvf/UWvFxbRQAg1BkfEylK+F4TTnZ9G9+8ZhjxAcTCovifAx+E68jDwKtSytuEKkK0CedhwPoxcI2U8rAQogpASrkLZb2NUPYHx4Dnw5dtBT4D/Oy8Y30WiEkpR4U23NuFEE9IKQ928lz/ZJz593/n0I//mQ01aX41UedEucFNQ27ikXkPqg1Ex0QyzzkJYRahGWUYsV5oZlclBJ8c0W57TU8SBGlkkMFKjkQ3a2hoeI/y8nLsjBIXM6xe6oeZGHKeWH8dml7Grl3rGDK4jyJNOkfQze7Ei6dGx+jatSs33KCCp+M4rFmzhqVLlxIEAb/61a/QNI3u3StpyA6nvNSPOoDKc/6AIlRKgWaUYJjd0a3uGLF+aobLPR6ROCVgxYciZR7POYSUeUWkbO2E4p3Dcw6FDiomjWmdkpIS9u3bx7ULJrR7f3SrJ5peimvvBQSG2U3VtEQcoaeQnkOz5EeQa1CT8d16s7OoiN37MgzsUxWpL14IMsxmNb0cIz6QzVtPsXHji8ybN5fqKhvPXoffLARndMEqmogQRmhXlUczuhB45/DdE0yfMpJNmzazas0+Jo7pFfnjGbG+GPHhPPfci+zbt48FV89i6ECHwNul2vVeHYF/Dt2oIPAzONmNBH4GTYvTnE06uR0IrQgnsxYpXeUKkxwRMuUPMmbUFaxdW8TS5XuYNa3luoWIYaVGo5s9W2VFB3HzO5GB2+qd0ACpeEs5taxrTZ34S+GSxEghRAkwE/glgJTSkVLWn7fZncBzUsrD4TYdfQvmAvuklIfCbXaEAet8SCAlVM6YQLUHOu/j8wEg993/y/h99fzDUo9Bma5k3Awv7nqR1w++G56h3+EX3c1tw06vIt/0DvmmFdjp1eQbl+Dl93d4HDWR39LFOXrsFDU1NeHQZDi/1qqLp+lJzHh/4sXTSJZdi9ArOHr0KNVVMqpdBd4Z7MzGsOPm49mH8d1axVq2LKZPn869996LpoUzWkHA8eO1/PLXr/EfrxyhrnEksaIpGLG+CC0eFsHdFsJnw5vkG9/Eze9RWtDF00iWXUe8aGIoRzKOROk10bR8s2yuso6uiDI43axi37799O3bF9u2iVv1bd4bISw0PRTAl35oxDgGKzGCRMmVxIunt1lWaHoJ0k8TeLVcddVsVq1ahROopfGlYMT6kLaH8OTTb9HU1MTdd91IVdl+3Nx2IEDo5YBQgcNrUMXv+EDixVcSL54S1bOc3A7uuONmli1bhhMMQDPKiRdP5+DRJP/2b98jn8/z9b+6nsF9a5F+i/devGR2yJ7fQ67xTWX6GWQiM02hJYkXTUY3KyPGvZvfS+DVYyVGhF3fzdx5x2dZt24dGbufGvWxakiUzsWweqlgFNjY6ZU42Y2K+6QnAINY0dRQGcBEBo0IQaigmb/ke/dBozMZUn+gFvi1EGIMsA74ppQy02qbwYAphFgKFAMPSyl/d95+bgee6MTxngVuAk6gMrG/llK2yyuFEA8CDwL07v3Bkrqa35RXxnTlQJUq/HmBw96zB5hXMxQpbezMRnSzMvK5B6LWfrOsR3SuF9AKam2lrOkpdu9+l8GDB6uicmCrTtd5S7Bon8Kgrq6WyspKzHjPsL0bqBZ9m+xGmSAqm+0qdLOampoaioqKiMfjTJo0iR07djBu3DgOHDjAc8+9gG3bdOnShREjRjB29AAM7UxIyqxX14eMhmyFMFrsuyNHDwGYBH5TaDtdSaJkZnjN6n3RzWr273+PIUOG0Ldvn3YBXjmniOg9MszuICyM+ACE0MIg67YaW1H3LN+rRWMV8+bN5ZVX3+KWGyfg5trqbbd8LimM+ChWrt7DwYOLWbBgPmVFdXjZd1q9zyZW6gp85yBufjd2ZgOB3x/fPUEsWQUYxItnkmtYDNKnJH6YcePG8fsnnuH+++/nsd8/yalTp7j55vn07n6WwN8T7tgKZ+3UyE8+93bEJWsNxWcaFgVfKzk2XA5K7Mw6EGZIK/Cw9ANMmzaNJ596nq9+9UFam4p6zkkVZFpzzPw0Qi9VGkvCwMKPBsaldLCzm5SZwV9Q0K0zAclA1XMeklKuEkI8DPwX4B/P22YCKgtKAO8JIVZKKXcDhMu8G4F/6MTxJqFU7XsA5cDbQog3pJRt0gwp5c+Bn4OqIXViv52GIVX2sGTaeI4I9eXUNY0BZa01izL4jguthrh1owLfO4sR66fap9LFyW7qQJdaoUUfW7Xcjxw5wty5c/HsXeHjFx/Q3b17N4MHDw6/kGraPFEyJ6rbyCAfdvD0kEt0jCDIopk9yeVyfPOb30TTNCZMUEF1xAi1rHQch3Xr1rF161aWLVuGZVn079+fyZMn0KXcV+MqQQ6kjwzdTtQ0/Fo0owLDrEa3uisulJYMZWdVq1s3a1TWo3Uhk8lw7NgxRo3oTXPSbcaHEPgN6FaP8zLF7m0m/z3nBEIvDgmLzR+/QA3EelRXOZSVlbFjd4bB/Sra1UrM+EBOnSlh8XOvMHr0aO68/Wqc7CZ8r2XRILQ4saJJaHoKTR+KZpQrqyqa/dTWq05ioDqnoDpWV0ubbx8/zr/8y7/Qr19fvvHVq/GdA20IjkKLY5pd8ezDrZofLdD0EqzU+A6mAbrjW2o5eH5N0bMPM3PGPFatWsWPfvQzbrrpJnr16hGJ+zW/j4HfFLkjE3LkNM0MFSG0lmtxz+A7f1kWd2cC0lHgqJRyVfj3s6iAdP42dWHWlBFCLAfGAM1894XAeillZ5TG70TVq1zgtBBiBTAR6Hjd82eALtWXPqkV4RseKRHnun6TWDBgIeCoLkbgoLfSMAaIFU2KlifNUKMY7Z02mhnQALrZg3PnzlFaWoqmaa1+hBdfw+/bt4/Pf/7z+M6+cPuqMNsqwoz3C2eUlG+95xzCyW5FN7uza9cuSkpKomXb+bAsi6lTpzJ1qqpFHTt2jJUrV/LYY08RBAE1NTVMnTqZmp6pVtbdql8feGdxvLOQ2xZ615cQePUIYeFkN6ugZFRw/EQtPXr04OjRo1w1qy+BE0NKGWYDocKhdy6s01S0CeqB1xAua5oi8mfzks5zTuDmtuLmdjB71gwee/x5+va5FoPVqHm8MqQ+gjeWriWdTnPrZ24kbhxsKUILi1jxlThppejgZDYSK56Om9uNa+8jUaIUGpUOU33I8FaRxmwsQ/ztD9H/8BzTr7ySJfPmoeuSwGstEaJgxoeim11UptdaAQANMzE0HP+4wOeTHK2uvxX7WtOLMOJjeG/lZoIg4MyZMyxduoTbbuwZkTWNWF9l2+Q3km9SN1rFTl+JlH7k/yalQDMUcdPJbgq1vtrLp/w5cMmAJKU8KYQ4IoQYEtZ85gLbz9vsj8APw7qPBUwGvtvq+Tvo3HIN4DBwlRDiMdSSbQqqe/cXgwg7j/0qB/Avs/8LfZI+V/ebRaJk2kVf5+b3q/RXL4l+VLpZ2eG2Ss/ICyfry9m9ew2DBg0KCYb14eMVFzxWJpPBMAxisRi5hubJ/7YZlToHNTLSvNwyzGrWr3+JwYMHX/J9UHfSLD179uTWW28FoLGxkRUrVvDCCy9i2zbdu3dnwoQJDBncA4LTrZZ2qisng2wYYEU4cmOgW33Yu3cvlZWVajbNO46UdjuDxzbLtRBSyoiLo6Bjxger4nSQwYwPxHcOEfhNBM4mrr76al55dRmfuWkCQgj2HfR4550XmTZtGoMHFOHmN+I5zQJvXcPJ/CSxktlI/xxCLyXfuJTAb0TTS/DdOjznOG7oEhv4aTStjNjzO/C/dT9mYyNeLMaouVNYmUxgHDmJtbs79nm+Bk52E4lSpVmlZHMb0I1KrNS4S6o5Ci2GlRyllmwIzPhgzjSU8trzixg2bBh33nknzzzzDLbtqqJ7GJB85xg55xjxktkYsf5R1hT4aSUxgyovCBlE/DEpPezMWuLFM/4igm6d7bI9BDweLr32A18QQnwFQEr5UynlDiHEq8BmVL73iJRyK0DYKZsPfLn1DoUQtwA/ALoCLwshNkopFwA/An6N6sIJ4NdSys1/4nVeFvZPncrILl2orBnEneOvxM3tiKRBLwTVHVHt1lhqYuQgciG06Bmpyfp9+/Zx8803R1mTIkFe+AuwZ88eFcD8bJS6G+cN8TajORsTWgKhl3Ds2DGuu+66i54fKJcMzvtxlJSUsHDhQhYuXEg+n2flypUsW7aMl15qoqKigvHjxzNmzEREcFZ14bzasDYiQ06RGjY+dOgd+vTpw+DB/Qh8JaFhnCeurxtdkLE+6Od1e4RmIX07nHQXePZ+1dE0lLaPYfVWSgtePd275OnSpQtL3z7Ali1b6NmzJ3fecQtasBM7swtC22orORLD6oMM0uSb3g67m/0BRa+QgU0+swGZ39NKvUFgnkrB1/8ZbclbaIAzcwbyhw9h9jT4xgYHsfAegp/1RF/1a3zjXMtnEmRDkbTRWKkJBN5Z1UjoZL1Gt2ow/QbQevDOu5s4eXIjN910U0Su/Na3vsV3vvMdjp8uolu5onJENc4gpzSSpAwzvJySWNFLkaFUstCSiufmnQlHm/ZiJS59E/tT0amAJKXciFo2tcZPz9vm28C3O3htFmiXJkgpn6eFAtD68TSq9f+hYdvNN9N74UL8d96J0uk2+kBBHoTVJmAIYWGlxuE7JzoletXaX81xHDzPI5lMkm86qZjZsf4Xff3u3btZsGBBS2G8A+Z4M5rVJQ2rF01NTUgpKSsru+Q5XgrxeJzZs2cze/ZsPM9j/fr1rF+/njfeeIPi4uJQ9+gaelYnQ5ukJjRRytlzNiUlJRw+fJgpV9TQasxPjcuIBEKLq0Fds2t4DfX4bi1GvL+66wfhfFiQwYwPxkwMA+lgZze2qCgKHYSGEIKVK1cqlYDSDNJZgd9cQA5yxIqno+tlePndOLmdSvbXaLFi0oxK3NwuBAHNhXvNKMc8UgQTZ2Dk87jFxYjv/HfcW4fSbOwpx3SFPn2I7d2L/+0XCf7rHFo7zLr5fQgtiWcfCiVlO188FkJwsq6UxYtfYPz48cyZM6fNCIhhlIQml8/zrW/ci5N5N3qtZx9UzG09hQzS6nrNcgK3DtAw4/0wE8MJgjwyvYrAb8TNbQ8Hp/+8LO4CU7sDNKsJuq4brdM954gSQNOs6AufLL8h6n4IzcSM9W1nodMRAr8pnDNT4v57d++nf//+4YjBKczEYDTtwmt2z/PIZDKUlpaSb1JayRerN0VLH6uad5avoUePHhfc9v3CMAwmTZrEpEmTCIKAb3/72+Tzef74x5f4xje+gWF1R0rJkiVLWLHix4wdO5ZUKoUmzka+vM3zfJpWhJRO1BXUzSo854hSHYj1x3PPIoSOEDrxktmAFg7Cno0+L82oIO8N5Fe/eA6AGTNmsH79ekaNGg4yfD9ivTFiA9D0EmXH7WcUv8lvIvBOohvFyvQys6FFu0oqdcnTZyyeevVJbh04kB4D++F9+wFkpQUyH3Y3PQLDIfHobwimzUB853tYN8/CHhZebCio1lzQdrKbO1T07AiO4/DGG2+QyWT43Oc+R3FxSxYrgzSecwIrUcLw4cNZvXo1L770DtdfMyCUnVFyvDLIKAWK+KCwkXCOvHM81G3qS+A34mTWt9LskjiZdSGL+9JUiveLQkDqAFWHDmGuWYPT1BS1TmWQjQhyAAj9fdPrAz+j2L16GUKz2L17N1dccQWuvREIcHM7cXO70I3KaExEaMXRHfTw4cP07t077HCdQNOsCy7XIAxIwkAzurBz505mzZr1vs67s9A0jZkzZ/LWW2/R0NDAd77zHbp37059fT1nzpxBSsmGDRsoLy+n7sxAulRUh0s7lT0oR1wfzzkakQuFiGPGByKERqJ4SmicWQ9ajHzjspbCsNCxEsN4d9VxVqz4LdOmTePKaYNBujQ2NtKUraSsxA7JjLUhR0dDt/oR5He2jFDkdqDpFbj53S1FYaoxv/cyb1bsZW02y/z58+nxwAIcuV+pQEDoe6arOleQwxnVDeMbX0N/+Ie4D/w92ts/QloS6WfRRAIt3h03vxfPOYru1GBYF/4cAfbu3cvSpUuZNm1ahxrkml6M1UoC58477+Tf//3fOTlxPFXl5Ug/R+A3IoTZZkRE11Tw990TOPl9+B3YJQW+Ummw/ows7oJiZAeY8u1vU3rttZjnzrUKOm3T6dYcDyk9giCLlAGdGcVRHJuYGsD0HeXW2sUCaYdduWIQBr5Xh5PdSq7hTXINr+NkN+O7tVH9SJktShUcLzCl3ZyN6UZXpBQ0NjZ2+EX+IHHy5En27dunhlmDgHQ6zcmTJ0kkEpSVlRGLxdB1nXPnzvHbxxbxwssHeeIPJ6nPDMCMD0LTz+NtCRMp87ihcL0M0iGJD/INb7ZaVlfgMpGfPfIamzZt4itffoCpE1PKCTi7iXlzp7N8+XIwRgIaMsiFvKIsulGmSJel85VHnvTJNy1X1AW9mNg2HSbfg/b//i9G//CHfOPrDzB8UBon2BMqPxgYsZ4IoYczZ30BtTyy/5+rcGpqiO3ahfXDN0NSpCRWNFG5m4SfnZPd2EampjVyuRwvvPAC27Zt46677mLYsGEdbnc+LMviuuuu4+mnn8WIDQtHUhRXzk6vavN9VYaTRfj2EWS7XEVTDjjOyUuy3/8UFAJSB9CC0IlV0xB6CZpRTrL8JpLl1xMrmqpE9FtZC/nOSXL1r+FkN5FvfOuS+5dBMw9E4+SpOrp3707gnQYkSAekHQ6r9sFKDEczKlQRNL8P197PsWPH6NWrVxse04XqDxGFwOrO7t27KSoqQtO0TgXOy4XneSxZsoSXX36ZXC7H8OHD0XWV3pumycmTJzl37hymaeL7aqEWi8U4cuQIx44d55e/foHfP72W3QeriBXPxUqOUlSGkFgqpY1n7yfftEIFC3u3qugIHSs5irWbNH78k98wfPhwvvqV24iJtVGGhfTRg+1ceeWVvLlkJWZ8GEIrjgZYNaMC3ShXHcJWSxLDKcH6r8+iz7qZ2N69OL17U/Gj/wHue4hQFlY3q0iUzFX1wOxmPPtIpG8OEMQk4uf/U53Gv/47Vq4aUOYBmlGmVDZRxWYnu63d+7p9+3Yef/xxRowYwbULRmFwhHzjm+Qal3fKHnvUqFFUVlby8ivvRSaXoEiqyt1Y1TRzjUtCMwILTbOi74hmlJMonYNu9UUGGcW/CpwOj/WnorBk6wDNP22paefVhczIR6w1mu9qnn0QTS/Gd+sQeir6IZ2P5q6YblZF5EbfDeee4gNVlyi7CSPWC92swkwMId/0LoGfoSlTREmJohU02yNdbOYo2sbsztq1z0aib4Ff/4EWKI8dO8arr74aSs7GmX/VAFKxOqZMmMTaDUcZPfYqXnnldU6cOEEmo0h51d0TLLz2VkCybNm7TJ48mePHj7NixQoWLVpEcXExw4YNY8qUicQSaTXD55xsxb/x0Y0q8n4ffvPL5/A8jy8/+EWK4oex06vQtCKCwEEIAyk9fO8MA/r2YNs2m6MnDaq76MggwHdP4mTX49lHw0HbPJpmEVtxDv/LX8c4fpxA0wj+89fx/3YhgaUGXwM/DcJSygt6Ejcs3Du5bcggF2bCCXzvNM4kAf/nH/ntyUbmOl3pXpRWUiBWT+VuayjypmcfUBIlZiXpdJpXXnmFVCrF3Xd9FuluwsmeafO+u/Z+rMSls6U777yT7373u0ycOJ6KolNRp1B1I8/gOUcV0dRPo2kWVmo0nnsO6Z1GM6rw3VN4tuqGBn6Wl3b8mkMZNxo8/6DY3IWA1AGaeUiyk2+yEeuLblaGd9st5JvexkqOQosP7HB73eyhZrG0BAcP/oFpUyfgZrYCKHkPLa6KjuGkvXKyVWnywUONDBw4MHSvtaPaUEeQgaNcRsIO3LFjx7j55ptDrtOZDyQgua7L0qVLOXr0KL7vM2RwDcMGgfRPICUUJV1mTy9HiC3ccO0Ynno2S0lJCa7rcu21V9GrRt2x775bdRUHDRrErFmzCIKAXbt2sXbtWn7wg58Qi8UYOnQoV145k2TcCVUXLFatO83y5b9m8uTJzLxyCG52HZ6jApbQk1jxgehWd/INS8JB1ENcPX8GTz39InffeTN476CkSJSZpZR5zHh/vNNJ5K0jsRwHe8QItJ///3CHJdWSR+jEi2fi2fvx3Vrs7HZlUhnrDwSKgGr1CQmUO6K6lPuVaUw5Ws2zzz7Lt775RZz0ciWZSxAtO5WNusfGjRtZt24d8+fPp6amRvmseW2DEQgMq3Ms6ng8zsKFC3nqqWf4xtfvbum6SU/dQEUcAgchJGZiKLrZE989QxDYeK3UFqSUPPDGL3h539v40kcXOjcNuYnHPvPYBxKUCgGpA4hWS7YgCC7IaG6GlHmEVhRKb3QN7ZQvnEor+6Mq0uk08XgcjZaxBjuzDpHdgtBLCLwzaEYlvncKkGogdf8hrr/+enxH/YAUj6njroda0kl0s5pz586haRpFRUW49sGIXfyn4PDhw7z++uskEgni8Tg3XjuCuHkM6QsMqw9B0BgND0vpUZI8zhfvv5Wf/vwJSkpKkHRMUwBVGB82bFhUKzl48CDvvvsuP/7xjzEMg/Hjx/Pee+8Ri8V48EtfoChxGC+/U2VPQsdKjIgCBGiYyTG4+R0EfiOmsZPJkyez7O11zJk5JHJ1FSKOlRzL2g2HeOutRVx///0M79Od4MvT8EQWITSEKEIGaQK/Hisxipz7BpqWxMlsCJnix0IftDSuc0jpqMsgFInLMahfwNouXVj/9/+LcffPxa1pGQk1Yv1I53rw/DOLqa6u5r777sMwDHzvHE6u/VIuXjz1shjUY8eOZc2aNbz6+mquvqqfUl3Qiwj8LPHUGHSrJ/mmFYpPJzQVSIWGDNT0gdBSLD1Zj7PoLb6z1+IrV6tzf3HXi7y27zWuGXhNp8/lQigEpA7QnCFZ8TiO47S3dz4Pbm4nVmIECKvVhPbFiZSgyI19+/ZVnJb44IirI6UDQZ580zsIYUQFaym6kc/voaioKGJnI2XIKSltd4dqzXRe8/YqevbsCRBqNF/8mlpDjRXYkYiY4zi8+eabnDp1iiAIGDG8P4P7pZHBseZXqMHj2HgCP43nHMPN7wbpYYqdxGIWp06dYtmyZdx9992dOoe+ffvSt29fAF544QVVnAbGju5O0liP7zhIKdCtKqzESHSjVGUvmfXEUhMwrJ747jF8P43vnWHIwB5s317PqbphVHebjH9gM+Kb32V5ZRc2X3EFDzzwBUqLzpLP7wbC7qp0sJLjsTNrcLNb0EursJLjlE6WzJNrfAszPgAjPoR845uRXjbCUhIpfiOefZA7645gfe975N9dgXjl/0XoOkZ8DKvX7mHPnkUsXLiQbt1aWPctlustMOMD23DjOot77rmH7373u0yYMI7y1DkCvz4qUQhhYCWGYqfX4GQ2RK/RjVJ0sytmpooB37yB51+3AZs/DDJY3M/Dkx57z+697HPpCIWA1BHCDMmMx5U0xiUCkm50hbBOIcMW8aXo/6C84lesWMGuXbvo168fQ4aMoktlEYF3Cs9V6/wgcBEo9uyxEy69e/duw85Ws2QnEFoCw6rBCnWXzmdn79mzh9mzZ6vL85vQL2Ct3RGE0BF6Et/32bp1K6tWrSKVSpFMJrjx2lEkYvWRfnczWvzBirASQ5B+I55zFN9r4rabx/PrR5dTVXV5rqnpdJrf/va3uK7LrbfeSH3taoYOjtPspCsQENjkm95BN7sQuGcwkyPw3FNYehFWcjS5ULPKzW/nmgWzePaZ/+B+20b/+7/HyGaZ2q0bs37xHRx3K76jhnXVe2ARL52DpiUJgkbc3E4151Y0FcPqSRDkQ383RQexkmNCqY/NxIomYaXG42a34nt1eLcNR/xrJfHVawge387JWz/Pa8+9zLBhw9pIwzTDjA8OM+4gzPrk+15ux+Nx5s+fH2qA34WTUTN8bn4PQi/Gc0606fRpehFmcgyG2YW6m+Yz9I212IbGf5sTsKR3WNQXGgMrOi5PXC4KAakDnHzhBY4ePIheWko+n6e0tPSi2ytX0rfVqIP0Qgudrhd9DSjZlF49K3BdwYGDR1i1ahW1tbVUV1czePBg+vQZh5BNCM0g8BvYu3qnmsiXDrrRRcmUAEgvFEw7hZ3JY8YGIKUbsbOFUO3+5uWPKmiWdfr9yOVyrFu3jp07d1JVVYUQgtGjBzKgphEZHMb3JJpepNr8YWByclvDoq6FlDnM5MjQtEBQVnyGL9x/G7979Hlmzpx5yYAPsGrVKt58800mTpzIVbOUhndQ3RXNKMYwu2PEh2CnVxB4auBVIIiXzFZiark3yfuNEP6o1Zvgkzyxk5sefhh9m1oOudddi/a9b5LPN6s9isgdpNldRbOSmPEh+M7xkPoRYKXGIKXAy+/Cze/GSo3DjPWNpvK1/D7VVQ0pJLJUR/zwn+GOr+L97T/wds7jlgcfvCB7XgidWGpspz+vS2HixImsW7eOxW+uZ+6sZiv4JuymlW3kSZqtnJqaMjz68x+RmDKFu4C/v9HkkaalmIFHXNO5rt8kru531QdyboWA1AH0ESM46zjELAvbbtZzPg7S73BGTQYugXc2lLjQ0PSLz6G1huLVZBg6dD5Dhw7F97OcPHmOXbt28fbbb5NKpRg8eDCDBg3i2LE3ufrqq9E0jXjJDKRUowyBV6f4IV6dEmRzTkRLRsOq5sCBAyQSiejOK4N8p2pIUsooIxo7diyVlZV4nstnb5mIqR1UngeoMQYzMQrdLFO8IOmC9HEy6zESw3GzG4iXzCWWuoJ80zIAyosOM3z4MH7/+9/zxS9+sc1xfe8syADd7EI2m+XRRx8lm83ywBfvpjR1AjuzJjquppdHYnCx5HhyjW8ptUWzG3b6XeIlczDjg5RsSGiAqZHEemQ1/OP/oovjkE6lWH3ffUz7l3uiGUMhLEU7sGpwspvw7AOh20dXhBYjXjKTwE+Tb1pBrGgimpakOdi52S0hF2lQ2MFrGcVU+x3N8WmSzMiRDNu6lem/+x1lf/d3nfq+fFC45557ePjhhxk/fjQl8VPhrFuzJHAxVmIs+m+eo/a3X+EXCxcyeepUrvrqV/HdY/zv9EZmH5rAvvqTDCjrzrze45QEi95xc+VyUAhIHcCyLBzHoaSkJApIbm47yja6fUDSrZ4kjFKk9MJaTueCkZQ+vlenHG9R9IF8w2IqipJcOaU7s2aMpimjs2fPXp588kmOHz/Or371K6ZNm8bQoUOjAKME0rrh5HbiOzK0u3GjDtz69S/Qv3+r2TjRonlzITQ2NvLKK69QWlrK+PHj2bBhA7NmTaRn11qkrCXww0KnMJUgnXMAw5qKlRwdTqGDJEA3KtCKpyODnOo4NV97kGHB3H784Cd72Lp1KyNHjgzfAw83vwcrPpT169fz2muvMWbMGObNGY6X30wQNPvXm6GpQsvnoRmlmAlVpPad4wR+hnzTOy2F9SCPGR/M6ZMxYt/7Al0dh8abbuQ/rppDPlXMrMQwtczVU5hW30gHyEqOUAqcQR7fO4tuVik5kvwe1EiFWropE84TiqvTtJIgyKjulWzR6xbGMBYvWUFTUxPjf/oTclcvoNe2bfCb38AXvtCp780HgWQyybx58/j975/moa/dGS3dzPhgzGMG7hduRl+xgq7AN77xDVJzpmNnVkdjU/P7jGd+n9Z7/GB4bYWA1AGKv/ENxhw9St2//EsUkBAmtNGtaYHyG7t0zeh8BF5dKM8aFjAlWMnh+M5JRYLM7yVh1TB6mOD0ScGxYwGZTIYlS5bwwgsvUF5ezsiRI5k8eTKWZWElhkJiKIGfURpF0gU0jhw5QlFREUeOHKGmpgaBQZTenAcpJevXr2fTpk1MnTqVDRs2YBg6t992Bfj7CHx1l9eNSnSzC0asD7nGJYqn4hzCsPqg2Qfw3TPIwMN3a9HNruTTy8LAIEJaQw7fOcCtn1nA08+8pAIsGXzvNFIfza9+8zgNDQ3cd98dVBSfxM2pIqv0XAyrB1ZqdAthMsgTBFl0o0I1B5xjik+jl0TCbJpMYjCEl17bwM6dO7nl3/6NiqSLMasrn4335+13j7F5y35GDB2Gm9uGk9uC0IswrO4IYRJLTURoZkjBqI2CkWKM50EqQqEZ66u4PeHUvJTpsGkxiMNHbZYseZqpU6cydEgf3Nwm9If/Hr70P2n89rcpuf9++ID4PJ3BFVdcoYahl2xg3uwRaKIc8YPfEPz3/47lOLhlZRg//Snxm6YqTzh5MTLkxW9wnUWnXEc+6vigXUekriOCgC0bNmB7HhMnTlTjIX7TB0omdO0DBF4DVnJku7k4GTj47imCIIeX30U220Rd3VkqugygstsUEMVs2bKV9evXc/r0aWKxGAMHDmTatGlUVrZ0X9avX88rr7yC53kMGDCAu+++W+nfCLNdJ/Ds2bMsWrSInj17YlkWe/bsYf68qVSW1KpZM9R4RrxostJy0hKARr7xTWX1IwzipXMJvCac7AblCaYlSZTODYc1N2KlxoUi+UuAAKGleOn1c9i2w913383mzZtZtGgRw4cPZ+HVY/Gd/fjeuTAbiymBtSAX1od0POcoTmYTQk+i6V2xksPxnMO42e1K4laYmLvzBF/6n+zTDVY+9BC3f/4WtGBXtDxTtIAZPPb7F7jttlsx5ToCvwGhJYgXTcXJ70TTy9Rn4hyMJvZVjWUooCOEhp3ZEpEHm2FYNQTaYBa/sRTP81iwYAGWfiK0HvJBSoLf7eAnjR5f+eu/pqTkL2vUmE6n+f73v8/XrrySxEMPYW1Wy0vvjjvQHv6/uIlToWvxxaGK+xefw2vGxVxHCgHpfEgJ4VJo986d1NbVMX369AtuHvjZUFys+LKHbXMNbyBEnHjJlZc4pYDAq8N3T+I5J4gXT8Wzj+K7x8Np+O4cPtrImjVrOHDgAJWVlSxYsIBevXrxy1/+Mlp+zpo1q0Nb6iAIWLlyJbt372bixImsWbOGoUOHMG5UMb6zF4SJQFlPB34jidK5bYKZ750h36gcW81Yb6zUBJzshsgTzIj1I5YaG6lp+u6ZUKXQCZ8fyr99T03lG4bBHXfcRlV5bUs9Ry9CBjkSpfMRwiTfuBTd6omVGIab34+T3YSZGA6Bje+fjZZomh/H+v5SxP/5Dprv41RXo61dhJM8TRDkQjkRAA3NKOX0uX6sWbOGz9w8j1zjUkDNCWqakpN1MmvUvGKQRjeriRdPiT4fL78X1zlM4DWGnJ0EVnIs+w82snz5cmbOnMnAAd2xM+uj82uG0OK8u0Zj9559fPWrX72cr9AHgj/+8Y8U//CHXPXGGzjdumH+6ld4c4fjZreEN55Ui+TtBRArmoxhdU5F4oOwQfr0oBVLOxa2/dXSx1BdLb8JocWiZZadfo/AbyRePO2SGtitoVr3TViJPpfctplIqcZIVAFXN7sgg2xoabObbuVxbrlxEq8vTrNh0xGWLVvGggULiMfj9O7dm6uvvrrNPg8dOsSbb77J0KFD2b17NwMGDKC6upotW7Zw4w1zSJp78Z1QcVj6mKmJGFY1+ca3cLKbiBVNivalG5WY8SF4zmE85wia2QUrMSqUmc2F4xA9EVoKx96Pl9/TfGWY8cHs3Kv0oAB69+5Jl+Jd+K7dav9d1diIfRgzMYRY0WSaLcGNWD9852joEEIk2mbt8Aj+039G36lIj96XvkjwT7fjxg9AQNSJs1LjlaNKdgs9uvUkkUiwa88JBvQdgpPbjSaSijWvxbBS48k1vBX6zZ3Ac06iaUnsbEuQ0fViNLMSTw7gxZfexDRN7rrrTnQOk2t4i46WNjLIM2NaX3avXc+JO+6g+gc/gC5/eoH4kmhsZO/p02zevBmmTqWsrIxxj/wEW+zEb8VDUsVuHbhwI+SDSmwKAel8hBwkKQTxMCDZ6TVYqbGh08bhMDh0QwZ2ZBXj2UcQIo6b30OsqMPg3wYd2Rx1Bi3SuCpAqezpLIHfiO8cZfL4OAN6V1JeWc2uXbvQdT2aX2vGypUref3115FScvr0aRYuXMiaNWuYPHkSs6/sie/ta6WDowwdm6V4jVgfnOwWdOeYmooPYSaG4LvHkYRdppIqYqlx2OnVSOnhZLeErW8TKSW6UQLmKB5/ahF1dXVce+21rFq1ispKNSnfbA6pm1XEUmNDSsNGjPjAiOMlZYCb34vfKuMQwsL69mto//d7mEGAU1OD+MX/wZ1UhpQtXCnpp4mVzkfXU63munYw96rpPPb48/TufTu6cTqqQTm5rcRSE7ASQ3Dt/YAeeqT5NAcZoSUxEqPZvbeO9957hquuuoo+vctxMu/hnifIb1g9MRMjyDe9E91YvrD6XRIvvIQXBBhPPcWfDdkswX//7zi//CWvPPQQc665hoMHD9L1S19CFsXwG5Q3n6YXoVu9cHM70bTiNpbvLdDbdBj/VBQC0vloFZBisZgqagtd6d2Ed+XmSWehxdCtHgReHUasBqEXYyVHdu4wflNIWixWUhBN7ynfeqt7G+2jjl/bUgNS2VMXdLMLZrw/VqqJ0sqTgMa+N9/BdV169eoFqHrBq6++yt69e5FSkkgkiMVivP7663zm5nn0qDqNZzeF15YMrzWL5xxGNytVvSa7XQ2TnldLE0LHSo1X2kTSw8luIFY0jXjxTPLpVVGRVwmcDWfH7jyLFv2KIUOGcN9990XuJ48++igNmYkUWSdUkHVP49onELiY8SHRmIxyOVnfKiAIzMQQDh7RSC99l3FS4n/9y/j/9RaCWBYhYkpJQZ0tVnIEup7CzqyL5tiQPtLdzpw5c3j99cXceMNs8o3LABlRBszE0CjwylZkUDM+gLxXw0t/XExxcTH33HMnwt9HvrG9i2wsNSHq4MVS48g3rVBP/I9b8V95HePpp+Huu+GGi7sjvy8sX45z771Yhw5hCcFXBw9Gmz6FqZOHRTed5k6l0Iox40ORfhOec6zd0k1oRUipnG1Eocv2Z0Ioi4EQUftfoFjYRqx/2PofEG0eS41r+3pxaZKflH6oDDlU1VS8JgK/SY2d5LapO1NsoEr/jYp2NAInu4XAO0eibGG7wKW83IrJ5/MEQUDXrl0RQrBlyxZWr17N/PnzGTx4MC+99BJSSurr6+nTS+Dl3iHwe0XjDrrVF3Bws9tVVpTbiZvbgW72JFZ0RSSXqt6qMGszKjDjg3Dze1Qgye3Ad09EX2JNLybQR/LYE4s4c+YMd911J9VdbZBZ7MxerOQorrvuOhYtWsQdt1+Hk34bhI6X30rgpzFifZCyN15+L05uB8qHDgyvBO1Uime2rOfo0aN85kc/xG/Yhz0mRvPYR+CnsZIj0PQUQktGzYlmLSIhYhEJsndPwbZtOvv219K31yDc/G7c/G4MqwZlN2USuGcjMqiZGMfW7UdZt+45rr76anp0j+Pk1kUuwOEXg+bWuO+dwYj1VnKz4ewd0ifoU0rwTw+h/8N3cB54AGv3bvgApIYBaGrC/7u/Q//Zz7AAZ9AgrMcewx/Xj1zDEqS0SZTMDc1IVafSd0/gO8ewkmOQSHy3DrV0U2YASC8aHJYfUIZU0EM6H0LA9OnUDhqEEALP8xB6EQKBpqeIpSagG2V/0iEC70xYl1Bta00vJlF2LfHimZjxQQBIv4F809vk6l/BTq/Dc46p0RTp4Xu14VDthbOogwcPYlkWPXr04Omnn+b48ePce++99O7dOwpS+bxabubtGJWVqmZhJZSHvPTPIDCj5acfOuOacSVELwMbu2mFmlFrBTMxLJLTdfO7lPqj0DHjQ9h1oJLv/+DXVFZW8q1vfoGupXtCN9zFePYB3NxOunTpwoABA1izdpfqYElJ4GfUcik5Dt85hp3dSnNWFNuQQUx+gMyc67Bcl7/+5n+i18BGnHHFIXG0+WMVitFt9WzTKTViqlOWKJ2LblQgpY2T28a8uTNYvnw5vuirlojSw06/S65xiZoFFALD6kU+GM/Tz75OXV0d995zJ1UVdS3cp7DJYcT6kyi9WmVpKJkazz6ihOPSa2htEOk8MAV73Bis2lq8//yfL++LdSEsX44zZAj6z35GoOvIf/xHzE3rsUeY4ZIxA2FWqxoPOlZS3Wid7KYWB1vpKHqLAAgwk61kTwo1pD8T4nF45x2WP/001wUBnue1kQT9IOC7p1T3xuiClDLstsXQrWr0sLYggxyalsJzj4fF4sPEUhNDvWa/nRPH+Xjvvfc4fvw49fX13HjjjUrCIhRQO3HiOJ+9eTSHDh9ix56A0vJKSspH4ub3RgRQN6cyI00vi9xUhF6M7zUoE4OQEOnmdmKY3dGMUpRr7jGCkCIAoGlxAm0Ejz6xiHPnznHPPXdRVdGosp/z4OZ3o1vVTJ06lUcffZRBgxZSZKXCuT0Nz94bSssWIzIO5v9+Af2nv1TD0IMGcdMV1bjZt2nORDSjFDNWE5Iy92Fn12Mlx7QJSEIIrMSw8IcYzvdJDy3YzsyZM3n99Te4/roryTcuj+pqml6MmRzPho372Lr1Ra655hq6VoKTXdrKftpHN7pixluWQlZqDHZ6NQB2pm1XWI3YOEhNQ/78/8Gfeh/Gr38Nd90Fc+d26nvVEfL5PK8vWsQNJ0/ijByJ9dhjeMOqsDNvg2xrle27p/GdwxixPuhmZWSVlGtcojqtWgrp16NbPbASo87LAD+YgFTIkC6A5uVaEHwwqWhrCC2OYfUO6yEeutkdKW3c3E7yjUvJNbxK4NWhWz2JF08nWXYtsdR4dLNb2ArXLupssnbtWg4fPozneRQXF1NTU8OxY8f43e9+R0mxxs3XVlDdzWHKFdX81ZdvJxaLcfRkMrTGcaKOFZh47lk0vSS0yGkKJTyaEEKLHGntzHo85zi5hjdw7T0hT0dgxoewc3853//hb+jatSvf+tZDVBY3M5yJZEKaa3OgPOWFkFx33XW88sqrGImxgMDOrMHJbgUpMd87hj75yxg/eQSpafj/9W8I3nkYr2eLKwgA0sGI9cdMjEDTiwi8c+Qbl+K7SpFBzftJAq8RKSW+dxrNKEMIC989Tb8+6rwOHDyHGR+orikxlIw7mieeVKqY99z9eSpKTioaQxiMNL0YMzGMWNHUNr58utmjnbkoaFiJ4STKFqAZVWh6Cr9/guC/qvZ/7ZNPdvp71QYbN7JmzRq++93vwrRpyFdfxVy3EnuAg51eqZxqUU0VReNQpQYnuyXUNFckXaEllKSy9BFCI158JfGiyUr5oU0poZAh/XkgJeTzxIXAcVqYqb5bq2o80gm5P5cenr0QjFj/yLhRsYDHqR+GXx+pIiJMnOwGfO8MutFVBS08zNhAdKNLG03vZgRBwHvvvcc77yhX0lgsxowZM1i8eDF1dbXcsHAQyVhd9N3RrR5oehELFizgySef5K47r4ecEu7Sze5YyRHY6dUIozu6WYGdXglS+b/Hi2eGoxqDcXM7sdPNxsYamlFOoA3ht4+/RGNjI/feexddy+vxchsQeiX4TehGF6zU+Kim0zyfpoTkd9KlywgGDhzImrW7mDh2MG5+F5pehvn//QfG/1UepPbw4WiP/BPOEAPII2QcKdWq27B6YyVHRUVwKzku8ltTbftT2JkNocJnLVZyJLHUJKVl7hzFzqzDzW5l3ryZPPHEH7j77juIFfVi1Zrt7N27moULF1JRmsdJv4VE0nxvNxODMeODO9SoEkJgJUeTd2ujifpY0eRwCFlgxvuEgm3gfHUmdYPH8dsjJ/lbz8MwOvlTra3F/drXMJ95hsMPPsj9//RPdO/eHd85ipNZGnG/lNqlGX4HSkJ1glVI6eJkNxMvmhyO5yjVAmWJfgWacaFB80KX7c+DujqoquKq0lJOb90aPey7J3Hze9VSS7/Qh9I5OJl1eO4pkqULEJq6Cwsh0I1ytZxoliSVDqDhe7X47klEPkGidAGG0Z7Ne+bMGV5++WUqKiooKiqivLyc4cOHs2TJEsaPH8WVk1ME/hnF9RMxrNSYqG2fSqkJ8HdWbGHmtIHKJto7R65xKdJvIl4yGN2owLdqQjuos4qRLJIR+VFBYMYHsWVHhtdf/w0jR47k/ntvws1txM2rwraVHIdhVqBbvVuK4VZPdPdYNCfl5vegWz2YMmVKtHQrK46zfVcT+46e4mbDQP73/wLfuA5HhuRJLQn4kQW0bvWI3tsgyKKbXVTRNnzv/PwpNb4Sssk1vRShWUq/KbwmKV0MuZcrr7ySJ598hlOnTjF69GjuuvNWvPwm7Eyz2L2munZmdRvBtJZubEsGqGkJzOTISG/Ize3Cze/C+P+3997hclRXuvdvV+p8zlHOOeechbKQhAFjkhDZxp6xZzxgbM+9d+6Mx577zWN7ggM2tjEwNkGASCJnCUlIAoRyzjnHo5M6VNrfH7u6Trd0BMIIEKbf5+lHR91V1buru1btvda73tdqG8wcgwvb1Ki4ojOdXosxZ86cj9aNkhKeegrn29/GPH0aNxLha6NGQfNycrXvN6CppJaovlsFETCs1nhWG1z7IL5bhZs7iBFpg2G1ClULMtVvE02NRTOaKv5bZkPB8S6MNdJ5BSQhRAXwINAXdX/9hpTyvTO2mYCyvDaBE1LK8UKIHkAhoaIz8K9Syl8LIa4DfgL0AoZLKVcUHKs/8EegDPUNDZPyjAXvp4WCsn+hLo0Z64Wb26f6myIfTWb8MBiRTkq0LLe7wfxUrm4VQhgYkc5EIx1UIts5ofqlzkhk+77P8uXL2bhxIxUVFWQyGa666ioWLVpEbW0t110zBkNux3cV41YzFK/nzLaR/v3788QTT3DidC8apZrj2ccRMke0bIJqm5ASK94f36/Bd0+r5ROgaSk1P9BTuKIPD81+iZqaGm677WaalJ/Crlta9D5OZqNaIgSfw8mpAGdYHfCdE8EdXKq7dGocl48dy/J//TGr27dH0zRm/ezfcf7lJtwWOaxYS0gfxYi0C/3u7cxmhIgEnmJdkH4WN7eXWPlUNKMM6Ts42S042Z3qe412USxvdOzsDuz0xpDBrZvNcenE1q1LOXToEI7j4NpHyNUuCKtLmtEocOuov0lIKXFzuxR/x2hMJDm8aMZkWB3w7AN4znF87xRCS4WKAHk1Adc+gO+e5KorhvLkP/6S2kceIfniiyrHeSYOH8b55jcxX30VE7AvuQTzz3/GbaNhV78dJtdBzcjNeF+lo53bF3CICM5FDzyvCunVYqfXopvNEJoVnlspbXJ1q9C0RNhKlD+Hn/SayON8c0j3AK9LKXsCA4DNhS8GAev3wJVSyj4EzrNSyq1SyoFSyoHAEFQNNu9WuwG4GnjnjGMZwGzg28GxJgANe8N8GgjK/r6UNG7cuGBchrqIP7TB8PygGU3RjArc7M6zZEBUYni/umCC14QwlLlAgdKAlJIdO3bw8MMPc+zYMXzfp1u3brRq1Yp58+YxaeJoJoxOoPubw94rK9aDaGpEg2qWQgguu+wy3njjTXSrO0KodZ2b20Ou9n3lZS9MDKtj0X6+X4sZ68WGbWXc+7uHaN26NXd+92Yq4ptC7/i8KaIat02ubg2eqxLE6sLYhZPZiBlwuBQZchji1VdJjRrF1Pvvp9HevXTs0JymZVtxWyhumBBaIHAvEMJAN5qg6UkQGr6fC2kHkeQwND2Oax8mUz0PJ7sbTa8gWjYOK94f6aXJ1izCSa9H0xJqqZIYxJ6DTXhizvP06dOH6667jkgkQp9+oxDBZSOERTQ1Ft+rVXrqGXWulS5UFVLaeM4RcnUripjMauk2iKBcFRJGNaMxkdQYrMTAoE8QvMwGZr71Jsm33sL/yU/O/jEtWYLTvTvmq6/ixuNw//0Yb79CrtkhZbwpHYT0lJqm2Zxo2QRlXBHtCULHyW7B99O49mGyNYvBd4IEey40sRRaJJR4kX46DEZCSxBNXYIV739OGeWPi48MSEKIMmAc8D8AUkpbSnn6jM1uBOZKKfcF2zRk3DQZ2Cml3Btss1lKubWB7S4F1kkp1wbbnZQXQgD6fBHMkHxNIxqNYhgGjuMEPWveednOfBRUvqBbIPpVLNzuezUQ9EKdy2sN4Omnn+aFF17ANE1s22bSpEmsWLECw4Drv9abVHQdXlAFySeNvSBvdSaklDjZ3cQjh+nbty/vvb8JM6p83N3cHtW7ZbUI/r873E/TU/jGMB569B0WL17CbbfdwtSJbbHr3g3Zz7rRjFjZJKz4wHA/zzkcVmjMWHeEMJTxo1tLNDWaiNML/9ZvweWXYx0/TqZvX7Rkkr79hoWBLRIfgBFpjx7pgu+ewk5vIlM1D88+FHjXp9CttsqeyGhCrnZ5QeJZBs24jbAzW5SGUr7/zUjim6N46ZVVbN++nZtvvokObSQd2+lcccUVbNu2p/7ilDbZ6gXkapdhp9chhKECN4o+EQYV+5BSy3ROBN9xXeDSmw9SfpB0P4XvHgvzigBS+PD7/6UMJ/7rv2DVqvA8njp1ivuWLCGnaThTp6Jv3oRz83gyNQvC7z4/Tt1IqfNUp0r7mp7AivVBIlTlT0okPtHyCVjxAeq7tvfVW21Z7YoKKYrKMAndvLAtLuczQ+oMHAf+LIRYLYR4UAhxprJ4d6CREGKhEGKlEOLWBo5zA/DEebxfd0AKId4QQqwSQvyvhjYSQvyNEGKFEGLF8ePHG9rkL0OBwD9ALBYjk8kgffVA6B+rb8fNHcDJ7jrred1sQ6z80rOqZbrRmFhqnHIlaYBnZNs227dvZ8eOHdTW1nLq1CmSySTLli3jyq+MoH+PSnz3QFAViRBJDiNaNhGEEbhknDj7mOk12Ok1OJktDBrYmf3793OyqhFCUyRL3WyDpqXI1b4XXNCKV7R+a5J7f/cIbdu25R/+/kYq4huKApbQEkRSY1RepUAWVYhIGFg0LY4RU8aVrr0L7ZUlOD16YDz+OJ5l4f7HjzEXP8iVP/whq1evCWYWGnZmC75Xi51egfRzONlt9fIYgZKjpkXwnGNkapaG/mxmtAux8qn4zkmyNUuCAOIjhEUkMYTd+8uZM+c5BgwYwFdmjEfay7AzG7AzG+jZoz3V1dUcOlrf8pNnzWtmM3SzLW5uv1In0OqDihARnMwmnNxOnNxBMtXzi8wWzWgXoqmxGFZbcnWrkdJBMxqr2R7gDm6K93dfR/N97JtugoceYv4rr3DffffRZ8wYkhs3or/yFNmKvarlpuD+bUQ6EyubqNySMQIRvz3q3OuNUQJ/lYqNbzTBDayZ8lyy/HjyszpNLyOaGkskMeBjN5OfDz6y218IMRR4HxgjpVwmhLgHqJZS/qhgm3uBoahZUAx4D/iKlHJb8LoFHAL6SCmPnnH8hcAP8zkkIcQPgb8HhqGWePOBf5FSzj/XGC9ot//u3dC5M5kWLYgdOcIrr7zC8OHDaRo0O35cqxcpXaRvhwL5fwmOHTvGzp072b17N67r0rZtW9avX8/p06cBmDx5MqNHj8bJrKlPMgudWGp8WBXJd8Vreopo2eSiz+G5p4IWCTXryXgDeeGFl7hp1uU46SUUahhpegqXXjw+52Xq6uq4cdZMGpUdPSO5rQTgjEgHrPgARBDEc7XvqaWJXg4yh262xMnuxPeq1AV+z/NE/vnfALCHD4P7/wknWKVaiUEsfW8PyWSS/n1SBdSE/MdVyWwj0hnNaIQdiMSBmiH60glmVR1xczuxM5sQKLa5YbXEpTtvvPk2lmUxefIkNLkXN7uNwuqRbjTFZgDPPPMMN998LW7dolCUL5IahZPZiqaX4zlHQyWAXHo9bnYnIDEiHTGsdkGriK+UGROD0AOxfunnyFTNUx5tXlW9PTgaluyMHPQVrP2KoLp88mR6zJ1LKpUIEuPbyLe4SL9WaZnHB4UzGCe7HSezA9/PEk0OQdMryNa+h/TTAe+olZopCYNoahRCWGSq3gLq1RqAULHhk+CTdvsfAA5IKfN13WeA/9PANieklHVAnRDiHVSuKU/jnQGsOjMYfcj7LZJSnggG/yowGBWYPn0EMyTb8zixfz/RaJRMJvMXfwnKNeQvu5O88MILrFmzhrKyMtq2bUs8Hqeqqor9+/fTsmVLampq8DyPXbt2MXJY++JgVDYpvMOC+lH5XjVmpONZn0W1fATVNa+GZPSYWrot28SoYd3V7MPPYEa7s2pdNQsWPMLAgQOZMmkgTmYtbi4TflYz3i9kNhc2DgshiCRHgPTJVM8LZHQV98eK9WLV2lOsqMrwzUaN0P/5btyvD0ZSn69z0usZM2YCs2c/Q9euVxHRD9b3xwFISSR1CYbZVMkN54Oi1VaZYhLBze3EtfeFDbNoUSKJAWzfWcXSpc8wadIkOrZvTK7ufbx8j1yQm/Kco3juCWLxEwwcOJClS1cydlRf7PQafK+KzOk3yYvwR5LDwmFZ0Z7KmlrmcHN78OwDGNEuCKGfRQ+QMocwKooqYvmkOSLJB9/5DsP+5V/IxGKk+/QhEXPIVi8oaoRWwnQphBZDCzoKlMW7j5Q5rHgvdKsNbm4f0k+jGY2w4oNUC1Okg6JD1K6gkFfk2QeRsd4IzfrEweij8JFXipTyiBBivxCiR5DzmQxsOmOzF4B7g4S0BYwAflXw+izOb7kG8Abwv4QQccAGxp9xrE8Xbdrwwte/jn78OPteeokuXbqcPwfkAmPtWsWQzmazDBkyhFQqRePGjUN76v379zNnzhxs20a32qJltyO0BL5XqVQHCvrshBAfKhRfKL/qZLcxaOB45jz5Cj16dKMi2Zqc25ZHH3mJTCbDHd+4lfLkcey6ZeR/uLrZAisxsKhqcyaE0PH9XDDbyiKPHUZ/YCl/aneQukyGG793J/zwCnJ6JQhPHTro85LSxcuuZ9q0abz66mvMvG4G2ZpFSOmjG2Uqx6GnyNYsV3kYLYIV64Vutcez92FntgFe6ApjRNrjyi688NJ8otEoN998I5q/p0gHCQyseD8Mq31w4VdjZzYyoP8k5jz5PCdP96Ii3jTI1/joZnOsWH0DsJQSO7OeQuH8vDNNJDWq4DlP9ctltiGlFxBUs1ix3hiRLhw6dIg5cx4g1aQJLebNY9nOnYyZdilOZlNRMNL0CiKJQbj2keB4mzCs9uTqVN4pVjYBoSew0+vDpbVaru0NEt3dQq+28DxZbVTSuoC68GnifK+0fwAeC5Zeu4CvCyG+DSClvE9KuVkI8TqwDjXHfVBKuQEgCCxTgb8tPKAQ4mvAb4FmwCtCiDVSymlSykohxC+B5aif5KtSylc+8Sc9X0SjHBo2jEQigZ7JcPDgwdA+6LNGs2bNOHXqFJdeeinr1q3j+PHjTJ8+nXbt2iGlpF27dtx999384he/YPfuPXTsOEopJfpZ3Nwe5aZqtcK1jwS9b+dOGQphEEkMDJYTEie9issum8GLL75E9+7dWbjwYYYMGcLkif2x02twc1mV1PZqFIu8gFfk+5kGbcRd+3DQMiGJvH4A+d0fY1ZWcsm3v03XX/0LbnYdbp7tLJLokRaY0R5ka97B92rxnKM0b9KGFi1asG7DHvr06Ilr78H3avAzW3CzO1QeSYuBn0UikTKDax9CyixC6CqvlRjA1u0neffdZ5g8eTLt25aTq3sXr1BeQ3oIPYmT2YxhtgmVDJAuTmYNM2bM4MUXX+Tmm67Gq10Y5G00CPrVpPSCXM3Zl9iZ4nZ23eowsCjjghRWfAyIGM899xxbt25l+vTpDBo0CN+rpuOECUElr1GovKmbLVRlUjMx9XKEMLAz63Gye4JZ0ECkTJOtej9cCioZFw0nvQGkH+TTJNJLI/SkMts8T9G1C4WSYuQZqK6u5o033uC6667jkUce4ejRo9xwww0NKi1+HDjZHar8Lt2g1O/V/y09JG4wE/CUZnS8L0899RSmabJnzx4ymQyG7tCtawcumz4ApBsuDTZt2sQrr7zC979/N3bdkoKKUQVWYgRebidmrPc5S7NSejiZrbj23oDM5ysbaNGPP/zhz2SzWb72tSvp0dnDDZpsFQw0PY6UHtGysUqsrG4tnnM4aCYNXE6CvIPvpbH3LkD/wR8xn38JAHv0KOQf/y9u62JmhxXrgxlTlT7POaFK0gQUiMQEZs9+mmuu+RoWK4tmCXkIPYWmxfDdUyHtwYh0wvY78sYb84jH40yePAHhKe3yfA5KnbdG6EZznOxWhJZUppeJwdjpjWEzsZUYxIpVh5FSMnRwc9zsHnyvmkhiQND/twrfq8WM9cZ3jmFEOwWaUFlAEE2NxXWOBnmqYMzCDJxO2rN3716efvppmjZtyqxZs4hEzDBXZMX7YUaV4oST2Ra62qqc3SDc3E5y6U1qye5n1VJQiwYVYkleqsWMdg/7FwthRDpgRvui6Z/OrKikGPkxUPvjHzNCSpgwgZMnT5JOpz+Ww2oh9u/fz1tvvcXUqVNpnNxUVP34MLi5vehWF44ePcqpU6cYPnw4A/s1ourkCpo2tcIqiW63xrDa0Lt3b9577z1eeeVVLr9sPJnqhYosGOuL0Cz0oEx9JqT0cXM7g6VCPl+jYcZ6sWL1KRYufJBYLEY2m+W995bRrUO97IpuNMNKDkZ6dcp+uW4dSBfPU/mZXN1yIonheM5RnMxGrPhA9Lnz0PNM4mgU8Z//intjP6RwEFo8zGn4biVObhdGpBNCMwMzgU7BMsPAt7cwdepUXn/9Ta65egq5mnfO+my+V6cuQOmGCd6t24/x7rvPMGXKFNq1SZKrWxJKo0hP+aZpWpRoahwg8AM5XDd3AMNqG2ghKXdhJ72BYcMm8thjz9C9e3cSloHwRUAYrU+Ee84RoqlxwezRUIl9ZBBgRdhUq1utA+KpyVNPPcXu3bu58sor6d27N55zkmz1qgLPu40BKzyOEe2q2NXeadzcXny3Ei8wOJDe6YIzogd5xEoi8UEIvQzPORgk3BVUe8igD+2T/LRRaq49A03+/Gfa33MPpNOUlZURiURCg8WPiwULFrB//37efPPNogbSD4NuteZUbS9+fc/vqa2txTKhSdleUvGjtGvXhng8FeQ3wK5bE/KibrrpJjZu3MiJUzli5VOJJAYjNAvPOVEkw5GH71aTrXkHO70hDEaa0QhPG8b/PLSAZcuW8c07bmPWdUNp1aolp05VokX61x9ACHznBLm61ehWR5VIFhBNXYJAx7MPBfIa7yFEhNwTz8MNN2CePo09dgzeiifI3tQTKZzgcKYqJyeHA1pgOXQC31VKi2asN5reGM/L4NkHaNVc0qhRIzZvORQ0vhZD4CtWcrQbnj6C515YyJ49e7jlllm0al5LtmZxGIyMSHvF/RIRtTS094dlbolq91B5GB8rzMtJvOwWpk+fzmuvvRbSEQqDkdCi6nwES1nNKD/DwlwitBiR5HCiyRFs376PX/ziFziOww9/+EN69epGrm5tuGSt380rkArJNzkHJEvpqVydWxmK7JnRrsTKJ2HF+6lgK8xQ+iSf31J0iMmfazCC0gypGEePEqmqQpaVsbG6mqNHj+I4Dps3b2bIkCFnbS59J2iSdBFasihH4znHmTqpJ88+X01lZSVWfEZBA+rZECKCEevHS698wLZtbyuhrxYuVSeX07RpGZqWQNNjShPIOaSm/9Iml15DJDGcaDTKxIkTefzxx/ne976H9B3szHrc3F7MWC9lkQSB7Os2nMxWwotH6FixXqxYfSLMFU2a0Bu7bi3SzHH7LeN4eu4qnnt+AVdf2UfJxjrHgqXaJQgRwYy2R9Mb4dp7w+P67kms+ECWrzrMor2H+JuBA6n41g14Nw7B94vVFtWSMq8f1BFNi6ObLcnWLEQ32+HZe/G9agyrMb5biZ1ex/jx45g9+2k6drwGXasXggMlvGYlBrN9x1GWLHkqmBXFyNUtRvpZpU0kjGBG0CxYZq1D05LY6fVopuq8N6PdcDKblR54eiORxEDMaE+c7HZcex9NKprTvn17Vq/ZTv8+PUJipNASRMvGIoSmWknsfTjpdeHyUX3ODlixvni+xqOPPsqhQ4e4+uqr6datW/AbOlnPdgeEFkM3GuPaB4ukQjSjPHDTPYDv1yKEFVgv9UDoUXRDdRz4Xho7vTbgQPnBeUphJQaH23zeKOWQCjF/PkyZwv4OHVj23/9NdXU1uq4zadJ4mpXvRzMq0IxGaHpjhBYlffoV1dMkdOIVlxcFpEz1Qny3kkhiMPc98BJ9+/Zl1NA4rn3wrLc1rHbsP5xk7nMv0rx5c26Y+VVwNhSxbdUPfAKaZilOT81ipT4ARJLDMCwlU/u73/2OXr16MX7cYLL5ihEasbIJSPygx6te31k3mmDLnjz+xFwymQw33Xg9ZYkDYaNr8O6Y8bH88tcPMnPmdbRovAchfXyZI1Y+CU2L49pHAuJlwOw9qaH96DEeGzCIqliMm268jlRsf4GTSAqkH8iqNA26zFV/nJvbgxARomUTVd4ktxtNjxFJDELTK8hUzVPLHLMVR062DJxCJgbLIA0z1h1Xtuett+YhhODSqRMR/rZ6qdozzieoPFe25h18t0ollo1m6GbTersiYSKERaxsIgihVBb9OoSwsJITeOTRJ7nmmquxWBXSESLJkehmS3K1y1QQEAKkW7Q02rRpEy+++CKdOnXiuuuuQ9O0cCmtHE6USYJSRxiE554IG3OFMImVT1H5IemFFIB8W4ySHz4C0kHTk9iZLWrMehnSq/1QZYJPE6Uc0vmiTt1h05EI2WyWVCrFZZddRiymkTm9ujhACDNssNT1iqJgJKWnuqgBO72BG2ddx31//B+GDP5bdHGCQstizerDM3Pf5sCBA1xxxRV06xzFziw+I98kgqRxvTKAlRishLOkh123VkmSaFFuvvlmfve73zF06FCi0