gan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "from keras.datasets import mnist\n",
    "import numpy as np\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers.convolutional import UpSampling2D, Conv2D\n",
    "from keras.layers import BatchNormalization, Embedding\n",
    "from keras.layers.advanced_activations import LeakyReLU\n",
    "from keras.layers import Reshape, Dropout, Flatten, Input, Dense, multiply\n",
    "from keras.optimizers import Adam\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "heigth = x_train.shape[-2]\n",
    "width = x_train.shape[-1]\n",
    "\n",
    "x_train = (x_train.astype(np.float32) - 127.5) / 127.5\n",
    "x_train = np.expand_dims(x_train, axis=3)\n",
    "y_train = y_train.reshape(-1, 1)    \n",
    "input_shape = (heigth, width, 1)\n",
    "classes = 10\n",
    "    \n",
    "generator_noise_size = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discrimantor():\n",
    "    model = Sequential()\n",
    "    model.add(Conv2D(16, kernel_size=(3, 3), strides=(2, 2), input_shape=input_shape, padding=\"same\"))\n",
    "    model.add(LeakyReLU(alpha=0.2))\n",
    "    model.add(Dropout(0.35))\n",
    "\n",
    "    model.add(Conv2D(32, kernel_size=(3, 3), strides=(1, 1), padding=\"same\"))\n",
    "    model.add(LeakyReLU(alpha=0.2))\n",
    "    model.add(Dropout(0.35))\n",
    "    model.add(BatchNormalization(momentum=0.8))\n",
    "\n",
    "    model.add(Conv2D(64, kernel_size=(3, 3), strides=(2, 2), padding=\"same\"))\n",
    "    model.add(LeakyReLU(alpha=0.2))\n",
    "    model.add(Dropout(0.35))\n",
    "    model.add(BatchNormalization(momentum=0.8))\n",
    "\n",
    "    model.add(Conv2D(128, kernel_size=(3, 3), strides=(1, 1), padding=\"same\"))\n",
    "    model.add(LeakyReLU(alpha=0.2))\n",
    "    model.add(Dropout(0.35))\n",
    "\n",
    "    model.add(Flatten())\n",
    "    \n",
    "    input_data = Input(shape=input_shape) \n",
    "\n",
    "    model_tensor = model(inputs=input_data)\n",
    "    is_real = Dense(1, activation=\"sigmoid\")(model_tensor)\n",
    "    output_label = Dense(classes + 1, activation=\"softmax\")(model_tensor)\n",
    "\n",
    "    final_model = Model(input_data, [is_real, output_label])\n",
    "    final_model.compile(loss=['binary_crossentropy', 'sparse_categorical_crossentropy'], \n",
    "                        optimizer=Adam(0.0002, 0.5),\n",
    "                        metrics=['accuracy'])\n",
    "    final_model.trainable = False\n",
    "    return final_model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generator():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(128 * 7 * 7, activation=\"relu\", input_dim=generator_noise_size))\n",
    "    model.add(Reshape((7, 7, 128)))\n",
    "    model.add(BatchNormalization(momentum=0.8))\n",
    "    model.add(UpSampling2D())\n",
    "\n",
    "    model.add(Conv2D(128, kernel_size=(3, 3), padding=\"same\", activation=\"relu\"))\n",
    "    model.add(BatchNormalization(momentum=0.8))\n",
    "    model.add(UpSampling2D())\n",
    "\n",
    "    model.add(Conv2D(64, kernel_size=(3, 3), padding=\"same\", activation=\"relu\"))\n",
    "    model.add(BatchNormalization(momentum=0.8))\n",
    "    model.add(Conv2D(1, kernel_size=(3, 3), padding='same', activation=\"tanh\"))\n",
    "    \n",
    "    input_noise = Input(shape=(generator_noise_size,))\n",
    "    input_label = Input(shape=(1,), dtype='int32')\n",
    "    label_embedding = Flatten()(Embedding(classes, generator_noise_size)(input_label))\n",
    "\n",
    "    model_input = multiply([input_noise, label_embedding])\n",
    "    model_output = model(model_input)\n",
    "\n",
    "    final_model = Model([input_noise, input_label], model_output)\n",
    "    return final_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    }
   ],
   "source": [
    "generator_model = generator()\n",
    "discrimantor_model = discrimantor()\n",
    "\n",
    "random_noise = Input(shape=(generator_noise_size,))\n",
    "noise_label = Input(shape=(1,))\n",
    "image_output = generator_model([random_noise, noise_label])\n",
    "\n",
    "#\tcheck upon the generator\n",
    "valid, valid_label = discrimantor_model(image_output)\n",
    "\n",
    "combined = Model([random_noise, noise_label], [valid, valid_label])\n",
    "combined.compile(loss=['binary_crossentropy', 'sparse_categorical_crossentropy'], \n",
    "                      optimizer=Adam(0.0002, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "              'Firefox 4 and 5 are also supported but you ' +\n",
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       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
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       "\n",
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       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
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       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
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       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
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       "\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",
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       "    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",
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       "\n",
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       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
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       "\n",
       "\n",
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       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
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       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
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       "        fig._resize_canvas(size[0], size[1]);\n",
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       "    };\n",
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       "\n",
