Assignment+3.ipynb

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    "---\n",
    "\n",
    "_You are currently looking at **version 1.2** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-machine-learning/resources/bANLa) course resource._\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# Assignment 3 - Evaluation\n",
    "\n",
    "In this assignment you will train several models and evaluate how effectively they predict instances of fraud using data based on [this dataset from Kaggle](https://www.kaggle.com/dalpozz/creditcardfraud).\n",
    " \n",
    "Each row in `fraud_data.csv` corresponds to a credit card transaction. Features include confidential variables `V1` through `V28` as well as `Amount` which is the amount of the transaction. \n",
    " \n",
    "The target is stored in the `class` column, where a value of 1 corresponds to an instance of fraud and 0 corresponds to an instance of not fraud."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1\n",
    "Import the data from `fraud_data.csv`. What percentage of the observations in the dataset are instances of fraud?\n",
    "\n",
    "*This function should return a float between 0 and 1.* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.016410823768035772"
      ]
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     "execution_count": 60,
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   "source": [
    "def answer_one():\n",
    "    \n",
    "    # Your code here\n",
    "    data = pd.read_csv(\"fraud_data.csv\")\n",
    "    \n",
    "    return len(data[data.Class ==1])/len(data[\"Class\"])# Return your answer\n",
    "#answer_one()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use X_train, X_test, y_train, y_test for all of the following questions\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df = pd.read_csv('fraud_data.csv')\n",
    "\n",
    "X = df.iloc[:,:-1]\n",
    "y = df.iloc[:,-1]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "\n",
    "Using `X_train`, `X_test`, `y_train`, and `y_test` (as defined above), train a dummy classifier that classifies everything as the majority class of the training data. What is the accuracy of this classifier? What is the recall?\n",
    "\n",
    "*This function should a return a tuple with two floats, i.e. `(accuracy score, recall score)`.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.96773598820058992, 0.012500000000000001)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_two():\n",
    "    from sklearn.dummy import DummyClassifier\n",
    "    from sklearn.metrics import recall_score\n",
    "    from sklearn.metrics import accuracy_score\n",
    "    model = DummyClassifier().fit(X_train , y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test,y_pred)\n",
    "    recall = recall_score(y_test,y_pred)\n",
    "    # Your code here\n",
    "    \n",
    "    return (accuracy,recall)\n",
    "#answer_two()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3\n",
    "\n",
    "Using X_train, X_test, y_train, y_test (as defined above), train a SVC classifer using the default parameters. What is the accuracy, recall, and precision of this classifier?\n",
    "\n",
    "*This function should a return a tuple with three floats, i.e. `(accuracy score, recall score, precision score)`.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.99078171091445433, 0.375, 1.0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "def answer_three():\n",
    "    from sklearn.metrics import recall_score, precision_score, accuracy_score\n",
    "    from sklearn.svm import SVC\n",
    "    model = SVC().fit(X_train,y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test,y_pred)\n",
    "    recall = recall_score(y_test,y_pred)\n",
    "    precision = precision_score(y_test,y_pred)\n",
    "    # Your code here\n",
    "    \n",
    "    return (accuracy,recall,precision) # Return your answer\n",
    "#answer_three()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4\n",
    "\n",
    "Using the SVC classifier with parameters `{'C': 1e9, 'gamma': 1e-07}`, what is the confusion matrix when using a threshold of -220 on the decision function. Use X_test and y_test.\n",
    "\n",
    "*This function should return a confusion matrix, a 2x2 numpy array with 4 integers.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5320,   24],\n",
       "       [  14,   66]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_four():\n",
    "    from sklearn.metrics import confusion_matrix\n",
    "    from sklearn.svm import SVC\n",
    "    model = SVC(C=1e9 , gamma = 1e-07).fit(X_train,y_train)\n",
    "    y_pred = model.decision_function(X_test)\n",
    "    y_pred_new = np.zeros_like(y_pred)\n",
    "    y_pred_new[np.where(y_pred >= -220)] =1\n",
    "    confusion = confusion_matrix(y_test,y_pred_new)\n",
    "    # Your code here\n",
    "    \n",
    "    return confusion# Return your answer\n",
    "#answer_four()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5\n",
    "\n",
    "Train a logisitic regression classifier with default parameters using X_train and y_train.\n",
    "\n",
    "For the logisitic regression classifier, create a precision recall curve and a roc curve using y_test and the probability estimates for X_test (probability it is fraud).\n",
    "\n",
    "Looking at the precision recall curve, what is the recall when the precision is `0.75`?\n",
    "\n",
    "Looking at the roc curve, what is the true positive rate when the false positive rate is `0.16`?\n",
    "\n",
    "*This function should return a tuple with two floats, i.e. `(recall, true positive rate)`.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.825, 0.9375)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_five():\n",
    "    from sklearn.linear_model import LogisticRegression\n",
    "    from sklearn.metrics import precision_recall_curve\n",
    "    from sklearn.metrics import roc_curve\n",
    "    \n",
    "    # Your code here\n",
    "    model = LogisticRegression().fit(X_train,y_train)\n",
    "    y_pred = model.decision_function(X_test)\n",
    "    #print(y_pred)\n",
    "    precision,recall,threshold = precision_recall_curve(y_test,y_pred)\n",
    "    #plt.plot(precision,recall,title=\"Precision Recall Curve\")\n",
    "    #plt.show()\n",
    "    fpr,tpr,_ = roc_curve(y_test,y_pred)\n",
    "    recall_value = float(recall[np.where(precision==0.75)])\n",
    "    tpr_value = float(tpr[np.where((fpr >= 0.159) & (fpr <= 0.16))][0])\n",
    "    return recall_value,tpr_value # Return your answer\n",
    "#answer_five()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6\n",
    "\n",
    "Perform a grid search over the parameters listed below for a Logisitic Regression classifier, using recall for scoring and the default 3-fold cross validation.\n",
    "\n",
    "`'penalty': ['l1', 'l2']`\n",
    "\n",
    "`'C':[0.01, 0.1, 1, 10, 100]`\n",
    "\n",
    "From `.cv_results_`, create an array of the mean test scores of each parameter combination. i.e.\n",
    "\n",
    "|      \t| `l1` \t| `l2` \t|\n",
    "|:----:\t|----\t|----\t|\n",
    "| **`0.01`** \t|    ?\t|   ? \t|\n",
    "| **`0.1`**  \t|    ?\t|   ? \t|\n",
    "| **`1`**    \t|    ?\t|   ? \t|\n",
    "| **`10`**   \t|    ?\t|   ? \t|\n",
    "| **`100`**   \t|    ?\t|   ? \t|\n",
    "\n",
    "<br>\n",
    "\n",
    "*This function should return a 5 by 2 numpy array with 10 floats.* \n",
    "\n",
    "*Note: do not return a DataFrame, just the values denoted by '?' above in a numpy array. You might need to reshape your raw result to meet the format we are looking for.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "array([[ 0.66666667,  0.76086957],\n",
       "       [ 0.80072464,  0.80434783],\n",
       "       [ 0.8115942 ,  0.8115942 ],\n",
       "       [ 0.80797101,  0.8115942 ],\n",
       "       [ 0.80797101,  0.80797101]])"
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   ],
   "source": [
    "def answer_six():    \n",
    "    from sklearn.model_selection import GridSearchCV\n",
    "    from sklearn.linear_model import LogisticRegression\n",
