\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.jointplot(v1, v2, alpha=0.4);\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 81,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"application/javascript\": [\n \"/* Put everything inside the global mpl namespace */\\n\",\n \"window.mpl = {};\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.get_websocket_type = function() {\\n\",\n \" if (typeof(WebSocket) !== 'undefined') {\\n\",\n \" return WebSocket;\\n\",\n \" } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n \" return MozWebSocket;\\n\",\n \" } else {\\n\",\n \" alert('Your browser does not have WebSocket support. ' +\\n\",\n \" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n \" 'Firefox 4 and 5 are also supported but you ' +\\n\",\n \" 'have to enable WebSockets in about:config.');\\n\",\n \" };\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n \" this.id = figure_id;\\n\",\n \"\\n\",\n \" this.ws = websocket;\\n\",\n \"\\n\",\n \" this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n \"\\n\",\n \" if (!this.supports_binary) {\\n\",\n \" var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n \" if (warnings) {\\n\",\n \" warnings.style.display = 'block';\\n\",\n \" warnings.textContent = (\\n\",\n \" \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n \" \\\"Performance may be slow.\\\");\\n\",\n \" }\\n\",\n \" }\\n\",\n \"\\n\",\n \" this.imageObj = new Image();\\n\",\n \"\\n\",\n \" this.context = undefined;\\n\",\n \" this.message = undefined;\\n\",\n \" this.canvas = undefined;\\n\",\n \" this.rubberband_canvas = undefined;\\n\",\n \" this.rubberband_context = undefined;\\n\",\n \" this.format_dropdown = undefined;\\n\",\n \"\\n\",\n \" this.image_mode = 'full';\\n\",\n \"\\n\",\n \" this.root = $('');\\n\",\n \" this._root_extra_style(this.root)\\n\",\n \" this.root.attr('style', 'display: inline-block');\\n\",\n \"\\n\",\n \" $(parent_element).append(this.root);\\n\",\n \"\\n\",\n \" this._init_header(this);\\n\",\n \" this._init_canvas(this);\\n\",\n \" this._init_toolbar(this);\\n\",\n \"\\n\",\n \" var fig = this;\\n\",\n \"\\n\",\n \" this.waiting = false;\\n\",\n \"\\n\",\n \" this.ws.onopen = function () {\\n\",\n \" fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n \" fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n \" if (mpl.ratio != 1) {\\n\",\n \" fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n \" }\\n\",\n \" fig.send_message(\\\"refresh\\\", {});\\n\",\n \" }\\n\",\n \"\\n\",\n \" this.imageObj.onload = function() {\\n\",\n \" if (fig.image_mode == 'full') {\\n\",\n \" // Full images could contain transparency (where diff images\\n\",\n \" // almost always do), so we need to clear the canvas so that\\n\",\n \" // there is no ghosting.\\n\",\n \" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n \" }\\n\",\n \" fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n \" };\\n\",\n \"\\n\",\n \" this.imageObj.onunload = function() {\\n\",\n \" fig.ws.close();\\n\",\n \" }\\n\",\n \"\\n\",\n \" this.ws.onmessage = this._make_on_message_function(this);\\n\",\n \"\\n\",\n \" this.ondownload = ondownload;\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._init_header = function() {\\n\",\n \" var titlebar = $(\\n\",\n \" '');\\n\",\n \" var titletext = $(\\n\",\n \" '');\\n\",\n \" titlebar.append(titletext)\\n\",\n \" this.root.append(titlebar);\\n\",\n \" this.header = titletext[0];\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n \"\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n \"\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._init_canvas = function() {\\n\",\n \" var fig = this;\\n\",\n \"\\n\",\n \" var canvas_div = $('');\\n\",\n \"\\n\",\n \" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n \"\\n\",\n \" function canvas_keyboard_event(event) {\\n\",\n \" return fig.key_event(event, event['data']);\\n\",\n \" }\\n\",\n \"\\n\",\n \" canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n \" canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n \" this.canvas_div = canvas_div\\n\",\n \" this._canvas_extra_style(canvas_div)\\n\",\n \" this.root.append(canvas_div);\\n\",\n \"\\n\",\n \" var canvas = $('');\\n\",\n \" canvas.addClass('mpl-canvas');\\n\",\n \" canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n \"\\n\",\n \" this.canvas = canvas[0];\\n\",\n \" this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n \"\\n\",\n \" var backingStore = this.context.backingStorePixelRatio ||\\n\",\n \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n \"\\n\",\n \" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n \"\\n\",\n \" var rubberband = $('');\\n\",\n \" rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n \"\\n\",\n \" var pass_mouse_events = true;\\n\",\n \"\\n\",\n \" canvas_div.resizable({\\n\",\n \" start: function(event, ui) {\\n\",\n \" pass_mouse_events = false;\\n\",\n \" },\\n\",\n \" resize: function(event, ui) {\\n\",\n \" fig.request_resize(ui.size.width, ui.size.height);\\n\",\n \" },\\n\",\n \" stop: function(event, ui) {\\n\",\n \" pass_mouse_events = true;\\n\",\n \" fig.request_resize(ui.size.width, ui.size.height);\\n\",\n \" },\\n\",\n \" });\\n\",\n \"\\n\",\n \" function mouse_event_fn(event) {\\n\",\n \" if (pass_mouse_events)\\n\",\n \" return fig.mouse_event(event, event['data']);\\n\",\n \" }\\n\",\n \"\\n\",\n \" rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n \" rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n \" // Throttle sequential mouse events to 1 every 20ms.\\n\",\n \" rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n \"\\n\",\n \" rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n \" rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n \"\\n\",\n \" canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n \" event = event.originalEvent;\\n\",\n \" event['data'] = 'scroll'\\n\",\n \" if (event.deltaY < 0) {\\n\",\n \" event.step = 1;\\n\",\n \" } else {\\n\",\n \" event.step = -1;\\n\",\n \" }\\n\",\n \" mouse_event_fn(event);\\n\",\n \" });\\n\",\n \"\\n\",\n \" canvas_div.append(canvas);\\n\",\n \" canvas_div.append(rubberband);\\n\",\n \"\\n\",\n \" this.rubberband = rubberband;\\n\",\n \" this.rubberband_canvas = rubberband[0];\\n\",\n \" this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n \" this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n \"\\n\",\n \" this._resize_canvas = function(width, height) {\\n\",\n \" // Keep the size of the canvas, canvas container, and rubber band\\n\",\n \" // canvas in synch.\\n\",\n \" canvas_div.css('width', width)\\n\",\n \" canvas_div.css('height', height)\\n\",\n \"\\n\",\n \" canvas.attr('width', width * mpl.ratio);\\n\",\n \" canvas.attr('height', height * mpl.ratio);\\n\",\n \" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n \"\\n\",\n \" rubberband.attr('width', width);\\n\",\n \" rubberband.attr('height', height);\\n\",\n \" }\\n\",\n \"\\n\",\n \" // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n \" // upon first draw.\\n\",\n \" this._resize_canvas(600, 600);\\n\",\n \"\\n\",\n \" // Disable right mouse context menu.\\n\",\n \" $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n \" return false;\\n\",\n \" });\\n\",\n \"\\n\",\n \" function set_focus () {\\n\",\n \" canvas.focus();\\n\",\n \" canvas_div.focus();\\n\",\n \" }\\n\",\n \"\\n\",\n \" window.setTimeout(set_focus, 100);\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n \" var fig = this;\\n\",\n \"\\n\",\n \" var nav_element = $('');\\n\",\n \" nav_element.attr('style', 'width: 100%');\\n\",\n \" this.root.append(nav_element);\\n\",\n \"\\n\",\n \" // Define a callback function for later on.\\n\",\n \" function toolbar_event(event) {\\n\",\n \" return fig.toolbar_button_onclick(event['data']);\\n\",\n \" }\\n\",\n \" function toolbar_mouse_event(event) {\\n\",\n \" return fig.toolbar_button_onmouseover(event['data']);\\n\",\n \" }\\n\",\n \"\\n\",\n \" for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n \" var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n \" var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n \" var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n \" var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n \"\\n\",\n \" if (!name) {\\n\",\n \" // put a spacer in here.\\n\",\n \" continue;\\n\",\n \" }\\n\",\n \" var button = $('');\\n\",\n \" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n \" 'ui-button-icon-only');\\n\",\n \" button.attr('role', 'button');\\n\",\n \" button.attr('aria-disabled', 'false');\\n\",\n \" button.click(method_name, toolbar_event);\\n\",\n \" button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n \"\\n\",\n \" var icon_img = $('');\\n\",\n \" icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n \" icon_img.addClass(image);\\n\",\n \" icon_img.addClass('ui-corner-all');\\n\",\n \"\\n\",\n \" var tooltip_span = $('');\\n\",\n \" tooltip_span.addClass('ui-button-text');\\n\",\n \" tooltip_span.html(tooltip);\\n\",\n \"\\n\",\n \" button.append(icon_img);\\n\",\n \" button.append(tooltip_span);\\n\",\n \"\\n\",\n \" nav_element.append(button);\\n\",\n \" }\\n\",\n \"\\n\",\n \" var fmt_picker_span = $('');\\n\",\n \"\\n\",\n \" var fmt_picker = $('');\\n\",\n \" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n \" fmt_picker_span.append(fmt_picker);\\n\",\n \" nav_element.append(fmt_picker_span);\\n\",\n \" this.format_dropdown = fmt_picker[0];\\n\",\n \"\\n\",\n \" for (var ind in mpl.extensions) {\\n\",\n \" var fmt = mpl.extensions[ind];\\n\",\n \" var option = $(\\n\",\n \" '', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n \" fmt_picker.append(option);\\n\",\n \" }\\n\",\n \"\\n\",\n \" // Add hover states to the ui-buttons\\n\",\n \" $( \\\".ui-button\\\" ).hover(\\n\",\n \" function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n \" function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n \" );\\n\",\n \"\\n\",\n \" var status_bar = $('');\\n\",\n \" nav_element.append(status_bar);\\n\",\n \" this.message = status_bar[0];\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n \" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n \" // which will in turn request a refresh of the image.\\n\",\n \" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n \" properties['type'] = type;\\n\",\n \" properties['figure_id'] = this.id;\\n\",\n \" this.ws.send(JSON.stringify(properties));\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n \" if (!this.waiting) {\\n\",\n \" this.waiting = true;\\n\",\n \" this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n \" }\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n \" var format_dropdown = fig.format_dropdown;\\n\",\n \" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n \" fig.ondownload(fig, format);\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n \" var size = msg['size'];\\n\",\n \" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n \" fig._resize_canvas(size[0], size[1]);\\n\",\n \" fig.send_message(\\\"refresh\\\", {});\\n\",\n \" };\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n \" var x0 = msg['x0'] / mpl.ratio;\\n\",\n \" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n \" var x1 = msg['x1'] / mpl.ratio;\\n\",\n \" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n \" x0 = Math.floor(x0) + 0.5;\\n\",\n \" y0 = Math.floor(y0) + 0.5;\\n\",\n \" x1 = Math.floor(x1) + 0.5;\\n\",\n \" y1 = Math.floor(y1) + 0.5;\\n\",\n \" var min_x = Math.min(x0, x1);\\n\",\n \" var min_y = Math.min(y0, y1);\\n\",\n \" var width = Math.abs(x1 - x0);\\n\",\n \" var height = Math.abs(y1 - y0);\\n\",\n \"\\n\",\n \" fig.rubberband_context.clearRect(\\n\",\n \" 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\\n\",\n \"\\n\",\n \" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n \" // Updates the figure title.\\n\",\n \" fig.header.textContent = msg['label'];\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n \" var cursor = msg['cursor'];\\n\",\n \" switch(cursor)\\n\",\n \" {\\n\",\n \" case 0:\\n\",\n \" cursor = 'pointer';\\n\",\n \" break;\\n\",\n \" case 1:\\n\",\n \" cursor = 'default';\\n\",\n \" break;\\n\",\n \" case 2:\\n\",\n \" cursor = 'crosshair';\\n\",\n \" break;\\n\",\n \" case 3:\\n\",\n \" cursor = 'move';\\n\",\n \" break;\\n\",\n \" }\\n\",\n \" fig.rubberband_canvas.style.cursor = cursor;\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n \" fig.message.textContent = msg['message'];\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n \" // Request the server to send over a new figure.\\n\",\n \" fig.send_draw_message();\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n \" fig.image_mode = msg['mode'];\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n \" // Called whenever the canvas gets updated.\\n\",\n \" this.send_message(\\\"ack\\\", {});\\n\",\n \"}\\n\",\n \"\\n\",\n \"// A function to construct a web socket function for onmessage handling.\\n\",\n \"// Called in the figure constructor.\\n\",\n \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n \" return function socket_on_message(evt) {\\n\",\n \" if (evt.data instanceof Blob) {\\n\",\n \" /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n \" * transferred with MIME type text/plain:\\\" errors on\\n\",\n \" * Chrome. But how to set the MIME type? It doesn't seem\\n\",\n \" * to be part of the websocket stream */\\n\",\n \" evt.data.type = \\\"image/png\\\";\\n\",\n \"\\n\",\n \" /* Free the memory for the previous frames */\\n\",\n \" if (fig.imageObj.src) {\\n\",\n \" (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n \" fig.imageObj.src);\\n\",\n \" }\\n\",\n \"\\n\",\n \" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n \" evt.data);\\n\",\n \" fig.updated_canvas_event();\\n\",\n \" fig.waiting = false;\\n\",\n \" return;\\n\",\n \" }\\n\",\n \" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n \" fig.imageObj.src = evt.data;\\n\",\n \" fig.updated_canvas_event();\\n\",\n \" fig.waiting = false;\\n\",\n \" return;\\n\",\n \" }\\n\",\n \"\\n\",\n \" var msg = JSON.parse(evt.data);\\n\",\n \" var msg_type = msg['type'];\\n\",\n \"\\n\",\n \" // Call the \\\"handle_{type}\\\" callback, which takes\\n\",\n \" // the figure and JSON message as its only arguments.\\n\",\n \" try {\\n\",\n \" var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n \" } catch (e) {\\n\",\n \" console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n \" return;\\n\",\n \" }\\n\",\n \"\\n\",\n \" if (callback) {\\n\",\n \" try {\\n\",\n \" // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n \" callback(fig, msg);\\n\",\n \" } catch (e) {\\n\",\n \" console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n \" }\\n\",\n \" }\\n\",\n \" };\\n\",\n \"}\\n\",\n \"\\n\",\n \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n \"mpl.findpos = function(e) {\\n\",\n \" //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n \" var targ;\\n\",\n \" if (!e)\\n\",\n \" e = window.event;\\n\",\n \" if (e.target)\\n\",\n \" targ = e.target;\\n\",\n \" else if (e.srcElement)\\n\",\n \" targ = e.srcElement;\\n\",\n \" if (targ.nodeType == 3) // defeat Safari bug\\n\",\n \" targ = targ.parentNode;\\n\",\n \"\\n\",\n \" // jQuery normalizes the pageX and pageY\\n\",\n \" // pageX,Y are the mouse positions relative to the document\\n\",\n \" // offset() returns the position of the element relative to the document\\n\",\n \" var x = e.pageX - $(targ).offset().left;\\n\",\n \" var y = e.pageY - $(targ).offset().top;\\n\",\n \"\\n\",\n \" return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n \"};\\n\",\n \"\\n\",\n \"/*\\n\",\n \" * return a copy of an object with only non-object keys\\n\",\n \" * we need this to avoid circular references\\n\",\n \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n \" */\\n\",\n \"function simpleKeys (original) {\\n\",\n \" return Object.keys(original).reduce(function (obj, key) {\\n\",\n \" if (typeof original[key] !== 'object')\\n\",\n \" obj[key] = original[key]\\n\",\n \" return obj;\\n\",\n \" }, {});\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n \" var canvas_pos = mpl.findpos(event)\\n\",\n \"\\n\",\n \" if (name === 'button_press')\\n\",\n \" {\\n\",\n \" this.canvas.focus();\\n\",\n \" this.canvas_div.focus();\\n\",\n \" }\\n\",\n \"\\n\",\n \" var x = canvas_pos.x * mpl.ratio;\\n\",\n \" var y = canvas_pos.y * mpl.ratio;\\n\",\n \"\\n\",\n \" this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n \" step: event.step,\\n\",\n \" guiEvent: simpleKeys(event)});\\n\",\n \"\\n\",\n \" /* This prevents the web browser from automatically changing to\\n\",\n \" * the text insertion cursor when the button is pressed. We want\\n\",\n \" * to control all of the cursor setting manually through the\\n\",\n \" * 'cursor' event from matplotlib */\\n\",\n \" event.preventDefault();\\n\",\n \" return false;\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n \" // Handle any extra behaviour associated with a key event\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n \"\\n\",\n \" // Prevent repeat events\\n\",\n \" if (name == 'key_press')\\n\",\n \" {\\n\",\n \" if (event.which === this._key)\\n\",\n \" return;\\n\",\n \" else\\n\",\n \" this._key = event.which;\\n\",\n \" }\\n\",\n \" if (name == 'key_release')\\n\",\n \" this._key = null;\\n\",\n \"\\n\",\n \" var value = '';\\n\",\n \" if (event.ctrlKey && event.which != 17)\\n\",\n \" value += \\\"ctrl+\\\";\\n\",\n \" if (event.altKey && event.which != 18)\\n\",\n \" value += \\\"alt+\\\";\\n\",\n \" if (event.shiftKey && event.which != 16)\\n\",\n \" value += \\\"shift+\\\";\\n\",\n \"\\n\",\n \" value += 'k';\\n\",\n \" value += event.which.toString();\\n\",\n \"\\n\",\n \" this._key_event_extra(event, name);\\n\",\n \"\\n\",\n \" this.send_message(name, {key: value,\\n\",\n \" guiEvent: simpleKeys(event)});\\n\",\n \" return false;\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n \" if (name == 'download') {\\n\",\n \" this.handle_save(this, null);\\n\",\n \" } else {\\n\",\n \" this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n \" }\\n\",\n \"};\\n\",\n \"\\n\",\n \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n \" this.message.textContent = tooltip;\\n\",\n \"};\\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 \"\\n\",\n \"mpl.extensions = [\\\"eps\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\"];\\n\",\n \"\\n\",\n \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n \" // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n \" // object with the appropriate methods. Currently this is a non binary\\n\",\n \" // socket, so there is still some room for performance tuning.\\n\",\n \" var ws = {};\\n\",\n \"\\n\",\n \" ws.close = function() {\\n\",\n \" comm.close()\\n\",\n \" };\\n\",\n \" ws.send = function(m) {\\n\",\n \" //console.log('sending', m);\\n\",\n \" comm.send(m);\\n\",\n \" };\\n\",\n \" // Register the callback with on_msg.\\n\",\n \" comm.on_msg(function(msg) {\\n\",\n \" //console.log('receiving', msg['content']['data'], msg);\\n\",\n \" // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n \" ws.onmessage(msg['content']['data'])\\n\",\n \" });\\n\",\n \" return ws;\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n \" // This is the function which gets called when the mpl process\\n\",\n \" // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n \"\\n\",\n \" var id = msg.content.data.id;\\n\",\n \" // Get hold of the div created by the display call when the Comm\\n\",\n \" // socket was opened in Python.\\n\",\n \" var element = $(\\\"#\\\" + id);\\n\",\n \" var ws_proxy = comm_websocket_adapter(comm)\\n\",\n \"\\n\",\n \" function ondownload(figure, format) {\\n\",\n \" window.open(figure.imageObj.src);\\n\",\n \" }\\n\",\n \"\\n\",\n \" var fig = new mpl.figure(id, ws_proxy,\\n\",\n \" ondownload,\\n\",\n \" element.get(0));\\n\",\n \"\\n\",\n \" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n \" // web socket which is closed, not our websocket->open comm proxy.\\n\",\n \" ws_proxy.onopen();\\n\",\n \"\\n\",\n \" fig.parent_element = element.get(0);\\n\",\n \" fig.cell_info = mpl.find_output_cell(\\\"\\\");\\n\",\n \" if (!fig.cell_info) {\\n\",\n \" console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n \" return;\\n\",\n \" }\\n\",\n \"\\n\",\n \" var output_index = fig.cell_info[2]\\n\",\n \" var cell = fig.cell_info[0];\\n\",\n \"\\n\",\n \"};\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n \" var width = fig.canvas.width/mpl.ratio\\n\",\n \" fig.root.unbind('remove')\\n\",\n \"\\n\",\n \" // Update the output cell to use the data from the current canvas.\\n\",\n \" fig.push_to_output();\\n\",\n \" var dataURL = fig.canvas.toDataURL();\\n\",\n \" // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n \" // the notebook keyboard shortcuts fail.\\n\",\n \" IPython.keyboard_manager.enable()\\n\",\n \" $(fig.parent_element).html('![](\\\"')
');\\n\",\n \" fig.close_ws(fig, msg);\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n \" fig.send_message('closing', msg);\\n\",\n \" // fig.ws.close()\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n \" // Turn the data on the canvas into data in the output cell.\\n\",\n \" var width = this.canvas.width/mpl.ratio\\n\",\n \" var dataURL = this.canvas.toDataURL();\\n\",\n \" this.cell_info[1]['text/html'] = '';\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n \" // Tell IPython that the notebook contents must change.\\n\",\n \" IPython.notebook.set_dirty(true);\\n\",\n \" this.send_message(\\\"ack\\\", {});\\n\",\n \" var fig = this;\\n\",\n \" // Wait a second, then push the new image to the DOM so\\n\",\n \" // that it is saved nicely (might be nice to debounce this).\\n\",\n \" setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n \" var fig = this;\\n\",\n \"\\n\",\n \" var nav_element = $('');\\n\",\n \" nav_element.attr('style', 'width: 100%');\\n\",\n \" this.root.append(nav_element);\\n\",\n \"\\n\",\n \" // Define a callback function for later on.\\n\",\n \" function toolbar_event(event) {\\n\",\n \" return fig.toolbar_button_onclick(event['data']);\\n\",\n \" }\\n\",\n \" function toolbar_mouse_event(event) {\\n\",\n \" return fig.toolbar_button_onmouseover(event['data']);\\n\",\n \" }\\n\",\n \"\\n\",\n \" for(var toolbar_ind in mpl.toolbar_items){\\n\",\n \" var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n \" var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n \" var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n \" var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n \"\\n\",\n \" if (!name) { continue; };\\n\",\n \"\\n\",\n \" var button = $('');\\n\",\n \" button.click(method_name, toolbar_event);\\n\",\n \" button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n \" nav_element.append(button);\\n\",\n \" }\\n\",\n \"\\n\",\n \" // Add the status bar.\\n\",\n \" var status_bar = $('');\\n\",\n \" nav_element.append(status_bar);\\n\",\n \" this.message = status_bar[0];\\n\",\n \"\\n\",\n \" // Add the close button to the window.\\n\",\n \" var buttongrp = $('');\\n\",\n \" var button = $('');\\n\",\n \" button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n \" button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n \" buttongrp.append(button);\\n\",\n \" var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n \" titlebar.prepend(buttongrp);\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n \" var fig = this\\n\",\n \" el.on(\\\"remove\\\", function(){\\n\",\n \"\\tfig.close_ws(fig, {});\\n\",\n \" });\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n \" // this is important to make the div 'focusable\\n\",\n \" el.attr('tabindex', 0)\\n\",\n \" // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n \" // off when our div gets focus\\n\",\n \"\\n\",\n \" // location in version 3\\n\",\n \" if (IPython.notebook.keyboard_manager) {\\n\",\n \" IPython.notebook.keyboard_manager.register_events(el);\\n\",\n \" }\\n\",\n \" else {\\n\",\n \" // location in version 2\\n\",\n \" IPython.keyboard_manager.register_events(el);\\n\",\n \" }\\n\",\n \"\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n \" var manager = IPython.notebook.keyboard_manager;\\n\",\n \" if (!manager)\\n\",\n \" manager = IPython.keyboard_manager;\\n\",\n \"\\n\",\n \" // Check for shift+enter\\n\",\n \" if (event.shiftKey && event.which == 13) {\\n\",\n \" this.canvas_div.blur();\\n\",\n \" // select the cell after this one\\n\",\n \" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\\n\",\n \" IPython.notebook.select(index + 1);\\n\",\n \" }\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n \" fig.ondownload(fig, null);\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.find_output_cell = function(html_output) {\\n\",\n \" // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n \" // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n \" // IPython event is triggered only after the cells have been serialised, which for\\n\",\n \" // our purposes (turning an active figure into a static one), is too late.\\n\",\n \" var cells = IPython.notebook.get_cells();\\n\",\n \" var ncells = cells.length;\\n\",\n \" for (var i=0; i= 3 moved mimebundle to data attribute of output\\n\",\n \" data = data.data;\\n\",\n \" }\\n\",\n \" if (data['text/html'] == html_output) {\\n\",\n \" return [cell, data, j];\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"\\n\",\n \"// Register the function which deals with the matplotlib target/channel.\\n\",\n \"// The kernel may be null if the page has been refreshed.\\n\",\n \"if (IPython.notebook.kernel != null) {\\n\",\n \" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n \"}\\n\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n 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\\\")
\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"grid = sns.jointplot(v1, v2, alpha=0.4);\\n\",\n \"grid.ax_joint.set_aspect('equal')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 82,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"application/javascript\": [\n \"/* Put everything inside the global mpl namespace */\\n\",\n \"window.mpl = {};\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.get_websocket_type = function() {\\n\",\n \" if (typeof(WebSocket) !== 'undefined') {\\n\",\n \" return WebSocket;\\n\",\n \" } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n \" return MozWebSocket;\\n\",\n \" } else {\\n\",\n \" alert('Your browser does not have WebSocket support. ' +\\n\",\n \" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n \" 'Firefox 4 and 5 are also supported but you ' +\\n\",\n \" 'have to enable WebSockets in about:config.');\\n\",\n \" };\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n \" this.id = figure_id;\\n\",\n \"\\n\",\n \" this.ws = websocket;\\n\",\n \"\\n\",\n \" this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n \"\\n\",\n \" if (!this.supports_binary) {\\n\",\n \" var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n \" if (warnings) {\\n\",\n \" warnings.style.display = 'block';\\n\",\n \" warnings.textContent = (\\n\",\n \" \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n \" \\\"Performance may be slow.\\\");\\n\",\n \" }\\n\",\n \" }\\n\",\n \"\\n\",\n \" this.imageObj = new Image();\\n\",\n \"\\n\",\n \" this.context = undefined;\\n\",\n \" this.message = undefined;\\n\",\n \" this.canvas = undefined;\\n\",\n \" this.rubberband_canvas = undefined;\\n\",\n \" this.rubberband_context = undefined;\\n\",\n \" this.format_dropdown = undefined;\\n\",\n \"\\n\",\n \" this.image_mode = 'full';\\n\",\n \"\\n\",\n \" this.root = $('');\\n\",\n \" this._root_extra_style(this.root)\\n\",\n \" this.root.attr('style', 'display: inline-block');\\n\",\n \"\\n\",\n \" $(parent_element).append(this.root);\\n\",\n \"\\n\",\n \" this._init_header(this);\\n\",\n \" this._init_canvas(this);\\n\",\n \" this._init_toolbar(this);\\n\",\n \"\\n\",\n \" var fig = this;\\n\",\n \"\\n\",\n \" this.waiting = false;\\n\",\n \"\\n\",\n \" this.ws.onopen = function () {\\n\",\n \" fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n \" fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n \" if (mpl.ratio != 1) {\\n\",\n \" fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n \" }\\n\",\n \" fig.send_message(\\\"refresh\\\", {});\\n\",\n \" }\\n\",\n \"\\n\",\n \" this.imageObj.onload = function() {\\n\",\n \" if (fig.image_mode == 'full') {\\n\",\n \" // Full images could contain transparency (where diff images\\n\",\n \" // almost always do), so we need to clear the canvas so that\\n\",\n \" // there is no ghosting.\\n\",\n \" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n \" }\\n\",\n \" fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n \" };\\n\",\n \"\\n\",\n \" this.imageObj.onunload = function() {\\n\",\n \" fig.ws.close();\\n\",\n \" }\\n\",\n \"\\n\",\n \" this.ws.onmessage = this._make_on_message_function(this);\\n\",\n \"\\n\",\n \" this.ondownload = ondownload;\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._init_header = function() {\\n\",\n \" var titlebar = $(\\n\",\n \" '');\\n\",\n \" var titletext = $(\\n\",\n \" '');\\n\",\n \" titlebar.append(titletext)\\n\",\n \" this.root.append(titlebar);\\n\",\n \" this.header = titletext[0];\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n \"\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n \"\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._init_canvas = function() {\\n\",\n \" var fig = this;\\n\",\n \"\\n\",\n \" var canvas_div = $('');\\n\",\n \"\\n\",\n \" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n \"\\n\",\n \" function canvas_keyboard_event(event) {\\n\",\n \" return fig.key_event(event, event['data']);\\n\",\n \" }\\n\",\n \"\\n\",\n \" canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n \" canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n \" this.canvas_div = canvas_div\\n\",\n \" this._canvas_extra_style(canvas_div)\\n\",\n \" this.root.append(canvas_div);\\n\",\n \"\\n\",\n \" var canvas = $('');\\n\",\n \" canvas.addClass('mpl-canvas');\\n\",\n \" canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n \"\\n\",\n \" this.canvas = canvas[0];\\n\",\n \" this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n \"\\n\",\n \" var backingStore = this.context.backingStorePixelRatio ||\\n\",\n \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n \"\\n\",\n \" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n \"\\n\",\n \" var rubberband = $('');\\n\",\n \" rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n \"\\n\",\n \" var pass_mouse_events = true;\\n\",\n \"\\n\",\n \" canvas_div.resizable({\\n\",\n \" start: function(event, ui) {\\n\",\n \" pass_mouse_events = false;\\n\",\n \" },\\n\",\n \" resize: function(event, ui) {\\n\",\n \" fig.request_resize(ui.size.width, ui.size.height);\\n\",\n \" },\\n\",\n \" stop: function(event, ui) {\\n\",\n \" pass_mouse_events = true;\\n\",\n \" fig.request_resize(ui.size.width, ui.size.height);\\n\",\n \" },\\n\",\n \" });\\n\",\n \"\\n\",\n \" function mouse_event_fn(event) {\\n\",\n \" if (pass_mouse_events)\\n\",\n \" return fig.mouse_event(event, event['data']);\\n\",\n \" }\\n\",\n \"\\n\",\n \" rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n \" rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n \" // Throttle sequential mouse events to 1 every 20ms.\\n\",\n \" rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n \"\\n\",\n \" rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n \" rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n \"\\n\",\n \" canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n \" event = event.originalEvent;\\n\",\n \" event['data'] = 'scroll'\\n\",\n \" if (event.deltaY < 0) {\\n\",\n \" event.step = 1;\\n\",\n \" } else {\\n\",\n \" event.step = -1;\\n\",\n \" }\\n\",\n \" mouse_event_fn(event);\\n\",\n \" });\\n\",\n \"\\n\",\n \" canvas_div.append(canvas);\\n\",\n \" canvas_div.append(rubberband);\\n\",\n \"\\n\",\n \" this.rubberband = rubberband;\\n\",\n \" this.rubberband_canvas = rubberband[0];\\n\",\n \" this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n \" this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n \"\\n\",\n \" this._resize_canvas = function(width, height) {\\n\",\n \" // Keep the size of the canvas, canvas container, and rubber band\\n\",\n \" // canvas in synch.\\n\",\n \" canvas_div.css('width', width)\\n\",\n \" canvas_div.css('height', height)\\n\",\n \"\\n\",\n \" canvas.attr('width', width * mpl.ratio);\\n\",\n \" canvas.attr('height', height * mpl.ratio);\\n\",\n \" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n \"\\n\",\n \" rubberband.attr('width', width);\\n\",\n \" rubberband.attr('height', height);\\n\",\n \" }\\n\",\n \"\\n\",\n \" // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n \" // upon first draw.\\n\",\n \" this._resize_canvas(600, 600);\\n\",\n \"\\n\",\n \" // Disable right mouse context menu.\\n\",\n \" $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n \" return false;\\n\",\n \" });\\n\",\n \"\\n\",\n \" function set_focus () {\\n\",\n \" canvas.focus();\\n\",\n \" canvas_div.focus();\\n\",\n \" }\\n\",\n \"\\n\",\n \" window.setTimeout(set_focus, 100);\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n \" var fig = this;\\n\",\n \"\\n\",\n \" var nav_element = $('');\\n\",\n \" nav_element.attr('style', 'width: 100%');\\n\",\n \" this.root.append(nav_element);\\n\",\n \"\\n\",\n \" // Define a callback function for later on.\\n\",\n \" function toolbar_event(event) {\\n\",\n \" return fig.toolbar_button_onclick(event['data']);\\n\",\n \" }\\n\",\n \" function toolbar_mouse_event(event) {\\n\",\n \" return fig.toolbar_button_onmouseover(event['data']);\\n\",\n \" }\\n\",\n \"\\n\",\n \" for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n \" var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n \" var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n \" var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n \" var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n \"\\n\",\n \" if (!name) {\\n\",\n \" // put a spacer in here.\\n\",\n \" continue;\\n\",\n \" }\\n\",\n \" var button = $('');\\n\",\n \" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n \" 'ui-button-icon-only');\\n\",\n \" button.attr('role', 'button');\\n\",\n \" button.attr('aria-disabled', 'false');\\n\",\n \" button.click(method_name, toolbar_event);\\n\",\n \" button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n \"\\n\",\n \" var icon_img = $('');\\n\",\n \" icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n \" icon_img.addClass(image);\\n\",\n \" icon_img.addClass('ui-corner-all');\\n\",\n \"\\n\",\n \" var tooltip_span = $('');\\n\",\n \" tooltip_span.addClass('ui-button-text');\\n\",\n \" tooltip_span.html(tooltip);\\n\",\n \"\\n\",\n \" button.append(icon_img);\\n\",\n \" button.append(tooltip_span);\\n\",\n \"\\n\",\n \" nav_element.append(button);\\n\",\n \" }\\n\",\n \"\\n\",\n \" var fmt_picker_span = $('');\\n\",\n \"\\n\",\n \" var fmt_picker = $('');\\n\",\n \" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n \" fmt_picker_span.append(fmt_picker);\\n\",\n \" nav_element.append(fmt_picker_span);\\n\",\n \" this.format_dropdown = fmt_picker[0];\\n\",\n \"\\n\",\n \" for (var ind in mpl.extensions) {\\n\",\n \" var fmt = mpl.extensions[ind];\\n\",\n \" var option = $(\\n\",\n \" '', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n \" fmt_picker.append(option);\\n\",\n \" }\\n\",\n \"\\n\",\n \" // Add hover states to the ui-buttons\\n\",\n \" $( \\\".ui-button\\\" ).hover(\\n\",\n \" function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n \" function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n \" );\\n\",\n \"\\n\",\n \" var status_bar = $('');\\n\",\n \" nav_element.append(status_bar);\\n\",\n \" this.message = status_bar[0];\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n \" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n \" // which will in turn request a refresh of the image.\\n\",\n \" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n \" properties['type'] = type;\\n\",\n \" properties['figure_id'] = this.id;\\n\",\n \" this.ws.send(JSON.stringify(properties));\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n \" if (!this.waiting) {\\n\",\n \" this.waiting = true;\\n\",\n \" this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n \" }\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n \" var format_dropdown = fig.format_dropdown;\\n\",\n \" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n \" fig.ondownload(fig, format);\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n \" var size = msg['size'];\\n\",\n \" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n \" fig._resize_canvas(size[0], size[1]);\\n\",\n \" fig.send_message(\\\"refresh\\\", {});\\n\",\n \" };\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n \" var x0 = msg['x0'] / mpl.ratio;\\n\",\n \" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n \" var x1 = msg['x1'] / mpl.ratio;\\n\",\n \" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n \" x0 = Math.floor(x0) + 0.5;\\n\",\n \" y0 = Math.floor(y0) + 0.5;\\n\",\n \" x1 = Math.floor(x1) + 0.5;\\n\",\n \" y1 = Math.floor(y1) + 0.5;\\n\",\n \" var min_x = Math.min(x0, x1);\\n\",\n \" var min_y = Math.min(y0, y1);\\n\",\n \" var width = Math.abs(x1 - x0);\\n\",\n \" var height = Math.abs(y1 - y0);\\n\",\n \"\\n\",\n \" fig.rubberband_context.clearRect(\\n\",\n \" 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\\n\",\n \"\\n\",\n \" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n \" // Updates the figure title.\\n\",\n \" fig.header.textContent = msg['label'];\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n \" var cursor = msg['cursor'];\\n\",\n \" switch(cursor)\\n\",\n \" {\\n\",\n \" case 0:\\n\",\n \" cursor = 'pointer';\\n\",\n \" break;\\n\",\n \" case 1:\\n\",\n \" cursor = 'default';\\n\",\n \" break;\\n\",\n \" case 2:\\n\",\n \" cursor = 'crosshair';\\n\",\n \" break;\\n\",\n \" case 3:\\n\",\n \" cursor = 'move';\\n\",\n \" break;\\n\",\n \" }\\n\",\n \" fig.rubberband_canvas.style.cursor = cursor;\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n \" fig.message.textContent = msg['message'];\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n \" // Request the server to send over a new figure.\\n\",\n \" fig.send_draw_message();\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n \" fig.image_mode = msg['mode'];\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n \" // Called whenever the canvas gets updated.\\n\",\n \" this.send_message(\\\"ack\\\", {});\\n\",\n \"}\\n\",\n \"\\n\",\n \"// A function to construct a web socket function for onmessage handling.\\n\",\n \"// Called in the figure constructor.\\n\",\n \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n \" return function socket_on_message(evt) {\\n\",\n \" if (evt.data instanceof Blob) {\\n\",\n \" /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n \" * transferred with MIME type text/plain:\\\" errors on\\n\",\n \" * Chrome. But how to set the MIME type? It doesn't seem\\n\",\n \" * to be part of the websocket stream */\\n\",\n \" evt.data.type = \\\"image/png\\\";\\n\",\n \"\\n\",\n \" /* Free the memory for the previous frames */\\n\",\n \" if (fig.imageObj.src) {\\n\",\n \" (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n \" fig.imageObj.src);\\n\",\n \" }\\n\",\n \"\\n\",\n \" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n \" evt.data);\\n\",\n \" fig.updated_canvas_event();\\n\",\n \" fig.waiting = false;\\n\",\n \" return;\\n\",\n \" }\\n\",\n \" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n \" fig.imageObj.src = evt.data;\\n\",\n \" fig.updated_canvas_event();\\n\",\n \" fig.waiting = false;\\n\",\n \" return;\\n\",\n \" }\\n\",\n \"\\n\",\n \" var msg = JSON.parse(evt.data);\\n\",\n \" var msg_type = msg['type'];\\n\",\n \"\\n\",\n \" // Call the \\\"handle_{type}\\\" callback, which takes\\n\",\n \" // the figure and JSON message as its only arguments.\\n\",\n \" try {\\n\",\n \" var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n \" } catch (e) {\\n\",\n \" console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n \" return;\\n\",\n \" }\\n\",\n \"\\n\",\n \" if (callback) {\\n\",\n \" try {\\n\",\n \" // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n \" callback(fig, msg);\\n\",\n \" } catch (e) {\\n\",\n \" console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n \" }\\n\",\n \" }\\n\",\n \" };\\n\",\n \"}\\n\",\n \"\\n\",\n \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n \"mpl.findpos = function(e) {\\n\",\n \" //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n \" var targ;\\n\",\n \" if (!e)\\n\",\n \" e = window.event;\\n\",\n \" if (e.target)\\n\",\n \" targ = e.target;\\n\",\n \" else if (e.srcElement)\\n\",\n \" targ = e.srcElement;\\n\",\n \" if (targ.nodeType == 3) // defeat Safari bug\\n\",\n \" targ = targ.parentNode;\\n\",\n \"\\n\",\n \" // jQuery normalizes the pageX and pageY\\n\",\n \" // pageX,Y are the mouse positions relative to the document\\n\",\n \" // offset() returns the position of the element relative to the document\\n\",\n \" var x = e.pageX - $(targ).offset().left;\\n\",\n \" var y = e.pageY - $(targ).offset().top;\\n\",\n \"\\n\",\n \" return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n \"};\\n\",\n \"\\n\",\n \"/*\\n\",\n \" * return a copy of an object with only non-object keys\\n\",\n \" * we need this to avoid circular references\\n\",\n \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n \" */\\n\",\n \"function simpleKeys (original) {\\n\",\n \" return Object.keys(original).reduce(function (obj, key) {\\n\",\n \" if (typeof original[key] !== 'object')\\n\",\n \" obj[key] = original[key]\\n\",\n \" return obj;\\n\",\n \" }, {});\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n \" var canvas_pos = mpl.findpos(event)\\n\",\n \"\\n\",\n \" if (name === 'button_press')\\n\",\n \" {\\n\",\n \" this.canvas.focus();\\n\",\n \" this.canvas_div.focus();\\n\",\n \" }\\n\",\n \"\\n\",\n \" var x = canvas_pos.x * mpl.ratio;\\n\",\n \" var y = canvas_pos.y * mpl.ratio;\\n\",\n \"\\n\",\n \" this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n \" step: event.step,\\n\",\n \" guiEvent: simpleKeys(event)});\\n\",\n \"\\n\",\n \" /* This prevents the web browser from automatically changing to\\n\",\n \" * the text insertion cursor when the button is pressed. We want\\n\",\n \" * to control all of the cursor setting manually through the\\n\",\n \" * 'cursor' event from matplotlib */\\n\",\n \" event.preventDefault();\\n\",\n \" return false;\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n \" // Handle any extra behaviour associated with a key event\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n \"\\n\",\n \" // Prevent repeat events\\n\",\n \" if (name == 'key_press')\\n\",\n \" {\\n\",\n \" if (event.which === this._key)\\n\",\n \" return;\\n\",\n \" else\\n\",\n \" this._key = event.which;\\n\",\n \" }\\n\",\n \" if (name == 'key_release')\\n\",\n \" this._key = null;\\n\",\n \"\\n\",\n \" var value = '';\\n\",\n \" if (event.ctrlKey && event.which != 17)\\n\",\n \" value += \\\"ctrl+\\\";\\n\",\n \" if (event.altKey && event.which != 18)\\n\",\n \" value += \\\"alt+\\\";\\n\",\n \" if (event.shiftKey && event.which != 16)\\n\",\n \" value += \\\"shift+\\\";\\n\",\n \"\\n\",\n \" value += 'k';\\n\",\n \" value += event.which.toString();\\n\",\n \"\\n\",\n \" this._key_event_extra(event, name);\\n\",\n \"\\n\",\n \" this.send_message(name, {key: value,\\n\",\n \" guiEvent: simpleKeys(event)});\\n\",\n \" return false;\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n \" if (name == 'download') {\\n\",\n \" this.handle_save(this, null);\\n\",\n \" } else {\\n\",\n \" this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n \" }\\n\",\n \"};\\n\",\n \"\\n\",\n \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n \" this.message.textContent = tooltip;\\n\",\n \"};\\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 \"\\n\",\n \"mpl.extensions = [\\\"eps\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\"];\\n\",\n \"\\n\",\n \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n \" // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n \" // object with the appropriate methods. Currently this is a non binary\\n\",\n \" // socket, so there is still some room for performance tuning.\\n\",\n \" var ws = {};\\n\",\n \"\\n\",\n \" ws.close = function() {\\n\",\n \" comm.close()\\n\",\n \" };\\n\",\n \" ws.send = function(m) {\\n\",\n \" //console.log('sending', m);\\n\",\n \" comm.send(m);\\n\",\n \" };\\n\",\n \" // Register the callback with on_msg.\\n\",\n \" comm.on_msg(function(msg) {\\n\",\n \" //console.log('receiving', msg['content']['data'], msg);\\n\",\n \" // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n \" ws.onmessage(msg['content']['data'])\\n\",\n \" });\\n\",\n \" return ws;\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n \" // This is the function which gets called when the mpl process\\n\",\n \" // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n \"\\n\",\n \" var id = msg.content.data.id;\\n\",\n \" // Get hold of the div created by the display call when the Comm\\n\",\n \" // socket was opened in Python.\\n\",\n \" var element = $(\\\"#\\\" + id);\\n\",\n \" var ws_proxy = comm_websocket_adapter(comm)\\n\",\n \"\\n\",\n \" function ondownload(figure, format) {\\n\",\n \" window.open(figure.imageObj.src);\\n\",\n \" }\\n\",\n \"\\n\",\n \" var fig = new mpl.figure(id, ws_proxy,\\n\",\n \" ondownload,\\n\",\n \" element.get(0));\\n\",\n \"\\n\",\n \" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n \" // web socket which is closed, not our websocket->open comm proxy.\\n\",\n \" ws_proxy.onopen();\\n\",\n \"\\n\",\n \" fig.parent_element = element.get(0);\\n\",\n \" fig.cell_info = mpl.find_output_cell(\\\"\\\");\\n\",\n \" if (!fig.cell_info) {\\n\",\n \" console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n \" return;\\n\",\n \" }\\n\",\n \"\\n\",\n \" var output_index = fig.cell_info[2]\\n\",\n \" var cell = fig.cell_info[0];\\n\",\n \"\\n\",\n \"};\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n \" var width = fig.canvas.width/mpl.ratio\\n\",\n \" fig.root.unbind('remove')\\n\",\n \"\\n\",\n \" // Update the output cell to use the data from the current canvas.\\n\",\n \" fig.push_to_output();\\n\",\n \" var dataURL = fig.canvas.toDataURL();\\n\",\n \" // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n \" // the notebook keyboard shortcuts fail.\\n\",\n \" IPython.keyboard_manager.enable()\\n\",\n \" $(fig.parent_element).html('');\\n\",\n \" fig.close_ws(fig, msg);\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n \" fig.send_message('closing', msg);\\n\",\n \" // fig.ws.close()\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n \" // Turn the data on the canvas into data in the output cell.\\n\",\n \" var width = this.canvas.width/mpl.ratio\\n\",\n \" var dataURL = this.canvas.toDataURL();\\n\",\n \" this.cell_info[1]['text/html'] = '';\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n \" // Tell IPython that the notebook contents must change.\\n\",\n \" IPython.notebook.set_dirty(true);\\n\",\n \" this.send_message(\\\"ack\\\", {});\\n\",\n \" var fig = this;\\n\",\n \" // Wait a second, then push the new image to the DOM so\\n\",\n \" // that it is saved nicely (might be nice to debounce this).\\n\",\n \" setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n \" var fig = this;\\n\",\n \"\\n\",\n \" var nav_element = $('');\\n\",\n \" nav_element.attr('style', 'width: 100%');\\n\",\n \" this.root.append(nav_element);\\n\",\n \"\\n\",\n \" // Define a callback function for later on.\\n\",\n \" function toolbar_event(event) {\\n\",\n \" return fig.toolbar_button_onclick(event['data']);\\n\",\n \" }\\n\",\n \" function toolbar_mouse_event(event) {\\n\",\n \" return fig.toolbar_button_onmouseover(event['data']);\\n\",\n \" }\\n\",\n \"\\n\",\n \" for(var toolbar_ind in mpl.toolbar_items){\\n\",\n \" var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n \" var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n \" var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n \" var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n \"\\n\",\n \" if (!name) { continue; };\\n\",\n \"\\n\",\n \" var button = $('');\\n\",\n \" button.click(method_name, toolbar_event);\\n\",\n \" button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n \" nav_element.append(button);\\n\",\n \" }\\n\",\n \"\\n\",\n \" // Add the status bar.\\n\",\n \" var status_bar = $('');\\n\",\n \" nav_element.append(status_bar);\\n\",\n \" this.message = status_bar[0];\\n\",\n \"\\n\",\n \" // Add the close button to the window.\\n\",\n \" var buttongrp = $('');\\n\",\n \" var button = $('');\\n\",\n \" button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n \" button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n \" buttongrp.append(button);\\n\",\n \" var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n \" titlebar.prepend(buttongrp);\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n \" var fig = this\\n\",\n \" el.on(\\\"remove\\\", function(){\\n\",\n \"\\tfig.close_ws(fig, {});\\n\",\n \" });\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n \" // this is important to make the div 'focusable\\n\",\n \" el.attr('tabindex', 0)\\n\",\n \" // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n \" // off when our div gets focus\\n\",\n \"\\n\",\n \" // location in version 3\\n\",\n \" if (IPython.notebook.keyboard_manager) {\\n\",\n \" IPython.notebook.keyboard_manager.register_events(el);\\n\",\n \" }\\n\",\n \" else {\\n\",\n \" // location in version 2\\n\",\n \" IPython.keyboard_manager.register_events(el);\\n\",\n \" }\\n\",\n \"\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n \" var manager = IPython.notebook.keyboard_manager;\\n\",\n \" if (!manager)\\n\",\n \" manager = IPython.keyboard_manager;\\n\",\n \"\\n\",\n \" // Check for shift+enter\\n\",\n \" if (event.shiftKey && event.which == 13) {\\n\",\n \" this.canvas_div.blur();\\n\",\n \" // select the cell after this one\\n\",\n \" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\\n\",\n \" IPython.notebook.select(index + 1);\\n\",\n \" }\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n \" fig.ondownload(fig, null);\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.find_output_cell = function(html_output) {\\n\",\n \" // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n \" // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n \" // IPython event is triggered only after the cells have been serialised, which for\\n\",\n \" // our purposes (turning an active figure into a static one), is too late.\\n\",\n \" var cells = IPython.notebook.get_cells();\\n\",\n \" var ncells = cells.length;\\n\",\n \" for (var i=0; i= 3 moved mimebundle to data attribute of output\\n\",\n \" data = data.data;\\n\",\n \" }\\n\",\n \" if (data['text/html'] == html_output) {\\n\",\n \" return [cell, data, j];\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"\\n\",\n \"// Register the function which deals with the matplotlib target/channel.\\n\",\n \"// The kernel may be null if the page has been refreshed.\\n\",\n \"if (IPython.notebook.kernel != null) {\\n\",\n \" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n \"}\\n\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n 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\\\")
\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.jointplot(v1, v2, kind='hex');\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 83,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"application/javascript\": [\n \"/* Put everything inside the global mpl namespace */\\n\",\n \"window.mpl = {};\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.get_websocket_type = function() {\\n\",\n \" if (typeof(WebSocket) !== 'undefined') {\\n\",\n \" return WebSocket;\\n\",\n \" } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n \" return MozWebSocket;\\n\",\n \" } else {\\n\",\n \" alert('Your browser does not have WebSocket support. ' +\\n\",\n \" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n \" 'Firefox 4 and 5 are also supported but you ' +\\n\",\n \" 'have to enable WebSockets in about:config.');\\n\",\n \" };\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n \" this.id = figure_id;\\n\",\n \"\\n\",\n \" this.ws = websocket;\\n\",\n \"\\n\",\n \" this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n \"\\n\",\n \" if (!this.supports_binary) {\\n\",\n \" var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n \" if (warnings) {\\n\",\n \" warnings.style.display = 'block';\\n\",\n \" warnings.textContent = (\\n\",\n \" \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n \" \\\"Performance may be slow.\\\");\\n\",\n \" }\\n\",\n \" }\\n\",\n \"\\n\",\n \" this.imageObj = new Image();\\n\",\n \"\\n\",\n \" this.context = undefined;\\n\",\n \" this.message = undefined;\\n\",\n \" this.canvas = undefined;\\n\",\n \" this.rubberband_canvas = undefined;\\n\",\n \" this.rubberband_context = undefined;\\n\",\n \" this.format_dropdown = undefined;\\n\",\n \"\\n\",\n \" this.image_mode = 'full';\\n\",\n \"\\n\",\n \" this.root = $('');\\n\",\n \" this._root_extra_style(this.root)\\n\",\n \" this.root.attr('style', 'display: inline-block');\\n\",\n \"\\n\",\n \" $(parent_element).append(this.root);\\n\",\n \"\\n\",\n \" this._init_header(this);\\n\",\n \" this._init_canvas(this);\\n\",\n \" this._init_toolbar(this);\\n\",\n \"\\n\",\n \" var fig = this;\\n\",\n \"\\n\",\n \" this.waiting = false;\\n\",\n \"\\n\",\n \" this.ws.onopen = function () {\\n\",\n \" fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n \" fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n \" if (mpl.ratio != 1) {\\n\",\n \" fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n \" }\\n\",\n \" fig.send_message(\\\"refresh\\\", {});\\n\",\n \" }\\n\",\n \"\\n\",\n \" this.imageObj.onload = function() {\\n\",\n \" if (fig.image_mode == 'full') {\\n\",\n \" // Full images could contain transparency (where diff images\\n\",\n \" // almost always do), so we need to clear the canvas so that\\n\",\n \" // there is no ghosting.\\n\",\n \" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n \" }\\n\",\n \" fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n \" };\\n\",\n \"\\n\",\n \" this.imageObj.onunload = function() {\\n\",\n \" fig.ws.close();\\n\",\n \" }\\n\",\n \"\\n\",\n \" this.ws.onmessage = this._make_on_message_function(this);\\n\",\n \"\\n\",\n \" this.ondownload = ondownload;\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._init_header = function() {\\n\",\n \" var titlebar = $(\\n\",\n \" '');\\n\",\n \" var titletext = $(\\n\",\n \" '');\\n\",\n \" titlebar.append(titletext)\\n\",\n \" this.root.append(titlebar);\\n\",\n \" this.header = titletext[0];\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n \"\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n \"\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._init_canvas = function() {\\n\",\n \" var fig = this;\\n\",\n \"\\n\",\n \" var canvas_div = $('');\\n\",\n \"\\n\",\n \" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n \"\\n\",\n \" function canvas_keyboard_event(event) {\\n\",\n \" return fig.key_event(event, event['data']);\\n\",\n \" }\\n\",\n \"\\n\",\n \" canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n \" canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n \" this.canvas_div = canvas_div\\n\",\n \" this._canvas_extra_style(canvas_div)\\n\",\n \" this.root.append(canvas_div);\\n\",\n \"\\n\",\n \" var canvas = $('');\\n\",\n \" canvas.addClass('mpl-canvas');\\n\",\n \" canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n \"\\n\",\n \" this.canvas = canvas[0];\\n\",\n \" this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n \"\\n\",\n \" var backingStore = this.context.backingStorePixelRatio ||\\n\",\n \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n \"\\n\",\n \" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n \"\\n\",\n \" var rubberband = $('');\\n\",\n \" rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n \"\\n\",\n \" var pass_mouse_events = true;\\n\",\n \"\\n\",\n \" canvas_div.resizable({\\n\",\n \" start: function(event, ui) {\\n\",\n \" pass_mouse_events = false;\\n\",\n \" },\\n\",\n \" resize: function(event, ui) {\\n\",\n \" fig.request_resize(ui.size.width, ui.size.height);\\n\",\n \" },\\n\",\n \" stop: function(event, ui) {\\n\",\n \" pass_mouse_events = true;\\n\",\n \" fig.request_resize(ui.size.width, ui.size.height);\\n\",\n \" },\\n\",\n \" });\\n\",\n \"\\n\",\n \" function mouse_event_fn(event) {\\n\",\n \" if (pass_mouse_events)\\n\",\n \" return fig.mouse_event(event, event['data']);\\n\",\n \" }\\n\",\n \"\\n\",\n \" rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n \" rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n \" // Throttle sequential mouse events to 1 every 20ms.\\n\",\n \" rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n \"\\n\",\n \" rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n \" rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n \"\\n\",\n \" canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n \" event = event.originalEvent;\\n\",\n \" event['data'] = 'scroll'\\n\",\n \" if (event.deltaY < 0) {\\n\",\n \" event.step = 1;\\n\",\n \" } else {\\n\",\n \" event.step = -1;\\n\",\n \" }\\n\",\n \" mouse_event_fn(event);\\n\",\n \" });\\n\",\n \"\\n\",\n \" canvas_div.append(canvas);\\n\",\n \" canvas_div.append(rubberband);\\n\",\n \"\\n\",\n \" this.rubberband = rubberband;\\n\",\n \" this.rubberband_canvas = rubberband[0];\\n\",\n \" this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n \" this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n \"\\n\",\n \" this._resize_canvas = function(width, height) {\\n\",\n \" // Keep the size of the canvas, canvas container, and rubber band\\n\",\n \" // canvas in synch.\\n\",\n \" canvas_div.css('width', width)\\n\",\n \" canvas_div.css('height', height)\\n\",\n \"\\n\",\n \" canvas.attr('width', width * mpl.ratio);\\n\",\n \" canvas.attr('height', height * mpl.ratio);\\n\",\n \" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n \"\\n\",\n \" rubberband.attr('width', width);\\n\",\n \" rubberband.attr('height', height);\\n\",\n \" }\\n\",\n \"\\n\",\n \" // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n \" // upon first draw.\\n\",\n \" this._resize_canvas(600, 600);\\n\",\n \"\\n\",\n \" // Disable right mouse context menu.\\n\",\n \" $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n \" return false;\\n\",\n \" });\\n\",\n \"\\n\",\n \" function set_focus () {\\n\",\n \" canvas.focus();\\n\",\n \" canvas_div.focus();\\n\",\n \" }\\n\",\n \"\\n\",\n \" window.setTimeout(set_focus, 100);\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n \" var fig = this;\\n\",\n \"\\n\",\n \" var nav_element = $('');\\n\",\n \" nav_element.attr('style', 'width: 100%');\\n\",\n \" this.root.append(nav_element);\\n\",\n \"\\n\",\n \" // Define a callback function for later on.\\n\",\n \" function toolbar_event(event) {\\n\",\n \" return fig.toolbar_button_onclick(event['data']);\\n\",\n \" }\\n\",\n \" function toolbar_mouse_event(event) {\\n\",\n \" return fig.toolbar_button_onmouseover(event['data']);\\n\",\n \" }\\n\",\n \"\\n\",\n \" for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n \" var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n \" var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n \" var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n \" var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n \"\\n\",\n \" if (!name) {\\n\",\n \" // put a spacer in here.\\n\",\n \" continue;\\n\",\n \" }\\n\",\n \" var button = $('');\\n\",\n \" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n \" 'ui-button-icon-only');\\n\",\n \" button.attr('role', 'button');\\n\",\n \" button.attr('aria-disabled', 'false');\\n\",\n \" button.click(method_name, toolbar_event);\\n\",\n \" button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n \"\\n\",\n \" var icon_img = $('');\\n\",\n \" icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n \" icon_img.addClass(image);\\n\",\n \" icon_img.addClass('ui-corner-all');\\n\",\n \"\\n\",\n \" var tooltip_span = $('');\\n\",\n \" tooltip_span.addClass('ui-button-text');\\n\",\n \" tooltip_span.html(tooltip);\\n\",\n \"\\n\",\n \" button.append(icon_img);\\n\",\n \" button.append(tooltip_span);\\n\",\n \"\\n\",\n \" nav_element.append(button);\\n\",\n \" }\\n\",\n \"\\n\",\n \" var fmt_picker_span = $('');\\n\",\n \"\\n\",\n \" var fmt_picker = $('');\\n\",\n \" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n \" fmt_picker_span.append(fmt_picker);\\n\",\n \" nav_element.append(fmt_picker_span);\\n\",\n \" this.format_dropdown = fmt_picker[0];\\n\",\n \"\\n\",\n \" for (var ind in mpl.extensions) {\\n\",\n \" var fmt = mpl.extensions[ind];\\n\",\n \" var option = $(\\n\",\n \" '', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n \" fmt_picker.append(option);\\n\",\n \" }\\n\",\n \"\\n\",\n \" // Add hover states to the ui-buttons\\n\",\n \" $( \\\".ui-button\\\" ).hover(\\n\",\n \" function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n \" function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n \" );\\n\",\n \"\\n\",\n \" var status_bar = $('');\\n\",\n \" nav_element.append(status_bar);\\n\",\n \" this.message = status_bar[0];\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n \" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n \" // which will in turn request a refresh of the image.\\n\",\n \" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n \" properties['type'] = type;\\n\",\n \" properties['figure_id'] = this.id;\\n\",\n \" this.ws.send(JSON.stringify(properties));\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n \" if (!this.waiting) {\\n\",\n \" this.waiting = true;\\n\",\n \" this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n \" }\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n \" var format_dropdown = fig.format_dropdown;\\n\",\n \" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n \" fig.ondownload(fig, format);\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n \" var size = msg['size'];\\n\",\n \" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n \" fig._resize_canvas(size[0], size[1]);\\n\",\n \" fig.send_message(\\\"refresh\\\", {});\\n\",\n \" };\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n \" var x0 = msg['x0'] / mpl.ratio;\\n\",\n \" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n \" var x1 = msg['x1'] / mpl.ratio;\\n\",\n \" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n \" x0 = Math.floor(x0) + 0.5;\\n\",\n \" y0 = Math.floor(y0) + 0.5;\\n\",\n \" x1 = Math.floor(x1) + 0.5;\\n\",\n \" y1 = Math.floor(y1) + 0.5;\\n\",\n \" var min_x = Math.min(x0, x1);\\n\",\n \" var min_y = Math.min(y0, y1);\\n\",\n \" var width = Math.abs(x1 - x0);\\n\",\n \" var height = Math.abs(y1 - y0);\\n\",\n \"\\n\",\n \" fig.rubberband_context.clearRect(\\n\",\n \" 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\\n\",\n \"\\n\",\n \" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n \" // Updates the figure title.\\n\",\n \" fig.header.textContent = msg['label'];\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n \" var cursor = msg['cursor'];\\n\",\n \" switch(cursor)\\n\",\n \" {\\n\",\n \" case 0:\\n\",\n \" cursor = 'pointer';\\n\",\n \" break;\\n\",\n \" case 1:\\n\",\n \" cursor = 'default';\\n\",\n \" break;\\n\",\n \" case 2:\\n\",\n \" cursor = 'crosshair';\\n\",\n \" break;\\n\",\n \" case 3:\\n\",\n \" cursor = 'move';\\n\",\n \" break;\\n\",\n \" }\\n\",\n \" fig.rubberband_canvas.style.cursor = cursor;\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n \" fig.message.textContent = msg['message'];\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n \" // Request the server to send over a new figure.\\n\",\n \" fig.send_draw_message();\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n \" fig.image_mode = msg['mode'];\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n \" // Called whenever the canvas gets updated.\\n\",\n \" this.send_message(\\\"ack\\\", {});\\n\",\n \"}\\n\",\n \"\\n\",\n \"// A function to construct a web socket function for onmessage handling.\\n\",\n \"// Called in the figure constructor.\\n\",\n \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n \" return function socket_on_message(evt) {\\n\",\n \" if (evt.data instanceof Blob) {\\n\",\n \" /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n \" * transferred with MIME type text/plain:\\\" errors on\\n\",\n \" * Chrome. But how to set the MIME type? It doesn't seem\\n\",\n \" * to be part of the websocket stream */\\n\",\n \" evt.data.type = \\\"image/png\\\";\\n\",\n \"\\n\",\n \" /* Free the memory for the previous frames */\\n\",\n \" if (fig.imageObj.src) {\\n\",\n \" (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n \" fig.imageObj.src);\\n\",\n \" }\\n\",\n \"\\n\",\n \" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n \" evt.data);\\n\",\n \" fig.updated_canvas_event();\\n\",\n \" fig.waiting = false;\\n\",\n \" return;\\n\",\n \" }\\n\",\n \" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n \" fig.imageObj.src = evt.data;\\n\",\n \" fig.updated_canvas_event();\\n\",\n \" fig.waiting = false;\\n\",\n \" return;\\n\",\n \" }\\n\",\n \"\\n\",\n \" var msg = JSON.parse(evt.data);\\n\",\n \" var msg_type = msg['type'];\\n\",\n \"\\n\",\n \" // Call the \\\"handle_{type}\\\" callback, which takes\\n\",\n \" // the figure and JSON message as its only arguments.\\n\",\n \" try {\\n\",\n \" var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n \" } catch (e) {\\n\",\n \" console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n \" return;\\n\",\n \" }\\n\",\n \"\\n\",\n \" if (callback) {\\n\",\n \" try {\\n\",\n \" // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n \" callback(fig, msg);\\n\",\n \" } catch (e) {\\n\",\n \" console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n \" }\\n\",\n \" }\\n\",\n \" };\\n\",\n \"}\\n\",\n \"\\n\",\n \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n \"mpl.findpos = function(e) {\\n\",\n \" //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n \" var targ;\\n\",\n \" if (!e)\\n\",\n \" e = window.event;\\n\",\n \" if (e.target)\\n\",\n \" targ = e.target;\\n\",\n \" else if (e.srcElement)\\n\",\n \" targ = e.srcElement;\\n\",\n \" if (targ.nodeType == 3) // defeat Safari bug\\n\",\n \" targ = targ.parentNode;\\n\",\n \"\\n\",\n \" // jQuery normalizes the pageX and pageY\\n\",\n \" // pageX,Y are the mouse positions relative to the document\\n\",\n \" // offset() returns the position of the element relative to the document\\n\",\n \" var x = e.pageX - $(targ).offset().left;\\n\",\n \" var y = e.pageY - $(targ).offset().top;\\n\",\n \"\\n\",\n \" return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n \"};\\n\",\n \"\\n\",\n \"/*\\n\",\n \" * return a copy of an object with only non-object keys\\n\",\n \" * we need this to avoid circular references\\n\",\n \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n \" */\\n\",\n \"function simpleKeys (original) {\\n\",\n \" return Object.keys(original).reduce(function (obj, key) {\\n\",\n \" if (typeof original[key] !== 'object')\\n\",\n \" obj[key] = original[key]\\n\",\n \" return obj;\\n\",\n \" }, {});\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n \" var canvas_pos = mpl.findpos(event)\\n\",\n \"\\n\",\n \" if (name === 'button_press')\\n\",\n \" {\\n\",\n \" this.canvas.focus();\\n\",\n \" this.canvas_div.focus();\\n\",\n \" }\\n\",\n \"\\n\",\n \" var x = canvas_pos.x * mpl.ratio;\\n\",\n \" var y = canvas_pos.y * mpl.ratio;\\n\",\n \"\\n\",\n \" this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n \" step: event.step,\\n\",\n \" guiEvent: simpleKeys(event)});\\n\",\n \"\\n\",\n \" /* This prevents the web browser from automatically changing to\\n\",\n \" * the text insertion cursor when the button is pressed. We want\\n\",\n \" * to control all of the cursor setting manually through the\\n\",\n \" * 'cursor' event from matplotlib */\\n\",\n \" event.preventDefault();\\n\",\n \" return false;\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n \" // Handle any extra behaviour associated with a key event\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n \"\\n\",\n \" // Prevent repeat events\\n\",\n \" if (name == 'key_press')\\n\",\n \" {\\n\",\n \" if (event.which === this._key)\\n\",\n \" return;\\n\",\n \" else\\n\",\n \" this._key = event.which;\\n\",\n \" }\\n\",\n \" if (name == 'key_release')\\n\",\n \" this._key = null;\\n\",\n \"\\n\",\n \" var value = '';\\n\",\n \" if (event.ctrlKey && event.which != 17)\\n\",\n \" value += \\\"ctrl+\\\";\\n\",\n \" if (event.altKey && event.which != 18)\\n\",\n \" value += \\\"alt+\\\";\\n\",\n \" if (event.shiftKey && event.which != 16)\\n\",\n \" value += \\\"shift+\\\";\\n\",\n \"\\n\",\n \" value += 'k';\\n\",\n \" value += event.which.toString();\\n\",\n \"\\n\",\n \" this._key_event_extra(event, name);\\n\",\n \"\\n\",\n \" this.send_message(name, {key: value,\\n\",\n \" guiEvent: simpleKeys(event)});\\n\",\n \" return false;\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n \" if (name == 'download') {\\n\",\n \" this.handle_save(this, null);\\n\",\n \" } else {\\n\",\n \" this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n \" }\\n\",\n \"};\\n\",\n \"\\n\",\n \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n \" this.message.textContent = tooltip;\\n\",\n \"};\\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 \"\\n\",\n \"mpl.extensions = [\\\"eps\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\"];\\n\",\n \"\\n\",\n \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n \" // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n \" // object with the appropriate methods. Currently this is a non binary\\n\",\n \" // socket, so there is still some room for performance tuning.\\n\",\n \" var ws = {};\\n\",\n \"\\n\",\n \" ws.close = function() {\\n\",\n \" comm.close()\\n\",\n \" };\\n\",\n \" ws.send = function(m) {\\n\",\n \" //console.log('sending', m);\\n\",\n \" comm.send(m);\\n\",\n \" };\\n\",\n \" // Register the callback with on_msg.\\n\",\n \" comm.on_msg(function(msg) {\\n\",\n \" //console.log('receiving', msg['content']['data'], msg);\\n\",\n \" // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n \" ws.onmessage(msg['content']['data'])\\n\",\n \" });\\n\",\n \" return ws;\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n \" // This is the function which gets called when the mpl process\\n\",\n \" // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n \"\\n\",\n \" var id = msg.content.data.id;\\n\",\n \" // Get hold of the div created by the display call when the Comm\\n\",\n \" // socket was opened in Python.\\n\",\n \" var element = $(\\\"#\\\" + id);\\n\",\n \" var ws_proxy = comm_websocket_adapter(comm)\\n\",\n \"\\n\",\n \" function ondownload(figure, format) {\\n\",\n \" window.open(figure.imageObj.src);\\n\",\n \" }\\n\",\n \"\\n\",\n \" var fig = new mpl.figure(id, ws_proxy,\\n\",\n \" ondownload,\\n\",\n \" element.get(0));\\n\",\n \"\\n\",\n \" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n \" // web socket which is closed, not our websocket->open comm proxy.\\n\",\n \" ws_proxy.onopen();\\n\",\n \"\\n\",\n \" fig.parent_element = element.get(0);\\n\",\n \" fig.cell_info = mpl.find_output_cell(\\\"\\\");\\n\",\n \" if (!fig.cell_info) {\\n\",\n \" console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n \" return;\\n\",\n \" }\\n\",\n \"\\n\",\n \" var output_index = fig.cell_info[2]\\n\",\n \" var cell = fig.cell_info[0];\\n\",\n \"\\n\",\n \"};\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n \" var width = fig.canvas.width/mpl.ratio\\n\",\n \" fig.root.unbind('remove')\\n\",\n \"\\n\",\n \" // Update the output cell to use the data from the current canvas.\\n\",\n \" fig.push_to_output();\\n\",\n \" var dataURL = fig.canvas.toDataURL();\\n\",\n \" // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n \" // the notebook keyboard shortcuts fail.\\n\",\n \" IPython.keyboard_manager.enable()\\n\",\n \" $(fig.parent_element).html('');\\n\",\n \" fig.close_ws(fig, msg);\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n \" fig.send_message('closing', msg);\\n\",\n \" // fig.ws.close()\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n \" // Turn the data on the canvas into data in the output cell.\\n\",\n \" var width = this.canvas.width/mpl.ratio\\n\",\n \" var dataURL = this.canvas.toDataURL();\\n\",\n \" this.cell_info[1]['text/html'] = '';\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n \" // Tell IPython that the notebook contents must change.\\n\",\n \" IPython.notebook.set_dirty(true);\\n\",\n \" this.send_message(\\\"ack\\\", {});\\n\",\n \" var fig = this;\\n\",\n \" // Wait a second, then push the new image to the DOM so\\n\",\n \" // that it is saved nicely (might be nice to debounce this).\\n\",\n \" setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n \" var fig = this;\\n\",\n \"\\n\",\n \" var nav_element = $('');\\n\",\n \" nav_element.attr('style', 'width: 100%');\\n\",\n \" this.root.append(nav_element);\\n\",\n \"\\n\",\n \" // Define a callback function for later on.\\n\",\n \" function toolbar_event(event) {\\n\",\n \" return fig.toolbar_button_onclick(event['data']);\\n\",\n \" }\\n\",\n \" function toolbar_mouse_event(event) {\\n\",\n \" return fig.toolbar_button_onmouseover(event['data']);\\n\",\n \" }\\n\",\n \"\\n\",\n \" for(var toolbar_ind in mpl.toolbar_items){\\n\",\n \" var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n \" var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n \" var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n \" var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n \"\\n\",\n \" if (!name) { continue; };\\n\",\n \"\\n\",\n \" var button = $('');\\n\",\n \" button.click(method_name, toolbar_event);\\n\",\n \" button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n \" nav_element.append(button);\\n\",\n \" }\\n\",\n \"\\n\",\n \" // Add the status bar.\\n\",\n \" var status_bar = $('');\\n\",\n \" nav_element.append(status_bar);\\n\",\n \" this.message = status_bar[0];\\n\",\n \"\\n\",\n \" // Add the close button to the window.\\n\",\n \" var buttongrp = $('');\\n\",\n \" var button = $('');\\n\",\n \" button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n \" button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n \" buttongrp.append(button);\\n\",\n \" var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n \" titlebar.prepend(buttongrp);\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n \" var fig = this\\n\",\n \" el.on(\\\"remove\\\", function(){\\n\",\n \"\\tfig.close_ws(fig, {});\\n\",\n \" });\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n \" // this is important to make the div 'focusable\\n\",\n \" el.attr('tabindex', 0)\\n\",\n \" // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n \" // off when our div gets focus\\n\",\n \"\\n\",\n \" // location in version 3\\n\",\n \" if (IPython.notebook.keyboard_manager) {\\n\",\n \" IPython.notebook.keyboard_manager.register_events(el);\\n\",\n \" }\\n\",\n \" else {\\n\",\n \" // location in version 2\\n\",\n \" IPython.keyboard_manager.register_events(el);\\n\",\n \" }\\n\",\n \"\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n \" var manager = IPython.notebook.keyboard_manager;\\n\",\n \" if (!manager)\\n\",\n \" manager = IPython.keyboard_manager;\\n\",\n \"\\n\",\n \" // Check for shift+enter\\n\",\n \" if (event.shiftKey && event.which == 13) {\\n\",\n \" this.canvas_div.blur();\\n\",\n \" // select the cell after this one\\n\",\n \" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\\n\",\n \" IPython.notebook.select(index + 1);\\n\",\n \" }\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n \" fig.ondownload(fig, null);\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.find_output_cell = function(html_output) {\\n\",\n \" // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n \" // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n \" // IPython event is triggered only after the cells have been serialised, which for\\n\",\n \" // our purposes (turning an active figure into a static one), is too late.\\n\",\n \" var cells = IPython.notebook.get_cells();\\n\",\n \" var ncells = cells.length;\\n\",\n \" for (var i=0; i= 3 moved mimebundle to data attribute of output\\n\",\n \" data = data.data;\\n\",\n \" }\\n\",\n \" if (data['text/html'] == html_output) {\\n\",\n \" return [cell, data, j];\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"\\n\",\n \"// Register the function which deals with the matplotlib target/channel.\\n\",\n \"// The kernel may be null if the page has been refreshed.\\n\",\n \"if (IPython.notebook.kernel != null) {\\n\",\n \" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n \"}\\n\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": 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DVVo/dwZWg85v99OJrQAvyOwgAD5bPMRuXnmatl8pEg/vrhPG7lpaHuJj4nSjwF/2HK4SGYs3yNfbXOdpFSF1kW928h9o7rIsI7peh8A8xFYgJ+pkYS7Z6+Td1bt14/bNouT+0d3ZSE7Aqp2aCmndU7XpwI5/+TWEhnJ9DRgJgIL8BO1iP2Zr7bp68gVl1eJ+vn184GZclV2psRHM2qF4Nh1rFjeWb1f5m44LBXGRS27ZSTJnad1kmtPzZJkzqMFmILAAkxWWVUt/122Rx6Zu1H25JXqfYNOSpE7T+8kWSkJjFrBEg4XlskHaw/K/74/IAVlVXpfakKM/HJIe7l9ZEdpn8almQBfEFiASaqrHTJr7X556OONsuGQ6zIm7ZrHyy3DO8hpndI4aSgsSY20zt94WGau2S+7j7t+IYiKjJBL+raVu5y/FLBOC/AOgQX4SI1Yvblyn0z9dPMP14dLT4yR60/NkvN6ttI/rACrq3b+KFiy67i8u3q/LNuTZ+wVGdYhTe4d1UUvjGedFuA5AgvwUmFZpby6bI++8K66AK/SLC5arhjYTi7u20biWGcFm9p2tEjeW3NA5m06LBXqZFpOap3WPWd2kWtPPYnvbcADBBbQRFuOFMk/v90u/1myW1/eRmmRGCNXnpIp405uxQ8fhAx10tIP1h2U99eqdVqu73U17a1O8fDLoe0lMZYF8UB9CCzAA2qdyvvO3+j/s3SXLNh8RJ8kVOmUniiX9msj53RvKVFRka6dQIhRr4L9ZMMheWvVPjlUWK73tUyOld+c3lluG9FJmsUTWkBtBBZQj6pqh3y7PVfeWLFX39yjVcqZXVrIz3q3kQGZnMsK4aO8qloWbDos/7d8n+zNdy2IV+sN7z6ji37lYfP4GL0PAIEFnEAtWP9uxzF5x/mb+szV++VAQZlxROSklHg5/+RWcnb3ltIyKZawQthSv3x8tuWIvLZ8r+w6VqL3pSXEyOQzOsvtIzpywXLAicBC2DteUiFzNxyS2d8flI+d97nFFcYRkZT4aBnVtYWc3S1D+rRhtAqoSYWWutahurj0TiO01As9bh3eUcdW62Zxeh8QjggshB31Q2Hp7uMyb+NhmbvxkCzedVzvc0t1RtWZXTPk9M5pMqBdikSoi7cBqJf69/P1tlz5vxV7f7jWZnx0pFw96CS5bURH6e/8dwSEGwILIU99i6vzU326+bC+4LL6jbvmeiqla0aSjOyUJkOyUqVH62acEhTwgvq3ps6lpaYO1x4oMPa6rnl42/COclGfNlzcHGGDwELIUd/SGw8V6pD6fMtR5/2RH1755Kam/oZ2SJPsk1IkO7O5ZCTHSY1BLAA+UP8G1x0s0Kd3+HJrrlQa/7jUv7uL+7aVnw9sJ6O7Zkg0r7xFCCOwYHvqEjXqyfxLZ1B9tU3dcuVgjcXpSkJ0pJzijCl169+uuXRJT3J+9xsHAfiNuubhxxsOy5z1B+VgjV901Lnj1AtG1PpGdd8xnWsfIrQQWLCd4vJKWbY7T77dkSvfbM/Vr/pTC9VrinP+ZqxOoTDQeevftrl0b5UskaylAoJGXYrn+wOF8vnWI3pk+Vitf7Md0xPk1KxUfRvkvA3MTNEXnwbsisCCpanFs2q6L2fPcb0YfdHOY7JqX/4PUw5uiTFROqb6tXPe2jSTbi2TuQYgYFHq37W6IPqKvXn6lyW1Xquqjh9FbZvHSe/WzaSX8990T+cvSepyPd1bJslJKQlcFxGWR2DBEtS34eHCcj3Vt875ZPu9837l3nxZtT9fn0W6ttbJsTLA+Rtu7zbJ+vQJanqBp1vAntSo9KbDRfoViBud9+ud//735Z84zV+TeoVi5xaJ0qVFknTJcN2rqyqoW4e0BEmK48zyCD4CC36lvr1KK6v1FJ66qbVR6uSd+/NLZW9eqb5IsrptO1r8k1f2uanRqV6tk6WH8zfYk9XNua3WbzhIKiBkqejafbxUdh0vkR25JbInr8T52Hnv3FdRawS7NnUZH3Vi4KzUBD3a1S4lzvlLmfPWLE5aOe9bJMXqE6OqRfcstIe/hGVgVVdXS3FxsfHI/8oqq+SZr7brqHD/dav/d//Nq3uH+p/zXq1T0MecN7V4Wz1WQ+dqSL2qWpzbani9Wj9W02Q/3qql0nlQPfGos5E77/Q+/eeM9+u66f+k5l6SpNYmqdF2fe98rKbWopzb+t59q/FY/bGa54ZSn5P7Y1RX3i9zBlWp83MuraiWgrIq5z7nB+4B9R4znU+KnZy/mWY579Vvol0ykqRNs3jXQQBhTz3PHCksl4OF6pe1Uv28eiDf9ViNehUaF6X2VFJclCTFOm/OX+TUxasTnduxzuiKjYqQGHUf7Xr+izae/1zPl67nQX1vPDep+5hIde6vTBmQmeraGQDq+beoqEhatWolkc7/PqwjLAPrwIEDcsYZZxiPAACwt5ycHElOTjYewQrCMrAKCgpk0KBB8uWXX/INCQCwrcLCQj1gQGBZT1gGlvqGzM7O5hsSAGBr/DyzLiZsAQAATEZgAQAAmIzAAgAAMBmBBQAAYDICCwAAwGQEFgAAgMkILAAAAJMRWAAAACYjsAAAAExGYAEAAJiMwAIAADAZgQUAAGAyAgsAAMBkBBYAAIDJCCwAAACTEVgAAAAmI7AAAABMFuFwMrbDRmFhoWRnZ0tOTo4kJycbe713qNTYAACgEa3ijQ0TmP3zDOZhBAsAAMBkBBYAAIDJCCwAAACTEVgAAAAmI7AAAABMRmABAACYjMACAAAwGYEFAABgMgILAADAZAQWAACAyQgsAAAAkxFYAAAAJiOwAAAATEZgAQAAmIzAAgAAMBmBBQAAYLKgB1Zubq6MGTNGFi9ebOwRefjhh6VPnz4ycODAH25vvfWWcVRk1qxZ+s8MGDBAJkyYICtWrDCOAAAABF9QAysnJ0cmTZoku3btMva4rFmzRh599FEdTu6bejtFhZg69vjjj8vSpUtl/Pjxcsstt0hJSYk+DgAAEGxBCyw1CnXPPffI5MmTjT0u5eXlsmnTJj2CVZd33nlHzj//fMnOzpaYmBi57rrrJC0tTebMmWO8BQAAQHAFLbBGjhwp8+fPl3Hjxhl7XDZs2CCVlZXy7LPPyvDhw2Xs2LEybdo0qa6u1se3bNki3bt319tuXbt21X8OAADACoIWWC1btpTo6Gjj0Y8KCgpk8ODBcvXVV8uXX34pTz31lMyYMUOmT5+ujxcVFUlCQoLedouPj5fi4mLjEQAAQHAFfZF7bSNGjJBXX31VR5aaAuzXr59ce+21P0wBqrgqLS3V227qcVJSkvEIAAAguCwXWAsWLJA333zTeOSi1mWpUSqlW7dusnnzZr3tpqYN1X4AAAArsFxgORwOmTp1qixcuFBvq1cQqhEt96sIJ06cKLNnz5ZFixZJRUWFvPLKK3L06FF92gYAAAArsFxgqVB64IEH5JFHHtHnv7r33nvljjvukIsuukgfHzZsmD5PljquphE/+ugjefHFFyU1NVUfBwAACLYIhxomCjOFhYX6NA/qPFzJycnGXu8dOnFJGAAA9WrlWvFiCrN/nsE8lhvBAgAATVNZ5TqVEayDwAIAwObKKgksqyGwAACwudKKKmMLVkFgAQBgc8WVBJbVEFgAANhccTmBZTUEFgAANldEYFkOgQUAgM0VEliWQ2ABAGBzBaWVxhasgsACAMDm8ksrjC1YBYEFAIDN5RFYlkNgAQBgc3klrMGyGgILAACbyy0pN7ZgFQQWAAA2d7SYwLIaAgsAAJvLLWINltUQWAAA2BwjWNZDYAEAYHNHiggsqyGwAACwudxipgithsACAMDm1MWei8o4m7uVEFgAAISAw0wTWgqBBQBACDhYUGZswQoILAAAQgCBZS0EFgAAIeBgIYFlJQQWAAAhgBEsayGwAAAIAfvzS40tWAGBBQBACDjACJalEFgAAISA/fkElpUQWAAAhABGsKyFwAIAIAQcKCgVh8NhPEKwEVgAAISAkopqyS/lcjlWQWABAGBzibFR+p5XEloHgQUAgM21SIzR93vzCCyrILAAALC5jKRYfb+HwLIMAgsAAJtzB9bu4yX6HsFHYAEAYHMtjMDaRWBZBoEFAIDNtUqO0/fbjxbrewQfgQUAgM21aWYEVi6BZRUEFgAANtc6+cc1WNXVnGzUCggsAABsLiM5TqIiI6S8yiF78liHZQUEFgAANqfiqp0xTbj5cJG+R3ARWAAAhICTUhP0/UYCyxIILAAAQkBWary+33S4UN8juIIeWLm5uTJmzBhZvHixsUdk7ty5ctFFF8kpp5wio0ePlueee06qq6uNoyLnnXee9O/fXwYOHPjDbevWrcZRAADCT5YxgrXuQIG+R3AFNbBycnJk0qRJsmvXLmOPyNq1a+W+++6Tu+66S5YtWyYvvviivPfee/LKK6/o44WFhbJ9+3aZM2eOrFix4odbly5d9HEAAMJRp/REfb/uIIFlBUELrFmzZsk999wjkydPNva47N27Vy6//HIZNWqUREZG6nBSI1xLly7Vx1WApaamSmZmpn4MAABEOqa7RrD255fJseJyvY3gCVpgjRw5UubPny/jxo0z9riMHTtWHnjgAeORSGlpqXzxxRfSu3dv/XjNmjWSkJAgV111lQwZMkQmTJggn3/+uT4GAEC4SoqN/uF8WGuZJgy6oAVWy5YtJTo62nhUNzUdeNttt0l8fLxcd911el9ERIT07dtXHnvsMfn666/1/jvuuENWrlypjwMAEK46tXBNE67el6/vETxBX+Ren23btumpwsrKSnn11VclOTlZ77/xxhvl2WeflY4dO0psbKyMHz9ehg8frhfGAwAQzrq2SNL3KwmsoLNkYH355Zdy6aWXymmnnSYvv/yypKSkGEdEP164cKHxyKW8vFzi4lwnWAMAIFx1yXAF1oq9efoewWO5wFJTfWpaUK3Duv/++38yjbh//3754x//KLt379ajWzNnztSvIrz44ouNtwAAIDx1NaYI1akaKqt+PL0RAs9ygfXvf/9bh9OUKVNOOM+VmhpU1CkcTj/9dLniiitk0KBB8uabb8q0adOkQ4cO+jgAAOGqXUq8JMRESmllNWd0D7IIh5OxHTbU4vns7Gx9Hi732i5fHCo1NgAAaEQr1wnXTeH+efbMO/MkIdE1PXjHrLX6VYQzrhgoV2WfpPch8Cy7yB0AADRdt5au0Fq+h3VYwURgAQAQQroZC91z9hzX9wgOAgsAgBDiDqxV+/IlDFcBWQaBBQBACOmYliAxkRGSV1op23OLjb0INAILAIAQEh0V+cMZ3TkfVvAQWAAAhBj3NOGKvZzRPVgILAAAQkxXI7CWs9A9aAgsAABCjHsEazkjWEFDYAEAmmz18YZvCK7OLRIlwnl/sKBMDuRzNuxgILAAAB5pSkARWcGVEBMlWakJens5C92DgsACANSrKVFVG5EVXJzRPbgILABAncwIJCIreLobgZVDYAUFgQUA+IF7tIowsr8fA4svZjAQWAAAjagKLeqVhGqh++7jpXKooMy1EwFDYAFAmGPEKjQlxUZLhzTXQvclu/kCBxqBBQBhjLAKbT1bJ+v7RTuP6XsEDoEFAGGIUavw0Kt1M32/cEeuvkfgEFgAEEYIq/DS2xjBWrzruFRUVettBAaBBQBhgrAKPx3TEyUlPlqKyqtkiTOyEDgEFgCEOEatwldkRIQMaNdcb3+25Yi+R2AQWAAQwggrnHJSir5fsOmwvkdgEFgAEIKsMmrVL9XYQNBkG4H13Y5jcqy4XG/D/wgsAAghTAeitsyUBOmUniCV1Q7537qDxl74G4EFACGAsEJDTu/cQt+/u3q/vof/EVgAYGOEFTxxhhFY8zYdlrySCr0N/yKwAMBm3FFFWMFTHdMTpGNagpRVVsuMnD3GXvgTgQUANmG3qGKBu3VERETI+D5t9PZz32yX6mqH3ob/EFgA4Ac1R5kau9XH07cDPDG2e0tJjImSjYeL5NPNnBPL3wgsADCJtzFU88/VvAFmSoyNkrE9Wurtp7/aqu/hPwQWAPjAjkG0MvenN4SHCX3bSFSEyMcbDssXnNndrwgsAPCCnaOqLkRWeDgpNUEu6NVab98z+3vWYvkRgQUATWCnsGosqmprytvCvq4dlKXXYuXsyZM3Vuw19sJsBBYAeMCOYRVMvILQutISY+TnA9vp7fs/Ws/lc/yEwAKABtglrNxRxQgUPHFp/7ZyUkq87M0rlVveXSMOB1OFZiOwAKAOdgsroCnioqPkwbO66gXvb63cJ69x8lHTEVgAUANhhXBxcutmcu2pWXr7tvfWyvajxXob5iCwAMCJsPoR4RY+rhiYKX3bNJOCskqZ8MpSKSitNI7AVwQWgLBGWCGcRUVGyINnd5O0hBhZuS9fLn8tRyqrqo2j8AWBBSBsEVaASJtmcTLlvB4SFx0pc9YfkjtmrWXRuwkILABhxw6jVoQVAkmtx3ro7G4S4dz+98Kd8pcvuJSOrwgsAGHD6mHljirCCsEwslO63Daio96+78P18s9vtutteCfogZWbmytjxoyRxYsXG3tEVq1aJZdeeqkMHDhQRo8eLe+8845xxGXWrFn6zwwYMEAmTJggK1asMI4AwE/ZJay85f78at4Ab1zSr61caZyE9PZZa4ksHwQ1sHJycmTSpEmya9cuY49IXl6e3HTTTfKzn/1Mli5dKlOmTJGpU6fK6tWr9XEVYo8++qg8/vjj+vj48ePllltukZKSEn0cANysHBu+jlY1FlNW/txhbb8Y0l6uILJ8FrTAUqNQ99xzj0yePNnY4zJv3jxJTU2VK6+8UqKjo2XYsGFy4YUXyuuvv66Pq9Gs888/X7KzsyUmJkauu+46SUtLkzlz5ujjAKBYLS5qBpW/oqouRBaaKiIiQm50Rpb7cjoqsp4jsposaIE1cuRImT9/vowbN87Y47J582bp3r278cila9eusmHDBr29ZcuWBo8DCG9NDRB/MSOo3Hz9nAL998F1CO1PRdYva0SWemXhnxds5tWFTRC0wGrZsqUeoaqtqKhIEhISjEcu8fHxUlzsOsNsY8cBhKdghVXtkHLfzBCszwlQ3JF1TXamfvy7jzfoxe9ElmeCvsi9NhVPpaWlxiMX9TgpKUlvN3YcQPgJVIT4K6RqI6xgFSqyrh/cXm4d3kE/Vqdv+OXbq6WqmshqjOUCS03/qWnCmtS0YLdu3fS2um/oOIDwEYgQ8XdM1URYwaou7d9O7juzi0RGiLy8ZJdcPiNHyis543tDLBdY6vQLR44ckVdeeUUqKipk0aJFMnv2bLnkkkv08YkTJ+rHar86rt7u6NGj+s8BCA/+DJGaQRWIqFIIK9jBeSe3kj+M6S7RzsqauXq/XPSfJVJczrUL62O5wFKvCJw+fbp88sknMmTIEHnooYf0bejQofq4elXhww8/LI888ogMHjxYPvroI3nxxRf1Kw8BhD5/hEigg0pxRxVhBTs5o0sL+fO4nhIfHSmfbDgsY6ctlrySCuMoaopwhOFqtcLCQn2aB3UeruTkZGOv9w6duCQMgJ+YHSOBDCq3YAVVU1/ZNyDd2PACryJsWKt4Y8ME7p9nz7wzTxISA7cWee2BAnngo/VSWF4lAzObyye/HCqtmsUZR6FYbgQLAGoze6Qn0KNVirefw+pjP70BwdanTTN5+qLekpYQIyv25svp//xW9ucz2lATgQXA0uwcVu6oaurn0FhMEVmwgq4ZSfL3n/WWNs3iZOPhIhn1/HdygMj6AYEFwLLMiqtAhVXNoPLmY28oqmqzamQxPRheslIT5G/je0nr5FgdWaP/vVAOFpQZR8MbgQXAcrwNlNr8HVY1Y8qXj7cpYQVYTdvm8fLX8b2llTOy1h8slLOckXWIyCKwAFiLlcPKrKByI6wQKjJT4vVIVkZSrKw7UCBjXlgk+aXh/epCAguAZfgaLWaHldlB5WZWWPkrznx5BSHCV2aKa7qwRWKMrN6fLxP/u0wqqsL3ZKQEFgBLMCOuzOCPoFLcUeWvKAKsQK3JmmKcJ2v+piNy88zVYXvtQgILQND5EjNmjFr5K6oUK0QVC88RSD1aJuszvqvL6kxfslumLDjx8nbhgsACEFS+xpW3/BlVil1HqzjBKMwwrGOa3Dmyk97+/Scb5b3V+/V2OCGwAASNt3Hjy6hVIKLKjmEFmG18nzZyaf+2evv6t1bK1iNFejtcEFgAgsKXuGoqd1T5I6yCGVX90owNwKJuGtJen/U9v7RSLn11mZRWVBlHQh+BBSDgAhVX/oiqmkEVjKhqqqZM2/HqQZgtOipS/jCmm6TER+tL6kz+YJ1xJPQRWAACKhBxZWZY2S2ogoX1V6hPy+Q4efCsrhLh3P73wp1hsx6LwAJgaU1db+VrWPkzqLYcKWzwFkyMXsGfBrdPk8sHttPbv5q5OiyuWUhgAQiYpsZPoEat/BVUir8CypP1V4EaVWL0Cp647tQs6dIiUY4UlctN74T++bEILAAB4a+48jas/BlVihVGpTzF6BUCITYqUk8VxkRGyOzvD+pzZIUyAguA3/kzrpoqECNV3oaVmUHGqBKsqHOLJLlhcJbevuv9tbLtaOieuoHAAuBXVogrf41W+RpU3jLz9Ay+jl4RcmiqS/u3k35tm0theZVc+8ZKqaoOzalCAguA3/gjrtT79PT9mhlVNWMqGFHVFEQPrCwqMkJ+O7qLJMZEyjfbc+UvX2w1joQWAguAJXgaV54wI6zsElO+YPQKwdK2ebzc/sOldDbIyr15ejuUEFgA/KIpo1dmx5U37BJUnL0doeLcHi1lZKc0qahyyKQZOVJQWmkcCQ0EFgDTmRlX6n158v68GbWyQ1A1laejSoxeIdgiIiLknjO6SKvkWNl0uEhuemdVSJ26gcACYCqz46oxTQ2rUIwqwK5SEmLk92O663VZb67cp8/0HioILACmCUZceSoUoqqx6UFGr2BH6mLQNw1tr7fven+dLN3VhCcSCyOwAAScr3HVlFErO4VV14xkYwsIL5f2a6vXY5VXVctF/1kie46XGEfsi8ACYApPR6/MiCtPhMKIVVMwegU7U+uxfju6q3ROT5T9+WVywctLpLDM3oveIxyhfjGgOhQWFkp2drbk5ORIcrLvvzEeCv1rVgINslJc+RJVRXvNPR9PUmYXY8szDY1gWWF6kLgyR6t4Y8ME7p9nz7wzTxISk4y99nWgoExufXeNHCupkAt6tZb3rz9Vr8+yI0awAPjE07hqjK9x5c2IlQqqmjez+ev91kb4IFS0aRYnj53bQ+KiIuXD7w/K5A/W2faVhQQWgIBoaPSqobjyZL1VU8LKn0FVH1//W2ad+4rRK9hBrzbN5P7RrtHff3yzXf40b5PethsCC4DXzJgabCyuGuLpqFUwoipQCB+EolFdM+TXIzvq7UecgfX3r7bpbTshsAB4xQpx1RgrRVVjH4e/X0HI6BXs5uK+beWGwVl6+64P1skrS3brbbsgsAA0mRlx1ZCG4sqTUatQGq0K9qVxiCsE01WnZMpl/dvq7V+8vVLeXrlPb9sBgQUgKOqLtMbiqjGhElae8DR+fD01AxAs6vQNNw/rIOef3EqqHSJXvL5c3l1tj8gisAA0iT+nBn2Jq1AatbIKRq9gBSqyJp/eWcb2aClVzsq6fMZyeX/NfuOodRFYADxmxbjyKqx2r278FkD1rb8K5vQgcQUrUefCuvfMLjKmW4ZUOiPrshk5MnvdAeOoNRFYADziz7hqSGNx5RFv4qn2n6l5sxGmBxEqVGTdP7qrnNU1QyqqHHLJf5fJR98fNI5aD4EFoFHehFFT1Dd65XNc+SuIasaWP96/B/w5wsToFaxKRdYDZ3WVUV1b6Mia8Moy+Xi9NSOLwALQoKbElZlTgz7FVaDDJ0iR1RhvRq+IK1idiqwHR3eVM7qk64tDX+yMrLkbDhlHrYPAAmCKQMRVo+utgjii5O1/14rrrwCri46KlIfO6iand06Xsspq+dl/lsqCTYeNo9ZAYAGolz+nBr2JqwZZdBTJHzwZZWL0CqFOR9bZ3WRkxzQpdUbW+OlL5JttR42jwWfJwPrf//4nAwcOPOHWp08ffVMefvhhvV3z+FtvvaWPATCHv6cG6+JVXAVz1Ko2G0cecQU7inFG1u/P6S5DO6RKSUW1nP/yElm+x4+/GTaBJQNr/PjxsmLFih9un3zyiaSmpsqUKVP08TVr1sijjz56wttMmjRJHwPgO3/HVV2jV17HlU0kZbouXhsIvHIQ4STWGVkPj+kuA9o1l/zSShk7bbFsOFhgHA0ey08ROhwOuffee+XMM8+Uiy66SMrLy2XTpk0/jGYBMBdxFXyBXn/F6BXsLj4mSh47r4f0aJkkR4rK5ewXFsmO3GLjaHBYPrA++OAD2bJli/z2t7/Vjzds2CCVlZXy7LPPyvDhw2Xs2LEybdo0qa6u1scBeK8pceWN+tZd1aXeuGrqlOAu59vWvlmENxd4biyGmjp6RVwhVCTFRssT558sHdMSZG9eqYx7abHklVQYRwPP0oGloulf//qX3HzzzZKc7HoiKigokMGDB8vVV18tX375pTz11FMyY8YMmT59uj4OwDtNjSt/rrtqMK480VhM1Txe+xZGiCuEmpSEGHnygpOlZVKsrD9YKJe9miOVVcEZgLF0YC1evFgOHTokEydONPaIjBgxQl599VUdWTExMdKvXz+59tprZc6cOcZbAGiqQMSVp1ODPsWVGZFUM7Zq3myAtVeASMvkOD1dGB8dKfM2HZY731+nlxsFmqUDa+7cuTJmzBhJTEw09ogsWLBA3nzzTeORi1qXFR8fbzwC0BT+nhZU/B5XgYggH96/WQvczRxxYvQKoax7y2T53dndJMK5/fx3O+S5b3a4DgSQpQMrJydHTj31VOORi6rQqVOnysKFC/W2egWhGtHiVYRA03kTV2ZNDXqsobgK9OiSif8ts08w2pTRK+IK4WBkp3S5aWh7vf2b/62TpbsC8NtkDZYOrD179kirVq2MRy5qROuBBx6QRx55RJ//Sr3C8I477tCvMATguUDFlU+jV43FFZqMuEI4mTSgnZzZpYVUVjvkyteXS2FZpXHE/yIcwZiYDLLCwkLJzs7WI2TuxfO+OFRqbAA2YXZcKXW9z5CNq/b9jI1asureX9cUYVNHsBoKI0av7KWViSta3D/PnnlnniQkJhl7UVOBM6pufHuVHCosl18Mbi8vTepvHPEvS49gATBfoOKqLuE4cuXvE4wSV0DDmsVFywOju+r1WC8v2SXvrt7nOuBnBBYQRryJq8bU9z49OedVuE4Lmjl65SniCuFsQGaK/HxgO72tXlVYXO7/qUICCwgT3sZVY6NXdfF0arBJWHP1E56OXhFXgMi1g7KkdXKsPgnp019tM/b6D4EFhAF/xVXApgatFFdNWH9lxvQgcQSYIzY6Um4c4npV4eOfbpED+f5dQE1gASEu0HFVe/QqpOLKj5p6egZGr4CmG90tQ3q2SpLC8ip5ZN4mY69/EFhACAvktKBi+rqrEIyrplx/sL44Iq4A70RGRMivhnXQ2/9duluOFZfrbX8gsIAQ5W1ceaKu9+3JuitLxNWula6bmXycHvT25KINIa6AuvVv21w6pydKaWW1vL58r7HXfAQWEIJ8iSt/rrv6iUDElTuoaodVffsbUt/6Kw8FavSKuALqFxERIef3cp3E/MVFO/12nUICCwgxwYgrr6cG62JGXDU1nHxRz8lFPVXX6JWvU4MAGjame0uJjYqU1fsLZNnuPGOvuQgsIIT4M67qY+rUoK9xFaioaoS/Ty7aEEavgMapk48O6+j67eaj9Qf1vdkILAAexVVd8WaZuApEWPlhetDs0SviCvBc9kkp+v6zzUf0vdkILCBEeDt65W1c1cXrdVfexlWwR6z8dO6r2ogrwHwD2zXX94t2HfPLmd0JLCAEBCOuvFp3Vd+idm8EMqyCPHrVGOIKaLrMlHhpmRQrFVUOWbjDgye0JiKwAJvzZ1zVx6upQTNfMRjMUSs3H0avzJwaJK4A76hXE/Zq7frlZ+W+fH1vJgILsDFv48pTdb1/r9dd1cUOcRXAUzPUxqsGAf/qmJ6o79cdLND3ZiKwgDDkz3VXfl3U7q+4aj/A2PCQh6NXvkwNsu4K8L8OaQn6fu1+AguAwZ9Tg/W979qjVwFd1B6MaUEfR69qY90VYC0d01wjWBsOFZh+wlECC7Ahq8aVR+uu7Lrmys2H0ava6osk1l0BgaEWuisFZVVypMjc6xJ6HFjHjh2Tm2++WU499VS57rrrZMuWLcYRl1NOOcXYAmBFlnjFoBXjqr7pwbpGrzw8a7unU4N1Ia6AwImNjpSMpFi9vT23WN+bxePAevzxx/Xw2RNPPCGtWrWSK6+88oTI8te1fACcyJvRK0u8YtDuI1f18OSVg2atuyKuAPO1ax6n77cdDVJgffvtt/Lkk0/K6NGj9f3ll18uv/rVryQvz3UNH/VyRwD+5c+4qut9mxZX3ghEXPk4emXm1CCvGASCo21z1zTh1qNF+t4sHgdWRUWFJCf/+MQxefJk6dWrl/zmN7/RjxnBAqzHl7iqi9dx1dTRq2COXPmwsN2TqUFvR6EYvQL8o60xgrU1WCNYvXv3ln/9618nhNTUqVNl79698uCDDxp7APiLN6NXnqjv/Ta2qP0ncVUfq8ZVU07N4OVJRVl3BVhfO2MEa8uRII1g3XffffLWW2/phe5uakRr2rRpsnDhQiktLTX2AjCbv6YGvY2rOpm17ioQAjA1yLorwB4yjcDaHqwRrJ49e8r8+fOlurpaNmzYYOx1Ph+1by/vv/++3H///cYeAMHGKwa9QFwBYcm9BmtvfqmUVlTpbTN4HFhKfHy8JCUlyaRJk2TChAnyxhtvSEFBgaSkpOhTNwAwX1NHr3jFYCOaetb2RvhrUTtxBQRGakK0JMREiloBtcPEUzU0KbCUZ555Rr755huZOHGizJo1S0477TQ9fbh06VLjLQCYxV9xVdf7DYu4qo+Xo1f+XNQOIDDUWRDc67DMXOje5MBSmjVrJldccYW8/fbbeuH7smXL5JprrjGOArAyT6MtJOOqrtGrIMQVo1eAtbgDy8xzYXkVWEVFRfLuu+/K1VdfrRe99+3bV1566SXjKAAz+GP0qr732eRXDIZ5XNWFVwwC9uU+VcOOY0EMrLvvvltGjhypg2rUqFHy+eefy9///ncZMWKE8RYAfEVcmczTdVcsagfCUnqi63I5+/PNOyNCkwMrOjpaXnzxRfn444/lhhtukPT0Rp4xAPiV2YvaawrZNVdKXaNXtRBXQHhIT4zR9wcKyvS9GZocWOpahIMGDTIeATBbU0evPFHX+/RkUfsJ7BpXXk4NEldA+EhLcAXWwWAGFgD/CeTUYG2NTg3WRlz9hDehRFwBwZccF63v80or9b0ZCCzApoK67irM46ou9YVSY6NXAIKvqtp1GcCYyAh9bwYCC7AIf0wN1sXndVfE1U9Gr7yNK0avAGuodAdWlHlZRGABFmCVqcGQiCtPmRRX9SGuAPv4MbAYwQLClj+nBk9Q16L2pjIjrlTUNSXsPBm9MjGu6gol4gqwlyNF5fo+1VjsbgYCCwiyYE0NBmRRe6B5EVd1Ia6A8LLlSJG+H9Cuub43A4EFBFEwpwZrsvTUoAqkutZP1eblyFXt0St/xhUAa9pqBNagLPN+AyKwAJvwJK7q49PUYIiOXAV6WlBh9AqwHvUKws1GYA3MTNH3ZiCwgCDxx9RgXe+zSVODZqy7UgK5sN0CceUJ4gqwpqW7j0theZU+2Wiv1s2Mvb6zbGDNmTNHevXqJQMHDvzhdu+99+pjq1atkksvvVTvGz16tLzzzjt6P2AXlp0arM3qo1cWiSvWXQH29dH6g/r+yuxMiY02L4ssG1hr1qyRiy66SFasWPHD7amnnpK8vDy56aab5Gc/+5ksXbpUpkyZIlOnTpXVq20wjQEEWEhPDRJXAHx0tKhcvtvheqK8ZVhHfW8WSwdWnz59jEc/mjdvnqSmpsqVV16pLzw9bNgwufDCC+X111833gKwtmCNXgVkajBQbBJXAKztg3UHRJ0Ca3D7VOnVxrzpQcWSgVVdXS3r1q2TL774QkaNGiWnn366/P73v9ejV5s3b5bu3bsbb+nStWtX2bBhg/EIgFJ79KomW08N2iiuGL0CrGtvXqm8uXKf3r7njJ8+R/jKkoGVm5ur11+NHTtWr8V68803ZceOHXoNVlFRkSQkJBhv6RIfHy/FxcXGI8C6/DF6VZegTg36c4E7cQXABA6HQ/7xzXapqHLImV1ayMT+bY0j5rFkYGVkZOgpv4kTJ+qYateunY6rr776Sv+llJaWGm/poh4nJSUZjwBr8mYRuiea+n4bHb2yKhPiSoWVr3EFwP7UuqvFu47rizv/e2I/iYgw7xI5bpYMLDXd95e//EXHlFt5eblERkZKv3799DRhTVu2bJFu3boZj4Dw1tDo1U/iyi5rr0yKq7rUFVcNYfQKsLfc4nJ55uvtevuuMzpLj1Z1Pzf4ypKBpRaxqxGsl156SSorK2Xfvn36FYQXX3yxnjY8cuSIvPLKK1JRUSGLFi2S2bNnyyWXXGL8acB6vBm98tepGX5QV1xZce1VEOKKqUEgNKmTij46f7O+9mC3jCR5eMyJa7rNZMnAatOmjbzwwgvy6aefyuDBg3U89e3bV/7whz9IWlqaTJ8+XT755BMZMmSIPPTQQ/o2dOhQ408D1uKvqcG6NGn0yg4aiysVVl7ElQorf8QVAGubtminrNyXL0mxUfK/G06VpLho44j5Ihw15+HCRGFhoWRnZ0tOTo4kJ/s+NHjoxCVhwAm8DazGRrDqer81A8ur0zKYMYJl2jUIa8WVHxezu/kaV4xewROt4o0NE7h/nj3zzjxJSGQtckM+33JE/jTftcTo7auz5dIB7fS2v1hyBAsIFcEcvarJVqNXKqwsFFeeIq4A61qzP18e/2yL3v7NGZ38HlcKgQVYkDejVzU16bQMblZYf2XCeivFzLhiahCwt61Hi+SBORukvMoh5/ZoKU+c38s44l8EFuAnjF41kQlxpcIqGHHF6BVgTfvyS+W+D9dLUXmVDO2QKu9df6pERwUmfQgswA/8GVc+jV41pHbMBEpdU4KKF3FVF0augPCkTsdw7+zvnfcV0qt1snz8y6GSEBNlHPU/AguwuSaNXlntvFd1hZUS5LhqCkavAOtRcfWb/30v+/LLpENagiy4eZikJsQYRwODwAJM5uvolbeXx1G8Hr1yM2MUq75oqqmhUSsT4kqFlS9xxdQgYF/uuNp5rETaNo+Tz24Z5rw38aWbHiKwABP5e91V7fff0OiV1/wVWe6oquuYUtd/t0ZcqbDyNK4aYlZcAbCe2nH19W0jpHOL4Jy+gsACQkSD571qqrpGkpqqZlDVF1VudY1a1Yqr2oIdV4xeAdai4upuI67aNIuTr5xx1SUjeOcGI7AAk4TE6FVtZoRWY+qKqxrqGrUirgDUdLTINXK1w4irr28f4XyeCO6JVwkswEJ8WX9Vky1OzVBXvHmx3krxNa4A2NfhwjKZ/MG6H6cFLRBXCoEFmCDYo1ceLW735RWEZo9i1fX+vIgrFVZmxBWjV4A9HVJx9b/vZXdeqWSmxMs3FokrhcACfGRWXJk1elWvWgHTZGZEVl2jVoqXcdUY4goIXQcLXCNXe51xlZXqiqtgLWivC4EFWFxTR6/8Oj3obWQ1FFY14kqFlRlxpULI7LgCYB0qrn7zv3U/nOdKxVXH9ETjqDUQWIAP/D01aDpfR7GU+mKpLg29bSOjVoo/FrN7i9ErwBrUtGDNuFKnYmifZq24UggswEtmxlV904OmrL3yl4Yiq7EI82BK0N9xxdQgYD96zdUHJ8ZVlvPeiggsINzUmpbziTukat/qU8d/25MpQSVYcQXAGtyvFrRDXCkEFuCFYIxemc4dO2bFVmPq+O94ut4qmHHF6BUQfPokorN/vLag1eNKIbCAJgrWuiu/Tg/6M7TqeN8qrDyNq8YQV0BoyyupkHuccbX7uOtUDF/eOtzycaUQWEAQBW30qj5mhlY976t2WCnerrciroDQVlhWKfd+uF6255ZI6+RY+cIZVx0s9mrB+hBYQBNYZfTK73yJrAYira5RK2/jyp+IKyD4Siqq5P6P1svmI0XSIjFGPnfGlVVOIuoJAgvwkNlx5cvoVUBePdhAKNWpkbDyZEpQ8UdcsagdsJfyymp56OON8v3BQkmJj5ZPbx4mJ7duZhy1BwILCIKmnLW9qaNXdU3B+cQdTo3d6tGUKUErxBWjV0BwVVZVy5/mb5Lle/MkKTZK5t40RPpnphhH7YPAAjwQqKlBs/47pkeWF+oatVICsd7KjbgC7KXa4ZAnv9gq3+44JrFRkfK/G06VIR3sOQRNYAGNCNTUYF18WXtVX+D4W0NhFcj1VsQVYC8OZ1z9/evtMn/TEYmOjJC3r8mW0d1aGkfth8ACLMJfo2Tu4PF3bDX03wjkeiuFuALsRcXVtEW75H/rDkqE8/F/JvWXi/q0cR20KQILaEAwR6/q48sCd3/EVmNhRVwBaMzry/fKmyv36e1/TugrVw3K0tt2RmAB9QhkXNX13/L3qRl8ja3G/mxDYeWPuFJhRVwB9jNrzX55ecluvf34+T3llhEd9bbdEViAzdQXLr7wJLZqvk1Db+frqJW3cdVUxBUQfB99f1Ce/WaH3n7grK5y/+huejsUEFhAHYI9ehVMtUPKffNEQ/FnlSlBhbgCgu+TDYfkr19u09t3jOgoU87rqbdDBYEF1GKF4GlsetAfo1i+aGzUirgCUNOCTYflyc+3isO5/auh7eXvF/eRiAi1vD10EFiAn/lr9MoKkdVQWCmNhZXiTfB4s95KIa6A4Pt8yxGZ+tkWHVc3nJolz1/SL+TiSiGwgBoCOTVohmBEljuqGgsrT0atvI0rbxBXQPCpkavHFmyWamddXZ2dKS9e1l8iI0MvrhQCCzAEemrQrP9ezeCpfTOTJ+/Tk7BSvI0d4gqwr483HJI/f7pFx9VVp2TKfy4fGLJxpRBYgJ/4e/TKEzVjy5NAqqmpf87TsPJ21MrbKUHiCgg+dQJR95qrXwzOkv/+fKBEhXBcKQQW4BToqcFAj5bVVDuc6rt5ilErAA15d/V+efor16sFbxneIaSnBWsisAB4pSlhRVwB4Udd/uaVpbvluW9d57mafHonfZb2UFzQXhcCC2HPigvbPQmXYHBHlacfny9hRVwB9lXtjKt/OMPqv8v26Me/H9NN/jq+d9jElUJgIawFc6rOTpoSVUqwRq2IKyD4KquqZeqnW2TWmgP68TMX9ZI/ndszrOJKIbAAE3kyeuVp1DUlaPzBHVWBDCtGrQB7K62okt/P3SgLNh+R6MgI+e/PB8idp3t3vVO7I7AQtuwwehXoyPImqhRfR4+8DSuFuAKsIb+0Qu75cL0s2nlc4qIj5d3rBsk1g7KMo+GHwAJM4q/TMtSMnvpu3jDj/fgaVgpTgoD9HS4skzvfXyfrDhRI8/homXfTUBnfu41xNDxZNrA2bNgg119/vQwePFhGjBgh9913n+Tmun6CPfzww9KnTx8ZOHDgD7e33npLHwM8EWprr+qKpcZuvjArrJgSBOxv17ESuWPWWtnhvG/dLE6+vm24nN6lhXE0fFkysEpLS+XGG2/U4fTNN9/Ihx9+KMePH5cHH3xQH1+zZo08+uijsmLFih9ukyZN0seAYLDCSUX9zR1VZsQNo1ZAaNh4qFDueH+tHCwsly4tEmXxr0dKv3YpxtHwZsnA2rdvn/Ts2VNuu+02iY2NlbS0NB1QS5culfLyctm0aZMewQK8wSsHPWdmVCmMWgGhY8XePJn8v3WSX1opA9o1l4XOuOqQnmgchSUDq3PnzvLSSy9JVFSUsUdk7ty50rt3bz11WFlZKc8++6wMHz5cxo4dK9OmTZPq6mrjLQFrs0MomBlVbt6GlUJcAdbyzfZcuf+j9VJSUS2nd06Xr24bIS2T44yjUCy/yF2dCfbpp5+Wzz//XH73u99JQUGBXpd19dVXy5dffilPPfWUzJgxQ6ZPn278CaB+jF7Vzx1V/ggrpgSB0DF342F5eO5GqahyyAUnt5K5Nw2VZvHRxlG4WTqwCgsL5de//rXMnj1bXnvtNenRo4de8P7qq6/qyIqJiZF+/frJtddeK3PmzDH+FGB9VogGd7z4M2IYtQJCy+zvD8rjn22RaofIladkyqzrT5X4mB9nm/AjywbWrl275JJLLtGRNXPmTB1XyoIFC+TNN9/U225qXVZ8fLzxCKhbuI9eBSKo3Bi1AkKPiqu/fem6aPOtwzvIqz8fKNFRlp8ICxpL/s3k5eXpUalTTjlFXn75ZUlP//GZWk0ZTp06VRYuXKi31SsI1YgWryKE3fgzJNzvu+YtEHwJKyVQHyeApvmwRlzdPqKjPDehr0RGhtelb5oqwhkpDmPbMv7zn//I448/LgkJCT+5dpEKKjWCpd7m4MGDkpGRoc+XdeWVVxpv0Tg1KpadnS05OTmSnJxs7PXeoVJjA5bl79ErM0/TUN/HavX48CWsFOIK4aKViRMu7p9nz7wzTxISk4y95vrIGVd/MeLqtuEd5B/OuAq36wp6w5KB5W8EVvixU2DZia9RpRBWCDd2CqzPtxyRP83frLeJq6Zh8hQhLxBrr8wIDbtwTwMSV0BoW70vX6Z+ukVv3zS0PXHVRAQWgAbVDCozosqNuAKsS13+5qFPNkhFtetUDM9f0o+4aiICC4BWO6TcN7OpsCKuAOvKLS6X3360XgrKqiT7pBR565psiWJBe5MRWAhpgTw1gz9ixF9qR1SgPnbCCrC2qmqH/HHeJtlfUCYd0xLk418OkcRYTiLqDQILCBOBjqnaiCvA+mbk7JHV+wskKTZKPrlpKJe/8QGBBZgoWPFSn2BHlcKUIGAPK/fm6cBS/nVJX+nRyvdX2YczAgshK5DTg1ZihahyI6wAe8grqZApn27Wl8C5OjtTrh6UZRyBtwgswGSBDpuaQWWFqHIjrgD7eO7bHXKkqEK6ZyTJvy7pZ+yFLwgswA/8GTpWDSo3pgQBe1FTgws2HxH1OsEZVwyUpDgWtZuBwAL8xKz4sXpQ1URYAfZSWVUtz3y9XW//YkiWDO6QprfhOwIL8KOmhFHNt615swviCrCfd9cckJ3HSiQ9MUaeuKCXsRdmILCAAKkrnmre7IopQcCeCssq5TXjVYOPn3+yM7Ji9TbMQWAhZPFD378IK8De3l2zXwrLq6RHyyS5YXB7Yy/MQmAhpBEA/sHfK2BvavRq5qr9evsP53TnUjh+QGAh5LlHWtw3eI+/QyA0vL/2gB696t4ySSYNyDT2wkwEFsJOzdiqeUPD+DsCQkN5VbW8t+aA3v79GEav/IXAAgyEVt34ewFCy1dbj8qxkgpp2yxOJg1oZ+yF2QgsoBZiwoWwAkLTrLWu0atfDesgMVFkgL/wNwvgBIQVELo2Hi6U7w8WSkxkhNw8vKOxF/5AYAHQCCsg9M35/pC+/1mfNtK6WZzehn8QWEAdwik0CCsgPJRVVsmnW47obTU9CP8isIAwRVgB4eWb7cekqLxKslLiZVTXDGMv/IXAAsIMYQWEp082uKYHrxmUJZGcmsHvCCwgTBBWQPg6WlQuy/fm6e0bhmTpe/gXgQWEOMIKwOdbjkq1Q+TUrFTp3CLJ2At/IrCAEEZYAVA+3XJY31+VzWVxAoXAAkIQo1YA3PYcL5ENh4okKiJCLue6gwFDYAH1sGOgEFYAavti61F9f0aXdGnFua8ChsACQgRhBaAunxuB9fOBjF4FEoEFNMAO0cKoFYD67DpWItuOFutL41zSr62xF4FAYAE2RlgBaIh7elCdWDQtMVZvIzAILKARVhwhYtQKgCe+3u4KrMsGMHoVaAQW4CF31HgaN7XfvubNW77+eQDhY19+qWw5UqxfPfizPgRWoBFYgJdqBlNdt4Y05W0VT98OANy+2Zar70d2SpMWSUwPBhqBBVhAzdiq6wYATfX1dldgTezXTt8jsAgsAABCTF5JhXx/sEBvX9Snjb5HYBFYAACEmMW7jutrD/Zp00yy0hKMvQgkAgsAgBCzcOcxfX9hr9b6HoFHYAEAEEKqqh2ydPdxvT2e6cGgIbAAAAghGw8VSlF5laTGR8upWbxKJlhsG1hHjx6VW2+9VQYNGiRDhgyRKVOmSGVlpXEUAIDwtGxPnr4/o0sLiYqM0NsIPNsG1l133SWJiYny9ddfy8yZM2XhwoXyyiuvGEcBAAhPOXtc04Pn9myl7xEctgysnTt3ypIlS+Tee++VhIQEycrK0qNZr7/+uvEWAACEn7LKKvn+YKHeHtO9pb5HcNgysDZv3iypqanSuvWPr47o0qWL7Nu3T/Lz8409AACEl/WHCqWy2iFtmsVJ5xaJxl4Egy0Dq6ioSI9c1eR+XFxcrO8BAAg3a/a7Ti46smO6RESw/iqYbBlYau1VSUmJ8cjF/TgpKUnfAwAQblbvd83inN4lXd8jeGwZWN26dZPjx4/LkSNHjD0iW7dulTZt2kizZs2MPQAAhA+HwyHrjfVXIzu10PcIHlsGVseOHSU7O1v+/Oc/S2FhoezevVuef/55mThxovEWAACEl335pfr8V3FRkdKnLYMNwWbLwFKeffZZfd6rs846Sy677DI57bTT9CsJAQAIRxsPFen7vs64inFGFoLLtl+BjIwMHVmLFy/W58C6//77JSoqyjgKAEB42XjYNT04iLO3WwKJCwBACNh21PUq+uyTUvQ9govAAgAgBGzPdQVW37bN9T2Ci8ACAMDm8ksr5Ghxhd7u1ZoF7lZAYAEAYHO7jrnOBdk+NUGaxUfrbQQXgQUAgM3tySvV9z1acbJtqyCwAACwuf35rsDqnpGs7xF8BBYAADa3L79M33dnBMsyCCwAAGxuvzFF2DWDwLIKAgsAAJs7WFiu7zunJ+p7BB+BBQCAzZVUVOn79mkJ+h7BR2ABABACMpJiJDGWUzRYBYEFAEAIOCmF0SsrIbAAAAgBJ6XEG1uwAgILAIAQkElgWQqBBQBACGhHYFkKgQUAQAho15zAshICCwCAENCGwLIUAgsAgBDQKjnW2IIVEFgAAISAVslxxhasgMACACAEtExiBMtKCCwAAGwuNipSEmOjjEewAgILAACbS0mMloiICOMRrIDAAgDA5tITYowtWAWBBQCAzaUQWJZDYAEAYHOp8QSW1RBYAADYXEpCtLEFqyCwAACwOUawrIfAAgDA5liDZT0EFgAANtcslilCqyGwAACwueR4TjJqNQQWAAA2xwiW9RBYAADYXBKXybEcAgsAAJtLILAsh8ACAMDmEqMJLKshsAAAsLmEWH6cWw1fEQAAbC4xhhEsqyGwAACwuTgCy3IILAAAbC4+ih/nVsNXBAAAm2MEy3oILAAAbC4umh/nVsNXBAAAm4tlitByLPkV2bNnj9x+++0ydOhQGTJkiNx6662ye/du46jItGnTpHfv3jJw4MAfbk8//bRxFACA8BIVGWFswSosGVi33XabpKSkyGeffaZvqampOrLc1q5dK7fccousWLHih9vkyZONowAAAMFlucDKy8uTjIwMufPOOyUxMVGSkpLkmmuukU2bNuljypo1a6RPnz56GwAAwGqCElilpaWyc+fOOm8xMTHy8ssvS6tWrYy3Fpk7d65kZmbqUa2jR4/Kvn375O2335aRI0fK6NGj5cknn5SysjLjrQEAAIIrKIG1atUqOeecc+q8fffdd8Zbubzxxhsyffp0eeyxx/Tjw4cPy6BBg2TChAl6+vDFF1+Ur7/+Wh5//HF9HAAAINgiHE7GtqWUl5fL1KlTZc6cOfL3v/9dL3ivz8cffyyPPPKILF682NjTsMLCQsnOzpacnBxJTk429nrvUKmxAQBAI1rFGxsmMPvnGcxjyUXuubm5cvXVV8vKlStl5syZJ8TVkiVL5IUXXjAeuagYi4838TsWAADAB5YLrIqKCrnxxht1iavpwaysLOOIS0JCgvzjH/+Q2bNnS3V1tWzevFmef/55mTRpkvEWAAAAwWW5KcJ58+bJHXfcIXFxcRIVdeKp/z/66CNp166dfpt//vOfsmvXLmnWrJlcdtll+jQOkZGe9SJThACAYGGKMDxYdg2WPxFYAIBgIbDCgyXXYAEAANgZgQUAAGAyAgsAAMBkBBYAAIDJCCwAAACTEVgAAAAmI7AAAABMRmABAACYjMACAAAwGYEFAABgMgILAADAZAQWAACAyQgsAAAAkxFYAAAAJiOwAAAATEZgAQAAmIzAAgAAMBmBBQAAYDICCwAAwGQEFgAAgMkILAAAAJNFOJyM7bBRWFgo2dnZkpOTI8nJycZeAADshZ9n1sUIFgAAgMkILAAAAJMRWAAAACYjsAAAAExGYAEAAJiMwAIAADAZgQUAAGAyAgsAAMBkBBYAAIDJCCwAAACTEVgAAAAmI7AAAABMRmABAACYjMACAAAwGYEFAABgMgILAADAZAQWAACAyQgsAAAAk0U4nIztsFFQUCCDBg2SL7/8UpKTk429AADYS2FhoZxxxhmSk5PDzzOLCcvAOnDggP6GBAAgFKgBgzZt2hiPYAVhGVjV1dVy6NAhSUpKkoiICGMvAAD2on6EFxUVSatWrSQyklU/VhKWgQUAAOBP5C4AAIDJCCwAAACTEVgAAAAmI7AAAABMRmABAACYjMACAAAwGYFlA/fee69cffXVxiOX7du3y7XXXisDBw6UkSNHyr///W/jiHWsX79errnmGsnOzpYhQ4boz+PYsWPGUet/Dnv27JHbb79dhg4dqj/+W2+9VXbv3m0ctcfXwK2kpEQmTZok7733nrHHxQ6fw9GjR/Xfvbr6gvo6TJkyRSorK42j1pWbmytjxoyRxYsXG3tEVq1aJZdeeqn++x49erS88847xhFr2bBhg1x//fUyePBgGTFihNx3333681Hs8DksXLhQf4ynnHKK/vgfffRRKS0t1cfs8jVQqqqq9HP/b3/7W2OPvT7+sKfOgwXrcv7jcfTs2dNx1VVXGXscjvLycsc555zjeOqppxxlZWWOdevWOZw/HB1z5swx3iL41MflfGJzPPfcc46KigpHXl6ew/mD3OF8otbH7fA5jB8/3vHggw86ioqKHIWFhY4HHnjAccEFF+hjdvj43TZt2uS4+OKLHd27d3e8++67xl77fA7qe//uu+92FBcXO3bt2uU4//zzHS+++KJx1JqWLVvmOPvss/Xf+aJFi/S+48ePO5zB4njttdf0v4nvvvvO4fwh6XD+wNTHrcIZ4/rf7t///nf9feEMK8cvf/lLx69+9StbfA7OIHf07dtXf687A8Vx8OBB/e9WfT52+Rq4PfPMM/r5//7779eP7fbxhztGsCxsy5Yt8vzzz+vfVmpaunSpPhP9r3/9a4mNjZVevXrp33Jef/114y2CT31c8+bNk1tuuUWio6PFGVh6FCU9PV0ft/rnoD7ejIwMufPOOyUxMVGf9V+NxjljRR+zw9dAUb/JqxEqZ2BJu3btjL0udvgcdu7cKUuWLNGjnwkJCZKVlaVHs6z291zTrFmz5J577pHJkycbe1zUv4fU1FS58sor9b+JYcOGyYUXXmi5z2Xfvn3i/KEut912m/6+SEtL06Of6vvFDp+Deo5xhodMmDBBX6nDGSXiDEW93y5fA0X921Ufr/OXIGOPfb6H4EJgBYkarlY/POq6OX9T18fVE/TDDz8sLVu2NP6Uy+bNm6VTp076yc+ta9euelg/kBr7HFSYqEs3XH755eL8bV5flPQXv/iF/rNW+Bwa+vhjYmLk5Zdf1pefcJs7d65kZmZKSkqKbb4G6gfl559/rsOp9mWhrPI5NER9jOoHSuvWrY09Il26dNERkJ+fb+yxFjXVOn/+fBk3bpyxx0V9Lt27dzceuVjt71vp3LmzvPTSSxIVFWXscX3v9+7d2zafg/uix+qasypA1HOoCi67fPxqWvx3v/ud/PWvf9W/WLjZ5eOHC4EVJGoeXf1mUtdN/fb1pz/9Sa8dqOui1Oq6UzX/0SnqsfqBGkiNfQ5ur7zyih6FUE8Mal2HWldghc/B049feeONN2T69Ony2GOP6cd2+Rqo0Ye4uDjjrU9klc+hIfV9jIqVPs6a1A9zNbpQW12fS3x8vGU/D8XhcMjTTz+tI139wLfb56BGfL766iv9i54aqbXDx6+ulatGbNVzpfoFqSY7fg+FMwIrSNRi3Y0bN9Z5U/9Y1G8kv/nNb4y3PpEaGVLTbTWpx2oaK5Aa+hzUiJWbegJQoz4PPfSQnmJTx63wOXjy8ZeXl8sf//hHeeaZZ+SFF16Q4cOH6/12+xrUxSqfQ0Pq+xgVK32cnlA/GNWIY03qsVU/DzXirKJk9uzZ8tprr0mPHj1s9zmo5x41+qmC5euvv7bFx6+eZ9Soshp1rs1uf//hjsCyoA8++EC/ukv9MFevnJo2bZrk5OTobTU10q1bN9mxY8cJr6RS67XUfqtQr8BTr3BRa3zcVKwoKrbs8DmoV02pJ7mVK1fKzJkz9asJ3ezw8TfGDp+D+ljUGpojR44Ye0S2bt0qbdq0kWbNmhl77EGN4Kopnpqs+j2za9cuueSSS3Rkqe99FVeKHT6H5cuXy7nnnvvD842ittW0v5pOs/rHr57/1Yi/er5Xtw8//FDf1LadvodAYFmSWvuzYsUKWbZsmb7ddNNN+lQHalstVFajFmrqR83Pq8WbarRrxowZMnHiROM9BJ9aq6TWzkydOlUPa6tYUSNBp59+uj5m9c+hoqJCbrzxRr2WQ00PqsXVNdnha9AYO3wOHTt21N/7f/7zn/UPe3WaDPXCDzv9PbupUzaoUFRT5ur7a9GiRXp0SIWMlagXcagXRqhTHKjnIvcLUxQ7fA4qBtWojvq+VmG1d+9eeeKJJ/T3zNixYy3/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\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# set the seaborn style for all the following plots\\n\",\n \"sns.set_style('white')\\n\",\n \"\\n\",\n \"sns.jointplot(v1, v2, kind='kde', space=0);\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 84,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [\n {\n \"ename\": \"FileNotFoundError\",\n \"evalue\": \"[Errno 2] File iris.csv does not exist: 'iris.csv'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mFileNotFoundError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[1;32m\\u001b[0m in \\u001b[0;36m\\u001b[1;34m\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[1;33m \\u001b[0miris\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mpd\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mread_csv\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;34m'iris.csv'\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m 2\\u001b[0m \\u001b[0miris\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mhead\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[1;32mc:\\\\users\\\\buono\\\\01_data_science\\\\99_venv\\\\coursera_env\\\\lib\\\\site-packages\\\\pandas\\\\io\\\\parsers.py\\u001b[0m in \\u001b[0;36mparser_f\\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\\u001b[0m\\n\\u001b[0;32m 674\\u001b[0m )\\n\\u001b[0;32m 675\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 676\\u001b[1;33m \\u001b[1;32mreturn\\u001b[0m \\u001b[0m_read\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mfilepath_or_buffer\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mkwds\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m 677\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m 678\\u001b[0m \\u001b[0mparser_f\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m__name__\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mname\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[1;32mc:\\\\users\\\\buono\\\\01_data_science\\\\99_venv\\\\coursera_env\\\\lib\\\\site-packages\\\\pandas\\\\io\\\\parsers.py\\u001b[0m in \\u001b[0;36m_read\\u001b[1;34m(filepath_or_buffer, kwds)\\u001b[0m\\n\\u001b[0;32m 446\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m 447\\u001b[0m \\u001b[1;31m# Create the parser.\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 448\\u001b[1;33m \\u001b[0mparser\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mTextFileReader\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mfp_or_buf\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[1;33m**\\u001b[0m\\u001b[0mkwds\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m 449\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m 450\\u001b[0m \\u001b[1;32mif\\u001b[0m \\u001b[0mchunksize\\u001b[0m \\u001b[1;32mor\\u001b[0m \\u001b[0miterator\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[1;32mc:\\\\users\\\\buono\\\\01_data_science\\\\99_venv\\\\coursera_env\\\\lib\\\\site-packages\\\\pandas\\\\io\\\\parsers.py\\u001b[0m in \\u001b[0;36m__init__\\u001b[1;34m(self, f, engine, **kwds)\\u001b[0m\\n\\u001b[0;32m 878\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0moptions\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;34m\\\"has_index_names\\\"\\u001b[0m\\u001b[1;33m]\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mkwds\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;34m\\\"has_index_names\\\"\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m 879\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 880\\u001b[1;33m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m_make_engine\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mengine\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m 881\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m 882\\u001b[0m \\u001b[1;32mdef\\u001b[0m \\u001b[0mclose\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[1;32mc:\\\\users\\\\buono\\\\01_data_science\\\\99_venv\\\\coursera_env\\\\lib\\\\site-packages\\\\pandas\\\\io\\\\parsers.py\\u001b[0m in \\u001b[0;36m_make_engine\\u001b[1;34m(self, engine)\\u001b[0m\\n\\u001b[0;32m 1112\\u001b[0m \\u001b[1;32mdef\\u001b[0m \\u001b[0m_make_engine\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mengine\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[1;34m\\\"c\\\"\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m 1113\\u001b[0m \\u001b[1;32mif\\u001b[0m \\u001b[0mengine\\u001b[0m \\u001b[1;33m==\\u001b[0m \\u001b[1;34m\\\"c\\\"\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m-> 1114\\u001b[1;33m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m_engine\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mCParserWrapper\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mf\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[1;33m**\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0moptions\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m 1115\\u001b[0m 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\\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m_reader\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mparsers\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mTextReader\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0msrc\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[1;33m**\\u001b[0m\\u001b[0mkwds\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m 1892\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0munnamed_cols\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m_reader\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0munnamed_cols\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m 1893\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[1;32mpandas\\\\_libs\\\\parsers.pyx\\u001b[0m in \\u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\\u001b[1;34m()\\u001b[0m\\n\",\n \"\\u001b[1;32mpandas\\\\_libs\\\\parsers.pyx\\u001b[0m in \\u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\\u001b[1;34m()\\u001b[0m\\n\",\n \"\\u001b[1;31mFileNotFoundError\\u001b[0m: [Errno 2] File iris.csv does not exist: 'iris.csv'\"\n ]\n }\n ],\n \"source\": [\n \"iris = pd.read_csv('iris.csv')\\n\",\n \"iris.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 85,\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'iris' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[1;32m\\u001b[0m in \\u001b[0;36m\\u001b[1;34m\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[1;33m \\u001b[0msns\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mpairplot\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0miris\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mhue\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[1;34m'Name'\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mdiag_kind\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[1;34m'kde'\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0msize\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[1;36m2\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m;\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[1;31mNameError\\u001b[0m: name 'iris' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"sns.pairplot(iris, hue='Name', diag_kind='kde', size=2);\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 86,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"application/javascript\": [\n \"/* Put everything inside the global mpl namespace */\\n\",\n \"window.mpl = {};\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.get_websocket_type = function() {\\n\",\n \" if (typeof(WebSocket) !== 'undefined') {\\n\",\n \" return WebSocket;\\n\",\n \" } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n \" return MozWebSocket;\\n\",\n \" } else {\\n\",\n \" alert('Your browser does not have WebSocket support. ' +\\n\",\n \" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n \" 'Firefox 4 and 5 are also supported but you ' +\\n\",\n \" 'have to enable WebSockets in about:config.');\\n\",\n \" };\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n \" this.id = figure_id;\\n\",\n \"\\n\",\n \" this.ws = websocket;\\n\",\n \"\\n\",\n \" this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n \"\\n\",\n \" if (!this.supports_binary) {\\n\",\n \" var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n \" if (warnings) {\\n\",\n \" warnings.style.display = 'block';\\n\",\n \" warnings.textContent = (\\n\",\n \" \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n \" \\\"Performance may be slow.\\\");\\n\",\n \" }\\n\",\n \" }\\n\",\n \"\\n\",\n \" this.imageObj = new Image();\\n\",\n \"\\n\",\n \" this.context = undefined;\\n\",\n \" this.message = undefined;\\n\",\n \" this.canvas = undefined;\\n\",\n \" this.rubberband_canvas = undefined;\\n\",\n \" this.rubberband_context = undefined;\\n\",\n \" this.format_dropdown = undefined;\\n\",\n \"\\n\",\n \" this.image_mode = 'full';\\n\",\n \"\\n\",\n \" this.root = $('');\\n\",\n \" this._root_extra_style(this.root)\\n\",\n \" this.root.attr('style', 'display: inline-block');\\n\",\n \"\\n\",\n \" $(parent_element).append(this.root);\\n\",\n \"\\n\",\n \" this._init_header(this);\\n\",\n \" this._init_canvas(this);\\n\",\n \" this._init_toolbar(this);\\n\",\n \"\\n\",\n \" var fig = this;\\n\",\n \"\\n\",\n \" this.waiting = false;\\n\",\n \"\\n\",\n \" this.ws.onopen = function () {\\n\",\n \" fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n \" fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n \" if (mpl.ratio != 1) {\\n\",\n \" fig.send_message(\\\"set_dpi_ratio\\\", {'dpi_ratio': mpl.ratio});\\n\",\n \" }\\n\",\n \" fig.send_message(\\\"refresh\\\", {});\\n\",\n \" }\\n\",\n \"\\n\",\n \" this.imageObj.onload = function() {\\n\",\n \" if (fig.image_mode == 'full') {\\n\",\n \" // Full images could contain transparency (where diff images\\n\",\n \" // almost always do), so we need to clear the canvas so that\\n\",\n \" // there is no ghosting.\\n\",\n \" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n \" }\\n\",\n \" fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n \" };\\n\",\n \"\\n\",\n \" this.imageObj.onunload = function() {\\n\",\n \" fig.ws.close();\\n\",\n \" }\\n\",\n \"\\n\",\n \" this.ws.onmessage = this._make_on_message_function(this);\\n\",\n \"\\n\",\n \" this.ondownload = ondownload;\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._init_header = function() {\\n\",\n \" var titlebar = $(\\n\",\n \" '');\\n\",\n \" var titletext = $(\\n\",\n \" '');\\n\",\n \" titlebar.append(titletext)\\n\",\n \" this.root.append(titlebar);\\n\",\n \" this.header = titletext[0];\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n \"\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n \"\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._init_canvas = function() {\\n\",\n \" var fig = this;\\n\",\n \"\\n\",\n \" var canvas_div = $('');\\n\",\n \"\\n\",\n \" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n \"\\n\",\n \" function canvas_keyboard_event(event) {\\n\",\n \" return fig.key_event(event, event['data']);\\n\",\n \" }\\n\",\n \"\\n\",\n \" canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n \" canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n \" this.canvas_div = canvas_div\\n\",\n \" this._canvas_extra_style(canvas_div)\\n\",\n \" this.root.append(canvas_div);\\n\",\n \"\\n\",\n \" var canvas = $('');\\n\",\n \" canvas.addClass('mpl-canvas');\\n\",\n \" canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n \"\\n\",\n \" this.canvas = canvas[0];\\n\",\n \" this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n \"\\n\",\n \" var backingStore = this.context.backingStorePixelRatio ||\\n\",\n \"\\tthis.context.webkitBackingStorePixelRatio ||\\n\",\n \"\\tthis.context.mozBackingStorePixelRatio ||\\n\",\n \"\\tthis.context.msBackingStorePixelRatio ||\\n\",\n \"\\tthis.context.oBackingStorePixelRatio ||\\n\",\n \"\\tthis.context.backingStorePixelRatio || 1;\\n\",\n \"\\n\",\n \" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\\n\",\n \"\\n\",\n \" var rubberband = $('');\\n\",\n \" rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n \"\\n\",\n \" var pass_mouse_events = true;\\n\",\n \"\\n\",\n \" canvas_div.resizable({\\n\",\n \" start: function(event, ui) {\\n\",\n \" pass_mouse_events = false;\\n\",\n \" },\\n\",\n \" resize: function(event, ui) {\\n\",\n \" fig.request_resize(ui.size.width, ui.size.height);\\n\",\n \" },\\n\",\n \" stop: function(event, ui) {\\n\",\n \" pass_mouse_events = true;\\n\",\n \" fig.request_resize(ui.size.width, ui.size.height);\\n\",\n \" },\\n\",\n \" });\\n\",\n \"\\n\",\n \" function mouse_event_fn(event) {\\n\",\n \" if (pass_mouse_events)\\n\",\n \" return fig.mouse_event(event, event['data']);\\n\",\n \" }\\n\",\n \"\\n\",\n \" rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n \" rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n \" // Throttle sequential mouse events to 1 every 20ms.\\n\",\n \" rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n \"\\n\",\n \" rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n \" rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n \"\\n\",\n \" canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n \" event = event.originalEvent;\\n\",\n \" event['data'] = 'scroll'\\n\",\n \" if (event.deltaY < 0) {\\n\",\n \" event.step = 1;\\n\",\n \" } else {\\n\",\n \" event.step = -1;\\n\",\n \" }\\n\",\n \" mouse_event_fn(event);\\n\",\n \" });\\n\",\n \"\\n\",\n \" canvas_div.append(canvas);\\n\",\n \" canvas_div.append(rubberband);\\n\",\n \"\\n\",\n \" this.rubberband = rubberband;\\n\",\n \" this.rubberband_canvas = rubberband[0];\\n\",\n \" this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n \" this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n \"\\n\",\n \" this._resize_canvas = function(width, height) {\\n\",\n \" // Keep the size of the canvas, canvas container, and rubber band\\n\",\n \" // canvas in synch.\\n\",\n \" canvas_div.css('width', width)\\n\",\n \" canvas_div.css('height', height)\\n\",\n \"\\n\",\n \" canvas.attr('width', width * mpl.ratio);\\n\",\n \" canvas.attr('height', height * mpl.ratio);\\n\",\n \" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\\n\",\n \"\\n\",\n \" rubberband.attr('width', width);\\n\",\n \" rubberband.attr('height', height);\\n\",\n \" }\\n\",\n \"\\n\",\n \" // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n \" // upon first draw.\\n\",\n \" this._resize_canvas(600, 600);\\n\",\n \"\\n\",\n \" // Disable right mouse context menu.\\n\",\n \" $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n \" return false;\\n\",\n \" });\\n\",\n \"\\n\",\n \" function set_focus () {\\n\",\n \" canvas.focus();\\n\",\n \" canvas_div.focus();\\n\",\n \" }\\n\",\n \"\\n\",\n \" window.setTimeout(set_focus, 100);\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n \" var fig = this;\\n\",\n \"\\n\",\n \" var nav_element = $('');\\n\",\n \" nav_element.attr('style', 'width: 100%');\\n\",\n \" this.root.append(nav_element);\\n\",\n \"\\n\",\n \" // Define a callback function for later on.\\n\",\n \" function toolbar_event(event) {\\n\",\n \" return fig.toolbar_button_onclick(event['data']);\\n\",\n \" }\\n\",\n \" function toolbar_mouse_event(event) {\\n\",\n \" return fig.toolbar_button_onmouseover(event['data']);\\n\",\n \" }\\n\",\n \"\\n\",\n \" for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n \" var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n \" var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n \" var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n \" var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n \"\\n\",\n \" if (!name) {\\n\",\n \" // put a spacer in here.\\n\",\n \" continue;\\n\",\n \" }\\n\",\n \" var button = $('');\\n\",\n \" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n \" 'ui-button-icon-only');\\n\",\n \" button.attr('role', 'button');\\n\",\n \" button.attr('aria-disabled', 'false');\\n\",\n \" button.click(method_name, toolbar_event);\\n\",\n \" button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n \"\\n\",\n \" var icon_img = $('');\\n\",\n \" icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n \" icon_img.addClass(image);\\n\",\n \" icon_img.addClass('ui-corner-all');\\n\",\n \"\\n\",\n \" var tooltip_span = $('');\\n\",\n \" tooltip_span.addClass('ui-button-text');\\n\",\n \" tooltip_span.html(tooltip);\\n\",\n \"\\n\",\n \" button.append(icon_img);\\n\",\n \" button.append(tooltip_span);\\n\",\n \"\\n\",\n \" nav_element.append(button);\\n\",\n \" }\\n\",\n \"\\n\",\n \" var fmt_picker_span = $('');\\n\",\n \"\\n\",\n \" var fmt_picker = $('');\\n\",\n \" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n \" fmt_picker_span.append(fmt_picker);\\n\",\n \" nav_element.append(fmt_picker_span);\\n\",\n \" this.format_dropdown = fmt_picker[0];\\n\",\n \"\\n\",\n \" for (var ind in mpl.extensions) {\\n\",\n \" var fmt = mpl.extensions[ind];\\n\",\n \" var option = $(\\n\",\n \" '', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n \" fmt_picker.append(option);\\n\",\n \" }\\n\",\n \"\\n\",\n \" // Add hover states to the ui-buttons\\n\",\n \" $( \\\".ui-button\\\" ).hover(\\n\",\n \" function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n \" function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n \" );\\n\",\n \"\\n\",\n \" var status_bar = $('');\\n\",\n \" nav_element.append(status_bar);\\n\",\n \" this.message = status_bar[0];\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n \" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n \" // which will in turn request a refresh of the image.\\n\",\n \" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n \" properties['type'] = type;\\n\",\n \" properties['figure_id'] = this.id;\\n\",\n \" this.ws.send(JSON.stringify(properties));\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n \" if (!this.waiting) {\\n\",\n \" this.waiting = true;\\n\",\n \" this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n \" }\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n \" var format_dropdown = fig.format_dropdown;\\n\",\n \" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n \" fig.ondownload(fig, format);\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n \" var size = msg['size'];\\n\",\n \" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n \" fig._resize_canvas(size[0], size[1]);\\n\",\n \" fig.send_message(\\\"refresh\\\", {});\\n\",\n \" };\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n \" var x0 = msg['x0'] / mpl.ratio;\\n\",\n \" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\\n\",\n \" var x1 = msg['x1'] / mpl.ratio;\\n\",\n \" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\\n\",\n \" x0 = Math.floor(x0) + 0.5;\\n\",\n \" y0 = Math.floor(y0) + 0.5;\\n\",\n \" x1 = Math.floor(x1) + 0.5;\\n\",\n \" y1 = Math.floor(y1) + 0.5;\\n\",\n \" var min_x = Math.min(x0, x1);\\n\",\n \" var min_y = Math.min(y0, y1);\\n\",\n \" var width = Math.abs(x1 - x0);\\n\",\n \" var height = Math.abs(y1 - y0);\\n\",\n \"\\n\",\n \" fig.rubberband_context.clearRect(\\n\",\n \" 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\\n\",\n \"\\n\",\n \" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n \" // Updates the figure title.\\n\",\n \" fig.header.textContent = msg['label'];\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n \" var cursor = msg['cursor'];\\n\",\n \" switch(cursor)\\n\",\n \" {\\n\",\n \" case 0:\\n\",\n \" cursor = 'pointer';\\n\",\n \" break;\\n\",\n \" case 1:\\n\",\n \" cursor = 'default';\\n\",\n \" break;\\n\",\n \" case 2:\\n\",\n \" cursor = 'crosshair';\\n\",\n \" break;\\n\",\n \" case 3:\\n\",\n \" cursor = 'move';\\n\",\n \" break;\\n\",\n \" }\\n\",\n \" fig.rubberband_canvas.style.cursor = cursor;\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n \" fig.message.textContent = msg['message'];\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n \" // Request the server to send over a new figure.\\n\",\n \" fig.send_draw_message();\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n \" fig.image_mode = msg['mode'];\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n \" // Called whenever the canvas gets updated.\\n\",\n \" this.send_message(\\\"ack\\\", {});\\n\",\n \"}\\n\",\n \"\\n\",\n \"// A function to construct a web socket function for onmessage handling.\\n\",\n \"// Called in the figure constructor.\\n\",\n \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n \" return function socket_on_message(evt) {\\n\",\n \" if (evt.data instanceof Blob) {\\n\",\n \" /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n \" * transferred with MIME type text/plain:\\\" errors on\\n\",\n \" * Chrome. But how to set the MIME type? It doesn't seem\\n\",\n \" * to be part of the websocket stream */\\n\",\n \" evt.data.type = \\\"image/png\\\";\\n\",\n \"\\n\",\n \" /* Free the memory for the previous frames */\\n\",\n \" if (fig.imageObj.src) {\\n\",\n \" (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n \" fig.imageObj.src);\\n\",\n \" }\\n\",\n \"\\n\",\n \" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n \" evt.data);\\n\",\n \" fig.updated_canvas_event();\\n\",\n \" fig.waiting = false;\\n\",\n \" return;\\n\",\n \" }\\n\",\n \" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n \" fig.imageObj.src = evt.data;\\n\",\n \" fig.updated_canvas_event();\\n\",\n \" fig.waiting = false;\\n\",\n \" return;\\n\",\n \" }\\n\",\n \"\\n\",\n \" var msg = JSON.parse(evt.data);\\n\",\n \" var msg_type = msg['type'];\\n\",\n \"\\n\",\n \" // Call the \\\"handle_{type}\\\" callback, which takes\\n\",\n \" // the figure and JSON message as its only arguments.\\n\",\n \" try {\\n\",\n \" var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n \" } catch (e) {\\n\",\n \" console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n \" return;\\n\",\n \" }\\n\",\n \"\\n\",\n \" if (callback) {\\n\",\n \" try {\\n\",\n \" // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n \" callback(fig, msg);\\n\",\n \" } catch (e) {\\n\",\n \" console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n \" }\\n\",\n \" }\\n\",\n \" };\\n\",\n \"}\\n\",\n \"\\n\",\n \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n \"mpl.findpos = function(e) {\\n\",\n \" //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n \" var targ;\\n\",\n \" if (!e)\\n\",\n \" e = window.event;\\n\",\n \" if (e.target)\\n\",\n \" targ = e.target;\\n\",\n \" else if (e.srcElement)\\n\",\n \" targ = e.srcElement;\\n\",\n \" if (targ.nodeType == 3) // defeat Safari bug\\n\",\n \" targ = targ.parentNode;\\n\",\n \"\\n\",\n \" // jQuery normalizes the pageX and pageY\\n\",\n \" // pageX,Y are the mouse positions relative to the document\\n\",\n \" // offset() returns the position of the element relative to the document\\n\",\n \" var x = e.pageX - $(targ).offset().left;\\n\",\n \" var y = e.pageY - $(targ).offset().top;\\n\",\n \"\\n\",\n \" return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n \"};\\n\",\n \"\\n\",\n \"/*\\n\",\n \" * return a copy of an object with only non-object keys\\n\",\n \" * we need this to avoid circular references\\n\",\n \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n \" */\\n\",\n \"function simpleKeys (original) {\\n\",\n \" return Object.keys(original).reduce(function (obj, key) {\\n\",\n \" if (typeof original[key] !