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lnksz/analyze_streets_to_edge.ipynb

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Explore data preparation for rivus -- OSM-STREETS --> (edge.shp, vertex.shp)
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analyze_streets_to_edge.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Look inside of edge creation"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import geopandas\n",
"import matplotlib.pyplot as plt\n",
"from rivus.utils.pandashp import match_vertices_and_edges\n",
"from rivus.utils import shapelytools\n",
"from shapely.geometry import (box, LineString, MultiLineString, MultiPoint, Point, Polygon)\n",
"#import skeletrontools"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read Input "
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fpath = \"./data/haag15/{}\".format\n",
"streets_filename = fpath('streets.shp')\n",
"edge_filename = fpath('edge.shp')\n",
"vertex_filename = fpath('vertex.shp')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CRS transform"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
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"\n",
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" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>geometry</th>\n",
" <th>id</th>\n",
" <th>name</th>\n",
" <th>osm_id</th>\n",
" <th>other_tags</th>\n",
" <th>type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LINESTRING (734017.7521000658 5341822.15924835...</td>\n",
" <td>1</td>\n",
" <td>None</td>\n",
" <td>4050378</td>\n",
" <td>\"maxspeed\"=&gt;\"100\",\"ref\"=&gt;\"B 15\",\"surface\"=&gt;\"as...</td>\n",
" <td>primary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>LINESTRING (736871.4876152406 5339170.68830887...</td>\n",
" <td>1</td>\n",
" <td>Hauptstraße</td>\n",
" <td>4050621</td>\n",
" <td>\"maxspeed\"=&gt;\"50\"</td>\n",
" <td>secondary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>LINESTRING (735627.0591133552 5338650.80274698...</td>\n",
" <td>1</td>\n",
" <td>Tannenstraße</td>\n",
" <td>4050749</td>\n",
" <td>None</td>\n",
" <td>residential</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>LINESTRING (733629.1316536779 5341877.42621907...</td>\n",
" <td>1</td>\n",
" <td>None</td>\n",
" <td>9660295</td>\n",
" <td>None</td>\n",
" <td>residential</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>LINESTRING (735678.8561374607 5339439.84541021...</td>\n",
" <td>1</td>\n",
" <td>None</td>\n",
" <td>9931785</td>\n",
" <td>None</td>\n",
" <td>service</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>LINESTRING (735532.1246327882 5339407.67760010...</td>\n",
" <td>1</td>\n",
" <td>Gerberstraße</td>\n",
" <td>9931784</td>\n",
" <td>None</td>\n",
" <td>residential</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>LINESTRING (734226.9737598393 5341329.16469117...</td>\n",
" <td>1</td>\n",
" <td>None</td>\n",
" <td>11538813</td>\n",
" <td>\"surface\"=&gt;\"unpaved\",\"tracktype\"=&gt;\"grade3\"</td>\n",
" <td>track</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>LINESTRING (736712.5506254345 5340021.60657790...</td>\n",
" <td>1</td>\n",
" <td>None</td>\n",
" <td>12956380</td>\n",
" <td>None</td>\n",
" <td>unclassified</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>LINESTRING (736725.7023614591 5339111.27784710...</td>\n",
" <td>1</td>\n",
" <td>None</td>\n",
" <td>12957323</td>\n",
" <td>None</td>\n",
" <td>footway</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>LINESTRING (736659.5552210254 5339085.82769116...</td>\n",
" <td>1</td>\n",
" <td>Marktplatz</td>\n",
" <td>12957325</td>\n",
" <td>None</td>\n",
" <td>residential</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" geometry id name \\\n",
"0 LINESTRING (734017.7521000658 5341822.15924835... 1 None \n",
"1 LINESTRING (736871.4876152406 5339170.68830887... 1 Hauptstraße \n",
"2 LINESTRING (735627.0591133552 5338650.80274698... 1 Tannenstraße \n",
"3 LINESTRING (733629.1316536779 5341877.42621907... 1 None \n",
"4 LINESTRING (735678.8561374607 5339439.84541021... 1 None \n",
"5 LINESTRING (735532.1246327882 5339407.67760010... 1 Gerberstraße \n",
"6 LINESTRING (734226.9737598393 5341329.16469117... 1 None \n",
"7 LINESTRING (736712.5506254345 5340021.60657790... 1 None \n",
"8 LINESTRING (736725.7023614591 5339111.27784710... 1 None \n",
"9 LINESTRING (736659.5552210254 5339085.82769116... 1 Marktplatz \n",
"\n",
" osm_id other_tags type \n",
"0 4050378 \"maxspeed\"=>\"100\",\"ref\"=>\"B 15\",\"surface\"=>\"as... primary \n",
"1 4050621 \"maxspeed\"=>\"50\" secondary \n",
"2 4050749 None residential \n",
"3 9660295 None residential \n",
"4 9931785 None service \n",
"5 9931784 None residential \n",
"6 11538813 \"surface\"=>\"unpaved\",\"tracktype\"=>\"grade3\" track \n",
"7 12956380 None unclassified \n",
"8 12957323 None footway \n",
"9 12957325 None residential "
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"streets = geopandas.read_file(streets_filename)\n",
"streets = streets.to_crs(epsg=32632) # EPSG:32632 == UTM Zone 32N (Germany!)\n",
"streets.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Filtering"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" text-align: right;\n",
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" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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" }\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>geometry</th>\n",
" <th>id</th>\n",
" <th>name</th>\n",
" <th>osm_id</th>\n",
" <th>other_tags</th>\n",
" <th>type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LINESTRING (734017.7521000658 5341822.15924835...</td>\n",
" <td>1</td>\n",
" <td>None</td>\n",
" <td>4050378</td>\n",
" <td>\"maxspeed\"=&gt;\"100\",\"ref\"=&gt;\"B 15\",\"surface\"=&gt;\"as...</td>\n",
" <td>primary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>LINESTRING (736871.4876152406 5339170.68830887...</td>\n",
" <td>1</td>\n",
" <td>Hauptstraße</td>\n",
" <td>4050621</td>\n",
" <td>\"maxspeed\"=&gt;\"50\"</td>\n",
" <td>secondary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>LINESTRING (735627.0591133552 5338650.80274698...</td>\n",
" <td>1</td>\n",
" <td>Tannenstraße</td>\n",
" <td>4050749</td>\n",
" <td>None</td>\n",
" <td>residential</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>LINESTRING (733629.1316536779 5341877.42621907...</td>\n",
" <td>1</td>\n",
" <td>None</td>\n",
" <td>9660295</td>\n",
" <td>None</td>\n",
" <td>residential</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>LINESTRING (735678.8561374607 5339439.84541021...</td>\n",
" <td>1</td>\n",
" <td>None</td>\n",
" <td>9931785</td>\n",
" <td>None</td>\n",
" <td>service</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>LINESTRING (735532.1246327882 5339407.67760010...</td>\n",
" <td>1</td>\n",
" <td>Gerberstraße</td>\n",
" <td>9931784</td>\n",
" <td>None</td>\n",
" <td>residential</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>LINESTRING (736712.5506254345 5340021.60657790...</td>\n",
" <td>1</td>\n",
" <td>None</td>\n",
" <td>12956380</td>\n",
" <td>None</td>\n",
" <td>unclassified</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>LINESTRING (736659.5552210254 5339085.82769116...</td>\n",
" <td>1</td>\n",
" <td>Marktplatz</td>\n",
" <td>12957325</td>\n",
" <td>None</td>\n",
" <td>residential</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>LINESTRING (736700.4427397507 5339080.82016933...</td>\n",
" <td>1</td>\n",
" <td>Marktplatz</td>\n",
" <td>12957327</td>\n",
" <td>None</td>\n",
" <td>residential</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>LINESTRING (736722.7920502152 5339158.32395773...</td>\n",
" <td>1</td>\n",
" <td>Zeno-Kern-Straße</td>\n",
" <td>12957778</td>\n",
" <td>None</td>\n",
" <td>residential</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" geometry id name \\\n",
"0 LINESTRING (734017.7521000658 5341822.15924835... 1 None \n",
"1 LINESTRING (736871.4876152406 5339170.68830887... 1 Hauptstraße \n",
"2 LINESTRING (735627.0591133552 5338650.80274698... 1 Tannenstraße \n",
"3 LINESTRING (733629.1316536779 5341877.42621907... 1 None \n",
"4 LINESTRING (735678.8561374607 5339439.84541021... 1 None \n",
"5 LINESTRING (735532.1246327882 5339407.67760010... 1 Gerberstraße \n",
"7 LINESTRING (736712.5506254345 5340021.60657790... 1 None \n",
"9 LINESTRING (736659.5552210254 5339085.82769116... 1 Marktplatz \n",
"10 LINESTRING (736700.4427397507 5339080.82016933... 1 Marktplatz \n",
"11 LINESTRING (736722.7920502152 5339158.32395773... 1 Zeno-Kern-Straße \n",
"\n",
" osm_id other_tags type \n",
"0 4050378 \"maxspeed\"=>\"100\",\"ref\"=>\"B 15\",\"surface\"=>\"as... primary \n",
"1 4050621 \"maxspeed\"=>\"50\" secondary \n",
"2 4050749 None residential \n",
"3 9660295 None residential \n",
"4 9931785 None service \n",
"5 9931784 None residential \n",
"7 12956380 None unclassified \n",
"9 12957325 None residential \n",
"10 12957327 None residential \n",
"11 12957778 None residential "
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# filter away roads by type\n",
"road_types = ['motorway', 'motorway_link', 'primary', 'primary_link', \n",
" 'secondary', 'secondary_link', 'tertiary', 'tertiary_link', \n",
" 'residential', 'living_street', 'service', 'unclassified']\n",
"streets = streets[streets['type'].isin(road_types)]\n",
"streets.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#skeleton = skeletrontools.skeletonize(streets,\n",
"# buffer_length=30,\n",
"# dissolve_length=15,\n",
"# simplify_length=30)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Create simple MultiLineString while I cannot install Skeletron**"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"300\" height=\"300\" viewBox=\"733992.7707061064 5341172.643005414 602.7890161784599 674.4976369030774\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,10683019.783647731)\"><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"4.4966509126871825\" points=\"734017.7521000658,5341822.159248358 734108.3439931418,5341644.481837075 734133.5023706679,5341576.129316445 734164.0345630101,5341477.507347351 734179.0100331899,5341436.377476257 734196.4696605101,5341404.199320424 734219.0497921659,5341366.977090854 734241.2434928293,5341340.993319948 734262.6336529693,5341323.347758788 734299.62123001,5341299.151269769 734331.4281196344,5341284.193240771 734377.6809868941,5341266.80060416 734478.7735741654,5341237.400662449 734506.0212389986,5341226.163555857 734570.5783283255,5341197.624399373\" opacity=\"0.8\" /></g></svg>"
],
"text/plain": [
"<shapely.geometry.linestring.LineString at 0x11207a58>"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"streets['geometry'][0]"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"300\" height=\"300\" viewBox=\"733903.6026794588 5339055.184350342 3082.0343563888455 2881.124318622984\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,10680991.493019305)\"><g><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"20.54689570925897\" points=\"734017.7521000658,5341822.159248358 734570.5783283255,5341197.624399373\" opacity=\"0.8\" /><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"20.54689570925897\" points=\"736871.4876152406,5339170.68830887 736860.0295636341,5339169.333770948\" opacity=\"0.8\" /></g></g></svg>"
],
"text/plain": [
"<shapely.geometry.multilinestring.MultiLineString at 0xb13c080>"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ls1 = LineString([streets['geometry'][0].coords[0], streets['geometry'][0].coords[-1]])\n",
"ls2 = LineString([streets['geometry'][1].coords[0], streets['geometry'][1].coords[-1]])\n",
"MultiLineString([ls1, ls2])"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(734570.5783283255, 5341822.159248358)"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"streets['geometry'][0].bounds[2:]"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(734570.5783283255, 5341197.624399373)"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"streets['geometry'][0].coords[-1]"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"300\" height=\"300\" viewBox=\"733031.5786090909 5337303.549908784 5252.367698524729 4849.608552210964\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,10679456.70836978)\"><g><polyline fill=\"none\" stroke=\"#ff3333\" stroke-width=\"35.01578465683153\" points=\"734017.7521000658,5341822.159248358 734570.5783283255,5341197.624399373\" opacity=\"0.8\" /><polyline fill=\"none\" stroke=\"#ff3333\" stroke-width=\"35.01578465683153\" points=\"736871.4876152406,5339170.68830887 736860.0295636341,5339169.333770948\" opacity=\"0.8\" /><polyline fill=\"none\" stroke=\"#ff3333\" stroke-width=\"35.01578465683153\" points=\"735627.0591133552,5338650.802746986 735606.9686760779,5338835.087106957\" opacity=\"0.8\" /><polyline fill=\"none\" stroke=\"#ff3333\" stroke-width=\"35.01578465683153\" points=\"733629.1316536779,5341877.426219075 733745.2698730684,5341827.424299685\" 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],