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       "\n",
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       "\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",
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       "        cursor = 'default';\n",
       "        break;\n",
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       "        break;\n",
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       "        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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OaKZwzBEHLFFvGHDBmw908d+jInmb5AACbwzATqnd0bGL5AACTiFQN26dQ1E5xDNg5CDoeVbw4YNNaKXPn16OXr0qBaDRpQPhq1dRA3LlSunW8QTJkzQM30ZMmTQ6+gOggggoofI/EX2MIpIz5s3T69DUKI0DApBZ8iQgT7WKYuF4ySBSEaAzimSTThflwQiE4HDhw8b2M6FyEPCBgy1/RC1Q49giEK0c4OQmz59ul7HZ+gbvGDBAnn8+LEEBQXpNnHNmjX1OtrGoXB0vHjxUOdPa/5BYJplZhBZdHd319qDyZIlo4+NTAuO70oCDiJA5+SgyeJQSYAE3o3Ao0ePjF69egmKQaPYsykAEcEzEzWQBVyyZEkJDg7W69j2bdeunZ7lQwYwijwj+ofMYBgiiNgOxh8ISbMGoHnGsEyZMrJ06VIVljly5KCPfbcp490kQAIfiQCd00cCzZ8hARL4+AQoAD8+c/4iCZCAMwhQADpjnjhKEiCB/0DAz8/PwDm+b775RrdzYSlSpNCkDmz7ousHEjhgqPkHQ5s4bPMiaohzgCgXg+3iGjVq6PU2bdpohA+lYDp16qSlYmDmFjMihYsWLZJ8+fIhWkgf+x/mjV8hARL48ATonD48Y/4CCZBABBHYunWrcfLkSVm/fr2WdIGdO3dOkzxg2AY2t4bxd1i1atW05h8SP7DdW7duXf0uMnthEH/e3t6SMGFCzRSGUIT4ix49ul7H96ZMmSIlSpSQrFmz0sdG0NzzZ0mABMInQOfEFUICJOCyBP755x8jNDRUrly5EpbFe//+fS0DkzlzZo0A4nwfBN2ePXuUA4Qdzv/hjB/OCiJyiHN+bm5ueh2ftWrVSvr16ycdOnTQkjF4HvoJwxBd/Oqrr7TMTJEiRehjXXZ18cVIwNkE6JycPX8cPQmQQDgEQkJCjDNnzkjOnDnDtmhx+++//y6NGjXSJA/U60PJFjNCiK3htGnTyogRI6R27dqa3Zs0aVKZMWOG/hJqBqIzCLKDkemLBBLUA4QQhCHCiGds2bJFChcuTB/LFUoCJGBLAnROtpwWDooESMAKAnv27DH69OmjtfuKFy+uj8yVK5cKvrNnz6JThyxcuFDOnz8fVugZW7nYIkY5mCZNmsjevXu1v6+5RYwsYUT40PoNz0HJl4EDB2K7V5+P+1BqBpHF6dOn08daMZF8BgmQgOUE6JwsR8oHkgAJ2IXA2rVrDdT0w7k+nOWDoXcvzvYh+aNQoUKyf/9+/Te2eWEQb6NGjdJC0agNiAggEkfMZBF0FcH9ly5dku+//14GDBggixcv1kgg7ODBg4JtZvy7devW9LF2WQwcBwmQwGsE6Jy4IEiABFyWQKxYsQyc4UPRZjNLF+IMIg9nA1G7D4KuQIECUrRoUeWADF5s7SKZw9wWxnlA9ACGrVq1SjOAIRx/+uknrR8IkYj7YYggolUc6g8OGDCAPtZlVxdfjAScTYDOydnzx9GTAAmEQ4ACkMuDBEiABN5MgAKQK4MESMBlCaRMmdJA27bPPvssrFUbtoBxTg/RP0TuULcPf0diiBkBRBQPZwWRJIIIIu738PDQ6/jsjz/+kC5dumj0D1FCRPzMTiCINCIJBPUBq1SpQh/rsquLL0YCziZA5+Ts+ePoSYAEwiGwYMECA3X+INKmTZumdx44cCBMAGJrN3/+/LoNjMQOGJI5Zs6cqaVgUNLlTfb5559r/UCzhiD+DdEHQ5mY+PHj6/eTJk1KH8sVSgIkYEsCdE62nBYOigRIwAoCLVq0MJD8sXLlSlm3bp0+Ep1BINJQ1++LL77Q7OBt27aFlXHBZ8juhbhDhBD3ojsISr3Ajh07pt/DWUIIS9QNhJA0k0hwH6KA6BjSsWNH+lgrJpLPIAESsJwAnZPlSPlAEiABEiABEiABErA3AQpAe88PR0cCJEACJEACJEAClhOgALQcKR9IAiRAAiRAAiRAAvYmQAFo7/nh6EiABEiABEiABEjAcgIUgJYj5QNJgARIgARIgARIwN4EKADtPT8cHQmQAAmQAAmQAAlYToAC0HKkfCAJkIBdCLRq1cpAT98sWbJoKRcY2rihlAv6+6LYc0BAgAQGBsrw4cP1Onr4lipVSvbu3StBQUF6LwpB4++wK1euSEhIiBQsWFDOnj0rK1as0FIwZk1AlI7BH29vb2nRogV9rF0WA8dBAiTwGgE6Jy4IEiABlyWATiC3b9/WIs1moWb0BcZnKPw8a9Ys7fFbp04dQccQmCnmqlevrrX9IPZixowZVhQafYLRHaR+/fqSOHFi6d+/vwo+sw7ghQsXtK7gwYMHZc6cOfSxLru6+GIk4GwCdE7Onj+OngRIIBwC8ePHNx4+fKjFnP9t2bNnl9GjR8u8efNQsFlSpUqlt2zYsEG+/vprqVq1qgo/3IOWb7FixdLry5cvl0WLFmlUEGISEUYUfk6QIIFeR0u4xo0bi4+Pj/j7+9PHcoWSAAnYkgCdky2nhYMiARKwgsAXX3xhXLt2LawPMJ4ZLVo0FWno4oEWbtj+vXfvXlgv4LZt26qg++abb+TSpUvyww8/SNGiRaVChQo6JHd3dwg7CQ0N1T7CY8aM0W1jUwAiOoi+wQUKFJAiRYrQx1oxkXwGCZCA5QTonCxHygeSAAnYhQAFoF1mguMgARKwGwEKQLvNCMdDAiRgGYGmTZsa6AOMM384pwdD1K9Zs2a6tRsjRgw5cuSIRgFPnz6t15s3b673IFlk6tSpenbw+++/R0KHXkf0MGfOnIKzgN27d9dkEWwHY5vYtFu3bkn06NGxbUwfa9ls8kEkQAJWEqBzspImn0UCJGArAnPmzDHc3Nzk22+/DUvSgKBLnTq1DBo0SBM78AeiD0kdsLFjx8rPP/+s9587d063jHft2iU4MwiD4EMm8J49e2Ty5Mn/43whno/tYWw9p06dmj7WViuCgyEBEjAJ0DlxLZAACbgsgcDAQKNPnz6yZcsW+e677/Q9Ifzu378vmzdv1gxeJH/MnTtXkiZNqtefPXumGcIQhjgnmDFjRlm2bJmeC4ThjB9EJQRetWrV5Pz58yr4unTpotfz58+v5wYhKufOnUsf67Kriy9GAs4mQOfk7Pnj6EmABMIhcPPmTQNbvdi2RSIHLFGiRPLXX3/JzJkz9XNkCG/cuFETOUxbvHixZgZjKxfJH7Nnz9a/wyAYIQzLly8vadKkUeGIcjJIGIHhGqKHSCIJDg6mj+UKJQESsCUBOidbTgsHRQIkYAWBEydOGCjlApGHc4CwVq1a6Rau+W+cDVy4cKHUqlUr7CcR5UORaJSQQUmXgQMH6ndgiATu27dPNm3aJJ06ddKzgw0bNpRp06bp9YoVK+oZwrRp00IM0sdaMZF8BgmQgOUE6JwsR8oHkgAJ2IVAQECAgYgftmiR8AFbunSpzJgxQw4fPiylS5eWtWvXSr9+/eSnn37S635+ftKoUSN5+vSpbvPiDGDdunX1HCBs9+7dmlCCKGGDBg3k119/1WdgmxkWNWpUrQ+YPHlyRALpY+2yGDgOEiCB1wjQOXFBkAAJuCwBJIHgzN/+/fvDCjkjgoeuH0OHDlUxiK3buHHjaj0/GLJ9cQ+EHP4LkbhgwQI9Lwj78ccfJUeOHPo9bAGfPHlSMmTIoFm/MLSEw58mTZrI/Pnz6WNddnXxxUjA2QTonJw9fxw9CZBAOAQoALk8SIAESODNBCgAuTJIgARclsC6deuMJUuWSJs2bSRv3rz6nojq4WxfjRo1tF0bEkGwpRsnThy9jgxh03Dvjh07JHfu3HLs2DH9GB0+cMYPUT5EEn///XfNDEbvXxiygWPHji1TpkxB8gh9rMuuLr4YCTibAJ2Ts+ePoycBEgiHgLe3t3H58mUVe2aZl9WrV0u3bt0kICBAQkJCdOs2ceLEYcIPbeFMwzbwtm3bJFu2bFoeBhY/fnwtDo1zhcj+TZIkibRu3VpFJQzJISlSpNDM4pYtW9LHcoWSAAnYkgCdky2nhYMiARKwgsD06dONU6dOaccOdOuATZo0SWsBIvIH++yzz8Tb21vKlSun/0YyB874ofsH6vkhonf8+HGNAsIQ+YMAhDBEFjBqBq5fvx5iT6936NBBr+OMYLZs2ehjrZhIPoMESMByAnROliPlA0mABOxCwNPT00C5l9q1a0uyZMl0WE2bNpXly5drRA9CDYZSL8OGDdO/jxo1SjOCTSFYsGBBjRaaghFdRVATEM+DOET0ECVgFi1apN8/c+aMdglB/cCsWbPSx9plMXAcJEACrxGgc+KCIAEScFkCCRMmNBImTKjn+BIkSKDviZp9GzZs0C1ffIbzf5UrV5avv/5ar1+9elUjhoj04Qxg165dpWzZsmGMatasqdFERA69vLz0TCAyhLENDLtw4YI+P3PmzPgufazLri6+GAk4mwCdk7Pnj6MnARIIh8APP/xgoBwL+vii/y+sVKlS8vfffwuEISKBKOSMM3uoFwjDucASJUpoizd8B2LuwIEDei8MZV9QUxARQSR+4A+umRHCVatWqXBEC7mjR4/Sx3KFkgAJ2JIAnZMtp4WDIgESsIJAmTJljHnz5gkSO9CiDdazZ09N1ECxZvT0vXjxogq6Gzdu6HV83rdvX5kwYYKgjzCKQaPQM6KIpkE8okD0uXPnVEwOGTIkLEnk6NGjukWM59y7d48+1oqJ5DNIgAQsJ0DnZDlSPpAESMAuBCgA7TITHAcJkIDdCFAA2m1GOB4SIAHLCMSOHdsw6/9hmxfm6+uLFm1a/mX+/PmCJA9k8O7cuVOvYwvYLPGCVm8464d/40wgDMkd6C+Ms4FFixaVoKAgTQQxs4jxHNyL5z98+JA+1rLZ5INIgASsJEDnZCVNPosESMBWBGLEiGFgQIULF1aRBsMZPdT3q1ixotSvX1/SpUsn5cuX17IvMJz/Q4kX9AMuU6aMZgo/f/48rJfwzJkz9V4zEQRiD9fNXsG1atXSBBP8BgWgrZYDB0MCJPAKAQpALgcSIAGXJRA3blwDZ/+qVq0a1skjZcqU0q9fPy37gk4gSNhABi/KxcDMDN6zZ8/KzZs3NUsYnT/wBzZixAhxd3dXYYjv4jpEICKBMPQNxtlBPHPUqFH0sS67uvhiJOBsAnROzp4/jp4ESCAcAvHjxzcgzoKDg2XcuHF65+bNm7V8C5I3Jk6cKOnTp9fEEGzZwiD00O0DW8EDBw7URJEBAwZoXUAYIokQhogcIsqHSCBE4NOnT/U6ysDgMwjNuHHj0sdyhZIACdiSAJ2TLaeFgyIBErCCQMOGDQ0INw8PDy3uDENEEP17sWVbp04d3eZFCRdkApsG0YhC0SgRg8LOuH/o0KF6GeKuY8eOsmbNGgi8sC1iiEAY+gKjhAwighUrVqSPtWIi+QwSIAHLCdA5WY6UDyQBErALAV9fXwOdPbAFbAo4iDa0g5s7d64MHjxYYseOrZE8sysIxBtKv6BnMM4L4kwg7jXrBKKNHARltWrVtGD0q1vBeG8kjOAZbdu2xRYzfaxdFgPHQQIk8BoBOicuCBIgAZclcO3aNQOJHiNHjhQ/Pz99z+HDh8uRI0ckbdq0kj9/filZsqQ0a9YsrFPI1KlTBdm/SOSoW7eujB49WqN5+C8Mn6O/MKKG+KxNmzYa9cMfGOoFIrsY/YEfP35MH+uyq4svRgLOJkDn5Oz54+hJgATCIUAByOVBAiRAAm8mQAHIlUECJOCyBHx8fAxk7CKpA5E+GLJ9u3TpIjlz5tQWcegDjLZtOAsIy5Qpk3zzzTda1w/lYNBJBOf7sKULQ1cQRAGfPHmiCSMbN27UhBHzOkrCoHbg9evXcd6QPtZlVxdfjAScTYDOydnzx9GTAAmEQ6BFixYGhBhq8/n7++udaPmGGoAtWrSQbt26oVafTJ8+XcUgDIWc8e8sWbJIvnz5VOThXB+SPmAo8XLlyhVtL4cyMJ07d9YMYNQHhOFeJIrEihULLeHoY7lCSYAEbEmAzsmW08JBkQAJWANut5QAACAASURBVEFg7969BhI80LkDvXlhZk/gxYsXq0jLkSOHlm7JnDmzXod4w9/xX0T2ED1cuXKlJoLAihQpouVjkiZNKlmzZtXoICKGpgC8e/euFobG535+fvSxVkwkn0ECJGA5ATony5HygSRAAiRAAiRAAiRgbwIUgPaeH46OBEiABEiABEiABCwnQAFoOVI+kARIgARIgARIgATsTYAC0N7zw9GRAAmQAAmQAAmQgOUEKAAtR8oHkgAJkAAJkAAJkIC9CVAA2nt+ODoSIAESIAESIAESsJwABaDlSPlAEiABuxDYuXOn8e2330qqVKm0HzAMNfwaNWqkBZ9RFLp58+bo2Ss///yzXt+8ebOWeEFrN9QANAs+nzlzRq9/9913Ur58eenfv79Ur15dBg4cqLUFU6dOrddXrFghJ06ckMSJE0uPHj3oY+2yGDgOEiCB1wjQOXFBkAAJuCyBzp07G5cvX9a6fugJDEuUKJFcunRJKlWqJA8ePNB+v19++aV07dpVr6N+X7JkycTb21sLPdepU0e7hfj6+ur1dOnSSYcOHVQA4ntly5ZVUYl7YaNGjZKbN29q3cErV67Qx7rs6uKLkYCzCdA5OXv+OHoSIIFwCMyYMcNAtA6C7sWLF3onCjwHBwfL2LFjZcqUKVroGcWb0cEDBuEGMYfi0OgKUqNGDcmQIYPkzZtXr7dq1UqjfSgOjeuDBg2SgIAAGTJkiF5HizmIxQQJEsjWrVvpY7lCSYAEbEmAzsmW08JBkQAJWEGAAtAKinwGCZCAKxKgAHTFWeU7kQAJKIEHDx4YOMd3584d2bFjh36GyB1s9erVgjZx2BqeOnVqWAQQ0T9EAbGV+9tvv8mhQ4ckWrRo0rt3b/1e06ZN5a+//tJ+wYj8nT9/XqJHj67nAGH79+/XHsJHjhyRKlWq0MdyLZIACdiSAJ2TLaeFgyIBErCCQEhIiHH//n1p3769pEyZUh+J/r67du1SgQbhliVLFjl69GjYz8WOHVu3i7G1O3ToULl165Zu73br1k3vwRbytWvX5NixY/qnXbt28vfff8umTZv0esmSJbUXMHoNz5o1iz7WionkM0iABCwnQOdkOVI+kARIwC4Ebty4YSCat2DBAs3KhY0YMUJOnz6tCSAJEybUhI4ePXqEDdk8Ezhr1iyN8PXt21fPA6ZNmzbsHkQJnz17Jvfu3dMo4dq1a1UwwiAsAwMDJUaMGMgQpo+1y2LgOEiABF4jQOfEBUECJOCyBJ48eWKgnEubNm1kz549+p5mxi5EXJkyZTRSlytXLhV7rxq2eVEqBlnASPb4559/9DKig8gkxr/37dunZV8WLVokKDcDQ0Tw0aNH8tlnn0nu3LnpY112dfHFSMDZBOicnD1/HD0JkEA4BLAF3L17d2nQoIGWgoEhkle0aFE5cOCAPH36VFq2bCmzZ8/WzGDTkiRJorX8UAMQ9f+w7VuzZk29DPGHjGBECHH+DyVisOW7bNkyvZ4xY0atIRgnThxkAtPHcoWSAAnYkgCdky2nhYMiARKwgsD169eNWLFiCf6YBtGGrVvUAYRQQ1LIpEmTNEoIy5Mnj27fxowZU7d5cV5w/vz5MmPGDL1erVo1FYRx48ZVcYjoIMz8DRSU7tixo5w6dQrnAeljrZhIPoMESMByAnROliPlA0mABOxC4O7duwa2aiHWBgwYoMNq2LChbu1iKxfbtDAIwShRoujfZ86cqcWeUUAaxZ7xfdyHxA/Y8uXL5auvvtII4KpVqzSSCKFYoEABve7n56eZw/jNhAkT0sfaZTFwHCRAAq8RoHPigiABEnBZAhSALju1fDESIIH3JEAB+J4A+XUSIAH7Ejh48KDh5eWlNfxq1aqlA82aNauWdUGnjlcNLeBgqBmILdwlS5ZoyRgYEkZQTxCGcjLYAkb3EJz/wxYwEktQVxCGZ3t6egrOEfIMoH3XBkdGApGdAAVgZF8BfH8ScGEC3t7expw5c7SW3+7du/VNsTWLM3/ff/+9Jm+gfl/u3Lm1JAzMrAO4YcMGFHIO2yrOnz+/XkfNP5wPxHchBMeMGaMJIGYhaCSdIEmkXr16cvz4cfpYF15ffDUScDIBOicnzx7HTgIkEC6B3bt3G+jMgSzfgwcP6r3p06fXDiCI8CVNmlTLvOCcH8rBwFA4evLkyZrFi89QMxC9f4OCgvQ6zgsi2odIoJubm9539epV8fDw0Ovu7u6yZs0a6dWrlzx8+JA+lmuUBEjAlgTonGw5LRwUCZCAFQR++OEHA4kcFy9elLp16+oj0akD27bFihXTEjBdu3bVSJ+5BTxt2jSt42catn+ROWwWgh44cKCKu+vXr0uNGjU02oeoINrFwZBsguQQbBH/+eef9LFWTCSfQQIkYDkBOifLkfKBJEACdiHQuXNnAx0/UJwZmbuw8uXLa/QOgg7FoUNDQ/XzJk2a6H/RNQQCDvX+bt68qX2BcS7QFIgoFzN+/HjNDO7UqZPWCkTRaGwJw37//XfZu3evFoe+ceMGfaxdFgPHQQIk8BoBOicuCBIgAZclMGjQIAPlW3Aeb9y4cfqeEyZMwNk83fqFIVkjTZo0Yde3bdum7d3QHzh79uwqArH9ayaBQAyi5h/Kv5iCEdvEZ8+e1X+jo4iZYBIcHEwf67Kriy9GAs4mQOfk7Pnj6EmABMIhMGPGDANbvVmyZNFafabhTGDr1q01qoczfIjWlS5dWi/jGpJCIArRB7hChQq61YutYNMePnyoiR+IEmLLFxHFLVu26GVca9u2rSaKjBo1ij6WK5QESMCWBOicbDktHBQJkIAVBCgAraDIZ5AACbgiAQpAV5xVvhMJkIASWLlypYFkDXT/wFYuDOcBUQsQmbswdO2YOHGilClTRv+NLeNjx45pqzi0iRs8eLDky5dPkBxiGqKB+IOM4nPnzmlWMfoKm4YkkunTp6OeIH0s1yIJkIAtCdA52XJaOCgSIAErCMycOdNAAWiUgmnRooU+Ehm9Znu4Zs2aqcjDH5R6gaH3L7aNsWWM+oGlSpXS3sEo/wLDtjHOA6KVHGoGIsmkQYMGsnTpUr2O2oCoCYjfLFOmDH2sFRPJZ5AACVhOgM7JcqR8IAmQgF0IZMyY0dixY4fkypUrLIsXWbv4A/EGMYds3lOnToX1Akakr3bt2hrdQyIIzggeOXIEJV30tdBHeOvWrSoCkSSCM4OIHkI4wlKkSKF/x7MTJ05MH2uXxcBxkAAJvEaAzokLggRIwGUJ5MyZ0+jSpYvgD5IzYKlSpVJBiExeFHJGpK948eIq+GAFCxbUbd3kyZPLihUr5Pbt25rkgexf2Nq1a7WOICJ/MWLE0GgfSskkS5ZMr2O7GRFFbBvPnz+fPtZlVxdfjAScTYDOydnzx9GTAAmEQyAkJMSAwENWrp+fn96Js3uzZs3SQs0QcriGnr/I2oV17txZUOwZpVyQBYxWb7g2c+ZMvY4+v2gTh+v4HraGse0LkQlDSRh0CkHruF27dtHHcoWSAAnYkgCdky2nhYMiARKwgsD69euNM2fOaBTvu+++00dCCGIbF3UAIeCQ5IEafoMGDQr7SYhEiDrchyQStHnDOT8YCj5XrFhRt46RQIIewogulitXTq9jC/mXX34Rb29vYR1AK2aRzyABEvgQBCgAPwRVPpMESMAWBG7cuGEgEocuH4jMwYoUKSI1a9bUdm8QbkWLFtXzetgahm3fvl23hmGIHk6aNElbvZlJINgeRkQQkUG0mEPfYJwhxJYwLFu2bPL1119rP+AzZ87Qx9piJXAQJEAC/yZA58Q1QQIk4LIEKABddmr5YiRAAu9JgALwPQHy6yRAAvYlEBoaakydOlXr/plJGsjcjRIlily5ckXP8uEMICKAZq9gZAC3bNlS70GEEDUCcS8SQWCI+iGJpHnz5rJx40aZM2eORhW9vLz0euXKlbU3MNrH3blzhz7WvsuDIyOBSE2AzilSTz9fngRcm8CtW7eMEydOSGBgoPTo0UNfFiVakL2bI0cOFXaoCYjzfKj9B8PZv+rVq+uZP2QKb9q0SbeEkTQCy5kzp96PfsHYNu7UqZOKy7Rp0+p1nCmcP3++rFu3DmVk6GNde4nx7UjAsQTonBw7dRw4CZDA2wgEBAQYSNiAyIsXL57ejjN7qN03d+5cTehAlA+CDr1/YegYUq1aNZkxY4Z+joLPFy5c0ILQMEQIkUCC8i84D4iewKgpePLkSb2O7GDUCMRvUgC+bYZ4nQRIIKIIUABGFHn+LgmQAAmQAAmQAAlEEAEKwAgCz58lARIgARIgARIggYgiQAEYUeT5uyRAAiRAAiRAAiQQQQQoACMIPH+WBEiABEiABEiABCKKAAVgRJHn75IACZAACZAACZBABBGgAIwg8PxZEiABEiABEiABEogoAhSAEUWev0sCJPDBCaxYscLw9PSUESNGSPny5fX3UBMQJVpgKN+CtnDo41upUiX9DG3fjh07Flbrr2rVqjJw4EAtFwNDG7inT5/K+fPnpVChQoK+wSgkbT7zyZMnsnXrVmnXrp1cvnyZPvaDzzJ/gARI4L8QoHP6L9T4HRIgAUcQOHHihLF27VoJDg7Wnr4w1PWDSINwK1mypH6Ovr3ffPONXnd3d9dagD/++KOMHz9ea/qVLVtWtm3bptfXr18v8ePH19qAZvcQfI7OH7DLly+Lt7e3Ck1PT0/6WEesFA6SBCIfATqnyDfnfGMSiDQE0AkE0bgSJUrIkiVL9L1XrVqlXTrQ8SNRokQo1qxiENFAGMRh7969pUyZMuLj46MFnxElhLCDQRyii0i0aNGkYcOGYSyvX78edn3s2LHyxRdfyLBhw+hjI81q44uSgLMI0Dk5a744WhIggXcgcObMGQMCL27cuNqxAzZt2jTdro0aNapu8967d0/7/pqGaCEEIKJ7t2/flsePH8v+/fvl1q1besvvv/+u28G7d++W9u3bq2A8fPiwHDhwQK8PHTpUv5ctWzY5fvw4few7zBdvJQES+HgE6Jw+Hmv+EgmQwEcmQAH4kYHz50iABBxDgALQMVPFgZIACbwrAX9/f+PgwYN6Zg9bv7ApU6Zo5A/9fg8dOqR/37hxY1gUEFu5OOeXKVMmjQLOmzdPJkyYIFWqVNHv42xgnjx5NCro5eWl31u+fHlYL2F8HhoaqtvOvr6+9LHvOmm8nwRI4KMQoHP6KJj5IyRAAhFBIDAw0ID4wzbu8ePHdQi1a9eWO3fuSPr06eXu3buasZsuXbowAQjxhj++vr5StGhRTe548OCB5M2bV78PwYfzg/jumDFjBEkmEIvY9oUdOXJE6tSpIwkSJJABAwbQx0bExPM3SYAE3kqAzumtiHgDCZCAUwkgCSRGjBga0cudO7e+Rvfu3TVLt0OHDvLHH39I1qxZVaxdu3ZNr0Ms4oxfypQp9TxfsWLFVPQhUviqXb16VfLnzy/379/Xa7ly5dLLmTNnliZNmkifPn1k165d9LFOXTwcNwm4OAE6JxefYL4eCURmAn/88YfRoEEDzfpFogYMSSDI6IXAg9BDdA91/EwzI3xx4sTR0jCoI/jo0SNNJIHh+sWLF/U548aN0+8iixi/AUuYMKHMmDFDTpw4gegifWxkXoB8dxKwMQE6JxtPDodGAiTwfgQuXbpkpEqVSmv/mZm+KOJcsGBB/SxjxowCobd48eKwQs74RWQJQ9RVrlxZzwAWL15crly5ooPp2bOnnDt3TubOnSuxYsXSMjHYEk6ePLleR1bwrFmzZNOmTagxSB/7flPIb5MACXwgAnROHwgsH0sCJBDxBK5fv278/PPP2rGjSJEiOqAUKVKoqMP5vlOnTsmiRYtk586d8vz5c72eLFkyFXbY4s2SJYs0b95cunbtqt0/YPiscOHCujUMEYgzf/HixZMtW7bodXQUQWkZCMGHDx/Sx0b8MuAISIAE3kCAzonLggRIwGUJrF271mjRooUmaISEhOh74rwfon6o24fuHjizFxAQoOf2YLgfEUF/f39t+4aCzvg+Cj/DkPyBrWNs9aKFnJubm4rL6dOn6/X69etrlBAC8vnz5/SxLru6+GIk4GwCdE7Onj+OngRIIBwCFIBcHiRAAiTwZgIUgFwZJEACLktg06ZNRseOHfXMnpnFi6xg1PIbPHiwlnhJmjSpbgUjsQOGZJFBgwZpKRicG8SWMHoBJ0mSRK+j7AvOCI4aNUpQM7Bz587aVQSRQRhaymXPnl3rBL548YI+1mVXF1+MBJxNgM7J2fPH0ZMACYRDICgoyEiTJo3egaQO2LNnz6R169ZapuXChQtaxgU9fVu2bKnXcX/fvn1VHCK5A+f7UC7m888/1+so/YKMYGwDQ+zh+xUrVlSRCYP4w5Yy6g8ePXqUPpYrlARIwJYE6JxsOS0cFAmQgBUEIADRkxdJGYkSJdJHQsihfAsSOczzfCgOjUghDOVbEOW7efOmRgDxfSR8mEkiHh4eWvoFQhL/RRkZlIBBoggMyR84L4hrTAKxYhb5DBIggQ9BgALwQ1DlM0mABGxBwM/Pzxg+fLi2coNggyHr14zyobwLInpo6bZkyRK9jqgeMnsbNWqkUUBkEGP7OHr06Hodz0L0D2VfsBU8efJkjfjhv68axCO3gG2xDDgIEiCBNxCgAOSyIAEScFkCNWvWNCDMsB3r4+Oj79mqVSv5/vvvwyJ8w4YN016+jRs31usoAYNo365du+TXX3+VZcuW6fk/M4sYwg5lZFBOBlvB6CCCMjIQhqZhuxmRxtDQUPpYl11dfDEScDYBOidnzx9HTwIkEA6BZs2aGRBmSPxA4WcY/ottW9T1Q0u4qlWrSnBwsAo6GCJ9CxculEuXLknbtm1VyCVOnDjsDCGSQyAO8RxEAlEXEKVj9u7dq9+H+EPEEOVhTp8+TR/LFUoCJGBLAnROtpwWDooESMAKAh4eHga6eWTIkCGs3RtEGyJ2yODduHGjCj4Ubz506JD+JK7v27dPv4OzgqgBiCxhJHfAsFWM77i7u+t5wXTp0mmfYdQWhCGxBBFH1Bv09/enj7ViIvkMEiABywnQOVmOlA8kARKwCwEKQLvMBMdBAiRgNwIUgHabEY6HBEjAMgILFiwwevXqpRG67t2763NHjx4tbdq0kfHjx2vkD8keyNr19fXV66gHiLIviO4hexiRQFw7e/asXseWMJI/kDyCreO8efPK48ePpU6dOnodCSYVKlTQNnGenp70sZbNJh9EAiRgJQE6Jytp8lkkQAK2IrBkyRID5/dQzBlbtbChQ4dqsWds4+7evVvP7N25c0dbw8Fu3LihCSI407dy5UqtFwhxZ9YRRNIHsn6xxYuWb3/99Zd+D8kiMD8/P0H/4WPHjknBggXpY221IjgYEiABkwCdE9cCCZCAyxJIkSKFgegcMnshBGEQbwcPHtSzehBwKPOSKVMmadCggV5HmZjSpUvLtGnTpF+/floWBoa6fjCUjEGSCM4K7tixI4zdyJEj9e/oMILvIrN4/vz59LEuu7r4YiTgbAJ0Ts6eP46eBEggHAKJEyc2zBZtaNcGQ6FmdPjAti9KunTr1k3/mNcDAwM1i3f27Nma2HHy5ElN/DDrCGLbF9HDmDFjap3ArFmzyq1bt6RGjRr6fEQS0RkEZWd27txJH8sVSgIkYEsCdE62nBYOigRIwAoCwcHBBs74oaTLmjVr9JG5cuXSMjDY0kU3kAEDBuiZPdNQ3gUCEWf+8Dm2fxEVNMvIYAvY/DvEH7Z6kVGMc4Uw/A7OCUJcPn78mD7WionkM0iABCwnQOdkOVI+kARIwC4E1q1bp2VgEK2DcIN5enpqyRfUAUT7NiSIIBr4b9u2bZsWjC5ZsqS2ekOyB2zPnj1ht+JZP/30kz6rSZMm+jnE4itGH2uXxcBxkAAJvEaAzokLggRIwGUJjB8/3kD0b9SoUWHviMgfsnhR369UqVIasfviiy/0M5i5Fdy7d2+5cuWKZgOfPn1ao4IwnCnEecCECRPKiBEjdOsXiR8oBg07fvy4/hf3//PPP/SxLru6+GIk4GwCdE7Onj+OngRIIBwCFIBcHiRAAiTwZgIUgFwZJEACLkugVatWRrly5aRmzZphZVzwsoj24Rwg6gBiG3fWrFla+gWGeoE4/4eyMcgORvQPZ/zQFg6GMjGIIhYpUkSTR5o2baq1AtFuDrZ9+3aNEOLfDx8+pI912dXFFyMBZxOgc3L2/HH0JEAC4RBwc3MzkNG7devWsF6/r9bzQ59g1AJEqzcIQhjKxTx69EhSp04tY8eO1e1jiESzzt/ixYt1u3fTpk3aBxhFo/EcbAfD9u/fr9u/SBQJDQ2lj+UKJQESsCUBOidbTgsHRQIkYAWBFi1aGEjwQDFnFH+GIUMX4swsAI2IHer2mcIQ96BUDIQjav7hjJ/5d1xDZK9gwYLi5uamkUQPDw9p1aqVFo2GISu4SpUqWivQw8ODPtaKieQzSIAELCdA52Q5Uj6QBEiABEiABEiABOxNgALQ3vPD0ZEACZAACZAACZCA5QQoAC1HygeSAAmQAAmQAAmQgL0JUADae344OhIgARIgARIgARKwnAAFoOVI+UASIAESIAESIAESsDcBCkB7zw9HRwIkQAIkQAIkQAKWE6AAtBwpH0gCJGAXAtOmTTPGjBkjBQoUkOXLl+uwSpcuLYULF5b+/ftrweZ48eJpPT+0hoOhFVyzZs20zl+tWrVk4MCBWgYmf/78en3Hjh3692rVqukzUfMPpWBSpEih1/HMZ8+eSZcuXVBehj7WLouB4yABEniNAJ0TFwQJkIDLEjh16pSRKlUqQfHmTp066Xuihl/VqlVl2LBhEjNmTDlw4IDEiRNH/wtDdw90+8Bn6AASFBQkCRIkkPjx4+v1O3fuqNi7fPmyCkf0Cx4yZIgWhDYN4g81BBcuXEgf67Kriy9GAs4mQOfk7Pnj6EmABMIhcO/ePQPFmseNGyfjx4/XO1evXi0bNmyQv/76S7Jnzy7Tp09XUQfBBkNbuDRp0qjwu3fvnkSJEkUFY+fOnfX63r179ToigIj2oWtIvnz5VFDCzpw5oy3k8OwkSZLQx3KFkgAJ2JIAnZMtp4WDIgESsIIABaAVFPkMEiABVyRAAeiKs8p3IgESMKNxugXs5+enZ/dgTZs21b6/iAjWqVNHcubMKc+fP5d58+bpdV9fX8mTJ49G9f7880/JnTu3bNu2LSyCiO1f9P7F2cCQkBBJliyZrFixQpImTarf79evn/Tq1UvWrFkjbdu2pY/lWiQBErAlATonW04LB0UCJGAFgX379hnRo0eXSZMmybVr1/SRXbt21cQPJIEgWQNicNCgQRIrViy97uPjI/v27ZNff/1VtmzZIrFjx5bAwED56aef9DoE4OnTp2XKlCm6lRwaGqpnAO/evavX161bp/eXKVMG28L0sVZMJJ9BAiRgOQE6J8uR8oEkQAJ2IRAUFGSMGDFCDh06JHnz5tVhFSpUSEVdz549JWHChBqtg4DD2T9YypQpVRQuWrRI0qVLh0QO/bxChQr637Jly+q5wN27d0ujRo00wxhRP4hBmLu7u5w6dUr/6+/vTx9rl8XAcZAACbxGgM6JC4IESMBlCfTu3duIFi2aNG/eXDZv3qzvOXXqVBVz2KJFssbo0aPl8ePH4ubmpteRNLJr1y7NAF61apXs2bNHRWOPHj30OsrEJEmSRLeVv/nmG/0MUT9sF8MgLG/duqVby//88w99rMuuLr4YCTibAJ2Ts+ePoycBEgiHQPny5Q2Uc0EWMM77wS5duiRp06bVbV7U8MMWMUq41KxZM+xJT58+1WzhX375Ra5evarn+44fP67XcT9E5dmzZ6Vu3bpy7tw5rRuIqCAMNQaPHDmi284PHjygj+UKJQESsCUBOidbTgsHRQIkYAUBCEDU9StXrpx07NhRHwmhN3HiRFm2bJmWccmQIYPWCGzdurVeh5BDRBD1/b788kv97OHDh2ECEWVjIAJxD7aOhw8fLsHBwWFnCE+cOCGtWrXSxJKTJ0/Sx1oxkXwGCZCA5QTonCxHygeSAAnYhcCgQYOM7t27aycP05C00aBBA1m/fr0KO4i5+fPna60/GEQhkjhQJBpRPHT7wJm+ypUr6/XffvtNo4FZs2bVmn/IMj527JhUqVJFr6PzyKhRo3Sr+OrVq/SxdlkMHAcJkMBrBOicuCBIgARclgAFoMtOLV+MBEjgPQlQAL4nQH6dBEjAvgQgABGpQ/QOW7amoe/v4cOH5ccff9QoIKJ4SPiAffLJJ3rGL1GiRDJ48GDZv3+/XLx4UWrXrq3Xq1evruVhUCbm888/lxcvXsjYsWOlYcOGev3+/fuadIKo4b179+hj7bs8ODISiNQE6Jwi9fTz5UnAtQlMnDjRQL2+zJkza2IGDBm+iRMnVkGI0i2o74ct4iVLluj1tWvXSunSpSVbtmy6/YutYmQEY5sYhhqCeBYyiSEWYSgJkzp1av07ysigNAyyiY8ePUof69pLjG9HAo4lQOfk2KnjwEmABN5GwMvLy2jbtq1G89DZAzZ58mTN3IXAQ1Fo9AQuUqRIWJIIijw3btxY70UG8MyZM+Xvv/9WYQhDhjAMySKIEEL4IanE09NTP0fJmO3bt2uXkBo1atDHvm2SeJ0ESCBCCNA5RQh2/igJkMDHIFCzZk3DzPZFUgds6NChmrBx/vx5LQ2TJUsW/QyiDYbCzy1atJBq1appoejevXtrGznUDYShniCSPlq2bKliEkkhEIX4DgyFpVFIGlHC+vXr08d+jInmb5AACbwzATqnd0bGL5AACTiFwJAhQwz07EVJFnO7Flu6BQsWFJRrwdk9ROog4I4ePaqv9eTJEzlw4ID2+MX5PghCiD/zDCCighB7N27c0H7AQUFBeu7PLAqNs4DIHkYXkU6dOtHHOmWxcJwkEMkI0DlFBSkHegAAIABJREFUsgnn65JAZCLw5MkT46uvvtKyLmYSCOoBFitWTAUhikIjooczfBB2MHT5wOe4H2f5IATRDQTlYWDY6kUtQYhEJI4g2QMFoxFJhGH7+ObNm1KxYkXx8PCgj41MC47vSgIOIkDn5KDJ4lBJgATejUBISIixePFi7dSBKCAMmbso/uzj46ORu6JFi+qWrSnw8F8kjcBMUYj7kNQBg3BEdxFEB1FDEOIQ0UNkEsMgCBEhjBs3LlrC0ce+25TxbhIggY9EgM7pI4Hmz5AACXx8AhSAH585f5EESMAZBCgAnTFPHCUJkMB/IBAQEGD4+flJpkyZpF27dvoE9PVF5G7jxo369+TJk4u3t7c8e/ZMr2MrF63ckCCCTh84E1i/fn3U9NPrKBmD7yDqh7OD2CJ+1RBpTJMmjUYKr127Rh/7H+aNXyEBEvjwBOicPjxj/gIJkEAEEVi9erUxevRoFXR79+7VUeD83507d/TcHrZxkR189+7dsC1iLy8v+e677+TChQt6f4kSJfRcoJlEgu3kPHnyaLkXCMhPP/1U7zO3mNFDGM9Aseht27bRx0bQ3PNnSYAEwidA58QVQgIk4LIENm3aZECwIfpnRgBR/mXbtm1a3gVn/3r27KmZvOgRDENpFwhERP9Q7Bmfo4YgzgHCmjRpIl9//bVG/vr166cZxegq0rp1a70OsQhxiTIw/v7+9LEuu7r4YiTgbAJ0Ts6eP46eBEggHAJbtmwx0NYN2bwo3AwLCAjQos9mYWeUd0G3EEQDYUjeQLQP/0YCCSKGiO6ZCSE5cuTQqB/E44wZM7Sg9NatW1H0Wb+P7GB3d3dBQekiRYrQx3KFkgAJ2JIAnZMtp4WDIgESsIIAzgBimzZq1KivZfkimodevt9++60sWLBARdu+ffv0J1EyJkaMGFofEOcBsUWMgtDoAQyrV6+eirvcuXOrWET2r5ubm6C/MAyt4CAaf//9d4kfPz59rBUTyWeQAAlYToDOyXKkfCAJkIBdCDx48MBA9A79e80zfBjbw4cPNYHj0aNHUrlyZe0KUq5cOR02ysN06NBBo3i1atXSyF/atGkFkT9Y2bJlNREEpWLQIaRu3br63QEDBuh1lJgZNGiQ9OjRAyKRPtYui4HjIAESeI0AnRMXBAmQgMsSWLhwofHHH3+oGEMyh2nY3j106JCgTdyYMWOkVKlSKvpgxYsX1zN9pUuX1n8jsofagfgDQ+9gXENUEJFEZAevW7cu7PmIFCKpBBHAevXq0ce67Orii5GAswnQOTl7/jh6EiCBcAhQAHJ5kAAJkMCbCVAAcmWQAAm4LIFmzZppBBB1/3bt2qXvmTNnTt3CRccPJHkggWPlypWyc+dOvY7tXtT+wzlAdP/A+cDEiRNrWRfY+PHjdRsY5wqRQYxzgNhOxv0wRAXRZxgRw0KFCtHHuuzq4ouRgLMJ0Dk5e/44ehIggXAI9OrVyxgyZIiKPrR8gyFpo0uXLprZC1FYsmRJLdlitnLDfxcuXCg//PCD+Pr6SsuWLbUMjFlH8NatW3o+cNasWSoKc+XKJfPnz9d7YJ07d9YyMDhzWLFiRfpYrlASIAFbEqBzsuW0cFAkQAJWEBgzZozRvXt3LdiMZA9Y7Nix9cwfEjcQAUSiB5JC0MMXlj17di0Lc/LkSY3iIcIHEWkWekakEDUAkRSC84HLly+XkSNHhp0RTJAggZ4LRNRw//799LFWTCSfQQIkYDkBOifLkfKBJEACJEACJEACJGBvAhSA9p4fjo4ESIAESIAESIAELCdAAWg5Uj6QBEiABEiABEiABOxNgALQ3vPD0ZEACZAACZAACZCA5QQoAC1HygeSAAmQAAmQAAmQgL0JUADae344OhIgARIgARIgARKwnAAFoOVI+UASIAESIAESIAESsDcBCkB7zw9HRwIk8B4EEiRIYKAmX8yYMbUYNKxt27bSr18/QX1AFIdOkiSJ1glE4WYYun+gs4fZ3ePZs2f6eUhIiP4XfX9TpkyptQMXLFggfn5+2vv32rVrej00NFSfgWfevHmTPvY95o9fJQES+HAE6Jw+HFs+mQRIIIIJ1KtXz4BwK126tKROnVpH07t3b7lx44aKtuLFi0vfvn3lwoUL0rBhQ72Ogs9o/wZxCCGHgtBZsmSR4OBgvV61alUVgGgnlzBhQvHx8UHLN30GDM8NDAyUBg0aoKA0fWwErwH+PAmQwJsJ0DlxZZAACbgsge3btxspUqTQNm/Xr1/X90TXDog59PtFC7dvvvlG9uzZI3fu3NHrAQEBsnHjRm33hn7B27Zt0y4fBQoU0Ov79u2TadOm6ecQe4kSJZIXL17IsGHD9Dr6BN+8eVP7ATdu3Jg+1mVXF1+MBJxNgM7J2fPH0ZMACYRDgAKQy4MESIAEGAHkGiABEohkBJ48eWIULFhQRowYoWf+YHny5JHHjx9L9OjRZdasWfrv8+fPy/379/U6egX/+OOPutXr5uYmS5YskaNHj0q+fPn0uq+vr+BcIHoIt2/fPozod999p3+fNGmS3r99+3YZNWoU/yc7kq05vi4JOIUAnZNTZorjJAESeGcCt27dMiDmxo0bJzVr1tTv4++HDh0Sb29vWbZsmXz99dd6fg+iDZY2bVoViBB5a9askXnz5smqVaukXbt2en3FihWa8FGqVCkVeUgWgZg8depUmIDE+cDly5dLo0aN6GPfedb4BRIggY9BgM7pY1Dmb5AACUQIgV69ehlI4hg7dqx4eHjoGJo0aaJRv/Xr10v58uU1qzdHjhwq5mDNmjXTz06fPi2zZ8+WS5cuacavmUWMhBJEAzNlyqTJJPg7xGDLli31+/he/fr1Ze7cueLl5UUfGyEzzx8lARJ4GwE6p7cR4nUSIAHHEnj+/Llx7tw5SZYsmZZrgSEhJHny5FquJWrUqJr0gSSPqVOn6nWIRSSIIAEE0cFbt27J8ePHVTTC5syZoxHENm3ayKJFiyRu3LhSsmRJzQqGIXoYI0YMzSYOCQmhj3Xs6uHAScC1CdA5ufb88u1IIFITePr0qYHs3AcPHkjPnj2VBTJ0Idrix4+v27x//vmnCkBk88Ju376tIu/gwYOaJTx+/HgVfIMGDdLrrVu31pqAODOYIEECPT+YK1cuLRkDixIlin6OEjGnT5+mj43UK5AvTwL2JUDnZN+54chIgATek4C/v79RrFgxiRYtmuzYsUOfhsjc5cuXNfoHIYeIH/4gygdDSRec50NSB0q+/Prrr/pZxYoV9ToSRyD2ICLN+1EiBlvCsKVLl6qg7NixI0rO0Me+5xzy6yRAAh+GAJ3Th+HKp5IACdiAACKAOJuHBBBs/cIg3lAbsE+fPpodXKFCBc0QNjt9IGKIqN7OnTsld+7c2iEEUcG6devq99H9AxE+CEhsE1+5ckWqVKkS9rZHjhxRsYjs48GDB9PH2mAdcAgkQAL/kwCdE1cFCZCAyxKgAHTZqeWLkQAJvCcBCsD3BMivkwAJ2JfAjRs3DLRp+/TTTwVbwTAkhSCK5+Xlpe3akMGLaODVq1f1+qhRoyRDhgyyf/9+efTokXTp0kUjfDgHCCtRooRky5ZNEidOrIkleD4SSvAbMEQFK1eurPewDqB91wZHRgKRnQAFYGRfAXx/EnBhAqdPnza++OILPQOIhA8Y2rchsxdbtDFjxtTCzxBsefPm1evDhw/XVnHZs2eXdOnSaQ9hJIDgGabAg8iDITMYZWIgFNOkSRN2Hb2C8TtMAnHhxcVXIwGHE6AAdPgEcvgkQAL/OwFsASMyh8zcCRMm6I34OwRc586dNYqH7N+mTZtKkiRJ9DoKQaP8C+5Dxi9q/c2YMSNMAJqRPghKZPpCOB4+fFjKlCmj34fgREIIzhzWqVOHPpYLlARIwJYE6JxsOS0cFAmQgBUE5s+fb/zwww8q+FauXKmPRNQPRZ+rV6+u2b5I6EAEENu4MHT1OHbsmGzZskUzhlHjD9FC0/AsiEBkBnfq1EmFJRJF/m0QiFGjRqWPtWIi+QwSIAHLCdA5WY6UDyQBErALgTlz5hh16tTROoAQdjAUbPbx8dGzfzgPiKLP6OYBwQfLmjWrCj4IPJzjgwhE+zezTuCrQhCiEVvDEIWvikAUg960aZNUq1aNPtYui4HjIAESeI0AnRMXBAmQgMsSWLBggVGkSBHdkjW3aAcPHizTpk2TSpUqSdWqVbWvL+r2mb1+0catdu3aejYQ/XxRFgZJIK1atVJOEHdBQUGSMWPG16KCrwpAiMZffvlFJk6cSB/rsquLL0YCziZA5+Ts+ePoSYAEwiHg5uZmINKHnr7Y+oVhaxYCDdu48+bNU3GHrdzRo0fr9RYtWmg0EOf7IPL+/vtvbfvWrVs3vY5IorkNjH8jUojaghCRpqF38NOnT3HGkD6WK5QESMCWBOicbDktHBQJkIAVBCgAraDIZ5AACbgiAQpAV5xVvhMJkIASqFevnoEWcAEBAf+DCCJ/3t7eenYPnT/MTiGI3iVNmlTixYsnHTp0kHLlyum/zdZvyZMn164hiCLu3btXAgMDtR4gysWYEcLSpUtrRDB58uT0sVyLJEACtiRA52TLaeGgSIAErCDg4+Nj1KpVS8/smeVbYsSIoYkfqPUHUYczfSj7ArEHu379unTt2lWvnTx5UoWceT+uQyzOmTNHs4OxbTxx4kQ9K4g/MNQRhOBs27YtSsrQx1oxkXwGCZCA5QTonCxHygeSAAnYhcCuXbsMCDWYmaTRuHFjLQmDzF4IOySJjB07Vmv5wRAVxJk+RPVQ3BkRQYhEnPWDQfT5+vpKjhw5ZP78+SoQ0TkEBaFhzZo10+4gEJUpUqSgj7XLYuA4SIAEXiNA58QFQQIk4LIEmjVrZiDRA0WdIfZgKNR8+/ZtWb9+vRaARkkYbOkmS5ZMr6ODB2r7oSUczGzzVqFCBf33zp07NZHE7AaCZ0MA3r9/X6+jzMyIESM0Qpg6dWr6WJddXXwxEnA2ATonZ88fR08CJBAOgYwZMxpo54ao3759+/ROZPlC5CGCh+1aRAMhAJEpDMudO7cKxDhx4miNP/wd4hDt42A4O4j7//jjD60fiAzijRs3yrJly/Q6egUfOHBABWeuXLnoY7lCSYAEbEmAzsmW08JBkQAJWEEAAhAROQg+nAOEYWt29+7dMn36dD3Ph2s47/fnn3/qdUQM0fsXkUFsDSPCh04hSPqAeXp6asJIzpw5tUTMkCFDVOyZreQQHUTxaXQgyZs3L32sFRPJZ5AACVhOgM7JcqR8IAmQgF0IpEiRwvD395cmTZrI1q1bdVjHjx/XM3wQaPjv5MmTVQRCKMK8vLxk//79Gulzd3eX/PnzS6xYscK2iBFNTJkypYo+PBviERE/FIyG4fwgnod6gbNnz6aPtcti4DhIgAReI0DnxAVBAiTgsgQoAF12avliJEAC70mAAvA9AfLrJEAC9iWQP39+A9u9KO3St29fHSiyfRG169y5s0b3cA4wceLE8ttvv+l1RAZ79+6tET3U/7t586Zs3rxZz/3BUO8PpWTQKi5BggTSp08flHuRQ4cO6fXMmTPLmTNnpG7duogQ0sfad3lwZCQQqQnQOUXq6efLk4BrE0AZmMKFC6t4+/HHH/VlUf6lePHimqmL83oo2YKagGYhaAi7cePGSbFixbQdHMq/YLvXFIgQfygpgxIwKP2COoCoFzhmzBh9PlrPnTt3Tp+7dOlS+ljXXmJ8OxJwLAE6J8dOHQdOAiTwNgIPHjwwcBYPCRyo5Qfr37+/Zu2uXr1aI3zI9EXfXmT1wnCer3379lrmBdm/uI5oYI0aNfS6GU2E+IOAxHX0DEakEJY3b165c+eO/ub+/fvpY982SbxOAiQQIQTonCIEO3+UBEiABEiABEiABCKOAAVgxLHnL5MACZAACZAACZBAhBCgAIwQ7PxREiABEiABEiABEog4AhSAEceev0wCJEACJEACJEACEUKAAjBCsPNHSYAESIAESIAESCDiCFAARhx7/jIJkAAJkAAJkAAJRAgBCsAIwc4fJQES+BgEjh07ZjRr1kwLM4eGhupPfvrpp9quDX9QqgX/fdVwvUyZMtrubeXKlXLv3j358ssv5cqVK3obagCiUPTp06dl165dUrt2bS0offToUb2O7yZPnlx+/vlnWbhwIX3sx5ho/gYJkMA7E6Bzemdk/AIJkIBTCDx9+tTYsGGDdusw6/RFjRpVLl++rDUAHz58qDUAIQSjRImir9WuXTv55ZdfBPdBPKIvMP6YnUCOHDkilSpV0tqAjRo1kixZsmjNQAjHVw29h8uVK0cf65TFwnGSQCQjQOcUySacr0sCkYkACkH7+PiIt7e3dueAIRoYK1YsFW8dOnTQYs7o5JEtWza9fvXqVRV1KPCMa4gCoj3crVu3wtDFixdPRSK6iaAzyL8NUUVEHGPEiEEfG5kWHN+VBBxEgM7JQZPFoZIACbwbAQrAd+PFu0mABCIPAQrAyDPXfFMSiHQEAgICjDhx4mgkb/jw4fr+J06ckB07dmiErnPnzrrle/DgQYkfP75ex1m+n376SbeE79+/LxcuXNBzf9jyhfXt21e3h3Hur2DBghohRP/gVw2/h5ZyRYoUoY+NdKuOL0wCziBA5+SMeeIoSYAE/gOBhQsXGhB63377rQo52JAhQ1TA9ejRQ3LlyqXn/wICAvS/sO3bt0vdunX1M2zvrlu3Tnx9fQU9hWHY9sVn48aNk6VLl0rcuHEFW8J3797V6xCLXbt21USQ4cOH08f+h3njV0iABD48ATqnD8+Yv0ACJBBBBDw8PIy0adNqwseKFSt0FIUKFZJMmTKpQEucOLEcOnRIz/0dP35cr7ds2VLPDELoZc2aVdauXStubm5hZwDHjBmj0cHx48fL5s2bNRMYYnHPnj36/V9//VU6deqkCSS1a9emj42guefPkgAJhE+AzokrhARIwGUJtGjRwti9e7dG95DUAStatKiMHDlSs4LTp0+vZV62bNmiW7owCENE93r27KnJItgafvz4sWYLmwLvt99+UxG4cOFC3UZGhvClS5f0eo0aNTSK2KtXL2nSpAl9rMuuLr4YCTibAJ2Ts+ePoycBEgiHQJQoUQxk5L5apmXy5Mka5TPLtuC8HiJ4EIMw3AtRmD17dqlfv75uB7do0UL8/f31+qxZs3SLF2cH8QfnCRFJhLCEIdr44sULiD8ZPHgwfSxXKAmQgC0J0DnZclo4KBIgASsIRIsWzfj888/l0aNHYY/r2LGjbs9CtKVIkUKWLFmiQg81AWGI7OE8X7du3fSMYPv27bVMjHnGr0CBArJ+/XqpXr26fmfTpk16BhCiEoYagdgGRgJJjhw56GOtmEg+gwRIwHICdE6WI+UDSYAE7EIga9asxoMHDwTnABHVgzVo0EBF3qRJkwTFmrH1iy1eJIbAUPQ5MDBQk0RwPvDw4cOC4s958+bV60gGgehDHUFkDH/11VeSL18+zS6G5cmTR9asWaPbwilTpqSPtcti4DhIgAReI0DnxAVBAiTgsgQoAF12avliJEAC70mAAvA9AfLrJEAC9iWQOnVq48mTJ+Ll5aXlX2AZM2aU0qVLy/Tp0wUZvTjjh2hdUFCQXm/cuLGWjLl48aK2jMMWMraKcTYQNnXqVN0aTpIkiUYIS5YsKaVKldJ7YcgArlmzpgwYMEAmTpxIH2vf5cGRkUCkJkDnFKmnny9PAq5NYNGiRQYKNbdp00a3a2Go2wfhB5GHBBEkgeCMoCnwkPyROnVq2bZtm0A8oq5f5cqVw7KIkRmMbWRk+aKANJ6BhBL8F4b7saU8d+5cJI/Qx7r2EuPbkYBjCdA5OXbqOHASIIG3EfDy8jKQtYuSLigGDcN5QET3zMLPOA+IrF3TkNAxePBgnN/T5BB0+cA5QWQPw65fvy5Vq1bVJA9TNEL0vfr3ffv2qSAsXLgwfezbJonXSYAEIoQAnVOEYOePkgAJfAwCdevWNVCsGXX+zCxeCDMkbqDsy40bN3QYEG+I7MEgGLEdjC4g+C/qBSIi2KpVK72OJJEzZ85oEWgIP0QKsf1rCkDcg+djy7lcuXL0sR9jovkbJEAC70yAzumdkfELJEACTiGQLFkyA10/fHx8VKzBIASvXbsmVapUkStXrmhWL7Zwa9eurdfd3d0lXbp02g0EpWIyZMigW75mJ5HPPvtM/vjjD/H09JTZs2drOZg7d+68hgRRRpSb+eWXX+hjnbJYOE4SiGQE6Jwi2YTzdUkgMhH45JNPDAi21atXa/IHDOf90Ms3ZsyYWsoF273oEgJRZxpqA0LAobtHsmTJ5NSpU9oWDobOIigEjf7A6BLSt29fLTEzZcoUvY6zhjg7OHHiRNQbpI+NTAuO70oCDiJA5+SgyeJQSYAE3o1ArFixDGTpLlq0SGv/wcwzf9jGzZEjh167fft2WK9fdPBABA/9gQ8cOKAdQVDnD/2AYej5i+3hoUOHaiIIIotoE4etYtjYsWN1e/j333+XEiVK0Me+25TxbhIggY9EgM7pI4Hmz5AACXx8AhSAH585f5EESMAZBCgAnTFPHCUJkMB/IPDFF18YiNCh1h+2cmEoCYMIXmhoqGB7GCVhUCdwwYIFeh01/xIkSKBZvsgIRpkYZABjuxiWMGFCTfjYuHGjfv7nn39q9NBsN1evXj3x8/PTEjG5cuWij/0P88avkAAJfHgCdE4fnjF/gQRIIIIILF682EByB7Z9c+bMqaNASZhChQpJokSJVNyhHRxKvqDHLwxZwkgOOXv2rBQrVkz/DTFo9gpG9rB5H5I/ICwhJs06gNhyhgCEMKxWrRp9bATNPX+WBEggfAJ0TlwhJEACLktgwoQJBjpzQJyZwq1OnTqSJUsWjfhB1CFpA+f80BsYFhwcrGcC48SJI5UqVZINGzaIh4eHnv0zhR8ihxCMyBJGV5E0adJoD2DYuXPnZO/evdpRJFmyZPSxLru6+GIk4GwCdE7Onj+OngRIIBwCW7duNebNmydz5syR3377Te9EMkfy5Mk1caN///6a6duhQwdZvny5Xt+5c6dm8ebOnVvWrFmjBaNR+w+iDjZo0CDdAkbpmKhRo2okESISLeNgiBYiOxhbzzFjxqSP5QolARKwJQE6J1tOCwdFAiRgBYHDhw8bEHoTJkwIK9MC0YYtWwi1GTNm6Hk/CDZk/8LQKg5lYbA9vGzZMr2OjF4UiIahyPP27du1fiBEHgTiqlWrpEyZMnod5wHxm+fPn0d0kD7WionkM0iABCwnQOdkOVI+kARIwC4EHj9+bCBSh76/KOgMw3nAXbt2afFm1PJDdA8lX/z9/fU6xOD333+vW8Hjx49XAYgzg+Z1s+8vtn8RTTx+/LhgW7l37976fdQTRDFp1A6sUKECfaxdFgPHQQIk8BoBOicuCBIgAZcl8Pz5cwNJHkjIQF9fGITcsWPH5OTJkyr8xo0bp59D9MHQFxgRQGT74jwfzvuhk4j5/ZCQEM0iRm1AtJjDddQNNDuJoL7gyJEj9c+kSZPoY112dfHFSMDZBOicnD1/HD0JkEA4BCgAuTxIgARI4M0EKAC5MkiABFyWwL179wy0ekPvX5zpg6GeH7J8sQ2Mjh0LFy7UTN8hQ4bodSSAoBcwegX36dNHawEGBgZq+RhYQECApEqVSlvJYbu4ePHikiRJEv0ODJnFKAPz999/4/v0sS67uvhiJOBsAnROzp4/jp4ESCAcAnfu3DEg2CACUfoFBuGHRI9SpUrJ3Llz9SwgtoKRuAHDGT8keGTLlk0aNmwozZs3V8F44sQJvY6s4sWLF0u7du30Osq/IBkkKChIrw8bNkwePHigvYO7dOlCH8sVSgIkYEsCdE62nBYOigRIwAoCKAODKB4SOmLHjq2PvHjxosyfP19FGoQhzvzhM9MQzatRo4bWDUS0EIJw5cqV2uMXhqQRZP7GihVLypcvL56enloWBlFGGH5n06ZNsm/fPiSE0MdaMZF8BgmQgOUE6JwsR8oHkgAJkAAJkAAJkIC9CVAA2nt+ODoSIAESIAESIAESsJwABaDlSPlAEiABEiABEiABErA3AQpAe88PR0cCJEACJEACJEAClhOgALQcKR9IAiRAAiRAAiRAAvYmQAFo7/nh6EiABEiABEiABEjAcgIUgJYj5QNJgARIgARIgARIwN4EKADtPT8cHQmQwHsQ2LNnj9GpUyftAoJuHjAUeUbR57x588rPP/8se/fu1YLQP/30k14fOHCg1gZs2rSpXL9+Xc6ePSv//POP3g/bs2ePLF26VItHo57gihUrtFbg559/rtfv37+vv3H37l18jz72PeaPXyUBEvhwBOicPhxbPpkESCCCCdy+fduAUCtatGhYpw8ItVy5ckm9evWkZMmS4uPjI3PmzNHizrDjx49LlChRJGXKlHLz5k0pWLCgFnZGsWfYhQsXxM3NTby9vcXd3V1CQkLkt99+0+/Atm7dKjlz5pTRo0dDQNLHRvAa4M+TAAm8mQCdE1cGCZCAyxJ48uSJAeGWOnVqCQ0N1fdEr19E+LJnzy4FChSQoUOHalQPUTxY+/btBVHDUaNGSZw4cWTGjBkqFE1DNBCt39AOrlevXlKrVi2NGpoCEPfh+atWrZLvv/+ePtZlVxdfjAScTYDOydnzx9GTAAmEQ4ACkMuDBEiABBgB5BogARKIZAQePXpkxIwZU7JkySJBQUH69o8fP9bt4AMHDui5Pmz94txepUqV9PqhQ4ckadKkev/27dulUKFCr0X3EFHEd7p06SILFy6UR48eyY8//ijVq1fX7yOyePToUd1WnjNnDv8nO5KtOb4uCTiFAJ2TU2aK4yQBEnhnAsHBwUasWLH0PB8SM2C3bt3S83wPHz6Uzz77TB48eCALFizQ83ymxYgRQ+/H+cCsWbPq9jDuheF7W7ZskZ49e0rQzvR4AAAgAElEQVRAQIAKQPyGeYawWrVqMnjwYP1OunTp6GPfedb4BRIggY9BgM7pY1Dmb5AACUQIgcWLFxuXL1+W3r1761k9GETbtm3bNEqXJk0azdZFJA/3wHBW8LvvvtPPkDBi3pcgQQK9fu/ePY0MTp48WapUqaJnBl81CEVEB48cOSJr166lj42QmeePkgAJvI0AndPbCPE6CZCAYwkcPnzYgFiDIPv222/1PeLHjy8bNmyQM2fO6NYutnr79++vgg+G7WEkf5w+fVrLv+DfEIL4Hgz3Qwwis7hcuXLi4eHxGh8kiEAE4rsvXrygj3Xs6uHAScC1CdA5ufb88u1IIFIT2Ldvn9G4cWM5d+6cJE+eXFkkSpRIateurVu2I0eO1DOB+fPnDyvzgrIuJ0+eVNEHEYdrgwYN0rN9sNixY6tA7Nq1q6xcuVKyZcum91+8eFGvY3sZ278Ql0+fPqWPjdQrkC9PAvYlQOdk37nhyEiABN6TwF9//WW0a9dOz+3hXB4MiR/4g0gehOCJEydU6JUoUUKvIzkE28IQcIjkQSii5AtKxsAgJPF5kSJFJF68eFoH0M/PT88WmobvohTMs2fP6GPfcw75dRIggQ9DgM7pw3DlU0mABGxA4NSpU8aaNWvE19dXu3bAIM4QEdy4caPWBERCBwQgzvaZBnGI+ypXrizTp08XT09PzQyGFS9eXKJFiyaFCxeWw4cPa72/MmXKhHUCwf0oDP1SGNLH2mAdcAgkQAL/kwCdE1cFCZCAyxKgAHTZqeWLkQAJvCcBCsD3BMivkwAJ2JfA8uXLDdT1wzZvjhw5dKA484cIXtmyZTXxA5E9ZPKadQJv376ttQCRzDF16lT9HD2Djx07pt9Hz2DUFUSEb8KECXo+EGVgcD/M399fpk2bJg0bNpTcuXPTx9p3eXBkJBCpCdA5Rerp58uTgGsT2LBhg4HzeWjn5uXlpS+Lun5I1MAWL/r+du/eXVu/Xbp0Sa/jDCAyfCESsS2M3r74Nwo7w5Ac0rlzZxWS2DpGvUBkAmMrGIa2cHPnzlWhOHLkSPpY115ifDsScCwBOifHTh0HTgIk8DYC27ZtM5DQMXPmTBk2bJjejvN8yNhFcWhE+zJkyCBNmjTRM4GwZs2aSWBgoJ4NxDk/lH9B5u//a+9coHUusz/+jKlERa4x7lFJhxBiGMml3HK/ZmIkh+NMYcYhicgoxkghSS2XqSSXNGGQFgrDUUg0RBy5lrswE8b6/dd3r/97Vubf/8zSPOl5X5+91lnnnPd53+d9fp/nWXvttfez95anT6LyMHfddZcVgj506JBbvHix++ijj6zUjETeQn1+5MiRrn79+ujY/7RJjEMAAj8JAZTTT4KdL4UABC4HgVmzZkUy+JSsIY+fRAahQrbK0lUYVwadkkGKFi1q4yrxsmrVKisWnZSU5K655hoz6Hr37m3jem+DBg3Mq1igQAF7TX/HOoWoXEyuXLkstJw/f3507OXYaL4DAhC4ZAIop0tGxgcgAIF4IdCwYcNoyZIlmXX5tO7z58+bkacQroxAdf0YPXq0FXyWFCtWzK1evdoNHDjQqYSMSr3UrFnT1a5d28ZLlSpl4V4ZjyoIrbt/qgkoL6Nk2LBhbsyYMWZQrlq1Ch0bL4eFdULgCiOAcrrCNpzHhcCVRKBcuXJRrKPHd5+7YcOG5hVUkkfVqlXN2FMPX8mMGTNc+/btLUwsz54MRNUQlOEoUTj430Vz7dq1y16WoagkEoWWS5cujY69kg4czwqBOCKAcoqjzWKpEIDApRFITU2NlPwRS9jQpxUKlodP3TvUJUQePHkCY0kg8gYqMUS9fmfOnGkeP90HVHeP2Odjq1CRaIk6inTo0MH+LlGihBWPVru5Zs2aoWMvbct4NwQgcJkIoJwuE2i+BgIQuPwEMAAvP3O+EQIQiA8CGIDxsU+sEgIQ+AEEJk+eHCl5QyVdUlJSbAbdzRs1apSrV6+eGzJkiN3jU9JG9erVbVw1/jp16uRatmxp9/xU9mXs2LGZSSLNmjXLXIk8h/pRSzl5CiWpqamWECKPYHp6Ojr2B+wbH4EABH58AiinH58x3wABCPxEBFauXBl17NjRTZ061Wr5Sc6cOWPhXPX3nTt3rps/f74ZeaoXKClTpoxlCsuAUyZw3rx5rcyLysZI9F6JagnqXqDmU7hXhqOkZMmS7vjx4+7jjz+WYYiO/Yn2nq+FAASyJoBy4oRAAAIJS6B3796RDDoleGRkZNhzqj6fkjaU9Xvq1Cn3u9/9zmr4yeiTKHlDZV+U/CGjTx5E9fqNydChQ60otF5r0aKFW7Fihd0RfOaZZ+wtlStXdpMmTXLnzp1T7UF0bMKeLh4MAvFNAOUU3/vH6iEAgSwIFClSJJIhV7FiRQvzSlQQWgkf1apVc0eOHHGq26eCzkePHrXxJk2amPeuVq1aVv5FWcLfbfXWvXt3MwAVVlZXEc2rpBLVC5QopKy2c8nJyTI80bGcUAhAIEgCKKcgt4VFQQACPgi0b98+Kl68uJV5UT0/iTKCJTLg5BVU6Fet2xYtWmSvK6yrn/vuu8/Cw7EC0rH16G6gDEfV+tOYvIVqB5eenm5vUe1AlYqRhzFHjhzoWB8byRwQgIB3Aign70iZEAIQCIVAo0aNoipVqpg3r27durYshWZ37txpNf5y5sxp4dt9+/Zl1vlTCReFg2MFnlUqJtY/WJ+fPn26eRTVEUSvy9jbv39/ZiHp2F1A9Qm+6qqr0LGhHAbWAQEIXEQA5cSBgAAEEpZA3759I4VrixQpYpm/kttvv92tX7/eFS5c2EK9uhNYqFChTAYKAcs4lGHXs2dPu+tXvnx5Mwgle/bssezg7du32x3CV1991bqA5M+f38blFZQRqezgPHnyoGMT9nTxYBCIbwIop/jeP1YPAQhkQQADkOMBAQhA4PsJYAByMiAAgYQlkJaWFk2cONFCtXPmzLHnVI0+JXxs2rTJSrx88803dg+wQIECNi7PX69evcyDd9NNN7mkpCS7I6hwseSBBx5wai8nz59Cwfq8agvGuoLoPqDuGS5dutS1bdsWHZuwp4sHg0B8E0A5xff+sXoIQCALAioErWLPStS44YYb7J233HKL27Fjh2X46jWNKVFkyZIlNq4afjLmZCgq7CvDUEaksnsl/fv3t6QPZRMrnKw7hbpHuHfvXhtXaZh8+fJZ8ejhw4ejYzmhEIBAkARQTkFuC4uCAAR8EOjYsWMkz58KPxcsWNCmlEEnD50KPceKPKskTCzbd9euXe7OO+80r556/MqYu+eee9yqVavs8yoerc/feOONlhCiBBIZlZMnT7bx3bt3u8cff9zuES5cuBAd62MjmQMCEPBOAOXkHSkTQgACEIAABCAAgbAJYACGvT+sDgIQgAAEIAABCHgngAHoHSkTQgACEIAABCAAgbAJYACGvT+sDgIQgAAEIAABCHgngAHoHSkTQgACEIAABCAAgbAJYACGvT+sDgIQgAAEIAABCHgngAHoHSkTQgACoRAYP358pLU0bdrUDRw40Jb14IMPumLFillruN69e1vdPvX1rVq1qo2vW7fOysaUKFHCagS2adPGSsboMxK9rj6/KhStzz3xxBNWGkb1ACXlypWzuUqVKuUee+wxdGwoh4F1QAACFxFAOXEgIACBhCUwZ86cSP1+1ad3wIAB9pxVqlRxPXr0cPv377f/VdBZ0r59e/u9YMEC16xZM+sOkp6e7nLnzu1q1KhhtQElMhr1mjqALFq0yIpAv/HGG65ChQo2rvept3D9+vXVRxgdm7CniweDQHwTQDnF9/6xeghAIAsC27Zti9auXWsFm9X9Q9K3b1/XpUsXM+Bk6P397393/fr1y+wEsmXLFpcjRw4z4N59910zHgsXLuyuvvpq+3xqaqoZe3rfU0895caPH29zfvXVVzYu75+8hX369HGVK1dGx3JCIQCBIAmgnILcFhYFAQj4INCuXbvoL3/5i4VsY71+Y2FftXPr3LmzW7Nmjdu+fXtmKzeFimXwyYB79NFH7ff9999vhp5Er2k+dfuQd/HFF180o1BzSNQ5JFeuXOYpTE1NRcf62EjmgAAEvBNAOXlHyoQQgEAoBDAAQ9kJ1gEBCIRGAAMwtB1hPRCAgDcCV199dRRFkcuZM6f79ttvbd4xY8a4u+66y82ePdsVKVLErVy50k2cONF98cUXNn7y5EkL4ZYsWdKlpaW5Vq1aWS/g7Nmz2/iJEyfcoUOHXOPGja1/8IgRI9zx48dtXsmRI0csUUS9hOkF7G0rmQgCEPBMAAPQM1CmgwAEwiGQN2/eSNm5CgMvXrzYFvbwww9biLd8+fKZ9/Zk8DVv3tzG582bp+xdl5KS4rZu3Wp3//T+PHnyZD7Yrl273OnTpy35Q2HgN99809WsWdPGlR08a9Yst2nTJpecnIyODec4sBIIQOA7BFBOHAcIQCBhCSQlJUWfffaZZe0qmUOi0i7K1NWdvTvuuMOVLVvWzZw500rFSM6fP+/Gjh1rmbzyHv7rX//KTAD5d1C6C9i6dWuXkZHhqlWrZsPXX3+9GzZsmDt79qzuHaJjE/Z08WAQiG8CKKf43j9WDwEIZEGgaNGi0b59+5wSPq677jp75zXXXGM1+xQGHjlypIWCly9f7oYMGWLjzzzzjHvppZfcjTfeaMafwrk/+9n/VZXHjh1zZ86csffKEFRWsKRSpUqWKKJM4ly5cqFjOaEQgECQBFBOQW4Li4IABHwQePvttyMZdLq/p7t9kttvv93Ku8hQO3DggN3nu+222zLrBCqjVxm+uuM3dOhQ8wYqnPuPf/zDPq/3yoOoGoAKEb/22mv2vooVK9q4QsbyAm7cuNHVqVMHHetjI5kDAhDwTgDl5B0pE0IAAqEQqFixYnT06FG7rycvnaRu3bpO3jt18VANQHnx5OGrXLmyjSvJ4+abb7Zaf/LuKXysULAMRYmMP9UWrF69ut0BlDH50EMPmTEpUULI4cOHLQTctWtXdGwoh4F1QAACFxFAOXEgIACBhCWQkpISycCTB3Dv3r32nKrPp4xfeQZVvFl1+5QlrGLOkmnTppnRt3v3bssc1t0+GYWxLOCkpCRr/1a8eHELJ6sYdHJyshmLkr/+9a82tzqEDB48GB2bsKeLB4NAfBNAOcX3/rF6CEAgCwIYgBwPCEAAAt9PAAOQkwEBCCQsgWXLlkXr16+3EG6tWrXsOfPmzWveupdfftm6g+iOn/oDq6OHpHbt2u6mm26yHsEq8aLwsO4PKilEorCxvIE9e/Z0n3zyifvggw8syeSdd96xcXkES5UqZT/jxo1Dxybs6eLBIBDfBFBO8b1/rB4CEMiCwPjx46Nf/vKX7tZbb83MApZBp+xehW3vvvtu1759extTWzhJ6dKlLQlEIeP09HQzDidMmODKlStn4x07drT7g0r8aNu2rRmWMjCVFCJRSRkZjDIUv/76a3QsJxQCEAiSAMopyG1hURCAgA8C8gDKyLvqqqsya/l9t6SLDDclhMgjOHr06EwPod6v8jG6AyhvnxJIVDNQop7AO3fuNKNw8+bN5hnUe3QnUCIPo4pOKxv4zJkz6FgfG8kcEICAdwIoJ+9ImRACEAiFwOjRoyN54+TtU5hWorCvRMZfzBuoriAq7yJR/b4dO3ZYoodqBerzffr0yXwkdRVRp5C5c+daaRjNo5ZwMWnYsKE7ePCgFZTetGkTOjaUw8A6IACBiwignDgQEIBAwhJYvnx5pBIw8sb16NHDnlMlW37/+9+7bNmymWdQxpruA8qjJ1HWb79+/azMizKE16xZ4z766CMzDCVt2rQx76DCvwoX655gp06d7Lfk+eeftxqAgwcPVpYxOjZhTxcPBoH4JoByiu/9Y/UQgEAWBDZv3hzJgGvSpElmqzYZfEry0J09GXAyDlXOZdKkSTaTjLnPP//cLV261N6nbiEq7BxrJaekEYV4W7Zsae9RLUDdC1RoWKKC0OvWrbO6gadPn0bHckIhAIEgCaCcgtwWFgUBCPggoDuA6gUsr16zZs1sylOnTqlFm2vRooV78803refviBEjMg083RlUMWfd5ZOHUAWdV61aZV5DyeTJk91bb71l9wNl+GkOhYrVFUQig1Ofv+GGG+QVRMf62EjmgAAEvBNAOXlHyoQQgEAoBDAAQ9kJ1gEBCIRGAAMwtB1hPRCAgDcCU6ZMiYYMGeL2799vyRoSZe0qnDtlyhTXq1cvt23bNksGUTcQySOPPOIWLFjgMjIyXPfu3Z2SOtQmLpY8Im+gkkZmzZpl75XHT51FYlnCv/3tb92ePXssMYQ7gN62kokgAAHPBDAAPQNlOghAIBwCHTp0iBYuXGgZwArLSpTcoRp+CgvrddXrUy1A3fuTKPFDiSOqFahwr1q7paSkZBaSlmGn/sJqIydDUH2Bb7nllswkEc2hNnAqQP3000+jY8M5DqwEAhD4DgGUE8cBAhBIWAL58uWzMjDy7ql4s6RVq1aW5Suv3ocffujKli3rGjRo4LZs2WLj9957r1uxYoXd/5OHT8adysCoFqBEnkRlBSv5Q9m+SiSRsbhhwwYbf++99ywTWEkitWvXRscm7OniwSAQ3wRQTvG9f6weAhDIgkC2bNkiJWMo01c/EpVrURmXMmXKWK0+SVpamlu+fLn9LcNNnj8ZjfIKqlyMEkJicu7cOTd//nw3ceJEawOXmppqIeRYK7lKlSqZMakOIe+//z46lhMKAQgESQDlFOS2sCgIQMAHgRIlSkRdunRxgwYNsq4dEnntdK9v1KhRrmTJkq5v377q2OG+/PJLG9dr7dq1s3p/Xbt2tddUQ1D3AiV/+MMf3Ouvv27Gn7yCxYoVszIzM2bMsHF5F3W/UEWiDx06hI71sZHMAQEIeCeAcvKOlAkhAIFQCBQqVMgMwKJFi1pCR0xUwkWePNX/U0mYWHhX4woZq1SMvHoy7vTe5s2bu5UrV9rHS5QooR6/9plvvvnG5paxp5ZyEnkOL1y4YHcJDxw4gI4N5TCwDghA4CICKCcOBAQgkLAEfv3rX1sW8NSpUy1xQ9KoUSO726e7e0oQyZMnj93r050/SSxbWPf69Lc+p2QRef0kzz33nNUNLFKkiIWVZQQqHPzpp5/auO4aKhO4VKlS6jqCjk3Y08WDQSC+CaCc4nv/WD0EIJAFAQxAjgcEIACB7yeAAcjJgAAEEpZAt27dIpVqOXLkSGanjsOHD1vtvnr16tk9wKSkJIVqre2bRPcB09PTrTewQrkVKlSw9m5169a1cb2uEjIqA3PzzTfb2M6dO621nER1AmvWrGljnTt3Rscm7OniwSAQ3wRQTvG9f6weAhDIgsALL7wQqSizyrbIIJMoVKvM4D//+c9W709JHzIIlfkr0R1AlXRRCPjZZ5+1Nm8qF6N7gJLhw4dbwojCwjly5HAKMav0i+r+SXbt2mUFpvWZadOmoWM5oRCAQJAEUE5BbguLggAEfBDYvHlzpKzccuXKueuuu86mHDdunCtYsKD7xS9+YXf5VBJm4MCBrmnTpjauZA8Vh1ZyiOr/yXjUe+UJlOiuYOvWrS3xQ11FDh48aB5D1ROUHDp0yH6rgPSLL76IjvWxkcwBAQh4J4By8o6UCSEAAQhAAAIQgEDYBDAAw94fVgcBCEAAAhCAAAS8E8AA9I6UCSEAAQhAAAIQgEDYBDAAw94fVgcBCEAAAhCAAAS8E8AA9I6UCSEAAQhAAAIQgEDYBDAAw94fVgcBCEAAAhCAAAS8E8AA9I6UCSEAgVAIVKtWLdq4caP1/FVvX8nYsWOtvp/KtXTo0MHNmzfP2rnF+gHrfdmyZXO5c+d2M2bMcHfeeae