== 'object')\\n\",\n \" obj[key] = original[key]\\n\",\n \" return obj;\\n\",\n \" }, {});\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n \" var canvas_pos = mpl.findpos(event)\\n\",\n \"\\n\",\n \" if (name === 'button_press')\\n\",\n \" {\\n\",\n \" this.canvas.focus();\\n\",\n \" this.canvas_div.focus();\\n\",\n \" }\\n\",\n \"\\n\",\n \" var x = canvas_pos.x * mpl.ratio;\\n\",\n \" var y = canvas_pos.y * mpl.ratio;\\n\",\n \"\\n\",\n \" this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n \" step: event.step,\\n\",\n \" guiEvent: simpleKeys(event)});\\n\",\n \"\\n\",\n \" /* This prevents the web browser from automatically changing to\\n\",\n \" * the text insertion cursor when the button is pressed. We want\\n\",\n \" * to control all of the cursor setting manually through the\\n\",\n \" * 'cursor' event from matplotlib */\\n\",\n \" event.preventDefault();\\n\",\n \" return false;\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n \" // Handle any extra behaviour associated with a key event\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n \"\\n\",\n \" // Prevent repeat events\\n\",\n \" if (name == 'key_press')\\n\",\n \" {\\n\",\n \" if (event.which === this._key)\\n\",\n \" return;\\n\",\n \" else\\n\",\n \" this._key = event.which;\\n\",\n \" }\\n\",\n \" if (name == 'key_release')\\n\",\n \" this._key = null;\\n\",\n \"\\n\",\n \" var value = '';\\n\",\n \" if (event.ctrlKey && event.which != 17)\\n\",\n \" value += \\\"ctrl+\\\";\\n\",\n \" if (event.altKey && event.which != 18)\\n\",\n \" value += \\\"alt+\\\";\\n\",\n \" if (event.shiftKey && event.which != 16)\\n\",\n \" value += \\\"shift+\\\";\\n\",\n \"\\n\",\n \" value += 'k';\\n\",\n \" value += event.which.toString();\\n\",\n \"\\n\",\n \" this._key_event_extra(event, name);\\n\",\n \"\\n\",\n \" this.send_message(name, {key: value,\\n\",\n \" guiEvent: simpleKeys(event)});\\n\",\n \" return false;\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n \" if (name == 'download') {\\n\",\n \" this.handle_save(this, null);\\n\",\n \" } else {\\n\",\n \" this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n \" }\\n\",\n \"};\\n\",\n \"\\n\",\n \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n \" this.message.textContent = tooltip;\\n\",\n \"};\\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 \"\\n\",\n \"mpl.extensions = [\\\"eps\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\"];\\n\",\n \"\\n\",\n \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n \" // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n \" // object with the appropriate methods. Currently this is a non binary\\n\",\n \" // socket, so there is still some room for performance tuning.\\n\",\n \" var ws = {};\\n\",\n \"\\n\",\n \" ws.close = function() {\\n\",\n \" comm.close()\\n\",\n \" };\\n\",\n \" ws.send = function(m) {\\n\",\n \" //console.log('sending', m);\\n\",\n \" comm.send(m);\\n\",\n \" };\\n\",\n \" // Register the callback with on_msg.\\n\",\n \" comm.on_msg(function(msg) {\\n\",\n \" //console.log('receiving', msg['content']['data'], msg);\\n\",\n \" // Pass the mpl event to the overridden (by mpl) onmessage function.\\n\",\n \" ws.onmessage(msg['content']['data'])\\n\",\n \" });\\n\",\n \" return ws;\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n \" // This is the function which gets called when the mpl process\\n\",\n \" // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n \"\\n\",\n \" var id = msg.content.data.id;\\n\",\n \" // Get hold of the div created by the display call when the Comm\\n\",\n \" // socket was opened in Python.\\n\",\n \" var element = $(\\\"#\\\" + id);\\n\",\n \" var ws_proxy = comm_websocket_adapter(comm)\\n\",\n \"\\n\",\n \" function ondownload(figure, format) {\\n\",\n \" window.open(figure.imageObj.src);\\n\",\n \" }\\n\",\n \"\\n\",\n \" var fig = new mpl.figure(id, ws_proxy,\\n\",\n \" ondownload,\\n\",\n \" element.get(0));\\n\",\n \"\\n\",\n \" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n \" // web socket which is closed, not our websocket->open comm proxy.\\n\",\n \" ws_proxy.onopen();\\n\",\n \"\\n\",\n \" fig.parent_element = element.get(0);\\n\",\n \" fig.cell_info = mpl.find_output_cell(\\\"\\\");\\n\",\n \" if (!fig.cell_info) {\\n\",\n \" console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n \" return;\\n\",\n \" }\\n\",\n \"\\n\",\n \" var output_index = fig.cell_info[2]\\n\",\n \" var cell = fig.cell_info[0];\\n\",\n \"\\n\",\n \"};\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n \" var width = fig.canvas.width/mpl.ratio\\n\",\n \" fig.root.unbind('remove')\\n\",\n \"\\n\",\n \" // Update the output cell to use the data from the current canvas.\\n\",\n \" fig.push_to_output();\\n\",\n \" var dataURL = fig.canvas.toDataURL();\\n\",\n \" // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n \" // the notebook keyboard shortcuts fail.\\n\",\n \" IPython.keyboard_manager.enable()\\n\",\n \" $(fig.parent_element).html('');\\n\",\n \" fig.close_ws(fig, msg);\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n \" fig.send_message('closing', msg);\\n\",\n \" // fig.ws.close()\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n \" // Turn the data on the canvas into data in the output cell.\\n\",\n \" var width = this.canvas.width/mpl.ratio\\n\",\n \" var dataURL = this.canvas.toDataURL();\\n\",\n \" this.cell_info[1]['text/html'] = '';\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n \" // Tell IPython that the notebook contents must change.\\n\",\n \" IPython.notebook.set_dirty(true);\\n\",\n \" this.send_message(\\\"ack\\\", {});\\n\",\n \" var fig = this;\\n\",\n \" // Wait a second, then push the new image to the DOM so\\n\",\n \" // that it is saved nicely (might be nice to debounce this).\\n\",\n \" setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n \" var fig = this;\\n\",\n \"\\n\",\n \" var nav_element = $('');\\n\",\n \" nav_element.attr('style', 'width: 100%');\\n\",\n \" this.root.append(nav_element);\\n\",\n \"\\n\",\n \" // Define a callback function for later on.\\n\",\n \" function toolbar_event(event) {\\n\",\n \" return fig.toolbar_button_onclick(event['data']);\\n\",\n \" }\\n\",\n \" function toolbar_mouse_event(event) {\\n\",\n \" return fig.toolbar_button_onmouseover(event['data']);\\n\",\n \" }\\n\",\n \"\\n\",\n \" for(var toolbar_ind in mpl.toolbar_items){\\n\",\n \" var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n \" var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n \" var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n \" var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n \"\\n\",\n \" if (!name) { continue; };\\n\",\n \"\\n\",\n \" var button = $('');\\n\",\n \" button.click(method_name, toolbar_event);\\n\",\n \" button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n \" nav_element.append(button);\\n\",\n \" }\\n\",\n \"\\n\",\n \" // Add the status bar.\\n\",\n \" var status_bar = $('');\\n\",\n \" nav_element.append(status_bar);\\n\",\n \" this.message = status_bar[0];\\n\",\n \"\\n\",\n \" // Add the close button to the window.\\n\",\n \" var buttongrp = $('');\\n\",\n \" var button = $('');\\n\",\n \" button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n \" button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n \" buttongrp.append(button);\\n\",\n \" var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n \" titlebar.prepend(buttongrp);\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n \" var fig = this\\n\",\n \" el.on(\\\"remove\\\", function(){\\n\",\n \"\\tfig.close_ws(fig, {});\\n\",\n \" });\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n \" // this is important to make the div 'focusable\\n\",\n \" el.attr('tabindex', 0)\\n\",\n \" // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n \" // off when our div gets focus\\n\",\n \"\\n\",\n \" // location in version 3\\n\",\n \" if (IPython.notebook.keyboard_manager) {\\n\",\n \" IPython.notebook.keyboard_manager.register_events(el);\\n\",\n \" }\\n\",\n \" else {\\n\",\n \" // location in version 2\\n\",\n \" IPython.keyboard_manager.register_events(el);\\n\",\n \" }\\n\",\n \"\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n \" var manager = IPython.notebook.keyboard_manager;\\n\",\n \" if (!manager)\\n\",\n \" manager = IPython.keyboard_manager;\\n\",\n \"\\n\",\n \" // Check for shift+enter\\n\",\n \" if (event.shiftKey && event.which == 13) {\\n\",\n \" this.canvas_div.blur();\\n\",\n \" // select the cell after this one\\n\",\n \" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\\n\",\n \" IPython.notebook.select(index + 1);\\n\",\n \" }\\n\",\n \"}\\n\",\n \"\\n\",\n \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n \" fig.ondownload(fig, null);\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"mpl.find_output_cell = function(html_output) {\\n\",\n \" // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n \" // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n \" // IPython event is triggered only after the cells have been serialised, which for\\n\",\n \" // our purposes (turning an active figure into a static one), is too late.\\n\",\n \" var cells = IPython.notebook.get_cells();\\n\",\n \" var ncells = cells.length;\\n\",\n \" for (var i=0; i= 3 moved mimebundle to data attribute of output\\n\",\n \" data = data.data;\\n\",\n \" }\\n\",\n \" if (data['text/html'] == html_output) {\\n\",\n \" return [cell, data, j];\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \" }\\n\",\n \"}\\n\",\n \"\\n\",\n \"// Register the function which deals with the matplotlib target/channel.\\n\",\n \"// The kernel may be null if the page has been refreshed.\\n\",\n \"if (IPython.notebook.kernel != null) {\\n\",\n \" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n \"}\\n\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": 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\"Week4_studied.ipynb\"\n}"}}},{"rowIdx":6427,"cells":{"id":{"kind":"string","value":"e91ddee0e4fbbdda00c14991e490dd96d91610f1"},"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Analysis of Crowdfunding Projects\\n\",\n \"\\n\",\n \"### Data Description\\n\",\n \"\\n\",\n \"Kickstarter.com is a crowd funding website where creators post descriptions of projects they want to create, and individuals contribute some amount of personal money to finance the project. In return for funding, contributors typically get a wide range of “Backer Rewards”. The rewards differ depending on the type of project. For example, a book/video project will typically issue a digital copy of the final product to backers that donate above a given amount. The scope of projects can vary widely, from the initial factory runs of complex hardware products. One creator simply requested ten dollars so he could make some potato salad and instead raised $55,000 and threw a crazy party\\n\",\n \"\\n\",\n \"### Data Source\\n\",\n \"\\n\",\n \"Webrobots.io runs a monthly scrape of all Kickstarter projects available on the Kickstarter website. The resulting data are available as both csv and json files, and each package covers a number of projects on the website from the start date (April 21st 2009) until the scrape date. From April 2015, Kickstarter began limiting the number of search results for each category. As a result, the numbers of historical results displayed are limited. Webrobots started running scrapes in multiple sub-categories to capture more instances, and as a result, there are multiple entries for several projects.\\n\",\n \"\\n\",\n \"### Dates of Study\\n\",\n \"\\n\",\n \"The scrape data that I'm working with was taken from Kickstarter.com in March of 2019. The scrape data includes both historical and current projects, so there's a wide variety of dates captured here.\\n\",\n \"\\n\",\n \"# Data issues\\n\",\n \"\\n\",\n \"### Inconsistent Dates\\n\",\n \"\\n\",\n \"Kickstarter.com limits the number of search results shown for each category of project (ex. 'Film & Video', or 'Publishing'). Because of this limit and because the number of projects in each category varies based on their relative popularity, the active dates for projects across different categories are not all the same. As we'll see later, the popularity of the Kickstarter.com website fluctuates over time, so different launch dates can affect a project's chances of success\\n\",\n \"\\n\",\n \"### Missing, incorrect or duplicate data\\n\",\n \"\\n\",\n \"The overall quality of the dataset is very good, with very few pieces of missing data.\\n\",\n \"\\n\",\n \"# Scope and Goals\\n\",\n \"\\n\",\n \"### Audience and Business Need\\n\",\n \"\\n\",\n \"This project is aimed at creators that are interested launching projects on Kickstarter's platform. Kickstarter campaigns are \\\"all or nothing\\\", meaning if a campaign doesn't reach the funding goal, all of the funds are returned to backers. For this reason, it's important for campaigns to carefully consider how their project is presented\\n\",\n \"\\n\",\n \"### Previous Work \\n\",\n \"\\n\",\n \"There's been a lot of work published using Kickstarter datasets in the past. Most of the past work has focused on trying to predict whether a project will achieve their funding goal based on characteristics such as funding goal, project category, and number of backers. While that's interesting from a technical/model optimization view, actionable information for creators is limited so far. \\n\",\n \"\\n\",\n \"Citations for previous work is given in Appendix B at the end of this notebook\\n\",\n \"\\n\",\n \"### Project Goal\\n\",\n \"\\n\",\n \"In light of the previous work done and the popularity of the dataset, this project attempts to approach this dataset from an original angle. The main question I'm trying to answer here is:\\n\",\n \"\\n\",\n \"* *Can we create a way for new creators to make their campaigns \\\"look\\\" like successful projects* \\n\",\n \"\\n\",\n \"This project aims to achieve that by running a segmentation analysis on attributes of successful and unsuccesful projects and try to uncover their differences\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Data Cleaning and Manipulation\\n\",\n \"\\n\",\n \"### JSON Formatting\\n\",\n \"\\n\",\n \"Many of columns in the original dataset were returned as JSON objects during the webrobots scrape. When exporting to a .csv file, and then importing into Pandas, the JSON formatting was lost. The cleaning script made for this project re-applied the JSON formatting, and then pulled the relevant information back out\\n\",\n \"\\n\",\n \"### Interpreted Data\\n\",\n \"Some columns were made with interpreted data. Time deltas were calculated between:\\n\",\n \"* Campaign launch date and campaign creation date\\n\",\n \"* Campaign launch date and campaign deadline\\n\",\n \"* Campaign launch date and date funding goal was reached\\n\",\n \"\\n\",\n \"Natural Language Processing was run on project titles and descriptions to better define project differences. The data from the NLP analysis was used to score project descriptions on their active phrasing and whatever other criteria I make up \\n\",\n \"\\n\",\n \"### Pruning and other cleaning\\n\",\n \"The remaining cleaning work has been selecting the data relevant to the project. The original dataset contained contained image url's, and duplicates of data available in other columns. These items were removed from the dataset used for analysis\\n\",\n \"\\n\",\n \"### Date formats \\n\",\n \"\\n\",\n \"All of the columns that are time/date related are in epoch time, and I've left it that way to make things easier to do math\\n\",\n \"\\n\",\n \"### External Data\\n\",\n \"\\n\",\n \"Data pulled from IndieGoGo has been merged in with the Kickstarter dataset for comparison\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"181302\"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"#Import libraries needed for analysis\\n\",\n \"import pandas as pd\\n\",\n \"import seaborn as sb\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"import datetime\\n\",\n \"import pdb\\n\",\n \"import time\\n\",\n \"import re\\n\",\n \"import os\\n\",\n \"import math as mt\\n\",\n \"\\n\",\n \"url = 'https://tufts.box.com/shared/static/939cuad4z6ieo8njf3ya58a603q4enyh.csv'\\n\",\n \"if not os.path.exists('2019KickDataCleaned.csv'):\\n\",\n \" df = pd.read_csv(url)\\n\",\n \"else:\\n\",\n \" df = pd.read_csv('2019KickDataCleaned.csv', index_col = 0)\\n\",\n \"\\n\",\n \"df.drop(columns = ['subcats'], inplace = True)\\n\",\n \"df = df.drop_duplicates(subset = ['id'])\\n\",\n \"len(df)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Project states\\n\",\n \"\\n\",\n \"There are four different states listed for each project in the dataset:\\n\",\n \"\\n\",\n \"| State | Description|\\n\",\n \"|:---:|:---:|\\n\",\n \"|Live | Project is currently actively fundraising|\\n\",\n \"|Cancelled | Project was cancelled by the creators before the deadline | \\n\",\n \"|Suspended | The project was suspended by Kickstarter for not meeting some criteria|\\n\",\n \"|Succesful| The project has met or exceeded the funding goal |\\n\",\n \"|Failed| The project failed to meet their funding goal| \\n\",\n \"\\n\",\n \"Since this project aims to analyze factors that make projects successful, I've filtered the dataset to include only projects that are either successful or failed\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Merge in External Data\\n\",\n \"\\n\",\n \"### Alexa Site Traffic Ranking data\\n\",\n \"\\n\",\n \"I snagged the Alexa global traffic ranking for Kickstarter.com for the last three years. Let's take a look and see if there's any correlation between the site's traffic ranking and the success rate of projects. I only had access to the last three years worth of ranking data, so some projects will be missing information\\n\",\n \"\\n\",\n \"I'm using the launch date as a foreign key to match with Kickstarter's Alexa ranking, so I have to convert the timestamp to M/D/YYYY format\\n\",\n \"\\n\",\n \"Once the timestamps are converted, merge the Alexa ranking data in with the Kickstarter data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"url = 'https://tufts.box.com/shared/static/1xr291wybxfardn6cteqdmq08khic84t.csv' #Read the Alexa data\\n\",\n \"alexa = pd.read_csv(url, skiprows = 6) \\n\",\n \"\\n\",\n \"#Conver the launch date timestamp (in epoch time) to M/D/YYYY format\\n\",\n \"df['launch_date'] = df.apply(lambda x: time.strftime('%-m/%-d/%Y', time.localtime(x['launched_at'])), axis = 1)\\n\",\n \"\\n\",\n \"#Merge the two dataframes together on the launch date column with a left join\\n\",\n \"df = df.merge(alexa, how = 'left', left_on = 'launch_date', right_on = 'Date')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Convert Foreign Currencies to USD terms\\n\",\n \"\\n\",\n \"There's a number of projects that originated outside of the US. For these projects, the funding goals are in a different currency. In order to clean up the dataframe for analysis, convert these into USD terms.\\n\",\n \"\\n\",\n \"Kickstarter.com was kind enough to include the prevailing currency exchange rate at the time of the project, so I'll use that for the conversion\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Multiply the goal by the fx rate\\n\",\n \"df['goal'] = df.apply(lambda x: (x['goal'] * x['fx_rate']), axis = 1) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" Unnamed: 0 backers_count \\\\\\n\",\n \"0 0 9 \\n\",\n \"1 1 36 \\n\",\n \"2 2 61 \\n\",\n \"3 3 1730 \\n\",\n \"4 4 183 \\n\",\n \"\\n\",\n \" blurb country created_at \\\\\\n\",\n \"0 A comedy web series about the inherently funny... US 1489891481 \\n\",\n \"1 Cabo y un Capitán, dos soldados villistas, tra... MX 1508479978 \\n\",\n \"2 We're building the new home of Māori Theatre a... NZ 1426499822 \\n\",\n \"3 Comic-book stories celebrate women who crack c... US 1482813229 \\n\",\n \"4 A Victorian “gaslamp” lesbian romance, set in ... US 1506228739 \\n\",\n \"\\n\",\n \" creator currency deadline fx_rate goal ... \\\\\\n\",\n \"0 915430313 USD 1494132705 1.000000 5000.0000 ... \\n\",\n \"1 1011928721 MXN 1512917074 0.052034 3382.2282 ... \\n\",\n \"2 1902194044 NZD 1429596000 0.685030 6850.2957 ... \\n\",\n \"3 121124061 USD 1489666144 1.000000 40000.0000 ... \\n\",\n \"4 484654302 USD 1510473540 1.000000 2000.0000 ... \\n\",\n \"\\n\",\n \" city creLauDelta lauDeadDelta staLauDelta source \\\\\\n\",\n \"0 Austin, TX 19 30 30 Kickstarter \\n\",\n \"1 Mexico City, Mexico 16 35 35 Kickstarter \\n\",\n \"2 Auckland, NZ 7 28 28 Kickstarter \\n\",\n \"3 Scottsdale, AZ 49 29 29 Kickstarter \\n\",\n \"4 San Francisco, CA 16 32 32 Kickstarter \\n\",\n \"\\n\",\n \" funds_raised_percent launch_date Date Metric kickstarter.com \\n\",\n \"0 10.160000 4/7/2017 4/7/2017 Global Rank 548.0 \\n\",\n \"1 5.226900 11/5/2017 11/5/2017 Global Rank 528.0 \\n\",\n \"2 78.343402 3/23/2015 NaN NaN NaN \\n\",\n \"3 243.617500 2/14/2017 2/14/2017 Global Rank 589.0 \\n\",\n \"4 195.550000 10/10/2017 10/10/2017 Global Rank 503.0 \\n\",\n \"\\n\",\n \"[5 rows x 31 columns]\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Check that the merge was successful and did what I wanted\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### IndieGoGo Data\\n\",\n \"\\n\",\n \"Webrobots.io also runs a monthly scrape of the indiegogo website. Since there's already been a large amount of work done on Kickstarter data in the past, I want to try and make some unique analyses using a new dataset.\\n\",\n \"\\n\",\n \"I grabbed the most recent version of the indiegogo dataset, and now I'm going to merge it with my Kickstarter dataset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Start by taking a look at the indiegogo dataset\\n\",\n \"url = 'https://tufts.box.com/shared/static/on2cmdxc4vqpm2dutaaf7l8764ktq0u5.csv'\\n\",\n \"indie = pd.read_csv('https://tufts.box.com/shared/static/on2cmdxc4vqpm2dutaaf7l8764ktq0u5.csv')\\n\",\n \"#Drop columns that are unrelatd to the original kickstarter dataset\\n\",\n \"indie.drop(labels = ['bullet_point', 'category_url', 'clickthrough_url', 'image_url', 'is_pre_launch', 'offered_by', \\n\",\n \" 'price_offered', 'product_id', 'product_stage', 'project_type', 'price_retail', \\n\",\n \" 'perk_goal_percentage', 'tags'], axis = 1, inplace = True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"| \\n\",\n \" | Unnamed: 0 | \\n\",\n \"backers_count | \\n\",\n \"blurb | \\n\",\n \"country | \\n\",\n \"created_at | \\n\",\n \"creator | \\n\",\n \"currency | \\n\",\n \"deadline | \\n\",\n \"fx_rate | \\n\",\n \"goal | \\n\",\n \"... | \\n\",\n \"city | \\n\",\n \"creLauDelta | \\n\",\n \"lauDeadDelta | \\n\",\n \"staLauDelta | \\n\",\n \"source | \\n\",\n \"funds_raised_percent | \\n\",\n \"launch_date | \\n\",\n \"Date | \\n\",\n \"Metric | \\n\",\n \"kickstarter.com | \\n\",\n \"
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| 3 | \\n\",\n \"3 | \\n\",\n \"1730 | \\n\",\n \"Comic-book stories celebrate women who crack c... | \\n\",\n \"US | \\n\",\n \"1482813229 | \\n\",\n \"121124061 | \\n\",\n \"USD | \\n\",\n \"1489666144 | \\n\",\n \"1.000000 | \\n\",\n \"40000.0000 | \\n\",\n \"... | \\n\",\n \"Scottsdale, AZ | \\n\",\n \"49 | \\n\",\n \"29 | \\n\",\n \"29 | \\n\",\n \"Kickstarter | \\n\",\n \"243.617500 | \\n\",\n \"2/14/2017 | \\n\",\n \"2/14/2017 | \\n\",\n \"Global Rank | \\n\",\n \"589.0 | \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \" category close_date currency \\\\\\n\",\n \"0 Home 2018-07-16T23:59:59-07:00 GBP \\n\",\n \"1 Web Series & TV Shows 2017-09-12T23:59:59-07:00 AUD \\n\",\n \"2 Photography 2013-02-13T23:59:59-08:00 USD \\n\",\n \"3 Home 2017-01-07T23:59:59-08:00 EUR \\n\",\n \"4 Health & Fitness 2018-07-11T23:59:59-07:00 USD \\n\",\n \"\\n\",\n \" funds_raised_amount funds_raised_percent is_indemand \\\\\\n\",\n \"0 10694.0 1.065059 True \\n\",\n \"1 30.0 0.003000 False \\n\",\n \"2 7060.0 0.415294 False \\n\",\n \"3 20.0 0.000020 False \\n\",\n \"4 250036.0 6.750033 True \\n\",\n \"\\n\",\n \" open_date perks_claimed project_id \\\\\\n\",\n \"0 2018-07-15T23:59:59-07:00 58.0 2397370 \\n\",\n \"1 2017-07-14T06:19:44-07:00 0.0 2152925 \\n\",\n \"2 2012-12-10T09:05:14-08:00 65.0 291347 \\n\",\n \"3 2016-11-24T01:51:38-08:00 2.0 1951140 \\n\",\n \"4 2018-07-10T23:59:59-07:00 532.0 2393782 \\n\",\n \"\\n\",\n \" source_url \\\\\\n\",\n \"0 https://www.indiegogo.com/explore/all?project_... \\n\",\n \"1 https://www.indiegogo.com/explore/all?project_... \\n\",\n \"2 https://www.indiegogo.com/explore/all?project_... \\n\",\n \"3 https://www.indiegogo.com/explore/all?project_... \\n\",\n \"4 https://www.indiegogo.com/explore/all?project_... \\n\",\n \"\\n\",\n \" tagline \\\\\\n\",\n \"0 Make a brew for two with one press using the A... \\n\",\n \"1 This street is the stage where the theatre mee... \\n\",\n \"2 Creating a photo-narrative book that captures ... \\n\",\n \"3 World 1st Charity Contest of religious communi... \\n\",\n \"4 Wake up gently every morning by stimulating th... \\n\",\n \"\\n\",\n \" title \\n\",\n \"0 2POUR An Accessory For Aeropress Coffee Maker \\n\",\n \"1 Hustle Street - A musical \\n\",\n \"2 The Thru-Project \\n\",\n \"3 Way2goodness \\n\",\n \"4 Sensorwake Trio: The scent-based alarm clock \"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"indie.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Making the Indiegogo data look like the Kickstarter data\\n\",\n \"\\n\",\n \"Since the two websites are unique, there's a lot of differences in how the datasets are structured\\n\",\n \"\\n\",\n \"The following few steps are manipulating the indiegogo data to look like the Kickstarter data\\n\",\n \"\\n\",\n \"### Date Conversion\\n\",\n \"\\n\",\n \"First up, change the dates that are in a YY/MM/DD format and convert that into epoch time (the standard for the Kickstarter dataset)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime # Library for working with datetime information\\n\",\n \"indie.dropna(inplace = True) # Drop rows that are missing important information\\n\",\n \"\\n\",\n \"# Define a function to strip out the extra information on the indiegogo timestamps, and then convert to epoch time\\n\",\n \"def epoch(target): \\n\",\n \" '''\\n\",\n \" This function takes a date from the indiegogo dataset (read originally as a string), converts to a datetime object\\n\",\n \" and then converts that date to epoch time\\n\",\n \" '''\\n\",\n \" target = target.split('T')\\n\",\n \" target = datetime.datetime.strptime(target[0], '%Y-%m-%d')\\n\",\n \" target = target.strftime('%s')\\n\",\n \" return(target)\\n\",\n \"\\n\",\n \"#Run the epoch function on the project close date and project open date columns\\n\",\n \"indie.close_date = indie.close_date.apply(epoch) \\n\",\n \"indie.open_date = indie.open_date.apply(epoch)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Renaming Categories\\n\",\n \"\\n\",\n \"The category labels for indiegogo don't quite match up to Kickstarter categories. I've made a dictionary to convert the indiegogo categories to match Kickstarter categories\\n\",\n \"\\n\",\n \"Some of the categories in the indiegogo dataset didn't have an obvious counterpart in the kickstarter data, so I've left those in their original categories\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"indieDictionary = { 'Home' : 'crafts',\\n\",\n \" 'Web Series & TV Shows' : 'film & video',\\n\",\n \" 'Photography' : 'photography',\\n\",\n \" 'Health & Fitness' : 'health&fitness',\\n\",\n \" 'Comics' : 'comics',\\n\",\n \" 'Food & Beverages' : 'food',\\n\",\n \" 'Music' : 'music',\\n\",\n \" 'Dance & Theater' : 'dance',\\n\",\n \" 'Podcasts, Blogs & Vlogs' : 'journalism' ,\\n\",\n \" 'Video Games' : 'games',\\n\",\n \" 'Art' : 'art',\\n\",\n \" 'Fashion & Wearables' : 'fashion',\\n\",\n \" 'Phones & Accessories' : 'technology',\\n\",\n \" 'Wellness' : 'health&fitness',\\n\",\n \" 'Camera Gear': 'cameragear',\\n\",\n \" 'Travel & Outdoors': 'travel&outdoors',\\n\",\n \" 'Film' : 'film & video',\\n\",\n \" 'Productivity': 'productivity',\\n\",\n \" 'Tabletop Games': 'games',\\n\",\n \" 'Audio' : 'music',\\n\",\n \" 'Culture' : 'culture',\\n\",\n \" 'Human Rights' : 'human_rights',\\n\",\n \" 'Environment' : 'environment',\\n\",\n \" 'Writing & Publishing' : 'publishing',\\n\",\n \" 'Transportation' : 'technology',\\n\",\n \" 'Local Businesses' : 'local_businesses',\\n\",\n \" 'Energy & Green Tech' : 'energy&green tech'}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Column Names, Epoch Time Conversion, and some Math\\n\",\n \"\\n\",\n \"Rename the columns of the indiegogo data to match the kickstarter column names. When the dates were converted to epoch time in the step above, the data was read as a string type. Convert those to ints\\n\",\n \"\\n\",\n \"Finally, calculate how long the fundraising campaigns were for each project\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Use the dictionary to re-categorize the indiegogo projects\\n\",\n \"indie.category = indie.category.map(indieDictionary) \\n\",\n \"\\n\",\n \"# Add an indiegogo tag column to separate kickstarter and indiegogo data\\n\",\n \"indie['source'] = 'indiegogo' \\n\",\n \"\\n\",\n \"# Rename each column header to match kickstarter\\n\",\n \"indie = indie.rename(index=str, columns={\\\"close_date\\\": \\\"deadline\\\", \\n\",\n \" \\\"funds_raised_amount\\\": \\\"usd_pledged\\\", \\n\",\n \" \\\"open_date\\\" : \\\"launched_at\\\",\\n\",\n \" \\\"is_indemand\\\":\\\"spotlight\\\", \\n\",\n \" \\\"tagline\\\" : \\\"blurb\\\", \\n\",\n \" \\\"title\\\":\\\"name\\\", \\n\",\n \" \\\"perks_claimed\\\" : \\\"backers_count\\\",\\n\",\n \" \\\"project_id\\\": \\\"id\\\", \\n\",\n \" \\\"category\\\" : \\\"fullcats\\\",\\n\",\n \" \\\"source_url\\\" : \\\"urls\\\"})\\n\",\n \"\\n\",\n \"# Cast the epoch timestamps as ints instead of strings\\n\",\n \"indie.deadline = indie.deadline.astype(int) \\n\",\n \"indie.launched_at = indie.launched_at.astype(int)\\n\",\n \"\\n\",\n \"#Calculate how long each fundraising campaign was run for\\n\",\n \"indie['lauDeadDelta'] = indie.apply(lambda x: (x[\\\"deadline\\\"] - x[\\\"launched_at\\\"])/60/60/24, axis = 1)\\n\",\n \"indie['goal'] = indie.apply(lambda x: (mt.ceil(x['usd_pledged'] / (x['funds_raised_percent'] + 0.00000000001))), \\n\",\n \" axis = 1)\\n\",\n \"indie['funds_raised_percent'] = indie.apply(lambda x: (x['funds_raised_percent'] * 100), axis = 1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Tagging IndieGoGo campaigns as successful or failed\\n\",\n \"\\n\",\n \"Since indiegogo creatores receive their funds regardless of how much the projects raise, the indiegogo dataset doesn't have a tag for the status of a project\\n\",\n \"\\n\",\n \"To fix that, I'll tag any project that reaches it's fundraising goal as successful and any that don't as failed\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/Users/chrismay/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n \"\\n\",\n \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\\n\",\n \" import sys\\n\",\n \"/Users/chrismay/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame.\\n\",\n \"Try using .loc[row_indexer,col_indexer] = value instead\\n\",\n \"\\n\",\n \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\\n\",\n \" # This is added back by InteractiveShellApp.init_path()\\n\"\n ]\n }\n ],\n \"source\": [\n \"inSuccess = pd.DataFrame()\\n\",\n \"inFailed = pd.DataFrame()\\n\",\n \"\\n\",\n \"# First filter out all projects that raised 100% of their goal or more, then add a 'state' column and set that to\\n\",\n \"# successful\\n\",\n \"inSuccess = indie[indie['funds_raised_percent'] >= 100]\\n\",\n \"inSuccess['state'] = 'successful'\\n\",\n \"\\n\",\n \"# Now do the same for projects that didn't raise 100% of their funding goals, and tag them as failed\\n\",\n \"inFailed = indie[indie['funds_raised_percent'] < 100]\\n\",\n \"inFailed['state'] = 'failed'\\n\",\n \"\\n\",\n \"# Merge the two dataframes back together \\n\",\n \"indie = inSuccess.merge(inFailed, how = 'outer', on = None)\\n\",\n \"del inSuccess\\n\",\n \"del inFailed\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"| \\n\",\n \" | category | \\n\",\n \"close_date | \\n\",\n \"currency | \\n\",\n \"funds_raised_amount | \\n\",\n \"funds_raised_percent | \\n\",\n \"is_indemand | \\n\",\n \"open_date | \\n\",\n \"perks_claimed | \\n\",\n \"project_id | \\n\",\n \"source_url | \\n\",\n \"tagline | \\n\",\n \"title | \\n\",\n \"
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \\n\",\n \"Home | \\n\",\n \"2018-07-16T23:59:59-07:00 | \\n\",\n \"GBP | \\n\",\n \"10694.0 | \\n\",\n \"1.065059 | \\n\",\n \"True | \\n\",\n \"2018-07-15T23:59:59-07:00 | \\n\",\n \"58.0 | \\n\",\n \"2397370 | \\n\",\n \"https://www.indiegogo.com/explore/all?project_... | \\n\",\n \"Make a brew for two with one press using the A... | \\n\",\n \"2POUR An Accessory For Aeropress Coffee Maker | \\n\",\n \"
| 1 | \\n\",\n \"Web Series & TV Shows | \\n\",\n \"2017-09-12T23:59:59-07:00 | \\n\",\n \"AUD | \\n\",\n \"30.0 | \\n\",\n \"0.003000 | \\n\",\n \"False | \\n\",\n \"2017-07-14T06:19:44-07:00 | \\n\",\n \"0.0 | \\n\",\n \"2152925 | \\n\",\n \"https://www.indiegogo.com/explore/all?project_... | \\n\",\n \"This street is the stage where the theatre mee... | \\n\",\n \"Hustle Street - A musical | \\n\",\n \"
| 2 | \\n\",\n \"Photography | \\n\",\n \"2013-02-13T23:59:59-08:00 | \\n\",\n \"USD | \\n\",\n \"7060.0 | \\n\",\n \"0.415294 | \\n\",\n \"False | \\n\",\n \"2012-12-10T09:05:14-08:00 | \\n\",\n \"65.0 | \\n\",\n \"291347 | \\n\",\n \"https://www.indiegogo.com/explore/all?project_... | \\n\",\n \"Creating a photo-narrative book that captures ... | \\n\",\n \"The Thru-Project | \\n\",\n \"
| 3 | \\n\",\n \"Home | \\n\",\n \"2017-01-07T23:59:59-08:00 | \\n\",\n \"EUR | \\n\",\n \"20.0 | \\n\",\n \"0.000020 | \\n\",\n \"False | \\n\",\n \"2016-11-24T01:51:38-08:00 | \\n\",\n \"2.0 | \\n\",\n \"1951140 | \\n\",\n \"https://www.indiegogo.com/explore/all?project_... | \\n\",\n \"World 1st Charity Contest of religious communi... | \\n\",\n \"Way2goodness | \\n\",\n \"
| 4 | \\n\",\n \"Health & Fitness | \\n\",\n \"2018-07-11T23:59:59-07:00 | \\n\",\n \"USD | \\n\",\n \"250036.0 | \\n\",\n \"6.750033 | \\n\",\n \"True | \\n\",\n \"2018-07-10T23:59:59-07:00 | \\n\",\n \"532.0 | \\n\",\n \"2393782 | \\n\",\n \"https://www.indiegogo.com/explore/all?project_... | \\n\",\n \"Wake up gently every morning by stimulating th... | \\n\",\n \"Sensorwake Trio: The scent-based alarm clock | \\n\",\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \" fullcats deadline currency usd_pledged funds_raised_percent \\\\\\n\",\n \"0 crafts 1531713600 GBP 10694.0 106.505882 \\n\",\n \"1 health&fitness 1531281600 USD 250036.0 675.003333 \\n\",\n \"2 comics 1373860800 USD 12904.0 430.133333 \\n\",\n \"3 crafts 1513227600 USD 78718.0 272.035000 \\n\",\n \"4 crafts 1524456000 USD 6135.0 232.300000 \\n\",\n \"\\n\",\n \" spotlight launched_at backers_count id \\\\\\n\",\n \"0 True 1531627200 58.0 2397370 \\n\",\n \"1 True 1531195200 532.0 2393782 \\n\",\n \"2 False 1369800000 151.0 413752 \\n\",\n \"3 False 1513141200 179.0 2276585 \\n\",\n \"4 True 1521086400 44.0 2352230 \\n\",\n \"\\n\",\n \" urls \\\\\\n\",\n \"0 https://www.indiegogo.com/explore/all?project_... \\n\",\n \"1 https://www.indiegogo.com/explore/all?project_... \\n\",\n \"2 https://www.indiegogo.com/explore/all?project_... \\n\",\n \"3 https://www.indiegogo.com/explore/all?project_... \\n\",\n \"4 https://www.indiegogo.com/explore/all?project_... \\n\",\n \"\\n\",\n \" blurb \\\\\\n\",\n \"0 Make a brew for two with one press using the A... \\n\",\n \"1 Wake up gently every morning by stimulating th... \\n\",\n \"2 Support independent comics by helping Antarcti... \\n\",\n \"3 The first smart padlock designed to protect yo... \\n\",\n \"4 Get 5 star results with our solid Titanium rol... \\n\",\n \"\\n\",\n \" name source lauDeadDelta \\\\\\n\",\n \"0 2POUR An Accessory For Aeropress Coffee Maker indiegogo 1.0 \\n\",\n \"1 Sensorwake Trio: The scent-based alarm clock indiegogo 1.0 \\n\",\n \"2 Antarctic Press...Keeping it Cold in the Antar... indiegogo 47.0 \\n\",\n \"3 BoxLock Home - Smart Padlock to Protect Delive... indiegogo 1.0 \\n\",\n \"4 SolidTi: World's first solid titanium rolling pin indiegogo 39.0 \\n\",\n \"\\n\",\n \" goal state \\n\",\n \"0 10041 successful \\n\",\n \"1 37043 successful \\n\",\n \"2 3000 successful \\n\",\n \"3 28937 successful \\n\",\n \"4 2641 successful \"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Check that the above operations were successful\\n\",\n \"indie.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Merge the two DataFrames together and deduplicate\\n\",\n \"\\n\",\n \"Now that the two datasets look alike, merge them together with an outer join (include only entries that in either dataset, but not both)\\n\",\n \"\\n\",\n \"Since the webscrape by webrobots searched by subcategory, projects can be listed multiple times if they had multiple subcategry tags. Use the drop_duplicates method from pandas to get rid of the extras\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/Users/chrismay/anaconda3/lib/python3.7/site-packages/pandas/core/reshape/merge.py:938: UserWarning: You are merging on int and float columns where the float values are not equal to their int representation\\n\",\n \" 'representation', UserWarning)\\n\",\n \"/Users/chrismay/anaconda3/lib/python3.7/site-packages/pandas/core/reshape/merge.py:946: UserWarning: You are merging on int and float columns where the float values are not equal to their int representation\\n\",\n \" 'representation', UserWarning)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"| \\n\",\n \" | fullcats | \\n\",\n \"deadline | \\n\",\n \"currency | \\n\",\n \"usd_pledged | \\n\",\n \"funds_raised_percent | \\n\",\n \"spotlight | \\n\",\n \"launched_at | \\n\",\n \"backers_count | \\n\",\n \"id | \\n\",\n \"urls | \\n\",\n \"blurb | \\n\",\n \"name | \\n\",\n \"source | \\n\",\n \"lauDeadDelta | \\n\",\n \"goal | \\n\",\n \"state | \\n\",\n \"
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \\n\",\n \"crafts | \\n\",\n \"1531713600 | \\n\",\n \"GBP | \\n\",\n \"10694.0 | \\n\",\n \"106.505882 | \\n\",\n \"True | \\n\",\n \"1531627200 | \\n\",\n \"58.0 | \\n\",\n \"2397370 | \\n\",\n \"https://www.indiegogo.com/explore/all?project_... | \\n\",\n \"Make a brew for two with one press using the A... | \\n\",\n \"2POUR An Accessory For Aeropress Coffee Maker | \\n\",\n \"indiegogo | \\n\",\n \"1.0 | \\n\",\n \"10041 | \\n\",\n \"successful | \\n\",\n \"
| 1 | \\n\",\n \"health&fitness | \\n\",\n \"1531281600 | \\n\",\n \"USD | \\n\",\n \"250036.0 | \\n\",\n \"675.003333 | \\n\",\n \"True | \\n\",\n \"1531195200 | \\n\",\n \"532.0 | \\n\",\n \"2393782 | \\n\",\n \"https://www.indiegogo.com/explore/all?project_... | \\n\",\n \"Wake up gently every morning by stimulating th... | \\n\",\n \"Sensorwake Trio: The scent-based alarm clock | \\n\",\n \"indiegogo | \\n\",\n \"1.0 | \\n\",\n \"37043 | \\n\",\n \"successful | \\n\",\n \"
| 2 | \\n\",\n \"comics | \\n\",\n \"1373860800 | \\n\",\n \"USD | \\n\",\n \"12904.0 | \\n\",\n \"430.133333 | \\n\",\n \"False | \\n\",\n \"1369800000 | \\n\",\n \"151.0 | \\n\",\n \"413752 | \\n\",\n \"https://www.indiegogo.com/explore/all?project_... | \\n\",\n \"Support independent comics by helping Antarcti... | \\n\",\n \"Antarctic Press...Keeping it Cold in the Antar... | \\n\",\n \"indiegogo | \\n\",\n \"47.0 | \\n\",\n \"3000 | \\n\",\n \"successful | \\n\",\n \"
| 3 | \\n\",\n \"crafts | \\n\",\n \"1513227600 | \\n\",\n \"USD | \\n\",\n \"78718.0 | \\n\",\n \"272.035000 | \\n\",\n \"False | \\n\",\n \"1513141200 | \\n\",\n \"179.0 | \\n\",\n \"2276585 | \\n\",\n \"https://www.indiegogo.com/explore/all?project_... | \\n\",\n \"The first smart padlock designed to protect yo... | \\n\",\n \"BoxLock Home - Smart Padlock to Protect Delive... | \\n\",\n \"indiegogo | \\n\",\n \"1.0 | \\n\",\n \"28937 | \\n\",\n \"successful | \\n\",\n \"
| 4 | \\n\",\n \"crafts | \\n\",\n \"1524456000 | \\n\",\n \"USD | \\n\",\n \"6135.0 | \\n\",\n \"232.300000 | \\n\",\n \"True | \\n\",\n \"1521086400 | \\n\",\n \"44.0 | \\n\",\n \"2352230 | \\n\",\n \"https://www.indiegogo.com/explore/all?project_... | \\n\",\n \"Get 5 star results with our solid Titanium rol... | \\n\",\n \"SolidTi: World's first solid titanium rolling pin | \\n\",\n \"indiegogo | \\n\",\n \"39.0 | \\n\",\n \"2641 | \\n\",\n \"successful | \\n\",\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" backers_count blurb country \\\\\\n\",\n \"0 9 A comedy web series about the inherently funny... US \\n\",\n \"1 36 Cabo y un Capitán, dos soldados villistas, tra... MX \\n\",\n \"2 61 We're building the new home of Māori Theatre a... NZ \\n\",\n \"3 1730 Comic-book stories celebrate women who crack c... US \\n\",\n \"4 183 A Victorian “gaslamp” lesbian romance, set in ... US \\n\",\n \"\\n\",\n \" created_at creator currency deadline fx_rate goal \\\\\\n\",\n \"0 1.489891e+09 9.154303e+08 USD 1494132705 1.000000 5000.0000 \\n\",\n \"1 1.508480e+09 1.011929e+09 MXN 1512917074 0.052034 3382.2282 \\n\",\n \"2 1.426500e+09 1.902194e+09 NZD 1429596000 0.685030 6850.2957 \\n\",\n \"3 1.482813e+09 1.211241e+08 USD 1489666144 1.000000 40000.0000 \\n\",\n \"4 1.506229e+09 4.846543e+08 USD 1510473540 1.000000 2000.0000 \\n\",\n \"\\n\",\n \" id ... city creLauDelta lauDeadDelta staLauDelta \\\\\\n\",\n \"0 1203770415 ... Austin, TX 19.0 30.0 30.0 \\n\",\n \"1 878861613 ... Mexico City, Mexico 16.0 35.0 35.0 \\n\",\n \"2 917345297 ... Auckland, NZ 7.0 28.0 28.0 \\n\",\n \"3 1702164653 ... Scottsdale, AZ 49.0 29.0 29.0 \\n\",\n \"4 1042930184 ... San Francisco, CA 16.0 32.0 32.0 \\n\",\n \"\\n\",\n \" source funds_raised_percent launch_date Date Metric \\\\\\n\",\n \"0 Kickstarter 10.160000 4/7/2017 4/7/2017 Global Rank \\n\",\n \"1 Kickstarter 5.226900 11/5/2017 11/5/2017 Global Rank \\n\",\n \"2 Kickstarter 78.343402 3/23/2015 NaN NaN \\n\",\n \"3 Kickstarter 243.617500 2/14/2017 2/14/2017 Global Rank \\n\",\n \"4 Kickstarter 195.550000 10/10/2017 10/10/2017 Global Rank \\n\",\n \"\\n\",\n \" kickstarter.com \\n\",\n \"0 548.0 \\n\",\n \"1 528.0 \\n\",\n \"2 NaN \\n\",\n \"3 589.0 \\n\",\n \"4 503.0 \\n\",\n \"\\n\",\n \"[5 rows x 30 columns]\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"merged = df.merge(indie, how = 'outer', on = None)\\n\",\n \"merged.reset_index(inplace = True)\\n\",\n \"merged.drop(labels = ['index', 'Unnamed: 0'], axis = 1, inplace = True)\\n\",\n \"\\n\",\n \"del indie \\n\",\n \"del df\\n\",\n \"# Drop duplicates from the dataset, use the 'id' column since its known to be unique only to each project\\n\",\n \"merged.drop_duplicates(subset = 'id', inplace = True)\\n\",\n \"merged.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Calculate the Funds Raised over Goal Percent\\n\",\n \"\\n\",\n \"Kickstarter and Indiegogo give a different gauge of percent of funds raised. Standardize on giving the percent of goal for each project\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"merged['funds_raised_percent'] = merged.apply(lambda x: (((x['usd_pledged']/ (x['goal'] + 0.0000001))*100)), axis = 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"| \\n\",\n \" | backers_count | \\n\",\n \"blurb | \\n\",\n \"country | \\n\",\n \"created_at | \\n\",\n \"creator | \\n\",\n \"currency | \\n\",\n \"deadline | \\n\",\n \"fx_rate | \\n\",\n \"goal | \\n\",\n \"id | \\n\",\n \"... | \\n\",\n \"city | \\n\",\n \"creLauDelta | \\n\",\n \"lauDeadDelta | \\n\",\n \"staLauDelta | \\n\",\n \"source | \\n\",\n \"funds_raised_percent | \\n\",\n \"launch_date | \\n\",\n \"Date | \\n\",\n \"Metric | \\n\",\n \"kickstarter.com | \\n\",\n \"
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \\n\",\n \"9 | \\n\",\n \"A comedy web series about the inherently funny... | \\n\",\n \"US | \\n\",\n \"1.489891e+09 | \\n\",\n \"9.154303e+08 | \\n\",\n \"USD | \\n\",\n \"1494132705 | \\n\",\n \"1.000000 | \\n\",\n \"5000.0000 | \\n\",\n \"1203770415 | \\n\",\n \"... | \\n\",\n \"Austin, TX | \\n\",\n \"19.0 | \\n\",\n \"30.0 | \\n\",\n \"30.0 | \\n\",\n \"Kickstarter | \\n\",\n \"10.160000 | \\n\",\n \"4/7/2017 | \\n\",\n \"4/7/2017 | \\n\",\n \"Global Rank | \\n\",\n \"548.0 | \\n\",\n \"
| 1 | \\n\",\n \"36 | \\n\",\n \"Cabo y un Capitán, dos soldados villistas, tra... | \\n\",\n \"MX | \\n\",\n \"1.508480e+09 | \\n\",\n \"1.011929e+09 | \\n\",\n \"MXN | \\n\",\n \"1512917074 | \\n\",\n \"0.052034 | \\n\",\n \"3382.2282 | \\n\",\n \"878861613 | \\n\",\n \"... | \\n\",\n \"Mexico City, Mexico | \\n\",\n \"16.0 | \\n\",\n \"35.0 | \\n\",\n \"35.0 | \\n\",\n \"Kickstarter | \\n\",\n \"5.226900 | \\n\",\n \"11/5/2017 | \\n\",\n \"11/5/2017 | \\n\",\n \"Global Rank | \\n\",\n \"528.0 | \\n\",\n \"
| 2 | \\n\",\n \"61 | \\n\",\n \"We're building the new home of Māori Theatre a... | \\n\",\n \"NZ | \\n\",\n \"1.426500e+09 | \\n\",\n \"1.902194e+09 | \\n\",\n \"NZD | \\n\",\n \"1429596000 | \\n\",\n \"0.685030 | \\n\",\n \"6850.2957 | \\n\",\n \"917345297 | \\n\",\n \"... | \\n\",\n \"Auckland, NZ | \\n\",\n \"7.0 | \\n\",\n \"28.0 | \\n\",\n \"28.0 | \\n\",\n \"Kickstarter | \\n\",\n \"78.343402 | \\n\",\n \"3/23/2015 | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"
| 3 | \\n\",\n \"1730 | \\n\",\n \"Comic-book stories celebrate women who crack c... | \\n\",\n \"US | \\n\",\n \"1.482813e+09 | \\n\",\n \"1.211241e+08 | \\n\",\n \"USD | \\n\",\n \"1489666144 | \\n\",\n \"1.000000 | \\n\",\n \"40000.0000 | \\n\",\n \"1702164653 | \\n\",\n \"... | \\n\",\n \"Scottsdale, AZ | \\n\",\n \"49.0 | \\n\",\n \"29.0 | \\n\",\n \"29.0 | \\n\",\n \"Kickstarter | \\n\",\n \"243.617500 | \\n\",\n \"2/14/2017 | \\n\",\n \"2/14/2017 | \\n\",\n \"Global Rank | \\n\",\n \"589.0 | \\n\",\n \"
| 4 | \\n\",\n \"183 | \\n\",\n \"A Victorian “gaslamp” lesbian romance, set in ... | \\n\",\n \"US | \\n\",\n \"1.506229e+09 | \\n\",\n \"4.846543e+08 | \\n\",\n \"USD | \\n\",\n \"1510473540 | \\n\",\n \"1.000000 | \\n\",\n \"2000.0000 | \\n\",\n \"1042930184 | \\n\",\n \"... | \\n\",\n \"San Francisco, CA | \\n\",\n \"16.0 | \\n\",\n \"32.0 | \\n\",\n \"32.0 | \\n\",\n \"Kickstarter | \\n\",\n \"195.550000 | \\n\",\n \"10/10/2017 | \\n\",\n \"10/10/2017 | \\n\",\n \"Global Rank | \\n\",\n \"503.0 | \\n\",\n \"
5 rows × 30 columns
\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" backers_count blurb \\\\\\n\",\n \"201554 6 The Manipulation :: of Light \\\\r\\\\nan Exhibitio... \\n\",\n \"201555 2 Wine tasting set shipped to your home. Amazon ... \\n\",\n \"201556 0 The chain pod for slow shutter speed, low ligh... \\n\",\n \"201557 7 Help us showcase our incredible community incl... \\n\",\n \"201558 21 Help Creep Cuts LEVEL UP! \\n\",\n \"\\n\",\n \" country created_at creator currency deadline fx_rate goal \\\\\\n\",\n \"201554 NaN NaN NaN CAD 1374292800 NaN 7095.0 \\n\",\n \"201555 NaN NaN NaN EUR 1546405200 NaN 35000.0 \\n\",\n \"201556 NaN NaN NaN USD 1396584000 NaN 5000.0 \\n\",\n \"201557 NaN NaN NaN CAD 1514523600 NaN 25000.0 \\n\",\n \"201558 NaN NaN NaN USD 1551502800 NaN 6000.0 \\n\",\n \"\\n\",\n \" id ... city creLauDelta lauDeadDelta staLauDelta source \\\\\\n\",\n \"201554 334244 ... NaN NaN 30.000000 NaN indiegogo \\n\",\n \"201555 2441088 ... NaN NaN 45.000000 NaN indiegogo \\n\",\n \"201556 683726 ... NaN NaN 44.958333 NaN indiegogo \\n\",\n \"201557 2218847 ... NaN NaN 60.041667 NaN indiegogo \\n\",\n \"201558 2454347 ... NaN NaN 60.000000 NaN indiegogo \\n\",\n \"\\n\",\n \" funds_raised_percent launch_date Date Metric kickstarter.com \\n\",\n \"201554 4.510218 NaN NaN NaN NaN \\n\",\n \"201555 0.280000 NaN NaN NaN NaN \\n\",\n \"201556 1.200000 NaN NaN NaN NaN \\n\",\n \"201557 3.280000 NaN NaN NaN NaN \\n\",\n \"201558 35.433333 NaN NaN NaN NaN \\n\",\n \"\\n\",\n \"[5 rows x 30 columns]\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Check the indiegogo data at the end of the dataset to make sure it was done right\\n\",\n \"merged.tail()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Reducing Memory Usage of the DataFrame\\n\",\n \"\\n\",\n \"I've occassionally run into kernel issues with this notebook, which is most likely due to running out of memory. Optimize some of the datatypes to reduce working \\n\",\n \"\\n\",\n \"### Re-casting integer variables\\n\",\n \"Ints have been automatically given a float or int64 datatype, downcast these to smaller variables to use less memory\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"backers_count uint8\\n\",\n \"blurb object\\n\",\n \"country object\\n\",\n \"created_at float64\\n\",\n \"creator float64\\n\",\n \"currency object\\n\",\n \"deadline uint32\\n\",\n \"fx_rate float64\\n\",\n \"goal uint32\\n\",\n \"id uint32\\n\",\n \"launched_at uint32\\n\",\n \"name object\\n\",\n \"slug object\\n\",\n \"spotlight object\\n\",\n \"staff_pick object\\n\",\n \"state object\\n\",\n \"state_changed_at float64\\n\",\n \"urls object\\n\",\n \"usd_pledged uint32\\n\",\n \"fullcats object\\n\",\n \"city object\\n\",\n \"creLauDelta float64\\n\",\n \"lauDeadDelta uint8\\n\",\n \"staLauDelta float64\\n\",\n \"source object\\n\",\n \"funds_raised_percent float16\\n\",\n \"launch_date object\\n\",\n \"Date object\\n\",\n \"Metric object\\n\",\n \"kickstarter.com float64\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"merged['backers_count'] = merged['backers_count'].astype(np.uint8)\\n\",\n \"merged['deadline'] = merged['deadline'].astype(np.uint32)\\n\",\n \"merged['goal'] = merged['goal'].astype(np.uint32)\\n\",\n \"merged['id'] = merged['id'].astype(np.uint32)\\n\",\n \"merged['launched_at'] = merged['launched_at'].astype(np.uint32)\\n\",\n \"merged['usd_pledged'] = merged['usd_pledged'].astype(np.uint32)\\n\",\n \"merged['lauDeadDelta'] = merged['lauDeadDelta'].astype(np.uint8)\\n\",\n \"merged['funds_raised_percent'] = merged['funds_raised_percent'].astype(np.float16)\\n\",\n \"\\n\",\n \"merged.dtypes\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Changing object types to categories\\n\",\n \"\\n\",\n \"A few of the columns listed as object types are actually categorical variables. Columns that have a low number of unique values can be changed to categorical variables, where each category will be given an 8 bit integer value and then mapped to a much shorter array.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"backers_count uint8\\n\",\n \"blurb object\\n\",\n \"country object\\n\",\n \"created_at float64\\n\",\n \"creator float64\\n\",\n \"currency category\\n\",\n \"deadline uint32\\n\",\n \"fx_rate float64\\n\",\n \"goal uint32\\n\",\n \"id uint32\\n\",\n \"launched_at uint32\\n\",\n \"name object\\n\",\n \"slug object\\n\",\n \"spotlight category\\n\",\n \"staff_pick category\\n\",\n \"state object\\n\",\n \"state_changed_at float64\\n\",\n \"urls object\\n\",\n \"usd_pledged uint32\\n\",\n \"fullcats category\\n\",\n \"city object\\n\",\n \"creLauDelta float64\\n\",\n \"lauDeadDelta uint8\\n\",\n \"staLauDelta float64\\n\",\n \"source category\\n\",\n \"funds_raised_percent float16\\n\",\n \"launch_date object\\n\",\n \"Date object\\n\",\n \"kickstarter.com float64\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"merged['fullcats'] = merged['fullcats'].astype('category')\\n\",\n \"merged['staff_pick'] = merged['staff_pick'].astype('category')\\n\",\n \"merged['spotlight'] = merged['spotlight'].astype('category')\\n\",\n \"merged['source'] = merged['source'].astype('category')\\n\",\n \"merged['currency'] = merged['currency'].astype('category')\\n\",\n \"try:\\n\",\n \" merged.drop(labels = ['Metric'], axis = 1, inplace = True)\\n\",\n \"except:\\n\",\n \" pass\\n\",\n \"merged.dtypes\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exploratory Data Analysis\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"| \\n\",\n \" | backers_count | \\n\",\n \"blurb | \\n\",\n \"country | \\n\",\n \"created_at | \\n\",\n \"creator | \\n\",\n \"currency | \\n\",\n \"deadline | \\n\",\n \"fx_rate | \\n\",\n \"goal | \\n\",\n \"id | \\n\",\n \"... | \\n\",\n \"city | \\n\",\n \"creLauDelta | \\n\",\n \"lauDeadDelta | \\n\",\n \"staLauDelta | \\n\",\n \"source | \\n\",\n \"funds_raised_percent | \\n\",\n \"launch_date | \\n\",\n \"Date | \\n\",\n \"Metric | \\n\",\n \"kickstarter.com | \\n\",\n \"
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201554 | \\n\",\n \"6 | \\n\",\n \"The Manipulation :: of Light \\\\r\\\\nan Exhibitio... | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"CAD | \\n\",\n \"1374292800 | \\n\",\n \"NaN | \\n\",\n \"7095.0 | \\n\",\n \"334244 | \\n\",\n \"... | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"30.000000 | \\n\",\n \"NaN | \\n\",\n \"indiegogo | \\n\",\n \"4.510218 | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"
| 201555 | \\n\",\n \"2 | \\n\",\n \"Wine tasting set shipped to your home. Amazon ... | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"EUR | \\n\",\n \"1546405200 | \\n\",\n \"NaN | \\n\",\n \"35000.0 | \\n\",\n \"2441088 | \\n\",\n \"... | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"45.000000 | \\n\",\n \"NaN | \\n\",\n \"indiegogo | \\n\",\n \"0.280000 | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"
| 201556 | \\n\",\n \"0 | \\n\",\n \"The chain pod for slow shutter speed, low ligh... | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"USD | \\n\",\n \"1396584000 | \\n\",\n \"NaN | \\n\",\n \"5000.0 | \\n\",\n \"683726 | \\n\",\n \"... | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"44.958333 | \\n\",\n \"NaN | \\n\",\n \"indiegogo | \\n\",\n \"1.200000 | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"
| 201557 | \\n\",\n \"7 | \\n\",\n \"Help us showcase our incredible community incl... | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"CAD | \\n\",\n \"1514523600 | \\n\",\n \"NaN | \\n\",\n \"25000.0 | \\n\",\n \"2218847 | \\n\",\n \"... | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"60.041667 | \\n\",\n \"NaN | \\n\",\n \"indiegogo | \\n\",\n \"3.280000 | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"
| 201558 | \\n\",\n \"21 | \\n\",\n \"Help Creep Cuts LEVEL UP! | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"USD | \\n\",\n \"1551502800 | \\n\",\n \"NaN | \\n\",\n \"6000.0 | \\n\",\n \"2454347 | \\n\",\n \"... | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"60.000000 | \\n\",\n \"NaN | \\n\",\n \"indiegogo | \\n\",\n \"35.433333 | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"
5 rows × 30 columns
\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" backers_count created_at creator deadline fx_rate \\\\\\n\",\n \"count 199618.000000 1.813020e+05 1.813020e+05 1.996180e+05 181302.000000 \\n\",\n \"mean 45.285666 1.446903e+09 1.074589e+09 1.455799e+09 0.999337 \\n\",\n \"std 58.656426 6.137708e+07 6.201507e+08 6.182117e+07 0.286660 \\n\",\n \"min 0.000000 1.240366e+09 3.000000e+00 1.242468e+09 0.009007 \\n\",\n \"25% 3.000000 1.407594e+09 5.377061e+08 1.415194e+09 1.000000 \\n\",\n \"50% 20.000000 1.446477e+09 1.073699e+09 1.456439e+09 1.000000 \\n\",\n \"75% 66.000000 1.497145e+09 1.612593e+09 1.507996e+09 1.000000 \\n\",\n \"max 255.000000 1.550094e+09 2.147483e+09 1.555301e+09 10.126885 \\n\",\n \"\\n\",\n \" goal id launched_at state_changed_at \\\\\\n\",\n \"count 1.996180e+05 1.996180e+05 1.996180e+05 1.813020e+05 \\n\",\n \"mean 8.219191e+04 9.750394e+08 1.452897e+09 1.453600e+09 \\n\",\n \"std 8.025752e+06 6.664825e+08 6.184694e+07 6.091841e+07 \\n\",\n \"min 0.000000e+00 3.271000e+03 1.240920e+09 1.242468e+09 \\n\",\n \"25% 1.514000e+03 3.718836e+08 1.412280e+09 1.414758e+09 \\n\",\n \"50% 5.000000e+03 9.693086e+08 1.453501e+09 1.453254e+09 \\n\",\n \"75% 1.500000e+04 1.554152e+09 1.505141e+09 1.504017e+09 \\n\",\n \"max 1.999812e+09 2.147476e+09 1.550207e+09 1.550119e+09 \\n\",\n \"\\n\",\n \" usd_pledged creLauDelta lauDeadDelta staLauDelta \\\\\\n\",\n \"count 1.996180e+05 181302.000000 199618.000000 181302.000000 \\n\",\n \"mean 1.485695e+04 45.970756 33.381253 30.847713 \\n\",\n \"std 1.170977e+05 127.614911 13.008452 13.090062 \\n\",\n \"min 0.000000e+00 0.000000 0.000000 0.000000 \\n\",\n \"25% 8.700000e+01 2.000000 29.000000 28.000000 \\n\",\n \"50% 1.248000e+03 10.000000 30.000000 30.000000 \\n\",\n \"75% 5.940000e+03 35.000000 36.000000 33.000000 \\n\",\n \"max 1.571227e+07 3303.000000 255.000000 93.000000 \\n\",\n \"\\n\",\n \" funds_raised_percent kickstarter.com \\n\",\n \"count 199618.000 81048.000000 \\n\",\n \"mean NaN 546.971474 \\n\",\n \"std NaN 73.028021 \\n\",\n \"min 0.000 428.000000 \\n\",\n \"25% 1.500 476.000000 \\n\",\n \"50% 100.000 550.000000 \\n\",\n \"75% 122.125 607.000000 \\n\",\n \"max inf 702.000000 \"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"merged.describe()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Data Skew\\n\",\n \"\\n\",\n \"From a first look at the descriptive statistics on the dataset, the data seems to be heavily skewed.\\n\",\n \"\\n\",\n \"The means and medians (50%) for \\\"goal\\\", and \\\"usd_pledged\\\", are off by an order of magnitude.\\n\",\n \"\\n\",\n \"I know there's a lot of different categories of projects, and some of the skew is probably due to different funding goals for different categories of projects (ex. A video publishing project needs a lot less money than the first run of a new hardware device)\\n\",\n \"\\n\",\n \"Knowing some of the skew comes from differences in project categories, let's take a closer look grouping by project category\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Drop projects that raised no money at all\\n\",\n \"\\n\",\n \"Let's consider only projects that had at least one backer in the analysis\\n\",\n \"\\n\",\n \"The indiegogo dataset has a few projects with fundraising goals over several million that didn't raise any money at all. Filter out those projects to make the remaining data easier to read\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"merged = merged[(merged['backers_count'] > 0) & (merged['goal'] < 2000000)]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Frequency of Success\\n\",\n \"\\n\",\n \"Start analyzing the data from the two datasets by taking a look at how success rates and funding goals differ across each category\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The relative frequency of projects meeting their funding goal is 0.5887230220078954 or about 58 %\\n\"\n ]\n }\n ],\n \"source\": [\n \"#Filter out projects that are live, canceled, or suspended\\n\",\n \"merged = merged[(merged['state'] == 'successful')|(merged['state'] =='failed')] \\n\",\n \"suc = merged[(merged['state'] == 'successful')]\\n\",\n \"fSuc = len(suc)/len(merged)\\n\",\n \"print('The relative frequency of projects meeting their funding goal is', fSuc, 'or about', mt.floor(fSuc * 100), '%')\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Comparing the Money Raised\\n\",\n \"The chart below compares average money pledged to projects in different categories against average raised in succesfully funded projects. It also gives a comparison of the medians for each category\\n\",\n \"\\n\",\n \"As was seen in the descriptive statistics, there's a heavy skew here. Based on the median pledge totals, it seems like a lot of projects must raise almost no money at all\\n\",\n \"\\n\",\n \"There's also a strange phenomenom with Indiegogo projects. A few categories (camera gear, environment and green tech, travel and outdoor, health and fitness in particular) are unique to indiegogo. It looks like there's some projects that raised incredible amounts of money that are skewing the averages\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"| \\n\",\n \" | backers_count | \\n\",\n \"created_at | \\n\",\n \"creator | \\n\",\n \"deadline | \\n\",\n \"fx_rate | \\n\",\n \"goal | \\n\",\n \"id | \\n\",\n \"launched_at | \\n\",\n \"state_changed_at | \\n\",\n \"usd_pledged | \\n\",\n \"creLauDelta | \\n\",\n \"lauDeadDelta | \\n\",\n \"staLauDelta | \\n\",\n \"funds_raised_percent | \\n\",\n \"kickstarter.com | \\n\",\n \"
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | \\n\",\n \"199618.000000 | \\n\",\n \"1.813020e+05 | \\n\",\n \"1.813020e+05 | \\n\",\n \"1.996180e+05 | \\n\",\n \"181302.000000 | \\n\",\n \"1.996180e+05 | \\n\",\n \"1.996180e+05 | \\n\",\n \"1.996180e+05 | \\n\",\n \"1.813020e+05 | \\n\",\n \"1.996180e+05 | \\n\",\n \"181302.000000 | \\n\",\n \"199618.000000 | \\n\",\n \"181302.000000 | \\n\",\n \"199618.000 | \\n\",\n \"81048.000000 | \\n\",\n \"
| mean | \\n\",\n \"45.285666 | \\n\",\n \"1.446903e+09 | \\n\",\n \"1.074589e+09 | \\n\",\n \"1.455799e+09 | \\n\",\n \"0.999337 | \\n\",\n \"8.219191e+04 | \\n\",\n \"9.750394e+08 | \\n\",\n \"1.452897e+09 | \\n\",\n \"1.453600e+09 | \\n\",\n \"1.485695e+04 | \\n\",\n \"45.970756 | \\n\",\n \"33.381253 | \\n\",\n \"30.847713 | \\n\",\n \"NaN | \\n\",\n \"546.971474 | \\n\",\n \"
| std | \\n\",\n \"58.656426 | \\n\",\n \"6.137708e+07 | \\n\",\n \"6.201507e+08 | \\n\",\n \"6.182117e+07 | \\n\",\n \"0.286660 | \\n\",\n \"8.025752e+06 | \\n\",\n \"6.664825e+08 | \\n\",\n \"6.184694e+07 | \\n\",\n \"6.091841e+07 | \\n\",\n \"1.170977e+05 | \\n\",\n \"127.614911 | \\n\",\n \"13.008452 | \\n\",\n \"13.090062 | \\n\",\n \"NaN | \\n\",\n \"73.028021 | \\n\",\n \"
| min | \\n\",\n \"0.000000 | \\n\",\n \"1.240366e+09 | \\n\",\n \"3.000000e+00 | \\n\",\n \"1.242468e+09 | \\n\",\n \"0.009007 | \\n\",\n \"0.000000e+00 | \\n\",\n \"3.271000e+03 | \\n\",\n \"1.240920e+09 | \\n\",\n \"1.242468e+09 | \\n\",\n \"0.000000e+00 | \\n\",\n \"0.000000 | \\n\",\n \"0.000000 | \\n\",\n \"0.000000 | \\n\",\n \"0.000 | \\n\",\n \"428.000000 | \\n\",\n \"
| 25% | \\n\",\n \"3.000000 | \\n\",\n \"1.407594e+09 | \\n\",\n \"5.377061e+08 | \\n\",\n \"1.415194e+09 | \\n\",\n \"1.000000 | \\n\",\n \"1.514000e+03 | \\n\",\n \"3.718836e+08 | \\n\",\n \"1.412280e+09 | \\n\",\n \"1.414758e+09 | \\n\",\n \"8.700000e+01 | \\n\",\n \"2.000000 | \\n\",\n \"29.000000 | \\n\",\n \"28.000000 | \\n\",\n \"1.500 | \\n\",\n \"476.000000 | \\n\",\n \"
| 50% | \\n\",\n \"20.000000 | \\n\",\n \"1.446477e+09 | \\n\",\n \"1.073699e+09 | \\n\",\n \"1.456439e+09 | \\n\",\n \"1.000000 | \\n\",\n \"5.000000e+03 | \\n\",\n \"9.693086e+08 | \\n\",\n \"1.453501e+09 | \\n\",\n \"1.453254e+09 | \\n\",\n \"1.248000e+03 | \\n\",\n \"10.000000 | \\n\",\n \"30.000000 | \\n\",\n \"30.000000 | \\n\",\n \"100.000 | \\n\",\n \"550.000000 | \\n\",\n \"
| 75% | \\n\",\n \"66.000000 | \\n\",\n \"1.497145e+09 | \\n\",\n \"1.612593e+09 | \\n\",\n \"1.507996e+09 | \\n\",\n \"1.000000 | \\n\",\n \"1.500000e+04 | \\n\",\n \"1.554152e+09 | \\n\",\n \"1.505141e+09 | \\n\",\n \"1.504017e+09 | \\n\",\n \"5.940000e+03 | \\n\",\n \"35.000000 | \\n\",\n \"36.000000 | \\n\",\n \"33.000000 | \\n\",\n \"122.125 | \\n\",\n \"607.000000 | \\n\",\n \"
| max | \\n\",\n \"255.000000 | \\n\",\n \"1.550094e+09 | \\n\",\n \"2.147483e+09 | \\n\",\n \"1.555301e+09 | \\n\",\n \"10.126885 | \\n\",\n \"1.999812e+09 | \\n\",\n \"2.147476e+09 | \\n\",\n \"1.550207e+09 | \\n\",\n \"1.550119e+09 | \\n\",\n \"1.571227e+07 | \\n\",\n \"3303.000000 | \\n\",\n \"255.000000 | \\n\",\n \"93.000000 | \\n\",\n \"inf | \\n\",\n \"702.000000 | \\n\",\n \"
\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Compare the money raised by succesfull projects against all projects in a certain category\\n\",\n \"money = pd.DataFrame()\\n\",\n \"grp = merged.groupby(['fullcats'])\\n\",\n \"sucGrp = suc.groupby(['fullcats'])\\n\",\n \"money['AvgRaised'] = grp.usd_pledged.agg(np.mean)\\n\",\n \"money['SuccessAvgRaised'] = sucGrp.usd_pledged.agg(np.mean)\\n\",\n \"money['MedianRaised'] = grp.usd_pledged.agg(np.median)\\n\",\n \"money['MedianSuccess'] = sucGrp.usd_pledged.agg(np.median)\\n\",\n \"#money.reset_index(inplace = True)\\n\",\n \"\\n\",\n \"ax = money.plot(kind = 'bar', title='Crowdfunding Funds Raised by Project Category')\\n\",\n \"ax.set_xlabel(\\\"Category\\\")\\n\",\n \"ax.set_ylabel(\\\"Funds Raised\\\")\\n\",\n \"plt.figure(figsize=(600,300))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"# Comparing pledge goals\\n\",\n \"\\n\",\n \"The same skew shows up in the fundraising goals for each project, there seems to be a few projects that have very high funding goals.\\n\",\n \"\\n\",\n \"What's interesting here is that most projects have significantly lower funding goals than the top projects. It's difficult to see, but it also seems there's a relatively small difference in the median funding goals between successful projects and all projects\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Delete old dataframes to reduce working memory\\n\",\n \"del money\\n\",\n \"\\n\",\n \"# Compare the funding goals for successful projects in each category vs. the funding goals of all projects\\n\",\n \"goal = pd.DataFrame()\\n\",\n \"goal['avgGoal'] = grp.goal.agg(np.mean)\\n\",\n \"goal['sucAvgGoal'] = sucGrp.goal.agg(np.mean)\\n\",\n \"goal['medGoal'] = grp.goal.agg(np.median)\\n\",\n \"goal['sucMedGoal'] = sucGrp.goal.agg(np.median)\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"ax = goal.plot(kind = 'bar', title='Fundraising Goals by Category')\\n\",\n \"ax.set_xlabel(\\\"Category\\\")\\n\",\n \"ax.set_ylabel(\\\"Fundraising Goal\\\")\\n\",\n \"plt.figure(figsize=(600,300))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Rates of Success across Categories\\n\",\n \"\\n\",\n \"Certain categories have much higher rates of success than others.\\n\",\n \"\\n\",\n \"Comics, publishing, dance, music, and theater all have success rates of 70% or more\\n\",\n \"\\n\",\n \"An interesting point is that these categories are tied to an individual creator, that have a dedicated fanbase they can draw from\\n\",\n \"\\n\",\n \"Additionally, projects that require a good to be produced (ie technology products, health and fitness equipment, productivity tools) generally score lower\\n\",\n \"\\n\",\n \"There's no binding rule for Kickstarter or indiegogo projects to produce the final product, all they require is that creators make a best faith effort. Since there's some inherent risks to these products being produced, it seems like consumers may be weighing that in their backing decisions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"del goal\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"catCounts = pd.DataFrame()\\n\",\n \"catCountAll = merged.groupby('fullcats').count()\\n\",\n \"catCountSuc = suc.groupby('fullcats').count()\\n\",\n \"catCounts['total_counts'] = catCountAll['source']\\n\",\n \"catCounts['success_counts'] = catCountSuc['source']\\n\",\n \"catCounts['successRate'] = catCounts['success_counts']/catCounts['total_counts']\\n\",\n \"catCounts = catCounts.sort_values(by = ['successRate'], axis = 0, ascending = False)\\n\",\n \"\\n\",\n \"ax = catCounts.successRate.plot(kind = 'bar', title='Rates of Success vs. Project Category')\\n\",\n \"ax.set_xlabel(\\\"Category\\\")\\n\",\n \"ax.set_ylabel(\\\"Success Rate\\\")\\n\",\n \"plt.figure(figsize=(600,300))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Funding success across categories\\n\",\n \"\\n\",\n \"The most popular categories generally have the lowest success rates, while things like comics, design photography, and games seem to perform better than average.\\n\",\n \"\\n\",\n \"These categories would likely typically have fanbases that closely follow the creators. Due to this, they have a reliable audience to draw from when try to source funds for their campaigns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Look at the number of successfully funded projects in each category vs. the total number of projects\\n\",\n \"del catCounts\\n\",\n \"\\n\",\n \"counts = pd.DataFrame()\\n\",\n \"counts['Total'] = grp.size()\\n\",\n \"counts['Successful'] = sucGrp.size()\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"ax = counts.plot(kind = 'bar', title='Comparison of Total # of Project and # of Successful Projects by Category')\\n\",\n \"ax.set_xlabel(\\\"Category\\\")\\n\",\n \"ax.set_ylabel(\\\"Number of Projects\\\")\\n\",\n \"plt.figure(figsize=(600,300))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Success rate of projects above a certain amount raised\\n\",\n \"\\n\",\n \"Because of the large differences between the average funds raised and the median funds raised, it seems like there would be a large number of projects that raise hardly any money at all. \\n\",\n \"\\n\",\n \"Let's take a closer look project success rate versus the amount of money raised\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'Funding Goals and Success Rates')\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"del counts\\n\",\n \"\\n\",\n \"minRaised = [1, 25, 50, 100, 200, 300, 400, 500, 750, 1000, 2000, 5000]\\n\",\n \"lowGoals = [1, 25, 50, 100, 200, 300, 400, 500, 750, 1000, 2000, 5000]\\n\",\n \"succRate = []\\n\",\n \"goalRate = []\\n\",\n \"lowGoalSuccess = []\\n\",\n \"for raised in range(len(minRaised)):\\n\",\n \" gotMoney = merged[(merged['usd_pledged'] >= minRaised[raised - 1]) & (merged['usd_pledged'] <= minRaised[raised])]\\n\",\n \" gotMoneySuccess = gotMoney[(gotMoney['state'] == 'successful') | (gotMoney['state'] == 'finished')]\\n\",\n \" if len(gotMoney) == 0:\\n\",\n \" tempRate = 0\\n\",\n \" else:\\n\",\n \" tempRate = len(gotMoneySuccess) / len(gotMoney)\\n\",\n \" succRate.append(tempRate)\\n\",\n \"\\n\",\n \"for goals in range(len(lowGoals)):\\n\",\n \" goalFilter = merged[(merged['goal'] <= lowGoals[goals]) & (merged['goal'] >= lowGoals[goals - 1])]\\n\",\n \" lowSuccess = goalFilter[(goalFilter['state'] == 'successful') | (goalFilter['state'] == 'finished')]\\n\",\n \" if len(lowSuccess) == 0:\\n\",\n \" tempLowSuccess = 0\\n\",\n \" else:\\n\",\n \" tempLowSuccess = len(lowSuccess) / len(goalFilter)\\n\",\n \" tempGoals = len(goalFilter) / len(merged)\\n\",\n \" goalRate.append(tempGoals)\\n\",\n \" lowGoalSuccess.append(tempLowSuccess)\\n\",\n \" \\n\",\n \"graph = pd.DataFrame({'Dollars' : minRaised,\\n\",\n \" 'Minimum_Raised' : succRate,\\n\",\n \" '%_of_Projects' : goalRate,\\n\",\n \" 'Success_by_Goal' : lowGoalSuccess})\\n\",\n \"x = graph['Dollars']\\n\",\n \"y1 = graph['Minimum_Raised']\\n\",\n \"y2 = graph['%_of_Projects']\\n\",\n \"y3 = graph['Success_by_Goal']\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"plt.plot(x, y1, x, y2, x, y3)\\n\",\n \"plt.legend(('By Minimum Raised', 'Cumulative % of Project Goals < X', 'By Funding Goal'),\\n\",\n \" loc='lower right')\\n\",\n \"plt.xlabel('Dollars')\\n\",\n \"plt.ylabel('Relative Frequency')\\n\",\n \"plt.title('Funding Goals and Success Rates')\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Funding Goals, Minimum Amount Raised, and Success Rates\\n\",\n \"\\n\",\n \"In the plot above:\\n\",\n \"'By Minimum Raised' Shows relative frequency of success in projects that raised at least the amounts on the x axis\\n\",\n \"\\n\",\n \"'Cumulative % of Project Goals' shows the % of projects that have funding goals less than the amounts on the x axis\\n\",\n \"\\n\",\n \"'By Funding Goal' shows relative frequency of successful projects that have goals lower than amounts on the x axis\\n\",\n \"\\n\",\n \"While a small % of projects have funding goals less than $1,000, projects that raised up to that amount have an outsized chance of meeting their funding goal. \\n\",\n \"\\n\",\n \"This idea seems to stress the importance of finding ambitious \\\"first movers\\\" from the general public to pledge support to projects and share with their friends. This supports the idea that project categories which have fanbases that closely follow project creators have a much better chance at reaching their funding goals. \\n\",\n \"\\n\",\n \"Let's take a closer look at the number of backers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Time between Project Creation and Project Launch\\n\",\n \"\\n\",\n \"Kickstarter allows creators to create a landing page for their projects before the project is officially launched. This time is for creators to raise awareness about the funding campaign, field questions from potential donors and tweak the campaign before making it live\\n\",\n \"\\n\",\n \"There's a clear relationship showing that a delay of between 20 and 30 days gives the most benefit \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'Success Rates vs. Number of Days between Launch and Creation')\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"del gotMoney\\n\",\n \"del gotMoneySuccess\\n\",\n \"del goalFilter\\n\",\n \"del lowSuccess\\n\",\n \"del graph\\n\",\n \"\\n\",\n \"# Plot the delta between launch and creation date vs. success rate\\n\",\n \"# Plot the campaign length against success rate\\n\",\n \"launchCreate = [1, 5, 10, 15, 20, 25, 30, 40, 45, 50, 60]\\n\",\n \"launchSuccess = []\\n\",\n \"for i in range(len(launchCreate)):\\n\",\n \" longTemp = merged[(merged['creLauDelta'] <= launchCreate[i]) & (merged['creLauDelta'] >= launchCreate[i - 1])]\\n\",\n \" longTempSuccess = longTemp[(longTemp['state'] == 'successful') | (longTemp['state'] == 'finished')]\\n\",\n \" if len(longTemp) == 0:\\n\",\n \" tempRate = 0\\n\",\n \" else:\\n\",\n \" tempRate = len(longTempSuccess) / len(longTemp)\\n\",\n \" launchSuccess.append(tempRate)\\n\",\n \"\\n\",\n \"graph = pd.DataFrame({'creLauDelta' : launchCreate,\\n\",\n \" 'Success_by_Delta' : launchSuccess})\\n\",\n \"x = graph['creLauDelta']\\n\",\n \"y1 = graph['Success_by_Delta']\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"plt.plot(x, y1)\\n\",\n \"plt.ylim(bottom=0)\\n\",\n \"plt.xlabel('Number of Days')\\n\",\n \"plt.ylabel('Relative Frequency of Success')\\n\",\n \"plt.title('Success Rates vs. Number of Days between Launch and Creation')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Project Launch Date and Success Rate \\n\",\n \"\\n\",\n \"Along with that, projects being successfully funded seems to have dropped off slightly in the last few years\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'Launch Date vs. Funds Raised Percent')\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"del longTemp\\n\",\n \"del longTempSuccess\\n\",\n \"\\n\",\n \"noOutliers = merged[merged['funds_raised_percent'] < 200]\\n\",\n \"x = noOutliers['launched_at']\\n\",\n \"y = noOutliers['funds_raised_percent']\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"plt.hexbin(x, y, gridsize=10)\\n\",\n \"plt.xlabel('Launch Date (in Epoch time)')\\n\",\n \"plt.ylabel('Percent Raised of Goal')\\n\",\n \"plt.title('Launch Date vs. Funds Raised Percent')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Alexa Site Rank and Success Rate\\n\",\n \"\\n\",\n \"Look at the success rate of campaign funding and the Alexa site ranking\\n\",\n \"\\n\",\n \"There seems to be a slight increase in projects reaching their funding goals when Kickstarter is more popular than other websites in general\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/Users/chrismay/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\\n\",\n \" \\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'Alexa Site Ranking vs. Funds Raised Percent')\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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e9+aWUixUu+8wP+OnFN7TVCE8mCA1BGLD/IU/n7HedxLNfuD+/uPy3XPSJaxjaPNw2I9yIfG+WKBtx+puP5YS/PpKNj2zm4o9dyU1Xu5/htMMINyLKhogIBx/zHM5+9ymsOmAlP/zq9VzysasojZUojnS2H+lb2MvZ/3wqL3nNEdx/60Nc9KHL+d2v/oBaJa7M3Qg3IpPPgCpHvvKFnPmuU9jr2av+/A3EgnCJHtHz8o51GI3I9mSJEyXKRlTTYW83MEYIMgE2toRRSLnYmQ6jEbneLNVylSgbdfVeB6Fxw/vEYgLTMSPciHxfjmolIZPPdMwINyLKRiSJxYQGhY4Z4UbkejPE1hDluvucs/mIymgBY5WkGnfMCE9GRAizIUm5QpSJKBW6V+Zcb5ZKqUKUCakUO2eEJxOEhjAKuaZwYVMG4kk9BwHtG4o1S7lQJshmOubdTIW1ii3FmMB01TjAxD1O4u7e6yS2qLUYA9VydwXjiqMlMIaki8YB3EhUAkPcRcNQozRWQTIZkm63qWIVEku1Q97zVKgqlWIFQbtqHMC1KUEpdzndJLYkcfP9h3+LyePxeDwN8QbC4/F4PA3pmIEQkT1F5Hpx8ph3i8hb0+2LxAmh35/+vzDdLiLyWRF5QJxs38GdypvH4/F4ZqaTI4gYeIeqHgAcBrxZnJD4PwM/VdV9gZ+m38Gtub9v+jkPp+Dk8Xg8nnmiYwZCVder6m3p3yM4jdaVuLVw/ic97H9wWgqk27+hjptwamOzWVra4/F4PG2kK3MQIrIaeB5wM04JbT2MC3TUJCxXsrP839p02+RrnScit4jILRXb3TdMPB6P56lExw1Euub+d4G3qerwdIc22LbL28Gq+mVVXaOqazKmuSUKPB6PxzN7OmogRCTCGYdvq2pNeWpjLXSU/r8p3b6WnTV198AtyezxeDyeeaCTbzEJ8BXgD6r6ybpdV+EkOkn/v7Ju+2vTt5kOA3bUQlEej8fj6T6d/CX14Th917tE5I502/k4bdxLReRvgMeAV6b7rgVejtN6LQCv62DePB6PxzMDHTMQqvq/NJ5XAHhJg+MVeHOn8uPxeDye2fGk/iX1vCw0GAbQk4fszOvZt50oRKMImljPvq2IQGC6ny4gmciVuQldjPYmLE3pBrQ/XVwdC2fWpmg7uRz09UA4D0u0ZTLIPLQpiSIklwXT5a5QdX76L5jabW/Ak3qxviW7L2Sw0k+lXO3oEr0Aks0SDvahJgARjID29kClgi0UoVMrUIpgchkIQyQwqCoShQhKUihD0sFF3YxgwnD8VTIRcZXaWjTuYLppmVUECQwoaBQhqiTFErQi0tNs0oGBIEAEVBn/H2tbEuhpGmMwuQjFlVkATfNhy1U6ttynCKavB3p7EeNWkCWXRaxixwpQ7uDCkCKYTOiKJmCyGbQnD9UqSaHk7nmn0k3blAkM1ipBJoOoEheK0Abdi4akz1DMhHMrRlCrE/Wsg0RZJ1i05qUH8pMrL2vqnCf1ct9r1qzRm2+6mRuuuoULP3Q5j9+3jmo5xrarAxHB9OQxfXWNZ/IhRtDEIjYhGS1AuzrOwBD05MY7jEbLiguuomm5glbat7qshAEYM155J2OMYK26pbir1fZ1nIFxhgGZMEaN8qdKUiq3tSFLFLrnnZZtMvVlbklFbiqCYNwwNMxXeh8EsJVq+zrNMCQY7EMzWYwx2Ab3WiRVjyuV0EKpbT3YZCM8Vbqilni0CHGbnnNgCPJZVMzUbSpN25bKaKlNK62q1hrrNG3KYK2dcv9cyPflQOCkN76U095yPEtWLn5qCAbVFOVqPHD7w1z80Su58Sq3reUluYOAcKAPstlxD6M5FLFKMlaESoueVxRictlZhVSMEWcUkwRbLLfWkAVM5JTVpuokdzml1nkJ2ErccgciUQhR6BpHk5cQESfrWq227mHXebDNNswJj0/mNIqSTARh4ByPJvM+3qHGcetpZ7MEg/1udNLk/a7VL0li7Eih5VFrzQjPuhNUix0roXNoU0Eui7bSpuK49dGMKhI4B9IEgk1m0abmaChqgl8Lly/gnPeewdFnH04ml6nb/xQ0EDWGNu3g+1/8Md/77A9I4qRpwRfJZAgG+yCYXWe1y3Vqnle5jG3G82ow5G0p3dpoZjbhJxFM5MJIcxnmjp+bJM152CLOcxYzpzKPd15qXZmbachGkHbE2YWJEUUz2Tfiwigi7Skz2pxhFkF6epD+qUfCzVDv2dvRAjQzaq2vX3Po9MSAJgqViuuwm2lT2QiiaFZGuMFl0jIr8VgTo5mdQkd22pHwjGm3cL/CTIgxwnOOOICzzz+d5/7lMxvq2j+lDUSNaiXmV5fdxLc/dDmbHttCuVDZ9WEJSD5P0N+XTsK2b2JywvNKw0+TO+wmhrxzQt1QuVEoZmKY33oFbsREKCbtvCYzHkaqeUvtSXd8zqA2T9HAw54pjNQqO12vkXHcKXTWvljzeJkBW6nsOg8WBJjBPsg6ffN2Vq9xJ6hYRBuNWttlhCenWxfSdR32pOdsDEFPds6Ox5SoYgsNRjO18osidKZNTVdvc71ZAF72+qM54+0nstvqZQ2Pq+ENxCT+cPP9XHTBFdz64ztRVapV68JIudycRgvN4BpyLfxUAKupYej8WzL1Q2Vbqox7c+3uJCdTP8lrKzEEBslGrvF0LNW6tK1CpYqtVMfDSJ0u804NuZogmdC9XNDh+gUT91ur1XR+oR+d40i4GcadoDjGjhYQA4jp+L12KFjFFoqotQT5nHuxocMTvjUj5SbTiy69VBa3bfOfjdJtEH7K9+XoW9DLWf98GseeeyT53uaWH/IGYgq2PLGNKz7/Ay7/5k3QRg+2aZKkc29nTIMI2GK57SOGpgiM+3S9qilaqqRxoC4mK0AUYQLT9folmQh6e7v+WrAI6PDohHHuJtZJ03YdgWTrUEcmlmdMWoQ99lvB33/m9Rxy7HMbhpFmOL8pA/Gk/h1EKyxZuYi//fCrCaKw+8YBmIdeEmDcm5sPh8AEwbwUW63ztrqetrq3Uuajfilggu43a1dW7b5xIH288/CTlVpZ56PMUS7itLeewJrjDpy1cZgNTzkD4fF4PJ7m8AbC4/F4PA3xBsLj8Xg8DfEGwuPxeDwN8QbC4/F4PA3xBsLj8Xg8DfEGwuPxeDwN6aTk6FdFZJOI/L5u2yUickf6eaSmNCciq0WkWLfvS53Kl8fj8Xiao5N6EF8HPg98o7ZBVV9V+1tEPgHsqDv+QVU9qIP58Xg8Hs8s6KTk6C9FZHWjfeJ++ncmcHSn0vd4PB7P3JivOYgjgI2qen/dtr1E5HYR+YWIHDHViSJynojcIiK3bN68ufM59Xg8nqco82UgzgYuqvu+Hlilqs8D/gm4UEQGGp2oql9W1TWqumbp0qWzTnjbhu38zwcuI6kmXZc5dsi8rBtT01fu5LotU2GTZF7SnY9F1GrU1MG6jaRpdz1dcanPV5nng1qVno9+pFqqcuUXfsDtP7uro+urdV2TWkRC4HTgkNo2VS0D5fTvW0XkQWA/YHZLtU7D/bc9zMUf+z43/+AORCCpJJje/Piqm51fmjhdrtcYUEHUugfb4WTH9RnCEO0NkDhGy5WmFa5axS0zrZgwxEYRKkAcu4VVO3yvJwyDOBEmFBsnHTcYteubXAbNZFzPVYm7ssKpGIMKaC7j9CdIdRO6sfS1VQgCdMEAVMpQKDkJzQ4ufT2evoBLRZyokK3Je3YuzfElzq1FMllEE7Qad32570fvXsv7Tv0oA0v6Oec9p/OS1xxBNp9tb5qdtD7pHMTVqvrsum0vA96jqkfWbVsKbFPVRESeDvwKeI6qbpvu+jMt953ECb++8hYu/MiVrHtwI9VStaEhkFwW6ckh0rraViPGO4xGa+OrOklCUTRuvxatSKq7YJwwUL2bo9Yi1SparkxId7aJWiMxuQw2k0GCYCJdVUgSTBy7DrsDnZdbm3/Xi6o6JTSNk7YbR+dgWEw+h2azu5Y5TpC4OnGf2yUYlBphyURoTx6ymZ3dWasYdfWr3foMtbQJA9TIzumqQrGMFEuItneF13HJ15omwyRUFSO4OtjuMqe6LlSqaHVnoSJVBZugSdJ2h8DJlur4/43I9TkdiBPOO4Yz3nYiS/dYPP0151sPQkQuAo4ClgAbgfep6ldE5OvATar6pbpjzwD+DYiBJD32+zOlMZWBGN46wtX/72dc/tkfElfipiVHiUKCvh6n8DYHoZXxitmsJ6NOgU3jZE6VetzDCA0qqVGYZvyrqlCNkWolbWytj2hq90tyWTTjZB6nQ611hqJSnZPntZMRjme+Rs1ISVrWudR/STtGzeWckzFDrEGTBJPE2Eo8p+c8bpB68th8DqIZAgGpWBVJKoE5l85LXD2zgXEKjNOVOa1fplhCK9U5pT0+Em6y89Vam7Jz03ceb1MotlRprBg4KV2sBU3SEVwbJEdnMSKKMiEY4eCXPIez33M6z/yL/bzkaI1H7n6cSz9xDb/63m8QEcrFFkXOA0PQ24POViO69hxavaXjlUtdnWjy2dQ6WIlCFHGjhtkmHceYuIotV114oIlRhRulpOGrTAaicNZzDc7Drs46FLNTB9tC49d0BGew2FmM4MZDZ5kIzeZaK7O1SBKj5eqsOi+pCRD15qEnN/vnrDoxqki06RHcuNceGqxprX4RJ5hSGS3OLvwkNTmPFjv42nN2fWzzDtCE1nlqGFroJ9VaBIutxs2PWlNjMFdhLxEh25Nh6R6LOfv80znyzBeSyUb1+58aBuLmm3/Db394BxdecCUP372WuBK3Lw4o4jzD/NSypDsNeds1rByv1DqtFzKepwZhpJaTHg8/lV1cu8G9rA11TS6DjTJIGDS40izTrXn2ceyG6UzRJtM5/qmG2q2mbdRi42TK0cx46Kxn1zDSXNIljpFqPN6RTaYWKpQodGGkXKYtz7k+/DRV3R03wqFBA9O2dKXk5ilkCoGhcbnaKcJIrVBrP7Xw01RlHndS4hhtpKneato2QePYtakGzteEXG1zztlsyPflMIHh1H88nlPe/DIWLl/w1DAQ+6zaTw+RYykXKs2HkVolE7lRhbg3NUTqhrydvIWTw0/pxLYEBhXjjEMHXqNw4acqUqm4BqPq0rTadBip5bSTBJMk2Ep1XBFOVV1n2cFJwPHwgLXjHYUYQREkn4NspiNlrqVr4hhbjdPQlTOC0pNDe3IQRTNfqLXEXfip3lAILnRmDAQzhJHmkC6VKqZYdjragJHZhZFaT1ondKTr21STYaS5pOvqV4KqTZUHZxmOngOZnKtDh524hn/9zjv+/A3EgnCJHhod391EgwAz0NfaMHsu1CqXpEP8LqVf8+ypVF2sOxN17ZVVtQrlkmuwXXxdddybN4Jks10uszNQhAH05J2Wd1cSduEnrLo0Z5pfaCdxjGwf6VjHPBWubltIYrQad62O1eqXVlsMgc8REeE6+52mDETXX3NtJ/Pxbj218Ee30xVxxikMuvJK7kSy7lVRMh3yYKdL2whqAoy1dLPrkNrkfn9v1+uYGAO9OTTsctMUcaOFcB7aVBhiohCbdLfDHH/TL0666oCM168pwk2dZjaDAr+aq8fj8Xga4g2Ex+PxeBriDYTH4/F4GuINhMfj8Xga4g2Ex+PxeBriDYTH4/F4GuINhMfj8Xga4g2Ex+PxeBriDYTH4/F4GuINhMfj8XgaMuXv+UXkc0yzooSqvqUjOXoSoLUVNrudLjhltqcY87ZamOr86Enq/KSt4FbLnYcbrulnflSA56dRPRnWwZtuwZc5yX2KyFeBE4FNNUU5EXk/8HfA5vSw81X12nTfe4C/wQkGvUVVfzRTGgrkerKUCuW5ZLVptCcHeyyhOtCLWEW2j2KGnWpWR9MVQftyaH8eK0JQidEdBSROZj55roi4paZrHVaSdHj5WoeKuKWtB/tcX1ksojtG3bo5ncYYTDZyS62rurzMIL7ULiQKUQtSSdwy211YNE+BpC8iXpTHBkJYsQTbiphS0pUOW4xgB/qgX9FCyS0F3o3OMxMh/X0ggqlUSIaGIV1ZtlOM61No2o66vLCbCQxRNoSx5o7vpKLcXwKjwDcmGYhRVf34pGOfCVyfb+9dAAAgAElEQVQEvADYHfgJsJ+qTtsbHHzwwfqvf/thLv34NRRGih1Z8lsFdEE/ssdSbCbcaRVVt7a8EhRKyLaxtnfYGgYw2IPNZdJlid322nLIxlrYUYBStf0N2bhF+ibX3/EloWurvLYZDQymJ4c1ZiexpvHlt6sxOjQM5fYv7CZhCFGQ6m/Uba/Ja9aWVm93hy2CRJHzoOuWj5/QEzdYEfdM2ogGQjyYpTqQdWnVZ0lBrCUYKhGOVjsyqhjXUqkvs4hTy6tUsSNjSLtXeBVxS6gH4U5tCsFpVSQJyfZhKLa3L9l5qe+dtT+6seR3banvw099Aa9696nsc9Be7VnNNdWLfjfwTCBX266qR093nqr+MtWkboZTgItVtQw8LCIP4IzFjdOdZIzh1L9/KSe/8Vhuve4uLrzgSh648xGSakLShPzktPkPDCxbiN1tERKkDXQSVgEj2P482psjqMSwdRQpVVrusBUgG8GCXmwYjOsh1C82qWlPYoMAWdTvRH5GS8hYee6eVxSAmPFKO/lqtXX6TXpPjICtzk1YRcEps/VkXblSGbH6VWtrZdZMhCxb5FbB3D4CheLcGpWIGy0g4wZp8i0cL7OqE5sJjVPum6uhMGYXI1yfdq38biSTetpG5rwUd5IJsIvyVPMBpla/Jh2jkraBJT1UFynRaIVgexnTJqGmevnRncqcjlQ1l3ErCFuLDo9BZY5OUBA4wyDpUuaT2lTNOmsYEixJ69foGHZkzHXsLdKMWFDtORtx2hTtFA3K9+UIwoDT33YCJ73pOBYsHZzV+c2sKfxt4BLgBOCNwLlMhIha4R9E5LW4ENY7VHUIWAncVHfM2nRbUxhjeP5LD+T5Lz2Qx+59gu986hp+fulNLcmOaj6LrFyCHezFBIZmVAprnVeSjZAVC5z279AYZrTYtOelAtqbg/4eCEzDDqPheQDGIIO92P48plyF4QIyGwMp4pZbZsKrm2lJ8dp+C66TE9B4duEnFZBsJhXiqfPaZypzbenzJQuwySAUCrBjdHZ6ArUw0rhEHU2XmcROiDbB7EcVUejuuTENjfAupAeoVQzOYBDMLvykQNIbkSzKk4Qyvoz5TKtcWwAjJINZqv0ZwnJCsK2EKc8u/CQG1E54yzNFLsYdgiBAFg6g1qJjRSiWZjeaiSLIZxFppU31Y/r7kHLZjSpm4QjV5Ea1GmPSejLT0t7jbcraVDmxNdlRESGTz7DbXst49XvP4IgzDiWcSbd8qmvNlIFUmu4QEfmdqj433fYLVT2yiYyuBq6uCzEtB7bgnsG/AytU9fUi8gXgRlX9VnrcV4BrVfW7Da55HnAewKpVqw559NFHG6Y9MjTGD756PZd9+loqpeq04ScFWNAHeyzFZqMJ7d85YHAPPBgtIkNTD5U1MC6MlM/uPORtkfHwU5KGn8rTeF5GnJ703JLcOW3XEqbtrNUYTE8WGwSz0/yeLl2rLiwxNOzEjaY6NgzGdaTbEV2t6XKPCzlN1WGLYKIIi6ae5NwSbzb8pMaFkeLBrBuBzClVh6giiRIOlQhGp/fsa3K57VBeNOLalJTL6Ghx6vCTCOSyEEVOC3qOhR4PbyaJm6coNe5LxsNImkwYg7m251lIGUfZCBFY89KDOPs9p/GMF+w79XXbJTkqIjep6mEi8iPgs8A64DJV3XvGi08yEFPtSyeoUdUPp/t+BLxfVacNMa1Zs0ZvuWX6ufQksdx09W1ceMGVPH7fOqp1mtUaGHTpAnS3RUgYOM+0zdQqdVCuuvBTOe28spEzDFE4pd71XBFwFXakiBTKE55XGDgPqZ062nWMd16Axs7rUnAjjZ7suGxrR6a/VJHEojtGYKzotokgmch10m0wSI0YL3NgJia13Y62GuHJjHfAgcEK4+EnGxmSxXmq+bAtjkcjak5QNFIh3FFGJoWfOiXHO+EEJdjhMajGrq4HASbv5q9q8rhtTxs3EtDhUXR0zCnD1cJISdIxudRa2KlRm8315TAinPSm4zj1LS9nye6LZi5HGw3EicCvgD2BzwEDwAdU9aomMrGanUcQK1R1ffr324FDVfUsEXkWcCETk9Q/BfadaZK6GQNRz4N3Psoln7iaG79/K9UVi4kXDbgb3/QV5oaoOu3fxLqG3aXX62pC7cGOAiR2PLTRaWqdl4bGhZFqCl5doBbyku0jTmO6DV57U+nWGnBNnrVL6YK730koVPYcIMkEnTPCk6g5QdnNRYKx6sSka1dQF06tJl19XdWIcz51w0Zsoegcjy5Ipo47BEaIshFLdl/EOe89nRefdTiZXGY212nPJLWqXp3+uQN48SwycBFwFLBERNYC7wOOEpGDcA7lI8Ab0jTuFpFLgXuAGHjzTMahFfY+8Gmc/403s33zMGe+8vNgtatSliri6rAxXesooS7OnCjQvcZbcz4kCsd/O9ItVHEFd5MEXeukx9OJwq6mC+5+21yEzYbp9+6ka9P5AlNwo+NuSuJC+pukLv+WoVZEW3Cj1G4YB5hoUyYwnPTG43jDx1/b0bI38xbTHriRw4twc1b/C7xVVddOd56qnt1g81emOf4/gP+YKT/tYMHSgS57OU9tpMsGcae0pbWJvrak2/VUHUZSX6DbdCqm1FTazOMvKrtPEAas3HdFxw1jM0ttfA24CliBe7Po++k2j8fj8fwZ04yBWKqqX1PVOP18HVja4Xx5PB6PZ55pxkBsEZHXiEiQfl4DbO10xjwej8czvzRjIF4PnAlsANYDr0i3eTwej+fPmGbeYnoMOLkLefF4PB7PnxDTLff9LGDv2u8dRORTQG0hj8+r6m1dyJ/H4/F45onpQkwX4JbFqPFS4BrgeuBfO5kpj8fj8cw/04WYVqjqDXXfh2trI4nIGzqbLY/H4/HMN9ONIPrrv6jqYXVfl3UmOx6Px+P5U2E6A7FORA6dvFFEDsMt2OfxeDyeP2OmCzG9G7hERL4O1CakD8HpQbyqw/nqKKVi5UmhB/vnwrhc6XykPU/rLzg9g3lJet5Wu5jXtS6eYs3ZJpahDds7ns6UIwhV/Q1wKBAAf51+DHBYuu9Jx+b12/nyh77P2Yf9G1Ksgmq7VRynJ3DLMGsAhN1LWAJBjZD0Rk5kpouF1lBIsKgBCZv52U17kEDQ0KBRkC6B3a2Ecc9ZrBNE6mKZiQxUE+JAXR3rEiKgBqoLM+lz7nLdDgwaCBJ0Md20DZmevBP36WKbEhHiasxFH/kebz38vdx87W1tU6DbJa0nsyfdzHLfqso9tz3KJV/8GXfc+ACqSlx1C8WqEbQ/S5LPdGzNfBGwAjZvGN0tpLwgAIXctpj+JyqYopOT7IQHJIFgUUrLMhSWRtjIEIxW6X90jOy6AsYYdI7SrA3TTYVpqosz7Ninj/KSDGIhv6HMwKNlTFV30Q5od9pxf4a4LwIjmLEymc0jyFCRwEhHVt6UwGBVSRb3UNxjgGQgi8SWzJYSuY0FxGrHVtDTSEhyhq0H9jC8Xx4NILsdBtcq4XCqQteB/kMCJ5FZXmIYWSHEPUJQtCz4Y4mBP5ac91ntlNAJaBRQGcyQ9Dq95WCkQnZbCSlVW1ZjmwlTqz9J4oSpVJ3mSbFIMlaY2N9m6pf5nrxCcL4vR74/z1nvPoWXvu5oevrzzVyvPXoQf8pMZyAq5ZhfXnsnF3/xp2zdMEy5VJly6K2AzUfoQK5tS3FL4FaLrS4KGV0eEvc09iTDkYSB9VWirW4dfW1HJxIINhJGl2coLwobjhikaulZW6D3kVGMBdpgKCRyOhPFVT3s2KuXpK9BBFOV7FDM4GNlwu1VTBuWxE4lrCETUO6LsPmwcUirmpDZPEKweQQj0hbjKKEzDJWVA5R270ezjcscba+Q31hACjFGmXPnVTP+5d0ybDmwh8LKTMMyB0VlYB3kN1hX5nbVL1HGVhrGlgnaaMSQKP2Plll0d4mgaJF47umKcQZJeyMqA5nxZc0nY0ox2aEisqPctjblFotVKFcgbqxEoNaixSI6Nuakitvg1dd0JpoR98r2ZAHluHOP4pXvOJkVT18+5bFPWQOxbfMI3//mr7nqWzdgE0up0LwmtQKaCWAwTxKYlgRXJHSVuLA8orA0RKPmhp6mYundUCW/vkIggs6yQdUaTzIYMrosotoXNBf3VyW7qUT/w2MEo1WMbaHzigxJIIzs3cvYqh40ai6sEhQSBtaWya9LG/Jsy5waYe2JqPRF7tk1g1WCoTEyG0eQatyaZx8INhNSWjVAZVmvkxttAlOo0rOx6HSdW+i8JDIkahndr4etz8lTHWxOa1hipXej0r9WMQkwS7WV8ZFwjzC8u1Ba1KQetiq5zTGL7ymR3VhxDsFsy5yOVJIBp4utzYbtYktme4lwWzHV8p5lujWpURQtVWhWu1RV0XIFimPYVPJ3tm1qLmqPYeSkfJ/5wv055/zTOejFz95lWfA5GwgR+aaq/pWIvFVVP9NSTjtMvYG4//drueRL1/Ob6/8AQLXSvMB4IzQwLvyUi2YOPwmogM2mYaSFQetxfqvktqThp8rMoRgJXSdZXpphbGmEzbYe8w6HXfgps6GJ8JNxAkjxYMSOffooLc+2PBEtsdK7vkz/Y2VMojCDoXAdBiT9EdXeCIIWy6yKGSuT3TQCO0oYM30nUkvXLsxT2GOAZHAOZa5aspuLZDcWmhrNaMZgQ1wYaf88NtN6mbNDMPi4Eo7qjOHNmhGuLDEMrxDi3tZj7eFYwsL7yvQ9UMSIgeoMHa4RNBAqg1kXRmq1TakSDFfIbisilcTN4k9T5lqYSGyClqtzmvXXagzFAknBtanpwk/ThZFaJdeXY3BxP2e/5zSO+au/JJvP1tKas4G4BzgepwVxFJOm+VR127QXFvkqcCKwqU5y9GPASUAFeBB4napuT6VJ/wDcl55+k6q+cabMrzlkjX7qg1/loi/8lHWPbaFSjtuu4KUCtieD9mV3CT/VvPZ4QcjI8oC4r40zg6pEIwkDT1QJd8S7hgcCwQYwtjxDaXEEbZygk0pC7+MFeh4dxSA7NWQJnTZuaY8eduzdS9wftS1dVMlujRl8rEQ4HO/aeQkQGSp9GZKeKcJILSKVmMzmEczm0XQ0M6nMqlRX9FNcOYDmmvPam8Iq0fYy+Q0FTGlS52XcPFllcciW5/UytmfjMFKrhGMu/JTb1CD8VBsJrzCM7eZClu1CYqX/4RIL7ykRVBSpm6eotSl6IsoDGWy2yZFwk5hi1c1TjOwafhrXk65WoTo3B3MytfCTHR1z6dQZChMINlE34d6heapcbw4RePl5x3D6W09g+aqlczYQbwHeBDwdeIKdDYSq6tOnvbDIXwKjwDfqDMRxwM9UNRaRj6QXevdk7epmWdS3Uo/a5/UUx8qzOa0lFNBsiA7kSMIADBSXhRSWha17c01iypa+9VXyGyqgkPSHjC6PqPa3t/HsglVyG4v0PzSGKVTR0DC6dy8jT+tFO1zmcCxh4PEyuQ1lp2PYE1Hui9Bsh1/PSSzhtjGijSOuk8iGFPcYoLJbX+sjlSYJRqv0bCpitpdQgcLeebYc2ENlYRsNUgOkqvRtVPrWKhKDzadhpMUdfuNNlfzGKovvLpHZlMqVDmSp9GeaDlO2ilQTMkMlgqFSqhEPWipDh6VDVdWlUxjDVqppZujaa7pRxjlWPyhd1J45CBH5oqq+qZXMTNfxi8hpwCtU9dWtGoiFPbvrYStf00rW5sTWw5dQWtB48reTSEXJVCBuozfXNNWYuGcOobMWyW1J6N1m0W7/jkKVOEyw+ajrv+EoLFMKS5lTuLAlrBKNCUm++/Vr2Z0xwah2vX6Z4SL5h7fOHO7qAPGmTR03SFPxE72sKQPRzHLfbxKRA4Ej0k2/VNXfzTWDOE2JS+q+7yUitwPDwL+o6q8anSQi5wHnAeSjgTZkY/Zo1N3fEoynGwg2K3TkfdwZaPhGUhfQUJzn3u0yi8yLcQCwOYPNdj1ZkPkxDuDmWNzrdF1GZN7krOdTt7xZZnRR0lDTt3HrLy0Dvi0i/ziXREXkvUCcXhecENEqVX0e8E/AhSLSsPdX1S+r6hpVXZMNe+eSDY/H4/FMQzNu4d8Ch6rqGEA6d3Aj8LlWEhSRc3GT1y/RNL6lqmWgnP59q4g8COwHTP8rOI/H4/F0jGaCnMLOb00ntLhwgYi8DLfG08mqWqjbvlREgvTvpwP7Ag+1kobH4/F42kMzI4ivATeLyPfS76cCX5npJBG5CPd67BIRWQu8D3gPkAWuS3+4UXud9S+BfxORGGeA3jjTa7Qej8fj6SzNTFJ/UkR+DrwIN3J4nare3sR5ZzfY3NCwpEJE353pmh6Px+PpHk29mpLqT3sNao/H43kK0eUXrT0ej8fzZMEbCI/H4/E0xBsIj8fj8TRkyjkIERlhmh8Yqur8/IzZ4/F4PF1hSgOhqv0AIvJvwAbgm7i3mF4N9HcldzMwL1oWi5T8c3YglRyl9T1uudcuoChxv1IJITskmLiLSyIoSAUInLRkt+Q7Faj0wqgR8ltSHYMuYWu//gnoqra0ClCFcBTiHro4xlcWLxph8eAo69cvYmxsZlWytmGh2AsZK2SGtWu3WwEbGUq79RNtHMVUulfBNI7RpIsVukWaWazvZlU9dKZt88HqPfbVgxe/CoVZCQPNHoV9FTnJovslqZCQYBMYfXCQ0Yf70GpnVhm1RqkOKpUFbp14RLEWsgVDZhOExQ41p7RamDrpxppyWxKADelYx6kCmoWkJgGgglVLz3bIr08ISx1KF9Ag1V6o214Ty6H26QA2gKTPUM04PQIVdZoXvVDuU7RDS2EZY9l9t63svXojURQTGCWxwthoDw8+uJwtWwbpVKElhqgsUE214a2TRs1vs2SHbMccAgWnNWHSpbbULfMdjZYJH99OMFLuSIlVFa1UoFDAlstImooyvT5FuwhCQxAG7HvI3nzmfz/YttVcbwC+AFyMK8bZwJtV9YXtyPRcWLNmjf76f2/kZ1fexiVf/Bk7hsbaayhChRdY5KQEHVTIsEtbEeu0Akrrehn+4wDxSKYtSScZJV6klHuUwAjJpBokABaCWMhugmhYkHaMZhSnw0BqEBpUDyOp9GMEsaEtXm6tcyYHiXGGYfLSbbUyRyXoWZeQ2dEeb1NJdZ0DScWhdi10TczFCSXRlj5TAZtxhiEO6qRTd0o3vQ9ZKPYrtkEdbIVctsJeqzax+4otBEaQBr2xTQKqseGRh5fzxBNLSJI2OEEKpgphGbCurk0uc4CQWEtuTMhujt2xbUBJBbaYqMOTEauYakK0djvhlrHxtjCndFWxhQIUCmBtQ82a2UiLzpZsPoOqcvTZL+KV7zyFVc9Y2T7J0XQp7s8Ah+Pu8a+Bt6nqI3PO+RypV5RTVX5384Nc/J8/4+7bHkGtJW51Cd8BhWMSODpxIh6ZJh6YCmohGc6y494BShvzzLYlK0rcC9XFShwqYppzLIy6SpXbKmS2CSZpoQdxfR+qUzeeXdKtLSwbOkPRSvjJdc5uxKBNlhfcwp+SQM+GhNwWbWkhUBU3WkiEKQ3DLumm96amCd1KZ60CcQ7iXuMMTrMnijOi5X514adZp60sGBxj3702smDBMEYEZOYbpzbAqrJh/WIefng5xWILS81aCCoQlFNj20QnWHMIwipkNydkRmbvEGh6Ife8pnZ6JmNSTenMxlHC9cOY6uyHM5okUCiQjBWcKFATGug1A9EOQ5Hvz5HJZTjznafw8r99CX0LJhY3fcpqUgOsf2wrl3/1l1x3udtXLlabu+Bqi5xo0WcnrsMIWrw3scHGwsgfBxh7rB+Np3ex1SiVAaWy0FUM26Lb4jwvJTcqRJuFsDRDc5oURmq28TQk9arjIB0JzJS0gGYgyaSdbotj7ADBWkvPNshtSGb0NpXUEGUMVprvMCZTO09M6uE3MaqwAdheQyU7tzKPKxn2QaVP3f2eNq+WFcuH2GevDWQyVYKgRcdJhcTC6HA/Dzy4nG3b+pmp0JJAVAYqzTsejTAKWMhtTcNPMxTBhZGcIt9clvM2gLVKZrjkwk9j00coVJ0inRYK2FIJMWYn9bjZUFtpfja3zASGKBuy6oA9ePV7z+Cwkw4hCHatIO0cQewHfBFYrqrPFpHn4hbb+2Dz2e4MUxmIGsWxMtddfguX/tf1FEbLjZXnAoWDLXKyRRfbhmGkVpHEhZ+Ka/sYvn+AZGxnec4kUuJFUO61DcNILacLzvOqCJlNQjTCeLwTSHvJWny/veHPWihGQ4gbzFNYA+SEJNCmvfZmMLj4daYA+XW7epuKmzfR0DhPvC2pOsaNTIPwk5sIhaR/6jBSqxjjHALNQ6kWfqojk6myes/N7LlyM0bABO0L6idJQLUS8NBDu7F+/WKsrXOCFKSaGoakdSPciFr4KT8Kmc0J4aT+upkwUiu4NqWYSkz0+HbCbYWdwk+qihZLaGHMjRzaGCaqSZJOZ+kyOde3HH7qC3jVu09l7wNXT3vNdhqIXwDvBP4r1WtARH4/W/W3TjCTgahhreXWX/2Ri//zpzxwzzqSakKST+CoBI5LkFDQbOdGUqKCtRBvz7H93gHGxrLEi6CaaT6M1CrG1oWftgqBldQ4tK/xNEy3dv1QqAau89IcE157x1JOw08x9KxPyG1TiGYXRmo5XSNY655pIhDn3fyCSmdfdkvn8dHQhZ96lo2xz16bWLR4O0GTYaRW0SQgUWXdE0t55OFlxMMZF0aifUahEePzURXIbkwIi+o6UtprkBphVNFEyWwYJnhiOzIyhh0dc8+/gwpxE/VrIvyU78sRhAGnv+0ETnrTcSxYOtjUtZo1EM28G9Gjqr+RnZW12qvo3WGMMTz/yGfw/COfwWMPbOS7X/kFP37RDZh86lV2+BUCFUUCiBYXkX1DSpsiav5tpwN81rjJhcoSyG1Oh8zQ0Y6SuutLrNg82J6Jsna+zEAGSrsFhGN2fA6942WueY0K5YUCWdllor0TaPqPVGG33AjPOOQBJ58tSqfvtgQJIbBq1WbW/34pUklfYOhoqhMhpEoOZIEQViec9k5Hza2Ic3x2H0Buu58gTdQm3alfNeOwZI/FnPfRv+KIMw4ljDrzmlsz759sEZG9qc33iLwCpwD3pGTVPst5+4fPJBww2LD78y/WGrr3pndduqqAdF25UwETzY+0orVuMrnbaatOhDm6TZiJCTSgLa/fzAaxVMvhhJHsJvP0cwKrgLUdHTVMRSaf4ZzzT+fFZx3eMeMAzY0g3gx8GXiGiDwBPAy8pmM58ng8Hs+fBM3oQTwEHCMivYBR1ZHOZ8vj8Xg8882MISYReauIDAAF4FMicpuIHNfMxUXkqyKySUR+X7dtkYhcJyL3p/8vTLeLiHxWRB4Qkd+JyMGtFsrj8Xg8c6eZOYjXq+owcBywDHgdcEGT1/868LJJ2/4Z+Kmq7gv8NP0OcDxOi3pf4Dzcq7Uej8fjmSeaMRC1GdWXA19T1Tvrtk2Lqv4SmKwtfQrwP+nf/4PTuK5t/4Y6bgIWiMiKZtLxeDweT/tpxkDcKiI/xhmIH4lIP7sukzMblqvqeoD0/2Xp9pXA43XHrU237YSInCcit4jILZs3b55DNjwej8czHc0YiL/BhYGer6oF3G+NX9eBvDQalezyzpyqfllV16jqmqVLl3YgGx6Px+OB6QWDnqGq9wIHpZuePunHcq2yUURWqOr6NIS0Kd2+Ftiz7rg9gHXtSNDj8Xg8s2e611z/CTdZ/IkG+xQ4usU0rwLOxU10nwtcWbf9H0TkYuBQYEctFOXxeDye7jOdotx56f8vbvXiInIRcBSwRETWAu/DGYZLReRvgMeAV6aHX4ub53gA90ptJ8JYHo/H42mSpn6jLSLPBp4J5GrbVPUbM52nqmdPseslDY5V3K+2PR6Px/MnQDM/lHsf8Ln082Lgo8DJHc5XxyiMFPneZ68hKSadXORySgyKzIMGh0DnVzGbAhvbri8NVKPVtfjnnG6s81JmjQ1W52FxIgUT2HlYZWz+6rabkpWuapbXqJar/Pjr1/PAHQ93NJ1mRhCvAA4EblfV14nIcuD/dTRXHWDdgxv4zsev4rpv/gJBqK5ICM8dhMMyGNP5hfukBDpmWfC9dbBjhG1H7gZZQ5LprCp9kLjlzvt/v4Oe3+zAPn0Z5SW9mNB0fDE5iS1BoUrPjeupLsswcshyJBCSsNNlVqy19P5hC5n7RtHVy4n7ckhgOrpwn+AMUjhaov/2DVSf1k9x/yWYwJCYzvYiJlE0sZSvLrDpZlj8GiFcqkiWznZgiaCxUr5bWPGr+9i2YCVjTx/EGEPS2ceMiS1UEjJ3rSfcnpA8fQVEARp0NmED2MQSbN6B9mQIilWsdkYudBdqIkJWue+3D/C2F/0Le+6/knPeewYvPHkNQdgGWdj65JrQg/iNqr5ARG7FjSBGgN+r6rPampMWmEkPQlW5/We/56IPfZd7bvwjSWJJJksHLjQEp/cip/UikUFbUFOcCrGgVUUeSoi/OQa/rYy/uKsCY88cZOj43SktzyKhUzhrF0FVoZqw8BebGfzfTQRjE+VO+jKUDlxBaZ/FiDHYNnZetcaT3TBK7rYnCDeNjvdPGgiFAxYxfMRKkr4IjdrbYZuqRcoxA79eR+/vNmMqEyYwGciTPGN3KrstcPq/7UwXV+Zow3bCe57ADBfH99nIUDpgKcU1K9FsiG2zcawZ4d5b1pF7YBtS10Flnw2LX2/IPc+taks7naCK0yYY/QEMfVuJ6943THIhhQOXMvK85Uho2uoQCEBsyWwr0XvjWnIPDo2P1BSwSweID1hJ0p9vu0NgrMUmSvjwRsIHNiDlSUqVqaBPTbehXTQjQ5rvz5HJRrziHSdzwnnH0L+wb/prtlEw6D+B84GzgHcAo8Adqjrvk8hTGYhSocxPvvlLLrrgcka2jlIcLc18sQjkqDzBuQOwxEC29aGjqToQ2HQAACAASURBVDoZTH5RIbl4DB6bfshfXpFj+7ErGH5O6nm16ASIglQtmW0VFv5oPX13Dk0bRtPQUNpvCcUDV0Bmbp2Xsc6Dzf9xC9m7NkwrzahAZWUfI0espLiqDwlaN46iQGzJbi7S/8vHyT24Y9rHppmQ6l7LqO7jOi9r5lJmi8aW6IGNhA9uRCpTy6QoUFk1SOnQPaks6YGw9c7LqDNI+Q2j5G9ZR7RhdNoyB8tg4VlC/8lOnYw5iGNpEewYDH1DGb4WtDDNsUYo7rOA0cN2pzqQnVOZA+tGwj0Pbqf3prVEW4rTHm/7ciT7raC6YuGcHQJJLFKqEPzhCYIntu5khKc8pwW50MnUC1BpkwXI5jOoKke96nDOfNcpPO2APabIXwc0qUVkNTCgqr9r+qQOMtlAbHp8C5d/+mqu+e+fgiqlRhKjzfCsiOi1g+jzIqcn0GSHLSXQssVeWkSvKcLo7GpH0huw4/ClDB21DMKAJNNcrxlY12H03zfCgh+tJ/f4NK22AQpU9xikdPBKKgvzyCwassQWU47J3b6O3ANbkVnG/OP+DKOH7cbIgWkoJmi2zE6gpe/ebfT9+gmirU04AXWoCPHuC4kP2B3bk4Umvc1aGCkolAnveYLgiaFZzynFC3OU1qyksM9iJzXb5AiuFjrruXcrPXduIBidXh95l7xnoe+lsOivhWAQJEdzTpAFrULlfmHb1yyFm5i1DlFleQ9jh+5OYdXArByCIHZGuO+2DfTcsZGgODutMg0D4tVLiPdZ4QxUkw5BbVQYbh3B/GEtZtv0RngqmvH+dzlHxImYzcW4BIYwE7LPQas55/zTef7xz8PUlb0jBiK98P78//bOPE6uqsz73+fcpZZespCFJAQIEHYkQEAERBYRwYVFREAdddxHHX2deRWZdxzHefXVeXUcnVFfl3FDQEVBEUG2cZctQNh3SMxGFrJ0uru6lnuf949zq7rSqe6uqq6qTjrn+/kk1VV16z7n3HPvec55zr3PD/5eVd/dcKlbzNKlS/Xee+/l0T8/yTX/5zqW//cjxLFSGmMk1xCzDd7FPchrM1Z8vEb4qRxGYnVMdGU//LkwsUQkgBroP3oGW86eR2FmaEMxNc5Or2hH7TP+tJFpv9uA3zfxepempckvmU9u/xmjdl4Gu/AcvjhI+v41BGv7Jhzmjn3DwJEz2X7KAuKMTxzUvpC9YgzFmJ671tL9wAbM0MQXZKPpXZQOm09xdu+oo81KGGljH95ja/C2DEzYbhx6DB05h8Fj50PgjTqDM6UYGSqRvW8t2ac3I6WJB8gyx9nwU3i4Ir5YbfaRJOpwg3cIm6+MKa6csFmibMDAMXPYfvQcO1v2dz5zBKAY42/P0/3n1WSe3lLXqH0sFIj2nk506HyirtHXo4yqbeeVGzFPr8PkGnPCo1F2EKOFn8pa7u1Yx8h0p8n0ZLjk4+dx9jvOINuTmbiDEJGXAF8A5gM/x97F9DXsQ2xfVNUvta4KzXHIAYfqSeGr2LRmM/nBfPtuZEgJ8soM3l/1QK+BtCCRoHEMdxaJru6HZ9tz58jQwixbz57H9kN6Kgt/phjj9xWZccs6eh7Ygim1vuJx6DF06GyGjrIjr9g3eLESxUrm2RdJP7gOb3uTM7QxUCC/Xw/bX76AofldwxdyEnfu+cNqMk9tacsdQnEqoHTQXIqL5thRrhFMbDuM8Pn1eE+vxwwVx99Rg6hAYdEMci9dSHF6GnxjZyqlmNTGQTLL1hCu2d6WtWZ/Acx4s9D9ahvSIFQ0J8R5ZevV0PcLJW6DAox6wuDBM+k/cT5RNiAOTCV0lv1LH113riZ8YeJOuBZxb4bokPkU50yrDAgkipFCCfPEavxVjc+EG0JAsA7BeIY46swCdyqbApRXve00Pvy190zYQdyNTbl9JzZl98eAq4F/VNXG5vNtYprZS09o+oHu5pBjQszFPejTJfQXOdjWmdvrSr0+W18+m8LcDNN/u57McwMdubtOBQr7TmfokNkEa7aRfmoTptiZW0eL01Nsf+lcNOXRc9c6wvWNhc6aRY1QWjCT4sKZ+Ks22w6jQ1KaxVlZcsfNxxRjsg+ux+9rvROuhWSh5zWQfRls/wUM/JGOSHkqUJjfTf8xc/A35+h6YD3eQOudcE3boU9pv9lE07P4z76A2TjxmfDugOcbbin+ZMIOYrmqLql6vwrYX3UybrKuzXR/Lz0+7qyDADBhUPeiUcttJyOOPQZVjEfbBeFrmp4MfWXABB6TcnP9JCKixC0InTWKqiLopLR1Oaw0GdyuP63LQYz1HERaRI5h+EztB14iScY+Vb1/4sV0OBwOx67KWA5iHfBvVe9fqHo/kWR9DofD4dgNGCtZX9NJ+hwOh8Ox+9Pmh+EdDofDsbviHITD4XA4auIchMPhcDhqUk+67zvq+czhcDgcU4uxNKnTQBarBjeD4dtde7FPVzdFkqrjx1UfHQB8EpgOvBvYmHx+hare1Kwdh8PhcEyMsW5zfS/wEawzuI9hB9EHfLVZg6r6JLAEQEQ8YA1wPVZi9Euq+oVm9+1wOByO1jHWba5fBr4sIh9S1f9ok/0zgWdVdaXInvXkqMPhcOzqjKsop6r/ISInAftXb1+PJnUdXAJcU/X+gyLyV8Ay4O9UdcvIH4jIe4D3AGSkqwVFcOzqTJJaqaOD7IltPFlpNhqhHsGgK4EDgeUMp+9SVf3bCRkWCYG1wBGquj6RMt2EPVf+BZinqn891j6me3vpyamzKbQhw2ZNjMHLZtAgQFDioQIUW5RafDw8gwl8e3RUiYvFjl1VJvQrOe3jYtQx/V/xDHgmkQqL0VKHjjVJeubIKrF1MkeQ8T0whoo8Wac6Ec/DpEMUQTS253bcmXqL59nOUkBLUcfsIoJ4vs3HJEpcKHbseFfEgCYhH1O6K8WN/VdNOBdTmaXA4dr6WpwD3K+q6wHKrwAi8i3gxvF2sOjIfXnj+efx86/cRBzF9SnHNYPv43dnUTGIkaRjFuss4hiKReJ8e04uE/pgjNWjiO1FJJ7BGJsSOioUoB2JxkQwgTdcJRFUwQT2giKKbd3bgAl9FBnOnS8ggQ+erXM7L2RJmrd8rDVO5DoBbVfCQBG8IEDR4XamnJhRbZnadKwl8CEMrUMsV894SDZjB0G5PETtyc8pfnJ+GaloqHhhYNUYVdE2Db7E8yC5ljXppMUYTGgQUaJ8sX4Jt0ZtJzbLh7pdGhAj8QMf4wlHnnIYl11xITeeflV9v6tjm0eAvbG5mVrJpVSFl0RknqqWbVyQ2B0TL/B4+z+/iTf/w4X8/tq7uOozP2Pjqk3kBwst8cqSCjHZDIipatDh7+2oR5B0ChMEiMZEg/mJj4BE8FIBChhjiOMds02W/1bAhCGAnVG0IMureMY6pKqRzU51xmYcjeOkw27FhSxiHYOS9NK6g7CKrbOVgZUwTOwWWzPaTAbro16oyUeebyplaskFLYIXBkmdAd3RfiWDrchwh9aKzlrEzhaMZ51QrCPaOHlFMNm0LVOxiBZaMFMXsRofVZOjHeqctLPxDLEYRFo3IDCBPdaVTrrWNaWClwpRLZ9frXOOIsPtW8u28YQ4Gl1UqBnS2RQKnP2O07noo69l3qK5jZW5jhDTb7B3Hd0DVJLTq+rrGy9uZZ9ZYBVwgKpuSz67MrGjwArgvVUOoyYjJUdVlcfveopr/s/13Hf7QxArxUbV5YzgZTIQhpWLp/562fNYVIlyQ1Bq8OQyxnYYUOm06v9pooMdx8RNKOqZwE8cUmMn5/AojObCT8Ygvlc5do1gnVgMUWRDEw1SrmujdR7WCm4u/CSeZ/81EV4oz3CacoyewaRTNoxUPWOoA2PECttoTDyUb3zW6tmwWcOj5bLzBuJSqfF6i2B8O/Bo9HhXrimNrZNqkIqKXIMp+iv9yARmFpnuNNneLJdcfj5nv/00Mt2ZETZaJDkqIq+o9bmq/q6RAreDkQ6imo2rX+T6r9zEjf/vVhRlqH8c4RXfx+vK2g7LMxMeIUoSntBCEc2PLVtoyuETM3G75ZNaIJkqj7G/qjBSMx10jd3ZkWEUjduBSOBb+y0YLVXXuZ7RZqWjaNAJ77wj+3sRqasDMGGQlLexgUfNfVVi2HWEn3wfSYWJ1vHEsG2s9ljXEX4yvkesjQ88atoud5jx+AMCMcaGyloQvrH7iIdDuuOdX03oUI+6rwb0qT3f4Pkei487kDf/w4Uc96qjd9ChHrHf1mlSi8h+wGJVvT0Z/Xuq2gYhwsYYy0GUyefy3HHVH7nms9exdVMfQyPWKSQMMV02jNQOKiOvOCbKVYWfaoSRWkrVyCsq7BiKESPgNTdqr4fhzkDRYtWFLFKZqbQj7loZeZXDEtUd53hhpInaNrXDB9VhpHYc7x06o2jHYy2p0C4+NzgTrtt2MgiiUNhxvaAqjDRhJ1yD4QGBJDdrDBswftDUTLhRaoafJKluO66pMaRJU9kQjeGMy07mjX9/HvseumDc/bVyBvFu7G2lM1X1QBFZDPw/VT1z3FK0mXocRBlV5cHfPsrVn72OR//0BJEX7Lw410bKIy/iGJKwRKfuYBAjxFWj+nZfPNV2y4u9w89ZdgaRJJ5dKkKslfhuuynbqYxg2+SEa5J0UKRTw510B9q5PAjSxCl3YtEVqq6fZEbTLidc03b5mopKaBR37PyqHhCk0iGprhQX/8/zOPddZ9I9vf7b/ut1EPUsUn8AOAG4G0BVnxaROXWXZBdBRFhy+pEsOf1I1j23nnee+MmdFufaiZYXFqoXpzpkvLLAmwzlOuEchu1SFTjvHKrJf8kx7pRkacVOMrXv6B2MCup59kYD7Zxkqr3TTCoztk7Zrb5+Kgvenbqek2tKk9Bip84vrRrknfnWU/nQf74Tz/PaZq+euEpeVStBdBHx2c2fa5l3wFyM5xLZdgpjJu8p+cl6Qn8yMwNM5vGeNPawKnuBz4FH799W5wD1OYjficgVQEZEzgKuBX7Z1lI5HA6HY9Kpx0Fcjs2w+jA2gd9NwP9qZ6EcDofDMfnUswaRAb6jqt8CyhlYM8BgOwvmcDgcjsmlnhnEHViHUCYD3N6e4jgcDodjV6EeB5FW1f7ym+TvbPuK5HA4HI5dgXocxICIHFt+IyLHAbn2FcnhcDgcuwL1rEF8GLhWRNYm7+cBb2pfkRwOh8OxKzCmgxARA4TAocAh2LuNn1DVDgkwOBwOh2OyGNNBqGosIl9U1ZdRR/pth8PhcEwd6lmDuFVE3iBONNrhcDj2KOpZg/go0AVEIpKjnLBQtbetJWsjUSnaLfRgpwqTeqj3wGZ2p/bUR2Olf9tA2+2MO4NQ1R5VNaoaqGpv8n7CzkFEVojIwyKyXESWJZ/NFJHbROTp5HXGRO1U07e5nx994ZdccsCHiJN88p2cGBljbFIz6GguKCnn5hFJ/nXM9A4ZRTtZZ+PZupZTi3cSm3rbJnHrZF4k4xmr3Z1k++wYkvxL8gJ10nbl+Cqjah+0g0q/kbx2sh8RIxTzRX7wTz/mk+d9nsfueqp9tupI9y3Am4FFqvovIrIQmKeq90zIsMgKYKmqbqr67F+Bzar6ORG5HJihqh8fbR/1pvte+fgafvKlX/H7n90NIhRyhbJBTCqw0optyplftiMiqO9DkEza8nnIFxDVtmW/LOeQN6mQOAisOHyphOTzxPlCokPReu3dsjCN+B5kMpAK7bB2KI/mcvb7Fsij1qJcJwk81CSJzEoRUioQF0pt14MwYQBBCL5v6xyV0HyxYVWxhmx7xor4dGUhk7adVilCSkU0ihDak1a+osgX+MRipWqJY6RQIM4NtbfO1eJH5c5Z1V5PWp/ATjNUCzXFxWJiVq2sdhS3r85V4lTVbSkipDIhc/abxWVXvIFT33giQSJMNebuWqgH8XWspPgZqnpYMqq/VVWPH7cUY+93BTs7iCeB01R1nYjMA36rqoeMto+xHEQcx9xzy4Nc8/kbeO7hv1AqRmM2nAQ++GUZyGZrVbW/cocR+MReohg3YpShqlAqYfIF4kKxZZ2XmOTiCUOreVFjdKNxDIU8mstX8vlPlIpDyqTRdNoe05F21QrMkBtCS1E5Xjlh29YJgxqTiCHVqHNkO814qNCyC7lS53QKghCpkV2z3M4USxX9golSccKBB9kspEZp5yjClErEhdY5qWEn7KOeP8r5pfb8GhqybdyK8zrpHI1nGM4kX+OaUsXAqAI7TdmuVrMbZVClmij8tdDusCzu+IO5THca4xku+PC5vP5vXs2MOdNG3baVDuJ+VT1WRB5Q1WOSzx5U1aPH2/k4+30e2IL19d9Q1W+KyFZVnV61zRZVnTHid+/BChix7777Hrdy5cod9jvQl+PWK3/PT/7tRnL9eXIjFOTGxRhMKrQXXxPiIxXBmFRgL546p70aRZhCgTiXb+pCLksTGt8nDgIrM1nHtLfSYQ/l7YnfROdVDiOZrgyaSls9gjrQYgnJDxHn8nYfjda57IR9LxnBSkN11kKh6c6r7IQlTCFhWFc7qypEMRKViIvNzWYqDimbRjNpJBh/tAh2QCClkq2zNNl5VZyw19j5VSwihbytcxO2K3VOHEO94ZyyNKqWoqZEsnZQKCyV6r42KiJGUjX4abQfKZe3CUW+MG3PiZe9/nguufx8DlqyaKdtWukg7gZOAu5NHMVs7AzimMaKvdN+56vq2kR86DbgQ8AN4zmIaqpnEGueXc9Pv3wTd1z9JxDID46tA11HATHp0J4gdShzVUbtvg9B0HRMsjzyYihfV+dVK4zUlN1klCuFPHG+ztmMiLWXSUM6NYE6x8hQnngwt0P8fjQq0/zAQ8VrOuZdqXOxSFwav/OqdsKEqbo7yZq248RR1Bl+qjjh7qydnTW5nlNd58pIeIxm3sEJG89qtjdb51Jkz6+h+gZBFT1mpBKmbcpuWcmx3N+O0+fVCiM1bRcq4af6rqnWyZYaIwTpgH0Ons+b/+ENnHTe8Xh+sk7UQkW5rwDXA3NE5DPARbQg3beqrk1eN4jI9VjVuvUiMq8qxLRhvP3cd8cj/OgLv+SJe54himKi4thi5g0U0IqyA4R+ErYwI+J/yQgj8FDPHzW00QhiBNJpNJVCiyVMeeQ1Io5clkrVIIBsgBozobVnEYEgsM4tHSGFAjo0ZKe2VRdyeaRvMik0lYag+U5yuC4Gshkkk7brMrlc4iR2jCWXwyqxMRB4ttOYiN3qOifhJ60Rfqp2wjpKGKlh28aACRE/QEvFYdnMkR2IiF3LyWaQspToROwmdVbfhziuhJ9Gdl6VEaznQeBN+PyCZE3KzyLpNJqEGa1Trjq/dgojNe8YqveJ56HJ+oQh0YkfESEoH4M4jtBSNOFljHK5Fez6jNh3I8NFO+iKx9qy5ZM4VvKDBZ5dvoL/+46vEqYDLvq71/Oa97yy/jrUE/8VkUOBM7FVvENVH2+61HZ/XYBR1e3J37cBn05svFi1SD1TVT822n5mZfbWk3tex9BAfiLFqR/PYMKw0oAmFRA3EEZqFi0l4achW0/xPTQIW9I5j2lXFfJ5dChfuZIkm4Z0uiWd5Jh2SyU7q6ius8iERrB12Y5jKBbRfAGS0WtlfaGN7WzDTxESRcTFEoB1wplM+9u5HH7KD9+8ge/BKOsLLbObhJ/I59FynRsMI03EtkGJK/rwjYWRJmJ3WEM7uaY6pOENkMrY/uumwasnNoMQkTTwPuAgrFjQN1S11KJyzgWuT04CH7haVX8tIvcCPxGRdwJ/Ad441k5KpahzzgEgiu3dGTN6Uc9HJziaqxfxPdS3dwMZVVQmPpqry67Y2QypFKATCqk0bDcZ2RP4mGIRpUPH2hhb3zC06wWdrLPvg+/bMJKfzEjbbjmpc5jceVWKJhTOaciuyPCNFAMDxMWSbecOVNrORgUkhihqy918o9mlfCdS4iA65RwA8rnGQu9jhZi+DxSBPwDnAIcBH2m6ZFWo6nPATovcqvoidhZRF5P1aLdUPc/QabvtvEV0VLvlzmsSEGOSsFZnn/4q13kyEgiI51WeKeio3WSGNhlIm2eGoxvu7HNBw2YFOjhzaJaxrvrDVfUoABH5L2BCzz04HA6HY/dirOFCZfm+haElh8PhcOwmjDWDOFpE+pK/Bcgk73f7XEwOh8PhGJ9RHYSqdj4I6nA4HI5dhslZkXI4HA7HLo9zEA6Hw+GoiXMQDofD4aiJcxAOh8PhqIlzEA6Hw+GoiXMQDofD4ajJbu0gJkN7Nw48Bhf0UJye6qjcsQKl3hRDs7Ko19ncAGoETYeo39k7nxXQbJqop8sm6uskIlb9r8N2FYjDAPW9jstpR9mAwt7dxEGH21mEqDeLdmU6LyGeCqGnu+MpRrSNSpKtZHIS7LSInhldhLHVXmg0CVWjRL1pCofPZWj+NEyiOmeKEemVfaTWDyJtamw1QmGvLMX5PWiiSje073RSmwfx1/ThDbXnIXcFm0W0N4MmOYkqCcb6h6BQbFsKGxVBu9PE3elKBlWdMwOzrR9e7ENKLUrpXgvPs1lUkSSluiKKzWjbxhxYagzSk7FpvctpolWhUEQLRaRNfYkCpelpivN7iUKrrZHfZzpBf55g9TZMf6F97ewb6M0SpxM1vERQKd64Bdnaj7RzBJjNIL3dw/nN9poJg4NEL26BQvMaEONRlifVeFgfYqSMaLsQEVLZkNkLZ0Gd+bjrSve9q7J06VL971t/y83f/Q0//fLNFPOlxhXkxkCB4rxeCkfsTbHXirSMPFpGbWOn1w2QWrUdL9+azisOPYrzesjPzNgUyCO+L2sHBLkiwapteNuGWnIhK0A6hJ4saqTmCLqcqlhyecgVWnYhq+/BtCxRKrB6BCN2a4VXFDOUh41bIZdvXecV+EgqtaPWcRWV9MyFIhRb45TLTthM6yYO/Np1TuRgJY7RXL5lAxH1hOLsLgpzuhHPENc4kBIrphgRrN6Gv3mwJU5KAVIBTMsS+15F16Qag5UMNn0D6MatSIuON8YgPV2QzewgW1pGxF5TUixS2rQFBgdbYraS1hsd1SFUUn43oSA3HkHKDvCOO+toLvnEBRx+4sGtU5TblalWlItKEX++8X6u+fwvWP30CxTzxYYlBsuob8gv2ov8oXMhMMR1qHfZk1pJbcuTen4bfl/jIy8Fou6Q4oJeil2h7Rzq+J3EikQx4Zo+go0DTXUiagS6EhlLrz5dbiNiL+RSCe0fQpoYYSug6QCmdRF7pmaHUfuHamcSG7cifQPNOQoRJB1a3YM6VAOhqsOOIjRfbCrOqQCZFNLbZTW06whjleUrJVZ0qACJnnejRGmf0vweCr3pus+v8iAoXN+P/8J2TKmJdhbQbAp6slBjoFULq++smHweXb8FBpscBIUBZloPGtQeeNS0jaJRTLxlG9rXR10/GoGVPNUdxIDGo6L33YJMr+nuNAK87v1nc8HfnsOsBXtVvtvjHEQ1zyxfwY+/eCN3/uoBRKAwVN+UMeoOKRw6l6GFM/A8IWoy/iyx4uUj0iu2EW4Yf+SlAsWZWQoLelHf2M66CcpC7akXBwnW9mHqmM2o7yG9WeLAr79zHkF55GViRQdyUCiNeyGrCNqVQnsyYOrrMGpRGW1u2Q6b++pzUp7BZNI2jFSnYxhJZbRJ0mHXYVeNIN1ZNJvMRpusdNk2+WJdoT4FomkpivN7KaX8ugcAIykPgsK+IfzV2/AGx7+u1DPQmyHOpOrunGvvqDwg2ALbBuqbtWbS1jFMID1/WS6UgQGizVvrmj2qKsZAXIqb0peHYWW9ZhxFujvNjDm9XHbFGzj90pNJZVK19r/nOogyW9Zv45ffuoOff+1W4igi17+zuJACpTndFI6cR2F6pmYYqVlMbEdemTX9pFZvxxR3PFHiwFCc201hdped5rfKLvZCDgYKNvy0fcdQTGWa35u1se8mHUMtyqEvySW62iP2q56xYaR0OLEOYwSV8FNuCDZsRfI11qR8vyLb2XJUIW9H9jt95fuYaV3EYdDSY12ezZgoIh4q7DRzVCMUZ2Up7N2DeELconoLQKx4+RL+6m34W3I7n1+hb2eFQe0wUrOUBwSydTts2rbzepQxSHcWurITcsIjqQwICgUbfsrldvh+J/3pFq4r1OMk/MDHeMKRpxzGZVdcyEtecfiY+hq7rIMQkYXAD4C9gRj4pqp+WUQ+Bbwb2JhseoWq3jTWvsZzEGWKhRJ/uO4erv7XX7Bx1WbyuQKxgfx+MykcPpc49G3H1SbKHXZ6yxCp57eBQmlBL4XuVN3T/GaRWJFiRLimD//FQSSbQrMZe9K10W4l/FQoEg/krYb0GHHnVlF2UKZUsusU/TlMyqqlGc80HXash3KHTTn8lAqQad0td8IjqczgVIlzBWIfSnv3UJiRafv5ZVTRSAlf6MNfP1A18Ki9ftUyu1UDAt2wBSlFdrYQhm091oAdDMQx8YtbiPu2Q2z1062+dpvPr1iH9cKBdFcKVTjnr8/gwv/xGuYtmlvXvnZlBzEPmKeq94tID3AfcD5wMdCvql+od1/1Oogyqsrj9zzDj77wS26VAqQD4ibDOc0ggORjvIJ2/PZJEyuZ1YN4CHEH21wEIiPgdV6FT0olzPpteFUXVCcwRohSIRL4nb9VNe2Rm9/V9gHASAwQvpDDi7Xjt59LoYTJFZJReyctK6Wnnx8OQ3WI8oyiZ2Y3b/vni3nV204j051pbB91OoiOPwehqutU9f7k7+3YG64WdMK2iHD4Sxfz6Ws/iulJd9Q5gJ16iy1IR+3C8FpqJ51D2a7ne5NT51jbPmuoRRyr1RHvqNXEtm/wWhgmrdsudrY6KRFr1UTnucNm42Qm0WEJYI2VMBPyjn+5hPM+cE7DzqERJvVBORHZHzgGuDv56IMi8pCIfEdEZozym/eIyDIRWbZx48ZamzgcDsfUpwODrklzECLSDfwM+Iiq9gFfV5bnMQAAGSFJREFUBw4ElgDrgC/W+p2qflNVl6rq0tmzZ3esvA6Hw7GnMSkOQkQCrHO4SlWvA1DV9aoaqWoMfAs4YTLK5nA4HA5Lxx2E2Huv/gt4XFX/rerzeVWbXQA80umyORwOh2OYycjFdDLwVuBhEVmefHYFcKmILMGu5a4A3jsJZXM4HA5HQscdhKr+EWo+/DnmMw8Oh8Ph6Cy7dbpvh8PhcLQP5yAcDofDURPnIBwOh8NRE+cgHA6Hw1ET5yAcDofDUZM9zkEUSxG3/OHxjufnKSMC0i79yDHQyn+dJ9Z4MlIx2fw8cWfz5JRR1dr36rUZI4p91nQSmIw2hknJ85UYniS7UMoX+f1P72Td8+vbamdK60FUs3nrAD+7dTnX3vwAURwzWChCKESAmKaEwRoiE5bwTMT5xz+O5oUb/3QIikcu316BeM9TkJj9DtjIgfus4Jnf7suWNd1EJY929yMmrZgwovfUbUQDKbbe24sRQ9Re+XCrfaBKboFhKCww46EB/IEIKWnbnaQGQtTlsfmoLrLbDZmVBTwRtNRew2EqRjXm+DOeI7uowG/uXkI+HzKUD9pqVwSMHzFt1gAHHLaSzctnsuGJaYgaovbIpQ/b9m2W3qG9AkyhRHptDoNAE4p3DdlN5LMFJdraB319Vo+jLOTUTttV2hCe7+H5hiNOPpTLrriQo087YkwNiB32s6um+24l9TiIJ59bz5W/uIc/3vcsAIXijgIjKkAgRCbJt97C4yECKb/E3OkDvOOMZZx51LMEvj1580WPO+47kB/8+jg292UYKrQ2NXQYxigxLzn+WY5/+SNMnzlQ+W7jimnc/4vDee7euYgYSoXWjYTEAF5Mep8iM87ZRNdRA/YzIMoZtt01nc13zELzhijf4glsIMQo2xb7bDvII0on9VIls6bA7Hv7Sa0awkPQqIVH27OaC/mFKTYe301uQVgZ1Xq5mOmPF5jxaB6jQLG111sqUyKVLnLWGx7kpLOfJJO1Km9xDA8+dgA33HoSq9buRRR5RLWEp5vED+xMZeFhGzjiFU8xc35f5buBTWme+fV+PP/7eQiGUr7FI20jxIEwMDckP8O34hAAsZLemKf7L4OYocgKKLXwcFfkZlG0UNxh31ooQn8/Uf8AxhPiFjqpetTl0t1pps/u5bIrLuSMy06pqSI3Yp97roMoRTG/v/cZvnfdXaxet4VCMRq341dAPTsCRCZ2XgW+IkScsHgtbzvtPo7cd/2os2BVeOjZvfnBLcex/Om5xOpRipq/oMKwRCpT4GWnP8yRS58lDEcfxuX6Qh6+5SAevPlA4sijONT8bMaEoBrTe9wA089+kdT80acJGsPAY91svnUOuTUhRKb52YzYdouywouH+fTvY8Ab/fj5fSX2emCA3ocHMCJQmMCFHBpiVfqO7OLFY7ooTRvjudNI6VlRZNaDefztMRI133kZDzyvxIJFWznnkvs44rjVGG/0na15YSa/uv1E7n5gMSKGQrF5xxykIpCYw05awcEnPkeme/R2LuUNK/84jydvXERx0Kc0gfNLksFb1OvTPzug1D12Cnm/r0jvqkH8F/MYmdiAoKwHTimqqRhYjUYROjBA3LfdildNIBW4eLbc5dd6SHelEBFe+75XccHfnsvsffaqud0e6SD6+nP8/LaHuOZXyygWIwbr1KKuRgE1QCjEkiiU1fnbTBghxFx44mNcfPJDzJk2MP6Pqnhhczc/+e+j+eWfDwYMQ4X6LihjwJiIeQs3c9KZy9l/8bqGwrJRSXjmrn1Ydt3hbN+UISp4dYfcvLSCFzPjrC1MO3UrXldjF0R+XciWO2az7YGehsJP5TBSfp7HpkM98ns11ulJMab3sUFm3duPl4+RQv3XgYaGKCW8uLSbbUdk0aAx2+kNJWY9lCezuohpYDYTpGLQmGNOWcmrLnqA+fttbchu/0Ca3/z5aG7+zXGUSgFD+foSKYiA50d0Tc9x1OlPse+Ra/H8Bo6XwoZHZ/LkDQew+bluNPaIx5dLt7aTdi7MChmYFRCnGjvWJh/RtSZHZk3OOoo6R/bDYaRkdtDgWpaqooM52L6dONENr7evrcwUGul8RhCkfESEY1/5Ei79xAUcduLBO4Sf9igH8dyqTVx1w73cceeTiAj5QmuCnyrYdQoZO/yUCUtM78rx9tPv4+xjniYd1Hn2j0Iu73PLPYu58tZj2T6YIjfKhRwENox05DErOeEVDzFz9vYJ2QV44ekZ3P/zw1n54GzAEBV39jRiAD8mNafEjHM30b2kH5ngUko0YNj6pxls/s0sKBmi0cISgW2H7Qd6bFnsE2UnGL5QJfuXPLPv7SdcV8DEUFMo2wixgcLeIRuP72Zwv4lrW/sDMTMfy9P7eB6DgWLtTiidKeH5EWde8DAvP/dxunp21lZvhCgS7nv4IG649SRe2DiNUjGoWWXrBGLmH/QiR5z2JLP3bcwh1WL7CxmeuXkRK/88x4afRgtvekLsweCckKGZwZizwrqIlPSGIXr+MogUkhlcDSo63yhxodSSxUnNF2Cgn6h/0Hb+tWYViTNopZY12P2lsiGzF87isisu5BUXv4wgDPYMB3Ho4UfpKRdczrN/2USpFBO16Y4VBdQXYj8ZWQC+pxiJOHr/Dbz99GUce8Dalt9MoQrLnljAD245jsdWzqrEkcNUCT8o8dJXPMLRJzxNOtP4TGk8+jeneejmxTx86wEIhsKQwQQ2jNRzdI7pZ28ivd/EOqpaaATbH+ph821zyG8IoGiF59WHKCVsPsyjf18P9Vt/B0mwpcSs+wfofmwAzwhaiJHAEKnSf1iWTcd1UZzR+kVfKSk9zxWY9WAeL6dIUREDQVBizvztvPqS+zj6ZSvtDQctZsWqOdx4+4k88MgiEEOxaAiS9atDXrqSQ096juy0oZbbLQ56rPj9fJ781f7EBRveFGN9c9zt0z8noNjTBiVCVYJtRXpW5fC35O0MLtaGwkhNm44i6B8g6ttu7UVxlc60IW7zHXeZ7jTGM5z/t+fyjk9fMvUdRM9eC/WoV36kY/bK4adFCzez5IAXePOpy5k/c+Kj9npYtaGX7962hCdemMEJpz7KgYetwZj2t12pYHjqTwu58+YjyBwxyPTTt+L3tucCGsnQqjSrblxA/5Y0mw/1GZrdGV1rk4+Z9sgAvU/k6Dskw7ajuhoObTSFKpkXIo5+bi37zXqRsy9azr6LX2y/XWBbX5Zb/nAsdz5yCItPXMGio9fgB+2/XVZjWPfgLO6+8khykmZgdkic7szd9yYX0ftcP6n1OesUOnRLtKqiA4PEmzdPyq3nfujz6/yPpr6D6J21rx555oc7bve7H7uOxQs6L3e6vmRYXkhTovP3uf9222EUtPPZ4Z9fO5snVyykw7K/k8qHl9zO6w5YPv6GLSYX+9yZW0Q0Cff33/izU1mzambH7Qabh5i5/MWJ3azQJNHadWixzfcCj8Lt+tO6HMQe96Ccw+FwOOrDOQiHw+Fw1GSXcxAi8moReVJEnhGRyye7PA6Hw7Gnsks5CBHxgK8C5wCHY2VID5/cUjkcDseeyS7lIIATgGdU9TlVLQA/As6b5DI5HA7HHsmu5iAWAKuq3q9OPqsgIu8RkWUisqw41N/RwjkcDseexK7mIGrdX7fDfbiq+k1VXaqqS4N0d4eK5XA4HHseu5qDWA0srHq/D7B2ksricDgcezS7moO4F1gsIotEJAQuAW6Y5DI5HA7HHknnH40dA1UticgHgVsAD/iOqj46ycVyOByOPZJdykEAqOpNwE2TXQ6Hw+HY09nVQkwNoar4XmerYIywaVuWWNMdtQsQSkgMSIebTQgIJCKQ9spX1iIbghHpeHaglOft8NopBNhW6CbSsRXB2oEvKRQwdLadBUM2myMVdr47CrMhRIqZaDrxBvF8A8YjzIQdtQuQztZ/bu3WDmLRPrN43ZlHkQ590qn2ToZSoU8YeJx9ymHMX/w9vOmfAm8/kAztFS8XIAPegcyc+RlOnn8l87vOxkiIkfZ2Ip5k8CTD/r0X87FDP8/rF1xCrz+dlGmvczQYAgnZL3sQ/3jCW/nBeRfx8v32J+V5BKa9HXZXENCbSvHe407glje/nfcvfSm9qRTZoL2dZmAMKc/j5fvtz6sP/SLzZ32LVHAcQhobbW0fIl14Zg5zp3+ci/f/LkfMOA9f0vjS3nb2JIUnIQf0vIJ/f9e7+Ke3nM0B82aSDn2r9tcmBMiEAQtnT+N/vufV/McvP8qZFywlTPmE6fa2czoTkkoHvPpNJ/KN+z7H+774NubsO4tMd5uvKc+QyoYcdMwiPvaDD9X9u906m2tZMGggV+BXv3mEK39xD4NDBXJNKMmNRjYdEPgel752Keef9RJ6uzOV71QVisvQ/m9A4W4gBlplOwQEUicjXe9DwiU7fJuPNrOi7yc8v+1KYo2IdLBFdsGTLIHpZfH0d7JPz+vwTbbyXawxj/U9yC3rfs6a3EoijYhblF02kBBQjplxIq+c+1rmZfbZ4fs12/v4zgP38aNHHkaAwVJrjrUAaT9gQU8PHzjhRM456GDCqplDMYq4+Zmn+Oq9d7O6bxv5aHwJ23rJ+LZDuuSIo/jrY45jQW/vDt8Xik+ydftXGRi8AcSgmmuJXfAQAsLgcKb3foRs+kxEhseLxTjHU9tuZfnmHzIUbafUMrsQSAYRj6NmXMQR088n48/Y4fuHn1/Hd2+5lz8/ugKAQov0GQLPICIcd/A+/PWrT+DYgxbsoLK2fdsgN199Jz/71m8pFkrkBlqnd5LpSpHpCnnje8/grDe+lK6eYYegqtx/+0Nc/dnreOLup4mimKjYmjqXHd5J5x3Pmz5+PgctWQTsYYpyZeJYuXP583z/+rt4esVGiqWIeBSR77EwIoSBx8J5M3jbhSdy6vEHjRvK0mgtOvA9yP0keXKjyQ5bsoBA9jIk+1bE23vMzWMtsm7gNp7e8i0GS2uItABNdNiCh4jP9NSRLJ7+bmZnXrbDxVOLdbnV3LH+V9y/5U5AKGqdeqEjSJk0vvicMfdcTp51Jl3+2M+3DBaLXP/EY3x92d1sHRpisNicowiNhwicsu/+/M3SEzhm3vxxf7P8hXV8fdnd/H7lChQoRM1dyNkgYFoqzd8sPYELDjti3BlKFG+hr/8qtvV/HdU8qo3J2ZaxMxKlK/t6pvd8gDA4ZMztVZU1g8u4/8UfsmHoMWKNUZpJUS34kqI7mMuxe72VA3pOwxsnZLlxaz/X/GY51/7+QWJVcvnm2jkT2ujCeScfyVvOPJb5e00bc/uoFHHX7Y9yzX/exupnN1AslJrrRzzBD3wOOHQel3zwLI4//TCMGbsfWf30On76xV9y+5W/Q0QYGmzOSWW603i+xwUfPpfXvf9sZszZsc57hIMQkY3AyskuxzjMAjZNdiHajKvj1MDVcWpQTx33U9XZ4+1ot3YQuwMisqweT7074+o4NXB1nBq0so679SK1w+FwONqHcxAOh8PhqIlzEO3nm5NdgA7g6jg1cHWcGrSsjm4NwuFwOBw1cTMIh8PhcNTEOQiHw+Fw1MQ5iBYgIp6IPCAiNybvvyciz4vI8uTfkuRzEZGviMgzIvKQiBw7uSWvDxFZISIPJ3VZlnw2U0RuE5Gnk9cZyedTqY6fEpE1Ve14btX2n0jq+KSInD15Ja8fEZkuIj8VkSdE5HERedkUbMdadZwy7Sgih1TVY7mI9InIR9rWjqrq/k3wH/BR4GrgxuT994CLamx3LnAzNrvDicDdk132Ouu3Apg14rN/BS5P/r4c+PwUrOOngL+vse3hwINAClgEPAt4k12HOur4feBdyd8hMH0KtmOtOk6pdqwqvwe8AOzXrnZ0M4gJIiL7AK8Bvl3H5ucBP1DLXcB0EZnX1gK2j/OwFyPJ6/lVn0+VOo7GecCPVDWvqs8DzwAnTHKZxkREeoFTgf8CUNWCqm5lCrXjGHUcjd2uHUdwJvCsqq6kTe3oHMTE+XfgY+ycAOkzyZTuSyKVtKsLgFVV26xOPtvVUeBWEblPRN6TfDZXVdcBJK9zks+nUh0BPpi043fK03Z2zzoeAGwEvpuEQ78tIl1MrXYcrY4wddqxmkuAa5K/29KOzkFMABF5LbBBVe8b8dUngEOB44GZwMfLP6mxm93hPuOTVfVY4BzgAyJy6hjbTqU6fh04EFgCrAO+mGy7O9bRB44Fvq6qxwAD2FDEaEylOk6ldgRArCTz64Frx9u0xmd119E5iIlxMvB6EVkB/Ag4Q0R+qKrrkildHvguw9PW1cDCqt/vA6ztZIGbQVXXJq8bgOux9VlfnqomrxuSzadMHVV1vapGqhoD32L3bsfVwGpVvTt5/1NsZzqV2rFmHadYO5Y5B7hfVdcn79vSjs5BTABV/YSq7qOq+2One/+tqm+paijBxgIfSX5yA/BXyZ0FJwLbytPCXRUR6RKRnvLfwKuw9bkBeFuy2duAXyR/T5k6jojVXsCO7XiJiKREZBGwGLink2VuFFV9AVglIuX83mcCjzGF2nG0Ok6ldqziUobDS9CmdtzlNKmnCFeJyGzs9G458L7k85uwdxU8gxWMeMfkFK8h5gLXW1+HD1ytqr8WkXuBn4jIO4G/AG9Mtp9KdbxS7C3Kir3L6b0AqvqoiPwE28GWgA+oamsUXtrLh7DnZgg8h20bw9RpR6hdx69MpXYUkSxwFkk9Ej5HG9rRpdpwOBwOR01ciMnhcDgcNXEOwuFwOBw1cQ7C4XA4HDVxDsLhcDgcNXEOwuFwOBw1cQ7C0VZE5AIRURE5tOqz/UXkkbF+1wK7/yAijybpFZaLyEuTz78tIocnf1/R4D6vT/b1jIhsq8qoeVID+5grIvckqSBOEpFLxGYdvV1EXioiX2pgX38Um4X0wWSfL2mkPiP29b9F5CM1Pv+MiJze7H4duzfuNldHW0nuM58H3KGqn0o+2x+b+fbINtl8GfBvwGmqmheRWUBYflq6art+Ve1uYv+nYbODvnaU731VLY3y3VuA01X1ncn724F/VtU/NFGOPwIfVNXlIvJu4EJVPafR/ST7+t/AJlX992Z+75iauBmEo22ISDc2Hck7sU+a19rGE5H/KyL3JqP99yafX5CMqkVE5onIUyKydzL7+IOI3J/8qzV6n4ft7PIAqrqp7BxE5LcislREPgdkkhnAVcl3b0lG4stF5Bsi4jVQ19Ui8o8i8ifgAhF5X1KnB0XkWhHJiMhS4LPY9CzLReSz2BTM3xaRz4nIK0Xk58n+ekTk+2I1Kh4SkfPHMA9wJ1VJ2ETkmyKyLJlFfXJEOT+VzGAeEpGDa9Tl/SLyKxFJi8gPy7ZH+62IzBGRO5L2+JpY7YXp9R47x66LcxCOdnI+8GtVfQrYLLXFSt6Jffz/eGxyw3eLyCJVvR6b6/4D2Pw5/5SkUtgAnJUk1nsT8JUa+7wVWJg4la+JyCtGbqCqlwM5VV2iqm8WkcOS/Z2sqkuACHhzg/UdUNWTVfVa4FpVPV5Vj8bqDLxdVZcBnwauSuxegX3S/k1Jear5FLBRVY8CjgZ+N47tVwM/r3p/uaouTX57VjmslrA+SWb3bayWSYUkzPQq4AJVHaphp9ZvP41t52OxT+7OH6esjt0El2rD0U4uxaZDB5vM8FLg/hHbvAp4iYhclLyfhs2J8zw2bcIjwF2qWs47EwD/KTZ1QgTsNAJW1X4ROQ54OXA68GMRuVxVvzdGWc8EjgPuFZtyI8NwwrN6+XHV3y8RkU9jBWt6gBsb3NcrSXL6q40DbxnNptj8UYJNvlfmUrFpF3xsh304NqUEwHXJ633YNAxl3gGsxIaqaobIRvntKcBnkrLeKCLbx62dY7fAOQhHWxCRvYAzgCNFRLHqVyoiHxu5KfAhVb2lxm4WYHU25oqISbJx/g9gPXZkbIBao1ySnDq/BX4rIg9jE5h9b6wiA99X1U/UV8OaDFT9/QPgHFV9RETehQ0lNYJQX1rmN2E7/n8F/gO4WEQWAx/GZqTdKiI/BNJVv8knrxE79gEPY1NiL8A6ilrU+m2tlNKOKYALMTnaxUVYJav9VHV/VV2InRWcMmK7W4D3i0gAICIHi82u6mNTpV8GPM5wOGMasC5xFm/FOp4dEKvbu7jqoyXU7vCKZbvAHcBFIjIn2cdMEdmv8WpX6AJeSPZ/WRO/vxX4YFIWkWGRm51Q1QJwBXBqsi7QC2wH+sRmMq1Xa3kZNqT3SxHZu4Gy/hG4OCnrudgZk2MK4ByEo11citVVqOZn7NxZfhs7Ar5f7K2v38COTK8A/pDc3fNR4F3JOsHXgLeJyF3Y8NIAO9MNfF9EHhORh7DhlU/V2O6bwEMicpWqPgb8L6yq3EPAbdjF7mb5JDZ19G0Mh3Ya4Z+xM6dHsOsULx9rY1UdBL4E/B02jPcYNjz3LeBP9RpV1d9hRXZ+JSIz6/zZPwGvEZH7sbPG9dRuF8duhrvN1eFwTAgRSQMlVS2JyCnAvycL5I7dHLcG4XA4Jsr+wDXJbcF5dtQpcOzGuBmEw+FwOGri1iAcDofDURPnIBwOh8NRE+cgHA6Hw1ET5yAcDofDURPnIBwOh8NRk/8PSTHo9sZbEMkAAAAASUVORK5CYII=\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"noOutliers = merged[(merged['funds_raised_percent'] < 200)]\\n\",\n \"noOutliers.dropna(axis = 0, inplace = True)\\n\",\n \"\\n\",\n \"x = noOutliers['kickstarter.com']\\n\",\n \"y = noOutliers['funds_raised_percent']\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"plt.figure()\\n\",\n \"\\n\",\n \"plt.hexbin(x, y, gridsize=10)\\n\",\n \"plt.xlabel('Alexa Site Traffic Ranking')\\n\",\n \"plt.ylabel('Percent Raised of Goal')\\n\",\n \"plt.title('Alexa Site Ranking vs. Funds Raised Percent')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## The Importance of Creators in Project Funding Success\\n\",\n \"\\n\",\n \"In the previous section, there seemed to be a relationship between categories that have dedicated fanbases and an increaesd success rate in reaching funding goals.\\n\",\n \"\\n\",\n \"To further explore that relationship, make some comparisons grouping projects by the number of other projects a certain creator has launched\\n\",\n \"\\n\",\n \"Count the number of projects that a certain creator ID has launched, and then add that as a column to the dataframe\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"creaGroup = pd.DataFrame()\\n\",\n \"creators = pd.DataFrame()\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"# Group all projects by creator id and then merge that into the dataframe\\n\",\n \"creaGroup = merged.groupby('creator').count()\\n\",\n \"grouped = creaGroup.sort_values(by = ['backers_count'])\\n\",\n \"grouped = grouped.reset_index()\\n\",\n \"creators['creator'] = grouped['creator']\\n\",\n \"creators['no_projects'] = grouped['backers_count']\\n\",\n \"creators.head()\\n\",\n \"merged = merged.merge(creators, how = 'left', on = 'creator')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Relationship between number of projects launched and success rate\\n\",\n \"\\n\",\n \"There's a small subset of creators that have launched multiple projects. From the chart below, there's an increased rate of success as the number of projects launched by a particular creator increases. Note that this plot is shown by total number of projects, and does not consider the order in which projects were launched by a particular creator\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'Number of Creator Projects and Success Rate')\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"noProjects = [1, 2, 4, 8, 16, 32, 60, 72]\\n\",\n \"suc = merged[(merged['state'] == ('successful')) | (merged['state'] == ('finished'))]\\n\",\n \"multi = []\\n\",\n \"for i in noProjects:\\n\",\n \" tempProj = merged[(merged['no_projects'] <= (i)) & (merged['no_projects'] >= (i - 1))]\\n\",\n \" tempSucProj = suc[(suc['no_projects'] <= (i)) & (suc['no_projects'] >= (i - 1))]\\n\",\n \" if len(tempProj) != 0:\\n\",\n \" sucRate = len(tempSucProj) / len(tempProj)\\n\",\n \" else:\\n\",\n \" sucRate = 0\\n\",\n \" multi.append(sucRate)\\n\",\n \"\\n\",\n \"multiGraph = pd.DataFrame()\\n\",\n \"multiGraph['no_projects'] = noProjects\\n\",\n \"multiGraph['success_rate'] = multi\\n\",\n \" \\n\",\n \"x = multiGraph['no_projects']\\n\",\n \"y1 = multiGraph['success_rate']\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"plt.plot(x, y1)\\n\",\n \"plt.ylim(bottom=0)\\n\",\n \"plt.xlabel('Number of Projects')\\n\",\n \"plt.ylabel('Relative Frequency')\\n\",\n \"plt.title('Number of Creator Projects and Success Rate')\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# This is where I left off with the memory reduction\\n\",\n \"\\n\",\n \"### Number of projects launched and percent of goal raised\\n\",\n \"\\n\",\n \"There also seems to be an increase in average % of goal raised as the number of projects increases. Above 15, the sample size for each category drastically decreases (ie the number of creators that launched more than 15 projects is very small. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Compare the money raised by succesfull projects against all projects in a certain category\\n\",\n \"projects= pd.DataFrame()\\n\",\n \"proGroup = merged.groupby(['no_projects'])\\n\",\n \"sucProGrp = suc.groupby(['no_projects'])\\n\",\n \"#projects['Avg%'] = proGroup.funds_raised_percent.agg(np.mean)\\n\",\n \"#projects['SucAvg%'] = sucProGrp.funds_raised_percent.agg(np.mean)\\n\",\n \"projects['Median%'] = proGroup.funds_raised_percent.agg(np.median)\\n\",\n \"projects['SucMed%'] = sucProGrp.funds_raised_percent.agg(np.median)\\n\",\n \"#money.reset_index(inplace = True)\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"ax = projects.plot(kind = 'bar', title='Percent of Funds Raised by Number of Creator Projects')\\n\",\n \"ax.set_xlabel(\\\"Total Number of Projects by Creator\\\")\\n\",\n \"ax.set_ylabel(\\\"Percent of Funds Raised\\\")\\n\",\n \"plt.figure(figsize=(600,300))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Number of Projects launched by each creator by category\\n\",\n \"\\n\",\n \"Visual and performing arts projects are most frequently have creators with a high number of projects. The median number of projects for each creator in each category is 1, so creators with multiple project history is a minority segment of the overall sample population\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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vidwrOucx07tyZFVZYgf/+978A3HnnnXV3DQ257777gHA30LlzZzp37rzINttvvz133303AG+++Sbvv//+Iif/XXfdlWuvvbYuA3n55ZcBGDBgADfddFNdhfX06dMB6NSpE7NmzQLg888/Z8GCBey3335ceOGFjB07tqrP3xb4nYJzbUBLakJ6++23c/zxx/P111+z5pprctttt1XcfoUVVmDbbbdl5syZDV6dn3jiiRx//PH07t2bJZZYgiFDhtTdpRTuAn73u99x+umn06dPH8yMXr168fjjj3PMMcfw5ptv0qdPHzp06MCxxx7LySefzMCBA9l9993p0aMHV111FUcddRQLFiwA4KKLLsrwiLQuqnRbpnC0VzWzD5ovSZX169fPmnOSHe+n4Fqi119/nQ022KDWyUitf//+XHbZZfTr16+q9z/00EMMHTqU22+/PeOUtW7lvh+SxphZowe6YvGRhRzjkWoSJamjpBclvSLpVUnnl9nmSElTJY2Lj2Oq2ZdzbvEzdOhQfvOb33DcccfVOiltSpLio+clbWFmLzUx9rfATmb2laQOwEhJ/zSz50u2u8/MTm5ibOdcK3LSSScxatSoeutOO+00hg8fXnXMvffem7333jtlylypJJnCjsBxcX7P2YAINxF9Kr0p3mV8FRc7xEfjTQicc23O9ddfX+skuISSZAq7VxtcUntgDLA2cL2ZvVBms/0kbQ+8CZxRrv5C0kBgIEDPnj2rTY5zzrlGNNok1cwmA12AveKjS1zXKDObb2Z9gVWBLSVtXLLJY0CveNfxNFC2tsjMBptZPzPr17179yS7ds45V4VGMwVJpwF3AyvFx12STmnKTsxsBjAc2K1k/TQz+zYu3gxs3pS4zjnnspWk+OjnwFZmNhtA0iXAc8C1ld4kqTsw18xmSFoa2AW4pGSbHmb2cVzcG3i9iel3zjmXoSSZgoD5Rcvz47rG9ABuj/UK7YD7zexxSRcAo81sKHCqpL2BecB04MimJN45Fw1atBdwunhfZhsvJ619PoUhQ4YwevRorrvuOm666SaWWWYZDj/88JqlB5JlCrcBL0h6OC7/GGh0UBAzGw9sWmb9uUXPzwHOSZZU59ziaN68eXWD2ZXq169f1R3frrrqKg499NAWM8nO8ccfX+skAMkqmq8AjiJcyX8BHGVmi+9g4845YPGdT6F///6cccYZbL/99mywwQa89NJL7Lvvvqyzzjr89re/rdvurrvuYsstt6Rv374cd9xxzJ8fClxuu+021l13XXbYYYd6fTcGDRrEZZddBsDNN9/MFltswSabbMJ+++1XNxnQkUceyamnnsq2227LmmuuyYMPPtjk/1tjklQ032lmY83sGjO72sxelnRn5ilxzrU6b731FieddBKvvvoqXbp04aGHHuLwww/nkksuYfz48fTu3Zvzz19kMIN6CvMp3HDDDRx99NF168eMGcOjjz7KPffcU9fPYcKECdx7770cccQRzJkzp16cwnwKL730EsOGDeNXv/oVs2fPZvDgwXXzKYwfP56f/exnnHrqqay88soMGzaMYcOGMW7cuLr5FCZMmMBRRx1VMc1LLrkkI0aM4Pjjj2efffbh+uuvZ+LEiQwZMoRp06bx+uuvc9999zFq1CjGjRtH+/btufvuu/n4448577zzGDVqFE899RSvvfZa2fj77rsvL730Eq+88gobbLABt9xyS91rH3/8MSNHjuTxxx/n7LPPrpjOaiQpPtqoeCHWEXgrIedcovkUDjjggIoxks6ncMopodFjpfkUhg4dWne1Xe18CnvuuScDBgyomOZCT+revXuz0UYb0aNHj7o4H3zwASNHjmTMmDFsscUWAHzzzTestNJKvPDCC/Tv359C0/qDDjpokc8BMHHiRH77298yY8YMvvrqK3bddde613784x/Trl07NtxwQz799NOK6axGg5mCpHOAXwNLS5rJwsrl74DBmafEOdfqLK7zKRQ+d7t27eodg3bt2jFv3jzMjCOOOGKR0VYfeeSRRtMCoZjokUceYZNNNmHIkCH1hgMp3l+S49JUDRYfmdlFZtYJ+JOZLW9mneJjxVhB7Jxz9fh8CsHOO+/Mgw8+yGeffVa3z8mTJ7PVVlsxfPhwpk2bxty5c3nggQfKvn/WrFn06NGDuXPn1n3u5tJo8ZGZnSNpBWAdoGPR+spz7Dnnmk8LakLq8ynAhhtuyO9//3sGDBjAggUL6NChA9dffz1bb701gwYNYptttqFHjx5sttlmdRXQxS688EK22morVl99dXr37l2XeTWHivMpAMThrE8jDFUxDtgaeM7Mdso/eYvy+RSc8/kUCnw+hfJym08hOg3YAphsZjsS+h5MrSahzjmXFZ9PIR9JWh/NMbM5kpC0lJm9IWm9xt/mnHNBa5xPoaE0N9ZctbVLkilMkdSFMAPbU5K+AD7KN1nOubakNc6n0BrTnIUkFc0/iU8HSRoGdAb+lWuqnHONStLU0i1+0jZTTVKngKTtJB1lZs8QRkhdJdVenXOpdOzYkWnTpuXSTt21XmbGtGnT6NixY+MbN6DROwVJ5wH9gPUIg+N1AO4CflD1Xp1zqay66qpMmTKFqVO9zYerr2PHjqy66qpVvz9JncJPCC2OxgKY2UeSOlW9R+dcah06dGCNNdaodTJcG5Sk+Og7C/eoBiBp2Ua2d84510olyRTul/RnoIukYwlzKd+cb7Kcc87VQpLWR5dJ+iEwk1CvcK6ZPZV7ypxzzjW7iplCHCb7CTPbBfCMwDnn2riKxUdmNh/4WlLGE8A655xriRINcwFMkPQUMLuw0sxOrfQmSR2BEcBScT8Pmtl5JdssBdxBmLRnGnCQmb3XlA/gnHMuO0kyhb/HR1N9C+xkZl9J6gCMlPRPM3u+aJufA1+Y2dqSDgYuAQ6qYl/OOecyUGnmte5AdzO7vWT9xkCjc8DFZqxfxcUO8VHa/XIfYFB8/iBwnSSZd9N0zrmaqFSncC3Qvcz6VYCrkwSX1F7SOOAz4Ckze6FMrA8AzGwe8CWwYpk4AyWNljTae3A651x+KmUKveNYR/WY2RNAnyTBzWy+mfUlTNCzZbzLKFZuNK9F7hLMbLCZ9TOzfoUJr51zzmWvUqbQocrXFmFmM4DhwG4lL00BVgOQtARhBNbpTYntnHMuO5Uyhbck7VG6UtLuwKTGAkvqHudhQNLSwC7AGyWbDQWOiM/3B/7j9QnOOVc7lVofnQE8LulAYExc1w/YBvhRgtg9gNtjB7h2wP1m9rikC4DRZjYUuAW4U9LbhDuEg6v8HM455zLQYKZgZm9K6g38FCjUBTwDHGdmcxoLbGbjCaOrlq4/t+j5HOCApibaOedcPir2UzCzbwlzKDjnnFsMJJp5zTnn3OLBMwXnnHN1KmYKsfPZXc2VGOecc7WVZJTU7pKWbKb0OOecq6EkA+K9B4ySNJT6o6RekVeinHPO1UaSTOGj+GgHdMo3Oc4552opyXSc5wNIWtbMZje2vXPOudar0dZHkraR9BrwelzeRNINuafMOedcs0vSJPUqYFfCzGiY2SvA9nkmyjnnXG0k6qdgZh+UrJqfQ1qcc87VWJKK5g8kbQtYbJp6KrEoyTmXrV5nl5/59r2L92zmlLjFVZI7heOBkwizpH0I9I3Lzjnn2pgkrY8+B37WDGlxzjlXY0laH60p6TFJUyV9JulRSWs2R+Kcc841ryTFR/cA9xMmzVkZeAC4N89EOeecq40kmYLM7E4zmxcfdwE+ZaZzzrVBSVofDZN0NvBXQmZwEPB3SV0BzGx6julzzjnXjJJkCgfFv8eVrD+akEmUrV+QtBpwB/B9YAEw2MyuLtmmP/Ao8G5c9TczuyBRyp1zzmUuSeujNaqMPQ/4hZmNldQJGCPpKTN7rWS7/5rZj6rch3POuQzlNvOamX1sZmPj81mEDm+r5LU/55xz6TXLdJySegGbAi+UeXkbSa9I+qekjZojPc4558pLUqeQiqTlgIeA081sZsnLY4HVzewrSXsAjwDrlIkxEBgI0LNnz5xT7Jxzi68kndd+IGnZ+PxQSVdIWj1JcEkdCBnC3Wb2t9LXzWymmX0Vn/8D6CCpW5ntBptZPzPr17179yS7ds45V4UkxUc3Al9L2gQ4C5hMaFVUkSQBtwCvNzR1p6Tvx+2QtGVMz7SEaXfOOZexJMVH88zMJO0DXG1mt0g6IsH7fgAcBkyQNC6u+zXQE8DMbgL2B06QNA/4BjjYzLxjnHPO1UiSTGGWpHOAQ4HtJbUHOjT2JjMbCaiRba4DrkuSUOecc/lLUnx0EPAt8HMz+4TQrPRPuabKOedcTSS5UzjDzP6vsGBm73vTUeeca5uS3Cn8sMy63bNOiHPOudpr8E5B0gnAicBaksYXvdQJeDbvhDnnnGt+lYqP7gH+CVwEnF20fpaPjOqcc21Tg8VHZvalmb0HXA1MN7PJZjYZmCtpq+ZKoHPOueaTtPPaV0XLs+M655xzbUzSmdfqOpSZ2QKaYcwk55xzzS9JpjBJ0qmSOsTHacCkvBPmnHOu+SXJFI4HtgU+BKYAWxFHLHXOOde2JJl57TPg4GZIi3POuRpLMnT2upL+LWliXO4j6bf5J80551xzS1J8dDNwDjAXwMzG43cOzjnXJiXJFJYxsxdL1s3LIzHOOedqK0mm8LmktQADkLQ/8HGuqXLOOVcTSfobnAQMBtaX9CHwLmFuBeecc21MktZHk4Bd4jzN7cxsVv7Jcs45VwuVRkk91MzuknRmyXoIRUnTgaFm9kW+SXTOOddcKtUpLBv/dirzWB7YnDCKqnPOuTaiwTsFM/tz/Ht+Q9tIuqDCa6sBdwDfBxYAg83s6pJtRBiFdQ/ga+BIMxvblA/gnHMuO0k6r60q6WFJn0n6VNJDklYFMLNzK7x1HvALM9sA2Bo4SdKGJdvsDqwTHwPx0Vedc66mkjRJvQ0YCqwMrAI8FtdVZGYfF676Y+X06/H9xfYB7rDgeaCLpB5NSL9zzrkMJckUupvZbWY2Lz6GAN2bshNJvYBNgRdKXloF+KBoeQqLZhzOOeeaSZJ+Cp9LOhS4Ny4fAkxLugNJywEPAaeb2czSl8u8xUpXSBpIHJm1Z8+eZffT6+y/L7LuvYv3TJrMphvUucy6L/Pbn3OLmXK/acj5d+0S3SkcDRwIfELoybx/XNcoSR0IGcLdZva3MptMAVYrWl4V+Kh0IzMbbGb9zKxf9+5NuklxzjnXBBXvFCS1B/Yzs72bGji2LLoFeN3Mrmhgs6HAyZL+Spin4Usz8yE0nHOuRipmCmY2X9I+wJVVxP4BcBgwQdK4uO7XQM8Y+ybgH4TmqG8TmqQeVcV+nHPOZSRJncIoSdcB9wGzCysb609gZiMpX2dQvI0RxlZyzjnXAiTJFLaNf4s7qhmwU/bJcc45V0tJBsTbsTkSsrjxlhXOuZaowdZHkraS9IqkryQ9J2mD5kyYc8655lepSer1wC+BFYErgKuaJUXOOedqplKm0M7MnjKzb83sAZrYi9k551zrU6lOoYukfRtabqAzmnPOuVasUqbwDLBXA8sGeKbgnHNtTKX5FLwjmXPOLWaSjH3knHNuMZGk85pzzrUpzT6qcitSqZ/CAfHvGs2XHOecc7VUqfjonPj3oeZIiHPOudqrVHw0TdIwYA1JQ0tfrGY4beeccy1bpUxhT2Az4E7g8uZJjmvJfLwm59q+Sk1SvwOel7StmU2V1Cmstq+aL3luceCZjXMtR5LWR9+T9CTQlTCh2lTgCDObmG/SXLW8ZYVzrlpJ+ikMBs40s9XNrCfwi7jOOedcG5MkU1jWzIYVFsxsOLBsbilyzjlXM0mKjyZJ+h2hwhngUODd/JLknHOuVpLcKRxNGDb7b/HRDWh0XCRJt0r6TFLZugdJ/SV9KWlcfJzblIQ755zLXpLpOL8ATq0i9hDgOuCOCtv818x+VEVs55xzOchtQDwzGwFMzyu+c8657NV6lNRt4jzQ/5S0UY3T4pxzi72KmYKk9pLOyGnfY4HVzWwT4FrgkQrpGChptKTRU6dOzSk5zjnnKmYKZjYf2CePHZvZzELvaDP7B9BBUrcGth1sZv3MrF/37j5VtHPO5SVJk9SKkDJkAAAgAElEQVRRkq4D7gNmF1aa2dg0O5b0feBTMzNJWxIyqGlpYjrnXC21hSFbkmQK28a/FxStM2CnSm+SdC/QH+gmaQpwHtABwMxuAvYHTpA0D/gGONjMrEmpd87VXFs4EbqFkjRJ3bGawGZ2SCOvX0dosuqcc66FaLT1kaTvSbpF0j/j8oaSfp5/0pxzzjW3JE1ShwBPACvH5TeB0/NKkHPOudpJkil0M7P7gQUAZjYPmJ9rqpxzztVEkkxhtqQVCZXLSNoa+DLXVDnnnKuJJK2PzgSGAmtJGkUYHG//XFPlnHOuJpK0PhoraQdgPUDA/8xsbu4pc845BzRvs99GMwVJHYETge0IRUj/lXSTmc3JPDXOOedqKknx0R3ALML4RACHECbcOSCvRDmXFZ+v2rmmSZIprBcHrSsYJumVvBLknHOudpK0Pno5tjgCQNJWwKj8kuScc65WGrxTkDSBUIfQAThc0vvxpZ7Aa82QNudchrwozSVRqfjIp8l0zrnFTIOZgplNLjyXtAKwWsn2kxd5k3OLCR8Z1LVVSZqkXggcCbxD7NVMgqGznXPOtT5JWh8dCKxlZt/lnRjnnHO1laT10USgS94Jcc45V3tJ7hQuIjRLnQh8W1hpZnvnlirnnHM1kSRTuB24BJhAHD7bOedc25QkU/jczK7JPSXOOedqLkmdwhhJF0naRtJmhUdjb5J0q6TPYrFTudcl6RpJb0sanySmc865fCW5U9g0/t26aF2SJqlDgOsIA+qVszuwTnxsBdwY/zrnnKuRJPMp7FhNYDMbIalXhU32Ae4wMwOel9RFUg8z+7ia/TnnnEsvSee1c8utN7MLUu57FeCDouUpcZ1nCs45VyNJio9mFz3vSBgT6fUM9q0y66zMOiQNBAYC9OzZM/keBnVuYH0LnmI6rzTneSzKxc4rbhax/VjkH7eh2K0tbp6xW2jcJMVHlxcvS7qMMGdzWlMI4ykVrAp81EAaBgODAfr161c243DOOZdektZHpZYB1sxg30MJQ3IrztfwpdcnOOdcbSWpUyjMqwDQHugONFqfIOleoD/QTdIU4DzC3AyY2U3AP4A9gLeBr4Gjmp5855xzWUpSp1A8r8I84FMzm9fYm8zskEZeN+CkBPt3zjnXTJLUKfi8Cc45t5ioNB3nLBYWGxVaCll8z5JmluQuwznnXCtSaea1TsXLkjoBJwLHAQ/nnC7nnHM10Gjro9jTeBDwCtAJ2MLMfpF3wpxzzjW/SsVH3YBfAAcBtwKbmlkL7vXlnHMurUr1ApOBqcBthCajP5cWdkI2syvyTZpzzrnmVilT+BMLK5o7VdjOOedcG1GponlQM6bDOedcC1DNMBfOOefaKM8UnHPO1fFMwTnnXJ2qMgWfT9k559qmau8UTsg0Fc4551qEqjIFMzs264Q455yrvSTzKZQrKvoSmJxkCG3nnHOtR5KRTm8ANgPGE0ZL3Tg+X1HS8Wb2ZI7pc84514ySFB+9Rxj3qJ+ZbQ5sCkwEdgEuzTFtzjnnmlmSTGF9M3u1sGBmrxEyiUn5Jcs551wtJCk++p+kG4G/xuWDgDclLQXMzS1lzjnnml2SO4UjgbeB04EzgElx3Vxgx0pvlLSbpP9JelvS2WVeP1LSVEnj4uOYpn4A55xz2Ulyp7AbcJ2ZXV7mta8aepOk9sD1wA+BKcBLkobG4qdi95nZyUkT7JxzLj9J7hT2JhQX3SlpT0lJ52beEnjbzCaZ2XeE4qd9qk2oc865/DWaKZjZUcDawAPAT4F3JP0lQexVgA+KlqfEdaX2kzRe0oOSVisXSNJASaMljZ46dWqCXTvnnKtGoh7NZjYX+Cfhan8Mya74VWadlSw/BvQysz7A08DtDex/cGwS26979+5Jkuycc64KjWYKsbJ4CKGyeX/gL0CPBLGnAMVX/qsCHxVvYGbTzOzbuHgzsHmCuM4553KSpH7gSMIdwnFFJ/AkXgLWkbQG8CFwMKH4qY6kHmb2cVzcG3i9CfGdc85lrNFMwcwOLl6W9APgp2Z2UiPvmyfpZOAJoD1wq5m9KukCYLSZDQVOlbQ3MA+YTsiAnHPO1UiilkSS+hKu8g8E3gX+luR9ZvYP4B8l684ten4OcE7SxDrnnMtXg5mCpHUJRT6HANOA+wCZWcUOa84551qvSncKbwD/BfYys7cBJJ3RLKlyzjlXE5VaH+0HfAIMk3SzpJ0p38zUOedcG9FgpmBmD5vZQcD6wHDCuEffk3SjpAHNlD7nnHPNKEmP5tlmdreZ/YjQ12AcsMjgds4551q/Js3RbGbTzezPZrZTXglyzjlXO03KFJxzzrVtnik455yr45mCc865Op4pOOecq+OZgnPOuTqeKTjnnKvjmYJzzrk6nik455yr45mCc865Op4pOOecq+OZgnPOuTqeKTjnnKvjmYJzzrk6uWYKknaT9D9Jb0taZLhtSUtJui++/oKkXnmmxznnXGW5ZQqS2gPXA7sDGwKHSNqwZLOfA1+Y2drAlcAleaXHOedc4/K8U9gSeNvMJpnZd8BfgX1KttkHuD0+fxDYWZJP+emcczUiM8snsLQ/sJuZHROXDwO2MrOTi7aZGLeZEpffidt8XhJrIDAwLq4H/C9hMroBnze6VXXyit3a4uYZu7XFzTN2a4ubZ+zWFjfP2E2Ju7qZdW9soyXSpaeiclf8pTlQkm0ws8HA4CYnQBptZv2a+r5axm5tcfOM3dri5hm7tcXNM3Zri5tn7Dzi5ll8NAVYrWh5VeCjhraRtATQGZieY5qcc85VkGem8BKwjqQ1JC0JHAwMLdlmKHBEfL4/8B/LqzzLOedco3IrPjKzeZJOBp4A2gO3mtmrki4ARpvZUOAW4E5JbxPuEA7OOBlNLnJqAbFbW9w8Y7e2uHnGbm1x84zd2uLmGTvzuLlVNDvnnGt9vEezc865Op4pOOecq+OZgnPOuTqeKVRB0gqS+tQ6HS4ZSZ0lXSlpdHxcLqlzrdNVC7E14BWS/iZpaOFR63Q1RtLGtU5DU8RhfjKPKelPWcct1eYyBUn/TrKuirjDJS0vqSvwCnCbpCvSxo2xL42xO0j6t6TPJR2aQdx1Y7yJcbmPpN9mEHcpST+V9GtJ5xYeGaX3ZklPSvpP4ZE2LnArMBM4MD5mArelTGvXSo+0Cc7rOwE8ArwHXAtcXvRITdJTkroULa8g6YksYgM3SXpR0onF+2jB3pb0pzLjvVXNzOYDm+c9FFCePZqblaSOwDJAN0krsLC39PLAyhnsorOZzZR0DHCbmZ0naXwGcQEGmNlZkn5C6NB3ADAMuCtl3JuBXwF/BjCz8ZLuAX6fMu6jwJfAGODblLGKPQDcREj3/AzjrmVm+xUtny9pXMqYYwi97wX0BL6Iz7sA7wNrpIyf13dijpldkzJGQ7qZ2YzCgpl9IWmlLAKb2XaS1gGOBkZLepHwO3wqTVxJs1h0FIUvgdHAL8xsUpWh+xCa2P9FUjvChclfzWxm1YkNXgYelfQAMLuw0sz+ljJunTaTKQDHAacTMoAxLMwUZhJGa01rCUk9CFeav8kgXrEO8e8ewL1mNj2ji4FlzOzFkljzMoi7qpntlkGcUvPM7MYc4n4jaTszGwkg6QfAN2kCmtkaMdZNwFAz+0dc3h3YJWV6Ib/vxNWSzgOepChDN7OxGcReIKmnmb0PIGl1ygxbUy0zeyve6Y4GrgE2jVfNv05xUryCMNLCPYRzxsHA9wnjq90K9K8yrbMIFzc3S9oeuBe4UtKDwIVm9naV6e0KTAN2Kt4dkFmmgJm1mQehk9zvcop9ADAeuCEurwk8lFHsi4E3CFcBHYDuwAsZxP0nsBYwNi7vD/wzg7iDgd45HONBwIlAj/jl7wp0zSBuX0KR33vx8TKwSUZpHlNm3egW/J24iHDn8QzhzmMYYSSBLI7FboS7pDvjYzKwa0ax+xCG13+TcJG3WVy/MjA5RdxFjinwfPz7Soq47YG9gYfj//BM4HvxN/hmFsckr0eb67wm6Tkz26bW6WgKSUsRir5mmtl8ScsCy5nZpynjrkk4gW9LKN54FzjUzN5LGfc1YO0Y71vCFZaZWarKd0nvllltZrZmmrhF8ZePAdPewhfHfAL4L6FYx4BDge3NbNcMYq9A/e9EJzP7JGXMN4A+Foazz5ykbsDWhO/Ec1Yy4nGKuCOAvwAPmNk3Ja8dZmZ3Vhn3OUJm82BctT9wppltLWmcmfWtMu4kQoZ7i5k9W/LaNWZ2apVx1wVuBL5nZhvHBi97m1naIuGF+2iDmcL5hCv6v1mGH07S7cBpFstM4w/2cjM7OoPYY81ss8bWpYi/LNDOwi1tFvFWL7fezCZnET9rkr4H/BFY2cx2j5V/25jZLRnE7gqcB2wfV40AzjezVAM7SlqGcHXZ08wGxvL09czs8ZRx7wNOMbPP0sQpibm+mb0hqez31bIpmspFvHC6GtiGkKk/D5wBfAhsbrHIsYq4y5nZV5kldGHcZ4j1hGa2aVw30cwya53VluoUCs4ElgXmSZrDwqvY5VPG7WOLVqJtmiagpO8DqwBLx1jFlePLpIkd4/8RuLQkI/uFmaVtgVQuc0md4UjqAJzAwhPscMKXf27K0EMIrY0KdUFvAvcRxt5KJZ78T4t3IQsyPBHcRqgb2zYuTyFUxKfKFAhFGG9Ieon6dQp7p4h5JmG+k3KtmIz65d9VifVAg4DVCeetwu861V2khYrkvRp4uaoMIfpjmTqgLwlFi4+miJtXPWGdNpcpmFmnePW2DtAxw9DtJK1gZl9A3RVi2uO3K3AkYVjxy6lfOf7rlLEBdjezujgxI9sDSJspjCUMeV7c4uZjSZ8Bx5rZmCrj3kgoP78hLh8W1x2TLrl0M7P7JZ0DdYM1ZtK6SVJv4A5C/QeSPgeOMLOJKUOvZWYHSToEwMy+yagp4nkZxKjHzAbGvztmHbvILYQr+DFk2DJNUnfgWKAXRb/nDEoAOgLrEzJygP2AV4GfS9rRzE6vMu7nktYiVuArTGb2ccq01tPmMoXYZPQ0wol2HKF881lg55ShLweeja0HIFQ8/yFlzC5mtqOk32ZZJlikvaSlzOxbAElLA0tlEPdfwMNm9kSMO4BQyXg/4YS+VZVxtzCzTYqW/yPplVQpDWZLWpGFP6StCVdtWfgzoQx6WIzdn4X1OGl8F/9fhTSvRTbNf0cD35jZglg+vT6hQUJqkg4A/mVms2Iroc0ILW1eziD8l2aWSTpLPEqoE3qabJtBrw3sZGbzACTdSGjx9UNgQoq4JxG+X+tL+pBQr/ezlGmtr9Y13Vk/CAe8IzAuLq8P3JdR7A2Bk4FTgA0ziFdI49icjsVZhFvgnxPad48Ezsog7iKtawrrCp+pyrhjCVfIheU1szg2hJPTKEJGMIpQfNQno2O8SAuVcuuqiPtDQguhqcDdhFZT/TOIO4ZQNLkK8AGhdczdGR2L8fHvdoQT7T6kbDEV/3ebEVpj/YlQ9l9Yt1kGaa76+9pI3P8R+jYVljsDb8TnL6eIu0b8W2h4ULcuq0ebu1MgdM6ZI4l4lfyGpPWqDSZpeQud1roCnxDaMxde62rpKhRfl/Qe0L2kI1wmrXnM7FJJEwh3SSJctWXRw3S6pP8D/hqXDwK+UOjavyBF3F8Bw2LLDRHKj49KlVJCRaekHQjzewv4n6WvpyiYJOl3hCaYEFoflWtF1SRm9pSksSxsyXOaZdOSR2b2taSfA9fG70jajnwFhSvtPYEbzexRSYNSxiytpyieejKL+orHJe1hsZ9Jhi4FxkkaTvj/bU+oZ1iWcFdSrYcImeHsonUPApuniFlPW2x99DDhRHI64QvzBdDBzPaoMt7jZvaj2Fyy0IO17q+lrOiKlc1PENo012MttzVPN0LZ9HaE4zASOJ9wJd7Tqu+YU2ieWzh5v2Gx6KvKWPtWet0y6AUaK+/PZ+GxGAEMslj3lDJ2HxYt606VZkkvE/qCXAn83MLEVxPMrHeauDH244RWO7sQTlLfAC9a/SLBFkWhR/OyhKK5uWTXMAWFzq5bxpgvmlnpdMRNibU+sBEhs/lV0UvLA78ys43SpLXevtpaplAsXh12JpRz5tIuuyWLJ8VLgJUIX8zMvvBZkrSTmf2noZN4tSdCSYXxjVYilPEXxlHaERhuZhUzjSbuK9PWR5JuJXTYepWFd19mKStA42/iF8AoM7skNsk83apsN18SexlC3dIEC72PexA6OT6ZQewVWXghYoQLkQvMbFra2HmRtDcLW9I9Y2aPpYi1D/BjwsVj8QCGswjDZzxb9o3V7KstZwpZy+PKLcZdh9DTdEOKWkxlcBfyNrCXmb2eLoV18a4ys9MlPUaZ4QusymaNks63MJZUuUHqsjgRPk5oFfVxXO4BXJ9FplDa+gjIpPWRpNfMLLPB1JqDpJ7l1lsc9iJl7KcId2GFsZ9+RqhjqWpIkbz7Vki6GNiCUB8EcAih3u2clHG3MbPn0sRodB+eKSST15VbjD2ScBV0JaHN9FGE/02q5oOSRpnZD9Kmryje5mY2Jl5tLsLMnslqX1kq7dyjMEDZeMugw4+kZ4HfWP3WR380s1StjyTdQugc+VraNMZ4uWToJfuYwMKi1Y6EQQH/l0XRhqQxZrZ5ybrRZtavofc0Em+whU6Bw8q8bGaWqq4i1hH2NbMFcbk9oYI5ba//joSGIxtR/wIy9XmooC1WNOdl6xyv3JY2s39LUqxHGCTpv6RvUz5aoQfrI9TvqFTV3Y3F/gfFJ/9Ypr6amaUeMTbWJ+zHondjF6QMPVxhOIp7CSetgwlDEGRh2UKGAGBmw2NlYlq3A89J+oRshhIpVIRflkHayiqtl4hX4cdlFH6YpIMJzZ4hDEfx92qDWfP0regCFBqiZDV/x52EMbF2BS4g3DFlUhJQ4HcKCWV95VYSexTw/witCP5DqKy72MyqbjUV4+ZVHDOcULa5BKEvyFRCmemZKeP+i4VDcte1GTez1OP9x/qK/xcXR5jZw2ljxrgPE5rSFrc+6mdmP04Z921CT+EJFLXoaqmNDxqijIZrKaoQLhyLdiwcOjpVPZmkbVn0QuSOauPFmIcQmtEOY2Hro3PM7K8V39h43JfNbFNJ482sj8IoAE+kvbOptw/PFJJRGP72MUKz1MwGgYuxtyDk9l2ACwktCv5kZs+njZ2Hoi/mMYS7hPMKX9KUcTMdwyVPku40s8MknUk4oRRaHz1DGPsoVesjSf/J9Ie+sGinrIy+x8UXBe0ILZC6WgaDA+ZF0p2EkYTHsfBCxDKqeO9BqFcQob9GqsEMY8wXzWxLhQECTyScj15MW/9YzIuPkruVMOxCvSu3tGJZ44Fm9ivgKzJol18UO6/yx7zmlnhWUm8zS9Pjs46kkRYmZymdSCWLVlibKwwMeAShNVOhqXIhflpvKEyI9BgZFP0BP8ogTY3pxMJjMI+Q9oeyCh6LKusNX2NmI1KG7UfoiJrJ1XGZiusp8e/KklZOW4ENDI7H4XeEVkjLAalnPSzmmUJy75tZ5nPZWhgWefNYn5D1bVte5Y8XEPpWjDKzl2KzxreqDVZ0FbsEcJRC57XUd2Nmtl3826natFVwE2G4jzUJQ0cUFDKHtFduSxOOwYCidVVPptJMxU7/IIzZ1YuF55azCQ00UlH54WueI33ntYmESXWyGj+oUlFn6s52ZvaX+PQZ0n/HyvLio4Qk3UAo3snqyq049uWEK6BMp9hrjvLHLKiBobgL0p7QJF1NaMudeVM+STea2QlZx82LwrhP1wIbAEsSJoOZnfKuqRD7f8AvCSfaTOtB4oXDFoQJcPrGzlznm9lBVcYrtMLqRJiE6UWyGzU2N8pxGPgCv1NILtMrtxJ5TbFXGMphhqSNCeWPvVLGRBlP9FE4aSgM+jbFzL6NTTv7EPoApDUW+F1M98OEsbBGN/KeRPLKEHIs+ruO0PrqAULRyeGEwduyMNVSdNBqRKbD15BjKyygVQ4DX8dyGAzKH00e5Op2woipheUVgFsziHtMjLU9MAn4DDgug7jPELrvv1y0bmIGcccRLlTWBt4h9Nv4R4bHuSthmOR/A2/V+v/eSFofIDQ6eIdQb/EkcHUGcQsDF44vWvdsRmnemTA72iHAvoVHRrEfJtypDyJ0Yns0i+8GoUVTu/h8XUKrug4ZxP1L/F3vFB+3AX/JIO5L8W/xby/TQf38TiEhSasSbrt/wMJu9qeZ2ZSKb0wm0wl8SlqBFCqur49/s2hDn9dEHwsszHWwL3CVmV2rMFZPVtYmjJrbC8i8aXHG1jazAyTtY2a3x0rnLAYz/FrSkoTB2i4llKVn8Z2A8F1bnzAnRl0HTzK4mzazn8Sng2KHs86EOp20RgD/L1be/ptQP3QQ6Yejbo3DwANefNQUtxFGSD0gLh8a1/0wg9hZT+BTqFhdj1AOW6gg34vwI0grr4k+5sb23YezcDasDmmDSrqEcNX6DuFW+8LiTLiFyqXoj9CCrh1hCPgzCJMl7ZdBXIBNLIOB9UqV9kC3bHvO5zVq7HxJa5nZOwCxMUYW8zWcSfg9rxX7N3UndOTLTpa3HW35QZlbtHLrqox9OKFV0IWElj1vAIdlEPdJ4pjrcbkTYXDAtHHXJAz/+zWho91IYPUM4m4IXAMcEpfXAM7OIO7xhNnXav49akKacyn6yznNN5PBPCMNxL6bMAJv1nFfJszR8DywUVw3IYO4OwPvE+oSniHMh7FjRmleglDXtDEZFHWVPrz1UUKSniZU8twbVx0CHGVmaWd0K8TfkFD2KODflkHPaUlvEK7eCjOvLUWYAGb9KuOV9lhemqKepWZ2RYrkuiKSfsGi/R5mAGPMrOorWS061zGQfvDFGPt1Qkewd8m+g+d/CHe9L1K/hV6qVkLKd9TYzIaBL4mbeQ/sevE9U0hGYQTI6whXFUaY4vNUy2AEyLxI+g2hg9nDhDT/hNDy5qIq4xXGYioUSz1K+MLvRRg6ItVcyspptNjWKNYh9CM0gYYwcc1LxHl/zezSKuO+QZm5ji2DIagbalps2TRJbVWDMEJuw2fk1gO7bh+eKSQj6XbCFURxuf9lluHohHmIPSyLx/xJXXEr6UlgPzObFZc7EU5Uu6WMm8tosa2RwgB++1mcn0HScoSxsX5CuFuoanBGSS+YWbVzaLcZynnU2LxO3vFuLLMe2OV4RXNyfaxoPBszm56mhVBzsdCtPm3X+lI9geJJi74jm0rQvEaLBUDSBhbnlpC0tbXQsaWi0mM8l1Bv842kNMUQwyT9idAiqLizVtbfkUyVGaoEQqub0cAvzGxSE0PmPWpspsNnFMm6B/YiPFNILusWQq3ZncCLCiOEFoqlbs8g7pzY0uQtSScTKrFXyiBuwWWSOhNabxxDaJfeUt0DPC/p0bi8F3CvwrDcaeqbCncJWc91nLcrgI8Ix0WEDnjfB/5HGJesf1OCWZlh4DOW6cm7pAf2a5Jy64HtxUcJSTocOIdwC2+Esvo/mNmdFd/YRmVZLKWFI46eBRSGE7mQ0Bb90mqv6CX1Aqab2cyidacCfwJ+amaZDdaWB0mbUzQPtqXshR0z3P3N7P5GN25hyhV7SXrezLaW9Io1cR5oNTxqbCaV47EvRWbDZ8Q6FRGm1z2r+CXgkiyLBD1TaII8Wgg5kPQasDvhCr4/JaOMmtn0Mm9LEncMsJOZfRmXTyV0TDqGMB1nS786zpykEWa2feNbtiySniPUNT0YV+0PnBkzhXFm1reJ8fIebyuXinGVmZ9CGQxbXy+eZwqu1uLJ+gRC/4cPWTjSaOGqrarWR8U/Fkl/BDYlVN5+rTLTOy4OJP0O+IbQia+4aWdVGW9ziU1Frya0/oMwQuoZhO/L5mY2MkXs7xOGbTHCMBKp5z3ImqQTCPMnrEnohFnQidCc9tDM9uWZgmsplPGIo7HOYwZhuOXNCJ2TPpG0AXDXYpopvFtmddUZb2unMCT3uYQZDwXsAFxgZremjLsvoahnpRg31RwesS5sBUKT7bOLXpqVdYbumYJrs+IYPwcSWvFMInQ+/IzQ1v8IM3uqdqlzTZHX2GMKw31vW+inEccVetbST4X7NrBXobVba7K4tp5xiwEz+w64q7AsqR/QmzBCaksf+ygXscHEIrLsEZuTvMYemwLMKlqeBXyQMibAp60xQwC/U3BusSLp2qLFjoQxesaaWbaDqmWsXGVyNRXMRe8tDNnSl3Ch8CjhDmQfwpzHx1cZd9/4dAdCk9RHyHhSrrz5nYJzixEzO6V4OZZVt4Zm1Z9LOpT6Y4+lGZqjMJLwO9SvuH20zLZNsVfR86/JZ1KuXPmdgnOLMYUZwsab2Qa1TkslrXHssdbK7xTcYiFOorIa9Qcna9FDO+ShZKyf9oS5mltDZ7bVSjt+xRFfU2UKsZNZubGPUvVhiWOlnVaou4rfv8tb+lhp4HcKbjEg6ULgSEIxQeELb4tp57XiTlXzgMlpW/A0hwY6bS2yroq4xc2SOxImHJpnZmc18JakcV82s00bW9cS+Z2CWxwcCKwVWyMt1szsGUnfIwx9DvBWLdPTGEnbANsC3Uvm81iecKeTSmEMpCKjJGUxHlKrHSutVSTSuZQmEsZT+qzWCak1SQcSxn4aTuhQda2kX5nZgxXfWDtLAssRzlWditbPJINpKOPJuqAdYaDA76eNC1wOPCup3lhpGcTNnRcfuTYv9k94lJA55DKyZGuhMHn8D83ss7jcHXi6qQPKNTdJq5vZ5Dh3hxXmmcgg7rssLFKcR5g284I0w2YUxW6VY6X5nYJbHNxOGHJgArCgxmmptXaFDCGaRrhCbuk6SXoZ6Aog6XNCr/SJKeNuSBhTaDtC5vBfwhwNWegKzDaz2yR1l7SGmZUbZqRF8TsF1+ZJesbMyo5aubiJE+z0YWF7/4MJTVJTVazmTdKzwG/MbFhc7g/80cy2TRn3fkJR1N1x1SHACtOrri0AAAUFSURBVGZ2QMPvShT3PEJR1Hpmtq6klQmzE/4gTdzm4JmCa/MkXUEoNhpKK5ptLC+x1+0PCMUaI8zskRonqVHl5kyoZh6FZow7jjAq79hCi6Osh7jOixcfucVBoRng1kXrWsNsY5mRNNLMtiua1rIwZ8WxkhYA04E/mdkNNUtkZZPisN+F3teHAlkUxbxcPDWrpK2AURnE/c7MTJLFuMtmELNZ+J2Ccy6z0UHzEjt/nc/CmehGAIOsaN70KuO+DqzHwk5wPYHXCXVPVc/AJumXwDqEAfsuAo4G7jGzayu+sQXwOwXXZpW0a1+EmV3RXGlp6cxsWiynb5Hiyf/UHELvlkNMgO6EWeJmEjKdc4FdctpXpvxOwbVZsbKvQWZ2fnOlxaUjaV3gl0Av6g9V0iKLAJtj2sy8eKbgnGvxYv+Km4AxwPzC+jI9kmuqOafNzItnCq7NknSWmV0a5xAoN+hZHsURLgetZU7t5pw2My9ep+DaskIP0qw6I7lmVjQMxWOSTiLMR1DcrLhFnWjN7EvgS0J/h1bJMwXXlh0EPA50MbOra50YV5Ux1G9C+4uS19ds3uS0fV585NosSa8BuxM6rfVn4YkFaHlXma5hkpZm0eEobjKzb2qasDbIMwXXZkk6FTiBcDX5IfUzBTMzv8psJRoYjqKLmR1Yu1S1TZ4puDZP0o1mdkKt0+Gql9dwFG5RrWF0ROdS8QyhTXhZUt0wJRkOR+FK+J2Cc67Fy2s4CrcozxSccy2epNUrvW5mk5srLW2dZwrOOefqeJ2Cc865Op4pOOecq+OZglssSfq+pL9KekfSa5L+EUfiLLdtF0knNncanasFzxTcYkeSgIeB4Wa2lpltCPwa+F4Db+lC6E2bd7p82BlXc54puMXRjsBcM7upsMLMxhHawv9b0lhJEyTtE1++GFhL0rg48T2SfiXpJUnjJdXNyyDpd5LekPSUpHvjDFxI6ivp+bj9w3EmMSQNl/RHSc8Av5H0rqQO8bXlJb1XWHauOfiViVscbUwYaK3UHOAnZjZTUjfgeUlDCUMgb2xmfQEkDSBMtbglYeiMoZK2B74G9iPMCb0EMLZoP3cAp5jZM5IuAM4DTo+vdTGzHWLsXsCewCPAwcBDZjY3w8/uXEWeKTi3kIA/xhP8AmAVyhcpDYiPl+PycoRMohPwaGGQNkmPxb+dCSf+Z+L2twMPFMW7r+j5X4CzCJnCUcCx6T+Wc8l5puAWR68C+5dZ/zPC3Lqbm9lcSe8BHctsJ+AiM/tzvZXSGVWmZ3bhiZmNktRL0g5AezObWGVM56ridQpucfQfYClJdVfhkrYAVgc+ixnCjnEZYBbhLqDgCeBoScvF964i6f+3d/e4FMRRGMaftxNR2wN7sAEiIlG60dmBRKmwAKERjZCIJagJURAhUaiEBYhIbn8UM5nuksu9Gs+vna//NPPmzJnMmQYugcUkE+22BegGr7wnmWuP7wHnDHYMnAKHv7xPaWhWCvp3qqqSLAM7STZpegkvwBawm+QWuAee2v3fklwleQTOqmojyQxw3XzIRB9YraqbtgfxALzSTHz7aC+7BuwnmQSeaV4NDXICbNMEg/Sn/M2FNEJJpqqq3z78L4D1qrob8hwrwFJV9caySOkLVgrSaB0kmaXpRRz9IBD2aKbFzY9jcdJ3rBQkSR0bzZKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSep8AquTPjbfA0qRAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"create = pd.DataFrame()\\n\",\n \"createGrp = merged.groupby(['fullcats'])\\n\",\n \"create['no_projects'] = createGrp.no_projects.agg(np.mean)\\n\",\n \"create['no_projects_median'] = createGrp.no_projects.agg(np.median)\\n\",\n \"create.dropna(axis = 0, inplace = True)\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"ax = create.plot(kind = 'bar', title='Number of Projects per Creator by Category')\\n\",\n \"ax.set_xlabel(\\\"Category\\\")\\n\",\n \"ax.set_ylabel(\\\"Avg. Number of Projects per Creator\\\")\\n\",\n \"plt.figure(figsize=(600,300))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### The total number of projects vs. creators with multiple projects\\n\",\n \"\\n\",\n \"The next plot shows the number of projects in each category and compares that with the number of projects with creators who have launhed multiple proejcts.\\n\",\n \"\\n\",\n \"It's easy to see in this plot that most creators in this dataset have only launched one project\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"createNumber = pd.DataFrame()\\n\",\n \"multi = merged[merged['no_projects'] > 1]\\n\",\n \"createGrp = merged.groupby(['fullcats'])\\n\",\n \"multiGrp = multi.groupby(['fullcats'])\\n\",\n \"createNumber['all_projects'] = createGrp.no_projects.size()\\n\",\n \"createNumber['multi_projects'] = multiGrp.no_projects.size()\\n\",\n \"createNumber.dropna(axis = 0, inplace = True)\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"ax = createNumber.plot(kind = 'bar', title='Number of Projects per Creator by Category')\\n\",\n \"ax.set_xlabel(\\\"Category\\\")\\n\",\n \"ax.set_ylabel(\\\"Avg. Number of Projects per Creator\\\")\\n\",\n \"plt.figure(figsize=(600,300))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, '% of Goal Raised and Number of Projects by Creator')\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"createNoProjects = pd.DataFrame()\\n\",\n \"noProjects = merged.groupby(['no_projects'])\\n\",\n \"createNoProjects['funding_percent_avg'] = noProjects.funds_raised_percent.agg(np.mean)\\n\",\n \"createNoProjects['funding_percent_med'] = noProjects.funds_raised_percent.agg(np.median)\\n\",\n \"createNoProjects['staLauDelta_avg'] = noProjects.staLauDelta.agg(np.mean)\\n\",\n \"createNoProjects['staLauDelta_med'] = noProjects.staLauDelta.agg(np.median)\\n\",\n \"createNoProjects.reset_index(inplace = True)\\n\",\n \"\\n\",\n \"x = createNoProjects['no_projects']\\n\",\n \"y1 = createNoProjects['funding_percent_avg'] = noProjects.funds_raised_percent.agg(np.mean)\\n\",\n \"y2 = createNoProjects['funding_percent_med'] = noProjects.funds_raised_percent.agg(np.median)\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"plt.plot(x,y2)\\n\",\n \"plt.legend(('Median Funding Percent', 'Median Funding Percent'), loc='upper right')\\n\",\n \"plt.xlabel('Number of Projects by Creator')\\n\",\n \"plt.ylabel('% of Goal Raised')\\n\",\n \"plt.ylim(bottom=0)\\n\",\n \"plt.ylim(top = 400)\\n\",\n \"plt.xlim(right=15)\\n\",\n \"plt.xlim(left=1)\\n\",\n \"plt.title('% of Goal Raised and Number of Projects by Creator')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'Dollars Raised and Number of Projects by Creator')\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"createNoProjects['usd_pledged_avg'] = noProjects.usd_pledged.agg(np.mean)\\n\",\n \"createNoProjects['usd_pledged_med'] = noProjects.usd_pledged.agg(np.median)\\n\",\n \"\\n\",\n \"x = createNoProjects['no_projects']\\n\",\n \"y1 = createNoProjects['usd_pledged_avg']\\n\",\n \"y2 = createNoProjects['usd_pledged_med'] \\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"plt.plot(x,y1,x,y2)\\n\",\n \"plt.legend(('Average Dollars Raised', 'Median Dollars Raised'), loc='upper right')\\n\",\n \"plt.xlabel('Number of Projects by Creator')\\n\",\n \"plt.ylabel('Dollars Raised')\\n\",\n \"plt.ylim(bottom=0)\\n\",\n \"plt.xlim(right=15)\\n\",\n \"plt.xlim(left=1)\\n\",\n \"plt.title('Dollars Raised and Number of Projects by Creator')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'Days to Goal and Number of Projects by Creator')\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"createNoProjects['staLauDelta_avg'] = noProjects.staLauDelta.agg(np.mean)\\n\",\n \"createNoProjects['staLauDelta_med'] = noProjects.staLauDelta.agg(np.median)\\n\",\n \"\\n\",\n \"x = createNoProjects['no_projects']\\n\",\n \"y1 = createNoProjects['staLauDelta_avg']\\n\",\n \"y2 = createNoProjects['staLauDelta_med']\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"plt.plot(x,y1,x,y2)\\n\",\n \"plt.legend(('Average Days to Goal', 'Median Days to Goal'), loc='upper right')\\n\",\n \"plt.xlabel('Number of Projects by Creator')\\n\",\n \"plt.ylabel('Days to Goal')\\n\",\n \"plt.ylim(bottom=0)\\n\",\n \"plt.xlim(left=2)\\n\",\n \"plt.xlim(right=15)\\n\",\n \"#plt.ylim(ymax = 2000)\\n\",\n \"plt.title('Days to Goal and Number of Projects by Creator')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Limiting the Results shown for Number of Projects by Creator\\n\",\n \"\\n\",\n \"Above 14 projects per creator, the number of unique sample points drops off sharply to just a few creators. Rather than view results for sample sizes of 1 or 2, the above graphics were limited to creators that made 15 projects or fewer to show a better representation of the population\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1.0 120551\\n\",\n \"2.0 17190\\n\",\n \"3.0 5724\\n\",\n \"4.0 2744\\n\",\n \"5.0 1730\\n\",\n \"6.0 1200\\n\",\n \"7.0 700\\n\",\n \"8.0 448\\n\",\n \"11.0 374\\n\",\n \"10.0 370\\n\",\n \"9.0 360\\n\",\n \"12.0 264\\n\",\n \"13.0 182\\n\",\n \"17.0 153\\n\",\n \"14.0 140\\n\",\n \"16.0 112\\n\",\n \"18.0 108\\n\",\n \"15.0 105\\n\",\n \"26.0 104\\n\",\n \"19.0 95\\n\",\n \"20.0 80\\n\",\n \"25.0 75\\n\",\n \"70.0 70\\n\",\n \"32.0 64\\n\",\n \"59.0 59\\n\",\n \"52.0 52\\n\",\n \"23.0 46\\n\",\n \"35.0 35\\n\",\n \"34.0 34\\n\",\n \"33.0 33\\n\",\n \"30.0 30\\n\",\n \"29.0 29\\n\",\n \"27.0 27\\n\",\n \"24.0 24\\n\",\n \"21.0 21\\n\",\n \"Name: no_projects, dtype: int64\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"merged['no_projects'].value_counts()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, '% of Goal Raised and Days to Reach Goal')\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fundRate = pd.DataFrame()\\n\",\n \"rateProjects = merged.groupby(['staLauDelta'])\\n\",\n \"fundRate['funding_percent_avg'] = rateProjects.funds_raised_percent.agg(np.mean)\\n\",\n \"fundRate['funding_percent_med'] = rateProjects.funds_raised_percent.agg(np.median)\\n\",\n \"\\n\",\n \"fundRate.reset_index(inplace = True)\\n\",\n \"\\n\",\n \"x = fundRate['staLauDelta']\\n\",\n \"y2 = fundRate['funding_percent_med']\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"plt.plot(x,y2)\\n\",\n \"plt.legend(('Median Funding Percent', 'Median Funding Percent'), loc='upper right')\\n\",\n \"plt.xlabel('Days to Reach Goal')\\n\",\n \"plt.ylabel('% of Goal Raised')\\n\",\n \"plt.ylim(bottom=0)\\n\",\n \"\\n\",\n \"plt.xlim(right=40)\\n\",\n \"plt.xlim(left=1)\\n\",\n \"plt.title('% of Goal Raised and Days to Reach Goal')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'USD Pledged and Days to Reach Goal')\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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KGPPLRNr49XOjVMQd1j+Thywa7bfPBBx8wadIkBgwYQGxsLOvXryc1NZUZM2Ywf/58Lr74YiorK/nqq6948cUXeeWVV4iKimLNmjVUVFRwzjnnMGHCBABWr17N1q1b617oc+bMITY2lrKyMkaMGMG0adOoqKjgscceY/369URERHDBBRcwdOhQAO6++27uvfdexowZw4EDB5g4cSLbt2/3+HqHDh1Kly5d6NOnDxdeeCFXXnkll112WYPtU1NT2bFjB1OmTGmwzUcffVSnLB0cP36cxx9/nC+//JKwsDCefPJJ/vSnP/GLX/yCH/3oR3z99decccYZTJ8+va7PI488wgUXXMCDDz7IZ599Vqdst2/fzvz581m+fDmBgYH8+Mc/5s033+Siiy7i4YcfZt26dURFRXH++eczbNhJC3q9hioXRVG8yty5c7nnnnsAmDFjBnPnziU1NZXJkyfz05/+lIqKCj777DPGjh1LaGgoixYtYvPmzSxYYCVKLygoYPfu3QQFBTFy5MgTLIXnnnuO999/H4CDBw+ye/dusrOzOe+884iNjQXg6quvZteuXQB8+eWXfPvtt3X9CwsLKSoqIiIioq6sodVRIoK/vz+fffYZa9as4auvvuLee+9l3bp1zJ4922Ufd/tjXXfddYSGhpKUlMTzz58Y771y5Uq+/fZbzjnnHAAqKysZPXo0O3bsoE+fPvTv3x+A66+/vk6JLFu2rO5eTJo0iZgYK878q6++Yt26dYwYMQKAsrIyEhISWLVqFePGjcORAmv69Ol198kXqHJRlHZKYxaGL8jJyeHrr79m69atiAg1NTWICE899RQhISGMGzeOzz//nPnz5zNz5kzAeiE///zzTJw48YSxlixZQlhY2Anfv/zyS1asWEGnTp0YN24c5eXlbl/otbW1rFixgtDQ0AbbdO7cmby8vBPKcnNziYuLAywlM3LkSEaOHMlFF13ELbfc0qBy2bBhAxdeeKHLujfffJO0tDSXdcYYLrroIubOnXtC+caNGxtUfg1dtzGGm266id///vcnlH/wwQetukxdfS6KoniNBQsWcOONN7J//34yMjI4ePAgffr0YdmyZYBlybz66qt88803dcpk4sSJvPjii1RVVQGwa9cuSkpKThq7oKCAmJgYOnXqxI4dO1i5ciUAI0eO5L///S95eXlUV1fz7rvv1vWZMGECf/3rX+u+b9y48aRxHb6Y7OxsANauXUtFRQU9e/bk8OHDrF+//oT+vXv3PmkMYwzPPfccWVlZTJo0qcn3LT09neXLl9f5YkpLS9m1axdnnnkm+/bt47vvvgM4QfmMGTOGt99+G7D8NQ4FeeGFF7JgwQKOHj0KWIpy//79jBo1iiVLlpCTk0NVVRXvvPNOk+VsCmq5KIriNebOncsDDzxwQtm0adN46623OPfcc5kwYQI33ngjl19+OUFBQQD88Ic/JCMjg9TUVIwxxMfH88EHH5w09qRJk/j73/9OcnIyAwcOJD09HYAePXrwq1/9ilGjRtG9e3cGDRpEVFQUYE2j3XXXXSQnJ1NdXc3YsWP5+9//fsK4Xbp04dlnn+Xiiy+mtraW8PBw5s6di5+fH1VVVdx3330cPnyYkJAQ4uPjT+h///3389hjj1FaWkp6ejqLFy+uu66mEB8fz2uvvcbMmTOpqKgA4PHHH2fAgAG89NJLXHLJJcTFxTFmzBi2brXSMT788MPMnDmT+fPnc95559GtWzciIiKIi4vj8ccfZ8KECdTW1hIYGMgLL7xAeno6s2fPZvTo0XTr1o3U1FSfrsITdyZleyQtLc3oZmFKe2X79u2cddZZbS1Gq1NcXEx4eDjV1dVcccUV3HrrrVxxxRVtLZZPqaiowN/fn4CAAFasWMGdd97p0jJrDq6eIxFZZ4xxPa/nArVcFEU57Zk9ezZffvkl5eXlTJgwgalTp7a1SD7nwIEDXHPNNdTW1hIUFMTLL7/c1iKdgCoXRVFOe/74xz+2tQitTv/+/dmwYUNbi9Eg6tBXFEVRvI4qF0VRFMXrqHJRFEVRvI4qF0VRFMXrqHJRFMWriAg33HBD3ffq6mri4+ObnCreOR39xRdfTH5+fotlmz179knO/6SkpLqEj0888QSDBw8mOTmZlJQUVq1aVSfLwIEDSU5O5swzz2TWrFkNypOUlMTZZ5/N0KFDmTBhQl1wpvN5PMGbKfFdXbevUeWiKIpXCQsLY+vWrZSVlQHwxRdf0KNHjxaN+cknnxAdHe0N8RpkxYoVfPzxx6xfv57Nmzfz5Zdf0rNnz7r6N998k82bN7N582aCg4PdJqdcvHgxmzZtIi0tjd/97nc+lftURZWLoiheZ/LkyfznP/8BrKh9Rx4xgJKSEm699VZGjBjBsGHD6jbfKisrY8aMGSQnJzN9+vQ65QQn/upvKO1+eHg4v/71rxk6dCjp6ekcOXKkSTJnZWURFxdHcHAwAHFxcXTv3v2kdkFBQTz11FMcOHCATZs2uR2zofT6TU2J/91335Gens6IESN46KGH6rYAAGtPGcd2BQ8//HBd+RNPPMHAgQMZP3583eZkrYnGuShKe+XTByB7i3fH7Ho2TP5Do81mzJjBo48+yqWXXsrmzZu59dZb+eabbwDrpXfBBRcwZ84c8vPzGTlyJOPHj+cf//gHnTp1qrMOUlNTXY7tKu1+586dKSkpIT09nSeeeIJf/OIXvPzyy/zmN7/x+NImTJjAo48+yoABAxg/fjzTp0/nvPPOc9nW39+foUOHsmPHjrr0/q74+OOPT0qv35yU+HfffTd33303M2fOPCH9zKJFi9i9ezerV6/GGMPll1/O0qVLCQsLY968eWzYsIHq6mpSU1MZPny4x/fCG6jloiiK10lOTiYjI4O5c+dy8cUXn1C3aNEi/vCHP5CSklKX2fjAgQMsXbqU66+/vq6/846Pzjz33HN11okj7T5YFoXDrzN8+HAyMjJO6usuvX54eDjr1q3jpZdeIj4+nunTp/Paa681eI3uUmedf/75pKSkUFhYyIMPPnhCnXNK/JSUFL766iv27t17Qkr8oKCgE/ZuWbFiBVdffTUA1157bV35okWLWLRoEcOGDavbS2b37t188803XHHFFXTq1InIyEguv/zyBmX1FT6zXEQkBFgKBNvnWWCMeVhE+gDzgFhgPXCDMaZSRIKBfwHDgRxgujEmwx7rQeA2oAb4qTHmc7t8EvAs4A/80xjT+E8qRekoeGBh+JLLL7+c++67ry4TrwNjDO+++y4DBw48qU9jKeEbSrsPEBgYWNff39+f6urqk/p37tyZrKysE8qKiorq/Dn+/v6MGzeOcePGcfbZZ/P6669z8803nzROTU0NW7ZsaTCP2+LFi+tS9tfHmynxjTE8+OCD3HHHHSeU/+Uvf2nV9Pqu8KXlUgFcYIwZCqQAk0QkHXgS+LMxpj+Qh6U0sP/NM8acAfzZboeIDAJmAIOBScDfRMRfRPyBF4DJwCBgpt1WUZRTgFtvvZWHHnropGmhiRMn8vzzz9f98nekMBk7dixvvvkmAFu3bmXz5s0njdlQ2n1PGTt2LAsXLqSoqAiA9957j6FDh+Lv78/OnTvrrCBoOL1+VVUVDz74ID179mzQunJHc1Lip6en120l4Lx19MSJE5kzZ07dlsiHDh3i6NGjjB07lvfff5+ysjKKior46KOPmixnS/GZ5WKsJ6fY/hpoHwa4AHDYda8Ds4EXgSn2Z4AFwF/FUr1TgHnGmApgn4jsAUba7fYYY/YCiMg8u+33284pitJmJCYmcvfdd59U/tvf/pZ77rmH5ORkjDEkJSXx8ccfc+edd3LLLbfULQMeOXLkSX0bSrvvKcnJycyaNYsxY8YgIiQkJPDPf/4TsDIr/+QnPyE/P5+AgADOOOOMExYMXHfddQQHB1NRUcH48ePrFiI0lUGDBjU5Jf5f/vIXrr/+ep555hkuueSSui0FJkyYwPbt2xk9ejRgLWp44403SE1NZfr06aSkpNC7d2/OPffcZsnaEnyact+2LtYBZ2BZGU8DK23rBBHpCXxqjBkiIluBScaYTLvuO2AUlsJZaYx5wy5/BfjUPsUkY8wP7fIbgFHGmJMWhovI7cDtAL169Rq+f/9+H12xorQtHTXlfnuntLSU0NBQRIR58+Yxd+7cZis3TzjlU+4bY2qAFBGJBt4HXD31Du3maoLQuCl3NaXnUlMaY14CXgJrP5dGxFYURTmlWLduHbNmzcIYQ3R0NHPmzGlrkRqlVZYiG2PyRWQJkA5Ei0iAMaYaSAQO280ygZ5ApogEAFFArlO5A+c+DZUriqK0G84999xGY2pONXzm0BeReNtiQURCgfHAdmAxcJXd7CbAYdsttL9j139t+20WAjNEJNheadYfWA2sAfqLSB8RCcJy+i/01fUoyulCR9tdVvEu3np+fGm5dANet/0ufsDbxpiPReRbYJ6IPA5sAF6x278C/Nt22OdiKQuMMdtE5G0sR301cJc93YaIzAI+x1qKPMcYs82H16MopzwhISHk5OTQuXPnNl+Kqpx+GGPIyckhJCSkxWP51KF/KpKWlmYcyfAUpb1RVVVFZmZmXeyHojSVkJAQEhMTCQwMPKH8lHLoK4rSugQGBtKnT5+2FkNRNP2LoiiK4n1UuSiKoiheR5WLoiiK4nVUuSiKoiheR5WLoiiK4nVUuSiKoiheR5WLoiiK4nVUuSiKoiheR5WLoijKKcqD721h1lvr21qMZqHKRVEU5RSkttbwyZYs/rMli8P5ZW0tTpNR5aIoinIKsutoEQVlVRgD72841NbiNBlVLoqiKKcga/blApDUuRPvrsv02VYKldW1FJZXeX1cVS6KoiinIKsz8ugSGcyPx53B3uMlrD+Q7/Vz1NYabnt9DZc+t4yaWu8qL1UuiqLUsWTnUapqattajA6PMYY1+3JJS4rl4uRuhAb68+76TK+f5x9L9/LN7uMcyC1l1b4cr46tykVRFAB2Zhdx86trWLTtSFuL0uHJzCsju7CckUmxhAcHMGlIVz7edJjyqhqvnWPjwXyeWbSTCYO6EBbkzwde9uuoclEUBYCDuaUAp+XKpPbGmgzL3zIiKRaAaamJFJZX8+V27yj+ovIqfjp3A10iQ3j66qFMGtKNT7dke1V5qXJRFAWA7EJr98qjRbqLZVuzJiOXiJAABnaNAGB0v850iwrh3XXemRr77Qdbycwr5dkZKUSFBjJ1WHeKKqr5esdRr4wPqlwURbE5UqdcKtpYEmX1vlzSesfg7ycA+PsJVwzrwdLdx1us/N9bn8kHGw9zz/gBpNmW0Q/6xREfEezVqTFVLoqiAJBVYL20HEpGaRtyiiv47lgJI/rEnlA+bXgiNbWGDzccbvbYGcdL+O0HWxnZJ5a7zj+jrtzfT5gytDuLdx4lv7Sy2eM7o8pFURRALZdThbX784Dv/S0O+sWHM6xXNAuaGfNSWV3LT+dtIMDfj79MT6mzihxMHdaDqhrDJ1uymy+8E6pcFEUBINu2XI4VqnJpS9bsyyUowI/kxKiT6qalJrLzSBHbDhc2edxnFu1kc2YBT05Lpnt06En1g7tHckZCuNemxnymXESkp4gsFpHtIrJNRO62y2eLyCER2WgfFzv1eVBE9ojIThGZ6FQ+yS7bIyIPOJX3EZFVIrJbROaLSJCvrkdR2jvZBeWIQFFFNWWV3ls1pDSNNRm5pCRGExzgf1LdZcndCQrwY0ETHftLdx3jH0v3ct2oXkwa0tVlGxFhakp3VmfkkplX2izZnfGl5VIN/NwYcxaQDtwlIoPsuj8bY1Ls4xMAu24GMBiYBPxNRPxFxB94AZgMDAJmOo3zpD1WfyAPuM2H16Mo7ZaSimqKKqrpFx8O6IqxtqKkopqthwsZ0SfGZX1Up0AuOqsLCzcdprLas2DX48UV/OztTQzoEs5vLx3ktu2UlB4AfLix+X4dBz5TLsaYLGPMevtzEbAd6OGmyxRgnjGmwhizD9gDjLSPPcaYvcaYSmAeMEVEBLgAWGD3fx2Y6purUZT2jWMZsmMq5ohOjbUJGw/mU1NrTvK3ODNteA9ySypZsrPxZcMV1TXcO38jheVVPDdzGCGBJ1tDzvSM7UQWXm5pAAAgAElEQVRa7xg+2HCoxbnMWsXnIiJJwDBglV00S0Q2i8gcEXGo6B7AQadumXZZQ+WdgXxjTHW9clfnv11E1orI2mPHjnnhihSlfeHwtwxNjAbUcmkrVu/LxU9geG/XlgvA2P7xxIUHN5oOJrekkuv/uYpvdh/n0csHc2bXSI9kmDqsB7uPFvNtVtP9Os74XLmISDjwLnCPMaYQeBHoB6QAWcAzjqYuuptmlJ9caMxLxpg0Y0xafHx8E69AUdo/DuVytm25HFXLpU1Yk5HLmV0jiQgJbLBNgL8fU1O68/WOo+SWuF42vOdoMVNfWM6mzAL+eu0wZozs5bEMl5zdjUB/abFj36fKRUQCsRTLm8aY9wCMMUeMMTXGmFrgZaxpL7Asj55O3ROBw27KjwPRIhJQr1xRlCbimBY7s2sEgf6iy5HbgKqaWjYcyGdkn4anxBxMG55IVY1h4caTFcDyPce58m/LKa2sZt7t6Vya3L1JcsSEBXHegAQWbjrcokzJvlwtJsArwHZjzJ+cyrs5NbsC2Gp/XgjMEJFgEekD9AdWA2uA/vbKsCAsp/9CY00ILgausvvfBHzoq+tRlPZMdkE5UaGBdAoKICEihKMaSNnqbD1UQFlVjVt/i4OzukUyuHsk764/UbnMW32Am+aspmtUCO//+BxSezU8veaOK4b14EhhBSv3Nj9Tsi8tl3OAG4AL6i07fkpEtojIZuB84F4AY8w24G3gW+Az4C7bwqkGZgGfYy0KeNtuC/BL4GcisgfLB/OKD69HUdot2YXldIsKASA+IlgtlzagLlllAyvF6jMtNZEthwrYdaSI2lrD7z/ZzgPvbWF0v84suPMH9Izt1GxZLjwrgfDggBbtgBnQeJPmYYxZhmu/yCdu+jwBPOGi/BNX/Ywxe/l+Wk1RlGaSXVBOl0hLuSREBJORU9LGEnU81mTkkdS5EwkRIR61n5LSnd99sp1/r9jP0aJyPt92hOvTezH7ssEE+LfMbggJ9GfykK58ujWbx6cOaXSVmSs0Ql9RFLILy+nqUC6Rarm0NrW1hrUZuR5NiTnoHB7MuIEJ/HvlfhZ9e4SHLh3EY1OGtFixOJg6rAfFFc1P86/KRVE6OFU1tRwvrqCrPS3WJSKE/NIqKqo1Sr+1+O5YMXmlVSclq2yMW8ck0SM6lJdvSOPWMX2wXN3eIb1vZ7pEBvNBMxNlqnJRlA7O0aIKjKFOuSREBlvlHXw58oGcUq/vK98Qq+ttDuYpP+gXx/IHLmD8oC5el8nfT5iS0oMlO4+S18CSZ3eoclGUDo4jxqVuWsye8+/IU2M7s4sY+/RiJj+7lM+2Zrc4Wr0x1uzLJS48mKTOzXfC+4IpKd2prjX8Z0tWk/uqclGUDo4j1X5Xp9ViAMc6cJT+3mPFAOSXVvF/b6xjygvLWbrrmM+UzJqMPEb2ifHqtJY3GNQtkgFdmpcpWZWLonRwsupbLo5psQ5suTjuycc/HcPTVyWTU1zJjXNWM+Ollay1p7C8xeH8Mg7llzV5Sqw1ELGmxhx7zDQFVS6K0sE5UlhOUIAf0Z2slCOdw4Lx95MO7XNx3JP48GCuTuvJ1/edx6NTBrP3eAlX/X0Ft7y6mq2HCrxyrjXN9Le0FhMHN8+fo8pFUTo42QVWAKVjSsbfT4gLD+rQ2x1nFVhLsx33JDjAnxtHJ7H0/vN5YPKZrD+Qz6XPL+OZRTtbfK7V+3IJDw7grG6eJZZsbfrGhRMZ0vSQSFUuitLBcQ6gdJAQEdKhp8WyC8rrfFDOhAb583/n9eObX57PFcN68PzXe1i6q2WZ1tdk5JLaO+akbYdPFfz8hGHNSCPToHJxpGhp6GiRtIqinDI4B1A6SOjgKWBc3RNnIkMC+f2VZzOgSzg/f2dTg9mJGyO/tJJdR4oZmdS8HGCtxbBe0U3u485yuRS4DCvP12fAdfbxCd9v0KUoymmMMeaEvGIOEiKDO+xqsYbuSX1CAv35y/RhFJRW8eB7m5u1kmxthuUoP1X9LQ6akwCzQeVijNlvjNkPnGOM+YUxZot9PABMbKifoiinD3mlVVRW17qcFsspqaSqxrOtdNsTDd0TVwzqHsl9Ewfw+bYjvLO2afvagzUlFuTvx9CeTbcMWpPmyOeJzyVMRMY4vojID4CwJp9JUZRTjroASheWizHW/usdjayCMoBGLRcHPxzTl9F9OzP7o21kHG9aws/VGbkkJ0Y1KzFkaxIV2vDmZQ3hiXK5DXhBRDJEZB/wN+DWJp9JUZRTjvoBlA7qovQ74HJkxz3p4qFy8fMTnrlmKAF+wj3zN1LtobW37XABWw8VNDmf2OlCo8rFGLPOGDMUSAZSjDEpxpj1vhdNURRfUz+A0kFCRMcNpHTcE08tF4Du0aH87sqz2Xgwn+e/3uO2bU2t4YXFe5j6wnKiOwUxLTWxRfKeqjSqXESki4i8Asw3xhSIyCARua0VZFMUxcdkF5Yj8n3KFwcOf8PRDujUP1JQjp9AfHhw442duDS5O1cO68HzX+9mXQMR7ftzSrjmHyt4+vOdTBjUlUX3jOWMhHBviH3K4cm02GtYu0A6NmLeBdzjK4EURWk9jhSUEx8eTGC9PUDiwoMQgSMdcFosq6Cc+IjgZu2L8siUwXSPDuXe+RsprqiuKzfG8NaqA0x+9ht2HSniL9NT+Ou1w4gJC/Km6KcUnty9OGPM20AtgL3tsG70oCjtgKxC18GCAf5+dA4L6pDLkbMLy+kaFdqsvhEhgfx5egqZeaU8stDajf1oYTm3vraGX72/hWG9ovn8nrFMHdbjlEtS6W08iekvEZHOgAEQkXTAO0l1FEVpU44UlNOrgTTv8REhHdKhn11QTt/45i+IHZEUy4/HncFfF+8hKjSQd9dnUlpZw+zLBnHj6CT8TtFIfG/jiXL5GbAQ6Cciy4F44CqfSqUoSquQVVDGqL6uVyt11Cj97IJyzjkjrkVj3D2+P0t3H+Ofy/Zxdo8o/jw9pd36VhqiUeVijFkvIucBAwEBdhpjqnwumaIoPqWssobC8uoGgwW7RAazI7uwlaVqW4orqimqaPieeEqgvx8v3ZDG0t3HuGJYj5N8Wh0BT1aLXQlcjqVcBgCXiciFIpLQSL+eIrJYRLaLyDYRudsujxWRL0Rkt/1vjF0uIvKciOyx85elOo11k91+t4jc5FQ+3M6Btsfu2zHsTUXxAtmFrpchO0iICOFYUUWrbfV7KpDdjGXIDdE1KoRr0np2SMUCngdR/pPvc4u9jDVVtlxEbnDTrxr4uTHmLCAduEtEBgEPAF8ZY/oDX9nfASYD/e3jduBFsJQR8DAwChgJPOxQSHab2536TfLgehRFofFI9ITIYGoN5JR0nKmxugDKFlouimfKpRY4yxgzzRgzDRgEVGC97H/ZUCdjTJYj2NIYUwRsB3oAU4DX7WavA1Ptz1OAfxmLlUC0iHTDymP2hTEm1xiTB3wBTLLrIo0xK4yVMe5fTmMpitIIjUWi1wVSdiCnfnMCKBXXeKJckowxR5y+HwUGGGNyAY98LyKSBAwDVgFdjDFZYCkgwDG91gM46NQt0y5zV57polxRFA/ILrCURkPTYvF2CphjHcip31A6HKXpeLJa7BsR+Rh4x/4+DVgqImFAfmOdRSQceBe4xxhT6MYt4qrCNKPclQy3Y02f0atXr8ZEVpQOQXZBGREhAYQFu34NdIl0pIDpOLEuWQVlRHcKPOUTSZ4OeGK53IUVpZ+CZX38C7jLGFNijDnfXUcRCcRSLG8aY96zi4/YU1rY/x61yzOBnk7dE4HDjZQnuig/CWPMS8aYNGNMWnx8vPurVZQOQmMbYjlSwnSkKP3sggq390TxHE8SVxpjzAJjzL3GmHvsz40uH7FXbr0CbDfG/MmpaiHgWPF1E/ChU/mN9qqxdKDAnjb7HJggIjG2I38C8LldVyQi6fa5bnQaS1GURsgurHA7/RMc4E90p8AOZblkF5bplJiXaHBaTESKaGCaCcAYE9nI2OcANwBbRGSjXfYr4A/A23byywPA1XbdJ8DFwB6gFLjFPk+uiDwGrLHbPWr7ewDuxLKqQoFP7UNRFA/ILihjQIJ7Sz4hIrhDOfSzCyo4u0dUW4vRLmhQuRhjIgBE5FEgG/g3lp/jOiCisYGNMctw7RcBuNBFe4M1BedqrDnAHBfla4EhjcmiKMqJVNfUcqzIveUC1pLcjhKlX1ldy/HiCl2G7CU88blMNMb8zRhTZIwpNMa8iOXUVxTlNOV4cSW1pvF4jviI4A6zWsyxUkyXIXsHT5RLjYhcJyL+IuInItehWZEV5bTG0618EyJCOFpUjgdu1tMeDaD0Lp4ol2uBa4Aj9nG1XaYoymmKpy/ShIhgqmoMeaXtP53g9wGUzUu3r5yIJ4krM7Ci5xVFaSc4cmg15nNJcIp1iW3HG1uBUwClWi5ewZPElQNE5CsR2Wp/TxaR3/heNEVRfEVWYTlB/n7EdnKvMOq2O+4AK8ayCsoJDfQnMtST2HKlMTyZFnsZeBA71YsxZjMww5dCKYriW44UlJMQGdzoxlUJdYGU7T/WJbuwnG5RIe1+h8jWwhPl0skYs7peWbXLloqinBY0Fp3vIMHOL9YRliNnF5SrM9+LeKJcjotIP77f5vgqIMunUimK4lOyC8o9ikQPDfInIjigQyxHzi4o12XIXsSTycW7gJeAM0XkELAPuN6nUimK4jOMMWQXljP+rC4etY+PDG73KWBqaw1HCssb3H5AaTqerBbbC4y3syD72XuzKIpymlJYVk15Va3HObS6RIS0e4d+Tkkl1bVGLRcv4i632M8aKAegXjJKRVFOE7IKrQBKT5VLQmQw6w/k+VKkNsexNFt9Lt7DneXSaP4wRVFOP+piXDx8kTqSVxpj2u1KKk8zFiie4y5x5SMAIhJnjDneeiIpiuJLmvorPSEihIrqWgrLq4kKDfSlaG2GBlB6nwZXi4nIpSJyDNgsIpki8oNWlEtRFB+R3cQcWo4o/WPt2KmfVVBOgJ/QOTy4rUVpN7hbivw74FxjTHesLMi/bx2RFEXxJUcKy4kLDyIowJNIBKdYl3bs1M8utGJc/BsJKlU8x93TVW2M2QFgjFmF+mAUpV2Q1cRgQYflcqQdWy5WAKVaLd7EnUM/od6KsRO+62oxRTk9yS4oJzHG88y/jhQw7d1yOatrY5vrKk3BneXyMpa14jjqf1cU5TTkSGHTLJfw4ABCA/3bbQoYY4ymfvEBja4WUxSl/VBeVUNeaVWTVkWJCF0ig9utcimqqKa0skaXIXsZzzx6iqK0C+qW3DbxRZoQEcLRdpoZuW5ptioXr6LKRVE6EJ5uElaf+FPIctmRXchlzy9j77Fir4yXXbcDpSoXb6LKRVE6ENnNDBa0ovRPDcvl0y3ZbDlUwE/mbqCiuqbF4zU1Y4HiGW6Vi4gMFJFnROQ/9vFHERnoycAiMkdEjjp2sLTLZovIIRHZaB8XO9U9KCJ7RGSniEx0Kp9kl+0RkQecyvuIyCoR2S0i80Wkfe/BqiheoLmWS0JECCWVNZRUtP1WTqv25RAVGsi2w4U8/dnOFo+XZd+TBF2K7FXcReiPBpYARVgp918GSoDFIpLuwdivAZNclP/ZGJNiH5/Y5xqEtbvlYLvP30TEX0T8gReAycAgYKbdFuBJe6z+QB5wmwcyKUqHJruwnLAgfyJCmpbGxRED0tZTYxXVNWw4kM9VwxO5Ib03/1y2jyU7j7ZozGw7qDQ4wN9LUirg3nJ5CJhpjJltjPnQGPOBMeZhYCbwcGMDG2OWArkeyjEFmGeMqTDG7AP2ACPtY48xZq8xphKYB0wRK3veBcACu//rwFQPz6UoHZbsgubtWeKI0m/r7Y43ZxZQUV3LqD6x/PqSsxjYJYL73tnUos3MsgvKdBmyD3CnXPoZY5bULzTG/Bfo24JzzhKRzfa0WYxd1gM46NQm0y5rqLwzkG+Mqa5X7hIRuV1E1orI2mPHjrVAdEU5vXHsE99UEk4Ry2XV3hwARiTFEhLoz3Mzh1FUXs3P39lEba1p1pjZhRXqzPcB7pSLu03BSpp5vheBfkAK1lbJz9jlrhL6mGaUu8QY85IxJs0YkxYfH980iRWlHXGkmcGC30fpt63lsmpfLmd2jSAmzHKxDuwawW8uHcTSXceYs3xfs8ZUy8U3uEv/0lNEnnNRLrixEtxhjDlSN4jIy8DH9tdMoKdT00TgsEMOF+XHgWgRCbCtF+f2iqK4oKbWcKSoolmroqJCAwkK8GvR9FNLqaqpZd3+PK4ennhC+fWjevHNrmM8+dkO0vt2ZkiPKI/HdASVquXifdxZLvcD61wca4FfNOdkItLN6esVgGMl2UJghogEi0gfoD+wGlgD9LdXhgVhOf0XGmMMsBi4yu5/E/Bhc2RSlI5CTnEFNc3cyldErOXIbahcth4qoLSyhpF9Op9QLiI8OS2ZzmHB/GTuhiataDvSxO0HFM9xl/7l9fplto8k3365u0VE5gLjgDgRycRaBDBORFKwprAygDvsc20TkbeBb4Fq4C5jTI09zizgc8AfmGOM2Waf4pfAPBF5HNgAvOLJBStKR6Wp+7jUJyEiuE0d+qv2WeuDRvaJPakuJiyIP09P4dp/rmT2wm08ffVQj8b8PoDS80Seimc0qFxE5CHgbWPMDhEJBj7F8pVUi8i1xpgv3Q1sjJnporhBBWCMeQJ4wkX5J8AnLsr3Yq0mUxTFA7KaGePiICEihD1eiopvDqv35dIvPoz4CNfxKKP7dWbW+Wfw/Nd7OHdAPJcP7d7omHVBpVEa4+Jt3E2LTQccEUo3Yfla4oHzsDYSUxTlNKK5ecUcJES2XZR+Ta1hzb7ck6bE6nP3hf1J7RXNr9/bwsHc0kbH/T6oVC0Xb+NOuVQ6TX9NxIpDqTHGbMf9QgBFUU5BDuWXEeAnxIU171d6QkQwheXVlFe1POVKU9meVUhRRTXpfU+eEnMmwN+PZ2cMwwB/+GxHo+NmFZQTERxAeLC+0ryNO+VSISJDRCQeOB9Y5FTXybdiKYribdZl5DG4RxR+zdzKN8H21bTFijF3/pb69IztxLWjevHZ1mwy89xbL80NKlUax51yuQcrAn4HVpqVfQB2PrANrSCboiheorSymk2Z+Y3+8neHI9alLZz6q/bm0Cu2k8eO95t+kATA6//LcNuuuUGlSuM0qFyMMSuNMWcaYzobYx5zKv+kAWe9oiinKOv351NVYxjd173Pwh2OFDCtvRy5ttawOiOXUR5YLQ56RIcyeUhX5q0+SLGbpcm6A6XvcLda7Gf1igxW8OIyhxWjKMrpwYq9x/H3E9KSWmC5RLZNlP7uo8Xkl1Z5NCXmzA/P7cvHm7N4e81Bbh3T56T66ppajhVr6hdf4W5aLKLeEQmkAZ+KyIxWkE1RFC+xcm8uyYlRLXJcx3YKIsBPWt1yWbXPyieW3kSrK6VnNGm9Y3j1f/uocZF37HhxJTW1Ri0XH+EuiPIRV+UiEgt8iZWhWFGUU5ySimo2HcznR2Nbkm8W/PyE+DaI0l+1N5fuUSEkxjR9ufBtY/pw55vr+eLbbCYN6XZCnSPGRS0X39DknSiNMbm4ThypKMopyLr9eVTXmib/8ndFj+hQMo43N29t0zHGsGpfLiP7xGLttNE0JgzuSs/YUF5ZdvJMfnZBGaCpX3xFk5WLiFyAtTmXoiinASv35hDgJ6T1jmm8cSMM6xXN5syCVot12Xu8hOPFFYxqpmL09xNu/kEf1mTkselg/gl136d+UeXiC9ztRLnF3nfF+cgE/gD8uPVEVBSlJazYm0NyYhRhXggUTEuKpbKmlq2HCrwgWeOsbkJ8S0Nck5ZIRHDASdZLVmE5Qf5+xIbpDum+wJ3lcilwmdNxKTDQGDPSGNN46KuiKG1OSUU1mzMLGN2v5VNiAMNt62ft/taZvFi1N4e48GD6xoU1e4yIkECmj+jJf7ZkcTi/rK78SEE5XaKCmzXdpjSOuziX/fWOA8aY1ptsVRSlxazJyKXGS/4WoO5FvzbD0x3Mm4/D3zKqb/P8Lc7cfE4SxhheX5FRV5ZVUE63SM0p5iua7HNRFOX0YeXeXAL9pc7i8AZpSTGs3Z/X7G2FPSUzr4ysgvImBU82RGJMJyYP6cZbqw7U7feSXaipX3yJKhdFaces3JvD0MRoOgV5LzFjWlIs+aVV7D3u2/T7K/da8S2jGsmE7Cm3nduHovJq3ll7EGMM2QWa+sWXqHJRlHZKcUU1Ww4VeG1KzIFj1dmaDN/6XVbtyyW6UyD9E8K9Ml5qrxiG9Yrm1f9lkFtSSUV1rS5D9iGqXBSlneLwt3jLme+gT1wYncOCWONjv8vqfbmMTIptdhZnV/xwTF/255TyxsoDgC5D9iWqXBSlnbLyuxwC/YXUXt7zt4C1Z31aUgxrfWi5ZBWUcSC3tNnxLQ0xcXAXekSH8o+l3wEaQOlLVLkoSjtl5d4cUnpGExrk7/Wx03rHciC31GdJLFfttawibzjznQnw9+OWc5IorbSCQNVy8R2qXBSlHVJUXsWWQwUtSrHvjrQk38a7rNqXS0RIAGd1i/T62NeM6El4cAAiEB/RvF05lcZR5aIo7ZA1GbnUmqZnEvaUwd2jCAn085nfZdW+HEYkxeLvRX+Lg8iQQG4b04eUntEE+usr0FfonVWUdsjKvbkE+fuR6sX4FmeCAvxI6RnNumZYLsUV1VTV1DZYf7SonL3HSlqU8qUx7hnfn/d/fI7PxlfcpNxvKSIyBytlzFFjzBC7LBaYDyQBGcA1xpg8scJvnwUuBkqBm40x6+0+NwG/sYd93Bjzul0+HHgNCAU+Ae42xvg2qktRThNWfJdDSq9oQgK9729xkNY7lhf/+x0lFdUe5y07XlzBeU8tpqrG0Dc+jAFdIhjYNcL6t0sEiTGhrNlnKSxv+1uc0ZQvvsdnygXrxf9X4F9OZQ8AXxlj/iAiD9jffwlMBvrbxyjgRWCUrYwextqkzADrRGShMSbPbnM7sBJLuUwCPvXh9SiKz9ieVUhOcSVj+se1eKyCsiq2HS5g1gX9vSBZw6QlxVCz2LDxYD7nnOGZ3O+tz6SksoabRvfmYF4Z6/bnsXDT4br60EB/OgVZx5AeUb4SXWkFfKZcjDFLRSSpXvEUYJz9+XVgCZZymQL8y7Y8VopItIh0s9t+Ye8hg4h8AUwSkSVApDFmhV3+L2AqqlyU0xBjDPfO30hWQTkbfntRi+M61tr+Fl858x2k9o5BxPLveKJcjDHMX3OQ4b1jeGTKkLryovIqdh8tZld2ETuPFLHrSBGpvWLUH3Ka40vLxRVdjDFZAMaYLBFJsMt7AAed2mXaZe7KM12Uu0REbseycujVq1cLL0FRvMumzAJ2ZBcB8G1WYYt/sa/4LoegAD+G9Yr2hngNEhkSyMAuER77Xdbtz+O7YyU8Na3fCeURIYGk9orxejyO0racKj8NXP1UM80od4kx5iVjTJoxJi0+Pr6ZIiqKb5i76gBBAdZ/RUc+rZawcl8OqT72tzgYkRTL+v15VLtx0DuYv+YgYUH+XJLcrdG2yulPayuXI/Z0F/a/R+3yTKCnU7tE4HAj5YkuyhXltKKovIqFmw5zRUoP+sSFseK7limXgtIqth0u9NkS5PqkJcVQUllTZ3k1RFF5FR9vzuKyod29smmZcurT2splIXCT/fkm4EOn8hvFIh0osKfPPgcmiEiMiMQAE4DP7boiEUm3V5rd6DSWopw2LNx0mLKqGmaO6kV6386s3pfrkRXQEKszcjE+jG+pz4gka0VXY/u7fLw5i7KqGq4Z0dNtO6X94DPlIiJzgRXAQBHJFJHbsLZIvkhEdgMX2d/BWu21F9gDvIy9jbLtyH8MWGMfjzqc+8CdwD/tPt+hznzlNGTu6gOc1S2SoYlRpPeNpaiimm2HC5s93sq9OQTbMSitQffoULpHhbCmEb/L/DUHGdAlnGGtJJfS9vhytdjMBqoudNHWAHc1MM4cYI6L8rXAkJN7KErrUlVTy6G8MpKauBXvlswCth4q5LEpgxGRutVdK/fmMLSZL+EV3+WQ2iumVfwtDtKSYlm1LwdjjMv4kZ3ZRWw8mM9vLjlL40s6EKeKQ19RTkvySyu54ZVVjPvjkib7S95afYCQQD+mDLMWOiZEhtAvPowVzXTq55dWsj270Osp9htjRFIMRworyMwrc1k/f81BAv2FK1MTXdYr7RNVLorSTPbnlHDli/9j/f584sKDeejDrVRWe+YvKa6oZuHGQ1yW3J3IkMC68tH9OrNmX67b9CgNsWpf6/pbHAzvbftd9p/sd6moruH9DZlMGNSV2LCgVpVLaVtUuShKM1i3P5cr/vY/cksq+fdtI3ly2tnsPlrMnOX7POr/0abDlFRajnxnRveNo6Syhi2HCposk8PfMrRn60a2D+waQURwgMudKb/49gh5pVXqyO+AqHJRlCby8ebDzHx5FZEhAbx35w8Y1bczF57VhYsGdeHZL3dzKN/19JAzc1cf4MyuESc5uNP7WlZAc5Ykr/guh7SkGIIDWs/fAuDvJ6T2jnG5Ymz+moP0iA5ljIfpYZT2gyoXRfEQYwx/W7KHWW9tILlHFO/9+Bz6xn+/v/vDlw3CYHjso2/djrP1UAGbMwuYObLXSQ7uzuHBDOwS0eRgyj1Hi9iRXcS4AQmNN/YBI5Ji2HWkmPzSyrqyzLxSlu05zlXDE32SOl85tVHloigeUFVTywPvbuGpz3Zy2dDuvPHDUSf5EBJjOvGTC/rz2bZsFu882sBIltUSHODH1GGuMxal941lbUaex/4bgAXrDuHvJ0wZ1t3jPt7E4XdZf+D7qbF31loZmq5OU0d+R0SVi6I0QmF5Fbe8uob5aw8y6/wzeHZ6SoNLfX90bl/6xYfx8IfbKK+qOam+pKKaDzce5tLk7kSFBroYwXLql1XVsDkz3/NS3PAAACAASURBVCP5amoN72/IZNyAeBIi2mbb3pSe0QT4SZ3fpabWsGBdJmPOiCMxplObyKS0LapcFKUR7p23kZV7c3jqqmTumzjQbdbioAA/HpsyhAO5pby45LuT6j/efJjiimquHdWwg3tUn86IeO53+Wb3MY4UVnDV8LazEELtFPkOv8vyPcc5lF/GdHXkd1hUuSiKG3JLKlmy6xi3j+3LNWmevSh/cEYclw/tzov//Y6M4yUn1L212opUd5cBOCYsiDO7Rnoc77JgXSbRnQK54Ky28bc4SOsdw6bMAiqqa5i/5iAxnQK5aFCXNpVJaTtUuSiKG77cfoSaWsPFZzctk+9vLjmLIH8/Hl64DccGqdsOF7DpYL5LR359RvftzLr9eVRUnzy15kxBaRWLvj3ClKHdW32VWH3SkmKprK5l6a7jLPo2myuGJba5TErbocpFUdzw+dZsekSHMrh7ZJP6JUSG8LOLBvDfXcf4fFs2APNWHyQ4wI8rGnDkOzO6X2cqqmvZcMC93+WjzYeprK7lag+tKl+SlmRZY49+vI2qGqNTYh0cVS6KzyirrKG2tsFtdk55iiuq+Wb3cSYN6dqsnFg3ju7NWd0ieeSjbzleXMEHGw5xydndiO7UeKT6yKRYj/wuC9ZlcmbXiCYrP18QFx5M37gwDuaWkdIzmoFdI9paJKUNUeWi+ITyqhrOfepr/vLlrjaVo6qmliOF5c3qu3jHUSprapk4uGuz+gf4+/H41MFkFZRz7csrKaqoPikivyGiOgUyuHuk23iXPUethJBXDU88ZRJCDu9tWS9qtSiqXBSfsGTnUY4XV/Lq8gyKyqvaRIaSimqufXkl455ewvHiiib3/2xbNnHhQXUvzOYwvHcs16QlsutIMWckhJPWhLFG9+3MhgP5Lpc0A7yzLtOKbUlpfJqttbgkuRuDu0dyqe422eFR5aL4hI83ZxEa6E9RRTVzVx9o9fOXVFRz86urWX8gn7KqGt5ee7BJ/curali84ygXDera4ujyByafRc/YUG4/t2+TLIzR/TpTWVPLehd7pVTX1PL++kOcPzCe+IjgFsnnTcYNTOA/Pz2XiBDXMTxKx0GVi+J1Siur+Wr7Ua5M7cHovp2ZsyyjSdHmLcVZsTw7I4X0vrG8ufIANU3w/yzbfZzSyhomDWnelJgzsWFBfPOLC5qcvHFEUiz+fuJySfI3e45ztKhtY1sUxR2qXBSvs3jHMcqq/r+9Mw+vqroW+G9lDpkgAyGQkAHCKAGVGQQBRRDrVBEVrANKfWq1Vauifa/WWmvtgK22WmcExBkFURARBZEwQyAQpkAgkIlAyETm/f7YJ+ESMt3kJvcG9u/79neGnGGdnXvPumvttdeqZEpCBLPGxpGZX8KS7cfa5N61Fcs1CV25fXgMR/NO88Pe+lOy1GZ5ciYBPh41BbycQYCPJxd1C6pzUP+Tzel06uDJ+D5mHonBNTHKxVAnBSXlnDrdvLGSpTuOEervzbDYEC7vFUbv8ADeWJNaM9+jtahLsQBM7B9OWIA38xOb5p6rqKxixe4srugbjpeHc78iI+JC2J6eR3FZRc2+U8XlrEjO4rpB3Zwun8FQH+aTaaiTmXM3ccvriXYrhKLSCr5LyebqAXqsQkS457JYUjILWL3veCtJW79iAfB0d+OWIVGs2pPNkRPFjV5rw8ET5BWXNztKzJEMjwumvFKx2WbcZXHSMcoqq4xLzODSGOViOIftR/LYcPAEuzPyWWOnQliZkk1JeRVTbGa0XzeoG+GB3ry++txcW46gIcVSza1DuyPo0sKNsSw5Ex9PN8b2CmsFae1jSEwwHm5ylmvMlea2GAz1YZSL4RzeWXsQf28PQv29eevHplVWrGZp0jE6B3gzJCa4Zp+Xhxt3jYpl7f5cdjajwmJDNEWxAHTt6MuEvuF8tPFIgylVqqoUy5MzubxXZ3y9nJ+6xM/bg4TIoJpB/X1ZBWx3sbktBkNdGOViOIus/BK+TMrg5sFR3DEimh/25rAvq6BJ5xaUlLNqTw5XD4g4J3PwbcO64+/tweurUx0ma2WV4t73NjWqWKqZMTya3KIylu3MrPeYbel5ZOWXOiRKzFGM6BFCUvopCksr+GRLOh5uUm8tGIPBVXCKchGRQyKyQ0S2icgma1+wiKwQkX3WspO1X0TkXyKyX0SSROQSm+vcYR2/T0TucMaznG/MT0yjUinuHBnD9OHReHu4Nbku/Mrd2ZRVVNU5gS7Qx5Nbh0axdEcG6ScbH/doCu+tO8RPB3L50/UXNapYAC7rGUp0SAfmJ6bVe8zynZl4uAnj+jg3w7AtI+JCqaxSrE/NZdGWo1zeuzOh/q4zt8VgqAtnWi7jlFKDlFKDre0ngZVKqXhgpbUNMBmIt9os4FXQygj4PTAMGAr8vlohGZpHSXklC9Yf5oq+4XQP6UCwnxc3XhLJZ1uOcqKorNHzv0zKICLIp9508neNikXAbldbXRzOLebFZXsY1zusyalG3NyE6cO6s/HQSVIy88/5u1KKZcmZjOwZWm8hL2dwaXQnPN2Fv3+z18xtaSsqy2H9f2Hjm7BrMRxOhNwDUJIPrRz1eL7gSm6x64C51vpc4Hqb/e8pTSLQUUQigKuAFUqpE0qpk8AKYFJbC30+sXjbMU4UlXH3qNiafTNHx1BaUcWCBn7tg67WuHpv3S6xarp29OXagV35cOMRThU3PyVMVZXiiU+T8HATnr9xgF1jD1MvjcLLw61O6yUls4C03GImuUCUmC2+Xu4MiurIrox8gv28GO9CVtV5y46P4evHYemj8NHt8PZV8PIl8EIU/KkLzLkIXr8clj8N+W0zh6u94SzlooBvRGSziMyy9oUrpTIArGX1N6gbYJu7I93aV9/+cxCRWSKySUQ25eTkOPAxWofTZQ3X8GgNlFK8vfYgfboEMDzuzGB8z84BXN47jLnr0hocCF+RnEVZZRVTGskpde+YOIrLKpm/vmFl1RALNx5mXWouT03pS0SQr13ndvLz4pqECBZtOUphacVZf1u2MxMRXLLAVfVkzusGdTVzW9qCDW9AaG94JAV+uQZmfArXvwZX/hGG3gsxo8E7ABJfhX8OhMW/0paNoQZnfUpHKaUuQbu8HhCRMQ0cW9fPUtXA/nN3KvW6UmqwUmpwWJjzw0sbYufRUyT8YTmr97atElyXmktKZgF3j4o9xxKYOTqW44WlLNmeUe/5S3dk0K2jLxdHdWzwPn0jAhnTK4x31h6qNyFjQxzNO82fv0phVM8Qbmlm5t0Zw6MpKqvk861Hz9q/PDmTIdHBLpWrq5qJ/bvQsYMntw5tWlZlQws4uhmObYEh90BgBEQkQM8rYNCtMOohmPgc3PAa3LEEHtoCF98O2z+EVwbDx3dBRpKzn8AlcIpyUUods5bZwCL0mEmW5e7CWlbn6kgHbN8ikcCxBva3a+atS6O8UvFOEwfRHcXbPx4i2M+LawedOzA+umcovcMDeOvHg3VOqjxVXM6afTlMSYhokovql2Piauqb2INSiqc+20FlleKFGxOaHYp7cVRH+ncNZH5iWs3zHDxeREpmAVe5UJSYLRd1C2Lb/02kV7ipkdLqbHwLPP1g4LTGj+0UA9f8A369A0Y+BPtWwH8vg/k3QdpP+hiloCBLj9ts/wBW/Rk++yW8NVG71xbeBlvmQWHT0xOhFGQlw08va6tp+wdQfKJZj9taeLT1DUXED3BTShVY6xOBZ4HFwB3AC9byC+uUxcCDIvIBevD+lFIqQ0SWA8/bDOJPBGa34aM4nPySchZvP4aflzvf780h/WQxkZ06tPp903KLWJmSxYPjeuLjee7cDhFh5uhYHv80iXUHchnZM/Ssvy/flUl5pTpr4mRDjOwRQv+ugby+JpWbB0fVO0ZTm0+3HOWHvTk887N+RAU3v19EhBnDo5n92Q42p51kcExwTbXIq/q7nkvM0IYUn4Cdn8LAW8EnqOnnBYTDlX+A0b/RQQCJr8I7kyGoOxQfh3LbCEmBwG4QHAtRQ+HIBtizVO+PHAK9J0PvqyGsN9j+gCo6Dqnfw/6VcOA7KLRC6r0DYct7IG4QNQx6TdLXCO119vltTJsrFyAcWGT96vQA3ldKLRORjcBHIjITOAxMtY7/Crga2A8UA3cBKKVOiMgfgY3Wcc8qpVxLddvJ51uPcrq8ktdmXMr9CzbzwYYjPHZV71a/77s/HcLDTb9w6+PaQV15cXkKb/148BzlsjQpg6hgXxIim/ZlFBFmjYnj4Q+2sTIlu0ljHNn5JTy7JJkhMZ34xYiYJt2nIa4b1JXnl+5mfmIag2OCWbYzkwHdgtpEmRtcmG0LoKJEu8Sag29HGPMYDL8fts6HQ2sgKEpbOMGxetmxO3jYuF6VgqydsOdrSFkKK/+gW6dYrWQ8vLUyydgOKPDtBHHjoMd46DEOArpCxlbYswz2fg3f/l63TrFayfSapMeI3Np2UnCbKxelVCowsI79ucCEOvYr4IF6rvU28LajZXQGSikWJB5mQLcgJl3Uhct7d+bDTUd4+Ip4PN1bz3tZUFLOx5vSmTIggvBAn3qP8/F0Z/qwaP65ch+pOYXEhfkDcLKojLX7j3OPnbVKrh4QwYvL9vDC17vx8nBjTHxovecrpXj6852UVlTxl58nNNnSaYgOXh78/NJI3l9/mHvHnGLbkTx+2waK3ODCVFVpl1j3EdDlopZdy6sDDJulW2OIQJcBuo19HE4dhb3LtLLZ+AaoKogcCuOehp7jIWLQuYqi26W6jX8aTqVb5y/Tz5P4Hwi/CCb/RSuZNsKEnbgIWw6fZE9WAbdZZXCnD+tOTkEpK3dntep9P96UTmFpBXePjm302BnDo/GqNalyeXImFVXK7sqDnu5uPHtdf/JLKrjj7Q1cOWc1768/XGek3JKkDFbsyuLRib1qlJojmD6sO2WVVfxq4VYAl0hUaXAiB76Dkwebb7U4iqBuMGQmzPgEnjik291fw9jfagXSmAUSFKmfYcYn8Hgq3PgGlJyCd6fogINT6W3xFEa5uAoLEg/j7+3BtQP1gPrlvTvTNciHBetbr4pjZZVi7rpDXBrdiYTIhqO8AMICvLl+UFc+3XyUvGI9qXLpjgxiQjo0K4nihL7hrH1iPHOmDcTH042nFu1gxAsreXFZCpmndN373MJSnlmczMCojswcHWf3PRoiPlyHXafmFNGzsz89OztOcRlaibJi+PEleHsyHN/n2GtvfAP8wqDvtY69bkvw8tMhz83F2x8SboYHNsDYJ2HPV/DKEPjhr1Be4jg568AZYy6GWuQVl/HljgxuHhyJn7f+l7i7CdOGdGfOt3tJyy0iOsTP4ff9LiWbtNxiHr+qT5PPuXt0LB9tSuf9DYeZNjiKnw7kct9Y+1xitnh5uHHDxZFcP6gbGw+d5K0fU3n1hwO8vjqVKQkR5J8up6CknL/elNDicsN1MWN4NImpJ8xAvqtTUQqb58Kav0FhFrh7w4KbYOa34O+A6QUn02DvcrjsUfDwavn1XA2vDjBuNgy6Db75Hax6DrbOg0l/1uM6tb+/BVl6jCdzuw6tbsZEUaNc7KCqSpF4MJeRPUIbP9gOPtmcTllFFbcNPXtAfdqQKP713T4WbjjCk5ObrgCayjtrD9I1yMeuF2ufLoFcFh/K3J8O4eflQWWVYsqAxvN6NYaIMDQ2mKGxwRzOLWbuukN8uPEIhaUVPHplr1YLwb2qfxd+e1Vvpg42KVVcksoK2L4QfvgLnDoC0aNg6rvg5glzr4GFt+j5Jl4tDMTY/I5+wQ6+yyFiuyydomHaPB119vUT8MFtOjBg0HTI3g2ZSVqpFNq44zvF6kg0O5HWrg7oagwePFht2rSpWed+ujmdRz/ezvv3DnOYglFKMeEfPxDk68mi+0ed8/dZ721ic9pJ1s2e4NCZ2bsz8pn8zzU8ObkP943tYde5q/Zkc9c7G/H39qBzoDcrHxnbKunfC0rK2ZR2kjHxYa1itRhcmKoq2LUIVj0Pufuh68Uw/n/1i7D6s7Z7CXx4O/SZAje/1/xoqPISmNNPD+TfssBxz+DqVJbrAf9Vz0PpKRB3Hf4cMRC6JOjJo10G6JDsijLE03uzTS7IRjGWix1MSYjgxeUpvLRiHyPiQhzyQl1/8ASpOUX8beo5AXSATlX/za4slidn8rOBLbcQqnl37SF8PN2aNct9bHwYPcL8OJBTxN0DYlqtrkiAjyfjeps8WucV+cfg+xfg9Elw97KaZ611Tx3plLUDwvrCtPnQ55pzXTd9fwZXPQ/LZ2tXz6Q/N0+mXV9Aca7zB/LbGndPGH6fHpPJS4OwPuBZTzqlZrgKjXKxAx9Pdx4Y15P/+yKZnw7kMqpny62XBesPE+jjUW+01Zj4MCI7+fL++sMOUy7LdmawaNtRpl4aSccO9n9o3NyEX47twezPdjhU4RnamIpSbRXkpEDOHt3y0qBzPx2yGjNaz8lwBErpZJBfPQYVZXq+R2WZ/vVcWVZrvRSCe+gop4t+3rBFMuJ+LXPif6BjtH5Z2svGNyCkJ8SObfbjtWs6BOvmYIxysZNpQ6J49fsD/GPFXkb2aJn1crywlGU7M5gxPLrOmfGgX+S3Du3OX5fvOWt+SXMoKa/kuaW7mJ94mIGRQTw8Ib7Z15p6aSRje4U1ODfG0MaUFUNBBpQVQflpKLeWZcVn1otyLEWSAicOgqoO/RY9yS8oUs+v2Ga5hzp2h5jLtKKJHqV99vZSdBy+/LV2Y0UNg+tfhZAGXLHVrvqmfreuel6H1y57EjpGaTdZUzm2DdI3wqQXwM0EzzoSo1zsxNtDWy+/+3wna/YdZ0wL6qx/sjmd8krF9GEN/zqcOjiSOSv2snDDYZ6e0q9Z99qfXcCD728lJbOAWWPieGxi7xaN4YiIUSyuROZOmHe9Vh4N4eYBwXHQuS/0v0G7QsJ661/u1S6RqiqtfA79qGeY2yqboO56Il+/67TScW+k7s3uL2HJw1CaD1c+CyMebHxsxN4fbG7u2sqZew18MhPuXAqRlzbt3E1vgYevTvdicChmQL8ZlFVUMe5v3xMW4M2i+0c2y3qpqlKM+/v3hAf68NEvRzR6/P0LNvPTgVwSZ0+o18qpC6UUH29K5/eLk+ng5c7fbh5oxjHONzKS4L3rwMMHxv9Oz4vw6gCeNq162zugcYVQm7OUzWrY/522hHyDtZXQ73qIHXO2X/50no5GSvpADw7f8F8Ib94PoyZTmANvTtB5vO75VrveGuL0Sfh7X0iYCte+3LqynQeIiBnQb228PNx4cHxPZn+2g+/35jTrZb32wHHScot55MpeTTr+tqHRfLUjk2U7M5tcP72gpJynF+1k8fZjjOwRwpxpg4y1cb5xbJu2WDz94M4l2ipxNG5uWjGE99PpTMpP6+SJu76A5M/1fAmfIOg9RVs04qatlcIsGPsEXPZY28wd8Q/TdVfevEJnJb59kXaT1ce2hVBxGobc2/qyXYAYy6WZlFdq6yXYz4svHhhlt/XyP/M3s/7gCdbNHo+3R+OWSI2lE+DDR/c1bukkpefx4PtbOZp3mt9cEc//XN7ThPOebxzdohWLd6Ce6xHceAofh1NeAqmrtKJJ+UqHtILOyHvDazpdSVuT9pO25CrL9JhR5FA91hM1VOfYcvfQ1tgrg6FDCNyzou1lbIcYy6WN8HR341fje/LEpzv4LiWbCX2bPhExO7+Eb3ZlMXN0bJMUC5wZ2H/h6xT2ZhXUO6lwb1YB89al8cHGw4T5e/PhrOEMjnF8JIihCVRV6RDXggxdq8MvBELidUqOlpK+GebdAL5BcMeXzRtodwSePlaK+Mk6CuzgD3qy48Bb6w9rbW2iR8J9a2H/CjiyHtLWws5PLHk7aIXXMRpOHIDLn3SOjBcARrm0gBsvieTfqw4w59u9jO/TucnWy0ebjlBZpeyuKjj10kj+/s0e3l9/mGeu7V+zv7yyiuXJmcxbl8b6gyeslCrdeOrqvs0KNTbU4uAayNxBTaFTpWqto8NnC7P1PI6CTN0KM6Gq4tzrBXaD0Hj96z6015n1gIimDWYf2Qjzb9Sp1+/80nHhwi3Fwwvir3S2FJqwXrqNeED/j06la0VzZINebl+o+7vfdc6W9LzFKJcWUG29/PaTJFbsymJiE7LqVlYpFm44wqieIcSG2pcvLMTfm0kXRfDZlnSenNyHk8VlLNxwhIUbDpNTUEpUsC+zJ/dh6uAogv2MUmkxx/fDN0/r9OVNwSdIv7ACukDoGL2s3vbvrJXP8b064eLxvdrnX1Zw5nzfYIi9TA+Ox47VEVy1lc3h9TD/5+AXqhVLkElb0ygieuylYxQMuEnvKyvSit/D9Upany8Y5dJCbri4G/9etZ853+7jir7hjdYa+XZ3FkfzTvPU1X2bdb/bhnZnyfZjTH1tHbsy8qlSinG9O3P78GjG9DJpUhzC6TxY/VdY/18dgXXFH3SddDd3m5e9tRTR624e2kVkD0ppC+f4Xt2ObtFupV1WEdaACB3uGztGt/xjOlmjf7hWLIFmAmuz8XJ8IljD2Rjl0kI83N14aEI8j3y0nW92ZTLporpn2ucVl/HSt/uYn5hGt46+Taq+WBfD44LpFxFI+sli7rkslulDo+keYqonAnqM4/QJHaVUkKkthWrXVNdLdAlZnwZKA1RVwpa58N1zutztxTN0PquAVsqYLAKBEbrFWbPDlYITqXBwtW6pq2DHR9UnaGvmjiX6HIPBhTHRYg6gorKKiS+txsvdja8euuws66W8sooFiWnM+XYfBSXlTBvSnUeu7EVYQPPN8ZLyStxEHJrIsk1RSruGUldpRRDcQ487hPRsPA1FRZmVsmQ3ZKfo+RenjliKJKvuMY4aBML766ihqOF62SlGv+QProZls3W52e4jdZ6qroMc+dTNQyn9jAdXQ95hGPkr7WYzGNoYEy3mBDzc3Xh4QjwPf7CNr3dmMiUhAqUU3+/J4bmluziQU8SoniH87zX96NPF/qJatbFnEqVDUEpHPZ1Mg7xDcPKQXi/OPTPbu3qmd33uhqLjOs136io48D3kW9XwxN0mBQl6kDqkp46qCumhB7/z0nQ68JwUrViqFYi46XTgnWJ0Piz/zuDfRVsa/tY4h3+4LhN7dJMezD2cCEkfwyarOrZfZx1plb5RD4xPnWvN1XAR96KI7t/OzXOjGgzOwlguDqKySnHVS6sR4OXbLub5r1JYvTeHuFA/nrq6LxP6Nj2arM0pLdBupPxjOmy2IAPyM3SEzclD+uVeVnj2OR1CtSLIS9PzCQAQ/YKuVjah8WcslIzt+hCfID1Y3WM89BgHgZH6Grn7z7Tj+yD3ABQcO3PdTjFnrltz/V72j3OAdn9l7z4TPZSdrGeZj3iwedczGC4A7LVcjHJxIEu2H6upxx7o48HDV/TidqvuvENQSo8FFGbpF7q7l452qU5X7uGlK/S5ewFK55kqzNJpMYqyteuoKMdaZlsKJePsiKVqvIN0Le+O0fqXfaeYM+sdo8/M1ais0GMEOZaLqtrCOL4Pqsp1UaeooVqRxI3Xrqam1t0oLdSKLrBby4tBGQyGFmHcYk5kyoAIlidnEurvzcMT4unU1HDgijJLCVgD0IVZusxooTUoXTM4naVf2C3B00+nyfDrrH/9x43Tg8MBXc8sA7o0faKfu8eZOQXY1B6vLNeuM3uuVRtvf/BufuZmg8HgPNq9chGRScA/AXfgTaXUC86Sxc1NeOW2S/RGjZWRbaM4ss5WIgXW9ukTdVxN9FwG/3DdwvqcPZbg4a2tlwqr/kVFqVUPo1TvA+v8zlqR+FutrUIw3T0htGfb3MtgMLgc7Vq5iIg78G/gSiAd2Cgii5VSu1rlhmk/6TBVVVWrqbO3T+fVb2W4e2tlERCuB6yjR56ZZFczGB0OfmH2Z681GAwGF6FdKxdgKLBfKZUKICIfANcBraNcEB2h5Oaul9XbZzXRKcarI5Vqlta6T5DrRCIZDAZDK9HelUs34IjNdjowrPZBIjILmGVtlorIzjaQraWEAsedLUQjtAcZwcjpaIycjqW9yNnbnoPbu3KpywQ4J/xNKfU68DqAiGyyJ+LBWbQHOduDjGDkdDRGTsfSnuS05/h2OsW7hnTAthpQJHCsnmMNBoPB0Ea0d+WyEYgXkVgR8QJuARY7WSaDwWC44GnXbjGlVIWIPAgsR4civ62USm7ktNdbXzKH0B7kbA8ygpHT0Rg5Hct5KecFN0PfYDAYDK1Pe3eLGQwGg8EFMcrFYDAYDA7nglEuIjJJRPaIyH4RedLZ8tSHiBwSkR0iss3e0L/WRETeFpFs2zlCIhIsIitEZJ+17ORMGS2Z6pLzGRE5avXpNhG52pkyWjJFicgqEdktIski8rC132X6tAEZXao/RcRHRDaIyHZLzj9Y+2NFZL3Vlx9aQT+uKOe7InLQpj9doJCQzoAiIltF5Etr267+vCCUi02amMlAP+BWEennXKkaZJxSapCLxb6/C0yqte9JYKVSKh5YaW07m3c5V06AOVafDlJKfdXGMtVFBfCoUqovMBx4wPpMulKf1icjuFZ/lgLjlVIDgUHAJBEZDvwFLWc8cBKY6UQZoX45AX5r05/bnCfiWTwM7LbZtqs/Lwjlgk2aGKVUGVCdJsbQRJRSq4HaGTavA+Za63OB69tUqDqoR06XQymVoZTaYq0XoL/E3XChPm1ARpdCaaoLDnlaTQHjgU+s/U7/fDYgp8shIpHAFOBNa1uwsz8vFOVSV5oYl/uSWCjgGxHZbKWtcWXClVIZoF9EQGcny9MQD4pIkuU2c7r7zhYRiQEuBtbjon1aS0Zwsf60XDjbgGxgBXAAyFNKVde9donvfG05lVLV/fknqz/niEjza6A7jpeAx4EqazsEO/vzQlEuTUoT4yKMUkpdgnbhPSAiY5wt0HnAq0APtCsiA/i7c8U5S52CAwAABgBJREFUg4j4A58Cv1ZK5TtbnrqoQ0aX60+lVKVSahA6S8dQoK660E7/zteWU0QuAmYDfYAhQDDwhBNFRESuAbKVUpttd9dxaIP9eaEol3aTJkYpdcxaZgOL0F8UVyVLRCIArGW2k+WpE6VUlvWlrgLewEX6VEQ80S/tBUqpz6zdLtWndcnoqv0JoJTKA75HjxF1FJHqieIu9Z23kXOS5X5USqlS4B2c35+jgGtF5BB6CGE82pKxqz8vFOXSLtLEiIifiARUrwMTAVfO4LwYuMNavwP4womy1Ev1y9riBlygTy0f9lvAbqXUP2z+5DJ9Wp+MrtafIhImIh2tdV/gCvT40CrgJuswp38+65EzxebHhKDHMZzan0qp2UqpSKVUDPpd+Z1Sajr29qdS6oJowNXAXrQv9mlny1OPjHHAdqslu5KcwEK0C6QcbQnORPthVwL7rGWwi8o5D9gBJKFf3hEuIOdotFshCdhmtatdqU8bkNGl+hNIALZa8uwE/s/aHwdsAPYDHwPeLirnd1Z/7gTmA/7O/nzayHw58GVz+tOkfzEYDAaDw7lQ3GIGg8FgaEOMcjEYDAaDwzHKxWAwGAwOxygXg8FgMDgco1wMBoPB4HCMcjGcN4hIpZVVNtnKPPuIiLTaZ1xEnmrGOdVZr5NE5AcRiXawTHeKyCtNOM5DRJ63MtxWZ+N9ugX3PSQioc0933D+YZSL4XzitNJZZfsDV6LnZPy+Fe9nt3KxGKeUSkDP0P6d48Sxi+eArsAApdORXIZOpGgwOASjXAznJUqnz5mFTrAoIhIjImtEZIvVRgKIyDwRqcmQLSILRORaEelv1d7YZlkZ8bbXF5EXAF/r7wusfY+IyE6r/boJYq7DJvmfiMywued/rVIRiMirIrJJbGqAWPuHiMhPlpW2oTq7A9BVRJZZVsmLtW8qIh2Ae4FfKaVKrP4qUEo9Y3NMnc8iIp9bSVWT20FiVYMzcfYMUNNMc1QDCuvYdxIIBzoAPta+eGCTtT4W+NxaDwIOAh7Ay8B0a78X4NvQ/YBL0bOs/QB/dIaFi+s45xAQaq2/BMyy1vsCSwBPa/s/wC+s9WBr6Y62dhIsmVKBIdbfAi2577T2BwE+QBoQVUuGBGBrA/1Y77PYyOKLnlEeUvu5TDNNKUV1EjKD4XylOpurJ/CK6Cp/lUAvAKXUDyLybxHpDNwIfKqUqhCRdcDToutafKaU2tfIfUYDi5RSRQAi8hna1bS1jmNXiUg4OilltVtsAvqlvlGnmMKXM0krb7asBA8gAl3wTgEZSqmN1nPkW/cFXWzslLW9C4jm7JITZ3eQyF3owlAhwMhGnuUhEbnBOjUKrahzG+kbwwWIcYsZzltEJA6tSLKB3wBZwEBgMPqXfzXzgOnAXeistCil3geuBU4Dy0VkfGO3s0O0cegXfjLwrM35c9WZaoS9lVLPiEgs8BgwQelxmqVoi0SoP+V5qc16JZzzI3I/0L3ajaaUekfpcZdTaOuozmcRkcvRyRZHKF1Ncasli8FwDka5GM5LRCQMeA14RSml0G6iDKXTxN+OfolW8y7wawClVLJ1fhyQqpT6Fzo5Y0Idtym3UtIDrAauF5EOVkbrG4A19cmnlDpt3fMXIhKMTlJ5k2VBISLBViRZIFAEnLKsncnWJVLQYytDrOMDbNKhN4hSqhid7fgVEfGxznfnjMKt71mCgJNKqWIR6YNOa28w1IlxixnOJ3xFV/nzRNd/nwdUp4r/D/CpiExFpw4vqj5JKZUlIruBz22uNQ2YISLlQCZnLAxbXgeSRGSLUmq6iLyLzhoL8KZSqi6XWA1KqQwRWQg8oJT6o4j8Dl2F1A2d1fkBpVSiiGxFWzmpwFrr3DIRmQa8bKVvP422KprK08AfgZ0iUmCdPxc4ppQ6VNezWC62+0QkCdgDJNpxP8MFhsmKbLjgsaKndgCXVI9VGAyGlmHcYoYLGhG5Au1ietkoFoPBcRjLxWAwGAwOx1guBoPBYHA4RrkYDAaDweEY5WIwGAwGh2OUi8FgMBgcjlEuBoPBYHA4/w8uhJpouWvpBQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fundRate['usd_pledged_avg'] = rateProjects.usd_pledged.agg(np.mean)\\n\",\n \"fundRate['usd_pledged_med'] = rateProjects.usd_pledged.agg(np.median)\\n\",\n \"\\n\",\n \"x = fundRate['staLauDelta']\\n\",\n \"y1 = fundRate['usd_pledged_avg']\\n\",\n \"y2 = fundRate['usd_pledged_med']\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"plt.plot(x,y1,x,y2)\\n\",\n \"plt.legend(('Average USD Pledged', 'Median USD Pledged'), loc='upper right')\\n\",\n \"plt.xlabel('Days to Reach Goal')\\n\",\n \"plt.ylabel('USD Pledged')\\n\",\n \"plt.ylim(bottom=0)\\n\",\n \"plt.xlim(left=0)\\n\",\n \"plt.xlim(right=40)\\n\",\n \"plt.title('USD Pledged and Days to Reach Goal')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### What's Happening here?\\n\",\n \"\\n\",\n \"There's an odd abundance of projects that reach their funding goal either very close to, or on the deadline date.\\n\",\n \"\\n\",\n \"Kickstarter.com has a section of their homepage dedicated to projects whose deadlines are within a certain time limit\\n\",\n \"\\n\",\n \"We don't have data for funds raised per day, but it seems plausible that the presence on the homepage plus the urgency of a project being close to its goal and end date motivates backers on the fence to contribute\\n\",\n \"\\n\",\n \"It's also plausible that towards the end of a fundraising campaing, projects push more incentive messages to their target audience\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Effects of Staff Picks and Project Spotlights on Campaign Success\\n\",\n \"\\n\",\n \"As mentioned above, Kickstarter highlights certain projects on their homepage\\n\",\n \"\\n\",\n \"The dataset that I have includes \\\"Staff Pick\\\" tags, for projects that Kickstarter staff favors\\n\",\n \"\\n\",\n \"The spotlight tag is a little more ambiguous, but based on the success rate of spotlight projects, it seems like all successfully funded projects are spotlighted at some point\\n\",\n \"\\n\",\n \"The success rate for staff pick projects is almost 90%, and the rate for spotlighted projects is 100%\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"staff = merged[(merged['staff_pick'] == True) & (merged['source'] == 'Kickstarter')]\\n\",\n \"staffSucc = staff[(staff['state'] == 'successful')]\\n\",\n \"spot = merged[(merged['spotlight'] == True) & (merged['source'] == 'Kickstarter')]\\n\",\n \"spotSucc = spot[(spot['state'] == 'successful')]\\n\",\n \"unstaff = merged[(merged['staff_pick'] == False) & (merged['source'] == 'Kickstarter')]\\n\",\n \"unstaffSucc = unstaff[unstaff['state'] == 'successful']\\n\",\n \"mergeKick = merged[merged['source'] == 'Kickstarter']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.8954918032786885 1.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"staffRate = len(staffSucc)/ len(staff)\\n\",\n \"spotRate = len(spotSucc)/len(spot)\\n\",\n \"\\n\",\n \"print(staffRate, spotRate)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"execution_count\": 41,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"staffMoney = pd.DataFrame()\\n\",\n \"staffGrp = staff.groupby(['fullcats'])\\n\",\n \"noStaff = unstaff.groupby(['fullcats'])\\n\",\n \"staffMoney['avg_raised_staff_picks'] = staffGrp.usd_pledged.agg(np.mean)\\n\",\n \"staffMoney['no_staff_avg'] = noStaff.usd_pledged.agg(np.mean)\\n\",\n \"staffMoney['med_staff_picks'] = staffGrp.usd_pledged.agg(np.median)\\n\",\n \"staffMoney['no_staff_med'] = noStaff.usd_pledged.agg(np.median)\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"ax = staffMoney.plot(kind = 'bar', title='Crowdfunding Funds Raised by Project Category')\\n\",\n \"ax.set_xlabel(\\\"Category\\\")\\n\",\n \"ax.set_ylabel(\\\"Funds Raised\\\")\\n\",\n \"plt.figure(figsize=(600,300))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"the number of successful projects that were not staff picks is 74943\\n\",\n \"the number of successful projects that are staff picks is 19228\\n\",\n \"the total number of projects is 153333\\n\"\n ]\n }\n ],\n \"source\": [\n \"unstaff = merged[merged['staff_pick'] == False]\\n\",\n \"unstaffSucc = unstaff[unstaff['state'] == 'successful']\\n\",\n \"print('the number of successful projects that were not staff picks is ', len(unstaffSucc))\\n\",\n \"print('the number of successful projects that are staff picks is ', len(staffSucc))\\n\",\n \"kickOnly = merged[merged['source'] == 'Kickstarter']\\n\",\n \"print('the total number of projects is ', len(kickOnly))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"execution_count\": 43,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"staffMoney = pd.DataFrame()\\n\",\n \"staffMoney['StaffCounts'] = staffGrp.size()\\n\",\n \"staffMoney['NoStaffCount'] = noStaff.size()\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"ax = staffMoney.plot(kind = 'bar', title='# of Projects that are Staff Picks by Category')\\n\",\n \"ax.set_xlabel(\\\"Category\\\")\\n\",\n \"ax.set_ylabel(\\\"Number of Projects\\\")\\n\",\n \"plt.figure(figsize=(600,300))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"execution_count\": 44,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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M/s/fT+X4EPIYrzQdwJVg4byl9m9fhJrZGM+0uuOIrRNZ0/DkIgmDGQdIfgXMsTdqsKLMfsIOZfaG+kvUMSfPh0wUWTSbATs9zHG7a2ze3bzHcjLp6flynqXwomiAIgvZIYyu3A8eb2eX9XZ48yfx2I+4GfgY+56VjL9tkOnwQ2NrM7unkHAMy1lkQBEFdpIr8FdxMeWU/F6cZ2+OmyQm4uWuXTk8k6UB8PuAVnSoZiB5NEARBUDPRowmCIAhqZboLQrngggvasGHD+rsYQRAE0xX33nvvq2Y2tD/ynu4UzbBhwxg7dmx/FyMIgmC6QlJRhIRaCdNZEARBUCu1Khr5oj1PyuMUNY3lJWkHSY/JV8VsOfEpCIIgmD6pzXQmaRAeNXVT3M1ujKRRZvZYLs3y+FoRG5rZ6ykkQhAEQTCAqLNHsw4eU2t8CslyKVOvsvl14EzrCiH+CkEQBMGAok5FsxjdQ3Vnk4fyrIAHjLxDvq76yGYnkrSfpLGSxk6c2BshoYIgCIK+ok5F0yzkeePs0Jnx4JIb4ytH/kbSVAvomNk5ZjbCzEYMHdov3nlBEARBh9SpaCbQfU2I/Bow+TTXmq8w+Qy+5OryNZYpCIIg6GPqVDRjgOXTehKz4ovmNIaYvoYUCj6tB7ICHjY9CIIgGCDUpmjScswH4utaPI4v5/uopKPVtcb59cBrkh4D/g58N62/EgRBEAwQprugmiNGjLCIDBBMDww7/M8tjz17wlf6sCTFTC/lDHqGpHvNbER/5B2RAYIgCIJame5inQVBMGMSPa/pl+jRBEEQBLUSPZo2iBZVEARB+0SPJgiCIKiVUDRBEARBrYSiCYIgCGolFE0QBEFQK6FogiAIgloJRRMEQRDUSiiaIAiCoFZC0QRBEAS1EoomCIIgqJVQNEEQBEGthKIJgiAIaiVinQVBCRHjLgh6RvRogiAIgloJRRMEQRDUSiiaIAiCoFZijCYIgqAXaTWmNyOP50WPJgiCIKiVUDRBEARBrYSiCYIgCGolFE0QBEFQK6FogiAIgloJRRMEQRDUSq2KRtJISU9KGifp8CbH95Q0UdID6bdvneUJgiAI+p7a5tFIGgScCWwKTADGSBplZo81JL3MzA6sqxxBEARB/1LnhM11gHFmNh5A0qXAVkCjogmCIJjmiGCqvUedprPFgBdy2xPSvka+KukhSVdKWqLZiSTtJ2mspLETJ06so6xBEARBTdSpaNRknzVsXwcMM7NVgZuAC5udyMzOMbMRZjZi6NChvVzMIAiCoE7qVDQTgHwPZXHgxXwCM3vNzN5Pm+cCa9VYniAIgqAfqFPRjAGWl7S0pFmBnYBR+QSSPpHb3BJ4vMbyBEEQBP1Abc4AZjZZ0oHA9cAg4Dwze1TS0cBYMxsFHCRpS2Ay8F9gz7rKEwRBEPQPtS4TYGajgdEN+36U+//7wPfrLEMQBEHQv8R6NMEMQ7irBkH/ECFogiAIgloJRRMEQRDUSiiaIAiCoFZajtFIWrVI0Mwe6v3iBEEQBAONImeAM9Pf2YA1gEfx2f6r4HNk1q+3aEEQBMFAoKXpzMw+bWafBp4G1jaz1c1sNXz2fkysDIIgCCpRZYxmZTN7INswsweBNesrUhAEQTCQqDKP5ilJZwO/x4Ni7go8VWupgiAIggFDlR7NHrj57HvA4cD4tC8IgiAISint0ZjZu5JOA642s3F9UKYgCIJgAFHao5G0OfAwcGPaXl3S1XUXLAiCIBgYVDGd/QRYF3gDIDkGLFdnoYIgCIKBQxVF86GZvdGwr3GlzCAIgiBoShWvs8cl7QDMJGlp4GDg7nqLFQRBEAwUqvRoDsQnaX4MXA28DxxSZ6GCIAiCgUMVr7P/4a7N35M0EzCHmb1Te8mCIAiCAUEVr7PfSRoiaU7gEeAZSYfWX7QgCIJgIFDFdPYpM5sEbA3cACwO7FlnoYIgCIKBQxVFM6ukmYGtgGvM7AN8vCYIgiAISqmiaH4DPA/MB9wqaUng7VpLFQRBEAwYShWNmZ1qZoua2RfNzIAXgE3qL1oQBEEwEChaYXNnM7tE0kEtkpxeU5mCIAiCAUSRe/N86e/QvihIEARBMDBpqWjM7Ffp7w/7rjjtM+zwP7c89uwJX+nDkgRBEATNKJ2wKWk23J15FWD2bL+Z7VdfsYIgCIKBQhWvs98Bw4DNgX8CywLvVTm5pJGSnpQ0TtLhBem2k2SSRlQ5bxAEQTD9UEXRrGBm3wfeNrPfAiOBT5YJSRoEnAlsBgwHdpY0vEm6wcBBuBILgiAIBhiVlglIf9+QtDIwGFiqgtw6wDgzG58meV6KT/ps5BjgRCr2koIgCILpiyqK5reS5gOOAq4HngJOqSC3GD7nJmNC2jcFSWsAS5jZn4pOJGk/SWMljZ04cWKFrIMgCIJphSrRm3+d/v07sGQb51az00056JGgT6VC3DQzOwc4B2DEiBGx6FoQBMF0RGGPRs68ue1ZJO0l6ZEK554ALJHbXhx4Mbc9GB/ruUXSs8B6wKhwCAiCIBhYtFQ0krYHXgeeknSzpM8A44Btgb0rnHsMsLykpSXNCuwEjMoOmtmbZragmQ0zs2H4qp1bmtnYzi8nCIIgmNYoMp0dBaxrZk9KWhu4HdjVzK6ocmIzmyzpQHxcZxBwnpk9KuloYKyZjSo+QxAEQTAQKFI0H5jZkwBmNkbSs1WVTIaZjQZGN+z7UYu0G7dz7iAIgmD6oEjRLNQQUHOu/LaZRVDNIAiCoJQiRXM+3QNqNm4HQRAEQSlFQTWn6WCaQRAEwfRBlQmbQRAEQdAxoWiCIAiCWimNDBBMf8QaPUEQTEtUWY/mQOB3ZjZJ0q+BNYDvm9nfai/dACEq/iAIZmSqmM72S0rmi3hQzG/g0ZaDIAiCoJQqprMsiOVmwPlmdm8KiBkEQRD0AgPd6lFFYTwoaTSwBfAXSXOTi8IcBEEQBEVU6dHsBayFL2L2jqQFgX3qLVYQBEEwUGipaCStmtt8D1hc0uJp+6NaSxUEQRAMGIp6NGemv7PhnmaP4ouZrYIvAbB+vUULgiAIBgItx2jM7NNm9mngaWBtM1vdzFbDzWiP91UBgyAIgumbKs4AK5vZA9mGmT0IrFlfkYIgCIKBRBVngKcknQ38Hvc22xV4qtZSBUEQBAOGKj2aPXDz2feAw4HxaV8QBEEQlFLaozGzd4GT0i8IgiAI2qJKrLP1gKOApfLpzWyFGssVBEEQDBCqjNGcDxwG3EvMnwmCIAjapIqimWRm19VekiAIgmBAUkXR3CzpeOCPwPvZTjN7qLZSBUEQBAOGKopmo4a/4G7On+n94gRBEAQDjSpeZ5/ui4IEQRAEA5MqXmc/aLbfzI7r/eIEQRAEA40qprO8p9nswFfwAJtBEARBUEoV09nP8tuSfgZcU1uJgn5joK/yFwRB/9DJksyzActWSShppKQnJY2TdHiT4wdIeljSA5JulzS8g/IEQRAE0zBVxmjup2vp5kHAJ4DS8RlJg/A1bTYFJgBjJI0ys8dyyS42s7NT+i2BU4CRbV1BEARBME1TZYxmu9z/k4GXzOz9VolzrIMv/zweQNKlwFbAFEVjZpNy6eeiS6EFQRAEA4SipZy3MrNrzexpSUMalEIVFgNeyG1PANZtks+3gEOBWYFNWpRlP2A/gCWXXLLNYgRBEAT9SdEYzVG5/2/p4Nxqsm+qHouZnWlmy+LLEBzZ7ERmdo6ZjTCzEUOHDu2gKEEQBEF/UWQ6U4v/qzIBWCK3vTjwYkH6S4GzOsgnmMEI77ggmL4oUjRzSPoU3uuZPf0/ReFUiHU2Blhe0tLAv4GdgK/lE0ha3sz+lTa/AvyLIAiCYEBRpGgmAr9K/7+a+x8qxDozs8mSDgSux73VzjOzRyUdDYw1s1HAgZK+AHwIvE6s3BkEQTDgaKloeiPGmZmNBkY37PtR7v+De5pHEARBMG3TyYTNIAiCIKhMlXk0QRAEvUY4c8x4RI8mCIIgqJWiCZurFgnGCptBEARBFYpMZ2cWHIsVNoMgCIJK1Op1FgRBEASVnAEkrQQMxxc+A8DMLq6rUEEQBMHAocoyAUcCXwRWwidffgm4HQhFEwRBEJRSxetsR+BzwH/MbDdgNcItOgiCIKhIFUXzrpl9BEyWNBh4CVim3mIFQRAEA4UqPZP7Jc0LnAeMBSYB99VaqiAIgmDAUKpozGz/9O+Zkq4HhphZKJogCIKgEqWmM0k3ZP+b2Tgzuy+/LwiCIAiKKIoMMCvuzrxwGpvJ1qIZAsR6ykEQBEElikxn3wIOBRYCHsvtnwScXWehgiAIgoFDUWSAU4FTJR1iZr/owzIFQRAEA4gqXmdnSvomXbHNbgF+Y2aTaytVEARBMGCoomh+CcyFuzcD7AqsCexXV6GCIAiCgUMVRbOema2W275B0oN1FSgIgiAYWFSJDPCxpGHZRvr/43qKEwRBEAw0qvRoDgNuk/QU7uK8HLBPraUKgiAIBgxF82jWM7O7zexGSSsCK+OK5jEze7fPShgEQRBM1xT1aH6FD/qTFEuEnQmCIAjapsoYTRAEQRB0TFGPZhlJo1odNLMtayhPEARBMMAoUjQTgZP7qiBBEATBwKRI0bxlZrf25OSSRgKnAYPwaAInNBw/FNgXmIwrtr3N7Lme5BkEQRBMWxSN0TzbkxNLGgScCWwGDAd2ljS8Idn9wAgzWxW4EjixJ3kGQRAE0x4tFY2ZbdvDc68DjDOz8Wb2AXApsFVDHn83s3fS5t3A4j3MMwiCIJjGqNPrbDHghdz2hLSvFfsAf2l2QNJ+ksZKGjtx4sReLGIQBEFQNy0VjaQN09/ZOjy3muyzFnntCowATmp23MzOMbMRZjZi6NChHRYnCIIg6A+KejSnp793dXjuCcASue3FgRcbE0n6AnAEsKWZvd9hXkEQBME0SpHX2YeSzgcWk3R640EzO6jk3GOA5SUtDfwb2An4Wj6BpDWAXwMjzeyVtkoeBEEQTBcUKZrNgS8AmwD3tntiM5ss6UDgety9+Twze1TS0cBYMxuFm8rmBq6QBPB8TAQNgiAYWBQt5fwqcKmkx82so/VnzGw0MLph349y/3+hk/MGQRAE0w9VvM5ek3S1pFckvSzpKknhhhwEQRBUooqiOR8YBSyKuydfl/YFQRAEQSlVFM1CZna+mU1OvwuA8DEOgiAIKlFF0UyUtKukQem3K/Ba3QULgiAIBgZVFM3ewA7AS8B/gO3SviAIgiAopci9GQAzex4Il+MgCIKgI2KFzSAIgqBWQtEEQRAEtRKKJgiCIKiVyopG0nqSbpZ0h6St6yxUEARBMHBo6QwgaREzeym361DcKUDAncA1NZctCIIgGAAUeZ2dLele4CQzew94A4++/DEwqS8KFwRBEEz/FC3lvDXwAPAnSbsBh+BKZk4gTGdBEARBJQrHaMzsOuBLwLzAH4Enzex0M4v1lIMgCIJKFC3lvKWk24GbgUfwhcu2kXSJpGX7qoBBEATB9E3RGM2xwPrAHMBoM1sHOFTS8sBPccUTBEEQBIUUKZo3cWUyBzBlmWUz+xehZIIgCIKKFI3RbIMP/E/Gvc2CIAiCoG3KlnI+ow/LEgRBEAxAIgRNEARBUCuhaIIgCIJaCUUTBEEQ1EoomiAIgqBWQtEEQRAEtRKKJgiCIKiVUDRBEARBrdSqaCSNlPSkpHGSDm9y/DOS7pM0WdJ2dZYlCIIg6B9qUzSSBgFnApsBw4GdJQ1vSPY8sCdwcV3lCIIgCPqXolhnPWUdYJyZjQeQdCmwFfBYlsDMnk3HPq6xHEEQBEE/UqfpbDHghdz2hLSvbSTtJ2mspLETJ8ZSOEEQBNMTdSoaNdlnnZzIzM4xsxFmNmLo0KE9LFYQBEHQl9SpaCYAS+S2FwderDG/IAiCYBqkTkUzBlhe0tKSZsXXsBlVY35BEATBNEhtisbMJgMHAtcDjwOXm9mjko6WtCWApLUlTQC2B34t6dG6yhMEQRD0D3V6nWFmo4HRDft+lPt/DG5SC4IgCAYoERkgCIIgqJVQNEEQBEGthKIJgiAIaiUUTRAEQVAroWiCIAiCWglFEwRBENRKKJogCIKgVkLRBEEQBLUSiiYIgiColVA0QRAEQa2EogmjxtlKAAAgAElEQVSCIAhqJRRNEARBUCuhaIIgCIJaCUUTBEEQ1EoomiAIgqBWQtEEQRAEtRKKJgiCIKiVUDRBEARBrYSiCYIgCGolFE0QBEFQK6FogiAIgloJRRMEQRDUSiiaIAiCoFZC0QRBEAS1EoomCIIgqJVQNEEQBEGthKIJgiAIaqVWRSNppKQnJY2TdHiT47NJuiwd/6ekYXWWJwiCIOh7alM0kgYBZwKbAcOBnSUNb0i2D/C6mS0HnAr8rK7yBEEQBP1DnT2adYBxZjbezD4ALgW2akizFXBh+v9K4POSVGOZgiAIgj5GZlbPiaXtgJFmtm/a3g1Y18wOzKV5JKWZkLafTmlebTjXfsB+aXNF4MkW2S4IvNriWBEhF3J1y00PZQy5gS23lJkN7eCcPWbmGs/drGfSqNWqpMHMzgHOKc1QGmtmI6oVL+RCru/kpocyhtyMK1c3dZrOJgBL5LYXB15slUbSzMA8wH9rLFMQBEHQx9SpaMYAy0taWtKswE7AqIY0o4A90v/bATdbXba8IAiCoF+ozXRmZpMlHQhcDwwCzjOzRyUdDYw1s1HAb4GLJI3DezI79TDbUvNayIVcP8lND2UMuRlXrlZqcwYIgiAIAojIAEEQBEHNhKIJgiAIaiUUTRAEQVArM5yikTSTpB164TzzSVq1N8o0EJA0SNKikpbMfhVkDq6yr7eQNI+kUyWNTb+TJc1TV379TZV3NHmFniLpj5JGZb++KmM7SPpkf5ehjBR6q20ZSSfVUZ5phele0Uj6W5V9GWb2MXBgq+Mled0iaYik+YEHgfMlnVJB7sQkN4ukv0l6VdKuFeTmlPRDSeem7eUlbV5BboWUzyNpe1VJR1aQm03S1yT9QNKPsl8FuW8DLwM3An9Ovz+VydHl2p5nzwr5rSDpXEk3SLo5+1XI7zxgErBD+k0Czi/IZ/6iX4Vydvrcl5U0W/p/Y0kHSZq3wvV18o5eAzwLnAGcnPtVopNrlHRj/nqSQry+QnZnS7pH0jer3o90/gMlzVc1fQ8ZJ+kkTR3XsSVm9hGwltR++C1Jc0maKf2/gqQtJc3S7nlqx8ymyx8wO5B9TPOl/+cHhgGPl8j+EPh/+GTRTG7+Cnnen/7uC/wk/f9QBbkH0t9t8Nhu8wMPVpC7DDgMeCRtz5Gdq0TuVjzW3P25fY9UkPtrLs/vZL8KcuOABdp4djsD1wGv43Opst/fgZsqyD8IfCNd41rZr+pzKNuXO/YMMD79/QgP7fFa+v+ZGp/7A/jUg+WAp/GAs6Mr3tu23lHgn1WfW29dY/69LNrXQnZ54Pj0zl0MbFpB5tiU/nJgJMnbtoLcW3hjJP97AbgaWKaFzGDg68CdwN146KwhFfI6OX0DuwHbZr8KcvcCcwKL5cr2h5480zp+dYagqZv9gUOARfGbnbUGJuFRo4vYO/39Vm6fAcuUyM0s6RN4a/iINsqatTC+DFxiZv+t2HhZ1sx2lLQzgJm9W7HVM6eZ3dOQdHIFucXNbGSVgjXwAvBmG+nvBP6Dx2XKt57fAh6qID/ZzM5qI7+MdyVtZGa3A0jaEHi3VWIzWzqlOxsYZWaj0/ZmwBcq5Nfpc//YfB7aNsAvzOwMSfdXEaT9d/Q0SUcBNwDvZzvN7L6K+XVyjR9LWtLMngeQtBRNQk81w8z+lXrnY4HTgTXSN/EDM/tjC5kjJf0Q+CKwF/BLSZcDvzWzpwuyOwWPZnIxXr/sBCyCx1o8D9i4SV5vAecC50r6DHAJcKqkK4FjzGxci7zmxxsxm+RPBzS9phwys3ck7QOcYWYntvGu9BnTraIxs9Mk/RJ/wY5pU3bpDrM9Gp+AeruZjZG0DPCvCnLXSXoCr9S+KWko8F4FuQ8kzUH6CCUtS64yKODVlDaT2w6v2Mu4U9KnzOzhCmmRdGj6dzxwi6Q/072yamqyMbPngOeA9avk04TrJH0Tb73l8ysLX/QN4EJ1jcu8TgVTHbC2mR2Qy+cvkqq8c50+9w9T42IPYIu0r6o55Ce0945+Cm9FbwJ8nPYZ3Su8Ijq5xiOA2yXdmrY/Q1fQ3JbIx5v2Ar6Cm2m3MLP7JC0K3EVBpWxmJukl4CW80TUfcKWkG83ssBZiI81s3dz2OZLuNrOjJf2gRRkHpfLthVtXTgb+AHwaGA2s0KJ8e7UqewmStD6wC77sCkyL9Xp/d6l6+gPu6lDuk3irb/fsV2MZZ8Nf7EFpey5g4Qpym+JmsIn4y/ossHEFuWWAm4B3gH8DtwPDKsg9BnyAt9geAh6m2OxyVNGvQn7b4pXgm3hP9C1gUgW5Z5r8xrfxPIZQwZyRS389cCRecSyFV5TXV5RtfO6LVJAZjrfWd07bSwOHV8xvwyr7cseeAGbt4fvdyTUuCGyOK9IFK+ZzW/pW52hybLcCuYNwq8f1wPbALGn/TMDTBXJ3pTpipvTbAbg7HWtqcsUbXb8FNmhy7PSCvFYA/kaXmXxV4MgK9+QzuMnte2l7maJ8+uvX7wXo8QV4C+6rVLS7Jpmj8PGAl/HB4JeAKyvIXQjMm9ueDw+tUyZ3X5V9DceFjyEtgLeQNq/6QebOMRcwuI30SzX71fjsxgEr9+G7snCqBP6StocD+1SQmx84Dbg//U6j2pjenLiCOidtLw9s3maZ5wNWbSN9W+8aPia3UA/uaeVrBFZKf9ds9qvxuR/d6j0uev9SpX0dPjY3Mf2/HD5WulELmbk7LGPb46p4aK+T6rpvvfmb9rpY7XMoXqFOlvQeXkGbmQ0pkNkOWA1/qHtJWhj4TYW8VjWzN7INM3td0hqtEktaBB+kmyOly4zXQ/APtCVmZpKuMbO1cC+uykg6DjgxK2vyuPmOmZV5nr1VcV9jfjcC2zfkd6mZfalE9GUze7zs/E3ymwU3g30m7boF+LWZfVgiegHesMjGLp7CK9rfFgmZm+QOljQEHz95u2JRz8db0huk7QnAFZR45Em6BdgSN4E8AEyUdKuZHVogs37KZ2jOpAn+rhW53C4MPCFpDN3NkFsWlTFHO9d4KG4ia+bVVmquS2NqP8YbQDPT9a2Xja0ubW6uzZ/rIjPbrej9M7PxdJkuG7m9xf7jmoxRvYnHd7y2oIxtj6ua2UeS1ipKM60w3SsaMxucXDmXxz3RqvCumX0saXKqPF6h3BEAYCZJ85nZ6+DurxTfwy/hYwCL4x9X3mGhqY23gbslrW1mYyqkzbOZmU05f1KIX8ZbnkXch/eiXk9lnRf4j6RXgK+b2b0t5IY2UcALVSjnWEmX4S62+UqubAD0LHzM4ldpe7e0b98SuQXN7HJJ30/5TJb0UVkhJX0K+B3es0HSq8AeZvZIiWinzhzzmNkkSfsC55vZUZLKnCRmBebG38fBuf2T8IZVK46qUJ4iKl+jme2X/n6uw7x+C/wfrthKn1uOVfIbaRyltIJO401fx02mU75zM9u7lQxeB62EK1twa8ujwD6SPmdmh7SQ63Rc9X75vKcrgP/lylj2DfUp072iSR/jwXhl/gCwHu7V9PkCsbHJD/9c/KV9G7inQnYn4wPmV6bt7YGfFqSf18w+J+lIMzu2wvkb+Rywv6Tn8Jcoa8GVTRQdJGk2M3sfIDkUzFYhv78CV5vZ9Unui7g76OV4pb5uC7mPOvQiGoKPI30xt6+Kp83aZrZabvtmSQ9WyO9/khag62Nej2recr8GDjWzvye5jfEouRsUCdG5M0fb3o1mditwq6QLGlvvJYylq+G1Al5J/qUN+bavUdL2wF/N7K3kQbYm7pFV5i31pplVLltqUPwAtyhMynbj45BVohxfC/wDH++sqtiWAzYxs8mpDGfhHn2b4mOerfhWKtNKkv6NjzvuUiG/Tr3V+pb+tt319Ic/vNnp8udfCbisDflhtGcDH45P+Pw2MLwkbVamwvGYAvmOxkzweTC3414oe6f/D6sgN7bVPornm4wEngcuSr/ngC/V+Mzvw1vS2fYyVe4xXqHdgSuXO3DTWemzp8m8kGb7mqTp1Jlje9wZ46zc9V1V8d6sgFdYNwA3Z7+C9D2ah9HJNZIcTICN8Ip8Kwrm89A1jnMCcBLurVh5bAc4vsP3rHTOWhOZJ/EeabY9D/BE+r/lXCHcvAe5cdVs30D4TffLBEgaY2ZrS3oAWNfM3pf0gJmtXiAjvLWwjLmr4pK4p0zTXo2kIeamjKazwa2FW62kS/CPYig+8W7KISr0TNQijIulnkOJ7GZ4r07ADZZ6KSUyN+CeL5emXTviFclIYIyZrVkguyDemxTuCVi63nlqQZ+Fe+B9MrmvbmklvT9Jn8fHBsan/JYC9rLU4yiRnRlYMck9aeXjOki6GlduF6VduwIjzGzrCrIL0HVf7q5yX3pC6tmdTYN5yVqYPSXdZ2ZryqM7zGE+D6Pw+2lyjrauUdL9ZraGpOOBh83s4mxfi/RFz9XMrOnYjqSVzOwJSU3fWyuZKyTpWOBOS/OnqiCfz3IkPm4ofBzxOHw+zY/N7Lst5O5r/L4k3Ws+RluU3+J4VIcN8Z7M7cDBZjahapn7goGgaK7GfdYPwbuPr+Pui18ukDkLnzOwiZmtnAavbzCztVuk/5OZbS7pGfxhKv/XCgYjk0PA9fjgbjesxMQh6eFcPrPjbq5PmtkqRXKdkpTFUXhLU/hL+xO8B7CktZhs1q7izsndCnwXH8hfI+17xMxKY1rJQ7RkCuMJS2bCFmm3LTqXldiz0/vxE7ruy214pfF6hXKuytQ2/rL8OlLASba0cmpIfz/wTTz6wD7mixM+bGafqii/Id7y/5889MyawGlF77akP+Fu91/Ax0reBe6x7ubQHiPpHDPbr4WiaqmgcvJv4T2M94EPqeZoRDJ7rpPS32NmjUvY59OuhI8hnYh/CxlDgO+WfetyR5yL6d4I2sXMNi2S62ume0WTR9Jn8a7qX83sg4J0WSvu/lwF92Bvv+i9TWqZ7W9m+5ek2xb4GbAQ/rJX+kB6UK62FHdOLuuN5p9Dy9a0pE3M7OZWiqNVBS7p/PTvQvi4ShYX7XPALWZWqIhy52nL60zSefh8iEfJTYa04sHkjhRwrrd9EO7cUmkya/pmvgPcYWY/k0/wPMTMDqpwiSQnhdXw6/wdPmN+WzP7bIHMnHgv+WHzmf6fAD5lZjeU5LUAXQ2hrPV+tJm9VqWsfYWkLenyiLzVzK4rSLsVsDXeEM0HM30L99y8sySvqb6XdnukfcF07wyQx3xAtAofyj1PsgHMoXRVBIV02ELN4jMNJ+cZV9QTaob5LOjCyjtxIj5rupLrsKRfmNkhkq6jySC+lbu6rpsp7pT+dUmzVsi6XU+bz+JKopnLacsBUEuzrlNLeriZ/Sdtf4LycEU98Tpbz8wqB1fM0UkIoXvp6v1C99ax0cKrMn0zt+a2x+PKqiqTzcxShXm6mf1W0h4lMgviTgh58/ATFfK6FO9NfjVt74K7pxeGA0rmxEuBy6045EyWvmOTm6QTgLXx8SqAgyRtYGbfb3Gua4FrJa1vZneVla0JWRDTS9L2zrhzwDTFgFI0bXA63uJbSNJPcffPKtGNm7ZQKffwOB9viZ2Kt6L3oqtCKMovPx9iJtwsMbFMjvbnp2Td7p+3IZOnU8XdzNOmZeRfMzsq/e00XMewTMkkXqZFSJAGOvU6u0vScDN7rM1ytu3qam2GVeqFxkXGW3Lvrl2Bz6T3oCxczp9pYhKmwQ25CfNb93BTx0oqHSfDews7ApdL+hhXTpdb67HOnsz3+TKwunmUeCRdiE/ybapoctwv6Vv4Pcg3Rgt7v7izzy/xugXcyaVMps8ZUKazdki20Wyw/G9VKmZJj3XSQs3s5nnbt6R/mNmnS+Tycxwm4x49V5lZYSwpSafhwf/anZ+SP8d8wBJmVhrkUtIu+Ie8Jh49YTvgh2Z2ecW85gJmMg9IWCX9bHirdhjde5ZHl8j9Ep9vdQleYewEjDOzb5fITWVWrWJqlQdVvA6PPPE+1Z1AlqFLkb1OUsBm9myRXJJtZgZ8EzdTvZJLt5aZ3ZtMZ1NR1TqQxiC/hjuL/CP1UDY2s99VkU/nqGoS/jneE8req+2AVbIGSMW8lsejt+9iZm2vHVPh/A/h1//ftD0/bp4te+ZX4L26r+GRDHbBo9DXtj5TXzJDKho19x57y0o8kCT9Fji53RaqpDvwoHpX4qaffwMnmNmKJXLbm9kVZfuayJ3fZHeVsYFbaJiRjtuYW85Iz8l2orjnxWNXDaO7wig03Uj6K155NnpWla6jkiriTMHfZmZXV5DpyOtM0ji8dfwwuR5e0UB5g3xbCjjJ/Bn3dMwGwDfGw9WvgI9nXNRCtCNSGd8zn6U+ZR5O2bfU5DxTeV01SZMNzmf3cia6JikWjkFKGobPS9oRf2cuq/i+bMDU72dLJSqfuHoCfv8zr7Pvm9mlrWSSXOaJ95CZrSqPfnG9lTsshNfZtIqkZ2kyAx4fRG05A74HLdS1gcdTPsfgHiUnmdndJXLNXB5LP8hOyb3s++K9maOyF79E7iIz261sXxO5bM2Oxor4whK5Sp5pPSW7hmTCHEaX19mt+FovhV5nkm4uqyhayHXUY0uy1wH7mtnLaXthuqIm3JbdN3V5NDal7Jnn8rsXV9zz4c9yLPCOmbWcbNjEJLwWbhYrC1nUEZL+iZvzrsAVzPiKchcBy+KNrqxBYxUaQp/Ax2mEzw96qUJe95jZOpJuw70AX8I91grHcTWdeJ3NqGM0nc6APw8Pd9KtYiwi2ax3MPeffxsfnymT2Qy39S4m6fTcoSFUWFdG0uz4ZM127b2drrfTUYgPYPYqvaUmtLucwe1mtlFqEecr1zJvvLXkUQ72wMfWMrf2TLaMJyRdjDdO2jFhXktXj61KJIE8wzIlk3gFWMF8nZh8L6N0pdaKNFsP5YESmcF03cfJ+P25qlJmbtLtFm7KzG4rEdvDzKo4GzQyAnceKW2NN3EcyHoUi0patMiBIHFOurYf4t5ncwOlq9vi4Z/yFowLJLUKc9NvzKiKZoR1X1/kBknHmdmhqTXZiufNrK311JNJYS1JqvLCJl7EK5kt09+Mt/BYT2VchNt7v0TO3ltBLltv5w6rsJaJeh7i4yJJX8cDMFZxxc1a4TMDe0kaT4WepZltlP4Obna8gLPxRskyJC+prCgUeHLlmCOVr90QO50uQAfwD7l3XT7W1m3JxJWPR9dOmJoipKnXQykb+xiNvzfD6KqDDscdbYoyahZu6i5aDM5L2tXMfg98WR7rrxvWYr2kHI/gY51VYo4VmeFKA4aaWRbU91aqxV3MmC68zmZU01lHM+Al/Qo3f7XVQpV0Mt4KayvwnaSZLcVMaodO7b2dIul4a+G+WSL3LTxW3Bt0tXCtlbkg9S5aUlZ5JieJS61NN1JJZ5nZN9qR6QmSzsF7B5V6bA2ywpXLhjBl0u1VrRo58nhvZwAr44E5BwH/KxrvaJBvex6OpCfxpdQfoY2xq9TQWBuPPrB6Ghf8iZnt2CL9/mb2a3V3qsll19wUqS5PvMHA6ngcxE4iW1cmmTiPAxY1s80kDQfWN7PCyOLJ+eKX+Lic4XEeD+7FhkSvMKMqmk5nwHc6yN6WXE/t5z2w93Y8I70TJD2Nz8FpKyyL3PV3gnm4oY1JkwUtF0G6hdweeKNiBdy9/TIzG1sk0xM6NWFKegwPzvgMbYwFdljGsbj33RW4qWh3YDkza8d0iqS5zOx/5Sm7TJkdlLXtcFNJbkMzu6NsX+5Yy8mmUOyRpw6XsJD0F9ISFma2mjxU0v1WMULDtM4MqWj6Grkv/cHWfb2WkwsUTU9b7vviNu9P4WuwzI27G/+6RK7jkDCdIA9vvpOZvdOm3AN4pTgMN/WNAla0grBDDfLz463+nfCGxfLt5N9GOTtyWW31/Iuee6fjUJLGmtmIvNOHpDvNrGyOUCa/Ph6+f24zW1LSarir8jcLZD6Pm3j+RnuWgbbDTSW5jpxqkrlxqsjWRUpD0m9wx4PMoWU34CMzK1zCQu1HyTiD4sZoO5Nua2eGHKORTyg8jKlbmnW5Era1YFqn3V519+bJnA6yme9zVThFJzPSe8JHwAPyWFT5CqfsI/nYfC2ZbYFfmNkZSlEJKrIcXmkMw5evrovlzGx7SVuZ2YXJMaA0uClwrDXx4sMrrab0YBzqHXkUhwcknYiPR1R5VzJ+gY8Fjkr5Pyj3zixiL/z+z0IbE5/NbJv074/TOzMPPobWFHW+GFzGbcCnU8Pwb/g43Y4Uh+/vqyUssp74hnjEkcvS9vZ0H9edJpghFQ0eHuIy3PPmANyrqMqM+/NxV8Lt0/auaV+ZK2G7C6aR0uVbp7PiH2aR/TyrZFbEbdmZ48IW+EdTRqeLL2WeZgvT3R23LMr0NenXLh/K5yvsTlc4mrLZ6Ej6GbAtHkn7MnwNlEJzWw/JWr5vSPokbsIcVkGu04W6ZsJD8LfTA90NdzE+EHc0WYKuEC+VMLMXGhonZWu3rNauSajx2orMVzk6XQxuSrbWvkfdR5KWtRTqJo1ZVVnL5lD8e11WPu9uaFEZLU0BkLQn8LmslyXpbHyJiGkLmwbWKujrH3Bv+vtQbt+tFeSmWp+i2b4maXbHvb6OwU0oTwC7dVDurYHjKqS7gbSmRdoejAcaLZNbBl/k6R18UuntVFv/5tv4uuqP4q7fD+fvbQ3PbzgeRmjntL00cHgFuQPwVTb76j3bF59f8hl8SYNXcLNSq/Tfxz0LJ+OV4aS0/RoV11TBG1FL9uE1Xon3Gu7DK/b/hztcFMmcS8laTr15bVXe4RZy9+OD7HfjEQjAIywUyXweX5vpFtyD7FlcEVTJb2a8kfFJ3CRYReZJfA5Stj0fHuG9T55/1d8MOUYj6W4zW0/S9XiF9SJwpZktWyJ3Ez7mkXcl3MvMilbzzGSH43blbOZ8RyabrOwlaZ7AW43ZCpuz4Qt1rdQifeNcljnIzbq2EjdQ+Qz4da3NKLrqfA346QJJ32HqeTdv4A2dli3jTr34kuzNeG/2Hrp7ODb1lGryDLL0lZ5Bcqw5DQ9sKbyRc3DRuyDpcXwiZFvODu1eW07uRmB76z5GeqmVTBDtxKMuyVVewqJBrq0oBElmL/z5ZZEgPot74l1QJc++YkZVNJvjK/stgY+5DMEfTuEcmRauhAdZhYXIOixnPm7VTPgA+GfNbP0SuSPwSZdXp3Jug3tYHd8ifeb+mZncrsU/ki3w2eRlA5l/Bza1Nl2xk0Kcag34MoWlXoqGXTdpTGYE7g4P8BVgDGlNeTM7sSF9jxbqSudoK3ZZp8+gJ3Ti7JDkOorLpiaLqjXb11t0qDA6ikKQZBeha5J5pUgEfc0Mp2iSvfsgMzu1NPHUshfiLZr8WMvPrXzGfUeou1t0FlTzHDMrHU9KlVU+plfpYLl8ftFXLcXWkjQYrxALJw/KY8CtiEflzQ/ql/WE/mlmraIwFMndTlc07C1I0bCtjeCKfUHqMX/V0vo1kubGTU3b4L2a4Q3pe7RQV8Uy3ZVvqHT6DHLyQ4GvM3XFOs1EEJaHydkmaxAmRXe1tZ4v13Fk604VRurlVYpC0CD3t0aLSrN9/c0M5wxgPlN/S7rCarfDqpaLb2Ue1qOWVlFiJpq4RVMhDHhq/Za2gBtYEp/Vn/EB1Qavn0+/WdOvKn+XdBLubZRXUGXlnsPM/iZJqRX8Y0n/wJVPKZJWthT0U9J6VhJzrgc03s8P8fGCdyU1M6fcmP7uYxXjcXXA7A3bnT6DjGtx68BNVBv07hhN7boN7pk1FvhOwT07Arhd7r4PPma2X0FWPVk2o3LYmgbaiUKQzdGaE1gw1QuZaXYIsGibedfODKdoEnfKQ8ZfRndbb9nH1ZH3WA9oyy26F7gIuEc+XyEzuRUGuEzl+gnQ1qS9RNaSHpE/HSXhOoD3khfSvyQdiDsuLNRGvj+XNA/u5bMv1dak6YSLgbslXZu2twAukc/PaDZG93184uSV+JILddBYAXb6DDLmNLPv9bhU1TgFH0+9GK9Yd8Ir5yfxOIQbNxMys7+mHv56Se7/rGCSsKWgumUmuRa0qzDyUQgek1Q1CsH++HyiReneoJxEhcX8+poZznQGU8YUGik1TUjaHa8MrsRfjh2An1ovh17P5fcgvrZFXrHdajXOFu7Q5Nb2pL0Oy5ZFUz4MD36aRcOeBzixVc9EHiL+v2Y2KbfvIOAk4GtmVimgY4dlXotcBAoriESQBq1nxsOe/KPxeNmgd8XyTJmomJT1dlZx3aAW5zsWuNPMRve0bBXymsrMl3Psabk+kFrM67EWwTjVOjJHqdNCqlsqh61J407Cl14/rCGvn5WZNSV928zOKEozLTBDKpqe0FveYxXz6lPF1inyMOzbAaOsjYgCqVdxFLn11fE1U5pOVJOHZtkM74lsDN0jKFvrYJz3Aptk501KZke8N3Nmb4x99AbyiZNr4j3LqRwwOmxhN+bRbRBc0m1mVjbBsuh82Rox7+OmwbKI2B0j6S7c5H1l2rUdvvLpeiqeRX9dbnN2YB18jKxVMM6OI3P0wGGhWfSCKkt07N4iv8oLz/UFM6TpTB0GsANIiqXO2eT5vH4nj0WVKbZt61RsPcHan7QHbu54BFeg4JMHz8cnVTYjH035XpgSRbksmvIsOSVzHLAG7iX3TlJ20wRm9gFuatug0eEj2eR7g8boAjdK+n9MbUZuqrQbsfYjEfSEXXBX6l+l7buAXSXNgU84bYqZbZHflrQEcGKL5N0USfLoWgd/v8aUeXS12xiQ9A08HuEy8tU5MwbjyzKXsXbu/9nxeTz3AdOUoun3iTz98QP+glduD1rXRKnCiVjxK7yfbU/aS3KdToA9q83yXY0rsBvxyY+LpP0rkybvTos/3PxyKPAJfC5HFZlt8aUd3qRrwuekgvTPNPmNr5DPSunvms1+/X3vSsquKt873qt8Hp87dyHu9bl3L9//eXCHm0vwuQR9PTkAABUXSURBVEzZb/52rqnhfKP6+x43/mbIHg0+O/xy+XoqmMfNqtVjZoBzAN7SXAxf8OkG4FsV5N6VtJGZ3Q5kkwffLROy9kP274g3LD7AZ+jfJOkVfD7LHm2eqy/5Mt5Sfw5X3lU4EdjCKiylDWBmS3dYtkNxz61m67C040xQGXUYa1DdA1DOhI+hVIk/9l1gDUtziuSxyO7Ee+KtaPf+v4krpZ2rpK/AO/iSJNMUM6qiaTeAXVCAuQdPUaDBVhwA/C5nvnqdGip+c5PU77NtSSPwyNb/snpjnbVFmjf1Y+sy3cyDx9U7kZJFwXK8XLWSS3l2ZOM3s/3S389VzasX6DTWYN4BYzJwibVYIqCBCXiPJOMt4IUSmbbuf09pmOszCO+ld+zcURczpDNA8qw6A48p9AgpgJ2ZPVQoGDRFHaxjk/d4kjQEwHJeYTMiec+p5K12MW6quUNpjaEK5zgNd6+9hgoh+FNrP2OKjd/MqgSdRNL2eBy9tyQdiZvOjrEK3ort0mzAv8gJoAf5ZCGZVscbJNfilflW+LpOBzSRycYVP0sb978Xypp3PpgMPFfWw+sPZsgejZndlx5QFo/oSStZmCgo5FzSOjYAZvaQPPxKS0VjvsbHgcDlM7qCyWHJFXdJ3FllMzN7VB47q+qg+xDcfFJp+Wgz+3Z+O/Uu2/Fq/KGZXSFpI3y5gJ/jThsdRxsooK1liwvclIHCBQSze/10+mVc2yRtRt7hoPL97ylmdmtybsqcAlouvd6fzKg9mkF43KlhdA+bUbaGeNAEtbloU07uh/iYTEceTwMNSeviS1t/gFdwQ/B1UHYEHrE+mBgpXyHyITNbuWL6bNnw4/EB9osbXah7sWxtxRrMuSln44WZAt0FeMdaLOU8PSFpB3w+2C14o/nTwHfN7Moiub5mhuzR4EEO38PD2X9ckjYop9N1bLJQOnnHgSI35R4jD9exBN0bGO2G6qkFM/snHgUZAHmopC/hXnOlrvdJpq3lo3vBxv9vSb9O5f5Z6n3N1IZ8OyxhDRMfkwNJU0WTjXXJl23eMHfocPmaL4WKJk2+bBbrrKWjg9pcTbcXOAJfbO2VlN9QPBxQKJppgMULus1B+3wLOAdYSdK/cRfZUueAHng8dYSkY4A98d5CVoHU4iHVG5hHEy+MKN6Ei/D1jr5EbvnogvT5eF6d2Ph3AEbiwWXfkPQJ3IxaB2cwdWieZvsamavBu3EDqq0imvf0mx1fEK4sQnlfh42aKVMyideoT9F3zIyqaP4i6YtmNu2tRDedkQb1R5jZF+QxvGayFP25QGYTM7tZ3ZdBmEJdA6d4pbhs8kKbZlHP1ulpa/noXrDxL0jy6kqmLXBF12uo50sy7wOcl/NufINqgWkbl0S+Q12BOVvR1/EQ/yqPEp6NW+2IzxOcpphRFc3dwNWpkqw1bMZAp2FQv2pAzc8CN9N9AHXKKalp4BT3MJwXX+lyWua3NFkjpiJtLR/dxMZ/hqR2bPx/pis6w+zA0niQy1WKhNqkR0syJ4WxWvJulLUIcdRIUhIZ2XpQi5SInYwH7e0WNqpKfp1gZt9NDbYsnt45ZnZ1Xfl1yozqDDAeXxb5YZsRb0Av0+mgvqRBZtZnE2XT/JlrcYVTJUJuv6AerBEjaV/gKnzezfl4Bf0jMzu7RfoH8XA83Wz81iJAZYX818QDqu7fiXzJuZcys+fk6ySZpXV+Ksi1FVMvJ/cMXSbWbD2oozMTXIFcn8VDzOU5t3Wte7ScmY2rO892mFEVzfW462g4AvQC6YNspNTUI+l5PHbZZcDNdSt9SY/iLtjdnECsF4JV9gbqWllzB9wk1OkaMe3k+bDlooGnXv6D1oMI4WoSILI3SD20i4Csp/EqsIeZPVIidxXeuMiWvNgNX+q8VUy9TG4OPA7ZRrjC+Qce/ui9ErmNgOXN7PykuOc2s2bfSK+RGgzP4HOvjreSZen7mhlV0VyAezb9hTZWhAx6l/Qhb4GvK7Im8Cc8Rlphi7EH+d1qZk2j604LqPnyFRlW5O2UO8e8wO5M7brfdIVH+aJnq9Jl498Jd28+rFn6JvL5MZOZ8Oe4gJl9qYp8O0i6EzjCzP6etjcGjjOzDUrkOproKely3Dz3h7RrZ2A+M9u+QOYo3MS2opmtIGlRfJXaDVvJdIKkOYEPLLd8ujxA55nAjmZ2RW/m11Nm1DGaLHhguytCBk1oMaj/Jm6abDkeYmbv4q60lyc30NNws0aVAd5OuDfN9xhFzT2FTrAUzkXSMtawWqSkqi7fo/ExyEqu+zkb/4a4qedsM7umjWLnx0wm42M2da3vM1emZADM7JbkgFJGRzH1cGWRNyH+PfUcitgGjw5+Xyrji8nU19vcjJv/XwKQtA3wDXyi6P/hC+hNM8yQisbSipBBr7EPPokuqwQ2xiu7FSQdbQXr58gjNOyIrzMzhq4lA+ogczNdL7dvWnRvbrbC5hXAWhVkZzezQ8sSSbrdzDZS1/LI2RoPX5f0MfBf4CQz+1XLk9BtddUhvlnscdhDxqfxwOx92hVvMJbRaUy9+5Vb6jtNqC2LkfaBmZmkbE5ZFUXYCXNYWrJA0n7A14HPm9lESSfUlGfHzJCKJtlND2PqSW3TWoUzvfAxsLKZvQxk6/2chYchuY0WIU3S2M4DeK/mu214rXWE9W0AyLaRtBL+Ts7T0EscQu49LeEiSV/HzZD5Xls3xwwz2yj9bdraVlek4kJFkxwszif1bCS9icdna3QN7g32Bn6Cj10Jf7f2KinfTKSeidqPqbcusHsaSwQPDfS4UmibFnPxLpdPYJ03PYe98RBNvc1ryUy3BL40wYpJyXyCadBKM0MqGtzmehmwOd7a2QOYWCgRFDEsUzKJV4AVzOy/kopiyK3WxkffMQ3jCFMxDY3NrYi/k/PS3fX7LbzFWoUPcHflI+g+KbWtaAtm9loaAynjPOCbZvYPmDIQfj7Vo023U6bXgaZjTQUyPYmpN7LN9OABeq/Ex3b+f3t3HitnVcZx/PsDWQIFWmRTFovIahUKFIRECWBQFiEICgiExWDERBC1SGRRkUUWDZQIBUUshAARBAoiBVnKvreURSCBFhdICFiggEgDP/84Z27n7tOZd+Z9587zSSbceWfemdNw7zzznnOe59kUOIW6ag8F+gZpquwF0u/GrXlab2fS//tK6dXNAI/b3kZ1rVKrvlBcZZIuJH3bq80L70cqsT4VuHm4K4l8ZXkUgxeuCy3Xkb/5DatqU6mSdrD9YJPnvghs79S6oe0k3T9woXuoYwW91yakbP2J9P99GXEmotnt902OsamWzAW87ydJ62zzbD/fzvdqRq8Gmoec+ozPAqYBrwDXVm1LYLeQJNLley1p7D7gutG2K+ddRPcyIDHRdrsWkytN/Rt0DTLczrEBrzETOND2e0WObYj3qX2YHgqsRNq1ZtJ620LbhX+rzt/YpzP492XEaboB+TB9Rtt+v5Rj62vJTP+Kz6uQuqMeUtR75fdbfaTH2xFEW9GrgWYv0gfc+qRaSauSGk7dVOrAutzSJo01ssW0oHEdb/vs4T7IG/kA7wRJIy5Q254x0uP5Na4nrfPcRf81mkL/jUVsxW7iPR+33ciGiIHnDZUPMz3veixqbKsBE4AzgRPqHlrUpiunWvBU3eHa/VFz2DqtVwPNwAqrq5OKArarwmpPWNqkMUmnAQ/YvqXN49rL9s3DfZA38gHeLar2b5R0WKvvXfft/RjSWurARNbRKlAMlQ8z3nY7dziGOr0aaAb1yxjqWBhZq0ljeWvtSqQF7LbVnJN0he1DJR1r+/wiX7sd8trVT4At6PJdkUOtWTTxGgO/vff70Brt27vqOpeOdKwb5Wnrg4ENbf9SqbDpOrYfKXlo/VSunHSHLJMTBIG+b0y9ugOvFXeSqvcCg5LGDm/g/NXy887MweWzjN7/vRnbKDXBOlLSBEmr19/a8H6tupJU2n9D0nbeBaQco1FJmi/ppYG39g119CG1+gK2N8zBZAtS47MnSdviL6Cx4p1zJPXlTjWYD9MtLiTlsH0r319E+qJXKb364drRCqtjWKtJY78l5eDsQuqdsoiUVT5lpJOaMJ1UU+3TpIXkgfPalZrPJpVwuTRfgc0GZmv08vQ129b9vCJpG2yZwbTIKZMZpCmwafn+QfnYaFNgzeTDdIvtbW8taQ709b+JPJoqsH25pMdYUmH16+5AhdUxqNWksY78kdieBkyTdJHto4t+/Tao5R69KmlP0q7I9Ro50fYbAw6dJ+k+Uj5HGVq+oqnTTEkYaC4fplssVmpNX6tEsCYV7Brck4EGIAeWCC6taTVprKN/JF0SZABOy7uYfsSSXZHHNXJi3bZjWNJDpR21thpV5BRVMyVh+lo6j1HTSK2+15J0Oqk/z0nlDmmwntwMENpjaZPGJB1MyrvYmjQFsj9w0mibCMLw1L/Pfa2Hyrm2Xyj4fTpebUHS30nZ9v2mwEhfTrp9CqxpSqWLdoW+/jcjte4uRQSa0DJJ5wJ/aGb6sRv+SDotZ8BfBKxte5KkzwN72z6tgXNrve0nsmTGwrZPLXiMHa+2kDd0jPSeY/nKZUiSzgeusf1A2WMZSQSa0DKlro5HkD7YLgOucoPtcsNgeeF/KnBxbcu9pKdtT2rg3FuBN0ll6uuz53/dpuGGEuW8qQOATUhTaNfYfqzcUQ0WgSYURtKmpIBzEGnu/Heu6x8SGiPpUdtT6nO7Gq2i0GhAKkq+gvo2gyuhR/JzB+Vt+vuRGtdtYHvjkofUT6/m0YSC5UX9zfLtdVKuww8lXV3qwLrT65I2Yskmif2BVxs89wFJTbdhbsIVwDrAV0hN69YjbVMPnfUZ0t/eROC5cocyWFzRhJZJ+g2wN3AHcGl9VrKk521vWtrgupBSN81LgB1JTbrmAwePtAZRywkhTV9uDLxEKtNSq7bQloXy2lWXcoViScsBs7qxikE3knQWKbXgRVJfpz/XSmtVSc9ubw6Fepq0W2yoisHbdXowY8C/SWtdd5GSLd8m9UwaaUF/rw6Mayi1nJ83JU0itRaeWNJYetF8YAd3qC1Es+KKJrRsQO5GzVvAy/V10EJjumlBP28EuQ74HPBHYBxwiu3pZY6rl+RyWhvTf43snvJGNFgEmtAySQ+RcmHmkaZqJuWfPw581/ZtJQ6v63R6QT90rxzojyWtjc0FvgA8WLWpy9gMEIqwAJhse9vcL2QyaTrty8DZZQ6sS3V6Qb9pks6QNL7u/oTc/iF0xrGk2oAvO3WynUwF29JHoAlF2Mz2M7U7OXFzsu0yqwZ3HUlPSZpHatD1hKTnJc2rO15Fu9cvPtteCOxR4nh6zfu23weQtILt50jVEyolNgOEIrwg6SKgtpX5gHxsBZYsFofRlbWg34pl8wfc/6Cvm+UKJY+pl/wrX1HeANwuaSGpCGulxBpNaJn6t8oVcB+pT8b7wErO7Z3D2CPpeNLW9stI26uPBGbajinTDpO0E6nH0622Pyh7PPUi0ISW5ETNGbYPKXssoRySvkpajwO43fasMsfTKyQtQypgW/mNIzF1Flpi+0NJa0pavmrfokLHzAGWI13RzCl5LD3D9keSnpS0ge1/jH5GeSLQhCIsAO6XNBN4t3awHaXiQ7VI+iZwDnA3adr0AklTbV9b6sB6xyeAZyQ9Qv+/vb3LG9JgEWhCEV7Jt2Uot8lW6LwTgSm2X4O+5nV/AyLQdMY4+m8iEXBWSWMZVgSa0LJa7xFJK9t+d7TnhzFlmVqQyd4g0iY66WO2Z9cfyJtzKiV+IULLJO0g6VlSt0MkbSnpwpKHFTrjVkmzJB0u6XDgL8AtJY9pzJN0dC6kumnOtard5pOqclRK7DoLLZP0MKkN88ylbdQVup+k/UgtvAXcY/v6koc05klaDZgAnAmcUPfQItv/KWdUw4tAE1om6WHb2w9o1PWk7S3LHlsIoXyxRhOK8E9JOwKWtDxwDHkaLYxNkhaRG7MNfIjU/2bVDg8pVFhc0YSWSVoDOJ+UtCfgNuBY22+UOrAQQiVEoAkhhNBWMXUWWpZzJ44idVbs+52yfWRZYwohVEcEmlCEG4F7SYl6H47y3BBCj4mps9AySXNtb1X2OEII1RQJm6EIN0uKZlchhCHFFU1oWd7qujLwQb7FFtcQQp8INCGEENoqps5Cy5QcIunkfH99SduVPa4QQjXEFU1omaSLgI+AXWxvLmkCcJvtKSUPLYRQAbG9ORRhe9tbS5oDYHthLkUTQggxdRYKsVjSsuTaVzmB86NyhxRCqIoINKEI04DrgbUknQ7cB5xR7pBCCFURazShEJI2A3YlbW2+w3ZUbw4hABFoQgghtFlMnYUQQmirCDQhhBDaKgJN6EmS1pF0taQXJT0r6RZJmwzz3PGSvtfpMYYwVkSgCT1Hkki75O62vZHtLYCfAmsPc8p4oO2BRlLktYUxKQJN6EU7A4ttT68dsD0XmCPpDklPSHpK0j754V8BG0maK+kcAElTJT0qaZ6kX9ReR9LJkp6TdLukqyT9OB/fStJD+fnX5+oJSLpb0hmSZgMnSpovabn82KqSFtTuh9Ct4htU6EWTgMeHOP4+sK/ttyWtATwkaSZwAjCp1nNH0m7AxsB2pO3cMyV9CXgP2A+YTPrbeqLufS4Hvm97tqRTgZ8BP8iPjbe9U37ticCewA3AgcB1thcX+G8PoeMi0ISwhIAzctD4CFiXoafTdsu3Ofn+OFLgWQW40fZ/ASTdlP+7GimYzM7PnwH8qe71rqn7+ffA8aRAcwSpRXYIXS0CTehFzwD7D3H8YGBNYBvbiyUtAFYc4nkCzrR9cb+D0nFNjufd2g+275c0UdJOwLK2n27yNUOojFijCb3oTmAFSX1XC5KmAJ8CXstBZud8H2AR6WqlZhZwpKRx+dx1Ja1FKr3zNUkr5sf2BLD9FrBQ0hfz+YcCsxne5cBVwGUt/jtDqIS4ogk9x7Yl7QucJ+kE0trMAuDnwDRJjwFzgefy89+QdL+kp4G/2p4qaXPgwbSBjXeAQ2w/mtd0ngReBh4D3spvexgwXdJKwEukabHhXAmcRgo2IXS9KEETQoEkjbP9Tg4o9wDfsf3EUr7G/sA+tg9tyyBD6LC4ogmhWJdI2oK0tjOjiSBzAbA7sEc7BhdCGeKKJoQQQlvFZoAQQghtFYEmhBBCW0WgCSGE0FYRaEIIIbRVBJoQQght9X8r4qzSLNWe4gAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"allKick = mergeKick.groupby(['fullcats'])\\n\",\n \"noStaffCont = allKick.usd_pledged.agg(np.sum)\\n\",\n \"staffCont = staffGrp.usd_pledged.agg(np.sum)\\n\",\n \"contributions = pd.DataFrame()\\n\",\n \"contributions['staff_picks'] = staffCont / noStaffCont\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"ax = contributions.plot(kind = 'bar', title='Contributions of Staff Picks to Total Funds Raised (By Category)')\\n\",\n \"ax.get_legend().remove()\\n\",\n \"ax.set_xlabel(\\\"Category\\\")\\n\",\n \"ax.set_ylabel(\\\"% of Total Funds Raised\\\")\\n\",\n \"plt.figure(figsize=(600,300))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Kickstarter staff picks accounts for staff_picks 0.451525\\n\",\n \"dtype: float64 % of all funds raised on average\\n\"\n ]\n }\n ],\n \"source\": [\n \"plt.close()\\n\",\n \"total = contributions.mean(axis = 0)\\n\",\n \"print('Kickstarter staff picks accounts for ', total, '% of all funds raised on average')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Correlation between Variables\\n\",\n \"\\n\",\n \"Pandas has a method to do a pairwise correlation calculation for all pairs of columns that have numerical data\\n\",\n \"\\n\",\n \"The only semi-strong correlated pairs are between the total number of backers and the total USD pledged. There's some other weaker correlations here, but in reality there's probably too many confounding factors to give a strong correlation.\\n\",\n \"\\n\",\n \"Factors that are probably affecting the correlations\\n\",\n \"- Differences in required funding goals to bring projects to fruition across categories\\n\",\n \"- Varying scales of projects\\n\",\n \"- Prior creator history\\n\",\n \"- Individual backer tastes\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'Correlation Heatmap')\"\n ]\n },\n \"execution_count\": 46,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"select = pd.DataFrame()\\n\",\n \"select['no_projects'] = merged['no_projects']\\n\",\n \"select['funds_raised_percent'] = merged['funds_raised_percent']\\n\",\n \"select['fullcats'] = merged['fullcats']\\n\",\n \"select['kickstarter.com'] = merged['kickstarter.com']\\n\",\n \"select['goal'] = merged['goal']\\n\",\n \"select['backers_count'] = merged['backers_count']\\n\",\n \"select['staLauDelta'] = merged['staLauDelta']\\n\",\n \"select['usd_pledged'] = merged['usd_pledged']\\n\",\n \"corr = select.corr()\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"sb.heatmap(corr, annot=True)\\n\",\n \"plt.title('Correlation Heatmap')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Appendix A: Data Descriptions\\n\",\n \"\\n\",\n \"| Columns | Data Type | Description | \\n\",\n \"|---|---|---|\\n\",\n \"|Backers Count| int64 | Total number of individual backers of a project\\n\",\n \"|Blurb|object| The short description of the project\\n\",\n \"|Category| list | A list of categories that the project has been tagged under\\n\",\n \"|Country| string| Two character country code of the campaign’s headquarters\\n\",\n \"|Created_at|int64| The creation date of the campaign in epoch time\\n\",\n \"|Creator| string |A list of the creator’s name and their creator ID on Kickstarter.com\\n\",\n \"|Currency| string| A two character string of the domestic currency of the project\\n\",\n \"|Deadline| int64|The end date/time of the campaign in epoch time\\n\",\n \"|Goal| int64|The funding goal of the project in native currency\\n\",\n \"|Id| int64|The unique project ID\\n\",\n \"|Launched_at| int64|Launch date of the campaign in epoch time\\n\",\n \"|Location| string|The main location of the project\\n\",\n \"|Name| object|The name of the project\\n\",\n \"|Pledged| int64|Total amount pledged (in the project’s home currency)\\n\",\n \"|Slug| string|An abbreviated description of the project\\n\",\n \"|Spotlight|string|True/False categorical variable of whether the project was featured by Kickstarter\\n\",\n \"|Staff_pick|string|True/False categorical variable of whether the project was a staff pick or not\\n\",\n \"|State|string|Successful/Failed/Canceled/Active categorical variable of the projects current status\\n\",\n \"|State_changed_at| int64|Time/date that the project changed states, in epoch time\\n\",\n \"|Static_usd_rate| int64|Static conversion rate between home currency and USD\\n\",\n \"|Urls| string|Various URL’s associated with the project\\n\",\n \"|Usd_pledged| int64|Total amount pledged, converted to USD\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Appendix B: Previous Work and Citations\\n\",\n \"\\n\",\n \"[Good Audience](https://blog.goodaudience.com/kickstarter-projects-prediction-of-state-steps-for-a-beginner-analysis-f4630a50b7fe)
\\n\",\n \"•\\tRuns logistic regression, XGBoost, Random forests, LightGBM and an Ensemble method to try and predict whether a project will be successfully funded or not. Based on the Kaggle dataset (limited to 49k entries). Mainly focused on feature selection\\n\",\n \"\\n\",\n \"[Towards Data Science](https://towardsdatascience.com/predicting-the-success-of-kickstarter-campaigns-3f4a976419b9)
\\n\",\n \"•\\tAgain predictions on successful funding, based on funding goals, category, rewards levels, and location data. Uses random forests, logistic regression, and kNearest Neighbors\\n\",\n \"\\n\",\n \"[Bentley University](https://www.bentley.edu/prepared/what-secret-perfect-kickstarter-campaign)
\\n\",\n \"•\\tFinds length of description and title are important\\n\",\n \"•\\tAlso the date of the launch important (winter is the best month)\\n\",\n \"•\\tHigher average donations, shorter campaign durations\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'Kickstarter.com Alexa Ranking over Time')\"\n ]\n },\n \"execution_count\": 47,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\\n\",\n \"•\\tRuns logistic regression, XGBoost, Random forests, LightGBM and an Ensemble method to try and predict whether a project will be successfully funded or not. Based on the Kaggle dataset (limited to 49k entries). Mainly focused on feature selection\\n\",\n \"\\n\",\n \"[Towards Data Science](https://towardsdatascience.com/predicting-the-success-of-kickstarter-campaigns-3f4a976419b9)
\\n\",\n \"•\\tAgain predictions on successful funding, based on funding goals, category, rewards levels, and location data. Uses random forests, logistic regression, and kNearest Neighbors\\n\",\n \"\\n\",\n \"[Bentley University](https://www.bentley.edu/prepared/what-secret-perfect-kickstarter-campaign)
\\n\",\n \"•\\tFinds length of description and title are important\\n\",\n \"•\\tAlso the date of the launch important (winter is the best month)\\n\",\n \"•\\tHigher average donations, shorter campaign durations\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'Kickstarter.com Alexa Ranking over Time')\"\n ]\n },\n \"execution_count\": 47,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"merged.head()\\n\",\n \"alexa = merged.copy()\\n\",\n \"alexa.dropna(axis = 0, inplace = True)\\n\",\n \"\\n\",\n \"x = alexa['launched_at']\\n\",\n \"y = alexa['kickstarter.com']\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"plt.scatter(x, y)\\n\",\n \"plt.legend(('Alexa Ranking', 'Alexa ranking'), loc='lower right')\\n\",\n \"plt.xlabel('Date (Epoch time)')\\n\",\n \"plt.ylabel('Alexa Ranking')\\n\",\n \"plt.ylim(bottom=1)\\n\",\n \"plt.title('Kickstarter.com Alexa Ranking over Time')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Index(['backers_count', 'blurb', 'country', 'created_at', 'creator',\\n\",\n \" 'currency', 'deadline', 'fx_rate', 'goal', 'id', 'launched_at', 'name',\\n\",\n \" 'slug', 'spotlight', 'staff_pick', 'state', 'state_changed_at', 'urls',\\n\",\n \" 'usd_pledged', 'fullcats', 'city', 'creLauDelta', 'lauDeadDelta',\\n\",\n \" 'staLauDelta', 'source', 'funds_raised_percent', 'launch_date', 'Date',\\n\",\n \" 'kickstarter.com', 'no_projects'],\\n\",\n \" dtype='object')\"\n ]\n },\n \"execution_count\": 48,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"plt.close()\\n\",\n \"merged.columns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'Distribution of % of Goal Raised')\"\n ]\n },\n \"execution_count\": 49,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x = merged[(merged['funds_raised_percent'] < 200)]\\n\",\n \"y = x['funds_raised_percent']\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"plt.hist(y, bins = 100)\\n\",\n \"plt.xlabel('Percent of Goal Raised')\\n\",\n \"plt.ylabel('Number of Occurrences')\\n\",\n \"plt.title('Distribution of % of Goal Raised')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/Users/chrismay/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:824: RuntimeWarning: invalid value encountered in greater_equal\\n\",\n \" keep = (tmp_a >= first_edge)\\n\",\n \"/Users/chrismay/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:825: RuntimeWarning: invalid value encountered in less_equal\\n\",\n \" keep &= (tmp_a <= last_edge)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'Distribution of # of Days to Reach Goal')\"\n ]\n },\n \"execution_count\": 50,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x = merged[merged['state'] == 'successful']\\n\",\n \"y = x['staLauDelta']\\n\",\n \"\\n\",\n \"plt.close()\\n\",\n \"plt.hist(y, bins = 100)\\n\",\n \"plt.xlabel('Days to Reach Goal')\\n\",\n \"plt.ylabel('Number of Occurrences')\\n\",\n \"plt.xlim(left=0)\\n\",\n \"plt.xlim(right=60)\\n\",\n \"plt.title('Distribution of # of Days to Reach Goal')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn import preprocessing as pre\\n\",\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"from sklearn import metrics\\n\",\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"from sklearn.metrics import confusion_matrix\\n\",\n \"from sklearn.preprocessing import MinMaxScaler\\n\",\n \"from sklearn.metrics import roc_auc_score\\n\",\n \"from sklearn.metrics import roc_curve\\n\",\n \"from sklearn.model_selection import GridSearchCV\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Index(['backers_count', 'blurb', 'country', 'created_at', 'creator',\\n\",\n \" 'currency', 'deadline', 'fx_rate', 'goal', 'id', 'launched_at', 'name',\\n\",\n \" 'slug', 'spotlight', 'staff_pick', 'state', 'state_changed_at', 'urls',\\n\",\n \" 'usd_pledged', 'fullcats', 'city', 'creLauDelta', 'lauDeadDelta',\\n\",\n \" 'staLauDelta', 'source', 'funds_raised_percent', 'launch_date', 'Date',\\n\",\n \" 'kickstarter.com', 'no_projects'],\\n\",\n \" dtype='object')\"\n ]\n },\n \"execution_count\": 52,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mergeKick.columns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\u001b[0m in \\u001b[0;36m\\u001b[0;34m\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mcm\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mconfusion_matrix\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0my_test\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0my_pred\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 2\\u001b[0m \\u001b[0mprint\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mconfusion_matrix\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mTypeError\\u001b[0m: 'numpy.ndarray' object is not callable\"\n ]\n }\n ],\n \"source\": [\n \"cm = confusion_matrix(y_test, y_pred)\\n\",\n \"print(confusion_matrix)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 87,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" country goal id launched_at staff_pick alexa_rank \\\\\\n\",\n \"0 US 5000 1203770415 1491540705 0 548.0 \\n\",\n \"1 MX 3382 878861613 1509893074 0 528.0 \\n\",\n \"2 NZ 6850 917345297 1427147079 0 NaN \\n\",\n \"3 US 40000 1702164653 1487077744 1 589.0 \\n\",\n \"4 US 2000 1042930184 1507662001 0 503.0 \\n\",\n \"\\n\",\n \" creator_projects state creLauDelta category \\n\",\n \"0 1.0 0 19.0 film & video \\n\",\n \"1 1.0 1 16.0 film & video \\n\",\n \"2 1.0 1 7.0 theater \\n\",\n \"3 1.0 1 49.0 comics \\n\",\n \"4 1.0 1 16.0 games \"\n ]\n },\n \"execution_count\": 57,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Make a new dataframe with only the columns that we'll use for modeling\\n\",\n \"kickModel = pd.DataFrame()\\n\",\n \"kickModel['country'] = mergeKick['country']\\n\",\n \"kickModel['goal'] = mergeKick['goal']\\n\",\n \"kickModel['id'] = mergeKick['id']\\n\",\n \"kickModel['launched_at'] = mergeKick['launched_at']\\n\",\n \"kickModel['staff_pick'] = mergeKick['staff_pick']\\n\",\n \"kickModel['alexa_rank'] = mergeKick['kickstarter.com']\\n\",\n \"kickModel['creator_projects'] = mergeKick['no_projects']\\n\",\n \"kickModel['state'] = mergeKick['state']\\n\",\n \"kickModel['creLauDelta'] = mergeKick['creLauDelta']\\n\",\n \"kickModel['category'] = mergeKick['fullcats']\\n\",\n \"\\n\",\n \"kickModel['staff_pick'] = kickModel.apply(lambda x: int(x['staff_pick'] == True), axis = 1)\\n\",\n \"kickModel['state'] = kickModel.apply(lambda x: int(x['state'] == 'successful'), axis = 1)\\n\",\n \"kickModel.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"| \\n\",\n \" | country | \\n\",\n \"goal | \\n\",\n \"id | \\n\",\n \"launched_at | \\n\",\n \"staff_pick | \\n\",\n \"alexa_rank | \\n\",\n \"creator_projects | \\n\",\n \"state | \\n\",\n \"creLauDelta | \\n\",\n \"category | \\n\",\n \"
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \\n\",\n \"US | \\n\",\n \"5000 | \\n\",\n \"1203770415 | \\n\",\n \"1491540705 | \\n\",\n \"0 | \\n\",\n \"548.0 | \\n\",\n \"1.0 | \\n\",\n \"0 | \\n\",\n \"19.0 | \\n\",\n \"film & video | \\n\",\n \"
| 1 | \\n\",\n \"MX | \\n\",\n \"3382 | \\n\",\n \"878861613 | \\n\",\n \"1509893074 | \\n\",\n \"0 | \\n\",\n \"528.0 | \\n\",\n \"1.0 | \\n\",\n \"1 | \\n\",\n \"16.0 | \\n\",\n \"film & video | \\n\",\n \"
| 2 | \\n\",\n \"NZ | \\n\",\n \"6850 | \\n\",\n \"917345297 | \\n\",\n \"1427147079 | \\n\",\n \"0 | \\n\",\n \"NaN | \\n\",\n \"1.0 | \\n\",\n \"1 | \\n\",\n \"7.0 | \\n\",\n \"theater | \\n\",\n \"
| 3 | \\n\",\n \"US | \\n\",\n \"40000 | \\n\",\n \"1702164653 | \\n\",\n \"1487077744 | \\n\",\n \"1 | \\n\",\n \"589.0 | \\n\",\n \"1.0 | \\n\",\n \"1 | \\n\",\n \"49.0 | \\n\",\n \"comics | \\n\",\n \"
| 4 | \\n\",\n \"US | \\n\",\n \"2000 | \\n\",\n \"1042930184 | \\n\",\n \"1507662001 | \\n\",\n \"0 | \\n\",\n \"503.0 | \\n\",\n \"1.0 | \\n\",\n \"1 | \\n\",\n \"16.0 | \\n\",\n \"games | \\n\",\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" goal id launched_at staff_pick alexa_rank creator_projects \\\\\\n\",\n \"0 5000 1203770415 1491540705 0 548.0 1.0 \\n\",\n \"1 3382 878861613 1509893074 0 528.0 1.0 \\n\",\n \"2 6850 917345297 1427147079 0 NaN 1.0 \\n\",\n \"3 40000 1702164653 1487077744 1 589.0 1.0 \\n\",\n \"4 2000 1042930184 1507662001 0 503.0 1.0 \\n\",\n \"\\n\",\n \" state creLauDelta art cameragear ... VE VI VN VU WS XK YE ZA \\\\\\n\",\n \"0 0 19.0 0 0 ... 0 0 0 0 0 0 0 0 \\n\",\n \"1 1 16.0 0 0 ... 0 0 0 0 0 0 0 0 \\n\",\n \"2 1 7.0 0 0 ... 0 0 0 0 0 0 0 0 \\n\",\n \"3 1 49.0 0 0 ... 0 0 0 0 0 0 0 0 \\n\",\n \"4 1 16.0 0 0 ... 0 0 0 0 0 0 0 0 \\n\",\n \"\\n\",\n \" ZM ZW \\n\",\n \"0 0 0 \\n\",\n \"1 0 0 \\n\",\n \"2 0 0 \\n\",\n \"3 0 0 \\n\",\n \"4 0 0 \\n\",\n \"\\n\",\n \"[5 rows x 223 columns]\"\n ]\n },\n \"execution_count\": 58,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dummies1 = pd.get_dummies(kickModel['category'], columns = 'category')\\n\",\n \"dummies2 = pd.get_dummies(kickModel['country'], columns = 'country')\\n\",\n \"kickModel = pd.concat([kickModel, dummies1], axis = 1)\\n\",\n \"kickModel = pd.concat([kickModel, dummies2], axis = 1)\\n\",\n \"kickModel.drop(labels = ['category', 'country'], axis = 1, inplace = True)\\n\",\n \"kickModel.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"kickModel = kickModel.fillna(value = 1000, axis = 0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 83,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/Users/chrismay/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:334: DataConversionWarning: Data with input dtype uint8, uint32, int64, float64 were all converted to float64 by MinMaxScaler.\\n\",\n \" return self.partial_fit(X, y)\\n\",\n \"/Users/chrismay/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\\n\",\n \" FutureWarning)\\n\",\n \"/Users/chrismay/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\\n\",\n \" y = column_or_1d(y, warn=True)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\\n\",\n \" intercept_scaling=1, max_iter=100, multi_class='warn',\\n\",\n \" n_jobs=None, penalty='l2', random_state=None, solver='warn',\\n\",\n \" tol=0.0001, verbose=0, warm_start=False)\"\n ]\n },\n \"execution_count\": 83,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X = kickModel.loc[:, kickModel.columns != 'state']\\n\",\n \"y = kickModel.loc[:, kickModel.columns == 'state']\\n\",\n \"\\n\",\n \"#Use a MinMax scaler to standardize features\\n\",\n \"scaler = MinMaxScaler(feature_range = (0,1))\\n\",\n \"\\n\",\n \"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\\n\",\n \"logreg = LogisticRegression()\\n\",\n \"\\n\",\n \"scaler.fit(X_train)\\n\",\n \"\\n\",\n \"X_train = scaler.transform(X_train)\\n\",\n \"X_test = scaler.transform(X_test)\\n\",\n \"\\n\",\n \"logreg.fit(X_train, y_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 84,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Accuracy of logistic regression classifier on test set: 0.71\\n\"\n ]\n }\n ],\n \"source\": [\n \"y_pred = logreg.predict(X_test)\\n\",\n \"print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(logreg.score(X_test, y_test)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 88,\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"'numpy.ndarray' object is not callable\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m| \\n\",\n \" | goal | \\n\",\n \"id | \\n\",\n \"launched_at | \\n\",\n \"staff_pick | \\n\",\n \"alexa_rank | \\n\",\n \"creator_projects | \\n\",\n \"state | \\n\",\n \"creLauDelta | \\n\",\n \"art | \\n\",\n \"cameragear | \\n\",\n \"... | \\n\",\n \"VE | \\n\",\n \"VI | \\n\",\n \"VN | \\n\",\n \"VU | \\n\",\n \"WS | \\n\",\n \"XK | \\n\",\n \"YE | \\n\",\n \"ZA | \\n\",\n \"ZM | \\n\",\n \"ZW | \\n\",\n \"
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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\\n\",\n \"\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"logit_roc_auc = roc_auc_score(y_test, logreg.predict(X_test))\\n\",\n \"fpr, tpr, thresholds = roc_curve(y_test, logreg.predict_proba(X_test)[:,1])\\n\",\n \"plt.figure()\\n\",\n \"plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\\n\",\n \"plt.plot([0, 1], [0, 1],'r--')\\n\",\n \"plt.xlim([0.0, 1.0])\\n\",\n \"plt.ylim([0.0, 1.05])\\n\",\n \"plt.xlabel('False Positive Rate')\\n\",\n \"plt.ylabel('True Positive Rate')\\n\",\n \"plt.title('Receiver operating characteristic')\\n\",\n \"plt.legend(loc=\\\"lower right\\\")\\n\",\n \"plt.savefig('Log_ROC')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### See if model performance can be improved using grid search\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"},"source":{"kind":"string","value":"stackv2"},"added":{"kind":"string","value":"2024-11-18T18:03:05.588009+00:00"},"created":{"kind":"string","value":"2019-05-10T03:28:06"},"metadata":{"kind":"string","value":"{\n \"license\": \"BSD-3-Clause\",\n \"url\": \"https://raw.githubusercontent.com/csong02/applied_ds/f6342fd7c2beafa219a6fd9455d8152df578e863/pezLyfe/ChrisMay_ProjectUpdate2_kickstarterAnalysis.ipynb\",\n \"blob_id\": \"e91ddee0e4fbbdda00c14991e490dd96d91610f1\",\n \"directory_id\": \"e8a04fd2f90b573a8fd90d9bb47d354af17764ec\",\n \"path\": \"/pezLyfe/ChrisMay_ProjectUpdate2_kickstarterAnalysis.ipynb\",\n \"content_id\": \"c79900b5e39abedf0ae8626e553229f2073def95\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"license_type\": \"permissive\",\n \"repo_name\": \"csong02/applied_ds\",\n \"snapshot_id\": \"84e7299e473ecd306e1950047671cf83f2f98a7d\",\n \"revision_id\": \"f6342fd7c2beafa219a6fd9455d8152df578e863\",\n \"branch_name\": \"refs/heads/master\",\n \"visit_date\": \"2020-04-27T14:36:36.018348\",\n \"revision_date\": \"2019-05-10T03:28:06\",\n \"committer_date\": \"2019-05-10T03:28:06\",\n \"github_id\": 174415550,\n \"star_events_count\": 0,\n \"fork_events_count\": 0,\n \"gha_license_id\": \"BSD-3-Clause\",\n \"gha_event_created_at\": \"2019-03-30T22:24:52\",\n \"gha_created_at\": \"2019-03-07T20:30:36\",\n \"gha_language\": \"Jupyter Notebook\",\n \"src_encoding\": \"UTF-8\",\n \"language\": \"Jupyter Notebook\",\n \"is_vendor\": false,\n \"is_generated\": false,\n \"length_bytes\": 855295,\n \"extension\": \"ipynb\",\n \"filename\": \"ChrisMay_ProjectUpdate2_kickstarterAnalysis.ipynb\"\n}"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":64,"numItemsPerPage":100,"numTotalItems":6428,"offset":6400,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc0OTI1OTUxNCwic3ViIjoiL2RhdGFzZXRzL2NvbW1vbi1waWxlL3N0YWNrdjIiLCJleHAiOjE3NDkyNjMxMTQsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.FevWM8fhNM1zLJ0zP3sPR5M8mEdyLYA-1jsDhc4zjd6fgjfz4fsDWPwUA87-t1VL6clhV59JGuyqRVkBSiHzDg","displayUrls":true},"discussionsStats":{"closed":0,"open":0,"total":0},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
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849941f28ad1fb7d3ea59abd961d39216997a763 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Parkinson's Disease - Hybrid Functional Petri Net (HFPN)\n",
"This script has implemented the following blocks of the HFPN for PD:\n",
"- [ ] Cholesterol homeostasis\n",
"- [x] Calcium homeotasis\n",
"- [ ] Energy metabolism\n",
"- [ ] Lewy bodies formation\n",
"\n",
"Implemented data and parameter optimisation\n",
"- [ ] Cholesterol homeostasis\n",
"- [x] Calcium homeotasis\n",
"- [ ] Energy metabolism\n",
"- [ ] Lewy bodies formation\n",
"- [ ] ER retraction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Add your imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"\n",
"# Only run this cell once to avoid confusion with directories\n",
"# Point this to the directory where HFPN.py is relative to your working directory\n",
"cwd = os.getcwd() # Get current working directory\n",
"root_folder = os.sep + \"team-project\"\n",
"# Move to 'utils' from current directory position\n",
"sys.path.insert(0, cwd[:(cwd.index(root_folder)+len(root_folder))] + os.sep + \"utils\" + os.sep)\n",
"\n",
"# Import HFPN class to work with hybrid functional Petri nets\n",
"from hfpn import HFPN\n",
"\n",
"sys.path.insert(0, cwd[:(cwd.index(root_folder)+len(root_folder))] + os.sep + \"parkinsons\" + os.sep)\n",
"# Import initial token, firing conditions and rate functions\n",
"from initial_tokens import *\n",
"from rate_functions import *\n",
"from firing_conditions import *\n",
"from visualisation import Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Initialise an empty HFPN"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Initialize an empty HFPN\n",
"pn = HFPN(time_step = 0.01) #unit = s/A.U."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define the places by module\n",
"Note - if a transition links two modules, put the transition under the module that contains the input places"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calcium homeostasis places"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"pn.add_place(it_p_Ca_cyto, \"p_Ca_cyto\", \"Ca - cytosole\", continuous = True)\n",
"pn.add_place(it_p_Ca_mito, \"p_Ca_mito\",\"Ca - mitochondria\", continuous = True)\n",
"pn.add_place(it_p_Ca_ER, \"p_Ca_ER\", \"Ca - ER\", continuous = True)\n",
"pn.add_place(it_p_ADP, \"p_ADP\",\"ADP - Calcium ER import\", continuous = True)\n",
"pn.add_place(it_p_ATP, \"p_ATP\",\"ATP - Calcium ER import\", continuous = True)\n",
"\n",
"# Discrete on/of-switches calcium pacemaking\n",
"\n",
"pn.add_place(1, \"p_Ca_extra\", \"on1 - Ca - extracellular\", continuous = False)\n",
"pn.add_place(0, \"p_on2\",\"on2\", continuous = False)\n",
"pn.add_place(0, \"p_on3\",\"on3\", continuous = False)\n",
"pn.add_place(0, \"p_on4\",\"on4\", continuous = False)\n",
"\n",
"pn.add_place(it_p_LRRK2_mut, \"p_LRRK2_mut\",\"LRRK2 - mutated\", continuous = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define the transitions by module\n",
"Note - if a transition links two modules, put the transition under the module that contains the input places"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calcium homeostasis transitions"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"pn.add_transition_with_speed_function(\n",
" transition_id = 't_Ca_imp',\n",
" label = 'L-type Ca channel',\n",
" input_place_ids = ['p_Ca_extra'],\n",
" firing_condition = fc_t_Ca_imp,\n",
" reaction_speed_function = r_t_Ca_imp,\n",
" consumption_coefficients = [0], # Need to review this \n",
" output_place_ids = ['p_Ca_cyto'], \n",
" production_coefficients = [1]) # Need to review this \n",
"\n",
"# pn.add_transition_with_speed_function(\n",
"# transition_id = 't_Ca_imp2',\n",
"# label = 'L-type Ca channel',\n",
"# input_place_ids = [],\n",
"# firing_condition = lambda a: 1,\n",
"# reaction_speed_function = lambda a: 1.44*1e8,\n",
"# consumption_coefficients = [], # Need to review this \n",
"# output_place_ids = ['p_Ca_cyto'], \n",
"# production_coefficients = [1]) # Need to review this \n",
"\n",
"\n",
"pn.add_transition_with_speed_function(\n",
" transition_id = 't_mCU',\n",
" label = 'Ca import into mitochondria via mCU',\n",
" input_place_ids = ['p_Ca_cyto','p_Ca_mito'],\n",
" firing_condition = fc_t_mCU,\n",
" reaction_speed_function = r_t_mCU,\n",
" consumption_coefficients = [1,0], \n",
" output_place_ids = ['p_Ca_mito'], \n",
" production_coefficients = [1]) \n",
"\n",
"pn.add_transition_with_speed_function(\n",
" transition_id = 't_MAM',\n",
" label = 'Ca transport from ER to mitochondria',\n",
" input_place_ids = ['p_Ca_ER','p_Ca_mito'],\n",
" firing_condition = fc_t_MAM,\n",
" reaction_speed_function = r_t_MAM,\n",
" consumption_coefficients = [1,0], \n",
" output_place_ids = ['p_Ca_mito'], \n",
" production_coefficients = [1]) \n",
"\n",
"pn.add_transition_with_speed_function(\n",
" transition_id = 't_RyR_IP3R',\n",
" label = 'Ca export from ER',\n",
" input_place_ids = ['p_Ca_extra','p_Ca_ER'],\n",
" firing_condition = fc_t_RyR_IP3R,\n",
" reaction_speed_function = r_t_RyR_IP3R,\n",
" consumption_coefficients = [0,1], \n",
" output_place_ids = ['p_Ca_cyto'], \n",
" production_coefficients = [1]) \n",
"\n",
"pn.add_transition_with_speed_function(\n",
" transition_id = 't_SERCA',\n",
" label = 'Ca import to ER',\n",
" input_place_ids = ['p_Ca_cyto','p_ATP'],\n",
" firing_condition = fc_t_SERCA,\n",
" reaction_speed_function = r_t_SERCA,\n",
" consumption_coefficients = [1,0], #!!!!! Need to review this 0 should be 1\n",
" output_place_ids = ['p_Ca_ER','p_ADP'], \n",
" production_coefficients = [1,1]) # Need to review this\n",
"\n",
"pn.add_transition_with_speed_function(\n",
" transition_id = 't_NCX_PMCA',\n",
" label = 'Ca efflux to extracellular space',\n",
" input_place_ids = ['p_Ca_cyto','p_on3'],\n",
" firing_condition = lambda a: a['p_on3']==1,\n",
" reaction_speed_function = r_t_NCX_PMCA,\n",
" consumption_coefficients = [1,0], \n",
" output_place_ids = [], \n",
" production_coefficients = [])\n",
"pn.add_transition_with_speed_function(\n",
" transition_id = 't_mNCLX',\n",
" label = 'Ca export from mitochondria via mNCLX',\n",
" input_place_ids = ['p_Ca_mito','p_LRRK2_mut'],\n",
" firing_condition = fc_t_mNCLX,\n",
" reaction_speed_function = r_t_mNCLX,\n",
" consumption_coefficients = [1,0], \n",
" output_place_ids = ['p_Ca_cyto'], \n",
" production_coefficients = [1]) \n",
"\n",
"# Discrete on/of-switches calcium pacemaking\n",
"\n",
"pn.add_transition_with_speed_function(\n",
" transition_id = 't_A',\n",
" label = 'A',\n",
" input_place_ids = ['p_on4'],\n",
" firing_condition = lambda a: a['p_on4']==1,\n",
" reaction_speed_function = lambda a: 1,\n",
" consumption_coefficients = [1], \n",
" output_place_ids = ['p_Ca_extra'], \n",
" production_coefficients = [1],\n",
" delay=0.5) \n",
"pn.add_transition_with_speed_function(\n",
" transition_id = 't_B',\n",
" label = 'B',\n",
" input_place_ids = ['p_Ca_extra'],\n",
" firing_condition = lambda a: a['p_Ca_extra']==1,\n",
" reaction_speed_function = lambda a: 1,\n",
" consumption_coefficients = [1], \n",
" output_place_ids = ['p_on2'], \n",
" production_coefficients = [1],\n",
" delay=0.5) \n",
"pn.add_transition_with_speed_function(\n",
" transition_id = 't_C',\n",
" label = 'C',\n",
" input_place_ids = ['p_on2'],\n",
" firing_condition = lambda a: a['p_on2']==1,\n",
" reaction_speed_function = lambda a: 1,\n",
" consumption_coefficients = [1], \n",
" output_place_ids = ['p_on3'], \n",
" production_coefficients = [1],\n",
" delay=0) \n",
"pn.add_transition_with_speed_function(\n",
" transition_id = 't_D',\n",
" label = 'D',\n",
" input_place_ids = ['p_on3'],\n",
" firing_condition = lambda a: a['p_on3']==1,\n",
" reaction_speed_function = lambda a: 1,\n",
" consumption_coefficients = [1], \n",
" output_place_ids = ['p_on4'], \n",
" production_coefficients = [1],\n",
" delay=0.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run the network and plot a time evolution of the system"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"pn.reset_network()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"pn.run_many_times(number_runs=1, number_time_steps=10000) "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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xYcraJMBs4tVbJ3HvvKHGOeHWAH45dyjWAK99gj/RIq/RaI7LzsIavnKLN6g68ptf2sj1L6w3xlzusr9dbk+pjp4s37apqKqhlaY2J7OHx+F0STYfVnnzMKuFOVnx7CmppayumbK6FgZEBzMhPZJ1ed6a9EGxoYxJjWDlgQpKaptJibQxND6M+PAg1hxUfyBSIm3MGKauf7iy0RuRD4unxV2S6RmbPTzefayi7SCL2Ug1ecYSI2yMSYlgYGyI8VpmDY9nTlY80zK9dix3TB/Iivtn+Cyijkm1d1lq5nhokddoNJTVNfOrhTnUu90SQfmd3/ziRiM690TkOwu9u7o95687TrVLx4h8UGwIQRZ39O3wRuQWkzA2B6VEBjN5YDRSwodbPdG2jUkZ0eSW1bP1SDUxoYHYAs1Mz4xlT3EtTpckJTIYIYQRbceEqkXO7BQ7Me5qHI/IegQdINU9NssQeW9EfnF2MrYAM4NiQ42x566fwFNXZhvH4dYAnr9hAhkxXuE3mQShQT1rqVOL/GmkK/3ki4qKuPzyywHYtm0bixcv9st9NH2TO17dxIfbCo3jl1Yf4u1NR3mzw25SDx6TrcIOAu7ZjOQR7M2Hq2lpdxrnJEZYWdch3z4wNpTxAyINQQfIjAtjTKq9g8jbyE61E2Qx8dZGNY+UyGDOdkfVy/aWkfwNYVbneKP0jscmk2DGUN+USlp0MJlxoYQFWQi3KTEel2bnkuwk5mR5r3nuyAS2PTqHKHcdPKicu8f6oDehRf400pV+8klJSSxcuBDQIq/5bj7OKeLuN7cax7XNbSzdVco9b20zxkxuP6bcsnoAn92Xx4vSO+4cjQ0LoqXdxfajNb7lisW1VDe0GqWIkwdGs6eklp2FNYCqZDlrUDTbjzo45N4Bag0wM3VwDAfLG4xzhieGkRRhVTXqdiXWnjHwRulnDY4hyGLyicjPH50IwJD4MGPspzMHc/NZ6YYHlcVs4v+uHsuEdN+F0CBL1+TM/U3P+lxxIj79NZTsOL3XTBgF5/3xW5/uDj/59PR0rrnmGj799FMsFgsLFizggQceIDc3l/vuu48f//jHHDp0iPnz57NlyxYeeeQRmpqaWLVqFQ888ABz5szhlltuIS8vj+DgYBYsWHBcx0tN36OyvoUHF+3g95eMJC5MieBP31AC/6vzhpFst/lE5B6/c09996bDSrw9Pi4Aaw9Wctu0gRQ6mjCbBBaTYO3BSs4aHE1VQyt3TB/IgpV5rMurpMjRRExoIOcMjeXvX+WydFcJDa1OUiKDGZ0SwVOfK//1cKuFCFsAs4bH88yyXFraXYZYzxoez5d7y7CYBPFhQQghmDU8nlfXHTYEXAjBnKx43tx41HBvtAWa+dcPx5PcQeRnDI1j1a9m+OTEL85OPu3ve09GR/KdoCv95D2kpaUZHvE33XQTCxcuZN26dTz66KM+5wUGBvK73/2Oq666im3btnHVVVfx6KOPMnbsWHJycnjiiSe44YYbTv3Fa3o0f/h4N+9vKTCOP9pexNJdpTzz5QFjLNhdKbLuoDdH7sHrd64i8IPlDZTVNhtRe3RIIBvyq2h3uiiobmRgTIiRdvFcJyspnKzEcNYeVGmX5MhgRqfYsQWYjS39yXYbY1Pt2IMD3IulSnRHJUcQ665ESbb7lism2q3GAqZnrKOA/2LuUN65Y4pR7QJq92jHqB3o1kXPnkDviuS/I+L2J13pJ+/hoosuAmDUqFHU19cTFhZGWFgYQUFBOByOb/05gFWrVvHee+8BMHPmTCorK6mtrf3WP0Ca3kFuWR2bD1dz1UTV6UtKyb9X5QPKlxyUKyMoHxcPIUEWGludrM2r5LLxKT4bj9YerGRkcgSFjiYGxoaoDUr5VQS6xfXy8Sk8tyKP3cW1FDqaSI60MS4tkv/9Yj87i1TaJSXSxpSB0byy7jBRwYGMHxBJoMXEWYOj+WKPdwORxWxi5tA43t9aaIi1ySSYNSyOtzYeNaL0+HArEwZE+vjaTB0cw71zh3D+qERjLMIWQHaq/TS+w30THcl3gu7wk/ekfEwmk/G959hjPazp23TsIwpwy0ub+NV7O6ioV5uLqjrUq3t8XjzR9e6iWqRUDaY9hl1rD3pz69YAE4NiQ1iZW0G700VxTTNzsxIIC7K4I3J3bn18ivGznsXRKYNUBczCzeoTREpkMDOHxdHa7jJKGqHzlSzXTRrA9CGxDI7zVrL858aJPHWVt5LFYjbx05mZxIR6fxc0nUOLfCfoSj/5U+GbnvLTpk3j9ddfB1Sj75iYGB3F9zLK6poZ+OBin1SMxzLXsyO0o7vihm+MVTa0sr+03ug3OjpFRetHqxpVRG63MS0zlg35lYYD4oBoZYW7Pq+SQkcTIYFmMuNCGRQbwme7S3E0tpFsDyY71U641cLq3EoCzSZiQ4OYmBFFmFUlBoxt/u4US2iHSpbpQ2KICQ3yicBHpUTwyi1n+GwOiggO8InkNaeOFvlO0JV+8qfCjBkz2L17N9nZ2bz99tv89re/ZfPmzYwePZpf//rXvPzyy6f1fprTz+e7S/l8d6lx7Kly+etn+42xyGBVzrc2TzXD6FjtsrZDBcywBJWTXnuwwojsL/dE5B1q0qdlxtDc5uKDbd6a9CmDosmraGBDfpVP/bnHSCwlUm3p99SkJ9mtmEyCALPJKFf0pGLiwqyMS7MzIDrY+PQbZg1g40Oz+t3iZ3fit5y8EGIo8HaHoYHAI1LK//PXPf1FV/vJHzp0yPj+pptu8unn6nkuJibGcMeMiooyOkl5+OCDDzp9P03XUlnfwrNfHeT+c71b23/0yiYADv3xAsDbDMMTmUspKalVY960i0qpjEhSi55SqsYXl41Pob6lnbV5lcb1ZwyNIzrkAOvcqZhRKRFMGhiNxSR4a4OqSU+2K+OtP3yyh11FtYaQz8lK4PmVKvfvEfDZWfF8sK3IZ1HzojFJfLKjmMw478Ln01ePNXaYejheulPjP/zZ/m+flDJbSpkNjAcaAW27qOn3PLcijxdW5/Pu5oJjnjvq7j3aMUovdDThaGyjsdVJQrjVqIAprG4izGph3ogE9pTUcqSqkTq38daUgdGsz6/iaHUjZpMgMUK1o1u2r4xqt/FWaJCF8QMiKXPn7JPsNgbFhhpb9z1pl3FpdiKDA3zGzh4SS6DZRGqUN7c+OyuezQ/PJrWDGVdqVLBPrl3T9XRVumYWcFBKebiL7nfa6Cl+8preidMlOePxL3h5zSFjzLPxaF+JWqSv7dCebm3esWWOnkVPgCsm+KZdPEZcUsJ7W9QO1pRIG2cOjsbR2MaSnSUkhKtSxDlZ8TjcO1U95Yodd3l6on7PmOcci9nE7OHxhASaiQlRC59h1gBeunki/2/GYJ/Xaw8ORNOz6KoSyquBN4/3hBDiduB2ULXhx0NK2aM+4vUFP3l/9ZPs77Q7XbQ6XQQHql8tj9HWox/t4sYz040xgE2HVJ67o6CvO1jJlRNSKXA0MibVztGqRtYcrDBcEedmJfDymkOqTt2hcutjUuyEBJp5Z6O3qUVKpA2TUHXvZ7gtbc8ZEofZJAy/F1CC/odP9vi8hvNGJvLc13k+vi0Pnj+cG6akYzJ5fw/PHBxz2t43jf/weyQvhAgELgLePd7zUsoFUsoJUsoJsbGxxzxvtVqprKzUonQakVJSWVmJ1dr7fDh6OvcvzCHrkaVG+WPHtIunht0j6ntL6qiobzHOSY2ysaZDbj3VXX/eMZJPiwpm0sBoVh6oMEoaAy0mpg+JNXL2yZE2IkMCjW36nhRLRHCA4WGe6h4bEB2CLcDMuSMSjHlmp9pZ8rNpPk0wIkMCGZUScZrfLU1X0BWR/HnAFill6QnPPA4pKSkUFBRQXl5+mqfVv7FaraSkpHT3NHo17U4Xb248ypUTUgyfk/e3qpTJ3pI6spLCfaL0nAIH4wdEUVDdRHp0MIcqG1mXV0mFOyd+xfhUnvp8P3kVDRQ5mpk3MoGUyGA+2VHMqtwKw1Rr1rA4oxLHI+BzsuL5dGcJwYFmI38+NyveqJLxcP3kAThd0qfePOe3c7GYfD8pD0vQJbd9ha4Q+Wv4llRNZwgICCAjI+M0TkejOTXanS6EEJjdgrh4Zwm/+WAnpTXNRlMIe3AAjsY21hysUCLfoZZ9TW4lY1LslNQ286NpA3lt3WHWHqzEFmDGGmBi/uhEnvp8Px9uK6LVqbxcpmeqlMiKA+UMjQ9TJY3D4xBCNeHw5M1nDlOpmGS7zUhtzhuRwJNL9hkllQDnjUrkvA67RkE1ttD0Xfz6ryuECAHmAO/78z4aTVcw8fEvuOlFb1s7z+YkTw05gNOp0jRrOpQ5RocEkpUYzpqDlZTUNvtsPFp7sNLYnJQRE0JihJWFnobRdhsDokMYGh+GlN6oPS7Mamwm8kTp9uBA5gyPZ0J6pDGX1Khg1jww0ycVo+l/+FXkpZQNUspoKWWNP++j0ZxuSmqaeXvjEWMtyOWSVDe2sfJAhTFW5I7Sc8vrkVJS09RGnbuJxvq8StqcLrdhl+oluvlItWGhm+Iey6toYNPhapI7bDwqqvHm1uHYaheAC0YlEmg2kdahXPGfPxzH//zA1w8pJjTIZ7FU0//Qn9M0muPwwPs5/Oq9HRwsVztPKxpajOc8Y558e3ldC/kVDcbx/NGJNLQ6ySmocVfAqJLG1nYXH7l3lybbbYa3S3ldixGlz+0QdX+zzLFj/fnNZ2XwxS/OJiLYu/W/J1WgaTpJUzXkr4Sd/kt29C4XSo3GD7hckj8u2cvVE1MZ6C4bbGh1AqpGfXBcmM8C6hrPmKOJ+PAgSmtbWH2wkgR316DLxqfwcU4xq3OVrcCsYXGckRFNoNnExzlK5JPcjaWHxIeyv7TeEPTJA6MIDbIQYBaEuNvIjU6J4K9XjPGpdjGbBGnR/dtCt9fRWAVH10PhFijdqXpj1KjUHEERMOJS8MMfai3ymn5PfmUDC1bksWRnCSvunwGA2f3Ltiq3guunpPssoK7OreCGKekUVjdx1uAY1h2sZE1uhVGPPio5gqzEcD7cVmg0wwgNsnDm4GiW7ysnNizIZ+PR/tJ6I5IPspi5dGwypbVel1IhhOEGqekFtLdC+V4l4mW71ffl+7yCLswQkwlpkyH+NtW4KGGUXwQetMhr+iFrD1ZiNglDlIvdTo1Hqrw+6x5RX3uwEqdLGnXq549KYNWBCprbnMpW127jzMExfLGnlMQIG9YAE9EhgcwaHsffluUC3rTL3KwElu8r98mtX5KdzHubCxmT4nVl/P0lI/346jWnlfZWKN8DxduhYBMUbVWi7nTbQJuDIHYIpE2BuJshdTIkj4MA23df9zSiRV7Tp2l3uih0NDEgOsQYu+b5dYDXDKzQ4RX3srpmokOCKHJXvBQ6mthVVENhdRMRtgDmjUhg8Y4Slu0twyXV4uiguFAWbi5g6a4SktwljHOzErwib5h6xfHQB75e6pnxYax7cJbf3wfNaaC1AUp2QkkOFG+D4hwo2wMuty2F1a4EfNCd7uh8NEQPAlP39orVIq/p0/x7VT5//HQvX/xiOoPjfNvCFdc0kRhhO8Yn5oyMKNpdkismpPB/XxxgVW6FUeY4ZVA0AG9t9LS1C2ZUcgRmk6DQ0cT0IWrX9sjkcBIjrBTXNPtY7947d6hP1K7poTjboTJXpVyOrIXDq1XKBffO++BoJeJT/h8kjobEbIga6LeUy/dBi7ymT1Hd0Eq4LcDYsLTtiGqVuDavisFxYUZtO8CqAxVcMSGVAvcCanObi1UHKox0yphUO0Pjw1iTW0lZXTMDokOIC7MyMjmcFfvVDuyUSBsRwQFMHhjF6txK42eFEFyUncQnOcWEW70VMN809NL0AFwuqDwAhZvVo2grlO6Cdve6SGCoyp+PuFQJe+IYCE/qkYJ+PLTIa/oMLe1Oxv7+c645I9WoFw8KUFXC6w5Wcv3kAccsoF4xIZXC6ibSooKJCQ1idW4FU927TFPsNqZmxvDqusOYheDMQWp8XlYCOwuVg2SiXVXUzBuRwOrcSp9UzL1zh3LXzEz/v3DNyVFb7BX0wk1QtA1a3G07A8MgKRsmuhdE40dC7DAw916p7L0z1/R7qhpacTS2Gtt6mlwAACAASURBVGWPnlZ3b244aoi8JxWz+mAFLpc0jtOiglmV6zYDczQxYUAk49Oj+HRnCasOqM5LyZE25mTF8x93s+yOtex//Xw/cWFBhmfNuSMSePqLA4zt0NYuwGzSlgHdjbMdKvapssXDayB/BdS6ffxNFiXio6+E5PHqEZ0Jpr71b6ZFXtNrufK5teSW1ZP3xPmYTMInt17f0k5okIVCRxMWk8DR2Mbu4lojkr9qYip/XrqPPcV1lLjz5h6fmP/mFBEVEkhwoIWJ6VFEhQRS1dBqiPyQ+FAyYkKIDvF6p8eFW9n8mzld+Oo1x8VTi354DRRsVFUvbe6FdVsUZEyHtLuUoCeMgoC+78SqRV7Ta/H0Qd1VVMuolAifKpn1eZVMHxJLaW0zV01M480NR1iVW0F1QyuBFhMXjUniz0v38f6WAtpdkmR7MAOiQxiWEMbekjoy45Sgm02C2cPjeGdTAcl2tflICME/rhvXW1KyfZuGCrUwemiVitLLdqtxU4BKu4y7EZLGqqqXqEF9LkrvDFrkNb2C/IoGZvxlOW/+aLJR4RJoMdHa7mJlbrkSeXckbzYJVuVWMCQ+DJeEsal2thyuZnVuBeHWAJLtNlKjghkYG8LCLeqje3KHVMzekjqfWvbrJ6dT5GgmM97bRGN4orbi7RZaG+DQasj9HA5+pRZMASw2tTg68geQdmaX16L3ZLTIa3okVQ2tWANMRoelrUeU0+M/lucyZVA07U4XTndjjlUHKrjznMEUOJpICLeSGR/KqgMVXmOvSBtnDY7h9fWHSY8OMQR83ogE/rn8oDrHPXbuiASe+fKAzwLqqJQIXrttUte8cI0vjqNwaKXaaFS4WdkBuNohIBjSp8LYH6qNRknZYAk68fX6IVrkNT2Scb//nIGxISz75TkAVLt7k+ZXKBdHj2VvWJCFTYeqaWp1UujulDR1cAz/8+letrrLJ5PtNmZnxfHC6nz2ldYZNr3nHkfkhyeGcffMwcwbqe15u4WGCpV2yf8a8r6GarXoTVC4Kl086x4l7mln9ot8+ulAi7ymx9HubpOXV96AyyV9FlULHU1UNbQax1dNTOXfq/LZcKiKopomxqVFqhLIT+Ft94alRLuVlEgbkcEBVDe2GamZ0SkRJEZYaW13YXP3UBVC8Iu5Q7v6JfdfWurVRqP8FUrUS3eo8cAwJeaT7lCLpbHD+2U+/XTgV5EXQtiBfwMjUVvFbpFSrvXnPTW9j6/3l/Pq2sM8d/14zCZBaZ3X1nd3cS0jk72LqlKq+nZPv9RLxibzytrDrNhfTrGjmeTRNrISw0m22zhS1ehT5jgnK969gOrdsHTXzEzDF17TBUipvF3yV8LBL1Ve3dmiPF7SJsHMhyHjHLVY2otr03sS/n4XnwaWSCkvdzf01t6ommO4793tlNW1sKuohtEpdh/RXXmgwi3yTUwdHENOgYNVByqMnPnguFDGD4jkw22FqkomUnnHzBuRwAur842oHeDCMUm8s6nAZwH12klpXfdC+yt1pZD3FRxcBnnLod7d7tk+ACbeCplz1aKpXij1C34TeSFEBDAduAlAStkKtPrrfpregZSSNzcc5aLsJELdfumJdhtldS2sPFDB6BS7kYoJNJtYeaCcn5wziCJHM6OS7YQGWViVW8G0zBhiQgOxBpiZOyKetXmq3Z6xgDrSLfIdqmSmZcay8v4ZPs03NH6gvUWVNR5cBrnLvCmY4GgYOAMGngMZ0yAyvRsn2X/wZySfAZQDLwohxgCbgXuklA0dTxJC3A7cDpCWpqOqvs72ghoeXLSDNQcr+Pu14wBoaVMNOlbsL+f/zRjss2Hp7Y1HqWpoNTYjjUwOZ8muElZ28JiZNyKBx/6r6qM9Ef74AZEMSwhjYnqUz/21wPuJqjzYv1QJ+6FVagOSKUBF6LMehUEzle+Lzqt3Of4UeQswDrhLSrleCPE08GvgNx1PklIuABYATJgwQfpxPppuoKJe5ddjQlV5m6cZxkq3dQB4vdu3HKmmvqWdguomokMCmZ0Vz6vrDrNoayGgovTJA6N5iJ0UOpoYkxoBqC5LY1LtbD/qIMnu3cS05GfTu+ZF9kfaW+DoBpV+OfCZst8FiB4MY69Xop4+FYJCv/MyGv/jT5EvAAqklOvdxwtRIq/pR5z5P8todboM73ZPvr2mqY3mNietThd1ze2cPSSWr/eXsz6vkkJHE0l2G2ekRxFoMfHG+sOAqndPiLAyLs3OliMOn1TM7dMGsmRXiVFXr/ED1YeVoB/4zButCzOkTIS5j8Pw+ToF0wPx22+ElLJECHFUCDFUSrkPmAXs9tf9ND2TVncVTHVDK5EhgT7+Muvzq4gLUxH+xdlJrM+vZOWBCoocTQyODcUWaGZ6Zixf7FELdR5RP29kIluOeKN2gAtGJ3LB6MSueln9AylVc4zdH8LexcroCyAyA7Kvg0EzIH0aWPXu356Mv8Oeu4DX3ZU1ecDNfr6fphupaWzjjQ1H+NG0DCxmk1HvDqpX6oVjkozmG+X1LazcX25YFGTEhDApI5rl+8oorW3hbHfzjfNGJvDFnlIsJkG8u1H2/DGJvLruMBMGRB07Cc33Q0qVetn9Eex8T21GEmaVehl/I2TOgxjtid+b8KvISym3ARP8eQ9Nz+HZ5bksWJFHYoSVS8YmU9KhGfWK/eWGyA+KUy6OKw9UkBatFkI9tr4Pf7BTHbuj9NnD4wkwK4H3NAJJjLAZDbc1pwGXSzk27vkI9vwXHIdBmFSUPu0XMGw+BOs/qL0VncDUnDakVOvm2446uGRssuHvHma1sOJAufJur25iRFI4GTEhPLF4L5sPVxNoMRETEsTcEfH85sOdSOk1DIsIDuC8kYnoFfnTjLNd7TTd81/Y+zHUFatqmEEzYPp9MPR8CInu7llqTgNa5DWnzDubjjIpI8pokl3ZoLZBrMpVlTOeXarXnpHGcyvy2FFYQ2VDK8l2G+cMjeOJxXtZvKOYZLsNk0kQF2ZlYnoUG/KrfBZVn746G6F9fb8/7S3KOmCPO8feVKXcGzNnw/CLYchcsEZ09yw1pxkt8ppToqGlnfsX5hAWZGHHY/MAbxem3LJ6ihxNxvGVE1N5bkWet/l1pI3MuFAGxYZwsLzBZ1fq5eNS2FVYY6RxAC3w34fWBsj9UqVi9i9Vbe6CwmHIuTD8Qhg8GwL13oG+jBZ5zSnhqW2va2lHSokQgkJHE1mJ4ewurmXF/nIKHarefVBsKJlxoXxg1LsHI4TgglGJPLMs1ydqv2JCCuePTjR2w2pOAWebEvSct+DAF9DepLoiZV0Mwy+CgWdrW95+hP5N0nSKpbtKWL6vzNs7tYO/zN6SOobEh1FS08yFY5Kobmzl6/3lNLQ6jSj9vJEJPLMsF4Akd/Pr8wyR943atcCfAlKqPqY7F6qqmPpSCE1QfutZFylrXm341S/R/+qaTnHHq5sBuPOcwaRGBfuYiH29vxx7cIC7jZ6N6ZmxLN5ZTHRIoNFBySPoZpMgwV0KOSwhjD9fPprp7nJJzSlQeRC2vwk73oXqQ2AOhMFzYNz16qsW9n6P/h+g6RQxoYFU1LeyfH85108eQGG1apA9KDaU5fvKmDAgElD59vhwK29vOkpdczuzhqvuTMMSwhgYE0JLuwuLWfmXCCG4YkJqt72mXktLPex6H7a9oYzAhAkyzlZVMcPmg83e3TPU9CC0yGuOobnNyX9W5XPLWRlGM41AtzB/va+M6ycPoMjRREKElRnD4vj3yjz2l6qm2sl2G2lRwYQGWahvaffxbn/kwiyqG7UR6SlTkQub/gNbX1MLqNGZMPu3MPoqCE/q7tlpeiha5DXH8NH2Iv68dB9NrU7unTeUdqfLaOSx5mAlLe1Ow1/m7CGx/Ovrg7yzyV05Y7dhDTAza3gcH24r8qmcOWdoXLe8nl5NQ6WK2nPeVhuWTBbIugTOuB1SzwBdeaQ5AVrkNcfQ1KqsfzcfVs2zS+tacLoks4fH88WeUjbmV1PkaGZSRhQT0iOxBwew7agDe3AAIe5F08vGpfBxTjFD48O67XX0WqRUG5U2vaDsBVxtEJcFc36novYw3X9W03m0yGtYsOIgg2JDjfx5UY1aVN1TUovTJY1F1svHJ7Nifzlf7CmlpLaZJLuNALOJeVkJvL3pKEkR3qh9+pBYtj4yh3BrQNe/oN5KkwO2v6XEvWKf2pg08VZVIZMwqrtnp+mlaJHv50gpeWLxXoAOdsDKjsDR2MbOwhpjU9PguFAmDYzig22FON2t9kA5QL696ahPagbQAt9ZCjfDxhdU6WN7EySPh4v/ASMu1RuVNN+bE4q8EOIe4EWgDtWUeyzwaynlZ36em6YLqG1uN76vamglKiSQwupGhiWEsa+0ji/3lhFkUYuuSXYb541MNBp+eKx+pwyKJinCyvAEnZrpNM42Jerr/qnsfANCYMxVMP5mSMru7tlp+hCd6cV1i5SyFpgLRALXA3/066w0fiO3rJ5nv8o1zMR8m2aXu8eaGZkcwYQBkXy2q4RCRxORwQEEB1o4d2SC4QbpqZwJMJv47Bdnc/eszC5+Nb2Q+nJY8Rd4egwsugPam+H8v8Av98CFT2uB15x2OpOu8Szfnw+8KqXcJbSZSK/lvoXb2XrEwazhcQxLCD9mU9P5oxIprWsm2W5jWEIYf/hkD61Ol5GKiQoJ5MxB0aw8UGHsXAX0LtUTUbQV1v1LVco4WyFjOsz/X8icqytkNH6lM7+Zm4UQn6Eacz8ghAgDXCf4GQCEEIdQaR4n0C6l1N7y3Yyn3v2rveU+Ij9lYDTL95VTWN2krH7d/VT/8Mke8sobmJsVb1zjZ7MzyU6161Z7J8LlhH2LYe2zatNSYCiMvwkm3gaxQ7t7dpp+Qmd+S28FsoE8KWWjECKak+vwNENKWXHi0zT+oKGlneBAs+Hk6HKnab7aW8ZPzhlEoaOZALPguslp/PSNrXy4rQhQ+fa06GBGJIWzq6jWp9Xe+AFRjNddmb6dtmbY+iqs/buyGrCnqR6o467XVr6aLueEIi+ldAkhSoEsIYQO3XoRVQ2tjPv95zxw3jDuOHsQ4K2c2XS4CkdjK4WOJhIjbMwYGkegxcQLq/MBr4nY2UNi2VWkSik1J6ClDja/DGv+BvUlkHKGqm0feoH2kNF0G52prvkTcBWqCbfTPSyBFZ24vgQ+E0JI4Dkp5YLjXP924HaAtLS0Tk5b0xkOVTYA8Kcle7nj7EG0O12U1DYzdXAMq3Ir+Hp/OUXunqshQRafptmeyP0ad8OPi7L1tvlvpXS3shvIeUfZDaRPg8ueV191vl3TzXQmvLgEGCqlbDmF60+VUhYKIeKAz4UQe6WUPn8c3MK/AGDChAk6XDyNFLujdpdUfjRVDa04XZJzRyawu7iWr/aWUeRo4sxBMQBcMDrBEHlrgPKsSY0K5uAT53fPC+jpHF4Lq/4XDiwFc5Cqa590u6pz12h6CJ0R+TwgADhpkZdSFrq/lgkhFgFn0LlPAJpTYOOhKgQwIV3lyztWzqw9WEmoVf1zp0YFc87QWL7cU0ZdcxvJ7tTMnKwEYHtXT7t3IaXqtLTyL2oxNTgaZjysdqbqZteaHkhnRL4R2CaE+JIOQi+lvPu7fkgIEQKYpJR17u/nAr/7PpPVfDdX/GstAPn/c77RqSnQbMJiFny5t5SJbvFPtlu5YFQi729RnZo8qZnQIAsPnj/M8J/RfINDq2HZ75W4h6fAuX9Si6mBId09M43mW+nMb/NH7sfJEg8scld1WIA3pJRLTuE6mpMkt6yezPgwihxNpMcEkxETwrI9ZSS6vWUSI2ykRYUQYQugpqnNp3Lm9umDumvaPRMp4dBK+PpJ9TU0AS74K4y9ASyB3T07jeaEdKa65mUhhA1Ik1Lu6+yFpZR5wJjvMzlN52lucxrff7GnjMz4MIprlInYrGHxLN1Vypd7Sn2cIs8flcCbG471nNGgxP3gl/D1n+HoOiXu5/4Rxt2o/WQ0vYoT2hoIIS4EtgFL3MfZQohTiew1p5G9JbVc/s81VDeoJhwd8+9fuhdPi9ye7zOHx2E2CbYccfg4Rf747EHcNjWD9GidbjCQEvZ9Cv+eBa9dBjUFynbgnu0w+Sda4DW9js541/wWtWDqAJBSbgMG+nFOmk7wwqp8Nh2u5r0tBQAU16hKmskDo9h8pJpCRxOVDa0k223EhAYxdbCqoOmYmhkQHcLD87MML5p+jZSw9xN4bhq8eTU0VCgvmbu3whk/ggDria+h0fRAOiPybVLKmm+MdcrWQOM/wtw2vhsPVQFQ6I7kb5iSjpTwxvrDgHdT08XuOvdkuxYrH6SEg8tU5P7WtdDaAJf8E+7arCwIdN5d08vpzMLrLiHEtYBZCJEJ3A2s8e+0NCeixB25r9hfQXObkyJHE0LArOFxJIRbeXODasfnSc/MHZFAXNhexqTqJs8GRzfAl79TC6rhKXDR32HMNXp3qqZP0ZlI/i5gBKp88g2gFrjHn5PSHMv6vEpqmtqMY0/k3tTmZM3BCoocTcSGBhFkMTN/dCJV7lx9x/LI9Q/O4gfjUrp+8j2N6kPw7k3wnzlQvhfOexLu3qLKIbXAa/oYnRH5a6SUD0kpJ7ofDwGP+XtiGi/NbU6uWrCOq55ba4wV1zRxSXYSIYFmPt9dalTSAFwyNhlQO+oTIrzpmX7vEF1XCp/cC3+bAPuWwNm/hru3waQ7wBLU3bPTaPxCZ8KWy4QQzVLK1wGEEH8HdM1dF+KpnNlbUoeUkjanpKyuhfSYEM4eGssXe8oIDbKQlRgOwIikcAbHhVLX3EaAuTN/x/s4zbXKNGzt36G9BcbdAGffD+Haj0fT9+mUyAMfCSFcwLmAQ0p5q3+npemIxzkSYEdhDZHBgUip8u0DY0NZvKOE8roWZg2LA1TE/ruLR1Bedyp2Q30IlxO2vAJfPQ4N5TDiBzDzYYjWG740/YdvFXkhREcjjtuAD4DVwGNCiCgpZZW/J9dfqWtuo6nVSVy4SrUU1Xhr4D/bVcq0TG855PgBkYQGWahvafcpj/SYjvVbDn4FSx+Csl2QNgWufVsbh2n6Jd8VyW9GWQWLDl8vcD8kulbeb9z4wga2HHGQ98T5mEzCqJwZlxbJ0l0lDIpTm5cS7VZsgWbOHZnAws0FPiLfb3EchaUPwp6PwD4ArngZsi7Wlr+afsu3iryUMqMrJ6LxsuWIA4CtRx2MHxBJsaOZmNAg5o9O5LH/7mZ1biXgLY+8blIaS3eVMCIpvNvm3O20NsKaZ2DV/6njmQ/DlLv0JiZNv6cztgYBQoi7hRAL3Y+fCiECumJy/ZVwtyXwkp3FgErXJEVYmTciAYCPthURFRKILVB5vo9NiyTn0bmkRvXDLfdSwq5F8PeJsPx/YOi58NMNMP0+LfAaDZ0rofwnMB74h/sx3j2m8QNOl6SxVZmNLdlVgpTS8KBJstuYlBFFq9NFYoSvgPXL8sjyffDKRarm3RYJNy2GK15SPVU1Gg3QOZGfKKW8UUq5zP24GZjo74n1F8rqmrnlpY2U1aoKmor6FtpdklHJERytamJXUS1FjmbDJviy8WozU7/Ov7fUw+ePwD/PhOIcZSB2x9eQflZ3z0yj6XF0RuSdQgij5kwIMRBvr1fN92TprlKW7S3j8cV7AO9O1uunDMAk4J1NR2lqcxoeNOePSiQ0yMLAmH7qHLl3MTw7CVY/DaOvVh4zZ/wITObunplG0yPpTJ38fcBXQog8VIXNAOAWv86qH9HsTs1syFcVqZ6+rCOTIjgjI4qFm5XLZEd7gk/unkpUSD8zzqotgk9/papm4rLg8qWQNrm7Z6XR9Hg6I/KrgExgqPu4041DAIQQZmATUCilnH9y0+v7eGrgi2ua2VtSS7H7ONlu4wdjU1iXp8S/Yw5+QH/yf3c5YcPzsOwP4GqDWY/AmXeDWa/9azSdoTPpmrVSyhYpZY770QKsPeFPebkH2HNq0+v7FDuaiQoJxGwSfLitiCJHM8GBZsJtFs4fnYgtQKUhkvtjDr5kh7IAXvIrSJsEd66Dab/UAq/RnATfteM1AUgGbEKIsahUDUA40KlaPSFECmrz1OPAL77fVPsGP397G6mRNn4xV30wKq5pYkRSOCYh+GhbEaOSI0iMsCKEIDTIwvzRiXyyo5iY0H5koNXeAiv+DCufguAouPwFZUnQHyuINJrvyXela+YBNwEpwF/xinwt8GAnr/9/wP1A2LedIIS4HbgdIC2t75e+LdpaCMA9s4dgNgmKapoZlhDOGRlR/PLd7TgaWxk3INI4/zcXZnHzWRmY+kv3psIt8OH/g7Ldytt93hNK6DUazSnxXTteXwZeFkJcJqV872QvLISYD5RJKTcLIc75jvssABYATJgwQZ7sfXoTtc1eP/j1+ZVMGBBFRX0LiXYrc0fEE7TIREOr06cPa7g1gKykfpCeaG+B5X9UVTOhcXDtOzBkXnfPSqPp9ZwwJ38qAu/mLOAiIcQh4C1gphDitVO8Vp/A080J4OOcYkprmw03yTBrALOGKxfJxP7Woq9gMzw3HVY9BdnXqNy7FniN5rTgN7NxKeUDUsoUKWU6cDWwTEr5Q3/drzfg8YVPirCyZGcJR6saAa+oXzQmyf18P1lkbWtWm5r+Mxta6uC69+DiZ8GmWxRqNKcL3VHCjzS0tLOnuNY4LnZH8j+aPpCqhlbe26Ly857drLOGx/PQ+cOZNzKh6yfb1RRtg+emqfTM2OvhzrWQObu7Z6XR9Dk61dBSCHEmkN7xfCnlK529iZRyObD85KbW+/ntR7t4d3MB6x+cRXy4leKaZoSAKyek8tRn+/lou0fkVSQfYDbxo+l93MHZ5VIdmr78HYTEwg/fh8GzuntWGk2fpTMulK8CfwGmojxrJgIT/DyvPkF+RQOg8u8AxY4m4sKCCAmycPHYJNqcknCrhZCgftI82nFEGYp9/huVc//Jai3wGo2f6Yy6TACypJR9uvLFH3isgD/cVsitUzMormkmwZ2aueaMNF5bd6T/GI3tfB/+e4+yBr74Wci+Tte9azRdQGdEfieQABT7eS59Dk81TU5BDXnl9RTVNDE0Xm0ZGJEUwaSMqL6/k7WtCZY8AJtfhJSJcNm/ITK9u2el0fQbOiPyMcBuIcQGwOgMLaW8yG+z6iMU1zRz3sgEluwq4cNtRZTUNHPOkDjj+VdvnYS5L29yKt8PC2+G0p1w1j0w8zfakkCj6WI6I/K/9fck+goulzR2ptY1t1Hf0k52qh1HYxtvbTxCY6vTx2gs0NKHi5u2vwUf/0J1Z7puIWTO6e4ZaTT9ks5shvr6eI+umFxvor6lnYEPLuY/q/IBb6om0W7j8vEplNa2uI/7+Ean1gb44E5YdAckjoEfr9ICr9F0I52prpkshNgohKgXQrQKIZxCiNoT/Vx/40il2tj0+493A1DkEfkIKxeMTsQeHOA+7sM5+NLdsGAGbHsDpt8PN/4XwpO6e1YaTb+mM/mCvwPXAAcAG3Ab8Kw/J9UbKa31WhbklddT4vaFT4ywYg0wc9WEVABSIvugyEsJm1+G52dAUzVcvwhmPgTmflIaqtH0YDr1WyilzBVCmKWUTuBFIcRW4AH/Tq13UdzBl+b9LYVYzAIhIC5MpWd+NnsI0zJjiQ/vY+maljr4+Oew413IOBt+8DyExXf3rDQajZvOiHyjECIQ2CaEeBJVStmHVwxPjZKaJkwCpgyKZtHWQs4cFE1MaJCxuGoLNDM1M6abZ3maKd4O794E1Ydg5sMw9Re616pG08PojFhf7z7vp0ADkApc5s9J9UaKa5qJDQviygmpFDqaeH9rIUkRfSxq9yClasn379mqDv7Gj2H6fVrgNZoeyAkjeSnlYSGEDUiUUj7WBXPqFXy6o5iWdheXjE0GoKRW7WadNyKB6JBAKhtaSeiLIt/kgI/uUg21B8+BS/8FIX3sE4pG04foTHXNhcA2YIn7OFsI8ZG/J9bT+cnrW/jZ29tobG0HVMlkYrhaZL12kupw1ebsY04Q5fvV4uq+xTDnd6qxhxZ4jaZH05l0zW+BMwAHgJRyG5Dhxzn1Kj5xm4+V1DQbkft1kwYAMCmjD7WtO/C5aqrdUqfSM2fdAya9NKPR9HQ6s/DaJqWsEb5mUn0sRD052pwuhFCp6bc2HuXckQnUtbQbu1kTIqxsf2QuodY+UELocsGKJ1VrvviRcM0bYO/7vXg1mr5CZ1RolxDiWsAshMgE7gbWnOiHhBBWYAUQ5L7PQinlo99nsj2F8roWpISBMSFsPlzNygMVAD45+IjgPuDR0lildq4e+Ew11b7gKQgM7u5ZaTSak6Azn7fvAkagzMneBGqBn3Xi51qAmVLKMUA2cK4QYvKpTrQn4amJv3PGYALMgme+PABAQl+qgS/ZAQvOgYNfwQV/hUv+qQVeo+mFdKa6phF4yP3oNG7/+Xr3YYD70SfSPJ7drSOSwjlvZCIfbS8C+pBlwZ6P4f3bwRoBtyyBFN0jRqPprXyryJ+ogqYzVsNCCDOwGRgMPCulXH+cc24HbgdIS+uZud6Glna+2lfGBaMSEUIYkXxCuJXbpw80RD4uPKg7p/n9kRJW/a9qzZc0Fq55E8L6Qb9ZjaYP812R/BTgKCpFsx44aeNztw1CthDCDiwSQoyUUu78xjkLgAUAEyZM6JGR/n9W5fPU5/txXi25ODuZ0tpmgiwm7MEBRIYEMnVwDLll9VgDevFmoLZm1bkp5y0YeZnq3hTQRz6ZaDT9mO8S+QRgDsqc7FrgE+BNKeWuk72JlNIhhPgKOBfVaapX0dLuBODdTQVcnJ3sbuNnxVNx9PTV2VTUt3bnFL8f9WXw1nVQsAFmPAzT79Wt+TSaPsK3LrxKKZ1SyiVSyhuByUAusFwI8dPOXFgIEeuO4HHvmJ0D7D0Nc+5yKt0Cviq3gkMVDZTWNPssskaHBjE07vvpVwAAExBJREFUIay7pvf9KNmh7IFLdsCVr8DZ92mB12j6EN+58CqECAIuQEXz6cAzwKJOXjsReNmdlzcB70gpPz71qXYfJbXNxIcHUVnfyqvrDlNc28S4tMjuntb355sLrEnZ3T0jjUZzmvmuhddXgJHAYuCxb+bST4SUMgcY+/2m1zMoqWlmVHIEwYEW3tl4lLqWds4fldjd0/p+rPk7fPawXmDVaPo431Un/0MgE7gHWCOEqHU/6vpbZ6jS2mbiw63cNi2DuhblVZPYW2viXS5Y+hB89hAMvxBuXqwFXqPpw3xrJC+l1MYkQHObk+rGNhLCrYxOsRMfHkRpbQuRIYHdPbWTp71F9V/duRAm/gjO+5O2B9Zo+jhayI/D4coGmttURU2ZuwF3vNuy4B/XjScxwtr7cvKNVfDKJUrgZz0K5/9ZC7xG0w/oAw5apxcpJWf/eTmZcaF8/ouzKan1bnwCGD8gkrUPzOrOKZ48FbnwxhVQUwiXv6Dq4DUaTb9Ai/w3cDS2AXCgrJ6C6kavyPfWBiCH18KbV4PJAjd9DKlndPeMNBpNF6JF/huU1nkbcv97ZT5JdiXuvbIB956P4b1bISIFrlsIUboNgEbT39A5+W9Q4valyYgJ4a2NR9hTXIctwEx4b/OG3/QCvHO98oC/5TMt8BpNP0WL/DfwLLQ+Mj+L5jYXi7YW+lgY9HikhOV/go9/DoNnw40fQUh0d89Ko9F0E1rkv4HHRvjMwdFcNCapm2dzkrhcsOQBWP4EjLkWrn4DAkO6e1YajaYb0SL/DUpqm4kMDiDIYua+eUMBCAnqBaWGzjb48E5Y/0+YfKdykTT3ge5UGo3me9HLEs3+p7S2xVhkTY0K5u3bJ5Nk7+GWu62NsPBm2L8EZjwE07XJmEajUWiR/wYeCwMPkwb28Hx2k0OVSB5Zp9r0Tbytu2ek0Wh6EP0+XfPOxqOc9/RKWttdgEfke0mHp/oyeGk+FGxSm5y0wGs0mm/Q70X+H8tz2VNcyxvrD9PudFFR39I7GnI7jsAL50LVQbj2LRj5g+6ekUaj6YH0e5FPjlT59r8ty+VQZSMuCXE9XeQrDsAL50FjBdzwoSqV1Gg0muPgN5EXQqQKIb4SQuwWQuwSQtzjr3t9H8pqW0iKsFLZ0Mrjn+wG6NmRfHGOiuD/f3t3HiZVdeZx/Pt2Q7O20NgNUVyAxIXFDdsJaJwYEw2uiAuiJkrU+MQ44zhmHkdGZmIyk1GTaNSoQVzGDZWIaAwqBgwmooICiqhIwJVNaTBCN0Kv7/xxT0PR0mxdt2717d/neerpW+feuuecOt1v3zr31Dn11TD6aU1TICLbFOeN1zrgJ+4+z8yKgblmNs3d34kxz5326bqNjDisN6uranh6wUogj6cwWDYXHhoBRcXRFXzp15IukYjkudiu5N19pbvPC9uVwEKgd1z57YoNNfWs21hHz9068h8n9d+Unpc3Xj+eBQ8Mh04l0UIfCvAisgNy0idvZn2IlgKcvZV9l5jZHDObU1FRkYvibLIqTEbWs7gDvbt34qenDGBQ793YvWueBfn3X4AHR0BxLxj9DJTsm3SJRKSViD3Im1lX4HHgCnf/0rKB7j7e3cvdvbysrCzu4mxhVWVYECR0z/zgqL5M+eejKSzIoy8SLZoKE0ZCSZ8owHfLqw9DIpLnYg3yZtaeKMBPcPfJcea1KxrnqcnbPvi3n4CJ50GvAdFN1uJeSZdIRFqZOEfXGHAPsNDdb4orn5b4tHFpv3zsg5//KEy6EHqXRzdZO/dIukQi0grFeSV/FPB94FgzeyM8Towxv+1yd2a9v4b6BgeiPvmidgV065RnE3nNewCe+BH0+QZ873Ho2C3pEolIKxXbEEp3nwnkUec2vLH0c0aNn8UZg/fixpGHsGpdNT2LO+TXXPGv3QNPXwlf/TaMmgDt83xyNBHJa23qG68rw6pPj89bxsKV6740GVniZt8ZBfj9h0VzwSvAi0gLtakgv2rd5vVbr5r0Jis+35A//fGzxsGzV8GBJ8PIB6F9Hv3zEZFWq20F+cpqCguM2849jAXL1/Lhmi/oWZwHwXTWOJj671GAP+s+aFeUdIlEJCXaXJAv7VrEyQfvyWmHRkv7dSpKeNWnV+7YMsBrNScRyaI2tWjIqsrqTVfu159xMN07F3HG4L2SK9DLt8GfroH+p0bzwSvAi0iWtakgX1EZzTgJ0LF9IdeeOjC5wrz8W/jTWBhwGpxxtwK8iMSiTXXXVFRupGc+3GjdIsDfowAvIrFpM0G+rr6BNetrKEv6RuuXAnyb+jAlIjnWZoL86qoa3KMZJxPz0i0K8CKSU20myGdOK5yImTfDtP+CgacrwItIzrSdIB8mI0tk/daXboXpP4VBZ8DpdynAi0jOpDrIXzVpPpc9PI/6BqeiKgT5XF/Jv3IHTPtPGDgCRoxXgBeRnEp1xPn9nGUAdO/UftP4+NJcrvo063fw3JhoHLyu4EUkAamNOu5OUWEBNfUNTJj9MQAlndtT1C5HH14av+h04MkaJikiiUltd826DXXU1Dcw9qT+nHV49K3Wv39Rm5vMX7olCvADhmsuGhFJVGqv5CuqotE0ZcUduPCovvToWkRJ5xwE25k3RzdZB56uLhoRSVxsEcjM7gVOBla5+6C48mlO4yLdZcUdKCgwxpzQP/5MZ/4Gpl8bjaLRTVYRyQNxdtfcBwyL8fzbVFGZw9E07vDCDQrwIpJ3Ygvy7v5X4LO4zr89jUG+rGvM4+Ld4c//DS/8LxxyrrpoRCSvJH7j1cwuMbM5ZjanoqIia+etqKqmqLCA3TrFGHDdozHwL94Igy+A4bdDQcLz04uIZEg8yLv7eHcvd/fysrKyrJ13dWUNZXEu0u0OU8dEE44d8UM45RYoSPztFBHZQmr7FSqqqimNqz++oQGe+TeYcw98/VIYdh3E9c9ERKQF0hvkK6vp3T2G/vj6OvjDZfDmo3Dk5XDczxXgRSRvxda/YGaPAK8AB5jZMjO7KK68tqaispqybF/J11XDYxdEAf7YsQrwIpL3YruSd/dz4jr31tQ3OBWV1XylW0fqG5zP1ldTls15amo3wMTvw5JpMOx6GHJp9s4tIhKT1NwpfPL15Qy57nkmz1vGmvXVNDjZu5KvWQ8Pj4Ql06MbrArwItJKpCbIL15VBcBPHpvPxFeXAlkK8uvXwP2nwoczYcQ4OHx0y88pIpIjqbnxWlVdS2GBcUCvYm6c9jcgC0H+sw9gwlnw+ccw8gHof0oWSioikjupCfKrK2voW9qFyT8+kodmfcRLS1ZzwFd22/UTLp8LD58N9bVw/pOw75HZK6yISI6kJ8hXVVPatYiO7Qu5+Oh+XHx0v10/2cIpMPmH0KUURj8DZftnr6AiIjmUmj75KMi3sHvGPZpJcuL3oOxAuPh5BXgRadVSdCVf07IgX1cNf7wC5j8czQV/2h3QvlP2CigikoBUBPmNtfVUVdft+o3Wtcth0oWwdBZ882o45mp9yUlEUiEVQX51VTStcGnXXVj5afF0eOISqN0IZ94bzQcvIpISKQnyNQA7111TXwczfgEzb4KeA2Hk/VC6X0wlFBFJRjqCfGXjlfwOBvl1K2DSRfDxy9E88CfcoP53EUmldAT5xu6aHemTXzIdJofumdPvgoNHxlw6EZHkpCrI795lG33yG9dF3TOz74Se/eGs+zU8UkRSLyVBvobiDu3o2H4rS+/V1cDc++AvN8AXa+CIi6Mpgos657ycIiK5lpIgv5VVoGo3wsKn4IXr4LP3oc/RUXDvPTiZQoqIJCDWIG9mw4BbgELgbne/Po58Gqc0wB1WzIPXJ8Bbk2DjWug5AM59DPY7TmPfRaTNiS3Im1khcDtwHLAMeM3MnnL3d7KaUX0dg/4+nSM6LIPbLoc1i6Fdx2jGyEPPg77f1ALbItJmxXkl/w/AEnd/H8DMHgWGA9kN8lbAFRtup9OGGth3KAy9DAaOgE7ds5qNiEhrFGeQ7w0szXi+DPh604PM7BLgEoB99tlnpzNxM27udxcHDRjI8PIWzDwpIpJCid94dffxwHiA8vJy39nXmxljz9diHiIiWxNnZ/VyYO+M53uFNBERyZE4g/xrwH5m1tfMioBRwFMx5iciIk3E1l3j7nVm9k/Ac0RDKO9197fjyk9ERL4s1j55d38GeCbOPEREpHkaQC4ikmIK8iIiKaYgLyKSYgryIiIpZu47/f2j2JhZBfDRLr68FFidxeK0Bqpz+rW1+oLqvLP2dfey5nbmVZBvCTOb4+7lSZcjl1Tn9Gtr9QXVOdvUXSMikmIK8iIiKZamID8+6QIkQHVOv7ZWX1Cdsyo1ffIiIvJlabqSFxGRJhTkRURSrNUHeTMbZmaLzGyJmV2ddHlawsz2NrMZZvaOmb1tZv8S0nuY2TQzWxx+loR0M7NbQ93fNLPBGee6IBy/2MwuSKpOO8LMCs3sdTObEp73NbPZoV4Tw1TVmFmH8HxJ2N8n4xxjQvoiM/tuMjXZcWbW3cwmmdm7ZrbQzIamuZ3N7F/D7/RbZvaImXVMYzub2b1mtsrM3spIy1q7mtnhZrYgvOZWM7PtFsrdW+2DaArj94B+QBEwHxiQdLlaUJ89gMFhuxj4GzAA+CVwdUi/GrghbJ8IPAsYMASYHdJ7AO+HnyVhuyTp+m2j3lcCDwNTwvPfA6PC9jjg0rD9Y2Bc2B4FTAzbA0LbdwD6ht+JwqTrtZ063w9cHLaLgO5pbWeipUA/ADpltO/oNLYz8I/AYOCtjLSstSvwajjWwmtP2G6Zkn5TWviGDgWey3g+BhiTdLmyWL8/AMcBi4A9QtoewKKwfSdwTsbxi8L+c4A7M9K3OC6fHkQrhj0PHAtMCb+8q4F2TduYaG2CoWG7XTjOmrZ75nH5+AC6haBnTdJT2c5sXu+5R2i3KcB309rOQJ8mQT4r7Rr2vZuRvsVxzT1ae3fN1hYL751QWbIqfEQ9DJgN9HL3lWHXJ0CvsN1c/VvT+3IzcBXQEJ7vDnzu7nXheWbZN9Ur7F8bjm9N9YXoKrQC+L/QTXW3mXUhpe3s7suBXwMfAyuJ2m0u6W/nRtlq195hu2n6NrX2IJ9KZtYVeBy4wt3XZe7z6F94Ksa9mtnJwCp3n5t0WXKsHdFH+t+5+2HAeqKP8ZukrJ1LgOFE/9z2BLoAwxItVEKSaNfWHuRTt1i4mbUnCvAT3H1ySP7UzPYI+/cAVoX05urfWt6Xo4BTzexD4FGiLptbgO5m1rhqWWbZN9Ur7O8GrKH11LfRMmCZu88OzycRBf20tvN3gA/cvcLda4HJRG2f9nZulK12XR62m6ZvU2sP8qlaLDzcKb8HWOjuN2XsegpovMN+AVFffWP6+eEu/RBgbfhY+BxwvJmVhKuo40NaXnH3Me6+l7v3IWq7P7v7ecAM4MxwWNP6Nr4PZ4bjPaSPCqMy+gL7Ed2gykvu/gmw1MwOCEnfBt4hpe1M1E0zxMw6h9/xxvqmup0zZKVdw751ZjYkvI/nZ5yreUnfpMjCTY4TiUahvAdck3R5WliXbxB9lHsTeCM8TiTqj3weWAxMB3qE4w24PdR9AVCeca4LgSXh8YOk67YDdT+GzaNr+hH98S4BHgM6hPSO4fmSsL9fxuuvCe/DInZgxEHSD+BQYE5o6yeJRlGktp2BnwHvAm8BDxKNkEldOwOPEN13qCX6xHZRNtsVKA/v4XvAbTS5eb+1h6Y1EBFJsdbeXSMiItugIC8ikmIK8iIiKaYgLyKSYgryIiIppiAvrZ6Z7W5mb4THJ2a2PGxXmdkdOSzHFWbWOVf5iewIDaGUVDGza4Eqd/91Anl/SDTWeXWu8xZpjq7kJbXM7BjbPEf9tWZ2v5m9aGYfmdnpZvbLMDf31DCdRON83X8xs7lm9lzj19GbnLeLmT1tZvMtmh/9bDO7nGhelhlmNiMcd7yZvWJm88zssTAnEWb2YUber5rZ13L3rkhboyAvbclXiebHORV4CJjh7gcBG4CTQqD/LXCmux8O3Av8YivnGQascPdD3H0QMNXdbwVWAN9y92+ZWSkwFviOuw8m+nbrlRnnWBvyvo1oJk6RWLTb/iEiqfGsu9ea2QKiBWemhvQFRHOAHwAMAqaFBXcKib6i3tQC4EYzu4FoKoYXt3LMEKJFLl4K5yoCXsnY/0jGz9+0oE4i26QgL21JNYC7N5hZrW++IdVA9LdgwNvuPjTzRWa2N/DH8HScu48LS7WdCPyPmT3v7j9vkpcB09z9nGbK4s1si2SVumtENlsElJnZUIimfTazge6+1N0PDY9xZrYn8IW7PwT8imiaYIBKomUbAWYBRzX2t4d+/P0z8jo742fmFb5IVulKXiRw9xozOxO41cy6Ef193Ay83eTQg4BfmVkD0WyDl4b08cBUM1sR+uVHA4+YWYewfyzRjKkAJWb2JtGni+au9kVaTEMoRXJMQy0ll9RdIyKSYrqSFxFJMV3Ji4ikmIK8iEiKKciLiKSYgryISIopyIuIpNj/A872Vrf6uvVbAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"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": [
