10_NDVI_NDWI_DistanceFromMaxNDWI.ipynb

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  "name": "",
  "signature": "sha256:50b5794b86bf0cbc1bbc660ee10cf5e00121284e5db4265c51709b4f35be7027"
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Use of Google Earth Engine to retrieve NDVI, NDWI, (distance from max NDWI) images"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To run this script, follow the instructions to run Google Earth Engine with IPython, put *integrateGoogleMaps.ipynb* in the same folder of this notebook then:\n",
      "\n",
      "-  Set *wd* to your working directory, i.e. where your output files will be saved.\n",
      "-  Set *country_name* to the name of the country to be analyzed, this name is needed to retrieve boundaries from Google Fusion Tables"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import ee\n",
      "from IPython.html import widgets\n",
      "from IPython.display import display\n",
      "from IPython.utils import traitlets\n",
      "from IPython.core.display import Javascript\n",
      "import os\n",
      "\n",
      "%run integrateGoogleMaps.ipynb\n",
      "#Set working directory\n",
      "wd = \"C:/Alex/script\"\n",
      "os.chdir(wd)\n",
      "\n",
      "#Set country name to retrieve boundaries from Google Fusion Tables\n",
      "country_name = 'Madagascar'\n",
      "\n",
      "#Set delete_temp to True if you want to automatically delete temporary files at the end of the script. \n",
      "delete_temp = False\n",
      "\n",
      "\n",
      "% reload_ext rpy2.ipython\n",
      "\n",
      "\n",
      "ee.Initialize()\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "javascript": [
        "require([\"widgets/js/widget\"], function(WidgetManager){\n",
        "    var maps = [];\n",
        "    \n",
        "    // Define the GoogleMapsView\n",
        "    var GoogleMapsView = IPython.DOMWidgetView.extend({\n",
        "        \n",
        "        render: function() {\n",
        "            // Resize widget element to be 100% wide\n",
        "            this.$el.css('width', '100%');\n",
        "\n",
        "            // iframe source;  just enough to load Google Maps and let us poll whether initialization is complete\n",
        "            var src='<html style=\"height:100%\"><head>' +\n",
        "                '<scr'+'ipt src=\"http:https://maps.googleapis.com/maps/api/js?sensor=false\"></scr'+'ipt>' +\n",
        "                '<scr'+'ipt>google.maps.event.addDomListener(window,\"load\",function(){ready=true});</scr'+'ipt>' +\n",
        "                '</head>' +\n",
        "                '<body style=\"height:100%; margin:0px; padding:0px\"></body></html>';\n",
        "            \n",
        "            // Create the Google Maps container element.\n",
        "            this.$iframe = $('<iframe />')\n",
        "                .css('width', '100%')\n",
        "                .css('height', '300px')\n",
        "                .attr('srcdoc', src)\n",
        "                .appendTo(this.$el);\n",
        "                        \n",
        "            var self = this; // hold onto this for initMapWhenReady\n",
        "\n",
        "            // Wait until maps library has finished loading in iframe, then create map\n",
        "            function initMapWhenReady() {\n",
        "                // Iframe document and window\n",
        "                var doc = self.$iframe[0].contentDocument;\n",
        "               var win = self.$iframe[0].contentWindow;\n",
        "                if (!win || !win.ready) {\n",
        "                    // Maps library not yet loaded;  try again soon\n",
        "                    setTimeout(initMapWhenReady, 20);\n",
        "                    return;\n",
        "                }\n",
        "\n",
        "                // Maps library finished loading.  Build map now.\n",
        "                var mapOptions = {\n",
        "                    center: new win.google.maps.LatLng(self.model.get('lat'), self.model.get('lng')),\n",
        "                    zoom: self.model.get('zoom')\n",
        "                };\n",
        "                var mapDiv = $('<div />')\n",
        "                    .css('width', '100%')\n",
        "                    .css('height', '100%')\n",
        "                    .appendTo($(doc.body));\n",
        "                self.map = new win.google.maps.Map(mapDiv[0], mapOptions);\n",
        "                \n",
        "                \n",
        "                // Add an event listeners for user panning, zooming, and resizing map\n",
        "                // TODO(rsargent): Bind self across all methods, and save some plumbing here\n",
        "                win.google.maps.event.addListener(self.map, 'bounds_changed', function () {\n",
        "                    self.handleBoundsChanged();\n",
        "                });\n",
        "                \n",
        "                self.initializeLayersControl();\n",
        "            }\n",
        "           initMapWhenReady();\n",
        "        },\n",
        "        \n",
        "        LayersControl: function(widget, controlDiv, map) {\n",
        "            var win = widget.$iframe[0].contentWindow;\n",
        "            var chicago = new win.google.maps.LatLng(41.850033, -87.6500523);\n",
        "\n",
        "            // Set CSS styles for the DIV containing the control\n",
        "            // Setting padding to 5 px will offset the control\n",
        "            // from the edge of the map.\n",
        "            controlDiv.style.padding = '5px';\n",
        "\n",
        "            // Set CSS for the control border.\n",
        "            var $controlUI = $('<div />')\n",
        "                .css('backgroundColor', 'white')\n",
        "                .css('borderStyle', 'solid')\n",
        "                .css('borderWidth', '1px')\n",
        "                .css('cursor', 'pointer')\n",
        "                .css('textAlign', 'center')\n",
        "                .appendTo($(controlDiv));\n",
        "            \n",
        "            // Set CSS for the control interior.\n",
        "            var $controlContents = $('<div />')\n",
        "                .css('fontFamily', 'Arial,sans-serif')\n",
        "                .css('fontSize', '12px')\n",
        "                .css('paddingLeft', '4px')\n",
        "                .css('paddingRight', '4px')\n",
        "                .css('paddingTop', '0px')\n",
        "                .css('paddingBottom', '0px')\n",
        "                .appendTo($controlUI);\n",
        "            \n",
        "            this.$controlTable = $('<table />')\n",
        "                .append($('<tr><td colspan=2>Layers</td></tr>'))\n",
        "                .appendTo($controlContents);\n",
        "        },\n",
        "\n",
        "        initializeLayersControl: function() {\n",
        "            var doc = this.$iframe[0].contentDocument;\n",
        "            var win = this.$iframe[0].contentWindow;\n",
        "\n",
        "            // Create the DIV to hold the control and call the LayersControl() constructor\n",
        "            // passing in this DIV.\n",
        "    \n",
        "            var layersControlDiv = document.createElement('div');\n",
        "            this.layersControl = new this.LayersControl(this, layersControlDiv, this.map);\n",
        "\n",
        "            layersControlDiv.index = 1;\n",
        "            this.map.controls[win.google.maps.ControlPosition.TOP_RIGHT].push(layersControlDiv);\n",
        "        },\n",
        "        \n",
        "        // Map geometry changed (pan, zoom, resize)\n",
        "        handleBoundsChanged: function() {\n",
        "            this.model.set('lng', this.map.getCenter().lng());\n",
        "            this.model.set('lat', this.map.getCenter().lat());\n",
        "            this.model.set('zoom', this.map.getZoom());\n",
        "            var bounds = this.map.getBounds();\n",
        "            var playgroundCompatible = [bounds.getSouthWest().lng(), bounds.getSouthWest().lat(),\n",
        "                                        bounds.getNorthEast().lng(), bounds.getNorthEast().lat()];\n",
        "            this.model.set('bounds', playgroundCompatible);\n",
        "            this.touch();\n",
        "        },\n",
        "        \n",
        "        // Receive custom messages from Python backend\n",
        "        on_msg: function(msg) {\n",
        "            var win = this.$iframe[0].contentWindow;\n",
        "            if (msg.command == 'addLayer') {\n",
        "                this.addLayer(msg.mapid, msg.token, msg.name, msg.visible);\n",
        "            } else if (msg.command == 'center') {\n",
        "                this.map.setCenter(new win.google.maps.LatLng(msg.lat, msg.lng));\n",
        "                if (msg.zoom !== null) {\n",
        "                    this.map.setZoom(msg.zoom);\n",
        "                }\n",
        "            }\n",
        "        },\n",
        "        \n",
        "        // Add an Earth Engine layer\n",
        "        addLayer: function(mapid, token, name, visible) {\n",
        "            var win = this.$iframe[0].contentWindow;\n",
        "            var eeMapOptions = {\n",
        "                getTileUrl: function(tile, zoom) {\n",
        "                    var url = ['https://earthengine.googleapis.com/map',\n",
        "                               mapid, zoom, tile.x, tile.y].join(\"/\");\n",
        "                    url += '?token=' + token\n",
        "                    return url;\n",
        "                },\n",
        "                tileSize: new win.google.maps.Size(256, 256),\n",
        "                opacity: visible ? 1.0 : 0.0,\n",
        "            };\n",
        "            \n",
        "            // Create the overlay map type\n",
        "            var mapType = new win.google.maps.ImageMapType(eeMapOptions);\n",
        "                \n",
        "            // Overlay the Earth Engine generated layer\n",
        "            this.map.overlayMapTypes.push(mapType);\n",
        "\n",
        "            // Update layer visibility control\n",
        "            var maxSlider = 100;\n",
        "            \n",
        "            function updateOpacity() {\n",
        "                mapType.setOpacity($checkbox[0].checked ? $slider[0].value / 100.0 : 0);\n",