1i9NrErWHU2mZxYsXu27dutnfKhj985//3MbPnz/vOnXqZH/Pnj0bHRvKYWAdEIDARQRQThwICEAgYQkkJydH69ats7p+MgIlTz75pLv11ltd48aNzViTIZeamprZK1gGnoxBFYh+/vnnrRC06v2p769Eht7WrVttThmFbdu2dZs3b3bFixe38RMnTljNQNUdPHjwIDo2YU8XDwaB+CaAcorv/WP1EIBAFgRWr14dqXCzDDkZZpJJkyZZ2zd17ChSpIgVdpYx95vf/MbG5Q2sX79+5vs1PmjQICsGLcmfP795AWMeQ42rLdy0adNsfNOmTW7u3LnmcTx37hw6lhMKAQgESQDlFOS2sCgIQMAHAQxAHxSZAwIQSEQCGICJuKs8EwQgYAQGDRoUVapUye7kFSpUyF7bsmWLhXF1B/DcuXPupZdeclOmTHEFChSw8apVq5o3TyHf/fv3u65du7o//vGP1iNYUqVKFQvvakz9ghUq3rZtm3kZJfqekSNHuho1argVK1agYzmLEIBAkARQTkFuC4uCAAR8EFiyZEk0YcIEt2DBAgvbSmTslShRwpUvX976+d59991m/C1dutTG3333XZecnGx/T5482W3fvt0tWbLEQsaSBx54wL366qvu2muvdadPn7b7gfqdkpJi40ow0fj8+fPdhg0b0LE+NpI5IAAB7wRQTt6RMiEEIBAKgbNnz0ZPP/20GWWxO3uPPfaYe/TRR82Ak/HWpEkTV7ZsWbd3715btn7/7W9/s98y/uQl1J2+d955x8YLFizokpKS3IABA1zt2rXNiMyePbs7fPiwjeu9bdq0cWlpae6NN95Ax4ZyGFgHBCBwEQGUEwcCAhBIWAJ79uyJ5P0bNWqU+/rrr+05leDRsmVLV65cOXfbbbe55cuXWwj3yJEjNi5vYI8ePVyOHDksc1ilYFavXu1mzpxp4wMHDrT3/vOf/7Qs4KeeesqyiRUWlmRkZJhRmDNnTpWQQccm7OniwSAQ3wRQTvG9f6weAhDIgsC2bdsi1f1buHChefwk+q0afyrvMmLECNeoUSMLD586dcrGc+XKZSFglXdRhu/gwYNdnTp1rPSLRN6/0qVLu7x581poWMakwsm7d++2cdUM1Py6Q7hlyxZ0LCcUAhAIkgDKKchtYVEQgIAPAqNGjYpeeeUVS9bQvT/JI488YokeCgvLizd79uzM+4Gx71SSiAxFlYe5//77XeHChc2rJ2natKnbt2+fGYoqKXPvvfdaYolekyisrO9T6Pj06dPoWB8byRwQgIB3Aign70iZEAIQCIVA8eLFo6NHj7rXX3/d3XHHHbasUqVKmWdPBpu8eMr41Wvdu3e3cYWKZcApvKs7gEoKadCggfvkk09sXHf7dJ/w+PHj9r727du7iRMnmtdPIkOxf//+7ttvv3VTp05Fx4ZyGFgHBCBwEQGUEwcCAhBIWAIYgAm7tTwYBCDwXxLAAPwvAfJxCEAgXALDhw+P5syZY3UAO3fubAtVJw+FePfs2WNdP9QWTl07qlWrZuMxT5/+7tixoyV5qGNITJYtW2Z1AVUHUD/qKqIkkWLFitlb5BU8e/asfd+8efPQseEeD1YGgSuaAMrpit5+Hh4CiU2gf//+0ejRoy0j97333rOH1f0/JWgoRKtSLs8995zd11P/XokMQ/0vqVixopWEUWmX3r1722tK8lDCSM2aNa12oH6rtqC+Q/Lll1+awdi3b1+FntGxiX3EeDoIxC0BlFPcbh0LhwAE/hOBnTt3Rir3IkNO3Tkk+fLls6xd3eGrW7eue+KJJ6wWYCxL+MKFC5YoonuDGlcZGCWQKGFE0rx5c8sqltdQ2cP6W1nBsSQTlYQ5c+aMagC6Pn36oGP/0yYxDgEI/CQEUE4/CXa+FAIQuBwEMjIyIiVwKOSrxA+J6gAqrKvXPvvsMwsBy2ArWrSojavGn4pF33fffa5hw4aWAazC0Sr3IlH5GM2l/1euXOkUYq5evbpbv369jcsQfOGFFyyxZMCAAejYy7HRfAcEIHDJBFBOl4yMD0AAAvFC4O23346Ukau7egr5xjyAKuWizF158Q4cOGA/sVZwKuissLDu9unuoAxD1f6L3QNUT2AZhbVq1XKbNm0yg/DJJ5+0lnIShYcVMlZB6YcffhgdGy+HhXVC4AojgHK6wjacx4XAlUTgwoULkcK0MtRU8DnmwVOYVkbbr371K0vaUKkXGXSSgwcPOpWOWbx4sduxY4cZfgr/Dh061Ma7dOli/YDl6Rs2bJjVAVQYOXYHsFevXkr+sDuFa9euRcdeSQeOZ4VAHBFAOcXRZrFUCEDg0gicPHkykkdPBl9KSop9eNeuXZbd261bN/MAKpyr8VgruBYtWph3UIkgMgBV169Vq1aZBp7+X7RokXv88cctdCwDUR4/FYyWyJuoJBO1kTt16hQ69tK2jHdDAAKXiQDK6TKB5msgAIHLTwAD8PIz5xshAIH4IIABGB/7xCohAIEfQGDz5s2RMnRfe+01C/lK5PVLTU11Q4YMsf9Pnz5t3UCU+CFRyFj/K1NYYVz1Av7www/dW2+9ZePy7MnjJ+9fyZIl3apVq6y24J/+9Ccb1x1A3R389NNPFXpGx/6AfeMjEIDAj08A5fTjM+YbIACBn4jAxx9/HClJQ/f8ZJBJlP2rBI9ChQpZmFdlX1TbT++R1KhRw+oDKqlDiRwqDK2/9bmYgai/1Qe4X79+FkKOtY+LPaYSR/T67t270bE/0d7ztRCAQNYEUE6cEAhAIGEJpKSkRBMmTLC+vidOnLDnlNH31VdfOdUHlDdQ0rNnT7d8+XL7u0qVKjY+e/Zsqxn4wQcfmJdQiSQS/T916lT7WwWglQii3sAxkXGYkZFhSSTJycno2IQ9XTwYBOKbAMopvveP1UMAAlkQqFChQqQCzzLalJghkTG4Zs0aq9snT+DatWvdvn373Oeff27jY8aMsTqBMu6yZ89uoWJl9iqULMmbN695A5UZLO+hkkDUUi42f9euXd3LL79s7/1ZzMJklyAAAQgERgADMLANYTkQgIA/AtOnT4+UyatQbe7cuS+aWB091Oxk310AAAYaSURBVB5Onj5l9MZawakG4N69e61lnAy7Z5991nXv3t28ehJ1B1HWsAw+3ResVKmSFZSO1QFUX2CJ+gFfe+216Fh/28lMEICARwIoJ48wmQoCEAiLwOHDhyOFc1W4WeHc78rkyZNdWlqa3fNTosfhw4dt+MEHH7RSLjII1eVDHsBXXnkls9Wb6v/pvqCSQeTgU0kYeRAfeugh+3yspdyxY8f0nejYsI4Eq4EABP6XAMqJowABCCQsgTNnzkS6n5cjR47MJA6FbRUCbty4sdX4U2i3bNmybty4ccZBHUPUyk0FoeXNK168uLWLU1hYosxfGX+SWD9geQeVCSy5/vrr3RdffGF1AUkCSdijxYNBIO4JYADG/RbyABCAwP9HAAOQswEBCEDg+wlgAHIyIACBhCVw7Ngx6wSi+3qxfAzd29uwYYO75557rFTL8OHDrRVcrI6fagQq9Kt7gK1bt7bQsJI+1D1EIk+h7gYuW7bMvIeae+PGjVb7T6L5dSewXr167uTJk+jYhD1dPBgE4psAyim+94/VQwACWRAYM2ZMNH/+fDd9+nS76ycpU6aMlW1RIWcZf507d7awbSyLV3cGZRjWqVPH7vapsLPCuaobKBk0aJDT/T61lBs5cqSFjFeuXGmGnyQ9Pd0SQBQWTktLQ8dyQiEAgSAJoJyC3BYWBQEI+CDQrl276P3337ekDdX/k8hgk4E3evRop76/uuOn0jCxWn7qCazEDnn6dEdQRmCjRo2sZIxEWcEqDyMjT7+3bt1qdwRVK1AyY8YMMy71e8uWLehYHxvJHBCAgHcCKCfvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE0AAzDs/WF1EIAABCAAAQhAwDsBDEDvSJkQAhCAAAQgAAEIhE3gfwA7BJkaPY+plgAAAABJRU5ErkJggg==\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py:490: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "  'Discrepancy between trainable weights and collected trainable'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n"
     ]
    }
   ],
   "source": [
    "figure, axes = plt.subplots(10)\n",
    "plt.ion()\n",
    "\n",
    "figure.show()\n",
    "figure.canvas.draw()\n",
    "\n",
    "batch_size = 64\n",
    "for epoch in range(1000):\n",
    "    indices = np.random.randint(0, x_train.shape[0], batch_size)\n",
    "    image, image_label = x_train[indices], y_train[indices]\n",
    "\n",
    "    random_noise = np.random.normal(0, 1, (batch_size, generator_noise_size))\n",
    "    random_labels = np.random.randint(0, 10, (batch_size, 1))\n",
    "\n",
    "    generated_images = generator_model.predict([random_noise, random_labels])\n",
    "\n",
    "    input_real = np.ones((batch_size, 1))\n",
    "    discrimantor_model.train_on_batch(image, [input_real, image_label])\n",
    "\n",
    "    input_fake = np.zeros((batch_size, 1))\n",
    "    labels_fake = 10 * np.ones(image_label.shape)\n",
    "    discrimantor_model.train_on_batch(generated_images, [input_fake, labels_fake])\n",
    "\n",
    "    combined.train_on_batch([random_noise, random_labels], [input_real, random_labels])\n",
    "\n",
    "    figure.suptitle(\"Epoch {}\".format(epoch))\n",
    "    random_noise = np.random.normal(0, 1, (10, generator_noise_size))\n",
    "    noise_labels = np.array(list(range(10)))\n",
    "\n",
    "    images = generator_model.predict([random_noise, noise_labels])\n",
    "    images = 0.5 * images + 0.5\n",
    "\n",
    "    for index in range(10):\n",
    "        axes[index].imshow(images[index, :, :, 0], cmap='gray')\n",
    "        axes[index].axis('off')\n",
    "    figure.canvas.draw()\n",
    "    plt.savefig('/root/photo/{}.png'.format(epoch)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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