Notebooks/MLE/MLest.ipynb

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    "# Maximum Likelihood Estimation (MLE)\n",
    "### by [Richard W. Evans](https://sites.google.com/site/rickecon/), January 2019\n",
    "The code in this Jupyter notebook was written using Python 3.6. It also uses data file `Econ381totpts.txt` and the image file `GBtree.png`. This data file and image file are stored along with the Jupyter notebook file `MLest.ipynb` in the public GitHub repository [https://github.com/rickecon/Notebooks/tree/master/MLE](https://github.com/rickecon/Notebooks/tree/master/MLE)."
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    "## 1. General characterization of a model and data generating process\n",
    "Each of the model estimation approaches that we will discuss in this section on Maximum Likelihood estimation (MLE) and in subsequent sections on generalized method of moments (GMM) and simulated method of moments (SMM) involves choosing values of the parameters of a model to make the model match some number of properties of the data. Define a model or a data generating process (DGP) as:\n",
    "\n",
    "$$ F(x_t, z_t|\\theta) = 0 $$\n",
    "\n",
    "In reality, a model could also include inequalities representing constraints. But this is sufficient for our discussion. The goal of maximum likelihood estimation (MLE) is to choose the parameter vector of the model $\\theta$ to maximize the likelihood of seeing the data produced by the model $(x_t, z_t)$.\n",
    "\n",
    "An example of an economic model that follows the more general definition of $F(x_t, z_t|\\theta) = 0$ is Brock and Mirman (1972). This model has multiple nonlinear dynamic equations, 7 parameters, 1 exogenous time series of variables, and about 5 endogenous time series of variables. A maximum likelihood model with\n",
    "\n",
    "Another example of a model is a statistical distribution [e.g., the normal distribution $N(\\mu, \\sigma)$].\n",
    "\n",
    "$$ Pr(x|\\theta) = \\frac{1}{\\sigma\\sqrt{2\\pi}}e^{-\\frac{(x - \\mu)^2}{2\\sigma^2}} $$\n",
    "\n",
    "The probability of drawing value $x_i$ from the distribution $f(x|\\theta)$ is $f(x_i|\\theta)$. The probability of drawing the following vector of two observations $(x_1,x_2)$ from the distribution $f(x|\\theta)$ is $f(x_1|\\theta)\\times f(x_2|\\theta)$. We define the likelihood function of $N$ draws $(x_1,x_2,...x_N)$ from a model or distribution $f(x|\\theta)$ as $\\mathcal{L}$.\n",
    "\n",
    "$$ \\mathcal{L}(x_1,x_2,...x_N|\\theta) \\equiv \\prod_{i=1}^N f(x_i|\\theta) $$\n",
    "\n",
    "Because it can be numerically difficult to maximize a product of percentages (one small value can make dominate the entire product), it is almost always easier to use the log likelihood function $\\ln(\\mathcal{L})$.\n",
    "\n",
    "$$ \\ln\\Bigl(\\mathcal{L}(x_1,x_2,...x_N|\\theta)\\Bigr) \\equiv \\sum_{i=1}^N \\ln\\Bigl(f(x_i|\\theta)\\Bigr) $$\n",
    "\n",
    "The maximum likelihood estimate $\\hat{\\theta}_{MLE}$ is the following:\n",
    "\n",
    "$$ \\hat{\\theta}_{MLE} = \\theta:\\quad \\max_\\theta \\: \\ln\\mathcal{L} = \\sum_{i=1}^N\\ln\\Bigl(f(x_i|\\theta)\\Bigr) $$"
   ]
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   "source": [
    "## 2. Comparisons of distributions and data"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "Import some data from the total points earned by all the students in two sections of my intermediate macroeconomics class for undergraduates at my previous University in a certain year (two semesters)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the necessary libraries\n",
    "import numpy as np\n",
    "import scipy.stats as sts\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download and save the data file Econ381totpts.txt\n",
    "url = ('https://raw.githubusercontent.com/rickecon/Notebooks/' +\n",
    "       'master/MLE/data/Econ381totpts.txt')\n",
    "data_file = requests.get(url, allow_redirects=True)\n",
    "open('data/Econ381totpts.txt', 'wb').write(data_file.content)\n",
    "\n",
    "# Load the data as a NumPy array\n",
    "pts = np.loadtxt('data/Econ381totpts.txt')\n",
    "# pts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a histogram of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http:https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http:https://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\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:https://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",
       "    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",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\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",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\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,\n",
       "                             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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"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\";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 = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\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.get(0);\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",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\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).html('<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/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<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 () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('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",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\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",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\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('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"639.9999861283738\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0, 550)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# This next command is specifically for Jupyter Notebook\n",
    "%matplotlib notebook\n",
    "\n",
    "num_bins = 30\n",
    "count, bins, ignored = plt.hist(pts, num_bins, density=True,\n",
    "                                edgecolor='k')\n",
    "plt.title('Econ 381 scores: 2011-2012', fontsize=20)\n",
    "plt.xlabel(r'Total points')\n",
    "plt.ylabel(r'Percent of scores')\n",
    "plt.xlim([0, 550])  # This gives the xmin and xmax to be plotted\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create a distribution around these data that we think fits it well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function that generates values of a normal pdf\n",
    "def trunc_norm_pdf(xvals, mu, sigma, cut_lb, cut_ub):\n",
    "    '''\n",
    "    --------------------------------------------------------------------\n",
    "    Generate pdf values from the normal pdf with mean mu and standard\n",
    "    deviation sigma. If the cutoff is given, then the PDF values are\n",
    "    inflated upward to reflect the zero probability on values above the\n",
    "    cutoff. If there is no cutoff given, this function does the same\n",
    "    thing as sp.stats.norm.pdf(x, loc=mu, scale=sigma).\n",
    "    --------------------------------------------------------------------\n",
    "    INPUTS:\n",
    "    xvals  = (N,) vector, values of the normally distributed random\n",
    "             variable\n",
    "    mu     = scalar, mean of the normally distributed random variable\n",
    "    sigma  = scalar > 0, standard deviation of the normally distributed\n",
    "             random variable\n",
    "    cut_lb = scalar or string, ='None' if no cutoff is given, otherwise\n",
    "             is scalar lower bound value of distribution. Values below\n",
    "             this value have zero probability\n",
    "    cut_ub = scalar or string, ='None' if no cutoff is given, otherwise\n",
    "             is scalar upper bound value of distribution. Values above\n",
    "             this value have zero probability\n",
    "    \n",
    "    OTHER FUNCTIONS AND FILES CALLED BY THIS FUNCTION: None\n",
    "    \n",
    "    OBJECTS CREATED WITHIN FUNCTION:\n",
    "    prob_notcut = scalar \n",
    "    pdf_vals = (N,) vector, normal PDF values for mu and sigma\n",
    "               corresponding to xvals data\n",
    "    \n",
    "    FILES CREATED BY THIS FUNCTION: None\n",
    "    \n",
    "    RETURNS: pdf_vals\n",
    "    --------------------------------------------------------------------\n",
    "    '''\n",
    "    if cut_ub == 'None' and cut_lb == 'None':\n",
    "        prob_notcut = 1.0\n",
    "    elif cut_ub == 'None' and cut_lb != 'None':\n",
    "        prob_notcut = 1.0 - sts.norm.cdf(cut_lb, loc=mu, scale=sigma)\n",
    "    elif cut_ub != 'None' and cut_lb == 'None':\n",
    "        prob_notcut = sts.norm.cdf(cut_ub, loc=mu, scale=sigma)\n",
    "    elif cut_ub != 'None' and cut_lb != 'None':\n",
    "        prob_notcut = (sts.norm.cdf(cut_ub, loc=mu, scale=sigma) -\n",
    "                       sts.norm.cdf(cut_lb, loc=mu, scale=sigma))\n",
    "            \n",
    "    pdf_vals    = ((1/(sigma * np.sqrt(2 * np.pi)) *\n",
    "                    np.exp( - (xvals - mu)**2 / (2 * sigma**2))) /\n",
    "                    prob_notcut)\n",
    "    \n",
    "    return pdf_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\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.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",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http:https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http:https://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\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:https://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",
       "    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",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\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",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\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,\n",
       "                             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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"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\";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 = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\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.get(0);\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",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\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).html('<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/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<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 () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('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",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\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",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\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('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"639.9999861283738\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a216a90b8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot two different truncated normal distributions on top of the\n",
    "# histogram of the data\n",
    "\n",
    "# Plot histogram\n",
    "num_bins = 30\n",
    "count, bins, ignored = plt.hist(pts, num_bins, density=True,\n",
    "                                edgecolor='k')\n",
    "plt.title('Econ 381 scores: 2011-2012', fontsize=20)\n",
    "plt.xlabel(r'Total points')\n",
    "plt.ylabel(r'Percent of scores')\n",
    "plt.xlim([0, 550])  # This gives the xmin and xmax to be plotted\"\n",
    "\n",
    "# Plot smooth line with distribution 1\n",
    "dist_pts = np.linspace(0, 450, 500)\n",
    "mu_1 = 300\n",
    "sig_1 = 30\n",
    "plt.plot(dist_pts, trunc_norm_pdf(dist_pts, mu_1, sig_1, 0, 450),\n",
    "         linewidth=2, color='r', label='1: $\\mu$=300,$\\sigma$=30')\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "# Plot smooth line with distribution 2\n",
    "mu_2 = 360\n",
    "sig_2 = 80\n",
    "plt.plot(dist_pts, trunc_norm_pdf(dist_pts, mu_2, sig_2, 0, 450),\n",
    "         linewidth=2, color='g', label='2: $\\mu$=360,$\\sigma$=80')\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which distribution will have the biggest log likelihood function? Why?\n",
    "\n",
    "Let's compute the log likelihood function for this data for both of these distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log-likelihood 1:  -1552.807450725983\n",
      "Log-likelihood 2:  -933.5573479356611\n"
     ]
    }
   ],
   "source": [
    "# Define log likelihood function for the normal distribution\n",
    "def log_lik_truncnorm(xvals, mu, sigma, cut_lb, cut_ub):\n",
    "    '''\n",
    "    --------------------------------------------------------------------\n",
    "    Compute the log likelihood function for data xvals given normal\n",
    "    distribution parameters mu and sigma.\n",
    "    --------------------------------------------------------------------\n",
    "    INPUTS:\n",
    "    xvals  = (N,) vector, values of the normally distributed random\n",
    "             variable\n",
    "    mu     = scalar, mean of the normally distributed random variable\n",
    "    sigma  = scalar > 0, standard deviation of the normally distributed\n",
    "             random variable\n",
    "    cutoff = scalar or string, ='None' if no cutoff is given, otherwise\n",
    "             is scalar upper bound value of distribution. Values above\n",
    "             this value have zero probability\n",
    "    \n",
    "    OTHER FUNCTIONS AND FILES CALLED BY THIS FUNCTION:\n",
    "        trunc_norm_pdf()\n",
    "    \n",
    "    OBJECTS CREATED WITHIN FUNCTION:\n",
    "    pdf_vals    = (N,) vector, normal PDF values for mu and sigma\n",
    "                  corresponding to xvals data\n",
    "    ln_pdf_vals = (N,) vector, natural logarithm of normal PDF values\n",
    "                  for mu and sigma corresponding to xvals data\n",
    "    log_lik_val = scalar, value of the log likelihood function\n",
    "    \n",
    "    FILES CREATED BY THIS FUNCTION: None\n",
    "    \n",
    "    RETURNS: log_lik_val\n",
    "    --------------------------------------------------------------------\n",
    "    '''\n",
    "    pdf_vals = trunc_norm_pdf(xvals, mu, sigma, cut_lb, cut_ub)\n",
    "    ln_pdf_vals = np.log(pdf_vals)\n",
    "    log_lik_val = ln_pdf_vals.sum()\n",
    "    \n",
    "    return log_lik_val\n",
    "\n",
    "print('Log-likelihood 1: ', log_lik_truncnorm(pts, mu_1, sig_1, 0, 450))\n",
    "print('Log-likelihood 2: ', log_lik_truncnorm(pts, mu_2, sig_2, 0, 450))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why is the log likelihood value negative?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do we estimate $\\mu$ and $\\sigma$ by maximum likelihood? What values of $\\mu$ and $\\sigma$ will maximize the likelihood function?\n",
    "$$(\\hat{\\mu},\\hat{\\sigma})_{MLE} = (\\mu, \\sigma):\\quad argmax_{\\mu,\\sigma}\\:\\mathcal{L}=\\sum_{i=1}^N\\ln\\Bigl(f(x_i|\\mu,\\sigma)\\Bigr)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. How to set up a maximization (minimization) problem in Python\n",
    "A minimizer is a function that chooses a single value or a vector of values to minimize the result of a scalar-valued function of that vector. Any maximization problem can be restated as a minimization problem. Because minimization problems are more numerically stable and well defined, most numerical optimizers are stated as minimizers. The [scipy.optimize](https://docs.scipy.org/doc/scipy-0.18.1/reference/optimize.html) library has many types of root-finders and minimizers. For our maximum likelihood estimation problems, we will use the [scipy.optimize.minimize()](https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize) function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1. The criterion function\n",
    "The first step is to write a function that takes two inputs and returns a scalar value.\n",
    "1. The first input is either a scalar or a vector of values (the object `params` in the function `crit()` below). This object is the value or values being chosen to minimize the criterion function.\n",
    "2. The second object is Python's variable length input objects `*args`, which is a tuple of variable length positional arguments. As you will see in the `minimize()` function, all the arguments must be passed into the criterion function in one tuple.\n",
    "3. Lastly, you must make sure that the scalar criterion value that the function returns is the value of the problem stated as a minimization problem and not a maximization problem. In this case of maximum likelihood estimation, you want the negative of the log likelihood function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def crit(params, *args):\n",
    "    '''\n",
    "    --------------------------------------------------------------------\n",
    "    This function computes the negative of the log likelihood function\n",
    "    given parameters and data. This is the minimization problem version\n",
    "    of the maximum likelihood optimization problem\n",
    "    --------------------------------------------------------------------\n",
    "    INPUTS:\n",
    "    params = (2,) vector, ([mu, sigma])\n",
    "    mu     = scalar, mean of the normally distributed random variable\n",
    "    sigma  = scalar > 0, standard deviation of the normally distributed\n",
    "             random variable\n",
    "    args   = length 2 tuple, (xvals, cutoff)\n",
    "    xvals  = (N,) vector, values of the normally distributed random\n",
    "             variable\n",
    "    cutoff = scalar or string, ='None' if no cutoff is given, otherwise\n",
    "             is scalar upper bound value of distribution. Values above\n",
    "             this value have zero probability\n",
    "    \n",
    "    OTHER FUNCTIONS AND FILES CALLED BY THIS FUNCTION:\n",
    "        log_lik_truncnorm()\n",
    "    \n",
    "    OBJECTS CREATED WITHIN FUNCTION:\n",
    "    log_lik_val = scalar, value of the log likelihood function\n",
    "    neg_log_lik_val = scalar, negative of log_lik_val\n",
    "    \n",
    "    FILES CREATED BY THIS FUNCTION: None\n",
    "    \n",
    "    RETURNS: neg_log_lik_val\n",
    "    --------------------------------------------------------------------\n",
    "    '''\n",
    "    mu, sigma = params\n",
    "    xvals, cut_lb, cut_ub = args\n",
    "    log_lik_val = log_lik_truncnorm(xvals, mu, sigma, cut_lb, cut_ub)\n",
    "    neg_log_lik_val = -log_lik_val\n",
    "    \n",
    "    return neg_log_lik_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2. The minimize() function\n",
    "The `minimize()` function is shorthand for `scipy.optimize.minimize()`. This function returns a dictionary of objects including the solution to the optimization problem and whether the problem actually solved. The `minimize` function has three mandatory arguments, plus a lot of options. You can experiment with the options on the [`minimize()` documentation page](https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize).\n",
    "1. The first argument of the minimize function is the criterion function (`crit()` in this example) from which the `minimize()` function will test values of the parameters in searching for the minimum value.\n",
    "2. The second argument is an initial guess for the values of the parameters that minimize the criterion function `crit()`.\n",
    "3. The third argument is the tuple of all the objects needed to solve the criterion function in `crit()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mu_MLE= 622.0539694388843  sig_MLE= 198.72367572486868\n"
     ]
    }
   ],
   "source": [
    "import scipy.optimize as opt\n",
    "\n",
    "mu_init = 600  # mu_2\n",
    "sig_init = 200  # sig_2\n",
    "params_init = np.array([mu_init, sig_init])\n",
    "mle_args = (pts, 0, 450.0)\n",
    "results_uncstr = opt.minimize(crit, params_init, args=(mle_args))\n",
    "mu_MLE, sig_MLE = results_uncstr.x\n",
    "print('mu_MLE=', mu_MLE, ' sig_MLE=', sig_MLE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      fun: 910.5500677663156\n",
       " hess_inv: array([[16067.52977213,  4683.90121207],\n",
       "       [ 4683.90121207,  1459.38093085]])\n",
       "      jac: array([0., 0.])\n",
       "  message: 'Optimization terminated successfully.'\n",
       "     nfev: 108\n",
       "      nit: 20\n",
       "     njev: 27\n",
       "   status: 0\n",
       "  success: True\n",
       "        x: array([622.05396944, 198.72367572])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_uncstr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\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.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",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http:https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http:https://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\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:https://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",
       "    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",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\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",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\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,\n",
       "                             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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"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\";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 = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\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.get(0);\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",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\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).html('<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/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<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 () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('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",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\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",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\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('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"639.9999861283738\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the histogram of the data\n",
    "count, bins, ignored = plt.hist(pts, num_bins, density=True,\n",
    "                                edgecolor='k')\n",
    "plt.title('Econ 381 scores: 2011-2012', fontsize=20)\n",
    "plt.xlabel(r'Total points')\n",
    "plt.ylabel(r'Percent of scores')\n",
    "plt.xlim([0, 550])  # This gives the xmin and xmax to be plotted\"\n",
    "\n",
    "# Plot the two test distributions from before\n",
    "plt.plot(dist_pts, trunc_norm_pdf(dist_pts, mu_1, sig_1, 0, 450),\n",
    "         linewidth=2, color='r', label='1: $\\mu$=300,$\\sigma$=30')\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.plot(dist_pts, trunc_norm_pdf(dist_pts, mu_2, sig_2, 0, 450),\n",
    "         linewidth=2, color='g', label='2: $\\mu$=360, $\\sigma$=80')\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "# Plot the MLE estimated distribution\n",
    "plt.plot(dist_pts, trunc_norm_pdf(dist_pts, mu_MLE, sig_MLE, 0, 450),\n",
    "         linewidth=2, color='k', label='3: $\\mu$=622,$\\sigma$=198')\n",
    "plt.legend(loc='upper left')\n",
    "plt.savefig('images/MLEplots.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log-likelihood 1:  -1552.807450725983\n",
      "Log-likelihood 2:  -933.5573479356611\n",
      "MLE log-likelihood 3:  -910.5500677663156\n"
     ]
    }
   ],
   "source": [
    "print('Log-likelihood 1: ', log_lik_truncnorm(pts, mu_1, sig_1, 0, 450))\n",