"PDanalysis = Analysis(pn)\n",
"# output_places=['p_RTN3_PN','p_RTN3_axon']\n",
"# output_places=['p_chol_LE']\n",
"output_places=['p_Ca_mito','p_Ca_cyto']\n",
"PDanalysis.mean_run_tokens_over_time(output_places)\n",
"# output_places=['p_SNCA_act']\n",
"# PDanalysis.mean_run_tokens_over_time(output_places)\n",
"# output_places=['p_SNCA_inact']\n",
"# PDanalysis.mean_run_tokens_over_time(output_places)\n",
"# output_places=['p_SNCA_olig']\n",
"# PDanalysis.mean_run_tokens_over_time(output_places)\n",
"# output_places=['p_LB']\n",
"# PDanalysis.mean_run_tokens_over_time(output_places)\n",
"output_places=['p_Ca_ER']\n",
"PDanalysis.mean_run_tokens_over_time(output_places)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
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"source": []
},
{
"cell_type": "code",
"execution_count": null,
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}
],
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},
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}
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2a7e2d4aac9b84b6bb19bcef0d9b66b56bf75048 | {
"cells": [
{
"cell_type": "markdown",
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"source": [
"# 02 - Autoregressive Integrated Moving Average Models\n",
"\n",
"Based on [Alejandro Correa Bahnsen](albahnsen.com/)'s class notes for this course.\n",
"\n",
"version 2.0, July 2021\n",
"\n",
"## Part of the class [Advanced Methods in Data Analysis](https://github.com/albahnsen/AdvancedMethodsDataAnalysisClass)\n",
"\n",
"\n",
"This notebook is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License](http://creativecommons.org/licenses/by-sa/3.0/deed.en_US). Special thanks goes to [TAVISH SRIVASTAVA]() and [Jason Brwnlee]()\n",
"\n",
"# ARMA Time Series Modeling\n",
"\n",
"ARMA models are commonly used in time series modeling. In ARMA model, AR stands for auto-regression and MA stands for moving average. \n",
"\n",
"## Auto-Regressive Time Series Model\n",
"\n",
"An autorregressive model of order 1 is defined as follows:\n",
"\n",
"`x(t) = alpha * x(t – 1) + error (t)`\n",
"\n",
"This equation is known as AR(1) formulation. The numeral one (1) denotes that the next instance is solely dependent on the previous instance. The alpha is a coefficient which captures the dependence of the values of the time series with their previous values. \n",
"\n",
"Notice that x(t- 1) is also linked to x(t-2) in the same fashion, and so on. Hence, any shock to x(t) will gradually fade off in future.\n",
"\n",
"The following graph explains the inertia property of AR series:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"sns.set()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"e = np.random.randn(30)\n",
"s = np.zeros(30)\n",
"s[10] = 1000\n",
"for i in range(11, 30):\n",
" s[i] = 0.8 * s[i-1] + e[i]\n",
"\n",
"ts = pd.DataFrame(s, index=pd.date_range(\"2020-01-01\", periods=30), columns=['x1'])\n",
"ts.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Moving Average Time Series Model\n",
"\n",
"A moving average model of order 1 is defined as follows:\n",
"\n",
"`x(t) = beta * error(t-1) + error (t)`\n",
"\n",
"If we try plotting this graph, it will look something like this:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"e = np.random.randn(30)\n",
"s = np.zeros(30)\n",
"s[10] = 1000\n",
"for i in range(11, 30):\n",
" s[i] = 0.8 * e[i-1] + e[i]\n",
"\n",
"ts = pd.DataFrame(s, index=pd.date_range(\"2020-01-01\", periods=30), columns=['x1'])\n",
"ts.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Did you notice the difference between MA and AR model? In MA model, noise / shock quickly vanishes with time. The AR model has a much lasting effect of the shock.\n",
"\n",
"## Difference between AR and MA models\n",
"The primary difference between an AR and MA model is based on the correlation between time series objects at different time points. The correlation between x(t) and x(t-n) for n > order of MA is always zero. This directly flows from the fact that covariance between x(t) and x(t-n) is zero for MA models (something which we refer from the example taken in the previous section). However, the correlation of x(t) and x(t-n) gradually declines with n becoming larger in the AR model. This difference gets exploited irrespective of having the AR model or MA model. The correlation plot can give us the order of MA model."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Autocorrelation Plot\n",
"Once we have got the stationary time series, we must answer two primary questions:\n",
"\n",
"- Q1. Is it an AR or MA process?\n",
"\n",
"- Q2. What order of AR or MA process do we need to use?\n",
"\n",
"The first question can be answered using Total Correlation Chart (also known as Auto – correlation Function / ACF). ACF is a plot of total correlation between different lag functions. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from statsmodels.graphics.tsaplots import plot_acf"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"e = np.random.randn(30)\n",
"s = np.zeros(30)\n",
"\n",
"s[10] = 1000\n",
"\n",
"for i in range(11, 30):\n",
" s[i] = 0.5 * s[i-1] + e[i]\n",
"\n",
"ts = pd.DataFrame(s, \n",
" index=pd.date_range(\"2020-01-01\", periods=30),\n",
" columns=['x1'])\n",
"\n",
"plot_acf(ts, lags=10);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Confidence intervals are drawn as a cone. By default, this is set to a 95% confidence interval, suggesting that correlation values outside of this code are statistically significant."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In a moving average series of lag n, we will not get any correlation between x(t) and x(t – n -1) . Hence, the total correlation chart cuts off at nth lag. So it becomes simple to find the lag for a MA series. For an AR series this correlation will gradually go down without any cut off value. So what do we do if it is an AR series?\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"e = np.random.randn(30)\n",
"s = np.zeros(30)\n",
"\n",
"s[10] = 1000\n",
"\n",
"for i in range(11, 30):\n",
" s[i] = 0.5 * e[i-1] + e[i]\n",
"\n",
"ts = pd.DataFrame(s, \n",
" index=pd.date_range(\"2020-01-01\", periods=30),\n",
" columns=['x1'])\n",
"\n",
"plot_acf(ts, lags=10);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Partial Autocorrelation Function\n",
"A partial autocorrelation is a summary of the relationship between an observation in a time series with observations at prior time steps with the relationships of intervening observations removed.\n",
"\n",
"The autocorrelation for an observation and an observation at a prior time step is comprised of both the direct correlation and indirect correlations. These indirect correlations are a linear function of the correlation of the observation, with observations at intervening time steps.\n",
"\n",
"It is these indirect correlations that the partial autocorrelation function seeks to remove."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The example below will load the Minimum Daily Temperatures and graph the time series.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"temp = pd.read_csv('../datasets/daily-min-temperatures.csv', header=0, index_col=0)\n",
"temp.plot(figsize=(10, 5));"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_acf(temp, lags=50);"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from statsmodels.graphics.tsaplots import plot_pacf\n",
"plot_pacf(temp, lags=50);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Intuition for ACF and PACF Plots\n",
"Plots of the autocorrelation function and the partial autocorrelation function for a time series tell a very different story.\n",
"\n",
"We can use the intuition for ACF and PACF above to explore some thought experiments.\n",
"\n",
"### Autoregression Intuition\n",
"Consider a time series that was generated by an autoregression (AR) process with a lag of k.\n",
"\n",
"We know that the ACF describes the autocorrelation between an observation and another observation at a prior time step that includes direct and indirect dependence information.\n",
"\n",
"This means we would expect the ACF for the AR(k) time series to be strong to a lag of k and the inertia of that relationship would carry on to subsequent lag values, trailing off at some point as the effect was weakened.\n",
"\n",
"We know that the PACF only describes the direct relationship between an observation and its lag. This would suggest that there would be no correlation for lag values beyond k.\n",
"\n",
"This is exactly the expectation of the ACF and PACF plots for an AR(k) process.\n",
"\n",
"### Moving Average Intuition\n",
"Consider a time series that was generated by a moving average (MA) process with a lag of k.\n",
"\n",
"Remember that the moving average process is an autoregression model of the time series of residual errors from prior predictions. Another way to think about the moving average model is that it corrects future forecasts based on errors made on recent forecasts.\n",
"\n",
"We would expect the ACF for the MA(k) process to show a strong correlation with recent values up to the lag of k, then a sharp decline to low or no correlation. By definition, this is how the process was generated.\n",
"\n",
"For the PACF, we would expect the plot to show a strong relationship to the lag and a trailing off of correlation from the lag onwards.\n",
"\n",
"Again, this is exactly the expectation of the ACF and PACF plots for an MA(k) process."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Autoregressive Integrated Moving Average Model\n",
"An ARIMA model is a class of statistical models for analyzing and forecasting time series data.\n",
"\n",
"It explicitly caters to a suite of standard structures in time series data, and as such provides a simple yet powerful method for making skillful time series forecasts.\n",
"\n",
"ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average. It is a generalization of the simpler AutoRegressive Moving Average and adds the notion of integration.\n",
"\n",
"This acronym is descriptive, capturing the key aspects of the model itself. Briefly, they are:\n",
"\n",
"- AR: Autoregression. A model that uses the dependent relationship between an observation and some number of lagged observations.\n",
"- I: Integrated. The use of differencing of raw observations (e.g. subtracting an observation from an observation at the previous time step) in order to make the time series stationary.\n",
"- MA: Moving Average. A model that uses the dependency between an observation and a residual error from a moving average model applied to lagged observations.\n",
"Each of these components are explicitly specified in the model as a parameter. A standard notation is used of ARIMA(p,d,q) where the parameters are substituted with integer values to quickly indicate the specific ARIMA model being used.\n",
"\n",
"The parameters of the ARIMA model are defined as follows:\n",
"\n",
"- p: The number of lag observations included in the model, also called the lag order.\n",
"- d: The number of times that the raw observations are differenced, also called the degree of differencing.\n",
"- q: The size of the moving average window, also called the order of moving average.\n",
"A linear regression model is constructed including the specified number and type of terms, and the data is prepared by a degree of differencing in order to make it stationary, i.e. to remove trend and seasonal structures that negatively affect the regression model.\n",
"\n",
"A value of 0 can be used for a parameter, which indicates to not use that element of the model. This way, the ARIMA model can be configured to perform the function of an ARMA model, and even a simple AR, I, or MA model.\n",
"\n",
"Adopting an ARIMA model for a time series assumes that the underlying process that generated the observations is an ARIMA process. This may seem obvious, but helps to motivate the need to confirm the assumptions of the model in the raw observations and in the residual errors of forecasts from the model.\n",
"\n",
"Next, let’s take a look at how we can use the ARIMA model in Python. We will start with loading a simple univariate time series.\n",
"\n",
"### Shampoo Sales Dataset\n",
"This dataset describes the monthly number of sales of shampoo over a 3 year period.\n",
"\n",
"The units are a sales count and there are 36 observations. The original dataset is credited to Makridakis, Wheelwright, and Hyndman (1998).\n",
"\n",
"Download the dataset.\n",
"Download the dataset and place it in your current working directory with the filename “shampoo-sales.csv“.\n",
"\n",
"Below is an example of loading the Shampoo Sales dataset with Pandas with a custom function to parse the date-time field. The dataset is baselined in an arbitrary year, in this case 1900."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-10-543f917c7e01>:2: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
" return pd.datetime.strptime('190'+x, '%Y-%m')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Month\n",
"1901-01-01 266.0\n",
"1901-02-01 145.9\n",
"1901-03-01 183.1\n",
"1901-04-01 119.3\n",
"1901-05-01 180.3\n",
"Name: Sales, dtype: float64\n"
]
},
{
"data": {
"image/png": 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mRE+HBWGGwc3LdFP2M04r2Gs0GgAAn8/Hjh078E//9E944IEHoNfrY+dYrVaw2WzI5XJoNJqExzJhsbgmLDogM4taLYHJ5Mz3MEgOzKZ72XrVFClvDAaTvielVIALXRYYhxxgx8nt9+ptAICwP/l14hHyIte7eNWEY+cN4HJYUIl5k/oZs9mshJPklGmckZEROJ2Rb84wDP72t7+hoaEBS5cuhdfrxcmTJwEAL7/8Mu6++24ASHqMEELyrWfQhTkV4pQLobQqEfyBMKwJOlTa3H5wOWyISjLPs1+rtffiYo8N83Qy8HnJ97CdjJQjtFgs+Na3voVQKIRwOIz6+nrs2bMHbDYbe/fuxZ49e8aVVwJIeowQQvIpEAyj3+RC86qalOdGtyjUW0agkk/c+Nvu8mW8ejYqGuy7Bx3oNTqx5TN1GV8jEymDfXV1Nfbt2xf32IoVK7B///6MjxFCSL7ozW6EwkzClbNjaVXXyi+X1U9cJ2Rz+TMuu4ySCHkQ8Dj4qHUQDHLf0vh6tIKWEFJUugcjD1XTCfZiIQ/SUl7Cihyby5dVJQ4AsFgsqOUlMNu94PPYmKtNXBmUCxTsCSFFpcfoQqmAC3Wa+7tqlKKErY5tLj/kae49G080lbOgSg4uZ2rDMQV7QkhR6Rl0YE6lJO08u1YlgsE8AoYZXxnoD4Tg8QWzTuMA14L9VLVIGIuCPSGkaARDYfQNueP2sE9EoyzFiC8Y22s2ypbh3rPxRIP9VOfrgTTr7AkhZDbQm90IhsKoSdIm4XrRh7QGs3tcYL+2ejb7mf3NSypRwudM2PB8KtDMnhBSNFK1NY5HM6b8cix7Dmb2pSVc3HKDJqvSzUxRsCeEFI2eQSdK+ByUl02smU9ELuZDKOBOeEgb3WVqMjn76UTBnhBSNHqMTtRUSOK2PkiExWJBqyyFwTwx2HPYrCnZVWoqULAnhBSFUDiMPqMro4ezURqVaGIaJ4u9Z/OJgj0hpCgMWkbgD4azehiqVYrgcPvh8gRiX8tm79l8omBPCCkK3YORh7M12QR71cSNTOxZ7D2bTxTsCSFFocfoBJ/HhiZJ//pEohU5Y9smRGb2M+PhLEDBnhBSJHoGnagpl4DNzjzHrpSVgM9lQ2+O5O0DwRDc3iDkIgr2hBBSMMIMg16jK63mZ/GwWSxUKktjM3u7a/I19tONgj0hZNYzWkfgC4SyqsSJ0qquNUSzjQZ7ekBLCClKr3/YhfOdlpxca2h4BGabJyfX6hmMrpzNPthrlCJYHT54fMHYgqrJtEqYbhTsCSE54Rzx47UPu/DWyb6cXO/n+1rxv39/aly5Y7Y69Q7wuGxoVJk/nI2K7lo1aB3JSauE6UbBnhCSE+09w2AAdOkdCF/XDjhTHl8QfUMu2Fx+/J+/X5zQXjgTPYNOHDmjx7J6JTjs7EPe2PLL2OrZ0pmxehagYE8IyZELXVYAgNsbhNE6kuLs5LoNDjAMsKxeiU8vm/DeWX1W1xnxBvGLfa2QlPLwQPPCSY1JLReCw2ZBb4kEe6mIn1HbhXyjYE8ImTSGYXCh2wqNMjL77dQ7JnW9q6Ov/9qmxVhSW4aX376ScGvAZGP67d/bYbZ78Y0tSyApnVx+nctho0JRCoN5JLJD1QzK1wMU7AkhOTBoHYHV4cMdN1VBKOCgY5LBvnPADo2yFGIhD1/dtBh8Hge/fO0CAsFw2td451Q/Tl0yYdvtczG/Sj6p8URplaXQW9ywT2Lv2XyhYE/IDOQLhPI9hHHauocBAEvrFJirkaJzwJ71tRiGQYfegXqtDEDkIehXPrsIvUMu/OX9jrSu0WVw4P8evorl9Uo0r6rJeizX0yhFMNk8sDi8M6rsEqBgT8iMM2Tz4Fs/eR/t3dZ8DyXmQpcVKlkJystKMVcrQ5/JBZ8/u19IQzYPXJ4A6nXXNhi5cb4a61focPCTPrR2JS/tdHsD+MW+VsjFfHx10+Kc5tW1KhEYBvD4QjNq9SxAwZ6QGedyrw3BEIMr/dnPnnMpGArjYu8wltYpAAD1OikYBugezC6V0zH6qSA6s4/avn4etCoRXjjQDseIP95LwTAMfvNGO4adPnxjy9Kc95qPblEIAHIJzewJIVMoGkT7Ta48jySiU++A1x/C4tpIsJ87GqSzfUjboXeghM8ZF1gBgM/j4H9sXgK3N4jfvtEetxzzrRN9OH3FjC/cXo96nWzC8cmqVAgR/aAgo5k9IWQqdRkiq0EHzJlVp0yVtm4rWCygobYMACAW8lBRJsz6IW3HgB11GmnchmXV5WJ84fZ6nO2w4N3TAxNe98qRDtw4X4U7V1Zn9b1T4XE5UMsjWxrSA1pCyJQJhsLoG3KCw2bBaPUgEMz/g9oL3VbUVkohKrmWMpmrlaFjwJ7xYiifP4T+IXfSWfnGpircMFeJ/3v4KgZGP924PAH84rVWlEkEePBzDVO6e1R0Je2sLr189tlnsXDhQly+fBkAcObMGWzevBnNzc148MEHYbFce3CS7BghJDv9JheCIQaN81UIMwwMlsktXpqsEW8QXXonltSVjft6vU4Ku9sPq8OX0fW6ByOrb+u10oTnsFgsPPi5Bgj5HPzy9QvwB0L49YE22F1+/NPWpeN+6UyFmgoxSvicSdftT7e0g/2FCxdw5swZ6HQ6AEA4HMZ3vvMd7N69GwcPHkRTUxOeeeaZlMcIIdmLpnA+c4MGADBgym8q52LvMMIMgyWj+fqo6MPVDn1mD5GjqZ9U+XaZiI8HP9eAfpMbT/zuJM51WLB9wzzUaRL/ksiVe9bMwWNfWZlVX/x8SivY+/1+PP7443jsscdiX2ttbYVAIEBTUxMA4L777sObb76Z8hghJHtdBgfEQh6W1CnAYbPy/pD2QpcVAh5nQnDWqUXgc9kZP6TtGLCjokyYVhXNsnoVNt5UhQGzG00L1bjjpqqMvle2BDwOysuyb6iWL9x0TvrpT3+KzZs3o6rq2g/TYDBAq9XG/qxQKBAOh2Gz2ZIek8vlaQ9OqRSnfS4pXGp19m1lyXj9JjcW1JRBUylDdYUEJodvWn++13+vi3023DBPBU3lxJn4/Joy9A650h4fwzDoGnRixcLytF/z8BcbsWSeGmuXaVA6xembmS5lsD99+jRaW1vxyCOPTMd4xrFYXAiHJ9c9j+SXWi2ByeTM9zBmBZ8/hJ5BB5bWKWAyOVFZJsSVftu0/Xyvv5dmmwcGsxu3L9fGHUO1WoS3T/ZDb7CDx02dRDDZPLA5fdAphBm9p+V1ZXA7vXA7vWm/ZrZis1kJJ8kp78CJEyfQ0dGBO+64Axs2bMDg4CC++tWvoqenB3r9tU50VqsVbDYbcrkcGo0m4TFCSHZ6jE4wDFCnicx6dWoRLA4fRrzBvIznwugK3iV1irjH67XS0eqh9FJN0fz+VNTHkzSC/de//nV8+OGHOHz4MA4fPozKykq88MIL+NrXvgav14uTJ08CAF5++WXcfffdAIClS5cmPEYIyU63IZL/jj6E1KkjMzh9nurtL3QPo0wiiHW6vN7cDB/Sdgw4IOBxoFOLUp9MMpZWzj4eNpuNvXv3Ys+ePfD5fNDpdHj66adTHiOEZKdr0IkyiSC2mKdqdIVpv9mFeVXTOxsOhxm0d1vROF+VsKa9TCKAQipI+yFtp96OOo1kUhuMkMQyDvaHDx+O/f+KFSuwf//+uOclO0YIyVyXwTFuD1WlrAQCPgcDQ9M/s+8xOuH2BieUXF4vurgqFX8ghF6jK6cdKsl49CuUkBnA7Q1gaNgzro6cxWKhSiXCgHn6yy+ju1ItThHs67VSmO3e2J6tifQYnQiFmXGdLkluUbAnZAboHl1Mdf2iIZ1ahH6Te1J7tGajrduKmnIxpCmagc0dXQnbmSJv3zEwuphKSw9npwoFe0JmgGiny1rN+PpznVoMlycAR4qZcy75/CFc6bdjcYIqnLHmVEjAYbNS5u079Hao5SUpf3mQ7FGwJ2QG6DI4UV4mnND35dpD2unL21/qG0YoPLFFQjx8HgfV5eKkeXuGYdAxYKdZ/RSjYE/IDNBlcMTt+xItvxxIs5Y9Fy50DYPHZWN+mhVA9VoZugzOhAskh50+2Fx+qq+fYhTsCSlwdpcPw04f6ionthCQiviQlvKmdWbf1m3FgioZ+DxOWufP1UnhC4QS9t+/Ojrrn5uk0yWZPAr2hBS4aKfL2gQdHXVq8bR1vxx2+jBgdqeVr4+KtitOtLiqU+8Aj8tGdTn1wppKFOwJKXBdBgdYrMjDznh0KhH0ZjfC01CR0xZtkZBGvj5KLY90sewciP+QtmPAjtpKCbgcCkdTiX66hBS4rkEHtCoRBPz4aZOqcjF8gRDM9qlvBHah2wppKQ9VGczCWSwW6rXSuDP7QDCMHqOT8vXTgII9IQWMYRh0G5yoq0ycz9aNVuQMTHFve4Zh0NY9jMW1CrAz3PZvrk4Gg2UEI97AuK/3Gp0IhpLvTEVyg4I9IQXMbPfC5QnEOl3Go42WX05x3r7b4IDD7U+5ajaeaDDvNIxP5UR3pppLZZdTjoI9IQWsyxBdTJV45isUcKGSlUz5zP7MZROAxC2Nk6nTSMECJuTtOwbsUEoFKJMIcjFEkgQFe0IKWLfBCS6HlbJSRacSJSxtzJXTl4agUZZmFZiFAi60alFsJh/VqbfTrH6aULAnpIB1GRyoLhenrFSpKhdj0DKCYCic8fcYMLnQZXDA6vAmfH0gGMKFTktWs/qoeq0UnXp7rI/PsNMHi8NHD2enSdb97AkhUyscZtBtdGLt0sqU5+pUIoTCDAatI6hSp18pM+z04bHfnkBozOpWsZAHuZgPmYgPmVgAmYiPYIiBPxjOqOTyenO1Mrx/1gDjsAeVitJYczR6ODs9KNiTccIMA5vTB4W0JN9DKXqD1hH4/KGklThRsbYJJndGwf5EuxGhMIOv3LMIoTADu9sPu8sX+a/bj8HeYdjdfgRDDEpLuFhYI8/27VxbXDVgR6WiFB0DDnA5LNQkWD9AcouCPRnnkzYjXnijHU8/vDa2IxLJj67YNoSpg6FGWQoOm4V+kwurUZH29zjWZsScSgnWLdcmPIdhGIz4glCpJPC4sq/l16hEEAo46NQ7cMsNGnTo7ZhTIUlrM3IyefRTJuP0m9wIhRn0DDrzPZSi121wQsDjQKNMvScrl8NGhaI0o7YJRusIugedWN2Q/JcDi8WCqIQHsZCX9LxU2CwW6jSRxVXBUBjdg7SYajpRsCfjmO0eAEDfNHZRJPF1DTowp1ICNju9BUw6lQj9GZRfHm8zggVgVUN5liPM3FytDP1DbnQM2BEIhqn52TSiYE/GiS65zyRokNwLhsLoNbrSSuFEValFMNu98PqDKc9lGAbH2oxYUC2f1ucz9VopwgyDd071AwDm0cx+2lCwJ+OYbTSzLwQDJjeCoTBq03g4GxV9SKs3j6Q8t9fowqB1BKuXpJ/fz4XoTP7UZRPkYj4tpppGFOxJjM8fgmMkAAGPg0HrCPyBUL6HVLQyeTgbVaWOtk1I/Yv6eJsRHDYLTQunL4UDAJJSPsrLhGAYoF4nAyvDHjskexTsSYzZEUnhLK1TgGEAvWX6NsQg43UZHBCVcKGWC9N+jUouBJ/LTvmQNswwON5uxNI6xaQfumYjWoJJ2xBOLwr2JCaawmmcrwJAqZx86jI4UauRZjTzZbNY0KpEGDAnv29X+mwYdvqmPYUTFW2PUK+jh7PTiersSUz04eziWgX4PDb6h2hmnw++QAh6sxuN85UZv7ZKLca5TkvSc463GcHnsXHjPHW2Q5yUz9ygAZ/Hpoez04xm9iTGbPeAx2VDLuZDpxKjb4hq7fOh1+hEmGHSWjl7PZ1aBIfbD8eIP+7xYCiMExeHcON8dcLNUKaagM/BumVaytdPs7SC/cMPP4zNmzdj69at2LFjB9rb2wEAXV1d2L59O5qbm7F9+3Z0d3fHXpPsGClMZpsXKlkJWKxIl8V+kzvWtIpMn1R7ziajG31Iq0+Qt7/QZYXbG8TqxflJ4ZD8SSvYt7S04PXXX8e+ffvw4IMP4vvf/z4AYM+ePdixYwcOHjyIHTt2YPfu3bHXJDtGCpPZ7oVKFnkgWF0uhssTgM0Vf4ZIMmN3+/HSwUu41Duc8txugyPrssRoX5xEFTnH24wQlXCxdBLdK8nMlFawl0iulX+5XC6wWCxYLBa0tbVh06ZNAIBNmzahra0NVqs16TFSuMx2D1TyyAKbaBkfPaTNjSOnB/Du6QG0/PE0nv7TaVzusyU8t8vgQF0Ws3oAkIn4EJVw4/a29/lDOH3FjKZF5bS5dxFK+wHto48+iqNHj4JhGPz617+GwWBARUUFOJxI3o/D4aC8vBwGgwEMwyQ8plCkP6NQKtPv3kcmx+0JwO0NolYrg1otgVAUmVUOjwSgVk+uK+FkXz/TMQyDk5eGsLhOgbXLtPjz4Sv4zz98isb5auxoXoSGMbNslycA47AHd66Zk/XPrU4ng3HYM+H175/uhy8QQvPNdVlfu9jv5UyWdrB/6qmnAAD79u3D3r17sXPnzikbVJTF4kI4TDnj6dBrjOSJhVw2TKbI/yulAlzqssBkSt1PPRG1WhK7XrHqHnRgwOTGnU3VWNtQjpvmKXHk9AD+fqwH3332AyypU2DrZ+pQr5OhrTvy6bdcKsj651YuK8FHrYMYGnKMewj61rEelEkEKJfws7o23cvCx2azEk6SMy693Lp1K3bv3o3KykoYjUaEQiFwOByEQiEMDQ1Bo9GAYZiEx0hhipZdKmXX+qRUqcWUxsmBYxciq1VvWhgpdRTwOGheVYPbG3V49/QA/n68B0+9dApL5yogLeUDQEZtEq6nU4vh9Ydgdfhi99PlCeB8pwUbm6rSbqxGZpeUiTu32w2DwRD78+HDhyGTyaBUKtHQ0IADBw4AAA4cOICGhgYoFIqkx0hhigb7sSs2q8rFMFhGEAhmvtUdiQiHGXzSbsSyeiVEJeNXqwr4HNy9ugZ7v7EWX7i9Ht0GJz5qHUS5XDipla3x2iacujSEUJihKpwilnJm7/F4sHPnTng8HrDZbMhkMjz//PNgsVh47LHHsGvXLvz85z+HVCpFS0tL7HXJjpHCY7Z5UMLnQFRy7a9EdbkYYYaBweIuyt2ELvfZ8MbHPXj480sh4GVXk365zwaby580yAr4HNyzZg7Wr9Dh/TN6KGXpt0iIR6eKBPsBsxvL50VWQx9vM6JCUYo5RXgfSUTKYK9SqfBf//VfcY/V19fjlVdeyfgYKTyRssuScTne6vJI7q9vyFWUwf69M3qc77Tgo9ZBrL9Rl9U1jrUZIeBxYkE3mRI+F3etqsnq+4xVWsJDmUSAgdGZ/bDTh0u9Ntx7Sy0tZCpiVH9FAIyWXV43oywvE4LHZRdl3j7MMGjtirQdOHSiD+EsFpcFgmGcujSEFQtUWX8yyFaVOrIoDgA+aTeCASiFU+Qo2BMwDAOT3RursY/isNnQZrj70WzRM+iEcySA5fVKGK0jONeRvN9MPK1dltHVqtlXM2VLpxbBYHEjFA7j+Og+s+lsb0hmLwr2BC5PAD5/aMLMHoikcvqLcGZ/vtMCFoD/fs8ilEkEOPRJb8bXON5mhFjIw+LastwPMAWdSoRgiMG5Dkta+8yS2Y+CPblWiSObuD1dtVoMx0gAdpdvuoeVlf1Hu/CDF45nlXYZ63ynBbUaCeRiATY2VeFiry2jTdi9/iDOXDFjZUN+VqtG2yb89f3Oad9nlhQmCvYkbo19VFX0Ie0MSOX4/CEc/KQPAyY3rvbbs76OyxNAp96BG+ZGWgzftlwLAZ+DQyfSn92fvmKGPxjGmjzlyTXKUrBYQL/JPe37zJLCRMGexDYtibcrUrQiZyb0tv/4wiBGfEGwAJy4OJT1dS50WcEwiAX70hIe1i3T4JP2IQw70/uEc7zNCKVUgPo89Wzn8zioKCsFgLxtUkIKCwV7ArPdC1EJF0LBxEpcsTBSxlfove0ZhsHbp/oxp0KCxvkqnLo0lHUq53ynBWIhb1wzsjubqhFmGLx9qi/l6x0jfrR2WrFqcQXYeSx11KlFedlnlhQmCvYEpjhll2NF2iYU9sy+vWcYerMbG5uqsHJROWwuf1apnDDDoLXTgiV1inFtBdRyIW5aoMZ7p/Xw+oNJr3HqYuQXzZo8VOGMde/aWnx985K87DNLCk9BB/twmJbpTwdLnLLLsarKI2V8wVDh3o+3T/ZDUsrDqoZyLJ+nApfDxsksUjm9RiccIwHcMHdia4+7VtVgxBfE0fODSa9xrM0InUoUa1uQLzUVEqxcRLN6ElHQwd5g8eR7CLMewzAw271QJ5nZV5eLEQozMFhGpnFk6RsaHsHZq2bc3qgDj8uBUMDFDXMVOJlFKuf8aD390rqJ+7/O08lQr5XirRN9CbuxWuxeXOm3Y/XiClqtSgpKQQf7HmNh54lnA7vbj0AwHLcSJ6o6uvtRgdbbH/50AGw2C7ePaWnQNJrK6RjILJVzvtOK2koJpCJ+3ON3rarBkM2D01fMcY9/0m4EQKtVSeEp6GDfnUFdM8nOtW6XiYN9haIUXA6rIMsvPb4gPjinR9Oi8nHb+DWOpnIyqcpxeQLo0NtjVTjxrFiggkpWkrAM81ibEfVaadzKJkLyqaCDfY/Bke8hzHrRsstkD2i5nNG2CQU4s/+odRAeXwgbm6rGfV0oiOyzeuqSKe1UTlv3aMllfeJgz2GzsbGpGlf67ejUj//7OWByoW/IRbN6UpAKOtgPu3xp1zWT7JiSLKgaq7oANzIJMwzeOdWPOo0U9dqJ9ewrF5Vj2OlLO5VzvtMCUQkXc1Ps/7pumQZCwcRFVsfbjWCxgJXUmoAUoIIO9gAyzrmSzFjsHkhF/JRdGavKxbC7/XC4/dM0stTauqwYtI5MmNVHNc5PP5UTKbm0Tii5jEco4OK25TqcvGiC2R75ZMQwDI5dMGJxrQKyBPl+QvKpoIM9j8vGVQr2U8pki/SxTyXaNqGQOmC+dbIfMhE/YXlhJqmcPqMLdrc/ab5+rDtuivyCeedUPwCgU++A2e7NW3sEQlIp6GBfVS6mYD/FLPb0gv3YjUwKwaB1BOc7LVh/oy5po7FoKqdzIPnzn/OdoyWXaQZ7pawETYvUeP+sHh5fEMfbjOBy2FixQJ3+myBkGhV0sK+tkKJn0Al/IJTvoeSc3uzGx62DsDq8eRtDOMzA4vCmVTkiLeVDJuIXzEPad071g8th4bYUO0hFFlixUqZyzndaMKdCklEKpnlVDTy+EN47o8cnF4fQOE8Zt+UEIYWgoP9mzqmUIBRm0D3oxIJqeb6HkzMMw+CXr1+IzZI1ylIsrlVgcW0ZFtWUTVvAGHb6EAozKR/ORlWViwui/HLEG8SH5w1Y1VCRMjiXlnCxtE6Jk5eGsP2OeXF71Yx4A+gYcOCzN2e2JWCdRooFVTL89YNOBIJhqsIhBa2wg31FJHXQMWCfVcH+cp8NfUMubL6lFiV8Ltp6rPjgrB7vnOoHm8XCXJ0Ui+eUYUmdAnUa6ZT1Q48+XEy2enas6nIx3j7Zh2AonJce7VFHzxvg808st0ykaZEaZ66a0al3YF6cLpQXuocRZpi08/Vj3bWqBs/+5TyEAg6WJSnZJCTfCjrYi4Q8VChKcaXfjnvyPZgcevtUP0QlXNyzZg4EPA7uXl2DQDCMjgE7LnRb0dZtxf6j3Xj9aDeEAg7+8Z6GKelxEl1QlawvzljVajGCIQZG6wh0o6tqp1u03HKeTobayuQlklGN89Tgci7i5MWhuMH+fIcFpQIu5mrTu974a6tQpRZjYY0cPO707jNLSCYKOtgDwDydFGevWsAwzKzoNWKxe/HpZRPuXl0zrtyRx2Vj0ZwyLJpThm231cPlCeBizzDe/KQXv9p/ATIRP+efbkw2D1gAFJL00zhAZCOTfAX7cx0WDNk8+G+3zU37NaUlXCypjfTK+eKG8akchmFwvivS5ZLDzvzTCpvNwmNfWYlZ8FeTzHIF/YAWiDSfcnkCGBqeHU3RDp+OlOptuDF5CkIs5KFpUTn+5xeWQyktwbN/OQ/jcG4bkVnsXsglAvC46f010ChLwWGz8rqRyTsn+1AmEWRc9bKyoRxWhw9d16167Rtywe5Kv+QyHjabNSsmImR2mxHBHsCsKMH0BUJ4/4weKxao034oKhby8D+/uBwA8JNXzsHlCeRsPKY0yy6juBw2NEpR3sovB8xuXOgeTlluGU8klTOxKidachmvpTEhs0nBB3uNSgShgDsrgv3xNiPc3iA23pTeg8WoirJSfPO/3QCL3YNn/3IegWBu+spbUmxaEk91uShvC6sOn+oHl8PGrY3ajF87NpUzdoHV+Q4LairEkIkFSV5NyMxX8MGezWKhXied8cGeYRi8dbIPNeXirHLvC6rlePBzDbjcZ8P/+ftFMFluuRcVDIVhdfqSdruMp6pcjGGnL6efMNLh9gZwtNWANUsqIC3Nrh1B06LxqZwRbwBXBxyTSuEQMlOkDPbDw8N46KGH0NzcjHvvvRff/OY3YbVaAQBnzpzB5s2b0dzcjAcffBAWiyX2umTHMjVPJ4Pe5MaId3oDTC5d7LVhwOTGHU1VWed31yyuxOfX1eHjC4PYf7R7UuOxOrxgmNQN0K6Xr5W0R04PwB8IZ/ypaKwb56vAYV9L5bRNouSSkJkmZbBnsVj42te+hoMHD2L//v2orq7GM888g3A4jO985zvYvXs3Dh48iKamJjzzzDMAkPRYNubrZGCACS1lZ5K3T/ZBLORNunfKprW1uGVpJfZ92IWPLyTfHi+ZWB/7TNM4edjIJBgK451T/VhcW4aaCknW1ykt4WFJnQKnLg2BYRic67RAKOCiXpd5ySUhM03KYC+Xy7F69erYnxsbG6HX69Ha2gqBQICmpiYAwH333Yc333wTAJIey0adVgoWa+Y+pDXZPDhz1YzbGrWTrsVmsVj47/cswqIaOX77t3Zc7rNldZ1Ma+yjpCI+JKW8aV1Je7zNCJvLj+ZVma1wjWflonJYHD506h2RjcVry7IquSRkpsmozj4cDuNPf/oTNmzYAIPBAK322oMyhUKBcDgMm82W9JhcLk/7+ymV12q567Qy9A65oVZnP7PLl/3HesFisfAPGxdClaMdjPY8dDMe+dkHeO6v5/HMt2+FNsO6d7c/BDabhQV1KnAyrGyZq5Nh0DqS9r2YzD1jGAbvfDqAmkoJ1q+aM+kSx403l+B3b17EG8d7YXP5sXa5bkb+ncoX+lnNXBkF+yeeeAKlpaW4//778dZbb03VmGIsFldsY+faCjGOtg7CaHSk7DdeSHz+EA5+3I2bFqjBBIIwmXK31eK3tt2Ap148iR/88iP8rweaIBby0n5tn8EBhUQAqzXzmvkKuRDvdlkxaLQnnRV7/UF4w4BMwMk6SF/otqLb4MBX7lkEszk3nyYW1ypw5rIJADBHLcrpPZnN1GoJ/awKHJvNGjdJHncs3Yu0tLSgp6cHP/nJT8Bms6HRaKDX62PHrVYr2Gw25HJ50mPZmqeTwecPFVQ/9XR8fGEQI75g2n1cMlEuF+Jb25bB6vDhub+cz6hCx2T3ZL1PanW5GIFgOO5CN5vLhyNnBvCTV87i2z/9EP/6k/fx4TlDVt8HAA5+0gupiI81Syqzvsb1oq0nqsvF4/atJWQ2SyvY/+hHP0Jrayuee+458PmRsrelS5fC6/Xi5MmTAICXX34Zd999d8pj2YourppJO1cxDIO3T/VjToUkbk+WXJink2H7hnm41GdDR4qe7WOZbd6MK3GiqtTXKnIYhsGA2Y03Pu7Gky+exL8+exQvvnkJerMb62/UYUGNHK8c6ciqVHPA5EJrpxV3rNClvco3HTfOV0HA4+DG+aqcXZOQQpcyjXPlyhX88pe/RG1tLe677z4AQFVVFZ577jns3bsXe/bsgc/ng06nw9NPPw0AYLPZCY9lSykrgUzMx9UBO9avyP0seSq09QxDb3bjq59rmNLl9LfcUIk/v9eB98/qMa8q9S8VfyAEu9sPdZbBXqsSgc1i4e/HevGX9ztjM/zaSgk+v64ON85XQ6cWgcViwR1ksPP/P4I/H+nAP96zKKPvc/BEH/hcds7vd2kJD089tBpS2j6QFJGUwX7+/Pm4dOlS3GMrVqzA/v37Mz6WDRaLhXk6WcYVOXqzG2USQV42lXjnZD+kpTysmuINqEv4XKxuKMexNiO+tHF+yvdqGd0wJdPVs1E8Lht1Ggm6B51omFOG5pXVaJyvjpsSqdVIsbGpCm+d6MO6ZRrUp/kJx+7y4diFQaxbps3oWUS6FNLsftERMlPNqJqzeToZTDYv7C5fWud3Dzqw5zef4NcH2nLy/QetI+g1pveAamh4BGevmnFbY25TEImsW66FPxDGJ+3GlOdmW3Y51r/d14if7VyHf93eiPUrqpLmvrd8pg4yMR8vHboUe+CeyjufDiAUYnDXyuqsx0gIuWbGBXsAuJpGbtrjC+L51y4gFGZw5qoZZtvkumYGQ2E8/afTeOy3J/C/f38KJy4OIRRO3KPm8KcDYLNZuD3Ftnm5MlcjhU4twvtnUz8Mjf4ssp3ZA5FPE+l+WhIKuPjSxgXoNbrw7umBlOf7AiEcOT2AxvkqVChKsx4jIeSaGRXsayok4HLYaT2k/f2hSzDZPPj65sVggYXDaQSZZE60D2HY6cPtjVrYXT78Yl8rvvf8x/jbsZ4JDx+9/iA+OKdH06Lyaav2YLFYWLdMiy6DI2UrA5PdCy6HDZl4+nLWTQvVWFJbhr+835Hyk9lH5w1weQI5WURFCImYUcGex2WjViPBlQFb0vOOnjfg4wtGbL6lDmsWV2LFQjU+OKuHL8uNyxmGwcETvdAoS3F/80L8x9dvxre23YCKslL8+UgHHnnuKH735kUMjJaFftQ6CI8vNKk+Ltm4eUkFuBwWPjirT3qe2R6pxIm3H+tUYbFY+P/uWohAMIz/evdqwvPCDINDJ/pQp5FgfhoPmwkh6ZlRwR6IpHJ6Bp0IBOMH7kHrCH5/6DIWVstx79paAMDGm6rg9gZxLMteMpd6beg1utC8qgZsFgtsNgs3zlfjO1+6EY8/uAprllTgo9ZB/OCFT/D0n07jzeO9qNNIstrmbjIkpXysWKDGxxcGE/58gEgaJ9tKnMmoVJTi7tVz8PEFIy72DMc95+wVM4zDHjSvqqENQQjJoRkZ7IMhBj2DE1MVgWAYz7/WCi6HhYfuXRxbaTu/SobqcjHeOdWfVWvgQyf6ICmN38SsqlyMf7ynAc88vBbbbpuLQesIzHYv7myqzkuwWrdMC7c3iE8vmxOeY85w05Jc2nTzHKhkJXjp0CUEQxOfeRz8pBdKaQluWpjZTlSEkORmXLCvT7Jz1StHrqLX6MJXP7d4XGkdi8XCxpuq0G9yZ9w4zGBx48xVM9bfqAOfl7iJmaSUj8/dXIuWb9yM3f/YhNWT7G6ZrYbaMiilJXg/QSrH4wvC5QnkrEdPpvg8DnbcuQAGywjeOtE37lin3oHL/Xbc2VRFzckIybEZ9y9KJuKjXC6cEOzPXDHj7ZP92HhTFRrjrIxcvbgCYiEPb5/sz+j7vXUysjvShjQX9nA5bNRWSvOWgmCzWFi3XIP2nmGY4lQgWaJll3ma2QNA4zwVbpyvwmtHu2LjASKzeqGAg3XLM9+JihCS3IwL9kBkdn91wB5LyVgdXvzmb+2oKRfjC+vnxX0Nn8fBuuUafHrFNC7AJOMc8eOj8wasXVoxo1ZbfuYGDVgs4IM4PWliNfaTKLvMhS9tnA8wwMvvXAEQeY5w8tIQbluuy8sCOEJmuxkZ7OdVyeBw+2GyexEOM/jV/jYEgmH8jy1Lki5gWj9a855OrTcAHDmjhz8Yxp1NM2thj0JagqV1Shw9b5iwFsBkH62xn8SCqlxQyYS495ZanLpswrkOM9462Q82izUlDeMIITM12EebovXbceCjblzqs+H+uxZAoxQlfZ1KJsSK+Wq8d2YA/hRlmIFgGIdP9WPpXAV0GfaKLwS3Ltdg2OlDa6d13NfNNi/4PDYkU9CCIFPNq2qgUZbi94cu4/1zeqxcVE5tDAiZIjMy2OtUIpTwOXjn0368drQLNy+pwNql6bXAvWO0DPN4W/K2AsfbjLC7/WheOTMX9iyfp4K0lDchlWO2e6CWCQuirJHLYeP+OxfAbPfC5w/RIipCptCMDPZsNgv1Wik69Q6o5ULcf9fCtIPXwho5dGpR0jJMhmFw6EQvqtQiLK4ty+XQpw2Xw8baGzQ4e9U8bsVqPssu42moVWD9Ch1WNZRjTiXtgkTIVJmRwR4AFs0pA5fDwje2LMnogR6LxcIdN1Whd8iFK/3x2y609Qyj3+TGXStn9sKedcs0CIUZfNQaWUzGMAzMdk/eH85e78t3LcQ3tizN9zAImdVmbLBvXlWDlm+sRW1l5qtUb15cCVEJF2+fil+GefCTXshE/LzVyueKRinC/CoZ3j9nAMMwGPEF4fGF8v5wlhAy/WZssOdy2Fk3GRPwOVi3TItPL5lgdYwvw4zujrThpqppaU081W5droXROoIr/XaYbYVRdkkImX4zP5plaf0KHRiGwZEz48sw3zoZ2R3p9sbZsbCnaWE5SvgcvH9WH1tkVUg5e0LI9CjaYK+WC7F8ngpHTutjTcMcbj8+ajVi7Q0aSEpnziKqZAR8DtYsrsDJi0PoHW19rKY0DiFFp2iDPQBsbKqCyxPAJ+1DAIDDn/YjGArjzlm2sGfdci38wTDe/bQfQgEXpSX5r7EnhEyvog72DXPKoFWJ8PapfvgDIbx7egCN81QpF2fNNLWVElSXi+H2BvPS2pgQkn9FHexZLBbuWKFDz6ATvz90Gc6RwKzc8zSyi5UGAPLW7ZIQkl9FHewB4OallRAKuPjwvAE1FWIsrJHne0hTYs2SSvB5bGiUtKcrIcWo6IN9CZ8bm/U2z/BFVMmIhTz88Cur8Nk1c/I9FEJIHlAvWQCfu3kOZGI+VjaU53soU6pCQbN6QooVBXtEdpm6ZzXNeAkhs1fRp3EIIaQYpAz2LS0t2LBhAxYuXIjLly/Hvt7V1YXt27ejubkZ27dvR3d3d1rHCCGETL+Uwf6OO+7AH/7wB+h0unFf37NnD3bs2IGDBw9ix44d2L17d1rHCCGETL+Uwb6pqQkajWbc1ywWC9ra2rBp0yYAwKZNm9DW1gar1Zr0GCGEkPzI6gGtwWBARUUFOBwOAIDD4aC8vBwGQ6SVbqJjCoUidyMnhBCStoKuxlEqZ97er2QitZp2oJot6F7OXFkFe41GA6PRiFAoBA6Hg1AohKGhIWg0GjAMk/BYpiwWF8Lh+FsHkplBrZbAZHLmexgkB+heFj42m5VwkpxVsFcqlWhoaMCBAwewZcsWHDhwAA0NDbE0TbJjmQ6czHx0H2cPupeFLdn9YTGJdt0e9eSTT+LQoUMwm80oKyuDXC7HG2+8gY6ODuzatQsOhwNSqRQtLS2YO3cuACQ9RgghZPqlDPaEEEJmPlpBSwghRYCCPSGEFAEK9oQQUgQo2BNCSBGgYE8IIUWAgv002bBhw7iuodmeQ/KP7uXsUUz3koI9IYQUAQr20+z6WcJsmTUUI7qXs0cx3Mu8BvvZ+AMtRnQfZw+6l7MXzewJIaQIFESw/81vfoNt27Zh69at2L59O9rb22PHFi5ciOeffx7btm3DHXfcgYMHD+ZxpJPH4XAQDodjf/b5fHkcTW4V030E6F7SvZxZCiLYb926Fa+++ir27duHnTt3Ys+ePeOOi8VivPrqq9i7dy+efPLJPI0yN2pqanD+/HkAwMcffwyz2ZznEeVOMd1HgO4l3cuZpSA2L2ltbcUvf/lL2O12sFisCRuUf/aznwUANDY2YmhoCD6fDwKBIA8jzV4wGIRAIMDOnTuxa9cu/P73v8eaNWug1WrzPbScKYb7CNC9BOhezkhMHq1fv55pb29nGhsbmdbWVoZhGGZwcJBZsGBB7JwFCxYwLpcr4Z9nAqPRyKxYsYLxeDz5HsqUKJb7yDB0LxmG7uVMVRAz+2AwGNvJ6o9//GOeR5NbL774Iv74xz/ie9/7HkpKSvI9nCk1m+8jQPdyNimmexmV12AfDAYhFArx7W9/G//wD/8AuVyO5ubmfA4p5x544AE88MAD+R7GlCqG+wjQvZxNiuFeXi9vm5cMDQ3hnnvuwdGjR4vmN+tsRPdx9qB7ObvlZWZfjB+hZiO6j7MH3cvZj7YlJISQIjAtdfYtLS3YsGEDFi5cOG4p9pEjR/D5z38e9957L+6//3709fXFjnV1dWH79u1obm7G9u3bx5V+JboemXq5vJfDw8N46KGH0NzcjHvvvRff/OY3YbVap/stFaVc/5t8+OGHsXnzZmzduhU7duwYtwiLFIjpKPk5ceIEo9frmfXr1zOXLl1iGIZhbDYbs2rVKqazs5NhGIbZt28f8+CDD8Ze8+Uvf5nZt29f7NiXv/zlpNcj0yOX93J4eJg5duxY7Lz//M//ZP793/99ut5KUcv1v0mHwxH7/7feeovZunXrdLwNkoFpmdk3NTXFyriienp6oFKpUFdXBwC47bbb8OGHH8JqtcJisaCtrQ2bNm0CAGzatAltbW2xWV+865Hpkct7KZfLsXr16th1Ghsbodfrp+/NFLFc/5uUSCSx67hcLrBYrGl6JyRdeSu9rKurg9lsxrlz57Bs2TLs378fAGAwGMAwDCoqKsDhcABE+laUl5fDYDBAoVDka8gkgVzcy3A4jD/96U/YsGFDXt4Dmfx9fPTRR3H06FEwDINf//rXeXsfJL68BXuJRIIf//jH+I//+A/4fD7ceuutkEql4HA4CAaD+RoWyUIu7uUTTzyB0tJS3H///VM8WpLIZO/jU089BQDYt28f9u7di1/96ldTPWSSgbwuqlq7di3Wrl0LADCbzXjhhRdQU1MDj8cDo9GIUCgEDoeDUCiEoaEhSt0UsMncy5aWFvT09OD5558Hm10QvfmKVi7+TW7duhW7d+/G8PAwysrKpvstkATy+i/LZDIBiHyE/9GPfoT77rsPpaWlUCqVaGhowIEDBwAABw4cQENDA6VwCli29/JHP/oRWltb8dxzz4HP5+dt/CQim/vodrthMBhi1zh8+DBkMhnkcnk+3gJJYFrq7J988kkcOnQIZrMZZWVlkMvleOONN/Doo4/i008/RSAQwC233ILvf//7sc55HR0d2LVrFxwOB6RSKVpaWjB37tyk1yNTL5f38sqVK9i0aRNqa2tjC3mqqqrw3HPP5fMtFoVc3kez2YyHH34YHo8HbDYbMpkM3/ve97BkyZI8v0syFi2qIoSQIkAJUkIIKQIU7AkhpAhQsCeEkCJAwZ4QQooABXtCCCkCFOwJmUILFy5ET09PvodBCAV7Ujw2bNiApUuXTmijvHXrVixcuBD9/f2Tuv6Xv/xlvPLKK5O6BiFThYI9KSo6nW7cArxLly7B4/HkcUSETA8K9qSobNmyBfv27Yv9ed++fdi6dWvsz06nE9/97nexZs0arF+/Hj//+c8RDocBAH/5y1/wpS99CS0tLVi5ciU2bNiA9957DwDw4x//GCdPnsTjjz+OG2+8EY8//njsmh999BHuuusuNDU14Yc//CFoHSPJBwr2pKg0NjbC5XKho6MDoVAIb7zxBjZv3hw7/sQTT8DpdOLtt9/GSy+9hNdeew2vvvpq7Pi5c+dQV1eHY8eO4Wtf+xoeffRRMAyDf/mXf0FTUxN2796N06dPY/fu3bHXHDlyBH/+85/x+uuv4+9//zs++OCDaX3PhAAU7EkRis7ujx49ivr6elRUVACINP/629/+hn/7t3+DWCxGVVUVvvKVr+D111+PvVar1eKLX/wiOBwOPv/5z8NkMsFsNif9fg899BCkUim0Wi1Wr16NixcvTun7IySevLY4JiQftmzZgvvvvx/9/f3YsmVL7OvDw8MIBALQarWxr2m1WhiNxtifVSpV7P+FQiEAYGRkJOn3U6vV417jdrsn/R4IyRTN7EnR0el0qKqqwnvvvYe77ror9vWysjLweLxxWyMaDIbYzJ+QmYyCPSlKTz31FH73u9+htLQ09jU2m427774bP/7xj+FyuTAwMIDf/va343L6yahUKvT19U3VkAmZFAr2pCjV1NTghhtumPD1H/zgBxAKhdi4cSN27NiBTZs2Ydu2bWld84EHHsDBgwexcuVKPPnkk7keMiGTQv3sCSGkCNDMnhBCigAFe0IIKQIU7AkhpAhQsCeEkCJAwZ4QQooABXtCCCkCFOwJIaQIULAnhJAiQMGeEEKKwP8D71SLhhNS7o0AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def parser(x):\n",
" return pd.datetime.strptime('190'+x, '%Y-%m')\n",
" \n",
"series = pd.read_csv('../datasets/shampoo.csv', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser)\n",
"print(series.head())\n",
"series.plot(figsize=(6, 4));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that the Shampoo Sales dataset has a clear trend.\n",
"\n",
"This suggests that the time series is not stationary and will require differencing to make it stationary, at least a difference order of 1.\n",
"\n",
"Let’s also take a quick look at an autocorrelation plot of the time series."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_acf(series);"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_pacf(series);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An ARIMA model can be created using the statsmodels library as follows:\n",
"\n",
"1. Define the model by calling ARIMA() and passing in the p, d, and q parameters.\n",
"2. The model is prepared on the training data by calling the fit() function.\n",
"3. Predictions can be made by calling the predict() function and specifying the index of the time or times to be predicted.\n",
"Let’s start off with something simple. We will fit an ARIMA model to the entire Shampoo Sales dataset and review the residual errors.\n",
"\n",
"First, we fit an ARIMA(3,1,0) model. This sets the lag value to 5 for autoregression, uses a difference order of 1 to make the time series stationary, and uses a moving average model of 0.\n",
"\n",
"When fitting the model, a lot of debug information is provided about the fit of the linear regression model. We can turn this off by setting the disp argument to 0."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" ARIMA Model Results \n",
"==============================================================================\n",
"Dep. Variable: D.Sales No. Observations: 35\n",
"Model: ARIMA(3, 1, 0) Log Likelihood -197.346\n",
"Method: css-mle S.D. of innovations 66.848\n",
"Date: Tue, 06 Jul 2021 AIC 404.692\n",
"Time: 17:25:44 BIC 412.469\n",
"Sample: 02-01-1901 HQIC 407.377\n",
" - 12-01-1903 \n",
"=================================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"---------------------------------------------------------------------------------\n",
"const 11.9920 4.143 2.895 0.004 3.872 20.112\n",
"ar.L1.D.Sales -1.0827 0.177 -6.126 0.000 -1.429 -0.736\n",
"ar.L2.D.Sales -0.5481 0.249 -2.202 0.028 -1.036 -0.060\n",
"ar.L3.D.Sales -0.1837 0.188 -0.977 0.329 -0.552 0.185\n",
" Roots \n",
"=============================================================================\n",
" Real Imaginary Modulus Frequency\n",
"-----------------------------------------------------------------------------\n",
"AR.1 -1.4841 -0.0000j 1.4841 -0.5000\n",
"AR.2 -0.7501 -1.7624j 1.9154 -0.3140\n",
"AR.3 -0.7501 +1.7624j 1.9154 0.3140\n",
"-----------------------------------------------------------------------------\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/david/.local/lib/python3.8/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
"statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
"been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
"between arima and model) and\n",
"statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
"\n",
"statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
"is both well tested and maintained.\n",
"\n",
"To silence this warning and continue using ARMA and ARIMA until they are\n",
"removed, use:\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
" FutureWarning)\n",
"warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
" FutureWarning)\n",
"\n",
" warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n",
"/home/david/.local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
" warnings.warn('No frequency information was'\n",
"/home/david/.local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
" warnings.warn('No frequency information was'\n"
]
}
],
"source": [
"from statsmodels.tsa.arima_model import ARIMA\n",
"# fit model\n",
"model = ARIMA(series, order=(3,1,0))\n",
"model_fit = model.fit(disp=0)\n",
"print(model_fit.summary())"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot residual errors\n",
"residuals = pd.DataFrame(model_fit.resid)\n",
"residuals.plot()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, we get a line plot of the residual errors, suggesting that there may still be some trend information not captured by the model."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0\n",
"count 35.000000\n",
"mean -4.793085\n",
"std 70.239323\n",
"min -132.091972\n",
"25% -42.505885\n",
"50% -10.017541\n",
"75% 26.455922\n",
"max 145.271301\n"
]
}
],
"source": [
"residuals.plot(kind='kde')\n",
"plt.show()\n",
"print(residuals.describe())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we get a density plot of the residual error values, suggesting the errors are Gaussian."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note, that although above we used the entire dataset for time series analysis, ideally we would perform this analysis on just the training dataset when developing a predictive model.\n",
"\n",
"Next, let’s look at how we can use the ARIMA model to make forecasts."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rolling Forecast ARIMA Model\n",
"The ARIMA model can be used to forecast future time steps.\n",
"\n",
"We can use the predict() function on the ARIMA Results object to make predictions. It accepts the index of the time steps to make predictions as arguments. These indexes are relative to the start of the training dataset used to make predictions.\n",
"\n",
"If we used 100 observations in the training dataset to fit the model, then the index of the next time step for making a prediction would be specified to the prediction function as start=101, end=101. This would return an array with one element containing the prediction.\n",
"\n",
"We also would prefer the forecasted values to be in the original scale, in case we performed any differencing (d>0 when configuring the model). This can be specified by setting the typ argument to the value ‘levels’: typ=’levels’.\n",
"\n",
"Alternately, we can avoid all of these specifications by using the forecast() function, which performs a one-step forecast using the model.\n",
"\n",
"We can split the training dataset into train and test sets, use the train set to fit the model, and generate a prediction for each element on the test set.\n",
"\n",
"A rolling forecast is required given the dependence on observations in prior time steps for differencing and the AR model. A crude way to perform this rolling forecast is to re-create the ARIMA model after each new observation is received.\n",
"\n",
"We manually keep track of all observations in a list called history that is seeded with the training data and to which new observations are appended each iteration.\n",
"\n",
"Putting this all together, below is an example of a rolling forecast with the ARIMA model in Python."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" predict real\n",
"0 363.496607 342.3\n",
"1 317.367824 339.7\n",
"2 396.243299 440.4\n",
"3 324.808534 315.9\n",
"4 388.153249 439.3\n",
"5 352.861656 401.3\n",
"6 402.763068 437.4\n",
"7 367.617142 575.5\n",
"8 408.465883 407.6\n",
"9 387.366086 682.0\n",
"10 420.164220 475.3\n",
"11 403.009558 581.3\n",
"12 430.245771 646.9\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/david/.local/lib/python3.8/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
"statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
"been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
"between arima and model) and\n",
"statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
"\n",
"statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
"is both well tested and maintained.\n",
"\n",
"To silence this warning and continue using ARMA and ARIMA until they are\n",
"removed, use:\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
" FutureWarning)\n",
"warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
" FutureWarning)\n",
"\n",
" warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n"
]
}
],
"source": [
"X = series.values\n",
"size = int(len(X) * 0.66)\n",
"train, test = X[0:size], X[size:len(X)]\n",
"history = [x for x in train]\n",
"future = [x for x in test]\n",
"\n",
"model = ARIMA(history, order=(3,1,0))\n",
"model_fit = model.fit(disp=0)\n",
"output = model_fit.forecast(steps=len(test))[0]\n",
"yhat = output\n",
"predictions = yhat\n",
"real_values = future\n",
"\n",
"print(pd.DataFrame({'predict':predictions, 'real':real_values}))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Running the example prints the prediction and expected value each iteration.\n",
"\n",
"We can also calculate a final mean squared error score (MSE) for the predictions, providing a point of comparison for other ARIMA configurations."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test MSE: 16994.680\n"
]
}
],
"source": [
"from sklearn.metrics import mean_squared_error\n",
"error = mean_squared_error(test, predictions)\n",
"print('Test MSE: %.3f' % error)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A line plot is created showing the expected values (blue) compared to the rolling forecast predictions (red). We can see the values show some trend and are in the correct scale."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(test)\n",
"plt.plot(predictions, color='red')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The model could use further tuning of the p, d, and maybe even the q parameters.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configuring an ARIMA Model\n",
"The classical approach for fitting an ARIMA model is to follow the Box-Jenkins Methodology.\n",
"\n",
"This is a process that uses time series analysis and diagnostics to discover good parameters for the ARIMA model.\n",
"\n",
"In summary, the steps of this process are as follows:\n",
"\n",
"1. Model Identification. Use plots and summary statistics to identify trends, seasonality, and autoregression elements to get an idea of the amount of differencing and the size of the lag that will be required.\n",
"2. Parameter Estimation. Use a fitting procedure to find the coefficients of the regression model.\n",
"3. Model Checking. Use plots and statistical tests of the residual errors to determine the amount and type of temporal structure not captured by the model.\n",
"The process is repeated until either a desirable level of fit is achieved on the in-sample or out-of-sample observations (e.g. training or test datasets).\n",
"\n",
"The process was described in the classic 1970 textbook on the topic titled Time Series Analysis: Forecasting and Control by George Box and Gwilym Jenkins. An updated 5th edition is now available if you are interested in going deeper into this type of model and methodology.\n",
"\n",
"Given that the model can be fit efficiently on modest-sized time series datasets, grid searching parameters of the model can be a valuable approach.\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| stackv2 | 2024-11-18T18:03:05.126300+00:00 | 2021-07-21T05:12:25 | {
"license": "MIT",
"url": "https://raw.githubusercontent.com/Felipe0812/AdvancedMethodsDataAnalysisClassGrupo4202119/a1ff702e7638236458c9a5cb13bc8db3e82cee40/notebooks/02-ARIMA.ipynb",
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"path": "/notebooks/02-ARIMA.ipynb",
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"visit_date": "2023-06-27T01:13:37.124900",
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} |
a76980e54d0f994b2e420e0e9f449deb11fb8507 | {
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import tweepy\n",
"from ibm_watson import PersonalityInsightsV3\n",
"import json\n",
"import pandas as pd\n",
"import time"
]
},
{
"cell_type": "code",
"execution_count": 230,
"metadata": {},
"outputs": [],
"source": [
"# Get tweets and favorites\n",
"\n",
"TWITTER_CONSUMER_KEY=\"q4qw7k6rY2mPxRUDEU4kPlNHn\"\n",
"TWITTER_CONSUMER_SECRET=\"0GskTiGx3H6cdbjyg8BNKo86CLlTegEggLquHJrFVs7Yrlk98i\"\n",
"TWITTER_ACCESS_TOKEN=\"1082776925767122944-Z4LXpd6qSUZwDzGXcxVpK0fKzWViuX\"\n",
"TWITTER_ACCESS_TOKEN_SECRET=\"M2gYYsh4Ih7u9mDvWJWvNGuRH2qCV4jwvor2E55PtJqYr\"\n",
"pi_url = 'https://gateway.watsonplatform.net/personality-insights/api'\n",
"pi_username = 'apikey'\n",
"pi_password = 'HJXndpm12XK3u2UOzNVHi7A8HmrgICguEARE39nHWve8'\n",
"\n",
"TWITTER_AUTH = tweepy.OAuthHandler(TWITTER_CONSUMER_KEY,TWITTER_CONSUMER_SECRET)\n",
"TWITTER_AUTH.set_access_token(TWITTER_ACCESS_TOKEN,TWITTER_ACCESS_TOKEN_SECRET)\n",
"TWITTER = tweepy.API(TWITTER_AUTH)\n",
"\n",
"twitter_user = TWITTER.user_timeline(screen_name='austen',\n",
" count=30,\n",
" tweet_mode='extended',\n",
" max_id=twitter_user.max_id)\n",
"\n",
"favorites = TWITTER.favorites('austen',count=30,\n",
" max_id=favorites.max_id)"
]
},
{
"cell_type": "code",
"execution_count": 231,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'word_count': 879,\n",
" 'processed_language': 'en',\n",
" 'personality': [{'trait_id': 'big5_openness',\n",
" 'name': 'Openness',\n",
" 'category': 'personality',\n",
" 'percentile': 0.641128765520318,\n",
" 'raw_score': 0.7601925881296464,\n",
" 'significant': True,\n",
" 'children': [{'trait_id': 'facet_adventurousness',\n",
" 'name': 'Adventurousness',\n",
" 'category': 'personality',\n",
" 'percentile': 0.8748702056311369,\n",
" 'raw_score': 0.5457086771424101,\n",
" 'significant': True},\n",
" {'trait_id': 'facet_artistic_interests',\n",
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" 'category': 'personality',\n",
" 'percentile': 0.6482328428881295,\n",
" 'raw_score': 0.6845896658047953,\n",
" 'significant': True},\n",
" {'trait_id': 'facet_emotionality',\n",
" 'name': 'Emotionality',\n",
" 'category': 'personality',\n",
" 'percentile': 0.6104802183530378,\n",
" 'raw_score': 0.6596336028830371,\n",
" 'significant': True},\n",
" {'trait_id': 'facet_imagination',\n",
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" 'significant': True},\n",
" {'trait_id': 'facet_intellect',\n",
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" 'percentile': 0.96300825102501,\n",
" 'raw_score': 0.6899486374453444,\n",
" 'significant': True},\n",
" {'trait_id': 'facet_liberalism',\n",
" 'name': 'Authority-challenging',\n",
" 'category': 'personality',\n",
" 'percentile': 0.9239959530519866,\n",
" 'raw_score': 0.588378034570022,\n",
" 'significant': True}]},\n",
" {'trait_id': 'big5_conscientiousness',\n",
" 'name': 'Conscientiousness',\n",
" 'category': 'personality',\n",
" 'percentile': 0.45900892143524114,\n",
" 'raw_score': 0.623250221420658,\n",
" 'significant': True,\n",
" 'children': [{'trait_id': 'facet_achievement_striving',\n",
" 'name': 'Achievement striving',\n",
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" 'significant': True},\n",
" {'trait_id': 'facet_cautiousness',\n",
" 'name': 'Cautiousness',\n",
" 'category': 'personality',\n",
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" 'raw_score': 0.4654035655329777,\n",
" 'significant': True},\n",
" {'trait_id': 'facet_dutifulness',\n",
" 'name': 'Dutifulness',\n",
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" 'percentile': 0.588599626236409,\n",
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" 'significant': True},\n",
" {'trait_id': 'facet_orderliness',\n",
" 'name': 'Orderliness',\n",
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" 'raw_score': 0.45793154185218277,\n",
" 'significant': True},\n",
" {'trait_id': 'facet_self_discipline',\n",
" 'name': 'Self-discipline',\n",
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" 'significant': True},\n",
" {'trait_id': 'facet_self_efficacy',\n",
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" 'percentile': 0.9272364150492991,\n",
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" 'significant': True}]},\n",
" {'trait_id': 'big5_extraversion',\n",
" 'name': 'Extraversion',\n",
" 'category': 'personality',\n",
" 'percentile': 0.2055868252938008,\n",
" 'raw_score': 0.5143331502708024,\n",
" 'significant': True,\n",
" 'children': [{'trait_id': 'facet_activity_level',\n",
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" 'significant': True},\n",
" {'trait_id': 'facet_assertiveness',\n",
" 'name': 'Assertiveness',\n",
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" 'percentile': 0.8995510357408202,\n",
" 'raw_score': 0.7079961858934074,\n",
" 'significant': True},\n",
" {'trait_id': 'facet_cheerfulness',\n",
" 'name': 'Cheerfulness',\n",
" 'category': 'personality',\n",
" 'percentile': 0.36231767396531656,\n",
" 'raw_score': 0.6063164290577914,\n",
" 'significant': True},\n",
" {'trait_id': 'facet_excitement_seeking',\n",
" 'name': 'Excitement-seeking',\n",
" 'category': 'personality',\n",
" 'percentile': 0.7031186651504222,\n",
" 'raw_score': 0.6262201730489432,\n",
" 'significant': True},\n",
" {'trait_id': 'facet_friendliness',\n",
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" 'category': 'personality',\n",
" 'percentile': 0.7232786746224931,\n",
" 'raw_score': 0.5902112942072308,\n",
" 'significant': True},\n",
" {'trait_id': 'facet_gregariousness',\n",
" 'name': 'Gregariousness',\n",
" 'category': 'personality',\n",
" 'percentile': 0.38442644274069065,\n",
" 'raw_score': 0.4382351703367174,\n",
" 'significant': True}]},\n",
" {'trait_id': 'big5_agreeableness',\n",
" 'name': 'Agreeableness',\n",
" 'category': 'personality',\n",
" 'percentile': 0.10021716695594202,\n",
" 'raw_score': 0.6913000703542063,\n",
" 'significant': True,\n",
" 'children': [{'trait_id': 'facet_altruism',\n",
" 'name': 'Altruism',\n",
" 'category': 'personality',\n",
" 'percentile': 0.797126984086461,\n",
" 'raw_score': 0.7342557642394623,\n",
" 'significant': True},\n",
" {'trait_id': 'facet_cooperation',\n",
" 'name': 'Cooperation',\n",
" 'category': 'personality',\n",
" 'percentile': 0.46519212465786747,\n",
" 'raw_score': 0.5741257798977777,\n",
" 'significant': True},\n",
" {'trait_id': 'facet_modesty',\n",
" 'name': 'Modesty',\n",
" 'category': 'personality',\n",
" 'percentile': 0.05980091815887584,\n",
" 'raw_score': 0.36624723546228927,\n",
" 'significant': True},\n",
" {'trait_id': 'facet_morality',\n",
" 'name': 'Uncompromising',\n",
" 'category': 'personality',\n",
" 'percentile': 0.3736087614250718,\n",
" 'raw_score': 0.610602986735108,\n",
" 'significant': True},\n",
" {'trait_id': 'facet_sympathy',\n",
" 'name': 'Sympathy',\n",
" 'category': 'personality',\n",
" 'percentile': 0.8839495071431671,\n",
" 'raw_score': 0.7091072128106586,\n",
" 'significant': True},\n",
" {'trait_id': 'facet_trust',\n",
" 'name': 'Trust',\n",
" 'category': 'personality',\n",
" 'percentile': 0.8760818382154567,\n",
" 'raw_score': 0.6334101458970721,\n",
" 'significant': True}]},\n",
" {'trait_id': 'big5_neuroticism',\n",
" 'name': 'Emotional range',\n",
" 'category': 'personality',\n",
" 'percentile': 0.43746679502697944,\n",
" 'raw_score': 0.45849316399490603,\n",
" 'significant': True,\n",
" 'children': [{'trait_id': 'facet_anger',\n",
" 'name': 'Fiery',\n",
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" 'raw_score': 0.5321321142727192,\n",
" 'significant': True},\n",
" {'trait_id': 'facet_anxiety',\n",
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" 'category': 'personality',\n",
" 'percentile': 0.5420626872832536,\n",
" 'raw_score': 0.6013156503532442,\n",
" 'significant': True},\n",
" {'trait_id': 'facet_depression',\n",
" 'name': 'Melancholy',\n",
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" 'percentile': 0.542642964023498,\n",
" 'raw_score': 0.4552220260868983,\n",
" 'significant': True},\n",
" {'trait_id': 'facet_immoderation',\n",
" 'name': 'Immoderation',\n",
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" 'percentile': 0.33066803476397866,\n",
" 'raw_score': 0.4837552476683349,\n",
" 'significant': True},\n",
" {'trait_id': 'facet_self_consciousness',\n",
" 'name': 'Self-consciousness',\n",
" 'category': 'personality',\n",
" 'percentile': 0.6172002784103465,\n",
" 'raw_score': 0.5624022890126389,\n",
" 'significant': True},\n",
" {'trait_id': 'facet_vulnerability',\n",
" 'name': 'Susceptible to stress',\n",
" 'category': 'personality',\n",
" 'percentile': 0.4513238515722376,\n",
" 'raw_score': 0.45982883152006954,\n",
" 'significant': True}]}],\n",
" 'needs': [{'trait_id': 'need_challenge',\n",
" 'name': 'Challenge',\n",
" 'category': 'needs',\n",
" 'percentile': 0.8171028574400874,\n",
" 'raw_score': 0.7723443842498605,\n",
" 'significant': True},\n",
" {'trait_id': 'need_closeness',\n",
" 'name': 'Closeness',\n",
" 'category': 'needs',\n",
" 'percentile': 0.09773938309348013,\n",
" 'raw_score': 0.7359008462457389,\n",
" 'significant': True},\n",
" {'trait_id': 'need_curiosity',\n",
" 'name': 'Curiosity',\n",
" 'category': 'needs',\n",
" 'percentile': 0.82251235774194,\n",
" 'raw_score': 0.8409948556613356,\n",
" 'significant': True},\n",
" {'trait_id': 'need_excitement',\n",
" 'name': 'Excitement',\n",
" 'category': 'needs',\n",
" 'percentile': 0.3261943394618946,\n",
" 'raw_score': 0.6494234581865365,\n",
" 'significant': True},\n",
" {'trait_id': 'need_harmony',\n",
" 'name': 'Harmony',\n",
" 'category': 'needs',\n",
" 'percentile': 0.26953040634082404,\n",
" 'raw_score': 0.7880480913318328,\n",
" 'significant': True},\n",
" {'trait_id': 'need_ideal',\n",
" 'name': 'Ideal',\n",
" 'category': 'needs',\n",
" 'percentile': 0.6988851860902994,\n",
" 'raw_score': 0.7158228490693465,\n",
" 'significant': True},\n",
" {'trait_id': 'need_liberty',\n",
" 'name': 'Liberty',\n",
" 'category': 'needs',\n",
" 'percentile': 0.5230752687029206,\n",
" 'raw_score': 0.7386796835597034,\n",
" 'significant': True},\n",
" {'trait_id': 'need_love',\n",
" 'name': 'Love',\n",
" 'category': 'needs',\n",
" 'percentile': 0.3819296484769044,\n",
" 'raw_score': 0.7540458758942005,\n",
" 'significant': True},\n",
" {'trait_id': 'need_practicality',\n",
" 'name': 'Practicality',\n",
" 'category': 'needs',\n",
" 'percentile': 0.49370221325678587,\n",
" 'raw_score': 0.7274322284078041,\n",
" 'significant': True},\n",
" {'trait_id': 'need_self_expression',\n",
" 'name': 'Self-expression',\n",
" 'category': 'needs',\n",
" 'percentile': 0.14438964800023035,\n",
" 'raw_score': 0.6384456910833018,\n",
" 'significant': True},\n",
" {'trait_id': 'need_stability',\n",
" 'name': 'Stability',\n",
" 'category': 'needs',\n",
" 'percentile': 0.2477628276376631,\n",
" 'raw_score': 0.7127438065635943,\n",
" 'significant': True},\n",
" {'trait_id': 'need_structure',\n",
" 'name': 'Structure',\n",
" 'category': 'needs',\n",
" 'percentile': 0.6550826907321118,\n",
" 'raw_score': 0.7068923629434621,\n",
" 'significant': True}],\n",
" 'values': [{'trait_id': 'value_conservation',\n",
" 'name': 'Conservation',\n",
" 'category': 'values',\n",
" 'percentile': 0.10503861351317995,\n",
" 'raw_score': 0.6089860675408036,\n",
" 'significant': True},\n",
" {'trait_id': 'value_openness_to_change',\n",
" 'name': 'Openness to change',\n",
" 'category': 'values',\n",
" 'percentile': 0.687704510573562,\n",
" 'raw_score': 0.8031382717544077,\n",
" 'significant': True},\n",
" {'trait_id': 'value_hedonism',\n",
" 'name': 'Hedonism',\n",
" 'category': 'values',\n",
" 'percentile': 0.143328855389612,\n",
" 'raw_score': 0.6701086041541615,\n",
" 'significant': True},\n",
" {'trait_id': 'value_self_enhancement',\n",
" 'name': 'Self-enhancement',\n",
" 'category': 'values',\n",
" 'percentile': 0.6109570768787038,\n",
" 'raw_score': 0.7186946890176534,\n",
" 'significant': True},\n",
" {'trait_id': 'value_self_transcendence',\n",
" 'name': 'Self-transcendence',\n",
" 'category': 'values',\n",
" 'percentile': 0.4524288141171109,\n",
" 'raw_score': 0.8310119465195638,\n",
" 'significant': True}],\n",
" 'behavior': [{'trait_id': 'behavior_sunday',\n",
" 'name': 'Sunday',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_monday',\n",
" 'name': 'Monday',\n",
" 'category': 'behavior',\n",
" 'percentage': 1.0},\n",
" {'trait_id': 'behavior_tuesday',\n",
" 'name': 'Tuesday',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_wednesday',\n",
" 'name': 'Wednesday',\n",
" 'category': 'behavior',\n",
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" {'trait_id': 'behavior_thursday',\n",
" 'name': 'Thursday',\n",
" 'category': 'behavior',\n",
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" {'trait_id': 'behavior_friday',\n",
" 'name': 'Friday',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_saturday',\n",
" 'name': 'Saturday',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_0000',\n",
" 'name': '0:00 am',\n",
" 'category': 'behavior',\n",
" 'percentage': 1.0},\n",
" {'trait_id': 'behavior_0100',\n",
" 'name': '1:00 am',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_0200',\n",
" 'name': '2:00 am',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_0300',\n",
" 'name': '3:00 am',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_0400',\n",
" 'name': '4:00 am',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_0500',\n",
" 'name': '5:00 am',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_0600',\n",
" 'name': '6:00 am',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_0700',\n",
" 'name': '7:00 am',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_0800',\n",
" 'name': '8:00 am',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_0900',\n",
" 'name': '9:00 am',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_1000',\n",
" 'name': '10:00 am',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_1100',\n",
" 'name': '11:00 am',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_1200',\n",
" 'name': '12:00 pm',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_1300',\n",
" 'name': '1:00 pm',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_1400',\n",
" 'name': '2:00 pm',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_1500',\n",
" 'name': '3:00 pm',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_1600',\n",
" 'name': '4:00 pm',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_1700',\n",
" 'name': '5:00 pm',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_1800',\n",
" 'name': '6:00 pm',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_1900',\n",
" 'name': '7:00 pm',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_2000',\n",
" 'name': '8:00 pm',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_2100',\n",
" 'name': '9:00 pm',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_2200',\n",
" 'name': '10:00 pm',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0},\n",
" {'trait_id': 'behavior_2300',\n",
" 'name': '11:00 pm',\n",
" 'category': 'behavior',\n",
" 'percentage': 0.0}],\n",
" 'consumption_preferences': [{'consumption_preference_category_id': 'consumption_preferences_shopping',\n",
" 'name': 'Purchasing Preferences',\n",
" 'consumption_preferences': [{'consumption_preference_id': 'consumption_preferences_automobile_ownership_cost',\n",
" 'name': 'Likely to be sensitive to ownership cost when buying automobiles',\n",
" 'score': 1.0},\n",
" {'consumption_preference_id': 'consumption_preferences_automobile_safety',\n",
" 'name': 'Likely to prefer safety when buying automobiles',\n",
" 'score': 0.0},\n",
" {'consumption_preference_id': 'consumption_preferences_clothes_quality',\n",
" 'name': 'Likely to prefer quality when buying clothes',\n",
" 'score': 1.0},\n",
" {'consumption_preference_id': 'consumption_preferences_clothes_style',\n",
" 'name': 'Likely to prefer style when buying clothes',\n",
" 'score': 1.0},\n",
" {'consumption_preference_id': 'consumption_preferences_clothes_comfort',\n",
" 'name': 'Likely to prefer comfort when buying clothes',\n",
" 'score': 0.0},\n",
" {'consumption_preference_id': 'consumption_preferences_influence_brand_name',\n",
" 'name': 'Likely to be influenced by brand name when making product purchases',\n",
" 'score': 1.0},\n",
" {'consumption_preference_id': 'consumption_preferences_influence_utility',\n",
" 'name': 'Likely to be influenced by product utility when making product purchases',\n",
" 'score': 0.5},\n",
" {'consumption_preference_id': 'consumption_preferences_influence_online_ads',\n",
" 'name': 'Likely to be influenced by online ads when making product purchases',\n",
" 'score': 0.0},\n",
" {'consumption_preference_id': 'consumption_preferences_influence_social_media',\n",
" 'name': 'Likely to be influenced by social media when making product purchases',\n",
" 'score': 0.0},\n",
" {'consumption_preference_id': 'consumption_preferences_influence_family_members',\n",
" 'name': 'Likely to be influenced by family when making product purchases',\n",
" 'score': 0.0},\n",
" {'consumption_preference_id': 'consumption_preferences_spur_of_moment',\n",
" 'name': 'Likely to indulge in spur of the moment purchases',\n",
" 'score': 0.5},\n",
" {'consumption_preference_id': 'consumption_preferences_credit_card_payment',\n",
" 'name': 'Likely to prefer using credit cards for shopping',\n",
" 'score': 1.0}]},\n",
" {'consumption_preference_category_id': 'consumption_preferences_health_and_activity',\n",
" 'name': 'Health & Activity Preferences',\n",
" 'consumption_preferences': [{'consumption_preference_id': 'consumption_preferences_eat_out',\n",
" 'name': 'Likely to eat out frequently',\n",
" 'score': 1.0},\n",
" {'consumption_preference_id': 'consumption_preferences_gym_membership',\n",
" 'name': 'Likely to have a gym membership',\n",
" 'score': 1.0},\n",
" {'consumption_preference_id': 'consumption_preferences_outdoor',\n",
" 'name': 'Likely to like outdoor activities',\n",
" 'score': 0.5}]},\n",
" {'consumption_preference_category_id': 'consumption_preferences_environmental_concern',\n",
" 'name': 'Environmental Concern Preferences',\n",
" 'consumption_preferences': [{'consumption_preference_id': 'consumption_preferences_concerned_environment',\n",
" 'name': 'Likely to be concerned about the environment',\n",
" 'score': 0.5}]},\n",
" {'consumption_preference_category_id': 'consumption_preferences_entrepreneurship',\n",
" 'name': 'Entrepreneurship Preferences',\n",
" 'consumption_preferences': [{'consumption_preference_id': 'consumption_preferences_start_business',\n",
" 'name': 'Likely to consider starting a business in next few years',\n",
" 'score': 1.0}]},\n",
" {'consumption_preference_category_id': 'consumption_preferences_movie',\n",
" 'name': 'Movie Preferences',\n",
" 'consumption_preferences': [{'consumption_preference_id': 'consumption_preferences_movie_romance',\n",
" 'name': 'Likely to like romance movies',\n",
" 'score': 0.0},\n",
" {'consumption_preference_id': 'consumption_preferences_movie_adventure',\n",
" 'name': 'Likely to like adventure movies',\n",
" 'score': 1.0},\n",
" {'consumption_preference_id': 'consumption_preferences_movie_horror',\n",
" 'name': 'Likely to like horror movies',\n",
" 'score': 0.0},\n",
" {'consumption_preference_id': 'consumption_preferences_movie_musical',\n",
" 'name': 'Likely to like musical movies',\n",
" 'score': 0.0},\n",
" {'consumption_preference_id': 'consumption_preferences_movie_historical',\n",
" 'name': 'Likely to like historical movies',\n",
" 'score': 0.0},\n",
" {'consumption_preference_id': 'consumption_preferences_movie_science_fiction',\n",
" 'name': 'Likely to like science-fiction movies',\n",
" 'score': 0.0},\n",
" {'consumption_preference_id': 'consumption_preferences_movie_war',\n",
" 'name': 'Likely to like war movies',\n",
" 'score': 1.0},\n",
" {'consumption_preference_id': 'consumption_preferences_movie_drama',\n",
" 'name': 'Likely to like drama movies',\n",
" 'score': 0.0},\n",
" {'consumption_preference_id': 'consumption_preferences_movie_action',\n",
" 'name': 'Likely to like action movies',\n",
" 'score': 1.0},\n",
" {'consumption_preference_id': 'consumption_preferences_movie_documentary',\n",
" 'name': 'Likely to like documentary movies',\n",
" 'score': 1.0}]},\n",
" {'consumption_preference_category_id': 'consumption_preferences_music',\n",
" 'name': 'Music Preferences',\n",
" 'consumption_preferences': [{'consumption_preference_id': 'consumption_preferences_music_rap',\n",
" 'name': 'Likely to like rap music',\n",
" 'score': 1.0},\n",
" {'consumption_preference_id': 'consumption_preferences_music_country',\n",
" 'name': 'Likely to like country music',\n",
" 'score': 0.0},\n",
" {'consumption_preference_id': 'consumption_preferences_music_r_b',\n",
" 'name': 'Likely to like R&B music',\n",
" 'score': 0.5},\n",
" {'consumption_preference_id': 'consumption_preferences_music_hip_hop',\n",
" 'name': 'Likely to like hip hop music',\n",
" 'score': 1.0},\n",
" {'consumption_preference_id': 'consumption_preferences_music_live_event',\n",
" 'name': 'Likely to attend live musical events',\n",
" 'score': 1.0},\n",
" {'consumption_preference_id': 'consumption_preferences_music_playing',\n",
" 'name': 'Likely to have experience playing music',\n",
" 'score': 1.0},\n",
" {'consumption_preference_id': 'consumption_preferences_music_latin',\n",
" 'name': 'Likely to like Latin music',\n",
" 'score': 1.0},\n",
" {'consumption_preference_id': 'consumption_preferences_music_rock',\n",
" 'name': 'Likely to like rock music',\n",
" 'score': 0.0},\n",
" {'consumption_preference_id': 'consumption_preferences_music_classical',\n",
" 'name': 'Likely to like classical music',\n",
" 'score': 0.5}]},\n",
" {'consumption_preference_category_id': 'consumption_preferences_reading',\n",
" 'name': 'Reading Preferences',\n",
" 'consumption_preferences': [{'consumption_preference_id': 'consumption_preferences_read_frequency',\n",
" 'name': 'Likely to read often',\n",
" 'score': 0.0},\n",
" {'consumption_preference_id': 'consumption_preferences_books_entertainment_magazines',\n",
" 'name': 'Likely to read entertainment magazines',\n",
" 'score': 0.0},\n",
" {'consumption_preference_id': 'consumption_preferences_books_non_fiction',\n",
" 'name': 'Likely to read non-fiction books',\n",
" 'score': 1.0},\n",
" {'consumption_preference_id': 'consumption_preferences_books_financial_investing',\n",
" 'name': 'Likely to read financial investment books',\n",
" 'score': 1.0},\n",
" {'consumption_preference_id': 'consumption_preferences_books_autobiographies',\n",
" 'name': 'Likely to read autobiographical books',\n",
" 'score': 0.0}]},\n",
" {'consumption_preference_category_id': 'consumption_preferences_volunteering',\n",
" 'name': 'Volunteering Preferences',\n",
" 'consumption_preferences': [{'consumption_preference_id': 'consumption_preferences_volunteer',\n",
" 'name': 'Likely to volunteer for social causes',\n",
" 'score': 1.0}]}],\n",
" 'warnings': []}"
]
},
"execution_count": 231,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# PI analysis\n",
"\n",
"# t = []\n",
"# for i in twitter_user: t.append(i.created_at)\n",
"\n",
"def convert_status_to_pi_content_item(t,f):\n",
" return {\n",
" 'content': t.full_text + f.text,\n",
" 'contenttype': 'text/plain',\n",
" 'created': int(time.mktime(t.created_at.timetuple())),\n",
" 'id': str(t.id),\n",
" 'language': t.lang\n",
" }\n",
"\n",
"pi_content_items_array = list(map(convert_status_to_pi_content_item, twitter_user,\n",
" favorites))\n",
"\n",
"pi_content_items = {'contentItems': pi_content_items_array}\n",
"\n",
"data = json.dumps(pi_content_items, indent=2)\n",
"\n",
"personality_insights = PersonalityInsightsV3(\n",
" version='2017-10-13',\n",
" url=pi_url,\n",
" iam_apikey= pi_password)\n",
"\n",
"profile = personality_insights.profile(\n",
" data,\n",
" accept='application/json',\n",
" content_type='application/json',\n",
" consumption_preferences=True,\n",
" raw_scores=True).get_result()\n",
"\n",
"profile"
]
},
{
"cell_type": "code",
"execution_count": 232,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1121157824204042240"
]
},
"execution_count": 232,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"twitter_user.max_id"
]
},
{
"cell_type": "code",
"execution_count": 233,
"metadata": {},
"outputs": [],
"source": [
"tt = [t.created_at for t in twitter_user]\n",
"ti = [t.created_at for t in favorites]\n",
"id = [i.id for i in twitter_user]"
]
},
{
"cell_type": "code",
"execution_count": 234,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(30, 30, 30)"
]
},
"execution_count": 234,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(tt), len(ti), len(id)"
]
},
{
"cell_type": "code",
"execution_count": 235,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[datetime.datetime(2019, 4, 25, 0, 57, 21),\n",
" datetime.datetime(2019, 4, 24, 22, 7, 25),\n",
" datetime.datetime(2019, 4, 24, 21, 10, 53),\n",
" datetime.datetime(2019, 4, 24, 21, 6, 47),\n",
" datetime.datetime(2019, 4, 24, 21, 3, 50)]"
]
},
"execution_count": 235,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get analyses for different time intervals and append them to a db\n",
"# this will most likely involve since_id\n",
"\n",
"tt[-5:]"
]
},
{
"cell_type": "code",
"execution_count": 236,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(308, 4)"
]
},
"execution_count": 236,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p = pd.DataFrame(profile['personality'], columns=['category','name','raw_score'])\n",
"n = pd.DataFrame(profile['needs'], columns=['category','name','raw_score'])\n",
"v = pd.DataFrame(profile['values'], columns=['category','name','raw_score'])\n",
"# df = pd.concat([p,n,v],axis=0)\n",
"# df['time'] = twitter_user.max_id\n",
"df1 = pd.concat([p,n,v],axis=0)\n",
"df1['time'] = twitter_user.max_id\n",
"df = df.append(df1)\n",
"\n",
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 237,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1121157824204042240"
]
},
"execution_count": 237,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"twitter_user.max_id"
]
},
{
"cell_type": "code",
"execution_count": 238,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(308, 4)"
]
},
"execution_count": 238,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 350,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>category</th>\n",
" <th>name</th>\n",
" <th>raw_score</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>personality</td>\n",
" <td>Openness</td>\n",
" <td>0.766004</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>personality</td>\n",
" <td>Conscientiousness</td>\n",
" <td>0.623164</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>personality</td>\n",
" <td>Extraversion</td>\n",
" <td>0.504791</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>personality</td>\n",
" <td>Agreeableness</td>\n",
" <td>0.691644</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>personality</td>\n",
" <td>Emotional range</td>\n",
" <td>0.462775</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>needs</td>\n",
" <td>Challenge</td>\n",
" <td>0.758457</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>needs</td>\n",
" <td>Closeness</td>\n",
" <td>0.738633</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>needs</td>\n",
" <td>Curiosity</td>\n",
" <td>0.820622</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>needs</td>\n",
" <td>Excitement</td>\n",
" <td>0.617777</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>needs</td>\n",
" <td>Harmony</td>\n",
" <td>0.772047</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>needs</td>\n",
" <td>Ideal</td>\n",
" <td>0.677488</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>needs</td>\n",
" <td>Liberty</td>\n",
" <td>0.714073</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>needs</td>\n",
" <td>Love</td>\n",
" <td>0.727049</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>needs</td>\n",
" <td>Practicality</td>\n",
" <td>0.723206</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>needs</td>\n",
" <td>Self-expression</td>\n",
" <td>0.615680</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>needs</td>\n",
" <td>Stability</td>\n",
" <td>0.686992</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>needs</td>\n",
" <td>Structure</td>\n",
" <td>0.703173</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>values</td>\n",
" <td>Conservation</td>\n",
" <td>0.594882</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>values</td>\n",
" <td>Openness to change</td>\n",
" <td>0.796705</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>values</td>\n",
" <td>Hedonism</td>\n",
" <td>0.667117</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>values</td>\n",
" <td>Self-enhancement</td>\n",
" <td>0.699532</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>values</td>\n",
" <td>Self-transcendence</td>\n",
" <td>0.821668</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>personality</td>\n",
" <td>Openness</td>\n",
" <td>0.749352</td>\n",
" <td>1130190636437479423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>personality</td>\n",
" <td>Conscientiousness</td>\n",
" <td>0.587062</td>\n",
" <td>1130190636437479423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>personality</td>\n",
" <td>Extraversion</td>\n",
" <td>0.516415</td>\n",
" <td>1130190636437479423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>personality</td>\n",
" <td>Agreeableness</td>\n",
" <td>0.668375</td>\n",
" <td>1130190636437479423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>personality</td>\n",
" <td>Emotional range</td>\n",
" <td>0.470652</td>\n",
" <td>1130190636437479423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>needs</td>\n",
" <td>Challenge</td>\n",
" <td>0.769503</td>\n",
" <td>1130190636437479423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>needs</td>\n",
" <td>Closeness</td>\n",
" <td>0.697555</td>\n",
" <td>1130190636437479423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>needs</td>\n",
" <td>Curiosity</td>\n",
" <td>0.836931</td>\n",
" <td>1130190636437479423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>needs</td>\n",
" <td>Self-expression</td>\n",
" <td>0.605069</td>\n",
" <td>1121571396625133567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>needs</td>\n",
" <td>Stability</td>\n",
" <td>0.700145</td>\n",
" <td>1121571396625133567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>needs</td>\n",
" <td>Structure</td>\n",
" <td>0.701437</td>\n",
" <td>1121571396625133567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>values</td>\n",
" <td>Conservation</td>\n",
" <td>0.593917</td>\n",
" <td>1121571396625133567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>values</td>\n",
" <td>Openness to change</td>\n",
" <td>0.788160</td>\n",
" <td>1121571396625133567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>values</td>\n",
" <td>Hedonism</td>\n",
" <td>0.666034</td>\n",
" <td>1121571396625133567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>values</td>\n",
" <td>Self-enhancement</td>\n",
" <td>0.708114</td>\n",
" <td>1121571396625133567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>values</td>\n",
" <td>Self-transcendence</td>\n",
" <td>0.822560</td>\n",
" <td>1121571396625133567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>personality</td>\n",
" <td>Openness</td>\n",
" <td>0.760193</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>personality</td>\n",
" <td>Conscientiousness</td>\n",
" <td>0.623250</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>personality</td>\n",
" <td>Extraversion</td>\n",
" <td>0.514333</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>personality</td>\n",
" <td>Agreeableness</td>\n",
" <td>0.691300</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>personality</td>\n",
" <td>Emotional range</td>\n",
" <td>0.458493</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>needs</td>\n",
" <td>Challenge</td>\n",
" <td>0.772344</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>needs</td>\n",
" <td>Closeness</td>\n",
" <td>0.735901</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>needs</td>\n",
" <td>Curiosity</td>\n",
" <td>0.840995</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>needs</td>\n",
" <td>Excitement</td>\n",
" <td>0.649423</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>needs</td>\n",
" <td>Harmony</td>\n",
" <td>0.788048</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>needs</td>\n",
" <td>Ideal</td>\n",
" <td>0.715823</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>needs</td>\n",
" <td>Liberty</td>\n",
" <td>0.738680</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>needs</td>\n",
" <td>Love</td>\n",
" <td>0.754046</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>needs</td>\n",
" <td>Practicality</td>\n",
" <td>0.727432</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>needs</td>\n",
" <td>Self-expression</td>\n",
" <td>0.638446</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>needs</td>\n",
" <td>Stability</td>\n",
" <td>0.712744</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>needs</td>\n",
" <td>Structure</td>\n",
" <td>0.706892</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>values</td>\n",
" <td>Conservation</td>\n",
" <td>0.608986</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>values</td>\n",
" <td>Openness to change</td>\n",
" <td>0.803138</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>values</td>\n",
" <td>Hedonism</td>\n",
" <td>0.670109</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>values</td>\n",
" <td>Self-enhancement</td>\n",
" <td>0.718695</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>values</td>\n",
" <td>Self-transcendence</td>\n",
" <td>0.831012</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>308 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" category name raw_score time\n",
"0 personality Openness 0.766004 1130352987082117119\n",
"1 personality Conscientiousness 0.623164 1130352987082117119\n",
"2 personality Extraversion 0.504791 1130352987082117119\n",
"3 personality Agreeableness 0.691644 1130352987082117119\n",
"4 personality Emotional range 0.462775 1130352987082117119\n",
"0 needs Challenge 0.758457 1130352987082117119\n",
"1 needs Closeness 0.738633 1130352987082117119\n",
"2 needs Curiosity 0.820622 1130352987082117119\n",
"3 needs Excitement 0.617777 1130352987082117119\n",
"4 needs Harmony 0.772047 1130352987082117119\n",
"5 needs Ideal 0.677488 1130352987082117119\n",
"6 needs Liberty 0.714073 1130352987082117119\n",
"7 needs Love 0.727049 1130352987082117119\n",
"8 needs Practicality 0.723206 1130352987082117119\n",
"9 needs Self-expression 0.615680 1130352987082117119\n",
"10 needs Stability 0.686992 1130352987082117119\n",
"11 needs Structure 0.703173 1130352987082117119\n",
"0 values Conservation 0.594882 1130352987082117119\n",
"1 values Openness to change 0.796705 1130352987082117119\n",
"2 values Hedonism 0.667117 1130352987082117119\n",
"3 values Self-enhancement 0.699532 1130352987082117119\n",
"4 values Self-transcendence 0.821668 1130352987082117119\n",
"0 personality Openness 0.749352 1130190636437479423\n",
"1 personality Conscientiousness 0.587062 1130190636437479423\n",
"2 personality Extraversion 0.516415 1130190636437479423\n",
"3 personality Agreeableness 0.668375 1130190636437479423\n",
"4 personality Emotional range 0.470652 1130190636437479423\n",
"0 needs Challenge 0.769503 1130190636437479423\n",
"1 needs Closeness 0.697555 1130190636437479423\n",
"2 needs Curiosity 0.836931 1130190636437479423\n",
".. ... ... ... ...\n",
"9 needs Self-expression 0.605069 1121571396625133567\n",
"10 needs Stability 0.700145 1121571396625133567\n",
"11 needs Structure 0.701437 1121571396625133567\n",
"0 values Conservation 0.593917 1121571396625133567\n",
"1 values Openness to change 0.788160 1121571396625133567\n",
"2 values Hedonism 0.666034 1121571396625133567\n",
"3 values Self-enhancement 0.708114 1121571396625133567\n",
"4 values Self-transcendence 0.822560 1121571396625133567\n",
"0 personality Openness 0.760193 1121157824204042240\n",
"1 personality Conscientiousness 0.623250 1121157824204042240\n",
"2 personality Extraversion 0.514333 1121157824204042240\n",
"3 personality Agreeableness 0.691300 1121157824204042240\n",
"4 personality Emotional range 0.458493 1121157824204042240\n",
"0 needs Challenge 0.772344 1121157824204042240\n",
"1 needs Closeness 0.735901 1121157824204042240\n",
"2 needs Curiosity 0.840995 1121157824204042240\n",
"3 needs Excitement 0.649423 1121157824204042240\n",
"4 needs Harmony 0.788048 1121157824204042240\n",
"5 needs Ideal 0.715823 1121157824204042240\n",
"6 needs Liberty 0.738680 1121157824204042240\n",
"7 needs Love 0.754046 1121157824204042240\n",
"8 needs Practicality 0.727432 1121157824204042240\n",
"9 needs Self-expression 0.638446 1121157824204042240\n",
"10 needs Stability 0.712744 1121157824204042240\n",
"11 needs Structure 0.706892 1121157824204042240\n",
"0 values Conservation 0.608986 1121157824204042240\n",
"1 values Openness to change 0.803138 1121157824204042240\n",
"2 values Hedonism 0.670109 1121157824204042240\n",
"3 values Self-enhancement 0.718695 1121157824204042240\n",
"4 values Self-transcendence 0.831012 1121157824204042240\n",
"\n",
"[308 rows x 4 columns]"
]
},
"execution_count": 350,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 247,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>category</th>\n",
" <th>name</th>\n",
" <th>raw_score</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>personality</td>\n",
" <td>Openness</td>\n",
" <td>0.766004</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>personality</td>\n",
" <td>Conscientiousness</td>\n",
" <td>0.623164</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>personality</td>\n",
" <td>Extraversion</td>\n",
" <td>0.504791</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>personality</td>\n",
" <td>Agreeableness</td>\n",
" <td>0.691644</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>personality</td>\n",
" <td>Emotional range</td>\n",
" <td>0.462775</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>needs</td>\n",
" <td>Challenge</td>\n",
" <td>0.758457</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>needs</td>\n",
" <td>Closeness</td>\n",
" <td>0.738633</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>needs</td>\n",
" <td>Curiosity</td>\n",
" <td>0.820622</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>needs</td>\n",
" <td>Excitement</td>\n",
" <td>0.617777</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>needs</td>\n",
" <td>Harmony</td>\n",
" <td>0.772047</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>needs</td>\n",
" <td>Ideal</td>\n",
" <td>0.677488</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>needs</td>\n",
" <td>Liberty</td>\n",
" <td>0.714073</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>needs</td>\n",
" <td>Love</td>\n",
" <td>0.727049</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>needs</td>\n",
" <td>Practicality</td>\n",
" <td>0.723206</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>needs</td>\n",
" <td>Self-expression</td>\n",
" <td>0.615680</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>needs</td>\n",
" <td>Stability</td>\n",
" <td>0.686992</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>needs</td>\n",
" <td>Structure</td>\n",
" <td>0.703173</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>values</td>\n",
" <td>Conservation</td>\n",
" <td>0.594882</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>values</td>\n",
" <td>Openness to change</td>\n",
" <td>0.796705</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>values</td>\n",
" <td>Hedonism</td>\n",
" <td>0.667117</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>values</td>\n",
" <td>Self-enhancement</td>\n",
" <td>0.699532</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>values</td>\n",
" <td>Self-transcendence</td>\n",
" <td>0.821668</td>\n",
" <td>1130352987082117119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>personality</td>\n",
" <td>Openness</td>\n",
" <td>0.749352</td>\n",
" <td>1130190636437479423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>personality</td>\n",
" <td>Conscientiousness</td>\n",
" <td>0.587062</td>\n",
" <td>1130190636437479423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>personality</td>\n",
" <td>Extraversion</td>\n",
" <td>0.516415</td>\n",
" <td>1130190636437479423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>personality</td>\n",
" <td>Agreeableness</td>\n",
" <td>0.668375</td>\n",
" <td>1130190636437479423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>personality</td>\n",
" <td>Emotional range</td>\n",
" <td>0.470652</td>\n",
" <td>1130190636437479423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>needs</td>\n",
" <td>Challenge</td>\n",
" <td>0.769503</td>\n",
" <td>1130190636437479423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>needs</td>\n",
" <td>Closeness</td>\n",
" <td>0.697555</td>\n",
" <td>1130190636437479423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>needs</td>\n",
" <td>Curiosity</td>\n",
" <td>0.836931</td>\n",
" <td>1130190636437479423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>needs</td>\n",
" <td>Self-expression</td>\n",
" <td>0.605069</td>\n",
" <td>1121571396625133567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>needs</td>\n",
" <td>Stability</td>\n",
" <td>0.700145</td>\n",
" <td>1121571396625133567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>needs</td>\n",
" <td>Structure</td>\n",
" <td>0.701437</td>\n",
" <td>1121571396625133567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>values</td>\n",
" <td>Conservation</td>\n",
" <td>0.593917</td>\n",
" <td>1121571396625133567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>values</td>\n",
" <td>Openness to change</td>\n",
" <td>0.788160</td>\n",
" <td>1121571396625133567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>values</td>\n",
" <td>Hedonism</td>\n",
" <td>0.666034</td>\n",
" <td>1121571396625133567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>values</td>\n",
" <td>Self-enhancement</td>\n",
" <td>0.708114</td>\n",
" <td>1121571396625133567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>values</td>\n",
" <td>Self-transcendence</td>\n",
" <td>0.822560</td>\n",
" <td>1121571396625133567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>personality</td>\n",
" <td>Openness</td>\n",
" <td>0.760193</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>personality</td>\n",
" <td>Conscientiousness</td>\n",
" <td>0.623250</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>personality</td>\n",
" <td>Extraversion</td>\n",
" <td>0.514333</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>personality</td>\n",
" <td>Agreeableness</td>\n",
" <td>0.691300</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>personality</td>\n",
" <td>Emotional range</td>\n",
" <td>0.458493</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>needs</td>\n",
" <td>Challenge</td>\n",
" <td>0.772344</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>needs</td>\n",
" <td>Closeness</td>\n",
" <td>0.735901</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>needs</td>\n",
" <td>Curiosity</td>\n",
" <td>0.840995</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>needs</td>\n",
" <td>Excitement</td>\n",
" <td>0.649423</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>needs</td>\n",
" <td>Harmony</td>\n",
" <td>0.788048</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>needs</td>\n",
" <td>Ideal</td>\n",
" <td>0.715823</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>needs</td>\n",
" <td>Liberty</td>\n",
" <td>0.738680</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>needs</td>\n",
" <td>Love</td>\n",
" <td>0.754046</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>needs</td>\n",
" <td>Practicality</td>\n",
" <td>0.727432</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>needs</td>\n",
" <td>Self-expression</td>\n",
" <td>0.638446</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>needs</td>\n",
" <td>Stability</td>\n",
" <td>0.712744</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>needs</td>\n",
" <td>Structure</td>\n",
" <td>0.706892</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>values</td>\n",
" <td>Conservation</td>\n",
" <td>0.608986</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>values</td>\n",
" <td>Openness to change</td>\n",
" <td>0.803138</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>values</td>\n",
" <td>Hedonism</td>\n",
" <td>0.670109</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>values</td>\n",
" <td>Self-enhancement</td>\n",
" <td>0.718695</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>values</td>\n",
" <td>Self-transcendence</td>\n",
" <td>0.831012</td>\n",
" <td>1121157824204042240</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>308 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" category name raw_score time\n",
"0 personality Openness 0.766004 1130352987082117119\n",
"1 personality Conscientiousness 0.623164 1130352987082117119\n",
"2 personality Extraversion 0.504791 1130352987082117119\n",
"3 personality Agreeableness 0.691644 1130352987082117119\n",
"4 personality Emotional range 0.462775 1130352987082117119\n",
"0 needs Challenge 0.758457 1130352987082117119\n",
"1 needs Closeness 0.738633 1130352987082117119\n",
"2 needs Curiosity 0.820622 1130352987082117119\n",
"3 needs Excitement 0.617777 1130352987082117119\n",
"4 needs Harmony 0.772047 1130352987082117119\n",
"5 needs Ideal 0.677488 1130352987082117119\n",
"6 needs Liberty 0.714073 1130352987082117119\n",
"7 needs Love 0.727049 1130352987082117119\n",
"8 needs Practicality 0.723206 1130352987082117119\n",
"9 needs Self-expression 0.615680 1130352987082117119\n",
"10 needs Stability 0.686992 1130352987082117119\n",
"11 needs Structure 0.703173 1130352987082117119\n",
"0 values Conservation 0.594882 1130352987082117119\n",
"1 values Openness to change 0.796705 1130352987082117119\n",
"2 values Hedonism 0.667117 1130352987082117119\n",
"3 values Self-enhancement 0.699532 1130352987082117119\n",
"4 values Self-transcendence 0.821668 1130352987082117119\n",
"0 personality Openness 0.749352 1130190636437479423\n",
"1 personality Conscientiousness 0.587062 1130190636437479423\n",
"2 personality Extraversion 0.516415 1130190636437479423\n",
"3 personality Agreeableness 0.668375 1130190636437479423\n",
"4 personality Emotional range 0.470652 1130190636437479423\n",
"0 needs Challenge 0.769503 1130190636437479423\n",
"1 needs Closeness 0.697555 1130190636437479423\n",
"2 needs Curiosity 0.836931 1130190636437479423\n",
".. ... ... ... ...\n",
"9 needs Self-expression 0.605069 1121571396625133567\n",
"10 needs Stability 0.700145 1121571396625133567\n",
"11 needs Structure 0.701437 1121571396625133567\n",
"0 values Conservation 0.593917 1121571396625133567\n",
"1 values Openness to change 0.788160 1121571396625133567\n",
"2 values Hedonism 0.666034 1121571396625133567\n",
"3 values Self-enhancement 0.708114 1121571396625133567\n",
"4 values Self-transcendence 0.822560 1121571396625133567\n",
"0 personality Openness 0.760193 1121157824204042240\n",
"1 personality Conscientiousness 0.623250 1121157824204042240\n",
"2 personality Extraversion 0.514333 1121157824204042240\n",
"3 personality Agreeableness 0.691300 1121157824204042240\n",
"4 personality Emotional range 0.458493 1121157824204042240\n",
"0 needs Challenge 0.772344 1121157824204042240\n",
"1 needs Closeness 0.735901 1121157824204042240\n",
"2 needs Curiosity 0.840995 1121157824204042240\n",
"3 needs Excitement 0.649423 1121157824204042240\n",
"4 needs Harmony 0.788048 1121157824204042240\n",
"5 needs Ideal 0.715823 1121157824204042240\n",
"6 needs Liberty 0.738680 1121157824204042240\n",
"7 needs Love 0.754046 1121157824204042240\n",
"8 needs Practicality 0.727432 1121157824204042240\n",
"9 needs Self-expression 0.638446 1121157824204042240\n",
"10 needs Stability 0.712744 1121157824204042240\n",
"11 needs Structure 0.706892 1121157824204042240\n",
"0 values Conservation 0.608986 1121157824204042240\n",
"1 values Openness to change 0.803138 1121157824204042240\n",
"2 values Hedonism 0.670109 1121157824204042240\n",
"3 values Self-enhancement 0.718695 1121157824204042240\n",
"4 values Self-transcendence 0.831012 1121157824204042240\n",
"\n",
"[308 rows x 4 columns]"
]
},
"execution_count": 247,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 250,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14"
]
},
"execution_count": 250,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.time.nunique()"
]
},
{
"cell_type": "code",
"execution_count": 364,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>category</th>\n",
" <th>needs</th>\n",
" <th>personality</th>\n",
" <th>values</th>\n",
" </tr>\n",
" <tr>\n",
" <th>time</th>\n",
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" <td>0.621326</td>\n",
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" <tr>\n",
" <th>1128348850727473156</th>\n",
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" <td>0.625688</td>\n",
" <td>0.702438</td>\n",
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" <tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
"category needs personality values\n",
"time \n",
"1121157824204042240 0.731731 0.609514 0.726388\n",
"1121571396625133567 0.714995 0.606459 0.715757\n",
"1121945823518941183 0.702164 0.606128 0.689289\n",
"1122715024303091713 0.725931 0.610440 0.718224\n",
"1123012734361059328 0.717461 0.598233 0.727263\n",
"1123427504826605567 0.709593 0.633440 0.714178\n",
"1124062010927460370 0.715081 0.622773 0.715351\n",
"1127315738358759423 0.697492 0.601927 0.703513\n",
"1127632473230196736 0.690027 0.621326 0.690548\n",
"1128348850727473156 0.713567 0.625688 0.702438\n",
"1129501761448734719 0.720988 0.608528 0.714461\n",
"1129897869761114111 0.702547 0.617224 0.708134\n",
"1130190636437479423 0.715667 0.598371 0.711270\n",
"1130352987082117119 0.712933 0.609675 0.715981"
]
},
"execution_count": 364,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gr = df.groupby(['time','category']).mean().unstack()\n",
"gr.columns = gr.columns.droplevel()\n",
"gr"
]
},
{
"cell_type": "code",
"execution_count": 372,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x12e521dd8>"
]
},
"execution_count": 372,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"gr.plot()"
]
},
{
"cell_type": "code",
"execution_count": 349,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"MultiIndex(levels=[['raw_score'], ['needs', 'personality', 'values']],\n",
" codes=[[0, 0, 0], [0, 1, 2]],\n",
" names=[None, 'category'])"
]
},
"execution_count": 349,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gr.columns"
]
},
{
"cell_type": "code",
"execution_count": 370,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x12adb2898>"
]
},
"execution_count": 370,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"df_p = df[df['category'] == 'personality']\n",
"df_p = df_p.groupby(['time','name']).mean().unstack()\n",
"df_p.columns = df_p.columns.droplevel()\n",
"df_p.plot()"
]
},
{
"cell_type": "code",
"execution_count": 371,
"metadata": {},
"outputs": [],
"source": [
"# df.pivot(index='time',values='raw_score',columns='name').plot(figsize=(6,30), subplots=True);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| stackv2 | 2024-11-18T18:03:05.138653+00:00 | 2019-05-24T02:29:14 | {
"license": "MIT",
"url": "https://raw.githubusercontent.com/pat-pyschographic-analysis-of-text/data-science/5eef0ae4ab44697c0b01a961828f0360f07ceba0/dash_viz_data.ipynb",
"blob_id": "a76980e54d0f994b2e420e0e9f449deb11fb8507",
"directory_id": "c439d5a5c8c574b3b0a032407d3e3ced761863b0",
"path": "/dash_viz_data.ipynb",
"content_id": "766900d037c1be2c81891714adb778f270aafb9f",
"detected_licenses": [
"MIT"
],
"license_type": "permissive",
"repo_name": "pat-pyschographic-analysis-of-text/data-science",
"snapshot_id": "b3bfc6d958b1c91152ce1a947bb3cc1498d749eb",
"revision_id": "5eef0ae4ab44697c0b01a961828f0360f07ceba0",
"branch_name": "refs/heads/master",
"visit_date": "2020-05-24T16:40:22.138321",
"revision_date": "2019-05-24T02:29:14",
"committer_date": "2019-05-24T02:29:14",
"github_id": 187366410,
"star_events_count": 0,
"fork_events_count": 0,
"gha_license_id": null,
"gha_event_created_at": null,
"gha_created_at": null,
"gha_language": null,
"src_encoding": "UTF-8",
"language": "Jupyter Notebook",
"is_vendor": false,
"is_generated": false,
"length_bytes": 148815,
"extension": "ipynb",
"filename": "dash_viz_data.ipynb"
} |
380ecae205979463e2a58d8e6ab162065b775b56 | {
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"ename": "JSONDecodeError",
"evalue": "Expecting value: line 1 column 1 (char 0)",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mJSONDecodeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-5-ecf2c52d93bf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 41\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mr_json\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'file'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 42\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 43\u001b[1;33m \u001b[0mm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMeowner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m<ipython-input-5-ecf2c52d93bf>\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbtn_update\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_cat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjanela\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmainloop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m<ipython-input-5-ecf2c52d93bf>\u001b[0m in \u001b[0;36mupdate_cat\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mupdate_cat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcat_img_url\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_random_cat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 23\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_tkinter_image\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcat_img_url\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m<ipython-input-5-ecf2c52d93bf>\u001b[0m in \u001b[0;36mget_random_cat\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 38\u001b[0m \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'http://img.recipepuppy.com/34464.jpg'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 39\u001b[0m \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 40\u001b[1;33m \u001b[0mr_json\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 41\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mr_json\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'file'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 42\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\requests\\models.py\u001b[0m in \u001b[0;36mjson\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m 895\u001b[0m \u001b[1;31m# used.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 896\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 897\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mcomplexjson\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 899\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\lib\\json\\__init__.py\u001b[0m in \u001b[0;36mloads\u001b[1;34m(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[0;32m 346\u001b[0m \u001b[0mparse_int\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mparse_float\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 347\u001b[0m parse_constant is None and object_pairs_hook is None and not kw):\n\u001b[1;32m--> 348\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_default_decoder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 349\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 350\u001b[0m \u001b[0mcls\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mJSONDecoder\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\lib\\json\\decoder.py\u001b[0m in \u001b[0;36mdecode\u001b[1;34m(self, s, _w)\u001b[0m\n\u001b[0;32m 335\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 336\u001b[0m \"\"\"\n\u001b[1;32m--> 337\u001b[1;33m \u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraw_decode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_w\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 338\u001b[0m \u001b[0mend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_w\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\lib\\json\\decoder.py\u001b[0m in \u001b[0;36mraw_decode\u001b[1;34m(self, s, idx)\u001b[0m\n\u001b[0;32m 353\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscan_once\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 354\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 355\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mJSONDecodeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Expecting value\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 356\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)"
]
}
],
"source": [
"import requests\n",
"import io\n",
"from tkinter import *\n",
"from PIL import Image, ImageTk\n",
"\n",
"class Meowner():\n",
" def __init__(self):\n",
" self.janela = Tk()\n",
" self.janela.title('Meowner')\n",
" self.janela.geometry('800x600')\n",
" self.janela.tk_setPalette(background= '#7e7d80', foreground = '#ffFF00', activeBackground = 'blue')\n",
" \n",
" self.btn_update = Button(self.janela, text='OTRO GATO!', command=self.update_cat)\n",
" self.btn_update.pack()\n",
" \n",
" self.update_cat()\n",
" \n",
" self.janela.mainloop()\n",
" \n",
" \n",
" def update_cat(self):\n",
" self.cat_img_url = self.get_random_cat()\n",
" self.image = self.get_tkinter_image(self.cat_img_url)\n",
" try:\n",
" self.label.destroy()\n",
" except:\n",
" pass\n",
" self.label = Label(self.janela, image=self.image, width=600, height=400)\n",
" self.label.pack()\n",
" \n",
" def get_tkinter_image(self, img_url):\n",
" raw_data = requests.get(img_url, stream=True).raw.data\n",
" im = Image.open(io.BytesIO(raw_data))\n",
" image = ImageTk.PhotoImage(im)\n",
" return image\n",
"\n",
" def get_random_cat(self):\n",
" url = 'https://aws.random.cat/meow'\n",
" r = requests.get(url)\n",
" r_json = r.json()\n",
" return r_json['file']\n",
"\n",
"m = Meowner()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
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"source": []
}
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a7e69dc428e67265bec71ffcfac391af2ac1f3b9 | {
"metadata": {
"name": "DTMC"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": "%pylab inline",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Populating the interactive namespace from numpy and matplotlib\n"
}
],
"prompt_number": 190
},
{
"cell_type": "code",
"collapsed": true,
"input": "import numpy as np\nfrom numpy import linalg as LA\nimport pylab as pl\nfrom collections import defaultdict\n\ns = defaultdict(dict) #states_transition\n# sunny & cloudy example\n# s[1][1] = 0.75\n# s[1][2] = 0.25\n# s[2][1] = 0.4\n# s[2][2] = 0.6\ns[1][1] = 0.7\ns[1][2] = 0.3\ns[1][3] = 0\ns[2][1] = 0\ns[2][2] = 0.85\ns[2][3] = 0.15\ns[3][1] = 0\ns[3][2] = 0\ns[3][3] = 1\n\ninitial_state = 1\nn = 20",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 191
},
{
"cell_type": "code",
"collapsed": false,
"input": "#create matrix array\nn_states = len(s)\n\n# this stuff is needed to sort matrix in form\n# [ Q 0 ]\n# [ R I ]\nabsorbing_states = [ i for i in xrange(1, n_states + 1) if s[i][i] == 1 ]\nstate_reindex = []\n\narr = []\ninitial_arr = []\nstates_to_enum = [ i for i in xrange(1, n_states + 1) if i not in absorbing_states ]\nfor to_state in states_to_enum:\n state_reindex.append(to_state)\n # append row\n arr.append([ s[from_state][to_state] for from_state in states_to_enum ])\n if to_state == initial_state:\n initial_arr.append([1])\n else:\n initial_arr.append([0])\n\n \na = np.matrix(arr)\nprint a\n\n#create initial array\ninitial = np.matrix(initial_arr)",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "[[ 0.7 0. ]\n [ 0.3 0.85]]\n"
}
],
"prompt_number": 192
},
{
"cell_type": "code",
"collapsed": false,
"input": "def noAbs():\n values = []\n values.append(initial)\n curent_matrix = 1\n for i in range(n):\n curent_matrix = curent_matrix * a\n values.append(curent_matrix * initial)\n\n for i in xrange(0, n_states):\n state_p = [ v.item(i) for v in values ]\n pl.bar(range(n + 1), state_p, label=\"state\", width=3)\n for iv in range(len(state_p)):\n pl.text(iv, state_p[iv], \"{0}\".format(state_p[iv]))\n pl.title(\"state {0}\".format(i+1))\n pl.show()\n\n# no absorbing\nif not absorbing_states:\n noAbs()\nelse:\n print a\n i_q = np.identity(len(states_to_enum)) - a\n print i_q\n N = LA.inv(i_q)\n print N\n",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "[[ 0.7 0. ]\n [ 0.3 0.85]]\n[[ 0.3 0. ]\n [-0.3 0.15]]\n[[ 3.33333333 0. ]\n [ 6.66666667 6.66666667]]\n"
}
],
"prompt_number": 197
},
{
"cell_type": "code",
"collapsed": false,
"input": "",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 193
},
{
"cell_type": "code",
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1a74f2c55355f18153f847de73205b98becc08c6 | {
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"In this notebook we reconstruct the Stabilizer decomposition of the state $|H>^{\\otimes 6}$ of *Trading classical and quantum computational resources* (2016).\n",
"\n",
"Here $|H> = |0> + (1/\\sqrt 2-1)|1>$ is within local Cliffords of $|T> = |0> + e^{i\\pi/4} |1>$."
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"%load_ext autoreload\n",
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"import sys; sys.path.append('..')\n",
"import random, math, os\n",
"import pyzx as zx\n",
"from fractions import Fraction\n",
"import numpy as np\n",
"%config InlineBackend.figure_format = 'svg'"
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"#Z^6 |K_6>\n",
"g = zx.Graph()\n",
"verts = []\n",
"for i in range(6):\n",
" o = g.add_vertex(0, i, 1)\n",
" g.outputs.append(o)\n",
" v = g.add_vertex(1,i, 0,Fraction(1))\n",
" verts.append(v)\n",
" g.add_edge((o,v))\n",
"g.scalar.add_power(15)\n",
"for i in range(6):\n",
" for j in range(i+1,6):\n",
" g.add_edge((verts[i],verts[j]),2)\n",
"display(zx.draw(g))\n",
"K6 = g.to_matrix(True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
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" o = g.add_vertex(0, i, 1)\n",
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},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
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"# |1>^6\n",
"g = zx.Graph()\n",
"verts = []\n",
"for i in range(6):\n",
" o = g.add_vertex(0, i, 1)\n",
" g.outputs.append(o)\n",
" v = g.add_vertex(2,i, 0, phase=Fraction(1))\n",
" verts.append(v)\n",
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{
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"# |E_6>\n",
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"for i in range(6):\n",
" o = g.add_vertex(0, i, 1)\n",
" g.outputs.append(o)\n",
" g.add_edge((o,v))\n",
"g.scalar.add_power(4)\n",
"display(zx.draw(g))\n",
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{
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"source": [
"# |phi'>\n",
"g = zx.Graph()\n",
"verts = []\n",
"for i in range(6):\n",
" o = g.add_vertex(0, i, 1)\n",
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"g.scalar.add_power(9)\n",
"for i in range(4):\n",
" g.add_edge((verts[i],verts[i+1]),2)\n",
" g.add_edge((verts[i],verts[5]),2)\n",
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},
{
"cell_type": "code",
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"metadata": {},
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"# |phi''>\n",
"g = zx.Graph()\n",
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" o = g.add_vertex(0, i, 1)\n",
" g.outputs.append(o)\n",
" v = g.add_vertex(1,i, 0)\n",
" verts.append(v)\n",
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"g.add_edges([(v1,v2),(v2,v4),(v4,v5),(v5,v6),(v1,v6),(v1,v3),(v2,v3),(v3,v4),(v3,v5),(v3,v6)],2)\n",
"g.set_phase(v3,Fraction(1))\n",
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]
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"sq2 = math.sqrt(2)\n",
"H6 = (-16+12*sq2)*ket0 + (96 - 68*sq2)*ket1 + \\\n",
" (10-7*sq2)*E6 + (-14 + 10*sq2)*O6 + \\\n",
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" (10-7*sq2)*phi2\n",
"H6"
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"H = np.matrix([[1],[sq2 - 1]])\n",
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"T = 1/(1-1j*math.tan(math.pi/8))*HAD*S* H\n",
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64530f1a4d1f58dfc1283000c05fe2047c612809 | {
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"# P300 with muse - Cats & Dogs\n",
"\n",
"I was interested in knowing whether it was possible to observe a P300 with a Muse 2016 EEG headset.\n",
"EEG channels on the Muse are not positioned ideally for P300. However, EEG potentials diffuses on the whole scalp and it is very likely that we will be able to observe a P300 ERP even if the electrode are far away from the region of interest. The real question is **How reliable will this observation be?**.\n",
"\n",
"So I designed a simple [Oddball Paradigm](https://en.wikipedia.org/wiki/Oddball_paradigm) using visual stimulus. I used two type of stimulus, frequency stimulus consisting in a grating with vertical stripes, and rare (target) stimulus with horizontal stripes. \n",
"The task was to count the number of times I saw the stimulus with horizontal stripes.\n",
"\n",
"Stimulus were presented for 200ms at an interval of 600ms with a random jitter of +- 100ms.\n",
"\n",
"I recorded 6 runs of 2 minutes, for a total count of 960 Non-target and 184 Target stimulus.\n",
"\n",
"### Record your own data\n",
"\n",
"To record your own data, you need a muse 2016 headset.\n",
"Use the script `muse-lsl.py` to stream data from the muse, then launch the following two scripts in another terminal\n",
"\n",
"`python stimulus_presentation/generate_Visual_P300.py -d 120 & python lsl-record.py -d 120`\n",
"\n",
"this will display stimulations and record data for 2 minutes.\n",
"you can do as many run as you wish, but 5 runs (10 minutes) is ideal."
]
},
{
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"## Errors\n",
"\n",
"Encountered on Ubuntu 16.0.4\n",
"\n",
"Trying to connect with `python muse-lsl.py -n Muse-41D2`\n",
"\n",
"1. `BLEError: No characteristic found matching UUID` https://github.com/alexandrebarachant/muse-lsl/issues/4\n",
"No solution found. Switching Muse devices solves the problem, though\n",
"\n",
"Trying to install psychopy with Python 3.6 `pip install psychopy`\n",
"\n",
"1. `Package gtk+-3.0 was not found in the pkg-co│der &)\n",
"nfig search path`\n",
"\n",
"Had to install gtk-build-essentials: `sudo apt-get install build-essential libgtk-3-dev`\n",
"\n",
"2. `GStreamer not available`\n",
"\n",
"Had to install wxPython with `pip install -U \\\n",
" -f https://extras.wxpython.org/wxPython4/extras/linux/gtk3/ubuntu-16.04 \\\n",
" wxPython` \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Questions about stimulus presentation\n",
"\n",
"1. Whats the reasoning behind the speed of the stimulus presentation? Is there any way we can slow it down to make it a little bit of a more relaxed experience?\n",
"\n",
"2. How do cats and dogs compare to gratings?\n",
"\n",
"3. How does varying the images within the group affect the results?\n",
"\n",
"4. Has anyone built ERP experiments that are more low-stress and naturalistic?\n",
"\n",
"5. Would it be to distracting to show accumulating ERP data while the experiment is happening?\n",
"\n",
"6. \"Bit confusing why signal quality is hard to get\"\n",
"\n",
"7. Should I be able to use my glasses?\n",
"\n",
"8. Serious eye strain during trials\n",
"\n",
"9. Should present feedback in between trials -- preview of ERP wave and signal quality feedback -- how good was signal quality at each electrode?"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"from collections import OrderedDict\n",
"\n",
"from mne import create_info, concatenate_raws\n",
"from mne.io import RawArray\n",
"from mne.channels import read_montage\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"from glob import glob\n",
"import seaborn as sns\n",
"from matplotlib import pyplot as plt\n",
"\n",
"sys.path.append('../muse')\n",
"import utils\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read data and convert them in MNE objects\n",
"\n",
"Data is read from this folder's data/visual/P300 folder. Data will come from folders with the model `subject {subject}` and `session {session}`\n",
"\n",
"Data is saved in csv file for more convenience. Then we will convert them into MNE data object so we can pre-process and epoch them"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating RawArray with float64 data, n_channels=5, n_times=30732\n",
" Range : 0 ... 30731 = 0.000 ... 120.043 secs\n",
"Ready.\n",
"Creating RawArray with float64 data, n_channels=5, n_times=30732\n",
" Range : 0 ... 30731 = 0.000 ... 120.043 secs\n",
"Ready.\n",
"Creating RawArray with float64 data, n_channels=5, n_times=30732\n",
" Range : 0 ... 30731 = 0.000 ... 120.043 secs\n",
"Ready.\n",
"Creating RawArray with float64 data, n_channels=5, n_times=30720\n",
" Range : 0 ... 30719 = 0.000 ... 119.996 secs\n",
"Ready.\n",
"Creating RawArray with float64 data, n_channels=5, n_times=30744\n",
" Range : 0 ... 30743 = 0.000 ... 120.090 secs\n",
"Ready.\n"
]
}
],
"source": [
"subject = 2\n",
"session = 1\n",
"raw = utils.load_data('visual/P300', sfreq=256., \n",
" subject_nb=subject, session_nb=session, \n",
" ch_ind=[0, 1, 2, 3])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Power Spectrum"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Effective window size : 8.000 (s)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/dano/anaconda3/lib/python3.6/site-packages/mne/viz/raw.py:614: DeprecationWarning: In version 0.15 average will default to False and spatial_colors will default to True.\n",
" 'spatial_colors will default to True.', DeprecationWarning)\n"
]
},
{
"data": {
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bjecTpm4LFzabjcrKSkNEac61btesWTPp+JTfsCzLTuA+4FbgKmARYAH6GSng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\ni/tYC7jTCl9Mhum6NNPADcBbsiz/P4q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t+66srCQej4vF22SY1B2pKMrq7P+AxOjXFEWZ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\n0Qh/A9NuCByHODtcAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2e01bcb5f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"raw.plot_psd(tmax=np.inf);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can clearly see the 60Hz (and its first harmonic) in the signal."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Filtering\n",
"\n",
"We filter data between 1 and 30 Hz"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Setting up band-pass filter from 1 - 30 Hz\n"
]
},
{
"data": {
"text/plain": [
"<RawArray | None, n_channels x n_times : 5 x 153660 (600.2 sec), ~5.9 MB, data loaded>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw.filter(1,30, method='iir')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Epoching\n",
"\n",
"Here we epoch data for -100ms to 800ms after the stimulus. No baseline correction is needed (signal is bandpass filtered) and we reject every epochs where the signal exceed 100uV. This concerns mainly blinks.\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"962 events found\n",
"Events id: [1 2]\n"
]
},
{
"data": {
"text/plain": [
"<Epochs | n_events : 944 (all good), tmin : -0.1015625 (s), tmax : 0.80078125 (s), baseline : None, ~6.7 MB, data loaded,\n",
" 'Non-Target': 803, 'Target': 141>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from mne import Epochs, find_events\n",
"\n",
"#TODO: print sample drop %\n",
"events = find_events(raw)\n",
"event_id = {'Non-Target': 1, 'Target': 2}\n",
"\n",
"epochs = Epochs(raw, events=events, event_id=event_id, tmin=-0.1, tmax=0.8, baseline=None,\n",
" reject={'eeg': 100e-6}, preload=True, verbose=False, picks=[0,1,2,3], add_eeg_ref=False)\n",
"epochs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Epoch average\n",
"\n",
"Now we can plot the average ERP for both conditions, and see if there is something"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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QjI1Kv9Y0baPqJ+W6eJk0ynYAhXJdcB2Up/y/bQfldlxsrukGejSKnhfvUJeu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\nOdlvluI19byutlN5hlTg7bw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733PziiYSX9Shezo/3vsYJuX518TR5szFeMEPGN39dyW9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"text/plain": [
"<matplotlib.figure.Figure at 0x7f2e02288ba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"conditions = OrderedDict()\n",
"conditions['Non-target'] = [1]\n",
"conditions['Target'] = [2]\n",
"\n",
"fig, ax = utils.plot_conditions(epochs, conditions=conditions, \n",
" ci=97.5, n_boot=1000, title='',\n",
" diff_waveform=(1, 2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see, there is a clear and identifiable P300 on electrodes TP9 and TP10. We can see a little bit of activity on AF8 but not on AF7. \n",
"\n",
"The reference of the Muse headset is on FPz, very close to the two frontal electrode. \n",
"This means that unless there is a left-right local gradient of the potential on the forehead, it is very unlikely that we will observe anything on these two electrodes.\n",
"This also means that any potential local to the reference will be projected negatively on the TP electrodes. This is why you will only see blinks on the TP electrodes and not on frontal electrodes.\n",
"\n",
"Anyway, here there is no doubt about the presence of a P300 potential."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Decoding\n",
"\n",
"By averaging the epochs, we can clearly identify an ERP. However this does not tell us anything about the SNR of the P300. I like using a classification pipeline to get a sense of the strength of the P300 response on a single trial basis.\n",
"\n",
"Here we will use 4 different pipelines :\n",
"- **Vect + LR** : Vectorization of the trial + Logistic Regression. This can be considered as the standard decoding pipeline in MEG / EEG.\n",
"- **Vect + RegLDA** : Vectorization of the trial + Regularized LDA. This one is very utilized in P300 BCI. It can outperform the previous one but can become unusable if the number of dimension is too high.\n",
"- **Xdawn + RegLDA** : Xdawn spatial filtering + Vectorization of the trial + Regularized LDA.\n",
"- **ERPCov + TS**: ErpCovariance + Tangent space mapping. One of my favorite Riemannian geometry based pipeline.\n",
"- **ERPCov + MDM**: ErpCovariance + MDM. A very simple, yet effective (for low channel count), Riemannian geometry classifier.\n",
"\n",
"Evaluation is done in cross-validation, with AUC as metric (AUC is probably the best metric for binary and unbalanced classification problem)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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MmMD48eMHrE2NDHW+bbcesEVmfiEzv9r602Yb+wMbAjcCt2TmRU3HDgFWoKyreifw7saB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\nkiSpBpMnSZKkGv4/Mh+FGglMS1YAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2dfddd4320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.pipeline import make_pipeline\n",
"\n",
"from mne.decoding import Vectorizer\n",
"\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
"\n",
"from sklearn.model_selection import cross_val_score, StratifiedShuffleSplit\n",
"\n",
"from pyriemann.estimation import ERPCovariances\n",
"from pyriemann.tangentspace import TangentSpace\n",
"from pyriemann.classification import MDM\n",
"from pyriemann.spatialfilters import Xdawn\n",
"\n",
"from collections import OrderedDict\n",
"\n",
"clfs = OrderedDict()\n",
"\n",
"clfs['Vect + LR'] = make_pipeline(Vectorizer(), StandardScaler(), LogisticRegression())\n",
"clfs['Vect + RegLDA'] = make_pipeline(Vectorizer(), LDA(shrinkage='auto', solver='eigen'))\n",
"clfs['Xdawn + RegLDA'] = make_pipeline(Xdawn(2, classes=[1]), Vectorizer(), LDA(shrinkage='auto', solver='eigen'))\n",
"clfs['ERPCov + TS'] = make_pipeline(ERPCovariances(), TangentSpace(), LogisticRegression())\n",
"clfs['ERPCov + MDM'] = make_pipeline(ERPCovariances(), MDM())\n",
"\n",
"# format data\n",
"epochs.pick_types(eeg=True)\n",
"X = epochs.get_data() * 1e6\n",
"times = epochs.times\n",
"y = epochs.events[:, -1]\n",
"\n",
"# define cross validation \n",
"cv = StratifiedShuffleSplit(n_splits=10, test_size=0.25, random_state=42)\n",
"\n",
"# run cross validation for each pipeline\n",
"auc = []\n",
"methods = []\n",
"for m in clfs:\n",
" res = cross_val_score(clfs[m], X, y==2, scoring='roc_auc', cv=cv, n_jobs=-1)\n",
" auc.extend(res)\n",
" methods.extend([m]*len(res))\n",
" \n",
"results = pd.DataFrame(data=auc, columns=['AUC'])\n",
"results['Method'] = methods\n",
"\n",
"plt.figure(figsize=[8,4])\n",
"sns.barplot(data=results, x='AUC', y='Method')\n",
"plt.xlim(0.2, 0.85)\n",
"sns.despine()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The best pipeline is **ERPCov + MDM** and yield an average AUC of 0.77. This AUC can be considered as good."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conclusion\n",
"\n",
"Based on this initial results, we can say that it is possible to observe a P300 with the muse headset.\n",
"Considering the AUC of 0.8, this can even be used for some BCI application, even if one should not expect outstanding results."
