diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..3c72f47
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
+.ipynb_checkpoints/
+utility_functions/__pycache__
+utility_functions/__pycache__/*
diff --git a/Project.ipynb b/Project.ipynb
index 266d5e3..6616f73 100644
--- a/Project.ipynb
+++ b/Project.ipynb
@@ -12,7 +12,7 @@
     "\n",
     "### Assignments\n",
     "\n",
-    "1. Generate a series of patterns of trasmissions on a network represented as a graph. In such graph the edges are weighted accordingly to the delay between transmittion and reception.\n",
+    "1. Generate a series of patterns of transmissions on a network represented as a graph. In such graph the edges are weighted accordingly to the delay between transmittion and reception.\n",
     "2. Implement euristic algorithms to lower the potential of the network, i.e. aiming at minimizing the distance of the communications from their final destination.\n",
     "3. Find out the most efficient algorithm\n",
     "\n",
@@ -20,6 +20,3916 @@
     "Leonardo Badia <badia@dei.unipd.it>"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction to Delay Tolerant Networks\n",
+    "\n",
+    "Delay Tolerant Networking (DTN) is a relatively new area of research which \n",
+    "is rapidly becoming the focus of considerable effort. DTN is an approach\n",
+    "to computer architecture that seeks to address the technical issues in \n",
+    "heterogeneus networks that may lack continuos network connectivity. The\n",
+    "final aim of this developing study area is to tackle problems which the \n",
+    "Internet is not able to face.  \n",
+    "\n",
+    "There's a great variety among the different DTNs: they range from deep \n",
+    "space networks to buses driving around mobile wireless hotspots in \n",
+    "villages to provide Internet service to kiosks. \n",
+    "\n",
+    "The big difference from the regular Internet Protocol (IP) is that IP\n",
+    "assumes a seamless end-to-end data path instead DTNs are built to account\n",
+    "for errors and disconnections. Unlike IP, where heterogeneus networks are \n",
+    "connected by assigning each node a common network-layer identifier (IP address),\n",
+    "a DTN uses \"naming, layering, encapsulation and persistent storage\".\n",
+    "\n",
+    "The situations in which we use DTNs are pretty challenging:\n",
+    "\n",
+    "* The throughput is generally low (we can think for instance about the acoustic in underwater modems) and may \n",
+    "be highly asymmetric or in the worse scenario a return channel might not \n",
+    "exist\n",
+    "\n",
+    "* These networks are frequently disconnected, not only by faults but due\n",
+    "to motion for instance\n",
+    "\n",
+    "* These systems frequently operate with a low duty cycle and the queuing \n",
+    "time can be very large; with these conditions TCP would time out the session\n",
+    "instead UDP would lose all the packets.\n",
+    "\n",
+    "We decided to use 2 different approaches in order to face to routing problem with DTNs. The first one is a greedy\n",
+    "algorithm while the second is an epidemic algorithm. \n",
+    "\n",
+    "We decided to model the DTN as a wireless network where at each time step a link has a certain probability of being shut down for a certain amount of time. \n",
+    "\n",
+    "Before going into the details of the two different algorithms there are some assumptions that need to be pointed out:\n",
+    "\n",
+    "* We are working with infinite buffers \n",
+    "\n",
+    "* There are no errors in transmission\n",
+    "\n",
+    "* There's a maximum distance between two satellites beyond which they can't communicate\n",
+    "\n",
+    "* We keep the same size for every packet and the same bandwidth for each link\n",
+    "\n",
+    "* This is a centralized system, in the sense that Earth knows all the topology of the network\n",
+    "\n",
+    "The simulated network is a satellitar network where the nodes need to send data to the main station on Earth, for the sake of semplicity all of them have a direct link to the the destination. This is not an oversemplification because given the fact that links often shut down the overall behaviour is similar to the one we would have in case of indirect communications.\n",
+    "\n",
+    "We repeated the tests done below also for the latter case but they were not kept. This is due to the fact that since some satellites are too far to directly communicate with the base station it is necessary to increase the number of nodes in order to have a meaningful configuration.\n",
+    "Increasing the total number of nodes is prohibitive because both the algorithms are superlinear in the number of nodes, meaning that it takes too much time to execute them in this particular case.\n",
+    "In most cases the results were very similar to the one obtained by the simulation, if not it will be specified below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#used to import the utility functions\n",
+    "import sys\n",
+    "sys.path.insert(1, \"utility_functions/\")\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "from matplotlib.lines import Line2D\n",
+    "import matplotlib.animation as animation\n",
+    "import math\n",
+    "import pylab as pl\n",
+    "from matplotlib import collections  as mc\n",
+    "import mpl_toolkits.mplot3d.axes3d as p3\n",
+    "from network_utilities import generate_network, plot_network, disable_links, adjacency_matrix\n",
+    "from greedy_routing import greedy_routing\n",
+    "from graphics import plot_idle\n",
+    "from metrics import throughput, delay, deviations\n",
+    "from animation_utilities import get_links, check_active, update_lines, get_vel, update_graph\n",
+    "import pandas as pd\n",
+    "from epidemics import epidemic, remove_duplicates\n",
+    "from tqdm.notebook import tqdm\n",
+    "import copy\n",
+    "\n",
+    "#MAIN PARAMETERS\n",
+    "################################################\n",
+    "n = 10                                         #\n",
+    "c = 3e8                                        #\n",
+    "T_tx = 10  # da regolare                       #\n",
+    "r_max = 60e9  # Marte                          #\n",
+    "tau_max = 200                                  #\n",
+    "delta_time = 10                                #\n",
+    "n_configurations = 10                          #\n",
+    "network_seed = 17363720                        #\n",
+    "################################################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nodes = generate_network(n,r_max,seed=17363720)\n",
+    "A = adjacency_matrix(nodes,tau_max)\n",
+    "sorted_nodes = np.argsort(A[0,:])     #sorting based on distance from Earth\n",
+    "A = A[sorted_nodes,:][:,sorted_nodes]\n",
+    "nodes = nodes[sorted_nodes]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support. ' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>');\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option);\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\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 overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>');\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\" width=\"700\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib notebook\n",
+    "\n",
+    "bg = [26/255,26/255,26/255]\n",
+    "fig,ax = plot_network(nodes,sphere=True,r_max=r_max)\n",
+    "ax.set_facecolor(bg)\n",
+    "rotation = True\n",
+    "\n",
+    "def rotate(num,ax,rotation):\n",
+    "        ax.azim = 0.5*num*rotation\n",
+    "\n",
+    "ax.set_axis_off()\n",
+    "ani = animation.FuncAnimation(fig, rotate, interval=20, fargs=(ax,rotation))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support. ' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>');\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option);\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\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 overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>');\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "At = disable_links(A,5000,1,mode=\"unstable\")  #demonstration with unstable mode\n",
+    "\n",
+    "n_updates = At.shape[2]\n",
+    "links = get_links(A,nodes)\n",
+    "active_links = [get_links(At[:,:,t],nodes) for t in range(n_updates)]\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = p3.Axes3D(fig)\n",
+    "\n",
+    "data = links\n",
+    "lines = [ax.plot(dat[0, :], dat[1, :], dat[2, :],alpha=0.5)[0] for dat in links]\n",
+    "\n",
+    "# Setting the axes properties\n",
+    "ax.set_xlim3d([-4e10, 4e10])\n",
+    "ax.set_xlabel('X(m)')\n",
+    "\n",
+    "ax.set_ylim3d([-4e10, 4e10])\n",
+    "ax.set_ylabel('Y(m)')\n",
+    "\n",
+    "ax.set_zlim3d([-4e10,4e10])\n",
+    "ax.set_zlabel('Z(m)')\n",
+    "\n",
+    "title = ax.set_title(\"Time = s\")\n",
+    "snodes = [100] + [50 for x in nodes[1:]]\n",
+    "cnodes = [\"green\"] + [\"black\" for x in nodes[1:]]\n",
+    "ax.scatter(nodes[:,0], nodes[:,1], nodes[:,2],s=snodes,color=cnodes,alpha=0.8)\n",
+    "\n",
+    "legend_lines = [Line2D([0], [0], color=\"black\", lw=2),\n",
+    "                Line2D([0], [0], color=\"red\", lw=2)]\n",
+    "ax.legend(legend_lines, (\"Active links\",\"Disabled links\"))\n",
+    "\n",
+    "# Creating the Animation object\n",
+    "line_ani = animation.FuncAnimation(fig, update_lines, n_updates, fargs=(data, lines,active_links,title),\n",
+    "                              interval=10, blit=False)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Greedy Routing\n",
+    "\n",
+    "In every network where data is generated from a node and needs to be delivered to a different one, may it be the postal network or the internet, it is necessary to find out the route that the data should follow in order to reach its destination.\n",
+    "\n",
+    "This is usually done by a routing algorithm, in the internet the most widely used ones are the Dijikstra and the Bellman-Ford algorithms, these two are used in order to find the shortest path from the source to the destination according to some metric that could be the number of hops, the end to end delay etc.\n",
+    "\n",
+    "<!-- The code below is html and I used it because it gives much more freedom for\n",
+    "controlling images-->\n",
+    "<figure>\n",
+    "    <img src=\"figures/routing.svg\" width=500/>\n",
+    "    <div style=\"text-align: center;\"> \n",
+    "        <i>\n",
+    "            Path obtained by applying routing between the two nodes\n",
+    "        </i>\n",
+    "    </div>\n",
+    "</figure>\n",
+    "\n",
+    "Classical routing algorithms do not work on DTNs (Delay Tolerant Networks) because they were thought for systems where links are supposed to fail very rarely and where the end to end delay is relatively small.\n",
+    "\n",
+    "In the internet the chance of a link failing is low and even if it does the end to end delay is small, so in case a packet is lost because of link failure sending the packet again is not too expensive.\n",
+    "In DTNs this is not true because:\n",
+    "\n",
+    "* The propagation time is very high, expecially if we are considering satellite communication\n",
+    "\n",
+    "* The chance of link failure is very high.\n",
+    "\n",
+    "On top of that it is not even possible to find the best routing algorithm for networks, this is because if also the traffic generated by other nodes is taken into consideration the problem becomes NP.\n",
+    "By analyzing the state of the art in DTN it is possible to see that the best results are mostly obtained by using greedy algorithms, i. e. algorithms that take a decision by choosing the best choice at each iteration without considering long term planning.\n",
+    "\n",
+    "The main underliying assumptions used in the implementation of the greedy algorithm are:\n",
+    "* Full knowledge of the topology and evolution of the network is given. This is not a very strict requirement as it might happen in real life, for example for satellite networks, where usually link failure is caused by the interference of a celestial body, the requirement can be easily satisfied.\n",
+    "* One central station (Earth), that fully knows the topology evolution of the network, calculates all the optimal routes beforehand and communicates them to each node. The fact that a central node perfoms all the computations allows for accurate scheduling that avoids collisions so no method has to be emplyed in order to detect them.\n",
+    "\n",
+    "The implemented greedy algorithm chooses the best next hop by using a slightly modified version of the dijkstra algorithm that allows to compute the best path from the source to the destination at a specific point in time.\n",
+    "\n",
+    "Since it is also necessary to take collisions into account, if sending one packet causes one, the sender needs to wait until it is sure that no collision will occur. After the idle time waited in order to avoid collisions the optimal path needs to be recomputed because, due to topology changes, it might not be optimal anymore.\n",
+    "\n",
+    "## Modified Dijkstra\n",
+    "\n",
+    "In general the Dijkstra algorithm is used to find the best path between two nodes in weighted graphs with positive weights.\n",
+    "\n",
+    "<figure>\n",
+    "    <img src=\"figures/dijkstra.gif\" width=300/>\n",
+    "    <div style=\"text-align: center;\"> \n",
+    "        <i>\n",
+    "            Dijkstra algorithm in action\n",
+    "        </i>\n",
+    "    </div>\n",
+    "</figure>\n",
+    "\n",
+    "Usually the algorithm is run on graphs that have fixed topology, unfortunately for DNTs this is not the case, hence a link that was active when the packet was sent might not be active when the packet reaches its destination.\n",
+    "\n",
+    "This has to be taken into account because a link failing while a message is being sent through the link will result in packet loss.\n",
+    "\n",
+    "An implementation of the Dijkstra algorithm for DTNs that solved this problem was proposed in [1].\n",
+    "With this approach the weight of a node is considered equal to $t_p + t_{tr} + t_w$ where $t_p$ is the propagation time, $t_{tr}$ is the transmission time and $t_w$ is the total time that the sender needs to wait before sending because of link failures.\n",
+    "\n",
+    "The pseudo code for the algorithm is:\n",
+    "```\n",
+    "Input = G=(E, V), s, T, w(e,t)\n",
+    "Q = V\n",
+    "L[s]=0\n",
+    "L[v]=inf for every v != s\n",
+    "while Q != {} do\n",
+    "    u = u in Q that minimizes L[u]\n",
+    "    Q = Q - u\n",
+    "    for each edge e in E between u and another vertex v do\n",
+    "        if L[v] > L[u] + w(e, T + L[u]) then\n",
+    "            L[v] = L[u] + w(e, T + L[u])\n",
+    "        end if\n",
+    "    end for\n",
+    "end while\n",
+    "```\n",
+    "Where G is the graph representing the network, s is the starting node, T is the starting time, w(e,t) is the weight of edge e at time t.\n",
+    "The only difference from the standard algorithm is that in the standard case w(e,t) is constant in t, meaning that by correctly computing the function w(e,t) the algorithm will be able to find shortest paths also in the case of changing topology.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Algorithmic Analysis\n",
+    "\n",
+    "In order to get a basic understanding on how the algorithm is working we decided to draw the following plots:\n",
+    "\n",
+    "1) The first plot represents the number of packets that passed through a certain node during their trip from source to destination. The nodes are indicated as a number and they are ordered from the closest (node 1) to the farthest from Earth (node 9).\n",
+    "\n",
+    "2) The second plot shows the occupation of the Earth, in particular the orange zone represent moments when the Earth is not receiving any data, while the teal one identifies moments when Earth is receiving a packet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d702f6ff0bc41a18dabd77e1919fe25",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=2000.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "np.random.seed(10)\n",
+    "#generating some random packets with earth as destination\n",
+    "packets = np.random.randint(1, n, (2000, 2))\n",
+    "packets[:,1] = 0\n",
+    "\n",
+    "ttr = T_tx\n",
+    "At = disable_links(A, 1000000, delta_time ,mode=\"unstable\")\n",
+    "#performing the routing algorithm\n",
+    "results = greedy_routing(At, packets, ttr, delta_time, with_tqdm =True)\n",
+    "\n",
+    "#finding out how many packets transited in one node\n",
+    "packet_distribution = np.zeros(n)\n",
+    "\n",
+    "#for each packet\n",
+    "for packet in results:\n",
+    "    #for each node in the route\n",
+    "    for node in packet.route:\n",
+    "        #if the node is not the destination\n",
+    "        if node != packet.destination:\n",
+    "            packet_distribution[node] += 1\n",
+    "            \n",
+    "#keep copies of the original data for later\n",
+    "initial_nodes = np.copy(nodes)\n",
+    "initial_A = np.copy(A)\n",
+    "packets_greedy = copy.deepcopy(results) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "_, axs = plt.subplots(1, 2, figsize = (15, 5))\n",
+    "axs[0].bar(np.arange(n), packet_distribution)\n",
+    "axs[0].set_title(\"Number of packets that passed through a node\")\n",
+    "axs[0].set_xlabel(\"Nodes id ordered by distance from earth\")\n",
+    "axs[0].set_ylabel(\"# of packets\")\n",
+    "axs[0].set_xticks(np.arange(n))\n",
+    "plot_idle(results, ttr, axs[1]) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1) From the first plot we notice a very intuitive fact, which is that packets tend to pass through nodes that are closer to earth. The two peaks in 8 and 9 are due to the fact that with this specific configuration the two exchange some packets because of link failure as can be seen in the final animation.\n",
+    "\n",
+    "2) The key observation for the second plot is that Earth is almost always receiving packets, this means that the algorithm is very efficient.\n",
+    "\n",
+    "### Transmission Time effect on the algorithm\n",
+    "\n",
+    "In next section the outcome of the algorithm is tested in case the transmission time greatly changes, this value is related to the dimension of the packet (L) and to the bandwidth of the channel (B), in particular $t_{tr} = \\frac{L}{B}$.\n",