W5BEtYPSHAB0U7ogMCK9WHV2uO8/fb/MGjQICZP7I2TWYdnZ8P3FVpM9S/l1nHFFZfzzDNzuevO23CCplm7bjVCRHDt/QitDIGO9cJO5F0/xqypYdrxkzR79WGc9Bo8p55XJL0MsfIpaHosII9mg4S5ph5CI1uzMHCb7RC0YijJFSV0thzPOUzbVq3ZlEiwdfsxunXuh6Y3Zev2Q7z77hOMHz+eTh2SONlVyp47PJ06Qhh4uT1oQeI8T1T03VOgKVeRet821Q1vRvuGY4gkBqtln7RxcxuYNm0ar732OtdeM41cjdLhdjIbcXM78Zyg90skMSItMGO9cRyf2Q8/HBZNOnbsqEbhVgUuJqfV9566BM85gmY2Q2gxDKs9bnYnvletgnhmk1qeCx0rMSgQkjuNm92JEemkEvaCAuE4EOhEyiagn1NK5PNDKYdUCFP92JKRCOPHj8c0TRyn4b5eWdAKoZ0x3fW90+SnxFLaJCL7GTRoEE/MeRYr0Z+8MNnO/S341T0Po+s6d3/v23Rqc1JJnBQFIy3Q3i7u8taCsmz+PXJ1a5BSUl5ezvDhw5k9ezZmrGe9Q4efQWhRIskRRFNjkcYo/vTwW7z77rt84xs3MWFMecBuNoPjlxEtm0A0NRaEju+n6dbZomnTprzy6tKwyuM5x8LKmzhai3HT7zC+8X3MmhrsSRNofO+d2HUri+S7dKMZsfJJCC1KtnY12epFCBFF1xsjfTegE6RRF+ToIB9mBp9VhgJwAHZ6HZMmjeHdd9/ldE05c59/i71793LzTdfTvlVlIL1xMuwr042mxMomIYSFnVFicyppvCAklupG47B1QmhxoqkxRBKDkbIubL3JJ9pB9aq1aAaNGzdm85YDoQyxOnZgsKmniCSHYMX7s27dRn75y1+STCb54Q9/SMeOHfG9GrLVS4K+utP5Xxl2Zh1WvD9OZkuwzJaB/I2G0FI42T3hrFvZUanvxc5sxs3tRqDMKNWH0bHifYmWjb8ogxF8CWdImepFIO0g0euB9JH4GGZrIkFAat28OaJdO9avX08ul0Pw4W0fmtGk6P9nGgG69n6mTBrFPb/dyMbNJ+nebRyPPjGXkydPMnPmTNq2ktjpd4Lln6mqPeGSZsg5RbGMSGc8+xCeeyIQZD+AEWnHlClTWLduHStWrGLwIHXXzOcrhGaxfPly5s2bx4ABA5g8sSdedh1eEHh9rxrdKFNMX71czcZi/bDT63Aym5l53Ve457f/w/DhQ2iUUC4UAoPI3G3Iu3+CUVuLk0ig//qnuFd3UnQGVLleSk8RDSMdQTrB8vE00qsmU/U6aAk03VKusUIQTY4M2cZqbGklWlfQB6bp5ZiGQUVFBffffz+XXnopg/q3wk4vDpP1QojgotTQrVZoehIj2gmv9rhyRHFrEELlaNRSqkXQH5cMnGlV9czJbkdosUA+xcSK98FzjiL9NHbdWiaMH8ejs5+kU6fr0PVDQSJaC5ZGPchkssz+0/3U1NRw0003hVQSzz1FrmZpUX6p/rd0GunVEUmOCJZ+B1UvpdCRfg1CCCUVYiipEDPWOyC4pkM6AORtl3oXsfcvRnzpApL0amnIo1ziQSJBTdOmpFqoloe8jC0f0UR4ZkLQL1weBHBzW7juuut4+OGHkVLStWtX7v7ed3Cza7DT9c3BQphE4oPRAunTkMvj28FSQy/YtnDp5qpeNbMpmhZj1qxZPPTQQ/Trd3ewNEqRTqeZPfshamtruf22G2lUdhg3U2/TI7QIkfhANL2MTPXbeM5BDKut6v+y9+O7JxH+Ri69dCpz5jzNnd+9Fc/eib3RJvo3/4guJfaUSWj3/RzRaQBUzavXDdKTWKmxaHoC1z6MnV4TJpeFXo5ptcWIdlWtGG41RrRjkdqBax/ETm+sT1wLAyvWh3SuCc89/SLHjh3DdV1Wr15F3x7di77jfPuFZx/FzmwKCKNt8GO98OyD6EYKzWiKFe9DXolTSW70xXNOhpwi9T1ksNMbiCQGhV352ZqlSJlDOpuZNGkSb745j69eMT7wclMNqStXruTNN9+kX79+XHbZZUGuyFVNtwWl90KoIO5iZzcTK5ukkv16eSALItGMChWw/Bx2Zj2RxJCA4FpvrZRv5zGsDp9628eFwJcuIKlWhHO8NmoUz/7Xf3H77bcD9QFJCA0RiNqfdTgtcZa+kHdGQNLNFmTcLrz00tOhUkAs4hQJfQlholttseJ9cLO7kDgYVuuwrUC5v7Y/y/ctLyNhp9eqnELdGiLJkbRq1YqePXvyxBNP8PWvfz28IPr378+UiT1xc+vw3UhRs6QQZtB/JjCjPeuXLUIQCQKf756mf+82rFieYP6ClSQSCRYvXsINt95Kh7H9cK/ujuQgMb8bVmJgWFnUg6pVfqYRnhurddCAq8ZgxQfgOUqGQ8ocQsTw3SqczBbAQ0owzGaY8YGsW7+d1asXMm3aNHw/y9tvL6auLo1m9sN3VQMraIHTbRTJcbVUq1tDJDUSpBsaCESSI8Ng5Lmn8N0qNKMCO7OxqOQutCi6qTzspfTQzeZBw+4eXPsA7dq0ZcMGkx27TtC9+zgymQyzZ6tZ0e23307Lli1wszvw9RRCaA0GI5X0HghoZGuWBHnC1URSY1E23dHAgilCpnp+wMzfhxFIhKgxdUD6NlZiQIOs+YsVX7qApHgYQnFlnKOh+4MVPVu5MZwhoabz0msgnxQIxRe2TeSTx/m70ztLdrJs2f8watQoOnbsyOzZs2nTrgeanguXd5HUKHSjCdK3sbPbEOhI38bJbAzv9k52h6qKnNFXZEQ64TmHlO63cxTpVSOMcr761a/ys5/9jJ/+9KcYhsHtt91Ao+QB3KxSJ5RIdLMFulGBk92u8jN+BumncTIb0Y0m6GYT1QTr1PdF+7t3cOvjj/Nc+/as7NWLr111Ga3bTyDrHAVcEAa+X4thtQ3lT5XBZEulwmgfQPoOVrwnRqRr0Z1bCB0hBE52G557AiH0IG8j0YxGROMDqcvEee7pl2jevDm33HILwj+End7MjTPHsGV7lkWLVzHhku5BEPOx69aDzOG5JwKjhONkTr9Z0GPmBwzo/mfPWIRJ3ka6cNnr+zbZ6sUYkbZKuyogOHrOIaZOncqf/vQnXn/9dWpraxk0aBDTp08Hv5ps9UK1JNWixMqmYATnJ4+zKRAdcHN7Q9kQM9oJo8Bxxor1C/XIc3Wrg+WkEehPiS/ErKgQX7qAVCQDUiCUb2c2Ek2NKdq2KCDpSTjDDlkdr6zoS88v13SzOZU1bZnz0FwikQjf/e53SSYNpJ/h6quv5tVXX2XI4G8FFRmfXO2KwP3Bwor2xM5swM1uL2JQW7FeRWMuHkcFvldDNDkqzDmtXr0a11UzsP59mlMWW4/nqoS5ZjTGig/ErluJEe2MbjZFM5qoC0FPYljtsDMbsUR/7PRafPcUQlpEHlqL/JefEslmmdSqFX/s2pUtm9fSoVVFcE51YmUTw1yFkj89jpQ5cnWrA4XBwdjpVWedu/CzGE3QzVYFVS6BGeuFEenG2rXrWL16NdOmTaNVy0bY6Q/CnJKd2UzvXhPYuHEjx072pnGyDCEknnMIhBbkr7rgZLeGfKGiNorM1rNnLNJB08qxEn0KNJBqsNNrEUKGbRdq+aZ6+iorKzl16hSe59GuXTtmzJiGk9kczAzVDFn62TAIek7QDNwAH8iK9QvyVFmczAZ0q2U44/Gc44qWkB+qn8bN7saMdfvQvsWLGV/MUV8gFJfqXdi8mVnf/S6MGAEo1458QNKNhpXxzrSUVl3vo5m3qJKHHn6c0aNH853vfIeYdZJs1XxytR/Qu1d3KioqePOtd7HiSpxM+umQh2NEu6oWiiIL5+6BuqK6gF37oNJ3Tq8jW/OO+rEHHJfa2lruu+8+Fi1axPTp0xFC0LX7kLDaopstiabGoRvlaEY5bnZH4HxhKOJkZjuucwjfPYlduxLfPYW5y8e49EfoP/xXjGwW59prcV56Cd0wKKtoi5nXBpcenl1fLlcX2oDAGqgnTmY9udqlqCrZ2fdD388U8HYMNL2cWNlEsk5rnnzyKU6dOsUtt9xC8yY5sjVLihLcisG8hunTp/Pmm29ixAYEyy0laG8EDcBmtDuaXo6mlxNNjUF6tfjuSaULLUx0sxVWvH/AWtbw/aqguidxMtvIVL0d6GErO3TdbEW0fDJGpAPz5s3jvvvuY8CAAXTp0oWpU6cGy1SlV5SHGe2KGeuB0CLKWqhsYoPkRKGZarYjdMxYr7Da6nvpQBYmn1NTFTSjAdb6FwlfuhlSMQoJYco/J1JXhzx9GoGaIVVWqiVVobZPIc4MSCdOVPLoo4+RTCa56667iEZ8cjVLAg6TBjKLnVnPrFmz+M1vfsOIESOIm40DJ4/dGFZrJWORGEK2egESiCRGYERU17X0c9jptUUES6EnQ1uhd99bzqJFixg8eDATx3dHyFpqa8fw9oL3+NbXJ2NnNimWtV8DWgoz0iVMHvteNbm6VUVVQmlniNz7HuJnv0J3XexmzbAefBDzyitpB9zRujUPPfQQY8aMRtMPBRIdm9SdPKAcKHcTM2h1UReQ0CJF505KiWfvx06vC3N1htURM9Y/nBVNnz6dli3KsOveV8s5LYqSCOmt+ri8Knz3JIn4Sfr27cv7yzYxcmi34MLNFiR+NSKpUXjOcXK1H6illnuSPBfJiHRBM5KY0S64ub3k6lZhp9egackicXshVOJbN9tw7NgxHn/8PqLRKN/97ndJxN2gSqnj+03x7P3B5xJEy8YVBZ/CKmperbnYFKAVujENoUUCuWFlmCl9B02Po2lJJf1ykVfQzgdf6hlSkdGd9EMeEkEJXFVCAhdUPRleYMWov+tt2rSJBx54gNGjR/Otb30LUzuoqlXuCcxod2IVyk7Zze0lFkkzZswYHn/88UCAS/0AncwWpHTQ9RRWrDdID9fZH4iZnSJTNb+Y7S10zEgXMl5ffv+HP7NixQq++c2bmTAmhZtZi5PdyYRxfcnlcqxcWxmU0X1y6fVkq99G4qvkau4A2erFRcHIjHbhyKE2pH9zP7rr4t1xB9b27XDllYDiIDVOHaJXr1488cQcrMQQ1MzHJ1e3MuQM5erWkKtdqgJtoNfsu5VhY6eaFb0f7OOEy5+M06loVtSscV14PpXxQQdiFVMxo12K+rnszEaGDO7Nnj17qKprGn5vbm6fYi6j+tOkXxcuifPMb989hZ1Zq9pmpES32ivXFT+DX2B8Y1htiZVPRjNa8/LLL/OnP/2JMWPG8O1vf4uosYds9aKAN1RvfZT/vfjuyQZlkD3nOJmq+WfZk4MK4L5XTbZmkaqySRdNszCjvQKZ4C9+MIIv+QypmDLvQyDEJm0bwdlynbrVFj/MPSjkPdpqa2t5/vnnue2222jVMkW2ZlHQ4BjFiCoeihACI9IRJ7uVXN1qxo2bzLp161i8ZBWjRqjqmueeDMvKRqRLwZLER/p2kdiXbjTBSgxmwcJlLFs2h1GjRjF2VHvszBo8p55cmatbzaxZ1/PnPz9C3953YFr7cXJ7FYelbq1yQMntQWgJkDZazkCL9OOZ599l3759zPrlL0l17Ig/uh2uux3T64eT3RC2Ysy4tD+/+d1cNm85QPfOytRR9ZttwHMOIYRONDUW3WwWJsg954iSkdWTONldKsgAutkKM96ftWu3sGbN/GBWlCRX956SBwlmEFK6QT4l0OY2KrDi/XCyO5XIXLB0e/31N5h1w2Xkapag6QnVDGxMUbpB0e549kGEiGIlBgXa3y6ufRjPzyH9moA8eVp9A261UteM9cCwWnHgwAGefPJJysrKuOuuu4iYNaqtJShqONltGFZrNKOiyPpILc11zGhnlHGEi5PdFKo4OJmtGGbrcObUUP9hXh3gi1RBOx98uWdIFOaQCmZIQSL4zLuYYbWv/4/QVbk6yPG89tpr9OzZk+ZNPTJVysXCjPUmVj4NK9YzDGxmtKuaevtp7PRGbr75ZpYuXUo62wQtoA+4uT14zlFVbk+OIpIYip3eVO8HL3SseH+qsz35zW8fZMuWLfzdd25j5BBN+XsFF61hKeKd9DM0Sh2nX79+PPHkC1iJwRiB866SrdgT/F2L9f5p9OF/x5qv3QbA3XfdTvvbb8EZ2RQ7vSFo7Dxa1Bfm5TZx3XVf5eWXX0bqnRF6OUJL4eZ2YMV6Ey2bHDr9Ku7UQEWOxCdX+z6+ewxdLyOSGELW68mTTz5PZWUlt9xyM00b1QTn82Rg4zNe5YMinYtmrL7vBhVCtST03OM0Kqujffv2rFm7GyPaRekmyWxgS+SHgdKI1H+vioJgIIRJ+vTbRT19ZrQj0dRoNKMFc+fO5dFHH2Xy5Ml8845b0f0tZGvq23OElgAkubpVwXsF1kd5pYbMRtzcfjJV88nVvR9+B+p7a43EC2fF2eoF9WYEWiRUB/hrC0bwJQ9IQouHBndC6GFAkkEi+8zWEU2PY1htw/YDM9o5DDS7du1iypQpSlcnOK0qh1B8ipXx3kDyLiBlSY+RI0fy2GOPYRUs3XJ1q5G+g++dIlv9Nm5uR9DF3ZhY2STmL9zOgw8+yIgRw/nWN6ZgsbqIkBlNjSGSHKraG4SB5xxj2tRhVFdXs3r1aiWjUTBD1GoEkR88i/mVb2AdOMDAkye57oqhuJn3yFS9XlR9kn5N2DYBarbSsslJOnfuzFNPPU0kPggZdPP7XnXRLFPlio7gFZS6dbMVVnIE6zaeYO7cuUyYMIGJ44fippfgZDYhhE4kMQQrMVS5spRfGrhemEg/R672A3I1S4r4TQBOZjNjxoxi/fr1ZJ3WQZBQGkVusK3QoirvFch5SDyEHkf6daELrWofUS0s+w8c4Re/+AVVVVV8//vfp2+vpsEyeq9SfNAiGJFORJMjAzmTKtzsjuD3kFDLcDSEFiNXtw7DaqV4UlqCPGcqkhyu8nm1y8lWLwqboY1Ih4Aq0KaBX/NfB77cAUloWLF+WPEBSocnCEhuJsO2bduIRqNks8WGuVZ8AJHU2LPW7FJKysrKEJqJ0NUywk6vaZBMaVitQzkQO72aiRPHk8vlWLN2W31/mp/ByW4jV/Ne4MOupvjVmZ7c85sH2LNnD9/9u9sZ2DurSs/B0iVfDcxzW6x4HzS9XC1jMmuZNWsmb7zxBtmcFlwcAuutA+hDv4X58ON4hoH///1/WCs/wBdVwWdzUTZBQ4lVXIoZ661YzQV3aM85wlVXjOTQoUPs2HUs9HVTfCKVl/K9NLnapcF5qT9mflZ0+vTpYFZUTaZ6QShcFiufghFpj/TryFarlhDlDnuATNW8UKCMAs0p3WhCtGwchmFx6aWX8tprbwaaRQrKRimvtdQL38tg160kWzU/zKNJmQ39x4TejKeffprHH3+cGTNmcOutM5H2KnJ19ZpCvldDJD6USGIgEjfsv8v3zAFBwryxWn4KD91siUAL9M998u6wdt3qgK5gFfXTfVba1p8XvtQ5JCDIJXRW/0m6LJ8xg2OaxqYXXsAwDGpra2nevHmoh/RRPwjdbImd3hAIpdeG+aAzYVjtcDLb8L1anMxmrr/+embPnk2/fj9AN1R/Wj65qRmNiSQG89a8d1m5ciUTJ05kyIBynOwH+EFS3ffqiKZGoRstyNYswrMP4+h7MKMdiSSHkatdju+epGnFKfr27cvs2bP51q23wh3fr5eTHTIE85GHcTrpuJlFYYe4EnofcAYjXZWjlQKigptdz9euuoLnnn+e73//e2jOIcXZqVup5GMzm+oVEqzWWLH+rF6zibVr3wpyRSlyde8WJNYFVrx+1ojQ8L3TgViaie8W+tXLgn65vkptMpiZtWnThsaNG7Npy2F6dOkcaAz55OpWEU1dEibXC+V5lfa2kkbZuXMnzz77LC1btuTuu+9Gk4fI1SzB9+oopFEJoWyPdKcZbm5n8Fwk4GCtIRowrSOJAWSqFoD0yNYsIVo2XgVoLRVKi+Q/kxnpqOgBn4IH2sWIL/UM6SwYBvY//zM7L72UsWPHUlNTg+/7zJ9//g5Mmp4MckTKUFHlg842shRaNHRAdbLbadUyQfv27Zk7d26wdDMADSvel5psL+75zYPs2rWLf/judxjYu04ZAgbBSDOaEC+fhBG0NOhGM3zpYGeUIJjvnAyDi5PdyfRpIzl9+jT//ZvfsGvrDtxoFP/Xv8Jatgx6dlUW3342VAeIJIc3aL9tWC3DPBWoSlWHNmnatGnD3LnPh1U3RSRUErdqVjSMrNuDOU8+R3V1NbfddhvNm2qhg6ymJ9HNVkGebZ3qxLePoGlxzJjypZcFAm+F5z5WPqVoKZ3HxIkTWb58ObbfIVRS9N1KMlVvYmc2Bl5mKiel1BMnIUUFjz/+OM888wxXXHEFN9/0NWTug6APrw7NKAvOQ7uww1/61fhuvsO/LOgjbITvVYdOH5peFuqFq33S+O7xwFLbCLZJES0bH/TXfTmCEZQC0lmIxxWx8MiRI0yYMAFN05g8efJ57ev7fpA4VU6oQs/nLBq2o9OtdmFvml23kmuu+Rq7du3i6LFqIokhxMon8faiXTz44IOMGjWK73znO6TKGqEVNJ2a0e5EU5eEF5NnH1AJ9Ui7gCi4Ti0r8nKthzxqVmwil8uRzmRYecfX8ZY/jnPHGND1oKlUSdVGEkM+0nXCivejUJ7Xtfdz3TUT2b17N/sPVIUXKqiZVrRsMms3HGXu3LlMnDiRiRMnous6utE4PI6VGEIkOQShxQP+jh12+ecVFKWfDlnrQphYiUFEkqPPachpmiZTpkzhjTfmFc1YC3WCJBqR1HiseH+2bdvJL37xCwC+//276dpRV4TIAk0spBME7KFBJ736XjyvDjPag2jZBKVpbjYDaQd9cenge+sRbp+rXRnk6HyEFsOM9SRaNumiUXH8LFEKSGdg7erV9P7gA0b8+MeMGz2atm3bNihfeyZM06SysjLQlo4o62LfJpIYdVZDbB6KBtAVTS9XrF9/D1OmTGHOnDlUVpnhrOjOO+9k5MiR4X5WvH948Z6pLKBbbZXjRvBjz2v8CF/DemgN+rBbyF5/E4P69yeRSFCbKkd2aBV4zx8NjxErm3Re+soqgA0oes7Lrefyy2fw9NNPo0e6oxvNiCSHk/V6MOfJueGsqFWrVgXHUfkkUFrhoBMrm6CUMKWP7+XI1iwtULtUmtma0YJo+eQiA8w829zJ7i4aV4cOHYjH42zdfiIsZgTvjhntQaxsPL5M8vDDD/PCCy9w7bXXcsP1l+GmlwRSHvV62UakI7GyyWHAFkJT1AGjEfGy8YqBL11ytR/U84qkG+TPZLh9/rOAItlGk8OxYr2+sK0fnxRfzk/9IZg4ZgwT3nmH1uvXw4MPhs61H4VYLMbx48fDUqwZaRskY1uecx/fT6uydyB56ub2MXToYAzD4IEHHghnRclkcQJdLffULEZKBwqkNpT8aiNydQVaODuz6NP+DfMH/w8jm6XZ8KFMGZrke3d+g8rKSvYfKUMIIxB5c4NjnL+Al/KCL/ic0qdXz/Y0adKEN9+ch5Uczdr1R3j22WeZOHEi48ePDM0uC6EZjRQ50avCyWwgV7ccN5gh6cYZ50CYilKQGlVU/g7NCzIb1CO9rmi/KVOm8O677+LKLpjRLkFrygSseG82bNjEL3/5SxKJBD/4wd20a5VVekmFDrnq3dG0RJi0Ds+D0ZhoYF/ue3Vka5fXi6eZzRFaVHGwgoJD3jFY0Tj6qX0LZr9fRpQ0tRvAwn/4Bybcey80acK7Dz9M4y5d6Nmz54fu8/DDD9O1a1dGDO1KLr0GTTOIlk1s8E6nKkR7cDIbCjzDOgad5CbptJrWx+PnFoZT5fOD6FbrIs0kJey/D01LIXO1WL9fgvjpPeiui9O0KeJ3/449tTVSumhGBXsPteTFF1/irn+4FukeVq0VWvSc73su2JmtgVNqu9DpdNOmTbz99ts0atSI1q1bc8klY/DtbTjZnUH7RHHrSLZmEZ5TiaZFkTJ71nsIoZ7XzVZEEgMDrzEbzz2GbrYOmmaVZG8hoqnRRa0/u3btYu3atVx11VWAJJezeeyxxzh16hQzZ86kTas4dno1vlerSve+jRAy7LyvP+4l6GZxj6P6bndiZzYhpUTTYlixnuhWOzznELnaDxDCCrSwI4F8by6kGHwZ8GGa2uc1QxJCVAghnhFCbBFCbBZCjGpgmwlCiDVCiI1CiEXBcz2C5/KPaiHE94LXrgu29YUQZw1OCNFeCFErhPjhx/q0FwDRG2+kZuhQOHmSIS+/zKpVqz5yn2QySVVVlarCySy+Vx0knovhe7XkapbUl761uLL/SQwK77jxePxDgxHkl3ttw2Dk2gfJVM8LCYtCL0PM+DeM//cLdNfF/fqteCsfJjeleRgEffc0XTqaNG3alNffXI+VGI6T3X5OwbCGkL+hmZHOxMqnEUkORQRGBNu2baO6upo+ffowbmx/nLpFAVdImQ1I6QW9Wbvxvcqg+fXMD6oHNkdCzSQSQwLeThTXPkymen7A13k7lBs5+5wXG0l27twZXdfZsWMHK1as5Fe/+hXNmjXj+9+/kxZNTpGtWVwvyCZz6GZjoqlxWPFiz7L88kspWW7Cc6oKWjs8hNAwY30xIu3xvdPB+AhyYuuD79H4UgWjj8L5pu/vAV6XUl4b2GkXXS1CiArg98B0KeU+IURzACnlVmBgsI0OHASeC3bbAFwN/PEc7/kr4LXz/iQXEAMHDeLVyy7ja6tWEXngAVr26cP+/fs/1L22vLycEydOILQIQk8gvZqiFgB151RNkXkm9ZnaN38JVK/YypBRLLQoGL15au4iWrdrz7iTp9Du/3dyw8shby5gNAFpq+pXZjM33HAV99zzR0aOGEgqeiBgQnfko9wofK8Wu241RrQLhtUaEehxnzp1ildffZXKykocx+HQgVV0a5+vMCn7bUVGXIf06vDc40pzOjUB00/jZLcX2HQPx7BagjBwMtsDpw+Bk9tTVCJvyL22/rUzl1wwdepUHnjgAaqrq7n88svp27sluZoF9UYIAQyrbTDjU1VGKz6ITPU8NC0VqEYKxbq2D+A6h0L3W9W3NwihKe3ufDBS579RUZWthHp85JUghCgDxgG3A0jVjXimBuyNwFwp5b5gm2OcjcnATinl3mCbzcHxG3rPq4BdQN1ZL34GiEaj9J41i33Ll9Phtde45LnneKqightvuumcglcVFRXs2bMHoZlIrwahKb3pXHoVkfhgcunV9RbXegorMQj9DC3uvwx6yO0x3zvKoXWneII1dOvWjQlz/oRbvR7HrAyDoKaniKYuwXePk61ZikBH93czfvx45jz5PH//netVo2vt8kBp8ezPW7gsQXr46Rp0oykSg2XLlrF161Yuu+wycrkcixYtot+A0QhtB2akE0a0C3Z6DW5ub9guIYSJEe2KEDpmrE+gEX4aTbPw3WNgtcSMdsfN7Q2DvGG1UY3I/rnze/mEe0PM5lgsRkVFBZWVlezdvYZu7etzZrrRFISBEemkgmEBND1ONHUJoAKRlRiEGe8bCOOlEVocK9YL3WqH754gW7MkWH7qIMCK9caIdPnCCad9VjifW3Nn4DjwZyHEAGAlcJeUsjBYdAdMIcRCIAXcI6V85Izj3AA88VFvJoRIAP8bmAqcc7kmhPgb4G8A2rdvf67N/mL07NmTF6+5hnZLlmAePEhzw2Dnzp107dqw3kzTpk2D3I8BiOBOHsF3TyvNazVqzGj3gOimZh9nNvB+XAghsHId8H7wW8xHnqS1ZXHrO+/QrH85ucwSMCV5TzHpp/G9GtzcLsxol0AobQ2uvY/hQ4excuVK3lu2gxFDumBEzubyQLDkrFtRpApgWO04fuIUr732Bt26dePWW29F09RSMm9BLmXHcHlpRrvh5vaTr5RFA2+3PCKJwYo4GAQq1z6iyv9B8MnVrSRaNiHQjn63wfOi5H77fSiRdeJEResYPHQcullfZdTMpkoMrwAqV3UK3WyO5xzBswOplbq1RJIjw0qjZrZBoJxlC/NNyoq7d2l59hE4nxySAQwG/iClHISatfyfBrYZAnwFmAb8SAgRzkmDZd6VwNPn8X7/BvxKSnk2860AUsr7pZRDpZRDmzVrdh6H/fiYNHMmL959N+7q1Yy+4goWL17coGwEQNu2bamrqwsqVBVAMau7sJojhI6UnhLuKnCG+EvgLV2K3XMg5iNP4hkG+j99n8adKwO31RhmtDO60UjZJFut0I2mYYLXiNR3lNt165h1w7UsWbIEl64fImehhcsjTU9hJS9h2crTvP76m1w2YyKD+wnsuvfOapkpTO5reopY2XglCywdnPQmtZQNXy/DjPVUrSJV88nVvlck86r6w7ajmy2KGmMBhBZTfWfJIecMRspJ9gBNy3Zw040zadeufdhfCEoCJk9mVduq/JydXkemSuWqhN4YzWgcBKeDGJF26FYb3Nw2sjVL6ukWgZSKlRhaCkbngfMJSAeAA1LKvA/0M6gAdeY2r0sp66SUJ4B3gEJyygxglZSyYYZgMUYA/ymE2AN8D/i/Qojvnsd+FxzJZJIOV13FonffJZFI0LlzZ9atW9fgtoZhkEqlWL58OZH4YPIXrtASQcf7hDBQec4JlYTNbsfJbj/LFOB8cfo//xPGjyd68iQH2rRh5X0/wr57Cr6eUa0PyeFI31ESsl4NkcSQsA/PtY+QKerbypGKH6Jfv348/vjjRe8jpR+OUdPjWLG+mNEeVGV689jjL2OaJjdcN5K4uUZdoM4xfPdsud9CaEZFSFB07f0qCAROvKBmUZpeXmQNJEQEKzFIaUrZ+5DSDwwkVVUwzw06l5he/QdScrq+V4WdWV/wufqEm+TqVivHj7qV5OpWolvtg4pYDZpeRiQxMNBg0vDcY3huZdiVrxLiOobVJqB+fDEcPy4GfGRAklIeAfYLIfIq+JOBTWds9gJwiRDCEELEUUGlUDhoFuexXAve7xIpZUcpZUfg18BPpZT3ns++nwYGDhzIkSNHOLZhA2PnzGHD/PmhrO2ZuOqqq5g/fz6uH8WK9QrMAUcESzQtSECvDas4+RaGfMXpfOG6LrtvvZWK//2/0T2Pw9dexamn/hedp3fFc6swrI5EU2PQjQolLSs0srXvB3kkl1ztcnK1hVIZKmHr2Ue5dOpQKisr2bRJfcX5C02REtUqXTPb8+4Hx5g3720u/8okBvTOqpleQbL+TPPMhqCbLYpUIwvPgxBakeiabrYkFhAgreQIdKsdIANC5eCzKpVnIi8W5zkncXP7w+Dj5vaGSzUj0ikct/TryNW+j5QusbLJROJ9FfsdgjaQHWqGGOuJ754MxPurg/NpEEkOC1puPj6F4suM8yVG/gPwmBBiHapq9lMhxLeFEN+GMEH9OrAO+AB4UEq5ASAIUFOBuYUHFEJ8TQhxABgFvCKEeOMCfJ4LDiEEl112GXV33IF+331c9u67zJs3r8Ft27ZtS5cuXXjyyScxol2JFtgVe84xJVMRtD7oZnOiqdGBREVNURXmw3Do0CEefvhh1nfvTi6Vou73P6Hsj7fSpWc7YrEYVqwbZrwfCDNoG1mBwAjYzurO7fv16T81q5iKEemspFyz67n22qt56aWXsHOV9Rea9PG9yvD9E4kE1189lJhen6xXAVYoa6jznBGYBRrQ+UbjPDSjAj3vsCFdEBaeW4mTXqNE84NtdbNFqLfUEKSfJVe7TCXx9ThGtDNGpFOojJCXesnbPanLQgdhEkmOUAG95n3cXP2y0c5sxnerlMtMwSyuXiKkFSV8fJSIkeeJtU8/Tb8bb0TzPOb97Gd0nTkz9GMvhO/7/Nd//RdXX3013bp1Q/pOQFaszymY8b6hcZ9rHyBXuxyltTy+iCxYCNd1WXvvvSwzTaqqqxk7egBDutjIeL1wv262RLc6kKt9B6QMlQ4BzFjPMFHre9V49tGAGd00eK6GTNXbgI8Z68VTz64gEolw5YxO+H4deqQ/i5es4ujRo8yYdglRY0fR8XWzJZHksMBsQIbmBR8FKSV23cqw095KDArVFZWVUhuy1Usw473RjeZkqt6ikGukztmHz8Y857jyNwOsWJ+w5O57daHJphHpEAQj1exsZ7aAdANd7X1KWleLoRnN8Ox9RccXIgpCD1xvP5185l8TPjExsgTof+21bJk6FaRk4gsvMH/evCLxtjw0TePqq69m7ty5uE5VQFbci9DioTtFYd+VbrZBt1oDMliynE3sO7JiBQeGDWPI3XfTatEibNtGZ38YjCKJYURTozCjndA0DSPSEc+tCpLDKjC4uQN4bj4ZXYYZ64ZuNsVzTuB7NYG4vervcjJbufbq6ezcuZPjp5tzoqozsx97jsaNG3P9tZdiiVVhMNL0JJHkMKx4X4QwsGK9zjsYQV5BckhwDsB3jpGrXUauboWaNUpJrGIaZqQjmh4vmlGBwHfP5hiBmhXlz6VmNA1NL53s1lCfKW+yqc5P4dKtC1og5uZkdyKlg6ZXEE2OJJqsH2seRqStaoYtBaNPjFJAOk8IIWj1hz+QSaXQ33uPSysrzylL0q1bN1q3bs1zz79B3rZG9TmNPEt2VAhBJK7UD89kd7u5HFvvvJPGl1xCxzVr8BJxRg3pjxCCVOPhCKFyP2cS/wyrA7pZgURHStU46ns1OOm1RVVCN7ePbM1icnWrcXP78Jy8vpCPkAcYMGAAf/rTQ7z11ltce+21DBo0CGQdxU2mnTCstucwQDg/1J8DK5DIPRxUp4YoxnkBQdOM9UTTU6E9khntVHQsxZHap4Tb8qqQQhBJDlOtJoHleP485JduQkSw05uCpZumJGDy+SurtZLO1WJka1fgOyr5rullRMsmYMX7oWlfHomQTxOlgPQx0KhDB47eeScAbX/zG+qOH2ffvn0Nbjtz5kx27tzF0VMtAbU0cwv0mQshtChmcKd2MlvxvWqOzpvHyd696fHb32Jls9jTx5N779dU/N1kJk2axNtvv4OVUA22TnZ7eHeX0gmMHStVG4a0MSId0PQEnnsc167nxuhmS4Sw8N2TyvHDz4R63es2OSxfvlz1mGWzlJWppk/daoeVGEQ0NTYgK+47JxXi40BZVAczDKETLZ+EEWl/Vi5KCJ1IcnRQtTy7AVj6dYGOtROoQtaE+1kxJZViRjoXHE9gJQYh8fG902HVTTcqQukU3z2NlDmyNYvx7P0gTMxY78BLreEldgl/GUoB6WOiw09+wqmOHREHDnCZpvHWW281uHSzLIuvfOUrPPPsK+gR9cO202uRfnGFTkoX3z2tXFqFjmb2ZPUv7qPZ9Om02LULp1lTco/+K85jdyHbNMVzjjByWAc0TWPpezvD5V6udgW+lyZTtUAtEfWkqrhF2oDQAq94cNIbArvvnCqzF/CDdKMZnjaS/3noLRYvXsyVV06nc2e1vKyuDipIQsOMdEQ3mwW6PeMuWElbObOYID28ggTymdD0+DnlOTQ9WW9aiR+U7/OzobbEKqadFeg0PRnI+RYv3dRsLIn002Sq5qmlrdGYaGoEVlA5LeHConRGPyaEYWD9+c+89E//hDlzJkOHDmXhwoUNbtuvXz8qKiqYv3A3ml4WmhXm4TknyFS9TbbmXcxod07V9GT2nMVsatSI2pYtyd5+DfYHv8K9bCCaXhaWyO30Om6cdR1Lly7F9jsjhBk0bK5RS8OyCUqdER8nvQEhoopEaLVDSodc7TIyVfNwMhuVMqQwsBKDWLEO7v3dg3Tq1Invfudaunc4zsxrhnHllVfyyiuvBKVzp8jM8EKqGWpGeaiiWahD/XFRKH7muydDpUbgnONtuOqmF+lwW/H+RFPjvvQSIZ8mSgHpL0BywgRaX3MNS5YsoX///pw8eZKDBw82uO2NN97I2rXryPmqspM3K/ScE2RrFiNlDlHdhAPXf4uFz7+O7/sMGd0f44Pf4f3iJihLhLKvqrdMBZ9kdB9DhgzhiSfmBolrHSnBjA9GN8rDVgbPPREu08zAWMB3K1WJ36tFt1qTYxh/fOBlVq5cyTe/eSvjR5fjZFYipY2T3UarFgmaNm3Khg0bUO67Z88ILwSk9IoE2NTS6+MvBxWHKW9aCU5mI76f/oh9RCgdLP1M/dLNbIoVH0isbDJmtNSD9mmjFJD+QgwePJgDBw5Q+dxzXN6iBW+88Qaedza5MR6P06FDB+7742PUpIM7cO372Om1mNE+ZGfvhH5T6fD884x95RVmXjOEDq0OIaMqcawcUqegGWVFDqiufZCJ43tTW1vLuo2VaEYjfPconr072K91WA1y0uvx3GqyNUvDcQlhEU0M5r3laf7whz/Rq1cv/u7bXyVprsFzDhdUjJS32Pjx4/jggw/IZHKfCtlP+tmAvb4jGJ9R7IP3MaHlSaEQMK7XnhXcPPe08lILAuy5qm5mtNM5pXFLuLAoBaS/EEIIrsrlaHzNNSR+8AMG9O3L4sWLi7bxfZ/333+fPXv2kMvlePHVjcoEQBgYhy0yU26nyZ13E6upIT1sMC3//Tos/TCgBwL7I4PqUL3AvmF1qLeGzq7nhpnX8uabbyKMXoAWdMArmZF89U5Kl2z124CqVulWa9LuIH5333Ns3LiRv/2b2xkzPIpdt1qVuI1GRBLDQ1de36sCbzcTJ07kjTc+Jf6qiCBELBxftHwKZrTTJ5qRFC7d8j1neUjfVklq50jgJKuQX7ppRkU4nhI+O5QC0idActYscs2bI1avZvC6dRw4cICjR9Vd9cSJE8yePRvXdbn55ptp2bIlp06dxjQHErl/LdrAqZS/9x6ZWJStP7gF+do/43dpgpRuwJ4+m+2rfM3eLcitWDRtdIru3bvz5NOvYMa6F4t/adEiQ0fdak0kOYZ33q3i/gceYvDgwfztNy8jZmwKLbvNaA9l16NZmNEuakeh49kH6di+KZqmsWPHjgt+LvOOtpHkiAvmyqrK9/VLN1VUUMFaaBZWVM2g8k7B+XFEksMDKdpSruizRomp/QnhP/442k034TdrRtXy5Tz52mu4rotlWVx99dU0bVovcfr73/+eio0bufH3vweg5rKpHLhrIs36dCAWiwVa2YPO0uBR3JrdSmg+sBJSuQ6VF7GSY/nVr//MlVdeQYdWB/G9Gqx4X1z7CL5XpciL8UEcP5nl8ccfJxaLceOsq7DEtrChVegp8DNEy6cghIWT2YKT3a5cXL0c4KploT6UJ56Yw6233oplfTFMC+30ppDfZVjtiCQVSVhKn2zNO4oiocWIlU9WVb4SPlWUmNqfIrRZs7CHD0c7fpzof/83J0+e5OTJkwghioIRqHzS9ubNWTF1Ks7zf0R79O9pP7QnsVgMw2oXuFicbQrguyex02tBumh6Gb50gqWIuvO7mfVcffXXeOGFFzFiKpltpzfge5VYsd5YiUt45bV3+NOf/sSYMWO44/ap6P4HYTBSS7TBICzs2hWBdfcuZe8sfcxY12AclVjaYUaOHMmCBQs+xbN6YWHG6pdurr2/YDakkt9CWIHTbonc+HmjFJA+KYTA+v3vkcG/7SpVW0JD3KTJkydTVlbGB1ddhTbjq+QZz5Hk8FCLuiHoZlOlN63F8L1qosnhQatIQNzzqujQVqN169Y8/8JCzGgXdLM5sbLJHD1h8atf/YojR47wD9+9g34904GMbv3xNT0VOGD0wnWOgxRIKdHNZkSSI7BivcK2EjuziV4923Po0CEefPBB9u/f39CQLyoIoRcxr3NBrgwCbaaKaQ2aS5bw2aMUkC4Ehgxh6+TJ6L7P+NdfJxZT+Y8zZUratWvHXXfeSbslS3CGXYrhqErWR/FtXPuIEiiTDlasD4bVOiA31su3OplNXHfdlezcuZMjJyrQo8N5du6rzJ49m8mTJ3PbzZcgnPdUZ750MKOdiKbGqOPn9gXtI1uVIL1fQyQ5mEhiILrRCCldpTEkdMDHTq8GJAcPHmTRokUX7jx+itCNRsEsCKSfKVIV+DI5w17sKAWkC4Tkvfeybto0Xp41i2g0Sr9+/XjttbM9CjRgytq1RNetw/yPZxBaFCezBd89fc5jq9lLZ2LllypN6sxW1atlF85OBKae5dJLL+WRRx7hZz/7GVVVVdx15zfoGShIFvagCWGhm82LmlUjyfGqMqfFcHMHFBHSz5Gr/YBc7Qeh3pH0s0ydcgnRaJTx48d/wjP32SG/dNPN5hiRhqWIS/h8UQpIFwhte/Sg/+uv46VSSjh+7150XWfz5s3FG+o6sUcewRcC7vkt1g4DxfVZWSTSplpK6ptmhZbEtY+RqXoLJ7MpFPYHDTPajVj5pSxfuTvkQ0kpadda4GffDZtvjUjHkDKg+ryqMWO9iKbGYkQ6kKtdgvTTWPGBeM5hnMwmMlXziqzAle/9ZDQ9Qbt27T7UieVigxC6EnL7EMvtEj5flALSBcaVV15JXNe5/KWXmNakCUuXLqWu7gzzlCFDcP/2b9F8H77zf9GNlqrTPxBps9PrSZ9+lWzNYuz0JjJVbygx/myxy4ZhtSNWPpWaTCvu/d0fWbZsGXfccQfXX389Qgg6dqnPm0DQs5XIqw/7gW60wHOOkK1ehO9VkatbhW42R7fa4WS3IQNXXE1PEk1dghHty9Kl7zN//nwuv/zyT/FMfjoQWqSUK7qIUQpIFxgdO3bkb9JpKp56CuPaa5nev3/YB1YI6+c/J9ekCcaKFUQeX4UQJk52G55zUnFlpKcE8LNbQ0XCvJ2OSlhPxIwP5pVX53P//fczaNAg7rrrLpo00ujRrVVQCVuGFat33LUzmxHCxIr3JZoaF/KM6mVHUB3vdYEKZJBbMaPdiJZN4nS14NFHH0XXdW6++WaixsWf0C7hi4USD+nTgG1zeuhQKtavh2HDmPejH9GsfXsGDBhQvN1TT8HMmbipFGxcSC6xFzWjafg7UQaDvTDMFuzcuZO5c+fSuHFjZs2aRSxqYmc24Ob2oOnlmPHh3PObPzJhwgR6d63G904jpcSK98EKu+EVPOck2Zp3zno/3WyOGeuFpjdi+fLlbNq0icsvvzykM0jf/lCboRJKaAglHtJnDcui7K23qG3WDJYvZ9Jjj7FyxQpOnz5dvN1115GbOJFqy8I9aAduGWcHI02vIJIcRTQ1Hl824pFHHuGZZ57h8ssv54477sAyqshUz8fN7UEIA9+rwkm/z8yZ16m2ErOvkl/VY3j2vrMMBTSjEYbVtuAZgRnrSSQ5khMnHebMmcP777/PxIkTi7hVpWBUwoVGKSB9StBatMB87TWcSATtySe5fscO5s6dSzabrd9ICCJz5rD0D3/goTVrePHVHWQy9VQBJQ87gmjZBAyrJTt27ODnP/85Qgj+8R//kV69euE5J5U7hp8JbHemYkQ64Hs1tGwm6Nq1K08/+zqG1R7pZwMh/bzHvIudXke2ehFOTi2/NKOCWNlENLMbCxYsYt68eWQyGSorK8/q1SuhhAuNUkD6FBEZMgTnkUeQQpD8+c+ZoWk8++yzxaoAFRV85atf5fjx42zespuVa2sBLRA/m4RhtQ6TsMuWLUNKiWnI0BlWMxpjRDoSSY4IbXcMq4Mq3dv7uOqqqzh06BD7DpkhWznvBZetWYqT3YnvnUYzKrBifYimxnPoSA2PPPIIFRUV3HjjjUydOpUuXbp8oUr8JXwxUcohfQZI/+QnHH3pJZq8/DK7jh5l7969XHnllSrQfOc7+Fu28OsRI3AaNWLixAkMHdKnQefY/ft3snv7fHr3KKdxy6+ctY2UPk52K05mG3nOkZUYxPadaV5++WXu/t4d2LWLUcvC+lyVZjQmkhiM50dZsGABp06d4rLLLgtla0so4UKilEP6nBH/8Y+Jv/wyz7z2Gt27dyeVSqnlzyOPwH334S9ezLBWrbjrrrtYt2496Uzx/lL62JktNE5sYMjAZsRiEfwCl1dAzXiqFwTLsXoCpJPeQM+enWjSpAmvvb40bDcBGepnR1Pj2H/gFI888gitWrVi5syZpWBUwueC0gzpM8S+fft45403uGHfPt41DEb/9KcYts3Gu+6iz69/DcDevXtZvnw51157bdjl7+Z2BiaPBNbcPcLeMildZZqYPbckiG62whPd+c1v/8gdd3ydVETJ2lqJQTiuwfz580mn08yYMYNEouQ/X8Kni088QxJCVAghnhFCbBFCbBZCjGpgmwlCiDVCiI1CiEXBcz2C5/KPaiHE94LXrgu29YUQQwuOM1UIsVIIsT74d9Jf9KkvQrRv356rnnkG49//nXE/+QmGbbN64EBWDxkSbtOhg5Ii2bx5M3Z6rXIQ8WpBGFjxvsTKJ4fBCAApce1i+Vwt6NvK61N7zmE0fz3jx4/niSeeVGzl1Bh27znCo48+SqdOnbj22mtLwaiEzx3n21V4D/C6lPJaIYQFFPHuhRAVwO+B6VLKfUKI5gBSyq0o622EMtc6CDwX7LYBuBr44xnvdQK4Qkp5SAjRF3gDaPMxP9dFi+R//AfOwoWYts268eN5a8oULi8ISABTpkzh0UcfpX3by4E9gCSaGtOgQ6vQTKz4QHK17yGEhRXvh261QwgRtHyoXJHvnmLY4AEsW+bzm9/+kaZNm5JMJrnpppvCZuASSvi88ZEBSQhRBowDbgeQqpfAPmOzG4G5Usp9wTbHGjjUZGCnlHJvsM3m4PhFG0kpVxf8dyMQFUJEpJS58/g8Fz8GDuTEq6/y7ksv4Y0eze0TJvDKK6/QtWvXUPAsEokwfvx45r29jBlTuwUM7qPntIw2rJbI+AAMqw1CiyB9m1zdetwCy2fDaodutsEwDE6dOoVlWdx0002fyUcuoYTzxfks2ToDx4E/CyFWCyEeFEKcObfvDjQSQiwMllm3NnCcG4AnPub4rgFWNxSMhBB/I4RYIYRYcfz48Y952M8XrSZP5ppf/5omTZqwfv16Ro0axXPPPYfruuE23bp1Q0rJvkMWmp7CydRbQDcEM9oZoUXwvepACUAFI6HFiKZGk/O789TTz9GqVSvKysrIZDLMnj37C6FnVMKXB+cTkAxgMPAHKeUgoA74Pw1sMwT4CjAN+JEQonv+xWCZdyXw9PkOTAjRB/gP4G8bel1Keb+UcqiUcmizZl9MT/VJkybhOA7Hjh2jT58+zJkzp4g4eemll7Jw4SIw+gKSXM3S0K++IXjuaezMTghmnUakM9GySaxdf5BnnnmGSy65hGuvvZZp06aRy+XYuXPnF0bPqIQvB84nIB0ADkgplwX/fwYVoM7c5nUpZZ2U8gTwDlDYuDUDWCWlPMp5QAjRFpVrulVKufN89vkiQgjBtGnTqKur4/Dhw4waNYrHHnss9HiLx+OMGjWKRYvXYEa7FDTb+ki/WJHSc46Sq12GZ+9BaHGiqXFknA488cTTVFVVceutt9K4cWPmzp3Lrl27uP7660tkxxIuOnxkDklKeUQIsV8I0SNIUk8GNp2x2QvAvUJJ71nACOBXBa/P4jyXa0GC/BXgn6SUSz9i8y88hBBMnz6dpUuXsnr1aq644goWLFhAKpVi3Lhx9O7dmw0bNnD0ZC+alpXjZLbg5gLjx2gXNKMpTibvGiIwoz0wot1ZsWIVGzduZMaMGbRsqdpOFi5cyMSJE+nSRXX55/8toYSLBefFQxJCDAQeRAWbXcDXgZkAUsr7gm3+MXjeBx6UUv46eD4O7Ac6SymrCo75NeC3QDPgNLBGSjlNCPEvwD8B2wuGcOk5EuXAF4eH9FHIB42RI0dimibLli2jvLyczp07s2zZMm655RY0DuPm9ih5kAJoejmRxGAqqzxeffVVOnbsyOjRo/E8jzfffBPXdZk+fTrR6IU3eSyhhI+DD+MhlYiRFxmy2SxLly5l//79jBw5kvLycjZt2sS6detwHIexY8cyfPhwNGpxcnvwnEOYka5oVmeWLfuA7du3M2PGDJo3b87evXuZN28eo0ePplevXp/3RyuhBKAUkL6QqK2t5YMPPmD37t0MHDiQDRs2sGfPHho1akQqlaKsrIz27dtTVlbGqVOn2LRpE7169WLkyJFks1nmzZuHbdsl9nUJFx0+LCCV7BYuUiSTSSZNmoRt26xZs4Z0Ok15eTkTJkygf//+VFZWsn//fk6cOEFFRQU333wzlmWxdu1aVq5cyfjx4+natSRkX8IXC6UZ0hcEUkr27t3LqlWrqK2tpV27drRo0YJYLEYul+Pw4cPs2rWLzp07M3bsWEzTRPqKQiC0Ut6ohIsHpRnSXwGEEHTs2JGOHTuSzWY5dOgQx44d49ixY1iWRfv27bnkkkswjPqv1Mntxs3tI14x7XMceQklnD9KAekLiGg0SufOnencufM5t5FS4jnH0I1Gn+HISijhk6EUkP6K4NpHcLPb0YxyPOc4vleNEXT8l1DCFwGlgPRXBCE0EAZOtp7crpVmSCV8gVAKSH9F0M3m6GZzXPsAnnMC3z2Fpld83sMqoYTzRikg/RXCsNqeYWtUQglfDJQ0tUsooYSLBqWAVEIJJVw0KAWkEkoo4aJBKSCVUEIJFw1KAamEEkq4aFAKSCWUUMJFg1JAKqGEEi4alAJSCSWUcNGgFJBKKKGEiwZ/FXpIQojjwN7PexwfgqYoR96LGV+EMcIXY5ylMX44OkgpG/Qu+6sISBc7hBArziVIdbHgizBG+GKMszTGvxylJVsJJZRw0aAUkEoooYSLBqWA9Nng/s97AOeBL8IY4YsxztIY/0KUckgllFDCRYPSDKmEEkq4aFAKSCWUUMJFg1JA+hAIIaJCiA+EEGuFEBuFEP8WPD9QCPG+EGKNEGKFEGL4Gfu1F0LUCiF+GPw/FWybf5wQQvw6eO12IcTxgte+WXCc24QQ24PHbRdijEKIjkKITMH73VdwrCFCiPVCiB1CiN8IIUTwfEQI8WTw/DIhRMfPY4xCiLgQ4hUhxJbgOD8veI+L6TwuFEJsLXit+UV2Hj+13+MnhpSy9DjHAxBAMvjbBJYBI4E3gRnB85cBC8/Y71ngaeCH5zjuSmBc8PftwL0NbNMY2BX82yj4u9EnHSPQEdhwjnF9AIwKjvlawf5/B9wX/H0D8OTnMUYgDkwM/raAxQX7X0zncSEwtIHnL4rz+Gn+Hj/pozRD+hBIhdrgv2bwkMGjLHi+HDiU30cIcRXqy9rY0DGFEN2A5qiL6cMwDXhLSnlKSlkJvAVMvxBjPMe4WgFlUsr3pPoFPgJcFbz8VeDh4O9ngMnB7OkzHaOUMi2lXBD8bQOrgI8SD/9Mx/gRuCjOYyEu9O/xk6IUkD4CQghdCLEGOIb6QpYB3wP+SwixH/hv4J+CbRPA/wb+7UMOOQt1Zywsb14jhFgnhHhGCNEueK4NsL9gmwPBc59ojAE6CSFWCyEWCSEuKXi/A+d4v3AsUkoXqAKafA5jLDxeBXAFML/g6YvhPObx52DJ86Mg6BSN5WI5j3wKv8dPglJA+ghIKT0p5UDUnXi4EKIv8B3gbil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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot edges and nodes\n", "ax = edges_proj.plot(linewidth=0.75, color='gray')\n", "ax = nodes_proj.plot(ax=ax, markersize=2, color='gray')\n", "\n", "# Add buildings\n", "ax = buildings_proj.plot(ax=ax, facecolor='khaki', alpha=0.7)\n", "\n", "# Add the route\n", "ax = route_geom.plot(ax=ax, linewidth=2, linestyle='--', color='red')\n", "\n", "# Add the origin and destination nodes of the route\n", "ax = od_nodes.plot(ax=ax, markersize=24, color='green')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great everything seems to be in order! As you can see, now we have a full control of all the elements of our map and we can use all the aesthetic properties that matplotlib provides to modify how our map will look like. Now we are almost ready to save our data into disk.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- As there are certain columns with such data values that Shapefile format does not support (such as `list` or `boolean`), we need to convert those into strings to be able to export the data to Shapefile:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "osmid object\n", "oneway object\n", "lanes object\n", "name object\n", "highway object\n", "maxspeed object\n", "length float64\n", "geometry geometry\n", "junction object\n", "bridge object\n", "access object\n", "u int64\n", "v int64\n", "key int64\n", "dtype: object\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", " and should_run_async(code)\n" ] } ], "source": [ "# Columns with invalid values\n", "invalid_cols = ['lanes', 'maxspeed', 'name', 'oneway', 'osmid']\n", "\n", "# Iterate over invalid columns and convert them to string format\n", "for col in invalid_cols:\n", " edges_proj[col] = edges_proj[col].astype(str)\n", " \n", "print(edges_proj.dtypes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can see that most of the attributes are of type `object` that quite often (such as ours here) refers to a string type of data.\n", "\n", "- Now we are finally ready to parse the output filepaths and save the data into disk:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "# Parse the place name for the output file names (replace spaces with underscores and remove commas)\n", "place_name_out = place_name.replace(' ', '_').replace(',','')\n", "\n", "# Output directory\n", "out_dir = \"data\"\n", "\n", "# Parse output file paths\n", "streets_out = os.path.join(out_dir, \"%s_streets.shp\" % place_name_out)\n", "route_out = os.path.join(out_dir, \"Route_from_a_to_b_at_%s.shp\" % place_name_out)\n", "nodes_out = os.path.join(out_dir, \"%s_nodes.shp\" % place_name_out)\n", "buildings_out = os.path.join(out_dir, \"%s_buildings.shp\" % place_name_out)\n", "od_out = os.path.join(out_dir, \"%s_route_OD_points.shp\" % place_name_out)\n", "\n", "# Save files\n", "edges_proj.to_file(streets_out)\n", "route_geom.to_file(route_out)\n", "nodes_proj.to_file(nodes_out)\n", "od_nodes.to_file(od_out)\n", "buildings[['geometry', 'name', 'addr:street']].to_file(buildings_out)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great, now we have saved all the data that was used to produce the maps as Shapefiles." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.6" } }, "nbformat": 4, "nbformat_minor": 4 }