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"source": [ + "### Arrows" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "\n", + "__tags__ = ['Vector data']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def sample_data(shape=(20, 30)):\n", + " \"\"\"\n", + " Return ``(x, y, u, v, crs)`` of some vector data\n", + " computed mathematically. The returned crs will be a rotated\n", + " pole CRS, meaning that the vectors will be unevenly spaced in\n", + " regular PlateCarree space.\n", + "\n", + " \"\"\"\n", + " crs = ccrs.RotatedPole(pole_longitude=177.5, pole_latitude=37.5)\n", + "\n", + " x = np.linspace(311.9, 391.1, shape[1])\n", + " y = np.linspace(-23.6, 24.8, shape[0])\n", + "\n", + " x2d, y2d = np.meshgrid(x, y)\n", + " u = 10 * (2 * np.cos(2 * np.deg2rad(x2d) + 3 * np.deg2rad(y2d + 30)) ** 2)\n", + " v = 20 * np.cos(6 * np.deg2rad(x2d))\n", + "\n", + " return x, y, u, v, crs\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def main():\n", + " fig = plt.figure()\n", + " ax = fig.add_subplot(1, 1, 1, projection=ccrs.Orthographic(-10, 45))\n", + "\n", + " ax.add_feature(cfeature.OCEAN, zorder=0)\n", + " ax.add_feature(cfeature.LAND, zorder=0, edgecolor='black')\n", + "\n", + " ax.set_global()\n", + " ax.gridlines()\n", + "\n", + " x, y, u, v, vector_crs = sample_data()\n", + " ax.quiver(x, y, u, v, transform=vector_crs)\n", + "\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Cartopy/LICENSE.txt b/Cartopy/LICENSE.txt new file mode 100644 index 0000000..57233b0 --- /dev/null +++ b/Cartopy/LICENSE.txt @@ -0,0 +1,110 @@ +2019 aug; osgeolive 13 edition + +These example notebooks include code snippets from the Cartopy documentation, +and are reproduced here, with the following Cartopy v0.17 LICENSE.txt + + +-- +Cartopy documentation and examples + +All documentation, examples and sample data found on this website +and in source repository are licensed under the + UK’s Open Government Licence: + +----------------------------------- +© British Crown copyright, 2016. + +You may use and re-use the information featured on this website +(not including logos) free of charge in any format or medium, under the terms +of the Open Government Licence. We encourage users to establish hypertext links +to this website. + +Any email enquiries regarding the use and re-use of this information resource +should be sent to: psi@nationalarchives.gsi.gov.uk. + + +Open Government License for public sector information + delivered by The National Archives + + +You are encouraged to use and re-use the Information that is available under this licence freely and flexibly, with only a few conditions. + +Using Information under this licence + +Use of copyright and database right material expressly made available under this licence (the ‘Information’) indicates your acceptance of the terms and conditions below. + +The Licensor grants you a worldwide, royalty-free, perpetual, non-exclusive licence to use the Information subject to the conditions below. + +This licence does not affect your freedom under fair dealing or fair use or any other copyright or database right exceptions and limitations. +You are free to: + + copy, publish, distribute and transmit the Information; + adapt the Information; + exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. + +You must, where you do any of the above: + + acknowledge the source of the Information by including any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence; + + If the Information Provider does not provide a specific attribution statement, or if you are using Information from several Information Providers and multiple attributions are not practical in your product or application, you may use the following: + + Contains public sector information licensed under the Open Government Licence v2.0. + +These are important conditions of this licence and if you fail to comply with them the rights granted to you under this licence, or any similar licence granted by the Licensor, will end automatically. +Exemptions + +This licence does not cover: + + personal data in the Information; + information that has neither been published nor disclosed under information access legislation (including the Freedom of Information Acts for the UK and Scotland) by or with the consent of the Information Provider; + departmental or public sector organisation logos, crests and the Royal Arms except where they form an integral part of a document or dataset; + military insignia; + third party rights the Information Provider is not authorised to license; + other intellectual property rights, including patents, trade marks, and design rights; and + identity documents such as the British Passport + +Non-endorsement + +This licence does not grant you any right to use the Information in a way that suggests any official status or that the Information Provider endorses you or your use of the Information. +Non warranty + +The Information is licensed ‘as is’ and the Information Provider excludes all representations, warranties, obligations and liabilities in relation to the Information to the maximum extent permitted by law. + +The Information Provider is not liable for any errors or omissions in the Information and shall not be liable for any loss, injury or damage of any kind caused by its use. 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The Controller invites public sector bodies owning their own copyright and database rights to permit the use of their Information under this licence. + +The Controller of HMSO has authority to license Information subject to copyright and database right owned by the Crown. The extent of the Controller’s offer to license this Information under the terms of this licence is set out on The National Archives website. + +This is version 2.0 of the Open Government Licence. The Controller of HMSO may, from time to time, issue new versions of the Open Government Licence. If you are already using Information under a previous version of the Open Government Licence, the terms of that licence will continue to apply. + +These terms are compatible with the Creative Commons Attribution License 4.0 and the Open Data Commons Attribution License, both of which license copyright and database rights. This means that when the Information is adapted and licensed under either of those licences, you automatically satisfy the conditions of the OGL when you comply with the other licence. The OGLv2.0 is Open Definition compliant. + +Further context, best practice and guidance can be found in the UK Government Licensing Framework section on The National Archives website. +Open Government License for public sector information + +-- + +https://scitools.org.uk/cartopy/docs/v0.17/copyright.html diff --git a/Cartopy/always circular stereo.ipynb b/Cartopy/always circular stereo.ipynb new file mode 100644 index 0000000..9a81177 --- /dev/null +++ b/Cartopy/always circular stereo.ipynb @@ -0,0 +1,108 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Custom Boundary Shape\n", + "\n", + "This example demonstrates how a custom shape geometry may be used\n", + "instead of the projection's default boundary.\n", + "\n", + "In this instance, we define the boundary as a circle in axes coordinates. This means that no matter the extent of the map itself, the boundary will always be a circle.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.path as mpath\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "\n", + "__tags__ = ['Lines and polygons']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def main():\n", + " fig = plt.figure(figsize=[10, 5])\n", + " ax1 = fig.add_subplot(1, 2, 1, projection=ccrs.SouthPolarStereo())\n", + " ax2 = fig.add_subplot(1, 2, 2, projection=ccrs.SouthPolarStereo(),\n", + " sharex=ax1, sharey=ax1)\n", + " fig.subplots_adjust(bottom=0.05, top=0.95,\n", + " left=0.04, right=0.95, wspace=0.02)\n", + "\n", + " # Limit the map to -60 degrees latitude and below.\n", + " ax1.set_extent([-180, 180, -90, -60], ccrs.PlateCarree())\n", + "\n", + " ax1.add_feature(cfeature.LAND)\n", + " ax1.add_feature(cfeature.OCEAN)\n", + "\n", + " ax1.gridlines()\n", + " ax2.gridlines()\n", + "\n", + " ax2.add_feature(cfeature.LAND)\n", + " ax2.add_feature(cfeature.OCEAN)\n", + "\n", + " # Compute a circle in axes coordinates, which we can use as a boundary\n", + " # for the map. We can pan/zoom as much as we like - the boundary will be\n", + " # permanently circular.\n", + " theta = np.linspace(0, 2*np.pi, 100)\n", + " center, radius = [0.5, 0.5], 0.5\n", + " verts = np.vstack([np.sin(theta), np.cos(theta)]).T\n", + " circle = mpath.Path(verts * radius + center)\n", + "\n", + " ax2.set_boundary(circle, transform=ax2.transAxes)\n", + "\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "main()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Cartopy/cartopy-citylights.ipynb b/Cartopy/cartopy-citylights.ipynb new file mode 100644 index 0000000..da45952 --- /dev/null +++ b/Cartopy/cartopy-citylights.ipynb @@ -0,0 +1,145 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Interactive WMTS (Web Map Tile Service)\n", + "---------------------------------------\n", + "\n", + "This example demonstrates the interactive pan and zoom capability\n", + "supported by an OGC web services Web Map Tile Service (WMTS) aware axes.\n", + "\n", + "The example WMTS layer is a single composite of data sampled over nine days\n", + "in April 2012 and thirteen days in October 2012 showing the Earth at night.\n", + "It does not vary over time.\n", + "\n", + "The imagery was collected by the Suomi National Polar-orbiting Partnership\n", + "(Suomi NPP) weather satellite operated by the United States National Oceanic\n", + "and Atmospheric Administration (NOAA)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import cartopy.crs as ccrs\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def main():\n", + " url = 'https://map1c.vis.earthdata.nasa.gov/wmts-geo/wmts.cgi'\n", + " layer = 'VIIRS_CityLights_2012'\n", + "\n", + " fig = plt.figure()\n", + " ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n", + " ax.add_wmts(url, layer)\n", + " ax.set_extent([-15, 25, 35, 60], crs=ccrs.PlateCarree())\n", + "\n", + " ax.set_title('Suomi NPP Earth at night April/October 2012')\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "ename": "SSLError", + "evalue": "HTTPSConnectionPool(host='map1c.vis.earthdata.nasa.gov', port=443): Max retries exceeded with url: /wmts-geo/wmts.cgi?service=WMTS&request=GetCapabilities&version=1.0.0 (Caused by SSLError(SSLError(\"bad handshake: Error([('SSL routines', 'tls12_check_peer_sigalg', 'wrong signature type')])\")))", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/contrib/pyopenssl.py\u001b[0m in \u001b[0;36mwrap_socket\u001b[0;34m(self, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname)\u001b[0m\n\u001b[1;32m 484\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 485\u001b[0;31m \u001b[0mcnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_handshake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 486\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOpenSSL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSSL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mWantReadError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3/dist-packages/OpenSSL/SSL.py\u001b[0m in \u001b[0;36mdo_handshake\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1914\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_lib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSSL_do_handshake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ssl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1915\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_ssl_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ssl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3/dist-packages/OpenSSL/SSL.py\u001b[0m in \u001b[0;36m_raise_ssl_error\u001b[0;34m(self, ssl, result)\u001b[0m\n\u001b[1;32m 1646\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1647\u001b[0;31m \u001b[0m_raise_current_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1648\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3/dist-packages/OpenSSL/_util.py\u001b[0m in \u001b[0;36mexception_from_error_queue\u001b[0;34m(exception_type)\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mexception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mError\u001b[0m: [('SSL routines', 'tls12_check_peer_sigalg', 'wrong signature type')]", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mSSLError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 664\u001b[0m \u001b[0;31m# Make the request on the httplib connection object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 665\u001b[0;31m httplib_response = self._make_request(\n\u001b[0m\u001b[1;32m 666\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 375\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 376\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 377\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_validate_conn\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m 995\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"sock\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# AppEngine might not have `.sock`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 996\u001b[0;31m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 997\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 365\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 366\u001b[0;31m self.sock = ssl_wrap_socket(\n\u001b[0m\u001b[1;32m 367\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/ssl_.py\u001b[0m in \u001b[0;36mssl_wrap_socket\u001b[0;34m(sock, keyfile, certfile, cert_reqs, ca_certs, server_hostname, ssl_version, ciphers, ssl_context, ca_cert_dir, key_password)\u001b[0m\n\u001b[1;32m 369\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mHAS_SNI\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mserver_hostname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 370\u001b[0;31m \u001b[0;32mreturn\u001b[0m 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7\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_wmts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_extent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m25\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m35\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m60\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mccrs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPlateCarree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3/dist-packages/cartopy/mpl/geoaxes.py\u001b[0m in 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wmts, layer_name, gettile_extra_kwargs)\u001b[0m\n\u001b[1;32m 370\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwmts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'contents'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 371\u001b[0m hasattr(wmts, 'gettile')):\n\u001b[0;32m--> 372\u001b[0;31m \u001b[0mwmts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mowslib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwmts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mWebMapTileService\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwmts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 373\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 374\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3/dist-packages/owslib/wmts.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, url, version, xml, username, password, parse_remote_metadata, vendor_kwargs, headers, auth, timeout)\u001b[0m\n\u001b[1;32m 175\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_capabilities\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadString\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxml\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# read from server\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 177\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_capabilities\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m 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later.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 514\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mSSLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 516\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mSSLError\u001b[0m: HTTPSConnectionPool(host='map1c.vis.earthdata.nasa.gov', port=443): Max retries exceeded with url: /wmts-geo/wmts.cgi?service=WMTS&request=GetCapabilities&version=1.0.0 (Caused by SSLError(SSLError(\"bad handshake: Error([('SSL routines', 'tls12_check_peer_sigalg', 'wrong signature type')])\")))" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cartopy/cartopy-ellipsoid-error.ipynb b/Cartopy/cartopy-ellipsoid-error.ipynb new file mode 100644 index 0000000..a26354a --- /dev/null +++ b/Cartopy/cartopy-ellipsoid-error.ipynb @@ -0,0 +1,171 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "from cartopy.io.img_tiles import Stamen\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.lines import Line2D as Line\n", + "from matplotlib.patheffects import Stroke\n", + "import numpy as np\n", + "import shapely.geometry as sgeom\n", + "from shapely.ops import transform as geom_transform\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The effect of badly referencing an ellipse\n", + "------------------------------------------\n", + "\n", + "This example demonstrates the effect of referencing your data to an incorrect\n", + "ellipse.\n", + "\n", + "First we define two coordinate systems - one using the World Geodetic System\n", + "established in 1984 and the other using a spherical globe. Next we extract\n", + "data from the Natural Earth land dataset and convert the Geodetic\n", + "coordinates (referenced in WGS84) into the respective coordinate systems\n", + "that we have defined. Finally, we plot these datasets onto a map assuming\n", + "that they are both referenced to the WGS84 ellipse and compare how the\n", + "coastlines are shifted as a result of referencing the incorrect ellipse." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def transform_fn_factory(target_crs, source_crs):\n", + " \"\"\"\n", + " Return a function which can be used by ``shapely.op.transform``\n", + " to transform the coordinate points of a geometry.\n", + "\n", + " The function explicitly *does not* do any interpolation or clever\n", + " transformation of the coordinate points, so there is no guarantee\n", + " that the resulting geometry would make any sense.\n", + "\n", + " \"\"\"\n", + " def transform_fn(x, y, z=None):\n", + " new_coords = target_crs.transform_points(source_crs,\n", + " np.asanyarray(x),\n", + " np.asanyarray(y))\n", + " return new_coords[:, 0], new_coords[:, 1], new_coords[:, 2]\n", + "\n", + " return transform_fn\n", + "\n", + "\n", + "def main():\n", + " # Define the two coordinate systems with different ellipses.\n", + " wgs84 = ccrs.PlateCarree(globe=ccrs.Globe(datum='WGS84',\n", + " ellipse='WGS84'))\n", + " sphere = ccrs.PlateCarree(globe=ccrs.Globe(datum='WGS84',\n", + " ellipse='sphere'))\n", + "\n", + " # Define the coordinate system of the data we have from Natural Earth and\n", + " # acquire the 1:10m physical coastline shapefile.\n", + " geodetic = ccrs.Geodetic(globe=ccrs.Globe(datum='WGS84'))\n", + " dataset = cfeature.NaturalEarthFeature(category='physical',\n", + " name='coastline',\n", + " scale='10m')\n", + "\n", + " # Create a Stamen map tiler instance, and use its CRS for the GeoAxes.\n", + " tiler = Stamen('terrain-background')\n", + " fig = plt.figure()\n", + " ax = fig.add_subplot(1, 1, 1, projection=tiler.crs)\n", + " ax.set_title('The effect of incorrectly referencing the Solomon Islands')\n", + "\n", + " # Pick the area of interest. In our case, roughly the Solomon Islands, and\n", + " # get hold of the coastlines for that area.\n", + " extent = [155, 163, -11.5, -6]\n", + " ax.set_extent(extent, geodetic)\n", + " geoms = list(dataset.intersecting_geometries(extent))\n", + "\n", + " # Add the Stamen aerial imagery at zoom level 7.\n", + " ax.add_image(tiler, 7)\n", + "\n", + " # Transform the geodetic coordinates of the coastlines into the two\n", + " # projections of differing ellipses.\n", + " wgs84_geoms = [geom_transform(transform_fn_factory(wgs84, geodetic),\n", + " geom) for geom in geoms]\n", + " sphere_geoms = [geom_transform(transform_fn_factory(sphere, geodetic),\n", + " geom) for geom in geoms]\n", + "\n", + " # Using these differently referenced geometries, assume that they are\n", + " # both referenced to WGS84.\n", + " ax.add_geometries(wgs84_geoms, wgs84, edgecolor='white', facecolor='none')\n", + " ax.add_geometries(sphere_geoms, wgs84, edgecolor='gray', facecolor='none')\n", + "\n", + " # Create a legend for the coastlines.\n", + " legend_artists = [Line([0], [0], color=color, linewidth=3)\n", + " for color in ('white', 'gray')]\n", + " legend_texts = ['Correct ellipse\\n(WGS84)', 'Incorrect ellipse\\n(sphere)']\n", + " legend = ax.legend(legend_artists, legend_texts, fancybox=True,\n", + " loc='lower left', framealpha=0.75)\n", + " legend.legendPatch.set_facecolor('wheat')\n", + "\n", + " # Create an inset GeoAxes showing the location of the Solomon Islands.\n", + " sub_ax = fig.add_axes([0.7, 0.625, 0.2, 0.2],\n", + " projection=ccrs.PlateCarree())\n", + " sub_ax.set_extent([110, 180, -50, 10], geodetic)\n", + "\n", + " # Make a nice border around the inset axes.\n", + " effect = Stroke(linewidth=4, foreground='wheat', alpha=0.5)\n", + " sub_ax.outline_patch.set_path_effects([effect])\n", + "\n", + " # Add the land, coastlines and the extent of the Solomon Islands.\n", + " sub_ax.add_feature(cfeature.LAND)\n", + " sub_ax.coastlines()\n", + " extent_box = sgeom.box(extent[0], extent[2], extent[1], extent[3])\n", + " sub_ax.add_geometries([extent_box], ccrs.PlateCarree(), facecolor='none',\n", + " edgecolor='blue', linewidth=2)\n", + "\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cartopy/cartopy-favicon.ipynb b/Cartopy/cartopy-favicon.ipynb new file mode 100644 index 0000000..66ae826 --- /dev/null +++ b/Cartopy/cartopy-favicon.ipynb @@ -0,0 +1,103 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import cartopy.crs as ccrs\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.textpath\n", + "import matplotlib.patches\n", + "from matplotlib.font_manager import FontProperties\n", + "import numpy as np\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "def main():\n", + " fig = plt.figure(figsize=[8, 8])\n", + " ax = fig.add_subplot(1, 1, 1, projection=ccrs.SouthPolarStereo())\n", + "\n", + " ax.coastlines()\n", + " ax.gridlines()\n", + "\n", + " im = ax.stock_img()\n", + "\n", + " def on_draw(event=None):\n", + " \"\"\"\n", + " Hook into matplotlib's event mechanism to define the clip path of the\n", + " background image.\n", + "\n", + " \"\"\"\n", + " # Clip the image to the current background boundary.\n", + " im.set_clip_path(ax.background_patch.get_path(),\n", + " transform=ax.background_patch.get_transform())\n", + "\n", + " # Register the on_draw method and call it once now.\n", + " fig.canvas.mpl_connect('draw_event', on_draw)\n", + " on_draw()\n", + "\n", + " # Generate a matplotlib path representing the character \"C\".\n", + " fp = FontProperties(family='Bitstream Vera Sans', weight='bold')\n", + " logo_path = matplotlib.textpath.TextPath((-4.5e7, -3.7e7),\n", + " 'C', size=1, prop=fp)\n", + "\n", + " # Scale the letter up to an appropriate X and Y scale.\n", + " logo_path._vertices *= np.array([103250000, 103250000])\n", + "\n", + " # Add the path as a patch, drawing black outlines around the text.\n", + " patch = matplotlib.patches.PathPatch(logo_path, facecolor='white',\n", + " edgecolor='black', linewidth=10,\n", + " transform=ccrs.SouthPolarStereo())\n", + " ax.add_patch(patch)\n", + " plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cartopy/cartopy-feature-creation.ipynb b/Cartopy/cartopy-feature-creation.ipynb new file mode 100644 index 0000000..d7c0644 --- /dev/null +++ b/Cartopy/cartopy-feature-creation.ipynb @@ -0,0 +1,110 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Feature Creation\n", + "----------------\n", + "\n", + "This example manually instantiates a\n", + ":class:`cartopy.feature.NaturalEarthFeature` to access administrative\n", + "boundaries (states and provinces).\n", + "\n", + "Note that this example is intended to illustrate the ability to construct\n", + "Natural Earth features that cartopy does not necessarily know about\n", + "*a priori*.\n", + "In this instance however, it would be possible to make use of the\n", + "pre-defined :data:`cartopy.feature.STATES` constant." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "import matplotlib.pyplot as plt\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "from matplotlib.offsetbox import AnchoredText\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def main():\n", + " fig = plt.figure()\n", + " ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n", + " ax.set_extent([80, 170, -45, 30], crs=ccrs.PlateCarree())\n", + "\n", + " # Put a background image on for nice sea rendering.\n", + " ax.stock_img()\n", + "\n", + " # Create a feature for States/Admin 1 regions at 1:50m from Natural Earth\n", + " states_provinces = cfeature.NaturalEarthFeature(\n", + " category='cultural',\n", + " name='admin_1_states_provinces_lines',\n", + " scale='50m',\n", + " facecolor='none')\n", + "\n", + " SOURCE = 'Natural Earth'\n", + " LICENSE = 'public domain'\n", + "\n", + " ax.add_feature(cfeature.LAND)\n", + " ax.add_feature(cfeature.COASTLINE)\n", + " ax.add_feature(states_provinces, edgecolor='gray')\n", + "\n", + " # Add a text annotation for the license information to the\n", + " # the bottom right corner.\n", + " text = AnchoredText(r'$\\mathcircled{{c}}$ {}; license: {}'\n", + " ''.format(SOURCE, LICENSE),\n", + " loc=4, prop={'size': 12}, frameon=True)\n", + " ax.add_artist(text)\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cartopy/cartopy-geostationary.ipynb b/Cartopy/cartopy-geostationary.ipynb new file mode 100644 index 0000000..d12a8a6 --- /dev/null +++ b/Cartopy/cartopy-geostationary.ipynb @@ -0,0 +1,145 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Reprojecting images from a Geostationary projection\n", + "---------------------------------------------------\n", + "\n", + "This example demonstrates Cartopy's ability to project images into the desired\n", + "projection on-the-fly. The image itself is retrieved from a URL and is loaded\n", + "directly into memory without storing it intermediately into a file. It\n", + "represents pre-processed data from the Spinning Enhanced Visible and Infrared\n", + "Imager onboard Meteosat Second Generation, which has been put into an image in\n", + "the data's native Geostationary coordinate system - it is then projected by\n", + "cartopy into a global Miller map. (requires net connection)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " from urllib2 import urlopen\n", + "except ImportError:\n", + " from urllib.request import urlopen\n", + "from io import BytesIO\n", + "\n", + "import cartopy.crs as ccrs\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def geos_image():\n", + " \"\"\"\n", + " Return a specific SEVIRI image by retrieving it from a github gist URL.\n", + "\n", + " Returns\n", + " -------\n", + " img : numpy array\n", + " The pixels of the image in a numpy array.\n", + " img_proj : cartopy CRS\n", + " The rectangular coordinate system of the image.\n", + " img_extent : tuple of floats\n", + " The extent of the image ``(x0, y0, x1, y1)`` referenced in\n", + " the ``img_proj`` coordinate system.\n", + " origin : str\n", + " The origin of the image to be passed through to matplotlib's imshow.\n", + "\n", + " \"\"\"\n", + " url = ('https://gist.github.com/pelson/5871263/raw/'\n", + " 'EIDA50_201211061300_clip2.png')\n", + " img_handle = BytesIO(urlopen(url).read())\n", + " img = plt.imread(img_handle)\n", + " img_proj = ccrs.Geostationary(satellite_height=35786000)\n", + " img_extent = [-5500000, 5500000, -5500000, 5500000]\n", + " return img, img_proj, img_extent, 'upper'\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def main():\n", + " fig = plt.figure()\n", + " ax = fig.add_subplot(1, 1, 1, projection=ccrs.Miller())\n", + " ax.coastlines()\n", + " ax.set_global()\n", + " print('Retrieving image...')\n", + " img, crs, extent, origin = geos_image()\n", + " print('Projecting and plotting image (this may take a while)...')\n", + " ax.imshow(img, transform=crs, extent=extent, origin=origin, cmap='gray')\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Retrieving image...\n", + "Projecting and plotting image (this may take a while)...\n" + ] + }, + { + "data": { + "image/png": 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tW8awYcNwuVyYzWbMZjMulwutVvu7nsJTpkwR21Um8QJ3DMkq39yVw1EpxHG5XISHh+Nyufjuu++4fPkyzZo1w9/fX1RBJcMlbe90OoWh9PDwwGw2i1DyViiVSjw8PPDz88NgMGAwGPD19aWkpAQ/Pz/atGlDrVq12LBhAzKZjNq1a5OVlUVERAQjRozAx8cHhUIhvG2j0YjRaBS5yYsXL5KXlyeOJ5PJ7phflNb/3HPPifP4/PPPf/W7zs7OJjk5mWvXrpGamsqqVauIi4sjOzubU6dOiYfhvYRUaV69ejXnz5/nm2++Ye3atXTp0oXk5GR27tzJwoUL8fHxEV5z48aNmTRpEsOHD7+nOcQ/w9P8T+O+rIpKXkNcXBw2m43AwECGDBnC7NmzGT16NOnp6ezfv58BAwZw4sQJ2rZty6JFi2jZsiW9evVi+vTpzJgxg/nz5zN8+HDGjRvH9OnTqVOnDhqNhgsXLrBjxw42btxIjx49aNOmDa1bt6a8vJzMzEyRGC8uLiY7O5uQkBBeeuklHn744Tsyx/fu3cvTTz+NTqejtLQUhUIhqrFGo5HmzZszadIkBg0adMcLdMqUKeJ1ZSNY+Ua2Wq3CmDmdTmEQpPyYBMnrcrlcIjFfWFjIli1bCA8Pp1evXuL7lbaXUNlzk6qhlQsLtWvXxuVy4eXlRXl5OXq9Hrvdzo0bN8RnSktL8fX1xcvLi+joaAIDA8nNza1CDWnUqBHR0dFVvEWAS5cuCS+tvLyc/Px8oOoDRQqb73bdSLjVuNntdrZu3cqXX37JsWPHaNWqFaGhoaSnp+Pr60tqaqp4cLZr1+6O+/+9yM/P58KFCyQnJ/PJJ59QUFAgiiNxcXH06NFD5CMrG5fk5GT8/PyoUaMGLpeLHTt28M477xAaGio6Wv4q3nnnHebMmYNer8fT05O4uDgSExPp27cvrVu3/o+Hwv9zBN2rV69y/Phx6tatS/369enZsycBAQFMmDCB0tJSzpw5w+zZszEYDKhUKg4ePMj27dtZuXIlFy9eZMWKFbz44ot4e3uTnp7OE088QVRUFKtXr6a0tJTevXtTrVo1nnvuOd599122bNmCTqdDoVDQqFEjatWqhUajISUlBbvdzqFDh371qWm329Hr9fj5+VUJZex2O9u2beO9996jTp06fPPNN1U+N2fOnCqeiRSKSfD09KxCqaj8tJXJZFVCU+m1UqnEZrPh7+8vQsp9+/aRkZEhEtSV9yW9lgi5UigqeUnSf2FhYQQFBZGdnc2NGzcoLy8XnQwajUZ4giaTCT8/P0JCQvD19RVGOTg4GIfDQWhoKH5+fgQGBopwWOqRLSgoAODYsWNiDbeGnvCLAb8bIiMjRdV88eLFfPjhh4SFhfHMM88QExPD559/zq5du2jSpAmJiYn07t37L0k8WSwWtm3bRlJSEsuXL6dRo0ZERkYydOhQunXrhl6vJz8/n+bNm//ufUre9ssvv0x2dvafWldl6PV6IiMjOXXqFPHx8ZjNZs6ePcuWLVv4/PPP6dOnD6tWraJ37940a9aMVq1a0bdv37983F/D/4xhc7lcGAwG8vLykMvl1KxZE7lczqxZs9i6dSsul4vDhw+L7fv06cPevXuxWq0EBgYye/ZsRo4cCUBJSQlt27YlLS2NmJgY6tWrR3p6uqA0pKenM2TIENq3b09CQgK+vr6UlpZSUlLCjRs38PPzQ6fTMXHiRFJTU4mJifnT55WRkUH79u0ZO3YsM2bM4OWXX65SDZQ8ldzcXPGeXq8HKgyW5KGZzeYquTPpCVs5NyaFlJJB8vLy4uOPP6ZOnTo89thjwC+kXGk/lQ1F5Y6C3zJskZGRgu7hcDiQyWSUl5fjdDqpXr268BoLCgoICAjAx8cHq9VK3bp1MZlMKJVK1Go1165dQ61Wo9PpyM/Pp6CggPLycrHGygbuVi9VgtRzazAYxHtRUVE89dRTJCUlUa9ePV577TU2bdrEtGnTGDx48D2pZDudTh577DEuX75M165dmTRp0l8iCJvNZpYsWcL8+fNRqVR88skndOnS5S+vs7y8nJEjR5KcnEyTJk04efIkMpkMDw8PCgoK+PDDD3nqqaeqfMZkMv2trWn/Mzy2Dh06cPz4caKjo0WuSqIpTJw4kccff5zx48ezYsUKOnfuzNChQ1m0aBGhoaFVqqMAfn5+7N69G6VSiZ+fH2VlZSxatIi1a9ei1Wp58MEHKS4u5ocffiAzM5PS0lLkcjlRUVFER0ej0+nQarUsWbKE6Ojov3RetWrV4vz58zRq1IjMzEwiIiLIzc0VF01JSQmRkZFERESg0Wi4fv06YWFheHp6otFoyMzMBCoKCd7e3oLWUdkgSTe/9B9U0D42bdoEQF5enjAKlfN40mvp89J7Uh5OOm5QUJAwmAqFQhgxl8uFzWZDLpej0WgoKipCLpfj5+eH1WrF5XKhVCq5ceMGwcHBBAcHYzab0Wq1WCwWDAaDKI6kp6djNBrFsSWJJ2l90porr7tevXpVwnTpIaFUKpk2bRoPPfQQcXFxNGvWjMTERFJSUvD39/9Lv2dubi7p6emo1WoKCgr47rvvOHr0KK1bt/5L+wVo3LgxaWlpTJ48mcaNG9OhQwdBt7m1sCF52bde+3eCl5cX33//PVu2bCE/P59nn32WgoICEhISCA0NJTQ0lCeeeILDhw+Tm5tLr169/mv9tvedYRs0aBByuVxUJm/evIlerycjI4Nhw4bx/fffs3DhQhYuXFjlc8nJyQwaNIirV6/y8ssv895777F582aefPJJLBYLRqORmJgY7HY7o0aNonnz5pw5c4YZM2awdu1aVq9ezb///W/MZjODBg3i6NGjHDlyhC5duvDYY48xcuRImjZtyty5c/90K0xQUBCtWrVi3759jBkz5rawR0r8KxQK4uPjxd+lC9rDwwMPDw+8vLzIyMhAoVAIKkROTo7gnUnGQArZpZv+qaeeEoZLoVBU8XxuDWklz0/an4eHByqVSqzRbrfj7e2Nt7c3ERERFBUVYbPZRCgdFhaG3W5HoVCISq3D4SAmJgaZTIavr684vtVqRS6XU1BQgM1mq8KtuzUf6HK58PT0FDw9Ly8vYUSl71hC7969AZg3bx7dunXjiSeeYObMmX/qt6uMCRMm8N1335GQkCAIzIMGDfrL1XAJGzZsYPv27WzevJmPP/6YESNGoNFosNls+Pn50bNnTxYvXoxarebTTz/llVdeoVatWvj4+NC0aVPef/99goOD77r/xMREFi1aJHpp1Wq1CHWVSuV/tcdWwn1l2Ox2OwsXLuS9994T74WFhQl2v9Vq5dSpU0RHR9O5c2c6depETk4O586d46effhLJ6GvXrnHixAkGDhwo6AqjR4/m/PnzFBUVMXPmTBYsWMCKFSuYMWMGH374Id26deOpp56if//+rFmzhs8//5yaNWvSrl07tFotXbp0YePGjbRt25abN28SFhb2h89v6dKlJCYmcvbsWVJTU+nWrVsV42YymYAKJQ2LxSJuFB8fH6pVq4bT6cRut2OxWKhevTpOpxObzUZBQQGxsbHAL8n19PR0srOzOXToENHR0QwdOlQ81SXPTMrRAbeFe9J2vwbJy4KKm0MysuHh4eTn54sujrKyMtRqNaGhocIDcDqdIveWkZEBwJUrV0Q4W3lNEs9NMriSFw8VOcjq1atXMdBQoeISHh7Oq6++yueff45SqRTtcHeDzWZjwIABnD59msaNGwv1402bNjFnzhyxrqKiIubNm8fw4cN/dX9/FvXq1aNevXq8+OKLHDt2jJKSErp16wZUeN09e/ZEq9Xi4eGBTCbjrbfeon///pSXl7N69WqaN2/O5cuX70pbcblcZGRkEBISglqtpry8nL1799KhQ4d7orhyL3BfGbaVK1cSHBzMgAEDgIof4IUXXmD58uU0bNiQb775RlxUgwYN4ssvv8Tb25uysjJcLhenT5+mb9++9OnTh6tXr4r5BD4+Pnz66acMHjwYnU7H8OHDOX78OCtWrEAul7N8+XLeeecd5HI5EyZMoHfv3nTv3h2o4Eh9+eWX7N69m6ZNm3Lp0iVee+01Zs+e/Yca3ZcvXw5U3HgPPPAAhw8fpm3btoLe4OfnR1BQEFqttoqihlSZ1Ov1grQq0TQ0Gg3l5eWUlZWh0WgICwsTHKijR4+ye/duoCIcrVGjhvDwpG0kVM63KRQKkaOr7DE5HA4KCgpwuVxERkYSGBiI2WwmICBAhMc5OTl4e3vj6emJwWBAq9WKnlKZTEZ0dDSenp5izSqVCpfLxY0bN3A4HNhsNmHYpPOWoFargQqPTKFQCC+vdu3aQm1ErVZTUlLCtWvX2LRpEy+++CJFRUVcuHCB9957D7lczoULF6hfv/4df6Nt27ZRWFjIwYMH2bNnD1OnTmX8+PF8+umn5OXlkZOTg16v5+zZs9y8efNvM2yVcWtoGxERQXJyMi6XC5PJhF6vJywsTBixJk2asGrVKs6ePUuLFi2wWCyC11j5PL/66iumT59Obm4uGRkZzJ49m/Hjx2OxWIiNjSUhIYFPP/30v6ZQ86uqdbm5ubz++utkZWX9pYPYbDa2bt36l/fzWzh+/DiHDx/m008/Fdr/33//PaWlpYwdO5bdu3dTv359IZu9detWvv/+ez744AOgQlHjlVdeYcuWLQwePJiDBw8SFhZG3759OXDgAPn5+ajVajZs2IBGo6FGjRrIZDK8vLyEK/7JJ58IowbQq1cvpk6dypkzZzh16hTZ2dl4enoSFhbGc889J/JXvwbJqCkUCoxGI5s3b0ahUHDw4EHkcjleXl5YrVa8vb2xWCyiQbqsrAyTyYRKpSI4OBiXy0V+fj5KpZKysjLy8vK4ceOG8PAyMzNFx0RMTAwvvvgigBCglNZQmfxZOeST/n1rwr5y7i0wMBCj0Yinp6foOZVybdLnJaNmt9uFXLpOpyMnJweoCB8tFgs3btzg+PHjqNVqioqKsFgs2Gw20dGgUqlQqVQolUoiIiLQarWYTCZKS0tp2bIlTZs2xWAwiPDUbrfjcDjYuXMnvr6+BAcHC76dTCajQYMGAFy4cOG23+js2bM8++yzTJkyhdjYWM6fP8/169cF8Xf16tVMmTKFb775hldffZWkpCTKysp+97V9ryGTyYQnX9kzKykpwWq1UlxcLOgjTz75JMOHD6dHjx68+eabzJw5k9LSUoYNG8Y777zD6tWrOX78OIWFhVy/fp2lS5cSGRlJYmIip06d+lvWbzQaOXbs2N3P79fi+tDQUFdBQQH/+te/+Oyzz/7QgR0OB//+97/ZsGEDP//8M0qlkqtXrxITE0PDhg3p2rUrgwYNwuFwCEmbP4vKyeBjx47xwgsvcOXKFTQaDS+//DLffvutkNxZt24dcXFx1KtXr0rCtLi4GH9/fzIzM2nZsiVff/01/fr1w+Fw8MYbb3D+/HlCQ0PJzc1l69atvP322+zdu5etW7eKRLKUE/otWK1WJk6cSExMDHPnzmXUqFH07duXBx544Dae2/z584EKFVUJRUVFrF69WvC0nnzySRITE/Hy8qpyfLvdjkwmw2q1UlBQgJeXFyaTSbD8jUYjAQEBWCwWSktLRQuVt7c3o0ePpmbNmgwbNowvvviCDz/8kPLycpRKJS6XSxBiJSN2a06rcnW1cnFCyvFJnQJSjquwsFBw56T8XnR0NHK5XLRnqVQq6tatK3pap02bxrVr17BYLJSVlREQEED9+vWpX7++aIyXiggqlQq1Wi0eKhKNxNPTE5vNVqVqrNfr2bhxIz/99BPjxo1j7ty5OJ1ODh48SHBwsAi3pdDUbDYTFhbG4sWLGTp0KACTJ09mzpw5tG7dGqfTSXl5OefOnatyzf4TQrY7ISkpiUGDBuHj48OoUaNQq9WEhIQQHBxMUlISrVq1wmaz8cgjjwhP+FY4nU6+/PJLZs6cSdOmTfH09BTV6oCAAJo1a8b7778vcpt/FImJiZSUlHDo0KE/TveIj493ffHFF3Tq1Ol3SxK7XC5mzZrFRx99RGxsLE888QQ1a9YkMTGR7OxsvvjiC3Jzc9m0aZPgkDVs2JAGDRpQrVo1/P39KSsrY9CgQdSuXQP9hVgAACAASURBVPuuP77JZGLjxo2cPXuWjIwMNm7cyPHjxykoKBB8sbZt2+Ll5cXPP/9My5Ytb9tH5fagyti2bRtTpkxBqVQye/ZsOnTowOLFi0lLS8NqtTJ//nycTidt2rQRCfVXX32V+vXr06JFC+rVq0eLFi2oWbMmAQEBZGdns3LlSsaPH09AQECVY128eJGVK1eybt06EhISWLdunbjJ5s+fL85fyp9BhTLJqlWraNWqFYcOHaJp06Y8/vjjxMbGikohVFQiy8vLsdlslJeXC29LEnmUKpPSjS0VDdasWcOuXbvE8YKDg5k6dSoeHh5ihGDlBL2k4nGrgZM8NanhXXptMpmwWCxCYLK0tBSZTEZwcDB6vZ6goCAyMjJo2LAhJSUlogMiLy+P9PR0rly5wrVr12jUqBGhoaFYrVa0Wi2XLl1i//79xMTE0Lt3bzw9PUU1VqvV4u/vL3TjpGNK35VSqUSlUqFQKESB49KlS2RnZ1NcXExKSgoLFy6kWrVqFBUViXms9erVw263ExkZyU8//UStWrUAuHbtGk2bNsVms3Hq1Clq1679O+6efw70ej3r1q3j8ccfryLd9UdhMpnYsGEDXl5ehIaGEhwcjE6nY968eYSGhjJv3ry7fvbEiROcOXOGhIQEatWqhc1mQ61WExYWxt69e7HZbPTs2fM/w2Nbv349U6dOZdu2bSIhfTdICeAjR45w+fJlMjMzKSkpQS6Xs2HDBuRyOe3atWPEiBH06dOHQ4cO8dFHHwEVYWfz5s3x9/cnOTlZdAfo9Xpq1apFenq6eOIMHz6crVu38s477zBu3DiaN29OnTp16NChQ5Xkt06nQy6XExgYyI8//kj//v3p3Lkz+fn5YtCKUqlk3LhxvPXWW3h7exMeHs6aNWuIiooiOzubWbNmsWvXLuGtSMRSf39/rl+/zpQpU3j77bdv+y6sVivdunXjscce4+mnnwbg66+/Bio8gspJ+dTUVBYvXky3bt24fPky6enprFq1CrPZLHJuUqgHFd6eRLqVwp+CggI0Gg0RERGCbuHl5YXT6SQ9PZ3Dhw+zdetWoGKYi0qlEiGoj4+PyHtJXLJb1wi/yHhXpoNIxYvK511SUoK/vz8hISFkZmZSvXp1PDw88PHxEWRd6SH20EMPiWS/UqmkqKioSs/q+vXrBbXhwQcfJDw8nFatWiGTydBqtWLOqsViwWKxCGMnrVGlUokbOS4urkqvLFQ89F5//XX8/PyIi4vj8ccfZ9SoUbRt25batWuzceNG5HI56enpxMXF0bZtWxEtuPELvvnmG2bMmEHLli25cOECdrud2rVriwr5c889x9NPP01MTAwGg4HMzExUKhUGg4EaNWrQt29fnn/+eYKDg/+4YQsMDHTNnTuXxx9//FcXabVaefrpp7ly5QopKSksW7ZMlMr/LFwuF5mZmRw4cICPPvoIm82G0Whk5syZ+Pv7ExcXd5sKxvHjxykpKeHs2bMMHDgQLy8v5s2bx5YtW7h69SoajYaysjKcTieff/45KpWKnJwcMjMz+eGHH9Dr9chkMhISErh06RLl5eWcPn2aXr16iVCtuLiY6Oholi1bRs2aNRk/fjw7d+4Uo+q+/PJLPDw8ePXVV9myZQsjR45Ep9OhVqs5cOAAHTt25NNPP2XMmDFVJjvt3LmTCRMm0Lp1a1auXMmKFSuAX3TF7HZ7lcpdcnIyx44dQ6/XU1paytdff42Xl5fwpqTtJY9NCjMLCgqQy+UYjUbkcjlhYWGYTCasVitWqxUfHx/Wrl3LqVOnGDBgAF27dsVoNGIymSgrKyM4OJisrCzUajUBAQEEBQVRVFQkQuVb+zElL006pslkEhW5srIylEol3t7eYjuj0YhGoxF5LZvNxrfffotWq2X69OmoVCqKi4vR6XScO3cOm80mqCx2u52ysjKsVismk4nMzEzy8/MxGo107tyZnj174uPjQ1lZGTqdjrKyMsLDw0Vuslq1akRGRqLVaklOTmbRokXUrVuXnj17YrFY2Lx5M1arlTVr1jB79mysViu7du3iwoULxMbGkpyczKuvvsqbb77JmTNnxPSvqVOnVqnUu1FxnWzbto38/HyaNWuGh4cHaWlp5OfnI5PJmDZtGiqViv79+1dpb7Pb7Rw9epQ1a9bgcDj44osv/rhhCw8PdxUUFNC1a1e+/fbbu7Kst23bRp8+fRg6dChz586toip7L+BwODh79ixRUVF/et95eXmcOHGC119/nbNnz9K2bVsxXOT69eucPn2aV155hUOHDmG32wkMDGTEiBH07NmTI0eOMGjQINRqNUqlErvdTnJyMtWrV+ezzz7D6XSyb98+EhMTmTNnDvPnz6dr166sWbOGadOmkZKSgoeHB+fOnWPgwIEiwf/ll1+KlpMff/yRCRMmUL9+fby9venatStarVZIaFdWm4UKw79kyRLy8/Pp0aMHLVq0oHr16nh6eqLX6zGZTPj7+2MymUQ1s7S0VEyllwappKenc/r0aUwmEzdu3MBut9O5c2eGDRuGj48P5eXl5ObmCoKx5PVJSX7pPYVCgVKpFMl5aY1Sy5QUgkpGSCLpGo1GDAYDQUFBeHp6kpeXR2RkJE6nk8zMTLZu3UqbNm3o168fUVFR+Pj4cObMGVJSUqrw0qRQV0KTJk0EwTc7O5tdu3Zx8uRJGjVqRJcuXZDJZIKTFhUVxfz58wkLC2PXrl2sXr0avV7PzZs3gYquDh8fH9q1a8fBgweJi4tj2bJlomBUVFTEV199JboTfv75Z1wuFyNHjmTFihVMmzatisCCG7+Nc+fOsX//fh5//PG7kqH//3zePxeKbt26ldmzZ7Ny5UpmzZpF//798fPzu01B9dChQ7z44os0b96cBQsW/GPHhLVp04Zjx47h7e1NUFAQ0dHRHD16lLCwMFJSUvDy8mL+/PnodDqWLl2K0WgkKiqKevXq8fzzz9OxY0e+/PJLxo0bV2W/Y8aMYc+ePcTHx7Nt2zZkMhknTpygR48eIsR9/vnnSU1NZdeuXaxfv56xY8dy48YNETJaLBaGDx/OpUuXuHnzJhMmTMDX15eYmBhRKPDy8sLhcJCSksLSpUt54403BAnWbDYTGRmJt7c3eXl5olooFRCKiopEi0tcXBw6nY6PPvqIXr16ERYWJqgXZrOZ2NhYSkpKCAgIoKioCH9/f4qLizGbzQQGBmKxWPDw8BBGMz8/X4SnarUahUIhKo5SqHenDoWSkhK0Wq1o9fL29sbhcJCfn893333H4MGDad68uQgfPTw8yMrKIj8/H6fTiZeXl9i3y+UiIiIChUIheHulpaVotVqys7O5du0a+/btQ6lU0rlzZ1atWkWbNm3Yvn07vXv35vDhw9SuXZtevXrRsWNHrl69ytWrV2nZsiVqtZqIiAhMJhMhISFVyNAS9Ho9MTExxMTEUFxcTKtWrSgvLxfzC9y49/jLvaJbtmyhb9++4mJ+8skn+fTTT6vclN9//z0jRoygfv367Nixg6ioqL/pdO4t9u/fT3x8/G39eRJVQbohS0tL2bJlyx35R0ajkbfeeovu3bsTFhaG1Wpl2rRpGI1GkWPZsGEDH3zwAU888QRarZYZM2Zw4cIFUW43GAy8/fbbREREsGvXLvbt20dwcDCjR48mLi6OK1euEBAQgIeHB6mpqSxfvpypU6eiVqtxuVyUlpYSGxsrKp6SkZLCRJ1OJ6pbhYWFLFiwgFdeeYVGjRphNBqBikKByWTCbrdz5coVcnJy0Gq1JCQkUFhYiFwu58aNG6SkpBAXF0dERAR2u11UWg0GA3K5XDDqJRJtZY02qXJaOTcm5SMljt0333xDy5Yt6dChg2iGv3jxIsHBweTn55OWliY8aEAYtsjISEJDQ7l8+TJr167lgQceQKPREBAQgN1u5/Lly+zfv5/OnTtz8eJFTCaTWFuvXr146KGH8PLyQqvVCkEAqTIrdVBIFVf4hWSsVCpp2LAhUBFhJCYmsn37dvz9/cnIyPiPD+f5X8GfMmwhISGuRx55hHnz5mE2m2ncuDHLly8nPj6eRx99lPLycjZu3MgHH3zAkiVLaNiwIUlJSQB069atSmXtfwESo91kMlG3bl3GjBnDxIkTb1P1KC4upmbNmhw8eFBwo0pLSwkLC8PlctGlSxdat27NmTNnOHbsGM8++6xI3peVlYmq3pw5c5gxY4YIC61WK2azWahfSAZGIocaDAaKi4u5fv06hYWFdOzYkSZNmlCtWjW8vb0xGAwolUrCw8NxOBzMnz+fw4cP43A4aNWqFSdOnECr1VJSUkJMTAwpKSlMnz5dyHsDgpphNpspLy8XVe7KQpd3ojpI71mtVvbv34/ZbGbEiBGC6xYVFYXT6eTKlSvs37+fa9euCWMi5Sp9fHxo1qwZZWVlpKWl8dFHHwkum0T5kNQ/GjZsyPnz53njjTeEFxoaGkrDhg3FgyE6Olrk+aTJWhaLBbPZLAoMUmQiFReaNWvGxYsXqV+/PvXq1WPZsmV/SJHDjT+GP9UEX1ZWxvz581m0aBFDhw4lNTWVwsJC4uPjMRqNTJs2jaSkJD788EPWrFnDkCFDSEtLw2QyiWrbP5Wr83fgrbfeErSHfv36MXbsWGHUrly5wvjx4zGZTKSlpVFcXCyMGiC8mYYNG3L48GGuX79OZmYmTz75pPgeJfIpVOS1atasydKlS3n00Ufx9PQUCrKZmZmEhIQQEhJCTk4OAQEBbNq0CYfDQbVq1ahRowYtWrQQzP2MjAxq1qwpaBmSGseBAwcwmUw88MADqFQqxo0bh1arFWKb7777LocOHaJJkyYoFArUajV2u52AgABSU1Px8/MT0uuSQu+d5Kulv0sh9tWrV3nyySex2+1ERERw7tw5YmJiKCgoIDk5maSkJOrWrYtOp2Pbtm0MHDgQtVqNj4+P6EHVarX079+f5ORkHnzwQc6dO4dGo6GgoIC5c+fSvHlzdDodBoNB0GFiYmIoKioSjd12u11M1HI6naK1S+LySe1Zt17jCQkJWCyWKiIAbtx7VFZhuRW/mgirXr06hYWFFBYWcuXKFWrUqMEHH3xAYGAgJpNJ6P6vWbOGZ555hmHDhvHSSy8JdVmJXHq/w2QysXXrVpYsWYJCoWDt2rVcuXKFqKgoFi5ciMvl4t1336WgoICYmBhKSkp44IEHqmhkffzxx4wcOZKcnBwhNPnII48IzbHi4mIxQ0CiWHTv3p20tDSRC5K8OqVSSVZWFmazGW9vb3bs2EFQUBCJiYm0aNGCkJAQVCoVJSUlnDlzhgsXLqDX68nOzhb78/T0FFXnY8eOMXr0aDp27EirVq2oWbMmBQUFVUI1id/m6emJxWIhOjpahHMGg0EUESrn16RoweFwUFZWxqVLlzh48CC9e/cmNjZWkHVlMhlXrlzBYrGQlZVFbGwsYWFhFBQUULNmTWQyGTVq1BCE2evXrwMVHlxJSQnff/89ly5d4sSJE0Kw0eFwEBgYiM1mw+l0UrNmTdHWVbNmTeCXljSoeJCYTCby8vKwWCyUlJTcJoPkcrk4efIk8IuxduPeY/DgwchkMl5++eW7bvOHeGwHDhxg0aJFNG/enA0bNjBo0CAeffRRQkNDycjI4PDhw3h5ebFp0yZWrlzJhx9++KsHvx+wdOlSJk6cSGRkJPXr1+fHH3/k6NGjjB8/ntOnTwPQoEEDVq1axfvvv49GoxG9kCtWrGDu3Lk899xzdOvWjby8PBITE1m3bh01atSgTZs2VXh2gOjTlLytOXPm8OCDDxIaGiqqk1arFYfDgcViwel0kpKSQnh4ONHR0YISotVq0Wq13Lx5k7y8PEGQleYSmM1mWrduzcSJE8nJyaFZs2ZUr14dhULBoUOH8PT0pHv37iQkJIjwU6pwhoWFiR5DvV5PTk6OaF6XmP6SKoeUb5MMT/fu3enatSs6nQ6z2Ux8fDxyuZykpCT0ej07duwgNDSUoqIiunTpgre3N/Hx8QQGBuLp6YnRaBQep06nY/fu3SQnJ9O0aVPi4+Np0KABDRo0EOu12Wx4e3sTGxsr8pQSsdfX1xeZTCZUQyIjI4UXJ6HyEBkpHG3cuPF/7Pr7X8Q333zDqFGj0Ov1BAQE3DuC7pIlS6qM+pL2MX36dD755BPUajU9evTg3XffpVq1avfiXP6RWLt2LZMmTWLZsmV069aN9957j0WLFqHT6Rg4cCAXL15ELpczdOhQJk2aJDhakrGaM2cOkydPplOnTrhcLiG1FBMTg0qlYvDgwSJhLX1GylGpVCocDgfnz59n69at9OvXj5CQEHFjSzy2w4cPU1paymOPPSZCtNTUVFQqFZ06deLmzZvcvHmTq1evUr16ddGRYDKZaNCggeCM5eTkUFRUhJeXFy1btqR27doij+bn50dpaSlGoxGn00leXp6QybFarUJpxOFwUL16dSEwABUM/XPnzqFUKunZsyc9evSgtLSUgoICAgMDhYz4xx9/THl5OeHh4WRkZNC3b1/RA+rl5UVERAQOh0MULaQKrsRZk+goXbp0EYZbmsYl5e8kmo9MJkOj0WA0GikqKhKySVLjvUwmEw8W6f9SOCqdl9u4/X5IIg3+/v6/i01hNpsZOXIkHTp0YOLEiXc0bH+KkzFmzBicTifHjx8Xbv/OnTtZsGABhw8f5vz58yxfvvy+NmpffPGF6CJ48MEHAXjttdc4e/YsKSkpLF++nOPHjzN58mSWL1/OlClTiIiIIDg4mB49elCjRg2ysrLYu3cv165d49ixY1VEI2vVqiWmOElKuFL/pZSP0+l01K1bl86dO7Nr1y6CgoKEmoIUAubn59OhQwc8PDwwGo0UFhaKFpUrV65QXFyMXq9HrVZz9epVSkpKMJvN1KhRg5s3b+J0OgkICCA2NpYaNWrQsWNHUlJSOHz4MJcvXxYdAA888AARERGUlZUJUc7y8nKaNGkivMGAgABKSkoEj+3ixYukpKTQrl07XnnlFbp27UpgYKAwgJIi7tdff41Go6Ffv34YjUbq1KkjKsE5OTlCmVg6D5VKJZQ+5HK5mKHQpUsXwsPDkclkeHt7Y7fbRd+ol5cXWVlZgsxsMBg4e/YsCoVCkJul7o3K8k0Wi0WQm6UK8J2kyN2oCr1eLxyil156iaCgICH2cCfY7XaSkpIYMmQI/v7+fP/996SkpNx1+z8tWySTyar0X86aNYvXXnvtrpIutyIvL4+NGzcybty4/3MFhtTUVKZNm8bx48dvaxvTarX4+vqyadMmatSoQWRkpJAPb9q0KWvWrOHUqVMEBQWxYMECxo8fT7t27YSiQlZWltC7l0IqqJiMJMllQ0VhR2oHatGiBWfPnuX06dPUq1dPEGslQq6Xl5eouko3tEKhIDAwkPPnz4ueUaio2Hp6eooHliSHZDAYRDuWTCYTJGApR6XRaET4JvVhSrSIqKgoYdCkm16hUHDx4kWGDx+Ov7+/GLZsMpmqJOsDAwO5efMm9evXp6ioiICAACH7HhERga+vr9in1A6l1+ur5L8kIjBUJJw9PT1FAcDb21vky2JjY0UvrSQsKslASQZcqVQK4yUZaEAYO8kIunE7DAYD48ePZ9++feTm5rJ+/Xpu3LghOgsqq+JUxnfffcdzzz1HZGQk48aN46uvvhIDthcsWHDHz9wTPbaLFy/SpEkTFi1axNixY39TDjg1NVUkegcMGCAa5CdPnszjjz8uugv+qQbv9ddf5/XXX79rL6xMJqsyxMLhcHDjxg2aN29OWFgYffr0ASrIwn369GH37t2EhobSoUMHWrRogU6nIzc3l5CQEKEtplarhaiip6cnkZGRIj8ll8vp3bs33333HVu3bkWj0fDggw9y48YNoqOjcTgcgoIil8vJzs5GqVSSkZGB3W7HYDBUMQ43b94kISFBKIJcvnxZUBwkVj1UUBxCQkKw2WwcOnSIhg0bUlhYSJ06dSgoKCArK4vU1FQh/x0TE4NGo8HhcBASEoJWq0Wv1xMbG4uXl5dQ2JWS9k6nE5PJhFqt5ubNmyLnJbVlhYSEkJaWxk8//SRC6zNnzuDl5YVOp0Ov16NSqUSRw9vbW1A3fH198fb2RqVSUVRUJCSKcnJyhO6bdP7SPIb8/HwhbSQ9eCTjKE0mk/h5J06cuKPwwv8qDAYDvXr1IiEhgUOHDnH06FEeeeQRkW+dMGEC4eHhWCwW0fvcp08fPv74Y1JSUti2bRstWvwScUoKyHfDPTFsjRo1wmazUadOHRo2bIharWbevHl3HCBx+vRpcWOvXbuWp59+mhMnTnD8+HHmzp3LzJkzMZvNBAcHk5iYyPDhw+nQocO9WOY9w44dO1iyZMnv3l4ul4txcdLcAgl9+/YlMjKSo0eP0qBBA2QyGWvXriUtLY1Ro0YJrTVpurokiS21+0jCidIEJUn9dtu2bTgcDjp27CjyXUlJSQQGBhIUFERZWZnwvCSPTQp1FQoFHTp0YPv27cIzkdqoKlf6vL29yc3Nxdvbm5s3b2Kz2YiOjhYKJhcuXCAgIEBMN1KpVOh0Omw2GxqNBh8fH6HC63Q6yc/PJyAgAIfDwfXr1/H29ubGjRs0btyYnTt3otVqiY+Px2AwsHDhQtFHGxERgdVq5dChQ6JlzeFwCKFKk8kk1E0kekdkZCQymYzS0lJBnr1y5Qp+fn7CeEkiDXv27CE8PJyQkBB0Op34LaV8p0RhsVqtKJVKsa7/JUhK01KebP369RQWFtK1a1dq167NgAEDqFOnDosWLUIul1OtWjW2bdvG1q1bmTBhAqdPnyYhIYH09HTCw8Pp168f7dq1Y/To0Zw+ffqu8kh3wz0xbMXFxezcuZP27dtz/vx5lixZwujRowUlJC8vjz59+tChQwf+9a9/0b59e9auXcubb77J5MmT+eyzz6hevTorVqwQF+X169fZvHkzjz/+OIMHD2bWrFl/64Tt3wspRJKqobeqi94NixcvJjQ0lLi4OPHejz/+SEREBLGxsaxdu5b8/Hzy8/PR6/W0adOGI0eOMGTIEGF4cnJyBP1BWovUaiU1vEuyPNIMUykPFBQURHh4uMgDSf2VSqVSqMlKfalyuZwDBw6IfUrDVSrrrUlDT4xGo1DGtVgsFBYWAohJ73a7nY4dO1JcXCxmRwQHB+Pp6UlgYCAlJSVERERgMBiYMmUKgYGBvP766wQGBlJYWEhubi5FRUU4HA5q1aqFy+WiXbt2YkZDnTp1MJvNOBwO6tatW4UIXPk3kwY4SwUCqf1LaoqXyWSEh4eLMPry5cvk5OSIYStlZWVkZmaiVqsxmUzUqlVLaLtlZ2ejUqkoLCwUiiMymYwzZ85UKbLdb9DpdDz77LOcPHmS69evi4KLRqOhdu3aIlRs3749e/bsYcKECVWk1/fs2cPu3bt5++23CQ0NZcyYMaInV61W8+abbxIWFva7Bs3cinti2DQaDf379wcQgxwMBgObNm1i8uTJQEWlTxKbbNCggajC3QrJUMTHxxMfH8+TTz7J2LFjqVatGo0aNcLHx4dnn332rvH43w25XM7q1avFnMnfUj6RIHlHeXl51KhRQ7yv1+sJDw/n0Ucf5YMPPsDT05OJEydSvXp1ZsyYIab9JCUlVSHPVp6qJAlKSl6XdDypQieXy8nPzyc4OJioqChBS7hy5QpQ4fVVq1aNjIwMMfouNzeX8PBwCgoKaNSoEceOHRN9mZJxk7w1s9mMQqEgOjoatVrN6dOnKSgo4MCBA1gsFgIDA2nZsqUYh1haWkp5eTkNGjRg9erV6HQ6UlNT6dq1Kz///DNJSUlCwbigoICTJ0/SqlUroRZsMpkIDAwkKyuL3r17i3yfpAhSmSNnMpnw8fEhJCRE8OkkmXTJsJnNZqxWKwaDAYfDwd69e8UUMAmlpaViFKBOp6OkpERII0lrkiB5rNIksPsRGRkZ9OrVi379+jFz5kxiYmJE25lerycwMJBLly6xbNkyoqKi2L17Ny1btuTkyZNs3LiRr776ij179rBv3z5OnTrF5cuXb/PK/kpL5t8y86BTp07CwG3YsIERI0awfft23n77bQoKCli4cCF+fn7MmjXrN/cVFBTE+vXrycjIICMjg/PnzzNp0iROnjz5u72le41hw4YJQU1JrPLhhx/+1bh/7Nix5Obm8vLLL7N27VoAId/jcDjo1asXzZs3x9vbW0hfv/HGG7z00kt069aN7t27o1AoOHfuHGlpaWK/ksb/rYRXifVemYaQlpaG0WikXr16HD16lJCQEIqKiujQoQN6vV6sRXpf8vak6iBQ5RiA6CxwuVxCKdnhcHDkyBEGDBjAd999x+LFi/Hy8qJp06bCIFerVg2Hw8Hs2bPJyckR/EeoEHjUaDSYzWaKi4sJCAggPDxcGNPt27eLNqpLly6hUCjw8fFBqVSKodMymYyioiLRTVG3bl3RnF9QUEB2djYBAQEEBASINfn5+bFv3z7RHSJ5tNI8hdLSUkwmk1AdljiBEsUkNDRURBUeHh5iOM39hmPHjtG/f3/eeustnnnmmSp/UygUYsJV3bp1hew+VDg73bt3p2/fvuzdu5eEhASuXr2KyWTihx9+EDNr7wX+tvJNaWkpY8aMYeDAgRiNRvR6vWgKfuaZZ5g2bdof2l+tWrXo0aMHL7zwAgkJCfTp0+c27a//JAYNGkTv3r2pVasW69ato3bt2nz77bdVBojciuHDh4uG+H379gEITX5vb2+ioqIICAgQVcKGDRuKliLpxm3UqBEdOnSool4r3byV9fkry3NLRFhJsmj79u0YDAZkMhlxcXHs3buXAwcOUFRUhEqlok2bNtSsWVNw7qTcUWVIx5CEIyvPppQIuz169OCNN94QOUYpFFSr1eTn56PVagkKCiIvL0+IAchkMlJTU/nss88oKioiOzubyMhIwZW7dOkSrVu3pnbt2nTv3p221wavMgAAIABJREFUbdvSvn37KnMfpPX5+fnh5+cnfpPS0lIUCgUREREitybJMElDVi5fvixoLpIxMxgMlJaWotfrhWcnGbCSkhIKCgooLi6msLCQsrIy0Y8LFSHX/YT169eTmJjI4sWLbzNqv4WpU6fSr18/li1bJoZTt2/fXlCC7iXuuceWm5vL5MmTWb16NQC+vr68/fbbTJo0SYRjfwVyuZw1a9YQHx/Pli1bePjhh+/Fsv8wVCoV77//PlChb5+cnMyQIUOYPn06L7zwArVq1SI6Opp69eohk8kwGo288MILDBw4UOxDusklj0FSi608Pbtz584sWLAADw8PunbtitPpJCwsjF69enHixAnR1C1BysFVDk+lvIbE7ZI8OUn22mw2Y7FYhHci6a9J3KzKahaAoHJIDxZpHqk0z7RatWrs37+f0aNH89xzzwn9uo4dO+J0Ov8fe+8e3nR9/v8/2iZp0vScpucDlBYKbSkHpYogZ1BEFHAwmc5N1HnC05z6HfhhermpH48MxCMTj5vOKaigBTnISY7SUlp6PqfH9JSk6Sltfn/097pN8byPDmTe18VFKTm8807ed+7D8yCzPPVabTYbycnJFBYW0t7ejt1u59NPP6Wnp4ewsDBGjx4t1aTL5cJkMjFnzhy0Wi319fViCu3pieopkaTmf9XV1fKlMXz4cHFq6u/vp7W1lerqahwOh3hwOBwOIfKr90q1vArmotgenjNH+AL+ocYBP/Vwu9089dRTPPnkk3z88cf/FrF/27ZtvP3224M+S3/4wx+w2WyDNp4/RPygiU052MAAlOHGG29k3LhxIufyQ4WPjw9PP/00N954IxqNRraspzMyMjIoLCxk7969rFu3jvfee4/du3eTk5PD9u3bufXWW7nuuut44oknAOQC8pTU9vX1paurSy5UjUbDddddx6RJk/jTn/7E448/zqJFi1iwYAE6nQ6z2SzCl06nU2SyVaWmqii1UVWtUVhYmCgSK5khNbtT1VFra6ts+NTsw5PnqWATKsEpOtL69esFKAsDRsx33HEHGzZsIDo6mszMTKms+vv7sVqtjB49WvTKoqOjsVgs6PV6Jk6ciMPhEBvA3t5eGhoaGDFiBJ999hl6vV6gII2NjeLVeqpEkvKK1el0ojSiQLnNzc1UV1dz/PhxGhoaZFPb3NwsMBE/Pz/xaVC4QIfDQWJiotgF6vV6SktLRQzUZDIRFBQ0SAb9pxyPP/4499xzD6+88gojR46kpaVFMIZ6vf5rxSA9o729fZB805EjR3juuedYt24dw4cP/0GP9wdvRZcuXcqePXv47LPPuOaaa37wpAYwZcoULr30Ui6++GLuueceKftPd3h5eTF58mT+/ve/s23btkEbHZPJxIsvvjhos6taRFVVKX9NpW5rMBgwGAykp6fz5ptv8vLLL7Nx40aOHTvGoUOHBPPldrsJCwsbVEF52tt5Jjl1oVutVgGaarVamT8phkBDQwNpaWkCOPWEg6jwPHYY+KAaDAbuvPNObDYbF154IR999BG1tbVMmjSJ1157TShfTU1NtLe3k5WVxSeffEJ3dzcZGRlYLBZ0Oh2tra3s3LmTEydOEBMTg8lkEppZcnIyEydOlMpKJffGxsZBowD1un18fKiurqa7u5uOjg4RFFD6cjAAgO7s7KSzs5PGxka6urqkta+trZUq2NvbexDFytfXl7CwMDQaDTU1NTQ3N+Pl5SX+pGdLXHrppTz22GPcddddpKSkkJCQwF/+8heioqK+M15v9OjRHD9+HBj4kpo+fTpPPPHEDzpbU/GDVmz/Kfnj8847j927dzN79my0Wi0PPvggzz333BmnpnDeeeexbds2brjhBu677z5F2pXlAXwBRVBCiKpCUgKMKvkYjUZGjRrF//zP/7Bq1SpefPFFWltb8fb2xt/fn7q6Onx9fQUhr4CPnhtUp9OJj48PVqtVBuLqeW02G0ajkTlz5rB7927OO+88ysrKvjT7UIBhVQ15Ytv6+voYM2aMzBFzcnLQarVER0eTkJDA/v37ef7555k4cSJ2u50NGzYQGhqKXq8nPT2dhIQEIiIiRGJcq9USHh4u+nNq0N/d3U1BQYEQ/a1WK2lpaTQ0NEi7qtVqqaurIywsjO7uboxGIy0tLWJ443a7sVqtQsfq6uoS31HVfitOqapoYcB7tqamBr1eL62nVqvlwIEDREdHD2rb9Xo9NTU1rF69mttvv/3H/bD9yJGSkkJKSgozZsygqamJ9PR0aR9LSkq+02NkZGSQnZ1NeHg4N998MykpKeJd+0PHT9IJ/i9/+Qv/+7//y+LFiyksLOQXv/gFd955J3/9619P96ENiuXLl3PVVVcxZ84cjEYjzc3NhISEUF5ejr+/vyQVnU4nW0B/f396enrEhGTUqFHY7Xbi4uJkydDX14fRaCQkJER8GWNiYpg9ezaPP/44PT09MttRbahyfvck0ys5ccU8UJVNfX09iYmJVFVVDSJ4q5mb8gL1TLw2mw2n00llZSXFxcUkJSVRWFhIRkYG5eXlVFRUMGrUKPbv3y/k8qioKGGgqCQRHR0NQHh4OC6XC5fLRV1dHX5+fvJcqsqsra0lLS2N8PBwcnNzCQ8PF48Hl8tFQEDAIKBtb2+vLGA2b94sifnw4cMiU664oUqGXLlbtbS0DJJaV2T5/fv309zcjMlkwmq1yvzRZrMRHByM1Wo9qwC7yqAGYOjQoYSHh5OTk/Od7jthwgRef/11CgsLmT59OqtWrfqxDvOnmdh8fHzo6uqiqamJ2NhYli1bxptvvil9/5kSkydP5s477yQ9PZ2JEycO8pZsaWmhpaVFuJulpaWcc8452O12KioqKC8vx263s2/fPpYuXcquXbtYv349hw8fpr+/n3/+85+Eh4djs9mYNGkSmzdvpru7m/DwcBITE6mpqcFqtYpMkrKda2lpkVmbMvJ1u934+fkRExMjKrFNTU1YLBZZNHhWaqq99hRcVM7pFRUVDB8+nPDwcAoLC8nJyRGneTVwV0N6ZRDt7+8v3hJqy6tav76+PsLDw/H29hZJ8/7+fhG7rKysFKpVZ2en8FcV5EVh37y9vYmJiSE0NJT8/HyZw6kvGpXwOzs70Wg0aDQamampLwnPZU9dXR3t7e1SGavzqGA2fX19gmM70zqJHyKqq6tlk+3v709NTY0Y/nxdTJ48mcWLFzN8+HB27dr1rdTL/0v8JBMbDAza1Yl86aWXyM3NxWw2M3PmTN59991B1nanM+644w4uv/xyARsqfTo1ZFbGuz09PWzdupWcnBxKSkoETBocHMzhw4exWCxMnDhR2krVYvX395OVlSVVSl1dHZWVlURHRxMVFUVeXp6oa6jtoKr4lE0fIAsANTSPjo7G4XAIXELxNhV/VfkYqASh1WqZNGmSvG41SwHYt28fRqNR/ALMZjNpaWnCD21vb8ff35/y8nIiIyOx2+1ERUXR399PYGAgFRUVYhOoTJXz8vKkWlVsAaXMoVRtVZWqSPohISFiy6c8IZRcTnt7uzjD9/f3y/ujHkcR4NX5UrdRMBitVisqwTBQEXsuDq6++mqxVDwbory8nLS0NIqKipg0aRKvv/4699133zfeJzIyko0bNzJlypTvtGz4v8RZIUOwZs0aRowYQX9/P1u3biUhIYHdu3ef7sOSGDJkiFwAarit1+tFD6yqqoqGhgb+8Y9/8Mknn4hPZ0NDgxiB3HrrrezevVtat7/+9a8cP35c6EyKIgQDQ1q73T4IvGq1WsW5Ss3iPF3QY2NjBeWv0WhEKRYQXqSqEJXKh7r4leKFt7c3DQ0NOJ1ONBoNaWlpjBgxQhKK0+kkMTGRmJgYmQP29fUJd1W5X02aNElwexaLhcTERFJTU/H19cVisRAUFERwcDA1NTVoNBrq6uqw2WwEBARQV1cnFaLigqrqacKECaKEq9FoiImJ4e2336axsVFeq0pYatGikreXl5fg8NTsUi0TlDOXkktSXxhqYwpf9lv9qUdJSQnx8fE0NTXR1NTEM888850AyZdddtmPntTgLElsU6ZM4fjx46xYsQIY4LBNmTLljPww1dXVDQLOqi3d+vXrsVgsxMXFCZE4LS2N5ORkZs2axSuvvEJwcDBPPfUUTz31FIsXLxajlZCQEJxOJ1FRUVx44YX4+PgMcnm3Wq2SRFSrpuZjMACx8Pb2Zty4cSKN1NjYSHh4uFRnqsUKCQmRrZ+fnx9tbW1S7VRXV9Pf3y+2fTU1NZSXlwtaf+bMmeINGhUVJQ5Xqs318/MTPw1FXTrvvPMYPXq0cEHPP/98iouLKSgooKuri7KyMkwmE263mxMnTsjGV8FRFN5q5MiR7Nu3j87OTvr6+hg+fDhms1mO3e0e8Cft6OgQwUrVkqpqTZ0LdcyqBe3t7aW9vZ3m5uYviU4qQdCvog/+VMPtdvPhhx+SmpoqX6oLFiw4o663n2wremrodDoeeughGhoaRHmjvLx80FzrTAmLxSJuVh0dHbz77rvAQIKOi4vj0KFDpKSkkJyczMyZMzGZTJSVlQFwzTXXiLKHt7c31dXVREZGEhISgtVqpampie7ubqKiomhvbxcpmNraWmkZVbWh0WhEB62pqUkI7HFxcRw4cOBLIFflchUaGkpnZycWiwWTyUR/fz9NTU1ER0fT29tLW1sbfX19BAUFYbfbMZvNorirxEeV2q5Go8FqtTJkyBBycnIICQkRHrGPjw8mk0kAxJWVlRw6dIjGxkZp4V0ulyjl9vf3c84551BeXi5taVBQEFFRURw6dEiMiNLT02lvb6ejo0O4rp5E646ODgwGg1TZqhpW0Brl8aBAyZ5/enp6MJlMtLS0CHcyKSlJyPlnQzzzzDMUFxezdu1aVqxYQVxcHKtXrz6jZMbOmsSm4qqrriInJ4d58+adkQq+gYGB1NbWilxQbm4uABdddJHMg0JDQzlw4AD5+fk0NDSwf/9+li9fzpgxY9BoNFxzzTUAUnFZrVYMBgNTp07F5XKRm5tLfX29uPioFqy5uVmG42r7p0QVe3t7CQoK4pxzzqG+vl7c5+vr6wkMDBTlhr6+PlpaWmT+pzwC1JZSHZNydo+PjycnJ4eysjIyMjLk/5Wix7nnnktBQQF79+7F29ubHTt2AAOS4QoAarFYsNlsmM1mEcKEgaG8zWYjKSkJp9NJSkoKW7duJTc3lzFjxnD11Vfz/PPPS6IDuOCCC3A6nXR1dbFnzx46OztFTLKuro6SkhJhFISEhDB+/HipcJUkuLqAVZIDRCXFYDDQ1tYm50xZ/p0tia2xsZH777+fI0eOyJfBhx9+SE5ODmPGjDnNR/dFnHWJLSEhgfr6ev7nf/7ndB/KV4bL5SI2NlZwYNOnTxe9uc7OToqKikSKCBBMWGNjI8nJyXR1dbFixQpOnDhBc3Mzfn5+Aizdu3ev8CGV5lhdXR07duzA398fo9EoMAZPPwFlvKKkdiIiIgShbzKZRLrIEwxstVppbW0Vf4FTrRYV9AEgMTGR48ePM2nSJIKCgkSyu6qqCoPBIC3f/PnzGTlyJHfccQe1tbVS1V555ZU4nU7xUnA6nVxzzTW88847nHPOOWRnZ4sMem1tLZmZmRw8eFD+reKSSy6htbWV1NRUKioqSEpK4sSJE/T393Ps2DGhdqnzrfwaVGWpFiUwUC17Ap7VjFG1n97e3qIAohR/f+phs9mYO3cuy5cvZ9iwYWLcc/XVV4uz15kSZ11iczgcZ8xG9NS4+uqrJekoCIC6IFpaWti/f/+gpGY0GgkKCuKCCy7gwQcfRK/Xk5ubi91ux263Ex0dTUBAAGVlZdJaBgcHi1GJ1WrFaDQSHBxMcHAw/v7+hIeH43A4KCoqEvyaRqOho6NDVG5VG9XY2IhGo8HPz090xuLi4uSi94QxePJSFSQkOjqa6upqafWUqkZKSgr5+fkEBQWRk5MjANr3338fLy8vSYpNTU1CmVKQEEWmf+655+jp6aG2tpbk5GTMZjNvvfUWl1xyCaGhoRw9enRQUoOBEcD06dNpamqisrJSlibHjx+ntbWVcePG0dzcTEVFBZGRkeL8pfTn4AterMLTqYSutqVqoQJfLIqUJPoll1zC5s2bf5wP148cTqeTefPmkZmZyQMPPAAMCDk0NDSwa9cuhg4dSkxMzGnjbp8aZ11iKy0tFTT5mRaeF4cnov2TTz7B4XAwbNgwgoKCKC4uxmw2s2TJEjIzM6Va0Ov1WCwWUbPt7OzEbrfT3d3NmDFjJFkpOezx48cTHh6O1WrFYrHQ09PDiRMnhPuoEP6qgurt7ZUKpaenR1pjpfoRExODl5cXTqeTuLg4uru7ZeNZW1uL0+mkurpaBu9+fn6yKDEYDPT29qLT6aiqqqKqqgqXy0VmZiY7duwgODiYhoYG7HY7mZmZ5Ofnc/nll7N+/Xp27dqF2Wxm1qxZ7Nmzh8rKSnx8fLj66qtFvXnlypVkZGQQERGBr68vN998M0VFRQwZMgSj0cj27ds5efIkTz31FLfccgvd3d0UFRUJYLeoqAiNRiMc14iICAwGgyQuhddT76FKVp76dwqcq6hcngq736T6cqZGe3s7mzZtYsqUKdxwww0MGTKENWvWDFJgueeee4iNjeWWW24hODj4/yxy8UPFWZfY9u7dy4QJE073YXxt+Pj4MGzYMIqLi/H29sZisdDe3s4ll1wimv9RUVGEhoYyd+5cPv/8c3x8fBg1ahTl5eXs3LlThuadnZ1MnjyZ/Px8dDodOp1OoBzKmb2+vp7IyEjCwsJwuVw0NDTQ0NAglV1nZ6eg/ZVstqpGAgMDqa6uJiIiQob9FRUVaDQaGbpXV1fLferq6ujp6SEpKUku6K6uLqFC2e12goODJTGlp6dz4sQJMjIyyMvLkznXLbfcwq5du9i5cyd3330369evF4DvvHnzaGtrY9OmTWi1WtasWcNFF11Ebm4uK1asoKioSBQ8qqurqauro7GxkSuvvJJt27bR0NDAAw88QGpqqoCXldFzQ0MDQ4cOpaenh5ycHBISEkQHztOIxtvbm/j4eBEYUDNJtd1V50/ptXmqfvxUYsuWLVxzzTWEhoZy/fXX8/vf/54HHnhAWurbbruNkpISYX+kpaWxbNmy03zUX8RZl9gUXeZMDKUiq8xS1q9fL4PlxsZGzj//fDo6Ogbp8WdmZrJp0yaKi4tlM1pRUSFqHQsWLKCnp4eoqCjGjRvHkCFDsNlsVFVVkZWVRVtbG2VlZcTHx8tW0s/Pj6qqKlHOVYqydrudsLAwoTzp9Xri4+OpqakRqe6QkBBsNhsFBQVysXteyIBwXVVlGhkZSUREBOeeey4Wi4VXX32ViRMnkpKSwpgxY7Db7ezatUu4qitXriQ2Npb+/n4ef/xx5syZQ3JyMmvXrsXhcDBp0iT6+vrYuHEjl112mVgGrl69mrS0NA4cOEBVVRUBAQE4HA58fHwIDg4WqElxcbHQnLy8vKirqyM9PZ0jR46g1+tFAVf5n8bHx+N2u4WVocDE/f39xMTE0NbWxowZMzhw4AB6vZ7LLruMrVu3MmzYME6ePMnMmTPZuHHjGSc8qVy/Tt1m1tTU8Mtf/pInnniC6667btCSRIXD4aCuro758+fT1dXFvn37vtVg5T8ZZ11iU4oLZ3LodDrB3BkMBm655RZGjx5NWloaFouFPXv24OPjQ2BgILGxsSQkJJCTkyNVWkBAgBDZn3nmGaEVabVaUlJScLvdsl3s6uoSJ6bhw4dTVlZGUFCQ6K11d3eLjZ+iAjU3NxMfH091dTVWq1WwcAaDQWAVCtc1d+5ccnNzZWalkoyPj4+AW5UG/t///nfRZRs+fDg2m03MXWDA9erKK69Eo9GwZ88e2traSEtLIysri6ysLGCg4j158iTHjh2jt7eXbdu2yXm1Wq2UlJRI0qyqqhIBztbWVo4dOybeBnq9XrahcXFxFBYWkpyczMmTJ0lJSSEgIAC73S5OX8pu0svLiwMHDkirqaq048ePYzQasVqtbNiwAZfLJRCbN95444ya+3Z2dvKHP/yBZ555hrVr17J48WKCg4PRarVUVVVx8803M2nSJPFr+CoYR3BwMEOHDsXHx4cPPvjgP/0SvjV++quaU2Ls2LFs2bLljJxpGAwGCgsLBw1f9+zZw+9+9zvGjRsHIJCJyMhIhg0bRmdnJ4mJiTQ1NZGdnc3KlSt54IEHmD9/PomJiUyfPl0UYUeNGkVQUBDV1dWcc845jBs3jqioKJqbm+nu7ubo0aM4HA4iIiJkk6mwWhUVFURHR2O320WVIigoSMxflOdnf38/KSkp+Pj4EBAQwMcff4zNZhP8mpI5crvdDBkyhGnTpjF8+HBaWlo4fPgwS5YsEZPikSNHkpaWJvxel8vFJ598IvdLTk7mxIkTcv70ej05OTnk5OTQ29tLQECAGETHxcUxdepU5syZQ15eHoWFhfT09Mjiwmg0kpaWJs+trP/6+/upra0VOhVAZmYmkZGRAKJgMXnyZA4dOsSOHTsEiOpyuSgsLMTlclFfXy/vnYLlKJ+G+fPny+zydMd7773HRRddRE1NDQC33norMTExxMfHU15eLu33v/71r298HC8vL66++mo+/fTT/8Rhf+846xLbhAkTCA8P5+WXXz7dhzIo9u7di6+vrxi8/vGPfxQ6VHNzM7W1tfT29pKWlsaCBQtEt1+JPZ48eZKmpiaZHzU3N3P++efT2dnJQw89xI033kh8fDy+vr44HA7a29vF/m7o0KGy/XS73RQWFkqFpyhC8fHxstW02+2YTCZsNpvo+ff19YlenEL9BwUFodFoRAdNgVYNBgPDhw8nNDRUWu2GhgaZ55lMJmpra6mpqeHw4cMYDAbS0tIYPnw4paWl7Nq1ixMnTgxSBx41ahTx8fGDzJBjY2MFWhEWFoafnx+ff/65LAGCg4OZPXs2qampBAQEiNuUav2VOm5fX5+4zA8dOpSioiKuvfZajEajSE3t3bsXp9NJbGysLBRUuw1faNP19vZy5MgRXC4XDocDvV5PXl4eERERpKWl/WgyPd8lamtrWbhwISNGjJCO4eWXXxZFl9raWmbPni2yTF8XdXV1HDx4kN27d7N48eL/1OF/rzjrWlEY8CvNyMhg7ty58s17uiM7O3uQfI2yyVOtoSKbx8TEsH37dg4cOEBubi4tLS3U1dUJKf76669n3bp1IgKZn59Pbm6uyORER0dTUFBAQ0MDXl5e8rdnOBwOqaxU++lyucQxXhHTY2JisNlstLe3i/aZaoU7OzspLS0dRLkKCQmhrq4Ok8lEaGio4JwsFgvvvfces2bNIjU1lX379gkFad++fezduxc/Pz9pS1taWmhvbwcGBvXDhg0DBkCwSh4IBoxcRo0aJVLdSo3Y19cXjUaDr68vu3btQqPRSFWpXrOSQs/LywMGLtaYmBjy8/NpbW3lrbfeEqHPtrY2DAYDQ4cOpaKiQtp7Jeip4DtK/cTPz4/AwEDa2trIyMgQzTlfX1/8/f3/Ex+3r4zo6GgeeOABVq1aJarEBw8eFMe3SZMmUVRUxJIlS3jnnXf4xS9+Mej+R44c4f333+fll1/mnnvu4a9//SsvvPDCf/x1fJfw+qaW7ZxzznEfOXLkP3g4P1zccsst+Pj4nDEabYphMHv2bCIjI8nMzMTf35+pU6eye/dugoODOffccykuLuaTTz4R0GdTU5MkHWVE0t7eTkREBBMmTKCmpoa0tDSMRiNFRUUkJyfjcrnYtm0bFRUVArNQpPfe3l78/f0HbfoUbCQ2Nhan00ltba2YLOt0OiGUe5o3h4aGEh0dzbFjx/Dz88Nut9PZ2UlTUxO+vr6iWwYDSVy1Z8eOHRNneyWqqZRHLBYLLpeL5ORkAdyGh4cLF1RBKvLy8nC73YwfP14EJaurq4XWpOSb7Ha7zFxjY2NFj82zylKQG9WuKuhKT08POp2O2NhYxo0bR0VFxSDlDk8eqUrsnoktMjISLy8venp6mDZtmnBqgdMOHs/KyuLxxx/nk08+4aqrrhJl44CAAD788ENqamo4cOCAdD2dnZ1YrVYuu+wyiouL+cc//sHYsWNJSUmR9/t0hZeX11G32/0lw4SzsmID+NOf/sS5557LjBkzzgjQ4KpVq3A6nRiNRplv5eTkcOjQIXx8fBgzZgwbN26ktraW+Ph4CgsLOf/889m2bZsIJiohxLS0NE6ePCkbrfDwcI4cOUJ3dzevvfYa4eHhtLe3y8bx6NGjQqFSm0ubzSYCispOrrq6WhJIYGAger2eiooKYEAmSafTUV9fj8FgYOTIkezdu5df/vKXHD16lOLiYsLDwxk6dChGo5FNmzYBiKdnfn4+UVFRxMfH097ejkajET/OlJQUjhw5gtFoFAmiwMBA2bIpHJhKbMnJyTQ0NHD06FFggG2Snp6O2+0mKCgIh8NBTk6O6LYBUsV5aqb19/czc+ZMsrKyBJM3ZswYKisrB7EOsrOzxd/Bk33Q19dHQEAAvb29jBkzhsbGRmEZNDU14ePjg9lsFvK9Ut093bF161ba2tooLS0lMTERGHifFLNlx44dQl9btGgR+/fvF3PqsrIyWlpaePbZZ8U0+kyMs27GpsJsNrN69WoWL17MvHnz+Pzzz0/r8dTX17N7926am5tJTEzEYDAwbtw40f/67LPPBC2/d+9empubKSgokBnQVVddRXh4OD09PRw9elTAksqMJS4uDo1GQ1JSEjqdjmuuuYa8vDzsdjtarRan04lOp6Orq4uuri5pzYxGo2w6Fdk8PDyc1tZWurq6iIqKIigoiP7+fhwOB6NHj2bevHlMmTKFX/3qV2zatImysjICAwPFdergwYOEh4czf/58LrroIoFIKDu7sLAwwdB5eXnx2WefSZurUP4K/PpVPgsajYbU1FSCgoKEuzlkyBAyMzNmAxfUAAAgAElEQVQHabAp7witVsvw4cPx9/fHYDAwZ84cXC4XPj4+7Ny5U5JdS0sLn332GTabTRR4k5OTpfJUGDXFtVVVnjLTKSoqor29XeZ4VqtVuK4KSH0mhE6no66uDrPZPOj3XV1d/OIXvyAmJob58+cTEhLCjh07xKLw/PPPJykpiXnz5lFSUiJOdGdinLUVGwxoP9XW1vLkk08ye/Zstm7dKtvH/1S43W6effZZcnNzmTdvHiEhISxevBin0yla/8ePH2fOnDkifFhcXCwyPnv37mXBggXk5+cTExPDhRdeyJo1a7jjjjvYtWuXVC1ut1vgCpdffjkwUKU0Nzfj6+uL3W6no6NDNMW8vLyora2VNlBVF3a7nfb2dhGtVFtTjUZDfHw8JpNJnJySkpJYtmwZGzdulMfZvXs38fHx/OY3v6GlpYWGhgZ6eno455xzcLlcZGdnY7FYBNSrjh0QD9OKigq8vb0xGo2EhoYOMpPp6OjA4XBgtVqJjo4WBY1JkyZx/PhxnE4nLpeLlJQUeSyz2UxAQABms5nCwkI+/PBDAIGZKH8JVaHZbDbGjh1LfX097e3tslFVbbRauISEhIia85EjR2R77OvrS3t7u8gdORwOhgwZQlVV1RmBzH/44YfJz8/nlVde4dZbb5XfFxQUoNVq2blzJ3PmzJHfK2msPXv2cODAAUaOHHk6Dvt7xVk7Yzs1Hn30UYqKili2bBmVlZVcfvnl0pb9mHHffffx0Ucf8cc//pG4uDhRulUMAS8vL0k4ymhYbRc7OzvZvXs3drud5uZmrFYrycnJNDc3S0uoOJsajYY777yT3t5ehg0bxvHjx/nggw/Q6XQCJlWVk6rgurq6pGLz8/MjKCiIxsZGdDqdWPB5VksjR44kMjKSyMhIxo8fj8PhICwsjOzsbDZs2IDFYiEtLY2+vj4SExP55z//KbM9RU9SCHzFM/VU41X/9pTiVnMuHx8fWltbaWlpQavVygA/ICAAb29vEhMTRdtOPX5nZycOhwOz2SwV2vTp0yksLGT06NHs2LFjkGikokKpVjUxMVGc3lXSTU9Pp6qqSvitgGyOTSYT9fX1guFTx6F8VA0GA319fXz88cc/+ufu2yI3N5cZM2bw2WefyXJGRUtLi8jET506lSVLlnDJJZeIK9iZFF83YztrW9FT49prr2Xjxo3cfvvtLF26lKVLl/5gj2232zl48CC5ubmyZYOBD8i6devYuXMnWq1WHMWbmppoaWmhuLhY1DgU+Far1VJRUUFHRwfNzc1MnDiR6dOnU1dXR19fH1VVVTQ1NaHRaLDb7YP0yNRs5/DhwwwbNky0zpSvgMFgICAgAJvNBkBQUJC0wsrxXLWE9fX1sglVm0ZVPZWWlhIcHIzD4eDmm2/mvffe48ILL6ShoYFLLrmEw4cPc+DAgUFqst3d3YOke05tN1UVpGZZyquzp6eHsrIyKisrsdvt+Pr6EhAQIJtG5Y166NAhTp48SX19PTU1NVRWVtLY2ChJRfmwpqSkYDQaGTJkCGFhYZK4ldqK2la73W4RzlSLlAsvvJDi4mLGjBmDVqulu7ubiRMnMnz4cJF48hSZVK9TSUDV1tZ+yeD6dMWIESMICAj4ShxaaGgoV155JW1tbTz99NP88pe/PCOT2jfFf01iM5vN3HjjjRw5coSMjAy2b98uFKXvE729vaxevZolS5Zw11138dZbb5GYmMh5553HkiVLmDNnDmlpaaSkpGCxWHC73eh0OmpqasjOzub48eM0Nzdz6NAhKisrOXLkCJ9//jknT56UaqW1tVUG5++88w4dHR1cfvnl+Pr6kpycLINtpRTi7e1NbGwsjz/+uMATDh8+jN1uFxiCqgKVOqxyNVfVnoJ6REdHy+ZTJUuj0cjSpUtJTk7m4MGDVFZW0trayiOPPMKcOXOoqanh/fffF7dwp9PJyZMnCQgIkGSmHK5UqISmwtPCD5CkZzAY0Ov1YheopIHUfQICAjAajWIqEhYWhtlsFizdlClTCAkJoauriwsuuIDHHnsMgLfeeou6ujqp0pSeXF1dnWxSFbe2r6+PUaNGkZeXx8iRI6mrqyMkJIS+vj6ysrIoLi6Wik8lcqW2olpVVameKcBxt9tNWVnZ18opPffcc7S0tJyxghLfFv81rSgMbAIvv/xydu7cCcC8efN47733BqmnflNkZ2dz++23o9PpuOqqq7BYLLz77rscPXqUzZs3M3fuXFpbW/noo48oLS3l008/JSAgQNb9am5mMpno7u4WoKzZbGbs2LEUFBRgtVrx8fER+EZ+fj6JiYmivVZcXCyaahdffDHvvfcePj4+6HQ6JkyYQGFhIf/v//0/tmzZQn19PUePHiU+Pp66ujo0Go24OzmdThlyK61+NWtTsAWlBDtv3jyGDx/Ohx9+iMVi4aGHHuKBBx4gLi6O7Oxsuru7qaiokBbu1FAJ7uuG5wpbpiATamGgoBT9/f2S3NS8DRj0fGpjqfipSgxTwUc6Ozu57rrryMrKoqqqahBgWWHwiouLMRqNtLe3Ex8fT3d3N263W5YQCvai/qjFheexAXIOFQVNXWOq5T7diyx1LHPnzmXu3Lncdtttp/tw/u34ulb0vyqxASxZsoS3334bGHCISkxMJCcn51uFAOvr60lLS+PBBx/khhtu+NZkePDgQa666ioee+wxfv/737NgwQJpnxobG7n44oulzdNqtXz++ee0tLTg4+NDfHw8ISEhOBwOEhIScLvdPPnkk1RVVaHVahk7diwHDhzA19dXnOVHjRrFrFmzePzxx5k2bRr5+fmMGDGCbdu2ER4ejtFopLi4WJRBgoKCaGpqIi4uDovFQmBgIEajkZqaGry8vPD396erq4s1a9Zwzz33CCYLBloVBdwtLS0dZHqiQumnwYDk+bdRbzwTlqfDvOfzGgwGsdlTyHjVugLiA+pyuQa1fAkJCTQ1NTFy5Ejq6+vJzMxk7969AOJ65ePjQ11dHbGxsbS2ttLT04PZbBbmh7IoVK2qwgSqaldh29RMTr0Wo9E4qOVWcJFjx4594/n4sePRRx/lpZdeYufOnd9qm3cmx8+J7f+PXbt2MW3aNPn36NGjiY6O5qOPPvrG++3bt4+77rqLgwcPfqfn6e3tZfTo0Tz44IODaCdpaWlMmDABg8EgihsZGRnYbDZiYmIIDAwUH1AFUt2wYQOLFi1i+/btInPtmQiUiq5Op2P69On09/czatQojh8/Tk5ODuPGjePDDz+UQbxqtxReTFUifn5+gu9SC4WJEyeybds2XC4XQ4YMIT4+XnBZFRUVMpT3ZDfMmTOHrKwsbr/9dqxWK2+88cb3eYtk3qXmX8q/1NPpyxNkq1gUyvlKzRFbWlq49NJL2bJlixjUeHt7ixKHcsfq7u5m6NChDB06lNdff52IiAhCQkIoKCgAvmiPk5KSpCVWiVfJO6mKVx2zmtMpaSRVZcNAW346q7aamhrGjBnDoUOHBMf2U43/+uWBiqlTp9LZ2cm1114LwIwZM/j444957rnnvvW+isrzXUKr1bJ27VqWLFmCr68vl19+OdHR0UIrstlslJeXM2nSJMrKyjhx4oT4a5pMJkJCQmhpaZEEt2HDBpxO55dUJVwuF729vSQmJrJmzRpxdcrLyxMn9y1btqDT6fj9739PWVkZMTExor6r1DiUoGR3d7dAEvR6PR988IG0gyUlJezZswen0ymzpFOTGgxQnSZOnMjq1au/V1JTj+O5QNDpdLJcUbdRVZmqjFSFp+Z4drudUaNGodfrRRVEqf663W4OHjwodKnExETefvtt3G43O3fuRKPRUFtbS1VVFVFRUYMoUCUlJVgsFtra2iRxtra2UlZWRmNjo4wEFGhVtcVKzNPTdOZ0hdvt5qabbuLaa6/l0UcfFQD22Rb/dYkNBi5YtRV96qmn+Nvf/sZDDz3E008//bX32bt3L5deeun3ep4ZM2bw4YcfMmfOHEaOHEltbS1Tp04lNTWVc889l4CAAE6ePElhYSHjxo2jq6uL4uJiPvzwQ2w2G6tXr2bDhg3AwAWttm7qYlYJzuVy0d7ezvvvvy+2e7NnzxbStzIpfu211zCZTLS2tmK1WnG5XJjNZtxutyQ3ldScTqckRoV70+v1zJs3T/wK1LF4xpgxY6ioqBCvhu8TqntQ+K++vj6BXyh/BtUWqqWHmsl5shN6enrIzc0V6Izaniqmg1IKvvDCC/nDH/7Axo0baW9vl60xINWyMo329fUlLCyM0NBQ/Pz8BJyrvoi6urpobGwc1HZ6ztaUq/3pdIVva2tj6dKlWK1WCgoKeOGFF86Ied+PET9qYjsVNX4mxYwZM4S/+cgjj7B//37Wrl3Lq6+++pW3b2ho+M6ziKamJq6++mq8vLxYtGgRlZWVhIWFMXv2bCZOnMj48eOJjY1l6dKltLe3c8EFF3DgwAFiY2NlUbB8+XIBkPb29gqWSg3M1UxHVW5Wq5VFixZhMpn46KOP5OJXW82mpiYsFosof6j2SrV4qnVSz+ep5+/pLLV9+3ZsNttXig/GxMSQnZ096Hffx/7QE+Gv5o/+/v6S0NTxqiQBX4B61RxO+acqrKBKdIGBgbS3txMdHY3L5eLWW28lKSmJRx55hDfffJPm5mZuuukmfHx8SEpKIigoCKPRiMlkQq/Xo9frZa7o7e0tSidGoxGDwUBkZCSdnZ1UVVVJMvb8AlKJ+Ku+DE6NvLw87r//furq6r7zufu6cDqdvPPOO/z5z38mNTUVk8nE5s2b+eCDD9BoNEKAP9viR2UerFy5EpfLxahRo5g3bx4mk+nHfLrvHWlpaXz22Wcy73r33XeZNm0aCxcu/JIKg3IQ/6ooLCzkwIED9PT0cNlllxERESH/19XVRU5ODr///e/x9fXl3nvvpbW1lYULF/L8889jsVi4+OKLOXnyJDt27ODTTz8V5L9er5dZl6pU4AsohLr43W43gYGB3HDDDVx11VWcf/753HHHHVLxqNmOt7c3ERERVFZWCmC3q6tLkoJaACjPAk9sl8vloqSkBB8fH5lVnXqBWiyWL52bhIQESkpKvvW98EwEnttTRRxXoeAe6lhTUlJobm6mvb1doDUKp6du39XVJRvpYcOG8fDDD7Nz507effddDh8+jL+/PzNmzOCFF16gr68Pm80miwo17Ffc2YaGBnH8Uv+nNqRxcXEUFRWJK31fX5+of3hucr8qsTU1NfHSSy+xadMmmeOWlpby5ptvfuu5U+F0Olm7di1eXl6cc845HDp0iL/97W8UFRVx55138vbbb3PBBRcAEB4ezvjx439y+LTvGj/q8qCtrY3s7GwWL16M1Wply5YtXHTRRf/24/3YodQsPv30U7HEU3H48GEWL15MQUHBIOLvSy+9xB//+EdmzpzJ3//+d9auXUtqaiqxsbEkJSXR1tbG+PHjsVgsgy7YESNGEBERIeDXpKQkPv/8c3x9fYUWpC5UpUir5kyeLZvCnKkLPSAgQND3SnXCM2lceeWVZGVl0dLSQl9fH9HR0dTW1spFrKowT0MSJcqoeJrFxcVfOVv7d0IlB/W83+Ym7glNCQsLo6OjQ76EFDxD+amqUK/D39+fUaNGccstt/Duu++ydetWIiIi6O7uJigoSCwGPSstpf7h5eVFc3MzWq1WpLA8Z4Iq7HY7DQ0N8vukpKRB50n97AnkBnj66af585//zMqVKwX+M3nyZNFy+7aoqqpi5syZZGRk8M477wBwww03sGTJEqZNm/al98rpdKLRaGSr/lON074VXbBgARs3buTjjz8exEM7k0KpqK5fv57f/va3gz4MbrebBQsWkJqayp///Gdg4EJauHAhixYtYsaMGSQlJdHc3Pwl7fcJEybgcrkGrfiVRI762WAwEBUVJVWSarfUrEklJzXIV3r+AQEBQo9S91XHqy5KT2UMk8mEw+GQTeg999xDVlYWBQUFkhgUbkxtTpXgpWrr1FLjq+ZFgYGBgyqmHzo8z4NKxkrRQyVJpbWmvFUVDevvf/87y5YtIzIykuLiYoGMqC8Nz0UEfPHFoFgeymdVVXOnJgvVcirV3pKSEtLT0790G7fbLS27w+Hg17/+NQcPHuRf//oX5513ntz2wQcfZNWqVTQ0NBAeHv6N52XKlClccskljBkzhgULFuB0Onn//fe/91z4pxanfSu6fPlyFi5cyOjRo/9TT/m9Q6fTcfjwYZ566ikWLFjApk2bBtmrPf3007z++uskJiZy/vnnExYWJnO0K6+8EpfL9ZWGFl5eXlJFKEK1Smre3t50dHRgtVqpqKggOztbnKACAgIEkqEYA2oo7ck9VJWgp0OSei6NRkNgYKBUFSrpqCT4yCOPkJ2dTUJCgvzO7XYTEBDAvffeK4muu7tbKknPhKZ+Tk1NBQbmbD9meM5t1bEq7TfVeiu8GQyoxhoMBrq7u7npppsYM2YM+fn5GI1GFi1aJPO5Uwf+ao6mSOwdHR34+fkJv1glsa9iUOj1ekl+iu+q1WoJCAjAYDDg5+cHDCS1BQsWEBgYSGlp6aCkBgO6bfPmzePZZ5/91vOiWCOHDx+mu7ub2NhYpk+f/rW3X7t2LYGBgcLEONvivw7H9l3Cbrfz7LPP8sorr2A2m7ntttuYOXMmgYGBuN1uNm7cSHBwMGPHjiU4OBgYmCVdf/31rFy58kuPpwjj5eXleHt7k5ycLFxMu91OSEiIDKIrKioIDAwUHwAlya22lp5zMFVduN1ugoODaWtrkwtaq9USGBiIl5cXAQEBPPTQQ0RGRrJhwwY2bdokdCrV8sJglQ1PJL1KJGq719HRIdpwAOnp6dTW1tLc3AwMiHy+9tprP0jlFhwcLG713xTqPHj+OzExkYaGBqZNm4bD4WD//v0kJydTWVlJb2+vWBAqBVyViBSLQUkr9ff3YzabJSGp+aOabyq+rVoOqPNSWlrK0KFDCQ0NlbGCWgK99NJLzJo1i6lTp7Ju3bqvBXzv37+fW2+9lX379vHWW2/x61//+ivB5J9//jkXXXQRn376KU1NTaSkpHxjlaek1o8ePXrG8Ff/nfi6iu2sli36dyMgIIB77rmHu+66izfeeINnn32W3/3ud8yfP1807a+44gomT54MDBihVFVVsXPnToqLi+np6eGKK65g4cKFglSHgWWFumgSEhJEmUMh2HU6nfgTeC4KDAaDGCyr5BIUFITNZpN2SSUqBSlwu92MHj2aWbNmsXbtWtatW0dgYCAjRowQ/JcnDkyh5j3NfT2t9VR4e3tLG6uiurqakJAQSWzPPPMMv/jFL/jnP//5f34v1Lzw28Izqb3xxhvceOONTJgwgdraWkpLS8Uztb6+HpPJRHl5uRhH+/n5SbuvoCNqnmY0GsUfwnMG6YknVOfPM7m53W7xdVUil0p/zmq1Mn78eH71q1+xevXqb3xdFRUV5ObmMm7cOAoKCjCZTF/ZXo4bN45Vq1Yxbdo0tm/f/o1JLTs7m5MnT5KZmcn48eO/0/n9qcXPFdt3jPLyct577z1gwLFoxYoVBAYG8uqrr9LX18fKlSuJioqSNuT222/n+eef55prrmH8+PHyba2qLp1OJxLZCpirgJyqglCJRw3w1RzJMwGdqugKA9VKaGioYLZ8fHyIiYkhLCyM48ePy/Dds8pRLZXnksJut0sbrBJwV1cXzc3NUuGMGjWK/Px8OU+//vWvefXVV0lKSsJqtX7nxPRDxMKFCyksLCQvL4/58+djNBrZsmULt912Gzt27KClpYXCwkKRNFJqJQ6HQ+aISoUkMDBQ/EU9FzDqy0WxIVRL6rlQUhzRvr4+8vLyGDVqlIwDent7+eyzz/j8888ZO3bst74mm83GK6+8QlxcHEePHsXb21tczr4q1q1bx5YtW0Rz7qsiPz9fRgfr1q3jt7/97Teat5zJcdqXB2dbdHd3c/311/PWW28xadIkZs2aRV9fn7j/TJkyheXLl3PFFVfI7EQJEcbGxgri28fHB39/f5qbm8X1SflRqj8qYZnNZnEcV+oTiohdX18vx6bI3Z5bUfhiBueZ0FRi9KwSTx3Oe1ZxquoJDQ2lv79fmAEKDhMWFsbQoUM5cODAD45h1Ol0xMTEUF5ePui1Pvfcc9x77700Nzczbtw4nE4n7e3tmEwmmpqapLJS893Nmzfjdg/IiCvMnsK/KbcrlaxUVa1YBZ6+B/AFpUstGbRarSgdNzc3U1ZWJlAQpXT8r3/9S97v7xObN2/mwQcf5IknnuCjjz5ixYoV0h6raGpqIikpSXjHXxeqlVZht9tPq9HMvxunfXlwtoWvry+vvvoqLS0tXHvttXzwwQesXLlSEPfl5eUkJycDCGo/ODgYo9FIXV0doaGhmM1mGhoaBIip2p7o6GgCAwPR6XSDgLYOh0PkcWAguVqtVpqamuR3qlpT91EXpUpqnjANTyMS1WapC/bU+Zq6j2qrkpKSSE9Px9vbm5kzZ6LT6fDz82Py5MmiJnvqhavVatHr9V+6GL8rGr+vr4/y8nIiIiIEROtyubj77rulDY6NjSUgIAA/Pz9qamqkqlLb65aWFnl+JeOt1WoJDg4mLi5OMIPq+dxuNyaTCaPRSHBwMEOGDJEZm0pmqv1UkkktLS3ExcURGRkpApiLFy/GbDbLfb6poPi6mDNnDsXFxfzlL3/hL3/5C6NHj+bUwsNkMtHR0fGtj6/GKKNGjQIGxi9fhUP8qcbPM7b/YxiNRn71q1+J/v8FF1zAtm3bqK6uFmVSZTQSGhpKSUkJGo2GQ4cOfUkmWrWkISEhREVFkZeXJ22jGnSrWZDasioAbmRkpAhFKokkb29vDAYDcXFxwMBFr9rII0eODCLTr1ixAm9vb8477zzuvvtuysrKBi0s+vv7CQ0Npa2tjYCAAA4fPkx0dDTjxo0TbJmPjw+HDx8WeSQFUFWVD3zBFAAGzfW+LoKDg+nu7mbRokXceeedpKen849//IP169fT0NAgSdFgMHDFFVewd+9empqapLWMjIzE6XSyfPlyysvLWb58Odddd51UrZGRkWKsrHwgVPj7+4tyr7e3NyaTif7+fuLi4vDy8qK6uloeR+noqUWDYrUouaOXXnqJqKgoSktLJUl+31Dv80cffcT27ds5efIkN9xwA1u3biUsLIzu7m4eeeQRSb5fF/fddx/XXXcdXV1dHDx4UKAxTz/99FmzJf05sf2Acdlll5GcnMysWbMwmUw8+eST3H///Zw4cQKHw8Fvf/tbrFYr27ZtIzk5mfHjx/PJJ5+QkpKCXq+nsLBQZmieW0AlJKlwdk6nU36vvArcbjeTJ0/m6NGjQrjW6/W43W7OPfdcKioq2LZtG5GRkVLFwUBi9Pf35+GHHx7Ujn7VplQl0+DgYHQ6nZg8a7VaLr/8cjZt2kRrayvDhg2jtrYWvV4vIGKDwSBYsaCgINrb29Hr9bz00kusWrVKPErHjBkjYOjXX39d6EwbN26kpKSE0tJSAJ544gmKiorQ6/X09/cLlQwGsHTBwcEMHToUPz8/br/9dsrLy9m8eTPPPvusGB339/dLclTgZmCQJLoygFbVrDpHnk5bNptNjlNhGdUXi6+vL1FRUdTV1REeHk5VVRUulwu73f5vof6VIOm0adOYOnUqR44cIS4ujueff56nn36amJgYdu3a9bX337VrF48++ih33303ixYt4uDBg5LMR4wY8b2P50yNn2dsP3Dk5uaSmZlJZ2cn8fHxtLW14XA4pDpoa2tjwoQJwMA8JD8/n4suuojm5mb5lnU6nYKXqqmpoaamhszMTBISEqioqJDhtWrtjEYjv/nNbwT0qZQ1SktLxb/A29sbPz8/wsLCOHDgAGazmYSEBI4cOSILA3Uxe1ZQpwJW1WxGtcbNzc2kpKTQ399Pamoqn376qWwbTSaTOMWrBYSaCz722GOi86ZAww0NDfj5+TFy5Ejy8vJk/peRkYHZbGb//v2SDLZv387s2bPx9fXFarVy/vnnAwPyUj4+PgIUVsBib+8Bt/iSkhLa2tqIj48HkMSmFgqq/VRgaYXMj46Oxul0CgBXr9cTExODj48PpaWlREZGinqHcspasmSJ4B5ff/11Zs2aJcl3zpw5fPTRR9+LvWGz2aS9V8KmAL/85S956623+OMf/8hDDz30tY+5d+9eJk+eTFxcHJWVlbS3txMSEjJoAaQ29D+V+HnG9h+K9PR0nE4nN954I1VVVQLJUDCJ+Ph4Ojo6ZNO5cOFCUXv1VItVlU5ERASxsbFkZ2ezf//+QTSeiIgIlixZwvDhw8nOzmbMmDHo9Xruv/9+ampquOKKK3jmmWfo7e1l8uTJVFZWUlNTI+3g0aNHvwRK9ZzBqYSgflYXuVKMNRqNaLVa/Pz86O7upqCgAKPROMgQ2dNPU83XDAYDq1atkp/V8N5sNqPT6aisrMRgMDB//nz0ej333nsvPT09aLVazj33XCH9v/jii0yZMgV/f3+OHTvG3r17Zdus2nyXy8XDDz/M6NGjsVqt6PV6hgwZwsMPPyxQD0VoV5totQzwZIBUV1fT3Nwsih/x8fFotVqGDRvG2LFjqa2tJSEhQTasnZ2dbN++ndTUVMaPH09iYiLbtm1j1qxZXHjhhRw8eFD03r5reFbxzz33nLxvDz/8MFu3buXPf/7zNybKtrY2fve731FZWYmXl5eYyuTn5/O73/0O4N+Syz8T4+eK7UcMt9tNQkICGo2GkJAQ8vPzmTp1Kl5eAx6WqiLR6XSEh4eL8obahKpt47Bhw8jLyyMvL4/o6Gi5GNXfBoNBQKDl5eUyh1PwEs/k5e/vL1Qpz21nSEiI6PlbLJZB8zzPz4hK0qrVUv6Z6jUoqz81hFewCI1Gg8PhoKenh46ODhnYG41Grr/+eqKjo1mzZg1RUVEcOXIEl8vF0aNH8fX15cUXX2TatGn4+m/NmaIAACAASURBVPqybt06duzYwejRo7n33nuxWCyEhYVx9OhR1q5dS1dXl8A2NBoNw4YN47LLLiM8PJwXX3xR+KBdXV3U1dURHByMRqPBZrOJIEB8fLycf086m7e3NzExMVx88cX09/fz6quv8tvf/paenh7Gjx8vS6FDhw4xY8YMduzYAQxUwPX19QwbNozKykpx73rvvfeYMWPG9/pMtbW1kZiYSGtrK2PHjmXjxo1SfX5TVFRUMGzYMDIzM9m+fTsGg4ENGzbw6quvilQ+QGVl5Xd6vDMlfq7YTkN4eXkxffp0YmNj8fPzw9vbm/b2doKCgoiLi2Ps2LEEBQXR19fHkCFDRMBRtURq49bQ0EBUVBRpaWn09vbS2NiIxWIhOzub0tJSJk2aRElJiTi5K30wT3FGVXkpXbKAgABuuummQSDTyZMnExkZSXR0NBdccAEjR44kJCSEyZMni/ijYkQoGR4/Pz9iY2PRarXU1NQQGBhIeHi4VDYhISECEWloaKCzs1MqvdraWlpbW1m3bh379++nu7ubAwcOMGTIEBGCfP311zlx4gT3338/f/jDHzAYDDzxxBM0NDTw7rvvYjabiYqKYty4cVx88cVSQaoKuLS0lMcee4xt27bx5JNPClzmhRde4LnnnhM/BqPRSFdXF5GRkSLlpKpUldhSU1Opq6tjy5YtmEwmVq1axZ49e8jIyKCuro6KigqCg4O58cYb8fHxoaCgAH9/f5YtW0Z6erpQvlJTUxk6dChTp079Tp+jF198ES8vLzIzMzl27BgrVqzgmmuu4YorrmDOnDmDTHK+KrKysrjgggt48sknZSvc1NTEb37zG3bs2MHGjRuFQvdTSmrfFD9XbD9yTJkyRS6Uffv2kZCQQHp6OpdccgnvvPMOjY2NBAYGCn1HwTsU5EC5sHvOtQwGA/PmzWPr1q0cP34co9EoWnFdXV0yF/OsyE4lxCvAcGhoqLSD06dP55133mH16tVs3LiR/fv3S2vX2dkpbvKn+n4q93iXy0VRUZFILRmNRsxmM3V1dXh7e6PX62UI73YPmB+rDWZ/fz9JSUn85je/4eWXX0av17Nz506uv/56SkpKuPPOO3nssccE86fX6/H39+fgwYN0dHSwfft27r77boYMGcLChQt5+umnxb3eZrMxZ84c7rjjDm655RYMBoNUx0oavb6+Hr1eL4h9TxygOod+fn48+OCD9PX18eabbxIQEEBSUhKZmZn89a9/5eGHH6anp4fe3l58fX3lnIWFhaHT6diwYQMtLS0sXryYpqam72wBefz4cTIyMrjqqqvIysri4YcfZsWKFZSXl5OWlsb7778vgNtTY9++fSxYsIC33nqLzMxMhg4disFgoLS09LSKXv5Q8TNA9zTFlClT8Pb2pr6+noKCAsaPHy/mwd3d3eJkrjibyrrNMyGdCp71NDBRblcjRowYpP7hyfX0BNz6+fnJVlW1qJ46/WquZzQaiYuLY+7cuWRlZVFUVISXlxetra3CQPD87Pj6+orps7qwu7q65P9VMlUzK71eL2oi6nyUlZUxYsQIOjo60Ol0ZGVl0dfXx5IlS/jggw+49NJLaWtr45VXXiE9PZ1JkyYxe/Zs7rvvPoKDg9mwYQNZWVkEBgayaNEili1bJgsNtTkNDg6mv79fWAWTJk1iw4YNuFwuIiMjByl6eMpEKfiIRqOhq6uLRYsWERYWRlRUFC+88ALTp09n2LBhdHV1kZSUhMPhICIiApvNhtlsltmdomvV1tZ+L2/bQ4cOMX/+fG6++WaeeeYZYmJiyMjIIDs7m5UrV7Jo0SK5rdvtpqqqiqVLl5Kbm8vbb7/NRRddxK5du7jyyiupqKgYxJTwDM9l0U8hfm5FT1OMGDGCoKAgCgoKSE1NZebMmYwfP56oqChR3lAfpp6eHrmYVKjko2ZVKhGptkZtOxWMwJNqpRKiEmbs7+8X8KZKdp6PqVqa+vp6sQ+899572bt3L9XV1dTV1WG32yUxqgtAXexqJnjqQgIYBBL28vKiu7tbqrfu7m6hk0VHR/PSSy8xefJktm7dKviwiRMn4nA4uPTSS7nrrrt4/vnnxb909+7dcvxr1qzhuuuu46abbsLPz4/XXnuNiIgIzj33XDo6OrDZbHh7DxhLV1ZWsmbNGnGkUnEqSFm1olFRUSxfvhy9Xs+OHTtISUkhMjKSO++8E7PZjNPpZMSIEWi1WlwuF/n5+Xh5eVFRUcHJkyex2+1oNBqCg4O/t8rNhAkTePXVV1mzZg1/+tOf6Ozs5I033iA7O5uHHnpIbudwOEhPTycjI4Pk5GQsFotoIE6ZMoWOjg4effRRsrOzef3118nOzmbnzp3Mnj0bu92Ot7e3bFt/yvFzxfYjx3XXXcf69evJyMhg4cKF0p6YzWaCgoLYvHmzaH11dnYOQvx7JgrPUDptiuxusVhobW1lyJAhsrk8tRVVyc6TvO3l5SXqrp7J0HMrqiotz8SlKhpPwr0nNUvd1/N1eP7e7XYzceJEoV0pjJyvry9z586lp6eHCRMmUFRUxNKlS7nyyivF4FmJAvT19bFs2TKeeeYZpk+fTm1trUBgMjMzWbx4MW+++Sbvv/++yALZ7XZZ1gAyA1TzT6XuAQi2Tb0m9Tq1Wi1TpkwRd/m5c+cSHh4umDUFYFZfKL29vfj5+eHv709ra6vo3On1etLS0r7352n16tV8+umn/Otf/+Ldd99l2bJl3HbbbTz44IPAQOt5++238+abb5KYmDgIutHY2EhERAQBAQFfEuOEATiJkt1SnxtFtTpTq7ifK7bTFFarlfj4+P+PvfOOr7q+9//zZJx9TnJOTtY52YuQEFYYCqKAigJFqwhWFAe4Wqu4qra9F2+17rbu1lYc9da2igoKVhGUgOwRkkASAtn7ZJ3krJyMc87vD+7n4wmOWge0v0fejwcPNDnnfL/n8P2+z3u8BjNnzqSjowOPx8OYMWPo6urCbrczNDSExWJh1qxZ0sxYtJNCHVdUViLxALK6GxoakoP2+vp66uvr6erqktAAcWOKOZqwqhOzvJOTmGh1RQIKBoNSKlxIKAFSulwkT/EnlJsqbo7Qzahwnjp48CAJCQnyXHw+H5dccgkul4vt27dTXV3Nnj17ePDBB2V7K7TOxGsLV6mioiJqa2t55JFHuPDCCznzzDNZtGgRr776Kg6HgwsvvJAf/OAHmM1mbDabXMDExsaOkPg+WapJ4PnCw8MZO3YsKpWK8PBwysvLUSqVtLW18fjjj/PQQw/hdDqlKEBaWhoWi4WsrCySk5PlcRITE7FYLGRmZn5jSe5rr72WhoYGnnjiCRYvXszEiRNHUKGsViuNjY2YzebP4dHWrVvHzJkzufrqqwHkZzl16lQcDoc8J41Gw86dOzGbzRiNRpKTk9m+fbv8XP5dfUxCY7Ri+55j4sSJlJaWcvfdd2MwGNDpdLS3t9Pe3i5doETV0t/fLx2UBB9RtJfij2jpxNYutDISGy+Xy0VPTw8xMTESowXI54vjqVQqKXgpXuvkCk/QbU5mIoiK5mSVkZOTmmh5hRKwiNDjCSmfrKwsrrrqKqqrq6mqqqKtrY2NGzeyYMECubiIjY2VFa7H45GCAL/73e/4wx/+QGxsLGq1mo8++oihoSEefPBBHn74YebNm0d1dTW7d+/G5XKRm5s7IpFlZWVx7NgxpkyZIs9fVL1CNTg2Npbe3l6ys7Opra1Fp9Nx1llnkZCQwMSJEyWhXqiyiFEDIKu52NhYaV2Ympr6ja6pmpoapk+fzuHDh+nr62Ps2LE888wz/PSnP0WhUHDjjTfi9Xr5y1/+Ip8jvty2bdvGzTffTH5+vvRTKCkpYcKECcAJ34qWlhbUarX8GcCKFSuYMWMG9913Hx6Ph2XLlrF48WLOOeecz3F/RZSUlGCxWL5XQ+bR5cFpiiVLlvDWW29x1113ERkZyVlnncXevXupq6tj0qRJKBQK3n33XYn8F8lGbNeEMKHgdYZyIwWgVKfTSZiIuBk7OjokbES0V4KHerJQ4clJRrSjJ38zn/y4k3FeYoMqxBpDYQiC+B36HgTFSiRIu93OWWedhdPppKOjA6vVSiAQoKmpSQ67bTYbjY2N6HQ6CcIVLWJ+fj6TJk3i6quvpr29nYsvvpiYmBhiY2Pp7+/HarWyYcMGrFYrZrN5RKuuUqkoKCiQ7baYkQkVY7GlhhPb0cHBQeLi4li8eDF+v5+Ghgbi4+M566yzsNvtqFQqnE4nJpMJvV5PWFgYsbGxdHV1yUp8+vTp3/i6+p//+R+2bNnCli1b2LVrFzfeeCNPP/00CxcupLS0lOnTp6PVasnJyWHChAlkZGRw3333YbfbR3gozJ49m61bt9LS0kJSUhIzZsyQQg433XQTf/zjH+Vjk5KS+OCDD4iPj2fNmjV8+OGHHDlyhMWLF3PppZdywQUXjGhZc3JyOH78OL/61a9YvXr1N36vXxWjie00hN/vJzY2lksvvRSv18ukSZPo7Ozk+PHjhIWFMX36dFJSUqirq0Ov1/P+++8zPDxMYWEhRUVFsiIJpTuJxCCUPzQajVSzjYiIYGBgQCbE/v5+mVy0Wi1Op5OBgQEsFguRkZEyEX1ZnHxthCa1k38eWqmF/j60ChQtrkhkgUCAjIwMiYQHJIRB8DnFHFDIEAmjFJHABX3p3HPPZfny5SQmJnLTTTdJE+P09HSam5tJSUnBbrfT09NDenq6VDZRq9XySwQ+AyALIYKamhrGjx+P3W6XtLfQilk8T6PREBMTg9/vp7CwkOTkZLKysqRfaUdHBwkJCZSUlDB16lQ5C/ymEQgEWLp0KS6XizVr1nDZZZfxu9/9TrpQeb1edu3ahdPpZPPmzbzwwgssXbqUN954g4suuogNGzbI19q5cydTpkzhiiuuYPbs2Wg0Gnbv3k16ejoA//3f/w3AlClTeOCBB7jgggvkl2NjYyNr167l1VdfJT8/n9dff53w8HCcTidbtmyR29rKykpSUlK+tLr7pjGa2E5DiBvg/PPPJy0tjYyMDEpLS0lLS0On08mKxWQyMTw8zLZt25g4cSLDw8MUFRWRlJREWVkZWq1WJje/34/ZbKarqwudTkd6ejr19fW43W4JPg2djQ0NDUkZ7GAwiNPplEN4wXWEkZvA4eFhIiIiMJvNOByOEXM00foJDFxfX59cKJyc0EQSEzNC0b6GbmNDW2NBEnc4HJx11llSH8xqtbJt2zaZ1ATbwuPx8Pbbb7N8+XI++ugjvF4vTz31FNu2baOjo0MCgX0+HzNnzmTt2rVYLBZMJhNjxowhKSkJh8PB3XffzdVXXy3fn/g7IiKCc845h56eHrq6ukhMTKS0tFRqtQm58JiYGHp7e+no6EClUuF2u7n00ktpbGykvLycSy65hEWLFnHfffeRnp5OcXExSqWSv//979/q+hocHOTRRx9lzZo1OBwOfvSjH7Fs2TLmzJkjoTdfNPS/9dZbee655zjvvPPYsmULr732GsuXL//S43z88cf85Cc/4e677+b5559n3LhxI9pcOAE7mjZtmuTUipkcwJ133smePXvYtWvXd85FHV0enIYIDw8nLS2N3bt309bWRkdHBwaDgdTUVDlMdzgcNDQ0EBYWxsyZM+Xc54wzzpDsAzG0joiIkGKTU6ZMkfANwWQQhiGzZ8+WwoaiMrLb7bS2ttLf309sbKxsY0PhHqFbz+HhYTo7O2VCFUkIkI5afr+f6OhoEhISPifDIy5eo9Eo22YRRqNRLi7MZjORkZFMnDiR6OhopkyZgsViAaC/v59ly5YxPDzM2WefjVKpHNHunnHGGWzevJl77rmH0tJSsrOzmTdvnhTBnDNnDuHh4dLVXSRQkbSuvPJKwsPDqaysJCIiApvNJgUDRGKuqqqS/NJjx44xfvx4/H4/Y8aM4fLLL6e6upr9+/dTVVUl54BDQ0Ps2rWLhoYGpk+fjl6v54YbbsBoNNLQ0EBiYuJ34rGrVCpZvXo1jz/+OG63mzfffJO5c+eSmpqKRqMhNjaW1atXf84P99FHH+Xw4cNs3ryZ5uZmrrrqqq88zocffsixY8e44YYb2LlzJ6+//jodHR0jHjMwMEBRUREffvghDQ0NZGdnM3XqVMrKyvjtb3/LL37xC+DEIumZZ57hyJEj3/r9f1WMVmzfc1x66aUcOnQIg8HA/Pnzqa2tHWFzJ5JUd3c3g4ODWK1Wurq6CAaDNDU1odVq6erqkjMesQUVHggKhQKn04lKpZJLB6vVKqWFtm3bJuW5RYVoNBrxer0YjUZUKhVGo3EEvg0+w50Bkpfq8Xikw/rQ0BAxMTHyOWq1WgKLg8Gg1IQTqrICsKtWq3G73fLzEebD2dnZUhHF4/Hw5ptvMmXKFJKTk/nwww/l5xIdHU17eztTpkxh6dKlnHXWWcTHx7N//35iYmK49dZbWb16NUuXLiUYDOJwOKQh9bFjx6R6yI033khKSgphYWG88sorXHnllezZs4cVK1Zw5MgRnn32WYaGhrj88svZtGkTLpeLvr4+ae4iVHa9Xq9sLd1uN1FRUfT392Oz2YiMjKSzs5Oenh4SEhLIzc3lwIEDLF26FOBrU6r+WQSDQd5++21eeOEFduzYwcDAAFqtltmzZ/OPf/wDOCEk+f7777N69Wqys7OJiIjA6/VSXl7Oxo0bqaqq4t133yUzM5Of/OQnnzuG+FL4zW9+w89+9jPOPfdc7r//fmbNmsWKFSv461//SlxcHAkJCfT19VFcXCyXViIqKip44okn6Ojo4B//+AeXX345N99887f6HEZb0dMYCxcupKioiKVLl2I2m/H5fLhcLslN1Gq1NDU1Sdu0oaEhibESlYPFYuGjjz4iNjaW4eFhSXa3Wq3U1NTI2Y9Op+PZZ59lzZo1dHZ2MnfuXJ544gm5WBAWgR0dHaSkpEgVDuG5EAoFMZvN0lhZr9djMBjo7u6WgN9gMCg9PYV/gJDWCb2uhK+nVqslNTUVh8OB3W5HrVaTkZFBRUUFM2fOxOPxcO2117Jy5Uo6OzvJy8sjOzubsrIyVCqVPPbPfvYzHn74YWpraxkaGmL58uWUlZUxPDzM7bffzk033UR6ejqffvop48aNIzExUapueL1eUlJSUCgUErh74MABEhMTJSRky5YtaLVaafj9yCOPcM8990gQMZxIyD/84Q954403pFpwcnIyOTk5pKSkYDQayczMRKvVYrfbyc/PJzIykpKSEuLj48nJyfnerrfBwUG2b9/O4cOHcblc7Nq1i66uLgloDoUNwQng7rZt2wBYtWoVTz311Je+9tatW3n//feJj4/nsccew2KxUFVVRXNzM0VFRaxevZrY2Fj27t3Lrl27pJzUyXH77bfz+9//Xm6SvylObjSxnca499572bt3L52dnVx88cVSLFLMtc444wyioqL429/+RlpamvTCHB4exufzodfrpYZ9XV0dMTExKBQKKioqyMzM5MiRI6SkpFBTUyPd0dPT01m+fDmTJ0/m1ltvpbS0VCL+xbbObDaj1+vp6+v7QsPgmJgYenp6gM8qONHGDgwM0NfXB5yo6E5W/bBYLNKDNBQiIgjYAtsm3mMwGGTBggU4nU727t3LkSNHuOCCCzj//PMZO3Yst99++whcmcvlIiMjg1dffZVXXnmFd999lwULFnD77bfz4x//WJq2OJ1OWeWFh4fT0dGByWTCZDLJmZ+oQG02G4sXLyYlJYUXXniBlpYWCZoWs7pQmlVMTAyJiYk4HA6mTJnCjBkzSEhIwGQy0dHRIbfAbW1t9Pf3ExERwZgxY/D7/VKT71SGGDNcccUVvPXWW7z66qvk5OQwY8YMrrrqKp599tkRI4VAICDpeidv0uGE4vMf/vAHJk2aJE3QX3jhBe644w5Jp2tsbJQKzqGxd+9e6QXy4osvcv3113+j9/Rlie0/R1HuPziUSqVsQ3p7e4mKipIkbuFa5Ha7CQQCWCwWYmJiKCkpkSBcn8+HwWCgt7eXSZMmMXPmTD766COp7nr22WfT2NgInAAE6/V6AoEAxcXFzJw5k8TERFJSUujv72f9+vUkJSXJCs/tdst5WOjmVa1W43A45Lyru7ubsLAw6S0QHx8vlToEB1JQrYQYYuhcTczpdDodTqdTLjdEslKpVMyZM0dWjbW1tRw/fpzVq1dzxRVXcPz4cSZMmIDf75cu9C0tLSQmJrJw4UIyMzO55ppryMnJ4YEHHuAXv/gFAwMDTJw4kc7OTjk/DAsLIyUlRc74vF4vg4OD3HTTTTzzzDO88cYb3HTTTfzoRz+Soo3BYFBCQMRsVDAYAoEARqORrKwsnnzySaZMmUJWVhbBYJDs7Gx0Oh1KpRKj0YhWqyU6OlryfE91iOOuXbt2xBDf4XB8bkYaCAT49a9/zf33309KSgorVqzA6/WycOFCzj77bODEHPXnP/85M2bMkInt5ptv5vrrr6eyspKampovxbBNnz6dYDDIPffc853MG0+O0YrtFMQHH3xAZWUlW7ZsoaqqisWLF6PT6aTKrNFoxG63y2pJtKY9PT0YjUbS09P56KOPZOXQ1tbG008/zc6dOykvLyc1NZWUlBRKS0txOp1MnTqVsWPHsm7dOumFMGXKFH77299SV1fHokWLmDhxIu+9954En2ZkZNDY2Cg3psLuT5DmRVsqhuPx8fEjNrCANJsRLAfRworHhA7lRRJ1uVxyZmWxWLjyyit59dVX2bNnD9OmTaO7u5vu7m42bdqEyWSiqqqKJ598Um6Gf/Ob3zBv3jyppSYcwEQSS0hIwO/343Q60Wg0EsQsWmebzYZWqyUrK4uXXnoJt9vN4sWL0Wq1HDlyRLbdy5cvZ82aNQBYLBa6urqIiIggKipKOo35fD4mTZrE9OnTiY+Pl9p6ycnJkjIm4A4FBQWn52L8mvHTn/6U9evXM23aNObNm0dxcTEvvvgiCxculNZ+a9eu5Z577sHhcFBcXExGRsYpP8/Riu00RlVVlbzoN23aRFdXF8nJyURFRZGZmSlJ0wkJCcTFxbF27VpiY2O55JJL2LlzJ3q9nttuu42XX36Z8PBw5s6dS3R0NPPnz6e3txeTyURcXBzjxo3D7XZz6NAh9uzZw5lnnsnBgwe55ZZbePzxxykuLua3v/0tx48f57XXXsNqtTIwMIDJZJJoesFlFJ6ioa2YyWSSSrbwmWmwmLeJEHSrUChJKHVLzFTETNDj8TBu3DhMJhPl5eUcOnSInJwckpOTWbhwITNmzOCRRx6hp6cHt9vNtGnTeOmll1CpVEyfPl0q2ooZkpBMnzp1Klu3bmXcuHES9S/abjhRlbS0tKBQKLDb7bjdbnQ6HRUVFaxevZrw8HC6u7upqalh48aNXHLJJSxevJjly5ePwOfl5uZSX1/PL37xC7mE6e7ulonM7/dLsQMxU/p3i7///e+sWrWKpKQkbrvtNn7/+99TUlIiyfptbW28+OKLPProo/I5PT09nHPOObS0tLBgwQJ27NghK/xvGg0NDRJG821iNLGdghCaYEqlkvHjx/P2228TFhbGzTffTFhYGB9++CEXXHABGzZsQKFQyFW5ILVbrVYOHTpEZmamfK3XXnsNk8lEMBikq6uLoaEhjEYjFRUVqFQq9Ho9eXl5DA4O4na7mTVrFh988AFWq5XY2FgyMzOZOXMmTU1NfPDBBxw6dEjCN9Rq9QivBoVCQUdHh3TKEsh7rVY7gscaSq4OZRqIDfAXAXtFRbRv3z6pYdbb28uyZct46aWXuPPOO1mwYAHnnnsutbW13HvvvRgMBqZNm0ZSUhK//vWvMZlMuN1uDhw4wKxZszh8+DBtbW1s3bqVlStXsmHDBhITE+nq6kKlUhEIBDAYDPT39zM8PExvby+1tbWcf/75bN68Ga1WS01NDXV1dVJOXFSgb7zxBmlpaRiNRkpLS+nr66OyspIlS5Zw8OBB1q1bJxVAkpKSCAZPGL+IUYLBYBgh8f3vEFu3buWmm25i5cqVpKenc+2113L++eePUCDZunUrMTExI8jzYiv8X//1X6xcuZJVq1bx+uuvf6tzSUtLw2Aw4HQ6/+ljv6rbHG1FT0EsWbKErKws2tvbSUtLQ6FQ8PTTTzN16lQefvhhfD4fNpsNlUolHaw2btwoJX1mzZrF5s2b0Wg0cq7ldruJjo4mIyOD+vp66UIfGRlJQ0MDwWCQ+fPnU1FRQU1NDY2Njaxfv55nnnlmxBxv8+bNuFwu3G635EUKF3MB7RBVWmRkpJQVVygUxMbGylldaEsqNqOhywixSBARSpwPBk9o04mlRH9/P1qtFp/PR3R0NAUFBdjtdhYtWiShF4FAgMLCQnJzcxkzZgyVlZWUlJTw+uuv43K5mDt3LgaDgU2bNjFhwgTq6urw+Xyywk1MTMTpdEp6Vn5+Pk6nk6NHj5KVlSVl1ydMmEBNTY2sFF0uFxdeeCGPPfYYZ555Jg0NDbhcLhQKBVarlWAwyGWXXcaECRN49913ycvL48wzz8Tn87FmzRomT55MbW0tq1atOl2X44h/g8suu4x33nmHNWvWsHLlyq987EsvvcQDDzzAwoULuf/++7n33nspLCzktttuY9++fSxbtoyjR49+KwDuX/7yF5YvX05TU9NXckxvvvlmysvL2bFjx2grerpCrVbLDZsw/Vi9ejUHDhxgxowZ3HTTTaxatQqj0ciSJUvYvXs306ZNY/fu3ej1eoqKirDZbGzfvp38/Hw8Hg96vR6tVktfXx/JyclSnSI1NRW/34/NZsPhcFBdXc3evXvJz8/HYDBIhdj9+/dTVlZGamoqPT09VFZWEgye8C/1+/3Ex8fT2NiI3++X/gWhLaXJZJI8UTFYVyqVhIWFSbhHqFKGSIDiZ6FfqMFgUCZQt9tNfX39iCRYXFwMnKD+FBUVSQlwAVFITU1lYGAAn8/H4xyW/wAAIABJREFUnXfeSWtrq6RBWSwWIiIiqKysJD4+nrlz5xIMBqmsrJTKK5GRkcyePZvXX38dnU4n54IC/Cyqh08//ZTo6Giee+45ib3bsWOHnIPW19cTFxfH2LFj6ejokC5ZghWyfPlyjh07RkJCwvd8xX292LFjB++88w5wQjXkq0KhUHD99dfzwx/+kLvvvpuCggIGBgZ47LHHAKSA6kcfffSt9NyuvPJKoqOjSUxM/NLHzJ49m23btvHwww+zY8eOLz7f0Yrt+4+xY8cCJ25gs9lMRkYG06dPZ86cOWzZsoWf//znrFu3joaGBnJzc7HZbLjdbgwGA42NjVRWVtLe3k5nZydRUVHS06C5uZkZM2bw3nvvMXfuXGmmInTOFi9eTEtLC319fbz00kuMHTtWehJUVFQQHR1NVVWVJN6PGTOG2tpali5dyo4dOyTAtaKi4nNzoZPVOgCZAIU6bei2VYB1hQ9paCUnrkGTyUR9fT3Hjh0DkOKObW1tTJw4UdoLJicn093dLbewXxWhsurJycmo1WoUCgUpKSns3bsXlUolOZ4TJ06koaEBt9tNTk6OPI8f/OAHbNmyRcIlAoEAOp1Oyqlv2rQJvV6PxWJBp9Ph9Xq5/PLLiY+Pp7y8HI1Gw5QpU3j33XexWq3ACbWM0xl+v5/zzjuPs846iwcffPBffn55eTk6nY60tDT5sxtuuIFgMMjTTz/9OXDudxkrV66kpaWF9evXo9FoRnFspysyMjIkJaq/v5+YmBhZGdhsNj788ENuu+02Jk+eTH19PTU1NSQnJzNv3jyeeeYZIiMj2bFjhzRA0el0TJ48mU8//ZQzzjgDrVbLjh07SEhIYHh4mB//+MdUVlaiUqnIzs4mEAhwyy23cNVVV7Fr1y6ZJILBIPHx8WRmZqJWq1m4cCF//etfpcjjwoULefbZZ/F4PNKfwGAw0NfXJzmjw8PDErMmQLBwokoV2m1iDncyWT6UmymSXyAQwOv1UlVVxTnnnMPhw4fx+/1fKIz4RRGKjxNJLT4+XppHiz8iMTudTlJTUzn//PNZu3YtY8eOxe12c9FFF/HBBx9IxZGdO3dK7it8Ru43m81YrVaioqIYGhrC4XAwfvx4FAoFbW1tNDc3S7qW0H5rbm7mtdde+x6utK8Ou93O3r17efnllykrKyMjI4NNmzZ9Z94Hzc3N3HHHHRw8eJC1a9dSWFj4tZ/7wAMPEBcXx0033fQvgXVHAbqnMeLi4lAqlRLDFB8fL9UlVCoVu3btYty4cbLNFPCHPXv2EB4eTlRUlOQ6ilmUUO2w2WxyfqTVajGZTCQnJ3PhhReiUCj4+OOP6erqwuVyodPpJB1LJJ2srCwsFgspKSnExMSwZ88e9Hq9tJQ7cuQIO3fuJDMzU7IfREJQKD5zdRftlWiJBU4q9KYRYpEnK4EIalmoCObevXsJBoPMnj0bo9HI9u3bJSD4yyIlJUXi+eCzak3MNQH5+kNDQzQ3N0sJn40bN3L22WdTUFDA1q1b+fvf/85rr71GXl4eBw8elHNGYS8YqjcXFhYm1T1mzJjB5s2bCQsLk+DmKVOmSKf7wcFBrrvuOq655ppvfV193WhoaOD555/nT3/6E9nZ2Sxbtozzzz+f/Pz8b4z4/6r45S9/ydtvvy1d679OXHjhhWzatIlly5bx6quvfm2s3yjc4zSH1+slIiJCanv5/X6mTp3K8PAwLpeL6OhoBgcHqaqqwmw2k5SUxIUXXojdbpetVGNjo2QADA8PMzg4SHV19QjNNrHx83q9dHZ2EgwG2b9/P5mZmSgUJ/xMg8EgarWa+Ph48vLysFgsmM1mNm/ejMFgID09nfDwcP72t78xYcIEzj//fOrr64mMjMRqtdLb2ysTsMvlklvDsLAwent7R5ieCEd7QLIQRPITm1OB3RNimqIF7e3tZdu2bej1emmz90Umw8JpS0gIiRA31cDAwAixTcGQEAyQ8PBwbrzxRlJTU3n77bdZtWoVd9xxB0eOHOHjjz9m6dKltLe3s23bNim6KYxpxJJBaMiVlpZis9nIy8vD4/FgMpl466235PsCeOWVV05ZYqusrCQvL48FCxZw/PjxEd4O31f8+te/5umnnyYlJYXy8nIptvlV8d5773HttddKSfNv0h6HxmjFdgoiJiZG3sShWmqCXF5eXk5hYSFjx44lKiqK4uJi/H4/cXFxGAwGCdYVN77ZbMZut0vqUl9fH5dccgmHDh2SZPqamhqZTG02GxqNBqvVisfjkVtFlUol+Z5jx45l5syZbNiwgRtuuIHu7m6GhoZ4//33Oeecczh06BBarZaoqCi6u7vRarXs2rWLnp4exo0bR2VlpZz9CZ1/AZI9eUsWChEB5AIi1E/B6/Vy7NixEckqPz+fpqYmZs2axcGDB2lvb/+nn71olc1m8wgMnTBMVqvVzJo1i76+PqZOnUpTUxN33HEH119/PcnJybS1taFQKIiLi8Nms1FeXk5YWBhGo/FzYqBarVYKAsydO5eOjg4GBgYwGo1YLBbOO+886uvrycrKknPX7zveeOMNrrnmmhEMk1MRVVVV5Obm8uqrr/5LSbykpASbzfa1E/BoK3oaIzk5WQpARkdHS4XZqKgoent7aW9vl76QcXFxOBwOPB6PrChEhSMqNSEZrlAoJLhXbBabmppQKBQkJSVJzBZ8ZuN3cohht2jRZs2aRW1tLf/1X//Frl27MBqNTJs2jY0bN0p1j4GBAdRqNWq1murqao4cOSLt7YQ7vFDU7e7uxmQyEQgE5LmL6kVsS8W21e/3yzY5EAggrr0vameioqIwGo00NTWNeC/Cum9gYEAuJ1JTU+XGVhw3LCyMqqoqUlNTpffE+PHjef3113G73RQXF/Pss8/KL6NgMChZBW+99RYKhUIyLRQKBQaDYcTWeNq0aezcuROLxSLFLoUpc0REBH/+85+/8+vsi+Luu+/G7/fz5JNPnpLjhcbevXu5+OKLef7550fYA36XMarHdppDmJgIbFgwGJStmsFgkDd/f38/Pp+PQCCA0+mUHp6Dg4MMDg7K+Zqo/EQSS05Oprm5GYPBILd/YmsphvOiDRSzLwFpEIkzEAhQVFREY2Mj9913HwcOHMBut9PR0cG0adO44IIL5LkJ5dnCwkKSkpKk/LXAdA0MDEidfY/HI8/X5XJJhy2xcBCtuUKhkDO5fyZIuGTJEiZNmkR+fj4xMTEsWLCA+Ph4qV8nRD7j4uI+5w8h2BKxsbE0NDR8Tl49EAjwhz/8gYSEBCkKGhYWRllZGW+++Sb33nsvOp1uRDJzu914vV5ZJQvFEIDS0lLa2tpoaGigubn5e77SRsbBgwd56qmnTgsoePr06XzwwQdcf/31IzxmT0WMzthOQRiNRslRHBgYYHBwUCaz1tZWhoaGyMzMJCkpiebmZikSWV5eLi9IkfQMBoNUpRAD+/DwcOx2O/39/YwbNw6n0ylFJAXp2+fzSQVa0TqJaj20KklOTqalpYWenh45/E5LS6OhoYEzzjiDefPmceDAAYmjKykpIT8/n/Lycrn1FUkrJiZGyisJ4K8Y1vt8Ppl4BQ5OhJA/NxgM9PT0fGHFJnibAAkJCVx00UWMGzeOwsJCDAYD8+bNIy0t7XN2gwqFQibbmJgY+dkUFBRgsVi4+uqr0Wg0zJw5E6/XS19fH4ODg6SlpdHY2IjT6eSJJ57A6XSSlZVFZGQkDodDVm5KpVKS9AcGBtDr9ahUKunZIDxgT1UUFRWdsmN9UUyaNAmTyURzczNZWVmn7LijFdspCEEij4yMJDo6mpSUFDQajUTyK5VKysvL6erqYvr06dTV1dHe3i6BncCIYbuYTXV2do5wrwIkLCL0Zm5oaKCjo2NEghAVUnR0tExyMTExNDc3Sy22YDAoz2HevHnY7XYpU24wGGhtbZUzMWEUI9pIUQ1ec801I8jyarWajo4OKTYptrMiySqVSlQqFStXrpR6dOJ3U6dO/cLPt729nVtvvZWYmBheeeUVjh8/jk6n49Zbb0WlUsllhthger1eqRgsxDkHBwfZtWuX5MMeOHCA+vp6jEYjt956q2yZRavt9/tpamrC7XZjtVpRqVTYbDYpCCrGBuILwuFw4HA4cLlcn1tyfJ8hhAtCxT1PdaSlpbF///5TeszRGdspiquvvhqHw8GyZcs4ePAgn3zyCbGxsWzbto2xY8eSl5eH1+uVqO0NGzbI2VVoSykG7KGt6NDQkETHJyYmSgWOQCBATk4O9fX1csYlbu5QjbTQLWVoiMfodDpJkH7jjTeYPHkyzc3NskJRKBR4PB6am5vxer1yk5ucnExNTY2kgAm4Q6jzk5jHhYZQAxab4K6urq/EWi1atEiKQ0ZHR1NTU0NeXh5xcXEMDw8zPDzM1VdfzfPPP09kZKRMPmazWX5uAmco3nNYWBizZs3immuuwefzUVJSwo4dOzh27Jj0hQitdMXiIDExkdbWVlmBCi5saIL+9NNPv+FV9M1i+fLlJCUl8cgjj5zS44r46U9/SmZmJnfcccd3/tqjM7bTHM3Nzfj9fh5//HFKSkoICwujq6sLQOLTfvjDH/L666/z0EMPcfjwYQCZ1ESCE+BTQUAOnWnZbDaJ7hc3XX19vYRUCAyZuIlDXdxDwacw0hdUrVbz6KOP8pvf/IZzzz2Xt99+mwULFqDX64mOjkav19Pb28s555yDXq9n7NixBINBOXcT1ZFYdoihu8CFCQkkYIRtX1hYGGlpaSPEKk8OvV7Phg0bZMXU3d3NrFmzmDVrFn6/n76+PgYGBnj++ecJBoPShd1gMBAZGYnFYsFoNKLT6dDpdFKCSKlUUlZWRktLi8TsXXXVVZx33nlkZGQQGxsrsVZi7jk0NERbW5vUoxO4xbi4OPn5fldg2H8lrrrqKnbu3HnKjyvik08+YdasWaf0mKMztlMU2dnZrFy5kmAwyMsvv8zhw4cZHByUcIBjx47x4IMPMjg4KKWDxM0vklQgEJAVkmgjA4EA9fX1JCUlkZCQIKubUDXb0KUBIIf/AvYgDI0VCgUJCQmo1Wrq6+vlts/r9aLVaqmrq+PNN99kwYIF2O12xo0bR2lpKUajkTlz5vDGG2+QnZ2Nz+eTMJScnBwJkQhd4xcXFxMREYHJZKK3t1c+XmDaZs+ezfvvv09kZCSJiYmyRRYhyP+rVq3iwIEDXHLJJfzxj3/E4/GQkpJCbGwsAwMDaDQalEolCQkJEgcnoDYC6hIMBmlvb2dwcFAyH7Kzs1EqlRw/fhytVktmZiYA8+bNo6WlhejoaDo7O9m4ceMI2Inf76enp0cuUsTc0WQyyc34qY4zzjiD8vJyGhsbSUlJ+crHut1uNm3axIEDB/B6vVx66aVMmzZtBB7xX4nh4WGamprIzs7+Rs//pjFasZ2iuPrqq6XT+9KlS7n//vu5//77UalUzJ8/H5/PR0xMDJMmTZLwCzEXghPJSIgjCls1IRUeHx8v/QjEcwTlSSS5UMyYoF6JNlBUcArFCUd2gcgPCwvD4/GgVqsZGBhAqVTS0tJCbW0tb775Jp2dnXi9Xj7++GPKy8sxmUw0NTVht9vJyMiQdCIBubDb7VRXV6NSqdDpdMyfP59x48ah0+k477zzuO2228jKymLRokUUFRVJeXSRfELR6LW1tQQCAR566CE++eQT7rvvPo4cOcL06dNpaWlBqVRyyy23SHmjG264gcbGRum9qlarJYsgNzcXi8Uik51CoeDYsWMEg0H27dsnAcBKpVIyQZRKJRkZGZx55pnk5+djsVhQq9UYjUYiIiLQarWyAvT5fLjdboaGhiSP9lRGVFQUs2fPlkbIJ0d9fT333HMPSUlJWK1W/vjHP6LT6YiLi+OWW24hJiaGdevWASfmuqLT+Drx4osvUlBQIKvYUxWjM7ZTFB988AGA3JqFhYXhcDj48Y9/zKxZs2htbcVms1FTUzPCpzMiIgKNRoNKpaKjo0Paz0VFRdHQ0EBERMQIeZfQ7Z9YKoRyNcPCwuTcze/3S935o0ePStK2CIF7E8/T6/VyJjVu3Dg6Ozvp7++XrupZWVkUFRXJrahoRwWkBD6b6U2dOhWPx0NFRQVJSUnk5+fT0tLC8PAwNTU1ZGZmSiNlhUIhVTQ8Hg+TJk2iuLgYg8EgpcSVSiXnnHMO8fHx1NbW0tvbS1JSEitWrJBJq66uju3bt3P8+HH8fj/9/f0sWbKEdevWSQaCaJmFuObQ0BApKSkkJydLbwqxoRZ4uYGBAZqamuR8sbe3V2rkAbJaNhgMALz11lvf89U2Mjo7O8nNzWXt2rXMnTuX/v5+Nm7cKDX2Vq9ezaxZs/jZz35GcnLy55gCTz/9NPfeey+ffPIJM2fO5Morr/ycr+gXRXl5uRR6CNV2+y5jlFJ1mmP//v1ye9bV1cXw8DAff/wxZrOZqKgoHA6HbP9EVWY0GiV0o6enh7a2NuAEfEQowYa2FqLtjIyMJCYmBrPZTGtrq9RX02g0DA4OUltbS0FBATabjW3btpGZmYnNZpNwDGH/J5JKRkYGNTU1DA4OEhcXR0dHh/zmFptTlUpFa2urlBIXszmhHBIdHS1VUbu7u6mtrSUxMVEa1QQCAWbPni2ZAWKDGh4ejs1mY8eOHbjdboknA0bMyzIyMrDZbPT29srKqqCggEceeYQXX3xR+mCKis7hcAAn5K0DgYDchoqEHDrbrK+vJzk5merqatxuN4WFhdJmTyRboT/ncrno7e2Vc07xGQ4PD8tjnupwOBz09PTw8ccfU1VVxcMPP0x6ejpjxozB6XSyYsUKfvGLX3wpr3PVqlXs379fusy//vrrXyuxPfbYY9x5553fOqndcsstdHZ28sorr3xt1ZDRxHYKQ0jfBINBjEYjhw4dwmKxSEFJoUlmMBgk4FNo6QvF197eXvmPm5qaSlxcnGwNxDZUtJAChZ+amorBYECr1eJwONDr9XR0dDBu3DgyMzPl80R7FQoSDgQC1NbWolCcMDdub28nIyOD4eFhoqOj6e3tJRgMSmycGPyLDa4wgXG5XHi9XmbMmCEt24TZh9vtxuVySV5ofn4+YWFhtLa2UltbS3V1NbNmzaKlpQWfz0d6erqssETCdrvdlJeXk5CQQFZWFn19fVJQsrm5WUJRSkpKpIGLQqGQ8kN6vV5ucR0Oh/xy8fv9qNVqysrKMBqNcgN90UUXSTCvUB6Oj4+nvr5ewmCAEX8LheBTHTk5OTQ1NfHggw9y/PhxXnjhBebPn/+FzlNfFk899RRxcXGSwfDCCy9w8803f+njm5ub+cc//iH12r5NxMTE8Pvf/56IiAj++te/fq3njLaipyhEy+f3+8nIyGD+/PncdtttbNmyhS1btqBUKtm9ezcKhYLS0lI5FxM4L1GNlZWVUVBQQGRk5IgLU7SNotUTzADRzgaDQc477zx27dpFeno6EydOZOPGjSiVStrb2yksLCQlJYW6ujoGBwcxmUzU1dVJk2BAVjOiCsnMzKS+vl4OxkW1FBcXRzAYpK2tTUIjAoEAsbGxI+Yz0dHRUhlEqJ84HA7OPvtsysvLyc/Pp6ioiNzcXIqKiuTrdnR0SCZGKL5MqJvcddddsnKsqKggLS1NVlVlZWUUFRXJhKvX6+XsTq1Wj8C7hUI/RHsderzExEQKCwult4FI7uXl5bS0tEj1jMrKSvx+PzU1NcCpb0W/ywgGg2zbto3FixdTWlr6pSq3L774Itu3b+d///d/v/UxhVpySUkJkydP5uDBg/J3o3CP0xxiI6ZQKKitreXhhx8mLCyMe+65h7/85S/86U9/ory8nPLy8hFaZUIZVmzXlEqlhGqEmqUIylZycjJxcXHSA1QAZ4PBIJs2bcLj8UiCe0JCAjabjeuuuw6n08nAwABVVVUcPnyYHTt2MHXqVMltjY2NHQEZAaipqZGtZlJSkrS4a2trw263o1QqiYyMlJvc7u5uucwQcJWoqCjpmK7T6SgoKKCyspJAIMCbb75JS0sLcALWYbfbaW5uxmazydcWyUcAYnU6nZxTDg8PM23aNNnelpWVMTg4KJkBBoNBSilFRETI6kt85mFhYZJJAdDR0YHD4ZDH6uzsZNu2bRQXF1NRUQGcWGq0t7ej1+vZv38/7e3tGI1GvF4viYmJp2V58F2GQqFg9uzZLFiwgJ///OccOnSIwcFBVq9ezb59+ygvL+fKK6/kl7/8JVOmfC7ffKMICwuTXgrFxcUsWLCA5OTkrzTFGU1spzAESHZoaIi+vj5UKhW9vb2yAhKI9lBeY1NTE52dnRw5coSmpibGjh0rKwbhcSAS3ZgxY2hvbycQCDA0NERnZydOpxO9Xs+SJUvQarVMnTqViRMnkp+fT3V1NX6/n+7ubnw+Hzt27CA7O1smn/fee4+Wlhb0ej1er1cyAURSFTf+4OAgdXV1GAwG4uPjZfIT5xHqZgWMSMr9/f309fXJSufQoUPY7XYJvzAajWzevJmBgQFZMdbW1mI0GlGpVJLFIWSb7HY7BoOBw4cPYzKZpCqJRqOhpqaGAwcOEBYWhslkIiYmRvppChaCmJHBiZs4Pj5eVqLp6elYrVbp29rZ2UlfXx8HDhxg27ZtvPTSS3zwwQfU19fLqrqjowOz2Ux2djYajeY/PrGJeO655zCbzVxxxRWYzWYefPBBqQpdWFjI+vXrue22276z4+3bt4/c3FyqqqpoamrC5XJ9ZSs9OmM7RXHo0CHGjx9PamoqbW1tpKSk0NbWJmWjPR6PXMeL4bnT6cTr9TJ+/Hj0ej1Wq5W2tjZ8Ph86nU6i3dPS0mhqaiItLY0ZM2awbt062UqJVtTlcrFq1Sreeust6urqOHDgACtXrmTbtm0kJiZSWVkpE5hwbBfJxOPxYLFYSE5OprS0VLZuJpOJzs5O4DOEvc/nk8kvFFcnkrhIckajUfJgQzmh3d3dsp0NDw+nra1NCgaIFtHv98uNXltbGzExMajVapxOJ1qtFoPBIBU7CgsLcTqd9Pf3k5eXR0NDA3Fxcej1eqkl5/F4pIOV8AkVS4FgMEja/wlVCmB0REQEarUajUZDa2urfA2xvRbVpqj+Jk2axI4dO5gzZw7XXXfd6bkAv+OIiori6aefBqCurg6bzUZJSQmRkZFMmjTpOz1Wf38/a9eupbm5Wc5m33333a98zuiM7RTGM888Q2JiIjt37mT9+vXceOON9Pb28u6772IymaQ/ZXd3N52dnfJGmjdvnsSSZWVlMXHiRJqbmyWqv62tjeLiYvr7+4mMjESj0eBwOIiKimLixImkpqZy9OhR8vPzyc/Pp6+vj08++YS4uDj6+/tle1xZWSlvWAFJsdlskrStUCg499xz2blzJ4ODg8ycOZOKigrpD2A0Gqmrq5OPtVgscg4nNsGhcyshrd3T0yNnW6Jy0ul08vHCFEXwPIUrlYBkiEWG0+kkIiKCOXPmMH78eJRKJWazWXJB29raWLduHTabTUJnhOyR2WzG5/MxPDxMYmIiERERUoVEPF8o/IplT319PVarldbWVjQajXysOJ+urq4RIOhAIMB77713ei6+7zkWL17M0aNHycvLo6Ojg/Xr138n1WlDQwNpaWnExsZKz4/77ruP++67Dxidsf1bxf79+2loaODFF1/k448/Zvr06dxxxx3cfffd3HPPPXKelJiYyJlnnkkgECAjI4OkpCQMBgNFRUX09PRgtVqpra2lvr5eciIFNEStVrNixQqOHDnC0aNHpSdkV1cXOp0Ok8mEWq2mtraW7du343A45MJBYNVEKxXaGn/yyScsWrQIk8nEzp075ZwrLy8PlUrFnDlzpDqJw+GgqamJrq4uWamdjBUT88OoqCj5eyE0KeZbYlssqE9iYSHYEgIEK7wk/H6/NO4ViPn29nY2bdpEfHw83d3dREZGSqd2AYYWczAxrxQ4wNraWqkIDMhKLO3/JI2EQbTADvb09GC32yWz4stgFP8/hZB2uvDCC9m+ffsInbxvE/fccw8XXHABnZ2dREREMH78eO68885/+rzRVvQURn5+PlarVW466+vreeGFF/j00085evQoiYmJpKSksHjxYvbs2UNOTg5Tpkxh0qRJVFRUyPYzISGBxsZGjh8/Tnd3t4QtCIiFWq1m+vTpPPvsszz44INoNBp5M9bV1dHf38/x48epra1Fq9Wi1+slcyEUjCvAxAKwqdPpZNUzf/58jhw5QnNzM2azmf7+fqKjozGbzeTm5hIbGyvd1VUqlWwlxWJEzEcEhUyQ+n0+n/QwFYoUApKhVqulN0J/fz9qtXoE2Tw8PBy3201ycjK9vb3YbDYGBgbo6+ujvr5emk0H/8/ExuFwyGVMqCClSOSCiSAq297eXsn7FOfudrtJSkqiq6sLvV5Pd3c3BoNBim4ODw9jNpvp7Oz8Sku5//S45ZZbuOKKK2htbQX41hSqYDDIs88+y759+3j22Wc5dOgQBw8e/Eqv0dAYTWynOMrLy7n55pspKipCrVZjNpuZM2cOer1eUnXWr1/Pvffey49+9COCwSAffvghra2tUqo6EAhw5MgR2WoFAgF6enowm824XC4SEhKk409vby/l5eXk5ORIkUeTyUReXp6sooR/qDBmiYqKwufzSUkft9uNXq+nq6tLtoxbtmxhzpw59PT0cPToUcaMGUNqaqok4ff19eFyuUaYvfT09Mj2UtCNXC6XVPKAExAQsUFOTU2VJHlRYSoUJwxqoqOj5fZRzMLcbjepqalUVFTg9XrRaDQYjUZJD2tpaaGtrY3U1FS6urpkpSYqR6GbFpqAhTCoYIAIXT2lUolGo6Gzs5Pw8HApux4XFydnh8JF3mg0kpGRgcfjOZ2X3vca06ZNY/r06TzyyCM88cQT35hbClBWVsYdd9xBT08Pq1atYtGiRfzkJz/52kkNRhPbKQ2Hw0FBQQHr1q0jNjYV+oRlAAAgAElEQVSWiy66iK1bt0pAqc/n46WXXmL37t0sXryY3bt3y1mZ0G7r6OigpaVFYq7UarW8ucUcKioqSpLNe3t7cTgcdHd3y5u0urqaiIgILBYLFRUVZGdnU1NTI3FaolJKSUmho6OD4eFhfD4fZrOZhIQE7Ha75JDm5uZSVlaGw+EgPDxcVlVpaWkYDAaqq6uZMWMG5eXlqFQqoqOjpS+DUP0A5CJEYMEiIiLo6emRVZFoFZ1Op/QbiI6Oxu12yzmbaHFVKpUU5BTc0IiICLkEEeq8QlwAkMsKEUNDQ5JrKySWtFqt1NULBoN0d3fLz1mAjLVaLRERERLkK4QOgO/Va/N0h0Kh4G9/+9u3fp3S0lLmzZvH6tWrufrqq0lPT+edd97hvPPO+9fOZ3R5cGqjoqKCO++8k7y8PG644QaGhoYoLS2lqamJDRs2YLfbmTZtGunp6ZJIDkgN/7i4OOx2u/TDbGxspLe3F7VaTXd3N4FAgAsuuIDY2FjZOrndbt577z2MRiMul0tuMM1mMx0dHdLl3WKxoNfraWlpkZtI0U6FhYWxfPlyNBoN+/bto6Ojg9bWVpmEAoEA8+fPR61Ws2nTJs4//3zJFDh27Bg5OTn4/X7KysokfxWQsBGdTidnVEKmfO7cuURGRrJnzx7ZQoq2O9QLQmDiBKD5wgsvxGQykZOTQ09PD5GRkZSUlGCxWKSqiJhLiteMjY2ViU20ouLcQs9T/F74NQSDQSkQIOacKpWKoaEhWSHrdDqcTid/+tOfTsMV958V5557Lmq1mjfffJOHH36Y4uJiybP+ohjliv6bRGVlJZs2bZJzK6GQkZ6eTnJyMq2trSiVSkwmE11dXVI3TK1WA8g2rrCwkPr6eikJLpQwhNBif38/LS0tdHV1UVVVRUxMDJGRkXR3d2OxWGhvb6enpweDwSAFKHt6eujt7ZWMBYPBgE6nw263S7UHYIQOmdFolAKLQrcsPj4el8tFYmIiLpcLpVIp9cqEn6qo0ATwWCTQUN02ca7imKHO88I3QlRzERERJCQkMGvWLAYGBkhISJBVVmNjI6WlpZhMJunwJY4tKG4na9GJOdrJblriT6jOXCguT3yWfr9fLhYEZnE0/nlce+21/PnPf8ZoNDJ9+vRvXAWOJrZTHAKjNTAwwO7duxkYGGDcuHGUl5dLld2ioiIKCwslmVvciMFgkDFjxtDZ2cmuXbtoaGggLCyM6Oho/H4/ubm5qFQqjh49itPpxOPx4PV6MRqNuN1uabknWAE6nQ6r1UpkZCROp1MOvaurqyUUQ2iNnXnmmQwODmK323G5XKhUKmm2nJGRIZNBT08PsbGxNDU1MWnSJPR6PW1tbXR2dsrKBpBofLFUCDW5ETppYlkBI82VBdFfJJFgMMiSJUvIzs6mrKwMnU5HdXU1Y8aMkdLcKSkptLe3S16sADUL8U74LHGJZCYYCwqF4gvNZUQyE8k6FH+oUqno6+sjMjKS2NjY00aA/0+L5cuXs3z5cjnz/KYb5dHEdoqjsrISpVLJ5ZdfTnd3NxqNhkOHDjEwMMDHH3/M7t27ueqqqzAYDPh8Prq7u4mJiSE2Nha1Wi3nOEKPPy4uDpfLhcFg4Pjx42RkZJCVlSUlgMQGUavVyqRRWFgoHeEFxEKj0UiohLhBXS6XnFNlZ2cTDAZxOBySBxkdHU1ubi46nY6Ojg55QyclJWEymTh27Bg+n4/s7GwKCgo4ePAgERERtLW14fF45EUrgK0ihKJIbW2tJL0LgUhR0YlzjIiIYPny5ahUKgkUDQQCJCQkSMxZeHg4XV1d0iWrr6+P6OhovF6vTJCiKhNJTfwt1IXFXO2LyO2iQuvu7ubDDz9k/Pjx5OXlER8fL5kXo/GvhehQvmmMJrZTHI2NjdLZSCDorVYrYWFhrFmzhhUrVjBp0iSsViuDg4MkJiZSXV1NXV0dEyZMoKSkBLvdLrFqYvbW19dHamqqdMESw3XR/sXHxxMIBLBarbKy8nq9cktoNptpbGxkcHBQLgjgM1UKOCE31NDQQGJiIgaDAYPBIPXI7HY70dHR7Nu3D6VSSW1trcSaVVdXo9Vq5WYytCoSLaZILMLzMxgMYjAYcDgcVFdXy4ooIiJiBEWroKBAclT7+/vJyMigp6eH/v5+KZ2+Z88eKUsk9OMSExMJBoMcP35cMgf0ev2I6g0+k0gPPTfRioZWkOK9NTc309zcLAnwYjv4ddzQR+O7i9HEdhpCbAUjIyPJyspCo9Fw1113kZWVxcUXXywlhPr6+ti5c6eEUhw4cIDW1lYGBgakX4Dw7hQ8VDHMT0tLo7m5mcHBQfLy8mhpaSEjIwM4gb8SeK3c3Fz8fj8Gg4GJEydSVlbG0NAQBQUFKJVKiouLZaIZHBwkKiqKw4cPc84550hFWI/Hg8/no66ujtzcXOnm3d7eLtVGhGikXq+XGDRAbhxDZ1fiZ/39/aSlpVFfXw8wIqkIp6nY2Fg0Gg1Hjx6VLfXQ0JCUZ2pvb0er1Uriu9BME4q3AnZyckILPZfQEIkMPuO8ikouPz8frVZLTEyMxMCJ34Wq/47G9x+jie0Ux3PPPcerr76KwWCQZr4CwDp37lwaGhrk4Fy0gHfddZdEvc+dOxer1SpnQwKxL0CucAIZ73K5ZNUzPDwsh/WiNRKqFsI8JSMjg4qKCsLCwsjNzaW5uVla84nZV3h4OImJidjtdvx+Px6PR250Ozs7pdKHaGHFPE3AR0TCEkwBQUMSvNHo6GjJ36ypqUGr1dLU1CSxZqEhEnh8fLxchMyYMQOz2Ux5eTlpaWmy5RWeDWJRIc5J0H/Eeft8PomVC01oIsmJ/wbkRlZUkkI2KS8vbwQOTjzngQce+L4uqdH4ghhNbKchpkyZwtq1a7nuuutQKpU89NBDNDU18cgjj/Dcc8/hcrkoKSlBr9fT1NQkk5rVaiU5OVkOzRWKE8YnXV1d9PX1kZ6eTm1trXRGCgQCcm4mrOaEq7xQwxgeHuass86io6NDyg/p9Xry8vKkLJEY1iuVSunq7vF4JM4sJydHQhpCk+vJJsih1ZBYjIQ6ZoljCxpXdXX152ZZoe2fwNcJ6IpIhoIqptfrMZvNUi5cOFkJbGBnZydjxoyhtbV1RKIVLecXJdPQv0XiCgaDI+TXgRGQlq+CVI3G9xOjie00xOTJk3nyySfZvn07aWlpHDt2jDPOOIPW1lYiIyMpKiqSczCTycQNN9wgk4HH45F4NIPBIJOJAIzm5ORIYUeNRkN8fDw6nU5uHwWFSZiLxMXFsX37dmJjYxkeHqa/v1+qeghbwMjISNm+NTc3k5aWRl9fHx6PB4fDgcfjISEhAb/fT3x8PI2NjXR0dIyARAjcF5wAvwrcWn9/PzExMYwZM4a2tjYsFotUrRXVpEiKAp4h3pvb7ebAgQPExsaSnp5OUlKSBCr39PRIa0Kj0YjVaiU8PFx+pjExMSQmJnLs2DFJrxKVocCwiSR6cnIV/y0eKyq0k9vWUFHO0Ti1MUqCPw3xu9/9jsmTJ+Pz+Th69ChWq1Ua895yyy2y1Qtt54RLUiAQIC4uTm74RLUjPA5EEhkcHCQpKYmhoSEGBwdJTk6moKAArVYrX3fs2LHSdamnp0cmsf3793PkyBHsdjtqtZpgMMjGjRslXUucjxiIm81m2UL7fD48Ho9MPvPmzWPixImyfRbHE6oX0dHRpKam4nQ6sdlsNDQ0EAwGJc1LEN0FTENUq16vFziBZ2tqakKv16PVauVcTsAsRHKPjo4mKSkJjUaDRqORAppiY2kwGCRvVCw1RBWm0WgkVi50UxoaoRWlSIri3+ZXv/rVqbq0RuP/YrRiO00xd+5cCgoKeOeddxgeHuaVV14hPT2dvXv38sknn3DBBReQlZVFeHi4nHXZ7XYmT55MZGQkdXV16PV6eXOKBCAMSYS2mEiGTqeT4uJiOe8S4FmhJGswGNDr9VRUVDBu3Dg0Gg379++XgGCXy8X777/PDTfcQHh4uISQWCwWCfcYGBigp6eHYDDIpEmTpBOUoIsJ9L6Y8wnlC6EmcvjwYYmvM5lMtLe3k5CQIKWMRGIEaGlpYXBwkMLCQoaGhqQhjFar5fjx4xK4azKZcLvdZGRk4Ha7OeOMMygtLZUULavVSlVVFTabDafTKTF6goYlQvwbKJVKyQ8NbUW/aAYX+v+jcWpjNLGdpnC5XKSnpzN//nysVitbt26ls7OTKVOmkJSURF5enmwF3W43Op0Om82GTqfj2LFjmM1m6dokFF3FplGtVkt1W1GdCIMXjUYj2zmtVisVY0ULaLFYpPuTUMnwer2S5/jyyy9z2WWX4fF42L9/PxMmTEClUlFYWMjw8DB5eXlyCymcnISqhUhuQgJItKfCB0HQunQ6HQ6HA4PBQHZ2Nj09PbS3t0tIhsvlYmhoCL1eL8n1Bw8eRK1Wk5mZKRVro6KipKm0MFgR5sWdnZ1y9piUlEQgEMBoNEoNNgHmDd1omkwmSf/yeDz09vaO2JLC5xPZ/8/80H/nGE1spzHa2trIy8ujvr6emTNn4vP5JOh0aGhIDrRFdaRWqyVsQfwt+ImCxC2qNaHfb7fbqa2txfb/2jvz6KjqNP1/KgnZimxkJRuEELIQtkCgISyGTXa1lQGcFlubsbWdcUZtHLFHRqW15TQDDm2D2nRLo7bSHJZmE4cGWQMk0qwhkASSkACpLGQrklSqKvf3R37v11shICgK4n3O4SShqm7d2t56l+d9nqgoJWlkMpmwWq1cuXIFs9msnJcuXLigMg/x7ezatSvu7u7U19cTERFBc3Mzubm5BAQEkJKSQm1tLU1NTdTU1Ki+X0tLC8nJydhsNoKDg5Xmmn4CCSgdM9nz9PPzo7y8nNjYWEpLS/nRj36kembCMUtISKCgoACHw0FjYyMOhwN/f398fHyIjIzE4XBw/vx5JVOenZ1NZGSkkkgXSSSbzUZQUJBalrfZbFRWVirFFZE7ErFE7f+7x3t6etLY2Kic6+X58vX1VdsdUo4aQe32wQhstwliX1ZSUqI+WOIgLoKRXl5edOrUiYSEBNXzKSsrU5I7gvDwcACVxUjTW/6+cuUKVqtVZW6iPybUh65du3LlyhXVbzKbzYSEhNDc3ExJSYnqXTU2NqreVHV1NZcuXVJSMrLvabFY1JqXKM1mZ2ejaRqhoaFKArxXr15UVFSorBNQZbH0+oKDg6mpqVHZXWhoKE1NTTQ0NKg+nn4TobGxkdTUVKxWK8HBwezevRt3d3dF8G1sbKRHjx6YzWYV0MrLywkMDETTNCVbJMFSpq7610QCloeHB/Hx8crtXmSSRMlDJsLPP//8d/OGMuACI7DdRkgJI7QA/XpP586d8fDwULucVVVVXL582cW+TpQu9Osn9fX1KluTzEOWywXSHBczYinDpNTy8fHBYrGoc5CgFx4eTkFBgdqx9PPz4+LFi8ojITQ0VGmdHThwQO16SrZps9mUzFJZWRl9+vThyJEj6rGIxE9tba3atZTBgaenJydPnlTlsmS2IqLp7+9PQEAAJSUlHDp0SJnmTJ06lQMHDuBwOEhLS6O4uBi73U58fDzV1dUqEAlp2NvbG7vdrtztxdPVarWqfqZMdE0mE9HR0VRWVhIYGEhTU5Naqpfs2cDtgTEVvc0Qgq6s3sh0T4JabW0t5eXl+Pj4EBsbi7e3t2qiy/QTXJvVUgqJb0FQUBC1tbWqVyTX6dSpE3l5eZjNZvLy8nA4HOTn52O32xk0aBDx8fHExsaqc7x48SIBAQH4+/sTERFBeHi4Igg7HA4uXLjAiRMnqKiooK6ujoqKCnx8fPDz81PUENkYiI+Px8vLy0U6W4KCh4cHXl5enDlzhrCwMLy9vdV9a5qmpsa/+MUv6NSpE3a7naSkJAoKCigvL8dms9GlSxfuuececnJyuHTpEg0NDezdu5fi4mKlT1dVVUVLSwunT59W/Tgp48WbVQYGwguU85eszs3NjejoaOVyL45Y33TX0cA3gxHYbiPeffddunbtSnh4OP369aNLly4EBwcTGxurAoaHh4fS+hIfgPDwcBUApAQTz1K5jX59CNpEI4OCgtRAwuFwEBERQVJSEvn5+bi5uVFWVqZKMmmOW61WtR3g4+NDXV0d9fX1VFRU4Ofnp6gQgNqACAoKUmTYqKgoPDw8CAwMxGQyKaHG3Nxc5UbfngsmJaCnpyenT5+msbGRsLAwRo0apcQff/7znxMUFITT6VRZZXp6OuXl5YwZM0Zln9C2ZdDY2EhjYyOlpaWUlZXx97//nVOnTlFSUoLT6eTMmTNq/1b8DsTYRsjI8hyLPV9jYyN+fn4AypUrMjJS3e4///M/b8O7ygAYpegdAeF4hYeHq8Y24NJ/Es+C3r17K40vCWJOp1PtJnbE0Pf19VX9KPmgCnlWSisxIZZG/4ULFwgJCVFlZ11dHWazmYiICKqrq+nRo4fq4YksUlNTE62trerD7unpqZrwUhrr5ZNEDy4zM5PNmzcrfpuUytLHGzZsmMpC+/TpowYBshbW2tpKVVUVMTExDBs2jICAANXzKi0txdPTk4CAAKWMIkrDTqeTmpoaNE1TxtKy3yrPH6DMXeTLRXqAwtuT1oFQQEJCQlxaBga+exiB7TbjlVdeYfny5dhsNrV/2F4XDFA0g9bWVqqrq+nWrRuAoiV0ZB4rH3qR5xEemSzNywc4KipKEU+FwyU+nU1NTcpRHlBmK/LBF28EIbLa7XasVqsKYHV1dS7ZpCydO51OrFYrpaWlmM1mYmJiKC8vV1lfS0uLGqYcOHCAsLAwCgoKGDlypBqMSBnq7e1NZWUlffv2xWRqE7p0OBxKJkkGIcKhEwkiOQ9x5uratauyL5QlfJnYCl1GSv66ujpiYmLU4Ka+vl7tjvr4+NxSs2ADNw8jsN0BaG/yIb0wPelT7+4kH1w92184V/Blv01fipnNZpXlSalVXl6uZIwkANbV1WGxWFRm06VLF8X2lwmlw+FQWw9OpxMfHx+VLcqxJQAHBQWpc29ublY7pHKOVquVI0eOqNJPv3AuXLv6+nq1GVFSUoLZbFY+oj4+PrS0tNCtWzeKi4vVapfsuYaFhVFbW6vUieUcgoOD1VBEgpzcXl4DTdOUIKY8FzIgkLJcDKNFOUX//Bu4fTAC2x0ACUj6rMtkMiltNVG52LJlC/369WPYsGFqgCBZXHt5awlWUtbZbDYlXinEWKFnSMklvbpOnTrR1NREc3OzapbLh1XIuyKu6HQ6CQwMVAYzUtoGBwcrYq/oz3Xu3JmePXtSWFiI1WpVK0xyPhLA9c9FbW2tGj44HA6KiorUloSbmxtFRUXU1NRgt9sZPnw4zc3NKlsU1yh5XMHBwSqzqqurIywsTCmdBAYGqn6h3s1eFFLky8bf31+pDYtBtbe3t5JWEgqNgdsLY3hwB0C4ThJgbDabyizy8/N57rnnWLJkCfn5+Vy4cAFos3YLCAhwUccAV+a7nh9mMrV5GNjtdnU9CZ6iHgK4lKmS3cl9iDSPnjoixFshFTudTqUiIkHCw8MDPz8/vL29uXDhArGxsaSnp6vHKyq37fcw9WKO/fv3Jzw8nKKiIiUcYLFYlPN8Q0MD+fn5NDY2Kg22y5cvExMTo0jAYj3YtWtX5SEaEhKiArnNZlOEYB8fHyIiIpTSrhCfhWIj/UmhxIj3KMCvfvWrW/wOMXCzMDK2OwT6MtLDw4OTJ0/y/vvvA20f8IyMDJKSkkhMTFTmKnrhRT3dQy9FLRlRQ0ODomlUVlbicDiUMoio2AoJWO89IJmhXjJbXxq3V7voqNcnu6BdunTBw8MDs9mMzWYjJSWFyspK1btqX8Lp7+f06dP06tWL6OhotmzZgpubG6mpqWrRf+DAgTidTuX5KWW6xWJRopLl5eUEBwdjs9mUT6qsgZWXl6vtArHSExklHx8fNX0WYU2TyaQUUYRyk5SUdIvfFXcOxPPh+7L7atjv3UH4/e9/D7R5K/7lL38hNDSU5ORkJk2apBRsZXG9veKsBCG90a9kcsKYDwkJwWQyUVFRAbQFHMnI9Gg/wJCSFr7UHrueVM+1YDK1KehK+SrSR/qgpv9dr2cm/xccHExYWBgnTpzAbDYzbNgwKioqFGE4JCSEkJAQLl++zL59+xgwYAAAFy9eVG714pUaFxdHQ0MDPj4++Pr60tLSQmVlpZoknzt3TkmlJycnq+moKBBLUNNz1saNG/f1Xvyvgc2bN/OXv/yF5557jkGDrnKgQ9M0zp07h9VqpV+/ft/ovnr06EHv3r3ZtGnTNzrOrYbJsN+786FpGhs3buTIkSOMGzeOrVu3uix9S99HX3bK7+2Dgxgf+/r6qma+KNcK1UEvV60PXMJL09NH2oso6u9TiMKaplFXV6d6Uh19w0sgk16fPI722mdyTvoMUQYSQqUQl/uIiAhyc3MJDg5G0zSOHTtGeno6gwcPJjo6mvPnz1NYWEiPHj2U7prQY4RkK2W22Wxm48aNfP7558TExCjqRm1tLYsWLcLf3x9/f39FyJVsTV/Of1289tpr2O12FixY8JXXfe+993j55ZeZM2cOkyZN4je/+Q0/+9nPgLaJ7ccff8yLL76oBEJnz57NlClT+NGPfqRev+rqaj788EPMZjMJCQkUFhbi7+9PXFwcBw4c4Pjx46q0LyoqoqioCIvFoibBTU1Nqs8qa2Z3Cu6cMzFAXV0dubm5jB07lk2bNjFixAj69evnYs6rN+4FVxVX2TaQxW2RLBJvzytXrqimN3y5wtWR3r/+uPpgI5dZrVYVnGR9SK4rYpjtS1U5Xkdl57XuR38b/f+JOrDFYuH8+fN07dqVs2fPUl1draSFLl68SG5uLkOGDGHYsGFERUVRWFiIxWJRlBYh+NrtdrZs2cKJEyeoqalh2bJlSjPO3d2d5cuX8+tf/5qZM2cydOhQ5eNaW1tLREQEmqYxcuTIG36tLRYLq1evxt3dnfz8fKqrq/noo48AqKioUF9IQpYePHgwly9fpri4mLq6OrKysti9ezdJSUk88sgj3H///SxYsIDW1lYuX77MyJEj2bNnD3379sVisfD000/zxBNP0Nrayv3334+/vz/vvPMOI0aMwGQy8cc//pGEhATq6+spLCxkyJAhpKenYzabCQ0N5cMPP2TFihUkJiYqg2p5X4qMVVJSEps3b1ZUpNsJoxS9gzBixAhiYmLYsGEDDz/8MHFxcQBXfeD1DXb9xFK0zSIjI4G2hrqsH0VERODp6Ymvry9XrlzBbrcrN3V9aQuuwVLuQyDrRNCWpYgiiT6AyQ6nfIO3D1L6n3p0dA7ye/tzkSzUbDYr9WBR3AgMDCQ8PJwBAwao9a6qqiq1KRAbG0tJSQktLS1UVFSwbt06Ghoa6N27N6NGjSIxMVFlcoDSlNu/fz85OTlUV1czY8YM9uzZQ2FhIaGhoSQlJTF06FCGDh3Kvffee83X2G63s2bNGp599lkmTpyIp6cnCQkJhISEkJ6ejp+fH+vXr1dmP01NTXh5ebFv3z66detGVFQUNpuNOXPmKGoKtA2cysrKcHNzIzY29qr2gjyP+/btY+fOnVitViZMmMCYMWOuea4dQegtZrNZ9R7l9Rg/fjy+vr787ne/o3v37jd13K+La5WiRmC7AyDyQTNmzGD06NHMnTuXyZMn06dPH3Udk8mkiKuAyuCkJJIAZ7FYiIyMRNPafEHl21+//tTc3OwS2AC1iaBv2AMumZL+n1j8aZqmlD2kNJZdV30wkoAomZK+DG5f6nZEW2l/bhLYZOvCZrPR1NREQkKCEsbcv38/3bt3JzQ0FJPJRGJiImfOnMFisXDw4EEKCgqIjY1l9uzZdOvWjZ49eyo5cTHEEWklQKkW7969m6NHj5KSkkJmZqZS8y0qKmL58uWcO3eOgICAq17nkpISxo0bR1RUFC+//DKjR4++NW+gOwQWi4UFCxbwySefkJSUREZGBkOGDKFPnz6sX7+eSZMmkZqaekvv0whsdyA0TePhhx/mr3/9KwsXLmThwoWcPHmS4uJipk2bRlhYGP/8z/+suGj6Hob0yQAlMmmxWAgNDVVBRDhbQk+QXpKsVUmPRL+F0D47av9/0JZ1CN+toaFBnYdQIaS3JmRcISAL7659hqi/Hz30/b325aict5ubm8oaxY1r6tSpPPXUU0RERHDixAmioqJITU1l0KBBLF68GKfTyZgxYxg+fDihoaGK9uFwOOjcubNSGXFzc6OmpsbluRY1EUAFtOnTp6tznjZtGtOnT+eRRx5xeSw1NTXMnj2bAQMG3PWOVc3Nzezbt48DBw6QlZXF6dOn6d27N9nZ2Tz66KNkZmbSt29fJXn1TWAEtjsQFy5coG/fvqxatYrHH3+cmTNn8r//+79AW1M6Pj6eiRMnkpyc7FKOti/ToC3rk6AmqrtC5ZDGe+fOnamrq1M7m0IA1pejkvl11AOTn/ryV4iznp6eatVKAoGUqtKHEQvA9gOJw4cPAyh3rNraWo4dO6YmtuXl5ao069KlC7GxsURERLicR6dOnThx4oTyahg8eDC//OUv2bp1K+7u7uzatYuCggIyMjJ4/PHHcTgcah/V3d2dgQMHuqh5QFsGIl8m+t1PkTICmDp1qvr/NWvW8PTTT7NmzRpGjRpFWVkZjz76KAUFBdTU1DBhwgQ++OCDH6zyR1lZGb/73e84evQo2dnZvPrqq8yZM0dVHV8HRmC7A1FSUsLw4cM5f/48CQkJ+Pr6cvz4cXX5jBkzyMnJ4emnn+7w9rLrCCh5cJkcwpcS4XoPUglsUmbJ4rocrz2doyMahhxTBhOapinTF1mklwV+IfcCSprJZDJRXV3NqVOnKCwsVGY0Fy9eJDExkZMnT5KUlKSUNwYPHqz03Kqrqzl58iRxcXHU1taqrFAe18SJE0lISKCuro7hw4cTERGB3ZDz9lcAABZuSURBVG6nrq5Oub47HA5SU1OVYCagKCihoaHq+dULEshPgNLSUqDNAEYCW1VVFaGhoTz99NO8/fbbaJrGT37yE4KDg3n22WeJiYm5o6aGtxtHjhxh3rx5Kvh7e3srwnlcXBxpaWk39AVgBLY7CFVVVbz55pv83//9H5GRkWzbto3Fixczf/58du3apThJTqeT2NhY7rvvPuLj49Xt5TVrbm5WvRwh1+r3OPWGI/Cln2dAQIDyMtCvAEnD+Vp+mO3/9vLyoqGhAavVyrlz57h06RLJycl89tlnNDQ0MGjQIFpaWqiqqsJkMhETE8OlS5coKyvD29ubhIQEEhMTiYqKUhPdPXv20KtXL5KSklyCqX6ocOHCBWXVJ/04Pz8/pQwsem/FxcUEBAQQHx9PQ0MDAwcOpLGxkcLCQqVOHB8fr7xWRSE4JCREPR9y/9LbBBQPUJ+ttba2MnDgQOrq6jh37hyvvvoqa9asYe/evWpKbcAVmqaxYcMGdu/erb58nE4nBQUFnDt3jn/6p3/iueeeo1evXtc8hhHYbhLSmO/Xrx/x8fEkJyejaRr/8i//4jKN+joYO3YsXbp0IT09nb59+zJ27FjeeOMNNm3axODBg3n77bfVdX/605/S2NjI4MGDAbh8+TLQxuKHtg+0BKKqqioVnGQpXnpPItUjab+sbNXV1akAoued6cs8ybw6deqk1ovkMnkj7tixg9TUVLKysvD29uaee+4hPz8fHx8fQkNDsdvtnDx5ktTUVFJSUpTJSvs1Kv0AoSNqiFwu5yAZpvwtWm4y1TSbzZjNZgYMGICHhwdFRUXU1dXR2tqqFE8SExPVcyIZm5Bx4UuRAn07YMiQIS6vaWtrK9HR0cybN4/4+Hhmz55Nbm6u4nwZuDmUlpayYsUKli9fjsViuebGg0HQvQkUFBSwbt06mpubOXToEIcOHVKXnTx5ktWrV7tcf9u2bbS2tjJp0qQbOv7o0aOZP38+a9asASA0NJTAwEDS0tKuWklKS0tT8tlikwe4GKdAGwdOLzDZvnSUtSmZisKX3DdN05R6LeDSJ5NmuZSeelJtc3Mz2dnZHD58mPvvv5/IyEh69+6N0+nEzc1N0U7E/6Bfv37YbDaXSaccS/9Tzl2v8qEPcuISJWW4XFd+ivmMkJSlZA4LC6NHjx7U19cTHh6u1s0qKiqU/JGnpyfBwcE4nU4qKioICwtzmXDW19dfFdTkOX7xxRdZtmwZvr6+rFy50ghq3wAxMTF07txZSVHdLO6ajC0rK4uDBw/y2GOPERQUxKeffkpISAhpaWlcvnzZpXfyVVi/fj0//vGPGTVqFI2NjZSVlREcHMybb75J165dKS8vZ/fu3dTW1pKRkcELL7yA0+lkwIABOBwOXnjhBbW0ff78eRYsWMATTzxBp06d6N+/v9o7/Otf/8q2bduorKwkNzeXnj17smbNGmVXB3D8+HGmTZtGcXEx7777LuDKKxNeEaB6Eu25YJKlyWRV6Bmy9O3u7q5+6vts0sOTzMrhcBAVFYWmafz5z3/m2LFj9OjRg8zMTPz8/NQEVB+I9BsF+vPTy5RLMJI+S3V1tUvQbR8IBUJgtVqtalKqaRo2m02dv0BEOSVYzpw5E0BloHJd8V0FSEhIcLk9QHJy8g2/jwx8fVRXV5OYmMiBAwdcXof2uOtL0Tlz5vDHP/4RgKeeeorly5e7XL5161bS0tLIz89n4cKFzJ8/X5V37VFTU0P37t3JyspSi83z5s3jrbfewtPTk/T0dDIzM/H09OTo0aOMHTuWBx98kL1791JdXc2SJUu4ePEinp6eyrkoICCA5uZm4uLiGDhwIJ988gndunVj7NixpKamkpaW1iGp8ejRo0yZMoWysjKAq6gCHTVY21MnvL298fPzU819mYa2trYqyoe4rAufDVAqsXqJbE9PT7KystiyZQszZ85UDlfC/9KXiIJrBTZ92Sk+ATJNFbd68UBt3/fr3LkzjY2NKvuU7E0GInqBAAlcEtzav+dnzZqlBhv6wNY+Uxg+fPhVz7WBW49ly5bx1ltvMWHCBJYuXXrd636vA5usu/z5z3+mpKSEyMhISktL6dOnD97e3pw+fVpxkUT5wt/fn3/84x9KQqdnz56cPn2aqKgopkyZwoYNG5g1axZ9+vRh+vTpV5WAixcvZtGiRQwYMICNGzcqNVrJJL4KmqZRWFhIYWEhEyZMULyuRYsWYbPZmDx5MgMHDrzm7Zubm1m2bBnvvPMOL7zwAnPmzFGXLVy4UP3efitB7lvQpUsX5YUpNAtRapC9TV9fX5qbm6mrq1PHEa20wMBAtTolxit79uxhxYoVzJo1S5Vtomar783JTx8fHxflj+bmZry8vAgNDVUimLKiJGWzl5cXly5duorkK3+LfV5VVRWtra0EBARgtVoVd6892Tc8PFyVpXI8CYaenp5KIVg/ENAPVkaMGHHd19vAN8fZs2d54IEHaGlp4U9/+hNDhgz5ys/aHR/YnE4nWVlZhISEEBgYyMqVKykqKqK8vJysrCySk5N57LHH6N27NxcvXiQ6OpoTJ07gcDjo2bMnDoeDc+fOUVtbq1RVbTYbPj4+TJw4kZSUFBcJnldeeYXy8nJycnIoLCxkypQpyg6upKSEkJAQ+vbty4oVK3j99dd54oknvpPnAdqyjmeeeYbc3FyeffZZ7r///quus3Dhwg4lgvSvp/S4nE6n+pAKsVY++FKmWiwWtf8nb6aIiAhMpjZzZQ8PD2pqajhz5gx/+9vfiI+PZ/z48dhsNrWgD1/2w/QZlo+Pj1KcjYuLUwbIDodD2fwJ307TNGU3qGka0dHRNDc3qwAmxxb3qsbGRqXI0djY6KJM4nQ6CQoKYtCgQRQWFlJeXq4eswQ1fYYmAxn9l8WYMWOMoPYdoLa2lvHjxzNt2jReeumlDt/bHeGOD2z/9V//xZo1a7hw4QIeHh5Mnz6dQYMGERoayqBBg4iNjf3W7ruqqooPPvgAi8VCYmIiSUlJWCwWcnJyOH36NMuWLfvWG8GappGdnc2WLVv45JNPiI6OZu3atV9JFfjtb3/r8ndgYKDL397e3i7DAr3XpZSmwqBvaGhQ/TRN0wgMDFTikDk5Ocor9N577yU0NJSWlhblgSCem+1Lxq8KbGICI1pt4ksgQ5ArV66oya0+29Lz8lpaWjCZTAQGBioHKXnMegUUu92uBgEpKSmcPXvW5bmqqalRvwcFBfHKK69c97k3cGugaRqjR48mNTWVpUuX3tSw4I4ObC0tLcyaNYuEhATmzp1LQEDAD4rMuGrVKv77v/8bT09PfvzjHzNp0iSGDx9+wy+wPrhFRUWp3/WCk9IDg7Y3knC1pM8GbZPVoqIizp8/j8ViUZlSVVUVycnJpKWlERwc7JL1yXGFmKsPnJId66et0dHRuLm5ERAQQE1NDWFhYVy6dImwsDCXlSk3NzdlCiPHEdMYh8NBYGCgWt+S/tnp06evGjDIY0xPTwfaymIJ5ODKT9OTo2UDxMC3jxMnTjBlyhTOnTt3Q20ePW4b3SM3N5ejR4/y8MMPY7fbOXv2LPHx8Uqob/Xq1cybNw9N0xg/fjzBwcHf9indNixZsoTnn3+ebt26UVFRoZRqO3fuzNq1a0lPT/9ao+25c+fy29/+1iWoAcpTVCBcNfhyz9HX15empiaqq6t55513sFqtxMTEKPnsUaNGERUVhZeXF83NzSqQiaGJ9L70+6JyXzcCCbJiGCPNfbvdrtax2m9CCJ1DMkChpnTv3h2LxQK4GuSYTCa++OILVVL6+vqSl5cH4DKB7tu3L8ePHzeC2neMvLw80tPTbzqoXQ/femDbsWMH//7v/84vf/lLampqCAoKolevXsTFxfHZZ5+RkJDAqlWruOeee77tU7nt6NGjB5rWJrHzpz/9ibFjx2IymfD39//GGercuXPV78uWLbvqcgk0km35+/sDcOnSJbZu3cru3buZOHEiY8aMUetDQp+QoOLl5UVtba2iX9hsNrUy1dLSogYE19pc0EMmn3I7CWrS4/Pw8FC9Nyl1S0tLXWgk/v7+Ktt0d3fHYrGoDEw2MQCVRe7duxdAZavy+KGtFWLg9iAmJoacnBwaGxu/0d6oHre8FHU4HFy8eJGYmBhMJhOffvopr732Gh999JHyr3z77bcxm81kZmZel6Nyt6Gmpobt27dz6NAhli5dyvHjx28ZL6qpqYnVq1ezatUq7HY70dHRZGRkuPghQNtAwOFw8Nlnn7F3717Ky8sZNmwYU6ZMUZNBvSqsyPfoA4b+cpPJREtLi2rai2gjuEoe6eHh4UGPHj2Uuqtke9Jf8/LyUn0zURARDTn40pZPbywj5w5tGYC+JJUsUm4PrsHNCGq3H6NHj+a+++7j0UcfvapPfD18Zz222bNn88EHHxAYGMhrr73Go48+yj333ENsbCxvvfXWdyZAd6fh3LlzpKWlMXToUK5cucLIkSOZP3++Wtv5pvj5z3/Ojh07WLx4MV5eXrz66qv06tWLlStXsnbtWpfrbtq0iQMHDjBjxgxSUlJUtqiXt5bVLcnehPIhQU2CVn19/VXeBPpNgvacNnANbEICltUwmWbKmlRTU5MaCIhRspeXlzoOtH3j648t0PfM2qP90MXA7UV2dja/+MUvOHr0KNu3byczM/OGbved9di6d+/OkCFDFM+ptbWV/fv38+abb5KZmcl9991HXFwcKSkphIWFER0dfVf31QSFhYWkpKSwdevWr9VHux62bdvGjh07+Mc//qFKzBEjRtC1a1f+53/+hwcffBCAtWvX4u7uTmVlJcOHDyctLY3a2lpKS0tpamoiLi5O9dyuVRK4ubmpJXCBLJ+3f1z6QKcPbj179lQEXPFN1Qc1q9WqeoCdOnXCz8+PqqoqvLy88PLyUsFLsrWLFy8CuEzOTSbTVQYmx44dMwLaHYYNGzZw6NAhiouLOXv2LNHR0S7bNF8XtzxjczqdTJ8+nVOnTuHl5UVQUBC7du0C2j6AIlVz+vRpKisrKS0tZerUqbz44ov07t37mzyWOxoWi4V7772X0aNHs3jx4hu+ncPh4PXXX+fJJ5/skHJSXl5ORkYGS5cuZfLkyS6XDR06lEWLFpGRkeHy/++//z5PPvmkKvUiIyMpKChg0qRJzJo166r7aGpq4tixY+rv9oobAmH5w5fbBu1vA9CrVy/MZjMtLS1qI0If0GTdSr8vqr+9XkKoPdrr7f/kJz+55nUN3F4cOXKECRMm8Mwzz9C1a1cmTJiguJc3iu8sY3N3d2ft2rXs3LkTTdOU/RnAhAkTmDBhgsv1a2trWb58OSNGjCAnJ8dFnuduQnh4OJs3b2bAgAF4e3szZswY0tLSruKp1dfXs3jxYmJiYggLC2P37t18+OGHbN++nX379qnrnThxgvnz57Nz504lJS6w2WysX79euZO3x2OPPcZDDz0EtPWmTCYTr732GosXL+5wMn38+HGXaWf7yaeenKsnVnakjOvh4aH6dBLU7HY7VVVValAgAa39bfWrXvp+mR4lJSWGYfH3BB9//DFPPfXUt/J6fStTUZPJ9JUmEfrJVmxsrFrxuZsRHR3N4cOHWbRoEQsWLODIkSOMHDmSuXPnKoejLVu28OqrrxIdHc3ly5ex2+386le/Ijs72+VYK1asoLy8nMOHDxMXF0dxcTFZWVls27aNLVu20L9/f9577z2GDRvW4bk4HA4OHz5MXl4eJ06c4IsvvsDHx4f+/furc3nmmWeuut31Vrjaq4NIiaq/TCSCOnfuTG1trVIVkV6jvC/0HLyOIFndCy+8cN3rGbgzUV9fzwcffMDWrVu/leN/JwTdS5cuqRQzIyODM2fO4OPjw6xZs9iwYQNdunRh8eLFDB069Bvf1/cJDQ0NrF69mjfeeIPw8HAeeeQR+vTpw9GjR1m1apVa/SkoKOD111932Rc9e/Ys//qv/8r+/fuxWq2Eh4czdOhQxo0bx9SpU1305I8dO0ZeXh52u528vDx27NhBXl4eaWlpJCUl0adPH/r378+QIUNumHbSUdDTQ99fk2mnpmmkp6erKatMbDsi1ephBK+7D7t27WLKlCmsXLlSVQ9fB7d180DTNN58801eeuklMjIyePfdd6mrq2PdunU88MADDBs27JY31L9PcDgcfPrpp6xdu5YzZ85w6tQpunfvzsSJE/nZz352XUqM0+lkx44dbNq0Scn3REdH07NnTzIzM3njjTfYvHkzw4cPp1OnTsTFxZGZmUlGRoaaLhowcDuQnZ3NxIkT+eKLL5TV5M3ijlipOn/+PC+99BKffvopZ8+evSm+yg8JDoeDnJwc1q9fz8qVK0lPT+fFF19UzHlN09i/fz8rVqxg69atxMTEMGPGDMXNKi0tJS8vj507dxIcHMzhw4dvGfHRgIFbiWXLlvGb3/yGFStWXNeP9Vq4VmBzUSZt/2/gwIHarYbD4dAiIiK0/Pz8W37suxGNjY3aihUrtNDQUG3Xrl3axx9/rA0aNEjr2bOntmTJEq2oqOiat3U6nVpjY+N3d7IGDHwNbNu2TYuLi9MmT56sHTt27KZuC3yhdRC7vvPA9ve//10bMGDALT/u3Y5NmzZpnp6e2oABA7SNGzdqTqfzdp+SAQO3DM3NzdqSJUu08PBwbeTIkdp77713Q+/xawW2GxM96gANDQ3XpBNcDwcPHsRsNrNp0ybFKDfw1Zg8eTLr1q3j4MGDTJ069Yb1qgwY+D7Ay8uL//iP/6CkpITnn3+eRYsW8Yc//OEqIYcbxXU/HVarlUWLFrF3715mzpxJTk4OL7/8MtHR0URERDBw4EBmz559FRP9enjooYcYNWoUS5cuJS0tjezsbJdVHgMdw2QyMXny5Fu2gmXAwJ0ILy8vpk2bxu9//3vef/99IiIiSE9PZ//+/fzhD3/g+eefZ/v27V9JB7puYKuoqODAgQM8+OCDREZGMm7cOHbv3s3nn39OQ0MD58+fJywsjN69e/Pkk0/y0UcfqfWWayExMZFf//rXbN++nX/7t39jzpw5BAUFkZKSwpAhQxg5ciQTJkz4WtmgAQMG7g6MHTuWgwcPUldXx0MPPcRPf/pT9uzZg7+/P/PmzSMmJoaVK1de8/bXnYr26tVLy8/PV39XVlYSEBBwVdZw9uxZNm3axL59+/j8889JS0vjvvvuo3fv3qSkpODr68vevXvJysrC39+fbt260b17d5KTk/H396e5uZn8/HwOHz7M1q1b+dvf/sbJkyeva5RqwICBHy7y8vKwWCxkZmZ+N3SPpqYmNm7cyM6dOzl16hS5ubk0NTUxdOhQMjIyaGpqori4mJKSEvLz8xkzZgxRUVFs3rwZp9PJ+PHjmTx5Mg888MDNP1oDBgz8oHDbeGza/19w7ojRXltby7p166ioqGDy5Mmkpqb+oIm6BgwYuDncNmlwER/sCIGBgTz++OPf9ikYMGDgBwaDM2DAgIG7DkZgM2DAwF0HI7AZMGDgroMR2AwYMHDXwQhsBgwYuOtgBDYDBgzcdTACmwEDBu46GIHNgAEDdx2MwGbAgIG7DtddqTKZTLdOF9yAAQMGbj2qNE2b0P4/rxvYDBgwYOD7CKMUNWDAwF0HI7AZMGDgroMR2AwYMHDXwQhsBgwYuOtgBDYDBgzcdfh/gT0autDA81cAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cartopy/cartopy-global.ipynb b/Cartopy/cartopy-global.ipynb new file mode 100644 index 0000000..0596d45 --- /dev/null +++ b/Cartopy/cartopy-global.ipynb @@ -0,0 +1,77 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import cartopy.crs as ccrs\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def main():\n", + " fig = plt.figure(figsize=(10, 5))\n", + " ax = fig.add_subplot(1, 1, 1, projection=ccrs.Robinson())\n", + "\n", + " # make the map global rather than have it zoom in to\n", + " # the extents of any plotted data\n", + " ax.set_global()\n", + "\n", + " ax.stock_img()\n", + " ax.coastlines()\n", + "\n", + " ax.plot(-0.08, 51.53, 'o', transform=ccrs.PlateCarree())\n", + " ax.plot([-0.08, 132], [51.53, 43.17], transform=ccrs.PlateCarree())\n", + " ax.plot([-0.08, 132], [51.53, 43.17], transform=ccrs.Geodetic())\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cartopy/cartopy-image-tiles.ipynb b/Cartopy/cartopy-image-tiles.ipynb new file mode 100644 index 0000000..2d21e42 --- /dev/null +++ b/Cartopy/cartopy-image-tiles.ipynb @@ -0,0 +1,73 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import cartopy.crs as ccrs\n", + "\n", + "from cartopy.io.img_tiles import Stamen\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def main():\n", + " tiler = Stamen('terrain-background')\n", + " mercator = tiler.crs\n", + "\n", + " fig = plt.figure()\n", + " ax = fig.add_subplot(1, 1, 1, projection=mercator)\n", + " ax.set_extent([-90, -73, 22, 34], crs=ccrs.PlateCarree())\n", + "\n", + " ax.add_image(tiler, 6)\n", + "\n", + " ax.coastlines('10m')\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cartopy/cartopy-img.ipynb b/Cartopy/cartopy-img.ipynb new file mode 100644 index 0000000..aacaaeb --- /dev/null +++ b/Cartopy/cartopy-img.ipynb @@ -0,0 +1,127 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " from urllib2 import urlopen\n", + "except ImportError:\n", + " from urllib.request import urlopen\n", + "from io import BytesIO\n", + "\n", + "import cartopy.crs as ccrs\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from PIL import Image\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def vesta_image():\n", + " \"\"\"\n", + " Return an image of Vesta's topography.\n", + "\n", + " Image credit: NASA/JPL-Caltech/UCLA/MPS/DLR/IDA/PSI\n", + "\n", + " Returns\n", + " -------\n", + " img : numpy array\n", + " The pixels of the image in a numpy array.\n", + " img_proj : cartopy CRS\n", + " The rectangular coordinate system of the image.\n", + " img_extent : tuple of floats\n", + " The extent of the image ``(x0, y0, x1, y1)`` referenced in\n", + " the ``img_proj`` coordinate system.\n", + "\n", + " \"\"\"\n", + " url = 'https://www.nasa.gov/sites/default/files/pia17037.jpg'\n", + " img_handle = BytesIO(urlopen(url).read())\n", + " raw_image = Image.open(img_handle)\n", + " # The image is extremely high-resolution, which takes a long time to\n", + " # plot. Sub-sampling reduces the time taken to plot while not\n", + " # significantly altering the integrity of the result.\n", + " smaller_image = raw_image.resize([raw_image.size[0] // 10,\n", + " raw_image.size[1] // 10])\n", + " img = np.asarray(smaller_image)\n", + " # We define the semimajor and semiminor axes, but must also tell the\n", + " # globe not to use the WGS84 ellipse, which is its default behaviour.\n", + " img_globe = ccrs.Globe(semimajor_axis=285000., semiminor_axis=229000.,\n", + " ellipse=None)\n", + " img_proj = ccrs.PlateCarree(globe=img_globe)\n", + " img_extent = (-895353.906273091, 895353.906273091,\n", + " 447676.9531365455, -447676.9531365455)\n", + " return img, img_globe, img_proj, img_extent\n", + "\n", + "\n", + "def main():\n", + " img, globe, crs, extent = vesta_image()\n", + " projection = ccrs.Geostationary(globe=globe)\n", + "\n", + " fig = plt.figure()\n", + " ax = fig.add_subplot(1, 1, 1, projection=projection)\n", + " ax.imshow(img, transform=crs, extent=extent)\n", + " fig.text(.075, .012, \"Image credit: NASA/JPL-Caltech/UCLA/MPS/DLR/IDA/PSI\",\n", + " bbox={'facecolor': 'w', 'edgecolor': 'k'})\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cartopy/cartopy-logo.ipynb b/Cartopy/cartopy-logo.ipynb new file mode 100644 index 0000000..1324b34 --- /dev/null +++ b/Cartopy/cartopy-logo.ipynb @@ -0,0 +1,106 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import cartopy.crs as ccrs\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.textpath\n", + "import matplotlib.patches\n", + "from matplotlib.font_manager import FontProperties\n", + "import numpy as np\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def main():\n", + " fig = plt.figure(figsize=[12, 6])\n", + " ax = fig.add_subplot(1, 1, 1, projection=ccrs.Robinson())\n", + "\n", + " ax.coastlines()\n", + " ax.gridlines()\n", + "\n", + " # generate a matplotlib path representing the word \"cartopy\"\n", + " fp = FontProperties(family='Bitstream Vera Sans', weight='bold')\n", + " logo_path = matplotlib.textpath.TextPath((-175, -35), 'cartopy',\n", + " size=1, prop=fp)\n", + " # scale the letters up to sensible longitude and latitude sizes\n", + " logo_path._vertices *= np.array([80, 160])\n", + "\n", + " # add a background image\n", + " im = ax.stock_img()\n", + " # clip the image according to the logo_path. mpl v1.2.0 does not support\n", + " # the transform API that cartopy makes use of, so we have to convert the\n", + " # projection into a transform manually\n", + " plate_carree_transform = ccrs.PlateCarree()._as_mpl_transform(ax)\n", + " im.set_clip_path(logo_path, transform=plate_carree_transform)\n", + "\n", + " # add the path as a patch, drawing black outlines around the text\n", + " patch = matplotlib.patches.PathPatch(logo_path,\n", + " facecolor='none', edgecolor='black',\n", + " transform=ccrs.PlateCarree())\n", + " ax.add_patch(patch)\n", + "\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cartopy/cartopy-ne2-cache-test.ipynb b/Cartopy/cartopy-ne2-cache-test.ipynb new file mode 100644 index 0000000..a3a2e03 --- /dev/null +++ b/Cartopy/cartopy-ne2-cache-test.ipynb @@ -0,0 +1,68 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import cartopy\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "## Natural Earth 2 cache test\n", + "## Live 8.5 * darkblue-b\n", + "##" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ax = plt.axes(projection=ccrs.PlateCarree() ) \n", + "\n", + "ax.add_feature(cfeature.LAND)\n", + "ax.add_feature(cfeature.OCEAN)\n", + "ax.add_feature(cfeature.COASTLINE)\n", + "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", + "ax.add_feature(cfeature.LAKES, alpha=0.5)\n", + "ax.add_feature(cfeature.RIVERS)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Cartopy/cartopy-nightshade.ipynb b/Cartopy/cartopy-nightshade.ipynb new file mode 100644 index 0000000..aeaccb7 --- /dev/null +++ b/Cartopy/cartopy-nightshade.ipynb @@ -0,0 +1,76 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import datetime\n", + "import matplotlib.pyplot as plt\n", + "import cartopy.crs as ccrs\n", + "from cartopy.feature.nightshade import Nightshade\n", + "%matplotlib inline\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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BPmdLULRrKdrSXtMXl1gCaDH3ls4DEWTp6uz50Rkz+TnAy7PryffLvt3Je5KsFAujM+DeHdGCcUjvWjFZO8uNvorpT/biP8qrvfHq9jf2RnpLnnPEcDX/Fo3xIr9aLqZZXs7e/Jxp56UxgohEm7UMgC+h/ouX4IRUlYgJtjDqggXR7/FuGSiQ+EzSG0F+Wv3R0T1eUe/xeeUXyvfnnD4ngYsgqisyC5/6z/Sns/TAhIgwm60+5/KcLc9yR+Bk1cUAKGjHOQ9YEBBJKTVngMCSp2JaxJOY21X/kgVB0Lb+bpdye9Kg5+tEIdJiUklZyxnCWta16p2U8l8If0dIujwvPwd5azugC8Nffu2CjzOHMSk0kTguS+qk5Jd2XrMMlEnnn6KvWnplSgXQUzN5Ai5reTvOQA9ALKeFiVI2vFDoUgAYEUWlbWNHKWia9EkRFOUKJVxOyNUyvsRQA+fv7OvAyR4oOt/GS0YNxOy2woz0sPF51JnnZ725lHXpFs+t1kK2W6TYIrlyfrR3+5LWM9SKLxR824blq9z21X3LHe9lHbL8aUWRiNuCMSxMpbbz2kgpW1GuCyTXmU995rGc3R2DG9uiXnE+yOsysZK8wCo4JkEs2vna9q2vvnI9S9rTfdEiz7re+2UjenZ9fVHRpc92EUFSY+XMEafZAqtv+9E3qhDGJQEPYwxGzHJwXeimji4H8KD4MMaLghvLSaGfi/tFd/LISXpH7HsxiR0eVYOo4r2PMrY4lzqqtHxf8emsETlP59xZ2WKCLKFzQIsWLvlkxLsvXVa1FtArCYX37TcASb/EIs+ho9k10W3jYrnYXmkFRbKgthIe7peTXzoC0Kuw88ZCvZOXi9LWs2iEQdXTXT+F/0TLd3epM9Uk/Zle+cJYLmlv+Xx/8qSXZGSevAyF0Gcg139gRTuXvnvlnd7si6yx1iJR/nz0mLXtSA3uqfA+2ut4a+LbekZw2SiHR4N0q5YvaRvoCSsX9QregxTypZI1gJQAc9nwaKuoW/lcgDFLjPgS/vXLdl4nC6U7qyylN8apD6sNc98QtAxc3q5eg7rVSP9Cv4rA+w4gRFpWd57Wts+F98IaiV9LI9nyPTWj9JL2FbaUU6EwJpJkJdZiTBtmXOxZEYLMlcf+eR9FKMCHpKNaZ3L44Mt3SQQ/IogYvAZDqCQDpXEdJhhjWt1WgJAMYHMn4x2NyyTVLP5JxCPbzp37eYCFEu0tL4Zvv8vqkHH3mVg8/bsgfz20sDC0cW4klqZWlMh9CRkJbfUu8biVtWxHjAn6JMlJtjmt3CR5y4Aj8blQclo2DigXaj5+L2MEksuE+qyY3NPczigjac1VhsVVFe+TjLaDfP5YL6HWWHa+l2N7B2ot05mgJQl5ScZIZqj3/ZoD4w2FCyiBghV6r6/HZcm9/jsW6luoO7Zjicsi5R1o93Jb78o66UzazsuL1XirDROf0mVF1MbiPbMivY+d1XkfmrTU1/VlaCeX6SvY/GxfCqNSwmQz24a3VputO6ci/FIY0LPqTasMU0y4zgD1HpYe8MtALH5Jk69URT0ohgDeKyJhoL3Xlnfqky7H+xZ1S8n2HM7qt8S0wndG50sD2gKm+Dx0FHlScrpQQe9jYZwWqR3jwvG5RIa0V75bqJSnvrEuV4kmT6LQARPj9KUuUXWhuJGwgo0r2jy+8X9jCgMeq1Tv8wT3yZAUnlNjwvUki4JgDYixhcoPnolk7Ft5idcBEYMRwXvFKETJiPeUxpUGph2GjtGgBUGtgYlGrpCVYEBieROGyZpgpLwGD0rtW2OfDGWQTYsxdOWt5IlIYbCCd6g7fpFLhZCfpQ1K3ZpKZn2S7pXARQvZiUCtkIS8Hly0NLlE8dZWllfZvP5Y9CstpXxZHsuqnneAZa+wFrong5BcKPEXUMkL02X1FyxrwSwShDr2rbR5mvpbvNpT2q/YSxNsdtuHKPcxnKReaRrX9qGnIJbxpLX7qU9nW5Csm6PZMXb1xuZzQAsMh1VmVHZVpgnRfjmj+X3xKAS2vNQ2PQuyRq5nM7DERZANT+SnyQxdvt4sBbN8dWdw4w3J9bc3WgVTdqNdT5cFVaVTJggVYRXVtyhLGaJ5xbRqlmnfwgCi3cnWCuzyWK4WV4RkuxZBQX5nqdSEGFJZbP3StvZuLuv9shylVtmkOHOnCQt19g1n7mPx6EL3OvIdnqmsySWdV4jG1RjbBSDpwbxKLd/aTuD05lWGLBiRtu1pBb0cjQRjrxHkFNHTBSWaZkTHCMcPxoTFiXdBSXnnuwazE/oq261Led3mAqR7MVwryYgFRWmtDZ4075Hcz3JhFBoZMEv0uqF4F+dZ1HClohaR4P1SsgI2RnKIpnEe21uIoeCc78z/rHRjuDQMezvbNfJeiF63gp8LMt6RhyKjSUB9ey/i4/h+U4hRqzO9T3pNixW6YG1qX9cclivmzhjFv9Zjs/h85N4S013eX6HK8/fY2p5nqXRk2ewVlSLUGm721Ztk7pc9Cf+3jo5UQlDtjmtHB8U50gl7Fe9dppueEy1nWU+1SgbH7TOtfUn/5XAUgjHEz7FjEah7wCUZkuDlyf2ktaFJxyiKOlfohCifae57T5DLtiOZX7EPZTpBWkQQr5PeV7JCF2VAtZWy1OGqep6gJQ98+mRMVsCp8vBSQugjMbnUltorn92bvcukTJSk+0MuSQkQUlJkWhnkKhKai21OqiE9240LL5elNHClgPocW8+wIy+YE/eVYjVHO5pRnxSDFLRou/rrtaP0BC0Yt8iz4okMMdIKqexLZ5Yl925Rr5YTlHZcpOWf6iIYyfUVjMxdl7Kyko+tMU4x/ta6tq7RtvMlK+P1OPkS3zUgsLxiLt+kbcGesUgfl39eFpCJr4msyaam+0JpBztPzqSUxLBqhVEqxFRFZFTOoUrXWld1KJjYp6qBNdIq9+Ru7s7TYB1br7CPYCeGt1Rp6jmN8y1b+oin0/Cup6JUVhTzOLxaYo5FUEvO+QyoWnAUZCp4ryQbWTEGvMOpy56NpOCCF67VJZlXBWPVB6DkFdSF0GwK47T5LW23SjXpW2blgqWyTXzueE1gQYpK8W5HpRcujG3qzM/saZHOWJTypPmfzpXOu8tnFiS8eH9rSLvyKks+Lb2/5HYnwT/pHE0gWcIioPCGJV2aDHSQL839T71I9iJ8bSdPa39M9maL2CU9D31PXoTwXTtjU45Ztn+rvAX9gV9RrEVO2rvUBWf5fYVObl8TlJIYzZ0OwEAxQgY2nrAYaO1UGgvfs1WmAB5JVTi89wHIp/aZVi8p3S6n/12x2KEA43EWkJ2JfUnLCjB5nM5i4nk5LQpOfVAWUBjIKOTaMl2REI+Nj5reCig937oiC0rGPSuhqGzKm6QBaTsajLGiTRNKGBM8LWl1WNplY3LsrzPxSwEqgJAkS9/C8J6S6xvG9NwiIAkTQhaEXnvPSeKZtnzUOIDaeyIYlg5aaO9neQ7Gokwg7QhO7G9Y7QWjZozpKFopQA+SPErJUEcZkP68bb1kpflUolHxUUGkZWbCJln7dLtT9tAUH4y2xlHRsGIrHtbi6Ywt+pMhv7JvSM6iPt8XqQR2Zbd8HqLwwSs55IAkg91rexwDIwlISZ5fPgIPh+Lm82zsjLFhRQUBlCT37qLKKJoZV28LLGohSlWZvHpKAmUkeE6IYYzUF/UaakzeI/UFAFWMab0JEtusqoht8wPU2Kxj3R2PTfpPCkBdBDmX6SA62RS9/vfnc2uAFzy0RfmOwe+Ne0YJhVdhQZloKQklsFjRyNzV9stCwFSL+3dKC806YyAKHVrmg5UGzvm+96doT/J+JDtT3NNO8WUoKdmdCGS1GwpMg5LG3kip7xNYkZD/kUEjnQVf8lJ434aOw5TpS8kSXVP0cUnPF64orb4TEWzc3ZQSrZN+HVQmekgVa+Mcjt7I1kui0fNisFWQNecc3jU5f6oaVNGbGTSxSQv01Mf4XudcK5VpwaItjzteXjHgfZ4vJoZ6c1+jjS09R6UGXEZng5Yo9OW8yhNJO9Ovy/IIGpJXISnShGoluq5a42dCnc7nyV6GMhIpPmuJsGLrgRDvaVS7K6HE0OQmlFahKK2eKPmIgubVo3T6Gl7jCrfuStYV6LEd3NLV1iqhIHxpi21/uFZHaIsO9F+e5SFNUIBUf5aM0AYpcwTKnRutykzsSMZKtTWICZwGsJMmU1GvpIlPfsZFQU2AWBSaHtA0JrjDm+jzFGAeRaBc3LdjlzpFRikprNDxuvTGs8ClxcUlvF6uJ7NLthSJ9LiVcD09akSoIovUK6PKYgZh50HdNAyHAWjaqDEUj8dSO6WJStgr1HGuqEIVlXU1HmXZT2VSMoQpXMX9vDT1Gt+XdkL1tiUKGGsxRqhsRZh/0a3sPU59nMZhlSfGRkCqNEmmfeCCMVJ4RmIoCkgZQh6yhyQvHJLRz/MpdussIx4HIclb+FrOvWVmpq2vXGmnfxff1SqPZUCwU0OvvvT0WQn/q2tsK23bm+Q9gSGW8Cfp2+JqX7mvfNfyCwtPFPOsO5+0e7t4YLkK63G8ABELCLEgnxdiXUWSajMJtERgY2K/nQ/2KiwaFU/KHTJBV6U2pIW7hBBGulR65LusSBqUDMPay9oXDkq4a0SwJnpCBKytMDb0xMfkf1SpGxfDhkrjmg5QzJ5sSXZcaeom8EcIczXaKGME5yLIE6gqG3VMkeKrig2xyDwsEan0PK5tG9pdnuV8jBxpV5Ndz98ZM/vcX3luQysJBUnZ2qBAUsNjGa/aaYDE1rYeV6W0+iIuo3JrbQtM0u4M2gEwYlD1kXGRkd7ltpmcg6BtWwv8kdruUztSd3KHw19MDCdta2VJv9v3dEmKATHR+5NBXJkcb9rVvce3jUjKpuPWVZLCbgWIBepMirzrRnGpXYUQJ8CSkyMTT3KRhIrj/V77QhWtezEDRDS7XpVgiBqv+MIlM7TBfVlHz4JXYW9jQAJEs8YxrZWNynBhwzBpHM7D7mZF7WEyb/AKLoJir0JdgCsfwWvOQWznRjt+rc1pVUVSehoVXGKnBv5J0XevUHtlb2RBlXElVNawbT1VZam9cnvquDy2jKxya+qonbI9HvCKK1sYIwyN4Joa5x2TiWd7c4OqqnDOcTxrgKA8jk5n1BjqCGCmtWNaO+ZOuV0rUwcpLigEsFRZAzZ4PIQqe9kSgGmcx7smrnoF7wUjNoPXJEbeuQBOvNLUNVnJSQCVXjXmpfgoLnVW0sQVYtjVEsoRgZdzhUrXJHNSvF8hAltbKERVD9aQkkjrplmcCIWcLrN2Cd52VIMURqUjGfFeT53ECdQpXdafv0u3FeXmhFVe33IM+nWX7+i/rWOwSyCT+9jr26Kt7Ri9PszQODlKr13eYRSrlWT1FvrWqXhJP9KX5KlV2i35y0Fmx2Om5QKl++6kqZusf1O4QuOcEIyGxbOiMQlVIGpOjZNH4+pLJOl4OiC61f6hH51QaWurycigxIzFre6aOXglEfAutC/tXiz1fEQhYYEsMZyjqT9Zk4U29kK6xgiurkPITtIuM+K8XHRctFwsBEjKABKksJVK+a62s8Z0+ZVhhCT5Wk5nJ+ICxobOu8jgFrwE8nn2x3gx7SqpJ08ZRpQgMxmINLiNc3RGkDTxiMY9rNBddH+JCYo2PeKLJVgCM2ICYs3xUmN7WE7bhMbl8wpY9PwslEpaQZKBE7x3OJJrOhrV0n3WIpTVA5Erl87XZETz7dw/ye8vt0wmI6ya+NStXwsdoW2Vue9pTJOYeR9crPsDCwonjefChmVslXt3hjT1HOfh2qRhZ2DZrTwG2BpXNHXDeDxia2uT4XCEoNw6OsVay6gyjDbG1NMpddMg1YCmrvHOc3pygvc+uEEVqmqAekdlLeoaUA0rBO+ZOZh4uH7qePxEuF1DFZXfZiVc2qgYDiusAK5hozKMBpbHDmZcn3ge3oMnjpXTRrm8M+RlexUVSu2VqxNP7ZS9jQGnkymV1lyfCa+8vM29l/dDW6K3xBjBNQ4xhsGgwohwenKKaxqcj0bYezaGA+bzGtc4Gu9gXlPXc5wYbk2VYRVXOI1jCAyNpTGGrYpcT2WE2sNM4fqkDdNVArWCUWiaAECEOBOzR9kAACAASURBVBdsNGthJ33YQovSOBfc0tiOPGhIKKJpHD7K72g0wlQWg+RtttAaFWtNAFEJiEdF3CYFJl3Szfladg6ORJCTDExlizyYhMxoQ0wtmVxGMiLVYqUMlbV5ziRwJ9KefbJo48+bty1pfJcveFO637XQmZ2cv+IVPp63YgQG1kQeSacMcWWeFiSJv/1dQfnrKjWWeRlW/eU7SnjXNzCt8VnkThe+tS9P4fzkBUTIIZjksW8d6oWS7h+vUYKehRaW3rvwn+uVS60cVINw1EIxIKqewWAAIjif8sKinTSSdWvrden5yGXx/3IRmotF+cx627RHXKR1nym2pyspzJq8mlr0eZnxT+9r+egan1B0weNeuT6bOnVBZkb+qsvLpTH3hW0XwiLZgEWwZ/ws4jmJuMH4OU3ojCxQOR0htaWIyyfBab0RIKIY0iRrkV/JliTQKZ6ZznvpTJCILNOE8j4EkoxJbuMkkKlMRKe+jenj6l4/KdqyPBhzpmrqQcVs/JNbTSJg08SflldB7srZnSZcKdm9dpTPxSqXXov8Soi5cb6jDFd1haggateGfppoRMaV8OC2YWcobAwMFmVnZMDVzBvP3vYG0/kcazy2EqpqwIsvbYTcDbFgDLO6QaqGrc0hp5MZN28fhlDJcIhv5sxnws2DA5rG4ZoGK2GcjbFUlUVUcY3DGMusbmiahmkzxznHVC0n8xk3ph4nlokTpmq5NBY+827LsDJ4wo6PDRzWgpWQ3GmMx3uH3WzYH1a8/PKIl122zBrPwWTO1sBwcW+L+emEuzYaau9REW6aAXPGvOrykLu3K4ZVBVVVKHPB2BBSaaL7tRqOEdOgKE0T3LVNPUesZe5CsGQwHrOxvY1vGi4OPDYqMINjXteY6L04qZWbdcV9OxUXxyasEpuan360Zndk2R8ZZrXjuAkG79JGxd27I3Y3RlnmvIeT2jGtG26eNhxPG6oqqgcb5qz3Ht8EICM2vHtggiencQ4TAZoxpvCMmOw9ETHUrt0q7p1mD2MnB827JeCA1kCJwVpiyMlkQ5fOqkiAJQCwaDzSoy2maauVApAXmw16Jj7IcJq72ZYtjxN3vMq9OVm+XmJdoV7J4YvcdxHmjc992RwaNgYhz6ey0Di4PW0CCI1Gbm8cvGons4bjmaMyhs4O0lW2hH6ZNjm4TFoN31udnfrT51p5vlO5ZTcvemJRmxJBRWh84amPcjMwaUkbbEOr41vw4cNKLLekMEN9bJPrkcT8JV333oetwMZGr4uL87cJVtHYcJZUGtPkeYnWa7kAly1YvJmApph2L81wOOibFwyEeZQxRuFLa3FmHK8zB3jl5QgrVrd2BZ5Z+C7n3Ejjk72RAW+4Ve3jDjwtXttzMiBM0tLFm17U3WqyvEeta90gvTe3ZyJozpoOwe8UdQz1Z2ChoLThD++Iiqt9b5k/0n4sp1cnyngGmxbneUccuqgnfyk9IQlYZMBCdN9F11u5EyafSxHvp9yQXEekzuqnaFBamObYbdycvyqc1O+bl7Ba3x9btgbC9kDYGwqHM8czp5733JqxVQmXxob7t4VpzqdVHrt2G0zFyNZc2B7TeGXQhGx07yaMx2OYTphOZzx7EDwHFkelDnyQLfWe4XDI9sZmAMoeGtdgYz5F3TRYIxxPa5z3zBrltBGeOTVMqJi4AdubA4z3vGSz4rhR7hor9+4I4+1dki51CtO6Ae8ZWoOxhqauuWsTHhxYqIaICBvecWl/i7rxSDVgtDukaWpGYrDWcMVITICTNocjapmkrK0Ej2AbOjUMbPBgDEZhfJL3q6oqjJgYHvU479lwDc411PMa75SRGeJdCNXsjSz3+oZ5PePazYbbM6WxYz7nJbvsV44hykSHjEYjajXsbVSMhzbMa/WczBpmdcPNKVxzDpNmlpLlTozBIgzHFuehaZrsKTG2Xdc5F3YdBPBi4yFrya1dqqLEh+jFS8Y9KuF8No8kOTYdefeqDIZVTEZO9ZSbXds57mL83hS7JPoejuxpdW2YNhuK3vxYRtqptChfGOt0Roaq5pBseF1IIt/bMNyeuMJ1H9r00P6QuVO2RpadccWFccXACr/31DGnc8e8cVzeGuK8Mqk909oxrgyjyvLyiyOuHjdcn7gCCK3oRKfdRE+xtv0rOjsaGKwI88a122x7lQugktO7SbhUVbEC++MKY+D6SZ3znDYsDCyMjHD3TsX1k5ob0yAbSuvxgpjI7TWe3CsQk1C9B5dzO7TduZT60VF6S8BDUSZt9xdjGQxNCJWq5hzBhI5S+CtJ33k8Lk1Gml8melhMBM55ER755tLZUKlNIq3NjLXl5HsIO4y0+762ay2g7DQq80az/encS59LxPqcqUXvAjFaUlQkgjV26ZNwBzktNp/smFxVlnhwAoOoMDS5g7XICu+5isIlzQzBdLf/JoOcD5tKCiDF5AjG3BjJyXsLK6Ief1eBwQwOnh/Hw7PxUWNMhvJlQmoKk4X3twJirMnhmzSBRYQqxizTrCpXZYlS+TRJ6rJnhVAtgKqyDiWvBioLl0aGwzqEQAxBYTywJVzeCElmO0NhczxiNB6hXvkUY/DNnNNpg/MNNyaeiSq3pp73HcEjFweIKpNGObw1ZeLgoBbu34CRUTanEy5e3OeeSwNeJD54XcSE0III8zoY59PJNGzbq2t803A6b2ic550HoWcTJ9R2BFh2BvDwnvDpd29wcPsIbwx7w2DomnrGYDBAVZnMPW4yYTTeoBoMsF7ZqoLhS0lk4/GYzEUhjlMFCkOr+fpwMIilBKfgGyUv5zQpbALwoAcYJayCfGFoJQFxhemszq7x7rMVZjigsmEbp4gwn5yEcqbBSsPFwYjdjcArZgccnnqePGzY2RixtzlnMBrx5C3PU4czbpw2qJgQMpMYR9fF+eLj7jIA54MsizUMqioba5+8QzEE5AvAtSCUYTKTDlILRiGttoEYokk5QyCIhlDktAmKfFgJop5542nUcGkMF8YVL760xeZowNNHNU8fnDJ38PGXKq5Ole2NEdiKWQOTusE5zxMHU+ZNG84qc0xGtp0zsyJqXU65llk9ri0pOxCwlbBdGfY3LDujitorN04aNkcVtycNO2M4mjlefGmDSuCZwxmPH0wZVZaDqeNVo4prpw17o5AzdWVryL27wu1pHbWkMm2Uk3kIjV8/mVMZoSpzZDpup+VUhrFKmUieqsk8ANB+2Kjf/QTWSn0sEq7dnIZcpJfvV9yzM2R3Y8RgWDFtNOxqcY77LmyxvTniV/74OjdPG3bHhtM6bdho0w1UNXj3JJxjYu0w9iMCCd+GyMq/PDj91duyDimIqQoexnriO3MGnfbFYtECZU8QcZEaq1SneO/auSBlmDLOGch2Ml9fyv/iNF5acJMAZPIupu53dlpBx7PW0UMrFsCrKdpCLfJMoXNeUoILCQ/pQvpCS+eEh4JLO7mrMqorXYZx8IwNqNdI3vMY6wgUEFV7UJd6H7eoFgcmadhVknpgrMnhIIg5GYULkKLuts39PiyWuWNaAhz6lHJoRMHRuhxNzJuJi0TEmMw/37iQqW5M5kkpICYjdsIuJm2z4pc0L5QrvgRlEsCSi/UGj1l4xmkIieCVuvEMVTl2QmUFVKgbz8wZNqkZyBDXNNw8qAHPeDhARBiOR6gOuNfOAMc9m8LFYUOjytg6prOaV961HSen5Ynbc47nytB65pMTqC235vOYa+SZTCaoiQcZesftaQMIt+cwHlimNRzMDUcMgLC6l3j2yO2J8v9OFH1mzmZl+LyHL7JhFWtsTAINzLFxFZP0iJq4tb48kyCvXPoynhR0YGLK6k+y2ALKyG/nQ5JwHKkA+E3rLZMWsCYFkg4IK2P26YyWhTyIuKra2tmLQN5xdHAzhxbGw4q6rsFaqi3Ps9OGZlBxeDLniYOaygqjGGZIcpXz09L8Slg7a1By/wSJ56oEXhkrqLaLmK58hnBe4o+1NuTSiCP/BgrB4Eisu/HKPZuWF13c4HTW4LzjiVsz7todcnlseO9Bwxe/+iLOVLz98esc1bAxMFw/mTGY1ty3bXn40h514xiNhrxsNMQYg3OhzX/wgUNunnpInmPa5MSki+auVep5Rb0EgEpuPwtUXmoU6iaAimsTB9T51NFK5uxtVvGsHOXRGxPSa8cRHCrwrqunzF2h0FWZu1BnzsURsDbI5DDJzMpWLdKCPRIYR/syi4t9W/Kq/7AsXu/Y7rTgI8h33XiuHU54+vYM8FzZqrh/fwtnoKmn1CczPuOhHeYeLm4OePfVY97+9AlehblCJSFRe9oolQleKyV6QyTMW43APKSpxEPsksDnabzaEi/YkYjeEojz3hcHQRZTpmuq6F9eOhJRR4iYePBe6WFqAUf6XH7vU2cHb2560UYl7ujTTrI70NmimfPNArppdWjBHY3u2UVAE8Oz5Zk6prtDMfEk7+A6g84JD0k8tbJ1riUfQTpLJZ0F4vPhcr0TPaPkpGTY5PKy8aRdIBuM9rHAZOfCdsrkUk+goDImruAK5hcMaGtZvPbCUXp3sUKM/yeD59Kk8LlURA6BF6YAca28S5swS0pqbKvPSFS7SWRD01UUzgfPwMMXDFZgVBkePfKcNDA2ys2ZUqswmUFlgsGcOoXGczCH8XHN5ZHiD2ZcGYNYy93bA6azGldPGY/GVFWFHY45uHGd8aBid6BcnSrPzuDadMD0+pydyjMysAHsboakT0uQi+FgwK2jU5qmYWAt14/mnLiQK3FjZqkVGi/M1aDYuMKIIFlgZyAMTFjdG2Bne4NH7tlhf3OA75jMGHr0YXtgGAbJW/essZh0WFOpXVdMnv5YdJZW8ZmQlEpclbQJmGlHlfPaHTDa55N7uN2pYALILMKLCcR4H/KURIStvYsBgDVhFWztKdsG9saeX3xvzdXTKSIwrBIgW+xu52DCFeGOfJqmtoAtLzSM4DUtcjTX453De8fmxiaSEywky3nmq4Zke1Q5qT3z6YSX7Q/Y2d7jytYpu9azMxKOpnOu3brN/Zd2eOTebcajAe8/mPH4rTkD8Tx2c07tjgHYHVdc2hpwPK25PQ1J3O8/mIEIQ2sYVGGHn/PFQXUSz4laUC79hNRFKnFfq9I08y/VD6DWUMXbtyYu/o6Q6dSVsoDw0FBsXwcO54F7+XeVNMllGpsWgPbbvNpEl50JNTS+u7pfBkL6FXYuL3kgSdd7D1O4xbE/FGbOsb2pwTNlK1xTM3LHXN7bZbAx5Pa0YdIoVWX41HtHDEW5Nam57SpunjpOG2Vo8lElneMzrDUxPGVau1Yidwr+xWteUz5ib8IUlL0hZcehezZDnk/dnCUbF1LprrVBcGyxQw6C1zYvJLRY7K/iefG5I6d9VJHzS5MiCA+10doWnJmAAjt1hyo1/7hs9tKVvCpQWjeHswUrWrDrLJt97om4oT9too/3jnTSp8ZGpR09EA/nsZJXuC7FignbXjNPElAB6tkM9Z7xeMRsXkdwIwwHIeYZVh8BLNkqrA7ntUOEkBzV7mNb6OwHBVhKO5b5LC0/EoNLF2lCsVH5SDSObbgnTVjNO6AAMvCPasbFT4aw06USZa6wUwVQsz8KhmxghEnj+aMDJe9tiHI1dfCBE8/uACannp1KuVCFtv6Jy4YLY8P1KfzKUzVbA+GhfeFV+wOmLpyH8m+erHEenpnCZqXcPJ2wMwjCffPaMWosO4NjnppYHMLDF4R6NueurQEbpuFFOwNQA+JRDOPNbXb3L3B0csx8VqMi7O9u4hqHNYbx2PHs0Yx3HzRM1WQ5+I9fusXOuGI8sDmObqoKWw2wVRVWyl6Z+aAM5s7n3IE0Pia6maqqjZV6TUmgLTAsvYhAsX20J0/ZK1CAl/jO7mnNSXILI06rJ7sqTOPvfLReH3L9mvukPkkJ7Xkp6plMJ6hTxhtjjDb8wqNTRjYo7/2hclhLEe5N7WhbUWiVjiy3heN7owbzLoQkTDzrAQ1ZK7n3miCJUA0HwCgY4HQqbtRQrglbpG1VZQ6LCCe1570HyjuuzbkwnvLAbsWz84arE3hws+F4Kjz27C2u7GwyU8egmfH4jQkGePm+5ZNfvM2Tt+cczDxPHsy4PU8hN8+oancu1Wm7uBSn0pZj3Q5FBGPtldbYdBVxZmnkcfaw0cpKp24he55Xggkp39/qh9bmlkIqUF7rv29JJ5djj6AvHAHUdQqfg37KRWi2ektokKekcNzAcaN84H2HuWrVAJrQ6yjwyosD3vCyEfNGuT6Zc+oCyGVasy2wt1Hx7KmyMwpzdNaEnXMi4JqUF0K7wIzNsqXubk348n0sHf1S9DcZ/Mij5OG2ts1TEdrdYWViMQLqw4JGoWPX7pSWDcWd2kDtfSjDQcve0DN52T6m3zLqLnqk0IV9/RMqKaLrZ9L557TQymdQWCa62TztvmxpJ2ZEWo3TBGdyiKd7qmAUyHjw1ObGBsZKzFsxMeFXmc9CeMpGd1Ld+DjIHu+UpqmJB/63qK/H1OdFHR3U1UZZSCkObSu3HUpYvSf9byStajV7Tkq+eoLrGMh7+TcjODmtlY1KuTAUbs7h4QuWcRUG+LTxfODE857bLiQkUqBj4KEdyxPHjlvz1F7h8ih4ZX7tacelsWdowmQ9aeBtN+DZ05qhEe7aEF6+FyaaNfBHtzzPzixPz4WHdix2DAdTx7WT4FZ8zYWQR3Ldj/mtazUv3hlw9bhmNLTMnGd/IMwOj5lTBc9OFQDI1cMJ/+7JEwbWUjvH3AsqA+IuakD5jSdOsXgq8WxYZd/WbI7HNBqmwMHJlJMGXnPvPoPRkGE1yMldKRu/666KGky7Ripa5XY1TCFLef5Jlo2kUBfc70ueTyuMPEdTdX0LIi1wKhdpSX0VEShQRQcBtDWNYzzeAGBzWPH0wYzPvAfefyzMPFw7hd2xZXsg3Jy49o2S5o10lmT9HSzJ0pYuamMsYg0af7MoG4EU9sogKFWtlC5i733Yqp4O/4rhG43HgasYjpqQwHxtAqf1nJdfGPDqi4CGM27UWsDz6PUJv/OBGcPhAAXeewSvnNZsV54HdytkuMVvP3HI07drpr7ot6SEeFILS2lYUCYJYJQuqQWHXFdpds8xKoa6rbD/svyS5S9IRiKdy2MWbi1QGsLk5g/PycIzfQibxu6sypdg2175O9DEpcEqQECQs3Cuk8Tdp7cmjrcchrOJpj7kVm6PhIsbY3YNHM+VrZHjJG4SrSScW5QaK0A76Jr1cRuCTQ7xNA/bRWq56CwX9BnCpvGW9oTvxJ+6CEE3WnS4lAW5I259VFAf02j5JZJIgvQaVXAX9HR3wK7Qpz06/5yWWKNbOHshBopi7SmRsfMyBdKhadEbkerwHvDhGOHtrU2MMZxOZiHWFQ+wMjYcFjccVExn83AqJ2GnxuTkNLrFbVe7F23v03lgpqMytJx8bX/LeWgKAFNWbMRANprBU5QNHK0+cxr+KgOvvmDZHQhjC8cN3D0WZl55y5Nznj0Rrk2Cu/T3rnk2bDhUbeaFSRMOTDJGqIoTkj1wa+p42Y7hvbeDkaqMcHOmcW4Jjx6FHg4iuBpa4cZc2Bka/EyYzF3Iy5Bwfo6NxmWAY2cA89oz94a7NytmXvEYPuXeEa++a5PTJuSJWIH9SmhmU05mytHtU7x6xsOKZ08ct6bKHMvcAVKRfoewdHA09RxnLbcbeGDbcuxhMoNLWxbXOLY3xgwVtjcqxpub2GpAHXMofPJcpCPw4/bFNv7aOtJN3A3Urogky61EIKqi4NtckyAArbCUBym19kjJiDLeaFfrkgFJf6nT+M7FbEXSKcLGCHXdUM/mKOEwuWpQYYxw/07FbFqDa2iwPLg95MnDhqvHKWGxrTLrzEJ5dgBLgjXxvWGh0WBs+GVkiXtvg4x70HKLsbRGsrNo0Xi6bhiTQVW1h2WVP2joXVitGsOpNzx70mB3R7x0f4QYw+bGmJ/8vWeYacUgAhaAgYF/+9gE55W7txpqP+X6lCIM29ICXihpJQpYcb1/rwQ759ZTfEkGsXi+A3wKY5fHMIHp4mXpEM1wtoswrEw85TWE5btewa4OL3VVSf2+LGvTWbRZQR29uX1cUz6+OYChEQ7nbRsFuDGLu/EsfOrdlstjYW8kVCiNg9MG/vDAcCvC5tNGo37pNtpI+r2eHm9pwV3Cp4m/JXODoU1JprTzMgI9RTu/5+V7Y7bQ4SW862Pb/vez6A6H4wWjzKPe1aRL84/RxMb7hS/tWCxLLk50fngI0MbFxiQomI4EJ69Kk25LCNVkZR8UVBN3Q6SMYREfDlOK7n60jq01VMMKa4S6CR6EpvFYWzGfTXFNjVfBVmGXSpAXzYq2Rerdg6CWDWBfEZyvn1LcrU2oTIdQJXd3sgch50RDNnjSinGlfWEsvHLPsj0MhunKpuGd1+e8/1C5OReujDxvvy7cvWn5rAeGbFqY+gBcpg6unTgOa0VU2R4YHrlsefG24fdvOt536KmssCEwc8qjR55XXrDcs2m4OQ3fH9qxIRkXzwPb4eySCuWxI8/vXQtxfz9Q7tpIvxmjvHp/yH3bFmMMTx83TD3sK4g0HE7nHE89HzgUfvcDp2wPDQMD104akACmZj5l1k951aUhzUnDwMCN08B9IzF/RyT+FkZLU6p4MKXw6KFHVdgfOu7e22BgK6bTKSfTht9633U2BwfcPp2CqTitPQOjjCvLeFCxvzXiyu4Go8pSWYMdjCH5A7KfPfxGR/CQdVfIzvl2t0TUTgHgSD6sLNTVtt5pkJq8DToKVFCKQVjKzX3lgV5pp5hP4MqHU6I9YVXoXINrGuazGSKwf/ESRmByeszVI8fhfMDpDB47goe2ptyc2wBss1JdWBhlOW2tbdjJQcpj8e1vBoXjwIO2iQdpxlwhExJzffrl4gBcWgdXSJL26sPZTVGb+3j0f/CMSTbCiazAzZlw4+qc3356BuoRbjOoqgVjEOZcOKzq6pQWiPWAYWjTGVqgAJfnr3bKOjpLoNWWf5WhKsaofCT3j+DNtiYsRvZGFfftDdkeD5g2yubQsjWw7I0NlXhGgwGPP3WVw2nDrUnNSSNc9dVCWHtl186hO9WhApzWZK9PSvtTwo7l3SGMKxAxbFrluAnfrTHsjYQNC/sjz6WNtIsmgA/vlZkGj/u4Ej7xogNjqNUwroRBjD0oPqz+VWnUMXPw7lvKcQ3Pni7vV2eX0VLm3BmXzrDDmWoXvEq1h7GFe7YMR3Pl9jwsTneHAZAfzMMZPYkq03r003sqWhkqWziQEC77cJFqspwtlWBYgPaHiIn6bXV954aHBIILOGqccptSSszKE8m3W5s8irogJDl+5z0+Knl1YbvbYDjCEM4rwNh40qnimqAga++yUnFNA1IFlzI+X5eefiihSifJ75x+9i+EqqT3a5tJBALbjQkKGoX0u6RNPLUyF08UjdDhTHnH9aDsZ07ZH8KxE1550fAiUd52w/LKC5YrYxgZw864YrOec9dGOC7+dC+sNt5xw+GBRw89924KD1+0PLhb0Xi4PIarp463PuN4z+0QRnrpjuEVe4ap81waGY5qOHXKsAqhvLH17AxDL6ZOUGO5dtJwdQrDw4aHL8KrLigXxgbvlQ2rHM6FqqpoNEymmVTURhALr31on62RZe7CEf2Tec3Bac2TB1NmzrNpXA6rpZ00iWfpJyP74zKISZxHtfJrjx1jUCpraDAgY6QBle3A9yoAIT/3mDmYScPe0Qk7I8vWwPDx91m8VPnQxKqy+QfH8gmryS2iKS5N/omAdoDbbfiIoI3PE7UFBd3lXBviae1ikq/WG6kxKTXyJK78nHP5oDNrK7Z3x2xsjFBVDg8OOD68zW4l3Ldd4TcaHtwSfuNqhRXFaYrRa17WpGalmaKquKZe2HbYgjftXEmHoiUwpq7BqAmeq8riXTgTOpE1ErxdgZuIMeHQLjHhWrRkQh/ACum3l0ZWEEm/jXL2CnT5+W+ShivyvGdUpFv2zBWudMstSG62GgXskPJ69zIsA5Ph+dqFLbbjSvjUB7a5srfFeFixW4XFSl03HE5rbt4+5uBEec9pw9HMMXUhaddjQIaIhPm6+IbnRyMThq1ZUYHTmBOjMKzCu7cGwbu7OTCMLGwNhLs2hQ0rCJ65C5sFagdbA2Vggkzcntu8Hd05yWGaxDclyLlVZSANs1nDLOpqMfH3dSScBTIU5cEdz8jCL75Pi7D0h5dUAyh/+JLhj2967t8RPvGKZdMqcyecNJ533YSPu2DYqITfvt7w4A6gwtMnyrOnYWfXuIJJEwDMafx1CyHkDSX5XjVGH05aAONpaqSF3xkG+9zwkLUJE6Wp2E52Tcq0o5zjfnjfHj6XNIsUvlmxNm9t8iS3WnsEuJG0u0bBe+b1LOyRz6uiAqeVS6ikHKQTzFnZv/L/DlBZwrXWvZXAmQ+KNp6u5LTYmtwTjAxzJICaWXReWWO4NgtnQrzrhmPaKA9fGlD5OUdTg6ugqWf80c2al17cYNJ4hiacRvuFDw1RhMYpp41jw8DYhFDIrann0duee7csL9kxvPug4W03XdhlpNCo5xOvVGwPhGk0so8fhXDOi3YrXrRjGVvBX6l44sjxO1drBlbYqAyNV9QIW0PDlQ2DxIPQrAjHTtjbHDKuQqiiaZrgfbLQjATZGfLQZsNjBzP+8CZUtohyZsZp1toLi5vkDclgWagjWDakXIokseGEVjHJuHluTRtuTkPy6v17I0a2pjLCYDRkOgsn0zp1DGyFtQbv2n1ICaAm4F661pN3MZ8vJN0zCUJLF1fcZQxXY75TmkOu8fnE2CJfDQQG1iImJasLvnEcn07D0freczRT3nVzzhzDXduWl+5XXBoKTx4rz04KqVey+zvt8NH0w6VSSH1/YVAYikVbH0Ccx4Nz8VyZQGF+h3luqwpP+NmFpm5AfAgPp0VCBItJEnxSK3ROpwAAIABJREFUOCKodLdrJ17maXuuYu52aBlgKataABF9NXTWa2Xhw8qv3eejzo0yPTDCq65scs+2ZWTggct7eO+oZ6e8+9opTx7MOJg03JqHk2U1eoGNCTvZqkF00S9ZT30wpATAMW20+D2p4h0KF8cwtsLuyPDQbujL0MZck9ye4F2f1woSvPIjPONBuNdoODZiw8K8LndRhUqaps65lWFtozgXzqNyXnGuTjscAI8dWhoXcmKeOUnPfIQoysLBNOjguzdgbMKCfVSFrdyvu6ti3gRwsj0QtqsQ3ro1a3e+PXIxnBmzM4ieoxgWf+/tAGaSl/WjhRamnQl6svvbSL1nFn+fo6X7X/awfsm3fO8L3rh8LbraTaFp6rom/BRLw3w+BwzGVvGHEnVphX09dZ5XZVnjymOEy0pkqUKTXCBMkIDg1WvenpjQosTV4dDCx1+27MWM9oExCI7DWjiaw7OnnoE17A0NB1PPjanjaK4MTELhyk4VXPRNXH2cNG0ILjp7EAkr9HI7dJUT7tpk6bqIIW5UQYl4VSZNu0tgZ0g4KTb2/7jWfM/FnTeNC21E0m9H+ZAzoWFCNR4aDBaXT8ac+5SoFkZNRKhiiKX/exUt79NKqtjq3lsNQ4xRk356Iq6gi2Ox299j8QFketgbwhc8fIXxeJTLtCesxBi1kpOow48DtoZMi3+8dg/lyg0tZCzicHI2jWr2ZJZgOZ2QK1J6cuB0MkWdYzqd0tQ1G6MBog2ekK/wvhsnaDVg19RMvAFb8a5Dm3MPS765eh48ISIY265hsi3JC48IrPr6Qoghozgm+dTZYrzS6bbRi9QeuhjnSTw/xzUxPGSruOgo2irF3CQYu7B6TwuJkGA+d5o9d4skRV2dD0sp/Np097nFQiufXnxv+rhKUXX0W9xeILAxsLz6onDvzohqOATXcHR8zFufmHLowhkuo8q2BklLoJVA9CLOKteUKL351JbvAObyXrzexFQka0LoYeba56yBKxvwafcGHZa9cskwafjhzOCnDno1gU/vfXsWkJRtCDtUVYOBluLnXlKqQONCGKiylkEsUzt4/5Hy5BEczEJ9r7gQfpfriSPl2mQRoKbFkTWLfEt8yB4mTfagvZ4SgM8DCkoIE++O4NY06JzknUq65lUX4d23iIcuhjBSymMs609ht5QiJnQ9LQsvfs4G80NPD91zhe/5qtf/rqr+if69c8NDLxQtnSwaQjspFJAPpRIwpmK8MQjKUottYbSTjDsUiAVaoQjLQ3eW/jhaqUFzHwpFEaVLinvpjIDjObzvwPP6B9MBXA1guTxW7t7wvGSvwrvweyJ4pcFydaL85gdcOGYcw82G7GWwwHjQbqmD5OmBC2PLXZsmJNZOPc+cuCjY4fRWI/AJVyw7wzDxx5UwFGXuPL/yZIMRYXtg+OwHBoyjJ8RrUEYDAx84qnn7dUejwRO3uzHg0ljYqGB/ZJnOG546brg2FfbHhv2xYXNgaJzjyWPPJ18acmsWYpcW5XTuODitmavEY9+1w980yt1Jlwx++lzuWGm9eoZufUJQhs6H8OLACEcNPHP9Jpcv7DHeGIccKtOe6opqTES2+YfMyoOYGueXelE6Dfbk39JqvRSS5TtRqla1XUk2jWMymTAaVBwdHzOfnjIej3B1zem8Zo7h+vGc+zcdQyO8eM/grOH2xPLoccWpC2OT5Dl4d8IvPAvhRyfTjzuWhQQQGwBTAsRlMm3b6H6/yZMy5bRlu59PwY0AMiLCtBW1qmznjJ2WjSl/KFTltD0yIMnAvMksJRvF50Op7Xfy/DJL1rnBolwseVcfXEBIVB1Z+PS7YVSFsZifHHLtaMYf3XScMKKyrb5ZiEgXgKs0bCVmUmIivgmGMB34BsFgDgxU8Qc5m3YYaXxrnO/dDt4MQ0iQFYk5FoSjEhB46lh5yb6gauLviBFSAFDSW4MHzmX5UO8y8EiHF6ravIgQaXekor5NaAeGgyAxs8bz5BEczeGp46BfX7orvOIiXD9VDmfKhbGwMwz9NhJ2booEfsxdWJgd15E/0o6nIejcDQsv3hUccO1UOZ6H83Q2B3A0C+WXheJKkljXpGnHKPxSO5lDGxb+owcDb29M4cmj8H9/fI2EZ8tdZSs3T38UApZAqxv2YQMtsBxcdFCrMVRVlYGEi75GRYO7OR6J3v89jDvie6lDeivaFImWVpv2HpHyMu3hOO2vNifkskyH1R4ubwifcq8NybleQWxMnApxamvCacKzRjmYKrWCqvDIlYrdgeXmpOE9tx3WBGVWR4UyMDBrlEbhrk3DS/fC0eADlP0xNLXjqcaHs3OssDcwHM08NyeekRge3BEa9ZzUnmdPPTtD4WDq2d4Ouzkm6Qf+LGyIcDp3/O4zYSeXinBhw/LZ91ZYQljvqdsnvOtQ2d8Y8ul3e8bGszEUDKEN94yV0/kpr7s0pBqOadQwbRynU+GtT82ZODqAQIrZ2Bp3XeBx8vQEeWlzpoCwuyWOTNM0IG3CqI9nu7ztesOr3W0u7jkGVdiV5FVDkrKtqKoq/FhjGv/YHtUQ3kjoygPqWwMfTuDUQu7C/861O5dCYmtMtC1cZE1cKjkXfl3W+XBI28HJHGYevKOqLHVTc+9OxY2phAP5nHLiXUh+FmGzKrZzEsF0Uwdvhq2K5WVhMRPjNbTXViav4NOM8OV4lCsJTT6koOFNPBG63EkUVsSaLa0xBoyhm/1SVJvGPK/uWy9IxxtyHhXdzPis6BOQw2V3tBjqg447aYos+1wsmAgG7K5Nw/3bBrGWw7mD09v8/nXPrVpoGOUDBxNbz2rvsnuND96pRy4ZjmrPe2/DtAkAKSx+glFMmwvSCt4KXNyA27Ow2n/9g6ZNANXoZhHBinJSw8wJW4P2mIvslZP0o5apdb4dEyX/nAPxewCtBcgXUHUdvqV/bk007xa7MRG2B/DKi3B5A4Ym5K7csxmqqYzyzEkIYYXTiTXX6IFZI/zhrfCr6Y0PPEpjZAS2hsIjl6N+8WG3U6PK3hCePoFnTuHJ4zsQC4l1JxyW/jQAxPcdwtXTMD7bg+AJf+YkLvITkOq95Dkv6D8G6FzQ4iL6XuJk+P9Ze5Omy67sPO9Ze+/T3OZrsk/0QLUAq4qkSyJFUhZN0ZYVQU3cDKyhx46wf4AHnlERtsN/wHP/AHlm0bZMy0HJkooUWSbBKqCqUOgKSGTm193mNHvv5cHa59ybCaBYpnwRmYnvfrc9Z5+13/Wud73r//ebYj4sz98EIVSe6ePO2VmehIkZ1WcNxexkHzaLY7GeFKAh8w+WAR7u45nMWb7ky094ZXrOkPQwvbo8NKvwe1/xnNeWHQ6JmRHI2UpLj3fK9z4bqSrPrz3wPFiX5wJjBHGZ24vAm3cqy1Io7rXiiiARrruBP3g/sQyJ79wxvVDllL+8KvOOxNTml9Es919eO+4tp4DnOKuFO4uK+8vIP/tY+fAm8t27Ndd95AeXyid70KwEL1S1iX1/7X7g5ZUjqmI5u+O9nafLcKdRWgd1cMSkxGw9ji3Ch2Og3sLlky13WzWNTl3zH371BDTz4dXA//MkMlgDgAlQj8oOR3sYZVc9IjXK8Z9KHQLkjAm3nYEtLTqsI7PDhDlxxt0F21xxdrKi8lCVWT9DP8xupJqTgZ3CuugXKD1z2ZNLFc1uR/POZoHt9IknTlytQyhlGzqoCfphYBgHyJGT9Zrgha7rqUJFHRyb6Nmqo9c968pxlRNPBxMVP4MlUrLs1TlC3cztnrNLb+GiXblekElafnxN5ZmF9DZqmWkeyvFWn1OcsbxmnUcuzH+e4zFFKHXJYrk+vd4zm/HhOE8lhi8Nys++/OE2bQjTsT8CKMfrSr/gOc+89he98C8YGz8Hyo4+r4iBhV99EDjxsOuhSnsu+sz/+bFjWXljKCdQzHOdFs8jsec+nmBHcR/hP/8l8/CZmu5/9Z61I/+P3zcwctnD770B3/vU/v/ltXKryZzWBi63o93/Bz9JBJd5cQVfu+3NwE+sQyW4UhY6isUmKH/2mtWj8zVfCkwxfIpTE+NX5nWV+2I2ewMvVgNRzdxeeE5b43K/cn4A0Me2HbEA7KjCeTOBcItnB9CuBA/fuWMfzrl5whDTklXVUpY0T6h1LeQseGdjae60mXWAH14cTsvEijuxY6RYOSfrs9qamOHFFbxxZoCr8ZMUwF7pu/cPgOizvb3u1GG0j7AdTPNyMx4tkedQ+hct22di7Jch+OeR/xdmAF/yvOl3X/Dmtka/HG79XNDiBO4snU0OTb/wNflvdfui94iFppuoUJfVOnaEo/k90wLXuVsglTlFtnno0eYy90/MLAuU+Q7l6jaQMy1e5kfC0V3lljnoD57xq1Fzqf2jDyOroJy3xpyoWgYegW2f+JNHkR7Pm6fKro9UyCzqbYPg1TaHWL5jVNOpCJmYTPx21jh+/QF8vDOVfVIbNvcbDwPfexTnrEBF+HALH2wSJzW8eur4pXPPJzsl5siPrxNdFO4sHWPKbJIxNJWzTdS+q52nVeUKdTm1vQrfuhP4cGsb2aKZLmrzOBEJOOf45XZEyDxoPWOMxKzEOJD6njHD7cbzxqm1eO/HzPlpw25IbAfrkLAzo7Mu5AuzzKNzNAv25OCVcOx4CcbuPFwqGiPvfDawuNhy5hNNcFR1zWK54ux0zTjGwlq5Z6/Pmb0rbb7TJlTo3am1k/LpP5eYH7F8lQ80zligJIkQFsguMXQdVxdP2HcdrlnSdSOVZIKOvH7rjH7RcLVPPOod1bHAWSDHSM4J5z3eh5nZMV8Ze+/gA4oxmt6ZSNZRupjE4Z2VbSc9rGoiJ7PFZyoDMSUTZYZSef1UPGem9n/rFDqasj4fDy2ltKODOz/n6KROZbVpLtqXnf+jX8h0/lXn6dOo6WIOkwV+DhB6/rWfe/1f5OGfe9rRItKyyX/rVodPNdeD0AbAwfkCHq6Ey/7Iu+V4pz9+sy/ZDGLZKFsHb962TU2PnpQz/JvP4MU1vHVHWATlolPeuxK+fddAS5c8dxa5dO9YrHz3Ah53ni4Lf/Yo8Y3bjiYci9QPR0ELYJkxnjAzk/b7aQMv5whbZ5MnmAikHMvrOrbDbvb7qasaQUjZEbNDdTJChVyKjtO6y2qsvRc/M+e57BcyfRB0TmBVyprWg1GoKwjm0PJhRqmClctUlZfXmY828On+UH5ywAtreGEFP3gK14MBlXVt+sLrYgbqgNUCHu2NBXqwtJh+hD8A0wx9urNS4pu3j0C4wpM9LPbw9tPJ98kA6XTL+ZAKHMezY/bmOI48c9Pn/j1+zBfd9/PuP7prSKal/LLbzxXivvXWm/rf/7f/iKtReO8q897N0ft8WSbz17gdwuYBgU5ySydwqzGk+e6l/T5mSoZgtzBzYubeOrl5HvwXJmgyTdM9tCTPQRQ7SweNhD1/ygiOtQZwMMaZHq96lLXpIaOBsnFnE37+1osOp4lPNpnL6NmNpl9xYnbTt1p48443lsLDaeOOZtjI7DxqVQSLdAp4nwnOUPiQjkz/BP639yOPO6MVHQeRV1b7bG+cOZ7sM9eDBc2/+bBiXSm3WnOtVU3843dHumjTdafA0gZ4de1wZNrguL+wHEQEfniZ+O6Div6ovy4XrxLnrHvHYXTqdCZFHGhmTIngnQ2scx4vyhiVH10l3t8eMswmHFiBrJ8vG05r65mZKXpkh390Qp3A0me8JtR5uuS40yhnuuNkueDlF+4xRgt8VQhU0+wsMQG2FPZvAnXTe0/Vnuff98uuu2m9ZVXGGElFSLDf3KA5se061us1+z4yDB1vX2YerD1v3atZVjVjjKirefdpx082bi7hpDjifDC2SXSmnM10rJw3JwXwTxmtCXvN68ZxueuNeRUQZ9qEqV88FwdsQUgpzd5F9vrlMD9Tkn3mr6Nvf3z/s0kDM+1Srk/3bPIxZefPx8gJGilKEGub38fiuaNKE4SbsYi2y3F5PjH5RW5f9pTPnenn73gWi+HIvHaSuezhq7c8r54ITjJR4X/5qaNPZa2VxOoLQdsziNj+ubuAr9+yVuPTamp9ncCACVV/eAlPO/i1B3BaK1e98tMboU/CndbsK5LCK2uZQUXjoArwaGMljL/x0Jmp2tHJOL7WJuDAfC0cTO4sCTFQEXNEsNb5yocDu6GZ4GznFefw4ogxEUJVvstx9D3E8rKo55LS0VIqOpvJ0iAftFgcr6/pNabX9Gg2Z2j73BktY26mTcA588m56pR/+oEQ1Tbl//Qb5sPSRSvB1R5WIeEoTr/J83gvvHnH2BIwQDNfBno45xPICM72EMrPxwNZHxfty8flHE2i/IU39mhyDVZsTxgzbK2CbM0gn1tfR7e/Agt8EY75ssclhd96QfjqS/f5nb/zd75QiPtXdg/93n/1+0cZ4hd8kn+L2yRkeriyDg7v4NUTOwLr6hDgTZ0uhWY8OJROMe26N/ah8TIHyE006/JttE2rj3A9GiuxrOx5fYLrUve0QGXPPzbkVGUeTDWV4J2zzyQYur3V2Pd4vLcT/uLa/vzpY+bs4KS2lsCYjZGwjKEMOxMoJvxktYF2dxeCF2Fdl7Hygnk0qLKsHHdaWIVJt/HsslB1pqssqzuU7zYm4WlndOEPnx4CkBejcpNajftvvxS421Im0GZyhsoLH2yUH1zATXlbX87fg4XVWJPC7dZxGuCqT9xampA65cxZY63SfbYAuayE61F498nAS2vHi+uKISpJhZwj9194iRgjcRzYXF/SjZGemrOQ6JKd/I+7gJfMu5f5IJLj2YvjGBAfhnwewKccHbqUlVfOAg9Dz7YfyeKoBbIItVO+8cZrOBFr0U1WIsrJgp3qNKH88BlUD1bfcARc+BKb6gJqpn+ngC7AzfUlXd+TxHOxj6wXLRHh/GTNkJWnm57YbxnUcT1Yl1d01eGlUwRnZcPpGplAfM557n6bGBPLUI0mEx8MiBztjgpFNCnFnuAA1iatz6Qdm6YoP/9lnxG7T6VZ5HNH59mOPpk3AwOQji7a67fBMl0zBVTOG7i/SEhO7LLjrXuBfbfnx1eeb90NbCKcNMKjTcfCw5g8lx18tAt8sgvUQWZPnueg0y9CsBwO1OGLPLvTTA/RqUNN5+83PSTl0iFVnhucxaqswncfwEmtvHctvHt5JKI9SliCm+4QhgS//gJ87UxnLw/b3g+gLmflH//IcW8J330gLHw62rR1Xj8xl2m8IsQ4Mr3S208DXz2HVe0OTGh5h2ldVVVTEhhFjKooyYeW4bjZZsqlRAhhXgO+ZN8pWTt0jGlOFkw3xVx+BF/AiT5jBmqzugrrLpBy4lASPTx2ig9JU0lyDYS1dT2fwDxNCBeoxNjLMUWcNyBlyYAHPDEmKq+ImGaujyZpcM4j4ssxLFYf3s3XxYGlsoTuc5HjKPmaQJj5N5m1eE6lKjE9PCu1PwCyMU8AzfbYQ+1IWQThkx38rz91Nh9KJ7NM5r8mqYYTqw7YXmdgbLrqTyrT2qWsh7bro/hbpnYUo0DbW9946T7/zT/87b9e95DHGITn62C/0O05BPZ8BtRHMxr6zReS2TI7V1pxzVRoClauHLBpszc9wMGUbN2UYOUDU0fsqhZePBGmiakmoLK6+rKx+8aovHupXPYQVejGRMymNFeKlodDSaH1sAjKzSA8XCezqG+UqqDpF1f2vJfXmdMmsAiO7z82Bf3feqj8i09sYbxxbkf2uofH+8zTLjNm+0zBGXB465bRfiKTi43Ql7ppJht7Y2dnDgbOTZVbOwjGujD7Gzzt4dHOWpS9g91grXC7CF89d7ywEr73aeJf/Szy5m3rOFkFq832SUzY5qBxOncTxAwf70CziXV/dJWsvfteYBeVq97mCf3FRWIz2vvdaoyledJluuR59AQ+3kW+eW6dTITAxZPPcKGyQYqrNaHvWKXI9a4jVC3Oe26HkajK/Vb4aCtWUigb5uTaOv8pG/AsES1MwFS2EbFSyconWoncub3iYj/yvc+Ul9ZCoxZAY0qkZHOvREwvdNgk5JnAqVNmVtaQ6HNwJU/AxuZpWVmmrNeUzbU3eLwTTs7OuBMCgvKVukFyZrPZMHY3nAo0fuCdCJ2a0V48qr1rsWt3PGsvAFMJlbmkeiyWBbHx35iDrWWRzwIewcTNzkmh9N3cYj4xS/Ocn/mvKeuYfrQX1ZkZfTZmHNrPHc6V8R7icWLAex2MZXxpZTqonDInree0Spy2DlWhS5F+NLbzK+eR/WiCzE2XWYbMPiZiGrnJC+4slWUdOauVz7rAo51ZBMyYQu36cfL8pz3+zIev8gxrc/Sc6e7g7Tr1R6Wp6XfOmZfR8VlTLNH48yfGmNxfJn7tgXW0dcnASZ8sU/5sBylbLLi/gmXIPN5n1pWi6shl87UsNxXwVPONW3B/mdkNqZgB2rU+ZgMQylFpsWiaRALfvmuxaYjKBIcUpY8DXmqq4NjtevqhK7FZqKqW4D1ZrYQZS/kn+EA39ObXUtWHYYflGNpMukxMiT5Gg0eqNKGegWbWVOwPMilZackVMVfKiVi6lRyOlBNTy7TNwDPQ6gqwd84xxDjH25jss3oX6LRn6oxrfYP3HlVhGEaLLRm61FNVnqGPiJjzc8qZ3X6HkqkrSzIWbjGvoWytpmWoa5pn2k2gP6eMzpohIWlmTCO1Vnjxdt0mJam1jzvnuIkGVPxRLHDiSJqO9mgbWLofM6+dCk+7Cin76kkteDKjmltvVqFPxgTdXxibeTUIP70ywfa/+4JJDkRglx1emB2dm+DYRuH9a2U7Kn3UGQN82e2vBC19acM9oPKfc3sOpDwPcKZ+fsE257tL+NZdM4qKCMxU9cSqHDJPOGQjrpy0qQyUFQMsagtZMb+UlEyUJeLYbHezIHDs65I1wOttQla+mF1VpJx53At/8cSxH21jfriEN84iyxDxzkop2ZYyjbdyzJ2Fsq4rsioxO/YRHixGbr8kdMmjjLx1p+LWwtP60kp74ojquB4cFz1shsyjvXKxV/7JT5V7C+WkNpGXF/ibD81IzlCpzELKqT6/j5bt//OP4OVT4bRWXlg5REzs6sRqqX/0kR3Q88YmQf/KfVO/Bwf3V573rjI/uYYne+U7dx13l8KdheNX7wMi/MXjyAc3Cgm+edfzg6cJyhrx3gJ8NyZevy28sLYl9sEm89lO+ejG0PaoNj7+rIbLPaSF8P0nytfO4dYiUanN1Bm1jKcvdZb1okVcYNsPdIMNg6yc41Zt1tZTSSMVimvOkI/os+DteE1Tx6cM3gFvP1WuWk+63nHVZXq35LPR8au3HUOZieX95K0C4izoO3GHmSN60F9Mie5xjmQgqvjTOM/VZk8smdd2u6OuK85PT1GU7W5LjMqyrbjY7GxieL4u87g6PrzseNwrN9ExEFDNeMmzfmMi3UOoS6yzzHdiD6exAfZx3Qz4cs5QBpUG50gpM6Riu19EtUyOvZTgOgHnwjyJHv3u+aCgB7zC/Dm+KFTZ8TaGq8yFco7TauDpDlQ8Xzvb0QTT3CzIdBGWdca7VITsEFC8OLJXunGkCbAbI10ciDkTnCN4z1u341zu6yPcXSTuFwPFXbRS7aPOcdNbNhlKHDkkqZ/XxMR0WA8TSywYAPiPvmZswKOd8C9/5ufzNrUXN6Xd+NizgxLzag+fRuH9jed3X468uAbEF8CojDnz8Rae9oGrztpx/4+NAb6/8VB5sMyEeXZOYThUGNRK8ULmphe6DKe1cLtJrOqDPkknZCZ23Rn74EAU1YTDMeaIKrR1g0gAFaJuqRc1cUiknIndnuArqiqUrjrBOwMpwVl3W4qJlJVQSsIpjeX97bo2QDEAwjCOZO1wWNKaMSbAOz9roGyWXqYNNdNeAtb1o+QCSk35FsThqsZKVmkkqVoXIhBzmhmFJlQF/auZPCYwXY1DZDJZFNBEVdWg0MWOPndUoS4km7Dt9lTeG2hKGed9OcxKLt1ZuWRbwQdLelTnzEOAIQ047HljGmwSu0CloZRup9ggZS1GK8FNjA02euf+0vFwmfnhZeSFE8+dFiaNEQChYugHMhZXa6e8f+14/8bW7KOdJd5BDNyt68MoH+cdKcFdMq+shDE7ogrvXWdC89fUtLz55pv6P/x3v88mChe98mQPP72x+QjTDJNn87aff3v9JPPKKnPSGGOxG4x56Ifean/eMY4jTaHg5oxZlbqqicnqheMwlpp6Yp7kK45hjLMh0VSWMXRpAx8Xi2Z23M1qQ9ri/HujuioJeO+pQgWYnXkm83h7Y4ZqwN31KbUPZBXGFOdx99NQyRK7Cpq1TLzynsrXhug1lwmgFsFUlTGOVpf0bjYkEhFuhszHG8/1CO9dGTMV85TFypylLSv45bsFwGEC3nsr+Msn8N61ZWxP94ceflXmLgQvJry7u1Ce7IXNCB9vlJNaeOVUeO1MqMpzUjGGe7SDe0trV3z/KvGDC2NsWq/8Ow9bXlsLmyHNWclfXkTeuSiAq2RudWnfngLxd+5afpEyfLy12vutGr56HhhjmRieLdMWDHQktbLZj68iH+9l3gzCkavT7FJcvm+ewLBMzNR0vO07jmrM01u34P2rkfPW8/CspV7dwYl17gDUVRn4pwc9zcTcGPMwtS0fsiCLLTrT4/0w4p1weXVF3TQs6kAae370ZEcjCfEVu+zYDpEBY1EGtQ4N5x3EyJQK+Co8w3JM4nA4lM5iSpYYTNcNdgxngaSlmUfWK5NmwjIqzbbRaNLCtkyL3d5jBihymMkVQiiAtrBfSqGTdf5cw5iY5pi1dSjXrtKPaW6nPQkDYxZaN3LWGFx67SywcAJ4hqEDF8GZbmXMERUK4Ip4cbNWYQJrZloJ1oViWTtO8JiTrBk1RtqqJYiVMmpflflVwtO9ctU7HneejzZm+lgdCR0d8J27kVdPom2sKfNP3mtIhSnejrYmlxV8525ARpFYAAAgAElEQVSm8YnT2jb9IfZ8sguIW/L6mdL6MoBPhNrbNXTRQVLHvWUm5jLnLUUTp6qWDdXiY+XASWY3wpPONqPZE+eIPXv/RvmzxzaN/T/5WmLIdi7+5DP7fr/2YLRy7DRHjiMLiZKV+qJbGVKkG3rqUKHZAC9ekTLmQTGvrpQyKSfqUNMNHU3dzGymATmD/Skl2rphiCPOW/x1hYpXERPClidNbIWVOq2DcVW38xoUhaiROlSA0A1Dib+2dlJKBB9I2DGdGjucOIv7ehincbZYmYu283jxpJiJKeN8TYoZkQHvbPjvmDLd2FGFCkHoYocg1AXIeLEybl1X7HaJrHvaZsl+v5utD9q2mv2mfNHUTKWq7bCncoHgDNCUK3QuA8UcSZrImllVS5IqXewOOqGiIXIiDGlkiOMM7saYyp7pCd5Tu4CXwDBE2rY+sBKaaSvBeU9f/HiceFLyjKMd52mPt0uxslDGQEqZB/fv8zt/93f/OuUhZT9ElpXj/NTxxqnylTPTRbx7yWy4E6bmDDmiRr/g9s6l451LxyIov3o38cqpLTq/qAFhGCJ1Xc/4rwp+1iF0fU9MVuucJvVWvjL6LxkCFXEzGLHnKb721uFQO4Y0mBahgKF9N7BqW1St9XhII+rBpcSQIkM/GGAq3jGKUrlAN0Q6RmKymmrlQmE8Dnb2Wa1klR2MKTGkEaFjVS/wYh4nIg4tNdMqCP5o6u2UxAxUqHc0KF+/UxxovQkjT2vlvFE+25kK/Y8fWdviS6dC420z//Zd4Zu3jc7+aAPXvZ2zk1qovRKTFZMerpR3L+FvPVT+9BF8BPwHrxvjFTMknYK8sQsvrzOpuFiKZLpk7dx/742GVRBuhmOqEd667Qku89711PZt2aQTA11v3RF+6bbQeuXH18bwdVH5/mP4yrnVrNOk+0QsIDjr+mmrwHcaz1dGO9b7qPzxZ6kg+skLwi7YVM6LaTX8HGePkkbuLBzfuOUYc+K0sXrP1WbLnfaUqI4QPG1dMelY0lC6GZwgRWxsa1XnjC4V/YsFU9vIsypNW7Pd7mkXC0B47+NPucg1Wxr68sGyCBDmnjfzmJEZjLlQWdmkbMhTZ9XUyTYFrRgjUnQt08nJExVbhBWHPojjQKcly5dZ+CjekcVYGJXDgNSDU5Hd54OjbhomMGpGdZYV+tIXnjUTKl/MykwkH1MmFiYVMXHqNgb67HG147XTjrZuqMQ0DuM4kPJIU3uGnLjqdvNMqbkEh4EkV+abZbWuqEmD43ygdm52e05xKOU0oRs6y/oLy5uyx4vjpHLcapWvnFvp9Wdbz4+u3Az6kkKQjt2Y+WRXc917grPrzqF8+87Iw5WyDMoiTL5NiZiV/Tjy0lpY1QNjVrp4GHPSjVCHivNGEDFGaSITg7d4FIu+x5gJ2PQD3lti9NIaxlyimtp5M01B5ocXwboNFYZcrleM+o8586SD2+2RhwpMNJOtR6SUWiCmkc2wp9VM41t8JSyqBZogpjj/EWd+SM47lu2S/dhReWsEyCi1q4g5MaZIzJZI5iT0acRpIjhPcN70Z6FiX8pPFr8rcELtK0SEMY7kbPfXRfflRFg17bzu+zjivCcVoDQBs5wzWWwmXsoRh2NRt2bpUGKMFGc31WyMjGZELOna9R3ixEpgRVA9gYCu74k50oaGuqoYx9I+rp6UI+oUvO1H0x5RBevAzBPzK1D7QBNaUoplrdo1F5wZfHrvET2AkpgTXhyreknSOMcoj+lrKhcYs7WaTQl9CMEWt0DWSKhhHC3x9q6iHwCpcepmPyFVtenwroiWJwGGCjHGeW9vm+rIA+rzt78CtAgh2AnoogXL8xburaz96g8/grPGTIaSCmPS2V74i95yGnI1qvDPfhb49pB5eakgmWHoWAQla2BbAkTwJraCYrsejYbPanW8UU0L0+fBskeFZbvEIYx9R0qZsd+TyWzGzmr63tNWNUE84hzX3bZQkErbtOScGYpbqCo0TYNDaKmQDE1oiMlObNRkXkoKKUWaujaPDQwEoZbZzte0wHW/5WxxYmxQCSrT74aiDah8NW8d9xfKC8spAFjNVUopwgKD8Nqp8nDp+GRrorzXTjPdaBdiLAJVzfDqiSAnE6MwkYNGxyYcv3xX2UVlVVsZyYmUWqSangEpLqGOITN3obxxJtxdwE+uhfO60LMp4r2jDkLKFqC/eW4teE8mv5ISILzAzzbK99TWz+M9XPazty1PusSDpWeIlgumYjtvjG4iNA2K8OJ5y/XlFe12w3fv1jza25wdFSmbeGl1nrpOysTk45lYAC8uM6nvyarcbj1ZpWzutjGH4OfyiulYCvOTtHgGHdZ8TGmmkr23GUgpRdJgAHyMphlQFXLs+cm4ZMQXtq0IHzlocabyw9Se6YJdJ1I27ykUTBqTw5U86U2KkDIpVV1RuQoXjF2UUBmo8R4fQhEJmkbF/Gl0znY1KeIOzIoYvVQ2rMwkwq2Cx9c1s6u1Hr7JxPagSl0ZmMqq7Hsb1qg5oykW5tR+vtuMvNxuuNuc4ICh37HZbUi5xwVhF/v5vZk8POajYJlxKADMlyA+ZVs52+Y3lnlTVwOoVKz9nlA6N1RNi5dyjxNHiIEmBFRHzurAw+VAzhXv3dQzfKucsSGbAT7cVozZ1u7r55Fv37VNJeZsnYS5UO8C66Yl5cxNb+0jkx7JlbKiamZI9l2dWAKoCpUPpEmr4TwxKjmNVuoo5YRNP1AHBziCM7DURfjzJ57rQbjdwt9+KZLywYr+wTKyGxNVqIs3Cgf9ifMzK97niBPHkCIxR/usZEQinpqrm+t5Y8wkmtAUoB/RMRO8pwnBAIoqTd2UTiJhWbd0cWAqey6bhZVo5lZjzxAjTbBYHDUyptFKQ3hyTCWOUjqUjIVb1hb7x2yDO6tQoZrpi0B2YmyCD8QU8cCqXZeRHhaPQ9mbcs7mecTEQoF3ARXFeW9MR86Is/JXLY3FOpdpF2uGoWcoICHGXEwta5at46bbEuOWpmpYteuZmYw5EmMs1xjs+/28BoIPpGxgOKvS9R1t1eDEsagb+jKzaYjjXLqZ2LF+HAjemzBakyUp+NLaba9dhRpVIeYR56203NTm1RTHyZ28yONQXClJxlQ2T/FQzEdVbXr9cTPM87efPzBRoHaeTR9xZcPK6ulHZV0plRN+8wXTRQBsOuWdS+Gj7aF8xBSgsOx5Ugi/tFZ+dOX4yyctD1aZ01q4LyNoZllXeB+ofEDaMtMmJm52G/rcG33rrL6dNBFJtJVRfA643F4boKD05zvBBUcQRx1q0w7EHoehclcmRmsBBTklfGXAwYY4KgmlCU0JGEYdBxFrS01G/XZDP5cdpgDjXbBsoYilVGHfdYAWi38rG3gfaLzVG/txAKyufjAa80alOSnbVjQzJSgTlpVbrZVb7AKzjTgVYIQI3ZhnCn56jCXYWihY+/dWK9xqrR1ZSJB5RhOCqm202KY1JGFZGcvzaDPgUFa15/E+s6jMGfLPHinvb8prHpLxsvEba/f2U7vbizEotk7g/nJyQC2D9opQRMTMCC+vr8k5E4elWer7wP0qcVIFXlsrl13kB1cQky+si1Ggk8BcOLS+K/CjK6E699xqoItC5YVlI/TdluX6jOCsFOm8I089zSIgiveOWKjhmBJ9PxCCJ4TAzWbPZrMhpYT3QhwHvLMule2+59Eukqvb1GVGi06REpj66YUJKBTKP0wdFZmhJAxDMlDaVgFX1VR1jYSaUNU2X8hX3Fo37MZpVpOdDClHYGJLZkMw+wmcGJifPDRUjQ1NCXGhtPUbXT2xPDkrknPxSSqA2002Am5mZ3M2DUbKpfPBWdnGNbUFuCz8/TcyLywSQ782qjlnUh65G7fElNj0O4ZxYBwGNEW6bmDX9wzDSOU93TgiCLGwod7Za1TzzCXlptuVllc7thcdXPZraq+swsAiZBYysgjWLYJExmQb8xgjn944nuwb6sIMZuBPn6z59fuXfP184OX1wOVQ0bjEWQubwUBgn6MNNXSBPk2D/+zgNL4iOE8XexwGIGOOaIZEpgm1bTgiJl6NPW3d0riGfd+x6/YgShUCYz/O192Hl60xAMXP5AcXNWP2/Mq9gVdOTYg/dS7bQvBAb5tSWJA04fFkzezHHgXb1NQM2WIuHUA+0I19AfQdi6ZFMA3IOJqFQE4ZEQMcZCU4T11V9rzCBIwpEry366+w3A6hEkcqwIsCkoJ3M4gJ3rRehkytZDGMZYPOyqpuZzZCVenHjn4ChmBMhnld0/iKPlpJd1m19LGnj6PJE4qUYNonun6kbcr1IVBVxs7ut/t5zprpiOw5gp0f7zx1qA0UV5FxjCiOReM4adcMaWTbbcm7jCRPVS0KUylmJ5GUlEeSGpCrxDqvunFABFbN0tgmKcxPuRYmwfSYU+FAagrBjgrUvi7scqIOLbMQP4+QzcwS9UCysQs5IXNXri+aoMFYmjKeYRwjEKmqipyUalETvMwSkS+6/VxNy9e/+U39nf/iH/F7ryspKlddZhWUReNBrGMGsXkRj3eZisxmUN7dNmyjmz0dXlzDKycm/NJS7+/HRFVZS+yYTJsSxMQ/sfg8XOxurJQjMGTbpL23co8TW5BOHK03f4pNtyMHmcVKJ4sVZBjGERcsX+0H20RiNurOVN7KqlngomXRQ4oFoFktU7NlbIt2YbXiQsHFlAqadaRsNOd8OLVk9aJ4FxjTiAAJxePwOJJknHiGOMz1RGNShCZUCG4GBwCLqiHGVDQdMmt+LMm1IG+i0Gkbep7xkpnyh4OT5nFZ79g9eCqJTQyDPdbRJSt/PNoql33mzds250iBf/kJvHrmGZPyw6eZVQVPOp21JPPOO920uC67g/mRE3N1fPM2vHwCqmZ65tw0q0bIKjzZJn62y/xsq1x1SpDE18+Er5/Brs98sjMm6u2b+jAa4Oi7zhSXGOsyAbMxKS+vha8te/ajsqg9beW598IrSJm6HJNpp6qqwntraRxjMgGeCjFF9vuOtm2pQ+DiasNnNxtyVj7ZJR6uPE+6zDZ5dpjmY+r2AZ2FutNxT9EyTYr2iwKmFQih5tZJw2+/FLjJgdfvnXDWOv7nD1r+3uuebhg5dyPXI5y2nlsnC8YcIGd+8uSGd25qnvTCpiDeaV1JEXhPHQxAKa1ZJ8Vut6MKVcmumAfgWUXlcKCnzVdKWcnNJ8HYl6kE/LnghF2DZ83Ib72wZ+1g0SzQHOnini727OOAc8bWppxtM0PYjSNC4nF3l8thCXFLGjN13rCWp9Qa6Qern2e1zdZKu65obVzppgmgiSYIf3l5m0+2CwP4ODQLlTMWJON4cbnh/qqjdUUzkM1csq0ahhSLWNtR+6kV3bLbWISUQzzYhimYXgPbPMU5Y/UQ1k3LECO7sacqyV0ofxpXM44D3dChTnHOs6pbxhgZcpxZmsoHm2YuHhGdO4g2Q0dTrRDKJPH5+rDNKmVlyCOV+Pn8aikxjTninW1OQ4r4sq0lMl6s1J9InFQrYkn0fCkBJc3klOYNv4/jvHZEmPWEFpzmuh8iJqD24uhSORc5E1wobtrl51IKnnjL6RrD2bpEoY/2/JiN/TSWXOhib7FeM6ftyiQIPlCHmpjsPKyrJUkNvDjMILLvRyrf4sQjOMJkUVG+zxhHmqqxdVa6l8Cuo2m8iKNG1YEmYjQD0OCFbuxYr5aIF1wp24Byc7NntVjYHqfWEbfpdsaSlLKhJiWEymZFYYwdAlWoyZpKubCwuyo0oSZHNUG8txEnIlYmBxMa5zwWdsmAX1VVM3Ma4wiqhMrcyO13nt1+S9uYNCSnhPgwd2ndu3eX3/iN3/n/rmlRhVqUP3jP1uegRlv/xkPhQZuL0tqBBN67iXy8rfDOaqXH3UZPO9gOymlQKpfoBssC49AzxtGygCqwG5ReTYtgc7aEXb/nZLEwl9CqIY4mMrP6X2Lf9wY0+sHQWq9kjXTZaPmYIpWvaGkI4mlcQMSTdTTarAzCS2Ni0bY4XLlQTDBUhdbqgUVfo8na16ggaCCNkaapwFeMkhjigHPm1BhTQhTEmQgLMZGVkhHnLVvXaPVLTcUXRam9dTEFgco1M2Ke6L5nxxPY/yeTqjMJDKfAd9gApoXILLqcgQpTn37JurCSytT+N8cKsce0DiQ4Tmrlaee4HoVzb11PGeHNW8KTvdKGwAc3mUXI3AxahqhZOWr+XAJ3VsK37nj2UbnqM62HF9ZK432hkXMRr1ImgNt3ubPw3Fk47i2UP3kU2QyeH92YKdurJ54PO8fTwdF6Sjv0YUOYNmeZ7z90GtVe+HSnPNrX/N6rpiGIqozDYIMKUyIEu+SN7YnEaCyBtSEmUjT27PJqw+Nthyi8fTOVJWqut2b8JlgHitWoC5NRGLjpMGkJSs55fNPStAt83VDVLVW9AOdYVcqfRWNaqgFWS+UfvmUAR7TmZm9Tn1NULjaQ00BVe+4vlV+6p/zzD/b86yftvGYm8XrOSh7HeXigc46UEilG2qI/y9k2DwHTxRyvQSmwVyEERyrrsOw5UBiX529WzoJWEq+dbKhUCdKQ04hK5Gp/wz6PRb+WkULLT10gtYM/v/w6F/2qrN3bqBOyOF5aPeW125/iyMQhstnuicPIbtdxcbPBI3NraUxGj4/ZsY/W/i8CJ6Gj9SOf7ddFTCzcbi5xOXI9KME7tn1f2FPL2Ps4FpBhTEHKmS6O1uXiJmv6YiCpsAiWbSasbLSoalJK7OPAtjdNTxNq1mGFYO6uT68v8I0nO2tTr0oytB07QilPRrX1GZwjSbme1M5bW63oxs4ckTUX8bIBqKSZIUZWzcKArYhpOZjapU0jAcyi5coFfAExuYCM0+WKzX43AxIRhyZLIK+7jYGvqmIYDfgE5yFTGGmDQlbaVYbYUxd9RhNq+jgW3YkNYDT2T+m6gSp46/ChlOLF4dR0hTlnE9sWAHk8TWYs7I0BGysXjWWUx6pZ4MWxHTraYBpLL8GS47rCc6guhFARo+074Sh3a0JjgKlcNl48bR1MuJ+Vrt8bi+IDbajsehodXdfTVksbqKuelJRm4cErKZXycDKtznboLK5naKu2lL4GcmnVbuvGzmUJjrb+TZax7XcEF/BS4yTgKl/mAhpp0e07nD+wqNNtv+/xQYmay9BL69KNMeIkUYUwM3kJx4lT8I5hiAzjl/cp/1zQsovCkA9cvoiVIr73SLldZ14/VW43yjBGHu8bKKxA5Z5tjd6OcJUdjztl5ZTglF2Cvgh3LveRq1Sh6vnle1ZSiXFk2++sQ8M5nCoXm2vGNOLEEY0bpQ6BkUzV1CTN9LGnqWvqLIw5MmhkN45kCuUoFQvvTciYlcGPbLotnRj7EVygDoF+tLqjKyxG7KzdrdRRGPqhbCyKaJzNgbz3ePWcNWsGNWR9021AxUS4mHisi72BCC8z+zJqsUSnqNVFSibi5i6L42F9Exqfgt1s1qWTZbXMm8kzw+1mBsVutnkeBtrNXgVYt8fklDht9mbBkKxtvTVH4usB3r+Bl9dmotd4eGWdeXUNXXR8/wl8eJOthXnOwOF2K/zua55Q2I6r3r7L9WAaitYXt1mm71rErOpLy7Lj/kL57ZcCn+4yP7rKvLur+bCzDLrx5Znz95eyYU6Nioesf8Iv0xEaklBXjvcvBxCPXD0FuU1dVaa3yrmU4OwZkyI+JqVuDNDs9z1NFfjRVaQKhxk9OUeCSNEcxJLtTB4n5VHOUbdL6nbJ+vQUCaW8M5+LwzndR+F+awM1F2HqInEMqaLvNoirEddwenqC9566CqRhy6PPLvmnn2x456Y+cnnWeaCjqAmeJ+PFfhjQnKmqUFp53ex78UUzuqZj6n0RvBYBod3ny2ym59heVcbsWTeR33zhE1biaMOKpLGwmwN4ZRlqJhVPFyOpbCRDUoIMPOnW1D7ObFXjE2+cfspZU7yPRAhNRevs+rrlHKddTxoSOo50u56xH+iGESeRV9c7vv90gSgs/A0vLZ5wUp2zj4F9bBnyGuGCqInWGTu4jwMLrQtbkJAsfHKzO5Q6CmvQuqZMri76J+dN18AhAdyPA00IdONAcEItnqVv2e22bMYt1sMIy9yyHweLjdmYiDqYzmXMSl3YYiduZhcmoamqElwoyU0mlnb+KeZU3pOzaYMSWnRvA8EHvDiGUsrpoyVqfek8MYCTCQ5u9luaULHtO/qxL2U60w7Y2htnfUUqQMiJlYtQZSht6uIMXMdSpupG07poYY5yNM1K8K4kGZgeRpXKG/sd1M9t2RObk9SSTQXaquZ2OLHz6AwwGQDL9GkgRIfgaKtq1i9GMVa4ch6NkEkMqSfuzetp6WtSHKmrBea9ElhUZivQxw4X3NwVlCSyWNQgjr7rLXkah1kTMvi9lcPKdetpSzgbTMvoLI4v6pZxHiJrDQRDHFGBRWWl3ZFEG2pErIIh6nDeKhqm2zEaQrN1+bqmYhwGfMDYLh+K2F+IccB5a9cOap1RuTAxeRLhuoaUIpe90ifHO4N5Ll30QjseteA9d/srHXH/wX/5+89GnyOEqFpmwJVywv2lBfvaKZ/sXfGJOHozDo6NMR8Y5NrZo5xz/Puv7Lja7rjuNpwt10VMVRCgc4xxJHhP3w/EnGhCRahMXLtsljgcXd9ZrVqsLlhVFb0OaMpc7m/Y9XsqbwBlUbdWGlBhM+4J4YBwjcfL+OBxU5NyQQ8pmgq7kZoqmOo9FqCTYqKuKxOPedilkZN2Qc6Zfhzo1URTq7olhMAQRyo3sT5Gc0rJ9sLRJuado/LVXIOf+91lmtGhMxgonx5BSo++FXumwDQNLaMwN9ZxYsBuypQVJYiwHa3ddVfchQXlsnf8+RMbhjYkY2bOG/jqmbKuhZdOAp9tI28/UT64gW/fhVut47z1/O8/jWaU52wC8VBa6K97nS2jp886ZvjaGdxthRfXjoxlgJVTfnzRsw426NF70+zsxsQ+O/71I53LFdOFakl4PhygI4gCB6GsFpGhc9Yh83dfFlqU0/PbhKYtXWI6v6a12VPEhJmbzY5+GNlsttw+aflwB28/HcwfpohUJ22IquJL9ocT6mZBvVhRL1a0y/XsAjpdprlkuDkXe3yYAWvKymkN563Vk9c1fP22pxXFpQEV5YdPEu/fOB53MLoa0UzArARCXRvjpnqg1ooGqiBfYwTywTRsMsWjZOReBDnywZeyPqdOp2Nr9FA2MtTKK23IrMOWW83Ii4srbjrPV+4uWTcLdvs9m2HDZrQa/Ki5uJSaJ01M5suyGWq2cc1n/V2uxxOcZO4vLni4vODecjAfEXTu8gMY4jS2YGIdTbBq5anM2I/sdnuurm+IfUfOjlEbdmNF4IJQXid426Sd2KZDYRKaUkaIKc2loMp50zL4ak46+nHEiVB5z1CcW71zBjLKsWuKInifIrULVOp5tL2kqgPLYLFoKluPKXNrueKsWdGnyCc3F9TekjKw1m0T9OtcPs1q7MjUKvssoCzsW/E3KfkbQzLQPcUWLefcO2E79DMDPF1yTopYu8QfP5V4nJtbiGNhZmKMxf024oP5iCzrlkSaxba5AJOkWhgTY7Eq563EWfaZMUbW7YIpKJiBoyeIte9uhw4EunEozSA2nNLKNjqXllSVRVWzrBb0yXSYznkr+ykkEvuxxHAc5urFLLL2xZZAMjjxBniizQUbU7Q9LYTSjNIYQzOOxGzWIGYRYt+tdk0BbgNZoQ0NMSpd39M2ASnt4EPqiy7SylJJ1SoQdV00llZ6te4iR8AXXZ7NWrP2bKsSzPYOZcq8JTjWTWVlpoCTqX3a/vKF8ev7EVWTfzjnTN+ixoY1xbDxf3rb881X7vFf/2f/3l/PEXeOPEf/6tH9ofxrjolKcJmFz3znTuZx5/jgxh20hJS6vcDXzyN9gove8duvGBp0RLb7nk23pW3NJRERGm/+Kje7Dd57dn2HqnK6XuOccGt9C6+Bbtdxtb22jFZA/ciYOKiR1XPmV3iv9DoiDrqxswupdEpouVAQu6ijKiKebhIseY9HiGr0I3kgaqLOFVUIaIwQYBhK2ctXVM5KVQpsh446BE7aZclkoXYHv5iJERnSiM8Z9XYxZM04NTGV0bam4J7A5OSJoaoE8XPwmI99nhTzypislmq/mOYx5ZnNmSh2xUzgWm8/LYIZfP3xJ8o7F0obTFgbCijejfB/f2LgJebIv/hY6bN5vPzg0piUyiVz1SzA4eHS7n//Rqm9mzeSXDaPxts8lOuVsG6Us2oglG6Mfa75yWXCXYy8uFBeO4FK4IfXGXFHU55VOYBza9vVPCkMDzWYqcRmtra5sDGef/WJ8su3Rvaffca9ew+oS4lInLXOjtFASxwj2/2e7XbPbrsBlA9uPO9cZbwqaUzz+4mItRPWLVW7ol2tqdpFCTIzXpi1NlOgdxzKWlPrPmAdPTlzsRee7CYgKvzZpz1BI+tKSC5wM9q6CQ5CHm3d+NqCTy51/gn5luNywHeltHfEqDhH8WExoFIeVpg+e9SxTw4Y2BYpn1mFjHCv3bOqRn7p/BqATVzy8p2Gpa+NBfSeUZQxj8VJ9DAQNcYRVeHR/hYfbF8mqoHYh8sLHq5ueGF1yZgcIqEYjxXgJ0JdOq8m1jJRgBdaygSZ7IV6veSF0xVZ4dQFnlxesNlseHRVDPZE2AwdlfPEnFg2DcM4shtHunFEKeZ25b1RtfcpJOAQY2Exctn0zVRx6hhsQ8V2GIia2Q4DWZW2qVAy27HjvFqRyVx1OxahKl1CEVR52m34bHvNum5NG6hujjEyM4STds6sIgQYc5rLS9Y+befUFda9iwNd0eFMI0naqirdKBYzKl/K6mrZvVBGhxQtni+29aZp0SKQNs2aqOCqigwsqqp4rUQ2ZUii6jDn0jlZqW0qozsEjgThYxHuDnGcwXNMiZCtDKi9bZxTR9G0bpMmFqEperCkEVAAACAASURBVDob39EE0xF5cdTBl1KZJ2liTKl4uGSGNNKGlqwGsMY0IDj2U0lQHMFlcrIOIdQE+vvUUVFRO2v992MuIltHJhUgZqxlT4dEb4J8jWy7DSIeH0zLqViyEkIgRzsPQ4ys2zUxmg7F49DShlz5cDimZU+qQs0wdCjFuDNF6uDwJLre9CrOGwivQ2VJsloSOO1Jhw5KMNRg3yeWoZ2VKFHNvfeVUyUeB4znbr8YaPk5t+m1ncCTTlA8P1NP68vEiQNTPCVrJIVv3e6IWfCFKks5sRkGbuKeZbskBBvuVgWrn/XjwJATTfA0dU1bN6VdrGLcRK73G/p+z/KkCAOT0YdJR7KL7LrOOonEUYXA7XDKtt8hZeRlziauHVNkUbUkMilaLbPvO3wI83yWUFVQjO1Ssk0lilGKKZs/iXfeELY6GmcXchbLIJpijnTSLOcsuRVD4ikdrNwnRmQS9ZlnivnHmC+BeX2YMMyzqBraYCZ8UmhnAyr5sPnArOS2TcgCyCSItG6UqdPJlm/CbMB/eu256BNP9s6mzyIUDVvJlm20/T7C//WhCbdePYVv3RauB+XfPIJRXflMJr5958KsnBe1Da3rC2OxqECLPfQyKE/3iT/8AL55K9Ml4fWTyK/cDrx1K/DRVvnxdebDTxP3Fkqv4ah0csjuJ0Ai6Dw3x6jDCSFoKR0dfiea2AyOP3pU8R9/vUWcY9/1B08hpdj6Z/Zdb2yVtw1xlIq3Lw0g5JwKoyKEumGxOqNZrQm1GTIdFa+AMoNEDvdMbcTOlTUndt6OA4I4T8Bo/UmxlLOAb7hBcCpUMiLBF7xmwrqJpcmF3p6PFZPmoGTE3jPNAJt/V7LllIonRVnPIlLmyk18X2FTy5yDnGHMntZHHi73fON0Qy3CulrjnHJ/baLVlKx1MqeBVRVQXbId+1KisBLfdmz4ePci1/EWVYDWZcbk+M7d96l8DbQ4l+nGOCcFkzfLLplTacY2zJxzEeHaMZQS2y73O2ofTBfSNty5f5fTO7c57zr6Xcenj58yxkSf7HMtckBEOF8s6Ap4SaXcUnnPPkYrW7jiZoqxWNZtSBHvZ9NORNiNAzFllvWas7blYr/jut/RuMCqaUmauek6ztoFVQhsByv7BO/RlGicp4+W/e+HnjZURQR5ODsOylowA8IgB8+aaV1KYUamVufGB8acMKG8mwW0rnj+OJHilhpg0r8UWjIWwzgX7XqrQyhMjKMK9o6mC1TzRBFHU1Wk0hpupmeTC7lpJLzzCBbHTSgq1lGWrfSxak2U3MfIqjVBc1QzohmTCZWdE5a+Zl9AUC4XqGhmWdXo0X/WZq4MaU9wgcp74mjMTBMqHFpMC0dqX+FF2I6JjBLTSFbPQERH05E4gTvL09mLLJdAGQdYLFY4Iu1SGIcRH9zsXp2jo+v3NlU+GRJYNMu5keRqez0bnUrluOluqEJF61rqtqEfuxJ3POOYialHcEQdGMaBpqqxHC2TyPxsA5vR8fIJVMG6eNumKa34UNd1aRSwRMj7QM6pxJlow1tzxKnS95Gf7Dy3F5nGC984hQ+eSXOevf1ioOXAoH/5Q5TDeHcgfcETHPDrDzpeXGV2sSDToWPIZSgVyiI0hrRzppuskkst7nx9Qk6F9hsqQ5GVZ9SesIh0rudyjLSDJyfl/OSMs9WalCIxZYYcyWPGSYOwYFnX1JUzpb1Xrvc3tFVTevQVDTWLUPNYrhmGgbGP3FqdUbuK4AJjGghVIISAZmUcUxlapgxpIKfEbtybiZEPbMY9J4s1USNX+w1dNG1L66rSLSTgYBetdbr29jkEAzRjjLMVdFblsruZabfgFtz0ey73G9qqoQ2VuVLOZYWDysgXJmY+V2o1WFEKuzCdU5vpctnDH35UE9yRQPT49JaXmkCpYAMWAT6+gQ+uM3WYghg8XP+/nL1ZkyTZeab3nNXdIyK3ylq6u7qBxkKApEiBMxpKNhrJNEYbSTTpVhe6kJl0JZsfMablx+hqrmTSrUwmmUZDSUYS3EFgCHQ3gEZ11pJbhG9n08V33COr0A3SGGZAdWVmRXq4n+U77/cu8LUd/PEXhWwV33uS+HCnHmzUMloyGlUyP7pJ/M2t5nYs/PWtnAR+cleD5JAcj85k5my4VGJiJ6dnHmy60tYgi7pHaVOLlPLWvVg+San3QinWE9C//OuR37gc+O4uYk8/YJxkcbq5uZVixWqapuXzF1ec7TZ81ot9fAqRbntCuzul3Z1hnVvqo4e1VUUnal6WgrJYlNXCUhaAjLMCl4eY0Mat7b+SizybunBARltz/IylQDU2fPjwFpRhuYil/bT+WC1uQ0pctJnrUa2E3EVVZL3HGJGaxpRXs6sFTn/rVcDZwu+/91PAs3EbDB25wOF+wDaKlES10TUNWWXGPKOMoAIuKcbKq1Al8MP9P8JqCaTLudQOYOHlQdPZEfGQkGKkVPREJLHiLzLUtm4xmn6O62U6o8XhVGs2vqEUaY18fnsr8LaSxGw6x0ff+IB5Tuzv90yHgVfXt5xtWqYkp9WTplkjEfowE5TctynFByVdWde8heQq8yHjtMYbwxjltK4QxHrTeO7HA8Y6WueYcyLHgtWKkBIvD3erAvPxdodG8fpw4BAmuZe1JWrVEvC3uBULiquBIYa6fsj8jzmz9Y0go9rQzxOtlSLNab1+lpeHfeUPwJjkRJ4qR0b8xhRzCXjrSClzPw1sm5Y4y8EtLcTQUqrqLDOEJG6tRnzBMgU0tLXFWkpcLeVzVSilLKnxG2/pZ2mFbLxlipHWavoQpT3hLK6Sl29GoRDIK8uakQtTqXyYGMhVcWb1wvcoDGGiqS06pRRzjFit8boa1eWyIjVzDZos9espRdpmAyha54khi6lcCTjvGaZrFC3hfkLrmrOnE9tuCyrStQ3zPDOXjDWW/dQTpkDbtDLHtMbUtcP7hpiC+G8ZxzQl5hD56WFkwmBRTMqxtR2PNoqPWih55lVf+MvblrlYfu9DseaIKVRXYtmXUsrsh1u8dRzGHqKptg+Sn3c1aO5D5PPBE7MCDJYqIUWB0nz8Pl/5+jsULQ+g4l/1+lt+ZIFeP+8tc048bguliLFRyomInMy9FQXDEEZ8fbBZCZGvs56pBIpSxDyLFCspUpm4nT1XhzNO2sLGCzzbz2LNHLLmYnPCOM+MYQA9cbO/o6jCxl/QtlupJHHEONMfBjrvSSpziInWOLKOmEaURIfxsNoYGzQqArlwiCNb2zIGMZ7CGXJJzEoiL3NM3M0jGIV3DcpqWt1IgaQXIm1Bo2uvUE4iIScaYxlzkI1Cga2PLiQx/EkPyLcpJw7VQl+KkVoJu1oJU1Bl6WFLiyqQalGhWcyqrC7sQ+EPXnicEeRjFf58VSH8zjhwBjzqraD4n97BZ7eCSKQM+yBuqPNC/lwIXyWSM3znQnPq4Ee3mptZrtloiZNYzvFzln7t1X4JfTs6di7XlNPSF+cripXlGssRdVGglCZEyWn58Y1hnOC75RW5GAqafv+aTdPQdBfkXDjbNIwh8DJ0NNuWs8fv4dpudYPNX/Jrl1BM6ZqVFSmTk59eT6fWaMZJ/BZ09Z9QS3G6ONoun03pY7vn3YdTj9YrrwnWPjN1LubKYYGl/ZS4m80KSlEyyoipmKu+GLm62b49II5tkGU9URXBa0zDbR94tPUYMqiEsorXfc+msby4v11zYEKJjHOAeqqnSFtA3Glld5TrFbM+Z0pNMZfr00rGmJwYpbqeouQQURT9HGmsqSfozBQTRgshcjkwWGPonCOjaIymn2dOupbDHHDO8ujROerxBY+fXfLm+o5wd89QzeGUkib4WdeynyaMVvQh1pao/M4lx2o5ZKQsSssxSBumcbZulEIKDVGiCmLKKMRsUzgqUuQUxAnVGcPrw57ztuPJbkcfZjrrKEoKllgLdKs1IWVcNQlDQWvFmO1mFLSp866qWAp9mEBVXguF29oakn9nVxO6nIXPY5WuIYV6RQTnFOVzUDgMo0ixq1pOhrVCVeVQZ129j7JB2tpSL/W+GWOYKim7cx6rxeBwjIG7aRC0AXHbTtVXaDEinGNiKgGnlygViZpYIgm0VquKqpSy2mWEFKXIqOifqWG/IutWWGXIWkw3l8JwDPPqEwSCWGnnGMPMo0216kgjzjQy/rW0r60NaAsxjRQiWlv2w11tvwV2mx1E6sF7wm3F8V3UgInGNCgkqd5YQ4qFcRwpaeSPb3YccovVUp7mXDg4+PajmZf7mZ/caa7mDTErvn0uyL82DRSF1oV5nirSklAkcrZA4fy0IYbEzRD5/153TEW6FjoHbD2IZaUg1tRr8+6p+O3Xry5aygMC1Vf9SH2T9Ct+BqQllAv88MbxutWY85nbYPhgEySUUddgP+cgFopyZApt0+DzskhJeJUxhtwCOBzw/7w6J+bIeXPD42aDQwx3/uBlYQ4J7zS/ng5cbDpso9i1Oy5PNaiENg0hJoZh4nT3CK3g9vaa2/6NqJaMxmOrLC2wHw44ZSgqYZPhxG2Zp4n9PJBrpW2VWbDWaiSmaduWMGVmldj4jWxSdfEpiB2zTIDEru1W8tcCr04p1iBAIQkvg18hpL5FBWCUJq49eyhZyITU9tMCqW8qp0Uj12kwK3u+FDnBxFz44yvHnBRL1tf60Hnn718xxh5umcvL6uO46gy0JhNjWpGFRdkjk00zRnjUFv79LQwh8+JQ+Is3hp2XzzrHyqupYW/1I731Z6oSx2XDfFivLJe/nBApy2m9fsi1kBLZ898ES2FmDJmYCv/Wsx0bA3MYuL2f+OD5+/y4tzx9/wmmJFRNSF4bdO/AK4ty4+EthSNPSUiLYlw3h5q1oqh8lweoWVGUUh2Yl4qLhT90fBKleqos8fVk4XF4J7wWceldvDBgDtWKXBtEAFAoJLSxWGsq36ZUd1DJnxri0hou9Vk+HDSFVBQbp8g1d8XZlikM4DKv7vccYqCYjru5F7dVZbCmEOrmmtaiorBxPSE35KLQLAoFRcoapxMbJ0XP9TBx2lo0ijFqMoY+Z96Mns4MNDaTMVU+K/fVLGNGiQJGUtYVVokUVmtFYzW5GPpZTuw5FrpNx3uNxz9/xuubW/Z3e25v9oQU2c9z5U1Iq1J8OiS9XddiVdUHoxSMIbBrHFOUg4mpzzPDyikRM0ZVW7Riab/xrjqYyn1fENWQIxtnJRU6BsZKSJ1ydUZNCW/MGgTorSXExNPdKQVBwG+ng7T6aytp8fYRh/KEU5qNc4wVuEol09ZMnTMnLZ03Q09jnRTHzlNKWhWBoYhy01XfF01NWK6IY64HspjEm0drTWMNsRoct1bUpOMc1vZrSIlAWup1pE0tYoNQCb1aGzKizPFVCAHSKp+i8MaKXhxdYz0UwD4NlRIgJolyy8VnK1MIYaLRcmCXw4ddfVmMrs9PSyTJOInxaUqZEie094xpIKtERsznxjjTNY0oiNJMTJGu23Df3wMSE6Cqd1djW2ISe4CUUnVIFl5UDAMxaX7WeyZaWrPM2apszfCD14qrYcOQDOctfOc88dEJtK5hHEfmqWfKirOu436MjEnagac6sm0bPnkT+Nld5CY2ZAVWBdKcqoinHmL0YjMgJq85HxHPd1+/Uj30wTe/W37/n/93WF9Jm5U8+aUb1K/auIo4tr7XRe5mzW0wOF140gbmrLieLP/k/ZnTRjNNE3MQ2VRQqZLXZGJoJDfiECdKbLmOLVdTx2U38fHJRGNa/vRKcx2Es+2tJiTFjBhprZB/CWQlvBdnDLum8K0np3x29YqPTyPffPKMVzev6cc923ZDiAWKBEXdjvecn54xzBOv+xve2z0WklWM/KJ/ReO9DP4CY4xsm5Z+HGtAlsV7gYmdEetli7DAG2NXz4Pbw57T3ZaNa6tLaCakyBxnClJ8NJUIZrQwzl01b7JKH1ENJRU81J4y0k/2xq4VPsCueg3IoqNrj13xV9eGT+883kjRKWPgK579V33vS8aG09K6qQAVGwu/+yyydbIoL6oJWVzerrpTKXy+V/zRS4Mux0A1+byKf/ux4psnmf/1J4WQJfl3HeHrWK/S2+M3ajjrWrY8+FDL5deyRtU2V0WOjZGYApRic3LG+4/PCLplqlwM58UTYQEZlvcWWoha391Ul1i1nC4rX0UKmkIIkdXBuJ6CF+L4Wze5oizrNS/3pt5TazTW1TTXelG5iF14znElSCstC27KFWktcs+8szU3SwrypR24Csa/aiw8GAPS2tB85+TnfG0DZ1Y2yMjM3Xgg6Mw0J5RRTOkYurlmvxRxce58S6bBGoUqt3y+f8zPDs9lya0FWypSpDZmIpWGhcbU6EDIlpgNl80Vp03ktBl5fqp5vb8nRCn0fS3KlpDY1mrup0DrLDfDTGvEXEvWmsSc8kqw7UM1zaxqkZIj16/vuHp1LZyq2hp4KDdWKEJO7LwjgbQAlao/Vdg2jn4WEzeLoh8nIpK3ZoyiNZZKGwJEfq+VorWWxpmHw4RSCodKTN44VwvhIrycXJhzFFI/4I3hMM3C9/Cei7ajwOq/kiqB+E3f01rLadvSh7lKuuG0a+nnmftpklZIXlpFrOodb6yILGA1qVsKgJyFzBqLEA+mKK2fpTWjFbTGEusHX8exUtVDRp6Pqy2IRJZkdcAgZPJd06FRK6dwjjPKGPbjIP8mJqyrxOIs0ROLytQaTVgORkgLUg6jx1bR1otd/5wkZ0crkcuMIa78NaMUj7Yn9IcB46rCRonR42GUPCWtNeSphgjDME845WrkTt37fMvd4cCLsWUMhmebkWHOnHaajbNYrbBk/s+rJxL1gqytR5WXGILGIsXwv/Ne4IMNzAF+cT/wJ286YhYUbsayZeB2zFAWPqX40liVyaoGjy4+Sm+VHL9cOBij+dr7T/jv/6t/9vdTD2kthnJlgaDVAyXAwyPqu6fuh4uUEsO5n9x7tCqrAd1n+6YSuwpz1ugyMcTItjWEmKAorIr0CVKJGK042+7Ypo4/fLXj1SC/p58Vn9w4bmfDXRLCUALGMVJSwJjI2PfigaFgMQUzxpJR3M+WP/zZgLc7fnR3z+vxFd977wStHRELuucQDB8+OqEdd9zcv8Ipy9Z3aArOKFKErW4xxTDmGaxmYz0b0xBMQOkWZQQSzVkY4q1rQCn6eWSqfiqqQNu18uC1xgNTSsTqW1CKbJQCs8og8NrgjLgTgrhaLovhQtIlSf/eIAx6V4skrTT9PNaJrytEbCsR0BCL7PpW5S9H3L5kk1IPvvxL/6iI4yxKIhd2rvAPn0ZOvagLljc6DqMHfAvENfncF06coQ9LSJycMHeu8MGuMIXCb51HfnDnGdODS6tV/MN259IKKr90wccNpf4UWj0c5LqGGRk2JydsTy8w1rFXhpIEGbG+Ief01rsuf1ncd5dYhTUrqJ5YF5kwRSIBWAoRXUnmtZBQRUzpZCN6Z1GojrZQ2LZeggJRjEu7ot4TkSsX9IralFWenWJEG9nQnBNYN4hRj+QxKUUqivMmcJjNLw8AxZd+rWQYgiYYTds13PR79nNgzol+DtylC/bTI95vfywQfRHlgVIaU6CxmqLEKfRu3jHGDZ/cP6sycHGiBYXTCa0KoXT85qMf83gTmULg0cZytY+k4rCqxxuRtH5xV2rbUczaYpa2jaAphTEWWmvwRnPWOrwxzNXF+6xr2E+Bfg6VH1GYY5LwzCwKkNPLc84uz3j9+ob7m/saMSA8oK13jDHhlRETvlzz3rqGu2mWYMiKtkjhvDz3WldmSDozjAlx4pVcKqukBdM4fZQGl8ybQfgdW++rg7AUJ/PixFs3n65uckY1ZKQVc6gtcCmIDEOU5Pr3TkTR2dewWWOEAzPHsPI/rJY1zaAZ5ghGURKM8wxrgZfq70qrZ8kUZ+YqfV7aHUvm1VIImWrzb+pnXpzCxxhRwFjdboGVxGqN5azb0dV/i1LcHw4YpEhe1trGWYoCpzRFC3LjrSB3MSWRRycp/J1zhPo7TZ3bQ5VRK6WIRdSSBUHOxF5CPI+GccI1VgIO81K0C/LivSHniR/enlF0S0qamAMhS4DukBw7F0hFU3LDq6kjK8Pr24nOKWya2LiBVwcPxvHxbuTnB8chmqU5UBdfsZvYevi184EnrnB/UJBnPt03KOP4xnnhh5/vaVvLTRCDR11DKb13WKuJqdTMsioW4G2PsIfr/bLqp1TXlq94/S3tIao1rxCxVOU7qBp7vubRfFnBwttfUwq8eXBpD/6eC3xxUNyOlp/sN3Q68/XTkdejxqlMwfJ0c2CcJ3RRnFjH83bP3bChGM313PFKxu6aOA2QkmREhGnGGI+2MmAebhhxnio/wJCTY88Zt33Gv4481oFixazqdT/ifcMH5xcc+lthmmeFdpY+TIxx5vHunHme8RiGJCSlzjmU35FUYZ8G2aCUcENiDMSc8XaRz1n6KATeuRTiPFNy4X6S1tPpZkupELLRgjyZSoZzxq4qhM776gRJVUQUokoPNmFWGWIs9cSihBMyhYC3CW8y720CiczGJj652x5Ri4cb0pegK2/9zJeOj3pyV4rfugxc+CTFxZH4UDOGhCex8A+gMMbMUCz/8FniL18brgY5ZZ36wu88SXilCBQe2YnfOs386U1D5GGLU/gipZJ8WdAI9XbL6DiNjkjGQy6KtZbtxWO2JxfifLnI+3Kqcndd0ZyHO/UyGSrip5aSSNUoA73+TwI1C0tauKqSy4ICLcXMipYomUSLwy7L+9Y2YeMdSmvmIBvoko8F1TgwxdVGWylVNwHE1ddaGieW6AXoZ2h0Yk6K01Y2v51P3Ax6vZXl4TN/OAiqY5dccuaDxyfszEuGuWeMgVRG9jHx5ze/gdKWIbV8vPs30oZtDXNW67iVTK1ELoZPby/ogxyIFiRMhpnizN/xuL3lF/17vL+5oQ8yT+7GzNYppnDgEBVTlBOyMwqt6gaItAUWH57GGuaU2bROQhWVqi0IaRMd5iiboxIJdWMFCctF3KGXbK/TtmH7wVP8R+9xd3vH3fUdh34kFVYENMfMxtsacQJbZ0kW+hCrajGi0KCh09JKV5WrI7b8hSkmNl4WX2fV+l4LWnVEsApbb5liqsnmgqxP1e8EpHDrg6gphYsT8NoQcmLjXW2nUV1zU01fF6VNRrGf56oYq9lACg5BUohbaxnCLO2oWtwt0Q5WWzSJtiouG2sZoxRV4oYso0srUR8tqrVcFv5YXrmCRiuhCxhXU8QTW9/RmIrasqzJIgqR8MUgRZAxtaVXyIo1LVmsJ1Ll1Ci8b4UXlCNWaaZZ9k5rLTEXGufke9auXjQWXe09pP1vrWIKM1Fp2raRuZ0yZ13LGDM/vjvlajqXmaZAK0lR/2KQMXAbHKt5oi5oEhlHSoEXveEnd2f0SSOJpwVr5PCjasGYkYL5tFF873HPzs7EtIPScxcir6cTPtzNzIceMsS5qgL1guBKntZyuCsVLS0VHT6uq+8uEg8LmK9+/er20De+U/7Tf/4/cKw8Ck3dEOd5wjcNqspu/05k3S8rah6gMsdDmWyvc1I82wx84+RaEIHR8+m9YygtH57B883I7TDy5zePV6OtHGZSjR6fpwm0wTm3WlhDVbioI3yutap92eW0KhMnoSnK8jvnP+NqdGQaWm55fvaY984f8eb2DbfjjfRBc0LVMLNM5sR3hBDpwyia/iJEwC/CLcYaYghYZ1cVwjSPeOtr4bFwXVTtRbq1NaAQNdX90HO+3QnvAXCo2v8VBcKiChAHSIFbS85SnKgV8QdkIVnujVWWjKTdbn1HY0XW+2p0/OX1iSzIRRb2xc/hK5/1w2f8zl81goaVIkaDzsDvf62XCAikBebqJrrAtVAkAC8J6z6VRFMdhL1rJbFYLVJZyR3KOfK//cwRiqkE3bIOuFVRs7SFyvH7K03knfmhrWF38YST88sHqc2yaK279TK24EgG5jhFxLysGrApxWIOaKoiZ0ELYhRjQ1B1cT7GBBzv5DLR6+cq8t/iAuowRldy7JKECzXFDDjKpguQwlwdd+V9tBHLgWlO9eCheL+75T60tA5+9/Gn9EHxo/2HbBqN14VPbyyH2fD2yPjyCrZUhZhGiHsFB0rjdOTZduLlwfL+9p4Pus84BI0qicbJ5jrHhNWZH99/l/u446PTAzu756/fPFmLNqsDO3fgdtox54ZTf+B7j3+EM47FBbpQOGkMN/1MKqI60QjHIaSMt14s86sZ0aI6en4h+Tz7KTIGeR4XG5krIWVe7UfGKE6rucipvHOWs85zmCNzTJy0si5RIPYH3ry6loTrup7mirRYrRjmSEK4NM5q7sbIHONKSu28oEKNFdTBW3nf62GktZaNr/4bBaYk6xtFeGvOaA5zwBtNzFLshCQ2+RvnsFrJZ1SLIaAQYOckaAHAnFINEZQCZj+HKjHPhCSIi9PiQ7PMi1Tbplvn6LwjZymMlliIIczYWox4YxiTSJWdFgPQlAsbb8QML4tjMEoOZmZBDUsmxEzbeOaUOO92tYVhaExdJwo03jPOM95KoeKVEY5Fhna7EduLSlDtuo5+6KFyanJOUE3irK7W98OEtRZvDeM4cxhGipbgXr/kItX7tsyPmMQywFvZY50x7Jxjngdu+sL39x/JnK2F4HIwWe7ngsi8DRq/A28+WIyWn9d18/RW/NMu24jXUWTm2tG4Lf3hFmUtr0bPT95kvrgLdK3wQd9+yYbuvcMZQyqylpeU65r8cA14uDS8vc5+/MEz/sf/+j/++7WHZP4cy4kQkyya2q5Eu9Vavv6f1YXwMM333dfDk/eDvy+1TyqKmOG3L3uedBO6OIZZ8yfXZxTjeNzOPNGvub5X/HyoAWYpkVOQhNcsA9r65hgS9eDX6XcvrEgbZfmsxojDrVEyAD/pT/lwM/Fmcoyx5ee3t3TWsG13tG3Lfn9LVAFnXA26wmHrogAAIABJREFUkwGYspBqr+7fcL45wSpHlxv2s2RJLFlDqURcTdAc5pHLzZY+JUAizhsnZMx+EqQmpSImTjnR1JPE/AB2E/gxrwoESddMtMbXLBhVSWAywHRt9Jd6LlreY45BigdteNQoTrxgFud+5nZ29NGRyvIeX1EhvzsGaoFa6ilWKWiNJIAfjfrlHi6TaYlFVxVZ8toTUmCeA7nmjiidMOqobFFaoUphzolvnsGPbzWh5quU6iZbZSTrKaCopYhZLvXBJ9Ka7dkjtmdSrOQiUubltGqUWk3GJFVbPqtSrIvD8jJaUdQx4VjGpPxGVRGdFCPG+XXzKrXNlCtMKTyIt+MZlpBF4TdIJECBNUNoOeUsfJlFKbQsfDlFlDbridOZljnWvCsURmVeTyc8bW/pHIwBtm3L95ovOO8Cje/45M2zdyb3u6/j15X4G9cCyuJ04tQP/Idfv6UxiX/5F8/48c0FZ/5Aa75gipoxhCp1z0xRCzm4KJ50e672rhZlkLLmH1z+GwwTJ23HH3/xlM4tQYgC52tVGEPg0WZH46w4Ks/SJgmp4KxjmKeqKIJd46qPi+LFbc/GS3E2hsTl1tPPCWcURsNJ6zhBiLC/uDusaOr9FPj6o+1aWFxuW/ZjIJ62vInA/YHh9o4pRDpr2DWCJnhrGELmfCtjYk41aLYOtFJbPmMonG8EKUk5c9J4mgo/x1yYglj5t65KnJUUKU7rejjIbLqWOSXGkHBGr6aPrhZupRTGkDjzhttBVFDeaFpn2c+B1hlOGsf9NHPWNTituO6ndYEXfpWircGRQ5AiSaIxZC5snEbh1jm4oFUU+RyutplyFpFCKbDxTta9ajy4mNRpY5hCoGtazhrHNCuMtUzzSONaVEVZTrsNJYtKKc4Rox0xRd68usY6zcluyzBO9P0bSimcnm7FBblkQkmMc8IomEOh8RIYOE7Ci+m2G+FGRVFKNV62XldR1SUNu05EGuuxSvPi+pafHbbcpAuULiuCuG6hRdbJpRgoD1rs8qVlc13OVMtm+LDYkR952s58fTcyxQJVGemtp98fcI3D+4Zvt4l//anYLggHZjn0LAe/svJzct0/ydLmW2b/8dqP0SlvbRN/CwDyd/JpORZDC+xTtfypEONYVQhVKmsMsSiRTZVjkvCXvt5a2OX3xAw7X3jcJZ7aA1d3hpdDy5u5wVtR/OwnzZ9NZ/SlQStNHPfEOJNqypXRhsY3y07A8jCX57X80uXkkZFTsq6clyPsrlDKcBfPeB1GTsw9r3JHzvf82auBb5zM7LzkdHjXQE7004GEWHdro6uTouZ+7iWPJBeoqo+NdzitOYTIXHMzxNpYk2NAlVJPlpEpHJOghe4iUei69kiVUgw1LVohceSxlBrwJYNzSBMKCSCba2pwLIkT3wHyrApldcGU4krTuQaVC79+HlBEGi08hjEZYjb86O6cIWq0Xp1FVtDhyweSfLMxMGc5cRgN/ZzQSpJepzRzmAagqhcqmW3xPOingVwkcVbi4iXW4bTZUNRxnJILrsxYZQnF1kcvrQwDMrGW0x+qdq7elkO3uzNOHj1FO1lkM1TjtCopV1L0LvCqIC2lqi+OZmtS4Ou1RflWQaOkH76gPcbat+ZbKRyLoTqQc8lrlg8FvLMSrLjEERTJEEoxoa09ttwUK28mJDGVC/OEa1ooct2+aaqL9GIwVkgZIpo3847vbq95tPVc9wNzCITo+cOXj7iZHM4cC7/jGHgXYn0wJEohJvj4cuDbjw58fDHwhz/fYo20G/701Uf81qWCciVQuy6kMOH9M84oHGLh+1dPiUkJ1I0ggac+E0qH0fCPP3zJYYqiZikRVz1fts2GL+5HnDGEnNl5y/UgWToxzuuibrQEaabKkYip1CJFCK6NM5QQeXOY2DVOnHRzIatMawUF0QqmmPnk9R5nNK01HKZIU9eJp2cb5m1L8/wJV1+8IvWDJMwXQTZaq9nPYhzWWk2vKlm9qlSU0py2rrYnMmPl0uSiaZ0mzplN5Uo4I4iM04qQMnPOawtLK8Wmbg3344zVQt48zOIs7rSm3Uhxa3TDzSAE3VQKO7+okhJnbUNrNaetZwiRm0EcbHeNzCOj5Xv9zZ4IeMBr8fLIKJ6edHJt1XCznyJr/k0dWBrISlRMuUjcSiZVPoqsHYdp4nSz4cJv0Mag6+bfGsm+0VmS0sdZDEinECSZe54pRbE72+BtwzhMZJXRTlorkqWk6JoGFSJzilAUXSu2+xlR3llXcMaT0oxGFLEpJ3Zdy91hL9fStqQosvDTzYYSM31/w5/efkTWLc5SD1YLRF4R3YfVy7rBPSgDHky5o5lmqcWOXn/EGfiNi577SYJeVSl0fsPYz7w+vORMnZMSjGnm/bMdL/exZgkdg3WXX5WLZJR55VEIiTqEeiBSRyXhula+uyz8iu4P/F19WlbY+0GFl8tqcS9rfq4StCiVY3WeLfqBX8Y717Xc+492hXO7p7WK/WS4GRLX94r/4+7xMazPSm8uBsk5ubvfE+MNpUgfURmLd6qmE1dfkLcsyVVVHdTnu5zoSwZl10yhZTBILkysmwW8Hh2v1BNKgQ83Azrf8YObCx67Ky63J1gUb4Y9gYgpoIphHA8VqRA4+j5NNMqwaTqx0FaaxnsutxvupomLzrMYNGmKBD1Wp/7WOZazv5gzFbR11TsgoUte5YFzyvRFWkFSpFTL5FyN+ZBp31hDq4STE2slvHUNU45ohPkukO2EVgGj5H36eto0ytAay29f/AKlFGM0vJ427EPDbfDkBzHFRj0wKwM6m4m5kLMho7lsZ+Y0EFJiPw9rJStnai+QcM5MOQEBV7NK5ixuykVJH1nssSXJVNURft7N/KYLTMnx/ddnaKSPK1EBWYrLBWkq1aCs3t/zxx/gOinqUi41tRuon38tctEPQJqyriHAkfuF9Op1RUXEjZYKmS/0UYUy8nNrO6c+saUIyrW4oUgLx1uzoiEpZyEAKlWLEyME3LJ4vixEt0RJiRRntHVY59+G7pNEGHSN4TBmUlHkYqBkii1c9RtiafhHz/fM2TDMmX+62/N//dTw8uCPfj5vvR5+8e2FKaP44HTiW49GfvjS89M7scz/z37tCqMiP3h1xr/7wZ4fvL7kDz5/ilWCCmolNoRKga0KbvGs0GxbhzOOxmqmmDhpPVMUTthNL143KSdOGsfrwyjorLJ4o/FGEZShrb4oX3+0JaTCdT9XtDNz2srJngIvbocq1y28Oky8f9YxR0EU3r/YYhcfpvrgnDHMMbOfIq1V+Bqj0DqDM5rnF1vu9gOvv3jFm9v9+owPU+LqfqKziufnHftJcry0ylxsxG7eGk1TFBtvuLof8UYKhKcntpK6ZfwqK4h2Zw0S5SrPSHOU4J+03dpVONvUoj2XmsmTazFl6ENkCCJxVUqx8Y7TVnF9GPn0zZ7WWT6+PGfrrRhP1vF4mCMb77jb90zFc7FreeoMu8bx5jBxM8yAorGiNkqlcNI2hFyIIbJrLH0UztdU1TveWuYshzKnVSVFi7PVyzdvON2cMQ0B1WhxEJ8i94ce5ywaTWudOKIXKTxDD8HNOCMk2QWoiMmgrSFFidfIJdX4hLKGMBYtqPscA51tKClibVULFmiN5FIRkpDl88xffT7xYjqlL8/obEJTI2NQKzIsT2rxtZJDinprs3/438tCtKzHat3vFNLZ+N7jAbRnt/FMY0AV+P4nf8l7jx+z3ZzTac2nt4E/umohHSDLmnB+fkrKMM0B+wD5LQXiHDBG11wlOTB+WT1ypGt8CeryJa+/vWh5WMmVY/jUemuWq6gnEqU03tsVJhe1RiX9PLgqZxSNKSgST+yBv7kxRN0QMmRaEhpTlUo5ZyiZMcxQCtM0UnKh8R3WOeGRLOjIL90V9eD/WYsQ+ZqSggW1toyOG2tZOSSoarRUAtpYbucdl25k5/bcRo+PI/sx0xqNVZ5hnhnCKGWRRtxx5R3pVaJTjs46DsPMfpp4pTVfO9/Qz3PdEcE7V/MXNHOYqwQx42vGQ6q7oioKrzSq9mdR1TVUSX+6ZPEEGKszo3hL6JXslmpTaA1drNcZckaXmpdCdT1UCoelqQxxgWplocg505jCR9tbwPBq7Op5RxGy4WpomPNingZ3syYXzXkz87QbeOT3xKIlG0UpvLHCFSq5brJzlVWW1QBPfkbRzxO5RJFyF5GdCiojYWhGORwjnS987WRmzprrUfqti98NitVGO+XC5vyS3fljqIVNqoqgApKRYmojq8gGpvUyvo/Tbi1c6kAqy2agpURSK/pX1s12aV8t8ld5n/zWuM2pBhdSVgfaIzok75VLxjpPlfhUHwQ5DYdpWH+X8c1aHC7zpBTwzrBpLHOUNuKzXeC0mcTIMBc+ufZ8+/KW+35PwZKKYusVv/f1if/pz7+GM4u66OHsezA/31mZvCn835+d83SX+IuXG37nvTvO25an24k/eXHKX78+4W76GneT49m253rwKA0xKUI1EnRaFlJnMuTM0xPHq33lphhNP4tE22iRpLZWs2k8wxw53zSIGEehdYNRcD9FnAas5c1hZuNlfBitMFkz58KTXcsYEgcVyLOMvdZaXt5P5FJ4frZhmCNnnRMVlFb8yc9ueHbacrnxnG0c+2HCVcdWrcSL6KyzPDrdstu2PL3f89lPv2AKif0UUBSRvaOJWbHvJ379+Wk12issTi+FzEUnvCartRxEHnCYVG0HKfVOyzzLIr+uk8f9kkURpOtB1ngptpw1XG4a+pDpvEQRKCXcn34OvLgduTlMvLofeXbasnOGuWQMhdPGUfKWOWfGICiKMYmbKZKrP8tSazVOCK3kxPnGE+rcm0KkqRySwzjX1b1UXyNF6y2Hw0h3smWjHYcQaXTDvpeMMNPIkX8OEmZrOkcoYr3RtQ0pBIYhoaxFlYxzguporWhaxzhPqCLE5DgJwTbXuZqRA8hhHLDGMedA6z2xPzBME8oJ3/D+Zubl4Pl5+pBtk7h0B14O7XEdqJunqs9GHL81OUkauNSiR07LW2DBu4gMx7JGq0KjhW8yTRM5JQ7jwMnZCSFlDDOxaIruhMuoDcM0YbTl9avXPLnY8f5lw+e3sk9rLZzNhXaw+MW8+6qfhsab1adMriF8CSZ7fP3dbfyh4jjql7++/rVmplgrF20M3jtSFlIhsH6A7+5uiUVzNTq+f30ilu251CiljC5JeCExU3IkhECOVf5mLN1mKxtszhWKzbSNk7TdXDf15apKhfTXg/9atbAEo5X8duHyYMtY/eClFVDoY8sQnmLVTFRnhMMdF83Ek87x8valGEXVqtNYIxu70cSUcFqRNKQYaLQlac0YJ96MmSnOFBSd1ewnYcrf9Xu8UVzsNlV2Ke+7HwOhOklao0kxVntyWcDF7j9hrGGcAwUhxC15SdboOrHy+km1EuMpUwm9OSfpoVQOhSTXpgqX17bHElxQxG3U1lTRR+1BBrASBUAq5/xs34rVOrBxhefdDefNXvrASZbZRZGwqIbEMCuLIoAiRVt1Dp2qgua06ejDSB9mnNarnLuUwhBHQJOAIQS+cbqnMYrXg+VFb7kaW1TJJCQa3rcbLt97jl8C2iqBVWR7tcBYiod6fYtJW6679GLYJ2OtFiPVPApKbUcVklJVfnlM6V6QG5Y/6zCW1qWum4ZeW5hiWrckLue1YNHaUDLYxlY1QGYOsWZyCTmdcgwtXOauFEKKTWMZ5sQQCo0p/MbFS97fHfDWcL7V/KtPL/jpjeZH4THatvwn37rmus+ctwlnMnMSR9olv+qt15ccpXSFrF/ce75zeeB/+evHeAtf7D0ve1GhvOwb5qSIRTFlg1OFx5uZbz26J5fMn74457SL/OPnLyt8bzjfVhJ7FhOyaY6MIbLxUpCZlPnibuC9047TzjPMCeFIK95rGt70QlC9uhu4n6RNopWmsWIFb7TibON4dtbyl5/fcNZ5Nt5JOn3KHGbZwDaNZQxiJ/9o4wmx0DnDq/sJ7wxjlDGhtcFbmGNBK3Gzvbw8Y7fb8uNPP6cPkZg1IRW+uBlItTgLMYksWQvSKKoUcE42hFzbVUuScq5k2oXkntPxGRVydcx+G8pPUVBI5zTG6CpAkNaNL0X4NdXnaT8nOmfYeM1J67m6m/BW0xjP/WHg5GxDK1bH3E1pPZxMCsY5cRgjKF1RU18tHxQxRDatXxf0mAR5L7VIOIxyuFHa0FiLVllQ2JQIJeCzuM7e9nc8chalC5tOeJF3hwNziWhvqpmmkOBjFBdc7aRtghE+kJizJVIaxSyulFVGLegtZCW+JVqJnHqMmRjh5rCvDsu6JjIbenXCjbrkzA3MSePVQEjturlbvZh/im/W4lyeKj+uVNMkBUeuSQEpTBfpp3q7vQTEAmOy2DDQ6IaQAkMYuNyeYrRmGnuuY8OZSTSqcAiRHDPN1jHc9nzxMkLcoJtTGqRtukSn5CWKYdlX1/+Wceqt2C1M8wTIWtq2Dm2+Gm/5leqh59/8bvln/+2/OH7g5dR0/MzrIuRqInPlNtYqXnHSymlo6+EwBvZTWScQRTaEEGZSTPIBK2FQa4V1XvpguiI1tdVTclmzUdaT7HJX6gUt9uPrJdYT9WJPvjz89XM8+LcLVJVrEfBuZdp5xQftT0k580n/LZyOPGquuVA9k9rwtTPL6/trUi3yQpJk2sZJf7V1joJGJfm8u6ajnwK2abibDjgjfcDOGz4439GI3etqQa2WijQm5pgZojhCXveTDASjuR3Gav8daZ0lpsKuaZhTBsSmewpyMtRV+qtW3wDNnBJDDKt3gEDGi6mZ3I8lCl4rsQl3xgp0XHJl9y/x84oxn/BZ/xytshgOFYVVgd9+dLWmF4ciCiynhB+/LACxRgwsfBxXF9qptoM2vqkup0HGFiIRRCla75mDWHhbrXkxPObxznPRJIbpJa/GMzAdV33HyaPHbM+fCHRaBOGQYi1KwVyLiLeH2wP+0zr21mmxjp2ccuWb1Hus1ZG7kiKl3tvyYKAtDrOCvNST1vID6sjTOjrSLhuMjP+ma1EKwjxL0VMVFSXn1T9m03q6xhOSeIoUKrepIhK/+/RzfuuDxH6UzKbHO83/+/Nz/vcfn3HRJWIWbtN/8zsv8IzMufB4Z/j8vuF//sFjQlYrYnqEWqnzQgqbXOA3nx54ce/53nv3fHTa05hUT9eyWBc0fdB8/3PHk03P+yeSQ3M/SozFnBIfX27oQ2E/pVXVcb4RgvthTkwx83jXoJXidpgxWrPzhj4kppgY5sSz01aMD6uEfI4SqPjyfuCs80wx8dHFFm9NjQY4Ks1iLuvC/Cc/v+Zbj08463xtAWnmmHlxN4mvU8482njGEDnvPFlpXH2msYgKSCFqpTEkWm9JKfJnn1zz6ac/J6XEZWc43zZCTC4VfSvHzWtZ42zNn1pkxKaOwRzLW+jgIsmWf1fXBHWEx9dNUVFVbfKZQxB30zFEppjYekvnLYXC1d3Efgp8eLHj1V4QY50Sh5h4vHWcOIuylpAzXWP5wec3eGuFN6MNqkDnWw5x5nYcaZuGmCLeS8TL1hs6Z3BGcdNH7seANoquuszuo8LGgLaKCGyLZXe6YwoQ4gFnbU3hlna4VoKChoowWmuZp4lYD3HL4TdnsFavh9kYM40TBDjGY/LeYY5sG0dWmtv7ntY7fG05tt7jrMNiKHPgpr+l3Wzog8c6x2c3Dd+8GNnoPXOM/Pntx0zRoXTh26dv+MndGVMQMvXbKsWaHVYx3OMzXH9iPYDnLAXQiSv8B897piEwx4BSip3X/Ow+8EfXT4jDPV23YRoOaKXZ7HaklJnnUNHnxOH+DusMTdvRtg3K2PXAF6tVcS7VZmHZe5V6a1WwRlMUfPPD9/kX/+U//Xuay0kD/zgJOO7vqlbq4rJpVmKOxNwnQLNJ99wFw32AN/sAWWzNQxA/khwTpfYCnXaY1kmYHWolHZU6+5TWK2lHPBmOD+HdV64TTFey2gLRSwflqFxZF9P1mR4XWKUEetdkqa6rDr2fC5+FJ7y3uZITSDHczVtGBZdtpB/hvN1yNw0SV99I2Fo/zfiax0EWv4DGWELMnDY7So7sp4g56UAJ6fHVfuC9sy3eSuUeYsJW2K01mtYYzlo5ae1sK9EDwHnrBCqdIieNQynN31wfGEaZOKmePIU9U6tgcRYnV8k3iBzaFCmallZKox0hSwS7rVV+rJHs1hiWxGhfJcsxZ2K2XPiejR24Gs/QqpAxWC2JtgsctiiEchHnyOXBGFUhbq1rLH3GYYg5cZhHdEGsv+s1zrmqXmZJb1VaTlvPzxJbfSNy+GJ4bxcwuufsvd/lhkdondfCTGst/iVKSbGRFoO9siIo66gpby8K8sfi8bEgIIpcpaOCj1T1Uk2cVlqygpZ2Z62JWHJaFiRk8fJYSL5xQc5WI7ujAkk+h3jwhHF4UDIIab5tPIcpEVKqc8ysxRgF/urVCd95dg9KWlFT0py7A7tmh3fwDy7vmFOhtQGrHWGcmZPh+enMbz458P0XJ4QijthHn94CqvCff/cNZ01gioqvn4/cz4bGZF7tE4ciChfIpKx5cTfw9KTh3/socpjgF7dyot41hstdwxhkXTEqcd5ZDmNg27Y01tSeeeT9M8cYEkNIfOvJiaAgufCmF1v9i41nTpnGGm6HmccnzWrEdtZ5Yi6cdn5VE4FIhpdactmwi4Jfe3LK1ltuDhOdlzn+pp9l3ULRGMUcM1tvGWPm5X3P1y9361K7WPXHGqkwjDOtszSbhudf+4A3L1+zM4mU6jr44BSbK0nAmMUriBV6t/UAVOpJXRk5VOolwqO2lPWS9r4WQA/GeoYYIihJkVZOfI+8kdyikBL7KXK5a/jmE8/NMHPT14iDXIgIUdopaJuGsRTmBGMfONts1xyim7EGZs6DKCa14bxtGefIUCXE/RzpnDh5n7ZO5OVxZte1HIJC55FiFVFptHYkFHdjQGc5SMYU8c5WUq1wtsYwE6ubdp5HUSQhpH05CymyKmyslcwtpRgJkmulCo+2TUWxFbvOM4RMSZkPLrYcQqafhJPXTwEfEq01gm62LTknTpuZYR757qORXBIJOex8c/sJn+2foHTHs11PHz0/udlh1OJFo9aCYMngWgqCVe2sljq1sDifK2CIipIDRUnr62448OLOEf0pNo/4tuVwOHD7+iWPnj6rbTeNtmZFoTe7nbTB9gdyShhr2e62xBgFPdGGFANTLGsbvjwolGXsZpz3D2wufvn1K4sWKdASKLvUbChAG/E1ab194Lq59K5KzSpJxFT4N31Ea4HxUwzSd409qkgUt3MO3+yg2tAXtciPS2XosP7e5bTwgGwi81VLYNNy86gbX1qIuA9PwurYHjoKrh6e/+rEfHCczmgMi626/MyYGr44XKCtGDWNqcV4RT+/IOSOx1bjlGXjPSFnaZGQBFKdxVxoHEe09ZxtTnl9f83JZsfF9hxvHddTzziNlKzYT4FHVrJPvDmG55V6narCasaYKqOWsDJnDH4jku+QwJPodltCVqQ4VaJuoVQPl8W/ICZp63Q1FC0msdPOZIzSTCmsOUWxZKjJ22GR6lnFVKv1hWfh9R1z6QjJ1IKxkIpspqr6hkgrqNT001IX78XOO6KUpTVeHD6Xz61EvjqVo326VtL7LkU8AsYUBJkApvkVY1Y0zrHxjpOTDedPHvPDe4+a5OnK+K6nFS0QddGWkmcS+cH6Xds9HLNMKJm6/6Pror6MYSHRZRZzPaUly4hVZn4cYzJkzYPCXaFKrqfhI+lXrvEod1+4LeL5IuMspcRUCYRiXmdWf6AhCOdDfN+OijoFjFHT+jPuxxtxgdXQmsh+MuSsuBvhp7eOf/LBL1C0xJTovMzFYU5847zn15/0/KvPTvnsRlqDShX6YPi9b7zhWxd7QoLsMrejxZtCRlLXxffD01rhcD09abg+zOxaIbU7o9g2jtNOfEGckYKvdRqrFTtvuJukGDQKWmf4/KbnpLVQMj+6usNqVf+9poRCP0ce7Roaq9m1FqU0l9sGqxU/urrn/bMNrauIMmK4trSUS4ExZBor77P1jsMU6GobqrGG08bWDUTajieNq0nSipNGEt2tMYSUVm6KRpFjrh4qmcutxxjN4/MN3N1w/eZuBd8KtfBfkNCFCFKLliXgT9LAFaEkXFbMKeMLLI7aqwx2KYgylUTBEeFDfJb0ig6Jv9XGam5T4hd3A6eNYUbROk0fxCoj50xMcNI4gnL89D5wGEagYIxj6wyqKPp5Jms4zCN3uXDWbeisw2nLYR446SQe42rsuR4ijS14DUpl3jvbcnU/MQyjhPo1HZumQ+XCYRhQZWTXOdpGE7IVI0EFIUbJEypgTcEquWdEISyrUg9AWeZ1LEXoywUa5yBEMoW7MdJ5aZ15q9k4TdDQOIMyUiilAlvvMEDXtoRpZj+MGGtQRlCsaQ6gwDhD6z37cSKkwtYrFJkTL8pQvahK6zh8eHxavq4edB7KulaxFg3SztVMKtO5lpgK//rqhJIT3mr6oeewv2ez2UobbxxRWuN9U4UDuUZ/aLbbHfM0EMNMv7+n227ECdl7zjrNIXopXuPiWJxJqdo1GI13GvMr2kN/K9JycrLDktF5JhRNCIE+yGlvPMS6YILWdpVsAoQwV0dXgVed8/i2A6fIdKtNr1LVZGz5QhaWxdKXFeZv3aBj5ZY8qBaXiZqyWr0stIEU0mrhvKR4gkywJcnULAZflKPsaoFf1hJVvrGkIZcKaVmtSOZUTrMUtHEMseVQvoFPgUfNCwya90+e8+bmilASxojWPStkJWgtY8rs717RWU8ME9Y17A/37EzLk+0JP71/RUoFpxTzFKv/iKpW7nrdDOUP6Yvbakgm7Q0Qw67C1y+2wq0pip+8nnnUOuZYpbl18AxzxFcouyCmWKBqLzwTc6yFxJK2A0tnAAAgAElEQVQlUR9bZdrv54Gdb2ideByIzbejdYpdfoFV8HI6w6qE1zNDnFBK+utlbT0dicHy6DKNtuSSuRn2WK3YTxO5wEnTAqwkx1IyGSEch5zroiJcmIhYck85Ms0DZ4/O+PbHX+PFvpBo8CaSsqAfWR8n9NKaNM6ic66Ex6VXq46wcQVkycsiIcmuSgn6KLdKs3CEJMhSQ5FgzsXjYIH5F/JsmGeMk2A5U8oqTVcscwSKEh+NdbIrwzCMq2fFwilLZEGTrLSjfOtrUSQFVSlU/pXi/W3PxyevuB0s37/Z8Yt9y+3c4LSYafxHH74kpMSb0bKPhQ8upEU4hUTJmiebmau+4bObFq2lCHr/ZOa/+M0vuNwE7seCt5pUzP/P2Zv02rZl+V2/Wa1i733KW777qojISKcznYkzMRgZGUsIBJJlgXv0TANE8QHcoo0EfA06CCRaSMamgZSSGzQsOW2ZLIiIF6++9TlnV6uYFY0x59r7vsyMlPOEIuLce8/ZxdpzzTnGf/wLOieF1HaIOGtxtuH9buTXn7fcHz0xV9JxZjcFNq2jdRKSFzNStJSyy8dM12gaI81PiAkfRJkjhSxsGodS8HY3iyeLkXUXY+YYhfA++ciQ5D776ZOLMu5E7veywYYkarD7wXPdi4HbujEMs5cAv5S56MSBtW8ssw8Ya9CukFWzrG+jpVMOQZR7KUryrcriy6SzRmu4XTVctPI8qn/KxXrDN9+8JOZYigrZt5YG0xYSZCwBqqE4Y+ci+U/QqOWFFIRGFDEsPAhK4SINo0IxlHT5++PIRWtpjUFpi20cK2X47c2a+4NY+T+M0sBanMidpdpBR0UcjnTB46whpkCbM1edZnYtr/YDyjRcrluxzo8inV03DVWetoqaJkrjcjd7UInD5LG2ZXN5jZ9GyVUKM9oabtaOiBNn7YOgNColsrYoJZ+R1YqMeMY4qwhJvHtiEhSu2qkZDONUwy9rm1VdX8u4zssarz47CbhabXjYH9gfD2QEcSBGET8oi1aZiBjWzcNIYxpaawlZkbPCqpn7w8TdsJJ9v3Iu1YmEW6cLCwCs6jlYDrf6Z9lEZISrGnoT+fLtAz8fbnE649qOh7t3xHnmcnO5iDt8FOfnUBAX6xyb9arca4GVMzISyoLQH4eR6e6Bt1phm4bWNbSrFTkHxuPAxbolYoQLGiN+HH5Yiixfv7JoSSmx3+/xXhxIldICZZfZeEhiN51yJPlDGQ/lYtBjaZzFtRupIhA/gOpYWKv5+l+5xrnWECwOnwUpUeq0KZUfPs3wVT3wTk6fJ9Oc8lycw2YCp1ekQgqb0mfUQijrpXapzyf1k1rg0pSQShzIOaKUlRyl3LAdBlZa0dp7nHasmwvJ34iJo1cQE7oRO/gwe47TgOlWJD9hjGHv94zR02SYfCTVri7pkyOqksdZFi5grC18Drl22tRR3Wk1q5y4bCzXqw6UYnuYaFoHOTPOoczmZbTRGEHZQkpIhq487slQUMYWPlXZn5Cgh2kUQ63iG2O0ZgqJIXhe9K9AGW6aHZ3rmIKEURp0MdtL1BykmCKBjA+iXElZ0kV30wRKcX88LOZKpuvFNIwoaaqI8VTKwusgQzKaroHVk9/grfkJ//jrjs+uJi7cyMO0kYK48Kaqv0rtToElCoISO+CMYn+MZ+uSUkAW07rSiXkvnjqV9JCzEIXJglxGL6hAAHQuRRKaUDKORIIsaziVTaqGJtanrXbzKDE4yzEIfMup+673mZAyazKzIoVw4ryUMLdXxxVvh0+IWXx5rM50NpW3mPlHv3jOZ9eBH10+8DuPJlbOk3Ni9mJ9njM402ANbJrIi8uZv/nxjpQS90cZezhryD7xxdtByKdFzr5uLc+vWnyE23XLFBMHI+6mt2tLzNC6mmsT2bSylU0+cPQJPSo2raGxTtRESojqzkrXD3CYZBzbF17E0Ufe7cVM7qPrrg6ygGobUF2pVRnHJUy5BTedZfCJi86V8YzwxhpblWHCpzMZ+sYsDVEq2VEp5qUQrYdKRXFczSbJmRwSJme5v7Xi6mpD237OL7/4WvgFGRSnn0/hhBjnWNKRKUo3VeE6pJY+h+RT+cuy59ZivEqKemuZU2LlGlETGY1PmWkQntH+cGSzFtJtVoYWSS9OKNZNxzTP+HDgyXqFXd8wzQnvPUYlgtHcrC3vQmJ3nBimidvNRhKhs6LpDNvtkZvbKzbIyGs3Tly1hpATq67n3cM9JszcdC0YxazgOI4o08iBC7RGrnUqpqCr1tIb4RYNCabRo6P44xzmwM3K8jDDNMuBPRdkxhhBVZSWhrhvnOwjWrFxhsZZ7o6ThKtGze44EmPgat0RlWM8HBhDZL1a1Sl5ITmDaVqM7TjOipf7K/bxhsMu8aMLxzMz8vo4k3VD7V3PJwZL01cAszrq02dbVU5JLBiU4Zs7z7t94stRMrxCGNg+3DPudlw/eoKvESAI2htTxFppdv08w6orzZyYmZqiNAwx0nQdrmnx00gKnuM0M00jYZ7Q1iGKMYu1SZqFac2f9/UXFC2Z3XZXHEdlQ0tBnF7rISmMcbVUW8ZYtLVLAZFSkuyBejGzXDUhClVYuxwItZAhlkq63nB6gb/rh3ByAmX5tKrnRsplbFTmxxVCl71AZo3kAMqgEPZyyvUp63OeVDW1dKnmPhazPG+KtRtXpOBJGG5XR7bphje+5T68Z4Wn9xNXqwviMMA84aNEtSsVuOwcx8kzzBONtaysobMt98cHHm0eMYwTg4+0ZYxTDevCXA8tcYTV2ixIEGWRLsTZfLoGRsHVquN+DJBFLUKKJWxMEWIuKgSzSOuyUosiSxeOhXhPCFxPId36wjFpjBV0RrZkDvNcxj6KJ/0DMWucdgx1ZFjGTa21dE40/TEnstYMw1HkzAjP5Zv797I+y4hRyG8z+zEQQ1icJcXhX8zylBLy46rrsU/+DV7GT5i95tPLI3/z2QP/6x89LmZquYrFUFqdDJRycRAuagVbJHquaeAwSJBhRbxSOdbzaW0siczl7+rqrgchpUBKKVHlyKl0w5XQtvxuhehLhV8RqsSJgB6XwqZywoTYLIjKaaTh5wmAMM80/WmjUFAIsGBVxtbxU90bUPytT3f8u5+8I8WZXzzc0BrFd1sYwjUosfnazo5//8fv+c0n4heSUISkSLlj9LAdxeAshMA4TlytJOvoOEemoGi84mrVELNlOB4YfKBvDff7iUcXPZcrxxwi7w4zRim6xnDZWY5zIOfEy4cDnTP0znLRi/HaVefYjoFVK5yry17s11ur2Y2B3kmyc7Wp12XsOHn5DDadXZyCa2EhXDXhEFkDq8IdUVkIrlZrkQmnfKbUKTL1nM7u2+LYvOw6FTqRf1P5BPXnmFBG03cNv/aTT/nyq+85Hodlb8opo4wSw7WKomCpJNtUuVSlIaVMLmMQO3mlC/k+Iwdbzqgsa2iOUd6XyeI6W/aGb97tWDvHpmsIMeO0ZOwELK5xzBkuiof95dUTlDG8ffMePXtiCtiuQWnNeFA42/Px7SXjJGjsRb9iOBwwzYq2leDLwzxgg8UYMWckK+Zxx6dXHdY6MIr9JCRyZx1T8OQkhUG0lpAUOXrWBjrVCPoE9FqhGlO8WuBR5+hbyxBmsMJfSrmYSyJoijWavrE01opXWcq0Th6jdYbt4DlOnmmauVr3RDSzD2y6NZfW8rDfio8QuqjIFGDZHwdQsA0vsDqUcZChVYFOHxhUs0wuakN53qicc0aWe7gWH4C2ltsm8i/uNoSk0HjCNLHfbok+sLm8FgFHSlReWsolfXwxWRH+VS5oJDmTkD1TG0MIwk9s+h7oURSBgBJ59HG3x1iZHMScub285M/7+pXqoY9+9Ov5P/wv/7sTN6Eu9gUekaIjluTNZUOrBzmVv1yeTNXCoSInp6t4XpRA4RHUx8unAmLpfHNFbE7dSa43di0+VHUjlcJkORyW5znf/OUwOhVSZyhL/SafFoIu6g9xWBTGuZD46+9mbtw71uodWTW8Dc/RaeIj95rGtkzzgGlbutYRk2x6+2EkeI+K0HUNucyUs9ZcdPDZ7UU5WFVBjSSKPae4OLRW6SqVqKykm6uLNWZobGVxF8KnlusTQwJt+H57ZPJRpMXkEua3gDpLXTcFsbFvnZhsCWlZeC41ATakKKGQ2pZcj0BrHTFFVk1DDcA7NyXKSNCkVvJ6d8NIX8hyPkb6RmSld4c9Kc/sxzWvwq+DrjW44mn3kt7BIax4N6whZ9r1hptnH9M4Kaj/9sc7nP+Grw6XbP0FL8crnJbnr2TG5cIpxHOoLOBFcpyFTF4PnJRENVW9Vup6rGZvFTmMScjnqrgdVvREGoTFnB2ldJE4S+Ky8BJql5TPivcziXgW0zVJQa5KOykM0rK7yX1grLhZ962jX/fLITz6k4Hd8nV2H2iVGYNZlFKdTYSk+PRq4OfvV0URNPI7T3c8WY1F4STF1W70vCqps50zHGdoDKydqD06p1i1DQ/HieMkuVhGZclSUrDuWhJisrc/jrRW0fcrtkcp+pUWR25nhK9RCbuijtKLeVyGMuLMS2FSADV2g+fRpmEOicYVcnnKWKvZHj3OKFaNpCjPhSwrtXteTAgrkqZK0RFDKpyiss5TLiGHJ5T5vOGoBUXdoyrys/gqlc8nlYYh58y3373m7m67rNMKz5/vWQpdVHancWyuh51sLMuBlFWVwkuRRBbvfNOYE0ptWAqccZ6YvajVxklhTMNwDHgfcLahc4bVasXr1+8xaeTq+prtYeDx9UYO964Vybk2DMOR0ZfMLG2wVkwgQ/Boo5m9Z86JnAONFVNSozPOWh6tHNaJzHw7RY4+MwwDfb8i+kjjLMkf2TSGpmkkN8nU0S0ozHLORYqTtFFkJfydcY682Y3i22M0L256bCFXH+fMcfL0TnPVOwGzcubL94M0n0Z4KylGhnHiMI5cdC23m5ZNEaK8O86AYRhnxAIk8UcPn5N0i7WW3778Q/GeGUf+5fZ3UNlLM1Ka0w/8WpYb+LQnCZCQWXeW2ScCRpzlY2Q8DIzDwGazAWPLfn9S7NYivIIHNRLEaHHShszhOBbbDYqYAdq2kZTy8tpk6yv7FYL01aT5z1885b//L/7uX049FGKtxlmcOBeouSx4bU6wYukVTpcqF4CxwlSlGKnExXy2M9blQikAZFRTHuksIVaqyfqTtcxIgCADKBa0RT4o8aKo7+P0WvPp8ZAXprXMwKvVe4XWKPwDKaY05FNKbyqdCRX+L+/ofXzOTl3xjD/mefcGry55PX3C5+77Ypkt83eyIvgRQyRrhWsafJgJObFpRfZtjME2jhREeqcyxBTwYaYScc8+AJRWEr6n1UKWUwUazCljtEDsZeWdWbWfjMp0KSBjQZ2qvFPrwnVRReqeBXGrq8AVwp8CrNI4Wxe4zO7nIE6JPsYzPx0p3DIi7Y5ZZNEhRnm+DFZbnLPEmEnR8/1uReAp2V5x1Wcu3T0pR47e8jBfQbrnyr3HN5rUP+Pm2Qu5PFoTk0b5V0zZ8deeKb7f3XPdT3Q2cT9afvHwuPiM1KV+JgnNlTGQQWlWqwZrND5Ihsg0zoh57CnTSuYD+mzsZJb3ncq9VImStbAwxqC1Wpj42omBoKiMSvJzrh/62UFY0MWcgyBCSjaOqig4d9qVEWtkmjKua5eNeiHlqnKfLO7G9f7SOCOFilKKKShiUliV+Hu/8Z7RKz6+nOjNQIiGGCXzRinN7bpl1TpGH0TGrwLf3h95vmno2w6lVElRlie0WpGSvB5nFeM8o1VinLwUC7rh5y8fMEqxajLTPIOWA3c7Ri77hmPX8Ghjy/WRtUXZf2LK6GKFYIpZ36q17CfJkknFrLBxcphfrRw5SWZOW5CslE77mDWlA0WXBk4X7pu8n2Lns0jr1VkzVQsZtYgF5PdV3SvreBFQ6IIugDIajebTFx/RNi2vX7/7oNs+3/VCCLJ+ivIl54qe1jwqlgYtZwValHPyojOqKkYqeougMroUxo1tcCXFOMRA22m6doNWQgq/e39PYw2Gnq5p2W4P7O92uMYx7Ee0k5Hmet3TtxZFLsocaUwTET95IWwqQYSzNlzrSOsMfpqxyqCRkMWHMZHCzMWqJ4RAq0HFidvLHtA4q2ikypfJQRYELcVI8IFu1ZOLOEQbBdnQOI2z4i0SYiwBlZneWVKOjLO4EB99kYWnyO3K8Wovo/ZxnEhBlEvrrqXrROasjWE3BQYPVkVC8LSuYdUawoMgglZDCIlX4YZ9CRKthcXZsQspn0644tyMVsQgxm9t48hhJquWNI8c9nvGYSTHSN91ZC1Ug/oYsnpknYbCZ3SLWjSRIqjZ0/UdF6tOpjQltFUphZ/DYvmg6p5aCpqUM5QGQyYYPwxiPH39xeZyxTuiohjUG7RWIfBB0ZJL5y7f1wtZyxTOfu5UwHxYSHCSe562zrObqMD1nH0gZXx1QoFYbsb6CQqnrFSiqp7V8iLqz2ktkrbatS6YSUrLxhFTFjVUiSqLMS7IEWV0s1yeFPB6xSv+KrfhDb16zTH9Bu/4MZ+uX7F9GOkbx+F45MmjCx6GmXEcOfhJusHiahvDxHFMXLWaTWMJIcmNmhONbqRAyaGcWoqsEqkQL1MslbfWImPMuZizCTlODgV5L9qI38CjVUsG7o8TCeiyEHNBM8y+IAKatrDG5xiIORa5rqU3dlnYFUOLxa3TGS1ZK0DXOgYvm3nMmc6akrNU3HetqNOerTcMs+c4B0y2mDzx5Z2i3fwaz9qRtX3A5AM5HPEpc6kCf+x/j7f+KW+mzNXNFdePPypSTpkV37YHrGu4sIbtMLJ2mYsmElPkm+0jrjrP0ZsP5vzamKXoqd2tVtA24oODNsVYTm7urMSkK1cieYlor7k/Yry04ITkpE64ZBafhVgsyqv6I6aIdQ4/+0XOWnuqGOJC6K2Lvhb/1fPGGovPfrnX6h2YUsJPk4TLDYOoGLTGWAfanAr8erOqzKbJPL+YsSqwmyzyig2fbLalITEipcxJjCONxprTGHgOsGoUfev46MIxREeaoxBrdcQZw2XfYI3Iur9+s2NVNvebNvPsakVWive7mU2rGefE67s7INJ1DR/fbuhd4M3DwOEwMvk1z64kw2yYfJGWI7k8sRQfUVAq8TFKgiQpiQHolShGnJXqXxoDFqdmo1XxLToVHynHkmP9oSmgQdyal1DTlAufqvyMkqKk5vnmsz1X7v0TEd4uFuny2C+ePcUazXffv1k+rlQgkejFy0gX4nsdG9UCR3yJTkR4UiKESIyh5FbpUhDqpcnMORPmRIiTWCAA8+SZx5nZJ1xjmWePSnL/dE2HSgltLdsh0nctaQo4pbi5vWYcfSnqXHHjjuTkGSqvL0PTdSijaZUo5LwPOJ1JKbDuJC08hEhjLJ2FqBQKQ6MD68bQOoc2QobWOZIjEBNJS/HhS5FpqkRcl0ZZa7wXjyCNYgqR67VbDEilUYPbTcP9MXAs/K7D5Hm0buitZTvOtBZs23LZu8XfCkR5dpyluYwxLSGVw3BAInMyVs3sfMfPDjdlHxaJNmdInaKuIRGoiF9KQiX5vFzToBV4OqKfGY8H9g9bnFZcXd9AIdRDXgpasVLIp0aeE0JcSeCN1aQkPMKQEjZFrLVYAz7FQvBWZc0WNaX4bIiYQokQ51d9/crx0PPPf5r/vX/wD2vZcPoqnUH92wqNnkYtJ0SkWq6fOtbT/58/puYEfZ5d9vLdD55/eR15gdQrN0AMuiokr3/wvPLIMoesj1nIZmUuV//+vEOpSAX1cXV9tXKQyYYhkq96bZZQPKpJXeLzi2/xuy0H/YgnVw163jGNBx73Lc9vLolkfIKgMu/2E7vJo1Li0hku1i2v94GVinz+5IKUVEkhlWpcyijZwGJKGCuKFKUEkTFFcl1l5LpUv/UCVxMga09o0VfvDhJTn5Kw16MsqGmeBHgyLM6QlM8OKZiJOeKsK3xBGRPV62qLp8IYRNopzy/y5FqB11FCiom2cRgtUPzruxm7/oyP1+95t9+Vmwa0MmzDtdiv+w3HIE6SFzdPuH3ymJjlgIpZ81eu3vP5xYHtpDE60lnFuyHTWcXTdeJfvVvTW8WrY8/91C9reblYdf0mGaF1XYsPp9nZPBzFdTTKSGdBGqloIqUz1Zx9BEvnWtUHUE2g1AL76kIyyurUQFQCfCzf17WXSqxDLgejHEQnl8raxZ+/srwoSAr3IcuhgiqOzm0vEG55vc9WIys389vP9ly1M61ztE4xzJHJZ1aN4dV2IGbFs8teiHkp8TB4OpPZTYmHg+d2s2K733PZG/rVWgLsppHtUXycNKB1SQWeZrbjzPMrx4vbFX3riBHe7UbZHEOkKwTITKZvLNsh8eXrB3ZDIJL5W7/+BACnBDkNBTk0piq7BHEyC/9uASVpnC1rDnQWw7SEKdlDslfEIA2XdaLECSGglIw3jNEQ5PNPMRXekTyXhEQWeD9J0SLrTZc9BhlHlqwrpeWzSilhFgREiJyvX73h2+9fCmKcJLk7hyTFtynoTN2rytqIOZFDXAqblEuUx0LiFOPHXEjcxmiabi1dNLJ/KKWYZvHmMDhijBynPdMkCd1DshjlSB5UinRO0zaK4Tjz/OktIUSOx1n4NSoTyKRsliw0lGQtyRg0YbWQcaOPNI2kYtfXK4neAZ0TTdsR5knea4zCoTgbv9azQrg75RxT9Z4E02gepsT2MEIKXPSWtbMLAqPKOYQS64bRJ37+el+UbZKRdd071q0Emy71v4LdFDnMkd0YICkmP8uOGideH6/Yqx+hdTnwtcIyMqYekocsZqRLAfGDw3IJaS1n4MW64WG7xbiO/cMdx8OROM88f/GCKebCP6Gca/WayB5TxS8L6rB8VdPYhLWWzcUFu+1WmmGtxXohCZXB6CJ0sHpRNS7oS3m0n3z2gv/hv/pP/vXHQ4Wi9QE8JBdBpJjhDIqWRV+tnCmEnPIBFvTjfMP/8Hk+vM4fHhJn3/2pyqVu3Ce4V/62IA5LGUSB9Mu/flDJVJb88pvyVGfFXDorWOSxciE11kKtdCBnb05UGLLJGA0hd7wZrtmYA+vOctPD+8Gz6RoZsVgxbWtiIlvF/eAljTlGfJZU2RQmgrU8HCca66BpsCT69UoIlVkKKldmvyiFDzPONcui06Z2SFXmekKmlJG/tVrz7uCZQ8SHJKoHBQGYZ1HxYM6cU5VeeEc+pgX2DiGVdZIWSWpMQuIbg7jo+uiXgrfKylFSCCvkNfkYiwV9oLv4NR41r3l1t6V1UsigNVYZSC/JuufdcEXKsLl9yub2KQbPFKUYMCrxs4dr/uj9pRip5YTR8KifeLo68vtf9/zW43fctAe203PuVfdnVOzyvmOINI07G2XIGooxoMoBuyQ//2D91r9bOFC6yiT1Iq2tBUvlgsk+oUmITX8sz6sKcqN0Jqkoh2DtpktXlMumVcdHuYxbTyGJlPVM2awEnjXGEiLMs+QVBT8Ly79pMa7h231PZs0XD5f87vMH/u2P92htEVM4Sd5tnS3XKPMwyIbYKkjKsu5bGhtJcebx1RpjO+63R1rj2B1kA+37FcM4kNTM+4c7UIrrjSVGQQXebGesylx0BqMVzaaRROqY8Mlwf5S1dbtpMUrCEr99t+ej6xWzlmybnJL4tWgZg3Y2MwVVCI7qg8+tUWJ+dvTw9XbDfnbc9iPP1vPidKzs6XqTKU6rBVxPgM3kkMXsEslNmrPj3ei4biY0Bqur+qyMGReelBSNKUUa05FzxLmGlItlfCGQP7q9wRjDV199S7XmV8agrV5Qlbr+KgFckNiIyqrkbcn9W30mEkK2tE0JxCVzOOzwIWJsiw6CBJFndmNDDhOSkqS4vFyjs+ZhP7CfBtAtGc3u6LFmhU8jx2EiB8/FZsPkPbZxeKV42B6Ek1WMIlNB1nWji08INK2VwNySzq21IwePRaGVJUxjado01jQyEsyxRMXI/lhH8VUxKPcVC9q5HzwxzFyuGtrSsCqtCBGMyUKNNBJn0jvoW8t+8DRG8fjC8eiiX+7xlDM+wm6MfHc/Eor0XSOIijOa9/6ag/oUrWscgKKxmp+sX/Kv7j4rSGqiRogoNPUoynDymyob67q33G/3KCVo+GF/IMfA40ePmGIue4rQH4QcXveDH4hfzrqtE8MjlzGQx1rNatWxu9/iuk48yYxZ7o+QEiqwIHq6rOeKfv4qMOVXj4eKbPZPFy6nwy4rsUsmsxQpH4xwknQwlUQGp++XShNd/LaKbLb+zFJclGfNZ7+lynfLj8jcvV7L6kUAZ6F4Z9f6JJ4uB3iu6MqfcbFqN7K8/cp1qJ1YQGlbkIbzgkggf4xC4TmECz65anB2YJwFJbleNayaRjqFwolonGPdKPZjksTSDLvRc7MRHfzD4Fm3it3+jk7DI6UI3tO6Vt5NoljZy2ZGzgvbu16bWnhJbkc8W4Oq2IInNp3l/uhRCgkhzJlV2wIwp4zOBQ7MEsGecizqLUmxBSV22ErQl5Qyq8Yxx8Bx9qA8ThtiktyRjASvCSwuK27ynk3bkZWiX20I6SXv9yPaWqzV9NaymxNTnEiqQRNwZuLFs1vM5SNCShyDdDb/zkcv+afffoTVgaa4X1Yvn5QVXz6sefA9P7uHx63j5WH9g7Ti8/VR1D51089SKPhpIMaILRB+ueDkD1Z8Xct5edTapVRuktG6xFecEEwhnefTaIm6McmIogaL1mK6SptVLQKVQqlUyLqyeVcinVIK55xkfNWxk5IRQvBVSm3F9yVK4WFdQ2OBHIlZ8S9eX/FbzzzWeFaNKQhs5sllT86ZOSQuOoHXtdJsQ8NwONDbRKRHpZZpP2NyS5wjjXIkHTjsD2yPB7CZttUobfn0+TVaizKkNYZVZ5d7MxXVhlKKu13gbu9preLzxyucmzlOnm/f73l62ZhJI6UAACAASURBVCG+F4Ct+S2JMWl+eddx0wUum0kMDLMEuBqd+eevNtyNlq+2K55fBD6/OvJkLblCdWcJuY7u6lEiTsMKLQT7ANlkWidusK/Glv/ru8dsveG2C/io+I8/fcfH60xIuuSZ1dFiQ4q+HKQltyeIqsQYJzyBLMq6p0+eYm3DL37xRRlxnLydlj0cQRTq+sz+9PkrZ2UctBQ0skZV4YzEFDE20TYN8zyLTcZxZnN1zdc/+yVaZS4ue26ur1DZkJOixdCtLbtxYkrgup6DT4Rk2B48OgfQI/fHka5pBGnNmuDFvj8kUbKEJCMQ7yN968SoMEi0h7ESJJhD+VyUwpn29H7JRB+E61VQ54pY+dlL9tjieZSk6FOGRs3CYdMak6sZn+yFKldFoy7onPCMUszMKXLRX7IbM84KUqS15vV+JoWAj17QTeA4HrHWonXijf8Yo0TuZBtNxtCYkaO3POve8vp4VXhlZ40zBQUpjSCqWCCgiLNkseWcebi7w2jD5vKSZBuJGin0hiUzrKBWy/m/FDHn/L4sTVEBCpSCHEUGHXPC1hshJVJJpK4TkswZHUTpZTT0p7lYZ7vwr1QPff7T/Lf/wT9cjvcK/59jIOqEe58VISfFhITKFY+Vs1GC0h+SYuu1Pr2yDxGoD7b8H77kD8+Ss78uW0btfhY05Oy1KnX2i6eiR6mzQZWi5P2o5XVXuM1Yu9zQ9TFOSqgCGZZuOGf4cf9HXK8aDnPiurWsrcWiuLy+ABTbKRDDzHA4krTmzWGmNcJF6ZwlhyBuoM4wK4NKiXXf0lor9vcxLk6VusCaMcVF5QHlQFO5HIyqFC0abewC38Wzavc4B3ZTZPTSjTwcJyl4yluORZOvkALWlxFFaw1Ki8+MLaxwH2M5mGUENAUhEl524hycy2w3Z+jaS55ft0x+Yj/M7GcJ/BMpnSZm8UfRCqzOtNYyBYtaf8Szjx4xxBUSGxdp8j138yUP/paEPVOnyUJTihPcUKrhP/vWUJAT83Aoo0FxgM5ZjMlOi/FsUZaCV1fZyBkaJweAPG7OCeecpEYbvXjGyLqUzysnGamlTCm4Ts8VU82CKvEBMVEJhPL5akEu2gYfIvPsz2TQpWkom0rXypixJgefbjzp0OvosXHCfXHdGkViCpr/+t/6jsYIeXI7BO6HIDETbcNxCjzdiKT4m3d7sna0rHn98nuOec/HHz0izjP7Qfg+NxvN00e32GbFn7yaIN6R0NyuHNYKtJ9S5rKTDf/JRScOp0bx6mHm7T5CSnRNy+wHOUi04mHwHA47/saPn+CspSoI//c/fs7rY0ujJfTx0Srym48P/PXH92xDw//2h89KkZjZe8d//ju/4Gpl0QV9jiEtn1mK5R6KZVyRS2eZIKrIV4eW//vrx0zR0GhBzaYo6/lRN/H3f/SKzvYQQRtLDB6jW0BhnWTiaCPInlEWZQ2ePS52J9S1WxNj4Ptvv+G7V6+lm60chSRrP4aAsoIc2tYyHkZUY8EpuiJPjfMMpuxtCpRLaKOwpuEwa4bDjk3fcpxE/ru7u6dvWlbrS5q2QxHIhTScQ8CnLMoYpegvLonzhJ89M5oYxZbBOInPHYcRQ2Kz7pmniX4lxUeIqajcxFZj1buC/Brp5FXlhnhRMxoL1boAabiVLuMhXcbMVq5NGIOohVJGGVn/2pQxd0EonRa3a60MiVjsPCiKK0VA0qeVhte7wE0vPl6SJp24XlmaxvInr/bEJCO1lDOEkcOY+X56Ae4Ka+wSjVNHlGQpFHKx35Zxnl4Ql6ZxNCaitOXSDbw+trJvTSPfffkV0U9cXV3RXz8u+40pjdBJDlMboVRJvGX7s9rIOLVeS6WWIgmE9N84g3WOEOISdSEu3ZIxqO0ZXrLUD3JepZT5ySfP+Z/+27//rz8eOm1VH+7e5we+AA5VMqiWC0tOKCPGYqlAzafiQRVJYH2xHz7fD/9Q93kZzZeNU7MoMWoh92e/A7k5RdbLB0XXqUD5kLNS32U9TGS30RV4KmiSbEi6bOi6GOlUPGl5T1lk0SixN9+0ikeXF3BMrPBcrzdSXU8RnyL740D0XrKBUqQFTIgkrXFKSJ/rzYZ32y2jjzjXMu8Dz64btoeR9aYjjTPKWkKIOKtRWUymFrVCuZb5g/eoz7gRhZRc3mPnpMi4WSlmH2mMEBNrdkRQmU3rSAgiY43GasMUI2tbcoWQnJbGGjEZRMY+omRSHH3k6UVH7zRdY9hNHSnD2s18935k1Tq0Svg4l+rcY60jZ0/Kht3c8+3hGr1+wV/76Ibevub56o6jjzyMHd+PH3OIV4is/aQaq5+/IB1+Kd7+3K+c8dMg46FC3tTGEbxfPu/zQrhxNftKrnuMUAMOdUE/KIVF7SKr4us0FjotWrmS6qTaULUwPVkLVKRENtRTiFuKUuSFGIkhsF61zHMgpixKgvL6BTWK5FjRm3LvVK+Z+prQzCHjTGaeBlE/aMObfeSqE1LhYQpYYMoZq0CRuB8NcdqTcLgom+ntbeavf/wTLjpH11pmPxNiprGKP3635h/9wTV/57NXvHtYsx9nrjfiakooUnCtaZ3EEojE2DBHAynx9v0WZxXDNOGcKIg2nbjcNtaQlaxJp2e+2fVsnBdfD5NZWU+vJ6zredKO/L2fvubtwfLL7Zof3zzwyW3D5NVpzFYSgGPMVGJsXQDyH82qSfzhXc//8cVTOpNoyoG5mxX/5tPI7zyamaaJ6BswFmudcAJ0szRCSmlc05V9MIlbs21IPkoaer8iERjjPcZ0vPj4ExKZ779/s+xdcs4ktC3FFpowRdquE2ferEl2QieLsoowB0yn0c6AXaO0ZYrw5vX3zH7g+LBlDglrFFYpVtc3JD8zpZm26cTTSCnu3uylm0+ZOWvUcSTlzDxO9Os13715zcXFBWk4ClJvG0zbMyVNwMIoxXbTWFbWYpXwhA77gwTiys1ZSLSKrulOjaR2VGJzRg5IVcZl2WS8D8QgJHClNNlkjGXx/6KOTbUkN2Py6VwwJ3m41mLa2TnDfoa3D3tuukt8klDAxkrRF2JmPxSDurJJHHzH9/NzAg2tsbIXL2qazG892fEHL68xWojiQsAXK4Smkcy5237gftAMPrMfDOP+PeM4Mg4DJgeuHj2mXW1O+0aKpTEXFpXVpxN1mbRITUaIfiEeo87qgNqcWXF+7vseax3b7U6K7sL7TH5C5YSyzVLz1PO2el2dHcR/6usvVg+dvWgNYkF/VmzI5pXKSELJm9EKtCkM+kKwPf2PFDJFlSSQ0hnisTzu2f+Xb06Tlyyzw1ISqnz+g8vD/Cl0JtdrkVkkevWHf/Dryw+q5fsfPFi52lKUVClreezlxwqrJidQFqcTm65lCJmHw5En1x1jkLC6yc/4DKOfcCW1+XLTs99PaCB6T2c62sYyzZN4ohgNKrHbH/nkek1nGt7eHXly1RUTKkMgE7OSDJeiXslUYnSWQlCX91ouaLWCt4VUFqKgPCFFnDW4kPjoas39MLMfPdeX/XJtD5Ok86YMc4xsxxkNvLhecfSBnBQP4yyFUHbcD1NZF5E5CCIUJzjOR6bpQAodIUXe7wM5ZSYf6FpLaxt8jITUsQuX3M+3PL5d8bu/nuj1F+zmjp8fr9nNPff+WSkEKlpYOViFWZ/BTwPkTNu2UuQu3cPpc1coYvSSoYIuhYhayMDL2lyWkCru0EpSnAuyglKoLOgUCjIa54xwn8omIgTIE6dF0DpTxq0nrpm8J8lgiko8KqrZXyh5HiIfTAUJgpwVbdswDEPlZSM8idPYaSyusWS1xMTX1Os6enJWQiyV0nRW8kKOAXQ+kFlz1bui9tC0Lpb1B1+9fM3z654YIiuTmbznt3/rxxyOMw9D4qs397x4csW6M+g8MvtEzIp/9v0NWj9i4wJ+/hIfM21j6buVEH9DpLWCNMWcuN8feHu346PrjqvL1cIHiDGwaRtIwlnpGsVX7w3/8t1zVjaUA1U6wl/eOX758JQUvuAPvtFMqiHoNccpkbzCxD2dCxjT8vRqxbqRRm0uhM8UAwpDJWtl4E/u1/yTr27o7UldlxP85q3n8/XETzaZeWWYw1o6e6XIWdM4JzEa2ggfy6TStTa4fkWYI06vCGkQW4SChgZ/ICTFR88/Yh4D7+8fRHlWSb7aYFwhbxPJUQi1RjX4YSQhcpZm3cu+2cD3r48cDx7vA9dXlrVb4VzC6R6tIu26wU8e2lIc5MQ8DsQgBpDz5DHWEKeJwTumcUJpxfbhjk3X0VjFHBU3N7dEEvv9yH440rQtrl9xPByYJk/KmVXfopG8MVXQE9v3wqkqqrWc5bDNSWGcQ2m5H7SVPTvMgZAFCXLWYp0FJWokbaWZOI5HXEEmq6uwKueGLaiLqLFkz9h0ltf7SAyR25Vh3Te8P3q+frvltz++JCbNdw/Vw6i4nueJr4e/IvuvPSEP9dTxSbOfNT++fMvXh1tiltRnpY2YQFrFhd1zd9DsZsU83JNiYnt/T5hmSIHHzz8C7fBZ5P/AmTmdoN0SaniiaJwIHVKYpYL+L0Z2VCGNvFJnDdYatGvIDztO3NGMbTtSCJgyicnnyHauIAJ/7tdfWLTUoifnAkendCLxIVW+ygmsyL70mckc9WDg9MbPISCWEUxBAOqGyBkQff6GzjCqyrQ555D8cJwEZ2eOOv1JyLGFfY/A3+e+GeePIIVV/X3OvwFO+vIUI9bZD4x862vQWnIslA4kNMdBDnJnLQmFD142oSiyyn5l6bqWY1S0TSJnxVXbMowjx71ntW5ZNZp9FGizaTrxQzCO5xcbvnz1kh89fyavPXmmrHh5f+Szq66gTiwFSvVTkc1TlQyU0zXLsEQehFkx+sRxjnz9/sCmlSyRdeuICC9lDJEYRUbqtKIxhuve0VhN13RorbleN8sBetEK2z9Ta13L3RC43x/IIbLuBCUAzTRPbHqLMYr9ZHk1vGBKHRlN0634G7/2mnmOvE8rfrb7K2TEOM2aKGstF4OtJGiEH4/FuEoDWqD2nMl+QimNdc2i0lk+T3W+RiXbSMImy88sC0Y6yYVEnRJd11MJfqKyqMWyPOboZaKblVruOymKcnle4ROkkBYFWywGf1ob/Cxwu3OOpOpnnE/IC9C1DWjDMAzM00zXdTROXGWrauB074n65eb6gv3oyVl4ApTiSyvh0KDAtJ1wQyIc5sB2OvLpTc+Ti473+4lN1+AjfPHd1zy97Hh+e833bx9QTSLPkZ99+Yo/fBn55vicp92R5+8OrNuG3/j0iscbzaqBY+xQMbOfLX/7eeLdPjGPSYysnCsNj6d3lsYAyvLidsOPn10yh4Az4smRksVHSTLWSlQdv9he8vPtNWsjRWlOEW0sLkeMivyff7xBdbdYA3jJG/tiu+Gbh8TG3vPRxQNvdpnHlw3XveGqa8g5YrQl+Dp+k4V+0cy82Ex8s+skf0iJyuWv3Xg+v9Lcz4m+X2NMJh+2NF1T/C0yrutFWq8tyk+oaMSePwrxX6tE21+grYOciIPYG8Q049sdz57dMPqJ4TgSx7EeWQXpT2DAOI0yQsZsTE+OiRgDkUD0ET9kxu17cvIlwLJlmCNt6ug2G9I8EifFqmvQVt5zCAEiuPWGS2fISdLdc8oE5QS9LKMDUmacJmLI3G+3NNbQNZZLJyq2+/fvWa1XaGXpW8vhOOJT4vLxNRRvp9mHAgSIP8g4zWitWG/WuLYhek8Ik4QgBlnTTeNQVmToOQAWlLYcx4l59phybkBeRsN5CXxSJS5FVGFjyLzczrx8f+CiSfzeT17wz79+R2sUN71FacvL+5FhEi7SPI80eN6PN9L4yGbDcb+nbdsSophRSgwWn/RbVm7i7XjNkDbodMRqxW62vAs9Pirm4z3zPLO/f49OiYvLS5qmJSu3gAgF9C5ncloiUerZUEfQeTnJ63Sl/Ez5WUUZ/5RrEDPcPexZ9V3ZK9QCTCzO4bXDX0YpdZ/Nvwpo+Yslz3/nH/xDVIHUFZl5nhECo8I1XTGxShjXiO6/sKLJ4tdSN8A6czuVEWdFAZXUKEm9tdhh+TcK0nKSc2YK43h5w2fwD5z9NmhbbfcpH3wxya7cglPpBGcjlOVXOOPfnJ6ifNiF/azO31vJopGyH2cl2uCT9Stum4GH40Sr4PllR6cMRhmZD7uWcZoo/FisUbx5v4UU6VqHVRbXGpRzZDS/fPUOazWN7rnSEdt0NNYxzQe6viMET00p9ingnCtjhby8fTHVSov9O1C+z2dduFquUcrw3f2IUVLM3BQbda0kiO44ByGpaUXrDCAmcqpU6K0rHhlKfFjEGT0VGWzgOAUao3h5v+Oibei7FXfHIzklnIU/2f4GKWu0PpW2xjqefPoTlBKvEJG0p6XASDES/Vz4ICeORyrS0mrgVg8WVzo2yBJwmU8oR/DiZSLwv1iehxDQBdpUH6yDCsPL67BWuDS2dG6qrJ/FuK78uY6NqueK0VIsaGsJPiyPW9f9UviUAkxrxTxNZDKheK9Y12KMZpxmyJIuXYm3uWxQfeu4ulyTlWGaZqyzWNfy20/ectvPNGwZfCvutAYejhbbNly2idf7xNE7Ni7w0yeGQltCq8zKab5/PzAc9vzTt38Vn2BlJn60eoX3Iz9723NoPhfCYTF13LTwH3z+Db3JHMcJayxfD0/4/V9esGkiYzRs7MRH65Hfu/2WkA0PQ6brDCnD/e7I508vOUxJHEd7x7NHV+SUaQy8P3qInkYr/vEXT3i9MyUV2xUC9JklwJ914y/bWEGTs+aGr7lML7lq4GrzjM5YMIlPPrGoUnRLIrXhMAT+lz98LiF4pehNxvK8C/z9n2Zuby6ZR8lpSTHS9h3KObRrSSHiD0cOw3cIvu1YtddY16KUYj5sFx8W72UUGFMkhozpDKqJ/PwXX7Hd7XBtSwoJbTMqG7TSzPOE6WQfHEbZt7vViuPUcDyOpHDgZrPmMAs6MPsjjTNcXmyIWA67PSFmDjsRAszBs1m37N++ZR72HH1GG4vO4svx5OaKYQ5C2uw6lOlwJJ7e3pCV5rDf44rxWpwn1psN4xx42G7pupZUVEVoCfTtm5Zi2YizhvVmRVMK+VjGpGTxKrGNI8yz/L2KSNSXZgySFTSHUIQmlQMowoVTSwqZiLKnpvf1biLHSFCGVaO5P8zsjkcSssZ8mOg3n2Die0gyLr2brrkbWowRMnVdXrrsQ8pYPrueaG3mx5f3vN0GYhiYleHHj1d8ubvi/33VMw1HQvDM48Tw8J7Lq2tcvxECbK4jyyKUybXgOk0x8tmJJ2NWIUOmoq5a3nN5gbZkDJ4LEqp60Rk5A+53A66koyslo8hUGq7GWlCniJiKIP/aJx/xP/43/+lfwhE3Fza2KdktWUhcFEg6piwksMK6XnJuYCFzLhu3rps4KFWNllj8OJSihBFW/Xo6vYgFYPmQH/Ch0Y1aaqFFRlWKpFzlYEqJVLSQtCivZRlRnR3c+fyJ/tTX6eDPsHAP6utUyogXQ/G8iCliUTSMTLNn3cjGOsVMSJGNkxn1cZyYDgO3T6942B5ojebJ7TXKSIJrSpCV5jh5UgpY57jWYnY1hMDzVY/3gaZtCEj4XqPl+Y0Rb4maGbWwvKlcpDOb72UBnS0ETmjI7dqJhDmfVcTlgjoj6E8lpdYMmE1rsVoXSZ1iDJHDnKRjVFIMtVZMshpj+ehG1sR+LDbuRnHRGcK9xhmxY5euRnH7/FOR2Sp5fSn6E8cEycuqVuDnZNn6+sVttdxsOUtXZSRLqxbgKcTC22iLt0b1thEuQO24qmotq9O6rYWLVrUjq74f5UZPp9+tCgJVF+EyfpR7sX4OVZZYSbIV/RH+khDr5mlavBtSzvhxEj+HolATt161bCZK64JAgGmapfnZ6Ds6rfHBioRVK+I8c6UznbIQDT+5tIx+h9GKL19mwijxDav1mpyl27/tLW/2YNLAXVJ89/Bc3p82uOxR2vLixvHybs/oW37/iyv+7k/f0DWOORk+ci95tFrxfjD0NjJGx1c7S8of89n6PVfNgTf7RKMiT68atoNniprOaXTO/Pyb95AjlOK5tYqmN3y97WkYcW1HrebV6UqTkPHbMkmFE6Ja4XGVeBs+5m16wuP8jvHuC3I2dLYD9VTGEVlhHbi45dFVw0+vd/zx+81SgFoi3x00//Mfwn/2WwOfrCKjzujVBankvaXjsaBpFhsaYprQJTYjBS9cpJxL6v1pX1RKkZVHZUeeEz/6/GN+8dVXDMNEyJmL7oJx8txvj1xcXeGDL1y8me1h5PUb4UQ4C23fQdMSjjuaRrPZPGXy8O5hIvqR1apjs7K0Dey2W8b9A/sthMOOT14859duNoKwzAFrGyGLxoC1mkTg3d2O7XbPzcUFrnGC/kaPDzPHcUIBx+GIAfwgo/TOWZq2IasVu90erRWNkyQhpZSoG0vTkmLENVbk/LOXWACK+qbEnpiCbBnrGI8HKVgaJ1lgSpGzGHrm8vg5UcwzJcvqftTsDhODhxAyL24uyMnzbnB8t3vEN++u2RiFZeLNcEWIYC1o01Dd1sks4Yk3a8MT95ZhOvL97gLiyMeP1/zBy55/8v9tGH0m55F5nhmPR+Zh4PGzFyjjim/NubNtUfYW5DklilxeirNzr7Vczkr5JxlTC51OFFnGSMES4kmdqhDjw6gNru1Qh4GQapK0LrE3kHUqDt9y/gkQmQrR/S/tiFtmXlmL8ZfWYAy5FC+SW1M9A1LdoWVylTPUYgCpoKpRE2emb9WyXaOWiyMHj/zcuWttRub85BM2Us6B5YBVVaFRXpP8u1oQkVDzY6id+lnRk0/mcrXIqddhea5i51/IIEVNpMrvnammyuahlJLY9WzYT4rHvWLdCOTY24bj7NmHzBUt0c/0qxXJRxojCIC1DTFKCnLwAe8nQgqEDGtrSFOgc5aYLeMwiDmUUhjlGf2MUY6MEIaTUhhlWDwHMwKDGlFbqOLcGkIu6Z15MX+rxCyRM4tDad+Yhb9CkuJpCorgRTo3+ijx5qVa9yEyp8TgM1MAHxPDvGPVNGhEwjiHRMqz5JFcrjl6XyyyE84YjD6hGAA3zz+l7VeyBmNino6wHP4ZlcWWHaQySgXZ+eFXdSslsySVkhOKdiHCGdfKeo+JTOEHLYVJuaAJGSnl85XEsh51gXkX76ICvRohrZT1fyJbVnv5itac+wPJmpTXcK4eyEWiaI3B2jVKK4bjseR6yONPfi79wMnkapy8FI+tOIpqa+ld5u30iJZXuLbHqUSKE58/ucBqJ69HwRdvB277FbebBsWOsYnMAeZ5x3ZIDNNI+0iTwkzrNE3bUKui2gEaY/lR/z3fvelI2fF6WvHl7pKfXD0wDYIa/Uc/fsP/8/0NL3cNVstI8k/erfhyt+GRfcfv3rwEpblYXZHCyN3eE73HdDdc5h2Pry6YQmSzaslZcT9MNFZhcXI9jV72gpAtjdVsbCIm8VsafLU5iGJ+dtaAWpPRtuFd/Iy/+zueV3cjhzEwTW/ZDgERPhs+fvKEpCy/sfmWf/byN1m7WHYjResUhwB5PPDLyRIxfLYyEkioFbrpiSHgjwdS9MsuFqYJZVj23IzYDZilMZGMtBgmMIqmb/jxTz7jiy++Zxjhq68POGtI2fLmzcA4juQszafRcNGv+eT5BdgO9IZvvv6ex09fMPvEF198w3B4gOTpu5b7dwpiYA4RpxXPnm6IIeMeX0FWpGhxTUPbmcWtVWktLq0aXrie/PQ5yjpiiLR9L+P8GGnblhgD19dXuLZlKnb4xlpIkdlPwrsziqZr6Ve9oCPBk3VLCKEUCJbJT2LUCMT/n7M3/bktTc+7fs+0hj29+53OOVXVVT3Z3Wk7jm0gYZJiEYGQ+IKQkEBCQkgICcRn/gA+5q/gI+IDSCBEUAgyikXA2I6dTjudbvdQXVVddaZ32NMan4EP97PWfk91dxxlSaU6dWoPa69nuu/rvu7r0iqresu6jimiDLR+BCX8QR/H7MU2zHw0rYXz9dh6Oi/ogo+ax2OPs5BUQQgn9p3h/uR42Vzw2DqsGXnbL4EVCo9zdkZO5yMnbyyFM+jU8sP9NcfulqAKXhSf8bpVvOxuib4jhAHvA+3xiNGa7dU1Hk3KkhNPr3M+rrJ7/LkLWOQQzqDAtLe9E8bnQMVkVHkcRWuLqZSUhURTSvgkZ7nklFlWIIk1R5iU82PMgZD82Wgj5/yvuP7S8tDv/af/zYyGyA/4kkLjXGphPrrf+cR0hsAnUzJU9sUha7LkyPUXgwQh+Mpnf1mz4vzwz62f7377GX7XpPnuyBHmeY48/Uz5HsVs266YW7Zn4u6M6iQmkGVib595E56EnsXFlLWsy8hzfsimKtgdG776/JLSFgyDJ54GyuVa/FzCKC2No0flNtTRe569+DXa42coCw9tz9XqQs7JzouDckzYQrNPgbf7Ax9tNiQ81uR6/0TOUVIOUub8289PLM3jZQyzSNcksmSUwsfsr5IkyDC5BDXxVFSGpkVISN7nAwwhcWjFGuDtocUoxamXgKx2RqBCNMeu42ZVcxoDrS/ZDxVDrGjCEpFWk2e9vr5lfflszozb4046eTLheFpwMRdu01SDzhPnTDJT7yJq898nQf6UcDjKqpy1VEKQ7CLOHTZZy2D67DwPBepN72QwCubgA1Jux9T59dItoLTCGsMwCuqjEPPICbJNMRLyhjQT9Z64qRfOMvqATmIqOLWGT/yYwjmMMTRtf/7tCYrSiUy9UsSkWC1KvnE78i/fvuZxP7KuNNuLRTaxUKANTRsxSbJVHwJ1WeRNPnLqB/7hp/DJoeZ1u2BRmjlLI51XqzWasnBAohsCwY84K8hcHwzP1yPfXN/xze2RmDTPrzb8dL/mR3eO772uqJ1sFP/JX33Jm7cPPJ4GXlyu2TUtm0o4ZV3X8tgMPNuuuVxbnl1t5BZAsgAAIABJREFUSDHyp58N3PcX3LWWVWV5aBR1YfiN7aeYKLYCyScIA5tVzWNc8zCUfL533PVL6sLg1MjJG56v4UV5ZDm+ZLusKIuCt49vWa5KbjcFRDgNPT//4sTxEPjOr6/5u59+yE8eKzbVOZitDOwHWTP/5V+LvFgbhqQxSpMyB0MTST7rfJgeFUUXhCjOy6KfFLBWgQokFeUfEqZUJJ24fxj40z/9Cbu7l1zfLDC2pBkVCoOOmjJz6bQtcG5N1/bsHh9YVp6rTU3SkZIFrixIyhPosBS4cpGDYo/3A7vHwDCK4vX2YkMMgTEbK1Z1BcoQQ6Suhe82+sDpdGIYRpyzVIXFWUNVFrRdx0QXsGYyGRVfI53bgzUJXdS8fP0W9AlUduIejZj6GY33Az4ErDHEOFIUDh9HUIZ9c6RwCVc4Tn2k0olFXeC9orQi1tYlEZHz0fDFwymLY4qjtDKWIcC2crw5DNC94buH36IwQuN9inpMp6ZWE8qnchk7im9bjLiioDI9VvU89qs5qPFjz+P9A8TAcrUiZG7m5DR/Pk+nbU2hmILkvP/zrm7adIlcyRSsnPdK0dJSYDRjP+R7Fhn+6YzVSrOoSpbrJS8/f4lxhVQfmM7cOAMdZ2CDuXLz9Q+e87f/q//gX0ARd4I+dRY3QtATEbASWPzpT53aId/9EObNPE2ISQyZUxBnFdQpgNBq6lOSDAE9tadFJpVbo562KE/oxiSJPm3A+UPVfBv8stv7haBNPf3d8kRDNo1MajqU9RxMQYYUQ0D8jwSBmaJVUpJ6rLVc1wdsbzi1DYuFQ5mCvh9Qg6debqgKRzccBcWKUaLR5AgpABZtHYOPuOw18un9icPhgIo9z9drFmWJ0nD3eOKyXvLpoeXDtc2oT8QWTsphSvhDKgqaMymnyvNQs3x3TGrmKPmYMEkxpkjlpMQ2+ojVBps1YLQVCD1GcFbmRspj0YyeymmWpQjWrbKFO/RYXczjblRgva1pRwfmEqJmP64Yo0HrSEBUKKvlmvXVc0iJGDxj14CW4G6SMicvjnODc8qLmRlpYppLOdiYyoZ5faOJlIXUmodRWmt9LnHNJpVkzYu8FSSYhQ0nEtvsvA3z+AoaYs/x0jyXhe8TYpx5MMF7JoFDpZQo1UwlOqY6NXmTyf5SwdMNI866TNiVjcgag7GGpm0zPDxNVsUwjAxjoiiKTKLThAjtEKgqy+ZiSR88Vit0NDzuGw6HPRfLBSOadb1gjNL+354e+bM3G/7s7SXWGtZLRUyT8Na7yY21jm/d9PzGbcP//cOBHz2sUUkI6jq0fHEPj917NPHIhX2grALf3NzxrQvFptjw3dcbTgPcrg3H45K6KBnHkYWLPLu5Jo4t//tfPCMpww9e1/xL6o42Jn76WPK9ty+o9MBv3B75aP2K8aqk7TzRa5aFwxE4DI0QqUNkle5ZVZavrUteHw+UzqD9iWAWLFSi3z+yWC0wxtKeTqyqC3aHlhASF0vHprDUH1kObc/Dw5F/9epzFuYFP9mVuUtMcV1F/t2vGT4+WMrakcIJkxLj0GGdFb+cGOeOQOulfTTl/VYXBWPXU7gCZRMme5wZZUjR44cRW5c45Uh9y9e+cYsrHFppNtHQ9Iq2VWhbU9UVRVnyycc/I4WAUonQKXo3sn1eELuIcYCqsakQhBCD9wOgsXrBzXU+rJOib6XFeX15hUojKMfpcKBrG/quFJ5YGHGFpXTIZ+f9+3BsSXHM3I+sqRI8CU+RW5uHYaCoat68fMUQDjy7vQQSg/c0TcsYe9q+Z72u0aag7zvK0nI6ntAGArKAe59wpaNyMt9jUtRu5PXJ8ed3W752sefrt47j0VM7Scximnh6gnS9eXhAxwHvbjG5y0fBO2vO6jN/bUpACmfwXvZlV5RYa/jmxUuOveYYtpkDp+j7DgWUiyVJWRRRSNlPUNgcRuS9Kc6lZNni1LznvXMuckZ8BbHOCVdGYqRkl5OkFJnUSKaPiSkwjCN1FJsZZcSGYUKaR+8zRD/tzPl8fqog/iuuf7ZOSz7JTOaBTKgEqHc9EPOPnoKGM8r1BD5XiRDFM2MiLU6/cA5qslPzGWnJ8PGU/SMZqskoD4j0b2T6b3WOfpQS++wJJVJK6v25DVu6hdJ8SEwDKBF8mHNlhUCJ8t3yOj+O+bzTxDSVCSaC54TCTGiFRleOEBNN23K9LFFR8f7Nhh9/fs+qKthoRxh69l2DKYWVrlL2fwkeksLqBIwoozidWhIB3XfcFFCYNUVZoJ2hBSyJY9exO/V8+8UtwyhqjnNZJIlW4/T8Zz8JJtTqDBP6jJ7EmESfJXcIpYiojkKuicpvN5lEPJkKhiiQX9OPHBoZF6MVZVXS+km+OVFXlu3CiXZL0iSl+d4Xm6z2CEafI3NjCzbXzxiaQ743GWPvBfGYumUmhGOeZU8W5vy3T1EVeOc1MSVKZ+n7LnN+NMm5OdCZ542SrDXDhk8QvSj6B3rqmMvdQVmYbYZck/CIJvHDwokcOdpM6ukSsCURM5Q5OMghkDc/4dWE+RmFMMwcrSlItdaK7YD39MOIMwYfovj1jB5tCjnwxCVQAmfn+GC9wzlL3yUedgdJNrzCe+ke+ejZJaNPRON4ODbomGj2b/h0fJ9/cvyAZdVjXUFImm2ZGIaeQzetuzy/Eny+g9+5PjJ0nphW0lU2SsnKWYMm8o9eLllVSyp+yLAx/PnbLdfrwL/99Qf+53+y4bsfdyzSgYcWvnK94O2u53/63pIXS8dPdzXOFdQ28cnpls3ikYsF/FtffYMxBXrco1KJ9oEyjtLhh3RFvv/sRhy8+w5MDUq0ZG7UEZMsQYGjo+siZVmhE3RNi4oBHR95trkkRM04OD7bveSr711QOMdmUTGcEjdlz4+im8vNz8qRX1t0fGNhSaOnDWCLgugHlBORwRTPgYtSWT5/DoATtnCEdEJrQ9N7+rHBWkdRGqp6Acqh04F/5Xe/w2Pb0HQ9xlTsHg7EqOnaEw930ghwOp24XDq0NRhtuNoULDdbSV4rLciM0oAgkylEUedNUmqVLTlibEFxsRHj0mQY20DXNpSFYVGuiEpjzBKlpLFg6Md85ErZc/AtRSHBXfSiChxjwliDT7IZlaXj/v6OZe24XrxHHEQ12CrLxYV4J61WJdZWdIMnWYsfR4y1GeFUjP3IZlVKiStrXSkSH+8qvvt6y94X/NZ7AT8eqc1AtXBEFK92HeMgXKo4dmjteBM/5NVhhbNmTuKMFvTfaNFWETd7g9aCVkyCoNZojDFU1hN8x9pFbso7XrVXTKhxURRSspmiBjV53oVz0KGYZTnm7f5Jfv/0UuqM/pz/8hzgTKh7GFP+70n9W4IXGWtFVVjaozg9K7IDeO5Eda4gxX4WrlMIKGBU7kF/qob6pesvRVqUkgVCDBRFwThMPVJPEAnO2fmZG5C7h3LNcHrdhIpoUkYmBBISBVdDjJ6JKyLvikzFHR8iJimCNsQgyq0qZ6EonVsw1TxogvzIZ41jZoGDtNAmP0NxU8kqPRnMqaOJlDIMP0F3ufyRF2EuqMj3TzWnPBMmLZC+98TYcZdu+ebFjqEXTsHN9YaHQw/Gs4mI38bg6eOYYUePNjAGODY94dNP2FyUqFKhQqIqHZ33dCqgome3a3n0ntXqkvbYcVWIr0U5BYlKxiSmNM+J5NUcfE44wdSJEnMQqDNZuctGdMd2pC6la0jmiUxUHxIv9x0fXi3Ytz21s1SF5qEdeLlrqaxmCOCcxShL1zUsK8d2UaKI/MV9zde2Az/bFfzk4UKwPA19cxQtCWtRWnNx+0K+NEPCUpfNmUoYSeisPHqeqzZvbjFNOjXp6Y/+cqIxT4SmG+dSZPQ+t1/LJfVsIyJ3U21+Gn+lzlpCZE7Ml4jeJhPcdeZRWKupyoIx5vUTeoKyWXsl825AhJmUFlJ5yl4xeQ1OgZtSmjCOgthkFeIEDOOYg+xAVJqyLABYrXOGGyPBR26vNmijab3i17YHToPi6rKA4DP8DruTtP4OY2BVLYlpZLlZcOwOfNz/Vf70dcm6ChTWcOoDt/WRz+6NdBJEjzJuXndC3Nd0PvJ23KC1kgAtiaZxjFECqxjoO8Xff/NX+Bv6c75Sfc7/+qPvCDrlFH/3Z8/ZVpf87s1L7k4tv/6159RvH/i/Pl5SVxUxeBpvKNKev3hb8rs3dyhdcGofpFXaKmxRUhdLFI62aWWP8gOltSw2F0JWXVd4a3l9eEvnO6KSdlrrRHXYmMB6teFw7Bl7g9+3rJYV+2MHBPrBUbqSsjjw/31xwR99XlM6PYtg/sldySenyO99JXFTxEyclsO3PTVPtpkpyDzDyInIMBxIKuGoQXnqqiDgccWGprX85JN7Qup53O3RJrKoaz77+Z5+EPfzFDxDD9vKcnmzQJsKNVZgBhQlpasgCgKsrJV9Ppc0Zm4fkvCipMRMlprXtgItwaCpljhVys6jDZ98+jnLRUnhSk5vHlitN9zfPxKiCKktasdViYi+FRucSvSDEJCtEsL30HmeP3/B67d3NI87iqLIJbOcjKas8rosGcYe6b8uGMcjwXvKwvLeszUhJFZV4me7EqsS33214jQaAppni4HPHiK/v3uO9wqjE33Q/Nr1wG/cPPDq8YBdbPjDl1+hMJGilA5FEYKzc/JmjJF9KkbaQVATW9aiu2IURkkJ3ipFsZBE0Hf3pCjUgKHvUMa+g5RPCfSE8k5JvqAs8+Euz/wpbyXXaKaS0fzniS6gxbcqRtG4UVphstXx6KXzdyp8xQS7w4kQI4WzeC90ia7Lyb8imyiKeaskdE+lWf8Fgxb5MbJBWic8gmm1nKHqcx/35EFkrcj3phSewGCcqfdKzSUhRcpmbioL80zEwJSFmVSGlKR0BGpmNE9kX0EFgpQ2cutZShCQDHYSE5wUXGWAzHmQYEaKzlFnxlny4E4R61xOmFAZJQJSQnI8i4zJJ+isICjttOPY89gbritFPwS2qwofFWEURcNT0+OMZRw80SbGrqFa1IQUKJ3FFbJBHI5HQkxcqIrruqbte/ZxpHCGq8KyOxzx48jFouTtm3ueXW6IQSziY4oiNY1+UsKYY7Inrc9KMC+lss5MZBgDh3YUv6FsBkkOALRSVIWmcoZdM9IOHh8Svdf8/L6ltlo0S1KiNiVN0zD6QDEaTv3AEDQ+Gi7KntI6EsLB8EOPthaj5d5XFzeY7JNxLgXJpi0eGULETnHEuUI2yyRdakaLtPq8LiYobRruJ3+VnvzllzORaQ6nKG7WcxfRFMTPwfwUqiNluemDIpIla/K80RSFm1WIY0xUZUE3lQj9KD4/JuL7I84YgjbyejV36QspNCs3p2wJT/5u7+McJE1qtgBN0wph1zm6riciLfpv7h7QRlPVS7pQUDipxDtTMvmgrJciWGiSpvMi1V8aIFk+LN/yg+oZPY6uO5GS4+O3gfW6FnE+fZYhUKSsGwP/4/e2DNFQF4aqKun7AYAhQVFY8dgxlu2qpjTwJ59sJHkhMQ49WsF9W/CDw1f4evEj/tEPX6Ldgm89W/O9Vy2DKjAk3rQ1t2tEsG0Yuchme1Zroo88tg2XmxWltThd0KsekuWwb6mrBafjQBh7jElstxtiCrTdOAuFlsUFp+ZEXTkKu+btqy8EHo9QL294uDuxrCPffdPxJ3fvUxifE8SIMhqrEp8dNf/P557/7N/4kNP9Sw6Pe2wBREXpMmckRZGpNxPBXuFHGYOkPLrQRK2IKrJcbvniVcdnP/8pCs9iUbKsBy62W2Ja8/yF5c3be/q2EWSsbTh4g78PXK0LtG6wusKpesLySUnNBHdZOerJHjiJiCaKssYWC4YxcewCx9MdKYgei1ZCcl6tV9RVgbOW9Xophq3jwLNnN9w9PKINJGX44r7lYmWoy57BJ3F5LhxhaEkhQgrc7XY8nF7x0Xvv49uIVyEHUGm2VziNI/3oKW3MCGNgtVhQlFnSICaCivwfP77G2UChE0bLOWRU4vv3G6xOFDbOa/yqGkhEEfNMkdt6z123obDSeCBcMunSiwl0jJz6TkTwrM2VhPMeEkIAVfDh9sjQDyxrw2N8f97DxqGnqpdMUlETifg8AjIYYrh5Dkz4UrAy//00eHkTzIUmtNY4p0U0NgFKAiODJLNTYCQ0CgVJmg7G0VPUhtGHGVmdzm1bZH81Ej5boZzv/RebJabrLxeXU+odZTyb25v10wBE6ZlUKy2g4pwbo9TeJ9RhpvukzMDP7YdK66zaObVTSRuW1tLbPZWPYhK2y0y8zaJgZKXOadEzVYieIDYxBJQ5S8pDylL25whPTfeqRcFUSWw6Z+1Tyeh8FMmfpxbrOUKdh16GfCq/xOD50eEDLhdvGFOiLGAVDPf9QDMmCqMI/ciyKBjDiHYVRhmRGteg0sipS3SDxyiDKmAYRsqyRvcdqS5ZFjVJ9dw/3LPvGp5fLLHa4tM4SWAwcXsmvZK53RdZeNPe40PEas2hHXlsBjaVGDI+NiNmZVhm6WmTlTcHHxm8kGovlyW1M+w68dM4ebBaY41htz8wjD3X6wpItN5zGix3reXHdzbD3TD2HSl6tJNatS1qiuVGSpUwc1cmNG+OPJRM7ZB9lIYxUBYFRqe8eKbXnUfpXPudC0XvoC/vUNXUeX3P+igzMUVElmbEcf4e6d6wWRFX1GWnrp9EGAei1lgl6rgpeFRW9IwpoU2UrD8FulAQ/IhSnrKq6fuec7U1zfCv1MjlM8pSJOCbphGyb15nWkmpTmTEZfYOgxAkC6fRaeSzfc13bk/4AE1MVCbRdCO99/T9KBwrVTC0Pd6KEuf/9rMbhqAgHUUNOLTUVZmRqimRSfODnEwgIwWrpSNh6EcpvRaF5StbuO8UlI6LwnNTfs6nrw581n+N0kX5zUl0Nd7barohsbq64tO3PSY4vnFz4lUdedkVDFFRqoGF9hz7gPLSgVe5gv2xQSWNdVrKqkQKk+hOAaUTRVnyuD/RHA9cXa4piwX7Q0dMge3WoY2maTyH4wFjCoJ3HI8Nxmp8SiQCtB5bOHanwNgLQmlsPgy0monjWisqHfF9C2mkWjiavqdvR6KGpStRubvPKCPdLypK6cUrdNL4OGCTxRrHw2PL688/5nbrePHiQw6nA9ou2R/g/u0dZbXEaBh95HR8YHux4tj2vL0/snsIfOc3L2GwUv5JZz7hpM/1VEBMkBMFSVFVG5oBula0Oe4edxA9i0WFC3B7fYWxIptfWsP+eGK33xPRxCjeZYvlCpKQZhORx2YAq/BdS2ETS1tmFBxSVIztgWc3WwgWH45kKhwqC6A5ZxnHQcwLnSAVy+WSopQjMeVuS43m129aBq/YdZYxQQiKV2NBaafzQJKNy8rz0eaEIWJWjmZM3J4+54AlsMZZiyucJM7jIMq9zmGDzaGBRA+lM5zaAXIXzUUVqbnDY/jx/gOGKF1WfugJfmRqYHunpDPlR3kPmIGDOUt/gq5M15y9y1oC4d9JF6J9Ah6Az/uXDyE/d83UuSb0DyE+r29W7A+7+VScxOuE46dx2WA3TKh9OnP/ftX1zxG0ZO5H/tKYohDBpF2EOXGdIrMU6Yc0i9EYbeY6mkLiDK0m9CFmt1ItvJl0brFVSFaeH/P5gcPMr5FgJc5BxBwYxTx4KUgGoHIpKgJP2q7kmlwn0wy15rfMAxM18+/P5AT5zFw3iCmiQjqnvPkHiJdDzAMh0F30iT/47Dnf2t5R246ri4qLesE//ekdy7BmaA6sypLFsmJIQpgbNcTkQVvG04GFcVhABYHQ2+bAclnx5njgVfeaUVdcLwo+ut7yo9evec84XFETQxauM9nqPgFJUKAcHxJiNjXU0m6LUqwqJ2TM0XPoBzEv7Hs+vMy+SUOgdMKFqZyYz516zxe7lmYIGebPB3wI1IVmWVYEFD6Bi7AswPaKHzzc0nqH708Y60haE734qWyeP5thzxhShhancXzaHwZT2SEkgai9D3ilSHFEaScGZCHOujHylkm/4InY4DtXeiIiOJHHMsI4QeJKOn+mMlTpbK5Pm0xqnRj5UuoxhRW5fm0wRuGHDt9HjHU54JAb8W2XS0KVrBcneg4xjGgCTdviijprRRhQImSljWboh6xRAc45QaC8pygcm41ogDRtJ90XSoiyV9sl3RgwtmDf93xyn0ghoYzmo4uOZVmxXSxp2wY/Bg77E25s+aOXW763u0ZrMFajlMVHhbElPiZKKxtxiCKLNrX7AihdoQj8jQ/2/MOf14zREZShHRI/buH3PnrJh5uRDy7hv/3991nYJc5mo8ggejbOWu4OgcJp2mj51vVbXh4c/+fHHzCqJQnYVpE3fc0Xry2PQ8Vfv3nJGAZO+4ABAkfG3uBsKaU4E2jbxM+/+D4ffHhJfXGFdjWuVFxeLhm9lDf6sUMlQ2UTZSHcnf3hIAhUtQAbud5sObWesetZa9iGHf/eR9/n9vYZ//0/fkYKat4vY0q8GRzd8WN6Y9ntB3SXUdlDz4GO1aaiXlj6cEAFjY4GayPRZJXXci3PZ1TUheV3fuvbhDGwPz1SFIa6vOS0u2O9XjOOidubWwCGugIFy6Ul5pJg124onCNYsCkw0uCUiN2pvOa0LdC2lIADy+7Y0O8bINJ2PaB5dn2BUggR3C14c79nu16gVKRvOwprpJwdRk5tx9iK8d8YAkXpMCZysS5RCh67Dp80q1FRa0FAnHGUCwPe0jEQrEbp3EUYBwnyrKNrdtxcXeNDZAyR4+nAVbnOIJLwTUDxNz96ZAiKP/p8zb/2wYlCDcQEbxrHP/jsgtZbtIZdb/hffviMEBW/9ezE1y/uKasF37af8apZ8tY/p+8HtDHSKacU3SDuzs4Z+n6kLi0q7Sk02GLNX7t9g/cjj8cerRJ3R+HWNIc9+/t7UdYGQvRMJNl8+DxJyiXpOocC0373ZGdLEiQbY6gKB7nhIGYxTh8jYUyzOvqUcFlnsTBzciZkxlnpMh1PDaUrGZSfMGcBCTIHBqQsHsJZnG/ox1+6+07XXx606MnVUYIQjUA/IvssCIk1mjE7eMYv6VOknMWTO4UCU/1akXe2HA1mQs4kCDd9/+TMDDADoMw104kAOpOHZkLjlEXnAZozUTI5LI/t1KmUyVF5vAVxeTLA58GN+X3ng2oKTs81uXNWnpIQokw24Rt8QOH5wd2a79wE+i7hCsXt1ZLHXcBHxTgGxsHjrKEdAjZEVssli7oW8SAN6Thil46h7blaX9KbyIcXBfHikoehZX/seP1wxAfPSCT0DWUxZRFCnlIkWcwpSqYXY3b2FLh2DBKojD5wbIX0trCGWFje2y5YlpbBRw7dSDskblclQ0gce083BmmN1hrvI+vKYmxB2zWcuoGydOICHWReLCx8c3vP994+B8W8AARpiKyvn0sQM8sBTV0zT6JypebRmucPKnOQZP5aK4hDDMKdEiG8qSX6SeDCJAiX5fKz9L6Q2eRgF6HMHFUkWbRiNhhntIV8AMlBnXLwItYE1gp5UfTdJMAfAVdIUBHRUkMOkZSN2mKEOPZMJOOoEmOIWFdlif3JnfXs9SOdXnKwmHyPzkngprSiObUMg2e1EJuBZV2yPxzQTnguf/yJQ5tnaK0ZcfzHv3nPN1Yjf/7Tt/z0sWTwhuPJoNwzPm2WVHWdA0aNc5bbaqRyiutF4uvbBu97jm2gZ8lttaOwmjFpxlSw1C1fu/K8WIy83MHv/2wDSbq2/t7PnrOtE791u0P5jqgtk/Hn9DyMtWjjaJqGP/7EsnFXPF90eCxaCTF88DJBChN5tVf80bjGxiPfufH4puMP365w9YK/HnbUztK1R/7RT9/Qbv4K760KfnY38JWqB2UJ0RPjiDMl69UK7wfUoiCliPeRutLEaLh/GGjayLIOFEVF03hUgtvLSw6nE51p+e31x5zsLY13fLT1bAopjf3sbYtvPBtXiqicNYTk8cDu4cDD28hHX32OD8K/0pVBY6Q0PnoRzVMJZStAuBs3N8+IBI6Hge3VDYef78CU9N0RpQyn02Mm60JR1Yz9wMNxRKlA25zY1Ja6KrCFZrMsZJ2YBaNPjCiOTU/bC6qnjGG5WFFUo6whpRjGgapeoo3h4e4t+1PHallTVBXESD8OOAPbVSX8jqKg6XpCiLhSUVYFMcLlpqYdIrvdDrusCSTswhKj8OGG4NFaOF9JCYJrlCaSWFQLfv52z9VS1LGl5f58tkhiISyN0kZ+76t7Qgjse81n+4KP9wuGaN45IpxOfLhpebE+MSaDVQMUDtc1oLTsYSkx5biTO/jQ9zgrprNfvYhUrkD71+z3e5puIIWB+/iMQEHyPafDHoCqXpHCKKUfM/f4cq4QTQ0D+p0g5Z0r5+Jaa/GcsjJmPohWz7npQETjvB+ZvkT0LqWUFrNUwyz3oIWRWlQFoZWOxplDihJOS4gM/YDO4IY0VPicHP7y65/Le0gbg8nQ0UQUE+JlyIJFYx7glIW4JBONUvRHIZs1WuXWaZjbsaZ6BTlAyUjLDFpktOMM0J/DgnSODeTB5UNH3paJwAmEEXkOTtQcmagnAUgONSbEWv/iCE8ts2d136nveZ7lTGZPk8rl0y4TCYKFJCoS6j0joktwc7FgCAOPjZSkvE9UhSOGhnKxQlvLq8c7NvWKl3dvWSXLYlkSXeQ4dpzaltuLa7qhZYzAOODLBS9WaxbW0g0DSj/RuEkTWiDRuQ6RpBAxoAwbdoOI/hz7lEmfUi764HJFXch4T6WIQycttaXVtINHaUvbt1wvK9zC4aPli7sHUvI8u9yg81DHCIvCopXiOFogQMoKzNkpt6hX1OvtPH7nyalBT+p2ZDTrXOqZwmeVkQwlH0AII1KnnQSUhDxotJmfC0nKPNZoJEKXElgEdEpZ50cxIXXSuSEBjjFa+CRKi9pmnnsmm63FmGbtFfLG5ZzFD4Oo9iZJD7TW2d34JS0SAAAgAElEQVRVSOrTj1Q6ZX8cBEI2ghpNBOMxK+fGKBuFZI4mB3Aao/Usi344HGmaFpQFPM4VtH1PPyZskmy9qkpM9skyCf7+J2sO7Wv+zk8+IGqXk4RIGsAVgd5rnNV86zbxm1eP3JQdCU9VaOIo2i3PFpbCDWhjSRrGMOCHE8tFzb5xLHzDH35+ncnHBYrE0iRC0vyDT7cQH4lRtq8QZO6ZTEiMMaGt5aEz3LeWh3ALBPpRXtuliHOOoe0Yo2Y4tHzRXfHYdwwnzxfhEj0sUCi+Yl4yRs33+R1Cs+b7f9zxX/zOF6zKtexNSYTMrC6IfcoSAAX92FBoKBaOGCynY8vgE/vmxHZdYg0MQ8dmc5HfE7kdW67Cz6iWS65WBuUW+F1Pv/fcLGt6P2J1wX53YLlaQBTzweuLFUMTcKVFWxj9kPfOgFELQhZC9KNn6EWbZAwDSpX8/NUJnwrGqHm8f4NKgcWi5nQwPD68Zr25YbVcceTEOEbRMUETdMnjqSfsGqrqPbohcGz24pOjpU2+rheSjGrD4/5ASom6Fj5MSnKfRVGAcbR9T1EtZH4njXFVllBIGGC9qigXK+leCz3NUdRfExGdIheLgqoUuwpSZIiJwkRAuGyjSrQp4IzF+IBGMv+LVYFzULiKFLsnB7siRiWSFErah7XW7FrLP3615OPDksIkaVJ4ckZE4PNjxfurHUvrKQtNxEin5SBnp9ETDxO0CvQeXLEQlNtHFmpHdxro2gFlFB9c1VyuL/iDT66IjdxHGD2LeoHSEoRLg0XM1Y75J8zUkEicaR5Th6gIOp4Rjin5T+fjjFxMmMXfhC84VVKmakI+k2NAJ31Wos9v9j5mrzWdTxz5zH5S7EYkHqY9yxiLtv+CQYtSirKq5IVWavJaa4a+o2na/NCltyfODMc0v1cpeybnyIkylcqAp9WUJzeYA6Lw5GyaP1edX/ME68pZljBQpjLE04hveoNMyElM50uIzhS65O/QTCiPZNlzkDQHOGf1j+m1slGcW6Vnwi7MrdbeB8YUMd7zdz77Or91fc83Lno0lqulItUFz69v6boebQrKUDGEEactl9sNQ9/x/PqSu+5IiSUOEUxisax4fXqLCoZLZbm8eYb3PURRra3KKiMD2dAqZN2PJF0pIQdzPiZ8SpyGgM2KxE4DUZQZFYgDq4+5ZCTiW0qprJqraXtPij3LasmrfUvfD1wsLO9dLUn5eZ76QO0s72+XhJh4aE58sn+GT47kB4G084ReXT0HMvE1jBIsPp0fTwLPacwUwr8asw3FPFeitGKGEFApoJUhTmJtwc/wJinhvWQw2khgoLSmyPVWH3xutXTYXPYBaZeUFnC5H1eUcquZoanI2ZWRDWIYerRShLHPCtN2znpCrhfL22POhDTNqZ15XSF4QgiZ0zHjkHP5b0IPrbGZN6BYr1fSthw8o1eU1RKtlCi6GisaG0HNUuZJ6SwnLkHauvJ860bx8uDBVpRqZNcn9r7kojT8+99+YOgTPjrp+ogG4wtKX+J1olqDNYa39w8cmiPGKJ5d32L1QLMbWFYLNosN//Vv9vzt79ZYJZpBSoGPisImivUFiogfBoyzuSPwrF3jXIHR0s3SdCOKxK9vd7y3VZRW82evl7zuRhbO0KUtz7YlD2lFKLfUSca8sy/QNzd8c3Xg+3+eOIWewmmckdNgvVhj1JqkDUPTYG2PwqKSoq62xDhChOgG3nt2Q9P09GPHy1dvuFgv2V7cMgwtZVWzezhxsyjZbBfYwvLqixOb5cC6KrgzI26x5nJ7zenxDmdHxsHT9CNOG/bHkaJ9pCwrVtsFrqhQyfKwO9F1Az70RAxt22O0pm0O1IsVX7z6C4xbcjy+BuNZrQrQBYrId77zTX784485Ng27hxNN23F9+0Lk8l0hsgVlRUjw8ef3lGXJOHqqqkIhOk3GiLWACoFlVTJZYkjgLmVB8XuylIWo1Vpn6f1I6AP1oiLFQDeMPL58JBGp65qmH9EqcXV7w/39I66wdF1HdJqoctnCJpoQ8GPDpqhxRvPmMHJdekJKLCrL5yfhzNl1xTDu2G6Wc0IOiZA8hROBx7o0fPpo+INPL9EKETN8J2k+nzRXtWdbRe6PUuYI0bDrF7JmY2JIiUWh+dr6DVZ1hKFBM2DdgpA0a2eprOOLoWfoAz96U+FOG+4bTfIdu/2O1foil3Cywnt+tjxJSlM2LxSdlemcTHkr0rmclJ6IvgrxNzTSpSYUAYNVYi3is29TiGdBzXMyRUaVo5SR83MRUc4KbQ3t8YDR7kllJeX4Qfgvo/egyGii41ddf3nLc/7imCO0cew5HY9oW6BilMMu5ZtUSoKHXwg4phY4PcPu+YSfuS6kd8+haQK8MymeYv/qF145X+eWazMP1ATpTzoqKgYSao46p0E7//ZzEDINwC+Zo9OLc9kof8iXPnDO/PPEmZCX6D0vmzXP64ahP9C0HRfbC6zRVGWFHzxGWyE5e2j7Bpsz8hLDaDxVIZ02oxqpXMEQRtAlQxhYlbIRTM8x4ufabgImGZ8QpUwUcpAVo6g67rM5n886EBeFYVnmNvVIdnz2IppVO2qneTiJyy7KcHc8QfQUNnGzWbDvBhKKhTOsSsumdjw0HYP3vG0rTr4mZI8fbUtSSqyunkkGHeWAngjb03OfB4aUWzAnopfKfhgT+pXOLcl5XvsxUJQaFfw5w0rTnIhYk6XPc3v5MAyMPmRirMPYbE0xcWC0BOfazJz9zLUwKGNmWHSCQUVd184k6EQmjGco1lnxB5r+/2zsGINsWH6UOn1K83dPWjyJcwCXkM2EqDIqM3JqOkBRlY62aVBFxalpKYpCTCFtnWFsIclNgVZM8NvP91gHv331muTfYKLi+ivXDLFDq8T+KARG5Ud0UCgfsWVJ0oqh7WiaBmcdm4uNZGXW4n0k+CSttEqjwsjf+8xhZ4Gs6TdNAZ20QBvrxN1aP12vOaO1Guksk23ue288nx4SdQGvH3ZUywsCiqIqUDo7fCvpylIKUugwx8/40T3shw+wRsbZOEvtaqIv2Dc7rDHUZYHyDp96TCGGpmiHUh7lHVYHysJTVFua9h4fPXeP9zjjWNU1Y1WyLKEqV5hyxde/ekHfDuAqvvkb30ApCWKvnr9P8IHldst7VlDu4/4RGNmsLmiajqEJQMSamsIlgm9QxIwwOtAFx6bng2eXnE4nig04V1IvLVHBoUvc7Q+o2nBZXvL4uCelxN2b12wvL/EhcnN1laUkBjZraVF3RWLw4tTsnGX0Zy8tQZZ9PgxFJiBEnztNfS7ZyxpXJKpKRDGLssBYw2JBXgcJPRQYA8dTw+3NlsPxRL2oabuGwsQcVAofbNf2FKWBPnDhIhcXFww+0g2ei8pSVyVFWebD079zCEloGikKK91hwfzCGfXLrsoGujHx0BS87q/pgiVh5n4YozXfWH5KGnd0PrHzNxTOcWkHbhbw0/0aqz1KR177LV5V+B30zYlh6OmalvpqSZ8TsujPulR6QvinA3Xac3QOZFC5izFXH1JuNkES2EjCKjGhFVsHef8w9tmFO+bS+HS2nUGK6eGkXMWYEOZxGHBIYHm5vcLHKK7fWbhUzWeMnJk2B7y/6vpni8vFSNd1rFYrxqGnbbyQ6/QUBU11f/XU3vCd2pmo6eYMKArr/Cy5z7l+OJ0/v7rT6TyhnkB4kAXKJgQlSRR0zm6zSujEfWHS71Dv8mW+FI2cW69Unrzpnd+VnvxhMsubOjLm78y3IKWrBEm+b/ocqwL3jeL/7a+41Hd8tC3wdDx0gaEZWdqKzeUNze6ROAbW6wXtOND2Itb1o8MrVq5ivdjgclnHGEPw0s4ofkWBopAxmKAtZbXUgXJnVdYSY2o501oxjJF+ENNBrSVI2XUjy9LgY8IaRdv4WYG194HRe5alY/SB1kcuagfJUpaWQ5/dhIG6EJfj0ScOXc/b7hkvm2u0Cox9g7EFPgRctaBeXmR+hs86EOdg8BchzDMDX4KYxORYLcKGki1ICceCdWKhEAJaZ68iBWfhwgTJE3yg7VRum7aZjCbclTET0CZNHxJnlXomzlMkJiU8FysZTvADk+EnOetBZQHECfUahUekEQ0LeZ8Xq4JR6sohxLM8dkxzaW96BikjaSqXJbVS7HYHjC3QCtq2B6XFJNKWjD6iTMlU+lMoFlXBoi4Yo+Lb24ZvbD1BlWxvCk4nT2wjPlT45iS+IU0LWuNcia1qtAsc2w5/PObxr6k3K5QreLbcoqym7Yas+ptwKvE//AD+/M6wLqaVds5qQkz4ccgdCKKrooBh9IQQsS43D2g9t5lrbVitFjRD4NRCuaww1hCSEr+uUfQ7tBbOhQJeHxKfP75PTIpFZWb0uLtPJBe5uClZb0oK64hhJKoRqxLKgB999jGyJNWBUlRVBcpyuV1xaoacDELTDVS149R4Tu09X/lwzWHXsrx8ji1K+tMeZRzaljT7O1xZoGPg7s1blusNSklLae8jaEt76jg1B4wuQGsWiwV97ghr25a6rhiGgc3FLRHHzULTHfdsbwuSUpSnyBep573Nltefn1DAxfaSx4cHdg8PrDYb7h93WCvlxdVykWkBIo9RVyVt02KNZd/3skY3K1IQ2f2irDieThSFY+g72V2T6G0lNMvlAmst+/1etlOtKIuSYRSHcms1pRMxtabtcNZwGry4x5eW3nsw8Gp3oC5E9G21rCgKx9u959SdWFUlwcNCF2IV4TsWiyKvI/KcyagpiTEofu26Y98X/JM3tcjx/5IMNgFvG8Prww1jKub1WJmek69ZOM+L6jWfPwwo9x6BgtuV5/PjkmXV0/p7fv440vgVhVtjVMySIKCtZTgc6Y874tUtWklp0o/icO20IL4x8zNnJ3nOVYKzlcu7h1lEwAfZoxzKaIxz6BjFriTvLXK8ymeEFJlMYJ8q1E/BUoqRMQp5d+hk/x/GHpTBOSsJHTGXC6XlWyk1z6Vfdf2zgxbZ9nh8fBSXS+TgLUor2icT5CR3CPMmfd5glJK6ZPQRhWRB1poMU02cijjzU94JYJ6gMPPTJ5dyvsQq0hMMll8x565J9Fq00hCFtyER5nR/TwKn+f4RfZL8FVNkOZUZUhAG/2T0Nd3yXBbKUaZkxxPqI79TPIoSpIgfJSBoVclpuOGL1xofFX/zoweKaqTtRmyzE14FEFuBWS+eL0Alip0cRMfDI2NMBAUmJp7VW1LUPIaeS2exLquoDsJBijHlTnGpNUYlGbUGljmgmFycfZTumIdmoI+RL/YdX71asms9g4+MMbIsRDxM9AdGfJBab2ENzegJveeiLui9bFoPTUfvB161z3gYPkQRUWmUjFmZWatksbkWNCFOQW6mOqs8B/RTUnae7Fn6XmLEHCzGmFElWUA+BHyGpWM46wOYJ2KIUaCoLHkvgkqa3D6YS55K69xGrrDOMWkrTPNe5X9rPcnyS6bic7vjjEhmhEjluTnzohB00hlF9HIgj97L5pERHRtVRmPIDqpndE/sAAxFWWCtxRhNP4w8e3bDfn9g8CnPbxG4K7J1gDFafK+U5mpTURSav/XhPR9eBuJguL9LoHravcL3LdvLS0zv2biFOA0vVsQYGYaBpmnRzrBYLaBycp8p4ruOcnOFWOUEysUCMfUzhOaO/+jXe/j1kf/u+xVfnCDEjJAi5Urrijn5Mbn+XTuLVdIarmxJYTVJGYYxYqMXYnBh5uDQZ8LyJKKptZUaf0ZzbErYrAtjjMD8/843OuKp5eb5C9r9gap2og+TICDZcewTWhUoW5EYGVOHjpbCLtHW8Xj/c4zTFEXBbt9gbY2zhUDtwfFPf/ATbq62NJ9+LPNJKRaLWnRerCEGz/2bA5uLNeMYOXUjVxdrtEp4HxhODc3xQFkt6MfAw/0bYhi5vP0KwUfu7t7y/osr3tzd0TZ3LOyaxaYk9Y7VRUVhGi6WmofTwNA53j60pF5zcXlNVRVS4h46UjQs6xJrRE4+ePHIaY492+0Fx7ZjUZeUhaNrOyLgg2b0h3kfdEVJ2zQslwtAEqau6xiGA8ZaQvAcd0euri4IPhACLBZLgh9FAzp6XFFwvVzw5m7P3f6Bi0phKs31qqTpOo5d4rP7I9F73rtcYBT0o5T69qcDVxcLyrKczws1nzPkRgU5lENU/OsfHTE68d2XCwr7y3GX2ngSPY++yGRbGGPJv/niE7SGpve8cV/nq9X3+fiu5i+aG9CK7zeOOF7KWeEbfLKYqsIaUYlNlGy2F3SnHTFKA8wwejmDlLTOq7xGROPs3XOJLE2BSvMZOv0DKus12ZkK0o8DfpySvayJlWkgU5v1GX6YRGyz/MdUIlITwpOoqoq+94AYLFp75u2FIKip1WKBMyzLX/ps4Z9HXC4InAdykI+jQOllWdI2QsOfDo305CfMJYkM/0wBiTY5CNAabaQjo+t9RisAfe7Bma8pPomcSbCcVfvmgIMnnR/p3SYvQXqkO0YzqTO+C7DM8VZKOWCR/yv8A3n91K0kXJ0plsqiZucQbn7dfINpZs1k+E5+5egTSvs5PXdKSka/eelpg2F5WTK0Ed9H6qIkGcU49NgCyrJiHAcqZdjUJX30tMcT0gGy5s3bHapeSl2dKCUEnzUgrJmpRFOrucCqcvkogUzvA4UVwSNrNdeLkrfHfs4grNb4kKgKLQ7NZORJacYQKXP7YgKcUfQ+chotb7sXPPRrNGdzQ6VkhacQqDeXGFfMqrbngHQaly+lOUq0EqqqYr0oeTh0kLLDad6FlMroBYoUA8H7bI1u57GdiNNaa1SSVkitNaVzQmaeshQ1IS8aW4hD8ITyyVdm5WEl0vshidx6jMIbEQVfn2X45SCfMb1MQDbWoJVi9NKJorWmqiqOp1a+33sm8m2anksii0KruSaNEv2SNEQhvrZ3HE/i1UJST7qcpD3dGhGNWy9LXFnxH377NddV5NQq/NjgNARdokLPxeaCul7RHvcMxmCAQ9MyDiPLVU29WWRRP59tNCT4V05D6EnKZC8ynzk6UkZtg2SF//nvKn7yMLDvPSEafvKg+fgoWdo5SDyX3LSxFKuSb19Hfue6oR88nx0r/vALebYqJ1g+k5Und2mVkxJJEOSzfZDsNiUJmq4WiRfVW1bFlrevXxG7QP3+C5mJRqFTJEU1ozuEiNIObSwhDAyjwsSCFze37A47fDfgdGQYGk7NQG0shYUPP7rhZz99RV0UXF9u0EVBiIn7xx2Xm7UIOdY1w+hphxE/jpyORzHAbI5cbDcUZUk3esZhx2pZsllf88nnb9BEvvHRC56/eM4//eGPKRcrjqeWZ9tbXKEYPVnXyHKzkXX2sF/x8vPHXHKN+BApreWUncO11dRlSeH+f9Le5Nm2LTvv+s1qFbs41b3vVu/lq7KSScnOlFImBSYSK4SgYxQmgiCCDiE6tGkBLdp0iHAQtOAfoAEENIwJmzARtowkI0tIJpVyKt/LV79bnWJXq5oFjTHn2vveVDodZke8d+89956z115rzjHH+Mb3fUNTuYrJT4QYqZ0MwNxtd5xdnGfpfZx5R9M4EEOgykMZARZtzXK5BKXY3t3StDUXF+dsdj3LpkIbOZBjSuwPB5SSwzB4aGrH7XZiqxUra2nqFmsTMRkuF4raVlhbEb2nrSwXS0dVSfHgQ5mmzrGYkMBDjOCcnjsD718N/Onzdk4IXn9d9w2JVibSJ3DGM3jDj24v+Nr9wKH/jF0f+HB8TG+WxATDfpNjqsYai3XSHrfZC2saZX8Iv86SQiBkpVDxRUmyoElJinilJWrq0wIJsNrmgjoTYBXEIMMhZT5QtugYJPb6cGybzQhyOlo+lOJMCi0Z1WNzUqqUYpw8dV3hrKMPA95L0TdNfuYnKqXxU2BKU6Zm/P9RD80zAGSzFwJgdzhgbJ4kq3KiMeNlaT5T5rM/QVQKlfkU5AMPFJUVTTgzApDmQ/3Vizn5bWkFzAjLyXuXG8wxwTma1xW7/fxPs59MmeZbJm0WVKUkICEUqfNRwopSlBlKSh+vQQhXxeJekpGCwJRDWCZBR1GLZJv1ILIRfnJreWfVsFoFht6zbivMYsFu1+HzVNdka2IYxLBt0eJ0hRsnlsualCLD9o6H9RlDHFEhcwwsJJtQQe5fiCEruuSQ81EQkgIDWmMwQe5J7wMrK2Z9d52ntjonO5HOJ1aNpbHCkt/10rtuK8OitvRjyItd48PAn2++AUmj8XMlST48YkpYbVmeXb0WPU5fstZO+UHl1daW3e6WlMTHIUYwSjx3jsid2HgHX3hI5a0SFI5T7v+mEEgxMcyJVWkfmVf6umW0A1G8TGShRIwVNQPBZ48W8nDNLM3XpV7J7qF5E5s5sI+ZQyOutU1Tsdnc4n02aIwnCYssxMzj4ZU9NGZnWR0izzrx4SkLOiF7USnmYLNoG5q64rZPXJnAdudg8LizmjE40tDw8vbHTGPD3c0dF6sFVVXRdT1KK67un2e0LvfAjUZpQ5wC2mbcy3ckbE4+NGkaSOOEUlZmWCmBu79+IWMDpq7nL18Yuuj5W3/U0hrpiZOrRTGoS/z6Wwf+yr2OKTWYZcsvPDKcLQ2//5mY0N3uRyFKp5Q/tyRuxgiq5WMZCpfb2QqmqHjzzHNeaSxrxu6WpCMxJ8FxSiST1YRKoY0D71FNg441Poqix4eOZrmiXbRMQ4fXE4fdxPXdAZUcrq4Yh8C7736FcYJpPPDi82fcv7qgbRcoIvtDjzVOPHesZRpH+mFkuVxwcXnJhOairen3HY8en9P3gaGfuH+x4OLyCuMc/eDBD0zDgeV5i6tEwXY4HFgsK7SBWhuqynC2epvfjxPPn+1Zuwu2m1vU2YJ2IeaXxzakoRsHQTO9WOaX/XV9fSNEWyOTo4s7c1U5tBHlV9/3bDY91tUYrVmsBFHZ7nYMo2e323Pv6hLvByonXL7NvkdbR9+P3Nzd4cNIheXibM3+0AlHbkos2pYBlw3lLIdJsYwJmxIKQzeI03dRyZRfjVH4QicANoOhtol/5+u3/J0fXfBa+AHI/i5pTnKGSWKGMYbd4YZ9NxIjdJwDgW6/IWFwNs+eygVPidHFu4zIkfOmZJ2W9yGj96VVoE3218kiiyN0q2YPNXLyknLmY6zNClLxZJJCTMi3x4JMeDBJHccllxg+t8iRVmvKZ2HwkWkKM00kTjKmRkKqzp9ZYpHGzonQz3r984m482/Kxaj5w5cDWAITxOPlv1YEn3w9Jy7A0dhr8mhFVqCIDrYoL06huvLWM46TjtW3TCkuENVR2iyXnomlIeTFqOc+nNZaoJ9XMMH8s8vPE7E52uSKNuUJl4j0S2YuHS8LOLaNUqmg87Vm919hdkeUkfdOZN8Oo0BpfEz8Hx+v+WtPNiz7A//nfsXnHfwbb9Z8dRXoUYy+J8RJTPl8ZIgdjXVopViu7zENB4axxwWHyvJZlWd/YCMT0zGLz0zukrZ3o2TWt/ueAKgJGmdorGHbj0zeoxAlkVGapAOjj1RGM/nIWeukpZFk5o81kV3v6f3ER7u35OCO0/GwVcLxKAqzy8dvSwVepojy2iJOQG7LHJeWJAub/QiqJkWBIFXu83LiLks6NYk7rtOyvEpSLTmMnjekyZtREpRAmf8DSUzIUCijZ/TDWkuYxOugVFHxlcCR90SKMhYhB5yUkAQ2J5cz4qcSN9cvMaYmpSlfazz5BKrczvnzeO/F96CgpQm0rVHIIV9VMkQxZTVHzAiPc4abLvFb3+jZ92dMYWQ7Bm7+5Lm0TrTmydWK5WolFbj3xBhZnS9JfpRqyRiICttIIEreo13eb9aBMSQvFWTKIzvyopLn4z2h7zgkSe5jMig0ldb8x9+a+NGtZgjw/zyH3icalxg93F+1uKrOi0TT7we+1Qx85zutXKtaQAx0XvHFNvJ7X2hue+g9/PW3n/N4PbGs4G//4IIh1lin+d6bE1+78nzyqebRwwWV6Vg9uc+432OMJLBaW3kaxmb0xaFIVHZN3VwSph5CYug3jFNH1a5xSnN1r2V9tubjT1+y6xM+aqpu4vx8BdWS+0pUYwrFly9uOF8taZqaaZhIKXC2XjF0B5qzC9plTeh6YvC4sxW1MyzMBKuWu34tHK7Rc3d9zbJdUrctwffUdYVOZ7QL4RWlmLIZoeHCGr7zS2/xO7/3IZvDl1SLmvXqAq0i/TAQovB1EonlYsGLly85X6/Z7vcslwvhR+S1FhOZ2wPDOKASVNqCVtSuYjdFDv2BGBPT0LNerxhD5OL8jO1uzweffsY7Tx7z7OUdrhIV4PXdljhF+v6ab3/9PveuztjsR67OanxIdH3AT158R1B8/vQaYw3bveadh+eEFOiHCaWEq3nXCZF2VWvOF5ZSt4cEhIHKahYLTYgKZ06sO0p4euUsCChlsBp2o0bFNXfpEpUmur0oBqt6IeMHYE7y545C5syFGGYH3apZSHvMupkXV6Y3l8ps9B4N2Tlb5xlH4fhvgThNhJCdjI2m73OsytwPpRUxJy+vtKz1ESKYZdZ5/IRCSeGXo3YRv4QQ6HOMdHkAsVKKxaJhf+izC7qQ4U/b7H/R6+dPeT6tdssPU2QjLTkIGmcZ/QlRNT+0U/+T09aR5kSWlU+HgudkqszxMDrNJ06ubZ7rUqD/o7HLfN2lNZVSpKocBdqfvT40MyIym+/I/IDZT6YMcpqZ6+WBxFdzueMHL/cmHjPGUo2g8uGWchuMnH8VozIgDyecEvzw7oy/7G54VPc8G1b88Qt47wxUHjiTUIxhwqBQxmKcYehGpqnHT14g9AjRKA6HHZeX50x+FO8OLa2P0lpICemJB6iMZjeKlFkOfvm1cYZ+Cvl55Gw6JRpXAQlrZBF7H0lJCHEahU+J7TBijWPwNbMXijJzho2CMAXqxQrXLIkxiCNxPCaQ8ypQ6pW1UO59iomk45wICtKSN/NcqmSSbTMSg+IAACAASURBVFlYeZPEvG50GS6WRMJdvjPFSNS5baTyMD+Vh0nOiEZEPGZSTo6z702UCl8bk9djemXhxBDzupSFlVLZayfEOW3Y7/f4SdAxUZNMczCBjD6e3Bg136+TP6vcuzZiLGetnQuGlCJVXTGOE+MQIGn23cg/++xDalcBFY8u1jKk87BnsbjKiZ2iqStRzegI9eIYN5KeE0WMmJ7JljCCSJW5TeSWa0okIz4r0t2LYCzJB5S1qCgo2FvnmnfP5dn+yoPAP36q+YOnmjfPLffPHEF5suENyhhsLWjONHq0CqjFgvNFzb1LxdcedLw8BO6GwPvryBAjUUf+vW8OtGf3iN5j7JLb2z2LBUyHXpCwvkM3DWkSsrapxZtHn6wtigRfG1xzRhwHXEoYu8QaS0CUmCsTWa0b7jYd4zQQtSPe7rk4XzMGiAQqk3B1zbPrOx69YTCuQnUjOoiVe1XX+GHAj6MYgJHQUZJEP3Z0Xtbu3d0eozyLuiIcdjJaAoVPW1ARtCANpwXhw/trLi4X7LZP8Wri0y8/prI1j954SNcfMFr8OG43G1EYjRNn6zXW2RnNdtYxTNO8Hq11WGvZdz0xRFarJTH2BJ8ygrzkcOhYLle8fHnN5cU559NSllVKjN5LWyklDn3PX/3FR7Ttkq6fRHKdY6w3GpU0zWLJ9e2GN+5fMI2eB5cVi2zbv240MQiyYozhzz+9YV3VKOW4OchsnZAS560MTPzxyxo9jwP5qWB0LGLzrzEl+lAzJcfoxywqkGnHwZeYqlCpjM2Rs62cYToj8UopXNMQxwGt6ywdjq+8LQmOJZncAxEiSKBNJRaWgimBihGtZMRB8ML3Cz4nOSdn6ekHPpIiyEX68bBOJ/+qTIVmRqSzKEZr9ocD4xTyQFD5t6F0Y37G65/fHiqBcwZZ5mwEkJ65VYbRZ36HOpEPv5aklO+fDWyiLwAayhiZPXCCjvAXXfYx/s4VaEl+KGhB/pnayLUVKC1keZVcVLk5RQJ9JNPOSAshV5BRHALnx1DIoMcDYv64c6yKOQnIPJdikJeECZ5SAGWEF5Vk4FdMikDCRIgqYBV8uXX89bdq7jYdIa7YjpqPt46vXiT6qKmqyKGXadU+Qb/fs9IOP2YlBgqvA2oQO/ftbovSUknLBkl5c6Ts04KQTzOvqLZHj70H64ZlbfnibqRxhi/3a4wKdL7l4Gsam5gC+Aj32y0X9S27wWGN597C0DjHutYs9hObQSysSzUBiHeK72nXF/MmNq6CSfrjx/2f/5+gzBB6fY0Wt9xjYpIrmCDzowqSJoZrx/aKSokQJ5nanCTQaLICKa+T4thMbkekvA9kBlPxLEhEPxIRN1rQmXgYs+z6CP+WtmUsqFJB5hKU4WRFbnxxdsV2t+fQDbISC6m67IefCp6vfU2Rrbo1dWUYx5EQhNPkrKGpKzEGNIbKaoiGD249f/Mb32R/t+P2dkPXdSQS6/USW+qpTHKXWWJK9PkF9TF5cnGQtQZpTljS5I+cpRhIwygFQ7PM9zoQOy+FhdKSUOZnMU6JAYWyjgf3Hb/1BnzzYeDxmWFRwRisEHSTIjkHReGnDWkUFco4BXa3O3QauNdYHi0t/fgQk3aEcUfUSybvGUePS444HmjrFuVHXFUTo0eZEbtckQbPFAPW6Yy0qHzdoNAkP5GUR1lDrZekkBi6HR/c1nzZW967l7isLZxZYnDsdntSqvjiacf9e/e4vb0jtq3wRpqaFy9f8M57X2Pc3TL5jAKHSdCX6DP6AzF6IX4rzdl6xScff8rtzQvhvawblFmw2WwJcQQdmDdS1EQlLfu8hfj2t56QQsf13cBtOBC15s8/+hEP7z3i7u4WZ51MUw+BkCJOV4A4fGujGQ4dSsmQSiUXx+31NYnEaiVIys3tNUpruqFjvVhR1xWbu404dktJDwnquuLpzS0P713xo48+IqQ9bfuAcZqwVvh0CRinhFYR7JIXmx1aia+N9yNffbLK5xZzTAkJnIG3HyxYLVqebyf23ZSN3zzOrvly17IdNK2N9OFoEnm6z8p5MKcOSmHwRC/oxjhONE2TfVZyG9tIoptSIKEpxPSUCx8/jnJ9Vc3t7TWuWZ6cWQrU8YwrDM85NqSUj7l00oXQcxGVEFS25NrFoPL4Op6tgrgUTmkW6Kh5ix0Bh1zQFbWlsD8UtrKzsVxKkrwmBFWyzh1Rip/x+rk+LU0lBlEy0h458CTuU1cVwzSijZ5lxdpoOWRUaX0c6bAqnagrtJWKJGV78dIqiVFIoyVxSWRnvXSyDkRZrvMDExe/kpnmuBkhFeWOKizprMyIkfnHJwTqR5HMkbAkFbdUfEpB0gqb+ScCtx3Rl2Ole3JoFolqqSLzoSdojcCQKZPSSp5sjcGkiSHI37ULy//4xUN+6WzLf/jmp/ztL+/xj75cYJPnreVEtAq7WnC7OwCJPkiADzbhp4nVYoHDgdL0hx26qTHOEtOUuSsx838VjZXPfpg8S6c5q+F3P/8qY1DEpNDPJGEISfO1iy+ZgmPjF1jj2fSauxCkqtKG/a7hw81DtI7EpGm3HX/tree82A+8e/aUP3r29rFKyfdLG42pFhhXix19Jj67SlQiR8Qk5Y13giSUDZEHg8kzzoRshEimYsiJwhG6VCnOUj/hHglJbU4cQNoSeYOWwZiUNZjbQzNemCTAawUx2+XPijNyYpIgxnH+7HMlpfX8+1iKg+wrE4n0Xcduu81XrudddUzej2o3+XORUcu6NNZm+SdMkxe1lzYQZI0/OTM8PSiWrXBwDpPiN9/s+FcWHR/88HMury5542qNVjrzP5T4IOW2m08eOkl43LqVNYP8R5fHQ/iAqx2x74gEdLuAmEgqoqoWU6/kOXS9VKK5Gju6TAM+SJETPKaq8H2Pdo5JO56cQVUtCGFAp4BSFcpWxDAAkiyCIlonyaI/0NSWRCZJhxFPTVIDVXXOs8/v+L9uEr/xtsKGW/7wow3fvL/k8qol9gf2txvW52fEcSfjTJwm7Q454ayE3GAtySiJc8qStPi3aKsYdM3/9GGi1ok/eAa//u4l375f8+L2QJwqrHFM3nN783Ie5vfRp9f0w4hVsLu9oVlfgDE4Zfj0489ZLippGU8D+95zfnnO+dVDhmFid7ejP+z4znd/CWcabl4+xXeBJ2/fJ6kRnWpsWODVgRQUpEiwHnRAG8V62fCN9x7z7OlLnraG7banqhUv9x9j7ZKGJevmjOA9RsF+t8lDdBOuskzDIHYK/V7an4q5zXpz8xJQNIuG282GfuxwtSX2gco1DMOBL58NoBKbQ8eqqbAKtrsObSJvPnjCR19uOF83rBolnMMI1ji+uD7Q1oHWiCChqR39oESOqyWpGoPmejswTGLU+ODMse1HhsmzHyacgqtVTddN1PGamCb68N5PJyzzASc+XNpajJJ43o1ephnnfSFJkKX3PSDeQ1pLi0UjPMoyxqTQHrTSLFY1z36yp2922GYhMUErQXmzIWVJKkphWKgIpciwRjGNIxgreyK715cPEzPyUhKVo2JXENF5iHLxdyCLbI4fX+JsOnHoBmKYJDnK12PyLCOQeVAh+3H9bBruv0B7KBGxeTKlMWY+hH2QPqDIeKWiEYtwSNi8IAXKLYMDX3+4xQa9VNYpf02TISROLIVPoPyU0+Ni8E86PiZ5KHP6PCNA5RWjRykxCUqK44HF66Z4av5aQrgbRepaDMvyl6Tilyck35fhupDlsehMNnwFYjsmOSUoex+Y/IiuRP7XdQPtcs0f3Fkwlvu2YwwNf/JS5KtXC4BJEJrMHLPGkKInpID3ntrV+MkLT8E6ru9u8QQe3zvPNweUztbQSWzIn+4rbrqWKcjQsGKplBI4Hfnx3SN5FkQ0EzHIbCXjqjyjCnEjRbxodpN8nqtlmw/snJZnxENQF8Pq4nJedyZziLyXXq6wg0sbJSe18yBKuf8RKRSV4iijL54rAErPBnPH+R9yD8omj3m4YXmPMuxQkqssndbClSjQ6TymIYnPQUoxIyxqRmsKH6qYNZ0GFkFmJlT27UiJGd1JUAgxWOdmUlw6Jb2/gqgoXtloBRHMXiNMt5jVA/yw5/k20LYVb181pP0LaneffvTsp8Rvf33kgTlws+159OghdS2+Jt4PpBSIZL8aFYhTj6kbUkyYypIm8Tsx1uH3nXy+PM5DEnoHZHWBNSjlMFETp5GoEmkY5e+clecYkfGSYyRpOZD6zZ7VpZP7rRTEgHM1KQgRmJCI4wHd6LnmLP47SglHTdsFSltSGoXz1HdUzuHjEqtaHl7B8rrnj561XN5+TB/PmQ4b4oVDuQq3kiQrhoCuBXfC1JANAFVMxCGgFi1KW8HsSRBG/AgPVpYYxVMkJfidTyzvrSxWKza7DRo4X68JTpyTN3d3LNqKYX9HMjDFgWWzJgVFP00M40TbVIzjgDOK9aph6Ec++uBj7r9xn5ubG1IK1O2SGAZcVXPZNuAO1PEiq7gAL2aOGjvH36QSRMWDB5e8vL7l8dWCs2VFN3jQ0trdH17S7wf85FkuVmituX//iv1uP69J62rq2s1ii+VyQXfYZ4O5xIeffMy278AOvPjijq+99Q7bwx33L654fnMNQB08t9sBn2B3d8OiaghBcbcbQCmMbghJ048RoyLLNsff4LG24np7ICTYdoFFbbDa0PeebgyidDGJpqpR2rCoc/zTjt3oqYxl1w2s6qzSUfncOT07cpICwnWTIkIKE1fXMhSyriQhiRFnLX4K8yFfOB0pSet0nq+nFcHLfnBNS7+7Y1k18j45PhujZp7K3JZVBYku15kYpzSjJDH7mcWcNJXURWJgTpYKFzOl+dyaUaQShZTCKkGXi0ghxKLEE15dVLlF7kOe+xbEi0rpMjUJrUVd+bNeP1c9NPpEhadpa0LwVHWDVprNdifVlLZzAE4pJxIqB0vIvaysDohRjOBKEpP/rbXlMopvi0T3AimVLlyZXVASiWNlmZifGpLZl+B0msjElIghz0F4pQeXP0E6qofKkygPpLRPdDnglChLFMxSudlQrmRgMMtpj7yKghIIcVR8QY6HjDLSp1RJZoUszUQ/aT7szvj+5WdsX3Z83i3Yfh75d9+9Y0yatqq42XYyzVhpdJhwToaqDSNZMy9tKZu9OFBglSbAzMsgBn7w/Iyf3C6IaJyOryVymYhMlqgniOgMw8PQyyRilQ918v1XqszFgTpL7ObVnv+wvnyDyadXk9OUN26KeW1ASXbKZlKkGaWY+TlRyLLFKjrGMKNaShbIyUGWTpIEeUZima+Olzivv5x45mBQ+rMpy4ZtVg1NY5g/WmY05FiRcuJLHh5W1mtGVGKSSctaE4P4g5SKp7DxX30gCeFivaqiUif3t1y31rAfPMSGr+unfN6PvH11xTZUsH/BzaDwaeK8gl95qHlgOra9Z7FYsGhqMKKIq1dL4aFoRfIeYsA0Ejh17YSLAhjXQD9BFJPClINTpYQwYVcLZrgzRvy4n1HUGKWdkIjEOIhEPFmwVvgllWNxeU6yGjUGUj9iKgsYKfeQNU8IJD+gTJXjSUlaNCRRt6QonyH0PdpZ/P4lalGj3ZJus+XXLvf8o13L9Pwa//b7JHNN6AOmMmL8FoLIRNtWJOiTPybjua0V+wO6yqoIpYWfQ8W+hzdX8MUeags3PfzhU8e/9kBz/+IMW4mSqHUy8kDlItD7gb/0tfd5+sUGTY0xlpA869WCfoo8fvJYDrj9LUOIPHjwmNEb4jSwWq3xfks/vkS32aU5GqZhEEWbNmhlSCaACqBDrtZl3RmjefPJG3z80Ze0lSQDb1WWL28O1HXDl09fYDXc7K5RLJj8yHKxJAwRrS3LtmWz3aKUxrmKTz75lIvzc6xxPH/xgpv+FmMNxhoerR23+xt00ry8U/goXkVVDGxvNyir8Wni4eUli7rlZnNAR5EvbztPbSI+Qu00lTXsR83kR1ZtTYiwPXgxxGzyoEQiVyvLw8sFWilBvJMC7Xi57RmGnroSl+pP9/dyMndaIOTblJjXu1aijmn1wPP9SJhGmqbFOCG/931PXdUZkQh5b5mjR1gqSL6ancyVgtXVPbYvnpOCEHK1UlTOMk6jnKcpllCZ5/nkczPHmtWypc8tmvnoVKVZXs7EOMf812PLTJEoKyMxk4UVYkZakJ1icleQZXJMKqNJQM9fT7lAiycCg9df6lWS46uvx+98PX3/P/rPUNmnRAY9Ccw9eTFYOwJK8kGV1kcOAFKpS+bH0SQsM6Tls8qv84htLzbOwjx+NYE9utSefABOKlNkaFTxWDlyY47Qls4yTEWBtxSlHQDkw+7YRtBavfJuJUGbX8e0dM6Qy9fVkV0LGZYrzHxtbWaW54zyxIXVZG8N+XNk4RLffSPw/j3F6Af+35eaP745p3aG7zzY8t7lns+uPX3fY4CztqZ1TnrHEbosd3WVY7vfYZ1hvW6LIa7wFZWn85b//YP7kAflMXM0mAmsgLDDY8gHdxkyWD6GHOimsjmTl5//G+9+gdMJHxWfbZf8ybM1baUISZQR9978KpubW2xVSWKkNZP3x2ojHzqFLFt8c+SpFA7LMdn0wc+HfGkHvoJ0zc8+QYZky4LSWkMMcliW1qI2md9yVBMJsVYqIPl9IviJEI8JbAkagjZI1RTjiZFdsd1Px+U0JzEceVEqzy2Z13T58IlcDR9J6ZLIyFrXWngcYrwnKI2xTibL+siyrVm2Fbs+8F/+6sA+VfgY2Tx7TmU0ldHY5Uq4KZUj9P2c58fei0wyRoL26LrOHiw17DuSF07HFAK2qeTvkkZZTfSeOE5y8ASP1hEdlHjU+EBSYKqKMPakOKGdQy+WKOtg8uh2RdzsuH1xw+6wZ7lacfXkAapdyb3zA2kcUZXLIyECcejAe3QrPiAxjrmYcQJT+1EmW487pmnARsfh7o5pt+NvffQmF41iNyn+068+5Xof8N2B93/5W8iB4gldj9WW/e0OCLilxlSOuJ8ychhRtUPVDRiN0jWVTvzgaeR/+cE4x44xKL5+tuNX3+i5sJHtbpAEqGrp9IILNihV88bjt8XVdxrZ3N6y2wohVuWJ8m3bME2eYRzY7Q4QB1Dw5lcvQE24cEWYpjyYMCtGomZC0DRlFAkPKmbZsKWUWD/60UccDuKtcrLtABimwDBFbjd7UlIcuol9NwIVL29uCMlhK5mQ3Y+e+xcXPH36lPOrhtoovvvNJ5ytGv78uWfX9Tx/uWO5sux3kaWtUMaiY2C9XLA77IRzGD1vPbxEGyuyZ5tQKlHXltvdQD8FGqsZY2T0GpsG7l0sGbz4zjhrud503FtXhKj44ubA2aqh6z3GgLM1+8OBwWs+6N4hpCLxnY+G+Zdp7KmqBmvA+8Q4DqQY2N5t5Ay1bt7wEmM0xgo5fuj7OcaITDpP9Q6Buq44dAPLRcv19Us2z77g8v5DbC1Fw6GXKdpFJdlUlm4Y51hTVw6tksyjSmQn7aIMSkekeEZEC8J8LCJVBiRIxR8mZ2kxZg5ejlHZXTrEjDYpaUHNxWYJInnhiCFoyQ8U7731kP/qP/kbf5BS+i6vvX4OEff4NIT8mxj9hM5qmMq5PDzpmHXx2uEQvAdl0SYjKERS0lJ9nx4iMWQujD6iEgUBKAsiZt/bnNod5aA5i0zhxFaYk5tTEg2BqUtGOZOCs3JIUJ44u5UefTjmd3llgUI5ZE6VU2lGao6H+bG/mHIyU9pfJeEr5kDGWgqcWOY9HTz8g+cNdyHwzbPAu8tbPI4Pdmt+74s1N4Pl7eULjGmZhokpJiolngmNdjitaesFt/ut+Lk4x9AHsIll5SCKJZAzpyni6fPkiD5B5jEc2yanAau8wiScg4T4pPzh0yt+9dENSiUeLgf+TC/x0dCqPYur9zhfwPautFw4egmUhTzf2uMmk8vLSYdSqJjmtVPIZzEd10AxBUypIPVlk6Tco5WNpkkoI7b+upDLU8pBXD6/wJnysspm0mVhxuv592lWx5SlkBMo0uw+G6aJ0goqra40b/S8zotXxIzi5SGCxyfFMUHP20chRUT+a6NLMixD09rKsmodQ0h8/Qr2qkE7Q9zsWC4XjMMkSJz3GFMRuw6dq7WUEn03ULfC9dFlkpUCVCI5SbaiD5i6gingh0msu7VUjfvDiDURa8EqJRwhJTNabNWQplz922ZGIwXJEIOxVFcsr87Y7XfUy0ZQGGJOnIOMqzhdmTEJxyQnglpLe8aZIFWibUmhx9olISXizqNtja09KxuYgqG18Mm45J2LwPMY6Z69pF22Yux2t2fIz067GhUyZ6hyMI1gNWkQ11wpmjx9NPziI8NFY/nv/28hQlYm8aPtmk/2Ld9c7fjGRc2HGximJT+8cfz2X6rpb57y6YcfYqyT1m6MjONA8J6qaXBWY6slm7tbhnGSeBIj6/MzqqphHHIBUGomFWYZfsKDlmF43sved1oRplyIaXj8+A1+/ONP5mWXyupTojJcVIbL1QU+JPrRs90NjFPgjct7JKV4frNnuViQUmTdKL751ls0TU1lRYL/xSbxk0+f4uPIYrEgeElCMIbKWeTCAouFpbINfb/HJw2jDMdMRoYiDlPCqIjThptDz6KRjsH9ixUpBlqnUbXlMMQ80gGGqDhbVPRj5DCOLKuKIQykOPFR9y4+GozO9+oIU8jZHWSekibiAzjV0XmJS2U8QT8M+RwqPDYZ9iljboI4ihuDSuBDeU5iWyFGkIq6WXB2dZ92scSjGIce56ocN6Qw7vuO4qaijAx+JEmst7ZYIBThQnmG6RjDCsqjSttcEHRZMgVRUMeYi6wPjSIpw3xqKkXiL0hYyksxWy7IvwmvAgOvvX5ue0im/ClCKkE5cx9sRdtUYAz94SCcBmPmql58KgzeC89lGPxMJlKqEHbk2DZapg5nndG8CU2G6xNljPcx2xOqUtkzUmmXg74kCXMmV175m1Me2qW0yoZfM1BPUUDJoSDfo/VJlklpB+UHIuav86It7/xTtzEjVLMTb4rMrTCQgzeVSzwOkhTpYRLr6NuKL/aKb196fulqxw9vWxbO8MOXK779cMPnm8TgD7h6zfV+oEo91UqClK4ddqyoKnGybKqKbhIVSls7DIZaBUJSIkrVOqsO5BpPp26GzDMphkOvKHLyc9EndyEBzw8tn+0OPGjvSMkRs6Q46iW2vYDxFqXdvEGnaaSMhii92SL/lTZQQqU8fTiJT1AqrQVt5/ctzPaSmMQg/B9JHos9PzPalmIUaa06+v0Uyd7xEx0lz3Jd8v1HsrAQxee1qQohO81JK+X9Z7lzGeioZ7m8oCw5iUak1MVRlIwolpbY66tOWkJKSICQp0+XewGVM6wXNYNP/PrbkW+/kUjVEhUnwjjQNi113eBHUceoppLPNYyE3RZtGpQSlRrWilld3jekiHKSDKsY0T7KLJ6U6PuJ2lmmCFSGaYykAKPWwifxnnbhiHEkhRHdrOl2B1zlMMmA90JlUglTG7RuuLx/xeLeFbqqc0vGZh5ZOpo5Kpj2B0ztwNWZJxWJfsCnBcqAwhOVJWpLXd/jbvMhpmpQoeb9Zs+f9hcsUuDvf9nwH7x7wDnNi2fXoh7Lwf/s/IzKOUwr8mOtAq5qSFFnXksi7raCttQ1qq7oguLRmaKygZWDzZCodMRj+MO7C/5snxijEEdN8jwfGx7ZxG4MYi+A4nDoRMqe2xejh9vrDVNUuKrGOUvwBm0c40EciUPyWNfg/YYY0swpEHubo1JOKwM6EYKYEIYQWC1b1qsF293hlTBbEuSyFKxRrNuKVevmQtJoxftPLrNSxxCioIopeD5+vqVpLS83A6jA+myNc5a+H7BWvJucc2CEn6hVwOqANRX96KmdzFdaLlp8iFn1A7c7Ub1NAZSyfPzlHW8/uWK772lrg4+KruvpakNlFFEbJt/L+JqMVMSk6eMCq3yJLDlRlw/tp4GUEm3VSKtQafaDtKd3d3dcXN0DY2jaBTGBHwdSTDNZ2ToLVaLrR1I+y0xW3xUzPoVwH5212PMLFosF+/2B9XrFoRtRyNnZOEXXH88p4YfmpCfz4rQuBb7EBc3R4oPC1Snfm+MegM7NmzLZSIYRB5ldpLQQba2dgQJyElQAiFdsGvJnD9l3KxVLkH/ZpEX02QGNfQUlKFnZZt+jNfhxpGnh0PUYm90sjcL7iYuLc/a7PU2t6foiozr24GMSboXW0iooSg1yVZhv66xOkuCe+QuR+daVG2r0qzd4NgksNwvRHaX8gPRsApe/K1eos/rkld5bTsbKLKCYxHQuf/14vTlJicf3LKWvyv+2qJ30nAnJ1OkjqTMnb7mVFSdP5eDGO36wv+Q3r3Zc2J7t1NDqwN/98B7febRjdRH48vktTdsSlWWMnnEcod/TtmeMY09T1wxh4un1HV95cgVI5esziRSiDChMJanKQ7nmlzq5/6d8iiPmElPEqkI8FQLvnzy/R6VX1FpIsDYl1vceoKYtob5kUW/Y7j2qquefJ4TWYopzRM0Siagyiz2Go9ouIRyWgo7l8r+07ow1M1JT5MbHLZJmVn95htoYIgoxqM8SZ1vWpsJYPQ/4SknNa4KcTEQf5p8/d6TytNRCshNUryQg2bm3MPczSTxkdAeVSD6TWkEg3JPncqoaKgmftRptLCrbip+talCabvT8W+8b/uo7a/b9hOr3TIeeytXgKlTlcIsFjIPYetaWpBR2sWLaHXC1I8WIHwdss4Q840rbhjj2857rD73wgIylWS3p9gdUZam1DLM7e3TFzecvuN1sOVsv8/42JGWJmy3V0hDxUAFtjfaJ4cWtJGWrBc39S/Et8hPaVPJsTEVKgpSkcYc/dKg4kTpP7Efs/Qv5e21Iw14OD60ZxpH27JKoLbZeEf2EtRW/+eaG8y+f87uHB0Q/8t/9eMXfeGfJWXpOVTcMfU+339IMkt/3swAAIABJREFUIzEmltn1tW7WBA4oa4ijR1UNalmDD8S7O+yDhSBmWvO9t2t+4xtL/pt/eMNdJ8+qsYndGPne246/+XbHf/4PFP/zDw54HrOqFEsHlYGHS/jawzMmEh8/3+Ks5ePnA4/PK57eDAxB8f2vrvnKeuDgDVARVYdnmB2cK+NIakIbRzcM+HGPNS0hSYskJfDZ1FAnw+PHD9n82YdERCh12kpPJ+t6zIVCiDJ7zAdZu9ebnm0/MXYDEeg8TF5ht9fs9h2rizMWRlqGl/ce8vzFLa2FqCaMtSzbhpBk5Ei7rKiNYpjEo+XQj/iY8CERUmTRWHaHgRA8lwvLRjeMY6AbxBqimzxtU0NK7KfINIopWwwTNQMYzWf+MVYFQtK8uQ58fB0w+XA3xlI3DU4rtntJ5Iy2vPzyGevzc0lYUFgjBnvb2xtpCWmR/gcs+66jzqKXMtQ3xUAMgpQkpakqJ5/Zj0zTBErTj1MmvJOfo+LQjSxa4a2IgbeaFbGF13g8s9SM/pYBuHIWlqREHIudlXl7Pqj5gIt5EK2co5GQny3BI1vezryZOSYV87yUBQWqFPzHoqrwUv+i189HWrKPR4nuqaTVBa0gobRlnHKVGMGPPdY6jLXZzhmaumEa9xQFR0oZMgIhIAHicWRmYuux5ZL/N99E+ZnCpdWUnHCuymFGiAqaXtqP87FXDtwZHhUJ7NzRKQmIUphiFpcrVR8yn+MkwTjNIFPOLuVuZUgtH2xHPPZVACxJljJXLJKoRUI8XnuMCe3EufNF5/jKeuSfXFuWKnCzT/zuJ2u+/66nWUamfkA56Ze6phIDujBRWcPkR5yGe6s1z67vWK3OuGgU132FxecD+NRrNZ3cO7kWnRvZKSWWTQVaPG2GYaSosfw4SP8284OsisTkuOsnnNPUiwW/8j7c7j0f761wbRaJ/TChkpDo5F6o+X3nK0mZK6LTyVWe5Ofz/S0V0TGglgFouvBETv8ut2xEykxWEkmCE8lJTBSUSOcqyPsRVJ4Snb1TUh6OaLQRRVXeMwrmgJGQ1llSUj2VmSyQN23Z2Hndl+nPKvv6lETsp7C9k3aY0QqlsqGb1jOqKFOBE4/PG2LSYlUwFV6KkyRDK3Gt7Q4wTaRJoauacd/RjaP0yJ3BVpbsu4+2FcmPx2qhtH3yYdVWDuyKZrHgbrNjUbekYUI7jWsdelFxuNuBFvl/tWhITpRIYp0dUcpCmHDLM6hr4hSlb64tEIgp5KGmDSl5ccTuB7kcY4jdRAqecDigKkm88BFVG6nicyJurEOliTQFkm341uWBPxsGhuSIUfN5l0h3N4TVGmsUZ+sVTV2JTD8mtDX4acRVC4IVE7gUA1FrVF2LPFUZjLJMceSXH2s+ufFcHyKDTzNq8W9/s+XXv1azfbqntYaQHDWBYfQcBkkOPr7V/OPPr4l+Em5B7DFa8afPepy1tE3F3/tQo94NPFluGMKEVSYn+B6tG9AyJHPwE+O4p82tFGnH5YIwiighAtpVLM/OuL3diDGZl2LEB6n0lTb0IWEQK3zvZT+UEPjgouGRagkp0U+Rfe/Z7Ca23YC1glWOY2CMgbjbk/yAacRczijFy82OdVvjqhqrhaMxHXqGFOi9tBh9dkJvKsO98xafwEfFwmludx0xBrpJ0BnlGnaDp7KGkILYH1hD0hWfdZd8fjhDEXnnKvFL92759PacygnxvHEye22aJrQxaGO4fvo5i9UaZRxog5882nuqusa5Bq3BqIhpGombWtN1w8z/lOcvqsUyxywqUQIpbThsr6UN76qs7JVzdAohJ54ipW7bhn4YZ0NTpST2uKxGFDHG0WYlxNIeEhuO0k6QZDDMyiSJj9nrRYlQWylZK6WVnygFGHNNW871WeF0Gr/SMX79rNfPH5iIZKoqqGN4zD8zZLvfyjmmcPy6qRpAbvxut4UUGYDVakFK0I9THh4n3BhdgjPMyUC57uKfUTI7pRLJmEzWSycoyTFDfP3mnCSP8zUeAY6ScfJKdpNKgE8p83Jy6ynJDynOqEJYOkleypsog8q9OQWzfftMVFLHhKpU1KeXoJCpoqf3JqaE8iOb6Ph7nzb8m2+NeDPwwYtEbS1RGf7+T6743uPPGAbFlKexVsuWWVoeFVY7kgqcLSzXL/ZUWvCt614WaGnPSeL70wtobnzkRa20wiclxmAnw7mclco8BJF+F+/htpb20PLyAZ9sa+7XGzQer1rqJfhwxxQjATM/uHJr9bwBMroR008t8liqlLxiC9x43B9prgxmNXxJYjKSUjZTmn+OvOesSDImr0dyIu5PNqd4AZUJpuUDyDOMM3eAJJD8kWB7XKLz2njt9h/XYP63qeyP+TvLJWSlW+ZpJVmzxVhw8pHGJN66cnT7A0w9qqpFuuz9UbaLtMsk4dLsbzfEyVMbTdXklpDKysAi901iSlZUXPWiIYWIchFUxDrDNIyMuz3LqwuUrlguFqzPzki5jeV3BwqeZCqHdg2kSJomwmFPc+8eqXIY26JVlxGpRJgOxL4njhPNG49kdtOUwGq0q4jdQJoJg+KQayuHcnlPzgllbmXHI4+orR3/+sUdf+f5JZWFP36R+M57b+H7A+M4YquGmGAYRpZtg1KI2VnTMuxk3pNRUSanM6DbmugntFOQIqta4ePI+1dClA4JhgDff6/isNvjY+TXzu/4ne0jOg8pTGThHpWVOBhNLjK1rPzlqs0ETPhip/n9zxy/9dWBSrUkahTCvwEI8SUomIKs5RAhIcl0WVshRrQ1UmgmePjwHtu7DcMke8GnhFWKkJR06opazgcqq47rOTGbkm6HyLbzjEkTk2YcPfeuzulHz6I1VFHji5zbexaVeAmFlNgd9tQR2kqL1LuqUUbR9x39OGBVyty7yKJtqSvNpy8ODMNIU1t8AFJg0Tb0XuwJ1o1FZ2nuwnh+/8WbjEHTWE9jFd96sONPP3hB9CvGKGjdrh+YopZEIAauv/xclKquFfVMyMZ9KTH0PVWVibejzJIrUmNykUwUG4livSAIRxFrCL/EOkd/ONCu3RxgZO0WibUM+3ROkDXhNsnQS61kOGjvJxT6WBApOXO1UjRNxTDIuBWrFeM0zdciZ6Ccn2RDQWvU8esxHRW6peicux6niNxflKP8bJQF/kWQFqTqSxltsflGkGOkQjFka/dTv5M5D0BBXoh9P2C0VDBET11VGC3+E957+t7P2aJCDmxp0RStuBidqZTnv8wQUqmqc/8ViWEFlipcAYVQfMpgtXlQU04kklIzmqKyXFF+avaKKBXszLDOFhpZAXI0ACpQV0n00hGZea2VcoTkTr6WVMFo5kwoIe8ToyZFcYn9X/98zdfveb7/1lP+7idPUHHCWcM//OxNvv3gBYw33PaKbuqpK8t5LdbTxhme395RVxW11gxRc3e7ZzNkJ9JMtorhRL6VE7+YEsvG0tQVPilGHxiCHO+lT1pXjmGchFBmDU1t+Mr5SD9BN2leHhK6WlA1K24PgRf7SyqjsFYShNVqwe7gYfKEPH9HWgaI62p+aSMeGWXi6PGVn1MBCF9JYsuqLElJ3qxGPrfRWpQjpkw7VUcZPCWhloQnpTzXKFcVpa0YgyeGPFG8cH8yh0kMPXPvNhU10vEzHZP2Y3WSOFYuOu85+YinCqrjNZT9E2JC6whRz7YHVis2neetM81v/+qaw+ZW0IuQULWCoSNZDXghknp5c98PhGmiXThSoyFE0hTBGJTVaFuhfASrYJLoJEifQvtICkZUP3U1K8x8gI9+/BOatuLy/BxVVSirWVyeoy7PCdsNmCRIkR9RQ8APWdLZtDJqIwzSahkHdtcbtNWYNNG8ccHh448xqwV20RBVTRpGzNkSU1dsP/gEd7HC1BXGNZJopSTIzDQSxhHtJ3bbLVW9oGlqbq8PPFk6xqcKh5h//bf/bMlvPKn5hfolwzSg6wbvA4skCkGnYOwPKA1DN9CsFsSUxD/JB6yVZIwU8MmxcBP//l9Z4bsbSR6B/fVe1GtJ8wv3Ku6HH9BUmk1a8b89v2RKdj5IlHa4yhJCyPOcYAqCWi5M5NNdzX/9TxoS8F9873NCsNzuelbtAlJNoKNSGlW3mXPlJSapnHCjsq+WkDlrW7M8O6Pb7zn0A3XVsOtGQvCsWsswBdrGoq1k0UpJO8dq6IJliop9t+MwBYYh4vuR83WDV4qvPjzj2d1A0ho/dXjvGWOgsZYpeLRRNK4hhJGhF7XLwiiGfsQoOF/WmSui8zT6ETVqHl42bHvLvhvRWnG5dNRVQzf2gAxhvFpV/NHTe3yyW1HbgPBWNb/2zoZnL254dr1l7zdz3IshMA6DtInqmqpZzshsiomx71EaaY0aM8cjo6FpFqSU6Ppu3tty1hWVYSBpQ/Ae5yThNsZQtUvWZxfcXL9EGysIj2Qr4pmkEj7Bdt9RhvOmFFmvFmhr2G22ok7KaIrSmn7oWbQ14zDmydsSi2Sor2PRaLp+JMTjxHYQf5iY7SmU0pl3d8xIBCU6mrY6K7lCrWGcPAk9t8gFZP6X9GlREhaJuTqVhKT0oWZQIh8CBUUop4T8ADHjkj9UWszpvA/EqMFH+hCxowx1a5uGbog0TYNSisPhkDPGTGY8PXIKqKKAbIGckkJlToDkHjlpyQ5+oUhJcwtH5ySLrObR86ecUw2KP4b8nPymsTQi8qTdxIwwyMdXcyIjhWhGS/K9k+Tl9PeCIqV88LxaSs9wTE4AcxJnDC2Bz649nzw7Q9dyz4MPGOv4wcsH/OIbCtXdMfpAXRtk1hJEn2jqGp8CbduwbjQLW/PR3szKJjg9HMsZqVhYQ13XDCHm6aevggHGOoZRXB8vV5avnG1Ymx2HqeHhqmNde7rQYO6/wQ+3kpw5o9BGEaKmP2zRWvHGvRXbzZYhVHT7jqL8ORWdKyRxKYfg/HotUS/EMvn9Kxk3x2nRQiaTeSXFdr+Y9iEoUm7/lJf46WS5vjpKmrU2oLQQhIOfkZUYw7yyNGlmLRYb6xgjyp60rsozR4E+JmbHttBrN/9k/WZAkHTyABVymzqf+OU3Kxwen3k40QfiuAcDZtmAD6Rs4473aJ3QrZPBbuNEGid5Cq5C08DYQ2VIUQkJN6/p8t4pRUxj82A4xThMKN+xaGvaRYuunFi/GytGbylXcFOAqccoxRSEa2WMzZ9LLANSDGitsLUjEKiX0kKwbSteSOMkj7BtwFoYJ8x6iW0b2Zs5UYcEfidDHRUcDkJIBsUwTpw/esK43/JkbfliF2iNonWGP3yZ+MZ7Z0x3L1gtV3m5KPw0Yp0lphNUNif2ISN2MshSkllxB8+BPgZR9Y3SblBaDqWzizOGoWcae+6rgX91fc0fjw/ZDxITtRZSbqVhsWiyu/ZxdcQEtRHi6//ww/tUKnC/8Xz3zQ1+CjgXCcrM5o4SA2XOz1w0ao0xThIt7zm7OOeTz59ROYPvejQRbWHXT1TGkB+5DPDL86B8Nmq8OVQsbcNKDQQf6KMnBMsb5yIymEKkG0fO2pY9HSlkFAEhqXZ+JPqY2QADRrds9wdWyyX7fhACq1YsKotPZCpDoBsCi8oR8l4bvbSCP3hp6f2Cf3qzYDfV1PaI2qYUebavsGlgk96QA19rhqFHG0fbWqxzhDzRXWklcdZP9EMgTfGn0AcfwIdJxi5oQ0p5Hpj3oA2K8v6C+PvsoptSpK5ramdpmoZxGBjHkWaxmGP1/8fZm/XKlmXXed9qdhPN6W6XTWVWl8UqUS6LMg0S9JtoQzAgWDD8atgv+gV+9A/wD7Gf/GgQ8IsBCzJNW7ZUZJGmiiqymsysyuZm3ua0EbGb1flhzrUjbrJYJSiAbO6558TZsfdaa8455hhjZlVDlsJCqgbD/jDReCP8rX5NypGSlEuHZY6FpNdRC//qkRZjOnYLtHBfuExG6CI5ZbwihfXcqUuwcUehwcXZitv7HV2niE4Wwoj37itWI2++fuPsIescpEgNwZURXIqmTjV5KWXJDqG2VlCHXEvrDcMU9EGA86LkkCm8YF3DOEceX52z9SN3h4lJoWxHIcTAMmBPX1bVF5Wkm1KSycWLOkg8JI6+LXZRAlnMQnaqrZraaSgFmZtQkfhcNEtUmGtxxq0PrqgXh1kStTryqbqqGvXOqIHj9FUXwfGTGRaiZUV86tvbYxupGEiIGqJawhSgtYUhOr44nHMZX7PWoWW7OeBLYbNaEWeBgTfrDSFmLtYtXXMMyIJMKeplDY239G2DMZYhVDRMLipmo7b8hhQjuWT6vuPt/ktMmPl4f8Un91vePfNcNDv+g/dbvvf1HR+Mn/KjF1s+uVux22VaLy0lgMNhxLiGzhbcVqrXaY4LR6reW5Eey8TwJVupuZY5SaR5s9F1bDvWDD9jnSflIgdPfjPRtZqRFg00Ms7iuB9q9XlkxsvXM2bxWKk93KPngVQ+oToKq0LrNDGSFpIGsZPOUEUOT/JK/Yw15dYnVI4ojHOi5Pu99zu+/6ywf5BKqnJBjIPihNBqWk3vMktlWJJwPyQZysiwqcT8cGD17NGCaFV37GOaVaTSNoD6xjTrlou3nokJV064VYeNEYOO1ChZkJcC89090YrdfrPuofXkYcDYgmsapsNEmgNtt2aaHzDOEg8ql0b5aylR2oZpN2BBWqZFpO0CozpIIs82zjEPI431MpnaFfqzM0opjHPiv/7OzB99CB/vPL4kpmwJxeJbS9u1rJ89ZR4GQSrChO22DLd3eGeUJlSLFcBkchx1j+spm2Zx54pJ0MYQKC7i+xXOep49ewbWE4YHfv9yJD5/4M/ClpiyVOBW5LW7wyDS63qWWOGwYApYx3vbma9fzDxaJVGHlsBPX2354OlAzELOTzESc8I5h286bIZhypBGUslMWVyRnz45Y/dwUBRFPL0ChraT833TylqYk+HF/Y772fHNJ2fsQ8ProeWDy8DtQVxxjfPiMoznrYsN1/c7nLfiO7IXB+GUDdM8gbWkIoF0ZRwxRy4vNjyMCYdh6x1t03J3GLhcN7x6ONB6z6Y1hDCxbqXA+PIBfnp9xd3cC/eLjG5xUpxlL6fEX79YcWWvMP0aM4uXjfeSmImjtTptqyAkpkSoE5dP0NNFkWMKMcpEa+PUk8kg92Bp2UirNWYZiumdZRxnnPNkDKuNODOn3QMpJVrnlmJFvJ7MInApCK1jnsXGIMSg5oTQNY0UBlGKgVXfstvLOhYlcATn9f2MAgDS7s5FPV5y5f2J1Ql6LxaEzUtSZDHgG1zTMYdA17WiwstJZN5frTxPXv9O7SFjjBDzYKkaFxv1cgJnqburiGtk8VorMNU4zuCaRSJc/VLkQ8ke9c4zHh4Y8kyImXeeXDHOgbsD5DyTi85LMdU3Rn6Ps2rEpTpy56wWegrd19HeirLUREOcVI8VaM0Nju0v/R9rFlPh2lSoKISgLBJ85O+rvFUDjL6xUSfQxdPlpFqusuJjm0hXNlBbVDXwlhNExui9ruBMUSfXnBONNbw+eN5/tBYjLyzDsOfJ1ZasvdVUDLtppO9b5lS4Ws384rajmgOBIBnOWhov7PXDFBWNEbWXAR71M1M2xATjHGnaljjPfPjScjc/4WxjMXnis5uGX8Rzbpu3ee0NHzzJ/P6717yzafjrF2s+vetpnDru+paUxefn6mLDHBNumHh42GtbcMlMNBmtbcSqWjhOSJXE5eQBG7O4VS4IRD4K7eTn5P2cMualSjUqPawmgHJvcoo0bcM8BzUM9LoB89IGMgaWblsF7HTyqiQsXlbP0pLU9mgpywTWqmarF73EPY5fk8RWP6O+k9U2qAwuM/zht1tcPsjBtV6rcZnBGCHU2rYhHQ6qt5e1UHKGlKDrKLudrLWmoRiD9YXiVfnnWozrWJKgpGiNPjK76injiPWWtlmRhpGSpA9eFJEtJSlfyODaFowDZ+iwmFWHmSPZZPYvb+m3Kx6ub9lsz3n9+obzlbR5re7fMkzQiyEZKWFDIhlJdsTgzdXNQ0kB07bMqrhoVp4wyQE+7Q847wnjyBQnvt/DR7unS8sOYL25oFuvGQ4Hxt2BvhcFUUmRpu8waRYHaW1FGyCPO043sVHouCjCZUqWKdemIsYIEpUK223HfVrzw7+8IzpZI603xNhytxtwXpCxoglrbTu2jSMV+Oiu5bef7LAkxmw4Xxl++smK7749MYbCMMycrVY0xkBJTBFcSbQ2cghRWvsO1uuWt777Pn/5Vx8iQFyk4EgxQFN4cTvy9uMzPrppScny2Z1nH1b8/LbhEAwXfWbaGLV09/TesO0FWWp9ZgyF3tbRJIlhmjHW0VnLIUsC3zWObddyv5+wVlxuCzKx/sXNA4e05nre8GTd8GgVcdbyo897Pt71HGbD63GDs8INqm7JufLn1d3WWMc8R17aK3wTmWZpA1cTVFPKgm7kkvFOqRSq+oOiQ2pl1l4uRV3aVf2Y8mIdcJrw59pJUO5YCBkZuGvJUZRU/bpfCsZWEaqcWAp6tBC3GInLjRgPeueXmG6dJc1iUOeVmuG9o3HiLdN1winyjWcOcTl5lrNKY2WVV9c43TjHGCTJEnVXw/nZlsM40jSOVS9E4oo253Gi8aeqyDdfv9YR991v/lb5T//Zfy9njmZTbeMY57hMtDUa8fuulVtcsljAO0OYA6GgKiN1A12KYbugJjUIH7XjcrNjLpQcudj2nPXw5YPApSFMwuzXd6o+ISBwfHVLrW2kGoQrxLcc+vrzS/LkDMsFLXdIz7R63SdEaDTbrEhTVQnJ7yzaeoAajOpU4HrP7MmAxlKq/LosB9oRhpNXJY8u0mh9D0Fz5ODFGGJItI3DOssfvP0FBtmMq85X6Ig6FinHzLVO13120fBHf/MunasTh6FRXkJdJzrnjO9efsq2GYjFcDeKwmBK8OnDdzg83ImNe10f2njr+xbf9Lz1jW8zz+IB8/YVvL0ZOO8iv/Nu4KcvG352c8FHNz0de+bhnrGcYY2w6L13NN2a+7tbUjomAblUWd0xkaiJXs5pWXfO6SyaIr4ISeXH1iqPJx83o1FvhHq/XXXIVWj6uHcydZZKXTAxiuRP/yVI0OJ+qwVARUGQBF6QFkFyzOK8q1yskzbTghkZo6iM7hlnlsBUkTtrDMYJTN02MtvoGxfwTz4QD42S4+JoOl4/SJXZNfiVzP8wpVBiXMi5WCcIiNETPmtSojJv41pUDqWyxyLjCEIUszdjiMjk9BIjZZ4pOePaVhIA74lOfi85U+aAaTvS/kBuHE3jKSFiUmGcC7/8+UeEDBfnWw67e771rXdxF2eUccLMQSqipsE0FmM8JiUJEtbiVp3eLy06UiEMA77d6DT2QBpnTJbqOajV/zxGiCP//MWGXw4dyTZcreCfPvqIVw8zjzYXmFK4vLqkWbXSvtABk2me8Odb0MBLU5NNt5wLGEMeB8p+IGOxiv4Y1yi5spDizP/+ieNPfnLL2hesl6nTMWZ80yxrxGn1XlWfIvOXNbpe9QwBVk3i/c2eD+82HFLDf/cHn5PpycOOZtVKcMwZg3h1VT+imDIPU+bRRoj1H378nNvra5zzhGSAQM6Wx1vD//TDJ7SNopA5gvXLMZuy4a3NyPceveAXL/Y8Whm+9+5TjMncTTBOkdv9wGEOJCzESXyZdKp00paJBzZ9wxwDX3u85WJl+Fefbvj5zRaKtCx+68meiy7yLz56xKZVdLae0/WorbxGY8gxcixV9fmUREqJlGTQZgxhiTXW1FimYcVURP9YxNZiWwYP2uN5DrSNl/dwcjbVSzKmUIyMhDHG0nWeaRROTtt1SwH8cHdHyZnHj694fX1P27U03vGw2+GMuGBbA1GRZO8cMUqbyHhJNFMUL6iUCjFFusbjvV+mdTsrSco8R4kNC+1Czq4qVqiJl9XzSK61Wc5igLbtBMEpiWmSuXRd3/Pes0f8D//sn/xKR9y/m+1SH5Ue3N45nJNM02qg9hqgV32L1ZkyTduqnlvGk3trsSSVgJ6+d1FiV15IcNX+v/I8rIHWe4Yx8umrA1+7MvSdoeCWVkhRAkslxlZ/l0yF4stx09aFowd/qRltvcHl5BtObwI1WakLrn6rZs+K3FCqh0xesmVj5KHVdllFeTKaVZZM5VTUd6tXWnkx1MRFN4EkduXEOyYv1yBDr6TcPl/Bw2EUHoAediLHa2m7jrbvWG23vHVxjilwvUt879mkBkzS06wVmiy+umgKjSu8HjIPY2GOiTFGXg/vMA8P1AGRxoB3FmcLfSMkse2jJxSgX63Znp3zejznR6/fIbDmwxcRX3Z8/+pjNuUF94OhPXuHpt/iuzWpeMZgGfb39N7ogTEzjQPzeOD+9QsOd68Z9vfM08A8TcQY9BYaUW9UKfPJdFFKXQPCyZHKyB6TxlwHkMladaLNX5ILmaPSHIEJfT+Z7HtMnHMWsys0aaqSRllf6gasSXR17pWfEddpccV0S7Iik8TVql99GKx1Om7DqCmeWZLaYQqEmPjTT2d+8IWht0mq/JjIhxlyxJ/1tFePsL7HtSuRPIP4EXQdpm/ln1WP9Z20H9pWArAxlGmgHAZJOHKmzJEcInGamQ8jMSdJfmJcDnOTC2RDnDKHYVQU08kE2gJlnvDrtVrtSxAxqw7TWN557x1WrSgpYsrYtpEzS6Wopu+EyzLMECOpFJIx2FaSq5KFK5BiJE0T4BRF8qIQTMLlcN6xurygPzvDGEQt5OuQUXh1gJfliveeXDCkifV2g+vED6fxFswEJeG7FsIkyWZFP3S/1MhTSoJJAqFdreT+9ivcaovp1jQePrvL/OyzW1ojCYCskTrH7XheZJUai/eH/AJRO0rrv7GFMTp+fHMBxuJtoXGeFEZpu2Tx4UpJridnSUSnmAmqehuDERRmtcY7zyE6duOMAa7WhTFZsvJfoo65SDEqYT3hTOL5rucnN29jHUypkE1hTIUpRB4ZXl1fAAAgAElEQVTGmXGaJDGxlstNz7OLNbZkkekDrW/Yhcj1fmSYMyVHPr1v+ehmg0GHn2L46HbN64Pn6SbotjcLBZNSrQpkbWa9XmDhYYQQsNbhmxbnq0WFOTmmNRZorKjzgpZzZok/GpGWeKdnlHMUBQSstQvH8NR1/fhf+buoU7VTSvSrnpwSd3f3bLdrusbhvFm4eW0r+6JtGhod0GpdKzL2JJ0MUUBJXOu7jrPtlmmeloLPN555nolZOFipFOLJuVYdwWu7HDLb7Urrh2aRSRuMKhTF8sEYK3y1HDXZ/tWvX0/E1RaErQzmUnDeCWejgPGOrW/o+pb9YaDrpLp+2N1ScKy7lpyTjqZ2J6jFm9mBPDR1EdX2USnoAV1NwCy/8yzxo93Pyf17DOFEXaGpW518e5o1H/kFcHQZrcRZSTAWKE6SbkXEC8vbaHJ2hNyPr1JE5mdOfmHRNtSpJHqZu1DbRXpCVYi/Jk3VR8NgJAGr72zMkQBsqrokLwZ2Mcw0TaseIDK88Nau+d6ZIcbI3W4g5cRmvcWQGUeRZXpfOOsartYbDlPgUfmEdfM+U+7EGVKTL7/MbCqs/EQpMwZDVNJk57dMaU2M93gdAGYMi6NkTJF103L16DEhzIRpzzQUwjzjGfmLuz3Pb+Fs09E3ntx0XJyt+MbZA+veslm1PF0XfvCLzMdfQjYdt19+TI5x2cRN2zMP4uhqG0H+cjGcX13RKAxaTlCSeZpkLZyuw0rHNrV2QYebZUpOuKalmjKhaE1JUcis1FaVpXikLZeTriXhb9kiMmdfDwZVZFhTlUBGUaLKC5FfJciLom/WLgeC1a+DWYqJ2ro0tQjQw8Q5KSY2nedRr1XizYN4cGxFQZN2B6xr8f1G9kZOOonZCGqhidLxALaa0Wdt+06QDWWO4J1K6aRyCzFggwaBrDwSYzG2EOcgRU2riI0efMUgraJ2jcsjJQjfI5PxnaXbPGIeB1zbsn/YkWPCzAFjHf7qQgjGN3eU1mFioHiHbQSlNTkLCVGJ3Aaw3mNIGJPJKeL6hhKFpJ9DoE7mzTGwsgljW6ZppGk7/pdPLvgvvnXONzefM84zXYpMw0zrDLY1FJshBpGQOwsmg+uhiHVESUH/qxN+fCPPuO1xvse5NePhNY0z/PwOXu5EZl2KcM7misIu61MWj3Pqk6OzoIqaPyVtHVkKnavWDIY5DKTkxGMoTtQCKYaAb4QvIcBaZgyBEOGsNzy7XLG/O+P++UtKiuzCBT9+1fH8zuJIgFuQwKUALDJaxZnMi4Png8tnkF/z8n6g7zypZCJijjiGiAO252t867kbIkMY2W63HOZAa4WHM6TMIa/5448uFhSzJmp/8LVrnIEnq5H/57MnsnV0LReV61dFYc4RMUyrBqbSvvHeUJJcF4oOJkVWje5taXUJYliHniZhW9ejY0GwT9F0Z80y2T4nmYScs3Q2pHCp4d4sPBKjZFtrLdNh1scunimHQQi6bdOIPYURjtd61YodQJSCPaYkbUAEzQxRwIm+bRDRiqPvOwqF/f5AzoWm8cRUzVYF2Mi5aGGma0/dhEsWzmmYZ/UeM0uXAE7vixjUpXSCcHzl9RuTllXfMYwTOI9TYpuzjq5rBDJKicM4Mk0zMUh/bbtek3JiGEZ802GMWEunKEPsTuVMRhdvKkakl9ZQipAjnc16U4QR9Ud/viPm9zDG4dwxqBTB4TTY6CIsYGrLRavmcvylb/BOzLIkMxRZENVqvSI5pwtr0ZtT0RbAaEKkG6eSKSu8b06y59PkbamuObKwK8+7gpLy88q/UcJerj143wvJ04lsPKVM23p831HizB9//A6r3vLdJwNn9jVdCzFk1mvPMEduX77i0XmPNfD4/JKfPb/mP/vgmo9uN3x4e8GcHZZM42fOu5Hbcc23Lj7hdkhawWdW7Tn70As03a7IudA2ns1KCK3jODLPM936nFcvXpBzol9tiSlxvlkDawJPeeeyx1sJ4pd+x5wG7uKKf3jxJeTI3c3Ib60nfvd3Vhjj+T+f/i7X94F1I+3JYhzONYQUuX39mpikrbLfD0zpQAiBfrMRVKtkvFPETh9oLgVbIkcnSDGKI81SMbcddXq0Val+0ZlZOR8VdCIT1QqaJAdFQRKWIn3tpJLoU65JhZflnZSUhyTQx0RF15ORUQJH4q9C066ilEdVVQFFAKU3X4B/82XhH7x7ge2HxWl39fSp+EmMA/Phgew8vusoecZ0a7Xud+R0kHtGrBAkIBJb5NyjWqgWMilIu9GvOqwqi6T9kyhRPnQcJlKG4XYiPkpsry5Etnl2CSlQ8oSxDabvFc5PWB/BNFy9/YQ8TfzH/8nvQoqibgLmYZDNbh3sZ8p5j9+cY01DOtyAdZQQBCW2DTkFOOzJ1lLCQLYdzrYUL0RUM03MMWJMoT+74B+WyA9uLH3TkVNm01j+148sf/juY96zt/TjTNu37B8e2HZnmKwzVSSbF8Li4UHITkJcgFQVO4AzGPWiKRhinvjrl5H/95PI/X6i7TaLydcwipFjnSODFg1mMYGsLZ5jcTVPsyJxkJHJws5khtDiTRS1XJGgLOPmCilpoDQZr4mRsfBwmOjajq5zPDuzXO9b/o+f9/StyuHt0WJC2pzK81iSdXn/L3YXPFvD9e41j+yWEAN94/G2Z96NrHziZj9hhsDVtmc/jozzSOtEum1dQ9e2vDqsmZJj3cr9iFi+//ia6+tX7PMFH+3fgRwVzZRkLs5BuYUW47Ke406RfElq1n3DujEkb3G+4S7Mi9IvG0RVhSAaQUdXGMqC0lZ6QlXfGI1ZBtisOhkeiqAmRWOBc169WYx2K4p2OgQBCtOE7XtKivSbLdZ79g/3EtNixDtVdLZmOa8e9uJYbfXcSBlCKATEaqKkRHaOh8MABTarht1+L15PTkZkpCQOx6VIcpIWvmvRrxnW6xWrvmMMiU5bit77RcQS1GndanxMWQff/prXr7fxz5nDOGiGJn1fY2DV92RgjpEwz0o0NMtGCFYuuO1aQkhLkK4H7mLIVqrpGksf0UlpJeZEOavsKio7u8e7GuJryrEUmoK0nCQnFcZHFU1y2FfE5cjTqFn/YlJnZfzbqWldLV7qAjuFXJY9pwvbKJJS+3gnf8uiO14yF2Wa54TBa7/vhLdiJOOvuRj6NecMqahbYb0TxuK8uBI7PSCsyQxj5q++WPGNPnOY7nh0sab3LX0Pjy639H0r6EaBECEkw99/NmLiwHW84J3twJP1jCPwMN9zPwjxFWQWiTErvnzYihNy09N0kgC8vr4hpUTjHe36jLOrxxTj6Kyox7bbM56dBz64kBHrf/rF2+wPAqe39p7H7cBDWPGvP0y8vdmz6Sw/fn3F7tUVrXc8HO5JbHn7YuKbZ7/k45sNP3nZY4msNmc0bcNhv8f7Rsh747DMiZF7ZsBIEl77ssZZRSystu9Y2jJimqboi8oBc0rElPR7BNWqU6krMlKjUEpiGV4F226BT6vqSA6jxlmViWoSe1KZGl3EMo7gpKCWb5T3UsffZenr4VhBx66x3I6FwzCAcaIOMoYyj+RJ2jeTNfTrXv/OUWIk5xHXnWHdllIClETJgZwFdSNpu8lYcZGNceGyL+q4KEo6FEGhQAmVkOzYnjVLcWOstoeMw5iMsZ1+LknYKULMMoBpG0zTUSZNAHKWFpBk/MRGzLzIBbxyGVKSBKgAccb1LVnRiDRN2KZhmg40XUvOQc8Uy2azIcTI4W7PP3p35l9+Ic7TFOgd/ODLlm+9N3B9X7gyjxiHge35ShJeTnhw0yiTqK2TycVrkWoba+U6SsZYT4qjrInDnn/xYeZ6hDJM+HbFwnGzla/25oF0NL6saEEtheq08Sq1hbqExpjoTD2/ECfXUqeGC+HaWUc0hdZ7pjCxbjuadsvTywfuD0/5059F+tZiXPumJ5ZeoPnqIarr9W50WLNhs7lj23u+GCYut5YXtyNX6xZvhSMlDruFbd/x6HwFOfP1J1s+vTkwToFHzUu+8+gpn9yL86wh85Prc84cXIdLSFFGlehn9tYTTcQ5QRYWqoFuw5wSXetZ9y2//fSGm7uXPN8/ItPTWMjG0DVCvPXOMozT8nkXLZ+1kkTq59WUBYfs5zdUqEjuLy2r2t5fbpYqGD1xmvCKKnvviOIUQJwDgwu06uvy+OqceRZUrLaaY8pC8D75ncu6cZUIK89qHKuxnAzZrKi0OE9bUo6s+55pDkjDR2JcCFG9ahLGOVZNS8nCzwvK2ak0DW8tbeeFgnKEGP7W69cnLSD90JRpOlXmWDUripF5GpWAKAmDDK4UOGiKia47juA+vYTadqpG8cdEuxwlphY5/HLSA/zYUlk4HMsb101Qf11ZbrAkdBKcagumyobfSB6O9/mYINT3NWZZeMBitFcTsEJ9iCq/1jO5GulITpaXjFwqcq2IlZ+Cci2MgZwti+q1IEHjJGGBet7XEGjqjV2q26ztvKbt1Ndh4he8j58zj2Pkbf85bbvCkGkbA63D+RaM5Zcv7nnv6QXfflr4wNyK7T2WmFouVoUxOAhCRLMFQrak0mJthBIZxkQIstEwlpASTy8fsV6twIoGv+97irF8/uC521/R8cA0TXh/BgZ+/PqcGFbCls8rvv3WI8LhFa/mC3Kasc7TeIsxe37y6cTfBEM2QcioNmOdE4a79XR9YZonun7FYb+n6zq8b1RRVgm1RU0UERJcFsJm9VwpGFHZWDm4UwjYOgn6xOofAOtwVn19UgIlC1qtMkUyKOibUbZ9XWegbqKl4Gola8rJgcWCABkjrStyPC4V/VeF3ksFH7Ogf1blqDlH8m6HLZli07K2ihHoe31+JovMSqVdYiaPB0qcsO1KCLeVRIh6oVRlmw6dWhJByuJPIVOCjXxviJQohmLZGHzf8urlK975znsIUd9jjFeOlqPkgJCeAyUH3UdZOVQZ4x3MBroG4xvSfpRWDIbWQLQZZyCnEWIiqWlW40VJJNPn5YxwzhOjuJUWnS5cIe1pmLh+fU3C8DsXM+ntt/iT5xZTIs57xuhwzYZhf8PLlzOrxi+cNmnZAcNIHkdM02NdJ6Z7RRHeXChYMA5vLF1j+atfXPO//WzmbrJ4Mtk7jEp9vXcE9U5fTiY9EqqCDsPSUqzwfS6FEoWIXjlS1hRuxpan3Z7inLqbWuVOiJym9Z45BOWFJZqm5/UhkW4+J7Tv8M8/h5v9T+lXHZXfV5HMZZkfT9CleDQFHIEhdczZi8OqgU3rmGPCW6d+PWK1P2R459EWb2GlRO73rtZ8+TDx/GbP184MX+zeRUeiErLlujzClEDKka4XOoN3hjkk2Y96Ph9Hnp7EAms4X0FrZqyBYYy4psPkie3ZFowULZVQap2TsQoYRUmWDB6W5yLPpG8bghZJMgpKKAWtdeRkjqi/vnJKUgRpjMxZVLMpiWQ4hYn12cVSrljvSNNMjJHLizMOw0iISescc/I5T+PO8X9OMDqp6/VsmgOQE23TcDgM+qil8LMIB6joM/beM4yjoJa+EUWUtuzR/IGURGb/79seqssKYxiHUQ9xi3eOVPLC+8hZbkqtYo21bNc9wyBOrOMUlr5bVfIIVFlOFkldINKiWRAHg/TSy5H1bjmqKCvaulQK1qA56lJRL4ofNWIpOSuWIW9QSXDuRGZVh1ZlVG7GCV+AimQUYjLHwKdGZTkJfyDmrLFAycGlniWGOoBy+cpJomutZtym+p/o95UjGYtSfT/kM3grfjAiOVc7ejVI67tWWkcxkjB8Phs+4306n3lrO/Ms3bIaHziMhbfONxQKnz2/5vHjLbcPA+frFTFkxlgY40zfO7796Jw5ZWLx/NXnEMOsipCZVb9iu61DBS1N63n69W9poiXEscOcsLaQwsRdcXj3GL/uKSkRQqBrHOSIazuerlvux5kpPRZHSO9xQMpJNp71uOZS1pdz4BwxJp3gDU3X4+0D0xS0ShayXU5C1F4qK4RV3xwflEiRS8GaIoe6Jp6VE2WNzvqwVoIOLOvbWhnkUCESkUJrBatrTNpKdSCnHBApavIaNSEtZRmwuexL7Vlv2TGrEVS/2vCw2xGzwsnO1ZQcZ448mFwK//k3d9hmImZHyeCKI8ZAyZbSZOL+VowKLTjfUkwhxUxrW8o4K6ojwTynjGu9JP0hUaaAaRtSCMwx0PhWWmMhYtqKACVSDgJPdx5ixjWWt77xjsgly7E6l704ktOoe0mSAikqHKWJkAOYTF61olyaZjAw7wecM0wpsHpySc4DxIRtW0zKmJWOKLAaYLKBKJB70yinLkzMByThNgXXeN565y3uH3bsUuEb8UNeXH2XX95bUgzYYvifP3rKP/76hm+sH7h5Xfj88xuevnWFp+BwuK4lNT1ht8e2EObIPIvfTAZe3T3w8V3mJy8Dr9OKhKdpVzJBF1GyxVzo2hZLInBaGBaqJOxU9k+Ratw7K20urXBdMfRNs9zrX9ytefudgWjAJS2yMnTeL4aD3noymfNVizeJ9y4t//KX7/B//9iw2awYzs5J83QMiKcKmuMhxumrlELbrbjoBr77zNP7wrefbvnh51u+/27m1UMkFcPb5x7vLK8fErth5tFW+GGxZEIqvHXW4SxMMfH7z37Bn776FlMy4hA7D1jf0Lb98utHVeH0bUPOmWE44JtOeJxIkk8JgMM7mKaIjYH3r2Z+8NFLLi8ucd4txbX3HooEX6uGqiYfzUtLSspmKDjbLIh6DAFKIRdPVgJ40onM2WRZ+yjP1Bl8acg5YKwXxMwZ8hQ1CXV4X315Mq9fvSLGwtnZGS9e3+CdY9V3HIaDxBZNNGqoQeOZPKWynBuNF8+vrEh03wl/J8ZI17UM44zTbkhI0mqLIVJyIgb5LHNIWDPjmlaM5BCFoyTGSZV7/J2v35i0CI9OPUSso2mswLeVLaxrMIW5TjwUn44o0M8Uk5pTvVEqasZd/UkEEp+1lVR0AUsHyUqyIX2UBUrKiyut3PBSpGo+Zo3HAC99y6wFyJtQWO2l1Z+pVbdVCN4ukMfy+MRsTgPwMekwC25Un/kpglQ/d9UIUVTufIR1lqBUde+/6iVB7uhtU2NHjOEo67Wy+cTVNS+eOE3TLJVYCoFhKvxs77i9epf3L2fOV/eMYYJiMCXw2f4JvdsR04HNtoPdXoaeuVaqnabl5vqWu/gBq5XBOo9DiLExS6XoXGZ18ZSQ6m+W63e1JWaM9IGNJUwTUQeORe0VOyeDyObqTWCOCV9tFWDMci8KEGKk7VtNMguX/Yi/6nl5pyqcLOQ6550eMGrelCTJzACZpd3jnBPeVE5C3Cxg2nZRaqQcl7bjInxWmah8aIs1aVkHORfa1guZzRrmeaZ6HJla3RRVK+kfK3lYCIJFkAVjuJ0arMkwDazWWy5Wltd7DRC5Jkt1mUlv/dzDRT8xGw1oloU3gykkkzEecomiHjIBipXJ7SHgnPq2YClpwjhHnCaY69DUhBmkmo05kmYh7aWovjWNwWKpBnTOWlzfY3sPvlHVn6WUmRRmjGvk56xIra1t5XcXmY0keHUrCNOwl0QGQaiaviWHiGs9JSUpDUMhESlekjox57CYJLwSYy0EbeNoW9k2YroVUsauCiZD1zrud/e0trC1E9DJGiWzT44P71t++3HPvhW10DjObDY9OUOYRuZhJOfE7rDHWMvm8VNKgS4H/sd/6zFNj28amtZgs7i+1hNCHHUz0AjBdnmZpZqrzuWL6R/o0NZ6PtZqz5wgIYXXQ8OcxTnXeUsKMrsq5ax27YXdEOhXns5n/s2Lc/7tc8frhyIcqFI4u3rK7Zef1mPz9Mj9238+vXoDrZ05xIb9zvODT7fcTZ53z3va5sDL+5l3LxuMgXUHD/uIsy3eW1pYZjud9R33h8CQHH//0Rf89OaKXezljFJeofh4ibJ1TnC+3XAYBgpChi8CgCufxJOzYe0nShrp+jV//pGhbVekXEQRpfs9hBnvPK5xTOOoR4AktKLKFNWOoOFZY5AgvTKBWVQ5Im/Px9YxRy+plMoS47RUxuKYx4G261idneGc1WnQ0kJw3jFPI957DOLy7Kyl6zoZqFgXSQ3VJS9nnFhKWEKMrPuWcYp4ZwkhKCHYMU2apJ6cXakkrErKW6fzpjBgxZrCObd4jxVtodcY/3e9fmPSkkv1enC4HJnnIDBOqXJegbzbpsUgLGRnhHDY9R1hjhgKYfEHsHoTkva7BRY6nTl0BDnLAtSVYjBalZZiTwKEyHxNrSnLcSMaKtpSE5hjgsFp8lM/i6sqDrtcQZLouMBo9ffK+4iJ0GlCJr4ecilHvrH8Qa6wVh4CRy7KksVUTH/iBEn86qsgm8TWAKa9coHt7OLmWrRnKx0rSyrHd/Bti5cbxM39gRfXhU235nE3MxwOpO4dwrTBmC3nzYEP0mesVxve3jZc7x4wpuN+N2Kt49uXey5XmSFaPIlNl7gfE3dzx/PDE87XV9S7n5XUPI8DGEEIUJg+J/FTyep6633DqvMcxhms15siyZ6YMolSSsahRxolIlqrzUdxV+L1zkBxKrmTQ2sOgZgyISg5Usm4kmwqJFrAl0zrWyGICdNJpPopETTZkeuRpEpMBFXuq2sHhfFRFLIA0zTRNC11GrTih1Q+VIbFUE4SIw0yekjVtouM+LCY9oxc4O5+ICaZtrx0E3WfOWdpvaXzhU3XEFIAq+1LmyAJHwaXiSmqt0jGmma5/uTk6xTIRj2RbCHNEQUUKRgSCeMsjWtxrsFZh/OJRMIaxzyPuLaRVqopQKBMEROCkm2jHmYGW8DgdQ8Zirq3nu5HYxwlD5RUSMxSqXqHw4s6qmkpQ5DExHrCcKBbr8Qu3QsJ1ahFeY5ibpficSJ3NGpRQKIg84OML/QrT/Y97893fNI+5m72y+Y97wrFZOYMTYls1ucLAdF4j2k7mA70Kwmm6eGWNO4ZYsZ2jzlft7ovitZmBo+giHV9ZUUbTW1hLmmz7PPF1bvGo1ILPvnO0+Kotvpf7lt+frPmm+f3+ORp2kaegTuiJtYYVr7w05sVf/ITyyE19L4sBW6/Pcddd6SgapY3EpWyrEk5G/XMLnK+f7nbcDv2zBGG6Oh94YsHz7ubQkL4Xt4Y1p3n2cUa72ydFEPjLa4UrncjXdNwvR9J8y2/8xT+7OXbzFFlxKpmqaMImrbjMAzsdw8438k9VLmxdw5LYQyR91Y3XGwu+PkXdyR/BdMo6yNMYKyMqEmZbIUHVhPDovyhXARp9jpM1llD23opmKpBG0bMEA2c+nLVFi/LaVqL2Lzsj5IzOcyEaWJuhOfkGydnZE6stxvuH/ZqUWC5OL/AWsN+v1eDSzkDGy/T6Vd9R4iBGK3Es2gIIUhhCxgr87XkWvLyiJVuLP9flbOmKp6021GOw2NFlVeW9/n3R1qKfNCEKB/0SwtKUg9F52SOQVGEYgoyw2UOsxrZgHEeb2Rs+SkRtnIyKmxYCYSgiiI99OGIRJgTfooUE0dOi9H3ATlwLPVQrMlMWbgW8vO1dqF+sDfMb5YkxOiIgsLSDqouuMu1LRD+UdZ2rGaqYVt9rGY5SOS+vslbqJAcVPOeI8u+CqyzYjspHXkVtbIGQzFWhks6gfRqsKuftUK2682Gyr25txfkldzvh1fP6Vc9u82Gv959h99unrO//wJMx2F/wFI479Y4bmmKYdXIgs6T4X6342uPLvhHv235lDs+urtSsyQxxHNth0F0++NwYBxGqlFanW3St54QAk0j0vmsElUQcmDRagVNVLLyE6wThMwalfWalqfbhC17AhKA123D/f29JMMG6jAxrFEzRH1KFoZpBsCajG9avBOullRiXnyJ9GBKOVfHfXkO6o9jSh2kmGRdlMQ0HjDGCqpkrLaATtDCE28g65xU2ubYHjXW4jB0LjCPD7z8EvAr6aUXTcSSJuJWVErZeP7b73/KIViMK2TispekmlRia+MVHodoxuOaAgITxlhclyjFkEPHlMRHo6pEyAhBvzWYKHvFeEujCju/8uz2DxgHTdfgXQdYcprJu73cwGLwzZriG8qJGokQxCW2BDJiR55ToKRMtlHlprI/5rzDNS0mFXLUUQ3zSJwnsjG0fU+4foC+lWeTsryXhTlnUpvAJMKcsWtBWYM54FZiMrc+axkOmWdbyz/1r/isPOJfv2goc+TH157f+5olEjDJ8PnnLyi58MF3v8MXX3xODiPjYeDj65nP8iU/Gy8x7VOIgauzlXIjhPhY7RyMYVGRGIqQZK2T4uqN6vTkXHsjABzx4drS9rUtbg2mQOsKP3j+hNtD4A+/LXOyfNNoUaHuqVZ4bj/8pME0PU2W9olR1KaUwubyEfcvn5+ccfXKjklVvbhcCq0X7kophiEKnaBxkDG82HdsbaBB9ljXih28+FbZRd9gdZ16Z/n89o7zrie1K764vuF72wN/efPtpVNQuwhNI27U0xRp2vUSl3ISdVxQBL5vG/7m9YbHA6z7js3ZOeXsEkdkHEdur1/hfbOoXStyfwzkEtuc8wvSa6x4DM37YYkrzqjJYow0TYdRYnpFI6rAI0Yh1PbrFd45bq5fgYFXX3zO2+9+jTEIXSCGuHCpbu92iwVHSYnr6xuhQPhmUQqXkqXwMXAYRrquI5cJkxQlShHvYI4isliveg7jyFHJqMaFw6TxXT7XOActXDPtMjm+IkdRPYUE2fa/qlrX1681lwORIKFwa5XX1d5XKhI8ol68t0JWc0g/v1YVjTN60BYlmBY9gOpiLvWJfAU2rIhGTcb10ZWMpWARiLRo4BK4NFPtXt0xHZL+uBOWfjGCoCSOC4vCsSqhMvyPTH+5zkogPr06+b8lSAFiWrV8uF8BhRZFRSR41WJj+XlzggaVKnddihGVssrmr8ROuU+VXyMXI4ZER9VUNfUpqtqoiVP9cy5mMfnJOdOvt4zTzN31NeMU+RORBowAACAASURBVKvrd8jr93GuEKY9jUuEOIrB2zDpePKGiLTdXu0HympNc+Lx4qy0pkqWhCKMA2kewYhZ27FdVximeSF9175pTpl5mhE9f1wSMZDkeRnxrvetPoPDBMV4TX6SEsUMbdvSqB+Gpr964ORlbVbli7ENMWaR6dmjokf4Qx6sRTgX4ithNGGRpEqnSDs1LbMe4xppufpGE7VT62pN1mviS01qzRvLKaXEYTZEu8F2Z8fkVbdDUXl81vtnyfz0tacx8WRdi9dQLJFiMsUakikko710i3oqVJmqrKdcCmSZ/dK1rUghKZT0ZkJfd/pCatVNXUym8Q2NbdWoD2wjDsYhBRKRVAZCeCCnkTg+kKeBnAMpHoh5Ty4zcT4Q40QmEtMECBqU8wQYYpoJZSL5zP6wk0q378VXqmSKLSQCyURMA+g/0UeKFcfggiQ9dX+mnKWtVoTs3jYt8zjxH10c+MdXn1CsZ4iW24PFeMNuPOBbIfz+9Mc/5uHll8zJ8KPnA39y+Do/jW/R9x0rb3B5Ug+NQIlB2w5mQX2cqr2Moqq/irRYjmHuja/Vr1dy5Gk7vAaQmtA8H54yp0S2Qn71ztE0rRgwWoczsG5l6nMd/FiXLrmw3l6cqFDevLrj9aCkc+i8U3fZSmo/tvh3oSEbmU7dqMtv5w2v7vf84tWOV/cjz+9GfvZyx5d3MhfpctszlcK677g835DCQdTlMSzrs8apMM/Lfnda5BrbYKxb9mXMmU/2l4zJ884WrvqJb128plDYrFr61UrtJ2QEQa62/PVJFClAk8aElERWXcd/1NgqMuYRiiSlOac39rzEhiNnxmII80TB8XB7g7doyxRSFFPAGkvsiSN4jVnWiEGsyQFDoWt1HpeRFu5hGPTZy/yhftXTdj2ttrmmeSKEwKNHl3R9h9FWUW211+fsjdxrsexXCoYVasM0TZQkqqGSEjHFX7Fu5PUb20NObXuXHr0xR5UER6Ji7ZeXAtM8k6cZY8D7lmK0ulQpcVYHOSEjVtMTWcjVw6Vmu6fL3CicVmHjr6Is1hpBMirkufRnyhuVykLQRUNAjeo1eaLKSzUYqgZ9gVIronFSxhhqJVGWjLPUE6CKlABMJR+ztKYKCVNUdqo4Su1h1g9fj6WKcmGEQc5yyfX3CpdF/rx8+uWeSg+13tljwC/6LJw1pDiL54l3bLbnhGlkd/uapl/xF/MZj9cbVm5kGm+47DNnK3EVJSciBVsKl6uO7eWaZDd8eCuTb0tOpAJN2zCOElAO44FiPNYdl+LSu9Xn5IwkPF7Z8a1XU7JyZKaXYlSaLxHVOUemkNOI9Q3eO+ZZHEOtJte+aUBrAYHf7TFJWBCy+l9NFjX5K+p5YaxWCUV8UWoPuhJxi/Zzs3JGUjxuxjoaQ4YxWpX/1+y1qk1YEk15VE65ByoXLrau4gXFqc+65mzWFU1GZf/84PkZb51dq1JBzaCMkWQTWUMpJjKGxpvjPSiSwFD5XNFoAA+LBbgkNPKLs0mY7LBGPmdxRREgg/GGpvW4xlJsxhiFiVNUwrO0NE2CRCSzwzgnnjFYin5+g7itVl6R0aQoZ/GkiXMSp2AHU5jUybaX+UpB5pmVNpOVIE2U789WEuyY6gGfMc7iWovzhuI8u93I2dqxv79lvTLsDzte3LVs8o5zN/Fy6vjoleGSe4b9IN/XNjTdmlflnL/4dMuNeUzTe3oKxUibSpRtRWTQyKC5kIJyHpwIIowkrNYoyldOtISm7uivMviWxXHylcrP07VSItDQuMJu9gwxEYvjzBmS00F3yNTncdYJ7WqcVs/MxXzRGtbnVzzcvOKrrxOdEwB914hSzTm8KYSShLTnpOUakuF62rAud1hrOSh94mtPzhmmgDUyjG8fooxcyIXGWUxbaDw0CdrNhnwb1XyvxqNa5Svxu2hr11RLARE24D0xBFbtxM0Af/Z5z6P+gRwj/+GT13x009H3HXEKEuHUmTyqZ1NF6mshL3uvnvPQta24ypbqal7jWdZDQBIPaw2Ns0zTLAmItbQN3I4iLc7Tge2jxxzGWSdF24X3aGtBewIGZLVWGKeJ4TCwWa8Js5z/UuzIPfDeMWurjwJt14oVyVyY56SE44omRznP1SKlxuikZ2ZJkVjExXicA7Mi2VGH3lqNb3/X699hYKLyU7zMQiBljJMN4yvCoEoKIdXoSVsW2whykQo4pbSoIDSXZZn7oVcpahl1wyxpMVSzlGXaLFb6/pVPc8w31P+g1O1bjgmGqYFbfpMg2MLCzhW9MKhfypvpUrFvAlJLRWKOMr4FtahwTeGNbVnzopMlI4HFOP3aMUjWxOQ0cB8f4rHqlmAYlSxViVlGCV92IREvPCGzvLsmNRpclKSMMcwhLjBlRRiarqftV0Ahjns+uxvxjcc3j3DtSo2pCr/37jVrc+AwzlytO56+/YTnD5GUZRaQNYB1MsG5ygt9J+2L5dnVJCxzvu7JBvq+Z4wFhwTYnMWAadjv1AegIYTAOM3YmGgaMXja7+5wvqVrHIdhZIyGdSNJt2+6xcfHWK/ma2VB7bI+CwlYcm3CGpAEtihDP6UkFXQ46jeW9Zfz8p7OComtGttVbyML6g2SxUSqZHKpbUZdU+gaBnJJaAdfVAhaSBzX63GtpVQT2YJt9Odz5vF6YpwHDkEG8Tldx1SlBGqUqAfmFDPeFLypBnYqxzaWlCK2FYk9XoirqECymAI+kus2RHwqipV2mW0NiYApEONISTUpbDAmU0wmlqSFDVCCuMoK9CH3Ox9PNwkKlQcUoVh5D52ZszvIsM11u2K2g1ofSOJZciGVRLJ5CVQ5FGwje3O9bRbPk2E2DENks97y6S8+4unTt0SRsrWkPOPbc/6ry59zG3v+bP91jHvM7WrLz+4t97uW5lDwiEDB51lckY0EF+8srDaUAudt5pvbmT/7vChKpd5BWnmXXIQ7pMqwmizUM6Pu+VOp7FdfOWWKywuJXUw+VQqLkOAP08z23DEEUS6lGPDOsl3D2UoJpdRRJUeCb0mF1faCh+tXJ4UVS0u+noVd4yjG0XLHPnfCn7QWrF8CLrbw+cOW7zx6xqevd/zw1fu0NvPNi2u2PpBy4WtXK/rLHu8sP/r8jm89PpNzw3maFsZZ2hUVCZnGQebpKEK6FMOItLrWEL5xdLaFdc8YwLvMs/aGP7/5liAIB2hs4PKsxRnY7ae6A7D25GxePrWY89WoZzTBKzqrq2nbBSSQWT+SEGz6TpAICnMUBCjFyMcffcE8jsyHe97++gdYZ2kax+39oHjA0dpjQeJ1fVT7jxwzT5884vbuga5pJDaqGjhnmWIvRq/CMRyUiGtswTsxmn142GGNoWm8erMAxYpr+gmPqhLEh2kWsr/yFGuRHhehz69+/dr2UNE3sEZGU8sdOCHPlJPwbqQHVqXNblkIcjE5iwOpKBSOkKEkEFUqerQArnLHxZTKWD0otRjVHp0E5to+qRB6WQ7K5aOX0wRAiavU7zlubnNyXfLfE9QE/R0nWeCSBFCW6q8+nCV7eePTHt/nyApn2TBm+Xs08XtTSVS5LtaIWse33fFK9H5Ja0Iq8CVTMkYTg5rIHK+jfk8OYUGJlvsOmlTK723ajm61pRTDOAwc7q9xZcSawg+fX3K+8ZxvVqw3K955tMXaC0LRtkGWEext2y1utIZjJu68VLHWwnbVgoXWOzyR/+bv3fAPntzxeDWz9kKY3K5bLs639H3DZrOm6xo1gGpIYRLYvm1Z+STTRZtWArmtLsUSrIXJL2iA9Cjs0lqqiUVdPzWpNcZJ4uWOh50x1U5bE1VrFQ6VlqR1MsvJe9nU9Tn6thN3XX0yzhxbq6UqDNB1r2tjuV5dO5y2SJekq7pwyr7IRVwsP7pZ89mul/ZeykwxK4xrVW4tdtwxFUJSZLOI/UDRwzzmoqqJJNNntV1RhMxCRhKxTCaZQjCZYCLJiDW61Db6OY0c6oLTZXIJFOWxYTJYRZmMfkaycGckb9KqWThciUxC7Okz4iLqnQMH69ZzvtmSTFzQE2zR35mxjcM2kuAVD4nAwp3TPZEK7McsLrjzjq9/8+t0fcft3QMXF1tFKgP7aHl7Ffkmn/PHN4/401cNY25Y+UKjyi9ywvoGq+2WzaojZEHMGmeIxXI/TARtZ5giJM46RqGYk6Gzx0j0xjnzxvFz8iU5X8xx/WAWUriQqx2dhxeHFTFO7OdMwvL6YaQYIXO/Hhr+8lMtuGqRqNdSL8P5ltXmbDnQZImetA61ZUGRqfO2TqBXk0Znrc74ESTfW8NhljW+C5b/78UzPt494xAcz+8Ddwf5u7cvVjxMkYcpsDscuB0Sr+926rZaf0eSwbv2WDjW2+WdYdU1OKctayOxcNtOfLnv+fn+a9gSmKPlrW0iFM/fe/wlv/X0nmkcdd+iRYjGI0RaUueK1cI2pnjkHOpZnVLSY1mgVmcN0zQRw0Qd8FpKYRwO7F59gcszj569Q8yJOURBsmvh88ZzPyYP2gyXz2wN9/cPWOsIOj4AhIZBKSKx9sJLtc7irXZhvJcRAd4s55p1XsazeI91KNIp+cCRRqztTuxyHfVvvPe/NtH+jZwWsOSS2e0PTONAinJw+DpKWhMU9MFg5MMsRmrqFFrnTThnNEOzUlUoHJNLUXWDXxZ/be/Uy09f+SBG/2Xq/3CygZfe+5HjIRvzmE4IjF8Pwpr12jce9PJ+RYKu0QsyRttTxiyKkJo01IfytzkxxwPwiJacVMmnidHxDwr/6u/Vb13ktF99hAtUrG948tlqxi9fUBRBP1uOR0v240EoCUtNgER5kDHO0a7WrLfnNO2Kw+5AChPZOD67BWsjT59ekgpMqRHeSgyEpMTNmq1QJPBbVW4pv6PvGrp+RTINxrf8l995zcMw8fG158kq8q1LgULH7EiKlPjG47woHZrGk43jbLumazyHObFaren7lpwzjSJTp5+13qNjm/PkZtc1polLzjLlNevBWg954zxdv1LOiqCFvmmW5OV0fdQWlnNy3XXgoPOerk56PU3aKYqspONzLJU7UtfHSfjIZalelm5XFsLyzdjyf33yFv/q82fsZ4FqRQknRMgYi5ptocQ42ccpy2C0VETen7QFkwWkobgiPA9NXMr/T9qb/FqWXel9v92c5javiyb7ZDJJFl1VLEgwSoIgW7AF2zNPPLHn/hs899R/gKceeuKJYRgowBNbggFLlm1JRVVRVSx2mWRmRGR0r7/3NLvxYK29z3lRyaKAugQzIl5z7zln773ab30fEHLR9lJKAP16MqLBlC26B8QwWidBjBBUSRBkjILO9YhbL9FbtkjQYQEnFZyQhQY8ZIWpq+6OSRHfemgSxhWMkV5rNGSXiCaCM9jGiEBcU7BMWgXEkJLlbpg5HAea1nP59sjbt1eMQ8B3kPJMf9ITQ+Anl4Y/uzuh95Z9a7Fo6wFLMJ5gO3KzwfqWfd/yX373lo+6I0OAcQ5c3hz4f34903WdBP2lEmss4zQtDqeudzU03xqslG+t7UxxnAUb1jiruC8ZP/3Tbx5zN1veHAKv3tzROsMcDdvO8pcvW97e2yWLzqs0rVxczmzPHr9zTaaapmWaKRK1hSnCiqHazlLttAZuhszLw44xWqXKSDy73fLz6yd89epSplet4dG2pXOW874BHDmMtHlSqIepQPblogybvmG/lfHp/baj61r1Z0KI6VxDSoaQDa+PWx7vM++dwtc3DSkavrh+RO8mlY9YWfa17S6VqPLFDGGWycliGoqKdoyJGCbI0G83Un0xDW9ev+Hq5XMuXz7n6uUzHr33ARfvfYTvd/r7meO44GCyKS3khxVcrb0AsgabzVZaQc4yh0jBsoKMNO82Pf1uU38v5SxtPQPGePpNL3bYiX6RYFumiqOxFuLK5nq3VLQeVAvyIgb8ba/fOT1ETszTKDdpnRoPI/IiVvgxphBU2EmUn/u+xxgYh7G+TanUOGCOgcZbQVBbR0yztjgE+Nu3nnGawUiUW5ltV3u+7AUo8UjBmMgPmBUfndVRPSlbLm9UWzvFsZsSzBQrnHV55PCUlkF9PCWIqf9dBO7W8VX5tRpkvRMULd8s2IflJ8w7iyjOJ9V/l8rLPEtfUHQhTL0ZuRal0surSHt1gQWwWx5yOVJFZGxt/8w6UwCarqPtOlJKHG6u+f/Sh/ze05nPNy33c+K8u8P7C+Ef0FHNaZrFCNjCgiHv3KoE+h88vubJ9i3Pv3xGFw9c3l6wOXmff/z9e86bwP008+ku8svbC3769oyn+4zJI8+u5N4v7wN/+CHEfOD+OHFvTvjs5IqfvdnjmoZZmT1BSpkZyMqDkrUUmk2qVbi6GiXzAe1QSAUxjSIqN8+TZiXaD0e4cURITJgrKUZYKyIyyqjVRu8V97OMWqaaBQtnQmnFoq0CVoGv0bUse6zG8iyBYusdTYzM0fKrqy3wlL//0RU3Q8DlGW8N98cDm84zRsOudeRsCFoF9RZIEafZoKiAS+soarDnnZWzEALGWcZgMUZE344pYwl4ZD/ELHuvtQacZ0oG75OM9QboGs+cRNk5RU+OI631jDqhZatTi4oHkWBkCktGWZyj9SjIEtBqi/THpH0UcySMGWsDrrGVxTSTyabh8nbmfkp0TcM8TVyOmbu7O05PzzgeB65vB84uzjjcTDx98pSzYeA/+IHlv/8LGJPn+08bfrA70jSeD9trXJiZxwNnJ+dc3d+ytydY2wOJw92RnDObtmWcAhhHmie6vmcaD5yenoomXJZEK5VqmNw0Bcj/7utBBrvEz4zTLNTu88hmd8I4HEjGcwwNP37zXf6zz5/xydOTCgZu/MwHp5l+05PDIHu4mMzyWfoBTdfRbHZMx/uaGKx+CGMsjQ3cDY55GjC+wTedVuBNrShiLBf9yI9vP8DZZZDD2cSUe+g+Yo639N6QQuZ86wnJ8vbg+dX1BdfT53g76/CC4Fa8TtXlDI0X2nnvDIdxRlqMCUNiiI6Pd9d8dbPDIK2Sx+09xxT45Gnip2/2dOaeL26eAr9muL5kd/6Y2ibNWUetyzO3dQ36rsU4K60qL9p1VoOl4e6KaZBpt2macAbOTvZSlbKO/fmTZWRaJSGsNXRe7FjSxGXlfNQ2ueofLB5r1SYZSCpuLPjBhpOTXtW5EyZbvBepA3LmqPT+bc5McxBJEyXY886w3e0YhpEykl/lUNS2yvXpNHEN6vlbMOKuskLxfpHCNxJjqAA+skhLd01D13eMs1CWB50hByBE2rajbRuMd0zjjHeeBMrcJyNhxgrpzaKZQc06y6GrY7u66THiStKSetQ2Xr2F8q134wV9WSMTRVZXcsGAFE4YU7PtdXZeHb2pe6J+4Npu1ECAArZ9WNWoca8pmBbq/ReV0BoMWYvJCmxSI+xFPYsQRlrVJKmpqT4QycT03kp2Xu61OrmHQcryyB4GbLBU2GKSKlnX9+QQ+ODpiXBkDJHvncP1dMtXN6f1XbabVoMkizUimhViFJHFZuRHT6+JxnG1TXz49DN6f+RfPt/xdD/gmjsen+4ZwoHPT2/4vUci7hfixEn3KVfDhk8uEt/fvWCer/nGXDAdduz8QOakgskhL0DctFDri4NiyYxWTwBKxi1PqATTWRe/bfu65taUCqO095y3FWycC9hP91WKMtFkFCcDuZI62TJOXbiDSkVTLzDXwFTbhq7w2AiRXiOc4BgkYCmMRkmlLX51dc5ZO/PJyZ1opoxCFhWAYZbn0ris7MKK/jeSqVpnNYtbGKqTZrE5S4Am2BAducxijForz2cMU01KorGQBNQZFBhpTVYxSuGIGId7Nn2jQfYykZi0epetDN1OQblndH9HPU+RYgO0wmCMBj1SyTHW4BsoODuN/zAGjlOS4CFlXl5ec36yxVLGZScuLnYMxzekDE1j6fue/mzH5TDwX/9+YG43nJoDNgzCHXR1T7aO/ekjApmzi6e8vJ15OVhsmphzSRCELbfrGhovUxmbzVamQrJQHaRYRkeXAYLS1nr3FK9fpW0YQsJ7aZNZxV7FqJgbk7gZG7662XC8vWHXZrbbluf3G/7Zb/aSjMQHdYWSLLNkkYbd2SOm4z0PL6rYocTdMZLxZCacJjCF+6gyr2fwNhOz0Bm8a5K+OZxyOZ0y24FPz47kHPnpmx0/f7vT340EDc7macRbT9/6hSLAGDDiqGdlosU45uQ4bSfmeSTOgiVpvOVff3PB739oMekNu84wxA7PRL/dcX95We3Jksk+DNisdTiTqxxIzokUouqaZdq2YbSeR48e0fc9t/cyxTOHSJpGSmu/aYQZV4oCRlqjKcn7qCAhBvUhpnKilUCm8takhLW+di6MkTOO7Az6thW19ttbYRPXsfemEQX3qOR43jn2uw3zNAg+ZVVxEv9XMKamYuhilMTEWZGkeOBj33n9TiBurZIbMbDOGJELL9m5tlsKI+w0T9K7sg5jIzmZasjHaSblRNu2KpdusVkYTPJqrKvvejKidRFXpF/rBV+qGUsuvGSW33IfrAKXd74OSoBTw1BTs2BdvuXBa3m7HKTy36WIz187TOvYb/2+pYqxPu7