+    "This helps us understand how the algorithm answers to low bandwidth networks or when it is necessary to send very big packets.\n",
+    "\n",
+    "The following plots show how the arrival time, the throughput and the number of optimal routes changes as the transmission time increases.\n",
+    "\n",
+    "In order to reduce the effect of noise we ran the algorithm multiple times with different network configurations and we averaged the outcome. The whole procedure was repeated with two different link deactivation probabilities in order to see how this impacts on the algorithm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b604a68d36e7428b9e0f1ff106c61264",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "062d9b359c8346a0815ff5bb3114db6f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "\n",
+    "#generating the axes as wanted\n",
+    "gs = fig.add_gridspec(2, 2)\n",
+    "axs = []\n",
+    "axs.append(fig.add_subplot(gs[0, :]))\n",
+    "axs.append(fig.add_subplot(gs[1, 0]))\n",
+    "axs.append(fig.add_subplot(gs[1, 1]))\n",
+    "\n",
+    "#generating some random packets with earth as destination\n",
+    "packets = np.random.randint(1, n, (100, 2))\n",
+    "packets[:,1] = 0\n",
+    "\n",
+    "#trying out the outcomes with two different link deactivation modes\n",
+    "for mode in [\"light\", \"unstable\"]:\n",
+    "\n",
+    "    #choosing the color to be used in the plot\n",
+    "    color = [\"blue\", \"red\"]\n",
+    "    color = color[mode == \"light\"]\n",
+    "\n",
+    "    #defining the transmission times that will be tested\n",
+    "    ttrs = range(1, 200, 20)\n",
+    "\n",
+    "    #outputs of the routing algorithm for each configuration and ttr\n",
+    "    outputs = np.zeros((len(ttrs), n_configurations))\n",
+    "    deviation_arr = np.zeros((len(ttrs), n_configurations))\n",
+    "\n",
+    "    for rand_iter in tqdm(range(n_configurations)):\n",
+    "\n",
+    "        #generate a new configuration\n",
+    "        nodes = generate_network(n, r_max)\n",
+    "        A = adjacency_matrix(nodes,tau_max)\n",
+    "        At = disable_links(A,500000, delta_time, mode=mode)\n",
+    "\n",
+    "        #for each transmission time\n",
+    "        for ttr_ind in range(len(ttrs)):\n",
+    "\n",
+    "            #calculate the route of each packet\n",
+    "            results = greedy_routing(At, packets, ttrs[ttr_ind], delta_time)\n",
+    "            #finding out the arrival times of each packet\n",
+    "            arrival_times = np.array([packet.arrival_times[-1] for packet in results])\n",
+    "            #the overall transmission time is the maximum of the arrival times\n",
+    "            outputs[ttr_ind, rand_iter] = np.max(arrival_times)\n",
+    "            #getting the number of packets that followed a route different from the ideal one\n",
+    "            deviation_arr[ttr_ind, rand_iter] = deviations(A, results)\n",
+    "\n",
+    "    #finding the mean over the different configurations of the arrival times\n",
+    "    arrival_times = np.mean(outputs, axis = 1)\n",
+    "\n",
+    "    #plotting\n",
+    "    axs[0].plot(ttrs, arrival_times, linestyle = \"-\", c = color)\n",
+    "    axs[0].plot(ttrs, arrival_times - packets.shape[0] * np.array(ttrs), linestyle = \"-.\", c = color)\n",
+    "    axs[1].plot(ttrs, throughput(packets.shape[0], arrival_times) * np.array(ttrs), linestyle = \"--\", c = color)\n",
+    "    axs[2].plot(ttrs, np.mean(deviation_arr, axis = 1), linestyle=\"--\", c = color)\n",
+    "\n",
+    "axs[0].plot(ttrs, packets.shape[0] * np.array(ttrs), linestyle = \"--\", c=\"black\")\n",
+    "axs[0].set_ylabel(\"Time(s)\")\n",
+    "axs[0].set_xlabel(\"Transmission Time(s)\")\n",
+    "axs[0].set_title(\"Mean arrival time vs mean idle time\")\n",
+    "\n",
+    "axs[0].legend([\n",
+    "    \"Arrival time(light)\", \n",
+    "    \"Idle time(light)\",\n",
+    "    \"Arrival time(unst)\",\n",
+    "    \"Idle time(unst)\",\n",
+    "    \"Total Transmission Time\",\n",
+    "])\n",
+    "\n",
+    "axs[1].set_title(\"Throughput of the system\")\n",
+    "axs[1].set_ylabel(\"Normalized Throughput\")\n",
+    "axs[1].set_xlabel(\"Transmission Time(s)\")\n",
+    "axs[1].legend([\n",
+    "    \"Throughput(light)\",\n",
+    "    \"Throughput(unstable)\"\n",
+    "])\n",
+    "\n",
+    "axs[2].set_title(\"Percentage of packets that followed unoptimal route\")\n",
+    "axs[2].set_ylabel(\"Percentage\")\n",
+    "axs[2].set_xlabel(\"Transmission Time(s)\")\n",
+    "axs[2].legend([\n",
+    "    \"Percentage(light)\",\n",
+    "    \"Percentage(unstable)\"\n",
+    "])\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Some insights on the plots\n",
+    "\n",
+    "1) What we get in the first plot is that the idle time remains almost equal to zero even if we increase the transmission time a \n",
+    "lot. The total transmission time can be expressed as $T_{tot} = t_{tx}n_{packets}$ and is the lower bound for the arrival time $t_a = T_{tot} + t_{idle}$. If we consider the light and unstable mode we can see that the results are very similar, the arrival time though is always higher for the unstable mode as expected; this is because if more links are being shut down it is reasonable to expect a longer arrival time.\n",
+    "\n",
+    "2) With the second plot it is possible to see that for small values of the transmission time the normalized throughput is very small, this is due to the fact that the arrival time is dominated by the propagation time.\n",
+    "In other words since $t_{tx}$ is small it means that the Earth has a lot of idle time, meaning that the normalized throughput is not too high.\n",
+    "With $t_{tx}>50s$ we can see that the throughput is basically unaffected by the transmission time.\n",
+    "Also in this case as expected the light mode curve is above the unstable mode one.\n",
+    "\n",
+    "\n",
+    "3) In the third plot we can observe something very intuitive: the bigger the transmission time, the \n",
+    "higher the percentage of non optimal routes will be; this is due to the fact that as $t_{tx}$ increase so does the queueing time, meaning that it is more likely that the path will be changed.\n",
+    "Here instead we can spot a big difference between light and unstable mode, given by the \n",
+    "fact that with unstable mode we are shutting down many more links than with light mode so the number of unoptimal routes must be higher.\n",
+    "\n",
+    "### Propagation time effects\n",
+    "The next plots investigate how the arrival time, throughput and percentage of optimal routes changes as functions of the distance between nodes. The distance between nodes is actually proportional to the propagation time $t_p = \\frac{dist}{c}$ where c is the speed of light so the two analisys will yield the same result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17561aca9c58434d9fd2398284d92f8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8ad85e9502d342e79540bbb35eeab326",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ttr = T_tx\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "\n",
+    "#generating the axes as wanted\n",
+    "gs = fig.add_gridspec(2, 2)\n",
+    "axs = []\n",
+    "axs.append(fig.add_subplot(gs[0, :]))\n",
+    "axs.append(fig.add_subplot(gs[1, 0]))\n",
+    "axs.append(fig.add_subplot(gs[1, 1]))\n",
+    "\n",
+    "#iterating over two different modes\n",
+    "for mode in [\"light\", \"unstable\"]:\n",
+    "\n",
+    "    color = [\"blue\", \"red\"]\n",
+    "    color = color[mode == \"light\"]\n",
+    "\n",
+    "    #constants that multiplied by the propagation times simulate the expansion of the configuration\n",
+    "    r_maxes = np.array([2**((i-4)/2) for i in range(10)])\n",
+    "\n",
+    "    #temporary variables used to store \n",
+    "    outputs = np.zeros((len(r_maxes), n_configurations))\n",
+    "    deviation_arr = np.zeros((len(r_maxes), n_configurations))\n",
+    "\n",
+    "    #for each different configuration\n",
+    "    for rand_iter in tqdm(range(n_configurations)):\n",
+    "\n",
+    "        #generating a configuration\n",
+    "        nodes = generate_network(n, r_max)\n",
+    "        A = adjacency_matrix(nodes, tau_max)\n",
+    "        At = disable_links(A,200000, delta_time, mode=mode)\n",
+    "\n",
+    "        #for each constant\n",
+    "        for i in range(len(r_maxes)):\n",
+    "\n",
+    "            #keeping the same configuration but rescaling the proagation times\n",
+    "            results = greedy_routing(At*r_maxes[i], packets, ttr, delta_time)\n",
+    "\n",
+    "            #getting the overall transmission time and the number of unoptimal routes\n",
+    "            arrival_times = np.array([packet.arrival_times[-1] for packet in results])\n",
+    "            outputs[i, rand_iter] = np.max(arrival_times)\n",
+    "            deviation_arr[i, rand_iter] = deviations(A*r_maxes[i], results)\n",
+    "\n",
+    "    arrival_times = np.mean(outputs, axis = 1)\n",
+    "    axs[0].semilogx(r_maxes * r_max, arrival_times, linestyle = \"-\", c = color)\n",
+    "    axs[0].semilogx(r_maxes * r_max, arrival_times - packets.shape[0] * ttr, linestyle = \"-.\", c = color)\n",
+    "    axs[1].semilogx(r_maxes * r_max, throughput(packets.shape[0], arrival_times)*ttr, linestyle=\"--\", c = color)\n",
+    "    axs[2].plot(r_maxes * r_max, np.mean(deviation_arr, axis = 1), linestyle = \"--\", c = color)\n",
+    "\n",
+    "axs[0].semilogx(r_maxes * r_max, np.ones(len(r_maxes)) * packets.shape[0] * ttr, linestyle = \"--\", c = \"black\")\n",
+    "axs[0].set_ylabel(\"Time(s)\")\n",
+    "axs[0].set_xlabel(\"Radius(m)\")\n",
+    "axs[0].set_title(\"Mean arrival time vs mean idle time\")\n",
+    "axs[0].legend([\n",
+    "    \"Arrival time(light)\", \n",
+    "    \"Idle time(light)\",\n",
+    "    \"Arrival time(unst)\",\n",
+    "    \"Idle time(unst)\",\n",
+    "    \"Total Transmission Time\"\n",
+    "])\n",
+    "\n",
+    "axs[1].set_title(\"Throughput of the system\")\n",
+    "axs[1].set_ylabel(\"Normalized Throughput\")\n",
+    "axs[1].set_xlabel(\"Radius(m)\")\n",
+    "axs[1].legend([\n",
+    "    \"Throughput(light)\", \n",
+    "    \"Throughput(unstable)\",\n",
+    "])\n",
+    "\n",
+    "axs[2].set_title(\"Percentage of packets that followed unoptimal route\")\n",
+    "axs[2].set_ylabel(\"Percentage\")\n",
+    "axs[2].set_xlabel(\"Radius(m)\")\n",
+    "axs[2].legend([\n",
+    "    \"Percentage(light)\",\n",
+    "    \"Percentage(unstable)\"\n",
+    "])\n",
+    "plt.tight_layout()    \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Some insights on the plots \n",
+    "\n",
+    "1) With the first plot we are visualizing the arrival time, the idle time and the total transmission \n",
+    "time as a function of the maximum radius under light and unstable mode.\n",
+    "What we see is that:\n",
+    "      \n",
+    "* Either with light and unstable mode the idle time is almost constant up to a certain radius. This\n",
+    "    constant is close to zero for light mode whereas for unstable mode it is about 300 seconds. Then from \n",
+    "    a certain radius on we see an increse which for light mode is very slow and instead for the unstable \n",
+    "    mode is very fast but in both case the trend is linear with the propagation time.\n",
+    "    The fact that at the beginning the arrival time is almost constant is due to the fact that the \n",
+    "    transmission time dominates the transmission while after $t_{tx}$ becomes negligible with respect to\n",
+    "    $t_p$ then the arrival time starts to become proportional with the maximum radius.\n",
+    "\n",
+    "\n",
+    "2) With the second plot we're pointing out the throughput as a function of the maximum radius;\n",
+    "what we observe is that the behaviour under light and unstable mode is similar but of course light mode gives us a higher throughput. Anyway for both modes the throughput is almost constant (for the unstable mode the constant is centered at 0.08 instead for the light mode it is centered at 0.09) like before when the transmission time becomes negligible with respect to the propagation time the transmission becomes dominated by the latter resulting in a lower normalized throughput.\n",
+    "  \n",
+    "\n",
+    "3) In the last image we can observe the percentage of packets that followed unoptimal routes as a function of the maximum\n",
+    "radius. What we get for the two different modes is a similar behaviour and they don't express a clear relation with the maximum radius as it looks like it is more or less constant.\n",
+    "\n",
+    "### Effect of the number of packets\n",
+    "Finally with the next plots we want to see how the various metrics change as the number of packets, and thus the network load, increases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d62f9ae6e2f54ceab8da66ccb1081c30",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b47ad03b56f746bd914061c1a4e283ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "#generating the axes as wanted\n",
+    "gs = fig.add_gridspec(2, 2)\n",
+    "axs = []\n",
+    "axs.append(fig.add_subplot(gs[0, :]))\n",
+    "axs.append(fig.add_subplot(gs[1, 0]))\n",
+    "axs.append(fig.add_subplot(gs[1, 1]))\n",
+    "ttr = T_tx\n",
+    "\n",
+    "for mode in [\"light\", \"unstable\"]:\n",
+    "\n",
+    "    color = [\"blue\", \"red\"]\n",
+    "    color = color[mode == \"light\"]\n",
+    "\n",
+    "    #changing the number of packets\n",
+    "    packet_numbers = [2**i for i in range(1,13)]\n",
+    "\n",
+    "    outputs = np.zeros((len(packet_numbers), n_configurations))\n",
+    "    deviation_arr = np.zeros((len(packet_numbers), n_configurations))\n",
+    "\n",
+    "    for rand_iter in tqdm(range(n_configurations)):\n",
+    "\n",
+    "        nodes = generate_network(n, r_max)\n",
+    "        A = adjacency_matrix(nodes, tau_max)\n",
+    "        At = disable_links(A,200000, delta_time, mode = mode)\n",
+    "\n",
+    "        for i in range(len(packet_numbers)):\n",
+    "\n",
+    "            #generating new packets every time since their number changes at every iteration\n",
+    "            packets = np.random.randint(1, n, (packet_numbers[i], 2))\n",
+    "            packets[:,1] = 0\n",
+    "\n",
+    "            results = greedy_routing(At, packets, ttr, delta_time)\n",
+    "            arrival_times = np.array([packet.arrival_times[-1] for packet in results])\n",
+    "            outputs[i, rand_iter] = np.max(arrival_times)\n",
+    "            deviation_arr[i, rand_iter] = deviations(A, results)\n",
+    "\n",
+    "    arrival_times = np.mean(outputs, axis = 1)\n",
+    "\n",
+    "    axs[0].semilogx(packet_numbers, arrival_times, linestyle=\"-\", c = color)\n",
+    "    axs[0].semilogx(packet_numbers, arrival_times - np.array(packet_numbers) * ttr, linestyle=\"-.\", c=color)\n",
+    "    axs[1].semilogx(\n",
+    "        packet_numbers, \n",
+    "        throughput(np.array(packet_numbers), arrival_times)*ttr, \n",
+    "        linestyle=\"--\",\n",
+    "        c=color\n",
+    "    )\n",
+    "    axs[2].semilogx(packet_numbers, np.mean(deviation_arr, axis = 1), linestyle=\"--\", c=color)\n",
+    "\n",
+    "axs[0].semilogx(packet_numbers, np.array(packet_numbers) * ttr, linestyle = \"--\", c=\"black\")\n",
+    "axs[0].set_ylabel(\"Time(s)\")\n",
+    "axs[0].set_xlabel(\"#Packets\")\n",
+    "axs[0].set_title(\"Mean arrival time vs mean idle time\")\n",
+    "axs[0].legend([\n",
+    "    \"Arrival time(light)\", \n",
+    "    \"Idle time(light)\",\n",
+    "    \"Arrival time(unst)\",\n",
+    "    \"Idle time(unst)\",\n",
+    "    \"Total Transmission Time\"\n",
+    "])\n",
+    "\n",
+    "\n",
+    "axs[1].set_title(\"Throughput of the system\")\n",
+    "axs[1].set_ylabel(\"Normalized Throughput\")\n",
+    "axs[1].set_xlabel(\"# Packets\")\n",
+    "axs[1].legend([\n",
+    "    \"Throughput(light)\", \n",
+    "    \"Throughput(unstable)\",\n",
+    "])\n",
+    "\n",
+    "axs[2].set_title(\"Percentage of packets that followed unoptimal route\")\n",
+    "axs[2].set_ylabel(\"Percentage\")\n",
+    "axs[2].set_xlabel(\"# Packets\")\n",
+    "axs[2].legend([\n",
+    "    \"Percentage(light)\",\n",
+    "    \"Percentage(unstable)\"\n",
+    "])\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# An insight on the plots\n",
+    "\n",
+    "1) With the first plot we are visualizing the arrival time, the idle time and the total transmission \n",
+    "time as a function of the maximum radius under light and unstable mode.\n",
+    "We observe a very similar behaviour under light and unstable mode indicating that even if the load of the network is increased the arrival time is not really affected by the number of disabled links.\n",
+    "One interesting thing to notice is that as the number of packets increses so does the idle time, this was expected because increasing the number of packets obviously increases the chances that Earth spends some time without receiving data.\n",
+    "\n",
+    "\n",
+    "2) With the second image we're pointing out the throughput as a function of the number of packets; what we observe is that \n",
+    "the behaviour under light and unstable mode is similar but of course light mode gives us a higher throughput.\n",
+    "Initially the throughput is low because, with a small amount of packets, the percentage of time in which the Earth is idle is very high since it takes time for the first packet to reach it.\n",
+    "When the number of packets in the network becomes big enough the two curves tend to overlap this is most likely due to the fact that with so many packets even if some links are deactivated some other nodes will still be able to send packets so the overall performance in both cases is similar.\n",
+    "We can still see that the unstable curve is always below the light one.\n",
+    "  \n",
+    "\n",
+    "3) In the last image we can observe the percentage of packets that followed unoptimal routes as a function of the number of packets. What we get for the two different modes is a similar behaviour. \n",
+    "As the plot shows the more the packets, the higher the percentage of packets that follows\n",
+    "unoptimal routes. Anyway as we could expect the percentage is always higher with unstable mode.\n",
+    "For both modes this value tends to increase because with high load also the queuing time is high, meaning that the chance of a packet following an unoptimal path because of topological changes increases."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Epidemic Algorithm\n",