    "    params = {'C':[0.01, 0.1, 1, 10, 100],'penalty': ['l1', 'l2']}\n",
    "    # Your code here\n",
    "    model = LogisticRegression()\n",
    "    model_lr = GridSearchCV(model, params, scoring =\"recall\", cv =3)\n",
    "    model_lr.fit(X_train,y_train)\n",
    "    output = model_lr.cv_results_[\"mean_test_score\"].reshape(-1,2)\n",
    "    return output# Return your answer\n",
    "#answer_six()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
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       "\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://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    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://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 overriden (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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\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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       "<img 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pG0QIDB4AQFWWgsIsNLcjCKQQ0CAlUPVnAIsPUCAAIFCBARYhRTSNggQGLzAsgZYdxrfd6GPEH5m9mfumQd/9QDoi4CLsS+VKGwdHiEsrKC2Q4DAYAUEWIMtvY0TIFCYgAArraACrDQ3owjkEBBg5VA1pxNYeoAAAQKFCAiwCimkbRAgMHiBZQ2w7lz/3EJPYP1182H3zIO/egD0RcDF2JdKFLYOJ7AKK6jtECAwWAEB1mBLb+MECBQmIMBKK6gAK83NKAI5BARYOVTN6QSWHiBAgEAhAgKsQgppGwQIDF5AgJXWAgKsNDejCOQQEGDlUDWnAEsPECBAoBABAVYhhbQNAgQGLyDASmsBAVaam1EEcggIsHKomlOApQcIECBQiIAAq5BC2gYBAoMXEGCltYAAK83NKAI5BARYOVTNKcDSAwQIEChEQIBVSCFtgwCBwQssa4D1v+oTF/oS9083f+KeefBXD4C+CLgY+1KJwtbhJe6FFdR2CBAYrIAAa7Clt3ECBAoTEGClFVSAleZmFIEcAgKsHKrmdAJLDxAgQKAQAQFWIYW0DQIEBi8gwEprAQFWmptRBHIICLByqJpTgKUHCBAgUIiAAKuQQtoGAQKDF1jWAOtnJr+w0EcIPzU91z3z4K8eAH0RcDH2pRKFrcMjhIUV1HYIEBisgABrsKW3cQIEChMQYKUVVICV5mYUgRwCAqwcquZ0AksPECBAoBABAVYhhbQNAgQGL7CsAdZdt99voSewzl/9Y/fMg796APRFwMXYl0oUtg4nsAorqO0QIDBYAQHWYEtv4wQIFCYgwEorqAArzc0oAjkEBFg5VM3pBJYeIECAQCECAqxCCmkbBAgMXkCAldYCAqw0N6MI5BAQYOVQNacASw8QIECgEAEBViGFtA0CBAYvsKwB1gnb77/QRwjPW/2Qe+bBXz0A+iLgYuxLJQpbh0cICyuo7RAgMFgBAdZgS2/jBAgUJiDASivoZgVYJ5xwwpZLLrnkKVVVnRwR44i4MiK+UFXVWdPp9NwDWV1d178cEY+OiJ+MiBtWVfXNtm0/Np/Pf3ttbe2re88xmUxObdv2cVVVHdv9rm3bf6yq6ven0+lb9/7ssccee6Mrr7zymW3bPjgitkbEf0bE30bEmU3TfOpA1uczBHILCLByCw90fgHWQAtv2wQIFCcgwCqupDZEgMBABQRYaYXfrABrMpmc07btAyPiAxFxbtu2XQDVhVG3a9v2tNls9vrrWmFd16+OiF+PiM9XVfWOtm3/rW3b21RV9diI2FlV1R2n0+l09xx1Xb8sIp4WEZ+MiHe1bbteVdXDIuIeEfGSpmmeufuzt73tbW/w/e9///y2be/Ytu3Zo9Hok/P5/P+qquoJEbGtbdsHzGazP00TNIrA5gkIsDbP0kx7CAiwtAMBAgTKEBBglVFHuyBAgIAAK60HNiPAquv6/hHxwaqq3jWdTh++eyVHH330YYcffviFVVUdtb6+vnVtbe1b+1rlZDK5Vdu2X4uIi+fz+fa1tbWdewRVj4mIN0bE25qmOaX795WVlduMRqMLIuKvm6a5a0TMd31+VNf1eRFx59FodOvV1dUvd/8+mUye3LbtK3adtnrW7rmPOeaYHznkkENWI+KyI4888pYXXHDBFWmKRhHYHAEB1uY4mmUvAQGWliBAgEAZAgKsMupoFwQIEBBgpfXAZgRYk8mkO3F14mg0ut3q6moXLF39Mx6PT6+q6qVt2z5pNpt1p6x+4Keu65+NiI9HxPuapnnIXuN3VFX1jxHx6aZpfqb73e7TWm3bPmg2m/3Rnp+fTCYPatv2/RHxiqZpnrrr8xdGxE/M5/Mf3TtEq+v6NRHxhKqq7negjzqmSRtFYP8CAqz9G/lEgoAAKwHNEAIECPRQQIDVw6JYEgECBBIEljXAuvuOX1zoS9w/cdEHDvqeua7r7mTVjZqmOWKP01BXVXFlZeVOo9HobyLivU3TnLSv0k4mk6Patv1GRHy5aZrb7PmZuq7vGxEfrqrqDdPp9Fd3BVKf6x5NPOSQQ4666KKLvrnn53fs2HGz9fX1SyPis03T3LF799UVV1zx3YiYNU0z2fv7x+PxQ7uTY3s/dpjQgoYQOGiBg74YD3oFJihSQIBVZFltigCBAQoIsAZYdFsmQKBIAQFWWlkPNsCaTCZHtm3bBURrTdN0L2+/xk9d1zePiH/u3m3VNM3tr22Vk8nkhW3bPjsiXnfIIYe88oorrvi30Wh0fER0J6RuUlXVnabT6T/tCrC+HRFHNk3zQ9272/eas6rrunsE8d+bprnp9u3bj5vP590JrI83TXPPvb9/MpncuW3bT7dte85sNute8O6HwMIEBFgLoy/7iwVYZdfX7ggQGI6AAGs4tbZTAgTKFljWAOtnf/yBCz2B9Zf/+EcHdc+86/TUJRHxpaZpbr13l23duvXGhx566HciotnXCag9P1/XdffC9ldGxGF7/PvfzufzR+z5Vwjrur68+yuCTdPceF9dXdf1v3VzdAHX9u3bf3o+n/91RPxx0zTdu7qu8TMej29dVdUXq6r66HQ6vXfZV4nd9V3goC7Gvm/O+hYnIMBanL1vJkCAwGYKCLA2U9NcBAgQWJyAACvN/mADrD0e2dtngHXcccfdZOfOnf+6vwCrruvfjIgXRsTHIuI98/n8m1VVbR+NRt1fGoy2bX+haZovdf951wmr/7qOAOvfI+KGXYC1xyOM+wywdr0Q/gsCrLT+MWpzBQRYm+tptl0CAiytQIAAgTIEBFhl1NEuCBAgIMBK64GDDbCOP/74Iy677LLvR8RXm6ZZ2XsV27Ztu8WWLVu+HhGfa5rmDvta5WQyuVvbtp/Y1ymplZWVY0ajUfcS94t2n/Da9c6tH76WRwi7v0TYPUL4naZpfmRlZeXY0Wj0DxHxl03T3GPv7x+Px3epqur8iHh/0zS/lKZoFIHNERBgbY6jWfYSEGBpCQIECJQhIMAqo452QYAAgWUNsO557IMW+gjhx75yzkHfM9d13b1I/Sa7XuK+vmc3rqys3HU0Gp0XEe9omubkfXXqeDx+SVVVz2jb9uGz2ax7ofo1fiaTyQVt2/7UfD7/ke6vCNZ1/ZmIuGNEHN00Tff44tU/O3bsuOX6+vrFu/9q4VFHHXX4jW50o+9FxNeu5R1dvxwRf1BV1Yum0+lzXEkEFilw0BfjIhfvu/srIMDqb22sjAABAhsREGBtRMtnCRAg0F8BAVZabTYjwBqPx++vqupBo9Hozqurq91fHLz6p67r50bEGRHx2KZp3rSvVdZ13b2o/QlVVZ06nU7fuvdn6rq+KCK2H3LIIVsvuuiir08mk5e2bXv6vgKvuq4fGRFvi4gzm6Z5VjdXXddX/dXCqqpuPp1Ou79QePXPZDJ5a9u2j2rb9l6z2ax7fNEPgYUJCLAWRl/2Fwuwyq6v3REgMBwBAdZwam2nBAiULbCsAda9jn3wQk9gffQr7z/oe+a6rrtH87rw50NN0zxgd6dt27bth7ds2fKV7oXq8/n8Vmtra9/d9W83m8/n3+5OU3WfHY/HD6uq6p0R8cmmaX52z78suLKy8lOj0ejzEfGNpmm27gqktkfElyPi74866qg7nXfeeVd2/37b2972Bt/73vc+GxHHRsSOpmm+tuvzj46IN7Vt+8rZbPaU3evrTmvN5/N/nM/nl8xms27OedlXid31XeCgL8a+b9D6FiMgwFqMu28lQIDAZgsIsDZb1HwECBBYjIAAK819MwKsXSHRmyPi1LZtz62q6gMRcUREPL47OVVV1UnT6fScXWHVKVVVnR0RZzVNc3r3byeccMKWSy+99KMRcbeqqj4zn8/fXlVV95cEJxHRBU43iogHNU3zod27HI/Hz66qqnvp+6eqqnpb27bdvf+jIuKnI+KpTdO8Yg+R0WQy+Uh3yqo7nVVV1cfbtr3prrn/76qq7jmdTru/VOiHwEIFBFgL5f//v7z7L6RLLrnkKVVVdc88jyOiS8i7v/Rw1nQ6PfdAlljX9U9WVfWctm3vEhHdn0vtjn52Y1/QNM2/7GuO8Xj8P6uqent3arQ7Fjqbzf7gQL7rQD4jwDoQJZ8hQIBA/wUEWP2vkRUSIEDgQAQEWAei9IOf2awAKyK6l6efFhGP2RU87ayq6rPr6+tnrq2tdS9Jv+pnPB7/QIDV/Xt3eur73//+E7rHArvQq/srghHRndD6dES8tGma7jHAa/yMx+OHVlX1xKqqjm/btjvJ9sW2bV8xm826AO0aPysrKz9UVdXTq6rq5t8WEd17sf6qe7xx9183TBM0isDmCQiwNs8yeabJZHJO27YPjIjuv0jObdv2hlVVdcc4b9e27Wmz2ez11zV5F0SNRqNPtG3b/ZfMK9u2/cZoNLpN27ZPjIiL5/P57bvjqLvn2PVffs9r2/aZEfFfXWIvwEoun4EECBAoWkCAVXR5bY4AgQEJCLDSir2JAVbaAowiQOBqAQHWgpuhruv7R8QHq6p613Q67dLuq36OPvroww4//PALq6o6an19fevu55/3tdy6rr8YET8xn89/cm1trfsTqlf91HXdpftvrKrqZdPp9Ol7/Hv30r6T2rZ9VlVVR0bEcwVYC24EX0+AAIGeCgiweloYyyJAgMAGBQRYGwTb9XEBVpqbUQRyCAiwcqhuYM7JZNKduDpxNBrdbnV19YI9h47H49Orqur+gsSTZrPZq/c17a6X9nXj/rRpmhP3/MyuZ6W7Y6VXNk3zo7tfulfX9cvm8/k719bWvljX9fMjojuN5RHCDdTNRwkQIDAUAQHWUCptnwQIlC6wrAHW//6Jhyz0Je5//g/vdc9c+sVhf0sj4GJccKnquu4Cphs1TdO9xO8af9VhZWXlTqPRqPszq+9tmuakfS21rusnRET3Z1V/q2ma3977M5PJ5C92vYyv+ysTq3v/XoC14Abw9QQIEOi5gACr5wWyPAIECByggADrAKH2+pgAK83NKAI5BARYOVQPcM7JZHJk27bdu6nWmqbpXt5+jZ+6rm8eEf8cEZ9vmub21xJgndX9FYmqqh4xnU67P6269xxviohHt23787PZ7MMCrAMsjo8RIECAwFUCAiyNQIAAgTIEBFhpdRRgpbkZRSCHgAArh+oBzjmZTI5q2/aSiPhS0zS33nvY1q1bb3zooYd+JyKapmm6P5H6Az91Xb9x11+yeMCefzZ19wfH4/Erq6p6Utu2D5vNZu8WYB1gcXyMAAECBARYeoAAAQIFCSxrgHWf405a6COEH/nye9wzF3Qd2MpyC7gYF1i/HTt23Gx9ff3SawuwjjvuuJvs3LnzX/cTYL0hIh4bEfsMsCaTyau6v0YowFpgoX01AQIElljACawlLp6lEyBAYA8BAVZaOwiw0tyMIpBDQICVQ/UA5zz++OOPuOyyy74fEV9tmmZl72Hbtm27xZYtW74eEZ9rmuYO+5p2Mpl0L3nvXvZ+8nQ6fcfen5lMJm/tXtAeET/XNM2f7f1778A6wGL5GAECBAYqIMAaaOFtmwCB4gSWNcD6ueMfutATWB++8N3umYu7GmxoWQVcjAuuXF3X34yIm+x6ifv6nstZWVm562g0Oi8i3tE0zcn7Wup4PH5cVVWvb9v22bPZ7MV7f2Y8Hn+yqqoT5vP5ytra2lcFWAsuuK8nQIDAkgkIsJasYJZLgACBaxEQYKW1hgArzc0oAjkEBFg5VDcw53g8fn9VVQ8ajUZ3Xl1d7f7i4NU/dV0/NyLO6B4RbJqmexn7D/ysrKwcOxqN/iEiPtY0zb32/MDRRx992BFHHPEvbdt+p2ma/ycifuB/vXACawPF8lECBAgMUECANcCi2zIBAkUKLGuA9fPHP2yhJ7D+9MJ3uWcu8oqwqWUUcDEuuGp1Xd+jC58i4kNN0zxg93K2bdv2w1u2bPlKRBw2n89vtba29t1d/3az+Xz+7bW1tW/t/mxd15+OiDvN5/Pbr62tfWH3v4/H4+7Rwu4Rw+fMZrMX7WurAqwFNx0YO3EAACAASURBVICvJ0CAQM8FBFg9L5DlESBA4AAFBFgHCLXXxwRYaW5GEcghIMDKobrBOeu6fnNEnNq27blVVX0gIo6IiMdHxPaqqk6aTqfndFOOx+NTqqo6OyLOaprm9N1fs2PHjp9YX1//VERc2f2ubdtvVFV1p4g4LSIumM/nd1lbW9vZff6oo446/Mgjj7zv7rFt2/5SRDw4Il5bVVX3uGK0bfsfTdN8ZIPbuMbHd/77vyz0fyk5mLUbS4AAAQL/LSDA0g0ECBAoQ0CAlVZHAVaam1EEcggIsHKobnzOUV3XXdj0mIiYRMTOqqo+u76+fuba2tr5u6e7tgCr+/327dvr+Xz+/IjoTnTdOCK6l7+//wY3uMGZX/nKV7oXxV/1s3379q3z+fyf9rPErzdNs3Xj2/jvEQKsg9EzlgABAv0REGD1pxZWQoAAgYMREGCl6Qmw0tyMIpBDQICVQ9WcIcDSBAQIEChDQIBVRh3tggABAgKstB4QYKW5GUUgh4AAK4eqOQVYeoAAAQKFCAiwCimkbRAgMHiBZQ2wTvzJhy/01SR/8qV3umce/NUDoC8CLsa+VKKwdTiBVVhBbYcAgcEKCLAGW3obJ0CgMAEBVlpBBVhpbkYRyCEgwMqhak4nsPQAAQIEChEQYBVSSNsgQGDwAgKstBYQYKW5GUUgh4AAK4eqOQVYeoAAAQKFCAiwCimkbRAgMHiBZQ2w7nfrRyz0EcI//vt3uGce/NUDoC8CLsa+VKKwdXiEsLCC2g4BAoMVEGANtvQ2ToBAYQICrLSCCrDS3IwikENAgJVD1ZxOYOkBAgQIFCIgwCqkkLZBgMDgBZY1wLr/rU9e6AmsD/39H7pnHvzVA6AvAi7GvlSisHU4gVVYQW2HAIHBCgiwBlt6GydAoDABAVZaQQVYaW5GEcghIMDKoWpOJ7D0AAECBAoREGAVUkjbIEBg8AICrLQWEGCluRlFIIeAACuHqjkFWHqAAAEChQgIsAoppG0QIDB4gWUNsH7xNr+80EcIP/DFt7lnHvzVA6AvAi7GvlSisHV4hLCwgtoOAQKDFRBgDbb0Nk6AQGECAqy0ggqw0tyMIpBDQICVQ9WcTmDpAQIECBQiIMAqpJC2QYDA4AUEWGktIMBKczOKQA4BAVYOVXMKsPQAAQIEChEQYBVSSNsgQGDwAgKstBYQYKW5GUUgh4AAK4eqOQVYeoAAAQKFCAiwCimkbRAgMHgBAVZaCwiw0tyMIpBDQICVQ9WcAiw9QIAAgUIEBFiFFNI2CBAYvMCyBlgP/KlTFvoS9z/6wh+4Zx781QOgLwIuxr5UorB1eIl7YQW1HQIEBisgwBps6W2cAIHCBARYaQUVYKW5GUUgh4AAK4eqOZ3A0gMECBAoRECAVUghbYMAgcELLGuA9eDbPmqhJ7Def8HZ7pkHf/UA6IuAi7EvlShsHU5gFVZQ2yFAYLACAqzBlt7GCRAoTECAlVZQAVaam1EEcggIsHKomtMJLD1AgACBQgQEWIUU0jYIEBi8gAArrQUEWGluRhHIISDAyqFqTgGWHiBAgEAhAgKsQgppGwQIDF5gWQOsX7rtryz0EcL3XfBW98yDv3oA9EXAxdiXShS2Do8QFlZQ2yFAYLACAqzBlt7GCRAoTECAlVZQAVaam1EEcggIsHKomtMJLD1AgACBQgQEWIUU0jYIEBi8wLIGWCfd7tSFnsB6z+ff4p558FcPgL4IuBj7UonC1uEEVmEFtR0CBAYrIMAabOltnACBwgQEWGkFFWCluRlFIIeAACuHqjmdwNIDBAgQKERAgFVIIW2DAIHBCwiw0lpAgJXmZhSBHAICrByq5hRg6QECBAgUIiDAKqSQtkGAwOAFBFhpLSDASnMzikAOAQFWDlVzCrD0AAECBAoREGAVUkjbIEBg8AICrLQWEGCluRlFIIeAACuHqjkFWHqAAAEChQgIsAoppG0QIDB4gWUNsB56+0cv9CXu7/7cm90zD/7qAdAXARdjXypR2Dq8xL2wgtoOAQKDFRBgDbb0Nk6AQGECAqy0ggqw0tyMIpBDQICVQ9WcTmDpAQIECBQiIMAqpJC2QYDA4AUEWGktIMBKczOKQA4BAVYOVXMKsPQAAQIEChEQYBVSSNsgQGDwAssaYD38Do9Z6COE7/y7N7lnHvzVA6AvAi7GvlSisHV4hLCwgtoOAQKDFRBgDbb0Nk6AQGECAqy0ggqw0tyMIpBDQICVQ9WcTmDpAQIECBQiIMAqpJC2QYDA4AWWNcB6xB0eu9ATWO/4uze6Zx781QOgLwIuxr5UorB1OIFVWEFthwCBwQoIsAZbehsnQKAwAQFWWkEFWGluRhHIISDAyqFqTiew9AABAgQKERBgFVJI2yBAYPACAqy0FhBgpbkZRSCHgAArh6o5BVh6gAABAoUICLAKKaRtECAweIFlDbAe+T8ft9BHCN/+2Te4Zx781QOgLwIuxr5UorB1eISwsILaDgECgxUQYA229DZOgEBhAgKstIIKsNLcjCKQQ0CAlUPVnE5g6QECBAgUIiDAKqSQtkGAwOAFBFhpLSDASnMzikAOAQFWDlVzCrD0AAECBAoREGAVUkjbIEBg8AICrLQWEGCluRlFIIeAACuHqjkFWHqAAAEChQgIsAoppG0QIDB4AQFWWgsIsNLcjCKQQ0CAlUPVnAIsPUCAAIFCBARYhRTSNggQGLzAsgZYv3zHX13oS9zf9revd888+KsHQF8EXIx9qURh6/AS98IKajsECAxWQIA12NLbOAEChQkIsNIKKsBKczOKQA4BAVYOVXM6gaUHCBAgUIiAAKuQQtoGAQKDF1jWAOtRdzptoSewzv7M77tnHvzVA6AvAi7GvlSisHU4gVVYQW2HAIHBCgiwBlt6GydAoDABAVZaQQVYaW5GEcghIMDKoWpOJ7D0AAECBAoREGAVUkjbIEBg8AICrLQWEGCluRlFIIeAACuHqjkFWHqAAAEChQgIsAoppG0QIDB4gWUNsH7lTo9f6COEb/3M69wzD/7qAdAXARdjXypR2Do8QlhYQW2HAIHBCgiwBlt6GydAoDABAVZaQQVYaW5GEcghIMDKoWpOJ7D0AAECBAoREGAVUkjbIEBg8ALLGmA9+qefsNATWG/+m9e6Zx781QOgLwIuxr5UorB1OIFVWEFthwCBwQoIsAZbehsnQKAwAQFWWkEFWGluRhHIISDAyqFqTiew9AABAgQKERBgFVJI2yBAYPACAqy0FhBgpbkZRSCHgAArh6o5BVh6gAABAoUICLAKKaRtECAweAEBVloLCLDS3IwikENAgJVD1ZwCLD1AgACBQgQEWIUU0jYIEBi8gAArrQUEWGluRhHIISDAyqFqTgGWHiBAgEAhAgKsQgppGwQIDF5gWQOsx9z51xb6Evc3/fVr3DMP/uoB0BcBF2NfKlHYOrzEvbCC2g4BAoMVEGANtvQ2ToBAYQICrLSCCrDS3IwikENAgJVD1ZxOYOkBAgQIFCIgwCqkkLZBgMDgBQRYaS0gwEpzM4pADgEBVg5Vcwqw9AABAgQKERBgFVJI2yBAYPACyxpgPe5//fpCHyF8w6d/zz3z4K8eAH0RcDH2pRKFrcMjhIUV1HYIEBisgABrsKW3cQIEChMQYKUVVICV5mYUgRwCAqwcquZ0AksPECBAoBABAVYhhbQNAgQGL7CsAdav/swTF3oC6/WferV75sFfPQD6IuBi7EslClvH3Xf84kL/H01hnLZDgACBhQnsXN+5sO/2xQQIECCweQJ/3Xx4Ke/9BFib1wNmIrDsAkv5X2LLjj6E9QuwhlBleyRAYAgCAqwhVNkeCRAYgoAAK63KTmCluRlFIIeAACuHqjlDgKUJCBAgUIaAAKuMOtoFAQIEljXAOu1nnrTQJzt+/1Ovcs/s8iHQEwEXY08KUdoyBFilVdR+CBAYqoAAa6iVt28CBEoTEGClVVSAleZmFIEcAgKsHKrmdAJLDxAgQKAQAQFWIYW0DQIEBi8gwEprAQFWmptRBHIICLByqJpTgKUHCBAgUIiAAKuQQtoGAQKDFxBgpbWAACvNzSgCOQQEWDlUzSnA0gMECBAoRECAVUghbYMAgcELLGuA9YS7LPYdWK/9K+/AGvzFA6A3AgKs3pSirIV4B1ZZ9bQbAgSGKyDAGm7t7ZwAgbIEBFhp9RRgpbkZRSCHgAArh6o5ncDSAwQIEChEQIBVSCFtgwCBwQsIsNJaQICV5mYUgRwCAqwcquYUYOkBAgQIFCIgwCqkkLZBgMDgBZY1wPq1uz65XWTxXnP+K90zL7IAvpvAHgIuRu2QRcAjhFlYTUqAAIHrXUCAdb2T+0ICBAhkERBgpbEKsNLcjCKQQ0CAlUPVnE5g6QECBAgUIiDAKqSQtkGAwOAFljXAeuIJT1noCaxXn/cK98yDv3oA9EXAxdiXShS2DiewCiuo7RAgMFgBAdZgS2/jBAgUJiDASiuoACvNzSgCOQQEWDlUzekElh4gQIBAIQICrEIKaRsECAxeQICV1gICrDQ3owjkEBBg5VA1pwBLDxAgQKAQAQFWIYW0DQIEBi+wrAHWk+721IU+QviqT77cPfPgrx4AfRFwMfalEoWtwyOEhRXUdggQGKyAAGuwpbdxAgQKExBgpRVUgJXmZhSBHAICrByq5nQCSw8QIECgEAEBViGFtA0CBAYvIMBKawEBVpqbUQRyCAiwcqiaU4ClBwgQIFCIgACrkELaBgECgxcQYKW1gAArzc0oAjkEBFg5VM0pwNIDBAgQKERAgFVIIW2DAIHBCwiw0lpAgJXmZhSBHAICrByq5hRg6QECBAgUIiDAKqSQtkGAwOAFljXAesrdnrbQl7i/4pNnuWce/NUDoC8CLsa+VKKwdXiJe2EFtR0CBAYrIMAabOltnACBwgQEWGkFFWCluRlFIIeAACuHqjmdwNIDBAgQKERAgFVIIW2DAIHBCyxrgPXUu5++0BNYL//Ey9wzD/7qAdAXARdjXypR2DqcwCqsoLZDgMBgBQRYgy29jRMgUJiAACutoAKsNDejCOQQEGDlUDWnE1h6gAABAoUICLAKKaRtECAweAEBVloLCLDS3IwikENAgJVD1ZwCLD1AgACBQgQEWIUU0jYIEBi8wLIGWKf/7NMX+gjhy/7ype6ZB3/1AOiLgIuxL5UobB0eISysoLZDgMBgBQRYgy29jRMgUJiAACutoAKsNDejCOQQEGDlUDWnE1h6gAABAoUICLAKKaRtECAweIFlDbCefo/FnsB66cc35wTWCSecsOWSSy55SlVVJ0fEOCKujIgvVFV11nQ6Pfe6GnQ8Hp9SVdXZ+2vi0Wh0q9XV1Ysnk8l5bdve9bo+X1XV+dPp9ITuMwfw+fc2TXPS/r7f7wnkFhBg5RYe6PxOYA208LZNgEBxAgKs4kpqQwQIDFRAgJVW+M0KsCaTyTlt2z4wIj4QEee2bXvDqqoeHRG3a9v2tNls9vprW+GOHTtuOZ/Pb38tv6/atv297nc3vOENj7nwwgv/Y2Vl5a6HHHLITff1+bZtj42I50fEa5qm+fU9A6yqqh68rzHz+fz/zGazz6YJGkVg8wQEWJtnaaY9BARY2oEAAQJlCAiwyqijXRAgQECAldYDmxFg1XV9/4j4YFVV75pOpw/fvZKjjz76sMMPP/zCqqqOWl9f37q2tvatja6yrusndGFUVVUPmU6n79vP+FFd138TEbe4/PLLf/ziiy/+tz0DrKZp5AMbLYDPX68CGvR65R7OlwmwhlNrOyVAoGwBAVbZ9bU7AgSGIyDASqv1ZgRYk8mkO3F14mg0ut3q6uoFe65kPB6fXlXVS9u2fdJsNnv1Rla5bdu2W2zZsuUfIuL8pmlO3N/Yuq6fEhEvb9v2QbPZ7I92f373I4QCrP0J+v2iBQRYi65Aod8vwCq0sLZFgMDgBARYgyu5DRMgUKiAACutsJsRYNV13Z2sulHTNEdExHzPlaysrNxpNBp1p6I2/J6puq4/GBH3vvLKK7d/7Wtf+8Z17XAymRzVtm0TEec1TfPze3527wCrOxn2oz/6o1decMEFV6SpGUUgj4AAK4/r4GcVYA2+BQAQIFCIgACrkELaBgECgxdY1gDrN+75jHaRxXvJx373oO6ZJ5PJkW3bfjci1pqm6V7efo2fuq5vHhH/HBGfb5rm2t5z9QMEKysrdxyNRp+pqurF0+n02fszmkwmr2/b9tSIOK5pmtV9BVi73o3VvZfr6F2//0pEvLppmjfub36/J3B9CBzUxXh9LNB3LKeAAGs562bVBAgQ2FtAgKUnCBAgUIaAACutjpsQYHUnny6JiC81TXPrvVexdevWGx966KHfiYimaZrJga6yrutPdC+Av/zyy2+x+11W1zZ2ZWXlmNFodFFEvKdpmkfu/bk9/grh5yOiC6u6QO3HI+L0iPixiDiraZruP/shsFABAdZC+cv9cgFWubW1MwIEhiUgwBpWve2WAIFyBQRYabU92ABrx44dN1tfX7/02gKs44477iY7d+78140EWN1fGRyNRud177NqmuZp+9tZXddnR8Qpo9Ho+NXV1S/v/fnuMcYtW7b8j9XV1Y9HxPru3+9ae/eOrRsfcsgh2y+66KLZ/r7L7wnkFBBg5dQd8NwCrAEX39YJEChKQIBVVDlthgCBAQssa4D1zHv+xkIfIfydj73koO6Zjz/++CMuu+yy70fEV5umWdm7BXe9iP3rEfG5pmnucCAtWtf1uyPipPl8fuza2to/XteYXSe8ugDty03T/M8DmX/Pz0wmk1e1bfvEiHhC0zSv2+h4nyewmQIHdTFu5kLMVZaAAKusetoNAQLDFRBgDbf2dk6AQFkCAqy0eh5sgNV9a13X34yIm+x6ifvVJ5y63+1xmuodTdOcvL9Vdi9YP/zww78dEf/UNM2x+/v8eDw+paqqs9u2fcZsNnvp/j6/9+/run5mRPx2RPxW0zTd//VDYGECAqyF0Zf9xQKssutrdwQIDEdAgDWcWtspAQJlCyxrgPWb93rmQk9g/fZHf+eg75nH4/H7q6p60Gg0uvPq6mr3Fwev/qnr+rkRcUZEPLZpmjftrwvrur5vRHy4bdtXzWazJx/A598XEQ9u2/Y2s9ns7/f+fPeY4Hw+v/t8Pv8/s9nsr/YRYL0tIh7Ztu3DZrNZd/LLD4GFCRz0xbiwlfviXgsIsHpdHosjQIDAAQsIsA6YygcJECDQawEBVlp5NiPAquv6HhHxsYj4UNM0D9i9km3btv3wli1bur/0d9h8Pr/V2trad3f9WxcqfXttbe1b+wiUnh8Rz2vb9ldms1n3bqvr/Knr+uKIuPmRRx55+AUXXHDF3h/evn371vl8/rXuRNd8Pr9Nt4bdnxmPxzuqqvpiROy8/PLLb7m/l8Xvby1+T+BgBQRYByto/D4FBFgagwABAmUICLDKqKNdECBAQICV1gObEWB131zX9Zsj4tS2bc+tquoDEXFERDw+IrZXVXXSdDo9p/vc7kf+ru0v/+1+/9VoNLrb6upq9yL3a/3ZunXrDQ899ND/ioivN02z9do+WNf18yKiC8a+GhFviYjurybu6N571YVrbds+0umrtP4xanMFBFib62m2XQICLK1AgACBMgQEWGXU0S4IECCwrAHWs+79mwt9hPDFf/Hbm3XPPKrr+rSIeExETLpTTVVVfXZ9ff3MtbW183d36P4CrPF4/OdVVd17Pp/fdm1t7QvX1dnHHnvsj11xxRXd+7e6F7gff12fnUwmv9C27ZMi4ra7wrV/adv201VVvbRpmr9zBRHog8BmXYx92Is19EhAgNWjYlgKAQIEDkJAgHUQeIYSIECgRwICrLRibGKAlbYAowgQuFpAgKUZsggIsLKwmpQAAQLXu4AA63on94UECBDIIrCsAdaz//diT2C96M837QRWlrqalMCQBARYQ6r29bhXAdb1iO2rCBAgkFFAgJUR19QECBC4HgUEWGnYAqw0N6MI5BAQYOVQNWcIsDQBAQIEyhAQYJVRR7sgQICAACutBwRYaW5GEcghIMDKoWpOAZYeIECAQCECAqxCCmkbBAgMXkCAldYCAqw0N6MI5BAQYOVQNacASw8QIECgEAEBViGFtA0CBAYvIMBKawEBVpqbUQRyCAiwcqiaU4ClBwgQIFCIgACrkELaBgECgxdY1gDruff5rXaRxXvBR850z7zIAvhuAnsIuBi1QxYB78DKwmpSAgQIXO8CAqzrndwXEiBAIIuAACuNVYCV5mYUgRwCAqwcquZ0AksPECBAoBABAVYhhbQNAgQGLyDASmsBAVaam1EEcggIsHKomlOApQcIECBQiIAAq5BC2gYBAoMXWNYA63n3edZCHyE84yMvds88+KsHQF8EXIx9qURh6/AIYWEFtR0CBAYrIMAabOltnACBwgQEWGkFFWCluRlFIIeAACuHqjmdwNIDBAgQKERAgFVIIW2DAIHBCyxrgHXGfZ+90BNYz/uzF7lnHvzVA6AvAi7GvlSisHU4gVVYQW2HAIHBCgiwBlt6GydAoDABAVZaQQVYaW5GEcghIMDKoWpOJ7D0AAECBAoREGAVUkjbIEBg8AICrLQWEGCluRlFIIeAACuHqjkFWHqAAAEChQgIsAoppG0QIDB4gWUNsF7wc4t9hPC5H/YI4eAvHgC9ERBg9aYUZS3EI4Rl1dNuCBAYroAAa7i1t3MCBMoSEGCl1VOAleZmFIEcAgKsHKqZ5zzhhBO2XHLJJU+pqurkiBhHxJUR8YWqqs6aTqfnHujXTyaTo9q2fUtE/O+IeFvTNKcc6Nj9fU6AtT8hvydAgMByCAiwlqNOVkmAAIH9CQiw9ie0798LsNLcjCKQQ0CAlUM185yTyeSctm0fGBEfiIhz27a9YVVVj46I27Vte9psNnv9/pZQ1/VJEfHaiDg0Im4kwNqfmN8TIEBgmAICrGHW3a4JEChPQICVVlMBVpqbUQRyCAiwcqhmnLOu6/tHxAerqnrXdDp9+O6vOvroow87/PDDL6yq6qj19fWta2tr37q2ZYzH40dVVfXWiPjD+Xz+/tFo1J3acgIrY91MTYAAgWUVEGAta+WsmwABAtcUEGCldYQAK83NKAI5BARYOVQzzjmZTLoTVyeORqPbra6uXrDnV43H49Orqnpp27ZPms1mr762ZdR1/ei2bf9jNpu9e/v27