        "            }\n",
        "            \n",
        "            var $checkbox = $('<input type=\"checkbox\">')\n",
        "                .prop('checked', visible)\n",
        "                .change(updateOpacity);\n",
        "            \n",
        "            var $slider = $('<input type=\"range\" />')\n",
        "                .prop('min', 0)\n",
        "                .prop('max', maxSlider)\n",
        "                .prop('value', maxSlider)\n",
        "                .css('width', '60px')\n",
        "                .on('input', updateOpacity);\n",
        "\n",
        "            // If user doesn't specify a layer name, create a default\n",
        "            if (name === null) {\n",
        "                name = 'Layer ' + this.map.overlayMapTypes.length;\n",
        "            }\n",
        "            \n",
        "            var $row = $('<tr />');\n",
        "            $('<td align=\"left\" />').append($checkbox).append(name).appendTo($row);\n",
        "            $('<td />').append($slider).appendTo($row);\n",
        "\n",
        "            this.layersControl.$controlTable.append($row);\n",
        "        }\n",
        "    });\n",
        "    \n",
        "    // Register the GoogleMapsView with the widget manager.\n",
        "    WidgetManager.register_widget_view('GoogleMapsView', GoogleMapsView);\n",
        "});"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x54bcfb0>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class GoogleMapsWidget(widgets.DOMWidget):\n",
      "    _view_name = traitlets.Unicode('GoogleMapsView', sync=True)\n",
      "    value = traitlets.Unicode(sync=True)\n",
      "    description = traitlets.Unicode(sync=True)\n",
      "    lat = traitlets.CFloat(0, help=\"Center latitude, -90 to 90\", sync=True)\n",
      "    lng = traitlets.CFloat(0, help=\"Center longitude, -180 to 180\", sync=True)\n",
      "    zoom = traitlets.CInt(0, help=\"Zoom level, 0 to ~25\", sync=True)\n",
      "    bounds = traitlets.List([], help=\"Visible bounds, [W, S, E, N]\", sync=True)\n",
      "    \n",
      "    def __init__(self, lng=0.0, lat=0.0, zoom=2):\n",
      "        self.lng = lng\n",
      "        self.lat = lat\n",
      "        self.zoom = zoom\n",
      "        \n",
      "    def addLayer(self, image, vis_params, name=None, visible=True):\n",
      "        mapid = image.getMapId(vis_params)\n",
      "        self.send({'command':'addLayer', 'mapid': mapid['mapid'], 'token': mapid['token'], 'name': name, 'visible': visible})\n",
      "        \n",
      "    def center(self, lng, lat, zoom=None):\n",
      "        self.send({'command': 'center', 'lng': lng, 'lat': lat, 'zoom': zoom})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class GoogleMapsWidget(widgets.DOMWidget):\n",
      "    _view_name = traitlets.Unicode('GoogleMapsView', sync=True)\n",
      "    value = traitlets.Unicode(sync=True)\n",
      "    description = traitlets.Unicode(sync=True)\n",
      "    lat = traitlets.CFloat(0, help=\"Center latitude, -90 to 90\", sync=True)\n",
      "    lng = traitlets.CFloat(0, help=\"Center longitude, -180 to 180\", sync=True)\n",
      "    zoom = traitlets.CInt(0, help=\"Zoom level, 0 to ~25\", sync=True)\n",
      "    bounds = traitlets.List([], help=\"Visible bounds, [W, S, E, N]\", sync=True)\n",
      "    \n",
      "    def __init__(self, lng=0.0, lat=0.0, zoom=2):\n",
      "        self.lng = lng\n",
      "        self.lat = lat\n",
      "        self.zoom = zoom\n",
      "        \n",
      "    def addLayer(self, image, vis_params, name=None, visible=True):\n",
      "        mapid = image.getMapId(vis_params)\n",
      "        self.send({'command':'addLayer', 'mapid': mapid['mapid'], 'token': mapid['token'], 'name': name, 'visible': visible})\n",
      "        \n",
      "    def center(self, lng, lat, zoom=None):\n",
      "        self.send({'command': 'center', 'lng': lng, 'lat': lat, 'zoom': zoom})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class GoogleMapsWidget(widgets.DOMWidget):\n",
      "    _view_name = traitlets.Unicode('GoogleMapsView', sync=True)\n",
      "    value = traitlets.Unicode(sync=True)\n",
      "    description = traitlets.Unicode(sync=True)\n",
      "    lat = traitlets.CFloat(0, help=\"Center latitude, -90 to 90\", sync=True)\n",
      "    lng = traitlets.CFloat(0, help=\"Center longitude, -180 to 180\", sync=True)\n",
      "    zoom = traitlets.CInt(0, help=\"Zoom level, 0 to ~25\", sync=True)\n",
      "    bounds = traitlets.List([], help=\"Visible bounds, [W, S, E, N]\", sync=True)\n",
      "    \n",
      "    def __init__(self, lng=0.0, lat=0.0, zoom=2):\n",
      "        self.lng = lng\n",
      "        self.lat = lat\n",
      "        self.zoom = zoom\n",
      "        \n",
      "    def addLayer(self, image, vis_params, name=None, visible=True):\n",
      "        mapid = image.getMapId(vis_params)\n",
      "        self.send({'command':'addLayer', 'mapid': mapid['mapid'], 'token': mapid['token'], 'name': name, 'visible': visible})\n",
      "        \n",
      "    def center(self, lng, lat, zoom=None):\n",
      "        self.send({'command': 'center', 'lng': lng, 'lat': lat, 'zoom': zoom})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Earth Engine processing (Online)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following cell contains everything is needed to process the images online with Google Earth Engine.\n",
      "\n",
      "A download link is printed at the end of the cell containing the **NDVI** and the **NDWI** image of Madagascar.\n",
      "\n",
      "The resolution can be adjusted by editing the *scale* value, inside the *getDownloadUrl* methods."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Function to calculate NDVI from a single image, input:image, returns:image\n",
      "def NDVI(image):\n",
      "    ndvi = image.normalizedDifference(['B4', 'B3'])\n",
      "    #addBands() adds a band named 'nd'\n",
      "    image2 = image.addBands(ndvi)\n",
      "    return image2\n",
      "\n",
      "#Function to calculate NDWI from a single image, input:image, returns:image\n",
      "def NDWI(image3):\n",
      "    ndwi = image3.normalizedDifference(['B5', 'B4'])\n",
      "    #addBands() adds a band named 'nd'\n",
      "    image4 = image3.addBands(ndwi)\n",
      "    return image4\n",
      "\n",
      "#Function to clip a single image depending on country name, the fusion table contains all world countries boundaries, input:image, returns:image\n",
      "def clipping(image4):\n",
      "    polygon= ee.FeatureCollection('ft:1XULuZoTOzBNiIguwxpKddIqt04SJGzSoeAdGucg').filter(ee.Filter.eq('name_sort', country_name))\n",
      "    clipp = image4.clip(polygon)\n",
      "    return clipp\n",
      "\n",
      "#Load GMW on the Madagascar zone\n",
      "map = GoogleMapsWidget(lat=-20,lng=47,zoom=5)\n",
      "display(map)\n",
      "\n",
      "#Acquire images from Landsat 7, code:\"LE7\", to retrieve the other codes go on earth engine data catalog, enter the satellite name and retrieve \"ImageCollection ID\" \n",
      "imageCollection = ee.ImageCollection('LE7')\n",
      "polygon= ee.FeatureCollection('ft:1XULuZoTOzBNiIguwxpKddIqt04SJGzSoeAdGucg').filter(ee.Filter.eq('name_sort', country_name))\n",
      "img=imageCollection.filterBounds(polygon)\n",
      "imgfilter=(img.filterMetadata('CLOUD_COVER','less_than',15).filterDate('2011-01-01','2011-12-01'))\n",
      "\n",
      "#Apply the NDVI and clipping function to EVERY image in the imagecollection\n",
      "ndvi=imgfilter.map(NDVI)\n",
      "ndviclip=ndvi.map(clipping)\n",
      "\n",
      "\n",
      "#Visualization parameters to use in map.addLayer, we need this later\n",
      "vis_paramsndvi= {'bands': 'nd'}\n",
      "\n",
      "#mosaic the imagecollection\n",
      "ndvisel=ndviclip.select('nd')\n",
      "ndviclipmosaic=ndvisel.mosaic()\n",
      "\n",
      "#Repeat steps for NDWI\n",
      "ndwi=imgfilter.map(NDWI)\n",
      "ndwiclip=ndwi.map(clipping)\n",
      "ndwisel=ndwiclip.select('nd')\n",
      "ndwiclipmosaic=ndwisel.mosaic()\n",
      "vis_paramsndwi= {'bands': 'nd'}\n",
      "\n",
      "\n",
      "#DISTANCE\n",
      "#vis_paramsdistance= {'bands' : 'distance', 'min' : 0, 'max' : 1500000}\n",
      "\n",
      "#lat=0\n",
      "#lon=0\n",
      "#Scale the NDWI image and find the maximum value(without geocoordinates) through Reducer.max()\n",
      "#reducermapmax = ndwiclipmosaic.reduceRegion(ee.Reducer.max(), polygon, 780)\n",
      "#maxvalue=reducermapmax.get('nd').getInfo()\n",
      "#print(maxvalue)\n",
      "\n",
      "#Scale the NDWI image (same scale as the previous function) and retrieve a List: [[id,lon,lat,time,bandvalue],[...],[...]]\n",
      "#x = ee.ImageCollection([ndwiclipmosaic]).getRegion(polygon,780)\n",
      "\n",
      "#\"Flatten\" the list, that is, from [[id1,lon1,lat1,time1,bandvalue1],[id2,lon2,lat2,time2,bandvalue2],[...]]\n",
      "# to [id1,lon1,lat1,time1,bandvalue1,id2,lon2,lat2,time2,bandvalue2,........] for easier manipulation\n",
      "#flat = x.flatten()\n",
      "\n",
      "#retrieve maxvalue inside the flattened list\n",
      "#valueof = flat.indexOf(maxvalue).getInfo()\n",
      "\n",
      "#retrieve the geocoordinates of the maximum NDWI\n",
      "#lon = flat.get(valueof-3).getInfo()\n",
      "#lat = flat.get(valueof-2).getInfo()\n",
      "#print(lon)\n",
      "#print(lat)\n",
      "\n",
      "#Create a FeatureCollection containing a single Geometry point and create an image with the distance function\n",
      "#maxfc=ee.FeatureCollection([ee.Geometry.Point(lon,lat)])\n",
      "#dist=maxfc.distance(1500000)\n",
      "#distclip=dist.clip(polygon)\n",
      "\n",
      "\n",
      "#Visualize the results on the map\n",
      "#map.addLayer(distclip, vis_params=vis_paramsdistance,name=\"distanceFromMaxNDWI\")\n",
      "map.addLayer(ndviclipmosaic,vis_params=vis_paramsndvi,name=\"NDVI\")\n",
      "map.addLayer(ndwiclipmosaic, vis_params=vis_paramsndwi,name=\"NDWI\")\n",
      "\n",
      "\n",
      "#Get the download URLs of the images(scaled)\n",
      "#print(s)\n",
      "ndvidownload=ndviclipmosaic.getDownloadUrl({'scale':1000,'region': [[42.495 , -12.082], [51.196 ,-12.082], [42.495 , -26.431], [51.196,-26.431]] })\n",
      "ndwidownload=ndwiclipmosaic.getDownloadUrl({'scale':1000,'region': [[42.495 , -12.082], [51.196 ,-12.082], [42.495 , -26.431], [51.196,-26.431]] })\n",