+    "A key problem affecting DTNs is the fact that the topology of network changes a lot over time. In some cases, it is not possible to predict these network changes.<br> \n",
+    "The epidemic algorithm suggests an approach that we can use to solve these types of problems,because it does not require a complete previous knowledge of the network topology. The algorithm works like an epidemic; if it is not possible to send packets to Earth, each node spreads the packets to all nearby nodes. So this procedure creates many copies of the same packet, in fact pure Epidemic routing has usually a high message delivery ratio. We can see below the main phases of the algorithm.<br>\n",
+    "\n",
+    "   * 1) If the link is up the packets are sent from the node to the Earth, if the link is down the packets are sent to all nearby nodes that they can reach(the topology of network is locally fully-connected, but it is possible that some links are temporarily interrupted)<br><br>\n",
+    "   \n",
+    "   * 2) This procedure is repeated for all nodes over time, until all the packets have arrived to Earth. But if a node has already sent a copy of the packets to its neighbour, it doesn't need to send it back.<br><br>\n",
+    "   \n",
+    "   * 3) It is possible that the packet is discarded. This happens when: the packet is sent but during the propagation time the link breaks or when the packet arrives but there are some collisions on the receiver because other packets arrive in the same moment.  In both cases this is implemented by checking beforehand if it is possible to send the packet, if it is not then the packet is just not sent which is equivalent to discarding the packet midair. In case of collision one of the colliding packets is kept because by using CDMA (Code Division Multiple Access) it is actually possible to distinguish between the different signals even if there is interference.<br><br>\n",
+    "   \n",
+    "Using Epidemic algorithm, unless a node always remains isolated, we are sure that all packets arrive to Earth. The problem is that many copies of the same packet can create a congestion in the network and an increase of total energy consumption for data transmission.\n",
+    "In this algorithm we assume to know the topology of network, namely if the links are active or not, only in the current moment in which we are. We have no information about the future, so we cannot route efficiently the packets, like it happens in the algorithms above.\n",
+    "\n",
+    "Obviously as the number of nodes and of packets increases the load of the network does too and it does exponentially, this means that in case of sensors networks, that contain many nodes, or other extreme cases this might not be the best algorithmic choice.\n",
+    "### Earth occupation\n",
+    "Like we did for the greedy algorithm we would like to find out when the Earth is occupied receiving useful data.\n",
+    "Unlike in the greedy case though here we need to take into consideration also duplicate packets as they can not be called useful.\n",
+    "\n",
+    "In the following section the Earth occupation is represented, in particular teal slots correspond to the reception of a useful packet while orange slots correspond to the reception of a duplicate or nothing at all."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "transmission_time = T_tx\n",
+    "slot_time = delta_time\n",
+    "\n",
+    "#generating new packets with earth as a destination\n",
+    "packets = np.random.randint(1, n, (250, 2))\n",
+    "packets[:, 1] = 0\n",
+    "\n",
+    "#generating the new configuration\n",
+    "At = disable_links(A, 100000, slot_time, mode=\"unstable\")\n",
+    "\n",
+    "#performing the routing algorithm\n",
+    "packet_arrive, packet_time, vulnerable, anim_packets = epidemic(\n",
+    "    At,\n",
+    "    transmission_time,\n",
+    "    slot_time,\n",
+    "    packets,\n",
+    "    return_anim=True\n",
+    ")\n",
+    "\n",
+    "#filtering packets that arrived to earth and removing duplicates\n",
+    "packets_to_earth = remove_duplicates(list(filter(lambda x: x.route[0] == 0, anim_packets)))\n",
+    "\n",
+    "plot_idle(packets_to_earth, transmission_time)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Some insights on the plot\n",
+    "\n",
+    "It is immediately possible to notice that, in contrast with the greedy algorithm, the idle time is dominant in the representation meaning that the algorithm is taking a lot more time to send all the packets that are needed.\n",
+    "A thing that can be noticed straight away is that most of the idle time is concentrated in final part of the transmission because a lot more duplicates are present in the network at that time.\n",
+    "\n",
+    "Just this graph shows that the greedy algorithm is probabily more robust to extreme situations and probably performs better in all cases. This is obviously mainly due to the knowledge advantage that it has if compared with the epidemic algorithm.\n",
+    "\n",
+    "\n",
+    "## Metrics analysis\n",
+    "Also in this case different metrics values are displayed in order to try and understand how the algorithm behaves in different situations. All the computations were repeated for 10 different configurations and the results averaged out in order to reduce the noise and the algorithm was tested both with light and unstable mode.\n",
+    "\n",
+    "### Effects of the number of packets\n",
+    "In the next section the dependence of the arrival time, throughput and packet fraction from the number of packets is investigated.\n",
+    "The packet fraction is a quantity that we defined that represents the ratio between the number of useful packets received by Earth and the total number of packets received.\n",
+    "\n",
+    "This is an important metric for this algorithm because it approximately shows how much useless information reaches Earth."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "40ff32437c3447ffb9c446b00c1228bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e540a44a1284544b2ac9d1375e38f32",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f27f2cb5d50>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#generate figure\n",
+    "fig = plt.figure(figsize=(15, 7))\n",
+    "    \n",
+    "gs = fig.add_gridspec(1, 2)\n",
+    "axs = []\n",
+    "axs.append(fig.add_subplot(gs[0, 0]))\n",
+    "axs.append(fig.add_subplot(gs[0, 1]))\n",
+    "\n",
+    "#seeing how the system behaves as number of packets increases\n",
+    "max_rand = 10\n",
+    "\n",
+    "#plots and analysis with 10 nodes and r_max\n",
+    "for mode in [\"light\", \"unstable\"]:\n",
+    "\n",
+    "    color = [\"blue\", \"red\"]\n",
+    "    color = color[mode == \"light\"]\n",
+    "\n",
+    "    #number packet that we want to send to earth\n",
+    "    packet_numbers = [2**i for i in range(10)]\n",
+    "\n",
+    "    #temp variables used to store data that will be used for the plots\n",
+    "    arrival_times = np.zeros((len(packet_numbers), n_configurations))\n",
+    "    packet_fraction = np.zeros((len(packet_numbers), n_configurations))\n",
+    "\n",
+    "    for sample in tqdm(range(n_configurations)):\n",
+    "\n",
+    "        #generating a new network configuration\n",
+    "        nodes = generate_network(n, r_max)\n",
+    "        A = adjacency_matrix(nodes, tau_max)\n",
+    "        At = disable_links(A, 100000, slot_time, mode = mode)\n",
+    "\n",
+    "        for i in range(len(packet_numbers)):\n",
+    "\n",
+    "            #generating new packets with earth as a destination\n",
+    "            packets = np.random.randint(1, n, (packet_numbers[i], 2))\n",
+    "            packets[:, 1] = 0\n",
+    "\n",
+    "            #performing the routing algorithm\n",
+    "            packet_arrive, _, vulnerable = epidemic(At, transmission_time, slot_time, packets)\n",
+    "            arrival_times[i, sample] = vulnerable[0] + transmission_time\n",
+    "            packet_fraction[i, sample] = len(set(packet_arrive))/len(packet_arrive)\n",
+    "    axs[0].semilogx(packet_numbers, np.mean(arrival_times, axis = 1), c = color)\n",
+    "    axs[1].semilogx(packet_numbers, np.mean(packet_fraction, axis = 1), c = color, linestyle=\"--\")\n",
+    "\n",
+    "ttx_tot = np.array(packet_numbers)*transmission_time\n",
+    "axs[0].semilogx(packet_numbers, ttx_tot, linestyle=\"--\", c = \"black\")\n",
+    "axs[0].set_ylabel(\"Time(s)\")\n",
+    "axs[0].set_xlabel(\"#Packets\")\n",
+    "axs[0].set_title(\"Mean arrival time\")\n",
+    "axs[0].legend([\n",
+    "    \"Arrival time(light)\",\n",
+    "    \"Arrival time(unstable)\",\n",
+    "    \"Total transmission time\"\n",
+    "])\n",
+    "\n",
+    "axs[1].set_ylabel(\"packet fraction\")\n",
+    "axs[1].set_xlabel(\"#Packets\")\n",
+    "axs[1].set_title(\"Packet delivery ratio\")\n",
+    "axs[1].legend([\n",
+    "    \"Packet fraction(light)\",\n",
+    "    \"Packet fraction(unstable)\"\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1) We can see that the mean arrival time in both cases increases linearly (the x axis is in logarithmical scale) with the number of packets. Normally one would expect to see an exponential trend, what is probably preventing this to happen is the fact that if a node knows that it can send the packet directly to Earth it doesn't send copies around. Even if the number of disabled links is higher than in the unstable case we do not see the exponential trend because the number of nodes that effectively receive the packet is lower since most connections are disabled.\n",
+    "It is possible to see that with a bigger number of nodes the arrival time becomes exponential, the analysis was not presented in this notebook because it takes more than one hour just to run one simulation.\n",
+    "This though shows that this algorithm can actually be used if the number of nodes in the network is contained.\n",
+    "As expected the light mode curve is below the unstable one.\n",
+    "\n",
+    "2)From the second plot we can see that the packet fraction value decreses as the number of packets increases. This was obvious because more packets means more copies in the network meaning more congestion and thus more useless packets reaching Earth.\n",
+    "\n",
+    "### Effects of the transmission time\n",
+    "The following cell wants to show the dependence of the various metrics from the transmission time, since the outcome of these measurements were very noisy the number of configurations used was increased to 30."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e988427a44ca421b842815512b7717d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=30.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "21f2cdbd665941f08064811368119e80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=30.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f27f8eedf90>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#generate figure\n",
+    "fig = plt.figure(figsize=(15, 7))\n",
+    "   \n",
+    "gs = fig.add_gridspec(1, 2)\n",
+    "axs = []\n",
+    "axs.append(fig.add_subplot(gs[0, 0]))\n",
+    "axs.append(fig.add_subplot(gs[0, 1]))\n",
+    "\n",
+    "#seeing how the system behaves as the transmission time increases\n",
+    "n_configurations = 30\n",
+    "\n",
+    "packet_numbers = 100\n",
+    "\n",
+    "for mode in [\"light\", \"unstable\"]:\n",
+    "\n",
+    "    color = [\"blue\", \"red\"]\n",
+    "    color = color[mode == \"light\"]\n",
+    "\n",
+    "    transm_time = np.arange(1, 200, 20)\n",
+    "    arrival_times = np.zeros((len(transm_time), n_configurations))\n",
+    "    packet_fraction = np.zeros((len(transm_time), n_configurations))\n",
+    "\n",
+    "    for sample in tqdm(range(n_configurations)):\n",
+    "\n",
+    "        nodes = generate_network(n, r_max)\n",
+    "        A = adjacency_matrix(nodes, tau_max)\n",
+    "        At = disable_links(A, 100000, slot_time, mode = mode)\n",
+    "\n",
+    "        for i in range(len(transm_time)):\n",
+    "\n",
+    "            packets = np.random.randint(1, n, (packet_numbers, 2))\n",
+    "            packet_arrive, _, vulnerable = epidemic(At, transm_time[i], slot_time, packets)\n",
+    "            arrival_times[i, sample] = vulnerable[0] + transm_time[i]\n",
+    "            packet_fraction[i, sample] = len(set(packet_arrive))/len(packet_arrive)\n",
+    "    ttx_tot = np.array(transm_time*packet_numbers)\n",
+    "    axs[0].plot(transm_time, np.mean(arrival_times, axis = 1), c = color)\n",
+    "\n",
+    "    axs[1].plot(transm_time, np.mean(packet_fraction, axis = 1), c = color, linestyle = \"--\")\n",
+    "\n",
+    "\n",
+    "axs[0].plot(transm_time, ttx_tot, c=\"black\", linestyle=\"--\")\n",
+    "axs[0].set_ylabel(\"Time(s)\")\n",
+    "axs[0].set_xlabel(\"Transmission time(s)\")\n",
+    "axs[0].set_title(\"Mean arrival time\")\n",
+    "axs[0].legend([\n",
+    "    \"Arrival time(light)\",\n",
+    "    \"Arrival time(unstable)\",\n",
+    "    \"Total transmission time\"\n",
+    "])\n",
+    "\n",
+    "axs[1].set_ylabel(\"packet fraction\")\n",
+    "axs[1].set_xlabel(\"Transmission time(s)\")\n",
+    "axs[1].set_title(\"Packet delivery ratio\")\n",
+    "axs[1].legend([\n",
+    "\"Packet fraction(light)\",\n",
+    "\"Packet fraction(unstable)\"\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the plots it is possible to notice some very awkward behaviours, in particular it looks like that when the transmission time becomes comparable with the propagation time the number of deactivated links becomes ininfluent.\n",
+    "This can be seen in both plots as from a certain point the two lines tend to overlap which is an unexpected behaviour.\n",
+    "The second plot looks strange because the packet fraction increases after a certain point, this is probably due to the fact that with very high transmission rate it is very likely that a link is deactivated before the packet reaches its destination meaning that not too many packets are forwarded epidemically reducing the number of useless packets that reach Earth.\n",
+    "\n",
+    "## Algorithm comparison\n",
+    "From the Earth occupation plots it is already clear that the the greedy algorithm is more performing. The opposite would have been quite strange since it relies on more information than the epidemic one.\n",
+    "\n",
+    "It is still important to compare the two performances in case the same conditions are used for both, on the plots below the arrival times are confronted. Only the unstable mode was used and the performances were compared firstly as functions of the transmission times and later as functions of the number of packets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "14ea5634a79c4a539ec7d64f216b9adf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f27f2cb5550>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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1jxd3Ke1jaiVLluTEiROe58nJyaxatYqgoKALjr2UtrJTp06ZTpFIHXv695Wd9rJ48eKsWbOG7777jrlz5zJp0iRPD6naT5H8Ky+3spBMzJ07l3vvvZddu3YRGxvL7t27qV27NitWrMhxXb169WLOnDnMnTvXc2fyxIkTVK1alcDAQL7//nt27dqVZR3BwcHEx8fn6r0AlC1bltq1a/Ppp58CTgO0ceNGABo2bOgZHprVcQCffvopycnJ7Ny5k99//5369eunuc51113HRx99BMDXX3/tGfJ54403MnfuXA4dOgQ4/7m42HvOTKdOndLMU0m5hrWWAwcOEBISkqt6RSRz2gNbUhSl9jG11G0lQOfOndMs4pI6uVywYAFnzpzhyJEjLF261DNqJkXqtnLLli1s2rQJgKioKFauXOm5zqlTp9i2bVuu3lenTp34n//5H0/SePToURISEjhx4gS33HILEyZMSPM+t23b5pmvKCI5Exfn2/qVHOYDs2fP5vbbb09Tduedd+ZqwZPGjRsTHx9P9erVueKKKwC45557cLlchIWFER0dTYMGDbKso1KlSrRp04bQ0FCeeuqpHMcAzt3YadOm0aRJExo3buyZ5H/zzTezfPnyix4HznyHli1bcvPNN/Pee+9dcMf0xRdfZPny5TRu3JjPP/+cmjVrAtCoUSNeeeUVOnfuTHh4OJ06dWL//v25eh/PPfccx44dIzQ0lCZNmvD9998DsHbtWqKiojxDaETEe5o0gXRTmqSIKkrtY2rXXXcd69ev9yy09vbbb+NyuQgPD6dRo0aeRWHAGSraoUMHoqKieP7557nyyivT1PXoo4+SkJBAw4YNeeGFFzxDcqtUqcLMmTPp3bs34eHhtG7dOtdDPR988EFq1qxJeHg4TZo04aOPPiI+Pp6uXbsSHh5O27ZtGT9+vOf4lStX0qlTp1xdS6SoshbGj4c6dcB9j8cnTAFf5DPHmjdvbtPv77N161bPPDjxvdtvv5033niDevXqZXrMfffdR9euXX22f9SlGjJkCN27d+fGG2/M1vH6HRPJnuPHoUIFePVVGDny0uoyxqy11jb3TmSFn9rH/GXIkCF069aNjh07ZnrMqFGjKFOmjGcl0IJg/fr1jB8/nv/93//N8HX9zolc6Nw5eOwxeP99uPNOiI6GUqUurc7M2kj1HEqeGzt2bK578vKL0NDQbCeGIpJ969Y5j82V0kkRN3LkSP766y9/h+F1hw8fZvTo0f4OQ6TAOHoUunRxEsORI+GTTy49McyKxsRJnqtfv/4F8wfTmzlzZt4Ek0sPPfSQv0MQKZRSOq5S7XgjUiRVq1btgu2V0hs1alTeBONFGk4qkn3btkHXrhAbCx98AP36+f6aSg5FRCTfiIlx5lNohxgRESnKvv/eGUJarBgsWQJt2+bNdTWsVERE8g2XS0NKRUSkaHv/fejcGS6/HFavzrvEEJQciohIPnH4sDN0RsmhiIgURUlJMHw4PPQQ3HAD/PSTM5omL2lYqYiI5Atr1zqPSg5FRKSoiY+HPn1g4UJ4/HGYMAH8sWOaeg7zkfnz52OMyfE+Q7fccgvHjx/P1TXLlClzQdnx48d55513PM/37dvn1S0l3nrrLaKjo71WX3bMnDmTxx9/PMtjhg8fzpIlS/IoIhFJLybGedRiNJJeUWkfU5s/fz4vv/yyT+rOzNKlS+natWuWx0yaNInp06fnUUQiRcOffzpDR7/+GiZNgokT/ZMYgpLDfGX27Nm0bds20819ExMT0zy31pKcnMxXX31F+fLlvRZH+sbvyiuvZO7cuV6pOzExkenTp9OnT58cnZMXnnjiCcaOHZsn1xKRC7lcUL8+lC3r70gkvykK7WN6b7zxBoMGDcr28XnVVg4YMICJEyfmybVEioLVq6FlS2daxZdfOvsZ+pOSw3wiISGBH374gWnTpjFnzhxP+dKlS2nXrh3du3enUaNGxMbGUr9+ffr160doaCi7d+8mJCSEw4cPM2LECCZPnuw5d9SoUbz55pskJCRw4403EhkZSVhYGAsWLMgylhEjRrBz504iIiJ46qmniI2NJTQ0FHB64Hr06EGnTp0ICQlh0qRJjB8/nqZNmxIVFcXRo0cB2LlzJ126dKFZs2a0a9fOc7d3yZIlREZGUtx9OyQmJobw8HDPtVJfp3v37txwww2e/QTHjRtHixYtCA8P58UXX/TE++GHH9KyZUsiIiJ45JFHSEpKAmDGjBlcc801tGzZkpUrVwIQHx9P7dq1OX/+PAAnT570PK9VqxZHjhzhwIEDufwpisil0GI0kpGi0j6mtm3bNkqUKEHlypU950RFRREWFsZzzz3n6dVM/xlA5m3it99+S+vWrYmMjKRnz54kJCQA8N///pcGDRoQGRnJ559/DkBycjL16tUjLi7O87xu3brExcVRqlQpQkJCWLNmTQ5/kiKS3scfQ/v2zr6FP/0EN93k74g05/BCQ4fChg3erTMiAt56K8tDFixYQJcuXbjmmmuoVKkSa9eupZl7bNW6devYsmULtWvXJjY2lu3bt/PBBx8QFRWVpo5evXoxdOhQHnPfcvjkk0/45ptvCAoKYt68eZQtW5bDhw8TFRVF9+7dMcZkGMvYsWPZsmULG9yfQ2xsbJrXt2zZwvr16zlz5gx169bl9ddfZ/369QwbNozo6GiGDh3Kww8/zHvvvUe9evVYvXo1gwYNYsmSJaxcudLzvgDuv/9+pk6dSuvWrRkxYkSa66xbt45NmzZRsWJFvv32W7Zv386aNWuw1tK9e3eWL19OlSpV+Pjjj1m5ciWBgYEMGjSIWbNm0alTJ1588UXWrl1LuXLl6NChA02bNiU4OJj27dvz5Zdf0qNHD+bMmcMdd9xBYGAgAJGRkaxcuZI777zzIj9UEfGm/fth714lh/ma2kefto+prVy5ksjISM/zIUOGMGTIEHr37s17772X5tjUn8HWrVszbBNvueUWXnnlFRYvXkzp0qV5/fXXGT9+PP/617946KGHWLJkCXXr1qVXr14ABAQE0LdvX2bNmsXQoUNZvHgxTZo0oUqVKgA0b96cFStW0LJlyyx/diKSMWth9Gh48UVo0wbmzQP3Py+/U3KYT8yePZshQ4YAcPfddzN79mxP49eyZUtq167tObZWrVoXNHwATZs25dChQ+zbt4+4uDgqVKjAVVddxfnz5xk5ciTLly8nICCAvXv3cvDgQS6//PJcxdqhQweCg4MJDg6mXLlydOvWDYCwsDA2bdpEQkICP/74Iz179vScc/bsWQD2799Pw4YNAWd4Tnx8PK1btwagT58+LFy40HNOp06dqFixIuDc8fz2229p2rQp4NxJ3r59O5s2bWLt2rW0aNECgNOnT1O1alVWr15N+/btPQ1Zr1692LZtGwAPPvggb7zxBj169GDGjBlMnTrVc82qVauyb9++XH0uIpJ7Lpfz6P6nLOJRVNrH1Pbv3+9pvwB++ukn5s+fDzht5fDhwz2vpf4MvvvuuwzbxFWrVvHLL7/Qpk0bAM6dO0fr1q359ddfqV27NvXq1QOgb9++TJkyBXCGj952220MHTqU6dOnc//993uuWbVq1RzP/xQRx5kzMGAAzJ7tbGo/ZQqUKOHvqP6m5DC9i9zB9IWjR4+yZMkSNm/ejDGGpKQkjDGMGzcOgNKlS6c5Pv3z1Hr27MncuXM5cOCA5w7grFmziIuLY+3atQQGBhISEsKZM2dyHW+JVL/BAQEBnucBAQEkJiaSnJxM+fLlPXdWUytZsmS2r536fVpreeaZZ3jkkUfSHDNx4kT69+/PmDFj0pSnNKIZadOmDbGxsSxdupSkpCTPkCCAM2fOULJkyWzFJyLe43JBQIDTkST5lNrHi7qU9jG1kiVLcuLEiWxdM31bmVGb+H//93906tTpgjmbWcVx1VVXUa1aNZYsWcKaNWuYNWuW5zW1lSK5c/Ag9OgBq1bBa6/BiBGQyUAFv9Gcw3xg7ty53HvvvezatYvY2Fh2795N7dq1WbFiRY7r6tWrF3PmzGHu3LmeO5MnTpygatWqBAYG8v3337Nr164s6wgODiY+Pj5X7wWgbNmy1K5dm08//RRwGquNGzcC0LBhQ3bs2AFA+fLlCQ4OZvXq1QBp5pKkd9NNNzF9+nTPHIm9e/dy6NAhbrzxRubOncuhQ4cA5z8Su3btolWrVixbtowjR45w/vx5Tywp+vXrR58+fdLcCQVnnkfqZFFE8obLBY0aQRb/t5ciqCi1j6mlbisBoqKi+Oyzz4Cs28rM2sSoqChWrlzpqfPUqVNs27aNBg0aEBsby86dOwEuSB4ffPBB+vbtS8+ePSlWrJinXG2lSM5t3uwsPLNxI8ydC888k/8SQ1BymC/Mnj2b22+/PU3ZnXfememqbFlp3Lgx8fHxVK9enSuuuAKAe+65B5fLRVhYGNHR0TRo0CDLOipVqkSbNm0IDQ3lqaeeynEM4NyNnTZtGk2aNKFx48aeSf4333wzy5cv9xw3bdo0HnroISIiIjh16hTlypXLsL7OnTvTp08fWrduTVhYGP/4xz+Ij4+nUaNGvPLKK3Tu3Jnw8HA6derE/v37ueKKKxg1ahStW7emTZs2nqGsKe655x6OHTtG7969PWXnz59nx44dNNekJ5E8Za0Wo5GMFaX2MbXrrruO9evXY60FnC2gxo8fT3h4ODt27Mi0rcysTaxSpQozZ86kd+/ehIeHe4aUBgUFMWXKFG699VYiIyOpWrVqmvq6d+9OQkLCBTdSV65cSadOnXL1/kWKoi+/hGuvhfPnYcUKyNdLW1hri9RXs2bNbHq//PLLBWXiOz169LDbtm2z1lobHx/vKR8zZowdPHhwnsTw6aef2r59+6Yp+/zzz+1zzz3nk+vpd0wkc7t2WQvWTp7s3XoBl80H7U5B+VL7mL8MHjzYLlq0yFpr7alTp2xycrK11trZs2fb7t2750kMMTExtm3btmnK1q1bd0H76U36nZPCJDnZ2gkTrA0IsLZpU2t37/Z3RH/LrI3UnEPJc2PHjmX//v3Uq1ePL7/8kjFjxpCYmEitWrWYOXOmz6//xBNP8PXXX/PVV1+lKU9MTOTJJ5/0+fVFJK2UxWjUcyjyt5EjR3qmXaxdu5bHH38cay3ly5fPk03ox44dy7vvvptmriHA4cOHGT16tM+vL1LQnT8PTzwB//M/zjzDDz8sGFMnjJM4Fh3Nmze3rpT/ibht3br1gmGHIt6k3zGRzI0cCePGQXw8BAV5r15jzFprrVLObFL7KPmBfuekMDh2DHr2hO++cxadefVVZ9G1/CSzNlI9hyIi4lcxMRAW5t3EUERExB+2b4euXeGPP2DGDLjvPn9HlDP5LIcVEZGixLoXo9H+hiIiUtAtWwZRUXDkCCxeXPASQ1ByKCIifvT773D8uOYbiohIwTZ9OnTqBFWrwurVcN11/o4od5QcioiI32gxGhERKciSkuBf/4IHHoD27eGnn+Dqq/0dVe4pOcwHjhw5QkREBBEREVx++eVUr17d8/zcuXMXHH/06FHee++9i9abmJhI+fLlc31+Xnn22Wf5/vvvc3TO6tWrGTZs2CVfOzk5mbFjx3qeJyUl0a5du0uuV0SyJyYGSpQA7actGVH7qPZRJD9LSHD2LBw3Dh591NnPMIM/LQWKVislf62MNWrUKMqUKcPw4cMzPWbHjh384x//YMOGDVnWlZiYSOXKlTl+/Hi2z09MTKR48aKzTlFmn5G35affMZH8pH17OHMGVq3yft35dbVSY8xVQDRQDbDAFGvtf4wxo4CHgDj3oSOttV+5z3kGeABIAgZba79xl3cB/gMUA9631o51l9cG5gCVgLXAvdbaC7OpVNQ+qn1MLa/ax/Ty0++cSFZ274Zu3WDzZnjrLXj8cTDG31FlX2ZtpHoO87k33niD0NBQQkNDmThxIgAjRozgt99+IyIighEjRnDy5EluuOEGIiMjCQ8PZ+HChVnWmf78xYsX0759e7p27UpYWBgA3bp1o1mzZjRu3Jj3338f+PtO64gRI2jSpAmtW7fm0KFDAMyZM4fQ0FCaNGlChw4dAHj//fe544476NixI7Vq1eLdd99l3LhxNCPKxAwAACAASURBVG3alGuvvdbT4PTt25f58+cD8NRTT9GoUSPCw8N5+umnM6178eLF9OjRA3D2XOrevTvh4eFce+21bNmyBYDnnnuOBx54gOuvv546deowefLkDD+L+Ph4IiIi6NevX5q7yYsXL6ZDhw50796dOnXq8NxzzxEdHU2LFi0IDw8nNjYWgIMHD3LHHXfQvHlzWrZsySpf/C9XpBBKToa1a4vkkNJE4ElrbSMgCnjMGNPI/doEa22E+yslMWwE3A00BroA7xhjihljigGTgZuBRkDvVPW87q6rLnAMJ7EsVNQ+qn0U8ZeYGGjZ0pk3v3Chs59hQUoMs2StLVJfzZo1s+n98ssvaZ5ff/31F3xNnjzZWmvtqVOnMnx9xowZ1lpr4+LiLngtJ1588UU7btw4a621q1atsuHh4favv/6yJ0+etA0aNLCbNm2y27dvt02aNPGcc+7cOXvixAlrrbUHDx60devWtdZae/78eVuuXLkLrpH+/EWLFtnSpUvbXbt2ecqOHDnieb8NGza0R48etefPn7eA/eqrr6y11g4bNsyOGTPGWmttgwYN7IEDB6y11h47dsxaa+3UqVPtNddcYxMSEuyBAwdscHCwnTp1qrXW2scff9xOnDjRWmvtPffcY+fNm2cPHDhgGzVqZJOTk9PUk1HdixYtsrfddpu11tqBAwfaV155xVpr7TfffGNTfsbPPvusbdu2rT179qw9ePCgrVixok1MTEzzWaT/jFI/X7Roka1QoYI9cOCAPX36tK1WrZp96aWXrLXWvvnmm/bJJ5+01lp711132Z9++slaa+0ff/xhGzdufMFnnv53TESs3brVWrDW/efT6wCXzQftzsW+gAVAJ2AUMDyD158Bnkn1/Bugtfvrm/THAQY4DBR3l6c5LrMvtY9qH1PLq/YxPbWXkt998om1QUHWhoRYu3mzv6PJvczaSPUc5mM//PADd955JyVLliQ4OJgePXqwYsWKC46z1jJixAjCw8Pp3Lkzu3fv5vDhwzm6VuvWralZs6bn+YQJEzx3P/fs2cPOnTsBKFmyJDfffDMAzZo189wdbNOmDf369eP9998nOTnZU88NN9xA6dKlqVatGmXKlKFbt24AhIWFec5NUbFiRQICAnjooYeYN28epUuXzrLu1J/TvffeC0Dnzp3Zt28fp06dAqBr165cdtllVK1alYoVKxIXF3fB+Vlp1aoV1apVIygoiDp16nDTTTddEP/ixYsZOHAgERER9OjRg2PHjnH69OkcXUekKIqJcR6L8jYWxpgQoCmw2l30uDFmkzFmujGmgrusOrA71Wl73GWZlVcCjltrE9OVFxpqH9U+iuQ1a+GVV+CuuyAy0lmRtDDOly86g+dzYOnSpZm+VqpUqSxfr1y5cpav+0J0dDQnTpxg3bp1FC9enBo1anDmzJkc1ZHS0IDzx3z58uWsWrWKkiVL0rZtW099l112mee4YsWKkZjo/N9j6tSprF69moULFxIZGcn69esBKFGihOf4gIAAz/OAgADPuSkCAwNxuVwsWrSITz/9lHfffZdvv/0207qzI/X1U8ebm/Mzi99ay5o1a9J8NiJycS4XlCoFDRr4OxL/MMaUAT4DhlprTxpj3gVG48xDHA38Gxjg4xgeBh4G0iRAmVH7qPYxo/PVPkphd+YMPPggzJoFffvC1KkQFOTvqHxDPYf5WLt27Zg3bx6nT58mISGBBQsW0K5dO4KDg4mPj/ccd+LECapWrUrx4sVZtGgRe/fuzbLe9Oend+LECSpWrEjJkiX5+eefiUm5vZ+F33//naioKEaPHk2FChUuGkNG4uPjOXnyJF27dmXChAmeRu5idbdr145Zs2YBTsNdvXr1NI15VlIWF8hpo5hax44d08zXuNhCCCLicLmcu6/Fivk7krxnjAnESQxnWWs/B7DWHrTWJllrk4GpQEv34XuBq1KdXsNdlln5EaC8MaZ4uvILWGunWGubW2ubV6lSxTtvLg+ofVT7KJJXDh2CG290EsPRoyE6uvAmhqCew3ytZcuW9O7dmxbuMVePPvqoZ0J8s2bNCAsL49Zbb+Wf//wn3bp1IywsjJYtW1KvXr0s661WrVqa8zt27Jjm9VtvvZUpU6bQqFEj6tevT6tWrS4a67Bhw/jjjz+w1tK5c2dCQ0NzPPH8xIkT3HHHHZw9e5bk5GTGjx+fad0HDhzwnPfyyy8zYMAAwsPDKVOmDDNmzMjRdR944AHCw8Np3rw506dPz9G5AJMnT+bRRx9lxowZJCYm0qFDhwwn94vI3xITYf16eOQRf0eS94wxBpgGbLXWjk9VfoW1dr/76e3AFvf3XwAfGWPGA1cC9YA1OHML67lXJt2Ls2hNH2utNcZ8D/wDZ8XS/jjzGgsNtY9qH0XywpYtzoqkBw7AJ59Az57+jsj3tJUFWjZZfE+/YyJpbdoETZo4d2L79PHNNfLxVhZtgRXAZiBlothIoDcQgTOsNBZ4JCVZNMY8izPENBFnGOrX7vJbgLdwtrKYbq191V1eBycxrAisB/paa89mFZfaR8kP9Dsn+cXXX0OvXlC6NHzxReGbH59ZG6meQxERyXMpOUgR3MYCa+0POL1+6X2VxTmvAq9mUP5VRudZa3/n72GpIiKSA1OnwsCBEB7uJIZXXXXxcwoLzTkUEZE853JB2bJQt66/IxEREfnbhg0waBB06gQrVhStxBCUHHoUteG1knf0uyVyIZcLmjWDALVC+Z7+hkle0e+a+Nu5c9C/P1SuDB99BGXK+DuivKdmGQgKCuLIkSP6oyReZ63lyJEjBBXmZa1EcujcOdi4sfDN3yiM1D5KXlF7KfnByy87c+KnToWKFf0djX9oziFQo0YN9uzZk+MNYEWyIygoiBo1avg7DJF8Y/NmJ0EsivMNCxq1j5KX1F6KP61ZA2PGwH33Qdeu/o7Gf5Qc4mwuW7t2bX+HISJSJBTlxWgKGrWPIlIUnD7tDCetXh3eesvf0fiXz4aVGmOmG2MOGWO2pCobZ4z51RizyRgzzxhTPtVrzxhjdhhjfjPG3JSqvIu7bIcxZkSq8trGmNXu8o+NMZf56r2IiIj3xMRApUoQEuLvSEREROD55+HXX2HaNChXzt/R+Jcv5xzOBLqkK1sEhFprw4FtwDMAxphGOJv3Nnaf844xppgxphgwGbgZaAT0dh8L8DowwVpbFzgGPODD9yIiIl7icjm9hiajzRxERETy0A8/wPjxztYVnTr5Oxr/81lyaK1dDhxNV/attTbR/XQVkDKw/DZgjrX2rLX2D2AHzv5MLYEd1trfrbXncDb0vc0YY4AbgLnu8z8AevjqvYiIiHecPg1btmhIqYiI+N+pU84cw5AQGDfO39HkD/6cczgA+Nj9fXWcZDHFHncZwO505a2ASsDxVIlm6uMvYIx5GHgYoGbNmpccuIiI5M7GjZCUpORQRET87+mnYedOWLq0aG5bkRG/bGVhjHkWSARm5cX1rLVTrLXNrbXNq1SpkheXFBGRDMTEOI/axkJERPzpu+9g8mQYOhSuv97f0eQfed5zaIy5D+gK3Gj/3jhpL3BVqsNquMvIpPwIUN4YU9zde5j6eBERyadcLrj8crjySn9HIiIiRdXJkzBgAFxzDbz2mr+jyV/ytOfQGNMF+BfQ3Vr7V6qXvgDuNsaUMMbUBuoBa4AYoJ57ZdLLcBat+cKdVH4P/MN9fn9gQV69DxERyR0tRiMiIv725JOwZw988AGULOnvaPIXX25lMRv4CahvjNljjHkAmAQEA4uMMRuMMe8BWGt/Bj4BfgH+CzxmrU1y9wo+DnwDbAU+cR8L8DTwT2PMDpw5iNN89V5EROTSJSTA1q2abygiIv7z9dfw/vvw1FMQFeXvaPIfnw0rtdb2zqA40wTOWvsq8GoG5V8BX2VQ/jvOaqYiIlIArFsH1mq+oYiI+MexY/Dgg9C4Mbz0kr+jyZ/8uVqpiIgUIS6X89ismX/jEBGRomnwYDh0CP7v/6BECX9Hkz/5ZbVSEREpelwuuOoqqFbN35GIiEhRM38+fPghPPssREb6O5r8S8mhiIjkiZTFaERERPJSXBw88gg0beokh5I5JYciIuJzx4/D9u2abygiInnLWhg0yJlv+MEHEBjo74jyN805FBERn1u71nlUz6GIiOSljz+GuXNhzBgIC/N3NPmfeg5FRMTntBiNiIjktQMH4LHHoFUrGD7c39EUDEoORUTE52JioE4dqFjR35GIiEhRYC08/DD89ZcznLS4xktmiz4mERHxOZdLmw2LiEjeiY52tqwYPx7q1/d3NAWHeg5FRMSn4uJg1y7NNxQRkbyxe7ezp+F118GQIf6OpmBRcigiIj6lxWhERCSvWAsPPghJSTBjBgQo28kRDSsVERGfiokBY7TpsIiI+N6UKfDtt/DOO85cd8kZ5dIiIuJTLpcz36NsWX9HIiIihdnvv8OTT0LHjjBwoL+jKZiUHIqIiE+5XBpSKiIivpWcDAMGQLFiMG2aM2JFck7DSkVExGf27XO+lByKiIgvTZwIy5bB9OlQs6a/oym41HMoIiI+43I5jy1a+DcOEREpvLZtg2eega5d4b77/B1NwabkUEREfMblclaKi4jwdyQiIlIYJSU5CWFQkLMYjYaTXhoNKxUREZ9xuaBxYyhVyt+RiIhIYfTvf8NPP8GsWXDFFf6OpuBTz6GIiPiEtVqMRkREfGfLFnj+ebjzTujd29/RFA5KDkVExCf+/BPi4jTfUEREvO/8eejfH8qVg3ff1XBSb9GwUhER8YmUxWjUcygiIt42ZgysWweffQZVqvg7msJDPYciIuITLhcEBkJ4uL8jERGRwmT9ehg9Gvr0gTvu8Hc0hYuSQxER8YmYGAgLgxIl/B2JiIgUFmfPQr9+Tm/hxIn+jqbwUXIoIiJel7IYjeYbioiIN730krMQzfvvQ8WK/o6m8FFyKCIiXrdzJ5w4ofmGIiLiPatXw+uvw4ABcMst/o6mcFJyKCIiXqfFaERExJtOn3ZWJ61eHcaP93c0hZdWKxUREa+LiYGgIGjc2N+RiIhIYfDcc/Dbb7BokbN9hfiGeg5FRMTrXC6IiHBWKxUREbkUK1bAhAkwaBB07OjvaAo3JYciIuJVSUnO3lMaUioiIpcqIQHuuw9q13bmG4pvaVipiIh41bZtTmOu5FBERC7V00/DH3/AsmVQpoy/oyn81HMoIiJeFRPjPCo5FBGRS7F4MbzzDgwdCu3a+TuaokHJoYiIeJXLBaVLQ4MG/o5EREQKqhMnnC0r6teHV1/1dzRFh4aVioiIV7lcEBkJxYr5OxIRESmo/vlP2LsXfvwRSpb0dzRFh3oORUTEaxITYf16DSkVEZHc+/JLmD7dmW/YqpW/oylalByKiIjX/PwznDkDLVr4OxIRESmIjh6Fhx6CsDB48UV/R1P0aFipiIh4jcvlPKrnUEREcmPwYIiLc3oPS5TwdzRFj3oORUTEa1wuKFcOrr7a35GIiEhB8/nnMGsWPP88NG3q72iKJiWHIiLiNS4XNGsGAWpdREQkB+LiYOBApw155hl/R1N0qfkWERGvOHsWNm7UfEMREckZa+HRR53tKz74AAID/R1R0aU5hyIi4hWbN8P585pvKCIiOTNnDnz2GYwdC40b+zuaok09hyIi4hVajEZERHJq3z547DGIioLhw/0djSg5FBERr4iJgUqVoFYtf0ciIiIFgbXw8MPOFkgffADFivk7IlFyKCIiXuFyOfMNjfF3JPmfMeYqY8z3xphfjDE/G2OGuMsrGmMWGWO2ux8ruMuNMeZtY8wOY8wmY0xkqrr6u4/fbozpn6q8mTFms/uct43RT0ZE8peZM50tK8aMgWuu8Xc0Aj6cc2iMmQ50BQ5Za0PdZRWBj4EQIBa4y1p7zN1g/Qe4BfgLuM9au859Tn/gOXe1r1hrP3CXNwNmAiWBr4Ah1lrrq/cjIiKZ++sv+Pln6N7d35EUGInAk9badcaYYGCtMWYRcB/wnbV2rDFmBDACeBq4Gajn/moFvAu0crerLwLNAeuu5wtr7TH3MQ8Bq3HayS7A13n4HkWkCLHW6QE8fhyOHXMeU75SP0/9/apVcP318MQT/o5eUvhyQZqZwCQgOlXZCNToiYgUOhs3QlKS5html7V2P7Df/X28MWYrUB24DWjvPuwDYClOO3kbEO2+CbrKGFPeGHOF+9hF1tqjAO4Es4sxZilQ1lq7yl0eDfRA7aSIZOH8eWfF0KwSuqy+P3cu6/pLlYLy5f/+uuUWePNNbX+Un/gsObTWLjfGhKQrVqMnIlIIxcQ4j0oOc87dVjbFudlZzZ04AhwAqrm/rw7sTnXaHndZVuV7MigXkSJm1y5YsiR7yd2pU1nXVbw4VKjwd3JXoYIzzzzl+9Tl6b8vVw5KlMib9yy5l9dbWfil0TPGPAw8DFCzZs1LCF9ERDLicsEVV0B1pR85YowpA3wGDLXWnkw9LdBaa40xPp0uofZRpPBKTobJk2HECGfoPzhzwsuVS5u41at3YUKXWYJXqpTmlRd2ftvnMC8avVTXmgJMAWjevLnmJYqIeJnLpV7DnDLGBOIkhrOstZ+7iw8aY66w1u53j6A55C7fC1yV6vQa7rK9/D0iJ6V8qbu8RgbHp6H2UaRw2rEDBgyAFSucoZtvvOHcvAsO1oqgkrW8HuF70N3YkYNGL7PyizZ6IiLie/Hx8OuvSg5zwr0Q2zRgq7V2fKqXvgBSVhztDyxIVd7PvWppFHDCPRLnG6CzMaaCe2XTzsA37tdOGmOi3Nfql6ouESmkkpPhP/+B8HDYtMlZDXThQmdj+fLllRjKxeV1cqhGT0SkkFm3zlmlTslhjrQB7gVuMMZscH/dAowFOhljtgMd3c/BWXjtd2AHMBUYBOCekz8aiHF/vZwyT999zPvuc3aiefkihdr27c7Kn0OHwg03OCtI9++vYaCSM77cymI2zlCXysaYPTirjo4FPjHGPADsAu5yH/4VzjYWO3C2srgfnEbPGJPS6MGFjd5MnK0svkaNnoiIX7hczqOSw+yz1v4AZPZfthszON4Cj2VS13RgegblLiD0EsIUkQIgKQnefhtGjoSgIIiOhr59lRRK7vhytdLembykRk9EpBBxuaBmTaha1d+RiIgULb/95swt/PFH6NYN3nsPrrzS31FJQaZdRURE5JJoMRoRkbyVlAT//jdERMDWrfDhh7BggRJDuXRKDkVEJNeOHXNWxVNyKCKSN379Fdq2heHD4aabnLmF99yjYaRFxrFjPq1eyaGIiOTa2rXOY4sW/o1DRKSwS0qCceOc3sJt2+Cjj2DePGePWSkCEhLg6aedeRw7dvjsMn7b51BERAq+lMVomjXzbxwiIoXZ1q1w//2wejXcfju88w5cfrm/o5I8YS3Mnw9DhsDu3fDAA86+JD6inkMREcm1mBi4+mqoUMHfkYiIFD6JifD669C0qdNZNGcOfPaZEsMi4/ffnZWG7rjDSQh/+AHefx8qV/bZJZUciohIrrlcGlIqIuILP/8M114LI0bArbc6z3v10tzCIuHsWXjlFWjcGJYtg/HjnU2F27Tx+aWVHIqISK4cOgR//qnFaEREvCkxEcaMgchI+OMP+PhjmDsXqlXzd2SSJ777DsLD4fnnnV7DrVth2DAonjezAZUciohIrqQsRqPkUETEO7ZsgdatnQ3tb7vN6S286y71FhYJ+/dDnz7QsaOz+tDXX8Mnn0CNGnkahpJDERHJlZgY5z8skZH+jkREpGA7fx5efdX5e7prl5MTfPIJVK3q78jE5xITYeJEaNAAPv8cRo1y7hJ06eKXcLRaqYiI5IrL5bRlwcH+jkREpODatMlZiXTdOmdO4cSJUKWKv6OSPLF6NTz6KKxf72xaOWkS1K3r15DUcygiIrnicmlIqYhIbp0/D6NHO39H9+xx5hXOmaPEsEg4ehQGDnTGEB86BJ9+6gwj9XNiCOo5FBGRXNi3z5keoeRQRCTnNm50egvXr4feveHtt326O4HkF9bCBx/AU0/BsWPOQjOjRuWrITjqORQRkRyLiXEelRyKiGTfuXPw0kvO3859+5wpZh99pMSwSNiyBa67zrkrcM01zjjif/87XyWGoORQRERyweWCYsUgIsLfkYiIFAwbNkDLlk5HUa9ezkqkt9/u76jE5xISnJ7CiAhnW4pp02DFCme7inxIyaGIiOSYy+XszVuqlL8jERHJ386dgxdfhBYt4OBBmD8fPvwQKlXyd2TiU9Y6XcMNG8Kbbzo9hr/9BgMGQED+TcHyb2QiIpIvWavFaEREsmP9eicpfPllZ27hzz87+xdKIff779C1K9x5p3MX4McfYerUAnFHQMmhiIjkyK5dcPiwkkMRkcycPQvPP+8khnFx8MUXEB0NFSv6OzLxqbNnnSVoGzeG5cthwgTnbmrr1v6OLNu0WqmIiOSIy+U8tmjh3zhERPKjtWvhvvuc9Uf69YO33oIKFfwdlfjcokXw2GOwfTvcdReMHw/Vq3v1EkuXLqVevXpU93K9qannUEREcsTlgsBACAvzdyQiIvnH2bPw7LPQqpWzjd3Chc6uBUoMC7l9++Duu6FzZ2fexTffwMcfez0xfPrpp+nQoQOjR4/2ar3pqedQRERyJCbGWWStRAl/RyIikj8cPw7XXw+bNjm9hhMmQPny/o5KfCoxESZPdsYPp+xR8q9/QVCQTy5Xq1Ythg4dyquvvuqT+lMoORQRkWxLTnaGTN19t78jERHJPx57zFlsZsEC6N7d39GIz61aBY8+6uxPctNNMGkS1K3r1UskJCQwYsQIoqKi6Nu3L4MGDfJq/ZlRcigiItm2cyecOKH5hiIiKT780NnIfvRoJYaF3pEj8Mwzzsqj1avD3Llwxx1gjFcvs3TpUgYMGEBsbCyV8niFU805FBGRbEtZjEYrlYqIODsWDBoEbds6OYMUUsnJMGMGNGgA06fDk086G9rfeadXE8OEhAQef/xxOnToQLFixVi2bBkvvfSS1+rPDiWHIiKSbTExznSKRo38HYmIiH8lJkLfvk5u8OGHUKyYvyMSn9i8Ga67ztm8vn59Z/PKN9+E4GCvX2rZsmW88847DBkyhI0bN9KuXTuvX+NiNKxURESyzeWCpk2d1UpFRIqyV16Bn35yhpTWquXvaMTr4uOdRWbeestZXWj6dOjfHwK827eWkJDAjz/+SOfOnbn11lv55ZdfaNCggVevkRPqORQRkWxJSoJ16zSkVETkxx+dOYb33gu9e/s7GvG6hQuhYUP497+dHsPffoP77/d6Yrh06VLCw8O57bbbOHToEIBfE0NQcigiItn0229w6pSSQxEp2k6cgHvucXoLJ03ydzTiVdbC669Dt25QqZLTNTxlivO9FyUkJPDEE0945hZ+++23VK1a1avXyC0NKxURkWyJiXEelRyKSFH2+OOwezesWAFly/o7GvGac+ec1YWmTXP2a5oxwyd7Fp45c4bIyEh27NjBkCFDeO211yhVqpTXr5NbSg5FRCRbXC4oU8aZjy8iUhR99JGz+MxLL0Hr1v6ORrzm2DFn5dHvv4cXXoBRo7y+PcX58+cJDAwkKCiIQYMG0axZM78sOHMxGlYqIiLZ4nJBZKRW5BORoik21tn3vE0bGDnS39GI1+zc6WT6K1dCdLST+ftg38KGDRuyZMkSAIYOHZovE0NQcigiItlw/jxs2KAhpSJSNKVsWwFOz2Fxjb0rHH74AVq1gsOHYfFiZ4UhL0o9txAgyAfDVL1NyaGIiFzUzz/DmTNKDkWkaBozxulYeucdCAnxdzTiFR9+CDfe6Cw2s2oVeLknb9myZYSHhzNp0iQGDx7Mxo0bufbaa716DV/QfQ8REbkol8t5bNHCv3GIiOS1n35yRhrec4/zJQWctc6cwpdfhvbt4bPPoGJFr19my5YtBAQEsGzZMq677jqv1+8r6jkUEZGLcrmgXDm4+mp/RyIikndOnnQSwquugsmT/R2NXLIzZ5wf6MsvO/sWfvONVxPDZcuWMW/ePAAeffRRNm3aVKASQ1ByKCIi2eByOUNKvTxHX0QkX3viCdi1yxmBWK6cv6ORSxIX5wwjnT3bGSc8bRpcdplXqj516hRPPPEE7du357XXXsNaS0BAQL7aoiK7lByKiEiWzp6FTZs031BEipY5c5zFK597zlmhVAqwrVudhWfWrYNPP4URI7x2tzP93MKlS5diCvCdVM05FBGRLG3a5KxWqvmGIlJU7NoFAwdCVBQ8/7y/o5FL8t13zh6GQUGwbBm0bOm1qjdv3kz79u2pU6dOgZtbmBn1HIqISJZSFqNRz6GIFAVJSc6OBsnJMGuWtq0o0KZOhS5dnEmjq1d7LTHcu3cvAGFhYURHRxfIuYWZUXIoIiJZiomBypWhZk1/RyIi4ntjx8KKFc4CNHXq+DsayZXkZPjXv+Dhh6FjR2cfklq1LrnalLmFV199NT///DMA9957L6VLl77kuvML3QsREZEsaTEaESkqVq+GF1+E3r3/3vReCphTp5wf3vz5MGgQ/Oc/Xun+XbZsGQMGDOD3339n8ODBhBTSDS/VcygiIpn66y/4+WfNNxSRwi8+3tnloEYNZ7N73RArgPbtg+uvhy++cJLCSZMuOTG01jJs2DDat2+PMYZly5bxn//8p1D1FqamnkMREcnUhg3O6BzNNxSRwm7wYPjjD1i6FMqX93c0kmMbN0LXrnDsGCxY4HzvBcYYSpYsyeDBg3nttdcKbVKYwi89h8aYYcaYn40xW4wxs40xQcaY2saY1caYHcaYj40xl7mPLeF+vsP9ekiqep5xl/9mjLnJH+9FRKQwi4lxHpUcikhh9sknMHMmjBwJ7dr5OxrJsS+/hLZtne9/+OGSE8NTp04xePBgvvvuOwBeffXVQt1bmFqeJ4fGmOrAAtdwiAAAIABJREFUYKC5tTYUKAbcDbwOTLDW1gWOAQ+4T3kAOOYun+A+DmNMI/d5jYEuwDvGmGJ5+V5ERAo7lwuuvNL5EhEpjP78Ex55xNkG74UX/B2N5Mj/s3fncTbX7R/HXx9Cm4ykVEq60X13S0hFspQWrbQrd8aWfUmypdKiwqBsicJtubOUirIT2ZmhYuyUslPZZ5/5/P74fKffqBlmmDnfc2bez8djHjPne77nzDU8Zr7nOp/rc13WwuDB8OijUK6c2zRaseI5PWXq3MIhQ4awatUqgJCeW5hVfu05PA+4wBhzHnAhsA+4G/jcu38sUN/7up53G+/+Osb9D9UDJllr4621PwPbgewbXCIiIn82oxERyY2Sk6FRI0hKcmMrChTwOyLJtKQkaN8eOnZ0yeHixef0TmbqamHt2rUBWLRoEa+88ko2BRs6Ap4cWmv3AP2BX3FJ4VFgDXDEWpvknbYbuNr7+mpgl/fYJO/8YmmPp/OYUxhjWhhjoowxUYcOHcreH0hEJJc6dgy2bFFyKCK5V79+bi760KHwj3/4HY1k2rFj8Mgjbt7Iyy/D1KlwjiWfU6ZMYciQIXTo0IF169ZRq1atbAo2tAS8IY0xpihu1a80cAT4DFcWmmOstSOBkQBVqlSxOfm9RERyi7VrXcWOkkMRyY0iI10Z6dNPu9VDCRG//OL2FG7aBCNGuFmGZ+nkyZNER0dz++23Ex4eToUKFbjllluyMdjQ40dZ6T3Az9baQ9baROALoDoQ5pWZApQE9nhf7wGuAfDuLwL8nvZ4Oo8REZFzFBXlPis5FJHc5sQJN7biyivho480tiJkrF7tNofu2gWzZ59TYrh48WJuvvlmHnzwQY4fP06+fPnyfGII/iSHvwJVjTEXensH6wAbgYXAk9454cA07+vp3m28+7+11lrveAOvm2lpoCywOkA/g4hIrhcVBaVKQfHifkciIpK9XnwRtm+H8eOhaFG/o5FM+fxzN8Pwwgth+XK4556zepqTJ0/SsWNHatWqhbWWL7/8ksKFC2dzsKEr4GWl1tpVxpjPgbVAEvA9ruRzBjDJGNPbOzbKe8goYLwxZjvwB65DKdbaDcaYKbjEMgloa61NDugPIyKSi6kZjYjkRlOnwqhRbmxFHt1WFlqshb59oUcPqFYNvvoKLr/8rJ7qyJEjVKlShR07dtC+fXvee++9PDGeIisCnhwCWGt7Ab3+cvgn0uk2aq2NA57K4HneAd7J9gBFRPK4P/6AHTugeXO/IxERyT67d8MLL8Ctt8Ibb/gdjZxRQgK0agVjxkCDBu7z+edn+WlSUlLIly8fYWFhPPXUU9StWzfPNpw5E79GWYiISBBbs8Z9vvVWf+MQEckuqWMrEhLg0081tiLo/fEH3H+/Swhff939p51FYrh48WLKly/P+vXrAXjvvfeUGJ6GkkMREfmb1GY0lSv7G4eISHYZMAAWLoQhQ6BMGb+jkdPavt2VkC5fDuPGwZtvZrlrUNq9hfHx8cTExORQsLmLL2WlIiIS3CIj3YsnNWoQkdxgzRro2ROefBIaN/Y7GjmtJUugfn2XDM6fDzVqZPkpFi9eTNOmTbW38Cxo5VBERP5GzWhyljFmtDHmoDEmOs2xN4wxe4wxP3gfD6a5r4cxZrsxZosx5v40x+t6x7YbY7qnOV7aGLPKOz7ZGFMwcD+dSHA5eRKeew5KlHBj8TS2IohNmOC6kF52GaxceVaJIcDs2bOx1rJo0SIGDx6sxDALlByKiMgpDhxwI6S03zBH/Reom87x9621Fb2PmQDGmBtxnbr/7T3mQ2NMfmNMfmAY8ABwI/Csdy5AX++5ygCHgWY5+tOIBLFOnWDbNje24tJL/Y5G0mWt21f4/PNQvTqsWJHl2t/FixezZMkSAHr16sW6deu0t/AsKDkUEZFTpDaj0cphzrHWLsaNZ8qMesAka228tfZnYDuuu/dtwHZr7U/W2gRgElDPmyF8N/C59/ixQP1s/QFEQsSXX8LHH0O3blC7tt/RSLri4qBhQ3j7bWjSxA23z0IWn3Zv4RteC9pChQpptfAsKTkUEZFTREa6sqtKlfyOJE9qZ4xZ55Wdpu74vBrYleac3d6xjI4XA45Ya5P+clwkT9mzx43jueUW189EgtDBg3D33TBxIrz3nhtAWTDzVfCLFy/m5ptvZvDgwbRr147p06fnYLB5g5JDERE5RVQU/POfULiw35HkOcOBfwAVgX3AgJz+hsaYFsaYKGNM1KFDh3L624kETEoKhIe7RalPP81SviGBYC3MmgW33w7ffw+ffQbdu2dpQ+iSJUuoVavWn3sLhwwZotXCbHDGbqXGmJK4vQ41gKuAWCAamAHMstam5GiEIiISMNa65PC++/yOJPhl9/XRWnsgzXN/DHzj3dwDXJPm1JLeMTI4/jsQZow5z1s9THv+X7/nSGAkQJUqVWxW4hUJZgMHwoIFrqS0XDm/o5FTrF0LXbrAt9/CP/4B330Ht92W6Yf/8ccfXHrppVSvXp0PPviA5s2bKynMRqddOTTGjAFGAwm4ze3PAm2A+bhN8UuNMTVzOkgREQmMvXth/37tNzyTnLg+GmOuTHPzMVyiCTAdaGCMKWSMKQ2UBVYDkUBZrzNpQVyiOt1aa4GFwJPe48OBaVn/KUVC09q18Mor8Pjj0EytmILHr7+6hjO33AI//giDBsHGjZlODFP3FpYpU4Y9e/aQL18+OnbsqMQwm51p5XCAtTY6nePRwBfexeja7A9LRET8EBnpPis5PKNzuj4aYyYCtYHLjDG7gV5AbWNMRcACO4GWANbaDcaYKcBGIAloa61N9p6nHTAHyA+MttZu8L5FN2CSMaY38D0w6tx+XJHQEBPjxlZcfjmMHKmxFUHhyBG3n3DQIHe7WzdXQhoWlumnWLJkCU2aNGHHjh20a9eOsCw8VrLmtMlhehc+b4P8NdbadV53tO05FZyIiARWVBTkzw8VK/odSXA71+ujtfbZdA5nmMBZa98B3knn+ExgZjrHf8J1MxXJU156CbZudbPTixXzO5o8LiEBPvzQdSE9fNitGr79Nlyb+XWllJQUOnXqxJAhQyhdujSLFi3SeIoclqmGNMaYRcaYS4wxlwJrgY+NMe/nbGgiIhJoUVFQvjxccIHfkYQGXR9Fgse0aW7I/csvuwaY4hNrYcoU+Ne/3JDJypXdjKSxY7OUGALky5ePI0eO0LZtW80tDJDMdistYq09BjwOjLPW3g7UybmwREQk0FKb0aikNEt0fRQJAnv3uv2FlStD795+R5OHLV0K1arBM8/ARRe5jqRz52ZpNtLJkyfp1KkT69evB2DMmDHqRBpAmU0Oz/M2yj/N/3dPExGRXGTnTvj9dyWHWaTro4jPUlKgcWO331BjK3yyZQs89hjUqAG7dsHo0W5ERd26WR5PcfPNN/PBBx8wf/58wK0eSuBk9l/7LdyG9+3W2khjzPXAtpwLS0REAi0qyn2+9VZ/4wgxuj6K+OyDD2DePPf5hhv8jiaPOXgQ2rSBf//bzQ7p3Ru2bYMmTdwG9kxK7URaq1YtUlJSWLhwIZ06dcrBwCUjZ5xzCGCt/Qz4LM3tn4AnciooEREJvKgo9457+fJ+RxI6dH0U8dcPP0CPHlC/Przwgt/R5CExMW6YZN++EBsLLVtCr16uTexZGDZsGIMHD6Zdu3a89957XHzxxdkcsGTWmeYcvuptss/o/ruNMQ9nf1giIhJokZFQoQIUKuR3JMFP10cR/6WOrShWzA2719iKAEhOdiWjZcvCa6/BvffChg0wbFiWE8OYmBg2btwIQMeOHVm6dClDhgxRYuizM60crge+NsbE4bqwHQLOxw3grYgb9vtujkYoIiI5LiXFNZN77jm/IwkZuj6K+Ozll2HTJldSetllfkeTy1kLs2dD164QHQ1Vq8LkyXDnnWf1dEuXLqVJkyYkJSWxZcsWChUqRPXq1bM5aDkbp105tNZOs9ZWB1oBG3BDdo8BE4DbrLWdrLWHcj5MERHJSdu3w7Fj2m+YWbo+ivjr669h+HDo3BnuucfvaHK57793K4QPPuhKSKdMgeXLzyoxjImJoVOnTtSsWZPk5GTGjBlDQXUQCiqZ3XO4DdhmjLnQWhuTwzGJiEiApTajUafSrNH1USTw9u2Dpk2hYkV45x2/o8nFfv0VXn0VJkyASy+FQYOgVauzbge7d+9eatWqxfbt27W3MIhlqlupMaaaMWYjsNm7fbMx5sMcjUxERAImMhLOPx9uvNHvSEKLro8igZU6tuLkSTe2Qnukc8CRI9CtG5Qr51YJu3Z15SUdOpxVYmitBaBEiRLUrFmThQsXam9hEMvsKIsPgPuB3wGstT8CNXMqKBERCayoKDej+LxM1ZNIGro+igRITIybmjB3rmuU+a9/+R1RLpOQ4FYHy5SBiAg3yH7rVujTB8LCzuoplyxZwm233caePXvIly8fo0aNonbt2tkbt2SrTE+VtNbu+suh5GyORUREfJCcDGvXar/h2dL1USTnrVrl3sAaMcLtM2zZ0u+IchFr4bPPXOnIiy+6f+g1a2DsWLj22rN6ypiYGF588UVq1arF77//zoEDB7I5aMkpmU0Odxlj7gCsMaaAMeZlYFMOxiUiIgGyebN7R177Dc+Kro8iOSghwU1MuOMO1wtlwQLo319jK7LN0qVQrRo8/TRccAHMmuWWZitVOuunXLJkCRUqVGDQoEG0adOGdevWUbly5WwMWnJSZguIWgGDgKuBPcBcoG1OBSUiIoETGek+Kzk8K7o+iuSQ6Gho1Mg1ywwPdxWPRYr4HVUusWULdO8OX30FV10Fo0a5f+T8+c/5qT/++GNSUlL49ttvueuuu7IhWAmkzHYr/Q1omMOxiIiID6Ki4OKLXe8ByRpdH0WyX3IyvP8+9OzpksEvv4T69f2OKpc4eBDefNPV515wAfTuDZ06wYUXntPTLl26lLCwMMqXL8+QIUPInz+/Gs6EqEwlh8aY0kB74Lq0j7HWPpozYYmISKBERcEtt2TLG8Z5jq6PItnrp59cN9IlS1xCOGIEXH6531HlAjExrotP376uPrdlS+jV65z/cWNiYujZsyeDBg3iscceY+rUqRTR8m5Iy2xZ6VfAKOBrICXnwhERkUBKTIQffoB27fyOJGTp+iiSDayFTz5xi1j587teKM8/r72F2eLzz6FjR9i7Fx57DN57D2644ZyfdunSpTRp0oTt27fTtm1b+vTpkw3Bit8ymxzGWWsH52gkIiIScNHREB+v/YbnQNdHkXO0bx80bw4zZ8Ldd8OYMWfdJFPSSkhwMwoHDXLlIZMnw513ZstTz5gxg0ceeYTrrrtOewtzmcwmh4OMMb1wG+3jUw9aa9fmSFQiIhIQUVHus8ZYnDVdH0XOwZQp0Lq1q3ocPBjatoV8mR60Jhnas8d1IF2+3K0aRkRAgQLn/LQnT57koosu4p577uHNN9+kU6dO2luYy2Q2ObwJeB64m/8vm7HebRERCVFRUW628fXX+x1JyNL1UeQs/PGHK2efONG9OTVuHPzzn35HlUssXAgNGsDJk+4fuEGDc37K1L2FX331FT/++COXXHIJr732WjYEK8Ems8nhU8D11tqEnAxGREQCKyrKlZRqX89Z0/VRJIvmzIGmTV3jzLfegh494LzMviKVjFkL/frBK6+49tMLF7rB9uco7d7CNm3akE9Lu7laZv93o4GwnAxEREQCKy4O1q3TfsNzpOujSCadOOFKSOvWdRULq1a5AfdKDLPB0aPw+ONuduETT8Dq1eecGCYkJNCpUydq1qxJUlIS3377LcOGDVMZaS6X2V/HMGCzMSaSU/dUqFW3iEiIWrcOkpK03/Ac6fookgnLlrkZ6z/9BC+/DG+/Deef73dUucS6dS4h3LnTDYjs2DFbykHOO+88oqOjad26NX379lVSmEdkNjnslaNRiIhIwKU2owm6lcM1a9zmo4su8juSzND1UeQ04uPdOL2ICChVChYtgpo1/Y4qF5kwAVq0cEuxCxeeczfSmJgY3nzzTdq3b0/JkiWZOXMmBbKhkY2Ejkwlh9ba73I6EBERCazISCheHK65xu9I0oiLg4cecq8ep0zxO5oz0vVRJGM//uhmFa5fDy+8AAMGQOHCfkeVS8THu6GQw4dDrVowaRKUKHFOT7l06VKaNm3Ktm3buP7662nZsqUSwzzotHsOjTFLvc/HjTHH0nwcN8YcC0yIIiKSE4KyGc348XDgALRq5Xckp6Xro0jGkpLcnPVbb4VDh+Cbb2DkSCWG2ebXX90baMOHuxrd+fPPKTGMiYn5c29hYmIiCxYsoGXLltkYsISSM60cXgRgrdWvs4hILnLyJGzc6PoXBI2UFOjf3w1rDv6Byro+iqRj2za3t3DFCnjqKZe/FCvmd1S5yNy58NxzbsD91KnZ8kf8jTfe4IMPPqBNmzbaWyhn7FZqAxKFiIgE1Nq1LhcLqv2G06fD1q3QpUuQLWemS9dHkTSshQ8/hIoVYdMm+PRTmDxZiWG2SUmB3r1dq9crr3SlH+eQGMbExPDLL78A0KNHD3UilT+daeXwcmPMSxndaa0dmM3xiIhIAIweDRdeeM69C7KPtdC3L5Qu7bruBT9dH0U8u3dDs2ZuUev++2HUKLj6ar+jykUOH3abN2fMcKuGI0eeU8Ou1L2FhQsXJjIykqJFi3JX8FdrSICcaeUwP3AxUDiDDxERCTG7d8P//udezBUt6nc0nmXLYOVK6Nw5VIae6fooeZ617m/JTTfB0qWuhHTWLCWG2er7712p/dy5MHSo6056lonhX/cWRkREaKC9/M2ZrsD7rLVvZfc3NcaEAZ8A5XGlOU2BLcBk4DpgJ/C0tfawMcYAg4AHgRigsbV2rfc84cCr3tP2ttaOze5YRURym0GDXIXSSxmue/kgIsLVnzVp4nckmZUj10eRUPHbb65v1NSpcMcdMHYslCnjd1S5zOjR0KaNayu9eDFUrXrWT7Vjxw4eeOABtm3bpr2FclpnersgpzZ9DAJmW2v/CdwMbAK6AwustWWBBd5tgAeAst5HC2A4gDHmUtx8qduB24BexphgeQ9cRCQoHTkCI0bA00/Dddf5HY1n0ya337BdO1frGhqCflOkSE75+msoX9792vbp4/IWJYbZKC7Ozf5o1szV/q9de06JIUDJkiW54YYbWLBggfYWymmdKTmsk93f0BhTBKgJjAKw1iZYa48A9YDUlb+xQH3v63rAOOusBMKMMVcC9wPzrLV/WGsPA/OAutkdr4hIbjJiBBw/7nq+BI3+/eGCC6BtW78jyYpsvz6KBLtjx6B5c3j0UbjiCtcTpVs3yJ/f78hykZ9/hurV4ZNPoGdPmDPHrRyehWXLlnHvvfdy7NgxChUqxNdff83dd9+dzQFLbnPa5NBa+0cOfM/SwCFgjDHme2PMJ8aYi4ArrLX7vHP2A1d4X18N7Erz+N3esYyO/40xpoUxJsoYE3Xo0KFs/FFEREJHfDx88AHcey9UquR3NJ69e91sw6ZNz/oFkB9y6PooErS++w4qVIAxY6BHD1i92t2WbDRzpttfuGOHW5bt3fusMu+YmBheeuklatSowfbt2/n1119zIFjJrfzYhXoeUBkYbq2tBJzk/0tIAbDWWrKxTbi1dqS1toq1tkrxEHrxISKSncaPh/373Tv9QWPwYEhODrINkCKSKi7O9Ym66y7XK2rJEnj3XShUyO/IcpHkZHj9dXjoIbj2WlizBh555KyeatmyZVSsWJH333+f1q1bs379esqXL5/NAUtu5kdLuN3AbmvtKu/257jk8IAx5kpr7T6vbPSgd/8e4Jo0jy/pHdsD1P7L8UU5GLeISMhKSXE9XypXhqCpKjp2zLU3fPJJuP56v6MRkb9Ys8ZNUNi0yfVF6dfvnCYoSHp++w0aNnTdSBs3dsMiL7jgrJ/uzTffJDExkQULFqiEVM5KwFcOrbX7gV3GmBu8Q3WAjcB0INw7Fg5M876eDjQyTlXgqFd+Oge4zxhT1GtEc593TERE/iJ1vnzXrkE0X37kSJcgBtUGSBGx1jWaqVoVjh6F2bNh2DAlhtkuMtKVkS5a5P4ejh59VonhsmXL2L17NwDjxo1j/fr1SgzlrPk13KQ98D9jzDqgIvAu0Ae41xizDbjHuw0wE/gJ2A58DLSBP/d7vA1Eeh9vaQ+IiMjfBeV8+YQEtwHy7ruhShW/oxGRNN5+2+0rfOIJiI52g+0lG1nruoPdead7t27ZMtedNIvv3MXExNC5c2dq1KjBG2+8AUCJEiXUiVTOiS+Thq21PwDpvRr4W/c3b/9hui3srLWjgdHZG52ISO6SOl9+6NAgmi8/cSLs2eM68olI0Hj/fejVy1U4jhoFmpGezWJioHVrGDcO6tZ1Q+2LFcvy0yxbtowmTZqcMrdQJDvoV15EJJfr1y/I5sunboC86SYtSYgEkY8/dr2hnnrKvW+jxDCbbd8O1aq57mBvvAEzZpxVYjh58mRq1Kjx595CzS2U7KRfexGRXGzjRjewun37IJovP2sWbNgQZBsgRfK2SZOgZUt44AG3mKXZhdls2jRXQr97txtZ0atXlrPvhIQEAO677z66du2qvYWSI5QciojkYkE5Xz4iAq65Bp55xu9IRAT3BtLzz0PNmjB1KhQs6HdEuUhSktvAWb8+lCnjWsDWrZulp0jdW3jHHXeQmJhI0aJF6dOnj1YLJUcoORQRyaX27HErAM2awWWX+R2NZ9UqN027UycoUMDvaETyvG+/dWWklSq5rsbnMEVB/urgQVc636cPtGgBS5fCdddl6SlS5xYOHDiQ2267jcTExJyJVcSj5FBEJJcaNCgI58tHREBYGDRv7nckInneihXw6KNQtqyr9r7kEr8jykVWrHCDZZcvhzFjXHfS88/P9MPj4uL+7ESakJDAggUL+PDDD7kwaPYHSG6l5FBEJBc6ehQ++gieftqNsAgK27bBF1+4adqFC/sdjUie9sMP8OCDcOWVMG/eWfVFkfRYC0OGuBrdQoVckti4cZafxhjDvHnzaNWqlfYWSkAFS1NzERHJRiNGwPHjQTZffuBAV0ravr3fkYjkaVu2wH33ufdo5s+HEiX8jiiXOHnSzSucOBEeecSNqwgLy/TDY2Nj6devHy+++CJFihRh5cqVWimUgNPKoYhILhMf7+bL33OPq2oKCgcOuNKq8HC9EhXx0c6d7m+DMS4xLFXK74hyiU2b4PbbYfJkeOcd+OqrLCWGy5cvp2LFirzxxht88803AEoMxRdKDkVEcpn//Q/27XOTIoLG0KGQkACdO/sdiUietW+fSwxPnHClpOXK+R1RLjFxItx6q2tAM2cOvPJKpsdUxMbG0rlzZ+68807i4+OZP38+DRs2zOGARTKm5FBEJBdJnS9fsaJ7ERgUTpyAYcOgXj244Qa/oxHJk377De69F/bvh9mzoUIFvyPKBeLi3B7q555z7V6//z7Lf3g7dOjAwIEDadmyJevXr6dOnTo5FKxI5mjPoYhILvLNN7B5M3z6aRDNlx89Gg4fDrKlTJG849gxN1pvxw43f/322/2OKBf46Sc3A2TtWve3rXfvTI/niYmJ4eTJkxQvXpxXX32VBg0aKCmUoKGVQxGRXKRfP7eH6Kmn/I7Ek5gIAwbAnXdCtWp+RyOS58TEwMMPw48/wuefw113+R1RLjBtmtvQ/dNP7uu+fTOdGKbOLWzsdTAtVaqUEkMJKkoORURyiWXL3EfnznBesNSFfPYZ/PprkLVNFckb4uPh8cfd34X//Q8eesjviEJcYiK8/DLUr++GQ65d6wZFZkJMTMwpcwtfCqoBtCL/L1hePoiIyDmKiIBLL4WmTf2OxGOtC+qf/3RLFyISMElJ0LCh648yapSbeSrnYPduaNDAZdpt2rjRPIUKZeqhGzdupH79+mzbto1WrVrRr18/CmvWqwQpJYciIrnA5s2uuun11+Gii/yOxjN/vpu0PWpUpjv3ici5S0mBZs1g6lR4//0gesMoVM2d6zLtuDjXmbRBgyw9/Morr6R48eIMHz5cJaQS9HS1FhHJBfr3h/PPh3bt/I4kjX794Mor3YsqEQkIa6FDBzd//a234MUX/Y4ohCUnQ69erptPiRIQFZXpxHD58uU8/fTTJCYmUrRoUZYuXarEUEKCkkMRkRC3dy+MH+9WB4oX9zsaz/ffu5XDjh0zXXoF8Pvvv9OoUSN++eWXHAxOJPfq2dNNjnn5ZXj1Vb+jCWEHD7qk8K23oFEjWLUqU6N40s4tXL169Z9/y0zQtI8WOT0lhyIiIW7wYLe/KKjmy0dEQOHC0LJllh7Wq1cvPv30U44dO5ZDgQUHY8xoY8xBY0x0mmOXGmPmGWO2eZ+LeseNMWawMWa7MWadMaZymseEe+dvM8aEpzl+izFmvfeYwUavTPOE995zH61auYV7/a+fpSVL3LDYpUtdWfx//wsXXnjGh61YsYKKFSueMrewTJkyOR+vSDZScigiEsKOHoXhw93oiuuv9zsaz88/w5QpLjEMC8vSQzt06MDw4cO56aabcii4oPFfoO5fjnUHFlhrywILvNsADwBlvY8WwHBwySTQC7gduA3olZpQeue8kOZxf/1ekssMHQqvvOKquIcNU2J4VlJSXFZ9111w8cWwcmWmN2ympKTQunVr4uLimD9/PsOHD1fTGQlJakgjIhLCRo50A66DalLE+++7V6YdO2b6IdZajDGUK1eOcuXK5WBwwcFau9gYc91fDtcDantfjwUWAd284+OstRZYaYwJM8Zc6Z07z1r7B4AxZh5Q1xizCLjEWrvSOz4OqA/MyrmfSPw0diy0bw/16sGYMer/dFYOH4bwcPj6a3jySbdieMklZ3zYypUr+de//kWRIkWYOnUqxYsX55JMPE4kWOnPh4hIiIqPhw8wwXbYAAAgAElEQVQ+gDp14JZb/I7G8/vv7kVVw4ZQsmSmHzZx4kTq16/PkSNHcjC4oHeFtXaf9/V+4Arv66uBXWnO2+0dO93x3ekc/xtjTAtjTJQxJurQoUPn/hNIwE2d6ha37rkHJk3K9Cx2SSsy0g21nz3b1elPmXLGxDA2NpYuXbpQvXp13n33XQD+8Y9/KDGUkKfkUEQkRH36qWtG07Wr35Gk8eGHEBPjumFk0okTJ+jSpQt79uzRCyuPt0poA/B9Rlprq1hrqxQPmm5GklmzZ8Ozz0LVqvDVV65jsWSBta4G9847XUnpkiVuCfYMNbkrVqygUqVK9O/fnxYtWvCqOv9ILqLkUEQkBKWkuJ4vN98M997rdzSe2FgYMgQeegjKl8/0w3r37s3evXsZMmQI+fJ2PdwBr1wU7/NB7/ge4Jo055X0jp3ueMl0jksusngxPP64+1WbMSOI5puGiuPHXWbdrp1bdl27Fm6//YwPGz16NHfeeaf2FkqulaevwiIioWrGDNi0ya0aBk3jibFj4dChLG2A3Lp1KwMHDiQ8PJyqVavmYHAhYTqQ2nE0HJiW5ngjr2tpVeCoV346B7jPGFPUa0RzHzDHu++YMaaq16W0UZrnklwgMhIefhhKlYI5c7Lc90nWr4cqVeCzz1x716+/hmLFTvuQ5ORkAOrUqUPbtm1Zv3695hZKrmRc5UreUaVKFRsVFeV3GCIi56RGDdi1C7ZtC5I9RsnJbgZYsWKuw18mM9Znn32WGTNmsHXrVkqUKJGtIRlj1lhrq2Trk2YTY8xEXEOZy4ADuK6jXwFTgGuBX4CnrbV/eAneUFzH0RigibU2ynuepsAr3tO+Y60d4x2vguuIegGuEU17e4YLvq6PoSE6GmrVgiJFXBXk1enuJpUM/fe/0KaN+wecNMn9Y55GbGwsr7/+Ohs3buSbb77RvELJNTK6RqpbqYhIiFm+3I3fGjQoSBJDgC+/hB07oG/fLC1lDhs2jBdeeCHbE8NgZ619NoO7/rYU4SV1bTN4ntHA6HSORwGZr+2VkLB9uysjL1QI5s9XYpglMTFuP+Ho0W5Uxaefwhn+7qxYsYImTZqwZcsWWrZsSUJCAoUKFQpQwCL+UFmpiEiIiYiASy+FZs38jsRjrZsNVqYM1K+fqYckJiaSnJzMpZdeyt13353DAYqEvl273Na4xESXGAbNXNNQsHWr69ozejS8+irMm3faxDC1E+mdd95JbGws8+bN46OPPlJiKHmCkkMRkRCyeTNMmwZt2wZRA4rFi90mqM6dIX/+TD0kIiKCW2+9lePHj+dwcCKh7+BBlxgePgxz58KNN/odUQj57DO3v3DvXpg1C95++4x/p+Lj45k4cSIvvPAC0dHR3HPPPQEKVsR/Sg5FRELIgAGupKxdO78jSaNfPyhe3A2QzoRdu3bxzjvvULp0aXX5EzmDw4fhvvtg926YOdON45NMiI93ZaRPP+1aun7/PdStm+HpsbGx9O/fn8TERMLCwoiOjuajjz7S3yjJc5QcioiEiH37YNw4aNIELr/c72g80dHuFWuHDnDBBZl6SJcuXUhJSWHAgAE5HJxIaDt+HB54wHUm/uorqF7d74hCxC+/uK5dQ4dCp06waBFcc02Gp6fOLezSpQtz5swBIEwtYCWPUnIoIhIiBg+GpCR46SW/I0mjf3+48EJo3TpTpy9atIjJkyfTrVs3rrvuupyNTSSExcVBvXoQFeWaagbNPNNg9803UKkSbNkCU6fCwIFQsGC6p6a3t/Dhhx8OcMAiwUXJoYhICDh2DIYPhyeecH1fgsLu3fC//0Hz5mecEZZq6NChlCpVim7duuVwcCKhKzERnnoKFi50kxcee8zviEJAUhJ07w6PPALXXeeG2j/++Gkf8vzzz9O/f3/tLRRJQ6MsRERCwMcfw9GjWZovn/M++MB1Ku3UKdMP+fTTT9m5cycXZLIEVSSvSU6G5593C2Affgj/+Y/fEYWAvXvh2Wddc6wWLdycn/PPT/fU2NhYkpOTufjii+nZsyetWrVSUiiShlYORUSCXEICvP++G811661+R+M5cgRGjHDNHjJRHnrkyBGOHz9OwYIFKVeuXM7HJxKCrIWWLWHyZNfnKZPV2nnbt9+6MtKoKBg/3v1dyiAxTN1b2Ml7Q6tSpUpKDEX+QsmhiEiQmzgR9uyBrl39jiSNESPgxIlML2V27dqV8uXLExsbm8OBiYQma91+4lGj3Ci+oKoSCEYpKW4sxb33urL2yMgMl1n/urfwmWeeCXCwIqFDyaGISBBLSXFD72+6Ce6/3+9oPPHxrqT03nvdO/ZnEBUVxSeffMITTzyhclKRDLz5pvu16tAB3nrL72iC3G+/wYMPwuuvu3LS1aszHP74448/UqlSJfr370/z5s1Zv369VgtFTkN7DkVEgtisWbBhg6uWMsbvaDwTJsD+/S6oM0hJSaFDhw5cfvnl9OrVKwDBiYSeAQNcctikiSshD5rf9WC0ZAk89xwcPAgffeT2GJ7mHywsLIwCBQowb948JYUimaCVQxGRINavH1x7LQRNFVRKihtfUbEi1KlzxtMnTJjAihUr6NOnD0WKFAlAgCKhZeRIePll1530448hn16ZpS8+3nUjrVXLjaZYscJt0EwnMVy5ciVt27bFWkupUqVYt26dEkORTNKfIBGRILVypWu+99JLUKCA39F4vvkGNm92GyAzsbwxd+5cqlatSqNGjQIQnEho+eoraNXKVUhOmAD58/sdUZCKjobbb4e+fd3onB9+gMqV/3Za6t7C6tWr8/XXX7N3714AjJZiRTJNZaUiIkEqIgKKFoVmzfyOJI1+/aBUKbfMkQnjx4/n6NGj5NNyiMgptm6FRo2gShX4/PMM57TnbSkprs72lVcgLAymT3dzDNOxcuVKGjduzJYtW2jRogURERFccsklAQ5YJPQpORQRCUJbt8KXX0LPnnDxxX5H41m+HJYtczPEzjv95ePnn38mf/78XHvttYSFhQUoQJHQcOKEG2xfqJBLDNWnKR2//AKNG8OiRVCvnqu5LV483VMTEhJ45plnsNYyd+5c7r333oCGKpKbKDkUEQlCAwa4lYT27f2OJI2ICLj00jMuZVpradmyJRs2bODnn3+moJZERP5krauM3LwZ5s51e4olDWtds6v27d3K4ahRrlNPOqWha9as4aabbqJgwYJMmzaN66+/XquFIufItzofY0x+Y8z3xphvvNuljTGrjDHbjTGTjTEFveOFvNvbvfuvS/McPbzjW4wxwdLkXUTknOzfD2PHutdDl1/udzSezZth2jRo2xYuuui0p3711VfMmzeP7t27KzEU+YtBg9yQ+3feyVRPp7zlt99cyXp4OFSoAOvWQdOmf0sMU/cW3nbbbQwaNAiAihUrKjEUyQZ+bgLpCGxKc7sv8L61tgxwGEh9a7oZcNg7/r53HsaYG4EGwL+BusCHxhht5RaRkDd4MCQkQOfOfkeSxoABrgauXbvTnhYbG8tLL73ETTfdROvWrQMUnEhoWLLEdSatXx+6dfM7miAza5Yb6Dp9ums8s2gRlC79t9NWrlx5ytzCli1bBj5WkVzMl+TQGFMSeAj4xLttgLuBz71TxgL1va/rebfx7q/jnV8PmGStjbfW/gxsB24LzE8gIpIzjh+HDz+EJ56AMmX8jsazbx+MG+f2/5xhKbNfv37s3LmTwYMHc94Z9iWK5CX79sHTT8P118N//6tZhn86eRJat3YtWy+7DCIjXTfkdFq3Dh06lOrVqxMTE8PcuXMZMWKEVgtFsplfK4cfAF2BFO92MeCItTbJu70buNr7+mpgF4B3/1Hv/D+Pp/OYUxhjWhhjoowxUYcOHcrOn0NEJFt9/DEcPepeGwWNIUMgMTFTS5l//PEHDRo0oHbt2jkfl0iISEx01ZLHjsEXX4BGfnpWrXIzU0eMcH9fIiPh5pv/dpq1FoA77riDF154gejoaDWdEckhAX9b1xjzMHDQWrvGGFM7EN/TWjsSGAlQpUoVG4jvKSKSVQkJrmt77dpw661+R+PJ4lLmoEGDSElJOeN5InlJly6u0e/EiVC+vN/RBIHERHj7bXj3Xbj6avj2W/eH7y/i4uLo1asXR44cYcSIEVSuXJmPPvoo8PGK5CF+rBxWBx41xuwEJuHKSQcBYcaY1GS1JLDH+3oPcA2Ad38R4Pe0x9N5jIhIyJk0CXbvDrJVw9SlzC5dTnvaypUrWb16NYBmGoqkMXGia0Lz4ovQoIHf0QSBzZuhWjWXHDZs6JrOpJMYpu4t7NevHwDJyckBDlQkbwr4Fdxa28NaW9Jaex2uocy31tqGwELgSe+0cGCa9/V07zbe/d9aV18wHWjgdTMtDZQFVgfoxxARyVbWuvny5ctD3bp+R+NJTHRLmbVqwW0Zb+lOTEykadOmNGzYUC/gRNJYv96NrbjzTvf7naelpLgS9UqVYOdON+Bx7Ni/1djGxcXRtWtXqlevzsmTJ5kzZw4jRowgfzp7EEUk+wVTt4BuwCRjTG/ge2CUd3wUMN4Ysx34A5dQYq3dYIyZAmwEkoC21lq9KhGRkDRrFmzY4Pq+BE2jitSlzBEjTnva0KFD2bRpE9OnT9cLOBHP0aPw+ONwySUwZQoUKOB3RD7as8fN5pk3Dx54wM0uvPLKdE89ePAgI0aMoFmzZvTv318NZ0QCzKRu8s0rqlSpYqOiovwOQ0TkFLVrw08/wY4dQfIi0lo3Z8xat/yRQcZ64MABypUrxx133MHMmTMxQZPZgjFmjbW2it9xhApdH7NPSopLDGfMgIUL3cphnjV5MrRq5TZVDxgALVv+7e9JXFwc48ePp3nz5hhj2L9/PyVKlPApYJG8IaNrpDaGiIj4bNUq+O476NQpSBJDgNmzITra7TU8TcLXvXt3YmNjGTRoUFAlhiJ+6tsXpk2D/v3zcGJ4+DA895zbaHnDDfDDDy5J/MvfiVWrVlGpUiVatGjBsmXLAJQYivhIyaGIiM8iIiAszO1NChoREa6L4LPPZniKtZZ//vOf9OzZk3LlygUwOJHgNW8evPqq+9Xp0MHvaHwyf74baD9lCrz1FixdCmXLnnJKXFwc3bp144477uDkyZPMnTuXO/NsJi0SPIJpz6GISJ6zbZube9ajBxQu7Hc0nshIVwvXvz8ULJjhacYYunXrFsDARILbr7+6pPDGG12j3zy3mB4bC927w+DBbrVw5Uqokn5l9yOPPML8+fNp0aIFERER2lsoEiS0cigi4qMBA1z+1b6935GkERHhumi88EKGp3z22WdMmTKFvLZvXSQjcXFuHGhiIkydChdd5HdEAbZmDVSu7BLD9u1h7dq/JYZxcXEkJCQAriR97ty5jBgxQomhSBBRcigi4pMDB+C//4XwcAiaLTY7drhXtq1buwQxHUeOHKFt27YMHjw4wMGJBK+OHSEqynUczlNV1klJ0Ls3VK0Kx47B3LkuQbzwwlNOS91b+O677wJQp04d7r33Xj8iFpHTUHIoIuKTIUNcA7/Onf2OJI2BA+G88067WapXr1789ttvDBkyRE1oRIDRo2HkSFceXq+e39EE0PbtUKMGvPaaWzZdvx7+kvD9dW/hHXfc4VOwIpIZ2nMoIuKDEydg2DB47LEgWmU4dMi9yv3Pf+Cqq9I9JTo6mmHDhtGyZUsqVaoU4ABFgs+aNdCmDdxzD7z9tt/RBIi1Lht+6SVXF//pp+k2r1q7di0NGzZk8+bNvPDCC5pbKBIClByKiPjgk0/gyBHo2tXvSNIYOtRtnHr55XTvttbSvn17ihQpQu/evQMcnEjw+f13t2B2xRUwcSLkz+93RAGwfz80awYzZ0KdOq42vmTJdE81xpCQkMCcOXO47777AhuniJwVJYciIgGWmOiqN2vVgttv9zsaz8mTbinz0UfhX//K8LRWrVqRlJREsWLFAhicSPBJToaGDWHfPjep4bLL/I4oAL74Alq0cH8vBg2Cdu0g36k7lFatWsWcOXN4/fXXqVSpElu2bOG88/RyUyRUaM+hiEiATZ4Mu3YF2arhmDFuGeQ0QRljeOaZZ2jYsGEAAxMJTm++CXPmuAX3W2/1O5ocdvQoNG7slklLlXK1tB06nJIYpt1bOGrUKA4fPgygxFAkxCg5FBEJIGuhXz8oXx4eeMDvaDxJSW6mRrVqUL16uqf06dOHvn37anSFCPDNN25/YdOm0Ly539HksO++g5tvhvHjoWdPWLHCDXJMY9WqVVSuXJl+/frRrFkz1q9fT9GiRX0KWETOhd7OEREJoDlzXEO/sWODaED21Kmwcye8/366d//000+88cYbPPnkk+pOKnne9u2uZ1Plym7VMNf+SsTHw6uvujeOrr/e1c5Wq/a3044fP07dunUpXLiw9haK5AJKDkVEAqhfP9e7oUEDvyPxpC5llivn9humo1OnThQoUIB+/foFODiR4BIT4yor8+d376lccIHfEeWQdetcBrx+PbRsCf37w8UXn3LKhg0buPHGGylcuDDTpk3j5ptvpkiRIj4FLCLZRWWlIiIBEhkJCxdCp06u+3tQ+PZbWLvWdSjN9/dLwuzZs5k+fTqvvfYaV2Uw3kIkL7DW5Unr17vJDddd53dEOSA5GSIi3CbKgwfh66/ho49OSQxT9xZWqFCBcePGAVCzZk0lhiK5hFYORUQCJCICihSBF17wO5I0+vVzffiff/5vd6WkpNCpUyfKlSvHiy++6ENwIsHjww9hwgS31/D++/2OJgfs3Anh4bB4MdSv7+YYFi9+yimrVq2iSZMmbNq0iebNm1O/fn1/YhWRHKPkUEQkALZvd2Vo3bpB4cJ+R+P58UeYOxfefRfOP/9vd+fLl48xY8aQlJREwaBZ6hQJvOXL4cUX4eGH4ZVX/I4mm1kL48ZB+/bu9pgxLkn8y2bK/v37061bN6666ipmz57N/bkyQxYRJYciIgEwYACcd57r/h40IiJcuVirVn+7KyUlhXz58lG1alUfAhMJHgcOwFNPuQkO48enW30dun77zdXKfvEF1KjhOmWVLp3uqeXLl6dJkyYMGDBAJaQiuVhu+hMnIhKUDhz4/zfjS5TwOxrPtm0waZKrcU2n5XzTpk1VSip5XlISPPMMHD7sVv7DwvyOKBvNnOlm6nz9NfTt6zZEp0kMU/cWvv322wDUrVuXTz75RImhSC6n5FBEJIcNHQoJCdC5s9+ReLZtgzp1XH1rp05/u3vZsmWMHTuWi//SnVAkr+nRw435GznSjfrLFU6ehNat4aGH3J7CyEjo2tW1YPWknVu4b98+zTcVyUOUHIqI5KATJ2DYMNff4YYb/I4G2LABataE2Fi3UnDNNafcnZycTLt27ShZsiQ9evTwKUgR/33+uZvg0Latm+qQK6xaBZUqwYgR7t2qyMhTst7U1cI77riD48ePM3v2bD788EPNNxXJQ5QciojkoFGjXEla165+RwJ8/z3UquUaTSxeDBUr/u2Ujz/+mB9++IH+/ftz0UUX+RCkiP82bYImTdzM94ED/Y4mGyQmQq9eUL26G27/7bcu8/1LI6qtW7cycOBAmjZtSnR0tJrOiORBakgjIpJDli51nQ1r1wbf+7qsXAl167pZGgsWQJkyfzslMTGRd955h9q1a/P000/7EKSI/44fh8cfhwsvhM8+C6KZpGdryxa39BkV5UbWDBni/g544uLi+Oabb3jyySepUKECW7dupXQGTWlEJPdTcigikgNWrYIHH4Rrr3V9X3z13XeuB/8VV7gVg2uvTfe0AgUKsGTJEhITE1VGJnmStW7FcNs2mD8frr7a74jOgbVuOGOXLnDBBTBlimu7msbq1atp3LgxmzZtYv369ZQvX16JoUgep7JSEZFstnatG5J9+eVuke6KK3wMZs4ct2J4zTWulDSDxPDYsWNYa7nuuusoW7ZsgIMUCQ4DBriupH37uhX/kLV3LzzwALRr5/YYr19/SmIYFxdH9+7dqVat2p97C8uXL+9jwCISLJQciohko/Xr4d57Xcv7b7+Fq67yMZhp0+DRR+Gf/3SrhxkEY63loYce4vnnnw9wgCLBY+FC6NYNnnwSXnrJ72jOwWefwU03uTeDPvwQZs065XffWstdd91F3759tbdQRP5GZaUiItlk0yY3IeKCC05bvRkYkya5fUZVqrgXh+nMMkw1ceJEli5dSnh4eAADFAkeu3e7eYY33ACjR7ueTSHnyBFo3x4mTIBbb4Xx409pkRwfH0/BggUxxvDiiy8SFhampFBE/kYrhyIi2WD7dpcY5svnEsPrr/cxmDFj4LnnXGfCefNOmxieOHGCLl26UKVKFZo2bRrAIEWCQ0KCq7iMjYUvvnDjP0POwoVQoQJMnOi6ki5bdkpiuHr1aipXrswnn3wCwDPPPKPEUETSpeRQROQc7dwJd9/tusUvWADlyvkYzLBh0LSpq22dNeuMr3R79+7N3r17GTJkCPny6ZIgec9LL7lmvv/9r6vADilxcW5e4d13u7EUy5fDG29AgQLe3XH06NGDatWqcezYMUqVKuVvvCIS9FRWKiJyDnbvdq/LTpxwb97/+98+BtO/v+tM+OijrjNhoUKnPT0uLo5JkyYRHh5OVd9nbYgE3vjx7v2ULl3giSf8jiaLfvjBlY5v2ABt2kC/fpBmNmlUVBTh4eFs3LiRZs2aMWDAAIqkGWEhIpIeJYciImdp3z6XGP7+u1sxvPlmnwKxFt5+25WTPfOMe8XrrRyczvnnn8+6detITEwMQJAiweXHH6FlS9eV9N13/Y4mC5KT3RtBr70GxYrBzJmuM+lf/Pbbbxw7doxZs2ZRt25dHwIVkVCkGiIRkbNw6BDcc4/rGD97tuv74gtroXt3lxg2bgz/+98ZE0NrLePGjSM2NpZLLrmEYsWKBSZWyRRjzE5jzHpjzA/GmCjv2KXGmHnGmG3e56LecWOMGWyM2W6MWWeMqZzmecK987cZY9RtKI3Dh92g+0svdb2bzguVt8p//tlls927uwqB9etPSQwjIyP56KOPAKhbty7btm1TYigiWaLkUEQki/74w23p+/lnmDEDqlXzKZCUFOjQwZWTtW4No0ZB/vynfUhiYiItWrQgPDz8z+YUEpTustZWtNamvu3QHVhgrS0LLPBuAzwAlPU+WgDDwSWTQC/gduA2oFdqQpnXpaTA88/Drl1u6oOvc0gzy1rXaKpCBVi3DsaNc8FfdhngOpH26NGDqlWr0q9fP2JjYwFXHSAikhVKDkVEsuDoUbjvPti8GaZPh1q1fAokORleeAGGDnUNKYYNc61ST+Pw4cPUrVuXTz75hJ49e9K2bdsABSvZoB4w1vt6LFA/zfFx1lkJhBljrgTuB+ZZa/+w1h4G5gFaQgLeece9qfPBBz6+sZMVhw65Zc6mTeGWW1xy+Pzzf87biIyMpHLlyvTp04cmTZrw/fffc8EFF/gctIiEqlAppBAR8d3x466Ca906+PJLV1bqi8RECA93betff911JzzDYLYdO3bw0EMP8dNPPzF27FgaNWoUmFjlbFhgrjHGAiOstSOBK6y1+7z79wOp611XA7vSPHa3dyyj46cwxrTArThyra+DOQNj9mxXgd2okVtsD3rffAPNmrkZhv37Q6dOp7wJdPDgQWrWrMlll12mvYUiki2UHIqIZEJMDDz8MKxe7aq5HnrIp0Di46FBA/jqK+jTB7p1y9TDkpOTSUlJYcGCBdSoUSOHg5RzdKe1do8x5nJgnjFmc9o7rbXWSxzPmZd4jgSoUqVKtjxnsPr5Zzf+s0IFGD48yAfdnzjhKgJGjnQBz58PN930590///wzpUuX5vLLL2fy5MnUqlVLnUhFJFuorFRE5Azi4qBePVi61PV7eewxnwKJjYX69V1iOHhwphLDpUuXYq2lXLlybNy4UYlhCLDW7vE+HwS+xO0ZPOCVi+J9Puidvge4Js3DS3rHMjqeJ8XGwpNPuq17U6fChRf6HdFprFgBFSvCxx9D167uHSkvMUydW1i2bFlmz54NwKOPPqrEUESyjZJDEZHTSEhw888WLHD9IJ55xqdATpxwy5Vz5sAnn0D79qc9PSUlhVdffZUaNWowYcIEAM4LmZaMeZcx5iJjTOHUr4H7gGhgOpDacTQcmOZ9PR1o5HUtrQoc9cpP5wD3GWOKeo1o7vOO5Unt2sHatTBhAvzjH35Hk4HERDee4s47ISkJFi2Cvn3/nFe6evVqbrnlFvr06UN4eDjVQmLDpIiEGr1SEBHJQGKiSwZnznTVXb5t0ztyBB580K0gTJjgauNOIzY2lsaNGzNlyhSaN29OgwYNAhSoZIMrgC+Nq3k8D/jUWjvbGBMJTDHGNAN+AZ72zp8JPAhsB2KAJgDW2j+MMW8Dkd55b1lr/wjcjxE8xo6F0aPh1Vd9LAc/k02bXJOZNWvcSJpBg+CSS/68+5133uH111/nqquu0t5CEclRSg5FRNKRlAT/+Y+r4BwyxDUG9cVvv8H997t5ZlOmuK6Fp7F//37q1atHZGQkERERdO7cGRPUm6skLWvtT8DN6Rz/HaiTznELpNt21lo7Ghid3TGGkuho13jmrrtc36agk5LiOg137QoXXwxffJFu3XqJEiVo3LgxAwcOVAmpiOQoJYciIn+RkuK6xk+Z4hoEtmvnUyD797uWqDt2uCz1wQfP+JANGzawefNmvvjiC+rXr3/G80VyqxMn4Kmn3ALcp5+ecQRo4O3bB02auFLxhx5y5eIlSgBubuEbb7xBmTJlaNas2Z8fIiI5TcmhiEgaKSnQsiWMHw+9e7uGgb7YtQvq1IG9e91QtrvvPu3pv/76K9deey116tRh586dFC2qeeeSd1nrVgy3bnWNPr2cK3h8/bV7B+rkSfjwQ2jV6pS5hY0bN2bjxo107NjR50BFJK9RQxoREY+10LGjewP/1VehZ81wb0kAACAASURBVE+fAvnpJ6hRAw4cgLlzz5gYDhkyhDJlyjB//nwAJYaS540a5bbnvvmmKykNGjEx0KYNPPoolCzp9hi2bg3GEB8fT48ePahatSpHjx5l5syZfPDBB35HLCJ5TMCTQ2PMNcaYhcaYjcaYDcaYjt7xS40x84wx27zPRb3jxhgz2Biz3RizzhhTOc1zhXvnbzPGhGf0PUVEzsRa6NIFhg6Fl1+Gt97yKZDNm11iePw4fPst3HFHhqcmJSXRvn17OnTowAMPPEDVqlUDGKhIcPrxR1cKft998MorfkeTxg8/QJUqbshi586wciX8619/3r18+XL69OlD48aNiY6O5oEHHvAxWBHJq/xYOUwCOltrbwSqAm2NMTcC3YEF1tqywALvNsADQFnvowUwHFwyCfQCbsfNgOqVmlCKiGTVa6/BgAHuRWW/fj4NyF63DmrWhORk18b+llsyPPXYsWM8+uijDB06lM6dO/PFF19w8cUXBy5WkSB07JjbZ1ismFs5zBcM9VEpKe6Py223uc7D8+a5zcyFChEfH8+8efMAuOuuu1i3bh2jRo0iLCzM56BFJK8K+J9Na+0+a+1a7+vjwCbgaqAeMNY7bSyQ2kmhHjDOOiuBMG8A8P3APGvtH9baw8A8QL2dRSTLeveGd95xHUkHDfIpMYyKgtq1oWBB+O67P4deZ2Tq1KnMnTuXESNG0L9/f/IHXbcNkcCyFlq0cFXZkyZB8eJ+R4TbM3z//a4c4aGH3BtA99wDuL2FlStX5sEHH+TXX38F4KYz/N6LiOQ0X99TM8ZcB1QCVgFXeIN7AfbjZj2BSxx3pXnYbu9YRsfT+z4tjDFRxpioQ4cOZVv8IhL6IiLcqmGjRvDRRz6tNCxb5prPhIXBkiVwww0ZnhoTEwNA48aNWbduHS1atAhUlCJBbfhwmDzZvdFTo4bf0eA6DFeoAMuXu0GpX3wBl11GfHw8r7zyCtWqVePo0aNMmzaNa6+91u9oRUQAH5NDY8zFwFTgRWvtsbT3eXObbHZ9L2vtSGttFWttleJB8VaiiASDIUPceLEGDdyQbF8SwwUL3OaoEiVg8WIoXTrDUydPnkzp0qXZsGEDxhhuvPHGAAYqErzWrIFOndy0ly5dfA7m5EnX8vixx6BUKVi71pUlGENiYiJVq1blvffeIzw8nOjoaB7MxIgaEZFA8SU5NMYUwCWG/7PWfuEdPuCVi+J9Pugd3wNck+bhJb1jGR0XETmjkSOhQwf3+m3cOJ9moM2Y4UrNrr/eJYYlS6Z7mrWW3r1706BBA8qWLcsVV1yR7nkiedGRI/D003DFFe532dd9hmvXur3CH3/s3nlasQJuuIGkpCQAChQoQNOmTZk5c6b2FopIUPKjW6kBRgGbrLUD09w1HUjtOBoOTEtzvJHXtbQqcNQrP50D3GeMKeo1ornPOyYiclpjx7qxYg895PYmFSjgQxBTp7rMtHx513wmg4QvPj6e8PBwXnvtNf7zn/+wYMECLrvsssDGKhKkrHXjAn/9FaZMcY1ofJGS4jpZVa0KJ0644Yp9+0LBgkRGRlKxYkVmzJgBQPv27dWJVESClh/vr1UHngfuNsb88H/t3XmcjXX/x/HXZwbDlC3ZGssUSVqkGIRK3UrdSaG90EaLFpW7hSK5SYs2pR8tKt1UtEhFi0orhshoQ1EkipjBGGbm+/vje50xw8yYYWbOnJn38/G4Hueca/2c65oz53yu7xZMZwEPAF3MbBnwr+A1wHvAL8ByYAJwPYBzbiNwPzA/mIYH80RE8jRliv8xedppMHWq7/+lxE2a5Is62rTx1Urz+UX72GOP8fLLLzN8+HBeeuklYmJiSjBQkdLtiSfgzTd9Hha2kVxWr/adzNxxhx+/8Lvv4NRTc7Qt3LRpkz67IhIRzDfvKz9at27tEhMTwx2GiITBG2/4nKxDB3j/fYiNDUMQ48f7YsvOneHttyGP4Secc1gwMPann37KGWecUcKBRj4zW+Ccax3uOCJFpH0