SfM5/NPCrAyFs3UBAgQWGIBAdYSF8/SCRAgsIfAsgZYL/z557SLLORz/vSF7pkXWQDfTWAPARfjkrVDXdfdyaobNU1zRETM91z+ysrKnUaj0d9ExHubpukeEdzvjwBrv0Q+QIAAgUELCLAGXX6bJ0CgIAEBVloxBVhpbkYRyCEgwMqhmmnOyWRyZNu2342ItaZpupe3X+OnruubR8Q/R8Tnm6a5/YEsQ4B1IEo+Q4AAgeEKCLCGW3s7J0CgLIFlDbBefOJzF3oC61l/8gL3zGVdCnazxAIuxiUq3q6/GnhJRHypaZpb7730rVu33vjQQw/9TkQ0TdNMDmRrAqwDUfIZAgQIDFdAgDXc2ts5AQJlCQiw0uopwEpzM4pADgEBVg7VTHPu2LHjZuvr65deW4B13HHH3WTnzp3/KsDKVADTEiBAYIACAqwBFt2WCRAoUkCAlVZWAVaam1EEcggIsHKoZprz+OOPP+Kyyy77fkR8tWmalb2/Ztu2bbfYsmXL1yPic03T3OFAluEE1oEo+QwBAgSGKyDAGm7t7ZwAgbIEljXAOvMXFvsI4W+d6xHCsq4Eu1lmAQHWklWvrutvRsRNdr3EfX3P5a+srNx1NBqdFxHvaJrm5APZmgDrQJR8hgABAsMVEGANt/Z2ToBAWQICrLR6CrDS3IwikENAgJVDNeOc4/H4/VVVPWg0Gt15dXW1+4uDV//Udf3ciDgjIh7bNM2bDmQZAqwDUfIZAgQIDFdAgDXc2ts5AQJlCSxrgPU793veQl/i/sw/PsM9c1mXgt0ssYCLccmKV9f1PSLiYxHxoaZpHrB7+du2bfvhLVu2fCUiDpvP57daW1v77q5/u9l8Pv/22trat/a1VQHWkjWA5RIgQOB6FhBgXc/gvo4AAQKZBARYabACrDQ3owjkEBBg5VDNPGdd12+OiFPbtj23qqoPRMQREfH4iNheVdVJ0+n0nG4J4/H4lKqqzo6Is5qmOX33suq6vk9VVd2YaNv22Ih4ftu2541Go9fu/sz3vve9P7v00kv/M3Urd9/xiwv9X0pS120cAQIECFxTQIClIwgQIFCGgAArrY4CrDQ3owjkEBBg5VDNP+eoruvTIuIxETGJiJ1VVX12fX39zLW1tfN3f/11BFgXR8Qtr2uZo9HoVqurq93nkn4EWElsBhEgQKB3AgKs3pXEgggQIJAkIMBKYgsBVpqbUQRyCAiwcqiaMwRYmoAAAQJlCAiwyqijXRAgQECAldYDAqw0N6MI5BAQYOVQNacASw8QIECgEAEBViGFtA0CBAYvsKwB1kvu9/yFvprkN/74+e6ZB3/1AOiLgIuxL5UobB1OYBVWUNshQGCwAgKswZbexgkQKExAgJVWUAFWmptRBHIICLByqJrTCSw9QIAAgUIEBFiFFNI2CBAYvMCyBli/e/8zFnoC6xkfep575sFfPQD6IuBi7EslCluHE1iFFdR2CBAYrIAAa7Clt3ECBAoTEGClFVSAleZmFIEcAgKsHKrmdAJLDxAgQKAQAQFWIYW0DQIEBi8gwEprAQFWmptRBHIICLByqJpTgKUHCBAgUIiAAKuQQtoGAQKDF1jWAOtlD1jsI4Snf9AjhIO/eAD0RkCA1ZtSlLUQjxCWVU+7IUBguAICrOHW3s4JEChLQICVVk8BVpqbUQRyCAiwcqia0wksPUCAAIFCBARYhRTSNggQGLzAsgZYZ/3iCxb6EvenfeC57pkHf/UASM8OPgAAIABJREFU6IuAi7EvlShsHU5gFVZQ2yFAYLACAqzBlt7GCRAoTECAlVZQAVaam1EEcggIsHKomtMJLD1AgACBQgQEWIUU0jYIEBi8gAArrQUEWGluRhHIISDAyqFqTgGWHiBAgEAhAgKsQgppGwQIDF5AgJXWAgKsNDejCOQQEGDlUDWnAEsPECBAoBABAVYhhbQNAgQGLyDASmsBAVaam1EEcggIsHKomlOApQcIECBQiIAAq5BC2gYBAoMXWNYA6+UPfOFCX+L+1D96jnvmwV89APoi4GLsSyUKW4eXuBdWUNshQGCwAgKswZbexgkQKExAgJVWUAFWmptRBHIICLByqJrTCSw9QIAAgUIEBFiFFNI2CBAYvIAAK60FBFhpbkYRyCEgwMqhak4Blh4gQIBAIQICrEIKaRsECAxeYFkDrFc+aLGPED75HI8QDv7iAdAbAQFWb0pR1kI8QlhWPe2GAIHhCgiwhlt7OydAoCwBAVZaPQVYaW5GEcghIMDKoWpOJ7D0AAECBAoREGAVUkjbIEBg8ALLGmC96sEvWuhL3J/0/me7Zx781QOgLwIuxr5UorB1OIFVWEFthwCBwQoIsAZbehsnQKAwAQFWWkEFWGluRhHIISDAyqFqTiew9AABAgQKERBgFVJI2yBAYPACAqy0FhBgpbkZRSCHgAArh6o5BVh6gAABAoUICLAKKaRtECAweIFlDbBe/UuLfYTwie/zCOHgLx4AvREQYPWmFGUtxCOEZdXTbggQGK6AAGu4tbdzAgTKEhBgpdVTgJXmZhSBHAICrByq5nQCSw8QIECgEAEBViGFtA0CBAYvIMBKawEBVpqbUQRyCAiwcqiaU4ClBwgQIFCIgACrkELaBgECgxcQYKW1gAArzc0oAjkEBFg5VM0pwNIDBAgQKERAgFVIIW2DAIHBCwiw0lpAgJXmZhSBHAICrByq5hRg6QECBAgUIiDAKqSQtkGAwOAFljXAes1DXtwusni/9t5nuWdeZAF8N4E9BFyM2iGLgJe4Z2E1KQECBK53AQHW9U7uCwkQIJBFQICVxirASnMzikAOAQFWDlVzOoGlBwgQIFCIgACrkELaBgECgxdY1gDrtQ85c6EnsJ7w3t9yzzz4qwdAXwRcjH2pRGHrcAKrsILaDgECgxUQYA229DZOgEBhAgKstIIKsNLcjCKQQ0CAlUPVnE5g6QECBAgUIiDAKqSQtkGAwOAFBFhpLSDASnMzikAOAQFWDlVzCrD0AAECBAoREGAVUkjbIEBg8ALLGmC97qTFPkL4+Pd4hHDwFw+A3ggIsHpTirIW4hHCsuppNwQIDFdAgDXc2ts5AQJlCQiw0uopwEpzM4pADgEBVg5VczqBpQcIECBQiIAAq5BC2gYBAoMXWNYA6/UP/e2FvsT9V9/9m+6ZB3/1AOiLgIuxL5UobB1OYBVWUNshQGCwAgKswZbexgkQKExAgJVWUAFWmptRBHIICLByqJrTCSw9QIAAgUIEBFiFFNI2CBAYvIAAK60FBFhpbkYRyCEgwMqhak4Blh4gQIBAIQICrEIKaRsECAxeQICV1gICrDQ3owjkEBBg5VA1pwBLDxAgQKAQAQFWIYW0DQIEBi8gwEprAQFWmptRBHIICLByqJpTgKUHCBAgUIiAAKuQQtoGAQKDF1jWAOsND/udhb7E/XHveqZ75sFfPQD6IuBi7EslCluHl7gXVlDbIUBgsAICrMGW3sYJEChMQICVVlABVpqbUQRyCAiwcqia0wksPUCAAIFCBARYhRTSNggQGLyAACutBQRYaW5GEcghIMDKoWpOAZYeIECAQCECAqxCCmkbBAgMXmBZA6w3PnyxjxA+9p0eIRz8xQOgNwICrN6UoqyFeISwrHraDQECwxUQYA239nZOgEBZAgKstHoKsNLcjCKQQ0CAlUPVnE5g6QECBAgUIiDAKqSQtkGAwOAFljXAetMjXrLQl7g/5h2/4Z558FcPgL4IuBj7UonC1uEEVmEFtR0CBAYrIMAabOltnACBwgQEWGkFFWCluRlFIIeAACuHqjmdwNIDBAgQKERAgFVIIW2DAIHBCwiw0lpAgJXmZhSBHAICrByq5hRg6QECBAgUIiDAKqSQtkGAwOAFljXAesvJi32E8NQ/9Ajh4C8eAL0REGD1phRlLcQjhGXV024IEBiugABruLW3cwIEyhIQYKXVU4CV5mYUgRwCAqwcquZ0AksPECBAoBABAVYhhbQNAgQGLyDASmsBAVaam1EEcggIsHKomlOApQcIECBQiIAAq5BC2gYBAoMXEGCltYAAK83NKAI5BARYOVTNKcDSAwQIEChEQIBVSCFtgwCBwQsIsNJaQICV5mYUgRwCAqwcquYUYOkBAgQIFCIgwCqkkLZBgMDgBZY1wHrrI3+3XWTxfuXtz3DPvMgC+G4Cewi4GLVDFgEvcc/CalICBAhc7wICrOud3BcSIEAgi4AAK41VgJXmZhSBHAICrByq5nQCSw8QIECgEAEBViGFtA0CBAYvsKwB1tm/vNgTWI96mxNYg794APRGQIDVm1KUtRAnsMqqp90QIDBcAQHWcGtv5wQIlCUgwEqrpwArzc0oAjkEBFg5VM3pBJYeIECAQCECAqxCCmkbBAgMXkCAldYCAqw0N6MI5BAQYOVQNacASw8QIECgEAEBViGFtA0CBAYvsKwB1ttOeelCX+L+y3/wdPfMg796APRFwMXYl0oUto7jbnGXhf4/msI4bYcAAQIECBAgQIDAQQl8+Rt/tZT3fgKsgyq7wQSKEljK/xIrqgKFbkaAVWhhbYsAAQIECBAgQGApBZY1wHr7o1620P9h/JFnn+6eeSk73qJLFHAxlljVHuxJgNWDIlgCAQIECBAgQIAAgV0CAqy0VhBgpbkZRSCHgAArh6o5Q4ClCQgQIECAAAECBAj0R0CAlVYLAVaam1EEcggIsHKomlOApQcIECBAgAABAgQI9EhAgJVWDAFWmptRBHIICLByqJpTgKUHCBAgQIAAAQIECPRIQICVVgwBVpqbUQRyCAiwcqiaU4ClBwgQIECAAAECBAj0SGBZA6w//JXFvsT95Ld6iXuP2thSBi4gwBp4A+Tavndg5ZI1LwECBAgQIECAAIGNCwiwNm7WjRBgpbkZRSCHgAArh6o5ncDSAwQIECBAgAABAgR6JCDASivGZgVYJ5xwwpZLLrnkKVVVnRwR44i4MiK+UFXVWdPp9NzrWt14PD6lqqqz97eD0Wh0q9XV1YsP5POXX375YRdffPFlu+dcWVm56Wg0ek5EnBgRN4+If6+q6vyqqs5YXV398v6+2+8JXB8CAqzrQ3mA3+EE1gCLbssECBAgQIAAAQK9FVjWAOsdp57VLhL1EW952qbcM08mk3Patn1gRHwgIs5t2/aGVVU9OiJu17btabPZ7PXXts8dO3bccj6f3/5afl+1bft73e9ueMMbHnPhhRf+xx4B1murqjpvX+Om02m3jnn3u61bt9740EMP/VxE3KJt29dFRPefb15V1VMi4siqqu42nU4/v8g6+G4CncCmXIwoCewtIMDSEwQIECBAgAABAgT6IyDASqvFZgRYdV3fPyI+WFXVu6bT6cN3r+Too48+7PDDD7+wqqqj1tfXt66trX1ro6us6/oJEfGaqqoeMp1O39eN3x1gtW37qNls9gf7m3M8Hr+yqqonRcRjm6Z50+7Pj8fjHVVVXRgRf980zbUFaPub3u8JbJqAAGvTKE20p4AASz8QIECAAAECBAgQ6I/AsgZY73z0Yk9gPfzNB38CazKZdCeuThyNRrdbXV29YM+uGI/Hp1dV9dK2bZ80m81evZGO2bZt2y22bNnyDxFxftM03aN/V/1sJMDqHm289NJL/9/ucMtRRx110/POO697tPHqn7qu/zQifq6qqp+cTqddmOWHwMIEBFgLoy/7iwVYZdfX7ggQIECAAAECBJZLQICVVq/NCLDquu5OVt2oaZojdj+2t3s1KysrdxqNRn8TEe9tmuakjayyrusPRsS9r7zyyu1f+9rXvnEdAdYhRx111A9deuml/7n3/CsrK8eORqN/qKrqo9Pp9N57/76u69+MiDP395jjRtbtswRSBQRYqXLGXaeAAEuDECBAgAABAgQIEOiPwLIGWO9+zMsX+g6sh77pqQd1zzyZTI5s2/a7EbHWNE338vZr/NR13b0w/Z8j4vMbeUxvZWXljqPR6DNVVb14Op0+e89J93gH1tsj4piIuGNEHBIR346IP4qIZzVN8y/dmJWVle5kWPcS+Tc3TfOYvdc3mUwe3rbtO6qqetl0On16fzraSoYocFAX4xDB7PnABARYB+bkUwQIECBAgAABAgSuDwEBVpryJgRYR7Vte0lEfKlpmlvvvYpdL1D/TkQ0TdNMDnSVdV1/onsB/OWXX36Liy+++N+uJcD6t+4F71VV/W1E3LSqqoe1bXuvLky7/PLLb9+NG4/HD6uq6p1t275qNps9eR8B21Xv74qINzZN87gDXZ/PEcghIMDKoWrOEGBpAgIECBAgQIAAAQL9ERBgpdXiYAOsHTt23Gx9ff3SawuwjjvuuJvs3LnzXzcSYK2srNx1NBp1f13w5U3TPG3vna2srBxdVdWtq6r6YtM0XXh29U9d1++KiIe2bfvC2Wz23PF4/NDu5fLXEWA9YNdfThRgpbWQUZsoIMDaRExT/beAAEs3ECBAgAABAgQIEOiPgAArrRYHG2Adf/zxR1x22WXfj4ivNk2zsvcqdr2I/esR8bmmae5wIKus6/rdEXHSfD4/dm1t7R8PZMzuz6ysrNxmNBp9Yff3jcfj7gXt3Yva39I0zaP3nquu60dGxNu6F81Pp9NnbOS7fJbAZgsIsDZb1HxXCQiwNAIBAgQIECBAgACB/ggIsNJqcbABVvetdV1/MyJususl7ut7rmSP01TvaJrm5P2t8uijjz7s8MMP795l9U9N0xy7v8/v/ftjjz32x6644opuPVc9stj9tG27GhEfb5rmnvsIsJ4XEc+vqurR0+n0LRv9Pp8nsJkCAqzN1DTX1QICLM1AgAABAgQIECBAoD8Cyxpgveexi32J+0lvPLiXuHcdMB6P319V1YNGo9GdV1dXu784ePVPXdfPjYgzIuKxTdO8aX8dU9f1fSPiw9f2yF83fjKZ/EL3zqt9BU6TyeRubdt+Yo+/Ojiq67p7xPHw+Xx+07W1tZ17ra9719bdRqPRZHV1tdnf+vyeQE4BAVZO3QHPLcAacPFtnQABAgQIECBAoHcCAqy0kmxGgFXX9T0i4mMR8aGmabp3Sl31s23bth/esmXLVyLisPl8fqu1tbXv7vq3m83n82+vra19a+9V13X9/Ih4Xtu2vzKbzc7e167G4/Enq6o6oW3bE2ezWfd44FU/J5xwwpZLL730o10gVVXVqdPp9K27Aq8XtW37rKqqnjydTl+1+/N1Xd8+Ij4bEZ9omqbbgx8CCxUQYC2Uv9wvF2CVW1s7I0CAAAECBAgQWD4BAVZazTYjwOq+ua7rN0fEqW3bnltV1Qci4oiIeHxEbK+q6qTpdHpO97nxeHxKVVVdMHVW0zSn7yPAuur9V6PR6G6rq6vdi9x/4Gf79u3HzefzT0fED3Xvtmrb9vNVVd24qqpHtG37U1VV/cl0Ou2CtKseZ9z1nq7uZNiPt2372oj4u6qqtkZE94L4+SGHHPLTF1100SxN0CgCmycgwNo8SzPtISDA0g4ECBAgQIAAAQIE+iOwrAHWex/3inaRig95w1M26565e1TvtIh4TPeUX0TsrKrqs+vr62eura2dv3uP+wuwxuPxn1dVde/5fH7btbW17mXs+/yp63pbRDwzIu4TET8WEf8VEV+pqurt0+n0jbvDq92Du5NfN7jBDZ7dtu0DI+LoiOjes/XRqqqeP51O/2mRNfDdBHYLbNbFSJTANQQEWBqCAAECBAgQIECAQH8EBFhptdjEACttAUYRIHC1gABLM2QREGBlYTUpAQIECBAgQIAAgSSBZQ2w3veriz2B9Uuv37QTWEl1M4gAgf8WEGDphiwCAqwsrCYlQIAAAQIECBAgkCQgwEpiCwFWmptRBHIICLByqJozBFiagAABAgQIECBAgEB/BARYabUQYKW5GUUgh4AAK4eqOQVYeoAAAQIECBAgQIBAjwSWNcB6/2mvXOhL3B/8+092z9yjPraUYQu4GIdd/2y7dwIrG62JCRAgQIAAAQIECGxYQIC1YbKrBgiw0tyMIpBDQICVQ9WcTmDpAQIECBAgQIAAAQI9EhBgpRVDgJXmZhSBHAICrByq5hRg6QECBAgQIECAAAECPRIQYKUVQ4CV5mYUgRwCAqwcquYUYOkBAgQIECBAgAABAj0SEGClFUOAleZmFIEcAgKsHKrmFGDpAQIECBAgQIAAAQI9EljWAOucxy/2Je4Pep2XuPeojS1l4AICrIE3QK7te4l7LlnzEiBAgAABAgQIENi4gABr42bdCAFWmptRBHIICLByqJrTCSw9QIAAAQIECBAgQKBHAssaYH3gCa9qF8n4i699knvmRRbAdxPYQ8DFqB2yCDiBlYXVpAQIECBAgAABAgSSBARYSWwhwEpzM4pADgEBVg5VczqBpQcIECBAgAABAgQI9EhAgJVWDAFWmptRBHIICLByqJpTgKUHCBAgQIAAAQIECPRIYFkDrA/+2mIfIXzAazxC2KM2tpSBCwiwBt4AubbvEcJcsuYlQIAAAQIECBAgsHEBAdbGzboRAqw0N6MI5BAQYOVQNacTWHqAAAECBAgQIECAQI8EljXA+tCvv3qhL3G//+890T1zj/rYUoYt4GIcdv2z7d4JrGy0JiZAgAABAgQIECCwYQEB1obJrhogwEpzM4pADgEBVg5VczqBpQcIECBAgAABAgQI9EhAgJVWDAFWmptRBHIICLByqJpTgKUHCBAgQIAAAQIECPRIQICVVgwBVpqbUQRyCAiwcqiaU4ClBwgQIECAAAECBAj0SECAlVYMAVaam1EEcggIsHKomlOApQcIECBAgAABAgQI9EhgWQOsP37iYl/ifr9Xe4l7j9rYUgYuIMAaeAPk2r6XuOeSNS8BAgQIECBAgACBjQsIsDZu1o0QYKW5GUUgh4AAK4eqOZ3A0gMECBAgQIAAAQIEeiQgwEorhgArzc0oAjkEBFg5VM0pwNIDBAgQIECAAAECBHoksKwB1rlP+r12kYy/8Kpfd8+8yAL4bgJ7CLgYtUMWAY8QZmE1KQECBAgQIECAAIEkAQFWElsIsNLcjCKQQ0CAlUPVnE5g6QECBAgQIECAAAECPRJY1gDrT5682BNYJ77SCawetbGlDFxAgDXwBsi1fSewcsmalwABAgQIECBAgMDGBQRYGzfrRgiw0tyMIpBDQICVQ9WcTmDpAQIECBAgQIAAAQI9EhBgpRVDgJXmZhSBHAICrByq5hRg6QECBAgQIECAAAECPRJY1gDrT5/ymoW+xP3nX/Fr7pl71MeWMmwBF+Ow659t9x4hzEZrYgIECBAgQIAAAQIbFhBgbZjsqgECrDQ3owjkEBBg5VA1pxNYeoAAAQIECBAgQIBAjwQEWGnFEGCluRlFIIeAACuHqjkFWHqAAAECBAgQIECAQI8EBFhpxRBgpbkZRSCHgAArh6o5BVh6gAABAgQIECBAgECPBARYacUQYKW5GUUgh4AAK4fqEs05Ho9PqarqVRHxP0aj0a1WV1cv3ozlewfWZiiagwABAgQIECBAgMDmCCxrgPXhpy72Je4/93Ivcd+cDjQLgYMXEGAdvOFSznDMMcf8yCGHHPKGiLhfRPxnRBwhwFrKUlo0AQIECBAgQIAAgf0KCLD2S7TPDwiw0tyMIpBDQICVQ3UJ5pxMJl9v2/bQ0Wh0Stu2v9m27V0FWEtQOEskQIAAAQIECBAgkCCwrAHWnz3ttW3CdjdtyH3PeoJ75k3TNBGBgxNwMR6c39KOruv6PfP5/NfX1ta+NZlMzhNgLW0pLZwAAQIECBAgQIDAfgUEWPsl2ucHBFhpbkYRyCEgwMqhumRzCrCWrGCWS4AAAQIECBAgQGCDAgKsDYLt+rgAK83NKAI5BARYOVSXbE4B1pIVzHIJECBAgAABAgQIbFBgWQOsj5y+2EcI7/MyjxBusNV8nEA2AQFWNtrlmViAtTy1slICBAgQIECAAAECKQICrBS1CAFWmptRBHIICLByqC7ZnAKsJSuY5RIgQIAAAQIECBDYoMCyBlh/8fTXLfQl7vd+6ePdM2+w13ycQC4BF2Mu2SWaV4C1RMWyVAIECBAgQIAAAQIJAgKsBLSIEGCluRlFIIeAACuH6pLNKcBasoJZLgECBAgQIECAAIENCgiwNgi26+MCrDQ3owjkEBBg5VBdsjkFWEtWMMslQIAAAQIECBAgsEGBZQ2wPvqMxT5CeK/f9QjhBlvNxwlkExBgZaNdnokFWMtTKyslQIAAAQIECBAgkCIgwEpRixBgpbkZRSCHgAArh2rP59yxY8ct5/P57Xcvs23bMyLix6uqenxEfGvXv188nU4/n7qV425xl4W+bDF13cYRIECAAAECBAgQKFFAgJVWVQFWmptRBHIICLByqPZ8zvF4fEpVVWfvZ5lva5rmlNStCLBS5YwjQIAAAQIECBAgsPkCAqw0UwFWmptRBHIICLByqJozBFiagAABAgQIECBAgEB/BARYabUQYKW5GUUgh4AAK4eqOQVYeoAAAQIECBAgQIBAjwSWNcD62G/8/kJfTXLPl5zmnrlHfWwpwxZwMQ67/tl27wRWNloTEyBAgAABAgQIENiwgABrw2RXDRBgpbkZRSCHgAArh6o5ncDSAwQIECBAgAABAgR6JLCsAdbHn7nYE1j3+B0nsHrUxpYycAEB1sAbINf2ncDKJWteAgQIECBAgAABAhsXEGBt3KwbIcBKczOKQA4BAVYOVXM6gaUHCBAgQIAAAQIECPRIQICVVgwBVpqbUQRyCAiwcqiaU4ClBwgQIECAAAECBAj0SGBZA6y//K3XL/Ql7j975q+6Z+5RH1vKsAVcjMOuf7bde4QwG62JCRAgQIAAAQIECGxYQIC1YbKrBgiw0tyMIpBDQICVQ9WcTmDpAQIECBAgQIAAAQI9EljWAOsTz1rsCay7v9gJrB61saUMXECANfAGyLV9J7ByyZqXAAECBAgQIECAwMYFBFgbN+tGCLDS3IwikENAgJVD1ZxOYOkBAgQIECBAgAABAj0SEGClFUOAleZmFIEcAgKsHKrmFGDpAQIECBAgQIAAAQI9EhBgpRVDgJXmZhSBHAICrByq5hRg6QECBAgQIECAAAECPRIQYKUVQ4CV5mYUgRwCAqwcquYUYOkBAgQIECBAgAABAj0SWNYA65PPfkO7SMa7vehx7pkXWQDfTWAPARejdsgi4CXuWVhNSoAAAQIECBAgQCBJQICVxBYCrDQ3owjkEBBg5VA1pxNYeoAAAQIECBAgQIBAjwSWNcA67zmLPYF1wgudwOpRG1vKwAUEWANvgFzbdwIrl6x5CRAgQIAAAQIECGxcQIC1cbNuhAArzc0oAjkEBFg5VM3pBJYeIECAAAECBAgQINAjAQFWWjEEWGluRhHIISDAyqFqTgGWHiBAgAABAgQIECDQI4FlDbDOf+4bF/oS97u+4LHumXvUx5YybAEX47Drn233HiHMRmtiAgQIECBAgAABAhsWEGBtmOyqAQKsNDejCOQQEGDlUDWnE1h6gAABAgQIECBAgECPBJY1wPqr5y32BNZdznACq0dtbCkDFxBgDbwBcm3fCaxcsuYlQIAAAQIECBAgsHEBAdbGzboRAqw0N6MI5BAQYOVQNacTWHqAAAECBAgQIECAQI8EBFhpxRBgpbkZRSCHgAArh6o5BVh6gAABAgQIECBAgECPBARYacUQYKW5GUUgh4AAK4eqOQVYeoAAAQIECBAgQIBAjwQEWGnFEGCluRlFIIeAACuHqjkFWHqAAAECBAgQIECAQI8EljbAev6b2kUy3uX5j3HPvMgC+G4Cewi4GLVDFgEvcc/CalICBAgQIECAAAECSQICrCS2EGCluRlFIIeAACuHqjmdwNIDBAgQIECAAAECBHokIMBKK4YAK83NKAI5BARYOVTNKcDSAwQIECBAgAABAgR6JLCsAdanznjzQh8h/JnnPdo9c4/62FKGLeBiHHb9s+3eI4TZaE1MgAABAgQIECBAYMMCAqwNk101QICV5mYUgRwCAqwcquZ0AksPECBAgAABAgQIEOiRwLIGWJ9+wWJPYP2v5zqB1aM2tpSBCwiwBt4AubbvBFYuWfMSIECAAAECBAgQ2LiAAGvjZt0IAVaam1EEcggIsHKomtMJLD1AgAABAgQIECBAoEcCAqy0Ygiw0tyMIpBDQICVQ9WcAiw9QIAAAQIECBAgQKBHAssaYP31i96y0Je43/nZp7pn7lEfW8qwBVyMw65/tt17hDAbrYkJECBAgAABAgQIbFhAgLVhsqsGCLDS3IwikENAgJVD1ZxOYOkBAgQIECBAgAABAj0SEGClFUOAleZmFIEcAgKsHKrmFGDpAQIECBAgQIAAAQI9EhBgpRVDgJXmZhSBHAICrByq5hRg6QECBAgQIECAAAECPRIQYKUVQ4CV5mYUgRwCAqwcquYUYOkBAgQIECBAgAABAj0SWNYA629evNiXuP/0s7zEvUdtbCkDFxBgDbwBcm3fS9xzyZqXAAECBAj8f+3de7glV1kn/nftbkPSSYAo4dI0pNM5u3Y3TSIY7ggEBRlFFEZQBNGgiD9AhovMDN64zCjoMIAoOl7QyIh4IXLJoIOA2ihyEcIlAn127ZOkURLAgAJBzaXPrnmq7c6v03anc2rv1bt21ef8o0/Oflet9XlXtc/+uqoOAQIECGxcQIC1cbO6QoBa4nTSAAAgAElEQVTVzE0VgRwCAqwcqsZ0AsseIECAAAECBAgQINAigWUNsN7/st+qFsn4wJ/4Qd+ZF9kA1yZwmICb0XbIIuAEVhZWgxIgQIAAAQIECBBoJCDAasQWAqxmbqoI5BAQYOVQNaYTWPYAAQIECBAgQIAAgRYJCLCaNUOA1cxNFYEcAgKsHKrGFGDZAwQIECBAgAABAgRaJLCsAdYHXr7YRwgf8OMeIWzRNjaVngsIsHq+AXIt3yOEuWSNS4AAAQIECBAgQGDjAgKsjZvVFQKsZm6qCOQQEGDlUDWmE1j2AAECBAgQIECAAIEWCSxrgPXBn7tooS9xv/8Ln+o7c4v2san0W8DN2O/+Z1u9E1jZaA1MgAABAgQIECBAYMMCAqwNkx0oEGA1c1NFIIeAACuHqjGdwLIHCBAgQIAAAQIECLRIQIDVrBkCrGZuqgjkEBBg5VA1pgDLHiBAgAABAgQIECDQIgEBVrNmzCvAuuCCCzZfddVVz0spPSUihhGxPyI+klJ65Xg8vuSWZjccDi9MKV10vBUMBoOzV1dX99Wf2759+8lf8zVf8+yU0lMjYkdETCNiXFXV7972trd9zaWXXnrjofGKoqhrzrqF8X++LMsXHu/6fk8gt4AAK7dwT8f3CGFPG2/ZBAgQIECAAAECrRQQYDVry7wCrNFodHFVVd8VEW+OiEuqqjo5pfS0iLhPVVXPmEwmv3qsGe7ateus6XR632P8PlVV9Uv1704++eRzLrvssn/evXv3aTfeeON7I+Lrq6q6eDAYvKO+XkTU4dn9U0p/NB6PH39EgLUlpfTMo11jMBis7t279xPNBFURmJ+AAGt+lkY6TECAZTsQIECAAAECBAgQaI/AsgZYf/Pzi32J+/3+6+wvcS+K4rER8ZaU0hvH4/GTD+2Kbdu2nbJly5bLUkpb19fXt6+trV2z0R1TFMWzIuK1KaXvGY/Hf1jXF0VRn5Z6eUS8uizL5x8a8/zzz/+aa6+99qMRsXswGNxndXX10oOfP3BqqyzL7Ru9vs8TOJECAqwTqd2jawmwetRsSyVAgAABAgQIEGi9gACrWYvmEWCNRqP6xNVjDg+NDs1mOBy+IKX0iqqqnjOZTH5xI7PcsWPH3Tdv3lyfjHpPWZaPOVRbh1pVVV0wGAz+y3g8vvLwMYuieFVE1I8yPm08Hv+mAGsj4j67aAEB1qI70NHrC7A62ljLIkCAAAECBAgQWEoBAVazts0jwCqKoj5ZdVpZlqcefBfVTZNZWVl54GAweF9E/EFZlk/cyCyLonhLRDxq//79O6+44oq/uzW1w+HwN1NKP5hSesJ4PL74aAHW1q1bt1x99dXXR8T6rRnTZwicKAEB1omS7tl1BFg9a7jlEiBAgAABAgQItFpgWQOsD/2P364WCXvf/3LhTN+ZR6PR6VVVfSUi1sqyrF/efrOfoijuGhGfiYgPl2V5rPdc/TuClZWVBwwGg/enlH52PB7/1K0x2rVr113W19f3RsT1KaWV8Xh87WEB1ikR8bsH35N1h4NB26VVVf3cZDKp39vlh8DCBWa6GRc+exNorYAAq7WtMTECBAgQIECAAIEeCgiwmjV9DgHW1qqqroqIj5dlea8jZ7F9+/bbn3TSSf/0b6+gKke3dpZFUfx5/QL4G2644e779u370vHqzj333DOuv/76d0REHZI9qSzL3z9Uc9hfIXx3RPxOVVVfHAwG31BV1Qsi4rYR8eyyLF97vGv4PYHcAgKs3MI9HV+A1dPGWzYBAgQIECBAgEArBZY2wPqfCz6B9YLZTmAdPPV09bECrIPB0j9uJMBaWVl52GAw2BMRryrL8seOt+F27do1XF9ff3tEDKuqeu6R79oajUbfFBHVeDz+i8PH2rlz57nT6fTDEXHD/v37t11xxRVfPt61/J5ATgEBVk7dHo8twOpx8y2dAAECBAgQIECgdQICrGYtue+MAdZ555136nXXXffViLi8LMuVI2dx8EXsn46ID5Vleb9bM8uiKH4vIp44nU53r62tfeqWaoqieERE1H+d8OSIeGpZln9wa65x6DPD4fBtKaXviIhHl2X5Jxup9VkC8xYQYM1b1HgHBO6z45ELfVZdGwgQIEBgPgLX76/f4eqHAAECBJZdQIDVrIOzBlj1VYui+GxEnHHwJe43ezH6Yaep3lCW5VOON8tt27adsmXLli9GxJVlWe4+TnhVvxT+dyLic9Pp9DvX1tY+crzxj/z9aDT61aqqfqSqqidNJpM6OPNDYGECAqyF0Xf7wgKsbvfX6ggQ6I+AAKs/vbZSAgS6LbCsAdaHX/n6hf4/xu/zYz8w83fm4XD4ppTS4weDwYNXV1frvzh4009RFC+KiJdGxNPLsvyN4+3Coii+LSL+uKqq10wmk+ce6/Oj0eg7qqr6o4j4VErpW8fjcf0Y47/7WVlZOWcwGHxjVVUfn0wmHzvyA0VRvCciHjqdTh+0trb2/uPNz+8J5BSY+WbMOTljL6+AAGt5e2fmBAgQOFxAgGU/ECBAoBsCAqxmfZxHgHXwMb53RcRby7J83KGZ7Nix43abN2/+ZEScMp1Oz15bW/vKwf92l+l0+sW1tbVrjhIovSQiXlxV1Q9OJpOLbiGU+lhVVZ9NKT2oLMsvHGv1h50A+8DWrVsfsmfPnv2HPlsUxTdHRP1i9/q0V/0XFG92eqyZqCoCzQUEWM3tVN6CgADL9iBAgEA3BARY3eijVRAgQECA1WwPzCPAqq9cFMXrIuKHqqq6JKX05og4NSKeGRE7U0pPHI/HF9efGw6HF6aU6mDqlWVZ1n8F8GY/h95/NRgMHr66ulq/yP3f/Rw68VWf0hoMBu89xsr3jcfj+gXtMRqNfquqqqdGRH0C6w31XyFMKX1DRPxISmn/+vr6t62trdUnsfwQWKiAAGuh/N29uACru721MgIE+iUgwOpXv62WAIHuCgiwmvV2XgFWRAyKonhGRPxwnRlFxPUppQ+ur6+/7PBw6HgB1nA4fEdK6VHT6fT8Y73TqiiKfRFx1nFW/PqyLC88+JlNRVE8OSLq+d0zIm5TvzcrIv4spfRz4/F43ExPFYH5Cgiw5utptIMCAixbgQABAt0QEGB1o49WQYAAAQFWsz0wxwCr2QRUESBwk4AAy2bIIiDAysJqUAIECJxwAQHWCSd3QQIECGQRWNYA69JXLfYl7uc/f/aXuGdpqEEJ9FBAgNXDpp+IJQuwToSyaxAgQCC/gAArv7ErECBA4EQICLCaKQuwmrmpIpBDQICVQ9WYIcCyCQgQINANAQFWN/poFQQIEFjWAOsjr/7f1SK79w3P+37fmRfZANcmcJiAm9F2yCIgwMrCalACBAiccAEB1gknd0ECBAhkERBgNWMVYDVzU0Ugh4AAK4eqMZ3AsgcIECDQEQEBVkcaaRkECPReQIDVbAsIsJq5qSKQQ0CAlUPVmAIse4AAAQIdERBgdaSRlkGAQO8FljXA+ugvLPYRwns/1yOEvb95ALRGQIDVmlZ0ayIeIexWP62GAIH+Cgiw+tt7KydAoFsCAqxm/RRgNXNTRSCHgAArh6oxncCyBwgQINARAQFWRxppGQQI9F5gaQOs1/zOQl/ifu/nPMV35t7fPQDaIuBmbEsnOjYPJ7A61lDLIUCgtwICrN623sIJEOiYgACrWUMFWM3cVBHIISDAyqFqTCew7AECBAh0RECA1ZFGWgYBAr0XEGA12wICrGZuqgjkEBBg5VA1pgDLHiBAgEBHBARYHWmkZRAg0HsBAVazLSDAauamikAOAQFWDlVjCrDsAQIECHREQIDVkUZaBgECvRcQYDXbAgKsZm6qCOQQEGDlUDWmAMseIECAQEcEBFgdaaRlECDQe4FlDbA+9ktvWOhL3O/17O/znbn3dw+Atgi4GdvSiY7Nw0vcO9ZQyyFAoLcCAqzett7CCRDomIAAq1lDBVjN3FQRyCEgwMqhakwnsOwBAgQIdERAgNWRRloGAQK9F1jWAOvjr13sCayv/1EnsHp/8wBojYAAqzWt6NZEnMDqVj+thgCB/goIsPrbeysnQKBbAgKsZv0UYDVzU0Ugh4AAK4eqMZ3AsgcIECDQEQEBVkcaaRkECPReQIDVbAsIsJq5qSKQQ0CAlUPVmAIse4AAAQIdERBgdaSRlkGAQO8FljXAuuyXf3ehL3E/71lP9p2593cPgLYIuBnb0omOzcMjhB1rqOUQINBbAQFWb1tv4QQIdExAgNWsoQKsZm6qCOQQEGDlUDWmE1j2AAECBDoiIMDqSCMtgwCB3gssa4D1t7+y2BNY5z7TCaze3zwAWiMgwGpNK7o1ESewutVPqyFAoL8CAqz+9t7KCRDoloAAq1k/BVjN3FQRyCEgwMqhakwnsOwBAgQIdERAgNWRRloGAQK9FxBgNdsCAqxmbqoI5BAQYOVQNaYAyx4gQIBARwQEWB1ppGUQINB7AQFWsy0gwGrmpopADgEBVg5VYwqw7AECBAh0RECA1ZFGWgYBAr0XEGA12wICrGZuqgjkEBBg5VA1pgDLHiBAgEBHBARYHWmkZRAg0HuBpQ2w/tcbq0U279xnPMl35kU2wLUJHCbgZrQdsgh4iXsWVoMSIEDghAsIsE44uQsSIEAgi4AAqxmrAKuZmyoCOQQEWDlUjekElj1AgACBjggIsDrSSMsgQKD3AgKsZltAgNXMTRWBHAICrByqxhRg2QMECBDoiIAAqyONtAwCBHovsKwB1id+7fcW+gjhPX/ke31n7v3dA6AtAm7GtnSiY/PwCGHHGmo5BAj0VkCA1dvWWzgBAh0TEGA1a6gAq5mbKgI5BARYOVSN6QSWPUCAAIGOCAiwOtJIyyBAoPcCyxpgffLXF3sCa/fTncDq/c0DoDUCAqzWtKJbE3ECq1v9tBoCBPorIMDqb++tnACBbgkIsJr1U4DVzE0VgRwCAqwcqsZ0AsseIECAQEcEBFgdaaRlECDQewEBVrMtIMBq5qaKQA4BAVYOVWMKsOwBAgQIdERAgNWRRloGAQK9F1jaAOs3fn+hL3Hf/cNP9J2593cPgLYIuBnb0omOzcMjhB1rqOUQINBbAQFWb1tv4QQIdExAgNWsoQKsZm6qCOQQEGDlUDWmE1j2AAECBDoiIMDqSCMtgwCB3gsIsJptAQFWMzdVBHIICLByqBpTgGUPECBAoCMCAqyONNIyCBDovYAAq9kWEGA1c1NFIIeAACuHqjEFWPYAAQIEOiIgwOpIIy2DAIHeCwiwmm0BAVYzN1UEcggIsHKoGlOAZQ8QIECgIwICrI400jIIEOi9wLIGWJ963WJf4n6Pp3mJe+9vHgCtERBgtaYV3ZqIl7h3q59WQ4BAfwUEWP3tvZUTINAtAQFWs34KsJq5qSKQQ0CAlUPVmE5g2QMECBDoiIAAqyONtAwCBHovsKwB1t7f+oNqkc3b9YPf4zvzIhvg2gQOE3Az2g5ZBJzAysJqUAIECJxwAQHWCSd3QQIECGQREGA1YxVgNXNTRSCHgAArh6oxncCyBwgQINARAQFWRxppGQQI9F5AgNVsCwiwmrmpIpBDQICVQ9WYAix7gAABAh0REGB1pJGWQYBA7wWWNcBavegPF/oI4c6nfrfvzL2/ewC0RcDN2JZOdGweHiHsWEMthwCB3goIsHrbegsnQKBjAgKsZg0VYDVzU0Ugh4AAK4eqMZ3AsgcIECDQEQEBVkcaaRkECPReYGkDrN9e8AmsC53A6v3NA6A1AgKs1rSiWxNxAqtb/bQaAgT6KyDA6m/vrZwAgW4JCLCa9XOnAKsZnCoCGQQEWBlQDRlOYNkEBAgQ6IiAAKsjjbQMAgR6LyDAarYFBFjN3FQRyCEgwMqhakwBlj1AgACBjggIsDrSSMsgQKD3AgKsZltAgNXMTRWBHAICrByqxhRg2QMECBDoiIAAqyONtAwCBHovIMBqtgUEWM3cVBHIISDAyqFqTAGWPUCAAIGOCAiwOtJIyyBAoPcCyxpgjV//pmqRzRv9wBN8Z15kA1ybwGECbsYObofhcHhhSuk1EXHbwWBw9urq6r4jlzkajc6uqurFEfHIiDgzIr4QEe8cDAYvOdrnN8rkJe4bFfN5AgQItFNAgNXOvpgVAQIENiogwNqo2L99XoDVzE0VgRwCAqwcqgsa85xzzrnjpk2bfi0ivjMi/iUiTj1agFUUxY6I+EBEnFxV1S9ExGpKaRgRz4+If920adP99+7d++lZliHAmkVPLQECBNojIMBqTy/MhAABArMICLCa6QmwmrmpIpBDQICVQ3VBY45Go09XVXXSYDC4sKqqH6+q6mHHCLDeWodcVVV9y2Qyedeh6Q6Hw0emlN5ZVdXFk8nkCbMsQ4A1i55aAgQItEdAgNWeXpgJAQIEZhFY1gCr/N8XL/QRwuL7H+878ywbTy2BOQq4GeeIueihiqL4/el0+uy1tbVrRqPRnqMFWAdPaV1dVdXeyWRy7pFzLoriE/VJ2cFgcOfV1dUvNl2TAKupnDoCBAi0S0CA1a5+mA0BAgSaCgiwmskJsJq5qSKQQ0CAlUO1BWMeK8AaDoePTim9PSJ+vSzLHzlKgFU/gvj0lNK3jsfjdzRdigCrqZw6AgQItEtAgNWufpgNAQIEmgosbYD1hgWfwPo+J7Ca7jl1BOYtIMCat2hLxruFAOvZKaVfrKrqpyaTyc8eOd3hcPiTKaWfiYgfLcvyl5suR4DVVE4dAQIE2iUgwGpXP8yGAAECTQUEWM3kCgFWMzhVBDIICLAyoLZhyGMFWEVR/ERE/GxK6Xnj8bh+gfvNfkaj0XOrqnp1RPxEWZYvb7oWAVZTOXUECBBol4AAq139MBsCBAg0FRBgNZMTYDVzU0Ugh4AAK4dqC8a8hQDrxyPiZccKsIqieF5EvEqA1YImmgIBAgRaICDAakETTIEAAQJzEFjWAGvyu3+00Je4D5/8Xb4zz2H/GYLAPATcjPNQbOEYtxBgPSsiXltV1U9PJpP6UcGb/RRF8aKIeGlEPKssy19pujQnsJrKqSNAgEC7BARY7eqH2RAgQKCpgACrmZwAq5mbKgI5BARYOVRbMOaxAqydO3c+ajqd1i9nf11Zlj98lADrooi4MCIeUZblnzVdigCrqZw6AgQItEtAgNWufpgNAQIEmgoIsJrJCbCauakikENAgJVDtQVjHivAOvfcc8+4/vrrPxcR+8qyHB0lwLo8IrZOp9M7ra2tfaXpUgRYTeXUESBAoF0CAqx29cNsCBAg0FRAgNVMToDVzE0VgRwCAqwcqi0Y81gBVj214XD4hpTSk1NKjx2Px287NN3RaPT4qqreFBG/WZbl02ZZhgBrFj21BAgQaI+AAKs9vTATAgQIzCIgwGqmJ8Bq5qaKQA4BAVYO1QWMuWvXrrOm0+l9D126qqr6PVb3SCk9MyKuOfjf943H4w/v2rXrLuvr6x9MKX1d/cL26XS6N6W0OyLqF7hfHREPKMvyC7MsQ4A1i55aAgQItEdAgNWeXpgJAQIEZhFY1gBr7Y1vXuhL3Fee9B99Z55l46klMEcBN+McMRc51HA4vDClVL+/6pZ+Xl+WZf1+qxiNRlurqnpxRDw6Iu4YEfVjhZesr6//t8svv/wfZl2LAGtWQfUECBBoh4AAqx19MAsCBAjMKiDAaiYowGrmpopADgEBVg5VY4YAyyYgQIBANwQEWN3oo1UQIEBgaQOs31vwCazvdQLL3UOgLQICrLZ0omPzEGB1rKGWQ4BAbwUEWL1tvYUTINAxAQFWs4auCLCawakikEFAgJUB1ZDhBJZNQIAAgY4ICLA60kjLIECg9wICrGZbQIDVzE0VgRwCAqwcqsYUYNkDBAgQ6IiAAKsjjbQMAgR6L7CsAdblf/CWhb7E/ZzveZzvzL2/ewC0RcDN2JZOdGweHiHsWEMthwCB3goIsHrbegsnQKBjAgKsZg0VYDVzU0Ugh4AAK4eqMZ3AsgcIECDQEQEBVkcaaRkECPReYGkDrD9862JPYH33Y31n7v3dA6AtAm7GtnSiY/NwAqtjDbUcAgR6KyDA6m3rLZwAgY4JCLCaNfQcAVYzOFUEMggIsDKgGtJL3O0BAgQIdEVAgNWVTloHAQJ9FxBgNdsBAqxmbqoI5BAQYOVQNaZHCO0BAgQIdERAgNWRRloGAQK9FxBgNdsCAqxmbqoI5BAQYOVQNaYAyx4gQIBARwQEWB1ppGUQINB7AQFWsy0gwGrmpopADgEBVg5VYwqw7AECBAh0RECA1ZFGWgYBAr0XWNYA64o3LfYl7jue4CXuvb95ALRGQIDVmlZ0ayJe4t6tfloNAQL9FRBg9bf3Vk6AQLcEBFjN+inAauamikAOAQFWDlVjOoFlDxAgQKAjAgKsjjTSMggQ6L2AAKvZFhBgNXNTRSCHgAArh6oxBVj2AAECBDoiIMDqSCMtgwCB3gssa4B15cVvqxbZvLMf/52+My+yAa5N4DABN6PtkEXAI4RZWA1KgACBEy4gwDrh5C5IgACBLAICrGasAqxmbqoI5BAQYOVQNaYTWPYAAQIEOiIgwOpIIy2DAIHeCyxtgPXmSxZ7Aus/fofvzL2/ewC0RcDN2JZOdGweTmB1rKGWQ4BAbwUEWL1tvYUTINAxAQFWs4aeLcBqBqeKQAYBAVYGVEOGE1g2AQECBDoiIMDqSCMtgwCB3gssa4C17y3/Z6EnsLY/7jG+M/f+7gHQFgE3Y1s60bF5OIHVsYZaDgECvRUQYPW29RZOgEDHBARYzRoqwGrmpopADgEBVg5VYzqBZQ8QIECgIwICrI400jIIEOi9gACr2RYQYDVzU0Ugh4AAK4eqMQVY9gABAgQ6IiDA6kgjLYMAgd4LCLCabQEBVjM3VQRyCAiwcqgaU4BlDxAgQKAjAgKsjjTSMggQ6L2AAKvZFhBgNXNTRSCHgAArh6oxBVj2AAECBDoiIMDqSCMtgwCB3gssbYD11gW/xP2xXuLe+5sHQGsEBFitaUW3JuIl7t3qp9UQINBfAQFWf3tv5QQIdEtAgNWsn9sFWM3gVBHIICDAyoBqyHACyyYgQIBARwQEWB1ppGUQINB7AQFWsy0gwGrmpopADgEBVg5VYwqw7AECBAh0RECA1ZFGWgYBAr0XWNYA69Nve3u1yOad9Z3f7jvzIhvg2gQOE3Az2g5ZBDxCmIXVoAQIEDjhAgKsE07uggQIEMgiIMBqxirAauamikAOAQFWDlVjOoFlDxAgQKAjAgKsjjTSMggQ6L3AsgZYf/d//nihJ7Du/phH+87c+7sHQFsE3Ixt6UTH5uEEVscaajkECPRWQIDV29ZbOAECHRMQYDVrqACrmZsqAjkEBFg5VI3pBJY9QIAAgY4ICLA60kjLIECg9wICrGZbQIDVzE0VgRwCAqwcqsYUYNkDBAgQ6IiAAKsjjbQMAgR6L7C0Adbb/2SxjxB++7f5ztz7uwdAWwTcjG3pRMfm4RHCjjXUcggQ6K2AAKu3rbdwAgQ6JiDAatbQu88pwLrgggs2X3XVVc9LKT0lIoYRsT8iPpJSeuV4PL7klmY3HA4vTClddLwVDAaDs1dXV/cd+txoNPqhqqp+JKW0u/5vVVV9KqX0v8bj8W8dOdbu3btP279//wurqnpCRGyPiH+JiA9ExMvKsvyr413b7wmcCAEB1olQ7uE1BFg9bLolEyDQSQEBVifbalEECPRQYFkDrL//4/+70BNYd3v0t87lO/NoNLq4qqrviog3R8QlVVWdnFJ6WkTcp6qqZ0wmk1891rbctWvXWdPp9L7H+H2qquqX6t+dfPLJ51x22WX/XP/vRVH8z4j4sYj4i4h4Y1VV6ymlJ0XEIyLi58uyfOGh8c4///yv+epXv/qeqqoeUFXVRYPB4C+m0+nXppSeFRE7qqp63GQyeXsPbxtLbpnAXG7Glq3JdFogIMBqQRNMgQABAnMQEGDNAdEQBAgQaIGAAKtZE+YRYBVF8diIeEtK6Y3j8fjJh2aybdu2U7Zs2XJZSmnr+vr69rW1tWs2OsuiKOqQ6bUppe8Zj8d/WNevrKzcezAYXBoRf12W5cMiYnpw3EFRFHsi4sGDweBeq6urf1v/99Fo9Nyqql598LTVTx6awznnnHPHTZs2rUbEdaeffvpZl1566Y0bnZ/PE5ingABrnprGuklAgGUzECBAoBsCAqxu9NEqCBAgIMBqtgfmEWCNRqP6xNVjBoPBfVZXV+tg6aaf4XD4gpTSK6qqes5kMvnFjcxyx44dd9+8efMnIuI9ZVk+5lBtURT1OM+uqurxk8nkjw4fczQaPb6qqjdFxKvLsnx+/buiKC6LiHtOp9M7HRmiFUXx2oh4VkrpO4/3qONG5u6zBJoICLCaqKk5roAA67hEPkCAAIGlEBBgLUWbTJIAAQLHFRBgHZfoqB+YR4BVFEV9suq0sixPPew01IHrraysPHAwGLwvIv6gLMsnbmSWRVG8JSIetX///p1XXHHF3x0WYH2ofjRx06ZNW/fu3fvZw8fctWvXXdbX16+OiA+WZfmA+t1XN95441ciYlKW5ejI6w+Hw++tT44d+djhRubpswTmJSDAmpekcW4mIMCyIQgQINANAQFWN/poFQQIEBBgNdsDswZYo9Ho9Kqq6oBorSzL+uXtN/spiuKuEfGZiPhwWZbHes/Vv5v8ysrKAwaDwftTSj87Ho9/6vAPFEXxxYg4vSzL29Tvbj+iOBVFcX1EfLksyzN37tx57nQ6rU9gvbssy0ceeaHRaPTgqqreW1XVxZPJpH7Bux8CCxMQYC2MvtsXFmB1u79WR4BAfwQEWP3ptZUSINBtgaUNsP5kwS9x/7bZXuI+Go22VlV1VUR8vCzLex25y7Zv3377k0466Z8iojzaCahj7cqiKP68PmV1ww033H3fvn1fOiLAuqH+K4JlWd7+aPVFUdSfP6UOuHbu3Pmg6XT619VYiNIAACAASURBVBHxtrIs63d13exnOBzeK6X00ZTSO8fj8aO6fZdYXdsFBFht79CSzk+AtaSNM20CBAgcISDAsiUIECDQDQEBVrM+3m3GAOuwR/aOGmCde+65Z1x//fX/uJEAa2Vl5WGDwaB+GfuryrKs/9LgzX4OnrD611sIsL5c/9HCOsA67BHGowZYB18I/xEBVrP9o2q+AgKs+Xoa7aCAAMtWIECAQDcEBFjd6KNVECBAQIDVbA/MGmCdd955p1533XVfjYjLy7JcOXIWB1/E/umI+FBZlve7NbMsiuL3IuKJ0+l099ra2qeOEmDV79y63TEeIaz/EmH9COE/lWV5x5WVld2DwaB+EfyflWX5iCPHGg6HD00pvSci3lSW5Xffmvn5DIFcAgKsXLI9H1eA1fMNYPkECHRGQIDVmVZaCAECPRdY1gDrM+94x5HvcDqhndz2H/7DzN+Zi6KoX6R+xsGXuK8fvoDDTlO9oSzLpxxvcdu2bTtly5Yt9TuurizLcvfRPl8Uxfsj4gERsa0sy/rxxZt+du3addb6+vq+iHhvWZYP2bp165bTTjvt2oi44hjv6PqBiPjtlNLPjMfjnz7e/PyeQE6BmW/GnJMz9vIKCLCWt3dmToAAgcMFBFj2AwECBLohIMBq1sd5BFjD4fBNKaXHDwaDB6+urtZ/cfCmn6IoXhQRL42Ip5dl+RvHm2VRFN8WEX9cVdVrJpPJc4/2+dFo9Iqqql5QVdWTJ5NJ/RcED7/e90fE6yPiZWVZ/mT9i6IoDvzVwpTSXcfjcf0XCm/6GY1Gv1VV1VOrqvqWyWTyruPNz+8J5BQQYOXU7fHYAqweN9/SCRDolIAAq1PttBgCBHossLQB1p/+6WJPYD3qUTN/Zy6Kon40rw5/3lqW5eMObcMdO3bcbvPmzZ+sX6g+nU7PXltb+8rB/3aX6XT6xbW1tfpRwJv9FEXxkoh4cVVVPziZTC462pYuimJnRPxtRHxs69atD9yzZ8/++nPnn3/+11x77bUfjIj65NausiyvOBhgPS0ifqOqql+YTCbPOzRmfVprOp1+ajqdXjWZTOoxpz2+hSy9BQIz34wtWIMptFBAgNXCppgSAQIEGggIsBqgKSFAgEALBQRYzZqybQ4B1sGQ6HUR8UNVVV2SUnpzRJwaEc+MiJ0ppSeOx+OL688Nh8MLU0p1MPXKsixfcJQA68D7rwaDwcNXV1frF7kf9Wc4HP5USum/R8RfpZReX1VV/d3/qRHxoIh4flmWrz6scDAajf5vfcqqPp2VUnp3VVVnRkQdZt0hpfTI8Xhc/6VCPwQWKiDAWih/dy8uwOpub62MAIF+CQiw+tVvqyVAoLsCAqxmvZ1XgBUR9cvTnxERPxwRo4i4PqX0wfX19Zetra3VL0k/8HO8AGs4HL4jpfSo6XR6/tra2kduaVXD4fB7U0r/KaV0XlVV9Um2j1ZV9erJZFIHaDf7WVlZuU1K6T+nlJ4cETsion4v1l/WjzeWZfnxZnqqCMxXQIA1X0+jHRQQYNkKBAgQ6IaAAKsbfbQKAgQILGuAddU737nQRwjv+i3f4juz24dASwTcjC1pRNemIcDqWkethwCBvgoIsPraeesmQKBrAgKsZh0VYDVzU0Ugh4AAK4eqMUOAZRMQIECgGwICrG700SoIECAgwGq2BwRYzdxUEcghIMDKoWpMAZY9QIAAgY4ICLA60kjLIECg9wICrGZbQIDVzE0VgRwCAqwcqsYUYNkDBAgQ6IiAAKsjjbQMAgR6LyDAarYFBFjN3FQRyCEgwMqhakwBlj1AgACBjggIsDrSSMsgQKD3AksbYL37XYt9ifsjHuk7c+/vHgBtEXAztqUTHZuHd2B1rKGWQ4BAbwUEWL1tvYUTINAxAQFWs4beVYDVDE4VgQwCAqwMqIYMJ7BsAgIECHREQIDVkUZaBgECvRdY1gDr6j9790JPYG395kf4ztz7uwdAWwTcjG3pRMfm4QRWxxpqOQQI9FZAgNXb1ls4AQIdExBgNWuoAKuZmyoCOQQEWDlUjekElj1AgACBjggIsDrSSMsgQKD3AgKsZltAgNXMTRWBHAICrByqxhRg2QMECBDoiIAAqyONtAwCBHovsLQB1p8v+BHCb/IIYe9vHgCtERBgtaYV3ZqIRwi71U+rIUCgvwICrP723soJEOiWgACrWT+3CrCawakikEFAgJUB1ZBe4m4PECBAgAABAgQIEGiTwIeveNdSfvf77F/82UJf4n6Xh3/zUrq1ae+ZC4F5CbgZ5yVpnJsJOIFlQxAgQIAAAQIECBBoj4AAq1kvBFjN3FQRyCEgwMqhakzvwLIHCBAgQIAAAQIECLRIQIDVrBkCrGZuqgjkEBBg5VA1pgDLHiBAgAABAgQIECDQIgEBVrNmCLCauakikENAgJVD1ZgCLHuAAAECBAgQIECAQIsEBFjNmiHAauamikAOAQFWDlVjCrDsAQIECBAgQIAAAQItEljaAOs9f77Yl7g/7Jt8Z27RPjaVfgu4Gfvd/2yr9xL3bLQGJkCAAAECBAgQILBhAQHWhskOFNxFgNUMThWBDAICrAyohgwnsGwCAgQIECBAgAABAi0SEGA1a4YAq5mbKgI5BARYOVSNKcCyBwgQIECAAAECBAi0SGBZA6zP/eVfLPQRwjs/9OG+M7doH5tKvwXcjP3uf7bVe4QwG62BCRAgQIAAAQIECGxYQIC1YbIDBQKsZm6qCOQQEGDlUDWmE1j2AAECBAgQIECAAIEWCSxtgPVXexZ7AushF/jO3KJ9bCr9FnAz9rv/2VbvBFY2WgMTIECAAAECBAgQ2LCAAGvDZAcK7izAaganikAGAQFWBlRDeom7PUCAAAECBAgQIECgTQICrGbdEGA1c1NFIIeAACuHqjE9QmgPECBAgAABAgQIEGiRwLIGWJ9/73sW+gjhnb7xYb4zt2gfm0q/BdyM/e5/ttV7hDAbrYEJECBAgAABAgQIbFhAgLVhsgMFAqxmbqoI5BAQYOVQNaYTWPYAAQIECBAgQIAAgRYJCLCaNUOA1cxNFYEcAgKsHKrGFGDZAwQIECBAgAABAgRaJCDAatYMAVYzN1UEcggIsHKoGlOAZQ8QIECAAAECBAgQaJGAAKtZMwRYzdxUEcghIMDKoWpMAZY9QIAAAQIECBAgQKBFAksbYL3vLxf7EvcHPdR35hbtY1Ppt4Cbsd/9z7Z6L3HPRmtgAgQIECBAgAABAhsWEGBtmOxAwZ0EWM3gVBHIICDAyoBqyHACyyYgQIAAAQIECBAg0CKBZQ2w/uH9f7XQE1h3fOBDfGdu0T42lX4LuBn73f9sq3cCKxutgQkQIECAAAECBAhsWECAtWGyAwUCrGZuqgjkEBBg5VA1phNY9gABAgQIECBAgACBFgkIsJo1Q4DVzE0VgRwCAqwcqsYUYNkDBAgQIECAAAECBFoksLQB1gfeu9hHCB/wjb4zt2gfm0q/BdyM/e5/ttV7hDAbrYEJECBAgAABAgQIbFhAgLVhsgMFdxRgNYNTRSCDgAArA6ohvcTdHiBAgAABAgQIECDQJoFlDbCu+Zu/XugJrDPv92Dfmdu0kc2l1wJuxl63P9/incDKZ2tkAgQIECBAgAABAhsVEGBtVOzfPi/AauamikAOAQFWDlVjegeWPUCAAAECBAgQIECgRQICrGbNEGA1c1NFIIeAACuHqjEFWPYAAQIECBAgQIAAgRYJCLCaNUOA1cxNFYEcAgKsHKrGFGDZAwQIECBAgAABAgRaJCDAatYMAVYzN1UEcggIsHKoGlOAZQ8QIECAAAECBAgQaJHA0gZYH3rfYl/ift8H+c7con1sKv0WcDP2u//ZVu8l7tloDUyAAAECBAgQIEBgwwICrA2THSg4U4DVDE4VgQwCAqwMqIYMJ7BsAgIECBAgQIAAAQItEljWAOsLH37/Qk9g3eE+D/SduUX72FT6LeBm7Hf/s63eCaxstAYmQIAAAQIECBAgsGEBAdaGyQ4UCLCauakikENAgJVD1ZhOYNkDBAgQIECAAAECBFokIMBq1gwBVjM3VQRyCAiwcqgaU4BlDxAgQIAAAQIECBBokcDSBliXfmCxjxCe/wDfmVu0j02l3wJuxn73P9vqPUKYjdbABAgQIECAAAECBDYsIMDaMNmBgjsIsJrBqSKQQUCAlQHVkF7ibg8QIECAAAECBAgQaJPAsgZYX/zoBxd6Auvr7n1/35nbtJHNpdcCbsZetz/f4p3AymdrZAIECBAgQIAAAQIbFRBgbVTs3z4vwGrmpopADgEBVg5VY3oHlj1AgAABAgQIECBAoEUCAqxmzRBgNXNTRSCHgAArh6oxBVj2AAECBAgQIECAAIEWCQiwmjVDgNXMTRWBHAICrByqxhRg2QMECBAgQIAAAQIEWiQgwGrWDAFWMzdVBHIICLByqBpTgGUPECBAgAABAgQIEGiRwNIGWB/7m8W+xP1e9/OduUX72FT6LeBm7Hf/s63eS9yz0RqYAAECBAgQIECAwIYFBFgbJjtQ8HUCrGZwqghkEBBgZUA1ZDiBZRMQIECAAAECBAgQaJGAAKtZMwRYzdxUEcghIMDKoWpMAZY9QIAAAQIECBAgQKBFAksbYH38Q4t9hPDr7+s7c4v2san0W8DN2O/+Z1u9Rwiz0RqYAAECBAgQIECAwIYFBFgbJjtQ8HUCrGZwqghkEBBgZUA1pEcI7QECBAgQIECAAAECbRJY1gDrHy/78EJPYH3teffxnblNG9lcei3gZux1+/Mt3gmsfLZGJkCAAAECBAgQILBRAQHWRsX+7fMCrGZuqgjkEBBg5VA1pndg2QMECBAgQIAAAQIEWiQgwGrWDAFWMzdVBHIICLByqBpTgGUPECBAgAABAgQIEGiRwNIGWJ+4dLGPEN7zfN+ZW7SPTaXfAm7Gfvc/2+o9QpiN1sAECBAgQIAAAQIENiwgwNow2YGCrxVgNYNTRSCDgAArA6ohvcTdHiBAgAABAgQIECDQJgEBVrNuCLCauakikENAgJVD1ZgeIbQHCBAgQIAAAQIECLRIQIDVrBkCrGZuqgjkEBBg5VA1pgDLHiBAgAABAgQIECDQIgEBVrNmCLCauakikENAgJVD1ZgCLHuAAAECBAgQIECAQIsEljXA+qdPfnShL3E/Y/e9fWdu0T42lX4LuBmXoP/D4fDClNJrIuK2g8Hg7NXV1X1HTns0Gp1dVdWLI+KREXFmRHwhIt45GAxecrTPD4fDh6aUfjwi7h8Rp1ZV9enBYPCHt7nNbV5+2WWX/fOsLF7iPqugegIECBAgQIAAAQLzExBgNbMUYDVzU0Ugh4AAK4fqnMY855xz7rhp06Zfi4jvjIh/qYOmowVYRVHsiIgPRMTJVVX9QkSsppSGEfH8iPjXTZs23X/v3r2fPjStoigeGxEXR8S+lNJrI+KaqqoeGhFPi4j3bd269eF79uzZP8syBFiz6KklQIAAAQIECBAgMF+BpQ2wPvWxxZ7Ause9fGee71Y0GoHGAm7GxnT5C0ej0aerqjppMBhcWFXVj1dV9bBjBFhvrUOuqqq+ZTKZvOvQzIbD4SNTSu+squriyWTyhPq/r6ys3GYwGNRh1kk33njjriuvvPLzhwVbPxsRPxERP1qW5S/PskIB1ix6agkQIECAAAECBAjMV0CA1czzDAFWMzhVBDIICLAyoM5ryKIofn86nT57bW3tmtFotOdoAdbBU1pXV1W1dzKZnHvktYui+EREjAaDwZ1XV1e/OBwO/2NK6Y8i4rVlWT778M+vrKycORgM6kDrY2VZfsMs6xBgzaKnlgABAgQIECBAgMB8BQRYzTwFWM3cVBHIISDAyqGaYcxjBVjD4fDRKaW3R8Svl2X5I0cJsOpHEJ+eUvrW8Xj8jtFo9Iqqql5QVdWTJpPJ7x3l8+P6oNbJJ59821nehSXAyrAJDEmAAAECBAgQIECgocCyBlhf2vvxhT5CePtdX+87c8M9p4zAvAXcjPMWzTTeLQRYz04p/WJVVT81mUzqRwBv9jMcDn8ypfQzhx4LLIqiPn1Vn8L6xvF4/NdHCbDqRxAfMZ1O77m2tvbJpssRYDWVU0eAAAECBAgQIEBg/gICrGamAqxmbqoI5BAQYOVQzTDmsQKsoijqd1b9bErpeePxuH6B+81+RqPRc6uqenX9bquyLF9eFMU7679UWFXVvSeTyceOEmAdeJ/WdDp90Nra2vubLkWA1VROHQECBAgQIECAAIH5CyxtgDW+bLEnsEbn+c48/+1oRAKNBNyMjdhOfNEtBFg/HhEvO1aAVRTF8yLiVYcCrNFo9Kf1y96PFWANh8O3pZS+Q4B14nvsigQIECBAgAABAgRyCQiwmsneXoDVDE4VgQwCAqwMqDmGvIUA61n1C9mrqvrpyWRSPyp4s5+iKF4UES+NiGeVZfkrw+HwTSmlx0fEQ8qyfO9RPv/nEfHw6XS6e21t7VNN1+IEVlM5dQQIECBAgAABAgTmLyDAamYqwGrmpopADgEBVg7VDGMeK8DauXPno6bT6Tsi4nVlWf7wUQKpiyLiwvq9VmVZ/llRFC+PiBemlL5vPB7/7lE+f2VEbLvhhhtO37dv33VNlyLAaiqnjgABAgQIECBAgMD8BQRYzUwFWM3cVBHIISDAyqGaYcxjBVjnnnvuGddff/3nImJfWZajowRSl0fE1ul0eqe1tbWvHPZXC3+jLMunH/75HTt23H3z5s2fjoj3lWX54FmWIcCaRU8tAQIECBAgQIAAgfkKCLCaeQqwmrmpIpBDQICVQzXDmMcKsOpLDYfDN6SUnpxSeux4PH7bocuPRqPHV1X1poj4zbIsn1b/9wsuuGDz1VdfvRYRXzedTnetra195tDni6J4bf2oYUrpKePx+A2zLEOANYueWgIECBAgQIAAAQLzFVjWAOvL5d8u9CXutyvO9Z15vlvRaAQaC7gZG9PlLdy1a9dZ0+n0voeuUlVV/R6re6SUnhkR1xz87/vG4/GHd+3adZf19fUPppS+rn5h+3Q63ZtS2h0R9Qvcr46IB5Rl+YXDgqpvjog/jojPppReExFfqKrqURHxfRHxtrIsHxcRM/0fCgFW3v1hdAIECBAgQIAAAQIbERBgbUTr//+sAKuZmyoCOQQEWDlU5zDmcDi8MKVUv7/qln5eX5Zl/X6rGI1GW6uqenFEPDoi7hgR9WOFl6yvr/+3yy+//B+OHKQoivumlH66qqpvjIgtEbGWUvrfp5122qsvvfTSG2ddggBrVkH1BAgQIECAAAECBOYnIMBqZinAauamikAOAQFWDlVjhgDLJiBAgAABAgQIECDQHoGlDbAmn5jpyZBZO3C74T19Z54VUT2BOQm4GecEaZibCwiw7AgCBAgQIECAAAEC7REQYDXrhQCrmZsqAjkEBFg5VI3pBJY9QIAAAQIECBAgQKBFAksbYK19arEnsFbu4Ttzi/axqfRbwM3Y7/5nW70TWNloDUyAAAECBAgQIEBgwwICrA2THSi4nQCrGZwqAhkEBFgZUA0ZTmDZBAQIECBAgAABAgRaJCDAatYMAVYzN1UEcggIsHKoGlOAZQ8QIECAAAECBAgQaJHAsgZYX7li70IfIbztjl2+M7doH5tKvwXcjP3uf7bVe4QwG62BCRAgQIAAAQIECGxYQIC1YbIDBQKsZm6qCOQQEGDlUDWmE1j2AAECBAgQIECAAIEWCQiwmjVDgNXMTRWBHAICrByqxhRg2QMECBAgQIAAAQIEWiQgwGrWDAFWMzdVBHIICLByqBpTgGUPECBAgAABAgQIEGiRgACrWTMEWM3cVBHIISDAyqFqTAGWPUCAAAECBAgQIECgRQJLG2BdubrYl7ifvdN35hbtY1Ppt4Cbsd/9z7Z6L3HPRmtgAgQIECBAgAABAhsWEGBtmOxAwW0FWM3gVBHIICDAyoBqyHACyyYgQIAAAQIECBAg0CKBZQ2wrt03XugJrNO3j3xnbtE+NpV+C7gZ+93/bKt3AisbrYEJECBAgAABAgQIbFhAgLVhsgMF8wqwLrjggs1XXXXV81JKT4mIYUTsj4iPpJReOR6PL7k1s9u+ffvJJ5100n+NiCdFxFkR8eWIeG9K6aXj8fiyQ2OMRqM9VVU97JbGTCm9ZzweX1B/5lZ8/g/KsnzirZmjzxDIKSDAyqnb47EFWD1uvqUTIECAAAECBAi0TkCA1awl8wqwRqPRxVVVfVdEvDkiLqmq6uSU0tMi4j5VVT1jMpn86i3NcNu2bads2bLl3RHxgIh4XUrpr6qqOicinhcRg4h4SFmWH6/HWFlZedimTZvOPNp4VVXtjoiXRMRry7J89uEBVkrpCUermU6nfz+ZTD7YTFAVgfkJCLDmZ2mkwwQEWLYDAQIECBAgQIAAgfYILG2A9enJYh8hPGs483fmoigeGxFvSSm9cTweP/nQrjgYSl2WUtq6vr6+fW1t7Zpj7ZiiKF4aES+qqur/m0wmv3boc0VRfGtE/HZVVb80mUx+5jg7blAUxfsi4u433HDDPfbt2/elwwOssixnXmt7dryZdFHABu1iV1uwJgFWC5pgCgQIECBAgAABAgQOCgiwmm2F0+cQYI1Go/rE1WMGg8F9VldXLz18JsPh8AUppVdUVfWcyWTyi0ebZf344dVXX/3Z+pHBsizrxw8bhXpFUdSntV5VVdXjJ5PJHx261qFHCAVYzfaIqhMnIMA6cda9upIAq1fttlgCBAgQIECAAIGWCyxrgPXVv19rFNbMqx2n3W1l5u/MRVHUJ6tOK8vy1IiYHj63lZWVBw4Gg/pU1DHfMzUcDu+fUvpARPxyWZY/Wtfv3r37pDPPPHO6Z8+e+l1ax/0ZjUZbq6oqI2JPWZbffnjBkQFWfTLsTne60/5LL730xuMO7AMETqDAzDfjCZyrSy