]
}
],
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"kernelspec": {
"display_name": "Python 3",
"language": "python",
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| stackv2 | 2024-11-18T18:03:05.148454+00:00 | 2018-04-24T20:23:12 | {
"license": "BSD-3-Clause",
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d04610cbe701ff271e31e388c7783cc1906bcee0 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Anlyse [`HiBench`](https://github.com/Intel-bigdata/HiBench) Measurements on [Gilgamesh](https://kb.hlrs.de/platforms/index.php/Urika_GX) (Cray URIKA GX)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prepare"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get access to Mesos monitor"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note**: To track the usage of Gilgamesh nodes, open http://127.0.0.1:5050/ in the [about:profiles](Gilgamesh profile). Authentificate with your username and password: `less /security/secrets/$USER.mesos`. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"!ssh gilgamesch 'echo -e \" login:$(whoami)\\npasswd:$(cat /security/secrets/$USER.mesos)\"' 2> /dev/null"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Clean up old measurements"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"bind: Address already in use\n",
"channel_setup_fwd_listener_tcpip: cannot listen to port: 8080\n",
"Could not request local forwarding.\n"
]
}
],
"source": [
"!ssh gilgamesch 'rm -rf report/hibench.report' #2> /dev/null"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get Measurements"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas\n",
"def read_hibench_report(filename, scale, ncores):\n",
" # recent_measurements.columns = map(lambda : str.lower, recent_measurements.columns)\n",
" recent_measurements = pandas.read_csv(filename, sep=\"\\s+\")\n",
" recent_measurements.rename(columns={\"Type\" : \"name\", \"Input_data_size\" : \"data_size\", \"Duration(s)\": \"duration\", \"Throughput(bytes/s)\":\"throughput\", \"Throughput/node\":\"node_throughput\"}, inplace=True)\n",
" recent_measurements.insert(1, 'scale', scale)\n",
" recent_measurements.insert(2, 'ncores', ncores)\n",
" recent_measurements['throughput'] = recent_measurements['throughput']/(1024**2) # convert B/s to MB/s\n",
" recent_measurements['data_size'] = recent_measurements['data_size']/(1024**2) # convert B to MB\n",
" return recent_measurements"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data Acquisition & Wrangling 1: Get data from multiple reports on cluster"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%%bash\n",
"# rm -rf ./data/*\n",
"mkdir -p ./data2\n",
"scp gilgamesch:~/proj/hidalgo/wp3/soft/HiBench/report/summary/hibench*.report ./data2\n",
"# for filename in `ls ./data`\n",
"# do\n",
"# NAME=$(echo $filename | cut -f2 -d-)\n",
"# NCORES=$(echo $filename | cut -f3 -d- | cut -f1 -d.)\n",
"# done"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>scale</th>\n",
" <th>ncores</th>\n",
" <th>Date</th>\n",
" <th>Time</th>\n",
" <th>data_size</th>\n",
" <th>duration</th>\n",
" <th>throughput</th>\n",
" <th>node_throughput</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ScalaSparkSleep</td>\n",
" <td>tiny</td>\n",
" <td>72</td>\n",
" <td>2019-10-10</td>\n",
" <td>17:26:48</td>\n",
" <td>0.0</td>\n",
" <td>8.801</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name scale ncores Date Time data_size duration \\\n",
"0 ScalaSparkSleep tiny 72 2019-10-10 17:26:48 0.0 8.801 \n",
"\n",
" throughput node_throughput \n",
"0 0.0 0 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"import re\n",
"import io\n",
"\n",
"measurements_loacal_folder = r'data2'\n",
"\n",
"re_filename = re.compile(\"hibench-(?P<scale>.+)-(?P<ncores>[0-9]+)\\.report\")\n",
"measurements = None\n",
"for file in os.listdir(measurements_loacal_folder):\n",
" match_filename = re_filename.match(file)\n",
" if match_filename:\n",
" recent_measurements = read_hibench_report(os.path.join(measurements_loacal_folder, match_filename.group(0)),\n",
" match_filename.group('scale'), int(match_filename.group('ncores')))\n",
" if measurements is None:\n",
" measurements = recent_measurements\n",
" else:\n",
" measurements = measurements.append( recent_measurements, ignore_index=True )\n",
"measurements.head(1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"measurements = measurements.groupby(['scale','name','ncores'], as_index=False).agg({'duration':'mean', 'data_size':'mean', 'throughput':'mean', 'node_throughput':'mean'})\n",
"# measurements=measurements[measurements['scale']=='large']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data Acquisition & Wrangling 2: Get measurements from latest run on cluster only"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [],
"source": [
"%%capture --no-stderr benchmark_data\n",
"!ssh gilgamesch \"cat ~/proj/hidalgo/wp3/soft/HiBench/report/hibench.report\" 2> /dev/null | sed 's/[ \\t]*$//;s/[[:space:]]\\{1,\\}/,/g'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'benchmark_data' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-f648e96c099b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m recent_measurements = read_hibench_report(io.StringIO(benchmark_data.stdout),\n\u001b[0m\u001b[1;32m 5\u001b[0m scale='tiny', ncores=288)\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'benchmark_data' is not defined"
]
}
],
"source": [
"numproc=288\n",
"\n",
"import io\n",
"recent_measurements = read_hibench_report(io.StringIO(benchmark_data.stdout),\n",
" scale='tiny', ncores=288)\n",
"\n",
"\n",
"recent_measurements.rename(columns={\"Type\" : \"name\", \"Input_data_size\" : \"data_size\", \"Duration(s)\": \"duration\", \"Throughput(bytes/s)\":\"throughput\", \"Throughput/node\":\"node_throughput\"}, inplace=True)\n",
"# recent_measurements['numproc']=72\n",
"recent_measurements.insert(1, 'numproc', 288)\n",
"recent_measurements.sort_values(by='name')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load and wrangle measurements"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] File b'/home/hpcgogol/Downloads/gilgamesch-HiBench.csv' does not exist: b'/home/hpcgogol/Downloads/gilgamesch-HiBench.csv'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-7b58d1568bcc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfilename_measurements\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'~/Downloads/gilgamesch-HiBench.csv'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mold_measures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename_measurements\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# measures = recent_measurements\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.opt_sources/pyevs/python3/study/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 700\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[1;32m 701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 703\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.opt_sources/pyevs/python3/study/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 428\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 429\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 431\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.opt_sources/pyevs/python3/study/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 893\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 894\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 896\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 897\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.opt_sources/pyevs/python3/study/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 1120\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1121\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1122\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1123\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1124\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'python'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.opt_sources/pyevs/python3/study/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m 1851\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'usecols'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1852\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1853\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1854\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1855\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] File b'/home/hpcgogol/Downloads/gilgamesch-HiBench.csv' does not exist: b'/home/hpcgogol/Downloads/gilgamesch-HiBench.csv'"
]
}
],
"source": [
"filename_measurements = '~/Downloads/gilgamesch-HiBench.csv'\n",
"\n",
"old_measures = pandas.read_csv(filename_measurements, header=0)\n",
"\n",
"# measures = recent_measurements\n",
"measures = old_measures.append( recent_measurements, ignore_index=True ).drop_duplicates()\n",
"\n",
"# # Filter measures from succeeded runs only\n",
"# measures = measures[measures.error == 0].sort_values(by=['mode','numproc'])\n",
"\n",
"# Store all measurements in file\n",
"measures.to_csv(filename_measurements, index=False)\n",
"\n",
"measures.sort_values(by=['name', 'numproc']).head(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot elapsed time via number of processes (version 1)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['GradientBoostingTree', 'LDA', 'LinearRegression',\n",
" 'LogisticRegression', 'PCA', 'RandomForest', 'SVD', 'SVM',\n",
" 'ScalaSparkAggregation', 'ScalaSparkBayes', 'ScalaSparkJoin',\n",
" 'ScalaSparkKmeans', 'ScalaSparkPagerank', 'ScalaSparkScan',\n",
" 'ScalaSparkSleep', 'ScalaSparkSort', 'ScalaSparkWordcount',\n",
" 'ScalaSparkNWeight', 'ScalaSparkTerasort'], dtype=object)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"fig, ax = plt.subplots()\n",
"measurements.sort_values(by=['ncores']).groupby('name').plot(x='ncores', y='duration', loglog=True, ax=ax)\n",
"measurements.name.unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot elapsed time via number of processes (version 2)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7efe330a39e8>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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/DEwHTMAaIBA4q6rqa13c7+vA1wFGjRr1UFlZWWfFBgVWu5WPr3zM5vObefvi2zSYG4gPjWdd9jo25Gxg1shZfjkzs9nSzOFrh13CfuL6CZewTxsxzSXsM0fO9N2EUQ4HHDkiRN9kglu3xFq/y5YJT3/JEgj2YqMkGdTUttbybuG7mPJNfFj8IRa7hRHhI1ibtRZjtpFZI2d1OwfEa0KvquoLPbphOwZjj74r2mxtvF/0PpvOb+Ldwndps7UxMmIk68etZ2PuRiYNm+Szs3FbrC0cuXbEJezHrx/H5rCh0+iYNmKay2OfmTTTP8cl7HY4dEhYOyYTVFYKkZ89W6yNO3cuTJ0qFoOXSO6T6pZqdl7aiSnfxN6SvVgdVkZFjsKYZcSYbWR60vQed/x8wrrpKUNJ6NvTaG7k3cJ32XR+Ex9e/hCrw8qYmDFsyNnAxpyNZMVlebV+rdZWjpYfdQn7Z+WfYXVY0Spapo6Y6hL2WSNnDb70zzabSLK2c6fYnxWhngQHw8yZbuGfPl0Kv+SeVDRXkHcxD1O+iX1X9mFX7YyOGs267HUYs41MGT7lvjp4AyH0OsRg7ALgOmIw9u9UVb3Q28oOVaFvT01rDTsKdrD5/Gb2l+7HoToYnzCejTkbWT9uPaOjR/d7HdpsbRwrP+YS9mPlx7DYLWgUDVOGT3EJ++yRswkPHGIDl9XVord/4IDYzp4VXn9QkBD+uXOF+E+fLs5Jhjy3mm6xo2AHpnwTB8sO4lAdpMeku8S9L57e+1ToFUXZBMxDxMbfBn6oquofFUVZAvwSEV75hqqqP+1lJZcDy9PT058rKirqzVsHNbeabrHtwjY2X9jMkWtHAJg+Yjobczaybtw6hocP75PPMdvMfHb9M5ewH712FLPdjEbRMDlxskvYHx71MBGB/Z9Z1K+oqRHCf/CgEP4zZ4TwBwaKNXKdwj9jhvT4hxDXG64LcS8wcajsECoqmYZMl7jnxuf2qTUrZ8YOEsrqythyYQubz2/m9K3TKCjMS5nHhpwNrM1aS2xIzxc1t9gtHL9+3CXsR64doc3WhoLCpMRJHsIuJ3z1ktpakVrZKfynT4uB3oAA0ct3Cv/MmWKwVzJouFp/le352zEVmFwds9z4XIzZwnPPjuu/hXSk0A9CLlZdZMt5MRv3UvUldBodC9MWupZJvLvXbbVbOXHjhEvYD189TKutFQWFCcMmuIR9zqg5RAfL2aJ9Sn29EP4DB4T4nzwphF+vh2nT3B7/rFkylNMPKaktcYn78evHAZg4bCLrstexNmstYw1jB6QeUugHMaqq8sXtL8Rs3PObKasvI0gXxNIxS1mVuYryhnL2l+7n06ufupZKG58w3iXsjyQ/IhdMH2gaGuDwYbfwf/65iPLR6UQkz7x5Yps1C8IG2cD2IKGoughTvglTgYlTN08BIsGhMcvI2uy1Xslq6xdCLz36B0dVVY6VH2Pz+c1szd/qWgQ5Jz7HQ9j7Ive1pA9pbBTx+87B3c8/F5E+Op1YYMVp9cyeLWfsepGLVReFuOeb+OL2FwDMSJrhEveUqBSv1s8vhN6J7NH3DXaHndO3TjMqcpTPrGkp6SFNTUL4nR7/8eNC+LVaeOght/A//PCALLk5VFFVlQuVF1zifqHyAgoKs0fNxphlZE3WGkZGjvR2NV1IoZdI/JnmZjh61G31fPYZWK0i/fLkyW6Pf84cmZjtAXFaoU5xv1R9CQWFR5IfYV32OlZnre6zSLe+Rgq9RDKYaGkRa+c6hf/YMbBYhPBPnOj2+OfMgSgZMXUvVFXl1M1TLs/9cs1lNIqG+SnzMWYbWZ25moSwBG9X855IoZdIBjOtrULsnVbPsWNgNotVtiZOdFs9c+ZAjBx4ByHux68fd4l7aV0pOo2OBaMXYMw2snLsSr9bPMgvhF4OxkokfURbm7B3nMJ/9Kg4pygwfrzb6nnkEYjt+dwLf8ehOjh67SimfBPbC7ZzreEaeo2ehWkLMWYbWTF2hV9HoPmF0DuRPXqJpI8xm8WArlP4jxwRTwEAubmewh/nX73Ye+FMEe4U95tNNwnUBrIofRHGLCPLxy4fNBMCpdBLJBI3FgucOOH2+A8fFr4/wLhxbo//kUf8clF1m8PGJ2WfYMo3saNgB7ebbxOkC2LJmCUYs4wszVg6KNN4SKGXSCRdY7GI2bpO4f/0UxHpA5Cd7fb4586FBN8clLTarewv3Y8p38TbF9+mqqWKEH0IyzKWYcwysnjM4sGXVfUupNBLJJKeY7UK4XdaPZ9+KmL7ATIzPYU/MdFr1bTYLewt2Ysp30TexTxq22oJCwhjecZy1mWvY1H6It9d5KYf8Auhl4OxEomPYrPBqVNu4T90SMzmBcjIcIv+3LkwYkS/VqXN1sae4j2Y8k28c+kd6s31RARGsHLsSozZRhamLez/NYd9FL8QeieyRy+R+Dg2m0jF7LR6PvlE5O8BSE93C/+8eZCU9MAf12pt5YPLH7AtfxvvFr5Lk6WJ6KBoVmWuwphtZMHoBQTq5IIvUuglEkn/YbfDF1+4c/UcOgR1deJaWpqn1TNqVI9u2Wxp5r2i9zAVmNhduJtmazOGEAOrM1djzDYyP2U+eq2+v76RXyKFXiKRDBx2u1h1y2n1fPKJyNEPMHq0W/gffxyGu9MJNJob2VW4C1OBifeL3qfV1kpCaAJrstZgzDbySPIj6DQ6r3wlf0AKvUQi8R4OB5w75yn81dWg0VD/xDzeWZGJKayMD6/sxWw3kxiWyNqstRizjTw86mG0Gq23v4FfIIVeIpH4DDXNVbyz7zeYTv6FPY4irFpIalQwKjkYH/0HZi56Fo0U917TU6GXz0QSiaRfqGqpIu9iHqZ8Ex9f+Ribw0ZKdArfzvouxpbRTN1xDI1pO/zimzD2v+Dpp+HLX+73KJ6hiAyvlEgkfcbtptu8ffFtTPkmDpQewK7aSYtOw5htZF32OiYnTvZcHLuhAbZtgzffFLH7Go3w8Z9+GlaulAur3wNp3UgkkgHhRuMNdhTswJRv4tDVQzhUBxmxGazLXocx28iEhAme4t4VRUXw1lvw5z/DtWsiz/7GjUL0p00TCdokHkihl0gk/ca1+mtC3AtMHL56GBWVcXHjMGYbMWYbGRc3rmfi3hkOB+zfD3/6E+zYIZKxZWYKwX/qKWnttEMKvUQi6VNK60rZnr8dU4GJY+XHALHo/LrsdazNWktWXFbff2h9vdvaOXxYWDsLF7qtnaChOSPWiRR6iUTywBTXFLsW6vj8hvgfnZw42bU4dkZsxsBVpqhI2Dp//jOUl4uVtJzWztSpQ9LakUIvkUjui8LqQkz5Jrblb+PMrTMATBsxzSXuqdGp3q2g3S6snTffhO3bxQIrWVlua2e4b67v2h9IoZdIJD0mvzLftTj2uYpzAMwaOQtjlpE1WWtIjkr2cg27oL4etm4Von/kiLB2Fi0Sor9ixaC3dqTQSySSLlFVlXMV51ziXlBVgILCnOQ5GLOMrM5aTVLEgycnG1AKC4Wt89ZbntbOV78KU6YMSmvHL4RextFLJAOHqqqcvnXaJe5FNUVoFA1zk+dizDayOnM1ieHeyzXfZ9jtsG+f6OXv2CGsnexst7XjxXz6fY1fCL0T2aOXSPoHVVX5/MbnrgHVktoStIqWR0c/ijHbyKrMVcSH+t/SgT2mM2vniSeE6C9f7vfWjhR6iWSI4lAdfFb+mUvcr9ZfRafR8Xjq4xizjawcu5LYkFhvV3PguXTJbe1cvw7R0e6oHT+1dqTQSyRDCLvDzpFrRzDlm9hesJ3rjdcJ0AawMG0hxiwjK8auIDo42tvV9A3sdvj4Y9HLf/ttYe2MG+e2doYN83YNe4wUeolkkGNz2DhUdghTvokdF3dwq+kWgdpAFo9ZjDHLyLKMZUQGRXq7mr5NXZ3b2jl6FLRaT2sn0LdXsZJCL5EMQmwOGwdKDwhxL9hBZUslwbpglmYsxZhlZMmYJYQHhnu7mv5JZ9bO3/2dEP2HHvJJa0cKvUQySLDYLey7sg9Tvom8i3lUt1YTqg9l+djlGLOMPJH+BKEBod6u5uDBae386U/C2jGbISdHCP6TT/qUtSOFXiLxY8w2Mx+VfIQp38TOSzupa6sjPCCcFWNXYMw2sihtEcF6mcK336mrgy1bhLVz7JiwdhYvFqK/bJnXrR0p9BKJn9FqbeXD4g8x5Zt4t/BdGswNRAVFsXLsSozZRh5PfZxAnW97xoOaixfd1s6NGxAT47Z2Jk/2irUjhV4i8QNarC28X/Q+2/K3satwF83WZmKCY1iduRpjtpFHRz9KgDbA29WUtMduh48+Er38vDyvWjtS6CUSH6XJ0sTuwt2YCky8V/QeLdYW4kLiWJO1BmO2kbnJc9Fr9d6upqQn1Na6rZ3PPhPWzpIlbmsnoH8bab8QepkCQTJUqG+rZ1fhLkwFJj64/AFttjaGhQ1jTaYQ9znJc9Bp5BLOfk1BgdvauXkTYmPd1s6kSf1i7fiF0DuRPXrJYKS2tZZ3Lr2DqcDEnuI9WOwWRoSPYG3WWozZRmaNnIVWo/V2NSV9jc0Ge/d6Wju5ue4JWfF9l3JCCr1E4gWqW6rZeWknpnwTe0v2YnVYGRU5CmOWWGJvetJ0NIrG29WUDBS1tbB5sxD948dBp3NbO0uXPrC1I4VeIhkgKporyLuYhynfxL4r+7CrdkZHjcaYbWRd9jqmDJ9y/+unSgYP+fnC2vnLX9zWzpNPCtGfOPG+rB0p9BJJP3Kr6ZZYHDvfxMGygzhUB+kx6azLXocx28ikYZOkuEs6x2bzjNqxWGD8eHfUTi+sHSn0Ekkfc73huhD3AhOHyg6hopJpyHSJe258rhR3Se+oqXFH7bS3dr76VbG/h7UjhV4i6QOu1l9le/52TAUmjlw7AkBOfI5L3LPjsr1cQ8mg4cIFt7Vz6xYYDJ7WTidIoZdI7pOS2hKXuB+/fhyAicMmuhbHzjRkermGkkGNzQZ79ohe/s6dwtqZMMFt7cTFuYpKoZdIekFRdZFroY5TN08BMGX4FJe4p8eke7mGkiFJTY07aufECWHtLFsmRH/JEpSAgB4JvZyhIRmyXKy6iCnfxLb8bZy9fRaAGUkz+MXjv2Bt9lpSolK8W0F/QVXF5nCI9AAOR+ebr1zzlXr05lpoKCQkwO3bYgA3L8+jZ38vZI9eMjA4hcCL/6Cq3c6F5iuYaj7FVHuYC21XUVCYHTwWY/g01oROZaQ22jf+sf3pmg9oyICj0XhuWm3Hc92d74trej3Krl1+1qNv3yvwlT9gea3vrnnrzwr4YhiYssV2yQCKCo+Uwa/yYU2ByvDGi8BF4K2+/fD7+Ud+0H9+na7/xaY/xcsf6uFLkVU9rItvCP3Jk+IHONRQFO/8sev1g/ofUtVoONlyGVPNIUyVn1DcdgMNGubHTOY7iQtYNWwew0Li+7ceDygGVquV8vJy2tra+uiPbYijqqLjYbeD1ert2vSaoKAgkpKS0OvvL9mdbwh9VJT4h6mu9jyflQWPPgqpqd5vxfv6foriWz0DP0dVVY5fP+4aUC2tK0Wn0bFg9AL+NftHrBy7krjQnnua3qa8vJzw8HBSUlJkbP4QR1VVqqurKS8vZ/To0fd1D68KfbvslWK9xvx8OHBAbAcPimxwBQWQnAzz5rm3lBQv1lriKzhUB0evHcWUb2J7wXauNVxDr9HzeNrj/OCRH7AycyUxwTHeruZ90dbWJkVeAoCiKMTGxlJZWXn/9/DZwViHwy38Bw+KfVWVuOYU/rlz3cIv/yGGBHaHnU+vfuoS95tNNwnUBrIofRHGLCPLxy4nKijK29V8YAoKCsjKyvJ2NSQ+RGd/Ez2No/cN66YzNBqxaktODrzwghD+ggJ3j3/3bjGLDGDUqI49fin8gwabw8bB0oOY8k3suLiDiuYKgnRBLBmzBGOWkaUZS4kIjPB2NQcdYWFhNDU1eZz77W9/S0hICF/5ylf69bNTUlIIDw9HURSio6N56623SE5O7tfP7A3PPvss//RP/0R2tn/MjPbdHv29UNWOPX7no83IkZ7CP3q0FH4/w2q3sr90P6Z8E29ffJuqlipC9CEsy1iGMcvI4jGLCQsI83Y1+w1f6NFa3J5aAAAgAElEQVR3JvT9jaqqqKpKamoqn3/+OQaDgR/+8IfcuHGD119//YHvb7PZ0Ol8t3/bHQ/So9f0W636G0WBcePgH/4Btm4VEwkuXIDXXoMZM+CDD+BrX4O0NGH1fOUr8MYbUFIyNON+/QCL3cJ7Re/xzM5nSPhFAov+uohN5zfxeOrjbP/SdipfqmSLcQvrxq0b1CLvy7z66qv84he/AGDevHm8/PLLTJs2jYyMDA4dOgSA3W7npZdeYurUqYwfP57f/e53ADQ1NbFgwQImT55Mbm4uO3fuBKC0tJSxY8fyla98hZycHK5du+bxmTNnzuT69euu13/961+ZNm0aEydO5Bvf+AZ2ux2AP/7xj2RkZDBt2jSee+45XnjhBQCefvppvvnNbzJ9+nT+5V/+hebmZp555hmmTZvGpEmTXPW4cOGC677jx4+nqKiI5uZmli5dyoQJE8jJyWHLli2u7+7snG7atInc3FxycnJ4+eWXXfUMCwvj+9//PhMmTGDGjBncvn27b38ZvcA/m7bOUBTIzhbbt74lxPziRbfV8+GHIlkQQFKSZ48/NVX2+L1Em62NPcV7MOWbeOfSO9Sb64kIjGDl2JUYs40sTFtIkC7I29X0Lt/5Dpw507f3nDgRfvnLB76NzWbj+PHjvPfee/zoRz9i7969/PGPfyQyMpITJ05gNpuZPXs2CxcuZOTIkbz99ttERERQVVXFjBkzWLFiBQBFRUX8+c9/ZsaMGR0+44MPPmDVqlWA6NVu2bKFw4cPo9fr+da3vsX//M//8Nhjj/HjH/+YU6dOER4ezqOPPsqECRNc9ygvL+fIkSNotVq+973v8eijj/LGG29QV1fHtGnTeOyxx/jtb3/Lt7/9bZ588kksFgt2u5333nuP4cOHs3v3bgDq6+s96nbjxg1efvllTp48SXR0NAsXLiQvL49Vq1bR3NzMjBkz+OlPf8q//Mu/8Prrr/PKK6888M/8fhg8Qn83iiLCM7Oy4PnnOwr/Rx/BX/8qyjqF3zm4m5Ymhb8fabG28MHlDzDlm3i38F2aLE1EB0W7FsdeMHoBgbpAb1dT0gPWrFkDwEMPPURpaSkAe/bs4ezZs5hMJkCIY1FREUlJSXzve9/jk08+QaPRcP36dVcvNzk5uYPIz58/n5qaGsLCwvjxj38MwMcff8zJkyeZOnUqAK2trcTHx3P8+HHmzp1LTIyIslq3bh2FhYWue61btw6tVuuq3zvvvON6Mmlra+Pq1avMnDmTn/70p5SXl7NmzRrGjBlDbm4u3/3ud3n55ZdZtmwZc+bM8ajjiRMnmDdvHnF30hE8+eSTfPLJJ6xatYqAgACWLVvm+vl89NFHffATvz8Gr9DfTWfCf+lS58I/YoRnj18K/wPTbGnmvaL3MBWY2F24m2ZrM4YQAxtzNmLMNjI/ZT567f1NBhn09EHPu78IDBQNslarxWazAcJn/9WvfsWiRYs8yr755ptUVlZy8uRJ9Ho9KSkprglhoaGhHe69f/9+oqKiePLJJ/nhD3/If/7nf6KqKn//93/Pf/zHf3iUzcvL67ae7e+vqirbt29n7NixHmWysrKYPn06u3fvZsmSJfzud7/j0Ucf5dSpU7z33nu88sorLFiwgB/84Ac9+tno9XpXeGz7n4838F+P/kFRFMjMhG9+U2SHu3lTRPX85jcwZw58/DE89xyMGSMGd598El5/HYqKpMffQxrNjWw6t4m1W9cS9//F8SXTlzhQeoAvj/8ye7+8l5vfvcnvl/+ehWkLpcgPIhYtWsRvfvMbrHdmoBYWFtLc3Ex9fT3x8fHo9Xr2799PWVnZPe+l0+n45S9/yVtvvUVNTQ0LFizAZDJRUVEBQE1NDWVlZUydOpWDBw9SW1uLzWZj+/bt3dbvV7/6Fc5AlNOnTwNQUlJCamoq//iP/8jKlSs5e/YsN27cICQkhKeeeoqXXnqJU6dOedxr2rRpHDx4kKqqKux2O5s2bWLu3Ln39XPrT4ZOj/5eOIXfKf6qCoWF7h7/vn3wt7+JssOHe/b409Nlj/8OdW11vHvpXUwFJj68/CFmu5nEsES+NulrGLONPDzqYbQarberKekBLS0tJCUluV7/0z/9U4/e9+yzz1JaWsrkyZNRVZW4uDjy8vJ48sknWb58Obm5uUyZMoXMzJ7l9U9MTGTjxo289tpr/Nu//Rs/+clPWLhwIQ6HA71ez2uvvcaMGTP43ve+x7Rp04iJiSEzM5PIyMhO7/dv//ZvfOc732H8+PE4HA5Gjx7Nrl272Lp1K3/5y1/Q6/UMGzaM733ve5w4cYKXXnoJjUaDXq/nN7/5TYe6/exnP2P+/PmoqsrSpUtZuXJlj77XQOK/4ZUDjaqK3rxT+A8cEE8BAImJnsI/ZsyQEv6a1hp2XtyJqcDER8UfYXVYSYpIwphlxJhtZObImWiUofvweD/4Qnilv9HU1ERYWBg2m43Vq1fzzDPPsHr1am9Xq88YnBOmfA1FgYwMsX39650L/6ZNoqxT+J2DuxkZg074q1qqyLuYhynfxMdXPsbmsJEcmcw/Tv9H1mWvY+qIqVLcJQPKq6++yt69e2lra2PhwoWuSB2JFPr7pzPhv3y5c+EfNsyzx++nwn+76TZvX3wbU76JA6UHsKt20qLT+O7M72LMNvJQ4kMyN4vEazijaCQdkULfVyiKsGzGjBGDuO2F/+BB2L9fDPqCW/idPf6xY31W+G803mBHwQ5M+SY+KfsEFZWM2Az+9eF/xZhtZELCBCnuEomPI4W+v+hM+IuLPXv8TuFPSPDs8XtZ+K/VXxPiXmDi8NXDqKhkx2Xzg7k/wJhtZFzcOCnuEokfIYV+oFAUEZ2Tng7PPusp/M4e/53p1SQkuHv78+aJSKB+FtbSulK252/HVGDiWPkxAMYnjOdH837E2uy1ZMf5R/ImiUTSESn03qIz4S8p8ezxb90qysbHe/b4+0j4i2uKXQt1fH5DRD1NTpzM/3n0/7A2ey0ZsRkP/BkSicT7SKH3FRRFzMBNSxPJ2FQVrlxxi/7+/Z7C377Hn5XVY+G/VHXJJe5nbon8KdNGTOPnj/2ctdlrSY1O7Y9vJ/FDfvrTn/K3v/0NrVaLRqPhd7/7HdOnT+/x+0tLS1m2bBnnz5/vsozD4eA73/kO+/btQ1EUgoKC2Lp1632vpNSeV199lbCwMP75n//Zde4f/uEfOHz4MBaLhStXrrhmx77yyisYjcYH/sz7xeFw8POf/5x//dd/7Zf7S6H3VRRFJFtLTYVnnuko/AcOwLZtomxcnHtwd+5ckY8/NNQl/vmV+ZjyTWzL38b5CvFPN2vkLP5z4X+yJmsNyVG+k+db4hscPXqUXbt2cerUKQIDA6mqqsJisfT552zZsoUbN25w9uxZNBoN5eXlnaZD6C1dpRt47bXXAHcjdKaXyeL6I82xqqrYbDZ+9rOfSaEf8nQm/KWlnj3+O8KvAudG6DBNCsSUYaMgwoyiwsPmBP5bs4A1EdNIcqTCrViwlkFsE8TEQGwsBAR48UtKfIWbN29iMBhcuWwMBgMgknh9+9vfprm5mcDAQD7++GOqq6v58pe/THNzMwC//vWvmTVrlsf9SktLOy1z8+ZNEhMT0WjEnIv2M3HDwsJ47rnn2LNnD8OGDWPz5s3ExcXx+uuv8/vf/x6LxUJ6ejp/+ctfCAkJ4emnnyYoKIjTp08ze/ZsIiLci9G8/vrr7Nixgx07dhAcHNzpdy4qKuKFF16gqqqK0NBQ/vCHP5CRkcFTTz1FeHg4J0+eZN68eaxZs4b/9b/+F21tbYSEhPDmm28yZswYzp07xzPPPIPVasXhcJCXl0dqaio///nPeeuttwD4xje+wYsvvsjly5dZsWIFkyZN4vTp00yfPp3GxkZXimRn+b5CzowdJKgOB6dP7cZ0+HVM9UcpUqvQqApzm2Ix3ohidaGGxBuNYgH27npmYWFC8J3C79y6ex0VJVYEk/QZ7WdBfueD77hstr5i4rCJ/PKJrpOlNTU18fDDD9PS0sJjjz3G+vXrmTlzJpmZmWzZsoWpU6fS0NBASEgIFosFjUZDUFAQRUVFbNy4kc8//9zDumlpaem0THl5OQ8//DBRUVEsWLCAp556ikmTJgFirdS//vWvPPnkk/z7v/87FRUV/PrXv6a6uprY2FhAWC4JCQm8+OKLPP3001RVVbFz5060Wq3LugkKCuKjjz5i69atroarM1tp/vz5/OEPfyAtLY3Dhw/zox/9iD179vDUU0/R1NTEjh070Gg01NfXExoaik6n44MPPuBPf/oTW7Zs4fnnn2fevHmsX78es9mMqqp88cUXfP3rX+fYsWPYbDamTZvG1q1bCQ4OJiMjg+PHjzNlyhRsNhsGg4G6uroufydyZuwQRVVVPr/xOdvyt2HKN3Gl7gpaRcujox/ln7ONrMpcRXxo/N1vguZmqKkRou/cunpdVib2tbVdJ3NTFIiO7lmj0P51O3tJ4luEhYVx8uRJDh06xP79+1m/fj3f//73SUxMdKUIdvaYm5ubeeGFFzhz5gxardYjPbATq9XaaZmkpCQuXbrEvn372LdvHwsWLGDbtm0sWLAAjUbD+vXrAXjqqadcKZHPnz/PK6+8Ql1dHU1NTR5ZMtunIwZ46623GDlyJHl5eej1XSfOq6ur49ixY6xdu9Z1rr39s27dOtdTR11dHV/5ylcoLi72uMesWbP4yU9+QllZGWvWrCE9PZ1PP/2UtWvXup4iVq1axaFDh1i4cCFpaWlMmXJPje4T+lzoFUVZBSwFIoA/qqq6p68/YyjjUB18Vv6Za0D1av1VdBodj6U+xiuPvMLKsSuJDYnt+gaKInrtYWFird0ef7AD6uq6bxSc261bYrWv6mrobim6gIDePTk4Xw8xe6m7nnd/otVqmTdvHvPmzSM3N9flb9/Nf/3Xf5GQkMAXX3yBw+EgKKjjQjHdlQkMDGTx4sUsXryYhIQE8vLyWLBgQYd7OOduPP300+Tl5TFhwgTefPNNDhw44Cpzt7+fm5vLmTNnKC8v73aAV1VVDAZDl559+/t+//vfZ9GiRXzrW9/i8uXLPPHEEwB8+ctfZubMmezevZsnnniCN954o8vP66yu/UmPhF5RlDeAZUCFqqo57c4/Afw3oAX+oKrqz1RVzQPyFEWJBn4BSKF/QOwOO0euHcGUb2J7wXauN14nQBvAwrSF/Pu8f2fF2BVEB0f3byU0GiGydxZ26DFms3ga6KphaH9cWOh+3RN76e6GQNpLfcalS5fQaDSMGTMGgDNnzpCVlcUHH3zAiRMnmDp1Ko2NjQQHB1NfX09SUhIajYY///nPrqX92tNVmVOnTjFs2DCGDx+Ow+Hg7NmzjB8/HhCRKCaTiQ0bNvC3v/2Nhx9+GIDGxkYSExOxWq38z//8DyNGjOjye0yaNInnn3+eFStW8OGHHzJ8+PBOy0VHR5OYmMjbb7/N6tWrcTgcnDt3zmOVqvbfxfmZb775put8SUkJ6enpfPvb3+bKlSucPXuWOXPm8I1vfIOXXnoJu93Ozp07XcsRtsc5wNtfa9r29I5vAr8GXCMEiqJogdeAx4Fy4ISiKO+oqpp/p8grd65L7gObw8ahskOY8k3suLiDW023CNQGsnjMYv5v1v9lWcYyIoM6T8PqUwQGipQPw4b1/D1Oe+leTw8PYi/1tJEYovZSU1MTL774InV1deh0OtLT0/n973/PV7/6VV588UVaW1sJDg5m7969fOtb32Lt2rW89dZbPPHEE532VLsqU1FRwXPPPYfZbAZEfnfnWq+hoaEcP36cn/zkJ8THx7sE8sc//jHTp08nLi7ONYjZHQ8//DC/+MUvWLp0KR999JFrYPluNm/ezPPPP8+rr76KxWLhqaee6lToX375ZZ555hl+9KMfsXjxYtf5v/3tb2zatAm9Xs/w4cN59dVXiYqKYuPGjS676/nnnyc3N5fLly93uO/XvvY1xo8fz5QpU7w3GKsoSgqwy9mjVxRlJvCqqqqL7rz+33eK/uzO9pGqqnt7cm85GCuw2q0cKD2AKd/E2xffprKlkmBdMEszlmLMMrJkzBLCA8O9XU3fpb291BOLyfm6O3spMFDMW0hIcO/bb+3Pxcb22VODTFMsxgmauvvdDDG8NRg7Ami/XHs5MB14EXgMiFQUJV1V1d929mZFUb4OfB1gVG+84kGGxW5h35V9mPJN5F3Mo7q1mlB9KMsylmHMNrI4fTGhAQPn5fk17e2lO5ZDj+jOXqqqgooKuH1brD9w5ox43VmctkYj5jR01xg4X8fHQzeDgxJJX9LnZpCqqv8P+H89KPd74PcgevR9XQ9fxmwz81HJR5jyTey8tJO6tjrCA8JZMXYFxmwji9IWEazvPNZX0g/01l5yPjncvu3enI1B+9dFReK4tbXz+8TEdN0QTJ4snjT0erENwfEF2ZvvOx5E6K8DI9u9TrpzrveYzSLPi0Yj/NCu9t1d666sc/MirdZWPiz+EFO+iXcL36XB3EBUUBQrx67EmG3k8dTHCdQFerWOkh7S/snhXvaKqgrB7qwhaP/6zBmxr68X73v/fc/xBo3GLfo6nef+7nNardf/3iW+xYMI/QlgjKIooxECvwH4u97cQFGU5cDyh0DkeOlvHqQBuY9Gplmn8v7wZkxJDexKbKRZ5yDGomXdzSiMtzN4tDqCAC6C5v+A8h9938D1Rdmh+tl9Xc/wcIiIEIvOdFfWYoHKSmhqQk1ORrHZhE1ktbr3ZrNoPLqY5o+idN8QtD+n08lGwQ940ImtPQ2v3ATMAwyKopQDP1RV9Y+KorwAfIgIr3xDVdULvflwVVXfBd6dMnz4c2Rnw2efdRwYGzMGZs6EiRPFH6fDIXo6quo+vte+r8r0oGyTamZ32A1M4eW8F36TFo2dOGsAT9aNxFiTyLzGGPR2xHuiurmf3S7+kX38++Jw9OZXLukhQf/931RPm0asTkcHGXYKs6J0HmWkqqLB6MvcNM4Gon2j0NXx/V7zVrn+uNaH91BVleqGBoIaG2HfPnfnohfzSXwrBYLNBufPw5Ej7u3KFVEoMBCmTIFZs9xbfHz3Nx4g6tvq2VW4C1OBiQ8uf0CbrY1hYcNYk7kGY7aROclz0GkG+SRkX26I/PB+Vr2e8vHjaWuXr6XDz7sn59qfdzhEB8K59cf/vqII68j5lNKb+vXk/L3OOX+WgwmHg6DLl0l69VX0tbUelxToUdSNbwl9Z9y8CUePuoX/5El3TyU93VP4s7PFH9kAUNtayzuX3sFUYGJP8R4sdgsjwkewNmstxmwjs0bOQqsZmLpIJH2Kc1yhq0Hm9q9v34aGhs7vEx7ecbA5NFTYTxaL2LffenKuuzIDgU4netJO+8u53X2uJ2Ue5FxAAAQHo8yfP0iE/m7a2oTYt+/1V1SIaxERMGOGW/inTxfn+ojqlmp2XtrJtvxt7C3Zi81hY2TESIzZRtZlr2N60nQ0ytCLjpAMcdraPBuA7hqHlpYHE7f+Fs/uyvjgeEZP4+i9KvTOwdj09PTnioqK7u8mqioidtoL/7lz4ryiQG6uZ68/NbVXv6yK5gryLuZhyjex78o+7Kqd0VGjMWYbMWYbmTp8KnL9VIlE4g38Quid9PnM2IYGMbDrFP6jR8E5TTohwVP4J0+Gu5Iw3Wy8ydsX38aUb+Jg2UEcqoP0mHTWZa/DmG1k0rBJUtwlEonXGdpCfzd2O+TnC9E/fFjsnSlGAwLgoYcon53LjjE2TOoFPr11HBWVTEOmS9xz43OluEskEp9C5qNvj1YrLJzcXPjGN8S527e5evAdtp/ZhKn1c46EHYWbkHMbfngzCmP0bMbFLYGY2WAY53PenEQikfQUrwp9O49+wD6zpLaE7fnbMRWYOH79OATCxOSJ/CRjFWvtY8k8ewMqjsD7h+FPu8WbwsI6DvJGRQ1YnSUSieRBGBLWTVF1kWuhjlM3T4nPHD4FY5aRtdlrSY/ppKFRVZH+1unzHz4MZ8+KWGRFgXHjPL3+9HTZ65dIJAPKkPfoCyoLXOJ+9vZZAGYkzXCJe0pUSu9v2tgIx497DvI6c5MYDG7Rnz0bHnoIuliEWCKRSPqCISf0qqpyofICpnwT2/K3kV8p1j+ZPXI2xmwja7PWMjJy5D3u0kscDigo8AztdK6XqdeLiJ72vf4uVreRSCSS+2FICL2qqnxx+wvRc883can6EgoKjyQ/gjHbyOrM1YyI6HqZsX6hshKOHXPbPSdOiAklAMnJnsI/fryYhCGRSCT3waAVelVVOXnzpEvci2uL0Sga5qfMx5htZFXmKoaF9WLZuv7GYhEpaNt7/TduiGuhoTBtmrB6Zs0SA77R/bz2q0QiGTT4hdD3dGasqqocv36cbfnbMOWbKKsvQ6toWZC6gHXZ61g5diVxoXEDV/EHQVXh2jVPu+fMGRHrDyJfT/tef0aGHOSVSCSd4hdC76SzHr1DdXD02lFM+Sa2F2znWsM19Bo9j6c9jjHLyIqxK4gNifVSjfuY5mZh8bQXf2eWuthYkabZKfxTp0JIiHfrK5FIfAK/FHq7w86nVz91ifvNppsEagNZlL4IY5aR5WOXExU0BOLXHQ64dMlT+C9eFNd0OpGb3xndM2sWJCV5t74SicQr+JXQj80dqz76H4+y4+IOKporCNIFsWTMEoxZRpZmLCUisO8yUPot1dXuQd4jR0QuH+dapCNHunv8M2fC6NFimbshuM6oRDKU8CuhV4YrKt9wv44IjCAyMJIgXRCBukACtYHdH9/reg+PnffSaXS+n9fGahUTuNr3+q9edV/XakVsf/t84PHxXR/fldhNIpH4Pn4l9CMyR6hf+83XMNvMtNnaMNvNmO13jm09O3a+ry9QUO6vseijBqf9ca9WpiovFxO6rl935wF35gJ3Ht+9VKOTiIjuG4L256Kj5QCxROID+JXQ99XMWFVVsTqs99VAdHts7/09LPa+Wa9Tq2h73UDEBMcQGxyLIcTQYYslmKCaBs8GoKtGobKy82XZdLruG4L2x/HxvVrbUiKR9By/yF7Z10nNFEUhQBtAgDYAAvvklveNQ3VgsVsevJHp5Pjuc42WRqpaqmiztdFma6O2rZa6trou6xaqD/VsADINGCYbMIRkYwh5xN1IBEVjaNMS22AloKq260bh4kVx7JwYdjdRUfe2jpz7iAj5tCCR9DGDqkcvcWO1W6lpraGqpYqqliqqW6tdx51t1a3VNJi7WPsTMW5iCDF0+aRgCI7FoAkltlnF0Oggpq4NfUV1141DdXXnHxQY6Pk00N0Tg8EgUk1IJEMUv+jRS/oPvVZPQlgCCWEJPX6PxW6huqVjg3B3I1HRXEF+ZT5VLVU0W5u7vF9UUBSGSAOGxDu2UXAyhpCHRMMQGI3BpsfQqsHQaCO2zkJMVQvaikrPRuH8ebG3dGGFxcb2bLDZuTC1fFqQDEGk0EtcBGgDSAxPJDE8scfvabO1ddo43N1AXG+4zhe3vqCypZI2W+cWj4JCdGw0hpHOJ4URGIInYAiJJVYThsGqx9CqYGhyYKi3YqhuIaqiEU1FpWgMvvhC7Ou6sK2Cg3s22JyQIBoQrfZ+fowSic8hhV7yQATpghgRMaJXyeNarC1dNg7tG4iyujJO3jhJZUtll4PbmgQNMSkxdxqGOAwhWRgCYzAQLBqGNo2wk+osGGraMFQ0EXmrFuX6dTh1Sjw52Gwdb6wowhoyGISd5NwCAjrf9/Tc/VyTDY7kAZFCLxlwQvQhhESG9DhttKqqNFubRSPQVQPRKq4V1xTzWctnVLVUYXVYPW8UCowGXZrONdYQG5yOQR+BQQ3BYA/A0KbB0AyGBiuGmjZialqJbHEQ0aYS0GaFlhaRnsJsFnZSZ3urtdPvcd9otX3fePTVNTkpzy+QQi/xeRRFISwgjLCAsB4vGKOqqisayfWkcHcj0Sr2FxtKXNfs6p3kchrAcGe7Q5AuiIjACNeEvojAOCKDIsW5gIh2x2FE6sKIUIKJ0AQRSRARBBKhBhDh0KOzOTwbhu4ajZ6es1jEHIl7le/s6eVB0Gp710Do9eI9Gk3f7PvyXt7ct2swzTYzTZYmmixNNFoaxd7c2OF1b+YNSaGXDEoURXGJcmp0ao/eo6oq9eb6Do1Dg7mBBnMD9eb6DscltSXUt7nPuxqKbgjRh7RrLO40HEGRRETc1WAERncsc+d9YQFhaDX3Yek4HEL0+7qRuVf5xkaxt1pFHex2sbfZxLn2m7Oc/d4/S2/jUKBZD42B0BQAjQF39l287kkZaw9/rRpHz+s5qOLoJZIHQVEUogIjidKFkR460i1Gdrvn1sU51WajxdIkRN/SSP2dfYOlkXpLIw22pjv7ZnFsa6ahsYWGunJu2QtpsLfQ4GilwdGKyr3DnsMI8HhaiFQDiVD1RDgCiFQDiHDoiLDribSLfYRdS6RNR4RNS4RVQ6RNR6hNQWN39Py79uLn0aMyAxjerQJmXd8JcmMAtPRiLmCwTSHMpkEFqgPtqD0IAAt0aEg1h5BmDiXNGkqaNZxUWzhp9khSlGiC+WuPPlvG0fs6Dkff/QMOZBlvf/79lvEBnL3EhkCoDxL7hkCoD2x33Ml5j3NBQozuhaJCuFVDpE0rGgDbncbAriXCriPSrifCoSfSIfbuBiVAHBNIBEGEaAJQtDpPS+Vui6U35zQa7BqFJo2NJo2VRsVKExaxVyziWDW796qZRrWNJkcbjQ7nvpUmeyuNdrFvsrdg68ETF4BG0RCuDyP8jmUYHhAu9oHhhAWGEx4Y0fH8nbKh+lAaLY1UNFdQ0VzB7abb3G6+TVl9GcU1xVS3es4hMYQYSI1OJS06TWwxaa7XieGJaJSux0H8K46+qQkOHvSdf3ZfEj9/ppt/4vv953cd3+313u99+rOO91lGo9USrtEQrtUyoqf36WRQ1O6w0wuVuSEAABaXSURBVGRp8rSc2jraT3cfV7fVc8V1vpYWa8u9f9WK1mUxtbeZ3BZVMBGBEWgV7R2fubGD/+zhQbc00mpr7fGfWog+pIPwxgYkkNz+3F2i3N3rQG1gt4kNzTYzV+quUFxTTHFtMfmV+RTXFlNcU8yVuiseIcQaRcOoyFGkRaexNmuth5CnRqcSGRTZ4+95v/hGj15R1AHpzw/EP78XBcKn6iijMQYNNoeNRnOjqzHosrFoq6fB0nWD4hQ/raIlPDC8a6HtpJfcnSiH6kPvb7ziHtS01riEvKS2xHVcXFvM9YbrHvZaqD5UiHdMmkvAnb3z5Mhk9Nr+mcHtXz36lBR48kkoLBQLbhQWdsybEhwMmZmQlSWW28vOFq8jInomPlJ4JJL7QqfRER0cTXTwg61nbLFbcKiOe/aWBwq7w055Q3mnQl5SW9IhX1RCaAJpMWnMT5nvFvM7wh4fGu8T36krfKNHf7dH73CIlLtO0b90yX1cVuY5gDN8OIwdK9ZWHTvWfZySIrIsSiSSIUuLtYWS2pJOhby0rtRjIp5OoyMlKsXlld/dQw8NCPXiN+mcwZumuLUVios9xd957FxnFYSHm5bmKf7OfVyczHkikQwCVFWlsqWS4po7vfI7Qu58fbPppkf5iMAI94BnlFvI02LSSIpI6t36Dz6Af1k3vSE4GHJyxHY3VVWe4u/cv/++Z1KsqKiO4j92LIwZI+4vkUh8BpvDRlldmVvI2/vmtcU0WTwX0xkRPoK0mDQWpS/qEMkSGxzr0xZLf+F/Pfr7wW4Xls/dTwGFhcIias+oUZ1bQaNGSZ9fIuknGs2NXQp5WV2Zx0S0QG0go6NHdxj0TI1OZXTUaIL1Q6ezNnitm76mqQkuX+7cCmpsdJcLCoL09M6toJgY79RdIvETVFXlVtOtToW8uKaYypZKj/IxwTEdhNz5ekTEiG5jy4cSfiH07WbGPldUVOS1enSKqoqUt50NCJeUeOYMMRg6t4LS0kSOD4lkCGCxWyitK+1UyEtqSzzi4jWKhpERIzsV8rSYNKKCorz4TfwHvxB6J343M9ZqhStXOreCbt1yl9NoRPRPZ1bQiBFyQFjid9S11XUZW36t/ppHbHmwLtgjcqW9kKdEpYglPyUPhBR6b1FfLwT/7gHhwkKR4tZJaKgY/O3MCoqI8F79JUMah+rgesP1LmPLa1prPMrHh8Z3OX1/WNiwITnwOZBIofc1HA64caNzK6i0VFx3MmxY51bQ6NFyjVTJA9NqbXVN328fklhSW8KV2ise6W+1ipbkqOROhTw1OpXwwHAvfhOJFHp/wmzufG5AYaEIGXWi00FqaudWUEKCtIIkgBj4rG6t7jK2/HrjdY/yYQFhHX3yO69HRY7yu9jyocTgjaMfjAQGutM63E1NTedzA/bsEQ2Ek4gIIfgZGWK2sMEgJobdvY+MlA3CIMDmsHGt/lqXIYkN5gaP8olhiaTFpPFY6mMdBkANIQZpsQxypND7OjExMHOm2Npjt8O1ax2toMOHRbTQ3bmCnOh07rVQu2oM2u+da6ZKBpxmS3OXQl5aV4rN4Y780mv0rtjy2SNnewyCjo4eTYg+xIvfROJtpHUzGFFVMfBbWSmsn7v3nZ2rqel6EYjw8J41Cs59ZKScXNYDVFWlormiy9jy2823PcpHBUV1OX1/RPiIfsngKPFtpHUzlFEUEdUTGirCO3uCzSZyBd2rUbh1C86dE8ddPTVotb17YoiLG7RPDVa71bXgRGex5c3WZldZBYWkiCTSYtJYOmaph5CnRqcSEywn5knuDyn0EoFOJwQ3Lq7n72luvveTQmUlnD8v9t09NYSF9bxRMBhEviIfeWpoMDe4wxDvGgC9Wn8Vh+qOqArSBZEanUpqdCqPjn7UQ8hTolII0gV58ZtIBitS6CX3j/OpITm5Z+XtdiH23TUKVVXiqcHZOLR2scqQVguxsT1rFJz7oPsTUYfq4GbjzU6FvKul4dKi05iZNJOncp/yiGa519JwEkl/IIVeMnBotb1/amhp6b5RcO7Pnxf76urunxq6aBTMhiiuhNspDm6lRFNPsb2K4pZyimtLOl0aLjkymdToVNfScE4hH6il4SSS3iCFXuLbhISIzKGjRvWsvN0uxho6aQxqqq5R3FBKSesNiu1fUGyrp7iulRLVQbkZ1HYRhqEWSK2FsU16ljTqSWuOJrUlgLTWYJLNweh1AaCrA/1Z0OWLiWw6nff2vSnrI5aXZOCQQi8ZNNgddsobyylpLKG4pZhiczHFtmJKKKFYW0xdZB2062yLpeEmMC88mbSAYaQSTZo1jLTmQOJrzChKNSi1EGSFKKsYsLZ2sbfZhM3UXZnO9u1nRA8UGo33GqXO1hZuf+zr5/y0kZRCL/ErWqwtXKm90mVseVdLw00fMd03l4ZzONwNRW8aiIHctz/2l8asP/GVRqcXkWpeFfp2aYq9WQ2JD+FcGq6zhFrFNcVdLg03PmE8qzNXe8z69Iul4TQaCAgQ22ClfWNms4nXdrvYtz/u7JzVKsJ4///27jU2svOu4/j3P/cZz8X27GY33vV6dyebtEtEICitUKooEoUmIquU0DYphBelaqhEecEbREUlBCqiiFeUFtGFhsCbVBU0aCOEEsEL0kpISbfqLakqxU6I7W42m/gyY3s8F8+fF3PxXD1nPOOZ8Zn/Rzqa4zPPucyu/TvPPOec58lmy6/N883L2pVz8l4vzxNVj7G+q/Kj5PPt/47UTz2MhmcPTJmhK5aKvLX5VuMtiRtLtTtaMvlMQ/nq0HDtBqKYDc/a4/u9KpXKQ2s2T4VC++X9Tv1sd2+v++c5jE7h2Wny+3srP6ht+/0HNhfZA1NmpJqHhqu/JfGgoeEeWHigIcjPT58/HkPDNYfnOIbmOIRnNdSi0eEGZg/h6UYW9OZQmoeGa763vNPQcPfN3ccTP/eEs6HhquG5mxnf0BxFeHYKtEGF52FqrxMYnseJBf2kqbZ7Ogiv/O42b2aWWdxeZmlnlcXsz1jMv81i4R2WirfIUqht1qPCvMZI7cV5tHCCVH6B1G6EVDbIxa0A07tUtvs25N+C/AujD08ngXbY8BzEV30LTzMgFvSD0EN4Dr2m2bzdpgtIGyFYnIHFWViaaZxfjkOpLmfCBUitQWodfm19f/7iOpxPCwFfAQIZCOQgkB5MeA66bdTC00yg8Q36QYXnUYVm/baP6uq71+s8wDqEZyngZzWYZzG4xZJ/m0VfmkXPBouss6RrrOlOwy5v8yVIheb4UHiO1NQ8qdg5LsYXSE1f5HR8DgkGLTyNOWbGI+h/+MPyCEn1ITqu4Tmsq+/Vh0scqA4N1+6WxHZDw51PnOfizPv5RNOoQjY0nDHuNB5BH4vB/fe33t/a7j7Yfk4AIuWOrUKh8j2o4fD+fKfXQbzn6++fuTo0XKd7yzsNDXf55GWu3Hll/7ZEGxrOmIk0Hn/xFy7At77lrGyxePDDEJ1OEE7eW1/v/F4/Fwa93q4niGIowEpMWYwWWAxnWQzusOTPsOjZZJF10jT2/X67f5ZU5Awfjt1D6uyj5YEokneQuu0uTkyfQSIRx98IjDHuNh5B3wufr9ykEo0Od7/NT+h1O3m0Wba9m2GpeIvF0rvldnLvarndPLTDm+Ecxbpmbv8eXKhc7Lx/rXzBM1W5AHphAyKFNWAN+FHnY/b52n/jOOplgYCNS2vMAUpaYiu/RTqXPvRU36NqN8cv6EfF7y9Psc5t2K1Dw73raGi4e2dTfLzpqc8zsTN4xdN4gunlm4qTR7/X1+HGjfbl+r1GMooTTChk32KGRbVzFwYHdWkwBk/i96NYKpIubFWmDOn8Vt3PWw7f2yJT2Oppv1HfFMngNLPBaU4Fp7k8s8DJ4Cxf4V8crW9B36PDDg33yJ2PtDy+PxOe6b7D6sXZePwIP1UbzU1kTvsMcbKsepJpV67T8IRO+XyDPXEEAvth5STUDgo5N61zjAJbgZwP0sH+p6y/v2MJFCGZhXNZSO6U55M7MJttP5/MwkwWgnvbwDbQeD3uKw73a0HfRv3QcM0XQDsNDZeaSTUMDZeaSbEwvXB8h4YbVRNZ9WlYpyeOXk82a2udyx3VA1pVIq29EXZ6dVKm0zrVO7aOej+HWaeHJj1VZVtzpEvZ8qS7+/M9/lxgsP+3HoQZzxRJT5RZb/m1eT7pjTJbKVOdn5Lg4PpmUoVPfcpR0Yns1Kx+aLh2d7K8u/NuQ/nq0HDNNXIbGs5l2l3oz+cHE3Iez8Rct9gr7ZHJZ/pqf07n0mTymYZK1VGJBWIkI0mS4SSz4dnG+XCSZKRxPhlOkgglxuLvfuI7NcsVcx3vLV9aX2o7NFxqNsVj73usFuTVQZvjwSE3m/RIVdkt7pItZskWymOsesTT8zTxvUCO6lvMmMjv5fsO53Qu3dB8OUxBb7BrYDe/NxOeIeANjOR4h+lYB/1adq0xyOu6u11Jr6Dsf1uZ8k+Rmk1xZ/JOHr7j4YYgX0gs4Pf22fhWR1UplApkC1l2Cju1AM4WKz9X5ru+77B8L1ffuznMCeKwkyBD3d9hj1FRVLX2WtJSy7JOr72UVSrlu5Vt2vZ2fpvN3Cabu5sNr/WDsIwDr3jxeXwUSoUDa+oe8dTCuT6U2wZ43XzEH7HKSgdjHfR7pT1WM6ttg3xxfZGN3Y2G8qejp7k4c5EHzz/YEOSpmRTJSJLd4m5LcN7cusmbG2/2FsQOgvuwXzkD3gBhX5iwP0zYFybij9TmE6EEt8duL79fKRPxRxrKh/1hBKGkpeFO9Fa+GlTNU7FUHP6xV4+J/poxBUFEaiew6ny7V1UdWc23F0FvkEQoQSKYIBFKMOWfavvZun3e6mvYF24N8KamkXgwPhbNIsNWLBXZ3N1kY3ejdsLuOJ/bYHN30/G2xyLos8Us1356raW72+ah4doJ+8LMxeY4HT2NRzzsFHa4fuM633nrOw1BXCgVDtxOJx7xNIRpc7Amw8n9kG0K5nblu73v9djtgaNSX6uun6oh5RFPrf05k8s0vPbavLHl8Pa6iD9CPBhvPwU6LG8zBX3Oh50zvStpiUwu0z2gq/O5yvzu/vxOYafrfqKBaO2kmwgmupavGouLsTInyu85L99LcPYatM3v+z1++zp4zKkqub3cQNqfs8Vs1/0JQiwY6zucY8GYdVcxBKpKtpg9dEBv7m6SzqW7fiOsfjuaDk3Xwro2H6zMhzrPx4Pxlt+HY3UxdmF6gS/+xhcdBXHIF7LgnRDV5o1BBLSTb3Q+j68lbE9FT3EpeamngJ4KTE1k08Oo5PfyDdcnujV3tAvrYungBwQ94mkJ4AvTFxrDuDm8m+ZHeav1wINeRC4CfwIkVPVjTtY5ETnBkz//5KAPxYzIsG+vC/vCtRpwNWwXphd6rkFbJWL4SloinUt3DGAntWkn37JigVhDKJ+Onuau5F2OAno6NM2Uf+pY/244CnoReRp4BHhHVe+uW/4Q8DeAF/hHVf2Sqi4BnxaRfz2KAzZHZ9i318UCrc0bc7E5x8EcD8aJBWKO75iqv1Ol+cJwsVQknUs3XJjt56LyoC8SH/k+evkcPV54r99HcxNJJpfp2uQR8oVaatPnEuccBXQiWG7ymPRrX05r9M9Qftq21rGCiHiBrwK/CqwAr4jINVV9bdAHacrq/1A2c5u1mtCNrRssby6znF5mJb3CWnaNjd2N2h+Tk4s8R00QooEo0UCUWDBGxB8BqAVAtYOnTuHWLojaBWBzufoyZl/9nTLDvIU26AuSmk05bu5IBBN2IXkAHAW9qr4kIuebFn8AeL1Sg0dEvgE8CljQt1Ftb66G9Hp2ndXMai2gl9PLLG+Wg/rG1o1RH26L6dB07Sus1+Nt+QNuFxjt7pNvLnfoMofY/2GOURC8Hu9IngM4qhCu3kVkJkc/bfRngOW6n1eAD4pIEvgL4BdF5POq+pftVhaRp4CnAM6dO9fHYRy9+lun3tt5r1Zzbg7p5fRy14s6g+L3+JlPzDMfn99/jc8zF5tjJjzT0rxhtSJjJtfAL8aq6nvAZx2UuwpchXJfN4M+jqq90h7pXJqb2zf3g7lNQG/le+s2tB9zsTnm4/OcjZ9tCOq52FztNqp4ME40ELXb64wxfesnRVaB+bqfz9Lch2af8nt5VtOrDYG8kl4p16Yry27t3BrkLg80E5ppqD3X16hPTZ2qhfRxv0JvjHGXfoL+FeCSiFygHPBPAL91mA1d/9l15M+OLhjDvnDbZo7q/Mmpk+WnB70D7ELUGGPGhNPbK58FHgROiMgK8Keq+nUR+RzwAuXbK59W1Vd72bmIXAGucHuH95GWgG5u7pgNzw60QzJjjHGbsegCYdj90RtjjBs47QLBntM2xhiXs6A3xhiXG2nQi8gVEbm6uem8X2VjjDG9GWnQq+rzqvpUIuG8X2VjjDG9saYbY4xxOQt6Y4xxOQt6Y4xxObsYa4wxLjcWD0yJyC3g/3pcLQEM6wxxVPsa1Hb73U4/6/e67gng3UPuy7Qa5t/BURmnz3DccmVBVU92LaWqx3ICrh73fQ1qu/1up5/1e10X+O6w/t8mYRrm38EkfAY35Eq76Ti30T/vgn0Narv9bqef9Yf5/2BaueHff5w+gxtypcVYNN2YySEi31UHfXMYYwbnONfozfF0ddQHYMyksRq9Mca4nNXojTHG5SzojTHG5SzojTHG5foZM9aYvonIR4FfB+LA11X1xREfkjGuYzV6M3Ai8rSIvCMiP25a/pCI/FREXheRPwZQ1X9X1c8AnwUeH8XxGuN2FvTmKDwDPFS/QES8wFeBh4HLwCdF5HJdkS9U3jfGDJgFvRk4VX0JWGta/AHgdVVdUtU88A3gUSn7K+A/VfV7wz5WYyaBtdGbYTkDLNf9vAJ8EPgD4MNAQkTuUNW/H8XBGeNmFvRmpFT1y8CXR30cxriZNd2YYVkF5ut+PltZZow5Yhb0ZlheAS6JyAURCQBPANdGfEzGTAQLejNwIvIs8L/AXSKyIiKfVtUi8DngBeAnwDdV9dVRHqcxk8I6NTPGGJezGr0xxricBb0xxricBb0xxricBb0xxricBb0xxricBb0xxricBb0xxricBb0xPRIR6yPKHCsW9Mb1ROS8iPxERP5BRF4VkRdFJCwid4jIf4nID0TkeyKSqnSb/Nci8mMR+ZGIPF7ZxoMi8m0RuQa8Vln2pIi8LCLfF5GviYi3Mj1Tt/4fjvTDG4P1XmkmxyXgk6r6GRH5JvCblLtI/pKqPiciIcoVn8eAXwDuAU4Ar4jIS5Vt3AvcrapviMj7KY+Idb+qFkTk74DfBl4Fzqjq3QAiMj3Ez2hMWxb0ZlK8oarfr8xfBy5QDuTnAFR1F0BEPgQ8q6p7wE0R+R/gPiANvKyqb1S28SvAL1E+EQCEgXeA54GLIvK3wH8ANgauGTkLejMpcnXze8BhatrbdfMC/LOqfr65kIjcA3yE8ji4nwB+9xD7MmZgrI3eTKoMsCIiHwUQkaCIRIBvA49X2tpPAg8AL7dZ/7+Bj4nIbZX1Z0VkQUROAB5V/TfK4+DeO4wPY8xBrEZvJtnvAF8TkT8HCsDHgeeAXwZ+ACjwR6r6toi8r35FVX1NRL4AvCginsr6vw9kgX+qLANoqfEbM2zWTbExxricNd0YY4zLWdAbY4zLWdAbY4zLWdAbY4zLWdAbY4zLWdAbY4zLWdAbY4zLWdAbY4zL/T/u+GKxXCEVpwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"colors=iter(list('rgbym'*10))\n",
"for label, df in measurements.groupby('name'):\n",
" if label in ('LinearRegression', 'ScalaSparkTerasort'):\n",
" df.plot.line(x='ncores',y='duration', ax=ax, label=label, loglog=True, c=next(colors)) # scatter\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot elapsed time and speedup (in [`bokeh`](https://docs.bokeh.org/en/latest/))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"cluster_ppn = 36\n",
"\n",
"elapsedtime_metric = lambda df: df['duration']\n",
"memory_metric = lambda df: df['ram']\n",
"io_metric = lambda df: df['io_in']+df['io_out']\n",
"def speedup_metric(df):\n",
" \"\"\"Semi-speedup\"\"\"\n",
" min_raw = df.loc[df['ncores'].idxmin()]\n",
" return (min_raw['duration']*min_raw['ncores'])/df['duration']\n",
"def node_speedup_metric(df):\n",
" \"\"\"Semi-speedup\"\"\"\n",
" min_raw = df[df['ncores']==cluster_ppn].iloc[0]\n",
" return (min_raw['duration']*min_raw['ncores'])/df['duration']\n",
"def efficiency_metric(df):\n",
" \"\"\"Semi-efficiency\"\"\"\n",
" min_raw = df.loc[df['ncores'].idxmin()]\n",
" return (min_raw['duration']*min_raw['ncores'])/df['duration']/df['ncores']\n",
"def node_efficiency_metric(df):\n",
" \"\"\"Semi-efficiency\"\"\"\n",
" min_raw = df[df['ncores']==cluster_ppn].iloc[0]\n",
" return (min_raw['duration']*min_raw['ncores'])/df['duration']/df['ncores']"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <div class=\"bk-root\">\n",
" <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
" <span id=\"2998\">Loading BokehJS ...</span>\n",
" </div>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"\n",
"(function(root) {\n",
" function now() {\n",
" return new Date();\n",
" }\n",
"\n",
" var force = true;\n",
"\n",
" if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
" root._bokeh_onload_callbacks = [];\n",
" root._bokeh_is_loading = undefined;\n",
" }\n",
"\n",
" var JS_MIME_TYPE = 'application/javascript';\n",
" var HTML_MIME_TYPE = 'text/html';\n",
" var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
" var CLASS_NAME = 'output_bokeh rendered_html';\n",
"\n",
" /**\n",
" * Render data to the DOM node\n",
" */\n",
" function render(props, node) {\n",
" var script = document.createElement(\"script\");\n",
" node.appendChild(script);\n",
" }\n",
"\n",
" /**\n",
" * Handle when an output is cleared or removed\n",
" */\n",
" function handleClearOutput(event, handle) {\n",
" var cell = handle.cell;\n",
"\n",
" var id = cell.output_area._bokeh_element_id;\n",
" var server_id = cell.output_area._bokeh_server_id;\n",
" // Clean up Bokeh references\n",
" if (id != null && id in Bokeh.index) {\n",
" Bokeh.index[id].model.document.clear();\n",
" delete Bokeh.index[id];\n",
" }\n",
"\n",
" if (server_id !== undefined) {\n",
" // Clean up Bokeh references\n",
" var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
" cell.notebook.kernel.execute(cmd, {\n",
" iopub: {\n",
" output: function(msg) {\n",
" var id = msg.content.text.trim();\n",
" if (id in Bokeh.index) {\n",
" Bokeh.index[id].model.document.clear();\n",
" delete Bokeh.index[id];\n",
" }\n",
" }\n",
" }\n",
" });\n",
" // Destroy server and session\n",
" var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
" cell.notebook.kernel.execute(cmd);\n",
" }\n",
" }\n",
"\n",
" /**\n",
" * Handle when a new output is added\n",
" */\n",
" function handleAddOutput(event, handle) {\n",
" var output_area = handle.output_area;\n",
" var output = handle.output;\n",
"\n",
" // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
" if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
" return\n",
" }\n",
"\n",
" var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
"\n",
" if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
" toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
" // store reference to embed id on output_area\n",
" output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
" }\n",
" if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
" var bk_div = document.createElement(\"div\");\n",
" bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
" var script_attrs = bk_div.children[0].attributes;\n",
" for (var i = 0; i < script_attrs.length; i++) {\n",
" toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
" }\n",
" // store reference to server id on output_area\n",
" output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
" }\n",
" }\n",
"\n",
" function register_renderer(events, OutputArea) {\n",
"\n",
" function append_mime(data, metadata, element) {\n",
" // create a DOM node to render to\n",
" var toinsert = this.create_output_subarea(\n",
" metadata,\n",
" CLASS_NAME,\n",
" EXEC_MIME_TYPE\n",
" );\n",
" this.keyboard_manager.register_events(toinsert);\n",
" // Render to node\n",
" var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
" render(props, toinsert[toinsert.length - 1]);\n",
" element.append(toinsert);\n",
" return toinsert\n",
" }\n",
"\n",
" /* Handle when an output is cleared or removed */\n",
" events.on('clear_output.CodeCell', handleClearOutput);\n",
" events.on('delete.Cell', handleClearOutput);\n",
"\n",
" /* Handle when a new output is added */\n",
" events.on('output_added.OutputArea', handleAddOutput);\n",
"\n",
" /**\n",
" * Register the mime type and append_mime function with output_area\n",
" */\n",
" OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
" /* Is output safe? */\n",
" safe: true,\n",
" /* Index of renderer in `output_area.display_order` */\n",
" index: 0\n",
" });\n",
" }\n",
"\n",
" // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
" if (root.Jupyter !== undefined) {\n",
" var events = require('base/js/events');\n",
" var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
"\n",
" if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
" register_renderer(events, OutputArea);\n",
" }\n",
" }\n",
"\n",
" \n",
" if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
" root._bokeh_timeout = Date.now() + 5000;\n",
" root._bokeh_failed_load = false;\n",
" }\n",
"\n",
" var NB_LOAD_WARNING = {'data': {'text/html':\n",
" \"<div style='background-color: #fdd'>\\n\"+\n",
" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
" \"</div>\"}};\n",
"\n",
" function display_loaded() {\n",
" var el = document.getElementById(\"2998\");\n",
" if (el != null) {\n",
" el.textContent = \"BokehJS is loading...\";\n",
" }\n",
" if (root.Bokeh !== undefined) {\n",
" if (el != null) {\n",
" el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
" }\n",
" } else if (Date.now() < root._bokeh_timeout) {\n",
" setTimeout(display_loaded, 100)\n",
" }\n",
" }\n",
"\n",
"\n",
" function run_callbacks() {\n",
" try {\n",
" root._bokeh_onload_callbacks.forEach(function(callback) {\n",
" if (callback != null)\n",
" callback();\n",
" });\n",
" } finally {\n",
" delete root._bokeh_onload_callbacks\n",
" }\n",
" console.debug(\"Bokeh: all callbacks have finished\");\n",
" }\n",
"\n",
" function load_libs(css_urls, js_urls, callback) {\n",
" if (css_urls == null) css_urls = [];\n",
" if (js_urls == null) js_urls = [];\n",
"\n",
" root._bokeh_onload_callbacks.push(callback);\n",
" if (root._bokeh_is_loading > 0) {\n",
" console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
" if (js_urls == null || js_urls.length === 0) {\n",
" run_callbacks();\n",
" return null;\n",
" }\n",
" console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
"\n",
" function on_load() {\n",
" root._bokeh_is_loading--;\n",
" if (root._bokeh_is_loading === 0) {\n",
" console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
" run_callbacks()\n",
" }\n",
" }\n",
"\n",
" function on_error() {\n",
" console.error(\"failed to load \" + url);\n",
" }\n",
"\n",
" for (var i = 0; i < css_urls.length; i++) {\n",
" var url = css_urls[i];\n",
" const element = document.createElement(\"link\");\n",
" element.onload = on_load;\n",
" element.onerror = on_error;\n",
" element.rel = \"stylesheet\";\n",
" element.type = \"text/css\";\n",
" element.href = url;\n",
" console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
" document.body.appendChild(element);\n",
" }\n",
"\n",
" for (var i = 0; i < js_urls.length; i++) {\n",
" var url = js_urls[i];\n",
" var element = document.createElement('script');\n",
" element.onload = on_load;\n",
" element.onerror = on_error;\n",
" element.async = false;\n",
" element.src = url;\n",
" console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.head.appendChild(element);\n",
" }\n",
" };var element = document.getElementById(\"2998\");\n",
" if (element == null) {\n",
" console.error(\"Bokeh: ERROR: autoload.js configured with elementid '2998' but no matching script tag was found. \")\n",
" return false;\n",
" }\n",
"\n",
" function inject_raw_css(css) {\n",
" const element = document.createElement(\"style\");\n",
" element.appendChild(document.createTextNode(css));\n",
" document.body.appendChild(element);\n",
" }\n",
"\n",
" var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n",
" var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n",
"\n",
" var inline_js = [\n",
" function(Bokeh) {\n",
" Bokeh.set_log_level(\"info\");\n",
" },\n",
" \n",
" function(Bokeh) {\n",
" \n",
" },\n",
" function(Bokeh) {} // ensure no trailing comma for IE\n",
" ];\n",
"\n",
" function run_inline_js() {\n",
" \n",
" if ((root.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i].call(root, root.Bokeh);\n",
" }if (force === true) {\n",
" display_loaded();\n",
" }} else if (Date.now() < root._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!root._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" root._bokeh_failed_load = true;\n",
" } else if (force !== true) {\n",
" var cell = $(document.getElementById(\"2998\")).parents('.cell').data().cell;\n",
" cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
" }\n",
"\n",
" }\n",
"\n",
" if (root._bokeh_is_loading === 0) {\n",
" console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(css_urls, js_urls, function() {\n",
" console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
"}(window));"
],
"application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"2998\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"2998\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '2998' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"2998\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));"
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
" <div class=\"bk-root\" id=\"8a4d6bb2-4a6a-4152-b881-819f5f7f3570\" data-root-id=\"3109\"></div>\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"(function(root) {\n",
" function embed_document(root) {\n",
" \n",
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" var render_items = [{\"docid\":\"a0dafb46-4aa3-43de-b41a-5b8f63cc7c05\",\"roots\":{\"3109\":\"8a4d6bb2-4a6a-4152-b881-819f5f7f3570\"}}];\n",
" root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
"\n",
" }\n",
" if (root.Bokeh !== undefined) {\n",
" embed_document(root);\n",
" } else {\n",
" var attempts = 0;\n",
" var timer = setInterval(function(root) {\n",
" if (root.Bokeh !== undefined) {\n",
" embed_document(root);\n",
" clearInterval(timer);\n",
" }\n",
" attempts++;\n",
" if (attempts > 100) {\n",
" console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
" clearInterval(timer);\n",
" }\n",
" }, 10, root)\n",
" }\n",
"})(window);"
],
"application/vnd.bokehjs_exec.v0+json": ""
},
"metadata": {
"application/vnd.bokehjs_exec.v0+json": {
"id": "3109"
}
},
"output_type": "display_data"
}
],
"source": [
"from bokeh.layouts import gridplot\n",
"from bokeh.plotting import figure, show, output_notebook, output_file\n",
"from bokeh.models import Range1d, axes\n",
"\n",
"measurements_advanced = measurements[measurements['name'] == 'LinearRegression'].sort_values(by='ncores')\n",
"\n",
"# Plot some metric for all measurements\n",
"colors=iter(['red', 'green', 'blue','cyan', 'magenta', 'black']*10) # iter(list('rgbym'))\n",
"\n",
"p1 = figure(title=\"Elapsed time (benchmark on Gilgamesch)\", y_axis_type=\"log\", x_axis_type=\"log\",)\n",
"p1.grid.grid_line_alpha=0.3\n",
"p1.xaxis.axis_label = '# of cores'\n",
"p1.yaxis.axis_label = 'Elapsed time, s'\n",
"\n",
"metric=elapsedtime_metric\n",
"for label, df in measurements.sort_values(by='ncores').groupby('name'):\n",
" color = next(colors)\n",
" p1.line(df['ncores'], metric(df), color=color, legend=df['name'].iloc[0])\n",
" p1.circle(df['ncores'], metric(df), color=color, fill_color='white', size=6, legend=df['name'].iloc[0])\n",
"p1.legend.location = \"top_right\"\n",
"\n",
"\n",
"# Plot speedup for advanced mode\n",
"p2 = figure(title=\"Speedup w.r.t. single 24-core node (Hazelhen)\")\n",
"p2.grid.grid_line_alpha = 0.5\n",
"\n",
"p2.xaxis.axis_label = '# of cores'\n",
"p2.yaxis.axis_label = 'Speedup'\n",
"\n",
"p2.ygrid.band_fill_color = \"olive\"\n",
"p2.ygrid.band_fill_alpha = 0.1\n",
"\n",
"p2.line(measurements_advanced['ncores'], node_speedup_metric(measurements_advanced), legend='`advanced` mode')\n",
"p2.y_range=Range1d(0, max(node_speedup_metric(measurements_advanced)))\n",
"\n",
"max_nprocs=measurements_advanced['ncores'].max()\n",
"p2.x_range = Range1d(0, max_nprocs)\n",
"p2.extra_x_ranges = {\"ClusterNodes\": Range1d(start=0, end=max_nprocs/cluster_ppn)}\n",
"p2.add_layout(axes.LinearAxis(x_range_name=\"ClusterNodes\", axis_label=\"# of nodes\"), 'above')\n",
"\n",
"# draw linear speedup line\n",
"p2.line([1,max_nprocs], [1,max_nprocs], color='gray', line_dash='dashed', legend='linear speedup')\n",
"p2.legend.location = \"bottom_right\"\n",
"\n",
"# Plot efficiency for advanced mode\n",
"p3 = figure(title=\"Efficiency w.r.t. single 24-core node (Hazelhen)\")\n",
"# p2.grid.grid_line_alpha = 0\n",
"\n",
"p3.xaxis.axis_label = '# of cores'\n",
"p3.yaxis.axis_label = 'Efficiency'\n",
"\n",
"p3.ygrid.band_fill_color = \"olive\"\n",
"p3.ygrid.band_fill_alpha = 0.1\n",
"\n",
"max_nprocs=measurements_advanced['ncores'].max()\n",
"p3.x_range = Range1d(0, max_nprocs)\n",
"p3.extra_x_ranges = {\"ClusterNodes\": Range1d(start=0, end=max_nprocs/cluster_ppn)}\n",
"p3.add_layout(axes.LinearAxis(x_range_name=\"ClusterNodes\", axis_label=\"# of nodes\"), 'above')\n",
"\n",
"p3.line(measurements_advanced['ncores'], node_efficiency_metric(measurements_advanced), legend='`advanced` mode')\n",
"p3.legend.location = \"top_right\"\n",
"\n",
"measurements_advanced = measurements[measurements['name'] == 'LinearRegression'].sort_values(by='ncores')\n",
"\n",
"output_file(\"pflee-hazelhen-20190924.html\", title=\"PFlee benchmark on Hazelhen\")\n",
"output_notebook()\n",
"show(gridplot([[p1,p2,p3]], plot_width=600, plot_height=400))\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <div class=\"bk-root\">\n",
" <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
" <span id=\"4671\">Loading BokehJS ...</span>\n",
" </div>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"\n",
"(function(root) {\n",
" function now() {\n",
" return new Date();\n",
" }\n",
"\n",
" var force = true;\n",
"\n",
" if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
" root._bokeh_onload_callbacks = [];\n",
" root._bokeh_is_loading = undefined;\n",
" }\n",
"\n",
" var JS_MIME_TYPE = 'application/javascript';\n",
" var HTML_MIME_TYPE = 'text/html';\n",
" var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
" var CLASS_NAME = 'output_bokeh rendered_html';\n",
"\n",
" /**\n",
" * Render data to the DOM node\n",
" */\n",
" function render(props, node) {\n",
" var script = document.createElement(\"script\");\n",
" node.appendChild(script);\n",
" }\n",
"\n",
" /**\n",
" * Handle when an output is cleared or removed\n",
" */\n",
" function handleClearOutput(event, handle) {\n",
" var cell = handle.cell;\n",
"\n",
" var id = cell.output_area._bokeh_element_id;\n",
" var server_id = cell.output_area._bokeh_server_id;\n",
" // Clean up Bokeh references\n",
" if (id != null && id in Bokeh.index) {\n",
" Bokeh.index[id].model.document.clear();\n",
" delete Bokeh.index[id];\n",
" }\n",
"\n",
" if (server_id !== undefined) {\n",
" // Clean up Bokeh references\n",
" var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
" cell.notebook.kernel.execute(cmd, {\n",
" iopub: {\n",
" output: function(msg) {\n",
" var id = msg.content.text.trim();\n",
" if (id in Bokeh.index) {\n",
" Bokeh.index[id].model.document.clear();\n",
" delete Bokeh.index[id];\n",
" }\n",
" }\n",
" }\n",
" });\n",
" // Destroy server and session\n",
" var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
" cell.notebook.kernel.execute(cmd);\n",
" }\n",
" }\n",
"\n",
" /**\n",
" * Handle when a new output is added\n",
" */\n",
" function handleAddOutput(event, handle) {\n",
" var output_area = handle.output_area;\n",
" var output = handle.output;\n",
"\n",
" // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
" if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
" return\n",
" }\n",
"\n",
" var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
"\n",
" if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
" toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
" // store reference to embed id on output_area\n",
" output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
" }\n",
" if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
" var bk_div = document.createElement(\"div\");\n",
" bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
" var script_attrs = bk_div.children[0].attributes;\n",
" for (var i = 0; i < script_attrs.length; i++) {\n",
" toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
" }\n",
" // store reference to server id on output_area\n",
" output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
" }\n",
" }\n",
"\n",
" function register_renderer(events, OutputArea) {\n",
"\n",
" function append_mime(data, metadata, element) {\n",
" // create a DOM node to render to\n",
" var toinsert = this.create_output_subarea(\n",
" metadata,\n",
" CLASS_NAME,\n",
" EXEC_MIME_TYPE\n",
" );\n",
" this.keyboard_manager.register_events(toinsert);\n",
" // Render to node\n",
" var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
" render(props, toinsert[toinsert.length - 1]);\n",
" element.append(toinsert);\n",
" return toinsert\n",
" }\n",
"\n",
" /* Handle when an output is cleared or removed */\n",
" events.on('clear_output.CodeCell', handleClearOutput);\n",
" events.on('delete.Cell', handleClearOutput);\n",
"\n",
" /* Handle when a new output is added */\n",
" events.on('output_added.OutputArea', handleAddOutput);\n",
"\n",
" /**\n",
" * Register the mime type and append_mime function with output_area\n",
" */\n",
" OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
" /* Is output safe? */\n",
" safe: true,\n",
" /* Index of renderer in `output_area.display_order` */\n",
" index: 0\n",
" });\n",
" }\n",
"\n",
" // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
" if (root.Jupyter !== undefined) {\n",
" var events = require('base/js/events');\n",
" var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
"\n",
" if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
" register_renderer(events, OutputArea);\n",
" }\n",
" }\n",
"\n",
" \n",
" if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
" root._bokeh_timeout = Date.now() + 5000;\n",
" root._bokeh_failed_load = false;\n",
" }\n",
"\n",
" var NB_LOAD_WARNING = {'data': {'text/html':\n",
" \"<div style='background-color: #fdd'>\\n\"+\n",
" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
" \"</div>\"}};\n",
"\n",
" function display_loaded() {\n",
" var el = document.getElementById(\"4671\");\n",
" if (el != null) {\n",
" el.textContent = \"BokehJS is loading...\";\n",
" }\n",
" if (root.Bokeh !== undefined) {\n",
" if (el != null) {\n",
" el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
" }\n",
" } else if (Date.now() < root._bokeh_timeout) {\n",
" setTimeout(display_loaded, 100)\n",
" }\n",
" }\n",
"\n",
"\n",
" function run_callbacks() {\n",
" try {\n",
" root._bokeh_onload_callbacks.forEach(function(callback) {\n",
" if (callback != null)\n",
" callback();\n",
" });\n",
" } finally {\n",
" delete root._bokeh_onload_callbacks\n",
" }\n",
" console.debug(\"Bokeh: all callbacks have finished\");\n",
" }\n",
"\n",
" function load_libs(css_urls, js_urls, callback) {\n",
" if (css_urls == null) css_urls = [];\n",
" if (js_urls == null) js_urls = [];\n",
"\n",
" root._bokeh_onload_callbacks.push(callback);\n",
" if (root._bokeh_is_loading > 0) {\n",
" console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
" if (js_urls == null || js_urls.length === 0) {\n",
" run_callbacks();\n",
" return null;\n",
" }\n",
" console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
"\n",
" function on_load() {\n",
" root._bokeh_is_loading--;\n",
" if (root._bokeh_is_loading === 0) {\n",
" console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
" run_callbacks()\n",
" }\n",
" }\n",
"\n",
" function on_error() {\n",
" console.error(\"failed to load \" + url);\n",
" }\n",
"\n",
" for (var i = 0; i < css_urls.length; i++) {\n",
" var url = css_urls[i];\n",
" const element = document.createElement(\"link\");\n",
" element.onload = on_load;\n",
" element.onerror = on_error;\n",
" element.rel = \"stylesheet\";\n",
" element.type = \"text/css\";\n",
" element.href = url;\n",
" console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
" document.body.appendChild(element);\n",
" }\n",
"\n",
" for (var i = 0; i < js_urls.length; i++) {\n",
" var url = js_urls[i];\n",
" var element = document.createElement('script');\n",
" element.onload = on_load;\n",
" element.onerror = on_error;\n",
" element.async = false;\n",
" element.src = url;\n",
" console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.head.appendChild(element);\n",
" }\n",
" };var element = document.getElementById(\"4671\");\n",
" if (element == null) {\n",
" console.error(\"Bokeh: ERROR: autoload.js configured with elementid '4671' but no matching script tag was found. \")\n",
" return false;\n",
" }\n",
"\n",
" function inject_raw_css(css) {\n",
" const element = document.createElement(\"style\");\n",
" element.appendChild(document.createTextNode(css));\n",
" document.body.appendChild(element);\n",
" }\n",
"\n",
" var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n",
" var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n",
"\n",
" var inline_js = [\n",
" function(Bokeh) {\n",
" Bokeh.set_log_level(\"info\");\n",
" },\n",
" \n",
" function(Bokeh) {\n",
" \n",
" },\n",
" function(Bokeh) {} // ensure no trailing comma for IE\n",
" ];\n",
"\n",
" function run_inline_js() {\n",
" \n",
" if ((root.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i].call(root, root.Bokeh);\n",
" }if (force === true) {\n",
" display_loaded();\n",
" }} else if (Date.now() < root._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!root._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" root._bokeh_failed_load = true;\n",
" } else if (force !== true) {\n",
" var cell = $(document.getElementById(\"4671\")).parents('.cell').data().cell;\n",
" cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
" }\n",
"\n",
" }\n",
"\n",
" if (root._bokeh_is_loading === 0) {\n",
" console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(css_urls, js_urls, function() {\n",
" console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
"}(window));"
],
"application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"4671\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"4671\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '4671' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"4671\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));"
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
" <div class=\"bk-root\" id=\"4b7e2b6a-5504-4d93-9b85-79f3cab087b3\" data-root-id=\"9189\"></div>\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"(function(root) {\n",
" function embed_document(root) {\n",
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"application/vnd.bokehjs_exec.v0+json": ""
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"application/vnd.bokehjs_exec.v0+json": {
"id": "9189"
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],
"source": [
"from bokeh.layouts import gridplot\n",
"from bokeh.plotting import figure, show, output_notebook, output_file\n",
"from bokeh.models import Range1d, axes\n",
"from bokeh.plotting import figure, show, output_notebook\n",
"from bokeh.layouts import Row, Column, gridplot\n",
"# output_notebook()\n",
"\n",
"from bokeh.palettes import Dark2_5 as palette\n",
"import itertools \n",
"# # Plot some metric for all measurements\n",
"# colors = itertools.cycle(palette)\n",
"\n",
"output_notebook()\n",
"\n",
"def scaling_plot(measurements, axis_type='log'): #linear\n",
" row = measurements.iloc[0]\n",
" title = 'HiBench.{1} {0} test on Urika GX'.format(row['scale'], row['name'])\n",
" y_axis_data = 'throughput' # 'duration'\n",
"\n",
" fig = figure(title=title, sizing_mode='scale_width', y_axis_type=axis_type, x_axis_type=axis_type,)\n",
" fig.grid.grid_line_alpha = 0.75\n",
" fig.xaxis.axis_label = '# of cores'\n",
" fig.yaxis.axis_label = 'Elapsed time, s'\n",
" fig.xaxis.ticker = measurements.ncores\n",
"\n",
" fig.line(measurements['ncores'], measurements[y_axis_data])\n",
" fig.circle(measurements['ncores'], measurements[y_axis_data], fill_color='white', size=6)\n",
" # fig.legend.location = \"top_right\"\n",
"\n",
"# fig.y_range.start = 0\n",
"# fig.y_range=Range1d(0, max(node_speedup_metric(measurements_advanced)))\n",
" fig.ygrid.band_fill_color = \"olive\"\n",
" fig.ygrid.band_fill_alpha = 0.1\n",
"\n",
"# min_ncores, max_ncores=measurements['ncores'].min(), measurements['ncores'].max()\n",
"# delta_ncores = .05*(max_ncores - min_ncores)\n",
"# fig.x_range = Range1d(min_ncores - delta_ncores, max_ncores+delta_ncores)\n",
"# fig.extra_x_ranges = {\"ClusterNodes\": Range1d(start=0, end=max_nprocs/cluster_ppn)}\n",
"# fig.add_layout(axes.LinearAxis(x_range_name=\"ClusterNodes\", axis_label=\"# of nodes\"), 'above')\n",
" \n",
" return fig\n",
"\n",
"measurements = measurements.groupby(['scale','name','ncores'], as_index=False).agg({'duration':'mean', 'data_size':'mean', 'throughput':'mean', 'node_throughput':'mean'})\n",
"plots_table = measurements.groupby(['scale', 'name'])['scale', 'name', 'ncores', 'duration', 'throughput'].apply(scaling_plot)\n",
"\n",
"\n",
"scale = 'huge' # 'large' # 'tiny'# 'small' # \n",
"names = measurements[measurements['scale'] == scale]['name'].unique()\n",
"# 'GradientBoostingTree' 'PCA' 'RandomForest' 'SVD' 'ScalaSparkBayes' 'ScalaSparkJoin' 'ScalaSparkNWeight' 'ScalaSparkPagerank' 'ScalaSparkScan' 'ScalaSparkSleep' \n",
"# names = ['LDA', 'LinearRegression', 'LogisticRegression', 'SVM', 'ScalaSparkAggregation', 'ScalaSparkKmeans', 'ScalaSparkSort', 'ScalaSparkTerasort', 'ScalaSparkWordcount']\n",
"from toolz import partition_all\n",
"L = plots_table.loc[scale].loc[names].values.tolist()\n",
"grid = list(partition_all(3, L))\n",
"\n",
"# output_file(\"pflee-hazelhen-20190924.html\", title=\"Benchmark on Hazelhen\")\n",
"show(gridplot(grid, plot_width=400, plot_height=400))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"gigantic\n",
"huge\n",
"large\n",
"small\n",
"tiny\n"
]
}
],
"source": [
"# measurements_avg = measurements.groupby(['scale','name','ncores'], as_index=False).agg({'duration':'mean', 'data_size':'mean', 'throughput':'mean', 'node_throughput':'mean'})\n",
"def fix_column_name(name):\n",
" re_col_name=re.compile(r\"\\(\\'([a-zA-Z0-9]+)\\', (\\'([a-zA-Z]+)\\'\\)|[0-9]+)\")\n",
" m = re_col_name.match(name)\n",
" if m: return \"{1}\".format(m.group(1),m.group(2))\n",
" return name\n",
"\n",
"def to_profile_tables(df, filed='duration'):\n",
"# x = pandas.DataFrame(measurements.pivot_table(index=['name','ncores'], columns='scale', values=['duration']).to_records())\n",
"# x.columns = map(fix_column_name, x.columns)\n",
" df_profile = pandas.DataFrame(df.pivot_table(index=['name'],#['name', 'data_size'],\n",
" columns='ncores', values=['duration']).to_records())\n",
" df_profile.columns = map(fix_column_name, df_profile.columns)\n",
" return df_profile\n",
"\n",
"measurements = measurements.groupby(['scale','name','ncores'], as_index=False).agg({'duration':'mean', 'data_size':'mean', 'throughput':'mean', 'node_throughput':'mean'})\n",
"\n",
"import os\n",
"from IPython.display import display, HTML\n",
"\n",
"# duration_tables = measurements.groupby(['scale'])['scale', 'name', 'ncores', 'duration', 'throughput'].apply(to_profile_tables)\n",
"# for df_profile in duration_tables.loc[measurements['scale'].unique()].values:\n",
"# print(display(HTML(df_profile.to_html())))\n",
"for scale in measurements['scale'].unique():\n",
" print(scale)\n",
" df_profile = to_profile_tables(pandas.DataFrame(measurements[measurements['scale'] == scale]))\n",
"# print(display(HTML(df_profile.to_html())))\n",
" df_profile.to_csv('/home/hpcgogol/proj/hidalgo/doc/D3.3/figs/%s.org' % scale, index_label=False, sep='|', header=True,\n",
" line_terminator='|' + os.linesep, float_format='%.3f')\n",
"\n",
"\n",
"# x=measurements[measurements['scale'] in ('small, ''large')]\n",
"# pandas.DataFrame(x.pivot_table(index=['name'], columns=['scale','ncores'], values=['duration']).to_records())\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
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"\n",
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" var CLASS_NAME = 'output_bokeh rendered_html';\n",
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" Bokeh.index[id].model.document.clear();\n",
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"\n",
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" });\n",
" // Destroy server and session\n",
" var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
" cell.notebook.kernel.execute(cmd);\n",
" }\n",
" }\n",
"\n",
" /**\n",
" * Handle when a new output is added\n",
" */\n",
" function handleAddOutput(event, handle) {\n",
" var output_area = handle.output_area;\n",
" var output = handle.output;\n",
"\n",
" // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
" if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
" return\n",
" }\n",
"\n",
" var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
"\n",
" if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
" toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
" // store reference to embed id on output_area\n",
" output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
" }\n",
" if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
" var bk_div = document.createElement(\"div\");\n",
" bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
" var script_attrs = bk_div.children[0].attributes;\n",
" for (var i = 0; i < script_attrs.length; i++) {\n",
" toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
" }\n",
" // store reference to server id on output_area\n",
" output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
" }\n",
" }\n",
"\n",
" function register_renderer(events, OutputArea) {\n",
"\n",
" function append_mime(data, metadata, element) {\n",
" // create a DOM node to render to\n",
" var toinsert = this.create_output_subarea(\n",
" metadata,\n",
" CLASS_NAME,\n",
" EXEC_MIME_TYPE\n",
" );\n",
" this.keyboard_manager.register_events(toinsert);\n",
" // Render to node\n",
" var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
" render(props, toinsert[toinsert.length - 1]);\n",
" element.append(toinsert);\n",
" return toinsert\n",
" }\n",
"\n",
" /* Handle when an output is cleared or removed */\n",
" events.on('clear_output.CodeCell', handleClearOutput);\n",
" events.on('delete.Cell', handleClearOutput);\n",
"\n",
" /* Handle when a new output is added */\n",
" events.on('output_added.OutputArea', handleAddOutput);\n",
"\n",
" /**\n",
" * Register the mime type and append_mime function with output_area\n",
" */\n",
" OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
" /* Is output safe? */\n",
" safe: true,\n",
" /* Index of renderer in `output_area.display_order` */\n",
" index: 0\n",
" });\n",
" }\n",
"\n",
" // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
" if (root.Jupyter !== undefined) {\n",
" var events = require('base/js/events');\n",
" var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
"\n",
" if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
" register_renderer(events, OutputArea);\n",
" }\n",
" }\n",
"\n",
" \n",
" if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
" root._bokeh_timeout = Date.now() + 5000;\n",
" root._bokeh_failed_load = false;\n",
" }\n",
"\n",
" var NB_LOAD_WARNING = {'data': {'text/html':\n",
" \"<div style='background-color: #fdd'>\\n\"+\n",
" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
" \"</div>\"}};\n",
"\n",
" function display_loaded() {\n",
" var el = document.getElementById(\"12202\");\n",
" if (el != null) {\n",
" el.textContent = \"BokehJS is loading...\";\n",
" }\n",
" if (root.Bokeh !== undefined) {\n",
" if (el != null) {\n",
" el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
" }\n",
" } else if (Date.now() < root._bokeh_timeout) {\n",
" setTimeout(display_loaded, 100)\n",
" }\n",
" }\n",
"\n",
"\n",
" function run_callbacks() {\n",
" try {\n",
" root._bokeh_onload_callbacks.forEach(function(callback) {\n",
" if (callback != null)\n",
" callback();\n",
" });\n",
" } finally {\n",
" delete root._bokeh_onload_callbacks\n",
" }\n",
" console.debug(\"Bokeh: all callbacks have finished\");\n",
" }\n",
"\n",
" function load_libs(css_urls, js_urls, callback) {\n",
" if (css_urls == null) css_urls = [];\n",
" if (js_urls == null) js_urls = [];\n",
"\n",
" root._bokeh_onload_callbacks.push(callback);\n",
" if (root._bokeh_is_loading > 0) {\n",
" console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
" if (js_urls == null || js_urls.length === 0) {\n",
" run_callbacks();\n",
" return null;\n",
" }\n",
" console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
"\n",
" function on_load() {\n",
" root._bokeh_is_loading--;\n",
" if (root._bokeh_is_loading === 0) {\n",
" console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
" run_callbacks()\n",
" }\n",
" }\n",
"\n",
" function on_error() {\n",
" console.error(\"failed to load \" + url);\n",
" }\n",
"\n",
" for (var i = 0; i < css_urls.length; i++) {\n",
" var url = css_urls[i];\n",
" const element = document.createElement(\"link\");\n",
" element.onload = on_load;\n",
" element.onerror = on_error;\n",
" element.rel = \"stylesheet\";\n",
" element.type = \"text/css\";\n",
" element.href = url;\n",
" console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
" document.body.appendChild(element);\n",
" }\n",
"\n",
" for (var i = 0; i < js_urls.length; i++) {\n",
" var url = js_urls[i];\n",
" var element = document.createElement('script');\n",
" element.onload = on_load;\n",
" element.onerror = on_error;\n",
" element.async = false;\n",
" element.src = url;\n",
" console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.head.appendChild(element);\n",
" }\n",
" };var element = document.getElementById(\"12202\");\n",
" if (element == null) {\n",
" console.error(\"Bokeh: ERROR: autoload.js configured with elementid '12202' but no matching script tag was found. \")\n",
" return false;\n",
" }\n",
"\n",
" function inject_raw_css(css) {\n",
" const element = document.createElement(\"style\");\n",
" element.appendChild(document.createTextNode(css));\n",
" document.body.appendChild(element);\n",
" }\n",
"\n",
" var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n",
" var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n",
"\n",
" var inline_js = [\n",
" function(Bokeh) {\n",
" Bokeh.set_log_level(\"info\");\n",
" },\n",
" \n",
" function(Bokeh) {\n",
" \n",
" },\n",
" function(Bokeh) {} // ensure no trailing comma for IE\n",
" ];\n",
"\n",
" function run_inline_js() {\n",
" \n",
" if ((root.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i].call(root, root.Bokeh);\n",
" }if (force === true) {\n",
" display_loaded();\n",
" }} else if (Date.now() < root._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!root._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" root._bokeh_failed_load = true;\n",
" } else if (force !== true) {\n",
" var cell = $(document.getElementById(\"12202\")).parents('.cell').data().cell;\n",
" cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
" }\n",
"\n",
" }\n",
"\n",
" if (root._bokeh_is_loading === 0) {\n",
" console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(css_urls, js_urls, function() {\n",
" console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
"}(window));"
],
"application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"12202\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"12202\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '12202' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"12202\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));"
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\"y\"}},\"id\":\"13028\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"12993\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":6},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"15140\",\"type\":\"Circle\"},{\"attributes\":{\"source\":{\"id\":\"15110\",\"type\":\"ColumnDataSource\"}},\"id\":\"15114\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"16639\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"grid_line_alpha\":{\"value\":0.75},\"ticker\":{\"id\":\"12956\",\"type\":\"BasicTicker\"}},\"id\":\"12959\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"16369\",\"type\":\"SaveTool\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":6},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"13000\",\"type\":\"Circle\"},{\"attributes\":{\"callback\":null},\"id\":\"13196\",\"type\":\"DataRange1d\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":[36,72,144,288,576],\"y\":{\"__ndarray__\":\"AAAAAFBNzT+rqqqq2ovNP6uqqqpyaM0/q6qqqkrEzD8AAAAAIGzKPw==\",\"dtype\":\"float64\",\"shape\":[5]}},\"selected\":{\"id\":\"14613\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"14614\",\"type\":\"UnionRenderers\"}},\"id\":\"14567\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"16506\",\"type\":\"Selection\"},{\"attributes\":{\"data_source\":{\"id\":\"14493\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"14494\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"14495\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"14497\",\"type\":\"CDSView\"}},\"id\":\"14496\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"16590\",\"type\":\"UnionRenderers\"}],\"root_ids\":[\"17244\"]},\"title\":\"Bokeh Application\",\"version\":\"1.1.0\"}};\n",
" var render_items = [{\"docid\":\"393b0a2a-37da-4114-acad-a28162d45961\",\"roots\":{\"17244\":\"63227378-c62b-4cec-83de-960fd6917cc4\"}}];\n",
" root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
"\n",
" }\n",
" if (root.Bokeh !== undefined) {\n",
" embed_document(root);\n",
" } else {\n",
" var attempts = 0;\n",
" var timer = setInterval(function(root) {\n",
" if (root.Bokeh !== undefined) {\n",
" embed_document(root);\n",
" clearInterval(timer);\n",
" }\n",
" attempts++;\n",
" if (attempts > 100) {\n",
" console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
" clearInterval(timer);\n",
" }\n",
" }, 10, root)\n",
" }\n",
"})(window);"
],
"application/vnd.bokehjs_exec.v0+json": ""
},
"metadata": {
"application/vnd.bokehjs_exec.v0+json": {
"id": "17244"
}
},
"output_type": "display_data"
}
],
"source": [
"from bokeh.layouts import gridplot\n",
"from bokeh.plotting import figure, show, output_notebook, output_file\n",
"from bokeh.models import Range1d, axes\n",
"from bokeh.plotting import figure, show, output_notebook\n",
"from bokeh.layouts import Row, Column, gridplot\n",
"# output_notebook()\n",
"\n",
"from bokeh.palettes import Dark2_5 as palette\n",
"import itertools \n",
"\n",
"output_notebook()\n",
"\n",
"def scaling_plot(measurements, axis_type='linear', y_axis_data = ('throughput', 'Throughput, MB/s')): #'log', ('duration', 'Duration, s')\n",
" y_axis_field, y_axis_label = y_axis_data\n",
"\n",
" row = measurements.iloc[0]\n",
" title = 'HiBench.{name} test'.format(name=row['name'])\n",
"\n",
" # Plot some metric for all measurements\n",
" colors = itertools.cycle(palette)\n",
" fig = figure(title=title, sizing_mode='scale_width', y_axis_type=axis_type, x_axis_type=axis_type,)\n",
"\n",
" fig.grid.grid_line_alpha = 0.75\n",
" fig.ygrid.band_fill_color = \"olive\"\n",
" fig.ygrid.band_fill_alpha = 0.1\n",
"\n",
" min_ncores, max_ncores=measurements['ncores'].min(), measurements['ncores'].max()\n",
" fig.xaxis.axis_label = '# of cores'\n",
" fig.xaxis.ticker = measurements.ncores\n",
" fig.x_range = Range1d(0, max_ncores+cluster_ppn)\n",
" fig.extra_x_ranges = {\"ClusterNodes\": Range1d(start=0, end=max_ncores/cluster_ppn+1)}\n",
" fig.add_layout(axes.LinearAxis(x_range_name=\"ClusterNodes\", axis_label=\"# of nodes\", ticker = measurements.ncores/cluster_ppn), 'above')\n",
"\n",
" fig.yaxis.axis_label = y_axis_label\n",
"\n",
" metric=elapsedtime_metric\n",
" for label, measurements_scale in measurements.groupby('scale'):\n",
" color = next(colors)\n",
" legend=measurements_scale['scale'].iloc[0]\n",
" fig.line(measurements_scale['ncores'], measurements_scale[y_axis_field], color=color, legend=legend)\n",
" fig.circle(measurements_scale['ncores'], measurements_scale[y_axis_field], color=color, fill_color='white', size=6, legend=legend)\n",
"\n",
" fig.legend.location = \"bottom_right\"\n",
" return fig\n",
"\n",
"scaling_plot_log = lambda measurements: scaling_plot(measurements, 'log', ('duration', 'Duration, s'))\n",
"\n",
"measurements = measurements.groupby(['scale','name','ncores'], as_index=False).agg({'duration':'mean', 'data_size':'mean', 'throughput':'mean', 'node_throughput':'mean'})\n",
"plots_table = measurements.groupby(['name'])['scale', 'name', 'ncores', 'duration', 'throughput'].apply(scaling_plot)\n",
"\n",
"# names = measurements['name'].unique()\n",
"names = ['LinearRegression', 'LogisticRegression', 'PCA', 'SVD', 'ScalaSparkAggregation', 'ScalaSparkJoin', 'ScalaSparkSort', 'ScalaSparkTerasort']\n",
"\n",
"from toolz import partition_all\n",
"L = plots_table.loc[names].values.tolist()\n",
"grid = list(partition_all(2, L))\n",
"\n",
"output_file(\"hibench-gilgamesh.html\", title=\"HiBench results on Gilgamesh\")\n",
"show(gridplot(grid, plot_width=400, plot_height=400))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
" <div class=\"bk-root\">\n",
" <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
" <span id=\"20989\">Loading BokehJS ...</span>\n",
" </div>"
]
},
"metadata": {},
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{
"data": {
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"\n",
"(function(root) {\n",
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" return new Date();\n",
" }\n",
"\n",
" var force = true;\n",
"\n",
" if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
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" root._bokeh_is_loading = undefined;\n",
" }\n",
"\n",
" var JS_MIME_TYPE = 'application/javascript';\n",
" var HTML_MIME_TYPE = 'text/html';\n",
" var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
" var CLASS_NAME = 'output_bokeh rendered_html';\n",
"\n",
" /**\n",
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" */\n",
" function render(props, node) {\n",
" var script = document.createElement(\"script\");\n",
" node.appendChild(script);\n",
" }\n",
"\n",
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" // Clean up Bokeh references\n",
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" Bokeh.index[id].model.document.clear();\n",
" delete Bokeh.index[id];\n",
" }\n",
"\n",
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" // Clean up Bokeh references\n",
" var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
" cell.notebook.kernel.execute(cmd, {\n",
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" Bokeh.index[id].model.document.clear();\n",
" delete Bokeh.index[id];\n",
" }\n",
" }\n",
" }\n",
" });\n",
" // Destroy server and session\n",
" var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
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" return\n",
" }\n",
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" // store reference to embed id on output_area\n",
" output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
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" var bk_div = document.createElement(\"div\");\n",
" bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
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" output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
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"\n",
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" var toinsert = this.create_output_subarea(\n",
" metadata,\n",
" CLASS_NAME,\n",
" EXEC_MIME_TYPE\n",
" );\n",
" this.keyboard_manager.register_events(toinsert);\n",
" // Render to node\n",
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" render(props, toinsert[toinsert.length - 1]);\n",
" element.append(toinsert);\n",
" return toinsert\n",
" }\n",
"\n",
" /* Handle when an output is cleared or removed */\n",
" events.on('clear_output.CodeCell', handleClearOutput);\n",
" events.on('delete.Cell', handleClearOutput);\n",
"\n",
" /* Handle when a new output is added */\n",
" events.on('output_added.OutputArea', handleAddOutput);\n",
"\n",
" /**\n",
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" OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
" /* Is output safe? */\n",
" safe: true,\n",
" /* Index of renderer in `output_area.display_order` */\n",
" index: 0\n",
" });\n",
" }\n",
"\n",
" // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
" if (root.Jupyter !== undefined) {\n",
" var events = require('base/js/events');\n",
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"\n",
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" register_renderer(events, OutputArea);\n",
" }\n",
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"\n",
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" root._bokeh_failed_load = false;\n",
" }\n",
"\n",
" var NB_LOAD_WARNING = {'data': {'text/html':\n",
" \"<div style='background-color: #fdd'>\\n\"+\n",
" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
" \"</div>\"}};\n",
"\n",
" function display_loaded() {\n",
" var el = document.getElementById(\"20989\");\n",
" if (el != null) {\n",
" el.textContent = \"BokehJS is loading...\";\n",
" }\n",
" if (root.Bokeh !== undefined) {\n",
" if (el != null) {\n",
" el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
" }\n",
" } else if (Date.now() < root._bokeh_timeout) {\n",
" setTimeout(display_loaded, 100)\n",
" }\n",
" }\n",
"\n",
"\n",
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" try {\n",
" root._bokeh_onload_callbacks.forEach(function(callback) {\n",
" if (callback != null)\n",
" callback();\n",
" });\n",
" } finally {\n",
" delete root._bokeh_onload_callbacks\n",
" }\n",
" console.debug(\"Bokeh: all callbacks have finished\");\n",
" }\n",
"\n",
" function load_libs(css_urls, js_urls, callback) {\n",
" if (css_urls == null) css_urls = [];\n",
" if (js_urls == null) js_urls = [];\n",
"\n",
" root._bokeh_onload_callbacks.push(callback);\n",
" if (root._bokeh_is_loading > 0) {\n",
" console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
" if (js_urls == null || js_urls.length === 0) {\n",
" run_callbacks();\n",
" return null;\n",
" }\n",
" console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
"\n",
" function on_load() {\n",
" root._bokeh_is_loading--;\n",
" if (root._bokeh_is_loading === 0) {\n",
" console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
" run_callbacks()\n",
" }\n",
" }\n",
"\n",
" function on_error() {\n",
" console.error(\"failed to load \" + url);\n",
" }\n",
"\n",
" for (var i = 0; i < css_urls.length; i++) {\n",
" var url = css_urls[i];\n",
" const element = document.createElement(\"link\");\n",
" element.onload = on_load;\n",
" element.onerror = on_error;\n",
" element.rel = \"stylesheet\";\n",
" element.type = \"text/css\";\n",
" element.href = url;\n",
" console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
" document.body.appendChild(element);\n",
" }\n",
"\n",
" for (var i = 0; i < js_urls.length; i++) {\n",
" var url = js_urls[i];\n",
" var element = document.createElement('script');\n",
" element.onload = on_load;\n",
" element.onerror = on_error;\n",
" element.async = false;\n",
" element.src = url;\n",
" console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.head.appendChild(element);\n",
" }\n",
" };var element = document.getElementById(\"20989\");\n",
" if (element == null) {\n",
" console.error(\"Bokeh: ERROR: autoload.js configured with elementid '20989' but no matching script tag was found. \")\n",
" return false;\n",
" }\n",
"\n",
" function inject_raw_css(css) {\n",
" const element = document.createElement(\"style\");\n",
" element.appendChild(document.createTextNode(css));\n",
" document.body.appendChild(element);\n",
" }\n",
"\n",
" var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n",
" var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n",
"\n",
" var inline_js = [\n",
" function(Bokeh) {\n",
" Bokeh.set_log_level(\"info\");\n",
" },\n",
" \n",
" function(Bokeh) {\n",
" \n",
" },\n",
" function(Bokeh) {} // ensure no trailing comma for IE\n",
" ];\n",
"\n",
" function run_inline_js() {\n",
" \n",
" if ((root.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i].call(root, root.Bokeh);\n",
" }if (force === true) {\n",
" display_loaded();\n",
" }} else if (Date.now() < root._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!root._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
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" } else if (force !== true) {\n",
" var cell = $(document.getElementById(\"20989\")).parents('.cell').data().cell;\n",
" cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
" }\n",
"\n",
" }\n",
"\n",
" if (root._bokeh_is_loading === 0) {\n",
" console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(css_urls, js_urls, function() {\n",
" console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
"}(window));"
],
"application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"20989\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"20989\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '20989' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"20989\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));"
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" var render_items = [{\"docid\":\"ffbc3279-ada9-492a-82ba-26bf44a3df11\",\"roots\":{\"26031\":\"53e44f27-fb27-42ca-89cd-b130644cff18\"}}];\n",
" root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
"\n",
" }\n",
" if (root.Bokeh !== undefined) {\n",
" embed_document(root);\n",
" } else {\n",
" var attempts = 0;\n",
" var timer = setInterval(function(root) {\n",
" if (root.Bokeh !== undefined) {\n",
" embed_document(root);\n",
" clearInterval(timer);\n",
" }\n",
" attempts++;\n",
" if (attempts > 100) {\n",
" console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
" clearInterval(timer);\n",
" }\n",
" }, 10, root)\n",
" }\n",
"})(window);"
],
"application/vnd.bokehjs_exec.v0+json": ""
},
"metadata": {
"application/vnd.bokehjs_exec.v0+json": {
"id": "26031"
}
},
"output_type": "display_data"
}
],
"source": [
"from bokeh.layouts import gridplot\n",
"from bokeh.plotting import figure, show, output_notebook, output_file\n",
"from bokeh.models import Range1d, axes\n",
"from bokeh.plotting import figure, show, output_notebook\n",
"from bokeh.layouts import Row, Column, gridplot\n",
"# output_notebook()\n",
"\n",
"from bokeh.palettes import Dark2_5 as palette\n",
"import itertools \n",
"\n",
"output_notebook()\n",
"\n",
"def scaling_plot(measurements, axis_type='linear', y_axis_data = ('throughput', 'Throughput, MB/s')): #'log', ('duration', 'Duration, s')\n",
" y_axis_field, y_axis_label = y_axis_data\n",
"\n",
" row = measurements.iloc[0]\n",
" title = 'HiBench.{name} test'.format(name=row['name'])\n",
"\n",
" # Plot some metric for all measurements\n",
" colors = itertools.cycle(palette)\n",
" fig = figure(title=title, sizing_mode='scale_width', y_axis_type=axis_type, x_axis_type=axis_type,)\n",
"\n",
" fig.grid.grid_line_alpha = 0.75\n",
" fig.ygrid.band_fill_color = \"olive\"\n",
" fig.ygrid.band_fill_alpha = 0.1\n",
"\n",
" min_ncores, max_ncores=measurements['ncores'].min(), measurements['ncores'].max()\n",
" fig.xaxis.axis_label = '# of cores'\n",
" fig.xaxis.ticker = measurements.ncores\n",
" fig.x_range = Range1d(0, max_ncores+cluster_ppn)\n",
" fig.extra_x_ranges = {\"ClusterNodes\": Range1d(start=0, end=max_ncores/cluster_ppn+1)}\n",
" fig.add_layout(axes.LinearAxis(x_range_name=\"ClusterNodes\", axis_label=\"# of nodes\", ticker = measurements.ncores/cluster_ppn), 'above')\n",
"\n",
" fig.yaxis.axis_label = y_axis_label\n",
"\n",
" metric=elapsedtime_metric\n",
" for label, measurements_scale in measurements.groupby('scale'):\n",
" color = next(colors)\n",
" legend=measurements_scale['scale'].iloc[0]\n",
" fig.line(measurements_scale['ncores'], measurements_scale[y_axis_field], color=color, legend=legend)\n",
" fig.circle(measurements_scale['ncores'], measurements_scale[y_axis_field], color=color, fill_color='white', size=6, legend=legend)\n",
"\n",
" fig.legend.location = \"bottom_right\"\n",
" return fig\n",
"\n",
"scaling_plot_log = lambda measurements: scaling_plot(measurements, 'log', ('duration', 'Duration, s'))\n",
"\n",
"measurements = measurements.groupby(['scale','name','ncores'], as_index=False).agg({'duration':'mean', 'data_size':'mean', 'throughput':'mean', 'node_throughput':'mean'})\n",
"plots_table = measurements.groupby(['name'])['scale', 'name', 'ncores', 'duration', 'throughput'].apply(scaling_plot_log)\n",
"\n",
"# names = measurements['name'].unique()\n",
"names = ['LinearRegression', 'LogisticRegression', 'PCA', 'SVD', 'ScalaSparkAggregation', 'ScalaSparkJoin', 'ScalaSparkSort', 'ScalaSparkTerasort']\n",
"\n",
"from toolz import partition_all\n",
"L = plots_table.loc[names].values.tolist()\n",
"grid = list(partition_all(2, L))\n",
"\n",
"output_file(\"hibench-gilgamesh.html\", title=\"HiBench results on Gilgamesh\")\n",
"show(gridplot(grid, plot_width=400, plot_height=400))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"palette = \n",
"import itertools \n",
"\n",
"output_notebook()\n",
"\n",
"def scaling_plot(measurements, axis_type='linear', y_axis_data = ('throughput', 'Throughput, MB/s')): #'log', ('duration', 'Duration, s')\n",
" y_axis_field, y_axis_label = y_axis_data\n",
"\n",
" row = measurements.iloc[0]\n",
" title = 'HiBench.{name} test'.format(name=row['name'])\n",
"\n",
" # Plot some metric for all measurements\n",
" colors = itertools.cycle(palette)\n",
" fig = figure(title=title, sizing_mode='scale_width', y_axis_type=axis_type, x_axis_type=axis_type,)\n",
"\n",
" fig.grid.grid_line_alpha = 0.75\n",
" fig.ygrid.band_fill_color = \"olive\"\n",
" fig.ygrid.band_fill_alpha = 0.1\n",
"\n",
" min_ncores, max_ncores=measurements['ncores'].min(), measurements['ncores'].max()\n",
" fig.xaxis.axis_label = '# of cores'\n",
" fig.xaxis.ticker = measurements.ncores\n",
" fig.x_range = Range1d(0, max_ncores+cluster_ppn)\n",
" fig.extra_x_ranges = {\"ClusterNodes\": Range1d(start=0, end=max_ncores/cluster_ppn+1)}\n",
" fig.add_layout(axes.LinearAxis(x_range_name=\"ClusterNodes\", axis_label=\"# of nodes\", ticker = measurements.ncores/cluster_ppn), 'above')\n",
"\n",
" fig.yaxis.axis_label = y_axis_label\n",
"\n",
" metric=elapsedtime_metric\n",
" for label, measurements_scale in measurements.groupby('scale'):\n",
" color = next(colors)\n",
" legend=measurements_scale['scale'].iloc[0]\n",
" fig.line(measurements_scale['ncores'], measurements_scale[y_axis_field], color=color, legend=legend)\n",
" fig.circle(measurements_scale['ncores'], measurements_scale[y_axis_field], color=color, fill_color='white', size=6, legend=legend)\n",
"\n",
" fig.legend.location = \"bottom_right\"\n",
" return fig\n",
"\n",
"scaling_plot_log = lambda measurements: scaling_plot(measurements, 'log', ('duration', 'Duration, s'))\n",
"\n",
"measurements = measurements.groupby(['scale','name','ncores'], as_index=False).agg({'duration':'mean', 'data_size':'mean', 'throughput':'mean', 'node_throughput':'mean'})\n",
"plots_table = measurements.groupby(['name'])['scale', 'name', 'ncores', 'duration', 'throughput'].apply(scaling_plot_log)\n",
"\n",
"# names = measurements['name'].unique()\n",
"names = ['LinearRegression', 'LogisticRegression', 'PCA', 'SVD', 'ScalaSparkAggregation', 'ScalaSparkJoin', 'ScalaSparkSort', 'ScalaSparkTerasort']\n",
"\n",
"from toolz import partition_all\n",
"L = plots_table.loc[names].values.tolist()\n",
"grid = list(partition_all(2, L))\n",
"\n",
"output_file(\"hibench-gilgamesh.html\", title=\"HiBench results on Gilgamesh\")\n",