    "print('Log-likelihood 2: ', log_lik_truncnorm(pts, mu_2, sig_2, 0, 450))\n",
    "print('MLE log-likelihood 3: ', log_lik_truncnorm(pts, mu_MLE, sig_MLE, 0, 450))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if this likelihood function is well behaved by looking at a grid over possible values of $\\mu$ and $\\sigma$ for the given data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\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.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",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http:https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http:https://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\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:https://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",
       "    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",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\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",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\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,\n",
       "                             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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"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\";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 = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\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.get(0);\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",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\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).html('<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/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<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 () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('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",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\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",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\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('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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57/8y7/oixZ9cP3R9+mnn66Rx26ive2Y9PJd47Ngw0tetxII1oo1a38x44XxAx/4gJIsCLJ/EYRo84IBqfr7v//7ReubFkXthQBnSWK4/v/xH/9RI/t8r2j+vNxvIHV+DTgH44PwQUppfMeJzvMdZz38XGZnZ/V7zotwNxFgPs9LAS+nea2dAPM9J2mU7z73Rb9Tw/UJjhDhNALMNc99wSecEoFmblzLvEQm7718x4l680L1tKc9TYdHBJ37XTvJ5R4BCf/nf/5n+bmf+7lOX0P7vSGwIgSMAK8IPvtwUQh0S4Cf8YxnaGQGYuIfSH6MnrjxUOHBD+nkwQ8xeO5zn7toKjx0iSwOggAT6WR7lOQtokpEkJKNBwsEnMghW7/tjQgPDzz/EO2VADMvtseTja1ZolhEfYhWEfkhKs7DjPEmG1IRHnZEGZEY8LAkwgQhYTs02SA7REt37dqlyU889PwaQVjYxk42IoKQ05UQYLaNiUhz/vZkH6LNXEuMhRcAv6XLli/yk27bX/3VX2kEHaL1ghe8QD8GweGaIxLO+qY1iA1SEnAhIvoHf/AHSqR5AaGtlAD/93//t/z0T/+0EhDIbvtaQI6J6jNuIniQdCJ1EFMfoe8Wg16+a5xzOQSYiDG4EiH0jWsOcpZ2HfO9hlTx4kjrlwQiiQlkjzFx3RNt/+Vf/mWNtBIdTq4h6wuZSzbWhhc9JE00iB4vjNyLeCFONqLAEOxuCDBElIg+6w/xzmrtBBiCDnH+jd/4DY3eJhvjhISnEWBkUIwv2fgOEU0msJCUGSHHecxjHqPnp5/2xrixMORP7j/M5Xd/93d1Z86aITBIBIwADxJdO3ffEOiWALMVjv7UP2CSA3jf+96nhNK7BkDuPvrRj2okA9KUbJAhtgUHQYDXrVunW6NEO4iMtjf/UMwDj4cE2/rJB263PsRp/XqdKYQBckskl23arIcWhJcoI1EriCbEj5cIXibam38w+ux/ou78nXVqbz66txICTHSK9fQkKNkHUWdeOtha5qHsCRLkAxLSbeNBzZyJBEOCkutABBhS5BvXIlFprilezNobUc6f+qmfylzLXjTASCqSUe+0+fASgCSDxrmJltMgj0gHWC8fqcvDo5fvGudZDgHmGkeikWz+XpB2HUPwIPZcP7R+EWBeWsCJaG+ahy4vEOyCJK8DXuTYbUi2dgyIvvOiC3HlhSLZeFFnJ6gbAgwB55ojosxLDjsprCEvQcmXh3YCzHx4gePlt/1FnKgwL4lpBBgdvL9u/Jj93CCyyVwCvwZJWRCfQbLCixr3Xz6TbGir2WGwZggMEgEjwINE187dNwS6JcB55CqLALON2r71PUgCzIMSEg6BhIi0J4h58klUlmhdWoPk+QSsXiPAaaS+nWTlPRgZT5IAo7FmDmxZQt7bWy8E2BPUlRJgIqpEsdtbFgHuhmS0n4t1JApGRJzoN5IGiDV4+EQzIr3oJYnysp5EhyEVEAR2Krgmky8uaWtJUiDrk4YJuwFsv3tShuwDHTcRfiL4aY3+zzrrrPlfQRgZCxpOSBvb2KwZkdasZEM+3Mt3jeOXQ4DTijbkuZm0Ezwf5U5bX7/dnsQ/64bFyzByAKL+vKyQhIpshvUG7+R3Kk9a0QsB9pHTbq9NCCsvY7xQMSe03OjZv/3tb88nzWYRYLTUr371qxdNHzLN9zwrCa5dU521vmkvIUhYeNnlBYHcC/Ia2K1BkkF+QbLPvj1E7ESGQBsCRoDtklgVCHRLgP22LCSkfUuXaCtRBR4sJD0NSwLBwxJ9LWNFAgAJhgz75h98ECailZ3aIAhwngSCiBzj9bKB1SSB4IELOWiXQHRLMpJrgcyB8xHF4yFOVJ7IFfII37zOFncC3BaSDfkN12AnAkwUngx7H7VOngPigBuHJ8BeHoC2PE0+0+laIhLHtjsvYcgPfEJV2ud6+a4NiwCztU6CXNp3yfsSdyLAEEskR+BCdD/ZkAdBHJdLgPslgUhbH6K6zDtZPKhfEoi070svBJjdFwrZEOFPOmggoSIYYQS40zfVft8PBIwA9wNFO8fAEeiWAHttZntmNpn76GpJOCEywk2XP9nGHUYSHA8LSCb6Sx7QkClvn0XUkO1oHg5s43uHAw8yv2dbHQcH2iAIsE+CQ6ZAhBxZhG8+4zy51c/2Kxnc7ckr/B+/Q3/tiaFfo0ElwUE2sWdqd1ggwolkISkBWInXKusA6SUaDwEmItZOGkk2RI7RvmVMsiHXI9roTgTYF/xoT+rz2LI2ngCzVc+YiNzyItWug4fgIr+B0BHhI1Ldnojk9e/sgvCSk9V6+a4NiwDTLy8JzJcovI9o8wLEuiDf6USAIdHIlnBY8A4WnBcZDESOe8tyCTBrwO4BdmPJJDiSGZGjdJMEx3eVNfVJrX69vIaXSCs7DbS0JDgkE3we8ul3o7i20VlzDQwiAszuBPcWnG2IpNPoE20xu09GgAf+SLUORKwSnF0FqwMBT4DxAMU+J61BMnjAo33joUbCSdIGjQdZuw0aUTIeDtyQ0bzy4OFhxsOEBBGfMZ+H0koIKA89Er94CLD97Mku/w9RI9kGsgxZqtfrug3OQ4n/81uQK+nfzytNZ+pt0NgGRS9NtBr82C4HV/700Ru24f1DDfswxuuttyAg7TZobCPzgOYhSxSVrWr0gN3aoGURf09MvA0aUbukDRrRUpIH223QlhMBpi8+R2QXggQ+PNCTDXKCjpOXLSKFkC7WEO05RBVcOhFgIuzgCdEiokeCES9GJBzRL9dFUpeK5RjElS1lItLe3ozPY3kHkeb64TriZYY1OPXUU9WBgJcdbLnY4vcWZFnXfq/ftWFIIBi719nyPUPKwMsjCWq8DCMP6ESAOQd+1nwfeJEjaQwtNy8A7DK12yX2IoHg3MgPSA7j2me9eCHh5aZbGzQi1LzocL/jWuPvJIGisWUnDNs/pDdpBJj/4zpAvw/55bvL9x25B/dL7oGsG/dEWt4LYy8RYL/7xj0O6RQvbuxekGDItW0EeHU8l1f7KC0CvNpXcETGn2Wmn5y+t1eCxPLQSxbCYCuXRCT/IPCf4zP4VLKVydY+0RhIAUSUh2W7rVca3CsloNgPQQIhMpAXor80SCXzgHTyd6J1eLvy0OBB5UncSvunr6xEKwhCshAGrgGQyrRCGIyR80CMfSEMtsnTCmEQccIBgYixL4TBljSkq5tCGHkEmN+xvZoshAG54+WJcycN+VcSAaYfyKz3DU5uNSevEzTC4EWUn3lDNpE08PIA4elEgDkXuwF8Bn0nUUxeIJA58GKSppPlmuLaQV5DlBOizNrxcohFINcRY4eIYJfG2nH9cf1D9hgvLy6dWi/ftWERYF5C+O7zPYH88gLhvZfb8c+aL5F6rk+S1TgHWCKpIgLcXjCnVwJMn2jJeTHxhTB4Ge+2EAaWYnzvuL54ySEyzcsYL4GsY7L4T1YhDL6znIMXb64VXqAIKDDPpKVkvwgwL3W4PKDD5vtBBJw+0a4TrTcC3OmbZ7/vBwJGgPuBop3jsEPAZ9NDAIn4WDMEDAFDYJQQ8HkAy/WJHiWsbK6rEwEjwKtz3WzUfUKArbdkaVhO6wthEN0iMtZe8rdPXdtpDAFDwBAYOgJE/tlV8FpcBkSEFlkE8gifNDz0gdoADIE+I2AEuM+A2ulWFwJswaF5RD/Hli/bcejd0NCh8UPzZ231IsCDnAc8P2grkR34nzyLr9U7Yxu5IdAbAsgmsPBDdoE8hARBiC/6X6z+8Di2ZggcjggYAT4cV9Xm1DUCJFygDyTKwY2fjH4SSahEBCm2tjoRQNtNkhY/aECJ5kN8Ib388HeSjPjxf/e/X50ztlEbAstDAL08SXgkh6IX57uDTh7yi4NEsqjF8nqwTxkChyYCRoAPzXWxURkChsAyEODhDeGF+BL9hez6KDAEl98nf3wXnvx6UuyJcZI0L2M49hFDwBAwBAyBQxQBI8CH6MLYsAwBQ6B7BNKIr4/2QojJlE+LZHnnEP6EKPt/07P/fBoptmhx92tjRxoChoAhcCgiYAT4UFwVG5MhYAh0hYAnrthUQVQnJibmpQ7+BESD0QD3spXbHin2xNgkFF0tix1kCBgChsAhj4AR4EN+iWyAhoAh0I6AJ6gQWyK3WDbhJUqJ6fa2HAKchngaKUY3jmsI/UKOs6LFtoKGgCFgCBgChxYCRoAPrfWw0RgChkAHBCC8PrkNUgrxvOqqq7R63yAJcNqwKJ5CVbCzzjprXlvsj8uSUECS/e9ssQ0BQ8AQMASGg4AR4OHgbr0aAoZAjwhAdon4oun1xNcTSdw8qPaGjdOgIsBpw8Un+p577tEy0MlmEooeF9cONwQMAUOgYASMABcMuHVnCBgCvSGQl+Dmz0QEeO3atYvKHPvf9UsCkTZq/KL5aSfAacd2IsUmoejturCjDQFDwBBYCQJGgFeCnn3WEDAEBoZAkvi2J6G1d/r9739fpqen5dRTT10ynjwXiJUOHvJLFHi55bL9vLwDRbsLhS/a0e5ZbBKKla6cfd4QMARGHQEjwKN+Bdj8DYFDDAHv7OB1vgyvk+0YhUxwgHj4wx9eKAFG/oAOeLkEOAv6rGgxRHnnzp2ydetWLdGdJMadMDrEltmGYwgYAobAUBEwAjxU+K1zQ8AQ8Agkia+PiHZL6q6++molhI94xCMKJ8CUzz7vvPMGvpDgc/DgQbnyyiuVcDNfmo8Gm4Ri4EtgHRgChsBhhIAR4MNoMW0qhsBqRSDN2aGXbf4f/ehHGg195CMfWSgBxgHi9ttvL4QAM7HZ2Vn51re+JRdccIGMj4/PF+7oJKEol8saRfdln3vBdrVeUzZuQ8AQMATyEDACbNeHIWAIDA2BlRJfP/Af//jHGgl91KMelUqAcY+A/PW7QYBvu+02edzjHtfvU6eer50AZ3XaKeHOE2GTUBSybNaJIWAIHIIIGAE+BBfFhmQIHO4IdOPs0AsG11xzjUZDH/3oRxdKgLdv3y633HKLnH/++b0Md9nHdkuA0zroRIpNQrHsZbEPGgKGwCpEwAjwKlw0G7IhsFoR6MXZoZc5XnvtteoPfNppp6US4Fqt1lMp5G77Xk0EOIsU8/95EgqIMRIKosUmoej2yrDjDAFD4FBHwAjwob5CNj5D4DBAYDnODr1M+yc/+YkWyTj99NMLJcD33Xef3HzzzarJLaKtJALc7fi8FZuPGDO/ubk5TTD02mGTUHSLph1nCBgChyoCRoAP1ZWxcRkChwECK3F26GX61113nVSrVTnjjDOWfIzoJr8jgtnvdjgS4HaMbrrpJiXA6KvbZRQcm3Sh8Ml27dHifuNu5zMEDAFDYKUIGAFeKYL2eUPAEEhFoD3BzSehQZj63a6//np1SDjzzDNTCTASiEEkwd1///1y4403yuMf//h+Tyn1fEVEgNs7Zn7gl5Zg2G0hD3OhKOTysE4MAUOgBwSMAPcAlh1qCBgCnRGA+CJHuPXWW+Woo45Su65B227dcMMNcuDAATnrrLOMAHdeop6OyCPAaSdql1BwPfhmEoqeoLeDDQFDYIAIGAEeILh2akNglBCA+PjqbSSkXX755eqPu2HDhoHDwDb93r175eyzzy6UAO/YsUOIPj/hCU8Y+BzpYFgRYF5o0jyWe5l0JxeKpK643bO4l37sWEPAEDAEukHACHA3KNkxhoAhkIlAlrPDF7/4Ra1YtnHjxoGjR6LW7t275ZxzzkklwIPSAI8CASa6zovNSglwVrQ4TVdMiWl2Do488kiVrpiEYuBfIevAEBg5BIwAj9yS24QNgf4g4Ikv0V5+kslQ9HDppZeqJGHz5s396TDnLNu2bZMHHnhAzj333EIJ8M6dOwUHip/6qZ8a+ByHFQEeJAHOIsVU9puenpaTTz5ZLdp8MwlFIZeZdWIIjAQCRoBHYpltkoZA/xDo1tnhsssu06S0I444on+dZ5yJYhREY4k4tzcI1KCS4IomwLgx/N///d98KeSBAyuiEg8wxJg5FcAAACAASURBVAatqHb11VfLunXr5JRTTpnv0iQURaFv/RgCo4GAEeDRWGebpSGwYgQ8AfE6X/6d5+zwpS99SSuzsY096EbCHZZkaI6LJMBEnalC98QnPnHQU9TzjwoB/sEPfqDacSLAeS2LFPMZrk0ixkkJhbdnG3RSZiEXg3ViCBgCK0LACPCK4LMPGwKjgUC7pVm73CENhSuuuEIe/vCHqxPEoNttt90m9957rzzucY8zAtxnsPFYprGWRbXvf//7smnTJjnppJN67tJcKHqGzD5gCIwkAkaAR3LZbdKGQHcIeGcHor78vRvi68/8la98RU499VQ5+uiju+tsBUfdfvvtcvfdd8v5559fKAHetWuXoFd90pOetILRd//RYUSAh0GAr7rqKtWOL4cAZ6HZSUKBjp1o8eTkpP6ZLPvc/QrZkYaAIbBaEDACvFpWysZpCBSIQNLZgehvL8TXD/NrX/uaPOhBD5Jjjz124CO/44475M4770wtSew1wIPY9h4FAkySH2TwYQ972MDX0XcAAd6yZYuccMIJA+0zSYpxEsEtxEe6TUIxUOjt5IbA0BEwAjz0JbABGAKHDgJZlmbLqd729a9/XTWcxx133MAnCPklCpxWkW2QBBjrNRK2nvzkJw98jnTgI8BEuicmJgrpcxgE+Hvf+55qxwdNgJMAercLn+znkz29pIJj/UtUstSz/7snzIUsinViCBgCK0bACPCKIbQTGAKrH4FunR16mek3v/lNOf744wshMXfddZfgBJFmR2YEuJdVW3osBBiSh5ylqPbd735XteNcP0W1Tm4XnSQUVsijqJWyfgyB/iBgBLg/ONpZDIFVicAgiK8H4lvf+pbqf/up48wCmcIJVINLc2MYJAHes2ePkLD1lKc8pZD1H0YE+Nprr9VCFEUS4O985ztyzDHHFLJ74BduOZHublwoeHlIRox9pHg5uyqFXGTWiSEwIggYAR6RhbZpGgLtCCzH2aEXFL/97W/L1q1bO1pZ9XLOrGNxgGALOy0ZjXlSypfWb9JB+WX0qkaA+7GKC+e48sorlfwWoR/3vfaL6CddKLj2siQUSWLsk+76fX32d1XsbIbA4YWAEeDDaz1tNoZARwQGTXz9AIjikclPItyg2/bt2wW3gjQt7iAjwJ4A028R5GUYEWB8jsfGxuShD33ooJdx/vy8PKH/JQpcVGOelF9+yEMeMpAu06LFvJihXafgBxi3k2L+XcR1NZAJ20kNgUMcASPAh/gC2fAMgX4h0A9nh17Ggo6TYgaDIhTJsVAEgwheWiR2kAR43759QsKWEeBerozOxyKfQTpThIWeHw12dlNTU/LgBz+48wD7dMTMzIwQ7SapEZlJMlrspRImoegT2HYaQ6ANASPAdkkYAoc5At7LF8uu6elpjTINwhKsHUakAWvXri0kcnj//ffLj3/8Y3nqU5+6ZDWLIMBIL3xVvEFeTsOIAIMrjhNFvMh47Cj3TFS0iCIqvs8f/vCHsmbNmkJ2LHyfBw4cEHZKSN6EANNMQjHIb5Cd2xBYQMAIsF0NhsBhikAywa1Wq8mXv/xlrZS2fv36QmZMOVuKChThH7tjxw6BwFx44YWFEuD9+/cLkW4jwP29pHAQIRKLhryo1m355X6Ox+8gdHP99OpCkSz73M8x27kMgcMFASPAh8tK2jwMgRYCac4ORHwvvfRSOe+881SWUETDHxdNZREldHfu3CkQmIsuumgoBBj3CQjHoNuwIsC8yBQpDfjGN76hOwd4ARfVVlJ+eblj9C4iy5XQmAvFcpG3zxkCIkaA7SowBA4jBNoT3Py2PAT4i1/8opxzzjmyadOmQmaMphJS+MhHPnLg/T3wwAOqxb344osLJcB+C/twJsDD0MZSRIWdA6rBFdWGUXxjEJUEu5FQeM9iZBfegcIKeRR1pVk/hwoCRoAPlZWwcRgCK0CgG2eHyy67TM4880w54ogjVtBT9x8lq572qEc9qvsPLfNIKrKRTHTJJZcsOQMyBXyCSXBCk0w0s1+Z9WkazmVOoauPDSMCPCwCzM5BUdcq4A+j+AY7F/gPpxVw6eqC6OGgZLT47rvvFqwDH/OYx8znA7QX8jAJRQ/g2qGrEgEjwKty2WzQhoBDwCe4NZtNgQT75LY0gvelL31JHv3oRxe2rYwrA+M67bTTBr5cbCVjnUUE2M8d3fO2bdvkjjvuUPLLv7Gd4sEOEeaHpCf+5PfLSWLzWfzJJKZBTnYYBHgYyWFf+9rXhJLERRLgYRTfIHnzxhtvTC3hPcjriO8EfZ911ll6D0n++H7bXSjaSz736yVykPO0cxsCeQgYAbbrwxBYhQgkLc38lmcnZ4crrrhC9bhFZdbjy1utVuWMM84YOML48WKd9bSnPU374gEP+UWDjJaUxD9wggATEU7+MEYe9p4Me2LMvzuRYk+An/CEJ0ilUhn4PBkrCWLYZuHMUEQbBgH+6le/qjsH+EgX1YbhPYx/NSW8Wc8i26233iq8NLIj1N66kVB4a7YkKTYJRZEraH31AwEjwP1A0c5hCBSEQDLBjegqrdsHD1E1ilIUVV3r+uuvl9nZ2dSHbL/hIpse66zTTz9dSyI3Gg1N2qKaGC2vEhyRYaQMSVLMuHmhwDbOR4s9MU4mux08eFAjz4c7AWbu2JIV1b7yla/ozkFRenXmNQzvYaQ5vKyRnFpk4+WQa57vS7fNXCi6RcqOWy0IGAFeLStl4xxpBNKcHbolvh44MutPPPFEOf744wvBkq1dSCXbrINu6BmJVEJaKaAAWfMRWbCD5NK63baFQDP2JDEm2kvzWmJIIX0Q6X784x+vlbwG3YYRAcbNY926dYUSYHYr2DnYuHHjoCGdPz+RdV4Qi9ohoWO0uHfddZc89rGPLWyedMR3k+/ESvX5nUgx37e0aHGn3apCwbDORhYBI8Aju/Q28dWCQJ6zQy9zIEJK9BcSXEQjEss2K84Tg2poYnmYE0mjsZUMWUu25RDgtPEScfeEOPkn50dqQb/JaPEgCPEwCDD2ckhIiowA41lNglZRln2s9zCs1+68805BBjHI70jatczuDPcVdNb9bl5Cwfk9QfZ9sLPCD/7O7RXujBT3eyXsfJ0QMALcCSH7vSEwJAS6cXboZWhs1fPgOfnkk3v52LKPZZsVe7Jzzz132efI+iARWnSMt912m2p3ifoSAaYUcjvx7BcBThuL1wAjt4CMexkFa8c4koSYv0OUu41Cp/U3LAIMES3qumG9iACzc1BU0RawHob12u233y44QRSxS5K8nkhQhYAWUaTG98u6esLPy0177oK5UPT9Nmkn7ICAEWC7RAyBQwwB7+wAyePvec4OvQydLHeSitjmLaJBUO+7776+6hvBg21josvgcuqpp+qWNVElNM4UFIBkJtsgCbB3Zbjgggvm+6U/tMFJTTERY9YTyUQy2a5XWzZPgKnoh51bEa3oCmm8PKABPvvss5dE8wc532Ek3uUlow1yrsMob8182gn/ciUUnZJTB4mdnfvwQcAI8OGzljaTVY7AcpwdepkyRv9E1B7ykIf08rFlH0t0Fm0uZK0fjWjyDTfcIERdIfFIOXxCGgQYAkNJ2XZ3hCIIcCdXBsaQjBBDiEnc87Zs7aQ4y5ZtGAS46AppSE1YS2QBvCAU1YaReLecZLR+4DEMZw/GjeMF132eM0yehIKXXl+4wyQU/bgSRvscRoBHe/1t9ocAAkni28nLdyXDhcjgakDUtIhGdjtbnkRHV9IgvBBffEtJ4ENu0B7lhVxCYPDjhTy2R4Ahmj6avpKxtH92JYTUE/NOtmyeHLN2RJFJ1ioyAlw0AWaORPORzjD3otowEu/YyeDaxZ+7yMaakmBYlKzFz20l8+0ULTYJRZFX0OHRlxHgw2MdbRarEIF+ODv0Mm2y+dGlDiLxJW0cZLcTBcYhYTktWcgC6QbEPSsiyLEkTmFHBlFcDQQ4C5M0r2Jvywa554UAzTMWYRBEytkOsl111VVakKKo5Enmjx4XZ4T2tRzUPIelO+bFDsJfRLnwJHbDKPtM//1OvutEirNcKExCMahv0uo6rxHg1bVeNtrDAAF/0+bBx3Yv/+7V0mw5MFDSln5Wan3Ubd9oddni7bXMK1HwZCELiO+WLVtyu4U0UekOst0eNQTfQUWAId64B+Dj2h557hanbo7jWkE2QclndKP0BSlmbklbNh8t7mdRjmER4EFjmsR9WLpjLPRoFKgpsg2j6h3zI/mOF7ZB7kKlSSj4/uAWg/wLiRT3QcaRlFOYC0WRV+Ch0ZcR4ENjHWwUI4KAd3ZgK5AtSH6KuvEWWZqY5UT/S4QLXW43jQcXMgcfFfOFLLqJ1vCAu/zyy1Vu0R4l9gQY7Ls5Vzdj9ccURYB9f0nJBdF8osHtyXbMl4d8ewGPdtlIt/OEAPMCcsIJJ3T7kRUdVzSmDHZYsosiCGHaYgyj6Afj4CWcFza+20U2XhaZM9IhvjdJezZ///2rv/ortYZ73eteV+TQrK8hImAEeIjgW9ejg4AnYT7iy80YQlEUqQDpIksT0x/+pvSJM0OnRiljtkf5k612ktx62doH18suuyzTB5gI8CAIcNHb9Z00x8wxzYECfNpt2YgWQ5Q72bIVvV3eaY6drqXl/L7odfRjvOaaa3RdKNddZENHDgnFFrHIVrSjiJ8bL4nf/e53dTcqeV9JSihe9apXqeQGImxtNBAwAjwa62yzHBICWQluePJi31VkEop3UMCDs4hGNBe7pac+9amZ3SULWYAHRGA5UgKI36WXXqoRnnbv2EFGgIsmTsshh0lbtmRlO6KekIF2r2Ls1ZKkuGgC7K3lOjlr9PMaHkbUeZgRUZIMyQVA211kK/pa8nOjIA+Jf7yMZ73wveAFL1CJxPve974iIbG+hoiAEeAhgm9dH74IdLI0K9qTF6SRXRBhxV+1iIbBPxGfiy66aEl3vpAFelYIGIb8Kyl7C95f/OIXVYvbXj1skAS46K3z5RDgtLVO2rIlSTFEEHuppC0b3q3HHHNMYbsVfrs66a086Ot1GKSbOQ2jzDT9DsPyjX6571GNkp8iG/ci5CZPfOITM7t95jOfqb//kz/5kyKHZn0NEQEjwEME37o+/BBIOjuw7UxLS3BDVwnxK3Lrc5CV2dJWEt9e5vm0pz1t/tfgk1bIotM2fDdXyhe+8AV1Dmgn0kaAu0HPHQPB9ppiT4whh6xPu1cx28Xeh7n7HjofiYSDHRISGgdRTjptBMMg3YyjaIs5+vSOF0WXmqZvyrGz63X00Ud3vhD6eAQFeW6++eZcS0bI7wtf+EL5vd/7vT72bKc6lBEwAnwor46NbdUg0KulGZFR9JdFZn8TbUWX26/CFJ0WB8eCK6+8Ui655BI9FEKMzheC017IotO5uvk9EWCi21imJdsgCXDRRRv6FQHuBk9/DGvISwVJdJBjiDFrCCmGBCeJcT9s2Xx5aSzt+ulmkTfnYfTJeIYhCSj6mk3ijmMKL/1HHnlkL5fgio+955571FmGHaK0xj2CctQkwL3yla9ccX92gtWBgBHg1bFONspDGAHv7OAT3LopXUw2NNGzIv0/2com+oq2soiG3IJkPyJ5WBDt2LFDjjvuuNRCFv0YD0lwZ5555hJdoxHglaGbZpnlbdmS0WJIpLdla48W90JkIdj02Z6wtLJZ5H96GH0yomHYkRWtW08iP4xy0/QP+SUnIUv+xXULMUf/ixbY2mggYAR4NNbZZjkABJZDfP0wyP7m86eddtoARpZ+SgpTEAUmslZEI+JL5jUvBJ0KWfRjPNignX766Us8gwdJgIv2jx1GBLhb3SYvgO22bPwbjLwtW5IYZ9my+Yx9tqQHIbFIu9Z8n1j29dsqL+/aRuqBEwwa66LawjV0nkxOVkRkn0g4K6HMisicSFCVIJgTkToGcRIEDff3oC4SN0WCSESaEgS11jEhQq/ET1nieExExkTiMYn1zwmJ40m5+gc3yikPOk3WrNkqElOwZryQaXPfIxGOF+S0xj2CNfjsZz8rz3jGMwoZk3UyfASMAA9/DWwEqwyBLGeHXnSsRVuSATHbgCTC5SWC9GMpfCEL+oIUnXHGGep4MehGIQxKyrZvrxoBXhnySCCI3C8ncakbW7YkKYYoQ0aRBhRJRtmtQK+e5xKwMhTTP40m9pRTTlnR9yOSAyJyn4jsEeHvwQEJg4MipTkJJJIAahq4PyVoStSsyb79u2XDhjUShjH/6QYX86f/0YP1xx0RS6zn8mQYXfisBAElxiHRFYljyCweu/y73DofH4YwQ5bnJIr3SrnMv1tdKjFe536ax0gYlCWKN0ocbZIo2iRxvNGda4WNexE676yAAzsaVFakAuFyK1eucIj28SEgYAR4CKBbl6sTgU7ODr3MCkkAD3p0Z0W1Xnx5lzOm9kIWRLZIPHnKU55SSDITpZCRlLR7mw6SAPuEItax3X5tORh2+swwIsArIcBp8wEzyEiygAd/97Zs2LDxb2y6SBTFFq+Xl8tOGKb93ttkca0W2RY0setxzhYJd0ushPaABDIjcUBUtqZkM9boLESyKYEQiW2Q0SZhEEgpDCQIyhIoWaxIQNRV/w6JhcLy4whsFFel0Tgg4+NEePl/yCvHVxYitlKSOCaqS4tctFcJ7/5WRNgTWKK9a0TiydY5/GeIGHP8HgkClwzsWxRNigikd0JE+4Ggl6VculOCkPn6c09JrXqR1OsXtKLLy1+ZTiWYyVfAfxxXDnaRrI0GAkaAR2OdbZYrQKBbZ4deuoAY7tq1S84999xePraiY7vx5V1uB8lCFieddJJGtdAaovkjkkdkb9Dtiiuu0KTC9mizEeCVIV/ENr23ZUOLi2UVVQRxgEizZfOkuJ9SBb6L6PK7rVqYjihk8gEJwgecrCCYcYRRiayLmAoyA6QEGkmNpF6flXI5lCCMJYhdtNWTTkdOfRQ2bkVf+W1JAlkr5VIspXC/SDzekhlUJFbSC6lMkl9HYF3Edp8EGpH1JJOILeSVHyK4pdZv6hLIQQnCvYsIL7/kuDheK3E85foJGFtVJNgvIeNpa3G0Rklyszkue/ftk/XrJ3XcsZQkZB7lOxdmzTiijVKvn9YivpDllbdOFffQCFMiHqkE9y9ro4GAEeDRWGeb5TIQ6NXZoZcuinZkYGx5vry9jD15bLKQBdZGJJIQwaPxO/xGSWZaTnGLXsdEX6eeeuoSi6VBEmDGSOS5KEupYUSAiyDAybUmGkckDjkC8036FBMZ5rqC/OJAkSzisRJbNvTq6PKdPIjo5QNKXpXUadSTv7uorItsEomFxBKJJcLZ+rtOBEKIZMBHXlsSA4n02AWaG0oU+2M5hmgqEVQIasPRX0hpgIY2kFDWaKS3VLpbgiCWGMIcT4mIi766KG7r8wEEdrZFYBdHYJvNSTl4MJSpqc0tohxLoLrfdAIbKYGF8EKQGSdkmmP3LSW7kONog0Qx9wCi0lUJwt0t8u9Jd0Xi6HgphfdKHG9Q8u2izPsljtdLtfociaL+SqZ++MMfqlsJ7jNpDYKMOw7XAVIIa6OBgBHg0Vhnm2WPCLQnuPloU7+2Yok43Hnnnbm+lD0OuePhRLlISrv44os7HtvpgGQhi3Xr1mkhi/YCFETvIIck3UFOBt2INlPJqT2haNAEmMgzyTXt8x/EfIdFgNkeLsq7FQIMYcmKxrKz4O3YvIzC27LxorVAirFoiyUsHxQJdrWin5AxLNxmVJPqE7yiqCbN5oxMTCAf8HKBFoFtRUy9hhZdLbRVYqKljuoqEZbQRWBVSwvZJWmMQ4jYQjJJHFu4KiCwahXOqfTAUJPFSAyD7kIe3TmOlVK4Q6OrEGRHRic18usitpDHWUdIIedtF14UITeAxDqC3GjMSCx7ZGKimkJgxySKIKUQazeGINgtYXgw49iNrWP59QEJw51KztsbY2g01sju3VU5YvM6KZfvcfNrNch1tfoMqdcft6BJ7uMXqJPfMi95+JVzz+rFsaSPQ7RTDQEBI8BDAN26PHQRWImzQy+zwpHhlltu0ehoUQ2do7/RL3f7OFnIgnMQ8UVykPZiAEnGmYGKXpCSQTfKuyK9IGEr2RgzYyEhb7nzzhs7BJhEv5VUsusWG1+yl2iVj7R3+9nlHoeVHdvCRRHgdDkCsoGdGpmNgz0iAdFHtLJEY6sSxTWJIkhaVeK4pZMN0MfGUilHUgqlRT5L+icJXa4hFyCSGkkUN1rXhyO2qrOVhoSBJD6vslttSkZbn3cRW87J55Af0DcOCotRpy+ivpyD0zTqZSmVJiUMAwnDA/PyBI3oRsdJKdwusUxIHE1LrNreWCQgAo62dkEvu0AkJyWK1qsm17VZKZU4FleH9rFMtMhu61iN6hL5TiOwkG6IcUWj3+64tP5LEkVElnnhhZhDnvep3rde2ywTE3fPD4IkN0fON0q1+v9JHA8u8trJbg4LxRe96EVaKXMQ94jlfvfsc4NFwAjwYPG1s68SBJIkCRLcjZfvSqaGxvGGG25YoeawtxEQLfvmN78pF154oZTLvWdW+0IWJDBBNIkK5tlUgeOll16qvsNEiQfdyOCGqB1//PGLuho0AUZ6QeLM4UqA++FUsLAgkKu9IkQKZZdIuFdEZnS7XnAuCOak2TggtcYBmVKLLiKpznZrQRPbOltMJLYVOW0le4VBU0UASAVoRF0hwZ6INiORKPJRW5EohgBDeDkTBBji6ghv6xQLhI2IbVSSSAkzx5WV5NLCMFKnhfYWxU7mwPGQ3gD9blCRUDW5i493RHyDRNGRTu+rEeOZFtlMObdKE9a3Irv0v0/C0u7Urxmk2B1bVnxLpV1SqbixJxu/d+4LLQIbOgKb1qJooyPbxKuDvRKWdqUe12yc1CL7DYmiXTI1fdCtVhxIrXax1GokHvrkucHcJTq9xH3uc5+T3//931ef9H7t8g1mJnbWfiJgBLifaNq5Vh0C/XR26GXyg0xIyxoHnqyQxF5dGdBgQtbREPdSyAJsqc5G9aUi5AFk1OM8wc/ih/pgI8AQYOyVitAODiMCnE2AIaW7JQghPvtbelMIGFFZR2aJyDoXALSvaGs9kXUr5KKmLYuuhJzASQb8KrZUs5BI3fJ3zgEQViK3uB+UIKP0ETaklCC8/gxKfPmMEl6stpxEQcluW4Q3+ZmmHguJdUlrHF8OHUFeSh6J7nI8E2OujkRnHhttlL17I5meXi+lktNCIHMgurv4+kX2sEHiaC1xZcXWSQ0Wa3sVz3i8FYGdULxJyEuTL3CsEt3IvZhqRJmflJtHFK11x0qYS3TjaEqi6AgnndDkud3SbJ4ipdKti6LKaIqj5nFSrz9Ff19E475HfkBWBbpPf/rT8uEPf1grVRoBLmJFDo0+jAAfGutgoygYAU98iWru27dPiZ2P+hYxFMgkujR0Z0U1Ire9uDJAtnCrQKu83EIWEOBzzjmnEHIIUcOrlsh0e0M3OigJBJjiP7y6CXAkEZFZ2ekijthw6Zb8jOzZc4+sWVOW8XHIFxpTnwCW1MhCYjXE2RK1+t8R1YQtOl0spFXUJ9YxSP13Sz/L1jvR2sXkz31ezxY3Wy4J7F6EqpMtl+aUkLY3SKgjvegd0L4ytrqUwjj1eAh1IxJpNEvSjJhnU8qlSCqlpZFg+uLYepMxcW6SvWoyVnbR46XEGKkBWln0vZFGVMNwIVIbNackio+QUukOcQlnGx1GQVVKpR2thLvFZ1VMVGqwRmJB2gPRRdecgkUyUqsR3cUE238iQmYRHaFEtxTuciQ29XxrlGQ7oku/Kclw0ZQ0PRlmzqX75jW/zeZJMjf7K+r9W1Tr9JL6oQ99SIgC4z9tbXQQMAI8OmttM9WHaKyVqdCE8iflgSkQgU61yEaiD/6qJKQVFXEggQq9aidXBo/Ltm3b1L6MBLcjjiCy03tDAoFHLgR60I1tTnSqaTZGgybAWCgVMcfuIsBESiGzu0QCiBaSg30SCxpZSFJVyqWqhCROEUUMiGxCLiMcrVyCV6tBrpwkiEQumCZEkmiu09I60kokkmPch1x0FuLpfRDwl4XEQmOd9yxKWY5fSnghraGSVyQCSochsepxu9DPmHfravXXjF2Elx80uNDjUslFeNsbx0Bg+QxjJXpcDptKjtsbv681A2loWNfR9rFyVlSX806LxJucDhhJgmpll46BJLZGY708sLMpmzZPSLl87xK7MYclLwBHaPQX318iv9kRXaLEGxRnd1w6Ia7Xp+TgzJRMr5mWUJPrlia40bfKJiIKUURSKm1PJeLuuM1OCqGJeNs1CozmOGmJBmGvVjfK9u1b5IjNL+5LcYtu7ye+WmOeV/fb3vY2LdvO/dHa6CBgBHh01nqkZ+o0eAtb4arhC0PVfBWdjMZCEHUmYnnRRRcVVu4VEki1NCodYQm09GEfy3333ScU6eAFAUcFIqorSQrJKk88iIuRBD+KYJx88slLTj9IAkzyHQU4BkeAiZKS+MWW8g656+6r5bjj10u5UmtpZ8mmd96yMYlXLZLZoqOtrXsitCUpBWyzc8yCI4Ejq61aX5rhVdYooJcZCHIBJbz4tkYtmYFPKlsgvaqNVRcE/q/UkjegdXXHpkdHIa1OZhAjG2jRb44NE5FdCCutEhLZraiuNqJMr+p9nSwhrRHNbUSc30kYOI6oblpzEWCOhWxXNaKbJl/gs9WGSD0SiaNQSnEoU5NL9bSOwOKqsKVlITbbioSScDcp9domqYy5pDAX0eW4aS12UQqJmILH0uakCxDOupQ0spp1nCOmLmkNgr1UMqEEtnmERPG6lt6Y8y3tk/GpNVk8ocUt0PumRYfr9ZNamm1eQCDNzukBIr99+/ly040nyGMeU1zxH/rmu48E4rGPfWymG80b3/hGDYb853/+5yBuTXbOQxQBI8CH6MLYsPqHQJ6zw6Cro2XNAl0tmtVe9bgrQQUJyjpGSQAAIABJREFUANnOaUlpaYUslpMo1z6+rPLEK5lH1meJqG/ZskUT9NrboAkwVcu6j5JXRcJ7JAx2iMT7JW5JDSg8wLa3s+fiT6pvQVy9btaxwGbENrpncQtuBmrJpWS13ioyUNbdhRKesJoMtphUKvnRKCgaV6erjSDPRFGJ+AbEUXFQcJ9NNo4lgjovM5CxltVXXcphOnHU8+sPfeEmgEtCI5Nkcm5IKSSSamckjnHuNILGeZEkaAQ4qEgpQL6QTvg4L+SVP5nXeDmdnDPfWrMs9SYvz00lzhybTkrHZW5ugzQaYDwj09PIR9KIZFnqjVNcUpzskXIZiUM64YyjrWp3FlDaOLw/h5geKRJPqWQhO+JMchsEtiLNaLuMjS11cHBEdVKi5pHOxaIEac4i15Qr3ujGVrpfr5lG4xQplW+Zn4wjzUcraa43LpAbb5hWT+eiK63hG02wgfteVkGe17zmNfrS/5nPfGYQtyY75yGKgBHgQ3RhbFgrR8BHfLmx8fc0Z4cdO3ao5yjOCEW2XvW4/RhbWlIa47jppptUBtJeyKIffWZVZ+vHudvPgdURUdg0s/vBEeBYrrzycnnYw7fIxo1s62M5hfSA4gkQ2gXNrMv6J1KYjBZ6bay30HKla5EecDxSAY2KJuQFqmttVexyulgXtUVm4FpD9atEO5facDldLFHOJOEl2hoGoZTDpW4DSnaJpBJ1bXmAuWOJvC49nhHUm6HUIyKdSAwqSlzLRJ5TyR4SA6g2bHchcazeHG9JNRZW2ml1KyrLQIrhxpB+NTmii7euI9sQ4qwodK3pyDbjGy9RWnipHMK9fFCpDIJIJB3969LKZ/piEU1Ivb5Zmk3kA3tkfHxW8d6xc5Ns3IQnsRtzvcZLytH6QhOobAFrtzRCjL3ZkRKwc5XQ07Yf6WzFkBvVpKQENn19ZmfXSaWCVGGvlEpUrkvrE9J8tCYOumjuUis1N9e10mwe6yzPGFviVOie52ZfIlH0IN1ZQsKDXKjIBunm3oAfeZbH74tf/GLdPfrIRz5S5NCsryEjYAR4yAtg3fcfgaSzQydLM18cgmS0orS4zLjoIhEeZZLSzj77bFm/fr2W/eQnq5BFP1YmqzhFP87dfg6KfGBF9uAHP3jJ6XshwJHqZHeIBNulRMIQJW3nK4ARTcXZYE6tqoisNpuQDTLknT7WNc/KWgSEqGeLzLrEL6KrjoQ6TW2LFAdQ3sVRTidFQCJAhLeVCKYkeKwVIa47LWvK9v6C+8FYq38nfyhnJHc5gglBHmuRXbbjnfQgjbx6khlHlOGFYDY0SpoVpUW+4CqckWhW03K+7a3WSNqUQYiD1njTSWkDct50emEvcUhN3tIocVmtzFjDiYxxgkG16SLKSEYmK+lRZ0f+8LLdoOdD/5olM2g0t0q9gWXYXimX02UGnK9axWt3rVQq+Pfi9JBGTL0cYbyl9U0n4c4RgopqkHAKT2RIIDRJjmjufk26SyfD9HlMiwxzLhcZVr128zhN4HN4oBverJZsJEzOzb1SouhY/d11112nf1KuvMjG7tZVV12llQWz7vHPfe5ztZjNO97xjiKHZn0NGQEjwENeAOu+fwgsx9KMmyPJD0VqcZmxLxJRlEeuR5mkNHxykX6g7cUaiMjHoMg/+lgismiJB93I4IbYo132jejo7l13yh13XS3l8gFZtz6SqemajE9Q8Ql9KpZduAOQMAURJfIKGVWa5swLdLu6VbJLI6+uqpdae7UIa1Ir6gkrlcKczVerSpi6H+jqtz7rTp8WpcXuy+li/Vj4XEm9ZvGqpbnILKSP/29qZLfZ8pt1zgRlKWviVBZx9BIDxoBUoiSVUrqWlUguUVJNTmsRTZLB0hrjqDWIAIOnkw1k6W6dWwNkm/PWVFZBglsa8YOQ8nteHMaI5ubMSx0aeEXI0fzqnBroiXl5qGfKG5BrRCpHqLSivum+uM5Dl4hpWcLwvvnksma0wTlLqF2ckxkQRW400PBul3I5PUpbnaM62noZG2/IxMRC1Lgd86jJ2EhoI4KcNTa0yJBhch/uysTOE3oX3SaRcmlTPJrH6DWs6vDSXUsOIhJdnX2VxDHRctcoNT02NqbFc4ps6YVVFo+AZOSnP/3p8qY3vanIoVlfQ0bACPCQF8C6XzkC7c4OPsGtG1I3DC2uewg6j1wSM4oooECfWL5BEsEFkohfbl4hi5WvjKjOGVuy9uIUKzk3dDQmQivb1bYLLSVVwfbsvUfGxmKZnCRcOSdxVJUmVcHUKxZtJpQocvrLqCyVUqzkQ+ONWgyBz3m7LR7t6FpxL4DA1pXMUbErzWfWaWKpgsVGvit6IFq5i0gnOld0senyAh1VSxcrEF5NZHNSgEy9a2NcAg0y1zTy7L1s03CFtHqSyfFEf7NkA57oEj10SWP1TPLKeYmUokYoh2UZK7uqaWmNpDEINHMdT9HSagKankek3qhIM0ay4CzL0qOgCzreUliRsRKR+fS+a62ENcZG1DdvjFiBlUsTUs5wb6AHXA4gePkR00BJc6O5TpMXw4zIarPJTgAvh6GE4UJktX0m1eq4HDgwJWNjDVm7FnlN+lwdGZ5qSRbSdb6NRkXm5jbL1KRIqNKG9HM1m1tVW+y0wIvlD1FEJH2LSigg1q4Ix049EVFgR363LDoxUjOSb9MkSiu5H3T6LJ7ryC9I/s1qBCJe8YpXyG/+5m92Op39/jBCwAjwYbSYozaVlRBfjxUJEnhEPvGJTyystKzvuyiLsGQhC6K+2Jr1k5DmXXckn+Cx3F6cYvFnoEW7idU6y67gfhHK3cboIWdchTB1NnBRV/27RmA1fqqsCpJKtBQii56SrH/vdOBK37LtjhaVaK/zGnDJX5BpSLFIs+nK4iqhLDmXhCwCptHWCM9YaKIjrIgXSiHa2AWHheQ8faJWLOMqkCCK7NwRsrSxLuLpEtVc5NP9palWXO1tIWnMkbwsD1s+R0TSa3Q5lqhrFjFEkoDuNpa6VMI4tW8lPkgHGk5CASEeL6fbkHHsXMMdq2sTBTI1vtQSzZ+z0RyXWGrad5YrAzhBsFnosTL9p0e9WbfZuvvdZIadGb8jSgsB1ORBJaZZ8oF1LduvGXVkSLZa/USJgrv0s+h3JV7Tsh3LkiyUJY6IrPKixecy9NX1aZk9uEYqY/tkaiormQ3JwrEScD2XqG6WPX5s1vKjx2WJmse6wiHhPRoJbyJzCHkBXWjqdhFtlnrtZ9W6rb3he87LfppLyyCfXVTdvO2224Ty4WmN5wiFbN7ylrcIWmBro4OAEeDRWevDaqZ5zg69TLSTNVgv5+r12EE7JCQLWeBQgNyBh1AxkgRIxg65/oavyJFbQtm0mUjogZYX7cFWRImoEkUVeNBD7Fp/evlBSzOrv1eSC+uDaqKDZaueqC2yAn7ZKh3bkhQkS98qGYT0EqFVpwMXitSqWoLlFbKCfDcCF0VFs0ukDmIXK8HMjk5CQNhed2Pl2KR3bfJaWYgeMz8imTgvLD63J8+cQ0lx5JwUwgBP32yNaoPkLiXPoZSROUDsUy5Ur3vtxhmh0QzVHSGSmkZzs+QNnGuWuhdxKOOlUCPEiwmTyP7quEyNVeWgSkrHlDRnOT0wRpLaomhMSsGcVNDw5s2lJafIw73eXCcloUjFdqlUnGVXe8OFotk8WtclDCGT6cSUyCeeuESG69G0BOFtqedzbgyTEgZ3S1ji+k/rk+IZR0kgcy5Km3FzaTa3SKM+LpXK/VIqp5+r2RyTRmOrVMpOV5zVmiprqKikIYswN5ubpNHYoqS6PWEPr+Lq3KuXRH59f+w+Ibcq6uXb90shHyRfFOTJIsCQ8o9//OPynOc8p9fbuB2/ihEwAryKF28Uh94v4uux43xEYokOoB8tsg3KISGvkMVKJAlxPCdxvFNi7LsETSIJYkSzZrSoAhEidJxOQ+sitlGTQguQRkdcHdmFOTo5AuQzSSJbJgOtQgoQXI5ofc4nfukiuQS0LA0tEU60sUR6I+8vq9HWdAkDZ/QJYOhofXNFErIjtNhpqUet2m+FSgaznBGaUSC1ZqxRYwYOMcsjZ40Iwo62Nz3Bbf4aJnGtCdnGmYBIaToxV9RaFcwg/hV1isggc61oLmS7jHTAy5dTviDVBlZhRM7rMkkBswy25qO+pTDQec/WRabHlkZq642SNDWhLlsPzDDQ+Wq0Ge1y2UXh0xr9Mm/6zLIxI/J/cGaTTEysabknZBPTZvOolrcukeHFPXLpzNVPcbKceL0EWmgiy9khlP37NsjaNVMSQCjbquD5M1PcAm1uCSu0zHOhPz5Wv39OspCOxczMGqlWx2T9un1Sbnshme8vnpCoebSEpT2LK9axa8D/h3e3fIupnonG/B7i6VKdhfwenXkLxaaQ3aAi8gGSgyD6iw74MY95TOrYuF9in/j5z3++cDegIp831tdSBIwA21WxKhDoxdmh1wl5Z4TBFTJIH1G/E8TAiEIWN9xwg5b9RefrSzz7EbSXCyaKh5Y2iu9HJSyRkETDA/uglEIqOx1whRWUbPoIrf/TlbhVGy7kB97ZQDWwTq4QR0gSIJ2BBEgDEgUYFh647m8kfhGldZrblmBUK2rlywS8RABiGUdjUqEkLVKHDA2tI7ps2bdK8sYUCnAR3bTmIo9EXXFSYCy1XHkBxMwlYeFHi2QgWxvLcUgBVHEclmS8vCCf8HpcT5K1epkvCFFKr0amOKrbwZg046aUMuQSfp5q/9VkTkgWOo8TYgXJzZJLMMZqHZximaw4uUl7m6m1iDg+vCKyRuUP6dirVKNZliB00eYsYgeOzAXyjzwkiww3musliDepXVdWBTSXzHaMAumioRlyimitFrmgrDE/jvyeLBLeOj8ZiLXERFaZw12tqnlL50oSG76/Ybgrs1SxuoBEx80nsmXqgKkGF29q6YDTq7yhPd67Z72MjVVl7dr0Yxgl9mZOn3yX1JvHSRjeuWTwqnWuPk/i+ITcWy/3Hjy6jzqKZLziGuXcZ2ZmMv2HDx48qGOCoJ977rnFDcx6GjoCRoCHvgQ2gDwEluPs0Cuig5YiZI2HaCza2Hx9bHezWVzI4kQ5+ZS1EpbvlZhyuFoWl1K4++XAgd0yNhbI2BhMqtaSEjiPZEhqSRO2iHg6O655kqpaWxLCnEstBRcgrIKfrP4fRFrpoYRUDaO0LD62KUxEiarKGfg8B7ix0LISv/idc0Vw+l7G4KO/fIYKXjgYLHZjQKeLM4KzyOI4NL1ZZIvzU/SACDDndtHcHFLcrLScDii6kC