/zp3rO5456yyfIIall+Fp03x7wh07fKZ6xRVgRmJiIn379mXp0qVcccUVjBkzRlVIRaRUyes7sjSMACQiUuxmzPAdzyQk+OdhSQwff9x3VHHmmT6IPBLDjz76iLZt27JhwwZiYmKUGIrsZuNGf6MnLg5eeCEMieGWLXD11dCrFzRpAt9+66skBIEsXryYTZs28e677/L8888rMRSRiKHkUETKvA8+gJ49oWVLX2JYtWoYghg1Cm65BXr08MUcVarkutqECRPo2rUr27ZtY+vWrSUcpEjp5xz07Qtr1/p2hjVrlnAA8+fD8cf7Lo7vussPVXH44SQmJjJt2jQArrzySn744Qf1RCoiEUfJoYiUaZ9+CueeC0ceCbNmQfXqJRyAc34gxbvvhksv9QOx5dLQMSMjg9tvv51+/frRpUsXvvrqK419JpKLRx6Bd97xj23alOCBMzL8TZ4TT4Tt2+GTT2DkSNIyMxk8eDDt2rVjyJAhZGRkYGZUDctdKBGR/aPkUETKrK++grPP9kMHfvghHHRQCQewfr2vejZihH988UWoUCHXVYcOHcojjzzCgAEDeOedd6hWrVoJBytS+n35Jdx5p6/NOWBACR749999L1Z33+1L/xcvhpNPJjExkRNOOIGRI0fSu3dvvv76a6LDMi6OiEjRyP1XiohIhJs/3zfti4vzHYLWrl2CB9+6FcaM8V3bp6b6X7MjR+bbMOrGG2/k0EMP5aqrrirBQEUix99/w4UXQnw8PPtsCbYzfO0131Y4PR0mToTevcGMFStW0K5dO+rVq8e7776rKqQiUiao5FBEypxFi+D00/0IER9/DPXqldCB09Ph//4PmjaFe+/1QSxd6qui5fJLduHChfTp04edO3dSt25dJYYiecjMhMsv9wni66+XUPXwlBTf++iFF0KzZr7TmT59WLd+PQBNmjThueeeIykpSYmhiJQZSg5FpExJSvJDjlWtCrNnQ4MGJXBQ5+Ctt+CYY/wwFU2a+Ppv06bBEUfkuslbb71Fp06d+OSTT/jjjz9KIEiRyPXAAzBzpu/wt1WrEjjg3Llw3HHw0ku+zfAXX5DWsCGDBw8mPj6ehQsXAtCnTx/1RCoiZYqSQxEpM376ySeGlSr5xDA+vgQO+tVX0LEjnHeef/3WW/D5577Tilw453j44Yfp0aMHRx99NPPmzaNx48YlEKhIZPrsM5+fXXwx9OtXzAfLyPBthDt08DUBPv0Uhg8ncfHirLaFF198MYcddlgxByIiEh5KDkWkTFixAk491RfizZ7ta3YWqx9/9Alhhw7wyy++OumSJdC9e76NoYYMGcKgQYPo1asXn376KfVKrM6rSORZt84nhU2b+o9YsbYzXLUKTjnFZ6IXXOA7nenUiWHDhtGuXTuNWygi5YI6pBGRiJWe7jue+fBDmDAB0tJ87/LNmxfjQdeuhfvu8z1ixMbC/ffDwIFwwAEF2rxXr15UrFiRe++9l6go3Z8TyUtGhh/95Z9//DA0xToyxOTJvkq4c/Dyy/7AQSZqZvTu3ZsxY8YoKRSRMk/JoYhEDOdg+XKfDH74oU8EN2/2v+HatIFnnvHN/opFSgo89JAfXG3HDrj+el/CUIBuUFesWMEbb7zBoEGDaNWqFa1KpNGUSGQbMcJ3KPXcc8X4uU5OhhtugEmToH17mDSJtLg4hg8ZQocOHTjrrLO49957sRLrGlVEJLyUHIpIqbZhg68m+sEHPiFctcrPb9zY1/zq0sVXJ61Vq5gC2LkTxo/3pYV//eUP+t//FqjeqnOOmTNncvnllwNw+eWXqxqpSAF8/LH/yPXu7TsMLRZffQWXXeb/qQwbBoMHk7hoEVd0705SUhJ33HEHZ511lhJDESlXlByKSKmSluZ/s4VKBxcs8CWG1ar5JPA///EJYdOmxdz+yDmYOtUPer18uW+L9OCDvohyL7Zv387EiRMZO3YsS5cu5YgjjmDGjBlKDEUKYO1auOQSOPJIePrpYvicp6f7Gzz33w8NG8Lnn5N2wgkMHzqU0aNHU7duXY1bKCLllpJDEQkr5/zwE6FkcM4c2LYNKlSAdu38Df0uXXxOVqGk/mPNmeOz0Llz4eij4d134cwz9/orddu2bcTGxuKcY8iQITRq1Ijnn3+eiy66iCpVqpRQ8CKRKz3dd0CzZYuvNl7AprwF9+uvvj3h11/7UsOxY6F6dd569VVGjhxJ3759efTRR9W2UETKLSWHIlLi1q6Fjz7yVUU/+gj+/NPPP+IIuPJKP3b8ySf70sIStXQp3HknzJgBcXHw/PO+Xlt0dJ6bZGZm8sEHH/Dkk0+ybNkyfvzxR6pUqcKiRYuIi4tTlTSRQhg2zA9d8dJL0KJFEe980iTfVtgMXnmFtJ49Wbx4MQkJCVxwwQU0atSI9u3bF/FBRUQii5JDESl2W7f6wrhQ6WBSkp9/8MF+XMIuXfzUsGGYAlyzBoYOhRde8F0ijhoFN98M+ZT2bd68mYkTJ/LUU0+xbNky6tWrR//+/UlLS6NKlSo0aNCgBN+ASOSbOdPX9rzqKgia6RaNf/7xnc5MnuzHJH35ZRL//psrWrdm5cqVrFy5klq1aikxFBFByaGIFIOMDFi4cFcy+NVXvoPPmBjo1Mn/8OvSBVq2hLCO5rB5M4weDY895oO++WYYPDjf3m0yMzOJiopi9uzZ3HLLLZx44oncd9999OzZk0qVKpVg8CJlx+rVvpbnscfCk08W4Y4//hj69vXVFYYPJ+3WWxk+cmRW28IpU6ZQq9h6sxIRiTxKDkWkSKxcuSsZ/Phj2LjRz2/ZEm66ySeDnTrlWxhXctLSYNw431f+hg2+DdKIERAfn+vq6enpzJgxgyeffJJOwaDY3bp1Y+HChRqWQmQ/7dwJF13kP5avv15E/yO2b/edST36qK+v/vXXbG3Rgnbt2pGUlETfvn0ZM2YMNWvWLIKDiYiUHUoORWSfbNrkO4wIJYTLl/v5cXFwzjk+GTztNKhbN7xx5pCZCa++6ksHf/3V12kdPRqOPz7X1f/++2+effZZxo0bx2+//UbDhg05//zzAahQoYISQ5EiMHgwfPmlr/XZrFkR7HDRIl8MuXQp3HADbvRo7IADOAA455xzeOCBB/j3v/9dBAcSESl7lByKSIFs3+477wyNOThvns+1DjjAj/Jw440+IWzevJiHmNhXH38Md9zhx8Zo2RJmzfI93+Tj+uuv5/XXX6dz58489thjdOvWjQol1mWqSNk3YwY89BBcd50vPdwvGRnwyCMwZIivGv7++yyoXZurO3bk2Wef5YQTTuC///1vkcQtIlJWhbO1j4iUYlu3+p5E77kHTjoJqlf3SeCIEX74ibvv9r0Kbtzof+DddJMfl6zUJYaLF0PXrr6U8O+/4eWXfYPI3RLDHTt2MGXKFDp27MjPP/8MwLBhw0hKSmL27Nmcd955SgxFitCqVb4z4FatYMyY/dzZypV+INQ77oBu3UhLTGTIF1/Qtm1b1q9fT3JyclGELCJS5umXjogAkJwMX3zhexX97DNITPRjjkVH+1qXN93kk8ROnSAihgD77Tef2b78sg/44Yd9j4WVK+dYbe3atYwfP55nnnmGP//8kyZNmvDHH3/QrFkzWhR5X/oiAr6Dqgsu8IV9r7++x8ey4Jzzn/EBA/zriRNZcNRR9O3alaSkJPr06cOjjz6qtoUiIgWk5FCknNq4ET7/fFcy+O23vppoxYp+wPlBg3wy2KGDH90hYvzzjx+K4okn/OtBg/zYhbn8ONy2bRvNmzcnOTmZM888kwEDBtC1a1eiwtqFqkjZd8cdvmr61KnQpMk+7mTDBujfH6ZN83etXnoJ4uN5d/hwNm7cyIwZM9S2UESkkJQcipQT69f7RDCUDC5Z4m+6x8RAu3a+mc7JJ/vnsbHhjnYfbN8OY8fCyJG+t5zevWH4cGjUKNsq25kyZQpffvklEyZMIDY2lmeeeYbWrVtz+OGHhzF4kfLjzTf96DE33QQ9e+7jTmbOhCuv9FXFR48m8eSTSfn1VzrHx3PXXXdx4403qrRQRGQfKDkUKaP++MMngZ995hPCH37w82Nj4cQTfd508sm+lHCfq3SFk3O+x9HPP/fT++/7N33mmfDAA37AtMBvv/3GuHHjmDBhAhs2bKBFixb8888/1KxZk4svvjiMb0KkfPnlF7jiCv9/56GH9mEH27bBf/4DTz0FRx1F2ltvMfzttxndoQPHH388c+fOpWLFikoMRUT2kZJDkTJi1aqcyWBoaImqVaFjR+jTxyeDJ5zgq45GnMxM3zV9KBn8/HNYs8Yvq1nTVyu7+WbfKUU2M2fOzKpa1r17dwYMGEDnzp2xUtdzjkjZlpbm2xmawWuvQaVKhdxBYqIfouKnn2DgQBb06kXfq67KMW6hPtciIvtHyaFIBHIOVqzYlQx+9pnvfwV25UnXXeeTwZYtISI72dyxw/cqGmoY+eWXvj0h+MEUO3Xa1UNOixYQtBPcsmULkyZN4uCDD6ZXr1507NiRu+66i2uuuYbGjRuH8Q2JlG+33eZHknn7bYiPL8SG6em+NsB990G9evDRRyysWZO2CQnUrVtXbQtFRIpQJP5kFCl3nPPVQkPtBefM8TUoAWrX9jnS7bf7ZPDoo7PypMiydSt8/fWuUsFvvoHUVL+sWTPo0WNXMhgfv8eYGcuWLePpp5/mhRdeYPPmzVx00UX06tWLAw88kBEjRpT8+xGRLK++6muC3nYbnHNOITZcsQIuv9z/b7j4YjaPGkX1xo1p5RwPPfQQffv2VRVSEZEipORQpBTKzPQdxmRPBv/6yy875BCfBJ50kn8stYPO782GDX7sjFAyuHChLyGIioLjjoN+/Xwi2LEj1K2bY9OUlBQWL15Mx44dAejfvz/jx4+nQoUKnH/++QwYMID27duH412JyG6WLYNrroH27X1HwgXiHDz7LAwcCBUrkvbii4xYtoynWrVi4cKFxMfHM3DgwGKNW0SkPFJyKFLCMjN9ord6tW8yF3rM/vz3331BGkDjxr6PlVAy2KRJhCaDv/+es73g0qV+fkwMJCT4TiY6dfK95VSrlmPTVatWMXPmTObNm8fcuXP5/vvvcc6xZs0aDjnkELp3785RRx3F+eefT/369cPw5kQkN6mpcP75vn3hq68WsL3z+vU+m5w+HU49lQW33krfO+/MGrewevXqxR63iEh5peRQpAilpfnqnrsne9kTwD/+gJ07c24XHQ316/umdEcdBaef7juOOflknxxGHOd8pxGhRHDOHN9jDvgecjp0gEsu8clgtu5SnXOsXLmSee+/z9y5c7n22mtp1qwZc+bM4dprr6VWrVq0bduW888/n4SEhKzqZGeddVa43qmI5OPmm2HxYnjvPWjYsAAbvPMOXHUVJCfjxozh3g0bGNW9u9oWioiUECWHIgWUnJx/ad/q1buqfmYXG+uTvgYNfC7UoIF/HZoXF+drTUZHl/x7KjLp6bBo0a5k8Isvdp2MOnX8Gx840Bd/Hnts1pt1zmFm/PLLL9x0003MmzePv4LtKleuzEknnUSzZs3o1q0bK1as4NBDD1VvhCIR4pVXYMIEuPNOX/shX1u2wK23+g1atoTZs7Gjj2Zdv35cdtllPProo2pbKCJSApQcSrmXmelrMeWX9K1Z43+77K5WrV0JXps2eyZ9DRpA9eoRWg00P6mpMG/ermTwq692naBDD/W/BEO9iR5+OJixfft2Fi1axNyxY7Oqh15xxRUMHjyYGjVqsHLlSs4++2zatm1LQkICRx99NBWDOmg1atSgRo0aYXzDIlIYP/wA/fv7fwP337+Xlb/+2nc688sv7Lj9dkZUrMg527fTGhg3bhzREX3nTEQksig5lDIrI8MXXq1dm/f0xx9+Sk/PuW10tO/4JS4OjjkGunbdM+k75JAIHTy+IHbsgD//3HWisj///nuYP9+vA7571Msv39WTaFwcmZmZ/PTTT6Rs2kSCGRkZGdStW5fk5GQA4uLiSEhI4MgjjwTgoIMOIikpKVzvVkSK0Natvp1hbCxMmZLPUDo7d8Lw4TByJDRsyMLx4+n7xBMsWbKE6JgYWrdurcRQRKSEKTmUiLNzZ85cJa9p3TqfIO6uZk3fvq9+fd+mb/ekLy7O14Qsc79JnIOUlD2Tvdxeb9y45/ZmftyMww7zDYk6dfJtBw86CIAPP/yQT556irlz55KYmEhycjJt27blm2++ITo6mlGjRlG/fn0SEhKIi4sr4TcvIiVlwAB/D2nWLH8TLVc//eQHtE9MZMdll3F//fqMuvZa6tSpo7aFIiJhpORQSo3U1L0nfGvXwt9/+zwnu1DeEkr6jj3W/ygJvQ5N9eqVwdK+zEx/UvJK9LK/3rZtz+0rVdp1gg4/3JcAZj9hoed16kCFCqSkpLBgwQLmzZvHr++9x7hx4wB4+umnmTFjBsceeyyXXnopCQkJtGvXLusw119/fUmdEREJkxdegIkT4Z57oEuXXFZwDp5+GgYN8kWLU6cy4c8/GTFgAL179+axxx5T20IRkTBScijFwjlfVTMtzU8bNvjqm/klfZs27bmf6Ohd+UnjxtCunX++e+JXp04Bu0iPJGlpvvgzvxK+/IpIq1ffdfISEvZM9kKva9bMahSZmprKmjVrdk0//MANp55KbGwsY8eO5cEHH2TNmjVkZmYC0LRpU7Zt20ZsbCxPP/00//vf/6hSpUpJniURKSWSkuCGG6BzZxg6NJcV/vgDrrwSZs1ix+mns/zuu2lx8sn027mT5s2bc9ppp5V4zCIikpOSw0KaNMl3ThIV5ROX7FNB5xVm3cLs08w3AwslZLlNJbl899K97GJiduUnRx4Jp56aM2cJJX8HH+zfb4kKZbXbt+f9RvNbVhTbbt/uu0fdnZnPhLMXke6e7IUeY2NzbLp161aWLVvmk74VK1gzZw5r1qzhnnvuoXHjxjzzzDNcd911exyyW7duNG/enLi4ODp37kyTJk1ISEigTZs21KpVK2s9jS8oUr7VrAlnnQVjx+ZSLX/aNOjXD1JTWXjHHfR97z3+vvhili9fTmxsrBJDEZFSwlx+v+DLoNatW7vExMR93r5TJ99Lf1kWE7PnVKlS7vMLuuygg3ImfjVq7GMPnunpvreDvU1bthRsndTUPZO1oFRsv0VH5zwZlSvnfaJyW3bwwXvWi61dO8/eHTZs2MDHH3+cVeq3evVq1qxZw6hRo+jYsSNvvvkmPXr0yFrfzKhbty5vvvkm7dq149tvv+X9998nLi6OBg0aEBcXR1xcHFWrVi2a8yFSwsxsgXOudbjjiBT7+/2Yq82b4aab4KWX2HH88dzfti2jxo+nTp06jB8/nrPPPrtojyciIgWS13ekSg4L6eOnfyIzZSuZRJHhovJ8DE2ZRJFBdNbr9Myc66Rn5lw/63WmkZnpawtmn/Kbl5m5j0lcJUel6AxiKmZSMToTc5l+Z4WdQkHkNm3f7hOyn7bAwv1I6tLSCnfBKlWCAw6AAw/0j6Gpdm2Ij/ela3klbYVN5nafv5892jjn2LRpE2ZGjRo12LBhA0+NHJmz2ueaNTzwwANceeWV/Prrr1x44YWAHyMwlOBlBFVO27Vrx9SpU7OSvnr16mUNFQHQqlUrWrVqtV8xi4hkmTMHeveG339nw2230XnWLJaMG6e2hSIipZiSw0KqdMM1fly3khAVtfcpVM80KsoXxe1LUhduMTE5E7fQVKfOnkldblN+6+TZh3rxysjIIGXTJpKTk6lYsSL169fHOcfUqVNJSUkhOTk567Fdu3b07NmT1NRUunbtSnJyMps3b+bPP/8kNTWVYcOGMXToUHbu3MnQoUOpXbt2VoKXkJBA06ZNATjqqKP47rvviIuLo2bNmnsMFl+/fn169uwZjtMhIuVJWhrcey889JDv3fjLLzmobVva/PMP/x05km7duoU7QhERyYOSw8IaNcr3rrK/JWlFMeW2/+zJYmGn/dl2b0lr5cq5J3WxsWFL4HITSsyyJ3CxsbF06NABgLFjx/L7779nLU9OTqZVq1bcd999ALRo0YJVq1axLVuvoJdddhkvv/wyZkafPn1ITU3NWlalShWcc/Ts2ZOYmBjMjIYNG9KiRQvq1atHXFwcnTp1AqBu3bps376dmJiYXGOvUqUKxxxzTHGdGhGRvUtK8kNULF7Mwh49uHHNGl6pV494M5577rlwRyciIntRen6V7yMz6wo8DkQDzzrnHijWAwZJghQN5xwZ6elER0djZmzevJlNmzaRlpaWNe3cuZP27dsDsGDBAlasWEFaWho7duwgLS2N6Oho+vfvD8CkSZNYtGhRju1r1KjB448/DsCgQYOYO3du1rLt27fTuHFjZs2aBcBpp53G7m1uOnTowBdBQ9Nx48axYsUKqlatSrVq1ahatWpWyR3Aueeey86dO3Msb968edbyxMREDjjggKxlFbIlxlFRUXz66ad5niszyzMxFBEJu88+gzPOYEe1atx/wQWMmjaNOnXqsHr1auLj48MdnYiIFEBEJ4dmFg08BXQBVgPzzWy6c+774jqmc46UlBScc2RmZmY9Vq5cmQMPPJDMzEzWrl2bNT801ahRg4MOOoidO3eyfPnyHMudcxxyyCHUrl2b1NRUlixZkmPfmZmZHH744dSrV4/Nmzczf/78PbY/7rjjOOSQQ1i/fj2fffYZ6enpZGRkZE1dunShYcOGrFixgunTp+dYlp6eTt++fWnUqBELFixg8uTJeywfMmQIDRs25KOPPuL555/Pmh96nDBhAvXr12fy5Mk888wzeyyfPXs2NWvWZPTo0YwZMyZH8uacyyoRGzJkCGPHjs1xzqOjo0lPTwfgqaee4oUXXsixvEaNGlnJ4bvvvsv06dOJiYkhJiaGSpUq0bhx46x1MzMziY6OpmbNzL5aaAAAEOpJREFUmsTExFClSpUcyd2gQYPYvHlzVvJWrVo1ateunbV88eLFORK63Y0cOTLfv58WLVrs5S9MRKTolOgN1LZtWdijB30XLWLJa6+pbaGISASK6OQQSACWO+d+ATCzKUB3oFiTw+rVq+8x//bbb+ehhx4iJSWFBg0a7LH8vvvu495772XdunW5Jghjxoxh4MCBrFy5krZt2+6xfMKECVx99dX89NNPdMllZOEpU6Zw4YUXkpSUxAUXXLDH8hkzZtCwYUOSkpK49dZb91h+yimn0KhRI5YtW8a4ceOIjo4mOjqaChUqEB0dzYABA2jYsCF//fUXiYmJOZZlT97Al4BVrFgxx/JQr7jNmzfnvPPOy5G8hapTAlxyySW0atUqa3locs5hZgwbNozbbrsta7vQFDJ58uQ93lt2jzzySL7Lczt32eWXGIqIlCYlfgO1cmXGV6vG35s2MX36dLUtFBGJQBE9lIWZ9QK6OueuDl5fDrR1zg3Ybb1+QD+ARo0anbBq1ap9PqZzjjFjxhAVFYWZERUVRVRUFK1ataJDhw7s2LGDiRMnZs0PTS1btqRly5Zs27aN6dOnZ80P7eOYY46hadOmbNmyhTlz5uTYd1RUVNY4cykpKSxatGiP7Zs0aUKtWrXYsmULq1atykrKQklc7dq1qVKlCjt27GDbtm17LA/tS0SkrCjvQ1mYWXtgmHPujOD1XQDOuVG5rV8UQ1mkpKSQnp6u0kIRkVIur+/IcpEcZlcs4ziJiEipo+Rw79+RRXnzVEREIkde35FR4QimCK0BGmZ73SCYJyIiInvhnBvvnGvtnGudvX21iIiUT5GeHM4HDjezQ82sEnARMD3MMYmIiJQGuoEqIiKFEtHJoXMuHRgAzAJ+AF5zzi0Nb1QiIiKlgm6giohIoUR814vOufeA98Idh4iISGninEs3s9AN1Gjged1AFRGR/ER8cigiIiK50w1UEREpjIiuVioiIiIiIiJFQ8mhiIiIiIiIKDkUERERERERJYciIiIiIiKCkkMRERERERFByaGIiIiIiIig5FBERERERERQcigiIiIiIiIoORQRERERERGUHIqIiIiIiAhgzrlwx1CizOwvYNVus6sDm3NZPa/5BwN/F3FoRSGveEvDvgu7fUHXL8h6+a2zL8t0/Yt/+3Bf/0i79lB817+kr31httnbeo2dc7ULeexyK4/vx8IozZ+PglD84aX4w0vxh1c44s/9O9I5V+4nYHwh5yeGO+bCxFsa9l3Y7Qu6fkHWy2+dfVmm61/2r3+kXfvivP4lfe2L+vprKrmpNH8+FH/pnxS/4lf84Y/DOadqpYF3Cjm/tCrOePd334XdvqDrF2S9/NbZ12Wlka5/4dcpK599KL6YS/raF2abSLxOIiIipVa5q1ZaFMws0TnXOtxxSHjo+pdfuvYieYv0z4fiDy/FH16KP7xKU/wqOdw348MdgISVrn/5pWsvkrdI/3wo/vBS/OGl+MOr1MSvkkMRERERERFRyaGIiIiIiIgoORQRERERERGUHIqIiMh+MLOuZvaTmS03szvDHc/emFlDM/vEzL43s6VmdnMwf5iZrTGzRcF0VrhjzYuZrTSzJUGcicG8g8zsQzNbFjzWDHecuTGzI7Kd40Vmlmxmt5Tm829mz5vZejNLyjYv1/Nt3hPB5+E7Mzs+fJFnxZpb/A+Z2Y9BjG+aWY1gfryZpWa7Ds+EL/KsWHOLP8+/FzO7Kzj/P5nZGeGJepc84n81W+wrzWxRMD/s519tDkVERGSfmFk08DPQBVgNzAcuds59H9bA8mFm9YH6zrmFZlYVWACcC1wAbHHOPRzWAAvAzFYCrZ1zf2eb9yCw0Tn3QJCk13TO3RGuGAsi+PtZA7QFrqCUnn8zOwnYArzknDs6mJfr+Q6SlBuBs/Dv63HnXNtwxR7Emlv8pwOznXPpZjYaIIg/HpgRWq80yCP+YeTy92JmLYDJQAJwCPAR0Mw5l1GiQeeMaY/4d1v+CLDZOTe8NJx/lRwWATM7wMxeNLMJZnZpuOORkmNmh5nZc2Y2NdyxSMkzs3ODz/2rwRetSHmTACx3zv3inNsBTAG6hzmmfDnn1jrnFgbPU4AfgLjwRlUkugMvBs9fxCe8pd1pwArn3KpwB5If59wcYONus/M6393xSYBzzn0D1AhuSIRNbvE75z5wzqUHL78BGpR4YAWUx/nPS3dginMuzTn3K7Ac/38qbPKL38wMf2NqcokGlQ8lh3nIrQg4mJ9b9ZkewFTn3DXAOSUerBSpwlz74AfRVeGJVIpDIa//W8Hn/lrgwnDEKxJmccDv2V6vJoISreAufStgbjBrQFDN7vnSWi0z4IAPzGyBmfUL5tV1zq0Nnv8J1A1PaIVyETl/FEfK+Ye8z3ckfiauBN7P9vpQM/vWzD4zs07hCqoAcvt7ibTz3wlY55xblm1eWM+/ksO8TQS6Zp8RVH94CjgTaAFcHBRfN2DXH2LYiq2lyEyk4Ndeyp6JFP76DwmWi0iEMLMDgWnALc65ZGAc0AQ4DlgLPBLG8Pamo3PuePz/pBuCamtZnG8zVKrbDZlZJfwN9deDWZF0/nOIhPOdFzMbDKQDrwSz1gKNnHOtgFuB/5lZtXDFl4+I/XvZzcXkvEES9vOv5DAPeRQB51V9ZjW7iuN1TiNcIa+9lDGFuf7mjQbeD1VTEyln1gANs71uEMwr1cysIj4xfMU59waAc26dcy7DOZcJTCDMVdHy45xbEzyuB97Ex7ouVH0xeFwfvggL5ExgoXNuHUTW+Q/kdb4j5jNhZn2Bs4FLgwSXoDrmhuD5AmAF0CxsQeYhn7+XSDr/FfC1D18NzSsN51+JTOHkVVT9BtDTzMYB74QjMCl2uV57M6sV9CTVyszuCk9oUgLy+uzfCPwL6GVm14YjMJEwmw8cbmaHBiVBFwHTwxxTvoI2Ps8BPzjnxmSbn71d2HlA0u7blgZBPwdVQ8+B0/GxTgf6BKv1Ad4OT4QFlqPEJFLOfzZ5ne/pQO/g5mE7fEcja3PbQTiZWVfgP8A5zrlt2ebXDmrLYGaHAYcDv4Qnyrzl8/cyHbjIzGLM7FB8/PNKOr4C+hfwo3NudWhGaTj/FUryYGWVc24rvpctKWeCuztKCsop59wTwBPhjkMkXIKeDgcAs4Bo4Hnn3NIwh7U3HYDLgSUWdB8P3I2vLn4cvnrgSqB/eMLbq7rAmz7HpQLwP+fcTDObD7xmZlcBq/CdXJRKQVLbhZzn+MHSev7NbDJwCnCwma0GhgIPkPv5fg/fU+lyYBul4PdhHvHfBcQAHwZ/S984564FTgKGm9lOIBO41jlX0M5gikUe8Z+S29+Lc26pmb0GfI+vLntDOHsqhdzjd849x55tbqEUnH8NZZEP2607WTNrDwxzzp0RvL4LwDk3KlwxSvHQtS/fdP1FRESkPFK10sKJuOozUmR07cs3XX8REREp85Qc5iEoAv4aOMLMVpvZVcF4MKHqMz8Ar0VA9RkpJF378k3XX0RERMorVSsVERERERERlRyKiIiIiIiIkkMRERERERFByaGIiIiIFFAwvu+iYPrTzNZke10pjHHdYGaXFnKbhmb26t7XLNC+bjWzytlezwqNR1kE+25tZv+Xz/J6ZvZeURxLRG0ORURERKTQzGwYsMU59/Bu8w3/GzMzLIGFQTB+3dHOuU3FsO83gSH5dYRmZi8DY51zc4v6+FK+qORQRERERPaLmTU1s+/N7BVgKVDfzMabWaKZLTWze7Otu9rMhpnZt2b2nZk1C+afamaLg1LIhWZ2gJn9y8w+MbPpZvaLmY0ws95mNj/YNj7YdoSZ3RI8HxjE8p2ZTcpn303NbFGwvIqZvWhmS4LlJwXzrzazqUFJ4DIz22N8WzMbCNQBPjezj7K9xxrBMZLM7GUz+9nMXjKzM8zsq2B/rYP1DzSziWY2Lzgv3YL51YEjQolhbu8jCOMtoFAlpyK5qRDuAERERESkTGgO9HbOJQKY2Z3OuY1mVgH4xMymOue+D9Zd55xrZWY3AbcC1wKDgH7OublmdiCwPVi3JXAksBlYCTztnGtjZrfhhxm6fbc4/gM0ds7tMLMawby89h1yE5DmnDvGzI4C3jOzw7Md/wRgJ/CzmT3pnPsjtKFz7tEglk55lBweAVwA/AgsBLY75040s57AnUAv4F5gpnOur5nVBOaa2YdAArAk277yeh+JwJBcji1SKCo5FCmFzGyUmXU2s3PN7K5g3kQz+zXb3cL2+7DfeDNLKuT6lxT2OCIiUi6tCCWGgYvNbCE+IToSaJFt2RvB4wIgPnj+JfC4md0IVHPOZQTz5zrn1jnntgO/4MecBZ80hbbNbikwKWiDuHMv+w7pCEwCCErp/gCaBss+cs4lO+dS8Qleo/xPwx6WO+e+D6rZfg98nEv8pwODg5LMT4DKwXHqA39l21de72M9cEgh4xLZg5JDkdKpLfANcDIwJ9v8Qc654/B3GvNsnF6E4gElhyIiUhBbQ0+CUrebgVOdc8cCM/EJT0ha8JhBUJPNOTcC6AccCHyTreQuLdt2mdleZ5J7LbgzgGeANsA8M4vOZ98Fkf34WfHu4/Z5xW/Auc6544KpkXPuZyCVbOctn/dROVhXZL8oORQpRczsITP7Dv+F9jVwNTAue1uNwByCO5pmdk3Q9mKxmU0zs9hgfl0zezOYv9jMTtztWIcF7RramFl0cOxQG47+wWoPAJ2C0sqBZnZU0B5iUbBeYb5cRUSk/KgGpADJZlYfn7Dly8yaOOe+c86Nwpc2HlHYg5pZNNDAOTcbX730YCC2APv+nKDNnpkdiS+xW16IQ6cA+9M76SzgxtALM2sVPP2BXSWY+Z2jZkCBawaJ5EXJoUgp4pwbBFwFTMQniN855451zg3fbdVu7GqD8IZzro1zriX+S+SqYP4TwGfB/OPx1WwAMLMjgGlAX+fc/GCbzc65NsFxrzGzQ/EllJ8HdzEfxbcJeTwovWwNrC7aMyAiImXEQnwVyh+Bl/DVIffm9qDzlu+ALcAH+3DcCsD/gn0sBB52zqUUYN9PAlXMbAnwCr7t5I5CHHc88FGoQ5p9cB9wQNAhzlJgWDB/KVA7W8czeb2PzsC7+3hskSwaykKklAlK7TKAucDNzrmrg/kT8dVMN+PbHwx0ziWZ2cnACKAGvprJLOfctWb2F/7uaVq2fccH+/0H6BHqGMDMpgLHAtuCVasD/YEdwO3OubOD9S4BBuO/6N9wzi0rptMgIiIigJkNAv5yzk3MY7nhSz7/7ZzbXJKxSdmj3kpFSgkzOw5fYtgA+BuI9bNtERDqfGaQc27qbptOxLdTWGxmfYFT9nKozcBv+Mb3oV7jDLjROTcr+4pmlmNfzrn/mdlc4N/4ntz6B1V3REREpHiMBXrks7wO8KASQykKqlYqUko45xYF1TV/xvfoNhs4I6jSmV8j86rAWjOrSM4xjj4GrgPfBiMYKwl8aeB5QO9sPZHOAq4L9oGZNQuqsORoQ2FmhwG/OOeeAN7GlzaKiIhIMXHOpTrnXsln+Trn3PSSjEnKLpUcipQiZlYb+Mc5l2lmzbONB5Wfe/BVRf8KHkPJ3M3AeDO7Cl9N9TpgLYBzbquZnQ18aGZbgGfxPZMuDKqn/AWcC3wHZJjZYnwJZQxwuZntBP4ERu7/uxYRERGR0kBtDkVERERERETVSkVERERERETJoYiIiIiIiKDkUERERERERFByKCIiIiIiIig5FBEREREREZQcioiIiIiICEoORUREREREBPh/dL3tZwa108QAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#generate figure\n",
+    "fig = plt.figure(figsize=(15, 7))\n",
+    "    \n",
+    "gs = fig.add_gridspec(1, 2)\n",
+    "axs = []\n",
+    "axs.append(fig.add_subplot(gs[0]))\n",
+    "axs.append(fig.add_subplot(gs[1]))\n",
+    "max_rand = 10\n",
+    " \n",
+    "#different transmission times\n",
+    "transm_time = np.arange(1, 200, 20)\n",
+    "\n",
+    "#number packet that we want to send to earth\n",
+    "packet_numbers = [2**i for i in range(10)]\n",
+    "\n",
+    "#temp variables needed for plots\n",
+    "arrival_times = np.zeros((len(packet_numbers), max_rand))\n",
+    "outputs = np.zeros((len(packet_numbers), max_rand))\n",
+    "outputs_ttr = np.zeros((len(packet_numbers), max_rand))\n",
+    "arrival_times_ttr = np.zeros((len(packet_numbers), max_rand))\n",
+    "\n",
+    "for sample in tqdm(range(max_rand)):\n",
+    "\n",
+    "    #generating a new configuration\n",
+    "    nodes = generate_network(n, r_max)\n",
+    "    A = adjacency_matrix(nodes, tau_max)\n",
+    "    At = disable_links(A, 100000, slot_time, mode = \"unstable\")\n",
+    "\n",
+    "    #getting the arrival times as the number of packets changes\n",
+    "    for i in range(len(packet_numbers)):\n",
+    "\n",
+    "        packets = np.random.randint(1, n, (packet_numbers[i], 2))\n",
+    "        packets[:, 1] = 0\n",
+    "\n",
+    "        #greedy routing results\n",
+    "        results = greedy_routing(At, packets, transmission_time, delta_time)\n",
+    "\n",
+    "        #epidemic routing results\n",
+    "        packet_arrive, _, vulnerable = epidemic(At, transmission_time, slot_time, packets)\n",
+    "\n",
+    "        arrival_times_greedy = np.array([packet.arrival_times[-1] for packet in results])\n",
+    "        outputs[i, sample] = np.max(arrival_times_greedy)\n",
+    "        arrival_times[i, sample] = vulnerable[0] + transmission_time\n",
+    "\n",
+    "    #getting the arrival times as the transmission time changes\n",
+    "    for i in range(len(transm_time)):\n",
+    "\n",
+    "        packets = np.random.randint(1, n, (100, 2))\n",
+    "        packets[:, 1] = 0\n",
+    "\n",
+    "        #greedy routing results\n",
+    "        results = greedy_routing(At, packets, transm_time[i], delta_time)\n",
+    "\n",
+    "        #epidemic routing results\n",
+    "        packet_arrive, _, vulnerable = epidemic(At, transm_time[i], slot_time, packets)\n",
+    "\n",
+    "        arrival_times_greedy = np.array([packet.arrival_times[-1] for packet in results])\n",
+    "        outputs_ttr[i, sample] = np.max(arrival_times_greedy)\n",
+    "        arrival_times_ttr[i, sample] = vulnerable[0] + transm_time[i]\n",
+    "\n",
+    "ttx_tot = np.array(packet_numbers)*transmission_time\n",
+    "arrival_times_greedy = np.mean(outputs, axis = 1)\n",
+    "axs[0].semilogx(packet_numbers, np.mean(arrival_times, axis = 1), c= \"blue\")\n",
+    "axs[0].semilogx(packet_numbers, arrival_times_greedy, c=\"red\")\n",
+    "\n",
+    "axs[0].semilogx(packet_numbers, ttx_tot, linestyle=\"--\", color=\"black\")\n",
+    "axs[0].set_ylabel(\"Time(s)\")\n",
+    "axs[0].set_xlabel(\"#Packets\")\n",
+    "axs[0].set_title(\"Mean arrival time\")\n",
+    "axs[0].legend([\n",
+    "    \"Arrival time(epidemic)\",\n",
+    "    \"Arrival time(greedy)\",\n",
+    "    \"Total transmission time\"\n",
+    "])\n",
+    "\n",
+    "arrival_times_greedy = np.mean(outputs_ttr, axis = 1)\n",
+    "ttx_tot = np.array(transm_time)*100\n",
+    "axs[1].plot(transm_time, np.mean(arrival_times_ttr, axis = 1), color = \"blue\")\n",
+    "axs[1].plot(transm_time, arrival_times_greedy, color = \"red\")\n",
+    "axs[1].plot(transm_time, ttx_tot, color=\"black\" ,linestyle=\"--\")\n",
+    "axs[1].set_ylabel(\"Time(s)\")\n",
+    "axs[1].set_xlabel(\"Transmission time(s)\")\n",
+    "axs[1].set_title(\"Mean arrival time\")\n",