"show(gridplot(grid, plot_width=400, plot_height=400))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"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"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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gHeCfIvInnIPj/YAvwhWfaR1KK7y8+3Uecz/LIWvHAVKT4ph6em9uOK0XvTracJQx4RDOPY5xwPXAGhFZ7ZbdD1wjIiNxhqpygFsAVHWtiLwGrMM5I2u6nVFl6rKzqISXlufyyhfb2HeonIzOKTx0yVAuG9Wdtol2eZIx4dTgf5iI/BF4GCgB/gMMB+5U1ZfqW09VPwFqGx9YUM86fwD+0FBM5tikqnyZs5+5y3L4z9pd+FSZPLALN43rzel9O0ZkOMqYY1EgX83OUdW7ReRSnD2Ey4ClQL2Jw5hQKa3w8s7qPOYsy2HdzgO0S4rjx2f04fqxveze3cZEQCCJo7LOhcC/VLXIvtmZ5rCj0BmOevWLrew/XMGALqk8cukwLhnVjTYJNhxlTKQE8t/3nohswBmq+pmIpAOl4Q3LHKtUlc+37GPOpzl8uG4XAGcP7sLU03tz2ok2HGVMNKgzcYhIN1XNU9V73eMcRarqFZHDOFd5GxMyJeVe3l69gznLctiw6yBpbeL5yfgTuX5sL3ocZ8NRxkST+vY4nhORDsASnIPinwCo6iHgUPhDM8eCbfsOO8NRX26jqKSCgcenMuOyYUwZ2Z3khNhIh2eMqUWdiUNVLxCRJGACzvUWj4vIVpwk8h9V3do8IZrWRlX57Lu9zPk0h4XrdyMinDO4Czee3psxfTrYcJQxUa7eYxyqWoqbKABEpA9wPvCkiByvqmPCH6JpLQ6Xe3hz1Q5eXJbLxt0HOa5NPD89sy8/HNuLbmnJkQ7PmCb53e9+x/jx4znrrLNC2u4jjzzC/fffX/X+9NNPZ9myZSHdRrBEVQOvLNIJ2KuqKiIJqloevtAatnbtWh0yZEgkQ2hVwjWtw7Z9h3nxsxzmfbmNA6UeBndtx43jenPxiG4kxbfe4SibJiN0juW+TElJobi4OKRtZmVlrRg6dOjoxq5f38HxscAMYB/OZIT/ADoBMSJyg6r+p7EbNa2fqvJp9l7mLMth0YbdxIhw3tDjufH03ozudZwNR5lm89aqHTz2wUbyCkvolpbMXecO4JJRTZt4+6GHHuKll14iPT2dnj17cvLJJ5OVlcVFF13E5ZdfzoIFC/jlL39J27ZtGTduHN999x3vvfceX3zxBbfffjulpaUkJycze/ZsBgwYwJw5c3jnnXc4fPgwmzdv5tJLL+WPf/wj9957LyUlJYwcOZIhQ4bw8ssvV0skjz76KC+99BIxMTGcf/75zJgxIxRd1qD6hqqexJkipD2wGDhfVZeLyEDgFdzhK2P8HSrz8MaqHby4LIdN+cV0bJvA9AkZXDf2BLq2t+Eo07zeWrWD+95YQ0mFM3vRjsIS7ntjDUCjk8eXX37J66+/ztdff01FRQUnnXQSJ598ctXy0tJSbrnlFpYuXUqfPn245pprqpYNHDiQjz/+mLi4OBYuXMj999/P66+/DsDq1atZtWoViYmJDBgwgNtuu40ZM2bw5JNPsnr16qPieP/993n77bf5/PPPadOmDfv27WvUz9MY9SWOOFX9EEBEHlTV5QCqusG+LZqacgoO8eJnufxrxTYOlnoY1r09j18xgouGd23Vw1Emsv733bWsyztQ5/JVWwsp9/qqlZVUeLl7/je88kXt5/cM7taOB75f9xD4p59+ypQpU0hKSiIpKYnvf//71ZZv2LCBE088kT59+gBwzTXXMGvWLACKioqYOnUqmzZtQkSoqKioWm/y5Mm0b9/eiWHwYHJzc+nZsyd1WbhwITfddBNt2jinq3fo0KHOuqFWX+Lw7+2SGssCPzBiWi2fT/kku4A5y3L4aGM+sSKcP6wrN57em5NOSLPhKBNxNZNGQ+Xh9j//8z9MnDiRN998k5ycHCZMmFC1LDExsep1bGwsHo8nAhEGpr7EMUJEDuBMVJjsvsZ9nxT2yEzUKi7z8PqK7cz9LIfv9hyiU0oCt03M4LqxvejSzv40TPOpb88AYNyMxeworPm9F7qnJTPvltMatc1x48Zxyy23cN999+HxeHjvvfeYNm1a1fIBAwbw3XffkZOTQ+/evZk3b17VsqKiIrp3d4bI5syZE9D24uPjqaioID4+vlr52WefzYMPPsh1111XNVTVXHsd9V3HYeMLppotBYeYuyyH+Su2U1zmYUSP9vzpyhFcOLwriXH252Kiz13nDqh2jAMgOT6Wu84d0Og2TznlFC6++GKGDx9Oly5dGDZsWNUQE0BycjIzZ87kvPPOo23btpxyyilVy+6++26mTp3Kww8/zIUXXhjQ9qZNm8bw4cM56aSTePnll6vKzzvvPFavXs3o0aNJSEjgggsu4JFHHmn0zxWMOk/Hda8ar5OqNt+RmDrY6bihVdspjz6fkrlpD3OX5bBk4x7iY4UL3OGoUSccF6FIW4Zj+RTSUGtKX4bjrKri4mJSUlI4fPgw48ePZ9asWZx00klHLVdVpk+fTr9+/bjzzjubtM1QCtvpuEABsB3npkpQ/d4aCpzY2I2a6FL9H2srd507gMmDOjN/xXZe/CyXLQWHSE9N5I6z+nHtqSfQOdWGo0zLccmo7k1OFDVNmzaNdevWUVpaytSpU6slDYBnn32WuXPnUl5ezqhRo7jllltCuv1Iq2+P4y/AROBTnNNvP9FgrhZsBrbH0XQ1T1cEiI0RYgXKvcqoE9K48fTenD+0KwlxMRGMtOWxPY7Qsb4MrbDtcajqHe59wyfg3AL2byLyIfC0qm5p7AZNdHnsg43VkgaA16ckxMfy9k/HMqJnWoQiM8ZEq4bmqlLgIxFZBVyNcwX5JuDZZojNhNH2/YdZvCG/1jNOwLnrniUNY0xt6ptypC3OfTeuAtKBN4CTbVbclsnrU1Zv28+i9fks3pDPhl0HAWdYyus7egTSJh00xtSlvj2OfJy9i1fdZwVGi8hoAFV9o76GRaQn8CLQxV13lqo+4Z6tNQ/ojXMP8ytVdb87LPYEcAFwGLhRVVc2/kczB0or+PjbAhZt2M2SjXvYd6ic2BjhlN7H8dsLBzFpYGe+2V4U8tMVjTGtW32J4184H/gD3Ic/xdkDqY8H+JWqrhSRVGCFiPwXuBFYpKozRORe4F7gHpzp2vu5j1OBp91nE4ScgkMs2pDPovW7+WLLPjw+Ja1NPBMHdGbSwM6M759O++QjFxKdmJ4CEPLTFY1pzXJycrjooovIysqKdCgRUd/B8Rub0rCq7gR2uq8Pish6oDvO8NcEt9pcnDsM3uOWv+geV1kuImki0tVtx9ShwutjRe5+Fq3fzaIN+Xy3x7k5Y/8uKdz8vROZPKgzo3qmERdb9xlRlacr2pkrxphA1HtwPFREpDcwCvgc6OKXDHbhDGWBk1S2+a223S2zxFFD4eFyMr/dw8L1+WRuzOdAqYf4WGHsiR25YWwvJg3swgkd7T7dxlQ5uAvm3wSXz4HULg1WD4TX6+UnP/kJy5Yto3v37rz99tucf/75PP7444wePZqCggJGjx5NTk4Ohw8f5sYbbyQrK4sBAwaQl5fHU089xejRo/nwww954IEHKCsro2/fvsyePZuUlJSQxBguYU8cIpICvA7coaoH/Ce+c28IFdS1ISIyDZgGkJmZSXZ2dijDjUqqytbCcpZvLWb5tkOs3V2CTyEtKZbTTmjL2J4pnNy9LW0SYgAv5fvzyN4f/HbKysqOif5sLtafodPUvkz//P/RLvczit79DQVj7m1yPNu3b2fTpk08+uij3HPPPfziF79g5syZlJSUsG3bNtLS0ti3bx8ej4fs7Gyee+45YmJieOedd/j222+5+OKL2bZtGz6fj9/85jc8//zztGnThr///e/85je/4bbbbmtyjOEU1sQhIvE4SeNlv4PpuyuHoESkK85BeIAdgP8cwj3csmpUdRYwC5wLAFvr0Eq5x8fnW/ZWnQW1dd9hAAZ3bcf0id2ZPKgLw7u3JyYmdDPQ2lBVaFl/hk6dffn+vbBrTf0re8oh7ytASdv0OmmHcyE2oe76xw+D8+u/IVJcXBx9+vTh4osvBuDMM8+kpKSE5ORkevbsSUZGBgUFBcTFxZGRkcGGDRu4/fbbycjIICMjg+HDh9OzZ0927drFli1buOGGGwAoLy/ntNNOC/vfTVOPzQSdONyzqvJUNa+BegI8D6xX1T/5LXoHmIpzd8GpwNt+5beKyKs4B8WLjrXjGwXFZXy0wUkUH28qoLjMQ2JcDOMyOjFt/IlMGtjZTpM1JlhFW6Fy0gtVKNwKHZv+wVxzGvSSkhLi4uLw+Zwp20tLSxtsQ1U5++yzeeWVV5ocT3NqzB7HbcBwEflWVa+qp944nCvO14hI5e2r7sdJGK+JyI+BXOBKd9kCnFNxs3FOx72pEbG1KKrK+p0HWbzBObC9elshqtClXSLfH9GNyQM7My6jE8kJNvOsMbVqYM+Ag7vgiREcuYWQQmkhXP5CyI51+OvduzcrVqxgzJgxzJ8/v6p83LhxvPbaa0ycOJF169axZo2zlzR27FimT59etUd16NAhduzYQf/+/UMeWygFnThUdSqAe4ptffU+ofrEiP4m11JfgenBxtPSlFZ4+WzzXhZt2M3i9fnkFTnfSkb0aM8dk/szeVBnhnRrZzdBMiYUMv8IWuOmTeqDzEfhoj/Vvk4T/PrXv+bKK69k1qxZ1aZN//nPf87UqVMZPHgwAwcOZMiQIbRv35709HTmzJnDNddcQ1lZGQAPP/xw1CeOOic5rKogskhVJzdUFgktZZLD3QdKWbwhn0Xr8/k0u4CSCi9tEmI5I6MTZw3qwoSB6VEx46yNyYeW9WfoNLovnzmj9mMgxw+Dn37S9MAC5PV6qaioICkpic2bN3PWWWexceNGEhLqOdYSRmGb5FBEkoA2QCcROY4jew/tcE6TNXXw+ZSsvCIWrc9n0YbdZO1wbp7YPS2ZK0f3YNKgLpzap4Pdi9uYcGvG5FCfw4cPM3HiRCoqKlBVZs6cGbGkEQr1DVXdAtwBdAP8p/44ADwZzqBaosPlHj7ZVOCcBbUxnz0Hy4gROOmE47j7vAFMHtiF/l1SbAjKmGNQamoqX331VaTDCJn6rhx/AnhCRG5T1b81Y0wtRuUMs4vW5/PZd3sp9/hITYxj/IB0Jg/szIQBnenQtuV+qzDGmNoEcnC8SERuqFmoqi+GIZ6oVtcMs707tuH6sb2YPLAzo3t3sBseGWNatUASxyl+r5NwzohaiTPzbat3sLSCpXXMMPubCwYxaVBn+qZH9/QAxhgTSg0mDlWtdu27iKThTLXealXOMLt4w24+/86ZYbZ9cjwTB6QzaVAXzqwxw6wxxhxLGnMB4CGgT6gDiSSP18dXuftZvCGfhet3V80w269zCj/+Xh/OGtSlwRlmjTHHjpSUFIqLiyMdRsQ0mDhE5F2OXHYZCwwCXgtnUM2hcobZRevzWWIzzBpjwkRVUVViYlrPF89A9jge93vtAXJVdXuY4gkbVWXznmL32op8VuTux+tTOqUkcO6Q45k8qDNn9EsnJbFZZpo3xjSDCfMmsLd071HlHZM6suSqJU1uv7i4mClTprB//34qKip4+OGHmTJlCjk5OZx77rmceuqprFixggULFrBw4UIeffRR0tLSGDFiBImJiTz55JPs2bOHn/70p2zd6tyV+y9/+Qvjxo1rcmzhFMgxjkwROR4Yg7PnsTnsUYVIucfHF1v2sXD97mozzA7q2o6fT+jLpIGdGdEjLaQzzBpjokdtSaO+8mAlJSXx5ptv0q5dOwoKChg7dmzVjLmbNm1i7ty5jB07lry8PB566CFWrlxJamoqkyZNYsSIEQDcfvvt3HnnnZxxxhls3bqVc889l/Xr14ckvnAJZKjqZuB3wGKcq8f/JiIPquoL4Q6uId8WlDJtxuJqtzotKC5jycY9LFq/u2qG2YS4GMb17WgzzBrTyjz6xaNs2LehUeve9J/a51Ed2GEg94y5J6A2VJX777+fpUuXEhMTw44dO9i9ezcAvXr1YuzYsQB88cUXnHnmmXTo0AGAK664gm+//RaAhQsXsm7duqo2Dxw4QHFxcVTfzCmQcZm7gFGquhdARDoCy4CIJw6AHYUl3PP6N3y4dhc7D5TWOsPs6RkdaZNgQ1DGmNB6+eWX2bNnDytWrCA+Pp4jikrpAAAbIklEQVTevXtXTafetm3bgNrw+XwsX76cpKTIz1cXqEA+TfcCB/3eH3TLokaZx8eCrF02w6wxx5iG9gyGzR1W57LZ581u8vaLioro3Lkz8fHxfPTRR+Tm5tZa75RTTuGOO+5g//79pKam8vrrrzNsmBPbOeecw9/+9jfuuusuAFavXs3IkSObHFs4BZI4soHPReRtnGMcU4BvROSXADVu0hQxArx96xmRDsMYcwy57rrr+P73v8+wYcMYPXo0AwcOrLVe9+7duf/++xkzZgwdOnRg4MCBtG/fHoC//vWvTJ8+neHDh+PxeBg/fjzPPPNMc/4YQQskcWym+gHxyjv21Xs/juZmxy2MMTV1TOpY51lVTVF5DUenTp347LPPaq1T8/as1157LdOmTcPj8XDppZdyySWXVLUxb968JsXT3AI5q+p/myOQpkiOj+WucwdEOgxjTJQJxSm3ofL73/+ehQsXUlpayjnnnFOVOFqiQM6q6g/8GujtX19VJ4UvrMB1T0uudlaVMcZEo8cff7zhSi1EIENV/wKeAZ4DvOENJzj9OyXx6b0nRzoMY4w5pgSSODyq+nTYIzHGGNMi1Dl5ioh0EJEOwLsi8nMR6VpZ5pbXS0ReEJF8EcnyK/u9iOwQkdXu4wK/ZfeJSLaIbBSRc5v8kxljjAmL+vY4VuCcflt5McRdfssUOLGBtufg3GK25n07/qyq1Qb7RGQwcDUwBOdWtQtFpL+qRtXQmDHGmHr2OFS1j6qe6D7XfDSUNFDVpcC+AOOYAryqqmWqugXn2pExAa5rjDEtWuX0Ijk5OQwdOjTC0TQskLOqLquluAhYo6r5jdjmre6taL8CfqWq+4HuwHK/OtvdMmOMMVEmkIPjPwZOAz5y30/AGcbq4052+I8gtvc08BDOUNdDwP8BPwpifURkGjANIDMzk+zs7GBWN/UoKyuz/gwh68/QCVVf6r59eF95Fd24gfi//rVRbRw+fJhf/OIX7N69G6/Xy/Tp03nssce46KKLyMzMJC4ujocffpjHH3+c3Nxcbr75Zq699loOHTrEz372M4qKivB4PNx5552cddZZTlyqZGdns337dsrLy6P/76byJiN1PYAPgC5+77u4ZR2ArAbW7V1XHf9lwH3AfTW2eVpDsWVlZakJnU2bNkU6hFbF+jN0mtqX5bt3a94Dv9f1w0fouqHDdN2AgY1ua/78+XrzzTdXvS8sLNRevXrpzJkzVVX1jjvu0GHDhumBAwc0Pz9fO3furKqqFRUVWlRUpKqqe/bs0b59+6rP51NV1bZt26qq6pYtW3TIkCGNji1Qa9as+Uob+Hyt7xHIHkdPVd3t9z7fLdsnIhXBJCkR6aqqO923lwKVZ1y9A/xTRP6Ec3C8H/BFMG0bY449ux55hLL1dU+rruXllOfl4S0ocAu0alnu9TfUuk7ioIEcf//9dbY5bNgwfvWrX3HPPfdw0UUX8b3vfQ+g6j4cw4YNo7i4mNTUVFJTU0lMTKSwsJC2bdvWOgX78ccfH+yPHXGBJI4lIvIezoWAAD9wy9oChXWtJCKv4AxrdRKR7cADwAQRGYkzVJUD3AKgqmtF5DVgHc5dBqernVFljGmiss2b8YX43uD9+/dn5cqVLFiwgN/+9rdMnjwZgMTERABiYmKqXle+93g89U7B3tIEkjim4ySLynsZvgi8rqoKTKxrJVW9ppbi5+up/wfgDwHEY4wxAPXuGQB49uxhz8ynKXrjDdTng4ojgyS9/lHzSoHA5OXl0aFDB374wx+SlpbGc889F9B6gU7B3hIEMsmhAvPdhzHGtBhx6el0feB3pP/8Z3UmkGCtWbOGu+66i5iYGOLj43n66ae5/PLLG1wv0CnYWwJRvzG/WiuIHMQZWgJIAOKBQ6raLsyxNWjt2rU6ZMiQSIfRamRnZ5ORkRHpMFoN68/QCVVfVu6BlKxaxYlvvRmCyFqmrKysFUOHDh3d2PUD2eOouu+GOLfUmwKMbewGjTEmUir3QEzT1HnleG3cM7neAmwuKWOMOUYFe+V4DDAaaJmnAhhjjGmyQM6q+r7faw/OabRTwhKNMcaYqBfIMY6bmiMQY4wxLUODxzhEpIeIvOneWyNfRF4XkR7NEZwxxpjoE8jB8dk4U4J0cx/vumXGGHPMKSwsZObMmYBzMWAg13C0NoEkjnRVna2qHvcxB0gPc1zGGBOV/BNHt27dmD//2Ls2OpDEsVdEfigise7jh8DecAdmjDGhcqiojDf/bwWHisqa3Na9997L5s2bGTlyJFdccUXVjZfmzJnDZZddxnnnnUe/fv24++67AXjhhRe44447qtZ/9tlnufPOO5scRyQFkjh+BFwJ7AJ2ApcDdsDcGNNifPXvLeRlF/HVv7c0ua0ZM2bQt29fVq9ezWOPPVZt2erVq5k3bx5r1qxh3rx5bNu2jSuvvJJ3332XCneak9mzZ/OjHwV1G6KoU+9ZVSISC1ymqhc3UzzGGBOwj1/7loJtdc9+m5ddeGTCJCBraR5ZS/NAoFtGWq3rdOqZwveu7N+oeCZPnkz79u0BGDx4MLm5ufTs2ZNJkybx3nvvMWjQICoqKhg2bFij2o8W9e5xuFOb1zbLrTHGRL0uvduRnBIP4hYIJKfE06V3eKba859OPTY2Fo/HA8DNN9/MnDlzmD17Njfd1PIHbAK5APBTEXkSmAccqixU1ZVhi8oYYwIQyJ7Bkpc3sPaTPGLjY/B6fPQ9KZ0zr238zLSpqakcPHgwqHVOPfVUtm3bxsqVK/nmm28ave1oEUjiGOk+P+hXpsCk0IdjjDGhVXKwnKHjuzPke91Y+3Eeh5t4gLxjx46MGzeOoUOHMmjQoIDXu/LKK1m9ejXHHXdck7YfDRqcVj2a2bTqoWXTgIeW9WfotIa+vOiii7jzzjur7hgYSWGfVl1EEnHuANjbv76qPljXOsYYYxyFhYWMGTOGESNGREXSCIVAhqreBoqAFUDTT4I2xphjSFpaGt9++22kwwipQBJHD1U9L+yRGGOMaRECuQBwmYgEfdKxiLzgToqY5VfWQUT+KyKb3Ofj3HIRkb+KSLaIfCMiJwW7PWOMMc2jzsQhIlki8g1wBrBSRDa6H+pr3PKGzAFq7qncCyxS1X7AIvc9wPlAP/cxDXg6uB/DGGNMc6lvqKo7R07FDZqqLhWR3jWKpwAT3NdzgSXAPW75i+qc4rVcRNJEpKuq7mzs9o0xxoRHfYlji6rmhnh7XfySwS6gi/u6O7DNr952t8wShzHGRJn6EkdnEfllXQtV9U9N2bCqqogEfRGJiEzDGc4iMzOT7OzspoRh/JSVlVl/hpD1Z+hYX0aX+hJHLJDCkVleQmF35RCUiHQF8t3yHUBPv3o93LKjqOosYBY4FwC29IuCoklruMgqmlh/ho71ZWhlZWU1XKke9SWOnWG4yO8dYCoww31+26/8VhF5FTgVKLLjG8YYE53qSxxN2tMQkVdwDoR3EpHtwAM4CeM1EfkxkItznw+ABcAFQDZwGLvfhzHGRK36EkeTro1X1bqmYz+qXfdsqulN2Z4xxpjmUed1HKq6rzkDMcYY0zIEcuW4McYYU8UShzHGmKBY4jDGGBMUSxzGGGOCYonDGGNMUCxxGGOMCYolDmOMMUGxxGGMMSYoljiMMcYExRKHMcaYoFjiMMYYExRLHMYYY4JiicMYY0xQLHEYY4wJiiUOY4wxQbHEYYwxJiiWOIwxxgTFEocx4XBwF90//Akc3B3pSIwJOUscxoRD5h9Jyl8FmY9GOpKWz5JwaB3cReKBnAFNaSIuVLEEQ0RygIOAF/Co6mgR6QDMA3oDOcCVqro/EvEZU40qeMrAU+o8Kkr8nsvAUwIVpUeei/Nh5VwEhZVzIbUrJLUDcb+nibivJYjX0nAd3PfV6tZWXt9rgqwv7s8UTP0gfiaJgSUzjiThCx6r/nup/RdW9+8xKuvWUT9cdRc9hHScmFLHCgERrXOD4eMmjtGqWuBX9kdgn6rOEJF7geNU9Z762lm7dq0OGTIkvMEeKw7uouQf15B8/auQ2iXS0dRN1e8DvLT6h7anrMaHem316iurKyGUUucHgTEtUPak58kYf7k0dv2I7HHUYQowwX09F1gC1Js4TAj5D61c9KfA1vH5jnzoVvvgrSzz++Bt8IO8nm/x/gnBU9r4n1FiIC4Z4pMgzn3EJx95btOhellckls3+chzXGL1deISqy8vPwizLwRv2ZHtxiXBTzIhpROoz/12qAG8Jsj66uQ39bmvNcDXGmR9nxtbMPUb2FZdP9OGBbBzNagXJBa6joQB5/n/Uuv4Xdf5R1BLUV2Vm7FunfVDXHftm7D9qzriClykEocCH4qIAn9X1VlAF1Xd6S7fBTT4tTfhQC4c7BTd35BDzedzPpS85eApd579H7WWlYG3wn3vvvYvK9kHK+Y4Qysr5sCBPGdbDX2L9/9wDJbE1vMBnARt04/+UK7tAz+YD/fY+Hr+mUPkvV9y1N6J+uDLWYEnZOM4uAuWPuYkDXCe89fCNa8cW//zoXJwFyz8/ZH+bIJIJY4zVHWHiHQG/isiG/wXqqq6SeUoIjINmAawKfNfFL77GwrG3Bu6yNSHeMsRXwXiqwBvRdVr8VVUWyZVy8rB66l6XX1ZzXoViM9TR73yqmW1b9eDhOCXXu3HxR1nxocAql58Wz6mIqU7GpuIxiWisSlockd8KYlOmVvui01CYxOqyqq9j0tEY5PwxSa6ZUnuOk5dYsLwp+cDyt0HABXuo/n02PwxSd7y6oXecko3f8z27OxmjaWlS//8/5Hq81Y7g8fn83Ig1P/zx4ja+rOxIpI4VHWH+5wvIm8CY4DdItJVVXeKSFcgv451ZwGzALKXzte07DdJa98eYhPcb9Hl7jdp/2/W/mX+38prKQvxBzOI8403NsHvEV+9LD4BYlOOrheXUGO9BLdOfD1liTXaj6+l7MhDDhfAEyOqhoAEiFUPsT96177VNcbtX1a9zM7OJiMjA4AkICNCIbVYCzeCr3rij/FVkHZgA2kZ1ptBq6U/G6vZE4eItAViVPWg+/oc4EHgHWAqMMN9fjugBn0e+PIFSEyp/iFZ7QPVfY5Pq1Hm/yHuV1brB3awH+JuWzGx4R8eaYrMPx4Zs66kvuCOdRgTDj/9pOqlfxI2jeTXn6VZWSua0lQk9ji6AG+K82EaB/xTVf8jIl8Cr4nIj4Fc4MqAW4xLgFu/sm/IjbH9C2dPy5+33Ck3xphaNHviUNXvgBG1lO8FJjeuUfuG3Gj2rc4YE6TWceW4fUM2xphmE03XcQStrOMg+H1RpMMwxphjSuvY4zDGGNNsLHEYY4wJiiUOY4wxQbHEYYwxJiiWOIwxxgTFEocxxpigWOIwxhgTFEscxhhjgmKJwxhjTFAscRhjjAmKJQ5jjDFBscRhjDEmKJY4jDHGBMUShzHGmKBY4jDGGBOUFn0/js3Fm7l67tUAdEzqyJKrlkQ2oBZowrwJ7C3de6TgU+fJ+rNxrD9Dx/oytPz78w8D/3DyUIY2uq1Ws8dR7Q/MBKyufrP+bBzrz9CxvgytUPZbi97jqGnKW1MAULSqTFVrrVtXnUDKj2qrrvUDaavay7q3F0gsTYqjFqf/8/R6l5vgWH+GzoR5E4iRGESEGIkhVmIRnNdV5RxZHiMxVctjJbaqvLZ1/NuNkZijy+po1/8RULsNtFVru7W0VVu92toNpahLHCJyHvAEEAs8p6oz6qufVqz84BMf/Xcoix7M8G/nyGtqf139pV+dQNatUa9aeSBt1VFeX/t1xdKUOP654Z/V1vHvzzWPT6m1XVO3l9a/VO299Wfj1deXXz06CZ/6qh6KHnmtig+/1+qrel+zrPK1x+epakNV8aq3znar1neXV9V1y+uLxaveamUtldT1jTwSRCQW+BY4G9gOfAlco6rraqu/bNFyjf/17SSWHyDeC4M2rG/GaFuHYXOHAXD8/lQuzprK2BUvWH82gfVn6BwLfVlbEvNPSDXLGkxINdvye33dgusAaFPejj/2+z/OnDC20bsh0bbHMQbIVtXvAETkVWAKUGviEBLZ3uN8Bm6aB8DBJUuaHEBd396b2GjUtjkuy8u4dUrb2HPY2bVvtf4s/vjjkGzjWGL9GTr19uUnn1avXOPfodr/cc3/laP+d+qr28h269vGUZs8eiRD8DsAHVS7tY2oCBBL713OTsKIXeeQcGIiTRFtexyXA+ep6s3u++uBU1X11trqf7Z4la58bb/zRpU2h3c1V6itxuE2x9eehKw/G8X6M3SsL0PLvz9PuvI4Tps0qtXscTRIRKYB0wCWLVoJ6iWxtIiUQ9uJGz6kaY2HJYdGT2KuTXJZKcUFXsokGSS2en8OGxzp8Foc68/QCbgvm/Llt7HrHrVaEO1onW+CjCG4dZNzsymO7UhZYvvGb9MVbYljB9DT730Pt6yKqs4CZgF8tnilgtCxcB0DN77CoPmtY9yzuS15eQNrP8kjRr34rD+bzPozdKwvQ8ezZw8f/r8P2XJImpZsib7E8SXQT0T64CSMq4Fr66rctmMCg0/tROHXA0mUgc0VY6tTcrCcoeO7k9rLS9GGcuvPJrL+DB3ry9CJS09H+g9jcIKPNhTtb0pbUXWMA0BELgD+gnM67guq+oe66q5du1aHDGni8JSpkp2dTUZGRsMVTUCsP0PH+jK0srKyVgwdOnR0Y9ePtj0OVHUBsCDScRhjjKldq5lyxBhjTPOwxGGMMSYoljiMMcYExRKHMcaYoFjiMMYYE5SoO6sqGCUlJcVZWVkbg1mnsLCwfVpaWlG4YmqObYWq3ZrtFBQUdCotLS1ojlia8/cQKY3pz+bS0vq/tr6Mpp+hpX2ulJaWDmhSEKraYh/AV41YZ1YzxheWbYWq3ZrtNGd/NufvIVKPxvRnM8bWovq/tr6Mpp+hpX2uNPVv81gcqnq3FWwrVO2Gop3GttGcvwdztNbQ/9H0M7SGz5WARd2V48EQka9UtdFXP5rqrD9Dy/ozdKwvQ6up/dnS9zhmRTqAVsb6M7SsP0PH+jK0mtSfLXqPwxhjTPNr6XscxhhjmlmLSRwikiQiX4jI1yKyVkT+1y0XEfmDiHwrIutF5BeRjjXaicgAEVnt9zggIneIyGMiskFEvhGRN0UkLdKxRisReUFE8kUkq5ZlvxIRFZFONcpPERGPe6dL40dEeorIRyKyzv3/vt0tHykiy92/069EZIxb3l5E3vX7PLgpsj9BdBGRHBFZU9lvbtk8v//5HBFZ7Vd/uIh85vblGhFJqrf9ljJUJc6NeduqarGIxAOfALcDg4CJwI2q6hORzqqaH8lYWxIRicW598mpwABgsap6RORRAFW9J5LxRSsRGQ8UAy+q6lC/8p7Ac8BA4GRVLXDLY4H/AqU4twuY3/xRRy8R6Qp0VdWVIpIKrAAuwbnFwp9V9X33lgt3q+oEEbkfaK+q94hIOrAROF5VyyP2Q0QREckBRlf+/dWy/P+AIlV9UETigJXA9ar6tYh0BApV1VtX+y1mj0Mdxe7bePehwM+AB1XV59azpBGcycBmVc1V1Q9V1eOWL8e5A6OphaouBfbVsujPwN0cfU/Q24DXAfv7rIWq7lTVle7rg8B6oDtOP7Zzq7UH8ipXAVLdL5QpOL8LD6ZBbp9dCbziFp0DfKOqXwOo6t76kga0oMQBzrc2d/cqH/ivqn4O9AWucndj3xeRfpGNssW5miN/QP5+BLzfzLG0aCIyBdhR+Q/oV94duBR4OiKBtTAi0hsYBXwO3AE8JiLbgMeB+9xqT+KMNuQBa4DbK788GsBJrB+KyAoRmVZj2feA3aq6yX3fH1AR+UBEVorI3Q013qISh6p6VXUkzjfhMSIyFEgESt1zkp8FXohkjC2JiCQAFwP/qlH+G5xvby9HIq6WSETaAPcDv6tl8V+Ae+yDrWEikoKzZ3aHqh7AGVG4U1V7AncCz7tVzwVWA92AkcCTItKuliaPVWeo6knA+cB0d2i10jVU/7IYB5wBXOc+Xyoik+trvEUljkqqWgh8BJwHbAfecBe9CQyPVFwt0PnASlXdXVkgIjcCFwHXaUs5ABYd+gJ9gK/d8eUewEoROR4YDbzqll8OzBSRSyIVaLRyj12+DrysqpX/01M58v/9L2CM+/om4A13CDsb2IJzXMkAqrrDfc7H+VysPKkgDrgMmOdXfTuwVFULVPUwzh1YT6qv/RaTOEQkvfIsHxFJBs4GNgBv4RwcBzgT+DYyEbZI1b55iMh5OOPzF7t/QCZAqrpGVTuram9V7Y3zz3iSqu5S1T5+5fOBn6vqW5GMN9q44+7PA+tV9U9+i/Jw/q8BJgGVwytbcY7PISJdcE7s+K55oo1uItLWPcEAEWmLcwyj8uy/s4ANqrrdb5UPgGEi0sZNLGcC6+rbRkuaHbcrMNc9OyUGeE1V3xORT4CXReROnLNcbo5kkC2F+wd1NnCLX/GTOEN//3X+j1muqj+NQHhRT0ReASYAnURkO/CAqj5f/1qmHuOA64E1fqeJ3g/8BHjC/UArBSrH6x8C5ojIGkBwhgKjcibiCOgCvOn+D8cB/1TV/7jLjjqmqar7ReRPwJc4x0YWqOq/69tAizkd1xhjTHRoMUNVxhhjooMlDmOMMUGxxGGMMSYoljiMMcYExRKHMcaYoFjiMCYAInK8iLwqIpvdaRwWiEj/SMdlTCRY4jCmAe7FaW8CS1S1r6qejDNnUpcA1m1J10oZExBLHMY0bCJQoarPVBa4Exl+4t7DJMu9h8FVACIyQUQ+FpF3cK/AFZEfinM/mdUi8nd3ws5YEZnjt/6dEfnpjAmSfRsypmFDce4PUdNlOBPsjQA6AV+KyFJ32UnAUFXdIiKDgKuAcapaISIzcSaUWwt0r7yfh9iNs0wLYYnDmMY7A3jFvXfBbhHJBE4BDgBfqOoWt95k4GScxAKQjHNrgHeBE0Xkb8C/gQ+bOX5jGsUShzENW4szq20wDvm9FmCuqt5Xs5KIjMCZIvynODfX+VFjgzSmudgxDmMathhI9L8hjogMBwpxbiIW696+dDzwRS3rLwIuF5HO7rodRKSXOPckj1HV14Hf0sBU1sZEC9vjMKYBqqoicinwFxG5B2eW1hycu9OlAF/jzCp6t6ruEpGBNdZfJyK/xbkjWwxQAUwHSoDZbhkcubudMVHNZsc1xhgTFBuqMsYYExRLHMYYY4JiicMYY0xQLHEYY4wJiiUOY4wxQbHEYYwxJiiWOIwxxgTFEocxxpig/H+vdjhPusgb4AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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zX/fat1veXvtO8hyv/FH7TlD+ZE78/Ylx033iizroH1ZxWO9xBff9uOJxlBeVU5Jf0ud3yJhsGrGBBODOv98Z9+AaOci6y8IH0mQH2UQH8vCyhAfyDOx/KLjqhatyXYR+EwQRiX52X4cHdQXntSA4/3reJ1o3/N67jfA2Baf7J9mgMcCS45Y4AcEzzlBRVNHngLMxQ9GIDiRP1D4R+QPPI895ljxEBJ/4In/44fTYR3iZT3w92/E8CqWw9zKi9xFv//H20e/903sfkdcx+4qk5cUvh+Bs58oXrkxYl7/9zG/jHkyjDqieA2z4wBwuZ7wDc6+DudsH3+tgHnsgj9lGoiCQa/MenJdw2eXVlw9iSYzJrhEdSF7/36/nuggjxlFVNv2ZMSY+a0ObiESnUdrplQNj9WkOFCO2RWJ/rP1np1dmltWnOVCMqEBy2OjD2HDZhlwXwxhjDijWtWWMMSYtFkiMMcakxQKJMcaYtFggMcYYkxYLJMYYY9JigcQYY0xa+gwkInKXiIwRkQIR+YuI7BGRL6ew3nIR2S0iNQmWLxSRZhFZ7z5u9iw7S0TeF5FaEbmhfx/JGGPMYEqlRbJIVVuAc4CtwEzg2hTWewA4q488/6OqR7uP2wBExAf8AjgbOAK4VESOSGF/xhhjciCVQBK+aPGzwB9VtTmVDavqK8C+AZRpAVCrqh+qahfwEHDeALZjjDFmEKQSSP5bRN4DjgP+IiJVQGeG9n+yiLwlIs+IyJFu2hRghydPnZtmjDFmCEo4RYqITFbVnap6g4jcBTSrakhE2slMC2EtcIiqtorIZ4AngFn93YiILAYWA6xatYra2toMFM34/X6rywyy+swsq8+hJdlcW78UkXHAy8CzwKsAqtoGJL4ZdIrccZfw66dFZKmIVAL1wDRP1qluWqLtLAOWAWzcuFFtYrzMsEkGM8vqM7OsPjOnpibu+VD9krBrS1U/AyzECSQXAG+IyGMislhEDk53xyIySdy7D4nIArcse4HVwCwRmSEihcAlwFPp7s8YY0x2JJ39V1U7cVojzwKIyAycs6nuEZFJqrog0boisgInEFWKSB1wC1Dgbvc+4CLgKhEJAh3AJerc6DooIt8BngN8wHJV3ZjWpzTGGJM1/Z1Gfj9wr6oudVsLCanqpX0svwe4J8Gyp4Gn+1k2Y4wxOZCwa0tEThKRl93urGPcCwtrgF0icpZ7aq4xxpgDXLIWyT3ATUA58CJwtqq+ISJzgRW43V3GGGMObMmuI8lX1edV9Y/Ax6r6BoCqvjc4RTPGGDMcJAsk3Z7XHTHLNAtlMcYYMwwl69o6SkRaAAFK3Ne474uzXjJjjDHDQsJAoqq+wSyIMcaY4SnZWVvjkj0Gs5DGGDOSbN26lerq6lwXI2OSdW014EyYGHTfi2eZAodmq1DGGDOUPLGunh899z47mzqYXFHCtWfO4fxjbC7ZsGSD7T8DGnFO870MOFRVZ7gPCyLGmAPCE+vqufGxDdQ3daBAfVMHNz62gSfWJZwCMCWhUIgrrriCI488kkWLFtHR0cHChQt58803AWhoaGD69OkAtLe3c/HFF3PEEUdwwQUXcOKJJ0byPf/885x88skce+yxfOELX6C1tTWtcg1EsjGSa9y5sBYCXwF+LiLP41zZvmWQymeMMVn1/T9t5J2dLQmXr9veRFeoOyqtIxDiukfeZsXft8dd54jJY7jl3CPjLgv74IMPWLFiBffffz8XX3wxjz76aMK8S5cuZezYsbzzzjvU1NRw9NFHA06wueOOO3jhhRcoLS3lzjvv5O677+bmm29OuK1s6GuuLQVeEpF1OJMn3g58ANw/CGUzxpiciw0ifaWnasaMGZGAcNxxx7F169aEeV999VWuvvpqAKqrq5k/fz4Ab7zxBu+88w6nnHKKU6auLk4++eS0yjUQye5HUopz35EvAlXAY8Bxqho/BBtjzDDUV8vhlB++SH1T7KV0MKWihIevHPhBu6ioKPLa5/PR0dFBfn4+3d1OgOrs7Pv+garKGWecwYoVKwZcjkxINkayG7gOeB34D+BD4HgRuVBELhyMwhljTK5de+YcSgqir4YoKfBx7ZlzMr6v6dOns2bNGgAeeeSRSPopp5zCH/7wBwDeeecdNmzYAMBJJ53Ea6+9FrnJV1tbG5s2bcp4ufqSrGvrjzhnZ81xH16K00IxxpgRLXx21mCctfW9732Piy++mGXLlvHZz342kv6tb32Lyy67jCOOOIK5c+dy5JFHUl5eTlVVFQ888ACXXnopfr8fgDvuuIPZs2dnvGzJiDMMMjJs3LhRjzwyeTPVpMbuQJdZVp+ZdaDVZygUIhAIUFxczObNm/n0pz/N+++/T2Fh0rt5pKSmpmZNdXX18elso7/3IzHGGDPI2tvbOe200wgEAqgqS5cuzUgQyRQLJMYYM8SVlZVFrhsZipINthtjjDF96ncgEZHjRWRyNgpjjDFm+BlIi+S7wJ9F5OFMF8YYY8zw0+8xElW9DEBEyjJfHGOMMcNNny0SEflLvDRV3d/HestFZLeI1CRY/iUReVtENojIX0XkKM+yrW76ehEZuiNMxhgzQKNHj851ETIm2f1Iit37jlSKyFjPvUimA6lcifMAcFaS5VuAT6rqPJw5vJbFLD9NVY9W1bTObzbGmIzY/zH8+mzYv2vQd62qkalThqJkLZIrgTXAXGCt+3oN8CRwT18bVtVXgH1Jlv9VVRvdt28AU1MsszHGDL5Vd8H2N2DVnRndbGtrK6effjrHHnss8+bN48knnwScm1/NmTOHr371q1RXV7Njxw5+9atfMXv2bBYsWMAVV1zBd77zHQD27NnD5z//eU444QROOOEEXnvttYyWsS/JppH/KfBTEfmuqv48y+X4R+AZ7+6B50VEgf9S1djWijHGZMYzN8DHG5LnCXbBzjdBu2HNr538viQXBE6aB2f/MKXdFxcX8/jjjzNmzBgaGho46aST+NznPgc4U80/+OCDnHTSSezcuZPbb7+dtWvXUlZWxqc+9SmOOsoZEbj66qtZsmQJp556Ktu3b+fMM8/k3XffTWn/mZDKYHuziHw1NlFVf5OJAojIaTiB5FRP8qmqWi8iE4CVIvKe28KJt/5iYDHAYy+/ydfveI5/PL6S02eWZ6J4Byy/3x+ZCM6kz+ozszJZn5XNTRR19J7d16ugZRs+VQSnmynU8CGBMYckLl9zEw19lE9Vqa2tJRAI8IMf/IDVq1cjItTV1fHGG2/g9/uZMmUKlZWV1NbWsnLlSo499lj27dvHvn37WLhwIVu3bqW2tpbnnnuOdevWRbbd2NjIW2+9RWlpab/qYqBSCSQneF4XA6fjdHWlHUhEZD7wS+BsVd0bTlfVevd5t4g8DiwA4gYSt7WyDODxVWt0d2uQ/3xtDxMnTrJbYabhQJvLKNusPjMro/U5877ky/d/DD89CqejBAQlP9hK/ldWQNnEuKuUABV97FZEmDlzJg888ABdXV1s2LCBgoICpk+fzkEHHQRAeXl55HPW1NRQVlYWeV9VVUVjYyMzZ85ERFi3bh3FxcWpfuqImpq450P1S59nbanqdz2PK4BjgbRPNxCRg3FmEP6Kqm7ypJeGTy1274myCOjXJ+0IhPjXJ2p4ePV2/vbhXna3dDKSJqc0xgyiVXc5XVpe2p2xsZLm5mYmTJhAQUEBL730Etu2bYub74QTTmDVqlU0NjYSDAaj7qi4aNEifv7znhGI9evXZ6RsqRrIXFttwIy+MonICpzb9FaKSB1wC1AAoKr3ATcD44Glzh19CbpnaE0EHnfT8oHfq+qz/S1kqz/I9Y/29HuWFvo4ZHwpMypLOWT8KKZXOq+njy+lcnQh7v6MMSZa3d8h1BWdFupy0jPgS1/6Eueeey7z5s3j+OOPZ+7cuXHzTZkyhZtuuokFCxYwbtw45s6dS3m504X/s5/9jG9/+9vMnz+fYDDIJz7xCe67r4+WVgb1OY28iPyJcJsOfMDhwB9U9YYsl63fHl+1Rpc88zEAkyuKeeiKk9myt42tDW1saWhj6942tu1tZ8e+doLdPZ97dFE+0ytHMX18T3AJvx9XemAGGeuKySyrz8w6UOuztbWV0aNHEwwGueCCC7j88su54IIL0trmYE0j/2PP6yCwTVXr0tlptpUU+LjuzLkcPH4UB48fxSdnV0UtD4S6qW/siASZrQ1tbNnbztt1zTy94SM8MYay4nxPcCllhifgVIwaOtM4G2NGvltvvZUXXniBzs5OFi1axPnnn5/rIgEpBBJVXSUik3AGvBXYnPVSpWFKCncvK/DlMb3SCQyx937sCnZT19jO1r1tbGlodwLN3jbWbm/kT2/vxNuAqxhV4HSXxXSVTa8spbykIEuf0BhzoPrxj3/cd6Yc6DOQiMg3cMYzXgQE+LmI3Kaqy7NduP6aXVnMazccl9Y2CvPzOLRqNIdW9T6fwB8MsWNfO1sa2tm2t6e7bPXWRp58KzrIjCstZPr4UZHAMr2ylBlul1lZsQUZY8zIkUrX1rXAMeHTc0VkPPBXYMgFkmwryvcxc0IZMyf0nq+yMxBi+752J7i4AWZLQxuvf7iXx9bVR+WtHF3o6SpzB//d7rLSIrvXmDFmeEnlqLUX8E7QuN9NMx7FBT5mTyxj9sTeQaajK8S2feFB/3Z3TKaNVzbt4ZE10cNNVWVFkZZLTyvG6TIrKfQN1scxZkh6Yl09P3rufXY2dTC5Ynuf3dhmcKQSSGqBv4nIkzhjJOcBb4vIPwOo6t1ZLN+IUFLoY+6kMcydNKbXsvauIFsb2iMtmHBr5sX39tDQGh1kJo4p6jmzzA0u4RZNcYEFGTOyPbGunhsf20BHIARAfVMHNz7mnOJvwSS3Ugkkm4keYH/Sfbb7kWTAqMJ8jpg8hiMm9w4y+zsDbNvrdJdtCw/+721j5Tu72NsWfV775PJipleWutfK9HSVTRtnQcYMPlWlK9RNZ6CbzkCIzkCIjkCIzkA3HV0hOoMhOt3njq5uz/KYvIEQfvf96i2NdIWiLwzsCIT4tydr8AdDTBhTzISyIiaUFTO+tJC8vAPrtP3Ro0fT2trK1q1bOeecczJyxXqqUjlr6/uDURDTW1lxAdVTyqme0nvesJbOQM/1MeHB/71tPFvzEY3tgUg+EZhcXuK2YtzBf7e77OBxoyjMj57cwLoOMmuo1Wcg1N1zwO7qdg/k0Qdv78G8V1pXiM6gEwz84XUj24het3sAk0mIOKfvFxf4KCnwUVSQF3kfG0TC9ndGX3wM4MsTKkcXMqHMDS7hIDOmyJNWROXoIgp8ySf4WPjwQvZ29u7NH188npe/+HL/P+QIlMpZW7OB7wHTvflV9VPZK5bpy5jiAuZPrWD+1N4z+jS3B3pdiLm1oY2n1u+kpTMYyZcnMGVsSaT1sr8jwJ83fBz5g61v6uCGx96mu7ub846ZiuD8oQMH5EWa/ZVqV0ww1B05OHdGfpX3HPCjfql7DuSRX/Uxef291u35xR8ayNEdKPYc0J0DvI+SgjyKC3yUlxRQ7C6LylcYJy0mOMSmF+XnJfxunfLDF6lv6j254uTyYh6+8mR27+9kd4uf3fv9kde79vupb+pg/Y6mXq14cL7P40YVelozvYNNvCACJExPRVtbGxdffDF1dXWEQiH+7d/+jeuvv55LL72UZ555hvz8fJYtW8aNN95IbW0t1157Ld/85jdpbW3lvPPOo7GxkUAgwB133MF555034HJkSipXtr8F3IdzL5JQOF1V12S3aP23ceNGPfLII3NdjCFLVWnyBJnwhZjh1/v9wb43EkMEN8BIJNAIbiJEpcXm9WRz0uJsC3e9ZNuKlCOSL/G+Iu9jtkXMfuNti6jPGH9bPfuBtdub6Ar2/hXtyxPGlRZGunYCoYEd3AvznQNyiXuwLo46OOc5B/J8H8Xuc0lhnvvs6503Ehyi1y0p9CU9uA+m2MAMTuvl/144L6VWXiDUTUOrPyrY7Grx8/Ke+2no2kJXqJtAqJtASKPm5ssv3ZJwm1OKqyn05VGQL86zLw9fnjB33FyuX3B9wvUeffRRnn32We6//37AmW/rqKOO4vrrr+eqq65iyZIl/OUvf+G1116js7OT6upqdu3SIMNdAAAXGElEQVTaRTAYpL29PWrK+Q8++AARGXDX1mBd2R5U1XvT2YkZGkSEsaWFjC0t5NiDx0YtU1UOvfFpEh3Slnx6NopGrpVRZyXUeYosC7938jgJ6m7fu9y7rfD+E29L3Tzxl4f3E84Zva2e94TzJtkWMZ8x3rbw7jfRtrqJG0QAQt3Kpw+fEPdgXhz7S70wjyLPwT8SNPJ9B9wYQDhY9HQV9n3xsVeBL4+Dyks4qLwkKj3w90re29cQea84rcSuUDeBoFKb5Kbi2/a29Urz5Qk1W+pYs+51JpQVMTFOt9r0mXNZufJfuP766znnnHP4h3/4B4DIfUjmzZtHa2srZWVllJWVUVRURFNTE6Wlpdx000288sor5OXlUV9fz65du5g0aVJKdZAtCQOJe5tdgD+JyLeAxwF/eLmqJrz7oRl+RITJFSVxuw6mVJRw9adn5aBUw1uirpgpFSX83wvn56BEw9/5x0zh/GOmZHSurWQtB4B5D85LuOz1rz3CrhZ/7261/X72tPipqW/mL+/ujmpFheV//i4e37aO5Zf/E9Pnn0hTe4Dfrq5nxkfdbNrVSltQaGj1M25UIXl5eQSDQX73u9+xZ88e1qxZE5lyvrOzM+06SFeyFskanOAc/tlzrWeZAodmq1AmN649c07croNrz5yTZC2TiNXnyFcxqpCKUYXMmZT4JFZVpdUfdIKMG3Q+2LKdNplCY+cs1laO592Xn6C9K8g9L9biG7WH1g1b6Pp4J3+94wXy84SPmjv58i/foGnDBoL7ffxi1Rb2bFrLtm3beO+jFkrG9T+YhE8E+d7J5cdVp1MJJL/Vbp9TxZuRJd2uAxPN6nNkGF88PuFZW6kQEcqKCygrLuAwd+ql53bVcO21V5KXl0dBQQHPPHAvF110Ef9zw+loURm/+vUW1q5p4vPnHsGu/X5+8Ms8yooL6Dr8k/ztv27guksXUThpFvnjpvKN37xJfvkO2rtCHH/HC4zu2kd9UwfXPfJWpFutqqzY7VoroqqsiGc2fOz5kZP+3WRTGWy/ME5yM7BBVXenXYIMssH2zDlQp+nOFqvPzDrQ67Mr6J44sN/PrpZwV1qn27XW09XW0OqPexq2SM9Y5k/OnsQFnzwurQG3VAbb/xE4GXjJfb8Qp9trhjt54/9LpwDGGGP6pzA/j8kVJUyuKEmaL9St7G2NDi679/u5e+WmpOv1VyqBJB84XFV3AYjIRJz7tZ+Icx91CyTGGDME+fLEuUZmTDHeLqyHV++IeyLIQPV5z3ZgWjiIuHa7afuAQIJ1jDHGDFHXnjmHkgxOnZRKi+RlEflv4I/u+8+7aaVAU8ZKYowxZlB4TwTJhFQCybdxgscp7vvfAI+qM0p/WkZKYYwxZlCFr8mpqalJe5aSVCZtVOAR92GMMcZE6XOMRET2i0iL++gUkZCItKSycRFZLiK7RSTupC/i+JmI1IrI2yJyrGfZZSLygfu4LPWPZIwxQ1tTUxNLly4FYOfOnVx00UU5LlF6+gwkqlqmqmNUdQxQgtPNtTTF7T8AnJVk+dnALPexGLgXItOz3IJzZtgC4BYRGZtoI8YYM1gCu3fz0a3f58PzLxjwNryBZPLkyTzyyPDu8EnlrK0IdTwBnJli/leAZHNynQf8xt3uG0CFiBzkbn+lqu5T1UZgJckDkjHGZFU4gGw+YxFNjz6K/733BrytG264gc2bN3P00UfzhS98gepqZ5KSBx54gAsvvJCzzjqLWbNmcd111wGwfPlyrrnmmsj6999/P0uWLEnvA2VQKvcj8V7ZngccD2RqlrApwA7P+zo3LVF6vPItxmnNsGrVKmprazNUtAOb3++3uswgq8/MymR9hpbdj374YcLlGgjAnj3Q2AgxM4G8d9EX4q4jhx6Kb/EVCbf5zW9+k7Vr1/LII49QV1fH4sWLqa2tZdeuXaxevZqnnnqKwsJCFi1axLnnnstxxx3HrbfeypVXXklBQQH33nsvt99++5D5TqVy1ta5ntdBYCtOS2JIUNVlwDJwpkg5kKdNyKQDfQqKTLP6zKxM1ufHFeX4SxJfId65dSvdra1xl5UkWK+oopxJScqXn59PYWEhM2fOjHo9ceJEzjrrLI455hgA5s+fj4hw1FFHceaZZ/Lee+9x+OGH4/P5+OxnP9uPT5lYJm7Jm8pZW19Pey+J1QPTPO+numn1OFOxeNNfzmI5jDEHqEk33ZR0eXDPHvYsvZfmxx5Du7sh0HMd9iH/7zcZL09RUVHktc/nIxh0bjj3jW98gx/84AfMnTuXr389m4fl/kvlrK2pIvK4e/bVbhF5VESmZmj/TwFfdc/eOgloVtWPgOeARSIy1h1kX+SmGWPMoMqvquKgW25m5gsrqbjoIqSoCAoK0tpmWVkZ+/cnuWNWHCeeeCI7duzg97//PZdeemla+8+0VLq2fg38Hgh3Bn7ZTTujrxVFZAVOy6JSROpwzsQqAFDV+4Cngc8AtUA78HV32T4RuR1Y7W7qNruRljEml8IBpepbV7Fn6b10rFs34G2NHz+eU045herqag4//PCU17v44otZv349Y8cOrZNYU5lGfr2qHt1X2lBg08hnjvXpZ5bVZ2YdqPV5zjnnsGTJEk4//fSMbTMT92xP5fTfvSLyZRHxuY8vA73v8mKMMSYrmpqamD17NiUlJRkNIpmSStfW5cDPgZ/g3GL3r7hdUMYYY7KvoqKCTZsyew+RTEoaSETEB1yoqp8bpPIYY4wZZpJ2balqCBhapwcYY4wZUlLp2npNRO4BHgbawomqujZrpTLGGDNspBJIwmdn3eZJU+BTmS+OMcaY4SaVK9vt5lXGGGMSSmXSxiKcqeOne/Or6m2J1jHGGHPgSKVr60mgGVgD+LNbHGOMMcNNKoFkqqravUCMMcbElcqV7X8VkXlZL4kxxphhKWGLxL3Pereb5+si8iFO15bg3Cxx/uAU0RhjzFCWrGtrCj2n/hpjjDFxJQskW1R126CVxBhjzLCULJBMEJF/TrRQVe/OQnmMMcYMM8kCiQ8YjTMmYowxxsSVLJB8ZBcdGmOM6Uuy03+tJWKMMaZPyQLJ0LsNlzHGmCEnYSBR1X2DWRBjjDHDUypXthtjjDEJZTWQiMhZIvK+iNSKyA1xlv9ERNa7j00i0uRZFvIseyqb5TTGGDNwqUzaOCDu/d5/AZwB1AGrReQpVX0nnEdVl3jyfxc4xrOJDlW1K+uNMWaIy2aLZAFQq6ofqmoX8BBwXpL8lwIrslgeY4wxWZC1FgnOXF07PO/rgBPjZRSRQ4AZwIue5GIReRMIAj9U1ScSrLsYWAywatUqamtrM1B04/f7rS4zyOozs6w+h5ZsBpL+uAR4RFVDnrRDVLVeRA4FXhSRDaq6OXZFVV0GLAPYuHGjzpw5c3BKPMLV1tZidZk5Vp+ZZfWZOTU1NWlvI5tdW/XANM/7qW5aPJcQ062lqvXu84fAy0SPnxhjjBkishlIVgOzRGSGiBTiBIteZ1+JyFxgLPC6J22se694RKQSOAV4J3ZdY4wxuZe1ri1VDYrId4DncCaAXK6qG0XkNuBNVQ0HlUuAh1RVPasfDvyXiHTjBLsfes/2MsYYM3RkdYxEVZ8Gno5Juznm/a1x1vsrYLf3NcaYYcCubDfGGJMWCyTGGGPSYoHEGGNMWiyQGGOMSYsFEmOMMWmxQGKMMSYtFkiMMcakxQKJMcaYtFggMcYYkxYLJMYYY9JigcQYY0xaLJAYY4xJiwUSY4wxabFAYowxJi0WSIwxxqTFAokxxpi0WCAxxhiTFgskxhhj0mKBxBhjTFoskBhjjEmLBRJjjDFpyWogEZGzROR9EakVkRviLP+aiOwRkfXu4xueZZeJyAfu47JsltMYY8zA5WdrwyLiA34BnAHUAatF5ClVfScm68Oq+p2YdccBtwDHAwqscddtzFZ5jTHGDEw2WyQLgFpV/VBVu4CHgPNSXPdMYKWq7nODx0rgrCyV0xhjTBqy1iIBpgA7PO/rgBPj5Pu8iHwC2AQsUdUdCdadEm8nIrIYWAywatUqamtrM1B04/f7rS4zyOozs6w+h5ZsBpJU/AlYoap+EbkSeBD4VH82oKrLgGUAGzdu1JkzZ2a+lAeg2tparC4zx+ozs6w+M6empibtbWSza6semOZ5P9VNi1DVvarqd9/+Ejgu1XWNMcYMDdkMJKuBWSIyQ0QKgUuAp7wZROQgz9vPAe+6r58DFonIWBEZCyxy04wxxgwxWevaUtWgiHwHJwD4gOWqulFEbgPeVNWngH8Skc8BQWAf8DV33X0icjtOMAK4TVX3ZausxhhjBi6rYySq+jTwdEzazZ7XNwI3Jlh3ObA8m+UzxhiTPruy3RhjTFoskBhjjEmLBRJjjDFpsUBijDEmLRZIjDHGpMUCiTHGmLRYIDHGGJMWCyTGGGPSYoHEGGNMWkZcIAns3s1Ht36fD8+/INdFGfasLjPL6jOzrD6HjlxPI59Runs3m//3l9DubggECLW2xeaIs5L2732cNI2Xp7/7SSFP3P30Skr/Mwb37iW4dCmbX/urU5fBIIGPPnIWikQ/O2+inpzFUW96rxN3O+msK3GS+r8diV3Wn3XjfBZwDnjBXyxl84svRr6bZuCsPjMnsHs37Np1MNXVaW1nRAUSWlpQvz/ydtPxx+ewMCNL7Wn9uk2M6cO7R1b3Cka9glgfzykFvTT34V1JSHGdVPP3cx0NBgk1NdG9f3+vHz+bz/5Mz5vwMvdZvT+uNCZPnPwpr9vHNiLreoua4ro9++9feaP3n3jd8A9EurvhzjsrSdPICiQxJlx7be9fvPF+NfbKksI6vVeKkyU2Tx/LU9r3APYTJ0+8/TTc/0sC27bFbS0ddMftPS0i7eMPkzhf3LhfeuKkxdmO94+jv+smLEOcckdeZ6b8TY8/TjDckosx/hvfSLBezEEwdl+x+eN85tTXic2fyufqY51U9zWAddrf+BvdLS3EUzR3Tsx3OvXg5SSF36TaEqXXstS3McB14wX3ZJ85ybotf/4zwY93uQkavyndDyMskETXx/h/vDxH5RieRn/iE+xZei/Njz2GhkLOLxZXxUUX5bBkw9PYSy9JWJ8TllyTw5INT8E9exLW59Sf/CSHJRt+xn/ta5G6BEmhbz65kTXYXj4GKSqCgoJcl2RYyq+q4qBbbmbmCyuRRYusLtNk9ZlZVp+Z461LRpc2pLu9ERVIpKqKmS+spOKiiyiaOzfXxRm28quqyP/WVVaXGWL1mVlWn5mTX1UFEyduT3c7ktIZR8PExo0b9cgjj8x1MUaE2tpaZs6cmetijBhWn5ll9Zk5NTU1a6qrq9M6M2lEtUiMMcYMPgskxhhj0mKBxBhjTFoskBhjjEmLBRJjjDFpGVEXJHZ0dLTW1NS8n2r+pqam8oqKiuZslinb+8rkdr3bamhoqOzs7OzX+eUDLctg/j/kykDqczANt/+DePU5VD7DcDuudHZ2zkm7IKo6Yh7Am/3Mv2wQy5aVfWVyu95t9bcu0ynLYP4/5OoxkPoc5PINq/+DePU5VD7DcDuuZOK7eaB3bf1pBOwrk9tNd1sDXX8w/x9MfCPh/2CofIaRcFzplxF1QaKIvKmqNuVvBlhdZpbVZ2ZZfWZOJupypLVIluW6ACOI1WVmWX1mltVn5qRdlyOqRWKMMWbwjbQWiTHGmEE2LAOJiBSLyN9F5C0R2Sgi33fTRUT+XUQ2ici7IvJPuS7rcCAic0RkvefRIiLXiMiPROQ9EXlbRB4XkYpcl3WoEpHlIrJbRGriLPsXEVERqYxJP0FEgiJiN3vxEJFpIvKSiLzj/n1f7aYfLSJvuN/RN0VkgZteLiJ/8hwPvp7bTzD0iMhWEdkQrjs37WHP3/xWEVnvyT9fRF5363ODiBQn3f5w7NoS57ZgparaKiIFwKvA1cDhwGnA11S1W0QmqOruXJZ1uBERH1APnAjMAV5U1aCI3AmgqtfnsnxDlYh8AmgFfqOq1Z70acAvgbnAcara4Kb7gJVAJ7BcVR8Z/FIPTSJyEHCQqq4VkTJgDXA+8J/AT1T1GRH5DHCdqi4UkZuAclW9XkSqgPeBSaralbMPMcSIyFbg+PD3L87y/wCaVfU2EckH1gJfUdW3RGQ80KSqoUTbH5YtEnW0um8L3IcCVwG3qWq3m8+CSP+dDmxW1W2q+ryqhm9D9wYwNYflGtJU9RVgX5xFPwGuI/oGtgDfBR4F7DsaQ1U/UtW17uv9wLvAFJw6HONmKwd2hlcBytwfmKNx/h+CmJS49XYxsMJNWgS8rapvAajq3mRBBIZpIAHnF53bFNsNrFTVvwGHAV90m73PiMis3JZyWLqEni+U1+XAM4NclmFNRM4D6sN/kJ70KcAFwL05KdgwIiLTgWOAvwHXAD8SkR3Aj4Eb3Wz34PRG7AQ2AFeHf0yaCAWeF5E1IrI4Ztk/ALtU9QP3/WxAReQ5EVkrItf1tfFhG0hUNaSqR+P8Sl4gItVAEdDpnhN9P7A8l2UcbkSkEPgc8MeY9P+D8wvvd7ko13AkIqOAm4Cb4yz+T+B6O9glJyKjcVpt16hqC06PwxJVnQYsAX7lZj0TWA9MBo4G7hGRMXE2eSA7VVWPBc4Gvu12xYZdSvSPx3zgVOBL7vMFInJ6so0P20ASpqpNwEvAWUAd8Ji76HFgfq7KNUydDaxV1V3hBBH5GnAO8CUdjgNquXMYMAN4y+2fngqsFZFJwPHAQ276RcBSETk/VwUdityxz0eB36lq+G/6Mnr+vv8ILHBffx14zO3yrgW24IxJGZeq1rvPu3GOjeETFfKBC4GHPdnrgFdUtUFV24GngWOTbX9YBhIRqQqfQSQiJcAZwHvAEziD7QCfBDblpoTDVtQvExE5C6d//3PuF8qkSFU3qOoEVZ2uqtNx/jiPVdWPVXWGJ/0R4Fuq+kQuyzuUuH32vwLeVdW7PYt24vxdA3wKCHfFbMcZ20NEJuKcJPLh4JR26BORUvekBUSkFGcMJHx24aeB91S1zrPKc8A8ERnlBppPAu8k28dwnf33IOBB98yXPOAPqvrfIvIq8DsRWYJzBs03clnI4cT9gp0BXOlJvgenu3Cl87fNG6r6zRwUb8gTkRXAQqBSROqAW1T1V8nXMgmcAnwF2OA5JfUm4Argp+7BrRMI9/XfDjwgIhsAwek2HLIzLefAROBx9284H/i9qj7rLus1JqqqjSJyN7AaZ2zlaVX9c7IdDMvTf40xxgwdw7JryxhjzNBhgcQYY0xaLJAYY4xJiwUSY4wxabFAYowxJi0WSIzpJxGZJCIPichmd8qJp0Vkdq7LZUyuWCAxph/ci+UeB15W1cNU9TicOZ8mprDucL1uy5ikLJAY0z+nAQFVvS+c4E7K+Kp7/5Ya9/4NXwQQkYUi8j8i8hTu1cEi8mVx7qezXkT+y52A1CciD3jWX5KTT2fMANgvJGP6pxrn/hixLsSZMPAooBJYLSKvuMuOBapVdYuIHA58EThFVQMishRncryNwJTwvUzEbiJmhhELJMZkxqnACve+DbtEZBVwAtAC/F1Vt7j5TgeOwwk0ACU4t0L4E3CoiPwc+DPw/CCX35gBs0BiTP9sxJmxtz/aPK8FeFBVb4zNJCJH4UyJ/k2cGw1dPtBCGjOYbIzEmP55ESjy3hxIROYDTTg3VfO5t3v9BPD3OOv/BbhIRCa4644TkUPEuZ97nqo+CvwrfUzbbcxQYi0SY/pBVVVELgD+U0Sux5mFdivO3ftGA2/hzJh6nap+LCJzY9Z/R0T+FedudXlAAPg20AH82k2Dnrv/GTPk2ey/xhhj0mJdW8YYY9JigcQYY0xaLJAYY4xJiwUSY4wxabFAYowxJi0WSIwxxqTFAokxxpi0WCAxxhiTlv8PXmkNygdwHyUAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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IxyJyUEQua4vgVPOKKmr53ZvZnPmXL/B0CUBaSnzbBqWUald82SM4zRhTCswB8oHBwC2BDEo1r67ezuIvdzDt0c/495pd/GxKOvefm0l8k+sB4qMjuWXWsCBFqZRqD3w5cNxQ5kzgVWNMiV59GlzehoXoFBPlPGsoLSWeW2YN49yxvYMcsVIqlPmSCN4Wkc1AFfA/ItIdcH8wWgWUL8NCnDu2N+eO7U1eXh6DBw8OYrRKqfbCYyIQkTRjzF5jzO0i8jBQYoypF5FK4Jy2C1GV19Txt0/zeO4LHRZCKeV/3vYInhWRrsBnwPvASgBjTAVQEfjQlN1uWLq+gIfe38yBshrOH9eb22Zn6LAQSim/8pgIjDFniEgcMA04D3hURHZhJYX3jTG72ibE8LR+VxF3L8tlw+5iRvdN4e+Xj2dsvy7BDksp1QF5PWvIGFNtjHnfGHOjMWYCcDNW8lgkImvaJMIws7+0ml+9soHznviKguIq/njhaJb+zwmaBJRqQ3feeSfLly/3e70PPPBAo/cnnHCC39fREmKM8b2wSCpw2BhjRCTGGFPrz2BycnLMyJEj/Vllu1Ftq+f5L3ew6JM86uoNP//JAK6fPpjEFl4RrJ3F/qXt6V/h2p6JiYmUl5f7tc7s7Oy1mZmZE1pTh8c9AhGZLCKficgbIjJWRLKBbGC/iMz2dxIIV8YYPszZx2mPfc7D72/hxMGpfPSrk7htdkaLk4BSHdWb6wuY+uAnDLj9HaY++Alvri9odZ333nsvw4YN48QTT2Tu3Lk8+uijXHnllbz22msAvPvuu2RkZDB+/Hj+7//+jzlz5gCwZs0apkyZwtixYznhhBPYsmULAIsXL+b8889n9uzZDBkyhFtvvRWA22+/naqqKsaMGcO8efMAKzE0eOihh8jKymL06NHcfvvtrf5cx8LblmYRcAfQGfgEON0Ys0pEMoCXsfoKVCts3V/GPctyWZl3iCE9EvnXzydx4pDUYIelVEhqOqhiQXEVC9/YCNDia2W++eYbXn/9db777jtsNhvjxo1j/PjxzvnV1dVce+21fP755wwYMIC5c+c652VkZPDFF18QFRXF8uXLueOOO3j99dcB2LBhA+vXryc2NpZhw4Zxww038OCDD7Jo0SI2bNhwVBzvvfceb731FqtXr6ZTp04UFha26PO0lLdEEGWM+RBARO4xxqwCMMZs9vWCMhFJAZ4FMgEDXG2M+bp1Ibd/xZW1PPbRVv61ehcJMZHcddYILpvcn6hIHfpJha+7l+WQu7fU4/z1u4qprbc3mlZlq+fW177n5TXuz10ZkZbM78/yfLj5yy+/5JxzziEuLo64uDjOOuusRvM3b97MwIEDGTBgAABz587l6aefBqCkpIQrrriCH374ARHBZrM5l5sxYwadO3e2Yhgxgp07d9K3b1+PcSxfvpyrrrqKTp06AdC1a1ePZQPBWyJwbfGmo5b52rHwZ6wzjC4QkRig07EE19HU1dt5ec0u/vjRVkqrbMyb1J8FM4fSNSEm2KEpFfKaJoHmpgfa7373O6ZPn87SpUvJz89n2rRpznmxsbHO15GRkdTV1QUhQt95SwSjRaQUECDe8RrH+2ZPZBeRzsBJwJUAjj6FsO1X+GrbIe5ZlsvmfWVMGdiN3589goxeycEOS6mQ4e2XO8DUBz+hwM1Iur1T4lly7ZQWrXPq1Klce+21LFy4kLq6Ot5++23mz5/vnD9s2DC2b99Ofn4+6enpLFmyxDmvpKSE3r2tQ1KLFy/2aX3R0dHYbDaio6MbTZ85cyb33HMP8+bNcx4aasu9Ao/HIowxkcaYZGNMkjEmyvG64X20p+VcDAAOAi+IyHoReVZEEvwWeTuxu7CS6/65lkufWU15TR1PXTaOf/9ikiYBpY7RLbOG+X1QxeOPP56zzz6bUaNGcfrpp5OVleU8pAMQHx/PE088wezZsxk/fjxJSUnO+bfeeisLFy5k7NixPv/inz9/PqNGjXJ2FjeYPXs2Z599NhMmTGDMmDE8+uijLf5MLeHx9FHHVcUeGWO89maIyARgFTDVGLNaRP4MlBpjftek3HxgPsCKFSvGp6WlHUP4oavKZufl7w7z6sYiIgXmju7GBVldiI1qm36AmpqaRrunqnW0Pf2rpe35cV4Jz317iIPldXRPjOLnE1KZMbhz8wt6UVFRQUJCAlVVVVx66aXcd999uJ7G3jDfGMNdd91Feno6V111VavW6U/V1dWtPn3UWyKwA3uAhlTn2kNsjDEDvVYs0gtYZYxJd7z/CXC7MeZMT8t0hOsIjDG8tWEvf3hvE/tLazh3TBq3nz6cXp3bdliIcD1PO1C0Pf0rlNrz0ksvJTc3l+rqaq644goWLlzYaP5jjz3Giy++SG1tLWPHjuWZZ55xduqGAn9cR+Ctj+AvwHTgS6zTRVeaY7j6zBizT0R2i8gwY8wWYAaQ25pgQ933e4q56785rNtVzKg+nXli3jjG92/b3n+l1LH597//7XX+ggULWLBgQRtFExzexhq6SazzRKcBlwN/FZEPgSeNMTt8rP8G4CXHGUPbgdDZn/KjA2XVPPL+Fl5du4fUxFgevmAUF4zrQ0SE3rdBKRX6vF666tgD+FRE1gOXAPcCPwDP+FK5MWYD0KpdllBWU1fP4i/z+esnedTU1XPtyQP53+mDSYrzpS9dKaVCg7f7ESRg3XfgYqA78AYwXkcdtfoBPt50gPveySX/cCWnDu/Bb84cwYDUsDspSinVAXjbIziA9ev/P45nA0xwnA2EMeaNwIcXevIOlHHP25v4fOtBBvdI5MWrJ3Ly0O7BDksppVrMWyJ4FWvjP8zxcGWw9hDCRkmljcc/3so/vt5Jp5hI7pwzgsun9Cdah4VQql3Lz89nzpw5ZGdnBzuUoPHWWXxlG8YRsurthv98s4s/friVospa5k7sx80zh9ItUc8pV0p1DPpz1otV2w8z568r+c3SbAb3SOTtG07kgfOyNAkoFUxl++CF06Fsv9+qrK+v5xe/+AUjR47ktNNOo6qqimnTpvHtt98CcOjQIdLT0wGorKzkoosuYsSIEZx33nlMmjTJWe7DDz9kypQpjBs3jgsvvNDv9x4IFE0EbuwpquT6l9ZxydOrKK2y8bdLx7Fk/mRGprXuCkallB+seBh2rYIVD/mtyh9++IHrr7+enJwcUlJSnMNJu/PEE0/QpUsXcnNzuffee1m7di1gJYv77ruP5cuXs27dOiZMmMCf/vQnv8UYSHrnExeVtXU8tWI7f1+xDRH41cyhzD9pIHFNxjdRSgXAe7fDvo3ey9TVwt5vwdhh7QtW+Ugvo/f2yoLTH2x21QMGDGDMmDEAjB8/nvz8fI9lV65cyY033ghAZmYmo0aNAmDVqlXk5uYydepUAGpra5kypWWD4bW1Y04EjrOG9hpj9gYgnqAwxrDs+x/5w7ub+LGkmrNHp3H76RmkpcQHOzSllKuSXdAwwIExULwLurV+qIqmw0ZXVVURFRWF3W4NcV1dXd1sHcYYZs6cycsvv9zqeNpaS/YIbgBGichWY8zF/g6orWUXlHDXf3P4dmcRI9OS+cvcsRyfrsNCKNXmmvvlXrYP/jyaI7dDMVBdDBc8D0k9/R5Oeno6a9euZeLEic7bVoI1dPUrr7zC9OnTyc3NZeNGay9m8uTJXH/99c5xlCoqKigoKGDo0KF+j83fjrmPwBhzhTFmLHBNAOJpM4fKa7j99e85a9FK8g9X8NBPs/jv/56oSUCpULXiYeuQkCtj92tfgatf//rXPPnkk4wdO5ZDhw45p//yl7/k4MGDjBgxgt/+9reMHDmSzp070717dxYvXszcuXMZNWoUU6ZMYfPmzQGJzd88jj7qLCDysTFmRnPT/KEtRh+trbPz4lf5/OXjH6iy1XPV1HRumDGE5A42LEQoje7YEWh7+leL2vOpE933IfTKgutW+icwH9TX12Oz2YiLi2Pbtm2ceuqpbNmyhZiY4NxpMKCjj4pIHNatJVNFpAtHhqFOBlp2p+gg+3TzAe59O5fthyqYPqw7v50zgkHdE4MdllLKF224sfemsrKS6dOnY7PZMMbwxBNPBC0J+Iu3PoJrgZuANGCdy/RSYFEgg/K3bQfLue/tXD7dcpCBqQm8cOXxTM/oEeywlFLtUFJSkvO6gY7C25XFfwb+LCI3GGP+2oYx+U1ptY2/LP+BxV/lEx8dyW/PHM7PpqQT00Z3CVNKqfbAl7OGSkTkZ00nGmP+EYB4/KLebnj129088sEWCitrueT4vtx82jBS9YpgpZQ6ii+J4HiX13FYdxpbB4RkIvgmv5C7l+WQXVDK8eldePGsiWT21iuClVLKk2YTgTHmBtf3IpKCNTR1SNlbXMUf3tvMsu/2clznOP4ydyxnjToO6yZrSimlPGnJBWUVwAB/B9JSVbX1PP35dp5ckYcxcOOMIVx38iDiY8J4WIiyffT+8BfQ8z8BudBGqY4kMTGx3QwOFyjNJgIRWcaRS/kigeHAK4EIJqZ0J5Sl+rTxMsbwzsYf+cO7mykoruLMUcex8PQM+nTpFIjQ2pcVDxN3YL11oc2c9jHolVLtgTEGYwwRER3rhBNf9ggedXldB+w0xuwJRDBiq4R3b4GpN1pXDHp45B8u56Wv88nbX8LMLnFcOrsPQ3va4cfdsLehnHG/vL3e5b2HMkfNr29mvqf1NFd/0zqarseH5e1N3tfXQvEuBAPfPm9dgNOpK8QmQUyi9dzwcL5PhNjkxu9jkiBSxyRUoWPakmkcrj581PRucd347OLPWl1/eXk555xzDkVFRdhsNu677z7OOecc8vPzmTVrFpMmTWLt2rW8++67LF++nIceeoiUlBRGjx5NbGwsixYt4uDBg1x33XXs2mXd0ffxxx93DkIXynzpI1ghIr2AiVh7BtsCGtGmt6yHF+nAbwBisA5UfRbQiCwS4eUhHqZHNjPfzfIRkUfPdzfN0+PHDVjX/jl24op3QV011JRBbbn1XNf8AFoARMU7kkRD0khu8t5DUolJckkwSZpUVGMtPHTpLgl4m36s4uLiWLp0KcnJyRw6dIjJkydz9tlnA9Yw1S+++CKTJ09m79693Hvvvaxbt46kpCROOeUURo8eDcCNN97IggULOPHEE9m1axezZs1i06ZNfokvkHw5NHQNcCfwCdYW5q8ico8x5vmARBQRBYNmwMT5zg1knRE+yD3Aq+sKqLIZZmWmcdHx/UmMi4GI5jaO7jbCkc3Md1dHO+h0dg7K1TAei2NQrms/b/wPV29rnBhqHM+1ZUfe15ZDTanLPMdz6d7AJpVGeylJoZFUtM/FvzwcunxozUNsLmzZ2DxXvX+V2+kZXTO4beJtPtVhjOGOO+7g888/JyIigoKCAvbvt25+079/fyZPngzAmjVrOPnkk+na1RqX7MILL2Tr1q0ALF++nNzcXGedpaWllJeXk5gY2iMY+PIfdQsw1hhzGEBEugFfAYFJBPY62LECzv4rJPVkxdaD3LMsh20HEzhp6FTunzOcwT2SArLqds/boFyufQWR0dbhok5+GGDPXVJxTSjOec0llXKoq/JtnZ6SirdDXa1JKqHU52KM9T/S6FHfzHtfyrTRMrVVUPCtdehy7QtQuAPikiAyFmp3g72yyQ80lx9q3tSWu5RzWabhEK0PP+ReeuklDh48yNq1a4mOjiY9Pd05/HRCQoJPfx673c6qVauIi4vzqXyrle0jtjR/GGS2qhpfEsFhoMx11Y5pgWPslH5wP7+quJzlmw6Q3q0Tz10xgVMyeujpoN7sWWP1Ebiqr7WmB4q/k4q3vRSPSaXcSiquy/qcVOK8H+oSgXX/sDZc616E6DhrGb9tPI/xfdNEHwwRUS6PyGN7X+LSvWiMdSgzoTvU13BbXQ3U1Vjf2boasNsarTZrQD+PIb2w7kMPc96C9x+yEk1UrHUTG+dzHETFWHu1i+dQsnwXPYqriV56DZ9uKmTnzp3wyf1W2YoD8NmDEBnD8RHV3PTROxR99hRJyZ15/Z9/J2voQMj7mNOmjuWv993GLdf/HKJi2ZCbx5ixY4+sPyrWagd/bcdWPIx0PqHVuxu+JII8YLWIvIV14Pkc4HsR+RWAMcb/P5GH25WzAAAaQElEQVTqa9nz/WeskjNZeHoGV05NJzYqjE8H9ZXLoFztcrTMyGiI72I9Wqu+zk0SaXroq+zIwzWJuCaVykKrEx+sDfHXf2vdhjAiCqLjW1mHmzoDsoybaQ2/0lvC3f0EbJVwxTL3h9zsjpMf6h0J4nUvAx5f/JKjXG2T52o305rMA7DXM29cMmctyifrpleZ0DeOjB4xsOU9K4bKQvjsD4A14uYd42uZ+NPr6RoPGamRdN6VB//6nL8Ms3P9m58x6plF1NnhpP6RPDWn6Q2uxJGMYq1E5JokGiWqWO/z6mzWj5NpJ7Ts7+HCl0SwjcYdxA09uX4/PrPRDODU6n8DMGlAFz65dBw9ktpoF0t1LJFRrU8qDRuuuvoj06Li4Mbvta+gJXw9dNkgIgIi4qy9MKyzgzydNcTwOS0Oq3ye9ZwKfP0r92WyH8XaO3PsrVxaXMj8+Cjqqis572fXce5Fc+C0E0mtq2HJNTVHklfD3k3Dc523edWNp1VWeF/O1LsPtgV8OWvobr+t7RjsKarWJKCC61g3XMq7Vh669Mcpoq0SEQkR8RAdz12/s25SX11dzWmnnca5P7+5bU8ocf5I8fFkjWb4ctbQUODXWGdtOssbY07xSwQe7C328RivUoESjD6Xjqy9H7p08eijjzZfKJDc/UhpBV8ODb0KPAU8C/hvX6QZeuN4FXQdaMOlOhh3P1JawZdEUGeMedJva/RBfHQkt8wa1parVEqp9sPlR0p1dvba1lbn7VaVDecDLhORXwJLgZqG+caYwtau3J3eKfHcMmsY545tl3fDVEqpdsfbHsFarPO8GnpAbnGZZ4CB/g5maGocX94+3t/VKqWU8sLj5XrGmAHGmIGO56YPvycBpZTqKBqGlMjPzyczs3VX/bYFX84aOt/N5BJgozHmgA/LRwLfAgXGmJaf7KuUUiogfBlU++dYZwzNczyeAW4DvhSRy31Y/kYg9IffU0q1K7YDB/jxrrvZfu55raqnoqKCM888k9GjR5OZmcmSJUtIT09n4cKFjBkzhgkTJrBu3TpmzZrFoEGDeOqppwBr2OoZM2Ywbtw4srKyeOst76MmhzJfzhqKAoYbY/YDiEhPrPsVTwI+B/7paUER6QOcCdwPeLhmTymlfGc7cIBDTzxJydKlGLsdbLbmF/Li/fffJy0tjXfeeQeAkpISbrvtNvr168eGDRtYsGABV155JV9++SXV1dVkZmZy3XXXeRy2uj2Oh+ZLIujbkAQcDjimFYpIc3+Bx4FbCcBwFEqpjmXfAw9Qs8nzMNSmtpbavXupP3TIMcE45+28/Gdul4kdnkGvO+7wut6srCxuvvlmbrvtNubMmcNPfvITAOe9CLKysigvLycpKYmkpCRiY2MpLi4mISHB7bDVvXr1OpaPHRJ8SQSficjbWBeWAfzUMS0BKPa0kIjMAQ4YY9aKyDQv5eYD8wFWrFhBXl6er7ErL2pqarQt/Ujb07/ctWd9cQmmyvOIAmb7dqisdDuvysNy1cUllDfzd4uIiOC1115jxYoV3HzzzUyZMoW6ujoKCgqoqqri4MGDVFdXO+O12+1s3bqVTz/9lB07drBkyRKio6OZNm0amzdvpry8HGMMeXl57Nmzh9ra2pD/7viSCK7H2vg33G/tH8DrxhgDTPey3FTgbBE5A4gDkkXkX8aYy1wLGWOeBp4GyMnJMXr1pn/olbD+pe3pX27b8+GHvC5Td/AgB594kpI33jjqkFDGa696WdK7vXv30qdPHyZMmMCwYcN49tlniYqKYuDAgaSmptKzZ092797tjLdh3tdff83AgQMZPnw4n376KQUFBaSnp5Oeno6IMHjwYKKiooiJiQnodyc7O7vVdfgy6JwBXnM8fGaMWQgsBHDsEfy6aRJQSilfRXXvznG/v5Puv/wfjwmhJTZu3Mgtt9xCREQE0dHRPPnkk1xwwQXNLjdv3jzOOusssrKymDBhAhkZGa2KI5jEuBxnc1tApIwjA4jHANFAhTEm2eeVHEkEXk8fzcnJMSNHjvS1WuWF/oL1L21P//JHezbsIVStX8/AN5f6KbL2Jzs7e21mZuaE1tThyx6Bs6NXrO7wc4DJx7ISY8xntM0t5pVSYaJhD0G1ni/XETgZy5vArADFo5RSqo0d65XFEcAEwD93Q1BKKRV0vpw1dJbL6zogH+vwkFJKqQ7Alz6Cq9oiEKWUUsHRbB+BiPQRkaUicsDxeN0xdIRSSqkOwJfO4heA/wJpjscyxzSllGrXiouLeeKJJwDrwjJfrh/oiHxJBN2NMS8YY+ocj8VA90AEs618G1kvZpH1YhbTlkwLxCqUUsrJNRGkpaXx2mvHdN1sh+FLIjgsIpeJSKTjcRlwONCBHa4O+CqUUu1QRUkNS/+4loqSmuYLN+P2229n27ZtjBkzhgsvvNB5E5nFixdz/vnnM3v2bIYMGcKtt94KwPPPP89NN93kXP6ZZ55hwYIFrY4j2HxJBFcDFwH7gB+BC4A26UB+fevrfLzrY9btX8f2ku0UVRdRb69vi1UrpULUt+/sYG9eCd++s6PVdT344IMMGjSIDRs28MgjjzSat2HDBpYsWcLGjRtZsmQJu3fv5qKLLmLZsmXYHMNavPDCC1x99dWtjiPYvJ415Li72PnGmLPbKJ5G7vr6rqOmCULn2M6kxKbQJa4LKbEpdI3r2ui98zm2CylxKSRGJ7bLMcKVCidfvLKVQ7vLPc7fm1d8ZLAbIPvzvWR/vhcE0ganuF0mtW8iP7loaIvimTFjBp07dwZgxIgR7Ny5k759+3LKKafw9ttvM3z4cGw2G1lZWS2qvzWmLZnmPGpyf8b94zNp3e0wvSYCY0y9iMwFHmvVWlrow59+SFFNEcXVxdZzTTFF1Y2f95TvIftQNkU1RdTZ69zWEyVRpMSlHJUkusR1OSppNDzHR8W38adVqmNz3XgB8KX11C2uG59d/Fmzy/dMT6b0YBVVFTYrIQjEJ0ST3D0w/6uxsbHO15GRkdTVWduXa665hgceeICMjAyuuio4Z9f7+9C5LxeUfSkii4AlQEXDRGPMOr9G4sZxicdxXOJxPpU1xlBuK/eaNBqe84rzKK4uprimGIP7QffiIuOOJIbYlEZJomts10bvG8pER0b78+Mr1aF42ng1TPfll/tnL20mZ+VeIqMjqK+zM2hcd06+1POon8YY6u312I2dOlOH3dipN/XY7dZzdWQ1xaXFFJQXsLdsLza7jW3F29hXsY+i6iJyDudgt9sprSllS+EWEvYlYPoZNm/fzNfffM1zHzzHx7s+dtbbsK56U99oWsN7d9Ocz26m1dnr3Nbnb74kgjGO53tc2xc4xe/RuOgW1+2YyosISTFJJMUk0Ze+Pi1Tb6+nrLbMmTgKqwuPJBKXhFJcbe15FFcXU2Yr81hfYnSi+0NUTfZCGpJHckwykRGRx/Q5lWpLxhhq7bXU1NdQW289u74+lmneXPvRtT5tCEdtOYPq3uXk915Pvz2jKMhN5M4l1zbauDs3mI7XzSnvXc6gjEHEHhdLTWkN5751LkXri6jKr+L7t78HYOfBnexes5vEskQASjJKqN1Vyx3rvN/9zBcREkGERBApkc7nyIhI53vnNMf0CDmmIeJ80uww1G2pPQxDbau3UVJb4nFvo7C6sNH74ppiqurc3z3JXX+Hu0NVXeK6+NTfcdSut4Ovu96qsVBoT2MMNrutVRtgj9PsbqbVNSlr974B90VsZCwxkTGU1Xr+ETWq+yiiJKrRBjEiIuKoaa4bQ08bTuezu2mtXD4qwopnwWULuPy6yznh5BPc1uda1t0017It6b/MevFIv8T9Gfdz9qSzW9UJ6sugc7FYdyhLdy1vjLnH0zIdWXRkNKnxqaTGp/q8TFVdFSU1VvLw1OfRsNfR0v6OlLgUr7ve2YeyEQQcXxdxvHA+S+P3DUSk+bJu6mxaxmNZl3JNpzlj8LBe13Iey7gr20yZhvfe2nNL4RbfNqzHsKH2VKa1YiJinBtj1+eG10nRScTEuZ/n67S4yDi386Ijop3t6rrxauqlM15q9edsC8XFxUycOJHRo0fzi5/+Itjh+I0vh4beAkqAtUDrv5VhKD4qnvioeHol+HZTa2MMFbaKZjvKi6qL2Fa8zbnn4c3cd+b646MohwuW+X4FalRElPsNaUQssVGxJEQn0CWui/eNbUTLNtQxkTEBOZQQrlJSUti6dWuww6BbXDe/dhj7kgj6GGNm+22NqlkiQmJMIokxifRN8q2/w27sjP7HaI/zF52yyNkx3nA40Pm+ocPc+WSOqWzTck2nNfBYp0u55tbbqGwzZZqeCGCM58/lrs4/rf0Tnjw27TGfNsoxETHaD+TgaeN1rP2BikaHJrOzs9e2tj5fEsFXIpJljNnY2pWpwGnuV9/JfU9uo0g6Dm+J4NT+p7ZhJB2D68ZLb/0ZWjwmAhHJBuyOMleJyHasQ0OCdbOyUW0TolJKqUDytkfQmyOnjqp2QHe9/UvbU4ULb4lghzFmZ5tFolpNd739S9tThQtviaCHiPzK00xjjOcDqEoppdoNb4kgEkjEeea3UkqpjshbIvgxXC8aU0qpcOLtnEPdE1BKqTDgLRHMaLMolFJKBY3HRGCMKWzLQJRSSgWHDkKilFJhThOBUkqFOU0ESikV5jQRKKVUmNNEoJRSYU4TgVJKhbmAJQIR6Ssin4pIrojkiMiNgVqXUkqplvPlxjQtVQfcbIxZJyJJwFoR+cgYkxvAdSqllDpGAdsjMMb8aIxZ53hdBmzCuseBUkqpENImfQQikg6MBVa3xfqUUkr5LpCHhgAQkUTgdeAmY0ypm/nzgfkAK1asIC8vL9AhhYWamhptSz/S9vQvbc/QEtBEICLRWEngJWPMG+7KGGOeBp4GyMnJMXoXKP/QO2r5l7anf2l7+k92dnar6wjkWUMCPAds0ruZKaVU6ApkH8FU4HLgFBHZ4HicEcD1KaWUaoGAHRoyxqxEb26jlFIhT68sVkqpMKeJQCmlwpwmAqWUCnOaCJRSKsxpIlBKqTCniUAppcKcJgKllApzmgiUUirMaSJQSqkwp4lAKaXCnCYCpZQKc5oIlFIqzGkiUEqpMKeJQCmlwpwmAqWUCnOaCJRSKsxpIlBKqTCniUAppcKcJgKllApzmgiUUirMaSJQSqkwp4lAKaXCnCYCpZQKc5oIlFIqzGkiUEqpMKeJQCmlwpwmAqWUCnOaCJRSKsxpIlBKqTCniUAppcKcJgKllApzIZcIbAcO8ONdd7P93POCHUq7p23pX9qe/qXtGTqigh2AK3PgINsunYex28FmC3Y47ZbtwAHq/vYE2z75RNvSD7Q9/Uvb039sBw7A/v39yMxsVT0BTQQiMhv4MxAJPGuMedDrAqUlmJoa59ttc+Y01OOudncr9G3asZR1OykQ62nFuh3LG5sN27591BcWgjGNZudffAkABpfpjYs0XsaX197q83H5xu+Ny2T/xWmaVuBjnKa+nvqyMkxFBU3lzTj1qGkevwO+zvepjuZm+2Mdgfkcpq6O+qIi7GVlR30PGv7XG1fhw3qski2KJ2BlfK7LlyLuCxmbjbpDh6gvLoYHH0z1LSjPApYIRCQS+BswE9gDfCMi/zXG5HpapjK+JzExycTWlgIQO2jw0RsOcD/tqK1Fk42J96K+r8fneNxsgDwu34p4XKZXZWdjLy2lJiaZ7BFXk5n7vLMtIxITj5R3/ZI2/cK6vnWZ1+gLedQyHuY1eu06uemX25flPcfZuD5fl28+tvKVX2IqKty2Z6cJE5p8Bg9/m4a5nv52vlfh+e/v63yfygTuc1SuXu3x+xk7aPAxxnks5Zov45e/z5HKAl6mcv167CUl1MQkUxvfw9eM6VEg9wgmAnnGmO0AIvIf4BzAYyKoj4xlR//TyfhhCQB9/vx4AMPreOoOHuTgE0+yej2UdB7UqC37PfdskKNrf7y1Z9pD3ndu1dG8taf+rx8b17bsGRnb6vrEp0zYkopFLgBmG2Oucby/HJhkjPlfT8t8/cl6s+6VIuuNMXQ5LiEgsXVURfsq3c/QtmwRr+2Zluh+nvKo6MejD7EB+v1sAdfv5riLujDllLGt2isIemexiMwH5gN89fE6EIg1lSRW7SUqeVSQo2tfUjvFU3awlpryems3VtuyVby2Z1JWsMNrd1Lj4yg7aNPvpx80+m76QSATQQHQ1+V9H8e0RowxTwNPg7VHADDkpMGcfOnRnUeqeZ+9tJmclXuJiAS7XduytbQ9/Uvb038a2hLfey88CmQi+AYYIiIDsBLAJcCl3hZI6BZN5km9qSyp8VZMeVFVVkvmSb1J6l9P2c5IbctW0vb0L21P/2loy05daje1tq6A9REAiMgZwONYp48+b4y531v5nJwcM3LkyIDFE07y8vIYPHhw8wWVT7Q9/Uvb03+ys7PXZmZmNj2N7ZgEtI/AGPMu8G4g16GUUqp1Qm6ICaWUUm1LE4FSSoU5TQRKKRXmNBEopVSY00SglFJhLuhXFruqqqoqz87O3uJr+eLi4s4pKSklgYwp0OvyZ72udR06dCi1urr6UFvE0pZ/h2BpSXu2pfb2N3DXnqHyGdrbdqW6unpYqwMxxoTMA/j2GMs/3YaxBWRd/qzXta5jbcvWxNKWf4dgPVrSnm0cX7v6G7hrz1D5DO1tu+KP72Z7PzS0rAOsy5/1trauli7fln8H5V5H+BuEymfoCNuVYxLQK4uPlYh8a4xp1RVyyqJt6V/anv6l7ek//mjLUNsjeDrYAXQg2pb+pe3pX9qe/tPqtgypPQKllFJtL9T2CJRSSrWxoCQCEYkTkTUi8p2I5IjI3Y7pIiL3i8hWEdkkIv8XjPjaGxEZJiIbXB6lInKTiDwiIptF5HsRWSoiKcGONVSJyPMickBEst3Mu1lEjIikNpl+vIjUOe7GpxxEpK+IfCoiuY7/7xsd08eIyCrHd/RbEZnomN5ZRJa5bA+uCu4nCD0iki8iGxvazjFticv/fL6IbHApP0pEvna050YRifO6giCdniVAouN1NLAamAxcBfwDiHDM6xHsU8na2wNryO99QH/gNCDKMf0h4KFgxxeqD+AkYByQ3WR6X+ADYCeQ2qSdP8EaXfeCYMcfSg/gOGCc43USsBUYAXwInO6YfgbwmeP1HQ3fTaA7UAjEBPtzhNIDyHf9/rmZ/0fgTsfrKOB7YLTjfTcg0lv9QdkjMJZyx9tox8MA/wPcY4yxO8odCEZ87dwMYJsxZqcx5kNjTJ1j+iqsu8QpN4wxn2NtgJp6DLiVo+8CdQPwOqDf0SaMMT8aY9Y5XpcBm4DeWG2Y7CjWGdjbsAiQJCICJGL9HepQPnG020XAy45JpwHfG2O+AzDGHDbGeL2nZdD6CEQk0rErcwD4yBizGhgEXOzYbXxPRIYEK7527BKOfCFcXQ2818axtGsicg5Q0PAP5TK9N3Ae8GRQAmtHRCQdGIu1138T8IiI7AYeBRY6ii0ChmMlho3AjQ0/BpWTAT4UkbWO+7y7+gmw3xjzg+P9UMCIyAcisk5Ebm2u8qAlAmNMvTFmDNav1IkikgnEAtXGOif2GeD5YMXXHolIDHA28GqT6b/B+oX1UjDiao9EpBPWIYs73cx+HLhNN1beiUgi1l7TTcaYUqw9/gXGmL7AAuA5R9FZwAYgDRgDLBKRZDdVhrMTjTHjgNOB60XkJJd5c2n84y8KOBGY53g+T0RmeKs86GcNGWOKgU+B2cAe4A3HrKXAqGDF1U6dDqwzxuxvmCAiVwJzgHnGccBQ+WQQMAD4TkTysX6wrBORXsAE4D+O6RcAT4jIucEKNBSJSDRWEnjJGNPwP30FR/6/XwUmOl5fBbzhOGScB+wAMtoy3lBnjClwPB/A2jY2dLRHAecDS1yK7wE+N8YcMsZUYvVjjfNWf7DOGurecAaLiMQDM4HNwJvAdEexk7E6mZTvGv0yEJHZWMe3z3Z8IZSPjDEbjTE9jDHpxph0rH+uccaYfcaYAS7TXwN+aYx5M5jxhhLHMevngE3GmD+5zNqL9X8NcArQcChjF1bfFiLSExgGbG+baEOfiCSISFLDa6w+gIaz204FNhtj9rgs8gGQJSKdHIniZCDX2zqCNfroccCLIhKJlYxeMca8LSIrgZdEZAFQDlwTpPjaHccXZCZwrcvkRViH2z6y/jdZZYy5LgjhhTwReRmYBqSKyB7g98aY57wvpTyYClwObHQ5pfEO4BfAnx0bp2qg4Vj3vcBiEdmIdUbhbcaYkB3pNQh6Aksd/8NRwL+NMe875h3VJ2iMKRKRPwHfYPUtvGuMecfbCvTKYqWUCnNB7yNQSikVXJoIlFIqzGkiUEqpMKeJQCmlwpwmAqWUCnOaCFTYEZFeIvIfEdnmuGT/XREZGuy4lAoWTQQqrDgudlqKNfLlIGPMeKwxb3r6sGywrrtRKqA0EahwMx2wGWOeapjgGFRupeP+DdmO8dsvBhCRaSLyhYj8F8fVmSJymVj309ggIn93DKAYKSKLXZZfEJRPp1QL6C8cFW4ygbVupp+PNeDZaCAV+EZEPnfMGwdkGmN2iMhw4GJgqjHGJiJPYA3ulQP0NsZkAuhNgFR7oolAKcuJwMuOcdv3i8gK4HigFFhjjNnhKDcDGI+VKADisYZSXwYMFJG/Au9g3YRFqXZBE4EKNzlYI4YeiwqX1wK8aIxZ2LSQiIzGGlL5OqwbhVzd0iCVakvaR6DCzSdArOvNPURkFFCMdVOkSBHpjnXryjVulv8YuEBEejiW7Soi/cW6n3GEMeZ14Lc0M+yvUqFE9whUWDHGGBE5D3hcRG7DGgUzH+vuWYnAd1gjNt5qjNknIhlNls8Vkd9i3S0qArAB1wNVwAuOaXDk7ltKhTwdfVQppcKcHhpSSqkwp4lAKaXCnCYCpZQKc5oIlFIqzGkiUEqpMKeJQCmlwpwmAqWUCnOaCJRSKsz9P3f0OmBh2ifCAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"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": [
"palette = 'o-', '^-', 's-', '<-', '*-'\n",
"\n",
"def scaling_plot(measurements, y_axis_data = ('throughput', 'Throughput, MB/s')): #'log', ('duration', 'Duration, s')\n",
" y_axis_field, y_axis_label = y_axis_data\n",
"\n",
" styles = itertools.cycle(palette)\n",
" row = measurements.iloc[0]\n",
" title = 'HiBench.{name} test'.format(name=row['name'])\n",
"\n",
" fig, ax = plt.subplots()\n",
" for label, measurements_scale in measurements.groupby('scale'):\n",
" style = next(styles)\n",
" legend=measurements_scale['scale'].iloc[0]\n",
" plt.plot(measurements_scale['ncores'], measurements_scale[y_axis_field], style, label=str(legend))\n",
"\n",
" ax.set_xscale(\"log\")\n",
" ax.set_yscale(\"log\")\n",
"\n",
" nthreads = measurements_scale['ncores'].unique()\n",
" ax.set_xticks(nthreads)\n",
" ax.set_xticklabels(nthreads)\n",
" plt.xlim((min(nthreads),max(nthreads)))\n",
" ax.set_xlabel('Cores')\n",
" ax.set_ylabel(y_axis_label)\n",
"\n",
" plt.grid(color='lightgray')\n",
" ax.spines['bottom'].set_color('lightgray')\n",
" ax.spines['top'].set_color('lightgray') \n",
" ax.spines['right'].set_color('lightgray')\n",
" ax.spines['left'].set_color('lightgray')\n",
"\n",
" legend = ax.legend(loc=\"right\", framealpha=0)\n",
"\n",
" plt.savefig('{}_{}_{}.png'.format('HiBench',row['name'],y_axis_field), dpi=300)\n",
" return fig\n",
"\n",
"scaling_plot_log = lambda measurements: scaling_plot(measurements)\n",
"scaling_plot_log = lambda measurements: scaling_plot(measurements), ('duration', 'Execution time (s)'))\n",
"\n",
"measurements = measurements.groupby(['scale','name','ncores'], as_index=False).agg({'duration':'mean', 'data_size':'mean', 'throughput':'mean', 'node_throughput':'mean'})\n",
"plots_table = measurements.groupby(['name'])['scale', 'name', 'ncores', 'duration', 'throughput'].apply(scaling_plot_log)\n",
"\n",
"# names = measurements['name'].unique()\n",
"names = ['LinearRegression', 'LogisticRegression', 'PCA', 'SVD', 'ScalaSparkAggregation', 'ScalaSparkJoin', 'ScalaSparkSort', 'ScalaSparkTerasort']\n"
]
},
{
"cell_type": "code",
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d1b3648289a92898b2bfeec8220d2e6c1d8b69a6 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/sam/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
" import pandas.util.testing as tm\n"
]
}
],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.ensemble import VotingRegressor\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.model_selection import cross_val_score\n",
"\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Age</th>\n",
" <th>Tonnage</th>\n",
" <th>passengers</th>\n",
" <th>length</th>\n",
" <th>cabins</th>\n",
" <th>passenger_density</th>\n",
" <th>crew</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>158.000000</td>\n",
" <td>158.000000</td>\n",
" <td>158.000000</td>\n",
" <td>158.000000</td>\n",
" <td>158.000000</td>\n",
" <td>158.000000</td>\n",
" <td>158.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>15.689873</td>\n",
" <td>71.284671</td>\n",
" <td>18.457405</td>\n",
" <td>8.130633</td>\n",
" <td>8.830000</td>\n",
" <td>39.900949</td>\n",
" <td>7.794177</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>7.615691</td>\n",
" <td>37.229540</td>\n",
" <td>9.677095</td>\n",
" <td>1.793474</td>\n",
" <td>4.471417</td>\n",
" <td>8.639217</td>\n",
" <td>3.503487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>4.000000</td>\n",
" <td>2.329000</td>\n",
" <td>0.660000</td>\n",
" <td>2.790000</td>\n",
" <td>0.330000</td>\n",
" <td>17.700000</td>\n",
" <td>0.590000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>10.000000</td>\n",
" <td>46.013000</td>\n",
" <td>12.535000</td>\n",
" <td>7.100000</td>\n",
" <td>6.132500</td>\n",
" <td>34.570000</td>\n",
" <td>5.480000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>14.000000</td>\n",
" <td>71.899000</td>\n",
" <td>19.500000</td>\n",
" <td>8.555000</td>\n",
" <td>9.570000</td>\n",
" <td>39.085000</td>\n",
" <td>8.150000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>20.000000</td>\n",
" <td>90.772500</td>\n",
" <td>24.845000</td>\n",
" <td>9.510000</td>\n",
" <td>10.885000</td>\n",
" <td>44.185000</td>\n",
" <td>9.990000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>48.000000</td>\n",
" <td>220.000000</td>\n",
" <td>54.000000</td>\n",
" <td>11.820000</td>\n",
" <td>27.000000</td>\n",
" <td>71.430000</td>\n",
" <td>21.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Age Tonnage passengers length cabins \\\n",
"count 158.000000 158.000000 158.000000 158.000000 158.000000 \n",
"mean 15.689873 71.284671 18.457405 8.130633 8.830000 \n",
"std 7.615691 37.229540 9.677095 1.793474 4.471417 \n",
"min 4.000000 2.329000 0.660000 2.790000 0.330000 \n",
"25% 10.000000 46.013000 12.535000 7.100000 6.132500 \n",
"50% 14.000000 71.899000 19.500000 8.555000 9.570000 \n",
"75% 20.000000 90.772500 24.845000 9.510000 10.885000 \n",
"max 48.000000 220.000000 54.000000 11.820000 27.000000 \n",
"\n",
" passenger_density crew \n",
"count 158.000000 158.000000 \n",
"mean 39.900949 7.794177 \n",
"std 8.639217 3.503487 \n",
"min 17.700000 0.590000 \n",
"25% 34.570000 5.480000 \n",
"50% 39.085000 8.150000 \n",
"75% 44.185000 9.990000 \n",
"max 71.430000 21.000000 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('data/cruise_ship_info.csv')\n",
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Ship_name</th>\n",
" <th>Cruise_line</th>\n",
" <th>Age</th>\n",
" <th>Tonnage</th>\n",
" <th>passengers</th>\n",
" <th>length</th>\n",
" <th>cabins</th>\n",
" <th>passenger_density</th>\n",
" <th>crew</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Journey</td>\n",
" <td>Azamara</td>\n",
" <td>6</td>\n",
" <td>30.277</td>\n",
" <td>6.94</td>\n",
" <td>5.94</td>\n",
" <td>3.55</td>\n",
" <td>42.64</td>\n",
" <td>3.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Quest</td>\n",
" <td>Azamara</td>\n",
" <td>6</td>\n",
" <td>30.277</td>\n",
" <td>6.94</td>\n",
" <td>5.94</td>\n",
" <td>3.55</td>\n",
" <td>42.64</td>\n",
" <td>3.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Celebration</td>\n",
" <td>Carnival</td>\n",
" <td>26</td>\n",
" <td>47.262</td>\n",
" <td>14.86</td>\n",
" <td>7.22</td>\n",
" <td>7.43</td>\n",
" <td>31.80</td>\n",
" <td>6.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Conquest</td>\n",
" <td>Carnival</td>\n",
" <td>11</td>\n",
" <td>110.000</td>\n",
" <td>29.74</td>\n",
" <td>9.53</td>\n",
" <td>14.88</td>\n",
" <td>36.99</td>\n",
" <td>19.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Destiny</td>\n",
" <td>Carnival</td>\n",
" <td>17</td>\n",
" <td>101.353</td>\n",
" <td>26.42</td>\n",
" <td>8.92</td>\n",
" <td>13.21</td>\n",
" <td>38.36</td>\n",
" <td>10.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Ship_name Cruise_line Age Tonnage passengers length cabins \\\n",
"0 Journey Azamara 6 30.277 6.94 5.94 3.55 \n",
"1 Quest Azamara 6 30.277 6.94 5.94 3.55 \n",
"2 Celebration Carnival 26 47.262 14.86 7.22 7.43 \n",
"3 Conquest Carnival 11 110.000 29.74 9.53 14.88 \n",
"4 Destiny Carnival 17 101.353 26.42 8.92 13.21 \n",
"\n",
" passenger_density crew \n",
"0 42.64 3.55 \n",
"1 42.64 3.55 \n",
"2 31.80 6.70 \n",
"3 36.99 19.10 \n",
"4 38.36 10.00 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fa74c734ef0>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"corr = df.corr()\n",
"# Generate a mask for the upper triangle\n",
"mask = np.triu(np.ones_like(corr, dtype=np.bool))\n",
"sns.heatmap(corr, mask=mask, square=True, annot=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Age</th>\n",
" <th>Tonnage</th>\n",
" <th>passengers</th>\n",
" <th>length</th>\n",
" <th>cabins</th>\n",
" <th>passenger_density</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6</td>\n",
" <td>30.277</td>\n",
" <td>6.94</td>\n",
" <td>5.94</td>\n",
" <td>3.55</td>\n",
" <td>42.64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>6</td>\n",
" <td>30.277</td>\n",
" <td>6.94</td>\n",
" <td>5.94</td>\n",
" <td>3.55</td>\n",
" <td>42.64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>26</td>\n",
" <td>47.262</td>\n",
" <td>14.86</td>\n",
" <td>7.22</td>\n",
" <td>7.43</td>\n",
" <td>31.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>11</td>\n",
" <td>110.000</td>\n",
" <td>29.74</td>\n",
" <td>9.53</td>\n",
" <td>14.88</td>\n",
" <td>36.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>17</td>\n",
" <td>101.353</td>\n",
" <td>26.42</td>\n",
" <td>8.92</td>\n",
" <td>13.21</td>\n",
" <td>38.36</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Age Tonnage passengers length cabins passenger_density\n",
"0 6 30.277 6.94 5.94 3.55 42.64\n",
"1 6 30.277 6.94 5.94 3.55 42.64\n",
"2 26 47.262 14.86 7.22 7.43 31.80\n",
"3 11 110.000 29.74 9.53 14.88 36.99\n",
"4 17 101.353 26.42 8.92 13.21 38.36"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = df[['Age', 'Tonnage', 'passengers', 'length', 'cabins', 'passenger_density']]\n",
"X_small = df[['Age', 'Tonnage', 'passengers', 'length', 'cabins']]\n",
"y = df['crew']\n",
"X.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Age', 'Tonnage', 'passengers', 'length', 'cabins',\n",
" 'passenger_density'],\n",
" dtype='object')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.columns"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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DW2omMarp5dL5Bfznk5Vsqmrh2qU6wyDiB/WkJe5VNnUzMBxi6SyNvY62cnYOKYkBXjzY4ncoInFLSVri3u5jnSQnBJhXFD/bUkYiOSHIheV5vKQkLeIbJWmJayMhx566ThYXZ5EQ0MdhrIvn5bO/oYumrgG/QxGJS/pWkrhW1dxN39AIy2bF7zKgp3LJvAIAXq5Sb1rED0rSEtd2H+skKRhgwYxMv0OJSstmZZGZnKBxaRGfKElL3Ao5x+66Ts6bmUliUB+F8SQEA6ydm8dLB5v9DkUkLumbSeLW4ZYeegaGWVaiWd2ncvG8Ag639HKsvc/vUETijpK0xK1dxzpJCBgLZ2T4HUpUu2RePgAvVqo3LTLVlKQlLoWcY09tBwtnZJKcEPQ7nKh23oxM8tKTdCmWiA+UpCUuVbf20tk/zLISzeo+nUDAuHhePi8ebCG8A62ITBUlaYlLu2s7CQaMRTOVpCNxybx86jv7qWru8TsUkbiiJC1xxznHrmMdLCjKICVRp7ojcfHc8Li0rpcWmVpK0hJ3jrX30d43pLW6z0BFQTqFmclsqmr1OxSRuKIkLXFn17FOAgaLi7WASaTMjHVz89l0SOPSIlNJSVriinOOXbUdzCvMIC1JO7WeibUVeTR0DnC4pdfvUETihpK0xJX6zn5aewZZplPdZ2ydNy69SePSIlNGSVriyq5jHRiwWBtqnLF5hekUZCRr8pjIFFKSlriyq7aTioJ0MpJ1qvtMmRlr5+ax6VCrxqVFpoiStMSNhs5+mroGWKq1us/auoo86jr6OdqqcWmRqaAkLXFjd234VPfSYp3qPltrT4xL61IskamgJC1xY9exTmbnpZGVmuh3KNPWgqIM8tKTePmQxqVFpoKStMSF5q4B6jv7dar7HJkZayvy1JMWmSJK0hIXdtS0Y8ByJelztm5uPsfa+6jWuLTIpIsoSZvZdWa238wqzeyuce5PNrOfevdvMrNyr/wiM9vu/ewws3dPbPgip+ecY0dNB+UF6WTrVPc5Wzs3D9A63iJT4bRJ2syCwLeA64ElwK1mtmRMtduBNufcfOAe4Gte+S5gjXNuJXAd8B0z07UvMqXqOvpp7h7g/NIcv0OJCQuLMslNS2TTIZ3yFplskfSkLwIqnXNVzrlBYAOwfkyd9cD93vHDwFVmZs65XufcsFeeAujiSplyO6rbCRgs0wImEyIQMC6qyGOTJo+JTLpIknQJUD3qdo1XNm4dLyl3APkAZrbWzHYDrwEfHZW0RSZdyDl2Hutg4YxM0rSAyYRZNzef6tY+jrX3+R2KSEyLJEnbOGVje8QnreOc2+ScWwpcCHzezFLe8AJmd5jZFjPb0tTUFEFIIpE50tJLR98QK3Sqe0KtrdA63iJTIZIkXQOUjbpdCtSerI435pwN/NmAlXNuL9ADLBv7As65e51za5xzawoLCyOPXuQ0dtS0kxg0bUs5wRbNzCQ7NVGTx0QmWSRJejOwwMwqzCwJuAXYOKbORuA27/hG4EnnnPMekwBgZnOA84DDExK5yGmMhBy7jnWwuDiL5ISg3+HElD+NS2vymMhkOm2S9saQ7wSeAPYCDznndpvZ3WZ2g1ftPiDfzCqBzwDHL9O6FNhhZtuBXwAfd841T3QjRMZT2dhF7+CIZnVPkrUVeRxp6aWuQ+PSIpMlopk0zrnHgMfGlH1h1HE/cNM4j3sAeOAcYxQ5K9uOtpOWFGTBjAy/Q4lJ60at4/2uVWPnkorIRNCKYxKT+gZH2FvXyfmlOSQE9DafDIuLs8hMSdClWCKTSN9eEpN2HmtnOORYPSfX71BiVjAQXsf7Za3jLTJplKQlJm070saMrGRmZb/hij+ZQGsr8jnU3ENDZ7/foYjEJCVpiTmVjd1Ut/WxenYuZuNdwi8T5fi4tC7FEpkcStIScx7ZVkPAYGWZZnVPtiWzsshMTtClWCKTRElaYspIyPGLbcdYUJRJZop2vJpswYBxYUWeVh4TmSRK0hJTXqhspr6zXxPGptDaijwONvXQ2KVxaZGJpiQtMeWnW6rJTk1k0UwtAzpV1nrj0q/olLfIhFOSlpjR1DXAE7vquemCUhKDemtPlWWzskhPCmrymMgk0DeZxIyHtlQzHHLcuna236HElYRggDXleWzS9dIiE05JWmLCSMjxk1eOcsm8fOYVahnQqbZubj4HGrtp7h7wOxSRmKIkLTHh2QNN1LT18b61c/wOJS6tnZsHaFxaZKIpSUtM+PHLRynISOaaJTP8DiUuLS/JJi0pqEuxRCaYkrRMe7XtfTy5r4GbLywlKUFvaT8kBgNcMCdX63iLTLCItqoUiWYbNlfjgFsu1ISxc/XgpqNn/djUxCD7G7r47rNVpCcn8F5N4BM5Z+p2yLTWPzTCg5uO8JbziijLS/M7nLhWUZAOwOGWHp8jEYkdStIyrW3cUUtz9yC3X1rhdyhxryQ3lcSgUdWsJC0yUZSkZdpyznHfc4dYNDOTS+bl+x1O3EsIBJiTl86hJiVpkYmiJC3T1vOVzexv6OL2Syu0JWWUmFeYTn1nP139Q36HIhITlKRl2rrv+UMUZCRzw8pZfocinvlF4TXTDzZ1+xyJSGxQkpZp6UBDF0/vb+K2i+eQnBD0OxzxFOekkJYUpLJRSVpkIihJy7T0/RcOkZwQ4H3rtMJYNAmYMa8wg8rGbpxzfocjMu0pScu009DZzyPbjvGeC0rJS0/yOxwZY35RBp39w+pNi0wAJWmZdr7zTBUjIcdH3zzP71BkHPOLwhucPHeg2edIRKY/JWmZVpq6BnjwlSO8e1UJs/O1eEk0yk1LIj89iecrlaRFzpWStEwr33uuisHhEJ94y3y/Q5FTmF+UwctVLQwOh/wORWRaU5KWaaO1Z5AHXj7CDefPOrEEpUSnBUWZ9A6OsO1om9+hiExrEW2wYWbXAf8OBIHvOee+Oub+ZOCHwAVAC3Czc+6wmV0DfBVIAgaBzznnnpzA+CVGRLKxw+/21NM3OEJ5fvo5bQQhk29uYTrBgPH8gWbWzdVqcCJn67Q9aTMLAt8CrgeWALea2ZIx1W4H2pxz84F7gK955c3AO51zy4HbgAcmKnCJL72Dw7x0sIVlJdkUZaX4HY6cRkpikJVlOTyncWmRcxLJ6e6LgErnXJVzbhDYAKwfU2c9cL93/DBwlZmZc+5V51ytV74bSPF63SJn5Jn9TQwOh7hyUZHfoUiELp1fwGs17bT1DPodisi0FUmSLgGqR92u8crGreOcGwY6gLHnuN4DvOqcGxj7AmZ2h5ltMbMtTU1NkcYucaK9d5CXqlpYNTuXGepFTxtXLioi5OCZ1/WZFjlbkSTp8XYuGLuU0CnrmNlSwqfAPzLeCzjn7nXOrXHOrSksLIwgJIknf9jbCMDVi9WLnk6Wl2RTkJHMH/Y2+B2KyLQVSZKuAcpG3S4Fak9Wx8wSgGyg1btdCvwC+KBz7uC5Bizxpb6zn1ePtnHx3Hxy0rS62HQSCBhXLirkmdebGBrRpVgiZyOSJL0ZWGBmFWaWBNwCbBxTZyPhiWEANwJPOuecmeUAvwE+75x7YaKClvjxu931JCcGuPw8nWGZjq5aPIOu/mG2HNalWCJn47RJ2htjvhN4AtgLPOSc221md5vZDV61+4B8M6sEPgPc5ZXfCcwH/snMtns/OmcpEalq7mZffReXLygkLSmiqwUlylw6v4CkYIA/6pS3yFmJ6JvPOfcY8NiYsi+MOu4HbhrncV8GvnyOMUocGgk5Ht1RR05qIhfPK/A7HDlL6ckJrJuXz5P7GvnHd4y9clNETkcrjklU2ny4lfrOfq5fXkxSgt6m09lVi4qoau6hqkm7YomcKX37SdTpHRjm93samFuYzrJZWX6HI+fo+LXtT+5r9DkSkelHSVqizu/3NjAwPMI7V8zCbLyr+2Q6KctL47wZmfxxr5K0yJlSkpaoUtvexyuHWlk3N18Ll8SQKxcXsflwKx19Q36HIjKtKElL1Ag5xy+3HyMtKchVi2b4HY5MoKsXz2A45HhKp7xFzoiStESNl6taqGnr4x0rZpGaFPQ7HJlAq8pyKM5O4dGddX6HIjKtKElLVGjvHeR3uxtYOCODFaXZfocjEywQMN62vJhnX2/SKW+RM6AkLb5zzrFxRy0Ox/rzSzRZLEa9fUUxgyMh/rBHC5uIREpJWnz3m9fq2FffxTWLZ5CbrvW5Y9WqshxKclL5zWs65S0SKSVp8VVz9wD//KvdlOSkamWxGGdmvH1FMc8daKKjV6e8RSKhJC2+cc7xj7/YRVf/MDdeUEowoNPcse4dK4oZGnE8safe71BEpgUlafHNxh21/HZ3PX9zzUJdEx0nlpdkU5aXqlneIhFSkhZfNHT280+/3MWq2Tnc8ea5focjU8TMePvyWbxQ2Uxbz6Df4YhEPSVpmXLOOe56ZCeDIyH+9abzdZo7zrxjRTEjIcdju9SbFjkdJWmZcj948TBP7W/irusWMbcww+9wZIotnZXFwhkZPLSlxu9QRKKekrRMqd21HXzlsX1cuaiI2y4p9zsc8YGZcfOFs9lR3c7euk6/wxGJakrSMmV6B4f565+8Sk5aIl+/cYUWLYljf7GqhKRggJ9urvY7FJGopiQtU+buX+/hUHMP99y8kvyMZL/DER/lpifx1mUz+cWrx+gfGvE7HJGopSQtU+LRnbVs2FzNxy6fx5vma9ESgVsuLKOjb4gnduuaaZGTUZKWSVfd2svnf/4aK8ty+JtrFvodjkSJi+fmU5aXyoZXdMpb5GSUpGVSDY+E+NSGV8HBf966isSg3nISFggYt1w4m5eqWjjc3ON3OCJRSd+YMqn+/Y8H2Ha0nS+/exlleWl+hyNR5sYLSgkYbNAEMpFxKUnLpHmxsplvPlXJTReUsn5lid/hSBSakZXCW5fO5MFNR+geGPY7HJGooyQtk6K+o5+//smrzCvM4Is3LPU7HIliH7l8Hp39w2x45ajfoYhEHSVpmXBDIyE+8eA2+oZG+O/3ryY9OcHvkCSKrSzLYd3cPL733CEGh0N+hyMSVfTtKRPuK4/tY+uRNv7z1lXML8r0OxzxyYObIu8ZL5qZxctVrXz+569xwZzcN9z/3rWzJzI0kWkjop60mV1nZvvNrNLM7hrn/mQz+6l3/yYzK/fK883sKTPrNrNvTmzoEo1+s7OO779wiA9dUs47z5/ldzgyTSwoyqA4O4VnDzQRcs7vcESixmmTtJkFgW8B1wNLgFvNbMmYarcDbc65+cA9wNe88n7gn4DPTljEErUqG7v5u4d3sHp2Dv/wtsV+hyPTiJlx2YJCmroG2F/f5Xc4IlEjkp70RUClc67KOTcIbADWj6mzHrjfO34YuMrMzDnX45x7nnCylhjWOzjMx3+8leTEIN9632qSEjTdQc7M8pJsctISeXp/I069aREgsiRdAoy+iLHGKxu3jnNuGOgA8iMNwszuMLMtZralqakp0odJlHDO8fmfv8aBxm7+45ZVFGen+h2STEPBgPGW84qobutjV612xxKByJL0eFsVjf0zN5I6J+Wcu9c5t8Y5t6awsDDSh0mU+NHLR/jV9lr+9pqFXLpA63LL2btgTi4zs1L47a46hkc001skkiRdA5SNul0K1J6sjpklANlA60QEKNFtU1ULX/r1Hq5cVMTHr5jvdzgyzQXMeNvyYtp6h3ipqsXvcER8F0mS3gwsMLMKM0sCbgE2jqmzEbjNO74ReNJpUCnmHWvv4+M/3sbs/DT+7ZaVBALaH1rO3fyiDM6bkclT+xvp0SpkEudOm6S9MeY7gSeAvcBDzrndZna3md3gVbsPyDezSuAzwInLtMzsMPAN4ENmVjPOzHCZhvoGR7jjh1sYHA7x3Q+uISsl0e+QJIZct2wmg8Mh/riv0e9QRHwV0WImzrnHgMfGlH1h1HE/cNNJHlt+DvFJFHLO8XeP7GRPXSffv+1C5hVm+B2SxJgZWSlcWJ7HK4daWFWW43c4Ir7RdTJyxv77mSp+vaOWz731PN6yqMjvcCRGXbtkJpkpifxsazV9gyN+hyPiCyVpOSNP7WvkX57YxztWFPOxy+f5HY7EsNSkIDdeUEpz9yBffXyv3+GI+EJJWiJ2sKmbT8sCOxMAABSZSURBVG54lcUzs/j6jedjpoliMrnmFWbwpnn53P/SEZ59XWsoSPzRBhsSkY7eIf7nD7eQGAxw7wcvIDUpeEYbKIicrWuXzqSxa4DPPbyDxz55GfkZyX6HJDJl1JOW0xocDvHRH22lurWXb79vNaW5aX6HJHEkMRjgnptX0t47xF/9cIvGpyWuKEnLKR1f8vOlqhb+5cYVrJ0b8WqvIhNmWUk2/37LKrZXt/PJDa8yEtIyDBIflKTllL75ZCWPbKvh01cv4N2rSv0OR+LYdctm8s/vWMLv9zRw9693axMOiQsak5aTenhrDf/6+9f5i1UlfOqqBX6HI8KH3lRBbUc/9z5bRUpSkL9/6yKtdCcxTUlaxvW73fX8/SM7uWxBAV95z3LN5Jaocdd1i+gZGOY7z1RR09bHv950PimJQb/DEpkUStLyBi8dbOHOn7zKspJs/vv9F5CcoC9AiR6BgPHldy1jdl4aX3l8H3XtfXz3g2s061tikpJ0nDndZVM1bb3c9/whslMTeefyYn61feyGZyL+MzM+cvk8yvLS+Jufbufae57lH9+xmHetLInorM9EXj743rWzJ+y5Jlq8tDOWaeKYnFDd2sv3XzhEWlKQD7+pgrRk/Q0n0e1ty4v55Sfe5CXrHXzgvlc42NTtd1giE0ZJWgA4eiJBJ/BXl80lO1W7Wsn0sLg4i0c+dgn/e/1SdlS3c/U3nuHDP9jMM683EdKlWjLNqaskHG7u4f6XDpOenMBfXVpBTlqS3yGJnJFgwPjAxeW8ddlMfvTyUR7cdJTbvv8KJTmpvHlhARfPK2BdRR6FmclxMQlyaCREd/8wnX1DBANGQsBICAYIaib8tKMkHedeO9bBz7ZUk5OWyO2Xqgct01tRZgqfuWYhd75lPo/vquPXO2p5dEcdP3mlGoCM5ATK8sIr5qUlBklKCJCUEE5eBpiFx7sNvNsWLvOOkxMCJCcGSU4IkJGcQGZKAqGQm/LLwPoGRzjY1B3+aezmaGsvx9r7ONbWR0vPIAPDoTc8xoDMlASyUxPJSUtiVnYKJblplOamanZ8FFOSjlPOOZ470Mxvd9czOy+ND6ybQ7rGoCVGJCUEWL+yhPUrSxgJOXbXdrDlcBtHW3upbu1lV20HtUMhBodDDAyPcC5nxb/+xH4KM5MpykymKCuFmVkpzMpJZVbO8d+pzMhMJiF4ZqOLoZCjsWuAg03dVDV1c7Cpxzvu4Vh734l6AYPi7FRKclNZNzef/IwkMlMSyUxJYHt1OyMhx0jIMTAcoqN3iPa+QWraenntWMeJ55iVncKCGZksmJHBnLx09bijiL6V49DQSIhf76hly5E2lpdkc+MFpSSe4ReIyHQRDBgrSnNYUZpzomzsrGfnHO7E8Z9uOwcOh3MQco7B4RD9wyEGhkbo6h+mq3+I2flpNHQO0NDZz9GWXjZVtdDZP/xnzx+wcC8/Jy2R3LQkctISSUoIkBgMkBg0BocdfUPD9A2O0NY7RGNnP41dAwyP+ushPSnIvKIMLizP5ebCMuYXZTC/KIM5+WknvUzyVLO7eweHOdbWR3VbL5WNPTx3oIlnXm8iNTHIspJsVpRmU1GQTiAOhgeimZJ0nGno7GfD5qM0dA5wxcJCrl4yQx9CiXvHT3GHb5z4zxuMN11jvEuTugeGqWvv41h7H3Ud/dR6v9t7B2nvHeJAYzeDwyGGR0IMhRxJwQCpSUHSkoL0Do4w0+vZZqcmUpiZTGFGMpkpCX82nt7eO8SWw21sOdx2Vm1OS0rwes+ZXLkI+ofCp9B3HetgR3U7mw+3kpmSwPKSbM4vzcE5Fxfj+dFGSTpOhEKODZur+a+nK0lKCPKhS8pZOCPT77BEYlJG8p8S4JnyawvYlMQgS2dls3RWNoPDIfbVd7KzpoNNh1p58WALj75Wy/rzS3jXqlnML9J3x1RRko4Du4518IVf7WLb0XbmF2Vw0wWlZKZogpiIjC8pIXBiiKB/aIQ9tZ00dPXzX09X8s2nKllSnMW7Vs3ihvNLmJmd4ne4MU1JOobVtvfxzacq+ckrR8lPT+JfblzB4HBIp7dFJGIpiUFWz8nlvWtn09jVz6M76vjV9mP8f4/t4yuP72NdRT7XL5/JVYtnUJKT6ne4MUdJOgbtrevku89WsXFHLQ74y0sq+PQ1C8hKSfTtVJqITH9FmSl8+NIKPnxpBYeae/jV9mNs3F7LF361my/8ajeLi7O4ZnERVy2ewfKSbO1QNgGUpGNEfUc/v3mtjkd31vLq0XbSkoJ88OJyPnxpOaW5aX6HJyIxpqIgnU9fvZBPX72Qg03d/GFPA3/c28g3n6rkP56spCgzmSvOK2RtRT4XVeSduD5dzoxF28bpa9ascVu2bPE7jKhxsp5v/9AIR1p6OdTcTVVzD8fa+nBAcXYK55fmsKY8l7Qk/Q0mIlOrZ2CY1xu62FvXycGmHvqGRgDISU2kvCCdivx0ygvS+eRV8zVbfBQz2+qcWzO2XN/iUcw5R1f/EG09g7T0DNLaM0h9Zz91Hf209gwCEDSjNC+VqxYXsbwkh8JMbdcnIv5JT05g1excVs3OJeQcDZ39HG7u4VBzDwcau9le3Q7Afc9XeZPTsk/8Ls5OUeIeQ0naZ4PDIY6193GkpYfq1l6OtPRytPVPP72DIyfqGpCXnsSsnFTWzMmlNDeN2XlpJCVoIRIRiT4BM4qzUynOTuXieQU452juHuRwSw8piUF21rRz77NVJxZtKchIZllJFotmZrG4OJNFM7OYW5ge14stKUlPgY7eIY609nDUS8Kjk3FdR9+fLUmYkhhgdl44+V4yr4DGrn7y05PITU8iNy0prt+sIjK9mVl4cZbM5BOLwPQPjbCnrpOd1e3srOlgT10nL1Q2MzQS/mJMDBrzCjNYXJzFopmZLPJ+F8XJZikRJWkzuw74dyAIfM8599Ux9ycDPwQuAFqAm51zh737Pg/cDowAn3TOPTFh0UcB5xytPYPUdfRT39FPXWd4daGjXhI+0tLzhiUCCzKSmZ2XemIyxZy8NGbnh3+P3aVHs7FFJJalJAZZPTuX1bNzT5QNDoeoau5mf30Xe+u62FffyUsHW/jFq8dO1ElLCjInP52KgjTm5KdTnp9GeX46s3JSKcpKPulSqdPNaZO0mQWBbwHXADXAZjPb6JzbM6ra7UCbc26+md0CfA242cyWALcAS4FZwB/MbKFzbgSfORdedH445Ag5x9Cwo9dbO7d3cIT+ofDv3sEROvoGae0Zor13kLbePx03dQ9Q19HP4JgdZxKDRmluGmV5aawsy2FOfvh4Tn4aZblp2shCROQUkhICLJoZPu29fuWfytt7B9lX38X++i4Ot/RwpKWXfXVd/H5Pw4me93E5aYnMyEyhKCuZoswUCjKTyEpJJCsl4cQGJMd/J3u7oSUFvfXUE8JrqicFA7731iPJFhcBlc65KgAz2wCsB0Yn6fXAF73jh4FvWrhl64ENzrkB4JCZVXrP99LEhH9q1a29rP/WCwyPhAg5GA6FCIW832cxqT0pIUDuqAXyl5dk89alMynOTvF+UinOTiE/I1m7yIiITLCctCTWzc1n3dz8PysfHglR19HPoeYe6jv6aejsp6Grn8bOARq6BqhsbKa5e+ANiTwSiUEjMRggIWCYGQFvO9Of/M91nDdz8pdHjSRJlwDVo27XAGtPVsc5N2xmHUC+V/7ymMeWjH0BM7sDuMO72W1m+yOK/uwUAM2T+PzRIB7aCGpnrFE7Y8sp2/m+KQxkMiz6AjCx/5ZzxiuMJEmP1yUc++fIyepE8licc/cC90YQyzkzsy3jXYsWS+KhjaB2xhq1M7bEQzunoo2RTBWuAcpG3S4Fak9Wx8wSgGygNcLHioiIyDgiSdKbgQVmVmFmSYQngm0cU2cjcJt3fCPwpAsvZbYRuMXMks2sAlgAvDIxoYuIiMS2057u9saY7wSeIHwJ1vedc7vN7G5gi3NuI3Af8IA3MayVcCLHq/cQ4Ulmw8AnomBm95ScVvdZPLQR1M5Yo3bGlnho56S3MerW7hYREZEwLV8lIiISpZSkRUREolTMJmkz+76ZNZrZrlFleWb2ezM74P3OPdVzTAdmVmZmT5nZXjPbbWaf8spjqq1mlmJmr5jZDq+dX/LKK8xsk9fOn3qTG6c1Mwua2atm9qh3OxbbeNjMXjOz7Wa2xSuLqfcsgJnlmNnDZrbP+4xeHGvtNLPzvH/H4z+dZvbpWGsngJn9jff9s8vMfuJ9L03q5zNmkzTwA+C6MWV3AX90zi0A/ujdnu6Ggb91zi0G1gGf8JZjjbW2DgBXOufOB1YC15nZOsJL0N7jtbON8BK1092ngL2jbsdiGwHe4pxbOeo601h7z0J4z4PfOucWAecT/neNqXY65/Z7/44rCe/f0Av8ghhrp5mVAJ8E1jjnlhGeSH18GezJ+3w652L2BygHdo26vR8o9o6Lgf1+xzgJbf4V4XXWY7atQBqwjfDKd81Agld+MfCE3/GdY9tKCX+hXQk8SnhBoJhqo9eOw0DBmLKYes8CWcAhvAm6sdrOMW27FnghFtvJn1bWzCN8ZdSjwFsn+/MZyz3p8cxwztUBeL+LfI5nQplZObAK2EQMttU7DbwdaAR+DxwE2p1zx7cZG3fZ2Wnm34C/A47v2pJP7LURwisP/s7MtnrLAkPsvWfnAk3A//OGL75nZunEXjtHuwX4iXccU+10zh0D/i9wFKgDOoCtTPLnM96SdMwyswzgEeDTzrlOv+OZDM65ERc+pVZKeKOWxeNVm9qoJo6ZvQNodM5tHV08TtVp28ZR3uScWw1cT3iI5s1+BzQJEoDVwLedc6uAHqb5Kd9T8cZibwB+5ncsk8EbU18PVBDe1TGd8Pt3rAn9fMZbkm4ws2IA73ejz/FMCDNLJJygf+yc+7lXHJNtBXDOtQNPEx6Dz/GWooXpv+zsm4AbzOwwsIHwKe9/I7baCIBzrtb73Uh4/PIiYu89WwPUOOc2ebcfJpy0Y62dx10PbHPONXi3Y62dVwOHnHNNzrkh4OfAJUzy5zPekvTo5UtvIzx+O62ZmRFe8W2vc+4bo+6KqbaaWaGZ5XjHqYQ/MHuBpwgvRQvTvJ3Ouc8750qdc+WETxs+6Zx7HzHURgAzSzezzOPHhMcxdxFj71nnXD1QbWbneUVXEV59MabaOcqt/OlUN8ReO48C68wszfvePf7vOamfz5hdcczMfgJcQXgrsQbgn4FfAg8Bswn/D7/JOdfqV4wTwcwuBZ4DXuNP45j/QHhcOmbaamYrgPsJz6gMAA855+42s7mEe515wKvA+114//JpzcyuAD7rnHtHrLXRa88vvJsJwIPOuf9jZvnE0HsWwMxWAt8DkoAq4C/x3r/EVjvTCE+qmuuc6/DKYvHf80vAzYSvqnkV+CvCY9CT9vmM2SQtIiIy3cXb6W4REZFpQ0laREQkSilJi4iIRCklaRERkSilJC0iIhKllKRFRESilJK0iJwzM/uimX12gp/zMW+rxxwz+/hEPrfIdKEkLSInZWZBv17bOfc2bwnYHEBJWuKSkrTIJDCzcjPbZ2b3m9lOM3vYW07wC2a22ds0/l5veUHM7JNmtseru8Eru9zMtns/r45aSvNz3nPs9FZAOv56e83su96m9L/zlk/FzC706r5kZl83s11eedC7ffy5PuKVX2FmT5nZg4RXsjtZG/+Xme03sz8A540qn2dmv/V2uHrOzBZ55T8ws/8wsxfNrMrMbvTKi83sWa+du8zsMq/8sJkVAF8F5nn3f93MHjCz9aNe78dmdsNE/duJRBW/9+jUj35i8YfwXuaO8G5PAN8HPgvkjarzAPBO77gWSPaOc7zfvx71+AzCS2heC9xLeHesAOE9bd/svd4wsNKr/xDh5QkhvC72Jd7xV/H2WAfuAP7RO04GthDe4ecKwjs2VZyifRcQTuBphPdNriS8jCmE98Ne4B2vJbwGOcAPCO+QFACWAJVe+d8C/8s7DgKZ3vFhwsv6lvPn+8JfDvzSO84mvGdzgt//5vrRz2T8qCctMnmqnXMveMc/Ai4F3mJmm8zsNcK7XC317t8J/NjM3k842QK8AHzDzD5JOHEPE07S1xJeI3gbsAhY4NU/5Jzb7h1vBcq9TUkynXMveuUPjorvWuCDFt6jexPhvauPP9crzrlDp2jbZcAvnHO9Lrw16kY4sWXqJcDPvOf9DlA86nG/dM6FnHN7gBle2WbgL83si8By51zXKV4X59wzwHwzKyK8qcMj7k/7+YrElITTVxGRszR2YXwH/BewxjlX7SWlFO++txPuEd8A/JOZLXXOfdXMfgO8DXjZzK4m3IP+inPuO6Of2MzKgdGL+o8AqYy/H/WJhwF/7Zx7YsxzXUG4J32m7YNwL7ndhff9Hs/oGA3AOfeshfeTfjvwgJl93Tn3w9O89gPA+wjvFvbhCGIVmZbUkxaZPLPN7GLv+Fbgee+42etxHh+TDQBlzrmngL8jPFEqw8zmOedec859jfCp6EXAE8CHvcdjZiVej3Jczrk2oMvM1nlFt4y6+wngYxbejxwzW+htHRmJZ4F3m1mqN1b+Tu/1OoFDZnaT95xmZuef6onMbA7Q6Jz7LuFtV1ePqdIFZI4p+wHwae81d0cYs8i0o560yOTZC9xmZt8BDgDfBnIJj+UeJnyaF8LjsD8ys2zCvct7nHPtZva/zewthHvFe4DHnXMDZrYYeMmbc9YNvN+rczK3A981sx7gaaDDK/8e4fHebd4EtibgXZE0zDm3zcx+CmwHjhDeLvW49wHfNrN/BBIJb+O34xRPdwXwOTMb8trzwTGv1WJmL3gT3h53zn3OOddgZnsJbz8rErO0VaXIJPBOPz/qnFvmcyiYWYZzrts7vgsods59yuewzomF9y9+DVjtvP2LRWKRTneLxL63H7+8ifCEry/7HdC58Mbm9wH/qQQtsU49aRE5KTPLJ3xJ1VhXOedapjoekXijJC0iIhKldLpbREQkSilJi4iIRCklaRERkSilJC0iIhKl/n+tcpz7si/JfQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 576x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for col in X.columns:\n",
" if col=='length':\n",
" fig, ax = plt.subplots(figsize=(8,4))\n",
" sns.distplot(np.log(X[col]))\n",
" fig, ax = plt.subplots(figsize=(8,4))\n",
" sns.distplot(X[col])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1.781709\n",
"1 1.781709\n",
"2 1.976855\n",
"3 2.254445\n",
"4 2.188296\n",
"Name: length, dtype: float64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.log(X['length']).head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Age</th>\n",
" <th>Tonnage</th>\n",
" <th>passengers</th>\n",
" <th>length</th>\n",
" <th>cabins</th>\n",
" <th>passenger_density</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-1.276402</td>\n",
" <td>-1.104984</td>\n",
" <td>-1.193956</td>\n",
" <td>-1.225331</td>\n",
" <td>-1.184588</td>\n",
" <td>0.318057</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-1.276402</td>\n",
" <td>-1.104984</td>\n",
" <td>-1.193956</td>\n",
" <td>-1.225331</td>\n",
" <td>-1.184588</td>\n",
" <td>0.318057</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.358105</td>\n",
" <td>-0.647310</td>\n",
" <td>-0.372926</td>\n",
" <td>-0.509363</td>\n",
" <td>-0.314095</td>\n",
" <td>-0.940676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.617775</td>\n",
" <td>1.043215</td>\n",
" <td>1.169614</td>\n",
" <td>0.782736</td>\n",
" <td>1.357341</td>\n",
" <td>-0.338017</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.172577</td>\n",
" <td>0.810215</td>\n",
" <td>0.825445</td>\n",
" <td>0.441533</td>\n",
" <td>0.982670</td>\n",
" <td>-0.178934</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Age Tonnage passengers length cabins passenger_density\n",
"0 -1.276402 -1.104984 -1.193956 -1.225331 -1.184588 0.318057\n",
"1 -1.276402 -1.104984 -1.193956 -1.225331 -1.184588 0.318057\n",
"2 1.358105 -0.647310 -0.372926 -0.509363 -0.314095 -0.940676\n",
"3 -0.617775 1.043215 1.169614 0.782736 1.357341 -0.338017\n",
"4 0.172577 0.810215 0.825445 0.441533 0.982670 -0.178934"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ss = StandardScaler()\n",
"cols = list(X.columns)\n",
"X = pd.DataFrame(ss.fit_transform(X),columns=cols)\n",
"X.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X_small, y, test_size=0.33, random_state=41)\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-0.94557494 -0.61609459 -0.63856439 -0.39738181 -0.64749158]\n",
"-0.6365909288733633\n"
]
}
],
"source": [
"lr = LinearRegression()\n",
"rfr = RandomForestRegressor()\n",
"ensemble = VotingRegressor([('lr',lr),('rf',rfr)])\n",
"ensemble.fit(X_train,y_train)\n",
"ensemble.score(X_test, y_test)\n",
"print(cross_val_score(ensemble,X_train,y_train,scoring='neg_mean_absolute_error'))\n",
"print(np.mean(cross_val_score(ensemble,X_train,y_train,scoring='neg_mean_absolute_error')))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['accuracy',\n",
" 'adjusted_mutual_info_score',\n",
" 'adjusted_rand_score',\n",
" 'average_precision',\n",
" 'balanced_accuracy',\n",
" 'completeness_score',\n",
" 'explained_variance',\n",
" 'f1',\n",
" 'f1_macro',\n",
" 'f1_micro',\n",
" 'f1_samples',\n",
" 'f1_weighted',\n",
" 'fowlkes_mallows_score',\n",
" 'homogeneity_score',\n",
" 'jaccard',\n",
" 'jaccard_macro',\n",
" 'jaccard_micro',\n",
" 'jaccard_samples',\n",
" 'jaccard_weighted',\n",
" 'max_error',\n",
" 'mutual_info_score',\n",
" 'neg_brier_score',\n",
" 'neg_log_loss',\n",
" 'neg_mean_absolute_error',\n",
" 'neg_mean_gamma_deviance',\n",
" 'neg_mean_poisson_deviance',\n",
" 'neg_mean_squared_error',\n",
" 'neg_mean_squared_log_error',\n",
" 'neg_median_absolute_error',\n",
" 'neg_root_mean_squared_error',\n",
" 'normalized_mutual_info_score',\n",
" 'precision',\n",
" 'precision_macro',\n",
" 'precision_micro',\n",
" 'precision_samples',\n",
" 'precision_weighted',\n",
" 'r2',\n",
" 'recall',\n",
" 'recall_macro',\n",
" 'recall_micro',\n",
" 'recall_samples',\n",
" 'recall_weighted',\n",
" 'roc_auc',\n",
" 'roc_auc_ovo',\n",
" 'roc_auc_ovo_weighted',\n",
" 'roc_auc_ovr',\n",
" 'roc_auc_ovr_weighted',\n",
" 'v_measure_score']"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import sklearn\n",
"sorted(sklearn.metrics.SCORERS.keys())"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"ename": "NotFittedError",
"evalue": "This VotingRegressor instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNotFittedError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-12-cfe307935755>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensemble\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_small\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/sklearn/ensemble/_voting.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 423\u001b[0m \u001b[0mThe\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 424\u001b[0m \"\"\"\n\u001b[0;32m--> 425\u001b[0;31m \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 426\u001b[0m return np.average(self._predict(X), axis=1,\n\u001b[1;32m 427\u001b[0m weights=self._weights_not_none)\n",
"\u001b[0;32m~/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_is_fitted\u001b[0;34m(estimator, attributes, msg, all_or_any)\u001b[0m\n\u001b[1;32m 965\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 966\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 967\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mNotFittedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'name'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 968\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 969\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNotFittedError\u001b[0m: This VotingRegressor instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator."
]
}
],
"source": [
"y_pred = ensemble.predict(X_small)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(10,6))\n",
"ax = plt.scatter(X_small.Tonnage,y)\n",
"ax = plt.plot(np.sort(X_small.Tonnage), np.sort(y_pred))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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| stackv2 | 2024-11-18T18:03:05.233362+00:00 | 2020-10-27T23:25:00 | {
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5897304c379d2967adf8b6666f86f3bfa9bd3357 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualizing Data in Python\n",
"#### Tables, Histograms, Boxplots, and Slicing for Statistics\n",
"\n",
"When working with a new dataset, one of the most useful things to do is to begin to visualize the data. By using tables, histograms, box plots, and other visual tools, we can get a better idea of what the data may be trying to tell us, and we can gain insights into the data that we may have not discovered otherwise.\n",
"\n",
"Today, we will be going over how to perform some basic visualisations in Python, and, most importantly, we will learn how to begin exploring data from a graphical perspective."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# We first need to import the packages that we will be using\n",
"import seaborn as sns # For plotting\n",
"import matplotlib.pyplot as plt # For showing plots\n",
"\n",
"# Load in the data set\n",
"tips_data = sns.load_dataset(\"tips\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Visualizing the Data - Tables\n",
"When you begin working with a new data set, it is often best to print out the first few rows before you begin other analysis. This will show you what kind of data is in the dataset, what data types you are working with, and will serve as a reference for the other plots that we are about to make. "
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"244"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Print out the first few rows of thelen data\n",
"len(tips_data)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"total_bill float64\n",
"tip float64\n",
"sex category\n",
"smoker category\n",
"day category\n",
"time category\n",
"size int64\n",
"dtype: object"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tips_data.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"sex smoker day time \n",
"Male Yes Thur Lunch 10\n",
" Fri Lunch 3\n",
" Dinner 5\n",
" Sat Dinner 27\n",
" Sun Dinner 15\n",
" No Thur Lunch 20\n",
" Fri Dinner 2\n",
" Sat Dinner 32\n",
" Sun Dinner 43\n",
"Female Yes Thur Lunch 7\n",
" Fri Lunch 3\n",
" Dinner 4\n",
" Sat Dinner 15\n",
" Sun Dinner 4\n",
" No Thur Lunch 24\n",
" Dinner 1\n",
" Fri Lunch 1\n",
" Dinner 1\n",
" Sat Dinner 13\n",
" Sun Dinner 14\n",
"dtype: int64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tips_data.groupby(['sex', 'smoker', 'day', 'time']).size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Describing Data\n",
"Summary statistics, which include things like the mean, min, and max of the data, can be useful to get a feel for how large some of the variables are and what variables may be the most important. "
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>total_bill</th>\n",
" <th>tip</th>\n",
" <th>size</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>244.000000</td>\n",
" <td>244.000000</td>\n",
" <td>244.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>19.785943</td>\n",
" <td>2.998279</td>\n",
" <td>2.569672</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>8.902412</td>\n",
" <td>1.383638</td>\n",
" <td>0.951100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>3.070000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>13.347500</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>17.795000</td>\n",
" <td>2.900000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>24.127500</td>\n",
" <td>3.562500</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>50.810000</td>\n",
" <td>10.000000</td>\n",
" <td>6.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_bill tip size\n",
"count 244.000000 244.000000 244.000000\n",
"mean 19.785943 2.998279 2.569672\n",
"std 8.902412 1.383638 0.951100\n",
"min 3.070000 1.000000 1.000000\n",
"25% 13.347500 2.000000 2.000000\n",
"50% 17.795000 2.900000 2.000000\n",
"75% 24.127500 3.562500 3.000000\n",
"max 50.810000 10.000000 6.000000"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Print out the summary statistics for the quantitative variables\n",
"tips_data.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Creating a Histogram\n",
"\n",
"After we have a general 'feel' for the data, it is often good to get a feel for the shape of the distribution of the data."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/envs/python2/lib/python2.7/site-packages/matplotlib/axes/_axes.py:6571: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
" warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
]
},
{
"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": [
"# Plot a histogram of the total bill\n",
"sns.distplot(tips_data[\"total_bill\"], kde = False).set_title(\"Histogram of Total Bill\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/envs/python2/lib/python2.7/site-packages/matplotlib/axes/_axes.py:6571: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
" warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
]
},
{
"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": [
"# Plot a histogram of the Tips only\n",
"sns.distplot(tips_data[\"tip\"]).set_title(\"Histogram of Total Tip\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/envs/python2/lib/python2.7/site-packages/matplotlib/axes/_axes.py:6571: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
" warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
"/opt/conda/envs/python2/lib/python2.7/site-packages/matplotlib/axes/_axes.py:6571: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
" warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
]
},
{
"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": [
"# Plot a histogram of both the total bill and the tips'\n",
"sns.distplot(tips_data[\"total_bill\"], kde = False)\n",
"sns.distplot(tips_data[\"tip\"], kde = False).set_title(\"Histogram of Both Tip Size and Total Bill\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Creating a Boxplot\n",
"\n",
"Boxplots do not show the shape of the distribution, but they can give us a better idea about the center and spread of the distribution as well as any potential outliers that may exist. Boxplots and Histograms often complement each other and help an analyst get more information about the data"
]
},
{
"cell_type": "code",
"execution_count": 40,
"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": [
"# Create a boxplot of the total bill amounts\n",
"sns.boxplot(tips_data[\"total_bill\"]).set_title(\"Box plot of the Total Bill\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"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": [
"# Create a boxplot of the tips amounts\n",
"sns.boxplot(tips_data[\"tip\"]).set_title(\"Box plot of the Tip\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"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": [
"# Create a boxplot of the tips and total bill amounts - do not do it like this\n",
"sns.boxplot(tips_data[\"total_bill\"])\n",
"sns.boxplot(tips_data[\"tip\"]).set_title(\"Box plot of the Total Bill and Tips\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Creating Histograms and Boxplots Plotted by Groups\n",
"\n",
"While looking at a single variable is interesting, it is often useful to see how a variable changes in response to another. Using graphs, we can see if there is a difference between the tipping amounts of smokers vs. non-smokers, if tipping varies according to the time of the day, or we can explore other trends in the data as well."
]
},
{
"cell_type": "code",
"execution_count": 43,
"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": [
"# Create a boxplot and histogram of the tips grouped by smoking status\n",
"sns.boxplot(x = tips_data[\"tip\"], y = tips_data[\"smoker\"])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create a boxplot and histogram of the tips grouped by time of day\n",
"sns.boxplot(x = tips_data[\"tip\"], y = tips_data[\"time\"])\n",
"\n",
"g = sns.FacetGrid(tips_data, row = \"time\")\n",
"g = g.map(plt.hist, \"tip\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create a boxplot and histogram of the tips grouped by the day\n",
"sns.boxplot(x = tips_data[\"tip\"], y = tips_data[\"day\"])\n",
"\n",
"g = sns.FacetGrid(tips_data, row = \"day\")\n",
"g = g.map(plt.hist, \"tip\")\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.15"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| stackv2 | 2024-11-18T18:03:05.241919+00:00 | 2019-09-17T02:22:16 | {
"license": "MIT",
"url": "https://raw.githubusercontent.com/rezapci/UofM_Statistics_with_Python_Specialization/edc31cadcbada20d385ae9b0304b8c0cb7ba83e2/Understanding_and_Visualizing_Data_with_Python-master/week2/Tables_Histograms_and_Boxplots_in_Python.ipynb",
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"snapshot_id": "1f7a131e7ff2ec8b550505b5514a183bff6bedf4",
"revision_id": "edc31cadcbada20d385ae9b0304b8c0cb7ba83e2",
"branch_name": "refs/heads/master",
"visit_date": "2020-07-27T01:22:14.129007",
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"filename": "Tables_Histograms_and_Boxplots_in_Python.ipynb"
} |
8b37fd74158e9625bc8f6c1cfe2e97548a33b083 | {
"cells": [
{
"cell_type": "markdown",
"id": "dedicated-bahrain",
"metadata": {},
"source": [
"# Yue - test suite code"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "exterior-collective",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn import *"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "overhead-airplane",
"metadata": {},
"outputs": [],
"source": [
"train_descriptors = pd.read_csv(\"train_descriptors.csv\")\n",
"train_mord3d = pd.read_csv(\"train_mord3d.csv\")\n",
"train_morgan = pd.read_csv(\"train_morgan.csv\")\n",
"train_rdk = pd.read_csv(\"train_rdk.csv\")\n",
"\n",
"train_crystals = pd.read_csv(\"train_crystals.csv\")\n",
"train_distances = pd.read_csv(\"train_distances.csv\")\n",
"train_centroid_distances = pd.read_csv(\"train_centroid_distances.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "animated-chancellor",
"metadata": {},
"outputs": [],
"source": [
"test_descriptors = pd.read_csv(\"test_descriptors.csv\")\n",
"test_mord3d = pd.read_csv(\"test_mord3d.csv\")\n",
"test_morgan = pd.read_csv(\"test_morgan.csv\")\n",
"test_rdk = pd.read_csv(\"test_rdk.csv\")"
]
},
{
"cell_type": "markdown",
"id": "contemporary-flashing",
"metadata": {},
"source": [
"## Preprocessing"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "explicit-chicken",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(13449, 1465)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Are there any redundant columns (all values the same)? These wouldn't contribute to the model at all\n",
"\n",
"for col in train_descriptors.columns:\n",
" if len(train_descriptors[col].unique()) == 1:\n",
"\n",
"# If so, drop redundant columns:\n",
" train_descriptors.drop(col,inplace=True,axis=1)\n",
"\n",
"train_descriptors.shape # Drops to 1465 - ~150 redundant columns"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "centered-dakota",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(13449, 891)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Drop all columns with NA - Last 2 columns are InchiKey and SMILES\n",
"\n",
"train_descriptors_full = train_descriptors.iloc[:, 3:-2].dropna(axis= 1, how=\"any\")\n",
"train_descriptors_full.shape # previously (13449, 891)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "possible-military",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(13449, 124) (3363, 124)\n"
]
}
],
"source": [
"train_PCA = decomposition.PCA(n_components=.95)\n",
"scaler_for_PCA = preprocessing.StandardScaler()\n",
"train_descriptors_PCA = train_PCA.fit_transform(scaler_for_PCA.fit_transform(train_descriptors_full))\n",
"test_descriptors_PCA = train_PCA.transform(scaler_for_PCA.transform(test_descriptors_full))\n",
"print(train_descriptors_PCA.shape, test_descriptors_PCA.shape)"
]
},
{
"cell_type": "markdown",
"id": "laughing-construction",
"metadata": {},
"source": [
"#### Choose the datasets to feed into the test suite \n",
"\n",
"Choose which dataset and target to use, and feed them into X_train and y_train below"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "adequate-provider",
"metadata": {},
"outputs": [],
"source": [
"target = \"calculated_density\"\n",
"\n",
"# Split data into training and test sets\n",
"# X_train, X_valid, y_train, y_valid = model_selection.train_test_split(train_descriptors_PCA, train_crystals[target], random_state = 0)\n",
"# print(len(X_train), len(X_valid))\n",
"\n",
"# Use full set if tuning with CV\n",
"X_train = train_descriptors_PCA\n",
"y_train = train_crystals[target]"
]
},
{
"cell_type": "markdown",
"id": "bound-bacteria",
"metadata": {},
"source": [
"#### Test suite for optimizing different models\n",
"In the test suite, we write one objective function for each model we want to test.\n",
"\n",
"**Parameters:** \n",
"\n",
"Random forest:\n",
"* `n_estimators` number of trees (default 100)\n",
"* `max_depth` tree depth (default ?)\n",
"\n",
"Gradient boosting https://machinelearningmastery.com/gradient-boosting-machine-ensemble-in-python/\n",
"* `n_estimators` number of trees (default 100)\n",
"* `subsample` number of samples (what fraction of the training set is used to train each tree? default 1.0, i.e. all)\n",
"* `max_features` max number of features to fit each tree (maybe less important? leave this out to use every feature)\n",
"* `learning_rate` learning rate (default 0.1, rate between 0-1; use log scale)\n",
"* `max_depth` tree depth (default 3; too large may lead to overfitting. Try 1-10?)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "similar-leonard",
"metadata": {},
"outputs": [],
"source": [
"# Define objective functions for each model to use\n",
"\n",
"# Set scoring function\n",
"scoring = \"neg_mean_absolute_error\"\n",
"\n",
"# Random forest\n",
"def obj_RF(trial):\n",
" n_estimators = trial.suggest_int('n_estimators', 2, 200)\n",
" max_depth = int(trial.suggest_loguniform('max_depth', 1, 32))\n",
" clf = ensemble.RandomForestRegressor(n_estimators=n_estimators, max_depth=max_depth, n_jobs=-1)\n",
" return model_selection.cross_val_score(clf, X_train, y_train, \n",
" n_jobs=-1, scoring=scoring, cv=3).mean()\n",
"\n",
"# Gradient boosting - maybe try XGboost implementation instead as it has better performance?\n",
"# Key parameters: n_estimators, max_depth, learning_rate\n",
"# GB doesn't have a n_jobs parameter\n",
"\n",
"def obj_GB(trial):\n",
" n_estimators = trial.suggest_int('n_estimators', 2, 200)\n",
" max_depth = int(trial.suggest_loguniform('max_depth', 1, 10))\n",
" learning_rate = trial.suggest_loguniform('learning_rate', 0.0001, 1) # Note - this is not an int!\n",
" \n",
" clf = ensemble.GradientBoostingRegressor(n_estimators=n_estimators, max_depth=max_depth, learning_rate=learning_rate)\n",
" \n",
" return model_selection.cross_val_score(clf, X_train, y_train, n_jobs=-1, scoring=scoring, cv=3).mean()\n",
"\n",
"# Define list of objectives to use below\n",
"def obj_XX(trail):\n",
" # Set parameters for optuna to suggest\n",
" # parameters = trail.suggest_XX\n",
" \n",
" # Set model\n",
" # clf = sklearn model function here with parameters\n",
" \n",
" # return a scoring function - have been using all data with 3 fold cross validation\n",
" # balance between time needed (higher CV folds = slower) and accuracy\n",
" # returns mean of CV score array\n",
" return model_selection.cross_val_score(clf, X_train, y_train, n_jobs=-1, scoring=scoring, cv=3).mean()\n",
"\n",
"\n",
"# Create list of tests to run"
]
},
{
"cell_type": "markdown",
"id": "above-yellow",
"metadata": {},
"source": [
"Create list of studies to optimize:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "radio-roots",
"metadata": {},
"outputs": [],
"source": [
"%%time # record time it takes to run\n",
"\n",
"study_rf = optuna.create_study(direction='maximize')\n",
"study_gb = optuna.create_study(direction='maximize')\n",
"# fill in other models etc. \n",
"\n",
"study_rf.optimize(obj_RF, n_trials=100)\n",
"study_gb.optimize(obj_GB, n_trials=100)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aboriginal-sector",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "second-oriental",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "recognized-pulse",
"metadata": {},
"source": [
"#### Below is just unit testing for a different model\n",
"\n",
"Not directly relevant to test suite code"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "gross-argentina",
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m[I 2021-08-15 21:27:32,174]\u001b[0m A new study created in memory with name: no-name-0f6c26ad-62af-4f19-ac6b-a90298b00144\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:27:45,804]\u001b[0m Trial 0 finished with value: -0.13947638888243055 and parameters: {'n_estimators': 71, 'max_depth': 2.6358572330388035, 'learning_rate': 0.022785434546836066}. Best is trial 0 with value: -0.13947638888243055.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:28:02,068]\u001b[0m Trial 1 finished with value: -0.21887384128155493 and parameters: {'n_estimators': 84, 'max_depth': 2.4155965813474567, 'learning_rate': 0.0007952688327350772}. Best is trial 0 with value: -0.13947638888243055.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:28:19,002]\u001b[0m Trial 2 finished with value: -0.11961728426717466 and parameters: {'n_estimators': 168, 'max_depth': 1.6303719801414625, 'learning_rate': 0.03915909340817165}. Best is trial 2 with value: -0.11961728426717466.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:28:26,554]\u001b[0m Trial 3 finished with value: -0.219156767811572 and parameters: {'n_estimators': 74, 'max_depth': 1.970993980293779, 'learning_rate': 0.0018252311817280903}. Best is trial 2 with value: -0.11961728426717466.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:29:18,443]\u001b[0m Trial 4 finished with value: -0.05361797559961646 and parameters: {'n_estimators': 114, 'max_depth': 5.965896920884274, 'learning_rate': 0.09962140846060119}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:29:30,993]\u001b[0m Trial 5 finished with value: -0.2223710073551616 and parameters: {'n_estimators': 27, 'max_depth': 5.820814449685189, 'learning_rate': 0.0005293314230162655}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:29:42,091]\u001b[0m Trial 6 finished with value: -0.22274499509100873 and parameters: {'n_estimators': 110, 'max_depth': 1.3701131897094603, 'learning_rate': 0.0004379700714491288}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:30:36,139]\u001b[0m Trial 7 finished with value: -0.11665636060477357 and parameters: {'n_estimators': 94, 'max_depth': 7.382235214566928, 'learning_rate': 0.009624744180282491}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:30:42,160]\u001b[0m Trial 8 finished with value: -0.21888487814791294 and parameters: {'n_estimators': 8, 'max_depth': 9.918656558710627, 'learning_rate': 0.003843594729245627}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:31:50,482]\u001b[0m Trial 9 finished with value: -0.06269388399970034 and parameters: {'n_estimators': 118, 'max_depth': 7.521522918554764, 'learning_rate': 0.023285796111345958}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:33:00,527]\u001b[0m Trial 10 finished with value: -0.07118916952592193 and parameters: {'n_estimators': 192, 'max_depth': 4.190320021856426, 'learning_rate': 0.6970793351318183}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:33:49,990]\u001b[0m Trial 11 finished with value: -0.05504563989463259 and parameters: {'n_estimators': 137, 'max_depth': 4.153809992507794, 'learning_rate': 0.16257863592075839}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:34:28,082]\u001b[0m Trial 12 finished with value: -0.05839745846463632 and parameters: {'n_estimators': 136, 'max_depth': 3.846214214930233, 'learning_rate': 0.2737570248595307}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:35:24,657]\u001b[0m Trial 13 finished with value: -0.054557262728302615 and parameters: {'n_estimators': 153, 'max_depth': 4.944267893666406, 'learning_rate': 0.1655005921744589}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:35:40,701]\u001b[0m Trial 14 finished with value: -0.08821675776931392 and parameters: {'n_estimators': 161, 'max_depth': 1.0186789481165366, 'learning_rate': 0.11820727964093074}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:37:09,644]\u001b[0m Trial 15 finished with value: -0.07549847766603024 and parameters: {'n_estimators': 200, 'max_depth': 5.395800870354057, 'learning_rate': 0.7676644593888118}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:38:21,203]\u001b[0m Trial 16 finished with value: -0.2217616253719602 and parameters: {'n_estimators': 160, 'max_depth': 5.666626004631373, 'learning_rate': 0.00011195464760098166}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:38:35,951]\u001b[0m Trial 17 finished with value: -0.08367617396679276 and parameters: {'n_estimators': 51, 'max_depth': 3.205163470127129, 'learning_rate': 0.07440036135622083}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:40:03,749]\u001b[0m Trial 18 finished with value: -0.057622127164433906 and parameters: {'n_estimators': 133, 'max_depth': 8.616013512028143, 'learning_rate': 0.33066033452762816}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:41:06,766]\u001b[0m Trial 19 finished with value: -0.057292943577395485 and parameters: {'n_estimators': 171, 'max_depth': 4.893241194811407, 'learning_rate': 0.05739704442470666}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:42:06,747]\u001b[0m Trial 20 finished with value: -0.09336222692432776 and parameters: {'n_estimators': 114, 'max_depth': 6.4423138982632935, 'learning_rate': 0.012739753329647439}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:43:00,998]\u001b[0m Trial 21 finished with value: -0.05455732368567151 and parameters: {'n_estimators': 140, 'max_depth': 4.067784213355413, 'learning_rate': 0.17830359063907444}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:43:43,488]\u001b[0m Trial 22 finished with value: -0.0578597885171081 and parameters: {'n_estimators': 147, 'max_depth': 3.17544141356158, 'learning_rate': 0.2604163361529052}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:44:50,481]\u001b[0m Trial 23 finished with value: -0.05464284959815597 and parameters: {'n_estimators': 182, 'max_depth': 4.693369773552185, 'learning_rate': 0.10092671099341279}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:45:28,290]\u001b[0m Trial 24 finished with value: -0.061501430098515934 and parameters: {'n_estimators': 129, 'max_depth': 3.6044045920545433, 'learning_rate': 0.40064871593591256}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:46:47,372]\u001b[0m Trial 25 finished with value: -0.05423760595823487 and parameters: {'n_estimators': 149, 'max_depth': 6.748765682225758, 'learning_rate': 0.04007310012968714}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:48:06,331]\u001b[0m Trial 26 finished with value: -0.05661015527465157 and parameters: {'n_estimators': 153, 'max_depth': 6.939697681873742, 'learning_rate': 0.030703811924057044}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:49:18,578]\u001b[0m Trial 27 finished with value: -0.10939981680954845 and parameters: {'n_estimators': 104, 'max_depth': 9.046446951004157, 'learning_rate': 0.009093757865691004}. Best is trial 4 with value: -0.05361797559961646.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:50:37,512]\u001b[0m Trial 28 finished with value: -0.05216031131024714 and parameters: {'n_estimators': 121, 'max_depth': 8.09254749619306, 'learning_rate': 0.05609515777851178}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:51:06,338]\u001b[0m Trial 29 finished with value: -0.12823080781262486 and parameters: {'n_estimators': 49, 'max_depth': 7.915906856751203, 'learning_rate': 0.015339129356656064}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:52:09,705]\u001b[0m Trial 30 finished with value: -0.14770057236346978 and parameters: {'n_estimators': 124, 'max_depth': 6.11883730129971, 'learning_rate': 0.004624879005715644}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:52:58,524]\u001b[0m Trial 31 finished with value: -0.05679559117333902 and parameters: {'n_estimators': 94, 'max_depth': 6.777887742981676, 'learning_rate': 0.049581992096135785}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:53:15,209]\u001b[0m Trial 32 finished with value: -0.0849659075040845 and parameters: {'n_estimators': 83, 'max_depth': 2.5578661902894364, 'learning_rate': 0.07832102519933908}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:54:33,803]\u001b[0m Trial 33 finished with value: -0.05696211154406202 and parameters: {'n_estimators': 175, 'max_depth': 5.05768076465137, 'learning_rate': 0.0356156324843993}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:56:13,053]\u001b[0m Trial 34 finished with value: -0.05395288496668125 and parameters: {'n_estimators': 150, 'max_depth': 8.495153751500075, 'learning_rate': 0.17203061508682607}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:57:36,566]\u001b[0m Trial 35 finished with value: -0.05842073929763716 and parameters: {'n_estimators': 121, 'max_depth': 9.619408049262375, 'learning_rate': 0.023828066011913722}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:58:17,120]\u001b[0m Trial 36 finished with value: -0.06391099797932585 and parameters: {'n_estimators': 67, 'max_depth': 7.8013769991594755, 'learning_rate': 0.5288103784430966}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 21:59:20,162]\u001b[0m Trial 37 finished with value: -0.05397798307523909 and parameters: {'n_estimators': 97, 'max_depth': 8.476177359280157, 'learning_rate': 0.04686516183113557}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:00:24,905]\u001b[0m Trial 38 finished with value: -0.05247169341546551 and parameters: {'n_estimators': 101, 'max_depth': 8.517884016754465, 'learning_rate': 0.11652556629968278}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:01:19,845]\u001b[0m Trial 39 finished with value: -0.05346359382371877 and parameters: {'n_estimators': 78, 'max_depth': 9.873238431803749, 'learning_rate': 0.10999205337707171}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:02:13,471]\u001b[0m Trial 40 finished with value: -0.053624385374278695 and parameters: {'n_estimators': 76, 'max_depth': 9.729651231634536, 'learning_rate': 0.0992840342844235}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:03:03,607]\u001b[0m Trial 41 finished with value: -0.05381825957409256 and parameters: {'n_estimators': 71, 'max_depth': 9.479748235498626, 'learning_rate': 0.12125794022211747}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:04:02,113]\u001b[0m Trial 42 finished with value: -0.05325225902888838 and parameters: {'n_estimators': 84, 'max_depth': 9.882427870195842, 'learning_rate': 0.07594326285247158}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:05:06,032]\u001b[0m Trial 43 finished with value: -0.05413638886785201 and parameters: {'n_estimators': 108, 'max_depth': 7.57936206803068, 'learning_rate': 0.23836640162321682}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:06:00,131]\u001b[0m Trial 44 finished with value: -0.07597587909714178 and parameters: {'n_estimators': 85, 'max_depth': 8.288827297692773, 'learning_rate': 0.02049714687521226}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:06:41,064]\u001b[0m Trial 45 finished with value: -0.05550367097809794 and parameters: {'n_estimators': 59, 'max_depth': 9.918713885832258, 'learning_rate': 0.0623925010151656}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:06:44,724]\u001b[0m Trial 46 finished with value: -0.21510149145237342 and parameters: {'n_estimators': 35, 'max_depth': 1.8992341302412943, 'learning_rate': 0.006626727545528588}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:07:35,376]\u001b[0m Trial 47 finished with value: -0.20619019483737744 and parameters: {'n_estimators': 87, 'max_depth': 7.0821265752402836, 'learning_rate': 0.0012139065710571949}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:08:28,383]\u001b[0m Trial 48 finished with value: -0.05965599468023255 and parameters: {'n_estimators': 101, 'max_depth': 6.265719320328941, 'learning_rate': 0.3923478180238797}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:09:41,524]\u001b[0m Trial 49 finished with value: -0.052575354607077913 and parameters: {'n_estimators': 111, 'max_depth': 8.864052381601157, 'learning_rate': 0.12507258221883769}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:10:57,590]\u001b[0m Trial 50 finished with value: -0.05284569701995332 and parameters: {'n_estimators': 113, 'max_depth': 8.74537637033004, 'learning_rate': 0.14073375961676984}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:12:10,862]\u001b[0m Trial 51 finished with value: -0.05293149884046949 and parameters: {'n_estimators': 112, 'max_depth': 8.518761573797649, 'learning_rate': 0.13033841123052575}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:13:27,613]\u001b[0m Trial 52 finished with value: -0.054716002369501804 and parameters: {'n_estimators': 114, 'max_depth': 8.841890594509223, 'learning_rate': 0.21864638111633194}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:14:43,677]\u001b[0m Trial 53 finished with value: -0.09140660436824499 and parameters: {'n_estimators': 126, 'max_depth': 7.766639095018165, 'learning_rate': 0.9925035099029577}. Best is trial 28 with value: -0.05216031131024714.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:15:48,177]\u001b[0m Trial 54 finished with value: -0.05199534420534335 and parameters: {'n_estimators': 109, 'max_depth': 7.247626859690884, 'learning_rate': 0.07659492746137277}. Best is trial 54 with value: -0.05199534420534335.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:16:36,280]\u001b[0m Trial 55 finished with value: -0.053819902694806986 and parameters: {'n_estimators': 107, 'max_depth': 5.859523667173857, 'learning_rate': 0.1511202460149405}. Best is trial 54 with value: -0.05199534420534335.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:17:33,492]\u001b[0m Trial 56 finished with value: -0.0617164744771189 and parameters: {'n_estimators': 93, 'max_depth': 7.4399236068300185, 'learning_rate': 0.4607620272109168}. Best is trial 54 with value: -0.05199534420534335.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:18:27,695]\u001b[0m Trial 57 finished with value: -0.05318313403051824 and parameters: {'n_estimators': 118, 'max_depth': 5.357133079629676, 'learning_rate': 0.13516714729457502}. Best is trial 54 with value: -0.05199534420534335.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:20:01,751]\u001b[0m Trial 58 finished with value: -0.05645836153063966 and parameters: {'n_estimators': 139, 'max_depth': 8.96335171348228, 'learning_rate': 0.28844991843195644}. Best is trial 54 with value: -0.05199534420534335.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:21:24,682]\u001b[0m Trial 59 finished with value: -0.05556260389474111 and parameters: {'n_estimators': 131, 'max_depth': 8.17152530311739, 'learning_rate': 0.02834706787695795}. Best is trial 54 with value: -0.05199534420534335.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:21:35,148]\u001b[0m Trial 60 finished with value: -0.22378856108903414 and parameters: {'n_estimators': 101, 'max_depth': 1.3703474925237795, 'learning_rate': 0.00023850325841387622}. Best is trial 54 with value: -0.05199534420534335.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:22:28,844]\u001b[0m Trial 61 finished with value: -0.05350314542738672 and parameters: {'n_estimators': 117, 'max_depth': 5.335885133613306, 'learning_rate': 0.13886991271773527}. Best is trial 54 with value: -0.05199534420534335.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:23:28,114]\u001b[0m Trial 62 finished with value: -0.0527499476006044 and parameters: {'n_estimators': 113, 'max_depth': 6.562917484017387, 'learning_rate': 0.07806562297006467}. Best is trial 54 with value: -0.05199534420534335.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:24:34,612]\u001b[0m Trial 63 finished with value: -0.05163768632120625 and parameters: {'n_estimators': 113, 'max_depth': 7.265364011095706, 'learning_rate': 0.08324081274091524}. Best is trial 63 with value: -0.05163768632120625.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:25:39,153]\u001b[0m Trial 64 finished with value: -0.05248128558796475 and parameters: {'n_estimators': 122, 'max_depth': 6.95495367551777, 'learning_rate': 0.07652393562711}. Best is trial 63 with value: -0.05163768632120625.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:26:43,213]\u001b[0m Trial 65 finished with value: -0.05284144414794739 and parameters: {'n_estimators': 123, 'max_depth': 6.6869645379724245, 'learning_rate': 0.06432255785068089}. Best is trial 63 with value: -0.05163768632120625.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:28:05,581]\u001b[0m Trial 66 finished with value: -0.05129906546550029 and parameters: {'n_estimators': 141, 'max_depth': 7.129754111218768, 'learning_rate': 0.08566224660821928}. Best is trial 66 with value: -0.05129906546550029.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:29:28,023]\u001b[0m Trial 67 finished with value: -0.05228659057508873 and parameters: {'n_estimators': 142, 'max_depth': 7.286535689143602, 'learning_rate': 0.04577103591169596}. Best is trial 66 with value: -0.05129906546550029.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:30:42,053]\u001b[0m Trial 68 finished with value: -0.05688995133518632 and parameters: {'n_estimators': 165, 'max_depth': 5.739561696069073, 'learning_rate': 0.03850974373930992}. Best is trial 66 with value: -0.05129906546550029.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:32:01,792]\u001b[0m Trial 69 finished with value: -0.052400224884291545 and parameters: {'n_estimators': 136, 'max_depth': 7.170606572863614, 'learning_rate': 0.04728448262876706}. Best is trial 66 with value: -0.05129906546550029.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:32:54,480]\u001b[0m Trial 70 finished with value: -0.07904941521489124 and parameters: {'n_estimators': 144, 'max_depth': 4.384291317915611, 'learning_rate': 0.021161094527604857}. Best is trial 66 with value: -0.05129906546550029.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:34:24,534]\u001b[0m Trial 71 finished with value: -0.06607380976228742 and parameters: {'n_estimators': 156, 'max_depth': 7.258975258335616, 'learning_rate': 0.015636975519090057}. Best is trial 66 with value: -0.05129906546550029.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:35:37,702]\u001b[0m Trial 72 finished with value: -0.053235300463872905 and parameters: {'n_estimators': 142, 'max_depth': 6.139330165290407, 'learning_rate': 0.04979396896146623}. Best is trial 66 with value: -0.05129906546550029.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:36:47,170]\u001b[0m Trial 73 finished with value: -0.05185752281513851 and parameters: {'n_estimators': 134, 'max_depth': 6.855689500947063, 'learning_rate': 0.08910709108690827}. Best is trial 66 with value: -0.05129906546550029.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:38:06,390]\u001b[0m Trial 74 finished with value: -0.0522458227740589 and parameters: {'n_estimators': 136, 'max_depth': 7.463507312887702, 'learning_rate': 0.050759264396036355}. Best is trial 66 with value: -0.05129906546550029.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:39:14,785]\u001b[0m Trial 75 finished with value: -0.05796083024945423 and parameters: {'n_estimators': 134, 'max_depth': 6.368451219522009, 'learning_rate': 0.031546100570889496}. Best is trial 66 with value: -0.05129906546550029.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:40:30,528]\u001b[0m Trial 76 finished with value: -0.052599706794686805 and parameters: {'n_estimators': 130, 'max_depth': 7.244493054306578, 'learning_rate': 0.046773137478719404}. Best is trial 66 with value: -0.05129906546550029.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:41:51,841]\u001b[0m Trial 77 finished with value: -0.051308397856081885 and parameters: {'n_estimators': 138, 'max_depth': 7.657803330755752, 'learning_rate': 0.09039008343409108}. Best is trial 66 with value: -0.05129906546550029.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:43:18,110]\u001b[0m Trial 78 finished with value: -0.05154344008285803 and parameters: {'n_estimators': 147, 'max_depth': 7.8464695567114395, 'learning_rate': 0.09276847390375122}. Best is trial 66 with value: -0.05129906546550029.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:44:48,561]\u001b[0m Trial 79 finished with value: -0.05136041821484236 and parameters: {'n_estimators': 156, 'max_depth': 7.924729434719671, 'learning_rate': 0.09451330529518341}. Best is trial 66 with value: -0.05129906546550029.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:46:21,501]\u001b[0m Trial 80 finished with value: -0.05132996409809671 and parameters: {'n_estimators': 156, 'max_depth': 7.897480740460265, 'learning_rate': 0.0940230397455829}. Best is trial 66 with value: -0.05129906546550029.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:46:51,893]\u001b[0m Trial 81 finished with value: -0.06890408489251312 and parameters: {'n_estimators': 157, 'max_depth': 2.8880868091016945, 'learning_rate': 0.0957468412248855}. Best is trial 66 with value: -0.05129906546550029.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:48:49,508]\u001b[0m Trial 82 finished with value: -0.054868933411844074 and parameters: {'n_estimators': 177, 'max_depth': 8.048076560441258, 'learning_rate': 0.2171648517526142}. Best is trial 66 with value: -0.05129906546550029.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:50:26,331]\u001b[0m Trial 83 finished with value: -0.05119613453555263 and parameters: {'n_estimators': 165, 'max_depth': 7.742003352230158, 'learning_rate': 0.08808430013447574}. Best is trial 83 with value: -0.05119613453555263.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:52:28,484]\u001b[0m Trial 84 finished with value: -0.05486158063855084 and parameters: {'n_estimators': 166, 'max_depth': 9.324120721315294, 'learning_rate': 0.18141881980227653}. Best is trial 83 with value: -0.05119613453555263.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:54:05,780]\u001b[0m Trial 85 finished with value: -0.05138449302236011 and parameters: {'n_estimators': 188, 'max_depth': 6.760908910627936, 'learning_rate': 0.08701163771913725}. Best is trial 83 with value: -0.05119613453555263.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:55:45,229]\u001b[0m Trial 86 finished with value: -0.051203484706031734 and parameters: {'n_estimators': 192, 'max_depth': 6.540215038200375, 'learning_rate': 0.08827771980124319}. Best is trial 83 with value: -0.05119613453555263.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:57:35,173]\u001b[0m Trial 87 finished with value: -0.05107531443090187 and parameters: {'n_estimators': 187, 'max_depth': 7.920281630913733, 'learning_rate': 0.06374800807435875}. Best is trial 87 with value: -0.05107531443090187.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 22:59:26,867]\u001b[0m Trial 88 finished with value: -0.05123656199667359 and parameters: {'n_estimators': 192, 'max_depth': 7.887391139237466, 'learning_rate': 0.06330350430297073}. Best is trial 87 with value: -0.05107531443090187.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 23:00:03,183]\u001b[0m Trial 89 finished with value: -0.07285849438083646 and parameters: {'n_estimators': 192, 'max_depth': 2.2108794522822732, 'learning_rate': 0.05974999269777788}. Best is trial 87 with value: -0.05107531443090187.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 23:01:44,339]\u001b[0m Trial 90 finished with value: -0.05694407687517591 and parameters: {'n_estimators': 191, 'max_depth': 6.565981420491316, 'learning_rate': 0.32303716797708637}. Best is trial 87 with value: -0.05107531443090187.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 23:03:42,842]\u001b[0m Trial 91 finished with value: -0.05330207258362662 and parameters: {'n_estimators': 199, 'max_depth': 7.84519194370893, 'learning_rate': 0.19222611804538717}. Best is trial 87 with value: -0.05107531443090187.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 23:05:32,980]\u001b[0m Trial 92 finished with value: -0.05134267930602599 and parameters: {'n_estimators': 186, 'max_depth': 7.91292160492773, 'learning_rate': 0.10069171161644537}. Best is trial 87 with value: -0.05107531443090187.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 23:07:43,948]\u001b[0m Trial 93 finished with value: -0.05285518397063649 and parameters: {'n_estimators': 185, 'max_depth': 9.228505604321875, 'learning_rate': 0.0648157550973326}. Best is trial 87 with value: -0.05107531443090187.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 23:09:10,441]\u001b[0m Trial 94 finished with value: -0.05452995614968831 and parameters: {'n_estimators': 171, 'max_depth': 6.106331186913661, 'learning_rate': 0.03367467223537263}. Best is trial 87 with value: -0.05107531443090187.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 23:11:09,774]\u001b[0m Trial 95 finished with value: -0.052478670744504775 and parameters: {'n_estimators': 182, 'max_depth': 8.200643351979796, 'learning_rate': 0.11141392650784503}. Best is trial 87 with value: -0.05107531443090187.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 23:12:32,062]\u001b[0m Trial 96 finished with value: -0.060511937975410414 and parameters: {'n_estimators': 186, 'max_depth': 5.592614394512464, 'learning_rate': 0.025457906512606435}. Best is trial 87 with value: -0.05107531443090187.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 23:14:41,637]\u001b[0m Trial 97 finished with value: -0.052520895163515637 and parameters: {'n_estimators': 197, 'max_depth': 8.418648870523993, 'learning_rate': 0.10525942498304738}. Best is trial 87 with value: -0.05107531443090187.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 23:15:31,596]\u001b[0m Trial 98 finished with value: -0.06225738985213979 and parameters: {'n_estimators': 177, 'max_depth': 3.6103148700543546, 'learning_rate': 0.06354245431834142}. Best is trial 87 with value: -0.05107531443090187.\u001b[0m\n",
"\u001b[32m[I 2021-08-15 23:17:08,733]\u001b[0m Trial 99 finished with value: -0.05334061529179348 and parameters: {'n_estimators': 163, 'max_depth': 7.828226462852263, 'learning_rate': 0.18308281243357885}. Best is trial 87 with value: -0.05107531443090187.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 1h 49min 36s\n"
]
}
],
"source": [
"%%time\n",
"study = optuna.create_study(direction='maximize')\n",
"study.optimize(obj_GB, n_trials=100)"
]
},
{
"cell_type": "markdown",
"id": "worth-woman",
"metadata": {},
"source": [
"Can use direct visualization (install plotly library) or write figures to image. Note: Also need to install [kaleido](https://github.com/optuna/optuna/issues/602) to export plotly figures\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "accessible-emphasis",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
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",
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],
"source": [
"optuna.visualization.plot_slice(study)\n",
"\n",
"# Uncomment to write figure\n",
"# fig2 = optuna.visualization.plot_slice(study)\n",
"# fig2.write_image('optuna_slice_plot_GB_PCA.png')"
]
},
{
"cell_type": "markdown",
"id": "broadband-ontario",
"metadata": {},
"source": [
"Information about the trails can be extracted from the study:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "better-prince",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"FrozenTrial(number=87, values=[-0.05107531443090187], datetime_start=datetime.datetime(2021, 8, 15, 22, 55, 45, 230322), datetime_complete=datetime.datetime(2021, 8, 15, 22, 57, 35, 173089), params={'n_estimators': 187, 'max_depth': 7.920281630913733, 'learning_rate': 0.06374800807435875}, distributions={'n_estimators': IntUniformDistribution(high=200, low=2, step=1), 'max_depth': LogUniformDistribution(high=10.0, low=1.0), 'learning_rate': LogUniformDistribution(high=1.0, low=0.0001)}, user_attrs={}, system_attrs={}, intermediate_values={}, trial_id=87, state=TrialState.COMPLETE, value=None)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trial = study.best_trial\n",
"trial"
]
},
{
"cell_type": "markdown",
"id": "solar-bangkok",
"metadata": {},
"source": [
"#### Submission\n",
"\n",
"Try creating a csv file for submission using the optimized hyperparameters\n"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "bibliographic-cycle",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Get hyperparams from best trial\n",
"trial = study.best_trial\n",
"\n",
"# Set up model\n",
"model = ensemble.GradientBoostingRegressor(n_estimators=trial.params['n_estimators'], max_depth=trial.params['max_depth'], learning_rate=trial.params['learning_rate'])\n",
"\n",
"model.fit(X_train, y_train)\n",
"\n",
"preds = model.predict(test_descriptors_PCA)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "derived-universe",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3363,)"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"preds.shape"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "solar-wisdom",
"metadata": {},
"outputs": [],
"source": [
"np.savetxt(\"task_1_predictions_GB_PCA.csv\", preds)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "vocal-canvas",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| stackv2 | 2024-11-18T18:03:05.244740+00:00 | 2021-08-20T14:17:20 | {
"license": "MIT",
"url": "https://raw.githubusercontent.com/JackS7806/DA_Summer_School_Hackathon/41b60e43314dbc6890c89f1352883eab760648cc/Yue_test_suite_2.ipynb",
"blob_id": "8b37fd74158e9625bc8f6c1cfe2e97548a33b083",
"directory_id": "0efcb25b14a32c85d4a9298ed8c6243c530fbb0c",
"path": "/Yue_test_suite_2.ipynb",
"content_id": "bc0535a5b8850e5b01132c7437e0834b2b425f42",
"detected_licenses": [
"MIT"
],
"license_type": "permissive",
"repo_name": "JackS7806/DA_Summer_School_Hackathon",
"snapshot_id": "b6b00453b864b0f4f65a9e77e6e2c4815f1d13ad",
"revision_id": "41b60e43314dbc6890c89f1352883eab760648cc",
"branch_name": "refs/heads/main",
"visit_date": "2023-07-20T07:58:31.214571",
"revision_date": "2021-08-20T14:17:20",
"committer_date": "2021-08-20T14:17:20",
"github_id": 392336094,
"star_events_count": 0,
"fork_events_count": 1,
"gha_license_id": "MIT",
"gha_event_created_at": "2021-08-20T14:17:20",
"gha_created_at": "2021-08-03T13:58:02",
"gha_language": "Jupyter Notebook",
"src_encoding": "UTF-8",
"language": "Jupyter Notebook",
"is_vendor": false,
"is_generated": false,
"length_bytes": 286646,
"extension": "ipynb",
"filename": "Yue_test_suite_2.ipynb"
} |
223530699452de71bcaf6569a7c8318878b81924 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Approximate and subdivide polygon chains\n",
"\n",
"This example shows how to approximate ([Douglas-Peucker algorithm](https://en.wikipedia.org/wiki/Ramer%E2%80%93Douglas%E2%80%93Peucker_algorithm)) and subdivide (B-Splines) polygonal chains."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from skimage.measure import approximate_polygon, subdivide_polygon"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create the test object"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"hand = np.array([[1.64516129, 1.16145833],\n",
" [1.64516129, 1.59375],\n",
" [1.35080645, 1.921875],\n",
" [1.375, 2.18229167],\n",
" [1.68548387, 1.9375],\n",
" [1.60887097, 2.55208333],\n",
" [1.68548387, 2.69791667],\n",
" [1.76209677, 2.56770833],\n",
" [1.83064516, 1.97395833],\n",
" [1.89516129, 2.75],\n",
" [1.9516129, 2.84895833],\n",
" [2.01209677, 2.76041667],\n",
" [1.99193548, 1.99479167],\n",
" [2.11290323, 2.63020833],\n",
" [2.2016129, 2.734375],\n",
" [2.25403226, 2.60416667],\n",
" [2.14919355, 1.953125],\n",
" [2.30645161, 2.36979167],\n",
" [2.39112903, 2.36979167],\n",
" [2.41532258, 2.1875],\n",
" [2.1733871, 1.703125],\n",
