{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Case: Employment rate map" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "**Goal:** plot an interactive map of employment rates across Finnish regions.\n", "\n", "**Required modules:**\n", "- Folium for plotting interactive maps based on leaflet.js\n", "- Pandas for handling tabular data\n", "- Geopandas for handling spatial data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "deletable": true, "editable": true }, "outputs": [], "source": [ "# Import required modules:\n", "import folium\n", "import pandas as pd\n", "import geopandas as gpd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Employment rate data \n", "\n", "Employment rate refers to \"the proportion of the employed among persons aged 15 to 64\". Data from Statistics Finland (saved from .xml-file to csv in Excel..)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/html": [ "
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seutukuntaseutukunta_nimityollisyys
0SK011Helsingin seutukunta73.0
1SK014Raaseporin seutukunta70.3
2SK015Porvoon seutukunta74.3
3SK016Loviisan seutukunta71.5
4SK021Åboland-Turunmaan seutukunta72.9
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" ], "text/plain": [ " seutukunta seutukunta_nimi tyollisyys\n", "0 SK011 Helsingin seutukunta 73.0\n", "1 SK014 Raaseporin seutukunta 70.3\n", "2 SK015 Porvoon seutukunta 74.3\n", "3 SK016 Loviisan seutukunta 71.5\n", "4 SK021 Åboland-Turunmaan seutukunta 72.9" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Read in data\n", "data = pd.read_csv(\"data/seutukunta_tyollisyys_2013.csv\", sep=\",\")\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Sub-regional units\n", "\n", "The spatial data for the [sub-regional units](http://www.stat.fi/meta/luokitukset/seutukunta/001-2018/index_en.html#_ga=2.189315043.975495398.1543480533-446957274.1543480533) (*Seutukunnat* in Finnish) can be retrieved from the Statistics Finland Web Feature Service `http://geo.stat.fi/geoserver/tilastointialueet/wfs`" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# A layer saved to GeoJson in QGIS..\n", "#geodata = gpd.read_file('Seutukunnat_2018.geojson')\n", "\n", "# Get features directly from the wfs\n", "url = \"http://geo.stat.fi/geoserver/tilastointialueet/wfs?request=GetFeature&typename=tilastointialueet:seutukunta1000k_2020&outputformat=JSON\"\n", "geodata = gpd.read_file(url)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/html": [ "
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idseutukuntavuosiniminamnnamegeometry
0seutukunta1000k_2020.10112020HelsinkiHelsingforsHelsinkiMULTIPOLYGON (((409963.522 6681658.341, 409969...
1seutukunta1000k_2020.20142020RaaseporiRaseborgRaaseporiMULTIPOLYGON (((306616.919 6665438.489, 306668...
2seutukunta1000k_2020.30152020PorvooBorgåPorvooMULTIPOLYGON (((427108.141 6694151.025, 427175...
3seutukunta1000k_2020.40162020LoviisaLovisaLoviisaMULTIPOLYGON (((444038.768 6703649.355, 444155...
4seutukunta1000k_2020.50212020Åboland-TurunmaaÅboland-TurunmaaÅboland-TurunmaaMULTIPOLYGON (((190999.717 6715878.622, 191021...
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" ], "text/plain": [ " id seutukunta vuosi nimi \\\n", "0 seutukunta1000k_2020.1 011 2020 Helsinki \n", "1 seutukunta1000k_2020.2 014 2020 Raasepori \n", "2 seutukunta1000k_2020.3 015 2020 Porvoo \n", "3 seutukunta1000k_2020.4 016 2020 Loviisa \n", "4 seutukunta1000k_2020.5 021 2020 Åboland-Turunmaa \n", "\n", " namn name \\\n", "0 Helsingfors Helsinki \n", "1 Raseborg Raasepori \n", "2 Borgå Porvoo \n", "3 Lovisa Loviisa \n", "4 Åboland-Turunmaa Åboland-Turunmaa \n", "\n", " geometry \n", "0 MULTIPOLYGON (((409963.522 6681658.341, 409969... \n", "1 MULTIPOLYGON (((306616.919 6665438.489, 306668... \n", "2 MULTIPOLYGON (((427108.141 6694151.025, 427175... \n", "3 MULTIPOLYGON (((444038.768 6703649.355, 444155... \n", "4 MULTIPOLYGON (((190999.717 6715878.622, 191021... " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "geodata.head()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Join attributes and geometries\n", "\n", "We can join the attribute layer and spatial layer based on the region code (stored in column 'seutukunta'). The region codes in the csv contain additional letters \"SK\" which we need to remove before the join:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "0 011\n", "1 014\n", "2 015\n", "3 016\n", "4 021\n", "Name: seutukunta, dtype: object" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[\"seutukunta\"] = data[\"seutukunta\"].apply(lambda x: x[2:])\n", "data[\"seutukunta\"].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can join the data based on the \"seutukunta\" -column.\n", "Let's also check that we have a matching number of records before and after the join:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Count of original attributes: 70\n", "Count of original geometries: 70\n", "Count after the join: 70\n" ] }, { "data": { "text/html": [ "
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idseutukuntavuosiniminamnnamegeometryseutukunta_nimityollisyys
0seutukunta1000k_2020.10112020HelsinkiHelsingforsHelsinkiMULTIPOLYGON (((409963.522 6681658.341, 409969...Helsingin seutukunta73.0
1seutukunta1000k_2020.20142020RaaseporiRaseborgRaaseporiMULTIPOLYGON (((306616.919 6665438.489, 306668...Raaseporin seutukunta70.3
2seutukunta1000k_2020.30152020PorvooBorgåPorvooMULTIPOLYGON (((427108.141 6694151.025, 427175...Porvoon seutukunta74.3
3seutukunta1000k_2020.40162020LoviisaLovisaLoviisaMULTIPOLYGON (((444038.768 6703649.355, 444155...Loviisan seutukunta71.5
4seutukunta1000k_2020.50212020Åboland-TurunmaaÅboland-TurunmaaÅboland-TurunmaaMULTIPOLYGON (((190999.717 6715878.622, 191021...Åboland-Turunmaan seutukunta72.9
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" ], "text/plain": [ " id seutukunta vuosi nimi \\\n", "0 seutukunta1000k_2020.1 011 2020 Helsinki \n", "1 seutukunta1000k_2020.2 014 2020 Raasepori \n", "2 seutukunta1000k_2020.3 015 2020 Porvoo \n", "3 seutukunta1000k_2020.4 016 2020 Loviisa \n", "4 seutukunta1000k_2020.5 021 2020 Åboland-Turunmaa \n", "\n", " namn name \\\n", "0 Helsingfors Helsinki \n", "1 Raseborg Raasepori \n", "2 Borgå Porvoo \n", "3 Lovisa Loviisa \n", "4 Åboland-Turunmaa Åboland-Turunmaa \n", "\n", " geometry \\\n", "0 MULTIPOLYGON (((409963.522 6681658.341, 409969... \n", "1 MULTIPOLYGON (((306616.919 6665438.489, 306668... \n", "2 MULTIPOLYGON (((427108.141 6694151.025, 427175... \n", "3 MULTIPOLYGON (((444038.768 6703649.355, 444155... \n", "4 MULTIPOLYGON (((190999.717 6715878.622, 191021... \n", "\n", " seutukunta_nimi tyollisyys \n", "0 Helsingin seutukunta 73.0 \n", "1 Raaseporin seutukunta 70.3 \n", "2 Porvoon seutukunta 74.3 \n", "3 Loviisan seutukunta 71.5 \n", "4 Åboland-Turunmaan seutukunta 72.9 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#print info\n", "print(\"Count of original attributes:\", len(data))\n", "print(\"Count of original geometries:\", len(geodata))\n", "\n", "# Merge data\n", "geodata = geodata.merge(data, on = \"seutukunta\")\n", "\n", "#Print info\n", "print(\"Count after the join:\", len(geodata))\n", "\n", "geodata.head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "## Create a static map" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Now we have a spatial layer with the employment rate information (in column \"tyollisuus\").