"text/plain": [
"<shapely.geometry.multilinestring.MultiLineString at 0x11f7fa90>"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"skeleton = MultiLineString([(line.coords[0], line.coords[-1]) for line in streets['geometry'] if isinstance(line, LineString)])\n",
"#skeleton = shapelytools.one_linestring_per_intersection(skeleton)\n",
"skeleton"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**This is a quite a strong distortion** in production either skeletron should be resurrected, or an alternative should be found."
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<shapely.geometry.linestring.LineString at 0x11fc5a90>,\n",
" <shapely.geometry.linestring.LineString at 0x11fae2b0>,\n",
" <shapely.geometry.linestring.LineString at 0x11fe0400>]"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"skeleton = shapelytools.snappy_endings(skeleton, max_distance=100)\n",
"skeleton[:3]"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"data": {
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],
"text/plain": [
"<shapely.geometry.multilinestring.MultiLineString at 0x11fc9278>"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"skeleton = shapelytools.one_linestring_per_intersection(skeleton)\n",
"skeleton"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11fd35c0>"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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Wk5h0gh0HD1CkX8aKtB9xtgyjlW0vPG164mwZQVt/r9PWE+MWwC8pu7irTU+E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\n3o2Na+IYcMPp2/zxi2dxynch2D+gni1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38M9DG2jvG0ygqxdDgiyv7XQJCmb1wxP4x/at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\ngTZzzUuAflr42tD9MPCDlPI0gJTyUnnbSndVy/oh4Fsba7Z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"text/plain": [
"<matplotlib.figure.Figure at 0x11fc95c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"skeleton = shapelytools.prune_short_lines(skeleton, min_length=55)\n",
"geopandas.GeoDataFrame(geometry=skeleton, crs=streets.crs).plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Put Geometry into GeoDataFrame with CRS(4326)"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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],
"text/plain": [
" geometry Vertex\n",
"0 POINT (12.18014738230067 48.1652135423499) 0\n",
"1 POINT (12.1883682 48.1663995) 1\n",
"2 POINT (12.19606713957078 48.15712345702804) 2\n",
"3 POINT (12.18007501864393 48.16522824633694) 3\n",
"4 POINT (12.1756668 48.1557562) 4\n",
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"6 POINT (12.15573914996715 48.18062360000686) 6\n",
"7 POINT (12.19349565563646 48.16369785138707) 7\n",
"8 POINT (12.18566450292734 48.15921207188419) 8\n",
"9 POINT (12.187404 48.15355780000001) 9"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# convert back to GeoPandas\n",
"edge = geopandas.GeoDataFrame(geometry=skeleton, crs=streets.crs)\n",
"edge = edge.to_crs(epsg=4326) # World Geodetic System (WGS84) \n",
"edge['Edge'] = edge.index\n",
"\n",
"# derive vertex points\n",
"vertices = shapelytools.endpoints_from_lines(edge.geometry)\n",
"vertex = geopandas.GeoDataFrame(geometry=vertices, crs=edge.crs)\n",
"vertex['Vertex'] = vertex.index\n",
"vertex.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <td>6</td>\n",
" <td>163</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" geometry Edge Vertex1 Vertex2\n",
"0 LINESTRING (12.13807510000005 48.1870267, 12.1... 0 62 121\n",
"1 LINESTRING (12.1394549 48.1855127, 12.14056790... 1 62 156\n",
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"5 LINESTRING (12.14527093227269 48.1866579163458... 5 19 63\n",
"6 LINESTRING (12.1486945 48.1864823, 12.14799653... 6 63 159\n",
"7 LINESTRING (12.1486945 48.1864823, 12.15573914... 7 6 63\n",
"8 LINESTRING (12.15573914996715 48.1806236000068... 8 6 132\n",
"9 LINESTRING (12.15573914996715 48.1806236000068... 9 6 163"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"match_vertices_and_edges(vertex, edge)\n",
"edge.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <th>geometry</th>\n",
" <th>Edge</th>\n",
" <th>Vertex1</th>\n",
" <th>Vertex2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LINESTRING (12.13807510000005 48.1870267, 12.1...</td>\n",
" <td>0</td>\n",
" <td>62</td>\n",
" <td>121</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>LINESTRING (12.1394549 48.1855127, 12.14056790...</td>\n",
" <td>1</td>\n",
" <td>62</td>\n",
" <td>156</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>LINESTRING (12.14056790847939 48.1869023644199...</td>\n",
" <td>2</td>\n",
" <td>19</td>\n",
" <td>62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>LINESTRING (12.1435042 48.1871218, 12.1449267 ...</td>\n",
" <td>3</td>\n",
" <td>19</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>LINESTRING (12.14527093227269 48.1866579163458...</td>\n",
" <td>4</td>\n",
" <td>19</td>\n",
" <td>61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>LINESTRING (12.14527093227269 48.1866579163458...</td>\n",
" <td>5</td>\n",
" <td>19</td>\n",
" <td>63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>LINESTRING (12.1486945 48.1864823, 12.14799653...</td>\n",
" <td>6</td>\n",
" <td>63</td>\n",
" <td>159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>LINESTRING (12.1486945 48.1864823, 12.15573914...</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>LINESTRING (12.15573914996715 48.1806236000068...</td>\n",
" <td>8</td>\n",
" <td>6</td>\n",
" <td>132</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>LINESTRING (12.15573914996715 48.1806236000068...</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>163</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" geometry Edge Vertex1 Vertex2\n",
"0 LINESTRING (12.13807510000005 48.1870267, 12.1... 0 62 121\n",
"1 LINESTRING (12.1394549 48.1855127, 12.14056790... 1 62 156\n",
"2 LINESTRING (12.14056790847939 48.1869023644199... 2 19 62\n",
"3 LINESTRING (12.1435042 48.1871218, 12.1449267 ... 3 19 40\n",
"4 LINESTRING (12.14527093227269 48.1866579163458... 4 19 61\n",
"5 LINESTRING (12.14527093227269 48.1866579163458... 5 19 63\n",
"6 LINESTRING (12.1486945 48.1864823, 12.14799653... 6 63 159\n",
"7 LINESTRING (12.1486945 48.1864823, 12.15573914... 7 6 63\n",
"8 LINESTRING (12.15573914996715 48.1806236000068... 8 6 132\n",
"9 LINESTRING (12.15573914996715 48.1806236000068... 9 6 163"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# drop loops\n",
"edge = edge[~(edge['Vertex1'] == edge['Vertex2'])]\n",
"edge.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <td>7</td>\n",
" <td>6</td>\n",
" <td>63</td>\n",
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" <td>132</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>LINESTRING (12.15573914996715 48.1806236000068...</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>163</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
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" <td>125</td>\n",
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" <td>160</td>\n",
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" <td>12</td>\n",
" <td>18</td>\n",
" <td>132</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
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" </tr>\n",
" <tr>\n",
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" <td>161</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>LINESTRING (12.1612266 48.17295960000001, 12.1...</td>\n",
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" <th>18</th>\n",
" <td>LINESTRING (12.16223287484881 48.1705480751595...</td>\n",
" <td>18</td>\n",
" <td>30</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>LINESTRING (12.1624808 48.16757969999999, 12.1...</td>\n",
" <td>19</td>\n",
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"</div>"
],
"text/plain": [
" geometry Edge Vertex1 Vertex2\n",
"0 LINESTRING (12.13807510000005 48.1870267, 12.1... 0 62 121\n",
"1 LINESTRING (12.1394549 48.1855127, 12.14056790... 1 62 156\n",
"2 LINESTRING (12.14056790847939 48.1869023644199... 2 19 62\n",
"3 LINESTRING (12.1435042 48.1871218, 12.1449267 ... 3 19 40\n",
"4 LINESTRING (12.14527093227269 48.1866579163458... 4 19 61\n",
"5 LINESTRING (12.14527093227269 48.1866579163458... 5 19 63\n",
"6 LINESTRING (12.1486945 48.1864823, 12.14799653... 6 63 159\n",
"7 LINESTRING (12.1486945 48.1864823, 12.15573914... 7 6 63\n",
"8 LINESTRING (12.15573914996715 48.1806236000068... 8 6 132\n",
"9 LINESTRING (12.15573914996715 48.1806236000068... 9 6 163\n",
"10 LINESTRING (12.1572016 48.18036679999999, 12.1... 10 125 132\n",
"11 LINESTRING (12.1604319 48.1661044, 12.15676416... 11 29 160\n",
"12 LINESTRING (12.15777632897053 48.1808760262701... 12 18 132\n",
"13 LINESTRING (12.16223287484881 48.1705480751595... 13 59 120\n",
"14 LINESTRING (12.1612266 48.17295960000001, 12.1... 14 60 163\n",
"15 LINESTRING (12.1604319 48.1661044, 12.1623088 ... 15 55 160\n",
"16 LINESTRING (12.1628662 48.1655761, 12.1604319 ... 16 55 161\n",
"17 LINESTRING (12.1612266 48.17295960000001, 12.1... 17 59 163\n",
"18 LINESTRING (12.16223287484881 48.1705480751595... 18 30 59\n",
"19 LINESTRING (12.1624808 48.16757969999999, 12.1... 19 83 161"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# if there are >1 edges that connect the same vertex pair, drop one of them\n",
"edge = edge.drop_duplicates(subset=['Vertex1', 'Vertex2'])\n",
"edge.head(20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"... wirte to file"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Final comparision"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x12519278>"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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WLVrkdikiIpIOtWo5NyV66y23KxGRjKIgnkvUrFmTjh07cuLECbdLERGRdHr1VfjwQ4iM\ndLsSEckICuK5RJEiRWjVqhUbN250uxQREUmn8uXh/vvh7393uxIRyQgK4rlIvXr1FMRFRLK5UaNg\n7lzYtcvtSkTkdimI5yJ169Zly5YtJCQkuF2KiIikU5EiMGIEjBzpdiUicrsUxHORIkWKUKJECSIi\nItwuRUREbsOjj8K2bfDjj25XIiK3Q0E8l1F7iohI9hcYCKNHw7PPgrVuVyMi6aUgnssoiIuI5AwD\nB0JcHMya5XYlIpJeCuK5jIK4iEjO4OPjrCk+ahRcuuR2NSKSHgriuUz9+vXZvHkzl3TVFhHJ9tq3\nh4oV4aOP3K5ERNJDQTyXKVy4MGFhYWzdutXtUkREJAO8+aZzo5/oaLcrEZG0UhDPhZo1a8bKlSvd\nLkNERDJA7drQoQO8/bbblYhIWimI50JNmzZlxYoVbpchIiIZ5NVXYdw4OHrU7UpEJC0UxHOhDh06\nsGTJEs6dO+d2KSIikgFCQ+Evf4F//MPtSkQkLRTEc6ESJUrQqFEj5s+f73YpIiKSQZ57Dr76Cn7+\n2e1KRCS1FMRzqYEDBzJ16lS3yxARkQxStKhzg59Ro9yuRERSS0E8l+rduzcrVqzg999/d7sUERHJ\nII8/Dps3g+bji2QPCuK5VIECBejTpw+TJk1yuxQREckggYHOxM2//Q2sdbsaEUmJgngu9sADD/Dx\nxx+TmJjodikiIpJBBg2C8+dh9my3KxGRlCiI52KNGjUiODiYzz//3O1SREQkg/j6Ojf5GTkSdBNl\nEe+mIJ6LGWMYM2YMzz//PBcuXMiS97x06RIbN27MkvcSEcmtOnRwljT8+GO3KxGRW1EQz+WaN29O\ngwYNGJVF0+xjY2Np3bo1MTExWfJ+IiK5kTHOqPgrr4AutyLeS0Fc+Oijj5g3bx5TpkzJ9PcqUqQI\nLVq0YO7cuZn+XiIiuVndutC2LfzrX25XIiI3oyAuFClShHnz5vG3v/2NmTNnZvr79e/fn6+++irT\n30dEJLcbPRrGjgWtVCvinRTEBYBq1arx7bff8thjjzFjxoxMfa8WLVqwdu3aTH0PERGBsDD485/h\n5ZfdrkREbkRBXC6rXbs23377LSNGjOCxxx7LtAmc5cqV48SJE8TFxV21/fTp00RHR2fKe4qI5FbP\nPQczZ8Lu3W5XIiLXUhCXq9SpU4fNmzdz4sQJqlWrxrx587AZfFcIX19fSpcuzZEjR67a/vTTTzN1\n6tQMfS8RkdwuONi5wU8WzckXkTRQEJfrFC5cmOnTpzN+/HhGjhxJp06dWLVq1W0H8sOHD7N48WIA\nihcvzokTJ656vUGDBqxevfq23kNERK73+OOwYQOsWuV2JSKSnIK43FT79u3ZunUrPXv2ZOjQodx9\n993MnDmThISENJ/r73//O9WqVeNPf/oTAIUKFeLMmTOXX7fWMmXKFDp16pRh9YuIiCMoyFnK8Nln\nIYO/5BSR26AgLrfk7+/P8OHD2b17N88++yzvvPMOVapUYezYscTGxqb6PA8++CBTpkyhUqVKJCYm\nsn//fvz9/Vm/fj0LFizgn//8J7t27WLbtm08+OCDNGvWjHbt2jFixAi++uqr60bPRUQkbe67z1lT\nXKvHingPk9H9v96qfv36dsOGDW6XkSOsXr2af/3rX6xZs4b33nuPfv36YYy5ah9rLUeOHGH37t1E\nRESwe/duNm7cyNq1a7HWEh8fT+nSpSldujQlS5bEz8+P8+fP07p1a4oVK8bcuXMpWrQoVatWZfXq\n1axatYrXX3+dRx991KVPLeIuY8xGa219t+vIKrpmZ45vv4WnnoIdO8DPz+1qRHKu1F6z9Z+hpFmT\nJk1o0qQJa9as4YEHHmD69OlMmDCB4sWLA04bytixY8mTJw9VqlShatWqVK1alebNmzN27Fjatm3L\nM888Q1BQ0E3fY/fu3ZQuXZpnnnkGgEuXLnH+/Pks+XwiIjlVp07w9tvwyScwbNjtnctaiIyEiAjn\nERwMfftmTJ0iuYWCuKTb3XffzaZNm3jppZeoWbMmEyZMICgoiGnTprFlyxbKly9/1f5HjhzhwQcf\nZO7cubcM4cePH2f8+PF07tz58jZ/f3/8/f0z7bOIiOQGxsBbb0H37jBoEOTPf+v94+Lg0CE4fvxK\n4N6z58qfBQpAeDhUqQLt2mXNZxDJSRTE5bYEBATw5ptv0q1bN4YMGUJkZCTvvPPOdSEcYNy4cQwe\nPJhChQrd8pzBwcE88sgjHD16NLPKFhHJterVg1at4J134KWXbrzP77/DtGnw2mtw5oxzTJUqTuju\n1cv5MzwcUrici0gK1CMuGWb58uW0adOGjh07Mnv2bAICAvjjjz8wxpA/f35CQ0NZs2YNlStXTvFc\nCQkJ+Pj4XNd7LpJbqUdcMtL+/VC/PuzaBSVLOtvOnXMmcn72GaxZA/fcAz/+CB984LS0iEjqpfaa\nrVVTJMNUqlSJWbNmERQURN++fbl48SILFiygQYMGvPLKKzRp0iRVIRycm/4ohIuIZI4KFWDIEPjH\nP2DZMrj/fihbFiZNgsGD4cgRGDoUAgOhY0e3qxXJudSaIhmmXLlylCtXjq5du9K3b18efvhhPvnk\nEwoVKsS9997L999/73aJIiLiMWqUMxq+eDE8/DCMHg2lS195/f334YknnL5yEckcGhGXDOfv78/U\nqVPZtGkT77zzDnfddRcA//d//8dXX33lcnUiIgJQogT06AGPPAL/7/9dHcL374cVK5y1x0Uk82hE\nXDJF/vz5mTdvHo0bN+bMmTO0bduWf/zjHzRs2NDt0kRExCNpJPypp67ePm4c/OUvkC+fO3WJ5BYa\nEZdMU758eT777DPeffddGjduTNOmTbUEoYiIF2nbFn75BQ4fvnr74MHwf//nTk0iuYmCuGSqtm3b\nUqdOHdasWUNuWaFHRCS7yJPHWVN81qyrt9eu7UzeFJHMpSAume77778nMjKSOXPmuF2KiIhco18/\nmDnT7SpEcicFccl0gYGBvPvuu4wYMYJLly65XY6IiCTTrh3s3Oncrl5EspaCuGSJjh07EhYWxoQJ\nE9wuRUREkgkIgG7drm9PEZHMpyAuWWbMmDGMHj2aqKgot0sREZFk1J4i4g4FcckytWrVol27dnz4\n4YdulyIiIsl06ADbtsHRo25XIpK7KIhLlnr22Wd5//33iYuLc7sUERHxCAiArl1B91wTyVoK4pKl\natasSc2aNZk6darbpYiISDJqTxHJeqkK4saYA8aY7caYLcaYDZ5trxpjtnm2fWeMKXPNMQ2MMQnG\nmL7Jti00xpwxxiy4Zt8Kxpi1xpg9xpgvjDF5PNsDPM9/9bweluyYUZ7tu40xHdP/K5Cs9re//Y23\n336bxMREt0sRERGPDh1g82b44w+3KxHJPdIyIt7aWlvbWlvf83yMtbamtbY2sAB4KWlHY4wv8Caw\n6JpzjAHuu8G53wTetdaGA6eB+z3b7wdOW2srA+969sMYcxcwAKgGdAI+8LynZANt2rTBz8+P5cuX\nu12KiIh4BAVBly4we7bblYjkHuluTbHWRid7mg9IftvEx4FZwLFrjlkCxCTfZowxQBsg6QuxSUBP\nz8/3eJ7jeb2tZ/97gOnW2jhr7X7gV6Bhej+LZC1jDH/961+ZOHGi26WIiEgy/frBl1+6XYVI7pHa\nIG6B74wxG40xDyVtNMa8Zow5BAzCMyJujCkL9ALGp/LcwcAZa2285/lhIOnGumWBQwCe16M8+1/e\nfoNjLjPGPGSM2WCM2XD8+PFUliNZYdCgQcybN4/o6OiUdxaRNFE7oaRXp06wcSMcO5byviJy+1Ib\nxJtaa+sCnYFHjTEtAKy1z1trQ4CpwGOefd8DRlhrE1J5bnODbTaF1251zJUN1n5kra1vra1fvHjx\nVJYjWaF48eK0bt2aLzX0IpJZ1E4oaRYU5ITxOXPcrkQkd0hVELfWRnr+PAbM5vo2kGlAH8/P9YHp\nxpgDQF+cC25Pbu4EUNgY4+d5Xg5IutHuYSAEwPN6IeBU8u03OEayiSFDhvD555+7XYZIrqB2Qkkt\ntaeIZJ0Ug7gxJp8xpkDSz0AHYIcxJjzZbj2AXwCstRWstWHW2jCcC/Ej1tqb/t3aWmuBH3BCO8BQ\nYK7n53me53heX+rZfx4wwPM1aAUgHFiXis8rXqRNmzasXbuWixcvul2KSE6jdkJJt86dYd06OHHC\n7UpEcr7UjIiXBFYaY7bihN2vrbULgX8aY3YYY7bhhPMnUzqRMWYF8CXOKMnhZH2CI4CnjTG/4ly0\nP/Fs/wQI9mx/GhgJYK3dCcwAdgELgUfT0AojXqJQoUJUqVKF9evXu12KSE6jdkJJt7x5oWNHtaeI\nZAW/lHaw1u4Dat1ge58b7H7tPn++5nnzW7zHdV9TWmsvAP1ucsxrwGsp1SDerUWLFvz44480bdrU\n7VJEcozk7YTGmKR2wh+T7TIN+Br4O1faCQGKAV2MMfG3+CbzcjuhZ9T7Ru2Eh9VOmL316wcffwwP\nPOB2JSI5m+6sKa5q2bKl1hMXyUBqJ5SM0KUL/PQTnDzpdiUiOZuCuLiqefPmrF69mvj4+JR3FpHU\nUDuh3LZ8+aB9e5g7N+V9RST9UmxNEclMwcHBhIaGsnnzZho0aOB2OSLZntoJJaP06weffgp//avb\nlYjkXBoRF9c1adKEtWvXul2GiIgk07UrrF4Np065XYlIzqUgLq4LDw9n3759bpchIiLJ5M8PbdvC\nvHluVyKScymIi+sqVKigIC4i4oV0cx+RzKUgLq6rWLGigriIiBfq1g1WrIAzZ9yuRCRnUhAX11Ws\nWJH9+/fjrHImIiLeokABaNNG7SkimUVBXFxXqFAh8uTJwwndT1lExOv07av2FJHMoiAuXkF94iIi\n3ql7d1i+HKKi3K5EJOdREBevkNSeIiIi3qVQIWjVCubPd7sSkZxHQVy8QsWKFdm7d6/bZYiIyA2o\nPUUkcyiIi1cIDQ3l0KFDbpchIiI30KMH/PADREe7XYlIzqIgLl6hXLlyCuIiIl6qcGFo0QIWLHC7\nEpGcRUFcvEK5cuU4fPiw22WIiMhNqD1FJOMpiItXCAkJ0Yi4iIgXu+ceWLoUYmLcrkQk51AQF68Q\nHBzMuXPnOHv2rNuliIjIDRQpAk2bwtdfu12JSM6hIC5ewRhD+fLl+e2339wuRUREbkLtKSIZS0Fc\nvEZYWJjWEhcR8WI9e8LixRAb63YlIjmDgrh4hcTERIoUKcKcOXM0Ki4i4qWKFoW774ZvvnG7EpGc\nQUFcvEJCQgJLly5l8eLF/P77726XIyIiN9G3L8yc6XYVIjmDgrh4BX9/f/773/9y11130bBhQ7fL\nERGRm+jZExYtgnPn3K5EJPtTEBevUbVqVXbv3u12GSIicgvFikHDhmpPEckICuLiNSpVqsSRI0e0\nhKGIiJfr10/tKSIZQUFcvEaePHmoVq0aW7ZscbsUERG5hV69YOFCOH/e7UpEsjcFcfEqDRo0YNWq\nVXyphWpFRLxW8eJQrx58+63blYhkbwri4lXCwsJ44403mD9/PomJiW6XIyIiN6H2FJHbpyAuXqVR\no0b4+PgwadIkfHz0r6eIiLfq1cuZsKn2FJH0U9IRr9KiRQv8/f05ePCg26WIiMgtlCwJdeo4SxmK\nSPooiItXMcbQqlUrli1b5nYpIiKSArWniNweBXHxOgriIiLZQ+/e8PXXcOGC25WIZE8K4uJ1WrVq\nxQ8//IC11u1SRETkFkqVgpo14bvv3K5EJHtSEBevU7VqVRITE4mIiHC7FBERSYHaU0TST0FcvI4x\nhs6dO/ON7p8sIuL1eveG+fMhLs7tSkSyHwVx8UpdunThW90pQkTE65UpA9Wrw+LFblcikv0oiItX\natu2LWvWrOHs2bNulyIiIino1w90Q2SRtFMQF69UoEABGjZsyJIlS9wuRUREUtCnD8ybBxcvul2J\nSPaiIC5eq0+fPkyfPt3tMkREJAVly8Kdd4LGTkTSRkFcvFb//v355ptviI6OdrsUERFJgdpTRNJO\nQVy8VnBwMK1atWLWrFlulyIiIino0wfmzoVLl9yuRCT7UBAXr3bffffx2WefuV2GiIikICQEqlSB\npUvdrkSyGL94AAAgAElEQVQk+1AQF6/WrVs3tm7dysGDB90uRUREUqD2FJG0URAXrxYQEMCgQYP4\n+OOP3S5FRERS0KcPzJmj9hSR1FIQF683bNgwPv74Yy7pyi4i4tVCQ6FSJVi2zO1KRLIHBXHxetWq\nVaNKlSrMnTvX7VJERCQFak8RST0FcckWhg8fzocffuh2GSIikoI+fWD2bIiPd7sSEe+nIC7ZQq9e\nvdixYwe7d+92uxQREbmFChUgLAyWL3e7EhHvpyAu2UJAQAB//etfmTBhgtuliIhICtSeIpI6CuKS\nbTz00ENMnjyZ8+fPu12KiIjcQt++TntKQoLblYh4NwVxyTYqVKhAw4YN+eKLL9wuRUREbqFiRShX\nDn780e1KRLybgrhkK8OHD+eNN95wuwwREUmB2lNEUqYgLtmGtZZ9+/YRERHBnDlz3C5HRERuoW9f\n+OortaeI3Iqf2wWI3Mzzzz+Pv78/efPm5dKlS3z55ZcEBgbyyCOPsGzZMnr27Ol2iSIichOVK0Pp\n0rByJbRs6XY1It5JI+LitcLCwgA4fvw4p06d4q233mLNmjU8/fTTTJs2jXgtUisi4tXUniJyaxoR\nF6/14IMP3nB7pUqVCA0NZdmyZbRr1y6LqxIRkdTq2xdatYL33wcfDf2JXEf/WUi21K9fP77UMIuI\niNc6eRI2b4bTp2HUKLerEfFOCuKSLfXr14/Zs2erPUVExEtcuuQsV/jCC9CggbOE4dSpUKsWHD/u\ndnUi3klBXLKlChUqEBISwqpVq9wuRUREgKFD4emnwVp4+20nfM+bB+++Cxs2uF2diHdSj7hkW506\ndeK7776jpabji4i4bvJk8LtBqmjYECIj4fBh5yY/InKFRsQl2+rQoQOLFi1yuwwREeHGIRzA1xc6\ndIBvv83aekSyAwVxybYaN27Mzp07OX/+vNuliIjILXTpoiAuciMK4pJtBQQEUKVKFTZt2uR2KSIi\ncgsdO8LSpXDxotuViHgXBXHJ1s6fP8/EiRPdLkNERG6heHGoWhU0v17kagrikq0NHDiQUqVKuV2G\niIikoEsX+OYbt6sQ8S4K4pKtVaxYkQMHDrhdhoiIpKBzZ/WJi1xLQVyytbCwMAVxEZFsoH59OHYM\nfvvN7UpEvIeCuGRroaGhCuIiItmAj48zaVOj4iJXKIhLtlayZEmOHTuGtdbtUkREJAVt28Ly5W5X\nIeI9FMQlWwsMDMTf35/Y2Fi3SxERkRRUrgz6ElPkCgVxyfaCg4M5efKk22WIiEgKwsIUxEWSUxCX\nbC84OJhTp065XYaIiKSgdGk4eRIuXHC7EhHvoCAu2V7RokU5efIk58+fZ/Pmzfx2gyn5UVFR6iMX\nEXGZry+UK6eVU0SSKIhLtpfUmvLuu+/StWtXZs6ced0+I0eOpHz58jd8TUREsk5YGBw86HYVIt7B\nz+0CJHe5ePEiMTExBAcHZ9g5z58/z1tvvcX58+epWbMmXbp0uW6fDz74gICAAHbu3Enfvn0z7L1F\nRCRt1CcucoWCuGSpXbt20aJFCwoXLkydOnWoU6cO9erVo379+pQuXTpd50xqO2nZsiWFChXC39//\nun2MMWzbto2nnnrqdj+CiIjcBgVxkSsUxCVL1a5dmzNnzrBv3z42b97M5s2bGTduHBs2bCAgIOBy\nKK9fvz716tWjZMmSKZ6zQIECAIwfP/6m+xw7doxNmzbRunXry9suXLhAQkIC+fLlu/0PlovExsZy\n9OhRwsPD3S5FRLKhsDBYuNDtKkS8g4K4ZDkfHx8qV65M5cqV6devHwDWWg4ePMjGjRvZsGED7777\nLhs3biQgIICgoCCGDBlC9erVqVGjBpUrV8bX1/fy+e655x7WrVt3y/dctmwZvXv3Jn/+/FhrGT58\nOFOnTiUhIYGKFSteHp2vW7cutWvXpnDhwpn6O8jONm3axPPPP8+KFSvcLkVEsqHQUI2IiyRREBev\nYIwhLCyMsLAw+vTpAzjhfPLkyYwZM4b4+Hg+++wztm/fzh9//IExhgEDBlC9enUiIyPZsWPHLc9/\n7733Xj7v559/zpQpU9i6dSshISHs2LGDzZs3s2nTJmbOnMm2bdsoUaIE9erVuzxCX6lSJQoXLkzB\nggWv+ktAbhQQEEBcXJzbZYhINqXJmiJXKIiL1zLGcOnSJRo0aMDo0aMvb4+OjqZYsWLUqFGDPXv2\nsGvXLg4dOpTi+ZIC9Pjx4/nss8+oVKkSAHXr1qVu3brcf//9ACQkJLBnzx42btzIxo0beeWVV/jt\nt984c+YMMTEx5M+fnyJFilCqVCnKly9P+fLlCQ0NpVKlShQrVox8+fJdfvj7+1OoUCGMMZnwG3JH\nYGAgF7QIsIikU5kycPw4xMVBQIDb1Yi4S0FcvNrevXupWLHiVdsKFixI2bJl6dSpE08++SQRERF0\n7tw5Vec7c+YMW7ZsoWvXrjfdx9fXlzvuuIM77riDQYMGXfVaQkICMTExnD59mqNHj/Lbb7/x22+/\n8fPPP/P1119z6tQpYmNjOXv2LGfPnuXkyZOUKVOG2rVr06dPH4YMGYKfX/b8z+706dP07NmTmJgY\njYiL5CLh4WAt+PmBv3/a/7zRNl9fWLQIevRw+9OJuCtVicAYcwCIARKAeGttfWPMq8A9QCJwDPiz\ntTYy2TENgJ+A/tbamZ5tQ4EXPLuMttZO8myvB3wKBAHfAE9aa60xpijwBRAGHADutdaeNs7w4r+B\nLsA5z3tvSufvQLzY3r176dWr13Xb27Zty7x58/jb3/6Wpjtr7t27l0qVKpEnT5501ePr60vhwoUp\nXLgwFSpUoEmTJrfcv2jRoixYsIC9e/cyduxY/ve//zF37twMXb4xs0RFRbFgwQJ27NjBiRMnOH36\nNFu2bKFPnz7MmDHD7fJEJIt8/z3Ex8OlS9f/eaNtqfkzb16IjEz5vUVyurQMzbW21p5I9nyMtfZF\nAGPME8BLwMOe577Am8CipJ09ofrvQH3AAhuNMfOstaeBD4GHcIL7N0An4FtgJLDEWvtPY8xIz/MR\nQGcg3PNo5Dm+Udo+umQHSZMorzVkyBCGDRvG008/TeHChYmOjiY+Pj7F0eYDBw4QFhaWSdVeLzAw\nkJIlS1KnTh169+7NqFGjaNq0KQsXLszSOtIiOjqal19+mY8//phWrVrRsGFDwsLCOH78OHny5CF/\n/vwaERfJRTLjUrV7t9pSROA2WlOstdHJnubDCddJHgdmAQ2SbesIfG+tPQVgjPke6GSMWQYUtNau\n8WyfDPTECeL3AK08x08CluEE8XuAyda5Z/lPxpjCxpjS1tqj6f084p1GjRp1w+3NmzcnODiYadOm\ncd9991GkSBFOnTpFiRIlbnm+06dPU7Ro0cwo9Ybi4+Mv96b7+Pjw5ptvUrZsWZo1a8bs2bNp0KBB\nCmfIWosXL2bo0KF06tSJPXv23PD3GR8fz7hx40hMTMTHRzfnFZG0K18+9be5/+MP6N4djIFGjaBx\nY+fPihWdbSLZWWqDuAW+M8ZYYIK19iMAY8xrwBAgCmjt2VYW6AW04eogXhZIPqPusGdbWc/P124H\nKJkUrq21R40xSangZue6KogbYx7CGWmnfPnyqfyokh0YY3jjjTcYMmQIf/rTnyhZsiR//PFHikE8\nJiaGggULZlGVzuhyoUKFrtr2xBNPEBISQvv27alevTovvfQSgYGBnD59mjNnznDy5EmOHz9++XH6\n9GliYmKIjY0lNjaW2rVrM3jwYHr37k1QUFCG1Tpt2jSeeuoppk+fftV669fy8/Mjf/78REdHa5lH\nEUmX8uXhp59S3u/0aejQAXr2hHbtYO1a+OorGDECLly4EsobN4YGDeCay614vPsuPPaY058v3iW1\nQbyptTbSE4S/N8b8Yq390Vr7PPC8MWYU8BhO68l7wAhrbcI1K0Xc6O+t9hbbbyVVx3j+wvARQP36\n9VM6p2QzzZs3JyAggF27dlGyZEl+//13atSocctjoqOjsyyInzx5ksTERAJu8P1rr169iIyMZPz4\n8bz++uskJiZe7j0PDg6mePHiVKxYkeLFi1OkSBEKFChA/vz5CQoKYtWqVUycOJFXX32Vb7755rrJ\nrOnxzjvv8N5777FkyRKqV6+e4v5Fixbl1KlTCuIiki7ly0NKU01iYqBzZ2jfHv7xD2f0u3nzK68f\nPuwE87Vr4ZVXYNMmZ43y5KPm1ao5E0Rzu7ffhj59nN+7eJdU/euZNAnTWnvMGDMbaAj8mGyXacDX\nXOkBn+4J4cWALsaYeJxR61bJjimH02py2PNz8u1JUzj+SGo5McaUxpkUiueYkJscI7lIw4YNWbdu\n3eUR8ZRER0dTpkyZLKjMWSbxySefvOnShb6+vtSsWZOpU6em6bwDBgxgwIABjBs3jqZNmzJnzhwa\nNUrfFImoqChGjBjBjz/+yMqVK1P9zVFSEM+IvwRIxtMEe/F2KbWmnD8P99wDNWvCmDE3bkEpV855\neG4RwaVLsGOHM9K+ejW88w4cOQL1618dzkuXzpzP5M1CQuDQIQVxb5Rig6cxJp8xpkDSz0AHYIcx\nJvn9rXsAvwBYaytYa8OstWHATOARa+0cnImbHYwxRYwxRTznWeRpPYkxxjT2XKyHAHM9550HDPX8\nPPSa7UOMozEQpf7w3CkpiJcpU4YjR46kuH90dDQFChTIgsrgueeeY8yYMTd9fe3atdSuXTvd53/0\n0Uf56KOP6N69O9u2bUv1cYmJiWzatIkXX3yRu+66i8TERFatWpWm9q2yZcumau12cVVra21ta219\nz/Mx1tqa1trawAKcCfZAihPsG+EMvvzdc+2GKxPskybNd/JsT5pgHw4s8TyHqyfYP+Q5XnKxkBAn\niNsbfFd96RL06welSsGHH6a+D9zfH+rUgeHD4dNP4ZdfnPcYOdJZpeWjj6B6dWfU/N57naC+erUT\n+nO6pCAu3ic1I+IlgdmeUT0/YJq1dqExZpYxpirO6MpBPCum3Iy19pRnRGa9Z9MrSRM3geFcGV35\n1vMA+CcwwxhzP/Ab0M+z/RuckZVfcUZX/pKKzyE5UMOGDfn44495+OGH2bBhQ4r7R0VFZVlrys1G\nwmfPns3WrVtZvHgxDRs2vK336N69O2PHjqV9+/ZMnjyZjh07XrfPpUuX2LhxIytWrGDFihWsWrWK\n4OBgevbsyYIFC264Kk1KKlasyL59+26rdslammAv3iR/ficcHz8Oyaf2JCTAffeBjw9MmuSsN347\nihSBjh2dBzjB/9dfnVHztWth2jT4+We4666rR80rV85ZE0EVxL1XikHcWrsPqHWD7X1Sceyfr3n+\nP+B/N9hvA3BdY6q19iTQ9gbbLfBoSu8vOV+tWrWIiIigfPnyTJs2LcX99+zZc/mOmm7Ytm0bDz/8\nMA899BB33nkn27dvv+1z9u/fn9GjR9OtWzeGDBlCixYtMMawd+9eVqxYwfr166lYsSLNmzdn0KBB\njB8//rbbcypWrEhERMRt1y6ZRhPsxesltackBXFrYdgwJ5x//XXmTCw0xrlBUXi4E/jBGRHftMkJ\n5vPmwXPPwblzTiBPCucNG0J2nhJTrhwcPOh2FXIjmsIg2VpAQAChoaFERkayc+dOLly4QGBg4A33\njY2NJSIigjvuuCOLq7wiMjKSOnXq8Oqrr/Ldd9/x1ltv3fY5ExMTiYqKYuHChWzfvp2FCxfi6+tL\n+fLl+X//7//RtGnTDJ9UWaFCBRYtWpTyjuIWTbAXrxca6gTx+vWdEP7007Bzp3MDoZtcxjNFUBA0\nbeo8kkRGXpkI+vrrsHGjE2aTgnnjxk6bS3aZCBoSAitXul2F3Eg2+VdI5MYuXrxIZGQk69evJyoq\niuDgYBo3bkzr1q1p06YNDRo0wN8zrDJhwgS6du2aZT3iN5I3b162b9/Od999x/HjxylSpEjKB6Xg\nwIEDWGtp27Ytbdte9wVSpqhYsSJ79+7NkveStNMEe8kOkk/YfPll+OEH55E/v7t1AZQpA716OQ9w\n7ga6c+eVlpb333daPerWvbqlpWzZW5/XLWpN8V4K4pKtffnll4SEhHD69GlatGjBY489RkBAAEuX\nLuXRRx9l7969NGvWjLx58/Ljjz+ydOlSV+tt2rQpL7zwAiNGjGD//v106dLlts+5bds2atW6rnss\nU1WqVIkDBw6k6m6mkrU8k+p9rLUxySbYv2KMCbfW7vHsdtUE+2THfgossNbO8UzWfD3ZBM0OwCjP\nfJ8Yz0T5tTitLmM9+yRNsP8n10+wf8wYMx1n8qcm2MvlIP6vf8Hnn8OPPzo93d7Izw9q1XIew4Y5\n286cgfXrnWD+v//BQw85o+vJR83r1nV64d2mIO699H9QydZiYmKoXr06a9asYcCAAezcuZMXX3yR\nrl27AnDixAmWL19ObGws//3vfzNkBPp2+Pr6Mnz4cIYPH8706dMz5C6fkZGRhISEpLxjBgoMDKRU\nqVL89ttvWsLQ+2iCvWQL5cvDlClOoF2xAkqWdLuitClc2FnjvH1757m1sG/flVHzGTOcUfQ77rh6\n1Dw83JmMmpVKlYJTpyAuDm5wawtxkYK4ZGt58+YlT548xMTEEB4ezty5c696vVixYvTpk+K8YlcM\nGDAgQ85z5swZV26sEx4eTkREhIK4l9EEe8kuZs2CrVshIsIZsc3ujIFKlZzHoEHOtgsXYPNmJ5x/\n8w289BJERzuTPzt2hKeeyprafH2d9dOPHAFdsr2Lgrhka3nz5uXcuXM0atQIX19fVq1aRUJCAr63\nu+ZVNvLTTz8RExPDxIkTqVKlCuHh4RQvXvymyydmlPDwcPbs2UOnTp1S3llEJJl585x+8Lx5naUC\nc6rAQLj7bueR5PffnRHz06eztpZy5Zz2FAVx76IgLtlaUFDQ5SC+Z88eSpYsydatW6lbt67bpWUZ\nX19f8uXLx9KlS5kwYQIREREkJiYSHh5OXFwc0dHRHDhwIMPfNymIi4ikxYYN8MADzhKF3bo5K5R4\n6yTHzFCqlHPX0KwWEgKHD6e8n2QtBXHJ1vLmzcv58+dp1KgRb7/9Nm3atGHp0qW5KogXKFCAtm3b\nMnTo0MvbTp48yZ49e+jatSunTp26xdHpFxERwUcffcTXX3+Nn5/f5UeePHkuP0qXLp2q9d1FJPdY\ntcq5s2WDBnDnnc4dMHNTEHeLJmx6JwVxcdXMmTOZO3curVq1olWrVlSsWDFNLRVJI+INGzZkw4YN\nPPzww0ycOJFnnnkmE6v2LrGxseTLl++qbcHBwQQHB7N9+3bsje4hnQGGDBnCN998w3fffUdUVBRH\njhwhMjKSCxcuYK0lMTERPz8/oqKiKFSoUKbUICLZT2wsJN3g+I47nDtbZtHKq7laSAjs3u12FXIt\nBXFx1eLFi7lw4QJLly7lpZdewsfH53IoT00wT+oRDw4OpmTJkhQuXJiVK1dy6dKly+uH53Rnz54l\n/00W3r3dO2he6/fff2fz5s1s3ryZTz/9lEOHDlG5cmWCgoIICQmhTJky5MmTh4SEBOLj40lISKBF\nixbUrl07Q+sQkewrNhaSbudwxx3OiLhkvpAQWLzY7SrkWgri4qoTJ07Qv39/+vXrh7WWX3/9lWXL\nlrF06VJefPFF/Pz8rgrmFSpUuCqYJ7WmgHOHySlTplC5cmXWr19PkyZN3PpYWers2bPXjYhnlP37\n97NkyRKWLFnC8uXLiYuLo06dOtSuXZs//elP/Oc//2HBggU0btw40yeHikjOEBvrrOABTmvK/Pnu\n1pNbqDXFOymIi6tOnDhBsWLFADDGEB4eTnh4OA8++OBVwXzJkiW88MIL1wXzIkWK0K+fs1RxUFAQ\ne/bsudwnnpuCeN4MumPEpUuXWLhwIfPmzWPJkiWcO3eOtm3b0q5dO1577bXr/iK0ePFiLl68mCEh\nfOXKlYSEhBAaGnrb5xIR7xUbe+XumblxRDw+Hu67z7mJUVZSEPdOWbykvMjVkgfxayUF8wcffJCp\nU6dy5MgRlixZQvPmzVmyZAnNmjWjTp06HDlyhP/973+cOnWK7du3U7NmTSZOnJjFn8Q9cXFxBAYG\npvt4ay0bN27kySefpGzZsrz11ltUr16d+fPnc/ToUaZOncr9999/wzahBg0asHr16tv9CCxcuJBe\nvXplyuouIuJdkgfxkBDnhj7R0e7WlJWio2Hhwqx/3+LFISYGzp3L+veWm9OIuLjqVkH8WjcaMd+z\nZw/Lli3j+++/JzIykgIFCtC+fXuGDRuWayYJXrhwIV1B/MiRI0ydOpXJkydz7tw5hgwZwpo1a6hU\nqVKqz9G6dWvGjRvHqFGj0vz+SWbNmsXw4cOZO3durvkWQyQ3Sx7EfXygalVnVLxhQ3fryipRUeDG\n/5p8fJzVaQ4dcibLHjrkPA4fvvLzmTMwZw4EBWV9fbmVgri4xlrLyZMnUx3Er2WMoUqVKlSpUoU+\nffowf/58/Pz8iIyMpHXr1ixatIh77703g6v2PnFxcQSk8p7FiYmJLFy4kP/85z/89NNP9OnTh/Hj\nx9O0adN0tZe0aNGCwYMHExsbe9MJo7eq5f333+fNN99k0aJF1KlTJ83vLyLZz9mzV4I4XGlPyU1B\nPGnVmKyUmOiE7mrVICHhynZjoG5daNcOOnVSCM9qCuLimjNnzpAvX74MWd3k0KFDFC1aFGstL7/8\nMt27d2f+/Pm5IohfuHAhxSAeFRXFxIkTGTduHAULFuTxxx9n1qxZBN3mFbdIkSLcc889vPbaa7zx\nxhupPu7nn3/mgQcewMfHhxUrVlA5J99aT0SuknxEHJwJmz//7F49WS06OmtHxK2FjRth3DjnZkKn\nTzttKu3bO+G7dWtI53iYZAD1iItr0tKWkpJDhw5RsGBBAgMD2bFjBx07duSbb74hPj4+Q87vzc6f\nP3/TVVN27drFI488QlhYGGvXrmXSpEls2LCBP//5z7cdwpO89dZb/Pe//yUiIiJV+0+ePJnmzZsz\ncOBAli9frhAukstcG8Rz24TNrGpN2bcPXn3V+YtO//5OP/7ChbBnj/PahAnQr59CuNs0Ii6uycgg\nfvjwYfLkyUP+/PnZuXMnmzdvJiwsjFWrVtGyZcsMeQ9vlJiYeF2PeEJCAgsWLGDs2LHs3LmThx56\niJ07d2b4muJJypQpw6hRo/jLX/7Cd999d8ulFJcsWcLIkSNZsWIFd955Z6bUIyLeLT4efH2vPM9t\nI+KZGcSPH4cZM2DqVPj1V+cOphMnQuPGTguKeB8FcXFNRo+Ig9M3HhQUxOzZs+nevTtTp07N0UE8\nqS3Fx8eHkydP8sknn/DBBx9QqlQpHn/8cfr27Zvq/vHb8dRTT/HVV19Ro0YN5s+fz/bt2zly5Ajn\nzp0jLi6OixcvcuTIEWbOnMn06dOvCuHR0dFcuHCBEiVKZHqdIuK+fPmcPvEklSvDgQNw8SLkyeNa\nWVkmo4P42bMwbx5MmQKrVkGXLvDCC07rSS65r122piAursnoEfG4uLjLd9SMiIigXr16vPzyy/Tq\n1YuOHTvi45PzOrEiIyOJi4ujVq1a7N+/n169evHll1/SoEGDy/tcvHiRXbt2sX37dnbu3MmBAwc4\ncuQIhw8f5tSpU5fvgBkcHEzNmjWpVasWFSpUoFixYjRv3jzFgBwfH8+iRYvYs2cPhQoVomHDhnTt\n2pUyZcqQP39+AgICKFiwIEWLFmXJkiV0794dcCbrTpo0ieHDhxMaGsovuem7aZFcLG/eq5fQCwyE\nJk1g2jT4859dKyvLREff/mTN+HhYssQJ3/Pnw913w6BB8MUXV7f9iPdTEBfXZPSIeExMDNZazp07\nx/r161m6dCnPPfccAwYMIDY2liJFilCxYkVWrlxJnmw+7HLx4kVmzZrF22+/jTGGu+++mxkzZlC1\nalUATp8+zaJFi5gzZw6LFi2ibNmy1KhRg2rVqtGjRw/Kli1LuXLlCA4Oxs/PD19fX44dO8bWrVvZ\ntm0bEydOJCIiAmst9erVY+jQobRp04YyZcoQHR3NoUOH2Lt3L/Pnz2fu3LlUrFiRiRMnUqZMGVq1\nasW///1vSifdOs9j0qRJtGrVCj8/P44dO8ZDDz3E/v37+fzzz3n22Wfd+DWKiAuSRsTj4+Hf/4an\nn4aXX4YhQ2DgwJw/Kp7eEXFrYcMGJ3x/8QWULw+DB8Pbb0PJkhlfp2QNBXFxzYkTJwgMDMRae9t3\nZjx06BAnTpwgPj4eay0AGzduJCEhgcTERAAqVKhAfHw8jz/+OBMmTLjt+t1w6NAhJkyYwMcff0y1\natXo1asXu3bt4oMPPmDz5s288sorfPvtt+zcuZMWLVrQs2dP/v3vf1MyFVfp0NBQQkND6dGjB6VL\nl2bVqlWMGzeO+fPnM3XqVJ588knOnDlD3rx5L98Bs2PHjrz44ouEhoZirWXLli2Eh4dzzz33sG7d\nuqvOv3r1apo2bcqqVavo378/gwcP5osvvgBgwIABxMfH4+enS5JITpcUxH194b33oEcPaN7cWU/8\nf/+Dhx92u8LMFRXlhOjU2rvX6fmeMsUJ44MGwY8/QpUqmVejZB39X09cc9dddzFy5EgmTZpEy5Yt\nadmyJS1atKBKlSppCubWWg4fPoy/vz+BgYGXe6InT55My5Yt+f3330lISGDdunVERUVRqVIlRo4c\nSYUKFTLro2Uoay1Lly5l3LhxLFu2jMGDB/PDDz9QpUoVXn/9dXx8fChXrhwFChSgW7dujB49mmbN\nmt1Wb3jSuuBBQUHce++93Hvvvaxbt45hw4axadOmq/75nD17lgEDBrBhwwbi4+Np374933///XXn\nXLVqFYUKFWL06NFMnDiRLl26XH6tVKlSHDx4ME03ExKR7CkpiBvjBPAVKyA83Fnho3dvpz3lNm4W\n7PVSs3zhsWPOqPfUqbB/v7PqyWefOWuta9JlzqIgLlkmNjaWTZs2UapUKSpXrszQoUMZMmQIe/bs\nYT0xeowAACAASURBVPny5SxfvpxXXnmF+Ph4WrRocTmc33XXXbcM5idPniRPnjwUL14cHx8ffHx8\nMMYwf/58Zs+ezcCBA2nXrh3GGAoXLszQoUP55JNPGD16dBZ++rSLiopi0qRJfPDBB/j7+/Poo48y\nefJkEhISeOutt5g8eTJ58uQhb968LF++nPDw8Ax77xvdoCcmJobChQtjjOHw4cPs3LmTVatW8cYb\nb1C6dGkGDhzI66+/DkCxYsU4evTo5faUgwcPsmvXLnx8fFi9evV1gTs8PJxff/1VQVwkF0g+WbN5\nc2d0969/hQYNnBvLTJgATz7pbo2Z6WY39Dl71rmr5dSpsHo1dOsGf/+7s9a3Jl3mXDlv9pp4raNH\nj/Lcc8/RsWNHgoOD6dKlC2+88Qa///47gwcPZsqUKfz222+sXr2azp07s379erp3706JEiXo06cP\n77//Plu3br3capLk8OHDFC1alEKF/j97Zx5XU/7G8U+7clPptmilpBIlbaQa2bMVGvv6G2MbTGM3\nhhHDGMTIvsxQRhrL0BiENI01klG0YEYbkva9bt37/P746urWbSPLcN6v13nVPcv3fM/t9Tp9znOe\n5/OoQVVVFcrKymjdujXy8vJgbm6OZ8+eQVVVVbz/mDFj8Ouvv4pTWN437t69ixkzZqBt27a4du0a\n9uzZg9jYWMyYMQNRUVHo3LkzMjIycPbsWWzduhX29vbNKsIB6d06CwsL0erFf4/Y2Fj4+fkhKSkJ\nHTp0wOPHjyESiSAUCiErKwtXV1dcunRJfGxMTAwcHBxw48YNqWLb1NQUCR+TfxkHx0dMy5YvizXd\n3FhEvIpVq4B16yRdVT40queIV1YCZ8+yXG99fSbCx48HnjxhqSgeHpwI/9DhhDjHW8PMzAxXrlxB\nUlISEhMT8fnnnyM7OxuLFi2ClpYWvLy8EB4ejrZt22Ly5MnYv38/Hj16hNu3b2PYsGG4e/cuPv30\nU/D5fHh6emLTpk24desWkpOTxe4cioqKYncUIyMjxMbGonXr1sjIyBDPw97eHiKRCNHR0e/qq6iF\nQCBAcHAw3NzcMGDAAOjp6SE+Pl68TkZGBgEBARg9ejT27duHffv2oVOnTigpKanXt7sp9OrVC/Hx\n8QCYP7lcdaNfsAi92ov/HgMHDsT58+dx8OBBxMXFIS8vDyUlJTA3N0d0dDRcXFxw/fp18bFVD1c1\nmwgRES5cuIBz587hl19+aZbr4ODgeL9RUXkptC0tgbw84OlT9tnGhonzbdve3fzeNAUFrKnR3LlM\nfPv6Mp/vBw+AM2dYwWoz3dbfCKtWsfnViIlxvCJcagrHO0FHRwfDhg3DsGHDALBUiKqCQJFIhLlz\n52LKlClQUlKCoaEhxo8fj/HjxwNgln2XLl3CpUuXsG/fPty/fx8aGhoAgJYtW6KiogJEBFlZWSgr\nK0NZWRnJycnic8vIyGDSpEk4cOAA7O3t3/q1V+fJkyfYvXs39u7dC0tLS8ydOxeenp5QqBECOXLk\nCP73v//h6tWr6NatGwoLC9G9e3csWrSo2Tpkpqeni1OAqiLb1bly5QrOnj2LHj16oGvXrujatSsc\nHBxgaWmJVq1aYdu2bViwYAFCQkJga2uLy9XCXJcvX8aqVavEn4kI58+fh6+vL3Jzc+Ht7Y3Y2Nhm\nuQ4ODo73m+qpKbKygIsLi4qPGsXWrVzJ1o0bBxgYvLNpvjEKCoDZs4ERI5jv93+lufDz58CcOcCJ\nE6yw9gN0BH4ncF8jx3sBj8fD9OnTcffuXbFTh5mZGfbs2YOKigqJffX09DB69Gjs2LED8fHxuH79\nOtzd3fH48WPcvn0bqampSE9PR05ODgwNDbFhw4ZaNomTJk3C4cOHIRAI3uZlAnhZfDlixAh07twZ\nOTk5uHjxIsLDw+Ht7V1LhD9+/Bhffvkl9PT00L17dyQnJ0NBQQEPHz5EaWkpVFRUXmsuN2/exOzZ\ns/HgwQM8e/YMgPSIuLKyMjw9PfHdd9+hXbt2CAsLw/Dhw6Gurg43NzfMnz8fBQUFSExMRLt27cQP\nP6Wlpbhz5w66desmPqeNjQ1mz56NuXPn4t69e5g5cybnI87B8ZFQs6FPVcFmFZaWgFDIcqQ/NFJT\nWfMia2tgw4b/hggnAg4fZnMmYpHwn39+17P6cOAi4hzvFTIyMnB3d4e7uzsiIyOxYsUKrFu3Dt9+\n+y3GjRsn1d7O0dERR48eBQCcPXsWGzZsQHR0NAoKClBUVAQnJyfMmjVLwibR2NgYHTt2xLlz58QN\nZt40+fn5CAwMxI4dOyAnJ4cvvvgCBw4ckMhfl0Z6ejoWL16Mr776CgBroKOkpASBQICioiIoKys3\n2fovJSUFv/zyCwIDAyESiTBx4kQYGxtDT08PgPSIuEAggJ2dnfjvU0Vubi6io6MRFRUlXhISEvDg\nwQOUlpYiKioKVlZW4hSarKws/Pvvv0hLS0Pr1q0BsL9HZmYmiouLmy3VhoOD4/1EmhAPDJTcx9yc\ntb1PSQGMjd/u/JqbnBzg2DGW/x0ZydaNHg2kpTFR+yaXKuFc13L7NksF6tqVvX2oGeV++hSYOZNZ\nKJ48yd5StG3LCms5mgdOiHO8t3Tr1g3nz5/HpUuXsGLFCnz11Vfo0aMHfvvtt1pR4yo8PDzg4eEB\nAMjJyRG7fMjJySE1NRXG1e7oY8aMQXBw8BsX4lVR/l9//RX9+vXD7t274erq2miLRgcHB+jo6GDR\nokUgIvD5fMjIyEBRURFFRUVITk7GoEGDcO7cuXrHefToEYKCghAWFoZ79+5h1KhRCAgIgJOTE2Rk\nZHD48GFUVlYCkB4Rr6sBk4aGBvr06YM+ffpg6dKlEAqFuHbtGvr374/c3FxcvnwZrq6u4v0vX76M\nTz75RCzCAUBOTg5aWlrIzMzkhDgHxwdO9WJNgInApCQgNxd4kWWIJ0+Yk8rixUBw8LuZ5+tQXMw6\nXgYFAX/9BQwYwJrumJsDd+8C8+cz0fumFhmZxu0XE8McWtLS2AODiQmL0rdvz5xdtm9nQvzIERYV\nz8sDvv/+XX+7HxacEOd473Fzc0NERAS+/vprbNiwAW3atMGaNWswefLker2yqws9R0dH3Lx5U0KI\ne3t7Y+nSpcjNzRXnmDcXAoEAv/32G7Zv346kpCRMmzYN8fHxtbpNNhZDQ0NERESgrKwM6urqANj1\npaWlISwsTOxQcvr0afTt2xf3799HeHg4BAIBYmNjkZSUhOjoaKirq2PHjh0YOHBgre9OXl4eQqEQ\ngPSIeHZ2NjQ1NSXWJSYm4vbt27Czs4OZmRlkZWUhJycHV1dXODg4ICEhAZcvX8aMah06bty4ge7d\nu0uMk5WVhZycHGhpab3S98PBwdH8VFYCffsC/fsDEyawwsLmoHqxJsBcQZycWL704MFAaSmQlcV8\nxTt2ZOt79Giec79JKiqACxeY+P7jD9Z2fuxY5n6yYweL+h8/zq7lyZN3PdvaFBcDjx4B//zDlrQ0\n4Px5oEsXVly6ZAm7xjFj3vVMPyy4HHGO/wxr165FXFwcFBQUsGnTJpiYmMDPzw+FhYUNHlslxKuj\nra2NsrIyTJw4sdnmmJaWJu40uWfPHvj4+CApKQkrVqx4ZREOsJQdZ2dn9OrVS7yuX79+CAsLg7W1\nNbp27QoAGDVqFPLz89G7d2/Ex8cjIyMDd+/ehYWFBa5evYr8/HyEhoZi9+7dYtFdhby8fL0R8ezs\nbImHG4C9dThx4gQGDhwINTU1uLq64ssvv0RgYCB0dXURExOD69evw8XFRXxMy5YtUVZWJv6cl5eH\nfv36YebMmVw0nIPjPUJODli7lkWrO3dmgvzwYSaUX4eaqSmAZJ54UhLrPKmqyqwMfXzeX4cOkQi4\ncgWYNQvQ0wPWrGEC/MEDZks4YQJw4ACwbx8QFgYoKr5ae/u3QcuW7O88bBiwcCHg789EOMDy2du0\nAT79lP1dOJoPTohz/Kfo0KEDoqOjoaSkBHd3d9y8eRMmJiZYsGAB7t27V+dx0oQ4AEyZMgUXL15E\namrqK8+psLAQgYGB6N+/P2xsbJCXl4fw8HBxQWZdaTSvy/Dhw1FWVoZRL6wGiAilpaUoLi6GoqIi\ndu/ejY0bN6J///4wMTFB165dcerUKXTu3BnJycm1It5ycnISQrzm9vLycrSo0e7O2dkZR48exb//\n/ovU1FT4+vrC0NAQoaGhiIiIwM6dO6Gvry+R0lK9kBMADhw4gA4dOuCHH35ozq+Hg4PjNZGRYaJy\n924WwZ08GQgIYJHxadNYSsOrtGNo2RLIyGDC+9w54LffgLIy9hNg+chV7QbGjGEPBAcPNttlNQt3\n7wJLl7JUjunTWX71zZssev/FF4C2Nttv3z7Azw+4eJEJ9eoe4v8VHj8Gtm5lc5869V3P5sODS03h\n+M+hp6eHS5cuoW/fvnB1dcW1a9ewf/9+DBgwAG3atMGkSZPQs2dPdOrUSXyMtbU1Ll++DIFAAEVF\nRfH6oUOHIjw8HF5eXrh48WKjU1RKSkoQFhaGw4cP4+zZs3B1dcWUKVNw4sSJ13IxaQoDBgxATk6O\nuEhTIBBATk4ORCQh/p2dneHj4wMNDQ14e3ujd+/eUsdrKDVFVla23iZIGhoa6NWrlzhqf/bsWfj4\n+EhEw6vmWb2w9K+//sLQoUMbnTPPwcHx9lFWZqJ4zBgmzH75heVwi0TApEks8mtk1Lix2rYFlJSY\nkFVRYcJcSQng8Ziwt7VlFoYAy2P+8Udm9TdiBNvnXZGczN4IBAUxC8IxY4Dff2dRZGm3rzlzWJv6\nq1dfFpz+F4X40qUsZSg6mvmdczQvnBDn+E+irq6O8+fPw93dHXp6eli7di1Wr16N33//HfPmzcOc\nOXPg5uYGT09PeHh4wMLCAq1bt0ZWVpbYGQQAlJSUoKurCycnJzg4OOD777+Hl5dXrSi2UChETEwM\nLl++jPPnz+Py5cvo2rUrRo4cia1bt0otYnwTVFZWwsPDA5qamrC0tIRQKMTSpUuxe/dutGzZEkSE\nxYsXS6TreHp6QlFREYGBgVi8eDH09fXh6OgIBwcHGBsbIz8/H7m5uXj69CnWr18PkUiEa9eu1Upd\nkZWVrdXVtD5MTEzw5MkTiUJNADAwMMDDhw8BABEREbh58yYCa1omcHBwvLcYGLB84cWLWRT4wAEm\nnm1tWdR8+HAmsOs7XsoLSont1f3Du3UD3N2BH35geeNvk+fPgaNHmfh+8IClZuzcCTg7N+yjXV7O\nRHe1fzkoKPhvCfGoKBbNHz2aRcO5eEnzI/O+tvlubuzt7enWrVvvehoczUxaWhq6deuGnTt3YsiQ\nIXB3d4dAIIC7uztMTU0RGRmJc+fOoaSkBAKBAB4eHvjkk09gYmICTU1NLFy4UBwxDg0NxZYtW5CU\nlAQrKytYW1uDiBAbG4s7d+7A0NAQbm5u6NWrF/r16ycumnybZGRkwMLCAtu2bUN8fDx++ukn7Ny5\nEyYmJsjMzMSVK1dARNizZw/S09NrHV9ZWYm4uDjcvHkTN2/eRHp6OtTV1aGuro6UlBTY29vD2toa\nwcHB6NKlC5YuXSo+tnPnzjh06BCsra0bNdeysjIoKyvj7t27Em8nBAIBLC0tMWbMGAQEBGDPnj1i\npxuOupGRkYkmonfbgeotwt2z/1uUlbHo8IEDwPXrTIxPmgQ4OgI1MtpeibQ0lq98+/abtzMsLGRW\nfUFB7FoGD2ZFl337Nq3dPBEwcSIgLw/s38/WBQaygs53nWpTUMBy/XV06t6HiOXuT5nC3n4Ihexa\nOBpHo+/ZRPRRLHZ2dsTxYRIZGUna2toUFhZGWlpaZGFhQdOmTSNlZWUaNGgQ3bp1i9LS0sjV1ZXG\njBlDU6dOpd69e5O5uTkBIC0tLdLU1KQ2bdqQqakpdenShbS0tMjNzY22bNlCFy9epNzc3Hd9mURE\ndO/ePbKwsBB/NjExoYcPH0rsExUVRV26dHmt8yxbtoxWrVolsa5r164UFRXV6DHy8vJIXl6eSktL\na22LiYmhqVOn0pEjR15rnh8TAG7Re3AvfVsLd8/+7/LkCdH69US6ukSyskSbNxMVF7/+uN9+SzR6\n9OuPI42yMqKTJ4lGjiRq1Ypo8GCiw4eJiopeb9zCQiILC6L9+9lnf3+iWbNee7qvja8vkbw8kYcH\n0bZtRElJtfc5coSoSxeiysq3Pr0Pgsbes7lnG47/PE5OTpg7dy58fHygpqaGiRMnYsmSJQgJCUHb\ntm3h6uqK7777TuzWERQUJD5WWlHi+4xAIICNjY34szTrxdjYWNy9exejRo1C//790a9fPxg0sU80\nj8dDbm6uxDplZWWUNsEuIT09He3atatV4AmwnP29e/c2aU4cHBz/DfT0mOvGvHnMaWXdOua+Mm0a\na+2uq/tq4y5axHy4m8vOUCgELl1ike/ffgM6dWKR7x07gBpOra8Mj8dSW9zdAXv7xuWIBwcDiYkv\n8+TfBF27Ar16sVSi06cBX19AS4sVlg4YwN5wLF7Mik1rGGhxNDP/HQXCwVEPS5YsQUlJCXJycnD8\n+HFs374d6urqOH36NBwdHREbG4sePXpAWVlZ4rj3TYRXPSHXha2tLYJfdLcQCoXIz89HRkYGysvL\nxfsYGRmhW7duGDBgAM6fP48uXbqgU6dOmD9/Pk6ePIn79+8jPj4eSUlJACDV/lFVVRVFRUUS61RU\nVGoJcSJCbm4uHjx4gCc1jHEfP34MQ0PDpn0Bb5js7Oxa3++QIUPw7NmzdzQjDo4PFzk5ZuFnbQ30\n6QNkZ7P29f/7H1CPyVWdqKgAmzYxr+tXhYgVHc6fz4pL580DOnQA7txhjXemT28+EV5Fp07M/u/T\nT4HMzPqFuEAALFsG9OzZvHOoiaws+/uMHMmccJ49Y+kzFhZsu78/K0Kt5pjL8YZ4v1QIB8crIicn\nh5UrV6K4uBhGRkaYM2cO7t+/DwcHByxevBiPHz+Gvb09MjMz3/VU66W0tFSi5T0RYeTIkVKLJDMy\nMiAjI4OhQ4eiVatWMDIyQkxMDIqKiqCpqYkpU6YgODgYGRkZ+Pnnn6Guro7p06ejW7duGDFiBLZv\n347w8HC0atUKXl5e+O6778QFmjwer5ZAz8jIwOrVq9G7d29YW1ujTZs2UFJSQtu2bTFw4EAcO3ZM\nYv/Hjx83ORL/JhEIBOjbty/OnDkjsf7GjRvv3QMZB8eHgoICa+8eFwe0a8caxZiavmwUdP580ywQ\nP/2UObQ0lYcPgVWr2IPAyJFM1F+4APz9N4vev+mYweTJrOj0zBnWsbIu9uxhDwY1hTgRm39BQfPM\nR1ZW0ptdVpbl87dtywpU169nDw8cbx4uNYXjg2HChAkoLCzEpk2bAAA2NjbYsWMHhEIhoqOjYWRk\nJOFf/a4pKyuDkpKShG1ffn4+eNX8ucrLyxESEiJVKJaVlcHQ0BD//PMPKisrkZaWBl1dXdy7d0+i\nMY6cnBwcHR3h6OgIVVVVJCUlYcuWLQCAU6dOAWCpIhs3bsTkyZNhYGAgNSKupaUFU1NTjBgxAlpa\nWtDW1gafz6+zu6m/v79E4553ja+vL/T19TFw4ECJ9SUlJbXelHBwcDQfrVoxAdq9O4tCL1sGLFjA\nUkLmz2dOHMOHs9+bs1lMejqzDwwKAlJTgVGjWPTX0fHduH9s386i4aGhrG18TQoLWUOgs2drb9u3\nj3XrXLaseeZSU4hX59tv2cNOhw7Ncy6O+uHCQBwfFDNmzEBhYSEmTJiArKwsZGdnQ0dHB6qqqhCJ\nREhNTW2SBV9NhEKhRBrIq7Jjxw6oqanB2toau3fvFovevLw8qFV7b1lcXFxnt8lHjx5BXl5e3Jyn\nXbt2UFZWRlFRkdRjsrKykJCQgPPnz2Ps2LEYOHCguPV8YGAgioqKUFFRAUB6RLxTp04wNzdHv379\nYGtrC319/TpFeEJCAu7fvw9zc/OmfzlvgKNHj+KXX37Bvn37JB58iFgTJE6Ic3C8WfT1WS7y3Lks\nL1tJiblxxMayXOi1a1l6SBVPnrAulIsXA7duNb6zZl4e8PPPLBWmY0cgJoaJ28ePgS1bACend2fB\np6LC8uevXGFpIDUcYrFpE9C798tullU8fQp8/XXz5mvXJcTv3QOOHweWL2+e83A0DBcR5/igkJWV\nRVRUFBwcHNC5c2ccOXIEy5cvh76+PrKzsyEQCBAaGlorKtoY9uzZg6VLl6K8vBw9evTAsGHDxNHh\npkBEWL16NaKjo5GRkYFt27bh66+/Rvv27fHll19K2CKWlJTU2SAoKSkJWVlZcHFxQU5ODszMzBAZ\nGYn09HRkZmbCz88PiYmJSEhIQGJiIiorK2FpaYmePXvC1dUV6urqEAgEyMzMRO/evcHj8aD9oh0c\nj8erFRHX1NREdnZ2o65x3bp1GDRo0HuR8hEWFoZZs2bhwoUL0Knh1VVSUgIlJSWJBkMcHBxvhs6d\nWXT600+BiAiWJiIjAyQksOY4VS0H8vOZZSCPxyLZEyYAOTmsiLBdO3YcEbPfKylhP//5hwn5hAQm\nZmfMAAYNYo2I3idKS5kH+U8/AZs3s/SP/v1ZOoi/P3voqA4R69Q5YwbLtW8u6hLiy5YB33wDtG7d\nfOfiqB/uvw/HB4eRkRF27NiBefPmITY2Fpqamnj48CFsbW0BQBz1bQpPnz7F4sWLcf36dejp6SEs\nLAxHjx7FvHnz0KJFC8jKyiI7Oxuenp6YNWsWevXqVae4y83NRWlpKTp16oROnTqhd+/eSE5ORrt2\n7TB79myUlJRg48aNmDZtGvLy8ur0K+fz+fjkk09w8uRJFBUV4Z9//oGysjJu376NuLg4tG3bFnZ2\ndhg3bhwsLS2hra3d6O6VKioqKCkpkVinpqaG58+fN3hsUlIS/vjjDxw8eBBr1qxp1PneFGfOnMGk\nSZNw/PhxdKkZZgJQUFCAVvUlbHJwcDQrffqw/OOBA5lHt6oqE6AREWx7SQkT4a6uwCefMJ/rw4eB\npCSWsvH99yzSra7ORLaKCvspFLKUk9BQtu19paQEcHNjqTknT7I3BMbGAJ8PjBvHHjSqc/w4c1B5\nUaPfbNQlxLdte3VXG45XgxPiHB8kI0aMQGRkJM6cOYPt27dj7dq1yMnJgbKysrirY1P4999/0bFj\nR1i8KCkfPnw4hg8fDn9/f1y7dg3r1q1Dp06d4OzsjGXLliElJQXDhw9H9+7dMWTIELSuFl4oLi6W\nyAMHAENDQ8jIyODff/+Fubk5zp07h9WrV4PP50NOTk6qTWFhYaG4sJPH44mFZvv27fHJJ59g/vz5\nTb7OKuTk5Gql8Kiqqkp1WKnJhg0bMH36dFhZWSE1NfWV5/C6BAUFYcKECdi8eTPc3Nyk7lP9O+Tg\n4Hg7TJoEpKQwwT1yJBPdlpbMMWT4cFbM+eOPQEgIS8cAmECdNYst/2XKyljXUBkZYNgw9h1s386s\nGe/fl9w3J4cJ9aNHWSpPc1KXEH/PjK4+Ct79e2MOjjfE+vXr4eTkBAMDA0yYMAGrVq2ClpYWvvnm\nmzqPSUtLw5EjR/D777/j/PnzuHTpEqKionDt2jWUlZUhNTUVz58/R0FBAQQCAVJSUnDkyBFYWVlB\nT08PX331FaKiohAZGYn27dvj1KlTaNeuHYYMGYKgoCAUFRVJTTcRiUSQk5ODhoYGeDwedu/ejX//\n/ReZmZkQCoUwMjKCk5MTvvnmG0RERKC8vBxFRUVSRWR+fr5EnvmrIK2dfWOEeFpaGn799Vf4+PhA\nT08PGRkZqKysxI0bNzDrLf4H3bdvH+bPnw9/f3+sWbMG+6va2tXgzp07700eOwfHx8Ty5cwT/Ouv\nmWVgZSWLCLdsycS3rCxgZsYi4R8KAgGL3Ovrv1ynoAD4+ABFRbWj4QsWsAeT5vBMr6KiguWcJyU1\nPu/+XVJUBNRoafHBwUXEOT5YZGRksGfPHnz22Wfg8XgQiUTo1KkTMjMzsW3bNsyePbvWMenp6Th2\n7BjKyspQVlaG0tJSlJaWIikpCbm5uXB2doZAIEBpaSnKy8tRUVEBHo+HgoICibQPExMTLFiwAABL\nfwgJCcEvv/yCWbNmwcnJSWwTWEX1xkJCoRBycnJiR5Lr169DTU0N165dQ1hYGBYtWoTExES0adMG\nGhoa2LVrF4yMjGBkZAR9ff1aBZ91UVxcLC5oLSwshIaGBrS1taGtrf3KQtzX1xfTp08X55q3bNkS\nBQUFkJeXR2RkZINzel2q8u8DAgJw6dIlmJmZoU+fPhg8eDASEhLw/fffo7y8HLt27cKpU6dQUFCA\ncePGvfF5cXBwSCIjA/zwA/P0njwZsLJi60+detlG3dCQtbYnencFls1JTg4rtpSWf62oKPk5LAy4\nePHV/NaruHSJFahmZrIc9OfPmTMLnw9oawNDhrz62G8DkQiYOJGlIn333buezZuDE+IcHzTy8vII\nCAgAn89HSkoKcnJykJ2djWXLlmHkyJFiwViFo6Mjjhw5Umuc33//HXv37hXb/VUhFAohFArrzb1u\n1aoVJkyYIHZy2bx5M27fvl1rnCohXllZCbkXpfFVjh5KSkpwd3eHu7s71qxZg5ycHMycOROZmZm4\nffs2Tp48iZSUFDx58gQlJSXQ1dXFp59+Kh4/MzMT0dHRiI6Oxq1bt3Dr1i1kZWWBz+dDU1MTqqqq\nyMvLQ0ZGBkpLS6GlpVUrUszj8VBcXFzndcbFxSEkJAQPHjwQr6uK9Ovo6CAjI6POY5uDyspKfPHF\nF7h16xauXbsmLsw0NzdHZGQkRowYgc6dOyMvLw/du3fHV199hcmTJ0O/eniKg4PjrWFgwPKfL10C\nRo8GXFyYUKxKw1BTYwI8P//9zvtuLNnZ7HpqZBnWoriYdSHdufP17Bz19VkRrJYWE97a2uzcddXQ\nHz3KvMrv3n31czYnq1YBGRmsRuBDhhPiHB8Ffn5+Ep99fX3h5OSEX375BT0a8d6vtLRUaqt2tQvD\nkwAAIABJREFUOTk5sWiWRlVUuUpk8/l8+Pj4YOfOnbX2qxpHKBSKCz3rEvitW7eGsbExunbtisWL\nF0tsi4mJgbe3N7y8vCAvL4+oqCjk5+eja9eusLOzw+jRo7FhwwaYmppKHT8vLw8JCQno2LGjxHoV\nFZU6hbhIJMK0adPg6+srkcsuLy+PiooKaGtr4/nz5xCJRCCier+zV6GyshITJ05EZmYmIiIiaqXs\naGpq4vz58+jQoQO++eYbcZqMhYXFe9V0iIPjY8TNjbmeLF/OnFX8/QFvbyZaDQxYVPxDEeIiEWvK\n06ZN3YJ4xQrmuf4K5l4SmJqypbEEBLCo/fvA8ePMhvLmzebPj3/f4HLEOT5Kvv32W2zevBkjRozA\n119/DYFAUO/+0gosG6KgoAAjR46EsrIyxo4di/379+Px48fg8/koKyuTSPOoHhGvSk0BACUlpTqb\n4tTlf21jY4MrV64gPDwcBgYGCAsLQ05ODsLDw7FhwwaMGjUK7du3r1Pkq6uro3v37rXSW8rKyqQ+\njADA8uXLoaioKPYlr0JVVRVZWVmIioqCgoICPvnkE3Tv3r2Ob6xhiouLpf6tZs+ejezsbJw6darO\n4ktFRUV4eXlJ2DLWd00cHBxvDxUVwM8POHGCCVFvb9Z2vSo95X3k8GHmd95YsrJYVLpvXxbptrWt\nbVcYFQUcOsSKVd8mT58Cly+znPV3TWwss2s8ceLjcHDhhDjHR4uXlxdiYmJw7949ODo6Ijo6us59\ni4qKmiTET506hU6dOqFVq1bg8Xjo3LkzQkND0aVLFyxZsgSWlpY4deoUDh48iLy8PHGkWCAQoLKy\nUhwRNzQ0RGJiotRz1BWlBwAdHR0cOXIE586dQ/v27ZvFzzs7Oxuampq11lc1ywkODq51nry8PNja\n2mLu3Lno2rUrZsyYgfDw8Cafu7CwEPPmzYO2tjasrKxw6dIl8baQkBBcuHABx44da1BUOzg44ObN\nmxLz4+wLOTjeH7p1Y23nLSwAGxvmMvIOzZfqJTgYiIxknuCPHze8f24u4OHB9k1PZ+3sq0esBQLg\ns8/YA0kT21O8Nlu3MgebgoK3e96aZGUBXl4st93O7t3O5W3BCXGOjxodHR2EhITAx8cHgwYNwowZ\nM6Q2rWlKRLyoqAhjx47FwYMH8fPPP2PUqFEgIvz666+IjY3F9u3bERcXh1mzZmHq1Kng8/kYMGAA\nrK2tYWJigpKSEnH3zs8//xzz58+v1VwHkB4RJyJkZmYCAPr374/S0lLcq6Pa58qVK/j9998bdU2A\ndCF+4MABTJ06FYGBgfD29q7lM25mZobffvsN0dHRuHLlCsaNG9fkNwuxsbHo2rUrsrOzkZycjI0b\nN2LUqFEIDg5GRkYGZsyYgcDAwEbZEOrr6yMhIUH8OSMjA7ofQ8iFg+M/RIsWrBvmmTPA7dvA0qWs\nK+fruHxUVgJ//QXMm8caCjUHHTsCcXHsQcHHp+H9s7OBqltoq1aAg4Nkvvj69SwVZ+zY5plfYykq\nAvbuZS4tBQXvzk2lspIV7fbs+fa/g3cJJ8Q5PnpkZGQwefJkJCQkQEFBAZaWlvD395doZV9XRHzr\n1q3o0aMHbGxsYGpqCiMjI3Tv3l2chgEAY8eORVBQEABg9+7dKC8vR3l5OTp37gwXFxc4ODigW7du\nuHnzJvr27QuhUCgW2DNnzoSdnR3at2+PuXPngojE5y4rK6slxG/dugVnZ2dUVFRARkYG5ubmtXLI\nq4iKisKff/7Z6O8pKytLQog/fPgQCxYsgEgkwqxZs2BoaIjcGj5ThoaG9RZ4NoSPjw969uyJlStX\nIiAgAFpaWvD09ERoaCh8fHzg4uKCCRMmNCrPH2BvKkaMGAGA/U2JqMkPBhwcHG8HOzvg4UPmmLFs\nGessGRDAIsdVVFayjpr37zNBnJnJhKVQyNrd//Yb8y3X1WUiXF2ddY5sDqysmBBfupTN4cyZ+vfP\nzq67Y2VCAktH2bXr7TvE7N/PxK+5OWuOJCXu81ZYvJhZVu7a9W7O/67ghDgHxws0NDSwdetWhIWF\n4dy5c7C0tERQUBBEIhHy8/OlpjD069cP69evR2BgIM6dOye2F6xesOjs7IwpU6bAw8MDGzZsABGh\nY8eOyMvLQ7t27fD8+XPMnj0b2traCA0NhVAoFJ9LRkYGO3fuxNChQxEUFAQvLy+x+4i0iLiDgwPa\ntm2Ln376CatXr0ZMTAzS6kiwrG6Z2BieP38udiLJz8/HkCFDsGbNGoSHhyMlJQXh4eG4VSPhkc/n\nS33D0Fju3LmDOXPm1LIYtLGxweLFi1FQUIAVK1Y0ejx5eXm0bNkSAIuG6+joNLrbKAcHx9tHV5e1\neP/7b2DTJuCXXwATE2DjRha9LShgQnvIEOa3bWnJjpGXZ9HmGTOYd/bZs8wqccUKlvLSHFhZAfHx\nLIK/fTswezbrnFkX1SPi1RGJgM8/B1auBIyMmmdujUUoBDZvBqr6v6mrsweYt01QEOs0+vvvta0c\nP3Q4Ic7BUQNra2ucPn0aP//8M7Zu3Yq2bdvi7NmzuHXrFqKioiQivKampujcuTN0dXUhJycHJSUl\ndOnSBUVFRaioqMDTp0+xd+9e7Nu3DwUFBejduzd0dXWhoaEBR0dH7N27F+7u7tDV1UVOTg6OHj0K\nWVlZxMfHS8zp2rVr4sZBXbp0wYkTJ+os1ly7di2+/PJLnDx5Ejdv3sTjx4/x9OnTWvtVLxBtDIWF\nhVBRUcHBgwdhZmaGzp07Y/r06XBwcMDevXuxcOHCWoKZz+cjKyur0eeoiaenJ549e1ZrfVJSEtau\nXYuLFy82KaLdokUL5ObmorCwECdPnpT6/XFwcLx/yMgA/foBFy4wsXb7NmuA88MPTGQ/eMCKOrOy\nWERXJAL+/fdl0WP//izC/u23rCCyOdIvzM2Z20tFBRvf0ZGl1NRFTo50If6//7Gc8XfRNbSqILKq\nhv5dCPGYGODLL9lc6npj8EFDRB/FYmdnRxwcTUUkElFcXBzNnDmTRowYQdbW1qSkpETKysokLy9P\nsrKyxOPxSEtLiwwMDEhPT49atmxJAEhWVpb4fD55e3vTmTNn6NixYzRs2DBydnYmGRkZ2rRpk/g8\nBQUFpKKiQkREu3fvJnt7e6qoqCAionv37pGBgQEJhUIiIrpy5QqZmpoSn8+n8+fPS533yZMnqbCw\nkPLz80lPT48+++yzWvvMmzePfvjhB4lrvXjxIq1cuVLqmAEBAdStWzfS0dEhHo9HDx8+bPD78/Pz\nIx8fn3r3uXv3Lvn7+4uvtzpxcXFkbGxMIpFIvK6yspJcXV1pw4YNDZ6/JkFBQaSjo0NKSkpka2tL\nEyZMaPIY7woAt+g9uJe+rYW7Z3M0RFIS0dy5RBoaRFOmEMXF1b1vRQXRX38RLVxIZGlJpKtL9Nln\nRCEhrzcHU1Oi+Hj2+5MnRHz+y881cXMj+vPP2usvXmTXkJ7+enN5Fbp1Izp27OVnFxf2Pb0tsrOJ\nTEyIDh9+e+d8WzT2nv3Ob7Zva+Fu6hzNga+vLy1atIjy8vIoMzOT9u/fT/369SNFRUXS0NAgPz8/\nysnJITU1tVrHnjhxgjw9PYmIxMLy+fPntGHDBtq2bRsZGxuLt/Xt25fWrl1LRES7du2iQYMGSYxV\nWFhIKioq1K5dO4qNjZU61wcPHpClpSX16tWLBg8eXGu7u7s7nTx5koiISktLydvbm8zNzWn16tVS\nRXFRURHJycnRjz/+SHw+v1Hf14EDB2j8+PH17uPl5UXq6upSr0MkEpGBgQHFVfsPu3HjRnJ1daXK\nyspGzeFDgRPiHBzSycoiWrWKSFubaPBgokuXiKo9u0vln3+ItmwhaiBO0CBDh0oKWX9/ok8+kX5+\nKyuimBjp4yxaRDRu3OvNpalcvcpEcPVb6eDBr/9w0lgqK4n69SOaP//tnO9twwlx7qbO8QbIzMyk\nfv36kZycHMnIyJCnpycFBwdTYWGheJ/8/Hzi8Xi1jj158iQNGTJEYt3atWupY8eO1Lt3b1q1apV4\nfUpKCrVq1YqCg4Pp5s2bJCsrSytWrCCBQEBERGlpaaSlpUUBAQHE5/PpwIEDEuNmZGSQlpYW7dy5\nk7KysojH41FxcbHEdjk5OYqOjiYioqVLl5KXlxeVlpbWee1CoZCUlJQoPDycbG1tJdbXFR3/448/\nyMPDo94xiYiGDRtGv/76q8S28vJyOnz4MOno6JC3tzcRseg5n8+nR48e1TnmhwonxDk46qekhGjX\nLqL27YmcnIiOH5cUmW+CJUuIfH1ffq6sJLKzIwoIqL2vri7R48fSxyksJDIyIgoPfzPzlMbw4URb\nt0quGz+eKDDw7Zx/6VKiXr3Y24oPEU6Iczd1jjdIdna2OJWkJkVFRaSsrFxrvUgkEgvPKtavX0/z\npYQDiouLic/nk7GxMQ0YMIB0dHTI1NRUnM5y9OhRcZT73r17ZGFhQZ999hmVlJQQEUt1adWqFWVl\nZVF4eDgpKiqSn5+fePyNGzfS5MmTxfNq164d3blzp95rfvLkCWlra1NISIhEhL6kpISUlJSkHhMZ\nGUkODg5St4WHh1Pr1q2psLCQli5dSr7V/psFBARQmzZtyN3dnb788kvy9PSk8vJy6tKlC+3bt6/e\neX6ocEKcg6NxVFYyEe7oyET5rl1MpL8JDh4kGjlScl1UFBPd2dkv14lERIqKRPXEOujECSILC6Ly\n8jcz1+pERBCpqREVFUmunz2bRfXfNMeOERkbEz1//ubP9a5o7D2bK9bk4HgFNDQ0IBKJUFJSgjFj\nxqCkpASJiYlITEyESCRCeXk5vvrqK4ljZGRkahVHamho1LL8A4B169ahd+/ecHR0REREBPr06YN/\n//0XnTp1AgDcuHEDTk5OAAArKytxEamLiwsqKiqgqqqKfv364bPPPsPYsWPF9oxVnDp1Ct7e3gCA\n+/fvo7KyEtbW1vVec3JyMoyNjZGXlyfhCtOiRQtUVlaioqKi1jH1FWump6fD2dkZPB4PmpqayKtW\nIdSxY0dcuHAB4eHhmD9/Pq5cuQJfX18YGBjgf//7X73z5ODg+LiRkwOGD3/ZbOePP1hh53ffNX8L\n9yrnlOrY2wMjRjBbwyqKi5mTS309xzw9WYMfP7/mnaM0fv6ZFZe+MJES87rFmiIRa1j09CnrjJqZ\nydxi8vKYw01R0cvOmcePv/3GRe8j8u96Ahwc/wX+/PNP3Lx5E2pqamjdujWGDBkCWVlZCIVCHD9+\nHAEBARg6dCgePnyIrVu3gogwfPjwBseVJsRLS0uxZcsWxMXFoaSkBJcuXYKdnR2WL1+ODh06AGBC\nvLptH4/HQ1BQEO7cuQOFFz2Kq/zQ7927B4FAACcnJ8yfPx98Ph9RUVFwc3MDAFy9ehU9e/Zs0Mbv\n0aNHMDU1RX5+PtTU1MTrZWRkwOPxUFRUJCHQAUBTU1OqfWFlZSXOnz8vPqeKigpKqvl+2dvbi383\nNDQEAGzevBmPHj3i7AY5ODgahYwM4ObGlvh4ZnnYvj0wYQLw1VdA27Yv9y0sBHi8pnt4V3dOqd4e\nfs0a1vDn+nXmSFKXdWHN+fr7s3m+SYRCIDycuc3URF2diehXJTgYmDKFuZ+IRGwRCiV/tm7NrvFj\n6ZzZEFxEnIOjEQiFQmRnZ+Pvv//Gli1b4O7ujsrKSrRo0QJCoRAKCgp49uwZNm/ejBs3boCI4OLi\n0uC46urqEpFggEWKNTU1YWBggA4dOuDGjRvYtm0bAgMDxZHn27dvw8HBQeI4GRkZ2Nraij8vWrQI\nQqEQqqqqMDExQWlpKfr27Ys2bdrA1tZW3IkyLi5OHGmvj0ePHsHExKSWEAfYg0BhYWGtY9TU1MSd\nQsvKysTrr127hsjISOzbtw8A0LJlyzob/zx69AhlZWWYMmUK1wWTg4PjlejYkUWB795lUemOHQE+\nny0qKqzTpasri6A3JWquogIQAcuXSzYaUlNjYnPGDNZ0qL5mPtUxMQF27Gj69TWFiAgWiZZ223/d\niHinToCxMbNjzMhgEfGcHDZmfj7QqxeL/E+a9Orn+NDgIuIcHI2gT58+6NOnDwBAIBDgu+++Q3Jy\nMp4+fQpdXV2UlJSgoqICc+fORW5uLkJCQhoVuZUWEc/NzYW6urr4s7GxMSIjIzF+/Hg4ODhg4cKF\nMDY2riWGa8Lj8eDi4oLz58+jU6dOaNmyJby9vbF+/XqJuSUmJsLd3b3Buaanp6Nz585ISUnB9evX\nERAQgGfPniEvLw9FRUX44osvoKioiJwX/8WcnZ3Rt29fKCkpoV+/fujevTvWrVsHAJCVlYWGhgaS\nk5Oxdu1aREVFITk5GZs2bcLMmTPF/t5bt27F2rVr4efnh+nTpzc4Rw4ODo760Ndn3uOff86a77Rp\nw9IlunYF5swBjh5lHTidnYGRIwEvL8k29NKwtASOHWPNhmbPBqZNY6J79Ggm/v39WVfQhiLiVSQm\nsi6h/fq9/vVKIzAQmDhR+rbmEOKZmUyEv+j/Jmb1ajb28eOvPv6HCBcR5+BoBKJq3R8UFRXh5OQE\nIkJISAg6duyIf/75B6amppCVlcWdO3dgZWXVqHE1NDRqRcRr5mADgJaWFkJDQ7FgwQJMnToV2dnZ\nyMzMbHD8vn37YuXKlXBzc4OZmRk0NTWhoKAAefmXz+D379+Hubk5Hj16hCFDhiA6OlpiDIFAgOfP\nn6OwsBCqqqoYP348Ll++jOPHjyMrKws8Hg9jxoyBh4cHRo8ejW+++QZLly5FaWkpli9fjrKyMtjY\n2MDX1xdEBH9/f8ybNw83btzAsGHDcOjQIYwZMwZz587F1atXYWlpib59+8LZ2Rk+Pj4QCoWNervA\nwcHB0Vjat2fiWEsLSE5mAnLUKCaonzwBJk9mueVt2wKDBgEBAXUL1MWLAQsLtn9iIsvznjULePiQ\nRbfXrgXu32+8EI+Le3Nt3ouKgJAQYMwY6dtfV4jLyrKHmKtXJdefOgXs28cedD62zpkNwUXEOTga\noKioCK6urliyZIm4MPHs2bNQUVHBkiVLYGtri/j4eJiZmQEAgoOD4eXl1aix1dXVG4yIVyEjI4Px\n48fDwMAAP/74IywsLDBw4ED4+PjAro5kux49emDXrl3o1q0bwsLCcO3aNezZswfOzs6ws7ODoaEh\nUlNTMX/+fFy/fh3t27fH+PHjERUVJe5YefnyZSxbtgw6OjpQVVVF586dYWhoCH9/f7StnmRZg34v\nwjl2dnaIiYnBvHnzcOfOHTx8+BDDhw/HvXv3MGTIEDx79gzDhg2DjIwMVFVVcePGDSQnJ+Prr78W\ni3B3d3e4uLhg3rx56NGjB5cnzsHB0WzExbGiyyp4PCbKR41iueN//AEcOQLMncvyzUeOBIYOZekn\nANC3L4uCW1gABw6wtIwdOwAXF8DJiQn5vXtfdq9sCB0dVuhYky1bmMitkZXYJE6cYPOqGa2uQk3t\n9TtrurgAV66wglmAdT397DP2AMBlF9aGi4hzcDQAj8fDihUrEBgYiJ9//hlBQUEQCoWIiorChQsX\noKSkBB8fH7Rs2RL5+fk4fvw4xo8f36ixW7VqhcLCQomIe15enlQhXkXPnj2xevVq3L59GxcvXkTP\nnj0xduxYnD59GllZWWCuSWyc3bt3Izs7G+np6bh48SJatGiBpUuXYuDAgUhPT0dwcDDU1NTg6emJ\n5ORkXL9+Hc7Ozpg4caJ4TufOnUP//v1RWloqThnR0dFBRkZGg9eXl5eHmJgYFBQUwNLSEg4ODpg0\naRLk5OSgp6eH0NBQREdHw97eHjY2NoiIiEBQUBAePnyIKVOmIDc3F9OmTUNSUhLc3d0xZcoUeHh4\nSG17z8HBwfEq3LsnKcSro6rKoscnTgBpaSzd5NgxwMiI5TofOsSKNK2sgMuX2TFt2rA0jJQUYMgQ\n4MYNlpteM0pcFzo6LLWjOlu3shQXPb1Xv06g/rQUoOGI+Llz7EGjPqqEOMAeZLy8mGNNYx9EPjoa\n43H4ISycJy3Hm0IkEtHChQupdevWZGVlVasLZkPweDzKz88Xfz579izt37+/3vN5eHhQp06dSEND\ngzIyMmjz5s3k5uZGampq4i6fysrKpKmpSdu2baPjx48Tn88nfX19ioqKqnc+ZWVl5O7uTo6OjpSW\nlkbW1tZ09epVcnd3p4sXLxIR0aBBgyiknvZrqamptGTJEjI1NSUTExNxB8+moq6uTllZWeLPAoGA\nvv76a+rQoQOlpaW90pj/VcD5iHNwvBFcXFib+aaQl8c8xIcMIWrVinWodHSUvq9QyLpq9u/fuLEL\nCoiqt6nYs4c1+0lObtoca5KWRqShUb+nemYmUevWdW/v35/ozJn6z1NayuZfWEg0YgTR1KmvNt//\nOo29Z3OpKRwcr4mMjAzWr1+P+fPnw9DQEMnJyVi8eDGWLFlSK9dbGmpqasjPz0erVq0AAAMGDGjw\nfKdPn8aJEyeQmZkJbW1t+Pj4wMfHB4WFhYiOjgafz4eFhQUWL16M4OBgpKam4syZM5g4cSKUlJTq\nHV9JSQlnz56FkZERrKysUFlZCQcHB1RWVopzy/l8Pv7++28QER48eIADBw7A3Nwc6enpePz4MR4/\nfgwAOHbsGA4dOiTVY7whBAJBLUtEBQUFrFmzBhoaGnBzc0NYWBhMTEyaPDYHBwcHwBxP4uKkO4jU\nh5oaMH48W/LyWE55bKz0fWVlAQMDVgzaGHg8Nq+iIuDkScDXlzmdGBs3bY41OXQI8PYGXrzYlIqa\nGnM3IZJu5diqFfMDr48WLYAuXQAfH+YpfujQ6837Q4cT4hwczURoaCh69uyJn376CatXr4alpSW8\nvb1x/fp1WFtbQygUYs6cOZCTk0NiYiKMjIzg4uIitjCs8st+9uwZysvLYWRkVGcutIyMjFSf8tTU\nVCxatAhJSUmQkZFBTk4OdHV1cefOHWhoaCApKQnt27dv8Frk5eWhrKyM7Oxs8Hg8tG3bFnl5eZgw\nYQIUFRWRkpICDQ0N2Nvbw8zMDF26dMHQoUNhYGAAAwMDLFiwAPHx8VBQUEBJSQla1uwa0QgyMzOh\npaVVqwkSACxYsAA8Hk8sxi0sLJo8PgcHB8ezZ6wBkLb2q4+hrs7SSWr0cJMgOZk1FWoMMjIsl/rQ\nIWDlSuDiRVZc+joQsYLTPXvq309BgQnpoiKWllMTVVWWbtIQioqsMDMuDmgg9vPRwwlxDo5m4tCh\nQ/j8889haGiIPXv24IsvvkBQUBAePXqEmTNnYuHChfjzzz+hoaEBc3NzXLhwATExMVBVVcXp06fh\n5+eH48ePQyQSib2/Dx06hHaNvXuDddm8ceMGAgICMH/+fLRp00bcxCclJQV8Pl+c510fCxcuRHl5\nObS0tPD48WN8/fXX8PPzQ1ZWFg4dOoS+ffvWK64VFBRgaWmJy5cvo7CwUFz42RQyMjKgXc9/xxkz\nZkBZWRn9+/dHYmJio66Lg4ODozr15Yc3hejour2xiVi+eFMi2jo6wIYNTDx37Ng88ysrA3r0aHjf\nqjxxaUK8MRFxAAgKYi4xnENKw3DFmhwczUBWVhZu3LiBgQMHitfZ2Nhg9OjRMDIywtSpU2FpaYlx\n48YhNjYWR48ehZeXF7y8vBAVFYXDhw/Dzs4OU6ZMgY+PD54+fYqRI0fC1tYWfnX0O2YpaJIIhUJ4\ne3tjy5YtCAsLw5w5c5CWlgYA4jSWxlBcXAx1dXUkJCRATk4OEyZMgKysLEQiEby8vOoV4UVFRYiO\njoalpSXOnDmDpKSkBj3PpZGRkQGdukr7XzBp0iTY2tpiT0NhnrfMF198gdTU1Hc9DQ4Ojgao6Zjy\nKggErHNnly7Stz99ytJNmvJiUFmZpYi8aF/x2ixdymwVDx8G/vqLdQMtLZW+b30Fmw0J8YQE1hSp\nZUtOhDcWTohzcDQDy5Ytg5mZWS2BmpubK85x9vDwQEhIiHibvb09njx5guzsbMTExGDOnDno0qUL\nHj58CBkZGfj4+KBdu3ZYt24dxo4di/Pnz6O8vBwAS4NRUFDAkSNHJM73448/Ijc3Fzdv3oStrS2y\ns7PFglBVVVVq90tp3LlzB2vWrIGqqir27t2L3r17Y+rUqVBRUan3uIyMDLi7u6Nbt27o2rUr4uPj\nUV5ejhEjRiA4OFjCHaYhnj9/3qAQB4AVK1Zg/fr1Ep07GwMRQVC9FV4zQUQ4ePCguHMpBwfH2+f5\ncyAykrVcX7eOdbjs358Jxeo0hxC/d491xKzr9vjnn6yzpqEhi0iPHctsECdPBk6fZo4qNcVtdjZg\nb8/yywGWDhIfD4SGMivEb74BBgxgAt/BgbWPrw8LCxaRDwkBli1jlovq6qzxUOfObKzPPgNWrGDN\nhNatYw4rBw+y5Zdf2PLwIVvqYts2Fsk3NmbjRUayNwIcdcOlpnBwNAN3795Feno6Zs6ciS1btkBR\nSihg4sSJ2Lt3r/hzhw4dYGNjA1lZWZw8eRJr165FUlIS8vLy0L17d/z000/4559/kJiYiCNHjmDF\nihW4c+cO9PX18ejRIwDA9evXMXLkSPGYR44cwffffw8FBQUAwMCBA6H3wu+Kz+cjOzu7UdczatQo\nLF++HN9//z2UlJRw7tw5WFtbIzg4GLdu3YK/vz/++usv3Lp1CxUVFZg9ezbc3d2xZcsWjBs3DitX\nroRQKISCggKePXuGv/76C8uWLcPatWvx448/olevXg3OoaHUlCq6du0KJycnLFq0CJs3b4acnFy9\n+9+/fx8LFixAREQESktLYWZmBisrKxw7dqxR301DZGZmQl5evlGFuhwcHG+GadNYJLptW7ZYWzPv\n75r2f/fuARMmvN65oqOBOlo5AGAFnSNHMtu/1FSWprJjByvEfPbs5TpFRWaLaGwMmJkxMW5jw7aX\nl7NtRkYsf/vhQ5bP7e3Nmgj17s2EsoGB9Dls3Vp7HRGQlcUaGFUt164x0R8d/fIhoEparWv5AAAg\nAElEQVRIE7H51tM+AhERLLddX5+l1UyYwHLOp05lv7du3Zhv9COjMdYqH8LCWWFxvCmePXtGampq\nlJ6eTkOHDiU9PT2Ki4sjIqK///6b9PT0KDQ0lGJjY0lBQYHu3r1Lx44dI29vb5KXl6eWLVuSk5MT\ntW7dmuLj40kkEpGfnx+pqamRlZWVxLnGjh1LPj4+1L17d3J1daV79+6Jt2VlZZGqqiqVlZVJnWdl\nZSXJyclRRUVFg9f0xRdfEJ/Pp4CAABKJREREVF5eTkZGRiQvL0/Lli0jbW1tmj17NhUXF9O4ceMI\nAK1fv15iHH19fUpJSSEiZrt44sQJMjIyovHjx1NGRka9c5g9ezZt3ry5wbkSEWVnZ5OrqyvZ2NhQ\nREREnftlZGSQgYEB+fn5UU5ODlVWVtKpU6dIVVWVYmNjG3Wuhrh79y517NixWcaqDjj7Qg6OZkUo\nJOLxmGXf6zB9OtGPPzbtGJGIqHdvoo0bX37OyiK6fZvo5Emi3buJdu0isrQk2rSJba9iyxaiv/56\nua6ykui774h0dIjqcZVtkPh4Ii0tImVlotzcph+fkUGkpsbmU/06w8OJxo5l28aOZZ+rX8+HSmPv\n2e/8Zvu2Fu6mzvGm2L59O40dO5aIiKKjo0lDQ4O0tLToxIkT9Ntvv5Gqqir16tWLTE1NCQCZmprS\nkCFDaN68edSiRQsqLi6m/fv304gRIyTG9fT0JD6fT97e3pScnEwikYgMDQ3FIr8mwcHBDXqYa2lp\nUXp6utRtIpGIioqKKCIignR0dOjp06fibb///jspKSmRnZ0dKSkpkZOTE9nZ2VFOTg4RERUVFVGL\nFi2ooKBAYkwnJye6cuWKxLrCwkJasGAB6erq0vXr1+ucq6OjI124cKHe66k5/6NHj5K+vj7Nnz+f\nysvLJbbn5eWRi4sLLVu2rNaxa9asoWnTpjX6XPVx6dIlcnZ2bpaxqsMJcQ6O5uXvv4kUFF5fFNrb\nE12+3PTjHj4k0tQkevSo9rYzZ4i0tYmmTCHq0qVx4129SmRsTDRnDvPybiz5+UTR0URt2xL5+RG1\nadP4Y6tz5AjR4MF1b8/KYg8sVlZE7dsTrVtHVMe/ow8CTohzN3WOt4Sbm5u4uY2vry8tWLCAIiMj\nycTEhKysrMjX11e8r1AoFP8uEolIVVWVcnNzaejQoRQYGCjeVllZSVpaWhQXF0eurq6krKxM3333\nHZmYmIgj1DWZMmUK+fv71ztXe3t7unbtWq312dnZ1K1bN2rRogUpKyvTH3/8Id4WEhJCrVu3phYt\nWlC/fv1IWVmZVq9eLXEtREQdO3as1SzI29ubgoODpc7l1KlTxOfzpTb7uXjxIhkaGjYqel+TzMxM\nGjx4MJmZmVGXLl3IwMCAzMzMSE1NjebOnUuV1cM1L0hJSaHWrVtL3dZUTp06RR4eHq89Tk04Ic7B\n0bwEBRENG/Z6Y5SXswhyYeGrHf/990Tu7kzIZ2ayKP3q1UR6emydUMia+fz9d+PGy8lhTXRsbIgS\nEl6uz8pi4+3dSzRvHtHAgUQdOxKpqhK1bElkYcFEeGgoUa9er3Yts2a9jPDXh0hEdP060f/+R6Su\nzv4GZ85IRtI/BDghzt3UOd4Cjx8/Jg0NDXE6iKenJx07doyIiPLz88nc3Jz4fH6tyG95eTmdPXuW\nNDU1aejQoaSioiKOLhMRRUREkK2tLRERDRs2jHx9fUlXV5d69OghdR4ikYj09PTo/v379c53zJgx\nEoK/CqFQSMeOHSORSCQh9L/55htSVVWlWbNm0alTp2jTpk1UVFQkdezMzEwSCAQS63x8fGjDhg11\nzufmzZvUpk0b2r59u3hdVlYWGRgYUGhoaL3XUh9CoZCuX79O165do9TUVLp//z5lNvD+uUOHDhQT\nE/PK56zizJkzNGDAgNcepyacEOfgaF7mzCH64YfXG+Pvv1n6yKsiELB5dOvGUjdUVZlArvZCklas\nIJo7t/FjikQstYXHY9FnLS02rqMj0aRJLBIdEkIUG8