modelisation_loi_de_Wien.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## loi de Wien : valeurs expérimentales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "T = [3500,4000,4500,5000,6000]   # température en K\n",
    "un_sur_T = [1/i for i in T]\n",
    "Lambda = [850,750,630,580,490] # longueur d'onde en nm\n",
    "\n",
    "x,y = un_sur_T,Lambda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modélisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Constante =  0.002939318610890894 m·K\n"
     ]
    }
   ],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "\n",
    "def lineaire(x,a):\n",
    "    return a*x\n",
    "\n",
    "params,covar = curve_fit(lineaire,x,y)\n",
    "\n",
    "m=[]\n",
    "\n",
    "for val in x:\n",
    "    m.append(lineaire(val,params))\n",
    "\n",
    "print(\"Constante = \",*params*1E-9,\"m·K\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
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       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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mLgKRKc+fMcxhgnRMWSxCxBrW1fytLZwtT+Hz2c9IiU0R6pPcQKEIVIUVBTUXCmgkGbhzD8FMf/mpEQSINZqjX1c2W3R8aYD3rRpUxPqZpVYfW23YeVfcGXJWk2yf07nmk62XYCwjVwl4QobczbChT/vt1YmOEa5UhOrcBte9htXAbniycKVxS+//NKsiFLQUsxwkoLhh3wf2f9cfeJnzP+w794Wqc/ieY8tAcLvDa7IhBeu8vBd4yolV8NUREAEgk1AAiTY/aPaiUBCCFjOKWe1GcMeXiyngTO/VhiPdY0VokXBQWeNxdq+M5ojaTloDJ+iY87C8CuudPBf+Ow8P7dmx+0C5LzzzgPDbWIVexy6WwHCGVw6mhQTLGw/OXHWP3yGmU4hVxKKOxiOvOz5HrGc0kgCJB6+sfrYzXazFBB0SO1OaXgf0AGl82kXIAyRYUhdrBJp29dI90QTIAwHohi2xF+ke50ekBiJC3N3aD+awGa72X6ujFAIWCVWX9vryVAqOs78Ryc6vDDngfbtAoRj1Ao1jMWYCeMU1LEKOdEmc7k4OcE2sG0M87LvjMVVU27+wHeAqy/cOYu5IXPnzi30iEh9Fs97bAmQ//znPyb8Krz4tWteLD76XAREwB8CEiD+cJQVEUgpArG2pi1uprk4ARIt1MYSIJwl5qoGC3eV4i5T/MddbMJLJAFCJ4fOTocOHYp1vChurFAvtwKE9eBsOeP9OWtMgcbZaRauunD1hYWOGme8OeNOgcB/XG3gSgFnpK2QmfATtWM5pcUJEDd8Y/WxGwHCZG6uBPEfVxoilUgChKdpUxTQaSyuMIyI/2KVSOPScty5EsBwL17D2fmyZcsac6wz6++HAKEI4cpYeLHEARlwlcAqsfraboeOO8Ou+I8hWOGFY5JttAsQSxhywwT7KlskjkzODl/NjMbbCuvi2GX4IFcVw0UiRTU3fOAGEBSA/Lxfv34YPnx4IbOR+iye99i+Cxa/g8KLBEist0efi0CwCEiABKs/VBsRSAqBWM6pWwESK0TIShK1h2AxTpxOhZsQLDpRTACmPa48OCnxCJBwu3RyGRPP0BiuwFAAMa+CAoTOGcNTwgtXUShW/BAg8fCN1cduBEi8IVicJWf4Fx10K0TLSZ9FuybSuLztttuM0zt69GgTGmgvDBfjKgzFhxcBEisEi7kRXN3zEoLF3BIKFjchWAyJo+BlGykE/CpWaNxVV11lBAhzecJzhay8H4a98Xq+y0yCt7bvtuoSqc/ieY8lQPzqXdkRgWAQkAAJRj+oFiKQVAKxnFO3AiRWkrS1fas9CZ3nE3DWlMnJnNm3F66KMAmdMe52x+ell14y+QrcpYtnUTgpfggQPsda0eAWvdyamGEyDEvhbPHUqVMLVYVhONYuYuEChOFmTNSnU2+dlWC/OZ4k9Eh8Y/WxGwHC+nEnLe74FClnhNufWknmdkffCh1jyEysVRAnfRlpXPK5fD7zjLh5gL1YwoF/8yJArCR0rjRwNSw8CZ0z/9xG2WsSOvOPKHIj5TFwy2SuvoQnoVurCVypCz8DxwnTSNcwn+n44483oZHMO6Hgp4i0n/3DXBfucMZNKRh6SbHHsK3w5PBIfRbPeywBEm9v6j4RCCYBCZBg9otqJQIJJRDLOXUrQOzbxNJRp7Cwtp6lY8gdc5iYSseb4TEsdOooMpi4yi1Hra1+6cgwnIThTyx2AcIZX87IMm+EYobOD7d4tQrDfRg6xZAua+cpNwKEziVzQBhqZU+kZaI5mXAlhMntXN3gjlnMieAMNBP2rTAybuXL8BRrB6FwAWKx5VbCkc6/iLUNr1O+sfrYrQBhDgG3oKWw4ja81rkSnLln33H3pnBHn/3N7ZnpuDJsJnx2nOFKdEYpKCMdbBn+EkQal3T8KQBom0nI1nhgMjXPKmGYklcBwvs7duxoNlCgkJo4cWL+cxg6ZW3/zPBCL9vw8r2hMGceCMe/tWrEEEa2nSIgXICwL7iayB20GBIVHmbFRHIKZPsBmrG+XPieUWRZ538wXJJj3174zvMcF/6XOTAUI8wHcdJn8bzHEiCxek2fi0BqEZAASa3+Um1FwBcCsZxTtwKElaLzQUeIoSAUFtZBhIxnj3YQ4cMPP2wOb+PuPXymdRAhRQnvj3YQIXfzoVigE0yHj44aHWDmY9AZosNl5RS4ESBWrgrFB0Or6tWrZ5wwzkgz4ZfnM3BnMGsXKGv2nc4zd+fhf/k5zzmgw0qnO1yAMF6e27ZyVyjyYvgM281EZ5biDiJ0wzdWH7sVIHQ0KSTJlisB9oMIeYgkBVWkgwi5ZSu3JKawpCNrba9M4UKRQNHIFSMnp5VHGpccb3R+KTS4YQB3cKOw4YoAxwBn8/ksLysg7BfmvlBocZtXigDrIEI6xuxv+9bP1kvqJgeE91DokuW8efPMmGB/WwcRMhxq7NixEQ8i5PkbDJNiPfg+UAxTCLPdDBWkSOZ74aZwIwdrtykKfbYvvNiTya0dsZwIEF7Dd9XNeywB4qb3dK0IBJ+ABEjw+0g1FAHfCcRyTuMRIKwkHVDO4nL2lg4hk2Y5I8vkXesU7vDGcJtUhjXRgaVjy52mGNPPkBOG7kQ6p4IOKw9KozPMnXroXFKMcEWByevcktM6+dqNAKGzx9h2roLQOWYbaIeOLWf/Gbtun6mnw8fEZ+alcEWH7aXjxoMYKTx4fkK4AOHsL//OlRTyYiiWfVa7uGRaN3xj9bFbAcJ+Y11HjBhh2stQITJnSBxtUVxEc/S5EkIHlvk+dOSZnM7kcO6aRFHD3CD7YY3RBny0ccm6MBeEooOChP3FlQqujHCraT8ECOtE21x546oeBS+FKsc1BWX4Dmm83q0A4T082dsaHxS9tMFxx2dQqEc6CZ33cQtqns/DFRGOW75LFNAUTQzRo7BxU6yzP3gPRSTfq/DCkDC+pyyRzofh34v7LnHzHkuAuOk9XSsCwScgARL8PlINRUAEREAEREAEREAERCBtCEiApE1XqiEiIAIiIAIiIAIiIAIiEHwCEiDB7yPVUAREQAREQAREQAREQATShoAESNp0pRoiAiIgAiIgAiIgAiIgAsEnIAES/D5SDUVABERABERABERABEQgbQhIgKRNV6ohIiACIiACIiACIiACIhB8AhIgwe8j1VAEREAEREAEREAEREAE0oaABEjadKUaIgIiIAIiIAIiIAIiIALBJyABEvw+Ug1FQAREQAREQAREQAREIG0ISIAEqCs3bNiAGTNmmJNv7actB6iKqooIiIAIiIAIiIAIZDSBnTt3YuXKlWjfvj1q1KiR0SzibbwESLzkEnDf888/j27duiXAskyKgAiIgAiIgAiIgAj4SeC5557DZZdd5qfJjLElARKgrp49ezbatm0LDugjjjgiQDVTVURABERABERABERABEhgyZIlZsL4s88+Q5s2bQQlDgISIHFAS9QtX3/9NY499ljMnz8fxxxzTKIeI7siIAIiIAIiIAIiIAJxEpC/Fic4220SIN4Z+mZBA9o3lDIkAiIgAiIgAiIgAgkhIH/NO1YJEO8MfbOgAe0bShkSAREQAREQAREQgYQQkL/mHasEiHeGvlnQgPYNpQyJgAiIgAiIgAiIQEIIyF/zjlUCxDtD3yxoQPuGUoZEQAREQAREQAREICEE5K95xyoB4p2hbxY0oH1DKUMiIAIiIAIiIAIikBAC8te8Y5UA8c7QNwtOB/S+ffvw22+/YdeuXcjNzfXt+TIkAiIgAiIgAulAIDs7G6VKlUKlSpVQuXJlZGVlpUOz1IaAEHDqrwWkuoGshgRIgLrFyYCm+Fi1apURH/yCzcnJCVALVBUREAEREAERKHkCnJzj7yVLuXLlUL9+fSNIVETADwJO/DU/npPONiRAAtS7Tgb0r7/+ii1btqBKlSqoXbu2ZnUC1H+qigiIgAiIQHAI7NmzB+vXr8fWrVtRvXp11KxZMziVU01SmoATfy2lG5iEykuAJAGy00c4GdDLly8Hv1QPPfRQiQ+nYHWdCIiACIhARhLgSsiPP/6I0qVL4+CDD85IBmq0/wSc+Gv+PzW9LEqABKg/nQzon376ydS4SZMmAaq5qiICIiACIiACwSSg381g9ksq18qJv5bK7UtG3SVAkkHZ4TOcDGh9kTqEqctEQAREQAREAIB+NzUM/CbgxF/z+5npZk8CJEA96mRA64s0QB2mqoiACIiACASegH43A99FKVdBJ/5ayjUqyRXOCAGyevVq3HPPPfjwww+xdu1a1KpVC6eccgpuv/12k0vBsnLlSjRq1Cgi/quuugoTJ04s9Bl31xgxYoT5O+1zh43u3bujX79+ce9M5WRA64s0yW+IHheVwLZt2/DQQw+hZcuW6Nixo0iJgAiIQCAJ6HczkN2S0pVy4q+ldAOTUPm0FyAbN27EUUcdhd27d+Oaa64xIoNfRmPHjjVJ3IsWLUK9evXyBUinTp3QtWvXQuiZb3HCCScU+lvv3r2NjSuuuAInnXQS5syZg6eeegr8+5gxY+LqOicDWl+kcaHF3XffjUGDBiEvLy8+A7qrCAG+J0uXLsUnn3yCqlWrOibUsGFDnHrqqXj66acd35PqF2Zim1O9z1T/9CGg38306ctoLWk3fKb56NNbT09KY534a0mpSAo/JO0FyGOPPYZrr70Wb775Js4///z8rpo8ebIRGpzBvfHGG/MFCFdFBg8eXGyXUrQ0b94cffr0wahRo/KvveGGGzB69GgsWLAAzZo1cz0snAxofZG6xmpukACJj1u0u4YMGYInn3wSn332GerUqePKeCo549x17plnnsGFF16IFi1auGqn/eJUanPcjdSNIhBQAvrdDGjH+FgtCRAfYSbJVNoLkPvvvx8DBgzAl19+ieOOOy4f69y5c83Kxfjx49GjR49CAoQihKV8+fIRu4GfDx06FHRO7GFbK1asMNv8DRw4EHTQ3BYJELfEnF8vAeKcVawrrdCrbt26RQ1bLM7Gjh07TJhi2bJlYz2qxD//4IMPcNZZZ5nVzcsvvzzu+kiAxI1ON4qAZwISIJ4RBt6ABEjgu6hIBdNegFB4tG7d2oRQjRw5EnQE+GV08803g44UP69UqVK+AKlYsaL5OwtDr7g6whUUe2nfvr1Z5Vi3bl0RoMwvYUz89OnTXY+GIAiQZL/EriHFeUNJCxCOKY6tTC0MfaPwqFChQkohkABJqe5SZUUgIgEJkPQfGMn2XZz4a+lP3VsL016AEM+ECRPMqgTzQaxy8sknY8qUKeZ0VJZVq1bhyiuvNKEWDRo0AE8cZ4L5V199hVtuucUknFuF4VVlypTB/Pnzi9A/5phjzEGBDNMqrjAZnv/sZcmSJeCsMu3STqSS6C/SZL/E4W184403TB+8+OKL+Oc//1no499//x1169Y1K1bMs2FeD1ei3n33XSMqt2/fblag+DnD4ZjjY5VoAuS7774zuSGzZs0yp+VyRevqq6/GTTfdhOzs7Pz7aeuuu+4yoVz2wlnxjz76yAhYq1DkMq/owQcfxK233mpELmfRX3/9dXPdHXfcYZ7H9jB34sgjjzQbIpx+euzY1bfeesuMRX75cSMEhgXx3vPOO888nkyOP/54/PLLL2YMUhBb5R//+Iepw+eff27Gl7XxgrUZw3333QdrFe///u//cOmllxYZgrGezxvC7bK+zBUZPny4EfThqwH268mCIZBcXeQGEQ8//DBOO+00UAjwHWabOAa4snnxxRcXqd+kSZPM2GC/sv848cANKE488cT8a9lftPn4449j7969ZmKCG0kcccQRJiSTn7EwR4U5XuHFGgesC+vHHBjy5ncC2fN5/K+9RFsBcVJf9vMDDzwAXktWPFDtoIMOQufOnYuMx2K/dPShCGQogUT/bmYo1hJttuWrWJVYvWmn+Z/1qxWOXElUTogEiPfuzwgBMnXqVIwbNw5cueCqBh0HOkVMTp8xY0bUWVn+8NMpZJz7Dz/8gMaNGxvi/C8dOyaehxeGda1fvz5/3/FoXWQ9LaaAAAAgAElEQVQ5xJE+z2QBQvFWu3ZtEx43bdq0QngeffRRk3cze/Zs8/mGDRtw+OGHG0eU/2VYD/uTTjKdfDqCVokkQOiIn3nmmUYs/Pe//zVigMLglVdeMRsWMH/IKm4FCO+joLnssstw9NFHo1y5ckZQ0cHm33v16mWcSIqQL774wjjIFLrFFav9FDPnnnuuEVgUarz/hRdeyBdsFLLHHnuscaTffvttY5LOK8USHffbbrvN/M1y/JnPRAeaK31cDaTjzXckXAQ6fb5ll0Kdq4TcmIF5IhQUrFM0AcKVQ/Yp2dOZ5zv6559/mrpfd9115u/sI+Zd/fzzz+adtJ9sTH4UfV26dDFJ7lxxYZ4KxQx3wGvbtq1ptyVAGJK5ZcsWcJc7Po9iYvPmzcY2n8P7KFLIjKK2Xbt25n72J/9RuLz00ks455xzzKTFb7/9hieeeMK0me9w06ZN87szkgBxWl9r7LL/OE4oMnmyM1dh2RYVERCB4glIgKTfCJEASf0+TXsBwlWOSy65xMwY2xPD33vvPSNIhg0bZmapoxU6wRdccEF+rgiv0wpIYgc+nXM6jlwhslao+EQ6X3Ty6BiyUCByBjs8l4Cz1q+99ppZ8aJjyRIuQBgSRCeS91LQ2G1YjuH3339vhA2LWwFCJ5aO83/+8598WHQYuWLx8ssvg6sRbgoFAp1trs5QCFiFDCjG+DlX8axVG64C0GnntRQrFBlc9Zg5c2b+NZZQ4D2sGwU5C51+jnGy5TWlSpUy9p0+37LLmXoypOi3l2gChOFZXCk58MADzeXWu8fnf/PNN/n1o3NP8WDPtbJCLSla7EKOoW9sFwUQ875YLAHClRSKNYoulm+//daET5IdRRNLcSFYXHELDymjgKLwuOiii8x3hlXC2+ymvqwT6//OO++4GTK6VgRE4G8CEiDpPxSSHb2hFRDvYyrtBQjP++AsMx2h8ELHg6FYnDGPVhYuXGicNyaV0+FhSacckJKeRYjE/dNPPzX9wm2OKUZYKDq48hRtlzI6y3Sc6ZAz/+bf//63caopMljCBYjVr5z15iqFvfA+rozQebfyf9wKEK5y0Bm1h3FZjjlnsjmLbzm+Tl7jRx55xISV0XGlM2svXKlhWNDixYvNCotVeDYHBQdXHiiI2C6uuljFqs/ZZ59tVo7shaFtZD1v3jyTQ+Xm+ZZdbmnNkK/wEk2A/Otf/zIrOVYhvwMOOMCMhY8//riQGW4QwYkBijkW5nSxv5YtW1Zk84j+/fsbQcvVjv333z9fgPDvDDuzl8qVK5vzfBjyxOI0B4SrLTt37jTbPLN/GcLJHyirhLfZTX25asTxTwFi718n40bXiIAI6CT0TBgDEiCp18tpL0AOO+ww0yucWbUXOgp0RhiWUVzCOFdQGNLBfBCGarBQiNBxSYddsIIoQNg3zMXg4Y4UIyzMC2BYFYUkY/Wt8txzzxlnkSFDFB/2QqeVzitLuABhmBVXxoord955p8kPYXErQLhyEylHiE4vcyE4q0/HnmKWjnf4KkF4vaxzZ4qrL8WGlb/A6xgKSNHGVYBnn33W5BfZiyUUGNZGgWEv1jbVDDEiJzfPt+zSybYcebvtaAIkXBBQVHIVhfVm/e2FYXpcpXn//ffNn7nKw1yg4or1vlorIAzL7NmzZ6FbWDcy5K5XLMUJEIpM5sq8+uqrRTak4Pi1VupoJ7zNburLFTrmRVGQcZywflxh6dChQ7Ht1YciIAIhAloBSf+RIAGSen2c9gKEs7AM5eCPuD0RlSE6zB2wZtQ3bdqEatWqFepBzmi2adPGzCxzZpUOMQtnkhkWEe0cEIZyWDPvboaEkyW9RH+RJvsljsaHWyczPI5J0YyvZ1gLZ73tTj0dP4Yy0RHjmS50ShlyRY7Mc2A+B3MBWMIFCB1rOv5cObByA8LrQifSyvuJJkC40kKRFCkJnblDkQrj9zkmmbxMB5d5L5yhD1+Jsd/LlSCG9FAQUzhHKgyxso9ha4zz2vCNFPg3Syhcf/31hc6z4WfhAsTN8+1J5ZHO1CkuCd1+vSVAmJ8Tfmgh+5qhVeTHwjwM9gM3MYhW+C5zDNmT0LnaYS/hdStOgDDxn7kl3LCA7Ll6whUvTk7w+yJ8TNgPX3RTX9aPq3tcAaHIpOjie8EVLrbXvsrm5vtG14pAphBI9O9mpnAMcjuT7bs48deCzCsIdUt7AcJEcf7wM8afs7h0KDlbzp2x6KwxtpzODHeUYRgFd81hUjJDKBjDz1lMOhScnY3kEDLfgI4NBQ5nTTmjypnVeIqTAZ3oL9Jkv8TROHEXIzqYZM9VAjp4nE3nrLpVOAtMMUgmdieMjjod5uIEiJVHEKlvI9WJY4Viw37wJK9j3zM/wo0Asdtn0nOrVq2MCGGYVLTCtlNEMI+BYzRWYZ0ogjneGUJIgUNn2RJkdgHiJATLzfNLQoBwMoAhWMwb4vtcXHEjQMiM4Xjh54D88ccf5vuDq2ThO6NxooP1KE6AuKlveFu4QtivXz/zPoSvesUaF/pcBDKRQKJ/NzORaaa32Ym/lumMYrU/7QUIATDenzsicUtdCgs6DtxJiLOtnF1n4e41FBwM1eJqCM9soNPLuHvGmocXzs4ylIahWWvWrDGihbOpTGhneE08xcmATvQXaVAECPkxYZthVRQg3B6VW6UycdgqXPUgM64ocAcsFq5aMUGZoVrFCZDc3FwTT08BQBFj366WdjjjzPAf7l7Fwm1VaZtjySrMj6CzybwKJwKEOQj77befsWsvdHC5I5d1/kyksUNxcsghhxgBwZ2twm0w3KpmzZrmVjqoFBUU3xTYHJtcsdu1a5dpa5UqVcx10ZLQrcRtSxRxPLt5fkkIEOtgUU4I8F22b8HMttr5uBEg7Bf2MXfX4kqHVTg+uOLBsEArTI+f0TZ3zos0JuwrIG7qy9CrGjVqFBoWDD2kIOYqIN8DFREQgegEEv27KfaZR8CJv5Z5VNy1OCMEiDskJXe1kwGd6C/SIAkQ7mhEQceQI+ZLWOE2Vg9ZThgFCldDKBwZqkPxSJbFCRDaoICgEKUzz/weOvgUJAy5Y6gTQ+ms3AzrTAiG3TD0hU42t2ilc09h4USAMCGbKzPMKWJuEsUN81SYeO1k5Yy7M3HmnDtzcUtfijEKajrJXDGyVlDoLPft27fQzm0U39wti86qlegdvg0vd81iYjxn+7lK+Pzzzxc6C8Tp80tCgLA/GXbHSQGKRYZe0mmnaKUooCiz8oncCBAKDYpT9jNXHTgWuTLHfxSOXPlkn3DskBnZMYyL9xW3AuKmvkzE50obV8q4GxbtcuMBjlvu4mUJypL75tKTRSDYBBL9uxns1qt2iSDgxF9LxHPTyaYESIB608mAzqQvUq4scYWKqxUMIYp0KNzo0aPBf9yCls4Zr6GjTWERS4Cw67l6wh3OGFfP3dK4OkZnkqtedMiZM8BCB5araAzvotBheBNzVLhqFu0gwvAcEMbtM+SLooMhUgwb49a2rDN323KycsaQIJ4/QfHEbWDpHHOliPks/EcnmI4qRVl4PoS1s5UlLCIdRGglajO5OjxpnRxiPZ/XlJQA4bO5IkChxJUfnpfBMUEe3JnKStp2I0Asmwyz4ljhqpB1ECFXVSj0uIMY+4Khbvfee69JmI80JuwrINbXjpP6csxwpz6uzlLYsE0c38xfC98RLUBfZ6qKCASGQCb9bgYGeppXxIm/luYIPDdPAsQzQv8MOBnQ+iL1j3emW4olFDKdj9ovAiKQHgT0u5ke/RikVjjx14JU3yDWRQIkQL3iZEDrizRAHZbiVZEASfEOVPVFQAQcEdDvpiNMqXtR7j7gk5FAhepAq8I7GyaqUU78tUQ9O13sSoAEqCedDGh9kQaow1K8KhIgKd6Bqr4IiIAjAvrddIQpNS/6YzUwpQewag6QUxa4eiZQ+6iEt8WJv5bwSqT4AyRAAtSBTga0vkgD1GEpXhUJkBTvQFVfBETAEQH9bjrClHoXff8G8GYfYNeWUN0rHAB0fQpo1C7hbXHiryW8Ein+AAmQAHWgkwGtL9IAdZiqIgIiIAIiEHgC+t0MfBe5q+DuHcCMAcD8pwvua3wGcNE4oGJoO/pEFyf+WqLrkOr2JUAC1INOBrS+SAPUYaqKCIiACIhA4AnodzPwXeS8gusWA69dCWxYGronuzRw5l3ACdcC2dnO7Xi80om/5vERaX+7BEiAutjJgNYXaYA6TFURAREQAREIPAH9bga+i2JXMC8P+GIC8N4dwL6/QtdXawx0fQKo2zL2/T5f4cRf8/mRaWdOAiRAXepkQOuLNEAdpqqIgAiIgAgEnoB+NwPfRcVXcPtG4I1rgR/eLbiuxWVAh+FA2Yol0jgn/lqJVCyFHioBEqDOcjKg9UUaoA5TVURABERABAJPQL+bge+i6BVc/hEwpSewbV3omrKVgI4PAc26lmijnPhrJVrBFHi4BEiAOsnJgNYXaYA6TFURAREQAREIPAH9bga+i4pWcN8eYNYQ4LOHAeSFPq/XCugyEajasMQb5MRfK/FKBrwCEiAB6iAnA1pfpAHqMFVFBERABEQg8AT0uxn4LipcwU3LgcndgV/m//33LKBdX+DU/kBO6UA0xom/FoiKBrgSEiAB6hwnA1pfpAHqMFVFBERABEQg8AT0uxn4Liqo4MJXgLduBnb/Gfrb/nWAzhOARicHqhFO/LVAVTiAlZEACVCnOBnQ+iINUIepKiIgAiIgAoEnoN/NwHcR8NefwNu3AAtfKqjsYecCFzwKVKgeuAY48dcCV+mAVUgCJEAd4mRA64s0QB2mqoiACIiACASegH43A95FDLViyBVDr1hyygLthwCtugNZWYGsvBN/LZAVD1ClJEAC1BlOBrS+SAPUYapK0gg8+uijeOqpp7Bo0SJceumlePpp2wm4SauFHiQCIpCKBPS7GdBey80F5o4GPrwHyN0bquQBR4TO9qh1ZEArHaqWE38t0A0IQOUkQALQCVYVnAxofZEGqMNUlaQRmDJlCnJycvD2229j9+7dEiBJI68HiUDqE9DvZgD78M91wNRewPJZBZU77qrQykfp8gGscOEqOfHXAt+IEq6gBEgJd4D98U4GtL5Ik9thWVlZuOuuu3D33Xe7fjBn6a+44gqsWLECDRuW/LaBrhsQ44ZPPvkEp59+Or777jscdthhfpuPaK9///5Yt25dRAHyzDPP4Prrr8eyZctQvXrwYoaTAkgPSUkCBx10EDZs2IAOHTrg+eefR7ly5QLbjpJ478NhuH3X9bsZsOH0w3vA672AHRtDFStfNZTrcUTHgFU0enWc+Gsp05gSqqgESAmBj/RYJwNaX6TJ7bCgCZBt27Zh5MiRmD9/Pr766ivjjP/3v/+NuSJw2mmnoXLlynjjjTccATzllFPw0UcfFXvt8ccfj0MOOQTPPfdcoeuWLFmCpk2b4sYbb8RDDz1U6DM6Dj169MARRxyBN998E/Xr13dUH+ui4gTIvn37jBA6//zzizzX1UPivJj9QRYzZ840orNChQo48sgjMWDAAJx55plFrK5evRr/93//h+nTp+OPP/5A48aN0atXL/Tp0wccd1Yhz0GDBpk+X7t2LbKzs3HwwQfj8ssvxzXXXIOyZcsWsp0Iu27HHetwzz334MMPPzR1rlWrFjimbr/9dhx66KGOCfNe2uHK12+//WaEZevWrTF+/Hhjk8VN3dxc67iSHi/My8szouPFF1/EO++8Y/77z3/+06PVxN0e7b1P3BOLWnb7rut3M5m9U8yz9v4FvH8XMG9swUUN2oZ2uap8YEAq6awaTvw1Z5Yy9yoJkAD1vZMBrS/S5HZY0ATIypUr0ahRI9SpUwfHHnss3nrrrZgCZPPmzahZsybGjRtXxFllaNPUqVONqLEcOhLm/z7rrLOiwv7ggw/M559//jnokNgLHSjmaUyaNAn/+c9/zEe5ubm47bbbzHO6du1qPttvv/1cd2ZxAoTGaP+OO+4wTm+VKlVc2/dyA9v18ccfo0uXLjjmmGOMY8y8lcWLF+Oxxx4zYsEqv/76K4477jhzzXXXXQfOgNNZf+2119C3b1/TDqu899575v+Tc7169UDna/bs2cZRPe+88zBt2rSE23Uz7jZu3IijjjrKhMqxzRyv/N4aO3asEVbM42E7YpUff/wRJ598MsqXL2/EFu/5/fffMXfuXIwYMcKIXxY3dXNzbaz6+f350qVLcfjhh6Nfv34YPny43+Z9sVfce+/LA1wYcfOu63fTBdhEXfr7UuC1q4DfFoWekJUDnDoAaHczkJ2TqKcmzK4Tfy1hD08TwxIgAepIJwNaX6TJ7bCgCZC//vrLhGoceOCB2Lt3L0qXLh1TgHB2lUKATq9dZJAkQ8s4u05nr0mTJo7hXnLJJSYJj/eFl1tvvdU4iAsXLkSzZs2wdetWM6PLmX6KAz7TmuHnygAd70ilQYMGxrm0l1gChDPvvO+RRx4xjn0yC0UBRYV9RWLnzp1o0aKFcZzXr1+PUqVKmSpxlWPMmDH49NNP0aZNm/xqXnvttUYoUrRwlai4wvbRxv/+97/8ELhE2XUz7ii22A6ucHE1yiqTJ0824pOrYlwdK65wVYCCi2KLoq5ixYpRL3dTNzfXRnsgbbzwwgsmvDJSWbNmDb799lt07OgunIQinatmDGvkik8QS3HvfbLr6+Zd1+9msnvH9ry8PODrZ4B3bwP27gx9UPmgUKJ5/dYlWDFvj3bir3l7QvrfLQESoD52MqAz5YvUyp949913MWfOHDz55JPgTH7btm3x+OOPmxnj0aNHY9SoUeAPPp08/p0Or1UYnsQQF/6Yb9q0ydzz73//24TE0HG3l59//tk4RZzho5N4zjnn4OGHH0bt2rWL5IDs2LEDw4YNw0svvWQcZIY28fqhQ4cWmtmNlgPi9P5YQ9OpAKHTsGrVKjNzHF7iESB79uwxqwt0wLg7VXg5++yzwThxzu6TD51QPp+rHnRAvZRYAoS2Oft+wAEHYNYsW3Kjl4d6vJcrGg8++KBhYIWcHX300eA4sN5n6xFffvmlCTGiUGPoUXGFM8CcLbevQiXKrr0escbd/fffb94xtoWCzCocfyeddJIJn2IYXnGFYWxnnHGGWd2hI79r1y4TelamTJli74tVNzftiPYghtnxeyRSH/E7h6s27FvmIoWHxxVXeYbhUTxXrVq1iPD2OAR9ub249976ruMkA/udfUzRzVVaCtLmzZvn18GP73bLmNN3PVN+N33paD+N7NwMTLsB+N4W+ntkZ6DjQ0D55K5Q+9ks2nLir/n9zHSzJwESoB51MqAz5YvU+pFq2bKlScjkDDpn8B944AHQyfrHP/5hZiEZmsEZdoYs1K1b18wGc7ckihWGwfzyyy8mDIS5ARQXDDdiiAxDXazCa/kDyRlqztwyZMSKOf/mm28KCRCGlTCfgn+/6qqrjOChY8mZ6P333998KdWoUcOYjiRA3Nwfa2g6cbboNNAZZ/gTncLwEo8AocN74oknmvYx/yS8MNyLjhQd0YsvvtjMXjP3hH0Zb2Fb+Y9OH508ik32c7iQpH32C8OTtmzZEvFzqw6ccaYwdVL4HArNeMq//vUvM97oYHKGm4WhNnSmuUpkL0zop1NFQUvxbS90avlv+/bt+OKLL8xYZb34ncAwpUTatdcj1rizRNQJJ5xgQse4AQPrePPNNxtRys8rVapULEprFY15SAMHDjSTEFw146oIxRzHX6QSq25u2lFcBdkWruQMGTLE1I+FDvepp55qxifFL7+n3BR+T3H1i+3kd1pxqz5u7Pp1bXHvvfVdZwlOhmBSNLL/2ddcKbVW/7x+t9vb4/Rdz5TfTb/62hc7qz4Pne2xZXXIXOn9gHNHAC0uC+zZHm7a7cRfc2MvE6+VAAlQrzsZ0JnyRWr/QePMqfXjZc0mUyTQWbMcLwqTW265BYyXZ24CHW6KEq5ScAXAKtaPPGfq2rdvb/7MWXWuaFCcXHjhheZvDAGh88ywEfsuWAwtosPBGX67E8SQC/74ckb6vvvuMzYiCRA398camk6cLStmm3H3dGzDSzwChHkNV155pWHQrl27Qia5GsVZfopBCrpWrVoZruGhX7HaFq2e9r9HS76nU8iVrx9++CE/TyDS86x8ACd1cZKUH8kOE8i5OsdZfI4lq1x00UVGYDBZnfk8VuGKEsOoKGzDxYnVV9a1dMYnTJhQyNFNlF1725yMO9aL7wnzQazClQHmHDnZoYzvIUUrxTzHGEUcJyC4KkQRRgFmX+20nuGkbvFcG6lve/fubfJa+N3D1UBOTHBMMaSQM/9uCjcwYH9ysoDJ9pFyq9zYS8S1xb33dlExb968fOH/+uuvg2OSEzrnnntuoe9Ffl/G891ub5vTdz1TfjcT0e+ube7bC3w6Evh4GJCXG7q99tFA1yeBGqG8rXQoTvy1dGhnItsgAZJIui5tOxnQmfJFav2ghYdrcCaZwuDOO+80uQtWsWbnrGRfxs9z9j88xIWrFZydpxDhtSy8lo5LeD6DZdMuQLiqwplrJn+HFzpKnLXkDC9LJAHi5v5Yw8eJs8VtaVnX5cv/PmE2zGg8AoTCjgIvkqjhsxhyxdWgP//8Mz+EJlZb/Pycs8jsX86aR5sp5/M4Q/vZZ585ejTDYtw6lVyB4fOZEL9gwQITAmgVhhgx/4UCjQ4sE6w5a37TTTeZFQ6O0fCxyz7kPzr1vJ8ChWKXs+6JtmuH5GTcUXSyHyjymVvEsULxTRE8Y8aM/JWgaPCt3CCGYVFEW4U5MxQy/A545ZVXitzupG7WTW6ujVRPTlJwBp6OOfuL/cIJkOLGXCQ7XImj+OAKClcNKbaeeOIJI/KDVIp7763vOvZ5z54986vN1eVq1aqZnCwKa/v3Yrzf7XYmTt/1TPndLPHx8sdqYEoPYNWcgqqceB1wxp1AqcK79ZV4XT1WwIm/5vERaX+7BEiAutjJgC72i5TLnRt+CE6LahwKdJkYV33sMcXWSgUNWTP6EydOND/+VmHoFYXE4MGDzVafDNviSoh9hyDrWjrHzCWxQlyiXUsHkrkOdgHCnZuYWByt0MlkPon9h9Z+Doib+2OBc+JAcaWoU6dOJp8lUvFbgNx7771GHHLmkzk1DCWhIOO2sckqVhJ0LAGSyPpwjHDcsu1cbeMKSnhhTgxDeawwMK7mMWSFfcJNBhjmV1xhCBCFIMWNPWE9UXatusQad1zlsJKV7asUdM7JhKuNDLEqrlDEUsxGCvNjSBf5cqUgvMSqm/16N9dGqyvD6viO8b9cgaXIclsYvskNBZi0T160x3FBYRqk4kSA2FeWrbozpIxjmt+j9u/F8GudfrfbmTh91yVAkjCSmOfxZh9g15bQwyocAFw4Djik6BbkSahNwh/hxF9LeCVS/AESIAHqQCcDutgv0vEnA2sXBKdFdZoDPT+Jqz6WAHn//fcLnaFg/Uhx1pH5H1axBAgdYIbfxBIgXK3gnvssvJaJ03QA7CWSAOG1XMWIliDMzylu7D+0dgHi5v5Y4GI5UJwhZ24LQ0K4s06kEo8AKS4Ug/k1nP2m8OBYZdIxz7dgqEWyYtqdhmVwhyXOOjspXPXiTK6TwjyfCy64wKxS0BkvbjckXst+4n+5OsBQQwpkzvAzfLC4QgecmyQw1IlttpdE2eUzYo07ii1y/f7774tUn/kAXMGItIJov5jnoXCGnJMEzIexF+aW8LuSbQwvsepmv97NtZH6gStVrBvDpzjOma/C7y0mqDstDFNkfhrfT4bocVWFuUa0Rwc9SMVJCFb49zXrH76ToNfvdjsTp++6BEgCR9LuHcCMAcD8pwse0vgM4KJxQMWaCXxwyZp24q+VbA2D/3QJkAD1kZMBnWkrIPEKkGghWNy6kasUjN/mzCOLmxAszlAytIuCJ1aJFILl5v5Y9mM5UPxx5ow6nUErhybcZjwCxApNCxeBtM2VDj6L+Rcs3AK4W7du6Ny5s0nEth+wF6t98X7O0BU677GS0BORA8I+4U5fXHlj290eKGfFzEdiG87Dqr89nDAaMz/txhp3dKhZeK6FvdC5tlYfYznXDEHq3r272WyA/7UX6wwR5huFl1h1s1/v5trw53AFhmewcOtlsuVqK0Wj1e/2vLPixjHFCu9nrpDVLgosti28fc8++6wJ92JyO/83BT03W2BYHidEKEg5AcOwS4aDMRSKwp/vAW1yPFq5WFwl5sGWVj8wlI/vZ3FbHhf33kcTFWx7IgWI03ddAiTeb9MY961bFDrbY8Pf73p2aeDMu4ETegPZ2Ql6aDDMOvHXglHT4NZCAiRAfeNkQGfKF6nXWTIrsfzVV18ttPUrdw7isj3j0LnqwWIlrDtJQucPNWecIzmIdLB4RgcTSVkiCRA398camrEcKMaVUxDQSYlW4hEg1nacXIGyRBztWytG3KHs5Zdfzn8k8xoYAkYnibtYJbrw9HHuxBVrG16/c0AYy8/df9h2JmFfffXVrppKfpz5ZpI1Vw+sDRY4S872hBdrp6hou5FZ1/ttN9a4Y8gfHXE65/Z8CCt/i84vQyVZOJa4XS1n/e3J+HyPmFdBwU473PGMxcoxYvglwzBLQoDwHBC2kStc/H7h/2bhigyFNr9bOAb4v4srdPaZvxN+LgrHDdvGsC77zmvcgIPCjOKagoerRAxr43vI7ySuFvG8H64+UpRwEoD2WV+KYq7cMvyNhbb53UCeDBllnzBkkStq0Uq0997+XZfsFRCn73qm/G66+sLxcjHP9vhiAvDeHcC+v0KWqjUOne1RN/7dDr1UKdn3OvHXkl2nVHueBEiAeszJgM6UL1KvAsS+DS9XOw499FDjMDDMIXwbXs4WWofFMRabMdj8YeZ2mtzdyp4Dwh9z/vgzGZY/6gy34laoDLPirj10vi3nKpIAcXN/tKHJnZLoQNDhZd24vRLLTUIAACAASURBVK3l7DD0hzOkrDt3ouKsJ5Nao5V4BAhtcYaXM6j2RGnLoaLIogC0Ch1Wij2GqHC2l3VMVHFzOJnfdbC2ZmUIUvisPZ/FcWPNQLN/GL7D3Z64axgT1el0Mh+ETpz9dHnuIsQxSmeS17Lv6eQytI7jj0LLWuFKlF3W38m443V0ZFlXnoHBd48heExCpyhjGBtzWyxH11rFibSjGc/4YR4RwyX5XnFLbSYzcytjjj3rTBU3dXN7baQxwnNA6PRTCISfa8P3m+ObO/QVdw4InXl+55AR84QsgcXnWe2m8KIgtQoT8rnTFkNMWSjqKXSs8334PL77DA0LLxQ5XGVhH1iF7ym/HzjmOIaslavi3otI7z2vL4kVEDfveqb8bvr9nRbR3vYNwBvXAj/YQgRbdAM6DAPKRj8wNCl1S+JDnPhrSaxOSj5KAiRA3eZkQGfKF6lXAcJupVMXfhAhZwgjHURIAUFnh04dBUVxBxHSyeCPP88h4Swjr2f4BOO4OStpbXcb7SBCp/dHG5pMwrUS3cOvsVZmGLpC54/hV0ykj1biFSDWQXH27ULJhKsdkeL2WQ9uu0lhyHuaNm2akDePScBMgueWrdy5KpmFTjdFWLRCJ8/asYrnYdCJ5ZalDJ2hY06BQkEZfiI9nUyOJeaKkCOdVp4jQmeQOwvZD7tLlF22ycm4s9rOunLFi/kR7AurfRTnXNmwSnEChNfQ2ee5H1wR4gYOZETHOXxTAzd1c3NttL6kiIp2rg3Ds/h+so+iFSZ083uI7wJ3QrMX690K3yWKWxIzB47ChYWrQJwssQQJV50YgsUtijnRwveR30/8vmGdGJLJRHmrMPyqQ4cOZhWT3xVOSqT3nveVhABx865nyu+mkz70dM3yj4ApPYFt60JmylYKHSrYzNsBs57qVEI3O/HXSqhqKfNYCZAAdZWTAa0v0gB1WICrwl2EGMpDQZWowrhyOst0EoNQmFTOWVy2nTO+KiKQLgQ42893jQLTOnyT20JzK3JrkwOKD17HMEBrhYain6tjFPyjR482p8uzUNBxq2OezcFVXq7COM3PCsJ77/Zd1++mxzdh3x5g5mBg9iiekhUyVq9VaJfLqg09Gk/N2534a6nZsuTVWgIkeaxjPsnJgNYXaUyMugAwhzByZpUhG4kqPIiQqz4MN3ESvpGoelh2mZjLEDrGvzs57C7R9ZF9EfCLAFc3uFrJ0DMWhjUyCZ2/B1byOkNL+T4yF4X5NlyJpKPOFUGGzzGMjytRXJFq06aNyQdheN8hhxxitvzl/U5KEN57t++6fjed9GyUazYtD51o/kto7AFZQLu+wKn9gZzSHgyn9q1O/LXUbmHiay8BknjGjp/gZEDri9QxTl0oAiIgAmlBgKFrDFezEu8XL15sJhfs20hzhYRnwPC/zAljmJgVqscT23k/E9S5DTJ3prPCsRiuyXAt5ulkp+nORfrdjPM1WPgK8NbNwO4/Qwb2rwN0ngA0OjlOg+lzmxN/LX1am5iWSIAkhmtcVp0MaH2RxoVWN4mACIiACGQoAf1uuuz4v/4E3r4FWGg7i+iw84BOjwL7OTsPyeUTU+5yJ/5ayjUqyRWWAEky8OIe52RA64s0QB2mqoiACIiACASegH43XXQRQ614tsfmFaGbSpUDzh4MtOrOQ11cGErvS534a+lNwHvrJEC8M/TNgpMBrS9S33DLkAiIgAiIQAYQ0O+mg07OzQXmPALMvBfI3Ru64YAjgK5PArUSs2uhg1oF9hIn/lpgKx+QikmABKQjWA0nA1pfpAHqMFVFBERABEQg8AT0uxmji/5cB0ztCXCbXascdxXQfghQunzg+7ckKujEXyuJeqXSMyVAAtRbTga0vkgD1GGqigiIgAiIQOAJ6HezmC76YQbw+jXAjo2hi8pXBS54FDiiY+D7tSQr6MRfK8n6pcKzJUAC1EtOBrS+SAPUYaqKCIiACIhA4AnodzNCF+3ZBXxwFzBvXMGHDdqGdrmqfGDg+7SkK+jEXyvpOgb9+RIgAeohJwOaZxxwD/hDDz00QDVXVURABERABEQgmAR4InxOTg4aN24czAomu1a/Lw0lmv+2KPTkrBzgtAFA25uB7Jxk1yYln+fEX0vJhiWx0hIgSYQd61FOBvSqVauwfft2s9e7dSJuLLv6XAREQAREQAQykcBff/1lDifdf//98w9tzEQOps15ecDXk4B3+wN7d4YwVDkI6PIEUL91xmKJp+FO/LV47GbSPRIgAeptJwP6jz/+wNq1a1GpUiXUqVMnbQ+OClC3qCoiIAIiIAIpSIDigye/79q1CwceeKD53czYsnMzMO0G4Ps3ChAc2Rk4/2GgXOWMxRJvw534a/HazpT7JEAC1NNOBnReXp450ZZfqDy1lsvKKiIgAiIgAiIgAgUEcnNzsW/fvtAkf5UqqF27NrIy9RyLn+cCU64GtqwOASpdATh3ONDiMp3tEedL48Rfi9N0xtyWEQJk9erVuOeee/Dhhx+a1YNatWrhlFNOwe23314ol4JfViNGjMDEiRPBe+rXr4/u3bujX79+RRx9N9c6HU1OBzRzQDZt2oRt27aBX7IqIiACIiACIiACBQQoNsqWLWtWPRh+lZHiY99e4NORwMfDgLy/fYXaRwNdnwJqNNFw8UDAqb/m4RFpf2vaC5CNGzfiqKOOwu7du3HNNdegUaNG4I4YY8eONV9IixYtyo8L7d27t/n7FVdcgZNOOglz5szBU089Bf59zJgxhQaDm2udjiINaKekdJ0IiIAIiIAIiEBUAn+sDq16rJpbcMmJ1wFn3AmUKitwHgnIX/MIkHsf5DGmJ43LY489hmuvvRZvvvkmzj///PyWTp48GV27dsVDDz2EG2+80QiR5s2bo0+fPhg1alT+dTfccANGjx6NBQsWoFmzZubvbq51g1YD2g0tXSsCIiACIiACIlCEAPM83uwD7NoS+qjCAcCF44BDzhQsnwjIX/MOMu0FyP33348BAwbgyy+/xHHHHZdPbO7cuWaVY/z48ejRo4cJxxo6dKjZLYOrJFZZsWIFDj74YAwcOBBDhgwxf3ZzrZsu0oB2Q0vXioAIiIAIiIAI5BPYvQOY3j+005VVGp8BXDQOqFhToHwkIH/NO8y0FyAUHq1bt8YJJ5yAkSNHomHDhiYE6+abbzY5FPycMaLt27c3qxzr1q0rQpU5Iy1btsT06dPNZ26uddNFGtBuaOlaERABERABERABQ2DdotDZHhuWhoBklwbOvBs4oTeQnS1IPhOQv+YdaNoLECKaMGGCWcFgPohVTj75ZEyZMgXVq1c3f2J4VZkyZTB//vwiVI855hjs2bPHhF65vTZaFzEZnv/sZcmSJejWrZupA5+pIgIiIAIiIAIiIAJRCTCK/osJwHt3APv+Cl1WrTHQ9UmgbguBSxABCRDvYDNCgEydOhXjxo0zKxc8wI9CgrtdMTl9xowZqFChgjkhlSsdTDwPLwzVWr9+vVk5YXFzbbQuuvvuuzFo0KCIH0uAeB/YsiACIiACIiACaU1g+wbgjWuBH0LRGaa06AZ0GAaUrZjWTS/pxkmAeO+BtBcgXOW45JJLwMFiJZET23vvvWcEybBhw3DrrbdqBcT7WJIFERABERABERCBZBBY/hEwpSew7e+w8bKVgI4PAc26JuPpGf8MCRDvQyDtBQjP+/j999/x/fffF6HF3A+GYr311luu8jqUA+J94MmCCIiACIiACIiASwL79gAzBwOzuVvn35uY1msFdJkIVG3o0pguj5eABEi85AruS3sBcthhh5nWLl36d2LW323n7sM8nKht27YmuZw5Ivfdd5+jXbDcXOumizSg3dDStSIgAiIgAiKQQQQ2LQ8lmv/69d+NzgLa9QVO7Q/klM4gECXfVPlr3vsg7QVIp06dMG3aNMyePRsnnnhiPrHXXnsNF198sdlSd/DgwWYHLO50Fe0ckG+//RZHH320ud/NtW66SAPaDS1dKwIiIAIiIAIZQmDBy8DbfYHdf4YavH9doPN4oNHJGQIgWM2Uv+a9P9JegDCp/NRTT0XZsmXNieZMIGcSOnfGqlatGr755hvUrl3bkOzVq5c5F4Qnobdp08aIFp6E3rNnT5PEbi9urnXaTRrQTknpOhEQAREQARHIAAJ//Qm8fQuw8KWCxh52HtDpUWC/ahkAIJhNlL/mvV/SXoAQ0cKFC3HPPffgq6++wq+//mqEx1lnnWVWPho0aJBPce/evRg+fDgmTpyINWvWoF69eujevbtJUi9VqlQh2m6uddpNGtBOSek6ERABERABEUhzAr/MD4VcbV4RamipckD7IcBxVwFZWWne+GA3T/6a9/7JCAHiHVNyLGhAJ4ezniICIiACIiACgSWQmwvMeQSYeS+QuzdUzQOOCJ3tUatpYKudSRWTv+a9tyVAvDP0zYIGtG8oZUgEREAEREAEUo/An+uAqT0BbrNrlVbdgbMHA6XLp1570rTG8te8d6wEiHeGvlnQgPYNpQyJgAiIgAiIQGoRWDodeKM3sGNjqN7lqwIXPAoc0TG12pEBtZW/5r2TJUC8M/TNgga0byhlSAREQAREQARSg8CeXcAHdwHzbJvdNGwHXDQeqHxgarQhw2opf817h0uAeGfomwUNaN9QypAIiIAIiIAIBJ/A70tDiea/LQrVNSsHOG0A0PZmIDsn+PXP0BrKX/Pe8RIg3hn6ZkED2jeUMiQCIiACIiACwSWQlwd8PQl4tz+wd2eonlUOAro8AdRvHdx6q2aGgPw17wNBAsQ7Q98saED7hlKGREAEREAERCCYBHZuBqbdAHz/RkH9juoCdHwIKFc5mHVWrQoRkL/mfUBIgHhn6JsFDWjfUMqQCIiACIiACASPwM9zgcndga1rQnUrXQE4dwTQ4lKd7RG83opaI/lr3jtLAsQ7Q98saED7hlKGREAEREAERCA4BPbtBT4dCXw8DMjLDdWrTnOgy5NAjSbBqadq4oiA/DVHmIq9SALEO0PfLGhA+4ZShkRABERABEQgGAT+WA1MuRpYNbegPideB5xxJ1CqbDDqqFq4IiB/zRWuiBdLgHhn6JsFDWjfUMqQCIiACIiACJQ8ge9eB6ZdD+zaEqpLhQOAi8YBTc4s+bqpBnETkL8WN7r8GyVAvDP0zYIGtG8oZUgEREAEREAESo7A7h3A9P6hna6sQtFx4VigYs2Sq5ee7AsB+WveMUqAeGfomwUNaN9QypAIiIAIiIAIlAyBdYtCZ3tsWBp6fnZp4My7gRN6A9nZJVMnPdVXAvLXvOOUAPHO0DcLGtC+oZQhERABERABEUguAZ7tMW888P4dwL7doWdXbxI626Nui+TWRU9LKAH5a97xSoB4Z+ibBQ1o31DKkAiIgAiIgAgkj8D2DcDrvYEfZxQ8s0U3oMMwoGzF5NVDT0oKAflr3jFLgHhn6JsFDWjfUMqQCIiACIiACCSHwLJZwNRewLZ1oeeVrRQ6VLBZ1+Q8X09JOgH5a96RS4B4Z+ibBQ1o31DKkAiIgAiIgAgklsC+PcDMwcDsUQDyQs+q1wroMhGo2jCxz5b1EiUgf807fgkQ7wx9s6AB7RtKGRIBERABERCBxBHYtDyUaP7r138/Iwto1xc4tT+QUzpxz5XlQBCQv+a9GyRAvDP0zYIGtG8oZUgEREAEREAEEkNgwcvA2zcDu7eF7O9fF+g8AWjULjHPk9XAEZC/5r1LJEC8M/TNgga0byhlSAREQAREQAT8JbBrK/DOLcDClwvsHt4RuGA0sF81f58la4EmIH/Ne/dIgHhn6JsFDWjfUMqQCIiACIiACPhH4Jf5oZCrzStCNkuVA9oPAY67CsjK8u85spQSBOSvee8mCRDvDH2zoAHtG0oZEgEREAEREAHvBHJzgTmjQsnmuXtD9mo2DZ3tUaupd/uykJIE5K957zYJEO8MfbOgAe0bShkSAREQAREQAW8E/lwHTO0JLP+owE6r7sDZg4HS5b3Z1t0pTUD+mvfukwDxztA3CxrQvqGUIREQAREQARGIn8DS6cAbvYEdG0M2ylcFOo0BDj8vfpu6M20IyF/z3pUSIN4Z+mZBA9o3lDIkAiIgAiIgAu4J7NkFvH8n8MX4gnsbtgMuGg9UPtC9Pd2RlgTkr3nvVgkQ7wx9s6AB7RtKGRIBERABERABdwR+Xwq8diXw2+LQfVk5wGkDgbY3Adk57mzp6rQmIH/Ne/dKgHhn6JsFDWjfUMqQCIiACIiACDgjkJcHfD0JeLc/sHdn6J4qB4USzeu3dmZDV2UUAflr3rtbAsQ7Q98saED7hlKGREAEREAERCA2gZ2bgTevB5a8WXDtUV2Ajg8B5SrHvl9XZCQB+Wveu10CxDtD3yxoQPuGUoZEQAREQAREoHgCP88FJncHtq4JXVe6AnDuCKDFpTrbQ2OnWALy17wPEAkQ7wx9s6AB7RtKGRIBERABERCByAT27QU+GQF8MhzIyw1dU6c50OVJoEYTUROBmATkr8VEFPMCCZCYiJJ3gQZ08ljrSSIgAiIgAhlI4I/VwJSrgVVzCxp/4nXAGXcBpcpkIBA1OR4C8tfioVb4HgkQ7wx9s6AB7RtKGRIBERABERCBwgS+ex2Ydj2wa0vo7xVqAheNBZqcKVIi4IqA/DVXuCJeLAHinaFvFjSgfUMpQyIgAiIgAiIQIrB7OzB9QGinK6tQdFw4FqhYU5REwDUB+WuukRW5QQLEO0PfLGhA+4ZShkRABERABEQAWLcodLbHhh9CNLJLA2cNAo6/BsjOFiERiIuA/LW4sBW6SQLEO0PfLGhA+4ZShkRABERABDKZAM/2mDceeP8OYN/uEInqTUJne9Rtkclk1HYfCMhf8w5RAsQ7Q98saED7hlKGREAEREAEMpXA9g3A672BH2cUEGjRDegwDChbMVOpqN0+EpC/5h2mBIh3hr5Z0ID2DaUMiYAIiIAIZCKBZbOAqT2Bbb+FWl+2EnD+wwAPF1QRAZ8IyF/zDlICxDtD3yxoQPuGUoZEQAREQAQyicDe3cCswcDsRwDkhVperzXQ5XGgasNMIqG2JoGA/DXvkCVAvDP0zYIGtG8oZUgEREAERCBTCGxaDrx2FfDr13+3OAs4+RbglP5ATqlMoaB2JpGA/DXvsCVAvDP0zYIGtG8oZUgEREAERCATCCx4CXi7L7B7W6i1+9cFOk8AGrXLhNarjSVEQP6ad/ASIN4Z+mZBA9o3lDIkAiIgAiKQzgR2bQXeuQVY+HJBKw/vCFwwGtivWjq3XG0LAAH5a947QQLEO0PfLGhA+4ZShkRABERABNKVwJr5wOQrgc0rQy0sVQ5oPxQ47kogKytdW612BYiA/DXvnSEB4p2hbxY0oH1DKUMiIAIiIALpRiA3F5gzCpg5GMjdG2pdzaahsz1qNU231qo9ASYgf81750iAeGfomwUNaN9QypAIiIAIiEA6Edi6NrS97oqPC1rV6mrg7HuB0uXTqaVqSwoQkL/mvZMkQLwz9M2CBrRvKGVIBERABEQgXQgsnQ680RvYsTHUovJVgU5jgMPPS5cWqh0pRkD+mvcOkwDxztA3CxrQvqGUIREQAREQgVQnsGcX8P6dwBfjC1rSsF1ol6tKdVO9dap/ChOQv+a989JegFx++eWYNGlSVFKDBw/G7bffjpUrV6JRo0YRr7vqqqswceLEQp/t27cPI0aMMH9fvXo16tevj+7du6Nfv37IycmJq2c0oOPCpptEQAREQATSjcDvS4HXrgR+WxxqWVYOcNpAoO1NQHZ8v7HphkjtKTkC8te8s097ATJ37lwsW7asCKlRo0bhq6++wvz583HMMcfkC5BOnTqha9euha5v0qQJTjjhhEJ/6927N8aOHYsrrrgCJ510EubMmYOnnnoK/PuYMWPi6hkN6Liw6SYREAEREIEEEmg3fKax/umtpyfwKX+bzssDvp4EvNsf2Lsz9McqBwFdngTqt0r88/UEEXBAQP6aA0gxLkl7ARKp/Tt27EDt2rXRsGFDLFy40FxirYBwNYSrIsWVRYsWoXnz5ujTpw8oZKxyww03YPTo0ViwYAGaNWvmunc0oF0j0w0iIAIiIAIJJpA0AbJzM/Dm9cCSNwtadFRXoOODQLnKCW6lzIuAcwLy15yzinZlRgqQ559/Ht26dcPIkSPRt2/fIgKEIoSlfPnIO2vw86FDh2L58uWFwrZWrFiBgw8+GAMHDsSQIUNc944GtGtkukEEREAERCDBBJIiQH6eA0y+Gti6JtSa0hWA80YCzf+lsz0S3L8y756A/DX3zMLvyEgBcvbZZ2PWrFlYs2YNatWqVUiAVKxYEdu2bTN/Y+jVjTfeiGuvvbYQt/bt25tVjnXr1hXpAdpr2bIlpk+f7rp3NKBdI9MNIiACIiACCSaQUAGyby/wyQjgk+FAXm6oJXWah0KuajRJcMtkXgTiIyB/LT5u9rsyToD88ssvOOigg9ChQwe89dZb+SxWrVqFK6+8EhdeeCEaNGiAX3/91SSYM0/klltuMQnnVmF4VZkyZUz+SHhhPsmePXvAMK3iytq1a8F/9rJkyRKzMmPlpXjvXlkQAREQAREQAXcELMFh3bV6UygXo361wlEBnnNC/lgVWvVY/XlBBU+8DjjjLqBUGXeV1tUikEQCEiDeYWecALn//vsxYMAAvPrqq0WSzcNxcqer008/HZ999hl++OEHNG7c2FzC/3Klg4nn4YUJ6evXr8dPP/1UbO/cfffdGDRoUMRrJEC8D2xZEAEREAERiI9AUgTId68D064Hdm0JVbJCTeCisUCTM+OrtO4SgSQSkADxDjvjBEjTpk1N6BRXH8qWLRuT4LRp03DBBRdg/Pjx6NGjh7leKyAxsekCERABERCBNCHgawjW7u3A9P7A188U0KHouHAsULFmmhBTM9KdgASI9x7OKAHy5ZdfonXr1q62yuUuWdzxiknlTC5nUQ6I94EnCyIgAiIgAqlBwDcBsm5R6GyPDT+EGp5dGjhrEHD8NUB2dmrAUC1FAIAEiPdhkFEC5LrrrjNndMybN88IESdlypQp6NKli8kH4YGELBQi9913n3bBcgJQ14iACIiACKQ0Ac8ChGd7zBsXOtV83+4Qi+pNgC5PAHVbpDQbVT4zCUiAeO/3jBEgu3fvRt26dVGzZk18//33Rcht2rQJ1apVK/T3nTt3ok2bNli8eLE5zJCnnbNwByzudBXtHJBvv/0WRx99tOve0YB2jUw3iIAIiIAIJJiAJwGyfQPwem/gxxkFtWzZDThnGFC2YoJrLvMikBgC8te8c80YAWKtZDAJ/bbbbitCrnPnzuABhTzxvF69emYXrEmTJplVDq529O/fv9A9vXr1MnkhPAmdImX27NnmJPSePXti3LhxcfWMBnRc2HSTCIiACIhAEAksmwlM7QVs+y1Uu7KVgPMfBo7qEsTaqk4i4JiA/DXHqKJemDECpFOnTmbb3dWrV5uVkPDyxBNPGMGxdOlScDWE54FwS12ebs4k9PCyd+9eDB8+3IRm8TwRipbu3bvj1ltvRalSpeLqGQ3ouLDpJhEQAREQgSAR2LsbmDUYmD2qoFb1WgNdJgJVGwSppqqLCMRFQP5aXNgK3ZQxAsQ7qsRb0IBOPGM9QQREQAREIIEENi4DJl8F/PrN3w/JAk6+BTilP5AT3+RcAmsr0yIQFwH5a3FhkwDxji0xFjSgE8NVVkVABERABJJAYMFLwNt9gd3bQg+rdCDQeQLQsG0SHq5HiEDyCMhf885aKyDeGfpmQQPaN5QyJAIiIAIikCwCu7aGhMeiVwqeeHhH4ILRwH6FN3dJVpX0HBFIJAH5a97pSoB4Z+ibBQ1o31DKkAiIgAiIQDIIrJkPTL4S2Lwy9LRS5YD2Q4HjrgSyspJRAz1DBJJOQP6ad+QSIN4Z+mZBA9o3lDIkAiIgAiKQSAK5ucDsh4FZQ4DcvaEn1WwKdH0SqHlEIp8s2yJQ4gTkr3nvAgkQ7wx9s6AB7RtKGRIBERABEUgUga1rgak9gRUfFzyh1dXA2fcCpcsn6qmyKwKBISB/zXtXSIB4Z+ibBQ1o31DKkAiIgAiIQCIILJ0OvH4NsHNTyHr5qkCnMcDh5yXiabIpAoEkIH/Ne7dIgHhn6JsFDWjfUMqQCIiACIiAnwT27ALevxP4YnyB1YbtQrtcVSp6tpafj5YtEQgaAflr3ntEAsQ7Q98saED7hlKGREAEREAE/CKw/n+hsz1+WxyymJUDnDYQaHsTkJ3j11NkRwRShoD8Ne9dJQHinaFvFjSgfUMpQyIgAiIgAl4J5OUB858Gpg8A9u4MWavSAOjyBFC/lVfrul8EUpaA/DXvXScB4p2hbxY0oH1DKUMiIAIiIAJeCOzYBEy7HlgyrcDKUV2Bjg8C5Sp7sax7RSDlCchf896FEiDeGfpmQQPaN5QyJAIiIAIiEC+Bn+cAk68Gtq4JWShdATh3BNDiUp3tES9T3ZdWBOSvee9OCRDvDH2zoAHtG0oZEgEREAERcEtg317gkxHAJ8OBvNzQ3XVahEKuajRxa03Xi0DaEpC/5r1rJUC8M/TNgga0byhlSAREQAREwA2BP1aFVj1Wf15w10l9gNPvBEqVcWNJ14pA2hOQv+a9iyVAvDP0zYIGtG8oZUgEREAERMApge+mAm/eAPy1JXRHhZrARWOBJmc6taDrRCCjCMhf897dEiDeGfpmQQPaN5QyJAIiIAIiEIvA7u3A9P7A188UXEnRceE4oOIBse7W5yKQsQTkr3nvegkQ7wx9s6AB7RtKGRIBERABESiOwNqFobM9NvwQuiq7NHDWIOD4a4DsbLETAREohoD8Ne/DQwLEO0PfLGhA+4ZShkRABERABCIR4Nke88aFTjXftzt0RfVDgK5PAHWai5kIiIADAvLXHECKcYkEiHeGvlnQgPYNpQyJgAiIcuskeQAAIABJREFUgAiEE9j2O/BGb+DH9wo+aflvoMMwoEwF8RIBEXBIQP6aQ1DFXCYB4p2hbxY0oH1DKUMiIAIiIAJ2AstmAlN7Adt+C/21bGXg/IeBozqLkwiIgEsC8tdcAotwuQSId4a+WdCA9g2lDImACIiACJDA3t3AzHuBOY8U8KjXGugyEajaQIxEQATiICB/LQ5oYbdIgHhn6JsFDWjfUMqQCIiACIjAxmWhRPNfvwmxyMoG2t0CnHIbkFNKfERABOIkIH8tTnC22yRAvDP0zYIGtG8oZUgEREAEMpvAgpeAt/sCu7eFOFQ6EOg8AWjYNrO5qPUi4AMB+WveIUqAeGfomwUNaN9QypAIiIAIZCaBXVtDwmPRKwXtP7wjcMFoYL9qmclErRYBnwnIX/MOVALEO0PfLGhA+4ZShkRABEQg8wis+SoUcrV5ZajtpcoB59wHHHsFkJWVeTzUYhFIEAH5a97BSoB4Z+ibBQ1o31DKkAiIgAhkDoHcXGD2w8CsIUDu3lC7ax4ZOtuj5hGZw0EtFYEkEZC/5h20BIh3hr5Z0ID2DaUMiYAIiEBmENi6FpjaA1jxSUF7W/cAzroHKF0+MxiolSKQZALy17wDlwDxztA3CxrQvqGUIREQARFIfwJL3wVe7w3s3BRqa/lqQKcxwOHnpn/b1UIRKEEC8te8w5cA8c7QNwsa0L6hlCEREAERSF8Ce3YB798BfDGhoI0N24V2uapUN33brZaJQEAIyF/z3hESIN4Z+mZBA9o3lDIkAiIgAulJYP3/gNeuBNZ/F2pfVg5w+u1AmxuB7Jz0bLNaJQIBIyB/zXuHSIB4Z+ibBQ1o31DKkAiIgAikF4G8PGD+U8D0gcDenaG2VWkAdHkCqN8qvdqq1ohAwAnIX/PeQRIg3hn6ZkED2jeUMiQCIiAC6UNgxyZg2vXAkmkFbWp2MXDeA0C5yunTTrVEBFKEgPw17x0lAeKdoW8WNKB9QylDIiACIpAeBFbOBqZcDWz9JdSe0hVCwqP5P3W2R3r0sFqRggTkr3nvNAkQ7wx9s6AB7RtKGRIBERCB1Cawby/wyXDgkxFAXm6oLXVaAF2fBKo3Tu22qfYikOIE5K9570AJEO8MfbOgAe0bShkSAREQgdQl8McqYPLVwOrPC9pw0vXA6XcApcqkbrtUcxFIEwLy17x3pASId4a+WdCA9g2lDImACIhAahJYPAWYdiPw15ZQ/SvUBC4aBzQ5IzXbo1qLQBoSkL/mvVMlQLwz9M2CBrRvKGVIBERABFKLwO7twLu3Ad88W1DvJmcBF44FKh6QWm1RbUUgzQnIX/PewRIg3hn6ZkED2jeUMiQCIiACqUNg7QLgtauAjT+G6pxTBjhzEHB8LyA7O3XaoZqKQIYQkL/mvaMlQLwz9M2CBrRvKGVIBERABIJPgGd7fD4W+OAuYN/uUH2rHwJ0fQKo0zz49VcNRSBDCchf897xEiDeGfpmQQPaN5QyJAIiIALBJrDtd+CN3sCP7xXUs+W/gQ7DgDIVgl131U4EMpyA/DXvA0ACxDtD3yxoQPuGUoZEQAREILgEls0EpvQEtq8P1bFsZeD8h4GjOge3zqqZCIhAPgH5a94HgwSId4a+WdCA9g2lDImACIhA8Ajs3Q3MvBeY80hB3eofD3SZCFQ5KHj1VY1EQAQiEpC/5n1gSIB4Z+ibBQ1o31DKkAiIgAgEi8DGZcDkq4BfvwnVKysbOLkfcPKtQE6pYNVVtREBESiWgPw17wNEAsQ7Q98saED7hlKGREAERCAYBJhovuAl4O2+wJ7toTpVOhDo/DjQsE0w6qhaiIAIuCIgf80VrogXS4B4Z+ibBQ1o31DKkAiIgAiUPIFdW4G3bwYWvVpQlyPOB85/BNivWsnXTzUQARGIi4D8tbiwFbopEAJk3759WLlyJTZs2GAqV6NGDTRs2BA5OTmeW3j55Zdj0qRJUe0MHjwYt99+u/mc9RgxYgQmTpyI1atXo379+ujevTv69etXpC5urnXaCA1op6R0nQiIgAgEnMCar4DXrgT++DlU0VLlgXOGAsdeAWRlBbzyqp4IiEBxBOSveR8fJSZAtm7diueeew6TJ0/GvHnzsHPnzkKtKV++PI4//nh07doVl156KSpXrhxXa+fOnYtly5YVuXfUqFH46quvMH/+fBxzzDHm8969e2Ps2LG44oorcNJJJ2HOnDl46qmnzN/HjBlTyIaba51WXAPaKSldJwIiIAIBJZC7D5j9MDBrKJC7N1TJmkcCXZ8Eah4e0EqrWiIgAm4IyF9zQyvytUkXIJs3bwZXHcaNG4ddu3ahadOmRmgcfPDBqF69OnJzc8FrKBooTJYsWYJy5crhmmuuMSsVVatW9dzqHTt2oHbt2maVZeHChcbeokWL0Lx5c/Tp0wcUJ1a54YYbMHr0aCxYsADNmjVzfa2bympAu6Gla0VABEQgYAS2rgWm9gBWfFJQsdY9gLPuBUqXC1hlVR0REIF4Cchfi5dcwX1JFyAUEAyx6tWrFy655BLUq1ev2FasWbMGL774IiZMmGBCtChOvJbnn38e3bp1w8iRI9G3b19jjuJm6NChWL58ORo1apT/iBUrVhhxNHDgQAwZMsT1tW7qqgHthpauFQEREIEAEVj6LvB6b2DnplClylcDLnwMOKxDgCqpqoiACPhBQP6ad4pJFyDPPvssLrvsMmRnZ7uqPVdGXnjhBSMcvJazzz4bs2bNAsVNrVq1jLn27dubVY5169YVMc9rWrZsienTp7u+1k1dNaDd0NK1IiACIhAAAnt2Au/dAXz5eEFlGp0MXDQeqFQ3ABVUFURABPwmIH/NO9GkCxDvVfZm4ZdffsFBBx2EDh064K233so3xvCqMmXKmJyQ8MIckT179pgwLRY310ar7dq1a8F/9sJwMwose16Kt9bqbhEQAREQgYQRWP+/UKL5+u9Cj8jKAU7/P6DNDUC2901UElZvGRYBEfBEQALEE77Q12VeHjcpz5xy//33Y8CAAXj11VdNgrtVGjdubFZDmHgeXpiQvn79evz000/mIzfXRiN79913Y9CgQRE/lgDJnPGoloqACKQgAf5sfvUkMGMgsHdXqAFVGoQSzesdl4INUpVFQATcEJAAcUMr8rWBECDUQDNnzjQO/qZNmxCuibKysoxo8KMw6Z1hVlx9KFu2bL5JN6sabq6NVmetgPjRm7IhAiIgAkkmsGMT8GYf4H8FK+ho9g/gvAeAcpWSXBk9TgREoCQISIB4p17iAmTx4sXo0qWLER/RFmMoQHjuhtfy5ZdfonXr1hG31VUOiFe6ul8EREAE0pzAys+AKT2A/2/vTuBtrvb/j7/PYIyMRSKZchtwaSBSGUqDKUfdW9e/ASHDlW6k1A03dDUbQmi4t2739kNESZdShgbcDEXJFEKIZIoz/R/r++0453COs/dZ3733d+/9+j4eHurs9f2stZ5rOWd9zndYv/zgdrRoKTfxaPDHGO843UMAgZwCJCD28yHiCciVV17pPFsxfPhwNWvWTGXLls2zV3Xr1rXubd++fZ39PMzrfU0ikvMwb7kaNWpUQG/BCqZsMI1mQgejRVkEEEAgTALpadLHf5cWPS1lZriVVmkopUyVKtQKUyOoBgEE/CLAes1+JCKegJgNB82C/rHHHrPvzWkiHD9+XFWqVNHZZ5+ttWvXnlLSvAHLvOkqv31AVq5cqfr16zvnBVM2mE4xoYPRoiwCCCAQBoH930sz7pW2fZ5dmXnIvMWjUnLRMDSAKhBAwG8CrNfsRyTiCUidOnVkrkyYDf9CecyYMcO51cs8hP7QQw/lWZXZm2TSpEnOTujmasySJUucndB79uzpbJyY8wimbKD9YkIHKkU5BBBAIAwCX82QZt8vHTvgVlaqknTLRKlWyzBUThUIIOBXAdZr9iMT8QTE7DL+8ssvO7dFmdfghuro0KGD89rdbdu2OVdC8jrS0tI0evRoTZkyxdkjxGyS2L17dw0aNEjJycm5TgmmbKB9YkIHKkU5BBBAIIQCxw9LcwdJX76eXUmd66UOL0qlzgphxYRGAIFoEGC9Zj9KEU9ATBfMot/sdH7nnXeqWrVqSko69f3p5rNYP5jQsT7C9A8BBHwvsHOVNK2b9NN3blOTikrXDZca95ISEnzffBqIAAKhF2C9Zm8c8QTEXJG45ZZbZAYzv8Ort2DZc4U2AhM6tL5ERwABBPIVMHt7fDZBmv+4lH7cLVahjru3xznu838cCCCAgBFgvWY/DyKegNxwww3OHiB//vOfZd6Ild9bsFq1amXfW59HYEL7fIBoHgIIxKbAoT3SzPukDf/N7l+jO6UbnpSKnhGbfaZXCCBQaAHWa4WmO3FixBOQUqVKOcnHyJEj7XsT5RGY0FE+gDQfAQSiT2DDAuntXtLh3W7bi5WR2r8gXXxL9PWFFiOAQFgEWK/ZM0c8ATHPfJi3Upk3YcX7wYSO9xlA/xFAIGwCacelD4dLS8dmV1mtiZQyWSp7XtiaQUUIIBB9AqzX7Mcs4gnIX//6V+cWrE8++USJiYn2PYriCEzoKB48mo4AAtEj8NNGaVpXaedKt80JidLVA6WrB0lJud94GD2doqUIIBAuAdZr9tIRT0DmzZvnbERYpEgR3XvvvTrvvPPyfAtWy5ax/951JrT9hCYCAgggkK+AedB81ZvSuw9KqYfdYmdWda96VG8KHAIIIBCQAOu1gJhOWyjiCcjJVz3MG69yHpmZmeItWPYDTQQEEEAgrgV+PSDNeUD6alo2w4XtpHZjpJLl45qGziOAQHACJCDBeeVVOuIJyGuvvRZQL+66666AykVzISZ0NI8ebUcAAd8KbFsmTe8m/fy928TkEtINo6RL72ZvD98OGg1DwL8CrNfsxybiCYh9F2InAhM6dsaSniCAgA8EMtKlxc9JH42UMtPdBp19sbu3x9m/80EDaQICCESjAOs1+1EjAbE39CwCE9ozSgIhgEC8C/yyQ5rRQ9qyKFviip7uruZFise7Dv1HAAELAdZrFni/neqLBCQjI0MffPCBNm7cqH379sk895HzMM+APPbYY/a99XkEJrTPB4jmIYBAdAh88540q490dJ/b3hLlpY4vSnVvjI7200oEEPC1AOs1++GJeAKyevVq3XLLLdqyZcspiUdW93gI3X6giYAAAgjEvEDqUemDx6Rlk7O7WuMa6ZZJ0pnnxHz36SACCIRHgATE3jniCchVV12lb775RlOnTtU111yjsmXL2vcqSiMwoaN04Gg2AghEXmD3Ondvj91r3bYkJkstH5Wa9pfifI+pyA8OLUAgtgRYr9mPZ8QTkBIlSmjo0KHObujxfjCh430G0H8EEAhawNyyu/xlad4jUtqv7unlzpdSXpaqXhp0OE5AAAEEChJgvVaQUMGfRzwBMRsP/uUvf1H//v0Lbm2Ml2BCx/gA0z0EEPBW4Mg+6Z1+0jdzsuPW/4N009NS8TO9rYtoCCCAwG8CrNfsp0LEE5AnnnhCs2fP1tKlS/PcAd2+i9ETgQkdPWNFSxFAIMICWxa7b7n65Qe3IUVLSTc/IzX4Y4QbRvUIIBDrAqzX7Ec44gmIefvVo48+KvMmrF69eql69ep5JiItW7a0763PIzChfT5ANA8BBCIvkJ4mffx36ZOnJP32xsQqjaSUKVKFWpFvHy1AAIGYF2C9Zj/EEU9AEk96ONC88SrnYV7Jy1uw7AeaCAgggEDUC+z/Xppxr7Tt8+yuNOsvtXhUSi4a9d2jAwggEB0CJCD24xTxBOS1114LqBd33XVXQOWiuRATOppHj7YjgEBIBb6aLs0eIB074FZTqpJ0y0SpVuxfHQ+pK8ERQCBoAdZrQZOdckLEExD7LsROBCZ07IwlPUEAAY8Ejh+W5g6Svnw9O2Cd66WOE6QzKnpUCWEQQACBwAVYrwVulV9JEhB7Q88iMKE9oyQQAgjEgsCOldL0btJPG9zeJBWVrhsuNe4lnXS7bix0lz4ggEB0CLBesx+nsCcg3333nerUqVOoltucW6gKw3wSEzrM4FSHAAL+FMjIkD6fIP33cSkj1W1jxQuklKnSOfX92WZahQACcSPAes1+qMOegBQpUkR33HGH+vXrp8suuyygHixbtkxjxozRf/7zHx0/fjygc6KxEBM6GkeNNiOAgKcCh3ZLM++TNszPDtvoLumGUVLRMzytimAIIIBAYQRYrxVGLfc5YU9APvnkEw0aNEgmqahdu7batGmjyy+/XDVr1lS5cuWc1u3fv18bN250ysybN8/57yuuuEKjR49W8+bN7Xvt0whMaJ8ODM1CAIHwCGxYIL3dSzq8262veBmp3Rjp4o7hqZ9aEEAAgQAEWK8FgFRAkbAnIFntmT9/viZPnqx3331XR44ccV61m/Mwr98tWbKk2rZtq549e6pFixb2vfV5BCa0zweI5iGAQGgE0o5LC4ZJn47Ljn/elVKnyVLZaqGpk6gIIIBAIQVYrxUSLsdpEUtAstqQlpamFStWaN26ddqzZ4/z5bPOOksXXnihLr30UiUnJ9v3MkoiMKGjZKBoJgIIeCewd4M0vau0c5UbMyFRunqQdPVAKSl+vv97B0okBBAItQDrNXvhiCcg9l2InQhM6NgZS3qCAAIFCGRmSiv/Jb03UEo97BY+s6qUMlmq3hQ+BBBAwLcCrNfsh4YExN7QswhMaM8oCYQAAn4W+PWANGeAZDYXzDoubC+1HyOVcJ8F5EAAAQT8KsB6zX5kSEDsDT2LwIT2jJJACCDgV4Fty9xbrn7e6rYwuYR045OSedMVe3v4ddRoFwII5BBgvWY/HUhA7A09i8CE9oySQAgg4DeBjHRp8XPSRyOlzHS3dZUukTq/LJ1V12+tpT0IIIBAvgKs1+wnBwmIvaFnEZjQnlESCAEE/CTwyw5pRg9py6LsVpndzFsPk4oU91NLaQsCCCBQoADrtQKJCixAAlIgUfgKMKHDZ01NCCAQJoFv3pVm9ZGO7ncrLFlB6vCiVPeGMDWAahBAAAFvBViv2Xv6NgExe4OYfUDi6WBCx9No01cEYlwg9aj0waPSsinZHa1xjXTLJOnMc2K883QPAQRiWYD1mv3o+jYBMbujv/fee86eIPFyMKHjZaTpJwIxLvDjWml6N2n3WrejiclSy0elpv2lxMQY7zzdQwCBWBdgvWY/wr5NQEaNGqWpU6fq/fffV+3atZ2ezpgxQ48//rjWrFlj33MfRmBC+3BQaBICCAQuYPb2WD5VmjdESvvVPa/c+VLKy1LVSwOPQ0kEEEDAxwKs1+wHx7cJiOnam2++qYEDB+rRRx91kpG9e/dqyJAh6t69u33PfRiBCe3DQaFJCCAQmMCRfdI7/aRv5mSXr/8H6aanpeJnBhaDUggggEAUCLBesx8kXycgn332me69916tXbtW3bp104svvqjk5GT7Xvs0AhPapwNDsxBA4PQCmxe5b7k6uMMtV7SUdPOzUoM/IIcAAgjEnADrNfsh9W0CcuONN2rZsmXOFZAGDRo4CciYMWOUkpJi32ufRmBC+3RgaBYCCOQtkJ4mffyk9MnTkjLdMlUaSSlTpAq1UEMAAQRiUoD1mv2w+jYBMbddmeSjTJkyTi/NVZC2bduqX79+GjBggH3PfRiBCe3DQaFJCCCQt8D+76Xp3aXtX2R/3ux+qcUQKbkoaggggEDMCrBesx9a3yYgeXVtx44duvnmm/Xll1/a99yHEZjQPhwUmoQAAqcKrJkmzRkgHfvF/axUJff1urVaoIUAAgjEvADrNfshjqoExHT34MGDKl26tH3PfRiBCe3DQaFJCCCQLXDskDT3IWnl69lfq9NG6viidEZFpBBAAIG4EGC9Zj/Mvk1A9uzZo2effVb79+/XBRdcoIYNGzp/ypYta99rn0ZgQvt0YGgWAghIO1a6e3v8tMHVSCoqXfc3qXFPKSEBIQQQQCBuBFiv2Q+1bxOQ66+/XgkJCTIPow8ePFi1atXSN998o+rVq2vTpk32PfdhBCa0DweFJiEQ7wIZGdJnL0rzh0oZqa5GxQukzi9LlevFuw79RwCBOBRgvWY/6L5NQMxtVuaZD/N3+fLltW/fPucB9PPOO895OD0WDyZ0LI4qfUIgigUO7ZZm3idtmJ/diUvvltqMkoqWjOKO0XQEEECg8AKs1wpvl3WmbxOQqlWravPmzSpSpIgqV67sJCMHDhzQddddp+XLlwfd8507d2r48OF699139eOPP6pChQq64oorNGnSJFWqVElbtmxRjRo18oxrXgE8ZcqUXJ+lp6frqaeecr6+bds2VatWzdkg0SRHSUlJQbfPnMCELhQbJyGAQCgETNLxdi/p8B43evEyUrsx0sUdQ1EbMRFAAIGoEWC9Zj9Uvk1Abr31VmcTQnMr1tVXX61nnnlGv/vd73TOOefo0KFDQfX8u+++c2KUKFFCd999t0xyY54x+fTTT50kok6dOicSkA4dOqhz58654teuXVtNmjTJ9bXevXtrwoQJuueee9S0aVMtXbpUr7zyiszXx48fH1T7sgozoQvFxkkIIOClQNpxacEw6dNx2VHPu1LqNFkqW83LmoiFAAIIRKUA6zX7YfNtAmJuuTJXPMxVienTp+vBBx90EofMzEwtXrw44J6b8o0bN5a5YvHxxx+rVKlSeZ6bdQVkyJAheuKJJ04bf82aNc7miOaWsBdeeOFE2f79+2vs2LFatWqV6tUL/t5oJnTAw0pBBBAIhcDeDdL0rtLOVW70hETpmoek5g9KScmhqJGYCCCAQNQJsF6zHzLfJiAnd80kD2b/jzvuuENnn312wD3/8MMP1apVK82ePdvZyPDXX39VYmKiihbNvVFWzgTEJCHmMFdM8jrM5yNHjnQehs9525a5ZaxmzZp65JFHNGLEiIDbmFWQCR00GScggIAXApmZ0sp/Se8NlFIPuxHPrOruaF79Si9qIAYCCCAQMwKs1+yH0rcJyLhx42QW+hkZGbr44ov1+9//3rnqYP6+8srAfyAOGjTIuc1q4cKFTmJgbpUyb9cyV0XMa36zYmUlIOYKSdYtXubWq/vvv199+vTJJd2mTRvnKseuXbtOGQHzPIl5XfD7778f9OgwoYMm4wQEELAV+PWAu6ngV9OzI13UQWr3glSinG10zkcAAQRiToD1mv2Q+jYBKVeunF599VXnVqa1a9dq5cqVzp/Vq1dr/fr1Afe8Y8eOmjVrlipWrKjmzZvr9ttvdx5oNw+kHzlyRF988YVTx9atW9W1a1eZ8uZVv6aMecDcPPBubv8ySUzWYcqbKygrVqw4pR2NGjVSamqqzG1apzvMQ/HmT85j3bp16tKlixPXxOFAAAEEQiqw7Qt3b4+ft7rVJJeQbvy71OhO9vYIKTzBEUAgmgVIQOxHz7cJSN26dfXVV185b8GyOVq3bq0FCxY4t2HNn5/9KslFixY5D6abh93feuutPKswz420bNnSeebEJD1mLxJzmL/NlQ5zNeXkwzyQvnv3bm3Y8NtmXfk0fujQoRo2bFien5KA2Iw45yKAQIECGenS4melj0ZJmelu8Ur1pM5TpbPqFng6BRBAAIF4FiABsR993yYgb7zxhrOIf/zxx6162a5dO82ZM8e5mnLXXXflinX++efr6NGjzmt58zvMsyPt27d3Xtfbo0cPpxhXQKyGhJMRQCCSAgd+kN7uKW1ZlN2Kxr2k1sOkIsUj2TLqRgABBKJCgATEfph8m4CYV+Sa26XOPfdc3Xzzzc6zH+ZPlSpVgup1r169nORh7ty5uuGGG3Kda16taybR8ePH841pbvkyz56Yh8rNMyTm4BmQoIaAwggg4BeBb96VZvWRju53W1SygtRxgnRBG7+0kHYggAACvhcgAbEfIt8mIObtUmbTQfMAunmewjz/8fXXX+vMM8887RWLk0mmTp3qbBA4efJk5++ch3mtrzm2b9+er+SMGTOUkpLiPA9iNiQ0h0lERo0axVuw7OcfERBAIBwCqUelDx6VluXYULXmtdItk6TSlcPRAupAAAEEYkaABMR+KH2bgJiHxs1mgeaNVVmHeSbjm2++cZKSQI+9e/c6D5Wb26aWLFlyYpdyc1uWuT0ra5dzs+9I+fLlc4U1t2c1a9bMeRZl48aNzm7n5jBvwDJvuspvHxCTLNWvXz/QJp4ox4QOmowTEECgIIEf17oPmu9e65ZMTJZaPiY1/bOUmFjQ2XyOAAIIIHCSAOs1+ynh2wRkwIABuummm5yrILaH2SzQvE7XvAXrtttu0w8//KAxY8bojDPOcN44ZRKLTp06OW/FMrdlmSsj5i1Yr732mnOVw1ztGDx4cK5mZN3aZXZCN0mKSW7MTug9e/bUxIkTC9VkJnSh2DgJAQTyEjB7eyyfKs0bIqX96pYoV0NKmSpVvRQzBBBAAIFCCrBeKyRcjtN8m4CYt1aZjQcHDhzoPANirnokJSUVusevv/66s++HeaVvyZIlncTGJBZm40BzmFu1TMLx7bffylwNMfuBmFfhmt3NzUPoJx9paWkaPXq0c2uWuYXLJC3mFi+z70hycuF2DGZCF3p4OREBBHIKHNknvdNP+mZO9lfr/1G6+WmpWGmsEEAAAQQsBFivWeD