\n", "Let's create a simple plot based on this data:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Define which variable to plot\n", "geodata.plot(column=\"tyollisyys\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adjusting the figure, we need to import matplotlib pyplot" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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y5UpSU1Npbm7m8GGtj/fff78nQXPz5s18+ctfHtTXokWLaGxs7PHTrFmzpkdSo1sXSErJa6+95lXw3el0cs011/DlL3+ZL3zhC/3O9ZUHWbduHZMnTw76s/OHHuejM+qRbpc2+/G4kBXHNOdyN4pBMzx5xSH3v/yipXz86WdcfNESDAYDf/jVL7j4yuu0Xdu5s/nqbV9C2tv5yq230tLWhkQwZ84cHn74YQC2bt3KI488wuOPP05aWho//elPWbRoEQA/+9nPSEtLAzQZi+uuuw5FUUhNTeWJJ54AtO10m23w0tJgMPDAAw+wbNmy7h1kvvrVrwJwyy23UFtbi5SSuXPn9khq9B3LCy+8wPr166mvr+9Zrj311FPMnTuX+++/n1tuuYUHH3yQhIQEHn/88ZA/P1/okho6Mc1QkhpSSs3QdPbuXkmPS9vVcjk0H09aTkgznr7s2LWbB//6CP949KEAr+gSNzNbEf6WgwFw3333ceuttzJ79uyw+4oEa9eu5YEHHuCtt94adE6X1NAZ80gpob150Ja5MJggs8DHVaExb85sll5wHh6PB4PBEMjouuqI2ZEGE1jiEMbQDeAf/vCHkK+NNM8//zy//OUvWbBgQdh96cZHZ3Ti6AwqVidc7rj1ltAu9Lg0QxSG8YklbrzxRm688caI9KU7nHVGJ6NJ5sLt8rrFfqajGx+d0UmgsT2xgFSjXrBwNKIvu3RGKaLr1X9GIe0dyD1boKUJklIQsxYhrIGnHEQNpyOi8q5jAd346IxOXA4GGh51zRvItW9pD3oX8s1/IpZegfK5ERZfdzuRqqpLcvRB/yR0Rh3S0QmOjn7H1DVvIFe/3M/wAOB0IFe/jLrmjZDv19nZydLLruzJmzp1uoyVV3+B6YvOZcbi8yg9eQqA2+/5FkWzFjDv/KXMO38pO3f31TrWdsBCkbEYCl+SGhdccEGPnEZeXh5XX331oGtPnjzJggULmDt3LjNmzOiJBerLt7/97Z7a8hBZSQ195qMz+hDQd9Yj7R3ajGcI5Nq3kOcuR1iD9xU98X//4prPX96zzX7b3d/k//37vSz/3FLa2tp68qEAfv+rn3P91T5mWS4HNpuNf/zjH5SUlFBRUcGCBQtYuXIlKSkpPPXUU5w+fZqDBw+iKIrfWu0AX/7yl/nxj3/M8uXL+41lw4YNPW2uu+66ngKEfcnNzWXjxo1YLBba2tqYOXMmV155ZU82/datW3sKHHZz4403kp2dzQMPPOB3bP7QZz46o48B8hlyz5bBM56BOB1auxDoJ6lx8JAmY/G5pUCQMhaqh8mTJkZMxiIQSY3W1lbWrFnjdeZjNpuxWLRkW4fDgdpHq9rj8XDffffx+9//PrD3FgK68dEZfQzctm5pCuy61gDb9cG7pEYy193yFeaffxH3/eQX/WQsfvKr/2TOuUu490c/8SJjIUD0BimGK2PhT1ID4NVXX2XZsmU9WkMDOX36NLNnz6agoIAf/vCHPbOev/zlL1x55ZXk5uYG9DmFgm58dEYVUvVogu99SUoJ7OLEANv1waekxq9/wea173OitJSn/vksAP/5859wYOunbP5ISxz93R//3L8zoxnRtXSLhIzFUJIa3Tz77LPcfPPNPvsoKChg9+7dHD16lKeffprq6moqKip48cUXveoRRRLd+OiMLgZ8swOIWYvA7Eerx2zR2gWJT0mNiYUYjUauuuIytu/S5CZyc3J6ZCy+cssX2bJte//OumKTIiVjMZSkBkB9fT2bN2/m8ssv9/s+8/LymDFjBhs2bGDHjh0cPXqU4uJiCgsL6ejooLg49MRcX+jGZxQiVQ/qB6/gef5h5OljIz2c4cVo0mqw90FY4xBLh959EUuvCMnZPKSkBvDR+g1Mn6rJeVRWVQFdMhZvv8OMaX0kNUxakmkoMhahSGoAvPjii1xxxRVYrVav762srIzOTk0FsrGxkU8++YQpU6Zw+eWXU1VVRWlpKaWlpcTFxQUkZh8suvEZZciyE3j+8H3UV/6OXPcWnt9+B/dvv4O67i1kRwAlYEY7HrfX1Arlc1ciVlw3eAZktiBWXBdWnE+3pAbQT1Jj9jkXIqXkq7fdCsCX7rqH2edcyOxzLqS+voGf3KcpEW7duYe7vvVvAD0yFt3SFX0rS9x///28/PLLzJo1ix/96Ec9MhaBSGrMmjVLG0uXpAbAc889N2jJtXXrVu666y5Ay0A/66yzmDNnDkuWLOH73/8+s2bNCvlzChZdUmMUoe7fhvq33/jWIs4rxPiTvw7voKJMX4kGKSW0NYEcvPTqRto7tV2t1iZI7I5wDi8VI3hJjb4IiE8OS1pDl9TQGVFkfQ3qI7/qKunrg4pS1IM7UabOHbZxDSse95CGB0BYbYhFF0b0tsFLavTBGh+2po8uqaEzosiTh4c2PN3tPngFxqrxGcFM9pAkNUwWhNm7v2W0oktqnIGIIu9qfgOR+7chTxyM8mhGCH+1uWIJxQDW+JEeRUyjG58YRbr6z3JESjrkTQjoWvWtf0ZjSCPPwPiemEWALREh9MdrKPRPJwaRh/fg+entqOvf7ndcWbgksOsPbEce3uO/4Sgi0I0Rd2srtS++RsVDj1P74mu4W1ujPDIvCBER7eaxjv4JxRjqx6tQn38YPG7U5x5CVpWhXH8XQjHAhMDLl3hefgzDD/84tiQc/Bigiocfp+rRJ1E7eiuYnv7PP5DztdvJu+euaI9OJ0jG0F/m2ECMn4SYd15PNKxc+wbqw/+hZW5/+n7gHZ0+htz0QZRGOfwIIYYU46p4+HEq/vhQP8MDoHZ0UvHHh6h4OPTSL4FKalx4yRWanMZ5SyIqY+GLjz76qCdWaO7cuVitVl577TVAy80qLi5GCEFdV0DkQIaS9/A1Fl1SYwwjxpdguOMHyI5W1L/9BnlkD3LfVjwPfB9qq4LqS33zGcSipQjTGFHQ8zGLc7e2UvXok0NeWvXok2TfehOGAB7qgQQqqbF+VVfci8HE9V++I2IyFr646KKLegIUGxoaKC4uZsWKFQCcd955XHHFFSxdutTn9XFxcT7lPXyNRZfUOAMQcYmIi6/tPVBx0ndwoS+aG5CfvBfZgY0kXvK6ABpXfThoxjMQtaOTxlWhzQSDldRobWkZdhmLl156iUsvvbRnLPPmzaOwsHDIayZPnuxT3kOX1DjDUWYtRnxu8LdnMKirX0IGEB80mnHVel9WDMQZYLt+1wQpqQHw6htvsOyipcMqY+EtlSIYBsp76JIaZzjS5ULEJYbXSVPd2PH9+NhBMmVmBHS5OcB2fQlGUqOb5156hZuuuQrpIy4p0jIWlZWV7Nmzh5UrVwZ9bff1feU9dEkNHSg/gbrqubC7Ud97EeljyTKq8JHakHrJMpS4ofO3lDgbqZdcHPQtg5HUAKhvaGDzth1cfs21frfbIyVj8cILL3DNNddgMgVfmNCbvIcuqaGDKJyMOGcFJKeF11F9NdQH56yOSXxstRsTE8n52u1DXprztdtDcjYHI6kB8OLrb3HFFVdgS/C+5ApFxsKXpEY3/gTDfOFL3kOX1NABwHDzNzH8+knE9Plh9SOryyM0ohHE3u7zVN49d5H33W8MmgEpcTbyvvuNsOJ8ApXUwGjm+Vff4OYvfrHf9eHKWPiS1AAoLS3l9OnTLFnSPwD1T3/6E/n5+ZSVlTF79uye+/cdy1DyHsOBLqkxSpDNDXj+9BOoPBnS9aJkFoZ7/yvCo4o+3RIN0uOG9ia/7T1tbTSu+gBnbR3mzAxSL7k4pBlPXwKS1DDbwBKnxSNFGF1SQ2dEEclpGL74LTwP3g9q8P4beWQP8th+xKTp/hvHIk57QM0MCQlkXH91RG89pKSGUDTZDJMfGdcwGKuSGvqyaxQhJk1HufW7IV+vvvus/0YxgnR0oB7dAVLVnOXuIGOcIswdt97iXctHKD2R1yO1ihhObrzxRvbv388zzzwTdl/6zGeUIWYu0nSMQ4jdkfu3Iz3umE96lA2VqPs3gr0dmT6ta7kVow+26obWBmTX+KRi0HR8LDFQHz7Gie2/Qp1BiPhElDt+gDx9DPluCNvwTifYIvNrl04H6mtPIQ/uQEydh3LdXT2lYUJFLTuEPPjZwDuF1Wf06TM+1eMzElunP7rxGYUoc89FtcWHZnzcTiAy38ry4A7kWq0Guqw6jUxORay8IfT+OlqQx3aGNabO5ha2v/4uzdW1JGdnMv+qS7Ele9/2jhoxPrOMFfRPaZQijMEHlAHgdGqG4sRBxNkXh7U7I/f2Lz+svvMs8uBOlJu+icgeF1xfHg/q1lXB56/14d0//JX3HnwYR3tHz7EX7/8VK++9h0vv+2bI/QaNbnwCQnc4j1YKihALl/gvljcAz8O/wPMfd6M+80fUv/4c2VAT0u2l047ctan/QZcTeWgX8sSB4Dtsawh4R8sb7/7hr7zx6//uZ3gAHO0dvPHr/+bdP4Re1aOvpMZH6z/WZDO6XrasfF576x0A/vLo45TMXYxiMkdUxmIofvCDHzBjxgymTZvGd77znR6ndyCSGgBPP/00JSUllJSU8PTTT/ccP3HiBGeddRYlJSXceOONOJ2aimQkJTV04zNKEWYrhjt+gOE3TyOmzgv8woreOCG5fxueX30D9aM3kB1tQaVgyDVv+Kx9rv7jQdT3X0aqgQu+y/bmgNsOpLO5hfcefHjINu89+DCdLaGpGvaV1LjowvPZ8fFadny8lg/feJU4m40VXRnu5521mPffeo0JE3zL3XbLWOzbt49Vq1bx3e9+t59sRTCSGhs3buSTTz5h9+7d7N27ly1btrBu3TptLOedxwcffDDkWBoaGvjlL3/JZ599xubNm/nlL39JY2MjAD/84Q+59957OXLkCKmpqfz9738HtN2u7npi4aIbn1GOiE9E+eqPICsvtA4cnagv/g3P92/E8+0rcX/nKjwP/aJfE+nxoH70Bu4H78f9i6/ivvd61Dee9t5fF+qrT+D5yVfwvPIEsrzU/zjCWG5tf/3dQTOegTjaO9jx+rsh9d9XUqMvL73+JpcuX9YrYzFnNoXFQ6tNRlLGQgiB3W7H6XTicDhwuVxkZ2drYwlAUuO9995j+fLlpKWlkZqayvLly1m1ahVSStasWcP1118PwG233dYjUhZJ9MXpGEDY4hGzz9bK5oSL243cuwV104coZy9D3fUp6qtPQk0I6RlN9cgPXsbzwcuQPxFl+fUoi5Z6b9vWFPKQm6trA2tXFVi7vgyU1OjL8y+/yr3fuqf3gFAgiGDDcGUszjnnHC666CJyc3ORUvKtb31rUHTxUJSXl1NQUNDzc35+PuXl5dTX15OSkoLRaOx3PNLoxmeMIEpmQUMNdHYgD2wPuz/15ceRn76PPBIhIfqyE6hP/gF5ZA/KF+5GDMzADsPxnZydGVi7nMDa9WWgpEY3lVVV7Nl/gJXLLuo9aLYF7MDvlrF4+umn+8lYrF27NuCxHT16lAMHDlBWVgbA8uXLWb9+PRdeGFjRRG9BkUIIn8cjjb7sGiMosxZjuOtHGL79K00DOlzaWyJnePogP16F+thv+h9zdiJb6kPuc/5Vl2KJHzp8wBIfx7yrBi+d/DFQUqObF159nauvuKxXxkIoEGCBwEjJWLz66qucffbZJCQkkJCQwKWXXsqmTZuGvKYv+fn5nD59uufnsrIy8vLyyMjIoKmpCbfb3e94pNGNzxhEueU7EBdeMmU0kfu2IZsbkPUVeLauQl3/IrQ1htyfLTmJlffeM2Sblffegy0peGG2gZIa3Tz30qvcfH0fmdsAZz2hyFj4ktQYP34869atw+1243K5WLduXVDLrpUrV7J69WoaGxtpbGxk9erVrFy5EiEEF110ES+99BKg7Yh506MOF7/GRwgxRQixs8+rRQjxXS/tlnad3yeEWBfxkeoEjIhLQBTPGOlh+EaqyF2fIqtLoSm0rf6BXHrfN7nyJ/8+aAZkiY/jyp/8e1hxPn0lNQBKT57idHk5S84/VzsgFDBboiZj4UtS4/rrr2fSpEnMmjWLOXPmMGfOHD7/+c8DgUlqpKWl8dOf/pRFixaxaNEifvazn5GWpmlH/e53v+N//ud/KC4upr6+njvvvDPkz88XQUlqCCEMQDlwlpTyZJ/jKcBG4BIp5SkhRJaUcsi/Kl1SI7p43von8p1/jfQwfJOWifj8DdA6tK7yofRpTJs0MeBuO1ta2fH6uzRX1ZKck8m8qy4NacbTF7+SGpa4qOZy6ZIaGsuAY30NTxdfBF6RUp4C8Gd4dKKPcskNcM7FyN2fgS0erDbUpx4A58hmh/fQUAv1tWCOrCPTlpTIubeGnuLhjSElNWDIemKRYKxKagQ783kC2C6l/MuA438ETMAMIBH4XynlP4bqS5/5DC/SYdeWOvu3IzevGenhaMycj5hcMmSTYGc+I4ItMap6PqOJYGY+ATuchRBm4ErgRS+njcAC4HJgJfBTIcSgaCshxNeEEFuFEFu7A6t0hgdhsaIsvgjltu+h/Nt/ImYsDComJSoc3APK0JNvIUeBTs4YL00UKMH+noLZ7boUbdZT7eVcGbBKStkupawD1gNzvAzuUSnlQinlwszM4GMudMJHCIEyZQ6Gb/4S5dKbRnYwbpdfA2hx26lvbo1tA+TRjY+Ukvr6eqzWwMINIDifz82ALym814G/CCGMgBk4C3gwiL51RgDlkhsgNQNZW4F8Z4RUDlvbhvT75LWeogKoq7MiIx/nFjGEJU7b9TqDsVqt5OfnB9w+IOMjhIgDlgNf73PsbgAp5SNSygNCiFXAbkAFHpdS7g1m4Dojg3LW55DVZXjWvQXtoSVehoM8cgAxa5ZPXWqT9DCh5cQwjyp4xIzzUXKLRnoYowq9eoUOAOqhXagv/C3k6hhhkZGNuHA5KGpI4vixgMgrRpl+7kgPIyYJ2+GsM7ZRpszB8K3/gEkjEJxYV4185f+Qaz4AafLrhI5FZMMYKMo4zOjGR6cHkZqBcsUtIzeAhhrkq/+H/PA9UA2jSxHQ3obsbBvpUYwqdOOj0w+RXxR+eeZwaaxDvvYv5PvvQnU9YAJLfMw7dGVD5UgPYVQxir5adIYDEZ+I4Y4f4nnwhyM9FGhqQH7yQe/PJhOUzECML4Kk+C4x/BiisRrGDR00qdNLbH+V6IwIsr1lpIfgHZcL9u9Evv9GTDqmZaPu9wkG3fjoDCaWA/oAiqfFpPHB0RG7hjsG0Y2PzmCqugSmUjJGdhw+EONjOJ6mtT72jXeMoPt8dAYhps3HMPtsSE3H84NbYm+WkZoK7s6RHsVg4pIgMQU6W7RMd6MFFP373Re68dEZhCjskxOcPQ4qT43cYLwRo8+zUjgT0b0j53ZqL4NRM0KjKWxgmIjRX6NOrCCmzB3pIfQnJQ2cMTjrSctBpHhJlva4wdEO9jbNGOlLsh5046MzJGLilJEeQn/yY1DbRwiUCX4iw1WPZjTtrVqNMt0I6cZHZ2jE3HMRF1/rv+FwcfQAKF7UBEcQkVuEsAUo2C8luOyxOXsbZnTjozMkwmRG+fytiPNWjvRQNOwdYBospj5iGM2IcUNXKfWKxxV7jvxhRjc+On4RJjPKNXeAJUYe+oaGkR5BD2L8NIQpRA3nMEpEjwV046MTECIuAcNPH4LCkfcByQO7RnoIGrZERNbgMsoBc4bPfnTjoxMwIi0LMX7oKprDwqnjYB75WZgycSYi3Dgel91/mzGKbnx0AkaePobc8clID0NjpCcMqTmIlKzw+/G4z9jZjx75pBMQnid+j9y9KXbqfhkNI2eAhECZMD1y/TntYI2PXH+jBN346PhF1lYit62PndiUhCRwto/Y7UVOESIuvCqo/VDd2gzoDIuC1pddOn6RR/bEjuEBxOyFIzceowmRH8LWuj/OwJ0v3fjo+CfWiuLlFYzYrUVBGFvrQ9E9+zmD0I2Pjn8iucSIBMYRKuBlS0RkT4he/85OUNXo9R9j6MZHxz+xJJA1ftKIpSYohTPC31ofCqlqCahnyO6Xbnx0/CKry0Z6CD2IabNG5sYp2YjU7GG4kdQM0BlQglk3Pjr+iaVEztSRqKwhUAqHuZ6Zo2PMO6F146PjFxEfIz4fkwncw/9AipzCyG6tB8oYz37XjY+OfyZOHekRACBmLtB2hYb7vjkjqBkdazuNEUQ3Pjp+UabOhaxxIz0MKJw0/PdMzkTEBajVEw3GsAb0mRVSqRM6zfUjPQKwWsAxXDMfgRhXHJ2AwmCIJX9bhNGNj86QyNZm5Ja14Bjh7OuMLE0LeThISEWZNBcRnzQ89xuKGC8RHQ668dHxi/ry4yM9BM3fE02MJpASMX665mAWIxTIOBB95qNzpqK+9X9a8NtIk5UD7o7o9Z+SjTJxNsJkit49QmEM+3zG7jvTCRv1wA7khndGehgaSnRnIsq4ktgzPEbLmF52jd13phM2IrcA0rMhJX3k5R6imcWekR8b/p2+KAYwWUZ6FFFFX3bp+ESkZGD42SPQ2Q5SIreuQx7Zg9z92fAPxhO9fCdlpHe0vGGyQKz4naKEbnx0hkSYzNAlISGWXYOcNg/PSBgftzsq83SRWTAy0ctDoRjAEGNLwCigL7t0gkJd++bw39RiBZs1Kl2L7MKo9BsWpui811hDNz46wTECW7/i8zdELcZHNlZHpd+QMZhG3r82TOjGRycolIVLhvV+4ryLQY1iMmlMSVcIMJ8Zsx7QfT46QaLu2zp8N5syC3Kzo5JMKvInIzLyIdAa68OByQyMbSdzX3TjoxMQUkooPYTcuXF4bjhnMWLy5KjNTEReMcIYQ05ds01bco3xHa6+6MZHxy/qvq2oj/3n8NXsEgpi6rToVfM0mmLL8IDmSzuDDA/oPh+dAJDH9g9rsUAx76zolhG2xFiBPoNxTOdw+UI3Pjp+US64bHhvWBxl8TJrXHT7D5YzZGt9ILrx0fGL3L5h+G4WnxDd3S1AWGLI+BhMZ+SsB3Sfj04gRNI/UjQVbHFQVwN1