0FUGKEjraJi0BDJspO4pDWIJBERpFPjJcDqZRcJbW05l0ROkVK1apMdbR1GStl961JbUgWxCWL5SXKHawTWY9VR5vn8ADZhfxNVdza7JlzSXDJ+dPvAWp+BCLrcskw8yipzrqSk/TmdMidIuwkqUFM8bjN1tvG0ZQ0IabBvky/XGbYaBwltcZGiUs3qaY7reGJK9ExEoREVp0jRFrDYQEHD0eY0yP0cbRGHRbC4D4JMkoe63clIkrLi2x6IQ36r1aPkHo9lOmp+xdV35v/7nP91k5xhVRUT7wgFUHeUq++TOLo1I43KqqxkX9AtLXIRkAA1x2qNaY1kuSwTsSmjfFZGx0EjACPzlqvqpkmE9yIZio5CtGRZsV2lj89kuDQx1IIosiG/Rp9nnTSSR27dVHRB1ruB0gPiNjulma0X6rVPRLHB6VSCaSi+99eatCy8nLWBkp2owhyB0F14gJkBOUwVJeCtC1YVzTBuRq4krkuCqpla9GqBo7MZEYEm0GrgIKLTBJpdL6u2QlSrlqYO15kvDXO9OOJ+vm+tRSvujs01XEh61LxjgNE1Ng+L5fSJQ70rtHcVsCbeWZtobtjS1JtXat52ljmNleHEJelVKrLVEI2gKPC/tqETFWoBucKSjBO1ikvqU2tx6hY1nJZyCLYqvVVXe6ElEtzuVFv9LuQ0zySy/lm62PSiJsyWXZjTCV1scj9B9y1sjaH5CJpOFDlemwqGc4j/6xLnrZa16SxUUJZl6trBYv9+9dpNHRiYm+Ow8KRajfmCPNChTTFoHayxOGtruSxktx9apmW1eLoKKnOiUxMEolOjzDj5sBxkNc8GUUcHa/VA9uLUCT7dkl4ODrckalhRsPrItroexdI7v2t4h2cr9ksS3V2i4yP75dy5YDUqs+XuHl2x/sXB3CfJdls06ZNXR3fr4N+8pOf6LMji9xu27ZNPYDvvvtuOeYYl4RobTQQMAI8Guu8ambZD2eHXidLZIIkCOQCRbbvfOdK2XLkhJx80rjEAckpuySQPRKr6wGJYvzZKok7Xw63FTENIs2eV0Krmk9YWiuqqgTVec26/yNK60rkOhobtMq/Ln7w8iDHjivpjKBbtxDmAFKZvUXuksSg1GNqM6aVqFpOBNkkJhQKIzRUoxvNb19nrYFuhzeYbEvv2Spjm0W4tOiCVkerdiCPLYsu/FsD9KnZllqQZ0g3JBaP2SwvXMZUbThCDA55sgFI9sEaaxlIHESybiIdAS/F4M9uotPg2okgqj62CbEnqpoRbYxFk9SYcycyTCSXRKrpsYUXFsa786AjvjQwJDIMGV4zlv3yxNjm6qFMVihGke5iwPk0Yh6LTOWcS4tCaGR1Rkv3Zr1Gq5NBjA54MclNrsjCuRzJ9eS3fdWQNAQyIRLemRnJrdfHpRweKwGSjIACLEsb1zDSB6Q3gRbbSL8+GLuS3BCSm0GqozWya/e4bNq0X/Xi6f3he0x/u6XZ3ChSWpB0+ONZ0+uueYTcfdeDZO3atfM/2JxNTTGGxVsc3BPIeTjrrLMKz7X40Y9+pGNKK5DDfH7wgx+oCxBuOdPT00U+AqyvISNgBHjIC2DdLyDQnuDmb6KDiPomcUebxpt/N5HY7teLLc7bRQKskfa1ksUOtNwPIHw1aTQOSliKJAxccQWfGOaSwxzRXZog5oUJeNnyd0qpEiV0EVlnC0ZIEVmD866l4RiAxKHcyjp3UVaswCCJXsKwUIY2T5PZjJxMgOZkBdmlY717gSaKseGsCVBEm9OR9N6yHF8Jx5SMJiNkdOu1npwBUozEwVuW5UU+G+pdqhSs9QKQQSR8lFQqUm5peLPWnbH4KHSexywvB3Mkd0UNJcPtkVIIoRZuKDmy2WgGKkOYqKQTGcbjiThB/6zIK8dVG0Txx6WSE/HlOIgk+OQVrvARX+YxPZYv5YAMQ/4hsp78tuNYa4YyWy/JRNkl0GW1vXNcw4FsmMy/dprNzVKmlHGO20HUJJKLV+6dmW4HrooahDlb+gAWB6unSiyzLZKbFcmdliDaKlLaLqG+1C5t+qIWHe/kCiV2dzKuTcohy4QE4R2Z3yGN5MZbpKRWaukkF4lNHB+fa39Wb5zgCpSohGJxhbhm/RyZ2f8sOXBgRvbv3z//U61SES9Uv98kMR4fH1dPcDS2/K7IBsHdsGFDpv0auRjPfvazhbLwg/ZFL3Le1ldnBIwAd8bIjhgwAv12duh1uCQ/YBPWnUF7VUltoLra/SIBpLblVytsW+PLib1Xy5RUI69uD91Zd/mtxVjdCYjcOr0tbamdleOZLtFM7bKa6Pp4wPtqZK6KV3v5XD2bktwJtcryn9ekHGHbn+IJ6X61ShHnE67GtP9QsNvKdxfgMxAHxlIqOTeC9Ie9I29sYYfBmPrmUtkrq1XrznM3QJssFRkrL2w/t39mvlpYB29blyiGnyneso3cxCmfYIUucyInUQyy4KORkNe8pLrZelmaMVHXmlqFZXnSQozpH3LIObMatmSQUiLSWbjzWcioyhoq+Ul0M3V3/UznRFVZ74M1V4luamypvRpyjt1z4xq5nGvEKvXIk5Ds46sRB7JuIpvk4jZRa47LWGku91xRk0SrcQmV5GZFtnE7OGY+iS2TdDaP1hdNR5gXjpqtniLN4Bb3H/FaEQo/hPdI2FY0wn9Ci63EaHJnlHhmtTg6WoK4LEFbf8njY0oOazIf0eUszfG4+gqHAe4R2dHlKDpBK8gldclqrxbvmZd6xM2W40Npu0TNB0mj+orW7tLiWWC96AkxEVX+DrHkHkUUGP0vXsCQY4hwEYSTEunYr2XlWvz3f/+3/Pqv/7oWChp0sKXXZ5MdP1gEjAAPFl87ew4C3BBJTkDj+81vflMzhNHEFn0Tok48N+SlJTq5cd8hQXirhMGdIrpFmfCtbSV9uSk6RwT3J6azrjSCS6fy5XAhvJBj74O7GBxHdkk042GBf6wjBLgJ8G90kW07i/MncGTXRXpxY4hiJAixktasaKsS6oiqW1Bvt2UNeco7vs7xMBthPNk2XgxMyUoD79yGEu48Ky11Q2iqOEPKYVXGy4kEvJgkqXGZqFR1bE5u4aaep4v1kgFXLtgdm6cLJlGM6PBE2VlwpTVPcjk388lLkputu2ITENc04sc5Hph1RJRINgQxS4bAWPZX3YvJdAci6TS+4zJRJhqXPw/wSGqQ249WucJcScYquDqkE0k+A/GnXyQN4MwlsvNgKGvGFz7DfGfqJU2TnEpIJNr75OWIuXKuvKjwXJ1rdY2MlQ/kXLNTrlJZjo5Wsd23ScqVUCYnd+bII3BX2KjyiGr9OGnIUmmA7r7EFI0g0Y0Khektjo6VanVWJiezSxzHEVpZrNRuz06G07LKWJblE28luQGlkrOKcKCfdxFoItrqHa0v9otb1HyYRLUXishU18827vE7d+4UtLgQYFwXcGWgIU1ol1BUKjlbAV33unDgt7/9bSW/Wfref/zHf5S3vvWtWiyj6GfPMqZjH+kjAkaA+wimnao7BNKcHSDASBCKNklnxFdffbWwRffwh5PJfKeE4U0Sln4iQXDzYlKr5JYw3JjKDNQFYV53S3RHy421ijJAnokOZ1VqqkitNiGNxphMThLNgXFBrom6ZJc65TjcClyBhRblDvB/dYQRIgkpa8+yV5KrBRHc7/KlAo5gckY8YLMKDtAfxK3adJXU2O6G6GWWVlUf3DFpRNVcT1bOC5li656IbyNuyLqJbH2qsyhb8MrNLBoQlaTWdFFvSGmmllJdG1zlOAoz5GHltbF5EV/mc7BWVs9arME8Qdw1t1ga4AiimwvWYHnk+kDVaac3IDHNIevMg98TYc4m/8gQYn3pyIvQMleuL4hpFia8IOyvluRArSkbJ7L7ZH2JfHOuPC/kA7UxfZljnlnjj6N1EsebWyQwq5gFBTSOVwlCVlli1okyx3FEolt25Hi2dpI0IhJyt0sQZliIqf3Z8SIp8oHkXXJudoNMjK8TyZM0xGtEkD6UkD1kRXvHnE64g8yi2ThearX7ZHp6cWljPyYKrNQaJ0kQohFeLMXgpapZfY1I3LuNGdHg73znO6q1JepL0MNHiIkS83d+eDZMTEwsIsUQ5LExdqKW13i2oP8lCpzWKIDxqU99Sn784x8vrwP71KpFwAjwql261TfwPGeHXhwR+jvzWLZt+5Js2rxNjjwSzRzSBtdcAhU33knNmndVx3w1MhgluluI7r5MrR1VpETWqubQnQtaWW19Jj0ao0SWcqdzrnJXZYwtYSK02ZIF5ztLo7pZRUphNZOkcJSXOBAtLgUUjch2QoAME5XzhBjCkkdy53AjIFGro2sCFcUo4FDVKGQ7qfLWWJwH8sU4JiplGc9xbVCHh5YzRN441WO34azBJirosNOvKnVs0Iiqi5TmRcdn6pCsSGUDeQSRrX5cINbn6FkhwftaGto81wTWfbZekcmy8xvOal6rnJfExmedbVlJppE05Oi099cccQWTZGPc9884Yk9UmJeTPGsz8EXfy7mydMJ+XKzD5qm8Nag4Eqheugvf43ZMouhoEUpma6JbOmLYjJFU1p4MV2tslbnGA5qIiQVgEB8vQbhLgpz+JMJLN5IgzC5QEUcbJRCIMNHejDFxD6E6nMoe0mVAugME0Q9xl8iSPYg0GydIqYTsYSFKrfr6xok6Br3/eU1yS67RrL5I4ujRy7r9YtPITtuTn/zkzCgr3x0iw+0SCv6fAIWXTfiIMf/XTcT2q1/9qjzqUY+SLJ/39773vYIXPLkg1kYLASPAo7XeQ5ltN84O6LTQhmVl6vZ/4PskLH1HSuG3tFqTi8BOa4nSWNgKbxVJaHnUOkcGorNZiS4TEscblCzjl+sqlR0QCdCVpY+eEqVzc9MyOclWJ+clYrwjpw+0sK4cbyxODsC5XcLYUlIKYaTYgxaoCKq5FcLUK1Y1vOVW1DebTPlENYYMcclLwKo1Aqk2naYzzwkBIqROCDEa35oSXqKDadj5imKegGUTNQg2CYXdRXxDKctEJf9FAFJHf8gQ8gjiQbb6g1DdEJINwnqgXtGkL++G0GkeLho+IePluVxJABXZfJJdroSjhttFU6bGsksoqzNF3TkrZEVomdeBqjomy/SYS7a678BSIsu5iGxDlvN0zLwYhFKRtePZLhzzBSNyt/zddj5a3PyEss0i8RoJS7dnE+F4UquilUr3SjOakIP1OX3pXdSwqYtP0CptQY71GeRVX1DDpRXk/PkcEV7fIsJZ95pJEXTCuUS4LDGyh9K9rkpjSnPuEidKEDygGuBa3Vm5tTfOI81HSNy8cNm3YSqx4cbwpCc9qadz8OxAMpFMtCNSjKwCqUR7st3k5OQiUuzdJ6gCRyJcWnvLW94i11xzjZJga6OFgBHg0VrvwmfbrbMDmbrcvAZtRB4Ed0pY+rKE4W0uKhtX5ODsnMRRTabXYPeFJi8rugKxRQdIFjMEmW15SCskN2uLHsK62ZFrbeh/MaVPP77ZDKRe3yiV8gYJ1Mt296KodHIBa2zTB2Wn/RVnUca2eabFE4lfkKQ4lgqerV2UzCUa6IonZLsRQNAgz1oyt4MEYk77d963aVv8kGuKJRBBhSBOlksyVs5OkEMHylY6/eZpWX0xCLLi2erPwog+IdiMLW8ubIGj8a0oX877AAAgAElEQVSEztUhqzkrL6cXJjY/Uw804tze5uplmWsg9ciXPuwlBxNlaI70QR0n6hTRiHIlDb5EcJ4VGdfWvtlQKpVIX0iyGslw6JnXj3dIrquN6+4EUfKsRhSaa2pTh2ivUOQhzNa1cv6oeax+18Kc6GsUrZd9eyuyYQOR3PTvZaO5Vg7WjhIJKASRXTktiCDCMx2IMFvxFLrgXOkoxJRR5oW6Y0R4a0sakf4dYQfKSSMotZwlDylJvfEQieSuVL/huHmqxPWXtfIblvcI2bFjh1x//fXyhCc8YXknSHwKUjs3N7eIFEOQ6/W6yit8hNhbshF5znOf+N3f/V0tlfwv//IvKx6bnWB1IWAEeHWt16oZba/ODkQHsM9hq6r/jUjXDRKGP9CEEidzmMl5+CBZWCeCqb0+oDh2Z45lEklvmyWWNS76q9pfSG5WBIeSv1tkbrYktfqcTE3NyNhYuiaP3hmLI9EURNilSSpkd5Oc197QB2OhReEMCG7WVjyfczIIJ8vAWSErasix3j3Al5rNWiN0ruhJOVcnycAB9b7FQaChUWS26TXq2EYo+T9+18lFADutWj2QNRPufFlNHSjwvu0QvUaDTHGJNNuy5Lkhf2CZp42FkO6eo0RzvvctRJ0EMIj/5Fi+9y1RVbSxeRFasAviQCaJ9mYAwpodrLl55mmAIaZoydMKVNy73zlGQL4P1OJcbS/DUMcHiHxOsYtag1eGrVIp78jRvhLFxMUgP+mMRDicFfJkD43GlATBVilpRHiBCOO6sr96lMTBvfrCHAjJYvjxrowIzx5cL6WwLBOTeQUzNkkga9T2LJssrxWJjpCgdFtqMRt3D5lwjhAlJBaLr6uouUZ1/OxaqUsFvsWhCwKwqxVXX5t4gV/e3Xn79u1yyy23yPnnn7+8E3T4FKQ46UDhI8bYstEgw+vXr5+PGPNvb7P5q7/6q6o7/uQnP9lxbPTzrne9S/7iL/5C7rnnHi03/4d/+IfyvOc9b9FnP/KRj8jnP/95La9MAuA///M/y8/93M8tOT+4/NZv/Zb87//+r47nWc96lrz//e/vqlgI7hp/+qd/Kn/3d38nnIcE8pe85CXy5je/eb4fXgr+6I/+SD796U8LMpTHPvax8oEPfEALkljj3uhc660ZAn1BwDs7sEXF39Wiq/WT1wEZwtzAzjjjjL6Mw58kCEho+7yE4bZF51VNLzd3mZJqdU6iCBN0orNZSSYktUBy17ZSz0hUQ66QPlykBJBczZbG7kxLkWaT3GZzgwSBk0K48y7NwKanZnSMRBEPsGlHyAO2ONO9Pjm+QaGJJhpXrMZyEonwvlXntrJGh8s5EV+vJ/VR0qwFgwwdrBFVxzM2P1lr96wjkXmRTe5U6IIhYehFs5LEGE+14Yg9SWx5ul2ix2r3lSNpcM4PoVaxm6xklyZW8lpzxDpJ4okAz7Si1G5srjTxZMUV6MhqEGHmmpfoRp/7qiVNcsOKLKu5PvNtzTy+4yUnBclqyEDmmgvjQvbQHgV3yXAu+S4vQj5To/x0JBsn865Nv+VPaeCs4g3IHiDCJG/llBkmmSwe66D/xUf3CCXC7PLMVE+Shty2GI55IoyuN11igIOLk0bs6awRDmpLvHaTHcbq/1vJjxpHbPGvaxXCSF+9ZnNa9u0dk42bduv3AnJfbWwVCbYvfCCekiDeqt7GUn+1SHzCiu/JVFm76667lIAV2fbs2SPf//73NcHaSym8Ldvb3/521QXzb6rTkQjXqRDGO9/5TvmDP/gDedOb3qRk/j/+4z8EssufP/3TPz0/tfPOO0//ToXRv/mbv0klwDwjzz77bH3uve1tb9MI9utf/3o59thjBW/iPH0zSYRPe9rTFNM3vvGNOr877rhDfxifb7/xG7+h/b/nPe/RYxg/Sd8k/B11VO/JjEWuXRF9GQEuAuUR6sNvTzHlboivh+bGG2/ULS0qBfWlQQxL/y6B7E5Y9nSK5MJmN7voL8rZgAIW2RpeV0QCkjup3r8uKpS1FQmBpsrUWqnVD0i5vEPK5WxbqZiKVCTFCEl29+rLRBQ/SGK5ZVFk2SXqEd2CQVAyNTuxTmUADTTBzdzEL/D3Ed+8oggcRyGDaiNSMppHdLxnbIkEq4nFnrH7VPLgimkwRiKbOEpkaYDpl+Nm6y6Jbe14dtljn8SGPCPPoYIoaLVRkUqIRCL7ClQ9botI5hHwg7UxqUc1fenwEo32s3qSyDF5koBqw8kt1o7nR7Z3UY9BJJdMsg7V+oRMVOZyXyAgrxCkvDUAi10HQ72WsNtLa5BqZBsTHebIiw1jyyP77qWVnY+87XxPhEnw4ruf3iKt0laSMFePu05ma8dKLb5egqwXQsh0fJySxSzpFLtCQXyiewnOSEzTUaK1DSDw2ePWnR8ly3k+wswtUEeIrBZH6J8npB6NSRyk2LnxwcazJYgu6MvtGGJ2//33K+Ersu3bt0++973vqfbYR3whnvz/X/7lX6ouGYJ8330ULHKEFb0wz6HXvva1i0goRBW/+Fe84hVKKH175jOfKXfeeaf88Ic/nP8/dkDpD2s1qoymRYA/+9nPyvOf/3zVHz/ykY/Uz5KId8EFF2hE+JJLLsmE6mMf+5i84Q1vUFlJlrsFLx0nnniifPCDH5RXv/rVei6esYzn5S9/ufACMOrNCPCoXwEDmD/6rG6yc5Ndsz2GTmzlEYL9Epa+IEH4jVQJghJGJa3oeMl03y0S7M3cHtatQz1+rOXc0Cnqu1UfLHgGO0KcRQxCqdW2yMxMQzZukFzNYNTcKA22OIlOB0Sc8uQSmyXioSb3S6WUnckP+YLAhFKS8XL2cRAYyDDH5/neMkskA/XIFSnI25b3ulgIM4lblVJ6ieV6syQH682ORSDY5odksjWf1y9b6vjy5kWjmQdb/SwbCWDZemq2xWPnhJChZwVfIttgl2drRp8HqljbRRoBz/MqxjGB8bfLRJJXGRZp1UZdNk9nR931RaPmXlryJRQlaTRiWT+59CXjQLUs1WZD0zd5aQH/POs2ZA8TJVc0I5OYxZtkdiaU6TVIjrK+OxMiEMHcJDCI8IktR4h9mf2pNALfixSyWK0fJTN1yiavlyBYK4IneNYFQQli7MFCvGQzXmzno8Z3SZBRhlglVDFODJRGzpZYxNFJreS17Bde5+JwIJNQ1xvHSZ0E1ZCX7LaoefQIkcZLWhUpV/6QuPXWW4Vo7Jlnnrnyk/Vwht27d2vEE/eJrAY5fu5znysXXnihkmF+kC187nOfW/SR6667Th7xiEdoshzRV98+/OEPy2te8xq5/fbblxTbyCPAL3rRi3RsSeLMOSGokOoPfehDmWPmOclu6Uc/+tHMY4hov/SlL9UCH0S4fUMmwUuB2b6ZBKKHr5Id2i0CvCn3qqwhQsBbNG+/y2tIB66UoPTvmIiJxEe6yGyA5RikNU9+sEa3BTds4EHHgzfnoYJLhJ6b6BHHZj9cSbKL463SbFLBbLuUK9kVzFRrpzpfZBgLdknN5lHSjCHTbkwu4nuMbuPykKSqVFaLIiI8rtJZJcy3tSKqyZnzEr8cGXa+rJCwbILiCJGXFmQVY9g9OyGNaE4JlOpnMwmPkxZQQIHCCnkOByTQQcLSdKoeJ8gfJHycBLu8CnTqvuAIZ160l+gl65KMRCM3gPz6qDh9chxkOc8JweudO/n7Eu2dKjtindXoD7nClhwiDDmfqTkc8sbl9b9es4umeV/NvQD4ptHlxoRMjeVHl/fMOplI3thr1fUyVlkrgZbzzSLC3hZsqa7Vf8I7HYSlfEK5d88GWbMmlkrF2achFSBy7YrfuBbER0oQUJHw3mzQ4/USCm+02c4SgsRAkB3cmlmoRPMPKKihSXBZu0pllSeAUbY0hBLLJ0nQlgSH7neuiYRqVvMWXIS69XIdrxep/44E84m7y7sjJz918803qwShaO1pp+Q7nlNEpUmEQwuc10jUJjp8xRVXLHKzwEf4la98pXzhC1+Qiy++eNEp8ggwiXkUf6IQR7I94xnPELTLl19+eepweL4i1UDre8MNN8i//uu/SrlcnifN3u4NOQXyB/TByYaGGZkEgSofFV/5Cq/OM1gEeHWu2yE96uUQYBIKbrrpJjVK771hbv8pCcIbUj/qLM6OVMsjR4iJzGZrHCkxKvFGLUwRBFgW5UWsjtBEEYhokri2D4QxNJtbJQymW3rgPJI97YhzVJFItuUkADGvY1UbKDrO9CxvxgIhg6B0si1zUoC4o4cv54JcoRcdz9GLchxEDMKTlEjsnAnUhss3rQTXwB6sKeM50hAIKSQRDXC+PRf62QlZOz6XW8jC25p1cpA4iHVZiVLD2SkTnryiJ949lx6l5aUAMj9RKsvkWPY1CEEnuQ5tbF4hDkj2WDmQdeP540LSsHVNvg6bYyDBeUQYz+G5Rl2TErMkL37NIfF5Y987F8qaUkXGx7OvW6d9JXntnszbAq4sQXyERl+z9d4Ur2kleGW6vLioMdHQ/bUpiSRdQhDEOEvMqv9vVkNDy7czCN3Welqbm52WSnmNVMZ4kc04SEssb2ol76YfgzVcEG+RQKPPWcfwsoBjxK3quFhtHO2S+pItnpQgPkrC6BIJ4gf1fhvO+QREDemB3+rv68lzTtYp+Q4CjPPQu9/9bnnhC6lwl92QD2Clhl4Xcunby172Mk2g+/u//3t5wQtesOgEeQSYBLqnPvWpKsVItl/6pV/SqnlEotPavffeq1XtSOQjek30mf973eteJ6effrpcdtll+jGkGl//+tdVJpFsn/jEJ/R3JMWtW4fMbnSbEeDRXfuBzRwxPxqoXhoaLLRQ3BCW12ZFgm3q9hCENyoZzWpxTNgKqcKkJqiI5Ol8ibIcreWIQ40k5xFXIr4kFqDRw+Iom5Sw9dpojEsUbZeJicUEAOgaEXrfbSIx9mnYL81oFDn7AcecjnNb9rpVm923L+yQ58vLadxx4zJeyi+qAfGD9OQVbOB8VENT7W6jkRv98/rTtTlRZjCaqU9IKZzLteeCMLP1jrQgX9tbcmWGx7LLICt5rTrf4ywJAuO6d4Zo9YRMVbLJN3gwLggi0eqsBtGcqZVl/UQjNwr9wEH3QsA8sxqEOorHZc2485BOa16zS/JaGhHm93cjjceDmsh9ztgphT3bKMu68exqen7LH9eUrMINjHP24GapVJoyNpZXXnizBMJ3Oi9qPN2qqpbtmLB/9hRXHly/RxkvKXEgoZykyWNBmJEEx1cRQh1iZZi9U0ShDJUh5BBq9RAOGh20v0SoZUkFt+Q612obZO/eCVm7MYPcN58ipejpy7sF53wKQke0cdA2l+1DIEkMLWyWtA5ZhifAeTIJZAkU3njxi18s//Vf/yWf+cxnhEQ3/v6qV71KE+mI5La7QXQiwMgucJRItl/8xV9U0op9W1pjPscdd5z+bNu2bb5KHpINpBw4TxBdRudLFbxrr712/jRIE4lYE+02AmwSiL5/0e2EVDHrnQCjU+ILn9RWrQzLvRIEP5EgvFYkuD6zUht9RM31sntPKBs3TuYmxXBsHEGGpyRUqUR2tal6vSzN5pEyPsaWKQ/kPDJ8tMo1XLJbXerNY5TELmlaaGOzSLBTwpy+XdW5o3V8SDqymit+MSGhzKm+N+84IqVYoOUdRwU2svrHK1FmdBDHAIga5BoSmeeCgD54tt7QxKg8Ozd0sbS8crnMdfdsKJP42XYgnJDmPEsz+pqpEVGsLToX5PD+mYpMtKQuPvGvk2MFEgTI5NqJ7J0GiDDJgnjj5skxdh5EGlGWNeM5VeEaJd3+7qZCW3upYshvkvzj8hGGzdwkPtV8N0RLI2fJYbAXo3Tw/8/em8BZttXV/esMd6ypq+d5eg8MMhg1mg8EiDI4ENA/IkKMggryBxkiiIgBDJCIJuBAAiKCBhBQxIRRUKaAAYJgCCKIwHu8eei5a7h16w7nnHy++9TpunXr7t+tqq66r1937c+nPt3v9alzzt7n3n3WXnv91sLuyx/3K81cmFa9vqBqzWKN+ewnJhDMUjSRdYV9YLnZPqKF5Db3eQoIyQhwfrHkDBWFAUlvN3s9hEmdCwSzbAFqLN/Q9d6hIPT0zcUrH5fCu73hFvkcBejGOm61hrjZnFaKZZyOSiEL+p5jskOKus91jPtmN8gNACSs5ygbulz0vL7i6j/+4z/Ws5/97KG3hPwBzS1+wTDFRWgGRXEvfvGL9cIXvlCf/OQnV+1gboUEAikJEgjAdq98AjBPmBRsNECdIrltCYT9aLcZ4KEf/e0D1jsCGwHArEaJQwYAb74uCTPUGxQEf68g/PJQ3a7Lug+QP9gsLlpgXB1C5/k7TNKwf0km4Wdx03RcrS7H8QKkqtwYefSBTvpALKol0WArdmyJFba33NGDWt693E2zE6ib5j6vlm8wTCn33+sicHp+ZcEY15tfYlQtFwSXntYeXuTGMYBXOy43D2wAzO/AvMPTCLpotNOhdmuun0tM6Jn5iiql1eAFOzgWBuPlxAT8jhEO5MIwfI3+zTUj7RzHqs5/3JlG/iyteGEXhJEEmqj4E+EA8Y5Br+SaZt81GQcYdssJpE2aXbTDdCdgx6MxV9f4JABucP/YwWk196lSPeWiwn0tTY4pjFgs+nXyWZrLGbBOa3cnNNdmrlhZLxA67S/+3n5XBUIrAidvusP/UJgrND2kmG5MgWw5gyuyzQ5I4U3GGFUV9B2TprHmF2uKS0tzVbZ8DKA36j4/1yZvQaPQiy37667bXGnFsFuluBrHB5+9JqDx6NGjKgDusPMV/45kDzAMoH//+98vZAvojfslBVtVBIelGQz0IACM5y8FdgD1P/mTP3H+w0g3sD1DOoFUg4UBRXLohQHNpOrt3bvXaYmvpbYNgK+lpz2ivqL1wqdwPY0MePRKj3jEIy5t6azn99d+bOqKT4LgSwrCL5ks7iXbJV6ITmPreyFLszOTiqKqxsfZ7rSK3aaFxjh0BWzLXr9JOql2lxi1JV2hS5vbt5RM57dFci9DHZICfD39mkTXFx1Slp1TFPm3kXOJQm5bVTbmQhhJ/G2HsbiFLpbjLZALeMWRAgmCFTG8FhcE2GqKtgDCFnMMWzpWKqneF1fc+5SLuOJhLO4dM8RN2+yy08YuyTHsojpsJdqmnAHwCugcVuB2thG688B8+xpjxf6EJcW4sJDbmVmscSGfGKbPDtDiBgvmln+nPemK4JAS+dpatK9JEmlxYY/GJk4r9KY1hjp/bkphja0Gv3QqxJ3B7fz4AXWYHcldZUK/5CHAyi3AKcf/fVVGvUDiku78CH8vGos1yCJwejijxfYJpQMszyjuC9OHKcoevPZpdJ1HomfFiQDgNsqGvSY1Kb6AJQqv0SUDlJE5rLfxnnv0ox/t5Aiwrf1tmA0ammEkCve73/3cr37uc5/Tgx/84KE2aPj7vve973USCJh1Gkl2T3ziEy+B+Ve84hUi5pkxxyqN6xAjTYNk6i9Ux1oN2QR9IlHvWmjbAPhaeMoj7uNGADAVqWwhPfzhD1e9zhb+KBpg+FuSPq80+1uVjGKuPBkOJuiCaVmWB2AcUaCWAjxGPd2gOj1ND2ludlbjE7G6Gd5bHhbZvTDRKNjaXlc1zvZlQOW4f/xmZiY1OTE5RCuZF5uVwuUt/UFnLNwNAMw+eQRsJPfTTkoaKxt60CULMs4JC2rpVHF7GIuHF5IBEgHCFuCca5UUh4nqZT9ILAq7Bll9wYwCILlfwCR/WmAfdnmulTpJgAXQOS86XEuysdAmrS7WdK1j2qedbkh76rYOmrEqh6FqfeMAA88zhOEt4pMnDX32WorgXEiE2/K3NbTCyotwi8DS/vK9DNyi0tdaizUtLuL0gixo9VG33TWt6uSi4mCfssBfTIY7QxQcUiYW0R5ZE4lzAeERtygIPURAFmrmwpR27Jz17+BcGqPbFIR+LbKyE5LpBBEo6X6bOtm3BtqvBel1itP/H+fgLZt0v/CFLzgG8siRI1t2jUEnxrqMVgDM/mP4d/TByCQK9wTrBkldQ+97/fXXuyQ4bMiweEN3SxJb0bAZA/zCCuPBi8sEjO2ePXsuySSKIAx2TCms478pZIOl7Q3CeOUrXyl+ALv4+tJgcCl4I4yDIjjuBSnGQx/6UL3nPe9xx0AqAZRxiaDojfcrxXIU8yGTgPX9+Mc/rg9+8IOOeAIwA+SvpbYNgK+lpz2ivm4EAPM72L5gg0aW+ygbK+GPfvRDesi/mNTk5NcktMM+L0+4ufYuNRqZJicv2mEW2c6c7XXs8WBPz5nZMYXlquIIfS/yCD+7pGxCAW4WzsXCOo7K3j1SQGCA5Rm8U2zdYrVkOV3AqCJXsPxzYf8anZKiIFG9h22kOAvwWYAOp4vt5OcyvYJdYV1V45VFE8xjBwb4trb6nWygFWpnPQ/s8DXn/zsEvAIGOc7ZlIX5391CoY8wAUxyLTvgInfSwOnBLEpbLKtebpvyAhYEPAPAvhcEdmFxS9o7Zqfjca6CxeWct8+sdOxwL9d2Ltew+pekNZXC3U6m43c5qCpwevWbhMpgYKNo1dl0Iffx764oxT/Xljx020iM5lUqL+++nDs/qbTaw9gmexRHmTuXrwXZLoUsSofKIupuIexrWTqhKJhyi1H/QUgn+J5a40gM+7TCKNcv97Y0rajZyc2q24tl1cZ6opezikrJLynQsk/sVsy9MJtIDQB3o2x43RJz7NMeA1xhcLEdK5c9ht49N0zx26//+q870Iuk4zGPeYxe9apXrQKOaHDf+ta3ruoqLkcA0aIBSIlCxkKNRVwRhdwLxmFxAadcs5dBp2bmBS94gT7/+c87TTAFcLhZ9L4/Yb+JQsZTmD7CLuNbDHguGtHLH/nIR9z/x5btWmrbAPhaetoj6itbKADa9TRAKIUFrMbRJI26YR2DTowVusQW5t8oCP+3M5v3vrzw4nXbuQ0p8FsZoVnMWeFFBeHdl1jhbrJX863zy8yzK5bBvB7Q6tcKu8p5HV3SFJ8xXpywxqRLnbMZNIrmMpivWxSFfha01SHoAi9ev1MCN1Pof2FyaYPAnUto61RVjhdNdwYXu0sBlQESucbFZqhKFLh78zXOg4Ri75jNvGJTVg4jVUt2tDAgsJPaulcK12DGLWuxxU6kVpI4RtgKwYARBuBahYMXmrHz9EXX62uMAYsCbNGs6/EcO0lJpWgw6AQcow/uL5Trv25uG4bkwZLyACjLQzx2sTvbNcQSrCKup/BbxqKi8M+9RaQ5nlsIFYQrF6jO83p+t+rjFxVF/rksyo4rcwtS/0Izl06cWXWN3nFykckuddJa2B5R6HagBseku/ORJhfyfV8+z2L7uJKeKOdWc69qtXl3P1HyhC2VPhR9xI0A1tSXWrZV8z1BE+hyfcAO0uWnf/qnnU5482tPtqpXaz8v7DJM733ve18XigFLzLu2eD/zb8hAcI3ARQKAzL+vN8hq7Xd0ZR25DYCvrOdxVdzNRgAwHWcyYmWag9DRNszN2SZbmY+OROKbStK/Vhx/RaEvDtUFVGCrBtsDm2pFHFNkUleSNtVOSaEbXPHtfEQDqrSM7ViGyIELALHFDrHnCxBuuoI9P6AvqZMQPnDeTlRD/9uVsCnzbeGjdZ0nqjhtmaEZRRwwTKJlU0bwQjstabLSNhlhGGd8eC1mEsZ2sVPSzrrNhLrkMuzABjhkFHZg9B+gD2PqA5P0EdCJfMICr64IDi2uWQQXaG5R2jOOrnPwk+R6uG3sHiJ5QBZRL2FTNhjgzS7Gmm13hbWbxbAXOmkrvINFWyiAIC4Hbe9nECCYJKdUKvtdHlwKnCtcsyKD9yzJIvyf9zSd0nxrvzoa7B/OTSbdqjqLNY1NoO333HZWVxQQXW65RdQUrUFeEWYHXXGb/1qVnDE3PH/zIrn97phucljtdAC7nNUU64GKsyduWtqbNWOzpU+KGq4Jo2xIEXifFNKB/msjF8AtAQnB1Qj6ikjmxz3uca6PFMg98IEPXDEMMNU4YcAk84y2AfAoP6Hb17rqRoAvHSvP9Ta2hshi79VSrfccGz2eCZoK5UOH0NHmje0jEoxYIR84UNf9vv2sKtW/Her4kL98Vha59d4XzG8r6bhtx073FlN7LJKlAl4a6H+NMc12KxBxrQBmP/uXpYc1P39RU1N+Fgl98nw7dYDUAqWFUwJFVr0sb+69G6oU5QsB/GcLRwjf88kTycqqldrmNR3rmuSMsA04yxor2bIBACdAfh8mGR5ww31x3EQlUNyzAMIODK/cogGqGQPbF3dJPjGEzYbFHSt1TcmDs34LpJ2Gk4WTfrTL2l33LxroH33ZP77yWTPGAOTi+bOg4e8Wkw2gLIU8GL9bAmE0YbDTdEJIklhJZ6+q1TvXIIvwJ6W5Z5NiG3aXwgELzYX2cc11blaYHlGS3q2SUQwZZoddgWrYxxT3fp6TNlX0TYWR3xEGZ4Y8RMOvab50jKF7ZhGAb3IQ+q0Y0+4RtZOOsnDA88hKqmQvVIit4ggaBAM7bKPe3UPigK61d17v7S7g77/8l//iEtWuRgBc9BUJCrZpMPC4QsAI4/hAcSKyiR/6oR9yumBkHddS22aAr6WnPaK+bhQAf/rTn3Yr9VEXSjAsbNExUXJ9GGyKDKgMrtVqzih9WZNFsdH/VRB+ymkSfc2FbbhtzdkVRXNJslvNhO3J3DyfKvUsOahSedZ0pIDVCUh9c3piv/G+sGNCz+eAsJ+JTrr7FEUlBYEVGpDbj+EGYQEfHB6QKgCEs1Q6s1S81T82zQ4Fgl2TTYRNBXBiUWbrdWOlWaqpql24BiMME2rpjV2IRBhrqubf6i6s2Liv8wu59dkg0Mx4DZc8hGolqQPxvgarSmQw924XypVUK3VMxtsV6WXSbjJVPI1FCul1h5eCoW6dWc2iF2w9z9kXW83pQ7xqnSTICIhwO3CHA54AACAASURBVBdsw/sBHEWnLCqC0M/iskAMND7ELQLpxEp9bDeZ0PnFtjLlTHO3E6taOuRkQH77wYpKOuicFLxOJUmohdkpTU7jyuIZbDyPYbGjW0xfZOcoEX7LYINLCpBWRYOPWWydUEd3KMb9pe+eS+mPKNbDR/JG4H0AuUHk8KiTxz772c86+cPKnb3lbr/+9a/Xn/3Zn3lT10YyQCO6CBJD+ktBIvZvNPTC6I5/+7d/e+TPZkTdNi+zDYCvhKdwld3DRgEwPsBMVBuxo7ncIWSFzFYZxQSwAfSBwgmYg8HMQObkEWH4cQVhXmnsBcMpQLirVE01u2z/DtL5URV/TIHmnGbXf7KSAqf/HV4Ih59oDoQNSzq8QhWaAML59QLshjDCrW6gU/NZbpVlFJvlbgMrPYH7+4s/baODhVcy1CkBcDusCG6hU9F0rWUCt7vmcpmCdS7swGbbufzDYo3pI0AxNtyEYHG5d0uusdgN3eJiVy019bpYusHiWmCZ/u2qB6oacc6wvgutsiZrfpkCrLlz6jACRUhZdFv+loTnkluCFSJRuEUYTgh8eFz4wym76NM5SlxQoHldaB5UJ1tdnBbpoNPPZkbQTKT9Ts8vg8XttkmlW1S54tf0LsyPa3y8ZGqjcykUnuSWfvqAu+de67Vu94CaybKlW+gi06ltmFentU8TJQrfts71off7zG4gFpfUdzC/jrINk1785m/+piM/egvTRnl/o7pWr6zh9OnTIiGP4kAWB/x5rbZtAHytPvkt7DdfNuQD622sTNkio1hi1A3wTZUs9w0A52ftpuB3KIg+riD4opd1xee32cEsP1M2JOQiB8LQjFaBW7yULuWXWrgxzCYViEp8w7aJwrXZSVUrJVWqfvBdyAF8Dg53Igso5QVW3cwGiTCS8+1Y1bhrF4h1c1/iXYbkwRVjLVZUK7XMpDqYZZhc7MAsr+G756UDE6sZaEDfqflQ5Th1LgiEaZjBD4nUSuyEOsb04mKkHVU72AK3CyQXFJz5GqCbyG4s0XyNZ3NmIWd6BzGUsNsLXanVqWiyakdgp8k+xdGc1+GEe2A73zmRGAAucE4pJcmwMeNzHAbo062giboCpEAhcqHBI0C0d7N1neYTnF58o1RSKTis1Fmd+Y6JVdJRpcJazHNMFisKjixZpg0+Jk1DdZp7VR2722CDY+VssKUNLuc7ROFNInmu0ZlQpn6ZRVXN2XGliz+s/XuXXQC2eq5lXgVkUmDFjtqoGu8hgK0lvXjJS17i3BWINL4WGr7IFy5ccI4XhGOgyR6169KVNM7bAPhKehpXyb1sFACTxlNIDkY1FHg6MilgRwM78T3f8z2XsSI+ryD8qILwcytY1zStqdGpK1MOLklbCoOKMqfr9ffUMb0wTUbhmrICCN9lApFcGkEKlfVSBzBj5I/1mB9894ZDFIwjzCGAsLfhbpAKRwJ/HwtQTdKYJXlY6JSUZh0zHAJwC1Ac5vIAW1oLA03W/FppAC7BD4eQVS89oztnyyrHywu7HMTnoNSWa4TOXcPSB8OqwvSi6bWDQMraWWubTC8AHkeJugGWGYMYDXGPbRoA/4bzub6aRjBLN+2aRXlSVbEO2GAxixQFx4Z458JHnsi1wT7PWweoC+9gv+tC7h2MBGG1zr2TTOpcs6ko2Ot2W7LAH1gRCfC+YLLBofa7KHGLDQ4di0tkoF/322xMKo5bZsRzHqDBgsOvMVZ6SJ2kro6+OfBLd/q2+2q8/KMjdWMgupcdtoc97GHOkWBUDSnbpz71KVN68bznPc+RHvj7Xs2NAAzYbjyB8QdmbHjf4U387/7dv3Myv2uxbQPga/Gpb3GfNwqA8WxEbuBL7dnM28YGBo0vZuVTU1Mu+QbwTSrQ5Tesij6mIPyssizQQme3Uq0uREGisNBINTaO/ZEFhDE/nzct2ZTBACGNwA3CqKDPdmp2JtXUjouGtpCbQceJnZIfIHScTCFzetgE21RPH5yOOLLZ0g7ayQ5saTrU9xeXBwvccZ5Ot6wdNbaOB49rzubmVmCWPhhgj1xjsZtbtg1qjEOzm2nHGpwgAJeWLGJuMVYp6pr9YzzRXcNk+xpML4uBQ5O2VOP2GenwVA7gv3o6T5brbzDLw3ybQ5wJ0LUbIA92NuSBmDsbU2o2pPFJSxs85grpLA2+08wDPJ0rS94jFiznFw6qvSR9CFRRKTioxGR618EGB7ZeNwoOm2ww3+G0u1dx2a/LT9OSlOxXXBrs+pKku9TsNhVpt7Lg1hWPkgXwlz7zEH3bfR8wUqcdghfY3cMDd5QJYwBb6koAeb5wpZ/92Z91Y/GGN7zh8qf9K/QMMPAEaRC2gT8x71d2N//xH//RWZ/xTNgBxaf5WmvbAPhae+Ij6i9fuvW2f/iHf3CrcV9u+3rPN+h4tL133HGHvvnNb7pJANcJMtC5NqC41yD88q93Ud3ss2plHwUiek/XaU+rWhlTJv+LD96Y5CwJOyY/k8SLPwxwsoDpNTx9F6dVrVacltjbMmwOAMK3e7WVMJdsqUP+WlpWwMdcO9J4OTGtwFwsb5brZ30tT5+raGrIFj3FbTgXWFpVwDK2YkgefGD5fEO6bS7X2Fo2ZgBTWHDioX2tnQRqdUPtqNmRz+eaOcC1NL2A811VO7KaZ0NQCMV7voYWmcUAx/gWMbD+kXYpjqzFWkmR2PK3gCCB13jn2kWa3fYhlct451qWaccUUGwXDmODWcgtqNE6odnOTauGIUj2K00vqFTxz1logx3zarCvkdPZzkqhn6EN8SgmKnkAO13cWO44wSLarx+em9mh8bGG4tKy3IXvTbOzX8nSYjvWcaW645J7TE1P06c/eUoPetCDXCzxqBoFV7gNfP/3f/9InRbY3QPYEa5UxAX39xlHBMbj1a9+9aiGY+TX4V1HQhwpcy984QtXXB+HI4JAYOff9KY3XVMWaAzENgAe+cfx2rggQLY/a3xYz5EiwBZ893d/97BDN/TvxF2y6iV2GZ0xK97C/Jz/z1bdd33Xd23o3NYvpdk5dbIPq6svwEN5D3WFKhSsWaCUgjWdUKZhkgfs0wgUGeYjDIjAgcIqsgE9HZSCG1cAJPxh75pf9sltdtjqTkxdLDKFnOm1C+XmW2WVI9vGzPn5dnOnBKsgDaCILMKSKbio32h5+794SN1E+tbF/H4ds9yJtHPMD16dnVurpImKHfnsopzLNiuO9Riwc7Jqh3sQtgE4t9hunhO630GAmmdy80WS9/JFhcWIA3CD4LxkBDaE2qdAeN/5P1O57pfYbtvhIQzw1l4u5lr15XG2ajuGJKnVlCb7da515yXXh/7zJN1YtdIhpcEt3u9ngNzDefn6j0ESUtI+dx4r/S7tTKtUsfpVX2K5/QlxSbeupFNRrZ6P85mzxDmvHHOSHgkYiYP9qmY/LezImOPQf46qnTt3Tl/5ylcuRQCP6rprYZ6x/+LnZS972ahua2TXKQrfKAQE6H/5y1920hfezbz3YH45hiQ7pBGEhiCNGCVLP7LB8FxoGwDf00/gKr3+RgAwkgSy06kW3syG/gmAy0QM6MXvtz/2Er9figPQAG9VS7O71M4+pER/Z14idFIG2CYDlKqi0LFtML1+gNRe3KF2O9HUlKEbdMlyJ5Ys0Sz5BH7ElUuOEfjH9odE5H6+w3Wxi91IaZZpomLbmMFOkn5m2W6x1V+PA40Z6WeAVxhay/MXZhmnBGQDBVgeJAtotCuqoNfs8QHuf6AAc8CzxWTDqs63I+2q+wE1rB4AHpBvsc8A+PGS7UM818o1vf12aP9wJtTOWv4ceHbOG9mwA82B4H6l5uIqUqxjeZGY1w4sUMRibkiBZpidlACUof9znmuD8Q1ezRgzhhcWjijNwlyKZEiESk6rbB9Dv/L0N4sxRrZ0t4LQf0xjdqcmJtH0+osWw6zQRXv6nrEzxIL4lBbaXWnA9ZJuSWdvfoLq1X1u5wuCAdnXqBquA5AbsJCjbGthnmGHn/a0p7k44qut9QPgN77xjXr84x+/opuw5M95znOcHJDF0TYAvto+Bdv9uUdGYCMA+NZbb3WhE0xKm9GQYQBssXxB54X5t8/om0rgU6dOOb3YVrcku0nt7D0OQPhbzvQqGML0atKZ2edA2H+2IIPpRfdraysp0JOlZeQS2VG1u2U1khu8F+ym+Vb/RNkP7vhlmFCK5CzWEY/aTP7EMs4DeKWwC+A2TDaAhZkl12i0I823kzzm2ANy0yxQo52J4j3L8u1iM9BkJTPBK8V2w6zcetlu36CjRT67kK0o3Os/tgi+ODKVj9Pd87BAq58RC4EDE6FiIxo7EjHgpBn6t+opEssdTfxa8iDbo9DtfJz3fp6cj69zihjGGNdc3Hhva7aP6EIr9+wONak4rA/U5Be/E2pCcTCuNPAztITOcExmHjOpSOhKTg3vl8GEE//spCmh36Gl1f42pTqlbIA8avHiv9C5O+/r4n4BPNRZMA9S/V/88N9bFQVMgTG1FrhAjLKx44e07eEPH+x3DEBE8vZrv/ZrQgt8tTaIH0A+Ouzf+Z3fce9BCuB43ngg8//+/b//9y4NrkiOu1rHor9f2wzwtfKkR9xPvB/5Mq2nEdUIQ0GxxOU0O8hi8JkB3/yMiqVg8k30JTVaf664bLCzqmi5eMY/noEDGngT+1+2hWNElt2kaCmlbeBoZHsVBJECz8s9SXboQqujUnBA3exGMyIaRhG3AatoDSZ0oRNrqtI1wSSaXvS8FvNKwRYFebDGvgY4x68YpteneQV0f/NcHh5hXQ/nhlihagaTTUEarKqV2gaAJ4iCIjQTwM8H2lnPhhbuTQxhg2HN+TzMtLpedwqAYEC8d2QAONUUEwPcV3C1cuzLLozBtBVzThFHhxSJhQoDdPCW9VjOiAawymGmNC3rdKOiFH/tSy1QJTihrls0+iVJ5eDkUoGc75hAJZ1YCsbwHxM7hta651BRcHyIdjpWGODQsnqhmyT71OieEkV9uURjufgt1F5N6AUKgtjZPGJHRtwtBAUSgVGAYggI5vbv/d7vvZxpfd2/C6EBAeIjVJiD8cGFGX3CE56w7vPfm34Bh6XnP//5TuaAtz0F3zwTxgj2+7d+67dGqs++UsZuGwBfKU/iKruPjQBgtspwgnjkIx+5odFgQrv77rvddtvwIIuVl6AwjsnycsH3em/8G9/4muLqF7Tv8FcvpcMNOgcV3PigDrNOm7kwrYnJpmKPYwHn7rSrypIdqtbYxjXuOANI4DO8cOmgLA11sbVH3SwHRaGmBSyPQ79cw9mFtUInd7DAHeAV6YFlF4b7AcdZXr7cF0VdgGAryhkQiPij1wqs6CjSB2QHANdWt6KdtdbQ4At0wlb/YLuRKVj3NNfKLdMmjSLAQqYwyLGhuH/GaWaxpH1jJBcOfsZfPoXuVzo4Yd93Y2Zak1MkGvolCGuRBYQ6LOm8adcXZgfV6ZxVuWIVv+135+gNfljVQ5ci19bs4rQayerCN44HuAfqKjOkRpE4T2t4MAYsuFEgh0tGqPk1FMhR5Lr8fevvV5gddTUChdSD5MWFzh4lWrYtzEE5FouJxvQMlYL7utMUdmQs8nslYBT/AoYLQMyfm8kUk6oJG7tVtR2+GYw5nR1F364e7wt2BvEA/oEf+IH1Tt33uuORF/7FX/yFY8VnZmZcuikpcKPY9bxSB2sbAF+pT+Zefl8bAcBs1fzt3/6tfvAHf3DdvUfvhc4XRmP9QRZyK+GvfvWresQjHrHua1/OLwC6z58/r+/5nvupnX1AXf3NkEI5ipDm16APxnIJ/aWf4QpcHC1bov6tZxK9Ah24JIuYb12nZnLjaryR7nL3VDKYZZhQgBlA0dfyYIicLTXBZDNQOUJD7D8X7Cy63/0T/mNgXs83K9pdXw59wAsXmURvcy4JVVs6wfUAm1ZYBTKF+XbmwLWvuXtazPXKlrzizHysnfWuKa/AExh7tn7Jx51zeTkmDDgLAZwiLNAdaqeiIFJmWJitTRZQV6SdygJ/cRexxLH2KiqvTmq7NGZZRWGw39mc+Vqrc8C5hXQyfyFooLLU3aWobMkdsEvbv4YCub0mEx6o5pw0FPr7HriiPioa/e4seWEbcpAz6nROqJmuBvjYoJWD6zQWPvHS8FAL8fnPf95JAoaF/GwmKEZfyry8le4+gz4D7OhBqhDBPKgB8ikKw6N4s+tOLuedsNm/e63JGtYzftsAeD2jtX3smkeACRQpwnoaq1Jsa1iNr1WP1htkcfDgQadv2ki0IwwF20RYwoyyoT2GtS70cUl2i9rZnyuVVWleOEHcotAKDdBusUmLjtjbsmh5y9iMTN6vdndSc91v+E+V5drZYbpYCtsAiZbut9kJ1EmzS6EMgy4KUEQWsRfdr8Fkn18oa7xiO0pcaOaAEBszEuMG3RsAHnAOY+pPGlsbgAd0Mk7WGMwsBs6ZYrzqX8TAhqNVtgC1s3pr5fdNozjvxgsrAW+ShqpFR5TIcC9QqHJwXIlV2OY+ccNkAXhbIAvgWn5ZT5jlFnzEiPtamB2TXPHbykIymNEzC3vVyU6rEh5TgvevUWzWbuxSfXxGMq6V992+n7zvdr9inXTFgd7FTRYoCpZkE94Cwkjo+ue79GuQDVys6fBFioJluzNAKATD933f9615fu0d942CYhb5zNMPfOADRzm1uoQ3iJHv/M7vHHhdGFEKomFE73e/+4303kZ1saIQDjb8Xe96lwP7vOsmJycd8/ujP/qjV23f1zLG2wB4LaO0fcy6R2AjAJiEGjLjYWH7XRr6b6A/yIIkm8upbMYB4m/+5m8c+0yRyKjaoMK/LEvV1efVzt5HXb73VtotnCB2qFxFpuC/40AnJWebhvDT01yR0ZhzghgINpNxx0rG4T4B0i1QAuAaFhOMDne+lZnJZ9wHAJfwCMsBwckGAthgP1CEnaXYzAKvAOov3Z2nv1ns891zuWzC8vtFpgAot+QcAOpOUtZU1b/dj0QBsGz5FMOaF+4VPk0zY3nHbKj946m+cc6vkS656N6zkvFZiZ0l3qxkedkuSQfwvPU1IrpDp1v3F3flxW+RFJz1nyebdhrXIFyWAeD5e7G9zIxGwbTiIF4hFeg/YaRpRUGs1GC5Ya8Z48zYNYlEUV/blE10WzsVRwuKy34P49wz2C+JaHVOKMnSJa/flYuEWvBIjYU/vKKLzHFoQPHj3ay2FlCM9AJCA7/dtRIbm3F/1JNgeekD3jDTsNJolNHFXq2NIsRnPetZ+vjHP+7031jgsTAA+FP8+La3vc0RPwVYvlrHYVC/tgHwtfS0R9jXjQBgijSwYmGLzpfcw3YOExasQm+QxeWCVtiRz372s3rUox41dHtwM4eRvjARD6pUzrKG2tn71dX/Ni+JbRoJXPj5+ltd+LMOd4vATgpAvQyW0fBeXDysdpqDYwcUwppS+beo+R2Y1GG6WI5h+71mhEegw+0kJe2o+e2iAK+EPuCHa7HBSALQBg8qbPvG2dyRAoYakFNEAg8aUwA1904hna8BTBc6VU2WF20pw5LNmeVTjMMFQRVWQd75RqBSnJnWa7dczK3O+q3QevsQalxxOKFUliygplKwW2mQuysMbmVXKMluhn+BhtzhyJACuXipENTwtM4ihQHfg5uUpnWdXgAY9oPLSNXwqDqZ5ZgSqxIcWSqQ8/WrpHJwcIgkAtnEXnN8km5ZlXiPFN5hgPsJhQH52CvdL9Jkl+a6SJcyJykJg+ySlhmni+nwVxQEK/VBo/Lj7QfFXJfdwFG7TyCJ430B6BvUWAww76J59rkDbeZcP+pzFZZmRB3D/r70pS9d4XbBAuEpT3mKG6M//dM/ddLBa61tA+Br7YmPqL98+ZgI19M4/mMf+5ge8pCHuC2a/saW1de//vWBQRbruc6gYwv2GXbElxp0udcY9PuszukT25K+1km+oUbnnSqVDa2uSktV9DcOYYMPq9u5oFLZzywrw4R/76UQjYX2dZrrrNb9lsOTSt3Wsp/FcrpY2UwoLGezU9FUxV9oxtjgdQt4tdhgJAqwz1b6WwFeD/Z8xACzSA7iMGeRC0B9cNzW4Z5tlDRd65iFbZwbOzUL5KNVhgy1fIOxlZtrp6ammVALFgKw2P2gk0UJxX1c49xCqAMTqfFZWXJKyAz3AiQjwQmlyguufC0vkCORzD8fRByjO4d44i4VgAVWgdxRzbRCLSR+i8FKeFRJhtev/zzl4JgSl6Lmv+dSQNIafffLOErIHUyf40DOJcKMUV7pkJEnvh1SJ1sGzmiZc53yrRoPnqxquFr3ek/58VLYzI4eiZtbVWg36LNH+EapVHJpn4MaARFIACBersbwhwIA421PYflv/uZvumGgPofFCAQSkpjHPe5xzg6NNLhrjQXeBsBbgWq2z+lW/OsFwHz5/uqv/soVJExPk2KWNyZNQKIVZHG5Q74W9vlyrzHo9y3nC8aD4jyYDF6yD/inF1Sd+AwqTu+t4OMb8kI2tnHTNNbiwi6NjQ+RTmRH1E0zXWjBBA4GOFhllcIdSuRnAnlhN1plTVTbQ10SAIqWtADwinRg15j/aQBeCY8YBAJ7f4sgDwrkAMzfPFfWxADngZnFUJUo07ghrwDgAjyt8Aj+vdGxtbowxoD8A0aqG/efe/RKceQfA4rcKKLrXSx8/ezKAr32Ak4gTUWxP6whSPco6c6obMg0KLjCx9dyU6CILgSRW3IHnE7w5TAlEUsFYJ7Pd7u7V+eabcUhcge/bCIOkFYQjOGXX0TBLnGE7RIxXO4QYVHogm38i048lSWin/3PItdE36YkPaBGd7BUqRp8l6aiJysI8DdZ2ag1YLcJgmGUDaYVQgPLsd62FvnE5fgU/93f/Z1jdtH5Dmof+tCH9MxnPtO9V0YpzRjV2BcAmOQ/ZIWvec1rVl0axyQAMsQTf24D4FE9ne3rXNUjwLYKK831Nr6ImJNjT9MfZMFKHgPvrWgF+4xnJJPuqBoFCV/84hdX2fAgyfja177m7GrYmuLlAUuRZqfUyv7EFdH4W9iTsGV7B0uwAYNBQJYGurB4QGFQUSe1gzbK4QmlMFIGq4a3LtICSxcLUIQN3cWur6FrxpWB4rdh8cbDQi8oDjs1X9akAfBwrkCHS5SwrxUBE0eXAiZ8x3EeHC6s++aYsZK9EIDprsWRxip+5hUpB+w6dmnosgHO/RKKLKkq7VRVHbvo7ZuLCS4jZfAvcnoZSP8olVQKDtpRwlms+ZkJTe5A++o5k8czmIXWmcZetdLTgletRYfVMcJmKNerhAfVNQpO6Vc52K/E8DkmGa8U7FIaGFIGZCXBmDLDpzvQDpGfp9DQO6d7tdAtOXZ6UNsZ/YLYmRnUKIRCcjVqx4P/83/+j3bv3q1jx0jHs9taQTGAughz8J2ReXXnzp06fhzv6NUNWcArX/lKYdN2uRK6Yf26J/69ALMvf/nLndcxgRePecxj3PuNdzPv1xe/+MX6yEc+ok9+8pNXtQ7aN/7bDPA98cm8Bq65UQDMF/E+97mP+3LeeOONTgtMgRuehVvZfOzzVl6Tc/cX32FQjzYL/8r9+/e77TtMy3tbXiT3aacPloztYJEghQuE31IJRW8oTPhXSycarWXpQzk8oiw7r8xM/SJlC+2oHwgAFGeXbM4siy+K33BJsDxzYV7BSGhjfQ22eKFd0e6xwcxaAQwpkIN5tYrfAJCwvJZzw9lGqFopNaUMXBOwZskd0D23u7Gma362n8XCOYC5kWoLG37HXD7m/nCQQNXwuLpoY1cTh8tD2zmkqHSHeUwuiRjmgnDc2aBZ8oIgOaIgQhLhB/gFI1ocs9A+rnOLK6UPteiEOpkt0cha+xVVTpmBLsvBGd5XqcrBiSEOGaGIWjYDQVRSSQeVeezdsD1rdO9WJZxWEqzU4FeCB2g6/hnvd4E5BRZ4K+PeB10c6zUceg4fhuVef9soKB523Te/+c36wz/8QyGVuJobu4xofXGAQObAO4X3HY5LLE5e+9rX6ud//uevShnIsOe6DYCHjdD2v29oBDYCgPlSAoAL30KAMNW5o1qdsxJmuwi2YlStKL5Do1WEcQB4seWBvbBamp1TK/tTpfq6cVigSCeVOR2ixQYfkLSYR9uiE+vu07lFquqXfwcf00q0T53UKEaSVA6vG+oUAfMKkLQiiQGBix071Q2AN7tY0c56yyw0KyQB/eCVMIgCGGKFhvzC8vFdaEdaTBLtNTYiuG8A/LEd/sfCQgB5xe66pcNdljtYhX30DUmETxuN1GO+PabJclcK/Fvs5SB3HciMIIZWY1zlckeliv88a3FBCJ1sgvvxM88cQ1CFdUweo9xRmi3o7kZJSbZaZlAi8CJsKZO/SDRt71Cl0jEXeI69FtHPfjeV3EXjlLkTUhL64ltNwI1VWn96XJpWhONJKq7PooU0O7y++ZyF2h3/sgv38LV7KpACoAULe+AAc8zmtLWAYmpGeH9w7UESh9/+7d/WX/7lXzogOKzxbnr1q1+tN7zhDS5BjXfTy172Mj3pSU9a8au/93u/J6QVOAqxu/fud79bP/7jP77q9CxESGD78Ic/7O6NQIrf/d3fHTrncyIs5f7Df/gPesc73uEWNIwrUc7EGRdt0DsTxpwwEn6HdywEy/Oe9zw98YnLXtHDxuFq+/dtAHy1PdErpD9MGLCZa23YshRb/rC9eDcOM2tf67nXetwnPvEJ3f/+93fm6KNqmNN/+tOfdkw321L4GMOUrBX0M85dfUbt7L1D2GAABxrMZauo1X0sCUeJNLtZ55s71M0GSyPK4TEnxbCK39BPRk5j6b8e2/PzrRyAWnKHC82Spqsdk53FJQHgagFqGGPSuQvAiyNCv462myD7yC555g76HABe75zNAa5137dezI+xWGWAMk4ZwxLr0EZbwHyuRXR1sooNh2m+6cKY4qihKJhSKSibVmA4oxhoNgAAIABJREFUQJSjcSUydg2yqtSdUFwxPktZRaWQiGQj9EFVxcFu8xhxjIYck1U13zqu2c4/er+2oVu8TatrOJfQ91I4pgQA62k4LMRBWalhy4ZLSm6V5k9H7CxOqlxuKzISG/t1wa32SS0kK6VPlRBv4ls1Fj1Ek9H/Z05b91QgBfMb8xpFcFvZmD+ZT4tCO5hPWr/7BMcgx/hP/+k/ueRR6k6Gtf/8n/+zXvKSlzgnBTTU73vf+wTY5U+KyIpWpKrBsmIvNggAA94J5+D9+Bu/8Rtu3n/Ri17kwDqFedbcj64Xn3ykLLg7AO6x0uSH++sFwM997nP1kz/5k5cIJQoRIXi4/qjfrcPG9576920AfE+N/FV+3bUCYDwiEeKzKmUCwI0BAMwKe9TtU5/6lLsu23WjaPSV9DmS4AC9TJpULW+kpdkZtbK3u+1Vf8MvF+eGG0y2tNH6djW7tyolutUAAqUQoGSkdSkSL+hOeqN5PdwXJiuZ6e4AeIUFtdwdYF7RD1vFaDDGrohsXLppxg8q8fotCuR8Y4AOeXc9UCX2+w+fWwg0Uc5U70uW6z0nkggWA2iDfQ0pR7MTa1fdL4lAq4wk4kiPJOLU3C610t6FTKR6eFRt0woszCURQwIvcllAwUAOvvN2Y69q46fNhULulGCfx+1ieK7V7U7pzvmG6vFRtVLrPIHq0Qm1cbbw6svpO1Zp1i4HVmmHlAT+sJpA2KBhE+eXA2VJTeV4QlmfxVnvSBI1jiolVaKZFhHJq59/OTiiPeWnOx9vq2EdyXyLH+8oG/PqAx7wgC2XsfX3id1ELNCYU3vdJwCGLAaYc2Fff+VXfsUxo/gFD/KfB6iyK4hM4Ld+67cuXeaxj32sk6pRbFe0Yvfy5ptvdrUbgwAw2uMnP/nJTnoB4ULDgpP6ExjhH/qhH/I+nj/4gz9w90thtEXUAKJhrF/4whe6c/E+5l0DgMbliH4CgtlthHzZqtqaUX7ONnKtbQC8kVHb/p2hIzAMALMKRePLthwBFkWQBWlsfCn571E3mIqjR4+6n61srPiLvuN2AQBGm3W5kxDa4I4+oU72F17XBvqVdHa5LeNqdfVWbifZqzMLZxSqqnK0V93MSqSTquH16jhA4ZdXLDZgu1oqG9vmuDsAAi3vXf4dmQLuBhbzevdcpP0Ticm8/t87pWmPH3Dx7Ck0q0ahJir+vgG4uXckCL5G/HGjY8cf59roinbV/VZwsLnIHSjGswIvbp/Nj8lU0qn5klIBnFa2qktHo2jRD6hzuzBS1iwrMLb8T5vSivbCTpWrs4pjywYN+cU5BYalXuicEgDTK3eWzs0f1Xz3VtfBWnRU3ewuM/XN9V23S4a+mIVb12mH/c++EpzchEVCJCebCPy2bVJF3c5xNdPBUqep+Ic1GT9y6JQF0QCYA4yOqhWyNnb0CGAYVQOIAoABtv0BSbg+oIf9oz/6o0t+8tRdAAoBwYQx9dZdsDMJkIYphn0t2ute9zoBpnmH9b8zLAD81Kc+1QWS9AJnzglgBlT/1//6X73DRAEj4R0UtVmtFwBjdQYYhu2mkBrQDwimj/wdCcU73/nOUT2aK+o62wD4inocV9fNUMjW34YFWfAl5cs7ykm6uEe0YOiitsoQnJcBOl9eRKy+AfkAYBJ6fN7HG/lE4MawmL1NmRFkkCSRyvHRFcEYWRbo7MJuddLlre1qdFLd9DYTUMTBXmeDZVlKpUlZ1dJ+55vqawA8GMy9ddt790KzrMlK2yxGAyijiR1UaIZcgjAIGFNihC3mlUKztThA3D4jnZi2gfncYkWT1ZYJzLFv2zPE6xirNPqFLMLXusm0mh3CEm7wHgNDCZi0tLFBukNpt6Vyza97zbf8Q2WGfVmoKeFNotDvZZ12x51sJir5tbrBEiOKVpnW7uzXXY2Vco1SiPwmUSK/vhhdcBi2lBq6YHx1pVlTE10KDi8tAPxyr7zw7bYhfsFFfPTqx5WmuzXTOq9aeGwVc48N4YHKi51Ty7AGkKONMva3AKIU3o3SXQeSASALYPQRC//m3/wbN9dTBIbbDq4RjNEv/MIvrBhKSBmkA4Q09fq1v+lNb9IznvEMpyMmQbS3WQD4e7/3e52rD+ETve1f/at/5Yq/cUIa1Fi80Be0vtiC/vf//t/de6QAzb2F4rxD+W/6xXuH437qp37K/T80xDDi7AZAvvD/uJdrzQKNMd4GwMNmje1/3/AI8IXlS1W0IsiCLznejKya+4sTmID4d1a5o25f+MIXHCC9/vrrN/3SuD3QNyYd+o4Gjb4X7hNoxzaTIcmy9lKK3F+bfcEBApcImLdG63rNtFcDpjjYqQhf1WxlGlXviZ2lFLZTQ+3SKJCzC/Ia7bLKUdsMjgDADpMNAHAvLObxx0UrwiAK5wiY17V4BqP5JTgDz2BfO92ItLOWmGltWJMRiGF5HRdyD8slotmJ1OwkzuJsUOt0d+rUQlvlcEJdYyHktLHhTnUt544kUr10UImR+sbzLwcHTLswLR1DWIOvJd1I7YVxTezwRyjDiMbao1S36/T8Xi0mqz+X7GBU453qENTiaaHGlvTOfs1vpCn32R/mFxypq9SIfY61L4+ONlxUSMTL3HdxpX1ks3VYi0spjBS+5fKMfF6djn9M4/HafH3ZAi/SMzd9gvOccC1AdCvuhQhkZAUQC9VqdeAlKDxDdvAf/+N/NG8BsMjcjF4XrW7Rnva0pzkWGfb0X//rf73iHBYARmZH0fPv//7vr/gdACrxxADxQY3QJOR5eBsDxGGf+X+//Mu/7KxDP/rRj176NVhmgDG1Nc9+9rPd4gPwDOvc67G/FWN/bzrnNgC+Nz2te9m9FgB4PUEWsKM4I1AkMOrGxMMk4UsO2sj9MBGzWmeiQnPG5NefNIf7BFt1W2H11s2+olb2Dkl+E/5Ak0rTPTrdZBvWZzsVqRYdVzu1k+Za8zudp2wU+beOYYyzbFFB6Gf6nOa1awdHuLS2Rg5wLUlEIQmgGO2G84PjhDkP8gsrahhWeawUaqzs7xuyCUC15RJB39Ar+23JAPeBLi5mLtrZ11gAoEPGe7i3AfLPNg5pIUF/GmosOq6WqXtd0saa+lnkLsO2/KXhsgCpHJwcYhcmJa1DKldJYvP0PiPR6n66e+FrZprdWHxcLXNRFilrTatcN7x3VVYl3Keu6YNcc4EwVnw0BXRRECsL/Cx4KFwtFiWCM9D+Jvs1017JcOPWkeqssxzcX/5lBYGRiNIzfF/+8ped3nMrFvi+z+hagOhG5tVhv0OhGzZoRB0PKviCGUVry8/Tn/507+lgiJmvf+ZnfkYf+MAH9Pa3v12QFfydEA3YVNjTfjeIYQD4UY96lHOU6G0w0mh7sSYb1Ng95B3CDxK6Qq/8P/7H/9ATnvAE5zwBu0wrCt0oqkO7jAcwgB/fYwBzUWjHnxAxay26Hjbu97Z/3wbA97Yndi+6X4Bv4WlLBfBagiwoToApHrVZO8PK6piCCV92/HqGnmKDm266yf2w+mbbsV+LVpyPLS8KU7aqSjrNZtTO3qFEg6vknfRg4ZDCoDoU4Oa6UDSf/vhjtsRz1szvEkDAQJbtUBz5WWUH5BZyza/lGXz3fC4bMP15F/IiOhhhXyQxrHIYxJqo+LWq6H3R/fayyv2fC2QT3BOSCF+jbxebsXaPdU3w7jS/E3b/sTk7NJn3j7bYPqxTzZXuC7XomLqZrXul+AvNr6ULdn7QOET0sZS9/cytwIYcoyNKB7CdveeJdVRZgBfw6ueRJIFuPldR0B1TqXZewVKE9aDxzgvfllnTQccE7YOKqlzL98Ry27Fc7+47JlIlOHzZxXGBC82oK9VpLbT2qY3jSl9jV2ZX6bGqx2svaPMlsq1nXlvvsRT6Asyocdhoge96r8nxAFyAJNH2g8AdQLZf6jDoOsgf2I1EKoCjQuEYQVEcIRIUmaE1/pf/8l+u+PWtkECwewhBA9julU/A8sLqwkZjh9bfYK55Du95z3uctJAivO2Wj8A2AN7+JGzJCBTFD5wc4LtWdhM7Fypr2ZoadaMqF83a5VRJ98YXF31HV2ytsNGWAZA5bqtaXiD38aUCuZUMZrN9QucWc/cIZAzSBaUGYxwFBF7UlWR2wAbFRjZgIOxhWpX4gh2R3MxZVx9w5b6RDSizi+j+7m5AcqQdNX+4AqwqoNtKfQO8wiqfGGKDdmq+on3jLTP1jWuReufz8KVvsNPolC2rNKV7FYbzUtbUXY0d6qSrLbjK4V4FQVOpcnZxUMt1wXjm+o+JiQgO8Mz1M/iRC2G5/GNyRrSZywd62lzzuM4WoRedHQqxeYv9yZPVENb0nDJj4VYNjyhBLmIUBuKO0ckI+vA7fwxnwQM1Z6c1scPPBCMXCdOTmukMLnwrBwd1sPqLAyOPfc8WQMg87EtG24q5h908irDYsh9l3DCFbtST9Gp2e/vHPM2ci70ZzOtaGx7AgGF28t7//vc7XS2EDcl0vW2riuB4djDQgwDwW97yFiF9eO973+vAPdZzeMnzPmXxA/uP7PA5z3mOI2MAzchDkORtNKRkreN2pR63DYCv1CdzFdwXkx/bNOvZXmGCgTXuX1GPYjjQ6LJlR8XyRlp/fDHbZ2vxW2SbikkIG7itbkl2o5rJf7skP0jSmk41AucKUbQowAt2Sl1cAnwtC9Rq7FR9gsp9/2G5DdptCkM/6Gx1aw6YWbpYrMJgVm3ZgHQOScSAyGL8dufaeXKc0/xO2rIJmFdYXiuy+NxCSTuqHVM2geUaPr8TRo0S4L0yJBSEAI4kS0yXjEATarYO6mzLH4wSBWMqh+Pqml63dVWiSXWNBQ7a4XK4Q0lwl/fhE5xSCnYoNY+pqxRMKDUjgimOIxs731FI0orumA2VZMuFeaVgSnGAEtcAlclEvtCIrAUAgHtBqaHVBXw6TbAZKpI7TZhJdt0jCkoUx60eQhZZ863dKoWTbhHZ3/ZVfk716NvXNV1Q48Ai+8iRI+v6vcs5mNoHwBdM7CgbHsDI6R760IcOvCwAGNBHmESvj+9a75Edvkc/+tHuHPj99rdhNmhohtFkFwWJuFI8+MEPHmqDBngF4CKBKKR0f/7nf+7CLAq2mvcJIR+AW8A6feXdhCQE4Ms7iTob/j+s8rOe9Sy9/vWvvyb9gbcB8Fo/8dvHrXsEKICAUV1PY+Ji5U6RwKgbwJuts/Xqj9cSX2z1ZVT2a8U9zM3drYvNN2nn7jO62Dyh+c4g7+BA9fjkUEkEHqTpkOSrtDulMOsqrvh1yFiFLXQyE+CirV2T5ncmjwfuTU/72pmSxsrLDCHnoYDMYl4vNAONlWwPX3TBXMcC5gBcdL+2VVqouXZqaocp6kNj7JNfAAzvnEOvOq125i80IxuwHh1WW5bFHZrvI+pmlj1X7pmbmBZeoSrBsGMilYPDSg1fXRhRXBmy4DbNNE/q/OJqUBiqrHq8V62lgrFB37m0W5K6FVXG/B7XUTChUkhgyOAgGM6LBCF0hW9+FjwOCNSZk4xkPeQiqVZHPqfJMV1s58+nHvE9XJZeVMJjOlB5zrqIBc6DFAHANoqFdjH2pKFR2IUWd5SNmgtAKKByUAPAwo4iX1jLvQGU0fuin4akwYYMeRtj2ptwB9vNdWGFYWF/6Zd+yTG2e/bsuUTqFEEYvB+RJ/Df6HIpcOsNwkCvyw9gl6JpGpZrFLxR3EcRHPeCFAOgj8SBRsAH7zL+H8V7aL+xVgMQA5b5k3cW1+d9B/kCW3wttm0AfC0+9RH1mS82E816GltXTCL9tjLrOcdGj12v/hhwj2QDg/m1xhcPujfs15hER7U1iR7sf/2vv9ZDv7+kO5ofXBF33H9/1eiwiFzOjPjXOJhWFERK5C8kSpNY9dIhdQ3QlWuR8yAL0+d3Xq44zGJnAbiEZsAq3z6DHnm1jpQ45koca4yIYE9DF9xKhhe1AYR7Ayj6T4e04q65QCem/Vvna3WkQPNL4Vv/GM00T+h8K1/MjMcntZjY4RJj0ckhxXFSnBxWFt1uarBHWRwXZtfrlpmbDZlCoPH4mFqpBdwjlbK9Uuxnr5WVVY12mQx3qLryMBi/jj3SDrdASo3I59whYvZSxDJ2hPOtHepkyzKWXJ+Nb3Oi/ZVnqhat36kGVwTst7ZSatX/uT916pSbH0ctaWPbn3Al7NcGNYAfTDiFz2vZ8UMz/Ou//usO9OLC8JjHPEavetWrVkkHKJZ761vfuuqS7GgCtosGQCcKGQs1dkiLKOReqeDLX/5yveIVr3DX7H03IGV5wQte4BhdNMEUwL3mNa9xtSawuh/84AfdvVF8TR0O4PuHf/iHndvFZsZRb/Q9eyX93jYAvpKexlV2LxsBwExMAEIMx0epGStW11Tasroe1ljhU7HLSnq98cX952YiY+JjJT6KhsyjKNxI49t018JblGR+RizIxpW0pUrdOGbJBq2bWUl0OAlcp05mO0mcXwg0XbOT4QCcLh7YkBZQrNZNIp1uJN6IZFhVgDCpb74GeD2N24Lh4w94PzVf1uHJtgnev3lOuu8uOx6ZAjqS6qwIZSWHFMV3XfKW7SaTun2uoazHxaMeHVUntYvact2rXbDWaexQfRwLL/9CIS+Os/WzpeCoUuHs4F8UB8khKbpTkaeoDb36XCvTYmLrcNe0AIhPmslxANGkuVO1CT8T7Oz/nEOEP/J5LUCZxLcoSJUFM0qSE5ppr/4elcP9qoY7tb/6cxuaJkYVSdx7czCUkARFRPCGbnwDvwQLy/Y//r2DGlHCFDsDzkc1726gGxv6FXTNML94FMMKI40A+ALc0SoDlIsf2GCY50EJeBu6+L3sl7YB8L3sgd2bbhf2FxC8npazk/9Lj3jEI0b+pWRShAW2tsS4P4AvW3sUFLAldrnVzTDeTEyj2oYCtH/iE5+4lD5HwdSdjT9yoMLbslD1eLURf//xBGfkXsB+prMUHlaWoaH0hysASl308WALT3dZrMQWO4F2j/mv9ZXTufzAshNz4LWRF75ZzPPZRkV7xuwgi1suSsembGkFx6BBtizXLjYjjVdsT+FI+xRglxUs6FzjmGY7q59fJdyjQE0lRqx1OaTwbdGMvnbBEWoq7StG633+MccETWXmMXsVqKHM0NguNiY0Pt5Z5TQSZDt0wwX8gTPV40NK0jPK+lLheu8nXwDgfOGXYeEQgU2a5TKi1n7FNfx5fd8OHCKOqSs/6+x8ksN9eQKdpwWqK9akZtoL6maDpRWHq89WPT6xnin10rFsrwP6cDAYVRvGxG7VfQyLfWYOxzIMImOU47FV/e09L9II3k3IKp7//Oc76zYK3tD9smtZePMDeiF80BTDEBcxzqO4xyvlGtsA+Ep5ElfhfWwEAPMlxRUBEErV6igbW2YUwg0q2Ciim2EWYGtJcWNFvRmNFTor8VElNNEXrNfYlizSmdKsozPN9+hi+9Nml2oRVlCwb35QUQ4PKssumglaUcDWMDG9fqs02NlGS9pjDDOygbvncvlBP0BBvnDHXG6PRuHbgSF2amcXIu2pJ4oNW1WutXvMBq94805V7MI3LNDCsGum0LEIoE8U0flB06TSZKdub/jBV+w0rVV1M/9YO91rVFGS+WUs7pigqsR4ZpEmFEcV51Pra2vzw4URTVY4TTQWj+nuhWWQXw33OPBvOZZUw/3KNKNU/sVW0tyhSn1OgVGoiQf2MPeHdHG/SkOAciU4Zmqmky5635Y6AwpQ69F9dbj2jA1POcyrWHqNMgiB+RJZGz7no2xs/zPP3f/+9x94WXbd8OJlN6zfl32U97kV10J2QuQxwB6gD7GD3peCN3TM/MkP71qkGARl4GqxnQS3FU9j+5zX7AhsBADzOyTabGY08FofAGwAXsBMjEUbFF+Mpmo9zhbDrk+RAhOWb7Ie9vvr/XdW+oRvUCDS601MX2898xE14w8rCC2Ai63bvMkY5lZp1aHpcYDlxCzGyj11Dw0Ju8C1gWN6ZQNfuksqEt8Yo/ML0k5iho00NzTIyCrGy/5RpRAN/bEFTOfboTqJXdQGwEWmYWmHYa85Bl20r800j6rVTdQyXDsoEKvFe9U2CsTwZuaYDn7BnsYx1XCvueXPMTDPSeB3EQlUXXKI8FvpBRpTHIwpC04ryHbphgurpQilYFKlqKQuOwqeVg6nFYpoZMO2Ld2lKF4wte6V8LBSJxfx72q153epOn7OZJTj7JiykEXkyhvOslCzi2PqZg2NxUfUzlYy+kdqz3FhNBtpfLcBwABRnx/5Rs477HfYUUOHSuHWKBsOC8ypEBWDGvHz2J9xb6OW2o1yHLavZY/ANgO8/QnZshEAaLHyXE8rooHZnqJKd5QNzRhWQeiPAbhFfDGyB6QORXzxZt/TZvgPr+eeijEmbKRgg3gRwH5jqn7yvmPS9F8qyfxxtM4qzdlpWV7AsSiis50E1qYLhsElXW1Y4Vvhl0sRHLZnYR/IwJGhHNrsLMwxKXO7PDHDjDWBGK1uHmTha4BX7sMKxMDa7dYZ6duMXWlYbir/s0EuCekhfWsmT3ybKB3Voln8RYFYnozmb4FIT2ubWu5AdTyejS1/7qcaoS+2nSYqwaE1xCfv08XFQGeatw287TCoqB5Pq536P4txML7EgvuZ6VIw7ez6UgMo46UszZu7GyXhOXy3ySg3Z6Y1PnVBQc9iLOme0MUe7e9YTIBH/qzq0bfpcO3n1/M1X3EsbCgSCObVzdq5WsvN4EYAy/rABz5wLYdv2jHDUu/Y9kciAAO6mWTGpnXgMk7Ec+a9y3NmEQDDjdyBH+R6vT+AfwrprtW2DYCv1Sc/gn5vBABzW2zPwxjAtI6yFQV4yC+YuK344s28L4An21FsT46qFfHLaI/Ry1GoQhIdjAmOFt10Vnc03qzFxNI1RqrFR9UZUvgWdg9Lse0kUA6PKXUpZP4FE4VvMLNDAzGwC7roD8Uoooh3GwobgGmzW9auetv7SCiOg3k+aSS+AV5vvpAXvln64m+dj3WfXXYqXCk4qVQrnR3Ozu/VbGfZhWCidFLNIe4PaykQy9PT0HL7P5H18KTaZjLa5sQnB9k+nZqvqWl+FmONlw6olQ4GyfQiVEW1aKfamd/9wfkkRzV1DSkIricA5cwAys6yTRdMr+Dm3A6NTVx0bHGaBppp1lf5D48tFeodrT9XpPlttKH7pwiOYrRRSsvYguc9sBnpmuvpO7IyCsHwYh/U8O4lHph5/moDwNjc8e4CAAN+kdcxpwN0+eH/81PI31772teuCvJYz1jfm4/dBsD35qd3hd/7RgEwDgWkx43asqUAwKyKAYZWfPFmDj2G7TCwo9TJITPBBogqbRgB+tpfDIIu+HTz3Zppf87sbj2+bqhfcKuxQ2PjC5JRtEQRVZ5C5t+qXuyEaiepkzL42k0XpJnFXKvrazC8M61Q+8b8Ug+K49AQHx+S+HbzRem6aRvg3jVX0aEJu4Du62elf7Lbdn+IRcDCLbnUIz2qb82s9vsdi4/mUgez+Gt4gRiAq5PeasYMk4zWHZaMFh5XIqKI/c+jHJxU0gfui6MvLhzWmebtmiqf1ELXsnYLNFk6rkWD4c49kA8N8UlGCrJLHRMowyhXhngF71KgRbPgL0j2KojOaW52p7rlwRrtqfh7dHjsSZc15aD9xF0H3f8oNa9IEQheYD4fZRsW+vF7v/d7Lk0NoHy1NbyJkfPxPmNXk53M4k/+TigGP+iAAcoUKvYn2V1tY+LrzzYAvlae9D3QT7baYR7W22AqkBuMKrGoiC8umFjAIFW0o2IGYGCZoNieHEVj8uNlSP+QduAx6dPBMTYX23+t001M1v1gEalDkp0eAnB3u3jcVH5pRSiABd6q/u1smNfzzcGBEIDWL5+Ssz272LTtzRhrktqODHF/uH0mt0CzbMnumAl1aCo1JRowwfj3WpHGd83F2jfedYV7vtacz10STjeqanQvDjysFu1XqotKteg9TzU8oDQ7r8xINIuSPcqi84oiv3VZhZhh9+z97H3Z6WfZbvafpxwcUze7VWGPBVqQ7dc3zi9/FqZKJ7SQ2Mz05FAWfLgUBKBciw+qk1mMck3laFKJkaoXBVOKFAzxAd6vuVZHbY+O+fw/PlhRcsCBlF4bq7WkTBYPHxkXwOhhD3vYZbvWrGeOGiZFWM+51nMsyWrM4Vh8DWqEReA29KlPfWo9p90+9iobgW0AfJU90CupOxsFwIAzzNp921eb2UfAINt0aF8B3ZiOk6AzSp0c18SBwpdatFn9RRfGlh9yBwAvtmtrDd9odL6mOxv/zQRUpXCXwgAN5WBQRj+cJ2o0pcRg16RYFQeo/fILgC7Fcf3WZfjs9gJVtMMHx2UWJRW+u5a+mGOwUrOS4+6czVlnK9IZycR0NQ/p8LVGu6opspENq7Bu+4ROLZ5VkvljfSvhLgVBWwlpZJ5W5pmpZdqkJa1xYciSGlZq2KTl9mbLkdr9l0QWoMCWBagLI3pWUZQvti4sHNTZ5p0rToXWuZXYDPcETHBis87DpSDoobH+s+3NqhFFgf6Cv1BjKoU1b1BMpBO6sDirUItKtDItsR5dpz36acfYsUvEn/Pz865iHylDLyguNJ+DHjW/CytKIAO60FG1YVKErboPSBScDfbtI41vdXvJS17iLC8JjbjaGp+N4oe+8bxx4oDthZCCjWcXgPcBf17LRYDbAPhq+/RfQf3ZKABmoka/xQS2VW1QfDGTwSB3hK26h+K8AFK2obYqLYnngBUOUgv6CMNNPCkLDKJR19payV26o/EH6qT+ivswqKsSTalrAtzIFUgNLY6LrlcnvcHcOr9rLtTBidSBXmQN/3AmZ397G84O6H0t8MoxFNBZzOtZHCLKdgEd5wG79rpP9I8vWuZSGGq65mfUQ00oCsrKgtVjjV70zrkxJVmgclhWx3BAcBZoUdl0Scht0nBSOO/9KMTO1YOz/G3fAAAgAElEQVS0v+V0sv6DnTYW6zJDwhIHLJJsWUCnOaFavatAUyvY397rjcUH1XU+wH7WOU+Eu9X0pB5b8gG25BnocNE6+xux0aQcWvHTVZXDqQFscajZ1pjayZxzxcD6rXfBcnzsmRqLV6a+IS0DBBdb2YBbGF52dNB4Fiwxf/LfABy2w0kQw+JxVDtbjBce59QWwMaOssHsPuABD3CWlYPa8573PFd38c53vnOUtzXya1H8+PrXv17vf//73WeAQrh3vetdTl74Z3/2Zy4pbyvfsyPv8DovuA2A1zlg24evbwSYZNbbttIXd1h88V/91V/pn/2zf+adONfbl7Ucv5YAjrWcZ9AxTHqAXV6YTHS8iHghfuYzn3ESk/W+mLrpnO5s/KErtLIAQX0NxXHEubaHANxKeMIBZStYA9/dPWPSDeekamnwXaEJnij7/53f4hjA64TBzuIiQdtlaJCxN0vTSLvG/Nv9WJvNLkrHjHQ5n1XY+XPTOg+TCrsTVFWPd6hlOCBwTDXaoXbml5WEyo/pWMcENVXCCXWt6F/cFoKyEvnBdKQpRWGobKkPg55YpJ2aaY7rzKIfVNaivQ5sWzKPsfiwOikJdf5n0Z2fVnXigrnQKorRrDCMOpppMwwDe7hd6pKYt9QiHdephWWGOV+MROpmF1WPjuv42LPXBFgBOgUoLphidL9812GGAT6wgMisYI5HBYKRXbDIpjBrVK2wfCMFDiJlUPu5n/s55zL0xje+cVS3NfLrQPKQCofc44lPfKK7/jve8Q5X9Ezfn/rUp7qd1te97nUjv7cr5YLbAPhKeRJX6X3wJSySZ9baxb//+793EzQr+M1sa4kvxh8Syx5Yi1E1tqYwbv++7/u+Tbsk4w7jC7hGB1dsexUXQGYCC7BWCUTvjVEcd2rhTzTb+VvzftdSHFcNCdawAW6eHGfri2Fn0fNOWclxHVc5ZqbLXbJAMwAuFmjNTqB9E/4EOmeBtpAX0Pkax9w1L93HdPuLRQRueilBLNa3zoRKo2VtfaBYE6UDWjQcEDhmLD6glqFp5Zh6vH+4V3C0Wx2tlCX09hEwne8CnPL2HSlMORrzhmqE2X7deKGhUhSpk/olNdUoLzTrlw70XtjpoZ3W2V+PUMWOzUU5+1l5xxYPc8YgKEaWzVysMNmlsMTYBJpv7dBispJVj4K6ymFdh2qP13hp48VjbHEXYJjkShhjGlviOAD0MsU4BWwFKP7sZz+rkydPOqA1qrYWy7ef+ImfcO+XV7/61aO6rZFfB6CL5ps0uBe96EWu4JmaD3YcAcC4YLz73e8WeulrMQWOB7INgEf+sby2LrgRALzZtmDriS++JxwoTp8+LUD/Ix/5yMv+cLDYQE6B1peXGvZDg5KfYGawmePltJHGdc4tfljnWn9p/notPqEOWsyewqb+Xyi7IqqzpmVUHOxWoPaKZLDe89xwXuokkZI0GRqf3O7aDhEk0MHOHpjwd41CPJjn44YFGpIMPH7vM3gX1p2cY26fCXX9Lj/wAigVCWLd7gndPDsIYAWaKh1Xc4jH70Tp2BCv4FDj0RG1+kIYekdiLQViRP9Ww31DAjMqqkTTAwseLzQO6nTzTpXCMVWjqlqG7KYc7lAcpt74YO69GmGp2FAqv0bZyRjSU2bQBawsmmDTHm4YCM5CdZu4okzoVE+yXe8Y4wF8/fgvbBooJR0MQMSWd690gr8DlCmo6wXE/H0z3CIoNMNacZSWluw6ssNFTQX2X4Ma0b8/+IM/qJe97GUbmf6u6N8pEt0Y+x/7sR9z4U6QIMj7nvzkJ+vGG2907wTA/1vf+lbhQ08A1Si14VfKAG4D4CvlSVyl97ERAAx4Y+seKcLltI3EFzNprFcbezn3yO/Cznzxi190ARyX04rgDuxtkDsgcfAVOKCzZhKEEbicNtP637q7+S7TIaKkA+pmpxXH/m1oAG4YtM0QgjSpKelItfrKWFsA6y0XAyeT4O8AXMsmDfAKwN1vAFz8e2GULQaXY26bla4fkteCNOOfDLG0rgYn1JXtbhB0juj2xll1jFjfqdJJLQzxAcYreDG1JCzSeHxiDYEZdoEYJY81F4RiaWNLqoR7V6TGtZqTurmxbIWHhGMsntRiuux33P+ZLYUTKkcldVK/9KIS7nRFgVYxXzU6oCRlMebXFjt7uMzWFtcj2yM5ywKlyXWa694w8Ot3fOwp2lF+0OV8NVf8LrtMxBL3F9oClgCMvUV2sMbMncgmekExrDH/bz1tmBZ3Peda67Frcbyg0Plnf/Zn9Yu/+ItrPe295rgCABOI8ZM/+ZMO5EKuvO9979OznvUsxwTzfIlApgAbYLwNgO81j3f7Ru9NIwC7wPbKehrVubCimLZvpF1OfDFbdqyWNyIN2Mi98jsAVxhZGImNbEPyAkNCwcSG3g53h2EvKgpi0AZuhj8nDhF3NP5Imfx6785iXeP1SGngdySIggnFAd6q/rQuZRUlaVWV0rKV2s0XS+qmy4AFVhUd7l4jPhgXifMLkQ5M+kE5z+a2GTvogmNuupjLGCxWEI/fb99jH4PeOTG22C+c36UzjVSlcVuvulYQvDmBGSfVkQWmAxfgYBc8RqoSiR3klmN3np3WXF+xXRiUNR7v0iJ6Xk+Lg5qq0bja2WA/XX4NtjgKUjMWGeY6zS6a9nC16Ig62R0KDMlEDSbY8zxbjZ2a0QVNDmDkiZH+J5O/rKA3Jm6jk8vS7yGFYn5Yi9Ui8yf64X5QzDzOrlI/KPbZsd1T8cvcN8V3SMoGEQDcF6FDL33pS4UW+GptECtPf/rT3W7gBz7wAVcIh/8x75oPfehDetWrXqXnPve5euYzn7ktgbhaPwTb/bpnR2AjAPhyXBEuN76YyYFAiOuuu25kA8eEDfB+9KMfva5tqN6CPgpbkDv4ij76O7PZhYaL3dt1R+ON6lrxyXj8xlUlRsoWhV+VaKe6pMJ5W6TZxZJ21hfV6ga6ZSZbFXkMwF3oVDVd8/vgOrA1Jx2bsoEpMoaTw8IwLkjXD0l7w6KNRDjLT7gS+EIjSrrlYkmtZEFamFZpbGWMbv9QTcRIHW4xQTksL6ERFnAvJYeUxQA9/9MYXiAm1dF6m9rYPDq53ZnVDTODnSbQKE+V92sBCzRPgy2uxZNqm2zxpHPhoNDM1wChRB6nBtuee1/bBXY+EHx+dlLdKGe5J0v5cyjakfpPaFdlcz3BmVMhFTa6q8Zcw86Sz46t0BQDjgs7NlhFGGBkF0Xq2CgmVTzV2fbH8WJQAwAzvwMGi+KwUdzXPXEN3iv/9t/+W0eyEEmNFOZRj3qUkz1AuLzpTW/aEOlyT/RlK665LYHYilHdPuelEWCrhYlwPQ2mglUrnpVrbXy5YUHZ6qPiGBZ0Ixo2mFEma35/VI3qbXwrH/GIRwxlbot7YpLH3QH2l3uF+V0Pe4xBPZqv+9///pvWTbafb59/g9roKD3NFUjF00Nt0tTZrbjiPw+nP9soabaVKAz9Owyn56VDQ4Iu1gKCzzYq2jtmJ7mRCHdihxQbNqvfupAzypbncDk46oreetnFNDmpGy4uM61j4SF1hhRtBa2diqsAZX+xHj63w6zC0uZuletnh+peh0Un16KT6qRWkpt0ceaozqeWZCLSVPmgFhIjoCKoaMw5Y/g/P3Ew7iQT3cxv64ZHcqCmqRuuhhTP3W26TPSD4LS9S2c7K+3tCPhAv10KpnS/qV9VGMSb9r3kRGx1A4JwRtisVtixFYV2gONeOzYs2ABcFBVjRzYqv9mi2JkCMB8Axh/4ve99rwOBV3vjvfj2t7/dFUXzrFgAII34qZ/6qXW9M67GcdoGwFfjU72C+rQRALyeojDANdo2ZBMA18uNL/7Sl750ySt3VMPIdiNMCVt2bDFarRfoo/FF6ztM7jDofDAATIS8nDazJWlDtzfe6EIIfA0mL0/Z8gMdGNw4Payg5Gf7krSiz9/eclpei6EkDOPQkGPOLcTaO9Y12VkS4Q5P4eHrHzEkExxj+Ql3kl2qxTNmsVU5OKRUpxQEXREMcuvFshZhf3vaeHxInfSUMnfM4BZ2diosXVAYWSD4iNrpHUOik4+pPYRRrkcnNBQEhyfVyQaD4Ei79NUz51RO9koVv94XbfGO8mEtJP7PT6iyxkvDJBN1lYKqksCvGy6jG1ZLaV9ARe9oO/kGaXCG1VotOr7kdiLNze9WM1gt8yHBblflgdpTHQzcLud7SuETC+3v+I7vuJzTDP1d5mNAFj8AbrbhaYUdW6/7BAB5PYv2oRdfOoBQId4HD3nIQwb+CnMoLj844WxUZrfWe7knj2P8ibn3hTpRn8P4c8y12rYB8LX65EfU740AYPwq0XBZq/MivpjVLX+HBcXW63In1K2yYLOGm4noE5/4hLOs4aUwqMG23HLLLa6SmwkNucPU1NSGnyLsMfKUrXghplnbpcY1ul817i9QHYcIM2BAqkXXqZ3eOBDgtrvX6eaZG0X4BCltFgg+08iPseQHRSKcFZhBYdy+MTvO+Gwj1o5aVzXjvVIOsDazU9FccpouKE0P6oaLg621xuL9SrILSg39dT3OC7us0Agij4P4vILQv1tTj446izRb90qBmC29qDnru9XSi4XWMd06d4v7zOysnNR819YWT5ePqpHkxw9quT3cPi0C7n0LBFWVdUKVqn53iHI4rVAk6q1MaVsNgrHq8y9GAMGZGrqrMVijHAd1fcf0SxSFhhH1Br/x7KgB/DZ7wWvdDoD785//vAOiyCcKppg/uRd2oJjLetPscG243Dl8mN4ZUI77zVe/+lU3j15trSho+9Vf/VVXz4LOl/cw482fBeDFHxjC6Ed+5EfcO/Ryx/3eOI7bAPje+NTuRffMl5Ev3XoaDhCsznFFGLRtxgSKVRrxxTg28OMrxFjPdTkWYAggpUhiVI3x+djHPuZeFLwM+hsTNv0FsAL0kXhc7mRF/DPM83d+53duSTezLNHdzT/VbPtvzPOvySs4uk6dPhAcalzfPL+oJMs/W+eb0p66DXABwXvH7EQ4jiE1rmLsQMMo767ZxwCUcaIgOc7XiA9GZ5oFK10teo+Pg326c7ajRtfPUtajvc49wwqEqEX7XES1VaiYNCdUrjUULsUQD7rv3Cps2Jb/+kFwqCn9w9nlwkauvatyUnNDQfAxNczdhkgTpf0mCM66JY1Xp9QxtOmlcFqROmZkdCU8oFRnTBAcpvfT2cWvaVB928Hao3V4bGu25CEKmGc2U/I0bOKw0ueYY3ulE/yd/8c83qsn5u9I2dYz30EUMGd+93d/98BbhB1mfscucj1JmMP6e6X8e+HpSwjG7//+7+stb3mLHv7wh7v3RwF+X/Oa17giwDe/+c1OCrHtAnGlPL3t+7iqRmAjABgdGXZk/ZrY/vhiwCDFX5vZeFHAXPgmz828VnEuVt8k0LEd11vEBkAFqCIJIbENy7LN2q4q9GBb2U/6dXbxAzrf+pg5bK25XRqbOmcyuFW2kN0WfL6VX7C/vSeeWQy1o5qakccA5emqDV6LWGQLBAOUSZazUuMAygBgK5wjDnY6djEN5gePUXpCt8zMKAxa6mZ+BrIW7Za0oMTwuc29cO3CrrL2KAsuSqHfBqzi2GuAnp8trkVH1cluMxP8epngxfZgf2OY4LmOrRuerpxQo+sPn8C3eBgIjpYcJDqGg8TaQDBjg43a6kV/FEzpzvkZlTq5xKN3xwK2+p/ufKlKoWFdchmTDwt7yAQ8eUfVqFOg1mAtAT+FHVs/KC7s2PqDOyzZ1zC5BzI3ACGa5VEW541q3IvrAIRxuWBX84//+I8d2w3z/uIXv9iB4pe//OWOHb4W/X+LMdpmgEf9qbzGrrcRAExh1//8n//TTVIA3GHxxZs5pEyeRWToZp532LnwYgSMUixCfyla4V6QOTBxbfZEjZQCjRgV2lvdzi1+1AFhq7nAjCGOBJXwiKu6D4OavnkOsLcagF1clHZUbQ0ux0xWpKrB8l5oSvWSDXAB09XIBrgcM14uaazsB5RRsEORUqXBsvdtPlahbpsZV6Mzq2o0rSjoqpv5beSqUe5zm2QeMO0CIfLCLhMoh3td4IjFKCfNSZVr82YBYm4VhmTCrz+uhsddCMrXzy0wAgM/IqMFwWM2E8yzCrobYoKD9ITubuZAvdTdJ5XReOdd3lt9sI6PP2HLvopo/gGNoyzuHVaMNqyzhR1br3SCv/NOoVaiHxQXu4As7iFLfEmiFBw/9rGPdcdcbeCPdwfjxp+QJczxSByQ1r3yla8UzC+7jThgEIpxrbdtAHytfwK2uP98Edl6WU9jgvvoRz/qJAFMUmz/82cR7rCe7bD1XJdjKajDSaLfMH6951nv8UxKD3rQg9xWH/1lDPDo3Qxd86B7YRuQF9Q//+f/fL23uqHjP/N//0C7j3+FUDNvqzq/WFhDv6tDOTyoZqeuW2YHBwhw8tlWzs5aDO5CO1Yp6mrMkCgAlCtDAC7HRIG0c3DglOtrFEwqDkMnQfC1SJOKwkCplmUAQXpcXzu/XExIvHApyNTJ+oHy8lkr+NyGiQmUCYSAUbY0rc4GLJgzQXDY3aUgvmCCYKzNukP8cpXcTzdc/Jq5A7AWELyjfFwLlymHyJngISDYySHWogleLowLVdOpRkeplplhnB8WErTQgR40/SuqOhZ/axq2YOhtR2nvOKwYbSM9BdyxQ9gPivn/kCWAYnbOAH8A4EEA98Mf/rCe8YxnCIbacqbgnKSlveENb3Aeyrx/SI570pOetOLWAZP46mKhifSCeOEf//EfX9U9xgNLMq7PdQGmv/u7v+tiia1mve+4L94RNN6zv/Zrv+bYXeQnzO2vfe1rHYHy+Mc/3mmeKZx+3eteN1It+Eae86h+ZxsAj2qkr9HrbAQAF5IA5AB8kTd7+996FOjC0JCRFDTKRhEcrAYT+7Fjx5zcYbN0zYP6AdBn8vRVSm9232G4d+w/o8nDnzdtuSrRYaUZwGHwognt722zE+qmF5Vkfu3sQidUJUrNQjSAMm4NsMG+RqAGLJ0FcBvtUO0kdfpiL8ANxlUKS0r6Ah56j480oSiECwYoB7pzdkqz7ZWguRxOKOt0FJb9/sblcEqlcBhQnl5KRfPLKgDBaTojRW1vv6ohRXjnzOQ0tbG0OzfwuQeq6JvnMo2XDmq+Y0cMX0kgOHeHWFyDRRo+wakindSdjdVFfVPlE6qG47rP1FM3+yu34nz4fjOfUi8xqsb8wny61Yts3jGA4iLiGclYYb0J81kU2SGnwP6MRLRXvOIVwhvZApdoaF/ykpc4rSzzJL8H2OXPxz3ucZeGsXCSgLB429veNhAAc208mCFyfuM3fsOB1Re96EWunoPENus+Pve5z616ZE95ylMcq8tzLdpznvMcvfGNb/x/7J0JlGRZXea/F3tkROS+VNaSlZnNCCLSLIOMgqKyCA3YqMguzWIzLAIHRJBFWnBExGFHQOCADd0MiMMoTbPY9owtNNis0uzQC117ZVXlvsQec34v6lVFRr5734vIyKjqqrjn5Mnuyrfce9979373u9//+7sgl3YyxhMn8yd/8ieuFIVEGFdddZUrSSHY0POO75Y9Xbfeu1bu0wPArfRW79iWewAwy0cftnjpi5EAsJrHpcBk4xL2mq0cx6DNFloY3Vor1zUdy0ANG4vcgQGNoLRutJfBn5+dBvo8T+QWDMZMwD/3i0mdLF2jmsy7AonILtUMDgnl8iW6bfF29cXQs66pXDNH768W6wDXFojGMbiaDVoY3I1SRKVq1Q2OswHlUkWasEg4o05G8QiZ7swBbRFlFIskVatl9cNTBpuvckKZZJ/KFkY5ESHZgwLY4iFFHHtgV6Q6ICeyrkjU/LwAwdXavGqO+TuvbAwqkV5UpMkjuVKa1o8X6yz3SHJaKyFAsN0dQhpKTIcIjMMdwpxsJer0KRlNqVyzWaQhJ0G6YV6I1ZNlzGl+I6aS4V29dOglyiWmOjGcGK+Bqw7WX5AJ3SqAX1jPbsisGtuExpcxdGxsbFPijre+9a268cYbXYIBlvgv/uIv3LrhhNBMNjBnkRDpyiuvFOd5BekE7YJR94oXdMYYxwLDjwH+5Cc/6UoOkKJ4gYgkqXjIQx7iMsKPfvSjQz8W7z4AdMAt5fDhw267yPTHvEn9WRBQT+YV3CBgw3keOHIAuOkD/p/34mItPQB8sT75LrU7LABuTl/MCpmADVbI3SyteBBvp160l3sR5MZ/Uxg8GcS6URgwAd3orHeieDZ1yDkYeBmQsWCCmVgv/dT1Cq7JDJgSEQbllU0OCRH16SenSirX6mAsHatrXstVs+Z1rVhPOmELVlsvSVECpVLmoC6uU65KYxaWFzBdKNft1kyFNiSifdZ0zxwzvz6qk3mzz23MySgdS6hYtSVy6AwIVimnZKoSwHYGM8HJCGDw8BkmuFZ1dNuJuEqRs+9BsjymWuJEoBwiCAQPJvZr3WKRFlFc2fhoQGrl4GQZycioajh5yMzIJyP30pG1H/tqofvj07rv8B/txCe46ZrYkQGCuul6AFBDZtDJ5BthOsoE9qkL7kKA0ZtvvtkFhtQRJhTi4eMf//iZBQLjFtIBgpNxI/IK8gECx9glbF5M2ADwFVdcIYB5I3D2xnxA9bvf/e4wTXOPIYUxrDQEhvc8P/KRj7gBb5ANMN7MnwDcV7ziFS7oRhZBABz/TowNPwR7U68LTQcduiPZZ6t5s28rZ/WO7fVAyB4IA4D90hezOgYMsp3TzUIAHNngGge9Tt+f7ToGWO4F6MWT8utf/7qr5Zqenu707Xyvh84Zx4udYLphGIg6Z8KhbbQRVw9P00yFNsp36NDq+60a03hkVI6zrtppZ4NK5RL9dOH2Te0hOCwWqahYNetiNwC4TkTZpFlbDAiuVKURC8vLMcWyNG4BuADl/sSgIhGz3hc9aCKaVQUnBZ8S1V796NS80tGU8lUbA5lVMhrfNghOnNYNVywBdgA9OThImIFeKjKpCi4KVi/cs4FxaJy/f2prwpRofkjRvoVtgmBHg4mpABCcUFI5VaKbs7I1PpK4k1M8StrkzRZtjcckCRqsLanmFHyf52qBAMZ+ty7NXtX3GrhCo6nOJqPxqwTb6AA2QHC3SpAbw07VAy0uwNBEnsD8futb33KDwYiDYLxn/AUsIg2gIC0AuBOM3ThGkjoY/fAXvvCFLT71NgAMM8tY+IlPfGJTsx/72Me6YJS6hC1eZr1/+7d/O3MKcgrkFzC6jQUNMzIOwO/FLHUw9W0PAId963rHtd0DfOB+xZa+mAEbtrCbmjXqiGaKe5OEo9PBdsgBkDsg78Dtga03L/EFDA3/1q0gFVKUEhSB1VynCnIO2kYbh4aGXAbFax86N9rWOCnlywd0cO29qlpkDHFswiIlVzJx+3xVxepW1jgZhemMWoFgvuyoUq1pyCp1kErVug+wqQCCq9WYhvss2ddI9xzNqWyx1KqD4IwvE3xqbZeOrx9TPNLXQRDsqGQBcYBg5BC2jGdogmtaNgI9+iwV3a0KKYhDWKQdXR7UYtGfwc45e1RwDgeA4BmtWuzP6hnj9ljTJju1uCKVlKJJs7tGPNKvmCNVZF5k1bXQW+3PEpF9OrBST9tMkN5a+azOGUeOB468So6fKXCnPsrT14HxJK6AMbVbJciNYafqQVsZa3btIpHM1gJYZPz79Kc/bawCsRhIttDrcrxXnvvc5+rDH/6wyxY/9alP3XS+DQATQPfwhz/c9eVtLHjwQhYAyMMUtLzIAtH6AsS9glSDMRY2+VWvepU7/sJWczzsL8AbSQjjMT+eBtgLoAtz7wvxmB4AvhCf6nnWpmYAHCZ9MStyBiAGjm4WBj4G0Ec84hEdC0Lz5ADIHQDVAF8Go0aAzbYdW1fdsimC+WA7jnZ2ohD9zEDOs6V9TLSN7YMBZjHTvAWbrxzWodX3qGLxuI05g6rWJvTThR8bq5qI5pSIxKwgGCa4WlMwCK5IoxapA9dZK0m7c+aei4QGwZuZ4Kgm9N0Tx89cuHMgOKd4BBBsc5DAHSIf4CU8rqqF7aTiYZJlJJyf0/dO/sQKcEdTM1oubs0Y19jrydKYail/Jp3j8AAeSExqvWJOqa1qXNnEoIqWBYvLkrt6aZsX8+7TSULO7jQUy5OaLxw9U+XBRN2zGCZ4NvcE7e7rTrAt3x+SMsadbhV2uSiMB90sAEEW32h4GwsB1QBf/G/R/AJu/QrjFMk3nvWsZ+m6667TNddc43q089/Pf/7zXWkBgLLZDSIIADPW4ijRWJ7+9Ke7MjhY6DAFcPv2t7/dZXob3SMAwDfddJPbLthkZB3eXMJY7yWLQu7g6Z1ZIOC2dDGXHgC+mJ9+l9qO/hMQ2Ji+mMABm80XW1C4InR78GT7ngGUbS/uv92