      "#distancedownload=distclip.getDownloadUrl({'scale':3000,'region': [[42.495 , -12.082], [51.196 ,-12.082], [42.495 , -26.431], [51.196,-26.431]] , 'min' : 0, 'max' : 1500000, 'format' : 'jpeg'})\n",
      "\n",
      "print\n",
      "print(ndvidownload)\n",
      "print\n",
      "print(ndwidownload)\n",
      "print\n",
      "#print(distancedownload)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "https://earthengine.googleapis.com//api/download?docid=911c8c8c3303fec3a646dd69e7e31ddc&token=449a76f3e1eb0fd87d847a6dee098914\n",
        "\n",
        "https://earthengine.googleapis.com//api/download?docid=5939ef545a9af6c3f10da11c825fd6a8&token=4aa372581c8fce14f6958b0e1c37f952\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "GDAL processing (Offline)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Since Earth Engine provides a rectangle image, we need to clip it with a boundaries shapefile.\n",
      "\n",
      "This script uses **GDAL** (*gdalwarp*) for this job."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First of all the script downloads the images from Earth Engine and saves them on the current working directory with the names *ndvi.zip* and *ndwi.zip*.\n",
      "\n",
      "Each zip file contains a .tif and a .tfw file, anyway, the zip file doesn't need to be extracted since **GDAL** can work with zip files.\n",
      "\n",
      "*Note*: Downloads from Earth Engine may be slow or corrupted sometimes. A filecheck may be needed."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import urllib, urllib2\n",
      "ndvifile = urllib2.urlopen(ndvidownload)\n",
      "ndwifile = urllib2.urlopen(ndwidownload)\n",
      "infondvi = ndvifile.info()['Content-Disposition']\n",
      "infondwi = ndwifile.info()['Content-Disposition']\n",
      "ndvizip = infondvi.split(\"filename=\", 1)[1]\n",
      "ndwizip = infondwi.split(\"filename=\", 1)[1]\n",
      "\n",
      "resultndvi=urllib.URLopener().retrieve(ndvidownload, \"ndvi.zip\")\n",
      "resultndwi=urllib.URLopener().retrieve(ndwidownload, \"ndwi.zip\")\n",
      "\n",
      "#print(resultndvi)\n",
      "#print(resultndwi)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ndviname = ndvizip.split(\".\")[0]\n",
      "ndwiname = ndwizip.split(\".\")[0]\n",
      "print(ndviname)\n",
      "print(ndwiname)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "shapefilepath = \"P:/3_Malaria_Risk/1_Data/5_Admin_Boundaries/MDG_adm/MDG_adm0.shp\"\n",
      "commandNdvi = \"gdalwarp -dstnodata 0 -q -cutline \" + shapefilepath + \" -crop_to_cutline -of GTiff /vsizip/\" + wd +  \"/ndvi.zip/\" + ndviname + \".nd.tif ndviclipped.tif\"\n",
      "commandNdwi = \"gdalwarp -dstnodata 0 -q -cutline \" + shapefilepath + \" -crop_to_cutline -of GTiff /vsizip/\" + wd + \"/ndwi.zip/\" + ndwiname + \".nd.tif ndwiclipped.tif\"\n",
      "os.system(commandNdvi)\n",
      "os.system(commandNdwi)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "SAGA processing(Offline)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is an alternative to **GDAL** since Earth Engine uses '0' for nodata pixels and '0' for some points inside the map. **GDAL** creates a map with nodata holes.\n",
      "\n",
      "This part of the script uses the combination of **QGIS** and its **Processing** plugin, together with **SAGA**."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import zipfile\n",
      "\n",
      "with zipfile.ZipFile('ndvi.zip', \"r\") as vizip:\n",
      "    vizip.extractall(os.getcwd())\n",
      "with zipfile.ZipFile('ndwi.zip', \"r\") as wizip:\n",
      "    wizip.extractall(os.getcwd())\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "sys.path.append('C:\\Program Files (x86)\\QGIS Wien\\lib')\n",
      "sys.path.append('C:\\Program Files (x86)\\QGIS Wien\\apps')\n",
      "sys.path.append('C:\\Program Files (x86)\\QGIS Wien\\apps\\qgis\\python')\n",
      "sys.path.append('C:\\Program Files (x86)\\QGIS Wien\\apps\\qgis\\python\\plugins')\n",
      "sys.path.append('C:\\Program Files (x86)\\QGIS Wien\\apps\\qgis\\python\\plugins\\processing')\n",
      "import qgis\n",
      "from PyQt4.QtCore import QFileInfo,QSettings\n",
      "from qgis.analysis import QgsRasterCalculator, QgsRasterCalculatorEntry\n",
      "from qgis.core import *\n",
      "import qgis.utils\n",
      "QgsApplication.setPrefixPath('C:/PROGRA~2/QGISWI~1/apps/qgis', True)\n",
      "QgsApplication.initQgis()\n",
      "import os, glob\n",
      "from qgis.core import *\n",
      "from PyQt4.QtGui import *\n",
      "from qgis.core import * # qgis.core must be imported before PyQt4.QtGui!!!\n",
      "from PyQt4.QtGui import *\n",
      "app = QApplication([])\n",
      "import processing\n",
      "from processing.core.Processing import Processing\n",
      "Processing.initialize()\n",
      "from processing.tools import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "shapefilepath = \"P:/3_Malaria_Risk/1_Data/5_Admin_Boundaries/MDG_adm/MDG_adm0.shp\"\n",
      "processing.runalg('saga:clipgridwithpolygon', ndviname+\".nd.tif\", shapefilepath, wd+\"/ndvi_sagaclip.tif\")\n",
      "processing.runalg('saga:clipgridwithpolygon', ndwiname+\".nd.tif\", shapefilepath, wd+\"/ndwi_sagaclip.tif\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "(Optional) Remove zip files"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "if(delete_temp == True):\n",
      "    os.remove(wd+ \"/ndvi.zip\")\n",
      "    os.remove(wd+ \"/ndwi.zip\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "% reload_ext rpy2.ipython"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "wd <- \"P:/3_Malaria_Risk/3_Variables/10_Landsat/\"\n",
      "library('sp')\n",
      "library('maps')\n",
      "library('spam')\n",
      "library('XML')\n",
      "library('raster')\n",
      "library('rgdal')\n",
      "library('ggplot2')\n",
      "library('fields')\n",
      "library('spgrass6')\n",
      "library('prevR')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -w 43 -h 20 -u cm\n",
      "ndvifilename <- paste(wd,\"ndviclipped1km.tif\",sep=\"\")\n",
      "ndvi <- raster(ndvifilename)\n",
      "ndwifilename <-  paste(wd,\"ndwiclipped1km.tif\",sep=\"\")\n",
      "ndwi <- raster(ndwifilename)\n",
      "\n",
      "par(mfrow=c(1,2), mar=c(5,5,2,5),oma=c(1,1,1,5),cex.axis=1.3,cex.lab=1.3,cex.main=1.6)\n",
      "plot(ndvi, main='NDVI 2011', xlab='lon',ylab='lat', col=rainbow(20, start=1, end=2/6))\n",
      "plot(ndwi, main='NDWI 2011', xlab='lon',ylab='lat', col=rainbow(20, start=3/6-0.2, end=4/6))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XorFv2YrCd1rQLCKRia0esDyp608hjh0LKSOBS5cuPX78+Dvf+c758+cP+lmE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\nw0fpjKDvmyRdY2ReknYZ4UtegSV4ZM7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v14d1WHHKsACrle3bE6VF5TqmJXdTRtEMD0a+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qdSr2ucIWvaF+K0eTbGSoOimWtp0bkPgYfAxx1PI8+UfGhtsqLbpTQM5ggr\nqtsvlPFUzL4DVJQkCdJ+/BXU6H4aRqVGiEvJsZnw1zqbbnkH7+x05lg6FsioZIePBchOHJyuGSFm\nLEJ9WIbmZLRJHlyll7O9NH+rFYWpYorlHWwXWN7B+THO/8irTN5uV64nl97hZpoT9QcAwzCeiwOR\nkbdv33axwgBcy68IK4BuRBTjNip17HSdDtR05G3xGGdIzgNTcxWkqh3F4VwQNhneGuPbPkUwICme\n2NKMDvQdI12F8Fl4UEu0OhyvETYnoQvDpVwZW3cXF7bqcKV0moL6Z+btxCe06gX/VW9ZP0108yjU\n902zIo2FHHlFdfuFMp6aPvCGL87Cg1BCZkJCq6JpzICiXuE/2W+cAl9dwntLCUswMuSiDcjI02ol\nXaFHVitpDlea5YDb67RI1GQSXhlybw+Hcx05mFbq47hVlndaxfno1VR5Vd501IdE/snzyNYrK65j\nGPvFwQa1nuasU+PpKGZOhdTAaIWSBiM9maPJ/CdIGsEbkl0hoCDt5+dkOttCJxnWobDscjiRSh/U\ncxDqz6hcbDKYlvVcpFRpzRoY+3cy9pmf7AqyzHbjbwIPw0fU2+x6ROb/Cvf11t3UJMMgjklFdfuF\nMvbI7HWqN/NHqYBPhwSsSoZkn2y6SPVUHQl6vALUhf2UkkRqJq+2KwumyTaieFQOyr1Bp0QWiuyc\nks1YUnjp+R3AG7NcJacPH1Z7rw0jPi+/1hwjyxuKmoow1+lFO/luGMb+cOC5kb0ODvq+xonjboa7\nUilnq0Oy1MjG88S/IHgyR9a0ItPRikx/lqNes1ErnrJxyiqMdKD24/hsrfw4WSTvkKAsCLtCW+WS\nOm5VEu+Q7jzO2jNjKqKz0gSTmmy+qTeBMwBCZ1LvIroLH7gET1ee5wLwDvB/vNXxRIbhOT4V1e0X\nytgr14HrpJecuIoS9LifRxmqxMhyhI9fndJ8Ns+4A8cglGdQCYdQejK6b8SCvMpJ6BzyCpEMBmVI\nSiKobLika8twG6XPhHSPwXmnE3ppjwo8+rq/sQ4RjhpC6kJAjut+Q5VTnmZFGsY+crAy8u233754\n8eJsNtvY2Lh27dpsNnOJLp9++umB3tc4ibyF2RBtvZzWkBx6+TckLUfxqznIisyAuu0t6UbaaT6Q\ntfUe60B9NhJDG31qpaclX5eFB1KAOR3LynXKaNW3kEemHpJzx1XUctG2MMlpvaLBELgPwLuUgsx0\nJA3JZPysHA9pBOELWxlZnw9jTxyHiur2C2Xsndl5r+XeBb5HCYG3wuIvnPQ4pYKrZSoqE0DfCx9R\nhhVdPvATJmHqYN+7cTKTv3bBNqZDl/BhNSiacBIqW+4e2Q8TKQc0Z9BdEtWd2i7wqEgo4SmwveTH\nB6nmJ0h91e+XX2sZKnXDMPaBg5WRd+7cuXLlCoCXXnrpJz/5CYCzZ89ubGx88MEHB3pf44Tikvqa\nDDkwytBkaArcB5VUDX2/mkZaX5Fr7bB311H1JRv5U1FAKVIqUW0gcayvjS5JriBfWaKlFpkH4tJZ\nDlh1w9LS4zLmHTLnkcB0hzy8UE4t0JYc7+qOnwBngBz4tnX5ME4O9gtlPB3n/6CNvZxSpKTrJJnM\nLSyBe/7YicB7NDOZ6xfpwCoMbeWyrlMK5NR1XOFngo67SrMO1H3FIOURtkbl8mko1vhyqSQUqVZ5\nokrF7k59Z5SpL9bavvHoebhe7SCcNg3lr9xp1dpFGsa+c+BBrS+99BKAc+fOffbZZ27k937v9+TY\nMAKy2X0gG7UaJhujBi5zWCYpGydjWDQ2GcZFWGuHswp1SuGuaCWZp1ZLhn7yYGTbIVUmR4e/6plR\nZGx4XyeHi6Y9I4KU1WOTtRalrH2hIw1Sb03PuQC8CTwBauAJcFklexrGMcd+oYynocR3fhQUNf0e\ncCPs5wHKBqxCZVgC1zsE26Tbb4uiWDkNkO27aUo99lN+Y5J+agIL3SgqlTcv10ZzrtLdJe9RVpNq\nOhKs66xLrcmDUkKScykrRsGuXAioDCYs75gbaRj7y8HKSKmz54KRXKUEw1jMeKUNvHQSsdUwHLJZ\nt0VlhmijWF2op1RwDdy02ruRXUqS8w9zpeIQXhKFeOpkxa6r6lBM6q/JZbs8UlKYmbdw4fpqZu3a\n9+mKxhfdcVdv0VvRr6RLO0fPlFOnEEd2FysW0Xqa+NJ/R9Zgaeeo9/Gs2C+U8VTMUGD7VaxS0OaN\nVOimRIRKIKg7q/P7kBJB09BdjErJRETqcRqqtb23t9ABsVFcq+wHXh4LFQJpLRKXVXRF3VBkb2VY\nxFUsXDf+aKmtwRPcG6m3wL04eaHKb86ZntMZfn+X92C8UPwcmAI/P+ptvMgcrIy8cuXK+vq6O752\n7doPf/hDAD/84Q8vXrx4oPc1Ti5vjZEDTRb4WuMMd7NQodWBSydSUwRVTOT+iVwc0kHSStO2IVIj\n0WSSu+lTkdrMU3OEYfto81OpGN2c0iBFnN4X2zZL3Nw1jex6GnSYr3ILHrxgxXWMk4b9QhlPzRs+\nLrKiNoSrVI7V0Q8VXVQgJ2qAIfAlUtdURkplWkYiE6oKzl40ZJddyZGrXHM16pnZp+BeEZCsbCP1\n5y78zk4wf0KLyJzzPwJuqMTIyDYdhO+RO3hK/GrpZyY6fBmG8TwcrIxcW1u7du2aK7t3+/bt9fV1\n19y5qpKZ5oYB1MjGbTirHwCAt8a+8qq3FovGz6lRNBgVAJA1PnuQhdYw1EAiHd00XQc13E9AMpWQ\nJyfdzujCPDUhWoddyrp9LaISm5X42sUZlwC2gKwJbs6vQcfk8kb4PwsWyEPgAgDgTSCHFdcxThj2\nC2U8NQPvbF33GukqcK8j85C9OI7njOCo1yqsy8oaqlJX6UaJCDWXrrgT+XYyP5ldKXMqSnTUfSzF\n7SuB7++0sbtalLqAVacYHy0FEa1XgdXwbUz1xXzL6G2yZ6o/3X9a/Rn+VL0OwzCeiwPPjbx9+7aU\n3ZOCewd9U+PkMlpplYp25rJRHNrq7MNmxR97uTM3JEUrkhKKGoG4wTjWFHT7vQd9IpR/SMnOrjhV\n7UPylmoAPmQX2ELQ2gQUmVt7acwhwO6rq110n8ThGVpeP6h+7pz+venHz4ShrYZxgrBfKOPpuO61\nyT2SbRNgM5R5A++HSRQon0JoFYrZCK8Go1BS3SgR4SlQ3Z3FxVonoY4taT9T0pBTOns9VLZQSnhC\ni7y3hHfGWB7HalNCfKWcz8Q/qbwipxbPj30sa+lfymr4bBL4q/chTzWhJMs+cGO392IYxrNw4DLS\nMJ4NqZRTA4UzGEPLrCnarL9gfp1SYvBhqwByZCM6pXVjl8LMU2cRzkE4OIxH5v1FchJz7trhbtGl\nAPh5R4n5vMYWPbQbz5q5vHTrPel4CP1ueM4ZP34BeOgXMSVpGMaLD3uDA9KH7K3JSJ8q4lTesut7\nmcOT+fheGA3r6CubLopfTdaOWVBQZuJLzoLUVhnKvE3/pBPvFg4olBehEnaDV4EPC6AfNAWRPVfU\nT7Kkx6lowieF7/ZxzyvLex1