VYMK5YmzlkS/dEws1UU/Q2c9oBsfnQAQ566Et/8Fbc3hdXT5dQhL15/clBItV9StIuwOZFsbNDdC7jiwt0QvkdRgQqQNRwmcADBaxrRkhj/O3HeuEzDCZEK55MbwOxogiyEECJMCiTZEbiZi6mTobAYJ2BIhOVObdSVlaPE44e4GCYEy9SyE2RZePxFBjPmsdX/oxkcnIETRtPD7aA+0DIyEzlZoroWEVGipg842zSglpYMxtFrpomQBIjk9pGsjjtl6xm2tD0Q3PjqBUVAEeeHVKZeNjcFf1FTTtwdoqddmQ0H6bcSEGSgZMVCBAzSBsDMga90fuvHRCQhhMKJ87urwOqmtjMhYsLdr9awSUgNrnz4OZVxxZO4dCcy2M37WA7rDWScIxMIliLLj2g9S1TR27J0QF68JjDU3DN1B1WmkXBQZcXa3E9pckJwFzTW+26XloeRODP9+kcJgPGOy1v2hfwo6ASPMFgzX3eVV3kKetwJ1x0bksQNQesh7pU23G1weMEfqz05qhiclG5q8SGMkpqGUzEPE0sN+Bm+tDySGfis6o4Lub+6B8TkZ2RiWXwPLr0G6nHD6BPL4QeTxA8gTh8ClBQ6Ktg5Ii7BeclM1pGT19w9Z41EmL4gtw6MYztiAQm/E0G9GZ9QgDIBvmQthMkPRFETRFOAqpNsN5SeQxw4ihUTbtoowTTWaAepoAVVFmXZObEUyg254BqAbH53gMZrBHXgKhDAaYUIJYkIJUkrUT9+IzriaarQgwkUrEbH4oEezAscoRN/t0gkeRdGic0NBiOj6PTyu8COxo0UsFF+MIXTjoxMaIeZ7CYCs8REdykDk/o3RWNiFj+rRZz990I2PTmgohpCraYrUrAgPZgCqB1l7Orr3CJUoSMKOVnTjoxM8Uob1LS7iol8dVB7ZjnQNnwBawMTimEYIv8ZHCDFFCLGzz6tFCPFdH20XCSE8QojrIz5SndhB9YC9jZB3rYZJwlSticHZj+qJvmTIKMHvbpeU8hAwF0AIYQDKgVcHtus69zvgvcgOUSfmCFPUXQAkZ0BzXUSG45OO1uj2HyqOdrAmnvEpFsEuu5YBx6SUJ72c+zbwMjBErLvOmCAS29gpUfb7ALQ3Rf8eoSBldDWqRwnBGp+bgGcHHhRCjAOuAR6JxKB0YpwILBuGJQCwoyU2d71Ay03znNnO54CNjxDCDFwJvOjl9B+BH0oph/yrFEJ8TQixVQixtba2NqiB6sQQkXhoznAhLQCcnWf01nswEc6XAtullN6KWy8EnuvKVs4ALhNCuKWUr/VtJKV8FHgUYOHChWfupz6akTIiwXLCYBqeWYmqxq5UqVS1SPEzNNk0GONzM16WXABSyh7NAiHEU8BbAw2PzhhBCO0V7je2YZjSHzwuUGJ4luVyaNHiZ6DzOaCvBCFEHLAceKXPsbuFEHdHa2A6MUwklgrKMKUVepP2iDWiVBI61gnoL0BK2QGkDzjm1bkspfxK+MPSiVki9aAM11IoRL3nYcVpP+PqtIMe4awTDB4XODoi09dwGZ9RMas4M7fedeOjExiqGtnUgGH6lld3foRUR0E2udupfcZnELrx0fGPlNq2cETTAoZpiaF6NAM0PHcLjyA0ksYCuvHR8Y+zc3RnY9vbUA9sHmYDJLoy/4Mwsm7XGRX3oxsfHf9E2PBIQC3dG9E+/dJYiXrq4PAZICH6ZP6LACVI5CjxUUUG3fjo+CfC2+KyuRaqTkS0z4AoO4Ssj1DtMH/0m/F0S5B0+XS6DZHwEus0GkIDIoRufHT8E0E9ZOlxI/dtjFh/Qd//0GZkZ1t0b6IYhvaPdRsib9lIqvuMcTzrxkfHPxFaCkhA3bU2In2Fg7pjjVZRIxp0L7fCwe2MzFhiHN346PgnArlcElAPbtFKHY84EnXHB8ioOHcjsIt3hvh9dOOjMzRShrcDIxRQDAhAKZkPqdkRG1pYuByom99FOjpHeiSDkeoZoXaoGx+doREieKF4xdDrTO3zIAmDAWXa2Sizl8RGvXKPC9nREtk+I1Ue5wxwPOvGR8c//gxFdxngbiOlerw7U9EWJSIhBWXx5TCuJLLjDAF5cn/kt98j4aA/AyKedeOj4x+vD1Of2JVuUfQgvvWFAGXCdJQFK2AkyxpHKletL6qnyxCH4/+R4OwY00GHuvHR8Y/B1DurEd0zHBm0wRmIAITFhrJgORTOjMhQg8bjRq04Fvl+pUrYNelVz5je+YqBhbdOzCMEWBO0xNIolH4RgCFvEjJvku9GTjvqsd3QGOEgQYMRZaj7jjQuu7bsjcXa82GiGx+dwBACzFbND2GPTkmaIRcpZivKtMXgcqKe2AN1ZZG5addOXFSIhOIjaLl1lvgxp/ejL7t0gmMEHwABCJMZZfICyMiPTKcuR/TyvUIsJz2IMbr80o2PTnAIwbDJYQxAArKjFXXfJ5Gb+QDqvk+Rjs4IG6EIRDr3ZQwGHurLLp3gidRyIkAkIOsrkcd3RafWeXMN6rbVkJiGMuuCyJjWSH9G3RnyY2jppc98dIJnuEu9SIk8tDk6hqcvrQ0R7CwKM8QxVmRQNz46wWMwRs6fEQBCCMgrHrb7RQTp0WxPJLWqx9jSSzc+OsEjBBhNw3rLmN4O94WUYYf69O9vbEU868ZHJzSGOfBWrY2cgzkqdCXQ9qD0yW2LFFGIsRpJdOOjExrDmBgqPR44uS/6NwqnfvxAHZ9oGYkxtOWuGx+d0DAYh6UgnwTUI9ujfh8AZca5/V3EwfhrVM/wRCGPIb+Pbnx0Qmc4nM6d7dBQEf37JKWDLbHrBzHg3wAZjix0KcfM0ks3PjqhMwzf9Grt6ajfA6GgTJqr7aoBPQ6toB9yOTyzH9346OhE3+usjJ8KSRlRvYcYPxVhS/AxgCCNyXA44nXjo3PGMwzLLoHmi4ma5k98MiJ3iG38QB/07lI4PkTUIopufHTOeLojbk2WqEY9CyFQ5n6u/ywkOQuRXRhuzyiT5iGGciz3M7BdkrKK0qtr1P2zr1I40WCMKBzquV06oaMYtB0vxdQlTWjoUgaM/NpDGAwo8y5GPbIVZfw0RFI6apiFB0XeJERCsp9Gos/bkd4DB4ddbFBqBiiS0dMjgG58dELHYNRe3QmPKtosSKqRj0cRCsIah2Hm+b3H1DCeeks8omCK/3axOstQPbrx0dHpQVFAmHs1jF32IC4WXbMMtfdn6M3ijlSksCVOy17PGo8IKFCyq9b68E9vhkZ6gOFNcYk0uvHRCQ9V7X02e2Qk+ojLB2o0hDLAZ9L1sA8hS6HkFSEzC8DRjrS3a7MtT5fvxeMB1Y20d0BnK1jiUHKLIDULEXQpIBHeLCtSGEzazNLjijlbGAq68dEJDyH6a8woiiZ9IaXmhHZ29DptvRoioV0T4g6OMJnAlIJISAnp+gDvEsW+A0AxahK23Q73MaLnrBsfnfDwJm5lsvRG4hpMXdrPUvvGdnvR5In1reORLF9jsoaXcxbD6MZHJ3S6Syn7cnx2V10QAhSJ5qMQoLq113DFxYTLSBofj1vbURxDCobdjG53uc7IIoRvwyPE4H8NBjAatG9yg2l0GB4Y2QdfdWvVK8Zg8UDd+OgML4qxt7zyqPFdjPCsw+PSjNAYQ1926USf7m/tvrMgo1mbATk6/MpESHsncv921NLDUFuJcp5WYllMnIwwDsOf8EjvtBuM2kxxjKEbH53o423Z0n3MbINON76ebgl4nv4jHNnTc0w9shcAw0/+AilpkR3rcKMY+gRpdpdYHhDzZLKN5Aijhr7s0hlZ/OlBd3b0Mzz9GDXLtiGQqvZSPYDsCkuQvYZHMY76SGZfjM13pTO6MFl9Zsirn33k+zplmHwx0UqxEIbBjuSBsVBj1PCAvuzSiQWE8BoNLaVEvvuc7+uGbeYTIYePYuiTrSFHz25flBi7ZlUnNumuvNlNjyzo4AdcnjispUn4whnlIoJ9CUe7SDHQUz5ZdlWgCDTtZAxusXejGx+d6KOqvVHM3UGHfXG0e41yVt/855Dden79LTyrX0baOyM1Ut9IFc0RHMRsq7utD+Ma2H1146OjEzqK4nvmIKXXbWQpJdT412+Wq1/G85M7cf/rIa3ETlTpWioFOgsSETAcY3hpphsfneHBV5SwxwNuL3E+Hg/EJQ4+7ovtH8NwzIBAmwUFYoAi4ag2js28LtCNj85I42PHShiNGO64L+BuxEVXQrwPEfho4M8ARULfulspcoyiGx+dkUN2xbX4EvVKzYAUP5UrhIALLkW5/KbhT4LwmfMlhjgXcOea8NkYTCjtRjc+OiNHV1a8bGtFOuxIVUXSxzVrtcH0eUN2oVx3F4arbh3Z7CtF6RVPAzSN5XB9NWPX0dyNHuejM3J0ZcWr695Crn0D3H2SJ7PzoXAyfLZmyC7UVc9jOOui4c39FEof1UaiF4TY2dYr1qYYNP/PGAo61I2PzsghBLKlEbl/W3/DA1Bdpr380dYCJ49AYUl0xjiQbg2iYZmY9KmWoXoAoQmzjRHGjhnVGZ24XFAVXklkzz/+iOxsj9CA/BApIftQMI0t57NufHRGDKl68Dz08/CXLS2NeH5xN7IygJlS2IyQL0YxDkuF2OFkbL0bnVGDrKtCKAbElDlgjUApZI8Hz3//AHXvtvD78sVIPvyqWxPmH0PoxkcnakgptdeJg8iBeVgGI+qOj1EuvhblS9+BCZHx2ahP/XfkDFC32mK30RmRbW+hia6ZbWMu5kd3OOtEHNnWgvrUH5DHD2oH7B2I5ddhuOaOnjYiNQMSzoKOVmRnB8qFl6M+88eI3F996r+RV9yCYenlgV80MEO+bz4aoCWGDrO/RzF0xfqMzTmCbnx0Io76yt+R+7drPygGxPLrwN6J+4e3IHIKUL58LyI9G2EyIROTMZy3EllX2VW/K0IPeEDVSLsQgdQNGwFfj2Hs+Xn6Mnbfmc7I0XfnSfUg925Bue5ODD98EOULX0OkZ/ee74qVERm5sHBpxJY28tj+wM1FrAYRj2HDAwEYHyHEFCHEzj6vFiHEdwe0uUUIsbvrtVEIMSdqI9aJecSSy/s/OJWnoLockZaFyC/qOSxrKlA3fYhaX43aXA/tzZGTkNi7BXX9uwEaoBi1PmM4tQICMD5SykNSyrlSyrnAAqADeHVAsxPAEinlbOBXwKORHqjO6EGZOg+x4AKUK74EZgskJHvNUJe7PkW++gTqQ79EGEwYVtwAlsiJpcvyUqir8d9Q9cSmHnTUJUJGlmB9PsuAY1LKk30PSik39vlxE5Af7sB0RjeGO34AgExKQZk2H5GeNaiNmDIb3vonVJ7E85tvoVxze2TTB7ZtwHN4D4afP+R/btMdQXwG5FTFCsH+pm8CnvXT5k7g3dCGozPWMJx/aY+PR0oVta0J9eBOZEsjYnwJhm/+Emzx0FyP+tQD/f1FYd/cgOG7vwl8URVrPpaRjKYeBgKe+QghzMCVwI+GaHMRmvE538f5rwFfAxg/fnxQA9UZnUhVhdZ6MFmRpXtQn38Caith0nSUS25EmbEQwy8eQ331CeSmDyJz05mLoLEO5ZIvIJJTgxltZO4fKTzu3ppeY5Bgll2XAtullNXeTgohZgOPA5dKKeu9tZFSPkqXP2jhwoUx9pvWiSRSStSdH0JbIzi6FAYVA9RVaf8/th/1zWegvQUx/wLkiYOh3Sg5DZob+h0yfP4W8LLM8z/oWJtpSHA7tSDDMUgwxudmfCy5hBDjgVeAW6WUhyMxMJ3Ri2yp1+Qg6iv6nzBZ++9mnTqK+upTKGZbYBnsXlCWXY36yhPaEuu+B7Tky6SU0PavIhlnFClcdi2yeQzOfgJa5Aoh4oDlaAam+9jdQoi7u378GZAOPNS1Hb814iPVGRXImlOom99GrT4x+GSdlwlxezPqMw+GdC/laz9GzF6szag8HmRjHSI5FRHog6oY+lejiDXD0013LfvuMkMet/Ya5QQ085FSdqAZl77HHunz/7uAuyI7NJ1RSWa+Vo2i5tTgcxlp2la6o4/Qu9s9WMsnQNQPXsFwz08x/OYJ1I/fQ4ybEFwHUsbgUssLzk7tNRCTdVQvyWLMva8z6mmq7f2mHoijA3HtzVBQ5P18sBw/gPxsDcJkwnDRFYi4IAXku2txjVa8lBwaTejGRyey+MuRcrQjzj4HcVEQSZ9D3W7t2+HtUcXa9nowjHI/0Cj+5HViCbWzFfXUAdTda/039rghNQFx+fXh39hkRh49ELoBCqYIYKzhso/0CMJilH7qOrGE9HiQDVXIw1uCc4RaDIib7gguA30glSdRH/vP0HPChDI6/D7ecDsjUCVj5NCNj07YSKnC4RA3OJ3tiGtvgZR0/2194fFATWVo1452v4+3aq+jBF3PRyd8mmp8O5kDwWNHLL8EnCpyzTvQ2hzwpcqNdyNKZiIbakM3IX3L4ASIdHQgK44jMvLAZEVEQgo2WIyWUb3bpRsfnfCpC6/6BIB0u+HkSa00zKRxcGx/QNepa9/CMHsxomiKVsHCFh+8ERIi4MwK2dmGrDqBrD6paRVVHgOTBWXWhcNrgIyWUV9GR1926YRNJMrWiOY22LkJ6qs1wzO+OLALq8uQR/dDUwOe3/17aMuvnox2P82qT6Hu+BBZeby/r8XlQD2yDfX4HtRjO7UI72gzyne6QJ/56ISBdNq1dAZ3+FUV5L6d/Q90tGsRyH0fch/OYfXJB7T/zD8fsnJDu79UkY3VKGk53s87HchTQ8zGWhuQrVqOmaw+CSnZKBOmIeKTQxqPX3Tjo3MmI8uPICuOQmdr+J0lp0F1ee/PdZVQPFObkHjcmiHqaIO2ZmhpGny9LR7D9XeG7PeRlceRJ/ehFs5CyZ3Ye9zpAGcn6pHtwZWuaapG7WhGpGQjxhUjbEEGQPrD7dKCDEexEdKNj05IyPpyzd8RCcMDMH2apuVz+njvsfIT4HT0OoQ9bkhIgnETtXPdJKdh+LffIMyhOV+lx4MsPwJSIk/sRiakILtSQOTRbaHnfDntyJqTyJpTiOwJiPwShCVCfiF19Mtt6MZHJ2hkezPqvo3e841CRCgCOVDK1Jsvqa1FmwGVzIQjewEw3PNTRFJoyxvpcSNPHeg3q1H3bCCy2j4SWV2KyAvQjxUobodWz2uUojucdYJCrTqBuumNiBoe0PR/qPMqFeVlEKpmeCZMhqJpqO+/ou2WBX1PFXXPBs2B3P9M0H0FRKQjkiMltj9C6DMfnYBRK472LE8ijRACOXkm7Njov3E3JzXpKAmo7S0ot/4bwhLE9rPbDfYIyrb6QbY1IZLCCKYcY+gzH52AUJvqkAc3QXOt1/OuDu9Bhs52Z0D9y05ncIZn4PUHd6GuejGoa4TJjCgaxipPkfKPdeNxjer0Cn3moxMQ5Q89jqeqnJyL52KK6y/lUL/zFM3b9pN+0SIaNmzH3drOuBsvpm79bhrXbEAYDJT87BvYMoZwthoM2ra9KzBj5Q358XvIhRcGpesjMsYhj24P+Z5BEY1td3s7GE1aftwok9jQjY8OoO34CIP32lXlv/9vqv78EAD1b79P5tWXkLZ4Crb0eFpONlDxz9cxZaRT9veXcJzSop0P7diNdPYakmN/eJK8L12FKTWRhNwkhEHbpZFSQlUdbFob/puwWCDTe5yOL+SpA+HfN0Cis+Tq0nl2O0ExdtV2Hx07YLrx0QHwaXgA2vfu7fm/2tFB9b9eoeZlC4kL5yGMBlxV1biq+juL+xoeAGdFJaW/18Qv4+fMIvfaZSTmJsPRwz27VmFj70R9+QmUG7+O8FL/S0oVMUA+Q1Yei8y9/ZGRj4hLiu49VDfYW8GaOCoMkG58dHziqqvn8BdvxX7k6KBz0uGg5ZNNIfXbvmsPlaoHZuSR6AhwhytA5LYNeI4dgMQUlEUXopx7MdLeAY4OZH0lFExFmEzajKu5dth2jER6aJHXQSOltqumGLWlWAwbId346PRDdblQTJrvoPXTT+nYE6FZyQAyp+UjiZKOTlMdNNUhx09C2jtQd6/TliWAbKlFpGQhG6qGd6fr5H5kajZiOMoyu52AU4sBMpqjf78Q0Y2PTj88bW00fbKR9u07qX7iqajdx6I4iXNENwFTbv4Ita4KZkztPdjRiuyI8K5TINjbkVWliLxJw3fPGI8D0rfadXqwnyqj+m+PIz0eqh7+G9IRfsLoQITRyLirl0fd8ADgciKP7ov+fQJElh0a3hsOxywrDHTjo9ODJT+PjK/cSt1zwcXLBEPWigvIElVR638QHjcxo1TodiGHU3kwmETYEUA3Pjo9CEWh+a13aFm7LsIdaw9//PSpZMUPn59l4P1jgnAUH4NFdff4umIR3eej00PV3x6n5h/PRLzfiTcsx6NCqqcGxR3ZnLCAUBTwxIZIvGxtilxmeyB43