2RgABriZplqgQIECBAgAABAp0XEGA1a/GsAdZoNDq9qqqvRMTawdNTN5tIURR3jYjPRMSHy7K879FmORqNfrCqqt9MKT1vOp3+a0T8WEqpPolVh2Efmk6nL1pbW3vnLa1wNBr9alVVPxQR55ZluXq0AOvgu7Hq93JtO/j7T0bEL5Zl+evN9FQRmK+AAGu+nkY7KCDAshUIECBAgAABAgQItEdAgNWsF3MIsOqTT1dFxMfLsrzXkbPYvn377U866aR/ioiyLMvR0WY5HA7/c0rpf0TExyJiS1VVrzoYet07pfTCiDilqqrvmEwmf3y0+pWVlXMGg8HeiPj9siy//8jPHPZXCD8cEXVYVQdq94iIF0TEnSPilWVZ1v+7HwILFRBgLZS/uxcXYHW3t1ZGgAABAgQIECCwfAICrGY9mzXA2rVr113W19evPlaAde65555x/fXX/+NxAqyfSin994j4/Pr6+nmXX375PxxazcrKyrcMBoM/jYi9ZVnWodO/+ymK4qKIuHAwGJy3urr6t0d+oH6McfPmzbddXV2t/8rh+qHfH5z7JyLi9ps2bdq5d+/eSTNFVQTmIyDAmo+jUY4QEGDZEgQIECBAgAABAgTaIyDAataLWQOs884779TrrrvuqxFxeVmWK0fOYseOHXffvHnzp+tHAcuyvN/RZjkajZ5TVdUvVFX1W5PJpH4M8GY/RVH8XUTc7cYbb7zzlVde+fnDf3nwhFcdoP1tWZb336jCaDR6TVVV/ykinlWW5a9stN7nCcxTQIA1T01j3SQgwLIZCBAgQIAAAQIECLRHYGkDrM9cvtiXuG87Z+bvzEVR1H9B8IyDL3G/6YRTvTtWVlYeNhgM9kTEG8qyfMrRdszKykr9FwwviYjfOdojgEVRfDAi7jcYDEarq6v1i9pv+hkOhxemlC6qquq/TCaTV2x0RxZFUT+i+PKI+ImyLOv/6YfAwgRmvhkXNnMXbrWAAKvV7TE5AgQIECBAgACBngkIsJo1/LQ5BFjD4fBNKaXHDwaDB6+urtZ/cfCmn6IoXhQRL42Ip5dl+RtHm2VRFHeIiM9FxGpZlvc88jNFUdS/u0NK6YzxeHztEeP/YUQ8oaqqe08mk/odWjf7qR8TnE6n3zSdTv9+Mpn85VHGfn1EfH9VVU+aTCa/10xRFYH5CAiw5uNolCMEBFi2BAECBAgQIECAAIH2CCxrgPXPV12x0BNYp951x8zfmYuieEREvCsi3lqW5eMO7YodO3bcbvPmzfVf+jtlOp2evba29pWD/60Olb64trZ2zaHPHgrBjgyShsPhk1JKvxsR7y7L8pFHCaD2RcRdTz/99C2XXnrpjUf+fufOndun0+kVEXHldDq9dz2Hw665K6X00Yi4/oYbbjhr3759X2rPjjaTPgrMfDP2Ec2ajy8gwDq+kU8QIECAAAECBAgQOFECAqxm0vMIsOorF0Xxuoj4oaqqLkkpvTkiTo2IZ0bEzpTSE8fj8cX15w498nfkX/4755xz7rZp06b69Nad679CmFL6RFVV56eUnhURX51Op9+4trZWh2E3/Wzfvv3kk0466V8j4tNlWW4/lkBRFC+OiJfU7+mKiN+MiPqvJu6q33t18C8cfr/TV832j6r5Cgiw5utptIMCAixbgQABAgQIECBAgEB7BARYzXoxrwArIgZFUTwjIn44Ikb1qaaU0gfX19dftra29p5DsztWgFX/fvfu3Xfev3//S6qqenRE3CkivhgR76wfQSzLsj5FdbOf+vM33nhj/f6t+gXu592SwGg0+o6qqp4TEecfDNe+UFXVe1NKryjL8m+a6akiMF8BAdZ8PY0mwLIHCBAgQIAAAQIECLROYGkDrKv3LfYRwq3bfWdu3W42ob4KuBn72vnM63YCKzOw4QkQIECAAAECBAhsQECAtQGswz56qgCrGZwqAhkEBFgZUA0ZIcCyCwgQIECAAAECBAi0R2BpA6zPfXqxJ7DufJbvzO3ZxmbScwE3Y883QK7lc/raJwAAD7RJREFUC7ByyRqXAAECBAgQIECAwMYFBFgbN6srThVgNYNTRSCDgAArA6ohncCyBwgQIECAAAECBAi0SUCA1awbAqxmbqoI5BAQYOVQNaZHCO0BAgQIECBAgAABAi0SEGA1a4YAq5mbKgI5BARYOVSNKcCyBwgQIECAAAECBAi0SECA1awZAqxmbqoI5BAQYOVQNaYAyx4gQIAAAQIECBAg0CKBZQ2w/uXzf7fQl7hvudPdfWdu0T42lX4LuBn73f9sq/cS92y0BiZAgAABAgQIECCwYQEB1obJDhQIsJq5qSKQQ0CAlUPVmE5g2QMECBAgQIAAAQIEWiQgwGrWDAFWMzdVBHIICLByqBpTgGUPECBAgAABAgQIEGiRwNIGWP/wmcU+QnjHbb4zt2gfm0q/BdyM/e5/ttV7hDAbrYEJECBAgAABAgQIbFhAgLVhsgMFWwRYzeBUEcggIMDKgGrIcALLJiBAgAABAgQIECDQIoFlDbD+9ZqrFnoC65Qz7+o7c4v2san0W8DN2O/+Z1u9E1jZaA1MgAABAgQIECBAYMMCAqwNkx0oEGA1c1NFIIeAACuHqjGdwLIHCBAgQIAAAQIECLRIQIDVrBkCrGZuqgjkEBBg5VA1pgDLHiBAgAABAgQIECDQIoGlDbC+8NnFPkJ4h7v4ztyifWwq/RZwM/a7/9lW7xHCbLQGJkCAAAECBAgQILBhAQHWhskOFJwiwGoGp4pABgEBVgZUQ3qJuz1AgAABAgQIECBAoE0CAqxm3RBgNXNTRSCHgAArh6oxPUJoDxAgQIAAAQIECBBokYAAq1kzBFjN3FQRyCEgwMqhakwBlj1AgAABAgQIECBAoEUCAqxmzRBgNXNTRSCHgAArh6oxBVj2AAECBAgQIECAAIEWCSxrgHXdP35uoS9xP/lr7+w7c4v2san0W8DN2O/+Z1u9l7hnozUwAQIECBAgQIAAgQ0LCLA2THagQIDVzE0VgRwCAqwcqsZ0AsseIECAAAECBAgQINAigaUNsP7p84s9gXXGnXxnbtE+NpV+C7gZ+93/bKt3AisbrYEJECBAgAABAgQIbFhAgLVhsgMFJwuwmsGpIpBBQICVAdWQ4QSWTUCAAAECBAgQIECgRQICrGbNEGA1c1NFIIeAACuHqjEFWPYAAQIECBAgQIAAgRYJLGuAdf2XrlnoI4S3uf2ZvjO3aB+bSr8F3Iz97n+21XuEMButgQkQIECAAAECBAhsWECAtWGyAwUCrGZuqgjkEBBg5VA1phNY9gABAgQIECBAgACBFgksbYD15S8s9gTW7e7gO3OL9rGp9FvAzdjv/mdbvRNY2WgNTIAAAQIECBAgQGDDAgKsDZMdKLiNAKsZnCoCGQQEWBlQDUmAAAECBAgQIECAAAECBAgQIDA/AQHW/CyNRIAAAQIECBAgQIAAAQIECBAgkEFAgJUB1ZAECBAgQIAAAQIECBAgQIAAAQLzExBgzc/SSAQIECBAgAABAgQIECBAgAABAhkEBFgZUA1JgAABAgQIECBAgAABAgQIECAwPwEB1vwsjUSAAAECBAgQIECAAAECBAgQIJBBQICVAdWQBAgQIECAAAECBAgQIECAAAEC8xMQYM3P0kgECBAgQIAAAQIECBAgQIAAAQIZBARYGVANSYAAAQIECBAgQIAAAQIECBAgMD8BAdb8LI1EgAABAgQIECBAgAABAgQIECCQQUCAlQHVkAQIECBAgAABAgQIECBAgAABAvMTEGDNz9JIBAgQIECAAAECBAgQIECAAAECGQQEWBlQDUmAAAECBAgQIECAAAECBAgQIDA/AQHW/CyNRIAAAQIECBAgQIAAAQIECBAgkEFAgJUB1ZAECBAgQIAAAQIECBAgQIAAAQLzExBgzc/SSAQIdFCgKIrfjogfGAwGD19dXd3TwSVaEgECBHoh4N/zXrTZIgkQIECgwwICrA4319IIEJhd4FhfeHbs2HG7zZs3/1JEPCWl9J7xeHzB7FczAgECBAjkEjjav+crKyv3HgwGL4qIh0TE7SLiixHxl1VVvWwymXws11yMS4AAAQIECGxcQIC1cTMVBAj0SOBoX3iKovjmiLgoIs6IiNMEWD3aEJZKgMDSChz573lRFD9w8N/yz1ZV9b9SSn8XEeellJ5RVdUgIh5aluWHlnbBJk6AAAECBDomIMDqWEMthwCB+Qoc5QtPHV69KyL+tKqql6SUPiDAmq+50QgQIJBD4PB/z/fv3//+wWDwDxFRra+vn3v55Zf//aFrDofD700pvTEi3l6W5WNyzMWYBAgQIECAwMYFBFgbN1NBgECPBI4MsIbD4bcPBoOV8Xj8mp07d541nU6vFGD1aENYKgECSytw+L/nKaUr19fXXxIRk7IsX3b4olZWVm47GAy+HBGfKcvybku7YBMnQIAAAQIdExBgdayhlkOAwHwFbumlvzt37twuwJqvt9EIECCQS+DWvsT9nHPOudumTZvqxwk/WZblPXPNx7gECBAgQIDAxgQEWBvz8mkCBHomIMDqWcMtlwCBzgrc2gBrNBr9j6qq/nNK6YXj8fjnOwtiYQQIECBAYMkEBFhL1jDTJUDgxAoIsE6st6sRIEAgl8CtCbCKonhKRPx2RFx2ww03PHDfvn3X5ZqPcQkQIECAAIGNCQiwNubl0wQI9ExAgNWzhlsuAQKdFThegFUUxYsion4v1t4bb7zxm6688srPdxbDwggQIECAwBIKCLCWsGmmTIDAiRMQYJ04a1ciQIBAToFj/Xu+bdu2U7Zs2VKfuvruiPjzG2644bv27dv3pZxzMTYBAgQIECCwcQEB1sbNVBAg0CMBAVaPmm2pBAh0WuBo/56PRqPTI+JPq6p6YET80tatW5+/Z8+e/Z2GsDgCBAgQILCkAgKsJW2caRMgcGIEBFgnxtlVCBAgkFvgyH/PD568ekdEPCQi/lNZlq/NPQfjEyBAgAABAs0FBFjN7VQSINADAQFWD5psiQQI9ELgyH/Ph8Phr6SUnpFSeu54PH5NLxAskgABAgQILLGAAGuJm2fqBAjkFzjyC8/KysrDNm3adObBK59ZVdWvRMSnUkovPjSb9fX196ytrV2Tf3auQIAAAQK3VuDwf88j4ovT6fTjEfHZlNJzjjXGtdde+ydXX331v9zaa/gcAQIECBAgkE9AgJXP1sgECHRA4MgAazQa7amq6mG3tLTBYPDw1dXVPR1YviUQIECgMwKH/3u+vr6+PaV00fEWNxgMzl5dXd13vM/5PQECBAgQIJBfQICV39gVCBAgQIAAAQIECBAgQIAAAQIEZhAQYM2Ap5QAAQIECBAgQIAAAQIECBAgQCC/gAArv7ErECBAgAABAgQIECBAgAABAgQIzCAgwJoBTykBAgQIECBAgAABAgQIECBAgEB+AQFWfmNXIECAAAECBAgQIECAAAECBAgQmEFAgDUDnlICBAgQIECAAAECBAgQIECAAIH8AgKs/MauQIAAAQIECBAgQIAAAQIECBAgMIOAAGsGPKUECBAgQIAAAQIECBAgQIAAAQL5BQRY+Y1dgQABAgQIECBAgAABAgQIECBAYAYBAdYMeEoJECBAgAABAgQIECBAgAABAgTyCwiw8hu7AgECBAgQIECAAAECBAgQIECAwAwCAqwZ8JQSIECAAAECBAgQIECAAAECBAjkFxBg5Td2BQIECBAgQIAAAQIECBAgQIAAgRkEBFgz4CklQIAAAQIECBAgQIAAAQIECBDILyDAym/sCgQIECBAgAABAgQIECBAgAABAjMICLBmwFNKgAABAgQIECBAgAABAgQIECCQX0CAld/YFQgQIECAAAECBAgQIECAAAECBGYQEGDNgKeUAAECBAgQIECAAAECBAgQIEAgv4AAK7+xKxAgQIAAAQIECBAgQIAAAQIECMwgIMCaAU8pAQIECBAgQIAAAQIECBAgQIBAfgEBVn5jVyBAgAABAgQIECBAgAABAgQIEJhBQIA1A55SAgQIECBAgAABAgQIECBAgACB/AICrPzGrkCAAAECBAgQIECAAAECBAgQIDCDgABrBjylBAgQIECAAAECBAgQIECAAAEC+QUEWPmNXYEAAQIECBAgQIAAAQIECBAgQGAGAQHWDHhKCRAgQIAAAQIECBAgQIAAAQIE8gsIsPIbuwIBAgQIECBAgAABAgQIECBAgMAMAgKsGfCUEiBAgAABAgQIECBAgAABAgQI5BcQYOU3dgUCBAgQIECAAAECBAgQIECAAIEZBARYM+ApJUCAAAECBAgQIECAAAECBAgQyC8gwMpv7AoECBAgQIAAAQIECBAgQIAAAQIzCAiwZsBTSoAAAQIECBAgQIAAAQIECBAgkF9AgJXf2BUIECBAgAABAgQIECBAgAABAgRmEBBgzYCnlAABAgQIECBAgAABAgQIECBAIL+AACu/sSsQIECAAAECBAgQIECAAAECBAjMICDAmgFPKQECBAgQIECAAAECBAgQIECAQH4BAVZ+Y1cgQIAAAQIECBAgQIAAAQIECBCYQUCANQOeUgIECBAgQIAAAQIECBAgQIAAgfwCAqz8xq5AgAABAgQIECBAgAABAgQIECAwg4AAawY8pQQIECBAgAABAgQIECBAgAABAvkFBFj5jV2BAAECBAgQIECAAAECBAgQIEBgBgEB1gx4SgkQIECAAAECBAgQIECAAAECBPILCLDyG7sCAQIECBAgQIAAAQIECBAgQIDADAICrBnwlBIgQIAAAQIECBAgQIAAAQIECOQXEGDlN3YFAgQIECBAgAABAgQIECBAgACBGQQEWDPgKSVAgAABAgQIECBAgAABAgQIEMgvIMDKb+wKBAgQIECAAAECBAgQIECAAAECMwgIsGbAU0qAAAECBAgQIECAAAECBAgQIJBfQICV39gVCBAgQIAAAQIECBAgQIAAAQIEZhAQYM2Ap5QAAQIECBAgQIAAAQIECBAgQCC/gAArv7ErECBAgAABAgQIECBAgAABAgQIzCAgwJoBTykBAgQIECBAgAABAgQIECBAgEB+AQFWfmNXIECAAAECBAgQIECAAAECBAgQmEFAgDUDnlICBAgQIECAAAECBAgQIECAAIH8AgKs/MauQIAAAQIECBAgQIAAAQIECBAgMIOAAGsGPKUECBAgQIAAAQIECBAgQIAAAQL5BQRY+Y1dgQABAgQIECBAgAABAgQIECBAYAYBAdYMeEoJECBAgAABAgQIECBAgAABAgTyCwiw8hu7AgECBAgQIECAAAECBAgQIECAwAwCAqwZ8JQSIECAAAECBAgQIECAAAECBAjkFxBg5Td2BQIECBAgQIAAAQIECBAgQIAAgRkE/h/Uqiv7pe7BrQAAAABJRU5ErkJggg==\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the following function to help visualize results from the grid search\n",
    "def GridSearch_Heatmap(scores):\n",
    "    %matplotlib notebook\n",
    "    import seaborn as sns\n",
    "    import matplotlib.pyplot as plt\n",
    "    plt.figure()\n",
    "    sns.heatmap(scores.reshape(5,2), xticklabels=['l1','l2'], yticklabels=[0.01, 0.1, 1, 10, 100])\n",
    "    plt.yticks(rotation=0);\n",
    "\n",
    "#GridSearch_Heatmap(answer_six())"
   ]
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "python-machine-learning",
   "graded_item_id": "5yX9Z",
   "launcher_item_id": "eqnV3",
   "part_id": "Msnj0"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}