NTrS1Kl91xupm97swDOMZMRlpHC/mpmKNomn9t7tSjlXHf3aMt50n\n+awErwrJGNRIE3ZFsUbmYXJjKiA2G3cX41kQlkryzpUUWmmCSF0pn1P74aFfsvBlh5zk1qr5At2H\nd5R0KXOaDy8p3fiZ1AswDMN4oRBhUgGbpJH6vvOHY0JepWNAwtL1CwGdAsWv9ukrj7BVyDd1JCu1\nLqaiBfmmCFM3Ky/lonGEc7hSzidLGPi3IamVpZfTTq8u78zd2pIWf33H71/Udkn26K5E8tp9llZc\nxzAOggORkb09cBD3NV4AtoBxgabAqMA4m2cDxll6ALy8ccpqruvqNglwnh5JZ+dZhbUScmHELEeT\nBtOYOhSBebhUV/hrHorGPLw11NdoA3kYl+sP3mrm6jEHLgO5V+BOXm6Fb7EGLngvUTu4yQDXGnhC\nHT4cTljessRI44Rgv1DGs7P8b1vL7nvAVa9Zbnlp9w3V47AkAekmb3onM4pcBYWYdpXG0e5il2Ls\nGh+E/6JLpNSqVF6dqK6SHEwaieTKR4+6+ffCQNx74fGHS0GjR3f38zvAFOd3sLwDTLD9qhfTpc8c\n1VV2+HgSfrqDVYtoNYwD4kBk5GwPHMR9jReAlYZq5wCFr7VzdyVQNi62c64rWc7lGGc0lz3J2hfg\nESFVK9m0IEEwGXSqsxyjLUWT61BD1vPHmR8rGzO4nCN4w0WccB7SSffpfF0WvNIAhd1FkJMpXyV+\ndUjv2F31xIvJaBHDOM7YL5Tx7Hzy+wDpPTHZxE+L+tvr0qYIlVifStdUoYBM5joOwrPRvRZ8dSQF\nqoSkVrSsqNxoS1AS9CrNF1ezCvfAiwy8RXl+B6/vtNPad8K5niWWd1LXR5G48P8BRAqeX9BebEzD\nMJ4aC2o1jhl1WwkGQPEucB9vjfwpUlNZM4/kHKV6geTAeMV7cP6q9rPL5eMJycTIve2fdxLvPPfp\nkaE6zZr5YNZQ5mS0ZrRnmZbPt3mftuwePeoCohI25/Goyu6d84QkcA488dLxAlAD3zIr0jCMF53e\ntpdATnG9M54n9UlEa9SNYjVs5Ohw6uZqOOiU5NVwjrQGYXW32JCL0GJykpow8bcuKS1T+6UIndWB\nj1MFmYqySb5EFln1d4mSPON99rG9FKpKuU3yAi7gI81S2pkzFDAM4wAwGWkcO1zTjiHQ3MLoWxiv\nxA07RD7NrbOcRKA364ompZx0QCm83mLpWMdKL1hhAeFO5l91kGokFJMabuFgVKzVcRmoqTatC22V\nS0RbSmCq+ydKUofiSmxvFGlbU6FXwzCMF57ZMnB+Z67oPixaEVWR3SXxomLuuTTIyOhjWSR5knIJ\nQqE4oa/ahIwquCZL7/CEQeiFsmjsez1ZqjRI2Qlo85xXyS1Dyu4OKPdCHes6f8hVfI/lMQnT6Gmj\nArXouN+gY9wwjP3BZKRx/KjbKqzsC7ZaiHSMmyOXtHi7L+e2H9xDssvZi47zboXUZdjxVXX4la7K\nGuDfpNbJ45mL7pKjyebFbIF5JHAkeIuxj+PNUft6qk4BcplWyZDcNRS3jgSqWZGGYZwe2iKiAEgN\nVv7gethfsU/1Th2izSpapEKArK9VHMhjQ4ctF0lKLTujIqscwuqOz4+DcqxM0qKcUuHZCfUO4ZXl\nnYhr2Ac+WQrk6BT+Vd4Lt4hQ1Er47IQu3gy15QCPCqdNZ1hSj2EYxv5gMtI4ZhQz5MjGPq61aVVQ\n7krvZHHlG/et8RErowwAxllraSZK1+iDyISsSUkmL9FEKnShDmwy4A9DuZbTVZEWTArLKD0yj85I\nZxPUwKhAU7Syc8urxyekIc8o95SX4qjeKML3vlmRxq+AGvgvR70NwzgsZsvA1ZT2cyaekz+ll0ml\nT54Ur9IZjxK5qhtVRImUUT+LaUpz7mq27SXkVQoCTYGvFu0xZxROqOZqn8JWudTqoOMRJm2qY2vb\nvrPTSusB8M5Oe9MSOD+mvXK4cCSFuSaP2JVX40ac53+kkk2N08YvgAnwX496Gy8yJiONY4mPwGwy\njItA0DVOTA4pchWo3bivSlqMSW1G6ieqP6OV0F7yElO7jRfpmJk14Xy+XWy/hjI40pZ8d59Q6h5O\nyudEKZ8I41fhR5LKl0Uuv0L31XWMXPCghmEYLybceGNCModtOjYqHaXPk1xNZSdGbSSlUA1SMaiR\npJp2i8kF8Z5IKaw+3fpdVWtnQAtGTqnuJBmd7XtNFylkvvX82SIJK29cavtM/XJ8Y9HuU/8MmOHV\n1IYMw9gfTEYax49s5vRQDWSjtlhrNmrFjGvm0WTAVqszcz+ejbEyRjEm6QVSYrXPGgSdqtsY2jh3\nUduAT6sn2deTWy9eNoqGRasP53KXJrchu37bbeQqWjEpT+w8w3HWZkKCiuWwDxk5stIaBLSjnCbL\nJW9ZRKthGKeJ2TIAlXYoNh282Cn9eOWtsnvAG74lhjAgNZQUkxPy+hBeyzxDSws2Od1XcVNBW4Lf\nBu+Ke1SWtHOOPE12cJwAHy4BQOXsx2m72vIO9fOoOt6FHPeVVSps+pFkTLBhGPuJyUjjODLOkI1Q\nNGhWgBzFGOMVACiaVj5lDTBstVMtNXRE8WyRyuJTmlCDxfOjuNa90OXrsYDM51G4ab+RpJt70mjl\nJkM2Tnmt/tBZtbL2fSq6A+ACkIc+ZOR0dkXy8tczZkUahnE6uRWGbnZ5fWIncnqkBKZKSZtBeJVM\nQ3gsmYC6RuseNeTiJMnKz5kAt/zOB6QPkzmZpfcY+/5xRIVWlCkKP5mLu24X7X3LaHMlbUi2zj5m\n0v3sk0+qy+MahrH/mIw0jiMFZvMqMjWQt8GrLii0kNIyrGNqkls5smbegLEpVJuLKNwzcg6TGYmL\niSJR6/BypdLmwpVXEA/RHzSZ18NDUpvwaZ907byekLvbVqyIP6bpT/y4DumN5HMkbJNuq2EYxqli\ndt7HToqFOPW6kdMmox6PrGtYPfZDNQQvgriFRkSU06iDWpMaqt8tOCX10V24Gco/1o1RoGxFBXWm\nXj3KU4gxKx7mhAroLO8AfW9Fwl8/CG/TVYVWm41TktqWEmkYh4HJSOO4UsxEQ44zNJnXMF7QuHaI\n2RiF7yE5zoJ8SMlCbI07JtScbecM7cS5aVpsdRGFgQqRvBRxCwBzuTu/Fmiyebla9/huh3MbM5Uq\nKSYk8rmS3erOY8yVboyeJg/Ua8sZ4CEwtBqthmGcVmbfUUNXVbQqKImPi9PI+D1/oCNCQZeIzSbR\np6KnVhdmRSJUmJGGZN9OUh/lFiXdneFERRlJqtMJPWyUVOk+t5fwaAmv75AalnTSPv2bhgVqZXXZ\n5SB8dwNgiu1XLTHS2BcePHjQ8zx+/LhrWlmWMu3mzZuHucMjxGSkcXxx+kp8tiECg8z937Z8K4BI\n7ehcxA4p2BTeG6zJtNw1GjYiOW3BtS5a1buIcYxrPa9PO99nNh+Pz7nUUI74zedJoLUvq8MxsNqI\nla+sT6NsTTf5iQ+UNQzDONVMfQDmKnBD+Y0I5c+qOssuX+SuXfcTrgIV8GMfL1qGTtuu4axT+ge1\nQw7NlSDbKownBVUG4mYkE3p80ZaRXygFhxAeu8K2yzs4Lz9pUWgsP16flojE5DR8HQPvh/axrGN+\nDOOpefDgwWuvvfbFF1/MZrNPP/303LlzSSXJYFLMAAAgAElEQVRZluVnn33mpm1sbKyvr7/99tuH\nv9vDx2SkcXzJMAOQNWGQqTfOVhqgnge7Ov/NFXGN4eIwfiRwIOFL7yzIFHzaIE7RZLXSkzndEcgw\nq0EzvQkYqb5Wdhbh+iHOjZyHwu4hDFXbtKwYw3cW7FG/ZsN4ZuzPvcaJY/Z94D+N575iGZ6ehnGh\n8KKrJCk19RJpotSQXOIU3RteuVWhdIxyA9FhTiJ1ahBq1wFFla6G4wBuqKI24lUOKAqXtSL8c5V0\nLGvOdz71yaPRG9Q75ihbbpbSpwn+ltsFDOO5+eCDDzY2Ns6ePQvg0qVL165d++53vxvNefz48Wef\nffbpp5+6aWtraxsbG3fu3DmC7R46JiON482wFTqt9ygZgORSZj5VsrXItvwXXdum9pVMa2SYtbGv\nYzpbq+OnSpVkyRUFjIL2Qzosa4Cml6tFIotV0kEzzgsNt8TScT4NgPcP9U6TMau8Zf1KdNqkYTwn\n9ude4wTDvqLWQRWZjRPgOn0d0EHUWgM0EgnRqTqICq5GuYS6XCrvfNKh3e5RxmMlVXBU9CivAx+U\nOghV4kBtrB/KwHZS1fEMSKnqyt+4ovzR8PWZG2k8N04fvvTSSzLyla98RevDs2fPzmazS5cuyYi7\nZMGfRF8YTEYax5tsBi8Mi8Y3hKwxLrxSCgvF5MC3Vzoq6Axp/hAj9NKOm2YrNZhEpTgG62+FX+t5\nEZ1MFQ2KNsUVdLpyNVlO383malqeeyu0SJMbj4J5ue6PjF8ALlhipLF/2J97jRPKDAUArHqBI3JG\ntJloRadxKuCPQv9MwkdLmg8vtFguVipDUPojDvwlXUVcuddlxKYKSZ2SZdqncSm+CtpbFQnClK0q\nS4mE/s7OvNtHO2PgtyLOJp+Nau1ErynqTHIVqNr/aAzjuTl37pwcs6RcwMcffwzA/Vq92JiMNI49\nW60QasNQAQA1cDcjawyt0FkZIQfuFlQfBm2SZENOZrsma6Muy7HuGH8GRI1txeIsUIlAk6FQuZHt\nNqIo1ZSejDTsx6Em1I5inbpW50zK+BOzIo39w/7ca5xsBiTwuCWGdvkGwDcAeE9Ssh854ZCZhAv2\nVcqinJqkbDxWXkmTT6jCs33/RGWYV4lU8ueUDtDRr3Ggdri9FO4epJsremB94yg9ki1dUM1c6/Zh\neP7qr//sz/7snyr2khbxxRdfJMcX/+48ePDgzp07Gxsbz7jhE8VvHPUGDGM33prV6LX6J0O9Qg0/\nHDWaApm3HN9qvMJ0435Wa9Z58eRSDdP+IZOM+NyLiqqVUMtV3VfOLwxjWcdFO5b7zY8z5EV7IG9g\nXlMnDGrdIq2oBbLWirmaEF2VvNw47fwaqIGf7cNK9ude44Qy+z56HwKV9yS50mlFOYQSQboJXAXg\nvTdWehyVWZKHKfGlC4qyJps6JpkqIRZFt3Izjy42SdANvHCb+qeOlGS07Xd2/OgUj5ZwvqRnmKau\njHasY3O5jWQfqGb4/e6tG6eHnwNT/MP//ru/+7t//Md/fDi3dDka165dW1tbO5w7Hi0mI40TgLPm\nihyoUWCufkRZud4eGVo99JY7ze0ZKfa1WWmLmmYS9ylzhruJpF0L7eQkIOvwK5Qsy1G7p5MJhf+/\nTdvjRBRjQTGrjOsh6SJjo4xKUa/RAW+Tt6PHo6uGPqj1lkW0Gp7/b4adnR3tH/7Wb/3WG2+8sevl\nC/7cu0Ainqo/9xrHHVcgJipCI9qnDP26e2Gkq0inyhtypW+uGGVFrqr6Oggn9FNCTFjQlgOUwSir\nwW8+eWHpNww6gM/V1OVUeRvbS1gezydtL2F5sXnY9yK7DJdj9SgjSBnBhvEs8N83ma7fJqchNzY2\nTomGhAW1GieAu732oG7TC0fZXNi0zSE5/7Aj1NOtMHbtPbaQjYBhh97qIplCGd2LY0Y55pamNcX8\nvrn7WqMp2r0VY+R+S7HvKo/Mx35lzrGs6ZmkaaR2FBckSTJDv4LLijQMpgf8vb/39/6h4h/8g39w\nQHc8bX/uNY4zPezMa5w6+y7KYKz82YnXjZs+cFRn/EktVilC0/cLLvAGocJfdb9HmbNrvGefZkoV\nG3ZK9XzJgRT7cKLmCMs77aRPCvTlK1QMbnQ9a0gd14qONpeG8RTcvHlTFwPnv3V+/vnnC649bRoS\n5kYaJ4DLAAKtKDVjnGXnTMgm83GtLKFA+YcryMYoRqSKuOZNR92aAF5WD0ZZi1vhVyIbqXYZNeoC\nAEYFVppWDWbUywSiEpN7QCAgEQbPfrzbYyVh31SOPzYr0lD0evjyl7/8L/7Fv3i2y+3PvcbJ5tES\nQCl+XE6Gwzv7/qwkCorH5uZcp6zAq8ANyieEMvTkcl6cmaZEI3uMkShNFomVy6O+IBMvjKf0XAgt\nzUj08ZM+WsJ5mtOpj8WBjMoEwb9cvrcclFZcx3hm1tbWol+Wixcvfv7555KW/9Of/vTatWv6wps3\nb66vr3/66aecwH8aMDfSOO40WazxMio/03ZTzLAV1uDR1mIb4DpsBV6ToVmhmYs1ZB7+k0F0fNUe\nH48MYzXYrIRT6naH4wxNQdVcOWexDgrzcDhr3imlFz1f1xNHI4bxnNife40XChFaUxJgrJrQYQxG\nIrCidVpB1JHoyLVqkuVmor3xV1kwWrOkOatKssk63LgRKY3aT+05KrnaV+Pp7YIMWandKpqzpAk8\nEtmghvFcvP/+++vr666mjkum+OY3vxnNefz48fr6+sbGxmnTkDAZaRx/shGA0PSrg0EnsVrZWAP3\n0axQ1Cbl/zXFvPhMNvKLgDTnHmvnIBGqOj+7WGyJrZoHYzmwMsaQxHA27l4pB/I4utUpyS1qnFnv\nFuQLkp3Jbeqo11upOFvD2Dtra2szz9ra2tmzZ92fe2XCrn/uNQ1pHCNuUPYjvNjRzmTpfbV+6NpF\nTSOZxVGscrtIfLFU64oSnagJlR+v/D+Q2oyqsJb0XAhtSRaTsn9R2gMxF/vY1p6hlph9dSp6X1H+\naLJCrmE8O5cuXdrY2Dh37lyv13P9jSVSpixL17v4hz/8IYD19fVeyGkoJG4y0jj21KTxIk2TAzWy\nMZqMlNLH3nhUTlwmxWxyXzdVbrEXTzLpMXL0p48+TcvRaP81AGRNK/xyzJthtiZk0U6AV4luBHnc\nIMRdUntPkp9MHujMU3qJbLKKED2DtpOnYewj9ude42TD8Z8OyYfs++hLkUXf+dE8SbL0aYeTlCCE\nv2SP6X5aiEYX6szG5AS386t+zZLaRcJPiO6ry/BEDiSLuz7wyRKWd3B+hxIjZTdRqCrCwX7qOedy\neYYlddYwngv+0ydnW1RVdfv27WgCcxoKiZ88GVmWZS/FUe/LODAue3/tsi+KMwSGyEZkLbJF9qdt\nt4xIy7UhrDWZdFvBhD1pyD2YltkCvy4ncevtwhy4jNZQHWdzE1LaeMiCeiRauA6fTx43980ek+7p\ngseNjp90P5lhPDMv0p977RfqVNH7MJReThmuUnyl2HoTYBOYAl99tW3mMfVtM9y/Vb/oKvl44gcm\nPTYtAgep8WgkWSYHfsOyggTZSplZVoa8bBQiy0arfFZACVz3D/K6r7ITPCrUA7OdOvVW5kApSf92\ntr/e/WyGYew/J0lGuoyaK1euaMX/6aefcpqN8UKxBQyB+ypYc0iqSOqa+oNWNDqGbX0d1MAQ47cw\nXgnbciBlLcLfTn/VEa0s0bZSci1Xws4Xw8kR1tTJ2xYmruFHpCGFyJDMmiBxUnbxMNx+HdqM0bPq\nLTNnnBtpGAfAC/DnXvuFOr1UPo/RBW3eI/uOdZ3ot6kXjRIjep0KtIpvyZVHuzL+WB8uKKbK43sv\nZSo2KbxSq3x3k2hNplTOpOxtbswma7FGhqxuYJJUqJRLGnibhmEcOCdJRn700UddlRXcH7M/+uij\nw9+VcaCM0ZsHoHr/sG2YsdWWnwGAHNmoVYltsOjIy6AhIOGsOVCjkJRIzlHU9WR4jpaXUPKLv3YZ\nm5zh6afNK7LWXgnX87njbK4Y3UHWYJzNv7qDSFVKMVvWliwy9xLgGs18ourLGoYh2C/UaaTyDp6k\nFE6A6wCA//i/tPmQYsdxFKvk95UUp9n3Om2QstywsPgq/IJdgpOLqV5XZwdkZuoSQSDXcKBsSe41\nAkq8jIq4VqQwt4tUFK8WjV2Rvsnbb1pEq2EcMidJRn722WcvvfRS19mXXnrps88+O8z9GIdALf/H\nVdNZ8QqqAIbetSuA+63EcVmFqDFeAe77a7dohQIAilG7QkuuJBcTKUwJG4WaJit0dfuI1JvbEivA\nLSBvN5kDRTO/osnUZLRvA0DWzJtM5qH4rX2zxzOhOl4YmRs/GcyKNIyF2C/UaaSkejMArnv5dxXA\nj4OIUKlDM6AL3YR7Xj3dI83p5tyie/XDT+3e7YrU0bmxMLRVVz/th80kEZbeke1Fg1OlJ2/4F7K8\nk8qnHKg7RQVtdcVYePmr7U3DMA6ckyQjo2p+EZ9//vnFixcPcz/GIbDSYFzM1Y+Ea7aFSeu2xI7r\nLTn2NWmaAkWD0bfoGtBkePdyq1s3giSXVoxI2Xm7yrIoINbfOohWHbaPoFdzmtk9uNy8oGu554cc\nuGWe+M8aeAe4AFzoqDUbWaqygpv/bC0oDeM0YL9Qp5FV4PUdrJJz5ro+VsBXl7AJ3AMmPoRV4lRL\nsuwcVVia9LoXSpsL9Z5GT46UHshylEu40g8vxXVSOSJXtHFSxHK7y4puN6CU0e0lPFryj83mJker\nDpQSnYYxtXKVe8XaYzUM42A5STLyypUr6+vrDx480KcePHiwvr5+5cqVw9+VcaCMMxSYtUKuniuf\neZmZet5ZsXDFaepWhq00qWYXokSHlF2ZhzOjOjp7CQAVogXZE+zQmfPKrpSr2RTzHh6iM9mKrMMR\n1znzfhgw+6GKaAXwIfDQ50zm3qvUCZOyZTfiJOgPYGVaDSON/UKdQmbLaAMpS6qRMwkdtZJyAsV7\nrDr0oTMk3/VfKxWkqmWhEMXB6saSsqbcekAzGdc9UuraQClMkLKLTMiBmjn1NYTEXFze8RuLiv9U\nfosIK8DKw1ynyfrBDMM4VH7jqDfwFKytra2trSVL3l28eHE2s/+B+wJSON3iSuxcRjYCVlph02TI\n8lY0NllrM2YNCTmnFbfQrIR9Jt/yX90pXcBUVoj05N7pio+tSVv6m0rtnyQiJuVYciMd7uytMJA1\nvEOwIx5xPPQHF+g4iow1jAS/Amrgvxz1No4H9gt1erkBXAUmwDeA74VphGLcwdtmfe+6bfpTZejg\nRR0dQaV3dPOPaRjpyV/lIJKXfRqcqDkT7wJW1A8zatkot46YhAqTH/wWcH4H20tY3sH2EraXsDxW\nq+jOIVP1KduNsKBWQ/MLYAL8HPgfj3onLywnyY10JIv1VZX9FeqFhjSQaKF5LGg9jwIdZ0A9r7vj\nPoOo0RzZXV+hZ4VK7/C9ooMF6MDQLVW2Jw9HkNJnyvAUVeyeJfIe3XouxjVaQDI3tf254Ibu1EOk\n0Z6uYRhJ7BfqNFJ64VR6WViR9hEpBZ8SWdKFCDUkgHv+YDWcpnMJ9WBXlOkgpbzYk5yoQblw4p8r\nWfKH0xi5Qk9FG3MaG1Ms7wBTLI+xPFZtQxxT5XLKVqLb67q09v/LDOOwOXky0jiN1HNVlI3m7ROD\nszlQoxhjVPhkRtFsyf6QOWrsSdHFO5FpeWpQRvJg24lpyUja3PuoeRupq0v/cN9I9xK2Us/xpCNU\nNdoLT4gcS21pGoZhGDFXfZOPCrgBVBR62fcKaOBjNlnROX2YtBBBGYmRgIoEYb9bIg5SIaZ89yhg\nlW839dJxqgTaJGVvgkxL0MoisD8p8MlSqjor/P0QyuIJHQzIwE2mePY7emsahnGAnDAZ6RpzOaIU\nFHfqqDZmHCw5MPR9PsLEyCCEFQCwMmrnNBkFf0a2YQ0AxTgxOCfpRkaRrlp4RdOiwWSkK4971RgZ\nj9FKUcMPnzoKUL6nDGqZrK3IKIyXx/8QAHDGolsNYzfsF+qUMvFZe6JlKmrjgTCCVKTQgKw8QTtw\nrAP7HY1AdOIiwpmR8HNfI3HIF0biTk6JBci7inxX0HNJjOvyDl7fwes7+GRJXRbRp8DfaGcS/qvN\nyYG5kYZx+Jyk3MibN2+ur69/8cUXrt90r9e7du2a60y9F5qmuXLlyt/93d9F43rEOHbkwH1kAHI0\nKwB8MmQdNu1AK8NWfGfIfCWoW9NOQCCkxhmKOqWuusy4SHZq4cWyDH6TdSAHJZkTtY+txVwIOp0c\npUTK8ThrI1rde6jn/mXwcHm7WDBY+5o6kbDUElEe7mt+zi2rr2MY3TznL9Rf/MVf/Kt/9a+iwZ/9\n7Gd/+7d/u88bNfYdJ4W+4aNbRdFMqSgrR67eoGl9Py0SkIPUOFLZjEhNSJ7qe0tv4CvKbvpb9FVn\nSKmtWvk8yQq4BbwbVm3lO3IELDOg2YMCjwqc3/EbikgW6okW0q9gAGCG/5C6xDCMA+QkyUjX3Nn9\nQgOYzWZlWZZluce0kyzLkjX0lpasX+3xpukBvrAqE0WQRsc5UKMGCsy/zq/yX5sVFE08mLiFJlmY\nJ5lXydGtw1bzZTmQY5yhyL2G7LidlNWRvpFF03qtUAKZ31Ay+9HBZVoXPJMcX+hOmzQMw/Gcv1