+    "axs[1].legend([\n",
+    "    \"Arrival time (epidemics)\",\n",
+    "    \"Arrival time (greedy)\",\n",
+    "    \"Total transmission time\",\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can observe the arrival time with the epidemic approach increases much faster than with the greedy one in both cases. This confirms our expectations, because since the beginning we knew that the greedy algorithm was the one with the strictest hypothesis but also the most efficient. \n",
+    "\n",
+    "In particular, as expected the epidemic algorithm performs really poorly in case many packets need to be forwarded because more packets generate a lot of duplicates this does not happen for the greedy algorithm so even if some packets might follow a better path (in the epidemic case), the extra load generated by the network is too much for the algorithm to compete with the greedy one.\n",
+    "\n",
+    "# Conclusions\n",
+    "\n",
+    "Here we want to sum up the results of our efforts by recapping which were the initial objectives and how the two \n",
+    "different approaches tackled the problem. \n",
+    "\n",
+    "So our goal was to find an effective routing algorithm in the scenario of Delay Tolerant Networks, where nodes are \n",
+    "located very far from each other and the links are not always working. \n",
+    "\n",
+    "The first strategy which we studied and implemented is a greedy algorithm which turned out to be very efficient and \n",
+    "probably the best type of approach when dealing with DTNs.\n",
+    "We can say that this algorithm is very efficient because Earth (which is the destination node) is always busy in the\n",
+    "sense that there are almost no idle times for the destination node which is always receiving packets.\n",
+    "\n",
+    "\n",
+    "The two main reasons why we also tested the epidemic algorithm were\n",
+    "\n",
+    "1) It needs much less information about the system to work properly.\n",
+    "\n",
+    "2) We thought that because packets are sent everywhere some packets would arrive to Earth faster than they would with the greedy algorithm, this because the greedy algorithm does not keep queues in consideration when computing the best path meaning that some packets may be forced to take strange routes.\n",
+    "Unfortunately the amount of duplicate packets was more than enough to overcome this effect making the epidemic algorithm actually way worse.\n",
+    "\n",
+    "In [1] the authors actually express very clearly the fact that in many cases the epidemic algorithm becomes unfeasible because it fills up the buffers and it generates too many packets.\n",
+    "From the simulations though we saw that for small networks where the topology can not be known in advance, this approach might actually work decently well as it takes all the packets to destination."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "packets = packets_greedy\n",
+    "A = initial_A\n",
+    "coord_nodes = np.copy(initial_nodes)\n",
+    "\n",
+    "n_pkt = len(packets)\n",
+    "coord = coord_nodes[[pkt.source for pkt in packets]]\n",
+    "path = [[pkt.source] + pkt.route for pkt in packets]\n",
+    "arrival_times = [pkt.arrival_times for pkt in packets]\n",
+    "start_times = [[pkt.arrival_times[j] - A[path[i][j],path[i][j+1]] for j in range(len(pkt.route))] for i,pkt in enumerate(packets)]\n",
+    "\n",
+    "delay = max([pkt.arrival_time for pkt in packets])\n",
+    "\n",
+    "next_hop = coord_nodes[[x.route[0] for x in packets]]\n",
+    "diff = next_hop - coord\n",
+    "dist = np.linalg.norm(diff,axis=1)\n",
+    "times = dist/c\n",
+    "vel = (diff.T / times).T\n",
+    "\n",
+    "#keep a fixed copy to be able to run the animation multiple times\n",
+    "fixed_coord = np.copy(coord)  \n",
+    "fixed_vel = np.copy(vel)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support. ' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>');\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option);\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\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 overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>');\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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width=\"800\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Animation: packets\n",
+    "%matplotlib notebook\n",
+    "%matplotlib notebook \n",
+    "\n",
+    "coord = np.copy(fixed_coord)##\n",
+    "vel = np.copy(fixed_vel)\n",
+    "\n",
+    "step = np.zeros(n_pkt, dtype=int) #current step\n",
+    "\n",
+    "done = [False for x in packets]\n",
+    "\n",
+    "fig = plt.figure(figsize=(8,7))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "# Setting the axes properties\n",
+    "ax.set_xlabel('X(m)')\n",
+    "\n",
+    "ax.set_ylabel('Y(m)')\n",
+    "\n",
+    "ax.set_zlabel('Z(m)')\n",
+    "\n",
+    "title = ax.set_title('Time = ')\n",
+    "\n",
+    "snodes = [300] + [110 for x in packets]\n",
+    "colormap = plt.cm.gist_rainbow\n",
+    "Ncolors = 9\n",
+    "cnodes = [\"black\"] + [colormap(int(x*colormap.N/Ncolors)) for x in range(Ncolors)]\n",
+    "#cnodes = [\"black\",\"lightblue\",\"lightskyblue\",\"lightsteelblue\", \"cyan\",\"blue\",\"darkblue\" ,\"nightblue\",\"darknightblue\",\"darkblue\"]\n",
+    "\n",
+    "pkt_color = [cnodes[pkt.source] for pkt in packets]\n",
+    "graph = ax.scatter(coord[:,0],coord[:,1],coord[:,2],s=20,color=pkt_color,marker=\"s\")\n",
+    "\n",
+    "ax.scatter(coord_nodes[:,0],coord_nodes[:,1],coord_nodes[:,2],s=snodes,color=cnodes,alpha=1)\n",
+    "\n",
+    "ani4 = animation.FuncAnimation(fig, update_graph, interval=10, blit=False,\n",
+    "                              fargs=(graph,coord,coord_nodes,start_times,arrival_times,step,n_pkt,vel,path,done,title,2))\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bibliography\n",
+    "\n",
+    "[1] Jain, Sushant, Kevin Fall, and Rabin Patra. \"Routing in a delay tolerant network.\" Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications. 2004.<br>\n",
+    "[2] Evan P.C. Jone, Paul A.S. War. \"Routing Strategies for Delay-Tolerant Networks.\""
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -44,7 +3954,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.4"
+   "version": "3.7.5"
   }
  },
  "nbformat": 4,
diff --git a/figures/dijkstra.gif b/figures/dijkstra.gif
new file mode 100644
index 0000000..b57a23b
Binary files /dev/null and b/figures/dijkstra.gif differ
diff --git a/figures/routing.svg b/figures/routing.svg
new file mode 100644
index 0000000..241e4ab
--- /dev/null
+++ b/figures/routing.svg
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diff --git a/link.mp4 b/link.mp4
new file mode 100644
index 0000000..5cfbd78
Binary files /dev/null and b/link.mp4 differ
diff --git a/members.txt b/members.txt
index 68de65c..5e960c9 100644
--- a/members.txt
+++ b/members.txt
@@ -1,5 +1,5 @@
 Surname Name
-Gottardis Marco
+Gottardis Marco eccomi!
 Mari Daniele
 Casarin Nicola
 Camaioni Martina
diff --git a/nodes.mp4 b/nodes.mp4
new file mode 100644
index 0000000..f4c69a3
Binary files /dev/null and b/nodes.mp4 differ
diff --git a/packets.mp4 b/packets.mp4
new file mode 100644
index 0000000..4785ab7
Binary files /dev/null and b/packets.mp4 differ
diff --git a/utility_functions/animation_utilities.py b/utility_functions/animation_utilities.py
new file mode 100644
index 0000000..61d8f4a
--- /dev/null
+++ b/utility_functions/animation_utilities.py
@@ -0,0 +1,72 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from matplotlib import collections as mc
+import mpl_toolkits.mplot3d.axes3d as p3
+import matplotlib.animation as animation
+
+#returns a list of active links
+def get_links(A,nodes):
+    n = A.shape[0]
+    return [np.array([nodes[i],nodes[j]]).T for i in range(n) for j in range(i+1) if A[i,j]!=np.inf]
+
+#checks if a link is active 
+def check_active(link, link_list):
+    for x in link_list:
+        if np.array_equal(x, link):
+            return True
+    return False
+
+def update_lines(num, dataLines, lines, active_links, title) :
+
+    title.set_text("Time = {}s".format(num))
+    
+    for line, data in zip(lines, dataLines) :
+        line.set_data(data[0:2, :])
+        line.set_3d_properties(data[2, :])
+        line.set_color("black")
+        if not check_active(data, active_links[num]):
+              line.set_color("red")
+    return lines
+
+def get_vel(node1, node2, c=3e8):
+    diff = node2 - node1
+    dist = np.linalg.norm(diff)
+    time = dist/c
+    vel = diff/time
+    return vel
+
+def update_graph(num,graph,coord,coord_nodes,start_times,arrival_times,step,n_pkt,vel,path,done,title,speed=3):
+    
+    time = num * speed
+    
+    for i in range(n_pkt):
+        if done[i]:
+            continue
+  
+        start = start_times[i][step[i]]
+        end = arrival_times[i][step[i]]
+        
+        if time < start:
+            status = "waiting"
+        elif start <= time <= end:
+            status = "midair"
+        elif time > end:
+            status = "arrived"
+            
+        if status == "midair":
+            coord[i,:] = coord[i,:] + vel[i,:] * speed    
+        elif status == "waiting":
+            pass
+        elif status == "arrived":
+            try:
+                step[i] += 1
+                vel[i,:] = get_vel(coord_nodes[path[i][step[i]],:], coord_nodes[path[i][step[i]+1],:])
+                start = start_times[i][step[i]]
+                end = arrival_times[i][step[i]]
+            except IndexError:   #no more steps
+                done[i] = True
+
+    
+    graph._offsets3d = (coord[:,0],coord[:,1],coord[:,2])
+    title.set_text("Time = {}s".format(num))
diff --git a/utility_functions/classes.py b/utility_functions/classes.py
new file mode 100644
index 0000000..8538962
--- /dev/null
+++ b/utility_functions/classes.py
@@ -0,0 +1,36 @@
+class Packet():
+    
+    def __init__(self, index ,destination):
+        
+        #final destination of the packet
+        self.destination = destination
+        
+        #arrival time at the next hop
+        self.arrival_time = -1
+        
+        #index of the next hop
+        self.next_hop = -1
+        
+        #index that identifies the packet, used for debugging purposes
+        self.id = index
+
+        #starting node
+        self.source = -1
+        
+        self.route = []
+        
+        self.arrival_times = []
+    
+    #function that is called when str(packet) is called
+    def __str__(self):
+        return f"id: {self.id}, next hop: {self.next_hop}, destination: {self.destination}"
+
+class Node():
+    
+    def __init__(self, packets, index):
+        
+        #queue of the packets
+        self.packets = packets
+        
+        #index of the node
+        self.id = index
diff --git a/utility_functions/epidemics.py b/utility_functions/epidemics.py
new file mode 100644
index 0000000..8417349
--- /dev/null
+++ b/utility_functions/epidemics.py
@@ -0,0 +1,289 @@
+import numpy as np
+from classes import Packet, Node
+from tqdm.notebook import tqdm
+import math
+
+#check if the link is okay, return true if the link is up, false otherside
+# t = current time
+# sender = node that sends packets
+#receiver = node that recives packets
+def check_link(At, sender,receiver,t, times):
+    cell = int(t / times["slot"])
+    return At[sender, receiver, cell] != np.inf
+
+def get_propagation_times(At):
+    return np.min(At, axis = 2)
+
+#return True if the packet arrives, the packet will be discarded that happens 
+#because the link fails during the propagation time + transmission time
+# t = current time
+# sender = node that sends packets
+#receiver = node that recives packets
+def is_arrive(At, sender, receiver, t, times):
+    propagation_t = times["tp"][sender, receiver]
+    total_time = times["ttx"] + propagation_t + t
+    
+    if total_time > At.shape[2]*times["slot"]:
+        raise Exception('I want more information about the next interval time')
+        
+    initial_cell = int(t / times["slot"])
+    final_cell = int(math.ceil(total_time / times["slot"]))
+    return np.max(At[sender, receiver, initial_cell:final_cell]) != np.inf
+
+
+# update vulnerable_time in this way: if the link is okay set the vunerable_time 
+#for the sender,if the packet arrives set the vulnerable_time for receiver
+# t = current time
+# sender = node that sends packets
+#receiver = node that recives packets
+def set_vulnerable_time(At, sender, receiver, t, times):
+    
+    vulnerable_sender = t
+    
+    vulnerable_receiver = times["vulnerable"][receiver]
+    
+    if is_arrive(At, sender,receiver,t, times):
+        vulnerable_sender = max(t + times["ttx"], vulnerable_receiver + times["ttx"] - times["tp"][sender, receiver])
+        vulnerable_receiver = vulnerable_sender + times["tp"][sender, receiver]
+    
+    
+    return vulnerable_sender, vulnerable_receiver
+
+        
+#check before starts the transmission there are same collision of one 
+#packet on sender and resciver side
+#return boolean value
+# t = current time
+# sender = node that sends packets
+#receiver = node that recives packets
+def is_collision(sender, receiver, t, times):
+    
+    propagation_t = times["tp"][sender, 0]
+    t_receiver = t + propagation_t
+    
+    #if the starting time is withing a transmission time from the vulnerable time
+    #then a collision occurs
+    
+    transmission_collision = abs(t - times["vulnerable"][sender]) < times["ttx"]
+    receive_collision = abs(t_receiver - times["vulnerable"][receiver]) < times["ttx"]
+    
+    return transmission_collision or receive_collision
+
+
+
+#send the packets to ather nodes,update n_packet and packet_trace
+#if the link is okay and the packet reaches destination
+# x = sender node in adjacent matrix
+
+def add_packet(At, t, sender, times, n_packet, packet_trace, anim_nodes):
+
+    n_node_tot = At.shape[0]
+    gen = [i for i in range(1,n_node_tot) if i != sender]
+    for receiver in gen:
+        packets = n_packet[sender]
+        time_1 = t
+        time = t
+        packet_counter = 1
+        for packet in packets:
+            if (
+                    not packet in packet_trace[receiver] and 
+                    check_link(At, sender, receiver, time, times) and 
+                    not is_collision(sender, receiver, time, times)
+               ):
+                times["vulnerable"][[sender,receiver]] = set_vulnerable_time(
+                    At, 
+                    sender,
+                    receiver,
+                    t, 
+                    times
+                )
+
+                if is_arrive(At, sender,receiver,time, times):
+                     
+                    n_packet[receiver].append(packet)
+                    packet_trace[receiver].append(packet)
+                    
+                    #using receiver-1 because packet_time does not contain the earth
+                    tp = times["tp"][sender, receiver]
+                    ttx = times["ttx"]
+                    
+                    if (times["packet"][packet][receiver-1] == 0):
+                        times["packet"][packet][receiver-1] = packet_counter*ttx + tp + time_1
+                    else:
+                        new_time = times["packet"][packet][receiver-1] + packet_counter*ttx + tp
+                        times["packet"][packet][receiver-1] = new_time
+                    time = time + times["ttx"]
+
+                    #data used for the animation it is just tracking all of the routes
+                    pack = Packet(packet, 0)
+                    pack.source = sender 
+                    pack.route.append(receiver)
+                    pack.arrival_times.append(packet_counter*ttx + tp + time_1)
+                    pack.arrival_time = packet_counter*ttx + tp + time_1
+                    anim_nodes[sender][packet].append(pack)
+                    
+                    packet_counter += 1
+
+    return times["vulnerable"], n_packet, packet_trace, times["packet"]
+                    
+                    
+                    
+#send the packets to earth if the link is up and update n_packet
+# x = sender node in adjacent matrix
+def arrive_to_earth(At, t, sender, times, n_packet, packet_trace, packet_arrive, anim_nodes):
+    
+    packets = n_packet[sender]
+    time = t
+    packet_counter = 1
+    for packet in packets:
+        if not is_collision(sender, 0, time, times):
+            times["vulnerable"][[sender, 0]] = set_vulnerable_time(At, sender, 0, time, times)
+            
+            if is_arrive(At, sender, 0, time, times):
+                n_packet[sender].remove(packet)
+                n_packet[0].append(packet)
+
+                packet_arrive.append(int(packet))
+                ttx = times["ttx"]
+                tp = times["tp"][sender, 0]
+                new_time = times["packet"][packet,sender-1] + packet_counter * ttx + tp 
+                times["packet"][packet][sender-1] = new_time
+                time = time + times["ttx"]
+                packet_counter += 1
+                
+                pack = Packet(packet, 0)
+                pack.source = sender
+                pack.route.append(0)
+                pack.arrival_times.append(new_time)
+                pack.arrival_time = new_time
+                anim_nodes[sender][packet].append(pack)
+
+    return times["vulnerable"], n_packet, packet_trace, packet_arrive, times["packet"]
+
+
+# At = adjacent matrix
+# A = propagation matrix
+# ttr = trasmission time
+# delta_time = slot_time
+# packets
+
+def convert_packets(packets, n_nodes):
+    
+    n_packet = [[] for _ in range(n_nodes)]
+    anim_nodes = [[[] for j in range(packets.shape[0])] for i in range(n_nodes)] 
+
+    for i in range(len(packets)):        
+        n_packet[packets[i,0]].append(i)
+
+    return n_packet, anim_nodes 
+
+# n_packet = every list is the code of node with the same index, and contains the ID packet that we want to send
+# n_node_tot = number of nodes, in our case is 10 including the earth
+# packet_trace = keep the trace of the road followed by each package
+# trasmision_time = time to trasmit a single packet
+# packet_arrive = [ID number of packets arrive to earth]
+# packet_arrive_dupl = [ID number of all packets arrive to earth]
+# vulnerable_time = [start_time,  collect the vunlerable time for each node]
+# slot_time = duration of each interval time
+# packet_time = each list rappresent a single packet: the value is the time it takes to get to the earth, the index indicates
+# from which node the packet is send to earth.