l4+WiJPIt/cUFsCQBnlVG0aSGyjjwtghbLpWYh6UfLQgaWz93CQLLXawf4rc/sapsmrJq2SQNVCNfHp4yfDXxg/1LLq9nzrnhN+bvYkxiv2mYc8OuA2cCbw/aT86ZY4B9E5kjwMzvf+8Dru4gNaf8+EXP+3vHx98L/Pz5FSEd2Ww6dj7hNg7sjj9ML8F6Oey3R3YnJ8RxwNiXfHFzKhMBFIBsWoDjyrArmV0mFi9XV7ZEpKt1LsFPbd/Ij2SzTDHlLJTvpYcv1To9U9YqOZ6szyJzgbZqdPV03aTvLwHPenUK/b8zC/ZJes6+Om7rpBXrvBPWaQ2OUcD1P3/2Hj960vPD02/I1mCtSND3XiaaxiAV0NYLlmU1lEcRzky6h8aQaXtLykZbVxbvDENItBp4BQxpDgRlXe29Y4wJYqRvPTFl5phpHAzjhPeWec4is2EVPIxMR8Ussgcpg8NqoKUZpC0A1bKOKguCU0CzBjIZRJWWGvC1Xt7EYhhDwtqWvs0MU+DxxZkmCJH9/gRnLaenF8RwQ9M0bLot2IY5TvgGNnmmm17z5uqaxnqaTcemFcK9PIxEk5iHkTe3jrdDh5tGciqAYrmmeZ5JFtq2I0TBORWDn/X8FRv4TiFjsd+pJHwPAxoBWgZRhrcNKQSMkQlEZw0Jy/O7DWfbt2y6hrON5X/98wveHj3ffzTyl78JIvWgQXheXUcJprrdHte0wpxejouRya+UsoxRZ5UDccpCbEvCKJfrMMQkE3mWRFjVaQESntvR8NM38HuPD7y463l2Kwr2EpwaTbiFYHDXd5WUc9YR3RhTde45GxovAYsJN3x507Pf9RhjCdOB823D907f8sWbhsfuJT+9OsE1O3anhps3rwnjgGs7NQzUc5ezVCOcQidSFrXkxlls2zKOk+D+GsHbtF3L3d0dvml1XBjmWQgnnTUM4yQ4qFYEP1OKnJ2e8PrtW3zTyARaiCuYRglYNUk2RkfW08rOyDOdQ1ScVuCQEuTAOM4S3ADeIRXTmOi7XrWPIre3NxXwjEJKyt4zOlFp1b5Z57DJAJZQq2r81tfv1h7S3nwpc+W6GYtRkgdRGPtC0gkHzTJLNF8JcSKEw0DXNQSlAJZsxlQjE3OSyouWqx5UKdYBQYlSMqvx50xVKXonUqnn9N3qS30JfsTksrjLD5rVVSwI+fWHsGTo2io1tfKiR1cj7hoa1Ladqej9dbUja7YOq6y+ODkMIUmLQTAUpk5uPcRpLAFXqRRJJWCZThKK+lTbQfWprK51FS7iS4uiPLWsnCg5c/boCdZv+ea+49nhCY+2A79/9jX/wP0rYvOY18OGf/jpLf/mxQk/efOIf/zpz7iaWv7q8jv8F3/wjJM2MgaYkmWOnl+/fMnf+fyC/+Y/esvldcuvv/4lP/z+pwxzr9m44aJv8NYwJwEyems5vTjheoh8dtawayfmdMf97BlCT7YOm2d80zCHgCtI9ZzZbrbcH0fmaRaOhFX6WPafwWDc0nNdwJpy2mOWErRznqgbseK8otTRU4yVy0FafHLWjK6vTBUF0ZHKuVZQVIS4npcidif7qaBSoXCqNCYzDAOz9VLy17HZWTketrsdm7blL9+c8kfvvab3nuMstAW7zhOwtCbTN5YxRIYE2ZcMyWjwZrEKOHQmM4aC37FMU2YKsVZerbFSBHSORvVG7qdIjjPOOa7vbtn1jVQOrSFFGYUdhpmmsUxRst+UkjA6m6TkivL+kwKJrSkCpUarCTNt0zBOI9ZGdp0nZmEUVjstwZIWhucxi0FOwjN1dX/P5eU9m00jDMFhECwSmXEc+M2vf8pHH3+PV29eEsPEpm9pvcclA8MRYw1bB9hA22xo+g2JIKX4u5FtDvxPf5Wx8S1TLomTLKezTsGMTqflDF3Tkq1lngONlSmSqDaiBsPvJlLw186tLaSDGaY50LWN4IRyqZFbTBr58mrHHz9OvH/q+V9+esb1wXDeT/zoyddc3Z7x1WWka2x1TpWmajkkbE8vuHn9QgDYdsHUhRBpWi970kjiNoeZfrORqkLOZBwfn9zgueePH/+KP7v6dO0YlpeB0zbwP/6bD+j9MhVVfq5pWwpRp4iiCg9U12/0nMl5CvNE03Q82sOzVwdss+fkpOXu+kreL8EwBP7k7pwnp5YxPeEH79+xsW94fmO5vvhA2mbNwkxdWsLeebZ9R1D/6ZUuYJpntk1D23Wcne15+ewrHj95j9vbe+ZplhZMBt94dvtdvXZroO165jDTdK0w+FrL0yePGYeR3a7nzdtrNl3Hbrfh5ZurqgTvFa8zl0BVE+e1KPAcpOrYWZH16tpGMG84xR4JxX8IgZwDGFHYiiEwh8im3zAMgzz3MmaAkwAAIABJREFUWhqmwiCycrKI6nbJ2v8WlZbyy05Pdte13B8ODw6Fmm2KcvHSs1rYG+WQyGrLH1KtaZtWRMa0Tts1lmGSB2idaC+gfeUSwCzvpZdglsNhy3g0hria5vm2Z7COad7d+6VnZ5DStHxe8fqZoptSyJkMi4x56SHWAESflWxeNST6NVuCMl3MEkgsBZJc77XEaOU6XQEqUio/8nvpgWFa/nTeUqZH671UB2ceBKKY5RrfDfKW99QgTtczA/vzRzqPn+nbzOVt5E+PT/jo/BFP81v+3nuv+L+//oS/entK11q+Pn7Id07f8Nn4Ba3dcpyFBfP565mLzcDHjx+RzY4vn0/89Gd/yX/yH36fu8NEzhKcGANTlFZCA7ReRuyMsWyazJQ9r24OeDPz/Ys3jGHH8/v3uB23ODUgySxswdZanBJoxTBzuD8u1RRjIOYy0U/pHxgWun2Ug0gmR5XIzhqmKdJ3HQUM55qGlCKu8RQdrXmey6IDmbZtQUu9BZxdyKpsqW7puUwlWlan0fiFeMw1nXLCOLCZEGWqw/tWpjOsUJ/fT5CzjK++PY4k67g+3PPhSS/OW5lTG2dFDBThw8BIW9VZsK5BZvMyMVmGaabxApqMScr7rRcA9RBkrNpbuXZnLX4jmam3cqruxolNymy2G24OIxvbCsszmTkbQjA6FSV7ICSDITBGllHLHGmdnGphLrYcJuH7yTHj7FLRSClrK8GIVooGPfuN5XhwBDwNhmBb7m/v6c9PiOGGzz//Ab/45a94dLqDdkvjJYlw1hIImDljvZyxKR0JxwnvGrGrLnGYE8Y0WN8Ky20GskqYpCh8GXr9rW8ZphnnnTxLLfrZembVnq0yN1MsxboMA3U/ZmT03VhDmqMUeZ0lROHrcSbz8+sLXg2OX7xpiMbwZDvxaOP5e58OPL9uawC9VHJWhgPo9meYt69YJsM0eVKMnPeeOYwyzadVmlhxg5n7yXNldnxyOvNDe8ufvzqjsStbh+yvl4eebVPIIZfPl5ckddM00TWekDPb3V74RbSlGqYD/WZL23pujgO+2+IsDMcjdfDBwpPzPR+cwWcnr3k5nPLRbuBXX1/z5OQJbW8Zbg5sGtHNyimTdHqmbT0JwYMaMZ3kDI1vmOfAZtNzd3fH4faO/cVTfNtqwCqK0Waecb7BeeFSaZ3ocDlncAY9h4Y5ylDB7f0gdAoY3l7f1GfvnYhnimlbBFVzzlV2IedM2zjGSZiIY0JtmqtrnNGpSTK+EV2wopHnG888DXV9SkySRWFFKnxOkihpR9o6TfXbXv8OQYs8WO8dfdcRM0I7PI6sJ2Psqq8hZfBSQbEC0FKzLqObhmEK+EaCEt80SksclXrY4g3MWQE6Rngdcg0GzMKsWXamWaoZ4spLG2S5h/rgvu1VKg7lns0y6bE+FtYaYl45MeUneFilKM5cApycSxn/YcBSgocCpk3rCk7NECTwKoRxULhpymTJ8txLAPEgKNFxcNHjEdNVvrdm3a1Pp7xXXrVCluAYWAGY69ORe9/sT7TlEgScGzNNt+EYMn/5xvJmf8q9ueG9k8zPLmEe7olh4u7uyL5J/PyV5em+Z9Nk3lxdc9FvSBi++uYtXz/7ms++c87dccI7OfBzlDK+tQIUbTSSl73oaFvP8TByunGk40A0ll1zw/fP7/jNzafcTCd1vzbeV07ChcPHaTawRP4yra9Pxmk5/MEBk7KtLJWAVGMURyjgMlWAdg3WirKybxpy1DXNUfeCBD3rQNYhji/rRF+dEjNGAzjZC33rySQleWpJMRA16JGfl70YY5CR5knEKX99e8EfPn6DsxnrGl7dHjEk5gw5GnJInG6UpyZTAwtjhKDMK/5gRpwHOROTYeOEtC5FqRqMMTFHqcw0SB9dhBllO2VrmBMcZ0PEyaTNkHC24+buCDZzthV9opASo4Ju69ZFKgVzmIX4LAlfxhSSluMhBDHozmRaL9iqxhkO03JyYpbyd4jSMjzYhpurA71JXJzsOdluuL97yaeffMjry5ntdoPzG47THZ3paHwr17VxxHGUM21ljSOROMzM+cDeZv706oTOCW26QapM5QSGmERwMs4iVqrU/SU2iGEkG49xFpMSSYn2vDXMIS+zB6uApUywVIZyNRkGo4jaBShutcX2528/Focy32PajrONDGN85zzxw6czf/7MsukMhYdjbWyzERvWn55zvH5T7VfSNpvzWh2yTblA2bMaCFmTuQsdV+MH/ODxV9hjWhK7tSnina+tTVQWvzCrEvKYI9iGaRrrNOY8TWAdXdcosFT81zgc1RBKS2oaRz49v8Mz8Oo2YPMrXtzdc7HfEGygc3Cn5yqWCExbXcYKlrNthM5/v+t4881bYna0fc8wDhxvrnny9D2ppFinTNpyY1HlQKxTwdcUmeeoAYNgU8Z5lpZN33E4DFjnaLyj6/bc3UvF1VoZRPBNS7w/aBtV90a5VgRz2LWN0vArmPZd62+kBZUzOCcUDViDyUbbRBIAq0sElFxPJ5USwo2kJ/lBt+Dd1+8OWoyUg/q+JySZCxfaZlsph8WRZowp+jqym2yWMlPpL1MMvPaxUpSyeeGucL5hGI6K5hYF3JwzIRus9QI402w4aqZXPv9BcG+lbCup0zI+nB/e1urvplYc8gNnvQBjyximQUe6K0thwZToIublkwqgz3uhVi/BRimNymXrddqlP1uvwQCrSk9W3E1NoPIC2lrf36r+ofe1Qs2Yopu0GK7yDGqQtXom6zhm+R4PlGO9brb9xRMRAcyJbD1xGqV/DPTec31IvL50jMHSt0Iw9xcvtrw8+yF/971ndMbx/MUtzmXOTz2TPyXeDQwH+P53nrI/3WEQ3MSmsWy93NccM/ez4/ow8OLmwKZvOe0s28bz8l5KlsY1dBhCksDtw/1L3GHDs9uGzsM4x5rd2ULjbaxUO6BO6pTA8kHgaSxFeyilBUsRo4bOFrzxyktgsMZrVq8KrzljvadzFms7offW71W209JzLIYcR9O6SpCGESNgjIz75hho+62MgGoZN5f0O6cqnll4OSDzZ0fHj7/6hMbBf/r9LzjtPCk77qdA4yyNE5Br620948dJ4IZTTNgx0TeGxjlMyoScaF1k20jZuWvg7SGJnUgZTCPYKisj1q4VtuLXN6G2RufhQEq6DhlCDvhkuboX3aY5qWZNyjqZIUZbKkhOqRS8AHaxzFHFT7OoWUcMGBEgbZwAl0XAVbANzkmFaNc7PkwGO89MhzveXF+y71o+fPqY1zeBl28uCeMNTx9NbPuO1ndMYaJxjtkFzNZjsiRtjhaTDMwT+33LP3l5yj/9IpGHGy3RFzsiZ3K/abg/HHHey7g3CmjWdWjavioMx6xs0HpWrS2ke2sQeamoahJU8iykinY4BgVkFjyQ2KQ832NzxnY91jte3rdcHjPz/Ru2TSbFLTl7zdBlLapd1KrCdn/OeHtZf0aUiAOH+yPbTUfnA1N0kOX8VFyhUb0lA//viyf80ZNX/MefTfzk1QWvDh2dT0twtn49yCOVL6ztqi2r5HVG5Dm2uz1957k9BL57ccccI8/uTlUvKNO6xMl+y8wpv3z5htfXlpwb/vO/43n0XsfPn498ed1wfxzIcRLJD61YlMuYZklUhGnW8vyrr/BNT9v3Ck+wbM4eM6WMTZl5HmoAKVIpFm8NwzhgkCk+AaTPIm1iBbvXdh3H4yDrGyKHGEkx0LaerMHUcZyxkwAqGi98bMYY0nzPFJt6nkYl4RMTZNS/PCwBWKS6GJJy78iOFIwUGbuqChpjmMaZpm0Yw4Bv+iVpN6a2vL/t9bsxLQjKeQ6L9ktxgqUVsiYoK5Pv8nsLz0oZ2USJfOyqsiCTBi3WUnvlZaPGQoNMcbLmgeOwxsoYdqlOZFYyA+saycN7kj/NMkKa159Bff/S9y0OKq+vxci/nS1B2/qw2urYkpYHSy9V7FF5z6yZjV6nBjPrqGpdPSkhBiAj6MpGufqh1Y0uEbM44WK0lusv0ccyQGgevs07Acs7D7C+T7fZiIAYGYywJxeiQCmPBqEn9x0nndWSaMKazO3RENlCnugbyzAHPnz0iMvrQKLlm+df0Pmex4/OSCnK2C0yFZJSZkiWr64HMtDbBpfhdspMMeKN4TDDdrMhjiPBgM2W1kX+0Xde8K9ePOWra5n6aV1W4Kkw/FqrLREjVcCUEi6bB5UnCZzl53Kyet+pBmuavi7j8jlXfp9SRYlJtFfQs1ToxZPu5YeZo56zwn3gDDkELTULx4ZQBUjGao0FKxM2y0hqroGx7FsFJVuPNxGD5a/efsz3L95gOXI/S1WldRa/EbrxkCRrnaK0eRtrmWPEO8f9GDhtM09OJSv7P3/Rc3W0HGfD7eTZdpZ/7+kdH5+Geo2bVsi8vrkL3A2B003HzdtLNie9tinkuTRNI0Yxx8rbFPNSfYyaFHkDs1mCfa+ZO8AUhaRsDBnvDHOUknqI0HpTq7plfTetYZwNm7bh4ydnDMeO+/tZRGFzy6+//jVDGPg7v/cReQ6yr52lsx4CNLZBWjsSrOUUwXqc84S25WeXBubjKvlZnXhjGcYRV0bnwywVQA08YjJgUt03urqa/UoQ2zih/i+jpSmLiu8cgiQluq2stQLwd4s0S0adWEq4tqs07TlFXt42fLW1GPMJ//qLmVbpA97tyZTMOiNq2/3uhDAceEf6mZQzwyzaXE3jCBHIErw7iwL04e2x45vDBeftJZ+ffkPfPuHZzRZn32kprN9egxNrFxV1SVBUJmGa2G23AvoOie8+mnniLwlu5Mv4CO8M3gT2m41gCZm5uj7g/AmGzL99fuR75weebD1/+nLPfu8Ybi61JS8s13VkJGsrzog4q296cL6212JKGM2gUpTkKeUkGm5Oq28a1EoSLQKDhXhQKs1WbYelbS3joFUS5yGX0WMRVLROJharcOQ0E6MXv5ZzlbExlW06U/Gl1SMiCZT6wvOTHffHkWma6Tdbus5zfX2j1BcBY6SzYZxbCP9gSex/e6Hl3y1oCWEmq9JkcWmCxZCphZA1yiLX0TGLAJ28bypOopSkyg0SS4k7U6ZjUtK4Jkv2g3GUolGpOlL6bixOuLwqx17NEtatjOWuSqxY2koL6Zf+URyLFkLEv+uCGVnklIrcuOJKSpVlhS0AFgKy+t7r8J8aHJVrWld6ANFg0apLNQo1QLKaMSzVmxLgkCGTNKNxNWOqr7xkXnL9lUSgfn/98NbhjBy4gvI37M+f1EmDnDPzOGqgJmtojCDQ52mApiXHWMfaN60jZ4f1liElhhCIqaW1kdPtni/sgY8/+hSnTKeNk70Uszj25zcT4xQws4ARj2HGYLkzmY1vOe160njg7XGkbzxJp7HuxpHPz17yyQncTUKbfj3OfHP/PlfDhjG2ytz7Lk9OrtlpCb5TFoCrsYY0zRhT1r+siz5nuwDX0XVb9sPSbsVaTMpVrK0kChWUmwo2zAg3gmvEsFivLKLi2NrGMk5xCah1jauh0b8UZWTjPAbDF29bXtx9zB+99wJn7khRetlzKxiQGCO4TONkEmuKkZgMZk483Rmm2PMvf9NweYB/8etGjTTsT7YcB8c//2rDf/WjZ2AMQ5AM/9lN4PXdQAqwGQ50PczGkaeZuzlw3svUTUozCSvBklvpu6SEU+frnbTRorY5xhgJ2dA3ntZZ7qYZb0XsMTmHSeAMNLmsgdGJKdnP3ibmKTMdDvT9juNhgBR5e32J7yzffe8JYYoYHVW9j/fs/QbXe4bDULFIKWUcCVIgbXr+7BvDb14faIgk42piZIz8bNs45tmpRo4SkGWZvIxZRlzR8x1CgBxru7pWa1QDRsbLE86J/ELTtqS0bIakGChjDGGeqzNTYyTOV5PXnDPjMPJ/XTcMwWJNS9d4bR0v+3ltbspXTy6ecPni16SQmGcZe48pYICmcTrd4zjvAldjLy2dFW/HnCxvjy1Pesv1PPKD09fEcMGbYbfES+9mqTVwMjU4bLSiH8OEcx7vDVOIzDHzuLnk9dUtm23HDy5e8+J+zxwbjrNwvMzHa5KVym9MmV++avnyZeSzp1KhMtbS9DucdYSUtLOgIqVGSFatE1ZxSlXCLAWAggsq9sarzZPpRF9vLCrzrlVeqeobTdE/U/mcHDG6v4z1dJ0nZ88wiiRDRicbo5D69ZueeZL2quBT1F5pAi8SIcjQii24pETrG6Zp4ni8ZxhmnG+ZppHheI/zAgdJKYIVTakwz0J5oguXoVIZ/LbX7x55xhDCwoq63shRZ9kXw7tk7VENegizjq4pMCpphSKLQRZpdU8aZO6861spq2dh3lyuo7RqTC2ZyzdMDShK7aL8vDhwU3/W6MhlWaTicAR7UK5RakW10lJ2e/HjWTZdwmCMqyQ4JUjSD19lHMt7lYwmrbLwEhwZVORNWw2FsVPfbmlVZSRC1fcqBkKqT+WzzRK8GKvPTPFEJVgrl4dGtcaQo6mBxMNnLt8vQZzM59dbIFtHv90CmRgSIUx1nFRaZJnDYcBZONlumGIiaQbnSRjXMCaYh4GP3n/KX/zFF7S54eX1NV98+VP+8T/8QzFkxtBZZWMFnl0fuDxOhMlwuL3nyfuP8THg7Yaru1t865ktjGnk8a7nvY3j67uZm+PEdtvz+jBjrbR0ZPJNWk7vb7/m472hcZF/9eIPmLPHNwmfRZU4JiU/S1JSLntsmsQAOJ2ddU4MsBxqrXTlVG2UNUtmokuA9Q1hHjFIyzQmUWg21pJj1EqNofIlZfBNqwmBqwrAWQGy9/dDPU9ljuxBEK/7ueyHlIRDxnkH2fKT159w4m/49PQtp9uZ31z2fHnZ8ubQMM2oNIGjazLvnQSyafjFs4E4HdntO4wRoqsS5IqyqwR9v3ibebwZcLajcZ6vrx3W9gy3z3hjPBe7LVsXCVlIE48xsMPSe8v1ELFWA/0oKvNYqZAlvaWU4TjOjEmy1K3xXB5GYs482XbcTYHjDLsWaeNkOM6BrnHsG4jZMUyZrmkZjwdctmxyz75x+N0Zwc303nN3gKbz5JgxTjLcfdgw3o20TcbtLSYZPC3BTRg8tvE0NvMnf+U42TaE0JKmsYqgWmPoW8NxHJXfZ2ERT1lwBs455hgUYyDO0noB986TOEgJXnO1b95L+9Zahwj7RSyObAzTFLT6IC14ax3WyGRJDEGIK+1SQQbIdkPfleSstIPE7hXNnHe5tYxrCUnsUdf1HEehnB/Gmc1GZElSCJy2X/CkP7Dv99D0/Pnr94nZ0NjMy2PLo85yPI6EBI/8c17zg2Vfl02ujj+tLF/WvT4GSTZt04rVj0c+Pblm0xjux3t813McI7iJmOUsHm+vxLfZhpQjJsoHeGdIseVqPiGnxMX5KW3T8urFC3Zn56SU6dsW76SSbK0lhCiU9UHbYOovZB+pbI4a+pS0ouKFNXyYBFOarYOUFfouSbRk69oCzirNoDgaLe0Sj6KV5t0SMGDA+hZvTcXTFRkZoz47p0wIM85Z8gP/LGKf8yx7bIo6xq4Wx/qGxnuGcayYmKwOy3vHPEfQvSUs+n+b9pApjpXag5QqSKqlRus0Gis/mAp1d9b5b7nwWHVb5M2iUjUDylJqYJxo2paYEyL8qltNd7x8qXrnVUuG+uDX/X8hjJOvyfj2UtBKpcRW6esXfo3ylhV/Up9H+dyC58lLkLJawoeuwQCqFZNtLRNW7RUUK6FGovx2yQhMCb7KO5tV9YcSkNUL/Gufv2ayWY6t3p8x1XgVR5gxkEV34oFWUfGsepacYjf2j57Q9luZ4ggCWIvYWuIzRsr6u22PsZbj7S1gMc7RtS3OQecDJ9YzzpZ5vmQOjmfPvuDv//vfgSQsqxa4HALPbo6ElDlMM84ZMIndXhhWnXUcp5HGO+Ic8dby6vKGPDR898mep7uOUUelG+vI2ZJSwJnMFAvwTx3ebPmDJ1/z9e37vD40tBVpL0Y9ZAF2ljYYGgQ7LyOHjffCr5Gz2hHFsKBBhROQLkbbOACazTqXIRus0uAXIcd127JgXVJOCHmgp6pLG8t4PCIq02Y5J6tWbt0f2mYtf89kGtsIcC5GvrrKvL77gLPuyFdvxNC1raVtjSYsCeM6vrpyhGkgjfd0m73cmyYPpeIkgblUj/785Qdc9AOH2XM3t4wzbDvLP/r0yBQM4zjwqNlyeRgYgwQIv745sm89wxw4BmmrnfctV+PMeW+4HQO7VhTKL28HvLM0xnI3ioxEysifG8EqSZtIWlz71skeyIbb48Smd9zPmU0TsAFsADMkjm7GRouNI3OGTeuxxjKp+KONYgNTSmSbmcepalAZKzpNnU/80y+3bDvhtMGgCuByAp2B++Mk/CclQMhZpizKXlMCOChtRFOBxgK5W+yyVOj8A9uWstg3a9CqZaqyDCmJUm8IUdXDc91bUqlZbMi6uFHmI8RmJ+VBWu81cN7z6On73L39Rio7pSKkvFQYg3OGb6bv8Pn5K4bxBp9mPjnp+dnbR/Q+MiVHMh2ZgotJS6K03txqq7bNyHFuefclmX1mipYfPfmaKYJJLffHI947vMu8PLSM48h4PIIRfpIF+5irXzR4efBEGjMS40y33RKnkabrJYnJRUZE1rvvO4bpqJIVYueNVsWFSFdsMdZV4G3W6ktmwdqpF1DwvVQxcgzMs2gANY1fcG2UdXeVSiGmiCvUJUqXUCZ9pTojSZP4KQ+kVaKe62i5Xdmnguk0OhmcVpIeksyLPw9BAiQJmCW1XmRq/vrrdwYtxYmWAAGlGQejIEAlNarOdoV3USeYsjhO0RkwmtFRqzOlpWRM6eMHnHfMShFclqS2X+r1rCsOpXeYRV5cf06EBReBxmqztWpQX/Vj1NCuAqGHYLASrKVSdnnnlNRPWpanaNkAGnZR1TVLNWZVISq/WQKaJZBYnkONUVjfxoK7eRAyVSdVcDfyb8E4lN61tswysqaGlTbHGhglK1wqbylFNvszqTyQVYbALhvQCM6iaxtCCDLx02+Zx4F5DpyfbDhMidfHUz44veKnP/sV3/n4ET/92S/59OM9J6cn5BiJWeTXf/7mjjmmOu7cWkezNUzJE4aZ68M9TddW6zkdBs53G37/43M6b9lPgeQtr47S451jwVUYJTIEm7X6YTMn7S0XTxv+6ZcfiMJ0jtVoWWNJJj1YBFGLFgE+4asoQd5DLAs5STaDllx1TUIQZ1Ez1iUaVQOTK5tu3a1J1sipAQ0xY3Igq9haTrnu7QccPsbqtZnl5/TypnEkzBMpQbfpmMeBZ3fC1yIKtLpPVSujaRzDzRtiTDT9TlsZkhBYp+VjLNM4iZaLMczGcHvcyD6ME33XcNEduR53vL8/MrUnXN4fmFMipsz1MAGZuzHQWpmwuRsmFX2M7FpJOvrGczPOXA8T+74Vlt2UyLPcv3eWt4dJWoVKXtkaARpb24k4XRDK/9txJs8znW1wkyhspwTx/ojzMJujOn9L66Si0tiGnKA/3UCTiMEgxIRCKtZ1kX/xbMP//vOZvhXmY+HNKc8VmXTy7ZLhLkccCWqWQYConECgYP9C84sQ0BU7EqvNlEA/Rat7KtXEyGjSaRXMWWj70TMhW9KSc3jgF9aYuPL3OZTR2MyCX5Hv7c8fc7h+QxwP6jty3YPeS5tpnB2/ePuUp70jXj/jw/ccf/yx58fPT3Am8fxuz/vNGzzQdY50t6ojv5M3NmbiyMOgpZzjOVl2XcBZD9EQorQ6msaT7SlvhhOIA7ZpSUGea/mI8j6CB6qWmrsBsJ5+s2EcBpxOIlolDSwVqDlEPYtLJlovvZp8U1m7wVTQdal8iIBmqlcUo7RfYpRxYqmahHrfpYqRcqa1KI+S1WA0UThKzMo2FJdorBGuKe1IWK0iS/C2+JJSZS73lrIA2p21tTth1Qd3bcukFWuMAsft4rPfff2NQUuZKCmVi5SEhbDWXkGiaZ3TLgspPy9gtKgboDCogqD0Q1oqGDJ/Lq+27UStNWaRBFDdApS8qhzissiJggsAdEpHFpRKHV6ubT2ivWYWzXkBwq5HnWtck8u0DUsmUwx9tSbrCKg8v/xtX65tgtqesaqOq29You5SKlvc0+ocmoeBSQm2SlZd7mXNfPgw6FuBjK3DF6IylkAQYx9cfg32WKov7f4U4zzj8Q7nG6wXEB9ZyohY+XfMy+hvHu6Z5syj81PGkPjRe2/57HQiTBvm6Yqz8x/x1ficO3/CL1/dsm89z24HxlDGgSUzBrifZ/Kkz8Zamn1HYzzTMEjw0hqigashctpDZw2tVeE/HQUWo+nw1mFSItpcDf3NCH1zyZPtGVdDR0Fg5ZxUibZgq+qil5CTeRZDIn5ADmpCxveLcuyD3aN7Vqpwy+80TVupt7MGkJbVOVAg96zMcxYdF6XsZ1mPEqTUvWUeJiXiiBbxUlGyzgzHsV7nHDIQqAGQJiRXb0ewjTDWxERhGTYmC8eIMSSbsNkRQyI7MWzeShbftA1ziDy/7UjGctYNDPPIEALeWlkbk9T4RY7zRFF6H5SU7Ju7iZwzr+9lvNg7yxQiMc2UtlrKiXnODESuxxFnDPuuZQqRQ/A82To2zmCsjGWf7hty9uwbuAkTXfZwTOTWYr2nzQn6RJMkY7XGYFqY7EzbOcZponECqLQN7DaW//Z/62gbR9/K78zzjHeu4kkkSZBnu0zLZnWO0pYutAO17af2RFrItr6XgHal3eCMlWpOFqdpVDAoad/AWhjHue5Z4zwpyARgykBKi1ZXLsDMJWguwUs5C2X6zaltKYzSXSsie/3pBcMwVHIxYy33d/dYa+m6hvOdBFQvhvf43uOZfQ/zfMU/+OiKv3jzAZfHDT98dE7DPcE07P2R27nHIAFnVPuagNt5r1QGJawyfLx/xWkfaUxgioH7oZiuRNf0jFNk299heCx2KyXmMK/8gSZ/2rKleZsrAAAgAElEQVTrOwG/99uNtI0tvP/kjMsrZHR/DlUUeJxmmWqLmbbtHviTpC1hqy2ikujElCpLelwlLlK91DNcAtQkFSznHdMk/C4YW7GNBsVBZbT9mGXPrOx937UEBfkX7MzSGjSV4p+cMWh5Wt9d2lm27guQACvGiLfClDvNk+6Nh7T9az/9ba/f3jjSV6mK1BJ43ZFiAEoSVwxIdZjW1VKzRO3FHQrpjSlZ5zvOOIaZOM9M04Qhs932tI2t711K80avoTCNFnrZXIMUU7MAs76uYhRM+R0JsNY3XAjjCslbcfZ1bJn1e/22l6mPK+l7lCWVVsEqMynfRLOY5R+/5a2X937QuioBxxKXvLMZ1HnEUDOSckjL1EUmk4xWvBRcVY2YqZ+AQTby5uScjKFpexm1y1n74aJ8XKI6GeMNtZ1yut+AMfxHnz3ns4vIb756xW9+8xu+99nH/OSXX/H+xR5j4c1x5tntwHGWcfoyzlrwNusRdYPBZqPgWZjGSYyMMTy/OfJXr49E47gJK6ig7oeLzjOFWfl9dKeasmMTn53fkbKtz9dosOl8EUdclqvsn7IQZc/Yd9dN36eUemXLFuMlSH/nne59NZCaOdd6nLVY30jg6b1Qw68cR1Q8QrnwnBJxDpicdcouS0IQlbhKnb2Ydjk4RfbAGglCclraBUnbngU4iCnjtPJ5Ka2eRV6C6JQk6Cv4Lpk8sLQeeh9VEC/TapU0I8RfYtBlHFmcelY7lBnDzBhmyqEV/IzgQMqeKOe5KnVnuJtm7qfA3ThxH2CYLeMwEhN0TsG+2dBtGyYfOA4TIQuO4TgPNDjyrLmcSYQccI0j5iD4HWvZbmDC8yf/1tM2gj+ZZ1EzzknbPqYe3Wov6j4t/zdLslefpTrUuJqgquB/ddBRStgYlBzMoCBNzXoVT+CcgJgfDBGU9DULp4qQJpZJmLLbSqa9LHfSNUCrWSDsrxmpMHTbE0JJWPXGRQcI7g9HXl9NHMeEzYEX94/5+sYQ88w4Dvze47c83t7y45dPeDVc0Dr4aP8ar+37oN2A8uAyYuVzNoRkmaPj49M7dk1kmAM5G1KSqahpnKSiag1YT9u1iF6RjLB3rSjRe6e0H1Has8ZKqy9p0aNpGnp7g2cEbA1AxinQNE1lBpY1i5W6ogSbJSjIZUNUD7pUS5a1W+0d/VtMikuxtv5uzipoWjGcRq9L/WgdOCj8Mgsre72MdxLfcs3yKnIiubYQRT9MfMhus5F2vNOgPC2Y2IqLrT73219/Y9CSyVXHo/Wu4jtSzCqFLg8ya6RnQA1IqqfOmaUUVQynMap4W25Ws4YScaQsI5RhmvXwhcWJGDWntoCEpCTsKnhHH4B91+eb+tCL811qFYaSOVZHppWJvHLc1RnlFX+A/ndtSNYO690rMKt/yHssfAnrNlTJgg3LxliCnpX1rddhHnzQut9a7leMivJ/6JrElIX2O6UqtJVU2ySXfmnKqwxgCXh807HZncgnlSCwVMDqrls2o9H1bZqWru8ZA+x7zzREPv/oPY7TPa+uD5zthKOnb3XUPiVa7+oeKMRq5SCX+/fG4hDukpAjeDmw0zATcmIIMz/+5pa39yPkLDpNSa710/MOKRUnphgUfyTO8jDBB7s7vI1Lu8danG/q3pOedglEltU3qwBZlK0VqAsK0M11fHkVXtbnSDESWimpmCdlkJX7EG6Z5X9Ajnous0rAa2adxQk7so6u5mqwZNvLGZrnmRhnUpiVHVYwSrkIyZX/i8eUM1ASEL32hwF12cGr86eGqvGeqFMk1lpOulkxbnJdIS2UBsZQJ3FqIK12JWW518V4m0piVyoVSdl0rXV1CqNkgCFGOhK3t2/ZnFxgicwhEVNkjElUuK0s8G635W4aOd1scdljkt6P0yqJquZGY2kb+Ncvev6Hf9Hzz74U3EMIWnnTc1eu991XZpkwc97plig4JbuM8OZUJR+KCrw+bQq0qAQ5Tm1LKpxZZvkeLE5mLR5bvh9CIIYyMCBTKXKdeUnIynvp30NUbIsGPeMkEyNN2+PaDay2eXF6XtlhD6O0Fa6Hnl/efMyLwwaTDa2b+eGj1zzq7/j55Rl/9vIpjzaB0+5ISJpcPDSJpGT57OQ57+8O7NuJlFuOQaZxBOQqDNTOC6d6SgmbLRsfoNnQNI79fsfJySmb/Rmb3Z5+09N1wi79+LSjcxOoWGjrLW8ODdHvGY53Kiboabxqi2myEmYZVnF2SZYKJ0q59gKGBapAptPzUva20XU0KFdPER51K96y+nPpwbMpPq8GD6skf92eNPpg3/WtBd+5SDTo/tb9WAKtcR4xSqGCUYK59Z4pe+5viFr+5vZQyZqMaAbJZEms1YBlYkhnxNWgLxe9MrRGFiWqsfNakspZCJ5KlzDnRI6ZGY/JgaBjn85lcii03JLxGX2K5fNyLhFoqplwOQulX1+dnOGBY10vgPz8MuFT2GRr7xVY2jvU+1wHC0vAsARPRqPtUpo3oCOwhrXRL5iTvDJjyyhh/chveallz4sRkdaAvHcR16uZW1m+EjinJQhYBeHyXmkBXgk4FE4u3qvVMlM2dongNZt2viHpSKprWlKGj5/s+eOPbjDxNXe3HXaCP/3i5zz9wRN675hC4pu7o/CsWMek/AM1cNQMLlbSLLm/8e7IlAIueJq2oQMmEt2mqCuLuZ9T1F5yrkb/Jy/v5KCnhFfHLcGo4f19y6aBY/B0Lune0TV1nsY5NeQSwIu80DIxFnScNoRi4BEDHmU8NcdlSstY0ZQy1pFT1N6waBAZ7fM6k8nqZI11CO5TRNdSFlEB3/YM41R5Y5wVIGjnnU44CXnXcRQJDeMKiFeyn8YrvkKBvqJn8+3bru5LU/YeNcgq5yDniFGRNcE+yTMqNOJYYfzsGs/G3sr1TSNdY+l8GZkvz084Wowaz5xN3QdJp6akCqBlZzkIojOUvbRBFLQtLVJHTom28UzjRGw2PLs58HjbkDB0jcVmw8tXwlLa7OH+9hZjJDA2VuQHkhF5CYdhMoBQ5PDf/R9n7DrBN23aorFS8E8y9fFuallMlbQKxW445zgOR6WRQJ8BGOux5LpPUIC8AWUDFntmG68VNShj/BmjIHz5/JQyfb/hcByYQ9CKtxgrkZMo9qw4QFsPgzHyntJ6WiVZaBALkKBtYBwHxuOR3eljrl5+peu6+BPIMoUyjqSY+O7TiW+O5zw/fMAm/SXJOHbblu+cXfF4c80QPf/s68/Z+MDGz8x5wUTIczCctAOn3cx5/gZ/6pjm4n/AZs+o4FvnLDtveP/RGYbMLy+PnPuZq3bD54+OmHjNPF7jtz3b3TkhZ1wc+LdfZwb3Ho0Tmn3v4Nw853X+mPOLx4Qw0TUt0zyRQsLoBI61geFwwDetJEPO1n1bJobKq1TRnNfJxKxnyMjET9TqaeHJimFW+YYkkiUo1qi29gT/4pRUcf3MapBrC8xjCXxWf6UEK9TCRfEjKmORxD6HGHHG4VvPNM1iG8tkozGrvcXfjlwuYwXvoMFEdRzVUNWdKTwmuVQJ7KrvChglQkKIdVKaASmnFcBn1gAnZundZSOKPsKMuH6i5sFhMBriS11i4RopQUmtp5Tf08pBCTYqNqBEqpn69RIISJCU6sTRUiYzy5/vZJeAsvCX90jVwOdswC4hTjHu8oW0rF65+vWK6pcr7CxnxXHW+srqrpeffehyHoJ/l8BpdVtQy3rGyIFpTSYmGW/b7E7ru9XuYU5LBqdgPu8lu/DecxwG5pQ4bQ7QnZKPgS9ef01zapnmyPVxqpweoCVOzGpabQGTWl2zQjOeGouJBueFGGnfd7TO8OYwgclK+S4TRbUfq69YiwfycyUQsgb2reOnr8/59PTAV7dbYoKtTw8yeu892TnmacSZ8l5CnudIuqd0f9VJgYd8BGXvSTa84LbEwJha8s/GgZMMsWRlmUzMVveCZdQqZd82mkFJVpPCzDwlom80uBIhPmkzrAMTizFS9i6TAaV1Zq2t3DSrkGWdE8CD/WYoXD1ln1stSe82jnB8y+geqW7Usu8aY2idJ6YgEzY5M80zrXdsHEpsl4SBd7UHy56umWoJpgyrvWOXK8ySGH18esbbl895/P5jNjolmbOwJZ9tW957/zHHIWIby/5kg/MXvHn7jIt+BwlMGR6xWVrXwP/8b3r2vSEEljYa68xS7eEq8y1H0GrWIBU6gzMJ77slo1gd1aT3UPaLiA9GUVZOsp9KVS/Gpa0kAnaLbYwpcX9/r+RjS1WrAITfxR5UW6P7u7SbqglR2/xgXciEccJk6LY7rG+JYeSBTdLrwlimaeLl8BhnEyEZur7ncByVmt5gTcAbeLK95+X9ntZF1i9JZjP3ocG7SKdjvbNLuOgZ5iQCqTEx58y2a9huO4ZZJt0uDw3e9Thvubq/58XdOa1/jHfAvcHmA1c3kOwpZyedgM2tZU6eN+Mjch5JRqpHxgTmaWSKhQJAQNTbrWeYZmxMJCNuWVjnl3tYJ4+UJDonCjSiOP6SnIn0S5LxbCRYbppWD4I8a9+0cr4fVHZLRm2WZHT1LJcdupz8nPJChVF/RkDNMcaKb7HOksIoqtNOpFOKvygj2UZpOn7b699h5FlKqhLBGTrvBYmPZAlo1o2RVpDwTiQFOMqDLKPROQa8/r70+GwNFPQeJSBJBe5YykSRGJX9My7tDUPWnlgBAmd1+FaNrXK66AaIyoNS+ryyCCUQ06xmFYzJ5diaPcZUqkde/1wvUunfr9YylwOzGldeBzpZNstCDJl5sENWAaI4u7w61sUArNaKxbGsL+PBYpZIefkIliCsfGF5NiWTNQZSzPQb8C7gT57i256sZFYLyZqKdhlhKrXW0LXCMzHOE6TIHz6+prGBaHucs1ze3nLytCdnaNQB14AXlM001R651ezf2uJY1M22lp5evofVaRO5p5JFeB3xK2XUMpKXUqp7Qlo4jpQyp73lX754ymHO/Oi9a3703pEhOP7Jry6q0F5jhVkSK2PcNhdmRyd7Rp+NNYm03qu84wS0JGxUw8fYwkn0EA+TSnVAs+XCipziTFYD4Z1M7UyT8CLc3VzLCGfbsz89q4qwMcx6XsA62bDOaoDkHClJVSZllPtj2XPfblhM3WrLRKG2IJJWW4xic6zh6nYkxo7tXgz0cTacdCMpW7b9huM04q0oTIcY6RQIeto3vD4MGCNjy3G1noWFspS6o44fe+OFiE6rEKVlZ43ldHNCGiY+/eRTTDrg2pZhDnReKBvCPGNywroNcwhcHQe+ef2SP/rBY4gRGy25cMpowPInP9nyizeWbV/OvwrnJXQCQ1a0rHF9equEROwnpDgT5gxWg9B3kpCUofSBUhawrdFnrsxTgkVCgKPEVEv9ZMG55AzGZvq+07aYtOWqBtA7udCSoJW9XECVdmWP1d6o7XHW0niHcxvGcWYOke3pBbdvXujPrZtkSsKGJYeJpsuQLS/GD/nu7gXX90dOTre0zmNM4H5q2TQTrQ3ch/7BlpSsXs7jGBPeeraNJ1o4jBOJRNe3kpw6ab223hGSpWsdjfcY13A57Gj7DY33HMaIdwZSR3dyJppSyicVU2bfjtzZLeMM/z9jb9ZrS5Zd532ri4jdnOb2mVmZ1YikRIqiKPjFsgDbTzYMG37xD/A/M2DAhv6BX2wIpg0B1oMEkBJ7kayimO3tTrO7aFbjhzlXRJysJIsblXXv3efs2NGsZswxxxyz9dD4zJU/El3i1e0NMUUeLpYmSIly2yaGKRPjoOJoNYfTgK1o+bqwaMv6OAyjtpYR5+pcIEdxXx7jSNM5TR3COA00rVOGR21InFvZNqymcg1MNDCM6SkYXPaKGryscwMaoGg1WN1nx3Ei50zXdYzjKPt4ZfBNzZisq21/+fX3aphYqUnppSI0j+hGHCBN6ubcsvJGtkZVqRARsJKMOA4Ccxnyk7z3vMAUrDajS0nMuAxZohmjQjI1xTFFGudZXYgkCyTHrMuCc3YuVZ7TM7XbdAUsqyhsvSzPehLN/VcmY0a5Am0QELOQIZU1mfek8pQokbfMqu3B9x7SzBxVuDOfkf746cHqmdeFZRlKPBlbs3Dqe2cyH2X1oZl9yhXUFU6T5aUfuXn2DGzGERnpNP9bhXdu3kycRuUxiRFbcI6f3Dwyxki2hS+/+YbLeODa71TdoxNAF+5cCqlkYV/0wgxLTt6sn6RuuFb9BsSfR7pAV0bDliVHvzzzCto0V+w8cYwEb8h4LhG2fuQy3vO7P95x3wdOo+hFvj1u+fJBLMQNBecb0iRlveK0uehmSlo0RTE+HXPLZq8U/CptKHniWp2iFWUr4CM6s4hX4WBKiWk4E6eJnBNNt2F/+xznPUajcDGVkg0mJbGerw3+6pyQfPSqXbzVpXKO9labrVldQ6V2Z6aoztmgb8v9liDA0LQbidKL5cXmNDdLnXLmOIzsuhbRbzh+7fkOKHy8TLTOcVamxWZxva1z2sxN+2T+WGVOlrlRFl0Hhf50JriR7WigiB/FVevnsWGtB7uBYeR0PPLtx0eu95l+jECkw8l8duIQ/v/+1Y4/+s5yvfVaKVl1fmVxM16m+jwC6mM1RgB2xhBTAdwMGOd1alVqWsthK8OkuARvEtbX1J+kH5JWpuRqEaH3LMdItoY4RQXb655aUrWyJrxlX7CkuFS0VBY8eKOVZss4qL/jvGcaRl2LM+3+isPdO8hpjtskIDU03hPzKJYKbYsh8t2xo3O3vOo+YI1Y2nsjlWhDClxiyw+9jJX+U6TMXRy5CoHTMJG0+mzbijFfnxJ9tDQ2cRylBULxAW/g2Xbg7bjHlMjnNyPEniGOHOItwyCp5SmPnIeRxmSsCXjraJqOfhohd7Rd5M32G9I08Lzd813/Cuc941h4sx246/cYTWHHGEWL5vwswBcR+zTLH7abdmZkpM2FCNXHcWQaRy7nMxg7F0oUBSMmGnxYXNZ/qbDECDNijCWOlxkw1Tmzjs6tFZ2qU0NM54x69KwcvXUt3223GFsYR+lbh7VM6tqd85Jq+ttefy/QIsIfRUox0QSPd4Z+TFjj0DBqTrPU6h6rG0bBquufNP+rk1fvzPJFRYKkxaVPbk7MBm8dUa2exY5aN521kIdlM1/f0hzVxtqIr0jlVORm2nkxKHmh2soTurYAtdFg0d9ZIqWqmq9K8CWtpL9RvneouioVRaYLAVLnNlUYVSlq3YJXx/2exqX+ZPW8558qrqobS71XZr7Oei/KE6z8VOCFlqfBQ3kOl0w+PWK9xxAVxNpZTIlRvxBkE5DNLkKzJ8cDL29uuTsXHi4H3vz4E0LTMM26mfos5Rk7I60SKhgBiDnNjEu9RDtXtBVl8+QVrBXH05R5sWl4HCeGmJZ0B/DF7YbDEOmc5brz9LGhcZY//zDys5tf0Mcdh3HPv/z3t/zk9shnV0d2jedntxfGT79hSA1fPl7zR2+vteMubNsdKSbtnwWFQJoGAS7BaLRjtQpHrkI85MRZ2MyAUQXRymrKAlHm8ea8mJaJ14GOLdcQfCugSUHQpD8vORN1ntoQCE0r4K6/iN+HbiYWI+JsLZ9Vwb8YeCmVPBcWaCQoZZp12ElqKRdwvlXRXZHNSVNp3gepINHn/t3YcXkWidkT7EQbAn2MxJR4td9y2/m5FLzzhsch0E8TjZVmmLEGUHoS1R5rFv4WGSPeidlWp80Gr9uGDkOOA75tJC2dC3Z2C86Qzlxvd1we3vPPfusL3n+8Ew2VEbAyAucp8dWj54++a9hvjZaLqqidojqWZcau2WC9jVSgWtD7a5cAb20+aTW9XMHHHPFqkYLxZt6gQKh6aVJuyXGal+EKnkMQFiwbSRlezkes9bPXVUxRAX+Na6Q55yzi1AWsoGahumsYXUecs8Qpcul7TocDxXgtpHBs9tecHu7mtcc6iy+FmCLZWIZeNDa3V9If68vDLdOmcDN94HrXUqzht1+/p58yf/LhM37n5Vv+/OMrjmPQSjTwJvOH737CP3vzHVuXuL8MWAqvb7dMKXPfT5iU6ELg2Bf+3befk6aRpnVgHC+3F8L4nt+5ueMyRP7i42eE9prgIl1jeNEceYxgPOSwJRM5Hw+03Y6UM883A+8vWwCce0csERMfIHacplucLQy5I+fE7Xbg090dXx5e8HhpxP14HMg5iYi5DRTjCI0XN1lj8CHQuogvlik76VcVAt2m1T3LEKeROEX2+x2XftBqLubgsgKY+VUKcRrZbDpSyozTpAGADLdcErNnixOQ752XtL4GV7WRa0rSQuIyTBgDTRBB8jj0Uv5dwc8qIPuh198JWuokiUorykSRixvGWvpbdA82cwmdU8OilMWNVujxGgmvynqesAV1Mpt5AZdN1ShCT7p5lZkWL/q95glbo59dAYG612fykxvylI/Rz6rB2IIhf+B4rADR92isxRdlWRCesi51RTfzfati5/q2WQ72tz6bJyf5vdeTJFJhjr6Kq94fFSh9/9zluGV1HFYRa0HOd3/7Eh+6FUBbhlhOSYCh0dLcLIIwVynCUkgEvjsGXl/vuVzO+OsdQxTfghoBV/iUEXS+nM+yCVULQQsznZtz4TxNS8SNCFuDMXhreHfpZyAWnKWxljf7likXtsFxswk8Dpk+Fh7Hic6LhXfnJ/bOctU+cN2egJZC4cN54LPrDZs0YW8O/OXHPddd4u7iOU1iOV/wFAyeEWNaclRju5jU8MvOd92pvoSCXLctyjAaDJKjrkr9UiqtrzbdOepGpGWspRCpJYXLuAjtdsV0oo3WwAdtsug8eYraj0hLi2dxcF4qFow8ifrsrTM6tOSbClZ74kxYMikq02EdJknnbRGlGkyMBN+yCYb7fsd1c2aMhi5YiDIMo2p6coFtKEzJUchsG89xiE/G8zwH6hxDzC0nBelVnDyMA9t2AynS7re0ITEMEybIRoURkbl0o/dYE3n54jWH0wVvIykHgte5ZDPfHHf8n3/a4b2VKss6WyWum89uzZKxmkMS7Mmi4ayARqMRb604moOaorPEKCerLIXVQokqqJ6ZsjpbcsSUREaqSmKMUiSRpW+b0/PNMYKXpqwCXKs/U2VQ6rWZ2dROQLXYZJTVAmydmRmmOI5sNluwjqEfoMDm6hmn+zuZ/7WwArMI3o187uHouNmN9GXDKV+xGb4hbhq6IOP5qjV8fn3krm/5dH/kL++ezXFiVoZvzJ6X20TBz5V8l0laPVig9RKQe5MgSL8c5ww37cjDY0+fodvsmXJLHnoO40Tsj/jgwfSYMgpocI6m2WJcw/kyEscCRIxzfHn4lMaJjme/a7icZI4eR89Pnz9w3Z7JOfHjq7ecupZzvuXj0TNET4wTJlgR1qYi85LCNE20jJSU6UdheK33BB+UsTY03QZnJw7Hk3jBhCCpZn4ZsMh6XzudayCpAYPT6rRgtZmprjHW1lT4orfMpcwZBQn+heBwzgCZtu1mp2djtH3P37H3/crqobyaUDVKykWpy6wQQzUAtSyrqvyrZqVStMF7atPEKuyqA7JSxvl7VT71ywtg1PRLTiPpSlDLbJeP1clbj13Kgo/q1F0/mrWgdRbrPvnZItzVcGK+PyUvTEVFKfNmXtTMaHUOy1UZJXXqsVi+Y4aD9e+r73iCVBbmZQ2y6oK2zkeJiItVOmS5wvncv/cy+jmzup8+NDSbHbCYn83XY8x8/yni9wJGbakNcUrs9/B7v3jBb37aEs+PbLYyJhrvNbKV80lKcdond2w1DhXAeCO+LDFnRi0F7ILYvBc9f2ssU85stYlg1LH3xc1Ouvtm+PLxROc9Gcv784BDyvy70HAcJorJODuxDYlSPGPKMCX6mKiOzqk0/OPXj/zumzN3l4a/uOv4+tBw1WRan/nDbzu2oeBCII3Sd6SCF6O7Wn1+RQfP8pwUcJe82A2YZcNzVp5TTtWZOGN8mOdEVkEemsee02VWIqNi1CMjJW28V2aDM2OYW3VYq03PylKGPFPLIlzDaA+kGgSEEKBIqXRRf4+UC8Yu3iRSSisszZ9//IzfefU1L7eDmspJMUDKmUmbv42xMMZMMIVd2wCGu36UhU9HijNmbSjAMEUyhV3jmbJYN3gFb8HA3cM9r5/tpWQ+Q2ctNovxG2qtkMeRw/FC0zi67QsO/QON8eRs+P13V/ybv7nClDM518pFeUZr9nBZgerm/PRVRbXWKWNk7dxUrgIfncYCJEqBrNeqAGuKcSkNp363dgHPiRAaShJGzxp9HrXEfJpUh4CUxa+AXwVMFWwV1IG1FELTknOSVM73Ug0xy7h1ztK0HRgrLS6ytA/xIdBttwyX08xco1bxuRSaECgkhsuJuLvlprnwMHT89FnD+7sTn73cY4ywbZ/te96eOxo78nJz4tnmzH3f8d3xGkzhvu+4aSfOU2GLNIbMZK5aabGxaze8OyZika12iplX+56dO3EJHe/yJ3zujly3Rx4uDZSEa3ZIKfvIfnvNvmkYp4kfv26ZpgEQMN4PUZoS5oSzE6+vM8N4pB8yj9MVm6Zw095zPI1YZ8gx482Fz/aR58Fxii13/Zbz1CgRkGcWwwB30Yv2yhpcK8ae0zTgSXTBUmjAg41Oyv6r86yp7UiWPaVWzaYVA261Mg31BUoUERgje0nORQpM6nqg43Rm56xoOHMujLVtNzJmqvziSXD/A69fybTIgKzi1jIjfwDnxO3SGSl59l7ErilnjdoKudoU663wwYm1cDWKqiKjop4wbv39ywQVLU0heKdCYIkkK3OwLpGqC+Fc+rgCLJinIqZ5Y9Z3S15UzBhYrN5WQEWfQEra50XPc41Ua8RRF9C5NHhWeq/Ak1mqkZbItS5ltbTz6UM082axHma1osbM4Ek8GPK8YcgSlvU+zAhjuecw6wG+z6IUoLt6NgvDAMr6p6VG3rqRqtAxxol+iOw3LYnA//BbdwI5oiEAACAASURBVDycer766gN+22H9QgnWtI6IcTOb4BmiUuuFubosqD11KmKSlldpLmfEyt0gjfBiSgTnBNQYy03nmHLhr+9PWo1kaTTP8dCPXDeBVMQrZkiZF9uNlMsa8V54e+y5aoO4rRZD6w2lON7sBm7bgdMIzvT8xvOef/I6YOLIbuP5r3/8gbfnHb/382tKRsqTDZiSGcaJpulI04Dz0vMllmWcLr5I8u+MkDAFZVFyZhqHpT9JqUBYIjHvGgEvWTdqY6hCVLmvtcwyz2yqNW4JWnKex5qxFluqHXeZ/WekeEn9GebSWkMpcQZA4zTig8xGSRssJb19P2LHSGgc//brz9g0mf/y81/werdh0zi+vj/z2I9s2wZrI13jmbJsNi+2nts+8FcfTxrlGa67QEyF4xi5aj3PNltiLrTe8ou7s0aXhcZ5La+39EMPPuh8iXOvNZsHptGQ6Xj23HE+ncjxTMBwGSLXV5Z//Ysrtk1hCg05iQu06I1UgFt+KPhYz14BLN5aQvAEZQ+u9zsuY6RpG06nfmZmSxZglnIkNK1olYxlquXcFHKxlZrBaTm7c4GowEC0L3YWcDrnsN4Th4vMwlLBVZlPt87VKlaXUuisG2TUNh6ShluDnaJz02hKVPp8LcL47uYZ/fkgY8PImIxJHXxtbfgYeLw/Mu52OJ/5gw+/zj98cSDHd4Rug6PQeHBm4j+8fY21hXfnPcYUnBUW/Zvjnq8Pe7wpxCI2ATW2/s9/9IFv70Yexz1V+2jJvD8FHi7P+bw58Hjq+ObjR350nSj+muMxMw0R46XJqYkf2fDIFF/w1XeBPgWmcWIbPrLbGM75JXfjFa8bx/HynnNfeBG+5B886yB50tmy91uCb7jv77Dek5LBmZGdu3Bzc8CbxMZ73l1afnH/nAlP8AtLllKa+1E5H8glMGXIWDo74JwjBM8w9AIia3Zg3gTMPB6r8aPJ4nJvrSW4ABimcSQh2iYBLVUmYqVHEsqymGUNq+Si1+OO40QTwpxFEX+wp9V069ev1LTU9tZ1FbTzBisCMWsscezptjvpPGos2SwVD7Il1ooSKftyzpHHcWZYjA7u6gNQNTHJIGXWVSuyehhOa87RiHwGZ6WmbBQg6PfO6ujKmhj1wtW/r7fnxe55OeZ6E6iA5vspozXIesKHrO5Z/Xn92LpImRmwLL+7EC9PUfBSWr78dv3+WQStJ732fanRWY5RS0AXL5sn51OeXoOMY0u3u5GFuB6vVP+Xeg80ZWMNKY6iU7EeQ2ZI8F/8KPLu7oizLW3XEPsH2tBIDb9uEHL6WrG0iiZqJZqzVd9TI0Cool2JtiTqaJzjMIzsm8B9P4gLqDEcBqluk5JD2aBfX+85jOIYehwGYQX1vo0pQSrsvaWxjn0b2Gr+v3VWIrYg5naTLZxHodu3Qeznp5hpG8+pt9z6A//db4z877//hk1TMCVLekYbFKaUEPf/xcAQljRk1hlvUI2A+hzUe1QBiamKHVOfzSI4n8N0XUAswtA4oxsRMrDWgEUWnqWtg5i05XnEUBJZmz9WZmEG6tXnIUt/F6OpY+f9DNPnYY44aVqbuEyeRMvb48jLXeCzmy3OWcYYsTaw9wVvPJcpMya517edVP2kLK0aMjKezlPkk6uWa295f5746bMtD7126M4jKUZ2V9L4cCiwDUY7ERcaL401m7bleB6wMZEQ1mLK0DSGxz6LcFuj3FpWXFYgfPa1Kguwn+edriWWxeU7pcxu06jou1bMQc5mZm1yKWCdBI2aWrSmBmKiOXBGNFAJO/viCAu1WLMXIzYA265hyBBT0rXfzeuPfeLYKaA3F+kP56wX0aa1swZKF4TVuJX5HFOa/VeKUtCFQttuCU3HOPQza1sX65TS7EFzOZ0A2F3vcWR+fr9n3PXc9Pfc3G7ZBkeztqu3edmM9U+DuOYag7jo6o//+P01l+jnOZbjhAstwRtiDnz68pY/ui+45qf8xu4/MZgz54cT1u9mIDb557wdr2V+9iO7XcCWzGnsOI6w2000wXF3crwdrkhmy/PdSD5/5HT/gZudY+oj537i2X7P5RzJY8HvHG1oiWmC0JCM4cVupAt3fHl4znFc2n1grBaw1FJ7AaRvtifM+MjlaLlMO3zTKIsqGrb6uzpSqYUDckhJnwXvMdZo+tgRjFpSzOZ4EgBJL1urZfZyXgJyk5qZWtI0zp3LHRCL9LuK8379y6+/0xEXpJLCGO0TouWiIgSUnDCIAGeKE1OcGMZRLIjNstAavYlWTXCsNVjvaEJgt9vKxooRV1UrNHQqkks2KwMqeQDMFse1jCvntNCSOvzKLH6T+vQZSMybonxHRSayKapzqbFz5ck8yFdzcE65laegxZhFjKvvMJtb1TNb3xMAM2cTZ2qs/vf0F+UYi4CY2bSvVO7I1utYbXa5bmoy6GrqSP6+Yqd4ypIsZ7xc9+bqdkZJSy7e6ECsFPhSaplLwbqAd+KwWgo0+cLL2w3jWLh7POGCw4cwX4/0mZH/nLGzjqHeiJhl8UxZKorqyT25bgWdlxixRmzaa1l2BYaLmZEIce8uI+dh4DwOJJgbmqX5/hnOqinYBEfjLc83AWst5yEyJdnkskbynbd0QftrOEdMUPIIznPbJX73E1HjF+tpNhIJ5gIilZS+P3XTq2r9KuKsfbNKEdfSGkhIbluo4phWwEGvW5oX+pnindthGIvzXoRzaNNGREBXDQlzTWOU/NTNWifBMIzEOJJjxDlH14r4rxjZULOa7hlWFvCYVeS+nkfyva0r/OXdK646w9ePAzEl+ssFijC2JidikuM02rtn48Wyv3rWTSnTOMm7n8Yk5nrW4qzjugu0ztE2ns1+I743zrBvpHmiMVKZErP0SprGnstwYpwS2RiygcN54uF45ve/vcYW8Z6StaEGTsys57wRV5Zzdb11jXFWBLPWGrwVe/9x6DFF0j3Oe7pNN1P1lSmExR28UI8nlXTyuzK7q3agAn2jovkmeJ5d72i7ACVrPyQ/A+JlPZA5nnJiGse5DDcpyxKnafVAV8CsLOuRtG94IvnX4MfQXYn3U8llZgqAJQWrOqthGJjGSYBQzvzi4RljgZItD5dIqAUf/PL4ql87Lx/L6XEchaWsDCbG4FWQYUziOGiFYkn85cMrgmXlcwSbTUfw0AaDsyKJOBwOYuhGZDg9cj5dROuHp7gtXWM55xv+5vQ5981vk7ZvaF/c8vzTKy4ukVsY80D/eCFOCWcCOVvG5DiNFmt6fnz1FVveKTlg5z01xhGKsFbeOm6aE2/PHf0olgwlZ6ahJ0WxM5B0oAKXokBRp2r1tpodyY3s41lvpgSr1fZ/EZxLC5dqk6L7RikapClhoWL7qOv6agv7pdevaJi4pEdksOgA0j4JwXkBC7ngMRhTS12hn6Z5dDg1R7Lea4lcpL9cBJExiCAnZfmuUtROXqJ5b40uwKhwp0Z2onsI3i4LNEJLi8eFxaovQk37zCWkepPlTzdPHQEdVVezsBrGmsVR17D8vjWsU/sVDOXVIrHKBFPZj5xXByrL7CkA83n+ANNRoBgRyxmUDaqDZ/7AMmAqjQtaRZBquwXz9DuLmt7VDWTZ+9dpSbY3z+V6yrpyaSWMXgEXq6i8pJGYVVyaI7//7op/+uqBF7eBh4eG11dXfP3hgeALyYjYM2XZXC4xPQGMBvPEbyflPF/Lmnqv52aNVJNYg3g9rBZRg6QnqlnTmGRTzinjFXTX0kHvHP0UeXnViXg3GCzymV1wXIxhiIlGnSuFHRLwHULAM+FNoQmOxlpSNvxXXzzy3/7sSCzw77+z/B9/FOicbJLVGl16msgDEMuMPLMjCUn3SdohzvNMRIOy2TjnJJ2r98jomE+5luyj97dWBzqskevL2idINlqh5kVAjNofJGkYiWyabdfNILBqKowBWyJTHHG+wVj17qHMwP8JQIfVOUnU/tcPG/bNc352e6AJjhI8wVnePpwpOYIL3HQeZwrttmE3Fp5tGh77iYcx8uZqQxccwRRikXTGTSsN+9IQ6YITY8EEw3Bhu7vleOnxvrBpZAyN2cAU6fszu+A0/+74eJzotpa35+f8/OF6DgGX6rwKkMsM9k1etF7eGmJMbBSEDP0gc9paNsGSsDw8PNLtrjGlcHh8wFqL6zZsd1v68wVYGMYxTpiyNiUT0G0xoKaP3otXTY1uS67mZh1jSqQpMVwu0ptqNWfqw8lrBKIBUNJS5XHUAMNo9ZmRFFWl+oUFkvlEI6XVpVTWTmZvt73haN5rlaeAF+cdcRxJwBSlbw/A8XDk6voKh/Qc+7PHX+fLvifkI223YdMUYoIhaUd4t1xLBqoSQRLmq2qt+jtqxhaT2HB4b/iTdxtalxhGeDcW7g+ZKQV2u07GhXUcj0fapqHkxKUfpSrLeTbbjof7eziduH12S2dH7h4PfPHFnmk68u30DGsNXz6+IudXxOL4zz75S2I287qdcmYYB8pQ2LQdKUaabce5P/NrLy58eThx4YoYI6+7D2xDFGNPCz//mzv+9c8dxrRkoCtH/vFPOh4OPR/iK5gmxY+iUYqlUJjExkEDDwpS4r4aF7Y2RqxjYh4uon2T9Kho5arfGwZsFrAcU6TrWsYpYpOUTQf3t/Mpf3d6yBhCExiGcTaxwjhxvrWWKWWCDzCLaJTZyEiJcsraE2iJiLN2fTYKFio1ldHSVvlF0fCWpbbHKG1cgVNNAciGudzDwlIuGDXiMaCfWwECs74pusSsgMqyVRdlJpZ31Y7gl1JDs4CSpcSyRib6v+8TJ8t7usgsHgxFn6084Oq/Uv1o1kjriW7HVP+YVZSh9Kf4ZRQVQy2RzuI/o9XrT65J/my3e3VYLIqqKztUK6vKfM6WRRtkTP2MUz+QhOWGwL14WoyFthguQyJYMD4Qy6ptoZFnX5F87WMxn4H5ZfaqwOIwWpDyVjXPqqnEqk2aKXVkktZJV23hn3Wib3i4JDoLrYZdU5J7H7ydHVn7qBb+RjaqIRqsk/kwaEVTMlaofGCIlkuMfHod2DZGy/jNfC8rnq2A2JTFuVcinmUQWStpVoxUGBkFciXXiiQ5xhqEYmReGIqWqgtAMRTSKI0Ha4PC2qm9MjdJ9QgGrSjMcg41NdSERjoEZyst6VlVILE8B4qa5ekwEe2aBEhiX5755tjxO2/OgOUyJY595jSJvoEEMYELYuB17MUMzztLsI7Oe65bjyNxHDNTNgTveH8c8Faax1mZWLjQ8ng6k/PIpt3QenHonrIhTRO7xrHfdPT9xJRhGkd2mw2lwFUbOY0tKIBca9MqkyLjVQXCuQpuLV0bOJ4vYGv6PYNzfHz3nqaTtMPpPNCElpQjPnimmOb1mAqO6jqgzxmK3mc14dTqLx+CPucRnLAzxtrFV6Usq+5y4sxx1hwwVJtnyiz6TRqFW/XaqIFMXWNqV3mTpXpkUEv5lAtWGeDu6obzw0e9XzqmWMatUfNQ5/wMXKyzeAeX1HEcLT/aWf6bX/vAlBq+PTnuL56ff2zRLYFNgPMkbIhFBLzBLUENmhbJCqBrBY0xLRTZyA2WKXqME+8eQJqWOvEqSTErWLdsu467j+9w1nO138qachjZ7a5pzCN9aecFTLIZ4IuY4FlT5nSJsxbTtYxl5P3hjs4HzvcXNqGBGHlZ/how/Px8zZ993LLvLPtw5rsPIx8PgTY0XD97zuPhwN3bb/nq9jf42fUDYbjn7WU3s6zGavrcunn9qZWNUmizrLaxFhIoK1N9qWqhhtg8FDWGrZYiRVsKZEJwYh6qouxFEPzDr78btJQ62LXE0S6UtbVOKXeJ+ow10jQM8EGEhMQom5Zu4NKLxgEOa6XxlrUF4zyNkVKumtKptGftgGuNxc6RDHM6AWNXHWpVvVwnmG7w4khbkNxp1Xygn4Fq0lSPLXNlEZ+JiZ7SpHo/14BFCRxqGSiGGaj80ku/2tpa+qXXpL4zeXWMmTYGqX6aAQOr86iL4rKA1Gh19RjnV6XuqrC6ntD8kRn8PY2AtzfPl/fX1zXfi7LQrStAhP7dO8c0jhzOE//yT1v++59Z3rzYcvcxEjG8OzzyO68+5+vHMzitZDBL5ZZludY5bUQFaLoprABMfQndrmaIZqHfDVJlNKZMsBKJdd4x6vipJX67xtE1HmMdXSPOrLU54mwFn9Pc1C9YoZNdBc5pUudcYQ4KBWMLwQdMkZJdW0Ze7Dd8fZdk05tRY20bUUFLkblWWRJTF3BJXxUFDVnZoyqIrFYDkmuG2li0fpGxooOZxhEBQ4v4W1JIVhnCPDt+osFBCP5perAUXEnkOHG59BQWfYbcBRl761Jfq0g5szzTyuh5W/h4aXh7avjsamLj4ZgKXeP57vFCSZlN2JFxnMcoYCJnGud4vvPkkjmOE9tg6YIlZgmSyIliPWNMkkLyLf1ocSFxHTa0wXIZE12j+XwkB//YD9jseHt34OXtnvMU+c03man0fHcIOm8UYdZJVPJM3Arwlii0YHi272Yflbqge+c4nc74ZoN1Xv2jNCXuW/rLBecDbSfeGUn7Qs1PVBeAyjiWgqSBcpQ0IIYSx3meGgPjOJFioqjR4BwQUNemp9VDMrfqvZHgU3xgLMVIWmSKcS6UsDMVbEhRvivFqJvXapCXzPb6lvPDR0CDrFLLupn3orIab8fDgf3VlQjXrTQ8vPJHvr2P7MPIF/uGL/aF1u64HzvGZPntlw98uLR8e/SMyfJiG/nmMRAxdK7QT3rtpeBVrJ7TRKIQfEMfhcFIcVSQHaT/U1ZzyjhirFM3XUdB+qe9fvOazXbLZYrsdlvatqWPI/f9FqfGkvW+O1N4GK75dP/IfV9dxoVt9cGz3W1JSYKxx0FaBWz3N/zBXx0wQZ77+/vCN5PDseH5sw0+tMQCm90ecubj3YVd85yduefl1vFwCRQjKcJ+GLHOEhqjfYsKJSWSWUW2LPtpBSmlaOChPxdGT/bqOTGs+5kxaI8k8foSy7clBf1Dr1/JtNScvr6hOasw1+WnlGkaURJ7L9TpNAmN3LYt3jlVi0eNrMSAyllP6BymTko1fLJOQNEwTsx+E1YGfUqSDqqGUXJaWUVlGm3YRQleN8yk0TmKGkXRLJGos5auC1gL535SlFujXjnOTFWWJWqqc0x+UP9dI806P1cjcAmC1DY+zpN4TlsUg6k9QoxdPrdO/8whMix+uvOvUtX+FdCsgiRKymQ9XsmRmT5ChJsV+M2/rx8OTUfTbfSrNW2gQzKXutEs31RBTDHMJe7OB6Zp4v7+ERNe8b/+8Rv+xy/e0m0aXufC6Tzw8ZKIU2TbtUxJnqssGurJU820lDFJRa3HdSOXtNxSNtc6S8yFaZVyq8cD0TtsQ5g7AXdejlnFvgZ4e4xcpguf326YkuhZMIaEiHktYsZlnCMgqaghFoZJ0kW71uGMpb+cKRiiCxgcp3EkGEs20lPmemv58mNSU7NaUbcAwipYdLqAVvapDkQBGkrTVnYxZzF8moFumZk6oYChaURQnFJU7xajzR2lZBwFbznFeSyuWZcYJVftlJnKpUjEXkSs2gZPnpJUGKZCyspBWHAKOKU8WD1hyKQE01R7HAnQ+b1fPOd//t1v+HgpXMaJm85w03peX3UEbzgOWQF5YoiFyUhVF8YwTJlxytxsApDFhM84bBqxYUNMlnHsaUPDw909+08+0R3rwsNJStPjmNk0cHP1nD/+y78h58TdZeLlVcfDeaDgpX9O1SLNwIE5FSZ6N8d+K74U09DzeB5xFmUQa9WWsrnU9I3RbuxptlKvZeUheHKBNMUlsi1lNgStVVslZ21YmqVqxkoXYGsMTfDSvV3BzRxJ13FUj6HrSYziDTTVtijGUOIk+hlFZzln7V7sl1QgyralSN8PT9ZMY+R7UxFX6W53xfn4iAGaJpCqhTR1bGoAoxKFx/sHaRipmp//yJ6UNkzDyO1m5GabSaawcR/5fHvmWdOwc2f+0YvA3THz1WnDT28Tf/4+0Gev/Zog58hxGJDZlZkipNxT0sjt7TVYT9s2Gixn8jTxZnfkw9mB32BKZhwmrq72mOeOttty498ylecUA+chchr2q2hZ71MxdGFiFw7c9WCY6KOldeISnF1h18FDf8X7yy23N0emeKQfJr54nvlQbjjdJ/a7LdZfUUphHEW313XS6mR3fcOma3k4TrwdNrx62eE99JOIsF/eBs5jIMaBiJMu47p+5Czz1XtpaVP1pDUEttbqmFQzV7ve12TtypS56td6i8Wy2284PB5nQ8Ufev09eg9ZdbbL842tGgbvVaswI62i+W4pTTbGYZwTUVctm1IRb6Gov4snlSh0vnM4Cud+mKl7qK3SE7WH0VPqyJCQ7rB9P4ijJ0gKq8ocKnbIiaI5VZlEsimOUxQQYYSOLtjlc/N9kO9ap4WKEhYzO1GZGVbgxpQFaMzvl9nNtUa/c3Bd6sYg3ylWLhqLKLs1R1MKimp6ZnmtSsEVFBW9MmNk061poHlTpD7jPIOZei7bm2fLTVgzOPoNwFz6/aRMuixRWc4ZHzz9+SyUeyr839++5F9cfUU/TDzrtlgHjykRkxi9XaZ1czBJuzgrlLJB+x3p99QIN6tA3CLfkSm82nUch8h5msS9VMXegicNjbf0U9SNOKvxmLA5KUU2TcAo5Z2LmNLlAsImW1KchErFcBkjY5E0UhVCJgm/xIhJjbyMa6SlhRE25XCRni/9SSKyJ1oCloq2+rRcTcXMz0nHiUb3VkFtUQBkLZg5T6wRtnZ2LTGqiFuf+8p7yHqrm48AszhNyzlp1GuNpeQJjNcqkUyMqmnISUBQSgo2NfVX2U2dI6J5Enq4ppNTylivXZmTpCTuTiONK+yajsNl4jhMhGgxLuBMAWdonGFKsrHHqEaHVvx5nJGqq8aONKHBNztSfCQaGRPdtqPvT9huQ2sLm62njxlrI7vdSy6j+PV8+skLfSZi8uXm/hkK+leMqAheV2uEEY3guZ/wPqjGTQCbrJOOaRoJTSOi6sicqkABddZgUbO9FFdZNEcaxgXU6pysHjxVDzJdzuBbvDW0bcPpfMY6T5wmaWFkLFWKkxRcrrU6c9l8XRaKmws0RPDtqF2+KWZeJyvrVj2uzPxvGTcGSd9tr2+5HB8pSL8a72ulHKsgr+rnHMZ7TJSxenw8EJoW7z1247gbAh/OwraG5pZvm1u+HSbacmC/MVzOI4aRjY98dvWSF+2JEy94PEVcu6WNB3y74/3J88lVJqbEu8MG6xt2YSSmSebseOBtbrjkLZ88cxyHzBDB+o4pC4+YpgtvLy2+ZfEq0+e6fuUCt13Pt8crxtSQiuG3Xn5LH+U+ZFoehsA3h2suk+f+0kG55Z+9+pr+BG82f83p6p/QBZT9scRpZJwS4xgF9KrkI5aCb1vuHi+8ujbsAnzsd9j0QGc9jzkwDBfarp3Z5c12AxROxwMxivlgt93NAJdSwa4g8BXvN8s3KhtNKZIKLobL+ULXtvNv/9DrV9v4m3n5olBpdqPt7WUhIUtvD+eCPAgtc500d2/IGKPOe6V6fIhRFYpmU5J+Q3Ea9XcsN9dbTueePGmn2cKc93fVeU8nUc6aG1M+3QJFxS5P9/Nqa10ttA2mgHMBZ6Ef8wJoTI1ea7XEfBAwc/2DviPTuW6ecuM0zVHTa/VnRpqmmWIXwKEDVxaexbRuBgamVr/URVCurdJsiw5i/bB1U/ue8LKCRokCzWzgNv9/KbroOawTi+15OMw5oEUjU9NyywRcfefqXJbrN7S+8P7iuH42MsSGvS28Px1pvKX1juAsQ8pQVs0Yc5G8MvL8UxJqct8GjuMkbEmQfPKbqy33l4H7XrpGx5xoZn8gbbxoDZdplDy/2vxvQ+AwDGpklmmdPLsxZoqJ3GwCKcm/nbU4sxgu5SxtLiZjsVG0IM4o65ChM7XU1FHiROMMYyp87APfPRacGWdgRJbOvKnIM6qdp3XVlsW6RrBqAlUNBrxzOO/nVOOsL1AWrlZkWWUObdNJ3jwKaBAjO61KSglyFkZUI+KKXWtFAMVQUsaUKN4teZkvwxjVSG4xn6xAawFbsGRoM8Z4STU4OX+v97f1FW5ZLmNkW6l3YxXESYGAzG+nPWWkf0997hgJUpyB0N5weLgjA5sgpdS31zeMw4SzgX68cL1p2TrD7npLLoEvv/mK3/jpF7x/7NmEwnkY2TVG3FONndPbNUNkdI2ZJ40ykdNw0UhVgzrv2G22GAOn85lSCsPljPNBHISNsCBMUXwxlA2eNQLG4EJQkCQBYb3J0gVcgEaaBqZxxLVbShEWo2jEm1WsZ43RNKCCZwU+dX2oXcXnqFq7jhc9n1r6LPKV2jNLGHLnHFEj+Qp+aipy0hYxOYtIqek6BmUohckQYFPXGMtSlVXXmxAack4cHx9puw1d17DtAjEJ0Cux51ICP+8tcCMprnJFKobbcGQshr53fHF7B43hWfuW76Yt+fKBw+U5r7sjPp74bNfy7YPj/cEwZceQHbf7a/7Fr0XSeODbg+XUd2TTYG0V8jpSKTTdFcMUV4HtkyWbYsQs8uvDXueKlHSMac91e2KIhcKF+76hj1780mIkZhiNxwXHdBEGb4iFkhNT8XgDTdOybR2nXhjtx8MjzrdAISV4e5/YtgVvB47lOXEcyQauN3CaCtZEgreM40AcByzSALN4MCWTkzBhoWkpJUtqDUdGzBxNDVTsAvClGWPGGQ2i8qQykh9+/UpH3E3XMdXoynjxEVgxLM44pJN23TTFwGcYeixgQ8DYhppWsAaGUWqzMTKYQ9NiFK1Jt0qDcY6P94eFzl5V9JScSVhCCLRdy+l41vSQA5IaZTGzMktLveU8jYIZ47wKDaXB1/Wu4XLpicXPLIQI2LRDqpFc/hoYC42tKShjZ7HwahzOLJFZbejGraKyFbAypWiH3aoTMaqHWOjUYq/RgQAAIABJREFU+jkQBoUKlGZqWo8lp0SOS38XAQ92BmfztVRx5Oqz+5tnGqXleVOpm02ei1vK/F5FzvLzar5Xf9HSbra8//Y7Xrx6ydZF3pbXvLk6ceGW6wfDYzwR+55TzHT7rRriabRYaWrEhO71ruOqDRxHobkv0yRsn3V8OI+83m94sdvw3eHClAXgWmOUKSlcd167SjOnjTpvuUyLWKwYw1VjMVZy26UY+iius62K+DDih3GJmWIdG1fo1OiptWIMtW8NU0pk68SvRBewNiT+t9/r2DQjQ5Iu2WsGRbWxFG1XP2lzt1wEwNV+PsJO1MaKeTZ7xDgKFqOeFMawbPKgkySKxXlWjZkRi4M6em1oZJMpEpzklPDWCHDTYTflLOmoUmiahsPpxHbTMY0SwBTQ860zQudrKUincLQTsIBqMbFaWLpLBGcyn1133Gy3nIYL3mX6mNi2DVMcSWo/va09TaL0qPJWIPNpSMQszO2+2fJw9xWvX77k63cf2V4/J12kLwtkTucDN7uG8+XM6TzxGAz3D2emlPibdw9cbQI+bGjLiSFbPrmKjKWjsz0hNFK8YDTVR21KJ2vj3cePbPdXlP6s+f7EpttiQ+ByOqhpn8G6IGuirQZs6m9jylxGWteAzgs4K6UQmsA4Tsv40e9OU8T4Btu4WfR4vvTQS9+anCK5oG66fg4UZvZXN5xqAiYCbmFRUkpzhWXKGatNGY2m5MWb42kQVdeqlKWHXdu09EOPMZYmNGyunjFcLpquzzPT4/3C8sS4CJ8NqFRBguf+cuH48ABGpAqhbQhehOqOogAn021aYkwcBqkAOvUd3x2VeTR7msbTeMcX1wd+/ZXj333zKR8eGygRfKFtO/6nn/2CP/zynn/1Z8+52b+kawK7K9HA1evOABZlWM2yyK5f31uAZa0WwPb7b1+Q86v5x9aIViqOI9Y6mmD5D9++5rp9zs+efcsu/jnDMBH9J3w4B86jJRXPcZLqsdP5RNtt9R4KG1ey4dRDYSKXB55vTjzbDHx1fkOwifu7R26f3WKdF/F745UZRiuCZSyN+hxD04oPVS7i4TZbJ6zYSGNxangqaWYFqH/L61eAFjT6X+5k0wRViGvEXuoAlAgjJokwAVwITyZWzokxSeTlfScP08oC5rzHu8jxcZr9RKwKYOvJ5CxozDjNu4Yw+8VkNbqxde9ePXQpay4zuhfgkpUCLZr+kijyeFbb7ihsj8aUMzhEo4U1QJ7t7BVUrcXL9STqAlKdRRdGpo7PWi2ysDLWPB3R1iwVJsur6OSH77NKBQFAKWmjSVM36Hpty9z5PuA3OoA2VzcCsOxC5+ruAKZ6hCzAorJMKdXqHWVilBWyxnAZeg6HA/urPX/6uOH19R2XmHh+dcP5w0Q/nunalhxV52AWQW5dQI0xnKfE811LSJYP54EpZW43LVMqPN91WCMGbx9XWiZnJDqX6iLLJSaCtXx23dIFx2Of2ATL/WXk+baRaNQ7LlPUSF9cRIepgJfnloshJpiyiBMtWjKI4XDs2TaWWMRNd4xZqnyKo3GZf/v1DZuQKbgZ8C/A06r4rczPqs5JytLyoDrA2lkhbebxXDQFKeXSi09P1g2+Pq/aFG9JfdbFpVBStS8wc9ljLgay9FERCYQaRDnH4+mMtZ5+mEillt/WzXMmFedNbg3E5/i9iO/HpmkoFDoPQ3Rs24aM4zJJ1N16y+NlkL4rTUOwzMZyfZSf13RHMVIF2DrP8fTAfr9jGCO5WDXKhHEaRGhtIRfL/eHMbrvH25Gua9mHgA9irnUZBkqBrml4PA38008e+ZPvWtB0G0kZ4soiaT6/aTfiW4TMMec9U8yUfKbve2pPnOpGSjEq9jbqYJtni4Ep126/bnagndcKBYhZK328lsiS4wwm1nM/RmVAqlCaJTiagUvdSFdlhqJx0TQhCma0ZH3R1yzsoLWWaRIPJeecNuGT/nIiGpcqmXZ3JSkybUmQcsJiResGs1bRCQ01axTHlGgbJ12Xg2gm0yQaoqQ6nnazo+dMu91w0pRsp8LmaVKBcEqSVhxH4mT5i1PDNB0YpwOlPCd4g3cNXWP4vb9oeNdf8fx2j7WWWOQ41V+mjv9Zn833bv769UPv6VveyjWr5+pc7h6C1zkQOIyW//jxE0x+oDEDjb0j2Z8QQsJkTxXpgzjZ17FkiLRtIJulHP7dseO7+8DLl4Zdayl5K4DFiyYqTuLLlEvBh6D9sQpttyHFif58omk7QtdIufYwrlLtZdYt1jUhl8K2bdl03S/fAH39CnM5o9oUARBt28yumKVkMaLJ8mdOSSycZ/dSq/b+EylNTOMwl0n6VeRglCYySFdgtMxKHl4tlTPzUxR9yyIIu1x6FRsKwJDW2Gk+JpU013Oa7f7VkKko81JNtJyTBcRYgymJKvGdF966SddoGJYUkFkMdtYjre4z8rXaIny1UMNixCcfMXMaZTalQkGBrRGzvqfvz6N8/jJZaLIyHKXeh7Io8c0PTIz6KqXQ7fdYH+a00nryyXXNN2U+p1moaQAkMowpq7hVNDkhNBwfJRf61SXwTbnlWZN59uIF1ljGc88UE0M/aN41zz1o9Otonbh7/vXHE189nElZUjudtnl4OE189yj6mc9vNwBMqTBlyUl7A/04cRomxiT55pjhPCbuzj0pF67awKtdK4ugF88J6Vwt3cuFSrcMceQ4jWAs/TRidfEMzuGNTOppGkWXRdI2FpH/6y9u+H/+DKnXVeCwgFej/bIMmuycNwYxfTNPxjhajSIPRjQFqch8SZpzF82LWqezHKOUCggXNhOgpDQzdLU9RDVPFO2QI5XKpMl4iDGy22zm1JWrDr2rYEJAe1YAY5YBVX+moF2qTKKa0WX+8N01XXD0/RlnPSlqCs8binEczj1eN/Up5XkeVefjWgmybz2b7RYDnMZERhZQbyXFXIxoQ46XAWsLPhju7488v7lRjVliipMavhlKGvAm8VsvP7D1k6SoSw2cZP2qjES9h+SC06aj1kqq/XzpKSbMbRgozFVZUKTdwzDMoDZWzwsWVlnmbk3bKThlaWchQFPWy3rdkuZVW33KzG4uC0pdaVZzvixdxmVMuhmAOuckFaJrdZUDOGPYdFLaKwJN8ZHxzisDI2XMIYg7dCmF62cvMQYNaiWIDW2Y11xnLaGRljFV99U4y+V8VADkaNuWdrOh3WzYbPfsrm7wTo4Xh4n+eORwf8/j/T2nw4Ok7kzGeTtrkaZpxOTI1/0LDrwgONE+diHjp3vu8mturq+RNFdkjHk2Xat3MBXRZo3JksvCvH//9YPrcln+SAW2IernDdZ5AebWqYg/M9Bx4Q13+ce8jT9lioWJTrGz0VR7Zrvp2G5avIe2aWm6djF9s579fsd2v+Pj3ZHYP3K1yVwuRw73d5we79WvLVPSqAQCc6bBh0ad8kfGccSHQAge7z2hCex2G9pOsjDWOX2eIi4fV9q5779+paYlxkjXNcRpYkpZDdukzM477R6ZVEVua2dnyStfBvF6iFGqTCyy/Pq2mamhnJI01EtCK/eXi2ywc/S+RGLWen1sZTbQKgX6y1kEe0W6uBYMJmfVtOhEq9R2LguDYjQP6yDHKD0eNNKQtiqrslsqsl0252WUoRFGZVXqv+oqzVxGWs9fZ/h8CKd+CVWHMIOLCl7qvZiHrsYuK8BS/zoLgldRu2w6LOH6k/OpUZMAHKO/212/pNpyYwyrQF5M8CqSm6+xnoQV34tSy1dFzJdUB+KbFuM879++47PPP+NffXPFP/9ky6fvHvnRi1e84RP+4v2fs9tuOD+e6LYbIlHFiNLkLZpEmZj1ONvgtXmhlLliMtYU3h57fvJ8xz96fc370ygl1RTx8GgsLhmmnDj0I0PMfDgPuvkXvj1ceLXr2AZPcMJmeDUf3Hee8xSxWK5ahwWO08SmCQw50w+GPA20wXM8n7jaNBK1FcdUEv/Lv7kGY/DeC6OE3s9s2XRWBXOTRHylKGVavWqWZyelyNIhWx5ffR/1vRDQkNME1mNLEf2JNaRipP+0ES0ZmLlsvKjzaFkDIgMGC0Y2mnHoqZ2fc5KeM8E7LsMgWh5jWEbnMtblPY3KC8yWAzr26gKG9ao9EpHqkAPDdOYyJoIztN6y7Tbcn8803tN4SypGtUjw5nngMhYe+8RhiLTO4QycLj2pCPDcNSJEHcYeCnz6LHB3LhjjOV2OvHj2gk3T8O5dIpbIECNb7+fWIFJNFihuZBgsF7E+YYqRJjRc2QeO5VZdgQFTsK4hl57GGfb7a2KG4+EB4xp8qYGIsG5VdyfUeiKEVtdH5pJhawzdplYRjYx9D8jG1HXSxFDmoQXfQkq6fCSMCxRjlBWQtyX9X5fIRasmYLUuW/I8hf1dqohqC4JpHKURKIbQNgxjnF2S4yjBiHcObw2nflTwIgz2OIp/Tdu20ARif+ByvhBTxDovjrIUtl0n+rCqpaFQ4sBYIOPFmbb1DFPGJAVEm3Zmh51zjONI6DpNpzqmYSCmQtQeT6LREe1lMVCmgWmCZLXlQfJcuMHZxGWMyhY8GejzuvyjqwuvtwMfL4H/9Ljjn39+z5QMf/Tuij5ZcjY0tpBWK/z8lwUr0rjCP3x+xx+8faXANmHUH2fS4MmgXc8N5JgYARjnTaTxshc/PJwJTUfbBLJ19FPG+UDJhb7v2W47tm2BsuPDSaqBvHPSp9gFHh8e+fi+5/VnPxItiw8wTcrMS9/C3W5LEwLjNBG8w1kj1WNOqmi3uw4wc6fxmMvccf6HXr+i5FkoO5MMm81mLkMuSvcFb+iHaYnSqFvpigkoku93qiyPBfIo1GgIQdMoyMKaC9VLBbPSfOi/pWX7emPXqLvrVFwqAMNbDyWKq6OyKFV6asxCQwI03iuAqgyKsjK6iVSjHDBaLrzcnlnMqhc6g40qgmNVbVTqJr+AhuU+y3u1P4dxhtrfuI7ZSgNWqnYRwq2+Yz63Kspc7lHR2aOQogL3GTxW/UAtM9/srgnt4nKaNfpdn/aTCVWqgHiJrGqjxly02aFW3ZQi0Vj2gZwSu8bxpw+Bz26O0Nzi80jsE831jo8fjlxv3/B4ucd1E1r8LRVIOZOR6Pl202AMPN80UvWTC11wfDgPXLeec0wch8R1G4gpM2WIU+Y8Ttx0Dfsm0E+DuuBCG7wKVgUq1M0DqreAALBcRKtirWW/aYhJ6O0UJ5o2cL6c2XWNNOTzDmcSU0kMU6Frq+Cxjj35vmHSRn2KM21Zpf1mnFiWRTWjkefK12Y1xCSFhaSPjFQSVb8eQ5aeQfqvoiZ8y3ysKauqsxCfJZBIKiep6gut6AJiTNoBfpGEL0dfA5fvpznrNKhAfXlPAgbHb7xcmIdSCtvNBtKkVWQi9BumLF461jLGQqNlz41vGMYBW/ui2cXVmAIT4lB7f84kAuNl5OXzG1IRDdKmazHGst9fkdJESonGW9mEU8SZLEyNy4yahh7HkY9mQ2seMcXhw444DcThTGg3XF9t2DSGL3YP/H+PEixlFJSquFrmfe3szbIAlRp0SXBmrWHsey6nI84p2PPayFbNyVzj59LhPI0Y38jnTdW6rfVwS+AzryG5LMEZy7MSB16xlqjuqfLMVKQJS8dpW69JxtA4jvPaJkBCUmMiEDYYHLZpKadzRXNU9+uCpMuwlmEsGO9FZOwcJY7yb037STQveixcNVKUedq2GiynKPoL57WPlqa39J4FD2HTzZKHYsX5OWWVGlADt9W6uJqzf32/4/254x88O/ObL8/8/5y9aa90y3Xf96thD919pme693ISZYmSaEuWYziOYwQBkhgJkjd5Efgr5Lv5K8QIECCwABtxLCWWB8IyaYrknZ/pnB72UFUrL9aq2v1ckqKQJi7Pec453b17V9Ua/uu//ut+yNwOwqv9G45r5LyqdXtaAv/u61uiF6453M4JSTzfujnz0aHwYjfx2fuO6IxTZPc1Gxey2EDTZgcc4AI4YTEhwd3+wDj0FNDRCs6DKGfPex1mWGKHuBUXYlPDrn6hG0em85MGf/1gyD5N+bainDlrm/q6JlbJKgxpU6azabrkXCU/+CXbcP34K8tDmtHpKhxPJ+ZlYU3FSHp6AVJUGlp5KBsk2iyuwbNd8LZ5dcvnVQlU3ptxtrLCdiivIs3NUrMVZbY/rTfKVY4FOt1UobBKONVtnm3ir2ZmUQmvgAuhHUAs22kdMfX9jU9ajWZVTW2fw77qx95akZ0zJKWWja4df0U5GkohG1GtdVts5asP9FBsjfQl7eey3R+xz0UlAl65DV8he7bApV0PsLu5N8Ldh86kfn8NFNWyghiapWTd0sTIGg9HtvvhvKfvex7fPxIcOnAun8lpZX+457dffJuvv/iccX9AlonluOJMzdQ7He/wbOz57t2eT252dMHzMPYEB6/2A5eUOa+Z2yHy6eOZQ/Q83w844Pl+x6vDDocSWWNw9EFJc4jON5qTtp923ropysYhyaJTcbWMWQ23I1C4zBdw2ha9HyLRqyhi342IZH76ruP//fRA38dtiKdzQBV8Uy+xrHW+R50RtZ2B6iSife36zgLG3BSDm6CZrWwIvpFrNQjdjJgL0TgOm2Cea/vDgmUrzznv2Y0d0WfNpJw6ohAcOatxV8qTvwqiP4io2z374CB8w0jVM1anwY+d46PhyLxowDBYTX22cSEebECc3pdpFaZVeLxklqTwvlBUgMzOfEG7wEbLOueUyRK56XtOpyPedywp8Xg60UWdhbWumcfjGZFMF1RFVoP+SAzgxFTDLcAIMfI4efaHG0I5qrwCuo/P5wtSFv7o2ef87W9V3hkf8MRALBitCUt1QptXLEUoaWWdZ7p+xLlgpWqboRQ7fOwa4ZKSCV1HsPdChDqrbVPT3qLeajs3k+4MJQlUEuFmo6q2F3RdbK3Pdfp48FUMsdi8q0AMOoeu2iHV/KilUkd/81wDomConomPrSmZnoeY7Ve9oWVdP1C6DiEoAmSJ6TLPTNPCdDlr6/npREmLds/hkVynVtekqxgXx5k2Tr8hAc4Iu3K1m9316mxW9Vu3M//od17zRx+d+FuvHtl1wrIGBi+83M18/2Hhk5uJt5dfgSW47Ytz4EPg4/0RJ2mbvNx1NbpsSF19ovotS0rNrvR93wYPJ5OHKKL2el0WbRZIKnjZxYhINgRW17LvO/a7HcPuBmzYa14Xc2fOXJtykVLSwCR0Pfv9gRC7tsd0H4rtZbVvuha/+vFXE3G9I/YjXYx0vWZdx9OJ47QyDDuWlACNpD66OZL9DY9Hm30COkDLou7VBIvq4SulMJ2VeLbb7UnrorNNrozXllhcO+ZqTGumaNvDOTqbbZTS2ghBgDp/+7thCMZEt+cLRGuBKLlYm3Nt15XtfczIllIJuTT0weE2ZrwRfFs7qtsMywbxWxZ6/VmltENb2w8reRgc3qsEen1+yYnWfiq0I15/0Eye4bmVrFz1VHBOScjtKJhz8444jPT72xaI1Lp4fX3N6rVFtpGF7VRUkatit66iVXrNhcqP8gDek5Lw5Zdf8/Bwx5vbH/A7fMqX7xa60PH73/o+IfRM88R3vvec29vnfPbuMx7LCcEzrTMFVZD9rV1HH3Rw290Y+cGLW5ZceD/pALfPjgvRaXv0YQiM0fHq5oHjvPLusvB+Xnm273lxGOm9lh6Cd0yL1lYrCdI5T8nCKo55PhOHDucVeu88dKHTgCZETqczeV0IY8fpPPPzY+Gf/fieTYBMW7qvlSRrQcV7h7N2a2fiYogGv2tWsTYMvSymNlsh+srl0J/perU2ahOQ80ZwziUhRWfpVPJ4tHZpAVzcxB+lqGbI05qNo2HdPxQb9RFMfdg3xA6uQ+XtZLezUYMj98tmXrtG4JO7jn/8+285l5UYeg79opO30czQCSZMBl2kdXnNS+Z2jNz0ji+PhfOqPLvgxDhJHk9mKYHohTFGXn/9Fe+7gZfPH8gZopETc0qs6QIEbm92iDjenya6LhJQxEvX3zFnzer1fhX6oef9ccL5gWFQMbcuRg5D5B9+61O+egp8Z/c5f8rfILhCuRKYit63zLkO/azlMj2flkiE2JCvek+99ybSqPdcy9sOfGz2yzIJvfNGHlaA7cPJyNVuO7sG51wbt6CvVddLSyRZRAP+EIzDFYidDvQUnAYpuIb8iGAJZWRdF6aLKqtTMn3QhoDzuzcbiOG1pX1ZAh7V8TpfNNihZFLJeB9JRfdn30Xirufx/RNKWte9pwFNRDqUR3GVNF8HIQgMnaK0OFUtXtZscgcf5qG5wMu9JuXvpqgds8nxX3//PWvS8R3Ra9dqKYuiaiVAThyXaCjgL6OrRRxjyPzuszMRx/NxYT9oA4APVg4cehgH5nlRW1wJ9WzIq34tLLLq/L6sPJN5mVmmGec9Y9+RinC42TNNE0Mf6fsBLCgPHkUYu57bh3vOxyc+ut2TEixJlFvWklhD6Ys2IUyLTit3bYK4dsoNV2NidmPPr3v8NcTlICerk/nA4XDg4i4adYlO7JSS+XyODPEJF/fmNwuItlata2kGdMu67GsRpKykddkyjIpe1ACnthO3a/I1oFTjgGoFCJDzalODdWSAw1nGvzIOg0Z9qzliI4ZWJ+/8xmRuWYarjt831KFpI1z/HjPOdu1XWAdb0ENbwEbo/eb9tsWtDqQGgC3xafB2oEnYO7ZOjKsARAPqyofZfqbfmJieZdbV+JWS2N99iyvY7Aoxsu4lzzZvwnkoxUpbSu6q901bDSzQbNm1PopogNT1HTk5nh6feHt6wf2rgc9ef8Xzb/8uuWSWdeUQIs4Ja5r56PCC+NTx1eWRlZVlFb77/PaDUfRzFs5L5ovjjM4A0ij/fhi0K8F7emCeF4IUnu9ii/C76OmDZzVdD+WxWJeHdUzkvHAY9hyP79kdBnyuLd6Cd6oHgwgXhBf3N+zGPfk04d2Z4ApJIjSnXtdFy4rOMjx1PnrglcBuQouiOiwVUr3eq/VO4+SD87Ip2NL2t8upITYuaEacWqRptqS2X4uQUmmTkx1aimnkTQuKQghNjVdLSNeon/wy4PKNcGZD9LZgS1zg1V4YQuJSIo7MmmfEj5oEiEDB1mYT1wMhFYhBlY93nee0Frqg67nvPU9TZt9HolekZpXCbtwz7g+MfWSaTrw7LRz2O/bdQJLM2DlOq8rdx2gTyaOKvPWsvDhk3r5JOhm4nR1NRKpqcN8Fius4dCfePV348vVrXj4MVA7Ctm7SyNhVV6h+vJR15lqRwr7vyUVLHUWqEJ0mCUr61dETxQXI1rlRtDtG32cbTtgQ2WvRTKwTrUow1Gu0ALkYylJK1iDAe8RrB0nXRYa+42xdIzUaENGuxkoPqHOYqrhlE6HDMfQDw+GOy/u3mpqZbVSKUGZN2YL5GexaY4hUOUbN+quWkvJ4CkJvekYhRuWzVBXoX/FwVk6KlkwE77Qzzu5lvSXewX/xnSOv9gujL/yTH30EAmNUFCd6UZtoSamHFiSGIHx2HIiubEflw4NMwetk8yjcjo7v3a/84ilwSRHvdFKyD9o0k71NfE+mn3QdXZm9oo4jyKtxoTy7cVDUZlEiPGkhOceyZgswNZErObM/mJbXuCOXopPeJda0vJG7k5jf9hA/3ETNf5X6uYuO1vh1j7+WIm5tPa7cjn7oid6zrIllWXh6/577h3syI+TCNF/MgDjVffBKJFuztoIWUTJPLWVczhdrI74ymFT/b+WE5vQsMHDb7+uQsXUtSE4Mu0Df62YsBcgJHwYrWRicayWuvoutpptyhgoNOhs0llVDQCztcDiybFOR1TF/sLPs70z9Vz601O1QXAVnCmDo86qBqzBwzbrc1Y2pARtc6bs0wGPjSGwtyDTngjkESQs+GpnZPmcXA27YM97cc81jqY6rBj45yxXKs7W9Xw+6+hA5cu0aLcGhdmA55+j7HiTyf3+24Jeev//8jp+9ec1ut9d2067j6ek9IWiN/tXzZ7i3kc/PR96eLrwex+YkbgZVVnw3LaolYhL9GohkvARO84J0nlTsM+PxQZ1kZxlHEjFRMzWoKjIXSHlm7HvWNLPf9ez6gTydFU20Lq3zUuhc0M4rH5lTYllO5BLUcWCOoOIqzr4XUF0VB5RGclUfvN3/1trqrpx+Q/3MwSBQjOQu+rNKmFSSq6/hsyrleq/dcg1RLC1IBS0vVWJdvV6oDjCroyzlA2SgRtpiqN43HcIH8YsIIhuvq4pSd13g411mniYcHb1P7G5GHmchOkgFxDumVTNynGWe9VgKdNHjnPAcnamyZh2iOXQaVPTRc77MTEuh73q899qWf1EhrWktdPYZgtfBniH0+BCVN1eEaV24HRzPx5mSR72fdgE1mKwq4vie227ld++fuHeO8nBgXpU8WwO2LXnTQbQtYXBYYmB/K3qPUt0TsiHAIQQjYCsqJuvaZhllm8clhoq6itLWziezC74S80UQv6HcLfexz1ZXV8sAaoNyKQTQCept76ofCfZaNXlJOVNKvXYLgvIKTrsBYz/S7w7M1hVUAzktebk222qzpdbV6oIFeYnpIto5ZdcaowbrpSgRfhw6zpfZZBbah9O/9bZ+XWjnxrt8RSFQBOWPPzrznduloai/fXfGOc+biydIYTaOTU2QcZac+8IldfzozS2IljqFq2sQ1QvM4tBxNAmH54cvTnzrduXPPr/lvalq1zUNfY8vivRN00wxVerWqWqvWxGv4BVxUWFK/ZOcEnEYOZ8nvNe90/cRXKCEwjRdSGviZtdzmQvO9Xin+1O30iauWNdtq2LUy1Dtt6rFY+bg1z7+6vIQpoObNshRa9We7DzjbqQfevaHA5/97Gcc7u4JIdomrjLmjlxW5cJ0PZK1biY5N/ceQ8THqDB5Ke0g6GHaNEGKIQ+qImmKgmi7IGhdunhHSoKURL5M3NzeQFENC6VcBN3tAAAgAElEQVS3KmIUvM6tWCzSLDkTMH6Lvy4hVRJsXeOrrhtRknFr0aYiFnWRftU93Rz41SnbSjYOKulx+8OrR910cpW9NsNYr0Fsk/sWbDhfpftVEE3iiJRs5Lmutc3dP7wwY1YDTDVR22dxliWV1vJYa6UV5tcyh6ZqtdX0mkRcjW8RYddFa1l0nI5H/s8fn/iT8Zb/5v5L7r7+jNelcPE79jf3fBIGYucYxo4Xz/b85deOh9t7ZIX/+MWZ774Y2feRd5eF3nsIjrHXzMUDQwwchg5fCmNXeFwCS/J0LlNCR5TM6TKroqw5h10ftcW4wuFOYUvJ2h0ynU9qTEqGENkPO4IXliWR15XHlMgpcb/recXKs4PncbZZJRa0q8M3o27ZL75rJRraIa/31ohuIjYAj03byCZO1241HWvh7G9sGq8zWNbWxNnrllQ7DvQ8FSOsO6RJcUtJxNgzDp6nUzbek4MqEIfHkfVnV/IIdSt/M7xHxJJra5sWm79jmfcPX3r++O7EXAaKX7ikjM+Fm75nzQVBB16OIbTuvvpewcG8iA7w6xxDcCzZocOjEy8PgS46Hs/C0/FCFwN97AlyoQAPN6O+Zs6c5hmXCvd3N5SSuCwLgZkE3AyBIXj+1ac9/9fPDuzG2NY3xsDtYceSHX3IvNpP/He/9RNOqwaSY7jhi9OJ//DuJbIeyW53lejU0mFhTdoxlYsiKd4Ju6Fnv+spoqTylgigpeDg7G9jVGQmBFJaQYq2QBd19loVMuI8OqhPrIxc0YkaJNRgZrN3VkovyhcMXnTspVNxt3VZWNeV/WHPbr9jWRa7L5Hz+UzsVL8jxkjxporba9v30+XCOHasy4JzjuH2GdP5aKWZ1Nq1q7iZd1DHha8VCbZHKbDk9KEdRfdIyUKIgeNp0kCvIUr6eWPwHG4OxF7Pfr1XlavngM4X/ucfvqULMC2ZCCTJ/JefHFlSposRCHROOTnFWv696fCU7NgH+Md/8CV/+sUtf/k4cEmBIRQNVByU4njYrbzcJZZkiY73PIxFhyqe74yXVD+z8fAExnEEQ+uOT0/NPldNniLC/uZgaIl1B3t9oWlOOF9HdCiCt64Tfd/TxZ6hH0hFuOkWFnZ0LjHliL9G3c3h1UR3G/Gg5aa8zI3GEbrR7tevfvwGnRYarCR2E0pODSUpgsFPK0iCGr2bPVZFVIMdnd+MNNLgblDClrPXv4IiWpCk0HadZ6EbsULbXQyMY6eloJrGs+mTTJdJCUIm6OodDINuvpSUD5GWVVV/RQ95CL69Tz2i0oKoD4MS54Ndcr2uFjpUOGTLSz6IQyyQuCLubtm0++A1N/Rm48PU7Pvqhn24sN5pW2ter8h9VRujIKJBo3Z15cZ9GQ93Wk5LuUneqvHyG0lZGti1BSR++31N2qpTrLC2xWUNYApe11SzMg2MQ+i4PL7ln0/f5U+nV8zdPT/4+EDHwvlyoRSdf+Oc5/svvs3TmwvLUiDDl6eJ05JYc2HstPTXu0rkDJzXxNNl5ulyYSkwhoiUFYK2PU9rsmnIkeC11BF9QMSz5ronBWQlBCWP+UAjs3mb7gqeaVkoAvO8orNXelIJLNKTbMKuvp7b7iHbPWs6Pg1BuTLAZpX0OVXO3xBCMxRKHNTAo2piIBZyGwzhnDONpVX1WKQiaI5rToptKNOHUWNyPk9Ux4qrIyYql8Vd7V07M7ZXqoFsWWT9HK7q1Bjh0asOzyEWOpcNXtYyz9gFghOGCDddYIiOPtbyna6RGOpXy+Y5q7HrvMdTtF1WAksOvHv3yH6IfOeTT+hiZDfueLi5UTQ5JURU/nzYRc7ThdfHiafLzDgElgLvpkQMiT/7+Q1d35vuSqDrO272I5D4ux99yT/6/qf8V9/5gvMqOBHuxgNfHxeKK9zvrRxWKsq1kRNz3tZY77Xeu9BFMo7zkpnOT6zLbGVj1TXRw+bbgEPdF6aa7ILaP+fIFpx454leeWGOKm4pVH0kH3SWXJs7JNIERrNpduVcuxW0e2Vd1ob6OLTjDDSYa+KIFnA7C0Bi7LhMF9WrQZqz7MedJr6GMtX+6xr8ViFKnNMGhDaF2rduobYrHUq4LcK4Nw2XcWDoowWv0Wbrwc1+VK4IG7esJpn1lHxys3BOyousTrVqfnnABUFM4j5GI+/atSaUkwLCnBx/95Mj/+333/Hb9xfm7Nl3RVuhBQ6dMHRafI3O0Xn1s3k5Umf6uKvPKULjtRTRakI0gcSu7zYUnM1GVzJ2KkpUN7NEKRpQihR2446UC/OycD5fSOvC2Hs+usltv2x2oOo7+WZ3NrOiCSLeq4J4F/joZuGuP/PrHn9195BlO646PRRSy0aCq0JCPkQoYhMfi9kx17KULNVwKLxc0CxWbMMKupiVob45xq3MUQ+JQ1iTOo2CssVTSpAXsgVU2wZV/YFlVicxzzpZ9Hw6W6ScKDnZCHVdGR+uNr4z0mlFC+oiXMUJFn9QcbyG6rJ554p4fwMOsedeMfbN+W/vTbuGli1zheK0CGAjGpu10KBynQkIOS82pVeXW6FURcykiezB/u65Pr2IBROG1KAbPl9BodscpPp56jXVwO3aUW1/X7OLYOtUa9erSfD3w8i4O/Duy8/4eXnGPzt/h6+Wgd/76Ibjmy9wruP9uzNpmXl+f8PLfs+YAx+ND5yfLpxT5pIyQ/R8cttzP6oK7duz3os3l5nV7sNlXXBBjT5S1JCGSOy0vo0UlqSZR52b4WwKNC421GhdVxXXKpus9jQtLEvSbHhQjYJj2vH+lHGSGTqVBtduE123rY2db2S0tq5uu+O+BokVfXE6fFS3p5gxr+VDa0ut0vxGiJOcTDxS106HE/pt/4pllGmlxch2VotxVgTRjoJ25g3tcTVgKg1VE+MIaWODqwenBe0tmIEWCM1Jjf+yZpwIndPAo3POgihtS63HIRj/SHV1NLGKfgv5aneVgrrC++PE/e2O+zvNUpcsFNGsW0oGAqsoqTZ2PeLVkXZ95N2UCU55e//hzT1iass5F3zQ772HfZj4w1dHXuw0IB684+Fwz2Wa+fJ44t3TiYzKnV+XYRWsEWIXr6Z7K8fPe+XTFEsO87rS9aOW8XDt7Cr6ocFtTtp55WvnhhkSfyU8mEWslV8sIbsKcA1N3WajVSTGZlX50PZYbWNWobnOzn8l8WoJschGQF9T1rIzmsQmI8WWvJWyShHunr/Stb4u4dSSwrVtvsqShNKuse44bw50zVbicY79Ycfd/T13D3fcPdxxe7Pn/u7A4e6OzJYM11cp4vg7r47cDYl/+O337EM2lFAn1DvR+94NHRjnKNp4ipQLJZWGUC1FOTo6U81z2xf+808e+d7dTB9K8xe/83wyZBebw6azub7/vOPlfm6NG9UPY4FgPVveeWKnSb5KjgSGoWMce9Y1QU62xr7tQ2eBb6gdhM4zp5Wqmqukfs+X7zKfv5nJ1Pb2D5PUxqHzDmRrCllT1qAwDkwlIPNrReR+zeM3zh5CCqWo4FBwfiNaWr09m2GLXYcYzO0BH1UsJ4vgfd0quvkDtQ1NuSfTvGrtvSgsXJyCzDF4stum+WoTgwYPKVmQ4BzLWgiho4vCfhByOnOcdyjBTDfOPE16MNE5OinpkLKchH7QWnda1wa36yRePYAprVCuskdnCa05LQyCqxrNNUBpqIvbjOZWZ/Gta0SDhgrV10jXHFnVzaZuvm0UQY2QPmzM1vq1KiR2mjFh7qXYmvrQNmQNsnwIHO5f6GZn0w0JVXOjvqWrwSQtKKlkW7guB+m1XxOQDZqis3Y5HFwup40jgx7gGDu6h+cslzNP5xP/R/8d/rd3O36/63j19Y85yQ1994zDbuAPf+/38CHw7vHIXXC8/eIR1wc+X2d+/5MDDzsVAjutqs0yrTOXJfP1cebZLnI3dhx6HSaWRQ3j6aylgqp+6pzJhYehIYvOKUHWh8AQO57OE+PYkZZM8cL+cGCeL/QRDmPAI/yrLwf6YUf0PZfTkSKe+9sb3j2eNFgQcDZ4TkRHDtQgRLLbgnqb3izONFyA2p4ZTfuoBqkaEFTUr3KabE9at0+dfzOEnmTfayKScRjnq6apspGOS9OGcHYvQPDN+aq8+jY+Q42mQ2rnvwjaHh82Y+uVI+JyJpfIi30g9oFh8OQScUHwomWv4IQimuzUJKfz+rpJ9HWKCMl2dedgHCMJeHw68vh04tn9Pc4F9kPkvEwchsC6Tmq0S2IIPV89PnHYRS7zwn7oiMHzrfuOP//yGX/y457gtM7f9bEleiJCxnOcCv/T33xLyQHnMx0qs/D49EQR6Cjc7J7x5z/vib3yjpBNxsGj91rtqHaVVMT1PC14V5hOJ3wc9TmltGFzznl8SXgn5LTg+z2+vjYWfIgGID5GAqY5pBlmK41IO76llc69dTjW9Y+hziraRgmEriNnRfqqmVytHTmlZEG20wDFK8o0DD1v3r7VwaLFSoZOZUmdg253IHQ9ZZ6UM5bShrBsES8fPjbuX23TjiGSc1ISrlaSySHqRGTzAR5PDHoWlzU3RffqCf72qyd+9/7ID+4vpBwYOgcFG84oOG9zhwpmhRMUT0lCjyObn9t1fuPle6GkYgGc4x98+z1jB28vHfte8GSOp21CsohrauEf9z/jzeUPcCjaUdZE6DpF1q3svJhAH2BEbSWqi62bErqz8YWytXhHhqG3TmBhf9hrp9GigpX1vs/TzP75cw6dlq8qIKH3wAQNXTBfGZoOlA+em27lP/vua75+PPJnX36X4dmeX/f4jeWh2qWS0wYx17ppSok0z8zThcN+b5usyhd7ui58YFC20EvJZaVkzmclMWJRYyXt4jaehwYsKg9Xg4RSEsFD9FbDLVr3PU7wNHUEb+9rTty5AC5oJ5S58pylaZGobLC02Ry1luyvJjXXKFkNAlffV2QHKsLTwkuuzpJsQco30RigQee1RluRkfoeG9rDFvzIFdm2/ictyL4ChWoQVH9oaIg9d3/7nE2/o2bzAcmpGTkNfGR73hXSU99DrhCByrhvKJKhR7VcpOq2qX22mn15M9bDOLI/3PD0+MgoEz+63PH842/zbN/x5t0j798/tuGb4zhyWU48f37gYXCwFr58+8hlLUrGLQqx3/Wq71FK5s1pYYiOw6jdQt5r+3vfd20NiomtOVSHJOerWRshEH1kmid2XSStM12EyzSzrjP7PuJ90LEQZP7962dEr9yQtCwE73k6npqTUx+v9zSEoPWdK4N7/ajGs7WtGkqjozVk21e2ptf9d/kK0q87wZuQlvee0PWKstRzaM8Vc2QtkL66vmroRYTOexNtpE3xrbwq3PX+8mxoam3xtyzWyiT/9qvE6bQyn06M407JmaVQxEqa4gku4J2n/g+nNqS3Lo/oYQyq1jonwYnu7VcvXzHuDhzGjmVRRG1JRrR1SrZ9/fbI8XJR5IXAz96t/PDjnn//+oE/+ekthzEwjoM6AkNJgpGfdR2E4xooomrN57QyzRdN/qTw1eMjj5fCkrjKkm3pzP4pudrIxd7T95H92G+Tnqml32LBv9mSnBj6XjkmNjKgKR5XhAVHjHU2mw7ClLperpb67byinT66b9SeVBS+rj1SibYqPZFtwKdXbFeRnqAcQim57VXvtM12mia62KuOh2yofZG6ZzPDzb1ub0P4rgzvdu/c9u9rzFI7BBW5Cl7LQF3ftd9n46rUpei7wJJsWK7f7HEBbiLsuoFaDimmIVO7VlVDKSJSSEUlKkRAkuDFSpWGBHado7dOts57Ap7O2Sic4ni+Tww+G2qtz+mjjgqZUsJ7YegGaygwvxQ0wGw8OcB3PXUzCUIXY5XaoWbczjkbJXHFRQTT2gksKbGuqnDcxdhKTM57jscj76egyrgiNLW9yqXjGz7JadB8nIV/+VP4N5/vDBD4FUavfoZf+xsqRI3BnVt2r7CfBiUlr+R1hRDJWdVxxy6yGzorQ2yFC+XeiC2ioRWmWVGDh67r6DpvkvsbfOhtY2sXgzrOlHXGiHEuTT/FdCbKJshWjJtRP3IlGxZRwlg1kDllyEqcdFIoaWun67rYvgqOOkn6Qy6KBRdsKAZ1P6AIR+soqJGFPmm76ZZhNE7D9Xp8cCqr3L67/oO/ajUtyOCD/wSFgXf3z7Zgg82wFcyBWsazla1sXa45PPXZ37gOhQW1JNTq4WAaDZFaxhBRUbZSMmtWHZi+73l6/8jj4xM3nfDTR3h26Bn6jv1uYBx6osHiY3/HPCe62PP63VsEdVKVF+WB+13P/X7Ee7gdFZFaVtWyEPuMnVdRruA3fkmyNemsu8T5QOg6Ui27uUIfA+OgYwT6XrPM4HQa8py3jg9E6Ma9hhqudvfoeqhQm+5/2ym/vITNGFdnoXe+EqRTTo0Iqo7EHARW4lKIEMDmz9BUnJ3TLpla6qnZdKmITA1q2/YVtFvEb2fdgjp7J9uurj1P/11JulzlQhZ4lUwXPUMf+cWT8L9/OrDzI8fjI8uSzdlGfHyGDyOLKXS28ybWcC21Ldj2uqBdZlJUewXh6XwkpUwuiVDPs4usa+IynbnMF+5v9uCguEBKE//8Z/f8i188MIZF14+rMradgpogeAe/eAwsJev8GgoxOvZDZBHhpve8X+5IebsH0hb1qrxnaObQR24OO3COlBZyUnTYu60jsuSMpzAMAzaBSZHxeq/L5oxiNIerA5fAkBTcxkXTz2JBZV6RlBoaXB3a1tGm8vxqM9T5dTHatcAyL20elZgtjiEwdJHQaRmp64LtD2/7TFpAIALD7gYfq/MN7X5fJ4sb+uwsgKCh+9H4PuPYs9+Pzf5s3Y8qllgHPuaaH1IDfFiy5/cfJualQFL0T8tBeo687SWxZNyLA9kCUOccrjjVGFprt52ibM5DiJq5OdHuopRBvHYV+baOsKSClIQTeH6IzMmzFr0HKijpG4cl9h29+bGKzsxrYp4X6iiEyg3tOxX8u9mNKtRXfTGa2AUr62uAZoGEU1Q+hkjHybTZMLrC5haaxxD9EE6EafW8Xl/id99BZyP9/2x5rvWrvosK59kcEx8iJStXZE2FpzevuXn2EdVwXuaV02Wi1jT7PiqhV9R4+Q+MmUafMarEsnIJ9MDvd70NztqmBlf0BVMQdU5VKqsDLWlR0g8F76JOhDZSzdhFhbScs0Po8L5jTRoF90PPMk+q/4JyLiSrml/NCqGSHC2o0vS2hWYlF6srXwX+Aq101AyjHqjNKLSVVOPTPudVXFIRj6tDVG/k9vxfXsd6fD/85UbM3d8/17W9uqKqmKvCULXHvzTnJiIt89AvGxRY3651pYioomj0ZkIy5/Os12D8mxC0BXle64fV7qQiwkeffMKnP/sZZV35ye0L/uAw8eywZ5oThziyXGacBNa10Hc7zscTH798wHtl62t2rUjOJWf66MjF84OXN6ziOC86dyU6B7ISvRLQBe04Esnshp51XfFBB0A60bPY9z3LfCb2e5akoyOCC7x7OvHx8+cUKUTf8+Y0k+cjWTqcw4T+dI8la2rwtQ3SnFdbOfP19d5nq02KaPbqrDbchANLRpyjWOAhXoN4kdzKrToQFHBb8NKFaO2fFbHZ9GHwqhTqnNbm26Ny0ta1XbPYNdbMG1cDoqtTITo922G1eacOeRgHYt+TxXFJQh/gX34hvEmf8N/f/JT+Yc+8JLpRyOk1pXimWYhuYT/u9FwbMhCtVLmKUDI4r1B9Fm1hFqfyDeesZRAnQp4mXNRGg13suLm946eff84f/PbHPC2Rf/36d3mcPLKelRsixuuRrT14WWa6rm8aKz95/8Anh0eeD4Vd6FglcpknXr974uvLLf/x9Q19Z3YMaWgbTh2EmHBbLnA6z2arAl9/fWI/dq2NNydFAu9uDhQR5ulM6Ee8N95StsDMeIpIRbO15b8GXsHr4Lo6h01ExQc1MPD4it7U0lLQ9Y0xtOA45USIQdFa5xGnSETsdJSA9xowFfSzTctCb5wy7f3wxDY3V1Gg69LM7uaB07uvzJnWxMRZ0Kuk3HKFJnlLBmpOdTjs9W/Mdla+XvSOrqpj23ulovtpDMLDkPjezcoP7i9cThpQCUASbSZAqRE4R5qNblDqrtdyMslKrPojXHTki3l1D75zDf3ICdbFNF16RV6KFSyWosrdwQX6KPTS8b/8/i8Y+siPvt7z51/uqb0RNRHNJhLZooim9WWdZMFze7O3+yFMayVS62RunCMn0XlNGZwrDIPtB++Z54Wvvvicjz5+SXAJse6gLZlXH6NBrjY5uKBUEFkXnh7PxH6g/BV4ym8sD9W5MVWquEnRG5yYU6KsM6lkq2lmizRtgFURNWhXkVqxaL8KICHaVl05L9q66TmfJ62jlTo5ekMAoNb0AKcQZ4gqbOTQOQbFMk+RonNG1mQIjIlRYWzqGFGeQGYcd8TY4cSg8IYuuAbFi/FfqJmrr2z60q7xG/HB9q1lJlI2A18zzKp46ezvPqi8XINq7sPXvH6b60z8CiHlmxdU3yPEwOH+GXyAoGwqitsLVNhR2vpsERObg7p6h4rIxeAJwaY8h8C8GLwspd3bZGq57X584Lxhf9izTBfl2OSVsdM698uXr3h48ZJ1nkhJoeVcCkPfEZ0jFWEXbU6RqF6Lg3ZN01qYl2KS+JndoLyVijSspbR16brY1ix0PWsqHJ9OjLu9BTmaEaW88u0XD2gXUeHp6TX/7ss9Q6+y5SVndkNk7D1j5xk6hfuvy3C1nVydVmE3dHgnW0aLZjsVmq0ddmqI1dEppwMq30WKmQKDZLezCo4rwmMNTq32XbeAcxvBUi9UIGe8lKYNooPbMkHzZMuopW3O+n3wqs/jvCd2kbvbPQ/P7untXgYje+YijNHxnx4dfr7w+PWZvhuZ50TJ2hgQpbDr9zgrX2gwXCeLe4YQGGIwBMaxCzuC71B+VdCp86tOou/7TqfxronFwfF05MX9AScrf/Hmhs9Pe9ZF9U6cBf5X8SVVyG9bQ/jkZuJhF+iNgD5fLuSSeHeZeDcdwIUW7Ld7jXFZcjF0YKDvB8QFvvtMuF3+E+OgUvLKf1Gl6a7rmdbMkgo+9ohYiTNnxGbKOEfjk2Do3hZ46RlfbaSFns+ta1Ocs5bmKupZS75VB8jst33WaoNEdDxGKdK0VXR/G0fGyLlbp6O0+1iRp2pbnXeMhxv1Sc43+1t9jPd1xpne06oJI1KaP1nWpHOFzAfVmWnRFKdTgaU45uL4eL/y914e+R+/94b/4Tvv+aNnZ0IWXNDgv85VCi7gi0MyUDmXlVaBqNxGbqbvKpqwAMYBBcpSeTD6u+CMe2ToSkW3eycMXoPVtNZqQmRe4A9eXHi5v2ACvhtI4KzZwwKVDWWFvusYe50HV21B3/cEa/W+9jlpTby8OVMk4oOKcooP9IPOH0ri28ieqgtUL0I/ukNKanYkpUUHLna6Z4O/erNvPH6juFzw2pIF2pZVzWqtY16Oj2p87FOpo9e/EVsdkW1h9RqvarSGPNRF83oCNIipRlP7pttgt2unrK9XKMUhubTI0VXtiyrcZTskVkgfrYcDbcDXsmaiTUDVrih1jMu6Nhir1ncrmtI+D5vxqJtEpA6q2xCTxjdwloTafdt0TDb2t9Rg8Srg2DQcth8LtTSm73z9nlIHUF3tOGerg8Du9rkGaWzCT6391K6xIh6t1dDuQ33v6xY2voHA1EF+IQZi0PkY59PJ0BlnPBa9lvoRasBT4/JchK4fWE5PSFpIlzOw43ic+eyzT3m4v+fFwz1vHr9imSeO5xM3t3vOcyK5nvtdRxecDTNUCDd4x+MlcZyFIUZ2nbpZESXs9kHr/97DMHRGJlXuR98Fc+ww7keWJSlXwNK2231PLsLj4zu6kPjstOPH7+9xQbsiHI7T8ZGCCjfG4MlZa9zzqkQ5j2xS9SLMy2SIX9cMee3YqEYxWyDsr1sOKwRLHXZYNjTMDEeRWnZN4PtWprB2lVZecU7VHYo5omS18ly0gwgTiJRsk2qb07MTIRB8aKW985zwsdPAYRg4TwtpncB3bXL8dcdTItBJB7PjcHsLTrhME8MwmIOrCBQ4F5rmBl7wQQhFW7ZDPJDSgitHUiks04QDxr5nXm2UiHdEL9wcOlxQjtzvPDvxk3cH1kURhLQuVCnyGmw3RxnM2eD4wbNHxthxCL3amM7z/umCd8K79ZY6xLGeycoDDE55C7e3Bz6697x5/8Sbo+Mnn585HzuGXTX+UsGsrfRQMxvRYDpLIbhN1buiAtf2K5jQWuUViatT7i0YrbumItXOq+GRmrAW09cQNo6NBRQibd/5oMG7wYKb8+Y6Oax7zvMhF9B4Tz6wv3/B+d3Xaq+tXbvao9zkC7aoybYzy7KwrAs34Y46ckIM+atlsJdD5vfuL+Ti+L0H7dhZE0yzBk01b9TjlRUds2suKX8QzBSLHByuDUBsgxCB1jdtgboTR0nKVfJOK7nBOch8EGjiHEN0uAKXlLWUXxTpXzJ8tD/z9bSvYV+9PEse7P5KwftIFxWFWm3ifBcDKWmFpJYJx2Fo6rq5wDInHsYjb+Y7gszcjwtP00DXjzqyxStHp84d0tEQdRq4NtM4hOADy7rQxagIi2zctl/1+A1By1WU661eFVXHZJ4X3n71FT4vjA+vrP1KrlRgNwfufFWR9dviUjeYV8XDuKnu1smxMSqZa15S0xdwUqztuuo92BRPQMThTLWv1g7brhCFAp2RrIKPFiTpNQSvEPnQ6WCoZVkplkFGXyN63YS1dNA2gr1PNbB65oz74yoadH1bt4y6BTkW3LVIATZjX21+Q5jqod7+9mq1rJwg7a381e9r8OIAFzw3z543kqpjM3i6grVjxZ4lNQDaArDa3lwzIRcqAqaE4hhNUtsJyzJzOZ9UcjtdoS14nfnkttCqvkdArO1RyV/Jj5TuwP2LV/zFFz9BXj+SkgX4E4QAACAASURBVLajBhcYxpH7tOfxPPHq2Z7D2KmwlhkA79RgCJqR3Q6Oklec65E8sRTHbrDuq1LYdz215V6/6IGakwWqBEQWRZF84Hg6g9Nrfni4JUjmz376LQpaUnKoYYjDDUgmF2GtRXMMji+FpZ6hUqxLz1AUYesgc9uMqpqZasmCZlj1FheF6AFEUbLaQloJx7kI4zDgnAmVNT6Zbr6cClX9WImmhS525KLKo95fdxb6q+B1Y7UpF0jP+/E8c3t7AKedR6fThWWe6PrR2ijV8Udbp9HDP3n9ff7XP4aHmx2ffv2X9KO1mfcj0zIjInRWAgCYp5UQLfNOWCdcYVnfI0mDsuyEse9wRSBlU8gGXwLnNTMOHV3wnBbhpnvihy96/vzzPcs8acBSCs7rrKolKUJVciJnDx6+fz8xuJXzRUghM4aIl8xxmnl1P/LuLx2dVyfgroxKZ3Y2FeHxeOL90bHMM0Ov7d9+uP1gT2qpx4KfqmzsPNpMoGc6V5typVDqvKesqwoLeuUlaTu9oilFVFcLKxvVJCMGpQwUKw8Fv3WLqD21wZmSiFHL8pVoLKVYdxKGZBfrOqzBn5ajFeHJFKTZQmdzy4oIu7sHpuNbI+QKDy+eK89xTbjLuX2W2KnkQCCzJnCorT2+fyIEx83NAQmBnIVX/YU/vD/xsBcE7dacV7UfCBqQr9biX7ayvgtbshoEyDoUODu7/+Jwu6D0AF/7OWlr5mXTNVNn4lrgqnpb2bgvAt4mtVuZL3rPTW/oUjDUHuF7d4l9/Jx/+cXHzS8UzKV4JWkk8w+5Uh3QZGRZFtOdWlVbp6LhIeLygqfwNO/xZeZvPPycPjr+4vhdduPEbjzw9u0jwXt2h735Hk8S2PXRNH00lljmmSWvOK8Il3OGsv76mOU3lYeusv+WSRRSLizzxHo5MuzvWj2wQGN5V/dTI/AKT1dczLkt+49mTIr16VdDuq6r9YdfdRm4QMrSDJTqsijvpjrSqmuy6T5skvRZauQohgTo9/OaKTkzryvzslDEkYsenDUVclHH6ZyJovk6fdcc/QYbtazB3I45443VjSEWTSyvZhRs96R1C109p97/pnnS4IktQLmOpurCfyPObv8+3L9QXoVdd71X1y9Tf1ZJvzWD08zQrolv7LCKPOXUygWX81kJZbVUKPX6/dX76n8NckZ0Gm2wgX+SuXMTeVk4Hk+8eHav7d3F8dlnX/D82QuGfsd//Pwz8LCUohodzngMwfHi0HHodQ+d18x5TSSDJ/Vz+jbHZutaUKg+ZdGWQBEkJ5pIn3gez4VPv37i3eMjIp73p4XPvviKf/qTlySp3B3dR8GH1gYK29qLZRib8d4I6sL28+vbXeHeaghTStt/OeuUbWnLcvV+V+U7E/qa5onL5dy6VFSZbXNkNThNaVV0U19MEVGnKEaMHVWdV0xR93pfYXt6N/b2+qa1ss4aXH2jbJrrawg8psA//XlP7CJeIpI9Ie6YzpOVmTWhyFnhddWY8ts5aJu60HXGSSvamh19oO8HOhcoBZIIo4ddV8vDwpKEjw+TIpnGO6uQ/bQkkKxImjceCJ6bbmLoRg7DyG7Yg3O8Pa+cLmd8d9DuRx+vOu22xCFlcEZGpSRFiddERkvvydqJta042RDVuo90b9LuY9kQFKl8Iw04KzEW0cy7dhGCSTpYgCyWMOm1KSLt/TZ8sNru2nVUEzeMRxFCaMThLFuDRBUdE2jnjaszsNmED+2Qd577Fx+zrgtpXXl6Oho/0ZnCelbHuK6a/FBR681+jsPAYNL8P7x74u8/e8+rfTJ/lhEn0DlyUKBD2KLLEB0uWLmmCGXNpviur66BXyDuniFBO5Rq91ayc14sqxRnCKlBZmL7Wc+82fysqFpedFBxRT+drYGzICcE5Q8ehsjDzvFbt2eicdeq3a1nyjlN2rzTzq4678mHbmtEQFvFK3fJm88FR/I7fvz4MZ8fd3hZWKVjLR3jbsc8L5weH5Uq4pQEv6aFeZqYpwvHx0dFYazbLnijdzhvQ4x/9eM3clquP1w1ckr4ysoP6IfNWVaj4LbnthsltWXvqnTBVdcNNEW/jRzkqQqP7WVkC3xAByRWgS/VEbASBrWEVWfiKPmRnFrnhMJcpXXrOLRHPWW9lqEzaNopMU3Escxri0uCpxl8t31ofYoFZVfFIoPkLMBhY5bXpzUeSwtc7PNe3UTnKklyg3frO1w9tcUzFbG4hjC8OZj9/fNmBCqKUz+Ls8yi6q5cXZCuW9nWwG2/aoFiHf63zDNVbrtIIYnpO9gzN0RMR5TrudCsrut7OtN6yEmN67MuazYihTxfcA6Weba/K3z+5Rec0oXntwOHoWuE0GpE62PstBSzJkUJkgXewcN5XvE+sOtVfGnNhWlVk5U1IaePnl0X6LvIp69PrNOEKyvHNfD10fE0C3Q3/PT9jrzMyrnJyttq12P3XkX2FKkpojowpWwBzBYs1r1lCUFVuq1L6+r91KRBjUAwboFrZPK25mytxTkrnB6iKo4WC9Qqv8jZder+qXOLzOglzaZD5TlZcF3LjMHRDCvOM3SdqlLbxlEHXNjt9uSytWGLXkR7787DX74X3p9Wurgjiwa5uSTtTnPaWRgtqcA5pDhaKVN0x3nvWYxr4EQdi/hAEnictfV56AJr0b/vnCJRU4Lbfm6aI81pe9WN0UnH3qaZe9ac+OIUWKcTJQvz5UQpK7ve8Vsf3/F+HpQn5YyThHasqVy/Sjw4f4UwOEcW5Tk5VBxwsx8VqbQ9gAqcVZXabwbCzflYUqpikm0rNRtdOzJrcuew7jbvdb+EbU9Z3PNBAlb3arVlxc5y65ij8oKExg+i2qRKGK3dTlecJVERusP9c3Y39zjvmKeJ6Xxhms6sq7Zb17Ku814nS199xqEzxWKn6rl//HAkBseSPCQNQsqSVTclJYokjRUM2k9XTQc6dd24QV4DgKqLldMM6OuUNZNXtd8lq+p4neheUVG1w34rG7ntrEnRUpiK1QmVo4RTArCIdhQtJta3i46/88mZb9+eSUVncNWkEl+nunf4oKNcYoz4qHvhWo5CYJsm7z2hi0bmzwwdHMuDBrhlJQblmB5ubijieHp6z+O7t5yOT7x//YactNW96wecuxqbYLbM+62q8asev3lgot2sEKoAFMobeP+O/cMLXbhrr2lfP3BLFfc0AyhGPNNNXvCE7UbWqF6s9ODUuYvoCdr+xlnm6wiugA+E4OhjZFmVYxCDdU1YdpstMEkqJwgY4cduWCUc15JRyomcrZedTMoJF2w8uWxEOyzg8LZxS66EsI0sXBefFsZsN8tMEBZ0N06ICDjJTfXSmQYFzuFMrfMDHM1tS/DB92wBTT2zh4cXJpO9GTOnCcPWxmz3vhoXZX3X+2SvXg247RPcNpE2FSPJSaHrOh4fH9nt71jWFYfByWWbAC5oErOhB4HVNBK02yby7WFmTo6xZF599JKnS+Lx8ZGSV9alY77MuOK4349UouC+V8O65sJx1pr9ED3zmrnd9SxZ1yM4ddQhBIZO0ZBUAnMSQuhIpeCdksrubgaWFDieZx52ifPi+bPPR75MHzP0I7Wds2diXiZiN4BlqpVM+0vlvrro1OC8lmc3JU7H5qDqPd8CU9eccs3AtmDHWumlZsjKV/AWdJfaglxMqVQcUCy+l00zxHRAapZcz6l2DykCkXO2PVS7NbZrjb0KCwreyJvCvMzsxh1Issw/UlAEpCGBdmt2URgjXHIC71gvEwFvgRXWAXXFPnQoR4BKoxAQh2TV7fR2V2sb8LRceLgZNDMX4WKlgCF6Oh84rdYJ5T3e17lRmkhVfRYNsjMhwpfHPTEKJa2knNgNO16fjoxD4fNHE6NznuKL2kkR1fUJyhPMuSjfQNTJBe+hFNYsxNjrfrLA2wctp9eSbU2SKsdJHajNbNMe4LY2XCEsMdafizU92vPtc4OWdKpvUO+syFy00o53JlPprvaNb8Zya6evqGtDEF1LCuEquN+utO3/yre5efaKN59dqNlzWgVMFyVloe87FRDFMVsSJcDd3QHxgVwyuXjTBzHbJQWXoSyZhUQY67DAjpJF+V8i+DBCXnWWUFBepUjRVnJDSSRPeAHfXdEHjIPkPaS52kAheMiLIo6xC5Sir+M7T1pVJdZ5z5IW7VISj+88rtTJ33rjS8l0PhAdnJPwfCf8+K20wGpDQjNd6FnqMM0W1GKdXNr9U4OVev9h4x+lLMQoqPqDcr1C7MipsDscmE4nXXaBw81W1syl7j3zH7ZvNfm5ii6/8fiNQUsRiF2Ff7XtabpMTE/vGfZ3unAIbcyh23gwqmQr7aKyAyW/1lkateRTM4FyxW24ykitDW1NqhejEZ0S9UI1tlKQlKlqjX2IqilgmhkiShDUeqBvUtXXaEH9rmb7Ip4QTIzLe9UGwKLhVoPVA79lGIJKFEeQ0sjGW7HSyj71ANphrHNTGkRiwUAq6pS81w2ljiYiRFKqo8IrDFvpxtcB0faowUWMkZuH5wBUtWEfo42O3/aLB4oX07PRywsx0jmte3qnXJX6tOBhXRZ87HCoQwoe5iVRUmZ/uCclk7y3AEHQmmzOpXEOYgx0fc80TVSyqZTC/uYGh3D78ECJI1+/v/DzTz/lsN9xuLnl3/zoR6zDe/7B3/tbHKcLu6GnlMLTvAWRuYjOVynC3diZAmjg/TTziOfQOW73A2sq1DbulITP3j/y4nDgaZpZU+JHv/iKd+fMZ9N3Ofk/IvqCo+CDjYuo6Y8IsR/NYZRW7tEA0gIW20ebuJhlnQgbY0+ak6jZr+5V3xa3kS81stHODKlxbp0LFLZr26JNNRilUAmWzglF9KsamE3rqFyVKLc5SOo0pSgvpFi0VT9rHT2/TDMO6IE+DFBWQuh4Mq6T8zoeQS+nsMwLpaiuindwSo7TeWLA8/j+hMwT+2cjkqA4wXkj8DqPi5v4nhRNpLwPFKdclhJ0Bk9JsKwLlEzJylPCKQnW2xn0rjD28ObSWc1dNGMOpuDrNMgTnHbYCMi6kkrhsOvxORAJvDme+Ohmx7/4T0f+/E2v97dou/iaN7tIWqny6Oua2+RsEZ3DhNRxHLa2zhmv0FmgWcjL2gZoOgcYdwCnk3eDcaMKtCBT7au1vVswUlWXfZuzpp+3FNUQKUauFBzSemw1IanDZrtepSVijKyXGQxZMu9oZRwtJ9efVwvWTCPVN1hAS4EwMBxu6YaRnFbSmsklK/KatMVbg7li5PnAYdczjgMJBzmzZHjWzyxFJQjymnFZVBBwSfgYYSl2zxfqodKA8dKCXk34PF60LVnQQKUmsWUpukeCBhgVh68dU85pwOFsiGVZMrELqi5PZtgP5CWznmf8rmtSB3nd0MhTygSn6IkgvLks3I8d37tb+fbtl3x+7PjTLx4oKbHOWup8Op0RgWE38iHxWVHVNnjXPifQBOsczjp/1Teo9pb6Rh8D6zwz7neAoofFbZPjK5Sk3CnAuJWlbPSSX/X4ayAt1SHqpkkWqfe7fcv2aj/8BsPTFpWqnilbtk81ghacZGt5w7LGOryvVq8UajXWtgl5ObwGAuawRWiloZKLynjjlXCLJ+cJ7zs7cvJBwMLVd95OeF2cmq2URjqlRf7Vu7urbFBLH+5KuM60SEvCObvdVZjuOmI1kbJ6QL0Z+or61JIJzlmnSs0mpQJF1Ntb/69mIsAHgc3Ni48JXbTuBtQo5Sqz7PGiejnOq56JckoKJSsJ2jkdVFnr3V3nmaaJvAr9MOBQPlIlPiPCfj8yr7kRtWvpo4rNBbt/MXr6vmNZZgv6Kts8EGLk7eMFCOzuD/z4pz/jb/3wB5SUuMwr8/LI937rOV+/O/GdVyPHKdmEZr0nSyoQlSA4Ri37OKck8WjIw7Qm7v0t0zIrJ6Q4zvPK0HV89f4dx1nnEZXwjF/k7zCHAx0r2mJcyGm94ipVdIOWwbRdd42QoQ6ilNoMqOvXCNJORf4w9GVDwFwjv+q9UxK5wRvWJWfigGx/d43S1IF3UnJTwK0k3Uaot+C7iKFjNdmwtmJnAXUtCdX29QrCUjtNBNvrmbSs9IMqya7r3PZE3asav6nmTXWQXXQ8zsK/fhP4m31SwceoStx9FynLqlmn8zgCtIkxzpIZ/VxTWimmQbWmwjopfH+73xOj5/G00oWZru9xznE3On7xvuf/+WLk3355IEQlodbmhJY01cRLtB22yr5Pq3CIC2uJ7AbPz14/8tXyknXNeL+VRdqAwoqSCAg25bf+u6ISiLUK277wcXtvsxneO7Mx1m5aM2VbGGmvqwG6mnmz5ShCVbep967pXdXX8M5MsGXMVfagBuM1CdSmCkWhVqfOrSJcdZ9UIbJa0gcjp7qrbrealJmGSzcMLKaPcnj2MY9f/hwXPJ3JGeSEdoMFT+x7DYAMXWqIsdfU5P0SCZJI2SFJ0VaXwYWCSILV47qKdNP8QTvFnc3VCU5bngOwFkS8tse7Ws6NOITIVffWqsGhSuGIVgGChyK40fhnKSJkQucRG4GCAaLZQQy6bkvWpCGLZ4yF+/2gSXiG05L5+JApxbEbYF2sxXuZoY2VEbou2nkJOO/a0GTEOkmrho3bSob6b0VjqlZPFztyWlFXtfHUquu8rkBU34TXBpwPreOHj78GpwWD8K2uVUSz7WG0w3GFilAPTI1NKtTnqh3dNmCFfO1nzlm0fxX4FCPh1bJUlepX8aAAV7wOb/yXIkbAdRWhcOB0Wm/DUloQ537pPyWX6pVdtzBeB34i3/wq1iqo9XjdpDWd1vEBgrWD1hY76+jYJl+q0SiyzZVoN7MGju7qIirsawgYtmFCqHNH9NOG2slhgU4cRg63D5BXJK9QVGenZCXIBqf3LnSD1uhDUO5O8Ax90PEIRvpT/ZvMdLkAMIw6otx51b/oYmxTnGNw5HVqASa2Ns46FYJxA/q+R6TY+Hfdaw4jDBqqltPKsmbG3Y43b99zPE+cLmc++dZLUvFcLo8fMPHnpIHjEPW9U1EiXPAebzXVMaqpPQwdP///OHuTHluyI03sszO4+70xvHhTZjKzyCo2uyQtBLUa0EJoCNBGQAuCFvoF+kla6R/0SksBDWij1oSG1KVqdZNVZFUPRbGYJJNvjngRd3D3M5gWZnaOx0uS2dAFMvO9yLj3uvs5x+wzs88+e3+L03nGvGakUnA/J3gCYhzw2c015jXhpx/+GEvdwyOBdY3SOsv9+KB7oEeTBNkj/CkPRc+OYQpnXWptuc2Z296VtuNGoHUiS24qn97L6ACZqmuEcYKNonhUTgJaWYGc02notZ27zWZrGUxY+lje/ChlzArOBNhKd4LpdXjlTLT4B4zgpR0yp/XR1u5nXuxBKaV3TDHj1dmjZCFZx/0EVC0nBiHHOu2Q2s4Ls04Y+5xclICLiqvLCU+vb0A+Ys0Vr96/b2RK7xg/eTXhH//bz/Hj18+Qte3USri1nf2u2mqAAizkw3/y/z7Hz28vkLni3Rzx5+//GG+OOziy7BVtnqFlGPRZVGtgMIOvdFhWoMs66DBncAOafRpxUxWH8Zc086QBQa25g1MzagpcJbOrdt/MGSTK9l6z1Rvb3p+2fJbuDJDTzBNXbdG3E6HZIeUgyT/KEdwQ9C2YtevaTwNubp4AWgokEKaLCwz7C4CldXtZVhjgnpcV80l4Ls3vmOOscp1JaQbsAB8cUATQxDHCkxedm1QkkwMGHFDQfRMF111EkIyJj5KJBlUpHzGDyDiZ5lsBggg3ljmjLhmcC3hVzZuVwacCx/rcCgswD3aWCXNKqFXE4fbeY00Ft4cD5lTx8bziOCfMy4p9cGD2+NNnD9hFhg9RusaqrNG6rJJBYdF+CcEhrYvaXkIMoipOIbQOQVsisV1ScjIbY4K0xi8LTTQVm11igZGsr539yt23f/r67pbnUgDnDfuj1orjw4P24+vFOgfU0hCsd538Kk13vEHQ3FPRarCdonDWNmZHvdvHDiq3h6Mp3qLKuShtsKEhNnHcBDhq5MCKqBNYe9eG3M9jp1DZ6vfomR9Fh2YDbXOS/Y7+XiOYti/YZmuMRMZqLCpcVVIrM8h7kOxgHefOjZhsBEb5GCPwEuCg8tAmyiSRkHXMdOOkcIyktEI1obJ01ICcSpFL2JFy0VSjRPdUEjJkInHKuR1S53SmDBH8EHoHUj5iZakf53WRshNkWu8w7lBKxW6KWNdVDWuG81J2K6VgXhaQZgfYiUpyqZIJiLsdnscPiPsbmeQ8DjifjtjtdkipILiA6hIupwlZeSu5MHaDZFWkS4SwjwHOyXTn6MUhH1fgZgr4cDgjDDvsRgGev3h/EqNWCio5vPnwDitfKU+gyKTxVTpf4jhpNKxrCgFmOYtirOMqqXSowYJG5rB9pTwUhrQWcmrOA8zalYOWgbFWbFEulY1WgSYeRWCAPyFSwzItLPuNJXNi4xosW9A4RgDIExCDpMBrhWvZPSuTcgPHTMIXIVR1Qt22xRgxjiPiEDDEiPPphLQuOJ/OKoRmwYlcY9HOQcE78hyiA76+B57+4Brnw4z3Hz5qdkBaKafRodQVIXHrIIE3kqgDiDHFAJcz1jVDeGEORafODiPhy2dPcDyeMA4ef/n6Gj9+9QyEiugYNnbEeY90PICxtT2uPVvR+5CfvzqNeP3LEct6DS4r4jBgmhir80gydOhRpsmcra1BU9/WtTO5edsLBmStpOZcANXSgKBoZhCGIMMChaBaUJw8E4aQ4Bk27JE0q2bdfNZmL3swuNiycZI90C8i/ZcSbgVASyBS0iqAt92PAEiu3EtYW84igGoZdM1COgDjEMEOeP/xQQFeJwI/ef45Pnzzt7IflZ9ByjFccoZbE+I4tixliBHMQq7/fFrxt8dL/NHuJOW3kuBDBAXfwCB7AquOEkgGqMr5zKIWrdddlgQ3BmQrs7DY5VoqXGGZOaP2GVV4Y015GoCfJGvq44TMGW4fwOeEmhlwFTwQ+MxwQXzBHh41Z322HgMRXAyYAAxjhCeHd+cFVB3GSPjBkwPeHFV40OyBZp5QqwYvMiTSN0oEI6vGk8khAKrto6rxMhLCgjAJfrKqzbcKgjPf3vKJspdd5/j1LO/vfn0HaKFNxCds7nVdkeYz4sWlyjF7zaio8fISsVmE2XpMeJOBMdiOvnamnYH2+yZMpDVjR80hVHSJ5UaABTSMMxVQQcy5MIKDILfMVkXTqH3zYIys15yEXImlMDfaQa3iZdbYuo8qG9v+d7ya36Am4Ca6G127wAyF3atSZ1ShlRtRqi2NDfPTFlYDNiISJiDAfs7M2F1c4ermBXKuiEHE9IyHY4x/5+OmfKBREKBt4NBSmOFjBqqkE63UQH7U62cM07792TIzDMK8CLPfh6jgx+qlRVOSrikyAoSSE4grfAiYLi7xcSk41xnvPtziyeUOzy8u8PbNz5H9isGd8Xf+6CXOVeu9zBicw6DAtrIIMQUtGzEYY/RwTu6RKeLu4QEXbi+6PR5Y1oLT6nA4H0G14snVJWjpQQNX0YmxfWOEbFQlZaomhfeiylqVO9G7iFiVgyVyZKYu/sUGPDXlrqTydtwNrVhAYM+MNTOi4ESVJno4zBDl3FqBWlq2TvaQ8BEsQ9rKUj321ayoR9H92LYli7EylU3JLvYuG9LS2eHwgMPDA0oRkrMdKovULWsDSCnZlQIfPKIHbueK6eISCRG4uwMRYU0J4zghpwUuiBYMlaSZvk5oTusqs8n0fmpNgB9auTKXhDmvGIcBS664Gc+otcJ3SppkbUpCSjKortXwnWUrekaplIqBWXl1FW7Ygcn4gdKJWOwB9rdvymvc0uht+bZAVh108A4pyVwf56R0biRZJq/KswVlPiOECB9H0SLSfcJkWTRd2yygw7orLSAeYsS6Ju3Kk+u1sptm91G4y0KYsnMcBrEhNmxRtWVIwbdJ9NtNGuAwgBFVhNGHgHlZQSQtuEXLdEQEN07YP7nB8f5jN7tqmz1JZ15aV23ZJYRBNI08AR+WiB/90YxzEsDHeVNBIBYSLbFMcM5VMh1BO4SgWXwGkKTVnecCBAcT/iMQkCVA4SyZGbO7whurGuzq2g0jaAggLkjLDFdJMjZLBvkgJRpSfiNLCdp7wloKHAmU5iqy/YkzroJDjA6Jq6jAO1bpEADON+5R0NkJzjmgJBQQfNB2bQ1oTBiQlRxuayY6PRklS6t5McCidghgOC8cqcpAG+qkH2Bu/FNZh09f/05EXKpF+Q0iyV+LbJRxGrGuuYEaArCbBpzPS2Mgi1HuGQt1881Jyn86KcmGVPWIRQyAAwnRruWXDVDIX0WER9pR+43Lm3OFpswdohr1qs5AIINFqjZMEY8OlD3Q7h62DxhtY7bVQ/vqT15m9DeHVVFm0cVnkD00aRN1EGEyWKW4X0gzGlSRi2xagmRtRA9hEGIUC///+vlnSGkVpwgzhJuIWrMlbViVEtdQM5hl81ZF0IAS83SzSotf1UDL5gZVUN2AU3Kta2AYJ+0yUY2eRgBFl3xWUJHTjGm3xzgMOM9nFBfB7JBTwvlwxtfLATEsuFs+4D/70Y9Q2MHXijnJc4meMHgpD8xJMi7W4ZSKgEVPhPssHWPPL/cCqODgecHLqx3mnHE9XuJ+Trg/HxvHpOakh7qn5hkAVBCvlp79y2kVcFoZrJFVUB0QR6R6Gz0683rNrvbNZmWObeZNiGvoKXWuNplLHCoUjEMBPqMZKXKuAe3KnZhtM8FM6t+MlmWEAIeK+q0sjmQhwmNQpYZriAO8dzg8PIjdcDJMr5XMWuDQ32vZS+eKtIWqPcsf32KeAQoBwQeM+wlrTXAZQJWAJA4eCB5rWuBjxGk5yfP0QM0J0xiVU2mZRtnrMXjVgALePkTkqgP0LKuq9s7S3nYge4ncPTK6onhc1QlKMFZyBoG1VTq3jifLxspnKu9PqGSQagAAIABJREFUbb7ZjMYBqJKJ4pJQ3aDckQgCI8ShzQ6STE5GWleM4yTdMim3kqLi5s55KVkz1GZf9Ow6kvL8xj47nVFk6yX6NNQ+FySaLrWIho0N2nPkwNQJ8lb6bVl1fY7eCYiPMehzLO1abKKya3aMsb/5DMeHB/U5DGblJxLJc1eJDGYWzpKCpQrCh+Swj4w6y7OoGmQBkPlAucJHv3n+4r+pSpu8DTLiwhLkpiJtyJqdqEkDMedApaIksaNxGoFiIA0gH+CDXFs5FpRjgttP8EI9RtGGFCHBSqes044bB+NHoQVKAzEe1oqBKwKAfXDYx4IQokoMSLl8iAFwokLNXLDqsOCWYiACfIDprZnPai8WyRAA0iQCIAbpArVRDSmtYu+4tuYd72SPi9o8t3P2+17fCVpKLRiHETJvZ0VOK6J3cCEgqWKkTJaVdOh5Tuq4ZBN7RVmCYbRTxoeWciPNZFTdsAQj9nQ55+Akcmqh2AYkAQDVDHYetWhkoRFeygXOyXVlAJTM6GhaMggxl5TJ7mC1YjS0b+ksDX8AKKue0YCEzeGhau3A+B0PXY+isRPt8zZGSAIf1ZrRu5NUuabd1OhtPAII4rS8dm4wCZ+fvG/1RYYMRXTDrmVECnpkazknAWDiiJxmgpgZcAHeSebFBy/vsOiPWYZMGmka1DgZDp2bIJ/PDfwV6yTIuYm5gVybDL6dPVFzxsvvfR8/upwRnnyJ29++xXF+QD5/xDCN+MFn13h2+QX+7NcJ7x8yhkDYD7K1x+AweNeIhIUZ0Vj3YDy/GnBeCw5zxvefjiCa0AURC17eXEpd/1zx/r6gLh9wrl9B5gAZIbWohgG1e2zcIoE3SFm6ZKqrQM59Iq/NFCEBgdMkrdElr7i82OO0JCzzIucvS/dBd5IQESo12MbyRxPr0v2pAMT2tGVGW7cRSPteKrwnAVwK3iXit9S3GnHbq9yjfrkiIeUaDKaN4YwxwHnCw92tdAHqlFmwpZsNjDNAm/EdJCRtAhC9h3cEnwv+u7+6wn/z5S0mFFTnsM4HDGHAKVdEL8TqZUkILBwmLgXnecZuN8IRsJtGAWlgFJay0fF4QoyMJ27AYUn4mBg/fX2BcVCSaq2tvCF7XgMW0jUw0FqtlELqNKtIQ7AI/ZU1NV5gBMDHIwo8oPLvFhiBtGOwPXMpx0QNSuCky66UBEeEuJPpz+u6yHPyXoNKCX5kqq9kUMhxyxATGLkWaR2HOHBURq3afl6tK5Jh04DNZlkWpjUiQOYNSUlaBEOH4HA4HFvLsMESz9wCF9N1CsPQsjRZVbCZGZf7CS5GzMuKikVLXpapFtn5dRWNkMvrJzh9fI+UpPRkWXIbDWFk8OPDAfuLPXxwiOTw1/dX+AdfHXCustYlF6ganNAnHaSlP2c47XQUXqE+i+YLpJOVQdIerU5ZSKqspTvJFOUlIx9X+L3oFoUQ4f0Iv3+Omg6gDIRplLObCzgn+OBRHQAv846q76m9oOCFiAAvytzVCx49ZeByijjPCT+8ecDbwxN8OGXsdxMIM5iBdU0YRyEUO7aAyrIsnVtp2LplLZnhPCHGKFwWJR9757CsqfE0QwzSOeUIAYxcKyo7OEtOkBPi7x9ItXwHEbdhLDHKzMjzGSHKsDHbVKWkZgztZWp63SFrjkU3PoDmUMm+Sg2YEcOIlLzj/CbC6ZfWUshEjbhpmR30/2x9PIiAWoUQm3NW4SV5b7XZRWyCRNRKTeAe5di1N3LsJqNkk0YBbDK+DV71iLRdUyf1QdOkrf0N1hlF7SFZFAMV+LLshDkMkHQuSf1eSnfOB1zcPG8A0pGBwxbTiAOhvjH7eso/5vja+uhaFTXEW6KwV+VEe17WdWVS3ZYyrLXK2HOY8VOHah1GLBwJR4SrfcQPhgfc3T9gTgDXgh987ynG3RWOmfD6/iOePZlEeVejtqDTQ4kk2zJFmU8yBK//dbg7JtyfMp7sx0YIDJp9iHHAu/sj7s9ZCNTrHWj/fXx9/iMVyVrautpeZIhxzCnrKAhJwzqv9XNNZweN3EMQQCVTsMUJDeMg02F9gAsB027ENE3SddFKSNrOa2erRbbSJls1SDCw287DBlRZu7TwyaQMsKbUsj09qjcQ5JrRl4CRNILdnmOTjUf73mGIEhEHKS2YPg8RNJqVPbPlbjU7oijXBL2KkqjvV8L/8PVTrNMTLOeEUgmZgcsnTzD5EVCSe84iPFdKxsW0w+CjKsmqdXMBQMTD0eM0zyjF4f1hwZLO+LPfPEfGKJCObYSBUwDZBwpa55Q9M0AibUcSaTqdwmznxOlE9bbvSXRunO/dW0aaltKNZjVQMQTlangplzSjBnHiaV0wxAGZCfO8SvCWMnJW4r/NSrIMWi3t7IHRxBgtuGx2Tu0buZ65M9Drvba7qz1xTkdFKAE7l4xpGhGHAT7GloFxzgmY1X3oQxByuXJvZF+I6CKcQzJ9LO8bwZ9Z9oTor8gaXDx5pi3VLb5tWbC+r9HKRTkl5JzwN+8nvJkjdjvJIkK1VuAJbnAIQwC8zFEDQUozToHr6KX7B0aJUJvv9EjCnrnxQhj5uKAuSW0jwcPLaABUIciyJAfiIPagarDDFRgvZXp3rqX7hubvtCTFMvPUB8I0iGbLcZbxNJcDw0P0fVKWjLCovkuXKBScdj+nPu+R9+qBK6DjSbS71CZqV2ZEBarGL7JsWq6AI//IF1VmDU46x+fT1x8ELcxamyTVMSAhV4ZhJ8BDjXCPMgx0oOlRNEEhzUsEbaMy2XvSMoLlYSxaba3i+sC6dL39vF9jNuABNMl/i+hRtVNHOSP9UJph6OCiWpbFYKQ6Z9oc0AZJ1CkwulYFoE790UO0f5G9qX2uOWZrmySCyt6jtROrX2r32speCmgqFzBpjRH9etWPgcC4fPpClE6rMdc7mDRCpz0TMHoXAvdDXnLqKFvXt7JF1K6BUYlisnQnYbtuHSJJZAIFWEYU3AgY2T3orw/DIIYtnXHO8kNCxeUuopQz5vNHvJnf4/Oba4yjR/QDEgc4iPKtIwMVwOBdc3zQKHw3eI2mGOc5YVkTjkvFbz4ckSvw9v6M41Jwzy/wz978Cc7ZtW4aIXvWBhzktvoMFjarBTTOAciDnEwlH4YB0xix309tRsuyzDidzri/v8f5eFTSoszqIJ2ULV0Wmi5X0qY5PAJaq7xlCPqe6JGSZZREQVeyKd5ZSyjUsbcwq/tHbAC8OeLN/1PTAIJeM0l24nw6NmKfETkty7Llg1mJ1oh9tNlzdvQHD5yqx//2asL10wtcX+wxDQG+FKAUBPZwSpUpVWapDCECrJ2EJOWCtCbMK+Hu9g5p3eGwJFReMNcdXh0uMMZOLhR9J+kkqVrK62dF3BXpuYzeYxgiQgjIJWs2jRrHw8B/TsIBGGLYHHK0+MZ5sb9eO7w6MPCtJduRlgm0xHqaRdum1ApWKXvj5tk+t0CS7TsBnSNVG6leDykAba6wNVAb14nDfaL4do+I7c3CqVHw7XxonW/DELHfTXKPjjAOsdnDoqrCNpNuWWVordnvoiVFUlRSQb17xRH2Tz4DVKfI9pvbAGI5F8CyrNL4oD7jL15doDqCHzwoEKon4a9EwI8XgGe4MYKiBmDOqfQYw08BUIVgNwS46EHRCYDwQo71McC5oFkHjyF66VpKNpaYUUtCXU/gdUFeVqzrAuciSD/bNByISUpUJNWAojYoaGmvEuCjBizOm4Q7HDFeHSMe1lH9Z084EAG7cUAIUZ6vro0eybbecqVCpt6KoUJtuMxQ6xIpZIKymlUz/iJvPguQTJGI8/1+0PLdOi3eY55lwmxaEy6vruGGsXcHeY8QPdJ50f5+GcFtJYaenbDoi3UKpi5ARYus7QCacZLUpMzXMAEaAyPcnx2aEy4FHEJ3tujRr3xXzwoYIazVUqEER10d0zh55NQJHbSgA6ttKahnLOznPd5t0ehm4W3ByO7D8hsG1J39rqGX/gBa/dzgQO2/ywBKAcKww8WT5woQGKRrQ0q+0ouGkfYIqhlAff4IAzBxO6jhLbWXJ7yXYXmSjVEn6iNMCKHpb7R7cFoXZxEmskwTZFgmM2v7qjjrYXeBq6Hg5Rd7/ObXGYfDASHf4+LFZwjDJTgs+JPnX2DOK7569iUQHQ73r3B1eQEAWIvIxFcGVq2nj1EQ/uUoxOOioG8apJsqxoqnVzs8nBa8Pk5wzuFf3r7ALmSAGNmcixMPbeUgQFoPbaeQRrUheNTgEXSW0jiNapwzhjgJ4I4EXhNyBsIwCsHXC+9j0QxIO+y1wAcHp6UUbk+wQ2alZ0sHT/NDbrPWwKCdVMWJIqidD9p8jhQotNikzqIFFQqCTbfCa/p8HCIuri5RmXF3d4+L3YSr62s83P1K5RJUY0b3bxepgz47p4JqFagO5CRdXxqGIuxcxc8fRozjLT58PMOfV9QgByfEAFc8fCD4ClAVwcN1XeFclZlGToTbHo5neLdg2gHn5YCf397gJ29fYIy1kRWdD41jFuKgOiDKOYFmOiDcGEdSJiilYByCCBrG0PRPSpXoOqcVyyxCa0JorjBVUCu9jCG0LDK01BSCCCQOg4ypKA5tsCxBOoFsBhJIh39C70PnZcHEKhsvR/eH962cFYOXbEmI0g1EHVAKP4oV4Er0LDPkesbbQcAViJDWBexEAmGcRnGuzgn9YJqEuFkrInpEn3TPy5Be6Vb0IYBrEq5Fla61WnWd1P6mnDHt98jzFc7HB82Ca9dSK3UKT4kZSKlgTUdcXl3ifg04rg7TTj4/VId5VYXjfIQjko4eQIIvm7ysZX83CFBh5beQNrISA1ygrcxqp30ECoRcWzPScUUYI+IuIB0+gokRxgn3xwPSfNcGhIKBNM8ilkpyFiUjpxIvZPdLWJNkq85LRogBVDLePiT88uMVDvcfMYyTXKeTDq7rywukWrEsC6ZpbMJ8Qkju3k/0VKIEHVo2G4JkNtd5RgkyboUgEhVcK8ZpwrKuQLXBosZt7b4w54r9bnqkP/Xp6zvKQ+K8k/Zynw8PiOMO4C6xv6aMeV4QNh0N8qLWxsSwsgm1tLAdTCGhSmqai84dUSdaNn/XZW4RM1pGBC0NSN7UcSVytDQUYL/P3SDbQTaQoO2Q3juRT7aMAyxl2q6gfZ8RyMQZ2P1YJ49+ujn+RxfSfqX9I/epoOeTLEefP/TpR/QsRn9/rzcCwPXLL/U9ls1SiFZNnbVnNZj6TJm6+XnvUFFYxfLdRJIG7sYPTTvGQAy1lI9G+uYOWbQtjA/USnvoqWcDM84PeLlfcRlmOB8wnx5wdfMc+3HCbr/DMA0AHFLNOJcZpQL73SAdAn6SiF9Fp8BoxtWRDEf0ZE5CGPmH04rTUnF3WBF4xl+9vcLffNgjenEO63xCSasSGpM8lc2a9Pi0ZweEXyXARtoEawM7y7pqhxEjxIhxGlrkzSxRpnXnGfi0jhW2HUmbnbSNisgyPdyuU95HMJJuYTRQbm82oG4zprhtKnVIThy1d6793RztMAyYdhOOxyPubm8b+Tqvq5bgLNOhrboa0FgkInajNiVRuycri/XSEmNwFR9mhxgzVsoIw4A2r8wBlUlLIyIlELyH91G4A+Thw4g1z9jtPHLOOKQBf/Fqj+hrbx1XYEIkRjUnjc61zGMrbx1XBMk8DdribYJmuQp4dk7AX04JDGpZaynAyT2XXDBp5kFKU0XI4xB9jTAIB2JdFpi2iYNmvkkia9JZVv2zVe9Gr7ude8jz5srgkpuGUX/exTi6yj9RW0+2bh38mB00Tg2r7Zl2ewzDgOA9ckoo64zD/UccDwfk+YQ0n8Bp0XJuL/cTRCsqxtCCTxtB4b1HVu6IzVCa14whSDbj4tlnsGC5bSKjqOv+UTaf2ByuOK2E3z54OE9Cdg2MwQEelu0TVlnSvbkAWCoj6fmSrLmACZnPSABrABgAGj3cTgjiIKelJ8nKDJcjSFAbqqvw0x7HdEaaT7h48kLU4r1kWFEZ1bqaIDKKc66YVSRUSlUOc3U4rqI0HJ1x3Ajn1TeCrMiUiB9bS8GyzML/cT3gRNsraHu+cZtAOpHcpuPIeU8pI+WqGRYPUzYX/9szNM2yaMDkPEkH7O95/UHQ0hDvumJZF6zzWdjDmiIkoHUvTLuh1cKlEwQt9UsWchLQZ1eYb+Z2IeZ0ATWadTPltrWAbrIs3UaDgTYTSH5opFVziFXT1r30QQBMOM9aj4sZEwU8ooOgwlz2YN3GmRPaIsh1W7pdL5F6xG2Ovkc2dqB+B0BpJ82mSXfg8AjpiIcBs4lblfZr++sbDNOu1VitxGUbr1Uoax+iBggnxghoPYaHdtxoG6Oz4YodsLFmsvLG11QziAaCCDonRrkXtTtBR9S+A3qlRIDzhLuzw1o8hnHCGB1Oh49YncPthyOWQ8KSzihM8G7Ax7t7FakDUplRq5UeGLshaklku9GBw3ltxnCMAt68A3787gt4T1gyAWWV9kPn4eMIEW/zDSibwBgbGNYNY1wZ+96cM9Z1Fa0alezOKWFVY1GKzlviXmpse8gyJdR+qs/fSHM2LND1bCZ1IcK+2/QJM4tIo2oqPPoFc3ZkowPMGRq4Vbn7QTgbUC2P3W7EvXYIydBGIEaPtM4Yxx2AHgi0rKpzGMcRrYSxAepiblRkrhYlg8sD8Q74p6+uMQaPaZrAXruwcsX57oj1MGNdMkqSoYDkPM7zDKaAtRBySaBQAecwRsb/8YsnoLBD1EGwQnGTtcy6NpLF+DR93c25lH0hOkJramGOnfd5PmOZFxCccv9scSUzQiQ8IPKijdSaC1jKNME7+DggpSTy9VUGk6Z1lci2Nf9twKba0V7mNgeuSuakpFbqnDrntbPLwCTR5jPQ1s9K8i0oJJI5baQdad630vy6rkgpY1kzcmHkVDDPCx4ejni4P+Duwwcc7j+iFiErk3JXhGbBrcTAkD0VoxC/27wn51BYSoLOB1w+fdF8CbApY9p+3ixfLQWDB/71+4vWjUsEuOgwTQExOhSFOStUXI4l6yqjFAAd1qRBK+CIMbiC4ApEcQ5IBIQRcKNDdQRED/YeFEbEi2dIReZJVQK4JkxhQDrdyVwjFZErjbxOwv9hAUrB9aA1RpkGHohwsR+xFsaH4wLnI3JFa3ZwTjq8NIkmtoJV5JTx6CzK/q4tcOBP9ob3QTgsLLaKq4H/bnWKZuDBpcWzj3hyYCzL7wctf7A8JE5fSGvH2/eYoqTeRNmu9+4zgFoYu2nAvBhTWLMltTQBtNavjoJShLlPzmsQLnWvNeVGZhXQYzXl7vztwDzy2/rHXkOT1KS0GqpBESQF435wFQ0Si1yTDkeTa+1RpUQMHfk3MlEtjV9gjgv6LIboWi3b9FVkzgg3o8BmJLR7pupcJkvD2e/0yFacFWu5zFLAXIvO++me2PmAq+efy4awiFwzR4/F+9AW0aInb9G8kpK910zBMEhkoxH/lpjVU+VQXgSjiQhZ2c1SA3qqCQw4EhDT1gHy3iqDKnNZMAXC65PHm3PB/f0dLq6egZCw3zHG4VJmefiKi2kCc8bp9AD34okYGGZMo8e8FJxTxcXYSV65MlJm7KLHpNm1pCAiuoIpMn729kqeJxGYFwy7C2llbnOEOnG8bUZ9JkQ2oA6wnb+uyZAv4hC1XbI0LlROZ5UUkIjKe9fArrSfizEyQ/yoNBl8c5pmoC06ZcaGq2Bwx8pxGo2Ta+AbdtY2QJ/0h83HBhU906gpBtH1KDnj2dOn+Hh3i7UAz66u8eHDe5Qqgl7Gm2gFU1J5/XVt32lYXvZQBbHoY4CBtSyIg6S1GcBfv59A7nP8w++/wcPbGTx6cAbu5zNCSQiDh0PEq/f3mHYTmIGyLFjLCvIOn91E/OyNwz/95XMM4wRXJEpsWUtAuAde7FNRiYItEGwAkOR5lVIxrwm7/U67IeRVqww6dM6CHNsuYk9yKfDkUMqKUqvMzqFOSE3LWTLfJGey5hkxjghxxDCOMn6D+sRmp+JvrMEjWaAAQEpv3H7uvJdOQl0PcBFCuK2UtQfXqsMTFWixRO8SSbOU0LxIuIst8cilaNNGaVpZpFPITTjObEBeM3I6wjmx5+M0ydw6EHLOUi7S0lKtWc+PacwAKcmIh1IKdldPcby/Q16Xtn/b/jdSuTrv3W4COcb9GrDqHKjJaWedA+CUJAsJXFNwWLM8i+gJ3gkw+9ntJYIrWAvh64crnEpQG2LnkuFJ1vbZtOI//fIBnhm5ZjCdEPZeJkuvHxGmAKCCB4dyZAALailS8YDYn1QZa5UuW5AQgT0Bcy4Yo0w0n5cF51Va3//q7Vc4zQcAauvVJ1km2Aj+Dw9HDNMEp5zFNvnbiaK0BTnSSOCxaOMBbM8Gj5zElocomUznAmpNagI38EOD1eg9Tscz/NMb/L7XHwQtRIScE9Iyg0tGvLyBzbqQEp62WpLDkpLUQMnAwQZbqeesRUegV0VsZFyWTUcCWQaAW5oYcI+Ms5q6djB58z/M0ZMKZDnqzsTURw0F0vaz0ImlgGQD7J2m+thKHZCNLqlKAniTBVKDm3Jq6L7WFfAW4Ws5hfX7SKIneZ6s3TLq4siyI50XA6hx4N7Kaoi2P26HJy+/J4qUhM3UmcdrayWBZjj1viVtKmvofNRD65BV4plZrrEpH2s2wRwmgzedKdhkceR7a+sQ0ztTY2PAisAYhwjvgVPKqOQwBcI0BkwT4WGtWJcHID5BKhmBAgoBp9uPeHJzhYspohRxspbB2U8epyTRRJDiPOZMCE6uN3jX6rqpJkyDQypSXgiOlUMSm1x633eEzQ6EtaSz3vw2s+ecVwApQDTnjFpcywLa5onD2KCPDTqUWU9FtYqAnIznVdFnxfTsoBN5DoTQuWAWjRnUJECzFh45ZYC004ggqrabMycDRs1H9/t1BKyZ8fTJJQ7HMxjAMA5Y04p5SQhxAKGKQ00ZXLKCZtsXTR5Evr9ueCLNZsiZKSwgyxHBbWY8vbws+PufH5AgAcPlfod3b95jBGE3XoA5o6aK59eXYAeclhWndELmCl8y/uzXT/Gv3l7AyhLY1NkJyvshavNxTJ5Bmvp6WdPIxWAWw8qi/5M0y9Ei/gbeoWXBLQdK7ReJkFpOK1yw7kkZa7EsMyqP+ozE6YcYBcSA4Im7TWg2BAC5zkVUQGbCX/Zf4xLImtgUdmuZtn1dYeM3Su0K5jVnMKSVVUTcIsgJZ6pu7tvyPFaaMVsuon2buTb6u8vp1LOMIDyczrIu44CURDLBzoVkxrnxpbhkXL/4HLe//RUYwH4aJAM2KyjULFEIMrQxVwFlhaXjEJ6AJLyRED18YuUfqX2B+LGHxHg/B/zy/gJv5n07H8ExRr9lmsmfCktG6DeHCW+WjB9eH3E/S5ebZ4dIjDdpwhfXBRkO8+EMH0g6guIAjbbbvnHMOC0ZV5NXnwAM3uO8AAyH14cFLy4CMu9wmpOcwzC2YDzGgJQT4iClU6ICAmMIvikwdxtnf6I25PY8LzCuqA8RjrwSeLlPbSaS4ctAAzx2BxYAe6/dcv3rvvX6TiLuvGQs55PKCo/ta4SQ9NgRORcQo0wftoNi7Y+WOoTyBrqDswgADc0LguwEN020tWuyaOzRi80o9+9VLlSPEM1w63e3soauRKuFo3M6ulO3KKo7CftOqVN7eG8TViVgJW8VbpmDk4voLDjnYZxZArX2y8riiNgZGNRJqhvdFgMbDNapuiK+1B8gsLt8guniSoyCcyILz13hg4Bm0OTw9ajclGjtmW2JzJYKtK4rE0OT51bbsweodQFIxsE1cMsAgspgO4K2PYtTOs2plWhKrViSpOPhAj67WHEZFzyEPZaUkPMtMj8HPKO4ilwrQgDSsmC/c/o8+953jvDZ9YifffOAr24mlOoxBrRrEmE+YE0JhUV15v/55qmCGs3+5KwaDb6rpRjAhO1x17hCtkcF5InhF+elLd2lonBpU4NJU/NOM5QXuxFF3zsr76Uq78DKV6gMeIZlDolMIwMtgtQl0e4JcX5GrIOWXUz0TAjYfc4Q61kprOMnStf+IcifY3A4z2eIIqwDccHH2w/wYQSRw93HA6InzQzKsDwTz3DOyOdy0LbZQtFF0QyG13NLQFU9nxBlBsqSKxgZFxce975inVeUpYB2IxYqqMuMq5srJC7w5DFMA3YuobDD69MFfvr6AoMHfIyte9AAvTizIN1m6tSrdkzqkjeQITatA/LgPbK2kTayuR5AAZdi2yyCNSdduDbSq83kGWIULsUqkbZ3yrtjEQdjkpJM1A4pC1Ws+4u0bKKmWJ2eKu+QzCAy/kgj6IsRazanckVJq8wJ89aNyM2WOc2a5MIylkGvQJK2G7I11FTZHxy1Mr34DeVJeRkZUDm2DsBSK4bBtbNAYInYLatN0iYvYzt0RAxG7K5usB7vsdtNYHKIIxBLRYgeyyIyBEm7lljl2cUVaRBCjFKBjILggaudx5plZlkujP/966e4WyY4B0TKzX7WWpFZzyJ3QGxnc/DAX76akHLGn9wsqDUg1IRvPjr85O4S/9Xze6z3M1wg0ChUAWfhq3ayeefgAqMQME0RFRUhEFKV2Vm1iF17/cD463cvsRxvQWHqnBRipXlImdHHoGrDQpNoc8Asu84iIFltyC7Qy9/Kl/JBdFoGfQY2nNdEFGV0g+wHyZrVts+C/7T0+vj1B0GL1ZEJjMubZ9IUSWKELUKG+UsGTmeZzOu9bTJqpZHKFcHb5rcFtEnE3Jy9kNTM/9kGpjYPqKdT0SJe14y+hQKSFeiaKdssi/IkmtFRoEO9bftR9MydyGWf5ZxFQ/LzqGRS1ApW4Zw+X4PhPTCvCSFE4QZwbU6CaWP0rGtH0/5ZySFiGyq8D1LaYEZeV1FNJJGgr5quJXK4fPYSemYEQGr03MSkiB7dD6CyyrCZHWjRrT4kEbBTwyszQzpYPkBmAAAgAElEQVTw623ils43KNS/y3kjYUkWSrI4XoUJZU1iDO39pQLLvOByN2IIjP/ke7fgyhiGCcE/4Or6KT4+HCWlmwumYUAKFXF0GIbQumAMVFKVdfwPv7pGqRV/82aGD9e4HlZcDRneE3J1CN7hx99c4p9/8xT7WJDXBaVk1UeR51S0TbWRVT8B0D3T2Lu7RA4d7eAzi8S7V1JwK10WxloE2BtoyykhpdRabn2Im24lqRFbqVWWy7R0KnLRjKAGAM1tVMlgpEfqxpssmAJjqCMQg5PRxj0oN22/24HrisM5a4o/4fY+gcJOCM8lIRftagijtuIqioZFxmggtiXlNCNkWYic7coJ5OS5l1IRHBCmHf6nv3mKz6Yj/uEPj0g5YXlyjbfJ46dvrnDIAfQhYx8Jo2fcLRMeZiAvR6AsGKfY1sPWjyvj6movXA8DHnp98zy3zFZVG8Es4lrQPW+aNyBC9H2mFytBPafV4qBGInXOqf6LRKfekwhtOCdRcAwAeYRhVCVUOVtrlnMd4iBBkbX6kuQ07HG3kEL3nw8abNTSSrjMCoqNuyRRE2rJGPwIGqYWyDDQCLcCTmrrDLWAobKACMkOdXvTxqXYGpM1abh2oaVY0AMFjWaqpawUvMP93QmUEsYhwoGQa8FSJACFttT64HD19CXezyfpSAvS+VJcRWaGjwGWy3YO+I++uMOcE5yLuBgJFAQgvTl4/F/ffCmaJiAMQTKxAFCWE0r+qPOGdENr+dDrbKVxmjZgVTZTJcI5e/yfv77B2znhv/jRR/z3/+wrXA6M//rfv8MQAb8LIO7NLESitxLI2psloLgcdY6U10G3zChEOMwL9uMO//KbJ7j9eIIf9u06ZP/J/jTAWtekwL0iRR2TYHkx1jPrrNSbAUY7HxJ42bpCtYEkwWGKLDFG5ahSA0U2iuJ8PmOIEWEbcH3y+m4Z/1Kl/bJVHYHmoajrfHhHUMpyy6oIYLXNZp5fa6Be2sLq9v+bw3PUwmSrpXXnwOgpRtnUtfbyEhvmsNZezbiITizrQaaG7p1eI4E0+BML3bIyDTzp82AWBK+dJ7luyZIezmtczuaIPdK6Su1QGdnCLXGgmtuwsODlO7IywBpBuaFhiWKQrb7sW8QMEEpawQw8+/KPpVVcv8u0CcQxGDeic3m2XIVPtXBIAZrxW2TTdkTcMzH9ELYVb2ukn6UgRdL9Ub6THwsYmbNlGNgCbp5c4WYqGJzM3Li9PeL0cIsnzz2mKcJ5iXSoqEqlOr2gra+M3tkD6BwbR/j3Xk74R3/xBLfngD99dsKLixWjywhU8JPXN9hFifJLMVXQipIrbJJvH7L5yZnABlijl+AAyyjYnpZ1MeDCmu3qmTptI41Rnlut8CQCdFZKTDkDJLOgnHMaNW/LDJs/tyuyDhBNv7MOxiQCWTlBnbEjp+eEN6Cir5lzANcV9/f3GMZL5JyQc5UMjB9Qa1Zj5hthn9tmRAMuUENcN+ukqEAAgTk5qL1QkEdOQXZZcSoOf7Pe4OLXK26XHX59uEBl7WJRUH87S/dMXU6oSYmww16FM7E5b/L3lCvIsU6rlZbwUouonAKqddxfRlgUW6U6OnlFqUH3oWq7FCEy2mBXs1PZItuSQXGUe4OWj73HmpIGdQ7G+yq6hkS+nc3KvQPPQKFpbfSdiYYQnX4PwcCXqF5babrkhCGG1qxgALtqmcg6Add1gVNg2iCmZkce5dD0/d8qb5MEVybLYEMntzpYPYMvn7u/usbpcMAyL4jjHtJHI3ZKMKhmMEPAzcsvcP/ut5h2k5QZnUcpGaXkHmCRx1+8usH8YsFVXPF877GWiJ++ucK7A6OsD7BmiUIeAl+cKnuH5idZMzWErmUWtPRlQnwWHBQimal1cvjxrwOmUPGj5zPeHD3G4BDjCrBMSF6y2IVpsGw1wW36T8hJ40ItQpEYAuN6F/AvfpVxd2SUkkSniPtaUEsCyt4J2hrOVcp8YRg0yHSqBNzPsIldUjWqAYvUht8QeDU7VBh4tks4pBELAgKEF2hCiWlZWoZmWRN+3+s7QUtaF52PoXgDHambQSTd/9ELCzkMgwyVUiPaGRmscufm0CosTWyOz0hAZuQeO9K6yYHYMZDPIc3IANpC6cThWIfLJsaUDWPI3QdxyFUdCuxA6wFpgIlb6YKrpNBTFoRor1JLu2cbGFeLDCwjEwBDP3iSEpcIfNUyCWpVefutfok95z4J1nsPG7YlKTeP6fIKu4tr5JwQoswgYS6tGyeEb5e/unMwl6qO2ICnghpbR3vm8rBqW3+gI3D5fU0X6v+tXMGZ23Pd+PV2PbanGNKxdrEbEYYBP7h6C84raLrGsjzg6U3E559dIi0MH5SAxtAWV8tM2XPbQAt6fJ3/7d9/g3/y8xv837+5QiTR1wARgrNujartqpLNMDGx9oGGzr71MhYRffLfDka3/k5AjPoQyI4OGp3XNcHGMUAjWAAauasmEAQ8m2OnPpjhUWbN/l5yEjC2EcEDS4nBOkmojTrX5bZzpFG5dw45JxyPJ4Q4qapm1Y4lj+3MItJnLs/PUs2bx6VlM2p7Ac2mgLgFHMpXb/uyZdGYEAJjpIwfv/8cHhXRM4RrJlpAVYFHOh+lYytMmhyt2F6MzFOR57SuOpmYK4oPm32uDtfeyVDbBpDJIDhtWggeNa0toJNhoKvYHSJZW4hxFTDoEELAfjdKGTStqHFUx4fGKwPJnrROvAoGlyoKy23NuTVFPJ4UDc2eipy8gQJHwhmCcuFsvwbvFQAasUXAn3GKxBZKR1WprLa4anyqhPG25mplNrwhR0BVW2vfaVlvDanaKeNml9A69fYXV0jrjJxX4X45tKxkyhmVNfiII+LuGqeH9+IDQODNnDORn/cgLvg3r54gBsYQGMeFsCxnoCZYx5wjt9k7DOeGDWgVYjDXx/v8fBRbHGKAj7GBRJvl93EO+PPf3mDwQMqEf/6bPV5MH1DY43oipJVVW4akfdl929LYLLNcgK/vEj67CJgT4935EiWdQX6Q+9Yn6jeG0agPIjKYVQjTAKWWvXNpYNO+VAKmzRwyZukea12gmpUjxtWQ8KfPP+Jv7y7w5riDM0I1NIALAankNmLnd72+o3uIEXyAG4OIgFGPlsyBWZTOgJZkhNQ3Ro8lZTg/tDRooIpkEIY6o521a8YcnsWw1q3TbKdFkbCooWclGFX5H3qAlTzUptCaQ7XD0SL63Iz9NrHCCj8NzFhqlzV1bFulPpqTQJpOFs/pNUvBtSDnimEaxVBqlCElDC+dSiRIlFyQdrQQIToYEuETCPARHmKomoUHQTpRBjz7/PuAc/BueESarHowZJ6QV9tRWzZEsjJ+AzIlMkvr2qcXK8ws24PanmXta1mkhJDLIhwo3ggIKfBptDTd+FYS6iAKuLrY4cWLJ7g9M/7ey3us4RJvPxBubi6x3h9wPFTEYYWfArJjxOAwK/ApDIzU942nvkLGH5FtQfjqesGX9yPuZ9/2FNeKvM6auhSjXKvMUWkpbdQNXtnmMeTvG/jXMnZ2qIXHZLwIavuZ2zMVtQ7WvUUubIjWjzu/BJhuNA8s88WW8SKQ90KSt64XeJGW514WBEhBpuoOwbVrFi5aRQwOJav0uTL/ZZ+SqHhWyRrlJqAln2wDNi2qtkel3Y0gEFzwTatmG5h4slKDWBzvrF2akHNRQ+eQVlHfjaGobL68Ly0yaoGIcHd3jzgMmHaiNUXOifx77Vkey0K1wZSQNk6nmiDz6SA/rzJ40QaDBnJtaJ2gmK7JMowTQCRiadpaGp0IcQUv0b10FQlHZxwiclpxnmdM4ySienruHDmwly4aBxJ7BEi5zzlUlnPanJjNuFL7J6C7E66bwasywwmN1wSQlW30DFkbMpyHYy2D+4Djw73M1UIPYNvYD1iw1kv1xouwTC9r51Kp1tkJFZvTz9PMTc/W90GaBOF/kbfhm5I9ryUj6YBGy6LUWvHssy/wIc2Y56P4NDuF6ttqFW4MlQXLKn7CaVDErF1ZpaKgX6vskj480MBLrwbodSLLNc92Nlg7fgRoxBhklEEMeHsQJP8//usXAIB/8IMH/PHNKmJ2gE4dV3ZLFAA7RI/jAtzPBR9PMybOOLgdfvH2Fm8/vEAchpZ525bhatHBiC2o1o7ZWpCWguwDgl6XgFALTjqx2wL1VqGwQ45uBwoDv7jd45vDHv/Biwe8OghRuVbpnPIhiD1w8VFM8+nrOzMtu/0OFQ7OJQUuHUxYn7c4Oeudl9q/WC3XzoQZW7GR3BCivFeeVIMs6hiaOq4aVkOmG9wt7+P+e5VlTHcbMlhF8yI0glnf+KQPt8vR63VVANQPm31mXwH9XkOa5DS9Lfdnf7cWb9YOCCExdn6Hc14mWitYspQ9SwgIcySAcC0IWQd3dTKX8SGefPa9nvJXsMI6jK/q5FWnEfJ2SjBIxdU0Qixao6xFumVkRWRNxDlSu3Y2w9PSsJv2bh91GXuRiA0Z6ku6D2oDphKBiZHaX044rYSn44riRpzLgDdv32FYKygvOJ0SPr++EiXbwmDn4FgktUvtXTQEG+2AnjnS70qF8Pe+OKIy4X/5+U3bVSWvsMmwliblKs9RPLnVcLabAthuEv703waIbf+xAXdu5RfYswZaN5bb1ITFWRbbdL2lWSPgtvZ6ZKS+rBCrSlbT+yDGk+1a9Eww6zwiwrIknbHT9zs50uyf14GnjYSmNygdGHkjWWAt780CKcbdGiQDCQBL0nUDBLf2QAilQKHt/u7BBLgipYLd7gpMDus8Y5lPCI6wpoJlWTFMO4xRFJB9CEgpS0cXsS5dN8AWSVtZJZeCbFwsjeItC+uIWtnOOmusJCOtyk4Biygam4SCIzknYCAOSs6uKn2uzxnMzd5W5Zck1ZEiAy2sE6Q18wm3KY03fhPaxHUDAK0zsWobrzloKEiB8jEIKCUjxqGV3FgNXM5Zs0aunYdGLIZkgR45b2c2vK8/9PfaepqJbnbWykVmv3s/pH2SCRjafTk3wEPUWI2RQRCJ/OnJUxwP93KPxqMB2lqKDeot7U2wj5rpV9BE7bzJvpGz4kzzRje9lVU7cNNnA2vC6HymlDJS8ghxRBg8omOcs8Pr44jv3yRUYpQi83xSQdsrgyOsBfjV3YwlCW/scjeAUeH9ICU/7rwxOYcafDovQd+SRYIC2pAB86u53WOIQbOBZh+6bRdSPRrI25b17BU9YSkOX3/cYwoVO7/iQ4qCJdQfeqJHU8Y/fX1ny3McBokYKErbq07dtLRhrgWwdDwJS5mZ4AmYphEpCalwHAJO5wzLgXcs9tiKMXo73sZsoR+DLeFWjQvVTVhrn221dJWlzgZRGY6CpsJIpcL1s7dWk7ffi75jmZuOii0+g3vrLzmdESFvk7KabwYipxVEWaS24ZTMxG0TA9uSmPIHoDooYGWhi3qwH0YAwOXTFxh3lzaD1y6zATwys8HfrhWK7dFW3FKaeBNMJE/BZIUCFQiwqPpmUjCzLWU148MqAE8bYOg2w/I2gEXeJ9crUWHExbDih0/egYlwOs3YTyPy8Q0CFezDKCPao6bqKyRjtcET3jkdxMctIihVAS1XeMd4e/T4819ftrJk1UmwlsK32TJbgqrsvG9HE93hbn70rZBh65ntwEvmgBSkk+TLpRMh+sbrMmCvWLDfqG1ZyyARVItmk15nbVHPsv5tDogZJgC76HA8HgCK6rjVEagOA5eMSqJMWkEgLgL0S8blPuLhOOt5Kxq1KRjdnKX2OLiDrVrq47Z8gsxVQS/fNAeGjdWFlDSEKsDKO8o4nk7I64oQR+SSMc8ziHwDF1JOIcQhgLKIdTmW8q5kr6iBqzavptYGCgywWEBm4z8sS+aca87ea5ko6WwZ70UMTQj6mh1Q4ND1qbIOB9SyJNv/B9aUNRDrZXcwYDNVJRIum6BNgh6bsgugZyxg3Dv5XusKJK4oTB1gMMO5gMKixcW1orqIVefi+DC0DJmBvK2uh6wTte+2ydDNRmjniqMevBjKdwTtcoN+R7eT4gJ6ULTNdLF2srgwAFWcrk0gHqc9nn3xFe5e/0YH/Llmf9tsOiI4D2QFxJ6cOlRr1a86xLBo8NAz8MpY7Hu/BRH96FO/ywYuoWTzWmW2VV5PmPaXSjDunzyNDvenjOAB5wJSWnHIhDkx3t+fUXLC5UB4xwGlLDjmwRa+2ebOIySAhOpgL8tiOe+0MVHE7EKVUmuIEuBu9YfMJxtAs79v/bdl8Qdf8eEc8V/+3Xd4fc/4cHoqFQIfEEjuf11W/L7XH860kLSOBe9RIL3g3hQKVe4Z5EBOHL8IEaEZ+nVNuL6+xLIkHA5HOPItJ2ybUddfFlQJrkLiEX5MbwfUVluL8KgjY6g8+3Y6LSCZEyhqkyC1ALVKxEzSWthbeQFwaRkIIgNX3KK57gJ6Zw1vDqZcFgOasu6LqZoBlQEVfipqGIWoqMCgASi9QbPPLoCcHB45yarEWiuG6QIXT1/AtG80rt5cj4ojgXTIWP85IB0YXsOIlimqrI5dM1fQtmTorCZ2G1DUs2xgqaEn3rRCW5TB9ky53ZcA1L7XBBcKYZEImLNHxAn3aYev395hx4TPLke4SshlwRivmyNx+l7yolS5rBXT6MA1Y0kFIYhBnrR1di2E//nfPsFfvr7EEHQtNRO2zGdUOCCLSqztK3DnaDR8Qt1jfAufYPuLUCAuNXTX0iTUgJAVzhxLCzbXgmVJkgULAl7XZNpDvWvAIn7bNoASjsmEG+WzLfrzIYA1dV5qhQNpJxEhjhdoLfUbICqdHEYWhZZLZD8yO9wfVzXUrKXVx+C/Pbft86il7w1nZ0pmlxkHzbduJbnHYYggFMzJOtG2GIZwf3+AdE1VMEtGIoSxRYA5V8TBq71x8IGwricQXCfGQrNxLCA2Ro/DcYYnUsE3Kd85Fdm0dnUr74BFT8U5AvmAdD4ihIi8iubKEIOw81hVUtnUfitCiCqwGcCcNZJ2rTzVsw1qt5rt4gaQbXAgqKJSgENBqa5lLexlZUaoXTXgUSor/834eaK5sdtNWGZRnq45IZeKYdhJeUYBhwBabvt7C89JDIpke/WaibqdtLlnrPN0TPkWEAAP+LaRRMZBnIdkPMxPSPApa6PWWvkR3nn4mAE4+GFEXles50M7g1nbh2W/F6SUFMByIzcX1j2pworkTQ2a23wkaEAmwZdcq/H2G8eTDXaRPocN75KhQTbB+wWgBb98F/Dq4zVeXi54eVHwd184jL4glRX/66+e4e88nfHDZzMGP+D98gw/e32F02xrWuBdas8SQGskaFVl+0clDZwGNHBALRLArDmBE3DhHM7npWd4TcYkxpY1bqR7+0YSnxO8clsZuBgYLy4J//mfvMaCPUZ+wK9uCf/qzTU++/8rLmevnLNmVtymc8LWmnv0AXOyVqdnpDVJu9zYyWRea4+WXpIgUhEys1VGJDUuvyEOr23uDZjYeIVWi6VN1ABzjgQEL8jY6YVa9NHgYC/58ObfxKwGXlN63AmmVoE3xVpJxasKMAitI0Y1Baw/vR00Fo5Kj8gtIrHrkEwGQFqr1/9JAJHHzedfdSdqB4H7r7XuIPTIRtapp1tLKb0jZBsd6URuQGTmvaMW+dqXts2un5t0jtTWiBAIPlADlo3b0aIvbaUuuTk3ELBkRmaHSMDgB9TTA4aLPSIIXAIqMggOJRU5XJCC5DQR1kLQAcg4pAE+F6zV47cPA96eIl4fBvz644AxcNuHEvWJcyAvkZMpvsqvUHcaG9kN68dv1/57Xtu0uC7g5v/p3BoS0F9Ll7EmJtWP4LZ3oNdj2j4WydhnqmkSA25rX6Vrj0jbVB2BtBGxpecAkM4UylkIpI1YLhYMIRBKEnE3ZkbNUP6DAkf9b0uxwCIt+5rHz6GVyNqZlpPUpAwgn7fb7zCfT0hFbUiPNlpLvvFivE7S3QK6xhtQpynS7zo4NUh3TFGpBHsfgbEucyvFgSvYOZ0UjZaFczAyZiewO+cRvOgSMVcMQ9QBdFkJiFV1kZySWp06y4xh0HEWZMJs4sRb0IYew8j+6XvPnllwRoh1ut2USEsWRKBrTsFKNCyE2rIZdcEVgHRByvdI59ow7Nq+bfZ4m2ExM9Hxaythw7IZ1DNA1kgBkwYwG8t9b2zPkYkU9nCt2/rHe0z3mWaozD49efk93L76GmlZmh+xCe2OKijI7CeD4M6RZk5Y96eJenYwKPYa+jO96/YcDKTYFXUKhP2vxvzR9VpmAQfSuRbwy2XEL94R/s17hxe7Ex7mgo8p4F98c4H7JeDN/YTb5VI7l2SW0/l0lnJ9a/K0Zy/fUVmne2upjiu3WEz8ZfMgcCTgjkiyr5XlHHkjqluQ1NZcKhMSiEl7fgVwMTocV+CwZHg/IB3fYZgmvD6/gPdbvuC3X/9OoMVaNFO24XC1HWpDZEz06IskRUiYlxVxiPAlC0Oc0TbPI4tmUab9gKsQENvm058puCFuTInNlRIk5aCbu7LOoLDBXQyQargAcBASlCFlS5fbwnao2NNqlj5um7xdkx1U6bwwJ2NXZXXynnIFwAXM1Mo6zqx6Q+M9ygG0nLLhOtx8/hV8iO1Qbx9pe6wN8PUywBbUyGGGkii9YjdqBohVkcrmzcgSbsmsj/dJI72VAmdtx8ytTVQXph0QiWCUn6C1UHIy1PBcHN7NN/jyfMRxPqPOM2bvQINwbYa4x1pnLZnJlORcGMcTw0eHNQGXI+Mf/eSlDDsshHOWLJF3LIDFtpaSXmtOABxqFqod2DqJ+oNj3X+fIpTfB1g6mOlRfM+MmJFS0NuckFmxbS2fOjBhA/tKrCZ6tGft8lxzWATyrp8NhnS1uNiuvTaArRk2HcTHkNLSsq642u+QirTtplK0pFibgyXdB48VmrdPiPvvVStPyDM17SSbb9W79gQgLOcj1izRvUmBM3SYnRr/EKQEsOUUWKAzDFHJhGKDrPvl0cFhwLRJyJF0WBGQ8yrdNmqV23gNcvDeuBnynIMXEBJC0PKJcH1MB8fKU1KyFEJjztJ2m4qpb0vGQWyHnI9aOzhu5709Y25O3BtxVO9rW3qG2hUDhrWla3QfbvlIWmIUMFUxjaK6S15mgLWGBBJbyrbWG4e1PQU9c7f9s16j2g47jwZ2ejn0MRBprc+brijbV/Z4AOOvaamrdeDaPnS4fvkV3v3mF3r+5busUYKI4PTwi0SEfgfJVdlwQD2am4xf3+9SDtuo/Lbzb39vl97XQH2SZfCYZYBm8B7kRO/suDjcz5fKrxIP8tdvJgXJcq0hDkJEd6FJKdjXeJL7t4GJpADXrqEyg7Q0bM8fLCu6m0bZB+Y3+wOXmU0WGEP+LMKasjdTAb56UvAff/YOdwtQUsXhfMar4xc4fRxwWGTfbRKC33r9YU4L8EjEyoeIkjKCloGItobnsckOTue4OCfZFhZyFzlGqfSoLx+MzinQqCkoe7kRvNRY94Ni1l3/RVZ5kjKMGWAxjhqDkzhImX9QFUX39KJlHPDJhqpgOO5H2w58e9lCbTpTnNtuYElrku/gR460V7SrGRnbHGhfAsOCBDTAAmZcv/gcw+4CdjC3GZZHeJAkgmnx+TajJZZQrtAmcIqnk/JIa5EUVO68yGK3UuYnUayHGnxmxBj1HrUMtu1DBjWHE5QYbM6DWYS11urx/atbfDn9Ld4errGkgh88e46ryeHy4ho1ZZSywkN5MiwdidEBwwCcK7eukKVIaaUCGMO3SXxoWSfgfDpKHbvqgM3a6m4NeMir85aaveH2cf3XdH+2b1WDJU4a2mJYWtTZhOYsQmPhE8HKo7Qp48GyAR3E2Fo0Q69goHIFZSm3Zn0/MwH63dtrIiWte0fqdAOWpQDO4/5wakZb0sfukUOxuTRxiPp3blkAAXudI7QFM45Uc8IOju4H50nPqaiwfvH0EhXAx9s7UeRgFgVp3atCIFSHbQEOASEIMTyrTXqk4kzWki0OnKupBAM+OORaZXBfrTJhWSPM8P9x9m49sl3JmdgX67J3ZlWde7PZZItq9cCGjfEYNmxYgDFvNvx7582A4fHDDDAwDNgWxg+DuUgaSVQ3RTZ5LnWqMnPvdQk/xGWtLJI6wmQDfVhVmTv3XitWxBcRX0RE66asgCUEpCjt4GNKKNtFiKApA1yR0hHbdgaFLMBG914GZJKn6tZ18WIGGTQ4KQN3KGQmmK8vyY6EMHhAktpKvj9mrIngHr6afpdQZu3HoYazdSHhHrT1PXMHV9VTAeJYQWWWAO6TQ6cGnVvVXh/scnZ1SHg21Neqnad79BJqBT0MRq/KIyQMXh2zR8wM+NjZHIBEGuaFlPHysy/x7tu/HSfboyZ6r2SEVBudwlOl6ujxM5vAybeQlSDTE4PT55EjreS5wvb6k8xmktTh5bI5GF4O0tQ0ouJcpfIxRdb+NUCOHXth1FpAyuF0vcuMlAJK2RDSAhuy2rusHZN1WhdSrNllWU/C6fEEihGtjVl/Ft0KwOhEr3ti+kjuC/i7+4j/9f4z/OlXj/iTzy44X4A/+yZhzeJMpLQgp5+HJp+MtBCk8qZZbwIt44sxoDVGDCyH2rxA/UxtFdYjDrCQobxnWRMu580F1zfXzibgA8mIrB3/MCw0GehhBsm9EjjgkPymeA4NZMx3ZvyIMKYL7hd9AhjMKBlb/2qNTAEo8haZv/YyGDZfyA6DREAsajPKyOjquoQhzPbvzfOXuH3xWnpKGAKeMMuVEzbdr5HguMmgxQhCUwUWo4Wip5izRwGgqFxWQyhE5JVEnU1xK/DTfLjxdViF2q4XtKR0RAfYFRSIsGQBc8/SPd7c3eC7j4xjWrCkjBQTPnz/DuV8wXJIONwddZJwQOKGH7aM379f8F//8gEpAQ1jxtS0HWNBGTJMLQScHz7qpPABxp++zGv1Km6+vubT77n2Nn/8NyvjLwYAACAASURBVGsIZsCTrtZewSZPjPzpi9iFYnYaJnKlngMBOPJmClHbb88QTDzR4B6Sek26t9TNsOuIB9a8G2vKKUSQRjhjsNJfKxkeU6tHlMfO3+A0eG+RScnpKYHNuPn89XOc94KP9x9RJ/7XmACv60jkzRNF7ILzruyMW+dYKIG+W1pTP9dKFW+6srY2X5BilKGCMXiH7qgk+xiV56K6oJeCfd+xLDKNOZJ0m9m3HYcbASVNz7yBuiWP/kySZrawvZSDuzOlG3+dZlWe4NBYA7wpKGBml1l2OZm4d/YZM9DqOC7r4q0jrLDBItS2vn4/NNLRsjcdx8OKvRvhV3WylbHAHDnVKYDrspFGHz/Bf9Y0hukQpxIYXUFkyOxOn8DOOD8iW4fbZ3j26pf4+O47P62WDvQmkFXs0dyF3RytQOP75Zxc2wfr+m57DQVZ187Q9ctSxe7s6R731tCIdOhow9aBXz07IxLw9rRgawGHzHhx6Pjd9zL/LuYRjWK1xa0Jt8cuPUer2IYME+nxEDkxW96YQdUg3dwcVAtPHKGNtSwd+OJux9fvM+4OkEGy3FHbjr/6sGLJQa6llISf0ZoA/iHpITPoakDzkvUhGSmLIYtq3UnfL82rtBGO9gWwDagNwH7Bze0NTqeLIlCyPZGFdb2s5aaT22pH68fGwFpqsy+gAwSe32V6frBS5PBLOkovPx19/W+93tMyLuPu2E8MBlTJ27Px9GDsCM2MkgrFxB8xIDeO7rj6crjBi8++uFZEGErHDRjbAZ4R/+BEhBDQSkGrBfmwAr3InJsYHQSJVyceqDH8/Wl618iSlNe1nbUjrT4jGGREUG1I5vfpnouVHM5pNymb32sFuCBQw/3jGdQj7m5fg0uRuTwv7vTgRwAJ+15wjA2PJeP/+PoVvv54QIyM788rss4Pwlh2f7W6g0LE48f3AEeEmFH2XQ2epEqCVhv16fMe4fgRIpquP4FSf4+hCP3s0yoLe6+kP8XbsSmr44xMyn76Pjt/5hxcw+bxOxl2F5RfBP/9GGzJ2tJe5JJVbgJYldggYovyhqYx5brBy6UZMWQ8njZEkllS9n2iE0xW2eVCjLWuBHfklJEi4RgI3799h1KaroW2ENDnoRC0caUshkXvAsHTq+ZsGYegFSllrUVSTTYLiBUQhRhQ9yKgmATwtc6waswYJYoRdBRDMvJtCCj7BuKmZGOZa1QaIy8HdR5GdNGcIzMQBi6Mj9K0k6q/EeI49ibXyEn4dmxRF2vB4QZHDbjKjJcUzw7SLL4EIEjvmJwXAYjz8SFLkYjxS5qi9pQy5Et6K1jXFZVnHmLwDsUuybOiHUfjSqZ52FRvRzDEfZQV+7Xc43TohgFZ5dcGYBjA7as3qHXH+eN7v8bg1GhkZCrXlue8btpnR9O5QQBAU3Ju4oI6mJqs2wQz3S4I4CGPYABSUNJqhVXAHlPBL2/f47PbAwJlHHMHtwu+/v6NcL8wQE+ApIZKNV7YtPGTIglkHE74GviLrXmlyJoB9ThdYjagtUvF7svlA25/ccAhnfH1+yOW/hH/4TvCv/67A0LoktJWTtFPOYz2+iRoCaQPh5kRPVpj5xxxiNZISA5NLQHLKq3pa2NcLhdpwmZIm4R9DHQsecFemiPPGSzYGlhrbZvzcPWaBD0AzpsxZWbK1d47opImQFGfjRGcBS9KdZCzaHi403396GcXxnEQebpB0UXs3z8UAbkhBGk/UzVskpqS38ec8frL3zirfn4uJ1Fp2bb8o2kBNQx+0GJEKTLCPuSIDiEBBpKSNuDpPjAqpFOo8HIiEJL+Rhc+SkQlafhUBv9JCipo0yBPSZmhZInQWJSsamphXRIO6YwcN/yzf/df4J9+9teo5YLzwzuknhGY0FiJdXVD7yesa8alE744bPjTzz/i5aHhX/7tS8Qw1micSQFbtWwIFLDvFzCEiMn7PnE75AMeMWJp2Q0aycG5a7MpLdY9nhXvvM+iyMYN2b7L/0Q59FqwrKtjHNb/M1VvPBeLGFo1HuE6Z+5dckXbo3Uho/o+M7vXayrTQIF9m/W7sRCwXF+jM/q7fS+IMeLVi1swxEF50FQS94oekoA/zfYKmX306FChQAgBa4rS2RUdp8cHbAU4IyCnRc+5Sl6MsEF8rITaEWHRIZWarlm03PVy2QZAcevQdWBbQKtNqw0Z+7Yja0UQ6zDAnKKAjyTvkXlAksYiqHw3Ri8XlH0HpUX2iAJq0U7VWnEJ2xfb0ykdC2afoCuEZzigszb3MUYt5zccTJ5+s+ZhXdPwQdCbRlHm5La1HaCpolO5NNoHSvbGeEY6KkDLqi1dAlhaXFO1tciMJGYY4hAw0z0d4rAgjGoWuDaQ54HyeXiaTm5zwPS2tXE+uXHWkwHWkkqzt4NQ6kt+BXhefPYFeivYzye/FyMa48rpUtFR1WKE9KviA5WtAB21QCMTwfBknPpA8iAj8mURKTHi0pa/ebSaO2Pfd4QgFWt/8+EVHveAXx5/wMKPuFsP+FikpQPDojJ2b7Lj5liYc2yOwtBXCqYnQObcIDAOi0TfZAabMgnYBiOS700MhOf5jN88+wMKZzzff4e//GHFH7Yj/tX2S6QYcDiYHpJocpvB50+8PtmnZVmyNh6CT+O1hzfl2BqjWYfVYDsqc3RK2dFa9Q32TVJlte0VS05KnsMVGrAcpZVU/RjS4Eooh0KXP5ABAczCNt8BnLAnWIe0CsFAhHkW1184TMfA7hRGB00vZ7U7VsGYgv/uQRjYeRoNGt+m9x0S3vz6t7Apmn5NezOzf/LqWo6ZyD12WJdaXw+o4hZicNMwuZSw21MK3h8lzBa9YB/vbuknUyDdDiJLpKX3p9EJVcJTSDmoG3uTN/HMQWj9EYgZ2cjAi4xeoBBw2c+IAAJltA4cA3ATu/JZFP37gtuadJTtgpiSRAXrPCRQ9k6KYRRMBEs9Rpcjf4inQHtWaPMvRYMN4AzAcksMNTYKOKDG1pQdzxcbmkZ/VLChBsujVqbs9ftNZgNEEfVWJK2jStC4WF2BZVLOx0gVSGRjlilLefbecXtI2LcN798XNI6odZO8NAWEkJFzBHOR2VoK0EZHZdEFKQYBBZcTahNeVeEMac4rhrLrdTtFj0gEhLE8+oxyZqxxHWHTdM0gN3YHgiFISLxDu6lqBEruf6TUovb0yJl0VpiVkIkSIq2gsNRozNnXuLPxMIbhtzNpa9q1wsiqMzsTQJJ6M73VugxhNL5S7VcCpX8ziRvpNRvgaqnIcSSU90TaPbx3r1L0oofpbEPlx6YCA0AwxwCQknhvRMfjDNgzG0nYdatxcUSOPQIzRXfGvU76zl9jLcaXjfu2H21K9AAjc0pV8RFFvPz8K7z9/V+h7pu+1wwdrs62dvu4shHuqLLdw4jODmg66dxJpwpIDB61ABEiExBIG/2NL7GIz7CpFZeqTRNjwnnv+OGUtBV/Fl2ge99au84WTMs2p9r8CfSBR9RJNo8CISJoabNEYmMUEFtb973dG/DbX1WgHVA+PgD5BX5fXuHudkWyRrTTzXRmJAr/6ZwWhpQojQcwXobDAz8Irshax+V8Ec5ECNisSYwtsqJnXyyShkmHNYG12gg0CJpWocRsYjnIWpM9vtLldmC5DwHyw8pwkpsbCdh7LKLTNVeNgRo18mLhewv6+JLbxtIgOw1wQQOg2IeeACHzKObDAfMwKOLNl38shD7X85PX3U3BjGM+zOF42XqCzeswhSEh5r1IjhKADvHDtVDpfT0dYDZ7EYQJ0OhYj2ADAY3EpsrDFEfU3xOzcxv22vCwPcOagdpF6RQAz1NE6cDDwwNyDDgsGTeHI6gDJVY81IRvTxn/4cMNlqk6aHCHGLVcJD1WC/ZtR2vsxLOZO2U58DA9389u4dM/XIGkCcEOJDEagqlxIYIS1iVqxco9sWZ/5o1dVR4pyCDWfkrqYcg8HnMyhIhuYyXQGTFmB52DpDdak4MwpRuCnxno/kUSGSml+DUeLxtubu6QYsRhXVCU2L5vG5hlUrI1YjMlHIN00o3UZZp17WiUULs2ftQIIrEM7gsxSfmmjosQL3gq1dSntW7X3Blb3cXoGoOczDuUNEspSkaG6RGNegVCZwFLKUXh8XVgzUG9/Sb8LpIUU2sd62FFLVJluaxHSbeSNrDTaiVrx9B5eP/cGTw7hbh2Pmw/ZqI+KTHSIh/+VoaUs1NAJP7xtex9wci4yvNTsNylpO/qy1k7Q7vYTXwSr17UdU9ZI4Ru7KwyRx7aI7/TgZl5TI4EuqW3JeLMPLpAwwCZg3qRcTt+sgRKbp76Bs2kcUzfb0AmhojXX/wGb38v0V3Xa9NyuJ2ZjqGGgwDMe6qA3D4YgvOJxN8xu8ATEMWwFfrB0q4pD/Or1o6lN3w8J3zNr/Ey/h5v989Q4zP0fsayRH/KWmSIDk+LNAAQ6/MH/1vVdOmIfJlYBFy23avVRtfoDurQwa0Jh0T4z15cEB/f41Iv+KOXhP/961/hsAq1YN+rRlXI79Gskg0O/qnXJ9JD7N6VuoBIoaE2eapWqqYfgLLtir7hC1B08UVPW8irC3wLU9SBgMtekWLSJmCiYHwcvG7ukqKXU7Y+dlAUgXxRx+SZmj7DMFogM/BmngdElkdUQpcZVMDHNBromYlG9l0S3rJfy1pd2TkDXSqNBrJ87k4YkaEwLuSHaDkcNTQnfzDSIEAaamYdWKdliphIUX6fGCFGWBhWCH/brlOce9FeJVW7RRpQNTAGGFfDHrD17h1MDdjMfzcPh3WvcrQ5HyPKJYcy4LBm1A7UlrHxC7xaPuB4OOByfsCL4y9QzhuIA26OB+S0yPj1vaLHhCVW/KvvXuHP72+xpH6FLDzV0XbElAUEnYunPI14y14NoKqxdTQFvOY1W+TFlIsp/aFoLOSsSzJF3iwzzAxQg3cLbjz4GVZZZ3NjSPk0RPDvNg9dgGjTfVAlr0C0tSndCRbOku1ZM26InQ0hfBILIpEzMNqiG6gHZF4KAdIGX1v/EwGvXr7A+XxGCAcxIUFKZSkE1CZGccmyhvt2kSocrdTZG4MRJ5DMY6FUlmMSz6zWKtfXCB6pIqUoe5Ac+FarBPAmhPNZMNmV60sIPaeEWhtSko9aJY/MCApYMmE9HlH2HXldhFug09oRkuit2nB89gLWBXpdkjS7U2NtXVe5d5QqOjFo+slsNnT9WIXGOAUhChmUNKVie0JBWv4/wdboSkS3SE+MYz/ngaYGDscIFhNSQtWeQWGKkoAGKLH5a0H1U1VAaWlP0f99RJXm5pea+pe2VjQMuaaWAfuuIRU0DtrklEi6w+Tb+EFhIiDHQFdG0i8+/xcRUsh48+s/xvd/+5fg2tCJfMyDAyaaP00gsj5lU48RXXPjSpE+o0V9hqNN10uOsQViC5qDyvlmBXQDl/MFMUXcbyve1j8GuKPXE7J2TEcgdB394HJhpPNuHYr7mDPHOjjUu77qOpKV7APoQHNOGoECsG8VdzcL/vNfNtDpP2LtjFssOB+O+Mt3X+L/+ZuABKk+bbpxIUAxhtpRv78Bwp++/n7QcgXR5d/zeVMD0xFIwqWtNTeW1jzJdmA46hYiHUYXpGVsMDKPVCS0LsMGCdInwQQ/RmlSs8/kbAZ4amI1gwrbYrb32J1MguKo0+9SL6WIWFJ94iFCSXHCGxBqIqmRU7YIYFBg2Gz9TsWQ1j2QyL0Vm9ng75sE9tUXX2G5uZkQP2F0lZpChDTCho6in3gV/mz6fMIr4PmMIaYFrcpgtKYEXCuG8nscK3v1swHU4Q2qQVDvP0w9ctwoh6mKCNotszd8dvsNvmsvkLQ/UD4ccbnsuGEgLBmnhx2EgpgWhADkteHPH+7w5/d3yHFuSnC9t3JQbIic9szoOyyKZk3OxjKxBwZlEGEAq+fjAI2mZ7bvIWBeMVG8c7l1H3I2swz1X6Ix98gaj9n7RWfM8kwOdGUoprYln++JIbLaGxASGDo7bNaWFh10aAz/XokydK+0a8xYUtAOlza+IyDkAwii9Kz3iBkM5o51lZTR+XJGRADQnHeBPqr6ZuVlTgaDdOxFRYI4KH6IdQ9sEnEt1UH5MHqDC9SmSI05XqSGPKegofog7fd7QYoHLDli3y7I6yr3VaEzWxqWdUW3z6+r34vIfNd5WOqguNGSvQ06GoH9sNguaOsGGIGaLGjhTpVtnVVNzkB9yKPIvHM6xLpfGVDXiZj4VqTzpmDetOibaPJlYF0/2/ok7zw95+Stz/cdwqR9GKM/ju6l8PR0ICIIHo122Rz6xvX48A+uOFhRI0jGFyS6Op0ia9PPMWW8+uI3eP93XwOt+aEmSyvqs8D0MVnEOQweUYzo2khwPo9DZ/+Ejr6yGcFl151A00UM7X3CuF0PKK1qpRn5fDqZdTU4KdbY05eKBt/Nxn4Acna5Nx9XIZoDk20lX2dSuSQAn78+4POb97hpD8hLxkF5ZH9zf4PvPgYcwg42TiCRpqnHgtj5EL13XYE1vz5JxO3ctWuk5oKN+AZG7ZtW9wCWZuitumKwczSqEnTRZ7TLJjDsIV3rhHtYVxyWjIfTGURC6uWuqJ/wo3yh7PT4h6frX71FD72RjX7ybXqB2pvmtgG0CrQGyssIeGruP2gTIPvSmfRl3qoc/DhB7GvejeSex2DH1198hfVoVTJ2W5MHAnv+WfB52B88URj2fwRXTlbJYOsyd6UNSrSMiVDKmMlhRsQW3zqyAvL5yqw6MXi4sbOEG6POL5JKi2ErrRJFUg4N77aXAEUcYsfeKp7HhMgFzAm9VsQcEJERI+EjM/7vb57hrx9fTE/745ccUI1Q1IqYMkq5DO8GPv4DBlNg6F/Le0FNOzxGB6fyd7Um6o1ch6BpKEmeFa89/Hinp+3sIKtHTkacszvTyI70Nomu/EbofVivKdADUi6IlSkb2BVnSwd6erWSpjNI5OiKcAxCJ2CvTXqfpAywkMaZAW4NN+uC2rvvb2uMD/cnhEDerj5qzwyLVEoZZndnIEUjyotC3vcLwCzgRdcj0NwYS6Y/m84Za66dvF1pk5NxiWxWk7ZaCDJzaj0k1FKRchZCrp6bbduRUkJnxl6lh4t9T4wRKQbsRQePQowJaWNJO68gAc1Jh9UJwVdGI9RavdOqTLmWdg0uQ2SRsOFwwKIBweJm8xkWMmeM0sk3RKkMsVL03iTiIphIjAmDEMEaRTO5HnptfD9fyeuQbB7GVj/gd6s6uxsPctYn+kzesBBzNBCuM8zgj+7bJu9ynrwgIhhfbNKFli0ywKXr6b24SOzZsh7x+ktJFfXeVI54ABRmL3Sgq7OhMkeEkMRB8HTljwzNjyM/ngmgAJCeC9U/FrX11FxvCClgSauS68Wh6CwAy3RBU56J3fusZ1iVhLWvEH5KhPGvAG1jQIZ3r40ma774l8fvgP0DWlpxjIT1sGDpjG+/XbDgAiDJWZlRm0gC2IbRQng58yy7p69Pg5bWB7FGx29TCLJhQb7QG0ZZ91Q3kozJuRwAwQ+dLIhlQ+aUQmfJ191vD3KANFTeuqDm1jqsJ4DloOfcqt8F/UhK/O8uu7BD6H9ykJXzglYu0nuEIigvgqLV7Hft7nvtbdvBduj05Kvpx+/VNQE3ECJe/eorLDd3V47w3CXUoS78/MF4O+Z5ERFqZY1EzUCRHWTKge3+vL0baVY5FGDsRT9vqoMBaA8dggJJHZRHbBCWkHPW5ljawC+Qp9paq6hF2j+zeisEAaYIGQvtWNYFnx/vkWjFi9sFGYSwyYDOnBfknHFqF/zz373G77dXWEO98lR+Ztv1cHZfkxAjeqvqKc65dgwvCpK+YlOswVrkj8qyQPL+MXdjAEliO6CzTIjxsjMS49O/a3dTU/wsIMnaDwBDiRkN4cqbx1AwPAu3ejk2pb2r8TJPkfUMpySAYK/16q6sgVtAFL5RiD4BHgo+c44otXhjNGZtd0+mLDvWRZqsldqBbiF9U7TyfMIpkYhgbx3UG/JyQJsADWvkJ6DL3K8wqoosvQmWgYjSa6o66dGDDlb1pimQpO3+GwnAzkvG/f09Fm0cZ04PIDyIWiooRkRmbJcNKWcZdtj61MqAtUx5hMRjIKw5YW/Sk6VWaZU/2uiPiKw0wwPm+bAuZc7mlP2IBOcQxSjExsNxlZQdM+plg40SkKGiejWvVhqcs9mJITshPGRe/nOOukxGdQI1pDrAHN1Ztw0ZHalXu54D8gmEG4KzWUFXaVddBy/CCAE2eoXUONs9jhfBw1iT7cjLAa+//GP88Pu/BtdBRLdPWlNOwPid2hyRJTFHusaY1mV+/ZQd8N01OyTEEb3sSLP3buM2tIsyAUwBKWVQ6w60hILQPZuRYgZolNJf3ZOlhIIMSMW8T1OZvjy7dn8OwLNnd6jnv8LLmwOeHQTybvWC1hq2Ij2IrMO92+rJXtusO3FQ2ijz/4nXJwYmwoWDoZUFGqrzCIo/LJ7q2+mgzRNjg5ZFmRFUXoCGdVuzsJReMmY3srWOceEpDYLRTHS0dtLmXcniRs2RDTAVFE3bDTMwJFEuICWd6AjrUStqBgyxAx10yNpTcDI6647yvhGaG8RlC3kGMBoIgSLefPkbLIcb+bzyP0zvDUIkzOb5Xo3Db97tKKsSQKShQr13mUwt3oiNRveKLZbvskiCVR/MfQPYQtedISMJ9L0gKfFsIxWm7afA3LHvFTEGxJRQGzvAKp3xKp4QwwaiM/6Xf/yP8eGHH1D7e9xwQQ4ZyFXKUkNE2ztu1oz/9pcNp+8qTsX8sXkfJ1nWqinpJpqwbWcsS0apAZumNHlap7GwCsChniHGSASTKhkOSbquWi1nSpig5atzCmrITNA+I8zjph2AmhI1bcw+y1uMthuMLmWZkxwIyJc9M9BvXiGrJwgAMS/oVlWgoI5C8EGKB+0gTWBslZHVU2eIAyHyNHtPGgKOGSkIWMBUlSf3EFGqPMvhsCDmjPPphN6BsheQpoU6GDklbTbJ4LyidOUKqOLbtzO28xkxLzgej7rOpN5jk1b8IeA2r2i1oIcs81NAIK4IMbtCpxCwHA4gksGZr1+/xsePH/Du3QU3hwV7B3jf0VrDuq7YLifUxlgPR5TLBSEmxBBxuTygd2krkJfF98OAQtDw/fl8xul0gqWclyVj3wVQlNphE+LnNIqJjhHcn3qGyYnvCTfHBaTRnMYQonPvWJbFz8oShVgZCWhI2PY67aWkRycqqwJTLYmf7CvsXmiOvAw5lwaclp65jiaao2Ow22TPHlcoI9PD63mwsxCYYY3ObK1mDpMNM6ytuj62SIuk3xhzgzjT3cwdeTngzRe/wdtvvga34uXWHjXVPk5Jz8WY9j0qPcme8AonTQBl/vX8PooIiRUwBHDo4/0afS17RUwRMSekEEFZrlt2aeC2l4J9V10QoqT8qF99r1XVSZ8h3U0FZJ07VpXD2g2synm5OR7RQHj34QH36z/Bs/2kDmHHpSTsPWBZ4thZXYw4RWu6e11WMs9YdMjlT70+0cafcDisqHuBtQ4HMIR1Rsjm/Q25VbmyHKX1EhFjF8IYD+DhNfXw7PqzEh9ekJYTOtN+TEeeIxusi06qiHNO0pskBDcaJkhWIullZDQ6XbYuB0IEidzTFQPgW+4rhie/skegqcnQCCWaVwwFQAmvvvgK6/EIy9Ma78LC/jOxci57E/2tYVAGxtHlSTnoOtKYOSKAiYQoCMiMoLkMnLQ7Z1PFwqSVJ6xOQJ8aew3Px2WFWf+u3ryumyhWViMmyumQIzZecDrf4JefJRz4gm/OD9jKe1wuO9a44BevX4FbUOJiw3aq+G36iM++2PB/vXshVUOxX4NQfXjPIBjSh1ao0FASRhuwB7iCo7aOxBgNlsiBiPWt8ENwJRcy3HD8ykwA+bUpDG9PPuEs8islb7/06FW3CjvDs4xrnTS4TubhBCOTEbQBWgesEVoM2upAPle24m3tU8ri4SXxWFubOBg8Fm2OCEq7c0tbWhn1FBFpTSvjZIr0cQ14PBNylPEXImMChrhXMWh6jtnA2+HoCs+91c5Svquy3loFaIBvAJqyCQqcgfVwkPLmGHB7PGArO2qVKqGuYKxUKRootWHbK9KyCvclCIisrUKiw4uAVRt2qZ6yNaMzkG9duwkBj6eLg8DhEEHAquvZMcPIKrz82QHvdwECSCuWiCTyMnhcyvUIJONWUkYgGXCbkhgrubakJgOAqh7M6M0ypST11DNZOnUC2VcYfaTEvdOtR0S0TYFer7PpSDufohNbrQLC52vGZOQ5P6cOCYjUXgA0ldyP2xrAwVsGhfF9DCCvR3z21W/xw+/+Clx2XecR6dMv0igPwcN7dgLDsDe2VnZYJgzl9sAPEtSGhjFNnNnu2d4r+8SdwYGHTMToYw5yChp0GI7L1WBb+zaiESnWZ0pBegxJC4GoeyMOXG0Nl31DzityaogouLSMva4I6MjRnlZkhzFF51znEuKio2707Mb0n5geYoi3XVuBsKPp6m+wDQNJeTEYIxQKr2PX+1WEFXRh1Eed0DCsC+gTDs6cNvLKDTZFDj0Y0uDJ0LL1DtlLhU+U1vcbERMWqvL9IVjPkM7dowkK84f+8MWdfumrIgo1zOUjoB/hGbLn0ldcFrz54jfIywGykip4CgadxKwK36NrMLAy523HAYA+lykSJ9HxQP4CZKQvhFSxzDfb/VCTvofVC7bKC1tAm41jhtFSTN0ImfYcMaIpiKBAaNrc6+5wBDPhbt2R8RH/4t++xetDwV1YcHMTcXfzAmUvQtatDQzhRFx24Jh27O0KYlwvtm2D7RsBh3UBuOOyFZ3EWz2SaOxb2wcBqcGvyWh+UdKvkK7Bo+urrSu5QdEIowE1tmFio0mX7Z8PILT1NNA6VyJN3YRZgTH6CK8bUS9MfYc8tO9T04Fa4eWuMSZJ90BKJMGaH49ZGrdplZU4rM42jAAAIABJREFUD80dCUDPpoJqD2Pbl+ihJS3RleeQ9PD5UhBKwfFwQK+apuMORrp2iHTtTaHFFKX6IRFyEO/ODLlED8n7MMUYUHcZMUG9auRQUrtJgcWi1RYpyTNRjDidzjgcjujcdZaa6MHeO2prSHkdRs5ALdhJoLVWBe5WXcKw4V1yrwZuhewYoniYs2MxKw8Bh90rr9zgE7Ck6GXaMarOVDkoWo1pa2mGdrfIBzr2LqBmzQmNGYWblvtHNLCDgpk/N4ymPPdViwSezs40q8t5FFMDT3t/cFkR3UsYsmvPaWeqWeqNddBpiPB+MLpOnSWNaMARpGlBS7nB0hWa/iLLICjEEC9AHK204M2v/wRvv/kadTsJAFbyKyWtfbfz5RtGoImiQE/+awYoY6zLJOwKsJplmIb6gqVUpH1DlVS76YbWYUmzdV1wubAQiqfvnCNR9t/Cx5N1j0miU4FkHEpQUC5gN+FyPoFxQEoL1jVjrx0f+A4AIwax7zx9j923S49GrIgJdZcZRlLhJRHBn3t9onqItWuotqpuM2pmyHCsaXOfgprrCJQgM7b0kio37irQCmT0+sMUuGVWVKuLQGJ4wUbqFNa2ec5FR2ovS0KvTVoJBwFFkazd/KRP9TsMSEUIYTbHNJjMfL0Bbij0/mehv/ZAaPy/PRvBp2jmwy1ef/EVxhA3OPFODqx1oZwQsGF2M0ZmkGZjoSrBopkGXpiHUR7PAfEmO7miYbbSNFsnMTTEpLwm2eTO1idDwKM9ts8xIm+lMQnEMKrM0lCocMCrdcPL/A5LjPif/4f/Dl//7luU/RukVnHaHrUkegf1iBBWUGr49w83+H/fPUOjgBx5ktFZ/gaPyEKgOUsjr/XmBoB4cIEIHz/cY9s21FrRWr2KSrgRZutnIJEkwuApJJ3DFCi54YkxguNg6FsfIlGqQXkFMuNEOgsTtm1HU0Oc4orDIaPUrmmRYZBnmTQCtEVgSA2nlZuaITPbYiRFARpA6xUhCN+EIGckxqmslaamYjryYQBBcyimahyGe3cxBCAaoOtqpmX/l5Rw/+EDEBasS8Th9k6MUROuTFMDnbUiIUaSmT5R+u3EGEEKuDyyCCAaIOgNaZUeKkk7fDOzpmOk8210eezguuO+VuQQcDpfPE0jDRg13WAAAGYkVC9By0HNwPeijoe8t8rC49ptkXNuCt3KbAdotnfz0AUaOrTULXJAXi3tQ97ddi8GjNhT84WBX98R/uYjcExQwvGC3gNKZ0ha4rrBIE9KfUR1h/FnVdDD6E7lv3jq7Km3rkbRWj6YjjBQZVF3ssis3ltVfgmpswyKGlkb0bbeuvTFlQ5p2iAwi7OlYMd2gEgjSEr4FjHWNN7km6W84NXnv8b9999iPz/IzDW2lK89n0SnzGg/nWjtu6n+xwAP46/uAKrj15rOyprW0gBS7x1F5uOgX86wC8cYsSx5ODDdIk42NFR6FAFCT5DooEbvpmfuGGT/QAyEiNPphNs143TZxNnopv90yLHqm2gOil3sqgJskOHngg0rEvm5198faWFpF5xiQogR+bDifL6gtQ6fkDp5F7b4w9j6+k17NoUPNRzgQo+pMQ8Dcz2//XLOe1ooiREUidqgrHEvtViPAb1EJzT9uxt2U7wmJEpGG5BUN1pjhy52ihpoODX6e3JhsnijGTsDGoEIlBIOd8/x4rMvEZPmP8Her0FKv7RElxQwgDQ9MZNyp9fklZonPumJoWR4CL4dqutRBeOAXYEgC+Gocg+aPhpuVAf7RO3R48UvKbvhe2xKSvpPVNxfEr66W/BwueDv3t7jfL4gc0OrOwJloCekmIAILLnjL+4z/uX3L3FIpiLm77GHfvI7fX6LFNUmodUYA0KMuH3xArcszc4eHx7Qm5BWi4Ia+WyTaB5Iyrhj0OgekHOGpe6EryIVHPu+6bmV6CUAxJjRmlSMzACgQMCHdCclbFtBzhlLjthKl4oS3U97aB8KqARrP/o0vDfm7njVBpI6P8GWqXcFbcWJnF2jdAhhAKBuZdvaOE13oLOFqLUKJUwdk1kYBDIEkRBTxrpGlP0EhkQLujLTrDyeTZExa3QFIEiKOIYAWhaN3oQB7GHGwIBq1GsAzmvS+1pyQlbibK07lhQQ8yIAUp0YqV6SnjDCT2oKnkQ32vRgF7luhQLC75JJ3lNKHKPiTo7BSKfJUe/zjvinbA/FXsknpYu3dlOtBbVLzw1rimigLzkqEO7F//RHHf/mrfzm//tOnL6uxrE3d0fxU8UMfZY91UMmC3O0QPTdaPUPjD1xJ04jH+pZuVzRvEL+O7gOMlA4pyHtZQN7Y1pgU7uh8mfNLI3TZc8RADSWfjpcpa9JoDwqWVhI6Dkv+MWXX+Hx/h0+fP/tpO/VhpFcTdoLPOU8mqFg19VP9SP572XtDFA5RcBSoyTt/WMICqIBGdEweJS1NmQFDlL2LaNIAuCdwI2naHJn6eax4HB7jkBopSCHgLvbIx42+f4QMPGvpl12W2EVwkOeLZ0pkcjB8+ytOQn4p16fBC29ATEHpBRwPB6AEHB6eEBpbnE8/Gz7ooAK7tz5BecfMG2YWFAn604ezPya4IpUbXR72JkIOJjwVnpKRHJgKQApoOxlCA7gCjUQYdB1p28lRkBQtBscq/mmwML409qBBgDD9DysHlUIuHvzOW6fvwagYXwVnpg0usNBiF/VKnkGKicHHfZ9fLVgDHg59kCP6r344ZB7Z4Z6mbofBjq7Kki9nnKlxBiTKIsOC4V3Tys4lqXrYKd7ThOghSqezhJ9AAjvzxVLWLBvJ9S6IwG4uX0ppedcUGtH2xtiCvjdaUUkQrSW2bPAPf33ycvBMyxPL92ZRXcG5HXFi2VxMNHKjhijVNOZrEIOWYoJFCWUG3PyvYZ6u61WlC7RqCaNFLxqg4jR+ujuKWZlyKXNdCr7riPiZY5P4+YG2ELcYKAO1xyedrXzMUWGjEfmjZxUxmRvA1JM0roeUg5q5FdLU5JFWoIAK1vP2DsqKccHw3CK3LLOm5KulzkSCA0fPz5iOdwCxEhBgOCaM7ZSAFaic92vogugoHNUhOjX9FBa08gr8MIKgvTa3CvW9aDntMscn8gIrB4ySWnw5XJG1qhKKbsUEiifyvZHokjRzzBIQLgDCwoj1azGgeZDwAZPNBLaZ+7B4CCYuI5IqHyee8eaIy6XixzaIB1w8uEAG3QaCSi1iGGBlFr/b39ZsOaID5vKDgEhytDHGKRHjemkzqNyZvApaConHmfJDJXJlDltT/kcDPl8h0UIB+i1z9n3BR5/91Sr6iCrSAMGBxDMCCmj1IaorSRMHgScFenca85XTOBehk41cNAaUo7mW8H6JvVWcXz+Gikl/PDN3wq4iFp+38QBCQryx6IMzWM8xaulMjBnUWlTxeaEWIBAI3L0ZE3MCbOIFitt4nw+Xzk3RECZWu3b98h3ByGuq54W/SQ2r7UGdMKSM25f3OHj4yNSPuj+i8xYs1Rg4gbyBFBokt1JXqTBZ5MqzlbRasHPvT5Z8mygYNsKLpd3GmUxAztW3HOPGCjrCnTwNWZxwGICRzSYywo2IoWrem0zcvaQNuNnyJn6LGp8RWiiKsiGEBi9VqScVJF1ZVJP9+fCMYcr5WvjFN63v4XpcI2QxshNjuM0QE6MC15+/kc43t75fc8Gi9vYVEaYQpn29dLZcXgvmLM9XtnSW9VQ+PC65SODCyHPwrAOtaaQ3FPmCWxPez7y4+yVJDbUayb7GgDwjp5KerSApBwMSYsgHtB7x7en5/hv3nyDt+eXOATC4ZDReUNjIVIfb4B/f1nwz//dawQwljBNYLYlmfUjDbBXmkw5bUyqzEibHI2yRFOx4/CzAA2SEl9o5CtQUG5BV+BlRmTkhlm9133fpZQUgPlevXf0IGm51ioOx1s1ENcpAGaAe/M5M8fDIhGPQNj3glKKK0DpFG2KYyyCNZ0zhWH8GrgMwBWtnydAKsoMPGEMTwW0f4veE8sDaQQxIIIwuoRikH2pY9srcs4gZpxOZ6SUcPvsBYxM2NR7tH8ZhJvDgpIlLZCyjn7AKOlurSMnIccy2OeksYKtxjaaQ6qfQrrF5XJGrRV3d8+wnx9RNqlA4pBQLjI0TxSxrMfw5JXcmCNAUdPCwTsFMzB6wijIs6nZ42zp+0o1bOuyZrrNHK6o/I2UInbda5uPBHTkGGXobAgotSGBse8N+/4A5o6YIkJMWJZFdAEDxB1/OAFARV5WdzDl3hUoRNGdpewgVrk0nev6SUCxtBJ5Gv0lly9LWj8FGaZ4jUcifyK99riGzzzTV2cpk7cU89DVyhWLyXuTSEVPUD2nYCtImpHUKHPvYEqgwBIhiYsb9OqGWPl4rYMRgd4Q1yPe/Pq3+PDd36KW3fcl6qGanVWTm2GnWECN8+f0PRwgvUsEfPXaMOEYPLWuRJLqLvuOvCyj3F8dhsN6wGXbdF1NlnW+FCzNCF1/bfhqrQeY5RZJ+q4EdHQkvL+/x7II6Ls9BFDf8X5Xq05m14cYDGd7REKFKFxwOZ/Ralf9JJ8vo2HWj16f7tPSWbtH0ng4VkIeuobacI3WniDqWTH6e2guTxts/kAEqGezl+J9F2Si6bgc+0bbYVBhNEzvDowsRrLhZzwIhKJ4gpd7Sqvv6P0IrETVhYQAwgilOwohO2fsv/MogkIGe996c4dXn/9akD2GYM2sbifnKTS1Lq0zOGMzrW6U53W2yMeoEjCA1W3dRimNKtj5G+w1knFPudx65+r56R7I7TqpVNIn7LnrAZ0IHnKeZKOXHY0ikDL+4sMt/sfnj9j2M2gh5LAKWOpdWsLHjEQ8wMp0rac/2302Br58VvH+EvCrZw3f3BO2qvDS9tJUB+s9aapurnYDAJtVVEqFt+5Wz5i9dFKMVakVIS4SrlZjbOkJhqSnYlxwOKxoDM+Pi3LUdGGXCq9AooRzkoZwMRJqJVX8UyLalESwMwGvtnEk2hkpRS3nt5yyAVpZ2BCkZ4kbkRBcFrr2Eul+z6Qhd/IJxL1Vb7QGMJZlQeeCVgoQs5wmIm+MZd4f608xyviOAOGSEHfkJCXLOWcULVWPymtDa8hGJlZSdavSvyfEiEiMy75jVYV8d3uH1ioupwcs6wEhLeqdB+9Ga8TPqnpDXQJwl9EB9rwiN3IyljzKz61RnBsrSIRSjsU4Yyav0XkZIi+ROhA6epOSVeMWMis/rzekJEbLRiowGx8pa3RFhFpI4dLxlELE4bCgte5RRAEIA6QyS0+kfkW+Z1Ouk/ND8G4p+vNst4bRNl2tq9hZpxFPDiMbT4ZcN/B8HQPlRL5Oft8KBtyBUg3QlV9jzlR3/WcwwJzdcW2HCSb7em6aEeC1YjDlBW++/C3u3/0BD+++18enq6iU3bvpfFu73kYKX2ZnMRAZxg20tCOTOYpmZ2haJ5UjS/NzH73MmHDZNozuuiON54urYEUitpjOvpVAB6xLRCs7zpdNiPlaDr0sCaeNsRf4sFuTad/2WQz0vsVGdHUcbD2FTmHp5Z97fRK0iBB1r+DyjdAvt4Zc1h9lbqR09SL7h1S5m0EfmykeG7nw2qRHIZMJWS5oL4pAgvkChoK1PXC+Cxv6n1HqMBxAQ8pZW2nr87SmaNwqOMxwMYCgoXWNrLCoH8vxG1DyUj4/drIuz958jtsXb9yr8im3GOmJEGRAoC3hldcC4zwMkp+s30TK9PeJZ2FeHWDMfhXuqU3yU9rTFU+GgDDNJLBrEUavEnt2U8ytNl9sS/lJeFbBoHsumrs12QiSkljDjnflDf7i7T1exYbvzhXPM+E2H5CT9P746nnB3gk5TPf+k6Inu5AC4+XS8V++vEelBX/87IR/9uEFmDMGJ8cEZaw5gTRXAxciCU0bDDMArjyd3gXkQyJPtVX1ashlKkbrf2CjBLTCozak9QC2rtIdwiczT33fxvWLdIUtuzaRmnyxQXacyKAwp4GGcmFGbSLbPerzUtCUH5xsKt65pqB6B2v4XUCdfDeFqTpEHZ29FgTIdZxkDkYtO0LM4iQoaAnjoIhhBWROUdvRakOlhEQMWhdZv6L9mqJEEQiMvUhkUQKTo2/GsmaAxdjUsuPmeMR+OeHmcIMG4HT/XuauBImcdCXOts5YskRaQrcGm7Ju67KgNJaIEgRQpkg+r8tD+AbgLSLXxShRk2e3CBcg0TaLbnjrANaBeSprczQ0qNwIYZwhTUvNuNnkZrjT2bXjcVPS8O3tEafHR+x7wfNnd2gQnWln2YTGIlaMARDI9owEEDBDMYs6JGy8Q1wZIIs8GQBy59IdN9Ujfph9eVyOr2bwwN5vqWu5hnMkFKR4KkV1T5iiYtB/zelky8+orjNdT+ju4Nn32mdDjLh99RnycsD9999cPe/1u/V+zclw22d+EzkosS609i1eNm3XJNU9+kPSpodRq3ygoIhZyfRdrjui8jomZLJB8lUEcJd0t35/uZzxcLrg9vZOKntI5HIxEGdVhOYsTfr0yrboHnNvOD18VFsbBoiabc/PvD4JWsCsVUKeYBgbYBvP2kBK1gExJF1Q24ixWfbfjpBh/QgGe1vW1shEokDXuKhgN/QqodKkI9TFSA6PRbgWcm/hahHNkgasOaNrh8y5jb27kVoxYv0p7PoGGaxR1vwaOW1/WgDAcrzBi8++RF4P48ApMmZIGsfvxRsBzkdqrPo18VYUxJxeAkQpSF8UmpacwKzK1IQXVumk62OHRZW+fZ6Vi2H3QXJ8QU/2U4DROHxEXt0J64bqiswqonis07YVHA+EtyfCy0PBh0vH61fP8R/fPuDDueO/+lXEN48LfvNSDMJtari0gKkKeHpNYI87agP+8ED4F+dXmiI6ChO+F/XQgt5/154zyp3SxnumBG1ScCDyCESfyGUIJHKp+1wfTmNtIGscY8DhuKC2LukX+2hM8j5mlF3mdPRelJfA3mcB0C7V2wZixovnd9hLQesSVXCdpjLh5dRsYj2Ung/nVEDWu/VEmUL9LnOjiZd5YMt6mKIkrHInKC9r+BjM4t0joDPheLyRcmHlpZijI6CdsJ1PsnytYyty7ZwIFYxEwOl8ASiIoY4R+3Z20NhqBWuY2cx7DBkIhBwTlhxxOZ+xHo84nc9A7zjePsNlF+7Y+fLRdYWQhQXUVZZeT1a6fNnriK5AesXIaIGOUnaEEGUydABCXrDtRe4xwtvmP7s94P7jg5wZlrEB5/MF6SDVbDkK+Zt7lx4p6ugINgjYLxcAGQwZ9DhSlKp7SYDftisPrFtkFwAxHh7O4ijkBaUpMRnDeO21D+eGosx+M0CmEaXWO3qVztZ7rQJO3VHqDgCGTEqFIlF3sbs6rgMaukEn+KQuB+HXVUgCRGpryjmzSDs7wLL3+cR2li7Kk/8Nj6iYo2VRJE31ju+yCHP39HApFSEQ8s0dXn35j/Dw7jtsp4cRCbFHY4uS0/So5uTq3ycns9WClLJGPeRKiaRik0giMiHYxWXQLYwHpV8ao/JnlJA42iAIX032hAHWrrkE7a3FON6sKPsZDQk3t8+clmB6I6aEVrtHIN1GdjnvFvF3FRPEmdsvFzCiFBqBAUrwmUO2dz/z+gdFWp5+fKRhLDTFCFMCYcYHsmzmkWISREWokjCTHhCmJD00N8LsDEVp0LbrDHC1yqB4hewGyWkgP3sWkxXJg1r6RENh0eYHybylkMQrbr0reJlSZcGaeal2mA6HP3cIuH35C9y9+sUVKNCbEYCl0Q+bBSFIuzkAm3bCPna1zszaEG0quRNE7TXG7sGQInPoWprnZFETWYeo+WL5rPTq6Igp63fRABumDPqEkmnco91TsOgUbO0HmLXvByRNUpooytIzNr7Dt48Nx0jA3XN82N7iZn2Gx+0RXz3v+JOXEf/6uxsc0hMFSPB1JRocjhxECURVQMbF8ZC5rsG6ZsQQkXOUvjQsSqvWJtPMiUAxQhq9spL4GnJOUuVJwlMhiri9PaK3LpyWIOTbFMV7z0pkNX5Q7x2BJQqYUlKPRuSi9aZeyhj2CMhgt9PpIlGGvDjJFnrfMQRtHzG8plmkOkZrfdi57AxEcuXp4keQqIhyY4hIwBVPnV7NSYApLuNqkToRwi9LUTp4SmO1wc/YLxcsKWArDU3HGuS8gCEt8c/7Re4nqty2irRkTdcJwAjWWl0r20opcpq0KrBsZwXVAXvd0TpjXVft6cR+do2TNMVLwb2iU/Q1CCTduVOKbiRjlA6+rQGlNEQFGr3sAAmR8bTvYGSkCBmTkFYwA8ebZ2AwcorY9x2NA6SzbhIdE4BA8nwpCWhOJM5ViuTTcy1KsG2bOESU/Hy6XnWFQlgXAT+1VoRewa2iVmkmaALAEGDAzF467uqsFRcVGZqrYJxHStLSFzR74ASXjfl7AsJEAVBjSCMKL5dmf06xJdH5LfKopnWeqt4x1R1X3wy/54D52rLwQZWXfIfp1q4DfqPqbUJIEa8+/wqXxw94//03YOP/Efl9sBy+ASAmm2gHiFifScuwB0NmFHl4VMzAgRp8GT4ZYVFjaywYYpikGW6DmMUBaLU6PDwsCefzCbVWLKu0b2ja1sGidpIttPSeXNdi/l7EwfApE701tFqFkqFcow51pizy5rvx069PTnlmF5opYDfdhAnTjzXifBlTfCN6Mt8Td0bzlRybYXlZ0tHXDmS0xJK1GZJVP1zN0HgCWGD36UKsBlXvXWbujJxciBotCkIItrBvq8JsNmPLAKDdXqXzp1zzcPsMz37xK8S8SK26GefJqKNbNQH5/XnTK/dWrl80f14/Mzfy6pqXHCfw+rCa13N1XCkgkNJ1TVEzo7WCCBH0rh4a0biJq+XlwbGxCBoBU3WXkaeDe3LXDyZhRhlGF3F/EaT+xfEB53aLz25O+N37it9vBb95sSGGHf/0K+CQgT/7uyM6ByRq0zPLzZLKhb1SihqelEiPAQbrh5B1towcqo69VGW0dzw+PihwC8jrQVi9YDBvnkMWQGdqWEK6AcC6ZuQUYe36ny0RH7cGMKHXi1R75CQcDRaidatFPTorg2cwV8xeiHA3Ohb18oWDIU0CLeXoERPXCOSy7xE1XG2e/DiBGJo+a7lvmeYuoy4Aa5Cn6SZ9b1QDFrgCvWO5e4aYMmopCCGiXE7Ytw3L8RZlO4FrQ9HoAYEQc0btXQexwsFGV4NijcMA8eDzehylyKSRB5axDWDGw8cPCNq5+P79ezy7vUWnqN1/h9x4tQOsTFYqhmyqddceHjYCw+yfgVsiabfAYJRanfTJAC5bgTa2kF48HciQUvm8CDfn4eEj4JOtDKgKIKu1oJQdx3UFQ0FSb6jKyTND26GOVcj4KSPAGByTUpsD4cdaASKsyyLOIg8DbyncfdvEEDbRmyEGHBfC+XQGIB3F20Qkj3ECGzCgYo4F/P7MoemQEnGyXIZFDljSYM7rm/Qha/TEUv1jxt2wU1fpmqcOrTvo7O+1FBsRnBpArTk9Qe7f9MuY5kyBcLh7js8ON/j49jtcHu4nsDXSPPZMTx1eMZHznqntMgdDHRF7DpGDgFql/NjnCmHwDgNppMybTbLbeCC4s2tP01rBtleshxW1luHQ6Pqu6+Jg8al0XUdoO7h11FqwbRddHyEy65u1x8vQQz8PWf4Bs4fmMJMtpnnIM0hx/OaRh2FIjWhk+bVABATNhTGP8kDD3HrxOac2FAoQSEl36ml4K/EwCaV5iZMiltu6ehoRO426WGgKkG6brdkgKvmlzdKJeZGN6AC3imYeXsxY0opXv/o14nIQ5d52hLw6M9rzwhAPzaqxAAb3htKMtDiAm0VBxnlT4NEx1njeNFgUZ4AcD0TNAqEH85AJl72BjYxFUhaaA+G8dekQiiIGgsXwh2S8DCtuYjeY1hm1dQZXWZtGhBTExNXatOxV7pq1n0XQz9RLQYoVHxvhz843QFzwu8cbfHHzHP/9y4ybdIfQLvg//+IPuF3e408/I9Re8H15hbenG+xtRe1VQuK1yS5bT5KY1PMkaUZG4snWskmarklJcUxJBj12mzYckPJBQJcqsH3btYJGG771JiHqtEIdcURKsqe1i0InMTL3jwHHTPgnbxb8m3dH5EjYtw3v374D1wJob49ShBeybRcJ30cBVHlZcDjeAASs2kAqq5y2JoPSqkUY1GNLSfcvGNFV9kM2YfRcEc4OIyXpN0PBcuNqbrybrexzBSFwl44CMYrsJAlV75eTzA5CQEwRj6cTlrxg3zdtjU/I6wEpBITjM1wuZwVujM0qicgMEvRZhCSbYsTedJ5Z1OaStaojI5ZqzRmIhFIqIjfc3L3AXirOpwtu757jtF3AXPQ4XAMXSfsp50gxW+2SrBDOj4TErTS4Q/lcGBV8EvnU8QfyB70+4/3HM2qTtdzOD+C+4LJZdUlE5+b8KAQpENi3Heu6gCiDFCi3UmDjI0ZZtUT0LDpR2+hQahFF1bIgMM7nizt8x9sbr+IyXWFDcgFx8A7HG+z7DooRZd+x71AgKyma4ayQRxLmYZlmNH/kyKn2Ems4lYjre9YE7Noszhw2ubZUiDmvBZPdYnNMMD6HwSczsr37tBO4GeUKVhEIEAUn43b/eQwfZIbLIIWE57/4EofbF/j49lvUfZuiCUNnD2Ch3z99L+uZtmaOAK76nQjXRZwXS1dZmbJdJ5tT45Ev2Xsj3gPsMtSZseSE+3c/4HjzDLUMZ9Bs6eGwSjWbRZCDct+mNe+touy7RIzN8YLKQ2+Tkz0EQBwC/L2vT5c8k+8j5gjLU2NpN+shKvWqm3qs1lXXW/eGqUTQD7jW15vGN+GdQIe9RH+OGTPkG63ARsPzo+KJUZsMMLOhi2bNrzAuixA09SLtOS2MZ6idOUAKLhKSvuvm2UvcPH+JtAhIaa1j2wv2vWj6KSLnRRvXQTeyjUX2zWN/9NZtM6fKCiuZsT0Zd+l/l3U+labzAAAgAElEQVSY2mozriMA+rjrknE6S0tqI0OaEDeGlunKp3btedC7DNHqvSPFpKWQUUahQzgPycijivAtD2/eqIFHSy30WZEGyacHMHJmRALenwjvzxF/8YHx2xc7Yj/jvhzxh+2AS0t4rEds50cwKrjvYJDzkYQr0jW6siugNimW+1mXFTGtuLs74HTeBsmYgjdXs0gYQXhIORJCyjqITHkcibHkrP1Y2GVbyKhBDPm+Y8kR5z3gz76VbrDnbUerBS9fvkCK0hRqLxVcCwIRtssZh8MBW2l49/0fcHnYsJ0e8frzLxG0L0czRayKvtbqYX9maRQZY0TVc+OzwDRtYuGCDkbUs1PVmwaUwM0EKZiR91uzOAZAHXh8fEBcDijaUZgZMrMH0pGTAnA+nyVi0Njpw3utuFwuYOsqnLJWJXUUGtw5OzNG8O7MWOOCUqrK4+Lp0DVnEMnguBQjSmXUywXcxUu8bDuM50Ww9gp+wGEGjhBgw15ZB4NStLlJQqhFjCgKmEh1iEfyWKbTz+XAFsIfw00T9m0M7QxRy+ibDjcM0gSvawQ4cPSUNWg4Q1ZSa7pyWRcn9ZYiusj+KP1bGK0xEJLse5CW8FW5NCFG18tm8KSVhHQi3/dd9L5W9CzLoqmx6lEf0TejlH52BEMwFT90q0XHSMGOtJ6RZ7xsVftPQR0s4VeJORgOWUwjyig+IrkP6zQE/bv7tqYRdI8sEilKS/5qbQhGZ+g5XTUcTdM9UTvj5uMt3vz6tzh9fI/Hd3+Qsmq9L/vM6OsF11HDaecrZGeYRWaG2Q9ayOFjIibwpeNFbK18H5TcC6VKhCDOY44BOR905IqcY6uoA8ugz9Y6Xr56AYuZDYehSyROI3Zio0jlZEJsbsL4CkjYM//c69NE3PkC039fe/e+1LC5HxYyIyLN9ylK1b817XgXVJkPxD0IgMYyN7Q9UDL8YLpAQqM9dH1H1tKcWTgEvVbs+ybkJlPUMxpSw2RgARgs765Kc0biFAJunr9S3oo8Z2ega64/5xWtFQmlsg5wI5J8vw6JA+DrM0NwURADhdtzzztsh519ZtAEaNRoWgUIqTK2Q0KsXWEpeV+IQBLkDKT5bRva5stEuJwfsSwreq2o3SojmoIqyVuWLoa6VhkeBwUqBIIXr/Sqjckkrxpi8GeLGn0rtaO1DSkv2gAv4s8/EIDnOIQNDYvcVwhofNZVCQBF6U8RJJUg8pRg84HkSaQKqLeuXVhlYF83wwSdYgyLBF2D45izANKY9MBLtU/VwytKS+f5KF8qQshrxB2BO7hVXDZp/pRyBpGQl5kCUs5IhxWtdRzubpGXFbe94/bFS7x/+wMe7+/x8f1bHJ89x83Nre8TQ8jBh3UFSMsqg3SPLXuR0RZqbE0Ptta0mkeesZF0rJWOm+Sp0+CTrqWJViDlOZiBC1Ylo3l+ELh1BK+qAXLKEqHYN+y7AcwqpcpEqF2UXNUGU2zDNtU4G6CHKuG9SMosqbz11jxkfdkrjocVIQQ8Pp6Qcsa2nRCjlFlb9YVHafWMDKdMQY2Vk2qkpHfpYhzYuvva8EiljtaGwakYAH0+2+41k/Hvgj+YDSi1lNshr+IAsvQHCjEJV4ctTRL9/syNER6D8C86S+rER07oc4gTTEDvSEHmMNVSYYMkRe90UJM9TTlJirw1H11g+lqIrs2fg5wgOnghprkk0hnckWKMiK3ofEMzuFKKQdNOBIkYWlTCGqsZcLMouUUKyapWTH68DYM5gt37zBhnU/TtcJg90qhO3QBOw+54pZXtS3fUIbbi2Ssc757j9P4tzh/fOvAHT7atWwidMVJP4oS5XQ1+Wfti2B8IhJhkDcwllzE8bQAZs3kdToQPGplrrWLbLsiHgztrALsjZxWHxm3a9nFO/T1V03uB1BHRrzPzZADPJcQfU+36z8OWT4IWbdKum6XkNIwDDRrkKBFcQX5BUwHWD8XuqtWGECREftWoSO985neArsmAFhwBGQqWJx9Tl2fwAV8V89AYkMqhw1FC+q2Cu3gFMSbkRYafCXEP/mXX92D3F3B89hy3Lz9DzEJWk74uFi1Ifo8pLQiLpD5668qLaej95P0lbGJuiEEm3vqmWUjS1rlPHts4UGB4e/85n+llfmRpOSulFK+h1iaMbuWxMJFQjzTkDliUafCFUl4BkJJz2bkcQsSER48YQuyV3iLirbVWNaSqHTb1YAWCeHzQUtAwQG/KUrK67xW9XZTrwei3NwDt6K2g7DtCOsB4ORJmHR0azfAq3leJZqA3rEtGrQ29FjxuDegNy3pAbc1DqhJCHh5WWo9AZ8QkoMtGLdTa0HuVuScx+jr3zppms73TyjGNVqVlcSNjIVuiID0MQkBtQD3vCEFy+q/fvMaL16/x8d173L/9AdvDA168fi3PHrPsA0PKi0la79cqaRtAOlxblMTC3DLBuYEE9iGliKI+UiB4GFgMhBD9pKQ7opEClKncVRxmkYWybWochIiXcwJTUCMsB3sv1Qm9rKDAAALpGYlJCKlNS3prkeZqOScUTUut64LzeUN7rDgeFjyeTs6nalVAZi3qBUJJ0Go8XU/oibdRFVcTcc1YgdG4Y7cSOYxKDlE9E2jhASauS4ABrSsbXwqASOcb6e/OF8m/hBBR901VE2EGDfJhBZcKsPdtR14yYoq4uTmCQnT9ABauSdIW8HvtAlh0GChD0n0pJXHEetPOwYSs76il4qQRn0w6NR0MaESWIakbSQkZz03K6q1lqmlNhTbmbWrKVeSz1YYcGaU1HX3R0Dm6Q2nmbwYSDjR4cP26A3vbQ7Md8EiNAB2MtjPMLhesm2ZQbP6Op7YiGOhTwNQm43188QaHZy9w/vAO54f3V+0PrK8PMOTS+uTEeC1D1zIr6cmUkz+LOX/gQZa333UF/f4KAffv3+Hu5gbLzRHn8yZ8JU1NMsscsxS1pJo7zucLqpZAu9PMWhHMUPA9SMgDhE50hydm+1OvT0da7GIODIIbY0O1PnfDQp1knwlafmtNseCTIwGrOrFBgwPl2svP5U88jnle1/fnP8i/VtobSBdP7ombAi/puyzArDPKLoqVfF6EIykRHJYw5/HZC9y9/MVo166vQHDyV3NPSYRKBlrZPYrBT5QlVKm3mlJE2Yt7QZYrtcoje2abGePPyYMNb+WxskbXlSGsXneKQVvRmwLWe1WPwQReBgp25CCt62MgzWMWWPOueV+stNbKQq0s1g+jPjv3pr05CJYSkrMuxioESFfIQBINat1L6ChE5FXEVtZKKm2M2GXdNFz2TAGow2OjGAAAmidmQNuXA1ybhsd1ZIPKQtYy8s4da8we/rfws0UQQwrYdyDl4HvWmhz0pn06kvJlCKRVJ92jFSbLonh1am+r3oywVuk7Qr2CGDg+e4aYEt794Ttf+96bzkjJMlOnaUVAGOlOZmsxwJDqP02pKD/GgW+IEnkLsp7btmFZMqR8mZUQP9KOZhDG2RHjHaNUmlTuUibNApiNIGgkRmbrUULupajTKQZAeWWAOgHMCDngsu2Iulfns3T/zCnishWJiOi6mLwHjdqYYXnq2ZkRnT1Td2bJnKrBAVPVDAOls/EaSASqR8bvLFHpSv/q+yYjqrq2cnVuid6hNyk0sESa6u3MyBp92i4XGS7ZqlanZazHI1qPKKX6NGr1flSW5JxUlQWLXllUBQBCiri5PYKYcb7soC4diXtn5znKvsr57gwEjca73ogTgdXW3/SF6zjppEwxCTCaaAjGzhk0A40M2LRX0zu2R2RGSp531q/yXTzahihB38GK6V3dQ5cJjKzA2PPZpvH0W/mvEBPuXn+G25evcbp/j9P9W5FRv2NcrYcBh/H9cG6oFZSMiJCCpj6GPkpGQ/ThPLpD3ksKSGToJrSqJ2J83bwfRECtHYFJHR9zBIdeHLOYRpTfiAlO7J4exZ7V+g/93OvTJc/+/3EACL1oaw0BuihqBG1IGCig9u4zI4CASOwhOLmMEnAVjc613gC85e/YdDU+U3TFrZE/tfxgVQsESMmZPsec97RniSkLmi8FgE60JclfW4QDMeHZqze4e/kaISRXsKZY5lsYBDSpBLIZNbOQi9HqLgis3m5cFlgnXgvnRTUYtUr42crOABVEUh6L8mhsXYbQy8/Nw4NWbaFGIhIkLTfyuMnnykStnlDBVQCyLAmlSKjQn8+U+f9P2pv0yJYlZ2KfnXPuve4eHhFvypdjZQ1ZrK5iVbFJUSSbbJKgAFGtAVBLC0EQpIUILbQRBP0JaSFAv0DLXghQQzttGi0tJLUaULM5oMlukFSxWVNmZb3MfFNEuPsdzjEtbDjn+ntZyYYcyHwRHu73nnsGs8/MPjPT+5acpR06SeEjcSuqFWQp3o11xRCgY1yGgACKwd2irVuWAiGkHimQEqmlwFYwmVIKMhQYQWucMMAsdX5iimDSDAdIFs+8GKjQNE+1foSLktX7JFyLTsEYIM9ioS2zbqTLMjRUoZk1pgTVGumTemfU2xS7DvNsGUPWNwuiPEifM0gZx9T3OJ4EZMeuR7fZqMUnlmdKSd3DVTGbWLF9YGQ+I1BD58m8HFxYQbNkg1ha72kU8t08Z32m2rBNeGOaIUaEECQUVlgaUBYE5HmCNWaLMWKeZ/SbjTaSzKs6OUzq7V3xF9SNrv+fZqmZElLEPE++95cs9TNESJcG2FsLAANHcPBp35W3LZvQj5tuCwbQpHgTS6q7GjhGPBUD3BS27vGmcKHpz0YY+f9dpbKFNUjX1rw3zcACeYaXAY+lQHk8s1dS5rJI/aognpNpHD1LzorgBa0+TgRshwG5QPep9CKS7sV6f/1H5CewDRHTacQ0T85pMxlpBlINgdikyNrGFFfay3kO5rUrDIr9mfeE7cOqJyyUYnOvStFuDqzJt3rGnR9yNu++KkTi8XGFq6emKkQYvQFn37W3TcY1Kqoq7hCxv/8IF9f3cLi9wd3zz1CW2b0TAgr8EVYy3fat+35Yw2EghCjGunmdiEwulDMeoei4kmeUAhwPR2wvIoYuOeF4mhZ9Zm0WOs9Sl6iYrlQ8wzZ/cCPYkYkBcwAgNqd/M1Xy+dKA4te9fiZoISIR7po/bdO1aIaLbSDJygiwMvjqhENsFscxlpwxn3BDsJYRENGUS/eaEw3ChVk5zQToZ21MQHXF+aFB+9HKE5HNb9lHJsTM+pHU1ot7D7HdX8u2KAXTdHTFE6x8OOSaRVeNIT0jqguMFDBZF1QG1GvljfcAsUbavj5ZKqAacjU1Lx2ApQCQxzc9LdaqrAbAsl1Y2tFbM7aWoFyKCPFogDCahTVLGXAwYuoxTSIAc2EcjmNVTEawhhFS4YRQO6zLsrhINiEkE6VcHj1YWQ9QUuFYNJZO0VJqFfACSgYmJXl3AEuhOIl3VSEVA9DFIERJyJwvWQCZA3rWmgGFfa8zAX3qRFkD0uFce/EsGehSdCWaohI5mXFzPGp8X9IPq3uV3HSJKUgGFjOoWI+U7Dygvu/c4yZTWPd/7HqkSAg0YdMH3M0TUtfVDsmaXhpDaMChnjt10+eloB96jOPkIQ0DehKS6wVALoSyZJSixOBSlP+xuJDJebaCDbJPUcAhSiioSD0RLtZXaNK9L4AtF9nL8zS5Ze+ARM+MeCujC2cKpM019ZxrNkcoEp5L0QCbKQap3L3TjIfTOIv3gE2LtMLUB9DA/ipAW0sZBdIWw4540PPEpjThMm91Absqw68m7bi44gHnBtbPmPBfeZWhXuTGGISu4aLgtE+SJo3YKcCXtQ5JGueFqJVUY0KKEfuLjaT9M0CFsSiwN2OgfY6qW8SrutltscFWydYGGs1IrfLbDVH1rpTFLH/7nFXcBWCgCtW4tZenbJRaAK7ey7zO7HKANNRneMfmjWBzDV8PIim0hlK90NS4BridCPvp7G+CZ40rVlvAmMxj3UfWj22zv8Zmf4XxcIfTzQss48G9Um7MSF0NPc+q6Es17EpmMIsH2rxYRsAVnS0lANq9SSRlGTabATcvXgKHO82SBfK8IEVJ4V8K0Pc1G7YW67PKy74AzfmTe9IKpRhvqEJFwQjhZ3pZgL9Gl2ewlr03JUPqdkJBnzrtOhtWRChxHcOttzZEYQ+TizvyYe65V6p3NP5YWVJy1Og3A/wA2OObW8qsNUeADUhqFQLZPVThhxCx3V9hc3kPqd+4JyhrGCxoXF2Y28Vddi15V6w5zemQU1UPCqo1VdpUU7WIDaSxQhnSAKtxTJwRnhkhyCamkNyqSCRkyJwz2FzjakW133exynJoChOieVdKEYtMrdOyLLrZo2/AGuu1BonVkqkbsi6lAT1pbLf4vpAD1UwOasE6hnCizCJMXaeEPQ0/NRaICBUBD5aGDsi6zAqaWlKpMefdYoM1Fqxt7qNyssbxBICEC1LEGzHNjBhIuAKqJE7Kqh/6TntcCd+i65JXwLWXpaEG9bSBxWXcBfFGhNRp0bCooE4OdBeAw+HotTWMu0IxiXeSoZ+FW5WpS5jHE6wCNEMIrPJ38ahITx/lJy0z2M+metNUgTBbhV3r2CzPsswzYpS9lpIaBJqGnJX7ghCcMG9x+cICmFl7q5AKY+mdY2T5arQZqTvnBV1KQgbXlOeUkq+lpaNakb1lkT5IxqUTsic7YG5lyvrlyFY9I/b9CjL0CL/2W2e4Z2VA+Z/az4RWgZqMw8ooe3WEbsrK/BM8PBpCgiU4OPBNEfMs/YccBLHuyRAx5epJ1j+5oWfKyOVrtCxN8ofr+w6Hu6OXdWjhnvMb2se2Q02VQ5kL6vd0LC2XsU4Z+89mOHhvO927tk+ZSIw8s5lQ57Tl1RhIsb3q4SpfR27uX72naJ9Ngc/PIpW2mLQ+bcDm4hLb/TW4zLh7+Qyn25coWsKAKDTXZF8X6QUYHRAb3yem2OBz4eEtizXT1cGGABSRfZfXVzgdj+As4biUonjNQ0If695krW0WiDAXbdViOtTOuM2FnnvTFcxQb7QYV7USbtFQ/StowF9f6Gkx1OMdmBlajbICD6Za1rsNFZg7HL7wuqEALxnNtnNC8AXmuhtgm+OV02o3USFn8cvMXLeThllkI+hzUI3XN5IKADBs99jur7HZXQDBskZU8CuRy9zaQS16s4CtnocNvJTs0sjCNzYOq9qYc0aMpoGFIGchAIulWnVPkzd1TuF8GalUWFEuq+IOuh5FQzRFM7bqc6sLnotbtKWQs9mFTFnjlQYAxOWt3hyzZgJ5OjSBfQ3XsncdI7fFK9C9gUpkzKVo7ykVjWp25XlCCUFLqFcBIfMja9ZWVnRBqxlqFODZHpxz5S+RdYJtOTYslUw1VJJi9FjwsiySjbMwno0nJ1czoKXtJ4ACuq7DdtNLpVLLLlHGfgxRn7d6M/OiPbYgwC6RGQ8MilIP5eZOvFzb7QYvXtzgeHeLTs9PDLoXYXwI0vTeEcNmK+uXM4glK2jWAnkxBMzz6GvsREYlINtkWjqqhVgY1dVuWVJ5Eh6J6RtRWkGvW888u5yvMgK8DgmT7g1bD9t3OWd0KaFACthZeFQ63GZECsg6/k47U2fNhhPieN3T7cuGYgCk6vMzDWffPXdjt9j7DKycv1lFGDVfPr+I/vTa69gYya/DYPQxSKYQAArRydwwBcrSbiNEI16rcrXsISXLsx0gGNCQ+XcvsYaSqjVdvxNikrAzCBEW4pVx11oyqFlb+og1wKPn1h+efO8wQ8I1oVrzwqusIVRAlHYk0uQD3V+lAhJuQSgpj4V1PdwgrlO9AhhtvS8zhltPkv6xUWPrtavbva4dn3E6mUGhw/7+Y1xcP8I8HnC8eYHxcAvjTa2MeFaPi4HXXBpdUjl2stah0Y3w8Jd5CC8uLzEeD+5JDoDyysRIDyEgdUlT6KV8ifOcyCIPmgEMMYTNeDecYM2WSQCB7DHnT30+0PviOi0WAnjdl6lOvrGfAWhGhYEEuBIU60dRsiEpthx85VMAstFU67Quv2ACDwRAW2jnAlOBJtxd+LlnRfeYjsGuwMzoNhfYXFxis9u7F8HS6vyA2FftHhCXqQzTOCAJ0OZ4DOtZJJ+ZTkcHXo4iFWlmcwg0FTOhB8+roLaLyOT7361fkGZ3RJjHCKV4QzLzirho4Nokq/YREQVnD2sCSlGpEellI8IkuzxT7NTzBNY5hGxUPfHFyGxNHLslL1ZgQ76JLWPhHHGTNsyUzATdhzGCkIEQpBJnbvggACh2MI/Q6TRCCL/CNen7Dl3fo+8H7SelYZJlxlIYKYjVsOk6HE4ztltJPd1upew6QsC+C8gsRa+sUd88zTieTji8eIrTbRKuR+xAIWG/v0AIJFlqmXF3d4cuimcudR1mXtQ6VMBaFozjiBgCju4VIjy/u8OmT7i6vAADGMcDuElvlrkruDlldMMGh+MJFhooXLDk4GeOQQhxkNAvSZaOFKmDFFljlj4yCqKEz9G42vU1K/fHBDC7orcDJKtdFDBbaNOI4QJczXKFe24MQNqzhxDqGYRUJ6VsQA1YzNgBBPzF6M9kJFx/tT/WxI1GgRjIoPaj6y/6BtX/WdioIhP/1WQSrcZhXECuH3gNoCLUkgw+PiL/mQBkbsIvgACM1TPpCaZaR8SuzwDGk4TqJNNT5USRFYtJs5OCctZs9CZDVL64saN8ldCERqAUALufXmC1j6Rya32fdN9ZiEi8fqxdxU3GQX7PVuMFDt5MCRjfyKCiGVcOAEK7rq2PRJ+Om1/B7vUJFX2slo0smkarLzZrWjlCVuOslYMGQCgEDLtLDLs9Ss4Yj3cY724wH+8cBIr+ExBj3ayZrdZXRq1/Zs0VRb8Y6JXxaH+rZUbqezAzUghgtmzD5MCIwdhutyh5wcIAqw4Lmn5fO4LXxw4UkFLEJvXi6SP1apeMORcN2ZNHBV73+mLQcjbJAOqEUj3ERKSpVAyURReQQDEhKlGHS9YiRCq4VsjSqgu2Cwqgye339wx1l5p+bbJR9LVVONS/ENx0ohAwbPbY7K/Q7y7EOjdErwixNBadKTFvhojqURFZKos8L9IQrfZmYKCIIk0aMxb0uyg3NdT50odYu2Lrz9VCqQKuzkV9mSI3hVH5EIaAAbNl3PWrn5MQmChKl9vNDVY/EdWr8pqlTyThuS6KUjGZSETgwCA2XkrLU2rHV8fmy+aWkcsKnxfSVF5wQey0USRXgjIRYRwnmfdlwXbo0XcDYoqIXY++k7o5h9OMLRUc5izhoa7HBozMhHEaUaYFTAnjtGgab8Bu02EuhBdj9rUQ8qrstd3FDrnvBchoSCZFqSI5j0dRqyXj8uoK0zRJT5soYYwlM3ianchbsqSjppiMnILN7kLrZUDXMWpBfZnXAkJh4bVY1WjLLMtzRoiMxaWVdglWQZkt7An1TGVGpPo+UfUY1v0r361VNgFGgWgf27m2d6keWN0fIvCgBozGtkPQOi2sZ1qBE9jX2Aq62d4xy1XmjrTWjsX9yf9exyMP0ZIp0Y7YZMnZn1kND27OcOXGtBsWZ1+sZ4vOPtCed/t/e86t0KEDK1trlU9WtVrOV6hggExB6tOy8QirnGQWL2hQi1dAgpR1L9DQah0gvNCiDY4InLW1QwjotB5Q0eJwpTGQbO6NjAqo+D2DCT5lTQIFodaZscQAAFpZvQJeB84wD65Pvq+fh7iZxOsNM96MCN7wCG1Zfa/VuWizoUw+yaB0367k91q2rp8/vLLm/hn9eggJ2/01dvtr4ZqdDhjvbnA63ABFmkt694OGM9juO+cm6dhZLEQAalAQ1UbGkN5n5PdX0Anx8IOkBlrQ57I6LtIyhSRsHIKHnkEij5Zlll5g84IQm1Y5WauYf87rr9EwUcRgnWxbOFV4Op8tX0J4ARYvrrHIYmEk/VLUU926xbzxn96sgUUOkgJZF2PUSQatNqEpOmJCNwzoNhcYdnt0my1gaN03kjjAatiobpLCuR4Q3dxktR00Bs8oyEwIVIuSoRHcZlER4F4WBjzjqmW9l5bbgjqcYpvPpVUVRvI9SCqkrQnXQ17XLsBmtGZG6ZzrM5dSy327275KZLl7oPqcPoRq5Yag7mmCv9cqBSc4GgLRexHEMoqNBWlLCbRh7+oSJYJmtQQN/S1glmZrVmisiwH9tsdmc4XUS7fwIUma7O1J0qZ5mfAcJBU9mZCnWSwIrYxMWnUWEKERSUIr4o2JrvyEmKmh0rzgeDwAISKlDvvdBuN4wjyNCETYbDa4ub3D3e0NclGy86j9XEisLqnZI2CvV8DiNX2Cedhkv2QEpCHVtSjZC+tRqfwVCgGxS77PSqn1g0CSrRZDRCHt3m5Cum6k+nsDWOp+Nbc9Ye2jPVP8jZUXfD1tv8kzlCYVd/1Z2UNZAYt5RSuIkKKHANZKfG3m+FGvLz57g0yUvOY8NWM+vzQABD+tqxej4QU0E2sjaoHguQo33hy1awCpGh2j7H/oXhW5QSCv5KWGFkyZr41Gu59hsKDZnQtbawf5OYbgYN8G4eFxTbOd51mL1FlYlgD3AKlhSY3yVtnWPi+jeAV2O/wmT4hqQoWPn0VfWVKHWqKNzjlfQws/Bd9bMrctkG1nv/2ZsAYedS3Zr6tvBj2DtjmaLebE3kYPNENZrfFqJCpHQ4wSKbi4xDXEqz8d7jCdbjGfTk05EqMjrJ0AdgBWJRyINGyuMo3ZieFtWMoSUAxgdF0nxoQmE0ANYVKuS9RkCjFMyMdlEQ5ZWy2jcH7YmtdfLzzkE1kzfoLWpTg/zBYvAwBYaIkgVTFjk/qp7sMCaOaBTFqKFk6Sg2DfsfLAADwcBIuvEZQEyl4qv9teYNjskIbtKsRQBUNDnmIgG8hgFrRq1qQqh5IlBS2GgDmzJSuBmJGxPlA2H6LY26yfulFai6WYGw1147SvzI0wQ3uR9VpZCKyBeavPeJpfKyyIEIK5c83KQHMvduvWXIJ+kVZK2zq3DwnyDSJW6+wAACAASURBVI52/A0WisGAqHw3eey9jtEFjjbONIvew1eQAy8N7KRuyK4DdvsrLLngdpwRmLHrI57fSZz2phCok6rIQ99hXjqtGiqgMXUB3XajBe3yK16u0zR7erh7iGAF3cTrFqKkSAv6XfD8+Qsww9vYT8sJRAnTommYSoy1RnGy5ycX+pkJoUifRoKkwDMzmMyzCHedZ6tFBHYivfdRClJHSHruGLHRhKbdd/b1tTPfhuR85dm3kQttUgXSegxk/mrFXTdHPByie5TZ+UvMWtVVz6a0/WBXfm3xMlLrLVJA5rwCD7Qa4LkaMrPIVRFe9zpXJOtrtL/a4bbzV2sqyTEg6frMkt5NWifHPm9BUzamaHsP0uvZe6aMVfAsWYyqISUvmxBifSZWbFWNpHbqxXAzQNWGxiJJ6wCRbeJ1u7s7VOMUWsdHS/5LfSNuiNbCF3GFCHgtHlm/qiyj9s4BgBQ7J8ZL4UUxqPokMoJZ9qRY6tpWokjl32hApNkfDPYS9zJ9DQm5Wb5WVpmSruhc5rF6iZqZZGi4s24+1tCqZQ4Zd8++b9lQskcqkA6kldxRic+S9ai1sUw2mozVMQzbHYbNDqDHABeMxwPm0xHT6Q7LdBJuUVDnAYz/prSMWJG3dWiH6UjYPwKCmQumMXvdNQc0ACgQur5zo8TWzXqidamThrDZ+iMRuthXpwO0cvznvL6QiGuWVylFyJZU+wc5GhUU44OsO8A2J0vqGMnEWljHNLwJV0mXrOEja6NtLv9zgAQixNihG7ZI/YA0bND1gyrZyvauilOukQsjmKBQb47VBmnlhJEuvU5IkAqMKUo9FE9X1oNlG3Ad612Pu1Xcr3MXfh7CPBNfr7zp9yCzENafprPvtbF0nyi8aj2YUA8a4nv9yKAk3DNrwH8jtzqBylMxC8LCGUbCBYzfVA9xa52GYBwftaTdVBE35aZPYBBuDqOENuYZsUt4epgxI4IQcH3R40v7gBSA7316xC+8tcdHLyc8uZ1UiQcFBdIwUjwSRWvlkJLNZLyz9VgyglnqZK6YcTodpAuvlTwHeRVdgJzj5bNRim+SOp/QkumLW2akWU3M6s4ngEuTvu9zWLPaLHMou+DWmV2ZhTLfpT1pVHdTxbKVfLg68ravGzwLAH2XXJ4AtRgiFKQUroaLECvlDKVIatOQ30A4HXk1nlIKNkOnJcXJHAv+L9qfm1fryTt/uaG2ljrNd16zz88vRg5DZI+ThKvyPFfeRnsd1rOgiLANn6xeQYC+ebRNJFuGBohNB8KMF6tuHnQ9ay+yulTtTvBnQjtvFWDZHgdbSMHaBsh+zdnSXKuCbe9jChkKkgzQSIVeGTOpQg9EiJASBVHDFj6koCFi8spX6gkQboQBXDMe7WUNL+2+3nuq2YeAll1AY0zqfjIZYHIq6HNY9ipghRJt/ti/LrWHasE4D5uilY9KM9Dvts/WmsFtqFHWXueXAjY78cLEKOOaT0dM4xF5PGEajwafhMAOSB0eB/eNQUHrHcKgJvuIVplzpWRtoRJVrqvXhiTsmLO0fwkaCw5EjgkMxHzemQS+KOUZdWHMq1IfoIIaV3qNYLHDYiln0A1enE6ngpmLD9QPhqFcJ27KosfYIfU9Ur/BsNmh32wdLLX54KDqxgJVq8zGbVa9c1m4WXwVugaizF1lZe6NIMswIqEeP71XaQEZbIOtn6udxnMc0C6WW7Gft0CNslhbgj9jxV97gVZcnQlit3rrfHRJwj/tAPxsNddpn8UrNqKp7Kgb1km5ZGnyQFNg25WEW4BEbuUxs1dt7JK4rKfMuDuNiDFgEzr0UToR39/1eHTR4WuPLrCnEd//7IQPbzMyM/7FT28xabHVkrN0KFUPWA5ixS1q0dX9UbSqq+ypXMQjUxbAwi4p9gpMqsdQQju2D3VeXQZpE0EGLGuGYCEz+a8YwOcaFnXw0Wwo66BawXsLROx+7XqbQIJ/z/gBqz1B7fdNyKMaIM1WCoFwvd+h63vMWuzNODJF67igMFJIajlnlMyVF2UFFhtPRVACe/XY6GebbsdGUm/H8wpgAdZ7v5HLrawz5exAqJkf3+uCChqZKDKh9k+rY3A+DlCfL1rmU+25Ju0filayFbkptZFUKev+EH5KdDkgeos8tFJTvCXJwWp3RK5cmJZrUp0/62wWe1JfdQOgAKjIepqbH1z7dhk59FwsRR2wraM/QKjhGhMzUnNI5mzONWSfF2mAGgkoCD5Xm6ETWc2MaZSCg8xwT07U7BcLjUo2n96DNAynQGcF5chSyeFKV7iMWhm59djDiL8VGFIgJ8kaQFk8aaMFT0JkjV4OwuqgVU5ia5iapnQQ2sx1+6m02Ur0wQ9FwTSdMJ+OyPOIZZ6wzCMcBevjV+ObYeU5bD9UwGWgSauH26hM9hMhaKd5QhF+a8OzsggLaD3+89fP9rQAILLDF1QIVoazCeHmhMui6aw52dMhFPthr65m2dTm1QgxIaYeIXVI/SCFtLoBsevQVkUlXTho2Ih9kwSNYROkSFxF86Ws23t7TJTa5VegwSQplZrBQ1qGHarYogIZezbPVmkk9srrxIaAK1HMnqO1PeoX2oWrdMHV31fapAUd7N97/d/PX5XBvnKX6mYzgto0Tuh76WqcNKujKkJaPXs7PtaHqbn7FY25YlRVsCjXiKjAKvd4kS0Sl24QaYf9psO4CAwupWDMWskXjN/9uUf48PkBqe/xVt/j8YZRQsL/8ucv8RdPbhFjxDRnXG+iNujTGdN9JPUHakVjKJHWlIeThZUXkln2MbjWfSGqKeJADc8FVW7W+dfCrqQKsvKaZX9kroCEUYVixQ02z03hL2oEGFpQ9Jqt8boXGZONVlumNSza7d2+upTQ9x36vkeICRlADpJVENWqIs6SfcgFCxcs2RSAXG3oEwIF9Nr00SCs3X/bdd7ZOUCytxh6bbB7mQh1Xols7ap+rHvVwlVy/RClAvDQd27Z3x2OanXrni2N4iDAeUghvmLLeTxflVjfywRKKJG8Iqx5l+d51pR/4HgaMfS9W9jWfTsFOS+W0ku65iHWbuQMODk8pejGg+23UMwYqc9loMmBhAKw83CIhRU16U4N26YjuhmLkDkxDo1lrAHiiYgpOTerUSW4HRfsepn7w2lGig01IQDjUnAxbGAct6VIfRoQSRYbERIBKW40PCogsNesmBgDUqplPcQLKWGRLkUUjpo4YRXNtZKy6sLQhNm6Hl5zhOxZSPbkuiy98YrMMAF6LzchdZ9Ok4aJS5Yoh3r6zdsrzVc1/GZzrNfy47kCoro3Ys1arUYMYdhcYNhcOCBjBpZpApcZ8+mIeZ6RZ+lCvywTTF+028GjJAo2jctWWMLRFjqS8JTk+xHJ/pY2JXGVUfx5YVrgr1FcrhRtCscSG3Rzw67O7Ato3oeikrcVCtHjnRGgiBAjYhROQUgJMXXeAdmEuB8MvYoRCy3DxV5GRJT0UO0yasJKN0cIwoKXNDkdO6qFaGEpD/eopUNQsp/W1RCApJlOCMAqzESSeknmgYESfrGS8OTssmol+x9fXQUYo7z+7k+OOsUtQFoL+AqCyH/mlcerAqmVVaj/SjG1WbIHiHD/aoe74xHzmGs8Fs296snxf1rLrw7VVZHuKde2sHiSfd6EnbVO71PA3axhOLUKLvqIqQDjnJGo4Be/dA/XYcI//N4t/o+XC+5fXWC3GXB7msEQxXi7cONNhKe8Vj5NkDL09mil3ZfcWJytxbF+vXoApU7GqiCXCpGWmMir69tHbX7lS+1qnbuH12N4DU557Zv1T/Uq9UOvWkBNBF8VXOp6sXIBb3oYKCIvM+Z58UwQaWUPEEXnq5BoeBfAKSXxQrCBV/nDvGSkxFiyKBALWYdm08i82RlhcDG50O5zap5E/w2S2mvNAueloB8S5vGAfthCu1G4fKvnWwo0cs4OEILWYTKviMiv4OemaLZOUtBCZN7GDiklTLmAYkJI0cNBy5KRRJAIcLK9qnLTwooxiOeBAyT1VkQJfIOQcRGsKJnKhqZITfEsojaJgEHKL4sxOA/NDFRbpyUXdFFSXGuWEhxkiNfVFKyA/Xmpnuq/9ZUH+Nabl4iB8D//4Y/x6e2IIQWMM2OTAr72YIfvfXILBvDgYsC+j7gdM0h7rrUVtCUTDUi214gw5SKlCmaZnD6ITICuE2fJpvQQT2M6RoQV+AqaVu7hHhZ6qnk0pnnxtiy22QwIRt1jAULvTLrWCdoZPkbvXG46bZ4XhES+h7yPn15XgE3x8dp9pR1MrokyqA4A9xAxo+t7MHfot3t5PvWOUpDCl8s8aW2qGWWekbMkLpQsneuthxKRtZ9B9Rouclaj9qArLIR7G2eMEUtpNfz69TNBSwgBu8tr7TfhxwIxdd4eHEROwCIlD1lVUplkc7nZYhnKkwktykY3N5ll5Nj8mevSesCYC87c48HIvHaoViM1xajQlqxnkpbNVw6KVFev5fRbCFmKlS0H6hatCLYV74Kqaw0CycpRQdkoJ9E77J8T8NKkoK1UzlpLtHdeARX7NOno7Hy1YECeyOfFv6zjquTkCjZknoFSyJv+jeMJx8MBqdvAvVRkYGj9/1XMXC9dPfIiRQ2MSKZKa02pS1HdsQCEr0LmkYG6YwEmwoOLHj98esDbV1t87X4P4hk3d0f8zne/iu9/eovf/+FTHKesJDQVGO302r5kITSaBap2kQOoNdBcL0EFGRWIeujS39Hd0ijO+h29AcF7A8l3m/utUEL7ZsGrAOk1rxbUGEbiNSBpR2xgyACuf7X5QrC0SVSFx1wV1DROkp5dWOSJGkOxSS8l0vovMYIg3rxeGzhaUTvjFDEXdBGYphkaUJPRafiOtUKnPBv7WEAin15Bazr/DmAhGTLD0KNgwul4wrC5EIE6TlW2NHMQTT4BSDF4JVKGEs5tPnRMkmQggKVPCaN68VIXcX+f0AXCto84zRnjUvDZ3azVhgnUyaKlqIUEC7vn08IP0jFcM3hCQFmKt/0Q4qxy81TgWqVSmNyFcFMMZImlLJ9PXUCXpLicJVe0tlfOBaRdqguEm+S7XfdEYWmvMM4LroaEhQugrSymXPBL793DkKSH3X/wN9/B9z65wxsXPX7y8oS3rzb46v0ef/TRHh8+PyIQ8DvfeAP/0+//CJ/ejbo2siadempN7BSW8M8b+x4XfcLlJqEw449//Bxp0ea+WmqAmg7HbfHJqF6uQMLBWRYpizHPjOM0y/uhhrAJhKzFSCVAIGu96RKmzOgjYVqkRQpYqlGnIF3VZY+oN0plNFFygJK1BUCXElj1TmjPJlVeW2FtVKoSxT7X0hj8nFhVcSL1sg9ACAj9gH7YwPqUCYiFni04uTcvQuYvWZrQStbj4p4hyfQ0XVwADenO84x+2OLzXnSeqdK+3v/Gd/g/++/+nleDDUGaC5pVaGmXjZxFVYKyRdqaDbZQrsC4jQNWcqbFU9G432AHnageTj0sZbHOrwYsqE4gw3P5fZFcOfsd/frV0rUulesQSPv5isMahc3wvwnoQn3DwYlo7uLeqEaEni2Hu/haLaOb1wlbbPFkqmDJ51kVIJpN+QoQkv8TCC7Pqc6JD58F8oT64KjT3WSS6f/kkSvvwK5hLkT72S4TY3VfL4q8+y5h00tX5XGx9gsidH776w/x4bMDntycMGbgP//VL+EiLPjhRz/B43e/jNT1KFmUWgHjok/47//hn6P3suP1VYr1olHmvLqF2NznSlQDVwBarDiTo4Bmzlj3HtrY00quvwLw/FrrD+jfeQWmV++/upiv++Vf4VVBlQO0MxdLBapBPRfr2hmgaiGSKra+k3BrLlzn34wJ/X5KUQtfsSsPqIe35IJsXhWwEI81M8Hd0rbXVT5VYWyAREMUITrgFTlS3enmWt/vemy3GxzngmefPEFM/RmxVDvshqDVPQV0xSgKnYLVnqh7mwLhOEkX7hgI71wNuJ0yPn5xwn/zm+9j6Dpo/XdILScSyzXP6FLAXz5d8A/+7AnGhXFcCvoU3PvUR/Iu8ybralFKeLExO35EpNy0qsxLI28IWmFZ5a40M5VNvGjIyb43Gf+DgV0f8etfe4gPHl3gapB5yIVxmjNOc8Yf/ug5Igkge/t6g3fvbbBkxt2U8fI4Y+gi3r4csOkF9KRImDPjNM7usUkxIAVtcqrPFYjQRQlnG1gqzPj+pwd8dpzxw6cHfPfde7jeJFxEwjhLRfIuKqgqGcc54/lpAbot/t8nt/jR8yPGJXvF14tevB0fPLrAW5c9/vLTO1z0Ed999xrPD7M80z7guBB+/GLC3//DD7HpIu7vOvwXv/H+yny0hWHlFkVdq2ku6LuA05Tx9G7GXAo+fjHiZpxxO8m++dOPXuLRfsC8ZHx8M6GLhO+8fYV//tELzE1CyLmzAIEETFE162UM8K7rngXk36vvRWIMXcRx1qrYTeavZO+Sn89Sioew7IIVTKnkUrBdTBbo3965t8V//bvf+gNm/tfPpdMXZA/ZT5UBLRUlJdYvB6t6IWTDW4ovVYXMjeJntbYDIWhMKy8VLLApDl4vrcGCigZRMwhiW0RJLSn3xBBgaJegdS5qPNYEnB1yB1g6DvP0vI67YS5rL7JUWIsRyd/d8UPVRl2yCK5SlL3uwM3QDvzaejcZZzj7vRlHDWeVWvBrdTTsO5+zzs1npJCUcJdsxolIiwNZiWXznLWa9fweBOs8C1MGMIBrQMjSlHUfFQk1xiSN264uBrHEMpCXCpwZkr78S29f4K8+ucW/9Q3hr4TpJX5yN+H+m+8qeVoUHgEILMqiFAbiGtiCAau9gwZgWZM/QNeZWd2q6uokWRjbP6s5tekpAcasFRxUz4GVEK8C4rULIz9qVskapoibvooYXl9jdYaqsKj/UuNdsyWU32Oo3IRmyA1Ag34farlHAOoS1o1vnsYYAoYuYZpG9JuNeCpsqM1ZskadVlq+HwacRrhAZGg2Iknqqtwm+npZ+XAysGmVF8jGKovi/aUoOQA2kC2tGuT5Ly52uBtn5KVg2GwBit7/K+cigGLK6JIWitQKsKTPG4KAiFyAuTCWIsTt3/7gIT56ccRPbhbc30T82998hGMmjAx0ZF4ZbWIahEuTc8ace7z7YI/f+40LEAF/9KMX+Oc/uZGwERg3Y8aSGdOSse0lQ+3lYX4F2EUFWdOSMXTyuZwruK5yBZjtdzJDk13ey76Xffk3Hl/izf2ACwUijy8HdMlABbDtA/Z9QIoDvvZo1+wrqS4OIrzBjIAtYgqY5uJcOnsN+w2Ox1EoBe65anZ4ADgIWLFIQCkFX3u0w9eJ8CvvXYOhZQksy6kI6f5wWrBQRJcSHl8PSDHhK/ceCRlf99+UC/oY0MXg2V1ff7hDTALKHuw6AYKl4GIT8Dc2Hf6r3/kqIhGGGDBnIdaap6eOX8Iv41ykOWVhHCdpg3G9iSAkvLUf0PfRn/d3fu4hdn3CTz+7xV+9mPDocoMlF/z+D54JkPWzUs1s9rNW9Vlx462e6xVfE2rOUvWOHcYZAUDXRSxQgnMgdEkKyMWkHeBZZHzJE7p+wNJ0/a48qkaXNvrM+Fyve31hnZYYAzqS/HBzbU3zJMSumJCXxcuGm4WflChkE+LCDhJqiKqkDdVbaXYA2vDN0BnDiGfyuaYvDleVWVzokFqzZj2YpejbWgvI6eQwgakCFhmnulWLZQs0gEIX1mLs7ONrFFujNVqrGoArAS4Fmz7pwYyrzevIV3/12irQNNvmkFbPkG3CahmZ5e6Khepc1DTKBojp5xWWwNLxlBXk7cxBFttmrSNQL/OKAozKWmkki6N43bQLF1AR4Gf9hIiAq/0Gp7mIlRO1R48Ky6wH43/8x99H33f4gx89x699+QEO4x0ePH5Lm+c1qp2BLhL+2YcvMKTgz1+pmmeQQ+eUNMXfHsjIiplZ06ANXDaAW+fL5qVWyrSwgI6plnCt1lAjYSrGb0GG3YtfcaK85inOQBA3bxs4a65v24GqsBeOp/GzqjfE5ihFE6LqBrdsP6rPZXNyOEoF4Pn2oC7vKoAZcqaIRKAn7ZzOFNANgyjdXOCtQpgRtWBVCNXS8/uRoO+g4zJQnqIhSS3WCGi2jgp3dWuHSLjYDjhO2tAySvfiomnrUfkq4i3RsDhJk8AY5ZyNS0HJQJ8ImySZa+/f3+JiiPilt/ZAjPjpzRElA5sYcG/XuVKKIWCcpNZMCoQHDx76GT4cZ3RdRBcDfvdbj/G3v/YA41Sw2yQ8P8zIzPj0ZsTDix4xEH768oRxKTjOBS9PM3Jh3I4ZxyXj8b7Hnz25xc0po092ZnSdfa+yF+eDTS1JraBtn7AfEr7zzhV+64OHdgrArKRUIpBW8M3qocklO0J3volZDlCZlpVIne3v8pWbmzvc3B3x5hsPwMyY1VvFrITPGNAlARRLLliykG3HpeqiFIMDmr5PiKFrxJPtI5ZwDxh9Cn6PLWqfpSVraGspCIuUwViUSBuD9e8C7m07fS44LmCIZ8rG4SUJ9NzXlOkKrpZccLpbXN+VvOB4d8Tz5zf4pS+/DWLGy3HBW5c9nh1nSS6olgEY6oljriFyIgc4WSsXG7C3ooC2nuZtEc5ZByLGOAmnJUYBVhXnkKdYM4AQO2muKMipyiFv7sju8TQ936f/H2X8ASlaZN4W61wbo3SKBEH6nYA0RCGCIAVL8TSrVR+HmtQmfQYRPtUCN2a+ITKpoicI1kCJeHtaMa3uqWBgp/JhoBvF3PahUbYOfhQIzaX2QDHVXour6Zi59RZUi3H1M7QSp97AgQQxwEqGIkIM1ndFrMRgG17VkPN0qA6A7H8WktGHkewEVKuJaspla5U40iYzn+T7q1LUzbyai9kUQ5eEKCVARyxfQ+LmeQBXUJVSFC+Njima1cesG16EZB9JqioScDtmt3iFoJd9Tbcp4G6SfXgaC04LsOkilkPBMs2gYfDru6onwvW2cy+APTY3e9HBrCp1awVb+S06/mDNxup+tTVow62rsKFKKw9XKHHUu2W/8noNCKlXq5uAm5AS6s+M9n1/NLSdLKWuWw0nEJniEGuri1boj90rBmhIhEzAy/iXnMXjguhVnW1z5qLdq7Wsd4xRhXoliztRECLEC5dmn0BDMEULgukYEACI1zeokF2y9EUhIlCq3sigFi6gXrJQOxMbtwSQZ04xYugTDpOEM1jnrU8dtn30UCETYeiTe99uxwU8MbZ9xDtXG/zN967x/v0ttl3EtgtIUebk2e2MXQLevkyYcsI4izKycJalgtuYqRTMRWQwKSGylsQn9J0o4vs76YD+1tXgivCNywElm2eK6v4DQGVBzxn/+Ec3GGerzg2cloIhVfPLvmFcmCkDv/fr7+Fq02E/RGw74aAsUgUTWcEG5yYEaLLWFDe3JHL7kIUt1MBTWR0C4eXtETFGvPPmQ5kvthpgIp8JVruJMc3Sd82UJkHCRrkw5jm7jBXgpPuDjKYgTxxjDVu359f0m43LXl3jFbJrlMI1MYPwahNOTQox3TY7y8FUv5xvM+6TpqoXAkI3oBTG++/tkJeM3Sbhzb7Df/rL7+KTY8GTmxP+xU9eagd0qQD+o2dHAITvvHMNEDAtBX/+0xtEBccxWV20Svuwap5S3VomQAwHUo9XhPEebdyTVhO3xBtpg0NaAqJg0d8j1R5l1mw3s5CcZ6UHvO71BdlD8t+8WMEmRbUQxStKSpSreBECiFiRbgZKFYi2MdtImqkHQ5jmIpL6FMU3sAi+tpJhrVvQEuFq+Xu5a2WsrxWTIMrGqd5oh7awj//LqOOm+i/A6LuobkYGN2XRQwhehwAlI3adpNOVBYW0I66GljZDwDjPMH5ASkmFb40xS8E6cUMHIk+DNda4pWWapZCiCLh5WWDdUM0jFRpl4+vcWFK2LjEGraaqbj0IAB3nCVwKUtcjRPJnMVBj1zSUngJht9t48SVAgFog8iJD0qmaccyMOTM2iZCSKKJ9H/BvfvAQ7z+4wO044X/73gv8xZNbfP3BHseF8d5VjwcXHWj/BroUMY4HTHNG7Adw6BDA+Cc/eIY/+MFTsKasCghtw3KNCFVBAbAXHKwgl32vVo9efdn+hX1edpz9anAI7EWc5ArqaHcPDBFptVSRnJpNvRqjEebswk318Qps/eMtN4ocQFfFVGtKSFMz8SCkGLwXlYGsPkTNApi1yKL0mSGScAhIXOi5SDn3QJVQWFiEdK+fdUI9qsUZgwIJ0jRQzabA0ONiiIiB8PRuajwBaowQobPmcZB9x6VgXoSvJIVUg5/sEDp0XSXLXm87fOutS7x9tQEgWWjvXvXYhAKEDs9uR9wsjB88O+KfffQCDOBmyohUcJwy/tt/95tICiCadlxe9XlWxX7vImn12g6bJAUyWc+lABJGlxQ6k4Qkky5zYOPeiMJz+VBYeCUMWAsR8zguyyJzuVSlcjxOCCniF7/6GL/6jbddBtt4P3x+xP/6pz8RTwIRtl3Er33lPt66HPDu9UbSi9WrdBitknPzvFzJwW24yXZb3c3VMyzguMpX8XoXnBbG0HfyLGogQ2WJ7R+o/GC9T4wBYqxLCvE8SZq4jE2+LxVmLWTD6smXOc9SS1XmpTlMUb3p1UA1aWmylLFkeBkOI/EWht/H6AkxAOOUMS/yn3iJRIYGBbgpamG9YJ7ves82jVr+T7jeD7jeA199uMHf/uC+JotVPiczcHucMSlIffCr7+G0FDw/znh+mPHyuAj4BuPPntzhdsq4GxdsuiAczBjAygFzCejyUUJpVLgSkFVmUCBsUsI4zYiRlcuSHRSJOhe6RC4F01ILtJ6//hq9h6DWjfZzMBHGFqNffRoAVi3oz61IbiYY3nywXfT297oobfraijALYD0KOyCt8pT3W08D+BxQtcM4t1Kra0zsHegJtJituAdjABACMvRA2wBSABfpoRJTH7kCFwAAIABJREFUhzxnj6N3XcI0L1KfRtNEi/KGJJQkHopgTRehVR9NYNE67GBEQCKpodNFqVooKLfm+vs01xnzw2A4b+iTFo6ST1g6eIipEVDSKdkVnh4kWz07V9NSsOjct/VdYiAsc23C93DX49tvXuAfff85xrkgM+PvfudtAIRpWTCPJ/ydr10hIuM3vnIP79zfaR8dcdfnwhg2O2w2ojDH+QB0W9zfJsxqCdrzOs+pASXynM2BaTvmqiA1S/usi8erL99vBsLP/uT7jWCeNWo2ZNNnsAIRv4DBH1S+U1g/R8Xq+n/ylZbwHOpZsmZ7MQowNl5RShGH04iUEsDFa9pMi3rOgqUnyj7cdhF/5+ffwraP+JMPX+Czuxm5FJyWgikzOrXwApGQGnWot1PG+w+2yIVxc5pxtenw8jQrR0N60Pw7334Tb+x7BCL8yYfPlXQp+/uz2wmfHWY83vfY9QnHecEf/fgFQAGxk/ldtFjXRR/BFHDRi8fnS/d3uNwkPLro8d69bQ1bkyi8U47YRgCl4O3LAd94vMfPPdxhYcYf//gFnh4XvP/ugJspo2+sYuj8mgJkAF3SMK16AgnkynXJQmSMqMqYAQQrlMfwazIDi3KuWs4B+d4Qz83d3RFEluq64HCcME6zdBrvN4gxCKhrdlcKhF0X8Le+/ABvXYuxse8iHu2lQOC4yHgs/6GoNW+gHjp3HnprPJ42/vYcOAmTsJLxnsqunwtqQOVSi/Otzi8JEy8EckDFAPouYPKebvBkAyvp7+tR6mmRy4qsDRzWZ5eM/2ifrl5YM4SCD05BVBDibynGtdTxBgHmzotiIKiXx50dqjUtagEtbFdKNTkY4tGUau/kpF4ReTK4rPK7SzXp5flhRt9FvLEf8Nb1BjkXHE8Sdvu1L9/HZ7dH/NMfvcQ//dFzdDECBQJgihn4VhMJyFl0kjkwrPhe6sXbsiyLe14iAcsiKfECVABwceN5rZjXr59NxA0kKX+FxV1Tgru4mKT/5+uc2NSgT9tyRNXae+Ubbu3ar/a5JvNI3aGOlGyjofkqvWIwu+dgPT4jD68/3z7AOmMI9cPcKC+9IVESS8pqUoQo/YnIFL24sWOUTbrdJOUERecKGZO/hm7kAQJJ8z+vKNuAayPICWtfNktEkngia0dYc+UF1oZqWUIwjYAR1C9jGPqk3hxJ2czqYjUSFcw1CtLDFXwMIsgsRCiDtPsUOzxULQYKVhhJFOdxKeinjI9eHAEA33zzEt966xLvPrgAgXF7d0Dst5gK4zff34O7Dp++uMPQJ5zGGfsLEbDLknE83Om8AjFt8cGjPX7xvRP++MMXr2zAc15HCMnBDYcmW6gUMSYiSXNKxqv7384co/k7u+BeEc4dYHNz+yqI/HtG7m6vYQKNGrHmW3atFPz86biKKRaQpq1Klkvfd9KZNQRMS0Eh4DhnXGwHsagnxlvXougiict9XsSl++X7G1ztBlxvEt6+2oABPPq5HoBwgF6eFhymBdsu4aMXBwwp4vHlBoMK0I+eH/H1x3sQgMMkHIs7zZSQGhYFD/YbVzS/+cEjrVos8fcUCdM0S00TncIvXW/w5HbE46stUiB878kNHl9t8LWHF3h6e8CD3QAOEZtk4WdtWKpnK8KyRYD9tsN20zkB9yuPdkgx4P17W4xLQQTjctepR5h8fRlihBSTY1SBvG0By6iw5g5QMZfzOoRioERaNjC6QA5s7Wy68igF46TeKBYy8DQDC3fY37vUrKaGp6ADYiJsE3Bvt8WX7m/1TItsmQrXUgOFQck4dHp/95LApKMdifUR8TeaECtXMGZbP5fsZ69LSQAOawG35soGIjzzCRpiVI9GzprpRLKuQeewTbyw61TRW0ft1Wv1e9ags57fCp4Ay1CV8RV9rmWxTDXh6hi1QUp5KKcLpIlywetRMReXnVENw9b4NoPKvUw6L/L8Ng54xhBQAbVEB0QGn2ZGKvqZKJmafcy4v0n4u7/wFh5dbnE3Z3x2O+IvfnqDPgUwSyHXU14UDErNnqEXcvyySP2YJa+Zg1YjJkUpi7Is8mzOEYPUqvm8189Mef7KN7/Lv/c//H3ttaNE3KZ53LxkT3mqGRTsAySQu9llkiuIaA/ja6P3/tbrN1VrqjbbZ3Wt9T3wyvt89rPco+4+G7ptdlMYVa3oBtKCTXJw4EQwUxR9V70krZAgqkTBNgvJNudaMdnhqcIQYO03wz43dl37lFkbDkxAWPQQ2WS0UNIEZZeCX9B4RbYGNVbsS1Dn6xUh0EghU56lFj2qQIzxpesBxMLSZwrITPjuO1f49uMthr6T0M+0wMjPN4cDhu0ePE8YOlmXeVkQQsQ4jq4c5nlB6nvcloS/9//80MFTO4bmHw+nGeiw1L3o1rM9g/zSniELD4qFXRt6mpVn/JcaDq8eORVjDnx8pzWKbRXO8sOAVWqhARh3u+v3YyB0XULXd0KEDbUYVWHG3bjgS/d3+ODRDt955xr7IWLXR3x2OwNEUnwrFry4PYLSIC57NutO4t4IAfMs8Wzos19skgpNnyaj9Oj82r8yN2YJEgjjvMieJAEv20EAZc5Fi6fJLI2TdNy29YkByEVDTiyAvU/RLcHCIsiNuOmWMpGHoouFCIKRChnHk3Aihj6qd/Vs79TjUF8tuGWs9guac2scDcA8gAZCalpvq7DEkHBpVQmcup7SMkL+umTheMxzxvObI66vL3DvwVU9u6XgdBix2w7Y77TAWqlpuObdsBCvhWqirqttx9ozDD6H1RitY/c935wJM178M43sbcNPMt8WPqvzXo8zaU0g1LVsAIWNzsEFrw0BEHn9prUH6+w+Js+4Pv9q2X0drNZV/YCH3NkybZtrKb8zEOkYldSs855VFpkMKa0hj7pJXqfaV6E6PotgNOMOJKGpu+OEu9sb9NtLHE8ZxyljFyfcu3cP4zTjdDph00sCxcWmx4vjgh/ezPjjD1/g04MYKYdpBgDvDwVIfay5aFq+z6H0oRvnGe/d3+G//O0P/tVTnhlwy5WixJWDNpnKyyIV7ZSIk7M8rSBjaoRwAyKIsPbA8OpeHrBhoCLKNfRYhYVeA4BeCfqcLVz711fAC51dqWIHAIrMdYPZY0I3k6VRRr1gtKZfuolc1wRyl2dKUa/fckGaTqRcU9IMeKw8QFyFVEVS5AqjfVIiYMqEXRdxbxNx0Q/oImHMwFuXA25PE16MBZdDwJQZH78ccXua3Rqx2htWC8YAWAR5heE2NdJn8XWWTLADKDshs2SU/cvPjpgL8FsfPMCvvXeJjqTv02FhlHHC85cTtkOqnBzNUp5ywTTN2G16hBDd/boss4TvYgeihIf7AYc543Lo3NvgE92Os9kPBHi2iKyPzGrhGhyydbE4PlHx5zRFtOSMssiAz/nO7d5jIzajWoJi7Wa/V9DqpV7ZuZqHcAIuoQGaorBDipoNJHVr8iIgmAD0KeLrb+zxi+9e4+sPN1gKcLmRSp0RWhmUhG91//pK5oOFy7HbJPQp4jTOmOYJhQm7TQSxtaOvrnirZ2J1KcSDJMoPJJ+b5qKNEgvGacHQCcdrt5Gw5DwX5TvJY0sYAtho6qmVUrclnhbp93ScFgdGgQgchdOw0dobrMCk1pKQ6tc513OUixWllJR3S4ZwwG5AsTGgzpZ7DVK5Aib2RqvVWLBwgj2LZ0oC7vXw5pMEly3m8XEiMgXMXHBzGLG/3OHBw3uemcgMjGOWquTBKoE3z6VjWZashUDZU5KrYbKWvS3BnbnOQv1sI5odwJLLOZNpHhJxrpfeR72PllVooRRmyYDMCuhM0fu9zcsLqsbJmUoJZPeBGziv6pg6L8ztPK3ldGGT3e26rpYZQL0Gg4EFbiAZX8ua3oQgesPCjhZqN3AnZQbYxMFrjfWa3IG1AdkaQTaHeUJKHYYuIeeMoe9wPBbcHQ5IkbC/vJRMw5Lx0afPEYnxOBL+o+8+xl99/AwvD0f87z9etMAf+frVorIiA+ZZgA2ThHzX9I/16wt6D0mce0JF+ibs7ZCypiICbeGY+uBefdInpD3I5FaIdSCVt8k35uunfT1G77mC8wPUfO5MKa2vURWHDJOaw9G6++SDUdtmE9WaJRIWqnHW1mPTpLyv7tm+V3Qe2u63r3jB/Lo6mLMDCJi3R4StfSepoviFt67wrTf3eLiLiKj1YSy2b92KCwjPjxn/4C8+w8txwZLFLZwV/S8sFRxTCLidFhgjQ40CVUZAcxz8JyKxDAozHlxI6HHTRbw8zbhjRhcY/+T7z/CDJy+wFKAfevwnv/wuxjmDQgIgSuQwHtHv9si5oN8M6GjApgseu88pIpcB4yQs9GkekbuId+/t8PRurHvVTVfHyg2nQUKgWmbFAQFBQJatYQHBiN2IqGvHlZBnCoQItYCfvhzQQubGSIhRhdOyCMfJgb6WUBfiJnTdhWMWm27cRvTOhRH6Tj0DAbs+4qKLuN51+MYbewxdxJAiLgch2N0ujB4snKJScH0xOCeDUYXG3XFBjAF9ivjs2Q1CkHDT/auNGDAUXHlGvUAg5TB49garciroU/RaD3Zery4k5dkcWyHUzI4lM4JbiUKw5MJSWRXAsgiYmRfJVEqBkCH78zgtYATsNgmnKWPTR6RIOIwSmpoX4YY5b8w3shTAMw9DNbbg7QlkHcurkktlgnvFbPF1I7XhYQMn9duaocG1xof83FTSVg0s5dJrIb9lyVhywbMXJ3RDwptvPnCljlIwzwW7PmIzKKg1OcNyz9m4B1Q9ul0Kbq2brDv3mLejd73hB830AwxmyzOWUueJ6+fNI8F2zyBnjpSbaHPjoAfk6ecWqmplNZibNZFrrsEKqrx1BIem+WQFkhWo6L9nz2ie4dzoypLX3BaSmJMbdESaTebhNyCQnZkKNo2ETIGAomuhm7BGP5r5a/QKEaGc67mKWxBiwOXlJXKWUON+K60lADlDfd8hBcbdcZb+VsuMEhOOpwlPnv4YZR7xJ08JkaxJIrshFkOUMCy0vELOoBDlfJ7V5zl/fSERd1pyrY2iiEoWzNxzQkC11NSsLcllzaluQF9C+IQR6ibwLa8TbgLcyDnVfSaLFpqD4mWLyUBS+5LPGY/l/EU2Fl20YegE5Woa4+vABqgVWPVVkS3hrYsOl6mAQ8SfPDnWnHUFZMziYaAGyIUgnU+tiy0zSYdi/W53tph2WOs4WDsA1/E83nX4qZb/frxh/PTZSxyOHd6+Gtx6XlhqTwxdxFwYeSm4umD8x7/8HkoRN/q2lwM/54JpWhBDREDBoOmOILHej1PGRy9O+Ed/+Rme3E4OdiJJVcv37w3497/9JrpE+OnNCc9OjP2Q8Hg/4HKTpEkYFXxyt+BmkuqLT+4WIWCq9yoQ4d52iy4SuijCc1qKzw8HwrMXRwcMhQKGPmEaR3zy4s4Pnk/SahGtfon8L68q2jbo4kwjOSCR/7kgjzEBDO0oW2Ecl2oIhFCt5S7U/Vs0tbjva18v23sAIS8ZMbELtqHv0GmzvBgCFgZ+/Sv38c03L9HFuv8tQ2LbSdrvNGUP4/ij6r+muGvIy5SqhBumacayZNy/twexygslepMqj3mRVgjMAC9WB6n2mLH9exoXEJmX0uZA5rTrksfIY5Cuz7ET9dJalFYzhRmIweZXlG/WcEYIwMUm+kLuhoTCBUuuGRYxknp7KhjIRXgrSy5OtBddo80GeW242TYx5WEhRUI1rip/rYJleQ557py5KimIl8n+biEr+a6AixAId8cFwKIEWSE6zkvG/QfXGIYOz54fMY8jdkPEbjdg2CatYQMPP4DhfZPA2nAxVv+RZUTJ8xWfN5sH2Wf2jKheViL3Grjc1/lxg60JozDr7BpQMAmbZRJmaIgh0ArsEgFYbC6rB4d0P7j+gXobW7DWvMz4cAOytH9bf04jka/ojJyzkp1lqY1vI/WBqicoGJhSb1dhgHT/xWApzxWsuHFtVlAzdptrG7etj7dH0UmyVZRrVg8/kfZkYuEwTjPQ96KXtn3APE/4+OOneH5zi6shoCBgjgM2XcT+asAfPzng49uAm0Xkm5FyM8O9gY68IaUuCEYcNsvu9a8vCA9ZTFyFZQhIqCmCZv5Q0Aq5LAcspdBepG5mXVwrQU8kD1AUjEAFeN0Q7J8z4SRyW2ssyODclWaHwD4v/zevjb1Fbk0I2SnUQjtdlP46qAeoNILI4+/MbvnZOCUWzv6MP//mPXz5MmAujK8/vsSUGR++PKELEfcH6fsx5oxpznh2ytgkwkcvJ7wcZYPf3wmH46MXJ9zbijL66c1oj+mejC5IOetKrmwK5RHwk5tJuAyB8KdPTnh3HzEuBe8/ugbAnp64ZCCEgsshIXdBFYspCWgzL+ma2m07OVwUBUxpvyUGodsQHl9d49vvXOLjFyM+u5twN2X84OkdSmF8+/EO9/cD+gA82HU4zMB+INydBOCACQURb1x2eDMIaS4pcOuI/KACYq1MXLRCJWGcCkJg5MU6tSbJbGEgcMH/+Vcv0HXJ90a7R6tpdfZ++3KhcKbarY6LXSLIvmvDOwQCh9rqIGfhD8UklnFKtYme357Z95fttZYrAyJc7QZ0MeB62+FmKrgZF5SFkULBv/fzj/HVR3vJ3FkaRQAgUcHpOOHpi1tsuoTLywuM86JZa5YBWAvKnce9KQRs++SpmLeHSdZJLXhLWW6+0ShaKJeLQdoYcx1O0HMXpOS4jTpFQmZoCKgK7Va2WCa5yEg9t7o+JUQfT1H5Ifwzq/QsmSZyf1Mc7MUwtWWSExltrLB1t+do9tD5jrGfbcuJHAUs/LGqYcUVvFjByxjglYPnuTiPjooUQlsK4/JicOV2GjNyZlxsB4CBZV6Ql4L9boNNT1o7p1aQtRHWXlJSpysosTME1OJp+gTMwKLjjiv53Tyz6YBS/G81RNt4KWzPqyw/Nw5s4lgvKsBOAKeP3b0jVc6bx8zCkAQdqyp9Xyf3QjS3bsZQvUr1IVmV8flw7RmzlncglbWczYg2PlfVe3bmgKrog8mPZij2/arqGrBMdSzeW6iRm1BvUOsZtH1bgXb9OeeCm9sDKAiP6/mzZ6BAWMYj/uhph7e7E+bhEn/2yUvshgF9Cnhyw7juE8KynIWjWjlkHm2TN7W/VWk8iOevL/C0qKb3XcTIWqY6UAALS03zzoVn0HV05tVg32h5qQxuI/Cd4dr6XbLKudX1txIGKlx9krH+jiF7+46ncobgGTItmCIS8pUVSbIQDRFwKmp9BWBIAZsUseuTAgQBVnNm73tx2Sd85fEVCgIiM97sJyQCPniwQegG5HkG8oIUCwp1oNgBnDEXIQ8epgUXHSGmhI9fjrjqCPe3AX/6yYhFwzhcCsY54y8+ucXHNzP2g+Qe+N91c1p2RmHGJ3cTxjniW29eYDzciYesiyjdgC7Ks8yLdOjMmTHPi5CtWUIg8yKW26YPXijMrGDDhEth3J1mEIAH2w4Ptx0IhH/t3SvkzFgKcDwtOOrxS4Hw8qB1a7Q4oVxLDiyTNBKze7EutKfVLxnTNANccDhN6gEoGE+jCHdmhK7HsXQ4ucu/bstXXg1IwNmP6zccBb+CgUzgiietXkCAjAkpKbonhzb5fjVoYM9plzaPWmFGKKLMCwj/4S++i/0moY8BN6cFH7884ThnvHu9xZsXEYdpqQaCDnWIhDkHLNTh0cOH6JWMavFxOw+mhGNDOAVEqORSME4zpIBkwTxnzDPUilNIrfc1EG2x92D2OldBunLD++wyRm29EJQ0awCilt638IAc+kplhVqSBCtgEwLrVWUOLbzaFglrORdF2cLeU8mvXDNmzob96p7RvVA/TysPhHsOGK5EwOyWdK3DosXmQE6QlWxAMzxEdnQpukcJBOEWUYe+k0zFTps4dok0vFi8lkj7ylyNPwvZme6xtFqGFRmUsVsGFimQqkepUfLUZrrZMzZzVdjPwcp+aIEDV9lugLpV0AVNhhGpcqRWade1Nu+GeTBfRyOQ81BBpBjYdZxVR8mFvccTakJGKQzK6/L9Kckudw6R7yUFWwC056eq4LoPzRBqtxy1v+jvrgdRQ9YutqiO2dbEuVCovfcCES4vBnz24oR/+dHHuH//Cvu+x+VuCzy9wf/9SUDALboYcJxPYGb0fcLzid2rXcF4fX6XS1w9QXXiXzlV/voC0MJ1QfUpY4ieiZBZLezI4BRXGz/od1r03KUa10sAONSmW05E1cJvlvkCMgZ/WKF0W7jWHWukx5SkKNbCsilCIDzaD17cpxTG0IlVcpykg+q8ZPzKVx/g3rZDHwmXmw6344LTXPDzj3caRxehAQDGlSXSCrTmbiR4mlsM1rNno5+TlMp+SACSkDaDCJ7MhI6k7Pf1ZlAUyvjSVSeTEhK+fk82A0BeIfZX3r+H692A/+svP0Mg4BtvXuLetlNARfj0bhRLKQB3pwVTLtikgG63EQ8LJF9+mrMid3aLP4SEfuh0PW2XA+O04HDK2G6SVJrMxddrsbBHYRfOkeAWMhcgwASlCIB5KRgnVUKAVmBmz5roIoNCRGFCFxnQGjaRCElrtIzjhDxPMJLn9vIKqe+9LgAD+K1vvIX9j5/js7sJL44znt7NUocB5EJc9uxZCrG9CADWNRteOS5Qa8KsHWq/bG0SoMKiZuJV72ArnamafKYgiXC97QFmTwu+f9FjKVKJ9SsPL2DKYFSgzboGG1NULFwQEWJZ5jlICfR5KVjmrO0uTMYRalZN9WwySwr4KnaeJZWxMIHMgxEt1FqFZptuakp9pVMao8ZAxbwU97qllWWpgKLU+W1n0vkGYPWSVEDGaLI6qApPU3R2oZXl11TyLX7OLQusAk9f82JkSdKiWvoJDcUYP6Y097FwVtamqjYnVtGZVHN3SXhIKYkSZGZshuSK1ABdCMA2VS6eeW9aom6B7BcDiRJ2lyJ7MVTPE4lwESWnhFiGZcQAxjE8w0E6xfX5bbWNg4JmH9kcrF6lnVs46PUSCgTPwpQUYlPuVXmLd932TR1jzVZasyHbIZhscn6nhXB0LzlfxTlH7DLTjnIKBHi/MLmDGTIGNE2xmxeN1UCrdAJ77wxs+3nyNz93Heozm/ypafq2/tNiOgH4/icvcSqEp3GPHz4HnvzoJV4cF3R9j/2QxPOSixgNrCoLaIrAmmFSvc0O0v3e5vk5k4Nnry/0tEi2S4FFYzkzMlhiiaEKHpEFVFGTDRSVYEsAmAo2UVZQKsSq4FBFz1Z+mRkgdhKkxPclxTEraW/TJ+nd04z4NBcsDJwWxu9+8zEKS3nrL11vlP9A2A/iEp3mjBeHWfkrGY+vpR120Zx3I3I9v51w+3LC9UWHTS+korvDhJxnXF1e6MbSPHwCui65NSTCQ/qQEEF7MtTFhD63EepyZnQx4XCc3DLqug4M4GI3qKCRPj0RwjdaSsG/8XMPAWjPExPGzHisxbgYDN4Pdkuc5uzbA/j/2vuSHsmS5LzP/C0RGZVZ1bP1jMghSC0kQQgCNCQgSLrqIkD6Cfp9Ouos6cQbL4QuAnThSYQAapmtuyozlvfcTQdb/UVkdrVmRuQA4UBVZka8xRdbPje3JeofLVpzg5jATQWh2Oqd2Q/72XeMMr5gQBFuBIxACd0NqB8ATUBV3wQlXc37YqnUCdOsc7Ja6vGCcSQQJAkRgbFcFhwvJ+BEGEjOvRsG0DBgmnYY51kEaPIjeNqN+Fd/9AOsLE6mf/2LF/z00wU/e77gr3/xgpMms5vVstBbP+BaNdSRDeEGg/HmJ9li2+dxPi9/OivLupA6EmqE1epCsuF3PuzxT37nPf7wy0cwy5qZ+CoEp7udprFn/RwsJxGFCKS8EEmyLP26RC+BLNRX+HEgs76EpB+oACNcgNtXFppv89SqKeJwFGSwW8KAqAYfXhPqaGwJ1FwAFwe8Rufm6mWKo5Ti1oPYOEXochaIblHS5ekcRvVewGSCrJ87wkI3KYVQC/mxOAEafs3+TLPK2D2kwoLSLpt1Ha0Kh1m3FrU0mlXABL5EcqqlyTO0pb5BNkWDVSzvlFgcUZj16VqJs9MLmMXSWqIisTwmnGNBWolbLb2l5KP1YAff3G4YidPSsOmDfAnQzTszw2Zc6IAcYMYmQPpBunvwau0wsGP8Blee7M8PC0pm8w58p3FJeQ/tja61Rz8xua8KgzWrLimfFafVuCfydsl45VuzvLFORhY9jHC2zbKr2wykeRS/VHLHeCmwWDCPhNN5AWPAMIqD+j/68R6trvjvP3vGf/iv/wt7dd5vtYKHQUvHCA2UEjX+ZF5TlnlLkWKU2+0A4yi9h459+0ZLCzezIlw/JKoaA1Wz8wmtKHpktRdwRvSElREB/EUUxJCYOjsPmWWkGEotRZkYngRr0R1LKQU/eNrh+4cJ3zuM+MmPHrBUiMJtC5gZl5XxuHvUyZXjnnEo2E0TlrSrqU3yhQiAajjsCo6nE1qdcDjsMA7APMoufxxkp2JhnZY2/HjWZGTqJ7Wsq1SR5cQoOmdyRi9HaLU1T9MvjNTg5/et4ZcfP2GaCN/74r2v09mSv+mOxX6cl+bMWFsSGIgdSClR+8GWZhiEmcQRUMYg/j3Q55tAbmgqpKR/savsjuBMSVdxsB7GSf1QVsAFJis4NWZsIGasK2NdZJx1XbCsFWWcUKY9QFCP8xHzNPe70g3JrsxYFpnLcSD88Q+f8Cc/lHH9/OWCXx4X/Oz5gv/y17/E16fFFcfkwuhaeCIJqMQ2N1ipdXN/4wKIFUOPjArhrLRARPjy3YQvHiY87Se8nwcchoLLeZX+dbs58ugtE+AgqTJsgnlUwJLHIw6YCi6UluoasMp2mLs5VTQHY9JU8WbqbY2BascBCpLALpyMp2F0pGOPqIcklAmeJZSZtSBpQDt7XjUZYcckWlogL4v5L9gOOKIy1HcHcA0k3TN5pv0eIzeJ+TVI9IeEYsnEAAAgAElEQVTWXlrZLYSEDOQFzNjO39bJNgkma1wWqB+PpKuX/lkm62EgjXCR7LmTZia1nCxSaVf8dqS+WhIwzocBLMchok8cXDR4lCCRrLv5qZS8hik/lJtESCpMF5IjJclknMDRRoOybo5s85P1RA7z3nKNWU8KhQWj21ywyFFDg9lIZuvAAErb0KHzRIzf5svkV2vsNYO27BzrKgtL+mxOKIjsQqeO+NzAaAYkTiOV/R3N3xcgNuFGt9qFs3ECWmmePJxbwf7aCMNgeWAI0zQIWNWyLR9fTvgHX+wkU/YwYGmxsUdroGGwQXRWIM9NoW8VsGe6TRdM18oxwE0hKu0baw+tLFGcOXeEf+8sbWLEUYd7AXP6nFkYQqWQDQGVY8dlOwJm4A9/8IB/8ftfYLeb8dPnCz7sR83pAbycFvz8JPV5HuYBXz5K3pF1bWAizANwrBYBwECZ8G5XcCDC6XxxATsVxmVZ8HKsvpM0pGtoXZ28MU8jQIyX4wmAEMbpdHZitSJdtTYczyvWdUGtFU+P7/yMeqHViToX7trtBkjm4Yr1WZSG1DWyfCbia9Ma43B4wFAIXz+fA/vZCpCZuGPRQ9GKYK16XEQQYDKNReshib+KXRvCVOqX2Jgz2JGXSp0lmYNVQBYRzpeqx3+M0/ki9Woqi9WEBYiWcdTqrCNOdcCwkyPHooqEGRgTAQ/MmF+n2JtGD2vS76DFyxrS7N1uxLt5xO982OMnP/5Com0goO9/fzpjKoR//5f/Q3b+9jwKQF5gx5hx9ADmkOcU73YOUnpjEiX1tB/xj798xHcOE4iBv//dg/qbEJg0f48qKBEUK85L7MbMWmnjlEKAsr7GqY0bFpIQ2HVtSfCTWwoBoYUyhKWGObJuWsG4y1L16NGAtQDeaRIaMkuBHXWwgSioNYEUiGQhDzj4MDp0gmNoRtngTSLZYY+687fK4wawa2NX9Dn5Wvav04867ZiPjoy/pW/2PYFUaMfRtjjwEsR53Pi8cUrmyN1r3F9ILCmmRCUDLADsdnDRao7NrQFjtXIdMjeW/wZIvJ/ACrpNkHxox2+U51wTfDmIpgATtvGR8baraCcgHKclizHSvMjgOQEIo0dwrJuHYmNz7JNAthpkO7whIM3Aj33HzmMiw6NWGwGoMgxdD5P50n+xFum4VejVnJCzxn06vcHfqX4Y8jM4IoPy9+5TxJbEz2TUbcDHHHyaETDl9TYZhJgnA7fmc2QZd+2Zw2BBMhKgQpoo8ng642dfHTFPE35RGv7Nn3wf//mvfiG1tFaxAn/3ccaXu4ZTK5jKgI+LHGdJVFfkG1p1U2tHocZjOT0GAZ4o8lZ7E7RMQ8GXTzt80lwdtTGOqZARGeXoi8wsZqKZvCBbOi8kYXibcNZd0momRZKKlA/zgH/5B9/BH3x3h9PK+P5uBgj49HLBZVnx/jDiBw8zHg8zxnHE80kqTu/GCUORHeA8yG5DvN2bh2XP086V8mWR+8axOBMOQ5xzMyyrJpQAgziVhFSYs6Ph2jSXwzBo1k4Bfh6R5M5qcv1aG8Yqu9/dPAZRkiFsRmYq5oZagxkzcZrJzZxWsznccnp4/gKVCMtqeS3Esc7R8w28SwQsS+xsG4fPwagEui4LdrMUgROjE+FweJCw5Ik0Jh+SQpqB88K4rBKmWZc4yjATepl2b5Hpr6WpzIU5Px6X8Gz/8RdSQO/f/bPfw0+fL/j6uODjcQEzME+Er44rPp4rPp1XvFyqVkIXwbibxHJyWqpmhLQXySCHUvDDxwk//uIBf/S9B/zuFwcAsqafjmdcLrI2hditjoZOTYgjuAzCd/JNXRtWVFwuyU6q/OnCSUGWKzASq6ocdajQBUBUMOqB9XlhLFVA/qgh0eYLYsU63eFY6bxQ8VBPGY90x3wlhNxidwtENWnAjpyDb/xYgBP92zXIWTaVRxo81JNZKo1fIQjkP4XP/P3qb2D8a8cnda1iDTLlDt9shhWghU+N+zvAjl1EOXIDxqmkhFuJMJ3HGEBBIdYCqaFkXTYAXsWBGRounNSr91E2LoXz8X3s8p2eCAj7FqmPVKSZ8PFyHAna80QhQcF7PraCg1+wADzz2/CK8siKuV8cO7K58umwyynJ7yabJDuCsVxa/jjS+SLqPg9n6XiOXub+JAE+EJYim6lstaNwTqZmm0ZC+EkVByZZntt744+YC9s8um5NP3tnYgpgwJEBV8iZYcXYZU4lAAMsBQ5fXo7gVvFyPOPw8IDWViyXC37//R7/9o+/g5+dGX/1sxO+c5gwEvDPfzRi3D0ArYLmg89XVdDabNOsPHSpUqLg03nFx9OKpcnG/LhUfHiY8Fp7M43/T/70z/g//vlf4LRUXKpEpfy3v/kaDHiUihVpWlJnlmapslmdTxXl6cI2XxwZ1IeHCe/3E77YT/iD7x3wfi+73nkkrGsVJ8smOUFO5wVDKfju+z2YJclRa8DpIhaMd/spgEHyirfzbje/KUUtNeruZKeh7pdMRGaBUBa15zFDPfotr0Ug5XGMiKZuJwRNkEVq7lUfhADPUYAwzOdKiMzBzIlAofcakAKgxwFx7l2bFuhKO2GzDpiiNiaMuQphZIDIKtICkWr8+eWIgRpKKZh3O1CR3C/L+aK5LwrGaYKd5zqzq6O11SISn4cxZRbuBipz4L4OyoyvUvKv1my5BgJQZbdyuljlYMI8E5bW8HKp+PlxQS0THnR+H3cSGWSMSSR+R+fVnBYZ//Bph3fzoGbf6ub4k0bO+A5XB2/CvZSC0at7mxUgOYSrsGDdnYefjmWqVX8IVShWUZYRZvR87AIAu7lgWVkSjo3h42K8HcI/aC9bLVb1uTIwY9XIa86xAcdlkv12iJ2hbxrIQDqcD/Ju15iFYl9l+w6X/ZxfdnPdOd0TRxhIn6VcaJ2/gQNARW8mvAFJemfJAe0dJjus5ozPrXcxOQgnsHKr62F546vxCo+TA48816EUs9w0IBhyIT87pi+sHPZ3TltvHfF3xRIlMWYjFSTRf2638NWnLlf1imzhZ6W1MuSqyImONs/x5U3WALOU+Pqma7JMtI2AHcVkunegWkwOZzqJWcplBvJmtLsIGQBSNxk5Em4oFpmla+O6L/jP+GddV/XJEZ12PJ6w1hWH/Q5lGPDVV1/LprYUPDxIPpZGA2gYcdjvsDKheUHKAuKWGDn6mfkxXEZ6HmXFB18cpptp/N8ELf/0T/+M/9Of/8VGcfky9TozdyZNovli5M9F4TRczmI1WauGUZci4bMlcqfkZrsbZhYHIKTdivqjWARRPh9V8oUdO5mjUghj26VkJyfpa+wgrQ824QEqujTRECIddfdFFCmZARHCzGJWviwN81RcONtzfVzabEds32/RivXPgFJark5QDEn5+JpQZpSETBAAKo+bASwWaUSyG75oWO2oQte4v7EJDQBU0LhiGEc0LeBm1Vjl+U2B5u2kQqxZE0/Hs0QHPewwTVM4d26Bza1nXM3ct2u/+PmLW6FsHUohHPajhwwLxwE0Tv0LE5tt7Vdsdao4dvFGN0QF61qxnFcUzUhKkKri4tulysMUOcUZfmM5hrE++zm9d4scMNoxiNH/qALPfDiICLtJiqHttDqzhR1XB48iExavnptT6sv3nXOxFhW16bHNQwCuG0BhS8AJKJH+Z8rH+PtN0kgyzVmHtu+yv3X1DKgQwoqik5r5pH8t3fx8MKCYgIYrxk5uZgATD7K9S6aoK5AfQwU4Hdj3xOAP6TZtaX7CUbpXPu4vgQB39p70H/T1DpD8uekKo/0uisffRf1apn7bfJl4zHK7JPmbN3+9Y3ICCRvwkoGKrbnTXIn1M5DCnI4V9dpsuTcQG33qhwMHXTEvCW6lvgdNmK6xdXV+0GeEpShvmi1/DXC5VDBXLBopNowDXl5egNbQuOLD938IALHh7PiG0wT9etpYCN9/mr997SFrPfF8nnKw2W6JoDv0AwDThGkYQesKy156OZ81AmS6qvRowpMIQMpq6swocFKuZWA9n68YNdjE/CoioZedR4cCkOev6Vzcfsp7A8UD0NTfhGHo5YAJeAMrTU2E0xhhhR7tQJFrIhNzz2AhRHwOGjaCBCBmr2YK9AzowpEARkb3BsgEAUcyvWBKc7IuJEzLmsfhcqlyll/kaMEyQRIB8zwL47QVDOB8XtGYsddsr8zAOM8Yhz50Pi0+UAqm3Swe7ePYX2d1VEq5olCjgbFIuOxrQv21JscATWt+QE2qwqitNpxOq0amiQCYhlHovpNM+dfN+xOdj06L7Gfp4zSK4G2WeVVCe8cdEggnjQAgzf8gu5XL0jys1ja+kTgMngRuGkNRdMe1RX2quOHTi0R9XRY5/pJIOnFuNr+WwZPKoQMQ7qRKBWsNX6kAtuRHoaZXoH3Jye1cmSm/UyGnc1OW8Hszx4eyvVLUeTmS8s1/b74WXlB/CFdwejBP+fH+/JA8+YW1NtRXOtNhic1mRCXYdRdNoZH/kZ4RD2UgHGg38xFdDmdQO57qlXMc17nlOXUmK07/LLQv7HDJL+FkKaDruXK6yACFTS/YIwJ5le6Ixn7X+VCrs9NMlqVpDG6RMPkMgAYLO0/94pCrJjMZ/UYRBEsZhJh+9qinmLDQmSUmontXdwRE8PnfHpl1x2b62bpqIVeVCc/HBVxX1CoGhN08AmdxnWBmfPcH3/N8K57JG6+D4990+yzQ8qs0evUPmWgaCePY+yxk5H6rZcWdWykF59MS15B4PxsaLonB8uacoWeg1CN5OwYxp1W71gqrAcC5VXVIlb/lTBAuPAoRns/NLUdmmTFHvQbulUkallXzdLQVr3GwkwV8dtLuPueITuiPSG23EJVu5Z9YPc4KQrojttYkqSCgRxakYdmi0FdP2S7WgGmScDjWOiiSvbZgvxsFyJGwm4Uf1nVFyWBVNY1lUZ6mqWNGZxydI428d78hQBwLhwIQazg4vV72HABa1YRmIGdwZsagDqwW0TOoyXkcCwbNaNsYqGV8FdznXdytz3v/JAFFApYLiuZCKgSPzFrX6oK+1UQTKpTmqeCyNDGPs/lIifQUn5QAy/m453Gcg95YjoJbbbiscu48GM0mmpYjYHXIZVvTeL7VFBkHC1mOQoo0BJmvK1/Nh9BfAG9Xvomgr2c8a9AAL/H5BlS+JXM2ICaD/G492UnWP3+zX+npWa5lWZX72AMidhDH8aj0c6Owbl54/SfZW11GUgccmbOMNlCUgUx+6/Vrsm62SwgATM4mq9LN3iY9Tvn4PPcdaf5Jj/EQa5O/l25ElnMHRYnQonBjHKlCaTuXTrENqRxnAufKmEfq6CtAp+keOKDJkXYhy80vTEfoCSvjuu2MMSITL7y/dozfsNamkX8VvF6wLBccz5Lc83xZsa4rfvT3vsRunsJR2Of/c0wXv7n2GwctbzZF8i2FSL2G3lprqOsKc1gaFElIojsxb68NGEwhKUVYwWP3adH36ev1JyNTlQGaYFzqmHG7m1hXdkbyoxzWXaX51gBdvgnJLtuP1XZutdnzTFjE7rgHJWGazuf9rTYPi4tNBHVCJ4+1tjWthcyF78yVyzW1jijmZK2QrjYXXXYUxDoeYUixfljOF9tbDjoGO55qK4NZjkJsjvM5tPlsuFOi9icWCiH8ifxnY6BqIULcPn3qWhkGz0S5m3WHcevCG+gjP54IOJ9W9+Gw6IOhEPb70X0KzpeqGUfVeXcuOF+q0AcrwEDVsGUZpPiDiOhrJDWhxkJgc6ytUiivsfgvXS4R/TMU4N2DCCPzCRsG8voopQCNK7jqmoEwDQSaRuzYQvrFssjMWpDQBHZ1oSabhVBytcYxp/Gx/W4Ox7emuDu2tblFCP8MxDtI0m3N7duUFfIVlNLp004h3Lr+trzKCtEcN1m/eE3GdUpa0GLCIAzwtbKwudlubLbYZIPtO+AXvyofauSMA0S75q1+U/9M0e/yJrPa5ufw5l75QTGmvOtKIMXmj/mWVUEtg3nyoSwECY238fv9pJFkmw2jyXVKk2uZgPMYLTpGjkLJo+lGCI9OxY5mgWEy93EL805ACb0/Sl4nArzgqAEnGVvMQwZqZompjdF8jtRhXgXbrNbY/Tjg8SlC6gmMy+WCv/k/X2EkxuX4gmEo2D2+93XjdptX/3+1v13QQvQmE+dWSgHpLhsA6ukMEdjAkgST1zYhwIo6miIzpQ7c0jfX/eDuvw3aTFsGuU0YwpztTIjL75SY01C55HZwMER2NqqvbAAVAxByvaeHpgRAYAwZQMxBTiAWf0+tEekBwHdzTILgi+VNQBZ0rOM0YCmfr7WJc6onntvUnLoldUyhmeBBOEtWnRsJ8YWPRwq/cYAVsO98arPjLxKzrR/3kQorxLs/p210Uw7rzGPKdHSTfFmjdxZV7JwcYWEhtzLXk/qKNGY0rUuy341Y1qa+PwBpFJp1b9Jjm1kF4d7KONSI2LNwWgPSzEKflvyrNQHP1uZJiwGOCows6gE5ekdSxZsPz1AKDnvp00WPiS6XtfPjejlJTSP3m6HEfwqSjV6zMnPlyoade7q3C0mfw/a3Xu+pw538YqxGe/kZna7TdSJ/BPtG4rX2KqzJlgObx7eek+fHKE7/s/xNeaPlPgY3ZGmWf/0M9DJQ6Pj6OCH3CS4XUlZZ+w7hE9h97veFDHTZ0oEEivVGRC/mTvucsV6fx6uDbBrBab5uBsi9XhfCipWPcC5Lc/70MgUcodqWmwaFpbK0js2SiFrPzUgsKRuU39WKb1M7z0XfV4AhlbMxXcIBdkFJVha6ljUkmwr3KaOQR9M4dCkXBpi/JzySbhp15olxOp3BrWFdV+zVQvuwl8zpl+dPOvEF4/7BZexrwP832f52QQvQLdQ3XypHBADQ5hlttSKNQaiGuAMc+GsghdPiA1N2IPXTcKYjR9T5eRnkUumpx7MAI3Y6hcIHxhig94sJEAL0URoA0rjirLRQJPba+taYgvW+6RjJfBhImMp2cQKsAKB5zSYUCQc1b3g7HxcLSfgbiNeKOHFmh2cz17u+oH5MLncYIErnbpAMq1aZ1wSt1FyR32u1GwOoDCVFN0DTXrNYClxVEOKo4hXa8rmuDErC0HES5FhDHKwJ3Coui+Tj2XmmYhGA61LRNIeBrcM0hEAhkOZjiMrdJqPHsfhR5TwO4JRx1hSUmXcB9VmpjHkqOOwnsI6bSGo8rS7EZK6MJoUuxKkaABZmlGXFOBSctYIuAaikGS4RwjBHOKzVlINmQmVJ4c+2+9REdrbOvqnwMQcNb/cEQTOh4sNiEbxofzgpJSCT6Q3pe19zUxIObpSeOD/odksQ6Iq2MjAJZWyvv35oviYsma7C3VowFvLkmkYPlJ6RH0TpC0If3WRfRk/Y57TvX38cFzhvO6EhH9PCeEfsvZ6ZNnWWlReE52NhMx4hINlz2d/aFZhE/C5yQJMltvC3GQapZWaAxzZJ0yhKehyFxyW5X2yoZBNgG0ikjahZksjpp2oVREa4HfjGoUlUIAGSvA0MqgGgrLRLLvpo8n2LWDzJHMM3aHnOxrE43WcrkVn0wSyRueuC6nXfZFMzT4RffvyEdZlxPJ3w9HjANI5ovKCsA6Z5B25VjsFe80P8DbVfD2jhUITmoGYMZUrWBJcXTzPlzZojwpMjmRIPQb2uFetSoWSdduikeQ1E+WnwJ+zHVjRkJzL9AC72jImM6Bt5KLBFQHRPTH21nZEpLVHWcpnXFiniUzB0YIf8GUUpjmBz6ZI3BKL3nX1+nMeZUTTe3/pTSE35Vc2m1awW5Mce1oscHnm1gwGBBhMU5GnTB51xM3MSGWDqd81ZQBDg9VQi5C6J95CqUtCxDAnMxby4bLQlJLVctebCyyxGpRSM0+C5EwgiHJ6fTyCoRWKwY8WCw24IR1hmcKtYa8V6EcZ+d9hjaQOoED487bWSrvhyrM4LcaQ3perHHiKr/V9LWLBsrswvZeUwq3t0klJDpiMaRLC9nJZEooTdLrN3rAmRmMoNiRgYO55XnDXEeFmrh8r7E7zf4utjAMRKU8h6VuS2LDW2AqSF+JDCQtNznWA6oJHVeY9qmI12OtWbScg/DNO7XGH1eBjwfCP2bbZ49urbfsoYiulmzircvkuFAE05d+DGQEKAhxyCatDOxmJzYaHrV/NGWiwWQX9JvHXyUDkUufX6kP0z2tzovXWl3dO0vddpJT1RHJXD30OFHRjqa0UxTttQZUXsY0p9dn8Q/c6KSIJiLW0jI9YSieys6my+m9SZ/NL83qVZxJyskRXbDT1nMjuqR2c9MA5DyjEUR/pr5c4H6/m4uGwz+rQ8KpHU0GSlzF0EVaR5ISAVOXMA3lpzXiuqSxzgsUXoEab5gImA0+mI03lBKRNKmfB7v/sFaq04Xw74+NUvceSCp6dH1KVhGgT5fPXzn4JbBZURIMLDww4PhwNoGNOGv8ewt/hqq6vfap8FWkx20GtPJmM+JeTsR6k1ZkjBixXY0ooHnr+l8SpOnMnJyNLXB1EqEEqCPqfHDi7Nql1ahCXb480xNiwFdg/FJX5Px4ibiTCCDbRtkxJE4kIzUIbPnX1uZ6uwxSab9545MtgJpkhKLIGo0VPpa5SS7vgz4MkWp+h7H3UETzCFPtGSdnTIO6UkdMzCZP48zJHIzABFBPghATN0RG8bD3eqLmY90VVTIo13IJlBGU2jmAQESVjud97v8em4utPxYVfwcr5gpEEtHlahVUzH43SQrMgAqIVPkp1nF4InayPk8cv8iXAK4BY1bHIWTAUr6m9iz0ynLSDS3EiL5LVhACNLFNZNJz2iRLtw2ssJAs+LZgols55IGnYDNKJY5F1DCUBYW+TqubKqaT+yxRBKi04AiRWA/qgDidavjj6yItvQTexbaHNdJEQbtl7s+r1XEE6vyApYOa7rRi90k1LRT0zh2M7d/S9uCFQpexDKO68mA+6jFwNPv5u8SGvfXZSng9JXN+R6PjrL72B9JnnNoX5+mNUC0KJYp+mPAF9BCxnUWH9cfiFkbzcb6YfxjR3LowX4y6DCEhvOU/HK9eITJteO6lRvDuSjMpzkGhOr6TgUcSKH1K2bp0FAztJcXs5T8cKRrbWQEU0d0C1SgGONgQBDIZeD2oQvN1SW+DmDxixHafOhjXYoAnBDDUl/d/OkSVFHB1OyOWl4/+EJx+MZp+Mn7HYP4CZK/vHxEU2PlMb5gN084OXTsz93bYQP759889XYEiMqhCeb5+abkBtCpGvfCFqEwYtPKBCVLc1kJXoqpTjOfDIU8TVgoIxJyBOwLuJwORZNZ68CtLaG2iryGbZZMUwIi4LSpDhD6Rk1Z3m0nUyxUN1sVTALgVxtiZ0A2+Fqf43ZE6N3Z51qaWga8moIOzOZDCOcdHPfRCAXkNUfSkKJAa8BU0gBgfVVI1fsGMJDEN2hV4SPJzxCkH3k1rA50zXV8xArGmmCweoBkQIfeR/AGfDpDsXM1zWdLxtoNKbU6YjU2DrgHA1hIcT2mVjW2M9jjY+LbZlJyhEslbHbDX6E0hqwn4vmD2G8HCt284DzIqUSTudVQXSR6t9Vq4+TOOJ6SnYSz3oDAeKAKvlLsrOoKzutSdVYKpx75leEwGqsfKWAZhyHpNwCBDYmz6QpfCGh9pYGfVWLXuS/KZosUaMdWo4kCIBRG3vqdfvMUtNTDb0ugKugFFnXwWqAGecZUNuC8iDlYB+foA1f0cba2mEU6ua2F+EAjCaMoDshZH0jvzctQgdS/Nb0on63yD6ndtMt3R8gXgv5pffG0TOuWpqaJMP0pwOSsJh191L/c/vHlQ86BeK4gVtw1b0NjuHNhUxpXShFVPPVaultW9+eDFpfASkEPyqzC40fSRfDNw0GWKq8ZzcXP8601BNrZZffqFFxuZSwaphDPKm+E5BTNLpSclPVJv5Gz8dV5AWuadT8bTZY2WvT2eYz9pB2fSgEt1inB+ekkkJXDILIJDA6J+Owcsaz7J1lGDBYKhHWkjK1YhoLvvp0wTw/4PBuxDxPWJaKcSyo9Yzj8QUfP73gfPqfYkUrhGn3Du/eHbDbHbBWSW1hiWHP5xdclhUPD3vUKqBonqcYYxlQ3ojwfBu0cMPl+SO++viCSx0wThPePx6wmwpeTid8+vQJXFc8HB4wDYTT6Yh1bTi8e8I0z5KbgxtOJ8mGumo+lv1+h2EahWdK0Ro3Bbud1KsppWAmqZ0yav6LZYnCfJI9krSYH6OQEN84Fpwuq+6iCrgx1pyEDrZrFYHPTNjNg+/qzCTXGrCbB1fsUT7eTHnBpY0jt4YBBjt3L2Rnq5H1dvBr4SY6l/OAo2z7vbXmuwA7Osiw2mqamIKrLTOxXcoe7+8WKgN56b+O0cbYiZsPRm0VXBu4rZJDh0oyV5uVQwqs1YRFSHcgBjYNBHcb504bhZNddjADgMsaKcSHQcbkpQp0N1XQ0NYV7/YTShlAqgxnXet5Kv6M89LwdJjBmDyngs9bJ6RljJbBFazh6Gy+SDEYy+lg7ygqUD1Rm4pjZgHJVqhwGIqGOKugt2VmLZY3hanbekbzgHka8HJasZuGqF3CQOPS0VlRa9DzSR3tIHQtFpWi5/1Vi+YF2Myp9s1aZI65ADx6aBqjAnOxUhGmRGxNM411uMSUMWXK7EFEJ+nxyhdBc5sXoCAsrvYIUiVoG4R8i5/TGy91/TLg2qI72lkGwDUGmZ1W80/QtnwCfH1LGDY7q43JJCLNH5WOTt0PKyZBaa9vofRic5cByA0s1d//GV/0oIv8vXFhbBAzeDNZbPLLMvL6M3XJ/aCcI5y383XxPkRl6nNytrV3esg+GWAKZW5VlsNfkdHWsLOZHAPYn5snXKaYgs7SXOTq0AD86DrdKdeVBDQQdHeFXPLd/g7yFzkNkcxpbJyDbgaCv4kIKFPBPIm/3ve+EKjQWtMM9YTjpWIaJjy8+4D9u+/iss7TpZMAAA6LSURBVCyYpgkPuwHn84Lj8QXgM86nivNJNozr5Yzdfo/dPGFdLgBLxOGKit1uh2EcUWsVf9VX2pughahgt3/AD3YHsCqueZTd3H6/x+Fhj4edFfWraO1Jfxcrw7IsOL4843y5YF0WnC5ix3p3OOD55YRpnvDh6R0+PB3wfFzw8nLGw36P00JSdK0xTpezZD8dR5zWkxPU8bRqwhvC2gjzXLAsFY/7SYDEYMpgxGWRmjfGKBJxQR0BLOsKmga02vD18RkFMwiaWpsYJNWDsK4VVCbP5FpIPlvWFefzBcu64ng84emdpLBvTUJOAQk3mx/fSdiZgiNGlBcAR3RApsvGEoYISKViI2vfechoxBpF5Lt3Y2h3vtQoJD8ThzC35fcwxbZKVTQcdqLkl3UVsEcsBeumWRTa2rA2xkrZ5yccTnezgbdQQNsw73VlF9A5iZ9dD6iZUsstCEiAK99plIKAYnkApiksCGsjEEsFbcsjkgWIhAPLTMp8s5u3ieOoB1lomMImgFuARImkCdBjP6cxkqntta7Uqn4uJoyGVtz6NwxFE7FJMwdqM0FbBlbbmhGZ3whwPDU3l/suXH/IUZdYfeaRfOxEhIOGJdXKmMfiNCcCRd5vtYPCWgk/ybW8K4P67gyFcLrUFFItCa3GgaSQqtGsK6BesTa1LikS0InohpMad591EEYXQtYaruzNapOdONPj4t4EVkzumEOnOf3n2woEuAg5swKAcO+XZeuPucgBU1IWaQMV5U4E9E3qP9T7U8A3S6GQ2Tt2C2RsrTx5DboNxK2Jpf7j3LZ6dINTHFYQ0jsziuufpvfE9VeDuaG3t/QE9NYtK9BpwpMBL7jZDYBjThOG1HHqwxw7sG+48mN8Kr1T2dJMsbFVlGW+JwZwmBnsbgYqq5xnYsOLNKOxfIycnIeR+yzjtdT9ubcmIwliLpTj4BjHNLHOqR7rqH/QwyRFVoeBMI87HB52YG6x6eEGHA64LKJf3KpTpD5UY6BeBKwc3qg99M3HQ2S7YtmhFgJG30UCta46GNadgnSOIOBgfHrEo15f1wXnyyrngbPU6j1fFvz05x8xTQMeZrlnXS/4+uMLeD3jdJbie9NUcDmf8fj4iLU27PcPaKsUcjpeGl6OZ+1HwbpKjZda5UztfHzBNBH284h5GkE8KwImWIhsrVXqJYDwsJvATSoU1yom8eP5Ga01LJcLGhOGQaZumGZM44jdXNDaiMPDjA9PBxQCzovUTRI0L6a3y7KilCogo0h15DKEUJzR5xkwcgOgDmwh5Y0PTEnaOpij6zAQSpOIIQMSRt+TVg8206FYgporJXHILHg5rQCVyH6qOWkGZvBQMCnXmJnRzK7M8g6rTWV8kY8oJOpl8DTzY2N3YMtKxuhv0QJbk4byvnsIi4YpXSkkmZwrOZyErRK1TSxDLEJ2jm3iUYoFylwNavrNSZoaS3bcRT3us6jIVVotp82g5ls7rpI+kabKj5DHZW0YFTSYdagosFh15zGMUWPJQBYpmLG1NPOvKUMBfuLQbjxq45U+Nqc/MYUDpv2sUKBZN22WamuSh4eBeRrw/LKofBAANClYJ0CAroI9SpaOXlT22ih0R1h4jHZdeXG+llzx+9MSwC+kx64stGT3bfUzpwf48xLQMhqxvtDmCfbXYEoyjanoS7vP4sFIG2CphI5ODyKOPyjupfScfgb9N59n2oC6pN23+YU2j7j992e0LR6hW9+9eXeA0h7gvPm2/lMdNAPiE5S/zAgnfeE+VQldEXcEt+2mg5LsXL1FLRksElLtKu0k6zEUGQ/nwTO7nDUgS+or1b3Mu2l/p/5SlJchv7a3Pll3w+qi1bvTYpqMHwfZiA1jWMbNMizuf2Mkv1Rr7W4XOso2NE7n9tnrhpZvsLRAKh7baJiblpbWiAFVdjZgOSssYSHw44VAdpkBG3fk49c/POzx/SS4bRFP54t0hSWRnDkIfuf9BMLOnQLHFJYmTr5Pnlk0JhadthezlFUvHkGFMCWhME17hKlPBRL3ZvJp3qddCmO3B/gxXx9Eaovm9JrmxM/vi4bgmtJQ5QQiN/UbwQFxtunKBgYmCaBUdI9cp/nOvUL9c2TiBIQBeHiYZT2L7CAn6ne2OtH+LmZ2R2yGHJ1NY/i32HVg6e9am/uG1Cpp4G03K8pbxzOQhNyl1JYFkimW9Zl2LOLEa78EZnItQDY3FEdQIRgC1Jh/jjhtB2O3y+pzbl76EZJuyRLZrSalyPjseIkZuGj2ZG6MMhTs5tELg+adDSBVw6uaKcYSx4U50Z8paqM3k6XBQxGB5H7hth5JEedjHJtKYxfjdTl2i/l4/zSndSUcdlLKwCxkYeJW4awvt/E53esDczSbWUtCt6S/ODngboS2X+X8lZTOZowGHvJROqmzgRl+7N64jzr6ybrM8m/ZLtPojdlSu3NsQoxsG4tOSfIgAytrDtq6+YjmcsCGmmcn8cUVzvmmFoL++iv0+v//tW2fsaXB2zfxdnCbJ+lq2fLfeCnnAXBc6zcQ5KJu7IkBkh4wuhWySMKHukVLdKkfkPoOplE7ZiGK6McMitD3ybrZTRxteDw3Qjif83aCSPmLUTeTbzxhiVCBiPYCES6XxZckV8P2ozY1GhAB86R8pBNnsva19o2WFksS1Q82O8RmeumvGjao1r9lTusfMzz4Obia0HjtSK8MQyxiDYfMXIxq8N1mACXxYyn+3FLCZ8QENEAYhj4cM7rL3dCKd0Le2gvTAApESOBCb1CrB6ZA3cwaDkfie9IQUQ5ltFBJjZQaomuDAxN5t5WBJ4JnZpTxxLyao9swSPKjZmPTibaESPaB6vjwzteWAacdXeV0zxkktKxjoA8Ew2SrWfNy3aHBr+d0b/xOQTZOi1sS9EvSPV04fjFplCuemrCXsbjlA9cg25odt1lUVV/cEv48K3WQQQSDNfcQ6bPKxoEROrfFI49EkQYYMsXvfEKxZkLvcCBYVIC1NA+EHIIKda7mfn69h71AsVQ7psSdpiFzZnlojNflvcq3iMg39znTpxX1frf56hoHrxGK1zfbXmfWJ5/LYETngzxvNGh6BmSkQOFfYnTINsfkMtBKc5iFcBotozR3oeMtzSmblU+f1eccSc7TSTcK31AAHZ/NnieS/k1rZ79slNeGTr1/evNW9m9b5xC8ZRFCAB1dt21USwjQDURJiGsLTfvXbPrEsqpEr3zv1+RZ2rbsdRILkOWaWzFz320Jb81D/t5+5zwfLEfTrht17Ui6cwtTuDywzylkcJaHlIgj9y/7y2xlp8kHX0O91i1BBLDyaX4Xw3LQ6N8J2PS81R+72Xcy5tdRyzeHPG8XS/96RX5ftTzBG91/g5b6DzyvhIn3xPBhzrce6ZS2OB6Q4x1T+MGMROLrEEqI9VlqbmOgagiWAQLf9VASmKSAxEASbIyKllmiO1oaV1eunUOoukNuMZghrbYmkUmNQVTdN4M5HB+zH4g9K9Bq7Ors3R4yrmPL1XWDuDfFIdUB0HaaPe0Fw7oC0PmJi3QlKecbSULcr+otctcbuxsCaNOyIHCBbUxTlNE2Yxg2zzXqysK7cC/uTaYaGQUYDnDozoZIO470ngxq3frFPX/FE0KJ3/JfsIRxMseI4xyb45ywmIGIHoh5cz8e21VwFlw9MNhaeGxOjIIyKA/ej3F0a+zfRXQEQcNqKSsGgjnDhEI25Rbzy4B5FgbA2zxnK9N8bdMPWw9beerWKp4wjgW2YbAokwIGqxIxnwWfNx1jdsz3MFQdCtsmjWXthH7EEtkaY6TIJZV9mUz+md+NWW6zsvT1THMWTak/yYQYaRJ2MeX9rZuvsn8VpXW7YuWtFaP7cd3DV9s3i4hNB16/gbfXMN/4rruho4srPanfu2XDYthvWK/gm3f5/bUpN7kqADd00za6bNsPkOk2ewzdvAYca2jrwIlur3uTcE5SCrfWnK8/0s9fBxjfCFosq+Dnt74LvPmFb3x5pfyunrZhrCQ8r+/oBb4rBLPGUHfp9s6o1Erh5PoaUTNzF56cO239ps2Xlm8jxKD2s3NQpXR9WLp6Zt/2yUVE17/+s1vj6FnBlbwvmAl8+yJM+9Yp84O52hFfrQ5fAVc7K+16cbUu+Y9eaXZTwTEKVyqdPLCdQlJs2AjENNGuMPS98q4gZBuL9YHyM/I8QfvQTX//HLuPkOd6+2D79Ura+zvsnfn4h67uT7/oVsdkKLM62SJggZmC44AnosXsUXk+Q1nFz9glyofZsrMd6w0jtn8vz+kBR6dDE3iKKd7O2pYrb13TX79h7zTWNOkJKLN3YvvU6LP0Nz7hTAv6VjvaDT5TZ/KS5hFilZFXmhUrj++GXHUav1qEbnSZr9/i0VtP2d7zlhS67uDNbl8/8LWuv3bP53x3i19c8159cuN5r1Dwa2O61Z9v0/JcfG67iRRuPKBTA5//glem8+Y7bi4Lvz4R3wharhy8qPvxRkuLe+Pi1+7/3Gm5LVxu3/8amvu72j6nr6/Bu/y9O1F/i3e+Smz586xUbwKoW+0GErHbLK30KzuD7lO6HucNvCagMwM4//oz+voaceX30itfpb4k14JNH15//mvjceHxhgD+HOvn1SWdkqRY25vAMZ2x3xj9WzMbFsqEYF5h1i0g2T7/1vTEezIR8NV1+d43l5lvdw8AYhuXhWFPa681u/RagW+AFOc9cL6KbtPRmxDlFdp/Y8GcBHjzwfae9FlHNt9G6H4bZfstH5Nn9df0mtdf9jkXfltF9Lnv4f7Xz33Ndlnf4q3Pbr+ysiUMbxwP0fXuOH1J9Je/6uvv7d7u7d7u7d7u7d6+ZfspM//r7YdvgpZ7u7d7u7d7u7d7u7e/K+2NwKJ7u7d7u7d7u7d7u7e/O+0OWu7t3u7t3u7t3u7tt6LdQcu93du93du93du9/Va0O2i5t3u7t3u7t3u7t9+Kdgct93Zv93Zv93Zv9/Zb0f4vscStpJEVtVAAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fig = plt.figure(figsize=(10, 5))\n", + "ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n", + "\n", + "date = datetime.datetime(1999, 12, 31, 12)\n", + "\n", + "ax.set_title('Night time shading for {}'.format(date))\n", + "ax.stock_img()\n", + "ax.add_feature(Nightshade(date, alpha=0.2))\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cartopy/cartopy-outline.ipynb b/Cartopy/cartopy-outline.ipynb new file mode 100644 index 0000000..48949e0 --- /dev/null +++ b/Cartopy/cartopy-outline.ipynb @@ -0,0 +1,110 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Custom Boundary Shape\n", + "---------------------\n", + "\n", + "This example demonstrates how a custom shape geometry may be used\n", + "instead of the projection's default boundary.\n", + "\n", + "In this instance, we define the boundary as a circle in axes coordinates.\n", + "This means that no matter the extent of the map itself, the boundary will\n", + "always be a circle.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.path as mpath\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def main():\n", + " fig = plt.figure(figsize=[10, 5])\n", + " ax1 = fig.add_subplot(1, 2, 1, projection=ccrs.SouthPolarStereo())\n", + " ax2 = fig.add_subplot(1, 2, 2, projection=ccrs.SouthPolarStereo(),\n", + " sharex=ax1, sharey=ax1)\n", + " fig.subplots_adjust(bottom=0.05, top=0.95,\n", + " left=0.04, right=0.95, wspace=0.02)\n", + "\n", + " # Limit the map to -60 degrees latitude and below.\n", + " ax1.set_extent([-180, 180, -90, -60], ccrs.PlateCarree())\n", + "\n", + " ax1.add_feature(cfeature.LAND)\n", + " ax1.add_feature(cfeature.OCEAN)\n", + "\n", + " ax1.gridlines()\n", + " ax2.gridlines()\n", + "\n", + " ax2.add_feature(cfeature.LAND)\n", + " ax2.add_feature(cfeature.OCEAN)\n", + "\n", + " # Compute a circle in axes coordinates, which we can use as a boundary\n", + " # for the map. We can pan/zoom as much as we like - the boundary will be\n", + " # permanently circular.\n", + " theta = np.linspace(0, 2*np.pi, 100)\n", + " center, radius = [0.5, 0.5], 0.5\n", + " verts = np.vstack([np.sin(theta), np.cos(theta)]).T\n", + " circle = mpath.Path(verts * radius + center)\n", + "\n", + " ax2.set_boundary(circle, transform=ax2.transAxes)\n", + "\n", + " plt.show()\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cartopy/cartopy-quiver-regrid.ipynb b/Cartopy/cartopy-quiver-regrid.ipynb new file mode 100644 index 0000000..633e484 --- /dev/null +++ b/Cartopy/cartopy-quiver-regrid.ipynb @@ -0,0 +1,126 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "\n", + "import cartopy.crs as ccrs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Regridding vectors with quiver\n", + "------------------------------\n", + "\n", + "This example demonstrates the regridding functionality in quiver (there exists\n", + "equivalent functionality in :meth:`cartopy.mpl.geoaxes.GeoAxes.barbs`).\n", + "\n", + "Regridding can be an effective way of visualising a vector field, particularly\n", + "if the data is dense or warped.\n", + "\n", + "### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def sample_data(shape=(20, 30)):\n", + " \"\"\"\n", + " Returns ``(x, y, u, v, crs)`` of some vector data\n", + " computed mathematically. The returned CRS will be a North Polar\n", + " Stereographic projection, meaning that the vectors will be unevenly\n", + " spaced in a PlateCarree projection.\n", + "\n", + " \"\"\"\n", + " crs = ccrs.NorthPolarStereo()\n", + " scale = 1e7\n", + " x = np.linspace(-scale, scale, shape[1])\n", + " y = np.linspace(-scale, scale, shape[0])\n", + "\n", + " x2d, y2d = np.meshgrid(x, y)\n", + " u = 10 * np.cos(2 * x2d / scale + 3 * y2d / scale)\n", + " v = 20 * np.cos(6 * x2d / scale)\n", + "\n", + " return x, y, u, v, crs\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def main():\n", + " plt.figure(figsize=(8, 10))\n", + "\n", + " x, y, u, v, vector_crs = sample_data(shape=(50, 50))\n", + " ax1 = plt.subplot(2, 1, 1, projection=ccrs.PlateCarree())\n", + " ax1.coastlines()\n", + " ax1.set_extent([-45, 55, 20, 80], ccrs.PlateCarree())\n", + " ax1.quiver(x, y, u, v, transform=vector_crs)\n", + "\n", + " ax2 = plt.subplot(2, 1, 2, projection=ccrs.PlateCarree())\n", + " plt.title('The same vector field regridded')\n", + " ax2.coastlines()\n", + " ax2.set_extent([-45, 55, 20, 80], ccrs.PlateCarree())\n", + " ax2.quiver(x, y, u, v, transform=vector_crs, regrid_shape=20)\n", + "\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "main()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Cartopy/cartopy-quiver.ipynb b/Cartopy/cartopy-quiver.ipynb new file mode 100644 index 0000000..dfeda60 --- /dev/null +++ b/Cartopy/cartopy-quiver.ipynb @@ -0,0 +1,325 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "\n", + "import cartopy\n", + "import cartopy.crs as ccrs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def sample_data(shape=(20, 30)):\n", + " \"\"\"\n", + " Returns ``(x, y, u, v, crs)`` of some vector data\n", + " computed mathematically. The returned crs will be a rotated\n", + " pole CRS, meaning that the vectors will be unevenly spaced in\n", + " regular PlateCarree space.\n", + "\n", + " \"\"\"\n", + " crs = ccrs.RotatedPole(pole_longitude=177.5, pole_latitude=37.5)\n", + "\n", + " x = np.linspace(311.9, 391.1, shape[1])\n", + " y = np.linspace(-23.6, 24.8, shape[0])\n", + "\n", + " x2d, y2d = np.meshgrid(x, y)\n", + " u = 10 * (2 * np.cos(2 * np.deg2rad(x2d) + 3 * np.deg2rad(y2d + 30)) ** 2)\n", + " v = 20 * np.cos(6 * np.deg2rad(x2d))\n", + "\n", + " return x, y, u, v, crs\n", + "\n", + "\n", + "def main():\n", + " ax = plt.axes(projection=ccrs.Orthographic(-10, 45))\n", + "\n", + " ax.add_feature(cartopy.feature.OCEAN, zorder=0)\n", + " ax.add_feature(cartopy.feature.LAND, zorder=0, edgecolor='black')\n", + "\n", + " ax.set_global()\n", + " ax.gridlines()\n", + "\n", + " x, y, u, v, vector_crs = sample_data()\n", + " ax.quiver(x, y, u, v, transform=vector_crs)\n", + "\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "main()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`cartopy.geoaxes.quiver`\n", + "\n", + " **Cartopy matplotlib.quiver PolyCollection.**\n", + "\n", + "Plot a 2-D field of arrows on a projected plane.\n", + "\n", + " Extra Kwargs:\n", + "\n", + " * transform: :class:`cartopy.crs.Projection` or matplotlib transform\n", + " The coordinate system in which the vectors are defined.\n", + "\n", + " * regrid_shape: int or 2-tuple of ints\n", + " If given, specifies that the points where the arrows are\n", + " located will be interpolated onto a regular grid in\n", + " projection space. If a single integer is given then that\n", + " will be used as the minimum grid length dimension, while the\n", + " other dimension will be scaled up according to the target\n", + " extent's aspect ratio. If a pair of ints are given they\n", + " determine the grid length in the x and y directions\n", + " respectively.\n", + "\n", + " * target_extent: 4-tuple\n", + " If given, specifies the extent in the target CRS that the\n", + " regular grid defined by *regrid_shape* will have. Defaults\n", + " to the current extent of the map projection.\n", + "\n", + " See :func:`matplotlib.pyplot.quiver` for details on arguments\n", + " and other keyword arguments.\n", + "\n", + " .. note::\n", + "\n", + " The vector components must be defined as grid eastward and\n", + " grid northward.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "`matplotlib.quiver`\n", + "\n", + " **Specialized PolyCollection for arrows.**\n", + "\n", + "Plot a 2-D field of barbs.\n", + "\n", + "Call signatures::\n", + "\n", + " barb(U, V, **kw)\n", + " barb(U, V, C, **kw)\n", + " barb(X, Y, U, V, **kw)\n", + " barb(X, Y, U, V, C, **kw)\n", + "\n", + "*Arguments:*\n", + "\n", + " *X*, *Y*:\n", + " The x and y coordinates of the barb locations\n", + " (default is head of barb; see *pivot* kwarg)\n", + "\n", + " *U*, *V*:\n", + " Give the x and y components of the barb shaft\n", + "\n", + " *C*:\n", + " An optional array used to map colors to the barbs\n", + "\n", + "All arguments may be 1-D or 2-D arrays or sequences. If *X* and *Y*\n", + "are absent, they will be generated as a uniform grid. If *U* and *V*\n", + "are 2-D arrays but *X* and *Y* are 1-D, and if ``len(X)`` and ``len(Y)``\n", + "match the column and row dimensions of *U*, then *X* and *Y* will be\n", + "expanded with :func:`numpy.meshgrid`.\n", + "\n", + " *U*, *V*, *C* may be masked arrays, but masked *X*, *Y* are not\n", + " supported at present.\n", + "\n", + "*Keyword arguments:*\n", + "\n", + " *length*:\n", + " Length of the barb in points; the other parts of the barb\n", + " are scaled against this.\n", + " Default is 9\n", + "\n", + " *pivot*: [ 'tip' | 'middle' ]\n", + " The part of the arrow that is at the grid point; the arrow rotates\n", + " about this point, hence the name *pivot*. Default is 'tip'\n", + "\n", + " *barbcolor*: [ color | color sequence ]\n", + " Specifies the color all parts of the barb except any flags. This\n", + " parameter is analagous to the *edgecolor* parameter for polygons,\n", + " which can be used instead. However this parameter will override\n", + " facecolor.\n", + "\n", + " *flagcolor*: [ color | color sequence ]\n", + " Specifies the color of any flags on the barb. This parameter is\n", + " analagous to the *facecolor* parameter for polygons, which can be\n", + " used instead. However this parameter will override facecolor. If\n", + " this is not set (and *C* has not either) then *flagcolor* will be\n", + " set to match *barbcolor* so that the barb has a uniform color. If\n", + " *C* has been set, *flagcolor* has no effect.\n", + "\n", + " *sizes*:\n", + " A dictionary of coefficients specifying the ratio of a given\n", + " feature to the length of the barb. Only those values one wishes to\n", + " override need to be included. These features include:\n", + "\n", + " - 'spacing' - space between features (flags, full/half barbs)\n", + "\n", + " - 'height' - height (distance from shaft to top) of a flag or\n", + " full barb\n", + "\n", + " - 'width' - width of a flag, twice the width of a full barb\n", + "\n", + " - 'emptybarb' - radius of the circle used for low magnitudes\n", + "\n", + " *fill_empty*:\n", + " A flag on whether the empty barbs (circles) that are drawn should\n", + " be filled with the flag color. If they are not filled, they will\n", + " be drawn such that no color is applied to the center. Default is\n", + " False\n", + "\n", + " *rounding*:\n", + " A flag to indicate whether the vector magnitude should be rounded\n", + " when allocating barb components. If True, the magnitude is\n", + " rounded to the nearest multiple of the half-barb increment. If\n", + " False, the magnitude is simply truncated to the next lowest\n", + " multiple. Default is True\n", + "\n", + " *barb_increments*:\n", + " A dictionary of increments specifying values to associate with\n", + " different parts of the barb. Only those values one wishes to\n", + " override need to be included.\n", + "\n", + " - 'half' - half barbs (Default is 5)\n", + "\n", + " - 'full' - full barbs (Default is 10)\n", + "\n", + " - 'flag' - flags (default is 50)\n", + "\n", + " *flip_barb*:\n", + " Either a single boolean flag or an array of booleans. Single\n", + " boolean indicates whether the lines and flags should point\n", + " opposite to normal for all barbs. An array (which should be the\n", + " same size as the other data arrays) indicates whether to flip for\n", + " each individual barb. Normal behavior is for the barbs and lines\n", + " to point right (comes from wind barbs having these features point\n", + " towards low pressure in the Northern Hemisphere.) Default is\n", + " False\n", + "\n", + "Barbs are traditionally used in meteorology as a way to plot the speed\n", + "and direction of wind observations, but can technically be used to\n", + "plot any two dimensional vector quantity. As opposed to arrows, which\n", + "give vector magnitude by the length of the arrow, the barbs give more\n", + "quantitative information about the vector magnitude by putting slanted\n", + "lines or a triangle for various increments in magnitude, as show\n", + "schematically below::\n", + "\n", + " : /\\ \\\\\n", + " : / \\ \\\\\n", + " : / \\ \\ \\\\\n", + " : / \\ \\ \\\\\n", + " : ------------------------------\n", + "\n", + ".. note the double \\\\ at the end of each line to make the figure\n", + ".. render correctly\n", + "\n", + "The largest increment is given by a triangle (or \"flag\"). After those\n", + "come full lines (barbs). The smallest increment is a half line. There\n", + "is only, of course, ever at most 1 half line. If the magnitude is\n", + "small and only needs a single half-line and no full lines or\n", + "triangles, the half-line is offset from the end of the barb so that it\n", + "can be easily distinguished from barbs with a single full line. The\n", + "magnitude for the barb shown above would nominally be 65, using the\n", + "standard increments of 50, 10, and 5.\n", + "\n", + "linewidths and edgecolors can be used to customize the barb.\n", + "Additional :class:`~matplotlib.collections.PolyCollection` keyword\n", + "arguments:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "matplotlib.__version__ => 3.1.2\n" + ] + } + ], + "source": [ + "print( 'matplotlib.__version__ => ' + matplotlib.__version__ )\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Cartopy/cartopy-rasterio-plot.ipynb b/Cartopy/cartopy-rasterio-plot.ipynb new file mode 100644 index 0000000..a52b809 --- /dev/null +++ b/Cartopy/cartopy-rasterio-plot.ipynb @@ -0,0 +1,221 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "from osgeo import gdal\n", + "from osgeo import gdal_array\n", + "\n", + "import rasterio\n", + "import cartopy.crs as ccrs\n", + "\n", + "## DEM plot via cartopy, rasterio, pyplot imshow\n", + "## Live 8.5 * darkblue-b\n", + "##" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "!mkdir sample_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "==" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "## Rasterio\n", + "## Clean and fast\n", + "## geospatial raster I/O for Python programmers who use Numpy\n", + "\n", + "with rasterio.open('sample_data/SanMateo_CA.tif') as src:\n", + " data = src.read()\n", + " data = np.transpose( data, [1,2,0])\n", + "\n", + "\n", + "## show selected data attributes\n", + "## -------------------------------------\n", + "# type(data) numpy.ma.core.MaskedArray\n", + "# data.ndim 3\n", + "# data.shape (1080, 864, 1)\n", + "# data.dtype dtype('float32')\n", + "\n", + "## NOTE: when using rasterio and matplotlib only,\n", + "## the column order for the trivial case of lat/lon/measure\n", + "## is NOT handled automatically.. \n", + "## column reordering is REQUIRED np.transpose()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'driver': 'GTiff',\n", + " 'dtype': 'float32',\n", + " 'nodata': -3.4028234663852886e+38,\n", + " 'width': 864,\n", + " 'height': 1080,\n", + " 'count': 1,\n", + " 'crs': CRS.from_epsg(4269),\n", + " 'transform': Affine(0.0002777777777780012, 0.0, -122.44000000003291,\n", + " 0.0, -0.0002777777777779992, 37.69999999999724)}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Rasterio supplies a simple dictionary of important GeoTIFF metadata\n", + "src.meta" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[203.4132 , 203.94624],\n", + " [198.2123 , 197.35855]], dtype=float32)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## numpy ndarray indexing example\n", + "## show the measure values for a 2x2 area\n", + "## no-data options are available using a masked array\n", + "## http://docs.scipy.org/doc/numpy/reference/maskedarray.generic.html#what-is-a-masked-array\n", + "\n", + "data[0:2,0:2,0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## pyplot Image-Show \n", + "## http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.imshow\n", + "##\n", + "## Example of using matplotlib to directly display a GeoTIFF\n", + "##\n", + "## Cartopy supplies a library of mapping transformations\n", + "## use an idiom to calculate the correct display bounds\n", + "##\n", + "## data[:,:,0] refers to a numpy ndarray: all-x, all-y, 0th measure\n", + "##\n", + "\n", + "xmin = src.transform[0]\n", + "xmax = src.transform[0] + src.transform[1]*src.width\n", + "ymin = src.transform[3] + src.transform[5]*src.height\n", + "ymax = src.transform[3]\n", + "\n", + "## Mercator etc..\n", + "crs = ccrs.PlateCarree()\n", + "\n", + "ax = plt.axes(projection=crs)\n", + "plt.imshow( data[:,:,0], origin='upper', \n", + " extent=[xmin, xmax, ymin, ymax], \n", + " cmap=plt.get_cmap('gist_earth'),\n", + " transform=crs, interpolation='nearest')\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "==" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Show the same content, but with a colorbar legend for the data\n", + "##\n", + "plt.figure(figsize=(15, 10))\n", + "ax = plt.subplot(111, projection=ccrs.PlateCarree())\n", + "\n", + "#elev, crs, extent = srtm_composite(12, 47, 1, 1)\n", + "plt.imshow( data[:,:,0], transform=ccrs.PlateCarree(),\n", + " cmap='gist_earth')\n", + "cb = plt.colorbar(orientation='vertical')\n", + "cb.set_label('Altitude')\n", + "plt.title(\"DEM\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%whos" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Cartopy/cartopy-shapely-katrina.ipynb b/Cartopy/cartopy-shapely-katrina.ipynb new file mode 100644 index 0000000..a6f2afb --- /dev/null +++ b/Cartopy/cartopy-shapely-katrina.ipynb @@ -0,0 +1,147 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.patches as mpatches\n", + "import matplotlib.pyplot as plt\n", + "import shapely.geometry as sgeom\n", + "\n", + "import cartopy.crs as ccrs\n", + "import cartopy.io.shapereader as shpreader\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def sample_data():\n", + " \"\"\"\n", + " Return a list of latitudes and a list of longitudes (lons, lats)\n", + " for Hurricane Katrina (2005).\n", + "\n", + " The data was originally sourced from the HURDAT2 dataset from AOML/NOAA:\n", + " http://www.aoml.noaa.gov/hrd/hurdat/newhurdat-all.html on 14th Dec 2012.\n", + "\n", + " \"\"\"\n", + " lons = [-75.1, -75.7, -76.2, -76.5, -76.9, -77.7, -78.4, -79.0,\n", + " -79.6, -80.1, -80.3, -81.3, -82.0, -82.6, -83.3, -84.0,\n", + " -84.7, -85.3, -85.9, -86.7, -87.7, -88.6, -89.2, -89.6,\n", + " -89.6, -89.6, -89.6, -89.6, -89.1, -88.6, -88.0, -87.0,\n", + " -85.3, -82.9]\n", + "\n", + " lats = [23.1, 23.4, 23.8, 24.5, 25.4, 26.0, 26.1, 26.2, 26.2, 26.0,\n", + " 25.9, 25.4, 25.1, 24.9, 24.6, 24.4, 24.4, 24.5, 24.8, 25.2,\n", + " 25.7, 26.3, 27.2, 28.2, 29.3, 29.5, 30.2, 31.1, 32.6, 34.1,\n", + " 35.6, 37.0, 38.6, 40.1]\n", + "\n", + " return lons, lats\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def main():\n", + " fig = plt.figure()\n", + " ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.LambertConformal())\n", + "\n", + " ax.set_extent([-125, -66.5, 20, 50], ccrs.Geodetic())\n", + "\n", + " shapename = 'admin_1_states_provinces_lakes_shp'\n", + " states_shp = shpreader.natural_earth(resolution='110m',\n", + " category='cultural', name=shapename)\n", + "\n", + " lons, lats = sample_data()\n", + "\n", + " # to get the effect of having just the states without a map \"background\"\n", + " # turn off the outline and background patches\n", + " ax.background_patch.set_visible(False)\n", + " ax.outline_patch.set_visible(False)\n", + "\n", + " ax.set_title('US States which intersect the track of '\n", + " 'Hurricane Katrina (2005)')\n", + "\n", + " # turn the lons and lats into a shapely LineString\n", + " track = sgeom.LineString(zip(lons, lats))\n", + "\n", + " # buffer the linestring by two degrees (note: this is a non-physical\n", + " # distance)\n", + " track_buffer = track.buffer(2)\n", + "\n", + " def colorize_state(geometry):\n", + " facecolor = (0.9375, 0.9375, 0.859375)\n", + " if geometry.intersects(track):\n", + " facecolor = 'red'\n", + " elif geometry.intersects(track_buffer):\n", + " facecolor = '#FF7E00'\n", + " return {'facecolor': facecolor, 'edgecolor': 'black'}\n", + "\n", + " ax.add_geometries(\n", + " shpreader.Reader(states_shp).geometries(),\n", + " ccrs.PlateCarree(),\n", + " styler=colorize_state)\n", + "\n", + " ax.add_geometries([track_buffer], ccrs.PlateCarree(),\n", + " facecolor='#C8A2C8', alpha=0.5)\n", + " ax.add_geometries([track], ccrs.PlateCarree(),\n", + " facecolor='none', edgecolor='k')\n", + "\n", + " # make two proxy artists to add to a legend\n", + " direct_hit = mpatches.Rectangle((0, 0), 1, 1, facecolor=\"red\")\n", + " within_2_deg = mpatches.Rectangle((0, 0), 1, 1, facecolor=\"#FF7E00\")\n", + " labels = ['State directly intersects\\nwith track',\n", + " 'State is within \\n2 degrees of track']\n", + " ax.legend([direct_hit, within_2_deg], labels,\n", + " loc='lower left', bbox_to_anchor=(0.025, -0.1), fancybox=True)\n", + "\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cartopy/cartopy-star-boundary.ipynb b/Cartopy/cartopy-star-boundary.ipynb new file mode 100644 index 0000000..6ba794c --- /dev/null +++ b/Cartopy/cartopy-star-boundary.ipynb @@ -0,0 +1,90 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Modifying the boundary/neatline of a map in cartopy\n", + "---------------------------------------------------\n", + "\n", + "This example demonstrates how to modify the boundary/neatline\n", + "of an axes. We construct a star with coordinates in a Plate Carree\n", + "coordinate system, and use the star as the outline of the map.\n", + "\n", + "Notice how changing the projection of the map represents a *projected*\n", + "star shaped boundary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.path as mpath\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import cartopy.crs as ccrs\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def main():\n", + " fig = plt.figure()\n", + " ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.PlateCarree())\n", + " ax.coastlines()\n", + "\n", + " # Construct a star in longitudes and latitudes.\n", + " star_path = mpath.Path.unit_regular_star(5, 0.5)\n", + " star_path = mpath.Path(star_path.vertices.copy() * 80,\n", + " star_path.codes.copy())\n", + "\n", + " # Use the star as the boundary.\n", + " ax.set_boundary(star_path, transform=ccrs.PlateCarree())\n", + "\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cartopy/cartopy-streamplot.ipynb b/Cartopy/cartopy-streamplot.ipynb new file mode 100644 index 0000000..a936936 --- /dev/null +++ b/Cartopy/cartopy-streamplot.ipynb @@ -0,0 +1,72 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "import cartopy.crs as ccrs\n", + "from cartopy.examples.arrows import sample_data\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def main():\n", + " fig = plt.figure(figsize=(10, 5))\n", + " ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n", + " ax.set_extent([-90, 75, 10, 85], crs=ccrs.PlateCarree())\n", + " ax.coastlines()\n", + "\n", + " x, y, u, v, vector_crs = sample_data(shape=(80, 100))\n", + " magnitude = (u ** 2 + v ** 2) ** 0.5\n", + " ax.streamplot(x, y, u, v, transform=vector_crs,\n", + " linewidth=2, density=2, color=magnitude)\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cartopy/cartopy-synthetic.ipynb b/Cartopy/cartopy-synthetic.ipynb new file mode 100644 index 0000000..2fc1993 --- /dev/null +++ b/Cartopy/cartopy-synthetic.ipynb @@ -0,0 +1,99 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import cartopy.crs as ccrs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def sample_data(shape=(73, 145)):\n", + " \"\"\"Return ``lons``, ``lats`` and ``data`` of some fake data.\"\"\"\n", + " nlats, nlons = shape\n", + " lats = np.linspace(-np.pi / 2, np.pi / 2, nlats)\n", + " lons = np.linspace(0, 2 * np.pi, nlons)\n", + " lons, lats = np.meshgrid(lons, lats)\n", + " wave = 0.75 * (np.sin(2 * lats) ** 8) * np.cos(4 * lons)\n", + " mean = 0.5 * np.cos(2 * lats) * ((np.sin(2 * lats)) ** 2 + 2)\n", + "\n", + " lats = np.rad2deg(lats)\n", + " lons = np.rad2deg(lons)\n", + " data = wave + mean\n", + "\n", + " return lons, lats, data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "def main():\n", + " fig = plt.figure(figsize=(10, 5))\n", + " ax = fig.add_subplot(1, 1, 1, projection=ccrs.Mollweide())\n", + "\n", + " lons, lats, data = sample_data()\n", + "\n", + " ax.contourf(lons, lats, data,\n", + " transform=ccrs.PlateCarree(),\n", + " cmap='nipy_spectral')\n", + " ax.coastlines()\n", + " ax.set_global()\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cartopy/cartopy-terrain.ipynb b/Cartopy/cartopy-terrain.ipynb new file mode 100644 index 0000000..d0ab171 --- /dev/null +++ b/Cartopy/cartopy-terrain.ipynb @@ -0,0 +1,108 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Map tile acquisition\n", + "--------------------\n", + "\n", + "Demonstrates cartopy's ability to draw map tiles which are downloaded on\n", + "demand from the Stamen tile server. Internally these tiles are then combined\n", + "into a single image and displayed in the cartopy GeoAxes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from matplotlib.transforms import offset_copy\n", + "\n", + "import cartopy.crs as ccrs\n", + "import cartopy.io.img_tiles as cimgt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "def main():\n", + " # Create a Stamen terrain background instance.\n", + " stamen_terrain = cimgt.Stamen('terrain-background')\n", + "\n", + " fig = plt.figure()\n", + "\n", + " # Create a GeoAxes in the tile's projection.\n", + " ax = fig.add_subplot(1, 1, 1, projection=stamen_terrain.crs)\n", + "\n", + " # Limit the extent of the map to a small longitude/latitude range.\n", + " ax.set_extent([-22, -15, 63, 65], crs=ccrs.Geodetic())\n", + "\n", + " # Add the Stamen data at zoom level 8.\n", + " ax.add_image(stamen_terrain, 8)\n", + "\n", + " # Add a marker for the Eyjafjallajökull volcano.\n", + " ax.plot(-19.613333, 63.62, marker='o', color='red', markersize=12,\n", + " alpha=0.7, transform=ccrs.Geodetic())\n", + "\n", + " # Use the cartopy interface to create a matplotlib transform object\n", + " # for the Geodetic coordinate system. We will use this along with\n", + " # matplotlib's offset_copy function to define a coordinate system which\n", + " # translates the text by 25 pixels to the left.\n", + " geodetic_transform = ccrs.Geodetic()._as_mpl_transform(ax)\n", + " text_transform = offset_copy(geodetic_transform, units='dots', x=-25)\n", + "\n", + " # Add text 25 pixels to the left of the volcano.\n", + " ax.text(-19.613333, 63.62, u'Eyjafjallajökull',\n", + " verticalalignment='center', horizontalalignment='right',\n", + " transform=text_transform,\n", + " bbox=dict(facecolor='sandybrown', alpha=0.5, boxstyle='round'))\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cartopy/cartopy-ticklabels.ipynb b/Cartopy/cartopy-ticklabels.ipynb new file mode 100644 index 0000000..2f7c31a --- /dev/null +++ b/Cartopy/cartopy-ticklabels.ipynb @@ -0,0 +1,105 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Tick Labels\n", + "-----------\n", + "\n", + "This example demonstrates adding tick labels to maps on rectangular\n", + "projections using special tick formatters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import cartopy.crs as ccrs\n", + "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "def main():\n", + " fig = plt.figure(figsize=(8, 10))\n", + "\n", + " # Label axes of a Plate Carree projection with a central longitude of 180:\n", + " ax1 = fig.add_subplot(2, 1, 1,\n", + " projection=ccrs.PlateCarree(central_longitude=180))\n", + " ax1.set_global()\n", + " ax1.coastlines()\n", + " ax1.set_xticks([0, 60, 120, 180, 240, 300, 360], crs=ccrs.PlateCarree())\n", + " ax1.set_yticks([-90, -60, -30, 0, 30, 60, 90], crs=ccrs.PlateCarree())\n", + " lon_formatter = LongitudeFormatter(zero_direction_label=True)\n", + " lat_formatter = LatitudeFormatter()\n", + " ax1.xaxis.set_major_formatter(lon_formatter)\n", + " ax1.yaxis.set_major_formatter(lat_formatter)\n", + "\n", + " # Label axes of a Mercator projection without degree symbols in the labels\n", + " # and formatting labels to include 1 decimal place:\n", + " ax2 = fig.add_subplot(2, 1, 2, projection=ccrs.Mercator())\n", + " ax2.set_global()\n", + " ax2.coastlines()\n", + " ax2.set_xticks([-180, -120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree())\n", + " ax2.set_yticks([-78.5, -60, -25.5, 25.5, 60, 80], crs=ccrs.PlateCarree())\n", + " lon_formatter = LongitudeFormatter(number_format='.1f',\n", + " degree_symbol='',\n", + " dateline_direction_label=True)\n", + " lat_formatter = LatitudeFormatter(number_format='.1f',\n", + " degree_symbol='')\n", + " ax2.xaxis.set_major_formatter(lon_formatter)\n", + " ax2.yaxis.set_major_formatter(lat_formatter)\n", + "\n", + " plt.show()\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cartopy/cartopy-tissot.ipynb b/Cartopy/cartopy-tissot.ipynb new file mode 100644 index 0000000..ef5eaff --- /dev/null +++ b/Cartopy/cartopy-tissot.ipynb @@ -0,0 +1,76 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "import cartopy.crs as ccrs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "def main():\n", + " fig = plt.figure(figsize=(10, 5))\n", + " ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n", + "\n", + " # make the map global rather than have it zoom in to\n", + " # the extents of any plotted data\n", + " ax.set_global()\n", + "\n", + " ax.stock_img()\n", + " ax.coastlines()\n", + "\n", + " ax.tissot(facecolor='orange', alpha=0.4)\n", + "\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cartopy/cartopy-un-flag.ipynb b/Cartopy/cartopy-un-flag.ipynb new file mode 100644 index 0000000..383d0b8 --- /dev/null +++ b/Cartopy/cartopy-un-flag.ipynb @@ -0,0 +1,225 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "UN Flag\n", + "-------\n", + "\n", + "A demonstration of the power of Matplotlib combined with cartopy's Azimuthal\n", + "Equidistant projection to reproduce the UN flag.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.patches import PathPatch\n", + "import matplotlib.path\n", + "import matplotlib.ticker\n", + "from matplotlib.transforms import BboxTransform, Bbox\n", + "import numpy as np\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# When drawing the flag, we can either use white filled land, or be a little\n", + "# more fancy and use the Natural Earth shaded relief imagery.\n", + "filled_land = True\n", + "\n", + "\n", + "def olive_path():\n", + " \"\"\"\n", + " Return a Matplotlib path representing a single olive branch from the\n", + " UN Flag. The path coordinates were extracted from the SVG at\n", + " https://commons.wikimedia.org/wiki/File:Flag_of_the_United_Nations.svg.\n", + "\n", + " \"\"\"\n", + " olives_verts = np.array(\n", + " [[0, 2, 6, 9, 30, 55, 79, 94, 104, 117, 134, 157, 177,\n", + " 188, 199, 207, 191, 167, 149, 129, 109, 87, 53, 22, 0, 663,\n", + " 245, 223, 187, 158, 154, 150, 146, 149, 154, 158, 181, 184, 197,\n", + " 181, 167, 153, 142, 129, 116, 119, 123, 127, 151, 178, 203, 220,\n", + " 237, 245, 663, 280, 267, 232, 209, 205, 201, 196, 196, 201, 207,\n", + " 211, 224, 219, 230, 220, 212, 207, 198, 195, 176, 197, 220, 239,\n", + " 259, 277, 280, 663, 295, 293, 264, 250, 247, 244, 240, 240, 243,\n", + " 244, 249, 251, 250, 248, 242, 245, 233, 236, 230, 228, 224, 222,\n", + " 234, 249, 262, 275, 285, 291, 295, 296, 295, 663, 294, 293, 292,\n", + " 289, 294, 277, 271, 269, 268, 265, 264, 264, 264, 272, 260, 248,\n", + " 245, 243, 242, 240, 243, 245, 247, 252, 256, 259, 258, 257, 258,\n", + " 267, 285, 290, 294, 297, 294, 663, 285, 285, 277, 266, 265, 265,\n", + " 265, 277, 266, 268, 269, 269, 269, 268, 268, 267, 267, 264, 248,\n", + " 235, 232, 229, 228, 229, 232, 236, 246, 266, 269, 271, 285, 285,\n", + " 663, 252, 245, 238, 230, 246, 245, 250, 252, 255, 256, 256, 253,\n", + " 249, 242, 231, 214, 208, 208, 227, 244, 252, 258, 262, 262, 261,\n", + " 262, 264, 265, 252, 663, 185, 197, 206, 215, 223, 233, 242, 237,\n", + " 237, 230, 220, 202, 185, 663],\n", + " [8, 5, 3, 0, 22, 46, 46, 46, 35, 27, 16, 10, 18,\n", + " 22, 28, 38, 27, 26, 33, 41, 52, 52, 52, 30, 8, 595,\n", + " 77, 52, 61, 54, 53, 52, 53, 55, 55, 57, 65, 90, 106,\n", + " 96, 81, 68, 58, 54, 51, 50, 51, 50, 44, 34, 43, 48,\n", + " 61, 77, 595, 135, 104, 102, 83, 79, 76, 74, 74, 79, 84,\n", + " 90, 109, 135, 156, 145, 133, 121, 100, 77, 62, 69, 67, 80,\n", + " 92, 113, 135, 595, 198, 171, 156, 134, 129, 124, 120, 123, 126,\n", + " 129, 138, 149, 161, 175, 188, 202, 177, 144, 116, 110, 105, 99,\n", + " 108, 116, 126, 136, 147, 162, 173, 186, 198, 595, 249, 255, 261,\n", + " 267, 241, 222, 200, 192, 183, 175, 175, 175, 175, 199, 221, 240,\n", + " 245, 250, 256, 245, 233, 222, 207, 194, 180, 172, 162, 153, 154,\n", + " 171, 184, 202, 216, 233, 249, 595, 276, 296, 312, 327, 327, 327,\n", + " 327, 308, 284, 262, 240, 240, 239, 239, 242, 244, 247, 265, 277,\n", + " 290, 293, 296, 300, 291, 282, 274, 253, 236, 213, 235, 252, 276,\n", + " 595, 342, 349, 355, 357, 346, 326, 309, 303, 297, 291, 290, 297,\n", + " 304, 310, 321, 327, 343, 321, 305, 292, 286, 278, 270, 276, 281,\n", + " 287, 306, 328, 342, 595, 379, 369, 355, 343, 333, 326, 318, 328,\n", + " 340, 349, 366, 373, 379, 595]]).T\n", + " olives_codes = np.array([1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n", + " 4, 4, 4, 4, 4, 4, 4, 4, 4, 79, 1, 4, 4, 4, 4, 4,\n", + " 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n", + " 4, 4, 4, 4, 4, 4, 79, 1, 4, 4, 4, 4, 4, 4, 2, 4,\n", + " 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n", + " 4, 79, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n", + " 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n", + " 4, 79, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n", + " 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 4,\n", + " 4, 4, 4, 4, 4, 79, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n", + " 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n", + " 4, 4, 4, 4, 4, 4, 79, 1, 4, 4, 4, 4, 4, 4, 4, 4,\n", + " 4, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n", + " 4, 4, 4, 4, 79, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n", + " 4, 4, 79], dtype=np.uint8)\n", + "\n", + " return matplotlib.path.Path(olives_verts, olives_codes)\n", + "\n", + "\n", + "def main():\n", + " blue = '#4b92db'\n", + "\n", + " # We're drawing a flag with a 3:5 aspect ratio.\n", + " fig = plt.figure(figsize=[7.5, 4.5], facecolor=blue)\n", + " # Put a blue background on the figure.\n", + " blue_background = PathPatch(matplotlib.path.Path.unit_rectangle(),\n", + " transform=fig.transFigure, color=blue,\n", + " zorder=-1)\n", + " fig.patches.append(blue_background)\n", + "\n", + " # Set up the Azimuthal Equidistant and Plate Carree projections\n", + " # for later use.\n", + " az_eq = ccrs.AzimuthalEquidistant(central_latitude=90)\n", + " pc = ccrs.PlateCarree()\n", + "\n", + " # Pick a suitable location for the map (which is in an Azimuthal\n", + " # Equidistant projection).\n", + " ax = fig.add_axes([0.25, 0.24, 0.5, 0.54], projection=az_eq)\n", + "\n", + " # The background patch and outline patch are not needed in this example.\n", + " ax.background_patch.set_facecolor('none')\n", + " ax.outline_patch.set_edgecolor('none')\n", + "\n", + " # We want the map to go down to -60 degrees latitude.\n", + " ax.set_extent([-180, 180, -60, 90], ccrs.PlateCarree())\n", + "\n", + " # Importantly, we want the axes to be circular at the -60 latitude\n", + " # rather than cartopy's default behaviour of zooming in and becoming\n", + " # square.\n", + " _, patch_radius = az_eq.transform_point(0, -60, pc)\n", + " circular_path = matplotlib.path.Path.circle(0, patch_radius)\n", + " ax.set_boundary(circular_path)\n", + "\n", + " if filled_land:\n", + " ax.add_feature(\n", + " cfeature.LAND, facecolor='white', edgecolor='none')\n", + " else:\n", + " ax.stock_img()\n", + "\n", + " gl = ax.gridlines(crs=pc, linewidth=3, color='white', linestyle='-')\n", + " # Meridians every 45 degrees, and 5 parallels.\n", + " gl.xlocator = matplotlib.ticker.FixedLocator(np.arange(0, 361, 45))\n", + " parallels = np.linspace(-60, 70, 5, endpoint=True)\n", + " gl.ylocator = matplotlib.ticker.FixedLocator(parallels)\n", + "\n", + " # Now add the olive branches around the axes. We do this in normalised\n", + " # figure coordinates\n", + " olive_leaf = olive_path()\n", + "\n", + " olives_bbox = Bbox.null()\n", + " olives_bbox.update_from_path(olive_leaf)\n", + "\n", + " # The first olive branch goes from left to right.\n", + " olive1_axes_bbox = Bbox([[0.45, 0.15], [0.725, 0.75]])\n", + " olive1_trans = BboxTransform(olives_bbox, olive1_axes_bbox)\n", + "\n", + " # THe second olive branch goes from right to left (mirroring the first).\n", + " olive2_axes_bbox = Bbox([[0.55, 0.15], [0.275, 0.75]])\n", + " olive2_trans = BboxTransform(olives_bbox, olive2_axes_bbox)\n", + "\n", + " olive1 = PathPatch(olive_leaf, facecolor='white', edgecolor='none',\n", + " transform=olive1_trans + fig.transFigure)\n", + " olive2 = PathPatch(olive_leaf, facecolor='white', edgecolor='none',\n", + " transform=olive2_trans + fig.transFigure)\n", + "\n", + " fig.patches.append(olive1)\n", + " fig.patches.append(olive2)\n", + "\n", + " plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cartopy/cartopy-utm-zones.ipynb b/Cartopy/cartopy-utm-zones.ipynb new file mode 100644 index 0000000..c5c4d8c --- /dev/null +++ b/Cartopy/cartopy-utm-zones.ipynb @@ -0,0 +1,102 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Displaying all 60 zones of the UTM projection\n", + "---------------------------------------------\n", + "\n", + "This example displays all 60 zones of the Universal Transverse Mercator\n", + "projection next to each other in a figure.\n", + "\n", + "First we create a figure with 60 subplots in one row.\n", + "Next we set the projection of each axis in the figure to a specific UTM zone.\n", + "Then we add coastlines, gridlines and the number of the zone.\n", + "Finally we add a supertitle and display the figure." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import cartopy.crs as ccrs\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "def main():\n", + " # Create a list of integers from 1 - 60\n", + " zones = range(1, 61)\n", + "\n", + " # Create a figure\n", + " fig = plt.figure(figsize=(18, 6))\n", + "\n", + " # Loop through each zone in the list\n", + " for zone in zones:\n", + "\n", + " # Add GeoAxes object with specific UTM zone projection to the figure\n", + " ax = fig.add_subplot(1, len(zones), zone,\n", + " projection=ccrs.UTM(zone=zone,\n", + " southern_hemisphere=True))\n", + "\n", + " # Add coastlines, gridlines and zone number for the subplot\n", + " ax.coastlines(resolution='110m')\n", + " ax.gridlines()\n", + " ax.set_title(zone)\n", + "\n", + " # Add a supertitle for the figure\n", + " fig.suptitle(\"UTM Projection - Zones\")\n", + "\n", + " # Display the figure\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cartopy/cartopy-wms.ipynb b/Cartopy/cartopy-wms.ipynb new file mode 100644 index 0000000..d974deb --- /dev/null +++ b/Cartopy/cartopy-wms.ipynb @@ -0,0 +1,71 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import cartopy.crs as ccrs\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "def main():\n", + " fig = plt.figure(figsize=(10, 5))\n", + " ax = fig.add_subplot(1, 1, 1, projection=ccrs.InterruptedGoodeHomolosine())\n", + " ax.coastlines()\n", + "\n", + " ax.add_wms(wms='http://vmap0.tiles.osgeo.org/wms/vmap0',\n", + " layers=['basic'])\n", + "\n", + " plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cartopy/examples/always_circular_stereo.py b/Cartopy/examples/always_circular_stereo.py new file mode 100644 index 0000000..318731d --- /dev/null +++ b/Cartopy/examples/always_circular_stereo.py @@ -0,0 +1,57 @@ +""" +Custom Boundary Shape +--------------------- + +This example demonstrates how a custom shape geometry may be used +instead of the projection's default boundary. + +In this instance, we define the boundary as a circle in axes coordinates. +This means that no matter the extent of the map itself, the boundary will +always be a circle. + +""" +__tags__ = ['Lines and polygons'] + +import matplotlib.path as mpath +import matplotlib.pyplot as plt +import numpy as np + +import cartopy.crs as ccrs +import cartopy.feature as cfeature + + +def main(): + fig = plt.figure(figsize=[10, 5]) + ax1 = fig.add_subplot(1, 2, 1, projection=ccrs.SouthPolarStereo()) + ax2 = fig.add_subplot(1, 2, 2, projection=ccrs.SouthPolarStereo(), + sharex=ax1, sharey=ax1) + fig.subplots_adjust(bottom=0.05, top=0.95, + left=0.04, right=0.95, wspace=0.02) + + # Limit the map to -60 degrees latitude and below. + ax1.set_extent([-180, 180, -90, -60], ccrs.PlateCarree()) + + ax1.add_feature(cfeature.LAND) + ax1.add_feature(cfeature.OCEAN) + + ax1.gridlines() + ax2.gridlines() + + ax2.add_feature(cfeature.LAND) + ax2.add_feature(cfeature.OCEAN) + + # Compute a circle in axes coordinates, which we can use as a boundary + # for the map. We can pan/zoom as much as we like - the boundary will be + # permanently circular. + theta = np.linspace(0, 2*np.pi, 100) + center, radius = [0.5, 0.5], 0.5 + verts = np.vstack([np.sin(theta), np.cos(theta)]).T + circle = mpath.Path(verts * radius + center) + + ax2.set_boundary(circle, transform=ax2.transAxes) + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/arrows.py b/Cartopy/examples/arrows.py new file mode 100644 index 0000000..771155c --- /dev/null +++ b/Cartopy/examples/arrows.py @@ -0,0 +1,54 @@ +""" +Arrows +------ + +Plotting arrows. + +""" +__tags__ = ['Vector data'] + +import matplotlib.pyplot as plt +import numpy as np + +import cartopy.crs as ccrs +import cartopy.feature as cfeature + + +def sample_data(shape=(20, 30)): + """ + Return ``(x, y, u, v, crs)`` of some vector data + computed mathematically. The returned crs will be a rotated + pole CRS, meaning that the vectors will be unevenly spaced in + regular PlateCarree space. + + """ + crs = ccrs.RotatedPole(pole_longitude=177.5, pole_latitude=37.5) + + x = np.linspace(311.9, 391.1, shape[1]) + y = np.linspace(-23.6, 24.8, shape[0]) + + x2d, y2d = np.meshgrid(x, y) + u = 10 * (2 * np.cos(2 * np.deg2rad(x2d) + 3 * np.deg2rad(y2d + 30)) ** 2) + v = 20 * np.cos(6 * np.deg2rad(x2d)) + + return x, y, u, v, crs + + +def main(): + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1, projection=ccrs.Orthographic(-10, 45)) + + ax.add_feature(cfeature.OCEAN, zorder=0) + ax.add_feature(cfeature.LAND, zorder=0, edgecolor='black') + + ax.set_global() + ax.gridlines() + + x, y, u, v, vector_crs = sample_data() + ax.quiver(x, y, u, v, transform=vector_crs) + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/aurora_forecast.py b/Cartopy/examples/aurora_forecast.py new file mode 100644 index 0000000..9fb7436 --- /dev/null +++ b/Cartopy/examples/aurora_forecast.py @@ -0,0 +1,119 @@ +""" +Plotting the Aurora Forecast from NOAA on Orthographic Polar Projection +----------------------------------------------------------------------- + +The National Oceanic and Atmospheric Administration (NOAA) monitors the +solar wind conditions using the ACE spacecraft orbiting close to the L1 +Lagrangian point of the Sun-Earth system. This data is fed into the +OVATION-Prime model to forecast the probability of visible aurora at +various locations on Earth. Every five minutes a new forecast is +published for the coming 30 minutes. The data is provided as a +1024 by 512 grid of probabilities in percent of visible aurora. The +data spaced equally in degrees from -180 to 180 and -90 to 90. + +""" +__tags__ = ["Scalar data"] +try: + from urllib2 import urlopen +except ImportError: + from urllib.request import urlopen + +from io import StringIO + +import numpy as np +from datetime import datetime +import cartopy.crs as ccrs +from cartopy.feature.nightshade import Nightshade +import matplotlib.pyplot as plt +from matplotlib.colors import LinearSegmentedColormap + + +def aurora_forecast(): + """ + Get the latest Aurora Forecast from https://www.swpc.noaa.gov. + + Returns + ------- + img : numpy array + The pixels of the image in a numpy array. + img_proj : cartopy CRS + The rectangular coordinate system of the image. + img_extent : tuple of floats + The extent of the image ``(x0, y0, x1, y1)`` referenced in + the ``img_proj`` coordinate system. + origin : str + The origin of the image to be passed through to matplotlib's imshow. + dt : datetime + Time of forecast validity. + + """ + + # GitHub gist to download the example data from + url = ('https://gist.githubusercontent.com/belteshassar/' + 'c7ea9e02a3e3934a9ddc/raw/aurora-nowcast-map.txt') + # To plot the current forecast instead, uncomment the following line + # url = 'https://services.swpc.noaa.gov/text/aurora-nowcast-map.txt' + + response_text = StringIO(urlopen(url).read().decode('utf-8')) + img = np.loadtxt(response_text) + # Read forecast date and time + response_text.seek(0) + for line in response_text: + if line.startswith('Product Valid At:', 2): + dt = datetime.strptime(line[-17:-1], '%Y-%m-%d %H:%M') + + img_proj = ccrs.PlateCarree() + img_extent = (-180, 180, -90, 90) + return img, img_proj, img_extent, 'lower', dt + + +def aurora_cmap(): + """Return a colormap with aurora like colors""" + stops = {'red': [(0.00, 0.1725, 0.1725), + (0.50, 0.1725, 0.1725), + (1.00, 0.8353, 0.8353)], + + 'green': [(0.00, 0.9294, 0.9294), + (0.50, 0.9294, 0.9294), + (1.00, 0.8235, 0.8235)], + + 'blue': [(0.00, 0.3843, 0.3843), + (0.50, 0.3843, 0.3843), + (1.00, 0.6549, 0.6549)], + + 'alpha': [(0.00, 0.0, 0.0), + (0.50, 1.0, 1.0), + (1.00, 1.0, 1.0)]} + + return LinearSegmentedColormap('aurora', stops) + + +def main(): + fig = plt.figure(figsize=[10, 5]) + + # We choose to plot in an Orthographic projection as it looks natural + # and the distortion is relatively small around the poles where + # the aurora is most likely. + + # ax1 for Northern Hemisphere + ax1 = fig.add_subplot(1, 2, 1, projection=ccrs.Orthographic(0, 90)) + + # ax2 for Southern Hemisphere + ax2 = fig.add_subplot(1, 2, 2, projection=ccrs.Orthographic(180, -90)) + + img, crs, extent, origin, dt = aurora_forecast() + + for ax in [ax1, ax2]: + ax.coastlines(zorder=3) + ax.stock_img() + ax.gridlines() + ax.add_feature(Nightshade(dt)) + ax.imshow(img, vmin=0, vmax=100, transform=crs, + extent=extent, origin=origin, zorder=2, + cmap=aurora_cmap()) + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/axes_grid_basic.py b/Cartopy/examples/axes_grid_basic.py new file mode 100644 index 0000000..a5b1d50 --- /dev/null +++ b/Cartopy/examples/axes_grid_basic.py @@ -0,0 +1,77 @@ +""" +Using Cartopy and AxesGrid toolkit +---------------------------------- + +This example demonstrates how to use cartopy `GeoAxes` with +`AxesGrid` from the `mpl_toolkits.axes_grid1`. +The script constructs an `axes_class` kwarg with Plate Carree projection +and passes it to the `AxesGrid` instance. The `AxesGrid` built-in +labelling is switched off, and instead a standard procedure +of creating grid lines is used. Then some fake data is plotted. +""" +import cartopy.crs as ccrs +from cartopy.mpl.geoaxes import GeoAxes +from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter +import matplotlib.pyplot as plt +from mpl_toolkits.axes_grid1 import AxesGrid +import numpy as np + + +def sample_data_3d(shape): + """Return `lons`, `lats`, `times` and fake `data`""" + ntimes, nlats, nlons = shape + lats = np.linspace(-np.pi / 2, np.pi / 2, nlats) + lons = np.linspace(0, 2 * np.pi, nlons) + lons, lats = np.meshgrid(lons, lats) + wave = 0.75 * (np.sin(2 * lats) ** 8) * np.cos(4 * lons) + mean = 0.5 * np.cos(2 * lats) * ((np.sin(2 * lats)) ** 2 + 2) + + lats = np.rad2deg(lats) + lons = np.rad2deg(lons) + data = wave + mean + + times = np.linspace(-1, 1, ntimes) + new_shape = data.shape + (ntimes, ) + data = np.rollaxis(data.repeat(ntimes).reshape(new_shape), -1) + data *= times[:, np.newaxis, np.newaxis] + + return lons, lats, times, data + + +def main(): + projection = ccrs.PlateCarree() + axes_class = (GeoAxes, + dict(map_projection=projection)) + + lons, lats, times, data = sample_data_3d((6, 73, 145)) + + fig = plt.figure() + axgr = AxesGrid(fig, 111, axes_class=axes_class, + nrows_ncols=(3, 2), + axes_pad=0.6, + cbar_location='right', + cbar_mode='single', + cbar_pad=0.2, + cbar_size='3%', + label_mode='') # note the empty label_mode + + for i, ax in enumerate(axgr): + ax.coastlines() + ax.set_xticks(np.linspace(-180, 180, 5), crs=projection) + ax.set_yticks(np.linspace(-90, 90, 5), crs=projection) + lon_formatter = LongitudeFormatter(zero_direction_label=True) + lat_formatter = LatitudeFormatter() + ax.xaxis.set_major_formatter(lon_formatter) + ax.yaxis.set_major_formatter(lat_formatter) + + p = ax.contourf(lons, lats, data[i, ...], + transform=projection, + cmap='RdBu') + + axgr.cbar_axes[0].colorbar(p) + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/barbs.py b/Cartopy/examples/barbs.py new file mode 100644 index 0000000..321b693 --- /dev/null +++ b/Cartopy/examples/barbs.py @@ -0,0 +1,32 @@ +""" +Barbs +----- + +Plotting barbs. + +""" +__tags__ = ['Vector data'] + +import matplotlib.pyplot as plt + +import cartopy.crs as ccrs +from cartopy.examples.arrows import sample_data + + +def main(): + fig = plt.figure(figsize=(10, 5)) + ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree()) + ax.set_extent([-90, 80, 10, 85], crs=ccrs.PlateCarree()) + ax.stock_img() + ax.coastlines() + + x, y, u, v, vector_crs = sample_data(shape=(10, 14)) + ax.barbs(x, y, u, v, length=5, + sizes=dict(emptybarb=0.25, spacing=0.2, height=0.5), + linewidth=0.95, transform=vector_crs) + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/contour_labels.py b/Cartopy/examples/contour_labels.py new file mode 100644 index 0000000..0d18b22 --- /dev/null +++ b/Cartopy/examples/contour_labels.py @@ -0,0 +1,56 @@ +""" +Contour labels +-------------- + +An example of adding contour labels to matplotlib contours. + +""" +__tags__ = ['Scalar data'] + +import cartopy.crs as ccrs +import matplotlib.pyplot as plt + +from cartopy.examples.waves import sample_data + + +def main(): + fig = plt.figure() + + # Setup a global EckertIII map with faint coastlines. + ax = fig.add_subplot(1, 1, 1, projection=ccrs.EckertIII()) + ax.set_global() + ax.coastlines('110m', alpha=0.1) + + # Use the waves example to provide some sample data, but make it + # more dependent on y for more interesting contours. + x, y, z = sample_data((20, 40)) + z = z * -1.5 * y + + # Add colourful filled contours. + filled_c = ax.contourf(x, y, z, transform=ccrs.PlateCarree()) + + # And black line contours. + line_c = ax.contour(x, y, z, levels=filled_c.levels, + colors=['black'], + transform=ccrs.PlateCarree()) + + # Uncomment to make the line contours invisible. + # plt.setp(line_c.collections, visible=False) + + # Add a colorbar for the filled contour. + fig.colorbar(filled_c, orientation='horizontal') + + # Use the line contours to place contour labels. + ax.clabel( + line_c, # Typically best results when labelling line contours. + colors=['black'], + manual=False, # Automatic placement vs manual placement. + inline=True, # Cut the line where the label will be placed. + fmt=' {:.0f} '.format, # Labes as integers, with some extra space. + ) + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/eccentric_ellipse.py b/Cartopy/examples/eccentric_ellipse.py new file mode 100644 index 0000000..c33ce78 --- /dev/null +++ b/Cartopy/examples/eccentric_ellipse.py @@ -0,0 +1,74 @@ +""" +Displaying data on an eccentric ellipse +--------------------------------------- + +This example demonstrates plotting data on an eccentric ellipse. The data +plotted is a topography map of the asteroid Vesta. The map is actually an +image, which is defined on an equirectangluar projection relative to an +ellipse with a semi-major axis of 285 km and a semi-minor axis of 229 km. +The image is reprojected on-the-fly onto a geostationary projection with +matching eccentricity. + +""" +try: + from urllib2 import urlopen +except ImportError: + from urllib.request import urlopen +from io import BytesIO + +import cartopy.crs as ccrs +import matplotlib.pyplot as plt +import numpy as np +from PIL import Image + + +def vesta_image(): + """ + Return an image of Vesta's topography. + + Image credit: NASA/JPL-Caltech/UCLA/MPS/DLR/IDA/PSI + + Returns + ------- + img : numpy array + The pixels of the image in a numpy array. + img_proj : cartopy CRS + The rectangular coordinate system of the image. + img_extent : tuple of floats + The extent of the image ``(x0, y0, x1, y1)`` referenced in + the ``img_proj`` coordinate system. + + """ + url = 'https://www.nasa.gov/sites/default/files/pia17037.jpg' + img_handle = BytesIO(urlopen(url).read()) + raw_image = Image.open(img_handle) + # The image is extremely high-resolution, which takes a long time to + # plot. Sub-sampling reduces the time taken to plot while not + # significantly altering the integrity of the result. + smaller_image = raw_image.resize([raw_image.size[0] // 10, + raw_image.size[1] // 10]) + img = np.asarray(smaller_image) + # We define the semimajor and semiminor axes, but must also tell the + # globe not to use the WGS84 ellipse, which is its default behaviour. + img_globe = ccrs.Globe(semimajor_axis=285000., semiminor_axis=229000., + ellipse=None) + img_proj = ccrs.PlateCarree(globe=img_globe) + img_extent = (-895353.906273091, 895353.906273091, + 447676.9531365455, -447676.9531365455) + return img, img_globe, img_proj, img_extent + + +def main(): + img, globe, crs, extent = vesta_image() + projection = ccrs.Geostationary(globe=globe) + + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1, projection=projection) + ax.imshow(img, transform=crs, extent=extent) + fig.text(.075, .012, "Image credit: NASA/JPL-Caltech/UCLA/MPS/DLR/IDA/PSI", + bbox={'facecolor': 'w', 'edgecolor': 'k'}) + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/effects_of_the_ellipse.py b/Cartopy/examples/effects_of_the_ellipse.py new file mode 100644 index 0000000..468dcfd --- /dev/null +++ b/Cartopy/examples/effects_of_the_ellipse.py @@ -0,0 +1,118 @@ +""" +The effect of badly referencing an ellipse +------------------------------------------ + +This example demonstrates the effect of referencing your data to an incorrect +ellipse. + +First we define two coordinate systems - one using the World Geodetic System +established in 1984 and the other using a spherical globe. Next we extract +data from the Natural Earth land dataset and convert the Geodetic +coordinates (referenced in WGS84) into the respective coordinate systems +that we have defined. Finally, we plot these datasets onto a map assuming +that they are both referenced to the WGS84 ellipse and compare how the +coastlines are shifted as a result of referencing the incorrect ellipse. + +""" +__tags__ = ['Lines and polygons'] + +import cartopy.crs as ccrs +import cartopy.feature as cfeature +from cartopy.io.img_tiles import Stamen +import matplotlib.pyplot as plt +from matplotlib.lines import Line2D as Line +from matplotlib.patheffects import Stroke +import numpy as np +import shapely.geometry as sgeom +from shapely.ops import transform as geom_transform + + +def transform_fn_factory(target_crs, source_crs): + """ + Return a function which can be used by ``shapely.op.transform`` + to transform the coordinate points of a geometry. + + The function explicitly *does not* do any interpolation or clever + transformation of the coordinate points, so there is no guarantee + that the resulting geometry would make any sense. + + """ + def transform_fn(x, y, z=None): + new_coords = target_crs.transform_points(source_crs, + np.asanyarray(x), + np.asanyarray(y)) + return new_coords[:, 0], new_coords[:, 1], new_coords[:, 2] + + return transform_fn + + +def main(): + # Define the two coordinate systems with different ellipses. + wgs84 = ccrs.PlateCarree(globe=ccrs.Globe(datum='WGS84', + ellipse='WGS84')) + sphere = ccrs.PlateCarree(globe=ccrs.Globe(datum='WGS84', + ellipse='sphere')) + + # Define the coordinate system of the data we have from Natural Earth and + # acquire the 1:10m physical coastline shapefile. + geodetic = ccrs.Geodetic(globe=ccrs.Globe(datum='WGS84')) + dataset = cfeature.NaturalEarthFeature(category='physical', + name='coastline', + scale='10m') + + # Create a Stamen map tiler instance, and use its CRS for the GeoAxes. + tiler = Stamen('terrain-background') + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1, projection=tiler.crs) + ax.set_title('The effect of incorrectly referencing the Solomon Islands') + + # Pick the area of interest. In our case, roughly the Solomon Islands, and + # get hold of the coastlines for that area. + extent = [155, 163, -11.5, -6] + ax.set_extent(extent, geodetic) + geoms = list(dataset.intersecting_geometries(extent)) + + # Add the Stamen aerial imagery at zoom level 7. + ax.add_image(tiler, 7) + + # Transform the geodetic coordinates of the coastlines into the two + # projections of differing ellipses. + wgs84_geoms = [geom_transform(transform_fn_factory(wgs84, geodetic), + geom) for geom in geoms] + sphere_geoms = [geom_transform(transform_fn_factory(sphere, geodetic), + geom) for geom in geoms] + + # Using these differently referenced geometries, assume that they are + # both referenced to WGS84. + ax.add_geometries(wgs84_geoms, wgs84, edgecolor='white', facecolor='none') + ax.add_geometries(sphere_geoms, wgs84, edgecolor='gray', facecolor='none') + + # Create a legend for the coastlines. + legend_artists = [Line([0], [0], color=color, linewidth=3) + for color in ('white', 'gray')] + legend_texts = ['Correct ellipse\n(WGS84)', 'Incorrect ellipse\n(sphere)'] + legend = ax.legend(legend_artists, legend_texts, fancybox=True, + loc='lower left', framealpha=0.75) + legend.legendPatch.set_facecolor('wheat') + + # Create an inset GeoAxes showing the location of the Solomon Islands. + sub_ax = fig.add_axes([0.7, 0.625, 0.2, 0.2], + projection=ccrs.PlateCarree()) + sub_ax.set_extent([110, 180, -50, 10], geodetic) + + # Make a nice border around the inset axes. + effect = Stroke(linewidth=4, foreground='wheat', alpha=0.5) + sub_ax.spines['geo'].set_path_effects([effect]) + + # Add the land, coastlines and the extent of the Solomon Islands. + sub_ax.add_feature(cfeature.LAND) + sub_ax.coastlines() + extent_box = sgeom.box(extent[0], extent[2], extent[1], extent[3]) + sub_ax.add_geometries([extent_box], ccrs.PlateCarree(), facecolor='none', + edgecolor='blue', linewidth=2) + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/eyja_volcano.py b/Cartopy/examples/eyja_volcano.py new file mode 100644 index 0000000..d228c19 --- /dev/null +++ b/Cartopy/examples/eyja_volcano.py @@ -0,0 +1,55 @@ +# -*- coding: utf-8 -*- +""" +Map tile acquisition +-------------------- + +Demonstrates cartopy's ability to draw map tiles which are downloaded on +demand from the Stamen tile server. Internally these tiles are then combined +into a single image and displayed in the cartopy GeoAxes. + +""" +__tags__ = ["Scalar data"] + +import matplotlib.pyplot as plt +from matplotlib.transforms import offset_copy + +import cartopy.crs as ccrs +import cartopy.io.img_tiles as cimgt + + +def main(): + # Create a Stamen terrain background instance. + stamen_terrain = cimgt.Stamen('terrain-background') + + fig = plt.figure() + + # Create a GeoAxes in the tile's projection. + ax = fig.add_subplot(1, 1, 1, projection=stamen_terrain.crs) + + # Limit the extent of the map to a small longitude/latitude range. + ax.set_extent([-22, -15, 63, 65], crs=ccrs.Geodetic()) + + # Add the Stamen data at zoom level 8. + ax.add_image(stamen_terrain, 8) + + # Add a marker for the Eyjafjallajökull volcano. + ax.plot(-19.613333, 63.62, marker='o', color='red', markersize=12, + alpha=0.7, transform=ccrs.Geodetic()) + + # Use the cartopy interface to create a matplotlib transform object + # for the Geodetic coordinate system. We will use this along with + # matplotlib's offset_copy function to define a coordinate system which + # translates the text by 25 pixels to the left. + geodetic_transform = ccrs.Geodetic()._as_mpl_transform(ax) + text_transform = offset_copy(geodetic_transform, units='dots', x=-25) + + # Add text 25 pixels to the left of the volcano. + ax.text(-19.613333, 63.62, u'Eyjafjallajökull', + verticalalignment='center', horizontalalignment='right', + transform=text_transform, + bbox=dict(facecolor='sandybrown', alpha=0.5, boxstyle='round')) + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/favicon.py b/Cartopy/examples/favicon.py new file mode 100644 index 0000000..b501166 --- /dev/null +++ b/Cartopy/examples/favicon.py @@ -0,0 +1,41 @@ +""" +Cartopy Favicon +--------------- + +The actual code to generate cartopy's favicon. + +""" +import cartopy.crs as ccrs +import matplotlib.pyplot as plt +import matplotlib.textpath +import matplotlib.patches +from matplotlib.font_manager import FontProperties +import numpy as np + + +def main(): + fig = plt.figure(figsize=[8, 8]) + ax = fig.add_subplot(1, 1, 1, projection=ccrs.SouthPolarStereo()) + + ax.coastlines() + ax.gridlines() + ax.stock_img() + + # Generate a matplotlib path representing the character "C". + fp = FontProperties(family='Bitstream Vera Sans', weight='bold') + logo_path = matplotlib.textpath.TextPath((-4.5e7, -3.7e7), + 'C', size=1, prop=fp) + + # Scale the letter up to an appropriate X and Y scale. + logo_path._vertices *= np.array([103250000, 103250000]) + + # Add the path as a patch, drawing black outlines around the text. + patch = matplotlib.patches.PathPatch(logo_path, facecolor='white', + edgecolor='black', linewidth=10, + transform=ccrs.SouthPolarStereo()) + ax.add_patch(patch) + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/feature_creation.py b/Cartopy/examples/feature_creation.py new file mode 100644 index 0000000..c310740 --- /dev/null +++ b/Cartopy/examples/feature_creation.py @@ -0,0 +1,57 @@ +""" +Feature Creation +---------------- + +This example manually instantiates a +:class:`cartopy.feature.NaturalEarthFeature` to access administrative +boundaries (states and provinces). + +Note that this example is intended to illustrate the ability to construct +Natural Earth features that cartopy does not necessarily know about +*a priori*. +In this instance however, it would be possible to make use of the +pre-defined :data:`cartopy.feature.STATES` constant. + +""" +__tags__ = ['Lines and polygons'] + +import matplotlib.pyplot as plt +import cartopy.crs as ccrs +import cartopy.feature as cfeature +from matplotlib.offsetbox import AnchoredText + + +def main(): + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree()) + ax.set_extent([80, 170, -45, 30], crs=ccrs.PlateCarree()) + + # Put a background image on for nice sea rendering. + ax.stock_img() + + # Create a feature for States/Admin 1 regions at 1:50m from Natural Earth + states_provinces = cfeature.NaturalEarthFeature( + category='cultural', + name='admin_1_states_provinces_lines', + scale='50m', + facecolor='none') + + SOURCE = 'Natural Earth' + LICENSE = 'public domain' + + ax.add_feature(cfeature.LAND) + ax.add_feature(cfeature.COASTLINE) + ax.add_feature(states_provinces, edgecolor='gray') + + # Add a text annotation for the license information to the + # the bottom right corner. + text = AnchoredText(r'$\mathcircled{{c}}$ {}; license: {}' + ''.format(SOURCE, LICENSE), + loc=4, prop={'size': 12}, frameon=True) + ax.add_artist(text) + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/features.py b/Cartopy/examples/features.py new file mode 100644 index 0000000..7bc5dc1 --- /dev/null +++ b/Cartopy/examples/features.py @@ -0,0 +1,32 @@ +""" +Features +-------- + +A demonstration of some of the built-in Natural Earth features found +in cartopy. + +""" +__tags__ = ['Lines and polygons'] + +import cartopy.crs as ccrs +import cartopy.feature as cfeature +import matplotlib.pyplot as plt + + +def main(): + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree()) + ax.set_extent([-20, 60, -40, 45], crs=ccrs.PlateCarree()) + + ax.add_feature(cfeature.LAND) + ax.add_feature(cfeature.OCEAN) + ax.add_feature(cfeature.COASTLINE) + ax.add_feature(cfeature.BORDERS, linestyle=':') + ax.add_feature(cfeature.LAKES, alpha=0.5) + ax.add_feature(cfeature.RIVERS) + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/geostationary.py b/Cartopy/examples/geostationary.py new file mode 100644 index 0000000..433a83c --- /dev/null +++ b/Cartopy/examples/geostationary.py @@ -0,0 +1,65 @@ +""" +Reprojecting images from a Geostationary projection +--------------------------------------------------- + +This example demonstrates Cartopy's ability to project images into the desired +projection on-the-fly. The image itself is retrieved from a URL and is loaded +directly into memory without storing it intermediately into a file. It +represents pre-processed data from the Spinning Enhanced Visible and Infrared +Imager onboard Meteosat Second Generation, which has been put into an image in +the data's native Geostationary coordinate system - it is then projected by +cartopy into a global Miller map. + +""" +__tags__ = ["Scalar data"] + +try: + from urllib2 import urlopen +except ImportError: + from urllib.request import urlopen +from io import BytesIO + +import cartopy.crs as ccrs +import matplotlib.pyplot as plt + + +def geos_image(): + """ + Return a specific SEVIRI image by retrieving it from a github gist URL. + + Returns + ------- + img : numpy array + The pixels of the image in a numpy array. + img_proj : cartopy CRS + The rectangular coordinate system of the image. + img_extent : tuple of floats + The extent of the image ``(x0, y0, x1, y1)`` referenced in + the ``img_proj`` coordinate system. + origin : str + The origin of the image to be passed through to matplotlib's imshow. + + """ + url = ('https://gist.github.com/pelson/5871263/raw/' + 'EIDA50_201211061300_clip2.png') + img_handle = BytesIO(urlopen(url).read()) + img = plt.imread(img_handle) + img_proj = ccrs.Geostationary(satellite_height=35786000) + img_extent = [-5500000, 5500000, -5500000, 5500000] + return img, img_proj, img_extent, 'upper' + + +def main(): + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1, projection=ccrs.Miller()) + ax.coastlines() + ax.set_global() + print('Retrieving image...') + img, crs, extent, origin = geos_image() + print('Projecting and plotting image (this may take a while)...') + ax.imshow(img, transform=crs, extent=extent, origin=origin, cmap='gray') + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/global_map.py b/Cartopy/examples/global_map.py new file mode 100644 index 0000000..db3874f --- /dev/null +++ b/Cartopy/examples/global_map.py @@ -0,0 +1,36 @@ +""" +Global Map +---------- + +An example of a simple map that compares Geodetic and Plate Carree lines +between two locations. + +""" +__tags__ = ['Lines and polygons'] + + +import matplotlib.pyplot as plt + +import cartopy.crs as ccrs + + +def main(): + fig = plt.figure(figsize=(10, 5)) + ax = fig.add_subplot(1, 1, 1, projection=ccrs.Robinson()) + + # make the map global rather than have it zoom in to + # the extents of any plotted data + ax.set_global() + + ax.stock_img() + ax.coastlines() + + ax.plot(-0.08, 51.53, 'o', transform=ccrs.PlateCarree()) + ax.plot([-0.08, 132], [51.53, 43.17], transform=ccrs.PlateCarree()) + ax.plot([-0.08, 132], [51.53, 43.17], transform=ccrs.Geodetic()) + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/gridliner.py b/Cartopy/examples/gridliner.py new file mode 100644 index 0000000..0063e74 --- /dev/null +++ b/Cartopy/examples/gridliner.py @@ -0,0 +1,44 @@ +""" +Gridlines and tick labels +------------------------- + +These examples demonstrate how to quickly add longitude +and latitude gridlines and tick labels on a non-rectangular projection. + +As you can see on the first example, +longitude labels may be drawn on left and right sides, +and latitude labels may be drawn on bottom and top sides. +Thanks to the ``dms`` keyword, minutes are used when appropriate +to display fractions of degree. + + +In the second example, labels are still drawn at the map edges +despite its complexity, and some others are also drawn within the map +boundary. + +""" +import cartopy.crs as ccrs +import cartopy.feature as cfeature +import matplotlib.pyplot as plt + +__tags__ = ['Gridlines', 'Tick labels', 'Lines and polygons'] + + +def main(): + + rotated_crs = ccrs.RotatedPole(pole_longitude=120.0, pole_latitude=70.0) + ax0 = plt.axes(projection=rotated_crs) + ax0.set_extent([-6, 1, 47.5, 51.5], crs=ccrs.PlateCarree()) + ax0.add_feature(cfeature.LAND.with_scale('110m')) + ax0.gridlines(draw_labels=True, dms=True, x_inline=False, y_inline=False) + + plt.figure(figsize=(6.9228, 3)) + ax1 = plt.axes(projection=ccrs.InterruptedGoodeHomolosine()) + ax1.coastlines(resolution='110m') + ax1.gridlines(draw_labels=True) + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/hurricane_katrina.py b/Cartopy/examples/hurricane_katrina.py new file mode 100644 index 0000000..3e74b94 --- /dev/null +++ b/Cartopy/examples/hurricane_katrina.py @@ -0,0 +1,98 @@ +""" +Hurricane Katrina +----------------- + +This example uses the power of Shapely to illustrate states that are likely to +have been significantly impacted by Hurricane Katrina. + +""" +__tags__ = ['Lines and polygons'] + +import matplotlib.patches as mpatches +import matplotlib.pyplot as plt +import shapely.geometry as sgeom + +import cartopy.crs as ccrs +import cartopy.io.shapereader as shpreader + + +def sample_data(): + """ + Return a list of latitudes and a list of longitudes (lons, lats) + for Hurricane Katrina (2005). + + The data was originally sourced from the HURDAT2 dataset from AOML/NOAA: + https://www.aoml.noaa.gov/hrd/hurdat/newhurdat-all.html on 14th Dec 2012. + + """ + lons = [-75.1, -75.7, -76.2, -76.5, -76.9, -77.7, -78.4, -79.0, + -79.6, -80.1, -80.3, -81.3, -82.0, -82.6, -83.3, -84.0, + -84.7, -85.3, -85.9, -86.7, -87.7, -88.6, -89.2, -89.6, + -89.6, -89.6, -89.6, -89.6, -89.1, -88.6, -88.0, -87.0, + -85.3, -82.9] + + lats = [23.1, 23.4, 23.8, 24.5, 25.4, 26.0, 26.1, 26.2, 26.2, 26.0, + 25.9, 25.4, 25.1, 24.9, 24.6, 24.4, 24.4, 24.5, 24.8, 25.2, + 25.7, 26.3, 27.2, 28.2, 29.3, 29.5, 30.2, 31.1, 32.6, 34.1, + 35.6, 37.0, 38.6, 40.1] + + return lons, lats + + +def main(): + fig = plt.figure() + # to get the effect of having just the states without a map "background" + # turn off the background patch and axes frame + ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.LambertConformal(), + frameon=False) + ax.patch.set_visible(False) + + ax.set_extent([-125, -66.5, 20, 50], ccrs.Geodetic()) + + shapename = 'admin_1_states_provinces_lakes_shp' + states_shp = shpreader.natural_earth(resolution='110m', + category='cultural', name=shapename) + + lons, lats = sample_data() + + ax.set_title('US States which intersect the track of ' + 'Hurricane Katrina (2005)') + + # turn the lons and lats into a shapely LineString + track = sgeom.LineString(zip(lons, lats)) + + # buffer the linestring by two degrees (note: this is a non-physical + # distance) + track_buffer = track.buffer(2) + + def colorize_state(geometry): + facecolor = (0.9375, 0.9375, 0.859375) + if geometry.intersects(track): + facecolor = 'red' + elif geometry.intersects(track_buffer): + facecolor = '#FF7E00' + return {'facecolor': facecolor, 'edgecolor': 'black'} + + ax.add_geometries( + shpreader.Reader(states_shp).geometries(), + ccrs.PlateCarree(), + styler=colorize_state) + + ax.add_geometries([track_buffer], ccrs.PlateCarree(), + facecolor='#C8A2C8', alpha=0.5) + ax.add_geometries([track], ccrs.PlateCarree(), + facecolor='none', edgecolor='k') + + # make two proxy artists to add to a legend + direct_hit = mpatches.Rectangle((0, 0), 1, 1, facecolor="red") + within_2_deg = mpatches.Rectangle((0, 0), 1, 1, facecolor="#FF7E00") + labels = ['State directly intersects\nwith track', + 'State is within \n2 degrees of track'] + ax.legend([direct_hit, within_2_deg], labels, + loc='lower left', bbox_to_anchor=(0.025, -0.1), fancybox=True) + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/image_tiles.py b/Cartopy/examples/image_tiles.py new file mode 100644 index 0000000..1666636 --- /dev/null +++ b/Cartopy/examples/image_tiles.py @@ -0,0 +1,32 @@ +""" +Web tile imagery +---------------- + +This example demonstrates how imagery from a tile +providing web service can be accessed. + +""" +__tags__ = ['Web services'] + +import matplotlib.pyplot as plt +import cartopy.crs as ccrs + +from cartopy.io.img_tiles import Stamen + + +def main(): + tiler = Stamen('terrain-background') + mercator = tiler.crs + + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1, projection=mercator) + ax.set_extent([-90, -73, 22, 34], crs=ccrs.PlateCarree()) + + ax.add_image(tiler, 6) + + ax.coastlines('10m') + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/logo.py b/Cartopy/examples/logo.py new file mode 100644 index 0000000..40d0485 --- /dev/null +++ b/Cartopy/examples/logo.py @@ -0,0 +1,48 @@ +""" +Cartopy Logo +------------ + +The actual code to produce cartopy's logo. + +""" +import cartopy.crs as ccrs +import matplotlib.pyplot as plt +import matplotlib.textpath +import matplotlib.patches +from matplotlib.font_manager import FontProperties +import numpy as np + + +def main(): + fig = plt.figure(figsize=[12, 6]) + ax = fig.add_subplot(1, 1, 1, projection=ccrs.Robinson()) + + ax.coastlines() + ax.gridlines() + + # generate a matplotlib path representing the word "cartopy" + fp = FontProperties(family='Bitstream Vera Sans', weight='bold') + logo_path = matplotlib.textpath.TextPath((-175, -35), 'cartopy', + size=1, prop=fp) + # scale the letters up to sensible longitude and latitude sizes + logo_path._vertices *= np.array([80, 160]) + + # add a background image + im = ax.stock_img() + # clip the image according to the logo_path. mpl v1.2.0 does not support + # the transform API that cartopy makes use of, so we have to convert the + # projection into a transform manually + plate_carree_transform = ccrs.PlateCarree()._as_mpl_transform(ax) + im.set_clip_path(logo_path, transform=plate_carree_transform) + + # add the path as a patch, drawing black outlines around the text + patch = matplotlib.patches.PathPatch(logo_path, + facecolor='none', edgecolor='black', + transform=ccrs.PlateCarree()) + ax.add_patch(patch) + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/nightshade.py b/Cartopy/examples/nightshade.py new file mode 100644 index 0000000..2130b99 --- /dev/null +++ b/Cartopy/examples/nightshade.py @@ -0,0 +1,24 @@ +""" +Nightshade feature +------------------ + +Draws a polygon where there is no sunlight for the given datetime. + +""" +__tags__ = ['Lines and polygons'] + +import datetime +import matplotlib.pyplot as plt +import cartopy.crs as ccrs +from cartopy.feature.nightshade import Nightshade + + +fig = plt.figure(figsize=(10, 5)) +ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree()) + +date = datetime.datetime(1999, 12, 31, 12) + +ax.set_title('Night time shading for {}'.format(date)) +ax.stock_img() +ax.add_feature(Nightshade(date, alpha=0.2)) +plt.show() diff --git a/Cartopy/examples/regridding_arrows.py b/Cartopy/examples/regridding_arrows.py new file mode 100644 index 0000000..155d762 --- /dev/null +++ b/Cartopy/examples/regridding_arrows.py @@ -0,0 +1,59 @@ +""" +Regridding vectors with quiver +------------------------------ + +This example demonstrates the regridding functionality in quiver (there exists +equivalent functionality in :meth:`cartopy.mpl.geoaxes.GeoAxes.barbs`). + +Regridding can be an effective way of visualising a vector field, particularly +if the data is dense or warped. + +""" +__tags__ = ['Vector data'] + +import matplotlib.pyplot as plt +import numpy as np + +import cartopy.crs as ccrs + + +def sample_data(shape=(20, 30)): + """ + Return ``(x, y, u, v, crs)`` of some vector data + computed mathematically. The returned CRS will be a North Polar + Stereographic projection, meaning that the vectors will be unevenly + spaced in a PlateCarree projection. + + """ + crs = ccrs.NorthPolarStereo() + scale = 1e7 + x = np.linspace(-scale, scale, shape[1]) + y = np.linspace(-scale, scale, shape[0]) + + x2d, y2d = np.meshgrid(x, y) + u = 10 * np.cos(2 * x2d / scale + 3 * y2d / scale) + v = 20 * np.cos(6 * x2d / scale) + + return x, y, u, v, crs + + +def main(): + fig = plt.figure(figsize=(8, 10)) + + x, y, u, v, vector_crs = sample_data(shape=(50, 50)) + ax1 = fig.add_subplot(2, 1, 1, projection=ccrs.PlateCarree()) + ax1.coastlines('50m') + ax1.set_extent([-45, 55, 20, 80], ccrs.PlateCarree()) + ax1.quiver(x, y, u, v, transform=vector_crs) + + ax2 = fig.add_subplot(2, 1, 2, projection=ccrs.PlateCarree()) + ax2.set_title('The same vector field regridded') + ax2.coastlines('50m') + ax2.set_extent([-45, 55, 20, 80], ccrs.PlateCarree()) + ax2.quiver(x, y, u, v, transform=vector_crs, regrid_shape=20) + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/reprojected_wmts.py b/Cartopy/examples/reprojected_wmts.py new file mode 100644 index 0000000..af1ac9e --- /dev/null +++ b/Cartopy/examples/reprojected_wmts.py @@ -0,0 +1,56 @@ +""" +Displaying WMTS tiled map data on an arbitrary projection +--------------------------------------------------------- + +This example displays imagery from a web map tile service on two different +projections, one of which is not provided by the service. + +This result can also be interactively panned and zoomed. + +The example WMTS layer is a single composite of data sampled over nine days +in April 2012 and thirteen days in October 2012 showing the Earth at night. +It does not vary over time. + +The imagery was collected by the Suomi National Polar-orbiting Partnership +(Suomi NPP) weather satellite operated by the United States National Oceanic +and Atmospheric Administration (NOAA). + +""" +__tags__ = ['Web services'] + +import matplotlib.pyplot as plt +import cartopy.crs as ccrs + + +def plot_city_lights(): + # Define resource for the NASA night-time illumination data. + base_uri = 'https://map1c.vis.earthdata.nasa.gov/wmts-geo/wmts.cgi' + layer_name = 'VIIRS_CityLights_2012' + + # Create a Cartopy crs for plain and rotated lat-lon projections. + plain_crs = ccrs.PlateCarree() + rotated_crs = ccrs.RotatedPole(pole_longitude=120.0, pole_latitude=45.0) + + fig = plt.figure() + + # Plot WMTS data in a specific region, over a plain lat-lon map. + ax = fig.add_subplot(1, 2, 1, projection=plain_crs) + ax.set_extent([-6, 3, 48, 58], crs=ccrs.PlateCarree()) + ax.coastlines(resolution='50m', color='yellow') + ax.gridlines(color='lightgrey', linestyle='-') + # Add WMTS imaging. + ax.add_wmts(base_uri, layer_name=layer_name) + + # Plot WMTS data on a rotated map, over the same nominal region. + ax = fig.add_subplot(1, 2, 2, projection=rotated_crs) + ax.set_extent([-6, 3, 48, 58], crs=ccrs.PlateCarree()) + ax.coastlines(resolution='50m', color='yellow') + ax.gridlines(color='lightgrey', linestyle='-') + # Add WMTS imaging. + ax.add_wmts(base_uri, layer_name=layer_name) + + plt.show() + + +if __name__ == '__main__': + plot_city_lights() diff --git a/Cartopy/examples/rotated_pole.py b/Cartopy/examples/rotated_pole.py new file mode 100644 index 0000000..21f393e --- /dev/null +++ b/Cartopy/examples/rotated_pole.py @@ -0,0 +1,45 @@ +""" +Rotated pole boxes +------------------ + +A demonstration of the way a box is warped when it is defined +in a rotated pole coordinate system. + +Try changing the ``box_top`` to ``44``, ``46`` and ``75`` to see the effect +that including the pole in the polygon has. + +""" +__tags__ = ['Lines and polygons'] + +import matplotlib.pyplot as plt + +import cartopy.crs as ccrs + + +def main(): + rotated_pole = ccrs.RotatedPole(pole_latitude=45, pole_longitude=180) + + box_top = 45 + x, y = [-44, -44, 45, 45, -44], [-45, box_top, box_top, -45, -45] + + fig = plt.figure() + + ax = fig.add_subplot(2, 1, 1, projection=rotated_pole) + ax.stock_img() + ax.coastlines() + ax.plot(x, y, marker='o', transform=rotated_pole) + ax.fill(x, y, color='coral', transform=rotated_pole, alpha=0.4) + ax.gridlines() + + ax = fig.add_subplot(2, 1, 2, projection=ccrs.PlateCarree()) + ax.stock_img() + ax.coastlines() + ax.plot(x, y, marker='o', transform=rotated_pole) + ax.fill(x, y, transform=rotated_pole, color='coral', alpha=0.4) + ax.gridlines() + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/star_shaped_boundary.py b/Cartopy/examples/star_shaped_boundary.py new file mode 100644 index 0000000..3419595 --- /dev/null +++ b/Cartopy/examples/star_shaped_boundary.py @@ -0,0 +1,36 @@ +""" +Modifying the boundary/neatline of a map in cartopy +--------------------------------------------------- + +This example demonstrates how to modify the boundary/neatline +of an axes. We construct a star with coordinates in a Plate Carree +coordinate system, and use the star as the outline of the map. + +Notice how changing the projection of the map represents a *projected* +star shaped boundary. + +""" +import matplotlib.path as mpath +import matplotlib.pyplot as plt + +import cartopy.crs as ccrs + + +def main(): + fig = plt.figure() + ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.PlateCarree()) + ax.coastlines() + + # Construct a star in longitudes and latitudes. + star_path = mpath.Path.unit_regular_star(5, 0.5) + star_path = mpath.Path(star_path.vertices.copy() * 80, + star_path.codes.copy()) + + # Use the star as the boundary. + ax.set_boundary(star_path, transform=ccrs.PlateCarree()) + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/streamplot.py b/Cartopy/examples/streamplot.py new file mode 100644 index 0000000..7f33982 --- /dev/null +++ b/Cartopy/examples/streamplot.py @@ -0,0 +1,30 @@ +""" +Streamplot +---------- + +Generating a vector-based streamplot. + +""" +__tags__ = ['Vector data'] + +import matplotlib.pyplot as plt + +import cartopy.crs as ccrs +from cartopy.examples.arrows import sample_data + + +def main(): + fig = plt.figure(figsize=(10, 5)) + ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree()) + ax.set_extent([-90, 75, 10, 85], crs=ccrs.PlateCarree()) + ax.coastlines() + + x, y, u, v, vector_crs = sample_data(shape=(80, 100)) + magnitude = (u ** 2 + v ** 2) ** 0.5 + ax.streamplot(x, y, u, v, transform=vector_crs, + linewidth=2, density=2, color=magnitude) + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/tick_labels.py b/Cartopy/examples/tick_labels.py new file mode 100644 index 0000000..c60ce85 --- /dev/null +++ b/Cartopy/examples/tick_labels.py @@ -0,0 +1,48 @@ +""" +Tick Labels +----------- + +This example demonstrates adding tick labels to maps on rectangular +projections using special tick formatters. + +""" +import cartopy.crs as ccrs +from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter +import matplotlib.pyplot as plt + + +def main(): + fig = plt.figure(figsize=(8, 10)) + + # Label axes of a Plate Carree projection with a central longitude of 180: + ax1 = fig.add_subplot(2, 1, 1, + projection=ccrs.PlateCarree(central_longitude=180)) + ax1.set_global() + ax1.coastlines() + ax1.set_xticks([0, 60, 120, 180, 240, 300, 360], crs=ccrs.PlateCarree()) + ax1.set_yticks([-90, -60, -30, 0, 30, 60, 90], crs=ccrs.PlateCarree()) + lon_formatter = LongitudeFormatter(zero_direction_label=True) + lat_formatter = LatitudeFormatter() + ax1.xaxis.set_major_formatter(lon_formatter) + ax1.yaxis.set_major_formatter(lat_formatter) + + # Label axes of a Mercator projection without degree symbols in the labels + # and formatting labels to include 1 decimal place: + ax2 = fig.add_subplot(2, 1, 2, projection=ccrs.Mercator()) + ax2.set_global() + ax2.coastlines() + ax2.set_xticks([-180, -120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree()) + ax2.set_yticks([-78.5, -60, -25.5, 25.5, 60, 80], crs=ccrs.PlateCarree()) + lon_formatter = LongitudeFormatter(number_format='.1f', + degree_symbol='', + dateline_direction_label=True) + lat_formatter = LatitudeFormatter(number_format='.1f', + degree_symbol='') + ax2.xaxis.set_major_formatter(lon_formatter) + ax2.yaxis.set_major_formatter(lat_formatter) + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/tissot.py b/Cartopy/examples/tissot.py new file mode 100644 index 0000000..dbe09b0 --- /dev/null +++ b/Cartopy/examples/tissot.py @@ -0,0 +1,32 @@ +""" +Tissot's Indicatrix +------------------- + +Visualize Tissot's indicatrix on a map. + +""" +__tags__ = ['Lines and polygons'] + +import matplotlib.pyplot as plt + +import cartopy.crs as ccrs + + +def main(): + fig = plt.figure(figsize=(10, 5)) + ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree()) + + # make the map global rather than have it zoom in to + # the extents of any plotted data + ax.set_global() + + ax.stock_img() + ax.coastlines() + + ax.tissot(facecolor='orange', alpha=0.4) + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/tube_stations.py b/Cartopy/examples/tube_stations.py new file mode 100644 index 0000000..5ff5513 --- /dev/null +++ b/Cartopy/examples/tube_stations.py @@ -0,0 +1,71 @@ +""" +Tube Stations +------------- + +Produces a map showing London Underground station locations with high +resolution background imagery provided by OpenStreetMap. + +""" +from matplotlib.path import Path +import matplotlib.pyplot as plt +import numpy as np + +import cartopy.crs as ccrs +from cartopy.io.img_tiles import OSM + + +def tube_locations(): + """ + Return an (n, 2) array of selected London Tube locations in Ordnance + Survey GB coordinates. + + Source: https://www.doogal.co.uk/london_stations.php + + """ + return np.array([[531738., 180890.], [532379., 179734.], + [531096., 181642.], [530234., 180492.], + [531688., 181150.], [530242., 180982.], + [531940., 179144.], [530406., 180380.], + [529012., 180283.], [530553., 181488.], + [531165., 179489.], [529987., 180812.], + [532347., 180962.], [529102., 181227.], + [529612., 180625.], [531566., 180025.], + [529629., 179503.], [532105., 181261.], + [530995., 180810.], [529774., 181354.], + [528941., 179131.], [531050., 179933.], + [530240., 179718.]]) + + +def main(): + imagery = OSM() + + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1, projection=imagery.crs) + ax.set_extent([-0.14, -0.1, 51.495, 51.515], ccrs.PlateCarree()) + + # Construct concentric circles and a rectangle, + # suitable for a London Underground logo. + theta = np.linspace(0, 2 * np.pi, 100) + circle_verts = np.vstack([np.sin(theta), np.cos(theta)]).T + concentric_circle = Path.make_compound_path(Path(circle_verts[::-1]), + Path(circle_verts * 0.6)) + + rectangle = Path([[-1.1, -0.2], [1, -0.2], [1, 0.3], [-1.1, 0.3]]) + + # Add the imagery to the map. + ax.add_image(imagery, 14) + + # Plot the locations twice, first with the red concentric circles, + # then with the blue rectangle. + xs, ys = tube_locations().T + ax.plot(xs, ys, transform=ccrs.OSGB(approx=False), + marker=concentric_circle, color='red', markersize=9, linestyle='') + ax.plot(xs, ys, transform=ccrs.OSGB(approx=False), + marker=rectangle, color='blue', markersize=11, linestyle='') + + ax.set_title('London underground locations') + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/un_flag.py b/Cartopy/examples/un_flag.py new file mode 100644 index 0000000..f2730d2 --- /dev/null +++ b/Cartopy/examples/un_flag.py @@ -0,0 +1,162 @@ +""" +UN Flag +------- + +A demonstration of the power of Matplotlib combined with cartopy's Azimuthal +Equidistant projection to reproduce the UN flag. + +""" +import cartopy.crs as ccrs +import cartopy.feature as cfeature +import matplotlib.pyplot as plt +from matplotlib.patches import PathPatch +import matplotlib.path +import matplotlib.ticker +from matplotlib.transforms import BboxTransform, Bbox +import numpy as np + + +# When drawing the flag, we can either use white filled land, or be a little +# more fancy and use the Natural Earth shaded relief imagery. +filled_land = True + + +def olive_path(): + """ + Return a Matplotlib path representing a single olive branch from the + UN Flag. The path coordinates were extracted from the SVG at + https://commons.wikimedia.org/wiki/File:Flag_of_the_United_Nations.svg. + + """ + olives_verts = np.array( + [[0, 2, 6, 9, 30, 55, 79, 94, 104, 117, 134, 157, 177, + 188, 199, 207, 191, 167, 149, 129, 109, 87, 53, 22, 0, 663, + 245, 223, 187, 158, 154, 150, 146, 149, 154, 158, 181, 184, 197, + 181, 167, 153, 142, 129, 116, 119, 123, 127, 151, 178, 203, 220, + 237, 245, 663, 280, 267, 232, 209, 205, 201, 196, 196, 201, 207, + 211, 224, 219, 230, 220, 212, 207, 198, 195, 176, 197, 220, 239, + 259, 277, 280, 663, 295, 293, 264, 250, 247, 244, 240, 240, 243, + 244, 249, 251, 250, 248, 242, 245, 233, 236, 230, 228, 224, 222, + 234, 249, 262, 275, 285, 291, 295, 296, 295, 663, 294, 293, 292, + 289, 294, 277, 271, 269, 268, 265, 264, 264, 264, 272, 260, 248, + 245, 243, 242, 240, 243, 245, 247, 252, 256, 259, 258, 257, 258, + 267, 285, 290, 294, 297, 294, 663, 285, 285, 277, 266, 265, 265, + 265, 277, 266, 268, 269, 269, 269, 268, 268, 267, 267, 264, 248, + 235, 232, 229, 228, 229, 232, 236, 246, 266, 269, 271, 285, 285, + 663, 252, 245, 238, 230, 246, 245, 250, 252, 255, 256, 256, 253, + 249, 242, 231, 214, 208, 208, 227, 244, 252, 258, 262, 262, 261, + 262, 264, 265, 252, 663, 185, 197, 206, 215, 223, 233, 242, 237, + 237, 230, 220, 202, 185, 663], + [8, 5, 3, 0, 22, 46, 46, 46, 35, 27, 16, 10, 18, + 22, 28, 38, 27, 26, 33, 41, 52, 52, 52, 30, 8, 595, + 77, 52, 61, 54, 53, 52, 53, 55, 55, 57, 65, 90, 106, + 96, 81, 68, 58, 54, 51, 50, 51, 50, 44, 34, 43, 48, + 61, 77, 595, 135, 104, 102, 83, 79, 76, 74, 74, 79, 84, + 90, 109, 135, 156, 145, 133, 121, 100, 77, 62, 69, 67, 80, + 92, 113, 135, 595, 198, 171, 156, 134, 129, 124, 120, 123, 126, + 129, 138, 149, 161, 175, 188, 202, 177, 144, 116, 110, 105, 99, + 108, 116, 126, 136, 147, 162, 173, 186, 198, 595, 249, 255, 261, + 267, 241, 222, 200, 192, 183, 175, 175, 175, 175, 199, 221, 240, + 245, 250, 256, 245, 233, 222, 207, 194, 180, 172, 162, 153, 154, + 171, 184, 202, 216, 233, 249, 595, 276, 296, 312, 327, 327, 327, + 327, 308, 284, 262, 240, 240, 239, 239, 242, 244, 247, 265, 277, + 290, 293, 296, 300, 291, 282, 274, 253, 236, 213, 235, 252, 276, + 595, 342, 349, 355, 357, 346, 326, 309, 303, 297, 291, 290, 297, + 304, 310, 321, 327, 343, 321, 305, 292, 286, 278, 270, 276, 281, + 287, 306, 328, 342, 595, 379, 369, 355, 343, 333, 326, 318, 328, + 340, 349, 366, 373, 379, 595]]).T + olives_codes = np.array([1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 4, 4, 4, 79, 1, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 79, 1, 4, 4, 4, 4, 4, 4, 2, 4, + 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 79, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 79, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 4, + 4, 4, 4, 4, 4, 79, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 4, 4, 79, 1, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 4, 4, 79, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, + 4, 4, 79], dtype=np.uint8) + + return matplotlib.path.Path(olives_verts, olives_codes) + + +def main(): + blue = '#4b92db' + + # We're drawing a flag with a 3:5 aspect ratio. + fig = plt.figure(figsize=[7.5, 4.5], facecolor=blue) + # Put a blue background on the figure. + blue_background = PathPatch(matplotlib.path.Path.unit_rectangle(), + transform=fig.transFigure, color=blue, + zorder=-1) + fig.patches.append(blue_background) + + # Set up the Azimuthal Equidistant and Plate Carree projections + # for later use. + az_eq = ccrs.AzimuthalEquidistant(central_latitude=90) + pc = ccrs.PlateCarree() + + # Pick a suitable location for the map (which is in an Azimuthal + # Equidistant projection). + ax = fig.add_axes([0.25, 0.24, 0.5, 0.54], projection=az_eq) + + # The background patch is not needed in this example. + ax.patch.set_facecolor('none') + # The Axes frame produces the outer meridian line. + for spine in ax.spines.values(): + spine.update({'edgecolor': 'white', 'linewidth': 2}) + + # We want the map to go down to -60 degrees latitude. + ax.set_extent([-180, 180, -60, 90], ccrs.PlateCarree()) + + # Importantly, we want the axes to be circular at the -60 latitude + # rather than cartopy's default behaviour of zooming in and becoming + # square. + _, patch_radius = az_eq.transform_point(0, -60, pc) + circular_path = matplotlib.path.Path.circle(0, patch_radius) + ax.set_boundary(circular_path) + + if filled_land: + ax.add_feature( + cfeature.LAND, facecolor='white', edgecolor='none') + else: + ax.stock_img() + + gl = ax.gridlines(crs=pc, linewidth=2, color='white', linestyle='-') + # Meridians every 45 degrees, and 4 parallels. + gl.xlocator = matplotlib.ticker.FixedLocator(np.arange(-180, 181, 45)) + parallels = np.arange(-30, 70, 30) + gl.ylocator = matplotlib.ticker.FixedLocator(parallels) + + # Now add the olive branches around the axes. We do this in normalised + # figure coordinates + olive_leaf = olive_path() + + olives_bbox = Bbox.null() + olives_bbox.update_from_path(olive_leaf) + + # The first olive branch goes from left to right. + olive1_axes_bbox = Bbox([[0.45, 0.15], [0.725, 0.75]]) + olive1_trans = BboxTransform(olives_bbox, olive1_axes_bbox) + + # THe second olive branch goes from right to left (mirroring the first). + olive2_axes_bbox = Bbox([[0.55, 0.15], [0.275, 0.75]]) + olive2_trans = BboxTransform(olives_bbox, olive2_axes_bbox) + + olive1 = PathPatch(olive_leaf, facecolor='white', edgecolor='none', + transform=olive1_trans + fig.transFigure) + olive2 = PathPatch(olive_leaf, facecolor='white', edgecolor='none', + transform=olive2_trans + fig.transFigure) + + fig.patches.append(olive1) + fig.patches.append(olive2) + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/utm_all_zones.py b/Cartopy/examples/utm_all_zones.py new file mode 100644 index 0000000..4679801 --- /dev/null +++ b/Cartopy/examples/utm_all_zones.py @@ -0,0 +1,46 @@ +""" +Displaying all 60 zones of the UTM projection +--------------------------------------------- + +This example displays all 60 zones of the Universal Transverse Mercator +projection next to each other in a figure. + +First we create a figure with 60 subplots in one row. +Next we set the projection of each axis in the figure to a specific UTM zone. +Then we add coastlines, gridlines and the number of the zone. +Finally we add a supertitle and display the figure. +""" + +import cartopy.crs as ccrs +import matplotlib.pyplot as plt + + +def main(): + # Create a list of integers from 1 - 60 + zones = range(1, 61) + + # Create a figure + fig = plt.figure(figsize=(18, 6)) + + # Loop through each zone in the list + for zone in zones: + + # Add GeoAxes object with specific UTM zone projection to the figure + ax = fig.add_subplot(1, len(zones), zone, + projection=ccrs.UTM(zone=zone, + southern_hemisphere=True)) + + # Add coastlines, gridlines and zone number for the subplot + ax.coastlines(resolution='110m') + ax.gridlines() + ax.set_title(zone) + + # Add a supertitle for the figure + fig.suptitle("UTM Projection - Zones") + + # Display the figure + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/waves.py b/Cartopy/examples/waves.py new file mode 100644 index 0000000..c9bfd5b --- /dev/null +++ b/Cartopy/examples/waves.py @@ -0,0 +1,47 @@ +""" +Filled contours +--------------- + +An example of contourf on manufactured data. + +""" +__tags__ = ['Scalar data'] + +import matplotlib.pyplot as plt +import numpy as np + +import cartopy.crs as ccrs + + +def sample_data(shape=(73, 145)): + """Return ``lons``, ``lats`` and ``data`` of some fake data.""" + nlats, nlons = shape + lats = np.linspace(-np.pi / 2, np.pi / 2, nlats) + lons = np.linspace(0, 2 * np.pi, nlons) + lons, lats = np.meshgrid(lons, lats) + wave = 0.75 * (np.sin(2 * lats) ** 8) * np.cos(4 * lons) + mean = 0.5 * np.cos(2 * lats) * ((np.sin(2 * lats)) ** 2 + 2) + + lats = np.rad2deg(lats) + lons = np.rad2deg(lons) + data = wave + mean + + return lons, lats, data + + +def main(): + fig = plt.figure(figsize=(10, 5)) + ax = fig.add_subplot(1, 1, 1, projection=ccrs.Mollweide()) + + lons, lats, data = sample_data() + + ax.contourf(lons, lats, data, + transform=ccrs.PlateCarree(), + cmap='nipy_spectral') + ax.coastlines() + ax.set_global() + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/wms.py b/Cartopy/examples/wms.py new file mode 100644 index 0000000..4f8cd14 --- /dev/null +++ b/Cartopy/examples/wms.py @@ -0,0 +1,27 @@ +""" +Interactive WMS (Web Map Service) +--------------------------------- + +This example demonstrates the interactive pan and zoom capability +supported by an OGC web services Web Map Service (WMS) aware axes. + +""" +__tags__ = ['Web services'] + +import cartopy.crs as ccrs +import matplotlib.pyplot as plt + + +def main(): + fig = plt.figure(figsize=(10, 5)) + ax = fig.add_subplot(1, 1, 1, projection=ccrs.InterruptedGoodeHomolosine()) + ax.coastlines() + + ax.add_wms(wms='http://vmap0.tiles.osgeo.org/wms/vmap0', + layers=['basic']) + + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/wmts.py b/Cartopy/examples/wmts.py new file mode 100644 index 0000000..f812b4d --- /dev/null +++ b/Cartopy/examples/wmts.py @@ -0,0 +1,37 @@ +""" +Interactive WMTS (Web Map Tile Service) +--------------------------------------- + +This example demonstrates the interactive pan and zoom capability +supported by an OGC web services Web Map Tile Service (WMTS) aware axes. + +The example WMTS layer is a single composite of data sampled over nine days +in April 2012 and thirteen days in October 2012 showing the Earth at night. +It does not vary over time. + +The imagery was collected by the Suomi National Polar-orbiting Partnership +(Suomi NPP) weather satellite operated by the United States National Oceanic +and Atmospheric Administration (NOAA). + +""" +__tags__ = ['Web services'] + +import matplotlib.pyplot as plt +import cartopy.crs as ccrs + + +def main(): + url = 'https://map1c.vis.earthdata.nasa.gov/wmts-geo/wmts.cgi' + layer = 'VIIRS_CityLights_2012' + + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree()) + ax.add_wmts(url, layer) + ax.set_extent([-15, 25, 35, 60], crs=ccrs.PlateCarree()) + + ax.set_title('Suomi NPP Earth at night April/October 2012') + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/examples/wmts_time.py b/Cartopy/examples/wmts_time.py new file mode 100644 index 0000000..120e09b --- /dev/null +++ b/Cartopy/examples/wmts_time.py @@ -0,0 +1,57 @@ +""" +Web Map Tile Service time dimension demonstration +------------------------------------------------- + +This example further demonstrates WMTS support within cartopy. Optional +keyword arguments can be supplied to the OGC WMTS 'gettile' method. This +allows for the specification of the 'time' dimension for a WMTS layer +which supports it. + +The example shows satellite imagery retrieved from NASA's Global Imagery +Browse Services for 5th Feb 2016. A true color MODIS image is shown on +the left, with the MODIS false color 'snow RGB' shown on the right. + +""" +__tags__ = ['Web services'] + +import matplotlib.pyplot as plt +import matplotlib.patheffects as PathEffects +from owslib.wmts import WebMapTileService + +import cartopy.crs as ccrs + + +def main(): + # URL of NASA GIBS + URL = 'https://gibs.earthdata.nasa.gov/wmts/epsg4326/best/wmts.cgi' + wmts = WebMapTileService(URL) + + # Layers for MODIS true color and snow RGB + layers = ['MODIS_Terra_SurfaceReflectance_Bands143', + 'MODIS_Terra_CorrectedReflectance_Bands367'] + + date_str = '2016-02-05' + + # Plot setup + plot_CRS = ccrs.Mercator() + geodetic_CRS = ccrs.Geodetic() + x0, y0 = plot_CRS.transform_point(4.6, 43.1, geodetic_CRS) + x1, y1 = plot_CRS.transform_point(11.0, 47.4, geodetic_CRS) + ysize = 8 + xsize = 2 * ysize * (x1 - x0) / (y1 - y0) + fig = plt.figure(figsize=(xsize, ysize), dpi=100) + + for layer, offset in zip(layers, [0, 0.5]): + ax = fig.add_axes([offset, 0, 0.5, 1], projection=plot_CRS) + ax.set_xlim((x0, x1)) + ax.set_ylim((y0, y1)) + ax.add_wmts(wmts, layer, wmts_kwargs={'time': date_str}) + txt = ax.text(4.7, 43.2, wmts[layer].title, fontsize=18, color='wheat', + transform=geodetic_CRS) + txt.set_path_effects([PathEffects.withStroke(linewidth=5, + foreground='black')]) + plt.show() + + +if __name__ == '__main__': + main() diff --git a/Cartopy/sample_data/SanMateo_CA.tif b/Cartopy/sample_data/SanMateo_CA.tif new file mode 100644 index 0000000000000000000000000000000000000000..eedb2fb143c6341c8660c1620bfcdc8dc56e8c3b GIT binary patch literal 3737701 zcmeF)d0b6h-#Gr$s7XRmgd{|%G@MiCoX-+oTTd&vKXYGCVS!-XP*Iw;wowN4J z$!Q`-iNoOx;&5c-I5M;pB1emVFI&rVII@2)>(STb{$Bp$IK_Wa9w;Nv(Us+Je3dyI zt$(q9$luHKCC<>lAM=uyb^paNUVkssW;yDs)Hr{{Bzih4Q`I<%w7i9uPpy{Y7|?Px zEf=m<49I+|oOo^!#)J{r5HBg;YKN*f#p90FCil;VEN2zM+ z5!Fj+$#TSI)D$Y5N}+a9CDd)|Eu|#K5s#r5Y8sV5ZKRG-m#N3pcWQ_{M?9Hwp=MJn 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