C/\n8zu/8yd/8ifR4A9+8IP19fV93qixr/S2AQAVsErapx+WqEHo1EXFYqJwzn4olybKDNTHUKpy0KEn\nK39qFXgXuE6mYEkVUiXOdtP3t3QbSzYgGZCGFAnNVuL5nfmzuTnbS1hOpkTyW6jCbiH68Tg98gZM\nRhrGoXOSglp1c+eqql5++eWytID4F5qaGjxuhWmBlBWpJZHzLeMQT8ZJuK35JU9RX2eBwoySHjF3\nBoPaPxxVG97OGa3ZOFCPWwhqBdXh0+td61dyAXgTuJCaHD2TPmWtPgxjMfYLdXrZpOqmInZEQ05J\nGQmScOgGq/CUhLNKk0kdxRmRLEATUdHKrpDPDS8aV4GrwC3glt+8+7xFD8IiuaS0T4RCt/Q7lxDf\n7SV/mRfQy2NlWeqv8v9xJMpWkjujx1s1K9IwjoSTJCOTzZ1v37798ssvv/3220eyJePAGfWwBVwG\nnG4cAr5C6Tw9knXm5fml2RiFj25NBHoOw69CFPW62JBcQCRKw0DWYNlwnSabC1HJAmWnkWu3rjTB\nHrt25CY8AT4Gnuz2EJEEvkA9zAzD6MJ+oU4hPezgG17XSIXRSOP0KcY1siKrVG6gK9AaxcdG7CrB\noGzDqOgO26Sum2Xf523eAlaBErgB3ADgS61u0iWbVAGoH1Zq5bs74bo8bp/8kyIdrJp+kuRxqZ6t\nD3xj8aKGYRwQJ0lGuubOevz27dt37tyxyJ8XkHGPEwtb53ALY6md46RV7jVhpAYlXzCSgvcBUE+M\nrTDBsisYdIGeFPjyYXz3efAql+dRmYvzaT4TUrZw1x9nTbt8k7X3JPmcKO6qszhlpn6sOhzP/Z+n\nDcNYgP1CnTZ62MFvU1LMPUojLCkkc6CEFsionIQuZd/7hJVXa+ho18Ekq58ucC+jQFl3/L/6SrMA\nbniLchWYAu/6x9EGoYxMget+fEK5oAPgqwU+XGqP57vt6o8pZyMHdhDOn0fNmhVpGEfFSZKRa2tr\nGxsbugIegNlsdvHixSPZlXFwNIUXY6FELEYomjAYVQQb9/aIKs/wOOhapAJANQtiQGUFth+3wj0w\nl/3MaLdD8khd6qZXiUXTXlf4yjpBM0zgfqgbn/glF+hirSGTkrm24jqGsQfsF+r0MZ2rHi67egO4\nF5YXleOKwjPLUFgi9CqjgqviW+q+GKl9dSIxqDoIVp7lDT8yBe75kN2BNy0nfvJUCeAb/u5cZcfd\n6J0xAJwf+0smoabs01qrfpVIPSersxqGcZScJBkJYG1tbTabXbp0SZ+qqmo2s/+x+wIx6mXjVIah\nL04zD0YdzguZRpqzeYuaKiJcKpKXyXKl0a0XwEGzvKCWqdFNuwJSc8AnQ4pi1NrTv4BgmehuXSJa\nK0wWovLVNKRh7BH7hTo99D4BXimC3hjsljmPbkJ+o06PXKU5kTMX9eqA15y6w2QUv7pAZE66ZVdU\nuYdXdg8iOZ9R5KpoPc7zFC+09HpylZ58eYzlnY599L2fm9yf9mQnAGa+jp5hGIfPCZORxqmjDnWg\nj2KN9A8HgrJs2gJGKymJKO6lS0FcUWVgNUklWasJLB0jbRlJSq79QxPmkpjG3aC7YsXnhYrNeZ+W\nHwbXpWNWk8fRc+hjw4j5FVAD/+Wot2EYh0vvkXfeKm/QCf3QSIQvsaN7W9zzNWz4Wj1tQBqSB0HL\nygaSelWSGKXiqeiyq+G9ot6PE+oOAv+8ERMqq+PuIlZk5d1XLjL1SUEPIHeVe2uh2O+QvxPgqhmS\nxkJ+DkyBnx/1Nl5kTryMLMvy5s2bR70LY78Z9RJ5g4iNtqZAs4ImQ1PMZ3KB1hxYGXckBdJx1lD4\nayTzZP5iyZUsmRNlKOb0FH7/gdTMQw1Z+73lyMatVB7RBFn7cjiywOOMeB7/1TCMXbFfqBeTzVBx\nIbTjQJGf3wNWQ8eSkxKnXo5CLRihWzVGqYJRQxFeqq+8TTkWWajl2NWU/zcg4So1ZmVLkvApC8pm\ntrmzGreb1CTboSDUsroFiGEYR8CJl5HGC8io58I0mxU0BUYSsSL+YY1sjHEBYK6v2glbyEZzOVWM\n0ymALDvn5PSPBzVJ+ZXUn8mrvF6Ni+6AKtDCb9Jf0hSofWlW+LTJrEHRzIviuIOHfkmtKqO97KoV\nrc+HYRgG03sFAFD6cqZ/BMBLtYFvjyHtKr5BQhEUsylirErlGcLPGfjVKjVhQu7lVF0YkWz2iLDS\nj1w4CPWtUPrJA9J3Ax+LKlZiRduL3MtHrlLrRBm4YqFqZagt2tLfdTrD19V8wzAOD5ORxvHDx2U6\nQbVCTTta49E7jZn0XYSvT8OwigoFUzYO+3xsdeQXJmVWUpYlyUPpyNfm7W4DrSguZea7g+Rte0xX\nU6fgnpkAgG9nwdpDlRsJdSwCcvFDvAm8ubenNAzDOEWwMdgH3vWhoQNglRw80X7X6UJuszEg9ShK\nkuvOiLqT0M5+qgyPDnZFSklGyE4ihdn3QapipYrzeS8UgFyVZ0CiUXYrGrsClsd4VADA+WRipDyw\n1rsSExxp5QF1IDEM42gwGWkcLxr0OCg0u0tdPbwEEhHlNFjj2mDUYWAqZypyviK8hOICqjkVVtXo\nQVaD6C7PU6eUZHgcZHX61VpP0vuucIqaMifFsRwCd7Po6s5NXQh3ERmSencfp57JMAzj1NLDeC78\n4IXPJim6zbArBkgcTcKCrgLrI1Z3PKeiBXV7D5GXE5rGInNx848IkXJvkDKU/XDmZ+X3JgbsJBy/\n53ufwEvQILo1gnMo++RD6lcGbBcz/Gn3UoZhHAYnXkZWVbW2tnbUuzD2jWwUumlDqrIDFGNkIwBo\nirnWyhrkIKE4DK/SVXAQFqWBl5Rc7QbhBCgxGelSnqZFpl4tuSw9qRDnT5KSlIe7ldqRNiQfpm5b\nqwO5m0W0GofGgwcPep7Hjx/vOv/tt98uy3LXaUeO/UK9aLxStIGpU6pfKuqu8s4kO5CbYWzngKJV\nQfVlItGoe1uwk+nQI1qR7mpLRnMGHQdSMFYMUhmRCVWoYOGLtX5/DPRVpmXkLpbAZqgeEVq0/I5W\nE8V+DMM4dE68jDReKL7dC0JJ5WBII0NkY2R3gS0vura8pxeVtKmVSAOtwwozqlED9VWvk5zZpRij\nsw5RuVFJHt5bjlrVbnVnRln66icLb56Hk3lQHusM8Cbwb2AYh8SDBw9ee+21L774Yjabffrpp+fO\nnVusJB88eHDnzp1D255hOHrYAUj+uL9jVKSppGOFJBZW3iSckjEo+ZNa4/GgKKUynAClGyODdEF7\nxa5jYeL/VWo110YS3nKUZpJ9fyzbmACrwHWRhP32qj6wPKbnjIoUDeiu/GCRXO5jewmv/5+p3RuG\ncaiYjDSODeMeLpNi1N4dy6bL5DrCS6JhW6aVi7XOL4yWzcNBDkDViYPJzfibxkGzSOlPvSCfivQc\nbSAHsgbjbG5C5r6+zhZtwe39vr9ah+gmrVA+5SZf8Af/t1mRxmHxwQcfbGxsnD17FsClS5euXbv2\n3e9+d8H811577bC2ZhgtvU+AV5YA8hIju69SPqFkDw68L7fqNSFLuEhMcsNGHoEfZ7sumtZXHUei\n4NioUuuApkX36tNZuYtsvk+PBqq+A68HudmJC2R9fQfLP/I6W8toMXYjiRzp5hLbr2KKGf4DDMM4\nakxGGscG7e9Fki9VK2a8gmalVVQu0hU5mZORLOQVhn4kJxG4wMCMjrVGzWmpXO05ilaN8hdJyUnC\np0hHh+RDwhux0YuJnEaEa2tq9VLhg4IN49B4/PjxZ5999tJLL8nIV77ylQVm482bNy9evHjt2rVD\n2Z1heO6RrJooW4/NRs5vLH2VGlCHjIq+us8oKxBh24yJavnYhc6EnISqEqrpCELvMUKcyWkogMUs\nrNT8q6QqK+D8Dpa5rE6lVoGyH/l5dIFa6/RhHCovas7FvmAy0jgejHqQPEC26epQ7kQOXu0H6rbV\nxzhLWYKiD/NwEEr48VUsp7RziHCEjcTkhgWlG+Nb+CjWnPp/5H5Q+kZqhctL6neQuk+wfR43JWkc\nMufOnZNjlpQRjx8/Xl9f/8M//MND2ZRhtPR+2x/poFBdQNQx8ZYbqMwMKBZUmIZOJhMJQqnvGunA\nAVmRGhZuzkgsQ+EK0rT9cFA0Z3JlCU2VfZbeg9UVWR8thZGvUhcosl/76gDUC2SAKWavpzZjGAeA\n5VwsxmSkcTzIgXpej7TVNFLzpg51GubjrtKMyKCiaW3JIJ0yqdZY8skIU6vPBfC2czL16nDz0X7Y\nC/Xbc9LRPZfr/DHOUHs3chhK1Kgq0M5vwLoAACAASURBVBOlT3fdeDTnCXA/VWzIMBbw61//+k8V\nf/mXf7mXa7/44ovkePKn+mtf+5qEvxrG4dB7D4DqbwFSiSIRJ6GgEkfxnldMlap3OiVJBUoY1IYn\nvALks1H7R33JwGdj8rTSlwKKJkdEDiS3dYysVDY83ddHSwCwKgsB58dhY0p0SGe+Pe/PN+U0K9I4\nRCznYjEmI41jwKjXHohm2+rIHozwhmSz4pXbFuCaZETZgQuUG8IekqCZUJM7YmvjENxh+HUrvMR/\njd1XWnOczd3IokHRoMmCsjoSNst3PtPx0PqxQJqXp7kV3rLESGMv/Bqo8d//H/zqV7/6c8WPf/zj\n/b3bzZs3AVjhU+OwqbwvV3mhKIKqH4rAAYm3yk+beDXFHRcjPcboaLgyLAmLsKWkrNNX7qWszxVP\nAdwAbnhddjW0A6OmlKx7Kz+CUAlWwCawSk0mz+/Md/JoqS2Kg4m/K4ftShEd0bhT9VoRPNjr3CbL\nMBbwc2AC/Ndnvt5yLnblN456A8apZ9QL0hSFXDlrqVNNRh7mll9H5z0y2n5k2zPpSUb3hVJyUKou\nChLdSjxIm+6ow0x9sK7kRm5RHZ1oLvyp+8BDVaynCy1BEV5rGHvkSz1kWfbee+892+UczspElqML\nZ+2yLg3jgOi954XPNFRxbAMyou7Y36vCYzYztYycqIPI7ex323gizSInD6naqCLcvgfcAFZ9exKk\nNs8KGSQv3eeqbxHptKirqSP+7aMlTIDXRYhHxmt0vwXpkRMAMxQdD28YB8JT5Vx88cUXi+3KFwxz\nI42jRno8clZh7ls7ih5i2UbSJ2uoeg3Heu4ajNqVSrjHS3I1XoeqkgfhpV5OTyHXKlnb1goiVprY\nnYVfNdpCdICULMzDzcoDnQl2YRgHws2bN6VcgTMYEYa2fv755/qqH/7whwDOnTvnLrxz585nn33W\n6/UePHhwONs2TilO6dzzXyfKBhRStWAAVYSGzyYTDgcLgz0ll3AQDvI2OE9yQnuOdiJxrU4i3gj1\nmpiuUu6Vu29IVuPUG5K8kympzbn1KmtFijZZqZafZ0BiMhl9axjd/NXf/dmf/dk/VcivzwIs52JX\nzI00jpoo71GMwaFXU1FKISvJpAG4R03Id++yHKNBHeAa5SCyONtS+6npEnqc1lCl/Wc5AGRN60be\nBy5n85vXoeEabVM/RzI4N/p6AXgIXAAAfMsiWo2DZG1tLQpMvXjx4ueff37p0iX39ac//akOCoqu\nevvtt3/yk59UVfJ/oRvG/iGqaRJGcrKskpBRDivldMFIc8pqXWl+SXnpKCkns2t+P+VzTpQEq0J5\neZXaXUKp2apDuPZ9AaE+JWGe38H2UhCIO4k2OiUlypuObiCvuASA7SIs+moYe+Af/sbv/u7v/vEf\n//GB3uTU5lyYG2kcKeMeoCJF0Uql7C7NrMN/CI3KJNqYi4iiUp8ZVoY16bxIedZhCVQfgjs3Hr3O\nHGcAWnnJF21Rr0gW1+7+T2gZ/UBsPEbjcu1DsyKNo+D9999fX193f991Ze6++c1vHvWmDAO9VwCE\n3SBZm2nPUNdQTbboiOriREt1zee7aJ0Z9cvQ1t2AtgR1UAKrwK3wEp4woAM2LbnM7FV/l+2l4KUF\nCjbZ2EPokwjmikYTAFje2a0qj2HsJ0+Vc3E6S4ibjDSOjru9ufeYp1ROjibrUD9OWvHlfCCfw1B1\nRYjCZGuvayY6rMs8pVcj7crRtnytvmMN1CgaAK0TK7OcgLwcOppyq6iobZd2PpMadFe96d1Iwzhk\nLl26tLGx4QJWXWl1+ZEuy/Ltt98+2u0Zp5Peh77oaOmDMSM5J26bNL2QJh9R5mTUudHlH5Zh/4sF\nKD21aA73YuRlJ5S7KNLOfb0HVMANyniEUpsTbzxOwuYloCYikf3IuZTB5qbKbJRnmKZUsujUG9Yy\n0jhQLOfiaTEZaRwdUXgnvGjEfGRec1WIIjuHSr8hpaWiTogsO6OY2MVEGhJKruX+XpH6rdVW9Tp8\nSR30wLzvH0JLYw71lYM8fJE5cAE442vwRGHC/PJuWUSrcRSsra3NPPyH3qqqbt++reffvn3bIlqN\ng+WdcVBIpk/CT2utSMWxoJqmDEApN7OXXL9kRZ+o86TAhmRk3TnnsCRJHIWt9lX3DjmQGFc3WFIt\n1TJsRHl+p42AjZNIp92ieYGMli1WwNUZXu2eaRjPC/8Sra2tnT171uVcyISunIsZce3atYsXL85m\nM8nUeIExGWkcEaNeHOQpROoLKhdQ2mls+RqmMlkk0RbNhwr0jLTcM4RyygaSp3LaQB7IvmaFJK4W\nrrlfNkcxBnJkDVYavNW0T5k1yKn8UKQh9Qbl82PgCfBmKMMthNUwDEPTwxjvFUF+oHR9nCr1hbAa\nKovMsqOwqi7Ao1s+gu7F+nPvKmyivnJe5USldDp/8nvAVZKFg7AfRx+4Sj07BhTa6rb9aAmv78RP\n1Ae9iEjCDpQ/mZTFJQzj0LGci8WYjDSOgrs9gFpYeDXTeo+RvsnDcNBctWGsw5lyjPBYG4kyyFcN\nU9P2QnSX0FZtuYzsLpoMcY8TsU+pL8i4QFMAvu1HNm6PaxKbWsZ2ZT/Ci8k3O8KEk4OGYRinjd57\nwCtFEFzpjitfslXXGZ2kBN4UuNch/ATn5k07nMYoFXPxUjJtqoTrgFRY5IjynBvUQ1LbpBOKaK38\n9ir/cljlPfINP8T2nJfG4TJEkUR2X1dJTOonMYxDxXIuFmMy0jgKOC4zUnQCj7M20zmH7PgtKCPD\nXyOxx0or0qh6P9FIHW5DRqJr5exlZGOKcYW/o/sctqeaDLmXjm3J1jzWjRza+iQUpOzFRi/mY/VA\nyeBcwzCMU0jvq95Y6yvXkfsdRgJPirVqJh1Oo1xb7aYzuVrs4lDurvo1ciELN9kwb+mGr9e66k9N\nQhMRqr3HxAtm2TDC+QOePVWiOZKzXVVo+8pdNYzDwHIuFmAy0jh0Rj0ATRFWrNG6KzrQsATdCsVb\nlBrIS+nEQdDXHM1Kx726wkbDhMbE7WoSjVt0ld4eqc1sjKxpR8ZZkPdYq+vqVJUdhNG+nBJ5Bqh9\nuR03882OyFjDMIzTReW9O9FIUSyoTjjs+wvZ6GOpqZ3GQXgtOiSoNhWvh/VgI/qkfnV/jmhLFe15\nQCOVtyX7wPdU7VlZcBq6pv3wRXE6aB9kXyKVErpKx3JW/E2XeTm1xEjDOG6YjDQOnSHgfLY8lcGY\nPIjEZCSSolhWFpPaulywq9pvLLqRXmrXxfX8pCOa+yjWyJWlx2+K9owLbYV/Z9p2XaC4SSbjDPAE\nuOANzDNeVX5MLUMMY0/8CngC/Oyot2EY+4gTLxK6Oei2xxCGtkaNIstUDOognB9VUuVpg46v1W7K\nE2F+JtT+eZBzI7l60AR41wfxJr1N0CVXKf5UlwJCslGHDv8FvUQuDjsgKW8YT8UvgCnw86PexouM\nyUjjcBn3EhJxQYkYYUhaq6bPZJGe5Ao6DZJPbanBaIXFiYPJC2slRLsiY9WumqINZM2aeWPJ+8r4\n1PGzei/8zpyGPBMqRlGkXe1ADMMwTgNto0hH1CWyr0SjLqgDqlCajMHsKrUqRNafiDr+Og3ndCUP\nRutP6XJtqA7CVEZh0ytJLVlLf23l38M7OwubXibl76RbTPP+ksmahmEcMSYjjSNCpM/9lIbMQ/VD\nwavNii92mlN6Ic+vyetjRTUkzVaHOixyArUpmvy6K3m4VFfSpv4Eqce6Lauz5c3LKBlzQTCqlppP\nvFYUE/IJ8NCftW4fhmGcdtj6mlJK4VRN01VFQT0zpmGx04iqY3ySag7JhVsrP43vqImrpIY1Znnn\nXT6ffl5eOar42j7stK1MGz9XV0AwKHpYJnCPzql/0dPdskINwzgCTEYaR01kMEYH4s5l7ddsTHGn\nW6F6HJK8BC0rE7jcK5RczMmTTIbFRgYmjye9ysUhtTKSxxpyvmGgKeZ7uayqCNXhMjLIUawOeSXi\nQ7oekhf8Cj8wDWkYhsF9KCKFxuorciZFMVZe+0yo6ozTYFdVqOoe4zR128m9X+KQ7XHBmr7fBlug\n+hbRS2DTlXMjl8fYLrA8xj2tchfYpiIU2Xjth7mUJTCZ4evJBzUM4wgxGWkcERzGmYe6R5eRAbLR\n/FRbnsf1YGTpxUVmoJReTYtrr7KmCdHIrtQpVanDWRebhox/G5IPyQV6tLB9klomVw96Brjg50ti\nZO0HR+jt4VENwzBeXCJBCN/4MWIaxrhGBVRl/mpwEW7sIah1wcb41nufz/fq0ydPm+y2ZqSc5VrO\n2PykQAWgj3d808i5eIw2HR1LwOrA27gVadzk8xiGcSwwGWkcIqMe0F1shlGphlxAtZWUObIRzdFW\nZIR260Dzdc4k7yHpQ+4aS5qUynrNDjPz2xlAJXiSmaTJ3EjeWley5wW1lxVzIw3DOMX0XqGqLqBQ\nSs6BFHuMGYQjErBahXGt1/eW4peUl3qQXdNd53fdlMvGRnM4k7Gv8hMnwPd3AktzLqSniZDauBVJ\nFKE6oZcur8w/4fbS3qSzYRiHjclI49BJqiYtusIcxWykYj6l0WJOxh2Hwia1X1eCYrQxvUK0uIx0\ne4mdp/ZCjncBeJFbNLjcIXi1DxolewpsWj70I08WGqWGYRiniyqsoCMMVDYg/GelZFHUCnEArAKb\nfn6kQheg5Z8uwTrpCBdlupzP6R7mcMUgkZ0D4MOl+SLysNtLAHA+NCTbVXjrpV8UfkX4J5G1+q0Q\nTxR6NQzjWGAy0jhEuvw9h5MyQ6XNtHvJl/tATylIk86ujG4d5Rfuqu60gES4SDSZLVb+uhWKzKQK\n9UiB1tqnhkp7lMtovyad1yGtKls4QyNcZcedfbdzF4ZhGC8+vVdUIZh+OOJ6eFz14301p8vNg496\nHYR5f0LSDJQLowlJPfVsXt2UyslWVNqmDANWB+rNyN6kv6MkiC7vAMCjpblL6UbaVaZk8kbmJru9\nAzInq7Ajp2EYxwiTkcZhMfKpdzXl+fGIYyul1nQ6oKbGuPCTt+iqLTVTr78VCj+EWlTrvcV6mLYE\neKkn698Pi/0sxNVrzb2k5N1J4VZu3aHruDoBmXvl+cR/1t6HdBssLKLVMIxTjlTQ6SpeOgE2KcgT\n3W6eHFd0wIINarKYirrrhpRvLUlnRfB+BuGgPnbIUpNwwYrOIvQReXBKEs8tfo8mLY/9S4hEsyjy\nisJ/y9bGnEvbyldtLYFyhlfV7g3DOHpMRhqHRVRyJjq1uCxNMtxURZzGgi5KIuQbJWVhDdxP7ZZu\nMT/QGZJaT8qynNE4DB9BR6YCqNFkGGetCZk1aLJAX+feP1xoZwYe6odqa7UPbbWIVsMwjJaoMYY2\n+rpqmXJTEE6JBB1L8KZmGhpyCNWgU1X3wgqxem+cz6k7kSSpUrVzEDYs0Qz8Brh/Zh/BuxvI7h1R\nzxDa2fYSlv9A3dLpyekMSzAM41hiMtI4dJLBnCoZMlGWhp1G7R+C8idZQA6D+NK2yuswpfrQIRrR\nEQcbRbpqNVaHn3mH5oxO+bM5xbWCRO5lfxBFtNahzrwA5MCbQA1cAN70ZXVcvdYzKTVtGIZx2uEA\nzqmXNjojsR/qvUEoHcWBE+l0tcPkjCRVSb5lNDkSmUmVqC3HrqTCkg4k75Hvy+5otNokFNuO13fw\n+g62C2wX6AOfLHUXgHUXr/pNVJgC21/H9tepTWfX8xiGcYwwGWkcCiNqJpGn1FpSzUQZkloiIhRm\nOtY0Kjgz9D0nt5Sii1zB6EDrrV3ll95kre7CG8D8AZ0JOZ9VI2twmQT4UD0EP4ccON04pNs69fgQ\neAKc8dryWxbRajwbvwZq4GdHvQ3DeD56r5BYirpR9MMaNlrgSPqeKKAoitOxuTD7MVp279Vcp/RP\n7x9h9iZTUdIj515qBTtRL0fyFuVfCTxaAqZY3mk3o0NzdcrpdgGU2H4V53cwBZbHYemdwdMUIzIM\nzc+BCfDzo97Gi4zJSOPgGfUCpeTUlFhp0Sk5lrNSn5TjUZMRoV3+oR+XNozp5EaZHJqcAdEtFojJ\npNm3pe6u1/flgooGowzjDOMCo6wdZG81DxMj1TLtnC2ViOqK6zz0Qa2GYRinlt4r9CVyGiXhMEoO\n5PEBqSmEQiti0hEpqov0dBVf5btH5qTuy9hXcxiRglz+p6RCO9z4sSsKt6SSPOd9cR3Q/La+TrTF\nPhUaqto558d0mXcpUc1QpO5tGMaxwGSksd+Me2h6GPVwt4dRL9CQ6JZqkXfHXiJ3TmRDki0+UnSj\nFYxW2lPjws/PAXgr0t99vOJllqYmFZo0NvnUAnS0atLn5Ed2W23QZFgZz68YZfOKONHTM+5pLviz\nrprr0DuQ8E0+3K3OwDAM4xTR+wS9R+i9h95X0fsQvff8CWlrIXDYalRQRyw4pLzHvtJd7OaxTXfL\na9FVPyKrrQLXaYUoZ3BBUdnFyLSKCtzoDSfL6vBZN36PBl13x/M78zkTqLYnCGzQ5TEw8MV1QK5o\n5XukmBtpGMea3zjqDRgvFqNeYCTqVEAdGqr1GFuISU8v95Gd2gzMsTKafyvG7eC4QBGts4XCfR16\nsXUfAVrvsYbkCNUuMZmUmvorS1ZaMGvQFMFY1DcyGQ48JFl6oX1QDIE3gY99LKtTkheAutvPNAzD\neMHofQJ8g75PQtsNoRgrSS9NQ09SLsdCr28SRoq68Rt0ybvh5RXdVGTeLeAN70+ytJuqY+mm0e/+\n2lcPUtHDTsMQVnkzbJMibAEie6iAcim4sATVpZ2EKrEPANsFpsD5H+HRq+gXWP5RuKIlRhrGccfc\nSGPfGKE3136R09iV+rhAgFFC43yQJ9SpsNJkvGuNYpRadggMcTcZ6dpF3nGQzJzU6ZfJyfw44eWu\n4Yd0DBFfNhsja2LZy5e6g4+9dHTH8PbjBa8hc+AHlhhpGMYpoIcf4R4pOilqGtmJ3NPCERXdSR5H\n14K6dIAElLh8XIVVSvJExVej5o1dxXWStXa04OQDvpfkfyLUeqA301XltR+6msvjVojOX+lAHcAL\n1imWx617ef5HWP4DmlYCwKPC2kUaxjHHZKSxbyRUmBZdu7Ig23Arjl+dj3Bgah0L12aFmjdKYKj3\nOS+D1szVJz9LHepDjkpNWo6LfcM61HyyPX9VU6DJKJ/TO41Fg6bA2I/v+mrP+BxIpyGfdCSTGsYR\n8uDBg57n8ePHXdMeP34s095+++3D3KFx4tl+NejZ2OV1TVMCksvJRBVKB0r7CZPwoJ86BYqP5UWu\nes256c921d3pyoHUu9LRsBED0pOS+cneY0lzIll7foztol3EnQoSI6PbwIvXeyQdBwCw/Wr7is//\ngRmShnHMMRlp7BvFKKVpImcy99GZQzVTJiAlzIbhUlGSIa+WSrMUMdYe1BhnaApgC9k4lJFJJZmH\nopF3mKutLhBnC5Iac8DVaPWXOytS8hsvh8ts0avSd5atcZMP0c7u7GXffNIwjpYHDx689tprX3zx\nxWw2+/TTT8+dO5dUkg8ePPja174289y5c8eUpPEUvNHRazGiD1wlXSfxolwQlWvwRGGfDhaicqyz\nKEGtF6HWuepXKH2q4SC8EN0Bt1FpWfZgB3Q5QqUmca3c63GiRO+A7lsCqy5Jso/lnfmybUTrrm98\n1cvo/gwFMMHyDtBvi7gahnG8MRlp7B86v1GOk84bn5VT7DTWXjBBFcKJvkYKM9xGNvbFdajKjqRN\noqasyGS4aUrvLTIBn9aDpffjtud8SDcit9pSmaRbtAAfSMDqGaqmIyu86SffBwqLaDWOAR988MHG\nxsbZs2cBXLp06dq1a9/97neT065cuSJfNzY27ty5c3i7NE46AxKBEawPpTUiUkZfZEjKcWQ8guZj\noSAaUKNI+L4gotzcypvh4rr4DW9p2jFH0Duchl/1DpFyaCWH001wlXKqaP3IukX4QgeuTcgMS8C0\nhzFV3/m3wOYMX1e7MQzjGGEy0tgnxmFiZNIjcwzV2WTCYZTiKMtGkaJDOo4WgaqDKjNzIEc2Sl2S\nhENeu1I9kxvgwaRVmzpwvT0cIoAldLfJ2gNu16FtVBfLeiaMYuXXsPdAY8M4OB4/fvzZZ5+99NJL\nMvKVr3xF60M37fd+7/dkZG1tbTazv4MYe6L3SFUf7QoBLTukINTlenyqbMm9dICcqFDYgdZj3UjV\nHK61w9ot0nE6XZFfhajQCSnq6BKXEslFX/vhhIGsJW+qDF9x2XqafQDoYaxSNrlMrWEYxxSTkcY+\nUYdaCx1hojK+QEnmFKea0wjCCyNLru6WlLkSfhwVuhdytcMFJFMPk+JTD9bxVU0xv6GrJlt363TZ\n1wUgB97xehJec37sa+3AZKRxnDh37pwcs6QUvvjiCwBnz56V3MibN28e3v6Mk85mStGxGmRRlEQr\nJfb9tPpyiGW3gOtqgitzuvfEwKQ8Thb+uSo1VLuX0ratKMlN6n5Z+nUmVGgnlr5TEqOy4gRAm0g5\nxzUPmZc8muH3u3dpGMaxwGSksU+IaKy7j6Fkksg8DtCsvWMp4wuyAN1BFPi62PrjCXvRUrvaj8lb\nJNdJzldu7V3nNzrLdNwmSRbNPKU0KT/l4AnwcZi56ZIkXZOPN/3kt0YwjH1hNpv9leKXv/zlXq51\n+lATpUd+/vnnAHq9nuRGfvTRR5YbaTwdTguJtGE7TuJCpYVj0tDjIjSlsh91COc01R4j+lqFSg97\n8DCjO0b3XZCQWHmrU1uUV/3xJLWfCVXc+f5Oe3lFsnkKPFqa1+BxKY7xKmH/zUe+Qch2ge2v+3fq\nXus9GIZx7LG+kcY+UafaGnITw2gy/GTRmUOfvYeOSFRmGE7Q6YuUPNkUyMahO7qF8QqKxi8euZ2L\no08j0zX5XEn0JqMDf/lb43awySCFWpusfSvOk9T9HmsvFx8CF4CPvWKU/V6gmwwBrFhAoLEPfLmH\nX/7yl1rRFUXx7/7dv9vfe7HmfP/991977bXbt2/v7y2MFxNJXyzJHhPzkPXOPYoOpR6HQcoiOlbo\nK3nWp34Ycq2Om2UGQAlsel3HM6fKO00+6YKiNtIqc6CeRW9Jd5h0+varS0AqazS4RDuw8h9ABVRY\nHgAFlscAsDymerXThTrYMIxjhMlIY58QMRY5bFqV8dctEmbJmE/HkPQQaBAUm8piLEplBJBj5KND\nszEwRNG0UnO8gmK8UB/q/S/WnFBndVStnM1TX/1g1rTXNkXc3CSpWN3gBS8ya7X8Qz9hj/aqYezK\n3/8S/tk/+2cfffTRs13O4ayMq7izYNBd+ODBg0uXLj3brY1TRF/Fl+qYz74ShBWVNl28OMJrQZVp\nJJCzDCM/B6GydaGtq8A3vKTapM4fFVmpWmFp+YpuISbFe7RZKC9HHmSg5GXfbybyP7l8az+6gO8n\n95hgWgBF2xdkewnLOxTR+mrH7g3DOEZYUKuxT3y7I8URqdI4Ojw10maRXdmVVbgVLsu3psGsAer2\nvLT3kBYgxehpwlblFtFB8vLk3vSD0La5S6TQFPNnZaUZha3yyBlfYqcGHgIP/akLXkN+y2q0Gs/P\nr4Aa+M9Pd9HNmzd1fiPbjC5+NSKZMIluFWoYAa5iS9QwY9AhESNJJvqTQzIXN2CEv4TXFIdzQcsN\nF8t5gwRnlHbY7/gU9mjjcaon27MS38sFWrnKzpRSK0u/VAmcH3f4iPy0Urh2AAwC37JtMinZlobx\nvPx9/N0/wi+X8HfPuY61NV7ACZOR/L8/Hjx4oE8d1cZOO6Neq3KK2VySSfSkV0pNkSoVqvMYIy0a\nFc7RDScjCXcfuE/xsUCToSnw1rh1HV0vjVZPdkXbJqm7DyLFyKG5+iyTFJa133Y2H5DnLpquK9rP\nj8OFL5CkNIwjx1VYdaytrZ09e/bixYssHX/6059eu3YtuspZjvxf+1J051B2vSfsF+p40nsFAPAf\nd+IA0U1VfoalXVc112iwpOMpBWaCnEzdvLEErnpx6zzPTR9Z2g8Fpy6UyrdDKiFTD047xuXukTbW\nq/Grq/xTSLxu+10navJjq54kyztY3gmqCW0vYdt8SOMYYW2NF3OSZOTNmzfX19fdf5az2ey11147\nJf8hHXdGvXn46N1eLFZI32RjNAUwjKM3m0KpLzm7FQjCdkSsTv6Uy53eugzkGK20980aNAWaov3a\nGUmb1Hu8q1pJxEhS5uFuoxDf6Ea8Qo4mC1t9NMgaNFk7V/T4KMPHqdvCJ0Be8KfOALVXlWfozrfM\nijSOE++///76+rr7bX7w4MGdO3e++c1v6mkbGxsffPCBfHXdJg9vl7thv1DHk1ZDvgH89lLCe7xH\nztsmacIFnp7MZ79OYjY5AbD0IpC1pRiM7wKv7wDAqtdjU8rGnFK+oggxtjcXbxIUYhrN1FdFYagi\neuVybx/GqhCU57m9NF/k/DhcTm5czi/YXvKnpEbrFMs7WP63FtFqHB+srfFiTpKM/Oijj+Q/SwCz\n2ewnP/lJWZaLrzIOllEP2C2kk1L6srGqo6MvGYZCkTt/iJaTCjq6rg/pvZXxfDAbUaEdVobJ5MZI\nDfJnMlUykpQLEib1tRRkK0GtrRVZz7cZpUTqdMszfoJLjHzoN+tSJR/6xEjzJI3jxqVLlzY2Ns6d\nO9fr9dzffeW/58uyFDG2trb2/vvvi9135cqVtbW1o9t1jP1CHUN6v01f+qGl1ieJJfIs0mls4nHi\n34TkE8dslmQYliQLJ2pBx3tLAMkokJ5EWD01yjmM8jCTJWmifiTsBWrXceDXHIRamrUx30JGJmGe\nZPuk/Bgd21oWqbnqI4Y3U/MN48iwtsa7cpJkZPSfJYCqql5++WX7nT4usEG2FX4FuWlhFRk4bSlE\nOq0OxV4eLlKnpCDJ16ZQ45F2XZAVqeNUu1QiC+bItFxAqFSzcfuPtzYu2gOqOxtZmAlYST70qZIX\nfBFXk5HGMYQjXTlOtaoqrsV6V/JUxwAAIABJREFU6dIlDog9ip12Yr9Qx5Fk/iEjAZVXKQCVLxEX\nTuZHlqZuFClGomitKBkSfuUoE1BXghVZ29/NUeQ9y3wu68oj/XAmSPdNOlI6OUsT3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ZkC\neTX96e78Wq0q/cbnCLnLiFZCyHGj6+uipkxFaUK6xok37IFsoZEJASYX7PrCcqwiXZ2EM/u57Z90\ns2AFlzYeo7GybvHopb7Y7UD0ohz7+3GPvG3DWeVN3fjTNuOxi7pRZNfaiiPZxWTsW7SZ2AGAEdY/\ns7sZiGGEkBWGMpK8EamNwGxWdOFV20lyWvwmmkPopqdW+A3tdCiHrlQTY1v1ius0JS6W6qceKSWu\nHCPPSJylKENhU6+FppOfRYKkQl5NJfaOzZZMqjpy1ViRqZWOwTZf2/Ouz0eLP0oIIUeWrDW2UyPF\n21V7ELTQkD+7okCMLM2aiSo1Y39ZKVkDrShr1WT+mRn37NZpMlH1rIGNPDWFZ514dkGmrh0lMpze\nqz9mYocyHHUE8YQZ0MXuGnZzANh9197GfUcG9vkgZOVZ+RI7g0E0p4EcOLUabBcrWu/tm6xofTfT\nTNK7OkS1YQvwaKUn7U15flhfTaJWZLDVVAi7wGkMFg+kbxk7M/R1I/wVSiDFELjgdGWCpJpe7Ils\nSRneq53Oobj5Casnn9sekoSQRcE31MIY+JVFNbIrozvQTSadtJNW3kj5kC7AFZgqLk1Q49Sd7FuX\nLlN+5ixI/SmTLUd+SCr8h922BwPxjAPx4JvA6QIv8ukDNvUj6aKWlaYXyDqwXmA3t61BRspp7QPb\nwDfnfE5CyHJBN5K8KTLKVAszrXUCbRY1D4VaSyobUyoUWrIDDMNSq9UGdnIgxcNLKC7FtgdRr6bp\nvvo4CFJtCeKVy+rMzEBC+1cviN0FhmtQULa0yaGBNg+eyVmRZ9gukhBybJG9EIM6L4ZAQxoGyoSE\nUmLR0FZYDTZQBXsuAjes3XfDjpRrSouvqSCQRnuY0v8M0jWDzQeep9TJLlXSnNy1ZVS7Qt86t9Nb\nf4zdNayLPivrn2F9D+uFyrxs0dmEkFWCMpK8EbrqTNogxtwY+bNUelKLt6HqSNkQtppU6AEPc1yq\nkO+g2hD3CjYQtfP0YP0IqVKDTZJS2pXaogzWT2tztQQuoG6L8gS4CdwESuCm2sIQeK5Uudvaa6AE\nTgCfMKKVEHJsGTcosRZrTtaeCaYHMaLmYOBnAqK5CaLUlo99w9OtIKXpvFakrK8jBXNTfGzQeVKf\nHwgbcyCe1zzy6QLninru6T1vHaMh1/eADLt57UA6ITrtlSmL/BBCVpiVD2oliyGfoOgAMX0VpSlx\nEZ6efJijByBHvuPnEEZXFpot2cEleybZiYz1QmSHkQERZJMS/QhDvw+kGxlEwMpnjKV0Fnltr94t\nUOVIUty0bTOLpPYq4W/5DPAcOAMAeO2bwSfESRbXIYQcTyaP0HnffmgJaoWvwfRViKt9kQw5ELGg\nXSu6BjHNJjtkjK0P1/dtPZlXGQ2CjlZ2lVczXxLDmor60YIAXem4OvnqHrPvNwUxFuWneR3vOgYG\na3XzDm+tMQCsZ3ixhtMjrGfYXfM9yTEwnjCilZDVh24k+WJEa9Ig9lGjBvSkwir9kXo1e6ZIUGyg\n2kBxyVqRMvqzV9fXmXZl3HdjgblaqktySzp8t/QPZNyvm2u3lxcA8LEduJOgSFAl9W5LUSz2jCgV\ndMKudMbP2dz3yQgh5BjRUmwGMeMR/ngZbup0VDfmVTYFaZoVjATdFArNVZy5qHbY7tJpJ0/HpsIX\nltH1gwKwI6Fm3bOYvikZcHpvqof7duXMNoDsGltS7SlD/cDGn6znw84khKw8lJHkbeAyDyXRMjZN\n8iydXo9cbYoj7QFpfTHZQV6ghN2Jk19DVAmSCkmFKm8IaoV/Po2JM226tgfKQkjHUkjA1N+eE88p\n8gIpkFd1+SLT6kM+tLQ/hzaEVd//hPUqCSHkWCNLhEr6sUYaTu/JKjVjKx1l1mKmputbmJM37KVt\nEQc7FsGrj4XpN2OY51gdBE/hGm1E41fNyb5/cizErWvJMbAfTX/IbfEgI7vhqY2ZYXdtWqAVwHrh\ndx2Bre46VxVdQsjyQhlJvjDRmM8mWhSXrf4arlQCsAowmDL0PUap/QSuuGsd1xrdrZSLgTiTujGQ\nwYEz6eiJAW7WUO0wRZUjr5AX9bC8qIclBaokUkRHmprn/e++tH0j6UkSQkgjQeyoLJ06EJ08Auk1\nsl0f3VztSQa4eNqu8PegivcM7KVoRGtQ2TXzPVLJWKhc+MfBmKDGTddKaLe9sW9OPrYjXZKnVoJ1\nUVaIoSPs5n4TErkPQsjKQxlJ3pR84ukuh1ZWgXILRJeTWClQIt+xyY2BFNQZj8NarfWAvAJSVBvI\nd+wleVNX8VU0bGwkCEANAlMDShQb/oPLRw7Ox6qsJoVIfHTbS6cuq7y5dDedUC1thR6IfpItaaqE\nEHLkmTzyPwdRnX3h2rnUx4C+EGwDa7s99qVge2xm144fWKNvO7Yr2UtDK8mxsg2bJJhuJSKfIhNj\ngsjYgXUgL1pPMrP7H9s9u00GmtYMMxrSLLi+B4zrdpHrhZg5zRCdIAchZPWhjCRfmNQ/cLqr9M23\ndmWTonC+otRPxoqMFl+9AAxrp9EkE3pSM/X30B5w2xTmqpWhckTzSl0KBHNUtTqxLeemQDn1XYMh\nUBoSSpa6Ka9j9ySEkONLkAoY5A2O/KvupwuLdb06pAZz1lyAi/aUmYrSAAxEo+wdEuQ6uuPMP9gX\n50m6SNpg5UxYjrD7cRWD5DoDUcjHpXfChsKetgVazTov1uoVd3Ps5qqCEMNZCTk6UEaSt0pUNaW+\nJIuFlV7Na0fRm2XVUrT4qquVmuzUf7y5ksCeK9UA/TFQd04hKzux9lGhzkcX10ZhIMJTLwQ3L6Yd\nQWSxVrPMJ/ZL6olUSXP1CgghhLTizDRJ8NHopUdFLbFkpdOsIWAToiTPwBp6zoqL2omB35g1iMlZ\n0N1KWjqISI+xawe7A3e1rwRw12pIw/oedtemmlkGuE6VpJsfDd4lhKwelJHkC6D1GJQvBnvgysyo\nijWF6fMhI1G13NLqqwcMUeVAD9WGMjxLpdCcOdlijZZq/9FYXD09sCLlSBmzqm+t1fUQSLGTo0hQ\n5dNRPT/oFbZMq9uyKatjirhu2K4hhBBCwkI7TWmNm74JaXhUAJmnx6IaT67ZtyGsZsFNkZSImKM4\nEqGnWUxVRhMd9TPKxaWelMG30cq0Y99ZDYJ1pQfr5LTcjNGQp/e8xdc/w3qB9cLfXx+777Y+BiFk\nZaCMJF+A/yGEWRC5WvqCqqxzFyNVcFLkBS4VMdNP9420mqmwkZ8mtzB5qOriuPHwz0insT2lU1L6\n44MxvWZpGswKvhaXFOoWtB+NEZsUdazv0N4neDLJc6nQEzaNJIQQALFgTvjFWl16pEtilB0dP83x\n/lodHSrrtbaUiXHBm9tWTzp9qFMTEbRUbNj5vjcNXEc3Vz6pXEpKZXd1JL6frlpWC2AzZr2oTUj3\nfa7vCRPTeZ1jYCSEJSFktaGMJF+Av5hICVQlVlClAKyTJhRm4t4dw4gy9BQp/CI3bpHe1M8scl/d\nRQ3MqCaE2Jg7nkUMp0oTSnqIkAIX/OeSZuxQ7FwKbLtylU9N3OCrMl/Dc/WILU9MCCHHiH4spdCp\noE174EJVjR0XVEzt+jVmoMrzRNswXvQL2AS4nQSuo9yeXjabv1OGE3Vu82O/bKxLgwzK6gTfAPwA\nVwghmgEY100+1vdqAXl6z8a1Dvzv3WhTBrUSckSgjCRvjTqpz+gYp6lKVCY4M0g4jJI2/AzGpICp\nztq0WqADU180No0Ptpf6e06VLg0sxLJBSbrkyWHzHoJ7lfbLnGaAesG2biPnxX5dmdboLggh5Fgx\nieaIX7T1cmTAZ1/0UXTng4qsQcioPoaQZE6Ltqu+IPrUHfQbJrZkOTp0vR8364ZN2myfawYMRKgt\n7PaCdFDzjLs51vcA60ZOq7aO8cLEr25bAToCMME3Z3gMQsgKQBlJvhjNSYayb8fUh2xJSoymRDob\nzjEESpQQlV2jUtMdtKjWplnBVqMasuWOUhMG5mogBzHVfC4N0k1JrWs7FA0hS1tZ54l/Z3ef3jxP\nTAghR5mxf2AE3g3gse2F2LeSb6DsQakGXe3Wgb9m15/lhvWF2+kGjxvEYUvx1Tcoa9qiEl27EZ0a\n2jRrbM1MY0L27WCXQrn+B1gvgEGtJNc/m859kddepfddsFIrIUcHykjyxchtDl7U0LPH0wDXAG05\nSuHXQ6W7gKSocqRA/lBdClaIepvBvVJf6UUVmJyi0yNlkGo02TKKG2njWhOXHVoCKYoET4SAPC9W\nPQ+ktqWHTgUdUkYSQggA0z0yaNHRBf6bvZxZldj3lZVDunMmprRvU/wyYNN2AYG4S9euJsM5g1BV\nfezIGgxDN1ibjS0EVYVGItrW6UA5ZmAVY7C3rt+hxIjJ9QKn96b+qTEhX7yL9QIv1nDa/eOx+xYG\nYglCyFGAMpK8bZSUSgrrRraLNKnE0tqMS3ZCJbaTo5QaMrqalIUqXtS7V5P8K31BC2VIDtWUFAAe\n5v6CTdLUzeqJYze+RF5NUx+1IC2BE8A9X8O6MXfB+jqEEAJAeX0DKwhlWR0jbQKNJ3s5bouQzq7V\nUQPgv/lVarpWmD0W60RL5rirwVabKujIEjhNJWf1R1koKIi/dZpupKTpwN9nYFc6Fb2b1/fbzacd\nPjIAGU7vASOrJGWobkuBIELI6kEZSb4wySSeNNhTZ6KUvhhKVW5fKVYbYmMH+Q6KS6guicUDR1HZ\noeE2ogYj6ltMB8wSxeqvc0mG70ZvJ+nFHt/K1yFwAjijvtqh/ZKuiO/GrfG6ecuEEHLcmNwSDSpc\nBuPIV1NRi28sJJM7A2tIOm3mvErjQN4GHtsMTNkBUvbhCOiK4NimDiISWS8n0KVyq0GorbQT5Sz5\n7MF5WdGnbxeRsb7A1JA19XXGwO4adtfw4t267o735P2GRyKErCSUkeRtkE888ZYq4RTVcu68a2dh\nRNJQGIluTCmW7SHfQfKwQTqmSr+VooRs4N9pWqRjC03uJXxFKsNoS3/nrmtICqR4mMS/QiMUA6U8\nVGcIIYTU9K2sClIfIQrG6FaQmZBefXH+segI4hjbMc6NlOJNEtxI2p6BhtSlXHU/ySai+ZbaF5VG\nZV98J11bxxV+aVbHOdsicjf3zpulTLFWcxBRtISQIwJlJHl7aPUoYzVTEcCpbToZSgoxC772K1Fs\n2PIycq481ooqumbTI7QQvapvp59CxrjKA7mZUlR8BS4AnwCwuZFm4Cf