CzoqJAJmHJwfw01x1u72dknrA4jOAM7GwxcqCxc+9gkUFcha/IJxC5agOrv6tKgZw5iiuQ8tDqtYKKuJKYCg4JMSciMoNVmLNh+bLdamDjQkuViJaK1atAW3hQXBWZUuGsWYmeGVjnw6u1hlDr9RBcFr5qnmrPhEJlkMkIwOKRqoBXsIjbkINe2DXhKq1BWvQWx0E1x0Q/Aosd6mS0nJA0Ntoqp4Iw1Zc3XDSBoJj6k+Ma6NiDnojDXEyllTJoqlORkbkOGtWGUhfdN/pdNE1kenv4OrWekXyI0pkVvXgsdfvSNpjv74CHGELxi5Tt8q58B5mjkEaQKY3WNDGcu211+oFL3hBYPMZizkfCdfTnvY0VwdMAVCTRAKpBPd42MMetulaOy2BoG1I5KgbThSNxZNAMM4iHWS3kYC/f/iHfxDtxg6NuY35iN/sdBIs97a3ve2iTYNM//UAcODn0Dtguz3gRaS2kr6Y7DUwiCYz8+3WyXQ+dcWS7Fd/9VfPbN+3cy/P/YBgCVgX2Bdb1jq2qgDG3STIx/wAACAASURBVEpVCiBHK9eu1MNLTvLTn/7UHUhhXPzSOHt9h6Yb/aFJ41ysHNeB1Xf7gmCndom+f/J2pZA6RB0rCI6pT06lJidhjswnUK1cdTSUNidoACgDcm2uDvwdScSklQlOKxkFBJsBmgeCpaq+e8Jf8xuOCc4p6faP3ScYSYmNca/70dr1vunorjqwt+h9G8Fg4zcUc8b1n8fn3H8aS89oKYDlDQbBjoaS+7RWNgcNqhZV2hm0630jdZa8bGHJU1H0votW8N8X3a8iWQtr0zq2sVXjTLt3RX5N99712+0MLS2fA3BCy4qmtTHTZMsXavGEc+E9zA4UYB93B1PyoMsvv9z1ef/Lv/zLUC3CGQgwzOIeKzFkC7CqzePdTgfBweITtOzHPhMEhzyDnbhGZvjZz3622F1kPu2VrT3QA8C9t2JHe4DBl4EQIMiWS9j0xYBlpBOsdrtZGpNw2ACdqU601wswA9B6coegNsCWACpJhtGNwnYgEdEE+7UaHAF7gH4Y9pfniawhSC8dRtPtD4IjOrCY1WqpDurqIDiiYtUcZKZyQrl0VgVLABkAt4jUwaL3BeAClm1Bb4Wyo4H4oKoRsxvDGZY3gAkulfbojhVzgg9Y3kQUmYe57bC8QR7AbsY42aUO6ei4m5SjJn/9Ps8iHd2tcu2YkeXlmDNgsCEBynphv+5YuuvMaz6entFiIAie0UrJlu44osHkHq2VN7Pnm76lakzRSkLRlNk6L4zUoS86qXJtzqpzzkQv0YGVO6CYtn7ONUc/n3yhJke748nLuAJjiQ9tO2Nau+MRulbAWLcCe6knBAaZ3pAsmAgH4h5+7/d+z2VzWynMDY985CPd8Y6As+YSZIOGZphx09vVZEwEiIe1QYO9Ro7Bbltz2zwbNNhpQC+JQJBqQOQgs0D2wXzk/TBec8xOes230rfn6tgeAD5XPX8R3ZdgCHwZW9l+g1kEpDFod7N4STgwb2/eQguqB/phdLDUmyAMdFdhwWW3AT9bYUwUBGYwEIYpTC48y0OHDrnBbEhYSK8apoQN8itW5nRg9V1nmGASCHz3xOYt8DAgOB7JKBVNqWCRDQBwKzV7kgsYXkcxDaRsQW9p4cwWT5lZZzKMJSJ9KpM0wqfg//uTUyTviClv3YbPqVouyombgWkqWk/XW7akRE5H61KHisXDNh2dULl6Uk7EbA/XF9urYpVgNTObnolOq1CtB39FnZxuPY5t2Objx9OzWgqQOoym6j7BplLX+45rvXJWc9t8LFKHhBNX2TFLaeIim15eVcfseJEJaHdMs8IRZLXiI99YmdT9xp4dmAEszHcV5hisr2APGdO8oNQw5233GOI4ID266eTD9j7AEn9zv7GJ8Z1+wMrshS98obWJSAcAkQQPwgITeEaQLztnjcFjMKyAX1hhrvnHf/zHLgBn58+TSXiJMHgWaI/5fzx82RVrTIRBumJ+cNBotMTkeO6JX/DHPvYx33qTnOOzn/2sy3x7AJgxGxKJuZc5mL8NDAy48xLg+0lPelJPArHdF713fq8HbD3ARw8L0UphoGGl62XZaeXc7R5LYACDRtiAEdqH/yK+jJiKI3fw7HTC1gVLMkDpAx/4wLCnbOs4T+v8G7/xG27Ah614Hs3UkWORabS6OGAy5Bwmk6ACCD64+m53K/rI8pAWC1tlAWFAsKeLLVgAJQAXLGZLhBHGA7hajiuTzFmTXJBcIe6QCGMrW1wpz+qH83e47hHJaEQFi9bZKaeUTkZVtgZk4ZGcV8XisJGO1l0dqhaWN1Yh3fG8NXtfJjalQpWsWuYnm43OKF+9U5XKjH4078/kwgQvWVheliIjqX1aLZmlDlEnoUx8UPlKXWLhV1yteIDUoVrIKJHMKxI1g/+sod0A8ZNrSRWr6xpBw1xuAsF3/Tc98N6PdIFINwo7aWhDATytjkvbqR9AEfDbTes1SAjGGoCnX6wFYxlyrTe/+c2ulMFWYFuRSTAXAR4vu+wy14O3OZCXYLmrr756y6WoQ6NVGTuDpELGQg0G1kuF3EgMwdSSpY57NjLn119/vevwQMrlxzzmMb7VZpcVAA6xwfhOv5PBjoUASTEgZugfQD3Hwkh/4AMfcMH4xcoE9xjg7XzhvXND9QAfmJeaMtQJkgsmu5GpzK8+6OXYpjLZ6HjnNCbvYMXNOc2Rx2HbC4BGZ4ZfZDcK8gUvkMM2KXqsNgMo4BVWIiyr3dgOopxhH8K6XACCD61+Wt+Z+4GxO+ogWFbNq+sOQSKOilk2kC/h81q1pjsmyQXBc7ZEGPXsYUmVa2Y5RMypB+pVGgL1CPL7yamoCpU641hvV03Fqtmaqx7QBstrcyUgU9maKhbv3kxsl8vy1ixa3khpRNHkvJXlzcamlT/N8poeGLKA75046Gtl553TVx1TJWF3dRhK7tJa+bDxvYg5afXFCBo0eyjjH+04BesCIaEx1RwYcDO7nfNpd9KZ1s+WzzLVw6lprZbr/5+N7dHq9+6r//pfH9Q1OUIQK7pT4w26fwJ/g8bRTt6f2AaC2FjY+8myGLMB5bCoANCLtdAPkFIXcxIMnn0PAF+sX0AX290OAO52SuLG7vDzrG3uLlbTyB1gbbcDDL3rsuLH2gaWphslKNjPS0nN1l67rHZjO5iUANqw42HLSvG4vnToXSpUzEAwHRs8nQjDAhaj/XJUVVXmjHEAXAhMmzsEx1CGLV7CAFwCqWyesTGnX1GnpqrqdXZqM/ruic2sKO2KRkoqVc0Aty82TEoRlWtm6UUmhm3ZoqqWrHvZ+B4VkQ0YHBuoYy4+pXzFzvLm4jPaqJhtyyK1GR1blRYKZi0vbHx/ZEKF6FkruOb3JeLENRAf1nrFfExdL403tFnqkI6OqoKeOmKWt2Tj+1SoHLSy205+TPHMWdC+XhjVUvFkQ7UdDaf2arV8UPfMPUk//vqqu9DtRspzKsEYhQSJAKpuMn3nwnrNs2M0ZbgE9LETBcnR7OIQdlzqHXfh9EAPAF84z/K8bUk7ALhbKYn9Os1m2QVwRJ9MPng0WehgO2GXBtvNNckN343CM8Evkvs1Rkt7KZrRJMOgsF0YVgpiqzdpQJFPtGprt1w4qi8dfpeKFQvTGRs6DRbNADday6hU3VAqYZbirBTqaZMHLO53+XJcQ6m00bKNPojDzjqkDjbXJ+54iSfWdWCxX8vFrUAtA8B18ipbZAzZ2JibpcwGcHPxScGo25JT5OL7lK8csrK8pO3dqJxN5OD3vHPxWeWrfrZljg4sDmixsKhdfdNaKNi0vBGNpCa1YmV5U8rEs8pXGoHm5holozwHO0seqwzJiS3JiZjfizDtjhbHFU3Pqbie0ymfBRuyiNH0tH6h/wp95eb/sAZpdfr7Z/cGnSrWju3s3rRbH3aYCOptdCVo91phz0M2x26aaRylL5Aw0B/dkpuFrXvvuO73QA8Ad7/PL7o7In8AcLVSkAOg5cKlIMhhoJXrhjnWz7EAYEjwF0FgADmAYScHdq5NBjUTcxGm3q0cAxPyL//yLy7j7GkR2SoF+MKisHXJT6e2yLZj87ZUOKwvHXq3SlVz9D5sKJpXG1iMKaeNyooylri95byUjjvKJMzb3mGcAhKRIUWcgqqn0zj7PZsEWe40oO80sb+Nx2bjY6rWllWx6HRz8V0qVk6oZsm+1h/fq0LlsL8rwekb9if2a6O8NV1vY33647PaqNxhZUT9QHBc+/TN43WHBkeOxtL7tFS0aXnjGkgOa61sY3kzp1NBm+UtfbFRVWsrVp1zvDwqJU5a2zSQqLfbVvrj+7VaqOhE3j/pRjZ/X01GH+LqL7sZkIYsgAUosoBuFc96DZDZLa0zbcMNgbGU/vUr7CyyCwWJESYeoVv91bvPuemBHgA+N/1+Ud21HQDs2XRhO9MpEBa205uz0GH7hdwBgMigSTKHTjMpnnVaY975sPVt9ziCMZgomKCQYBB5zPYgLG2nt2e36+u8kD+gmw//rcvimkomPqpaDc2r+ZhatU81Z12pmLnX5jekXEJWoFxPimBPGBEmccJK/hIdXz2oikWi0J/YpWL1hGoyB2QNJPZoo3zECnAHE1NaL9tlDAOJGa2fzlRm6qEwYDAXr2uCvbKwPqmDK2fdGSKKaiQ1oeWSJTlFJKVcPKt1C8ubCsHyhlkgDCSmtd6Qptiv7UHthtVfLfRpleewpUS0b+MpWl0saW2tvpuBIwO2ZN4P/9/pcYX7kPQGm8VubvlDeCAl66bUg7ayk8buock9iEBePII5phM7W+2Ovb3zzo8e6AHg8+M5XNC1gG3EKaGVwiSBhiyMS0Er1w1zrKdXhQFlwIRVwPaLAK4gx4Qw1/c75lxIPnC7wK6N9jFhwYwQsLITjDv+lyyEtuNzPL9xp24+/F6Va2YLsL7oqMqVBTkxM1hcK8aVjpesIPjkel3rawPKcQ0rElm1JkVIRcZVk3/iBBJCfOvYnAYTu5SHwbUA3KHkHq0HANyhxJRWAwDuUHJaawHZ1wYTs1oPYDsH0PtWLVpeRdyAr0LtoOLOiL55bKv9W9SJazAxpNWy2bEhGckoGYuqYEnukYmx8FmxsuQDiX1aL9u1vEOJWa0FtRuWvHLWw7jxW447szq0cki5xKA2mtq0q+9+ut/oM87ocXGZYYxjS54fIvMBv8iRGkEx8qrtfo/Yc5GtEU/YbpVz5TzBLhrkyf3vf3/fpkJuYP1If3dCutat/uzdZ2d6oAeAd6Zfe1dt6IF2ADADKIEKSAJsGdR2oqNJ3MEASfAI94YRbdX2q9V6ITvAOB7JRzcK/YtGj61KL4VxWD/gduoHg+55UrZzvnfOyY3bdPPh96mK8a6hOMWsEnjyWvxrAbgkwUhEzbU5vlrPBGc7pprnXuuKRM0aUhJGVHwSRpAQ4rbFOpgaTu7VWskuURhOBgPc4WTdK9dmSTacnNVaszVXUzcMJWa0VrEBXEf96IarZhkD/sl9sTGtFJP68by/5jceSSsTS2ujYnZs6IvVHRts8pb++KQKBMVZAvmGEnU3BlvfDAX0jaOI0FTnq5tdKOrWZwkVqhuu33MyltjkPPLgiRdpKDnjgl0/PS4EgQeGcV4BxPFvfJMA4kZgHNZ723ukBNcCDLHE6lY5V84TSBtwuPnFX/xF36aS5RP7L8b2nWDbu9W/vft0pgd6ALgz/di7iqUH2gHA283I1u4DQS8HEIURBfhimbNdBiZMXbabmjjMPTgGwMs2IRMFfUx6T5junS4wUCwqTMxMK/c/tvYD/ceRD1oZ0/74bhWrx1SzAKK5NWkiI9dKzVTypYz6k3lrAFmsMiZFTyhiuQ5pgUu1s0FmUSer7xxbU7UhIcRoar+Wi3YN7sjpZBA2EOd6zwYklQgDggcT+7VuYDvpL0BfNj6hQtUsY4g7A7prMaWlklnLm47iy4vtmzloMBcfd+3lqjIvfIaSU1or2SUetHs1APwHtTuihPpiAyo2pLdORaZ1x9JZkJ+ODspxiq7NWi6+W7+y62XuOAKwxRLQZNPlvYd8pyxSPVDMb4Ax3yzMZSNLDDi2ycTQvfLNd9NTPciPt5XvvZVjGWeYb4jR8Cv//M//rJe//OWux3w3xvVW6t6JY2k7wP73f//3z3gB4/wBkQOJg+yD354nOzubrS6oOlHP8+UaPQB8vjyJC7geDOawf60UziFIC71WJ4PNjEAnn3cD3Jgs0L8yyXQzCx0THL6ZO6l5ZvJFigA7g5wDVojf3TCqp2+ZFDsVeX145dv62rG/r2exMJSh1JTWSwetulhY3t05KRoxv511hwT79nl1fVDJ7KLdLqswplj6hHtMuTyjH5zayrC6aYELZisxahkvDCmSMXsNc8xoalbLgSB4v9bK/tv5dYAbUaIypFrCP3sdx0QUFwxtkdTAPsWpzeqHJ4+7MoZ8pZ7O2q/k4iOq1lat8hZSHQfpnN0FQtHO8o4kZ7RStqVVjgrAna/aMsplXJs1krVQNorjWihs7gMXtFfnde/h39HebD0oi4UuO0ztaP0ZExtlE4wZNj2xB/Bwl4EFZiztVgkL9DtdH7TOADqT3zjJLf7n//yfrlPEhQiAvf589atfrb/+6792/xfvdqR7xLIgtXPHkHjc/TeSgXBcoxNQp5/J+Xy9HgA+n5/OBVK3dgAwTb/xxhvdrSx8aHeqsGImKpsBEeALc8AgwWDRzQnDS01MnvpOr8jZSgWAMhECdrFuY/BDY0265ubMRjvR1zDOnexT2vS1n35Gc/GbrNUNw5geWpZmBmUFr2GssDLR/SrW7AyuNgDBJ/WjE1GVDcknJtKzWizaHQeGE1Nas8gP6JSR1H6tlOwAdzC5W+tlf9cCt2NrUSVqOSludlog8QQJQEq15mMc3bmQ1VJhRdn4kOQUrE4eg8ldWi8etyae8NpkY8AB/3YG3NEQkpNy3ZXCr0SdpPpiWWsq7aTr9JFXLNKvO5f8GW7Y+F+e+ENFI3XrkU4HpMEIs7BsZIqRAMAIe7IJmGQWvSzouwX6Ot3OsGMSiwvGcWIb/Mr73vc+ffzjH3ddMS7k8qEPfUj/63/9L/33//7f3bgLGGCeCRKQj370o3r+85/vLsauuuoqveIVr9Bf/MVfdNUj+nzp+x4APl+exAVcj3YBMBrVnWQoGRCw/WKCABSyHcQEcS7SMCMPuOmmm1xmqFPBGfQ7jDaBfIDqZus2m99xp19HHCbo7+1muqNNsFk8N9o0MLui29Y+b63uaHpWy0W7ddeBJekSMJolne8gutgghwRswnx9cM9WMaF76daTP7ID9/herVYtwFTSWHq/lgMA7lByt1YtADeimPoTo9qoHDPWx6kmlElmVbRkVku43sdlVRrSM8edffr60bMgczA5rkJlXlWZLRFT5SFVEwvW5zCWntGyNWUyDHhdC20qtDuXGNGGNZkG0gypXDMnWemLTqhUSenouv9C45L+X9N9Rs5mHOtGQBq7bY2gGKDDQt/TEzfKJ3ZK99+Ndvo9W+QlpBZuTCPceNxb3vIWd5zFoeJCLsj3kHpceeWVW5pJOmfajwvQG9/4Rn34wx92CZJOEy93h/7tAeC7w1O6AOoIyGy1kNOcAC1+OllgSNCKAaT8AsC6nZSCtgVlZmu1/TDKyB1ghmBDmBCagz6++tWvusk8TJNFq/e0Hd+JRQUsFsF0eETTJthr2vSjU1/QD+c/Z63uWPoSLZdutx5zYFG6x4i91eEcEuwg+OjyiKJOv+ZtGdEkJYuDUtrMvCJRGE5NatWSMCLixNQfH9W6BeDGnJT6Yn0qWAFuv6KRqio1s043FR1TrbZ0xhVjYX2P7lreHCw2mtqrlYBgv6HYHq1VD1tBMFKRJQsIpm+GkpOBKZOdqiMnZvaXTkXJtodW23/8ijpprRfHtFz01x4/fO+rhJezV4ISNXTym/Ouxe4L4wG7P42BdoBisjM2AmLY007YTp6LwDvaS8Y72mna1Xrd617n7vZdf/31O9HV58U1iV/huV533XV69KMf7cZ98OON/+ysPu5xj3NjMpAZkhKaOfFiLD0AfDE+9XPQZgAeH2ErxS8hRSvnNx/LgE9qX9hItgdhRBn8mwu2YAyS3fbNJDMbkdp+dQrb7sYUxgQ8wAQwGPqVW265RaOjo8btwrD3DHMc/Y7XcTupnr3nxjMheIPnhmeqV3ivbj3xv3XHkp3VGYMJDtDFFkoDyqXM6XO55yCesRUzu8gxA2RNa/DB9eqacCb19aNoSx1N9O3VYtG8DQ+I64+OKK+zaXab+xomcyA5Yk0L7AHcvBXgksK5prIF4JI6uKZla9a5TGyPitUjIhHI14/6OztMnM4EZ2Pbd/XNaqFgk4E4Gk0Dpm0yhoRy8X5tWHyEnUpayZhUjZi9o7OxSRWr/g4Tqcisbl+6Q+PpWS01yVbGUj+nX5l83qZHxjfAt9DOdxDmO/M7BnaP8fc+97nPmT/zTfnpidkBAwQ3gmK2z1uVTgQlpGi3LUHnEUfBwpiFvV956Utf6gI/5AEXagHMspPIuHj11VdvSj8PufMnf/Inrg4dEgg5yMte9jI3KPBiLD0AfDE+9XPQ5nYAMHZBJGnAqWC7xcsRD0BE7gBLYBrUYS9gGtHjdqswWH3xi190I7UHBwfbui3aZepNuwC+Qdrp5oQfbd005EntsurohmGyGdRpExOb33Or1ar6xrGP6dDqN601GsFKrGK27uLkoaQ9OAzwOoivrPU6EWHN1WyXtZaf0m2L9ftHHJJBjGu5ZA62ijoJDSQGtWbxynWqcaUiCVVj5nTRSZwWnJpKFoDbFxtRrbZqzZqWjdXdNWx2Y7n4fq0UHP3glHmRMJmZ1bwV4EoT6f1aLJo1zLDbg8mRgGxxWSWjjopVs4whqSE5UdptdpgYwBHDJ0vecn5QK6U6Sz9BAGPxbHDdg8av0O7MZjuucwEMkQtRgtKQMzYinWiUTzTriT1gHCTTCkpIEXLYaPkw4hrwMzcluXjuc5/rjq8f+MAHWr723ekE5IO0FTafxRbyPp4lu37sCLzzne/U0572NL3yla905Q+4Y1yMpQeAL8anfg7a3A4AJiEFA23QwG1rDtvmaGABh8gdyOQWpHsDLHNvHBm6WdiOwiUBDVsrhTaymkd3B/uBPCDMNiZ2bzCpLAh2uhCAR7DhQx/60FC3YjJmYGYiZfCmjkEatWqtrK8e/oDmNsz62jNb46QFNhSOGUhMat16TLBTQLNDQszJ6VtHV1VrcK6IR5LKJXJaK5801gdf2XQsqXzF7PwQq/Up5lRUi5mlRiSMqNTsDC4et8UAP93++JQ2Kma7MWzCTq7t1bF1eyDfZGZa8wWbTjeq4dS4ViwLBHyE++ibqi0d8rBUW1dF5m3e/sRebZSxqTO/ns2JMtLRPbptsfE9cjSW2qvl0kGRoe6R+17jLnIay7lwZGABiRVWO9850rXmIDu+Tb7FRpaY/+YeXkHyxOK1E7aHoQaM0wcB/Aj6MjkHPelJT9Iv/MIvuE4QF3pBQgjTzVzG/EfgM/PDi170Ij3mMY9xATF/Y7FwsaaF7gHgC/0rOE/aR9Q+226tFNLnUkym5rZrER3NIIzVFyt+QHRYq5duefI21x8JBIN3EHPrndcoDaCNSANaSWFMJDSD4nYWGGGfJ8wX0hMSm9gKTDjbcbBWTKhMVq3Y4JUqeV3/gzepljIDoogT10B8WOtsaxtKxEkoGxtUvmrOUoZTQDqaVbFmtglDI5o67ZBQq8zq1hNbQWEqCksZUd6S7Qzf2YhKKtXMelW0ppXaopXJTNZGVIvObztlsk0LHdeMbjl6p6ZyM5rbsNmNRTTWN6mlonkxwgIhSzrksrmP0/SNUwqRKAPm2izDGsYerWS3oCOZxtpp+Uutuk9H1jZLMMhuN5AY0r7s/XTPoa1JbVgEssDulB1gmO+PcRQioRM7aXyfSAgaXSfQFzMWIZXwQDE2aADlSy+9NEwVO3IMdQAA43ZhkpFddtllLrHx+te/viP3vLtcxEuq0jh39BKBMBy0Ksy8uzzxXj3Pqx5oBwB7Dg2kDW2lsNrlXAbEdtL7dsOT1689ZL4DjJKOOKjgqIDcgX6ljSZpgO06t956q8sUAzJ3uqB9hIm3+Z8ysfLcYLJhsUnQ0c4g/YUbrpNzyX9qvWLWzmLd1RdPqVA1s6rxSJ8S0YRKFnYxHskqHnGsTgGuQ0JE+tGJktbL/gDWtQnThko1G0s5rlKAi8JgYo82KkesQC+Kj3Da7rQAGMTxwlZwxVj3yRZ3eHlEx9dOCTJ1d3avTuXNbhaxCBKPAa2Wzc8qTKKM/viEirWT1uQow8n9Wi3ZbeqCEmW4uuz4hMpa1V1LK74u1KloTo/c+1Kl41ulTOeCGWWhCyDcqYQ3jLOA4EZQzK4UxQPEniVbO3risGMTYyESiAc/+MGbYgQazycd9BVXXOHqXi/0wjzIriKLEYgRZH+M9d3wfb+79G0PAN9dntTdvJ7tAGC0Sny8YRNSMOgCoGBYcDYARDVuy4XtQoJDGEjJ1gRD2q2CNQ11ZsvfVNiSBEhib7bdFMaYxrP+bYdhb7VPPOsy+rS5UAeYMZ43EyaDdCtMdvP1GPR/4X730K1rH1G+bA5oS0b7FYtUVK7ZtLPYe9nZRfxgK5UVRWIWey/nHvruyYMqV80ShaHkLm1UTgSAuL1aLR6SY0ncEcYrdzC+X+tVs76WPuWYDesxjgZcwH0W4CYju/TVw2ct1WKRmEZSw1oqmpl0WPREzFG+Ytbp9sfH3IWITac7kprSSkAmuE55BKejU7pz+ae+n8GezH30kMln+f6NHSkkBd1kRpE6sYvSDbcXr9GMLezCEcPhSSgYuxiPG9M68713aoxlAY3GlUBiP40y4wySjNe85jWuPvZCLjg9EOzG+0Z/I0HkebA4eMMb3hB6Tr2Q+4i29QDwhf6Ez5P2sR3GB9hKCWudxXX50DkelwBY1O0AKAZq2Fi262EsulVstm8M3ugH0cVSJ0Aik8t2iscgd2Myho1gK/bhD3/4pip72emYvBq9mLfTrhtuuMGd6BK5ov790DtVqpoj/NHFVgOcDfpi46rUFlSzBEnV8lkl+zaMKZPnVidULEe1WjqmmsxSoLH0lJYMllpenyQKQ1IAg+u6EgQ4Xoyk6lv+xlKTYsUBRfvMiwh0zpn4kAqnpSIbxf364anNwDoVy6gvFtV62ZwJrj8xokptReWaOWPkCBZqgH+LTjdMu0k5bUsQgkQmFx/SRsUE2iNu5rdCZdlXkvJrk1dqV8ZfV4+TCe96Nxad3nMl2JVdJdK6d6v4yS6aUzvDGDN2A4CbUzu3Q1zAQmODxrjtdz5jKDKQ97znPW6q4Aut0D4ChOn7Zz/72e4O6J/+6Z+6zx4Cih2/t73tba7mF4s0G9FyofWNqT09AHyxPOlz3M52ADABUPyYAqc8vSgBYBSkABMTEy1bS/lKzwAAIABJREFU9jR3DXVFj/uQhzwktG64E91r8uUFJAJWYaZJDMJE1qotkV/96Dcm424EqiBrYCvWCyykj2F8eb7IN7zsdJ3oR54doJ6B/uT6T3XzkfeqWjMvvvoTu1XAJ9cxA9NcfI8KAdKCvshulXRkC0BLRMZ0y+H6Fv/u7H7N5+3M666+Gdcj2JrtLDGllQA3i+CEEY6Gk3usiTLIBJes9kkJMzsbczJKRqNugotvHc2rUtvajwOJYVW1rlLVLPEYSe3RSvGwld1udlrwe1/G09NasibBiGogMWb1Ro5HMkpEIir5JMHoi+3TT+YPisx1+fKcag3vTSY2pMv2v1qOgaL3syTrxDtvuwZ2h/jidhPwYLMF82iTXTB+s2vXrCfm3wnObQTF/H+QHCoo/TLXBQx++tOfdv1xL7TCYgJJ21vf+lZ98IMfdNtJXEhj4bn8zu/8jpsJDgcI75wLrS/CtqcHgMP2VO+4bfVAOwCYbX4mDD/dKKt95A4kRWCQ5SeM80GYRnTCkizMfZqPafblZdXugcTGFMbtXNvvHPqW7cluBOSgWSZL06Me9Sg3IhlAz4TGAI0XcSdLcwrtwyvf1teO/T15fY23GUpOaa1sdjbgRPx/18o/swLTgXhdF9sIXkulGX3v5FmmdV9uVic2ghwS6iDYVibS01osml0UwiTKwGYtG+CVG6ml1JeIq2SxEnPKGVVKA/rByhFjlcfSuwMZ8FRpSNWUXZ/MAmHB0jf1du/SatlcF9wjCE4sWt0jCCqE+d8sbalW9ujIWj1wj2x8S8WzuuL7DD9G9x7evMvR2CEsOmHmmoFJJ9//5msFeePuxL3blV0AyBr1xIxPgGTGCk864f3G37yRCAhKvwwDzaKY/uimD/NO9K/fNT0wi76Z9+yf/umfXMcj5hH6CVac/3784x/vBlvjhNEDwL0guG69nxf1ffjQAMGtFL9t88ZEDwAnWN/GpAitXN92LDrSBzzgAR0HZ7Z74nsM68E2HUFjDGIMYEyWrVqjhemHTqUnDnMvz1kDhp7n2opdW5jrNx5DvnskItzLK7cv3uQmy7CVkdSsVst2YNpsheV3vUaHhIiT1K3HKipVN7/7+7LTOpG3J9PY1TelhaLZsziiYJuwmJNUNpHVRsXsopAgkC/qWAFuOiAjGv1w5MS45qsnrAF4e7MzOpm3A/vx1JSWyuZ2OyIJxh4tFy3BdSHa3RcbVrm8JCdq3h0YwO+5fPDMgiYR6dcdi5ulHG7SDlJtK6LHTb9O6djW5Dree+It/Bi3ulWQVrFzFNZdphP16qTsAtDWbMWGphVA18gS829I4Uw7hgBkxh30ybbAXyDR3/zN3+h973ufG2vBePxnf/ZnevKTn7ypa9773vfqc5/7nCAuiDv51Kc+pSc+8Ylbuo/4BxJwfP7zn3eBPJnX3vGOdwS629h2+aiXl+zD7zjuw05Yc8wFEpFnPvOZrhXai1/84h4A7rlAdOJz710jqAfaAcCwuwyksIYUBhJAIR93mEQPQXWy/R0WkcxJjSBqO9cLcy6ejLDY+DPiodmY7jfM+a0eE1Zj3ep1m49nQiFwjwxY2LUx+YS1pGvn3iY3je+e+Cfdtvh/rZccRRcb4H7QaIVluthgYspNlBHTjL5+dCvgA8RNZnZrvmC2ACPRw3BqRCsls11bPJJSJpbWhsUjGFeCWLSqUtUc7BfOIxipyFE5PlZiqcge3Xz4sAD2cxsBwD65V4sNgXN+fTjRt8+aJS/mJJRL9Gvd4p+cjg4oEimqXDNrwCOFfsXSy1ZWv9EZgsxvty1uXSRN9E0pGx/Qr0xeYX2/AF946AJIu1UIrt2JnRZb/cniSZDuTjgOMJ4064kByMwxgEFY3kb3CW9nkPEOaRSyK5se+i1veYte+9rXirTJBNSRJAKwy2/YU6+QtIiCfOujH/2oLwCGsCGIG3D+V3/1Vy4Di/QAOQrPxQZy6cPmAniF8GGu8ArXAMyS2ILCvMk9kIS84AUvcOtH3AhzCnWAOf/IRz7ivhOebrhb7+L5dp+eBOJ8eyIXaH3Y9uPjb6V4dmRsVwGg+ICROrCK75TcwVSfm266yV3578QA7ndPBm80wGz/eSmMdzoAD+cF/HkZ5HeqMDEx6dMu2viIRzyiLWeOVuqHFygAo/HZMdCXyyV9/fjVOr5R95f2L46GU3u0Vjazi/VEGbu0jibYeJWYsvFR3T5f0IkNf6u1eCShwWS/VkrmJBjJaJ9S0VigR7Aju1NFLj6mcm3B1emaCo4O6+XD9mQQ2KP5eOWuFab0o/k6a7s/N6Nj6/bguv7agIoJc3BdDD/dJPZo5r7BHi0agV03eyPznPIEs1n03YMk9qjaswMOJ6e1Wj6g+fW01stbFxJRJ6aH732uJjL2rJUEIvFddzPxAAtCrCQJEO5Wufnmm902dotA4PtmR4vdJRwvmDuImaCw8KYPmDs+/OEPu2nuTTtqAFV2Fq+88kpXS+uVxz3ucW4QMhparzCnQcZwfeYkPwb4k5/8pJ7ylKdsYp2RYBBfAiPcihbZuw8AHYcHrwCAYaxf8YpXnPk3APJf/uVfuuw0TDl1BRDzTN797ndfkBKQdt7tHgBup9d657TcA+0AYD5YQCEfONt33kq25Zu3cQLbhvv37+9K5LTnW8zgC3PxS7/0Sx0JcgtqdqvZ2YKu1/h3wC4TDYM2kyCMC2w+LhBBmfhauY/fsY12ckyM/FAf93e1pP84/v6AFLu4AJAEw+xNSxKMvlhWhapZWpCK7NWPT61ptWT2Gu6L5ZSI1pSvrBqbnYsTQLamcs1soQbQKwRYqA0l92mtYTvf74YjyRktBySDgBFda5CKEAT3tSPrmzLc7c3u04mNzUkiGu8Xc2IaSPRrrTJvbHcyklEyJhUszPVAYkKF8olNgWjNFxxNTWu5ZNduu8y/xRHDQW6SuId+uPBj3/rihPE7s+bgN++knfbkba4c7zzgD53/dl1jWvkuzwXrDMOL1ArpGsXTE2NpCeOJOwK7iCxA6A8swRhrSXnvAWLiSmBGSUvv7TxyLZwjYFkhDWC2G4sNAOM5zDNvBM6cC2AGVANGw5Y3velNLisNg01QYyMABhS//OUvd/+JuZYxFiYYWR11hnxiPvut3/qtHScgwrbnfDiuB4DPh6dwEdShFQDMoI3GCdYXUIgUofGD70Z3mRwZOnlvHBiQdACA8ehku4yBqtXEH+3WKWx2tlavj+MDWkeeI3IHGG3PW5nJJiilcav3az6eCY8Jhm1GD/hyDAspfgqVVX358Dut6YdJgoGzgZ8LgHe/RCSnWKSmcs0fvGIJdmgZlrOgosWKbSg55gZjVWrmHZKRFAFkR6362tHUlJYDLNSCnSEkwCAg2FwcDcEWn5YxRDWrrx3dLAuo+/8OabFgSXARIwOerOA/Xs4qkliTIuYAxrHU5kA0v3oH2aPVU2Tv0polcC6iaS3m55WvbLVzu3T00brv6CMCX912g8MCL2w4gPef3awHPehBOyo7ar79uWCdWXATMEeAl1/5whe+oOc85zmClQUYooeFIf3EJz5xxp4R5hQATf0bg69xVXje854nrgGIDAuAAdgwz9yjsTz2sY91ZRzodMMWrPMA6uxwNRbGNP4dwgh5BPWDEW4G6mHvczEd1wPAF9PTPodtBQwBZoMKW1eAJ7bOATEMamzRm1JbBl2v3b8zOLKVthNblSwGWJXTNtoF44AmFsCPVKAbrgz0S5jsbK30HwM6gB6WpTkRCWCfiZhJxc+kvpX7BB0Lew/jjASC945tymat3WrphAuCixZ2kcAvx1lTRWbmtS86poq2OgXEnD5940jdEmw8PanV8pzVim2Xq3lFdmEGehN901oo2JnMifSsFov2QL5gEOxoKLlba2WzPhn3iEysX4XqSd250K/5/FZQCEOeiEobZTO7PZIcV746r0rNIs2ITGjDOW6VZriBaAVbu+uBcyslW+BcSn0xsgNuTaPNYuf2xRX1J0ZVqpCQ42x90XP/ziWvVcYn81vzuwrwYjerW+CEMZfvAb3qTkuqvLaGSUkc9A2383fGT0gEU4AbEoWrrrrKZVC98cDbIfIs1ph3iFNAK4uO1iskzkA+8fGPf1xPfepTQwNgZHTser3//e/fdM4znvEMd55jQRSmIJ1Bv/x3f/d3LhBvLLDMOOxAMsD80j4kggTt8a7RHn6QwDCnAZIB5u14LYep693pmB4Avjs9rbtxXYMAcKPlF16NREljkI4bA+wFH243CwMTAwWyi04WtqUY+JiYuDYgzRuMAcT8ncGpG+X48eP6/ve/724BbqfwbA8dOuQCePqMCah5wUJgH8zFTiYX8SYz2HsmQt4Zb/Bvtkyivafyd+irR9+nqg18JXYrX7Ezr7nYXjcbWqQhO1tMs7rlyFlAtje7XycD/H/3Zmd1Mt8Je7T9VolH3SZsQqvlo8bHjsQjE8sqb5F4JCP9SsdG9OVDZrZ4JDWujbId4O7O7Nd8we6NvDszq1NWgCvlNKpi1KwZJnCu7ohhll30xYak2vqWRU9fdFY/Xqg/m/H0lBYLZy3z9mR+Xr+5L1xmMWRARO93a0eL7w7NqSk72na+e9O5YVIS78R9GcsAdc1jNswoY90//uM/6h/+4R/02c9+1ldiBuHCnPOsZz3LTRRxzTXXuAsH/hvfXBbxMLnNbhA2CQQAmLgHHCUay9Of/nSXLAC4himvetWr9Pa3v90lF/zmwpe85CXuGMy8wjFcF+kZxArvAOQEf2OMBORzTDddQcK08Vwc0wPA56LXL8J7mgAw/85WPOCJwQd3h8YAhWZP1251HZotz4KsE/dk8GHAQ9oBO8nA2CwFQMPGwNQtj8rm5BTttBPGmokH5p7AM5gtv8hm2o892a/+6q/uiG1do86XOrGQYOLjh8GfZ4kGkh9AMQCdyfLw6rf1zbmPWpvuZUyzJaZw1ocVz50FVnfND+hkfnOQ1/7+WR1ftwPcfbkZndiw24SlywOqpmzZ2aIaSo5qtWx3j0jH0ipUzfrkVHRA1cqGFLXs3FR+Tj+cv8PK4O4B/G/YAe5UblZzAd7IsOQLRbOuOELSDqVVjZkZ53RsQBGnoHLNnJBjMLFX6+XNGeeW8v1aLp5luV1Afnqx8rA9z9JU7j6hPp9uJ6WAFeSefHc7rb33OuBcgG7ubQowvPbaa103hKCC/AH5GWMHjgrogCkExZFRjSAzFvEPe9jDNl3KBoA7IYFgbGNHjbrhROFXkH6w8PB+qCO7l3/913/tjn88E++H8ZFMeDsdSB7U3+fD33sA+Hx4ChdJHfgQG4uX4YwPEkAIeGrO9uMX0d+N7sK5gIFnuylLPXaUpBOwkMgdAGB+ha0rAtOIEO5GwReTLdlmTVuYe3vpp/HdhElg4WKTNuxUdj2P9WXblR9P59sIwhn4eddwEeE3YJ3zSJcNIF7P/kBHyjdbmz2WvkTLpdutxwwl64kyks6kvnLYn13dl53Sibzd4zbIHs2pRTSQHNRG1RJAFq1nZyNdr6nAdjrOhhUMxssDUnxZjo8GF5nH1w5vaG92SnMB7Pb+3KyOBYD/3X17NW/19sUZYlCrZZuuuF8R1xHDbH3W54yqHDlpz7SXmtXK6VTSmdge/Wh+qxwEn+aN8oJ+7x6vU8SJhvls3KBewIzn4RrqpG0cBNsH6wwg6hbgORegmy5i145tfphcv/I//sf/cMc7SJUwBbICMMzc9JnPfEbIFiANmne3djoIjpgGds782GdTO5hnmLvQO/eKuQd6ALj3dnStB7wtGH4DCNmyQQIAc2gCT+jXAMbd0sx5nUE0MMBpO2mCAVqwo0wIDKIwv7Z0nvQHgJLBrhsFQIjXJAC4ldTKAGfaBeAE+IaxOgIw33DDDR3Vczeyviatr18/Um+ejccQLy4taqX/FlVyZlszydFIap9rhWUqrj1acpdOrcf1g5P+jGfUiWo0PaKl4pzxOoloSrlEWmsW94gUyStiVRUtFmC5+KjK1SVVZQ6uG0zs1kaAxKOvtkul6LEtgDGuGX3lcJ2tnh2c1dE1O7sdxhkiVUuqHDP7FadjwdZnQ8lJrZdIbW3WUycKw3Iy5gUEbRpN7ddK6S5FNKW7lrc+d6zaHjD+CN131Jz5rfkhd9seLCg98E6MMx7oRu8flL64k/cPSr6BjIC4h//zf/5PS7dl7CKFO7IV/H6bS5ANGpphxkvGSgpjLrt8YW3QYK+RYyDjCKPjxnUC7+E3v/nNm6zRWmr0RXJwDwBfJA/6fGgmDDAsJ+l9AbysUoO8KRksAFimVf1OtQuADkhiIGm1wHZ6KYzRM6NJCxP41emgtKB6ez7LDO5h2KFGGQeWOoD6sIEUnUwv7cf6bneiXdtY0S3H/06rVUtiCsWUjmRVjmwNkvL6Ohkd0u2nolooWPxrYxmloo42rNZng5KzoVLVHIA3mJxQvnJSNZkzmQHaV4pnM5n5vRNjuD4EJP8YTc1q+TQj6l3j4OKQjq2dlVDs79+ruQ1zkFk8EtdQsl9LRbN1XKySVCYdVcEC7Iddt4bjVoA7kZ7RYtEuJRmMTmrDMeugsT4bSkzoJ4vHVK1VfT+n37/HqzSQHAv61M78HTaP+AacUbpRYDCRBvilk9+p+7OwRk7APVtZWG+3PkHJN174whe6gPzqq6+23grJBHpfJASwwASeIU9DStLI3MMmA35hhbn2H//xH7uaYZ6tJ5PwEmEgTSCwjv/HwxfipzERxhvf+Ebxg5cxY6tXOJ574hf8sY99bEu9SWcMacL92Ilj5xL/X3YbeQam3cbt9vWFcn4PAF8oT/I8bweghcEfdiAMG+o1h0GGbWrO6WZhUGFgwysybKGNXrY6AGWr2Zf8Uj+HvXc7xyE9gWEP8ub1bOnQMDOwEuTWjqcoFkL0Z9Cix9YWj/U1yR3a6QfvnEJlRV86/A6tly3MYDmhZCwixfw1pAnnEn1vbl5y8iqgnzUUgsPylQWr9dlYCveI46rJH3xx6fF0PeDNpk8O4wwxnp7WUsmWwQ1niMkzNmHp6KT+/cBm8JiIJDSczmqpaO6/bDyniFNRoWJOXjEKwK3MWdu9q29GCwEA1z2mYAbBANx4OS0nZdYMV1aGtahVVSJbddATfTN6/MyLWnrlcELB1nEnUpv7VYQUvQTddmtXiTqwQwQQa9bKttRRbRwclPL5D/7gD1wWF09fW4FtBUQCepFJXXbZZcKDtzlwkWA5PzBNuxutyiA2SIXM+MeCwEuF3PgO/Pmf/7ne8IY3uPdEIuOV66+/3vULJuXyYx7zmC3VJkCPuhFDw9wK+OY45B7dktm08ajOm1N6APi8eRQXfkUIdiMqlWC3sIXtHC84Luw5nTiu1SxpyByYaDBibzeFMZMVOrZGA/ZOtMV0DQInYCHIF296JrSL7TsvixDsRLtsK44eWLy1M/kDfAG93m8/a7NO9NVK8Zi+dPidVl1sojYoRdHFbgWmd80Pam5jUZOZPVosHLWCOILDTgVoZ/dkpnWqYE8tPJmZ1XyAQ8LEaaBs6iPXBzc1rrXyMWM3xpyUvMC5Umla/zm3tV4DiUE5kYIV/I+lJ7VStAP7vdkZnczbGdzJzLTmLX0TgcFNjWkVOYShOJWEMumESlV/ELyWH1KlElFB85sSfXC5e0YfpnsOP9jVhPL9hGE7u+2Py6Ka3ahuxRXQL9yTHbSHPvShnfgkQ18jaHHxhCc8wXXYgYntlV4P0AM9ANx7D7rWA2wDAWJaKWTvoWw3GK2Ve3IselxW40RP20pjMBjADtY3jE7L75qAZ7bZWtXktto273jPmgzGAma3sfCcYMH58drVfEyr98X0HS/LVrZ/wwS5tVqPoOPn1n+o/zj2Qasn71Byv9bKm5nXar5f3zx1NuhsV2K3Fqs2XbE03X+Jjq3bg+vCOEOMp/doqWTz7SXz2rDWyhbtMck/YlEVqyvGLkrHht0gs28c3VCx4u/dSxDfUhHwb9bgTuVmdNyWLlnSvty0TmyYwT+gfTQ9rmULwE0SDBiJqGBpUz/Z5ContqRLzsTG9cNT9f7al53RiQZAHlVMl5afqLXlDTfyHkcXgDA7I/xmod8sDzoXWdlgH9mm75azDH11Lu4Zpm+xIwMEv/rVrw4aAnp/v0h6oAeAL5IHfT40Ez0TgLGV0olgtFbu5x3LIM6WP+yoqSCRoH6ARXR9aJXDsECm67WqyW2nXY3neNZkMDVs9XkF3SCsL8/LC3LbTru867ZqaddukNt2+4V39Gt3/G+diH7VeqnR9CVaaXCGKJan9Z3jmwHbcG1Y63F7sNVU/37NrZttwki0MBwf0VotyOM2o42KzdaMzHVVlSzJP7LxUVVqmxM9NHdCNnpP3Xz4J9YFwsxAsOvDVG5ax9fNADfiRDTeN67FgpnBTQFwYxHlK2bQPpAYV6F80pou2S85SCoyo58snGWhGwH5zw0+SL+258nujgR6Ub5dL7ASaZHnMgIY9oAxLDE7Lt3MysauG4v5VqRc2/1+uB/a2W75mVNfxir6lns2jmVeW3ge6HNf9KIXuT+90usBeqAHgHvvQdd6oB0AzPYd2+/tBKNtp2GAW7yAYQ2aC8wpwJetPiQBBEuEDQaz1cnT5HYjXbA3acDKepn2Gt05cN1Ad91J71C2fwHUBAbayrlgfRvBv5vGWTXF99+uudJ3rHUdSU25zhAkjvjW0coWVhTwOtk3ofmiGcTVnSGGtFQ0A9xoNa5sMqmCIe0ylczECJzLW+UbA/E621lzzDsxw8l6m0y64rmVcUWdrA6t2l0fZgb265gF2EediMbSo1oomFnp1OmAwbwlYNAFuFWCAW1a6WktBmTRa9RBI584thpXvnJW693o4PG46RdpV8bfbosFFN+yB4j5zZhBXAB/IwAKb1mA8U6nBSfomHGqm+Mn8jHkXN3KaMm7T4A1Dhsw3X47VYwpxC6gl0UL3Cu9HugB4N470NUeaAcAI0PA/oXVezcLLCi2Oo0euY0pjNniZEDld6dKN9MFU2fagy4Xdoh7w3gzIdOu7QSqmfojjKfzuWJ9vUyEMGaAfzcFdqSmrxz5Wzf9sKlEnbiyiUGpltOXDvoDwlQ0rVwiqZWS2T0iXk0qEa+p4piTTgwmx1SuLqpcM9uajaT2aBUphGN+K0eTU1oumQEuZ46nZ7XU5PrAv6eiw7rprjqjPTMwpaNrZlu4mBPVeGZYCwWzby/pkuORmvIVs/XZsBsweCoglXRdD2wLBgwKigP0DiRHtF6ZUy4+pe+f3No20j8PpQZ0+exLWtrtYXFJcBgLZ4Av2nrAMO4wnmzCk060q7H3e+LIHxjLHvCAB3RqmAq8DrIpQD+JG7pVgryHGVf4rglau/zyy7tVrd59zvMe6DHA5/kDupCqx4APCG6lwGDw0+2AClhnTOsJSGNCQp8LMwiTg63Znj17WpoAw7R5p7Ol+dWByGQmXhgrQB8RyJ2cgBvvyRYlAYL0XXM5l6wvDBnAxMv812gdlC+v6EtH3u4mPDCVVLRfJ9ZyunPJrMEdSo2oXF1RqWoGuEOxUeV1yqqd3Z2d0rwlkQZ1nMxcovmCXVfssp1Fu+uDC6bLm23NItVZffV0iudEJK7hdL8WC2Zbs1y8X7FoWXmb60N6Qmulk1aAGy4obkbzFtcHNyguIENeMopMpKJSeUgHV/2f5y9PXqYHjreePtzLkEZAGgtNQFsjS8z/IzViUd0onQAktytBwlaLbxvtfbcKu3a0tZtxG/QjjkEm72EW+6QQZsfLJmvrVh/17nN+9EAPAJ8fz+GiqEU7ABgtGRHF3fSx5GF4cgSANywKujZscEjasVPbll62NE+SsJMvBRMC7aJvmWxha9oN3gtbT2yKkIyQEKSxNFqb8e875fDQXE+2TbEPAgDjM82PH/hfKhzWl4+8S5WaP3hNR0f1k1NRLRVPGP1iuXdQFjiOGY9Parlm9qblmJHoLq05ZklFHQTbHRIIIBtJTWilZL5XzEkqWsPyrW7nxjk/PJHSUuGshdlgclA1YflmTi28q29SyyW76wNBZnMBKaCDguIAuMPpUa2UzCmgUx7AtfoMT+nHC4cMz9LRs37+tXXWv8WC6wpetYwpfmMI3z9WVl62QkAdi2IWZo0Bdl4a7zC3PxdglG+KsZ6A4G6VIL9jxnOkJ+zqdVMO0q329+7TXg/0AHB7/dY7q40eaAcAd9sb12uWJ0dA2+ulMN4JWUAzECT/PHKPnTQwx6gen04mV57Jfe9731DZ3Np45JtOQaMH+PWy+nmsL3VoJZPbduvBvbykI4B+JB9+gTON9zmy+p/6xpy/gb5Tm9UtR+5ws6EdCdDFzg7M6khAxrQ96b06VTInlKBeAxpSIWZmpSNOTMPJIa1Y0ga7DgnRiPA/NpVIqU+JVFE1p6x0ZK/+7cDWeu3J7tGpPG4XZteHmYEZHQtwfdidsreboDg0w7YsemkAbrRizZA3mNylDUCyIVNcLj6rlYJ0fGOrpGVf9ud0+SXPa+sVBIR97Wtfcz15w8QM8J7CpDYH2LF4zWQym1hi/t+PJQaMAqx5x7tVCKClfeyUdasQs4GEy+Taw/dOfVjwd9tTvlt90LtP6z3QA8Ct91nvjDZ7gIEbrWUrxdPiIkVodxuwlftxLCwMABEmBlaQAXOnZAHNdQMAw1C045Ub1E76ngng4MGDZ9hsQGmYwLSga4f5O5ISzNlhgT0/351IaGGrCwsb5A5IWpB8AMbDvlc/nL9eP138102XhxX9wVxKK6U6K3rJ4IwOr9r9a/f3T+n4ulk7G3Ec7crs0nzezM7GIwn1JzPWdMlJJ6NYrKpyzZyQY+C0BZgtgGyYQL/SAa0X9up7J/2B+Wz/jI4EANygoLiIIsqoT8WoOTFFJlYHuLZEGsNuwg57KuSJvlktGryTVwuDOpVf1FRuSnMbm5/TI6eepnucmbASAAAgAElEQVQOtaenDdqmD/MN8b00B9jxThNg50knPE0xrhPIthi7cKnpVsG6EtlGN4EmAJd4EXbP/ArjHuMq8SRkTOuVXg/QAz0A3HsPutYD7QDgblqDwZTcdtttIoqZQZLBkqjidrKetdupaNRgZDs5SAM2aQvAD2YGNgg9HAXzeCYqtgd3urD9S7sAnR772y25A/cD+PN8eZ6A/lYlH7VaVbcc+5DmNn54pqsysSn9v7vOgiScDXZnx3ViwyxRIGPaUDqnJYt2Nh3rUyoW1UbZZu81rIpWVbboihOlnJRckRMxP91x0gYX7rQGkI33/RfdeOdtqtTMLO/+3F4dt6RCjkViGu0bsLY7UU0qmYqoaMmiN04ijdIxq1Z6sm9W80W7S8VgdEIb2iyXyMYn9P2T9X9jkTGYzGqlVA/6i0eSes4vXOX+ezuFnRcS3aBBDbvoCnMfdnI8ltj7zVjmJbcBjKK9ByCHSXke5p62Y0hehExjdnZ2u5cKfX6Q9Rr6YNx1kKFs1888dKV6B573PdADwOf9I7pwKtgOAA6TrWy7PdQIEJkgAEcka9hJNtZU57BWYWHbDDsEC4QtEZMgk1Ijm016agLfmnW5Ya8f9jj6mO1fQCd1QAMJCOgkEDDVBcaMPiDICA03YL/d+5YqG/r3I2/XWqnubLBW2Kfvnzy46dYZrLvijtZKFiYzklHNKajqmINCx9K7tFZGV2z2zp7MTGmhYGaTqdgYrg8V+zEj8b1arZplF33RWd25WNDRNXOgXzKa0EAyo+WiWZrRT6Y4Z0PFasH46uzq26OFgl1SsS83qxM+EoXGi+4K6JuIYkopoUr0rKY5HZnWjxbOBgcOJofd4EWcN+49/GD95r7fD/vKbzkuSKfa9oWbTuRbY9wEDMOKNkrPkPo0BtgBBtv9Fkz1/eY3v+navLHT060SZL3GuPrkJz/ZHQO6tZvXrbb37tN+D/QAcPt91zuzxR5gYIataKUQqMTghbYLnVunCwMizCiTE8AMyYPHkuwEGxtUf5tTQtC5jX9vtGyD8YT19eu/r3zlK64rw05OVl6QGxHpTFSehhGdM3XjN8C40xOxF+iHLRMTMtvAsGHbLaTx/dLhtwud7c0H13yVr+N9dWeDigW8TqR2acmSeph6TvXPaC5AWjCVm9VcIBjcr4WCOdmGao7S1ayqCX/G+cjKmObWVpVLJLRSXDJ24UhqRMXqskpVs9RpT2aP5otmIM3F9+eCE2nszU7ppMURIxZJKJfIaN3i4BGvZhWLF1RVSQTRHW3y/qUu3n1+95IXaXfW3/s3zDvFIpSFGBrgbpVvf/vb7vfFrktzgB1jKztCzQF22/X+ZqHLIpOg4W6VILeL6667Ti996UvdgNdOjzPdamPvPp3vgR4A7nyf9q5o6IF2ADDsxQ033NBxKQLXhR0BHBHcRsRyM0DEt5bACXSr3So4JTBZeYFi7dwX7TKBKLC/gD4b44kul8QUAP9OFz9rM/6NRQfbwdST3wT6MOkChj1A3Eqku1+9Yb/oAxZc9AHSi05OfEfXvqtbT9ykLx00240R+HV0za4Hnh24REfW7JZl+/uD0wbvye7VqbyZwcWveCA5oLWyOdlGXGnFVFU1upmdTWhQNx2qexjvyoxrtXjKCuynSYCxZgHbrodwcFDc3uw+ndjYzK43PuswOuhcfFjl2rKqsjDtqSktFQ9oMDmlW0/4M+X3Gv5FPXb6mdt6hwjUIiitm5aONja2OcAOgMyikcWoxxLzG9a4FdaUMYVdpW6Om2h8+dbvc5/7+A5j1157rd7ylrcIoNzJcaDTY2bvet3tgR4A7m5/X/R3g3VopQCYSNZA+lBPt9rK+X7HekwMINiW6hd5AMCwm0yGFyjGBNJqQfeH7RG+yUw+AL8gyzbYGgLukEd0srRibcZEDBj2ADHglfPRLDayxGH8UHmmLGpgmgH2LGC2y2iZ+uX/3fUF3Xjgi9ZuCxcUt0/H181AD13xeN+YFgpme69kNK1MPKH1spmdzcaHVNO6yjXzNziU3K11rNEaHBLWlkd16/JZ4LwnuUvzFbsN238ZmtXBFbsGd3//Hs1tmJngRCSpbDylNUubBpOjKlUXVamZAe5EX539DkqSsZgv6WfL/ouIX5l8lH5l96O29YkcO3bMfTdNgVrburjh5FbYWMBvszcxUgrAr1+AnQlIsohHatTJOIagvoFZpz6M537l/e9/v6655ho3u2ev9HrA64EeAO69C13tgVYBMJW78cYbXVP17Q6oAC2scggI81L92uyIuiEPaO78W265xd2ubxWQekFuTFbIHcK6SBAcAuvaqYjtTlibMREDghtZYtgdwHwjIG4O6kHG4qYxrtXciZB+3MlSrVV1zfc/pJ8snA2Ka75fRI76nYzyFmeDZDSp/mSfViza2WysX9Fo2ep+MJQaU6GyoKoFDA7Fd2utesQKBjc7JDj60ck+LeQ3Z2qbiAxrOVIPDvMrtHtvbpfmNsxOFgT6ZeJRrVkC/UhcUajYAe6e7H6dytsZ58mMPUkG/sCrxT6d3PBfZPzhfV6tweTItl4nnApYmHUzqyWBp4x17Qa54hzTHGDHv/EtNmew88ZSAmthYsOOQdvq1NMnBzlP/M3f/I0rpYPU6JVeD/QAcO8dOCc9AJABoLRSwqTQtV0PQAUrCjsKaELuwNZeUGkXjAZd1/Z3ACl1g0EJUwD1aJjRtqFh5qeVSG+i0pF+bNezs1Hu4D3fTgW5eX6ojYCYwDaKlz4WpgoATDAf9mZhfFbD9G/QMeulNb3vP9+mhbwZDBIUl4hJG2Vbut9RFatLKlu0sxN9u7VcPGp1P9iTndGpvF12MRiZ1EbEnmxjvG9KS4UDysb36oY7t7KisUhUE+khzRfMkop0NK1UzNGGJQtcVjlVY6uqGTx56f8wSTL2ZvfrpAUE15NkjGilNOf7SAcTs7pt4YSiERYZm23j9mSm9dR7/VHQqxD4d9Js41ZA6vFuFWwOWUyzG9KJwreItKrZm5h/9wLsSF7kWSt2S26A1hlAbnKe+LM/+zPXAvJzn/tcJ7qhd40LpAd6DPAF8iDvLs1oBwBvRxcLaEILClAEVCJnCDsoo58DMIcFo514BgzkbPWbtvK8ezDhAOq9TG5hkjn41Q/LIuySgu5na5snd+imtRlSByQTTLaw3yxyKPSdpyPmN8+vFf1iO8/w6Oph/d133mkFr7uzu7WQt4PXqdx+zW3Ymczp/uDgsKmcPauaI0djfZNaKuK04F9iTkLZeFaLhay+dcxfFzuQ7FdEReUtWeBytX6VYsukkTOWMHrg/f3TOrZmTt1MkozR1LCWS2ZA3hfrV8QpqlTbmrWuUBrT0bUT2pPZq1OFzYD/kVO/p0vHfrmdV2PTOdjwIYNAztWtAuOJFApXm50qfIueNzHjLVpnCgvxRscJ/jtIktVuHSEOPItFv2u87GUvc4MAP/nJT7Z7i955F2AP9ADwBfhQz+cmsX3mgZWw9WQbb2JioqVALYA24BDGBZcDGM5WB99OgMOwbfSOu/XWW92Jw5a5yQvwQq9Hu1oB9c314X4ARFPwSBDwPVcJLXi+BBQBftFL84O8xtMR85tJmbYx8SKd8OQTrb4HYZ7hf859Q//442uth14yOKvDQZniQmST25eb0ommBA2NNw6TMS0ZrfsM5y1Z4EiSccvhZa2XzYk09vfv05xFv0y9pvr2aa5o1jhzTFCSjBiZ7dKDWrIwzshEnEhBJYvF2mhqr5ZLhzZJQDKxYX3/5FkGvzFbX9SJ6gX3vUqpWF+Y18B6TJBV17Zv4HOBbssRGN8B3Q94wAPcpEeN8gmAsrdA9YBxpxao7NYxDjLW+5U//MM/dMeBD37wgzvRzb1r3k17oAeA76YP7u5a7XYAcCs6VQAZW42AI5hNgGS7KYzRlcEWtwMO230+ZKBjgUAyjObSmKiDLU2YHc/sfifuFwR+vRTG3U5jDIvG82Uy5fkyifoV3rXG4Dr+mzoT5d7IErN1G3ZXwHQfFlv/fuIGHY7aGdzZgf06YnFIIAvcZGZCp/KWRBpohhNprZbqzgx+pS+Wk2p5VR2zHdkQKYHLc6o5dfa8uWTjM7pjvqpDq/Y23XN4VgeW7QFv0/17dWzd4lJRi6o/kVJeZplIf2JI1dqaSpbEH7szU5oP8EXe1TetheJZNjkXm9F3T56VjUBW78lO6mT+qP7L4C/q8kuuaPfz2nQerjMwpPe///07cr2gi/BdonsFjO5kavXGeiCPIJD3IQ95yKaxyXN/ARDzHfKbBTzfnRdg59mxhQl2bW57kNQDD2B2ud761rcGdVvv7xdRD/QA8EX0sM+HprYDgMMysWxxEQTF4IoOFG/b7Wx/cy2Yxvvd735d6zr0vDCZzfdkW5H6UNAwd2pLs9U2+lmbdUrrG9TJnt4ZrS+6RoJ7Wnm+1B1WuJElRjsM494IiPnvsM4RPBcvw969fv6e+scD1+jAsnmrPhFNaCiV1VLBrBnOnk73m7doZ4dPB7zZ3A8GIqMqRMySAPp7MjOreUNK4IX1Sf3w1FHda3hGP1s264oB7VO5XTq+bkndrLjS0aiKzlb5gffcc9F+VZ01VR1z4g+0vicCZCL7+2d1fN0MyElfPRAb0oZOubdeWM9pqbjZ/xjddjxa06Onn6R7DPpbawW9r81/D/KqbfV6QcezkCZ+ghTAYWIegq4X5u+AWthYfNuDviEW9M0Bdoy3nNfsTRyk6cc/nXHRFPh62WWX6eEPf7j+/M//PEwzesdcJD3QA8AXyYM+X5rJoAcL10qBFQW84AThV7gmk8vPfvYzVwcGM9qJdJewjACmBz7wga1Ud1vHNt8TMAzA8rb6AfatBLkFVYb7MWnBEgWVRmszD/RuhzkNup/3d+6LlIUgRiZGJrpW0xib7sWE6wFi2DkmZIADrHAjKG5O1MFCjr6DjUZ+4WXYWy4s6m+//VZrFjiSRRSqy1bN8GQmTDa0aZ3YMINt2jyVu0RzG3afYb+MaanogP7vz+qWajEnosnsiE5s1LWdfiUbzygZldYtgX7j6TGtluetme2m8TzesAfx7Urs1kLFrF9GAoJt3KLFNi5aSSqZkDLJEd0653+tfdn9evI9/lDxWD1rYSuLLb8+4v1lEWcax8J+D2GPY1wEGP7SL/2S+z53o/D9sGP367/+6y33lxfs2hxg5yXOadQTE7jrjT2cB9CHNDDt9gHIn/nMZ+rlL395N7qhd4+7SQ/0APDd5EFdKNVsBwADAJk4mrcOGfhwP+DvDIadZEbpb0D1qVOn3AmkW+W2225z3QwIlCFohq11BntkGKat/u3UjUkZAAhLZCqdsDZrt46Ac1hqFiJYtaHx20nQzWTrZczyXCcaE3WwlewFIHoSm2Z27baFH+vq7/2d1a1hmqCudTt4DZMkA8B4LCBTHAF483kzYIy7frt9Wq+cTWGcjl6iG392FjgPpQZUreVVqJg9hPe697EH+oXTQU/rqCXgzak5yjh9KkXNcolsvF8OAW9VM+M8lt6rYiWmH837P4f7j/03PWrf5ZtcaxoXfq2CYhZMjH82fX+734nfeSyekQb88i//ckcIgTB1W1hYEDt2v/EbvxHm8MBj+B69ADtPOoHMAhLAk07wm0Bnxky/MZLvlQX+q171Kl155ZXWcQ67tPe9731ucC3jDe4RyCcay3vf+17XTQKmG0/5T33qU3riE5+45bosjsk+9/nPf95dDPz2b/+23vGOdwT62fOOINX4yEc+4gY6Q+pcfvnlesMb3rBJysIi/PWvf73+/u//3h3DcRd55zvf6SufC+zoi/SAHgC+SB/8uWp2OwAYkAYYaYyeZusa4MsA1I79V5j2wyjj3ckE0q2CTpDBlwGegR8HCqy9dgr0BYH8RocH+qBbcgcvlTOJA0iAgn6vE2mM23mOXqIOFkMsuJh4KLZEHf/vwL/oxrs+b71dGDBIkJk9SUZUWSetQqRuC+dXCOBKRR3lK2bAOJAYU6E6r5rquzMHFod1aGWzTGN2cEpHVv0dIbz73nNoVgcCEmDMDkzpyJr5OvFIXCNpZAlmmQh64Ep1TeWaObX6WHJSKxWzLCPqxBTVlO4y1PfZ93mx9manXQDM+8iPtxhs7GcPCAcBYsYrynYcV1p5f0163Fau0eqx3Uj3zK5No3TC0/Z7PuGNAXaMozwzxtF3vetdetKTnmRsEpniXvva1+p1r3udm6zkn//5nwXY5ffjH//4M+d5Ps4EIH/0ox/1BcDMc5AK1PWv/uqv3DHjla98pbuAh5W3jeeAWs4B8DL38N685jWvcVNof+YznzlTjz/6oz9y7w9YZheK+rP4IHalU7Z3rT7/u9vxPQB8d3tid/P6In9gcGilAAqRADDwMAnx/wA3truYTHZqew8GlsjtbqUupW+wQWMSwfWiG6CvsW8bn8m5sDbz7s/kBusL8ETOQl/s1AIgzHtIX/D+kUTFs6jj32yJOpBPfObwp/RTS5IMMrztyozqVN7fm5a6paIpZRNJrZbMGd7STkaxeNnqfjCW3q3Vkp2d3ZWZ0ULhTmXj4/qXO/zr9PPDs7ozIOBtLNKv1ciyGZBHk8olU1opmts0nBpxfZFtGucweuD+ypCKybPMdmOlhlP79e1jB7Q7N6GTG5uDDgeTw3rJA1635b3zHGwaA0D9QDGMn7dY9KQTvNMAsu16bod5ZzmGBTSZ4ABOQRrasNcMOo7vhF0sguC6VTzdMYwtxAiAmH9jJ+eNb3yjy4gyrsIA4wbhN5YAVNEPwxA3Bso97nGPc3fiGjPI8Q7wTCFIyBTqxwBjt/aUpzxFyPc8xp/ESvQLjPCjH/1oY/cgc+O4q6+++swxgNtXv/rVLvBnR5BAb2JcAPUvfOEL3eNoL/WhjW9+85u71f136/v0APDd+vHd/SrfDgD2gCiAkEkEAA0wYpW7k8AIJhYJAnq2nS4ecwJTQJt+8zd/c6dv6V4fgM9g6qVnPZdBbo1pjAG97VjXdbrT2EYG+BLsRuCdX2ClX6IOJqOyU9K3kl/RhtXZoL5VX7B46Y6nJ7RaPmnVzobJhjaVm9Xcht2tYTIzraVCRF866H8cAW/7cuM6vm5Oy9wX61Nf3NFaycw4T2QmtFI8IbLpmcr0wIyOB8g7CHg7tmZpU03qj+ZUjGwOcuOe1eK47lqfU388JydS2vQMHrrnEXr4/seGep08ZjiIJYbJQzYDUNuunjhMxbajxw1zfb9jGDPZtu9mtju+ta9//et62MMediY+grGEeQN5AH7uLARgxNlNQtJG/Z7whCfo0ksvdZvBs0FC98UvflGPetTZlNfvec979OIXv9gdJwm6bSw2AHzFFVe4bGxz6mUAKqD63e9+t7GLGWOQPABuvfKBD3xAz3/+810ADOGDPOI5z3mOK9GjTV559rOf7WqwYYF7JbgHegA4uI96R3SwB9oBwAw+6OeYYBgcWCEHRRh3ospsdzOQED28UwXmAYCF1IIBlm11GJRugG7a1Mhyn0vWF+0gixveDxY6nXK5aPe50Ree3RoBcEyOrew0eBHuP537sa4/8Y+qygz09uX26cRGkE/urI7agJ6kMEky9mT36lTebEeGHvjoypDuWjbbsA0myaJoT4ARRg98j8FZHQr0RbbrgfHpHUkPWf2Bc4kh1bSmcoN9WlQJ3b5YOqPTHnGGtRY9K7l4waWv1Hhmsq3Xx48lhpUEZLFo94CUH0vc1g0NJ3Vajxumbuci2YfXTsZMP0KEmAokAjfccIPLDqPdxVv+D/7gD85ogmGI0QljG9c49uIb/LznPU9f+MIX9Fu/9VuhATAgG2neJz7xiU3nPPaxj3Vdfv71X//V2J1vetObhBYZZhmgzvzwtKc9zd2J/PCHP+yeh5wC+QNjVGPhPGQc7J51Y5EV5p04n4/pAeDz+elcgHVjcvA0lEHN84KNPPCLHoqt5W4VVtewB42MQKfu3ehXjGMF22S0rRugu7ENsL8AbqKkvW3dbjo8eN7GTJwY2cOOdWu71vQsm+3WWHRtZ6fhP458WZ+9/X9bX509qUmdKtvTE49EhrUWMeti6+4Ho1os2CQVfUrHo9oob2VEqeBAYq++cWRDG5UllapmqVIoPfDwJTqwbHegmBnYp6NrZvCPHng4ndOyRQ+MXKFUWVG5ZvY8bmbIR1Iz+saxzW4TuxPjmq/OKVPp1wPLv+5+j40/7Sy6PckWUqPdu3e7oIh3yaQlbtQRbxfAMH6xBQ8z2q0CKwrgDOMq06k6BemOqRMyCH7zLfsVWGQCXNHeAi698tznPtcFnR//+Mf11Kc+NTQAZhyDOHn/+9+/6ZxnPOMZ7kKfFPS2gv6XH94Tyu/+7u+6YNp7B5FqkHAEcNxYPvShD7mgHqDfLeu7Tj3Hc3GdHgA+F71+Ed8zLADmAyayF00XwIjBixX4doBIq92OxhOmoNP3RZtH29jOYqBs9LNlMGdw3AnQ7dd+mGcGZCYsmE40it3qY9oKK8ZED8PabsKSVp+r6XgmG0/2AttLndDbbbdw3U/86Gp9/+R3jJci+cJgJKsNSzBbTHENpvuseuBcfECOU1DR4n4wmp7UWumYr0tFtTqtmw/9TPcesXv/0pBdkSEtRvz1tfydNk0P7NaxNbMDRSqWUi6RtOqBR9NjylfmValtzx94U4ro2m7dubS5XjEnqvHMsC4dfZDulbr0jD2eZ43Hu+ABYsBSoxWX34Nly50dJMYwFrjNuxqeZKIxuM4DPN71tsMSI9sBILG47Vbpttcx7YIF5b7NumPmEDTJyNhgUGF+/UAhsgSkKc961rN03XXX6ZprrnGZV/4b2QHPEfDZ7AZhk0Awrj/iEY9wHSUay9Of/nT3mUCsmAqyCwLxrrrqKteCEwIINwrmIU8XDMj98pe/7I6fjcVjrHlnd8I1qFvvUbfu0wPA3erp3n3cHmCAZ9vfVGCHGbBgBGFM0IGyZUQAwSMf+ciOeuAGPRJYAWyEGMg6wUoy0TFQ42zAZMhWf7NfMdt5DNSdBt3NbfUYKNqITo1JmjZ63rde2uBOeg57dfDSVDNxwcjAiu3EfYKeb+PfmeSYTOh/Jq9OO2/kyxuuP/BCvp58wa/0J/rlqKiCBbwOJ0aUry0G6IGndCpvd2vw0wPjinDLYUcb5TqTeu+R/frZsjkLHEF8u3OjOrFuZpzD+APvyuzSUmFONYtMhPTERy3JLajvsDOitYi5f0mnPJTqV7lW1g9O+gfpjaRH9Lz7/pH6k4NnHpFnxeUFPQKseF/4XryEDd73AkPnyWd4n/h3wG+YjI1htcRhWWK+L8YaT9/fyvfQ7rE49jBedzN7JrtY+IRjA9ZYrr32Wr3gBS8IbAryBzyEYa4ByuiAKQTF/emf/qle8YpXuD7DzUy6DQC3K4GAtccpgqC3l7zkJWfqjhMFmmWAM2RFTwIR+FhDHdADwKG6qXdQp3rABIA99o3VLnY2sG+euB9whnUM3pJhJpJO1dW7L5qw7VpwMbjC+rLlD/A1ORswuZJKdCfBvl9CCxYeTPDeDwwCBSaUSdz72U4/NLsp8IzP9TadJ0Vh0dXpJBvN7+HhlYP6wHfeaWUyw+iBB6tDyifMzCv3DaMH3p3do/n84TPV7E/s1/W3nQW8qWji/7N3HmB6VNUbP9t7r0k2ZXcDIYIUgQjSFQQRAf+K0otUURSpCqKEJk1EqgiIdGkiCAGUjhQpoUgvCeltN9ney//5TTjJ3W9n5pv5+m7mPs8+m+w338y95965973nvuc9UpFfIGt6nJ9VkVsm/cOd0jfovKmdUlwnq1zSIFMBL3zgaSXuknDZ6dlSlI2H3DlFdFlOpeRllsobKz61nSYaSqbLsVv8JOwUAsjTNNv6GwCrG1poNJzsEK8QDZXBi5fYSYYtGQFpeDepM+92ogpBd1DHnLTMAbQEjDG3eekL7MZ8zUYY2TFoC3jTQ+eqeATBEawHkH/uuecs9Q4txMHAY7733nvlgAMOsILgoGdwihYEwUU+0gIAHLntgm9GYAE7AGxSAoi050U3Jyo8hk8//bR1lBeLI2mv1Y7Fc7kHoB4vBQsik6oblxBbcLSFCgQbgVgW1TPV33q8avcM+MB4h01QDEgGAJteYgCyl0UFQMDiiIcDj2+0aapjYRe8eNA/ADD0C1SbeNM/Xln6gjzy2d9dq1+TUSWtac5Z1/jyhNwaWTPgrMSAd7Yqv0Jaep3vk5dZINkZIr1f6AN39U2WN5aP5ONOLKyS9r5mGXRRa5heNlUWtzt7iqnvjPIGWeginwZdYkrxRFnR5UyXyMvMk/ysTOly4C9bXrvcaunsb3INOizOmiEfrfnItg++u9EPZNYE/7rfgD7oRGykKHiHGfOaZtvkEkfzXpteYvN9NhujHH6AHF7gRCbyYZNP2xMl9Ua78XLzDocmSlKb3H///RaFgFNFP+83cyCOCOYFAs5CSzgZNDjD2EN1n6HTEcfiJoMGZYNAySuuuEJ+8YtfrHvkAw88YCXbACCjh68yaNAloGlQWDtYOwMZNO+rQwCAvdsquDJGFsB7QjFTGDtRAriOiYgI3kQHwelzOUL066lkcWJBBPRpxjC8qOEKoIzdfyy8zvqsWEibcQ/V2FRQjNamucCbx8Dms5msORqNJa82nB3dPqctLIYE/wFM8FbFInW2lzpZfOAP/irvNb/jeDlgcGJhjTT3OANcL/rAucP5kp7VJ0PiHMxWkz9JWvuWSHZ6njy3oEcGvgi6MSs3MaNcWlyC77h2k3J3zjDyaXWFVbKq240uUSiZGUPSM9DlaJva/AnS2u8eLDi1qEGWO9AlirLL5e0Vq2V62WRZ0jES7KMocdZ2syU/yx/vG1AKCAMQQZ3RdOWaZltTbXOqwpwC1z6US+xlE+lkFDsvMc8BjFMHAsC8JuvwMobdrnnnnXes9mGDRJVwKQmyX9MAACAASURBVKYJYoOLqxlDneoFZYL5l7qzebjhhhsszXkoaRMmrFcEQWaMvsYrjAbvqaeeanGGWcOUJqGJMHAaEFjH/08//XSL1mcmwkCnmB+ocRqgB9DFa01CDLzarCHwgdmkE/imY4VEGLfffrulW8x3L7/8cosiQeBjkAjD2+gLALA3OwVXxdACAGCOrPC+MTGzQybdo1MBNPzrX/+yJoOKiooY1sT9VjyXiYgjKT8BWpq+V/mkfrydsfA6m62Kp7SZ0iZ0gec3i7HSJgCVeDQ0o10iPKzhBgcgHq9MolIr29VnLR/4clnT45bpLLw+cE1+rbT1uXNny9OrpCvd3Zs8pahRugeGZM5nI1URzLpvUj5ZFrY7qzVkp2dJZX6RrHZpU1lOqQwNd0mfIUcWah8vFJAKqZCuLGeuL/ecVDhJmrrX0zv0OeU5DfL68nmCN7kwO0M6+tdn0Nuk/EtyxGbOqXLt+pLxRKAb3l64vvBG3eYxxp3JJeb7bCJDucTReIl5BiCIeVZpRqGqE9GkdHZ7x+DTshEmsCxRBWBL+5xoF+jp/uMf/7C0gt0KwW8XXnihBXqZw/bee29Bkox5yywEy5lJKvQzwC9cYS04QEiFjIQa9tZUyOYadu6551pqDzwT7y2Fk7cLLrhA/v73v1vcZsAsiTMAymYgJWuFXSpk1TZOlP3H8nMCADyWe28M1h2AxI6a3TOTJJQHLwFQTz31lHz5y192BcrxMAeeZwIkvOjSmlnq4GUxIeMN8VPwFKARGYnXORT46hEp9UqEtBnPYfIG+DP5sxBToHywgVAPMYt9NB4vP/bUa1VSD69vslMrUycoA39++yrXRBBTiqfIyi73YLaGkkZZ2ukuNTa1eJqs6Prc1Wy9PXXyzmpnfeD8rDwpys6U9j57+TRuXlsAXcJdraGxdKos7XCnS4TjA+MhryueIE3dzp7g/MwCyUwflt7Bkd7kjt4yWdm1ltM8uWiSNBkc6B9ucphsWf0VT8PLjFlgbAN+IwGtppdYucR4b9k86vuCt9gr1Yj3DkAYWielToRmsDMbGwsvMd5RnBmhSSM8GTXCiwD72J50x3YFUAt1ABpdUAILjBjzw6G6K4F9AgvE2QJM0EySfhILQAvgCIgjpEQWhNHxUIc7UgL04VlkQdPr/fDNTKCG15kjNS+UCTtbxNPrG872AGA8+xwlsiDRBtNDzOeAX0CwBtaxwEcCHsLVRT/HI0/f8BtuIseZkfSN1+d5uQ7P30Nv3y/vDbzpenk4MKhAbpWNt1NvnImWbq6zlm7mcK68umhY0jMHpd+FLjGteKKs6nZPp7xJeYN8HiZV8sZl02RRuzMgh79cU1Ahq3ucPdfFWcWSlo7c21o6lV2ZWDBZmnvWe61Lc6pl7vKRFIxNKhpkQds8yUrPll9vf55kZ+SE7T5OPpjD2MQzxmN5ssG7y8bRDLBj3JrvjNInzIBgNs4clXPi4kXFxEtK50i8xDg3sAdKBokqqNiwluBMsSsoOUDDwgsclMACAQAOxkBSLcACohOw14oQGIZXIZGeBeoGX4uJ1WlCN2XbmPgBWJEI5pt2AABHQveIBdfXa3+EXmfyINnckKraDtSyUCsg1qNg5UWaahN4zqMFqdSJCHH4dRw7sjFJpIqI0+aEY014iwCZd7Jelc9a1wZO2RW0aasKymRNT5PjNaQezslEvsw59XBZbqX0Dq6xlU8rymqQRz+dJxMyS6UlzVlBgQpsWtEg88MAXPi1i93oEhlZUpFbKC29zuoS5bnl0jvYJoPDzvzlqcVTZWW3uzfZTJVckt0gb64YmTYZbvKkoipBEeOgmYeHHf6oA+Bx5B3nRMrPJj7szR0uYI4JVZzgPcJLrJtHgC8nafB9I9V/9SvDZneKg1wlQa7hHAaR2sLue/Beeb+VQhB6zU9+slbVwy6QLZb1CO419iwQUCDGXp+N+RpHAoCJoAVYMbkmsqADTGBLKPA2tT4BehyB+uEJu7UBCgQLmRsvOvT7prQZn7kpPMTafgBZvL4sygBfv/VWXqQG18GnBGCYahN4jL1QZbRtqiyCJ5pNCQtytIA6WruZqhN4DtlUdfZ3yDVzL5OOfmdqAWCwb6hdBoacM51NLJgkq3tHc17NOgMGV9gEhy1sKpHPO1utS79cVS+ftjhzgaEf1GYXS+uwvY4u9wCQ52elj+DXhtqutqBa2vuaXCkg5cNl0pXlLvfWUOqeKnmt9m+JlUluTXehrOkZbefy3DI5YJPvycyKTR27WDW8kaOKhbxZNGOJdx2vMO8Lnk2VLFQvsRlgF82Gz4uX2JxreL+YL5kDvFDGorGB+V3oDZwMhnJ19ZrDDz/c+vzaa6+N1SOD+4wTCwQAeJx05FhqRiQAGG4ZEzvHe4ksAG80e82gDo6vOQJF0gvvMJ/FktPqlXaBHdTrq9y+RAJfM40xYC6cxJvXfgMAm15iaBMUM1AIb7Hd4s6iTYS2JhthMY4GBHits9t1ptawnerEZ2s+lr+++yfbzGx63/qSelnW6QxMuc4LH3hyUZ2s6l7P9c0YzJNnFnevq35ORpZUFxRJU7dzgF5JVqFIWq/0u6QenlI8SVZ2uQPyGWUNsrB9pEc21I71JVNkWaczDxrt39LcfGkPq/2bK68ts+c4k43ugp3Ol8z0TNtuBGwS6KYJHhIZiOs0rqgLnmjAr2qm828zwI7305QtZOzhHY5mrnLzEvM8wKjWR58TzfO8vH94nZmDTaUG83ukESaj2sUXX+zldsE1G5AFAgC8AXV2qjSViRLA5qe89dZbFpBRTUU/343mWiKHAVuAOyZ/PEAEUvG3WKXKDa1fONqFCXxVAikRQW5mPQH/quJhJi2JxtZO32Ws6BGweon1CFgDhfgN0KROAGhNNhKP+vi5J3WhTtTbTWv435/PkecW/dv11g0lU2Vpp/ORP8f5NQVVsqbHWWosNzNfcjPSpOcL7d+MgYnyzJKR2rtQAlp73YPZNi4Pr/07Na9WVvQvd2yTl1TJeZn5kpeZLl0D69UaQm9YnV8jHf2rXDPJ1ebPlDeWj0wbq/eZNWGWHPylg0bVUzcu6HgDehnn8eSqex1XJD+A0w5NiIxrdhJ+6iU2qROcigBGAcF6usLvaJLbqJdYT4FoAyoEoZkzo0npHM4uSIO5eZ3R8kWB4ayzzgp3q+DzDcwCAQDewDo8FZobCQDG28GkDu8ukQVZHxYYjtBZdAA0TLYcqcXrSD0c3zmZQW7KeSbanKNgryoesewz8whYPcV45SkAFDxBABYW91iksI6k7qoDDXgCcECRcdMaHhwelJvfuUYWtjkHh+Vm5Ehhdq509K+lK9iVkpwyGRzucKVLFA6VyED22nssaauUz9aM5hdvVtUgn7W4e2c3rZwm81ud65suaVKeWSAd4gxei7OLJD2tX3oHexzbNKlwojT3OCfI4IuNpc5qGGmSJis68qQ6v1yWdIz2Ah+/5fEys2KTEc8nmJVTHsAmFBpOOOL1vnsdX4BNuONoWOPx9HvypPx7ExTzPuNYMBUn/Ki0MM7htKM5zEkZcyNg11SgCZVgo726YY+FlxjpMShjZkY006YEFJMSGd3coAQWMC0QAOBgPCTcApEAYBYjwKdTtp94NQLPM54TflgEWQzj7QUiFTIgLjSoI5lBbtiXQBsizTVVNQAz2YVjXzYmHAlroCLeKP4OYAB8mmoTiUh4wThlvGo6VTjkXsDT6p5muXbu5a5gsLagVlp7V7jSJaYW18uKLne6BNd09rfJnE+d9XQ3qaiTBW3O0mikSi7Py5eWXufAucq8cukZaJMBl2C2CimVrkz34LuNShtkUYczIMebPKlogjT3jJZGq8ydJK8sXSLF2YWSmT4o3QPrKR8FWQVy3o6zJSM9Y91Q5nSDMcU4Z8OdyOyTTu+T0jAA5nh9ncCen/cxNLkNwJh5jrGqdCP1FAOSQ8cw4JnTDTYJnLi4KfR4SekciZeY+wKACRq2S1ZEG7EXurpwgYMSWCAAwMEYSKoFONIGBPspeD4ANqSBTFQB8JHZiPKVr3wlYUk4kBJCUN+U9Umm1xdwCfBF9okgxNBU1YnqD/M5jCF4vlBS8M6Hqm+wMConUnmRgAf1diko9qqv6qWNGhiJrbgvXl+/OtBvr5wr9310u+vjppc2yuIOd+3faSVTZXkYukRp9sby+Dz7lMBUoDi7QLIzh6Wz3zkz2+SiGlndQzKOYcc6kwYZqTG3Up1WJu0ZzgFvXqTRSrJLZDitW/pDEm0UZdXLmyvWbggaS6fI0o71nOLtJ24vP5z5A+szxgz0JpRDSF7D+xdv/mq4caUnCYwp1RaP5waceZn3xpQuVC+xGVxHvTiV44QF76vfTYJfxQnsZNcX1A0KBMmK7OpAPenLW265Rfbff/9w5g4+38AsEHiAN7AOT4XmRgKAyZRDXnvSIce7EK2vOp94FQBNAOBEFQL+eC5KAcn0+qrYP8ebLC5wIBMh+xTOzizOeOhYrPE8eYk4py30q3KI+a36qqbaBP+ORMaOTYIGRpJKFXqIF6+vXVvv/+hOeWvl645msBJBFE2wNHmdSo5Fl8ixvLxOpaVrsnzeulz6XNQlNi6fLItcJM2492aVjTKv1R2Qh5VGS8+Skuw86Rhwrq8XabRpeL+713u/0yVdlrRlS+fAeorFTCNt84lbnSgbl29keT4BdLH0sIYbx+E+Z3wzpsiaGWu94XDP1s/N90apExqUynsC7QH1G+USRzrmdQOinmKd90LTFIQm6+Cd5sRshx12cAyKxZmAtOTXv/51r80OrttALBAA4A2ko1OpmUxy7Nz9FHhvePt23HFHP1/zdS2TLc/A2wwAxYMH1zXRnmd4xwSmwKdjs6CBJolUeFClCxY9AJ3XY3xfBvd5MbZA05c+ioXqhOqrKig2UzmboBjahNPCzphRagjeXsaMX29YqBl6B3rkGitVsjM9oTi72FJi6Bt0TgRRJMXSn9EmAmIOKSXZVfLQx6tk8+p6+XSNO13CizRaQ2mtLOt0DngrIPguM006XbSKJxbUSksYekdVWqW0ZzhrItNMlCOWf5FBrzKvTl5ZMpLGkZORLWW5eZYE27k7/FaWLllqcVgBSmzyItkA+RzKYS9nLKI8wTsPDSNSbd+wD/JxAZsDNp4k/WGDhyyheop5l/BMh3KJ/UgXhlbF9BIrpzgUEDNPzZ071wLA9Fuol5hNLnQy1CkSeXrow6zBpUm0QACAk2j8DfXRkQBggCgBRbvuumtczMaxHx4g1Y3VoBc8zwAcAikSVchsxMLBsT4TfiKBL8/jCJijYDw7eFgTwZsNZ1sWXfiGjB1ASjykqDSVsynBhmfXXNhZ4DVIyAyU4rico9ZoPGCmDRa1LZAbSZUsQ46mIRHEii73RBB4RJfb8IHzMxtlzqdrvbabVU2WeS3rM6aFPjA7I1NqCopdpdHKc0tkWLqld7DPsb6V6aXSke7O9Z1R3igL29y9yTXZFdI65Lw5yMvMk/ysDEs5ojBrmry1YnSgXl1RrcwobZCNeqdbgI53LZ6BreHGt37O+4eMHxs95iA8v9GASK/PDXcdNgKQcxoGIDepPeolNpVaNA06J0YmKHbbTIarA5+bXGKlZgGCAbf67pleYvWes3Y4pUr28tzgmvFpgQAAj89+TelWRQKAmcjg4+6+++4xbZtq2SqXNFQ3FjCI9xkPQ7yLHvsB9AD8AFB+TNAVzzqwaPHsVEoZjNdXI98RukdKLFHKDvQHAW3KIea3pnLGQ89nAIJYJkEx+/fZhf+WJxfMce3yxtJpsqTDWYkBBYQJhdWyumfFiPt81lwqi9rXgtGirHzJz06T9j7nTHJ1hdXS0tckg8POgHyT8mmy0CXFMc+aUV4vC9qcPc5IufGsVd0j62tWvigLLVt35YjyzArpTWuRha0Z0uMAynfP2lGmFNSNAnTxfMfc7h2q7Qu9INmFdwAnAHx7ToF4/7zwonlvQ7nEbBjZTCqXWH9HAvBNQM77x3to0ibUU0wQM2vG0qVLHXWCk23j4PnJs0AAgJNn+w32yUxOTIZ+ChH1HGPtueeeMfOyAaoBfEzoeBU5Ag0tZFrCG7Pzzjv7qa7va80gN44TmeD1aF5BF55HBcSRclXtKsaGhEWOH/i0qZA8gnrS5/QPJd5aw147DG8Tx8D0Dd4sxrGZylmpE9AgovUGc0T/l3euk89dPKIc5xdZ0mjO3NnSnDIZMKTRSnKq5aGPRmoFzyivk0XtzooP2Gfzqkb5pMXdOzuzYqosaHP2SmelZ0plfpGs6XEOeKvILZO+IXcpt3BBftS3bKhG3u20B9K5kiM/bTg6JQLdqCsBpowrxo2Ttq/XMRqr60xADsj0k+ExtA6hm0m8xcolxktsBtiFoxypFrsTIFfqBCmSDznkEOtdJYiQ64MSWMC0QACAg/GQcAtEAoDxJpDxh918tB5AlakCAKOl6aZlS+AdgSi77bZbXOzkJchN9TtNriqgS48XVdEAr6Rf0MVCBMgEyEF3iGaRi5WBaC9eX/RFNe1sJF6iWNVH78N4YSyo1xdeJv2nqZyVOgGNJtpUzvrMNT2rrVTJbjq5peml0pPWYsv11ftMK26Q5V+kQc7PaJQ5n40GslvVNMhHq92lxqaXT5DF7c7Bd/lZeVKYleGaBnliQY209K5ypXdsXNYgi8JkiQvn/S7JrpflnS2y2gZsz8hslG9N/Ma6hBDRHs1HOtai1faN9LnhvocUHJQwt2Qb4e4R7nPmMECwmb0O0G2+OwBjNv7M+TgGqBPXsyF28pBj05tuuknOPPNMOfDAA+W6666Lmpcfri3B52PTAgEAHpv9NqZrHQkAxvNGhjSAaKSpbXkudAYCXlSmKlxwCZ4ZjtHIJhTrEqm0GRO8gi71FIdKfOEppo1OgJjFRyWf4D7Cj0uF4B/NMKdeedqR7MLCC4eQzZAXGTgWcXOzwuaNYmoSO6VytmsrihAoQ7iV+uJpsqzLmQrBd6cWT5YVXYvk06YSWdwxOplGVnqGTCoqlZVdzvzaitxiGZQeV65vbVaZtImzh5e6zCxvlPlhuL6NpXWy1CZxhdoBpYui7Bxb73dGWoZFf6jIK7OVaTtsygFS1FdgBXEpz9vMjgboivemS7V9GV94fVNhrDO3QHeAh4zcIePdC+UhFu+oeonNRB28O/wdIK7gWE+D7OY2APVJJ50kjzzyiFx11VVy9NFH+3YKxKItwT3GhgUCADw2+mnc1ZLJzE8B4D399NOy0047RbSbZ2LEe8CiA9jzqmoAwESXN5bUCy9eXz+2cZL4YgFX77AGovA3QCaeTAuIzJwZl4AyP/XnWkAAXl+4esnKMGdXZzZAeMjhLgJSwm2Y7O6hfEgTFNNevI6m2oTThoX+vffD2+V/TW86mhXu7ISCKml2SYNMWuGKvEq5/4P1OrihN5xSVCVreptdub5fqqyX+a3uyhFfKp8mn7vwganv5KJqWdHlzPUtzCqQ7IyhEYkrQuvrlCWuOn+yvLR4bWDftNxqWTG4nvJRmlMiv9vlN5KetjZjGSdCpu6tHs3rhkX7KJqUwWa9eSbjnGP5VFKewA4EuuFsYKzHI9DU77zAaRAUNGIxeF8A6KaXmLryjkJRY4N66KGHWtfcc889CU+a5LdtwfXJt0AAgJPfBxtkDQC0oZI2boZgUvvXv/5l6QD7yUCmnk48Gpqq089CptQLPMCx8Aip15f2aDpQv7QFLwMG+5pqBvybokeJLLxwfVNB4YEFDEBO3eAa+ulfL7aI5Boz5XMkaWfdnskYYOE2NYn5P+03uZBmKufu/i65eu5l0tbnrKJQ7oE7W5GziTz62YeuJglHhbA2TpVT5PNWZyCdnZ4p1QVFtvQDfTje2b7BDul30SFuKJkqy1wSenCv6aUNsjgkS1xR1jR58wv1h3R0k4srZVX3Wvm0r0/ZSX448/8cbcCcYSoa8G8zZbBuJgHIfr2j3Ec1xlMlxTKGUA4ybQL8RnrKFsm75vQdzTTHhp2NOnJmFHPD8sADD8ill15qaYLjJSaG4bTTTpNvfOMblqPDb//Esv7BvVLfAgEATv0+Gpc19AuAMQJi5qS89OqZIEUngSUADoCVl4QJocbGY0ymIUTUo8nApF5fFtdES5vRJuUyA7YJtIFCwaIBAFYhe36ziMQDkNsNYpNagIQYx62x2GRE+8IwbvD6Qglh3NilWI32GaHf530w1SZCUzkDTN5f8T95rvMJ10c3lNTL0k5n7+zHTcVSXVAhHza7KDEAKstrZXG7s65vYVa+5GWlSUe/m3JEjbT0rbL0dp3KjLIGWRCG67tx2TRZ5OJNJrCuPLdIWvvW0i6gP3y+Ok16jfTLNQUV0tXfIgPDg3L6rJNkelmD5y7UDYvpJeb9AVwxNhQQ89ttjqB/8bCy0UFKLBWSypgcZN4/NnuJev/dOoDxj+oP84Fbpjn49qeeeqo899xzVrIibMuJHYFyzGf8HVsHJbCAnQUCAByMi6RYIBIA/NRTT1mTWbhALdWHBPQBrEjkEGngHN4Gcs1zxOY3ra0a1uT68jf1/CbC8NgC/qoG/LHAsXBTJ8C96YXU4C2TNqGat7GuqwaUARgSBTLDtYENAfxwjqeTnQrXTOWMd45+oszP/kgWpodJg1xcJ8u7Ris6lOVUywMfrpTcjCypLiiQpm5nb3J1/lolhr5B54Q1k/OqpGlglatZvWSJayydJEs6ljjeB13fgqwM6ezvcLymNr9WWvtWWCmZ84fK5J2W0RzkTSsbZHVP8zr6Q7jx4PY5mzdNta3eYt1QmoBYQW4qavty6gAgZ45gXk0FDjLzEso7zFmkOOeUymlTTAzDYYcdZr0bd999t3zta19b12XISL7yyisWdS3SeTua8RF8d2xYIADAY6Ofxl0tWUA0w5nXxrGbR4eSoC27wuSJcgAgBs8mR3nReu9Y1J588klLB9gv/zPSIDev9nC7jmdr8hDNUBbO42QeLcJ9NuXXTFAcTbAcGx8WN5KLxJpaEI3dNPiOxTZVaBhmoCIayPysaV0j9yz8q7QOOgeZFWYVSkb6oPQOdo8wSUFmg/zzk7UqD2RuW9G5FjA6FS9Z4maWTZbPO5yTaMD1nVpcJSu6Rsqumc8syS4SSeuTXpesdlOL6mRlt7tM2+S8SbKqf4lk9FfLR+2jn0dCvO/N2FP23WivaIaK49yjG0r1FAMwGU9seOlLgsrYWEXz/sSq4rx/nHJAs2GejOZ0K1Z1MlM/Q3lwm+cfeughOfHEEy1K3O23324rYRmregX3Gb8WCADw+O3blG5ZJAD4xRdftILXCJIKLRxJQnfAKwNIjlVWLoAk1AsywQECvZRYB7l5eaZ5DZ5ceIaAWLzf2CuSY81Yy69pymBTRsxv22J9vUqu4XVKpeA7zUwIcAKQl5eXr2v6so4l8qe3/iCDw4OO5qjOqpa24ZEg8NPmUlnYtt7ru01to7zf7O5N3rSyTua3OgPPnIwsqcjPlzU9o1UltHI1+RXSNdDiWt+NwtAcuNeMsnpZ2O5M3UgbFuFZ7zd3SNeAfZDt2dv/VGZUNMZ6GNnejw2octuJO2BDyaYfCpKpOBELzWivDWI84SBgvDM3xGqe9Pp8p+uwDZQHyhZbbOFID2EDfc4558j1118v5557rvzqV79KCdpUtO0Pvp8cCwQAODl23+CfGgkA5kgL+gNcNS1M6Mj28APHF4kcP0FuXjqC4Lutt97aE/c42V5fZN44GmSBxYsSy+M/U36NY0cAdqj8GpsEPOUm4NaUwRzle5ER89InsbiGRBtsmqCEADK9bnBi8Wyne5hJSQj6cZKn85IlrjK9XDrSV1uPypNieXT+yGQZmenpMq2kQpZ2ONMYSnMKJCtjSDr7R3qTzfrXl0yQ5V3O2sBc64UKsVHZFFnU7hxYR9KP0pw8aetzBtt1hfXy2rIFtioWJTlF8sfdz7XUH+JZNHshp1Emr9Z8f9RLzKkL9CyTNqG6t7GuIx5qQCabPni1qRBsShuhHLFRYG5nznKiq6EEccQRR1iKEHfccYcVlxHJxj7Wdg3uN3YtEADgsdt3Y7rmTMIsFH7K66+/blEaNKc7x9YAGO7jJozu5xl213rhHocGucVT4cGujmYaY+zD8WG8FweVkDKz1rHIcuyrHi4WfYCAVxpGtH3l5ftmeuVUSrShpxiAonBJSfD+3vT21bKo3TnrWm5mruRnZkrXQIek902QJ5eMBqm1eaXSNdQuA0PO7+LGxRNlcfdSV9NuXt0gn6xxT6LRUDpBlnU6A+WCzHzJyRTpGuhyfFZd4URp7nGuS2n2NOkdzJBP1oz2bH996g5y5Je/72WIRHwNfQivlvkNXm24TZXSjhQQq+4tm0jTSxxtog6VXSOAmLkyFWgYvIdIwRGr4aaIwTyDE+KYY46xgPudd97pSI+IuOOCL26QFggA8AbZ7clvdCQAmIQUcNU4umPi1GAlKA+RBrl5sQRBcAoq7a5PptcXgEmADR5wlTaLtQfci430Gjz7eIfZnHAETD9TWNA1jbOfJBB+nu3lWsA6myYKXt9UCfzBq4Xnnj4E/HrhZDZ1r5Jr514u/UPOacUnFU6S5p4lMn9Nmcy3CQzDDg05ZbJ8yD1xxYziWlnU7awKQRKN2sISaepe63G2KxV5pTI41Cl9brJnpVNkaYezF5j7Ti+eKku6RgP/dEmXJe050tXfI9NKqmRZ50gKyC+3O1G+VLmRl2Hi+xozeCsabV8z+FFBMScojAfTS8z75EUxRUEm7yIgEx55vDfGXoyn3mjqB6h1itVg/rjwwgvl97//vZx++uly/vnnx3Wu91L34JrxY4EAAI+fvhxTLWHiU3DkteIq0o6XBZBH8EYijvH+85//WFy50FzydlzfROpOmmmMiZbmCDHZi5sZfAe3ETBHXytlQjVV8WiZBQofuAAAIABJREFUgXXx5kGaAWX0I5smLwDC69iM9Dr42gByOJD0IZHvfvrwv0tflH9+dr/r46eXzpDb/veR4zUEhzUUV8iybucMcIVZeVKQnS7tfc6yZ1OKa6Spe6VrYB1KDPNbnT3FVHJG+TRZ0Oac1S59OE3KcvKla2hkXWry6+Q/i9bylScUVEh7/3reMUk1rt5jtmSkZ0TaVY7fi6e2r56ymBJ5zH8UQKPpJQ7d+DKmmDO5B97oaAOCY2U45UaH80YTM3DUUUdZbbj11lvl29/+tq93I1b1De4zfi0QAODx27cp3TK/ABiPwWuvvWaJoOPJAJAmCmy+/PLLlgg7UdxaEpXQwq4TsZ1mRwIwYY9UONKkb+Dywa11Cr5zk19jMVcvcSzl1wAPgEy8a6EBZcl6STQbGIoYtJt6ReK55z63vfdn+WSNc3KLspzp8urSJlnV5Sx7VptfJj3hZM9yyqV5yNnDiy03r260pR+Ydt64bJIsDiN7lp+VLp0uGsOTCifImp7lI8B2WU69vLp0fZCcWZddJm8nR2/xw5h3dzK0fZ0SdTB+NJEKXmO0cEn+48arjblBXG5oBuC5ZeNkTON0OPLII63AVLK6mXNvIuscPGt8WyAAwOO7f1O2dV4BsAYFAfiY4DkKRJEhkeXVV1+1AuAaGxstbwo/1IsfQLgfj1209QZcIl9EHVjYOG5NdtHjX1IZI7UGmPMTfIcOqalHjOcKm2qSAfUU+wX55kZh0qRJltc3nlQZr/1Ae+lD+hIgEO2xdFtvi1w191LpGbAPVFvUWiFDQ9mytGMkYAyt7+aVU+RTl+xuXD+9oEqW9jkHzWWkpcvk4nJZ0bU265pdKcspFknrkd5BZ+pGfclkWdbpLK/GfWeUN8jCtrXe5DRJk5UdedLSu54/jGe7vrRGlnWukNNmHSebV8/02kVhr2PMz58/36Ie0X/JPFEwE3Uwpgg25XSNd8j0EIdL1BG20VFcgCQcAXh4y90C8Hhnr7zySpk9e7Ylc3bZZZelRFa6KJoefDWFLRAA4BTunPFcNcAjk6Fbga/57rvvWpM5YI9JlGMxtB8TWd544w0L2LHIKfBNdJAbtgJgIl/EEX40yT1iaTuO8AFzUBuwT7Rgjro5ya9Bk1AwjKeYDZHT5oP6mAGSXrMHxtI2dvdi/OIl16BA2hSL8uaK1+SBj+8adavi7DL5+4dr+b3bTmiQ95rc6QcNheWyvNfZy5uflSMlOdnS2tvuWO2qnBLpGm53zQA3s6JeFrQ5S5px843LpspClyA/MsBV5hVJS+8aqc6fKC8uGh0cRwY4kV65cvffSmZ6ZixMbZ1CMS9BRSCgLFxinpg81MNNNHsam3LqZb5HvA+AS6UeKTBmXov3Bl41h3l3oa05bWQB78cdd5wgd/nnP/9ZfvCDH8S9bh7MGlwyji0QAOBx3Lmp3DQ3AKwpclEPAOypFBQSXxzr7bjjjgltGsF3eA6hGiQjjTEeHdUTZWELF1meCONo0hFAOYsp9WJxjUcJlV/DW4wXFT1hk0esiT44LWCcoITB2EkFry9jmsBNwAAnCRzpxhJ40B93ffAX+aD53RFdUJbTKPd9sFYRITs9Q+qKS2R5pzPArcovlsHhHukZcPbOblxWJ4s7wiSlyCqXJnGnS2xcPkkWtztngMuWTMnLzJBesdf0pU0TC2qlpXeFVOTWy8tL7MH9vhvtKodttl9MhiYZDNnwMdYAc5HQVmJSEeMm9D1zI/q+jHnmqVB+O9cA2M10zpqow/QS82+/Jy1O7THTLIfTHEbhh6xuPP++++6z2hCUwALxtkAAgONt4eD+thawA8AaQAVQ0BS5ZpS+ZjbbddddE2JVpTtQHxYYFj0AF3Xix0ukfjQVhcfHs1l0AUxoiiaK9+xWbxZOQAAeJwAm9IJYgrlwNjMDgzglYFFncVfbUBeyzLF5SgXw29TUZNkLYAFo8ptRMJw99PP2vja56o1LpNuQEVvZUS3vrFyvhtBQWiMru1bK0LBzBritaurlo9Xu3tktq+vl4zXO12SkpcmkwjJZ1eMMgkuzCyUtnQxwblSIOlnW6Q62oUK8tXylNHfbp0o+Y7ujZdsJm3k1o+11prZvPDYwkVaOjRUnHXhPOSUjVsFrYX5RQMymkvdZE3WYihOcVvh9vzkZgvIQLs0yz8PbS0KLQw45RK6++morUUhQAgskwgIBAE6ElYNnjLIAIIYJWIuCKg2gAvCFgj08oW+//bbsvvvucbeoKW3G4qfeE9W85XiRhUEBMb+j1erURvFstDEJkMLDBKc2XqDJjyHV04TXlw1APJKO+KmPXqueJqTE2KTg/YJHTH35f7Lk18wscwRtApzivYH536o35Z4Pb7NMU5hVLP/4aGTyC/6+bW29vNfsDnA3q6qTeS3OwDMvM1vKcnOlpXf0/bVfJhZWSVtfs21SinXXZJRJS3oYCbYwqhDVebWyqK1XVnWNvg/JM27+9vnC70iLX23fSJ/j93sagMfGCpWHaIEj7xHvjeklhu7B/U0vcbhEHczTUETCpVkGcP/0pz+Vxx57zAK+KD74Bdp+bRZcH1jAtEAAgIPxkBQLKABWHVs0UAEqbgFUgGMC0vbcc8+4TZR20mbK9zUBKouiBm4BikOP5GlLJPw6U0kBwEQUdLxBk5cBgAoHXkzazfEknqZUWKxYsFls2UwByMkGSDH1VLWf8JYlSn6NZ1IvCl7fRNFWGL9/+/BWea/pbanIbZB73h9NC0AIbEJeoawesPeYUueKvGKLO9vtkFKYa7xQIbyoQkwtqJRVfc5BcyT0KMzKlI5++/rW5DfI4rYuWdm5YpQE2/aTtpBTZh3pZYjbbtLh3LMRZVzhYY0VPSCiCn3xJfoY7W+oPnDuOYWJ1xyhiTpUhg3Qam2uvjgNU2DMRp16Kf3IzIBn11bejUMPPdSaQ+69914r/XFQAgsk2gIBAE60xYPnWRZgsuRoX8ELGqjhQBWT70svvWR5gONxtG1Km1FHrwoPeiQfmhGNOprSXvzbaaEyObV4WABzfpQU4jWsqBd8Wha28vJyCwSkAu/RTADiRQpOI+XNPuKY1vRusWmJVn6NeikISJY6QGdfh1w19xJZ0loory+3T15Booim7mbXQLUta+rl43BUiDDXQIWYXFzhqgpRmJFnUSEGxTkbXU1WhbQO2+sUd/SWydKONbJVdYN8tHok4D9528Nkh7qv+H492Cyx4YO+wtyUiMyKXirJRo85Ey9tPLNfOtWF0zA2naYuMXXSjQHjH74vtCg7nW3ew9tvv11OOeUU+c53viM33nhjyugTe7F/cM34skAAgMdXf46Z1jBpkt4S0IsHwwufFprE888/L3CAYwnC1OvL5B6rIDfaZ3qIQ6W9AFsacGJ6V1FSSDSn1mnQaHpl7I7X12+ShngNRuoFCMDrHi5lsFsdYi2/pt5owBMnGclUnnh31f/k2Dl/EWemr8isiY3y7qrRKYNNm21WNUnmtTgHquVn5khpbra0uKhCTCqqkpZed7A9o3SKLOp0zwA3MatCVoeA4NKsMnl1+VrqQ05GllTl5cvqnlbr/5npGXLz3hdIflaur6Go6jNsYJHsipZa4OvhLhdzAkZSCE4xoDzEK+jUT33NuA3sBRDm/VQZQzaU6LcTuIwmMcAXXd/LL79cfvKTn8TNc+2nDcG1G64FAgC84fZ90luOR5dIfq8FUPn000/LTjvtFJNFyQvdwWvdwl2n2dBMvVu8JQB/2gXHF49OKmRrol7q9eXoFw+Yn34KZ4tIPzfrhfQU9fKycfL6PE0wYPYRfzPl1+y43n690V7rE+11Zz97qzy14C3H22Smp8vUkjJZ1uGcAa4yr1iGxF0VYkZ5nSxqdw9U27y6QT5Z4y7BtlHZRFnSMVrKTBtQkJkv2ZnD0m3oHef1l8vbbesD7aYWVMuq3rVBf1vVzJSzvnacZzOq5jj6vio1mArZApmn0BumXlCi8LDGi/Lg2VhfnOJpvcwgXbjvzO28R1A1Dj74YMtrzLvDXHfCCSdYf2NzkShKyc033yxXXXWVRWdhjkVL/uGHH7aae+aZZ8ojjzwixBAA3Nnsn3rqqXLggQeOMIcd5QtQT7xGUMamBQIAPDb7bVzUmsmQyd1rYYHCa4wOcLQpkM0gt1h5fb22g+s0OxkcO/h0HMebHFUN3IokAttPPUKvZaEiqpx6ATCZ4FOB64tXiXphJ7y+1CveRaWjTNoEXmNAtwY/sjFgkU9kvby0G6/9y2+9Lhd99qh0DjmrLDSUVsvKrlUJUIVIl0nFZbKqyxlsV+SVysBQp/QPOeuDTy+dKos7FqwzQf9ApcxvHckfnpZVJiuH18je5dvJLpO3tfqK+cINzDLe8a5yGoP3XrnkXmwdz2tMzWG45KmQ+Ib28h5wCsN84VYv3qG///3v8pvf/MaikRDXMHfuXOtdxoN99tlnWz/xLOeee6784Q9/sJ7z1a9+1VLMePzxx+WGG26wHksgHqBXZS7vv/9+uemmm+TOO++0gLoW5sGTTjppxN+YC77yFf8Um3i2N7i3dwsEANi7rYIrY2wBvwCYxz/xxBOyzTbbRHy8nEivr5258CjiNcHDCqVA6R/Ui8XXpE2w+CnYUsAFWI6H98f0fsXDuxrp0DE5yCz+gN9Yen391CtUfo1od1UyoX/gSCvYigdH3UtdqSOBW2jCsolaXjggs18anSDDvNesCY3ybpM7FWLTykkyv9WdClGSmyWtvc6BdRPyyqV1YLUrLWPTygaZ3xrOUzxZFrUvkpKcUnlt6ej0zihUlGZny0/qvy/9Hb2WN5LCKYsGbdFPSqMiFgFAhmcw0pTUXvrG7zXwjwGZnEBAeYgl7ctvXczrlYrB5tytXoBkQCc83/POO8/ytOrcxTz33//+13pntt1222iq4/pd9NOp45w5c+Sb3/ym5+fssMMOlt1xuJgAmMx0p512muf7BBemtgUCAJza/TOua4fHE+Dlpzz11FOWxyESD2Cyvb54Egmsoc14V8N5mQDApvcRLyheLDP5QzjPlhfbAhAAAIC5aDi1Xp7l5xo2BNQLb2YqeaM1+x124zgaYEJQEn2lWqqALbOfEgFe6D/GV3Nzs+XNgktO+dWzf5VnF77jaPqsdNIXl4ZNkDEw5J6+eJPyybKwPUz64uJJsrDbGUiTvrihtFaWdTofKxejH5zWL5V5k+SZBfZgeee6LeTM7deqP7DppF9MzVvmHrz3ADL6E8pDMtMZm51jBlKiZ81PPDa9ft5FrjXVJ8JRMdjgH3HEEUIyo7vuukt22WWXpJwkAboffPBBa0Pop+y7776Wd/uZZ54JALAfw42xawMAPMY6bDxVNxIA/Nxzz1kLFcdpXkuyvb6qB8tioMoAkXgIsZfpIVbPFp4rU+vWK6+OhRZvNEf4XpQUvNo72uvoL/h4SOMRSAYoTwUOsgb8KI/QLvtdsuTX8EYDfgHabBDNwK3m7nY56KGLpa23y7FrGktrZbmNjJj5ha1qRqsshN5w8+qp8uma9RSF0M9JX1xTUCTNPc7avzX5FdI10CKDw86qEBuX18uitm75qNkeKB+35f6y30a72LaXftTkJIBj3hc2m2wuTQ8x75XXdynaMa/fB4xDxeA3nks8pKlQTPUJvORO6Z+xLfSCY489VrbccksL/DK3JKsAvDk5oi5oDTN/QqH74x//aP1NizpHcDL885//tOp/xx13yPe///0RAJj5iI0U7xdynHiE2QwEZWxaIADAY7PfxkWtAYYsQH4KeeLx1niddCKVNvNTJ7drASZkc8ODA2AyM9tF+wynoC3V6FTahJ33kUkc7yp9AMAM542Otq5ev4+3l3qxEOH1TRXlCdO7ygaMMeiFG63ya2ZgHW1kA2R6iAFbkQRc0X94t5YuXWp5Cp28hU/Me0N++8Idrt0wa0KDvNvkTj+YWTFBFrQtc7xPUVa+5GWJdPQ7g+1pJRNlRZdzsBs336yyUea1OtMyCjILpGegWOa32tfllr3PkeqC0eBR5QaxmVJ9ALlOWdHMd0lpE1763et4N68zqRhsYlJh00f9GLtkdaM+BK45qU+wQT///PPlyiuvlF/+8pcC9zaSjX4ktnP6DichvBuchlx00UUWfWr27NnWpp+EPqrP/eSTT8oee+xh3YY6X3PNNXL88cePuC0e7X322cc6fYSaQlsZOyRniuW8Hsv2B/dyt0AAgIMRkjQLRAKA4Y0B1hBadyvxkDbzYygWVBZZIoTJAkZ9IwE4fp6J91ETdCh1gnoAgNVDDNCiTnhYVYIu0V4uuzbRX6SbZlFKJb1h6gowwbvKwh/qXfXTP3qtnfwan5mefDyR4bjOZrINvIVugaHY94xn/iIvLFqbnMOu5GRkSm1hoazqGs2r1esnFJRJ10C79A8NON5nZvlkWRCGCvHl6gb51EUVIj0tTaYUV1oBenZlclGDvL2iWfqGuqRvcGTQXGPpJLlqj9E8TTNtcDjdcc2KZm5ceJdMTj7gCapLtPQEnsVcwQlRKqVZVv49JzHhEm4wpxx55JHWe3LbbbfJt771LU8bxEjeHz/fYbNK/QGseK4py5YtszaK8JLPOOMM62/QHTjZwTFAZjo8xH/605/k6KOPdnwcmwIC4ADWeh8/dQuuTb4FAgCc/D7YYGsQCQB+/fXXLaBA8JgTkGLiZlFRhYnQTG7xNDjPXLFiheX1BXgmU9os1PsINxTwRQEs4QEDGMdiEY/GppoGm0UolbLMAZhYFFnc2cDYpeeOpt36Xb/ya2bAIlQgbOZlc7Wqq1UO+sfF0tHf41jtGeUTZVG7u3d269pG+aDZPWiuPr9CVvQ7Kz7kEqgGd9o1lXK1tPauGpXdjcrnZdTJmysWy9a1DfJB80iv9SGb7iUHf2nPEW0kcAsQBICNJG2wGQCpoJjNJuCX+cj05vvZUMJzh/LAWKNeicoYGG7cmpsFgKNTzAV2eeGFFyzwC6j829/+Zm34U6Wg+gAfOVSqDPoD2eduvfVW26qi9gCQZ9y4vVvYhk0x2sZBGXsWCADw2OuzcVNjFn5AsJ/y1ltvWYsYwDK0JDvIzUxjDGBiIYjWO+THNk7XYme8IHh9oRTAY1OdTkAndTQXcP7tBVBFWzc9jtajSPo0EcFiXurNZgEqBmCGRS6R+swqvwbQUk++yq9xJA9ook/duJhObXz4k1fkopfcF+ttJ9TLe03zHc2Ed7axtFKWdNh7Z/liWW6RpKX1SY9LKuWNyupkcYe7frAdFSInI1veWzUgA0NDkp4mMr2sWha3r9X+pVyzx+lSX7o2RiBU2xePYKzeSeYuM7COf6tutMkldpIyxBOJSoGmgA/n8fcybmNxDe3Au8nYh/LglJGStv7+97+XCy64wJISu/TSS8OeWsSifn7uATCHkxwKgAG/eG9vueUW29v99a9/laOOOsryFrtxmJmz2LgEANhPr6TOtQEATp2+2OBqEgkAxovDosbErCXZQW4qPQWQA6AwKaZK9igAFECOOgKYQoNq8PSoggGAC2DMtapioNSJWC/OqqTAs/Hmp0r2O8Yk/QgdA48vR9KxAkzRvODYixTLLMhsTqhnaNAWoCsc55K+Pelf18vryz9xrE5+Vo6U5WbLmp52x2umFldJc497drfNq+rl0xZnIM3NN6uaJp+1fO74nOz0TKnML5SW3vW0jLqiqfLk/PWBdpOLqmTNF5nmagsq5KZvnW0dv2Mz5gs2DInQ0GVeMqUMeZ/YFAMkVR6P38wNbEjhpgLIiWeIF6/Yz5hTGhJ0jHAnCwQRHnfccfLKK69Ymrnf+973UqINoe1F0/eAAw6wvOyMAQoygTgoLrnkEjn55JNtTXTMMcfIfffdZymqOL1TOGOQ5Lz44osDaTQ/Ay2Frg0AcAp1xoZWlUgAMNQCFratttrKMleyvb56fA9wZDGDK5cKixneKRZZgJxmtQoHjrCnSkap9xGAyr3MbGiAYjy1kbTT1KkFsNkpKSTrPTA5tWwWUiWwBe8v3Eo2M9AdACd23kcAmBf5tSXtTXLwQ5dKbwh31rT7ZpWTZV6ru6SZHf0gtO82qZgoC9ucKRWFWfmSkzUsXf3djt1eHxI0V5ozTV5ZMhI0b1PbKO83fybf3XhXOWaL/SwaEjbDc5/MgDIAMONKPcWq3MK7A60AGhKgONkBb4wn7AWwJSiW+ACn8uqrr8rhhx9ubaYBicx7qVqYz2bNmmVxfPFUs5GH+wuvH3oTm0qk0gDJbHhVBQJQD7DlMwqpm1HMQVWCPmNjdeGFF1pxAW+++WbKUFdStR9StV4BAE7VntkA6sWCjQfST8E7x4LCzlu5vtxHeb6RgDI/z9dreSaUAiZQgBKLhlN0dCT3j+Y7eC1YzLBFtEBOj+P1KJ7fBAOxYJvSa3i+w9keMEC96L9U2iyYuquASzzSXjYL0fSR1+9qAB7H0AA5p+NoDdoydaN5tzQAks0G/cVGhn66671n5arXH3Ktxta1U+WDZmdJswxJk6qcPGkbclZ8qMkvk55B96C5L1VOk/mtzl5gKrlpZb3Mb50v6WnpsrAlSzr613LZtWSlZ8jEomI5ZZuDJLO53/KuotEMDSncuPTaF9Fcx3tEnaA8KN1ItYkBn8wdJg1J+ymaZ3r9LuAQJQNOOjhZ4122K4yx66+/3kpugSICgWJO49HrsxNxHe/QL37xC3n00Uet9QYQS2Y4NpNslPACv/zyyxZNgveEAMlTTjlF9ttvv3XVQxqNYDdAM/YiEJtAP0C122YhEe0LnhG5BQIAHLntgm9GaYFIAPD8+fOtiYrsQckAvjTZTBfMJJoqUl0qicURnwrVx5rLy0KOB97kp4bKegG08LwpdUAXf45W8VACylNls0Bf4s1hYcQbnSqpZulLFlsoD5EE4IWTXysuKZbz/vd3+ajF2Ttbkr3WO9vR5+ydbSipleVdzkkreF+86AfPqJgkC9tcMs1l5UlBVrqU5JTJ05/b84Y3LZ8sBxZ9lWMhi5cZbbr0KKe3dV+nLwG+ADFNUKKgnH4KpU3wfrEBM3nEsUh4E9oePY1hnOGNZhPvNF/gwT7xxBOtzGjXXXed5QFOhY1FrPoouM+GaYEAAG+Y/Z4SrfYLgE2OGsf6HMHFY2FwMo6ZOCKV0gVTX44uWWRZwAByiYwmN2W98EBqYB19gzeJxZO/4Vn1qp8b7wHKWEILFA8+iz9eHz/R+/Gsn1IxABh4fWMF5PDcm7JenzQvketWviCDLsmJt6qZJh+tdvfOfqW2Xj5sdub6ZqSlydSSClne2eRotoq8Eukf6pL+IecToRnl06R3IF2eX2SvVbxF7mQ5eqM9LCCXKh58xj0BZfQloJwNYLgS2k9Km7DTJA53L6fPAeXQyfCAMvY1a6Dd9dT/0EMPtd6Pe++912pHUAILjAcLBAB4PPTiGG0DIITJ3ktRri9gC+qBBmzxXT3exfPoJRDIy/NCr+F5HN+nWuIIPJd4VvEUJkpvOJz9sBH2QtcUYK5ydCz+Jm0iWbxHM8UyYCmStNrhbBDJ5+YGSzMGxtqDb9YLfuS1rz4sd338vGt1N6+aJJ+2uHlnc6QkJ0taezsc7zO1uEaaulfaSprpl75c1SCftrgn4ijJmir/a7KnZfxis/1l383ts79F0h/RfEcVTng3dYMVKSinnwDS5uZF6S2mlxiA7CVgE54roJY6QnlwAuV8jkzYqaeeKvvvv7/ccMMNCVVDicb+wXcDC3ixQACAvVgpuCYuFvACgN2C3FQ/1eQ9xlrBwAwmw0sCdzVVPIVkmcPry8IKrSBWnsJoO5tNCvWCiwwPEzCnC7j2FXaFP6jZ6vgNLSKex6omKOH0AE95skB4qI0BJVAxsB19mSgqRt/ggBz+z8vl89YVjt1empEjQ+n9MiBDjtdsVjVF5rUsdB064agQaSJSX1otyzrXS5qZN6wtqJb3V/VI/1D3qAA+1CL+8f0LJC8zJ9rhG/X3Aadslhn/eFf9pG338nClt5gSbGzqVBVEQTG/Q+cq5SHDYWX8O4Fy7gdvFhWFK664Qk444QRP4NpL/YNrAgukigUCAJwqPbGB1kMTM4Q2PxJpMzxoHBeagBigpUeH6n30qjWrtAJAGYtFqIRYsrrMTNBA5DIcUS+en3jXlz7TJCCAW4CcnRyc8h7NfiJAjght7SN+ewms89omMwBPlRTiCba91ktpPQR3AnrxSMdaci5cXd5aMU9OePxq18tmFlXLgh57YKpf3Lx6iny6xhkE52ZkSUV+nqzpaXN81iQkzXqabD3Fk4sa5d/zP5Ntahvk/ZDkF1+btKlcuMsx4Zoa989VQxdgiXc1UXKIzAmm2gT1YD7UuQ8vL4AcHjKg3E2tBk7wYYcdJnD7SWyBikJQAguMRwsEAHg89uoYapMdAI6VtBn3MT2PAC4WCjyNACz9CZX0MgEmwWRowcbzKNpPd7GA4V0FJCU6QYNbPaGyUC82DdjLb/R9aGAdHigNBDLTOPvtB8YAQZPwHVMtAM8E5eFS8/oZI5Fce8nL98qDH7/s+FUSTmxUVi2LjIQToRcXZ+VKRsaQ9A4605o2Lq+Txe3uyS82r2qQT2yoEAODVfLpmlWCp7gut1CaB9dTLk776g/l243bRdL0mHyHcUbGMaQHOSmC7+53rMakIl/cxEzlDPAloxl1xCOs7xMe4tBgVTy+JLXYbbfdhGQQqbLpj6VtgnsFFlALBAA4GAtJtQDASTmi6vWF2sC/Yy1tpkeHAGH9AYCbkl6aOYq/4fVNFVoBdgLEAYAjUQWIZyfj9QX8srEAlDvJKPmpg1MgkMl5hDbhRkcxQTlUjFRJOIAdVKcWWxHolmxVDJQeDvzHxdLU7eydnVZSJU3dTTI0POzYlQ25FbJ80DkFMl/cvHqqfLrGWV4tJyNLyvPypMVIk1yaUyKvLGld99xpxVXS1LPyOPTeAAAgAElEQVS2LmmSJvd/d7aU54UPMPMzBr1eyzgj2QweWDz4bpnDvN4zVtex+YOOAZBl3lDFCQ1MJZHFnDlzrKxozImPPPKIJfd12mmnJfxU6eabb5arrrrKUj8BmG+33Xby8MMPW6ZAj5e6Ef/BusApDtzkAw88cISpcF785je/scA7bSQVMnJtZuKkWNk2uM/Yt0AAgMd+H47pFigAjpXX148xeCZeOCZ+vCRwagHf0AlIF6weYjyHyTouN2kFeKoBmF4iyf3YIdJrTVCuXt94UTHs+N56xGsG1im9RT3lbGQAmLEA5ZHayfyeebqQSjq11PHhd1+Ui96437WZsyY0yLtN7oFqG5OauMOZLlGclS9ZmYPS7ZomebIs7lifiKMyo0b+s2IkT1nrsmnlNLnmmz+PRff4vgdzB1nGGGeoI6SKLi7vBkASSUSnbHPQw1577TW544475KWXXrIUUXjPAJc77LCDfO1rX5N99tknIUGi5557rqXNi8YwoBWPNSmMCbyj4JWmXvwwJ+KpJlnFnXfeKQcffPC6fuO62267zUrRDD2M9MxkbKOPUmlj4nugBV+IiwUCABwXswY39WoBQBSTLhN2MnR9mUwJDCFaG74eoES9jyxuBCeZR/GhGrde2xnJdXin8foCzOMNMP3WzwSYyQDl5hGvcok1Ml7T4MJzTPZRtGlXFnU8hRyNe5XE8tsvkVyPLeEg4117sOcDmbvaWdIsJyNTagsLZVXX+tTEoc+sK6qU1t7VMjjsHDQ3JatMmmSNa3U3rZwq81rXeooHu0vkk871HmD+lpeZJRV5efL9TXaWg770jUiaHvF3mDfQJCc7GNJ+gMx4bf78VhLuLioPvA+MMydJRNqA95eUxiQWQvGBuebFF1+0fgDFgMxvfCO+tuX0iHpSl29+85uemwtIZ85Gm5gC2Id6hRcZzWIKFLj6+nohtTGZ3YISWMC0QACAg/GQNAvgcWCC3X777a1dP5NZIhcRFgomX47KAL52GrUaXKKUCSZUAJZKr2n2pljy/ZS3igcHjxJUjFTyYKp+aCpRMbAZUnDYjMI4YiNjch7pK7zniRxj1AWACTcUjmi8EpRE+hKzwcM7xjjHUz6Qky4HPnSxdIVkWjPv/6WKOvm8zZ3Hq6mJ3eo1rbBcVvaudrykKDNPJK3Pyv72XtOADNpQL0jZfM4OB8u00tpITeD7e2xMUewg4JbNH5rgqVLYmLLJYqxTN6eASvp79uzZFlg866yzLNqAnSKEUtHi2T7oDQ8++KDlhPBT9t13XwvgPvPMM9bXbrnlFvnRj35kneaZ3OWjjjpKXn/9dWucByWwQACAgzGQEhbg+A2pHX4z0cJD49hNj9/ggcWDesCz8HYBSvymMebYEMCsgJhFkPtRV/MoPlKpNCgZgHK8hZEEk8WzY/EOwSdkUQUspQoVgxMEPJiLFi0aATCxpZmxDrCnUlEqvxbvRCpmpjkACdSaVCimJBySWHBXdcze/+F/5PL/PuBazXBpkrMzMqWmoECau0d6bc2bTiqslLY+d0/x1JxKSc/KlGeX2Gebw9t8/3fPiss8YWcAwBXgV1NTJ5u7rXVULz7vAPMGx/9OcycbxSOPPNLaLN5+++2W1zUe86zXcU5qYhRQttxyS7n66qutdxanCNxd/qZFaXK8x6QmPvbYYy36xve//33rkjPOOMOiP8B7Nstll11mUSuYDxK9+fVqg+C65Fgg8AAnx+7BU7+wAJMaCgAEYzz33HPywgsvyH//+19rsiJwYccdd7QAMRMik2S0EzWTJx4SvL/wycjjHs09AV+h0mv8DXCoIAtgHE7ayqRi4O3F65soCaVwg9HkrXKcyE+qLCRsRgAkAAAAplvUunrzFRRrhi02L+rJp68i3byYdtRNFsAcD6EJMMPZO96fa9AWmzg79YnBoSE59rE/yvtNzpJmZTkFkpExKF39PY7V3aSiThaG8RRvVdMoH63+zPEeKD6USpW81brK9pqDvrSLnLzt/vE2mTW+oDuQPTCVpAdpOPMn3k3mTKgEjGG7wph89tlnBY8o1KC7777bOvVKdmEehoaGegYBeMyVeKexNe+PUjiefPJJ2WOPPazq4q2+5ppr5Pjjj19XfQAx6wcnVGbhlJHPmCt414MSWEAtEADgYCyklAWYpDli5Mjq+eeft35efvnldceNeIgVFBPU4BW8qroDvD0ACZNuPJIgOHFTAbOhSR/U8CxgeH0BJHAJUyVdMPVD1gyvL6AwlWTXTEBCogEWdL+ZtjSwzsywxd/oK9Ob79fLBxABlOP9VYCZKi8Z/ckGkDbhxXcK2vp49RI56pErXHm829TWy/suKZBp81Y1U+Wj1c6KDySwqC4okNU99p5i6A/Dw7WysG2ZrfrET+p2lVmTZqyjJHnV+PbTH/QnAJNNMzZLFS8+beBUhv5kww34ddpoM64JCPvd734nP//5zy0+bCw2en7s6HQtcx6ncbwzzDEUvNRstM877zzLs0vhfcJrDZB97LHHLA/xn/70Jzn66KOtzwG5//nPf6y51Cw33nijxXNmw5sqp1axsFtwj+gtEADg6G0Y3CHOFsBz9+abb64DxExyChaVMrHTTjs5AkcmTBYJ7oMnLpGcPTPpg9Im8MCxUKtnA94engkm/1SJIofqwWLDQpRqHi8zaxqeco7wY1EA1dzbpE2YfaWgGIDsdrysmsOApXgAskjaqjQR0lN77c+rXntI7nr/WdfHzayslQWt9vQEvliaky+Z6UPSNeDsKZ5eOlGWdi61fU5d0UT517ylsv2kBnln1Uj1iYLMHLnxa8dJR9vaNMFmX6lHP9pkKibApD/jsWmOpD8Zq8RQwCsHKMLHdxqTtIEgMJwKSI1997vf9ew4iKRufr9D/AftCKUuQH/YYostrOA8u3LSSSdZlAfoYlCbAgqEX8sH1wcAOBgDY84CLOZ4ZPAOc+TFD7qqBBipd5jfACMmRXb9euyXbK+HUj6oLzxkFm1KPLOg+e1geI54fVlUUinFsplsgE0M3tVw1BK/bTevV91o9RCzgcFbzxhSgMVvNi+MSTxPgI1U0xxWHjKbGkCc0xF5qK0IhDv4oUtkeaezWsOU4kpZ3bNahlwUH7asrpeP1zgrS/DcxvxKWdbfNKq76gob5F/z51mKD1UFudLc3b7umj2mbSUX7HK49X99r0xvvplMRfvLK+fb5NQCLgGZXk+bohlzXr7r1SONTaCTHXHEEdZceO+991pjM9UKfGQkz0IBMOCXuBCC2+wKWr/M62zSOQ3kOrzBnHIEQXCp1supWZ8AAKdmvwS18mEBFisiiOG3KSAmGATgiwcOnchDDz3UAkzJ5q6yKFE3jvxYjPFgsrCqlBe/deE2OcSJUC8AIMG5UzkhFv5Yqlv46NJRl3L8jBcfD20ykw1AzzE9xKoKQoWhYAAwWIz90jGisY3Td3WsKQ+Z8e93A/jConfl9Kdvdq3erAmN8m6TM4+XL29SAY3B2VNcllMoaWl90hOSRa6rr0w+b10LwKFTfGwk0Dhvp0Nlz4atHevmlEzF5HzzjoVuohhrbLDpazdObTz6LNw92ZxSN02g4nTCwJwIRxZ1B5QRrrzyypQ5jQhtI5q+BxxwgNUuNmgU5iDmn0suuUROPvlkW7Pg1b7vvvss1QfeN523aPcJJ5xgfYf5ghOPQAYt3MjaMD8PAPCG2e/jttWAE7IY4R1AvxIwgp4lgBPunnKI8RAz2SYSqJggDs4q3FU7r5KpQwwgBmQB3E0OsVdPlteOVo1anoPX10k71Ov9YnUdII6FjQ0OdWLDkEq0AuoFrUBTyqoqiBkEaQeyYmUfp/sA3szsZAR7Rlp++cwt8uzCdxy/jjYwig9NLooPk4uqZA2Z28Q5i9yW1Q3y8Zr1NIfKvDJ5fuFI7/PWtZPlw9WLJCMtXR7/4flSnJPvuVkasGp6idn0QTtSDzHXMFfwf+aHeJ4weK74Fx5ugvCIYQhHYaF9P/7xj+Wpp56yOLKHHHJIyniv7dqMzWfNmmXNcxdccIFlc7i/UMOgYUH1QCoNkEzbVQWC4Da4zHymhUQYKFuQCANN4Msvv1zeeOMNi18cJMLwM+I2jGsDALxh9PMG08r99tvPmjDhusEto6jkkwbV4SXmuBoQiboEKhMAYjhneMhifdRpyq5xNIcH0w+Ig7us0mssbvybEqpFHAmYd5IQS4UBw1EvVAzazIaBKPFY902k7WSxxmOF/Uz1CTxvoaogCrKUQ8xv+j9ebVHeKsAOD6bfIL5Qm6zsbLHSJHe5ZG7btLJO5re6awNvXdsgHzQ7Z5FLF5H6sipZ1rFW8WFaSYPM+XTk9bUFJdI71CmbVU2T6/f8SaTdt25e4LRFufkcndOfnHrwnuqGMxGnL24NUd1hxhygHDUcp0LWs8MOO8ziKuMd1aCyqAyVgC8DdpHEfPTRR61YDaTRyAxHsDJ0MbzABENDk2De4zTjlFNOEeZ7s+A8sEuFDJ0iKIEFQi0QAOBgTIwrC+AthO/m5rkBkDLhmoCYzEkABkCzAmKyI7GQRANUWGDxxPE7FrJrdJZTWmAWak3f7MXryMJP3SgslF65oYkYMCx0bFI46k2l4ECTh4xHiT51oxVwfWhgHYCGcWV69KMN1tJxgUeadyDWcnX3vP+c/OG1f7h2/VY1U+Sj1c7SaTnpGVKUmSldw72O95laUiNNXStlWIYlJ32ivLlidHDc9pMaZbepm8nBm+4ak6HIu8n7z0YFYMX7pV5iPX0BdJk8Yr9UkkgryjtK3djEIAvpRnng1IvTL3Rxr7/++kDxIFKjB9/bYCwQAOANpquDhjpZAJDCQqP8YYDx3LlzLXrEtttuuw4QA44ByV4AMZ5AgtzwRkO9wOsbrwhy9Tqa3FQWc5XzUs+jLp56zEv9NI1rqnB98eCgosAGBT4tx5he7J2I0U0AHEepAFqoGDU1Nb4fy1jDs21yvqHGMNZMLWKlVHh9gHqkGQt4CWNNYRkYGpQfPfoHQR7NqVTmFcvgcLf0DvY7XuMli9yWNQ2yuG2pvLm81zb7W2Zamjx8wK9kSkn06h/ozzLeeEfp01Bgq4lvTNoENmbDYgZCRutlDzUY4wQdXOYPzR7oFL/AeMRDSjY1uL7IgSU71sHruA2uCyyQTAsEADiZ1g+enZIWYPEBUMAdJjkHsmtkqwM4arY6KBPwie2y1XEEjvcSwIRHCaCUSBCnXkc92uU3x4os0oBiTQCRipqmUB7YKFC3VEn/rGmWAUrx4CGbnG+AlgbWhWYXdEpVi4QUvFU80oy3SKgwXl7Ed1ctkGPn/NHyzjqVbSc0yHtNzjQHvvflqjr5rMWZLpGflSPTS+vk4U/sA+umlVTJIz84y0uVHa/hXaY/URDAi19XV+fpHTV1vhUUx1p+TROVcP9wqZaZZ6A88J177rlHtt7aOSgwKoMFXw4sMA4tEADgcdipQZNiawGVWEJSSJUmzGx1Splg8bnqqqsswHzddddZXt9UCKJRQI8agGpmAgDMY3i8xG76trG16Mi74WXj6B5vXLgAn3jWw+7eAAtABvzQRCUpMb2ObF7gfAO8QgPr6FcoLGxoEqWMcfHL98o/Pn7ZsRsy0tKkvrRClnaMljTTL1Xnl0jvYKf0Dw043mdG2Ux5Yv7IhAZ68eGb7SJnbB959jf1lmM/ONLRZAeLtfwaoBduOZ5oKA9OuuA8F8D7s5/9zMqO9pe//CWlKEyJfk+D5wUWiMQCAQCOxGrBdzZoC7D4AIzwCiuPGNCLlxWawTe/+U3ZZ599rMA6P9nq4mVUTQQC6OWYl+NevNOhx/Cqb6s8Yjyw8T5KNdUn8PrCtUyVolnTku2RNiku6nVkrFHYYHFEji6yV3pONPZt7e20AuLW9HQ43mZaUaWs7HEGwHxx69pG+aDZ3sObLmmyqC1HppWUySdrlo16zs17nyhfnbSR72aoogjKApqeOh7ecvXoa+CqnrjoBkapEyYlykydjTeaoE+ndw8KjSZ9ILMbwWPxfk99Gzv4QmCBMWCBAACPgU4Kqpi6FmCxQ7qHhQjZNWgRyO4AiAF38FjxEPPjlq0uHi00s0WFSxds6tsCjOEVwgsOlV6L1UJr8pCV45gqPORIsqbFo//s7qlH93jLCfbEZoBiQBFg2OwvAFc8qDePfvqqnP/i3a5N/nL5RPms3T67G1/MSk+XiUUlsqprdJKNyUUT5PF5y6S+pFKau1ePSMdcmJUrLxx2vmRlZPoyOV516DUoZEATcZIg9HVTjxeHk1+jn+C8A5ihPLjJdSGFdvjhh1vtuPvuu61NdlACCwQWiMwCAQCOzG7BtwILWBZAbu2cc86x9Db33XffdVZh0SNgCg5xaLY6pUzwu7GxMS7eG7xOPJ+FH6+vm3SSXVfiYTSD6rgfYEqj4TWwLhLQanqkTQmxVBhSard4BZNF00bqxvG4Ht2b3nI8+mbGOpQNdAOjoJij/kj6K7TOPP/Hj18jb6105voWZ+dLbuawdPR3OzZ5k/I6Wdg+mgs8rbhBHv1s7b13rGuQN1esf86e9VvI73c/0pcZGW/Yjc0btIJkc8s1PTr9BZAlkQN/wxut75VmGNQNJ58/8sgjVoIHNHPRuk1kSndfBg8uDiwwRiwQAOAx0lFBNVPTAgBdOIXhou41Wx2AGNoEHmJUGEhQgNdYPcTRZqvjOXiJiCDn3hylxkKySXmpSpsAVLAoa1YtXbjdnkXdEPLnJ5Z1i8XIoG7YDNtRNwKj4nE8HkldTUUAr3Vzy4JmgqxIx8a8NcvksH9ePsI7G9q2cLq/XL9l9cjsbvytf7BCPl7dbN2uICtbynOzZfUXlIsLdzlI9tt4liczaiY8+OV4fOnTWGwAPD08zEWqTQ4dg7rBfWeDY8qvEWeALi6xBZzIPPDAA5bG7a9//euktIPNPjEO1Jn3frvttpOHH37YqvcVV1whjz32mPUZY4o6X3TRRVbQsFnsTiQIEg5NgxwLGwf3CCwQzgIBAA5noeDzwAJxsAALIEDQBMRmtjoFxH6y1QHE8foCfvD6ckQerwJgBASbXmI2A3jXTC1i5TmygBOwxVE9AVup5L1Chgy78Ru7pVLdsBd1o28jlV5jDIRqR9N32l8mbcJPgpYr//ug/O3D5x2HWJqIzKiokYVtKxyvqfhCOq3vC+m0stxi+c+ithHXz5owTd5r+lzSJE2ePXS2VOQVhR3WnGAw3qAhMd6iyYQX9mE+LzDpGNQNABxauOb111+XO+64Q1588UVLDo13jhMTaA/87L777hFJ8fmsrnX5ueeeayWmOPvssy2tdOz6+OOPyw033GCNTwLxSLm88847W7EQf/zjH62TL5R0TBAMAD7ppJOs9PRaoO6EAuVI6hh8J7CAXwsEANivxYLrAwvEwQIaoKOUCbzEodnqAMVbbbXVqGx1AN5FixZZgNpLcoY4VN9anAG5Kr0GMGYhJDALTyoAjuxaqZZeFi4tXitAIOAiXlrNkdicDFjwVuGIYjc/4DTc80LlvEypPFOP2CmwjgDBuf97S36/9GlpHXCmOTSUVsuKzlWu0mmmp7ixtF4e/mT+qOpvVTNR8jKz5M79Tg7XNGtTpkoKqDygbpIqhfeAxBYAQTc6BvMBqYyPPvpoi7MM5QGqBCdH+gPI/MEPfhD3pjEPYcc5c+ZYAb6hBboN7TEVK9i4NTQ0yJ577im33HLLuq9w3WWXXWYl7AhKYIFkWyAAwMnugeD5gQVsLBCarY5FTzNCwQHEAwQghiN44oknygEHHCDHHXdcyngvqT8LNuASfipHz3i1AHFmSuBEKBfYDTA2DYBL6ghNxKsObCIGK3bCbmjUJioZCP2FB1wpLoBI+s1UBgEYAyahiUDf4dh+fnqbnPPC7a5mCacNjHTalOIyWdG1Wkqy6+SlJaN5wVOLK+TgTb8qx221h+OzTKpIOCWFRPSj+Qzqpkk3OPLH8+tEx6D/L7nkEuuHdL8XXnjhKBoT92MTkwhKx5lnnmkl2YBK4qcQFMzm94knnggAsB/DBdcmzAIBAE6YqYMHBRaI3AIseIASjhVVeg21CQrewa9//euWCgXgOFl6vto6XewBcQRqqWc1VHqN/5vKBQDjWKQEDmdlAo8Av4BxbJdKHkIN2MJTFq1GbTg7hPscL54ZWIeHn0LdOGng6B4u6MlP/1leX/aJ4+2KsvIkL1uko8/ZUzyjfKIs71wpHzaJ9Aza6wM/csCpsmlVne1z2NBwFI/9oqGKhLNJJJ9DNcGLikcfHvKkSZMc1TlQg8Dr++abb1qeUwJr46Hk4acdu+yyixVEu+WWW8rVV19tjYntt9/eojnwN7uCV5gskyhWkJ1OC21BhpF+4r3DQ4xHGCWYoAQWSLQFAgCcaIsHzwssEKUFAG9HHHGERXk4/vjjrWA0gLGfbHVRVsHx68ipUT84guE8qwqwlDbBoqkpgdVL7DclsFu7ACKAcjxx9fX11k+sZN2itadywvGuplrAFm3DZoA4NihsajjKB8RQunNELp7/WJiAuHr5oHk0tcG026wJM+WeD+yTX1TnF8srR5xrCwYZa1AeyHTIpiHWaYmj6Vs2DZzc0L9QHqCz2BU+hy975JFHWv1PkgsoBKlQAO30P8CdwDY2rbNnz7aCRkmuYxcAjDbx9ddfb21KOMXQwryFRjpecD47//zzLe/222+/HSTySIXO3sDqEADgDazDg+aObQuwUOLlnTp1qpVtTgO2NCMVkePKI37llVesoDNAgVImUJzAmxMPrxIeLkAStAa8vn49q2ZKYEAxIAuAanJSAV+RHPsC1lhwsRNe33CqHYkcJXjCqRsbgFQLwuM4npTB9G3ohsbUt73lg6fkX6veczXbzIoaWeASELdx2cby/KL50j2wNsmHWX4w86tyyW4HjvgbfcmGgY2gakmnyoaGikJhYTNIMCr96qQqApUBzyoKD8cee6wVbJZKXHQyIBKgyxjlvda2sYE877zzrKQcZrnrrrvkkEMOkWuvvdaiZ7kVNgcEwAGsQ++TyHcweNaGaYEAAG+Y/R60egxbAHAIgHMDsZqtjkhyBcR4mJAsYjHW5ByxyFZHsJuCJHSN4YbGAmADvkyVCfU4hmoRu8mVmdJr4ZKBJGNIIP/EpgFPN+AiloFu0bbH1M9lE+XkveQ5Xf29Voa4lV0tjo+tKyiTNX0tjgFxbT3FMq20St5YPjpD3PV7HiV7NW6+7t6qjoGHlQ2NX53raG3j9n3zpAHvqRu/nHcZbV9SrKOocNBBB8Xk3Yll+1B9WLBgwSipMugPW2yxhdx6663rHvfvf//b8vD+/Oc/l0svvdRTNRj39CFe76AEFkikBQIAnEhrB88KLJBECwBU33rrrXWA2MxWh2dY5ZXwpnkFsKgB4OXiGJNFzA0kRdt0lfIyQTEAl2ea0msc0VLwqOK1AizFWxbOb9vUswoAxsPmx+Z+n+X3ejZPAB68fn7oGE9//pac9dx6MGT33I1yy2TJ4Ojsb7UFFfLk/GYrQ1x9aYks6Vh/TWZ6usz90YVSlJ1r3ZIxR79ywgAwT6VNA2MOryb9C+XBLbX33Llz5bDDDrPacd9991mBcalYoGUgeRaq1Qv4xXurKg+vvvqqFYew3377WaoVXucQ3k36MQDAqdj747tOAQAe3/0btC6wgKMFNFudBtXBI+aoGzBmeojtstVBVwAgwQ3E4wtfMdHHzyrlZUqvATwAFAByPJgAYxZXBcWpMBwA8AA47BXOs5ro+sLhpm56UgBX02sBOP/s33+S15Y5qwUQEJeTOSRdA70jblubWS1PLl1p/W2rmjr5eM16JYjtJ02Xu/b7iaV6wJhDgSLVONzUm3cH7WHk/vBqOiUZoR0klUBdARkzqEzJzk7n1sf333+/pTIDz5pNLmXJkiXWO49Sxcknn2ydYpDqfZtttpF//vOfnpPvsCHnOxdffHEgjeb1RQuui5kFAgAcM1MGNwosMLYtYGar0/TNgA0i/hUQs8jxN4JczjrrLPnOd76TMnxaABgBUQS6aSpgQD5BUab0Gv/36p2KZY+adAyOxfH8RsJnjmWdzHuZnlWATiTBZPNbVsihD1/qO0Nc5mClvNXctK46m5WUyfzutV7gM2btLUduuqMFwOBLs2kAZKZK0fdm8eLFYb358Np/9rOfWRnUyKp2zDHHJGUs+rEd7xBxB9T9ggsusDaTcH9RrNB3DRDLCROJO0zuP1xmtMspl19+ucXZRlWC2AU2Wki8Mc5QvUglXr4f+wTXjl0LBAB47PZdUPPAAnG1gJmtTuXXyEiF53Lbbbe1uH677babBUhSIW0wCzJ0DILwFMCZ2rZ4iqFDsIArZYLfiZCNA7gB4KgP3sF4ZunzOygAcETzk0wlFp7VP772kNz9/rOO1UhPE2ksq5Il7ausa3Izs+Wt5f0yMDS87ju1eYXSPdQlA8NDMnvqzjIhu9AKDCP4E9slaxMT2ij6FcoDnnMoD24gjrF56KGHWhn4OO4fS9nPeLfY9JKaGaALiCVYD44z/GXmAbtCf6EWQcEzTLAboBkwTT9+61vfskB1KmXq8/v+BNePXQsEAHjs9t24qDlHmngGUCzAI0DWI36bheP2c845x+KVAWIAXL/73e8svplZgjzz8RsSHFXCVyRxBJxAPK2AYhZ1eI7bbbfdOqUJu2x18auZWHxLTRzBsSyUDCc6BoBFOcSMJQBpaLIHOMWxonOwidBAt1TMNoenHGDOO8amIRae1c6+HjngwYtkdU+7Y7dvVDZBlnQssz6fXjZF/vHRwlHX7jylQRa3rJLz6na0PIb0CX0H2AQMm+ogidCPDq0goBDKA+PfLcMhY+Duu++2AsP22msvi/4QeDvjOSME9w4s4M0CAQD2ZqfgqjhZ4KGHHpKf/vSnVn55Mg3hjQoFwEjp3HbbbdZxGQCZoAsyE4hFKZEAACAASURBVL388stBnvk49Yt5W7ymeAZJg4r4vS7emq0OIIzSRGi2OmgTBNZxPEqgUjxoB8qn5d6AELegIztTAabMoDo8U9ASaKOpRRwJVUHVMQBK0B1IDBAPG0QyBOg7ZLpQ7wD0EogUS570o5++Kue/eLdr1baunSYfNH8uk4vq5bHPRmsEZ6eny3610+XCbx40YswxHs2MdaofTd9rn/HvWG1iQhthcpHDqZ6w4SLt75133mmpIkB/iFe9IhkHwXcCC2zIFggA8Ibc+ynQdhYTXRDwLCLbZQJggi04RuO47aSTTrJqzOJNBDKgDACtJcgzH78OBSyFO6Y0s9UpZYJId8AjIFgBMZudaGkHjBv4hByvxpJPC2g1s58RDMa4QqZMaROAq3CUDwAa45jr4q2O4bfXTWAeLlmJ33vr9UPDQ3LcY1fJu6sWON6iPLdQ0tL6ZElbjiztsPcW37L3EfLtje2zjemNTf1o+o5NjPaZbmToM6egND9tBHxDefDCReZ0i1MTxsLf/vY3QWklKIEFAgukjgUCAJw6fbHB18QOAD/22GOy9957W0fcLNZa8Kpcc801VrS6eq4CAJxaQwhAjE4r+sPqIUYqCQ4kNAkNrAMYAFC8eke5J+AS7y182nhqwFJXExCjLEG7QqXXFFzFC5jHqmdNbV84q/FUH/iweZEc9cgfHHV/adOsCRvJ3963T6OcnZEhH51woRRk5fhqPpQY2qn9xm+VyzNpE36TTWiQIH3Ppsbp+4wPgtx+/OMfWymDoW7Fc4z6Mk5wcWCBwALrnWbDvK1BCSyQAhawA8BQHf7v//7PyvYEt1PL2WefbQVUcIRLIAYlyDOfAp3oUgXNVgcIJnAGL7GZrU49xE7Z6vg+gVoEbAEo0E2N5bG9F+sBpNh0mdJrgGSAJD8ALwBYqiVnwHZ4ywliJKUtm8lIaB1ebGRe87uX7pGHPnnF8WublDfKOytXy+L20drAu0zZWO77vx/7feSo6025PAXFeMEJpDOpLgRP2m3CzE0Np07wzJ02axqvQBrgc889V371q18lxM5RGym4QWCBDdACgQd4A+z0VG2yHQAmyESzBKGZqYUAuKefftryLuJloQR55lO1Z+3rZWarUy1ieN2ASDipAGEFxXh7kYzaf//95Xvf+55Fx/DqMY6nVRRckTgCHVjqRLsAUwquoE7EiwPtpW0c2/MeRaLt6+X+bte09HRYAXHtfd22l+VnTpDe3iH5xCZF8nk77ycnfGXXaKsw6vv0D7xhk/ut6iChgXUAZYIEOXUg+LaiosKxPmzOmIPYaMD5/frXv54SYzTmBgxuGFhgnFggAMDjpCPHQzPsADDtQmJHFxW8vQTB/fKXv7SO0vEgwim1K0Ge+bE3KjRbnQJizVaHtw6u71FHHWWB4FTJnEZ9SQKwatUqy6uKdzVUeg3wDgA2AbGTtzHWPUa9AL9wriPV9o22Tvd88Lz84dUHR90mLzNH3ljWK4PDIltXVcuHLWsTYWh58fBfykbl3hNxRFPP0MA6AC+xCYBlqA4EMSLbZec15xpSALNBw8aAX8ZBUAILBBZIbQsEADi1+2eDqp0TAMa7Riai1157zbIHQXE/+tGP5Le//a0FjPm/UwnNMx9L2TWeCbjgmBOOK4AclQoE7oOAl+iHLp5gAh+RkEIvFCACIEZWDACs6ZvxEk+fPj3h0fVIwcFFhv+Ld9COT6u0D1N6jQAqvmOmb4ZXGkuPdqy1faPpzYGhQTns4ctkfuuKEbeZkFUm/1qylvpQX1Iuq3taZGBoyPr/5KIyef1H58TUJl7bQJ9Bs2HeIfiRfsF7TuH/bGSQBWRjTh9CxULKkbgENG3DBUh6rYef65BWY94hVoI6IksID5l6X3HFFUIsBZ8x7rbeemurzqE6xGzmfvOb38hf//pX6xQGxwKqL3DFgxJYYDxaIADA47FXx2ibnACwNgewi3cNLzCTOsoQpOJ1K6F55mMpu4aHmcxo3/72t61obxY+VA9YYHbfffcx2gupU+2dd97Z0h0mu5Rmk1JgpxxieMSarU4BMdJr8IPjJTdFHeDSApAA4n7BN95G8/gdbyNjxzx+B8REWn/V9gXQ4JEEpCW7/HfpR/Lzf/9pRDUqM6rlmWXrvb67TWmQV5fNs6454stfk8u+cUDCqw2HF8oDwBHbacISNrcaWAc4PuSQQ6ykKqSKhg+OlCM6v0jdJbrANWYuJC4C0MrG7PHHH5cbbrjB2qDtsccelsOA94kxAajlvYE+ZoJg2oDc5O9//3sr3gLZNoA+9iAbZFACC4w3CwQAeLz16BhuTzgArE3DgwYgghNMSk6nYpdnPpayawAuFoq77rprDFs9dasOtQD7uqXk1Wx1JmUCgAJXE2443mE2KbHKVqfgEloDAMmNE+rVsqEyXoAvwK8JiFHJCBe0hi3YEBIYSpAgm79YSH95bUe460576ib5z+L31l3W0lMsi77wrPLHoqwcKc3NlNU9nXLbd46WvRo3C3fLmH4OkAXsQVdhvDiNO+zMBgwaFn0COCSVL3rPjNeDDjrI8rAmovCOUNc5c+ZYOt2hhfGKBxvKjRaAO4F8e+65p0Uno6jcJF5kdNcpyMkR9Ae14+KLL05Ec4JnBBZIqAUCAJxQcwcPC7UAHl0mb8q1115redbw7lJIt4kHBrkzAADeFbzAfA5AePHFF9flnY8kz3w0smssPAAMMwgv6N3kWwBwwmKugDg0W51Kr+H5Ahx6pR3ofTlGBvTGOnGEaTlUJEKl1/hctYgVGJvg1uQic0ICB9Vr2xLRa3hPn37jJbl88ZMyODwkEwsr5Il5zaMevfPkevnfqoXy4fEXSmG2P/mzSNtB3+LNhx7FHAPf18n7zgYabysb7xNOOMGiPkDNUdoE443vwlVPRDnzzDOtpEAkEfJTCCLm1OGJJ56wvgYQxkvMiYuZDZB2oM3OxiAogQXGmwUCADzeenSMtQdAi5fBrjzzzDOy6667Wkdy1113nSxevNgCH8iinX/++SOOdiPJMx+N7JouGHDr8ARx1EiQ1qmnnrouYccY64pxWV2ACYFgJiCGuoKXb9asWevSN2+77baOSg14aNnwoAObDHDJ8bspvQaY5G+qRQwQ5t1w4yInq3NNcAkof6rzE7njvWdkRnmj3P/hZ6OqlWbRH7aRa/c6JCFVpm/h8bPhYFMDpcGpQC04/vjjLR76n//8Z+sEKtmbDJwEePu33HJLufrqq612cPIBzYG/2RW8wgD9ww8/XK688krrkjPOOMOiP8CvN8tll11mUSvwGkdKyUlIRwYPCSwQgQUCAByB0YKvjA8LRCO79rvf/U7OOussy1tC8IsGnbCgwFmFIxiU1LOAZqsDxACK8di98cYbjtnq4IyzEeNYm6Nm1BSSXUxdW7zdnKJQOObWwDqVXktmXe3AZWd/j/zgwYukb6BYXlqyxLZ6F+2yr/x81m5xrzobCTZDbBwI9DJpAqEPZ4wceuih1knUfffdt057PO6VDPMANmTQXthcQLtAF3v27NnWSRlUIE1bbt7mF7/4haBTzKYd/jrl2GOPtd4F6DNmuemmm6zPsBUnELEqvIfJ3jzEqi3BfcauBQIAPHb7Lqh5lBaIRnbtwgsvlF//+tdy+umnW8EiWvbdd1/rKPX999+PsnbB1xNhAadsddAQ4ElCyUF2jY0Nm51UWbTxyAFgCKAj4A/wBodVg+sAn14TPcTDzsqnhR7AxsEEl3M+fV0Oeehu6ftC8SH0+a8eeYZsWjUhHtWy7qkJVaANhEsKwmbjxhtvtE552NTiZU2FTZAaB7oG8w1jAcUbCmnLOVWDpoFn1yzEK9AO6GbK9VUAzKaQkw6z0PbjjjvOOoHgxCGawjuFmg/3Q9UlAMHRWDP4biwsEADgWFgxuMeYtEA0smt4UFhAoEDstdde69oPUMIjrFJXsZJdQ5rIiVdIMAtR30GJ3gIsygRPcrxNYottttlGyFxHfxL0hsIEPGICIOGnJwMQq7YvsmvUCTqHWWgDXmFTek0TPZjSa3w/1vXXoMR58+Y58mmHhodklzuulLnLF43qsImFJfLxCb+Neb30QXCl2ZzCdYXy4KZuAOhDhu/RRx+1gC8c2VjbK9oRi+oD/OVQ6gL0hy222EJuvfXWdY9Aq3ifffax1CrMTTsXJIICgVoLJynV1dVW4DAbtAAERzsCgu9HY4EAAEdjveC7Y9oC4VQn3GTXiAJHBzQUABMgQ2AKAIQgk1jJrgF68EaahSNO5fGxqAUleguQXRCQgLQdMlIARrypBAIpZYLAR46E8bxqUB3AON7Z6eD90ufwffFO4+XzCsgA8Kb0GjxQxift0wQdePii4XmijIEnEuCINxKg41ReXjxPdr/76lEfH/7lr8r1ex0YfUfa3IF6QXlAuQHKg5snl3ZAeaDce++9jnzauFTUx02Zw9j8hgJgwC+BnqrywCaOwLf99ttPbr/99lHjhuuOPvpoi+cezyA4ggZ5PjEcnJYFJbBAMi0QAOBkWj94dlItEA4Aa+XsZNcARXiP8AoxqWsBPAFQ8CJSYim7Fmos9D8R3ud5gU5nbIYSIOmRRx6xPFX/3965x1hVtXd4f9YY79FPjAoxraCmElM0GP+oMSIBxQs0iBq0CoF4QQ2KmiAW1GiFglH/QERElIuSkQhIuGhAEExFsRgVNcRIhCB/GBW11Rr7YRubZzVrujmcM3Nmzp69z+VZyQQzc85eaz1rH89vv+tdv7eSuCSKuGPHjlD8hLxJto6JKPbr16+9OAeCmAIt1QrUzkZPqgMn8dlGJqWgXG5nZ9dI/537lzSFmDaB5RXCkBzXGCUm57Mz67V4TeaPaCTVodqKc2PXLEmWf/HxQcNeMnxMMupvz+/KVDp9LVFGPiOkPPA5oVhNpXnxWnL4yZPls0wKAEzqtS1fvjykFXBvwJ1GXjgPSLNmzUomTZoU0hqwAmQ3g8PC5azxog0ajju4W9C457B1664NGg9smzZtCoECHrb4LHCfYWHJ54MHTFJkbBIoioACuCjy9lsIgaxs1xg86Q5sHVI9ia1IvlzYKsWWiLzR0laL7Vo5WByAQWRt2LChEJZ2+n8EeMhB/KWdJrKqVpe2XyPlgqhzT3j7IurT1mux8llaEPPfpVXOmDvpDuyWIJYQXtVGkb/+jx+T8178l+Qv//PfgeNhf/pTsveuf07+fFR2Bw15YEAA4tELu969e1e8bfl/Ay4ur776aoL7AYUhqp1LUZ8FRCZuJghLHoY5BEfuL/PFso9IP8KX9UXYp6PeiM9YYIbxM1+is7ju8P8VHuw5/Me93Z0HbB7QifTycMQDGw9djG/ZsmUhD/mDDz4IRYOYQ7UPWkVxtt/mJKAAbs51dVYVCGRluxYvj90QP0SYiABSFpm0hHKtFtu10uuxJY91F1uXXNdWPwRitToixIhiIsTkaSIiyB2OxTk6ikQym5ivWoT9Wqx8FqPEpHwgxqP1GoKGHE5cAxCO3S0K8ui/vpE8se2tsHgXnvbXyeabJmW2kIhCUh6IPJLyUK5UdeyM1BKqOSL8EcC4ujRKQ+wSsSZXmXsGazRSsXhAjqla5eaCyOX/h7EhUMuVQiadojuNHHoELqkkFNKI+b6sC+cW2GFIn10wH7g7lH1PLQQUwLXQ870S6AKBWmzXSru57777gpURXzJZ2hN1YTq+tEoCfLEjNGLKBGkTCC5yLaMgZksYkRYjrBxYIiKLtzQpDx2JtyqHUdPLEPVpL2KEMb8jckeuL160pE50dUv7Pw/8Jfm7BdOTb3/9JZn695cn/3TR/x8orWXAbOkjzhkbkd/SyHW8NmvDjg0HWikVzKGxLKr71TL2ZnkvOeHYReIvTKoGOckxJYyDwjhRcHaBvGS8i20SyJuAAjhv4vbXsgRqsV1LQ+NLBCN7vjT4YrE1FoFYsjgdIaYYAw8ypNKwTczhSqJ6ROQqibciZh2j25zoR5yT8xsP1xF9JCpc6kXcWR70y5//WzLhzbZk8z/ek1zY+29qmhaRa4QvKSidFS1BoGFlSFEL0gY4vFrvKQ81wSngzTzscR9TpvmRRx5pf0Div0kzwZ0EP3W8i02DKGCBWrxLBXCL3wBOPz8CtdiupUfJwZIhQ4YkK1euTEaOHHnIBLKyXuPCRC5J60CsEQE8++yzQ56khT6yu29itbpVq1a152+ST0sUk/xOUiaIEHdUrS670VS+EqkOHLZCOBKVRuimI6nkm6at13gdEeG09Ro5qKWCGFu0f3jt+WTVtbcnf3XYYd2eCv2T8oAIJpre0c4IAp5UpX379iVtbW0hbaAzod7tgbX4GzlE9/777ycrVqwIBxBhfvfdd4f0CO4jIsA2CRRBQAFcBHX7bEkCnblOdGS7lgaGXRFfJqQ/lNtyzsp6jegMQgJhQISMrWHEwksvvRT6pyS1LRsCHKDk/iDfki1jhGKlanWkTXCqn2hxOUGZzYgOvgr3Gv655P5icUaUuqOGqOf+iTnE/Bu9sUut17i//v2/fktOOPKobg+d4g8cduMexd+30kFBxrV+/fpQ3QzW+NF254BXtwfaQm+MOb2UkMayD8FLbjUOOawVh3cp5EHzIFwL3Rh1NFUFcB0thkNpbgKdCeA4+3K2a/FvRNX4wkZ8vvjii2WBZWW9hgBDaOGNi5VRbAggDj1xmtuWDQEeLPbs2RO24Uu3gtPV6qIXMb6uiAYKHkQvYv7FqSHLSCZ9YB9GNJqyubVYu3HvpqvVYbNFekep9VpX0hAYH24HlANmd4LUoErzxxECp4Snn346cGbbvZ7SS7K5k+rrKvH/RViysZOEtRxOHOT+kpttk0CRBBTARdK376YnkKXtGrA4sIP43bhxYzC276zVYr22efPmZPDgwcEKiQMssRGBJP+TAgG2/AnE6Co2UqSm8KDCFjP3GlvKMWWi1mp1MaUA4chOQNZ+uOQMpwUx7gCI1yiIiTbzUyk3lPmS8sB1OhsfOcF8FohiE2GnFG+WDwr53wWN1eP27dtD6hQPUewg0Vi3nrD0aywyjrZIAgrgIunbd9MTyNp2DdP7rVu3Btu1aiJltViv8QVFhJGo2ty5c8P2MlFf/EKxL0Ic2+qDANFVHlSiF3G6Wh1CmBzirlSrI6LKYbLOUgqynD1CG7u1tPUa1yeXN6ZNIIgRTemUDHYjOkp54CAWnwO24bE4I4pty5cAkXqca3homz17dshtt0mgaAIK4KJXwP4l0IMEarVew2OUkqV8cdHI/Vy0aFGolGarXwKxWl0UxKXV6qIXcWlKA4fYKLmNCMVFgUh/UZFSRFO0XotFOvgdYpf5kQpE2kMl6zVeS7oDxRjuuuuu4DrQWe5y/a5o44+MByqcazgAR5GMou6rxifpDLIioADOiqTXkUAdEqjFeo1cZAzr+RerIramV69encybNy/BsWDYsGw8W+sQW9MNKV2tjogoPxwcI7ofc4iJrrLOOCIgFuvNX5qUBw5QcbiOghzkEBM15iAgY+f3jJltdqqP3XbbbSE1hHLG1157bWGCi1x9op7kKjM+DoLxOaKxo0Iq0bZt20IeM9zxyE23SrtIHILkfY3UJk6cGFJaqKJpk0DRBBTARa+A/UugBwnUYr02Z86ckLdHugVleGPDeo0oIfmXsWVpvcaBK7ZLSbNAuHEAj2p7bGHbsiEAV9aMSmFEifEdJsqKTRVRVQ4/kjLBQaV68Gf9/vvvQ0leHsJIeSCSGw8HRus1ypDj6kDxCw63kSK0YMGC4EFbVLSRSCdV2aZOnRpcO3BE4L5+/vnnw0KS0sQ68Dd+15EAnjFjxkGHUXkI4EBqI7X0Ad1GGrdjbU4CCuDmXFdnJYFAoDPniY6s16iOxZf17t27D6LJ6fknnngi4ZBUbFlZr7Ftff7554drT58+PTnyyCPDaX1EDh60RVdEa7bbihQDIqVEJClOcMopp4TocDXV6vJgEYU6vr19+/YND0EduTxwX7I7wfvYueD+Zk5UeZs0aVKovJdXw+qLQ4lvvPFGEOHlWloQMq+OBPBrr70WItnN0BTCzbCKjT8HBXDjr6EzkEBFAp0J4PjGctZrWBdRiQzjeqJqsQ0fPjyIYqqXxZaV9RqHlMgvJrqMeKAREe7Xr18oq0pVKVt2BIgqEjVlKz4dTYzV6ogOR6eJWK2OLfzoNIE7CDm5PRFhJaWBh57obkHp6EoNIc8DGx6/lNgdO3ZsGBP3LmKeeVC8hch2Xg2rNVxbsJGrprWSAK6Gh6+RQE8TUAD3NGGvL4GcCWRlvUbqA9vNiM8pU6aEPEu+0J977rnwM2HChLIzq8V6DdHw8ssvh3zIdLvgggtC/iSexLbsCHCYjB9KGnfUYrW6KCb5d8eOHSFC3xPV6sjhjRF/HoQqHXRjzLzupptuCmkP5NPGB6fsKHXvSuRS9+rVKzipkJ7BLgaHwEjn4XelrTMBzLVIocCZg+ppPKB29FDQvVH7Lgm0DgEFcOustTNtEQJZWq9x6GjatGnJhx9+GA4dUbkJG7Tx48dXjPrVYr2GQT7pFMwh3Yg4EnXm4JateAIIYizLoiDGZQIbNkQcDysxQkxuK2kr1UaIuS755aw/5aB5+Kr0Xl7LwxL54ghCcmjr6eAeLho8yPXp0ych0k7eMuk8zG3Xrl3hgTLdKglg7nmcLDiQyntwZCE9iJQQCqLopVv858ERNCYBBXBjrpujlkDdEqjFeo3ta0QwOZ9UjKIhvLHjIk0Dv1tb/RFAjJK3jf9wTJlAnOHSQLQzehHzLyKunKhlbYnmst7sPBDxrNToC+FLxBerszvuuKMqX+w8yfGwyAE3Du/F9BLELHnMlBafPHlyVQK43JjXrVsXqqqRunL99dfnOS37kkDTEFAAN81SOhEJ1AeBWqzX8J/FxmrgwIEhzYItdmyhOABEpAsRbKt/AulqdbF8c0fV6hB05H/jEUtVN9a9UsNO7Oabbw6CGwFYr0UViH7v3bs3oQpduvFAMGDAgGTx4sXdFsDwJdpN3jOpEDYJSKDrBBTAXWfmOyQggQ4I1GK9xmXfeuutZNy4ceHwG40T/Ihi8n/37Nkj+wYlUKlaHcU4OKx21VVXBXcRvInLRYgRfStWrAgpOIMGDQoFWeo5B5bPAS4qpQIY8cvhwYULF9YkgLFBo8CHArhBPxAOu3ACCuDCl8ABSKC5CHTmPNGR9VokgR0ap+eJBLJljDgi4tXW1tYOi6jw0qVLQ+4ph4PIF2Ur/Pbbbz9oOxwbKnxYsaUilYKtcyJnpe3JJ59M8D5GsHCQCksqhJatZwiQDjBq1KhQ3ILUCA7V7d+/P+S2pss3kwt84MCB5MEHH0woKkH+K7sC1ZQC75mRV3fV5cuXB59f0jpI6aDxUMf8EK3YsqVbpRzgcr2tWbMmVGhsJmu06qj6KglkR0ABnB1LryQBCVThPRwhlbNeKweQEqpEzSjWMHjw4PaXYMdF9JDCHHi9bt68OVilISwQrzS23YkgjxkzJjgFbN26NfjdcmDqlltuab8W4pcqaBxWIjpH9TD8ZMljrRdXgWa6uXgIotAGB8TI42UdsdLDao0c4nS1OnLBsUTjEBlpEqxntYfqimTGQxzpGb/88kvy+OOPh/GT+0t5cdI4qGC3c+fO8ENDLHOfYjPI36644orw+yj2Sakgf5p7kvucQ3bc37hf2CQgga4TUAB3nZnvkIAESghkZb3GZbFCQ9xS9YuoIOLhmmuuCaI03agOlq5Qx9+I7pI7jOUU1lmICKLDnJyPjcIPa9euDRXuiCKyNY+A5vdswdMQLwhf8lERXbZsCcA3PoQgDMu1WASDNAIi86TAEMFvpIbYxbuaHGfs5rBGozIc4pVGpTicIUobDwTRCYWo99y5c8OBOj5nPDTw0Mf76sn1opHWxbFKAAIKYO8DCUigZgJZWq9RsGDTpk0JB+LY/kaYEtWtpiQvtlhE0bCfIj8UgTBz5syDCmgQYSS1AWs3DtsROSay/NFHH4UqdLEhMJ566qlg99UIEceaF9ELSEACEmghAgrgFlpspyqBZieAWOagVNxmxn6K1Ilhw4a1T53IMZXtEMukRRBd4zAR0bWjjjqq/XXkV2IxxQGtRos8Nvs6Oz8JSEACtRJQANdK0PdLQAJ1QYCILpW2yPGleAf5vuSZkidJSkVseNNiqUZFLmy3OFRFoQHyTNNt48aNydChQ0MaBqkQNglIQAISaB4CCuDmWUtnIoGWJYBzA4eEiNRu2bIlCNwogLdt2xb+ViqAZ8+enUycODEIYPKMSz2GsWO77LLLkk8//dSDcC17ZzlxCUigWQkogJt1ZZ2XBFqEADm65PQSwaUk70knnRRmzun6rqRAIIDTBRgqpUBkab9G+gU2bRzSwwJMW6sWuWmdpgQkUDgBBXDhS+AAJCCB7hJA9F5++eXJrl27QqoDp+djw92hJw7BZWm/FlMzcAVYsmSJAri7N4Lvk4AEJNBFAgrgLgLz5RKQQH0QIJcXezRK7fJTLk8XGzQs0RDHsU2YMCFZvXr1ITZo/B7HCBo2XVwPK7RSG7Ss7NfoB6svrNiii4YR4Pq4txyFBCTQ/AQUwM2/xs5QAk1JgIpv8+fPD969F1988UFz7N+/f4j+xkIYVKfDXo284IcffrhiIQwKDFAIY8GCBcnKlSurLoTRHfu19IAVwE15izopCUigjgkogOt4cRyaBIoi8Mcff7R731K+Fa/cU089NbnooosOsgoranz0i0fw3r17yw6B8cYyxuTYUuUtXQoZ27N0Y76xFPK3334bIr8I60svvbSqKXbHfk0BXBVaXyQBCUigRwgogHsEqxeVQHMQQAQ+88wzoarar7/+miAOSQ0YMmRIEMOkGKQPjjXHrLs2i+7arymAu8Y5y1dTXQ0XEEoSs1NALjZpMbRly5aF8sy4h1BQhbLalCMubZQ45vfLhijRFgAACstJREFUly8P1QQppsJnJZ2HnuWYvZYEJJAtAQVwtjy9mgSahgDpAJRqnTx5cjJixIjktNNOC965VEejKht5sgsXLkzGjh3bNHPu6kRqsV9TAHeVdjav556mHPHUqVODPR6lsim3TGlm2nXXXRfKDvM3fldJAF999dWheiCfB0Q0qTU///xzsM1LF1TJZtReRQISyJqAAjhrol5PAk1AYPfu3SEqRiW0OXPmtM/o999/DzZjN9xwQzJ8+PAQ9Tr66KObYMZdn0Kt9msK4K4zr/UdpMGQ3kJaDB7P5Vo8mMjfKIFdTgBjW8fnY926dcmVV14ZLvP1118n/fr1C1FgDlTaJCCB+iagAK7v9XF0EiiEAFHftra2ZMOGDck555yTIHwpLvHVV1+FyBjRs3vvvbeQsdVDp1nYr3VVAGflP/zNN9+ECChry3oed9xxIZ2FiP+ZZ55ZD3h7bAwPPPBA8vrrrydffvllVX1UEsBUGySFgugxr4mNnPFjjz02WbNmTVXX90USkEBxBBTAxbG3ZwnUJQG2cfkiP/fcc4MbAsI3CuBnn302VE2jgMPIkSPbbbzqciI9NKis7Ne6KoCz8h9eu3Ztcs899yTjx48PpaN/+umnZMaMGcmePXvC9j3V9Jq1XXLJJUmvXr2S8847L0RqsciDAak9/K60VRLA7IwQ8SVPON04XLl+/fqQQmGTgATqm4ACuL7Xx9FJIHcCbAHj+DB69OgQ5YqNL3yKThABJoJ44oknJmm3iNwHWlCHWdqvcYAOCzS8he+8887k/vvvD1vrJ598coJYS7es/IcRfUQpDz/88PbLc22EL24ZRDebtVFwhINtffr0CaL/iCOOSB599NGwBhRTOeGEEw6aeiUBPHTo0JAHT+5wuk2bNi08HBIZtklAAvVNQAFc3+vj6CSQOwEOt5HX+Nlnn4Xt4jPOOCPZt29fMnHixFA84p133gm/a9WWpf0a/sSLFy8+BCXid8uWLZ0irtV/ON0B+au4e8TDYJ123oAvOOuss0J09vPPPw9lsmmkhHA/P/bYY+HAZ7p1JIB5gHjzzTcPej2pQfPmzUt++OGHBqTjkCXQWgQUwK213s5WAlUR+OSTT8JBN8QBUTMihETHsIhCRNjqg0Ct/sNxFjzgIALxQp40aVJ9TK4HRsHuBd7RuHekG+kPAwYMOORhxBSIHlgELymBOiGgAK6ThXAYEqg3AqRCUGL4448/Dt6/VFtjyzh9Sr7extxK48nCfzjyIp/73XffDYfDSG1p1kbEnbSFUgGM+KUCILZ+1USAPQTXrHeI82olAgrgVlpt5yqBKgm0Ym5vlWjq4mVZ+Q8zGdwfyF1dtWpVsLZr5kbRCnx+Se/hkCeNSod9+/ZNZs2adUj0uzMbNFIghg0bFq5DFJ3raIPWzHeQc2smAgrgZlpN5yKBjAkQ7aUddthhGV/Zy3WXQJb+w+Qfjxs3Lng9cwivXMvKfo1rUzTlvffeCwfR2E3AkxfxXcmTt7uMKr2P/PYLL7wwoYobbiaMgdzf7777LlSFO+aYY5KdO3eGHxpiecyYMeHBgL9R+TA2CmGwO5IuhMHaWAgj61XzehLoGQIK4J7h6lUlIAEJZE4gS/9hSv+OGjUqHPyaPn16xbFmZb9GBzfeeGMyaNCgUDDit99+SyhJjGfu5s2bQ4pNHg2xi4c1RSyw9+PAIa4m5LrTqBSHM0Rpo8QxbhGxYRcYSyEfOHDAUsh5LJ59SCBDAgrgDGF6KQlIQAI9RSBL/2GcPNi6R5AiQjtqWdmvleuDiCyH7xjL/Pnzewqd15WABCRwCAEFsDeFBCQggQYgkJX/8BdffBG8hnv37p288MILwc82tuOPPz7p379/pzSytF/jgOXAgQMPOYDW6SB8gQQkIIEaCCiAa4DnWyUgAQnkRSAr/+FFixaFvN9yrVr/4Vrs1zhgSeSXfFlcFx566KHk7bffDhXZbBKQgATyIqAAzou0/UhAAhJoAgK12q9RXvvWW28NJKhIt3Tp0mTEiBFNQMYpSEACjURAAdxIq+VYJSABCRRIIAv7tf3794fDZPxLYZW2trZQcTDtsFDgFO1aAhJoEQIK4BZZaKcpAQlIoBYCWdqvpceBxdiePXtCeWKbBCQggbwIKIDzIm0/EpCABBqUQJb2a6UIsB2bOXNmQh82CUhAAnkRUADnRdp+JCABCTQggSzt18pNf+jQoaEaWyw+0YCIHLIEJNCABBTADbhoDlkCEpBAXgSysl+j8MSSJUsSKqidfvrpyY8//pi88sorIf+XPODRo0fnNSX7kYAEJJAogL0JJCABCUigIoGs7NfwH54yZUqyffv2cACuV69eCR7A/A77NZsEJCCBPAkogPOkbV8SkIAEJCABCUhAAoUTUAAXvgQOQAISkIAEJCABCUggTwIK4Dxp25cEJCABCUhAAhKQQOEEFMCFL4EDkIAEJCABCUhAAhLIk4ACOE/a9iUBCUhAAhKQgAQkUDgBBXDhS+AAJCABCUhAAhKQgATyJKAAzpO2fUlAAhKQgAQkIAEJFE5AAVz4EjgACUhAAhKQgAQkIIE8CSiA86RtXxKQgAQkIAEJSEAChRNQABe+BA5AAhKQgAQkIAEJSCBPAgrgPGnblwQkIAEJSEACEpBA4QQUwIUvgQOQgAQkIAEJSEACEsiTgAI4T9r2JQEJSEACEpCABCRQOAEFcOFL4AAkIAEJSEACEpCABPIkoADOk7Z9SUACEpCABCQgAQkUTkABXPgSOAAJSEACEpCABCQggTwJKIDzpG1fEpCABCQgAQlIQAKFE1AAF74EDkACEpCABCQgAQlIIE8CCuA8aduXBCQgAQlIQAISkEDhBBTAhS+BA5CABCQgAQlIQAISyJOAAjhP2vYlAQlIQAISkIAEJFA4AQVw4UvgACQgAQlIQAISkIAE8iSgAM6Ttn1JQAISkIAEJCABCRROQAFc+BI4AAlIQAISkIAEJCCBPAkogPOkbV8SkIAEJCABCUhAAoUTUAAXvgQOQAISkIAEJCABCUggTwIK4Dxp25cEJCABCUhAAhKQQOEEFMCFL4EDkIAEJCABCUhAAhLIk4ACOE/a9iUBCUhAAhKQgAQkUDgBBXDhS+AAJCABCUhAAhKQgATyJKAAzpO2fUlAAhKQgAQkIAEJFE5AAVz4EjgACUhAAhKQgAQkIIE8CSiA86RtXxKQgAQkIAEJSEAChRNQABe+BA5AAhKQgAQkIAEJSCBPAgrgPGnblwQkIAEJSEACEpBA4QQUwIUvgQOQgAQkIAEJSEACEsiTgAI4T9r2JQEJSEACEpCABCRQOAEFcOFL4AAkIAEJSEACEpCABPIkoADOk7Z9SUACEpCABCQgAQkUTkABXPgSOAAJSEACEpCABCQggTwJKIDzpG1fEpCABCQgAQlIQAKFE1AAF74EDkACEpCABCQgAQlIIE8CCuA8aduXBCQgAQlIQAISkEDhBBTAhS+BA5CABCQgAQlIQAISyJOAAjhP2vYlAQlIQAISkIAEJFA4AQVw4UvgACQgAQlIQAISkIAE8iSgAM6Ttn1JQAISkIAEJCABCRROQAFc+BI4AAlIQAISkIAEJCCBPAkogPOkbV8SkIAEJCABCUhAAoUTUAAXvgQOQAISkIAEJCABCUggTwIK4Dxp25cEJCABCUhAAhKQQOEE/hfFvBNn+sV0iQAAAABJRU5ErkJggg==\" width=\"639.9999861283738\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'log likelihood')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm\n",
    "cmap1 = matplotlib.cm.get_cmap('summer')\n",
    "\n",
    "# mu_vals = np.linspace(300, 635, 50)\n",
    "# sig_vals = np.linspace(30, 210, 50)\n",
    "mu_vals = np.linspace(0.98*mu_MLE, 1.02*mu_MLE, 50)\n",
    "sig_vals = np.linspace(0.98*sig_MLE, 1.02*sig_MLE, 50)\n",
    "lnlik_vals = np.zeros((50, 50))\n",
    "for mu_ind in range(50):\n",
    "    for sig_ind in range(50):\n",
    "        lnlik_vals[mu_ind, sig_ind] = \\\n",
    "            log_lik_truncnorm(pts, mu_vals[mu_ind],\n",
    "                              sig_vals[sig_ind], 0, 450)\n",
    "\n",
    "mu_mesh, sig_mesh = np.meshgrid(mu_vals, sig_vals)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca(projection='3d')\n",
    "ax.plot_surface(sig_mesh, mu_mesh, lnlik_vals, rstride=8,\n",
    "                cstride=1, cmap=cmap1)\n",
    "ax.set_title('Log likelihood for values of mu and sigma')\n",
    "ax.set_xlabel(r'$\\sigma$')\n",
    "ax.set_ylabel(r'$\\mu$')\n",
    "ax.set_zlabel(r'log likelihood')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3. Constrained minimization using the minimize() function\n",
    "The `minimize()` function has many methods that can be used to find the parameter values that minimize some criterion function. These methods are called using the `method='MethodName'` optional input argument to the minimize function. Three of those methods allow for constrained minimization by providing upper and lower bounds for the parameters being chosen. These three methods are `'L-BFGS-B'`, `'TNC'`, and `'SLSQP'`.\n",
    "\n",
    "Suppose you were trying to estimate $\\mu$ and $\\sigma$ of a truncated normal distribution as is the case above. The value of $\\mu$ need not be constrained. However, the value of $\\sigma$ must be strictly positive. You could include these bounds in a constrained minimization by using the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      fun: 913.1743336854444\n",
       " hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>\n",
       "      jac: array([-5.99811756e-02,  1.13686838e-05])\n",
       "  message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n",
       "     nfev: 45\n",
       "      nit: 12\n",
       "   status: 0\n",
       "  success: True\n",
       "        x: array([420.        , 129.03980187])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_init = np.array([400, 80])\n",
    "results_cstr = opt.minimize(crit, params_init, args=(mle_args), method='L-BFGS-B',\n",
    "                            bounds=((380, 420), (1e-10, None)))\n",
    "results_cstr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that you must set the lower bound of $\\sigma$ equal to some small positive number close to zero. You cannot set it to zero itself because the bounds are inclusive. That is, the minimizer might try a value of $\\sigma=0$ is the lower bound includes zero."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. The variance-covariance matrix of ML estimates\n",
    "Davidson and MacKinnon (2004, Sec. 10.4) have a great discussion four different estimators for the variance-covariance matrix of the maximum likelihood estimates. That is, we want to know what is the variance or uncertainty of our estimates for $\\mu$ and $\\sigma$, and how are those two estimates correlated. The four most common estimators for the VCV matrix of a maximum likelihood estimate are:\n",
    "1. Empirical Hessian estimator (H)\n",
    "2. Information matrix estimator (I)\n",
    "3. Outer-product-of-the-gradient estimator (OPG)\n",
    "4. Sandwich estimator (S)\n",
    "\n",
    "All of these estimators of the VCV matrix intuitively measure how flat the likelihood function is at the estimated parameter values in the dimension of each estimated parameter. The Hessian is a matrix of second derivatives of the log likelihood function with respect to the parameters being chosen. The Hessian matrix therefore captures information about how the slope of the log likelihood function is changing in each direction.The empirical Hessian estimator is the most commonly used. One really nice property of Python's `minimize()` function is that one of the result objects is the inverse Hessian.\n",
    "\n",
    "$$ \\hat{VAR}_H(\\hat{\\theta}) =-H^{-1}(\\hat{\\theta}) $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VCV(MLE) =  [[16067.52977213  4683.90121207]\n",
      " [ 4683.90121207  1459.38093085]]\n",
      "Standard error for mu estimate =  126.75776020475993\n",
      "Standard error for sigma estimate =  38.201844600129554\n"
     ]
    }
   ],
   "source": [
    "results_uncstr\n",
    "vcv_mle = results_uncstr.hess_inv\n",
    "\n",
    "stderr_mu_mle = np.sqrt(vcv_mle[0,0])\n",
    "stderr_sig_mle = np.sqrt(vcv_mle[1,1])\n",
    "print('VCV(MLE) = ', vcv_mle)\n",
    "print('Standard error for mu estimate = ', stderr_mu_mle)\n",
    "print('Standard error for sigma estimate = ', stderr_sig_mle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mu percent 0.2037729303762825\n",
      "sig percent 0.19223600036977828\n"
     ]
    }
   ],
   "source": [
    "print('mu percent', stderr_mu_mle/mu_MLE)\n",
    "print('sig percent', stderr_sig_mle/sig_MLE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Hypothesis testing\n",
    "Can we reject the hypothesis that $\\mu=400$ and $\\sigma=70$ with 95% confidence? How do you answer that question? What does the figure tell us about this answer? In this section, we will discuss four ways to perform hypothesis testing.\n",
    "1. Two standard errors (back of the envelope, approximation)\n",
    "2. Likelihood ratio test\n",
    "3. Wald test\n",
    "4. Lagrange multiplier test\n",
    "\n",
    "Davidson and MacKinnon (2004, Sec. 10.5) have a more detailed discussion of methods 2, 3, and 4."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1. Back of the envelope, two standard errors (assuming normality)\n",
    "A really quick approach to hypothesis testing is to see if your hypothesized values are within two standard errors of the estimated values. This approach is not completely correct because estimates in the log likelihood function are not symmetrically distributed. But it is at least a first approximation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mu_2= 360 , lower bound 95% conf. int.= 368.5384490293645\n",
      "sig_2= 80 , lower bound 95% conf. int.= 122.31998652460958\n"
     ]
    }
   ],
   "source": [
    "lb_mu_95pctci = mu_MLE - 2 * stderr_mu_mle\n",
    "print('mu_2=', mu_2, ', lower bound 95% conf. int.=', lb_mu_95pctci)\n",
    "\n",
    "lb_sig_95pctci = sig_MLE - 2 * stderr_sig_mle\n",
    "print('sig_2=', sig_2, ', lower bound 95% conf. int.=', lb_sig_95pctci)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2. Likelihood ratio test\n",
    "The likelihood ratio test is the simplest and, therefore, the most common of the three more precise methods (2, 3, and 4). Let your maximum likelihood estimation have $p$ parameters (the vector $\\theta$ has $p$ elements), let $\\hat{\\theta}_{MLE}$ be the maximum likelihood estimate, and let $\\tilde{\\theta}$ be your hypothesized values of the parameters. The likelihood ratio test statistic is the following.\n",
    "\n",
    "$$ LR(\\tilde{\\theta}|\\hat{\\theta}_{MLE}) = 2\\Bigl(\\ln\\ell(\\hat{\\theta}_{MLE}) - \\ln\\ell(\\tilde{\\theta})\\Bigr) \\sim \\chi^2(p) $$\n",
    "\n",
    "Note that this is a joint test of the likelihood of $H_0: \\mu_0, \\sigma_0$. The value of the $\\chi^2(p)$ has the following interpretation. The area under the $\\chi^2(p)$ pdf from $LR$ and above is the significance level or $p$-value. It represents the probability that the null hypothesis $\\tilde{\\theta}$ is true given the MLE estimate $\\hat{\\theta}_{MLE}$. More precisely, it represents the probability of null hypotheses with LR test statistics greater than or equal to (worse) the LR statistic from the null hypothese $\\tilde{\\theta}$. When this $p$-value is small, it it highly unlikely that the null hypothesis is true. You can calculate the $\\chi^2(p)$ significance level by taking one minus the cdf of $\\chi^2(p)$ at the $LR$ value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hypothesis value log likelihood -913.1817396241\n",
      "MLE log likelihood -910.5500677663156\n",
      "likelihood ratio value 5.263343715568908\n",
      "chi squared of H0 with 2 degrees of freedom p-value =  0.07195805797802624\n"
     ]
    }
   ],
   "source": [
    "mu_new, sig_new = np.array([420, 130])\n",
    "log_lik_h0 = log_lik_truncnorm(pts, mu_new, sig_new, 0, 450)\n",
    "print('hypothesis value log likelihood', log_lik_h0)\n",
    "log_lik_mle = log_lik_truncnorm(pts, mu_MLE, sig_MLE, 0, 450)\n",
    "print('MLE log likelihood', log_lik_mle)\n",
    "LR_val = 2 * (log_lik_mle - log_lik_h0)\n",
    "print('likelihood ratio value', LR_val)\n",
    "pval_h0 = 1.0 - sts.chi2.cdf(LR_val, 2)\n",
    "print('chi squared of H0 with 2 degrees of freedom p-value = ', pval_h0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Linear regression with MLE\n",
    "Although linear regression is most often performed using the ordinary least squares (OLS) estimator, which is a particular type of generalized method of moments (GMM) estimator, this can also be done using MLE. A simple regression specification in which the dependent variable $y_i$ is a linear function of two independent variables $x_{1,i}$ and $x_{2,i}$ is the following:\n",
    "\n",
    "$$ y_i = \\beta_0 + \\beta_1 x_{1,i} + \\beta_2 x_{2,i} + \\varepsilon_i \\quad\\text{where}\\quad \\varepsilon_i\\sim N\\left(0,\\sigma^2\\right) $$\n",
    "\n",
    "If we solve this regression equation for the error term $\\varepsilon_i$, we can start to see how we might estimate the parameters of the model by maximum likelihood.\n",
    "\n",
    "$$ \\varepsilon_i = y_i - \\beta_0 - \\beta_1 x_{1,i} - \\beta_2 x_{2,i} \\sim N\\left(0,\\sigma^2\\right) $$\n",
    "\n",
    "The parameters of the regression model are $(\\beta_0, \\beta_1, \\beta_2, \\sigma)$. Given some data $(y_i, x_{1,i}, x_{2,i})$ and given some parameter values $(\\beta_0, \\beta_1, \\beta_2, \\sigma)$, we could plot a histogram of the distribution of those error terms. And we could compare that empirical histogram to the assumed histogram of the distribution of the errors $N(0,\\sigma^2)$. ML estimation of this regression equation is to choose the paramters $(\\beta_0, \\beta_1, \\beta_2, \\sigma)$ to make that empirical distribution of errors $\\varepsilon_i$ most closely match the assumed distribution of errors $N(0,\\sigma^2)$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Generalized beta family of distributions\n",
    "For exercises in this section, you will need to know the functional forms of four continuous univariate probability density functions (PDF's), each of which are part of the generalized beta family of distributions. The figure below is the generalized beta family of distributions [taken from McDonald and Xu (1995, Fig. 2)]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "# Download and save the data file GBtree.png\n",
    "url = ('https://raw.githubusercontent.com/rickecon/Notebooks/' +\n",
    "       'master/MLE/images/GBtree.png')\n",
    "image_file = requests.get(url, allow_redirects=True)\n",
    "open('images/GBtree.png', 'wb').write(image_file.content)\n",
    "Image(\"images/GBtree.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The lognormal distribution (LN) is the distribution of the exponential of a normally distributed variable with mean $\\mu$ and standard deviation $\\sigma$. If the variable $x_i$ is lognormally distributed $x_i\\sim LN(\\mu,\\sigma)$, then the log of $x_i$ is normally distributed $\\ln(x_i)\\sim N(\\mu,\\sigma)$. The PDF of the lognormal distribution is the following.\n",
    "\n",
    "$$ \\text{(LN):}\\quad f(x;\\mu,\\sigma) = \\frac{1}{x\\sigma\\sqrt{2\\pi}}e^{-\\frac{[\\ln(x)-\\mu]^2}{2\\sigma^2}},\\quad x\\in(0,\\infty), \\:\\mu\\in(-\\infty,\\infty),\\: \\sigma>0 $$\n",
    "\n",
    "Note that the lognormal distribution has a support that is strictly positive. This is one reason why it is commonly used to approximate income distributions. A household's total income is rarely negative. The lognormal distribution also has a lot of the nice properties of the normal distribution."
   ]
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   "cell_type": "markdown",
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    "Another two-parameter distribution with strictly positive support is the gamma (GA) distribution. The pdf of the gamma distribution is the following.\n",
    "\n",
    "$$ \\text{(GA):}\\quad f(x;\\alpha,\\beta) = \\frac{1}{\\beta^\\alpha \\Gamma(\\alpha)}x^{\\alpha-1}e^{-\\frac{x}{\\beta}},\\quad x\\in[0,\\infty), \\:\\alpha,\\beta>0 $$\n",
    "$$ \\text{where}\\quad \\Gamma(z)\\equiv\\int_0^\\infty t^{z-1}e^{-t}dt $$\n",
    "\n",
    "The gamma function $\\Gamma(\\cdot)$ within the gamma (GA) distribution is a common function that has a preprogrammed function in most programming languages.\n",
    "\n",
    "The lognormal (LN) and gamma (GA) distributions are both two-parameter distributions and are both special cases of the three-parameter generalized gamma (GG) distribution. The pdf of the generalized gamma distribution is the following.\n",
    "\n",
    "$$ \\text{(GG):}\\quad f(x;\\alpha,\\beta,m) = \\frac{m}{\\beta^\\alpha \\Gamma\\left(\\frac{\\alpha}{m}\\right)}x^{\\alpha-1}e^{-\\left(\\frac{x}{\\beta}\\right)^m},\\quad x\\in[0,\\infty), \\:\\alpha,\\beta,m>0 $$\n",
    "$$ \\text{where}\\quad \\Gamma(z)\\equiv\\int_0^\\infty t^{z-1}e^{-t}dt $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The relationship between the generalized gamma (GG) distribution and the gamma (GA) distribution is straightforward. The GA distribution equals the GG distribution at $m=1$.\n",
    "\n",
    "$$ GA(\\alpha,\\beta) = GG(\\alpha,\\beta,m=1) $$\n",
    "\n",
    "The relationship between the generalized gamma (GG) distribution and the lognormal (LN) distribution is less straightforward. The LN distribution equals the GG distribution as $\\alpha$ goes to zero, $\\beta = (\\alpha\\sigma)^{\\frac{2}{\\alpha}}$, and $m = \\frac{\\alpha\\mu+1}{\\alpha^2\\sigma^2}$. See McDonald, et al (2013) for derivation.\n",
    "\n",
    "$$ LN(\\mu,\\sigma) = \\lim_{\\alpha\\rightarrow 0}GG\\left(\\alpha,\\beta=(\\alpha\\sigma)^{\\frac{2}{\\alpha}},m=\\frac{\\alpha\\mu+1}{\\alpha^2\\sigma^2}\\right) $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last distribution we describe is the generalized beta 2 (GB2) distribution. Like the GG, GA, and LN distributions, it also has a strictly positive support. The PDF of the generalized beta 2 distribution is the following.\n",
    "\n",
    "$$ \\text{(GB2):}\\quad f(x;a,b,p,q) = \\frac{a x^{ap-1}}{b^{ap}B(p,q)\\left(1 + \\left(\\frac{x}{b}\\right)^a\\right)^{p+q}},\\quad x\\in[0,\\infty), \\:a,b,p,q>0 $$\n",
    "$$ \\quad\\text{where}\\quad B(v,w)\\equiv\\int_0^1 t^{v-1}(1-t)^{w-1}dt $$\n",
    "\n",
    "The beta function $B(\\cdot,\\cdot)$ within the GB2 distribution is a common function that has a preprogrammed function in most programming languages. The three-parameter generalized gamma (GG) distribution is a nested case of the four-parameter generalized beta 2 (GB2) distribution as $q$ goes to $\\infty$ and for $a=m$, $b=q^{1/m}\\beta$, and $p=\\frac{\\alpha}{m}$. See McDonald (1984, p. 662) for a derivation.\n",
    "\n",
    "$$ GG(\\alpha,\\beta,m) = \\lim_{q\\rightarrow\\infty}GB2\\left(a=m,b=q^{1/m}\\beta,p=\\frac{\\alpha}{m},q\\right) $$\n",
    "\n",
    "The statistical family tree figure above shows the all the relationships between the various PDF's in the generalized beta family of distributions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. References\n",
    "* Brock, William A. and Leonard J. Mirman, \"Optimal Economic Growth and Uncertainty: The Discounted Case,\" *Journal of Economic Theory*, 4:3, pp. 479-513 (June 1972).\n",
    "* Davidson, Russell and James G. MacKinnon, *Econometric Theory and Methods*, Oxford University Press (2004).\n",
    "* McDonald, James B., \"Some Generalized Functions for the Size Distribution of Income,\" *Econometrica* 52:3, pp. 647-665 (May 1984).\n",
    "* McDonald, James B. and Yexiao Xu, \"A Generalization of the Beta Distribution with Applications,\" *Journal of Econometrics*, 66:1-2, pp. 133-152 (March-April 1995).\n",
    "* McDonald, James B., Jeff Sorensen, and Patrick A. Turley, \"Skewness and Kurtosis Properties of Income Distribution Models,\" *Review of Income and Wealth*, 59:2, pp. 360-374 (June 2013)."
   ]
  }
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