DHrdNZnPmc4TWs+AqB9/wEsRRNxHB9c5tiYno51UuiF/qp3niDdXYkiRygVIIZK48i6cv+NIkkszfoGEDu/FZ1hx15aytHbv0rln/+KYrVimzJZS3PogyE1FXNeLvbjJ0g85+yQ7tNx6AhlHWnB5YrHJUDJLE27Jz/MCqex9AC2NiDbW4bvfjFkeAcSuyPTiTpqRyfS6aTsP3/HoWu/wKEbv4iloKBfG0NCvBbrIyWuhoaQ71X73jpaM6Zo+VuBkDsejuzRRMPKjkNeIRTPCM2QxJLxcdlR932MbA+8QkdYGGM330s3Pmcwwtjr8nNV1+AsPYkxK6NfG2NaWs//pSt0Z2n8rOkk1h2C1ExNHrVomu/GeYXQPMDQVZTC0X2QUwDp2cHdvK4xtvwebheyMbKR3V4xmPSZj05soA7It3LVDY61ad+xi7hZM0k452wQAsXaq4/jrKzCkBxCZrbBQP6MLG3Du6Ycju71HYOSkavNjup8bMdXngJrHMQHqIlsMiMb6pAuN6Rkx8wsSJ4+GP0qFzFe00vf7RpjeNxu3B0dCLcbc59Zi8duRzGZaPrgQ5KWXIinsRHF7GVK7nL1pFRYi4uRfXaJZGcncXPn0Pqp/5yujIsvwBxvobOhldS8JOIcA4zJycPa0qryVO8xoUBqBjT5eSjLT4AtHpLTodlHW2sczFkMuVkIRQAqNFWDLREstl5Rek+XoNhwb0lLibr3Y8jIRxlXgoiPcNKpyRrzQYa68RljNFVW0rhlO+OXnI90u5FSophMyE47nadPY5s1E3dzMzVP/qProfSN/ejghNKOAwcxpqfjrvf+0BvT0yhYthCrYsfqqIYMwNkxuKHLCa1NkJKh5WKBVhZHqtBQ4/+NdrZrSzBvxqdkJsyY4X3F0dnqXdQrMVVbpjTVhFTBNGTqylDrylDmLYtchQvFGPOzHtCNz5hDdblxp6Rw6LobMWdlMuWFZwFwnDpF+e8eoOXTT5FOl6aDPISMhi88TU1Yiyf5ND7u+gZa6tqxZQawvGlrgYwcbanVWKPVXO9o74pKDoDSQ5CU0r+UzqxFUDwx+NVVa6P2b0o2tDVBQopmiKKl59wPEVkh+FFSyVQ3PmMEZ2cnZpuN5spaWmtryf76V2l+6G+07dyF/fBhOvbso23HTqS9T8i9J7S4G0dVlSZL4cMBXb/mE1qyMpm6ZCJGf0GF3b4doUBKOlSeDHwgUmqyEd2ctRQxLkhn9ECaqnv/TUjVHNXtTdFNVbDGD6mnFBRCxPxyqxvd+IwRKvcfou5EKf/82nfInDSRaTOmMAmJvayM9h27qHnqH2HfQ86fR4cw8Oa6TVx10TnYtm/3acBcNbUcP5hJ8WQLihrATEaq/n093nA6QFEQF66EtAj7Tdq6ZkNGsyaBGsz2eGKa1j4QjWWvImNCi8wGtNmXCKwvZfQ80qNnpDo+qT95mgMffkTV/kNYkxI5uXUH6VJl5vyZtH+6iYY33gr7Hp4FC3j63Y+QXQX0/vXm++Tk5XDhgnkk79+H2jl4htNx4DB7TlgouuKciIuG9aAYYMVVEBfFrXS3UzOySekQyA5VciaGGedqn1VHC7KlAdlSC811/ZeUBhPYEhBp2X1mK7KrSKEcUCo6wNmMYfQ80qNnpDo+OfbxRhRFYdrFF3Fk3ccAxMdZcSUlUd3cRlxBAYQRIGgsKuLvb3846HhVRRUvVFRx+2UXoWzfMei8dLuRbW4q95aTUGJCRNp/kpED516AMA5DxIj0aIYnKQPaGoasYipSNR1poSiQkIJISIG8IqRUoaNVWy5a4hB9Ay79zmr8fHaKQXMyjyIRed34jHKklKSML8CamsKWf75Afam2db1hw2d8snErqseDUBS+dNnnMG/d2nOdkpSItDtQiiYi2ttxny7zeY/OPlv23jjU1MYQIYO07z1Ax8yLiHfUBfXe/GK2DI/h6UtLnTYDam3UlooDMZgQWQWDj4OmHx1qBQtv99J61RzMo7Buux5kOMoRQjBu1nTGzZjGvOuuwprU6z9Qu/wxUlV55q0PaFuwEICjk6fx2OHTrI9L5rgw8np5Hca8PJ/3sNqHdhp/9tl2DH4MlEmNcLVOgMpTmhbzcNNSr/l0vCByJyKildLQ17gIofl3bAmaT2qUGR7Qjc+YwNnewSs/+CmfPP40cogdrOff/oDDxVNYt/5TAA4fPMIHH31CVUUVb9W1+DRA6uEjLFjou7iex+Ohs8h38qcSH4fZ1RbguwkCKcEzQlKhLXXatnxfhEBkT4zePbsDlxSD9t5Vd8g7lrGAbnxGOeV79/P6j/+DbS+8yoHVa3C0Dy3WteHjz7z3c6qMF05WYpwyZdA56XSyoK2ZiZMKffa7u8y3OqE1f9yQYwqLkdTpaa4Fa29goMjIR1ijqNWjejTD09c/5OwY0v8Uy+jGZxSiqmrPkuqxG25j09P/0o6H8C1oMPc6PQ0pKbx48DjGqYMNkKepiRWp8aRleF9u7Nq5F2NXRnzy2QuYduMFpC09FxQFa06G12sigivAgMRoIFXIngAZ2vsWOYXaMcWAJt0ahaWQN8e0oz3mxeK9oRufUcjb//FfNJwu4+CadTSVVYTVV9HZi8ibOZ2SJefTVFZBp8PJkxt3cGr6TIwTC/u1dZeV84XJhT4N0F6zjcQFcykcp2J1NjEhtZVpXziP9NzozQZEGJn2kUDJLUKZNBuROR7R7QdSPWi7U8NkEKQ6Kg2QbnxGGW319UxdtpTqQ0fY+85qnB1e8qYCJCEjg5aqair27ufIuo9xOxx0NjXjdDp5f83HPPrZbhwLF/a7xn38OAtmT/fa3yefbKa5IK1ftVCrs4kERwC5WqESRm33sMmfglAUhMGIUjJv5MYBmsFzdIwqA6Qbn1HG8Y2bOf7pZgDKdu4JaanVTVtdHUk52RTMn4MteXCUrVRV3vx4K0pSYr/jRU31GAwGjKbBuzpPPvIKj22qxp0WZppDgPRLFxlOFANK/uQAGg7jLpTqjvmKFX3Rjc8owNE1u1FVldd/8isQ0NHc2nM8HI6s/4TT23cRl5rq9XxzUxPvuxWYOxdjjhY856mr5645k7lt2Xlea6Lv3rKXnz/8AfZM7/EuJKeh3P0TlKtuCz8i108YQNQQSmDb28O9Be52aAZoFMyAdOMzCrDExSGlRAhBS2UVZTv3EJeSxImuGVAkSBmXi8nqPRu69PhJ/r5qLa9U1mMsKUE6nbgrKmHnTu5ccQHJqSmDrmluaOLP//oYaRmQrR2fiOF7v0Upno5ywUoM3/gpJISYk1U0FcZPCO3acJFqbBof0Gq129uGCEyMDXTjEyV2vvZWwAFwTrud2hMnqTl6bNA5KSUt1TWs/eujPP2VuymYP4d977zPx489FdHxHvtkE+PmzByyTW11LU9v3YuYq8X8CLMJQ2sbl5230Gv7k0dPscvVP6JXjC9BxPcu48SEEgzf/g/IDmE7fs4chHWEgvTTcgNrJ9XA87IiiVTBGYXAzgiiG58oMem8s7G3BlYT3NHaRtmuPex6451B59obGrEkJlB18DCb/vEs9aWnSBmXy46XXg9pXJaEeC646mLu++aVTD23v9EQAXxL2zs7+fvqDbQvXMiB/EKe2XmQF9/zXWTwiYdfpiNzfO+BgsFBeCI9C8O3f4nyha8hllyOWLgEUTJz6CWZyTyyQb11ZZrujz+EouWFjQQe1/AWKQwSMSLh6cDChQvl1j65RmMN1eNBMRioOnSE9Y/8nRse/C+v7drq6zUDkxCPq9NOZlH/h7OlugajzUrjqTL+Z+lltNc3YImP9xtM6Is7v34185TezOxKQzLvHmpm+/sbKFy8gNLN20LqdyhyCnL4fzcvQmltwvDtXyImlAR0naypxPOPP0LV6cEnM3MRF1wY2YEGi9GMsvjSoV3KQhnZ5Y8QmqTqCOZ+CSG2SSkHTY/1mU+UaG9sAuD0jl1kTS7mwz/+lRe++0Mq9h/s166zuRWT1UZSVlY/w3Pww7XsePVNzHE24pKSGDdzOjf95QGAkA1P6rhc5ij9s9tzPc0sKEoPqb9AqTpdxWsHWxEXXBqw4QEQWbkYbv0OGAfPgMTMEd7aBk1qw190sVRHtoKElODs1KRjnZ2BaQINE3pWexTwuN0kZqRTdegI+959n5zpU3nt/p+jGAzMufrz0CdMJrOoEKfdjstuxxIfD0BjeQWv/+RXdDQ2kT3lnySkp5GUncX8669m7zur+eyZ50Ia19KLz0JhsB7NHKWeGRechVNE/s8hbXw+y797D2ffdDUGL9v5/hDZ41AuuQn17X9pD3J6Nsp5K1DHj0dUDNaYHlYSUvsIfg2BEN7jDYVB24nv0gqLrmGQmrF0OzVj2J0JP4LoxicKGIxGKg8c5Nlv/DsF82YTn5rCyvu/R8WefUxZen6/tg2nTtPe0MC4Wb3O3sp9Bzj39i+RPXUyyTlZ2JKTqTtRSsbEQq7+r1+y953VtNcP1ufJnVxM1ZFjPY7us5ctobm9ndNHjjN55jQmD/EtfceseF6vsXIkQp8BQGJmBt9b9QIZE3xsuQeIsvQyxFlLoLYK8idqyoUn90dolGGMq2h2YA0HujaEAVA1X1D3KcngvK1oIVUtINESP6LiY/qyK0qc2raL1IJxnNq2E5PVwoVfv4MVP/weZbv29LTxuN385Yov8OlT/+L4ps20NzTgsttJKRjHwQ/XcvDDtfxy+mI++svf+PQfz+J2uag/fgKpqqSNz6dkSa8hm3b2Qi42C26aP4Mbz5nHRSuWkutxcm7pUS4vHs+cIwd6VAi9YZEurstsJzk3MsGBJquVe154ImzD042wxSPGT9LiijpaoTySZjI06t58TxPjx08ihVTpyfVSDF1Gx8sV3Ymjw4WjXVuOjZDfVzc+IeBxufC4XLR5KbrXTf68WWx59iWSc3PInTGNna+/xT+//h3aumYs6x5+nENr1jPloguZvOR8WmvrMVmtHN34GftXfYDJZsPdFb378vd/QlxaKv+1aCkHP1xHR2MTDafKOLLuYwoXL6Bk4TzOLT2Kua6WuPIyEk6coGj3TtIOHgAg4WQpiqrSuHEb7ZYMPD6m2wZULvjcooh8Rpf/5PsULY68X0YC6tHBqonDTcupRkp/8mv2XXkD1c88R+N7mtJj254DuBq9aT3LruWXHx9RtwHqSU6NMlId/pplXejGJwQMJhMGk4mEDN+O2typU8ifM4tDH63nH3d8k3V/fYyqA4c4sHoNLoeDPW+tIm/OTC6856sk5eWy5813+fstd/Gvr30H1e1h/vVXM/HshViTErn7tWc5uv4Tynbt4a1f/LbffWbkZLBACeybS+3s5Ni729j77kGq3Fle25yf4iAlLyfwD8PXvdxRzDZP8T724aKzroPqN9cDYD9ylFO/+A1t23cBUPb7/6Hykce9XyjVwGYZqqc3OVVEKTu+L91BiYGWLIoQus8nSigGA/HpqdQcPkr57r3EpaaQWpCPJTGB/avXsPed1TzzlXuYdcVKdr3+Ngfe/6jn2ld+8FPO+tKNNFVUsejm63nr5/9JxZ59g+6Rk5dD3oH9qAHGEwF4mlsAqFr9MRlXzhlU2iZBtfPda+bx0KoD1BwrDe3NA3WlXrbHI4AAlILJqGWHwu6reuNhpNtDxlmTMVoCX+4c/e1juGpq+x2reeZfdB4+QutnW7AOUAPQ6PYsB8lwLYlUj7YMM1qGre6XbnyiyM0PPcjW517izZ/+mlv+9r+Y4+MovuBcag4fJTkvl32r3mffqvcHXXf2l2/GkpjA8Zde59Aa3wF8VRVVVKxYSs7unUGPreiaCzEOLGHcRYbaxs9WFOAShZSJJN7fV8vutZ8Gd4NoPjQR2rpu3LCVjr37ad2ziOJ7rvRbwbW9uo3KVz7C3TR4WSUdDlrWa+L9LRs/69nA6kERoIb4mQjD8AUqursSU02WqMcF6cuuMPCXUZ41aSLLv/8dbn7of5h4zmLi09NwdXRyeO3HNFdU+rwuOS+XdX99DJfdf3h8vCm47w/zuDxsJZNI8mF4+mKSHiaqjXxtmpFrvnxlwPeIT0/jS3/+z6DGFTRea10FhrPVQcvJBtSuxNzWTVtwO4ZecnQ2dFD/6V7NsDiH9pE4Skv7GxqhhG54AAabsujidgyLH0g3PmHQHcE8lJEwWSxcePedpOWPo2DOLNobG4lLT+mJ6fHGnjffRfESWDcQoSi0OoMLn1esFvIXBp+MuSy+kUtvujygtqpbi+6OFgIQRb41pf3RXtHEsV/8Cfvx0p5jzmbvv8PWsmaOPvIWx37/FHUvB1j/TEoqH30Sd1uX6oAIccnV01+ASazhIhTN0S2UYdny92t8hBBThBA7+7xahBDfHdBGCCH+JIQ4KoTYLYSYH7URxxD21layiov8JpA6Ojpob2jAYNJC3CedcxYrfvBvXuUoACr2HQjIYStVlQ/XfYrBS1a5zzEfO8HRlz+m3ZIZ8DXdXJraygQfQmJ9GT8/dMMQOGE8zB4PcsDnW/na2kHN7E12jv3yz7R8sglneXCKkeV/eJAdC8+nefP2IUX9YwKhAF07caqn69/oO5/9Gh8p5SEp5Vwp5VxgAdABvDqg2aVASdfra8DDER5nTGJNTMRlt1Pb5xt0IG6nk7b6elrr6tn87EvEp6XidjpJyc/DEMDsxh+2uOAlSqXTSdXR4IsItipW8if4z+aeuCj6qQ+ydG/oF3sx+p62Xm0kt8NDe3UbrUerUANY+vrE5eLwzbex68IVuFvCrN4R1fwwL5KvUkbd2R3sX/8y4JiU8uSA41cB/5DaFGCTECJFCJErpfTt2BgjWOLjySr2XTZGCIHRZKZs5152vf4WboeDNQ/+lbNv+yJfeeZR/n7T7UHVnlKMRgoXL9CWHgYDkzNT8Wz8JOhxt+zYgyyYiSDwP+o46WR2fgptnzuXQ1v3YG9pZfryi1j5/W9iMBo5tG4jB9d+QkL60DW8IkJccmBZ5QMoX70LT1sHyReeR/P63s+tfdcedt9biXQ6MGWkY0xNpW1bZOKJXBWVHPn6t5n6rycCUg7wSaSTVLsjqn39/bmdmuM5SgRrfG4CnvVyfBzQd2+1rOvYmDc+HpeLzpYWTJmZeNzuntlM+d59tFTXMvnC80jMymTv2+/haGnlxKebqdh3gFd+8FOmLlvaFXim/fIVoxFrUiIdDY397pE3azrWxEQUo4HaY6Uc3/gZQlG47dKLMHR2oCQloba0BDlwD05zAhZn4NeZpIcZ1DNjioHSc67HdOkNTJjXmxZSfPYCLv/ht3E5oi/lqeQUotYM/A4cCoFUVWy5GZz83yeQXsbortMqqnpaWoHS8MeYlEjK0iUIk4mk887G0+nAGBfGNnYkZiLd+WQ9sURD4LJryy9zXFR8TgEbHyGEGbgS+JG3016ODfqkhBBfQ1uWMX78+EEXjEZO7diNJSGOpMxMDEZjj/jXc9+6j/EL5pCQkYaqqjjb2zn7tlvY+dqbPdce/HBtv75ypk3G3txKztTJHN+o1ddKGZdHc2U1FXv65zJdeP5ZmE+exFNXF8TcpT8yDJGromUXoczzLj5mskTv27KHhBQtLynQwLjkDIRQqF31L6+GJ9Jk3HwDuV+/E2vBgEKMYeVvyfBmP93aQsHYMI9by4Y3WQNLog2CYHq7FNgupaz2cq4M6JvEkw8M8tBJKR+VUi6UUi7MzAze4RmL5M2YStr4Alx2Oy6HA1dnJwkZ6STlZCGEQu6M6UyYN5cLvnkX4xfMoaXKdyWH+LQ0Gk6d5vinm8nsWspZExNpH5DGUVg0gZJTJ/DUhVf7vNURmpEQ561EzD0nrHuHiwAoGKpCfB8Ug7aEaK2n8Fu3RnNYAKRdfSXjf3zfYMMDkdtFCtYQKIbQjZbHpRmgCBPMO7gZ70sugDeAL3ftep0NNJ8J/h7QfD7WhARMVismiwVzXBwGo5Fzv3IL59z2RaTbjctuZ89rb7Pur4/1zGgGkpidRWW31o+USI9KQka619I486ZMQobjCO2idntoUcKioAhhGZ4o2KFQ8oogYwj5VWHQShqbLNDeDB43FpvKjMd/iyExwfd1YZJ7z10YbEN8Pt27S6HQbUDUru337q3xnn676JugGolsedUdcQMUkPERQsQBy4FX+hy7Wwhxd9eP7wDHgaPAY8A3IjrKUYTL6aStoQGP203DqTJMVitt9Q1kTS7h0EcbvAYmGs1mEjPTaavtncnUnShFSknDqf5pCoVFE8g4FhkdG1teaBnssqosIvcPm7ZmRGoOpI8brE1jtgESmmo0+Yg+WCweCr79lagN68DVN6IOVUlVqtrYws1gl7J3a7yn3y5UT6+Ri9Rsy+2MaPxPQMZHStkhpUyXUjb3OfaIlPKRrv9LKeU3pZSTpJSzpJRjVx/VD4qiYDAambBwPgc/XMv+99cQl5qCvbWVir3eNWgmnr2Iir0HBh33ptkze3JR8M5lb+O02Ugd771cjl+a6pEdoakpRhSLDXlkG9SXA1Kb5SC0xFOPq/chH4A0WbHmZRI/LZC6W8GjdnbiaQ1ga73HQEQJH+8/LCJYF0zP7YowBqOR1po6Njz6JOPmzORv19xC8QXnUHP0uM9r7IH8oXaR29xIJMK/VLsdowitJ7lzI3LWYsScxREYSegIkxll7jLUPes1Y9NUrS2xmrz71ao+2oN1/Dgqn3mZzmPB7JQFR+EDv8WUlhJg65FUwQ8BjytiukO68YkC6x56jOObthCfloKjvZ19qz7w2dYWF0ecM7BvE6PJhCfISFtfWEuKqauTiMwM4u0hOK5t0au/7g+pqtpDYLKANa5/hQZf38xC0PzpDiqeeD46gzIYsE2bgiU3l4yrLgvsmuFSLowoImKzNd34RIHz7vwy4xfM4/Uf/4fftp+/6FySDx7gsKIMqTQIMH36ZERbk9/ERn8kLFpI25at2A8fodlmY9JV5wZdT1194xnk3HMwXHx1WGPxjfd8KM+pg9AtpxGXBEkBBjRKScE9X+Tgd34RsRH2H5iHCb/8CYlzZwV4QW9816jCZI5YzI+eWBoF1vzvwzz5pbtoOHnKb9vkI4dQ29u5dNkFQ7YrKp7I4sZa1BArV3Rjysmhfeeunp/Vzk5KP9yNKoIUE686jVz1Qlhj8YliQHPIDtgVEgKMfcbZ0QJVpQF3a7BFOf7IE8xW9ig0PABGc8S60o1PhJFSkjFxgpZE2oUwGLAkJmJNSup33GA0orZoQmC5e3bxtc+dQ8mU4kF92uLiWDZxXNgzHgBL4QSkq38mvKu2ljoZXPkccfG1GH70x7DHM7jjPksRtcth2p1tjUDJm4SYdSFYgl/22Stq/TcKgeJH/syste+REPCspwupDq9mc7gYzRF1kOvGJ4JUHz7CO7/6HRd+4y7i+mSaF59/Do7WVuwtLdiSkyi+4FzMcXGUXNA/UM+z/wBLVQfXX7aMhYvmkZqWQnZONl+eXoS6J4xEyi7iFy6gbZP3OKOqDzbRYQk8J0vu/gySo5HD5WVG0CfbWgAiMRUygxem7zztX8MoFI792/ex5OchDCE8TsMtGh8OES61oxufCJKcm8OUzy3hpXt/RGuXzGZmcREt1b1B4W119RzdsBG308mxT7dgSO5fy1xtbSV5+zbmnD7B9RmJXJ2bhrssQnE1Q0g7eFpbOfb+HuzmlMD6qilH7tsemXH1w/9yRKDldpEcnJZz1rIFGKOQ9CodDlo+3YKzpg6PfYjNg57gQtH/2GhwOgsl4mV2dOMTQSwJCex+611Obt3BpPPOJqOokJRxeVQfHFzmRe2KfG4r9l3BU21pxV0ZuUBxZ5W3zJhe3PUNHFt3mM6uGZCvKhc941sXoLhWMMjAgu+E2Yoy/eygvo0Vdyf5X7s5nNH55PCtd3D49q8z5NZ5j6hY13sUYaQ8DDdRmJ3pu10RRErJ0fUbqdi7HykltuQkGk75nrWkpqeSYO8k2t97puxsLBPG07bVfx12Z1U1B5+rxjZpIhgMTJkZh+jalXGaE6lrsZBn7dqaP3UUWXYCkT9xiB5DITBnrBACZe5FqNtWo8xdhmypQx7fNeQ11two5RQKQdatX8Tn2MWAbfXRMNvpSxSMjz7ziSCt1dUUnXsWE89ZjFAUOptbfCoSzp43ixsKsvDsG1yVItJYiibStnmL/7rifeg8doLOw0fZu76cFnMOnZY0nIoVFKhTcvEYtF0PdeemCI1S9M4GghinsNhQFl4CcQmInEK/S4O4NDNT//QL5j773xT9/Lte68CHhJRUPvI4itnLblB3NvloJgqR2LrxiSAJmZlc/O/f5ranHyF9gm/JkISMdM7LTscybhzmceMiLlUwEOeA/LBgcNfVU7rqM2qqXLR3KrSVVnH65dWUnpI4zMnIY4PTQkJCdOUghfCQCrOlx5Miiv0o+EqVuFQjiruTlOIMZj3xW5SEXr0aJT4OY8DRyf1xni6j7I8Peb1nVNMohoOR1PPR8c/uN99l5qXL+eh/H6buRKnXNgaTievjDDj37MHp8WCbOYP4rEzULo0ZY3Iy7oZGOg8fjkwQmsGAMoRYfSB4mltoWLux37GWzds5sMtM5ufOJf6VN8m49vNh3SNSiNTsoCJoTMLB7Md+hZBuLThalbhFHNUvvUPzug2o7e0oCQmobb0pMNaSYuxHvCf3duzdN9y1JqKPYoxKTXfd+ESQwx+tx9nZyek+9dgHYo6zobhcPYalc6+27DLnj8NZWdWzI2XOy8U5RHmdgPF4ECYjwmyOSJxQX6TDSc27a8nJyERefblPQfzAOouM41UoCiRlQEvgKSNKV+FEc9fTYLZ6mPTH34KE9gOHMSYl0rp5G57OTpIWL8Td2Ejbzr2YszNp+Wwr9a+81rNUnPCLH/tQ1pOj1yAZI7vF3tNtVHo9Q1n5o+9x+KOPOf6J91gagM7mFpzjS7DU9g94c5aV9/zfPGE87prIBcTZjx7DNmUyHRGIFRpI/JyZpC4sQdaVIbJCVacMs7TMwN5yi5BBGJ9BOO0gBMJgIGGWVq3DOqF/XFHSOVpSbca1n2f8T77P/mu+iONEKR0HD2EZP66foZGAunU1pGYh0vMQyZmjyxBFOL6nm1G+EI0tUnJzqT58xKsAWDdFc2cNMjz9MBiw5OURN2sm8fPnET9vLiJMp6h0OHA3NWHMzAirH2+079pL7Uc7kSf2DDIfgZuTCGjb9EEkBRet3Q/FAOl50BJ4dQ9jYiIpyy7CWjSRuhdfHTwDtLdrmkJVpch9G5E+su5jEsUYtZph+swnwkw8axG506dS7qW2OsD09GSoGsIB7PHQ+mn/HaT4xQuxHzmGp7HRx0X+MWVm0r49MtUYBpI4c5Km9dveDPFa0KS0d6DuXIOyYIXmL+iOUlYMiEH+g8AEr6SUAVV/kB1h6B2pHpRxJYi4xKAuG/ftrzP+R9/DXnoaVBUpFLB3IJ2dyAFC93L/p3hyJ6FMnBn7M6AoLblAn/lEnOIl52FN0vK4BpKUlUnWoYNB99m+eSu2ySUoIdTo6kaJkqi7ZXwBKSVapLF6Yq8WQudyou74EFQP6pZ3UTe9ifrZ26hbVqF+9o7X7H3pcSNbG5Eu734pqaqoh7YguwTjpcuBbBwcNCkBeTSMyGshwBy8RKwhQXPqWwsLtLEe3w2Kgjy2E6q9aAdVHkMtPxL76aVRcDR3oxufCOJ2OpFuN+fecStG8+BvjHFFExABVCL1Rttnm5EeD3EzZxA/d07Qhqh18+BUjkgQN6UYoXR9f7fUoe5ai7rlXd8O5Pgk1EObUevKke0tmj+k9hTq9g9Q96xHnjqAtPdm7qsVx7R2J/dBQyWyvssJL0E9uR+18niPMVM9bmRDFTjC0BpOHweRKPVceUz7HIaahZ3cj3pyf+waIGtCVEME9GVXBDGazbQ3NOLs6CBn2hTKd++js7lHeTbsnXPpcNDRtTuWsHBBQBHLPXg82KZOpu2zLb7bKAqpy5eChMbVa/qfMxgQRiPS4SDr5msxxtto2bGfjM8t7N+uvZkhMVmgsRrZWO31oZPVpZCQqonoN9Uiyw4jS/fR7UGSpw8h03Ohoxk6WjRfU80pRNZ4bSYUjj8lJRslawIizAdOtgZRDba5Fpx2pNkae0uwKMcm6TOfCONo76Dq4GE+/6sfkztjKtbEXt9BcYItYvdxNzYF7Qh0VlT6vMaUncXE+79B1vLFZCwdHKg37mu3MPX3/07cjGkkFOeTdf4UJn3zahLzg51NBeCzaaxC3bcReWK3Jlre10w52rXZUd/lWXsz8sQezfAkZWhR0gPpdmhbun4HKVmQ3J1qITT956ZqpMEQ9kxEBKN509aEuvW9iGojR4QoFQrsi258Ikz6hAIKFy9k3MzpfPHhP+LuU6DuSGsHR2bNpXzWnLDvYz9xIqhdsPg5M8m65pKe6ZcxPQ0lQSsfY84fx+SffY3UaTnEZyfgbutflif14iVknzsZW1ocU+77IklFGSgGBYMpyOWJ2Qodrf7bNVQOWaZFVh7XhOO90VIH8UnavWxd5XFsiZrjNDFNO56UoRmqznbtWHJvXJDcs16bcQX3zrRrpcRzeCtq9YngLzYOQ6HFQDGao+po7rlN1O9whmEwGjnnyzfTUlvLhkefxN0nsO/Q5m0cAmaedxZDVJsKCFNWFq6qwPRphMlE0T3XYYo30X7xUho/WEvm51eQu2wm7k43CDBae/8UjAm9DlfblBLG37ysty+DQISyQIhL0pyXwSxJQqWtERCQmqwtHcw2TVze7db8Od2zDGeH9hqA3L8RKQTK/BVB1SeTTTVQV+6/4VB9dP3r9xMesnKp6CrDHULgplC06qTDgD7ziQKqx4PqcpM6LtfreWMEHJru1taAHaOpK5Ziite+yQpv/RzT//xjkmcWamOxGfsZHoCE/GQm/r9vYZtSwqR7b8ZgjsCfiVSHx/D03lDbvu9o0QwPaIXvAl3eSIm6fbVfXe2+iMQQSxGh+YlkZxvq9g+RNad71Ru7iwIOLAgo1f7Lon5xUrLL8HgxYf4KFlqiv9zqRjc+UUAxGEjKzqKxvJLkvMEGKMkS3pRWmExIlxNTACWnLePzyb7k3N5rFQVrio24bN8VO4UQpE7LYcpv/x1zfAQ0exNSoTPw8kCRJYwHSUrU3esCX4IZzYii0JbUcu8GLTzB3oY8ub83Lqq7KGC3sekrPtZ3B8NbnFS3EelrxIYqWGgwDauqom58ooSUkknnLvb6zVnf3klbYWHIfaecs4Cp156NJce78TGkJJOx/EJKbvoc0xekYrM3hXQfxRShPw+nXYuUHW6a6zTHcjh0tKBuWYUMoLyRAERGnqYxHYLMaw8uu3eDJ2VwS6me0sp9jFg3AwsWGozD4ufpi+7ziRKqqjLnqsvZ+epbbH/ptX7n9m3cjG3p+YTqds7IS8DmqKZkIjRNWUFHqwtHSwcCSB2XRKK7BkVthK7nRW5cg7j5TnAEOftwdIA1XksPCAdnp+b8Hc7Zj9mmPVxNQ6s3BoTLAebAHMLCaEYkmlHdLmRDpRb5HQp9osWjRrfUhxDDsrs1EH3mEyVMZjONp8sH1VoHmLxoPtPqQnsoLPnjiHf0XpvirCTPUsfEaUkUZnaQ7KxC8RZBvHlj8H9cnW1aoFm4WOKGf9k1pEM2eNQ9G4LaAROpWShnXY4oWRDa/Q5uHp7gQyGG1c/TF934RInKw0dZ99BjlG4evCU81wjmuuCz1hPnzyb//GnevRgmP76ZYwdAhLCd21StxcAkZ2o7VqEQQpmbsBAC+claaI1gPfnWBtRTB1FbG3tSPIYcQvcrM19LVA0WRweypT7464IlwuVwgkE3PlFi50uvUXPk2KDj1qREqhKCf4hTzltM8QRJksPHjCmAB0Lu2Bz0fQHNADXXhv5H6nYNj8/HbIOWDhAKIisPKiNU9aObskPIPetRt3+A7GgNaGYiAGXyQm1sQSL3bQzI0IWEYtC21CNYBDDoIYzYnccwHreb1rp6rzXaLzprPkVHgksuNY/LY3zuEDEduRMC6+jYgZAegh7am4L/Y7UmaDKxapQeom5MFlBBfvAmOFXIzYeCAD+XYHE5UHeuQT22E0/NKWRnu+YLdruQbU2DjJIQAmX6OV67GpJoyq+abdpnNoLoDuco0NncjMWHdGladRUGu93rOW8Is5nCC6ZgcPgQx5o0DY7tD6ivWpFLYk0rtpQwfu3xydosyGLTvjmF0Jyj3rZ6kzO12J4QssQDxmSBdjty6ydQrQX4ybdegOxxiCmzID3JR1Kd0MTj3S4v5wLE0QlNh1Ed7dpuUd9ZSmo2Imu8Jh4GyLam0O4RrXruMaAprRufKGBLTubEJu8JnDIAiQJhMTPhqqVIKTEZZD8Hs5fWAY2p3ZJJ2QsfwCurKfrFvaQUZ4T2h91cq+2MWOO1/4NmZJoH+LDMVmhr0oxSS53WRqraA+vwLbYWFIoBuW4NVA1YXqkqVJ5GVp5GnPM5KJzYu9Nn6XKg19Yg62oQRYWh319KcHT5lQYuj7qTZ63xKPMvJlSlRtnejEiKcKFDo2VEHMyDhjHSAxiLdDQ0cnLLYE0Zk9WKEsA3bd5lS0h1dUlH+FutBLijU7rhYI/O8PFfPEjeXV8kZ+nM0HaEBqYluBxajEjf92ZN6K+j3Nc4GS1gUMKTvgDtsxloeAYgP12DkEsgLwdMFm3Xr6NV8wfFJ8C06aEbw0CMt70d9fA2LV8tlFs0VEIkjY9QRny51Y1ufKJA6dbtONraEEIwb9l5TMrXZD2rmzpIlx5cVjOO094fmsQFc8gkgJytuASwxQcs9hQ/aTyuunpkV6JrxeP/AnkT2csXIlyBLwO90tGi7YR1Gx9b4tAC7m4HxGWEZ3wMJuR7rwXUVG7diJizSJuN9S31096G3PwpYt780Ir4tTVqfTr9fH51YTi+q08iC2dETm7DbIuJWQ/oxicqJOflUnLheYzPiOOqjHagK6ep29dbMoE292RqT7fRtGlbT8WKzBVLyEtoRQw5GxFQNA1KD0FHG9QHFi9UmN7O4UmFtO8/1HOs4u/PUfH355j37B8Q7jAlHfqG5Xe2+X8ow61V5nFB0VTYs9V/W7cLuW2j93PHDkJ+IaQHGdCXkKottToDyNIPB49Lu08kFAUNpqgqEwbLyHudxiA5UyczYXwWV0z0Ea5utZHgqGViViczrj+L7MsvYtJNF5Mf34Aih1qWCc3BfHx/0N/UHoMZ++kKr+fcjgg4NfvWWFcUzSfki/gUiEAMi5g5KyIpAXLdKlCCWIokpWv3jbbh6UI9dTACAYehycNGE934RAGz1cqVP/4upqu/4qtB73+dreSZ63zH7/RDgr0TEoIPu7cbk/G0en9Y9tz5I1RjmEJn7U2QmK4FJCqGoY2Ly6lp6gSDYqDHud4t+yAEYsXVIQ64P/KN5wNLqrQlaOMYzgoUlcdQ934cXsyPxRYTO1x9ia3RjCGMJbORxTO9n0wIrjJCP8pPaMutIAIVJVC2cwi/g8dD24kwcqCEgIQUcNm7dHP8FCd0dgS/BW9L1GZU8cnazMNl13LO4kyQkx/62HvG5AARQAyTyRJUQcKI0VKPunU1MpQdSqM5arW3wkE3PlFEScuEybMGn3CE6eBVPZAZeMi+FCY8nUPfs/ypl3CpIe6CJGdq2+rBlKyJSwxcW0cx9MYSuZxatno3qgexZEVQw/WF/Ogd/40627TZ3Ug4bT0u1IO+C1J6pVtMLQbRjU8UkE4HsssnI5IHFLCbOA0qT4V/kxMHwdfMagCKdDFlURbZl12EEu89z6rz8HFa9h0PfhyJaaEljQ6UdPDXtnu72dnJoJgZISExAhng9bVg8VPX3uXQZne2RG3pqBg153NKdvj3DwClYGpwF4xg+oQ/dOMTDUxmhGKAzg4omNR/Z8fl0L69I0FTPaQFpldj8DjIs9SRccFin20c5SEsvVzO0OJk2lu0hFN/Dzt01V73oYJoiUe+9H/Q6qdqRiBIifxwVWAyoh0tml8rIVmblUUwg35IhnLkD8RojpmYHm/oxicK9FTVbKxFJqdDXp8co+YISona4oLuzxxvQpi9fxvWvf0hMthvSoMxxD9wqe0WKcrQjl5LvObM9rXfI9XwUiQGkpqmxSEFhNQMUGLasFWfUI/vDmznaxi1mENFNz7RJCkVseNjKOtTzSAlgvXSTx/TYl2CIFOtYvK15zLumuWDziXOnxV8ykV7k2Y8QvUrdLZqy5aBYXTmOE2FUKpDZ+wLBVLDqM0+sLtJU4P7DKzxmvM8nBLNwVBXphVG9IFES8mIpWBCX+jGJ0pIKZG2OMTVX9GCArux9nlIzRbIyAWLVdPjKZ4Jaf51mfvR2hT4mIQBuyUZs7sdm2XwA2bOzvAT4OijX4+kZt2uoK/roaVL7tSWqJWxMcdpEdxNNUOW0AHA0Y5YsjL0ew+kMcj4I3tX+Z0RRgLS0Ym6c432QwwFE/pCNz5RQgiBYrYi0rMR567oleGsOKH5gcZN1HKt6iq1b8/xxXB0r+YHsdogpyCwKOAAKyY4TYkc3tfOgec+Zs9LWzj63GC5j+pnX+PUC2uRfabrzSebOP3yBqrWH0D6qC1lb2in7KF/4CaMaX5TtRa411ynbcUHE0cTwW94+dk6MAfhV1GU0FIzwmBgRVQpJerxPajbViMS0xBZ44d1PKES++ZxlCJVD9LlRHa0I9e9pc1snA5ob9NefWmu114AlSe1GdDRvTCuECpOeXdmWqyaETt9dMhxeAwmpDBw9LMyHCf977LVvf4eTRs+I/eLV+OoqqXmpbd