+def epidemic(At, transmission_time, slot_time, packets, return_anim = False):
+         
+    n_node_tot = At.shape[0]
+    n_packet_tot = packets.shape[0] 
+    n_packet, anim_nodes = convert_packets(packets, n_node_tot) 
+    #this is used to copy the list
+    packet_trace = [queue[:] for queue in n_packet]
+    
+    packet_arrive = []
+    vulnerable_time = np.zeros(n_node_tot)
+    #slot_time = delta_time
+    packet_time = np.zeros((n_packet_tot, n_node_tot-1))
+    times = {
+        "ttx": transmission_time,
+        "vulnerable": vulnerable_time,
+        "slot": slot_time,
+        "packet": packet_time,
+        "tp": get_propagation_times(At) 
+    }
+
+
+    #start time
+    for t in np.arange(0,At.shape[2]*slot_time,transmission_time):
+
+        #if all packet are received exit the cycle
+        if len(np.unique(n_packet[0])) == n_packet_tot:
+
+            # returning data for the animation if requested
+            if return_anim:
+                anim_packets = get_packets_for_animation(anim_nodes)
+                anim_packets.sort(key = lambda x: x.arrival_time)
+                return packet_arrive, packet_time, times["vulnerable"], anim_packets
+
+            return packet_arrive, packet_time, times["vulnerable"]
+
+        for i in range(1, n_node_tot):
+            #print('tempo che scorre',t)
+            if len(n_packet[i]) != 0:
+                if t != 0:
+                    for p in n_packet[i]:
+                        if packet_time[p, i-1] <= t:
+                            packet_time[p, i-1] += transmission_time
+            
+                # if the link to erath is down 
+                if not check_link(At, i, 0, t, times):
+                    if ( (t >= vulnerable_time[i] + transmission_time) == True):
+                    
+                        times["vulnerable"], n_packet, packet_trace, times["packet"] = add_packet(
+                                At,
+                                t,
+                                i, 
+                                times,
+                                n_packet,
+                                packet_trace,
+                                anim_nodes 
+                        )
+                    
+                else:
+                
+                    if t >= vulnerable_time[i] + transmission_time:
+                    
+                        #this is all just one line
+                        times["vulnerable"], n_packet, packet_trace, packet_arrive, times["packet"] = arrive_to_earth(
+                                At,
+                                t,
+                                i,
+                                times,
+                                n_packet,
+                                packet_trace,
+                                packet_arrive,
+                                anim_nodes 
+                        )
+                    
+        if len(n_packet) == 0:
+            break
+
+    return packet_arrive, packet_time, times["vulnerable"]
+
+def get_packets_for_animation(nodes):
+
+    packets = []
+    
+    for node in nodes:
+        for p_type in node:
+            if p_type is not None:
+                for packet in p_type:
+                    packets.append(packet)
+
+    return packets
+
+def remove_duplicates(packets):
+    packets.sort(key = lambda x:x.id)
+    previous = -1
+    result = []
+    for packet in packets:
+        if previous != packet.id:
+            previous = packet.id
+            result.append(packet)
+    result.sort(key = lambda x:x.arrival_time)
+    return result
diff --git a/utility_functions/graphics.py b/utility_functions/graphics.py
new file mode 100644
index 0000000..57fec98
--- /dev/null
+++ b/utility_functions/graphics.py
@@ -0,0 +1,39 @@
+import pylab as pl
+from matplotlib import collections  as mc
+import matplotlib.pyplot as plt
+import numpy as np
+### pkt_plot function
+#This function takes in input the times in which packets are recepted at destination and the transmission time
+#It enables us to visualize the moments in which we receive packets
+
+def plot_idle(packets, t_tx, axs = None, seed = None):
+
+    if seed is not None:
+        np.random.seed(seed)
+
+    recept_times = [x.arrival_time - t_tx for x in packets]
+    end_recept = [x.arrival_time for x in packets]
+    seg=[]
+    for i in range(len(packets)-1):
+        diff= recept_times[i+1]-end_recept[i]
+        if diff>0:
+            seg.append([(end_recept[i],0),(recept_times[i+1],0)])
+    
+    lines=[[(x,0),(x+t_tx,0)] for x in recept_times]
+    
+    lc = mc.LineCollection(lines, linewidths=15,color="lightskyblue", label = "Packet reception")
+    if axs is None:
+        _, axs = plt.subplots()
+
+    axs.add_collection(lc)
+
+    seg_c = mc.LineCollection(seg, linewidths=15,color="dimgray", label = "Idle time")
+    axs.add_collection(seg_c)
+    axs.set_xlim(0,(recept_times[-1]+t_tx)*1.01)
+    axs.set_ylim(-0.5,0.5)
+    axs.set_yticks([])
+    axs.set_xlabel("Time(s)")
+    axs.set_title("Active time vs idle time of Earth")
+    axs.legend()
+    
+    plt.show()
diff --git a/utility_functions/greedy_routing.py b/utility_functions/greedy_routing.py
new file mode 100644
index 0000000..91a22b2
--- /dev/null
+++ b/utility_functions/greedy_routing.py
@@ -0,0 +1,316 @@
+import numpy as np
+import math
+from classes import Packet, Node
+from tqdm.notebook import tqdm
+
+#used to check if the time exceeds the maximum time of the system
+def check_time_step(A, time):
+    
+    max_time_step = A.shape[2]
+    
+    if time >= max_time_step:
+        raise Exception("Maximum time limit exceded")
+
+# used to check if the link becomes unavailable before the packet is fully transmitted
+#weights = time series of the weight of the considered link
+#start = starting time
+#end = ending time
+def check_interval(weights, start, end):
+    return np.max(weights[int(start):int(end)]) != np.inf 
+
+#finds the next useful interval to send the packet if the link is down
+#current_time = earliest time when the packet can be sent
+#delta_time = time interval 
+#e2e = end to end delay
+#distances = weights of the edge during time
+def find_next_interval(current_time, delta_time, e2e, distances):
+    
+    current_delta = math.floor(current_time / delta_time)
+    #the last timeslot has to be considered as well so ceil is needed
+    ending_delta = math.ceil((current_time+e2e) / delta_time)
+    
+    #if we already have a good interval then the packet can be sent immediatly
+    if check_interval(distances, current_delta, ending_delta):
+        return current_time
+    #otherwise iterate until a good interval is found
+    else:
+        #time of the first np.inf after current_time
+        i = current_delta + np.argmin(distances[current_delta:ending_delta] != np.inf)
+        
+        #number of timesteps required to send a packet
+        delta_e2e = np.ceil(e2e / delta_time)
+        
+        #keep increasing i while either the max length is reached or a good interval is found
+        while not check_interval(distances, i, i + delta_e2e):
+            if i + delta_e2e >= distances.shape[0]:
+                raise Exception("One packet cannot be sent before the maximum time")
+            i += 1
+        
+        return i * delta_time
+
+#used to find the next node and the arrival times
+def find_next(At, starting_node, previous, times, delta_time, starting_time, max_time, earth=0):
+    
+    result = earth
+    current = earth
+    good_until = [] 
+
+    while current != starting_node:
+        result = current
+        current = int(previous[current])
+
+        #time slot when the node is reached
+        current_delta = math.floor(times[current]/delta_time)
+        #evolution of the link between source and destination from current_delta onwards 
+        future_evolution = At[current, result, current_delta:]
+
+        #if the link is disabled in the future
+        if np.max(future_evolution) == np.inf:
+            #the maximum sending time is equal to the starting time + the remaining time after the packet arrived
+            max_sending_time = starting_time + (current_delta + np.argmax(future_evolution == np.inf)) * delta_time - times[current] 
+            good_until.append(max_sending_time)
+        else:
+            good_until.append(max_time)
+
+    if times[result] > max_time:
+        raise Exception("Maximum time reached")
+        
+    return result, times[result], min(good_until)
+
+#performs a modified version of the dijkstra algorithm that adapts to this specific problem
+#A = adjacency matrix
+#starting_node = node sending the packet
+#ttr = transmission time
+#delta_time = timestep dimension
+#earth = earth node
+def DTN_dijkstra(A, starting_node, starting_time, ttr, tps, delta_time, earth = 0):
+    
+    N = A.shape[0]
+    
+    #setting the arrival time of the packet to each node to infinity
+    distances = np.ones(N) * np.inf
+    distances[starting_node] = starting_time
+    max_time = A.shape[2] * delta_time
+    
+    current = starting_node
+    visited_nodes = 0
+    
+    #previous step in the shortest path
+    previous = np.ones(N)*(-1)
+    previous[starting_node] = starting_node
+    
+    #if the source is the destination do nothing
+    if current == earth:
+        return starting_time, -1, np.inf 
+    
+    #keep going until the shortest path to earth is found
+    while current != earth:
+        
+        #arrival time of the packet in the current node
+        current_time = distances[current]
+        #time slot of the packet arrival
+        current_step = math.floor(current_time / delta_time)
+        
+        #checking if the packet arrives after the maximum time
+        check_time_step(A, current_step)
+        
+        #arrival times to each node if the message started from current node
+        new_dists = np.zeros(N)
+        
+        #compute the arrival time to each node
+        for i in range(N):
+            
+            #computing e2e delay as tp + ttr
+            tp = tps[current, i]
+            
+            if tp == np.inf:
+                new_dists[i] = np.inf
+            else:
+                e2e = tp + ttr
+            
+                #finding out when the packet can leave the current node to reach the next one
+                next_good = find_next_interval(current_time, delta_time, e2e, A[current, i, :])
+            
+                #finding out the arrival time of the packet to node i after passing from current
+                new_dists[i] = next_good + e2e
+        
+        new_dists[current] = current_time
+        
+        #if the arrival time is improved then the previous node is changed
+        previous[distances > new_dists] = current
+        
+        #setting the correct arrival time to each node
+        distances = np.minimum(distances, new_dists)
+        
+        visited_nodes += 1
+        sorting_indexes = np.argsort(distances)
+        
+        current = sorting_indexes[visited_nodes]
+    
+    return find_next(A, starting_node, previous, distances, delta_time, starting_time, max_time, earth)
+
+    #returns the queues of packets at each node
+def get_nodes(N, packets):
+    
+    #generate N empty queues
+    queues = [[] for i in range(N)]
+    
+    #add every packet to the queue corresponding to its starting node
+    for i in range(packets.shape[0]):
+        pkt = Packet(i, packets[i,1])
+        pkt.source = packets[i,0]
+        queues[packets[i,0]].append(pkt)
+    
+    #generating the Node objects
+    nodes = []
+    for i in range(N):
+        nodes.append(Node(queues[i], i))
+    
+    return nodes 
+
+#function used to sort the nodes based on the one that has the packet that
+#would arrive first to the next point
+def sorting_function(node):
+    
+    #if no packet is queued then the node has to be placed at the end of the sorted array
+    if len(node.packets) == 0:
+        return np.inf
+    else:
+        return node.packets[0].arrival_time
+
+#runs djikstra on each node assuming that a packet is queued and that
+#it is sent as soon as the sender can send it
+'''
+nodes = list of Node objects
+first_free_moment = array indicating the first available moment for a node to send/recieve 
+a packet
+'''
+def dijkstra_on_nodes(A, nodes, first_free_moment, ttr, tps, delta_time, next_hops, valid):
+    
+    #for each node
+    for source in range(len(nodes)):
+        
+        #if the node is not empty
+        if len(nodes[source].packets) > 0:
+            destination = nodes[source].packets[0].destination
+
+            #if the previous route is still valid then just update next_hop and arrival_time
+            if valid[source, destination] > first_free_moment[source]:
+                next_hop = int(next_hops[source, destination])
+                nodes[source].packets[0].next_hop = next_hop 
+                nodes[source].packets[0].arrival_time = first_free_moment[source] + tps[source, next_hop] + ttr
+
+            else: 
+                #compute dijkstra on the first node of the queue
+                next_hop, arrival_time, valid_until = DTN_dijkstra(
+                    A, 
+                    source, 
+                    first_free_moment[source], 
+                    ttr, 
+                    tps,
+                    delta_time, 
+                    earth = destination 
+                )
+                #update the information of the packet based on dijikstra result
+                nodes[source].packets[0].next_hop = int(next_hop)
+                nodes[source].packets[0].arrival_time = arrival_time
+
+                #update the best route from source to destination and its validity
+                next_hops[source, destination] = next_hops[destination, source] = next_hop
+                valid[source, destination] = valid[destination, source] = valid_until 
+
+    return nodes, next_hops, valid
+
+#checks if there is one node that is completely isolated from the rest
+#note that this does not notify the case where two nodes are connected
+#but are separated from the rest
+def check_nodes_connectivity(tps):
+    
+    #matrix saying if an edge is up or not
+    connectivity = tps
+    
+    #setting the diagonal to inf so later i can check if a whole column is equal to inf
+    connectivity[connectivity == 0] = np.inf
+    
+    #if there is a column that contains only inf it means that the node is disconnected
+    if np.max(np.min(connectivity, axis = 1)) == np.inf:
+        raise Exception("At least one of the nodes is never connected to the graph")
+    
+#packets = source and destination of every packet
+def greedy_routing(A, packets, ttr, delta_time, with_tqdm=False):
+    
+    if with_tqdm:
+        pbar = tqdm(total = packets.shape[0])
+
+    tps = np.min(A, axis = 2)
+    check_nodes_connectivity(tps)
+    N = A.shape[0]
+    nodes = get_nodes(N, packets)
+    #at the beginning all of the nodes can transmit a packet theoretically
+    first_free_moment = np.zeros(N)
+
+    #data needed to save some computation
+    #cell i,j constains the best next hop between i and j
+    next_hops = np.ones((N, N)) * (-1) 
+    #defines for how long the path will be the best
+    valid = np.ones((N, N)) * (-1) 
+
+    #number of packets that reached their destinatio
+    arrived_packets = 0
+    final_packets = []
+    
+    #while some packets aren't arrived yet
+    while arrived_packets != packets.shape[0]:
+        #find out the next step and the arrival time of all the first packets in the queues
+        nodes, next_hops, valid = dijkstra_on_nodes(A, nodes, first_free_moment, ttr, tps, delta_time, next_hops, valid)
+        #sort the nodes from the one that has the packet that would reach first the next hop
+        nodes.sort(key = sorting_function)
+        #packet to be eventually sent
+        pts = nodes[0].packets[0]
+        
+        #boolean value that tells if the packet was sent or not
+        sent = False
+        
+        #propagation time from current node to next hop
+        tp = tps[pts.next_hop, nodes[0].id]
+        #if the first bit of the packet reaches the reciever when the reiever is free
+        if first_free_moment[pts.next_hop] <= pts.arrival_time - ttr:
+            
+            #set the reciever as busy until the packet is fully arrived
+            first_free_moment[pts.next_hop] = pts.arrival_time
+            #set the sender as busy until the packet is fully sent
+            first_free_moment[nodes[0].id] = pts.arrival_time - tp            
+            sent = True
+            #send the packet by removing it from the sender queue
+            nodes[0].packets.pop(0)
+
+        else:
+            #if the reciever is not free set the sender as busy until it is sure that 
+            #when he sends the reciever will be idle
+            first_free_moment[nodes[0].id] = first_free_moment[pts.next_hop] - tp + 1e-7
+        
+        #sort the nodes back by id in order to add the packet to the reciever queue
+        nodes.sort(key = lambda node: node.id)
+        
+        #if a packet was sent
+        if sent:
+            
+            pts.route.append(pts.next_hop)
+            pts.arrival_times.append(pts.arrival_time)
+            
+            #if the packet reached its destination then remove it from the system
+            if pts.next_hop == pts.destination:
+                arrived_packets += 1
+                final_packets.append(pts)
+
+                if with_tqdm:
+                    pbar.update(1)
+            
+            #else append it to the queue of the reciever
+            else:
+                nodes[pts.next_hop].packets.append(pts)
+                
+    if with_tqdm:
+        pbar.close()
+
+    return final_packets
diff --git a/utility_functions/metrics.py b/utility_functions/metrics.py
new file mode 100644
index 0000000..f77be73
--- /dev/null
+++ b/utility_functions/metrics.py
@@ -0,0 +1,67 @@
+from scipy.sparse.csgraph import dijkstra
+###DELAY FUNCTION###
+# 
+##This function takes in input 3 parameters: 
+# 1) scheduling is defined as a list of lists in the sense that it is a list that contains the scheduling of 
+#every node which corresponds to the istants of time a node has to forward packets that are in its queue. For 
+#every node these "forwarding istants" are compactly put in a list.
+# 2) routing is a slightly more complicated data structure; Here for every node we place a list whose elements 
+#are the lists containg the hops that every packet in the queue of a specific node has to go through in order to 
+#reach its destination
+# 3) n_pkts is the total number of packets that has to be forwarded in our network
+
+def delay(A, scheduling,routing,n_pkts, T_tx):
+
+    #The waiting list is obtained as a result of the concatenation of all the lists present in scheduling
+    waiting = [wait for node in scheduling for wait in node] 
+    #The routes list instead of having all the forwarding times, it contains all the hops every pkt has to do
+    routes = [path for node in routing for path in node]
+    
+    longest = 0
+    
+    for i in range(n_pkts):
+        path = routes[i]
+        #Here with tleft we are not taking into account the fact that a packet may be queued in some intermediate 
+        #nodes on its remaining way to destination. Anyway this does not represent a problem given the fact that 
+        #our scheduling provides us not just with the information about when a packet is sent from its 
+        #source node but also all the instants it will be forwarded by intermediate nodes.
+        #As we see tleft is made of the sum of transmission and propagation time for every remaining hop
+        tleft = sum([T_tx + A[path[j],path[j+1]] for j in range(len(path)-1)]) 
+        
+        
+        #The total time is given by the sum of the time we wait before forwarding and tleft which can be seen as
+        #the minimum time it takes to go through the rest of the path
+        t_tot = waiting[i] + tleft 
+        
+        #Here we are looking for the last packet to arrive at destination
+        longest = max(longest,t_tot)  
+    return longest
+
+###deviations: given an optimal routing (that in our case is the one determined by Dijkstra) this function returns 
+#the fraction of times a packet doesn't follow the route which was pre-established by the algorithm
+def deviations(A, packets):
+    
+    indices = [packet.destination for packet in packets]
+    _, predecessors = dijkstra(A, directed=False, indices=indices, return_predecessors=True)
+    n_deviations = 0
+    
+    for packet in packets:
+        #print(packets)
+        route = packet.route
+        correct = True
+        for i in range(len(route) - 2, -1, -1):
+            #if the route is correct from the second hop onward than it must
+            #be correct because having additional hops would increase the distance
+            #print(correct, route[i] != predecessors[packet.destination, route[i+1]])
+            if correct and route[i] != predecessors[packet.destination, route[i + 1]]:
+                correct = False
+                n_deviations += 1
+
+
+    return n_deviations/len(packets)
+
+###throughput function
+#the throughput enables us to evaluate the performance of our routing algorithm 
+#the function returns the ratio between the number of packets sent and the total time elapsed
+def throughput(n_pkts,delay):
+    return n_pkts/delay
\ No newline at end of file
diff --git a/utility_functions/network_utilities.py b/utility_functions/network_utilities.py
new file mode 100644
index 0000000..b184dc2
--- /dev/null
+++ b/utility_functions/network_utilities.py
@@ -0,0 +1,134 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+### Generates a random network of n nodes (the first one corresponds to earth)
+### r = max distance
+
+def generate_network(n,r_max,polar = False, seed = None):
+   
+    if seed is not None:
+        np.random.seed(seed)
+    r = np.random.rand(n,1)*r_max
+    r[0] = 0 #Earth
+    theta = np.random.rand(n,1)*4*np.pi - 2*np.pi
+    phi = np.random.rand(n,1)*4*np.pi - 2*np.pi
+    
+    if polar==True:
+        return np.hstack((r,theta,phi))  #polar coordinates
+    
+    x = r * np.cos(theta) * np.sin(phi)
+    y = r * np.sin(theta) * np.sin(phi)
+    z = r * np.cos(phi)
+    
+    return np.hstack((x,y,z))   #cartesian coordinates
+
+   
+def plot_network(nodes,sphere=False,r_max=0):
+    fig = plt.figure(figsize=(7,7))
+    ax = Axes3D(fig)
+    ax.scatter(nodes[1:,0],nodes[1:,1],nodes[1:,2],color="cyan")
+    ax.scatter(nodes[0,0],nodes[0,1],nodes[0,2],color="cyan",s=200,alpha=0.8)   #Earth
+    r = r_max
+
+    if sphere:
+        u, v = np.mgrid[0:2*np.pi:20j, 0:np.pi:10j]
+        x = r*np.cos(u)*np.sin(v)
+        y = r*np.sin(u)*np.sin(v)
+        z = r*np.cos(v)
+        ax.plot_wireframe(x, y, z, color=(88/255,88/255,88/255),alpha = 0.2,linewidths=1)
+    
+    plt.show() 
+    return fig,ax
+    
+## disable some links for a certain amount of time
+##
+## delta_t = time between steps (in seconds)
+##
+## modes:
+## light mode: few links, short time
+## heavy mode: few links, long time
+## unstable mode: many links, short time
+## extreme mode: many links, long time
+##
+## priority: random, near, far
+## prioritize disabling of links involving nodes near or far from Earth
+## 
+## custom_n_targets = number of targets
+## custom_offtimes: array containing possible values for off times (in seconds)
+
+def disable_links(A,total_time,delta_t,mode="light",priority="random", custom_n_targets = None, custom_offtimes = None):
+
+    n = A.shape[0]
+    n_updates = int(np.ceil(total_time/delta_t))
+    time_max = np.max(A[0,:][np.isfinite(A[0,:])])    #propagation time from the furthest node to Earth, used as a reference
+    
+    At = np.repeat(A[:, :, np.newaxis], n_updates, axis=2) #add temporal dimension
+
+    sorted_nodes = np.argsort(A[0,:])
+    t_nodes = np.arange(n)
+    
+    if priority == "random":
+        np.random.shuffle(t_nodes)   #nodes targeted for link removal
+    elif priority == "near":
+        t_nodes = sorted_nodes[:int(np.ceil(n/3))]
+    elif priority == "far":
+        t_nodes = np.flip(sorted_nodes)[:int(np.ceil(n/3))]
+    
+    possible_links = []
+    possible_links = np.array([[x,y] for x in t_nodes for y in range(n) if x!=y if A[x,y]!=np.inf if [y,x] not in possible_links])
+
+    if mode == "light":  
+        rate = possible_links.shape[0] * 0.05  #disable 5% of possible links every time_max 
+        offtimes = [time_max * 0.25]   #selected links stay off for around 0.5 * time_max
+            
+    elif mode == "heavy":
+        rate = possible_links.shape[0] * 0.05  #disable 5% of possible links every time_max 
+        offtimes = [time_max * 2]   #selected links stay off for around 2 * time_max
+        
+    elif mode == "unstable":
+        rate = possible_links.shape[0] * 0.3  #disable 30% of possible links every time_max 
+        offtimes = [time_max * 0.25]   #selected links stay off for around 0.25 * time_max
+        
+    elif mode == "extreme":
+        rate = possible_links.shape[0] * 0.2  #disable 20% of possible links every time_max 
+        offtimes = [time_max * 2]   #selected links stay off for around 2 * of time_max
+    
+    else:   #use light mode in case of a wrong "mode" value
+        print("Wrong value for \"mode\", light mode will be used")
+        rate = possible_links.shape[0] * 0.05  #disable 5% of possible links every time_max 
+        offtimes = [time_max * 0.5]   #selected links stay off for around 0.5 * time_max
+        
+    n_targets = int(np.ceil(rate * total_time/time_max))
+    
+    if custom_n_targets != None:
+        n_targets = custom_n_targets
+    if custom_offtimes != None:
+        offtimes = custom_offtimes
+ 
+    indexes = np.random.choice(possible_links.shape[0], n_targets)
+    disabled_links = possible_links[indexes,:]
+    
+    for link in disabled_links:
+        start = np.random.randint(0,n_updates)
+        end = start + int(np.ceil(np.random.choice(offtimes) / delta_t))  #offtime expressed in number of updates
+        
+        if(False):   # TEST
+            print(link)
+            print("start {}".format(start))
+            print("end {}".format(end))
+        At[link[0],link[1],start:min(end, n_updates)] = At[link[1],link[0],start:min(end, n_updates)] = np.inf 
+            
+    return At
+
+### Creates the weighted adjacency matrix of the network
+### Entries represent propagation times between nodes
+def adjacency_matrix(nodes, tau_max, c = 3e8):
+    
+    n1 = nodes[:, np.newaxis, :]
+    n2 = nodes[np.newaxis, :, :]
+    
+    A = np.linalg.norm(n1-n2, axis = 2)/c
+    A[A>tau_max] = np.inf
+
+    return A