" [2.07782258, 1.16666667]])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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EyfaPX3V6brkQ4pwQ4oIQ4klLX4DSe8vigjhTUktRjXamS17JovY9fO3V3ZNWpMPNxYmYYO2Mg9iiQNvmAzmYJKxdoM0FWx2yK+o5klPN7QmjNH8HqxXmNBcMwE+llLHAbOCHQoi4Lo7bL6Wc0v7xHIAQwhl4GVgBxAGruvlexYaWxQYD8PWZMjtHYp7xIT4E+bjbLfGnFtYQG+qrqfovOiuXa9A1tvLOkXy+NzGUUcM9rXYeS3j3WAHOToLvTx9p71AcRo+/yVLKEinl8fbP64AzgLnD5jOBC1LKbCllC7AdWNnXYBXLiA70JjrAi10ZjpH4hRAsiglkf2YFBhvXYDGZJKeLajUzf79DbZPBqi3+t47k0dBi5OFF2m7ttxhMfJBSyFXjgwjyte2iQ0fWqyaMECISmAoc6eLpOUKIU0KIHUKICe2PhQEFnY4pxPw3DcWKlsUFczi7irr2+eBat2hcILV6A6faK2TaSm5VA3XNBiZqaGAX2lv8VhrY1bca2ZKUw8KYQCaM0NZ1X27P2TIq61tYNVMN6vaG2YlfCOENfAD8REpZe9nTx4EIKeVk4EXgPx3f1sVLdbkSRwixTghxTAhxrKLCfjM4BotlscG0GiWJmZX2DsUsC8YE4iSw+eyeNA3tsduZNfv4/51SSGV9C49ovLUPsO1oASG+HiyK0W79IC0yK/ELIVxpS/pvSyk/vPx5KWWtlLK+/fMvAFchRABtLfzOb8UjgS7X4EspN0opE6SUCYGB2tjhaCCbFj6UYZ6u7HaQfn4/T1emhg+zeT9/aqEOD1cnxgR62/S8PdFZadtFo6lt853JI/2Yo/EZMkU1TSSer+D2hJF22wjGUZkzq0cAm4AzUsq/dXNMSPtxCCFmtr9uFZAMjBVCRAkh3IA7gU8sFbzSdy7OTiwZH8Ses+U27zfvq0UxgaQW6aiqb7bZOdMKdcSF+uKioVIFJpOkvtk6m7DsSC8hr6qRRxaN1nzZg/eS23qRb1dz93vNnN/mecBq4KpO0zWvE0I8IoR4pP2Y24B0IcQp4AXgTtnGADwGfEnboPB7UsrTVrgOpQ+ujg1G19TKsTzHmNO/KCYQKeHABdt0TxlNkvRiHZM0UpGzQ12zASktX65BSsn6fVlEBXhpfj9ko0ny/rECFowNZOQwbc860qIef3OklAfouq++8zEvAS9189wXwBd9ik6xqgUxgbg5O7E7o8whFr5MDPNjuJcb+85V2KSgXHZFPY0tRk0t3IJOq3Yt3OJPulBFelEtf7hloua7ThLPV1Cs0/O/16vZ4X2hnftXxea83V2YM9qf3WfKkFJbG590xclJsGBsAPsyK2yyUUvHil2tDezqrFSLf/2+LAJ93LnZAYqcbT+aj7+X26U1KUrvqMQ/yC2LCya3qpGsigZ7h2KWRTGBVDW0cLr48olllpdWpMPTzZlojQ3sWqNOT1qhjgMXKnlwXhQertqoQNqd8jo9X58p59bpIzW1qM6RqJ/aILcstm0anKPM7ll4qXxDudXPlVpYQ/wIP811e1ijFv/6xCx83F24e7b2d676IKUIg0lqvky0lqnEP8iF+g1hwghfdjvIKt4Ab3cmhvlZfVqnwWjidHGt5hZuQefdtywzuJtX1cCOtBLumh2u2RLdHaSUvJucz8yo4YzW2J2YI1GJX2FZbDAp+RdtOk2yPxbFBHI8v+ZSArSG8+X1NBtMmuvfh29r8Vuqxf/q/mxcnJx4aF6URV7Pmg5nV5Nb1cidqrXfLyrxK1wdF4yUsOes9btPLGHRuECMJslBK07rTNNgKeYOuqZWnAR4ufW/xV9Z38z7xwq5ZVqYQ9S62Z6cj4+HC9dNDLV3KA5NJX6FCSN8CfH1cJh+/qmjhuLj4WLV7p7Uohp83F2I9Pey2jn6qlbftmrXEiWIX0/KpcVoYu1C7ZdnqGlsYUd6KTdPDdP8ALTWqcSvIIRgWVwQiZmV6FuN9g6nRy7OTswf0zat01rTUNMKdcSH+WmyvrulCrTVNxvYeiiXa+KCHaK//KMTRbQYTNw5Q/sD0FqnEr8CtPXzN7UaOZRl303NzbUoJpASnZ7MsnqLv3aLwcSZkjpN9u9D23ROS/Tvbz+aT63ewCOLRlsgKuuSUrL9aAGTR/oRN8LX3uE4PO3unqzY1JzR/ni5ObP7TBlLxmu/0uGicd9O6xwXYtmdsTLL6mgxmjQ5owegVm/o94yeFoOJ1/bnMCtqOFPDh1koMus5WVDDubI6fn/zRKueJ6eygeTcaqueI3y4/beIVIlfAcDdxZmFMYHsPlPGb2+K13yBrlC/IYwL9mFfZgXrFlq2xXppxW6Ytmr0dNA1tRLk07+umY9PFlFaq+cPt1o3kVrK9qMFeLo5c+OUEVY7x5mSWm7fcIg6vcFq5wBwdhIceXopAXbcx1glfuWSZbHB7EgvJb1Im/PXL7doXCCvJ+XS0GzAy91yv8ppRTX4DXFl1PAhFntNS+pvV4/JJNmQmM34EB8Wx2i/BHp9s4FPU4u5YdIIvC34/9xZflUj924+ipebC9vWzmaop3XWM+RWNnLPpiPsyihj1Uz7jVWoxK9csmR8EE4Cdp0pc4zEHxPIxsRsDmdXsdSCNVtSC3VMGumn2bue/tbi//psORfK6/nHHVM0e42dfXqqmMYWI3dYaZet8jo992w6QqvRxDsPz2FssGW7DjsLGzqE8OGe7EgvtWviV4O7yiXDvdyYHjHMYVbxJkQOY4irs0WndepbjZwrrdPk/H1oi6/ZYOpXi3/9vizChg7h+kmOMRd++9F8xgX7MHWU5bvedE2t3LvpKJX1zWy5f4ZVkz60zaBbER/CwQuV6Brtt+2pSvzKf1kWG0xGSS1FNU32DqVH7i7OzB3tb9HEf660DoNJandGj76jQFvfbtaTc6tJybvI2gVRmtpcpjsZxbWcKtRxx4xRFr87aWoxsuaNZLIq6ll/z3SbDXIvjw/BYJJ8fdZ+DSzt/88rNrUsrq3L5GsHWcy1aFwgeVWN5FRaprpoavseu/EabfF3lGvoa1fP+m+yGObp6jC7Vr2bnI+bixO3TLNsqehWo4kfvnOcY3kX+fsdUy4V/7OFySOHEurnwY70Upud83Iq8Sv/ZXSgN9EBXuxykO6exe2bbO87Z5lyE2mFNQz3ciNsqDYHdnX92ITlXGkdX58t5765kXhaoNyDtelbjXx0oogV8SEM9XSz2OuaTJJf/DuVPWfLeX5lPNdPst5Moa44OQmunRBCYmYFDc3WnUHUbQx2OauiacvigjmcXUWd3n59kOYK9/ckKsDLYt09qYU6JoZpd2C3PyWZNyRmMcTVmfvmRFo4KuvYkV5Crd5g0fLLUkqe/zyDj04U8bNrYrhndoTFXrs3VsSH0GwwsddCDZbeUolf+Y5lscG0GiX7z9tmb9v+WhQTyKHsqn6Xm2hqMXK+vF6z/fvQ901Yimqa+ORkMXfMGMUwL8u1nq1p29ECIv09mWPBxU4v773AlqRcHpwXxQ+XjLHY6/ZWQuRwArzd7NbdoxK/8h3TwocyzNPVYWb3LIoJRN9q6veKy4ySWowmqdkZPfBt4u9ti3/T/hwksGaB9ksvA2RV1HM0p5o7ZoRb7O7rrcN5/OWrTG6ZGsb/fi/Wrnd1zk6CayaEsPdsuV3qY6nEr3yHi7MTS8YHsedcOQajyd7h9GhW9HDcXJzYd65/3T1phTUATBqpzRW70FauAcCnF7N6ahpb2J6cz42TRzBymKe1QrOo95ILcHES3DrdMoO6n6UW88zH6Vw1Pog/3TZJE8X3lk8IobHFSKKVNxXqSo+JXwgxSgixVwhxRghxWgjxeBfH3C2ESG3/OCiEmNzpuVwhRJoQ4qQQ4pilL0Cxjqtjg6lpbCUl76K9Q+mRp5sLs6KG97ufP7VIR6CPO8G+9ltK3xNdUyvuLk69Kkv85qE8GluMPLxI+6WXoa2O0L9TClkaG0SQT//3CEjMrOCJd0+SEDGMl++ahqtGprHOGe2P3xBXdtqhu8ecn4AB+KmUMhaYDfxQCBF32TE5wCIp5STgeWDjZc8vkVJOkVIm9DtixSYWxATi5uzkMDX6F8UEcr68vl/rD9IKdUzS8MAu9L5cg77VyOsHc1kyLpDxIY5R1XL3mTKqGlosUn75eP5FHn4zhdGB3rx23wyGuGmnjr+rsxPLYoPZdaaMFoNt76x7TPxSyhIp5fH2z+uAM0DYZccclFJ2NA0PAyMtHahiW97uLswe7c+ujDKr1by3pMUd1Tr72N3T0GzgQkW95ktV9LZcw/vHCqhqaHGI0ssdticXMMLPo99z68+X1fHg68kE+bqz9aGZFt2c3lJWxIdQpzdwMMu2Eyl6dc8jhIgEpgJHrnDYQ8COTl9L4CshRIoQYt0VXnudEOKYEOJYRYXt+7yU77o6NojcqkayKiyzOMqaRgd6EzZ0CPsy+zY97nRxLVKi6Rk90Dad09wEZjCa2Lg/m6nhQ5kZNdzKkVlGQXUj+89X8P2EUTj3ox++8GIjqzcdxdXZiTcfnGWRLiNrmD82AC83Z5t395id+IUQ3sAHwE+klLXdHLOEtsT/y04Pz5NSTgNW0NZNtLCr75VSbpRSJkgpEwIDtV8xcDDoKHzmCN09QggWxgSSdKGK1j4MSKe2D+xqdcVuh7bdt8wb2P0ivZSC6iYeWTRa091Xnb2fUgjA9xP63mlQWd/MvZuO0thiYOuDMwn31+6AtoerM1fFBvNVRplNJ1KYlfiFEK60Jf23pZQfdnPMJOA1YKWU8tI2TlLK4vZ/y4GPgJn9DVqxjRFDhzBhhK9DTeusbzZwvA8D0mlFOkL9PDTbMuxQ22Qwq8UvpWT9N1lEB3pxtQUrl1qT0SR5/1gBC8cG9nn2UZ2+lfu3HKVY18Tm+2cQG6r9cY0V8SFUN7Rw1MobwHRmzqweAWwCzkgp/9bNMeHAh8BqKWVmp8e9hBA+HZ8D1wDplghcsY1lscEcz79IVX2zvUPp0dwx/rg4iT7N7klrX7Grdeb28e8/X0lGSS0PL4zWxNRFc+zLLKdEp+fOPq7U1bcaWbv1GGdL6njl7ukkRDpG99bicYF4uDrZtLvHnBb/PGA1cFX7lMyTQojrhBCPCCEeaT/mV4A/8K/Lpm0GAweEEKeAo8DnUsqdlr4IxXqujgvGJGFvP+fI24KvhyvTIob1OvHX6lvJrmzQfP++ySSpM7OPf/2+LIJ93blpqmWLm1nT9qMFBHi79WlvBYPRxI+3neBwdjV/+f5kh9g+tIOnmwuLYgLZmV6KyWSbiRQ9dhZKKQ8AV2wySCnXAGu6eDwbmPzd71AcxYQRvoT4erA7o4zbpmt/staimED+78tzlNfpze62SW+vyDlRwwu3ABpaDJhkz+UaUgtrOJhVxVMrxuPuop3pi1dSXqvn67PlrFkQhZtL7+bZSyl5+qM0vsoo49kb4hzqza7DivhQvjxdxomCGqZHWL88tDZWMiiaJYRgWVwQiecr7LK0vLc6pnUmZpo/PS6tfY9drXf16Mws17B+XxY+Hi7cNct+Ozz11vsphRhNkjsSet/N88edZ3nvWCE/XjqWB+Y5RkmKyy0ZH4Srs2BneolNzjdoEv/+8xWkFtY4xJx0rVkWG0xji5FD2VU9H2xncaG+BPq496q7J7VIx8hhQxiu8eJl39bi7/5GPaeygR3ppdwzOwKfXhZysxeTSfLesQJmRQ0nOrB3m8iv35fFhn3ZrJ4dwRPLxlopQutzEhDs68E3NupS1X5Rbgsor9WzetNRoG3Py+XxIayID2Fa+DCHGfiyp9nR/ni6ObM7o4wl47TddyqEYOHYQL4+W4bRJM2aC57Wvseu1unMqMy5MTEbV2cnHpgXaaOo+u9wdhV5VY08sSymV9/3bnI+f9xxlusnhfKbGyc4zJTVy+VWNrBm6zFKdXp+d3O8Tc45KFr8HfVmfrhkNONDfHjzUB63rT/ErD98zf/+J42kC5UOUYzMXjxcnVk4NpDdZxxjFe+icYHUNLZempt/JTWNLeRXN2p+/j502naxm66e8jo9Hxwv5NZpIzU/LbWz7ckF+Hq4sDw+xOzv2ZleylMfprFgbAB/u32Kwzbgki5UsvLlJKrqm9n60EwRQuTzAAAgAElEQVTusECZCnMMihZ/St5F3FyceHxpDG4uTtTpW9lztpyd6aV8kFLEW4fzGerpytWxwayYGMK8MQEOMyhmK8vigtl5upT0olrNlzVYMCYAIWBfZkWP+6imtQ/sTgrT9sAu9NzHvyUpl1ajiXULHaMYG8DFhhZ2ppdy16xwswvPHcyq5MfbTjB51FA2rJ7e68FgLZBS8sbBXJ7//AxjAr159d4Emy40GxyJP/8ik0f6XfoF8fFwZeWUMFZOCaOpxci+zAp2ppewM72U91MK8XZ34arxQayID2HRuECH2KbO2paMC8RJwK4zZZpP/MO83Jg8cij7Miv4SQ/dB6kOMrALnTZh6SLx1+lbeetwHiviQ4gK8LJ1aH324YkiWowms3fZSivUsW5rCpEBnmy5f4ZD/m22GEz86uN0ticXcHVcMH+/Ywre7ra9Dsf7qfWSvtVIepGOB+d3Pdo/xM2Z5fEhLI8PocVgIimrkp1ppXyVUconp4rxcHViUUwgK+JDuSo2qNc7Hw0U/t7uTI8Yxtdnyvifq3vXF2sPi2ICeXHPeS42tFxxx6n0Ih0R/p74eWr//7W2qRUhwKeLJPHOkXzq9AaHKsYmpeTd5Hwmjxpq1grbrIp67ttyFL8hrmx9cJZF9+G1lcr6Zn7wVgrJuRf50VVjeGJZjF26qQZ84k8v0tFqlEzv4ZYfwM3FiSXjglgyLojfGeM5mlvNzvRSdqaX8uXpMlydBfPGBLAiPoSr40I0PwvE0pbFBvOHHWcprmlihEY3I++weFwg//z6PAcuVHLD5O43004t1DE1XPvdPNC2CYuPu8t3EkWzwcjmpBzmjvbX9CYylzueX0NmWT1/vGVij8eW6Jq4d9NRBPDWmlmE+DnOGEaH08U61r5xjOrGFl5cNfWKv5fW5nidY73UMbA7rZeLIlycnZg7OoDnVsZz+KmlfPCDudw/N5IL5fX88oM0En67i1UbD7P1UC5ltXorRK49y+LaVlR+7QBF2yaNHMpQT9crTo+rqm+mqKbJIWb0QPflGj4+UUxZbbNDtfahbVaOp5sz1/eQAC82tLB601Fqm1p548GZDtWV1eHz1BJue+UQEvj3I3PtmvRhELT4U/IuEunvSYB333dVcnISTI8YxvSIYTx9XSyni2vZmV7KjvQSfvXxaX718WmmRwxj+YS2LqNRw7VbDbA/Rgd6Ex3gxa4z5ayeE2nvcK7I2UmwYGwg+zIrMJlkl7fTHQO7Ex1gYBe63oTFZJKsT8xiwghfFowNsFNkvVenb+XTUyWsnDLiiv3bDc0G7n89mfzqRrY+ONMhZl91ZjJJ/vH1eV74+jzTI4ax/p7pBPrYf4e3AZ34pZQcz7/Y7w0dOhNCEB/mR3yYHz+7dhwXyuvYkVbKjvRSfvfFGX73xRniw3zb3wRCGRPUuwUpWrcsLpgtSTnU6Vs1v0BoUUwgn54q5kxpLRNGfDdhdKzYjQ/TfgVHaJvOefkY064zZWRXNPDCqqkONY/9k1PFNLUauXNm99MXmw1GHnkrhbTCGtbfM53Z0f42jLD/GpoN/M97J/nydBnfnz6S394cr5nZggM68edXN1JZ32LV2hdjgnz40VIffrR0LPlVjew8XcKO9FL+8lUmf/kqk7FB3pcGj+NCfR3qj7Mry2KD2ZiYzf7zlVw3MdTe4VzRwvYW8L7Mii4Tf2qRjuhAL82/gXXQNbUSHfBtQ0JKyfp9WYwaPoTrejEHXgveTS5gfIgPk7vpZjOaJP/z3in2n6/kz7dN4poJjnV9BdWNrN16jMyyOn51fRwPzIvU1N/+gE78Hf37tih6BBDu78m6haNZt3A0Jbomvjpdxo70El7ee4EX91wgfLjnpTeBKSOHOuSik2nhbX3nuzPKNJ/4g3w9iAv1Zd+5Ch5dPOY7z6cV6pgd7Rile6GtZEPncg1Hc6o5kV/D8ysn4KKRDcTNcbpYR2qhjl/fENdlMpRS8szH6XyeWsL/uy6W2/tQv8eeDmdX8ejbxzEYTbz+wEyL9jhYyoBP/D7uLsQE+dj83KF+Q7hvbiT3zY2ksr6Z3Rll7EgvZUtSDhsTswnx9WB5fAjXTghhZtTwfm0zZ0suzk5cNS6IPefKMRhNmk84i8YF8mpi9ne6pspr9ZTW6jVfkbOztt23vr2G9fuy8Pdy4/sOlhi3Hy3AzcWp2yqaf9uVyTtH8vnB4tGsdaDFaABvH8nj2Y9PE+HvyWv3zdDsQPSAT/xTI+xfjyfA2507Z4Zz58xwdE2t7Dlbxo60UrYdzef1g7n4e7lxzYRgrp0QwtzRAZpfibgsLpgPTxSRkneRWRrvd10cE8gr32RxMKuKazt1F1xasesgM3paDCaaWo2XBnfPltay91wFP706xuwVr1rQ1GLkPyeLuC4+pMt5+JsO5PDingvcOWMUv7h2nB0i7JtWo4nnPs3gzcN5LBkXyD9XTdX0mp8Bm/jr9K2cK6tjRby2uiP8hrhy89SR3Dx1JA3NBr45V8HO06V8crKYbUfbapYsiw3m2vgQFsUEavKPemFMIG7OTnx9tlzziX9axDC83V345lzFfyX+1EIdTqKtmqcjuLxOz4Z92Xi6ObN6ToQ9w+q1L9JKqNMbuhzU/fB4Ic9/lsHyCSH87uaJmuoTv5LqhhYefTuFw9nVPLwoml9cO17zd/ADNvGfLKhBStv17/eFl7sL35sUyvcmhaJvNXLgfCU70kvZfaaMD08U4enmzJJxQSyPD2HJ+CCbL+vujre7C7NH+7M7o4ynr4u1dzhX5OrsxLwx/iRmViClvJRM0op0jAnyxksjP9Oe1Haq01N4sZFPThVz35xIh1u9+m5yAVEBXsyK+u+xla/PlPHzf6cyd7Q//7hziuYTZ4ezpbWseeMY5XXN/P2Oydw8VfubFcEATvwpeRdxEjB5lGPcynu4OrMsLphlccG0Gk0czq5iR3opX50u5fO0EtxcnFg4NoDl8aEsiw2y+x/81bFBPPPxabIq6hndyxrqtrYoJogvT5eRVVHPmCAfpJSkFupYpMFBt+5cKsk8xIXX9ucggDULHGvTkQvl9RzNrebJFeP/qzV/NKeaR98+zoQRvmy8N0GTd7ld+fJ0KU+8exJvdxfee3gOU0Y5znjRgE7840J8HWaqXmeuzk4sGBvIgrGBPL8ynpS8i+xIL+HL9FJ2nynHxUkwZ7Q/y+NDuCYuxC4LQpbGBvPMx6fZnVHG6EXaTvwLY9qmdX5zroIxQT6U1uqprG92mP59aCvXAGAytbWab5wyQvNlMy73bnI+Lk6CW6d92yrOKK7loTeSCRs2hC33z9DMXe2VSCl5ac8F/rork8kj/dh4bwLBvo5VQkLbo4h9ZDRJTuTXMD3Ccd6Bu+PsJJgZNZxnb5hA0pNX8fEP57FmQTQF1Y38v4/Smfn73dy+/hCbD+RQVNNks7hGDB3ChBG+7HaA8g0jh3kyJsj70q5clypyOlDi72jx/+dkEU2tRocrz9BiMPHB8SKWxQZfaqjkVTVw7+ajeLu78OZDs/Dvx+p6W2lqMfLYthP8dVcmN08N492H5zhc0gczEr8QYpQQYq8Q4owQ4rQQ4vEujhFCiBeEEBeEEKlCiGmdnrtPCHG+/eM+S19AVzLL6qhvNmi6f78vhBBMHjWUJ1eMZ+/PFrPzJwv48VVj0TW18txnGcz74x5WvnSAV77JIreywerxLIsNJiXvIlX1zVY/V38tignkSE41TS1G0gp1ODsJhxnYhW/7+L88XcrS8UHEBNt+inJ/7Mooo7qhhTtntk09La/Vc8+mIxhNJt58aCZhDnD3UlzTxG3rD/JFWglPrRjP326f7DDdUpcz577KAPxUSnlcCOEDpAghdkkpMzodswIY2/4xC3gFmCWEGA48CyQAsv17P5FSXrToVVzm0sKtcMdZnNNbQgjGh/gyPsSXJ66OIbuinp2n2yqJ/mnnWf608yzjQ3zat5kMJSbY2+KzJK6OC+afX59n77kKbpuu7UGtxeMC2XQgh8M5VaQW6Rgb5O1Qf7Qds3pajZJHFjtWax9ge3I+YUOHsGBsILrGVu7dfJSq+hbeWTubMXZYZ9NbKXnVPPxmCs2tJjbfN4Ml47W9BWlPekz8UsoSoKT98zohxBkgDOic+FcCW2XbvnyHhRBDhRChwGJgl5SyGkAIsQtYDmyz6FVc5njeRQK83Rk1XPutCEuJDvTm0cVjeHTxGIpqmtrLSZfwz6/P84/d54kK8Lq01/DEMD+LvAlMGOFLiK8HuzPKNJ/4Z0QOx8PViX3nKkgrrOHq9kqjjqKjq2d6xDBmRDpWg6agupH95yv5ybKxtBhMPPRGMlkV9Wy5f6ZDDIi+l1zA//tPGiOHebJ9XcKAqL/Vq5EUIUQkMBU4ctlTYUBBp68L2x/r7nGrSsm/yPSIoQ4zD9jSwoYO4aH5UTw0P4ryOj1fnS5jZ3opGxOzeeWbLMKGDuHaCSGsmNi24Xxfp84JIVgaG8RHJ4rQtxo13YL2cHVmTrQ//zlZRE1jq0Ot2IW2cg2Aw/XtA7x3rAAh4JapI3n07RRS8i/y8l3TmK/xaqIGo4nff3GWzUk5LBgbwEurpjnEhj3mMDvxCyG8gQ+An0gpay9/uotvkVd4vKvXXwesAwgP7/uGwxV1zeRVNXL3LNtsWqx1QT4e3DM7gntmR1DT2MKujLY3gbcO57E5KYdAH3euiQtmRXwos6KH49rLEgzL4oJ5+0g+h7OrWDxO27e/i2IC2dten3+Sg5X3DfJxZ1r4UJY6WBeDwWji/WOFLBgbyN92nWPvuQp+f/NEzdd50jW28ti24+w/X8lD86N4asV4zZcn6Q2zEr8QwpW2pP+2lPLDLg4pBDoXDBkJFLc/vviyx7/p6hxSyo3ARoCEhIQu3xzMcTzftoXZHMlQz7a6Lt9PGEWdvpW959r2Gv7weBFvH2nbcH5ZbDAr4kOYP9a8DefnRPvj6ebM7jNl2k/844Lg0wxcnQXjQ7Xfr9zZE1fH8JNlYx3uLnZfZgWltXrcXJxIrG7k59eO4y6NN8oulNex5o1jFNfo+fNtkxyuSJw5ekz8ou03bRNwRkr5t24O+wR4TAixnbbBXZ2UskQI8SXweyFERxa+BnjKAnF363jeRdycnbosw6t8y8fDlRsnj+DGySMubTj/5elSvjxdyr/bN5xf0r7h/OIrbDjv4erMwrGB7M4o5/mVUtOJKdLfk/DhnvgOcdFMXfTe0PLPtjvbjrb19OZXN7JmfhSPanxges/ZMn687SQers5sWzeL6RGONZ5iLnNa/POA1UCaEOJk+2NPA+EAUsr1wBfAdcAFoBF4oP25aiHE80By+/c91zHQay0peReJD/PVdH+z1nS14fyX6aV8lVHGp6eKcXdp33B+YghXjQ/+zi5Qy+KC2Xm6lNPFtZreIUkIwT8dqByAoyur1bP3XDkAt04bydPXxWr2zUtKyYbEbP6082zbCuLVCQ63QK43zJnVc4Cu++o7HyOBH3bz3GZgc5+i66Vmg5HUIh33OVjhKi3pvOH8b28ycTS3mi/TS9l5uu2NwNVZMHd0x4bzwfh7u7NkXCBOom2utpYTP8DUcNUFaCv/TinEaJIsiw3iT7dOtHuV3O7oW408+UEq/zlZzPcmhfKX2yYzxG1gNxy1vz66F04X19JiMKn+fQvp2HB+7ugAnr1hAicKatiZ3rbD2JMfpvH0R2nMjBrOivhQogO92X2mjCeujrF32IpGuDk7cU1cMC+smqrZgdFSnZ6H3zzGqUIdP7smhh8uGaPZuxJLGlCJ/3j7wq1pqlVncd1tOL/zdCnPfnL60nGlOj0hfo63hF2xvLULozW9kcrJghrWbT1GQ7OBjaunO9z2jv0xoBJ/St5FRg0fQpAD1s5wJN1tOJ9WpMPDVZstO0Xp7KMThfzygzSCfd1586F5jAtxrFle/TWgEv+pghrcXJw4VVDDZAdYEThQdGw4ryhaZzRJ/rzzLBsSs5kT7c+/7p7GMC/H2tPAEgZU8+zeuZFUNbSw8uUk7thwiD1nyzCZ+rwkQFGUAaRW38qaN5LZkJjNvXMi2PrQzEGZ9AFE24QcbUlISJDHjh3r0/fW6Vt5N7mATQdyKNHpGRvkzdqF0aycMsIh524ritJ/OZUNrHkjmbyqRn6zcgJ3zxp4M/+EEClSygSzjh1oib9Dq9HEZ6nFbNiXzdnSOoJ83HlgXhR3zQr/zjx0RVEGrsTMCh575zguzk68cvc0ze8T3Vcq8XcipWT/+Uo2JmZz4EIlXm7OrJoZzoPzowb0Ag1FGeyklGxOyuV3n2cQE+zDq/cmMGq4p73DshqV+LuRXqTj1f3ZfJZaggBumDyCtQuiiRvhOBtyKIrSs2aDkf/9KJ33Uwq5dkIwf7t9Cl4OsK1jf6jE34PCi41sPpDL9uR8GluMLBgbwLqF0cwfEzAoFm8oykBWXqfnkTdTOJ5fw+NLx/L40rGaXTVsSSrxm0nX2MrbR/PYkpRLRV0zcaG+rFsYzfcmhfa6PLGiKPaXXqRj7dZj1DS28tfbJ2u+/LMlqcTfS80GIx+fKGbj/mwulNcTNnQID8yL5M6Z4XgP8NtDRRkoPj1VzM//fQp/L3c23jt90FXoVYm/j0wmyd5z5WxIzOZoTjW+Hi7cPTuCB+ZGqtXAiqJRJpPkb7syeWnvBWZEDuOVe6YT4O1u77BsTiV+CzhZUMPGxCx2ppfi4uTETVNHsG5htENsDK0og0V9s4En3j3Jrowy7pwxiudWxuPmMji7aVXit6C8qgZe25/D+ykF6FtNLB0fxNqF0cyKGq4GghXFjvKrGlmzNZmsigZ+dX0c986JGNR/kyrxW0F1QwtbD+Wy9VAe1Q0tTB7px7qFo1keH6I29lAUGzuYVcmjbx9HSvjX3dOYN0bbG7fbgkr8VtTUYuSD44W8tj+b3KpGwod7smZBFN+fPmrAb96gKPYmpeStw3n8+tMMogO8eO2+BCL8vewdliaoxG8DRpNkV0YpGxKzOZFfwzBPV1bPieS+ORH4D8KBJUWxthaDiV9/epp3juSzdHwQ/7hzCj4eqvxKB5X4bUhKybG8i2zYl83uM2W4uzhx2/SRrFkQTVSAaokoiiVU1Tfzg7ePczSnmkcXj+an14xTXayX6U3iV5PU+0kIwYzI4cyIHM6F8npe25/N+8cKeedoPtfGhbBuUbTaEUxR+uFMSS1r3jhGZX0z/7xzCiunhNk7JIfXY4tfCLEZuB4ol1LGd/H8z4G72790AWKBQClltRAiF6gDjIDB3HcjR2rxd6W8Ts8bB3N563A+uqZWZkQOY93C0SwdHzQolo4riqXsTC/hf947ha+HKxvvnc6kkWqDpe5YtKtHCLEQqAe2dpX4Lzv2BuAJKeVV7V/nAglSykpzgung6Im/Q0Oz4dLeAEU1TUQHerF2QTQ3Tw3Dw1UNBCtKd0wmyYt7LvD33ZlMDR/Khnumq0WUPehN4u9xpYOUMhGoNvPcq4BtZh474Hm5u/Dg/Cj2/XwxL6yaiqebM099mMb8P+3lpT3nqWlssXeIiqI5jS0GHtt2nL/vzuTWaSPZtna2SvoWZtbgrhAiEvjsSi1+IYQnUAiMkVJWtz+WA1wEJLBBSrnRnKAGSov/clJKDmVVsSExm32ZFXi6OXN7wigemh81oOuEK4q5Ci82snZrCudKa3n6ulgemh81qBdl9Ya9BndvAJI6kn67eVLKYiFEELBLCHG2/Q7iO4QQ64B1AOHh4RYMSzuEEMwdE8DcMQGcLa1lY2I2bx3O483DeVw3MZSHF0YTHza4CkspSofk3GoeeTOFFqOJzffPYPG4IHuHNGBZssX/EfC+lPKdbp7/NVAvpfxLT+cbqC3+rpTomtiSlMs7R/KpbzYwd7Q/axdGszgmULV0lEFj29F8fvVxOqOGefLqfQmMDvS2d0gOx+Lz+HtK/EIIPyAHGCWlbGh/zAtwklLWtX++C3hOSrmzp/MNpsTfoVbfyrYj+WxJyqW0Vs+4YB/WLozmxskjBm3RKWXgazWa+O1nGbxxKI+FMYG8uGqq2hO7jyw9q2cbsBgIAMqAZwFXACnl+vZj7geWSynv7PR90cBH7V+6AO9IKX9nTlCDMfF3aDGY+ORUMa8mZnOurI4QXw8emBfJqlnh+KpVisoAcrGhhR++c5yDWVWsXRDFkyti1aKsflArdwcAKSX7MivYmJjNwawqvN1duGtWOA/MiyTUT20Srzi282V1rNl6jJIaPb+/ZSK3TR9p75Acnkr8A0xaoY6N+7P5PLUYJyG4cUrb3gDjQ9Qm8YrjMZkkV/99H7omAxvvna5WtluIRefxK/Y3caQfL66ayr6fL+Ge2RHsSCtl+T/2c9/moxy8UIkW37wVpTv7zleQVdHAM9fHqqRvJ6rF74BqGlt463Aerx/MpbK+hfgwX9YuiOZ7E0NxUZvEKxp37+ajnCut5cAvr8JV/b5ajGrxD3BDPd147KqxHPjlVfzhlok0thh5fPtJFv3fN2w+kENDs8HeISpKly6U15GYWcHq2REq6duR+sk7MA9XZ1bNDGf3E4vYuHo6oX4ePPdZBnP/uIf/+/Is5XV6e4eoKP9lS1Iu7i5OrJo5MBdpOgpVlnkAcHISXDMhhGsmhJCSd5FXE7P51zdZvJqYwy3TwlizIJoxQWpBjGJfNY0tfHi8iJumhKnNiuxMJf4BZnrEMKavnk5OZQOv7c/m3ymFbE8uYFlsMA8viiYhYphaEazYxfbkAppajTwwP9LeoQx6anB3gKusb2broTzePJTLxcZWpoYP5eGF0VwdpzaJV2zHYDSx8M97iQzw4p21s+0dzoCkBneVSwK83fmfq2M4+ORSnls5gar6Fh556zhL//oNbx3OQ99qtHeIyiDwVUYZxTo9D8yLsncoCqrFP+gYTZKd6aVsTMziVKGO4V5u3DsngnvnRDLcy83e4SkD1G2vHKS8rpm9P1us7jStRO25q3TL2UnwvUmhXDcxhKM51WxMzOYfu8+zfl8W358+ijULoojwV5vEK5aTVqjjWN5Fnrk+TiV9jVCJf5ASQjAr2p9Z0f6cL6vj1f3ZvJtcwNtH8lgeH8K6haOZMkrtb6r035akHLzdXbg9QdXj0QrVx68wNtiHP982mQO/XMLDi0az/3wlN72cxO0bDnGyoMbe4SkOrLxWz6epxdw2fSQ+qrqsZqjEr1wS5OvBL5eP59BTS/nf78WSUVzLS3vO2zssxYG9dSQfg0ly/9xIe4eidKISv/Id3u4urFkQTVSAFybtjf0rDkLfauSdI3ksHR9EZIAaN9ISlfgVRbGKT08VU1nfoqZwapBK/IqiWJyUki1JuYwL9mHuaH97h6NcRiV+RVEs7mhONRkltTwwL1KVCNEglfgVRbG4zUk5DPN05aapYfYORemCSvyKolhUQXUjuzLKWDUzHA9XZ3uHo3RBJX5FUSxq66FcnIRg9ZwIe4eidKPHxC+E2CyEKBdCpHfz/GIhhE4IcbL941ednlsuhDgnhLgghHjSkoEriqI9Dc0GticXsGJiKKF+Q+wdjtINc1r8rwPLezhmv5RySvvHcwBCCGfgZWAFEAesEkLE9SdYRVG07YPjhdTpDTwwL9LeoShX0GPil1ImAtV9eO2ZwAUpZbaUsgXYDqzsw+soiuIATCbJ60m5TB41lGnhw+wdjnIFlurjnyOEOCWE2CGEmND+WBhQ0OmYwvbHuiSEWCeEOCaEOFZRUWGhsBRFsZV9mRVkVzbwoGrta54lEv9xIEJKORl4EfhP++NdTd7ttgCAlHKjlDJBSpkQGBhogbAURbGlzUk5BPu6c93EUHuHovSg34lfSlkrpaxv//wLwFUIEUBbC39Up0NHAsX9PZ+iKNpzobyO/ecrWT07AldnNVlQ6/r9PySECBHtS/OEEDPbX7MKSAbGCiGihBBuwJ3AJ/09n6Io2rMlKRd3FydWzQy3dyiKGXrciEUIsQ1YDAQIIQqBZwFXACnleuA24AdCCAPQBNwp2/ZzNAghHgO+BJyBzVLK01a5CkVR7KamsYUPjhdy05Qw/L3d7R2OYoYeE7+UclUPz78EvNTNc18AX/QtNEVRHMH25AL0rSYemB9p71AUM6nOOEVR+sxgNLH1YC5zR/szPsTX3uEoZlKJX1GUPvvydBnFOr2que9gVOJXFKXPtiTlED7ck6vGB9k7FKUXVOJXFKVPUgtrOJZ3kfvnRuLspGruOxKV+BVF6ZMtSbl4u7vw/YSR9g5F6SWV+BVF6bXyWj2fpRZz2/SR+Hi42jscpZdU4lcUpdfeOpKPwSS5f26kvUNR+kAlfkVRekXfauTtw3ksHR9EZICXvcNR+kAlfkVReuXTU8VUNbSoKZwOTCV+RVHMJqVkS1Iu44J9mDva397hKH2kEr+iKGY7klNNRkktD8yLpL02o+KAVOJXFMVsW5JyGObpyk1Tu91TSXEAKvErimKWgupGdmWUsWpmOB6uzvYOR+kHlfgVRTHLGwdzcRKC1XMi7B2K0k8q8SuK0qOGZgPvHitgxcRQQv2G2DscpZ9U4lcUpUcfHC+kTm/gAbWR+oCgEr+iKFdkMrVN4ZwyaijTwofZOxzFAlTiVxTlivZlVpBT2aBa+wOISvyKolzR5qQcgn3duW5iqL1DUSxEJX5FUbp1vqyO/ecruXdOJK7OKl0MFD3+TwohNgshyoUQ6d08f7cQIrX946AQYnKn53KFEGlCiJNCiGOWDFxRFOvbcjAXdxcnVs0Mt3coigWZ8xb+OrD8Cs/nAIuklJOA54GNlz2/REo5RUqZ0LcQFUWxh5rGFj48XshNU8IY7uVm73AUC3Lp6QApZaIQIvIKzx/s9OVhQG3HoygDwLajBehbTTwwP9LeoSgWZulOu4eAHZ2+lsBXQogUIcQ6C59LURQrMRhNvHkol7mj/Rkf4gt3WYAAAAX2SURBVGvvcBQL67HFby4hxBLaEv/8Tg/Pk1IWCyGCgF1CiLNSysRuvn8dsA4gPFz1JyqKPX15uoxinZ7frIy3dyiKFVikxS+EmAS8BqyUUlZ1PC6lLG7/txz4CJjZ3WtIKTdKKROklAmBgYGWCEtRlD7anJRD+HBPrhofZO9QFCvod+IXQoQDHwKrpZSZnR73EkL4dHwOXAN0OTNIURTtSC2sISXvIvfPjcTZSdXcH4h67OoRQmwDFgMBQohC4FnAFUBKuR74FeAP/Kt9YwZD+wyeYOCj9sdcgHeklDutcA2KoljQlqRcvN1d+H6CmqcxUJkzq2dVD8+vAdZ08Xg2MPm736EoilaV1+r5LLWYu2dF4OPhau9wFCtRS/EURbnkrcN5GEyS++dG2jsUxYpU4lcUBQB9q5G3j+SzdHwQkQFe9g5HsSKV+BVFAeDTU8VUNbTwwLwoe4eiWJlK/IqiIKVkc1Iu44J9mDva397hKFamEr+iKORUNpBVXs8D8yJpn4mnDGAWW7mrDDxzx/jjq2Z2DArRgd4cfOoqvN1VShgM1P+y0q2nVsTaOwTFhgK83e0dgmIjqqtHURRlkFGJX1EUZZBRiV9RFGWQUYlfURRlkFGJX1EUZZBRiV9RFGWQUYlfURRlkFGJX1EUZZARUkp7x/AdQogKIM/Gpw0AKm18TltT1zgwDIZrhMFxnZa8xggppVn71moy8duDEOJY+85hA5a6xoFhMFwjDI7rtNc1qq4eRVGUQUYlfkVRlEFGJf5vbbR3ADagrnFgGAzXCIPjOu1yjaqPX1EUZZBRLX5FUZRBZlAlfiHEZiFEuRAivYfjZgghjEKI22wVm6WYc41CiMVCiJNCiNNCiH22jM8SerpGIYSfEOJTIcSp9mt8wNYx9pcQYpQQYq8Q4kz7NTzexTFCCPGCEOKCECJVCDHNHrH2lZnXeHf7taUKIQ4KISbbI9b+MOc6Ox1rm9wjpRw0H8BCYBqQfoVjnIE9wBfAbfaO2dLXCAwFMoDw9q+D7B2zFa7xaeBP7Z8HAtWAm73j7uU1hgLT2j/3ATKBuMuOuQ7YAQhgNnDE3nFb4RrnAsPaP1/haNdo7nW2P2ez3DOoWvxSykTaksCV/Aj4ACi3fkSWZ8Y13gV8KKXMbz/e4a7TjGuUgI9o2zzWu/1Ygy1isxQpZYmU8nj753XAGSDsssNWAltlm8PAUCFEqI1D7TNzrlFKeVBKebH9y8PASNtG2X9m/l+CDXPPoEr8PRFChAE3A+vtHYsVxQDDhBDfCCFShBD32jsgK3gJiAWKgTTgcSmlyb4h9Z0QIhKYChy57KkwoKDT14V0nVA07wrX2NlDtN3hOKzurtPWuUftufvf/gH8Ukpp/P/t3L9rFFEUxfHvgaRQ7AzYaIhYaKWFgoIK/ihE/4cItlZ2AQstbKwsrSLYiBYS1EIEOy0Uq2CKgAQFWbDRwkCsgsfijRJhdWdxmXV2zqfbZYp7d5bDe3ffTlksTqQp4DBwFtgGvJL02va78ZY1UueAZeAMsA94Luml7fXxljU8STsoq8Arferv9yVt3TG9AT3+vOY0JfhPNFnbKA3os9HsSfD/7gjwoPrgZ4ALkjZtPxpvWSPVAz7b3gA2JL0ADlHmjpPiEnDTZXC6JukDcAB4M96yhiNpmhIU92wv9bmkB+zZ8no3ZZfTGjV6RNJBYBE4b/tLk/WNSo0+G82ejHq2sL3X9pztOeAhcHnCQh/gMXBS0pSk7cBRysxxknyk7GiQtAvYD7wfa0VDqn6fuAOs2r71h8ueABer0z3HgK+2PzVW5D+q06OkWWAJmG/rrrROn01nT6dW/JLuA6eAGUk94DowDWB7Iub6g3q0vSrpGfAW+A4s2v7r8db/TY37eAO4K2mFMg5ZsN22pzweB+aBFUnL1XtXgVn41edTysmeNeAbZafTJnV6vAbsBG5Xq+FNt+/BbXX6bFT+uRsR0TEZ9UREdEyCPyKiYxL8EREdk+CPiOiYBH9ERMck+CMiOibBHxHRMQn+iIiO+QHlpF6eeMTN5QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(hand[:, 0], hand[:, 1])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Subdivide polygon using 2nd degree B-Splines"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"new_hand = hand.copy()\n",
"for _ in range(5):\n",
" new_hand = subdivide_polygon(new_hand, degree=2, preserve_ends=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Approximate subdivided polygon with Douglas-Peucker algorithm"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"appr_hand = approximate_polygon(new_hand, tolerance=0.02)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Show"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x288 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(12, 4))\n",
"\n",
"ax1.plot(hand[:, 0], hand[:, 1])\n",
"ax1.set_title(\"Original\",fontsize=16)\n",
"ax2.plot(new_hand[:, 0], new_hand[:, 1])\n",
"ax2.set_title(\"2-nd degree B-splines\",fontsize=16)\n",
"ax3.plot(appr_hand[:, 0], appr_hand[:, 1])\n",
"ax3.set_title(\"Douglas-Peucker\",fontsize=16)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(hand[:, 0], hand[:, 1])\n",
"plt.plot(new_hand[:, 0], new_hand[:, 1])\n",
"plt.plot(appr_hand[:, 0], appr_hand[:, 1])\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
"autoclose": false,
"autocomplete": true,
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 1,
"hotkeys": {
"equation": "Ctrl-E",
"itemize": "Ctrl-I"
},
"labels_anchors": false,
"latex_user_defs": false,
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a7c67cfaded6b6bfab197c53c38e8103a1f92b77 | {
"cells": [
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-02T10:01:51.863444Z",
"start_time": "2020-05-02T10:01:51.670443Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"C:\\Users\\00073294\\Dropbox\\1_Jupyter_notebooks\\map2loop\\notebooks\n"
]
},
{
"data": {
"text/plain": [
"array([[ 500000.00000001, 7453468.06957902],\n",
" [ 500000.00000001, 7454065.12819134],\n",
" [ 499866.35160956, 7454612.48820315],\n",
" ...,\n",
" [ 494271.69034647, 7480320.86623232],\n",
" [ 494187.02347317, 7480312.92871178],\n",
" [ 494098.38788314, 7480290.43905619]])"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"array([0. , 0. , 0.56539567, ..., 0.99273367, 0.95170834,\n",
" 0.98161545])"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"403055.6435328834\n",
"500515.34088441276\n",
"7452102.357823384\n",
"7511450.0\n"
]
},
{
"data": {
"text/plain": [
"(5000, 2)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(5000,)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"''' \n",
"In this example we generate synthetic scattered data with added noise\n",
"and then fit it with a smoothed RBF interpolant. The interpolant in\n",
"this example is equivalent to a thin plate spline.\n",
"'''\n",
"import numpy as np\n",
"from rbf.interpolate import RBFInterpolant\n",
"import matplotlib.pyplot as plt\n",
"np.random.seed(1)\n",
"\n",
"# observation points\n",
"x_obs = np.random.random((100, 2)) \n",
"#display(x_obs)\n",
"# values at the observation points\n",
"u_obs = np.sin(2*np.pi*x_obs[:, 0])*np.cos(2*np.pi*x_obs[:, 1]) \n",
"u_obs += np.random.normal(0.0, 0.3, 100)\n",
"# create a thin-plate spline interpolant, where the data is assumed to\n",
"# be noisy\n",
"\n",
"subset=5000\n",
"methods=(\n",
"#'phs8',\n",
"#'phs7',\n",
"#'phs6',\n",
"#'phs5',\n",
"#'phs4',\n",
"#'phs3',\n",
"#'phs2',\n",
"'phs1',\n",
"'mq'#,\n",
"#'imq',\n",
"#'iq',\n",
"#'ga',\n",
"#'exp',\n",
"#'se',\n",
"#'mat32',\n",
"#'mat52'\n",
")\n",
"import os\n",
"print(os.getcwd())\n",
"contacts=np.genfromtxt('raw_contacts.csv',delimiter=',',skip_header=1,dtype='f8')\n",
"x_obs=contacts[0:subset,0:2]\n",
"display(x_obs)\n",
"u_obs=contacts[0:subset,4]\n",
"display(u_obs)\n",
"print(np.amin(contacts[0:,0]))\n",
"print(np.amax(contacts[0:,0]))\n",
"print(np.amin(contacts[0:,1]))\n",
"print(np.amax(contacts[0:,1]))\n",
"\n",
"xmin=np.amin(contacts[0:,0])\n",
"xmax=np.amax(contacts[0:,0])\n",
"ymin=np.amin(contacts[0:,1])\n",
"ymax=np.amax(contacts[0:,1])\n",
"\n",
"display(x_obs.shape,u_obs.shape)"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-04T00:49:29.032196Z",
"start_time": "2020-05-04T00:46:17.319865Z"
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"phs1 0.5 1.0\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"phs1 0.5 0.9\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"phs1 0.5 0.8\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"phs1 0.5 0.7000000000000001\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"phs1 0.5 0.6000000000000001\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"phs1 0.5 0.5000000000000001\n"
]
},
{
"data": {
"image/png": 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orJTm9ss2UisrKiZWjWVp/FDzzDM+r31Lhl9dWeKQc2ew9alRmrsztM5vZWzQ44cX3M22lSMIAa94/WyEJShVNLNn2QQB2Jbm+nOv4/FHzLV7xrL0V7IMVo1Lr99v2uOYymrnGIgjBE7kJtvTH7gcpRSPqQRDKsmQSrIlaGat38Iqr5lRZTOq4rThmJh9jSlI3PttXzEp1yNJzOvWM778ftpbijQnzWw9I5Mn7Y/xyzfcQaI5wSEXL6H7iC5UqNnywDZW/WolsxcmCGoBHV021YLPyYsDLrusxLTpDmvWeghHMPewNratKuCVfFSgSbUlWHreLJacO4Nl129mw0M7kLagMuLxzu8czNylTY3YxWh/jRu+spZV9w+hA4XW0NQElSrMnW+TbbVZucznjI8s4oFfbOHg1+3Huvu3M/vMRfTct4FtD21D2pCZkmTm0imsvqsXWysWzHdYs87HTllUyhodChACHQQIyyLR2cSBn3olp5wUtXlxxuj3mzgis4EWq0RXlApcb1bZEpkl4U5diI2lUReRZ7zxlMJ608s6rghpkZXG+QDaoiaRzTIZV83HxDwPL2X67+Ilrr7h1r1PgF04c/s+Sf+dtI+dlm0msdGqKdKbkclTdpp463Vn8Ou33MlDX36YoBoiLcHsxTmWnpDj2UdHmDLN4eBjm/jFv27krKMyHLjQZeWzHpYjuOTnxzN1YRN3/Hcvj122jKmLmuhe0sbYjhI/e9sDTJ2XQVqS0liIk01w+UdX4zqC6YsyFIc9tqwqRI8IGiHAD6BUMqm+o1UXN9fEwhMlt35zLYdfvD/SluS3lTnxtLnUCh5hzyDD/R4jPVX8Qm+jg3EQGveW8hRa2bS+4jjaDj0Op7mNoFQgv+IRnvz4zWyaneSia8+mp2aSAaQw7fOHVJouq4iD3q0rcB0J9EYFj7syHGbHj4uaTfaHOeY74/UrDj6uEPSHZbqs7G7niImJeemJpptJz6R8tNxDmQgALU6FRNrhouvP4rBXmr5WWim2rByj95kxTnn9FLpmuvz8Cxv5/GezfOfbRYbyikpFce6/LqVrUTNCCJZceCD/dNcbKIz4LLtxE0//fjuhrxjeUWNwS5n5x3fx2m8ejZaStlMOQB+0P5tWlxGd7YTCQgmwLEi4UPOgUNB4OOSHFU5nE6f/6xH0P5Pn0Z89y2nfPh3LtdCBorldMmdhgmRKUCkpmlokfih4dp1P5zRB4NnMeuN76DrlXNyWdoQQONkmOo49nblv+zD5LTV+fNoNdCVGOa/1CdqtYuPepEW9CeSeDVwfTVI8d3wJTIuWxvHaZmCCwLjP9UeJiYn5mxIi9nrbV0xKi0RrCHzj968F5v8t0bokANKSfPSr03ny1Vl+9OnNjAyGPP3oGD3PFDn5JJcTj7P53GfHSCYkW3t8Lv336Zx6QY2VFdNuJSEDEmnBxb85h6s/8CdGV2xlxmFT6FzUzLQD29j4aD+/fPsfWHTJkcx73RJW/+RRmk48hNLy9ehQo4WFJsSSYFvmiaHloC56HtpKz1N5sl19zD9nAYd98iTcrOniuP2BDezXJalUwbIFQQClQki21aZWDuntFbQsPpzMzPl7vCfJji7ajzmNwT/ezY8ufJDkNUezKNlLuywB4/7RnJQkMPesTIAlBFWteMobr1PZ4Y+nN/cFTYwGaXKWcW/Vuw4DtFgldoQmLhMyLlpdz11iExMT8xKiYZ8KxN4yKYUEZW7c4EiWXM4IyEM75jGzKQ/A1OQYm70ODj0BfvNwN/feXuJXP8jT26O45+4alYqmtVVyyqtc3viZ2SRTZuZrs81k2HANWZID/t/reOzinzG4rUqxCBuXF2hbMp2T//t4Uh1Ztv5+LRtveYYp7zyb8qpNgIYAQinIpkFXNIEQZKZled1v3rzHX2fHE9spbRtj3Yhm0aFp2jsserYECARaQTprU/MkbYce+7y3pXXJ0Qw8cDtbVtT42EG/x0nAr26fzsw5Rqxyu7i1ctJhOFp0KyO83RbO6gvGg/eFMEnOqjIY5Jhim5jLDr+FLie/02eSImBEBTRHdSVxvCQm5m+L0rGQ/MWE5WhoOUjYO1de91WbWJDqZ7PXwczMCK98dZZXvjpLUgSoKH4hhMDXkoIKAfP5tBzvwjvsmUyowUKOmV+6mI3v/x61MZ855x5AdnYb2x/YxObbn6U6UqPrkxcycMXtNB+5H0O3PYEKAoJQIqWmVtNIGbDiimUIKdj/DQdiJ83YVajYev8W/vwfD3DGeRmeeLjC8oeKzN8/weZ1Pm7SwnEFBx/fwh+uH8HOPX8hpJ02riaBsdp0CG86tRcNtLaAVgIh4NyzUnz3a62k7DQ5ocirgNl2gQ0ThKMuIn/OzyEbtWLZTjMAvXJ8HEc3rafLyfNUdQZHpDYBsCGwmWdXG2ISExPztyG2SF4iisUURSCTqVL0x5+oE9K4qR6rzOWIlCn4q2qbpHz+OACMiwhAKuFDZ4Kut51MzxX3sPVPA9hPF7CyadLnnExaCvp/8jvSczoob+hD1QJAgFI4tkTraG3KIGTllctY+bPlzDpmGpZr0besj6acKVC873dFDjwiRVC2WLPSI5GxERKK+YCOGQmEZeGPDmOnnrt6PyhG7eWj75WU5qXSMDwM6bRGhXD19WWuv6VCoWhEta0VTjolwSVfyrDD6gKgHCbYVn1+4QIYDjMMhxmSwqc3aKLNKtNl1ShrcJQXV7zHxPwN0YjGyqmTmcnpl1C7K3CpND5hZR2PLbUJPv9g9/oKX+/5V6sqh+5Ufrf99nEnIQB/IE9p5WaKT22k7wc3MXLdvUw5YylaayrP9BhTAAXKtI1vaRY4Djg2+OUA7flsum8LfY9sIW37DPVU6d8R0jLFYmzA59hTU7gpi0xnimopYM5BGUYHPbQKGH7yoee9LSPLH0HYDpYFCJPtJaR5LSxQyryu1WDxgTbZjEALGB2DG66vcd6S9fzn/1mF1nonESk+R0+vOhOX+x0OxyvvR/XuywDHxMS8dNQtkjjY/hcgFNh5M7TQ1iA1TS1lgmgZ3HwtSY/VgoVifrKfgaCJfDTBHZTYuRdVTlbxtcWQytBilcCC5aWZtLkmSC2EZmQkg7RA+xCMlkBrUnOnkpzTQTBWYftP7yIlMqRqKapodFTh3ZQTjBW1Sd0NMZO7D+kszF3osHVjQDItcKTm4g+0EISab352CD8U+LUi6SSc897p/PgT6zn7NTa3/uZJmhctJTtv/93uSWXHVob+dC/ar6GipYQTCYHvaQQgjZGE60AiAY896TN7hs2WbQFeCMmEEZ6n/pDnM29ZzymXHcSWQiuh2llwXdtYdNPSBbqSo2ytttGdyLNfYgcllaDFKrPWb2KePUZGykalexwriYn5WyAIn+OheDIxKYVkp9iSHE+iHihm6ciOZw9tqbQxP9nfWOccIK9S5KJVA+uLQDkT1jUPtSTds4lf/8dmChtGKNcs9LQuWt96trHPfIVAUFnVQ9MqQTstLOZVJEUajWaYflbwR0BRqmi0NhN4qIybSdpQKcLqJ2ssODjBUSek8DzNT741wtiowg8BS2KJgNd9dBYP/M8AJ5yapLtbQlhk641X0rz4cNqWHovbMgW/MMrIU48y8sRDoBSWhDCUOAmF52nQYEkzhkALLGWWA64F0NUp6B8UqJrZZ0kjJvmVfax+oka2sWKMoeLbDSHZXs7RlTSt1XpqLRye3rDTsbvWqyhULCYxMS8xxv8x+f9dTUohEQrssqA2p7bbewNFE3DuSpvMortHDmBeerDxfrczQiFaebBFjqcMz7TzjJQtPvqOPlYv93jtuSkWnezS1x9yzbUbGfvS902hIQILi4M5iimia+dxCUE7U1mij2EFD+PVQoIw8nZh/uhSgpWAVALWPFXj6eVVwgDclEWi2cX3arS0aU5823TuuaqfubMFZ78mzWc+OsL5F6W48VdlRp/6M6MrH0eHIcKyQID2AyypCIVAuEbzkhmBX4MgarLjOJJqxYimlPDoEz6WBZaAqmesklCZVOEN/3knC7/1TvM5qdjR14wOJEG78ccmXZ+xwLgTO9zixNvAHHuMgjJZYqE2S/rmR0O+99Mh7ry3RM+OECEgnRKMjoUUy6YLQBgaIZvSKjntpBQCzZRWh7e/uYmD949jLTExeyIOtv+VJDYl8FqMVTE21ozdMS4MPcVmurOjOx2flD75MN2wQCYKiVKaD7x5O13tktXLushlx1X+c59u5qtfH+P7PyyilcAlSTtTn3NcbXSSIEU5MBOscASOMC6uhCuoVjVVBTrUODYgBEpBebBCOiOwpWb7imFe93qXZ5/x+ezHRjj9/Az3/q7MFddO4Z2vHzSBdAlahQhtrLRQgXAFrq1JpySlom6omJaRsFSNdSIwx9tRPKVeT2hZUZPIzaYx5dBgDjToYPx+JN3x2MeuIlKnnmqslOYjX+zjp78ocNKxCfabY+H5IctXB4SBsYLqAptIgA5gOK+47uYSWoPvw/cuH8W24JOXtvD5j3U8532PiflHQ+vYtfUXI3Ypzp54Hy17/M2JYpKUPpsr7aSlx3RnBNi5UnvFPUMU84qf39SB4+ys8I4j+Oynm1iztsadd4ZMoWuPqzA2xicEHXo6vfazKCShr7CiS9VqGixB1Qc37WC5krDok80J3ITLUL8HYcgzTym2bvTpnG7jOPD0shqf/3or3/hSgVPf2ML9N+YJMJF0LQXoECk0rq1wXEGtqhATLCGEwC8FuK6J0wTaWC22BeVaXUgEYDK5CJUREfZORIYmVLkPhVlOTu0A4IOfGuS6W4poIXj48RphCJWqNsa4MAIohOlMWiyZ1/4uLR9UAEEA//aNPP/2jTzNzXDKcWl++NWpdHbYFFV1p8W56ksD77qaY5xBFvO/ERVbJH8ZQkEy8laFifFJLnAS+BKSbRWCSF02F1obPavq9PqtDTGpc8VlBT76wdxuItK4phB8/CMt3HHn0F519ddoTjwmxaNPVCkGJgW3tUWSzyv80ATAm7ICNwmpmSlqVc3g1irJhOTA/W1WrPIpjCmkFOy/2KUwBp/7yAinX9SBkNDabpFIhPRuM4F0BNHYNZWyxhaRgGgzMaczkrZmzUC/Qlpga7ClaeFi2QKhNUIYEdHaWCuj19xPZuH+ZJtnAuC1KTx//CshxXPfCV9rnnisxtU3lbCk4D3vzHDQgQ75EcXPryqzfkOAFtrEbwAXCKPMMh0lJigNE2+2jGI9o6PwmzvK3HL7RjSQShpBvPS9Of7vR9oY08bSlBP+gZV1QDEsNroeO5FfORGtWe/rMBaamJcdJmtr8lskk36E7tj464mWycDY7o0DU9b403Sv39p4nRQ+WzaFHH/c86e5Ll3iglAM0MPzdUXWWtNPD3c/UMF1BcKySCagUFDUPPOErQLID/sM9vlse7ZC77NlpnVKgkCzbrPGSdkkUjDQF/LwH2rkCzD3wCS3XtnPhkdGmDvXIptz+OqPOk1AP4TQ16gAiIL7ng+ehmRKYhEy0K/QysQhpIyKFgE/FCQcQRAaV5KIMrxqtz5I33e+z/p//wxDf7wPd1hSyI+n924ut+32u3fbI3TbI2iteevbRzjp+ARPL5vG5z7dzAXnpXnnO7Lcd2cn3/1mC44tCE3SHSqqt3Fs47KzLeP2QpqxIs0xlm3GJwAlzRe0UoGxAnz5WwXa5m3mI58cMpYfUK4ovv1fIxx53AAHLO5n4ZI+XnlmP707zHfB12G8lHDMyxjj2trbbV8xOS2SENwxTak7Wrt9DLw9LMUxMJblpFnrn/dc9RoIIY37BIxfX8rdLROtNZYFfuAxyHY6mL7Hcw6xA194SFcyPKIR0ghI1ZMIS4JUSK0IahqtNdICIQXbtits16I4FqC1JpEQWK6mo91i+4Yqm58xk/9Af8jhx6UYGgj4908O8rUrp/PoHyrccfMoY0MmACIFJNIWQoWkXE2pBETiIm1MJ5cQlJQ4wmSLaRWlCUf1J0eEJ2HjMhD08vSdt1FavZoZ3/knapFV4sqQZFT42SLNglsllWCOM8ptN3pks5LLf9S2RyvvgvPSrHnW50c/KeF7Gi3AtcELjHAEoYnXaBWlTkfFlfUzBaFZ15a++9YAACAASURBVF5Z4GCOVQo8D372ywpXXdvD9y9v5aPvH2X2gWne9KlZtE116N/qcevP+jns+H5a2wU33dVFS9qIjheWmWcbF1lKPP9DRUzMZCDO2vorqGfrOsXxVGBlg3KjRo7O+CTw+MAMZjWNFxi2ueOK86BayMGpbYBZL+Q9H8zzzDM+1bLCTVlYNiRdOOzQBO9+ewpLajIZQaka8FTtUQ7WR9LB+FrqWmsG2c5K6zGaT1rEyANP4ybBq4K1/xzcvjFkxsUOihQ3FXnFITYf/0Arc2c6vPOD/Ty7IcALFTJyU+WaJLWaZrhfUW/am0pDsahZ/niNJUekeMv70lz25WGslM2/3nIEN313PQ9dP4gtQaoQKaFY0HieKUp0HDPZhpZ5mneEMpNwQCPDTEpwcHCFcfVMZQZZ3cyj2+5iw0d/xKzPvZVgaIy8P8KGOe3Myw417ukcx8SkvvytUT7wvsxzugoBLnlH1iQwYMZiWZB2JOWyGZNjA4ERE6FB2CYRoJ4QIKXZH4bQ2SkZGdFoNEFgfsf3vn2E17x7CguWZPj+xzbi1YzrzrKgfZrLQK/H8Yt7ue3udqbOid1aMS9PwrjX1l9OYkxRa9m5NYCbNze0WvdaRTUmW8ZMlXZdUOqdbOvce8MwT6/RHPKWBfzTV+eRmZIkv7XIsl+vY9VNG6l1tHHpZ4YISh5BqFlwwlTy60ZYvfkxbO0wRU8DYIDtBPi0nXEwbkcz4ewWCptGsBzB9ItOInvAjMY1T63eyXWfW80/f3wIpXTDtaNDSGYFQagZGVbUKsZisSMRqFaNeLbNcHlqRUjrgM35n5jPnCU57vzpVpbfOUJrq8ByJSODCisp8KuKRApqFfDqXpx6HEKYSReMFeLYxoW0X3AYE2N4GZFjGrPp3bqZje//Nk57E78rV9FeQPPMLO+81SGVMilgWmvyecXhr3j+p/rODov2douBgSglWQhCZWpvLLlL2rQw9yBQ4/uDIBovMDisefdbc1xxVQGNRkSieMuPBxEMkssJQt8kGjgW9Pf4WMJYaGe+cpgTT7L57uWdDIQeHZa7x/HGxEw2NOJlESOZlEIiQ01ixKd5QvbW6NxxUXGXuxTmmTfzrvHpW7ZC5QRKC0YDs4DTyU1P8+zyCpd/tY83XHEa7fPGrZXWWTlO+cShzDtxOjd96EEu/e+l/PZbG9iyaozRbSWOPiXD8j9Xmbs4y9bHt7Blc0CtCqmZzRQeW0tyfhfpqVkolvBGa/Rd+yz64/vhusZ/Vpy9iLN+vggAsW4jt37taXpWDJvYRBVmzrYZHlKoUOPVTMzDskBaEASKTavLdM5KMj1n88cbdvBfH3oa2xFYQiEdiefBvP0T9OdtwqCGCHy6Zwt6NmuEa5mUXgQhIbajG3GHIIBuNY8uMXO3+z5Nz6KPrQSeT3JWG10ffAPeln76fnor8xYPElZ8bBsyaUCYNOfnQ2tNtarRCrDADzTSMo0lEeOCQZTeLKSIfoj+zq6xRup8+L0t/PRXBZN8IM17TmTFnHK8g2XZ3Hpn2QTtPY2TELgJjVfT3Pt7n6P37+H+FV0U3JAOaTLSrMjabJfP3eMsJmZfol4G6b+Tf4TPgTsyPvSJKcFrCuP1H71+KzdcOczh7zhgJxGZyOyjprLwjFn89NJVvOPbi1EhjPbV+PODRU5/+zQ2PlngpBNcbFsgmtKku5rwx6oUV27DTtpkWhMmGD481hARgI3F8V5gzQd0ceHPTuEjj53PCW+bQTIr6e0JCYJoksUEmZWCdItDe3eKUCu2ry/zx5v6WHb3ECow/b0KY5pqWZNISnq2w9iOCrmMYunRaXQgmTNXcs6rQfshBAEiNBNz4EPoWRyiTmSROGyP98JmfC2S8p/Xsf3TPyIxZyqzvnwJ7vQp2K4gUKZ3V7Go+Z8by8/7N3riSd8ICcYycmxBzce0kqm72uoHR73DlDL7NKZnmIzqUBbMsZk1w2Fqh0UYjltwSpnt5ts9PvOJJpY9OJ1pXTapJFSrGi+U1DOHy2U4dvEOwlBhCdEQkZiYyYpC4Glrr7d9xaQUEhEonKEymQ2jJIY9EsPebsdYFUj1muHX8glq+QTL1swCjJis6mvlxiuHeejWUQ46b+7zXm/p6+dRK4U8cdsOFp/WSbWiCZSNCjXaC3jkEQ8/MFFqS5jJbe45i+h7dCvKC0z7kSXdlEZSjQ2MmDwxMJOnx7p4eqyLZ0vdLLj0NF5/+0Wcf/clfO+xo/nJqmPINEtEVERYHlP0bSyjA8i2OCbTKqqzqFQ0liOo+YJ8QVAe9cllNB1TLVY9XqFUVAwOapKZJOdd1MxdGxZyzKuynPXuLoSWHMtZtIvO57wPoww33F2WBdXeEfLX3Yt0bDrecQbKckxLFttYUNddX2H9hj13Ww5DzRe/MkYQTfoSqNRM+pauu6+iTUSxkMDT5rUwqcuZ9Phx111uugyokIZFUxeZ+njf9+FBZnTb/O7aTpQ2QpOwFHb0LZcSvAocs7SfgRCW1ZoYCCErXGrab2wxMZMJhdzrbV8xKYWEYPd0zal/KtO6xm9sySj+m1iexu0ff5LeOtbCE1ev487zr2TF3YNIR5Jsen6feK4rjQZ+991NdMxOMOWAdgZ6fX75xU2c+SqXDRtDaiHs909Hkn+6Dy2guHWEuUd3Uh72UBpyZxw/fkKp2ZZvYVvexG62F3e2hiqhGe/m2hSu+vImLEvyts91G8uhGiItM2EWRsyk1j0/he2aSvkgAL8a4o15dHZoHFewfYuP75k4zHlvzHDXbRVe/65WNj1bY/kjFdJZB4RkK8+d4aa1ZrNYi7Kiey+jWpM7HkP5AakDZyMdC8eJOg5jnvjPOneA399dRU1YWHrDxoC3vGOIZcs9Qt9U/GtNIzVZ1wvyI8tEKxC2UQUdWRi2bWI7ng8L5lnMn+vyzFqPoRGTYFBPYQbzWgpYvtKsRzOj2+bs09MmOyzSBeGKRnfk6mjIaacOA9CzS+foerFjTMxkQGteFum/k1NIADFSGH8dauz+MdJrjXooR5DpC2hfHdDxlHkirovJ4K2PM3Ld/Vx41emcf9nJ6FBTye/es2siY70lrLRLKG02P1Ug150jMy2Hpy2uuLJM2RMccOnJDD+8no75WdyMS1J7jKwfploOCVo6sEgi8w6UbCjsPhnVxaQuImDSkO+5ajtjwwFXfqHHTHQSQt9MjG5CkExJBrZWyaUlxx6RIO2aqnHHhsEditJYSKWs6Z5pM2c/m9t/U+FLP+lmZDDkk2/v4XUfmcE9V/ejdMAWnqVf9+w2Nq01T4snqVFBBUYQBGaCTiWgtmkHQgisTMJYJFFgXGvI5xXv/udhDjx0B6953QAnnNbHia/s48GHa5RKupHT6/mR6yqkISxBYK7hJASBp6KxmN9NY6rhZ8+Apx6Yg9aar3zH1K8EalxkZFSYqbQ5V6Foxn/ScQkSrkBFTTXrtZV1b9aOjVV+9f0+AGo6aGxjarytTkzMvkegXsS2r5iUwXbt+wQ926FnO85c464i6u2UXjtE0NlEdYqLjB6eU/2AgESvZs2V9/DmX7yS1tlm4p52yBSeunEDR158wHNeb9kNmyCdxs7mWP+nTRzy3rmM9VUQUlDqKzH3TYey7brHael02PH0KPOOnELP8iGUFyJsi2mf/XDjXNIXKEdTHEojPEmladwtNzicZUrbeNuR37ztBpQWSAlSWcbTIwIUxn1ULWtcR5NKmZYojz5RY0qXjSyBkxQM9vhIy6QS9+0Imb0wyXFnuHzv3wYYGVScelEX9107YLK1UuBXQlbyJ3KilW41FxeXohhjq1hHQIByNIQSdGhcTIBtC7QfEpaq+MNFHBnFc7RJN7YtKJc1xaJmcMgDDe/5UIqrrqhiC025atxOREWJE6lX2ftRcaElwXGjbsohLLt3Oov2SzFWUHzuq0PcckeJIISEHRVdRq1XbAuwBCrUpJLmH1OtNh7MVxMsoCicDyH84vtjnHhhN/nWTvZ3jajMtOK4SczkwTxQTdrn/QaTUkgmogeGEB3tqJ7tjX2WY5MZMYHeWndTw6+fX/MkXQe20DZ33F1x7PuXcOM/38ecY6fRuWj3FQE33N/L+j9sx/cUmZkzSM1spvfR7bQs6mR4RS92ymbz1U+Qzgp2rCpgWYLNjw0gtSJs66LzU5cipVtfzRcwYuIMG8vDn+A5Cat2Q0y2/OIPFNeNoLXANGGPYg2hmerq3r1QweiYxnLMZLijJzTFjGmLZBq8moVfNUWIq5+ssm1LQPv0BKlcyG1X7EAp8D1FImnhV0IUijExREmOGVeS1GipUH5oRCRUJJKYnFsJY2MhLV1tjN77JEIK/ECifGVEQBkxkVFVuuuYHl8//m4V0KZ63Ya2ZkGlAn6ocR0T81EhJFyo1GDuTButNb19IZUaJF3BwoUO3/5RgeGRUW6/p4xtQ7VmPu/5O4uE45iq//YpAtc1X4brbipR86JUYyv6x6hDqC8EZguUF/LuY1dx1apDGn+jraHmoMn/7zbmH4g4/fevIUpnCgsFKBSQKRPAVpUK1rZxUaG7ifaV5qm/r2cLC0/fuRq9a8kUpi6ewjUX38PSCxey5Pw5jTqSJ6/bwJq7ehDNWXJLD6J0z5/Y/5IDWfnzVaRmtdFxcCfpaTn6Vg4z7agOOhd3sH61R2JqMxx1PNK2CYdd0CADgfQEVuRFq/+f9SnqyRQ2ECZsRten6L/64YYbJwgEEolCYwkItEaHAhlNkABhAGiw3Wg9+poymWQiRFmgsFGBZnQooJgPTBGl0GghTS2Gp5AOKF+jQwhkgIz6kZj4hml+JSK3UhCCBTQfMhtv+xBDV9+LVIEZoy0IorVQok+aP1nUDiXrdlJSg1h2SK0KQ8O60c246gumzU3SPS/BcJ/P+qfKbOoJ8Wq6ERvyQs2ajR7Lor+rlKa1iwYq1XERkZaJpeSabKqlgC/8i3lQuP/hKitX+2btF0CLhh0y3t9LCJAa5WsuPnwFdywfT8io2SawEsdLYvY1GlPSMNmZ/FI3AVV5Af+1gtDfPVB//mUngmPx5DXr+PmFd/H9E2/m2vc/xMY1PqGWuAvnY7U2k2hyeOpnK1n6iRPZdudaFl6wP2EtpOmYA9h071bmvHIOs991CvK4k5H2zhqsbD0uHhOYuM8umnVWem+82ky6SMJAkCSNQGIh0dpCIrGiSa6enYQGKyEIfLNfaWO1NHWmmLogh7QDpGUaLSql8aoKaUVuKV8RhJpE2qG1K0qhQqNUaNKKo6py2zFWRKNViSWplEO2/b9fInVg2pZYxkoKNNhpaSwTc7pGNtW83BHo0Hz5k0ljLZn6GPCqmko54OyLO/jiNYv4z7sPYvGxOZKZ6KsYdS94zSUdXLvmEK5ZtYQLPjCFYEJsRVrjNTedHRbVUsArljq85Q1Z/ufmEm98+4ApfAxBOgKlLCyhTJKAEljJ6G8nzRIAlTHFOYeZhbsyctwVGWdwxUwGQuReb/uKl5WQ1K0SMJZKWCjgPrSqseWm788zt27e/XNS8tr/PBEVaqw5s3BmdqGbWmDKVDo+/HbsqR3kr7qV7PQsh37yJJ7+6WMsfN0BJFuT9D3eS9tZr8DLV7jx08t3Oq+1PoU7YjW2xAi4+WgbHd86Hw9pfVqT26ppXqcpPvkYAoHERqGoUCKwQ0JbEcoAhSKsl8boeuAddGBMABWtM6ICTVj1qQ1XsITATQizDklg3D06UFiOIAgEVsImmxPomqIpC/VOkJYMkEJhSWMNhX4UBAd8bVF9dhu2DggDjWuZQLeOalNsFS1YFfVAsaJMr67sfiSdLLYtmDHbagTmXde4s/Lbff7tzWu56MAneGZZkY//cD6zFiaxLXAJCbTFDT8c4KLFy/nFf/Rw/iXT+OwVc7HtaClhx1hyCRsGBkLaWgTZjMW8Q3r44L8MU6maljFYFhobxwoIfdBInFYXtEaikVLiuuafwNiI5szF61mz1WZE1RpbTMy+xCSSyL3e9hUvGyFRlUrDIgkLhZ3215k30k25r8amh3p3+3zT9Axojap6BMMFrI52wuEx+r56OaXb72PaCbPQGp782h/Y7zULWPKuQ1n1y5Vkl8zFacuBgPLy9Wx/2sRmwrzLnup/du287o6ZHU5550VWNJoAL8qlFaY3SDAhIj2hqWTd8yQAy4GW5mh/CKW8cWWp0ESUtWWRzkG2WRDqqDNxRqBqAbYK8GsaP4DmJkF7u+BVp9mN1i1gJvp5840rCc8n9FUUZ9AoxlN4bZvGMsOWiCrM1fh5pLSpeZodvSEIEytJuFHoJfoZDT/42CZ+f3Ufb/jgdOxoUneazAODEHDPtUN85nXPsnBphmNe3UooTFylWoPOk2Zz3i/OoZhq4s4HfYZHFGMFjZIWwpE4Voj2fXwPQGJnLZSnUFoZ6ybUCEsgHJNZUC7CW07cyN13mxY7WTF5Pb8x/ygIwhex7SteUEiEEIuEEMsmbGNCiA/vcszJQojRCcd8fsJ7Zwoh1ggh1gkhPvW3+CXqIqOXPcMBuTO59RMPsea2TcZtE/HU/6wjeejBTPvsP9P5iUtILd4fZ9Z0CAOmH9ZB58JW9n/tIi645c3MO2sBT/7gcdbetoGud59J4c9rScyfQe7kV9B32e2EeVOX4hTALpstPSFsIzS0rPVoWeuR7vNJba+S2l6laX2ZzA5/wtrmUcQ68t/riRGHCSlOSo3rSjppKrS1jjKcNPhe1FE3gPY2yGUk2bTF8UfZeKUA7ZlGkfm8wo+6706fJumebrN5K5xxVoJDDnF420VpFh/i0NTkkEoLk0WmjKj4NbNZCYtUwohHEERpvKH52Q/BTtj4YZWylyeZECSSYkLXZWhtlY1fUUTZXD//Si8zFyXxPeOuE8USoe2O34JawOVf2MZJ57aSTFmmFb0Fvfdu5uZ33sr5/30mlzxwIcd/6ghwQAch2leRgICdcbBSEh2a9yQC6VhMXZgznYfrwgag4J8uGeHZFUG8fknMPuflYpG84COX1noNsBRACGEBPcCNezj0Aa31ORN3RMdfBrwK2AY8JoS4RWu9+q8d+J4Idchw32oUcN9X/sz9X3+C2Ud0oEPNukeGaHnreQC43VNxu6cSjIxRWf4MPY/20vun7bQfPJU1N69leHU/TUcsYO5XL8ZuStN//cPkTjsamXQo3vs4waptJGfNwmvRuHmBG6346xQgmTfiJaN11NUu6aT2XU+gGoJRr9kwQRAbhzDqtqgaaWAa6YBXD+Jb4xOwHf3fV2ZSVxpGh6OCPRSjIya1uFyOUnZD414S2tRb5Jo027YFdHdbpJLw0B89ps+0eGq5T7ZZkswI9psl6O3x6dkRBb3LIZ4eT91FmLXgfTuFcATC89hSWoFlSYQMTWuWALJNkqCqGBlR5HKSSslYBTKytG78wXakZUQRqdEqREdB9sGhkKF7Rzn67BZsW1DTgJQopdCh4henXcvb73kTnQdPwU04eL6PsIVZKkBKdPRAoRVIKQiVwHEF5bxvrDYkQqjxtsMhnHb+MIMrcjQ377u2EzEx8PJYs/3FSthpwHqt9e6BiD1zJLBOa71Ba+0B1wDnvchr7hUDupf7uJltrEcRUisGVPIeGx/oYcsfe81CVVEHwLBWY9uHvkLPR7+Cv60PpWxkezNy6cGkzjiWBT98P90feS0Iweb/uAGSabLHLUGVq7jN7Qzffmvjuu6EZePrIjKRuqAA2Dvq7e7H99WtE4HEx0MRRqnAunFcXYsE41lLWhkLwLLNRB6GmGSDcHzzPN0owAsDyDWb4j/LhaFhxbq1AZ4HTz/jM1rUbNoY8OdHPZN6bElmz5Bs2RIyVhQkkzD1c/8Xd/9FSEtgWZEoNTXR9i+fpvV1FyCUIuu0smHsMbr2czj6+CSZrKSpWZhVHW3zOTATuu+D6wpsCQ/9ZsS0pI++kUJKk14MTJ1msfiABPffnCfwdSOon81KbEdiobnjI3cTVIwySVeiA00YKsIgRIWKMDDyLVMOtiuYsX+OWjlEBMG45SoFWBLpgvJDFp6w5cV8BWNiXnK0Fi+5RfJCXiIhxCwhxL1CiCeFECuEEGe/0DlfrBP4zcDVz/HeMUKI5UAv8HGt9SqgG9g64ZhtwFF7+rAQ4j3AewCSpPd0yB5ROuRJHiLPIBILgUYxPqHXwvrqST6lR5eTPPoQtr/3iyipsZtb6H77e7FyTWz85hfZfuVdpA+YSeGJDfhDBUpPbSJ7/FKm/p+zEJZF6YGnaDnwKHbcfQP2QEhLT5ROJQSJ4Z17TiU2DYMQVOa14T69CYBgdILq1NNRo7GaeLVAoRHRE4iUmlBFT+lR9paQJi3X11E2lzLhFdc1lopXEzSnNMWqQCERKsRxzJN9tSqwHYHrSHRVEYamrcrAgGZoKDRWhpQUC4o5bbBlk28yrXwTeB/8r58y9QPvw27aua1Idd16hq+7HqF8xoIRps5PUBqq0JeyCAONUjC1UzI4EDJvP5uhfkVFaWxn/E54VdO/JJE01eiW1jiuWevFdSGXlTz8SAHfH6+8t2yoVRRuAnYsG+SZ324kqIVopXCTAq+qES4ILdCBQlqKqbNSjA3U6FlTIvAUSguENHEgs5wj1GPsgyMhd95T5PRTd1+NMybm78VLWZC4l16izwLXaq1/KIQ4ELgVmPN8591rIRFCuMC5wKf38PYTwGytdTFSr5uABbBHm0zvYR9a6x8DPwZoEm17PGZXKrrEQ9yBABIksXEQSArkqU/N0hdoIcFW1J7dRM/7/hVQCGkz892XYjeZ2oPmw4+hOrqN5JFLUYUSiVmzab3k9VgZE/gtL1+Lt62P5jOPYMfv/4fR+++jZf6p9cHvNK7EcLWxP7V+aBcBAQuHkPHUUolAY2ISNnYUK4FQhYj6T3UrpGbERJo6Rqr100RDsCxNzYNACSwRYjtGiBwHPMslqNTMOQA3KbAtwf9n783j7DirO+/veZ6qukvvrZbU2i3JkhfJ+76AWewQkwXMawJJyAxJSCBkf7OSZCZ5h5B3sk6GIQnLZCaBSYAMhBCWEAw2xgbbeJMX2Za171Kr9+671K16njN/PHVvt2RJloEJlumfP9fdqntvVd1b1XXqnPM7v1/uPN6F0lhj3NFVhf17M6wJWc/AcMLiFRFPPTLBwf/0/9N16UWU1p+L5hmNx7aQ7juIEAJW32LB15qsXRexd1dOo+5BhJEjjkpVaDZhYtwHc60IskxxhZKKLYG2lMwJcTHh39MNoyNKT9kFp0UXGkaRgUolfGdt//ftn99J0hWTtyxGMyQO6yMxlLotJrKM7JwNel+tLJwXQBSZsF4cqC14zAoObv2xwxx8fBWLBiPiheb7Av6NoQjZt1fVt1MlAhCRdpVofiBRoH232EdIDk6LF/KXcSvwiKoePfEJVZ2e9/vnReQvRWSIEPHmG1+sPJOdOhM0tc793EHQxoxokQaZj6KiqMX/vSgGIc4qpNQRDCqG7osu6wQRgEWvfA27/vj3MDZi4M23kKwKcvS+3mTmKw8z9am7WfW6HyefHseWq0xsf5g17UAyD+0goiOjwPEMs862WMoIwbkx5FAmyJEQkZN3MhIpggsoq375XYw/9Glq9zzVmaK3iQUK3fVIiIzHFM1vco9NQl/Ee/DWFP7yMbFk1GagHClpU0MOZIS4RCgHIdz0A73c9pODbH+iwQfePcLMeMbmi2P2786YfOwxalueBBQjPmQ9CQz0R1jxHBtzoVdTzI4YUeJYWLzMcvSQC4yv4hi1gyYCrhUysMhox0Gxt8+yZFHEE0+38BqUg70Regu/+6HhiOnxvJi0V9Qp6lw47tbiMofmjjQXbDQ3lKhYTEF59t7jvMVEWhAMwjBnoKXBhhsOM/bUypPfFi1gAf8XEZrtL+jEGxKRh+b9+4PFTXobZ1Il+j3giyLy80AXcPPzbfSFBJIf5hRlLREZBo6qqorI1YRK9xgwCWwQkbWEJv2bgR95Ads8Kbx6HuQuHHkIDCgRESUq5OSk1AkcqHC5UmtJ80Db1eLC29i5naOf/SR9l11NaflKTJxQXbuR/OAoR37/f2K6KphSTDYyTtc5G1jzQz9DefFyDn/5U5RXrKG+byfSyOgemRuAjMZrnd+VkwcRgPO5vBNI2q11weBwCIYyZRRoUkcQPJ6D7/1z1vzJ7+NubbDvd38PkynaClsSDJKHkJrjA9U26CsWZTGh1JNAHi6qKjA4KIyPtaVXFJcpWQuWLI9476fW0j8UTg3vII6Vvl7D/h0ZYmDzebBnT4tms3A2rAppphzen4dym0CpHLbdlkQpVeDw/jz4kOThDLGi7WpSIA0UPR8hEAlEYGrKk1gXjqMHZw3d1dAXiksRtWmHjQTNgueJ80KShAa7umDypa3wfu+0k21Ya4qSJzhnwDhUCkZCW9u+kBeuT+X85C+M8pH3LftWT90FLOAF4wUOGo6q6pWnef5MqkQ/DPyNqv6piFwHfERENqvqc5vABc4okIhIlVBTe/u8Ze8AUNX3A7cDPyMiOdAA3qyqCuQi8nPAvxJK+/+j6J18SzjGITJalKnSohkmxMkJSlJuXtcBUI/kDsHi8RjCXXx12hA9sJ0jDzwEpZihN76J/utexqG//SDDr3kjlaFleJeT9A0SdfUAMLP9Saaf2UJl+WpwSrM2QY/rRu0JB3ps4pRBBCCRhHk34h3WFhhikk7fJKFMRtCt8q7B4ofhwGP3IFnoJ4TXFf2UIqCGaXnF5UqpFCjCTpW81iK2HktodDcbSpKEzaYZdHeHcPYrf7S8E0RmJh3/4a37+I+/28Prb6sGleCnciYmPIsWGSYmHD/51inqsyEgJVUbnB7V0aiFbWcuXJNrha+8UkylE3o7Umwf5jzaoyiIRcaRkKbK/kM5aQ42snRXlXIMWSEhE+RcQp3PJCWMBqZBrgbTVYFmkzyJce403gAAIABJREFUoJUXMy6hjuZcEBNzRpFIQeLA3JIQVHB63F/XRz9d56ffNs1lFwfq9wI1eAH/Fvi/IJFyJlWinwS+F0BV7xORMjAEjJxqpWcUSFS1Diw6Ydn75/3+PuB9p3jv5wnNmm8bDrCroMcG/qklwuFoUismxsNtcchWHBZLUcBhMcvZwEWUpNLeP0bTw2z9u4+w6LbbwVhG7vpnqivW0n/JtbhSmebIISYef4DGwV0Mv/aNHP7nj6LqSO+8G9t/aWe/3OTkSff3ZLiJ13E3ny72q8gqEJayksUsQzBMcIwD7ETIyMnp/8ZRHt7x5bDfUBTEICLBk5NQxuPJCAMUrZajVAk02DxVWsUFO4rahb/A7qpWhN/6xUH+9ENTXHztHNHhX//3BNdcE/P628IyEeHCTfP1p2J+4qdy/vsHa2S54p2jXAmFuWolBAqKOZf2pL4IqAQmmnMhqHT3CwNLLAe253PeJao0G0qzFab6SwlY49BMmKwpPQOWmYmcLCtYu0AceTTLaTmLiSPcbKMoURmkWoI4QmfqYbiz4B2rGiSJMS4HhTxrh4+5MCKVEtpIee0PjTL17Dl89ouz/NrvHWbfwZBhVatCEsGH/mwpr7+154zPgQUs4EzwbTasepDnrxLtIzB0/0ZELgDKwLHTrfSs7B7WaLdkwl15ixRBKFGmSg/TjBMRk9IkpoTBklJnMSvYxFVB0LC9BgnB5Uq9iW/80//G9vXiZmeJ+vsZvf9LuPosttJFz6bLWPSyV3PoU/+LvhXn0ziyl1prCl+vY6rHs8x0vtH4KRBLPC8rCft+AVewi6c5ZvYjPmItF3Ad38MWvs40Y2wbuQv1vpN9hPcaPI4yXcX3kZJQIiOoHJZKDicW38pxJsiKuOLG2+VQLgkf/cAyIgtDy6Ljvps7/mGSP/nj018Yf/QtFT70gRpigqthK1WSKBCg2rpYcUzoYwPn37yCo09NMHu0ThKHfsXyc0oc3JkGFV8fAo13UOkSxCrNRtDryhVQJSkLs5MOIhP6Hk6D+GUjx2GwSYTGMZI7VCxRTxU3VSPqL5O1MiTPEGsCxVgVkxWUZ0fIYLGouDlZ44JMUW961l+xk5mG8Mu/2M1tr6/Q02N4/ImMD3ywxo/97FFWLR/lqXvXnvoLW8ACXgCCBNG3LyNR1ZNWiUTkPwEPqeo/A78CfEhEfpnwZ/vWosJ0SpyVgURRqvR0egiCIaGER0lpsJRVTHCMmFC7aVLHYFjHhcddKOejQheRN7QmxhATM7N1C3H/IH2XXYNEMY09Oxm9+wssvfBljG37BrGWaPrQE/H1ov9yBgFkPi7jRh7la3iUjAYPczfGFtWV2LA1/QalRDm3dS1TjHF48smgvlv0f6T4L6EcWFiUyGnh8SQkNGkwNQEbb+wjPTTNvn0OEU+5BFFsqNc91iq3vqrCw4+3mBzNUdXOdzQ2krN23elPkWXLAqOkVLG0Uo9VpZkGyq4t1HwLXUh6FyXsf+gYrhma485BqcdyYEeKOiXTQC8wAthQDrMxoedSFciDvItXwVhwmQ9SLkYQHyi+8dIB3MQ04nxQBCjHBCFkJRub4YK//w2e/n/ejeY57bKgF+YpCRhU/NyEVRKFaXgj+EzZd1T4zCf7ufLKudLWVVcmXHVlwof+e43//EczbLxuF8/et+4FnQsLWMCp8O1W/z1ZlUhV/+O8358Cbngh6zxrtLbaaGoDT06ZanE5DV4eTWqk1JlligPsLLSsMlKaxcW2RJfM3V07dYzqEZ7Wh7lHP8/dfIa88AQRVTTLKZcH8QdG0f0j9PWvY8XltzL+7IMMD24mTSeIfdyRZ3leZeKTYJEMA4qNPHl7rsSBZkFUPiopOYZt0f1U6CYjJdAFAokgJiEmoUyVJk1KVOhlsAig4TMbLBOHmpyzRujqj3AaLGfVK9ddn+C80H/eTg4eStFMeWZLs7N/lWqY/TgdpqfDBbtUtUSxpVwR4rLQakGzGWZQ0NCUT+s5rt7qTJrnHpozjrylpCmIC2wtEUgiiOLwfZSr0NcFkRVUbJh/8SGoYAzGCsQxtqeMm5gmKlvUWEw5wVQS8ul6cH5URazBVAomAu3mezGkY4K0PKIhumih+4IJTC4EnHLbG09ewvypt3Wxfp1l/yHH//zY2As+HxawgBMReiQvfomUsy+QUMMQMc7RwMA6AaGZLtSZwRWBweNoUONL+onO4y4+xRbu5SC7yWlRokwXPRgskST0VZZTqhsmdz7KxM4tHHroc9Se3crGNa9lenIvJS2TUPqWPkuj4hAJ6rwAVmKsJGFALzXkaShcqTE07BSWqMNSM0RkpChKjWkWs4wxjhBTKj5Hu08Ex3bV6R8QokqwE65WQj/myGGP15BN/PA7jzEx7njXj+7hzn+aRFW56pXdfOyjpw+Qn/pkg8HhhKzpiWIJviKASYRqNbDHSkkoo7k0HC8ROP/84KeepiHYRPGcXL66YKXrfOiP+AzGR5VaMxTv2ugdCLUwX2houWaG9xq+T2Mw1RJuuoZ4hVKMLcekB0bDDrQ5yG0fXimUBNrWjxiILBJHQbKl2HGxoY/yG+8aP+57ePyJjJ/9hQn2HXAYK/zCf5jgxu/fy+zs8YOqC1jAC8VLQrTxxQaDIWe+T0SbtWSxRPM0qnjOa8JvQrsRL8XHd+Q0aVBjBksE3uNmp6lNHWTD2tdy+aYf5+pL38ni/o3s2PF5SjWhrjMMMfwtfZYHGp9FVbFWqVQ85WqGjVthnkEMYMhaUK6G0k1Oq0NpDpMQMQbDAIuZYZJFDDPCARrUaVDDkRV0BOFzn05pNDxxIoyOe/r7DAcPOK66pkRt1hOXgjeHAP/l1w/zS6/fzQ++dYB/+HiDJ584uS/HgQOO//a+OpNjGUjQ9bIRxCWDxDH1tDCqyoosI5aOQZVIzBveUGbxYhjo76jXFHpYkDZDMEmbYS4m18A2gzCXEpVjyMP8ShiU8WANtruCVJIwcFhvhuRCBVGl/3uuYOwz92P7+okHBosTQoIksRqEKNQVUbAgkUWdx2qhP6M+aGwKfPgjKdu2BVLDn/zZDG9+yziL15Z4939bzL/7mV6ufXmZPUdzhi/ZzaWv2kOanpI5uYAFnBLtOZIzfXyncNYFkm76O5mIzAsiFbqKDGRODHEO7X8H+6iIiCGGWcwyIhJsMU0uGEqU6WeIpp9lRWMZx3Y9wJYtf80jj36Iid1b2NA6n5pOIRi6ea5175liTEdwNgMEFaGZCfU0JnOBTVRO8kBLxdKsOWw5HCpr56RVFE9GxihH6GWQaSaKwldoyHfRS4UuylTwzpKNpiSx60i+x7FQqQS3w+GVMT4P19Ekgf07Un7+B/ZQrylvun2cD/xVjYmJ8L3Xap6/+0id214/TtoSugcistSjeaAF1+uepBJhIosWMyXL15U4/6ouVq6wGAPbt2d85SspiwYjUOHKyxJe/wMVerqFyy6N6e0RSsUMirHB68RIyD7EGsQ5mg2PKRlMqRheyTxab6L1oHGiraKTJBLk4pOImW/sIJ+YxHR1h4jV6SEqKoqUE4hiTKWKtloYUVwmtIfaTRKCJMBrf3Ccj36szsc/2eRP/3oJ99/d4Ld/9hgHnm0y0AWRCEaErc9mDJ6/k/0H50yzFrCAM8PZUdo665rtRswJcSLcnSeUCzbXXNA4/oWBFryeTaxgHVZCk9ir5wj7eIZHcWQklJhijDVsZAdPstgt53wuRRDGGeFpHi6KYAOddbxQqCqP8lXERIjx4C3e50ipmGb3Qo5QinMazhYXzzD/4EWoVJRGw+MRSiRU6OII+4pAWCGhTJM6JUpU6aZBDUsUcq5jnhXLhSNHc77/B8rc87UWCCxbGTFbN0wcbbFq2JAksGevo5QoszXlve+d5U//eIZS2ZCmSu9ARK0GSUXIM6FcDRf3kRFP94p+fCMN37kV6g0lmc7Z/lgLUyRUfQMGW4k4ejTDOWXb9oynns5IW8oTT2a0WjCwOGK25hla38vYzpliNkbxuaeeCiYyWDWIFoOHucfnHlOuINag9Qa2rxs3PYvpqjJ111bERPTfeC1T995NvGQp2ZHDoXdiFKI4ECZE8M06EY48E7CK5qF/UqpYGtM5JoZ6TXn3e6b49fcM8atvG+GnfrrKj39sgFJJOsf5vq+3+Jm3T1Krw9pr9vJXfzDE236s/5SkjwUs4ET4s0BS4azLSOajnUUMspQJjjFXwmr/NJ2sxWLZyMWslg3HBQAjhuVyDpu4CotlnBF6GWSEg1zFK+mil0Ps4SC7sViWcw51ptnARd/0ft/PHVibYH3EqkVXsHHpK1iz+GqsjxCfgAHng895qRREDwVCjT6yeA9dXaH30yr6JF30UqaCxTLLJKs4l3GO4XB00UNCiQpdKMrBQ8ECd8PGiKRqefn3dnPkoCMuW5KyYWzc4R309xmyUpkfvPsdXPaHr8MJ1OuerKWMjWSUuiyNGYdr5cTGYU1oZteP1WlOtgDtSLlPHnP0dgvDSyK6uixjY8r0WM5115QoVw0zDenIHNcbgT7cSIXeRSXGds5w8/dV+d0/GWTFqohzz4uxkeJyT9b0uMwhzmHEgWggP0zNhFuM3BEvHybqHSSfmKb32hvIZ2fouuhibLVKWzlSHWiahTpc2oLMB+fHCHCFZIqxiAS1TF8kvxMT8LlPzPLv/32Fd/xMdyeIhMMlXH9Dib//2GBwiUR4x++McuFNe/F+odS1gOdHm/57po/vFM66jOREBMHGKm2pkDYtVos43h5KNBiWc2p+/xJWsJ3HcdQ5xkEA7uOLDLOafhaR0eIw+xCEXgbpl0WnXNfpUNNpmqbB0v5NXLj0e7Bm7hBsXPwKnjn2JQ5NPIlKi0bLUi15UiSo4yYWY4XlSyyosLue43VujiaU9iKWsppD7OEczmcv2+hniEUso8ksFss0E5QSw/s/UMepcO3LyzzxcMrMdINK1aBpodcFrH/jpRhrWHzJctZ86Lc58Gvvw0xM4RzUJ4JvSbPmmGkSGtwDPQx0w+T+GsYXNGUbAYYDB3Ki2NPTA8MrY85dZbnn6ymZU4yHZj1Qhm2hTpLVMzJxrN8Q8fDXGtz35RppC5LumCSxoI4894gI6hxaBFr1HqKYnkuvIHTvlWR4GYt/8Hamvn4Pzd27GH7bT3Hwv/wpYi2mtws3PsfEMhaCmkD4iRWwUfB/iROwGQaH9wKqPPT1Jn/1F0tOecw3bY655tqE++5rkaWwY1dGz4ZdHNmyhp6e+JTvW8ACFCH3L35PnLMyIxk+bsIfmswWvx3fG5k3m8xiloey2CkgIgyzev4SQDjCPnbwZCHJUsFguJjrvul938J9dFeWsHn41uOCCIAxlguWfA+91WWAxRduhsUOBk/0NKe3T1i+1NLdGyZK6syQk9OgRkqTUQ6znHPYzw4u5npKVNjNU8wyTUyJhBLjo5barOdHf7qXf/zIDOdc1E2pGpHO5qxaYRkcMESxMGX62T05yJY9K/H0sOZ9v4o9/5yO1Emz5nHGEi8bxGHp7RWmDjdwCknFBlpDOQpBJjF4YGYW+iqeJ55M8UCWB9fESrnomUeBVNVMhbFRz7YnWowdy5me8jQaysxYi6Elhg0XVSmVgwgnNkaiGCklxIv6wTuyY0epb32CfHKS2hOPc/Cv3ouqsvydP0t965NUVp1D3NuHm5jGLh8Mfw0mTBJ7FYgtEgkYizUhhjRncoxziJVC7yWQDLq7T/+n9JrvLVFKQMoR3gnNujJ00R5e86Z9C9nJAk4Lj5zx4zuFszKQXMicJplHmSAo7c4PI4EGPCfcGP59erRZXAZLLwMsYw2DLMVgOMJ+phinziyjHPmm9z2zGesWXX/KGrmIsG7gWowz2MgXuoGKKUd4r3T1J5SSQv49U7rpLybZQ4O5HewOs5cLuYJn2YLHcTWvYphVOHKiYvYkbxk+9qFJKoMlnvz6NJo5+vqEVhpYVEdHlb5rNjI1OTe5nzVihn/1HZieLnINEtelvjImbYL3jO+tkeeKeI9vZmRe8I1WJxCCECWWxx53jI4pzVRIbBCByPIQnDI1YSoeRUwgVjWaQYI+pPCGVlyhVelh5dXDQSMLQ2V4NdXla0n6l2JKFXzaZOmP/wR9N95A/82vZvW7fpvFt99Oc/cuJu+4g0WveA1udhYTx+ix2WBqZaQwfYkwNgTAweEYI0rLJ2iuWOMDW9gIIlBv8rzBILIQJSYMWxrFRIa8BV+5L6W8eic/967niGovYAFnDWvrrCxtGbGcr1fyDA9xfPhoi67PiRm2GV5jHD1uavtkaM9hXMkrjhtebGmTZ3iUMY7iyNnKN9iq32AN57GeTafNdE6Ecy0Gu9ac9jWD3WtwvoUINFOgbIlLNjAzGi2eeUbpqjrUeS7kcspUuJd/wRZy+l30kJMxxlG66WMV5/IwX6WHflazgQpdNKlzkN2MjR/lmYdnsUaIjadcMjhVdu7O6Nl0AY3JNZ37nGQq/JZ3Ket+493s+v3fRltNaqMpGME7JSkJifeoL4qLSUxUquDGZzAVg/OOrBlou1kLjHqyYvI9DsPrxOJJvRAbRdvEKg09I597fBRxdGeNal4m7kvo3jjMzLNjuLRB8+gh4t4+JLLkU9Mc+dCHqG7eTHn1aqYPH6b22GO4mVmW/8hPko2PUupbTHP0EL7Q2/cGwBPFIQOROGZqzOPVQr1FHOd4D44IsYqoQ4xwxaUjPPr4qengX70nozZTcJtFUBODT8lziBLhr/9+mr/9xDRb7ljN+nO+tfmkBby08J1kY50pXvx7eAqslHPYVMjoz3dEbENOSPNaNIuG/Mkxo5PMMsW13HJcEAFIpMxFXEtPh+4bvra9bONO/pEv6Sc4qLvPbMdFTjpIOR9aDCuoK/yaCyXaWBybN0VsOr8UZjPyKr0yQCJlruQVOBwtmrRI6WGAw+xlFeeyha9xLpu5VG5gsSynW/roZ4jzuJQLuJzWrKC5wxpoNpWjIx6tLqL/NW856f6ZVvhu1/3Oe1AHlhzNsjCqnmZolpOJxcWlIGsyMUO56mk1QuMlKtlwZy4ghf2uNYXNioc0N0QmTK5HEnrfSKHqbsK+4hy1nceoPXuY2vYRRJR8ZgZTrlBZu57eq29A05S+K66jNDhMa+9B3LFxFr38Ftb+0m9hu7o49oXP0H/ORUiSUOlfgokEk4Q5ojzzpPWM5nQLshYmb9EzoKgKTqJAFTYgEsgQk5PC9HR60u/rwP6cu77cpDxQDn0VEdTlwbI4DoKaURw+34WvOEDpnO383LuO0mwulLy+6/ECspGFOZJvEstkFTfL7SxmxXHL2x4e88tZjpzHuY8pHT9xNczqNI9wD/0soiQnlwcXEdazKQwsniQQPM3DfFk/xfNom1Glh5Hp7ad9zcj0s9gosLdUle6+GJOmrFoKE2PKY0+m1GqGq92c30yvDHA1r0Iw1JghpUGFbsY5yiBLWCYhCxrTIzyi93APn+NB7uIZHqVMlbQJM7PKZD2ifMXNLH3rrzBwuMSix2DZPeHzVkbCo3c3lMfCY+2bf5HMmaCiG0Vk3qBdPdhyCbEx6gSrSqulmMQi1pI1XUeZJBIl98FfxBYaWziPy0PPpNkKg+amGEY3qlgTLuio4lLH4NVrOfcdN+JaTUySkB0bp7VrJ2ItM489TGPns3Sfv4nBG19NPLCIsS9/gf0ffC/Dl95CY+Jw6IXESagzO4KJigfvpZjMD5L8M1NClrczpJB1OW86WcYlmyZwJ+it7dqV86M/MolHqE22UO8RFLE2kAOMgAlMtWYdjPFYgb/55CwDm/by5W9MMOubLOC7E8rZ0SM5K0tbJ+ISuRYI+lkPczfThGBx4pR7TsbDfIUe7WcpqwDhGAeZYgyPch6XnXY7/Qwdl020fUTaBTXF8WU+yc3cfsp1rNXz2DnyVYb7zsea5zJ2nM/ZMXIPjhZxAj6H2ljKwICwZ09OHEGXrmRT80qiE6xfe2WAG/S1fJ1/YYJReunnEHvYxNUA7NFtHGAn69nEJVyPFUuuGYfYw06/lXVLbmXZiiuR3DB9IMHHkEyHz7v4ofAZ075wsiZtAeblqxm4+lVM3H8XOMV2VVBncY0MEY/kOXFFyTKDegXvScrBMyWIcIW+RKPpGBwwTEx5oiRkKI2U8DzBU11tMK7Cacdu2KU5Yw/uZerpo4HFVZtEycknplDv0dzR238Ok3feSWt6DBPF9K66kI3f/7M0J48xfeCZYFmcFlYLWR6a9uUKvtWg0RLIJUinmDxIzovivSDqMZHBZwYxivee89Yf42d/votyWbj/gYwHH2jhVcgLyRpjCgFIHAbBxgbvJTDNUFop4CFWT1wyvPbNk/zX92S844cX/E++W/GdzDTOFC+JQNKGFcvVvCp4WVAnp0WZLmJJ2KVPs4uteDxTjBe+7nNlMYt9TjnsREjbTIO254kWEiQAc7axd+qneAWvO2nvZCkrOZLv58Hdf8fFq15HNRnoPNdoTfL4gX8mT+DVn/4FWhpT2zPKoXv2kk7nrDy6mvW7ezFiyZsnb/iXpMQ1egsPcAczTOJxdNPLuI5wgJ1cxSs7Xizhc0cs5xz6WcSj++9gaKabqu1l8FmYvunc565/SimPz9OPOgjula8lGztGY9djaD1FSQPlttvjrNBqhXKOGCVODJo5MmcR6zEWsqanFAc3xJ5uw0wN4tjjHOSZx5nCc8QJkQlClpEEnTATgYrQ88rL6HvFpez5lb/E1WaJh/uJFi+i/vh2RrZ+laHzr2fRy95EXO0lnR7l6JN3M7VvK9HiIcraTW10F8ZDLmFfMYV0SssHtpmacPy9BzzqA9XYuTCoKBKOvVN4//vrgW1nDLk3SClGW0HTSz3kLcVYj1Oh1B2FbK0YztdCLzJLQV2OivBLvzXFgX3C7/56OFcWvOO/e9Butr/YIc9XivlOoFcG9Rp59bd9vdv1SfbyzHHLpAgAlogNXMxKObX894xO8iB3Fb7w5nl7HZdzE4Oy+DnLVZUdbOWA7KK7PEQl6aeRTTLTPMbQ2itZdssbmLxuLpuyx0LmsuKrc8tKn/nGabc9qaM8yr0IwrXcwja2MMQwK4rPV9MZ9rGdI+wL+4SnQjfd9HGRXEO0aBF+zVzz2FXnsqfshNkHHwuNIcuur3yUiT0PhXKNgoks3gcTLTGhLCV4vBjy3BBXhEqk1GYdkVW6q6FnMDkF3d1K32DM4YM5zbrHRILmihohjooLcmYwtpBNKSX03rAZyjHTdz4CCtW1i0Ggtn2E0sqV5KNj+DQl6umhdN655EeOYeo5zckRotiTNTNULeAwpVLg/NXTIrAEgyxEMBH4TDt+JSKF9Lxqx8FLijJee44lWAwIVjxEFp8rlcVdaD2lWQ+Cl1kjR1Q7YsR5IXFm49BXunij5Z7PrlgIJC9y2GXbH34eu9szRv/5S/RlH3rTGb/+sy9/37dt2y8EZ3WP5IVig2zmlbyeXgY7y9ph1JGzl214PbVs+m6eLgYd5zfMT3238Ah3c1QPPme5iLBBNvNy/T7WlS9hEUtYcsUtXP7G/4/lr3kjYk5PVe569LnrPBH9MsQmrkLxgZ3Fkc6czJge5SHuokSZ63kNV/MqLuRK+hlilCPs1x3kY2OYA8911rS1Ob2oqOGIigZ6ZdSxafMPser1Px0urBpYXCY2RSNfEfV4sTgXYayCQqOeE5dD871WK8y6rDAzrRzcmwWfdWPCfEkcKMGdINKhRxdZzvYpZu5+MkypK7QmU6S3j+TcZbQOH8I1G9juLnzWovH4U7ixCZrTxzAmL6TtY0ypFIKDFUpL+ildcW4IJK7IVFRRZxAbI1LQhY2H3GPLFTqkQO/wWY7mDp97rAF1YVre54pElmyyHr4jzcGAKVSGTSxtu3gqFXAZtJqOR5/K6V67h1/8ncPP24tbwEsDbavdF3uz/bvu1sZKxNW8CoBZneEb3Ikv1IRbpDzGfVyk1xDJ3F23V89OtjLKkU4/JGBOz6utKlxo23YCzRPcx5DedlJdLiuWRZNdRMvWM7F6baf/AFDZHrzBGxtalEfnlp9JEGljiawg00vZxpYgVykRqTZ4kge4hOuL/XuAOrP0MoAjR4CdPEVZqyweAUYC082WQo3ep02qK5Z3ttHaMEx1/yymkZEPVFm5YiMrX/eHfOMzv4tr1nCZAwOthkdKZcQ7xDhMJEjmqHRFNBpKJFCuCJNTSqUvIq+Fu/Rl6ytsezxFW3kxbS94BZt4XEbIEowPbK9jhznvrb9J3qix/e/+jGxkAjKDLZXQ1CGxkk9NElXLEBlcY5pyb0JWE7yLiHq6yBs1MBFSKpPXMkqLe0m9h8ggKmhRggoKl4KKok6xlRjXaM6dGrkAHmwoy4VMxgRRzL4qpCnqhVbDYZOIvOHQIPOFNYKxSksMzUI1WHOwXZ68LvzVh2f5Hx/dwZ5HVrGob6F38lLHgtbWixzd0sOr5HXcLLdzBTfhyJlklK/yWbbqg+zVZ3lWH+OrfKYwy/Idn4+Atkth0PSKiDt9kvn9lrv41Gn3Iz98hIEHR+g+mNN9MEfmJUVdT4eAUpoAk3oaFy6jceEyog3rz+gzrpC1nMtFeDxNDbMjS1hJTs5j3MdK1nEDt7KacxlmNevZzBDDPMXDZHq8Wq1Pj2cPaa1ONNXENOZk5rsOBv+Sy2/7PZKuXkxiMJHFJBZDRlQJd/XGuTAZnyp4R2TDQGJSFpozOddcn5BlSlyyHSOsOBFsHDxDXIvCN6S4chvwecbef/4bbKnK8A3fBwikGemRwwSv3xiRCMlaRK5FkliaEw7XMpSXrcKlhfeKQrxiBT03XE+8dCksGQLnURd6Q+o9WtC4NfNEvSVMVEE8tM+N67mVAZYiNohwmthS6U+IYkMpcmQNR6upYYq/ZIP9STjUOK+dzMbYzirJauAyxUaGLINlm/fzXz+a2/5qAAAgAElEQVT4XBbiAl5C0LNjIPG7OpDMx4As5ma5nVfK67mQK5lklJ1sZR/byck6fZHABJtrsVOEEgqtqy56WMNG1nAePfR3KMiP6Needx+6nw6uekvvOkpl3shLaeK5r22sHXjuwlNgtZzLEMMcYCdHOcAwq9jKg1zCdSjK/XyR7TzBFGOMM8Ioh4lJ2MaWzjpOFkQA/KNPkT+znfyZ7UQ7DhLtOEjf3TtY9OWdvPyC/5flfVcF9pYRbGxxtYw4CuZXuYLPgh1P7oJEu4ktpRL0D1puurWH7Y/XkSRGbETeMrjMIFjABpFGo6Eng2DUYMSw/W//M40j+zBWMJJiYkPc1w14TKVCPLgKTw9eeuhaex5d52+iOX4I29dH8O5VoqV95PUJvKux5C3fT1sTRvDECcRlIeoqB7fF1JLXaui8c+Nx7udybqTc6kZV8S1PPpshwPRIikeCmVdiaNVyIlFwgW7svQSKsZtXvpI5r5ZWI1gMewe/8QdjXPyqM5xhWsBZh4XJ9rMYw7Kqo+fl1fNVPkNOdkJfpG0Z1V4irGMT4xxhL88WvRRDhW7qzDDOYbz6U07B59t3AtBthNkLFtF1JKfrCDQH5kpilX1Tc69/5vSzKCdiAxfxIHcBwjQT9DHANBPsZwebuZq+eSKUmbbYyZMcZh9NrVOW6nHryg8eOuV2/HTQPdOLNhDNNNi86JVcMPByvjbyMRoz+8P7cwN4ItEOSynPQTMlEccrbilx5xeb3HJ7H6XFPeTT4CZm0cLxMrgVhveJBlqxBbxzNEYOsPrmHyGrTTG17RFysZhYMJKhqrhmnbynm1Z9CrGW/NBOyhs3YvsHcJOTRY4JzYe3QSQka1Ywc8f9tE3mFSFXQV0GZEiU4PJaJxPFBnryjJlEVDhPL+FRdy85YTZGJciqWAnGWa7lMfhASjDg1CAouQMxYVkHCr6taEnoA2UpPLsrx67YzpYvreSiCyos4KWFs4G1tZCRPA+MGK7llpM+JwiWGClEIXexlTFGOhItiqc+zyPlTv7xebeXb9vR+V3t8SdQuqL3m/4cVenhYq5D8YxxhAGWsIunuJyXHxdEAGJJOI/LGGIZz/L4GW+jHUQA7MycRa81lmsufyc3vfwPuPqqX2HjBW+hNLSezIETwVhDpWro7RbWrYu4+0spr76tl8/+Q52Zow3yqdlijqNYYTQ3Cq8UDXhiVDyGiP1f/ji1g7sAweSGJFpKOkvw+1XIJyeCm2JvDxhLumsX2bFjYT4FQbzipmepLl5DV88aBs69irU/8cudz9MucQFo3natDOKOndElEbx6BlkKCNdc+st0l1djjAQ2mwqu4dDMddhZIOQuDGl6B5GBdpIjhTukqA89FzMn6JkF6xcue/UBklXb+fMPLPjFv1SgCM6bM358p7CQkZwBylLlev1e7uOLHRHINjJSIhKOcQh/Et2vE/El/QQ38TpiObV8eNf2MOOSruylczNSFM1db2iuSttHIz+5De7JMCCLWa0bOcxeJhljmNVUpOukrxUR1umFPMidOHVnZOI1v/zl5wVEALst/OwBeq1lJVdyR74TREmiUNYRK+RYJBHu+NeclrOYri60NYVqzsb3v5Nn3/6Xcxr3ECbM4wQyB7EF71l9zsswNmaCx/A+w842EBeCu6ggDsRE0MwwEix21aUYwDuPopjMo3uPML5tK3nWIIorhEjmQzrhi1kTFPEC1qPFNkhicI797GQ15yLA4ZFHuPqKt9NqNbnn/ncHc8ZiqFIixatBVYPOWBpsUrwWFsQSbksokhGxhQCAo+MMLIXdvDXwm+8Z54//coKn711Lb8+LX4J8AafHQrP9JYSqdPNqeQMXFvpeAeFO1BT3xHM18jkYLHP+8OGEuJtPM6mjz7vN0oHpEECKIBLNPNeqVaK4E1TOBKtYT0bKLJMsZtlpX9slPSSUqTNzxut/IRDCvEkjtdTyhCOHla1POibHHWnUDzaBequ4QCulZUVfKIkhijCVKqarG9tdxlRsSAhci0N776NRO0Z7YLThphlgUSfbMJliM4/kivURNFMijfBpihS3A44WUUvAeSpxP1YtEQVDylHMjLjQhBeCg6IC1iAazordPM0ExzBY9h/4GgcPP0gUx7z65e9h47lvAOZKXbHxaKa0siAWqRRBRAvWl4KJBFtO8MqcxEwhaqkK2DDGkmVwZES57OZ9eL9AEz6boQvN9pcmlhf6XjCnNdxuxge059wNEXHB55J5zwQ8xFd4QO8k13lT4u01jIwhI2PBKbEkncfUhm5aA2VaA2XskiHskqHQgdUzF/eLJWEzV5PRQs7g8LeD5LcT6lx4tPtMzkPqMKUK6gVMjE6n+OlZXL2G+HBbvvMvvhpW0AryK5o20EaNfHIa1ONTh4iwsnIhyVSOFEFckRAMpQj46oh9jE8bUK/jWw2saytFWxBDRJmpVlCMLkc9VEuDqC2OsTWhvIYQ0gSH6e+FOAoXeBOMtnJa7OIpPB61yvbd/8K9D/wRT+34FNO1g8SlReStUJpKWyG7EBNUkV17GDGa6wmZcgmjOb7IPoBOD0VNkSi5OfbX3gM5H/6Hub7aAs5OqMoZP75TWAgk3yRCMAl/zeaErzH0TkJJwXUa9J3LZud1M4zzFf6Jr+kXSLVx3Dp0OPQtbHr8RbwxNFeNdEefOzB4JhiSZZSpMs7p359qkyZ1qnR/U9s5Fbx6juj+eUsE9Q6pp0gOUS5kE+OIV8TPecrU77gXk1TAWDQLoolBKNngWh6M4tSxt/EEFdtDJeql7ZjZIg25hswFxnCpT2lvwUqMjRJEoGK7cZrjNGc6H2U6GyXuLQZZteilVMqYahnT3YPWahhrMUkJzRxtYc9pJtjARVxnvh+nLTJtcmRkC4ePPkyzNY3EQGLnWMwSBi8LU8nO8qi3istyxBeFDmOYX21s366oCRW+kMVY3v5rx6jXTz1ku4AXO86OgcSFQPIt4Ga5HYM94Y59brakLcynJ7xCMMQkxCRExDSpcw+fY6SYgs/Hjm+WniqY2KVLOo8Xik1cxUF2keupeywH2MESVh43nPmtYkYnuZfP82yHWhyCRFuxWV2Gy5shw8Djjevcdhsx+LyJKVWKAQuDSJB9x3vExxgbs+LcmzjAblJXw4sHa4glIUz8gdgE4oiq6adkuqhE/WgkREkXaoXIJIWsiyVOKmie41st0onCfMoHAxXNsvC7y7GlMprlYSjRz2WZHmW1bKC3PMgitww0R9WFhr3PiEpRUD1ObKiW5SEbaffzTXc5MMXqKSbPabVdM50hkiLLFTrnmDEG5yikZBzOwZqrFujBZzMWMpLvArxKbpsXJsKBNFhckBY8LoCYYt4kJukss0RUqCIYHuc+alownzzHqdXbVMnL0nk0Nq+gsfl4+fwXgh7pZykr2cLXaOnxPhqqykHdxUH2sJbzv+ltnIim1nmEe1A8w6wpvociQyjOxDAPXmQIcaFvVZTuFEAjNEuRuBRKTLb9LpBY8C5l5MDDXHTD27noxrcjxuCMCzr1hJ4EkUVjQ1TtJqn2EVd7MaUKLgZUyfMmTjNUPV4dEseUh1fStWxOh01dhuQObWZoI8XPNoqmRnBrnI+duhWM4cr+WxFX9MqsRYwla3qcF2wSY6sJJOG9SVmII0FaKUmk+JYjywqyWi6oCVlGKy9aRlbCzIGH7u4wh5IkgTQxU1OOHD1zUsYCXjw4W+ZIFgLJtwGbOta/2vlpioykrcvVFoc0GCIi1nAeG7iYpawkpUlMDAhbefCk26gvNqQnmUH8VgLKeVxGH4N8nS+wVR9in25nlz7FfXyR/ezkCm46Javrm8E+tlOizArWcYyDhfaXx0iMFH8EYiLaWUqQQAkihlJ4s2DyUNLKWuFO3DmsjTGmvY5gGvX0Qx8mSXpQVVQ9LoJ2P8bkHokitKuEr8T4njKloWFco0YlSzCYkKmpwzuH5hl2JkVm2+XH0BtRFDUexeNNjhpTnALzg4lnN9s638HN5/xSeF5ArMHEBluyaJaHzCaJkUpMq6lkqdJqFNLyxeiS9xaJCqYbIcbmKlhCB75UVur1djceEIsovPM3F6x8z0roHJniTB7fKSzQf78NWCqrOKx7O17u7Sa8FNPu4V/BaGsDF7OCtcdZ/q7XzTzB/UwyyjRjePXw9LN49ey+oJvGgd2wD8oXbKR6/gWIMYgP5aaukW++/i0ibOBi1uhGDrOPOrNYLBdwOf0MndaWWFVJaeDxlChjn0eR1qvnEHsAgqw9CSMcIjJl1g1dz/K+zVibMNM8yu7R+xmv7cFpHlR3PUXarhBbrlz7Yzz07IcJV2PBuVYxMOhBhDSdYXBwIw/d9UfgPSYqEZmEvH1knCOZdvjGNJk2MWLocb10+y5Sk6IKOQXvFpA4oZlNYW1C+JPJO88Ff/eOUiPtyZYTPn146dAgFrhm6Vt44Ojf4TUoI2vuMaUIdQaf5hgcNjHkraDVpYQAJdYDLjC1CnaWxEJslVYedsW3gglXrkpUULq8wlM7nkvqWMDZgbOB/vu8gUREzgM+Pm/ROuA/quqfn+S1VwH3A29S1U8Uy/4Q+L7iJe9W1Y+f+L6XAi6VG7lXP0+TekdOpQ0pKMIrWHtSmXorlov1Or7OF3A47uVznKsXsy1+HPvwEJXLL0Rzx+gX/gn38Y8w8AM/SH/Pdd9SEJmPRMqsYeMZvdar5yC7OMBOWqSdYFnRboZYxmo2kMhzPcczwuBeD/1MMEqVHtIo48a1b6MUzzXzB6qrGFi9it1j97Nj5B48eVBrl3aJSxnoXgUiSLHMxGVAcC5DIot4wRjL6tWvYN+eO9E8J44qtJjT5vKR0MprOM3J8cwYxdgI78Kxk7bSrwaNLa+euNILjenOvhoivKNo0At51sRIHG4EcHMCCICbmsIW7on9RFy0/ofZuuvvURMhhRy9ZjlRTNDmEiWqWPK4CtMF/brwK8kcEIGJw/BiKwsDi9WqEAvU6mCNUqsH5QAxcGw8TM8b8+K/KC1gDgrf0d7HmeJ5A4mqbgMuBRARCxyE56oQFs/9IfCv85Z9H3B58f4ScLeI/IuqTp/4/pcCbpTXco9+npR6p5xFp9murD7NxdqIYbVuZCdP0iLlKR5i+LfeSWndqs5r+t9wC83Hn+XYX/wvxrNPccNrfh9r/+0Gzrx6nuB+Mlqs4lz2sI2YhF4GmGGS/exgL8+SaJl1XMAy1nQkYSy2YyLmyJk2k2xa9trjgsh8rF10LQcnH6eejeONQXIHJQsZ2JHJ0DhXIIqIFw9TWb6auKefsfvvIunqY+TIFpLSQLjYq6c5HcTLRCxGIpxrFRd8AMH7PEiXRCVsHJG2psPNgCrqPSZKyBpTzDWufIcF5vN5g5ia044eNolwaU5CkC1xs7PY7vB5V8wO0t/377hn7MNobBBXDDg6hwVauRAvWkSUp/hyCSJDlDVCmQsgDzORLgrvS2LFeE8zC3ewkUIUGdR7ssww48ssuWQvo0+c862fCAs4Kfz8pua3Dd/Z3seZ4oX2SF4N7FTVvSd57ueBT8JxnNILgbtVNVfVGvAY8L3f1J6eJXiZvJa1XEjg0bRLHRARP0ez6kQMMIQgrGA9BoObrR/3vIhQueQ8Fv3UDyFJxNf+5V086b7O4Wv+bSqUe9iGw7GRS9jJU2zgYnoZZIwRYkqYgo2meLbzOF/lM2zXx0m1SSQx3fQxwySCISNjqPvUJmIA5wxejdEo0IDLEeqFPt9faH2FonB7wDAfHSPdv5e4q5csreGTCE1THBmignc5gqEq3RBFROVurE1w5DhybFSiVB3AG8Wrx7sw/GkIwxm+1SgGIwu09UnUofjiWBeT7TYCa3BZeP0GNnfe5mZnkZ4QTLpKvXQnS8FLoAzHJXxXP9q/pPA7ySH1mKQEaYoAUSWCwjEyicFaJTYei1JrCpkGF0nvoNXyReYirPr9tzE5pSw674VptC3gzOFewDzXC8HZ0CN5oYHkzcBHT1woIiuA24D3n/DUY8CtIlIVkSHglcCqE9//UsN6uZCb5XYWzZsc9/jnNSNqDzUuZzWD/4e9946W67rOPH/n3HsrvaqXI95DJBIzQFKkmERSpKxoSVbLtizL3ZLbY2t5HHr1cuju6W5rPNbY0rR7qXva9tjtHNpBpEzZskSJmWIEA0iCBJGBh/ByrnzD2fPHuZWAh0QCZnofV4Gvqm6+t84+O3zfpp/pr/3Fsstlrr0cnbJ8h4Xv3cv0n//VhTv40x2bGI5zkM1cxSF2s55LKZEnzwJJUgiCi0eaNvoZJk0bgjDBUR7n2zwjD9BOFw4uAT4JN2NDU2dA2uuwy3iWaKeCiKu5Of7WodaRUBcqRDMz+GMncKuKqFzE7enF9GRAa1zlIkYwGJQBE1bJdA1jtCGZ7CCd7ManQqZ7hJ7+ywj9hmaYpZUmbLFEk8aW7bSlbGmU46KSSXBdnO52UMaqp8TiikNqbct5heMThOM2n3b9+s/ZVI/rWlZiuYxZWkQ7LpIPUCkPcRROwsEohYQhbiZJJOD7VuwyNFAJbfrfESEMlC0g0xAaTe9PfJDkUDe5my5noeTwoz9z7j1tVnB+uBjG5B1V/quUSgAfB76+zNdfA35VpLW9oIh8D/g28CTWAD0FLJv1U0r9tFLqOaXUcwHV5RZ522G7upk7+KGYXS0scubeEZMcJyIiRyfr2IITwdhv/sEpyymtSV+1GeXZRkn5I8/zmuy8WKcBwBJzJEnh4rHILIOs5ij7ydJOggQl8qxlM70MMcsE69jKFVxPmiwuLhEh44wSETDDGH5YPKthrYR5O9uPNE4gvJe78JRHVSpWeypmqRdL0wSlJaqlBUpLk4iJCBZmKE2fQIwQ4KNjrkWRJfrMIDMTu+jpvZT1mz7IJVs/xsYtHyM/P8rk2PNEMQ8kQQoHt57jaqgXxIgM+D46YYuWlashvwShYMKzTw/D8Qn0vlHWh5sQbUuCtZdAeUmcjhyREyGOQldLOCYgMg496zvQiQRKa5yERmlF5x1XEPmWEV+tWsMSKQWJJAM/83G6PmxlfdJb16A8l298q7TSYfECIZCQIFanmDJlpkz5LGucH6yn8dY3JOcTE/kw8IKILFdHeB3wN3GVTy/wEaVUKCL3isiXgS8DKKX+F7Csby0ifwD8Adie7edxXG9pOMrhTj7FU/JdDrCLa+W2Zauhak2nOuhCK4ecdGGIkD2j5J/cSe6m7a0riC3x1JYbx7h7kLawnTXq3BpenS9CQjwSFMmTpYMFZmijnWnGGGIdLgky5NjNc7yH9zPFcY6wlw1cxhyTzDBBjk4CqlSpYDDMFg+fMbx1fOFFIhOwwVzGerbWr1utyZiVQLEVcSYKMLU5SgRRpWST8wqMEoyKUJHGIExxnHbTjYxPcnTqQNwZUlON8ohy4syWNX5JUlhmkEtIjYvRlEUHpNwwMBGtop7ngkvUFZT8AlOJSUxQxe3IoSRA+T4OIcoE+OLiJQz5+Qh/sYKjBUcpHAcWdhwmffla+j//YfLP7EYiQ2brGtq2b0I5jbmiqfgQGVwP3v+pIzz89+vP6zhXcHqciGxBxER0aqHJG0Vk3lk5kh9jmbAWgIisF5F1IrIOuBv4WRG5VynlKGU1ypVSVwFXAd97g8f8tsT13EWeBV7iSSrSyH2ICPMyzQ4eQpB66CYkQOOwgUtZ+P27W2aQEkWUX9qLjgesMLRk6kO8wiPyTR6Wb/KsPERwBtb6+SJFmmIs3mgw+FRwccmQY5oTrGYjxzjAOrbiU+Ewr3E1NzHKvnrIK8CnnW7ayGEk4rWJ7+GHpWX3d2x+J5Uwj9YeCZItxneSY+iY3qmVQ0K3YTA4eGgcHBwkiNBGUEqRSrZbyXnbwhCjYZ5p5pnGj4qYKKAaFdDJtK2gsh1CSJLGp0KSTJMRgZNLfGs5kuacWA36HOdqV6r3stG/DFP10fk5dH6ehBMSVCMCNK6OyAx2YlQStMJLOoQGXE9hFopU9p3A6+ug78fupP9zHyB73ZYWIwKw9P1dVqZHwWNPh7zvk8ulOldwPjgYVjkYVskbzf6gnby58Ibk7eCRnJMhUUplgA9Ao6GGUuqLSqkvnmVVD/i+Umo31tv4nMgyKoXvAjjK4SY+zCyTPMl9PCMPslMe5wm+w4s8AShu5sNWygOY4Cg9DLKK9QjC0Z/99fq2ik+8gIQRoW8IQ4XGoZ9hruQGruP2mCCp+D7f4gX5/gU5/qzqIEmKgCpFbNGdTxUHlzJFsnQwwziDrOE4hxhhIyc4RBe9zDNNL0NoNAWW4vJajeulePrIn3J07gWCqIKIsFgeZ9fYtzg0+yS57CpAmKXVCfap4JEEFCJCaKqknByOTsTyNA6eWFFFMSFVvwiAdjy046JF43oZAjeCtjQm5aBcD/EDHKMRZRnqSZKkyeKRwKdCq+ymUCMmKlxs10Z10jKKbvrO+RqvVZtok3aqgSXJh6HtmNg+lCUyLqW8Q5Avk0g66CjC9RTFggGtUJ7HzN8+ctptF57dQzAxh6Kh1fXsTp//+2vTp11nBWfGnqB40fchnLsRecuHtkSkBPSc9NnJifXa559v+ruCrdxaAZBUSW6Tj/MI95JngRwd5OhkI1fRphplsFWpMMo+ruK9JFQSVzyCUpnRn/o/yNx6HZWnXsREgSWpobmaG+lRg/X1s3TQzzDH5AD72cXDci93qE++4eNfz6XsYSc9DLLEAkXyOLhoNEHMKXGVy4yMcTU38wKPsZqN5OhkhnHa6WKSEzYUZYRSZY6RoRuYKY6yd9/9GDGkEx2sGriW1V0jjJ74PihNhUbcOZQAK/MekSZDgE9ESBCVcfGIgC76WGKuXo4pYmz/jtDHSSQRpWhP9FKqzFAt5tG4tKcGKKtFAgLQSVTVp0ieAUY4xqFlrkZNsFMhce6kpmJQg0Ixwpkr08De7wO8XM+hSSj42go5Gi9FdSJCcAgW8yTaXKhWCSJBTGST8IGVUFl8aCcSRfT+8G243bYJmqn4LDz4ArN/8zAmjHCVoeqrOmP6P31lgf/wb87d2L3bMRE1CjFmo9YqzOdKtXu974Lu8+0Q519htv8zw1Uu18ht7OQxfKps5AoyWBkSEWGWCfawkxEuoVP1IiJ1djyBofTQM/VtaRxWs7HFiDRjtdrIjIwzyyQPyje4U33qDR17n1qFL1X28zIaTRs5qpTJ0ckUYzi4lKRARESFElk6mOAYq1hHmRKzTFBj/ENEZELGpl6gvW0Vl2/6YVLJDir+ImNTz1MsT+Mlc4Tl2cb5YzkoltjYTkhIB93MMUmCJIKxRgWfIFb7VSjExGW5CFEUorVLNRESVHxcL00U+RRlkcgxRMagxJZu9zPMGEfqhqLhhTS01WocoeUh9LD8vQF7v3fxNDOMI4CHLUdWous9WLQCEho35RIsFDAl26zd8TQmEvxAWcKm7yMhLD36EkuPvkRi9QDac6mOTqBSCSslo2yZcGh3bhWCFXzr/jwf+0DudT8X7yYk40rD6kUq9T0F8g4hJK7gwqNb9aHFoUqZXTyDQpEQGzZKkGITVzKgbJX0LBNx1ZAlHjZXDtX+LknB8iOWwRo2s4AdjC+EZzKs1tMt/RxlHxMcJyIgIKDKXgZYzXEOkqaNEgUUijIFFpihg26mqeCRwBDFxkGITIjb1s6x6R2EQRkv0UZ793rCuZBScTo+80YE1sXDYMizQD8jzDPFajbZbRFxgsOUyNNBr5WbQepCkAoNynoQAT4kk0RRiNfWjU6nCRemcLRGqgHt9NSN4FEOUDMetTYANWn7VhPSeJckjSBnlJl5iSeZY5IkaQICIoI4y9IgSgaFom19UlV4WiiHGkcpCAU/csBRmEDiiJqLVANwNdVjk3bfSkOpjJYAR2G9EVubjBs3wvrv/3NhxZCcJ5JNpetj4TIieBcSF9glUUp9CPhvWMnTPxSR31pmmR8BvhTv/SUR+eyZtrliSN4kvI8f5GH+noiQK7iBJCk8krSpxg86lJB9vERU1306dRZ0lP2Msjd+2BSXcz1DqkHVydJeH+4iHfKk+S43qQ++oWNPqza2sJ0tbKciJV7lWRaZi9sNR/QxzBzTMflQUaFUl1KxSXFbUquUAwrmZvYhUUC6rY9KdZHCsScREdJ9qylPHiVFI4RgzYqikz7mmWINm1hingVmYgqowVNJCrJ4yu9PEFuZ4LgEhUXcbNbqW7kCOkKCAIkMjmhCAvoZrofHVN37MDbHE4exGt0xW70VwTBwBspUUfLMMUUaWyhgK8fiMuJ4X7WthRGEkdXQcrUhNC5GOSjHek8GAYHVw9dz7MSTtv43NLgJm1jXCip+3E7esZpkjgtBaMNb4TmUKq/AlvoumsZEbld1NVXTaLEw7ndclP1eSI8kViD5HWzO+zjwrFLqH0Rkd9Mym4B/D9wsIvNKqbP2qVhR/32T4CiH9/FxBGE3z5FnkWQspSEizMokO3iQCraqyYqP29vVaN1LfWBzcHFw2csLPCTf4FWxKsK2E2IMAyXyFC6gQk1KZbhW3cbNfJgOujEYxhlliVlSZGijHesthAQEJEjEYSjBMw6O0XjZTnQqQ37pONXqEpn+tbSvuZTK5DGiqMoAI/X9KaVQ2mVRzTHIGk5wmCXmyNFJmjbb2lgU7boHcR2U0nFFlcSenUJFESqIiPJLEBikWMYfH4MwVgomYiNXUCLPNDGLvgm1Cq3afdD1RLtdLktXvI0rT3vd9vMygqFMiQrleO2anwN1ef3GmVuOSBUczwUT2qZftRa/wKb1H27ReAuNJkITiLI9ebFGRDvWwDgawgDed9OFU3h+pyI4qUboWGgnfEl98eX5LzCz/XrggIgcEhEf+BvgEyct878BvyMi83b/ctYOeiseyZuIhEqwTW7hRR7nALvYz8skJEEY5wEiwjiZXRteak2zaro+thOjR4LVbKSD7pj4d9ApxrUAACAASURBVJRJjrEkc3Qz0JhVx6J/T/M9XLEhIg+PDnptruY04bFzQVKluIobERGWmOMEh5nkOBJ7IDVfIsKgETyShPioSJOZi5hXBdKZHpRyKIwfpIcB3EgTAf1NhsSXKhEh2bZBTpRH6TK9dEo3Jg6y5fUSvvi0my7EBFYOReUIgzy1loPWsBpMKKiwlsivdUFRtNFutc5YwxDr2M1zdS/hZK+j0QGzgTJ5+hnBUafXQVtiHo8UGkWVSn17Tb0O69kkaDJl2oNQIaFYmRZl6n139RMvsT15Jy9UH7LnExqisHULSiuUFjwnXs2BX/7ZixyaeYehZkRqSOqg7plMVNov6L5eh2hjr1Lquab3fxBz9GoYBprbkx4HbjhpG5sBlFJPYMNfXxKR+8600xVD8iajVw3SJh2UyCMIVSr1magTk+IUCo8UPpV4aLEDooNDL0Ncznvq4ogAPQySl808z6Mc4wBglTx8H1IpqMQag70MUqVCgQWe5n46pIdt3HRWSfgzQSlFBz100MNWuYZR9nGY1yiRr4e3IsJYeKQNF48lM0uGDB2FdhSaCppZxnBw6KC35dzmmCTVNUClsMBA/5VkEt1MTb9CGFVJJnKoIiTS7SxU5nCNR2QE4zb6gyjtISbC/kRNHDYE7SRRymBCHxAyZAnwOcSrgDXcDWNS8w6Xz5FEhFzGtWe/VtgSarv9Gjel4ZO00h5jmMAeiaNRJkKMXb7GV+n2O+gZuYSFiX1ERqNMHCZzFI6KMLFzEhli1WAol4W2M8vAvethEAricyJsQ8eTh06nxMul1ZQjj92LtqgiNBc4yCPA+RmSGRG57gzfL7exkx8zF9gE3A6MYCkcV4jIwuk2uhLaegvgRvWB+pBkhxOHWrteGzjx8OPwR80bUVhF3ZONSA051clWrom9gQaiuJVGkjQFlmgjR4IUSVIsMcdjfIuSFCjIEgdlN0/J93hI7uVB+QYPyd/zhNzHhBw/p/PSSrNebeV2PhHzKWzwxsHFEOFTqXssOToI8KlSIiKyopVEXMH1rcdPSLJ7gIiIOSY5MvkkHd0bGBl+L9mOYSITUa7M0b3qirpnZ0IrqaJjXolyHXQyRSrXSyLbhdJWXl5Ce3HKlEmQYpG5elGA5YnopmR7zYNpftWg6gbqdOigxyb8oWU7NeNR48OALTCwz0TMjfF9CCOMaeyzjRwPyD08IHcze3wfW67L8Bv/uAlbcWZQJrJCj9rKqPghpLMKN6H52E+saG+dC7Kq0dn0WNhgQ6QdG96qTOeZfuDVC77fCxzaOk6r3uEIMLbMMt8UkUBEDgN7sYbltFgxJG8R3M4naAwkpp7APfnZMPFMWqEZYeOyRqSGfhqdE2vTkEwbeB6004VGM880AVVCAjLkcPF4hgd4mvs5wSHKFOvHZIgoU+AVnuYBubv+OiYHznhuWmm2q1vZzvtQKEICIiIMhjLFmKi4yAIzLDBLgQUiIq7lNpIq1bKtFBkqM2N0rL2c6sIUXT/wIZbWuoy3T5Ffl8Dr6gZjmBx/AddJoQxox8X6EC4aBwlDpFqhkp/BL8yhDRDZbpaO9gAh9IiZ+D3YfIWJDbnGI4lHAjdumdzIkTTCSAcyB3BHTt+5civbqBlWVb9DjTtu77I1YiE+hjA2CrXvpWX5PPM0G7PdT5f4jx+1nRm3Xp+ha5WHQWEcxWXvzfLV71xJW4fV7HruVWOVgldwWpQkoCQBY2EXu6qrWYgyLEQZlsIU4+OGHb/8LZ743J+z/48vgubdyfOVM73OjmeBTUqp9bF+4meAfzhpmXuxArvEYrubYVkyVR0rhuQtAle53MYnWsyHHTxqVUNxsjSer2o0Oc5cJaKVJoPNe9Qmr0qBoxTjjFJgiYCADVzOrXyMG9Sd3MJH2M6tZGmniz7exw+yhW241KpTVN1bqiX497OLB+TuRr/506BL9XI7n2AjV9arnSIiiixRphSHvQLSZLmFj9CuTo3dd9FPUFiga/O1SBgx9/37KR3eh85mMCYkKOVRaCSK0CgcNF7ggHJIJTtwlUda5TCAQwJQZGjHwSGbHkAlkkRE5KNZOlOryLT1UKuWs4O7DYdFymCUraZr5E4amKgetPe1p4XHW0dSpellVTxpaJQV1wxSM7Hx9Dg1StF4ThrYs6PE7ImAoCq0tWl+4b9vpFKI8KuCk9SIUvzif1xhuJ8Nk5H9EfU4jee8MFnirz/9T+RfPkwy6+Lp5SV/Xj8uLLM9Vhb5OWzfqNeAvxORV5VSv66U+ni82HeB2ViR5GHgl0Vk9oxH+VZUAW1X3XKDuvPNPow3DfvlFUbZU3+vUCRIUaWMxsEQ4eKxmW2sOkmi/GQ8Lt8hcIp4HlR8RWe7UCyB62fJ0MY801zO9QyokZb1Igl5lodYyxaG1FqKssQzPEitWqmLPgZZjcZhgVnGOIwgvJcPnHPS3ohhjimWmEeIyNFFD4NnTFIDfF99B8kmya27jPzMETpufx/hwgJKKfzpaYKde2gLM1SkiI9PlhxLagGlHFZ1XsFc/ghhWMYjSVVKtKV6aW9bxURxH0FQtkx47dKRGkSLZqZ4yPaSN1YqP8S3DbMIcfCIVAi6SWZea1CaDw79LNGaPrRvw1zm+VdOOZed8nhM1LSTBBV7P+cPVfdsllu/ZvoVKq45C2kf8ijMCSYSvIRL5eCa17HfdwdqciiBaF6uNjzNf339S4SlCIkM2rUdNgnD58+SpzhnJNePyNCv/9w5Lz/6L//9Bdv3+WDFI3kLYpO6grvUp2MBRzs8NOLptrInJGSMw2fcTkEW8angODbBnk4KVR+CQOFTZru6lau5iVd5lsOyp2VdR7lcwhX1ZH2bameEDSg01/A+tqtbGFJrGVAjbFFXcysfJUcnO3jwnM9TK02vGmSDupRL1BX0q+GzGhGAIbOaoFxgfu/zuOIxe883CCYn0ek0Ti5HNSwiXTkCFRARUlB50pIBDROFfWQyvWwYuo11w7eycfhOnGSaydIBuldfTSrXa4d0V+N3uBS9Ml5bL2JCxLX8EqU0ET5KOZZpr7U1IlpZnoYCjHBkXbFuRE6H7eoWtnEL58s6U8v8dFU9BKfrn9QMSBvtZMiicXFxUGiK44IJQ5xsljAUdu+tnLLNFUBVAgLRBNJ6zX/zi0cI8rZiwc1kcXIdOMn0hT+ACxvauihYMSRvYfSpIe7gk7SRw5LfnKasibDEPDMyvuy6Rgx7eREwRAa0Y7vqmRC6pZ+agepRg6xlM0fYwwGxM+ZAfA7LHvaziyXmeVju5WH5JuOMYrA92yfkKL40+sa4ymM7tyAYZs9edv6GsJqNYAzKcVDtabzOHir7D7H42ONUDxxGuZpyMM+m7Z+OlXIzOIkMIoZUex+ZNZs4UdnDoYVnGKvuo2PTNQxf+UFmjr1AUFpEoTGhT6B8Kvlpwsq83XEYIESIto2ojBN3wYwC+0syYsugEJCIfU//EUvFibOeT68aZFus+nzu3khrcr+5oqwRDrX/JkhRJE8BO7HwqdbDikSgs+1ghCtvP8bv/9mZe+a821CNFbQ7dCuP5JUTWV57dAblebYIIoqQcgVjzjxxeH1Q5/F6c7AS2nobYa+8xHEOtKTga1Lzw2zAU4k6j2MfL5NnwYbBXJtgNwY6qiMsMkM73VytbgKsaODjfBsAD4+IiAw5fMokyVCl3FKtlKWTiIA8i/QzzCauwsFlkmMc5jUCfDwSdNBt+S1q+TzBG8HD+h8RDcnuAfylWdxcO2EhTzLXQ3VpFiMhSkAiId09RGnmOEppktkugvISPSPbyHYNEwUVpo/tJAqrtA9tJH98P9XKIkKE291DVCjg9fXjHzva2LmDbZiu7BWh1i8i5cascptNAXA8xfV3/BpeIkNmRyNfGU7PnHJOz8jD5KmFopct/m1C4/ua32HqCgg1SqP1PCJCdNOcsVYRp2MPxmBse2AR0smIoX6Hlx9eRzr97p1nlpsmSWNRQN7Y6sd7FmzU6Pc/dT9zR6q42iMKfTydIpIApV2CsHhhQ1tf+vlzXn708//uTQltrfBI3kbYoq5mC1fX34cS8jyPcIjXOMRuEpKMq6EiIiI8DxJxpMj3HS6NrkNh9buuaOIgJVUKR2zupSZJP8MEq1jPcQ6xirWMM8pWrqGPVXX9KF+qHGY3z/IwgiFFGpcEIQFZOggJ2cnjpCXLpVxDu+pCRChRoEyBAktUKRHGvJIUGbroI0fnGTWqADqiThbUPMHiHMlsD229q1GDCUqTo0gYojyFFZMKqS5MkhvZRHHyMFG1RLpzEOMYFhePoh2Xns03UJw6Qv74fgK/aCnfxmAC3xptZSCdgnLFeh7NGo4o0KCSHhLGfduVqk8Qo0B45rHf4uY7/097z5YxIDXcoO7gAbmbeKunMSP2Gx3nOmpYbllDhIplXBSK1WxkhEtIqTRGDFOc4AC7rIKydtAiRCbixJRhzbUHOfD0Bjrazx5qfKfBnOQV1owIwPrUNAdLPcwdqaIiQ4IkZfEBBaJwjOaCc93fenP9U7BiSN7GcJXLDdwFwANyd11uXaG5lGsZCtZCYHMRRgwTHGUPO+mguyUXYcTWIg2xjhnGSZOlgx4WmWUDWznEa2zn1lOqqBIqyRa2Y+R58ixQosA6tjLMBhzl4EuVoxzgOAessRFTZ8ZYo+XWnXFTbw5lZ8yuuGziKgbV8gngjVzBDvMIRCEJN4OUKigd0d65FkRRmD2CVinQDsb4BAuziCOYMIKyT6F4kMAvoJRGa5furs0UxZDIdSHJEiaRwB+fx+nuILFpLalbryH/V9+ySsKO1QgjiqzR8NyGETEOSEgsI4YBTKXC87v+iFvWfBbXdev92pfDtdzO8zxyhrteq+gzTZ8IqinkSfwUqKa/ruQGetVQfR2tNIOspkcG2MGDlIMiyoF0UmFEWFiCvssOkUpAsal7bGcH/Pin2vjCZ7rYftVFyAe8iWi+ptORT6o+mWkdJvf+xU4kCgCN27aIWzagC6Q8RaXqLyeJ9/px/oTENwXvXt/1HYb305CIFwz7eInH+Ede4Rlelqd4jH9kLy+SpZNr1W0t684wjkIzwxirWMc4o/QySIFFQNFJ77KluDVs4DIKLLGWraxRm3CUQ1GWeJr7mWEMF48sHbTTTYKUrczCo4veupqvZe8n6/1NDIbdPM9Dci/Ty+SB2lUXaZ1FNERJTWHhGAtTe1lcPEJ69VqUlwCB9PAahAjfz6NwcPrbiZwIv5onmx2ms3M9rptmfukwqb5VVAuzJPpz5G64FO05mEKJcGyM/D33NRLZERBGll2uFcoYCAwqipNQsVCiNFUFVyYOEh07O/GvS/XG9/BsYa1WNJbXTZ9YkZVu+luMSDM8lWAL23Bw+exnMnzp19r5lV9q57KtLokEBJENi9aQL8Lv/VmR6z54HGdoP5/94ol3RP/3gqkwE5XqL4BKfF45HZLTIW0q5Nb0QR76szHa2hTbrvT4468NMPbyBo48t47f/rUeRoYu/JB6gQmJFwUrhuQdAq00d6lP199HhIQETOkTTOkTeCS4kQ9yvbqjZb1QAvbzMhEhVWxDrSplDIYOelhgpoXYuBySKk0bWdqwGkRGInbyON30o1AMsx43Zq/3MkSeedawkTzzBAQoFCkyJEhipV/cOK7vAIZdPMXDci9zJyXxLw+3I2GAb/L41QI6m6Fanmfh2CukL9+E25YjnJlFeUlUArzONMHsPGo4C54D2SSSy5DqHyFSIYXZIwz8wBaKh2fouONqQOG0Z/GPTpC+ciO5H74D0bX8hwbRSCRIhC3/ldCGxWIqhzU09m/jRzw59bcAuOvOXLI9fNZmWA2vo/WzUxWiFZpVrDvj1mo9U37hF7J85kcz/NS/buP+7/bxu/+jC8dRKAUJ25ASMWCUosaD/cY/lRjZfrDOsn+2unxg575ygvvLLvuDwimvici+dvlldvnlOGtjTgkxXSwUjK1Wq4jUjUcNtfdNIgIEpYhPfijLjvtW88kPZ+nqdOjvdfmpz3Xy0kNnvrevCytVWyv450azMVFoG7I3ioAqs0wQiQ3wNysMVynj4MakQ6l3HZQ42FTrhXImNPcmn+QEadpYYIatbGeMI6znUiY5Tie9aBwWmSNLZ9zMtiNu2VuKS1htkyfbXGqENWymhwFe5HGek0cx8Tl0ql6ydBAszOF0t+Neupa2W67F2zBC6dW9BEvzDP/wTzLyL76AqUSExQrKgfKBUVKbhwgGhHJ6nqIZx4RlVn3sUgqjc3R9+Hqk4qOzbYTT86S2bcYd6ic6Om1zJQ6x4ahVaYF2VMwfAddVOI5CjLT8wMq6SOie/Se36Qyqwa04eeRYfiRJkFr28xqUUrh45JdaB+4PfTDFr/xSlkRCEcV5fMcFrQSniT85Myt89Mcb3tasKdZfB8MC95Ub0iJHws5lj2E6ajxjx8OL38K2hl1+mcOhcDgU9ged7A862eV3MBrm6q8B7TLk2Fdp1DYE++0v9fCXX1/iC78wzhd/eYL7H7UkxWzbRRhSRZ37603CSo7kHYi71Kd5QO6ulwyb2D/Zx8vs5UUSkiQkqLPo17CZbvoRhClOMM4oh3kNQRhmPQvMtEi5n4xILDu95pFMcpRO+gjwY60vy8TPkKVMgW4GOM4BEqTwqVLFIUmKRMw0Fwzt9LGV7fUe9mDLkl/hGZ7nMa6T21FKcXl0Dc9UHiF95SUoN8JU8qQ2DtD3kx9k9i+/y7H/9fus/Vc/z/Cn/hXH7/kT2m68isqLrxEcHSc11IWbS9K2dRAvt47x771Gautaen/0dib/6D4y26+mvOs1Sk/tQrfZfIDjuURFgZjVXnMKTGST7q4jKBFC4yBaE4UBSmO9lijimbG/4ebVP3HG++cqj6vlZl7iiTfwFNSg4kZf3addIpKQEJ++vlObW/34ZzN89b8UahxLapKhIRqlDLU0wsNPlAkCa4gOBQk2eJb3tD84db9Hwk7WuVb/byLKUpHyKcscD4uMuBdf3l4rwcQD8FyUpdtpVWdIqSCeTNllvva7S3R1ata9Z5SBXgdjYHY+4q/uyWOkIYh6IaHeBpHDFUPyDsVd6tOckKO8xg7sQK7qAoS1Hicd9LCdW3BVIwjewwDrZAvP8jBOLGMywVE2yGUtg3ozxhlFo+sNqAJ8dByuigjx8OJ4fa2fik1s1sqEXVxC/HqcP0NuWTFKTyW4Wm7mGR5gihMMMEJOdeLiEeybICwXyFy9mXB8lonf+HOixSJRscDh//lVcluvItHRSbj3IMYPre6Ul6Cwf4rCwWnSm0fo/+kfJHPlOioHxlh87BWG/t2/ofLKHpRj+6TX8p46oTG+QWmxvT1oKGBprQmNxhiFIwERDq4bEcSVXnmZxpizh2z61BA3yAd4loeodcJs5oqcitY6rxo5MSLgKPsZlDWnrYSb4BiXXOLS23uq55nLaTZe4vLaXhuyqh25qwyhgsAq9SMGPvb/pvmN/73A1xcaQptBkxpuMuZijFc6yHkVNqStLEshsh7T+uQ0nU5DYmTELfCib0fmoklwXdJeh2TT83q+mDVFenQbs6bm9Sh000g9F1lVhj7X9uz5o99b5E//qzV6IopIhLaM8Le/P4jWwu/92RLz8xrHEV7aHRK5QtBKOXljeJNDVueKFUPyDsawWsMwa4gk4mH+vv55TVX4am5qMSI1pFUbV8mN7OQxxjhClk5e5HGulptJqGTLsjMyzj5ewsVlgRm66MMjicFQoUyKDGWKpMhQZIk1bOIIe0jTVi9VBheXBBpNSMhaNp9WjFIrzQa5lKPsr3tJ/cEgE/lxsusvI61X4ZgE3ddtJ9W3ioN/99/o+vg1EBki3U1592Fya7oIS1Uqh6fo++wddLzvSnQ6QbhUYvYbTzD7zafo+dyPgEA4Z8mIJgzRrkZrcBIOvjFICBEK7SibdHcUfsWAVjiEREajHUMQNsnDR8LLo9/gCvWes96/nOrg/fwQvlQ4zB4qFMnSxVo2U6ZAmWIsxt+OR6LFUIgIYxzmNV6gRJ7D7GEDl56yj4Is2i6cB0O2XzfOjqf68LzWYaFajc2kgONqTGSotSwXsYVrouCFrx/hHz73AQ4Werkk21rmfLTUTdattnx2qNxXNyYAh6t9bM+MArAUpXjZt+TIommdwFQleN3GpEe31f8/a4q8UFlDPkox7tuQ29FyN1EYMfONp3n4dw6A0mjHIQpDotDmocpVh0/+5CQStVbIiSO8/4e6eOju+dd1bMvjzQ1ZnStWDMm7AI5ySEmGCuWYmCb0M3yKUWhGB90kSFOmwBL2B/0E36ZfRuikl4iQCY5Rpcw6tnCUfbzM02yXWxhkDcc5SIhPlQrtdDPHFL0MUWCJkIAu+llkliplKpRJkiJCKFOsJ39Phx4GeZVG755LuJxxc4LKwjiF0T0kOrpBa/y5abyRVcz83ffZ8NUv0P/DtzD6la+Tf+4gXnc7USHPwmN7mfyj+1DJBBKGtG2/koGf/xkSI6uY/at7SK/eQOXYKB1BF3PRJCIOJrKs+sxgkuLxEsaI5fMZhauFMDJEOGjXoDW2p7qmPp2fcsbOq0Q0oVJsYVvLZzk6ybF8vgFs3mOYDeSkix08yBH2MCsTrGOLzSvhM8YRxjgS800UkxOwZv00/89XMnzux60g6OhoyNFjYT2NH0WmTpXB2CS8iaxBmRst8OePbeW67YstxuRo6fRhtUPlPvoT+ZbPlmIP5e8Wruej7S/ycOEyAAbd5xhxTv/Mni96dBvTcZOqPYVBMo7P/IE5/unz37TE0iYoNNrTeDqkGtrrqxyFGIPrCmEARPDktxcv2PHVseKRrOCtgu3cylN8N36nyHHmrnhKKXLSQZlCPbQCNow1x1R9EIsIOcLeeDCKeJaHyNBOQJVOetjDC1zCFbzCDrZyDXvZyQCr6+GwWg/3MqV6q+HzRVKlGAyHmVucRzsu7vpVuN3dpFyH4tM7MUHIoV/5E9rvvJr8k3vA1YTFMmiH4PgkfT/zebyRVTiZtJU+KZWZ+/o/4O87jJtuhzCMZTBAueAmXXLDWSqFeIatwUjctRBwkg7aRASiMUEcdDKxUTEgWl1YrsEZ0K66uE0+wff5FovM8go7qFV41aqialVyNfzyr5b4yldLXLo5zf4DIVFkq5a055Agsi1/4zoDp5bnxb6f+4tv8WLnjyGLCXayAUnZZyfVVSGbbngkaa9R3TWQaeQl9hYGAFibsZOXZ8qXkHGqlKIku/1+dgM3paYpiM3B1DyMc8WSKXMsah2Zn5q/BICHn+lg7Bf/pG7z3TgkW2utLKHB1wpPRwTGwdMRkbJeqZsQQh+qpYsw6q8YkhW8VdCmcijRMRFQE8YikGdC2MLR1fEPTKhSpkotQapiEncjfl9kkRQZ5pgiQYo97GSAEV7jOTrpYZbxmJltYs0nWwJcpUyCBLNM0Meq0x7XLBOnJI+3yjZeqj5JkFAk9y6Bk0eJJpqZwmZkQha+tQONSypMUSqXIDSYojD3l/eg0ykSa4Yx5QqV/Qdpu2QruUu3kd+5AwRmGQMUJhR8Mcy+1qSqbWIeh7ZXIgqFSGx2WhmDOFjuiRMvK4KInJW9f6HgKY875JM8wjeptVeO4sGReu6sptdlSaPMDjDz1BCdQJVjLDKLVhFhLdISgXYtJ9MYMJGma9BhfudeZPHUXFplPkVQdenqbK3IKvkJ4tQahSBBd9IatNHYi+ntyFOKGl6IL05LMv/1YLWj6sakZkRMaBj/t/+DWlcYhUOopR67U0bbKr1I8JJAGBEEGq1tmC9ybC4MhPBC50jMSmhrBW8h3Kk+FVdziS3JlUtPO5gF4rNALc5dS4+3omZClhPqqCX0a424jmP7c8wwgYMXFxs7ZMgR4lOmiCD4VHmV52iTLDk62cDlLSE4I4Yj7GUdW1uOxVEO28zNnKgc5khlH1WKMblR4ZIkCKvUzF0PQ1TDw5YPLhCVyiS7+vFUFrd3gNzqyyi8spPCsWNoXILQDloabWXCo9qVaEpwuxoVNq6lUgojEeICoQ19RaE1xfpN6CFVa3GsceIJgr13GkWtN7zBkCDFddze0gpghA3kZYHng0cJ4smF41jKTBjasVa7CifhIUEJb+csye4+u99UI9fld7gsjNmw1Vwizrt0hJSrjXzHUbrINXkuE6UcI202XDQbWO/jro5XmYqd5HWe9Vyc+D5s9c7soUTxcqsc+NBL/7L++dR3dkIU1rvDgKDi+1ST3q898eUyJJJ24iAo6645EUZA1Nk00s4fb4eqrRUeybsMt/AxJOaKnDiNDL2IsJ+Xmz9p+b4h4FjrfVF7cdJyVuVJ47S0/I0IqFKO8y+zpGmjhwGIOSuGkDJFxjnKE3yHp+R7GDEE4vMyT5IkdQpJsiQFnuGBWDuqghM34grjBr7EHlNERCVuouXg4BiFDgVVqFI5sJ+lnc9R3LmTTKoPFYJTjurn2xziU03/2ZOyWxdlwBWMMnaEDZU1ImhqGWpjon82b6SGZmVgFYcUa+z3mnCjxuFa3rdsP5mc6uQqbsTBJZm0nojvgxjbAjjTrlicC0E7yL5TWzG7Tf2enGqTwV10KedP5bn4kYMfc0uOF1sbuB2oNnJoDlI3IueLy3omuaxnEoDxP36sfk20JygPdMLgeBFGQojVFwT7+9BKkU5azzKRAglitRznIoW23uKExBWP5F2GlEpxm3yCR/km+3iJihRZzaZ6S9ui5DnIq8wwfooPYmXsDY0S1FMTkrWBqibNsYp1eCTIs8BR9uNTbRmQgbix0/JKxnNMso+XeIRvAjDEWrZwdX0gtsKVj1JkEY3LCJewyCwFFhlkLcc5GHslHiERHh7TnECh6WGQOaYQI6i5PAFFRAz+wjR6dolEIBTN0inXYbleIDV9Cq2Jw1aCdkHEEIXNjPOmrPs/IyzZtDYBqI06reXE7XTRptpPu40ud4/3XgAAIABJREFU+qxIYTVEofFUAuNFOAlD/0iSydIgamyOxIEpRhZmUYUKC+9tSLOUhlTdiKQnG9ewGnj4Mx6J9Tbp7kenliEfL3YwV7UxsHKU4LXiEIFo/u3g/TxZsuGpa9KjLaq9lUWXL/2XWV7d49PRrvm1X+pmdezIHggaXtBlPZM8VyyB0jg6Ngr2ohFGDso1OEoQExFFtlglMoJ2FOl0zCFCrGNylvvwTsWKIXkXwlMed/FppuQEr/IcR9lPQlJ1T6U2MzsZy3WRV+j6shLP2mqlxd2qv75cF32slo3s4QUmOGp7YTRBo9nGzS3rKKXoYZDrpZcdPIRCc6m6pv59SfI8w4MYDEnStNPJEGs5wSFydNZj/wYTqxKXCKjGxw19DDPHJA4OJrJlN7VwWKW6iInf17bTytNQdaNpOTo2Pm5E2aQBDZFgtLTYjvYzkAMvFpRSDMgw04w33cPWe2m9wjNvo1sGOMEhOkc6iKIq2hg6upPMFHsoj00ilQpuTwpVsPyPthNVisM2NJk91vBEgtjpUU35hGC/NWLlNgPtAck2n3K1AxNpcm0VFuNijOHMYr3JVM2I1LA7UGzC8PHPTPLsCwE3Xpdi4waPg0cCbvzIcZJJ+O7d/Vx1hccvDX2Xr+y+kXt+4h9xXHBdS7AMfOtd2FxHhBbrUWpl8DwhCCDTplhYEP7zb7bzZ39QZvxYYCOeF8EreDuEtlYMybsY/WqYfoZ5Uu6jRKNypubCQ5wXiJm9LgkCqnWzYf9tHpRU3atoNgj17SrFVrmGOaYo05p07WZg2XXAdmvcItt4iSfrSepQQp7hQRQKFxfBsJbNTHAUF5cUGaY43hSGE5KkcfEosYTCYUnNsUrWMc04PQwwwwRlCmgcuuilSpU8C03nR31bUGuLK/XrJAhiTv7Vq1ahJgzbuXXZ87zYuJTrmOReThZ9bJAXz96UqeZNVvMFux0vybx3CaWjh+luW8tC9SDZKNsystSMSXrGXodyr91/sxFpzi8BsORBm4+JTvX+HhndSH+H9V7e236o5TtjhOt+YJyRXo99Tw0zPNQ4kBPjIZ//xQnu+qEpLr9UsXuvwZh7IIJMWhH4QjXANkPTgovNA1V9hdYG7VgmvEKztGAYHoFPfaadu/+6CsoSGzPtHv7MBRaSX+GRrODtgO3cwhPcV3/f6nXYsIzUq3qaBQOlPlCrprDXKtafdl9KKdbKFvbxUt3zcfEYPsM6YD0aheYERxhhPbt4Ok6CRngkCajSTjfHOUQYc1xqnJk0WUrUuAoKJ+40OaaOcqlsI0mGg7yKi0c7PQiGeWbqJbE2b9M83bR1PTU3o2ZSFRoPLx6SG10Ka7kkjaaTfrw3wMx+I3CVy7VyG8/zaMvnDg4hhkmOcYlcftr8jRHDNGMAVEoKJ5khKpVplzyrOq5gfOJ5IhOQHisTKkss1Mft8m03XlHfjldsVHWVe/QpA6VbssajahqJ8/kuB4mNSiLrM7WYo78jzyvFhnSPowwP/dUYjlF8569XkUy2GqHhIZdv/cUqRrYf4pXdwk9/IceP/0iWrk7N7j0Bv/uHSzz6RJUoEkJRuJ71JNNpoVzWGGVwFeAoch3wT98foVIRRg8GIDaUWS1caCPCSvnvCt4eSKssvbKKmXiQsLA/7uZBtFax0zwTr4W2akYkSfq0Uio15OiscYHrn3mceR2lFK54nOAgq2QtC8wgcXLexcPHhlJq1V8buIwTHKJCiTLFetq/ZhizqpNUboBXFl/AUI2NktWcai17Ph2k7oPV5NqB2ISYetjLjgO2zNZguEbdcg7bvnjoUn3cLB/kiTqnyMJew2pdemY5HOdg/b63eT3k2lZBRjE3v58omidlMkT4p1UlWA7pWYNxHaodp19GGbWsZzK1mGOPa73Yre1TaISH//AYv/2fek4xIjXc93CRKILHvjPIlk0Ngz7Q53DHrSn+vz9Z4te+vAihEBqNpwxRBKmUsTpartVTe3SnvUbf/vsSStnig84Ol3LhQtb+xngbGJKVqq0VALBN3VSvdLKwQ2BESIJUnEDnpO9rs/LmgTQ4a3+KxkDd2GKRpTOuE0mITyVu6XucWuvYiIgqZbJ0MMM4BRbZzNXk6MAnQOPgkSBJmjQZBhnBIJQkz8ziXgS/bmQGWEMGG7x3zjDHUk3HbQ2G/UtieUypeyqm/jdQb0L2ZiOtci0q0bWWAQbDqzzLcTlUV1gGe+2PyF4O8ErsaSnWlEZIT1bITFVYH2zENQ5l8mxhe8u+JAyQMEB9fycAKlr+2Ug2EcKTc/aVmtakpjXJWYU7kai//KVk/TU6283obDff3nc5ADNTAR+96/QlwL/6f83wW1/qajEizfjiF9q5+kov1uOUeqV3TT9LNVJg7Hy2ym9/eRHfFxAoLkWUTtWffMNQcu6vNwtn9UiUUluAv236aAPwn0Xka8ss+x7gaeBHRWzPUKXUV4GPYo3W/cAvyjuhE847EHeoT/Ck3E+Jxq9aoeK8iKkP3s15kYbnHZe2YuqaW6fDGIebEtjWsIyyn1Wy/gzCgkfRaNaxlQmOAYo0GTQOGk2aNvayE8EwyBrKFJqOybYeVsA2LmOCE3VjpuK8zrXcFpccH6HWxbEW2moNa6nYE9JxwO/kUGATtANGSJPhWt5HSmVOe03eDNRUog2GeaZjo6fZzy4O8Aqd9CEIC9g+MJZ0aSVS9vIiKdrsxAFL4EuTpZvl81wA+klbUp4GuM4O/ESCaHtdMtPgZ+OwVocisQT+SUVkOgC94GI6GzP/KLC1Ul9+4SOIPIF7mtKpmdmIY2MRn/7Eme/DF3+ynZ/7pVmqvmCU5Zma2K5qBf1D8LOfn+WFpytEBkJfELFSOG4CwtfPlVweb4PR8qweiYjsFZFtIrINuBYoQZMCYAyllAN8BRo+s1LqJuBm4CrgCuA9wG0X5tBXcDFwk/oAN/OR+J0dJq2kYqqeKWl8p+qmpNYX3GDYx8v1vicnY0nm63H25l9IlTL7eXlZb2ZJ5tnHy4QE9KpBO1MkwMQmokKJGSbw8UmTxVEOh3kNB5cMWbJ00EUPWdpJqTQDDNc9BY1mC9vIqU5eZUd8VHKS3lfzeUv9PGt5kebr0YwNZgt38kPcrD70ljMiNWzkShrnp+peVUTIHJPMMxVf59ByZIz13obZQC+D5OiiSgUXh/dw+/nxY5q8E2WoEwABkotC+2hE+xGhc398r5oijnrBzoFrRqSGtpzLg4+XWA5TMyFdnZp0+szD3oa1LkrHpdzxMYXx42wMzM4YdjxZoadD8c0/HWDtiG0n4DhcnC6F70AeyZ3AQREZXea7nwfuwRqLGgRIQdxoAjxg8nUc5wr+GZFWGe6Uf8Ej/EM84wzrhqI5VNMME4c8wFBiied5hM1yNR30oJQiEpsAbyTZWxERcoLDzDHFGtlElg5CAsYZZZLjGAw5egDI0c0UY4SEJEiSIEWVCmHcFRJglknStNXJkC6J+n4v4TLGsY+wAAOMxH3rTUuIrhm1jFCtCLqu+Fo3Jq2l0ZdwBetVK/v+rYh1agtLMstUS34sDss1j+xiO0KC1R07xv56fugy3sOQWn3WfUnUmFzonXvs/wf7SScaxiCxYAd5v9OGnnQQ52Saui0HJYVTAY55RCmPandTRd11N/Ib/3UHH7mzDa1bjVpXp8PSkiEIBM87vcEbn4xsJ0hjw0UikMpAUIXf/WoPH74rS2e7QzJpt7F/h+1o+R++PM3ffrPI0WNnr347V7zZIatzxfnmSD4D/P/snXeYJFW5xn+nUucweWdmZ3Nm2SUsLBnJIF4EQUWCmAUExXBFREExAlcBFQMqqEgUJSgSBUSSkjMLLJvD5NC5K5z7R3V3dc/MJhmW3dl6n+fsTldXV1dXd533fOn9bhi+UQjRDhwH/LJ6u5TyceBBYF1p3COlfHW0AwshPiOEeEoI8VQ519/HuwchBAeJ97MTiyurVFFyBImalXf13+4v3sYmxQDP8ggP8zcelXfxz1LP+GpxwOGwsciQ4nWe42n+yfM8TierSimnksWlNsFtTK5EJIoUsLGxMBG4vVCGZB/VPTxEqc4jxQC2tAmIUIUUQ0RQhFpq9OURxshU2OFLPq8qvLpIUyBoY8p2QSJlLBD7sIj3UD0dKKiuyq/UQFKKkHmuPg2DmezMQRy7WSSyITjru9B60yO2GwMmxoBH5sagd+3VYc2jAn3eb7DxsPfx6psmH/t8J9msR4SOI7nlrykUBf5+38YDGVf/MUWh6GZuCdW1NBxLoOsQi2m0NGkVEqnG989v4pTjRzYHe9sYTx0ShRAGcAxw3ihPXw6cK6W0q01bIcQMYC5U0kDuE0IcIKV8ePgBpJRXAVcBxEX9dsDBOwZaRQetuBNFWqboYz06ARqYgI7OEp5jNcugZJE00UYHM8mRJksKE5MIUaIkeZWnybPxNqoSB2uYxaJh8B5xjPdY6ISUODlnCKRLKK4QpY3E4Q1eREErtRB2s5GK5IlRx3pW0M40ykKTZQumNrgu6aebjaM266wcM0lQzzyxaBOv3faQFI0cygcqj/tlN2/xCqZ03YWz2aWi1qxjoIq3V8PtFL1AgujuRe12RTCdnWvTwMOdpf1KtThmQqd/+shpq0wmoW6d8DHf5PY7vsNtd73FRz4QY2qHxq9+n2bNWhsbyVfO72fvPQM0j9LI6+/3ZnnoXwWkxFVz1lxptWLRff+vf7eXIw4Kj9pSd8Uqk5/9duxl5MXoToBtClvi2joKeEZKOZprahFwY4lEGoH3CiEsYCbwhJQyDSCEuAvYCxhBJD62fURFjCi1K6457MqcYZk6LhpHbNmPo5DS1btSUBigj2W8TD89jObgDRFhLw4fddLazd6XR/S7cVCQjkPADqETIEeGIfoplwlKTFQ0YiSRSN7kJYIyQiNtdLIKkwKDso+EqEeTWolYytXq5bjHaHdy7fk6ODTSxi5in9Ev3naGOtHE7ls7nOlIjPVe9p4TNrAStTpc+qBJ/Rve41xDlVtsyEE4EpoSLDjguzz296/z2+uHXMejo1Rclz29NosPXse3vpbkg8eGCYcVlq+0+NXVKX5zbRrLcrscipLsv1UsF+lK1nZa7Pe+1fz8kmb2XhRw3ba25K5/ZDn9K92Y1juktbWNQ2xuApUQ4kZc19Q1m9jvd8DfpJS3CCE+DHwaOBL3jrwbuFxK+deNHSMu6uVicchmnZePHReDCZOnMn8FFJSAjtBUrMFM6cZzYxdKxfYQGLgyMBZFQsQYog8FQZQkiziQ9azmVZ6mXG5YDj5vjj5WEx0sFIvf2Q+8g0AJh119kp1mAGAlghjrSgSTdgPpMummafft3oAx5H03omS5CAfkY8/wSO+N5WcqNmTZrRmNCLI5iaq6gXVVhewocfpqdQRwU4CjYUEsptDcpLJylYVtSzI5t/YEx3laSjkmZmmwvUNO+tyXNnv/N87/0pi995ZgsywSIUQYOAz4bNW20wGklL/c0OuAW4CDgRdxv8O7N0UiPnxsLhoCbbTkZ9Btv45iSzRFoITBLCpgU+r74cmXlGVZFBRS9FGu9MgwxJM8xEx2pgm3MLMsf1+LsoVSGy+ZxUImiZlb50PvCCg19FC6XHkaowuIhiskAiAG0shklIZ7lnqvS8ZxlnvKw0qynoXswws8Uar3USnL5Suo5DIuoZiOUxIk1ZnGtEp76NUsrWoHjcsgpd9UKi9JZWzWrrdLYUIBQsXQJcWxDvFuBxbJZhGJlDILpZQZb9uoBCKl/FjV3zZV5OPDx5giGGBn4zCeWNdHrpDGtC3UQBBp5lANDduykYqGLWxURav0EXEUUIJRFEfDyqSxMUnRz/M8BkBZ/qSsQeURSnU43pWE2ZODiYuNd5v08d/BWuNlkmntIxudiYE0VmeXt6H6b8DpzKNj4BWIykq6iKvY4CZxqGh0MIPp1MrDTJPzeJ3nWcMyN+Ov2ntTlYshcBctSHvsScQ98W0evkSKj+0W9ho3J3QPeSBLnZdYU3wLiq5kvF20EUIDx0HTQth2kUBdM0LXoWhR7O9Fle4UE6WBycyijiaKpeLLTlbTw3p2Y39XEXgU2Y/lcgnLWcIC9trKn3x8ozoQX0Y1qWwJvPbNJan3kjXiZSGq1NE0gkTAzVycJReSYqDU5M2rJfIOKZA12VJjHxnfHtJ/fSLxsd1C2jZCVVGFxix9N6ZZOzFEP0WZZwnPYsmiKx9v5dxq9J60SzKl2pgWJjOd+TUkUdb8elY+wiwWblQ3rJ2pvMUrWNJCE/6ttC0iJCKltFiv+qesg1YupJ3MrA0WUgohmCrn8AJPjExdl+V/JEItxVHGroRku4L/6/exXaNc5CY0BU0LUM8EpG1T5zTyGs/Swzp0AkgkebKoqLQymWnMQ9uACq8t3bqUCBtu8gSgCwNVqpgU0PxbaZvFdOazlBfxlBjK0S4FC5NErdd+BJI0ltxgXrh+uL9JFRJdd8M75hgLAPuuLR8+thKkVXv3GiLAAvaiKAsVHakwMVd5eBMyHhkGKxpjG4MjnVLLXv822pYxVcxmqXwRqFWuLsdMvMy80VFbnDq8nYILyyrnCIwkmbeF7aSy3b8DfIxrGCKwQVn0MtRobY9yK9WFjcU6VmxUfLKbtSioZGfVE4xOBkaq29ovuEIO2iT3HGTcVaa1X1rivnd9HXZf/xZ8Ih//DQ7gGB7mjspjBwe1pA3dxRpambzB13axplKMCV5bgGrC8JrB7Zh1JL6MvI8dGkoo5MZaImFExBVWDEyfiUChizUMyN5RX2fKIm/wIjY2z626lfyEkdLlxYYw2vSpaNPdau0yiQCosRhqzC3uVOvrUOv9zK93EoYwOIBjqNWrdiVulvEathxduseSJst4paQeXdt7hlL2l1LqO6OWdKPHHNuBaKNPJD52WCih0IhtIhImEqin7At/nkdZIV/HlG4mkSMdOuVqnuQBiuRRAhpWPsVQ/+oaMik21Kr9VpOIj3cHhjA4VJzA3hxOWSvNxqFAlqf5J2lZK2+SkgM8yYMUcb/7cri+DKXKxaWhMYU57M0RY3rOgnHSj8SHj/ECJexO7kqs5MqKeJO9jAQxS5O/bShoy+M42SwCQTdreYtX0KSOhUmAIHlyCEUh2Bgks97ipTduZNrnzwPqUHMQ6nGPW593A/ZqqlZpsLhoFgB6X5WAoO/i2iqIiDiHcgJSSp7mYQbpJc0Q/+EBwjJKkBBZMuTJQslq8bK9XBeWgkBFw8ZCQ2cPDnYzxGDsLYMxPp4Q4kjgCkAFfiOl/OEG9jsB+BOwh5TyqY0d0ycSH+MeQitJksdqYyEoSqXdnVllQWQn6LQuPJTV//4rjpRYstz7xKrI5SuqgTBs8v15EAJraJC+xx+hfm+3lW6u0SWTnoXu5KJnveOHem3UrI0dVGp6UqpzZyG6XFea1Tu6S83H2EEIwSIOREpJN+tYy3IKZMiRIUuqvFfFlVUuUC33brFK7RVmsdAjkbHGGFsapb5RV+IqlawGnhRC3CGlfGXYfjHg88C/N+e4PpH4GHcQmo5QPTE/EXKF/5xUGmVSe2W7HXW3m3UB8vXurTA0yfX2Rlr2gRfuBAuUYAIllcNyLAJGjECyBTO9kuT0JGkzjF1wsDp76LvvL6Tfeoqmsz+DYhiEejRSU8AJOgTXe17kdJtGqNt9v+BaGJrrWi2J5/uQzaVU1BKRKIYxaoGej7GDEIJm2mjGq55/QN6Kgz1KDx5R2abg/saaN5HM8bYxtjWOewJvSinfgoqG4vuBV4bt9x3gEuArm3NQP0biY1yiuolSGWLyyBverAvUPI6v9O7apk9+EscpYBsDSMVGDxnYIkdkapbYpCgZM0h+dTfOUAY1EEILB7DeWkX3V7/J0KOPMDDNwQmOnAWcABSS7t9lErF1QaE9Tm5ygtzkBMUj94C9FqJMbEObNgU1kUBNJN7OJfGxBdiHI6tkHssoR7S97qAGwVFVD8YSWxgjaSz3dSqNzww7XDuwqurx6tI27/2E2BXokFL+bXPP0ScSH+MO5ZqSajIpZ2TJUg3J4PyGESQC0DdPwYqCFYXofi0Qj5MdzKIENWxpYUubnrV5coFGcqt7iCzcCUXRsLIpjIhGY0cQXRdkb76D9f93IfllywAoLMyS7bDJdtjkm93zKpOJXerW52gj61us5gQMum4WofkOhK2FoAixmEOH1YuUvqeSpQKSInkc+Q43DNmyrK0eKeWiqnHVsKONVkRV+ZBCCAW4DPjylpyi/8v0MS4hdPenrYTDUJcAVUGGXbkTM+b+X0h4P//MhNo1VbHOgc4QzWccz/pLrsUJRJAUkJksxf4cwbYIoXlzyb+4hNisBRSyq4gySDRsoysO2DZ19UWW/vYXZHeeRd3pH0atB7vPJa9cS6nCWnrvK1XvfAKDDrmSSy6abXafB9T2ZkSvl13032pQ+dg0oiLBIfJ43uQlVvA61dZJdYV8D+torl3Ujx3GPq13NVDd0nIi1PRZjgHzgYdKhbsTgDuEEMdsLODuWyQ+xi3KWVoA2A5WzMAqkYhwZKV3BYCo8oQV67wVZninaajREKot0IwQSjiMounkX3yVgBOhYY/3kHr9WVSnQOtknUSjjm4IMinJYL+kqUWlcWg5XV/4AUN3P44S96TQpQ6FqvKRfFKQT45cMBaaIxSaRwnmam+vS6GPTUMIwUyxM4eK4zlUnMCh4gQSNFFuryxxWMJzFGR+0wf7b89hbNN/nwRmCiGmlrrenghepaaUclBK2SilnCKlnAI8AWyURMC3SHyMU1STiAxoONEgZtT7uatFSbZZxVFFzWReTSIA7U39RM4/gpe+fhsgiE2fR6C+BZAMvfk8mTUWyUN2xnnmRVY8b7LX+xrRzAKDPSZrV1uc87UET70AfZ1pkk/eT8/DjzLjG8fRE3f7lxSDOsVGkLqD0enmcOWbBC1PgVMSAsy26KX/XV+YkYoR7HblW0RTErXLTRt2yu1qcxvvSe7j7WEP4XaOfFTeTY40JkWe4D6mybkbrZD/rzGWiitSWkKIs4B7cNN/r5ZSviyEuAh4Skp5x8aPMDp8IvEx7qA1VbX5NQycUnaWsCRyWBxCsWuLzLSUgtloge4SSt7USS6cROvR81l35wvYopeCXUAJB0gcNo/ikuXYT7/I1AURCoMFXnq4j70PCGLmLN54yWH6bJ377styxEfqMQfzrFtV4NmzfkdgSgvRz30cVa1H6iN97Ov3UoiucM8rMFjlUikZIfmmQIVM7OY61K5+hOFaW6phYA+Ofe9wH7XYVxwJwGr5Jm/wEm/yEkt4fszfZ6wLDaWUfwf+PmzbBRvY9z2bc0zfteVjfKNYxA6o2AF3BhYb6aldrE6KMr1bI2/qTD/zYOa/byL5N9dQXLKMwrOvwpPPMHtijkgMUutzNLYaxKLwzGMZJrSpHHBoiIE+h1BEZdaCMKuWW3z5ogY0Q7DPgiy9X70Y65l7aZgwRMMEr1e51NxzLAxTTZHDPFn5Ji9ZwG72JVbeLUwUMzhIHMtB4lgO4QNj/wbbgUSKb5H4GBeorhtB0ypV68U2jx2cgEIx5hGEFRSYpRrFQn1pH0MSaPBcQzPrugFYGFsNF83hjfkmf7h4HYGIijmQo3e5w/T5EZY+O0j3WyZ19Qr5tOQ/D+e47KoGfnF5ij3f20g+62AYggkTdWbOM9j7kAhD/Q5P/vF+Uv96kfYvHos627Uo8oNBlCGNQqMk3+zODqFSHYqegUKyVIPSJzEjbuwk1GOhz5yE1N391CUrK5/BF4XcehBCjO2E/i4TxObCt0h8jAvU1I1USZ8oRQel6LqOqkkEqJBINZSiwCx466uhYrDy94zAeo46tYkf3zmbgGpTzFmkuvOse3WQjg6Vla/nyWds1q4wOfVTMfr7HJa8YrLf0QkeuXOAvQ50tb3qGlRyGclJZ9YTjavYazp565yr6L7xAZxcgWAij1QkUnFnkGDX6Ldpvt5zyeUatQqJANAyUrVYMTbcpMvHtgmxhePdgm+R+Bg3KMcIqjsLmQlv8hSlUEQxImoeA9hhBxlwNyiAWdDYf/pS6o0M4JJIGa2TA7zxbBs///UQP/hRiqFe6FknUBWwiw5nfinO2jU2F/7vAOf9agrLXs3z0n8yfOtH9UgpWbnU5OiTNGYvCFLMSzpmhpi7k8oDf/sXK+9+lDlnHUih9aiaz1aVJTyMABWckjFWjEYI9bmfwYjqMNEtdtTue2qjJOJXz2/j8C0SHz62HkbLVlILtRXuZRKpRnliFgX3djACFkbAlRXvK7quo3+lZpOXBhGlQEQpkJU2Z346zopX2jjvrCRWQVIsStavtbn424O8tUblomunsnZZge99djnf/HETkajCK88VSKccdt4zhJTuDDFnjxihsEp9o8q5F8RY+euHGPjFJQz950HswCD5OXlyrTa5VpvBOTaFpDezOMPiJrn62lva0QRyv12w95yHMrkDbeb0Ggl730rZ9rE9qP/6ROJjXMHJ5SAYwKmP4dSX+n2UyCTUPbIH6nD3VplM6qJevUeZTAB67Vjl76y0URTB+V9sZM2zUzntQwkG+hzqmjXWryzwzVOX8cjtvVx8VTP7HBSmt8viu1/p4eSz6lEUwZLn84RjKrohUBQ4+Ogoy9+0+Mk1jbC+h136HqLrSz+geNffaZzUg9pQQOoOVp1FZqpJts0mNd0m3wj5qkS13p3czzC8Ut5scc+9uGgWuX3n+CSyvcAPtvvwsfVQrUWlZEv9QxrdeIlwJGZExamaO8tZUWaD19RIRCxak27qbERz02s7gn3uY6VAmzYAQEAoJBQvfhIOq1z5g2a+89V6jj51LatWFvjwJ+MceHiYYlHym8v6uf26FO//aJKjPpRESsn1v+jnsJMaeeKufj79hQSrlptk+iXzFhhMnalx7Pt1vvKVMCef8BjP3/oEDYcxJls2AAAgAElEQVTvgnHkBxCailC9WcNM2OiDKvlSWESxYGC690HVUswn1OdQaAigFFz3l5g9rbKP1jNQ+dta57nxysrJw1sZ+9hKkLUu2G0VvkXiY1xgU4KGZsT1AalVbdjVYvm1xcpQNId0wU2r7Qj2VUgEqJAIUEMi1aiv03j0jg5+e2kL99+S5vQT1vHNs7rp7ZVc8seJnHJ2A9m0wxUXdLF+nU3btCCpPos9Dwyz5IUCE6e4k/5uewZ483WLaTM0fnZVHcmEILLqDbq/dhFWj5tJptYXK8OKOeQmeCM1mcooI1evjHB9ATiBKjH7UBCteWSgvkwoPt4F+BaJDx9bB04mi5pwg8tS8yZLO1Dr5pGqu2IHN75gByUCsEurdjVgEdAtTEflsd7pzI2vp9VwLRS1Ss97Yy1VFUVwxEERXn04zBnndnHtLSk611jcd+sQ/T02j92fYcF+Md778RZ+dd4KvvXTZvp7bR5/IMN5F7UCUCxK4mH3nHfbQ6e+QeE9n5nCg9esYPmFP6L1fz8EOy3w3rOxgF2qfRGDtZN+rrmqq18RCkmdwIAk01pHsN/9THqdq4xsBxW0tA07TcLodd17IuuyrwwHYLVrrQhNw+71SHY0tWUfY4N3M/axufAtEh/jAmUSqUa5oh1Ay9f6B8wwWBH3DpVrvZa7dlXqb38hVCERgF7bi5WknU1rK6mq4Kr/a+H+m9tZ/kqeW38/wPK3LHY7KMbyl7PcedVavnNlCzPmBjj/M+v5yCdiJJIqjiN56J4ce+xV0gUTgl120ehdleOYr8xED6qsvfQm+m66z3sv3ZvIZcJzQ0m9KjBfxS+FYZpedlDBDg4Trqxq9iXDrpUm6pLeezbUj/jMvuXyDsC3SHz42DqwensrLhnR3Y+c0IiSM4FaMjHDVZNlVZxBKB7R6Io3KevCNV/mBdcAMOCUSSdXIZPoBtxcZey7Z4i1z03j2//XwyVXDkDR4r3HR5g6y+Bf96X5xhlpjjsxwme/6JLh3bdliEQEu+7uTcq5nCSsK3TsFCea1Bjsdsje+zCFx54i+bFjiOwxm0jcPZ/sqhhWa5UPrztQQyJlFJKCbIsbN2p+1hq5Ay6ZVIfks7MaMfrjpWsLTJngXr9nXqvso7W34fS7bkAnW7JqNN2Ps/yX2B4sEp9IfIxb2NEAUvVW3mZEqbi1rCAoeWVE46lA2JvsGoNZRkOTmqFszG+KRKpx4Vca+dzH6/jBT/v43dWDtLSp7LlfgOv+1kJbh0YhL7njTymuumyI3/6xnpKMN/m85OEHC5z9WdcCCMc18hmLfMYhrORJ//JG0r83aPnQ3ujv2Y9wh9u/JDPonltmbsFzdzWBPuieu5alknzQN1ujbHzpWS+n2A5A1BgusW8QzJnIgLefMnsqYiDtPa5LVsjEt1LeBraTynafSHyMGzjpDErUdT/ZUdcVE+grMjTFm+w90cNSP4migIl5AgGPQLSSdTJQDNJpJmjRPfeWSyIutoREymhsUPnRt5r41EkJzrmwizv/lGXpaxZGAJ5/usicuRrX3FDP7Dne5HvNrzNM2jlOY0eIQtame2UePSCITwhhZQpMn6kyd7bg9t8+hPP7fxE5YFeaTjucSCJfIROZMCtkYiYc9EGFXKtDoNcjiWICqjx52CUpr3y9SrCvNgaSn1ibNx3KFJDJKGIgjdNYcn81JhEvv7HF18jHMPhE4sPH1oNQFGQ2h7XrzJrtelZihj3LxAoNfyUUC+4kawRq3S+qcOixvNqRbjvCIuPty7TPnWVwzw0TWbq8yF/uTPO9K/o48dQwp58dJRJxJ/f+fodrrsrwl1uLfO6PbmD9qTvWMXVekFVLiziWw8TZUXbb2eKJf+a54FsxrrshT98Lz7Di40/Tevg8Zpy6J32GG8CnEQaH3LiH3QS6bpNX3Mdqzr0+uWaQhiS0rqTtlXI1yfJ1CnrWndFsw91Xz5QkXAZszCaXWLRhXRzVei+mYnV1v+3rtqNBsH24tvxgu49xBRGPoWZH+uLLk+BwErHDta6tMqEANAYzbAj/jTUyGqZPMfjfz9XzxJ2T6Fwm2X/3Lk78QC8nf7ifQ/fv5vnVYc66fg/qWoO89cwAd/90KYoKMw6cgG06qCrMmmuw5DWLD3wgyLrVFhf9uIFQAIxXX+PxU69hzfdvIPvGWgKaFwepDs5XQxq1s1amqs29bYgKiVQjn9xAgy1VQFUShJpMjr6fj41jPATbhRCzgZuqNk0DLpBSXj7KvnvgdtT6sJTyFiHEQbj9f8uYA5wopbzt7Z22Dx8jIeKe5VAmEzusIwWYEeEWdlXNg1IFpVTJXl72zZjgrprrA1lmRzsr+76Sb2d2YB3NapplFkzWXKskqgQ2mgq8uZgz0+DWq9t5fWmRA49fQzrvsP/Jk5i2qI4Vzw9x251LePM/A8xdFGbNakmEHHu+v4XHbljD/IsaUVQIBhUWLzboXGfzgZMjZHstVBXmtK3lrvN/R19zmPknzGTF/KNAdxlVJErFNLlADYlUS+qnS41ZSx8ZpzRr6GmBMQTxVRb9M4MEhhzi/XnyE6NIBUIr0zjhAISbECvcbq5qMok9MOA1HpPSb8S1CQi57ZskmyQSKeUSYBcAIYQKrAFuHb5f6bmLcTtvlV/7YNVr64E3gXvH4sR9+KiGmkwis+6EpHT2U5zRAkCu0cAs6WvlmjwWCQwI8g0lV02/gVpfqDleXyEMpTBAo5aqbO+yozSrblA5qgQYa8yabrDmmckceeJaHvr9Sv5z61pCYYVQVMG2JSkzQGyCwM6ZSNNh3/cEeeFZkz0Xu1Fz25EIAe85PMx3/reXsz8f5ffXZLj2hjo+dko/8ulXWffzF4jPayN6xGIaFs9AqAqBCf0M5VwrKzUUohh2pwZhOJBV0QZdq8MZNmMU4zDUoSFsKMQVhubE0bOulWcl3OujDRYQVS4urT6Js74LtoMJ8l3HdhJs39Kl1CHAUinlilGeOxv4M9C1gdeeANwlpRw9FcaHjzGCTHsuKXOYSKNaKv/ITrRxQg5OyKlYKcGgiSJkZSxJt9SQSBlddvQdIZEyFEXh3psn8o3PJ0kPWPR2mqSyCsmOCKteGGDCRIOO2WFe+UcnXzg3xu9/leK0j4bJ5SRPPG6y00IDTQfbgsOPCPDM0yY7L9A59PAAe+9t8LOfxcm/vgbttjt54/QrSf/nFXKr+5GlHvaxeI661iHqWodQNBslXsTpyI0gkTKKcchOACsMji4oJFQKCZX0JJeYrEQAJxauGUpzE0pLM0pL8zt2HccLtgfRxi0Ntp8I3DB8oxCiHTgOOBjYYyOv/fGGDiyE+AzwGYAg4Q3t5sPHqBBxL4vInlCHLKXOavnqu8vdpuZHpv4KISlUFSMmSwH1TtP18bTogzhVfjFbVlW5i3cm1Pj1LzRwyP5hjv3EegbXZGmfGaZuToin7ljHgYcG+eEVSb79v/3Mna1y+BEBfvubLDstNGifpHHT71LMnqOhaSAE2DYce1yIn12e5owzGjj+hCLRkGDaVJXzL7iDQFQDw6Dt2F2Y96E5DNi1WVlDgyEKE6o0yTQHsapUMFnaXIwDCJRSiCowJMk1u4RrBFXUksaXNlBi89I1LCsR26mRpO2D8WWRCCEM4BjgT6M8fTlwrpRy1AieEKIV2Jkqt9dwSCmvklIuklIu0nnnVns+xikyucpQl64d8XRN1lZpnaLklcooI2nkKiRSjTKhANSrWYZkYcQ+7wQW7xZi9dNT+PRJMda/mSG1NsM+++n0rDP5wid62WexxiWXxvnjtTl+/osMX74wSbEgufkPaU45Ncxzz5m0tasYhiCeUMjm3Fnp1I+GufHmHCedGOHED4X4yAkG1/wsSOCxJ7n/g9ey5tJbCS95if0mLAUgnsgRacxUhrQU8q0W+VaLXIdFMSkpJiXpSd6sZwUEhYRCIaGQ6vDKGq1kEBkLIeMRZNxTCygTio9abA8WyZYspY4CnpFSdo7y3CLgRiHEclwX1s+FEMdWPf8h4FYppV/a6uOdQyzqDkDNWe6vu6p1nBX2SKQMJ+jgBB2kFEgp6MlH6MlHcKRSGqIyilIjLIrkHY28dOhycli88xpTqiq44rvNdL88jVOOjfPME0WWL7U46iiDrm6H/fbt4S+35/nVjc00taicd1YPs2epLN5L5+pfZznpFPdDv/6aycR2N9YxbZpKT4+DZUk+9fEoN96YY9dddP58Sz1zp0NzoZOnvv9PbvyfP5O97g6a0stGnJeRKGAkagnVGBTYBthV5fBCSoSUmHGtMqxkCCtZCvg3N7rDl7UfiZL67+aOdwtbQiQfYRS3FoCUcqqUcoqUcgpwC3DmsMysDb7Wh48xQcxzxVgz3ZxVNeO5YvScRB1maAyvaq9GXyFEX8HLFa7TMySVkeG9PmfrdRYMBhW++7VG1r84jUu/2cTDD5rccXue/Q8Jcdj7wtz8+xTv22ctzfWCy65I8NMrMrz6qsmJJ7lNtK67NseHTnQ/08CAxDBAVWH6dA3LkgwNSXRdcMEFcV57xeTuBxt433s1Mv98icc/dT1dX/wxvTc/iNWfJtLoxaGGk4lZJXs2asZR1aYymQCQHKmX5oPxkf4LIIQIA4cBn63adjqAlPKXm3jtFKAD+Od/e5I+fPw3sCPez9sMC2JrHFLtCvawWpJIa5oNIVHKeXWkwno7QUMpY8su3bQRZeuXYimK4JQT4px8fIz7H85y5nld3H9nltlzNT53VoR8Ho46rJfGZoVrb6gnGhNc/P00ioCDD3bdxn++JcfRRwURQmBZkkJeEih5lOfvpBONKrz2is3XLojz8gsm3/xalLvuyfHco4+y8taHic1opu6sDxForWeoO4IxMUtxyMCU7jU346CsFBRjAqMU+siV2jkGBhycKoVmJ1EyE22JsnAeYunKynN2esPfzY6AcVWQKKXMSikbpJSDVdt+ORqJSCk/JqW8perxcillu5RyO2jP4mO7Raa2eHA4iQC1JCJBybk//8y6KPm8Tj6vs2YgUXFlxY0CXWbtKrm3Kgj9bpBINYQQHHZghDcem8pDf+lgUpPBlT/N8Nfbc5zysTDnfSPKPx8qcOzRvTz17yLX/L4OVRWsXm1z1S8zfOoT7me5+948C3cxCFcJWiYSglzOQQjBSadFuPGWLL+6sp5oRHDZZQlalX5Wf+lK1v7kdqLJIQCMeMk6c9yRnujOgMVS6KPa/VJMDFvD2t5sWZ0q7AM3TXpzx7sEv7Ldx/hBiUy0pWsqm6qD7MMtEfDIBKjR20oGPJn4MpmYUsOUGuut5LtOIsOxYF6Am65qY+kTU/nsyXXcelOeT502wI1/zHLW2RH+cls94bDg5puyHH9cL58/K8ruuxlksg4/vjzFaad5waNCQbLsLZu2Ujxl/gKdZcstdF3wqY9HuP22PNddX4eug/3Mi7z5sUvJLyslOGxguVisiqObUQUzqpBpN8i0GzUkUkaZTISqVsaOivEWbPfhY9tHJgMRLxNIz0pXHmXYTRbor2r2lFMwDKsScI/qRSxHwXIUzNKYaPTVvN6UsjK2JdQlVb742TpefHAKt/+uncmtAb74hSF226WLXRd0cdedBX56WR2f/HiEZ54t8sETe5k/X+eY//EkX/72txyz52q0T3SthkxGEgi412ufvQIsWWKRTCqccEKIxQeESCYcui78Jbm/3kv9Tj3ItjyyLY8dcSrZXFKDXL0gX1ctLeCO3KQouUlRrEQAqavYzXVgGIhIGLWxwd11R22ctSXxEZ9IfPgYI1SRCEB0uRvj0HMSLetKpw+HE3Ao5DyNrf7CKKZLFWJKjgFnpObUtob9Foe49Zo21j4/le98tYEZUzWWLLH4/sVD7LlPF585o59jjglx6aWJimT9Sy+bXPTdFKd/3nPh/f2OHIeWYivFIpR1GRcvNijkHAxdMGe3CP23P8Yb51+Pk3WtOSXhJSKYUVm7Yh5l0jMT3ndgJ6vSgktksqNie8ja8tV/fYwfVJFIdcaWFXQnyWCfJF8vKmRS1pNScwpOXGA77roqohcpOipTo72YUiWuuRNjTPHSvjJSJ7iVakneLuIxlTM+VsfppyV55fUiS5cV+f5P+hlMWyiK5NlnTdJpyW2357jnnjzf/mGCPfdyiWPlcos/35Tj7jvcpmF33pVjr73dNN1CQaJqgikzdSbtHKGn06L3tVV0fv0ymr55OorWAiVRTGEJiqWujMWEQE+BXgprCcdbz2qJUgqwA2qdV7ujlHqbSNve8ayTbcvoHRW+ReJjfMAZ/W5LTx7durCrxHudgPtaMzdyXVUmkReyHaw161hr1mGWmpr0OgF6nQC57YRQhBDsNDvAMUfGePzODn7+gwm8+LTDmWcMcM4XB1ADgr8/2MSRR4ewLMm9d+U47cO9nP/VGNOmanR121xzbYaPftSNp9x1d4Fd9goy2O/QMSNI//oiOx+QoH2ioOvrl5N99gX3fa2S9TbM/WJG3DECG1hZ73AEUsL2ECPxLRIf4wepdKWepDprS8tJrFBV0H0YiWgtnqUhHcFQwd0hnvQC7jnbc7t0W3E69P6at87JAiGx/SgyCCE4aN8wB+0bxnEkV149wBW/GeChf/RQV6/Q3ekwZYrGjy+u47BDgjzzXJGzv9jPyaeEmTNX5+WXTR59pMiHPlfPby8fZOL0AIGwwpw94ix/Lc/aN7KkbrgB28wT3besmjT6unVwukJiqcse6XYDY8j9u5ioJ/ZyD7DjkohLvtu+SeITiY/xg2i4ot9UXtWW/cZ6RjIwozSRle5Lx3CD8Nb6ENoEj0xM27U4nuydxKSoRxhhxbM88lUKhqYsEBAaBWkSENtfW1lFEZz9qTo+94kkTz6b57JfDXDPigzSllx3Y4bvXzJI/4DkrLMjnPiREPfem+dr5w7x5e/W89vLBjj0Q/U8fvcQux5ch2VKAmGFmXvEaZ0c4OG/3IpQi0T22hczXpXiOyzGlJmgoKdBK0iKccUjkzbXvWUM1lf23dEaZI2bOhIfPrZ5REcX+oyucB3xZlig5byeGuDGRsoYza01P7mu8ndcy9M3TMgQIK64JDIeoCiCxbuHuPGqVlY9O40vfKKeNasdXlti0TFJ4Z8PF9l/vx4u/VGGj3w2yV9vypDNKxz0gTruvLaHQ06ewNP39TNrjwShqEbH7BBaQGHoz/dSWPqK90YbmBjNKOQaSnGUuEIxXiZ+iWyoq+ynzp31Tl2CbRN+1pYPH1sfdksSLVNEyxRJT4pgBd2mVnbAHcIpK9W6ZKLmFJAC21KIhAsEdZOgbvJs30SKjkbR0egpRgkKk6wTIOsEMISNIWzyUqPfKZCVJtlxJCUXjSicdHycp+6ZzON3dtDTKbnv3jzpLHR12vz52jQ77ZvggGPruPCjyzj2rHaGek161hZYcFA9q15N0zI5xMTZUWb8z0z6f/1nzOAQdr05oiulVlVLalZxtXAg3RGoZHPJhjpkQx12IoQ2feoOIfJYrmzf1mMkPpH4GBeQqTQyla7xJ9shvSY2og0X9S2t4hxDohieD34oP7KNbkQt0md7keFex/s78A7JyG8r2HXnIK89MpX/u7CRQt4hXwBFF/z5V93cd8sgp100lUhc5xdffpOP/3AWbz49hGNLZu0eJZe2adm9FadQoPOSXyMtC0I2+SanEmw3o94AKMZcaZUyCgkVOxGqjDJEc+PWvhRbH1IinM0f7xbG9x3gY4eB3dcPjkQOpVHX9mEHtdJNWJVjL906knL/DCcgKxlb0hZI25u8hvJB5iS6iKhFIqpbD1FwdNaZSVJOkLyjs8ZKMuBsvOZkPOHsT9bx4gOTaa5X6VxdZNrCGLEGnd9duJy7/7CeM382j4a2ANec9zonnDORnjVFOpfnaN6llUhbAmEXGLz7QcipOBEbs8nEbDKxorIywCOVbKPACrgj2xYk2xYkN8Elebsuil030tU4LuG7tnz42DpQkyVJjahrKWgZEy1jVtq+llGWNw/2gJYRaBnX7eXk3TiH4wgM1cZQbZ7raaczH6MzH8OUClE1T1T1MrkipeB7py3ptCUF6WBKqzLGI6Z06Lz+yGSOOjzAa/8eJJdzOPqMDt535iQeu62T753wHMec3s4eR9Zz049XM/W9M1ENlXxflvqj9yB196MUu9+qOaYV9b4jJwBmzB1QK61STpwQVqk2xXFQk0lvjFNXl+/a8uFjK0FEwohI2BOvcxx3QIVMFGv0KmA7aiMckFmNoD46AfQVagseVCHJS6M0VPJSZb2tUZBWZaSd/KjH2t6haYLrf9nMTy+u4/V/D/LAtWu57+rVNLXq/PCuBczdM8ZPz1nKihUw/9OLWPefNajxCPG95yAE9Fz+e+zBFEKRCMWd/cpkYsacyiiUPFfFWO331bN7DOEMKzYZr+nBErdGanPHu4TxkW7iY4eHHHK1ykW8dlVanoCMtEMxrqLlwQrWPldBwKGrM0Eo7hFAU5Ui7pqCmznUqG+etHlUGRlrGU84+YNRZk7TOObUXnrXmeTygifuHmCg22Tq/8zmwHN3wzFtnvnJf6g/Zj+wbJSAhiIdur57ORN/fDZaNAQxKGZ1rCiIAS99Wk+5FgpAZoIn2igkFOtdl6KudQCgdrkKxCKXR1rjJ+kB8CvbffjY2igTCoATqmrvWh10z7ujBoGR5dQBw7NOwpo3OfWYUXqtkf75iPD2Hy8pwZvCnrsH+fONdVi2xK6vZ/rHF/M/t53E/E/sxqoHl3HXJ/9KYMFMkgctYPCxV0nu0kHD4ikkmzVWn3M5heXrK8cSw3wz5iiequHum/Rk11K0m+PYzfHxRyL4ri0fPrYa7FTKc2tZVoVErJCokEjZpeXo7qh2cZWD7dIWmKaKpjrYtkLGNNAUh6KjkrECBBSLmaFO8lJnjVnHGrOOASeEiiQvVdLSQhViq7TgfTdhSqcyonNinH1ugs7nOnn8+49yy5HXcfOh1/LyHStoPPVwJnzicOxUjr6//oeJ718IEqbs20ZAtVn3rV+Te2M14VieULSA3ppFb80iYy4pl+MlwyfKQp1Goa4U1zLUytCmT60Mxks23Rj3IxFCHCmEWCKEeFMI8bVRnv+SEOIVIcQLQoh/CCEmb+qY4+RK+/DhQkTcwkQ1lQcp0dMOwgZRmtftUVRMjB4Nkavtd5HLey6WwYLnouor1sZKygH3XscriBx0xt+qeDjiSm222mGnNDN1moLe2sDki05h3o1fZfK3TyW+eDbZN9ay7BvX0nLwbGJzJtD/zApmHNaBWXA47LQ2er53Nenr/4q0PavQiBZr4iXpiVCIVw+FQlxBLW5g8rRK1uE4IJOxtEiEECpwJXAUMA/4iBBi3rDdngUWSSkX4LZOv2RTx90x7G8fOwTKJALghHSE7RDsylFocIkg0zpyUnGqFE1ETkWGPEsil9cpBN1bJKoXyNhGJRUYXBJJ2UFSaoh6Jc2QE2DIgel6lkHHJKC6B1fG6XotroQYcnKknCCKApf8oZ0PLl7GyotuINDegFYXpdjZj5POM+nDi2g7ZgGd975CrCVM44wkjiXZ54RWHvzDaow3X6fr+11MvehkFNW9Xt3UqhWYcdDdUEiloDTcA4WGAIHeKuFMa1jChFA86ZztDWOf1rsn8KaU8i0AIcSNwPuBivSAlPLBqv2fAE7Z1EF9IvExLqCEQjjlgPu0DpRMASfimh9WyJ2YqnuR2AG3nkS1oNDkgO5ONGrQQtPcv+ui7gvCukse9br7+NVMGx3BPtJV6o99TpR6pdTPverOH68kUkZcCXFYyOK5Yh4i8MPL4nz1nCFa9p9KoDlGoD5CYn47CEHXA6+x7Kp/ctyVB7DqyU4aJoVITgjQPjvCsWe0cvU3lvHyaT9hyteOJza/A6PdLXkvpAIU4yCGNMw4RJd71zSfVEr/hxAO1GU9a1DLe+RidXZtpSsytnAr27eISRqFEE9VPb5KSnlV1eN2YFXV49XA4o0c75PAXZt6U59IfIwrKNFIZRqvJpNq2AHINYNSrHpdXsEJloQC8xpG0KoQSBl9ZrhCJmVUCzlWkwmMfxKpRptqsdbWOPCwECeeZXDjT54g3JYgseskeh55g74n3iIYVTn2ZwfQODPJ7Z97kAM/0ooQAtuUBEIq7/t0G4//vY9l376Rts8eib6PqxociBUopNzvUUgollq6GwPe+5djXYWmMIHuYd3LrO08XrVlxlSPlHLRRp4frSPbqEwlhDgFWAQcuKk39YnEx7iANMvl6hJR1QPcTAQwUjZWWB0RH3GM2sdKXoEoOLaCWRz91ugzXXfL7LAn6Jiv8o8FNYv1doCkUqTfcSe0hBIc96QSU3SmlWbzs043yK0Lcf8/sjQGs0Qag+x9zJ5M2LmBbE+ee89/jJBus8/xrQx0FuhclmXSnBB1zTp3/mYdJ104nZu+ezf1jz7N1POOZVBpwBwc2bikmIRQz8hzKTS535EWaUfvcq3U8rdp9fa+I5//ncQWWiSbwmqgo+rxRGDtiPcU4lDgfOBAKTfdcGd8/7p97HCwB4cqfzthjym0rI2elpURf8uTT8EWSAV39AVQS26u3myE3myEgq1RtDU04VTG6mI9eamTl1UkonhulYEqlhocp4WJ1TCrYhBNap6vfKuO4483ePHmN1j20CpevX0pd37xn1x3wp20ToAzf7ETmqFw71Ur2OvoBkJRj7gXHdVMIKQwqTHPy+fdQr0+iD4xUxmFjiJmXGLGJanJgtRkQSFRGnUqhTqVYsxNnjCbY5jNXh6x1rCdte3dEnmUzeObJ4GZQoipQggDOBG4o3oHIcSuwK+AY6SUm+UT9C0SH+MD1cHUUvaPksoTTOXJzHYnj1CvTb7Oy87SU2CFINAnMKMCJ+S+zi5Lypfmn3QhQDheu5LNWAF0YZMoBV4G7DBhWaBD884j5dgkFfdYQz2AO4MAACAASURBVE5uRKbTeELtZ8tRb9j86KtRprXaXHzxALMXhpi8Xx07/WgGoZhGLmVx50+X8tqjfVxwo5s09PzDA0zeOYaqCWbtmWC3/YL0dQ0wcNtj7HLCPFZY7e7hIzm6Bppq3r/s7tLy7to43GVRqPNIXp3YgrJ+FPNlm8fmp/Vu1tGktIQQZwH3ACpwtZTyZSHERcBTUso7gEuBKPAnIQTASinlMRs7rk8kPsYVlHBtpo8MGWgZ10duRTwSsQ3XVazlXDIBUHJKhUwAbFvBKEmmrM95q9qkkSeu5RkoubliSr4mVlKNAceqkMmOBgXBR06OkEyqXHjhel58oIfXHu0jn7FZ8ng/8/dN8M0b5hGr1ynkbO77QycnfmsmAI4lURTBsZ9o5MrznueVP75A3eLptHx4P0KTGtEmuoH4bCyA1u9e3+HNsirnUQqROBMat0syGWtVXynl34G/D9t2QdXfh27pMX3Xlo9xgzKJOGvc+IUMGZh1oYrEtp5yScE2RE3fCzvkTjaKDU7CAkWCIkn1j94sa6DoZWuVyaTcp2SNlayMyj7O+BRwHA1lfTGlKqZ7zPuC/OPhBqKGRd/KDLvuH+MHdy7grCtmEm/QGegqctnprzN5YZzZi5MU8zavPT7ArF0izNk94rYCfmAnDto1zevnXkvqlTWVYwcSBeyogx11UExITXZHMaZWRj6pgqa4w3HQmhrRmhq3D5FHWasPt6nxbmHHXCr5GHeQto2dSlXIRBrqqEJ+jlZe4QmEXVugaBsjV37pXABNdVhdTNIc8+RXco5OqBQTKfcpqVfdVXJQuNsLUqCXqsQUBGknP+71t4Z/Pl24VmBjDB74WxPnfHWQP353BUueSpFs1lm/vMCrTwyy/4dbef85UxFC8K+b1zFtfpjWKQF61xfRNEGsTuPYz05g8uwgP/naLcz9zedQg67rqsdUcQa9mFR5Qs3Xu2QW7JPkJ7i/i/BK9zmZ32T8eNvBdtCz3bdIfIwrONnsqNvtgIIdUNBztTelWppPKiSSV91RgqaOXOYFNatCItXosyMVEgEqJFKN8aoIvCnoQiUZU/ndL+q5+Acxnrmvj94um3kHNvC9fyzmuC9Pw7ElD123hvt+vYpPXdAGwON3D7LTXp7lsOt7kkycpPDsCT9i5Q/+xNBLbkmEkijWrMoHZnkWUZlQytiuSAS2i34kvkXiY9zByWbRSj0rlKKNFdGrelmAWlseMsISkaoEs1TolvNWumkjQDRQIG9prM/HSRhuy8VsyawJqwVWmQ00aG4tSUZ6hBSr0t7aQdoxARAQXsC7HJD//IkhprUYnHluP6//Z5AVL6SwTIeX/9XPxOkBLrpuOm1Tg6QHLf72u27OvGRqzTEPO6mZhsYedt0nxXUX30z4sD2o//ChsKBIpj+EMlhSFChWkUnS/S6MmRMB0Nb2u0+kUrxdSCnpp5vVvMUQfW/7eMMxxum/7wh8i8THuIHQ9MqohpbxrAQ7UBJwLOlvSQF6RqBY7tD7VbeeZBhU3SWCdMHzhQ0WQwwWQ4TVAmHVW+X2Wq468IA90o2l+7ccAIe8J8Qrj7WwcIbgzX/3MrFd4aJrp3PRH2fQPi3I0peyXHjKUhYfUcfcPWpjGZG4ilmUvP+0eq68fRLZe55g1dd/g9k9CICTqLUWxSj1iFZb3Zh8Dkc6vMJTvMYz1NHEbhwwJsetwRiLNr4T8C0SH+MCw8mDdBaiYbSV3ViTmtAyJvmmAEqpWDHcI0lPcCd1YXvyKeXawupK9zKJlN1ceav2vbJVgZZ8SV/LEBZLiq3MNtaRLHVVLJNIThYIiVHUI3cw6KrGHde18P2f9fPrq7t5/uFBgjGNoT6L9KDN+z7VwuEnN4943arXs7S0u1NXU6vOZ77ezPW/7GPluVfR+r0z0BsTEC1CT7SGRBSrdqK12upg+Yq39RmW8hIFcizmUNRy64CxnM8lW1rZ/q7AXx75GBeQlom0baRto06eCMEAWDZ2ewPCdhCl2hIt66Bly50T3VFGmUSELZACREEBIbEtBdtSyBQMMgXX1ZW2DNKWgabYDJjhSvYWuCRSOSYKfXYYU0qy0iYr3Zktt+li4XGNkAgQEDphzeCic5pY8dRkrvh6kvT6HLsenOSKf+zMEae0UKpjqMC2JA/c1MMRH/Sy4g44Ok7/ugL7H9dA5zd+TvblZQDkOiyyUyyKCUhNFgxOV+jdOUyhwaiMsuT8iIXIZsCURdawjJ3Y0yORMYZAIuTmj3cLmyQSIcRsIcRzVWNICHHOBvbdQwhhCyFOqNo2SQhxrxDi1ZLG/ZSxO30fPkZBOjNikx2svdG1ql7u5f4klcead0PahZETRJlMAAaKXiHegBmuIZFqDIxS45CThR2eUMowDMFhB0S5+LsJHrqpi9Vv5kbsY1uSq85fzpRZBrMXetfdCChEEhp7HDMBWTTpvux6Uk+/UXneqvO+k3xVYbtS8H4D6uSJW3zOXayhnhYC4h3OxBsPri0p5RJgF6ho2a8Bbh2+X+m5i3ErJqvxB+B7Usr7hBBRtgtDzcd2iXJ1u2XDgCuVYrd7K1dHr103KaZ744U6BWbMIxM15036dswlEzVgYVpuwLYpnkarStrPVbHQM+nJTDCGqC8F3LvsWOX/nQy3G6AurIqby8TZ4d1c1Tpkxx1YT+HbOmecvIQ9j0iy+L0NBCMKS1/McP/13TS3qnzjyvaa12fTNulBi7q2ILP3riM5LcljP72RxnMjBGeUZaW8qS7dqqEWJKCi5dzfh/jXs1t83gVyhLdG6sR2EGzfUnvsEGCplHI0x+LZwJ+BPcobSg1TNCnlfQBSys1rdu3Dx1ggFEQbLOBEXAtCy5Yq3MO1Tay0nKRQP9JikDpIy91uo0JV693BYpCEUZvKayje831WtEImxrBob3XAvUEZKUa4o+PE42IctF+I314/xO+/t5y+IYed9wxz9rebWbBXeIS764Hbh5izdx2hqIZmKDRMitA2J8a6S35L9JC9iH/wCHItDoE+97q7JPL2oWGQZmDTO74djNMYyYnADcM3CiHageOAXw57ahYwIIT4ixDiWSHEpSXLZQSEEJ8RQjwlhHjKxDf3fbwNhILuAKSuIoo2omjj6AJHFyimgxkRlWr2bItAWFSGYgoUU6BmBaKgIARgK5imimmq5Io6qnBImwZp0yBlBkmZQXoLUVJmVY8SK4opNTJOgIwTIFUScjRxauIlPkaipUnj61+o5/G7JhDSYPf9IyzcOzKCRFa+WeDaK3o46BOTcWzJW88M0jwjzvwj25l/cBPqy89TvP9+9NYs+RabfItNapIgNUmQbREU6nQKdTr2QbujtU7YonNspo1u1mLJd1a5YFzESMooKUUeA/xplKcvB86VcsSdoQH7A1/BtVSmAR8b7fhSyquklIuklIt0dmxT38fbgxwYdKvabRsl5y1KtNzoSzt1AzWCshwrqUoHVkuijNVpwGWULZKsY2BKlUY9xYDtBeEz0mC9HaW76i4pSLMyzHd4QtoeUR8JcPd1bVx/WRffOXM1Lz+VJTVgs/qtAtf8XzdfOXEl7//fGUzbNcEL/+gm0hCgdW4C1VAQiuDkS3em+6Z/0XP59cjM8spx/7+9M4+zpKgT/DfyeGfdVV3VTXfTDU1zNPd9uYgHCqgDMoyCjLoci4Iun3HXdWEdZ1ZZ3FHW8R4FGQfGC1kY1EUZBEQEQW6bQ6TpppumgD7qfndesX9k5Mt8Va+qu6lqqroqvp9PfF6+eJn5Ml68jF/G74qEtzZ2Ie6QXREmGZGji17W8wxydw7ie4CNZFdmJGcAT0optzb57BjgZiHEJuBc4J+EEGcT5r5/Skr5kpTSA34GHDXNa9ZodogshGolUfOmFCa+HT7hRsIkyv4urXE3ZdWoC5GIpDBJqrXscaqspDCxVWBiZHzf6IXRkUbT9YY0AGsOSPPsb1aQ913+/j/185FTNvCZv+5nayHNFT88mmP/YgnrHxvh5s+v47RPhZmEX35ikMWrW1iyfytLD8hz5LLtbLv6Bvxtz08qRCJ2RZgcxNGMMcTTPMyIHJx5gSIlBMHOl1liV2wk59NErQUgpayHngohbgTukFL+TKmxOoUQi6SU24G3A483O4dGM1NIlWNLjoxCbxcAwvURKo7ALvjYBZ/CihSGLzH8cBA3ShA5ZAllGzHc8DVIS5yyjWFJnFSsnR0q57CM8AbuUDaTQTe0e4x5GQ7IhQb2brOAK01cTDJq5uGq6xwIqrSrCHB7N7mR7ul0dpj86qZl/J/vjPC5a4fY+/AO+lblef7BIW7+/Dq2bihzzhePYuWxPYxtrfDCb7dw1n8PgwN7921h9WEWJ53ZyZc/fiPFd7+DlredSLbYwfAB4cNAa79HqrLr6WssYXO0fCv9vMRzPIqzO9Tye4CNZKf+tUKIHHAa8LFE3ccBpJTj7SJ1pJS+EOLTwL0iVG4+AXxvWles0ewkIptBlKoE+YnumUHKwKpIvOzEmYBZY8JqikZNINonPm2mzPiJdsTJ1IXJ+FnJoN9Ktxmm40gx8Sl4VLp1YaJpjhCC/3Z5J0sO8PjYpQNUqtC2OMsRf7kPB71zCZZtMLqlwo8uf4S3XrgP+Q4V8zPkkG1Jc9jJbaw5Nk/x5Yfp/5+/p+OTF5FnXwAKyyzyixcht2zf5esyhcUK9mdvuRoPl/sb14mafrvni9eWlLIMdI+raypApJT/cdz7u4HD3uD1aTS7TFCpIEwT6TiYXZ0YJfWk2ZUlSMXaXMODVEHitAqCcXdCPZljwqkq8BqFTiREjERyxpTpMeqGMQ49qSKv1jpJGx4dZplB5Qq82Gye32lUug1Pn/M9U/Ab5ZS3Zrj9pjQXXDbI2LYc3StaeOr2zWx+coh1D2zl1Iv35dSLQyVJYXuNTU8Oc8RXQ5fhU97fzR9+NcxfXrqIr33mOrLn/meyfWEMSZBLwb5LMbYOw0jojSUsG6MtdvGVatZidHbgb91Wn/1CKOhsUjOfPHG+CBKNZo8lCCAVPumbVQ9pTnzqzw4EOK2hgDF8qCXSMAlP1O0lQc3ESPuMjeSwsx4VIJt2KDppWlKNKo2U4dFi1kgnDPARr/rtdBhxSH0mCAejJWZ8O5pC20wmo8/M0HcybHxiKYe/7VWev7ufvv1aWXFoK+//7IHk2sM+DnzJHV/6E285q4ucWnq3pc2kWg447rR2TjhtlN//+Ot0HHI0S484NxQgCaJod5HLIRNZpY3OODZJmGaDMJlxJDDDC1vtDrQg0cxL6ikvUhMTOHr5uM5X43uUh8kogtMaD+Ljje6RMBFqFlJ1bFKmz5AXGtS70uGAU/VtXir3cFDLlobjI5XXSJBrECbJlPNaiExNWtjUpIttC267uYuzPzAMfp7VJ/aQa7eRUrLpyWHuu34DpufwkWtX1Y99+YUKvcvCTj/zwz386YkKHdnNvHL/jfS1nwWbwkWzkilTyofs1fD90o76ZzmZXz2BME2Ckw57Q0GNO2Z2vbF2Fi1INPMOI51QCXkJ1YMfEKRMzKpHeXG4T31ZkcSS45EHl5+Jje4AwgtnLdIzINeYYTZK6DimVk9sU7aS7U6ozkpmB84ZNVLCZ7vfxr72AA4Gjkykmh83biz0yPdmROnpj1jezu/+n8W3byjyrQ89jGkLpIS2LovTL+jhtPOXY6fDfvM9yT0/GeBvvroCgCUrUhQGHT5x+xF87b338/iWn7Cy+3jEtiFae1eSsZoHi0Yp6TueL8KRa+r1Vl8v3tZtM99YLUg0mjefoKb02OkMlMqQV0vwJrytrGo48HuZ2GYS2I0zAbMKZlXgZ8atV2IFuDULOx2qrZotfjXmZOrCxDJiYWYLvyHSfcTP1LMDF5T7ZqvR6JWvswVPzd49eT73GZNT35LmgksHueIb+3LIia0NwYu+J7nusy+zdFWG1YeH/4fhbR7ZVota0aOtJ0f/a5t5oTiEKWzKr/6W1tQi0lmB83gFwzDJt62hb+mJwKKG7w+y8TBq9fVC4yR0+mhBotHMHkGtiuF5MDoG6eYDsVUN8DIGZi1cmwTU2u1K5ggfrJJAmhCFgwhLIgR4Tnj7eL5B2UlhmT5d4+zjI8rwXvAyHNrSD8AWt73++RbaOTAdrjGfUSlVRpRO3BUuLZE7sNZ2TUmLkeGMt2S44VqLS67YyKH/oY1Tzu4i22ry0rNlfv2jARbtZfPpb66sC5h7bx1i/1P6+M65D9M2tJiTOYG0CPvLw6XfeYl+nufKq7tYtJfN3T9fy7/f+iCp1In0nfx+RvdrpWVzfA2963ZDw7SNRKOZIyghYo1W8fPhdpSwMUgJrFqAnzaxqhIvI+pCJCJa6NBwBIFaTdGvmZjpcGbh+CaW8uAquGla7VCNNeZk6EqXSZvhzOVVZcVflYnVH0k34UJg0Wo0RrcXpRcLE80OOec9LZxyQparvzrIN//rJnqWpli2KsPHrl7GmmPjFCsbni3zm9uGWLxfF51Dy1jlH9ogrC1hs5IDyLhZvvu/13Lzg0s59OgMH7iojcvOeYjNTzxF7zkXw94r68JE9igj/IzOSGScjHQOo9cj0cxvkjMRL8AcrWCOhinKg9TEx3yrKus5uPwMGA6YlbBA4/Ktfq1p2jgKbvydnjQoeSlKXoqKHxYIBUjS8A7witfFa14Lr3ktbPHiaPgaPoNBqZ5KRTM1Pd0mX/9fvXz57zoY2+aw6pAsKw/MIIRgdNDltu9s5eqLNvLOvzmQzWuHWeEdOOm5+uRynDGbJx8K/wDLVtpcdW0vvR0O2370bYYevI/C0rAf+0/vof/0nplv0DxLkaLR7Hm4blwAmbKQKQuz6iNcWS+GF5fQNjLxVFKo4okwK7AEv2pSKqcpldO4nslAIY/jWQxW8ozUsnXje5I/V5ZQDlKUg1R9fffIi8sgwFDBJCUJ1cTgUJTOhHNpJufS87u446a9eObOIS487jkuOPwZLnvb86x7yeCjN5yAlTLptnuxpggEFULQWV7BH34Tr49y/Kk5nIrkQ5d34jx+J/3fvZoxBjBrjTm8ZoRItbWzZZbQc2bNvEaWyghlbMcA4cWqI6vi42UbZxXjDe5JrGL43OXkfIQ58aZ1HIuUSjVvGrE6IjK811TUYy2wGPVztJtlRvwcHaZyGZaNt6PJxAW6itKpeyxpdsxxR2Z44PalnHpeP8VcO+/73KG0LgqFe//TIxh+81llElNa1BLrbJmmYO/9UuxzQJpUSnDCuzLc/+N/oP3gt7L4hPfNfCO0sV2jmV2k6yHVIleiJw4kM9xwoE+pV0e5dIazksZEjhCquSC0l9iDNn4uPC7IBPiuiaEES62awk87+IFJPuVQclPk7XAmUfDStFrhI2s1sKkGodG9oE6+Ot2oXC8owVMIoD2ZFJIKWZ2ba6dJpw1+/sMeLvzkMF85/T72O7mXlp40r6wdYbTqIpET0tMnqWQHWHFA4+88OuTT0mZyxEk5Vh6UZ/Akhz8/ej/r1v1h5huwBwgSrdrSLAhEJo0oNdc7RHm3psKsxkb3JEbVqAuRySi5cVR7wZvcjXcsyI57P/m+FelS0faSnSaXNbj5hi6OPS7N8HYXa3kvB198JHaPyTCT59eqyQrbgy286+w4TcqmFx0Gt3kceES2bgd/90cW0704hfBmWLclZX1JhJ0qs4QWJJp5jcikEZl4QBalWl2gBCmjnntLGuHKeWZNYjphmfK8vkD4omlepbKTouyk2F5soerZVD2bspeql63VVl6vtVNUmSGThvexIFsXKGWZYovfyhaVo6sZ0brveu33HVPB58rvLGflEpe133+a4XVDrDxjBc9Zj1KSYxP2d2SNP2Uf5AMXt9OmZqyeK/nWNQO854IOpIQnf19i1eEtrFyTY+B1l1PO6pxwnmmzBxjb9bxYM6/xR0cxsuHA3GAf2TJC0JHHz9m4Lc1tDplhidMWqjykAZYyWQRp8LPxTeu78fOYnYm/ozUTDu5JewlQTztf8DJsddtpt0IbiZ14oiwHaRZb8TKuW5X6q9VwCAKvHrQYLdtr6GfCKRlVs7cjO8b4x+u7efrxKp//9DoKYwE9ffDElntZZCymy12GwGCQLWwzN3Pm2S1c/OkOpJSsfaTKP39tiHybxXkf7+GuW0dZvDLDstVZtvfXSGUEx7yjnbt+PDizF78HqLa0INHMe4JKpS5MgIb8W2bZxc9YGIkFjpxWI84GrO5h4YfZggGEpCH9fOCEg7hIKXvLJEZ3iIVIxOu10E4SCRMAU0weN1AIUmTMKoUgoNUwKCdWVdRG+MnJCINqIh6j7fDlfOXu5dzwu7157taXaO/bzli/w2jtVex0Hn/VEtIDkjv/7WUe/m0J15G0dpq87687OOODHdz7s1H+5SsDXHVT6Dr80B0DHP22NkxzpiNHZ9cba2fRgkSzIAgqlfiZ3XHxF3dN3MeKB4FIaFjK4O4ljO0AZi3c10/Leg4uPANfpUupuCkyKRc3UJ5eKsoxejXI0mJX6U6VqQUW25w2MulGm0erEaq+OswyJRnbWWyVLdiVHmmV7DFvGA3qLZ1ShYaYm0iIPFDZu1435OXpPHARb/nbRawf6qa4LlZLSdWlpbVPM/bzW2jPuRx1co7NGxwueudGWrttrrzxQPY+MMfg6zXu/sE2/sf39uH3v2zMIDxtJEgdkKjRzB1kuVxPB25uGWpYgjcpRJoZ3q1JjO2RQAHAiI8TRpgZOCJpcI8oupl6zAhAIchQCOK4kxG/edLA+jkT7sKlWVxmda5ii6lde7us2L16v65YHSVcgVkN+zV/+GEs/tsvwP77cPfPi5Rcm098fTVfuO0Qlq3O8tivh/jC+c9z1iWLWLQ0xX23Ds18Q3QciUYzh3DVNEMtTiSyGUynjNuRxi6FnwWWQZASSMPAzYl6/i2AhPaJQMkIt0XdvEqIBCUbbBVQWM0ilcdx5AoMsChbIlD5OLY7sTfQiJtjUapAkVCYLEmNUA5SDWlU2oxqg7CxE2qwvFJz2Ri4MnYRDvdbWLd6oAS0LUxc6TPkN29/2bMZKOUZHGkhaE14PUVjciY8T9cVH4I7/sDDt9zDE/cM070kxdAWh77laS753FJWHZrjixdv4O3vzXP7v0403E+LPcBGomckmgWFSDXODGTaIr0lXLEwsOLbwaqEA4hZm3gTJ1dTtCoCo2KAK8KihAhGWByV2HGgkKfq2lRdm2KT2Yk9zi6SXHVxxM+N370p42clC9VFOCCgHDj1MhzUGAvSjAVplluDjPg51lf7WF/tY6AUz/qMlsRvlQnqQiSi670nsOiay3A8wfb+Gse/q53j3tnGg3cM8akznufAgy2efWo3uP8Gwc6XWWJhPaZoFjTNhEhEUoiMx6xJ/LRQ+yWOjzIEe2B6BhjgRYNP4p52HIu2lipeEH9H0U3RogIVmwmRPnu0oS4SJuMN8YYfv2+1SpSCYMI+2QVkhA9obHtRzdIyyuhVVR3YYxcYcCe6VRstLkFx4u9VLafI5BwySxex9Pqr2Pjha7jnlkHaO006eyz6lts8cG+VZWvagN0gTOY4WpBoFgze0DBmS/wEKtxYlWF48QBkuAaBLTDVA2pghckcvayoL4SVXPwqPGji95kZD6mER6UWDk7ZtEvFC7crno0XGLTYCVuN00arVaXo99IZuQUnVFsp36fVTOTrSFBQkq1DNAamefh4Uq0vry50Pnp4jY+led2PPdpe8+KsBpuqcWLF3nyRoWqOvu4xBgvhf8PNiXDxsgSBZ1Iey4IvMEYszNYeGC5QKPkMDzoIUwCCwNkLmFn3X7kH2L+0akuzoPCLifxVpXhANsuhhHDzFkgwHElgNc5AmiXkM6s03EWillgoyzfw3bB4nonnmRRKsX3DUIr4otvoYVXwMg3Co+GzIMNrbme9JCkHNuWgydO0TMSnKFXXfM8ivNGTVKVZLxkRJ7zstOP/wFB1otowGQtUp2xC2cQYCf8QVq4Fz6+xrLSKtNmGsHLYbd0UH3txhluyC8GIOiBRo3kTUG6UfiG0iVi5LFFeRJG2sUZ9pKkWNsqY5LZ4uK2x54+bF5hqPPKTS/NWRN1dVBoG1AyCVFC/r0UqwK1a9QHq9aF2ctkaWduj6tm0pquUPZueTImSlwrtIzb1yPei30tvKjTgRgkeI17zQmHSalboFiVVlyFnxILCFz4FtYxvl2HjKsGS9GrakwMaI5sIxIGH0dA2orIEJNPPFL0MI24oQCJb1EAhj0jYpaSvvCyCKO9a+PtkBsP36c5e8purvMZGDnaOJe1k8Ms+aTI8wC9nrnF7yMJWe+6/R6OZBsI0wZ5cvWNVfaxq/CQfWI0zkmRCR9nkLjKcuFImtoUhEcrDq+I2PscZQtYHtkiIRBT9ienokxT8LGPBxH0C2RgglwxgjAbfPZliUJ20HSPjcpcBbPfa2O7EtpGW1MRpZrTyJdDg0g3xDLX96BMYtUdZzWH8mSd5hkfoZwPrWPsGWrEDZLDzZZbQMxLNgkKYidgCtUYJba2ISjQYNQ4+wbg7xKyBn46W4FWnmSTcQ0SpU/Lh4C0Sg5KUglwq/P5IeIzU4u9utysTcnENB3k6zDIFf+IACeGsZDDI022EFzZeiEQ0CBPfIwDSwiCTmKHM9YDGyB5SlI1qqHIQtzlaMCxiu9fG86W9SCvD+7pCuPZ6S6rGAGEnNgiRBFZZqNfwfb57OemlyxjYvJXjg9MYZZAKRQQmW+mfZutiJCBneEYihDgd+DpgAjdIKf9h3Odp4F+BowkNPh+UUm6a6px6RqJZUEjfj0upPOFze3sRwwkwnAAkmE5cDFdiuBK7mBAIIoyCj4pIlDoVE6TAd018Nxysq45N2bHxA0HFs+sG+IhXSp2UvDQlL1Jvha8bq4vqaefHEwmYwSDPa14bJWlTkjYFadZLWcp6gQbnsgZbiis9XOlN8IKaCmlyhAAABu5JREFUC+zMNVWlhZ3ohI213gmzuq50Gcc3cXyTlmyNwDfiB3sZFlExsQcnCpfUGBx60AW4LSaPGPdRYowWOsjQXMi/YaRs/M/uoOwIIYQJfBs4A1gDnC+EWDNut4uBYSnlfsBXgS/t6LxakGgWNElhIlPxE3lgh7eGWWsctKQZu/1GD/wTVsZTMSThdiK5o6PSpJRCN+Qo8r3iNAoRQ0h6MkW1HX9/Ut21I2EC1AMXmxnhfUTT4TgpTAIiYTN3hMnOXMtIIv1+JEzSRqNzwYCTZ91wOCMZq8W/pWnH5xfVxP8hMZFV5ipMK81Bp1zKAQefw2i6yAvm02zgTzvfmJ1lZlVbxwHrpZQvSSkd4GbgrHH7nAXcpLZvBd4hplqwhTmq2iowPHCPvPXl2b6OGaYHGJjti5gF5m67C+NeZ5652/bdy0JtN4RtXzFTJyswfNc98tZdWQg+I4R4PPH+einl9Yn3S4FXEu/7gePHnaO+j5TSE0KMAt1M0adzUpBIKRfN9jXMNEKIx6WUx8z2dbzZLNR2w8Jt+0JtN9TbvnKmzielPH2mzqVoNrMYb4TZmX0a0KotjUajWTj0A8sT75cBr022jxDCAtqBKbNRakGi0Wg0C4fHgNVCiH2EECngPOAX4/b5BfBRtX0u8Bspp452nJOqrXnK9TveZV6yUNsNC7ftC7XdMMfbrmwenwTuInT//b6U8jkhxBeAx6WUvwD+GfiBEGI94UzkvB2dV+xA0Gg0Go1GMyVataXRaDSaaaEFiUaj0WimhRYkO4EQwhRCPCWEuEO9/5EQ4gUhxLNCiO8LEebkFiHfEEKsF0I8LYQ4KnGOjwohXlTlo4n6o4UQz6hjvhEF/gghuoQQd6v97xZCdI6/rjeD8W1P1H9TCFFMvE8LIX6q2vGIEGJl4rOrVP0LQoh3J+pPV3XrhRBXJur3Ued4UZ1z4kpQu5kmfS6EENcIIdYJIZ4XQlyRqJ/XfS6EeIcQ4kkhxB+FEA8KIfZT9fOtzzepfvmjULEYk/XJfOz3aSGl1GUHBfgvwI+BO9T7Mwl9rQXwE+CyRP2dqv4E4BFV3wW8pF471Xan+uxR4ER1zJ3AGar+y8CVavtK4Etzoe2q7hjgB0AxUXc58F21fR7wU7W9BlgLpIF9gA2ERj5Tbe8LpNQ+a9QxtwDnqe3vRr/vLPf5hYT5hwz1vneh9DmwDjgo0c83ztM+3wT0jKtr2ifzsd+n9dvN9gXM9ULoZ30v8HYSg2ni808B16jt64DzE5+9ACwBzgeuS9Rfp+qWAH9O1Nf3i45V20uAF+ZC29VgcJ+6pqQguQs4UW1bhFGwArgKuGr8fqrclai/ShWhjrVUfcN+s9juR4H9muy7EPr8BeD4RD99cb71ufreTUwUJE37ZL71+3SLVm3tmK8Bn4GJSX5EqNL6MPDvqqpZ+oGlO6jvb1IP0CelfB1AvfZOtyFvgGZt/yTwi+jaEjSkVQCitAq7+pt0AyPqHMn6N5Nm7V4FfFAI8bgQ4k4hxGpVvxD6/BLgV0KIfsL/e5Qtdj71OYTR278WQjwhhLhU1U3WJ/Ot36eFFiRTIIR4L7BNSvnEJLv8E/A7KeUD0SFN9pFvoH7WadZ2IcRewF8B32x2SJO6N9L2Wf1NpujzNFCVYeqP7wHfjw5pcpo5276pmKLtnwLOlFIuA/4F+MfokCan2eP6PMHJUsqjCDPjfkIIccoU++6pbdwtaEEyNScDfyGE2ESYJfPtQogfAggh/h5YRKhPjpgs/cBU9cua1ANsFUIsUd+1BNg2M03aaSa0HXgO2A9Yr+pzIgxagsnTKuzqbzIAdKhzJOvfLCbr837gNrXP7cBhante97kQ4pfA4VLKR9Q+PwVOUtvzpc8BkFK+pl63EfbxcUzeJ/Op36fPbOvW9pQCnEqsM74EeAjIjtvnPTQa4B5V9V3ARkLjW6fa7lKfPab2jQxwZ6r6a2k0wH15LrR9XH3SRvIJGg2vt6jtg2k0vL5EaGex1PY+xIbXg9Ux/5dGw+vls91uQnXORYn6xxZCnxPbPvZX9RcDt823PgfyQGti+yHg9Mn6ZL72+xv+/Wb7AvaUMm5Q8Qi9T/6oyt+pekG4aMwG4BngmMTxFwHrVbkwUX8M8Kw65lvE2Qa6CY2eL6rXrrnQ9nH1SUGSUYPBekLD9L6Jzz6r2vcCylNF1Z9J6BG0Afhson5fdY716pzp2W430AH8UvXrw4RP6Quiz4H3q7atBX4b9e186nP1/WtVeS66tsn6ZL72+xstOkWKRqPRaKaFtpFoNBqNZlpoQaLRaDSaaaEFiUaj0WimhRYkGo1Go5kWWpBoNBqNZlpoQaLRaDSaaaEFiUaj0Wimxf8HV7+h9rs+aQoAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"phs1 0.5 0.40000000000000013\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"phs1 0.5 0.30000000000000016\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"phs1 0.5 0.20000000000000018\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"phs1 0.5 0.1000000000000002\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mq 0.5 1.0\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mq 0.5 0.9\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mq 0.5 0.8\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mq 0.5 0.7000000000000001\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mq 0.5 0.6000000000000001\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mq 0.5 0.5000000000000001\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mq 0.5 0.40000000000000013\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mq 0.5 0.30000000000000016\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mq 0.5 0.20000000000000018\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mq 0.5 0.1000000000000002\n"
]
},
{
"data": {