+rT5Rm6IVSQghAZPT6lTX\nOopOy3VjVlmg9Aa2xE60Q+PAVpMZiII6s1RqhW8wQhzrdpH7ZldKnNPoNOdIydGBLb0jcztdNOxA\nzM18J9bErJrKOobTn4Vn6mnuC2JcKyFHCspI8pYwhiQAiLo4DhuSCjRUxEn9bEN3KVikp84HQjT1\nh6V1SZ5IsGsL8/p62jUN5KKWzS4O1W21BFJUSb1nM++1beOR2uI6AE4Az216pFvvHnAPOAP8BRMj\nCSHEZ/Io1pxD9kIcqZxACMlzUZQ5RcwYBNAHbgC3xS00XRVAmwkPcCw+ZrEw10x9bEIWB0Jska74\nBpxKHAkB2RXr6G1MbVz7ReyuYfddO2IAjLG+hxdrdr6pazRmtw9CjhKUkeTtkU+QTJDWPQ9rMZnW\nx04g1X+k2JPiMGonGoZAidz0EemJkbKUzlDYeqgvJTu+utNldRwtXmXT3CBaVV6VZqy/fv1tyNzI\n3rTnx04SmSSt2TPCeCxtC5BojzRCCCEAJresmJQ+YTfmGfbtn0xYcO4S/BhXmSR5W8jLpsaP0ZI2\nENtw4jZoJonYxyakX+qmZOJY7kTG7l4EoDp0ZL6iPrcHZHUdIRfU6kzIWk+O6rm7a8Bt4AYTIwk5\nelBGkrdN4rlhRi85RVTltbwsAsfSWZEyB1IitVSvoa+FzIGEKNUT1XXz5kCm6meweBm7UbCCPZ8U\nIqPRPktSoQSqBEPgCZCfxIoiAAAgAElEQVRav1HqazfphFDNPeAM8NxGwBJCCIkyueUrQCcmu1Yx\nOqE4tirLhX1mNosyE1JQ51X2RV+NgMAkdEQVo8O5he2Bsk2FgloMzJF9aogmJRmwbaXyyI9r7Tsr\n0o8DNsbj+h7W97BeYL2od7NeYH3Ppl2OJvATKQkhKw5lJDkAkonRii66Na+QVFMR5UW9ynDToOqM\nVIPm5wUAsfBX+BVWe/5BL5ad+FbQ+ZlpTGrKW7v41Q1v2EPhQF5Qk6REfe1fem4f/crbfjhCCDl6\nTG7ZI6chB/YAVhxJY9DhpNNAWHlavI2Axw3xsVJ/OhWmq/s00d4vpKV+jSwSK4vrQKR6ZkL9ymYn\n5tINkSq5m0+3YkzI3TVrPI6F1DZ8S6SWEkKOFJSR5ABJqjrAtaa0DSSNeorWNYXvGfasSLI/iw1/\nFnz9ZmzGJ8ok1OvLu88iv9w6AcFqJQAUeSyB05+bVCjMv8z2AOBSgSJBCiQF8sob6W77iY1oNbwG\nXgPnxaP/LRMjCSFkdqQSc30sRqKajsSII6mGuip8NBq5ORaOn2wWEi2f0415klGFGZyMupHRiTca\nCskObLZj19fJY+BbvjlZG5djwCrJ9QLr/10I1rFd7vs2UpbFdQg5alBGkgMhKXwBaZBRne5qNDbV\nHciqPCUA5EXDePfzCXBhmpPpWZEQenI/mReh589tkpQpnrioXSlu4anZnQQp6nxI8zOvkFf18QXb\n20Omjp4RD2owZ86z4QchhMxO0P9jJII2R76Q00IrSC+UC0LNcoVzRnYKfAknW27Iq3rDejPBvYKJ\nXXXghOu5Pa+QT7CaOXNDDAjcxIF7sFHdJRLGojRz+mLdrviybIdJQshR4WcWvQFyRAkkllBfSeF7\ng3IAVJlTLfaiRW6Ck/YgqRqsyHkTI/XGgu356jQF7gIf59Z9TcMpLjXUyOlaVJsMyRQA7gJnVIFb\np0lNXOsZsSNmRRJCyHzIUM+BiG4diAFRcdhvEHt9MUzW2hn7B31fMWZCUjatnM2W+hjgUjHdgVnn\nxRq+D9wW0bwj8XMM3BDNSwbCdN20H81j7OZY36tl5PoednOsF+KW7q5j4PEE/1/zRgkhKwndSHIw\nqNqkkZOSaFkd+PJMO4Fp7GMgFMvYML3PlgfZN69SLWvu6SJ4w92W02I50zRRO6auamuTHgOb0dWg\nPSHmmdBWVmolhJA5GAv9JsM4962GOlKhp64zIvYLQM1inuS+WZFyvL5vdLeyewd8dXp6LxKyC6FU\nR8DpPawL07IvxtRLDbBuGkUWtZhc3/P9TVkM9+IMT0gIWTEoI8nBo5XYvnos9aM5L4ircnDgakoZ\nCaXA3BntZJoz0co98i66oI4ugAMAuFnZMjkuttYX0hvVNEL248Sb7sq0psBz4BMAwHNfq54AXtuT\nzxuelRBCyHwMfBcxijvfV+czX61pjZdZ/Sk7bXR9MSk3IJtttAhOfbLr24Hwzcz31yIhtVLfbgMv\n1uquj7f2pnfv2/aYfqJk3SWyDnB1jTjHIpd0FqFMCFkxKCPJgRHImmHsalDnRouh1M4thRJTMaKe\nnBvWnl7ocKbqDKxYjV6S+9Qxulq+CqlcJKJMTiqmpEJS2kTOVN5I3PCEvfQcKJVcPGEvueObrK9D\nCCEzIht1ROvNtJOpDpBOGcoiqFI9uSTBvtWTfVWzx4W5QgnaFuNRMxKaMHAjR8Am8DgmLwFsWvvQ\nGJKn92oxKfc2gNCpA/sHdbePqQk5El8B6+sQcgShjCQHQz4JrULZxbFJ0UGcdA6hTApMp0JxalfC\nXy0FhkgeAkMbMioFaunfYl8XTydnBpRTiWj+7OS4C1xN6mI5HydePSF3XApl/TBHlaPK65zJITAE\nXvtfldSzxuo8AZwXapMQQsisSC+xG2v/iFbtY7SfLLHjXDdz8kbMroQt1rrtV+hxWxqL/pDBBmYP\nf3XI6FknFy8C5wr8TVFv74afHbpth3WB3TW8WJuKWKMZ181Epyn79uc2dtcajNF5900IWQ0oI8mB\nMWxQaC2iJ/UPghI7PSEXUxQ5MLTdF0ulP1MgRVKJtpP6vnMlSc4wZicBgI2i3uZdYCfxJ6VwHTWd\nEL6gCgy9Ft0go7mZT+wzuSl3aUUSQshsTB4BsA0pNPs6Z131MbMi0LBp14cVk8GUTH3s+6V9oMqo\nZupqlGjGo5GsF4GLwGPb+PEGsOl/A0HHx/W9WtP27YP0AWRY/3/8m1mBu14IPe098wTvNmyXELLC\nUEaSA2NDCJuWeNEoUjk5HVha/QSgrJesy9j01GoylBT+z3a0RVkqQdvARoGdBEjr2abtyMf+4yQF\ndpKpyTq0jzj0u11CqERn0772H24oKu4QQgiZncmtWO3TJm0m9VWg08wirj2GGbAt7M2RVWtjIbtk\nBGym1tQfozdtIloiqAs8tqai6fS4LbYq594ALprN21UGwMXacbTPKb1RmRIqq/E4v5URrYQcTSgj\nyUHilOTs5V90zqFtq1ifsYIw2fEzEoP6NzpoNprlOMsAXcsnOrEESmwUANADrtqptdAtp3JQKtwL\nvlCUu3DRqieAEjhhD16r2q2EEELmZfKoucFGQNdqs74SkyPfSHS46NCx0lyygqkLDg1q80gpmKn4\n2H33LPWwDG29DQB4sYZ7a14p174oyfMtO3E39+41NSqD+Nq+f0Z+F11gNME399suIWQloYwkB0yu\ngi2dTmoJefUjVKdKzB9Z5UAq4lphLbyybpsx1Z9ycSizEXH56l2NZlEGYbEpABQJLlW4C7FzOXJY\nV9+xOwUwPSNd2Ne2dk5gQp4QZ177KaKEEEJmZ3ILgN/mMKiPGiAlmb4UaD+zoLQiZWCqTp6E0qhS\nrLY7kLPTB24DV/ZqbRw8i/ROoeTxdA+Z+MrGtdKty7SaHEpnxb6tfRNClg7KSHJYBOmOs+ciliiM\nGpRRnj2b+pgiKUQtGuvNJQWSHXEpiEoNjnX2YTCypSaQm5hOT1cJSiAvpm7qTo6HyfR2Q/tAJjC1\nSOpcyqEtvppauWiU5BlRccfglCSAS0yMJISQN0PLnLmEzxjYFOGphkws4pRY3443+YNX/eRJSabU\nbH9mRRZkVELp4ZHvGnbt+s6Y3RbHzqgc2VDY+vLYWqhu3Nj2jRyJDEmGsxJylKGMJAdPUEYVojNH\nk5j0z+dF7S56vtvQrtBScDUoUDN7bG3rfgxFHkuhBIbATfHEV3M8AXrApcqbblpHXgA2Kq/+amlD\nWKVQNNrytRDFZ/xLhBBC3pCuOOiKnhy6div8M7KmTtDbYyCUVNfvpCiHIeY96gMdNIv9VOVFlSTp\nFOmtPXxnrRaAjwrcELuFEJPu1mbn90zV1pHtFrKttHIQ6SuXI4QcQSgjyaEgqqc6L9GrnqqdPd8k\nNO5i2Pwj9d3CIE3QXRqK43n3rLdnqc3GIKK0xEY1nfFxUmvFpLLdR1DX4HmY4C5wF3iY1JWDhuJb\n0cZnaZ3JFDgjnMk/mueZCCGEODqf+imLrhtkkNDoGAuDDtbE+xZw2w9nzYBNq6ScadcVNh7E4kEZ\nGin8grtLRdYVP6OMRD8OiPtuAxjjBtAFvg8gw2Nx066twfNoDwBO7wHArQJ9YBNYL8Q42UjEbnR3\nzX4vA7Moa7QScoShjCQHj1F3T2KXUn8MhN1WCiUZRKWWIh/yieiMEQ09lcu2oJMh4Rd6DVIo3Sa1\ndi1xt8AFoMrrUUlV9wIx26sS3K0iO0r9+5zwd5TaTMihGPwXmOSMaCWEkDdick60ahyLGqqyIaTE\naMKBEJ9OBEqzsW8XccmDsrFklEys6Rg3j3caFb6shchPlA8iu1b+1xwwsnAPn65NSwc5UXpvDbBR\nrOf28Gnu2ZVTzOrBnfruJ4vrEHK0oYwkB4/x6y7Yj6X6iQZbEtabc8NsFGud9FgKP1AuGNTv2VdA\nBgOCXUUzJ5uOgSqvkzmTCilwswCAjQJJVQ8ugSqpv4/UNvxIRYmd0raOBPDaeo8ATgDPrZ58zlYf\nhBDyxeh8B4CvFYPuF/IgEx9NzOdACDbYth9uwNh5cna6rOXjFGYUHYwa4G7a9Q9MimY0CNYN61uJ\neG+trt36WIhBM90oSYNTp3gsHqYvFWN9+zp/EqLdJCHkyEIZSQ6MolP/AapchHSitfdG6UvKUgnC\nJl9RZl3K1fbNh5SiUUbJus00OZzwB9vVkp268mqRYKMQV22zyBRIKuRFXWLHLTC0fmMqun3AKknY\nijtGXp5gViQhhLwpnffReV/ow0wl8WkbcByGcIaVTgfKzHRLOcMzepdop8dASTaJSZ1y6Yr9dIXI\ndKViBzbRcQw89qNTXeQthGAMtuotJ2NhYY1aB7MiCTniUEaSg8QqoaSKnJxS+lqutW5qdSnWLETa\nhqWSmi0Ltp+Ui8io16jnKUeWgGnjITdmj0vUO7xrZwxVySFZX0ciu4CcaBhDCCFkf6QaHMdMQl31\ndOQ7k0FfELmgPgm/X4hMiZRFWfVcHewKUSRV7sot1VWCVmZImq4eA7+6rGMsq8KOAWu9ntsTBmO/\noedm336V1JCEHH1WXka+evWq0+k8e/Zs0RshzZgwVL9OqVcaB7FLUVx7D30LPV16idESO01ascmB\nlOfLqbsYbqAEgKTAToIi8Vdw8hIA0BMFekpbO+ePgD8DzotJJ+xPV2JHl3IlhCwhfEMtO4HKgpVn\n7qMWWv2Y3oM6E2i/sV1t3Ko8x7HzY/VRm5NBnZ6+bf8IURNIquJv2caVskCrrBsEUy8nqzeTAZ+u\nAX3s/k/s/k/fvc1EPR+z3GOhywkhR5afWfQG5qDT6TRdOnv2rDmYTFhuZGmIqcEqQVL56qtUQi5V\nqkwrw9IWNm1aZyjyDpviWqP3CgbMGBPr77DKkexgo+GOVQKjLnviIe76kvJShTJBClyqUCR1LSGj\nG5+3htkSQhYC31CrirTNjF7a9vt5uBo8mdCQEAOkBO2qpMfgo1u5b8cPxAotMa4B0R4kTnNmwhQc\n+Kp4BNwAbgNX/ekuXndkt2cSHXfXMAau7NVVWM36u2sYvzuNp+2+i9Of+d8CEyMJOfqskhv59OlT\nAFtbWxPBy5cvzSXzcdF7JIJSKUAX3RroRj/m06t9GtiJhqHQkxDTg+BYcd+2Tc5+1ZXzMbe+EJlh\nriY7drpKztwxQjqtn8MwFD9NpGuR4GaFHlAkGAIbFe5W+IsC51H/vKr+PwAhZIHwDbV6jFXwp7T+\nxrGf27FoTSf5LopY00zUax35qm+szDz4+ZPBSFlI1k0ZK4HqauQMgG/Z3o1dUQHHOau37RQZxzuy\nU1wK5e5aLR03AYzrbh+nC4xF5w+nFu+9i0/fxafv4sW7ePFNvMhxL1ffFCHkSLFKMvK9996bTCbX\nr1/v9/mvXKtDe8GboJ5NdHrqi8NSDdCFVYMysG+LQOIOfX3bUvhHsFFN5eUFu6QRpFetUM0rDAGU\neALkVZ1L+XECpHUfkR6QFDQkCVki+IZaebTec+clOvo080c6yedaULqRiNmMsgqORhqM0TaSmVKS\nY3v82K96k/lmqdOQsgHkNureHiPgNrC+h/W9esEX+fQxXuQYAeeK6QrSp5XJn4SQo8sqyUjDZDL5\n2te+1ul0Xr16tei9kHmY0R7UlFO1WSWxATomVp9/i+gES5l72fMPVF3Z6SOk03zRmwCAu8Bz1O1A\nUmAnRw+oElwFPs7rZZIKQyCvsJMjKUAIWSr4hlo9pIhCLDtxRrp+Z0jz0+m6pjjVkRCfLTd1K/Tt\nRzlS25jSwHQ7ccISNnNypMRepr4Qt+5p8coxz/VpXq9gnr0vxPMIk1sNz0IIOSqsnowEcP/+/a2t\nrVOnTt25c2fReyH7odMa56TaQJXXE+tg0V5z24/gpu23a7o6m68YGZCiyvFxjp0cDxMhJi2V0X6+\na/oxcEFEyPaApJoG6t4FntjhAHZMExHTfJIRQ4QsH3xDrQyBZov26kBDpZjMvyrH9GeQoEHmpJyu\nXUcIN3KgXM1xTIJqyWoCXO8Cf1PgRuy+UI6iuWqMx9qEXMOna9Nw2bEo3gPr5XqFXgkhR5lVKrEj\nuXbt2te//vVTp0794Ac/WPReSANBPCqU9mtvm2GpS/IEyFntkrL0VWVLQZ19N9a0jmEIAEmKm0CR\nY6OK3KL2D0UuaA94YuNajcreqOr6rqY8UCp6SxqX0jxRD0jAVCtClhG+oVYAJ3gMLRmPM+Lqzcw4\nsSvMQBeeCt8OlVoxaDGiQ2SD+0ZblVwFunnd7QOxeF2nADNbSsecebGG01ndZxIiFHZgQlszZGve\nxkbA6Zm+BkLI6rKSbqTh5MmTrFiw1Gzs979ONCVSC0JZqKanEhSjU6JXgxzL6HRdzifYBoQIdH/K\nUHnm0a4kgiKvZxnD8mNrOabA1aSusuMEpBkAYGiG9VinlZBlh2+oJScechm4ghrZeCMTdU2NKuuL\n432rrUoJJ4NUMyERW5zJJppmyejW20pABk7spq0nNLATT+8BwK0CVwpc2fM084scn65NPcku0MXk\nXOuzE0KOBKvqRjoGg8H+g8hC2OmEaicVSqxJCGn1Jeu4DtXVKFG3sMmHlMdD0SCkqeTP7B9Nbw+b\nTrIjLErXOrK09uNrIAU+Ac74QrW0+7pZoDIrDIEUec7/h0rIssM31NLS+Y461fXTFKOMlUmIWCXV\ndpqiT8e+gITQhE3Vd6K4HEW5bGBjQlRwHYiPAEbAth1prq7vxeXpwA7YtLmX8JcihBxpVsyNvHPn\nTscSNHQ2l9qnj0aj/63gPxgfFPsKxSa/bpakx/YBLU5ge+VY3WRSK+Eg2zN6CytEaw2ZorI1crz1\n01pYBro16F3ijpMKVdLaCZMQsji+yBvq3//93/Xr6Z/+6Z/4hjoomrIT541lhWgpKY3EfacE2YNB\n9w5DoCr1DoNCOy5/skkPy/Gu6E5mm1hmflpj17YG2V2LlfERNx3YVMlurGwPIeSIskpu5J07d65f\nv/7y5cuTJ08C6HQ6ly9fvn///ozTq6r6wz/8Q33+537u597mLomj9A01xGRb08T2803plLOEeurq\nO6V/HL1pNNTWVWfdd2MlEiBPsZNgo2FTf2aTJAMr0mWYVjmGwMaOSaDk/7MkZLn4gm+ooij++I//\nODj5D//wDz//8z//ljdKDJmv5WavCjNWB7CmXKbsSu0BBl0x3BRdO0duFXaY6yCiK8rqEkF9UfZG\n3ivA6Mm79tjh2p9sA4M13MiRGWdSiEa38mNg09ZoZUQrIceDVZKRP/jBD7a2tswbGsBkMun3+/1+\nf8aooSRJom/0v/7rv36buySOjQmKzv6a8A1on9vz5VdAIGsR8xuDFMpSiMaWikFQE4NhJTZScTX1\nrpqpV4G7dgtPRBvJHCgS9IBiYxoQSwhZHr7gG+o3f/M39Rvq7/7u737/93//LW+UAAAmt9B535d8\nozeyIiGaZOjpUU0Y1XKaaOsRJ+10AR7XqEObgVrNyruYk7ftdDnetaC86E9wBqYZsG2V8Jh9Pgg5\nRqxSUOsPf/jDr371q/LMYDD42te+xl7Py0tZJ/LN6kO2L9WOzCNETA3qdaLVdHTaZBrTkO0bc1Gq\nZezuQakee4cLdu/Pxa5L1IVbASQ7yCsgoRVJyNLBN9RK0vdF14y9InWB02h51SCXMHARu6JnRjDG\nCcVMndTDukpnugFBemSwh2DZplq1LkPyKrBe1E+4vhfanmO1E0LIUWeVZOQ777zz+eefByfv37//\nta997YMPPljIlkgbH9tEoKYcwnnLjUoTT5+X9mMg/6A+zhgWG7MNG0VpcLKMfHQNMOU6VYKkwk3g\nLlACr4ETdohrJlklrdsmhCwavqFWi8491P0PMU91HENTJ0kt/2Rhmr5/FXYDstuHPBir8VBjIORf\nV2ws83M+A6Xq9hk8lBwWjHm0h/9ljgZAVpdmHYhMyIzhrIQcO1ZJRn7jG9+4fv26Pn///v0HDx5E\nL5FFcnMCtCq3wBgMmFFkBkGqOl8RSte1rNyypWj+pM601GPEx6QCjJh0ytBeugA8tz7kazvPmJMm\nijU1g1mglZClhG+o1WJyxZaQGSvfb14CPWYW6ccuGbqqoM5YiMZoLGsgdIO0TMSU7cgGuHYbHFf9\nsCNbbsdljbqI7HtrWC/sncZ1Jdi+NVRH80txQsjqs0oy8tq1a1tbW7oCHoDJZPLOO+8sZFekkYf7\nFM710GmKUQ8zVa6gFnVS70lbMlV6cpYtuXWicbml7eK4b6ysSLNMiulPpNNNGRPyjD0IvoykYrtI\nQpYXvqFWi877AFSaYntLxihaQ5ozI3VpJA5GtqaOs/Iy4V46kRkIs0Bhupo6QX3UsX8JSrXKRdxd\nRrZL5GMAogvIVA9nAPAiB7Jaao5FQqb+KgghR51VkpEArl27NplM3nvvPX1pMBiwMPqSEqivGc3A\nlo9NarBsuOREYNSr1LRbi3JYKpRqFL1nX5FWSW029qwJWaoCt4UdQyuSkGWGb6iVxEVyvpmZ5rzH\nJsmnD5pEVyYkKGbwSEexA1f31Z2JduDQXUO6QhaaKZuiq+TAzsmAT9c8kWzLzLK4DiHHjRWTkWQl\naYr83Hd8cLKp8qr+2GIGzkKpDmaJudVRr6myFNVgc65K6lrrmp4LZx22bZkQQsib0NKcYxac8GuK\nYg2SHvsNYaXdZrnY4pF2xR/YIjd6HZk22fKMmT94IFa7YUesF14KpV2QGpKQY8jKy8h+v3/nzp1F\n74I005QSKbWljgJN1Z8mBagXjw4uZ1CzwZaie9YKM1gcMT3ZcCOTJAlbWadpvXoZ9ookZNXgG2qp\n0R4d5s/xi6YsBrGyQX3UsTAkZUxpsMIs2xgJ/3CWvpfa3nSaMIi5hVC2m7CJkXamdFZZipiQ48oq\n9Y0kK0ZQ4DRAF1aVl9wKuj5qEEcaVYxRtBRsmtgyYMaaOm6Mvqk4mVS4mwBJ+KDmwPT/GAIbFcNZ\nCSHkbeMq0ER7Lc6FtPi0hgx6RQYFb3TG40Bd0ovLgyATEjbBcuQPyMRmHMGDy6/iSgFkuOIP2M1r\nl3Ib6AOPgS4mj8LvgxByHFh5N5KsDNp1dBoyjV1FTIIGfuAsQaraJ5wlVzNaJifYmzyIupFuHV02\nFgBQJXgOvAaeeyV48AnwCVACGxV6AEpUmKdeESGEkH3R0lF33ZiRzLcc9SXdXaNlV0HvkPYMSWcG\ndm2NHChfVLblgNiMtlLD72QcGqPrezhX1IvYDMzOvebHIYQcXSgjyYGxMdknHlUSTXrUNVfdpZ49\niC7VlJEYvZGs5qr30HQpGDOMFfhpr81jD2WBVnPlDHAFyCvbFARIQDeSEELeGrWB5uRcV1h20fRC\nTeYPDppGGqLrBBqyPShU9wKRt5M1Wl1ZncyvASvXGSuFKR8nuj3Dpzl2bRrGi3yqV7uA6Z5CCDl+\nrHxQ62Aw2H8QWRT5BIW10YJyNdoSjCpJHQvqVmtyEZuCV9sFbTCrZaQbM5zNtJRBuf6yT+yVwI41\nB05Dss8HISsK31DLzORvbNsPKBdOx5rqbhky/jOoWzMWqlLKtr4txiNzCwdqtegi0X6SettQ+jBo\njCnd0ahNGrSa3M3r+NVtAHl9Ux1tSwg5ftCNJAdMoMp08KdOoUz94+CnW1ZblHKdGZzAxt3qqNTo\nmFRNadGu5mfPmyW3bBzWjQoblXefpJq5wCwhhJB5ifbDQLOp2NQoEg1Kzx33rUIb+/VsZJMP+L6o\nvEWwH12DR0bkdlWeZHTDTaG2Wp26XpeP7Zl295IQcgygjCQHjKwv2pRACCUX24upRmWVDCVtCnZt\nunvTyKb96OnRxYMDVaJWtvCod13iYVJX1kmtXcn6OoQQchBMHvk9MwK5hYZQVROGOlIDnMUX/Mla\ntZZs1+GK5eg9aDEZ9OeQCwabGfkrNJWBDZ9xBGDa4cOsc0NE4W4CjGgl5BhDGUkOHqMknX/YLibT\neliV11GdVb6fPQhfsKUxJamdTH13+GpWjg8GlEpqBtuTXqUY8DBBkQBAsYGHCZ4DV4AeUAI9IClq\nbZkC94AnwAXU4wkhhBwEdZKk9tZ0t0Z3ZuRrP6hhEMJSBqzKVEyD1Hgj+2ccMySbcCvLfo8Du4jU\njU52Blmdbv0btTLEDeBWAXRxL8eneb2gCcc1EblGTDJkm5DjDWUkORRMuR1De4imHZZUSCoASApU\nyX7upS6TU4qPacyrjKZK6sRIXS/H3aInlkrVlIbqrHkB2Io85soF4AKQVyhyVAleA/fs8iWQVyCE\nEHJwTB7FsgSjdIW/N/KrpEpVmYljrTaDMNquCG2FnS7bSMpURsRsSbly1wpX6VXK6S6wVuIGbAMA\nzu1Nzw/sH6Mh1z/DlaI+g9m+NELIEYUykhwWufUk0RxTGq2qmiIpmt0/PaslYDUV6rGp02NgSPbs\n4GCiLvkjj6O3SAHgYQ6k3n2Sqi6okxcYAifsHzO7ohtJCCEHTK0k4Xdf1MhuHFoZdtXgbszSDD7q\nyjruIBrFKs8EQbB93/wMsi7dstJF7NqTZqvfB7rAizUgw6d5rZkH1ns891k9wcnmPgghxxbKSHKI\ntGtIOQZCuQUxpYFg6yntNxQjnfeoh6UxL7EU9w2iUnXhnyYhGhz4T50CHycoUSvGnhCKRY4SeA28\nBgA8AXJMWF6HEEIOAy0OA3Rk6cAXadFqPWNxFVbCBY1GzJ++LztlbZ7omrKeqouGdbcbNIfC6lqv\n5hbre1jfw6MCp/eA8dQm7doys27C6T1c2QMwOddwC0LIMWDlG36QVcIkSRadfZQkfPcPUyVWJUig\nZJ60B3XmZJNnKD9G0yndssOY/nR+o9aNweLltMtlaT8NgavAEEhsc8ikwt2kFpBngJ6dlLNjJCGE\nHDyTWwBsCxDdfgPKSByI5hkyJlb28JBogSq9xEArjn1ZG6wPXyLKCqtdZUJ2rZ04UlGyEBNvwHNC\nnRXpRppsSQC7Oa9+ZWAAABlDSURBVG6rZyGEHD/oRpJDZ6jONOUoumNbtMZkS04lnNSQ0XqqutRN\nEHFqBOHQvxrIS/g30tvTdYPkre0iVYLUakjz81IFlEgqlMDDBK9tOKvTkPi4A0IIIYeGLKLTRLQh\nhzMVRzENCRW/2rc3cotsi49u8ZHKnBz5A9yYkQ1qDc5DlMORFWURq7KzmwMjvMinU4wE3bQTdvOp\nz3lRPSMh5DhBN5IcOpcmKHx1FGgwU3ZGJUNWCZJKNc+IGolmfG6bLqbKyXRE8ypnCSQNRjblZ/ob\nM0821ZBiwBA4j1pnAkgqFAnym7QiCSFkEUSbSQYuZVD+1MWaQgjRzPqBcnqmhrmT0ftGN9D1p49s\n7Z++P3Lkby/zp0u9aj7svgsA3/IHn9vDpzkg8ir7mJxu3Soh5KhDGUmWg1Qdq9qnSeFdrXL/jBtp\nhWLi67Spk5nGrMVSKUz4jqU8gwZzUuthe2wiV68CsG7kToKNClWC3/WXdNVZGc5KCCGHzOSWjWuV\ndNWxrqYTCEIZ6TqwJU+DqjxjtfKoQamOVagqlGXqNjb2z7i0SRkl6/YjZfCLtenKm1YxPirqHXTz\n+swYyJgVSQhhUCs5fIpYoKZTXEP/TLQqzxCwXRYNdYfJxAtDrUvXBFGpaDAbW9I15UStGEXMbWQp\n8RRJhbt2+E3gHvAwmT7uedvhQz4RIYSQw2SaGyk7MQYO4UjEiwbnDUFWYVPxnpEwDDNxjNj4sZ8P\nCVViR+9TrpOJx4F4roH1MMfA6QIZcLrAuT2c+8wOGwFjvMi9+q4s0EoIoRtJFkCugloNsziExtbb\nsD6kDWetO0xWXrDr1I1sClKNVfEJC/a0RL02x9OG4+1mLtg0zNdACXwiFoONaM2L/dYkhBByQGQx\ntQar0FoSJvXVsYhTHTfoQ5eCuG9V1aYOjSOldQNfNBpqKzfjfM57Oa58Nl35VjE9djVpx3bbhJBj\nD91IsgREC+QgdqZEUtkMSTGlSpTfKP80rdkUjJrGPEY5wDWTjD6I+Fjl9RnpLg6BEvgj4Io/byg2\nkzCilRBCDh1TrzWCDBANTjpGMbGnfcJMNG8c+MP0ymN1INcJJG7Xr+4TaEj48q8PXFSRrm43997F\nvbz+9CL3FgEm62ozhJDjB2UkWRwtUaZBjKhWdO36MFq1NXrcZCfqpEeVsVnl/hnfunRtPOSZFLjg\nr2fK6pyxGrIEqgQfM6KVEEIWRZPvJ8Nc5YEraQN4kZ96naYWIPq806tNm4F1CLUFatiMKc/gTGBg\nDoBP3/VWdGV1+ratZR94tNe8J0LIMYIykiyCPGa1lc1CsTWzcZoDmfpjol4iVEhqewSp7g4i9lDl\n/sbEUlJAyjNPgFIUZU2BK8BzuxEjMm/SiiSEkAUxudUaQTpSB4OGMfBzLIMSOEFo64xI7Tq2t3MH\nhkx5mxCX5KNJVWmmnNsDRnjxbu2sjoFP38WVYvqYI0ywBkIIoYwkC8MoSZkPGaQaakMSYpggqabl\ndopcRKUKvIo1qS84h2H65XQ/0bhWO9j0e4wXAZKmpbIlX7u2kGKs+ZkUEf1JCCHkMPGUpNRdUhDK\nk2hQbiOrM41+69sFo5GxsnWHJKjW4yrr9O3V4I5jay1u+uczf7WuODNVtgOg7wXcyvUHwI3YYxJC\njiUssUMWR7N5KKWXVICJizVNbVtI+9H8zNUZc5CISgHeVY2YNR3Qi1XcKZEDxQbyIrJa3eVSPMUQ\neAKkwAkgBZKqDnMdAlfc06dAQiuSEEIWzdgvt+MUl6Ev9JjUmSMxYNSc2dgVCY3Z1OiL9/yQd3F6\nz7UPkU5mULh1G9gEtv0Nm4fqq5I5m8D6Z0B/2vbDrf9pbkoETR6pb4kQcoyhG0kWhzQkDSXg68ag\n70WVoEpqL6+uy9pU07WpX0gsy9GbgtDJjAzA9NalW0p5pGbDOwmqZLrT1HaPNCfdvFoAU0MSQsgy\n4Gy3sf1pxF4Q7+oMxsxvqiEzG10pVFe7NWCWMrDBYDRPkUV9zhWhrToWdmgXuAH0gQxY3wOAF2uh\nFtXHhBACgDKSLBgtzFwt1obeidOYz6hENFIzFycDTdjSoiMIYdWOZUyO9oAdt89S7Nnet2eHX6qm\n5VjdM7qI2iphdVZCCFkabjd0/nCZkEZASk8PfuFT2elxZNWabs4BoQYz34ocCxEbDHMm5EhFybqS\nPyPgRY5bRd1WZCx2CD+jcgDcWwOA00UtmMfWKc3q48YatoSQ4wplJFkortPFMNR7ZaxKjTfRjXMH\nJSAUmiNMjGxZ05iBQWajzo10wbc5kgIbhfhY2dul9WbgP9kFc0WITFNWhxqSEEKWi+/bA6nTXJGb\nQLnpkjmBYDM/B35JnmixVikXM+FhOn3o5OUmcNFe0vqwC1wF3s+nInYs9p+JsNt68T5e5J7pOq4T\nOyeuPxUhhFgoI8lCyScYAj1btNRQAiXSBjeyjmsdNixYKpcytapSlt5JI3+qBKg6XmBtWidVFgmq\n3ErKnqiyU0zvUlugbrqoxAPbyaMHbFRIKlQ5imS6TVlxhxBCyMKZPBLdEV0yYVCmVZqNIzFyIIJg\npd/oxndjJyVdoRsz22/D6U9XcHVb1M7pWjUbWJ0j4KIQupm/lBONAxvR6m5nH2pybr8vixByLGGJ\nHbJoLk0A4OPOVKEZH69odQ57KuI0iI+1Qae1IMwBo+hSf4wo5+PUZp21mCC34/MCSFEkSBNPOkov\nNIHnQNa6VNyqBHqo1WNe4UmCmw+BS5ONWb4lQgghh44pKtN5X+jDkWjaYZDHLhly7GvFAF2bR14a\n2yBY+OVYXXWczFZ/NZduA33gyh7urU3vK+sDXfXrsg78FM1MOKWw9Xu+D3QxOd367RBCjjd0I8ly\ncHOCSxMMm5WhIOpSTgf7rqYLcJWRrtNWk9ZRNCan0XhOhNbOp1g/2fHvkvofpZNpI2whNO9QiN+b\nO1ZCE0IIWWImjzB5ZJWhrqAjkfGrTc0n5bCu70kG6tRFwPaFgJQTZS/KF2u48t/DW8AOgCgt2xdP\n0RVea1/4nwNqSELIPlBGkmXi0gRPWgcMp400jN1Xh4/KmqfyJ+puH17NnlLV6ZGUU5dRWpSmm0i1\ngcIFr5beXUxca5Xbc3cBYEeUYy2BDUxMqw8kE2xQQxJCyMrgtbuQ2k+WQu3PX9R0JOJX3Zo6ZzIQ\npSM/zTIDHgP3/l+vr+NIXDUS8aJdx7SUvCEGAJPT9ZqTWwxkJYTsD2UkWTJuTiK+IqxV2PP7fMg4\n1bQ5xdAWSE0Kawj6+ZNJJbpupOKKlYDOk0wqpKjlotfo0gbNJgXyHeAJcAFJgY2deoipo4Oik2PC\nrh6EELKK1ErShaRKAWmE2UC0Z5yFkQ0idRM31RjnK8pkS4mzKI227IuA2JF/i9s2LnfbfoT92EXn\nO5hcYXNIQsisMDeSLB/Gpis6dfUdS91ZUeQ0lnXOY0PTSKdCh/akC1cd+lGpQ2DDHqRIRKhqkQA5\n8qq+XSrL6ohb1CfzCXK3J3MGyfTQv0QIIWTVmGZLdkX5nJFff1X2h4xW0JGMRDnWsdWBAAbADTu9\nbx3FbVHOJ7PBriM7Bmobbm+Z6tjB4quEkC8GZSRZVowk2+mgh50EG0ZDWmFZiTKnU6SN6YebesJS\nikmg2kDimkRuIClQJbbAzxC5nZv0kAcRsOZSTmuREEKOF7WY/I66IBs/OrEnC7oGFXeMT7ip1OZI\n2JKbwDaQAdvAJrC+h++s1Ze2xS1iepXWIiHk4GBQK1luUqDExkPRjHEIDL0uGtOR0mDsifNqTde9\no9hAUqDY8MTmtEhsKkriPPGzLvMJkgk1JCGEHHcykdkI1WEy6kYGYbGyb8dFMdcgEyBNWw7Y0jju\n1t8HbkxNS1MTiBqSEHKg0I0ky43RaSY0NAHKjtOHSYU0KNkatABRvT3cx2mg6Q6Q1mrRLFjkyHdQ\n5XVR1moDyY7oOVmCpXEIIYQAfpjoOWtOdq3rKN1IowCdOHRRr673hmvm4RIgMzEYfqDsrT18Zw2Z\niHq1UjOMXCWEkAODMpKsFL6ESx52bO0aACp41VA2DCinP5MCaY4iQY5JXkyVarVRO5NVbkv7UEMS\nQgiJISVc530Afn/IIJzVVWQd2Y8j38ncFu03XK8OUyAnW6sFp6vKs40Jcx0JIYcLg1rJKnNpgmSC\nxC/uGkSxBg0eTVXVobha2h4eAKoOYDuCpNMysIkpr0oNSQghZAbqsFIjLHXiYqa6g2S27UfXasu+\nMCRho1hhFWbXepjre5N1akhCyAKgG0mOBPmkVoAQIawmBrVnjwGUdWcOYzACdenUvABS5MO63I5X\nITaZsLwqIYSQN8MoyTreVYpJY072/ZNjv5+HDGR1iZRuygiTdQBrIISQRUA3khwVnC0pezmKGjl1\ns0fU5XmSyh7soMiBEtVGfWZqYLLBIyGEkC/MNN4185Mex6oPZNcPf+3a4NWBnWKqsz5mGiQhZMHQ\njSRHC2FL1r0crYaU/R6LHHkFDJEA1QZyF8JaikKshBBCyFvCsyUNst+j9CQzcTCwB2MrQQeYXAFO\nH/B2CSFkPygjyZEjsUpSVGpNXJhrDzBRrAB6QIkSyEtUORJMgA5SakhCCCEHw1ioRJMDaSNUva4h\nWaxTyABgLVZCyNJAGUmOIskERWcqI4MCrbCRrkOgVydGJgWADgUkIYSQg8P0cux8xzYFca0+ZC8Q\niGPjWI4ACkhCyJJBGUmOKEYQ7nSmiY7lVEyaNMi6jQczIQkhhBwidYDrPeE6Ru1HfzwhhCwVlJHk\nSGNadBSdWkD2UCWiuI4pqEP1SAgh5NAxXTo6n/pn/Yo7FJCEkKWFMhL//M//vL6+bo5/8pOfdDqd\n9vHLxn/8x390Op1V3PaXvnRohYJ/tvXqz824Cr/qQ2MymQBYua8aQJIki95CGz/96U/PnDmz6F2Q\nWVlbW/vbv/1b84aaTCY/+clPVu53eRX//sHhbnuf99P/Pes6/KoPjVXcM4BOp/OVr3xl0bto41/+\n5V8+/PDDRe+CzEHH/N81Yvjt3/7tv/qrv1r0Lubj3r17v/Irv/I7v/M7i97IfKziV/0nf/Inv/RL\nv7S5ubnojczHKn7VDx48+MpXvvL+++8veiPzsYpfNVkV/v7v//4P/uAP/vRP/3TRG5mPb37zm9/9\n7nd/7dd+bdEbmYPxePzd73734cOHi97IfFy+fPnDDz88derUojcyB69fv/7www///M//fNEbmY/f\n+73f+/a3v/3rv/7ri97IHPzjP/7jt7/97cePHy96I+RIsXr/mkIIIYQQQgghZIFQRhJCCCGEEEII\nmQPKSEIIIYQQQgghc0AZSQghhBBCCCFkDigjCSGEEEIIIYTMAWWkx8/+bHvl7WXky1/+8pe//OVF\n72Ju+FUfGvyqD41V/KrJqvDlL395FXsMfOlLX1q532V+1YfGl770JX7Vh8OKftVkyWHDD49//dd/\n/YVf+IVF72I+/u3f/m0V/0bjV31orOJX/dOf/hTAz/zMijW2XcWvmqwQq/gf2CruGau57VXcM1Zz\n26u4Z6zstskyQxlJCCGEEEIIIWQOaHATQgghhBBCCJkDykhCCCGEEEIIIXNAGUkIIYQQQgghZA4o\nIwkhhBBCCCGEzAFlJCGEEEIIIYSQOaCMxAcffNDv9+WZZ8+edSyvXr1a1Mba0dvu9/tu23fu3FnU\nxlrQe57l0sLRe3v16pX7qj/44INFbawFvec7d+64PT979mxRG4vS8ZFf6dL+Mrbsefl/E8kKwTfU\nocE31KHBN9RBwzcUOSQmx5unT58CeOedd4IzL1++DI6XCr3td955x211a2sLwOXLlxe3wQh6z7Nc\nWjjR/0Lkx5X4qs1/EvK/6qdPny5ugx4vX75s+i1b2l/Glj0v/28iWSH4hjo0+IY6NPiGOmj4hiKH\nxnGXkUZLB2+7ra0t9/Hy5ctL+DsWbNv8lSH/2jV/Oyxod3H0Vz3LpYWz738hK/FVv/POO/I/46X6\nr9q8faOXlvaXsWnPK/GbSFYIvqEODb6hDg2+oQ4avqHIoXGsg1rv3Llj/vJyZ169evXDH/7wq1/9\nqjvzG7/xGw8ePFjE7hrR2z558uRkMnnvvffcGfMIyxNiofc8y6WF0/RfyNe//nV35tq1axP7XlwG\nlvn7jPL5559Hd7vMv4xNe17+30SyQvANdWjwDXVoLPP3GYVvKEJaOL4y8tWrV9evX/+zP/szfenU\nqVPuWP4dsQy0bFvyySefADh58uShbGofWvY84+MshOjezD/mnTx5cjlTC5q+z2984xsPHjwwr4pn\nz549ePDg/Pnzi9hghB//+Mc/+tGPmr7P5fxlbN+zZKl+E8kKwTfUocE31KHBN9ThwDcUOTSOr4z8\n3d/93a2treCXx/wVrFmef6qJbjvA/C1sYhWWgZY9z/I4iyK6t88//xxAp9Nxhv4PfvCD5alh0PR9\nXrt27enTp6dOnep0OmfPnn358qX898jFYv751n2fP/7xj807b5l/GZv2HLBsv4lkheAb6tDgG+rQ\n4BvqcOAbihwax1RGmt+oa9euLXoj8zHLtp89e3b27NnLly8vydO17HmZ/1do35t8f3z00UdLEsfS\nsucPPvjg7Nmz5o3y8uXLU6dOLc8/UU8mk8Fg4D6eP3/++vXrC9zPLMyy52X7TSQrxDL/3dgC31CH\nBt9QhwbfUIS08DOL3sACMGEV0X9JkvEJkmX4x8iWbTvM3wtbW1tL8vdCy55neZxFse/e5H8P5r+Z\nZ8+eLfZfT9u/6gcPHpicewAnT57c2tq6fv36kvxHEuC+z2X+ZQzQ/w0s228iWSH4hjo0+IY6NPiG\nWiB8Q5GD4zjKyL/8y7+Eeh93Oh0TUwHg5cuX7i8CEyKyDLRs2/zVcOfOnevXry/V3wstezZfbMvj\nLJCWbTclPzS9UQ6Nlj2bY53C8erVqyV84RncbpfzlzGK2/MS/iaSFYJvqEODb6hDg2+ohcM3FDkQ\n3k7B11Xm8uXLK1dOfaK2baLbl6fVUpRgzzNeWjjB3oLvuaUa+AKRe9Y1vpdnz+17W85fxvY9r8Rv\nIlkh+IY6NPiGOjT4hjo4+IYih8kxzY1s4aOPPrp+/bqsGPbhhx8uelP7YMJFtra2Fv6vpMeBra2t\n733ve+7j9773vSXPUD958uTly5fPnj1rPr569cpEsyx2VwazN1MszvDJJ5+4vS3nL2PLnvmbSA6a\n5fylaIe/F4cJ31BvEb6hCGmHMjLkvffe29rakhXDljaswmHCRa5fv97xWYaKYUePa9euffTRR+5L\n/sY3vrH8kSH379+/fPmy2fCpU6eWKprl/v37ANz3ef78ebe3pf1lbNozfxPJQbO0vxQt8PfiMOEb\n6u3CNxQhLXQmy9SXlhBCCCGEEELIkkM3khBCCCGEEELIHFBGEkIIIYQQQgiZA8pIQgghhBBCCCFz\nQBlJCCGEEEIIIWQOKCMJIYQQQgghhMwBZSQhhBBCCCGEkDmgjCSEEEIIIYQQMgeUkYQQQgghhBBC\n5oAykhBCCCGEEELIHFBGEkIIIYQQQgiZA8pIQgghhBBCCCFzQBlJCCGEEEIIIWQOKCMJIYQQQggh\nhMwBZSQhhBBCCCGEkDmgjCSEEEIIIYQQMgeUkYQcIJ1O59mzZ4veBSGEEBLCNxQh5ItAGUkIIYQQ\nQgghZA4oIwkhhBBCCCGEzAFlJCGHxLNnzzqWO3fuuPMmrMhdYogRIYSQQ4ZvKELIvFBGEnIYPHv2\n7OzZs0+fPp1MJpPJ5Pr16x988IG7evbs2ZcvX04mk62trbNnz7569WqBWyWEEHKs4BuKEPIGUEYS\nchh873vf29raeu+998zHp0+fPnjwwL2Mt7a2Tp48CeDrX/86gJcvXy5qn4QQQo4bfEMRQt4AykhC\nDpxXr1798Ic//OpXv+rOnDp1CuJl7C6ZVzUhhBByOPANRQh5MygjCVkAfBkTQghZTviGIoTMAmUk\nIQuAuSWEEEKWE76hCCGzQBlJyIFz8uTJd9555/PPP3dnTLCQCRwihBBCFgXfUISQN4MykpDD4KOP\nPrp+/borlX727NnLly8zcIgQQsjC4RuKEPIGUEYSchi89957T58+PXv2rGm9tbW1df/+/UVvihBC\nCOEbihDyJnQmk8mi90AIIYQQQgghZGWgG0kIIYQQQgghZA4oIwkhhBBCCCGEzAFlJCGEEEIIIYSQ\nOaCMJIQQQgghhBAyB5SRhBBCCCGEEELmgDKSEEIIIYQQQsgcUEYSQgghhBBCCJkDykhCCCGEEEII\nIXNAGUkIIYQQQgghZA4oIwkhhBBCCCGEzAFlJCGEEEIIIYSQOaCMJIQQQgghhBAyB5SRhBBCCCGE\nEELmgDKSEEIIIYQQQsgcUEYSQgghhBBCCJkDykhCCCGEEEIIIXNAGUkIIYQQQgghZA4oIwkhhBBC\nCCGEzAFlJCGEEEIIIYSQOaCMJIQQQgghhBAyB5SRhBBCCCGEEELmgDKSEEIIIYQQQsgcUEYSQggh\nhBBCCJkDykhCCCGEEEIIIXNAGUkIIYQQQgghZA4oIwkhhBBCCCGEzAFlJCGEEEIIIYSQOaCMJIQQ\nQgghhBAyB5SRhBBCCCGEEELmgDKSEEIIIYQQQsgcUEYSQgghhBBCCJmD/x+26F10fMu0yQAAAABJ\nRU5ErkJggg==\n"
      }
     ],
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    {
     "cell_type": "code",
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     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
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   ],
   "metadata": {}
  }
 ]
}