7zlU89k/mPvvfKO7+s5Ha9z4GYN83fsGMh3+BUYYQSmCydNXb6osgoGzwSMpOqCpyz07ElJLA2itG/7OzCCO6RPElIGvLkEe20/M5JUZuCRpt9JlPFJAuJ2pHB6K6DGGxad/o7QGG4pstWswMQHkpFE/XDM0A7JYUOLpPi3gegMdgxm200mLO4eAnFRzb2xKQ4enG3dDE6b881c/wdHP8j//qt0WuGm3UvbEagMIf3E3F8++FEEkrtCXXQCNisoAp3r8/SfEgPn9TkPccgn3boTPAaGK3c3ilSBNSEckZyPYW1C3vdVVu7frcbAmIvOLhGUcE0Gc+UUCYzNr6u70NMlSU8y9BPRGgeqHT0V8m4shebdY0scsRajDQWOug7K315C9bSLK7Dqc5jnZPHHEGBxKF8gO1tO09gGI2o3Z24qwMrLJpILR8to36CxaSOKMIo9VIR3nvEsCYGEfBzRdryZDBkJLlvcpEUwu0tiAP7EJc8vmht/TNAsZPglODpWtDQR7cg5i/ILAtdJdD8/tEO/I5KR2lZCHqvk+hecCy1GRBmXsxIsbyt4ZCNz5RQKoelJ0bkdY42LAKdeocLR6nIcR8IJdTCyrsoqUxEWt+LqUvrCZt5TIaXvlQOyEEhsREPC3azovaGZ3lwMkH/gaAeVwO1nE5PcfbD5cSnz45MjcRCvKz9VosE4DdBdY43wZIqohzLkRGyPhw4jBMng7x/sIIhDYzG47qHEYz6rb3Bh9XDChzPoeID1Hgf4TQl11RQCgGxDnLEVn5iOxxGMZNREydG7H+x2W6SRyvPfTO+qbeE1L2GJ7hwFleRcvmnT0/K1YL0hjKN2+f5VZtIzS3g8nWa3gA+dq/kG+9MnRMkNsOGZGLNJar3wDMfpZ9UqsyWhmB+mBDoRh8xjMpM85HpAS5SxoD6MYnSgiDEZGQiPrmM6jbNsB5K8PXsOnCbkii9mOtJE/nseMYs2LjD+/U/zzG3nt/h8cQZMyPy6PtMNXUIzesRn70DnLd6v5tpAouP8s5qcK4CIrGSxX5yjPI556CpjbfiadtnfDpGuShI0hVIl1qaAmg3hCij1LA4GWdmDAdkR0lofwooy+7ooTqdKCkZSGKZyKf/m+Ycy4UTdd2scKkrV3ibmwCwNPYiDkvF1N2dte2r4qrOsrfwkNQcPeXMHiCXO411CLffrH3Z1X1XvbGadec0I4O7WE0mruqidq0qGSPGxqj43eRazTnu7j4SkiyddUiUzRtaEdXysy+7bB/p2YEp89DTpkcfhRAUobvqquKAZEXoWXuCKDPfKKEMBpR3U6UZdcgVt4AuzZGxPDYLalUvvlhv2POikpcNTWobW2Yc3N8XDk8xE8MoUCevxlNN6qKXLMaLAnID95DrnoTzFbk268gN23UBMFqIudc90q3cZMC+co/QZr670Z2O6j370DUhCmYlpI9WJi/D6Jozqjz8/RFn/lEGOnxID0ehNEIq19Cra9B5I5HxicGvt3uBZcxjrJylfYjB7w3kFITCxMCY1Ym7prgK6JGgro1m8m8+CxUlwezKTCjIo8d8t+om+py5Mv/p+0KApRXQksTtDQhT58Y8tJIIGsqERMKNDeVy4l89f98BjnK7Zp0q1h8ITItqTfnLxDikvzUmRejJpjQF7rxiTCe5haExYxiNsPKG2H926gv/i2sPlst2ZR+sAN3nf9v0vYdOwEw5+XirKruqVgxXNS8torOU+VkrLgAc34AomlGMxwProhij+HJLwrOcEUErRCyXP1G72fry7/Tqe3MySMHIGccMi8XjAaEMoQREkKTmfWTHiGmnY0ItXx1jKAvuyKMMS0VQ3w8qsuFu7ERceHlgVcU9YIUBkrf3x6Q4emLs6ISJW6Ya6QDnuZWmtZ/hnS5A0t7UEyhRygrBk3ZcTg5fRxUJTgJjYqTsH0jvPUyvPcWNLb2prAkpmmvhFQt3snYFek91HIrrxhlXIAR2DGMbnwijLuhEVd9Pe7aOkq//0Mq/t/9OAxxeEKcIgvpIeuC+UFdo8TFETdrJmrbMMSeeMPjofbdtYEZlcaBKRXBEKVqnv7u+tq/oCkAKRMhBocGdHZoioltbdrsprVBe7U1avls/soYxacgioP7e4hV9GVXhDGmpeKsq0N1u0i/9RZOfe8+KmpqsWSmM23lVER78HE4GcYGmmdOo32vD3/PAKTLhSEhIej7RBJrgR/Hs2IEgwF5JIxs+MhVs4oOl1yt7UC+/XLvMaNRk1UxGYeWChlIXBLKgpVgtgbnO4phdOMTBQzx8bibWzDabLgbtG92R209VTWQmyCCXmYYPC4mlxjZV5et+XH8IF0u5DD7evpS/JUrEUYD0hyHEArU14PFijy4Wyt0eGBn4DtcQzIyM5+AEAIsZk3idcVV0NKiHcvJBGTwW/Bmm5YnOIbQjU8UMNhsGONsyORkUi67hMY33gKgavUnJN91FXH1wWslSyFw+yh9443OAwfAZIrQQx4cjYdOMT5XheeORs88CDHszvSAscYhFp2PFF2qlglW7dVDkJbHYESZfm5EhxgLBOTzEUKkCCFeEkIcFEIcEEKcM+B8shDiTSHELiHEPiHE7dEZ7ujBnJuLdLno3Le/3/G6fSd7tX2CQEhJfKAyD4CnpZX4WYEJzEea+k93omaMi+5NpIy5IngAGE2Ic5ZCZmrEZIbElMWIuDDKLcUogTqc/xdYJaWcCswBBjofvgnsl1LOAZYC/y1EIEWQxi6dR49R9/yLOMv7Vwmt/3QnFbUGOjMnIbsrMgSA22Ch42hwOzvCMnK/AqVbFiSanDwESYGJqQ0bcxZDagQNRdZ4lFEkkxEMfpddQogk4ELgKwBSSicwsPyCBBKF5glLQCtOHkZ5xdGLp6MDYTZjSIinafX7qPbBuxfVaz6jeg0gBOmLZzN+nPTrBzJ6HKQunkvdB+sDHov0U6vLG8JiwTqhAE9rW8hSHJaczOHJ8s7IhbYIVK0IFOFli10xaEUL4xKhsw1RMgWc7ZG5n9mGMu0c/+1GKYHMfIqAWuBJIcQOIcTjQoiBOpN/AaYBFcAe4N+kHK5aIrGFIS6O5o/WcuLe+xj3wx+QdOH5vhtLSf1nu9jz/jGqHSnI1KHL4GSlByfXaT92HGEJbomXe+kFTJqbztTz8jCmhjarcFTVIlOHIdm1ugyy8qKnV2yLh8IpWvqEULR7jZsIE6doKpPp2doSuuKkljpTXor88N2I1coSE2YgYrj0TbgEYnyMwHzgYSnlPKAduH9Am5XATiAPmAv8pWvG1A8hxNeEEFuFEFtra0cm/D+aqHY7FX/6K537DpB+y01U/O73Ae1OuVvbqXhnA8e3nNaEw3xgcbYQN20KppxsFJv/nQ9PkHE+CbOmk6nUYXJ1IKQHU7pv4yNMJhIWL0Lp2tKPmzkDQ7JWuM9aPAmXJQJF/ALhxCFN73rSDMiKsJ9JCKg6rW2Pp2dpxq78hHbP8lKorwb7AH2h2krkB+9p6pXhYDAhxo3N5VY3gRifMqBMStldp/UlNGPUl9uBV6TGUeAEMKimi5TyUSnlQinlwszM2JCBiCSe1lbctbVYSiZR/svfoLrc2A8fCfj6lj2HaRRDl9aZMtXMzHOymHn5dPKuvhjrRN/R06bMDPD4ni0pNhuJ8+cAYC0qZOLUBBSptVdUlZK5qWQsuwDTAMkO65TJWCZMoG3zFgASzjkbxWbDNm0a8fPnYUhMpHr3Sc2vNRyJj+Un4Ni+Xv9PQvLg4L5gvb+KAqmZmnFpb4O6IJagDTWaLlEYiMIZiBiuNhoJRCC6I0KIDcBdUspDQohfAPFSyvv6nH8YqJZS/kIIkQ1sB+ZIKX3qGyxcuFBu3bo17DcQS0i3GyklrZ98yuGbvxRyP7mXnI8pwYbRZiHZXTv427UPdksypVsr6Dw6ePs++eyF5I83YVfiOealWkXRjctJdNdR40wj3daGyeX9Pm6DhVPlCm67E6SkfWfggYEz7r5aE/trqYaOKPqBklK14oxOuyZDmzUOSg9pfiGrTZtRBiJla7VB/iRwdGrLraP7Qh6SWHIJZKQGXc1UjJ+OKFkwZoIJhRDbpJQLBx4PdLH8beCfXTtYx4HbhRB3A0gpHwF+BTwlhNiDFsTww6EMz5jFYEBtacFVX4+wWJCO0KpYVq76uOf/qQtnUjhe8emQtjiaSSwe79X4JOSmYnZVYaaNwhtX9DwDTrsHW5xCvNqEorrJMdbAEOFARo8De2k1jsrgZWD3PfIaAIrNyrTbLsFcHSGZ04EkpcLBnb0/d/uBUtM1HeyklB69I59k52s13yMgfQIg162CGfMRM2ZoQvOWBFDdQ6RQCETRbMTE2WPG8AxFQFvtUsqdXcul2VLKq6WUjVLKR7oMD1LKCinlCinlLCnlTCnl/0V32LGH7FreCKMRta0tZMMzkMate3FlT/R5XgC1H3zs9Vzt5n10WrSlSKqzkjS39sox1pDsrMLoDnw3bNyyRUGNeyBqp50Tq7ZqTtxIkztB88H0xe3qKkHUFWfV0tS/eKM3HPawZjpe2bcduWEtmOORu7aBr1WwyYoy/2KUojlnhOEBPbE0YjS9/yGnfvEr6l97g+aP1vY4YiNBzf6KIc8nL/aeaOgsr+DoB/twmMNz/qrpuZx6fW1YfQB0lJbhSMoNu59BxCUMXtqcPKzNYPoeH8L/BUBqBEtZ96XyNPK5J2D/TuR7rw32R9kSURZfhkiLwmcTw+jpFREicfEiTv/yV7jr6mh67/3eEwaD/z96P9Rs2EbeLecg2vonpXqS0zE012PLSKRpwDWWCeNJnlaELcmMQQ3B+RmXgDMpm4otx2hetR41hJghb4hwEy76xtoUTdd8M8cCnK3UVkDxDE0/yGTSZkxC0XazXA5oCc9JHBCtzWCygqMrFiguEWX6uQjbyCYCjwS68YkQSmICieeeQ+Pb72LMSNf0dxSF+Hlzad+qib1bS4pxVlSgtg9Rf8obqoorIRNzH+MjjWaeXHuCryzKxtXqJH7WdNr39KZyOE6eIv3sCVidlYHfx2imM6WA02t20H5sT3BjDBCTvSn0i9OytLia+CRNM7midEhn/CDaWrRllcmsLbFKD2nxOidKQx9TSAgwW1Gmnj3q1QjDQTc+YeI4XU7n8eM0vvEGyZ+7iLp/PYd10iRsJSVIVaV9954ecXdHWTm24kl07AneoenxDPADGAzUVNSwzV5EcaaNujWf9DttysrEgdV/9XSLjRZLNooiKPtwK52ntgU9tkApuOZiREsY8V0GoxZ3Ey6uPgH6XqrBDgcip+iMNjygG5+wcLe0YCkYh3Q66Dx4mIxbv0TSBefRsuET7Md6d3VcfYr3OSsqMeXk4KoKLnWh8XgVtpReDRjVGkfFqUqeeexVEhITueH8hXQaDAggbvcuXAFqOKuJqZx+dQPOuqagxhMsybOnkJauQH0Yge/JadBU1994hIswEHBN+EixZzfi7CuG734xim58wkEIXHX1mHJzSDxrMSfu+RYT/us3tGz4xOcl7vqu5diC+bRv2x7QbYwTxlPW5KLZnEZxgQWPhEMNvalzba2tPPHuRz0/3/n5i4lXVeydKuacSdiayjSfhheUugqmfOFCmms7OfXS+17bhEPeZReSUZCIoaES6oNYAnrj6N7wKr8OZMLkwP1FkcJqQ7n+q4ghItnPFHTjEwLS40EYDBgTE2n8eBUVf/wz7rp6hNFA/Suv+e9AVRGmwD/65swsnntrcJCgL554ew1fXbKY9je2UwHMuOdqTO31dJrTcbs8GIwKjUfKUV1u8uZOACSnXvnQX7chkZkfh1JzKnIdpmdHzviMBAXFiEingYxSdOMTAsKgbZV2Hj9B/Suv0XngILJLtMtxKjCfROfBwwirFdkn6904cSIYDaBKapNTqG1tY9+BI6QfKcVsseAMMHZIqipvHTnF50uKcR85yol3t5I0ZQJV7702qG39pzuZ87VLSDx7Ea3bdoAjgksagyEKgl8RXB4piraUaw5AjzlCiIlThu1esY5ufILEcfo0loIC3O3tOEpP0rZ1W4/hCQZPczPWKZMxpacjDArt9Y08+fFWrwamoS74h6OirIL2udOxoMXXdJR6qQDaRX2NE8fhQ5jS0vC0taMGoZg4FEIRSEOYCZa9ncGEEmiO4Hb4iYNapvowIqYvGNb7xTK68QkSU5Yme6GYzSQsWkDON75O9WNPaKJhwXzLS4kxORlnZSWOk6fYkjc+4JnNUAghkFIyf8EcbPv2EciIyl5b0/N/c0EBzra20MvZ9CFrySIMkVpySRVqyiObH1ZQDIrQ8r/qwvRH+UJRUG7+Vpfsqwdl8uzo3GcUohufIFEsFqTHQ8fBQyhWK/Hz55P9TQtCUejct5/ap58JqJ/4RQtp+2xzz8+JyZHJ/v7S5cuwHjiAggt3n122QDFlZeI8HYHtbKCspo2dnRYuLTQhImE00rIgrxCqTmkxO+HS0qAZtZam8PvygXL911DOWxm1/kczenpFCAiDAUteHtJuJ27aVBLPOZv4uXMC0tgBiJs9e1BmeGFDZPSNzB4Pans77vKhUzJ8IUyRWSZVzZjNc59s452XP8ARnxKRPik7ruVsRcLwgObrSUzVIp0HpjxEAOWKL6Es/XzE+x0r6MYnREzpacTPmomztg7H0aO0b9mq7WJZh1CeE4KEsxbTsXv3oKoS1ooKLl6xFKGE9yt5fetumueH7ldo27wF27RBUkwB0T5hAo2TtWtP2h042zswGA0okRS1DKbWVSCUn4DKk2A2Q8nM4HV/fCDOXYFy2c0R6WusohufIFBdLjr29o8LMaWnkXzhBZjGjcNZVUXcNN+Z0wlnLe631BrIhN07uXLJOZjjQq/P1NbWQXpFecjXo6ooFgueuHgap0xFDTACuHzWHDY7JW9u3snuaTM51lXgcPrcqZjrKpAWW/jCYuOL4XSUJDnsnZr0RnH4FT/E4os0P4/OkOjGJwgUk4m4mTP6HTMmJNC6cRNtn30GHnXQ+W7M+eNo2/SZ13N9yTiwj2vmTCUxM7QM6yUXnoU7yOjpgbgTE9lVMIFX1m3i/fhkOsaNo2Oc79gU1Wzmw483c3zHblx2O1s++pjOZm1ptH/nQfbET2RNjYHmuPSwxkVbizY7iYYsR+EUlDvuw/D1H8H8IXS3/ZE7HuXGb/SEY+j4Rnc4h4j0eJCqinS5iT97MbX/eo6m91b7bG9IToaywGYkCSdOcOGc2bwdRKWKbrYdPMqsC8+hcf2nPccUmw3FZsPdENiW/ZH6Jnas2whA2f5DPKcoCEXh+kWzsdXVYWzt9bk409IwtrVhiY/rl0bSjcft4Z0Pd1K2ay+nzp7NV87NQ7RrW/nV6RPJdDSgBFqBoqFGexVMiuwMqKAIw7d+3lPKz3DzN5DT54PBiMjJRzY3or70KNT50eNWDBju+CHCFhe5sY1hdOMTAB63G4PRSHtjI/FdFR26v9kq/vRnpMdDxlfvwH78OPYjR7324aoMbis3/dQJhKIEXfa47mQZ1s/PIfvSi2ivrCOpMJsMgxYbs/ddO2qH/yzwhub+cT5SVZGqyodlNSSkJjOnYDz1ioFWu4Mje/Zz7lnzSS2vpq2uflBftuQkrEmJGEwmtm3cSdGkcZxXkIXDZONXv/wH3/7ezUwhyPI3TZEJChQXX4Nc9w7KhZf3qyEqBIi5Z/f+nJkDF1+Luu4trc76gR2DO8srxPD1nyAyzyxNnnDQjY8fXA4Hpq7yM92GR3o8qB0dNK/fQPWjfyf1isuwTphA2jVXU/3Y43gamwb1Y87P76nbHgimllaMFovX2YQ/yhwmFlrrYCJANd3BPuOvOJ+Tr6xBuod22nbavccb1Z8uo/50GScHHP9w9VqffeXPnQUIDCYTboeDl/7vHV6Usket791Vm5myZIhAP8UA6gA9pLZmdhhyyUi0UNBUOuR78YkQKCuug5VfCKh6sVh4AYYFF4AAz/23abtufYd5+Rd1wxMkus/HD6YBda88bjfCYEB1OrEfO07ud76JMSsTV3k5itns1fCA5mxOv/7agO/rsdkwhVhxdONu74F9qa5KZl85m5KbP4cxNcXn9R0BzI4Cpe54KUhJfFcZnu6CBd3/lh45qUUvd+HMGIeamIo9s4AH15azW8lC2hJwpeX0tJFGIx+s2sQb72xGhlB6umsAIKU2ywmguYCetmJ5/9+jcuM9KPPOC20cZzBn3MzH7XRiNAf+UHvcboQQKAYDjvZ2VLcbg8WCq7wC2+zZOE+fxjJxIuW//T05//Yt0m+4no7de+g8eAiA+LlzsE2fzrh/v5f9l1we8H0NnZ1ctHBOj98ns3A8503Mp0IYaGxpo+rkKVprBy9zADKzUhlcVFZDkS4S7LXkXbyY5tMNmhGtqMZVVw9dM6L2jsBmW93R1D1jNpnwuFwYLRZypk7GmpxIc0U1R9Z7z/JPzMokPj2NfaYcZnhqaE4v4Pd/ep3mxt5lmMNo5dE9+xFC8K17b6IgQeHRlzZx8qhmYN1XzMbUEIKDPS1Lyz0LAWXpFXga62DThyhX3oayRJfHCIUzzvgEY3gAmiurMJhMJGZl0lJdQ+mW7cy/9kqM06bSuWEDDa+8RsLiRbR9tpnG194k+dJLsU6ZTOUDDyLdbiY88DscJ0/iOHUKR+nABcvQlHbYScnNYXlRPpaWZiwH9pMO2LOzeXuIonQHdhzAk1+MYYgkzHR3JeldqwQ5pZD/edvBguJJtDldWKrroXrooEchBIVnLUJ1uxAGAyaLhZrDx8iYVEhLdQ1luzQlxJypkzHHx+NsH1xCODk3h7Jde3j4wCHSs9Kor/loUBtzlxa2lJI//8+zg86X2k2UDDFO5UvfQX3xMU1uFaBkFoZLb4S0zIBmPN4QBgOG6+5EFs9AOVePXg6VMWt8+vpqVFXF3tJCXEoKso+/IRCSc3Owt7YiVRWhKNQfL0VKiae5GZPZgqelhYQF84mfN5f6F17E3dBA0vnnkbBoIfbjJ7BOLKThlddwVlZhzs/HmJZKx+7AJEqn42HcrKlYy05h7rNTtS85naZdhwa1F4qCEIIbP78YA95nRd6QJgsFE/N46Z1eWY2MiYUoJiPxaamc2LQFo8WCu0/uWdbkYk5sGhyz1Dxgm7/q4GGf97UmJfb8v77GuxNZKAqTzjubk1u243YOns396X+eZdEF87n2omkk1GjGXbn2dsTk2Xge+AFi5kIMuQV4XnsawzW3Q1ZuyEan37gMRsQ5KyLQ05nLqPf5dLZo276ndu7iwAcf0dnczMEP1yKlRErJ7rffo+HkKUxWK+1NTRz5ZCNqnx0k1cduUkdTEx63m4aycsr27KOtvl6bNSkCR0cHxoRETLm5JJ5zFi0ff0z6jV/AlJ1F/Px5OE6fRjqdZN15Ow2vvUHd8y/StnkLlsLxjPt/PyT18ssCem9Jx49RsHtnP8MDYBgQBZ2al8tlN1/O/9x9Ib+4ZzkzgjA8AEpbE6YB+kJ1J0qpOXwUt8NJfHoaqQXjKFnSx68RgcTT8l17MflJSTn2ySYUo7GfoeqLlJLN67fxp6fWaBIZuRMQ5y5HZGSjXPVlqDiFyB6H8ev/DxEhw4NQwBIXsWjoM5VRb3xsSUm0NzbScKqMk1t38J8LLuSPF1/Jmj8+hKOjg8LF8zHatCjd+JQUrAmJNFdVoaoqTVXVg2ZB7q60B2dHJ6rbTUpuDic2bsbe1oYwGjjx6RZsSUkoNitxk4rI/8n/I/cbd5P15S+R//9+SMK5Z5Ny2SXk/+zHpCxfRse+/aRfdzW2kmIQgrhp00j+3FLtQQmR8XgwmExcdO1KfvytK/jVVcVcltKCSXpI8wxe3vijIrWQD9/yHlN0escu0gvHU3v0OPaW3uRQX8YgGDpbWihc7D8VpOHUaa/b+H2pOFnBxx3JyJvv6TEwYtES1P1R0KTWDU9EGJXGx+100lpbS9nuvdSdKKVy30FqDh/HkhBPS5Wmcnfg/TUc+nAdTeWVfPL4P7C3ag9O5qSJ1Bw9QcPpMlJysvn/7Z15mBTV1f8/t6rX6Z7unp59YxZmQPZt2DcRZDHirjEuxCWaRGOiZjMxyfv6xuRn1BiTGI1GSVxiNIqCiiKKG4iA7DPAAAOyzr5Pz/Re9fujalZmEBRswPo8zzzdfau6+nR11XfOPffcc5VIBCUaRVVVDm0twaRPrLQ6HUgmE83V1fSfPIH6fQfZv24D1zz5CMG2NipKdxIJhxFmMwFJou7wYZKuuBzniOG4J03Emp2Nb8MGrLk5JN9wHUogiH/XbqqeWkikpQXPnHMBkGzHX8DcW7qDX9/yDS5NbSM9epw5Mr2wbc/Rk+cObNgMaEKU3D8fR1IiBzYe+5LJfWFxOFD7mPeVmNuPzGFDyBg6mLrPji1W9sI/X0PtMiomTCakOZcd/U3HOqHUZAHZou1/Eiahfh05rcQnqo/GmCwW4pOTqd93kKS8XACGnz+HtoYmMoYNRghBxbZSnrvpNl760c9pOHiIaDRCJBLBV1dH3rgxmPWbXjabiYRChEMhTDYrAZ+PYFsb+9ZtwN/YhCslhdxxYwgHguz7dCOh1lYEsH/9Jg5s2oKiKOxY8RFbFr2GqqpEIhGClZVU/O0x4kaPwj54EEpbG2o4TKSqGsXno/ofCzEnJ5N4xeVY8/O+0Exyl3oCVkSVJFTT8QXga/bspbW27riTH3uSlJeLqiiUfbS61+3enGwOF2+jvGR7r9t744anH8Ns6/59eo3vyWaQTIDQxeRYQp9CW3LHehKmdnxNOa0CzrKpu7n5k8ZRubsMT1YGxW8sA1SyRgyj/+QJvPvHvwLQWt9A1a4yJElm3q9+imwy89Idd2F3uZjxw++RkJmBqihY7XaS83KJhELIZjPO5CQsjjjq9u1HBeISPCT1zycSDlO95zMyhg5GspjZt34jzRUVxCV48Dc3Y7bZiPoDJF44HwIhlNZWpPR0InV1mJKSqHt1Cbn330fTRx+Rct01NCx7h/jJk2h6dwXB41g/Kig5sPDFa+QcSsjlvt8/iyRJfca9TiYNh8uRTX17EJHA8ZVzdSR6GXLu9M+P6QhJExwha0k7igKy1FHsq/vKp7o4KRGtXUUrPmZwQjitPB+AUCBA+fZSmmtr+Xjhs2xdspSVf19I0NdKqM1PSmF/9q/fhC1eS+nvP2k8zZVVrHryaSp37MSdmsKlD9zLObffQoM+10pIEqG2to6bcPXCZ1n/wiIsdjst1TWE/X48GWkIASaTmXDAjyQgOacfm15egjMpibSBAzi4qRjZbMbSLxvbwAFEGhuIHzcWNRKmZd16FL8fS1oaks2Ge/o07IWFOEaOoPofT2EfPAg5XoujmNPS8F50AZa+JnPKMlKfi34fG6oK86+YHRPhAYiGQiTm5vS5vbVBywbPnzgOR6L3c4/3necex+HuJQ7VJYERSQazVetCSboImS3aa7MNTFbNC5JMYLZr64QJobfJXypOZ3Akp9XZ9Dc3I8kyW5Ys5c177mPxXf+Dv6GJjYuWsObp5xl92UVkjRpOw8FDeLLSyRw+lIObtWHtpPxc3v3jX2mqqsZXV8/25SuIhsMEWlqw2O1Y4uIwW61YHQ5cqamMumQ+4UAAu9tN1e49NFdW03/SeCSzCV91PVaXi0gkwpgrLyF71HBsbhdZI4eiRCJEw2FCFVU0vrsC//4DCJOZ1JtvxDVtCqHD5ey/627sgwahhCPUL3qFlBuvxz5kECk3Xo9jzBhMyUnYhwzC5PFgTkvFPfOcbgHOpBmTcARrv9S5zG7cxzkDPF/qGF+WxkPlpA4o6HWbJMtYHHHs/WQd5//vL47YHtcjQzvQFtSFRnSJyQitiyWbO+M17WIkSZoCR6NajaB2YZFkTXQkGSSzJkwWXYgMTiinhfgEW1sJB4PYXS5MZjNTb7qOg5uLUVWVtsYm6vYdoLmymo0vL2b5H/5EW0MjtXv3k9w/j4A+FF+9q4xdH36Mr6aWfWvXU7FtB4VTJlG5q4xgjwQ4IQuEbEIym8kcNgR3SgrhgJ/mymrik5OwxjuoLN1FTdkeLFYbrow04pMTiXO7CQUCVO/5DGt2JvbCQsxJich2O4mXXoLnwvkkXXctrpkzUAPaJM+4ESNoXP4ugZ27kYcNZS0y2wYMwvfJOtJ/+XOGvPs2bcXF3Ya2Hd4ew9Nxzi9UZsIvx2a1znbMcXbkPqaQVO7YSXJ+HikDCtj86htM+e4NeDI754BNvfl6XKlaPW2rw6ElNdqcmmfTHhRu93LMNq3rJGRNxEN+rbslSZ0ekKKASRcbk1n3eKROj0eSjRGuE8xpIT5IEs2VVSiKgqqqVO0q4+DGLWQMGUTpex/i8CbQ1thIU3kFO5a/hzMpEUmWkUwyrtSUjuDyhAVXokQibHvrHda/8Arl23bQb+RwlGhnF6atsZHP1nzKo/OvoOHQYS2Td+xoMkcMQ9WvvdyiUfSfMhF3Whpmhx1bXBytdQ3sW7eeXe99RHNlFa2NjXgvnK+NZkkCJeBHbfZBIEjLqtWYEhLY9sk6lry3iu2ji9hoj2dL8Q6KP/mU9//2D14u/Qx/Sgqll1x+xOqj/ubu8RAxePRxTxUIJGfx2HNHZhR/FaQOLCR9yFn4ausoL+49oCyZTDiTk7A5HZSXbCdr2BCue/pxkvJymfDtq2iqrGLgOdNwpaUy/Qc3MenGBZoH0y7S7V2o9tdWh3aOVFXzfpSo/lx0ej1C6DEfvU02BOdkclqIjyzLJOb0Q9IzeJsrqwi1tTHiom9QMHUi0ZCWm6MFncHf2ERCVgbjrrqCuAQPkVAIs92O2W6ndMWHzL37J3hzsknIyaatqQm7S6uw56uto3p3GUKWKZw+maqdWnmMSDiMyWQif1wRJosFm9NJc3kl1ngnkUCQDx99knXPv4grPQ1vTjYmq5WyVWvYt3mLZm+Lj2BDI62bN9Gydh15Dz+IJT2NZf/vIQ5s2Mzuj9fy3sOPsmnREsZedTmF0yYz845biLzwIoFdu484H8279qEKAYVDERcs0G6i46xrXBsxcXDPiSkUf7xY7HYqtpWi9JhdXzhtMqb2rPRIBFdaCiqCQIsPu9uFJc7Gd156Gnd6GvN+9VOGzJ3FzS89Q8bQwWx97S0i0aju8dg6PRZZ7vRw2rtkavRIYekQIP3xBCRRGhyd06Ij23M+VuH0yQyZO4vx13wTFcHej9fS1thIq16yoqWmFpsrnrJVn3DNk3/l0OZi4rwJpBQWUH/gANFQmIvv/z9MZjN2fe4QQPHSZVSU7mLWnT+gpaaWZD0gGud201xdg00PCIeDQRAQ9Pn4ZOFzJBf2Jz45keaqGmr27uX9hx/jkgd+i5Bktn+yjvzxY4lPSsR53+9BUVAliXAwyPz/u5uVjy9ESBKJuTlMuO4qlv/hT6iKSkb5ISqf+ifm1BQijU2oXaY2KKEILPgpvPIE6sQ5sOzF4z6ntU2Bz9/pJHFw81Yyhg3u5vXkjS9izJWXokSj7Pl4DQDr//My33rsIXa+9xH/vf3n/HD5YhL6ZTFkzizcaakMv/AbhPwBPlu7nkCLj5d/8isufeBezIhOcQkHte4XdIqN5ShJgu3thsdz0jktxKcnzsREbnvrFUAb/codV0TF9tJu+9Ts+YzccWMomDyRgskTAfjkmedxJiURbGnh0MYtqJEIhVMn09rYiMliYcSF3yBr1AjiPB7NG+ry38+Vktzx3Gy1IhC89bsHKXljGZNvXEDJ+k1ceO9wEvv1Y8zlF5M/fqyWQxQOo0QihINBqsv2EmhsIrF/Lh8/+QwJmekk5ucim82s+de/iYbDNB4qZ8E/H0XetZPcB++n9uVFhLuUX7Xm55Pz4H2ID16AYePg+b8cUVvmaIS9aaw5FGD50mNffvlkoIQjCEkiKS+HKTdfR/bI4bz/yBM0Hi7HZLEQCYW44N5fY7bHEefxEOdx89x3fsCdH77Fij8/ypyf30FcgpuUgv5M+/6NWOPiqN5dhmw2d++Cds1j6ktQ2rtf7fsYXs9XwmkpPl2x2GzkjB3FntVrqN27j+T+eaQPGsiWJUuRu8xXqtixkw8eeYLp3/8Ow86bTfaoEURCIcp3lGK225H0shkWu01LTBOiowvQk0goRLC1lZm334orJYUNL73K4DkzcWdlYHc6iUYibH97BVmjhvP2fX+ivGQ7tngnk25cQHN1NfakBCLBIKMvvxi7282Khx+lraGR7NEjmP3T2+g/ZRJvvb2CKXl5SPFO3DfdSKsQKMkpWAYNovjTDdhJYkRt5XEJT9SdyLulDSxdFFvhAW3CacHUSZStXI0nI4NwIEh58XYCLT4GnTuD4qVvU7WrjH5jR7N9+XtM+d71WOxx1O3dx7f/9Rj+xkYcXi+SLGON08qWJublIrULT7ugtMdxRB8Rhp5C01WIDE4qp734AIy98lKGnz+XDYuW4PB4aKmuIXfCWMz2OD549B+cfctNfLbmU+oPHKJg6kRCbW1Y4uJoqqgkMTcHWZZpravn+Vvu5KYX/4VsMnULQoMmOE3lFST0y8ZksWBPcHNw41amfvc65v7yx7Q1N1P85nIOfLqRtU8/z6A5M5nuiOOTf/2boM9HQlYmO955j6Cvldd/dS9nzTyb3R99TOaIodjindz00jMc3LiVluo66vcfoKm5hS3vvMeWvYcofXZRN1tyx43h1u/Mg02fcDzIzQ20tn25/KAvg5AkrA4H/YpGsuv9lez9ZB2XPfR7fHV1rHzin1zw27tZcvdvqT9wiBm3fZfybaU0HjzMwBlT6T9xAhXbtmOy24hzu4lzu7sdOxwIdAwsoCjdBaQ34VHVzv0kqVOgDOH5yjgjxMfh9eLwepl1+62oisIHjz/FzuXvU/rO+5Su+IBVT/yLYefPZcSF5+Gra8CR6EW2WNj1/kr2rF5L3oQiJt+wgHN/8kMkWUaJRjv/g+qYLBasrnhQVaLhMMm5uXjS0zFbrVqXqnQ3ldt28MFf/g6AGlUoWfo2QghGX3YR7vRUMkcOZ/OiJaiKwuB5s7A6HYTa/ORPGk8kHGLr628y+rIL+fPsi6nf3/cywyNnTsG+tfcCXX2i57VMGJXHB2+tOu5z/GWZsOBbjLzsQnw1tbjT0xh63hwCLS0k5efhSk1h2e8eJOQPcPOiZ9nw31eZcvP1vPPgX5DMJq589CFkk4kB0yYBEPL7sfSYDW/uOkfu85IBu45ydXS3TouxlzOKM+qMq4pCU1U17pQUrn3qEUxWC7LZzKEtxdjdLmb86PvEJycSDgTYt249Vo+LQXNnkVxYQDQaxZ7gZtdKba5Rb5m/Tt3Nl/W5WO31gtRoFLPVgsUehystlfiUZAbOmIojOZFx136TjGFDsCe4kWSZnLGjufrJv5I5YhhN5ZUIWSalsD+HNpdwzh234M3N5pbXXuDcn/6IAWdPxZvTr5sNkiwzaNKYo8Yloik5RFNzu7zOZs8uP2UHYNFrnd7StKkTuOS8mVw075wvfM674k7vnNQ5fP48AKxOJ0KSGDR7Jna3h5wxo0gffBaDZ5/D1Juvx5mSREJ2JqOvuBi720Vu0Wguvu8edrz9LnnjxqAqCpU7d3f7PXoKz3FjBJVPCc4Iz6cdSZZxJnpxeD0Uv/E2N730DAuvvpEti5eSOXwImYMH4W9pofiNZbz+63uxueJJH3wWiXk5pA0aQGphAaHWNqr37MXbLxuLzUY0HO4Qm674m5porW9EURVS8vPIGjmcpPxcRl12IVWlO6n5bD9544rILRpN2pBBKJEIgaZmfDW15I0dgycjnayhgwm1tZGU24+CqZOwe9xseuU1Nr28mC2Ll/b6HZVolA0rN5I153LUlW9qGbr+LkmSdgflJYeJBiNYE+JpKTtI62dalrdkt3N2YQEzUlMR4RCRXVqQvvELrHAal+ChrUu9aiEElzxwLyVvvs2G/77K4LmzsHvcDDt/LsEWH0PmzcLp9aIoCkIIhBCax7irDHd6GvPv+SX2Ll2p5spqMkcMJWfsaDzpaUimM+pSNeAMEx/QvBFPdhb5E8drM9jHjyUpL5fG8gqi4TAr/vQ3WuvqmXj91VidTpb/4WEKp00iGghiSjKTMXQw4UCA1rp6om5Xt6H4rlgcDuxuN21NWkkL2WTCEhdHUm4/0gr7E41GsehdgWgkQigQwJ2WSnxqMpLJRP3hcuzxTqxOJ5IkaRM8o1FaKqvJG1fEzvdWdmRn92T01CLYvwFmXopAQX39WVAUlJRsip95D8Xf+zC64vdDL1UUkxrqMZnNxLucnDthNKs2b6f8cN9L/UgmEwVTJ1Fesp3avfuwOhyMveYKkvrnkj1qOEo4Qv7EcRzcsBlHYgLD589DNploqavrKFlyeNsO1EiUgqmTkCQJZ2LngoJqNMr59/wSyZhLdUYj1BgNKxYVFanr168/KceOhMNIsqwtcaOq7P14DTlFo9i/YTN7Vq3hwMZNDJgxnUHnno3d48budmO12zuCzJIs09bUpM1QD4ex2O1HxIC6okSjNFdWEYlGscc7O5IWJVmmqboam9PZMSLT2tREsLkFV2pKt/wlJRpl36cbuH/iLFIHFNBUUUWgpfv6WZ7MDGbcciOz8h1QW4E6fjY1byzDI7fgb4uyf/EHRHqsudWBLGteUh8IsxlVL6Rmys+nzetlW0UNGzf0XbfnhuefYueKDznn9ltwJCXiSk6isbwCT0Y6h4pL2LniI6b/4GYseve0t1iNwZmPEGKDqqpFPdvPOM8H6PjviiQho2XOSrJM2uCBZA4bjGw2I5vNHTd/uwB3FRiL3Y6QpCOWzlGiUZRotJtwSLKMMyW583N1IqEQkWAIc5LmAYWDQRxuNxartdtnqapKNBLBqntZVbs6Fx40Wa1Ew2FURWHkxecz4oLzYPdapDnfJKBaCfoVSp5dTNzQodiHDSdcWUmgrPtqniavF9fZ06h/ZXGf56xdeAAie/di2buXMcOGsREw2+1HrB827Py5NJVXMHjeuYT9Aa0wWzSKNzsLgMyhQ0jsl90hPOFg0BAeg26c8X5te9HxcCCAOyUFZ2IidpermzfTM80fNJHZu3otOz9cSTgY7OYVKYpCS20toS43ZLvwRMJhfHV1qKqKyWIhMTuLaCSCr64esy4kssXSEexUVVU7thBEgkEKp03Gm9OPs2bN4JIH7iV71HAmXn81oK3+mWJVIehHKV4HteUobW14Zs/CNrCQlk/WYM3LRVityB4tfiLZ7fS79x7CVdXHfe6U4mLOGj6EH3/0FqMvvxjoLM5VXbaXAdOnMOri+eSOHd1xbtqRZLnbCFRPETcwOCM9n660eyh9dZskWUb0EltY8ae/4aut4+Cmrcz7zc/IGjaE+KQkQEtsNFks3WbDR8NhTUBCIWSrFSEEh7ftIBIIkD5kEEGfD2eiF9ls1sq26rOqVX3xOrPFQsgfYNZPbsPhTUDIMv1GDid/4nhK3lzGRX+4h6HzZlO3YRvezHxtpYr0fjQsfQtH0RhaS7aRsuAa6l5djH1AIf4dWjDZkplJW0kJrZs2f+65ck2fhu/T9dgK+uMcW4R94AAyHA5MHg8HNmzCk5mB1ekg6Gtlys3XkTVqRMf36IkQAkucsWa5Qd+c8eLzRRk8eya7V61m+EXzGThtUkd3q13EhBDY4zuLVwlZRpIk/E1N+OoasA8ooG7ffnKLRiPLMnaPu+P9kVCowytQo1Gt9KrfT8Ohw/iqazBbrbz52/uZ/fM7KFu5mvP+5y5aGxrxZqRT/s47hMdeQNWTC0n97kCSr70a57giDt7zOxqWvkm0sYmoy9WxJLJzXBH1i1/vc4121/SpWDIzsWRl4poxA6XVhyk5iXBlFbb+/TF7E9i45E2GXXAe3qxMlvzyHq545AGm3LDgqHEwA4PP44wSn2BrK1bH8de16a3Ob8awwTSUl5MzZgRAh2j0loTY1thInMcDaPPOzHY7BzZuxmy3U7f/II6kRK3YvO4htMc+WurqMNtsCKC5oopAUzN1+w6QNWo402+9CSFJtDU2Icsykj487Z41k22z5qIEAtQtehUlEMCcmoIlPRVrv340LFuOyesleOAgabd+j3B1DY5RI5EccQR2l2FOSUaYzLhmTMdfuhNLXh5mtwtbbi7WrEyEJAhWVWMvLMCSmoqQJBJyszh7/Ghe/P6dTLzhGqbddP1xn2MDg56ckaNdJ4qWmpqO+UPt+T69ZT93JRwIoCgKZpuN5qpqZIsZk9mMyWrtXMRQXy1DURRMZjOqoiDJMh8vfJaMYYMItvpJzsshMacfrU1NONxuGioqcaemIIRg8/AxRBsb8V5yES2rVpP5618QbfHhW7MOVIX6V5cgxcWR+bMfE66uofLRv3fYZz9rIO7Zs3BOGIeQTVT85W9YsjPxfvObuIYORu4jtWD3Rx9jdsSRUpB/xNQGA4Oj0ddolyE+n0PXlU+Pl3ahUqJRIqEQSjSKzensEBRfbR2SSe7wmvZv3IInK4Pqnbu1+WD5uaQX9D9C7FrWfUrrtm04RowkUlODEgwiWa2UP/QwbVuLcc+cgWf2LJre/xDZ48FWUEC4vBw1EsE+ZBC2/HzCVdWoJhlLZibh+gZshf2xp6Yi9xKniYbDtNbX40pNRVEUI//G4Lj4Wg21nyi6TVbU6en5qKra4bmEg0H2r99EweQJQGeQW5stbyfg8xEOBIgGQxwsLsFis+LO0EqDHiwuweZxsf4/L+OrrWPmnbfiTEjo1S7HyBFYsrORnQ4iCW7qX3+DcE0dofIKCp9ZSLTNj+Lz4Zw6lUBJCYrPR9K1VyM7HESaGrHn5iI7nYTr6oj4/cSPGokaCiH3MRQejUSw6d6OITwGJwrD8zkOAj4fti7dkp6vw8EgssnUOYTfZSpBO+2BayUSQTKZOkbjQoEANXs+IxIMEJeYSGJ2Vq83uhIIINlsRNvaaPl4NYf/+DBtW7YCYEpIYNjaVYQrq2hZv4G2XbvI+OEPCNXXY/UmghJFyDKqLGOO//IrjhoYHAuG53MC6FlRMeBr7SY+PbtnvYlHuzD1PNbhrSX46uo565zpfXbzVFVFstkIHi5n11XXIkwmlNZWnOPHIVmtuM6eBqqK7HGTMHc23gvOR/H7sWdkgBDIX2B1VAODk4UhPsdBT8HwpKV+oeP0FrDOG1dEyO8/enxJVVEBYZIZ+v47CD1PSAiBGoloOTdCYNKndwCgj/617SglbtBZX8heA4OTgdGBP4Xoa/pBe3a10AvoW1JTCddoK1q0d+mEyYRksSD1sfSyITwGpxqG+JwG9OYpWVK/mNdlYHCqYIiPgYFBTDDEx8DAICYY4mNgYBATDPExMDCICYb4GBgYxARDfAwMDGLCMYmPEMIjhHhZCFEqhNghhJjYyz5nCyE2CyG2CSE+PPGmGhgYnEkca4bzn4FlqqpeJoSwAN2mPgshPMCjwFxVVQ8IIVJOrJkGBgZnGp8rPkIIFzANuA5AVdUQEOqx21XAK6qqHtD3Of6CwQYGBl8rjqXblQ/UAP8UQmwSQjwphOhZLnAAkCCE+EAIsUEIseCEW2pgYHBGcSziYwJGA4+pqjoKaAXu6mWfMcA3gDnAr4UQA3oeSAhxsxBivRBifY0+N8nAwODrybGIzyHgkKqqa/XXL6OJUc99lqmq2qqqai3wETCi54FUVX1CVdUiVVWLkpOTv4zdBgYGpzmfKz6qqlYCB4UQA/WmmcD2HrstAaYKIUxCiDhgPLDjhFpqYGBwRnGso123Af/WR7r2AtcLIb4HoKrq31VV3SGEWAZsBRTgSVVVS06KxQYGBmcERhlVAwODk0pfZVSNDGcDA4OYYIiPgYFBTDDEx8DAICYY4mNgYBATYhZwFkLUAPtP8GGTgNoTfMwvwqliB5w6tpwqdsCpY8upYgecXFtyVFU9IrEvZuJzMhBCrO8tqv51tQNOHVtOFTvg1LHlVLEDYmOL0e0yMDCICYb4GBgYxIQzTXyeiLUBOqeKHXDq2HKq2AGnji2nih0QA1vOqJiPgYHB6cOZ5vkYGBicLqiqGvM/YCFQDZR0afMC7wC79ceELtt+AZQBO4E5XdrHAMX6tr/Q6dlZgRf19rVAbpf3fFv/jN3AHcD7aDPytwE/ipEtZWgTeLfodtwTw3Pybb1NBjYBb8TSFmCffozNwPoY2vJ9tPIypWjXy8QY2fFz/Vy0/zUDt8fq9zmu+z7WwqN/gWloNYK6is/9wF3687uAP+jPB6PdlFYgD9gDyPq2dfpFIIC3gHl6+y3A3/XnVwIvdrlo9+qPCWh5R9P1bfHALv3zYmHLZ/qjWf/BJ8TIjr36453A83SKT6xsOQAk9bh+YmFLC/BDfZsF8MTy9+nyD6ISyIm1LaeN+OhfJJfu4rMTSNefpwM79ee/AH7RZb+39ROWDpR2af8W8HjXffTnJrRkKtF1H33b48C3urxeApwbS1vQivVvRKuRFCs7bgVWAOfQKT6xsqWGI8XnK7UFcKF5GN+KpR09r1lgNvDxqWDLsfydyjGfVFVVKwD0x/YVMTKBg132O6S3ZerPe7Z3e4+qqhGgCUg8yrEQQuQCo9C8jljYchh4EK07+o6qVZKM1Tn5LvAztFpN7cTKFglYrtcKvzlGtuSjic+dPeqax/SaRfNK/hOjc9LzPZ/LqSw+fSF6aVOP0n7c7xFCOIFFwO2qqjbHyBYVeAjIAsYJIYbGyI4BQIuqqhuO8vlflS0Aj6iqOhqYB9wqhJgWA1tMaJ7CarXvuuZfhR0d7XqhvwuAl45ix1diy+d8fgensvhUCSHSAfTH9uV4DgHZXfbLAsr19qxe2ru9RwhhAtxAfR/HqkITnn+rqvpKjG0pV1W1EfgAmBsjO4YCZwkh9gEvAOcIIZ6L4TkphY7lmV4FxsXAlkNAG7BGf91e1zxm1wmaGG9UVbVKb4+lLcfGsfbPTvYfR8Z8HqB7wOx+/fkQugfM9tIZMPsULTDbHjA7T2+/le4Bs//qz710Bnbbg7wvAA/3sO2rtqUALfjtBezASuD8GJ4Tr77tbDpjPrGwZR/QT9/mAFajiXIsbAkA4/Rt/6vbELPfB+26vf4UuH+8x3zPx1p09C/xH6ACCKOp6Y1ofcoVaEN4K7p+KeButCj9TvSIvN5eBJTo2x6hc6jQhuaOlqFF9PO7vOcGvb0M+D2a27iVzqHL82JgywE08dmqH+M3+vZYnJOuF/TZdIpPLGz5GdqN056CcHcMbfkNsF7/jRaj3Xwx+X3QBiXqAHeXfWJ6rRzLn5HhbGBgEBNO5ZiPgYHBGYwhPgYGBjHBEB8DA4OYYIiPgYFBTDDEx8DAICYY4mNgYBATDPExMDCICYb4GBgYxIT/D+1addcW7k9bAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Adjust figure size\n", "fig, ax = plt.subplots(1, figsize=(10, 8))\n", "\n", "# Adjust colors and add a legend\n", "geodata.plot(ax=ax, column=\"tyollisyys\", scheme=\"quantiles\", cmap=\"Reds\", legend=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create an interactive map\n", "\n", "Next, we'll plot an interactive map based on the same data, and usign the folium library, which enables us to create maps based on the JavaScript library leaflet.js." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Create a Geo-id which is needed by the Folium (it needs to have a unique identifier for each row)\n", "geodata['geoid'] = geodata.index.astype(str)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a Map instance\n", "m = folium.Map(location=[60.25, 24.8], tiles = 'cartodbpositron', zoom_start=8, control_scale=True)\n", "\n", "folium.Choropleth(geo_data = geodata, \n", " data = geodata, \n", " columns=['geoid','tyollisyys'], \n", " key_on='feature.id', \n", " fill_color='RdYlBu', \n", " line_color='white',\n", " line_weight=0,\n", " legend_name= 'Employment rate in Finland').add_to(m)\n", "m" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "We can also plot \"tooltips\" on the map, which show the values for each feature." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "folium.features.GeoJson(geodata, name='Labels',\n", " style_function=lambda x: {'color':'transparent','fillColor':'transparent','weight':0},\n", " tooltip=folium.features.GeoJsonTooltip(fields=['tyollisyys'],\n", " aliases = ['Employment rate'],\n", " labels=True,\n", " sticky=False\n", " )\n", " ).add_to(m)\n", "\n", "m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a styling function with a classification scheme:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "import branca\n", "\n", "# Create a series (or a dictionary?) out of the variable that you want to map\n", "employed_series = data.set_index('seutukunta')['tyollisyys']\n", "\n", "# Setl colorscale\n", "colorscale = branca.colormap.linear.RdYlBu_05.to_step(data = geodata['tyollisyys'], n = 6, method = 'quantiles')\n", "\n", "#Define style function\n", "def my_color_function(feature):\n", " \n", " employed = employed_series.get(int(feature['id']), None)\n", "\n", " return {\n", " 'fillOpacity': 0.5,\n", " 'weight': 0,\n", " 'fillColor': '#black' if employed is None else colorscale(employed)\n", " }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the polygons, apply the classification scheme and add tooltips" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Based on :\n", "#https://python-visualization.github.io/folium/quickstart.html\n", "#https://github.com/python-visualization/folium/blob/master/examples/Colormaps.ipynb\n", "\n", "\n", "# Create a Map instance\n", "m = folium.Map(location=[60.25, 24.8], tiles = 'cartodbpositron', zoom_start=6, control_scale=True)\n", "\n", "# add geojson layer on the map, which visualizes the polygons based on your style function, and additional parameters \n", "folium.GeoJson(\n", " geodata,\n", " name = \"Employment rate in Finland (2013)\",\n", " style_function=my_color_function,\n", " tooltip=folium.features.GeoJsonTooltip(fields=['nimi','tyollisyys'],\n", " aliases = ['Region','Employment rate (%)'],\n", " labels=True,\n", " sticky=True)\n", ").add_to(m)\n", "\n", "\n", "#Add a legend\n", "colorscale .caption = 'Employment rate (%)'\n", "m.add_child(colorscale)\n", "\n", "# Create a layer control object and add it to our map instance\n", "folium.LayerControl().add_to(m)\n", "\n", "#Show map\n", "m" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "outfp = \"employment_rate_in_finland.html\"\n", "m.save(outfp)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.6" } }, "nbformat": 4, "nbformat_minor": 4 }