9dqpvExCTMGTt/WFueTKbA15yySXOrU8vvfSSfc99GIEJ7cNBoUkIRJvA5kXSjB7SwR1uy4uWkm5+Vmrwh2jrCe1FAAEEfCnAes1+WHybgJzcNXOVwSQkJhkxO6TH4sGEjsVRpU8IhEkgPVVa+KS06BlJmW6lVRq5e3uUd6/0ciCAAAII2AuwXrM3jJoExL6r/o/AhPb/GNFCBHwpsH+LNL27tH3Zb81LkJr1l1oMkZKL+rLJNAoBBBCIVgHWa/YjRwJib+hZBCa0Z5QEQiB+BNZMk+YMkI794va5VGWp0yTJvGaXAwEEEEDAcwHWa/akvk4gfTl4AAAgAElEQVRADh486Oz9YV6De/IeHvZd918EJrT/xoQWIeBbgWOHpLmDpJVvZDexThup44vSGRV922wahgACCES7AOs1+xH0RQJiXn9rEo2cf0zSYV6Xm3WYPUBi/WBCx/oI0z8EPBLYsVKa1lXat9ENmFRMuv5v0hU9pBx7J3lUG2EQQAABBHIIsF6znw5hT0COHz+umTNnygxeVsKxdetWZWZmOn/MUbp0aV100UXO5oHmzVfm7xYtWtj31ucRmNA+HyCah0CkBTIypM9elOYPlTJS3dZUvEDq/LJUuV6kW0f9CCCAQFwIsF6zH+awJyCdO3fW22+/7SQbxYsXV9GiRfXLL7+ocuXKev7559W4cWNn5/J4PJjQ8Tjq9BmBAAUO7Zbe7iVtXJB9wqV3S21GSUVLBhiEYggggAACtgKs12wFpbAnIGb38ZYtW+qZZ55RnTp1nOTD7DI+efJknXfeeRo3bpyzA3o8HkzoeBx1+oxAAAIb5rvJx+E9buHiZaT2Y6WLOgRwMkUQQAABBLwUYL1mrxn2BMRc3TA7kN9xxx25Wr98+XLdd999zq1ZKSkpztWQKlWq2PcwiiIwoaNosGgqAuEQSDsmLRgufTouu7bzmkqdXpLKVgtHC6gDAQQQQOAkAdZr9lMi7AnI6Zpsbst68cUX9dhjj8k8dP7EE0+ob9++SoiThyqZ0PYTmggIxIzA3g3S9K7SzlVulxISpWsekpo/KCUlx0w36QgCCCAQbQKs1+xHzFcJSFZ39uzZowceeEBvvPGGLr30Uk2aNEmNGjWy763PIzChfT5ANA+BcAiYl3Gs/Jf03kAp9bBbY5lqUqfJUvUrw9EC6kAAAQQQOI0A6zX76eHLBCSrWx9//LF69+6t9evXKzX1tze+2PfZtxGY0L4dGhqGQHgEfj3gbir41fTs+sxzHu1ekEqUC08bJDUf/aFT16JBLcNWJxUhgAAC0SLAes1+pHydgJjupaWl6bnnntPAgQPte+vzCExonw8QzUMglALbvpCmd5N+3urWklxCuvHvUqM7w763BwlIKAea2AggEO0CrNfsR9D3CYh9F6MnAhM6esaKliLgmUBGurT4WemjUVLmbxuuVqondZ4qnVXXs2qCCUQCEowWZRFAIN4EWK/ZjzgJiL2hZxGY0J5REgiB6BA48IP0dk9py6Ls9ja+T2o9VCpSPGJ9IAGJGD0VI4BAFAiwXrMfJBIQe0PPIjChPaMkEAL+F1g3R3qnr3R0v9vWkhWkjhOkC9qEve1ZCUdWxdv2HXX+s1r5ErnawjMhYR8aKkQAAR8KsF6zHxQSEHtDzyIwoT2jJBAC/hVIPSrNGyItn5rdxprXSrdMkkpXjki7SUAiwk6lCCAQpQKs1+wHjgTE3tCzCExozygJhIA/BX5cK03rKu1Z57YvMVlq+ZjU9M9SYqJv2swtWL4ZChqCAAI+FGC9Zj8oJCD2hp5FYEJ7RkkgBPwlYPb2WDZF+uBRKe1Xt23largPmp97qb/aymt4fTceNAgBBPwlwHrNfjxIQOwNPYvAhPaMkkAI+EfgyD5pVl/p23ez21T/j9LNT0vFSvunnTlawhUQXw4LjUIAAZ8IsF6zHwgSEHtDzyIwoT2jJBAC/hDYvEia0UM6uMNtT9HSUttnpfq3+aN9+bSCBMTXw0PjEEAgwgKs1+wHgATE3tCzCExozygJhEBkBdJTpYVPSouekZTptsXcapUyRSpfM7Jto3YEEEAAASsB1mtWfM7JJCD2hp5FYEJ7RkkgBCInsH+LNL27tH3Zb21IkK66X2oxREoqErl2UTMCCCCAgCcCrNfsGUlA7A09i8CE9oySQAhERmDNNGnOAOnYL279pSpLnSZJ5jW7HAgggAACMSHAes1+GElA7A09i8CE9oySQAiEV+DYIWnuIGnlG9n1XnCD1GG8dEbF8LaF2hBAAAEEQirAes2elwTE3tCzCExozygJhED4BHasdPf22LfRrTOpmHT9E9IV90oJCeFrBzUhgAACCIRFgPWaPTMJiL2hZxGY0J5REgiB0AtkZEifjZfmD5MyUt36KtaVOr8sVb4k9PVTAwIIIIBARARYr9mzk4DYG3oWgQntGSWBEAitwKHd0tu9pI0Lsuu59G6pzSipaMnQ1k10BBBAAIGICrBes+cnAbE39CwCE9ozSgIhEDqB7+ZLM3tJh/e4dRQvI7UfK13UIXR1EhkBBBBAwDcCrNfsh4IExN7QswhMaM8oCYSA9wJpx6QFw6VPx2XHPq+p1OklqWw17+sjIgIIIICALwVYr9kPCwmIvaFnEZjQnlESCAFvBfZukKbdI+1a7cZNSJSuGSxd/aCUmORtXURDAAEEEPC1AOs1++EhAbE39CwCE9ozSgIh4I1AZqb7at33Bkmph92YZapJnSZL1a/0pg6iIIAAAghElQDrNfvhIgGxN/QsAhPaM0oCIWAv8OsBd1PBr6Znx7qoo9TuealEOfv4REAAAQQQiEoB1mv2w0YCYm/oWQQmtGeUBELATmDbF9L0btLPW904RUpKNzwpNbqTvT3sZDkbAQQQiHoB1mv2Q0gCYm/oWQQmtGeUBEKgcAIZ6dLiZ6WPRkmZ6W6MSvXcvT3OuqBwMTkLAQQQQCCmBFiv2Q8nCYi9oWcRmNCeURIIgeAFDvwgvd1T2rIo+9zG90mth0pFigcfjzMQQAABBGJSgPWa/bCSgNgbehaBCe0ZJYEQCE5g3Rzpnb7S0f3ueSUrSB0nSBe0CS4OpRFAAAEEYl6A9Zr9EJOA2Bt6FoEJ7RklgRAITCD1qDRviLR8anb5mtdKt0ySSlcOLAalEEAAAQTiSoD1mv1wk4DYG3oWgQntGSWBEChY4Me10rSu0p51btnEZKnVX6Ur+0mJiQWfTwkEEEAAgbgUYL1mP+wkIPaGnkVgQntGSSAE8hcwe3ssmyJ98KiU9qtbrlwNqfNU6dxLkUMAAQQQQOC0AqzX7CcICYi9oWcRmNCeURIIgbwFjuyTZvWVvn03+/MGt0s3PSUVK40aAggggAACBQqwXiuQqMACJCAFEoWvABM6fNbUFIcCmxdJM3pIB3e4nS9aWmr7rFT/tjjEoMsIIIAAAoUVYL1WWLns80hA7A09i8CE9oySQAhkC6SnSgtHSYuelZTpft3capUyRSpfEykEEEAAAQSCEmC9FhRXnoVJQOwNPYvAhPaMkkAIuAL7t0jTu0vbl/0mkiBddb/UYoiUVAQlBBBAAAEEghZgvRY02SknkIDYG3oWgQntGSWBEJDWTJPmDJCO/eJqlKosdZokmdfsciCAAAIIIFBIAdZrhYTLcVrcJCA7d+7U8OHD9e677+rHH39UhQoVdMUVV2jSpEmqVKmSQ5Kenq6nnnpKU6ZM0bZt21StWjV1795dAwcOVFJSUi7tYMoGOkxM6EClKIfAaQSOHZLmDpJWvpFd6IIbpA4vSmdUgA4BBBBAAAErAdZrVnzOyXGRgHz33Xe6+uqrVaJECd19992qWrWq9uzZo08//dRJOOrUqeNg9O7dWxMmTNA999yjpk2baunSpXrllVecr48fPz6XdjBlAx0mJnSgUpRDIB+BHSvdvT32bXQLJBWTrn9CuuJeKSEBNgQQQAABBKwFWK9ZE8Z+ApKZmanGjRs7Vzc+/vhjlSpVKk+1NWvWqEGDBurXr59eeOGFE2X69++vsWPHatWqVapXr57z9WDKBjNETOhgtCiLQA6BjAzps/HS/GFSRqr7QcW6UueXpcqXQIUAAggggIBnAqzX7Clj/grIhx9+qFatWmn27Nlq27atfv31VyUmJqpo0aK59IYMGaKRI0dq06ZNqlGjxonPNm/erJo1a+qRRx7RiBEjnK8HUzaYIWJCB6NFWQR+Ezi0W3q7l7RxQTbJpXdLbUZJRUvChAACCCCAgKcCrNfsOWM+ARk0aJBzm9XChQudJMLcVpWQkOBcFXn22Wd15ZVXOopt2rRxrnLs2rXrFFXzjEjDhg31/vvvB102mCFiQgejRVkEJH03X5rZSzq8x+UoXlZqP1a6qD08CCCAAAIIhESA9Zo9a8wnIB07dtSsWbNUsWJFNW/eXLfffrt27NjhPJB+5MgRffHFF86tVeaPuSqyYsWKU1QbNWqk1NRU59YrcwRTNr8hMg/Fmz85j3Xr1qlLly5OG0ydHAggkI9A2jFpwXDp03HZBc5rKqVMlspUhQ0BBBBAAIGQCZCA2NPGfALSunVrLViwwLkNa/78+SfEFi1a5DyYfuutt+qtt95SrVq1nLdhmSskJx/mgfTdu3drw4YNzkfBlM1viIYOHaphw4bl+TEJiP3EJkIMC+z9zn3QfNdqt5MJidI1g6WrH5QSc7+tLoYV6BoCCCCAQIQESEDs4WM+AWnXrp3mzJmjV199VXfddVcusfPPP19Hjx51XssbzFWNYMrmN0RcAbGfvESIM4HMTPfVuu8NlFKPuJ0vU83d0fy8JnGGQXcRQAABBCIlQAJiLx/zCUivXr2cvT7mzp2rG264IZdYkyZNZCbR8ePHeQbEfi4RAYHQCRz92d1U8OsZ2XVc1FFq97xUolzo6iUyAggggAACJwmQgNhPiZhPQKZOnepsJjh58mTn75yH2Q/EHNu3b3ceUB81alRAb8EKpmwwQ8SEDkaLsnEjsPVzaXp36cBWt8tFSko3/l1q+P/Y2yNuJgEdRQABBPwjwHrNfixiPgHZu3evqlev7txitWTJkhM7mpvbssztWd26dXN2PjdvwDJvuspvH5CVK1eqfv36jngwZYMZIiZ0MFqUjXmBjHRp0bPSwlFSZrrb3cr1pJSXpbMuiPnu00EEEEAAAX8KsF6zH5eYT0AMkdlY8P7773fegnXbbbfphx9+0JgxY3TGGWc4b5yqVq2aI5l1u5bZCb1Zs2ZOwmJ2Qu/Zs6cmTpyYSzuYsoEOExM6UCnKxbzAgR+kGT2k7xdnd7VJb6n1UCm5WMx3nw4igAACCPhXgPWa/djERQJimF5//XVn34+1a9eqZMmSuu6665xbrswmg1lHWlqaRo8e7VwRMbdlmVu0zG1bZi+R5OTkXNrBlA10mJjQgUpRLqYF1s2R3ukrHd3vdrNkRanjBOmC62O623QOAQQQQCA6BFiv2Y9T3CQg9lShj8CEDr0xNfhYIPWoNG+ItHxqdiNrtpBumSiVruzjhtM0BBBAAIF4EmC9Zj/aJCD2hp5FYEJ7RkmgaBP4ca27t8eedW7LE5OlVo9LV/aVEhOjrTe0FwEEEEAghgVYr9kPLgmIvaFnEZjQnlESKFoEzN4ey6a4Vz7Sj7mtLl9TSpkqndsoWnpBOxFAAAEE4kiA9Zr9YJOA2Bt6FoEJ7RklgaJB4Mg+aVYf6dv3slvb4HbppqekYqWjoQe0EQEEEEAgDgVYr9kPOgmIvaFnEZjQnlESyO8Cmz9x33J1cKfb0qKlpbbPSfVv9XvLaR8CCCCAQJwLsF6znwAkIPaGnkVgQntGSSC/CqSnSguflBY9IynTbeW5l7q3XJWv4ddW0y4EEEAAAQROCLBes58MJCD2hp5FYEJ7RkkgPwrs3+LuaL592W+tS5CuGiC1eERKKuLHFtMmBBBAAAEEThFgvWY/KUhA7A09i8CE9oySQH4TWDNNmjNAOvaL27JSlaVOL0k1r/FbS2kPAggggAACpxVgvWY/QUhA7A09i8CE9oySQH4ROHZImjtIWvlGdosuuFHqMF46o4JfWkk7EEAAAQQQCFiA9VrAVPkWJAGxN/QsAhPaM0oC+UFgx5fStG7Svo1ua5KKSdc/IV1xr5SQ4IcW0gYEEEAAAQSCFmC9FjTZKSeQgNgbehaBCe0ZJYEiKZCRIX02Xpo/TMpIdVty1u/cB80rXxLJllE3AggggAAC1gKs16wJRQJib+hZBCa0Z5QEipTAwR+lmb2kjR9mt+DSe6Q2I6WiJZ2vNR/tfrZoUMtItZJ6EUAAAQQQKLQA67VC0504kQTE3tCzCExozygJFAmB7/4rzbxPOrzHrb14Wan9WOmi9rlaQwISicGhTgQQQAABrwRYr9lLkoDYG3oWgQntGSWBwimQdsy93crcdpV1VG/mvuWqTNVTWkICEs7BoS4EEEAAAa8FWK/Zi5KA2Bt6FoEJ7RklgcIlsPc7aVpXaddqt8aEROnah6Xmf5ESk/JsBQlIuAaHehBAAAEEQiHAes1elQTE3tCzCExozygJFGqBzEzpy9fdV+ymHnFrK1NNSpkindckV+1ZCUfWF7ftO+r8Z7XyJXKV45mQUA8a8RFAAAEEvBBgvWavSAJib+hZBCa0Z5QECqXA0Z/dTQW/npFdy8W3SG2fl0qUPaVmEpBQDgaxEUAAAQTCLcB6zV6cBMTe0LMITGjPKAkUKoGtn0vTu0sHtro1FCkp3Thaatgl4L09uAUrVINDXAQQQACBcAiwXrNXJgGxN/QsAhPaM0oCeS2QkS4telZaOErKTHejV64npbwsnXVBULWRgATFRWEEEEAAAZ8JsF6zHxASEHtDzyIwoT2jJJCXAgd+kGb0kL5fnB21SW+p9VApuVjQNZGABE3GCQgggAACPhJgvWY/GCQg9oaeRWBCe0ZJIK8E1s2WZvWVfv3ZjViyotRxgnTB9YWugQSk0HSciAACCCDgAwHWa/aDQAJib+hZBCa0Z5QEshVIPSrNe0Ra/nJ2pJotpFsmSaUr2UbnfAQQQAABBKJWgPWa/dCRgNgbehaBCe0ZJYFsBH78WprWTdqzzo2SWERq9Vfpyr5SYqJNZM5FAAEEEEAg6gVYr9kPIQmIvaFnEZjQnlESqDACZm+PZVOkeUOk9GNuhPI1pZSp0rmNChORcxBAAAEEEIg5AdZr9kNKAmJv6FkEJrRnlAQKVuDwT9I7faVv38s+s8Ed0k2jpWKlg41GeQQQQAABBGJWgPWa/dCSgNgbehaBCe0ZJYGCEdj8ifuWq4M73bOKlpbaPifVvzWYKJRFAAEEEEAgLgRYr9kPMwmIvaFnEZjQnlESKBCB9FR3Xw+zv4cy3TPOvUxKmSKVrxFIBMoggAACCCAQdwKs1+yHnATE3tCzCExozygJVJDA/i3ug+Y/LP+tZIJ01QCpxSNSUpGCzuZzBBBAAAEE4laA9Zr90JOA2Bt6FoEJ7RklgU4nsGaaNGeAdOwXt1Tpc9zX69a8BjcEEEAAAQQQKECA9Zr9FCEBsTf0LAIT2jNKAuUlcOyg9N4gadW/sj+94Eapw3jpjAqYIYAAAggggEAAAqzXAkAqoAgJiL2hZxGY0J5REuhkgR1fStO6Svs2uZ8kFZPajJAu7y4lJOCFAAIIIIAAAgEKsF4LEOo0xUhA7A09i8CE9oySQFkCGRnSp+OkBcOljFT3q2f9zt3bo/IlOCGAAAIIIIBAkAKs14IEy6M4CYi9oWcRmNCeURLICBz8UZrZS9r4YbbHZV2l60dIRUtihAACCCCAAAKFEGC9Vgi0k04hAbE39CwCE9ozSgJ9919p5n3S4T2uRfGyUodx0oXtsEEAAQQQQAABCwHWaxZ4v51KAmJv6FkEJrRnlPEbKO2YNH+Y9Nn4bIPqzaROL0llqsavCz1HAAEEEEDAIwHWa/aQJCD2hp5FYEJ7RhmfgfZ+5z5ovmu12/+EJOnah6XmD0iJSfFpQq8RQAABBBDwWID1mj0oCYi9oWcRmNCeUcZXoMxM6ct/SnMfklKPuH0vc567o/l5jePLgt4igAACCCAQYgHWa/bAJCD2hp5FYEJ7Rhk/gY7+LM25X/r67ew+X3yL1PZ5qUTZ+HGgpwgggAACCIRJgPWaPTQJiL2hZxGY0J5RxkegrZ9L07tLB7a6/S1SUrpxtNSwC3t7xMcMoJcIIIAAAhEQYL1mj04CYm/oWQQmtGeUsR0oI11a9Iy08EkpM93ta+V6UudXpIp1Yrvv9A4BBBBAAIEIC7Besx8AEhB7Q88iMKE9o4zdQAe2SzN6St8vzu5jkz5S68el5GKx2296hgACCCCAgE8EWK/ZDwQJiL2hZxGY0J5RxmagdbOlWX2lX392+1eyonTLRKnOdbHZX3qFAAIIIICADwVYr9kPCgmIvaFnEZjQnlHGVqDjR6QPhkjLX87uV62WUseJUulKsdVXeoMAAggggIDPBViv2Q8QCYi9oWcRmNCeUcZOoB+/dvf22PON26fEIlKrv0pX9pUSE2Onn/QEAQQQQACBKBFgvWY/UCQg9oaeRWBCe0YZ/YHM3h7Lpkjzhkjpx9z+lK8pdX5ZqtIw+vtHDxBAAAEEEIhSAdZr9gMX8wnIli1bVKNGjTylunXrpilTpjifBVouK1B6erqeeuop5/xt27apWrVq6t69uwYOHKikpMLtOs2Etp/QMRHh8E/SO32lb9/L7k6DO6SbRkvFSsdEF+kEAggggAAC0SrAes1+5OImAenQoYM6d+6cS6x27dpq0qRJrgSkoHJZAXr37q0JEybonnvuUdOmTbV06VK98sorMl8fP358oUaGCV0ottg6adPH0ts9pYM73X4VLS21fU6qf2ts9ZPeIIAAAgggEKUCrNfsBy5uEpAhQ4boiSeeyFcs6wpIQeVMgDVr1qhBgwbq16+fXnjhhRMx+/fvr7Fjx2rVqlWqV69e0KPDhA6aLHZOSE+VPhopLX5OUqbbr3Mvk1KmSOXzvoIXO52nJwgggAACCESPAOs1+7GKqwTEJBfmKFGixClyOROQ05UzJ5rPR44cqU2bNuW6vWvz5s2qWbOmHnnkEY0YMSLo0WFCB00WGyfs2+zuaP7D8t/6kyA1f0C69mEpqUhs9JFeIIAAAgggECMCrNfsBzJuEpBSpUrp0KFDjpi59er+++9Xnz59TghmJSAFlTMntGnTxrnKsWvXrlNGoFKlSmrYsKHef//9oEeHCR00WfSfsPr/pDkDpOMH3b6UPkfq9JJU4+ro7xs9QAABBBBAIAYFWK/ZD2rMJyBbt25V165d1bFjR1WvXl07duxwHhxfvny5HnzwQedBcnMEWs6UNbdXFS1aVCtWrDhlBBo1aqTU1FTnNq3THTt37pT5k/NYt26dunTp4sQ1cThiWODYQem9QdKqf2V3su5NUvtx0hkVYrjjdA0BBBBAAIHoFiABsR+/mE9A8iIyb7Bq2bKlFi9erPXr16tWrVp5SuZXzpQ3VzrMg+cnH+aB9N27d2vDhg2nHZ2hQ4dq2LBheZYhAbGf2L6O8MP/pOndpH2b3GYmFZPajJAu7y4lJPi66TQOAQQQQACBeBcgAbGfAXGZgBi22bNnq3379po0aZJ69OiRr2Re5bgCYj/x4jJCRob06Vhpwd+kjFSX4KwLpc5TpUoXxyUJnUYAAQQQQCDaBEhA7EcsbhOQ1atXO2+yMg+Lm4fG8zvyKsczIPYTL+4iHPzRfb3upo+yu35ZV+n6EVLRknHHQYcRQAABBBCIVgESEPuRi9sEZMaMGUpJSXGeBzEbEuZ35FXOJCyjRo3iLVj28y8+Iqz/QJp5n3Rkr9vf4mWlDuOkC9vFR//pJQIIIIAAAjEkQAJiP5gxn4Ds27dP5cuXzyV19OhRNWvWTF999ZU2btzo7GIeaDkTyLwBy7zpKr99QFauXKn69esHPTpM6KDJ/H1C2jFp/lDpsxez21n9KvctV2XO9XfbaR0CCCCAAAII5CnAes1+YsR8AtKpUycdOXLE2fG8atWqzluwXnvtNefqhbmKMXjwYEcx0HJZ5L169XKeHzE7oZtkZsmSJc5O6D179tTEiRMLNTJM6EKx+fOkPeul6V2lXb+9DS0hyd3Xw+zvkZjkzzbTKgQQQAABBBAoUID1WoFEBRaI+QRk6tSpTsLx7bffOlc5zD4f5hW3Ztdy8xB61hFouazyaWlpGj16tHML1/bt253kpnv37ho0aJCSk5MLhM+rABO6UGz+OikzU/ryn9Lch6TUI27bypzn7mh+XmN/tZXWIIAAAggggEDQAqzXgiY75YSYT0DsicIXgQkdPuuQ1HT0Z2nO/dLXb2eHv7iT1PY5qUTZkFRJUAQQQAABBBAIrwDrNXtvEhB7Q88iMKE9owx/oK2fS9O7Swe2unUXKSndOFpq2IW9PcI/GtSIAAIIIIBAyARYr9nTkoDYG3oWgQntGWX4AmWkS4uekRY+KWWmu/VWri91flmqWCd87aAmBBBAAAEEEAiLAOs1e2YSEHtDzyIwoT2jDE+gA9ulGT2k75dk19ekj9T6cSm5WHjaQC0IIIAAAgggEFYB1mv23CQg9oaeRWBCe0YZ+kBr35He6Sf9+rNb1xlnSR0nSnVah75uakAAAQQQQACBiAmwXrOnJwGxN/QsAhPaM8rQBTp+RJr3iLTilew6arWSbpkolTo7dPUSGQEEEEAAAQR8IcB6zX4YSEDsDT2LwIT2jDI0gXZ9JU3vJu35xo2fWMS93crcdpWYGJo6iYoAAggggAACvhJgvWY/HCQg9oaeRWBCe0bpbSCzt8cXk6UPHpXSj7mxy9eSOk+VqjT0ti6iIYAAAggggICvBViv2Q8PCYi9oWcRmNCeUXoX6PBP0qw+0vq52TF//yf3FbvFSnlXD5EQQAABBBBAICoEWK/ZDxMJiL2hZxGY0J5RehNo08fS2z2lgzvdeMXOdDcVrNfZm/hEQQABBBBAAIGoE2C9Zj9kJCD2hp5FYEJ7RmkXKD1V+miEtPh5SZlurKqXSylTpHLn28XmbAQQQAABBBCIagHWa/bDRwJib+hZBCa0Z5SFD7Rvs/ug+Q8rfouRIDX/i3TtYCmpSOHjciYCCCCAAAIIxIQA6zX7YSQBsTf0LAIT2jPKwgVa/ZY05wHp+EH3/NLnSJ1ekmpcXbh4nIUAAggggAACMSfAes1+SElA7A09i8CE9owyuEDHDkrvDZRWvZl9Xt2bpQ7jpJLlg4tFaQQQQAABBBCIaQHWa/bDSwJib+hZBCa0Z0FbCHUAACAASURBVJSBB/rhf+4tV/s2ueckFZPajJAu7y4lJAQeh5IIIIAAAgggEBcCrNfsh5kExN7QswhMaM8oCw6UkSF9OlZaMFzKSHPLn3Whu7dHpYsLPp8SCCCAAAIIIBCXAqzX7IedBMTe0LMITGjPKE8f6OAu6e1e0qaPsstd1s298lGkRJgaQTUIIIAAAgggEI0CrNfsR40ExN7QswhMaM8o8w+0/gNp5n3Skb1umRLlpPbjpAvbhqFyqkAAAQQQQACBaBdgvWY/giQg9oaeRWBCe0Z5aqC0Y9L8odJnL2Z/Vv0q9y1XZc4NYcWERgABBBBAAIFYEmC9Zj+aJCD2hp5FYEJ7Rpk70J710vSu0q417tcTkqRrH5aaPyAlJoWoUsIigAACCCCAQCwKsF6zH1USEHtDzyIwoT2jdANlZkpf/lOa+5CUesT9WtnzpJSpUrUrPK6McAgggAACCCAQDwKs1+xHmQTE3tCzCExozyiloz9Ls/tLa2dmB724k9Tueal4GQ8rIhQCCCCAAAIIxJMA6zX70SYBsTf0LAIT2iPKrZ9J07tLB7a5AYucId00Wvr9n9jbwyNiwiCAAAIIIBCvAqzX7EeeBMTe0LMITGhLyox06ZOnpY+flDIz3GCV60udX5Eq1rYMzukIIIAAAggggIDEes1+FpCA2Bt6FoEJbUF5YLs0o4f0/ZLsIFf2lVr9VUouZhGYUxFAAAEEEEAAgWwB1mv2s4EExN7QswhM6EJSrn1Heqef9OvPboAzzpI6TpTqtC5kQE5DAAEEEEAAAQTyFmC9Zj8zSEDsDT2LwIQOkvL4EWneI9KKV7JPrNVKumWiVOrsIINRHAEEEEAAAQQQKFiA9VrBRgWVIAEpSCiMnzOhg8De9ZU0vZu05xv3pMQiUuuhUpPeUmJiEIEoigACCCCAAAIIBC7Aei1wq/xKkoDYG3oWgQkdAKXZ2+OLl6QPHpPSj7knlK8ldX5ZqvL7AAJQBAEEEEAAAQQQKLwA67XC22WdSQJib+hZBCZ0AZSHf5Jm9ZHWz80u+Psu0o1/l4qV8mwcCIQAAggggAACCOQnwHrNfm6QgNgbehaBCX0ayk0LpRk9pUO73ELFzpTaPifV6+yZP4EQQAABBBBAAIGCBFivFSRU8OckIAUbha0EEzoP6vRU6aMR0uLnJWW6BapeLqVMkcqdH7axoSIEEEAAAQQQQMAIsF6znwckIPaGnkVgQp9EuW+Tu6P5Dyt++yBBav4X6drBUlIRz9wJhAACCCCAAAIIBCrAei1QqfzLkYDYG3oWgQmdg3L1W9KcB6TjB90vlq4idZok1bjaM28CIYAAAggggAACwQqwXgtW7NTyJCD2hp5FYEJLOnZQem+gtOrNbNe6N0sdxkkly3tmTSAEEEAAAQQQQKAwAqzXCqOW+xwSEHtDzyLE/YQ2t1qZW67MrVfmSC4uXf+EdHl3KSHBM2cCIYAAAggggAAChRWI+/VaYeFynEcC4gGiVyHidkJnZEifjpUWDJcy0lzOsy509/aodJFXvMRBAAEEEEAAAQSsBeJ2vWYtlx2ABMRDTNtQcTmhD+6S3u4lbfoom89c8TBXPoqUsCXlfAQQQAABBBBAwFOBuFyveSookYB4DGoTLu4m9PoPpJm9pCM/uWwlykntx0kXtrVh5FwEEEAAAQQQQCBkAnG3XguBJAlICFALGzJuJnTaMem/j0ufT8imOr+5dMskqcy5heXjPAQQQAABBBBAIOQCcbNeC6EkCUgIcYMNHRcTes96aVpX6cc1Lk9CktTiYemqB6TEpGDJKI8AAggggAACCIRVIC7WayEWJQEJMXAw4WN6QmdmSv/7h/T+YCn1iMtS9jwpZapU7YpgmCiLAAIIIIAAAghETCCm12thUiUBCRN0INXE7IQ++rM0u7+0dmY2wyUpUtvnpOJlAqGhDAIIIIAAAggg4AuBmF2vhVGXBCSM2AVVFZMTeutn7t4eB7a53S9yhnTTU9Lv72Bvj4ImBJ8jgAACCCCAgO8EYnK9FmZlEpAwg5+uupia0Olp0qKnpY//LmVmuN0+p4GU8rJUsbaP1GkKAggggAACCCAQuEBMrdcC77anJWM+AdmyZYtq1KiRJ1q3bt00ZcqUE5+lp6frqaeecr62bds2VatWTd27d9fAgQOVlJT7AelgygY6YjEzoX/eJs3oIW1dmt31K/tKrf4qJRcLlINyCCCAAAIIIICA7wRiZr0WQdm4SUA6dOigzp0756KuXbu2mjRpcuJrvXv31oQJE3TPPfeoadOmWrp0qV555RWZr48fPz7XucGUDXR8Y2JCr50lvdNP+vWA2+0zzpJumSjVbh0oA+UQQAABBBBAAAHfCsTEei3CunGTgAwZMkRPPPFEvtxr1qxRgwYN1K9fP73wwgsnyvXv319jx47VqlWrVK9ePefrwZQNZnyjekIfPyLNe1ha8Wp2l03S0XGCVOrsYBgoiwACCCCAAAII+FYgqtdrPlGNqwTEJCHmKFGixCn85rORI0dq06ZNuW7Z2rx5s2rWrKlHHnlEI0aMcM4Lpmww4xy1E3rXGmlaN2nvt253E4tIrYdKTXpLiYnBEFAWAQQQQAABBBDwtUDUrtd8pBo3CUipUqV06NAhh97cenX//ferT58+J4aiTZs2zlWOXbt2nTI8lSpVUsOGDfX+++87nwVTNpixjroJbfb2+OIl6YPHpPRjblcr1Hb39qjy+2C6TlkEEEAAAQQQQCAqBKJuveZD1ZhPQLZu3aquXbuqY8eOql69unbs2OE8ZL58+XI9+OCDzkPn5jC3VxUtWlQrVqw4ZZgaNWqk1NRU59arYMvmN+Y7d+6U+ZPzWLdunbp06eK0wdTp6+PwXmlWH2m9m5Q5x++7SDf+XSpWytdNp3EIIIAAAggggEBhBUhACiuXfV7MJyB5EZk3WLVs2VKLFy/W+vXrVatWLeePudJhHjw/+TAPpO/evVsbNmxwPgqmbH5DNHToUA0bNizPj32fgGxaKM3oKR367WpRsTOlds9LZnNBDgQQQAABBBBAIIYFSEDsBzcuExDDNnv2bLVv316TJk1Sjx49uAISyFxKT5U+fEJaYh7Sz3TPqHqFlDJZKnd+IBEogwACCCCAAAIIRLUACYj98MVtArJ69WrnrVfmwXLzgHkwz3UEUzaYIfL1hN63yd3R/IesW9QSpKsflK4ZLCUlB9NNyiKAAAIIIIAAAlEr4Ov1WpSoxm0CMmPGDKWkpDjPg5gNCU0SMmrUqIDeghVM2WDmgW8n9Oq3pDkPSMcPut0pXUXq9JJUo3kw3aMsAggggAACCCAQ9QK+Xa9FkWzMJyD79u1T+fLlcw3J0aNH1axZM3311VfauHGjs+O5eQOWedNVfvuArFy5UvXr13fiBFM2mLnguwl97KD07oPS6n9nd+N3baX2Y6WSuU2D6SdlEUAAAQQQQACBaBXw3XotCiFjPgHp1KmTjhw54ux4XrVqVectWK+99ppzpcNc8Rg8ePCJYevVq5fzTIjZCd0kKEuWLHF2Qu/Zs6cmTpyYa3iDKRvovPDVhDa3Wpm9PfZvdpufXFxqM0K6rJuUkBBolyiHAAIIIIAAAgjElICv1mtRKhvzCcjUqVOdhOPbb7+VuRpi9gMxr7g1O5ybh9BzHmlpaRo9erRzW9b27dudhKV79+4aNGiQkpNzP+cQTNlA54YvJnRGhrR0jPTh36SMNLfpZ1/k7u1R6aJAu0I5BBBAAAEEEEAgJgV8sV6LctmYT0CiaXzCPaGbj/7Q4Vk0qKXLdHCX9HZPybxmN+u4vLt0/RNSkVN3j48mW9qKAAIIIIAAAgh4IRDu9ZoXbfZbDBIQH41IuCd0rgRk/Txp5n3SkZ9ckRLlpA7jpd/d7CMhmoIAAggggAACCERWINzrtcj2NjS1k4CExrVQUcM9oU0CUjTzuBbU/0j6PMczLuc3d99ydWaVQvWDkxBAAAEEEEAAgVgVCPd6LRYdSUB8NKrhntBdnnxNjx97VnUyt7gKCUlSi0ekqwZIiUk+kqEpCCCAAAIIIICAPwTCvV7zR6+9bQUJiLeeVtFCPaGzbrkyjWyW9oUeP/aMSiQcd9q8I+FsDSv6gNYm1c1+JsSqN5yMAAIIIIAAAgjEnkCo12uxJ3Zqj0hAfDTKoZ7QOROQczJ+1JSjA3RmwlH9N6m5ninaU4cTznA0TjyU7iMbmoIAAggggAACCPhBINTrNT/0MdRtIAEJtXAQ8cM9oR8b+YSK61cNefhv7O0RxDhRFAEEEEAAAQTiVyDc67VYlCYB8dGohntCn/IaXh9Z0BQEEEAAAQQQQMCPAuFer/nRwLZNJCC2gh6eH+4JTQLi4eARCgEEEEAAAQTiQiDc67VYRCUB8dGohntCk4D4aPBpCgIIIIAAAghEhUC412tRgRJkI0lAggQLZXEmdCh1iY0AAggggAACCNgLsF6zNyQBsTf0LAIT2jNKAiGAAAIIIIAAAiERYL1mz0oCYm/oWQQmtGeUBEIAAQQQQAABBEIiwHrNnpUExN7QswhMaM8oCYQAAggggAACCIREgPWaPSsJiL2hZxGY0J5REggBBBBAAAEEEAiJAOs1e1YSEHtDzyIwoT2jJBACCCCAAAIIIBASAdZr9qwkIPaGnkVgQntGSSAEEEAAAQQQQCAkAqzX7FlJQOwNPYvAhPaMkkAIIIAAAggggEBIBFiv2bOSgNgbehaBCe0ZJYEQQAABBBBAAIGQCLBes2clAbE39CwCE9ozSgIhgAACCCCAAAIhEWC9Zs9KAmJv6FkEJrRnlARCAAEEEEAAAQRCIsB6zZ6VBMTe0LMITGjPKAmEAAIIIIAAAgiERID1mj0rCYi9oWcRmNCeURIIAQQQQAABBBAIiQDrNXtWEhB7Q88iMKE9oyQQAggggAACCCAQEgHWa/asJCD2hp5FYEJ7RkkgBBBAAAEEEEAgJAKs1+xZSUDsDT2LsGTJEl111VV6/fXXdeGFF3oWl0AIIIAAAggggAAC3gisW7dOXbp00eLFi9WsWTNvgsZZFBIQHw34G2+84UxoDgQQQAABBBBAAAF/C5hfGP/pT3/ydyN92joSEB8NzN69ezVv3jydf/75KlGihI9aFntNyfrtBVebwj+22IffPKtG7CNjj3tk3E2t2GMfCoGjR49qy5YtatOmjSpWrBiKKmI+JglIzA8xHcxLgPs3IzcvsMc+cgKRqZk5Hxl3Uyv22EdOgJpPJ0ACwvyISwF+KEVu2LHHPnICkamZOR8ZdxKQyLljH1n7aKidBCQaRok2ei7AgsBz0oADYh8wlecFsfecNKCAuAfEFJJC2IeENaCg2AfEFLeFSEDidujju+N8Y4zc+GOPfeQEIlMzcz4y7vwWPnLu2EfWPhpqJwGJhlGijZ4L7Ny5U5MmTVLPnj11zjnneB6fgPkLYB+52YF9ZOxxj4y7qRV77CMnQM2nEyABYX4ggAACCCCAAAIIIIBA2ARIQMJGTUUIIIAAAggggAACCCBAAsIcQAABBBBAAAEEEEAAgbAJkICEjZqKEEAAAQQQQAABBBBAgASEOYAAAggggAACCCCAAAJhEyABCRs1FSGAAAIIIIAAAggggAAJCHMgIgLp6el66qmnNGXKFG3btk3VqlVT9+7dNXDgQCUlJRXYpq+++kqDBg3S4sWLnbJXXXWVRo8erUsuueSUc0NR9oMPPtC0adO0YsUKrVmzRqmpqdq8ebPOP//8PNv+xhtvaOzYsfr222+VmZmp2rVrq0ePHk6fExMTC+yvVwWi2f3IkSP6xz/+oXfeeccx/+mnnxzvtm3b6pFHHlHZsmWtxt4r4/zixIt9YcYJ+/y/j9l6XnPNNfrkk0/0pz/9Sa+//nqoqXPFj5c5n7PT//73vzVu3DitXr3a+V5fq1Yt3Xfffc4r38N5xJu9X37GhnOMo70uEpBoH8EobX/v3r01YcIE3XPPPWratKmWLl2qV155Rebr48ePP22vvvvuO11++eUqX768+vXr55QdM2aMfv75Z33xxReqU6fOifNDVfbuu+/Wm2++qXr16unYsWMySU5+CciIESP06KOPqk2bNmrfvr3zQ2n69On66KOP9Je//EVPP/102EYxmt2Ncf369dW8eXPH8uyzz3YSQJPEmkTE/PeZZ55ZqLEPxwDEi32w44T96b+P2XiahN3Mu8OHD0ckAYmXOZ81hx944AG98MILuu2222QSP/O93vwMKl68uEaOHBmOqX6ijniy99PP2LAOcpRXRgIS5QMYjc03v71u0KCBkzyYb9ZZR//+/Z2rBKtWrXIW9vkdnTt31vvvv69169Y5V07MYa6iXHjhhbrxxhv1f//3fydODVXZH374QRUrVlSxYsWc5MJ8A8wvATEL5erVqzvJUUJCgtO2jIwMNWrUSFu2bHESp3Ac0e6+d+9e7dixw0lCch4vv/yyunXrpmeeeUZmAZB1BDP2ofaPJ/tgxwn7038fK6yn+b5St25dDRgwQA8//HDYE5B4mvNmDr/77rvO1dh//etfuv3220M9rU8bP97s/fIzNqKDHoWVk4BE4aBFe5OHDBni/DZo06ZNqlGjxonumAV8zZo1ndtpzII+r+PQoUOqUKGC/vjHP+q1117LVeSuu+7Sf/7zH5kf2KVKlVKoyp7croISkBIlSqhVq1aaM2dOrlPNb/HNDwqzqA7HEWvuWWa//PKLypQpo65du2rq1KnOl4MZe+wL/reUn1Fe9l6U9XJM4mne53Tr06eP/vvf/zpXZ80vSsJ9C1a8uV999dUyt8stX77cufJhvgeVLl3ay6kccKx4s/fLz9iAB4iCjgAJCBMh7AJm4W2ucuzateuUuitVqqSGDRs6VzjyOj799FPnli1z+1avXr1yFTFfM5edTZkmTZo4f4eibLAJiPmt2Ny5c51brTp06OD8cDJXacwPCXOvsLk/OBxHrLlnmZnnan73u99p8ODBGjVqlPPlYMYe+4L/LeVnlJe9F2W9HJN4mvdZbuZ2xCuuuML5pYe5KmyuvIY7AYknd5NsmF+CmO/l5sq4uSV4//79KleunHN11vzCrUiRIl5O69PGiid7A+GXn7FhG+AYqYgEJEYGMpq6YW6vKlq0qHPP/smHuS3JPNBtrgzkdZhnJ8ytNeZB5Hbt2uUqYr5mFvjm4fCUlBTnOYtQlA02ATGJ1v/7f/9P8+fPP3GquSd48uTJ6tKlS9iGLtbcs+DuvPNO5+HaL7/80rm1zxzBjH04BiCe7PPzzGucsC/4+1iwnub2TvMLmHPOOUezZs1yTo9EAhJPc37lypXOL85M8mH8H3vsMVWtWtV5TnDGjBlhT/7iyd7Mb7/8jA3H97NYqoMEJJZGM0r6Yt4KYq50mAfPTz7MFYvdu3drw4YNefbmn//8p8xCZt68ebr++utzlTFvpjK/+TFlzMI+VGWDTUDMb8IeeughHT9+XDfddJOTYJmHQz/88EPnB5RJksJxxJq7MXvppZect8uYZz/MMyBZRzBjj33B/5byMsrP3ras1+MRT/Pe2E2cONF57mPt2rUnbnGNRAIST+7mbYzm5RjmMG8cy/pv8//m9lvzvf7rr7/WRRdd5PX0zjNePNkbAL/8jA3L4MZQJSQgMTSY0dKVWPvtzOmeATGvQjS3QpgfCG+99daJITK3YZlXB5tbWMwD9OYe1lAfseY+c+ZMJ3kzSZ35LWNycvIJQq6AFPybdRuj09mfPI+DKRuKfwPxNO/37NnjPHjet29fDR8+/ARnJBKQeHI3V/Mvu+wy52185lnGnMerr77qvO3xxRdfDNvttvFk76efsaH4/hXLMUlAYnl0fdq3WLs/9XQJiHnVbsuWLZ2H482rGXMe5jf2Dz74oJYtW+b88Ar1EUvu5mqXeaWxuWL23nvvOa+5zHnwDEjBz3UU1qgg+5zjEEzZUM3/eJr35s2C5i1MCxcuzPVLDfNqcnN7qnkOzdwmlNeeOV77x5O7uQXI3PLWuHFjffbZZ7kozfOM5jkc82IV84KVcBzxZO+nn7HhGNtYqoMEJJZGM0r6Yr4Jm4eFQ/EWLLMJlNmgLpC3YBW27MnMp0tAzC1Wd9xxR56vZvz73//uPDid9dB8qIcvVtw//vhj5we6+S3fggULnLE++SjoLVg5xz7U7iZ+PNlneQYyTtjn/QayvOZnIJ4dO3Y88dxHfrZmA1jzi49QH/E253O+Ej6nrdmn6N5773We+TMbz4bjiCd7P/2MDcfYxlIdJCCxNJpR0hfzBizzwF5++4CYB/rMXg/mWYmNGzc6bxcxv13KOswD5uYZkG+++cZ50M8cWfuAmN/8mFtLQl02J/XpEhDzYLR5sP6GG25w3oSVdaSlpTlXPcwtWOa2ibwW0V4PZyy4f/7552rdurVzb7tZkJm3zOR3BDNPvLY+OV682QczTtgX/H0sUE/zywyzR9HJx6233uo8l/DnP//Z+d56wQUXhJrdedNhtH+fD9TdYJrn/EaPHq3Zs2c7b2Uyh7k9yFylNbdomecazS1a4Tjiyd5PP2PDMbaxVAcJSCyNZhT1xbxCd9KkSc69sc2aNdOSJUucndDNA8XmIUpzmE36zELT7O9h7qPNOsyi3TxXYfYDMT9QzWFee2iufJjN/sw90KEuu3r1audNXOYwb7cyi2Gzq7m5tcH8MfdgZx3mh5HZpMrsjNupUyeZ5CPrrU2PP/64hg4dGraRi2b377//3lnQHDx4UE8++aTzIoOch/n/6667rlBjH44BiBf7YMcJ+9N/H/PCMxLPgJhxjZc5b/pqHoQ2v1TauXOnzKa65557rvPc36JFi3K9Ijwc8z3e7P30MzZc4xsL9ZCAxMIoRmEfzCLc/LbIXJ7evn27cyXDXJ4eNGjQiYeJ80tATHdNAmDKmsTFHOaBbnNL08m7ZIeqbNaDhXnRm13PTduzjmPHjjkPIJo3X5nbzszbsMzbUMw748N1ST6rLdHsbu5rb9GiRb6z3SR4pkzOI5h5Eup/RvFiX5hxwj7/72NeeEYqAYmXOZ81f03yYXadN8+lHThwQLVr13Z+GRWuvZ5y/juKJ3s//YwN9feyWIpPAhJLo0lfEEAAAQQQQAABBBDwuQAJiM8HiOYhgAACCCCAAAIIIBBLAiQgsTSa9AUBBBBAAAEEEEAAAZ8LkID4fIBoHgIIIIAAAggggAACsSRAAhJLo0lfEEAAAQQQQAABBBDwuQAJiM8HiOYhgAACCCCAAAIIIBBLAiQgsTSa9AUBBBBAAAEEEEAAAZ8LkID4fIBoHgIIIIAAAggggAACsSRAAhJLo0lfEEAAAQQQQAABBBDwuQAJiM8HiOYhgAACCCCAAAIIIBBLAiQgsTSa9AUBBBBAAAEEEEAAAZ8LkID4fIBoHgIIIIAAAggggAACsSRAAhJLo0lfEEAAgTgVGDdunF555RWtWbNGd9xxh1599dU4laDbCCCAgP8FSED8P0a0EAEEEECgAIEZM2YoKSlJ7777ro4fP04CwoxBAAEEfCxAAuLjwaFpCCCAQDQLfPLJJ2rZsqW+/vpr1a1bNyxdGTx4sHbt2nVKAvKPf/xDf/7zn7Vx40ZVqFAhLG2hEgQQQACBvAVIQJgZCCCAQAwJHDp0SE8//bRWrFih5cuXO4vxu+66q8ArAi1atNDChQsDkrjmmmsCKtu4cWPVqVNHr7/++om469at00UXXaT7779fzz33XK76TJLQo0cPXXjhhXrnnXdUrVq1gNqTs1B+CUh6erqTBLVr1+6UeoOuhBMQQAABBKwESECs+DgZAQQQ8JfAli1bVKNGDZ1zzjm69NJLNWfOnAITkP379+vss8/Wo48+qlq1ap3okLmt6e2333YSmkqVKp34uvnv66677rQdnz9/vlPms88+k0lEso4333zTeUbjtdde05133ul8OSMjQw899JBTT+fOnZ3PSpYsWSjY/BIQE8zEf+yxx7Rz506VLVu2UPE5CQEEEEDAXoAExN6QCAgggIBvBI4dO6a9e/fq3HPPVVpamooUKVJgAvLGG284ycCOHTtyJRpDhw7VsGHD9N1336l27dpB9fEPf/iD/ve//znn5jwGDRqkp556SqtXr1a9evX0yy+/6I9//KPef/99JzkwdSYkJJw4pXXr1lqwYEGedVevXl0m4cp5nC4B2bZtm8w5Y8aMUd++fYPqD4URQAABBLwTIAHxzpJICCCAgK8EAk1ATLKwdetWffrpp7naX9gEJDU11bnCcM8998i8nSrncf3118s8G2JuFTPJg7klytRtrnqYqx+2x+kSEBP7kksu0VlnnaWPPvrItirORwABBBAopAAJSCHhOA0BBBDwu0AgCYhJFsyC3NwC9fDDD3uSgJjbrq688krnuRPz/EnOw9zqZa5CPPnkk7r11ltVqlQpzZo1Sw0bNrTiNH01f8xVFPPcy+TJk523YpkrQDmPbt26ydwGduDAgVM+s2oAJyOAAAIIBCxAAhIwFQURQACB6BIIJAHJelbD7J9hrg7kPAp7BcTsx9G1a1fnSkfz5s1PhNy+fbvzYHmVKlW0e/duXX755c4zJjmfLymscFZbc56f18P3I0aMcJ51Wb9+vfOAPAcCCCCAQPgFSEDCb06NCCCAQFgEAklAzKtpzYPqmzZtOqVNhU1ARo8e7VxROTmpMfWYW65Kly6tgwcPavbs2Wrbtm1YLLIqmThxou677z4tXbrUuUrDgQACCCAQfgESkPCbUyMCCCAQFoFAEhDzxqwOHTro+eefD3kC8re//U1//etfNXPmTOc1vOYB9GXLlqlmzZph8TCVvPjii+rTpw8JSNjEqQgBBBA4VYAEhFmBAAIIxKhAQQmIeRNVgwYNnLdMmQ0DTz4KewUkv1uwUlJSnFuuTOKxYcMGNW3a1Hntr3n43TwLEo6DW7DCoUwdCCCAwOkFSECYwvDVywAAAtxJREFUIQgggECMChSUgJjFuNkbY8+ePUpOTvYsAcl6CN0kInffffeJuOZKh6nHPH9hDvP63y5duqhTp06aNm1artfvhmpIzLMp//73v3kIPVTAxEUAAQQCECABCQCJIggggEA0ChSUgJgNAk1SYN4KlddR2CsgWa/hNcnH+PHjndDmrVPm1by33Xab/vOf/5yobsCAAc7tX8OHD3feYBXq4+KLL3Y2XeQ1vKGWJj4CCCCQvwAJCLMDAQQQiDEBs/fGzz//7Oww/vjjjzuvuDVXGczRvn171a9f33lVrXkblbkKcfvtt3uagJhgZm+RFStWOLdamePjjz/Wtddeq1GjRsns1ZF1mCTJ7A2ycOFC59kQ075QHWxEGCpZ4iKAAALBCZCABOdFaQQQQMD3Aueff76+//77PNuZdVuU2Sejd+/ezu1X5spEXkdhr4CYWB9++KFatWolczuWudJirnKYqx1z587VDTfckKs604bLLrtM+/fvd8pfdNFFITE2O7Cbh+DNju/lypULSR0ERQABBBAoWIAEpGAjSiCAAAIxJ2Beh3vkyBHnAfRQHU2aNFHt2rX1+uuvh6qKgOOmp6erbt26zmuAn3vuuYDPoyACCCCAgPcCJCDemxIRAQQQ8L2A2avDbATYokWLkLXVbERo3q719ddfO4v/SB7//Oc/1bdvX2e/kwoVKkSyKdSNAAIIxL0ACUjcTwEAEEAAAQQQQAABBBAInwAJSPisqQkBBBBAAAEEEEAAgbgXIAGJ+ykAAAIIIIAAAggggAAC4RMgAQmfNTUhgAACCCCAAAIIIBD3AiQgcT8FAEAAAQQQQAABBBBAIHwCJCDhs6YmBBBAAAEEEEAAAQTiXoAEJO6nAAAIIIAAAggggAACCIRPgAQkfNbUhAACCCCAAAIIIIBA3AuQgMT9FAAAAQQQQAABBBBAAIHwCZCAhM+amhBAAAEEEEAAAQQQiHuB/w9UNSsBz9T6/wAAAABJRU5ErkJggg==\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titre_abscisse = \"1/T ($K^{-1}$)\"\n",
    "titre_ordonnee = \"$λ_{max}$ (nm)\"\n",
    "\n",
    "plt.plot(x,y,\"+\",label=\"valeurs expérimentales\")\n",
    "plt.plot(x,m,label=\"modele {} = {:.3f} × {}\".format(titre_abscisse, *params,titre_ordonnee))\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "plt.title(\"modélisation de la loi de Wien\")\n",
    "plt.xlabel(titre_abscisse)\n",
    "plt.ylabel(titre_ordonnee)\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": 2
}