uEe/XYzqZNRLNnv9q1dOxIdOtW047Jz2dz\ntbdnDxwrVxK9yGb8z9PYezZXrMnB8RocPXoUnp6e4m6VqampMH5hFtuqVSskJCTAzMwM/fv3x+rV\nq2FoaIhTp04hJCQE5ubmaNOmDWRlZSErK4sNGzZg1apVkJeXx6FDh+Dt7Q0iwqVLl7BlyxZcvHgR\nc+pozRYXFwdFRUWYmZnVO98OHTogMjISE2pUJ8nKymLEiBES66KiorBlyxZs37691v7S4PP5tdYZ\nGhoiJSWlzmMcHBxw5coVeHh4IDU1FWvWrMHnn3+OTz/9FP3792/wnHUhKyuLbt26NekYZ2dnXL16\nFdbW1q98XgBo0aJFkx1cODg43j6RkUAd7rCN5tIlwMnp1Y9XUAD8/dnvaWnMweSPP4A2bV7uM3ky\n4OjI3EgaYwkoI8OKVePjmR/5xImsSLWO/nASJCTUbcNYH8+fs2LPph7bqhWb67RpwJ07wL59gK0t\n+04//xwYPJh9Rx8ynH0hB8dr8Ouv/2fvvMOiuLo4/A6iaFQUu1gRsWLBFgsaOxorlhiNsetnjRoT\nWyRqbIk9URN7jKLRYEcx9i72LqgIKAqC2Cii1Pv9cdl1kQUWWAHNvM/DA3vnzsydNdn9zZlzfmcL\nPXr00L4OCwtLYFunKApNmjThm2++4fz58/zxxx9Uq1aNa9eucebMGT7//HPq1q2Lj48P58+fp23b\ntjx48AAXFxcGDBiAp6cn5ubmmJubc/nyZdq2bat3HQMGDCAsLIzGjRvTpUsXhg0bxtSpU1m2bBln\nzpwhLi6OsLAw7t27x4oVK7Czs2PNmjXJ2vcdOHCAoUOHGiTCAby9vRPZE5YoUYKAgIBk9ytXrhyn\nT5/mxIkTNGzYkHv37jFnzhyDzmlM7O3tOXnyZLqPY25uzvPnz42wIhUVlffF69fSurBOnfQd56+/\noGdP46zJyQlGjEjcgdPKCmxtwdXV8GPt3QsrVki7Q0NFOMCpU2nr4nn8ODRuLLuUppWaNaX94cOH\n8j1dtEi6xEycKH3PP1YMEuKKotxXFOWGoihXFUW5GD82Q1GU6/FjBxRFsYwf76QzflFRFHud4/yi\nKMrN+J8eOuNWiqKcUxTFS1GULYqi5IgfN4t/fS9+e1mdfSbFj99RFCXtoTMVlTTy4MED7t27R4sW\nLbRj+qKhZcuWJTQ0FGdnZw4cOMDYsWMpWbIkISEhPHnyBFdXVwoWLMj+/fupUKECjRo1on379hQr\nVowTJ07QuHFjDh48iL29fSKf8ri4OGbNmsW1a9coUqQIs2fPplevXlSrVg2QtoqDBw+mTJkyVKlS\nBTMzMwICApg/fz7//PMPVatW5datW3qvr0CBAgQFBaX4PkRFRTFlyhTq16/P7du3E2wrUaIE/v7+\nevfTFe2FChXi8OHDPH78mMqVK2ufMGQkrVq14uDBg8SmZMibAhUrVsTLyyvdx1FRUXl/XLokO1am\npyHv9esyEqzzFZBmrl2Tonn8eP3b+/eHtWsNO9adO3L+gQOyMc+2bYbtFxcnrRB//BHu3jVsHw3H\njkHTpqnbJyk++URaHZ44IT3YY2KgYUNo3lx27PzoHjgakr8C3AcKvTNmrvP3N8Dy+L/zAEr839WB\n2/F/twMOIr3LcwMXNccA/gG+jP97OTAs/u/hOsf9EtgS/3cV4BpgBlgB3kC25K5BzTdUMTZz584V\ngwcPTjDWqlUrsW/fvgRj48ePF5988ok4e/asOHDggJg+fbqoVKmSMDMzE/Xq1RPm5uaiXbt2IiAg\nQMTFxYkyZcqIWbNmCSGEGDx4sFi8eLHo1auX+P333xMc19/fX3z++eeiYcOGYvfu3aJatWpJrvXW\nrVvi/PnzicbXr18vcuXKJZydnRNt8/HxEUWKFElUlPkuvXv3Fg4ODsLf3z/RNm9vb1GmTJlE487O\nzqJOnTqJjh0SEiIqV64sFqfWC8xIVK9eXRw9ejTdxylTpozw8vJK/4J0QM0RV1ExGiNGCDFwYPqO\n0apV+o+hwcFBiCVLkt4eHi6EhYUQej5mExASIgsvV66Ur69eTZhznYS7rZZSpYSYMkW6uTg5CRER\nYdj605IfnhoiI6UrS6tWQhQqJMTEiVnfAtHQz+w0C/F3tk8C/tAz3gDwjP/7e2CKzrY1wBeAAjwF\nTHX22R//936gQfzfpvHzlPjzTdI5lnZeUj/qh7qKsalVq5Y4fPhwgrE+ffqItWvXJhjz8PAQRYsW\nFSVKlBDZs2cXXbp0EaNGjRI9e/YUHh4ewsbGRkyePFkUKlRIeHp6ikWLFolvvvlGXL9+XRQsWFDk\nypVLKIoibt++LYSQhZnOzs6iSJEi4scffxSRkZEiOjpa5MuXTwQGBqb6OhwcHESBAgX0urEUK1ZM\n+Pr6Jrmvr6+vKFKkiIhI4tP69evXIkeOHAmO/eLFC2FhYZGkb7eHh4cAEjmwZATLli0TXbp0Sfdx\n2rVrJ3bs2GGEFb1FFeIqKsYjd24h0ltTXbWqEHpiGKnmwAFp5/eO42oiBg2ShZZJERsrRMeOQgwd\nmnDs3DkhJk2SYnnYsLfbfvpJCnxdypaVdooPHwrRvbsQ5cpJR5PkCAxM7B/+PvHxkZ7pyXw1ZQkM\n/cw2NEdcAAcURbmkKMoQzaCiKLMURXkIfAX8qDPuqCjKbWAvMCB++BrQVlGUTxRFKQQ0A0oBBYGX\nQoiY+HmPgBLxf5cAHgLEbw+Jn68d17OPFkVRhsSnx1wMDg428FJVVFLm7t27+Pv789lnnyUYL168\nOI8fP04wZmNjQ2hoKJ6ennzxxRd06NCBzz//nODgYG3qxsyZM2nRogWXLl0iOjqaTZs20bp1a7p1\n68bNmzdp1qwZ//77L48ePcLR0ZGff/6Zffv2MX36dHLkyIGpqSlNmzblyJEjqb6WNm3aEB0dzfXr\n1xOMR0RE8PTpU86cOZPkvjly5CA6OlpzQ5yInDlzkidPHp4+faodCw4OpkCBAtr0GX37FChQgCFD\nhhAdHZ3q60kPffr04dixY8kWmBqCra0tN2/eNNKqVFRUjMnTpzJHfMqU5OclU0IDyG6X+jpqxsXJ\n4xtCXJxMR5kzJ+VCzP794c8/k24ZP2OG7Mb5669y7d99Jztt9u0r91mzRuZga9i/X3bS1MXERK6p\nZEn45x9YtgxGjYKuXWXutj6MkR+eGqysZG7/1asZc773jaFCvJEQohbQFhihKEoTACHED0KIUsBG\nYKRmshBihxCiEtAZmBE/dgBwA84AfwPuQAwywv0umv/MktqW3D5vB4RYKYSoI4SoU7hwYYMuVEXF\nEMaOHUv+/PmJiIhIMP6uEI+OjsbU1JTKlSvj4eGBjY0N9+7d0wpwc3NzYmNjefPmDcWKFSMoKIjq\n1auTJ08evL29Wb58OeXKlWPSpEnMmzcPOzs77OzsuHjxIrVq1Upw7pYtW3Lo0KFUX4uNjQ0WFha4\nvlMJdP78eSwsLBJdoy6WlpY0bNiQDRs2JDnn3YLNmJgYTE2TNmy6d+8e1atXp2jRovz888+puJL0\nkydPHvr27cuyZcvSdRxViKuoZF1cXORvO7vk5/XsCevW6d8mhHQJKZEoBCiLQJNrea/Lpk1gZiaF\nbko0aCB/nz2beNuuXVJob90qhXT37jJX/OhR6YQyZw7Ur/+2bT1As2Zyuy4aIa6hTRu4eROqV5fv\n1/z58G58xJj54YZSs+Z/TIgLIQLifz8BdgD13pmyCUj0n5EQ4gRgHR8BRwgxSwhRUwjRCimmvZDp\nJvkVRdF8M5cENN/aj5BRc+K35wOe647r2UdF5b0TExNDyZIlqV69Ot9//z2rVq1i+fLl/Pvvv+zb\nt48+ffpgY2PDsGHDACnMbty4gbW1Nffu3SN79uw8evSI+/fvkzdvXl6+fEnRokUJCgqiQYMGPHny\nhJw5cxITE8O2bdvo168fL1++5PDhw0ydOlVvMWPLli05ePBgktHppLC2tiYmJobdu3cnGD979iwl\nSpQgLCws2f1/+OEHZs+eTWRkpN7tlpaWCYR4rly5krX38/b2xsbGhpUrV7J8+XJWrlyZiqtJPyNH\njmTt2rW8evUqzcews7Pj/PnzRlyVioqKsVi/HgoWlEWByTFjBnz/PVy8mHjby5dgago6Jllazp0z\nzI3lzRv44QdpS2iIq4miSCvDd4s2PTyk1d+2bXJNzZvLItRt26BixaSP17SpFNG6vCvEAXLmhKlT\nwd0dDh6EWrWku4oGVYinjxR9xBVFyQ2YCCHC4v9uDfykKIqNEMIrflpH4Hb8/PKAtxBCKIpSC8gB\nPFMUJRuQXwjxTFGU6shCzgPx844C3YDNQF9gV/xxd8e/do/ffiR+/m5gk6IoCwFLwAZQv/VUMoS4\nuDguXrzIzZs3efDgAQcPHuTcuXOYmJhQtmxZrK2tsbOz4/vvv6dq1aoAVK5cmZMnT/L06VPc3Nw4\nePAgr1+/pkmTJjx9+pTSpUtToUIFAAoWLEhsbCwODg5cv34dGxsbOnXqRHBwcLIe1xUrViR79uxc\nvXoVu5RCPTpYWVnx/PlzXr9+zenTp2nUqBEg7QtNTU0pXbp0svs3aNCA6tWrs2jRIiZOnJhoe8GC\nBRPY+X3yySfJRtnv3buHtbU1pUqV4tixY3To0IHDhw8zdOhQLC0tyZ49Ozly5KB48eJkew/PQsuV\nK0edOnXYs2dPAmvK1FClShXCw8Px8/NL8f1TUVHJOAID4cYNsLdPeW6VKtICsGtXuHABihR5uy0g\nQH80HGTE2pA2BkuXyihz48aGrR2kJ7itLSxeDLlzyxuCzp1h7lyoWxdmzpSpGxs2JIx+66NBA+nW\nEh4OefLIMX1CXIONjXR22boVvvwSWrWS6S8BAWnzHk8PNWvKc38MGNLQpyiwQ5G3a6bAJiHEv4qi\nbFMUpSIQBzwAhsbP7wr0URQlGngN9IgXz9mBk/HHCQV66+SFTwA2K4oyE7iCLOQk/vcGRVHuISPh\nXwIIIW4pivIP4IFMbxkhhFC9wlQyBB8fH/LkyUPx4sUpXrx4io1jvLy82LRpE7du3WLOnDns27eP\np0+fEv//AtHR0Rw5coTRo0eTI0cOrTD/7LPPWLVqFWXLluXOnTtJeohrUBSFHj16sHnz5lQJcTMz\nM8qVK8fw4cNxdHRk0aJFuLu78+zZM2JjY7G2tk7xGEuWLKFOnTp07do1UVMhT0/PBO9Rvnz5ePny\nJXFxcZjo+abw9vbWzrexseHixYv89ttvODk5ERQURExMDJGRkYSHh1OvXj2++eYbOnTooH0/jUGr\nVq04depUmoW4oih89tlnHD9+3GAfdhUVlffP1q1SUNraGja/Sxdpddijh4wGa7LqkkpLASnE4x+G\nJsnhw9ImcOZMWLlSCuqQkKR/Hz0K1tbSE7xBA9i+HXr1kj9t28pIOcD589L6LyURDvKJQK1aMk+8\ndWs5lpwQBxmV795dpqxMmybFf6FCcp+MyhEHKFcOnj+HFy/AwiLjzvteMKSi82P4USvwVYzF3r17\nRevWrQ2ae//+fWFhYSGcnJzE/fv3hRBCmJiYiBg95eU1a9YUDRs2FAULFhQnTpxIsC0qKkqYmZmJ\nNyl4T127dk0UK1ZMXL58Wa8LSlKMHDlSTJ8+XZw5c0Z89tln4uuvvxZBQUEid+7cIiQkxKBjTJ06\nVQzTLckX0uHFwsJC2NjYJBgvX768uJiE15Wtra24evVqiud7+vSpcHFxEVWqVBEtWrRI5MLy8OFD\nceXKFXH79u0kXV2S4p9//knWPaVfv36ibNmy4q+//hJBQUEiNDQ00ZylS5eKAQMGpOq8yYHqmqKi\nkm7s7YVo3lyI1asN3ycmRjqsjB37dmztWiH69Ek8NyREOrJERSV9vP37Zfv5bNmkM8ngwUJ8950Q\nM2dKC8MNG4TYvVuIEyeEuHZN2g9GR7/d38VFiGbNhJg8WYimTd+eKy5OuonEf9UYhJOTtALUYGsr\nRBKGVnqZP1+IihWFsLYW4u+/pUtLRtGokRBGcJt9bxj6mZ3pH7YZ9aN+qKsYi9WrV4t+/foZNLd2\n7dqiVatWCcbMzMzE69evE82tX7++GDNmjLCxsRHHjx9PtH3y5Mni+fPnyZ4vLi5OmJiYiEKFCone\nvXunKMbPnz8v4uLixOXLl0WpUqVElM63x65du0Tjxo2T3V+X27dvC0tLywSi99SpU6J8+fLCzMws\nwVqcnJzEmDFjEh0jNjZW5MqVS4SFhRl83ujoaLFs2TJRpEgR0alTJ9GvXz9Ru3ZtAQhra2tRvnx5\nkStXLtG6dWvh4uJi0A3Krl27RIcOHfRui4yMFKampsLd3V30799f5MuXT2zatCnRvJs3b4py5coZ\nfB0pkRohjrScvQFc1eyHLJy/Hj92ALCMH++kM34RsNc5zi/AzfifHjrjVsA5ZJ3PFiBH/LhZ/Ot7\n8dvL6uwzKX78DuCQ0jWon9kqxubhQyEKFBCiVi0hTp9O3b7Pn0srP41d4YwZCQWshkOHpNjXx7Nn\nQvTrJy0Ct2+Xgl3PV0GKvHkjRK5cQhQrJsSTJ2/H/fyEKFIkdf7ahw8LUb/+29fVq0vv8dRy6JAQ\nderI9/bAgdTvnxZGjBBi0aKMOVdaMPQzW21xr6KSSrJnz05MTEyK83x8fPD29ubixYsJcqRz5Mih\nt7V8pUqV+PXXX3n69CkWep61zZo1S++4Loqi4OTkRI8ePbhx4wbbt29Pcu6aNWuoV68ex48fx87O\njvLly7NOxyJg7dq19OrVK8Xr1FChQgVatGiBlZUVw4cPZ8OGDcycOZPhw4eTN29enjx5op3bsWNH\nDh8+nOgYAQEB5MuXjzyahEUDMDU1Zfjw4dy+fZsvvviChg0b8uuvv9KtWzfGjx+Pl5cXz549o1+/\nfsyePRsHBweD7Anl52hCAgMDmTdvHtmyZaNcuXL8888/+Pv701NPj+sqVaoQFhaGn5+fwddiZJoJ\nWRyvKRubJ4SoLoSoCezhreXsYaBG/PgAYDWAoijtgFpATeBT4HtFUczj9/kFWCSEsAFeAAPjxwcC\nL4QQ5YFF8fNQFKUKMrWwKtAG+D2+bkhFJcP45x/o1El2jaxcOXX7WljAjh0wZowsEkwqNeXsWfj0\n08Tj27dDtWqyuPPGDXB0lOkx+hxQUsLMDIoXl2kluoZw58/LVJHUZOk1aCDXEx4uX6eUmpIULVrI\n80+cCMOHQ8uW+otcjcnHUrCpCnEVlVRSuHDhBK3fQ0JC2LNnD9OmTWPYsGH07duXcePG0blzZyZP\nnkyNGjW4cOGCdn727Nn1OoysWrWKyMhInj9/nqTHtiF8+eWXbN++nVGjRvHPP//onXPt2jUmTpxI\n1apVORZfNr9w4UJ++OEHvvzyS4YMGcLZs2fp06ePwedVFIX169dz4sQJnj59Sv/+/dm/fz+9evWi\naNGiBAYGaudWrVqVu3fvJmoDf+/ePcqXL5/6iwYsLCzo1asXgwcPplGjRnTs2JH9+/cD0qmlZ8+e\nnD9/nubNm1O3bl02b96c7LXoY86cORw7doxff/0VR0dHTExMyJ07d5LH0OSJZwWEEKE6L3MTb/kq\nhAgXb+86tOPIDsbHhRAxQohXyF4QbRT55jQHtsbP+wtpVQsyuv5X/N9bgRbx8zsBm4UQkUIIX2Rk\n/F33LRWV98qWLVIw5smTtrzi6tVlgWWXLuDrq1+Ily0LHTu+fR0YCN26weTJ8kbgt9/eFkY2awZp\naP0AwKFD0p1FI6BB2hTWS+X/VblySavF06fl67QKcXibP+7hIa+5Y0eZW+/llfK+aUEV4ioq/1Eq\nVarErVu32LJlC506daJUqVIsXryY6OhobG1tadq0KUWLFmXkyJGMGzeOunXrJhDidnZ2WvGri6mp\nKdmzZ0/3+vLnz8+LFy84dOhQkjaBYWFhPH36FH9/f76LLz2vWbMmHh4elCxZko0bNzJ06FA+Scnf\nSw+3bt3C3d2dpUuX0rJlS4oWLUpwcDC6Xv4xMTFky5YtUbFmeoT4uzRr1ozjx48Tp/OtYmpqysSJ\nE/n333+ZOnUq/fr1S9GeURc/Pz+GDRtGhw4dqF69OiVLlkx2ftOmTfX+W2cAahM2FRUdfH3lj4VF\n6qPhuvToIV1Ujh59K6h1+eoraNJE+oyvXw81akgLwatXId6QSos+H29DsbKSloG6PudTpkg7xNTS\ntOnbdaRHiGvInh2GDpUCvHp1GXUfNgze6XWXbqpWlU83knDO/WBQhbiKSio5e/YsAQEBLF68mC5d\nuuDn58ehQ4eYNWsWI0aMoH///owfP54hQ4ZgYmJCjRo1uHHjhnb/ChUqJEjTMBYxMTEsWbIEGxsb\nzM3NuXbtWqKmPxrs7e3JkSMH48eP16aBxMTEsGLFCpydnbUR/tTi6enJ//73P7Zv346pqSmlSpUi\nKiqKFy9eULRoUe28hw8fUqpUqUSRZ2MK8ZIlS5IjRw4ePXqUaFutWrW4fPkypqamej2/Q0ND9abH\nBAYGUqxYMSwtLXFwcEjkEPMu9vb2uLu7a1+nx5s8lahN2FRU3mHRIincqlQxfJ/HjxML09mzISZG\nRn9XrEjY4EYIOHAASpWCn36Sdn+zZkkv7ndp1AiuXIFk3FyTZexYaWOo+2AxLc4lun7ixhDiGnLn\nljcGd+5IhxZbW3mzEBJinOPnyiXdUzw8jHO8zEIV4ioqBhIVFcXw4cOZMmUKVapU4aeffqJv377k\nz59f7/yYmBicnZ1ZuHAhe/fu1Y7nzZs3QYMbYyCEoGPHjuzYsYN27dpRrlw5PD096du3b5L7DBgw\nQJsaEhAQQNOmTTly5AgXLlygWbNmqV5DSEgIjo6O/PLLL9StW5fw8HDy5MlDQEAAxYoVS+D5nZS/\ntjGFOMiuno+TCMPkzp2b1atXM2fOHNq3b8/MmTO1ufu+vr6UKVMm0T6PHz+meOkIwJEAACAASURB\nVPHigLSdTOkJRqVKlfD19eXVq1d89913dO/ePZ1XZBhCbcKmopIAKysZrfb0NDwivmePTH/QeaAJ\nyIjvL7/IyPfff8vI7KZNMgJuZwfjxsHo0dKjOzkn2dy55fE1aSGppWFD2Zhoz5607a+hfn3ZPTMs\nzLhCXEPBgrBgAVy+DI8eQYUK8qYomb5uBvMxpKeoQlxFxQCCgoJo3rw5/v7+XLlyhfbt2yeIdOqj\nd+/erFy5km+//TZB5Ld///4GFXumhvv373P58mUOHjzItGnTsLa2pnz58ty7dy/JfQYOHMjatWvZ\nvn07devWpU2bNhw8eJBSpUoluU9SxMXF0adPH1q0aEH//v0BGf3NnTs3/v7+iVI4NBHxdzG2EC9S\npAgppTh0796dixcv4u7ujp2dHTt37uTOnTt6o92hoaGYm8t6xZiYGExNk2/FYGZmhqWlJY0aNeLm\nzZtMnz497RdjIIqi5FYUJa/mb2QTtpuKouheUIImbPF53LzbhE1RlILx4wmasAGaJmygvwkb6DRh\nix//UlEUM0VRrFCbsKlkEh4eKQvx6GgYP14WHe7Yob/4sls3cHWF16+lKB87VkbK58yB69dlR84k\nykcSkJ70FEWRaTKDB6dtfw25cslOoKdPvx8hrqFMGZlKc/iwvOaKFeGvvxJG9FPLxyDEDWnoo6Ly\nn+bevXs4ODjQq1cvpk+fjomJCfXr12fVqlXJ7rdixQry5cunFaka4VapUiV++eUXo64xNDSUIkWK\nkC1bNipVqoSzszNz585l69attGzZUu8+tWvXxtTUlB49ejB48GCmTJmS5vPPnDmTZ8+e4eLioh3T\nCPHKlSuzZMmSBPP1RcSFEEYX4tmyZUuQI54UpUuXZs+ePezcuZO5c+dy7tw5goKCsLCwoGXLllrx\nHRcXp43sx8bGGtTZMy4ujmrVqqEoCqNHj+b06dNGbT6kB7UJm4pKEnh6Jp+a8vCh7BqZL5+M4BYq\npH9e2bJSvD54IFNQLl+WnTdTW+bTrFna8ro1vHkjm+ukF02euIlJ+oSxIdjawu7dcOoUTJgA8+fL\nG5h27VLn+AJSiLu6vp91ZhRqRFxFJRkuX75MkyZNmDBhAjNmzNAWFxYuXJjDhw8zZswYevbsSfPm\nzRM5lOTLlw8AExMTbTfJ90Xp0qVZvHhxgrGOHTvy77//JrnP69eviYiIoEuXLuzcuZNw3fL7VLBn\nzx5WrlyJi4sLOXLk0I4/efIERVEoUKAAtWvXTrCPvoh4UFAQn3zyifZ9Mxb6bAj1oSgKjo6OnDlz\nhjx58lCrVi1WrFhBiRIlaN68OfPnzyc6OjpBR1RDimtr1qzJjh07uH37Nvv373/fIhwhhI8Qokb8\nT1UhxKz48a5CCNt4C8MOQgj/+PFf4ufVFEI0EEKcih9/I4SoEv9TXwhx9Z1z1BNClBdCdBdCROrs\n0z1+vJ4Qwkdnn1lCCGshREUhxL73+iaoqOghOFiKTJ1ylQTs2yft/zp0kOkeSYlwDf/7nyzYzJ9f\ndnlMS629xj4wFTXjidacCnOrJNHkiWfL9v4i4u9iby/F+KxZUpAXLy6fRDx7ZvgxatSQKUAGfsxn\nSVQhrqKSBHv27MHBwYGlS5cyZIjWeIIdO3bQuXNnoqKiKFSoEO3atWPUqFEMGjQoSZeSAgUKJPAS\nNzYWFhY0b948wZi1tTUBAQFJpsGMHj2apk2bsnnzZpo3b868efNSfV4vLy8GDBiAi4uLNndaw5Ur\nV5g+fTrVqlWjdevWTJkyhQsXLvD69WsePnyYKCJu7Gg4vI3Kp4bIyEjevHnDrFmz2L9/P4GBgXz7\n7bf4+PgQHR1N9erVGT58OJcuXUryGFFRUbi5udGgQQPc3NxwcHDg7Nmz5M2bN72XpKKikkY0aSnv\n3gvHxMCkSTBkCLi4SC9sQ1rEDxoE9+7JSHJaHWdz5ZLi/9Sp1O/75ImM8DdpkrZz61K/Pty6JUV4\nRglxkP8WHTvKdJ6SJWHjRpnP36ABzJghvciTW0/hwvJm6P79lM+1dm3qRH5GoaamqKjoYcWKFUyf\nPh1XV1fq168PyJzgyZMns2XLFtzc3Gjfvj2DBw/WuoH88ssvuLu76y10LFy4ME+fPqVChQoZdg3Z\ns2enSJEiBAQEJBK9oaGhbN68mUePHqEoCrNmzaJWrVoMGTKEEvrMcfUQFhZG586dmTFjBg0aNEi0\n3dramm+//ZZKlSoxePBgTp48yd69e7lz5w4xMTHkyJGD06dPU7VqVapWrcrt27eNLsTTkov/bnFp\n7ty5ad++Pe3bt2fZsmV4enri5ubG2rVruXfvHrdu3cLOzg4LCwtCQkK4ceMGN2/epFq1arRq1YqG\nDRuiKEoiq0YVFZWMRV9air8/9OwpXT0uX07YICejsLaW7idt26Zuv717oVUr0HkQmWZy5pQ3BD4+\nEBqa8nxjky2bjO5XqCALR+/eBTc3+Ppr+bShTRv5/rRuDQUKJNxXkyduZZX08R8+hO++gy++eL/X\nkRbUbwYVlXc4fPgw06dP5/Tp01oRHhERQZs2bbh+/TqXLl2iXr165M+fP0G6SfPmzfV2iwQZEX+W\nCbfipUuX1ttF8sCBAzRq1EibBlKmTBkGDRqEk5OTQceNi4ujf//+NGzYMMHTAl1CQkIoWLAgtWvX\nJm/evEyfPp0rV67w8uVLNm7cSM+ePYmKimLDhg106NCBIUOGsGPHDhwdHZk4cWIiS8G0ULNmTS5f\nvpyqfZ49e0ahJJ5JK4pClSpV+O677/Dw8CA0NJRFixZhZ2dHgQIFqFKlCjNnzsTHx4dTp07x008/\nUapUKaJ1/c1UVFQyhXcdU/bvl3neDg5S9GWWY2b37rIrZWrdTffsgfbtjbeOr74CPz/4+WfjHTM1\nFC4s8/NXrZKdORculP9mmm6lGzfK3PxGjWDmTLh0SUbLDSnYXLlSXl8qmjZnGGpEXEVFh4CAAL7+\n+ms2bNiAVfztdXR0ND169KBo0aKsX79eGynVJ8SdnJyYOXNmouPmypUrybSV90nJkiXx9/dPNO7q\n6kpH3fZvwOTJk6lQoQJXr16lZs2aSR5TCME333zDkydP2LhxY5I5zyEhIVqhr/HfBsiRI4deGz8/\nPz88PT0JCwvj7t27vH792uDrTIr69euzdevWlCfqoOuMkhI5c+bE3t4ee3v7JOdkz55dFeIqKlkA\nT08pumNiYNo06eCxeTN89lnmrsvBQeZor10Lo0YZtk9kpOyuuXx5+s8vhIzI//KLzHevWDH9x0wr\n334rbRknTnzrOmNlJR1shg+XxaknTsjo+VdfwcuX0o3Fzw/GjNHfMTUqClavlm4tWRFViKuoxBMd\nHc2XX37J0KFDadGiBSAjvwMHDiQ2NpZ169YlcMmwsLDgxYsX2tf29vY8ffqUQYMGUb58eQoUKEDe\nvHlRFIWAgAB8fX0z/JpKlCiRSIjHxcXh5ubGjBkzEozny5ePmTNncvHixWSF+I8//oi7uztHjhzB\nzMwsyXm6kXhdIZ4UpUuX1ustnh7q16/P2LFjiYuLMzg1RPcGwhioQlxFJWvg4SELMFu2lIWVly4l\nXbiZ0UyYIKPBQ4caVvR57Jh0H0lvFD88XOa6e3nJyHOdOrJ4NLOwsYHGjeHPP2HkyMTbc+aU6Smt\nW0svch+ft7nkZcrI4s22beVPzZoyB33nTnlzkZpGThmJKsRVVOL54Ycf+OSTTxLY+M2ePRtvb28O\nHjyYyCHj3Yh4zpw5OXToEFu2bOHZs2d4e3sTFhaGEILg4GCePn2aYdeioVy5cly/fj3B2I0bN7Cw\nsNAregenYEi7cOFCXFxcOHnyZIpiNTQ0VNvsyNfX1+huKIZgZWVFgQIFuHDhAp/qMwPWQ0hIiMER\ncUN4/vx5kk2fVFRUMobQUFnc2KmTbLf+ww9p60L5vqhfHywtZYpMp04pzzdGWoqXFzg6Qr16slg0\nZ075Pt25I7t9fvJJ+o6fVr7/Xka7hw6FFFo1UK4crFkD27bJJx43b8r3sEcPeZPRpo2MhM+alTFr\nTwuqEFf5zxMcHMz8+fPZsmULly5d0kZOr1y5wm+//caVK1f4RM8nUv78+RNExEHmWo8fPz7R3GHD\nhunt1Pi+adGiBfPmzUMIoU0hOXr0aKo7Z8bFxTFjxgzWrVvH8ePHSan9eHR0NFFRUdri1ALvVtdk\nIF988QVLly41WIiHhoYa9abB39/f6EWoKioqhhMZKf26o6NlA5nWrTN7RfoZMECmy6QkxIWQ3tnp\n6ajp6goDB8po8pAhMnIcFiYLP6tVg3Pn5HuWGTRoIK0Md+yQ+fMpYWIiI+F378o0HwcH+PVX6Wiz\naJEsyE1tIWxGohZrqvxn6dixI6VKlaJChQqEhoZy8uRJbZFeVFQUffv2Zf78+XpdROLi4ggODja4\nEPDhw4cGu5EYk8qVK2Nubs6uXbu0Y/v370+VED9y5Ag1atTg6NGjuLu7G5Q+EhQUROHChQ1qePO+\n+e677zh37hwbNmwwaL6xI+L+/v6Z8m+voqIiPabr1pXCzsQEbt/O7BUlzRdfSCvEJ0+Sn3fzphTO\nVaum/hyxseDkJPOtd++WXuiaMp+nT2XqTuPGcPJk6o9tTL7/HubNM9wf3MZGRvh1KVdOpts4O0PB\ngsZfo7FQhbjKf5avv/6an376CR8fH/74448EAnP27NlYWVnx9ddfJ9rPy8uLWrVqceHCBUIN8HkS\nQnDp0iVq1Khh1PUbgqIoLFmyhJEjRxIQEMDly5e5du1aokJNfYSHhzN8+HD69u3LzJkzOXr0aIp5\n3hoeP36cyFc8s8iTJw/bt2/n+++/Z8mSJSk2+Hn27JlRI/iqEFdRyXhiY6X7R8uWMG6cjACXLi2L\nNN95kJllMDeXzYQ2bUp+niYtJbW9wZ4/l90rT52SOdXxpmBadIV4WnzNjUnHjrIQ09AbgqCgxPn+\nGzbICH+PHsZfnzFRhbjKf5bu3bvTv39/LPSUWT99+hRLS8tEjiAeHh7Y29szdOhQ+vXrx507d/jp\np5/4+eef6dKlC+vWrePhw4cJ9rl37x6mpqaULVv2vVzH/v37mTZtWpLbmzVrxrBhw3B0dGTy5MlM\nmjRJb6qNLqGhoTRp0oRXr15x48YNOnXqlKqOkIYUZ2Yktra2nDx5ko0bN2Jvb8/tZMJid+/eNarf\nuyrEVVQyFm9v6YSyf78UnH37StFaq5b8eadOPUvRv79MT0kOV1cp2FPDlSuyENPWFg4e1F+kqhHi\nDRvKSHIa2jAYDRMTeQNlaJ+5Bw+ktaGGV69kHcCiRam/YcloVCGuoqKHGTNmsHv3bo4cOaIde/ny\nJe3bt2f+/PkMHTqUggULEhkZSXR0NM+fP8fNzY1du3ZhZ2dH/fr1OX36NADHjh2jcePG7621ubm5\nOXv37k12zuTJkwkODubgwYP07t072bmRkZF06dKF+vXrs27dujQVGmaliLgGGxsbTp06hb+/P61a\ntcLf35+IiAhiY2MTzPP09KRSpUpGOWdUVBRPnjzJcu+FisrHiBDSL/rTT6FrV1mkp1uaY2Mjxej6\n9TKfOCvStKmM2F+5on/7kyeyA2ZqLBfXr5d58T//DPPnJ10AqRHiBQvKpwcpeXO/b/r0kf7qHh7J\nzxNCCnHdf+u5c2XH0Xej/lkRVYirqOjBwsKCtWvX0qdPH4KCggBZcNmuXTttukqtWrUoVKgQM2bM\nYO7cuVhbWzN9+nQeP37M2LFj6datG4sXL2bHjh0GpYKklerVq3Pr1i2ioqKSnOPr60tYWBh2dnbY\n29uze/duvfNevXpFr169yJcvH0uWLEnzzUNWEuKaawcZqQ8LC2PIkCG0bt2aRo0a8b///U87NyIi\ngsDAQK2HfHq5evUqlStXTuS4o6KiYlwCA2WUePlyOH4cxo5N3KbexgYeP4bx42UOclbExERG8JOK\niu/bJ9NtknGO1RIVBSNGyOY3x46l3FVSI8Qha6Sn5Mol179wYfLzXryQ75smZvToESxdmnmNiVKL\nKsRVVJLAwcGBvn370qtXLw4fPoy7uztz587Vbi9UqFACS0JLS0seP35M9uzZ6dGjB+fOneP333/n\n0KFDfP755+9tnblz58bGxoZLly7p3S6E4IsvvuDHH3/kwoULNG3alC5dutChQwf++OMPFi5cyIQJ\nE+jXrx92dnbkyZOHjRs3pqvQ0s/Pz+ie4Gnh9OnT1KxZE0tLSxwcHOjZsyedO3dmypQpFC9eHE9P\nT37W+bT28vLC2toa05Q8swzE3d2dBg0aGOVYKioq+tm6VXpG29nJlIqkihg1BX2jR0uv7Kza4KVv\nX5knri+2Ymhair+/jK77+8OFC4YVduoK8alTpatKZjN8OGzfLm+gkuLdaPjkydL6MAt8BRmEKsRV\nVJJh+vTplC9fnvbt29O0aVNy5Mih3fauEC9WrBiBgYHa16VLl2bSpElUqFDBqC4c+mjTpg17kvCy\nOn78OG/evGHkyJEoisLly5eZM2cOx48fZ8aMGfj5+ZE/f36aNGnCypUrWbduHTlz5kzXenx8fIwW\nVU4PJUqUwM3NjYCAAIYMGUL16tUZNWoUiqIwduxYTExMiNFJhLx8+TLVqlUz2vnPnDlDw4YNjXY8\nFRWVt7x8CV9/LYXXzp0y91vnIzoRFSpIIW5mJnOPx46VRZ1ZDWtr2Xzm3Y90TTfNlOI6J05Ip5j2\n7aWINdSN9enTt+4ixYpB3rypX7uxKVQIevWSEe6k0BXiFy7I92jixIxZnzFQhbiKSjKYmpqyYsUK\n5syZw+3bt6lRowYrVqzg5cuXFChQgBcvXhAXFwdAkSJFePKO79S5c+cYmAFhhb59+7Jq1SoWLVrE\noUOHuHr1KleuXGHbtm2MHDmSbt26oSgK27Ztw8vLi7Fjx3LlyhVy5sxJ+fLlmTRpEgMGDKBp06ZG\nyWX39fXNEkK8bNmyNGrUiLx589K1a1eWLVum7Rrarl07RowYwaRJk7TzT548SZMmTYxy7tjYWE6e\nPKkKcRWV98Dhw1C9uhSZV64YlgtctKhskf7iBXTpItuhr1nz/teaFvQVbZ44AZUrQ5Ei+vfRtKrv\n3l12ppw8OXF6TnL4+8umQlmNsWNl7n94uP7tGiEuhJw7Y0bWuIkwFFWIq6gYwJgxY3B3d2fhwoUc\nOnSIsmXL0qdPH3LlysWzZ88AKFq0qDafXMPBgwdp2bLle19flSpV2LVrF7dv32bWrFn07duXfv36\nsWbNGu7du4ejoyMAq1atInfu3JiammJtbc3hw4eZM2cObm5uRltLVFQUjx8/zhKpKSnh5OTEwYMH\nOXHiBAAnTpwwmhDX2D2qzXxUVIzH69cytaRfP1i9WkZKc+c2bF9FeZueoigy9/jHHyEk5L0uOU10\n6yaFt85D1mTTUl69kpHj9etlMx4Hh9Sf09cXskD8JBHW1jLNJqmbJo0Q37pVivV+/TJydelHFeIq\nKgaiKAqtW7fGxcUFb29vGjZsSEREBAcPHgQSC3EfHx9evXqFra1thqyvQYMGrFixgqNHj3Lt2jWu\nXbvGqFGjqFOnDjVq1ODZs2e4u7vz7NkzbTqGlZUV8+bNY+XKlUZbh5+fH5aWlh9EgaK5uTlLlixh\n8ODB3L9/n2fPnlG5cmWjHPuvv/6ib9++RjmWiooKLFsmuz4GB8P166nvkPnihYygaxq/1K4tOy7O\nnm38taaXPHmgc2fYuFG+1nTT1NfW3stLPhHImRNOn05o42coQsD9+1lTiAN89520ItRnqfjggWzY\nNGGCvLnKAn3kUoUqxFVU0kDBggXp3r07iqJgGf8s710hromGvy/bQkPYtWsXnTt3BsDFxYW2bdtS\nunRpbt26pZ1jb2/P2bNnU2x0Yyg+Pj6UK1fOKMfKCBwdHWncuDGff/45lpaWmKTmWW4ShIWF4erq\nSq9evYywQhUVFYB69WS6xqZNMq0ktUydKtMvdDswzpolI+s+PkZbptHQpKcIIS384uLkjYgurq7Q\nqJF0F1m7VjqNpIUnT+S+WTWl49NPZfHl1q2Jtz14IIt0q1WD5s0zfm3pRRXiKippZO/evRQtWpQX\n8W3aihcvzmOd0u6DBw/SqlWrzFoecXFx7N69m06dOgHg7OxM7969qVu3LufPn9fOK1WqFCYmJty/\nf98o5/X29sba2toox8ooVq1aRZMmTQgx0jPqv//+m6ZNm1K4cGGjHE9FRUUWINrbp23fBw+ktaGl\npWx7r8HSEr79VloaZhTPnkmf85Ur5bqSonFjmXJy6dLbaLgmrvNuq/qhQ9PXuCarpqWAdI958EBG\nxfW1vb9/X96cGdr8J6uhCnEVlRQYNGgQTk5OnDhxQluYCbBv3z5sbGwIDg4GZERcU6wZExPDkSNH\nMlWInz17lvz582NjY8PDhw/x9PSkdevW1KtXL4EQVxSFtm3bsimlvsoG8qFFxEG+B8uXLzfKzYgQ\ngmXLljF8+PD0L0xFRcUoTJsmUxbMzRP7Y3/7LZw5IwscMwIzM+jUSfqd160LlSrJvHc3Nym8NZiY\nyHzndesS5oc/fy5F+cmT+lvVpwVf37SltLxPhJBuOFWrwm+/yWuOiJCe6BpevZLuOX36SFecDxFV\niKuopEDXrl2Ji4tj5MiRlC9fnt27dxMbG8vhw4epXbs2D+JDGoUKFSIsLIzw8HBcXV2pXLmyNm0l\nM1ixYgX9+/cHYPv27XTs2JEcOXJQr149Lly4gLu7O+vXrwdg3LhxLFiwgJcvX6b7vB+iENdgDP9w\nd3d3IiIiMqRIV0VFJWU8PWHvXplTXrYsvGvtnyuXTO8YNgxcXN7/evLkkcJx40ZZjLlxo3R0+eUX\naRvYsqWM7l6/Lu0ZN22CmzdlweLVq7I7aJUq0qZPX6v6tJDVIuJXr8o0kylTZG3AggX6297H19nz\n44+Zs05joApxFZUUaNu2LbNmzeL69eusXr2a7777jpYtW1KkSBGaNWvGhQsXACniatasydmzZ1m6\ndCkjR47MtDXfunULNzc3BgwYAMDWrVvp1q0bALa2tty6dYvOnTuTL95g9ty5c0RERPDHH3+k+9wf\nshBPL0IIZs+ezbBhw4ySa66iopJ+fvxRpjW8eiWj0fryy8uVk63fv/hC2v5lFCYmsmh08mQZIff3\nh1GjpDB2dJQ3CGFhMhVl0yZo1QrmzJHC1Eh9x4CsI8SfP5eNhNq0gR49pCDXLcrt3VvaVd68KV83\nawb790OBApmzXmOgflOoqKSC5s2bc/36dYQQ5M+fn08//ZTz588THR0NQLdu3Zg/fz4eHh507do1\nU9aoid47OTlRsGBBHj9+zI0bN7QR2pw5c1KsWDFWrlypzR+PjY1l5syZuLu7p/vcXl5e/1nLvrVr\n1/Lo0SNGjBiR2UtRUVFB5lefPg0jR0qRmy3b21bouvzyiyxYdHGR6Q+Z1ejH3Fymrfz+O3h7w65d\n0ink9WuZDz5okBSoxiarOKaYmUGpUnDnjsx7f/dmI2dO+W+5YMHb1y1aZPw6jYkqxFVUUomm6+SP\nP/5I4cKFqVixIsePHwegf//+HD58mJEjRybowpmRzJ49m9jYWG2O8pgxYyhcuDAhISFERESgKArt\n2rXj4cOH2n0GDRrEoEGDOHbsGGFhYWk+t5+fH/ny5SO/vm+6j5x79+4xYcIENm7ciJmZWWYvR0VF\nBfjhB5ne8MknUogLoV+Ig5zTrZtsipMVLPCEeNucpn592TVy2TJ5o2BsskpEPHdumc+fXDfQoUPl\nDUpAQIYt672iCnEVlVQSERHBxYsXtY1fOnfuzM6dOwHpvlGlShW++eabTFmbq6sry5Yt4++//8bU\n1JSgoCAOHTrEixcvsLKy0qbR1KpVi0uXLhEXF8fdu3cByJ8/P40aNUpXcx8PDw+qVq1qlGv5kIiJ\niaF37944OTn9J69fRSUrcvw43L0ro8hhYTKy/OZN0kI8q7FkCTx6JNcdFCTt+erWlakYxiQ2Fh4+\nfNsmPqtTsCB8+aUssv0YUIW4ikoq2bdvH1WrViU2/tmlRoifOHGC+fPns3v3bvJmghnrkSNHGDhw\nILt376ZEiRIATJs2jT59+vD7779rhTZA7dq1cXd3p1WrVnyr82nWtWtXtm3bluY1eHh4UKVKlfRd\nyAfI7NmzMTc3Z9SoUZm9FBUVFWQ0+YcfYPp0yJFDRsNLlJBNfT6EfOIrV2Q0fOxYsLOTQjk6Grp0\ngR07jHsuf38oVEimeXwoWFqCjvnXB40qxFVUUsnvv/+Oh4cHxYsXZ+nSpVSqVAlFUejUqRMbN26k\nTCaEFc6cOcOXX36Ji4sLdevWBeDGjRts27YNJycnunfvTs6cObl48SIA1tbW3L17l88++0wbzQfo\n1KkT+/fv5/Xr12lax39RiJ87d45ly5bx559/qgWaKipZBDc3Kbo1PbU0QtzbWxZmZmXCwmQe+G+/\ngZ+fdHkpUUKmj3TuLB1g4suSjEJWSUsxlNhYWL9e2jp+DKjfGioqqcTf35+jR49y+vRpFsRXjFhZ\nWdG7d+9M8Q13c3Ojc+fObNiwgc8++wyQ7h0jRozgp59+okCBAri4uPDo0SOcnZ3x9vYmf/782Nra\n4uDgkMCyr3DhwtSuXZv9aXz2eevWrf9UakZ4eDhff/01y5Yt0z6FUFFRyVzi4mQ0fObMt7neAQFS\nzN65k/X9pkeOlM18evaEc+dkfriNjewIWqIElC8v026MxYcmxF1dZQ5548aZvRLjoApxFZVU4Ovr\ny4sXL6hVqxY1a9bk8ePHvHnzhm7duiVo9pNR/PXXXwwYMIDdu3fj4OCgHf/zzz+JiIhg8ODBgEw5\nKVmyJG5ubvj6+gIkauyjIa3pKUIIPD09/zMRcSEEo0ePpkGDBlprSBUVlczHxQWyZ5fRYw3+/jIl\nJToaihTJvLWlxIYNMuXit99kes25c7K9u40N3Lsn5zg6wvbtxjvnhybEgtpscAAAIABJREFUFyyQ\ndpTp6SSalTCiC6WKysfP/v37ad26tTYFIXv27ERHR1OhQgVcXV0zbB1CCH7++WdWrFjBsWPHqFSp\nknZbQEAAEyZM4NChQ2SLDwdly5aN9u3bU7p0aVq2bMmTJ084fvw4e/bsISYmhrFjx2r3d3R0xMnJ\niaioqFQ5vzx69Ig8efJgoc+k9yPkjz/+4Ny5c+m2fFRRUTEuXl5SWOoKNY11YcWKWVfA3b0rCxAP\nHZLuIb6+cs0lS76NiIMU4s2awdKl0oc8vfj6yuN9CJw7J/PlM8kd+L2gRsRVVFLB0aNHtX7cISEh\nCCHImzcvFSpU4Pbt2xmyhkuXLlG3bl1+/vlnTp8+nUCEgywc7N+/PzVq1EgwnjdvXo4fP86UKVMo\nVKgQrVu3JjIykjp16iSYZ2lpSeXKlTl69Giq1vVfyg8/ceIE06dPZ+fOnZlSmKuiopI0Y8dK//C9\ne9+O+ftL9xHdtJS4OHBwkB7dmU1kpHQCmT4dNB/dmrQURUkoxCtWlE2JjFWs+CFFxBcsgDFjjNvM\nKLNRhbiKSio4e/YsDRs2BODu3bvY2NigKAply5blxYsXhISEGP2cQgjc3d1xcnKiXr16dO7cme7d\nuwMyIq9LVFQUW7Zs0XqI6/L06VOuXr1KsWLFUBSFbt268erVK8aPH8+LFy8SzO3QoQN79uxJ1Tpv\n3br1nxDifn5+9OjRgw0bNvxnGxepqGRlcueGVatky/rQUDnm7y+LICtWfDvPw0MWb+bKlTnr1GXi\nRChbVq5ZgyYtBRIKcZBRcWO5p/j6ynNndXx94fBh2XnzY0IV4ioqBhIQEEB4eLhWfF2/fh1bW1sA\nTExMqFKlCjc1fXeNhCb/vHfv3gghmDVrFvfv32fChAk4OjqyYcOGBPPd3d2xsrKirJ5P1bZt21K+\nfHlatWpFXFwcw4cPJyYmhilTppA7d+4Ec9u3b4+rqytCCIPX+l/wEI+IiKBz586MGzeO1rp9l1VU\nVLIUzZvLaPfEifJ1QAAEByeMiJ86lTUK/lxdZc736tUJ02bOnn0rxMuWldcQFSVfa4R4Kj6i9RIZ\nKd+XkiXTd5yMYPFi6Qn/sT2EVIW4ioqBXL16ldq1a6PEf1KePHlS68sN0KBBA44dO2bUc06YMIHY\n2Fg8PT2ZOXMmrVq10uZ9DxkyhN9//52YmBjt/IsXL/Kp5pP7HZ48ecKpU6c4c+YM2bJlw9PTE2tr\na6ytrQkPD+fPP//UuqVUrVoVRVG4deuWwWv92FNThBAMHjyYKlWqMG7cuMxejoqKSgrMmwe7d8Ox\nY7Ihjp9fwoj4qVNgb59pywNkpH7wYNi0KaG/eVQUXL8OmszB7Nll63cfH/m6Vi0poj080nf+Bw+k\nE0tWT/V4/lxaFmZSr7z3iirEVVQM5OHDhwk8ws+dO6dNUwHZ2GeHETstPHz4EGdnZ9asWaO3aLJh\nw4aULFmSLVu2aMcuX75M7dq19R6vfv36WFlZ0b9/fwAePHhAtmzZ+Oqrryhbtiyurq7a8yiKQrt2\n7di3b5/B6/X19cXa2jo1l/hBMW/ePG7fvs2qVau0N2MqKipZl/z5ZUv4Dh1ksxofH5nioeHkycwV\n4rGx8NVX0q5QJ6YDwNWrYG0NefK8HdOkp4wcKUV4587pd0+5f//DyA9fsUL+O36MLrGqEFdRMZCA\ngAAsLS0BiIyMxNfXl4o64ZXGjRsTHBysbSOfXv7880969uxJwYIFk5wzfvx4Fi5cqE0huXz5MrVq\n1dI7t2jRovj6+tK9e3dsbW2pU6cOhQsXplevXjx+/Jjt27fTTKd0vnbt2ly/ft2gtUZHR/Ps2TOK\nFi2aiiv8MIiMjGTkyJGsXLmSHTt2kCsrJJSqqKgYRKdOsrAxLEy2Rtdk4fn5ySLNzPQUnzVLup5M\nmpR426+/yvXppp7Y2ICnpxSlZmbG6bL5IRRqRkbCkiXwsT6IVIW4ioqBREVFYWZmBoCPjw+lS5fW\nvgYwNTVlyJAhrF27Nt3nio6OZtWqVVof8KRo27YtUVFRTJs2DWdnZ3x9falcubLeuXPmzMHBwQE7\nOzvWrFlDYGAgJ0+eZNy4cYlyxEGmp3gY+NwzKCiIwoULa9NmPhbu379P48aNCQgI4NKlS5QuXTqz\nl6SiopJKRo+Wude6ovv0aRkNz6yHWydOwB9/gLPz26ZDuixdKiP6U6a8HbOxgRs3oHBhuW57e3j0\nSEa108qHIMT//huqVn3rJvOxoQpxFRUDMTc3JzS+BD8wMFAbHdeld+/euLi4EBkZma5zrV69mkqV\nKiWyIHwXExMTXFxc8Pb2Zu3ateTNmzeRk4qGBQsW4OrqyuTJk/n0009TFM2VK1fm9u3bBjUq0n1a\n8LHg6urKp59+Ss+ePdm2bRv58uXL7CWpqKikgXz5ZGRZV4i3aAFz5mTOep49g969Yc0aSOpj08IC\n9u2TzYmWL5djNjbSa1zTkChbNpmusXNn2teS1YW4EG8b+HysqEJcRcVA8ufPz8uXLwFpBVioUKFE\nc8qUKYOtrS1ubm5pPk9gYCBTp05lwYIFBs2vVKkSzs7ODBs2jMbJWACYpLLzQ968eSlYsCD3DQi3\n+Pv7fzRCPCYmhgkTJjBixAh27NjB2LFj1ZxwFZUPmMBA+Vu3ULNIkYSvMwohYMAA6N4dPv88+bmF\nCkkx/tNPsujUxkYKZ93OoOm1MczqQvzAAfn7YzapUoW4ioqBWFpa4u/vD8CLFy+SjJB+/fXXiWwF\nDUUIwYgRIxg0aBDVq1dP1b6+vr5YGfkTtUqVKgalpzx48IBSpUoZ9dyZwZ07d2jatCnXrl3j8uXL\nCYpxVVRUPkyCguTvzMwH17B0qXRKMTQab20Nu3ZJ7+zHj+HFi4TuKi1bwrVr8ORJ2taT1YX4ggUy\nN/xjjoWoQlxFxUBKly6Nn58fERER/Pvvv1oP8Xfp1q0bR48eJVAThkkF//zzD56envz444+p3tfP\nz8+oOcxCCIoXL86RI0dSnOvj4/NBO6ZERkYybdo0GjVqRPfu3XFzc9P7xENFReXDQ19EPDO4elVG\ntzdvBj1GWElSty78+Sd06wbm5gn3zZlT+qXv3p369YSHw6tXkFVr7K9dg5s3oWfPzF7J+0UV4ioq\nBlKgQAE8PDwoVqwYcXFxfPXVV3rn5cuXj+7du7NmzZpUHT8wMJBvvvmGdevWkTNnzlSvLyQkBAsL\ni1TvB7IQddu2bbi7u7N9+3Y6depE/vz5+fvvv/n111+pV68emzdvTrLBz+7du4mIiEjTuTOb48eP\nU6NGDa5evcqVK1cYPXp0qtN4VFRUsi6BgdKHW8d9NsMJD4cePaQbSloa8rZvD9OmQUgIxGdIaklr\neoqmo2ZWjTYvXAijRkmHmI8Z9dtGRcVAChQogKmpKX5+fuzcuZMiuol67zB8+HBWrFiRoNlOcggh\nGDZsGAMHDqRevXppWl94eDh5dE1nU7mvs7MzY8aMYdmyZTg6OuLt7c2bN2+IiIhg+vTpzJs3j4YN\nG+Lu7p5o/+DgYObNm4ejoyNnz55N0xoymufPnzNo0CC++uor5syZw86dOz+K9BoVFZWEBAVJ8ZuZ\nTWtGjZJe4b16pf0Y//ufLOI8dUraMWr4/HPpiR7vJWAwWTkt5dEjGeX/3/8yeyXvH1WIq6gYyOvX\nr8mVKxf58+dPcW7NmjUpVaoUrq6uBh3777//xsvLi6lTp6Z5fYauTR8FChRgx44dnDt3jsOHD9Ov\nXz9taoaZmRlt27blwoULDBs2jC+++IIWLVqwbNky/P39tU4yfn5+tGjRgp49e9KkSRP27t1rkONK\nRiOEYMuWLdja2pIzZ048PDxwdHTM7GWpqKi8JwIDMzc/3NkZ3N2lF3Z6efJEpqh06SL9tUGmq9jb\ny8LO1JCVhfiSJfD11wnz4T9WVCGuomIgMTExTJw40eD5w4cP5/fff09x3pMnTxg7dizr1q1L4Eue\nWjZt2kTz5s3TvH9KmJiY0KdPH+7evcvIkSM5e/Ys1apVo0GDBhQpUgRzc3NGjhyJl5cXw4YNY9y4\nceTLl4+NGzca/GTgfePn50eHDh2YMWMGW7duZenSpZibm2f2slRUVN4TEREyepxZ+eFeXjB2LGzZ\n8raZUHpQFOk/bm4uhWpsrBx3dEx9l01NakpWIywMVq+GMWMyeyUZgyrEVVQMxNLSkvHjxxs8v1u3\nbly/fp07d+4kO2/cuHH06dOHOnXqpHeJGUKuXLlwdHRkw4YNBAUF8dlnn2FjY8M///zD6tWrMTU1\npWfPnixevJjy5cuzcuVKKlSowIoVK3jz5k2mrDk2NpZff/2VWrVqUb9+fdURRUXlP0JmOqZERsKX\nX8rcbmM2o8mWDTZuhOBg+OYbaYnYqRPs3w+p+YjNqhHxNWukz3u5cpm9koxBFeIqKu8JMzMzBg4c\nyMqVK5Occ+zYMU6cOMG0adMybmFGICwsjLi4OLJnz05ISAg9e/akRo0azJ07lzFjxhATE0NQUBC2\ntrYcP36c9evXs3v3bqytrVmwYAHh4eEZttbw8HDatm3L9u3bOX36NFOmTCFHaiwLVFRUPlg0jimZ\nIcQnTYLSpWH4cOMfO2dOaWvo7i6dWIoUgerV4fBhw49x/37WE+IxMbB48cfbzl4fqhBXUXmPdOzY\nMUn7PyEEkydPZs6cOXpbzGdVrl69SvHixalduzYHDx5k79692NraUrFiRc6dO8etW7cYOHAgAQEB\nFC9eHAB7e3v27t3L3r17uXDhAlZWVkybNo1nz56917U+f/6cVq1aUbp0aY4cOULFzPYvU1FRyVA0\nEfGM/l9/717Ytk1Gd9+XK4m5ucwLd3aG339PnXuKEFkzIr5tG5QqBZ9+mtkryThUIa6i8h6xs7Pj\n7t27eiPA+/btIzQ0lB49emTCytLOH3/8wejRo5kyZQoDBw4kJCSEIUOGEBwcjIWFBTt37uTmzZss\nW7aMwMDABAWbNWvWZPPmzZw+fZpHjx5Rvnx5GjZsiJ+fn9HXGRgYSNOmTWnUqBGrVq0iW7ZsRj+H\niopK1iYwULa4L1w4487p7y8b8Gzc+P6LDYsWld0nZ8+WFo27d7/NG0+O58/BxES6sGQVNO3s/0vR\ncFCFuIrKe8XMzIzq1atz4cKFBONCCJycnJg+ffoHIxCXL19O7dq1OXnyJGPGjKFr16788ssvFClS\nhFatWtGjRw9iYmLInTs3p0+fpnLlypw4cYJq1aoxevRonJ2dCQ4OBqBChQqsXr2a9evX4+3tTaVK\nlahXrx7Xr183ylrv37+Pvb09PXr0YN68eWqLehWV/yhBQTIanlEfAbGx0Ls3jBwpnUwyAisrcHOD\nGTNklPzUqZT3yYrR8JMnpUd6hw6ZvZKMRRXiKirvmQYNGiTy1t65cydxcXEflG1enjx5mDt3Ltev\nX6dwfHjp1q1bhIeHa/Ouf/jhBwBy5syJlZUVkyZNYvny5ZQoUYJdu3bh5eWV4JgvXrygRYsW3Llz\nBwcHB9q0acPnn3/OiRMn0rxOX19fmjRpwpgxY/jhhx9UEa6i8h8mo60LZ8+WvydNyrhzgswP37oV\nAgJk06Akeq9pyYpCfP7/27v3OJur/Y/jr8WMOzWRFIqUXArJCBFiSCZySco98kOlU50cv/xKjjOi\nOkUXRFJEcmKkcsqkUCd3hy4uMbrJlCTllsvM+v2xvqNtDOay9/7O7Hk/H4/92Huv/f2u71ozs7/z\n2Wuv72c95TLM5JOxqaDxMb29SMHQuHFjZs6ceeJ5WloaI0eOJCEhIV+t4NizZ89TyooVK8Yll1zC\n+PHjee2116hduza33XYb9evXP7FNs2bNaNasWaZ1fvXVV1xxxRVUrlyZ0aNHM2LECGbMmEG/fv24\n9NJLSUhIyNYCRykpKcTFxTF8+HCGhOIKKRHJV376CQJORyH18cfwwguwfr0/wWSzZvDAA5CQAEWK\nuGkx5cpB2bLuFvg4KQlyuP5bSGzdCitXwpw5frck/PJPFCCSh3344YccSV9dIYPGjRuzYsWKE8vD\nv/nmmxQvXpz4+PhwNjEkdu/eTdeuXZkwYQK//vor48aNo1evXhw8eJBq1aqxcOFCvv7660z3PXbs\nGGvXrqV6wHBVsWLFGDhwIFu2bOHWW2+lc+fOdOrUiS+++OKsbfnll1+Ii4ujf//+CsJFBAjfiPje\nvW5KyrRpcNFFoT/e6Ywe7VIYHjwIGze6/OWjR7uc47Gxbr78jz/Ct9/Ce+/B7bfD55/71950Tz8N\ngwdDiRJ+tyT8FIiL5NKqVavo3r37ifnPGVWqVIlixYqxZcsWUlNTeeyxx/j73/8eEVMmjhw5QsWK\nFRk7diyNGzfmwIED7N69m+nTp3P33XdTtWpVrr32Who2bMjYsWNJSkoiKSmJ22+/nWrVqnH06FFa\nt259Sr3R0dEMHDiQbdu20axZM1q1akW/fv1Om4d8//79tGvXjvbt22dr0SURiWzhCMSthTvvhC5d\noH370B7rbIyBokXdiHiFCnDlldC8uVuJc+BAN2Xmn/90I9A//QRXXw1xcS7jyrp1/rR5926YOxfu\nvtuf4/tNgbhILhUvXpzp06dTqVKl027TrVs3Zs6cyTvvvEOZMmVo06ZNGFsYOkeOHKFIkSLcdddd\n/Oc//+GJJ56gZcuWrF69mlKlSvH888+za9cuxowZQ0pKCgkJCYwaNYr58+czb948PvzwwxPzzTNT\nvHhxHnjgAbZv386hQ4fo2LEjhw8fPmmbo0eP0rFjR66++mrGjh0bER9wRCQ4unWDGjVCe4yJE2Hn\nTnj88dAeJ9hKl4Zhw2DHDmjRwi0K1L69y00eThMnwq23ulzoBZGxZ5vRHyEaNGhg165d63czpIDa\ntGkTrVu3pn79+nTt2pW+ffv63aSguOWWW+jZsyddu3YFXL7wNWvWUKJECZKTkzkvk9xdGzdupHv3\n7mzevDlbxzp+/Dh33HEHZcuWZdKkSSfKBw8eTEpKCvPmzcs3GWhywhizzlqbP5ZfDQKdsyU/2LDB\njSivWAGXXeZ3a3LnyBGYPh3GjoVq1eCRR9xoeijHNg4fhipVYNmy0H9gCresnrM1Ii4SBrVq1aJC\nhQokJSVx6623+t2coPnggw/47bffADh8+DDVq1enVq1axMTE0L17d/bt23fKPomJibRt2zbbx4qK\niuLFF19k/vz5bN26FYCXXnqJpUuXMmPGjIgOwkUk7zl40C1hP358/g/CwU1pGTQItm1z893vustd\nAPr++2fPwpJTM2ZAw4aRF4RnhwJxkTCJi4ujVKlS+WoVzTP5+uuvOXToEMuWLaNy5cqcc845/PDD\nD8yfP5+YmBiOHDnCVVddxb/+9a8T+6SmpvLaa69lmoElK2JiYujZsyczZ85k1apVPPzwwyxYsIAy\nZcoEq1siIlly773QuDH06OF3S4IrOhr69YPNm2HIEJeJ5dpr3WJBwQzI09LcRZoFbQGfjBSIi4TJ\nwYMHT+TZjgTjxo2jaNGi1KxZ80TWmPfff5+qVasyd+5c1qxZQ7ly5ejfvz+LFi0CYMKECVx88cVc\nc801OT5u9+7dmTNnDl27duWll17SsvUiEnazZsGnn8Jzz/ndkqyzFr75xl0YeejQ2bePioI77nBZ\nVYYPh8ceg3r13P5ZWb3zbN55x81Tb94893XlZ8ojLhIm27Zto127dn43I2i2bNnCzJkzT8wPD1St\nWjXGjx/Pxx9/zFdffUWHDh2IiYkhLS2NVatW5eqCypo1a5KcnMw999xDhw4dctMFEZFs274d/vKX\nvJeLO6N9+2DVKli9+s9b4cJudLtJk6ynCixUyGVd6dTJreD55JNw/fUuK0tuPPUU/PWv4Vv1NK/S\niLhIkK1fv54XXniBtLS0k8qTk5OpVq2aT60Krp07d/LZZ5+dMRf6wIEDmTlzJnv37mXAgAH88ssv\nbNiwgctyOZkyISGBokWLnnaRIBGRUDl61M0LHznSjQ7nZdOmuQsvDxxwU03WrYMffoDERDhDkq/T\nMsZlVVm6NPdB+OrVLpd5JuM4BY5GxEWCrGTJkrz++uu8+uqrxMfHc/XVV3P06FG+/fZbqua1NYVz\naM6cOXTu3JlixYqd8trq1asxxhAbGwtA0aJFmThxIitXrmTp0qX06tUrx8ddvHgxM2bMYNiwYSxd\nupRu3brluC4Rkez63/91QWx+yHn94IN5d/71P//pvlWIUhSqQFwk2K644gqWL1/O22+/zcqVK5k4\ncSLgplQULVrU59YFR2JiIiNHjjzxfOLEidSuXZvmzZvz1ltvUbx48ROBOEChQoV49tlnGTBgQI4D\n8ZSUFPr27cusWbM477zzFISLSFgtWgT/+pdLWVjQp1PkxjffwAcfwNSpfrckb1AgLhIChQoVomPH\njnTs2NHvpgTV0aNHuemmm/j0009Zt24dderUoUKFCixYsODEaP/PP/9M3bp1T9m3SZMmJCcnk5aW\nRqFC2ZsVl5qaSs+ePbnrrrto2bIlqamppKSksHfv3kxzlYuIBNOuXdC/v7tQUaec3Bk/3v0slezK\n0RxxEcmyIkWK0K9fP6Kjo/nyyy+pWbMm119/PevXr6dMmTKkpaWxbNmyk0bD00VFRVGyZEn279+f\n7eOOGTOG1NRUHn30UQAKFy5MbGwsK1euzHWfRETOJDXV5dUeMsTl1Zac+/VXlzt86FC/W5J3KBAX\nkWxp3rw55cuX57XXXuPHH39k2LBhlC1blvbt21OjRg0uvPDCTANxgHPPPffEAkBZtXz5cl544QVm\nzZp10qI9TZo04ZNPPslVX0REzubxx13qv4cf9rslwbdjh8uHHq5F1qdMcRd85uRi0UilQFxEsqVk\nyZI0bdoUcBdixsfHs3XrVnbt2sWsWbN49913T5ue8Nxzz810tc3T+fnnn+nRowfTp0+nYsWKJ712\n8803M2/ePGy4/oOISIHzySfw/PPw2msu9V+kqVTJpTicMCH0xzp6FJ59Nu9eQOoXBeIiki0xMTHM\nmTPnlPISJUoQGxt7xpVDS5YsycGDB7N0nLS0NPr27cvtt9+eaf712NhYjh07xoYNG7LeeBGRLDpw\nAFq3hsmTIcM4QMQoUgTeeAPGjIE1a0J7rDlzoGbNvJ/2MdwUiItI2ERHR3Ps2LEsbfvMM8+wd+9e\nEhISMn3dGEOPHj2YqkvvRSQE9uyBc86BW27xuyXBcfSoC4YzqloVJk2C225ziwCFgrV/LuAjJ1Mg\nLiJhExUVlaVAfNWqVYwbN47XX3+d6Ojo02537733MmfOHH744YdgNlNEhE2bIJMEUPnWmjXwxBOZ\nv9ali/vAUbs2jBgBycnBPfaCBZCWBm3bBrfeSKBAXETCJisj4vv27aN79+5MnjyZKlWqnHHb8uXL\n079/f0aPHh3EVoqIuEC8dm2/WxE8H30ELVue/vWnn4bFi+HwYWjUCOLj4fjx3B/3+HG3GmlMjPKv\nZyZLgbgx5htjzOfGmA3GmLVe2WhjzGde2WJjzEVeeceA8rXGmKYB9TxhjPnSGLPZGPOs8a7oMsZc\n49W/PUP5ecaYJGPMNu8+xis33nbbvWPVD/YPRkSCLyoqiuNnOLNbaxkwYADx8fF07tw5S3U+/PDD\nJCYmsn79+mA1U0QKkO+/h1q14K67YPp0+OorN5Xiyy9deaRYuvTMgTi4Dx5PPw07d8Lnn7vFd3Lr\nH/+A2FiXi/2553JfX6TJzoh4S2ttPWttA+/5k9baOtbaesA7wKNe+RKgrld+J/ASgDGmCXAdUAe4\nEogFmnv7TAIGApd7txu98uHAEmvt5V69w73ydgHbDvT2F5E8rnDhwqSmpp729UmTJpGcnMyTTz6Z\n5TpjYmJ46qmn6N27N3/88UcwmikiBciFF7qsKHXruhHh1q2hfHlITIycQPzIEZcdJWMe9H37XFaY\nxx47ubxoUbdw0e+/5+64n3wCL77oViT98EM3T/zll3NXZ6TJ8dQUa23gr6ckYL3yA/bPfGInyr37\nYkARoCgQDfxkjLkQKGOtXeHtNwNIvzSiI/Cq9/jVDOUzrLMSONerR0TysDMF4hs2bGDkyJG88cYb\nFCtWLFv19uzZk5o1azJixIhgNFNECpCoKKhfH+65B15/Hb77Dtavd8F5o0Z+ty44Vq2CGjXcxafW\nwqefQt++UKWKC5YzGykvVQqymOQqU/v2uYWQpk51H3YuuQSSkuCRR1ymFnGyusS9BRYbYyzworV2\nCoAxJgHoDfwGnPg1GmM6AY8D5YH2ANbaFcaYj4AUwADPW2s3G2MaADsDjrUTSE8UdIG1NsXbP8UY\nU94rrwh8n8k+KYGNNsYMxI2Yc/HFF2exqyISKoULFyYtLe2U8v3793PbbbcxYcIEqlevnu16jTFM\nnjyZOnXqEB8fT8uzff8qInIGlSu7W6T46CNo3NhNDZkyxY2QDxwITz4J55+f+T6lSrkUjjlhLfzP\n/8DNN7u55umqV4f33oO4OChRwr1e0GV1RPw6a2193JSQu40x1wNYa0dYaysDs4B70je21iZaa2vg\nRrBHAxhjLgNqApVwQfMNXj2ZTd0/2wodWdrHWjvFWtvAWtvg/NP9pYlI2Bw/fpxChU4+7VhrGTx4\nME2bNuWOO+7Icd1ly5Zl6tSp9O/fnwM5/e8hIhKBpkyBadPcSPhzz8HWrS6V4JlCo5Ilcx6Iv/qq\nu9g1sywtV10Fb78N/fvDkiU5qz+SZCkQt9bu8u53A4lAwwybzAa6ZLLfcqCaMaYc0AlY6U1dOQD8\nG2iEG80OXOy0ErDLe5w+dQXvfrdXvhOofJp9RCSPOnz4MCVKlDip7JVXXuG///0vzwXhKp6bbrqJ\n6667jrFjx+a6LhGRSDFkCHzxhZt606JF1rKX5HRqyrZt8NBD7ljFi2e+TWwszJsHt98O//lP9o8R\nSc4aiBtjShpjSqc/BtoAXxhjLg/YrAOwxdvmsoCsJ/Vxc8J/Ab7n0OoYAAATbklEQVQDmhtjoowx\n0bgLNTd7U0/2G2Maefv1Bt7y6l0I9PEe98lQ3tvLntII+C19CouI5F0ZA/FNmzYxbNgw5s6de0qA\nnlOjRo1i8uTJ/Pbbb0GpT0Qkvxsxwi3ckx05mZpy9CjccYe7+PPKK8+8bbNmMHMmdOrk5uQXVFkZ\nEb8A+MQYsxFYDbxrrX0PGGuM+cIY8xkuOL/P274LLlDfALwA3OZdhPkmkAx8DmwENlpr3/b2GYzL\nrrLd2+bfXvlYIM4Ysw2I854DLAJ2eNtPBYbkpPMiEl6HDh2iuDdEcujQIbp168a4ceOoHcRkvZde\neilt2rThZV2aLyKSYzmZmvLoo1ChghuBz4q2bd20mfbtXbrIguisF2taa3cAp6wtZa09ZSqKVz4O\nGJdJeSrwP6fZZy0upWHG8l+AVpmUW+Dus7VdRPKWwBHx++67j3r16tGvX7+gH2fAgAEMGzaM+++/\nP+h1i4gUBNmdmrJkiRvh3rAhewv33HKLO07btrBsGVSrlv225mdZzZoiIpJr6SPis2fPZtmyZaxb\ntw4TgqXWmjVrxrZt29izZw/lypULev0iIpGuVCmXyjEr9uyBPn3glVfOfAHo6fToAYcOuRzuy5dH\nVsaas9ES9yISNocPHyYlJYX77ruPuXPnUrp06ZAcJzo6mmbNmvHRRx+FpH4RkUiX1akp1sKAAe7C\ny7i4nB/vrrtg6FBo1Qp+/DHn9eQ3CsRFJGyKFStGnz59GDVqFPXq1QvpsVq1asWSApobyxjzjTHm\nc2PMBmPMWq9stDHmM69ssTHmIq+8Y0D5WmNM04B6njDGfGmM2WyMeTbgQvxrvPq3Zyg/zxiTZIzZ\n5t3HeOXG2267d6z64f+piEh2ZHVqyosvwvffQ0JC7o95//3Qqxe0aQN79+a+vvxAgbiIhE2JEiXY\ns2cPgwcPDvmxWrduzQcffBDy4+RhLa219ay1DbznT1pr61hr6wHvAI965UuAul75nbgL5zHGNAGu\nA+rgruGJxWW7ApiEWyztcu92o1c+HFhirb3cq3e4V94uYNuB3v4ikodlJWvKpk1upczZs6FIkeAc\n9//+D2680d1+//3s2+d3CsRFJCzefPNNjhw5wrZt20IyLzyjK6+8ksOHD7N169aQHys/sNYG/ksr\nibcImre2g81Y7t0Xw6WgLQpE8+faDmWstSu8/WbgFm8D6Ai86j1+NUP5DOusBM5NXyNCRPKms01N\n+eMPNx1l7Fi44orgHdcYGDcOGjRwq3IeOhS8uvMiBeIiEhbGGObMmUO4Vrk1xtClSxdmz54dluPl\nMRZYbIxZZ4wZmF5ojEkwxnwP9ODPEXGMMZ2MMVuAd3Gj4lhrVwAfASne7X1r7Wbcysg7A4610ysD\nuCB9TQfvvrxXXhH4/jT7nGCMGehNj1n7888/57jzIpJ7Z5uaMny4W7L+zjuDf2xj4PnnXe7zzp3h\nyJHgHyOvUCAuImHRpUsXYmNjw3rMQYMGMWXKFI5E8lk8c9dZa+vjpoTcbYy5HsBaO8JaWxmYBdyT\nvrG1NtFaWwM3gj0a3OJsQE3cysUVgRu8ejL7OsNmUhYoS/tYa6dYaxtYaxuE6wObiGTuTCPiixZB\nYqLLAR6qLzgLFYJp09wHgu7d4dix0BzHbwrERSRi1apVi7p16zJ9+nS/mxJW1tpd3v1uIBFomGGT\n2bjF1zLutxyoZowpB3QCVnpTVw7gFlprhBvNrhSwWyVgl/c4feoK3v1ur3wnUPk0+4hIHnS6OeI/\n/QT9+7uc4TExoW1DVJSbf37kCPTrB2lpoT2eHxSIi0hEGzNmDKNGjSowS94bY0oaY0qnP8atfPyF\nMebygM06AFu8bS4LyHpSHzcn/BfgO6C5MSbKGBONu1BzszflZL8xppG3X2/gLa/ehUAf73GfDOW9\nvewpjYDf0qewiEjelFkgnpYGffu6dIXXXx+edhQpAvPmwQ8/wODBLl1iJFEgLiIRrX79+sTHxzNy\n5Ei/mxIuFwCfGGM2AquBd6217wFjjTFfGGM+wwXn93nbd8EF6huAF4DbvIsw3wSSgc+BjcBGa+3b\n3j6DcdlVtnvb/NsrHwvEGWO2AXHec4BFwA5v+6lAFhfAFhG/lCx56hzxZ5+FffvcUvbhVLw4LFwI\nGzfCgw9GVjBubCT15gwaNGhg165d63czRMQHe/bsoVatWiQlJVG3bl2/m5Mjxph1AakII57O2SL+\nstZNDfnjD4iOdkvXx8XBqlVw6aX+tOnXX6FlS+jYEUaN8qcNWZXVc7ZGxEUk4pUrV47Ro0dz7733\nUlAGH0REcsOYPzOnHDrkUhWOH+9fEA5uTvrixTB3Ljz5pH/tCCYF4iJSIAwYMIC9e/eyaNEiv5si\nIpIvpE9PeeABl9e7Rw+/WwTly8MHH8CkSe6W3ykQF5ECoXDhwjz22GM8/vjjfjdFRCRfKFUKFiyA\npCR44QW/W/OnihVhyRIYMwZmzPC7NbmjQFxECoyOHTuyY8cONm3a5HdTRETyvCJFYORImDULypTx\nuzUnq1rVfUAYPtxlVcmvFIiLSIERHR1N3759mTZtmt9NERHJ81JSoFMnaNTI75ZkrkYNt7jQkCHu\nPj9SIC4iBUr//v2ZOXMmhw8f9rspIiJ52oIF8M9/+t2KM6tXD956y+U3X7rU79ZknwJxESlQqlWr\nRsOGDZk9e7bfTRERydOaNct7U1Iy06iRy6TSrZtLr5ifKBAXkQLnr3/9K//4xz80Ki4iEiFatIBX\nXoEOHdzCP/mFAnERKXBatGhBgwYNGDFihN9NERGRILnpJpg4Edq1gy1b/G5N1kT53QARET9MnjyZ\nhg0bUq9ePXr37u13c0REJAi6dHG5z+PiYPlyl10lL1MgLiIFUtmyZVm4cCEtW7akevXqNMqraQFE\nRCRbeveGAwegdWsXjFes6HeLTk9TU0SkwKpduzYvv/wynTt35ptvvvG7OSIiEiRDhsCgQS4Y373b\n79acngJxESnQ4uPjGT58ODfddBP79u3zuzkiIhIkDz3kMqm0bQu//up3azKnQFxECryhQ4fSunVr\nunTpwtGjR/1ujoiIBMljj0HLlu5Czv37/W7NqRSIi4gAzzzzDCVLlmTQoEFYa/1ujoiIBIExblGi\nOnVcasO8lrVWgbiICFC4cGFmz57Nhg0bmDBhQtDrP3bsGA8++CDbtm0Let0iInJ6xri0hhddBF27\nQl764lOBuIiIp1SpUsyfP5/HH3+c5cuXB63e33//nZtvvpnNmzdToUKFoNUrIiJZU7iwW/CnSBHo\n0QOOH/e7RY4CcRGRAFWqVOGVV16hV69eHDhwINf1ff/99zRt2pSqVauycOFCSpcuHYRWiohIdkVH\nw5w58PvvMGAApKX53SIF4iIip2jXrh0tWrRg5MiRuapn8+bNNGnShF69ejFx4kSiorR0g4iIn4oW\nhcRE2LED7r0X/L4kSIG4iEgmnnrqKWbMmMHmzZtztP/WrVtp3bo1CQkJPPTQQxhjgtxCERHJiRIl\n4J13YPVqGD7c32BcgbiISCbOP/98RowYwQMPPJDtfbdv307r1q0ZPXo0vXv3DkHrREQkN8qUgffe\ng0WLICHBv3YoEBcROY0hQ4aQnJzM4sWLs7zP119/TatWrXjkkUe48847Q9g6ERHJjbJlISkJZsyA\n8eP9aYMCcRGR0yhSpAhjx47loYceIjU19azbf/vtt9xwww387W9/Y+DAgWFooYiI5EaFCrBkCUyY\nAFOnhv/4CsRFRM6gU6dOlC5dmmnTpp1xu++++44bbriB+++/nyFDhoSpdSIikluVK7uR8VGjYPbs\n8B5bgbiIyBkYY5g0aRIjRozgs88+y3SbhQsX0rBhQ4YOHcrQoUPD3EIREcmtyy6D99+HBx+EBQvC\nd1zl0hIROYurrrqKiRMnEhcXR58+fU6kNVy7di2TJ09mxYoVzJs3j+uuu87nloqISE7Vrg3vvgs3\n3ugyq7RpE/pjakRcRCQLbr31VqZOncrTTz9NTEwM5cqV48EHH+SCCy6gadOm/Pe///W7iSIikkv1\n67s84z17wscfh/54CsRFRLKoQ4cOVK5cmcaNGzNo0CDS0tJITEzkqquuonv37n43T0REguC66+D1\n16FLF1izJrTH0tQUEZFs2LhxI9OmTeOPP/7gmWeeoWnTphQuXNjvZomISBC1agUvvwwHD4b2OArE\nRUSyoUyZMtx///1+N0NEREIsPj70x9DUFBERERERHygQFxERERHxgQJxEREREREfKBAXEREREfGB\nAnERERERER8oEBcRERER8YECcRERERERHygQFxERERHxgQJxEREREREfKBAXEREREfGBAnERERER\nER8oEBcRERER8YECcRERERERHygQFxERERHxgQJxEREREREfKBAXEREREfGBAnERERERER8oEBcR\nERER8YGx1vrdhrAwxvwMHAT2+N2WECuH+hgJ1MfIEMw+XmKtPT9IdeV5OmdHFPUxMqiP2ZOlc3aB\nCcQBjDFrrbUN/G5HKKmPkUF9jAwFoY+hVBB+fupjZFAfI4MffdTUFBERERERHygQFxERERHxQUEL\nxKf43YAwUB8jg/oYGQpCH0OpIPz81MfIoD5GhrD3sUDNERcRERERySsK2oi4iIiIiEieoEBcRERE\nRMQH+SIQN8ZcYYzZEHD73RjzF2PMaGPMZ17ZYmPMRd72HQPK1xpjmgbU1ccYs8279Qkov8YY87kx\nZrsx5lljjPHKzzPGJHnbJxljYvJCHwP2izXGpBpjukZaH40xLYwxvwVs/2hAXTcaY7Z6fRkeUF7V\nGLPK68sbxpgiXnlR7/l27/UqeaGPAf3cYIz50hizLNL6aIx5KGDbL7y/1/MirI/nGGPeNsZs9H6P\n/QLqypPvx1DKwc9P5+wI6KPROTsi+mh0zg7v+9Fam69uQGHgR+ASoExA+VBgsve4FH/Of68DbPEe\nnwfs8O5jvMcx3murgcaAAf4NtPPKnwCGe4+HA+PyQh8DtvsQWAR0jbQ+Ai2Ad06zbzJwKVAE2AjU\n8l6bC3T3Hk8GBnuPhwTU2x14I4/08VxgE3Cx97x8pPUxw/Y3Ax9GWh+Bh9PfN8D5wF6vT/ni/ZgH\nfn46Z0dAH9E5OyL6mGF7nbND/H7MFyPiGbQCkq2131prfw8oLwlYAGvtAev9RALLgbZAkrV2r7X2\nVyAJuNEYcyHul7XC228GcIu3T0fgVe/xqwHloXTWPnruBeYBuwPKIq2PmWkIbLfW7rDWHgXmAB29\nT6c3AG962wX2JbCPbwKt0j/NhlBW+ngHMN9a+x2AtTb9dxlJfQx0O/C69ziS+miB0l5bSuFO6sfJ\nP+/HUNI5+086Z+f/97rO2ZHRxzxzzo7Kdvf8150//ygwxiQAvYHfgJYB5Z2Ax4HyQHuvuCLwfUBd\nO72yit7jjOUAF1hrUwCstSnGmPLB7MxpnLWPxpiKQCfcH39swL4R00dPY2PMRmAX8Fdr7Zdk3sdr\ngbLAPmvt8YDy9D6e2Mdae9wY85u3fSiX681KH6sD0caYpUBpYIK1dgaR1cf010oANwL3ZGyvJz/3\n8XlgIe7vtDRwm7U2zXuf5of3YyjpnI3O2UTOe13n7D/l5z7mmXN2vhoR9+YcdQD+lV5mrR1hra0M\nzOLPPxastYnW2hq4Tyaj06vIpFp7hvKwy0YfxwN/s9amZqwik2rzax/XA5dYa+sCzwEL0qvIpNqz\n9TGs/c9GH6OAa3CBR1vgEWNM9TO0Nz/2Md3NwH+stXvTq8ik2vzax7bABuAioB7wvDGmzBnam2fe\nj6Gkc7bO2elVZFJtfn2v65x9svzaxzxzzs5XgTjQDlhvrf0pk9dmA10yFlprlwPVjDHlcJ9gKge8\nXAn3aWin9zhjOcBP3lcSePeBXymGQlb72ACYY4z5BugKTDTG3EIE9dFa+7u19oD3eBFuFOJMv8c9\nwLnGmKgM5QTu471+Du6rqFDJ6u9xJ/CetfagtXYPsByoS2T1Md1JoxREVh/74b6uttba7cDXQA3y\nx/sxlHTO1jlb5+z818d0OmeH4f2Y3wLxwLlKGGMuD3itA7DFK78s4GrW+rgJ+L8A7wNtjDEx3lWt\nbYD3va8U9htjGnn79Qbe8updCPTxHvcJKA+VLPXRWlvVWlvFWlsFN+9qiLV2ARHUR2NMhYDfY0Pc\n3+svwBrgcuOu0i6CO1ks9OZtfYT7J5exL4F97Iq7+CSUo0tZ6qPXvmbGmCjva8Brgc1EVh8xxpwD\nNOfkv61I6uN3uHmJGGMuAK7AXeSTH96PoaRzts7ZOmfnvz7qnB3O96MN8dWrwboBJXBv6HMCyuYB\nXwCfAW8DFb3yvwFf4r52WAE0DdjnTmC7d+sXUN7AqysZN3co/Qr+ssASYJt3f15e6GOG/V7BuwI/\nkvqI+wrpS9yV2SuBJgH73AR85fVlRED5pbgrm7fjvpoq6pUX855v916/NC/00XvtIdxV+F8Af4nQ\nPvYF5mRST0T0Eff15mLgc+/1nvnh/RjKWzZ/fjpnR0Af0Tk7kvrYF52zw/J+1BL3IiIiIiI+yG9T\nU0REREREIoICcRERERERHygQFxERERHxgQJxEREREREfKBAXEREREfGBAnERERERER8oEBcRERER\n8cH/A8IM1oI9/DouAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x124f25c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(1, 2, figsize=(12,8))\n",
"streets.plot(ax=axes[0], color='black')\n",
"edge.to_crs(crs=streets.crs).plot(ax=axes[1], color='blue')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"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": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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