"image/png": 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pP44wZ2mSujpBXZ2ktdnGdiSB67Plrj3YDVnGVZrFl7yMM/7zHLoe3MWjX3uQN//PcbilgFW39DLSs+8ncKU0d3x3B5WiomRSOLAsjRdonnrK4w1vTfPzqwqseN1s9qwfo7YjTf2iesYHXb53wT10bhhBCHjZm+YhLEGhpJk318b3wbY0N5//Cx79o3Fwd+fSDJZTDJaNldLv1uyzTc8svxITglg4TLavLzinzFDZqEoxFGQYCjLs8RrZ4rayqdLOUJBmKNj/MOJfuWRDRMTfLCYh8cCXg8WMnI8kvrBDd/zrP9BYn6cmYTr02ekxUt4YP3/zHSRq46y4dDkdx7ahAs3uBzrZ+PMNzFsSx6/4NLfZlHMeZxzp893vFmif5bB5i4twBAuObqBzYw634KF8TbIhzsrz57L8dbNZc+Mutj/Ui7QFpRGXd/33ESxYWVP1XYz1V7jpK1vYeP8Q2ldoDTU1UCrDgkU2mXqbDWs8zvnYUh746W6OeOMhbL2/h3mvWkrX77fT+VAn0oZ0U4I5K5vYdHc3tlYsXuSweauHnbQoFTU6ECAE2vcRlkW8pYZDP/0KTj/dyEJLbJx+t4ZjMzuoswp0WGbEcaKsS22oHuVpVYihoK2qiGyqdFS3TRS9nEAKRZ0sUjNlfbNVDs9tURuGBO8rqTEi4m+dFzP898jlMX3TbQceALtkTs9BCf+dsWnM0jb6Ol4O8xjSYxSdWt56/au4/m138tC/P4xfDpCWYN6RWVaemuXpVSM0tTsccVINP/3iDl59fJrDlsTY8LSL5Qguu/oUWpfUcOdPunn0u2toXVpDx/IGxnsL/PgdD9C6MI20JIXxACcT54qPbyLmCGYtTZMfdtm9MRc+ImiEAM+HQsGE+o6VY8SyNSw5TXLbN7dwzKXLkLZktLPIaWctoJJzCboGGe53Gekq4+W6qxWM/cAMbylXoZVN/ctOpuGok3FqG/ALOUbXPcKaT93KrnkJ3n79ufRWTL6JI3wSwmNIpWi2CsSFJiEElpisMDyBBXR7+7Zoeqfkr0yU4R+ghkWxvmnrHaEoBGAxGRJcI/eONouIiHhxCLubGc+MfKTcR5oIAHVOkXjK4W2/PJejX2HqWmml2L1hnO6nxjnzTU20zYlx9T/v4Aufy/Df/5VnaFRRKile98WVtC2tRQjB8osP4+/uvpDciMeam3fy5O96CDzFcG+Fwd1FFp3Sxhu+eQJaShrOPBR9+DJ2bioiWhoJhIUSYFkQj0HFhVxO4+IwOqxwWmo4+4vH0v/UKKt+/DRn/dfZWDEL7StqGyXzl8RJJAWlgqKmTuIFgqe3erS0C3zXZu6b30vbma8jVteIEAInU0PzSWez4B0fZXR3hR+cdRNt8THOr19No5Wv3ptsKAD7C+Uta0FCPHv2en5Kpr2nLfr9SeFxxN6GczwqpxIR8Rdnum/02ZeDxYzsCbQWBJ4Z9y97xhlc50w6eaUl+fhXZ/HEazJ8/zO7GBkMeHLVOF1P5Tnj9BinnWzz+c+Nk4hL9nR5fOjfZvHyCypsKJkyJXHpE08JLv31eVz7wT8xtm4Ps49uomVpLe2HNbBjVT8/u+QPLL3sOBa+cTmbfriKmtNWUFi7DR1otDAziFgSbMs8MdQd3kbXQ3voWj9Kpq2PRect5uhPn04sE4buPrCdQ9okpTJYtsD3oZALyNTbVIoB3d2CuiOPIT1n0T7vSaK5jcYTz2Lwj/fw/YsfJHHdCSxNdNMcislEN5+VEid8PiijsBAUNDxanqwM3elO1hPrcWsZ9ZJkworKUzPts7JMlze57wSH2KUD/zIjIiL+bDQcVIE4UGakkEz0ioPDWTJZMzb/YO9C5teOANCaGGeX28xRp8KvH+7gvjsKXHP5KF1dinvvqVAqaerrJWe+MsabPzuPRNKIUoNtOt2uiskTkZbk8H+9gFWX/oTBzjL5POxYm6Nh+SzO+MkpJJsz7PndFnb86ima3nUuxY07AQ0+BFKQSYEuaXwhSLdneOOvL9rnx+ld3UOhc5ytI5qlR6VobLbo2u0jEGgFqYxNxZU0HHXSs96W+uUnMPDAHexeV+ETh/8OJw7X3tnOnPlGrLJyuoHZIGN0B164rUROTR+G6nEnh7TyQYyM5TLgZmiOmfvU59fQ+oxSLCnh0heUqZPmpxMX+583JiIi4oWzv2roM4mZKSSALoZNy0LMmT4k01euYXGyn11uM3PSI7ziNRle8ZoMCeGjQv+FEAJPS3IqAMywT0pOThI17JqopIF8lrn/einbP/A/VMY95r/uUDLzGuh5YCe77nia8kiFtk9fzMCVd1B73CEM3b4a5fv4gURKTaWikdJn3ZVrEFKw7MLDsBNhocRAsef+3Tz29Qc45/w0qx8usfahPIuWxdm11SOWsHBigiNOqeMPN45gZ589EdJOZQATMqw16AAuPLMHDdTXgVYCIeC8Vyf59tfqSdpx6qTPcKBZ4gzylDdZs2dCRB4fmkPamT55Vl95ckjruPodtNrjrC7N55TU0wBs8epY7IxWxSQiIuIvQ2SRvEgU8nEKxEmmXcbdyezqeFhN99HSAo5NmoS/srZJHMBETxMiApCMe9ASo/3vTqfzivvY86cB7CdzWJkUqfPOICUF/T/8Lan5zRS396EqPiBAKRxbonU4N6UfsOGqNWz48VrmntiOFbPoW9NHTdYkKP7+t3kOOzaJX7TYvMElnrYREvKjPs2z4wjLwhsbxk7uP+zWz4fWQfi7ktK8VBqGhyGV0qgAfnFjkZt/VSKXN6JaXw+nnpngrf/SSJdsxxEBFWXTVXx24QIY8rIMeVmS0mWn10SbPUaHPU5RS2I6IBmVno+I+IuhEXh6/+WaZgoz0tmO2luBS4XJulMZx2V3pbH6vtffOxppIqlu7/UWHcnRvdZbJ5+BALyBUQobdpFfv4O+y29h5Ib7aDpnJVprSk91GVMABcqUja+rFTgOODZ4RR/teuz8/W76HtlNyvYY6irT3xtQ12QxPuBx0suTxJIW6ZYk5YLP/MPTjA26aOUz/MRDz3pbRtY+grAdLAsQJtpLSPNaWKCUeV2pwJGH2WTSAi1gfBxuvbHM21Zu4IoPPoHWepqIFLzY/i4JQHKKJTc1wmtUiUhEIiL+gkxYJJGz/c9AKLBHjQr7tgZLk60r4isjDqOVBHtsU2L9kEQffX4to+F0tofHp08vnJVlAiTDQYpGy5RYWR3MoyFmJo+SaIZGM0gLtAf+WAG0JrmglcT8ZvzxEj0/upukSJOsJCmj0SgUipqsYDyvTehugOncPUhlYMEShz07fBIpgSM1l36wDj/QfPNzQ3iBwKvkSSXgvPfN4gef2sa5r7W57ddPULt0JZmFy/a6J6XePQz96T60V0GFUwnH4wLP1QhAGiOJmAPxODz6hMe82Ta7O33cABJxIzxP/WGQf3vHJo7+5hF0j9USBNMF17HNMGBjukB7epyuUi0dyTEWJ3opqjgNdp6NbhtLnH6y0q9WB46KOkZE/CUQBPt5KJ5JzEghmYY1Gc46mM/QlJkMed1TqueQRB9xMTkD4ahKkg2npJ0o1z51MihX22S6tnPt1/eQ2z5CsWKhZ7VR/9ZzjX3mKQSC0sYuajYKGqnjSF5JQqTQaIbpZx1/BBSFkkZr04EHygwzSRtKedj0RIXFR8Q5/tQkrqv54X+OMD6m8ALAkljC540fn8sDvxzg1Jcn6OiQEOTZc/NV1B55DA0rTyJW14SXG2Nk/SpGVj8ESmFJCAKJE1e4rgYNljRt8LXAUmY64IoPbS2C/kGBqph1ljRiUtjQzdbVBVKLJq0LAM+1q0IyVEjTnjZDaV2lWo7PbJ227zPnio8qBEdEvPiY8Y9ISP48NFglQWW+u9emwbxxOLeljHVxz/ChHJIeqG7vcEbIKdOhTUxDCzDLHmekaPHZd/axaa3LG16XZOkZMfr6A667fgfjX/6OSTREYGFxBMfTJNqmXVsIQSOtLNcnso6HcSsBfhCOdplmIyVYcUjGYfP6Ck+uLRP4EEtaxGtjeG6FugbNae+Yxb3X9LNgnuDc16b47MdHeP3bk9z88yJj6x9jbMPj6CBAWBYI0J6PJRWBEIiY0bxEWuBVwA+L7DiOpFwyQiAlrFrtYVlgCSi7xioJlAmK6//+b+n49/eH+2oq3WmEL8g3G/PYjgWMuyavpClRmHobWOL0k1M2tdIn0Jq4sBkdC/jGFUPcdV+Brt4AISCVFIyNB+SLpgpAEBgha6qXnHV6EoGmqd7hkotqOGLZ859AS6GmZdcrFHllHiKiRMmI/ytEzvYXSHxnDK82rAQ8VoNsmSzZ0VWopSM9Nm3/hPQYCjLEwuS8qUKilOaDF/XQ1ijZtKaNbGayA/r8Z2r56jfG+c738mgliJGgkdb9tquBFuIkKfrGOhKOwBFmiCseE5TLmrICHWgcGxACpaA4WCKVFthS07NumDe+KcbTT3l87hMjnP36NPf9tsiV1zfxrjcNGke6BK0ChAYtQqsnJojZmlRSUsjrqoppGQpL2VgnArO/HfpTJpI8Lct06EFnLwBuXwqhQfiTP1Y7NjmfyTNFZIKJ0vNKaT7x5QGu+mmB00+Kc8h8C9cLWLvJJ/CNFTQhsPE4aB+GRxU33FpAa/A8+J8rxrAt+KcP1fP5Tzy/+dCmismEiABU9MTUxkE0y2PESxatXxpDWzOyhSKY/l7LKaU+LIUVlk/pKkwOzSSkx45SE51uI2XlUFYOOZWoLvffUyY/qrj6isZpIgLgOILPfaaGV77C5EQ00bbPWRir7ROCZmbh2GDZEu1pLGk66UpFgyUoe2ClHKxMHCkF2aygqTVGuagp5gKeWu9y/c8KdHcrHAeeXFPhC9+o5z++nOPlb65DWqCERAsLZdlgCaQDcVsRi0GlrBBaTz6rCIFX8InFJpMk0ea1508IiZjYFQKF22f8SgciIsN+pro8UlpYXf/xzwzzs+vyaAEPP17h5rtKrHnSZ+JyWhhBDDTkC+D7UK6YpeKadk6s++J/jGC1b6Fx2RbeeFkX/QPPHoE3psqMqfI+qxIXJ4QEFZXAj3hJMzFdxIEsB4sZaZEIBcmwkruKCSY6QG8kTmBpYg1lvMA443eONyDF9LIg3V49s5yRaeuu/G6Oj384i+Ps+2YLIfjkx+q4866hA6rqr9GcdmKSVavL5H3TIdbXSUZHFV5gOviajCCWgOScJJWyZnBPmURcctgym3UbPXLjCikFy46MkRuHz39shLPf3oyQUN9oEY8HdHcaRzqCsO2aUlFji3DWlrBEfCotaajVDPQrpAW2BluaztqyhREdYcKBtTbWSv7me8gsOJRsejYA5Ubw/EmRdWSwj09u8BCsf7TE9beUsKTgve9Kc/hhDqMjiquvKbJtu48WRmAVEAOCMLJMh4EJE2I3gQx9PaNj8Os7i/zqjh1oIJkwgvix99Xz+Y83ktN7C0NRBxSVsQwBPC2Airl3KDqDAllpHkCaQgslSqaMmOmYqK0Z+bw/jRkpJFNxxqFaa3CK430ol6YxO33YJWlNOt27vXra7ImquB67dwaccvKzO4NXLo+BUAzoLpbo5fu1SrTW9NPF7gdKNNRLRFmSiAfkcorKFLfO6LCHtASiz8MrK+bNs+nuCdi6S+MkbYT0GOgL6O0JmHNInAWHJbjtqn4OPdxhwQKL8Zzkq9+v5zN/349WEGCEAAVBWDQSC5JJiUXAQD9oZfwQth0mLQJ+IMjGoeJpfN902EEAxXseZIy76bZsmk56Jc1Hn0GlIY5sMENEu/L1zMtMF+Q5saHqPbj0nWOcfkqcH17eQDw+ea/e9c4MN91a5COfGMULNJZpMoIwTDoMEAiUsVRkuF1psGwTASeAQIKloFQyn+VL/znCV789wsUXJvnqlxqw41AsKX54dY6rflJmfFwjJMyeBZdf0UBrmx0KynQGQwulw4qEJGKm89IY2pqRQiICiI9rcrNNJzBNTKYwlEtz2rxtz3quRBjRJaQZQgEzri/l3h2M1hrLAs93GaSHZmbt85xD9OIJFxmTDI9ohDQCUnYlwpIgFVIr/IpGa420QEhBZ4/Cjlnkx3201sTjAiumaW606NleZtdTpsMc6A845uQkQ1rqh0sAACAASURBVAM+//bpQb521SxW/aHEnbeOMT5kHCBSQDxlIVRAMqYpFAAFrmcix9BGUJSUOMJEi2kVhgmH+SfHBqdjE2Mg6ObJB25nfMsmmr52GcozP9yE5ZMMEz8nyssUVJxFzgD33FImk5Fc8f2GfVp5F5yfYvPTHt//YQHP1WgBMRvcUMj8wAwFahWGTofJlRNn8gMzr72ywMHsqxS4Lvz4ZyWuub6Lb13RwKf+YZR5h6W46J/m0tDq0L/H5bYf93P26YPUNtj86K4FxMISOa62WRTOdb/AjtEV5Gi3TAJoVBI/YiYSRW29AOREFNJkpC/aBhULOwRnMoFudf9s5tRMJhj2OJN+kz8Ey1iR2g2Y+ULe++FRnnrKo1xUxJIWlg2JGBx9VJz3XJLEkpp0WlAo+6yvrOIIfRzNzKpaJlprBulhg/UotacvZeSBJ4klwC2Dc9hcdO84MhMj5uUZ21HkZStsPvnBehbMcXjXh/t5eruPGyhkOEyVrZFUKprhfmWSCYFkCvJ5zdrHKyw/Nslb35/iu/8+jJW0+eKvjuWWb23joRsHsSVIFSAl5HMa1zVJiY5jOtvAMk/6jlCmE/apRphJCQ4OMWGGeFqZTUbXsqr3bro+823aPv1O/OFRcmKUHXPrWZCdtEoWOSZC7uv/meOD70/vd6gQ4LJ3ZkwAA6YtlgUpR1IsmjY5NuAbMREahG0spYmAACnN+iCAlhbJyIhGY6wq14UPXDLMa9/TxOLlab7ziR24FWOxWRY0tscY6Ha5YOXT/O+dC2iel5nWtgmrpCcoVMUkImImEkS1tv58YuMB5brpzYuNmhtamihIGwr1nnGTpT0hKBmrMu24+24a5snNmhVvXczffXUh6aYEo3vyrPnFVjbesoNKcwMf+uwQfsHFDzSLT21ldOsIm3Y9iq0dmnQ7AAP04OPRcM4RxJprCebVkds5guUI5r3jVGoOn5ws6qz8nfz0c1v4wCeHUEqjwlIqOoBERuAHmpFhRaVkLBY7FIFy2TioG2bHWL8uoH7A5vWfWsT85Vnu+tEe1t41Qn29wIpJRgYVVkLglRXxJFRK4E64NSb8EMJ0umCsEMc2Q0iH+Ecz1TeXFlnamUd3zy46P/V1nMYaHiyW0a5PenYN7/+tIBk+2WutGRtVHPOyZx8qbGm2aGy0GBgIQ5KFIFAm98aSzwibFuYe+Gpyve+H7QUGhzXveVuWK6/JodGIUBR/9YNBBINks4LAMwmhjgX9XR6WMBbae1+1k2NOS/PZ/13AQJCm2Zo+JNoTFOiwss/6WSIiDgY6nFZ7pjMjhUQEmviIR+2UKTDGFkw2NfZEjPFDzMZRx0Qe2U6AygoUkvHA5BCcnn2SLeuKXPHVPi688iwaF06Oj9XPzXLmp45i4WmzuOUjD/Khn6zkN/+5nd0bxxnrLHDCmWnWPlZmwZEZ9jy+m927fCplSM6pJffoFhKL2ki1ZiBfwB2rsPO67cz+9GJTuwvILVjG638eZqg/vYNbv7qZ3evGUAoqZZgzz2Z4SKECjVsx/gLLAmmB7yt2birSMjfBrKzNH2/q5X8/8iS2I7CEQjoS14WFy+L0j9oEfgXhe3TME3Tt0oiYBXriRxhgO2EhS0zn3KEW0iYmy8pP0K7n0scefNcjMbeBtg9fiLu7n74f3caKFYMEJQ/LhnTKBCeUy88elqC1plzWaAVY4Pna+IzCMazq1Cl6IrpLhG/C7zlmrJEJPvq+On7085xxoId+Hie0Ys48xcGybG67q2ic9q7GiQticY1b0ay6O8+FR67nmscPYzzWSFtY1TgWFvRskFGGfsTMRL0EfCQzv4X7IT482XTbmextNo9PVrjt9uq5+cphjnnnodNEZCrzjm9lyTlz+dGHNvLO/zoSFcBYX4XHHsxz9iXt7Hgix+mnxrBtgahJkWqrwRsvk9/QiZ2wSdfHTYc4PF4VEYCdhclaYE2HN3PZT0/hC6tfxVnvmEUiI+nuCgj8sJMldDIrSNU5NHYkCbSiZ1uRP97Sx5p7hlC+qe+VG9eUi5p4QtLVA+O9JbJpxcoTUmhfMn+B5LzXgPYC8H1EYDpm34PAtVihTmOpOHqf98Jm0vlcfGwrPZ/5PvH5rcz998uIzWrCjgkCZWp35fOaX95c3Od5Jlj9hGeEBGMZObag4mFKyUwMtU3sHNYOU8qs05iaYTLMQ1k832bubIfWZosgmLTglDLLrXe4fPZTNax5cBbtbTbJBJTLGjeQiLDmXaUIFx+9iSBQxAiqIjKViZIvEREzAYXA1dYBLweLGSkkwlfYgwVS20aJD1WID+39z22VINVlml8ZTVAZTbBu81zAiMnG3jpu/fEwD98+yuHnL3jW661800IqhYDVt/dy5FktlEsaX9moQKNdn0cecfF846W2hOncFpy3lL5Ve1Cub8qPrOhgeChTXcCIyeNDc9iUb2dTvp3Npdkc9tHTuOR3F3LJ/W/lysdXcN2TK8nUmpBnARTHFX07imgfMnUOfmCis3wfSiWN5QgqnmA0JyiOeWTTmuZWi42PlyjkFYODmkQ6wflvr+Xu7Us48ZUZXv2eNoSWnMSraRQt+70PYwxXh7ssC8rdI4zecB/SsWl+5zkoyzFVB2xjQd1wY4lt2/ed6xEEmi99ZdxEkIX+mlJFgzLiqSfUQpshN6HBd03UlRAmdDmdmtzvhitMlQEVULVoJkRmor3v/+ggsztsfnt9C0oboYlbCjv8lUsJbgkuPnYzQyrFukoHQypFg+UzrNzqEhExk1DIA14OFjNSSPD3flJs/VOe+s1udUmY4BuSa5PE+yaHvfaM17Hm2q3c/YYrWX9PP9KRJGqevbptti2FBn77rZ00z4vTdGgjA90eP/vSTl71yhjbdwRUAjjk745j9Mk+tID8nhEWnNBCcdhFaci+6uTq+aSt2DHawI5R48zZk59ern2iLHS3V89V/9qFtGze8fkOYzmUA5OMGEBuxFg4HYuS2DGTKe/74JUD3HGXlmaNExP07PbwXOOHOf/Nae6+vcSb3l3PzqcrrH2kRCrjgJDsYf8RblprdoktKCu892F47tidj6I8n+Rh85COheOEFYcxT/yvft0Av7unjJoysfT2HT5vfecQa9a6BJ7J+Nd6MjRZTyTkh5aJViBsowo6tDBs2/h2XA8WL7RYtCDGU1tchkZMgIEIi1SCeS0FrN1g5qOZ3WFz7tkpEx0WGokiJqrVkctjPhe9ohOALq9+2n1IiYP3VBcR8Uy0hkDLA14OFjNTSAAxPBmJJZTGGhgnuXUQABWTpPt8Gjf5NK0Lp88NxWTo9scZ+eUfuPias3n9d89AB5rS6LMPV4x3F7BSMQJps2t9jmxHlnR7FldbXHlVkaIrOPRDZzD88DaaF2WIpWMktMvItmHKxQCvvhlJAjHqQNFGje8tXBNiMnVuAaU0d1wzwPiwz1X/3GU6OgmBZzrGWFyQSEoG9pTJpiQnHRsnFdMmGsuGwV5FYTygVNR0zLGZf4jNHb8u8eUfdjAyGPDpS7p448dmc++1/Sjts5un6ddde7VNa82T4gkqlFC+EQSB6aCTcajs7EUIgZWOG4skdIxrDaOjivd8YJjDjurltW8c4NSz+jjtFX08+HCFQkFXY3pdLxy6CqgKix9m3Dtxge+qsC3ms2lMjsm82bD+gflorfnKf4+gtcZXkyIjw8RMpc25cnnT/tNPjhOPCVRYVHMiZ3UiNWhgZ5FbLt8TfieTy5jafxJmRMRfnwPPao8y25+B9n38vn7o68eea7KusU1Tk1sHCZprKDcnEGGnlwxrNsa7FU/9+B4u+ukrqJ9nfCLtK5pYf/N2jrv00P1eb81NOyGVws5k2fannax43wLG+0oIKSj0FVjwlqPovOFx6locep8cY+FxTXStHUK5AcK2aP/CR6rnEq5AxzSjQxm0KynUTjpve4Zrmds0XH1/3Vt/i9ICKUEqy4z0CB+FGT4qFzUxR5NMCiplxarVFZrabGQBnIRgsMtDWiaUuK83YN6SBCefE+N//mWAkUHFy9/exu+vHzDRWknwSgEb+BNZUU+HWkCMGHkxzh6xFR8f5WiTBagDM8QE2LZAewFBoYw3nMeRoT9Hm3Bj24JiUZPPawaHXNDw3o8kuebKMrbQFMtm2AkdZrJPYSLL3quYDZYEJxZWUw5gzX2zWHpIkvGc4vNfHeJXdxbwA4jbRpSkMGJjW4AlUIEmmTD/TJXKpDNfTbGAQnc+BHDL5X2cenEH9+pDWBrrAaDD3ndtsYiIg4F5oJqxz/tVZqSQTEUNDCGbGwl2Tz5JW45NetQ4eiuzpky09PQTtB1WR8OCScf6Sf+wnJs/8Hvmn9ROy9K9ZwTcfn832/7Qg+cq0nNmk5xTS/eqHuqWtjC8rhs7abPr2tWkMoLejTksS7Dr0QGTcNjQSstnP4QlY0z12wpXYA+HVkntpDXklW06R+qYXT/K01c8xOjTY2htUtUVoa8hMF3dxOheoGBsXGM5pjPs7QpMMmPKIpECt2LhlU0S4qYnynTu9mmcFSeZDbj9yl6UAs9VxBMWXilAoRgXQxTkuBlKkhotFcoLjIgEingCE3MrYXw8oK6tgbH7nkBIgedLlKeMCCgjJlKazjrmgK/hB98qAxrbMqLTUCsolcALNDHH+HxUAPEYlCqwYI6N1pruvoBSBRIxwZIlDv/1/RzDI2PccW8R24ZyxRzvetNFwnEg8DSNTYJYzAjJDbcUqLhhqLEV/jPqwNjgAoQtUG7AJ055jO+tO7H6HXX5aebP+P+KiL8lovDfFwFVKqF2dyJsE1GkfQ+1Z8rkVbNqaVxvOuvent0sOWd6Nnrb8iZaj2ziukvvZeXFS1j++vnVPJInbtjO5ru7ELUZsisPp3Dvn1h22WFsuHojybkNNB/RQqo9S9+GYTpOaKT1yGa2POkTb61BnHAy0rbxhuKgTOFD6QmskunIwoRwgq1ptG16PRtwEw67tmTpuuZP1WEc3xdIJAqNJcDXGh2YIo2BZ44NfEwRxlg4H31FmUgyEaAsUNgoXzM25JMf9U0SpdBoIU0uhquQDihPowPwpY+07LAycfi4rjTCniirAhZQu2Iebs8QQ9feh1S+aaMt8MO5UMIjzXejQWrIxFooqEEsO6BShqFhXa1mXPYE7QsSdCyMM9znsW19kZ1dAW5FV31DbqDZvMNlzQbj+JbSVAnWQKk8KSLSMoZqtsamXPD55380Dwr3P1xmwybPzP0CaFG1QybrewkBUqM8zYePX8UNTxxW/c0MKeOAa5RRomLEwUUjUC+BhMSZL3VT0L73HDtA4O09xv36754GjsUT123l6ovv5jun3cr1//AQOzZ7BFoSW7IIq76WeI3D+h9vYOWnTqPzri0suWAZQSWg5sRD2X5vFwtfsYAF7z4d65TTkfZ0Dda2rorIVKbmRtp5gVWU9PzyWtPpIgl8QYIUAomFRGsLicQKO7mJ6CQ0WHGB75n1ShurpaYlSeviLNL2kRZIoVFK45YV0gqHpTyFH2jiKYf6tjCECo1SgQkrDrPKbcdYEdVSJZakVAzo/NefIbVvypZYxkryNdgpaSyT8N5PRFMtzB6LDsy9SCSMtWTyY8Ata0pFn3MvbeZL1y3l2/cczpEnZUmkw59iWL3gtZc1c/3mFVy3cTkXfLAJf4pvRVqTOTctzRblgs/LVjq89cIMv7y1wJsvGTCJjwFIR6CUhSWUCRJQAisRfnfSTAFQGQ942zEbAaiRkwUhh1Q0zBVx8AmQB7wcLF5SQjJhlQCoYhFVLOI8tL661LQt46nbdu11nJSSN3z7NFSgsebPxZnThq6pg6ZWmj96CXZrM6PX3EZmVoajPn06T/7oUZa88VAS9Qn6Hu+m4dUvwx0tccM/rQOmRB1tS+OMWtUlPgKxMbPERyeXlkc19Zsgu1tTt0VTfOxRBAKJjUJRooBvBwS2IpA+CkUwkYypJxzvoH1jAqhwnhHla4KyR2W4hCUEsbgwxQ59M9yjfYXlCHxfYMVtMlmBrihqMuGHUAGW9JFCYUljDQVe6ATHBAaUn+7E1j6Br4lZxtGtw9wUW4UTVoU1UCYKMbZlDiHhZLBtwex5VtUxH4uZ4azRHo9/uWgLbz9sNU+tyfPJ7y1i7pIEtmUSBH1tcdP3Bnj7kWv56de7eP1l7XzuygXYdjiVsGMsubgNAwMBDXWCTNpi4YouPvyPw5TKpmQMloXGxrF8Ag80Eqc+Bloj0UgpicXMv0BuRHHRivVs6bQZVbq6REQcTEwgiTzg5WDxkhES7XtVi0QVJxPhlDsZ979weBbFvgo7H+re6/iaWWnQGlV28YdzWM2NBMPj9H31Cgp3/J72U+eiNTzxtT9wyGsXs/zdR7HxZxvILF+A05AFAcW12+jcZCbL8kfj7Ot7E2r6+9h4WDesML1T0mh83DCWVpjaIP4Uj/SUopITI08CsByoC91CKoDCqBnKUoFRN21ZpLKQqRUEWiAsi0RaoCo+tvLxKhrPh9oaQWOj4JVn2dXSLWA6+oWLzFASrkfgqdDPoFHsXV1YKRPF5YS5JRPnkdKm4mp6u00pX8s251bhR7PCwpKXf2Inv7u2jws/PAs77NSdGlOZQAi49/ohPvvGp1myMs2Jr6knEMavUq5Ay+nzOP+n55FP1nDXgx7DI4rxnEZJC+FIHCtAex6eCyCxMxbKVSitjHUTaIQlEI6JLCjm4T2nP8nv7zFWSXbmjyhE/J9HEDyP5WDxnEIihFgqhFgzZRkXQnz0GfucIYQYm7LPF6Zse5UQYrMQYqsQ4p/+Eh9Cua5Z1m/m0PQrue1TD7H59p1m2CZk/S+3kjjqCNo/9wFaPnUZySOX4cydBYHPrKObaVlSz7I3LOWCX13Ewlcv5onLH2fL7dtpe8+ryD22hfii2WTOeBl937kTf9REYjk5sAtmSU+JqhUK6p72qHvaI9Xrk+otk+otU7utRLovmFJpNvRYh+P3eqrHYcrTsFKTupJKQLEYhsnGzG6eG1bU9aGxAbJpSSZlccrxNm7BR7umUOToqMILq+/Oapd0zLLZtQfOeXWcFSsc3vH2FEeucKipcUimhIkiU0ZUvIpZrLhFMm7Ew/fDMN7AvPcCsOM2XlCm6I6SiAviCTGl6jLU18vqRxRhNNfVX+lmztIEnmuG60S+QGDHJm9BxeeKf+7k9NfVk0haOLY5tvu+Xdz6rtt4/U9exWUPXMwp/3QsOKD9AO2pUEDATjtYSYkOzDaJQDoWrUuypvLwhLABKHjvu0fYtq5Ck5V6YT/MiIgXyEvFInlOZ7vWejOwEkAIYQFdwM372PUBrfV5U1eE+38XeCXQCTwqhPiV1nrTC234vgh0wPDAZhTw+688xv3fWM28Y5vRgWbrI0PUve18AGIdrcQ6WvFHximtfYquVd10/6mHxiNa2XzrFoY39VNz7GIWfPVS7JoU/Tc+TPasE5AJh/x9j+Nt6CIxdy5urSY2JoiHKS9ODhIjRrwmKhgra/pTQuy2x1BVwZjI2TBOEBuHIKy2qKphYBrpgBv6WixrsgO2w7+eMp260jA2HCbsoRgbMaHFxeLkPB+xmMmpyOU12RpNZ6dPR4dFMgEP/dFl1hyL9Ws9MrWSRFpwyFxBd5dHV2/o9C4GuHoydBdh5oL37CTCEQjXZXdhHZYlETIwpVl8yNRI/LJiZESRzUpKBWMVyNDSuvnyHqRlRBGp0SpAh072waGAofvGOOHcOmxbUNGAlCil0IHip2ddzyX3voWWI5qIxR1cz0PYwkwVICU6fKDQCqQUBErgxATFUc9YbUiEUFPmIYbXvGGM/nU11IbWX1RmPuJg8VKYs/35/necBWzTWu/tiNg3xwFbtdbbtdYucB1w/vO85gExoLv5PbfSyTYUAZW8T2nUZccDXez+Yzda62oFwKBSofMjX6Hr41/B6+xDKRvZWItceQTJc05i8ff+gY6PvQGEYNfXb4JEiszJy1HFMrHaRkZuv6163fhk3mRVRKYyISgATvfEzpPrJjoogcTDRRGEocC6ut+EFgkmo5a0MhaAZZuOPAgwE14Fk4vr6moCXuBDttYk/1kxGBpWbN3i47rw5FMeY3nNzh0+j61yTeixJZk3W7J7d8B4XpBIQOvn/x+xZUuRlsCyQlGqqaHhHz9D/RsvQChFxqln+/ijtB3icMIpCdIZSU2tMLM62uY4MB2650EsJrAlPPTrEVOSPvxFCilNeDHQ2m5x5KFx7r91FN/TVad+JiOxHYmF5s6P3YNfMsokYxLta4JAEfgBKlAEvpFvmXSwY4LZy7JUigHC9yctVynAksgYKC9g2amd1e9Jsfd3GxHxl0Zr8aJbJM81SiSEmCuEuE8I8YQQYp0Q4tznOufzDf+9CLh2P9tOFEKsBbqBT2qtNwIdwJ4p+3QCx+/rYCHEe4H3AiQ48CEFpQOe4CFGGURiIdDT/ukrwcTsSR6FVWtJnLCCnvd9CSU1dm0dHZe8Dytbw45vfomeq+4mdegccqu34w3lKKzfSeaUlbT+/asRlkXhgfXUHXY8vffchDMQUNdpLAktBYnh6TWnEjuGQQiKixqIr98BgD82NmWP0GIJ22r81QKFRoRPIFJqAhU+pYfRW0KasFxPh9FcyrhXYjFjqbgVQW1Sky+bSshCBTiOebIvlwW2I4g5El1WBIEpqzIwoBkaCoyVISX5nGJ+A+ze6ZlIK8843gf/90e0fvD92DXTC2CWt25j+IYbEcpj3B+hdVGcwlCJvqRF4GuUgtYWyeBAwMJDbIb6FSWlmYid0IBbNvVL4gmTjW5pjRMzc73EYpDNSB5+JIfnTWbeWzZUSopYHHrXDPLUb3bgVwK0UsQSAresETEQWqB9hbQUrXOTjA9U6NpcwHcVSguENH6giekaVWj9DY4E3HVvnrNfPn0uk4iIvyYvZkLiAY4SfQ64Xmv9PSHEYcBtwPxnO+8BC4kQIga8DvjMPjavBub9f/bePM6O8yrz/573raq79N7aWrslS7Jsyfu+ZHNssrAkBkMCgSEQQjaWYYCBADPkMyHzG5Yw/JgMkGSYAUJIYBICCdkTO4732LJl2bIta9+lVu/dd6lb9b5n/njr3pZkSZadhFimH3+u1V333rp1q6rr1DnnOc+jqjNF9PonYC2cMic7JRVGVT8CfASgVwbPii7T0Br38mUEKFEmIkYwTDNB+9JsMkHFQORJn9nDwXe+D/CIiVj+9l8k6g2zB31XXU9z8gDlay7DT9corVjJwNtux3aFxm/9se20Dhyl77VXc+Srn2Lyrm/Qf/7NYd+cxO4pjaXtL0V1x+hJAQQsMY5ZKrNBUEJPIiIqeiXgvEPav7WzkDQEExPmGGm2V1NsgrVK2oLcC1YcURwCURxDyybkjTSsA0jKQmSF3Hm8C6Wxxpijqwr792ZYE7KegaGEBUsjnnxknIP/5f+j67KLKZ2/Bs0zGo9tJt13ECEErL4Fgq81WbU6Yu+unEbdgwjDRxyVqtBswviYD+ZaEWSZ4rQIDCXQlpI5IS4m/Hu6YWRY6Sm74LToQsMoMlCphH3W9n/f/oWdJF0xectiNEPisD4SQ6nbYiLL8M6ZoPfVysJ5AUSRCevFgdqCx6yhxPVTRzm4JWHeYISRufLWHP51ocgJskrfAXSqRAAi0q4SHR9IFGjfLfYRkoMz4vlkJK8DHlHVoyc/oapTx/38BRH5MxGZT4h4xxtfLDubjTobNLXOA3yVoI0Z0SINMh9FRVGL/3tRDEKcVUipIxhUDN0XX94JIgDzXvUadv3h+zA2YuDNt5IsXwSArzeZ/sYmJj9zF8vf8DPkU2PYcpXx7ZtYWQSS49EOIno06La46elnvWYeixgmlE1CDmWCHAkROXknI5EiuICy8hfey9FHP0vz3q2dKXqbFG7oCkRCZDymaH6Te2wS+iLeg7eGJBHExsSSUZuGcqSkTQ05kBHiEqEchPCKH+zltrcNsv3xBh9+/zDTYxkbL4nZvztj4rHHqG1+AlCM+JD1JDDQH2HFc2zUhV5NMTtiRIljYcFiy9FDLjC+imPUDpoIuFbIwCKjHQfF3j7LwnkRjz/VwmtQDvZG6K3AsaOO+UMRU2N5MWmvqFPUuXDcrcVlDs0daS7YaHYoUbGYgvLsvcd5i4m0IBiEYc5AS4O1Nx5m9Mllp74tmsMcvosIzfbndeLNF5GHj/v9I8VNehtnUyV6H/AVEflFoAu45bk+9PkEkh/nNGUtERkCjqqqisg1hEr3KDABrBWRVYQm/ZuBn3gen3lKePU8xJ048hAYUCIiSlTIyUmpEzhQ4XKl1pLmgTKsxYW3sXM7R//l0/Rdfg2lJcswcUJ11TrygyMc+b3/g+mqYEox2fAYXeetZeWPvYvygiUc/vpnKC9dSX3fTqSR0TUyOwAZjZ7oz3GqIAKwnis6gaTdWhcMDodgKFNGgSZ1BMHj2f9nf8J5v/97uNc02Pv+92EyRVvhGwkGyUNIzfGBahv0FYuymFDqSSAPF1UVGBwUxkbb0iuKy5SsBQuXRPzpZ1bRPz+cGt5BHCt9vYb9OzLEwMYLYM+eFs1m4WxYFdJMObw/D+U2gVI5fHZbEqVUgcP78+BDkoczxIq2q0mBNFD0fIRAJBCByUlPYl04jh6cNXRXQ18oLkXUphw2EjQLnifOC0kSGuzqgsmXtsL7vdNOtmGtKUqe4JwB41ApGAltbftCXrg+mfO2XxrhYx9a/O2eunOYw/PG8xw0HFHVq87w/NlUiX4c+CtV/aCIXA98TEQ2quppG4VnFUhEpEqoqb3juGXvBFDVvwBuB94lIjnQAN6sqgrkIvILwJcJpf3/XfROvi0c4xAZLcpUadEME+LkBCUpd1zXAVCP5A7B4vEYwl18dcoQPbidIw8+DKWY+T/6JvqvfxmH/vojDL3mR6nMX4x3OUnfIFFXsGGdvmk7XQAAIABJREFU3v4EU09vprJkBTilWRunJ+vGxyceaBkZIz9NEAFIJOG4G/EOawsMMUmnb5JQJiPoVnnfYOEmZf/jdyNZ6CeE1xX9lCKghml5xeVKqRQowk6VvNYith5LaHQ3G0qShI9NM+juDuHsV/9gSSeITE84/tNb9/Gff7eHN95WDSrBT+aMj3vmzTOMjzve9tZJ6jMhICVVG5we1dGohc/OXLgm1wpfeaWYSif0dqT4fJj1aI+iIBYZR0KaKvsP5aQ52MjSXVXKMWSFhEyQcwl1PpOUMBqYBrkaTFcFmk3yJIJWXsy4hDqac0FMzBlFIgWJA3NLQlDB6Ql/XZ/45zrvfvsMV1wSqN8liZnDHL7b+C5IpJxNlehtwGsBVPV+ESkD84Hh0630rAKJqtaBeSct+4vjfv4Q8KHTvPcLhGbNdwwH2FXQYwP/1BLhcDSpFRPj4bY4ZCsOi6Uo4LCAJazlYkpSaW8fI+lhtn78Y8y77XYwluE7P0t16Sr6L70OVyrTHD7E+JYHaRzcxdDrf5TDn/0Eqo70jruw/ZfRrmDmY+Nn/R1ewRu4i38utqvIKhAWsYwFLEYwjHOMA+xEyMjJ6b3/IEf2fz1sNxQFMYhI8OQklPF4MsIARavlKFUCDTZPlVZxwY6iduEvsLuqFeG3fnmQD350kkuumyU6fPn/jnPttTFvvC0sExEu2nD8BTTmZ9+e878+UiPLFe8c5UoozFUrIVBQzLm0J/VFQCUw0ZwLQaW7XxhYaDmwPZ/1LlGl2VCarTDVX0rAGodmwkRN6RmwTI/nZFnB2gXiyKNZTstZTBzhZhpFicog1RLEETpdD8OdBe9Y1SBJjHE5KORZO3zMhhGplNBGyvf96DDTz6zmc1+Z4dfed4B9B0OGVa0KSQQf/eNFvPF1c97vc/jO4jtsWPUQz10l2kdg6P6ViFwIlIFjZ1rpi1608VSo0W7JhLvyFimCUKJMlR6mGCMiJqVJTAmDJaXOApaygauDoGF7DRKCy1X6Cr71T/8X29eLm5kh6u9n5IGv4eoz2EoXPRsuZ97LXs2hz/wtfUvX0ziyl1prEl+vY6onscxOnwF2EEt8XFYStv1CrmQXT3HM7Ed8xCou5Hq+j83cxxSjbBu9C/W+k32E9xo8jjJdxf5ISSiREVQOSyWHE4tv5TgTZEVccePtciiXhE98eDGRhfmLoxP2zVf/YYI/+sMzXxjf8pMVPvrhGmKCq2ErVZIoEKDaulhxTOhjA+tvWcrRJ8eZOVoniUO/Ysl5JQ7uTIOKrw+BxjuodAlilWYj6HXlCqiSlIWZCQeRCX0Pp0H8spHjMNgkQuMYyR0qlqinipusEfWXyVoZkmeINYFirIrJCsqzC8dOsKi4WVnjQiWy3vSsunI70w3hV365m9veWKGnx7Dl8YwPf6TGT73nKMuXjPDkPaue8/jPYQ5ngyBB9J3LSFT1lFUiEfkvwMOq+lngV4GPisivEP5s31pUmE6LczKQKEqVnk4PQTAklPAoKQ0WsZxxjhETajdN6hgMq7nohAvl8ajQReQNrfFRxMRMb91M3D9I3+XXIlFMY89ORu76Eosuehmj275FrCWahahfR7LlLALI8bicm3iUe/EoGQ02cRfGFtWV2LA1/RalRFnTuo5JRjk881RQ3y36P1L8l1AOLCxK5LTweBISmjSYHId1N/WRHppi3z6HiKdcgig21Osea5XX3Vxh05YWEyM5qtrZR6PDOatWn/kUWbw45GOliqWVeqwqzTRQdm2h5lvoQtI7L2H/w8dwzdAcdw5KPZYDO1LUKZkGeoERwIZymI0JPZeqQB7kXbwKxoLLfJByMYL4QPGNFw3gxqcQ54MiQDkmCCEr2eg0F/7db/DUj7wfzXPaZUEvHKckYFDxsxNWSRSm4Y3gM2XfUeFzn+7nqqvKnX1w9VUJV1+V8NH/VeO//cE0667fxTP3r35e58Ic5nA6fKfVf09VJVLV/3zcz08CN578vjPhnOMzNrWBJ6dMtbicBi+PJjVS6swwyQF2FlpWGSnN4mJboktm766dOkb0CE/pJu7WL3AXnyMvPEFEFc1yyuVB/IERdP8wff2rWXrF6xh75iGGBjeSpuPEPg4y98Xj+WKeDAGKjTx5e67EgWZBVD4qKTmGbdEDVOgmIyXQBQKJICYhJqFMlSZNSlToZbAIoOE7Gyzjh5qct1Lo6o9wGixn1SvX35DgvNB/wU4OHkrRTHl686z6baUaZj/OhKmpcMEuVS1RbClXhLgstFrQbIYZFDQ05dN6jqu3OpPmuYfmtCNvKWkK4gJbSwSSCKI47I9yFfq6ILKCig3zLz4EFYzBWIE4xvaUceNTRGWLGospJ5hKQj5VD86Pqog1mErBRKDdfC+GdEyQlkc0RBctdF8wgcmFgFNu+9GJU+6Lt/9cF+evtuw/5Pg/nxx93ufDHOZwMkKP5MUvkXLuBRJqGCLGOBoYWCchNNOFOtO4IjB4HA1qfE0/1XncyWfYzD0cZDc5LUqU6aIHgyWShL7KEkp1w8TORxnfuZlDD3+e2jNbWbfy9UxN7KWkZRJKz/r854N6kiIS1HkBrMRYScKAXmrI01C4UmNo2EksUYelZojISFGUGlMsYDGjHCGmVHyPdp8Iju2q0z8gRJVgJ1ythH7MkcMeryGb+PF3H2N8zPHet+zhjn+aQFW5+lXdfPITZw6Qn/l0g8GhhKzpiWIJviKASYRqNbDHSkkoo7k0HC8RWL8++KmnaQg2UTwrl68uWOk6H/ojPoOxEaXWDMW7NnoHQi3MFxparpnhvYb9aQymWsJN1cKcTynGlmPSAyNhA9oc5LYPrxRKAm3rRwxEFomjINlSbLjY0Ef5jfeOnbAftjye8Z5fGmffAYexwi/9p3Fu+oG9zMycOKg6hzk8X7wkRBtfbDAYco73JWmzliyW6DiNKp71mvCT0G7ES/H1HTlNGtSYxhKB97iZKWqTB1m76vVcseFnuOayd7Ogfx07dnyBUk2o6zTzGfq2vsu3Wl9EVbFWqVQ85WqGjVthnkEMYMhaUK6G0k1Oq0NpDpMQMQbDAAuYZoJ5DDHMARrUaVDDkRV0BOHz/5zSaHjiRBgZ8/T3GQ4ecFx9bYnajCcuEbSygP/+Hw/z79+4mx966wD/8PcNnnj81D4wBw44/seH6kyMZiBB18tGEJcMEsfU08KoKiuyjFg6BlUiMT/8w2UWLICB/o56TaGHBWkzBJO0GeZicg1sMwhzKVE5hjzMr4RBGQ/WYLsrSCUJA4f1ZkguVBBV+r/vSkY/9wC2r594YLA4ISRIEqtBiEJdEQULElnUeawW+jPqQ/VS4G8+lrJtWyA1/NEfT/PmnxxjwaoS7/8fC/h37+rlupeX2XM0Z+jS3Vx28x7SdE5iZQ7PH+05krN9fK9wzgWSbvo7mYgcF0QqdBUZyKwY4izavwf7qIiI+QyxgMVEJNhimlwwlCjTz3yafoaljcUc2/Ugmzf/JY88+lHGd29mbWs9NZ1EMHTzbOves8WoDuNs2F4VoZkJ9TQmc4FNVE7yQEvF0qw5bDkcKmtnpVUUT0bGCEfoZZApxovCV2jId9FLhS7KVPDOko2kJLHrSL7HsVCpBLfDoWUxPg/X0SSB/TtSfvEH91CvKW+6fYwP/3mN8UJLrFbzfPxjdW574xhpS+geiMhSj+aBFlyve5JKhIksWsyULFldYv3VXSxbajEGtm/P+MY3UuYNRqDCVZcnvPEHK/R0C5dfFtPbI5SKGRRjg9eJkZB9iDWIczQbHlMymFIxvJJ5tN5E68VQaKvoJIkEufgkYvpbO8jHJzBd3SFidXqIiooi5QSiGFOpoq0WRhSXCdL2wkpCkAR4/Q+N8YlP1vn7Tzf54F8u5IG7Gvz2e45x4JkmA10QiWBE2PpMxuD6new/OGt5MIc5nB3OjdLWOddsN2JOihPh7jyhXLC5ZoPGiS8MtODz2cBSVmMlNIm9eo6wj6d5FEdGQolJRlnJOnbwBAvcEtZzGYIwxjBPsakogg101vF8oao8yjcREyHGg7d4nyOlYprdCzlCKc5pOFtcPMP8gxehUlEaDY9HKJFQoYsj7CsCYYWEMk3qlChRpZsGNSxRyLmOeZYuEY4czfmBHyxz970tEFi8LGKmbhg/2mL5kCFJYM9eRylRZmrKn/7pDB/8w2lKZUOaKr0DEbUaJBUhz4RyNVzch4c93Uv78Y007HMr1BtKMpWz/bEWpkio+gYMthJx9GiGc8q27RlPPpWRtpTHn8hotWBgQcRMzTP//F5Gd04XszGKzz31VDCRwapBtBg8zD0+95hyBbEGrTewfd24qRlMV5XJO7ciJqL/puuYvOcu4oWLyI4cDr0ToxDFqHMggm/WiXDkmYBVNA/9k1LF0pjKMTHUa8r7PzDJf/zAfH7t54Z5+89X+ZlPDlAqSec4339fi3e9Y4JaHVZdu5c//6/z+bmf6j8t6WMOczgZ/hyQVDjnMpLj0c4iBlnEOMeYLWG1/zWdrMViWcclrJC1JwQAI4Ylch4buBqLZYxhehlkmINczavoopdD7OEgu7FYlnAedaZYy8UveLsf4KtYm2B9xPJ5V7Ju0StZueAarI8Qn4AB54PPeakURA8FQo0+sngPXV2h99Mq+iRd9FKmgsUywwTLWcMYx3A4uughoUSFLhTl4KFggbt2XURStbz8td0cOeiIy5akbBgdc3gH/X2GrFTmjd98B5f//htwAvW6J2spo8MZpS5LY9rhWjmxcVgTmtn1Y3WaEy1AO1LuE8ccvd3C0MKIri7L6KgyNZpz/bUlylXDdEM6Msf1RqAPN1Khd16J0Z3T3PL9VX73jwZZujxizQUxNlJc7smaHpc5xDmMOBAN5IfJ6XCLkTviJUNEvYPk41P0Xncj+cw0XRdfgq1WaStHqgNNs1CHS1uQ+eD8GAGukEwxFpGglumL5Hd8HD7/qRl++qcrvPNd3Z0gEg6XcMONJf7uk4PBJRLhnb8zwkWv2Iv3c6WuOTw32vTfs318r3DOZSQnIwg2VmlLhbRpsVrE8fZQosGwhNPz+xeylO1swVHnGMGl6n6+whAr6GceGS0Osw9B6GWQfpl32nWdCTWdomkaLOrfwEWLvg9rZg/BugWv5OljX+PQ+BOotGi0LNWSJ0WCOm5iMVZYstCCCrvrOV5n52hCaS9iESs4xB7OYz172UY/85nHYprMYLFMMU4pMfzFh+s4Fa57eZnHN6VMTzWoVA2aFnpdwJofuxRjDYsuW8zKj/42B379Q5jxSZyD+njwLWnWHNNNQoN7oIeBbpjYX8P4gqZsI8Bw4EBOFHt6emBoWcya5Za770vJnGI8NOuBMmwLdZKsnpGJ4/y1EZvubXD/12ukLUi6Y5LEgjry3CMiqHNoEWjVe4hiei67ktC9V5KhxSz4oduZvO9umrt3MfRzb+fgf/8gYi2mtws3NsvEMhaCpEr4Fytgo+D/EidgMwwO7wVUefi+Jn/+Pxee9phv2Bhz7XUJ99/fIkthx66MnrW7OLJ5JT09cxPyczg9FCH331HRxu8KzsmMZOiECX9oMlP8dGJv5LjZZBaw5IzqrSLCECuOXwIIR9jHDp4oJFkqGAyXcP0L3vbN3E93ZSEbh153QhABMMZy4cLvo7e6GLD4ws2w2MDgiZ7m9PYJSxZZunvDREmdaXJyGtRIaTLCYZZwHvvZwSXcQIkKu3mSGaaIKZFQYmzEUpvxvOXne/nHj01z3sXdlKoR6UzO8qWWwQFDFAuT0s/uiUEe3bMMTw8rP/Rr2PXndaROmjWPM5Z48SAOS2+vMHm4gVNIKjbQGspRCDKJwQPTM9BX8Tz+RIoHsjy4JlbKRc88CqSqZiqMjni2Pd5i9FjO1KSn0VCmR1vMX2hYe3GVUjmIcGJjJIqRUkI8rx+8Izt2lPrWx8knJqg9voWDf/6nqCpL3v0e6lufoLL8POLePtz4FHbJYPhrMGGS2KtAbJFIwFisCTGkOZ1jnEOsFHovgWTQ3X3mP6XXvLZEKQEpR3gnNOvK/Iv38Jo37ZvLTuZwRnjkrB/fK5yTgeQiZjXJPMo4I8CJYSTQgGeFGw3PHdXbLC6DpZcBFrOSQRZhMBxhP5OMUWeGEY684G3PbMbqeTectkYuIqweuA7jDDbyhW6gYsoR3itd/QmlpJB/z5Ru+otJ9tBgbge7w+zlIq7kGTbjcVzDzQyxHEdOVMye5C3DJz86QWWwxBP3TaGZo69PaKWBRXV0ROm7bh0TE7OT+1kjZujX3onp6SLXIHFd6itj0iZ4z9jeGnmuiPf4ZkbmBd9odQIhCFFieWyLY2RUaaZCYoMIRJaH4JSpCVPxKGICsarRDBL0IYU3tOIKrUoPy64ZChpZGCpDK6guWUXSvwhTquDTJot+5mfpu+lG+m95NSve+9ssuP12mrt3MfHVrzLvla/Bzcxg4hg9NhNMrYwExoGNMDYEwMGhGCNKyydorljjA1vYCCJQb/KcwSCyECUmDFsaxUSGvAXfuD+lvGInv/DeZ4lqz2EO5wxr65wsbRmxrNereJqHOTF8tEXXZ8UM2wyvUY6eMLV9KrTnMK7ilScML7a0ydM8yihHceRs5Vts1W+xkgs4nw3Py6fCuRaDXSvP+JrB7pU430IEmilQtsQlG5gZjRZPP610VR3qPBdxBWUq3MMXsYWcfhc95GSMcpRu+ljOGjbxTXroZwVrqdBFkzoH2c3o2FGe3jSDNUJsPOWSwamyc3dGz4YLqY+f19mu0lTYd3lVWf0b72fX7/022mpSG0nBCN4pSUlIvEd9UVxMYqJSBTc2jakYnHdkzUDbzVpg1JMVk+9xGF4nFk/qhdgo2iZWaegZ+dzjo4ijO2tU8zJxX0L3uiGmnxnFpQ2aRw8R9/YhkSWfnOLIRz9KdeNGyitWMHX4MLXHHsNNz7DkJ95GNjZCqW8BzZFD+EJv3xsATxSHDETimMlRj1cL9RZxnOM9OCLEKqIOMcKVlw3z6JbT08G/eXdGbbrgNougJgafkucQJcJf/t0Uf/2pKTZ/dQXnn/ftzSfN4aWF7yUb62zx4t/C02CZnMeGQkb/VDaoclKa16JZNORPjWmdYIZJruPWE4IIQCJlLuY6ejp037Db9rKNO/hHvqaf4qDuPrsNFznlIOXx0GJYQV3h11wo0cbi2LghYsP6UpjNyKv0ygCJlLmKV+JwtGjSIqWHAQ6zl+WsYTP3soaNXCY3skCW0C199DOfC7iMC7mC1oygucMaaDaVo8Merc6j77U/ecrtM62wb1f/zgdQB5YczbIwqp5maJaTicXFpSBrMj5NueppNULjJSrZcGcuIIX9rjWFzYqHNDdEJkyuRxJ630ih6m7CtuIctZ3HqD1zmNr2YUSUfHoaU65QWXU+vdfciKYpfVdeT2lwiNbeg7hjY8x7+a2s+ve/he3q4tiXPkf/eRcjSUKlfyEmEkwS5ojyzJPWM5pTLchamLxFz4CiKjiJAlXYgEggQ0xMCFNT6Sn314H9OXd+vUl5oBz6KiKoy4NlcRwENaM4fL+LXnmA0nnb+YX3HqXZnCt5/ZvH88hG5uZIXiAWy3JukdtZwNITlrc9PI4vZzlytnA/kzp28mqY0Ske4W76mUdJys96HkLJ6Xw2hIHFUwSCp9jE1/UzPIe2GVV6GJ7afsbXDE89g40Ce0tV6e6LMWnK8kUwPqo89kRKrWa4xs36zfTKANdwM4KhxjQpDSp0M8ZRBlnIYglZ0Kge4RG9m7v5PA9xJ0/zKGWqpE2YnlEm6hHlK29h0Vt/lcFDJeY9BovvDt+3MhwevbuhPBoeq978y2TOBBXdKCLzBu3qwZZLiI1RJ1hVWi3FJBaxlqzpOsokkSi5D/4ittDYwnlcHnomzVYYNDfFMLpRxZpwQUcVlzoGr1nFmnfehGs1MUlCdmyM1q6diLVMP7aJxs5n6F6/gcGbXk08MI/Rr3+J/R/5U4Yuu5XG+OHQC4mTUGd2BBMVD95LMZkfJPmnJ4Usb2dIIety3nSyjEs3jOPciQOxu3blvOUnJvAItYkW6j2CItYGcoARMIGp1qyDMR4r8FefnmFgw16+/K0xxv2JPjdz+LcD5dzokZyTpa2TcalcBwT9rE3cxRQhWJw85Z6TsYlv0KP9LGI5IBzjIJOM4lEu4PIzfk4/80/IJto+Iu2CmuL4Op/mFm4/7TpW6QXsHP4mQ33rsebZjB3nc3YM342jRZyAz6E2mjIwIOzZkxNH0KXL2NC8ikhOPHy9MsCN+nru44uMM0Iv/RxiDxu4BoA9uo0D7OR8NnApN2DFkmvGIfaw029l9cLXsXjpVUhumDqQ4GNIpsL3XfBw+I5pfzhZk7YA85IVDFxzM+MP3AlOsV0V1FlcI0PEI3lOXFGyzKBewXuScvBMCSJcoS/RaDoGBwzjk54oCRlKIyU8T/BUVxuMq3DasRt2ac7oQ3uZfOpoYHHVJlBy8vFJ1Hs0d/T2n8fEHXfQmhrFRDG9yy9i3Q+8h+bEMaYOPB0si9PCaiHLQ9O+XMG3GjRaArkE6RSTB8l5UbwXRD0mMvjMIEbx3nPB+cd4zy92US4LDzyY8dCDLbwKeSFZY0whAInDINjY4L0EphlKKwU8xOqJS4YfevMkf/yBjPf8+EkK03P4N4PvZaZxtnhJBJI2rFiu4ebgZUGdnBZluoglYZc+xS624vFMMlb4us+WxSz2WeWwkyFtMw3anidaSJAAzNrG3qGf4ZW84ZS9k0Us40i+n4d2f5xLlr+BajLQea7RmmDLgc+Sx/Dqz/4SuUbU9oxw6J69NCcdy4ZXcP7uPoxY8kOHT7mNJSlxrd7Kg3yVaSbwOLrpZUyHOcBOruZVHS+W8L0jlnAe/czj0f1fZf50N1Xby+AzMPWKNc9e/4RSHjtOP+oguFe9nmz0GI1dj6H1FCUNlNtuj7NCqxXKOWKUODFo5sicRazHWMianlIc3BB7ug3TNYhjj3OQZx5nCs8RJ0QmCFlGEnTCTAQqQs+rLqfvlZex51f/DFebIR7qJ1owj/qW7Qxv/Sbz19/AvJe9ibjaSzo1wtEn7mJy31aiBfMpaze1kV0YD7mEbcUU0iktH9hmasLx9x7wqA9UY+fCoKJIOPZO4S/+oh7YdsaQe4OUYrQVNL3UQ95SjPU4FUrdUcjWiuF8LfQisxTU5agIv/pbU+zfp/zOr4fSarc5ddY8h5ce2s32FzvkuUox3wv0yqBeK6/+jq93uz7BXp4+YZkUAcASsZZLWCanl/+e1gke4s7CF948Z6/jCl7BoCx41nJVZQdbOSC76C7Pp5L008gmmG4eY8F5VzJ0648wccNsNmVHQuay5Juzy8qf/dYZP3tCR3iUexCE67iVbWxmPkMsLb5fTafZx3aOsC9sE54K3XTTx8VyLdG8efiVs81jV53NnrKTZh98LDTmW3Z94xOM73k4lGsUTGTxPphoiQllKcHjxZDnhrgiVCKlNuOIrNJdDT2DiUno7lb6BmMOH8xp1j0mEjRX1AhxVFyQM4OxhWxKKaH3xo1Qjpm64xFQqK5aAAK17cOUli0jHxnFpylRTw+lC9aQHzmGqec0J4aJYk/WzFC1gMOUSoHzV0+LwBIMshDBROAz7fiViBTS86odBy8pynjtORZ1wVTZiofI4nOlsqALrac060HwMmvkiGpHjDgvJM5sHPpKG9cZ7vjc0FwgeZHDLt6+6Tnsbs8a/esX6ss++qazfv2/vPxD37HPfj44p3skzxdrZSOv4o30MthZ1g6jjpy9bMPr6WXTd/NUMeh4fMP89HcLj3AXR/Xgs5aLCGtlIy/X72d1cjHz/HwWXXErV/7I+1j8mh9DzJmpyt2bT3bGfDb6ZT4buBrFB3YWRzpzMqN6lIe5kxJlbuA1XMPNXMRV9DOfEY6wX3eQj45iDj6bnGBrs3pRUcMRFQ30yohjw8YfY/ltPx8urBpYXCY2RSNfEfV4sTgXYayCQqOeE5dD871WK8y6rDA9pRzcmwWfdWPCfEkcKMGdINKhRxdZzvZJpu96IkypK7QmUqS3j2TNYlqHD+GaDWx3Fz5r0djyJG50nObUMYzJC2n7GFMqheBghdLCfkpXrgmBxBWZiirqDGJjRAq6sPGQe2y5QocU6B0+y9Hc4XOPNaAuTMv7XJHIkk3Uwz7SHAyYQmXYxNK2i6dSAZdBq+l47EnHvNUH+OXfOfycvbg5vDTQttp9sTfbX1KlrbOBlYhruBmAGZ3mW9yBL9SEW6Q8xv1crNcSHefJ7dWzk62McKTTDwmY1fNqqwoX2radQPM49zNfbzulLpcVy7ypHqIFq5hYvoZm/2xcrz6TAFBf16I0MnuCnE0QaWOhLCXTy9jG5iBXKRGpNniCB7mUG4rte5A6M/QygCNHgJ08SVmrLDgKHA29A1sKd8E+bVJduqTzGa21Q1T3z2AaGflAlWVL17HsDb/Ptz73u7hmDZc5MNBqeKRURrxDjMNEgmSOSldEo6FEAuWKMDGpVPoi8lq4S198foVtW1K0lRfT9oJXsInHZYQswfjA9jp2mAve+pvkjRrbP/7HZMPjkBlsqYSmDomVfHKCqFqGyOAaU5R7E7Ka4F1E1NNF3qiBiZBSmbyWUVrQS+o9RAZRQYsSVFC4FFQUdYqtxLhGc/bUyAXwYENZLmQyJohi9lUhTVEvtBoOm0TkDYcGmS+sEYxVWmJoFqrBmoPt8uR14c//Zob//YkdPLFpESv7e8/6fJjDuYk5ra0XObqlh5vlDdwit3Mlr8CRM8EI3+Rf2KoPsVef4Rl9jG/yucIsy3d8PgLaLoVB0ysi7vRJju+33Mlnzrgd+bER+h86Ss+BnJ4DOXJcUlR9OgSU8jjYpqexfojG+iGiC57dvzgVlsoq1nAxHk9Tw+zIQpaRk/MY97OM1dzI61jBGoZYwflsZD5hqN6FAAAgAElEQVRDPMkmMj1RrdanzRN+11qdaLKJaczKzHcdDP4lV9z2PpKuXkxiMJHFJBZDRlQJd/XGuTAZnyp4R2TDQGJSFprTOdfekJBlSlyyHSOsOBFsHDxDXIvCN6S4chvwecbez/4VtlRl6MbvBwTSjPTIYYLXb4xIhGQtItciSSzNcYdrGcqLl+PSwntFIV66lJ4bbyBetAgWzgfnURd6Q+o9WtC4NfNEvSVMVEE8tM+NG3gdAyxCbBDhNLGl0p8QxYZS5MgajlZTwxR/yQb7k3CocV47mY2xnVWS1cBlio0MWQZrLz7K//+RZ7MQ5/ASgp4bA4n/pgPJ8RiQBdwit/MqeSMXcRUTjLCTrexjOzlZpy8SmGCzLXaKUEKhddVFDytZx0ouoIf+DgX5Eb33Obeh66lQSlp0x2HKx1WVyuPPfm1j5dlL2K+QNcxniAPs5CgHGGI5W3mIS7keRXmAr7Cdx5lklDGGGeEwMQnb2NxZx6mCCIB/9Enyp7eTP72daMdBoh0H6btrB/O+vpOXX/gfWNJ3dWBvGcHGFlfLiKNgfpUr+CzY8eQuSLSb2FIqQf+g5RWv62H7ljqSxIiNyFsGlxkEC9gg0mg09GQQjBqMGLb/9X+jcWQfxgpGUkxsiPu6AY+pVIgHl+PpwUsPXasuoGv9Bppjh7B9fQTvXiVa1EdeH8e7Ggt/8gdoa8IInjiBuCxEXeXgtpha8loNPe7c2MIDXMFNlFvdqCq+5clnMgSYGk7xSDDzSgytWk4kCi7Qjb2XQDF2x5WvZNarpdUIFsPewW/811EuufksZ5jmcM5hbrL9HMaQLO/oeXn1fJPPkZOd1BdpW0a1lwir2cAYR9jLM0UvxVChmzrTjHEYr/60U/D5znAx6DKG2kUL6D6S030EGvNmS2Ll/VOdn91Tzzyv77SWi3mIOwFhinH6GGCKcfazg41cQ99xIpSZttjJExxmH02tU5YTqaf5wdOX1/xM8LHXDWuIphtsnPcqLhx4OfcOf5LG9P7w/twAnki0w1LKc9BMScTxyltL3PGVJrfe3kdpQQ/5FLjxGbRwvAxuheF9ooFWbAHvHI3hA6y45SfIapNMbnuEXCwmFoxkqCquWSfv6aZVn0SsJT+0k/K6ddj+AdzERJFjQnPTNoiEZOVSpr/6AG2TeUXIVVCXARkSJbi81slEsYGePG0mEBUu0Et51N1DTpiNUQmyKlaCcZZreQw+kBIMODUISu5ATFjWgYJvK1oS+kBZCs/syrFLt7P5a8u4+MIKc3hp4Vxgbc1lJM8BI4bruPWUzwmCJUYKUchdbGWU4Y5Ei+KpH+eRcgf/+Jyfl2/f2fnZR3KCAky69IXXw6vSwyVcj+IZ5QgDLGQXT3IFLz8hiADEknABlzOfxTzDlrP+jHYQAbDTsxa91liuu/xdvPJlH+Daq/4DF6x/C6V555M5cCIYa6hUDb3dwurVEXd9LeXVt/XyL/9QZ/pog3xyppjjKFYYzY7CK0UDnhgVjyFi/9f/ntrBXYBgckMSLSKdIfj9KuQT48FNsbcHjCXdtYvs2LEwn4IgXnFTM1QXrKSrZyUDa65m1c/+Suf7tEtcAJq3XSuDuGNndEkEr55BFgHCtZf9Ct3lFRgjgc2mgms4NHMddhYIuQtDmt5BZKCd5EjhDinqQ8/FzAp6ZsH6hctffYBk+Xb+5MNzfvEvFSiC8+asH98rzGUkZ4GyVLlBX8v9fKUjAtlGRkpEwjEO4U+h+3Uyvqaf4hW8gVhOLx/etT3UsprL+ujcjBRFc98bmt4mCQV13zp7170BWcAKXcdh9jLBKEOsoCJdp3ytiLBaL+Ih7sCpOysTL9+YDR5+244TnjPbwr/dxWMJl/M13QmiJFEo64gVciySCF/9ck7LWUxXF9qaRDXnpo//O+75ib+Z1biHMGEeJ5A5iC14z4rzXoaxMeM8hvcZdqaBuBDcRQVxICaCZoaRYLGrLsUA3nkUxWQe3XuEsW1bybMGUVwhRDIf0glfzJqgiBewHi0+gyQG59jPTlawBgEODz/CNVe+g1aryd0PvD+YMxZDlRIpXg2qGnTG0mCT4rWwIJZwW0KRjIgtBAAcHWfgYCkc4utvfmCMP/yzcZ66ZxW9PS9+CfI5nBlzzfaXEKrSzavlh7mo0PcKCHeiprgnnq2Rz8JgmfWHDyfEXfwzEzrynJ9ZPjAZAkgRROLpZwcNkySdoHI2WM75ZKTMMMECFp/xtV3SQ0KZOtNnvf7nAyFkXI3UUssTjhxWtj7hmBhzpFE/2ATqreICrXQt6QtvTGKIIkyliunqxnaXMRUbEgLX4tDe+2nUjtEeGG24KQaY18k2TKbYzCO5Yn0EzZRII3yaIsXtgKNF1BJwnkrcj1VLRDG/4ShmRlxowgvBQVEBaxANZ8VunmKcYxgs+w/cy8HDDxHFMa9++QdYt+aHgdlSV2w8mimtLIhFKkUQ0YL1pWAiwZYTvDIrMVOIWqoCNoyxZBkcGVYuv2Uf3s/RhM9l6Fyz/aWJJYW+F8xqDbeb8QHtOXdDRFzwueS4ZwIe5hs8qHeQ63FT4gV0eCQ8RHCl2cfkmi7SgRLpQAmzYD5mwfxg6HSSvtOZEEvCRq4ho4WcxeFvB8nvBtolIXEeUocpVVAvYGJ0KsVPzeDqNcSH2/J7P/hIeGMryK9o2kAbNfKJKVCPTx0iwrLKRSSTOVIEcUVCMJQi4Ksj9jE+bUC9jm81sK6tFG1BDBFlJltBMboc9VAtDaK22M/WhPIaQkgTHKa/F+IoXOBNMNrKabGLJ/F41Crbd3+Rex78A57c8RmmageJS/PIW6E0lbZCdiEmqCK79jBiNNsTMuUSRnN8kX0AnR6KmiJRcrPsr70Hcv7mHya/K8duDv96UJWzfnyvMBdIXiBCMAl/zeak3Rh6J6Gk4DoN+s5ls/O6acb4Bv/EvfolUm2csA4WhYl4m554EW/Om61GuiMvzMNiviymTJUxhs/4ulSbNKlTpfsFfc7p4NVzRPcft0RQ75B6iuQQ5UI2PoZ4Rfysp8zM5+/DJBUwFs2CaGIQSja4lgejOHXsbTxOxfZQiXppO2a2SEOuIbOBMVzqU9qfYCXGRgkiUDFdOM1xmjOVjzCVjRD3FoOsWvRSKmVMtYzp7kFrNYy1mKSEZo62sOcU46zlYq7zr8Vpi0ybHBnezOGjm2i2ppAYSOwsi1nC4GVhKtlZHvVWcVmO+KLQYQzHVxvbtytqQoUvZDGWd/z6Mer1s7/RmMOLDefGQOJcIPk2cIvcjsGedMc+O1vSFubTk14hGGISYhIiYprUuZvPM1xMwbuxE/m+pwsmdmhR5/F8sYGrOcgucs1O+5oD7GAhy04Yzvx2Ma0T3MMXeKZDLQ5Boq3YrC7D5c2QYeDxxnVuu40YvGtiSpViwMIgEmTf8R7xMcbGLF3zCg6wm9TV8OLBGmJJCBN/IDaBOKJq+imZLipRPxoJUdKFWiEyCSKCGEucVNA8x7dapONF4PbBQEWzLPzscmypjGZ5GEr0s1mmR1kha+ktDzLohkBzVF1o2PuMqBQF1ePEhmpZHrKRdj/fdJcDU6yeYvKcVts10xkiKbJcmbVwM8bgHIWUjMM5WHn1HD34XMZcRvJvADfLbceFiXAgDRYXpAVPCCCmmDeJme1pWCIqVBEMW7ifmgbbYFENukvFwzY9eVk6j8bGpTQ2niif/3zQI/0sYhmbuZeWnuijoaoc1F0cZA+rWP+CP+NkNLXOI9yN4hliZbEfigyhOBPDPHiRIcSFvpUWdFcAjdAsReJSKDHZ9rtAYsG7lOEDm7j4xndw8U3vQIzBGRd06gk9CSKLxoao2k1S7SOu9mJKFVwMqJLnTZxmqHq8OiSOKQ8to2vxrA6bugzJHdrM0EaKn2kUTY3g1ng8dupWEOGqrlsRV/TKrEWMJWt6nBdsEmOrCSThvUlZiCNBWilJpPiWI8sKslouqAlZRisvWkZWwsyBh+7uMIeSJIE0MV1Tjhw9/Q3DHF68OFfmSOYCyXcAGzrWv9r51xQZSVuXqy0OaTBERKzkAtZyCYtYRkqTmBgQtvLQKT+jvtCSDjx7+bcTUC7gcvoY5D6+xFZ9mH26nV36JPfzFfazkyt5xWlZXS8E+9hOiTJLWc0xDhbaXx4jMVL8EYiJaGcpQQIliBhK4c2C5KGklbXCnbhzWBtjTHsdwTTqqYf/hiTpQVVR9bgI2v0Yk3skitCuEr4S43vKlOYP4Ro1KlmCwZD7DNThnUPzDDudIjPt8mPojSiKGo/i8SZHjSlOgeODiWc32zr74JZlvxCeFxBrMLHBliya5SGzSWKkEtNqKlmqtBqFtHwxuuS9RaKC6UaIsbkKltCBL5WVer3djQfEIgrv/s05K99zErP3kmf1+F5hjv77HcAiWc5h3dvxcm834aWYdg+/BaOttVzCUladYPl7vm7kcR5gghGmGMWrJ396O149ey7qpr5/N+yH8kVrqa6/EDEG0XDoqsdeeP1bRFjLJazUdRxmH3VmsFgu5Ar6mX9GW2JVJaWBx1OijJUzn0pePYfYAxBk7UkY5hCRKbN6/g0s6duItQnTzaPsHnmAsdoenOZBdddTpO0KseXKNT/Npqf+inA1FpxrFQODHkRI02kGB9fx8J1/AN5johKRScjbR8Y5kimHb0yRaRMjhh7XS7fvIjUpqpCLo829ljihmU1ibUL4k8npUCek6HKHb0l7suWkbw+AWRDmda6d/+M8OPJJvAZlZM09phShzuDTHIPDJoa8FbS6lBCgxHrABaZWwc6SWIit0srDpvhWMOHKVYkKSpdXeHLHs0kdczg3cC7Qf58zkIjIBcDfH7doNfCfVfVPTvHaq4EHgDep6qeKZb8PfH/xkver6t+f/L6XAi6Tm7hHv0CTekdOpQ0pKMJLWXVKmXorlkv0eu7jSzgc9/B51uglbIu3YDfNp3LFRWjuGPniP+M++bcM/NAP0t9zw7cVRI5HImVWsu6sXuvVc5BdHGAnLdJOsKxoN/NZzArWksizPcczwuBeD/2MM0KVHtIo46ZVP0cpnm3mD1SXM7BiObtHH2DH8N148qDWLu0SlzLQuwJEkGKZicuA4FyGRBbxgjGWFSteyb49d6B5ThxVaDGrzeUjoZXXcJqT45k2irER3oVjJ22lXw0aW149caUXGrPqAoYI7yga9EKeNTES4zVc8DsCCICv1+FwuJj3IVy88s1s3fcJ1ERIIUevWU4UE7S5RIkqljyuwlRBvy78SjIHRGDiMLzYysLAYrUqxAK1Olij1OpBOUAMHBsL0/PGvPgvSnOYhcL3tPdxtnjOQKKq24DLAETEAgfh2SqExXO/D3z5uGXfD1xRvL8E3CUiX1TVqZPf/1LATfJ67tYvkFLvlLPoNNuVFWe4WBsxrNB17OQJWqQ8ycMM/da7Ka1e3nlN/w/fSnPLMxz7n3/LWPZP3PC638Paf72BM6+ex3mAjBbLWcMethGT0MsA00ywnx3s5RkSLbOaC1nMyo4kjMV2TMQcOVNmgg2LX39CEDkeq+Zdx8GJLdSzMbwxSO6gZCGD6Mh4aJwrEEXEC4aoLFlB3NPP6AN3knT1MXxkM0lpIFzs1dOcCuJlIhYjEc61igs+gOB9HqRLohI2jkhbU+FmQBX1HhMlZI1JZm2WfYcF5vNZHTKvOe3oYZMIl+YkBNkS32p1Zn6WzvTR3/UW7p78OBobxBUDjs5hgVYuxPPmEeUpvlyCyBBljVDmAsjDTKSLwvuSWDHe08zCHWykEEUG9Z4sM0z7Mgsv3cvI4+d9+yfCHP4V8b3tfZwtnm+P5NXATlXde4rnfhH4NJzAKb0IuEtVc1WtAY8Br31BW3qO4GXyelZxEYFH0y51QET8LM2qkzHAfARhKedjMLiZE726RYTKpRcw7+0/hiQR933+vWzN7+PItf86Fco9bMPhWMel7ORJ1nIJvQwyyjAxJUzBRlM829nCN/kc23ULqTaJJKabPqaZQDBkZMzvPr2JGMB5g9dgNAo04HKEeqHP95PvO0C7eNweMMxHRkn37yXu6iVLa/gkQtMUR4ao4F2OYKhK9/9j782j7DrPMt/f9+29z1Tn1DxJVZpnW7Yk27HjKbbjpJ1ASEJIICShO6G5kMVi6D8IdN/LhSwuNJ00rJtmNdDQ0JALdAI4wYaQOPE8W55kWx40S6WhJtV85j187/3j22eSSiUpkeOpHq2jVWefPe9zvvd7h+d5wXVxU1kcJ0FESESI4yZJZrowWjBiMJElf2osOcP45ZgYGaOmTyIRgomfdcxsd1xwNFFg19/E9vpmxvfRHVbqpi2ZI5foB6NsybCXxLR1Ip39cb+TEKoGnUhCtYoC3LQLccfIhAeOI3ja4CAUK4pAbBdJE4Hvm9hzUaz63Z9jbl7o2XLwEn0blvHDwlshR3KxhuSTwNfOXKiUGgJ+HPgfZ3z0IvBBpVRGKdUL3AasOnP7txs2qMt4n/o4PU3McYM5bzOiGqlxJavppp/TX/mbRdfLXH05OmX5DnPfvYvTX/27S3fy5zo3MZzkMJu5kiO8yjq2USJPnjmSpBAEF480bfQzRJo2BGGc4zzGt9kt99FOFw4uAT4JN2NDU0sg7XXYdTxLtFNBxA5ujD91qHUk1IUK0dQU/ugp3KoiKhdxe3oxPRnQGle5iBEMBmXAhFUyXUMYbUgmO0gnu/GpkOkepqf/MkK/UD8HSytN2GKJJo0t22lL2dIox0Ulk+C6ON3toIxVT4nFFVeoNS3XFZ6eIjxtlQ2uXfUpm+pxXctKLJcxC/Nox0XyASrlIY7CSTgYpZAwxM0kiQR834pdhgYqoU3/OyKEgbIFZBpCo+n9mTtIrugmd8PlzJUcfuoXzm62tow3L95W5b9KqQTwYeAfF/n4K8BviLS2FxSR7wHfBp7AGqAngUWzfkqpn1dKPauUejagutgqbznsUjdyGz8es6uFeZbuHTHBSSIicnSyli04EYz+/p+ftZ7SmvSVm1GebZSUP/ocr8meBjX6dcACMyRJ4eIxzzSDrOI4B8nSToIEJfKsYTO9rGCacdayle1cS5osLi4RIWOMEBEwxSh+WDyvYa2EeTvbjzROILyb9+Epj6pUrPZUzFIvlk4TlBaoluYoLUwgJiKYm6J0+hRihAAfHXMtiizQZwaZGt9LT+821m26gw1bP8TGLR8iPzvCxOhzRDEPJEEKB7ee42qoF8SIDPg+OmGLlpWrIb8AoWDC808Pw9NTqCMnWRduQrQtCdZeAuUlcTpyRE6EOApdLeGYgMg49KzrQCcSKK1xEhqlFZ23bSfyLSO+WrWGJVIKEkkGfuHDdH3Qyvqkt65GeS7f/FZpucPi64BpU2TaFM+/4kXAehpvfkNyMTGRDwLPi8hidYTXAF+Pq3x6gR9RSoUicpeI/B7wewBKqf8NLOpbi8ifA38Otmf7RZzXmxqOcridj/GkfJdD7OVquWXRaqha06kOutDKISddGCJk3wj5J/aQu2FX6wZiSzy15cYx5h6mLexg9RI9538QhIR4JCiSJ0sHc0zRRjunGWUFa3FJkCHHqzzLu3gvk5zkGPtZz2XMMMEU4+ToJKBKlQoGw3Tx6JLhrZNzLxCZgPXmMtaxtX7fak3GrASKrYgzUYCpzVEiiColm5xXYJRgVISKNAZhkpO0m25kbILjk4fizpCaapRHlBNntqzxS5LCMoNcQmpcjKYsOiDlhoGJaBX1vBBsUNsp+QUmExOYoIrbkUNJgPJ9HEKUCfDFxUsY8rMR/nwFRwuOUjgOzD19lPTla+j/7AfJ734ViQyZratp27UJ5TQmFqbiQ2RwPXjvx47x4D+tu6jzXMa5UTMg49GlH8wj8/bKkfw0i4S1AERknYisFZG1wJ3AL4rIXUopRymrUa6UuhK4EvjeD3jOb0lcy/vIM8eLPEFFGrkPEWFWTvM0DyBIPXQTEqBxWM825v7szpYZpEQR5Rf3o+MBKwwtmfoIe3lI7uZBuZtn5AGCJVjrF4sUaYqxeKPB4FPBxSVDjtOcYhUbOcEh1rIVnwpHeY0d3MAIB+ohrwCfdrppI4eRiNfGv4cflhY93onZPVTCPFp7JEi2GN8JTqBjeqdWDgndhsHg4KFxcHCQIEIbQSlFKtluJedtC0OMhllOM8tp/KiIiQKqUQGdTNsKKtshhCRpfCokyTQZETizxLeWI2nOidWgL3CudoV6Nxv9yzBVH52fQednSTghQTUiQOPqiMxgJ0YlQSu8pENowPUUZq5I5cApvL4O+n76dvo/836y12xpMSIAC4/utTI9Ch55KuQ9H10s1bmMi8HxMM/xME/RGA4GSfLmwgVULxRvBY/kggyJUioDvB8aDTWUUp9XSn3+PJt6wKNKqVex3sZnRBZRKXwHwFEON/BBppngCe5ht9zPHnmMx/kOL/A4oLiRD1opD2Cc4/QwyErWIQjHf/F36vsqPv48EkaEviEMFRqHfoa4guu4hltjgqTiUb7F8/LoJTn/rOogSYqAKkVs0Z1PFQeXMkWydDDFGIOs5iRHGGYjpzhCF73McppeVqDRFFiIy2s1rpfiqWN/zfGZ5wmiCiLCfHmMvaPf4sj0E+SyKwFhmlYn2KeCRxJQiAihqZJycjg6EcvTOHhiRRXFhFR9O1vUjod2XLRoXC9D4EbQlsakHJTrIX6AYzSiLEM9SZI0WTwS+FRold0UasREhYvt2qjOWEfRTd8F3+M1ahNt0k41sCT5MLQdE9tXZImMSynvEOTLJJIOOopwPUWxYEArlOcx9fcPnXPfhWf2EYzPoGhodT2zx+c/f+X0ObdZxtI4Hr4+qtjNEC7ciLzpQ1siUgJ6zlh2ZmK9tvyzTX9XsJVbywCSKskt8mEe4i7yzJGjgxydbORK2lSjDLYqFUY4wJW8m4RK4opHUCoz8nP/F5mbr6Hy5AuYKLAkNTQ7uJ4eNVjfPksH/QxxQg5xkL08KHdxm/roD3z+69jGPvbQwyALzFEkj4OLRhPEnBJXuUzJKDu4ked5hFVsJEcnU4zRThcTnLKhKCOUKjMMr7iOqeII+w/cixFDOtHByoGrWdU1zMipR0FpKjQELUMJsDLvEWkyBPhEhARRGRePCOiijwVm6uXGIsb27wh9nEQSUYr2RC+lyhTVYh6NS3tqgLKaJyAAnURVfYrkGWCYExxZ5G7UBDsVEudOaioGNSgUw5w/1FiVCod4qZ5Dk1DwtRVyNF6K6niE4BDM50m0uVCtEkSCmMgm4QMroTL/wB4kiuj9xC243bYyzFR85u5/numvP4gJI1xlqPqqzpj+v780x//5Hy7c2L3TsWAa38UZ06pBt7u0Mf7r0mqbvRXi/MvM9h8yXOVyldzCHh7Bp8pGtpPBypCICNOMs489DLOBTtWLiNTZ8QSG0gO76/vSOKxiY4sRacYqtZEpGWOaCe6Xb3K7+tgPdO59aiW+VDnIS2g0beSoUiZHJ5OM4uBSkgIRERVKZOlgnBOsZC1lSkwzTo3xDxGRCRmdfJ72tpVcvukTpJIdVPx5Riefo1g+jZfMEZanG9eP5aBYYmM7ISEddDPDBAmSCMYaFXyCWO1XoRATl+UiRFGI1i7VREhQ8XG9NFHkU5R5IscQGYMSW7rdzxCjHKsbioYX0tBWq3GEFofQw+LPBuzz3stTTDGGAB62HFmJrvdg0QpIaNyUSzBXwJRss3bH05hI8ANlCZu+j4Sw8PCLLDz8IolVA2jPpToyjkolrJSMsmXCoT24VQhW8K1783zo/bnv+3vxTkI6FjAtX8Kw8ZKQtwkhcRmXHt2qDy0OVcrsZTcKRUJs2ChBik1cwYCyVdLTjMdVQ5Z42Fw5VPu7JAXLj1gEq9nMHHYwvhSeyZBaR7f0c5wDjHOSiICAgCr7GWAVJzlMmjZKFFAoyhSYY4oOujlNBY8Ehig2DkJkQty2dk6cfpowKOMl2mjvXkc4E1Iqno6vvBGBdfEwGPLM0c8ws0yyik12X0Sc4igl8nTQa+VmkLoQpEKDsh5EgA/JJFEU4rV1o9NpwrlJHK2RakA7PXUjeJxD1IxHrQ1ATdq+1YQ03iVJI8iSMjMv8gQzTJAkTUBARBBnWRpEyaBQtK1PqgpPC+VQ4ygFoeBHDjgKE0gcUXORagCupnpiwh5baSiV0RLgKKw3YmuTceNGWH/0P+eWDclFwhoUO8E5EXa/vge7xC6JUuoDwH/DSp7+hYj8l0XW+Ungi/HRXxSRTy21z2VD8gbhPfwYD/JPRIRs5zqSpPBI0qYaP+hQQg7wIhFxv9WmsEkNxznICPvjL5vicq5lhWpQdbK014e7SIc8Yb7LDeqOH+jc06qNLexiC7uoSIlXeIZ5ZuJ2wxF9DDHD6Zh8qKhQqkup2KS4LalVygEFM1MHkCgg3dZHpTpP4cQTiAjpvlWUJ46TokHktGZF0Ukfs0yymk0sMMscUzEF1OCpJAWZP+v3J4itTHBcgsI8bjZr9a1cAR0hQYBEBkc0IQH9DNXDY6rufRib44nDWI3umK3eimAYWIIyVZQ8M0ySxhYK2MqxuIw4PlZtb2EEYWQ1tFxtCI2LUQ7Ksd6TQUBg1eC1nBh/wtb/hgY3YRPrWkHFj9vJO1aTzHEhCG14K7yAUuVlWMyahorBC5X1VKQR3hrzO16XY15KjyRWIPljbM77JPCMUuqfReTVpnU2Af8JuFFEZpVS/efb77L67xsERzm8hw8jCK/yLHnmScZSGiLCtEzwNPdTwVY1WfFx+7garXupD2wOLg4u+3meB+SbvCJWRdh2QoxhoESewiVUqEmpDFerW7iRD9JBNwbDGCMsME2KDG20Y72FkICABIk4DCV4xsExGi/biU5lyC+cpFpdINO/hvbV26hMnCCKqgwwXD+e7RPiMq9mGGQ1pzjKAjPk6CRNm21tLIp23YO4DkrpuKJKYjnNiyAAACAASURBVM9OoaIIFURE+QUIDFIs44+NQhgrBROxke2UyHOaUc6cEtYqtGrPQdcT7Xa9LF3xPq445307yEsIhjIlKpTjrWt+DtTl9RtXbjkiVXA8F0xom37VWvwCW9bc0aLxFhpNhCYQZXvyYo2IdqyBcTSEAbznhkun8PxOwYnIFsWk1Osf4rrEzPZrgUMickREfODrwEfOWOf/AP5YRGbt8WXpDngseyRvKBIqwU65iRd4jEPs5SAvkZAEYZwHiAjjZHZteKk1zSKeKdtOjB4JVrGRDrpj4t9xJjjBgszQzUBjVh2L/j3F93DFhog8PDrotbmac4THLgRJleJKrkdEWGCGUxxlgpNI7IHUfIkIg0bwSBLioyJNZiZiVhVIZ3pQyqEwdpgeBnAjTQT0NxkSX6pEhGTbBjlVHqHL9NIp3Zg4yJbXC/ji0266EBNYORSVIwzy1FoOWsNqMKGgwlrytNYFRdFGu9U6YzUrWMurPFv3Es70OhodMBsok6efYRx1bh20BWbxSKFRVKnU99fU67CeTYImU6Y9CBUSipVpUabRd/fpvezUt7DHPGKvJzREYeselFYoLXhOvJkDX/jFRfoTLOOcqBmRGlIqqHsmY5VL65V8H6KNvUqpZ5ve/3nM0athCGhuT3oSuO6MfWwGUEo9jg1/fVFE7lnqoMuG5A1GrxqkTTookUcQqlTqM1EnJsUpFB4pfCrx0GIHRAeHXlZwOe+qiyMC9DBIXjbzHA9zgkOAVfLwfUiloBJ7570MUqVCgTme4l46pIed3HBeSfiloJSigx466GGrXMUIBzjKa5TI18NbEWEsPNKGi8eCmSZDho5COwpNBc00ozg4dNDbcm0zTJDqGqBSmGOg/woyiW4mT79MGFVJJnKoIiTS7cxVZnCNR2QE4zb6gyjtISbC/kRNHDYE7SRRymBCHxAyZAnwOcIrgDXcDWNS8w4Xz5FEhFzG1ee/V9gSarv/Gjel4ZO00h5jmMCeiaNRJkKMXb/GV+mRHnqHNjA7cYDIaJSJw2SOwlERJnZOIkOsGgzlstC2tAzcMoBAQiYih0T8nelzF3iutI5ClGLvnJVDCs0lDvIIcHGGZEpErlni88V2dubXzAU2AbcCw1gKx3YRmTvXTpdDW28CXK/eXx+S7HDiUGvXawMnHn4c/qh5IwqrqHumEakhpzrZylWxN9BAFLfSSJKmwAJt5EiQIkmKBWZ4hG9RkgIFWeCwvMqT8j0ekLu4X77JA/JPPC73MC4nL+i6tNKsU1u5lY/EfAobvHFwMUT4VOoeS44OAnyqlIiIrGglEdu5tvX8CUl2DxARMcMExyaeoKN7PcND7ybbMURkIsqVGbpXbq97dia0kio65pUo10EnU6RyvSSyXSht5eUltDenTJkEKeaZqRcFWJ6Ibkq21zyY5lcNqm6gzoUOemzCH1r2UzMeNT4M2AID+52IuTG+D2GEMY1jtpHjPvkG98mdTJ06wJZrMvzuv2zCVpwZlIms0KO2Mip+COmswk1oPvQzy9pbFwKvaYJ1PGx4cVnHzswqk3km73vlkh/3Eoe2TtKqdzgMjC6yzt0iEojIUWA/1rCcE8uG5E2CW/kIjYHE1BO4Z343TDyTVmiG2bioEamhn0bnxNo0JNMGngftdKHRzHKagCohARlyuHjs5j6e4l5OcYQyxfo5GSLKFHiZp7hP7qy/TsihJa9NK80udTO7eA8KRUhARITBUKYYExXnmWOKOaYpMEdExNXcQlKlWvaVIkNlapSONZdTnZuk6998gIU1LmPtk+TXJvC6usEYJsaex3VSKAPacbE+hIvGQcIQqVao5KfwCzNoA0S2m6WjPUAIPWImfg82X2FiQ67xSOKRwI1bJjdyJI0w0qH0QdyVKzgXtrKTmmFV9SfUeOL2KVsjFuJjCGOjUPtcWtbPM0uzMXv1qRK/+aO2M+PWazN0rfQwKIyjuOzdWb78nSto67CaXc++YqxS8DLOCRP/OxV18EJ1FTNRlpkoy3yY5tQo7P7Cv/LoZ/6GA3/5wqU/+JnzlaVe58czwCal1LpYP/GTwD+fsc5dWIFdYrHdzbAomaqOZUPyJoGrXG7hIy3mww4etaqhOFkaz1c1mhxLx2O10mSweY/a5FUpcJRijBEKLBAQsJ7LuZkPcZ26nZv4EXZxM1na6aKP9/BjbGEnLrXqFFX3lmoJ/oPs5T65s95v/lzoUr3cykfYyBX1aqeIiCILlCnFYa+ANFlu4kdoV2fH7rvoJyjM0bX5aiSMmHn0XkpHD6CzGYwJCUp5FBqJIjQKB40XOKAcUskOXOWRVjkM4JAAFBnacXDIpgdQiSQREfloms7USjJtPdSq5ezgbsNhkTIYZavpGrmTBsZ9+7tzOjsXvRdJlaaXlfGkoVFWXDNIzcTGc+PsKEXje9LAvqdLTJ8KCKpCW5vmV/5oI5VChF8VnKRGlOJXf3OZ4X4+TEa28KXPbRSrFCZKfPUn7mHhxaMksy6eXlzy5/vHpWW2x8oiv4TtG/Ua8A8i8opS6neUUh+OV/suMB0rkjwIfEFEppc8yzejCmi76pbr1O1v9Gm8YTgoLzPCvvp7hSJBiiplNA6GCBePzexk5RkS5WfiMfkOgVPE86DiKzrbhWIJXD9LhjZmOc3lXMuAGm7ZLpKQZ3iANWxhhVpDURbYzf31aqUu+hhkFRqHOaYZ5SiC8G7ef8FJeyOGGSZZYBYhIkcXPQwumaQGeFR9B8kmya29jPzUMTpufQ/h3BxKKfzTpwn27KMtzFCRIj4+WXIsqDmUcljZuZ2Z/DHCsIxHkqqUaEv10t62kvHiAYKgbJnw2qUjNYgWzVTxiO0lb6xUfohvG2YR4uARqRB0k8y8tirMdwx8HrO6H+Xb5WbPq2ddyx55LCZq2kmCir2fi4eqezaLbV8z/QoV15yFtK/wKMwIJhK8hEvl8Orv47jvDIxHdpLki/CiP1Bf/omr9xOWIiQyaNd22CQMnztPnuKCkVw3LCt+55cueP2Rf/ufLtmxLwbLHsmbEJvUdt6nPh4LONrhoRFPt5U9ISGj55FiKMg8PhUcxybY00mh6kMQKHzK7FI3s4MbeIVnOCr7WrZ1lMsGtteT9W2qnWHWo9BcxXvYpW5ihVrDgBpmi9rBzfwoOTp5mvsv+Dq10vSqQdarbWxQ2+lXQ+c1IgArzCqCcoHZ/c/hisf0N75JMDGBTqdxcjmqYRHpyhGogIiQgsqTlgxoGC8cIJPpZf2KW1g7dDMbh27HSaaZKB2ie9UOUrleO6S7Gr/DpeiV8dp6ERMiruWXKKWJ8FHKsUx7ra0R0cryNBRghGPrynUjci7sUjexk5u4WNaZWuSnq+ohOF1fUjMgbbSTIYvGxcVBoSmOCSYMcbJZwlB4dX/lrH0uw8IXwT9j0v2bPz9KkLcVC24mi5PrwEmmL/3BL21o63XBsiF5E6NPreA2PkobOSz5zWnKmggLzDIlY4tua8SwnxcAQ2RAO7arngmhW/qpGageNcgaNnOMfRySlwEIxOeo7OMge1lglgflLh6UuxljxMaJOcK4HMeXRt8YV3ns4iYEw/T5y85/IKxiIxiDchxUexqvs4fKwSPMP/IY1UNHUa6mHMyyadfHY6XcDE4ig4gh1d5HZvUmTlX2cWRuN6PVA3RsuoqhK+5g6sTzBKV5FBoT+gTKp5I/TViZtQcOA4QI0bYRlXHiLphRYH9JRuJ+MAIScWD3X7JQXKzrQit61SA7Y9XnC/dGWpP7zRVljXCo/T9BiiJ5CtiJhU+1HlYkAp1tByNccesJ/uyrS/fMeaeiW7fqar1wop2XH5pBeZ4tgogipFzBmKWLLL4/qIt4vTFYDm29hbBfXuQkh1pS8DWp+SHW46lEncdxgJfIM2fDYK5NsBsDHdVh5pminW52qBsAKxr4GN8GwMMjIiJDDp8ySTJUKbdUK2XpJCIgzzz9DLGJK3FwmeAER3mNAB+PBB10W36L6jn7Yn5APKj/BdGQ7B7AX5jGzbUTFvIkcz1UF6YxEqIEJBLS3SsoTZ1EKU0y20VQXqBneCfZriGioMLpE3uIwirtKzaSP3mQamUeIcLt7iEqFPD6+vFPHG8c3ME2TFf2jlDrF5FyY1a5zaYAOJ7i2lt+Cy+RIfN0I18ZTp8dct4tD5KntnzR4t8mND6v+R2mroBQozRazyMiRDfNGWsVcTr2YAzGtgcWIZ2MWNHv8NKDa0mnl+eZALOmRD42EF9bsH2B/t+PPMrU0Squ9ohCH0+niCRAaZcgLF7a0NYXf/mC1x/57H98Q0JbyzyStxC2qB1sYUf9fSghz/EQR3iNI7xKQpJxNVRERITnQSKOFPm+w7boGhRWv2t7EwcpqVI4YnMvNUn6KcZZyTpOcoSVrGGMEbZyFX2srOtH+VLlKK/yDA8iGFKkcUkQEpClg5CQPTxGWrJs4yraVRciQokCZQoUWKBKiTDmlaTI0EUfOTqX1KgC6Ig6mVOzBPMzJLM9tPWuQg0mKE2MIGGI8hRWTCqkOjdBbngTxYmjRNUS6c5BjGOYnz+Odlx6Nl9HcfIY+ZMHCfyipXwbgwl8a7SVgXQKyhXreTRrOKJAg0p6SBj3bVeqPkGMAmH3Y1/ixtu+aJ/ZIgakhuvUbdwndxLv9RxmxH6i41xHDYuta4hQsYyLQrGKjQyzgZRKY8QwySkOsdcqKGsHLUJkIk5NGlZffZhDT62no/38oca3O/JNXsbG5AQHyn1MHa2iIkOCJGXxAQWicIzmknPd33xz/bOwbEjewnCVy3W8D4D75M663LpCs42rWRGsgcDmIowYxjnOPvbQQXdLLsKIrUVawVqmGCNNlg56mGea9WzlCK+xi5vPqqJKqCRb2IWR58gzR4kCa9nKEOtxlIMvVY5ziJMcssZGTJ0ZY42WW3fGTb05lJ0xu+KyiSsZVIsngDeynafNQxCFJNwMUqqgdER75xoQRWH6GFqlQDsY4xPMTSOOYMIIyj6F4mECv4BSGq1durs2UxRDIteFJEuYRAJ/bBanu4PEpjWkbr6K/N99yyoJO1YjjCiyRsNzG0bEOCAhsYwYBjCVCs+/8r+4cc1P4yY8wrHxcz7Tq7mV53hoiadeq+gzTUsE1RTyJP4WqKa/ruA6elWjHFkrzSCr6JEBnuZ+ykER5UA6qTAizC1A32VHSCWg2FBOp7MDPv2xNj73yS52Xfk65APeJKhKQFJ5i372yl+/hEQBoHHb5nHLBnSBlKeoVP3FJPG+f1w8IfENwbLv+jbBe2lIxAuGA7zII/wLL7Obl+RJHuFf2M8LZOnkanVLy7ZTjKHQTDHKStYyxgi9DFJgHlB00rtoKW4N67mMAgusYSur1SYc5VCUBZ7iXqYYxcUjSwftdJMgZSuz8Oiit67ma9n7yXp/E4PhVZ7jAbmL04vkgdpVF2mdRTRESU1h7gRzk/uZnz9GetUalJcAgfTQaoQI38+jcHD624mcCL+aJ5sdorNzHa6bZnbhKKm+lVQL0yT6c+Su24b2HEyhRDg6Sv4b9zQS2REQRpZdrhXKGAgMKoqTULFQojRVBZfHDxOdPJP3dTa6VG/8DM8X1mpFY33dtMSKrHTT32JEmuGpBFvYiYPLpz6Z4Yu/3c6v/1o7l211SSQgiGxYtIZ8Ef70q0WuueMkzoqDfOrzp962/d+rsVR8t/bo1h457fDe9ATf+etJ2toUO6/w+F9fGWD0pfUce3Ytf/jbPQyvuPRD6iUmJL4uWDYkbxNopXmf+nj9fURISMCkPsWkPoVHguu5g2vVbS3bhRJwkJeICKliG2pVKWMwdNDDHFMtxMbFkFRp2sjShlUuNhKxh8foph+FYoh1uDF7vZcV5JllNRvJM0tAgEKRIkOCJFb6xY3j+g5g2MuTPCh3MXNGEv/ycBcSBvgmj18toLMZquVZ5k68TPryTbhtOcKpaZSXRCXA60wTTM+ihrLgOZBNIrkMqf5hIhVSmD7GwL/ZQvHoFB237QAUTnsW//g46Ss2kvvEbYiu5T80iEYiQSJs+a+ENiwWUzmsobF/Gz/iicm/B8Bdu3TJ9tB5m2E1vI7WZWcrRCs0K1m75N5qPVN+5VeyfPKnMvzcv2/j3u/28Sf/vQvHUSgFCduQEjFglKLGg/3mv5YY3nW4zrLfFxQXPcajFXi8YhiPCme9CqZCwVQ4FuY59kPoOngulKVKuamABBrGJGi6r37J8NEPZHn6nlV89INZujod+ntdfu4znbz4wNLP9vvCctXWMn7YaDYmCm1D9kYRUGWacSKx8d5mheEqZRzcmHQo9a6DEgebar1QlkJzb/IJTpGmjTmm2MouRjnGOrYxwUk66UXjMM8MWTrjZrYdccveUlzCaps82eZSw6xmMz0M8AKP8aw8jImvoVP1kqWDYG4Gp7sdd9sa2m66Gm/9MKVX9hMszDL0iZ9l+Cc+h6lEhMUKyoHyoRFSm1cQDAjl9CxFM4YJy6z80DYKIzN0ffBapOKjs22Ep2dJ7dyMu6Kf6PhpmytxiA1HrUoLtKNi/gi4rsJxFGKk5QdW1kVC9/w/uU1LqAa34syRY/GRJEFq0eU1KKVw8cgvtBqhD9yR4td/LUsioYjiPL7jglaC08SfnJoWfvTTDZmVqgT112RU4NGmquLDweKiXlOmkVmYNZea1HdunIry9dehwHAoMOz1yxwPq/VXVqfo0hm6dIapYy6OA3/4xR7+9h8X+NyvjPH5L4xz78OWZ5Jtex2GVFEX/nqDsJwjeRviferj3Cd31kuGTeyfHOAl9vMCCUkSEtRZ9KvZTDf9CMIkpxhjhKO8hiAMsY45plqk3M9EJJadXvNIJjhOJ30E+LHWl2XiZ8hSpkA3A5zkEAlS+FSp4pAkRSJmmguGdvrYyq56D3uwZckvs5vneIRr5FaUUlweXcXuykOkr9iAciNMJU9q4wB9P3sH03/7XU787z9jzb/7ZYY+9u84+Y2/ou36K6m88BrB8TFSK7pwc0natg7i5dYy9r3XSG1dQ+9P3crEX95DZtcOyntfo/TkXnSbzQc4nktUFIhZ7TWnwEQ26e46ghIhNA6iNVEYoDTWa4kido9+nRtX/cySz89VHjvkRl7k8R/gW1CDiht9nbv5UiQhIT59fWc3t/r0pzJ8+Q8KNY4lNcnQEI1ShlpNxIOPlwkCa4iOhj7rXPvc9gdnk1MPBxk2eNZYjEcunfrs9PSsKdGlX38lSQdFFBvgaZOh5wxmelK1Guf/+sezdHVq1r5rhIFeB2Ngejbi776Rx0hDEPVSQr2BnsaFYtmQvE3xPvVxTslxXuNp7ECu6gKEtR4nHfSwi5twm5KKPQywVrbwDA/ixDIm4xxnvVzWMqg3Y4wRNLregCrAR8fhqogQDy+O19f6qdhkca1M2MUlxK/H+TPkFhWj9FSCHXIju7mPSU4xwDA51YmLR3BgnLBcILNjM+HYNOO/+/8RzReJigWO/s8vk9t6JYmOTsL9hzF+aHWnvASFg5MUDp8mvXmY/p//MTJXrKVyaJT5R15mxX/8D1Re3odybJ/0Wt5TJzTGNygttrcHDQUsrTWh0RijcCQgwsF1I4K48CcvpzHm/NnYPrWC6+T9PMMD1DphNnNFzkZrnVeNnBgRcJyDDMrqc1bCjXOCDRtcenvP9jxzOc3GDS6v7beDfe3MXWUIFQRWqR8x8NH/nuQPf6nM1+YaQpuBNPaZig3GaKWTTq/EhpQNVeaN9ZjWJybpdBoD+a0p4XBYiNdx2ZlY2rO6EJi4iq25WMFpChFOG/sdXuXaENuf/Emer/yB5QKJKCIR2jLC3//ZIFoLf/rVBWZnNY4jvPhqSOQKwdIc1IvDGxyyulAsG5K3MYbUaoZYTSQRD/JP9eU1VeEd3NBiRGpIqzaulOvZwyOMcowsnbzAY+yQG0moZMu6UzLGAV7ExWWOKbrowyOJwVChTIoMZYqkyFBkgdVs4hj7SNNWL1UGF5cEGk1IyBo2n1OMUivNetnGcQ7WvaT+YJDx/BjZdZeR1itxTILua3aR6lvJ4X/4b3R9+CqIDJHupvzqUXKruwhLVSpHJ+n71G10vOcKdDpBuFBi+puPM333k/R85idBIJyxZEQThmhXozU4CQffGCSECIV2lE26Owq/YkArHEIio9GOIQib5OEj4aWRb7Jdveu8zy+nOngvP44vFY6yjwpFsnSxhs2UKVCmGIvxt+ORaDEUIsIoR3mN5ymR5yj7WM+2s45RkHnbhfNwyK5rxnj6yT48r3VYqFZjMynguBoTGcRQX6biqMoz/zjCnT/zXg4UBticbSVijpS6ybmt+YfDlf66MQE44vdzVfoYAHNRhsOh3UfeXLphqsanqRmTZ6r9FE2SicDq1h0p9xGFEZN3PsndfzQKSqMdhygMiUKbhypXHT76sxNI1FohJ47w3h/v4oE7Zy/Z+dbKit/sWDYk7wA4yiElGSqU4x+S0M/QWUahGR10kyBNmQILWLbz43ybfhmmk14iQsY5QZUya9nCcQ7wEk+xS25ikNWc5DAhPlUqtNPNDJP0soICC4QEdNHPPNNUKVOhTJIUEUKZYj35ey70MMgrNHr3bOByxswpKnNjFEb2kejoBq3xZ07jDa9k6h8eZf2XP0f/J25i5Ev/SP7Zw3jd7USFPHOP7GfiL+9BJRNIGNK26woGfvkXSAyvZPrvvkF61XoqJ0boCLqYiSYQcTCRZdVnBpMUT5YwRiyfzyhcLYSRIcJBuwatsT3VNfXp/KQzelElogmVYgs7W5bl6CTH4oKQYPMeQ6wnJ108zf0cYx/TMs5atti8Ej6jHGOUYzHfRDExDqvXnea/finDZz5tB9aRkZDjJ8J6Gj+KTJ0qg7FJeBNZgzJ9rMhfPHo51++cbTEmI6Vzh9UOV/rpT7R27JyLrFfwtblr+FD7CzxQtAZwpbOXfuf7b752JjSa06EN5z27sIZur8TUoVm+/unvWmJpExQa7Wk8HVIN7f1VjkKMwXWFMAAieOLb85fs/OpY9kiW8WbBLm7mSb4bv1PkWLornlKKnHRQplAPrYANY80wWR/EIkKOsT8ejCKe4QEytBNQpZMe9vE8G9jOyzzNVq5iP3sYYFU9HFbr4V6mVG81fLFIqhSD4RAz87Nox8VdtxK3u5uU61B8ag8mCDny639F++07yD+xD1xNWCyDdghOTtD3C5/FG16Jk0lb6ZNSmZl//Gf8A0dx0+0QhrEMBigX3KRLbihLpRDPsDUYibsWAk7SQZuIQDQmiINOJjYqBkSrS8s1WALtqotb5CM8yreYZ5qXeZpahVctvFOrkqvhC79R4ktfLrFtc5qDh0KiyKq/aM82dapWLXlfaSstJireo4bpr/4rz3R+EjOfYDcbIWW/O21dZXKphkfSlmj8PZhpVGq9UlgJwLq0JW4+Vd5ARvuUTIJXgxyvBnBdspGISC8xGVoMBsPJsLmybIAHZ7cC8K3dfYz84tfrNt+NQ7K11soSGnyt8HREYBw8HREp65W6CSH0oVp6HUb9ZUOyjDcL2lQOJTomAmrCWARyKYQtHF0d/8CEKmWq1FhqKiZxN+L3ReZJkWGGSRKk2MceBhjmNZ6lkx6mGYuZ2SbWfLIlwFXKJEgwzTh9rDzneU0zflbyeKvs5MXqEwQJRXL/Ajh5lGiiqUns8BEy962n0bikwhSlcglCgykKM3/7DXQ6RWL1EKZcoXLwMG0btpLbtpP8nqdBYJpRQGFCwRfD9GtNDHUT8zi0vRNRKERis9PKGMTBck+ceF0RROS87P1LBU953CYf5SHuptZeOYoHR+q5s5pel80hMD3A1JMr6ASqnGCeabSKCGuRlgi0azmZxoCJNF2DDrPP78PMn51LK86mqWZcejtay4Pz1VTdkCz4KXpTNidytGxldXq9PCXT2F9FPEbCAmvc73/oGnbb6sakZkRMaDj+y39KrSuMwiHUUo/dKaNtlV4keEkgjAgCjdY2zBc5NhcGQnipcyRmObS1jDcRblcfi6u5xJbkyrZzDmaB+MwxFb+rpcdbUTMhiwl11BL6tUZcJzkMwBTjOHhxsbFDhhwhPmWKCIJPlVd4ljbJkqOT9VzeEoIzYjjGftayteVcHOWw09zIqcpRjlUOUKUYkxsVLkmCsErN3PWwgmp41PLBBaJSmWRXP57K4vYOkFt1GYWX91A4cQKNSxBao6vRViY8qt2JpgS3q1Fh414qpTASIS4Q2tBXFFpTrN+AHlK1FscaJ54g2GenUdR6wxsMCVJcw60trQCGWU9e5ngueJggnlw4jqXMhKEda7WrcBIeEpRwn58h2d1nj5tqJNv9To/JURu2Ml6sE9bpUwkaw9AIXS2ey2ipg1VttsPrpN8OwAc6XmI8dpLXuq1hsbXu2ZVnLfchTu6scNK89+VP1JeP/+sLEIX17jAgqPg51aT3a9/4chkSSTtxEJR115wIIyDqfBppF4+3QtXWMo/kHYab+BASc0VOnUOGXkQ4yEvNS1o+bwg41npf1F6csZ5VedI4LS1/IwKqlOP8yzRp2uhhAGLOiiGkTJExjvM43+FJ+R5GDIH4vMQTJEmdRZIsSYHd3BdrR1Vw4kZcYdzAl9hjioioxE20HBwco9ChoApVKocOsrDnWYp79pBJ9aFCcMpR/XqbQ3yq6Z+9KLt3UQZcwShjR9hQWSOCppahNib6oXkjNTQrA6t61VKtANzE1UwOV/OeRfvJ5FQnV3I9Di7JpPVEfB/E2BbAmXbF/ExoZaYPnN2K2WmSWdHVpqKAuQT5+bNDmn7o4IfWCJ0otuaC9lXP7a1eDC7vGufyLitXc+IvHqvfE+0JygOdMDhehJEQYvUFwf4+tFKkk9azTKRAglgtx3mdQltvckLiskfyDkNKpbhFPsLD3M0BXqQiRVaxqd7Stih5DvMKU4yd5YNYGXtDowT17IRkbaCq1nIaCAAAIABJREFUSXOsZC0eCfLMcZyD+FRbBmQgbuy0uJLxDBMc4EUe4m4AVrCGLeyoD8RWuPJhisyjcRlmA/NMU2CeQdZwksOxV+IREuHhcZpTKDQ9DDLDJGIENZMnoIiIwZ87jZ5eIBEIRbNw1n1YrBdITZ9Ca+KwlaBdEDFEYTPjvCnr/kOEJZvWJgC1Uae1nLidLtpU+zn30UWfFSmshig0nkpgvAgnYegfTjJRGkSNzpA4dJpVc7OoYoXZ6xqFE+UVDSOSmmzcQz9MUphOkl0/F78/uwz5RLGTabfN7idK8FpxJYFofn3gPh4trwPgXamRlm1m5yK++AfTvLLPp6Nd89u/1s22bXbIOxA2QruXd43zRLEESuPo2CjYm0YYOSjX4ChBTEQU2WKVyAjaUaTTMYcIsY7JUg/hbYxlQ/IOhKc83sfHmZRTvMKzHOcgCUnVPZXazOxMLNZFXqHr60o8a6uVFner/vp6XfSxSjayj+cZ57jthdEEjWYnN7Zso5Sih0GulV6e5gEUmm3qqvrnJcmzm/sxGJKkaaeTFazhFEfI0VmP/RtMrEpcIqAanzf0McQMEzg4mMiW3dTCYZXqPCZ+X9tPK09D1Y2m5ejY+LgRZZMGNESC0dJiO9qXIAe+XlBKMSBDnGas6Rm2PkvrFS69j24Z4BRH6BzuIIqqaGPo6E4yVeyhPDqBVCq4vSlU0SbE205VKQ7Z0GTb8YYnEsQRqGYuYnG/9TwWsgbd7pPK+BSrCSKj6chUqBXVDqfnCMQaopoRqWFfUGQDGe74qZPsfr7K9dek2Lje4/CxgOt/5CTJJDzwjSF2bs/whcF7+f1Xr+Prn/k2jguuawmWgW+9C5vriNBiPUqtDJ4nBAFk2hRzc8Jv/X47X/3zMmMnAhvxfB28grdCaGvZkLyD0a+G6GeIJ+QeSjT6rddceKjV20s8q08QUK2bDft/86Ck6l5Fs0Go71cptspVzDBJmdakazcDi24DtlvjFtnJizxRT1KHErKb+1EoXFwEwxo2M85xXFxSZJjkZFMYTkiSxsWjxAIKhwU1w0pZy2nG6GGAKcYpU0Dj0EUvVarkmWu6Pur7glpbXKnfJ0EQc+avPo6h12HYxc2LXufrjW1cwwR3caboY4O8eP6mTDVvspov2P14SWa9DZSOH6W7bQ1z1cNkTWueomZM0tP2PpR77PGbjYg6g9xuFhKQ8YnM2d7fvce2MNhhcyPXtx9u3c4I299/jKFejwNPrmVoRWOIOzUW8tlfHeeWj57i8sscXt0XYszdSASZtCLwhWqAbYamBRebB6r6Cq0N2rHyMArNwpxhaBg+9sl27vxaFZRCKyHT7uFPXWIh+WUeyTLeCtjFTTzOPfX3rV6HDctIvaqnWTBQ6gO1agp7raR1ltgMpRRrZAsHeLHu+bh4DC2xDViPRqE5xTGGWcdenoqToBEeSQKqtNPNSY4QxhyXGmcmTZYStRJThRN3mhxVx9kmO0mS4TCv4OLRTg+CYZapekmszds0TzdtXU/NzaiZVIXGw4uH5EaXwlouSaPppB/vHPLkrzdc5XK13MJzPNyy3MEhxDDBCTbI5efM3xgxnMYqGFdKCieZISqVaZc8Kzu2Mzb+HJEJSJ8sEiqbf3NPWA2utuu31/eTKDSuv9zjcKZhi0o2QFQyjVzNVJeLCa1RSbX5jM+3M9ixwAvFRpsBjeHevx1HG8V3vraSZLLVCA2tcPnW36xkeNcRXn4l4uc/l+PTP5mlq1Pz6r6AP/mLBR5+vEoUCaEoXM96kum0UC5rjDK4CnAUuQ7410eHqVSEkcMBiA1lVguX2oiwXP67jLcG0ipLr6xkimaZ83jW2DSI1ip2mmfitdBWzYgkSZ9TSqWGHJ01LnB9mcfS2yilcMXjFIdZKWuYYwqJk/MuHj42lFKr/lrPZZziCBVKlCnW0/41w5hVnaRyA7w8/zyGamyUrOZUa9nzuSB1H6wm1w7EJsTUw152HLBltgbDVeqmC9j364cu1ceNcgeP1zlFFvYeVuvSM4vhJIfrz73N6yHXthIyipnZg0TRLCmTIcI/pyrBYkhPRxjXoXpubiUqUnUj0ozx+Xb2eTYct7Xdkh/v/ctR/vA3e84yIjXc82CRKIJHvjPIlk0NgzbQ53DbzSn+x18t8Nu/Nw+hEBqNpwxRBKmUsTpartVTe3iPvUff/qcSStnig84Ol3LhUtb+xngLGJLlqq1lALBT3VCvdLKwQ2BESIJUnEDnjM9rs/LmgTQ4b3+KxkDd2GORhcVXjhFJiE8lbul7si51ERFRpUyWDqYYo8A8m9lBjg58AjQOHgmSpEmTYZBhDEJJ8kzN70fw60ZmgNVksLNgZ4k5lmo6b2sw7F8Sy2NK3VMx9b+BehOyNxpplWtRia61DDAYXuEZTsqRusIy2Ht/TPZziJdjT0uxujRMeqJCZrLCumAjrnEok2cLu1qOZXwf4/voh58HQJ0V+rNIzjX+Ts3Er0mH1KRDclrjjifqr0o+WX8dnerh6FQP/7zfKiZPTQT86Pvaznntv/H/TPFfvtjVYkSa8fnPtbPjCi/W45R6pXdNP0s1UmDseabKH/7ePL4vIFBciCiVF93tDwQlF/56o3Bej0QptQX4+6ZF64HfEpGvLLLuu4CngJ8SsT1DlVJfBn4Ua7TuBX5V3q6dcN7iuE19hCfkXko0ZB4UKs6LmPrg3ZwXaXjecWkrpq65dS6McrQpgW0NywgHWSnrlhAWPI5Gs5atjHMCUKTJoHHQaNK0sZ89CIZBVlOm0HROtvWwAnZyGeOcqhszFed1ruaWuOT4GLUujrXQVmtYS8WekI4DfmeGApugHTBCmgxX8x5S6vVXs70Y1FSiDYZZTsdGT3OQvRziZTrpQxDmsHpYlnRpJVL28wIp2uzEAUvgS5Olm8XzXADO43sByAByzWUAKBEkVvnNnAY/a5+/36FILIB/RhGZDhR61sN0NbzGILChsC8+/2OI7MY9R+nU1HTEidGIj39k6efw+Z9t55d+bZqqLxhleaa1brtaQf8K+MXPTvP8UxUiA6EviFgpHDcB4fm5vheHt8BoeV6PRET2i8hOEdkJXA2UoEkBMIZSygG+BA2fWSl1A3AjcCWwHXgXcMulOfVlvB64Qb2fG/mR+J0dJq2kYqqeKWl8puqmpNYX3GA4wEv1vidnYkFm63H25l9IlTIHeWlRb2ZBZjnAS4QE9KpBO1MkwMQmokKJKcbx8UmTxVEOR3kNB5cMWbJ00EUPWdpJqTQDDNU9BY1mCzvJqU5e4en4rOQMva/m65b6ddbyIs33oxnrzRZu58e5UX3gTWdEatjIFTSuT9W9qoiQGSaYZTK+z6HlyBjrvQ2xnl4GydFFlQouDu/i1ovix6imZ62MtHgriXmhfSSi/ajQdSB+VkFj33rWehQ1I1JDNudy/2OL9zOZnArp6tSk00sPe+vXuCgdl3LH3kcYf52Ngekpw9NPVOjpUNz91wOsGbbtBByH16dL4duQR3I7cFhERhb57JeBb2CNRQ0CpCBuNAEeMHH2pst4MyGtMtwuP8FD/HM84wzrhqI5VNMME4c8wFBiged4iM2ygw56UEoRiU2AN5LsrYgIOcVRZphktWwiSwchAWOMMMFJDIYcVjYjRzeTjBISkiBJghRVKoRxV0iAaSZI01YnQ7ok6sfdwGWMYb/CAgwwHPetNy0humbUMkK1Iui64mvdmLSWRm9gO+tUK/v+zYi1agsL8v+zd97xclR1/3+fKdvb7bk3vYcQQg2hIx1EEQQVAUV5fBQFFH18RERFEQv4k6JiQQUVpShKeeggINKkhRoIJKTf5Pa2fcr5/XF2d3bvvWlyKbmZT16Tuzs7Mzs7u3M+59s+3x46qW0DLHEVcVRWqI6QoHTH1vJGJT40n0W0islbfC9pe9dUW7JM/Z3QRNj0BvZgn3pcqFNEoVvqmkY2eCRihwV6HlgbwAkGKDR4193da18uvuwp3n9YFE2rJbW6lM7goItlSUxz04S3ocNRnSBd5S6SEkIRsArwi0sbOObwGKmETjCojvHGU6qj5Te+38VNt2VYs3bL2W9bi3fbZbW12NYYycnADcNXCiEmAicAv6peL6V8AngI2FBa7pVSvjragYUQnxVCPCOEeKac6+/j3YMQgkPEh9iZxZVZqig5gkTNzLv6sfrFOzgM0c8SHuUR7uAxeTf/LPWMrxYHHA4HmwxDvM7zPMs/eYEn6GBtKeVUsrjUJriNqZWIRJECDg42FgLVC2VQ9lLdw0OU6jyG6MeRDkERrpBimCia0EuNvjzCGJkKO3zK51WFVxdpCgRtTNsuSKSMhWI/9uJ9VA8HGrpS+ZUGSEoRMs/VZxBgNrtwCMdvFYlsCu7GLszuke15g30WwT6PeAID3rXXhzWPCvZ4v8HGIz7Aq8stPvXFDrJZjwhdV3Lz/w2haXDX/ZsPZFzzpyEKRZW5JXRlabi2wDQhHjdoaTIqJFKNH1zQxGknbl6i5T/CeOqQKIQIAMcB54/y8hXAeVJKp9q0FULMAnaCShrI/UKIg6SUjww/gJTyauBqgISo3w44eMdAq5hMK2qgSMshetmISZAGJmBisoznWcdKKFkkTbQxmdnkSJNlCAuLKDFipHiVZ8kzek/vMiQu9jCLxSDA+8Rx3nNhEtYS5NxBkIpQlBClg8TlDV5Cwyi1EFbZSEXyxKljI6uZyAzKQpNlC6Y2uC7po2sLV6Y266wcM0lSz3yx1xb2fe8hJRo5nA9XnvfJLt5kKZZU7sK57FZRazYJoIu3VsPtFjw20Lp6MbtUq4LiLlNrtgt3qO8nUop6F1Mm/TNHDltlMgl3mUSO+xa33f49br37TT7+4TjTJxv8+g9p1rc7OEi+ekEf++4dpHmURl533Zfl4X8VkBKl5mwoabViUX3X37i4h6MOiYzaUnf1Wouf/27sZeTF6E6A9xS2xbV1DPCclHI019RewI0lEmkE3i+EsIHZwJNSyjSAEOJuYB9gBJH4eO8jJuLEqJ1xzWN35g3L1FFoHLHmAI5BSqV3paHRTy8reYU+uhnNwRsmyj4cOeqgtYezP4+a9+CiIV2XoBPGJEiODIP0US4TlFjoGMRJIZEs52VCMkojbXSwFosCA7KXpKjHkEaJWMrV6uW4x2h3cu35urg00sZuYr/RL952hjrRxJ7vcDhTOg6BDV72nhsNYiVruyIG+i3qX/eeZ6vIIDjgqjhLU5KFB13M43d9g99dP6gcj65WcV129zgsPnQD3/l6io8cHyES0Vi1xubX1wzx2+vS2LbqcihKsv92sVykK2nvsDngA+v4xaXN7LtXULltHcnd/8hy5le7sOy3YQ68HUyrxdYmUAkhbkS5pq7dwna/B+6QUt4shPgY8N/A0ag78h7gCinl/23uGAlRLxeLw7bqvHzsuBiIZHmmeA+goQVNhKFjD2RKN56KXWgV20MQQMnA2BQJE2eQXjQEMVLsxcFsZB2v8izlcsNy8Hlr9LGamMyuYvHb+4F3EGiRiNInWTAbACsZIthemumnVSBdplSads9eDQQHvO+mHLAXLsjHnuXR3nLCqVe5VHZrxqKCbE6i6yqwruuQHSVOX62OACoFOBYRxOMazU06a9baOI4kk1O1J7jus1LKMTFLQxMnyylnfWWrt3/jgq+M2XtvC7bKIhFCRIAjgM9VrTsTQEr5q03tB9wMHAq8hPoO79kSifjwsbWoN1tpKc6gi+VojsTQBFoErKIGDqW+H558SVmWRUNjiF7KlR4ZBnmah5nNLjShCjPL8ve1KFsotfGSOezKFDH7nfnQOwJKDT20TlVcEuwEouEKiQCI/jQyFaPxniqJlEQcd/XaylMtVc+u7MeLPFmq99Epy+Vr6OQyilAs1y0JkprMYEalPfQ6VlS1g0YxSOk3NZSXDGUc2jc6pTChAKETMCXFsQ7xbgcWyVYRiZQyC6WUGW/dqAQipfxU1WOHKvLx4WMsIUJBdgkewpPdfeQKGSzHRg+GkFYOPWDg2A5SM3CEg64ZlT4irgZaKIbmGtiZNA4WQ/TxAo8DUJY/KWtQeYRSHY5XkjB7cygJsflukz7+M9jr1lceGxNHSseL/jR2h9fznerHgNvZhUkAr0BUVtJFlGKDSuLQMZjMLGZSKw8zQ87ndV5gPStVxl+196YqF0OgJi1IZ+xJRJ34ex6+RIqP7RblQWQveSBvspT1xZVQVJLxTtFBCANcF8MI4zhFgnXNCNOEok2xrwddqiEmRgNTmUMdTRRLxZcdrKObjezBgUoReBTZj1VyGatYxkL2eYc/+fiGWxxZ0Wevbx9lyy3Da99cknovWSNeFqJOHU0jSARU5uIcuStD9JeavHm1RN4hBbImW2rsI+PbQ/qvTyQ+tnvowmA2C5khd2aQXorkWcYL2LKo5OPtnKpG704rkinVxrQwlZksqCGJsubXEvkoc9h1s7phE5nOmyzFljaG8G+l9yLCIlpKi/Wqf8o6aOVC2qnM2WQhpRCC6XIeL/LkyNR1Wf5PIvRSHGXsSki2K/i/fh/jBrrQK9IsdbKZ11hCNxswCSKR5Mmio9PKVGYwH2MTKryOVHUpUTbd5AnAFAF0qWNRwPBvpfcsZrKAFbyEp8RQjnZp2Fgka732I5CiseQG88L1w/1NupCYpgrvWGMsAOy7tnz4eJcQEEEWsg9FWajoSEWIK+XhLch4ZBioaIxtDq50Sy17/dvovYzpYi4rpNL5qlauLsdMvMy80VFbnDq8nYKCbZdzBEaSzFvCdlLZ7t8BPsY1AiK4SVl0YZRkOFpqBSbtdZ042Gxg9WbFJ7toV9k/M+sIx0qFdE6tb8N5RRU9GG2takVCpa3ar72h3juZxBkY+yI2H7U4iON4hNsrz11c9JI2dCfraWXqJvftZH2lGBO8tgDVhOE1g9sx60h8GXkfOzT0+pTyRYSCagECC+Yj0OhkPf2yZ9T9LFnkDV7CwWFJ+63kW2Mjt2mIYkyZhDGlRGQJbxstHEYLq0CwnkyiJ5Nj/Ml8VCMgAhzEcdTqVSuJm5W8hiNHl+6xpcVKlpbUo2t7z1DK/tJKfWf0km70mGM7EG30icTHDgu9fpRuSqEgkXAjZV/4CzzGavk6llSZRK506ZDreJoHKZJHCxrY+SEGB9bVkInVMKwnRmIk0fh4ZxEQAQ4XJ7EvR1LWSnNwKZDlWf5JWtZahkOyn6d5iCLquy+H68vQqlxcBgbTmMe+HDWm5ywYJ/1IfPgYLyjP+kVcDeoy6Q32bixEoV5ZJG5AQ1+aQGayCARdtPMmSzGkiY1FkBB5cghNI9QYIrPR5qXXb2TmOecDjeg5CHeru7o+r2pM9MFapUF7kRJ1NPuqBARf8l1c7wSiIsHhnISUkmd5hAF6SDPIUzxIRMYIESZLhjxZKFktXraXcmFpCHQMHGwMTBZxqMoQg7G3DMb4eEKIo4ErAR34rZTyR5vY7iTgr8AiKeUzmzumTyQ+xj20gErhLRNIBaVKZaBCIgDZJp0Jux9B++O340qJLcu9T+yKXL6mBxABh3xfHoTAHhqg98lHqd9HtdLNNQrC3ZLu3ZQ2mZnx3jvcbaPnbJywgdnnnY4xdxayoxsAp7+qZaCPtwVCCPbiYKSUdLGBdlZRIEOODFmGyltVXFnlAtVy7xa71F5hDrt6JDLWGGNLo9Q36iqUUsk64GkhxO1SyqXDtosDXwT+vTXH9YnEx7hEOZAOICKqqZQcSsNkr2GVk1AximJdgFyDEv9LT1KDRqx5X1hyB9ighZJoQzls1yYYiBNMtWCl15CamSJtRXAKLnZHNz0P/J2hlc/QdPZn0QIBwt06Q9PACUvCVf000hNNwl3q1gu1w+A85WJLvtiNaCmJXZaIROg60tlBixPeIQghaKaNZrzq+QflLbg4o/TgEZV1Guo307yJZI4xw9jWOO4NLJdSvgkVDcUPAUuHbfc94FLgq1tzUD9G4mNcorqJUgWTWkasKtaVCg5Ls77YOm/61/TZM3DdAk6gH6k5mOEAjsgRnZ4lPiVGxgqRX9eFO5hBD4YxIkHsN9fSdd63GHjsMfpnSJzwyOmkE4JCShFLmUScoKDQliQ3rY7ctDqsoxYhFi9EnzwJY9pU9HgcPf429LrwMSr24+gqmccyyhFtrztogNCoqgdjiW2MkTSW+zqVls8OO9xEYG3V83Wldd77CbE7MFlKecfWnqNPJD7GLarJRERLrW5LrqyBBXUeiVR2gL65AisKVhQSBzQjknGyA1m0kIEjbRzp0N2eJxdsJLeum+iuO6NpBnZ2iEDUoHFyCNMU5P56Gxsuu5D8ypUA5HfLkZnikJnikG9WFkaZTJxSkyQ3MPJ2dFqSyMGStLrpOxDeKYREmMUcPqxepPQ9lSwVkBTJ48q3uWHItmVtdUsp96parh52tNGKqCofUgihAZcD/7Mtp+j/Mn2MSwhduR20aARRnwJdxw2rOIidVARSjHsDd66p9v6y6hzojNBy1om0//BPuMEokgIyk6XYlyPUFiU8fyfyLy0jPmchhexaYgwQiziYmguOQ119kRXX/ILsgjnUnXkyej04veq9cxPK9653Dq7u3Y7BARe7lB4cy3qWlD6xBdHjBeXt9g1v9VL52ARiIslh8kSW8zKreZ1q66S6Qr6bDTTXTurHDmOf1rsOqG5pOQlq+izHgQXAw6XC3QnA7UKI4zYXcPctEh/jFlqsKrjuONjJQIVEhK2WMqq70Fl1XkwisvN09FgY3REYgTBaJIJmmORfepWgG6Vh0fsYen0JulugdapJstHEDAgyQ5KBPklTi07j0Co6z/0Bg/c+gZbwsrRcEwpVwsGFOkGhbuSEsdgcpdg8SjDXeGtdCn1sGUIIZotdOFycyOHiJA4XJ5GkiXJ7ZYnLMp6nIPNbPth/eg5jm/77NDBbCDG91PX2ZPAqNaWUA1LKRinlNCnlNOBJYLMkAr5F4mOcooZEAiZOLIQd8QZeo+CSaTZGDObVJAIwqaGP6DeP4MXzbwcE8ZnzCda3AJLB5S+QWW+TOmwX3OdeYvULFvt8oBHDKjDQbdG+zubcryd55kXo7UiTevp+uh95lJnfPIGehOpfUgybFJtAGi7BDpUgkG8UND8DsiQEmG0J1PwNDCUIdin5Fq0phdahUr/cLlU8Wd3G1sfYY5FQnSMfk/eQI41FkSe5nxlyp81WyP/HGEvFFSltIcTZwL2o9N9rpJSvCCEuAp6RUt6++SOMDp9IfIw76PG46pEKiHAIJ6batWpFiRuonfFrw2LyxoCO1WghTLV/3jap23UKE4/dmfV3voQjeig4BbRIkOQR8ykuW4Xz7EtMXxilMFDg5Ud62fegEFbO5o2XXWbONbn//ixHfbweayDPhrUFnjv794SmtRD7wqfRjXqkMdLHvnEfnfhq9Tg44I0kbokLC03BCpm4LXVoHX2IQEnyJWDiDA3h4+3F/uJoANbJ5bzByyznZZbxwpi/z1gXGkop7wLuGrbu25vY9n1bc0zfteVjXEPm8rghHTdUipkUN31XFqvEfqXl3Rp522TWWYewywcmkV++nuKylRSWvApPP8fcSTmicRjamKOxNUA8Bs89nmFCm85Bh4fp73UJR3XmLIywdpXN/1zUgBkQ7LcwS/d5l2A/dx8NrYM0tFb1Ki9N7/L1tefnDvNkFZq82he3xW+u9W5hkpjFIeJ4DhHHcxgfHvs32A4kUnyLxMf4g2lUig+tyZ5EuBMUFON61XOwYiAcyDeWJPcCkki9F8eYlVQFgrsm1sL35rBsgcUfL9lAMKpj9efoWeUyc0GUFUsG6HrToq5eI5+WPPVIjsuvbuCXVwyx9/sbyWddAgHBhEkms+cH2PewKIN9Lk//6QGG/vUSE798PNo85boq9IfQhwwKQcg3q/MKb1DEZmagkCrVoPRJrKj6nKEem8Ccqbim+nz6a6u9z+kXN75jEEKM7YD+LhPE1sK3SHyMO1RXsGt5By2v4h7VJAKKREbsWxQUi978atAKVR7PCHRyzCeauOzOuQR1h2LOZqgrz4ZXB5g8WWfN63nyGYf21Raf+Eycvl6XZUstDjg2yaN39rPPwSoLq65BJ5eRnPKFemIJHWd9B2+eezVdNz6ImysQTuWRmkRqagQJdY5+m+arAvP5BqNCIgBiwkjV4nImm4/tB2Ibl3cLvkXiY/wh7/URsVKe+0dz1MBcjKqBWVTF1d2QxA2pWIUBFIsGB81YTkMgCygSKaN1apAVSyZy1W8G+cFPBhnsge4NAl0Dp+jyha8kaF/vcOH/9nP+r6ex8tU8Lz+V4Ts/qUdKyZoVFseeYjB3YYhiXjJ5dpiddtZ58I5/seaex5h39sEUWo+p+UiyigOsqrrELJoXN4lHCfeoDxWMGjBR+er0fzy7eRIRGrzdtRA+/nP4FokPH+8u9HytPHiZRKpRHpi1fMl9ZDqYphqQe4qqkPHx9BwsaRDX8sS1PGlpc9Z/J1i7dCLnn53CLkiKRcnGdodLvjvAm+t1LrpuOu0rC3z/c6v41mVNRGMaS58vkB5y2WXvMLJUHDlvUZxwRKe+Uee8b8dZ85uH6f/lpQw+9RBOcID8vDy5Vodcq8PATg7F5MjgexllqZcyHFOD/XZDLt4FfdoUjJnTVQpzSTaGt7kq28dbx/ag/uv/inyMPwQDUJdUCx6ZRDYUR2xqDVMdKZNJQzxTWVcmE4Bex/OHpaWNpgku+HIj65dM5/SPJunvdalrNti4psC3PrGSR2/r4ZKrm9nvkAg9nTYXf7WbU8+uR9MEy17IE4nrmAGBpsGhx8ZYtdzmp9c2wsZudut9mM6v/JDi3XfROKUbvaGANCRWnUNmmk221WVopkuhEQqN3mfoWaDIxDFrb2+7RV0Pe9E8CgfM90lke4EfbPfh451DjfsmqwLmUldMoVkuVlzVjZRRLCXAsdc7AAAgAElEQVQ6FRs8H5eIWrSmVOV4TFfE0xZSdRoRrcAEQ2VXBYVGUvPiJ5GIzlU/bOZ7X6vn2E+0s3ZNgY/9V4KDj4xQLEp+e3kft/15iA99MsUxH00hpeT6X/ZxxCmNPHl3H//9pSRrV1lk+iTzFwaYPtvg+A+ZfPWrEU496XFeuOVJGo7cjcBRJyIMHWG4UFBEUEy6BAY08iUy0Szon+W59PRCKTjf60BjE3peubH0+bO8z93lyRDbnV1VF7VENr7r692BrC2Wfa/Cn5L4GBfYUiDZiqvB1Mh70zatFErRk8XKohmSdEENwm2hvgqJABUSAWpIpBr1dQaP3T6Z3/24hQduTnPmSRv41tld9PRILv3TJE47p4Fs2uXKb3eycYND24wQQ702ex8cYdmLBSZNU+e5x95Blr9uM2OWwc+vriOVFETXvkHX+d/F7lHxGr2hUFmsuEtugrcMTqOylJGv18nXj7xOMlSlORYOYTQ0jNjGt17eRfgWiQ8f7wyk41T6jqB5g2VZCLFcJe7qolKEKHVwguruc/KltNmQQ8BwKDoGj3bPYufkBloDykLRq/S8N9dSVdMERx0S5dVHInz+vE6uu3mIjvU2998ySF+3w+MPZFh4QJz3f7qFX5+/mu/8rJm+HocnHsxw/kWqt3uxKElE1DnvscikvkHjfZ+dxkPXrmbVhT+h9X8/Cjsv9N6zqYBTqn0R/WbN+WSbq7r6FSGfChDql2TaGpSVApipKeo6hHSMtA1MxexR7j2RVYwrI0FYrWSZRCCA3TN6G2IfY4t3M/axtfCnGT7GBSokUgWZ8GIbRrZW+sSKgR0t3aHrPeuiTCgA/YVwhUQAuqrjI+6WZUh0XXD1/2vhgb9MZNXSPLf8oZ9Vb9rscUicVa9kufPqdr53VQuzdgpywWc38vEz4iRTOq4refjeHIv2KemCCcFuuxn0rM1x3FdnY4Z02n98E7033e+9l+l9PpnyyvWlWRWYr+KXfKo2WdQJ6TihYenRVe2CZURZaaLJs1Z8y+Udgm+R+PDxzsAtFtFKarluRyfaxNbSTNobDI2sgxXzBktZPeZp3l2oa1WWR8lBPT+4HoAeRx1vlpGpkElsE26uMvbfO0z78zP47v/r5tKr+qFo8/4To0yfE+Bf96f55ufTnHBylM99WaXr3nNrhmhUsPue3sify0kipsbknRPEUgYDXS7Z+x6h8PgzpD51HNFFc4km1Plk18SxW70UaDqDNSRSRj4lyLYYOAFoec4euQGKTKp3zc5pItiUQMuq+JGYrppByWdfqWxjtE7A7VcE7OZKxZ1+ivF/jO3BIvGJxMe4hRsPITVv5m3FdPTS+GqHwMgJ7GGNpwIRbzbfFM4wGpr1NOXyry2RSDUu/GojZ326jh/+rJffXzNAS5vO3gcE+fMdLbRNNijkJbf/dYirLx/kd3+qpyTjTT4veeShAud8TmmmRBIG+YxNPuMS0fKkf3Uj6T8EaPnovpjvO4DIlCGkhOygOrfM/CJiwLvVA/2KQY0sOCVDrmeeQbBUAG/mPLK1gxAL1loZhbog4WwRN+hRjD5vFqLfiyFpqWSFTHwr5S1gO6ls94nEx7iBm8t5VklcDaLB7hxDMzyXVNkKyTerv3pBYE/KE6wiEL1khfTmw3QWEzQHvAFSkYjCtpBIGY0NOj/5ThOfOSXJuRd2cudfs6x4zSYQhBeeLTJvJ4Nrb6hn7jxvkL72Nxmm7JKgcXKYQtaha00eMyhITAhjZwrMnK2z01zBbb97GPcP/yJ60O40fvJIIol8hUxk0q6QSTHlEujXyLa5BLu9Qb6QokImoEgEoFBnEOyrtVhyk2rzpsOZAjKVQPQPIhtV10fRmIKXX9/ma+RjGHwi8eHjnYWby6HtuaBmnZFzscPegGlHhu8FxawauKstEgBNSLqtOJQG1U4nxqJgGheJWwq+by7wvinsNCfAvTdMYsWqIn+/M833r+zl5E9EOPOcGNFS0WRfn8u1V2f4+y1FzvqTCqw/c/sGps8PsXZFEdd2mTQ3xh672Dz5zzzf/k6cP9+Qp/fF51hzxrNMOHI+Mz6xmF5TBfBpgMFBRbRWI5gBh4JQF8POqvfMN4EbpNJjPjCoNMrydQGMnBrRXFO9ZmbV82CfjdWsyNo0aq+F0egpT9pd3dt8nXZ0CLYP15Zvc/oYV9BjMbT0yEC4kVOD/nASsaO1fvsyoQA0hrKbfJ+YFtzka9uCmdMC/O9Z9Tx55xQ6VkoO3LOTkz/cw6kf6+PwA7t4YV2Es69fRF1riDef6+een61A02HWwRNwLBddhzk7BVj2ms2HPxxiwzqbiy5rIByE4Kuv8e9PXEP7D68n+0Y7IcOzKoyAM+r5uMHaUStd1UvPNUWFRKpRqBt9PioNDRKe5aInk9tyaXyUMR6C7UKIucBNVatmAN+WUl4xyraLUB21PialvFkIcQiq/28Z84CTpZS3vrXT9uFjJPSqZlZlMnFLvUismIZwQFaNg1KAntVw6qgo3k1rVbPm+lCOObEO3NIOL+cnMzfUTpOe5g1bZ4ZRCrSLANoYxADmzQ5wyzUTeX1FkYNPXE8673LgqVOYsVcdq18Y5NY7l7H8qX522ivC+nWSKDn2/lALj9+wngUXNaLpEAppLF4coGODw4dPjZLtsdF1mNfWzt3fvJb+5ggLTprN6gXHQEBZJjJZssCywRoSKVaN+ekppetbipvL0qhhpAXBQYivseifHSI44GL2ZpXbSxOE1wyqbK9IENaolsB6MokzMIAW9NyCfiOuzUPI975JskUikVIuA3YDEELowHrgluHblV67BNV5q7zvQ1X71gPLgfvG4sR9+BgOJ63iF3p7B+5O0wDIN4exYmqgz7R4A36gDwplr0tfAOpr5VN682Eo8VKj6cVFupwYTaU4SUyMTDl+q5gzM8D656Zy9MntPPyHNTx1SzvhiEY4puE4kiErSHyCwMlZSMtl//eFeHGJxd6L1bk4rkQIeN+REb73vz2c88UYf7g2w3U31PGp0/qQz77Khl+8SGJ+G9GjFlO/eDZC1whN6GMor6ysoYEwVqQ0NARcyOqYAyoAL4eNGIUEMMVEOFBIagzOS2FmS9ZfSpGV0Z9DpBKVoLtRl8Ld0DHm125cYjsJtm/rVOowYIWUcvUor50D/A3oHOU1gJOAu6WUm/YX+PAxBqjuDlgmkTKM0qw6O8nFiailjGC4Nj6ydKi1hkTK6HJibwuJlKFpGvf9ZRLf/GKKdL9NT4fFUFYjNTnK2hf7mTApwOS5EZb+o4MvnRfnD78e4vRPRsjlJE8+YbHzrgEMExwbjjwqyHPPWuyy0OTwI4Psu2+An/88Qf719Zi33smKM39O5t9Lya3vRbpqxIoncyRbB0m2DqKZDlqyiDMlN4JEyigkIDtBuQ2dgCCf0smndIamKKvDToWRyRgyEaks2oSmyuJj89geRBu3Ndh+MnDD8JVCiInACcChwKLN7HvZpg4shPgs8FmAEKNEQ3342Axq/O9tLZUadDNTFQNpLM2Ic6DnNJxwbXykkDNBJRyRCip3y4aSj6c5MIhTVXhi48UYzLcpZ+UbX2rgsAMjHH/GRgbWZ5k4O0LdvDDP3L6Bgw8P8aMrU3z3f/vYaa7OkUcF+d1vs+y8a4CJUwxu+v0Qc+cZGAYIAY4Dx58Q5udXpPn85xs48aQisbBgxnSdC759G8GYAYEAbcfvxvyPzqPfqW3WMjQYpjChKnPLkLBOxZPKSgGqw6SnHBAYlOSalZUTCOlohVIVfX/JlVVy2ZSViN2sP8ccFduBRbLVd4AQIgAcB5w/ystXAOdJKZ1y7vuwfVuBXahyew2HlPJq4GqAhKjfDi6dj/cSnAGvAp2BAcTeu9S8XqyyTGzlcUHPeevKtFAmkOHoLCaYG1R+/pSWZcC1SGqjVPmNMRbvEWbds9P4yoVdXHPDIA1NGvsdYNK9weJLZ/Rw+ulhvnBWlD9dl+MXv8zwm780UyxI/vLHND/8YYLnn7dom6gTCAgSSY1sKfPqE5+M8LGP9PLykgm89LJFKCI48sgQF130NA/89Wla95zA7ONmc+Cezfxrw0ziiRzFKost1x2pIRZ9SLm+iilIrFBjgBNUGV8AhaRJcrm6ylYqhOlWkXh3L6AIxSeTkRhvWVvHAM9JKUdzbu4F3CiEWIVyYf1CCHF81esfBW6RUlqj7OvDx5hATyYrlomWKYAQSOEF2O2wRyJlOGFXWSYlX3RPPkJPPoIrhVrQKktemoSERV6a5KWkwylivQPV2rouuPLiZrpemcFpxyd47skiq1bYHHNMgM4ulwP27+bvt+X59Y3NNLXonH92N3Pn6Czex+Sa32Q55TQ143/9NYtJE9WAP2OGTne3i21LPvPpGDfemGP33Uz+dnM9O82E5kIHz/zgn9zwwb+Ru/52mjMrR5yXkSxgJAs168xBUSKQqpWla2slzMriJMI4CfVlaBOa0SY0I0Jjkwk3rlBS/93a5d3CthDJxxnFrQUgpZwupZwmpZwG3Ax8YVhm1ib39eFjLFDt2tJ3ngNQkwZsZtxKfKSM4a6tavQVwvQVPNapMzKktJGz5V53dGmRtwOhkMbFX29k40sz+PG3mnjkIYvbb8tz4GFhjvhAhL/8YYgP7NdOc73g8iuT/OzKDK++anHyKaqJ1p+vy/HRk9Vn6u+XBAKg6zBzpoFtSwYHJaYp+Pa3E7y21OKehxr44PsNMv98mSc+cz1dX/kJPX95CLtviHCjdy2Gk4lycZUwymy6eoZdJhMAkfLTg0fFeEj/BRBCRIAjgM9VrTsTQEr5qy3sOw2YDPzzPz1JHz7+E7jRYGXQsqIaydU2A1MNnFLmafk1Y+Jm6kV0RUaW1Gm362gqSclbJTMnor3zd6+mCU47KcGpJ8Z54JEsXzi/kwfuzDJ3J4Ozzo6Sz8MxR/TQ2Kxx3Q31xOKCS36QRhNw6KFq1v+3m3Mce0wIIQS2LSnkJcGSQbBgZ5NYTOO1pQ5f/3aCV160+NbXY9x9b47nH3+Utbf+k/isZurO/ijB1noGO2OYk7JYg8FKT2ArDsIRWHEwS7kPuVI7x1C/W1FlBnDjymISUqIv3An5xirvtdww9t/BMK4KEqWUWSllg5RyoGrdr0YjESnlp6SUN1c9XyWlnCilr9jm4+1DTYwERSJlWKVK8RoScVUNCYC9PkIxa1LMmmzoTeK4Go6rETWLdBZrpUC6bG+6/W6QSDWEEBxxcJQ3Hp/Ow3+fzJSmAFf9LMP/3ZbjtE9FOP+bMf75cIHjj+3hmX8XufYPdei6YN06h6t/leEzZ6iA+j335dl1twCRiDccJJOCXM5FCMEpp0e58eYsv76qnlhUcPnlSVq1PtZ95Sraf3obsTp17c1EyTIpzY7Tk9X1KXehrM4sKiZr40vVtRJaQz0+qiDl1i/vEvzKdh/jBmUycV7x9J2sqh7tzijSWGUyATBDnpsqURV0L5OJJXVlmVh17zqJDMfC+UFuurqNFU9O53On1nHLTXk+c3o/N/4py9nnRPn7rfVEIoK/3JTlxBN6+OLZMfbcI0Am63LZFUOcfrqXKVkoSFa+6dBWiqcsWGiycpWNaQo+8+kot92a58/X12Ga4Dz3Ess/9WPyK1Wfkk25V6pbGhdjGsWYRnpikPTE4KgFdz6ZeNge0n99IvExruAMDNTES8y0i5l2R/iSgz3eYz2joQccXFfguoKIWcR2NWxXo+gaFF2DSYHemvexpLcU3kM5JHUpnS9/ro6XHprGbb+fyNTWIF/+0iB77NbJ7gs7ufvOAj+7vI7/+nSU55YU+cjJPSxYYHLcBz2WveOOHHN3Mpg4SXm+MxlJsJR9td8+QZYts0mlNE46Kczig8Kkki5d3/kV+Tvuo37nbtzWAm5rASfqUqiTFOokrgH5ekGhqg9KefDLTomTnRLHToaQhobbnIKAiRaPYTTv4HUm2xIf8YnEh4+xwXA9p8gKJWdbDrYPD7gDOGGJk/PChf258MiNqhDX8vS4nussKN7+NOD/BAcsDnPLtW20vzCd732tgVnTDZYts/nBJYPsvV8nn/18H8cdF+bHP05WJOtffsXioouHOPOLXh3JXbfnOLwUWykWwShdqsWLAxRyLgFTMG+PKH23Pc4bF1yPm1XWnEh4BGvFZU1W0Wiz52p3l5uq6iOzg5PJ9pC15av/+hg3qCYRbcjLJCor/0a6XHKNGmYptl4sFR8aGUEhIZCOGkzDAYuCYzA11kfBMYibamCMa567K+sGCIl3LmPrrSAR1/n8p+o48/QUS18vsmJlkR/8tI+BtI2mSZYssUinJbfeluPee/N890dJ9t5HEceaVTZ/uynHPberwfzOu3Pss6+q6i8UJLohmDbbZMouUbo7bHpeW0vhgsto/OaZ6EZLJTNOOIJCXbmmRGCmwSy1exGuN581ksoyElISSHrxKNGjLELpjC42Oa7x3vKijgrfIvExfuC6lUVIiZCS3IzUqJvaVfESJ6TuVCc/cl5VJpHn0lNpt+pot+ooljKTep0IvU6EHjeDW/r3XoYQgp3nBjnu6DhP3DmZX/xwAi896/KFz/dz7pf70YOCux5q4uhjw9i25L67c5z+sR4u+FqcGdMNOrscrr0uwyc/qeIpd99TYLd9Qgz0uUyeFaJvY5FdDkoycZKg64IryD7/onrfEkHjlpYSrKhaRpznJoLGOySJsH3ESHyLxMe4gTM0hB5XUd3qrC0z62JVZSQNJxGnuVgW/8V1NAbzaoN4nWeB5BzP7dJtJ5hi1sZM+twcddrmXWLvJQghOGT/CIfsH8F1JVdd08+Vv+3n4X90U1ev0dXhMm2awWWX1HHEYSGee77IOV/u49TTIszbyeSVVywee7TIR8+q53dXDDBpZpBgRGPeogSrXsvT/kaWoRtuwLHyxPYvqyaNPm/tn6mRWqEYJj0pQHBQPS4mG4m9rKT7dlQSUbGP975J4hOJj3EDPRbzbrry35IQoZl26JuryKA8c3MDym9vbAxgT/DUfx1HDXhLuicxKealFQc1z+efr1IwtKQkKDQG3Dx12vanE6dpgnM+U8dZZ6R4ekmey3/dz72rM0hH8ucbM/zg0gH6+iVnnxPl5I+Hue++PF8/b5D/ubie313ez+EfreeJewbZ/dA6bEsSjGjMXpSgdWqQR/5+C0IvEt1nf6ykNyAKt1ZKKT1BI5AGvSApJLQKmVhtyqI0+xsq29o9Pe/AVXnvYNzUkfjw8V5HdS+SakSWK8vBDmsYWdWnvLJPrmowy+sMx7w6Tw0oZuZVp8ThxxdFguOkJ7mmCRbvGebGq1tZu2QGXzqjnvXrXF5bZjN5isY/Hyly4AHd/PgnGT7+uRT/d1OGbF7jkA/Xced13Rx26gSevb+POYuShGMGk+eGMYIag3+7j8Lypd4bbWJgLMYg11CKoyQ0ConSdZUSmrx0YGPe7LfrErw34Wdt+fDxzkO0tqAN5dGG8uRm1OMENYRLRQNKOJ6Mh54TilAcAbZGKFokYNoETJsXu9vIOwZ5x6A7HyOiF8i6AbJuAA2JhiQvTbodl6x0yMrx436JRTVOOTHBM/dO5Yk7J9PdIbn/vjzpLHR2OPztujQ775/koOPruPCTKzn+7IkM9lh0txdYeEg9a19N0zI1zKS5MWZ9cDZ9v/0bVmgQp96qke4HMDLeY7tqPiBcGJoSopgqSfY31UNTPU4yjDFtakU1eDyjXNn+Xo+R+ETiY1zASadx0mmk61aqfN1IsKZXuzFMCaWcMumaQNAb3DK5keKBMbNQY5H0Vsmsv9eKE8cau+8S4rVHp/P/LmykkHfJF0AzBX/7dRf33zzA6RdNJ5ow+eX/LOfTP5rD8mcHcR3JnD1j5NIOLXu24hYKdFz6G6RtQ9gh3+xixcCKKfKwY6poUQooxgXFuGctFpI6TjJcWcrQdoS0YCkR7tYv7xZ8IvExruBms7hr25ERVTE9/EYz0xKtlLVbrVIrXbWUkckFmVPXRcwsEDNVKnHBNVhfqGPAjpCXJu12Hf3u+J8Vl3HOf9Xx0oNTaa7X6VhXZMauceINJr+/cBX3/HEjX/j5fBraglx7/uucdO4kutcX6ViVo3m3VqJtSYRTYOCehyCn40YdrCZLLXGJFZfYMU9SxYpDrslTEs62hsm2hslNUETi1sdw60d3Z447+K4tHz7eWZR7gYvBLGIwi5mpdTc5oVKtSJeqYzAzyirRsipG4roCXXfRdZcXO9rYmI2zMRun6BrE9AIx3atPiWjqcbsdot0OkZeCQTdXWcYjpk02ef3RqRxzZJDX/j1ALudy7Ocn84EvTOHxWzv4/knPc9yZE1l0dD03XbaO6e+fjR7QyfdmqT92EUP3PEax682aY9pRj8HdABVLBaCYqK2EBxBW6TuVEj0eryzj1dXlu7Z8+HiHIAwTYZhIx1GpolUmRplMNHv0KmA77iIk6Bkd0xw9ztGbr03t1XDJu6ZapFmyUGLkpVNZOp2RbXrHAwxDcP2vmvnZJXW8/u8BHryunfuvWUdTq8mP7l7ITnvH+dm5K1i9Ghb8915seGo9eiJKYt95CAHdV/wBZ2AIoUlEyS1YJhMr4VaWfClRq5gQNYNk117JESmx4zY9WKIyD7d2eZfgp//6GBeQtkrNFcYwVVlH3VyBQZtCUsfISuyImuUOL0x3TUmmPYaW8lKBraiXzbUur1JRGwJb18WvWR/frpdTPxJj9gyD4z7RQ88Gi1xe8OQ9/fR3WUz/4FwOPm8PXMvhuZ8+Rf1xB4DtoAUNNOnSefEVTLrsHIxoGGJQzJrYMRAD3vdnDoFTirNnJlRl1UmwUorYjbnTANC7lBSOuyZf66McD9gOQnC+ReJjXKFMKAAy4lkRdtSbMxlZiZGtvTtdc+TdagY9pgkb3nF7ihG67cSI7UPCqno8Mp14PGLvPUP87cY6bEfi1Ncz89OL+eCtp7DgjD1Y+9BK7v6v/yO4cDapQxYy8PirpHabTMPiaaSaDdadewWFVRsrxxLDkhZGybYeMaimp6nSeKcphdOUGn8kwvbh2vItEh/jDtJxKiRSTSBld1Y5wF4jIuh4vnjX0jFCFo6tkS0GSATzWI5O1g5SF8gwNdxDXhqss1RtQ1Qr0KBnyEuTXrdIvSbJS4ewVERkivF3m1W3GI7Ni3POeS5X/bSDDc9sQNr/wrUc4gsm0/iJI0ksnos9mKX3/55i52+9n467XmTa/m28eutyNnz3N7R849PEd2oFwC5J+RfTARgMVMgk2F/7/vkGz3KRAY+0jRnTKo/tN1eN7Yd+tzDGle1CiKOBKwEd+K2U8kfDXv8K8BnABrqAM6SUqzd3TN8i8TG+UCoOFINphJSYA0WELRF2SU9rlLbgwS4NLV9baW3nvYFqsOBpqvQVa8WhIppyg/U43vreYVXbltw+xB23BYlhcjBHnNbM9BkaZmsDUy86jfk3fo2p3/0EicVzyb7RzspvXkfLoXOJz5tA33OrmXXEZKyCyxGnt9H9g2sYuv4OpOORUyBWrImXpCep2h9vERQTAr24CQvEHj/XfCwtEiGEDlwFHAPMBz4uhJg/bLMlwF5SyoWo1umXbum442+q5GPHRXWFeTgEtoO5cRDRpKa16Ykjf+7VxKLlBW7IuxvtvIkdVkQRDxTI2iaRKhdXRCsyYEdI6Vnq9TSDbohBN8RMs4deV1RcXcFxaJGAIpNBN8eQG0LT4NI/TuQji1ey5qIbCE5swKiLUezow03nmfKxvWg7biEd9y0l3hKhcVYK15bsd1IrD/1xHcHly+j8QSfTvnsKmq6+x25qs7CKCQioTscUStlc4W7INwYIdXtxrfFEIm9DWu/ewHIp5ZsAQogbgQ8BFekBKeVDVds/CZy2pYOOz1+4jx0TJXeL0dIMQ2mIq2C3E1E/czNTRRIhgbDBsCHfBLJkgLhhB01Xx4knVApv0FADUyqgni8damNiuJ+BqoGu14lRr4/M0hqvJFJGQgtzRNjm+WIeovCjyxN87dxBWg6cTrA5TrA+SnLBRBCCzgdfY+XV/+SEqw5i7dMdNEwJk5oQZOLcKMd/vpVrvrmSpZ/6KdO+fiLxBZMx21RSQ3EoQDEO2qCBnYDoKm/CUEjppb9hhAupnEcoRt4rWLQ7u96hKzK2UJXt28QkjUKIZ6qeXy2lvLrq+URgbdXzdcDizRzvv4C7t/Sm4/tX7mPHRhWZVMMOCbItAq1qEqtnBU5E3bBuXkcLORUCKaO/GK6QSRmRqoMMJ5PxTiLVaNNt2h2Dg48Ic/LZAW786ZNE2pIkd59C96Nv0Pvkm4RiOsf//CAaZ6e47ayHOPjjrQghcCxJMKzzgf9u44m7eln53Rtp+9zRGPvuDUAgXqQ4FKi8V7mPTKAqblKOdxWaIgS7arPqZPG908HyP8K25Q90Syn32szrYpR1ozKVEOI0YC/g4C296Y7zS/ex48D17jwnFcUYKOJETexQ7T3kBmp307MCJ6YC79LahOR5UcUGZkc7K+syVd0S24w+1tsJUnqODkfJ0Kc04z3bRXGsENdMZpRG87PPDJDbEOaBf2RpDGWJNobY97i9mbBLA9nuPPdd8Dhh02G/E1vp7yjQsTLLlHlh6ppN7vztBk65cCY3XXwP9Y89y/Tzj6dfNGIPjiw2LKYg0j3yXApNalsjOlG5NlFRZQCnv3/kDu9xbKNFsiWsAyZXPZ8EtI94TyEOBy4ADpZSFoa/Phw+kfgYv6hK/9UzFsFBbzAPDkL/DDW8CMcjFbPHxG5WVsZAVu0fCRXJO0ZNfKS9kKTBrFIbpDb9t98J01SqfO93bRLjPK2lOourSc/z1e/UkUwN8MdfvUHL/BT9qwZ49pqXaV/SzeLjJ3DCV3fGCGjcd/Vq9jm2gXDMYKhXWYB7HdPMrT9ZyZTGPK+cfzMLLzuZ/ETvuyskAmj96lUqAroAACAASURBVPngNDU5CPap1zRLDWmaDUbWxpqg0rSNbqUCradS2xeZjH2M5GlgthBiOrAeOBk4pXoDIcTuwK+Bo6WUnSMPMRI+kfgYd5BWySU1MIQ2MIS9YBoAoa4ChXrPDAkMgR2BUC8U4+CUk7Mypdsirgglmw8QSda6R4bsEKZwSZSawA84YSxNR6/KKR5wDZIlYa819hAtupf9Nd4slNosrhz1AYeffC3GjFaHSy7pZ+6uYaYeUMfOP5lFOG6QG7K582creO2xXr59o0oaeuGRfqbuEkc3BHP2TrLHASF6Ovvpu/UJdj1pPmvtNnX4WJbOvlrBxkKd+qsXFGNHO2yKdd53bUxpRWzYHuMkckzTf6WUthDibOBelKF2jZTyFSHERcAzUsrbgR8DMeCvQgiANVLK4zZ3XJ9IfIxriEgYfUgRghP3BhYnqAYcI6vIBEDPV5EJ4NgaRkkypSPjVcclQzliZoFeS+0YM/I1sZJqVJNJGeONRDaHj58aJZXSufDCjbz0YDevPdZLPuOw7Ik+Fuyf5Fs3zCdeb1LIOdz/xw5O/o7qNeLaEk0TnHBGI1ed/zyv/ukF6hbPpOVjBxCe0ogxWVmD2UQQo08NY9W1QNUop37L1qbtkkzGWtVXSnkXcNewdd+uenz4th5znBvcPnZElF0XIhJGNiQRrotwXYwBFbNwglqNTLkdVq4QzQYr5YAmQZM4vaMUnQADVbpbZTIp9ylZazVUlsr2rjdfM8d5xftoYpXHfSDEPx5pIBaw6V2TYfcD4/zwzoWcfeVsEg0m/Z1FLj/zdabummDu4hTFvMNrT/QzZ7co8/aMqlbAD+7MIbunef286xhaur5y7GCygBN1caIumgVDU9RSjOuVpVBnIA0NaWjgSoyGBoyGhu1D5FHW6sNtaXm34FskPsYlRMActZ7ANURJf0ug2RI7XFXRHhixOcWsiWa6dPXHSCW8bKC8YxLSlburp1R+3WAOAV6sJCNNQiVBL11At5snKUxiWojxiuGFisHStWiMw4N3NHHu1wb408WrWfbMEKlmk42rCrz65AAHfqyVD507HSEE//rLBmYsiNA6LUjPxiKGIYjXGRz/uQlMnRvip1+/mZ1+exZ6SFl23ZaO2+99eeUBtdxtMdwjybco0oiU6rPd3Hakzrwd9Gz3LRIfOwTcsIkbNjGyw7rz5Uopv6VxSMtraHnvttDMkdO8gO5USKQaPVZ8mN7WSCIbkNt5Kup/CFPopOI6v/9lPZf8MM5z9/fS0+kw/+AGvv+PxZzwPzNwHcnDf17P/b9Zy2e+reIhT9wzwM77eG7F3d+XYtIUjSUn/YQ1P/wrgy+rkggtVayZlffN8SYIZUIpY7siEdgu+pH4FomPcQlZskZE3sKNhxC2GmE0R6IXhwk2DrdEdIlmqcHHyXrxjGwgQCRUpOjobMzGSQWVqyzrqG0iusVaq556XfnvM1UHjlfFSQrDyGQ8x0yqP1vZWvniyWFmtAT4wnl9vP7UAKtfHMK2XF75Vx+TZga56M8zaZseIj1gc8fvu/jCpdNrjnnEKc00NHaz+35D/PmSvxA5YhH1Hzscdi2S6QtXMrrK3yFAvl4NdYE5kwAw2lUWl7t665ScNwcpJX10sY43GaT3LR9vOMY4/fdtgW+R+Bj30IbylcflIHs5JuLqAiOtUoCFA8E+gZYb5bYwFBFl8x459BdC9BdCRHSLSJWF0utE6XWio3ZPNIdNG8cziWwOh70vzNLHW9h1lmD5v3uYNFHjoutmctGfZjFxRogVL2e58LQVLD6qjp0W1coARxM6VlHyodPrueq2KWTvfZK13/gtVtcAAG6qlqjFKK1K7Lb6MfkcrnRZyjO8xnPU0cQeHDQmx61BqXX0Vi3vEnyLxMe4hDuYRkvEcFauQZ8+BW0oT7E1UcngCXfaDE0uzVwd0EoF6Y4JREHLabjhkp+kRCJaSeY8XzQh5LlHyhaJeqwC9CHN4tX8RHYKrSelqVlvmUT63AJJTe2TkwXCYvSg/niHqRvc/ucWfvDzPn5zTRcvPDJAKG4w2GuTHnD4wGdaOPLU5hH7rX09S0tJN62p1eSz32jm+l/1sua8q2n9/ucxG5MQK0JPrIZEhluidls9rF7LW8EKXqZAjsUcjl5WMhjL8VyyrZXt7wp8i8THuIMei6GFglC00KdOUpXupWp3I2tjZJWbKTjkEhyqqoIv8YHmAJpEKwglqeoIcASFgkGhoAaLrBUgawUwNJe+YoS+omd9hDRvRmxJnS4njiUFWamRleqWG3B3zFhJGWERJChMIkaAi85tYvUzU7nyGynSG3PsfmiKK/+xC0ed1kKpjqECx5Y8eFM3R30kVVl30LEJ+jYUOPCEBjq++Quyr6wEIDfJITvVoZiEoamC/tkaPbtEKTQEK4sxYxrGjGkIfduz6SxZZD0r2Zm9PRIZYwgkQm798m5hi0QihJgrhHi+ahkUQpy7iW0XCSEcIcRJVeumCCHuE0K8KoRYKoSYNnan78PHSDjpkeKJMlTrQiqTCSgCqTIqaptc5UYOMGUygeES85EaEqlGvzvS6hhwrR2eUMoIBARHHBTjkouTPHxTJ+uWjwyIO7bk6gtWMW1OgLm7etlhgaBGNGmw6LgJyKJF1+XXM/TsG5XX7ZRnluQbvePpBW8SoU+tVg3ZOnSynnpaCIq3OQtvPLi2pJTLgN2gomW/Hrhl+Hal1y5BVUxW44/A96WU9wshYmwXhpqP7R1lMtGntlXWSbN23qSVXB2xDZJCUuCWyMTIVtWYxFBkEnZwbbV/IplD17yfcb6KhZ4dmMqE0AB1pnJnbbRTlb+7hVTuqSnyFTeXhSDt5sd1SvCWoFXNZ084uJ7Cd00+f+oy9j4qxeL3NxCKaqx4KcMD13fR3Krzzasm1uyfTTukB2zq2kLM3beO1IwUj//sRhrPixKaVSYIj3jSrQZGQQI6ek59P+LR57f5vAvkiPAOtFMeh8H2w4AVm+iWdQ7wN6CizVJqmGJIKe8HkFKmpZRvPU3Ch4+tgDBMRH8aYbsI20XP2OiZkSm5RtatkEg1XAOELRC2gHytZZIujLQwApo38+2zqlxdwqpJC64OuLdoai6Xdr2EgB0dJ58Q57V/TeHgGXDT91dx+dnLWblkgHO+28z3fz+ZSKz2u3jwtkHm7VtHOGZgBDQapkRpmxen59LfMXDTPUhXkmtxcQ31nSoSeeswCFDkbf7eyjGSrV3eJWwrkZwM3DB8pRBiInAC8KthL80B+oUQfxdCLBFC/LhkuYyAEOKzQohnhBDPWGxRbNKHj01CGCbCKDGDaULBgoKFNDWkqaEVXayojmZLNFuSbtMRNpVFK6rFyFKKkyhCcYs6blEnXzDRhCRbDJAtBkhbaukthElbXlZXnxXBlaJS9V7O4rKojZeU4ZOJh5Ymg298qZ4n7p5A2IA9D4yy677RETGTNcsLXHdlN4ecMRXXkbz53ADNsxIsOHoiCw5tQn/lBYoPPIDZmiU/wSE/wWFwqmBwqiDTIijWBSjWBXDft4fqY7MNaKaNLtqx3+YOmOMiRlKGECIAHAf8dZSXrwDOk1IOT7QzgAOBrwKLgBnAp0Y7vpTyainlXlLKvUx2zCwWH2MDaVuVhYyn0KtnRtfDMjZRn1a2Uqrb8IpR0oDLKFskGSeIJXUazAy9tuf6yLhB1tt1dFUJemWlXVks3+s7AvXRIPf8uY3rL+/k/7d35mGWVFWC/52IeGvuS2UtWVAFRUFZyCa72IgLCqiNoqMgrQ7LoKLj187Yjoxt9yiD00o74tYK0i7thgwM6qA0AiKCILvFIlZBLRRZUEvuL98W250/4saLeC9fZVWRWWZWZvy+734Zcd+N++K+m3FP3HPOPffKywd4+pEShVGPgU1VvvvPu/jE+Vs59+8O49DjOnjirl209GRY+ooOzLSBGMKFVx/Frp/ey+A1P0YVt9TqNWMyO1WIBMG+CJOs5Ommj+d4ErU/B/EDwEayLzOSs4HHlFI7mnx2AnCDiGwB3gX8i4i8nSD2/eNKqU1KKRf4GfCqad5zQsJeo+yphYmX1sEbtTAJhUejqsuoSE2IhMSFSVytlW4I0hgXJmntjxoa3ze5gVBJ6W2C/USYTGLtERme+s0KWjyHf/xPA7z/9I188m8G2FHI8LEfHs+Jf72U5x4e5YbPbuDMjweRhJ9/dIglq1tZengb/Ue0cNzyXey88nq8nc/sVoiE7IsweQXHM84wT/AAo2po5gWKUpHX4d6kWWJffNYuoIlaC0ApVVt6KiLfA25VSv1Mq7G6RGSRUmoX8HrgkWZ1JCTsD6S1JThwnNrqdmusgjVWYeLQ9kC95QUPf3oCKl2BKiuUBaYdzEbcnEKVLJSl8DKR0CgUs1ja8N6SDlSyQ9VAcBScbG0DrA6zRMVPUSFFXoJyFb2/7y7fpk0LmBSRoFvIBvhGujpNfvX95fzzN0f5zNXDHHxMJ4tXtfDMfcPc8NkN7NhY4rzPv4qVJ/YyvqPM+t9u59z/FiwO7Du0ldVHW7z6nC6++KHvMfHmN9D6ulPJTXQyckTwG7duc0lV9l2lbkmK49VrGWATT/MQ9v5Qyx8A7xZ7JUhEJA+cCXwwlvchAKVUo12khlLKE5FPAHdJoNx8FPj2tO44IWEfUBPFSJjE8LMWVsnDzU822Zll8OpjD2KVhWrH5LdN04ye8qKdqQmTxlnJmJenwwz8TMwmYVoLyqwJk6BM85DoCxkR4e8u72LpES4fvGyQcgXal+Q49p2H8Io3LsVKGYxtL/Ojyx/ktRcdQktnMGOcGLbJtWY4+rR21p7YwsTzDzDwP35P50cvppVDgzL9Fi1LFqG273uYeVMsVnA4B6vVuDjcwy9mtt0HgNfWXgkS7WnV05DXVIAopf5jw/kdwNEv8/4SEqaNmtCqrZ4O/Gz0L294inTBpdqZxkvVD9ymVnW5sSgn4jaU0ULEkOhBz1ou404GyNCbKfJitYOU+HRbE4x5QWX9qZGm91lQJmYYh0tBv5nYCptx+muz3PL9DBd+eIjxnXl6VrTy+C1b2frYMBvu3cEZlxzKGZcESpLCripbHhvh2C8HLsOnv6OHP/xqhHdetohrPnktuXf+Z3KLg/hbfj4Nh/Zj7BiBsSDcilgpjPZINanKgV7M6OrE27ET5UXCX0RIkZ754InzRZAkJByoKMdBssGALGUbMSe/6ee3u9idwczEcIVqV8y47oHSkxapGqiMjzOSgayHA1hZl2I1TUum3vaSNjxarCopPfso+JGaapvTRacZecG3SHDtMitS3psIjgpD0Bt1ay0WOovNLItPg82P9nPM67bxzB0DLD6sjRVHtfGOT68h3xGoDH1PcesX/sRrzu0m3xZ0Ymu7SaXkc9KZHZxy5hi//8lX6DzyeJYf+65AgMQIPf8kl0PFIgYbXdGqejHNOmEy4yhghje22h8kgiRhXhMKkRBjoorfGuV5epFiuCA94yjs9kiQqAbNVyhMRMfd8mwTx/IZdQNdWJuOCOz6BhsLvaxpr9/yOgwtP+rl64RJfFZjEn2/KYkAaSQjKarKIZUSbr6hm7e/ewS8Flaf2ku+I4VSii2PjXD3dRsxXZv3X72qdu3z68v0LQ9UXue8r5c/PVqmM7+Vrfd8j772c2FzEHur5j4OlF9ZvwDST4f9cxDZXz2KmCb+q49G7n18P7R2dr2x9pZEkCTMb5yYrcLzIJ3CKDtUlncAYLiTH9LQg8vN1UeOrYUld0z8fL0NJFRzTdiBkGrVtpJBO7DPtFjB+RiQMRzS4rLD7WB1ekdgdFdQadyuV68raRWLvBEMfsnMJCCMmnzsQR387v9ZfOP6Cb7+3gcwU4JS0N5tcdaFvZx5wUGkdMRnz1Xc+ZNB/vbLKwBYuiJNYcjmI7ccyzVvvYdHdvyElX0nIy8N0bZoJVkz6Duluz2U9RU9e+18ZgKOW1u7J2txH+6O+heHGSERJAkJs4tXKGC26TDk6egt0yoGUxC3JcrzGpaGWGVwG/ZxB1CWwq+aGNp7K25wD5mwMzVhYsWM6ynxSMc2vBr187XowKN6/5LOBoEyoVzyBJ/5+IkwaeDg3hY+80mTM16T4cLLhvjYVw/llae21S1e9FzFtZ9+nv5VWVYfE9iqRna65NosqhMu7b15Bl7cyvqJYUxJUXrxHtpSi8jkwX6kgmGYtLSvZcmyU4FFdd/v56Jh1FrcB9tnuIGJIElImH28QgExTZqFVLCKDm5LCqvioyTcq0Tha+O74QTJt8DVzl++pUDAt4MaPc/AsS0M04+HdAJg3A6k0LiT5eiOYK/xXW60v8Yut40jMi8B0Ra9wzoUvWPYtGnX4pKfuAVPRauR5ezXZLn+aotLP7aZo/6qndPf3k2uzWTTUyV+/aNBFi1L8YmvrawJmLtuGubw0xfzzXc9QPvwEk7jFDISdKCLw4CziYHyM3zqym4WLUtxx8/X8e833Uc6cyp9r3kHY4e10bo1uoe+DfuhYYmNJCFhblALET5WgJbgbVS8YID20yZmxcXPpEmVfZycURMiIb5+Ssyy4OWCh1oqBiob1OF5RiBEgJKdIp8OBMKEnaE7W6qtM3mpGqjTVuUj9YcZc/EZ97O0G/VhUgq+URMmCXvmvLe0cvopOa788hBf+69b6O1Ps3xVlg9euZy1J0YhVjY+VeI3Nw+z5LBuuoaXs8o7iphpCktSrOQIsk6Ob/2vddxwXz9HHZ/l3Re38+Hz7ueFRx+n77xL4OCVNWGiFnUFBzM6I1Gg5n7/J3PkhHlN3T4TtgMjY0EiECKNpMo+hqMwHIWbDWYjVjmym5jlmEdXpfnjU7IjdZmrDCqeRcWzKLppim6gojJRNSEy6gVTnRecHra5nWxzO9nuddTqqChhhx+FUkmYmt4ek6/8zz6++A+djO+0WfXKHCvXZBERxoYcbv7mDq68eDNv/Ns1bF03wgp3zW7rWqwOwh5P8dj9wT/A8pUprri6j75Om50/+gbD991NoT9QcQ6c1cPAWT27retlM89CpCQkHHAoz6slADJpyKQxSjaG49eSxJJVAatJ/ERlBMlwBcMVUIKUTZxiCqeYwnMMCuM5bMditJhjopKpGd/j/KmwrBbIsduawEcY9lrpNIuYojC1VbfgpymqSCgV9DhRVlWqypm093tCPZdd0M2t31/Gk7cNc9FJT3PhMU/y4dc9w4ZNBh+4/hSstElPqg9riu2ORYSu0gr+8JvI/ffkM/LYZcV7L+/CfuQ2Br51JeMyiFmpj+E1I4Sqrb1Ns0Si2kpYOJgGuNEbvVGs4rfUD/R+Onq3kobnMhVux9sCfhODi2+bGOlAYFkxA3xoeLe1jswmiAzclSrVuQFXVP1iNsOaPDAUFHQk7397zUnHZbn3ln7OOH+AiXwHb/vMUbQtCmxMA0+MYnh73hnRVBbVWGBP0xQOPizNIUdkSKeFU96U5Z4f/RMdR76WJae8beYbkRjbExLmJuJoj6tRvZajMxAooVoLwIxF/Q09t3wL0kNGbcW7n1X4joEy9bqSioWfdbFdk1zGoWinaUnb+EoYtzO0a0+uqm+xvdrOkgxM6MoPz9Yr1ws6sGPBz9BpRCPZDiq0GJOFSWaKN+uFTCZj8PMf9nLRR0f40ll3c9hpfbT2Znhh3ShjFQeFmhSePk45N8iKI+qHyrFhj9Z2k2NfnWflK1oYerXNnx+6hw0b/jDzDTgABEnyapOwYPALk7fgBVBpq27r3WaYlcjoHseoSE2I7I6iHfkVjzdRdYWMevm684Kfm1QmtKsUfZ/iLEZ7PdDI5wxuuL6bE0/KMLLLwTqojyMvOY5Ur8kIu4+vVVVldvnbedPbozApW561GdrpsubYXM0O/ub3L6FnSRpxZzhoo1LB+qe9TbNEIkgSFhR+YaImUFTaQqUD6aAMwax4Qar6dft5N8Nwg9So/gJwqhZO1WJ8PIftmNiOSdlN1dKOUis7Ku0U3UCopMQjFYaX9/I1gTLuZdnmdLHN6aozzscZ86u1lDA1ZTw+/a1+DlnqsO47TzCyYZiVZ6/gaeshimp8UnlbVflT7j7efUkH7XoRousovn7VIG+5sBOl4LHfF1l1TCsr1+YZfMnh9HO7Zv7GDwBje6LaSlgwKDtai+G/8CJGdxe05vHam6/LyA16VDv1PiGmkCoE+V62PjqwOLH3sWz0VpjNBd9nGvUPeLhAccLNsNNuo11b9lOxZfQlP81iK/AuM0WxTXtxdRolPF9o09GF43ceN74naq56JrS323EdRb50XQ9PPFLhs5/YQGHcp3cxPLr9LhYZS+h2liMYDLGdneZWznl7K5d8ohOlFOserPCv1wzT0m5x/od6uf2mMZaszLJ8dY5dA1XSWeGEN3Rw+4+HZvbmDwDVViJIEhYckomplyZKSC6F5USDuN3Rim9pnbl+hoN9S4I8qdQLEqMa5PuZoHDc6L47IRLyUrkDctSECYARK2M2THlG/TxLzHEKvkWb4dY8uQA6YuUSoVJPRgyqsfUYXcf0c80d/Vz7u5U8fdMmOhbvYnzAZqy6jVSmBW/VUjKDitv+7/M88Nsijq1o6zJ52990cvZ7OrnrZ2N890uDXPH9wHX4/lsHOf517ZhNgoJOj9n1xtpbEkGSsHDQK9eV7WAuXzbpY5WKPHjCGFxWJfjrZoMBIgziaGpNkpeJwsubruCGRnfHwMu46HWPuNo7yNXr60erivZ0lc50mbKXouylyBr17rxtenFitzVB0Y+E3xDBuhNHVWpBIPOGV6fe6jCSEPRxYVrSu4DfX15eyxvyWulas4jX/P0iNo70UFgfqaWUnmQW1z3B+M9vpCPv8KrT8mzdaHPxGzfT1pPiU99bw8Fr8gy9VOWOH+zkv3/7EH7/y+ZbBLxsFKhkQWJCwtyhto874A28WLcFb1yIWOXJD65VUZMiAUMkUCAafAAwFE41ek8rO5Pf2cbtTN3sY8zNMeZGU5146PlmjMc+LzXzR17gpGTq36THjJwvVnVF6ijDltrC05ZjjmbJ338ODj+EO34+QdFJ8ZGvrOZzN7+S5atzPPzrYT53wTOce+kiFvWnufum4ZlvSLKOJCFh7hEKE2U7YI9CVwtGQUuElImZsVACbosJMbdQK4r6jqe1ReF27KEQMSdM/JRWcZVNnO4gP5t2asKkK1dG6ZCyw9Vo98ZxJ0dPulgzwi/LjjLhZetsJ51msU7AhPG5API6+GMKRcWLXIQnlE1eLDylMEXIyfyfrfh6f9qUmDjKY9hrPtRVXIudxVaGRlrx24Lf2YNoPY8Og9P9sffCrX/ggRvv5NE7R+hZmmZ4u83igzJc+pl+Vh2V5/OXbOT1b23hln+bbLifFgeAjSSZkSQsaCSTxtim3T/rZiV6YWGT2Ul8ZmKWwSxJFNwxFRpVFBhBlGCAQiFY8W47FiUnPalOqyGeVnymsqeZSUjjrCTuIuzpwWihCJGSb9fSiF9l3M8w7mdYZo0w5LWyvrKU9ZWl7CxGbr1GS0y1mPVrQiSk+62nsOiqD2O7wq6BKie/qYOT3tjOfbcO8/Gzn2HNkRZPPb4f3H99f+/TLJHMSBIWLJKJDeip3atBrLKPm9P2lVix8DhwBRaUCV5WCxJf21QAv2qSbrXxvOi9reSkyacCr65mQmRZZgwfwSAMlxIIk8b93uPG+HajSsk36zbJAmiJuQ2XVf1AN98ESzgTCQm9tbLay62iFwP1pgoMOm00YrQ4+MXJzgmVUpps3ibbv4j+665g8/uu4s4bh+joMunqtVh8UIp776qwfG07sB+EyRwnESQJCxZlR2+gYkcLEsXxUZbUVrj7lmCVfJy8STiOh05WXhr89OSdFAFU1kdpT6/QXpLKuFTdoHDVzeEqg7Z0NPD4qpVWq8qzbh+dqTKG+HXCw8SnbTcBnca1Qb6z4XNPKUrobXt1iNu8BPfjxIJApuTAHg4aheRLXtS2F91oe9wtld7acV/LBMOVPIt7xxkqaCcGX1BevbLGd0xKYznwBXPEwmzrhZEChaLHyFC4hbPg28uAmXX/VQfAwtNEtZWwYPELheikFIQg8TpyiFIYjo9vSeQGTOTBFce064VI6AocVCaIYyCOgXKDZBejWVBofik0rHafcDNkTLdOvRVS8LO86HTWUt1nKktBTVaDlWJvtAU9KM33KMKbXUVFmbWUlWgNUYcVhZsZruQnXZvKNfltyiaUTcyRQNha+VZcr8ry4ioyZjti5Um19zDx8LMz3JJ9WIyYLEhMSPjLozyvJkzMXBbGChhaxeXn0+RfKuO0RwO/3WZgVrU7cK7eCB8a25UIZkXwM6rmROOnVTAQ5QK7y9hQK1bOIZ12GXbytGRsqo5Fd75EyU3VVFNFvcHV5nKG3nTgYdShLf7hKvdtTuCy2maU6bGCMi+4nbTEBk7fKBOKzG7TpaLdSVP4ODWjtIGrIqO+hXnAzFAc5VLWrr6jfigEgnsf8nWUgFi4mZKXZtgJZh/hbz1YaEFiKsFwJhmqKE29ZUBmODjPdPXRsrXCi2zmSPtEMnYWr+SRIcu9/HLmGneAbGyVzEgSFix1e5U0YJRsjFJsMDYFqxw90HHtkWryFMVnJoYdm6WIqsVVse36gdoQVRvYCk79zCL05AKahkop+DkK3uTYXI2U/OheSmrq2EzOATBrGffLNSECEF8PGAqROLvcdnbY7bXz1vRke0bcbZuGBaVhvLWO409hLDXGao7mzzzGkzzIABvZwLqX2ZIpUP7ep1niwHjlSEjYD4R7lJgdHahSqO6oVxf5DSuVrbLCzUmw0l0vQ3Bam69mFifI91r0Ax5/41VCJhMMgKGKa7QSCYL2VLUmTCzDwxKPUSdHh1WqW2sSp80oM+S21mYmuyMuTIri4SvIiEs25urcZQRCdq7aUCb8QJIXGoRdwTdix/W/0y63naeK/aS1ZgxGmAAAB7lJREFU4X1jIbCVtKarDIaLPKvN22iVRP8Nzlt6DiLTv5zBrTs42T+TMYYoM4FgsoOBabYuQgFqhmckInIW8BXABK5XSv1Tw+cZ4N+A4wkMPu9RSm2Zqs5kRpKQEEO2DyK2GxjflcJ0/FhSmI4iMx69+Skjcv01nCiYoxEb34yyEai6tZ0EAc82qVZT+L5B1TVrBviQgYkOSm6Kkht4EBXcQKg8X+5hwmvuaRUOnENuK9vdDsb9rE6ZWioqq5YMqdeaVGI69rJyKCuHamygnisbae3NfVSUhRHz4Npc7auF6w/pzpSouhZV16I1V8X3DD1yE6i0/GDjstTQZOGSHoOj11yI02ryoHE3RcZppZMse54V7hNK1W3Otqe0J0TEBL4BnA2sBS4QkbUNxS4BRpRShwFfBr6wp3oTQZKQoJF05PYZrnQ3G8LL+2a0qZUytP7cDlLtWiNmM4k9YVLVJxPBwOTZ2nvLrnc3NUTRmyvWjkNCYQLsUZgAlLQXV0mlKan6tSseRlPVe1yYeFqFNpeESfj9fhP1XkjBj9oaCpNMQ/iZETvPhpFFQfmYs4OZioRPrb+o38gsPabLWhnW/tVlHHHkeYxlCqw3n2Ajf9rHFu0FM6vaOgl4Tim1SSllAzcA5zaUORf4vj6+CXiDTLVhC3NUtVVgZPBOddPzs30fM0wvMDjbNzELzP12jzac75yxmud+2/cPC7XdELR9xUxVVmDk9jvVTb17LlkjKyKPxM6vU0pdFzvvB16InQ8AJzfUUSujlHJFZAzoYYo+nZOCRCm1aLbvYaYRkUeUUifM9n38pVmo7YaF2/aF2m6otX3lTNWnlDprpurSNJtZNE7v9qZMHYlqKyEhIWHhMAAcFDtfDry4uzIiYhHsUDBlNMpEkCQkJCQsHB4GVovIISKSBs4HftFQ5hfAB/Txu4DfKDX1asc5qdqap1y35yLzkoXabli4bV+o7YY53nZt8/gocDuB++93lFJPi8jngEeUUr8A/hX4gYg8RzATOX9P9coeBE1CQkJCQsKUJKqthISEhIRpkQiShISEhIRpkQiSvUBETBF5XERu1ec/EpH1IvKUiHxHRFI6X0TkqyLynIg8ISKvitXxARF5VqcPxPKPF5En9TVfDRf+iEi3iNyhy98hIl2N9/WXoLHtsfyvichE7DwjIj/V7XhQRFbGPrtC568XkTfH8s/Sec+JyKdi+YfoOp7VdU7eCWo/06TPRUSuEpENIvKMiHwslj+v+1xE3iAij4nIH0XkPhE5TOfPtz7fovvlj6LXYuyuT+Zjv08LpVSS9pCA/wL8GLhVn59D4GstwE+AD8fyb9P5pwAP6vxuYJP+26WPu/RnDwGn6mtuA87W+V8EPqWPPwV8YS60XeedAPwAmIjlXQ58Sx+fD/xUH68F1gEZ4BBgI4GRz9THhwJpXWatvuZG4Hx9/K3w953lPr+IIP6Qoc/7FkqfAxuAV8T6+XvztM+3AL0NeU37ZD72+7R+u9m+gbmeCPys7wJeT2wwjX3+ceAqfXwtcEHss/XAUuAC4NpY/rU6bynw51h+rVx4rT5eCqyfC23Xg8Hd+p7iguR24FR9bBGsghXgCuCKxnI63R7Lv0In0ddaOr+u3Cy2+yHgsCZlF0KfrwdOjvXT5+dbn+vv3cJkQdK0T+Zbv083JaqtPXMN8ElgUiAbCVRa7wP+XWc1Cz/Qv4f8gSb5AIuVUi8B6L99023Iy6BZ2z8K/CK8txh1YRWAMKzCvv4mPcCoriOe/5ekWbtXAe8RkUdE5DYRWa3zF0KfXwr8SkQGCP7fw2ix86nPIVi9/WsReVRELtN5u+uT+dbv0yIRJFMgIm8FdiqlHt1NkX8BfqeUuje8pEkZ9TLyZ51mbReRZcB/AL7W7JImeS+n7bP6m0zR5xmgooLQH98GvhNe0qSaOdu+qZii7R8HzlFKLQe+C/zv8JIm1RxwfR7jNKXUqwgi435ERE6fouyB2sb9QiJIpuY04K9FZAtBlMzXi8gPAUTkH4FFBPrkkN2FH5gqf3mTfIAdIrJUf9dSZjKU4N4xqe3A08BhwHM6Py/BoiXYfViFff1NBoFOXUc8/y/F7vp8ALhZl7kFOFofz+s+F5FfAscopR7UZX4KvFofz5c+B0Ap9aL+u5Ogj09i930yn/p9+sy2bu1AScAZRDrjS4H7gVxDmbdQb4B7SOd3A5sJjG9d+rhbf/awLhsa4M7R+VdTb4D74lxoe0N+3EbyEeoNrzfq4yOpN7xuIrCzWPr4ECLD65H6mv9DveH18tluN4E65+JY/sMLoc+JbB+H6/xLgJvnW58DLUBb7Ph+4Kzd9cl87feX/fvN9g0cKKlhUHEJvE/+qNM/6Hwh2DRmI/AkcELs+ouB53S6KJZ/AvCUvubrRNEGegiMns/qv91zoe0N+XFBktWDwXMEhulDY599WrdvPdpTReefQ+ARtBH4dCz/UF3Hc7rOzGy3m2DrxF/qfn2A4C19QfQ58A7dtnXAb8O+nU99rr9/nU5Ph/e2uz6Zr/3+clMSIiUhISEhYVokNpKEhISEhGmRCJKEhISEhGmRCJKEhISEhGmRCJKEhISEhGmRCJKEhISEhGmRCJKEhISEhGmRCJKEhISEhGnx/wFfjWZbGwhXnwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"for method in methods:\n",
" for eps in np.arange(1,0,-.1):\n",
" #method='phs2'\n",
" sigma=.5\n",
" print(method,sigma,eps)\n",
" I = RBFInterpolant(x_obs, u_obs, eps=eps,sigma=sigma, phi=method, order=1)\n",
" #print('done')\n",
" # create the interpolation points, and evaluate the interpolant\n",
" x1, x2 = np.linspace(xmin, xmax, 100), np.linspace(ymin, ymax, 100)\n",
" x_itp = np.reshape(np.meshgrid(x1, x2), (2, 100*100)).T\n",
" u_itp = I(x_itp) \n",
" # plot the results\n",
" plt.tripcolor(x_itp[:, 0], x_itp[:, 1], u_itp, vmin=0, vmax=1, cmap='viridis')\n",
" plt.scatter(x_obs[:, 0], x_obs[:, 1], s=100, c=u_obs, vmin=0, vmax=1,\n",
" cmap='viridis', edgecolor='k')\n",
" plt.xlim((xmin, xmax))\n",
" plt.ylim((ymin, ymax))\n",
" plt.colorbar()\n",
" plt.tight_layout()\n",
" #plt.savefig('../interpolate.a.png')\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-17T07:23:45.016654Z",
"start_time": "2020-05-17T07:23:45.008845Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([3. , 3.9, 4.8, 5.7, 6.6])"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"array=np.arange(3,7,.9)\n",
"display(array)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-17T07:47:17.423769Z",
"start_time": "2020-05-17T07:47:17.407177Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1. ],\n",
" [ 1.04811313],\n",
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" [ 1.1513954 ],\n",
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" [ 1.38949549],\n",
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" [ 1.52641797],\n",
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" [ 2.02358965],\n",
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" [ 2.6826958 ],\n",
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" [ 3.23745754],\n",
" [ 3.39322177],\n",
" [ 3.55648031],\n",
" [ 3.72759372],\n",
" [ 3.90693994],\n",
" [ 4.09491506],\n",
" [ 4.29193426],\n",
" [ 4.49843267],\n",
" [ 4.71486636],\n",
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" [ 5.17947468],\n",
" [ 5.42867544],\n",
" [ 5.68986603],\n",
" [ 5.96362332],\n",
" [ 6.25055193],\n",
" [ 6.55128557],\n",
" [ 6.86648845],\n",
" [ 7.19685673],\n",
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" [ 7.90604321],\n",
" [ 8.28642773],\n",
" [ 8.68511374],\n",
" [ 9.10298178],\n",
" [ 9.54095476],\n",
" [10. ]])"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"array([[ 2. ],\n",
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" [ 4.85486025],\n",
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" [ 5.18449095],\n",
" [ 5.35760652],\n",
" [ 5.53650261],\n",
" [ 5.72137222],\n",
" [ 5.91241481],\n",
" [ 6.10983653],\n",
" [ 6.31385036],\n",
" [ 6.52467642],\n",
" [ 6.74254219],\n",
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" [ 7.20034093],\n",
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" [ 8.76882139],\n",
" [ 9.0616215 ],\n",
" [ 9.36419852],\n",
" [ 9.6768789 ],\n",
" [10. ]])"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xcoords=np.geomspace(1,10).reshape((50,1))\n",
"ycoords=np.geomspace(2,10).reshape((50,1))\n",
"display(xcoords)\n",
"display(ycoords)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-17T07:57:16.110239Z",
"start_time": "2020-05-17T07:57:16.099503Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1. , 2. , 1.04811313, 2.06678209],\n",
" [ 1.09854114, 2.13579411, 1.1513954 , 2.2071105 ],\n",
" [ 1.20679264, 2.28080823, 1.26485522, 2.3569668 ],\n",
" [ 1.32571137, 2.43566839, 1.38949549, 2.5169979 ],\n",
" [ 1.45634848, 2.60104309, 1.52641797, 2.68789464],\n",
" [ 1.59985872, 2.77764625, 1.67683294, 2.87039476],\n",
" [ 1.75751062, 2.96624025, 1.84206997, 3.06528611],\n",
" [ 1.93069773, 3.16763922, 2.02358965, 3.27341 ],\n",
" [ 2.12095089, 3.38271258, 2.22299648, 3.49566489],\n",
" [ 2.32995181, 3.6123888 , 2.44205309, 3.73301024],\n",
" [ 2.55954792, 3.85765935, 2.6826958 , 3.98647063],\n",
" [ 2.8117687 , 4.11958305, 2.9470517 , 4.25714024],\n",
" [ 3.0888436 , 4.3992906 , 3.23745754, 4.54618751],\n",
" [ 3.39322177, 4.69798947, 3.55648031, 4.85486025],\n",
" [ 3.72759372, 5.01696911, 3.90693994, 5.18449095],\n",
" [ 4.09491506, 5.35760652, 4.29193426, 5.53650261],\n",
" [ 4.49843267, 5.72137222, 4.71486636, 5.91241481],\n",
" [ 4.94171336, 6.10983653, 5.17947468, 6.31385036],\n",
" [ 5.42867544, 6.52467642, 5.68986603, 6.74254219],\n",
" [ 5.96362332, 6.96768272, 6.25055193, 7.20034093],\n",
" [ 6.55128557, 7.44076784, 6.86648845, 7.68922286],\n",
" [ 7.19685673, 7.94597405, 7.54312006, 8.21129843],\n",
" [ 7.90604321, 8.48548227, 8.28642773, 8.76882139],\n",
" [ 8.68511374, 9.0616215 , 9.10298178, 9.36419852],\n",
" [ 9.54095476, 9.6768789 , 10. , 10. ]])"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xycoords=np.hstack((xcoords,ycoords)).reshape((25,4))\n",
"display(xycoords)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-17T08:16:16.131511Z",
"start_time": "2020-05-17T08:16:16.124679Z"
}
},
"outputs": [],
"source": [
"import math\n",
"output1=np.zeros((25)).reshape(25,1)\n",
"output2=np.zeros((25)).reshape(25,1)\n",
"output3=np.zeros((25)).reshape(25,1)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-17T08:16:16.901585Z",
"start_time": "2020-05-17T08:16:16.892795Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.],\n",
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(output1)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-17T08:16:27.442526Z",
"start_time": "2020-05-17T08:16:27.431790Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.0823087 ],\n",
" [0.08876712],\n",
" [0.09576738],\n",
" [0.10335814],\n",
" [0.11159267],\n",
" [0.12052932],\n",
" [0.13023197],\n",
" [0.14077062],\n",
" [0.15222197],\n",
" [0.16467006],\n",
" [0.17820703],\n",
" [0.19293385],\n",
" [0.20896126],\n",
" [0.22641067],\n",
" [0.24541523],\n",
" [0.26612097],\n",
" [0.2886881 ],\n",
" [0.31329233],\n",
" [0.34012647],\n",
" [0.36940204],\n",
" [0.40135116],\n",
" [0.43622852],\n",
" [0.47431365],\n",
" [0.51591332],\n",
" [0.56136421]])"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"np.subtract(xycoords[:,2],xycoords[:,0], out=output1[ :, 0])\n",
"output1=np.square(output1)\n",
"np.subtract(xycoords[:,3],xycoords[:,1], out=output2[ :, 0])\n",
"output2=np.square(output2)\n",
"np.add(output1[:,0],output2[:,0],out=output3[ :, 0])\n",
"output3=np.sqrt(output3)\n",
"display(output3)"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-17T08:16:29.016904Z",
"start_time": "2020-05-17T08:16:29.005193Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.0823087 ],\n",
" [0.08876712],\n",
" [0.09576738],\n",
" [0.10335814],\n",
" [0.11159267],\n",
" [0.12052932],\n",
" [0.13023197],\n",
" [0.14077062],\n",
" [0.15222197],\n",
" [0.16467006],\n",
" [0.17820703],\n",
" [0.19293385],\n",
" [0.20896126],\n",
" [0.22641067],\n",
" [0.24541523],\n",
" [0.26612097],\n",
" [0.2886881 ],\n",
" [0.31329233],\n",
" [0.34012647],\n",
" [0.36940204],\n",
" [0.40135116],\n",
" [0.43622852],\n",
" [0.47431365],\n",
" [0.51591332],\n",
" [0.56136421]])"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"for a in range(0,25):\n",
" output3[a]=math.sqrt(((xycoords[a,2]-xycoords[a,0])**2.0)+((xycoords[a,3]-xycoords[a,1])**2.0))\n",
"display(output3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"hide_input": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| stackv2 | 2024-11-18T18:03:05.261394+00:00 | 2020-05-25T07:49:37 | {
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} |
71c51c59fef7c4db18302e64763892754717a02e | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Merge and Data Preperation\n",
"This Notebook is Merging different Dataframes containing features of patients together.\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##### REQUIRES THE DATAFRAME FOLDER TO BE NAMED 'Cohorts', WHICH INCLUDES ALL PRECOMPUTED DATAFRAMES #####\n",
"import fiber\n",
"from fiber.cohort import Cohort\n",
"from fiber.condition import Patient, MRNs\n",
"from fiber.condition import Diagnosis\n",
"from fiber.condition import Measurement, Encounter, Drug, TobaccoUse,LabValue\n",
"from fiber.storage import yaml as fiberyaml\n",
"import pandas as pd\n",
"import pyarrow.parquet as pq\n",
"import numpy as np\n",
"import os\n",
"import matplotlib.pyplot as plt\n",
"from functools import reduce\n",
"import json\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.impute import SimpleImputer\n",
"import category_encoders as ce\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#load data with baseline characteristics\n",
"Case_EF_ICD = pq.read_table('Cohort/Phenotyping/ALL_Matches_1yr_HF_EF_ICD_Notes_Cohort.parquet').to_pandas()\n",
"Case_EF_ICD=Case_EF_ICD.set_index('MRN', inplace=False)\n",
"Case_ICD = pq.read_table('Cohort/Phenotyping/ALL_Matches_1yr_HF_ICD_Notes_Cohort.parquet').to_pandas()\n",
"Case_ICD=Case_ICD.set_index('MRN', inplace=False)\n",
"Case_all= pd.concat([Case_EF_ICD, Case_ICD], ignore_index=False, sort =False)\n",
"Case_all.index = Case_all.index.map(str)\n",
"#load all dataframes that should be merged to the cohort and add them to the array: \n",
"df_forMerge=[]\n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Unsupervised_ALL_HF/HF_ALL_Drugs_Count').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/vascular_cognitive_impairment.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/acute_myocardial_infarction.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/anemia.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/angina.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/atrial_flutter_fibrillation.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/cardiomyopathy.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/chronic_kidney_disease.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/COPD.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/coronary_artery_disease.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/depression.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/diabetes_mellitus_type_I.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/diabetes_mellitus_type_II.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/diabetic_nephropathy.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/dyspnea.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/dysrhythmias.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/edema.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/hyperkalemia.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/hyperlipidemia.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/hypertension.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/obesity.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/peripheral_artery_disease.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/pulmonary_hypertension.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/rheumatic_heart_disease.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/sleep_apnea.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/stroke_broad.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/stroke_hemorrhagic.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/stroke_ischemic.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/transient_ischemic_attack.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/valve_disorder.parquet').to_pandas()) \n",
"\n",
"#df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/oral_diuretics.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/angiotensin_receptor_blockers.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/beta_blockers.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/entresto.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/IV_diuretics.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/long_acting_nitrates.parquet').to_pandas()) \n",
"df_forMerge.append(pq.read_table('Cohort/Feature_Extraction/Supervised_ALL_HF/mineralocorticoid_receptor_anta.parquet').to_pandas()) \n",
"\n",
"df_forMerge\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"col_for_dropping=[\"age_in_days_icd\",\"Note_ID\",\"age_in_days_term\",\"Term\",\"HF_Onset_age_in_days\",\"HF_Onset_Type\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"col_patient_information=['age_in_days',\n",
"'date_of_birth',\n",
"'month_of_birth',\n",
"'gender',\n",
"'religion',\n",
"'race',\n",
"'patient_ethnic_group',\n",
"'deceased_indicator',\n",
"'mother_account_number',\n",
"'address_zip',\n",
"'marital_status_code','medical_record_number']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def merge_dataframes(df_master, df_list, col_for_dropping,col_patient_information, final_name):\n",
" df_patient_information=df_list[0]\n",
" df_patient_information=df_patient_information[col_patient_information]\n",
" df_master=df_master.merge(df_patient_information, left_on='MRN',right_on='medical_record_number',how='inner')\n",
" df_master=df_master.set_index('medical_record_number', inplace=False)\n",
" for x in range(len(df_list)-1):\n",
" x=x+1\n",
" print(x)\n",
" df_merge=df_list[x].drop(col_for_dropping,axis=1)\n",
" #df_merge=dataCuration(df_merge,df_list[x][1])\n",
" df_master.index = df_master.index.map(int)\n",
" #df_master=df_master.astype({'medical_record_number': 'int64'})\n",
" df_master = df_master.merge(df_merge, right_on=\"medical_record_number\", left_index=True, how=\"inner\")\n",
" df_master=df_master.set_index('medical_record_number', inplace=False)\n",
" df_master=df_master.drop(['Note_ID','age_in_days_icd','age_in_days_x','HF_Onset_Type','age_in_days_y','date_of_birth','month_of_birth','Term'],axis=1) \n",
" #saving the dataframe and a sample: \n",
" df_master.to_parquet('Cohort/Feature_Extraction/'+final_name+'.parquet')\n",
" sample=df_master.head(1000)\n",
" sample.to_parquet('Cohort/Feature_Extraction/Sample_'+final_name+'.parquet')\n",
" return(df_master)\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df=merge_dataframes(Case_all, df_forMerge, col_for_dropping,col_patient_information, 'ALL_HF_cohort_supervised_only_ever_diag_drug')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_supervised_merge= pq.read_table('Cohort/Feature_Extraction/ALL_HF_cohort_supervised_only_ever_diag_drug.parquet').to_pandas()\n",
"df_supervised_merge=df_supervised_merge.drop([\n",
"'gender',\n",
"'religion',\n",
"'race',\n",
"'patient_ethnic_group',\n",
"'deceased_indicator',\n",
"'mother_account_number',\n",
"'address_zip',\n",
"'marital_status_code','HF_Onset_age_in_days'],axis=1)\n",
"df_supervised_merge\n",
"#df_supervised_merge=df_supervised_merge.replace(1, 'yes')\n",
"#df_supervised_merge=df_supervised_merge.replace(0,'no')\n",
"#df_supervised_merge=df_supervised_merge.fillna('no')\n",
"df_supervised_merge.to_parquet('Cohort/Feature_Extraction/ALL_HF_cohort_supervised_only_ever_diag_drugFORMerge.parquet')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_supervised_merge= pq.read_table('Cohort/Feature_Extraction/ALL_HF_cohort_supervised_only_ever_diag_drugFORMerge.parquet').to_pandas()\n",
"df_supervised_merge\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"df_supervised_merge=df_supervised_merge.replace(1, 'yes')\n",
"df_supervised_merge=df_supervised_merge.replace(0, 'no')\n",
"df_supervised_merge.to_parquet('Cohort/Feature_Extraction/Supervised_True_false.parquet')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_supervised_merge"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Remove MRN which do not have any Lab Values "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#load supervised for clustering\n",
"clustering=pq.read_table('Cohort/Feature_Extraction/ALL_HF_cohort_supervised_only_ever_diag_drug.parquet').to_pandas()\n",
"clustering.index=clustering.index.map(str)\n",
"#load supervised for merge\n",
"merge=pq.read_table('Cohort/Feature_Extraction/ALL_HF_cohort_supervised_only_ever_diag_drugFORMerge.parquet').to_pandas()\n",
"merge.index=merge.index.map(str)\n",
"#mrn without labvalues: \n",
"mrn_without_lab=pq.read_table('Cohort/Feature_Extraction/Unsupervised_ALL_HF/LabValue_after_onset_HF_ALL_mmm_0_8_missing_values').to_pandas()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mrn_list=mrn_without_lab['medical_record_number'].to_list()\n",
"len(mrn_list)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"clustering"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"clustering_wLab=clustering.drop(mrn_list, inplace=False)\n",
"clustering_wLab\n",
"clustering_wLab.to_parquet('Cohort/Feature_Extraction/ALL_HF_cohort_supervised_only_ever_diag_drug_wLab.parquet')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"merge_wLab=merge.drop(mrn_list, inplace=False)\n",
"merge_wLab\n",
"merge_wLab.to_parquet('Cohort/Feature_Extraction/ALL_HF_cohort_supervised_only_ever_diag_drugFORMerge_wLab.parquet')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"merge_wLab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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ce0cd210f68b85e0fb01e8e07782fbb675a60d98 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>alcohol</th>\n",
" <th>chlorides</th>\n",
" <th>citric_acid</th>\n",
" <th>density</th>\n",
" <th>fixed_acidity</th>\n",
" <th>free_sulfur_dioxide</th>\n",
" <th>pH</th>\n",
" <th>quality</th>\n",
" <th>residual_sugar</th>\n",
" <th>sulphates</th>\n",
" <th>total_sulfur_dioxide</th>\n",
" <th>type</th>\n",
" <th>volatile_acidity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9.4</td>\n",
" <td>0.076</td>\n",
" <td>0.00</td>\n",
" <td>0.9978</td>\n",
" <td>7.4</td>\n",
" <td>11.0</td>\n",
" <td>3.51</td>\n",
" <td>5</td>\n",
" <td>1.9</td>\n",
" <td>0.56</td>\n",
" <td>34.0</td>\n",
" <td>red</td>\n",
" <td>0.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>9.8</td>\n",
" <td>0.098</td>\n",
" <td>0.00</td>\n",
" <td>0.9968</td>\n",
" <td>7.8</td>\n",
" <td>25.0</td>\n",
" <td>3.20</td>\n",
" <td>5</td>\n",
" <td>2.6</td>\n",
" <td>0.68</td>\n",
" <td>67.0</td>\n",
" <td>red</td>\n",
" <td>0.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>9.8</td>\n",
" <td>0.092</td>\n",
" <td>0.04</td>\n",
" <td>0.9970</td>\n",
" <td>7.8</td>\n",
" <td>15.0</td>\n",
" <td>3.26</td>\n",
" <td>5</td>\n",
" <td>2.3</td>\n",
" <td>0.65</td>\n",
" <td>54.0</td>\n",
" <td>red</td>\n",
" <td>0.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>9.8</td>\n",
" <td>0.075</td>\n",
" <td>0.56</td>\n",
" <td>0.9980</td>\n",
" <td>11.2</td>\n",
" <td>17.0</td>\n",
" <td>3.16</td>\n",
" <td>6</td>\n",
" <td>1.9</td>\n",
" <td>0.58</td>\n",
" <td>60.0</td>\n",
" <td>red</td>\n",
" <td>0.28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>9.4</td>\n",
" <td>0.076</td>\n",
" <td>0.00</td>\n",
" <td>0.9978</td>\n",
" <td>7.4</td>\n",
" <td>11.0</td>\n",
" <td>3.51</td>\n",
" <td>5</td>\n",
" <td>1.9</td>\n",
" <td>0.56</td>\n",
" <td>34.0</td>\n",
" <td>red</td>\n",
" <td>0.70</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" alcohol chlorides citric_acid density fixed_acidity \\\n",
"0 9.4 0.076 0.00 0.9978 7.4 \n",
"1 9.8 0.098 0.00 0.9968 7.8 \n",
"2 9.8 0.092 0.04 0.9970 7.8 \n",
"3 9.8 0.075 0.56 0.9980 11.2 \n",
"4 9.4 0.076 0.00 0.9978 7.4 \n",
"\n",
" free_sulfur_dioxide pH quality residual_sugar sulphates \\\n",
"0 11.0 3.51 5 1.9 0.56 \n",
"1 25.0 3.20 5 2.6 0.68 \n",
"2 15.0 3.26 5 2.3 0.65 \n",
"3 17.0 3.16 6 1.9 0.58 \n",
"4 11.0 3.51 5 1.9 0.56 \n",
"\n",
" total_sulfur_dioxide type volatile_acidity \n",
"0 34.0 red 0.70 \n",
"1 67.0 red 0.88 \n",
"2 54.0 red 0.76 \n",
"3 60.0 red 0.28 \n",
"4 34.0 red 0.70 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wine_qa = pd.read_csv('data/wine-qa.csv')\n",
"wine_qa.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>alcohol</th>\n",
" <th>chlorides</th>\n",
" <th>citric_acid</th>\n",
" <th>density</th>\n",
" <th>fixed_acidity</th>\n",
" <th>free_sulfur_dioxide</th>\n",
" <th>pH</th>\n",
" <th>quality</th>\n",
" <th>residual_sugar</th>\n",
" <th>sulphates</th>\n",
" <th>total_sulfur_dioxide</th>\n",
" <th>volatile_acidity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>6497.000000</td>\n",
" <td>6497.000000</td>\n",
" <td>6497.000000</td>\n",
" <td>6497.000000</td>\n",
" <td>6497.000000</td>\n",
" <td>6497.000000</td>\n",
" <td>6497.000000</td>\n",
" <td>6497.000000</td>\n",
" <td>6497.000000</td>\n",
" <td>6497.000000</td>\n",
" <td>6497.000000</td>\n",
" <td>6497.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>10.491801</td>\n",
" <td>0.056034</td>\n",
" <td>0.318633</td>\n",
" <td>0.994697</td>\n",
" <td>7.215307</td>\n",
" <td>30.525319</td>\n",
" <td>3.218501</td>\n",
" <td>5.818378</td>\n",
" <td>5.443235</td>\n",
" <td>0.531268</td>\n",
" <td>115.744574</td>\n",
" <td>0.339666</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.192712</td>\n",
" <td>0.035034</td>\n",
" <td>0.145318</td>\n",
" <td>0.002999</td>\n",
" <td>1.296434</td>\n",
" <td>17.749400</td>\n",
" <td>0.160787</td>\n",
" <td>0.873255</td>\n",
" <td>4.757804</td>\n",
" <td>0.148806</td>\n",
" <td>56.521855</td>\n",
" <td>0.164636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>8.000000</td>\n",
" <td>0.009000</td>\n",
" <td>0.000000</td>\n",
" <td>0.987110</td>\n",
" <td>3.800000</td>\n",
" <td>1.000000</td>\n",
" <td>2.720000</td>\n",
" <td>3.000000</td>\n",
" <td>0.600000</td>\n",
" <td>0.220000</td>\n",
" <td>6.000000</td>\n",
" <td>0.080000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>9.500000</td>\n",
" <td>0.038000</td>\n",
" <td>0.250000</td>\n",
" <td>0.992340</td>\n",
" <td>6.400000</td>\n",
" <td>17.000000</td>\n",
" <td>3.110000</td>\n",
" <td>5.000000</td>\n",
" <td>1.800000</td>\n",
" <td>0.430000</td>\n",
" <td>77.000000</td>\n",
" <td>0.230000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>10.300000</td>\n",
" <td>0.047000</td>\n",
" <td>0.310000</td>\n",
" <td>0.994890</td>\n",
" <td>7.000000</td>\n",
" <td>29.000000</td>\n",
" <td>3.210000</td>\n",
" <td>6.000000</td>\n",
" <td>3.000000</td>\n",
" <td>0.510000</td>\n",
" <td>118.000000</td>\n",
" <td>0.290000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>11.300000</td>\n",
" <td>0.065000</td>\n",
" <td>0.390000</td>\n",
" <td>0.996990</td>\n",
" <td>7.700000</td>\n",
" <td>41.000000</td>\n",
" <td>3.320000</td>\n",
" <td>6.000000</td>\n",
" <td>8.100000</td>\n",
" <td>0.600000</td>\n",
" <td>156.000000</td>\n",
" <td>0.400000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>14.900000</td>\n",
" <td>0.611000</td>\n",
" <td>1.660000</td>\n",
" <td>1.038980</td>\n",
" <td>15.900000</td>\n",
" <td>289.000000</td>\n",
" <td>4.010000</td>\n",
" <td>9.000000</td>\n",
" <td>65.800000</td>\n",
" <td>2.000000</td>\n",
" <td>440.000000</td>\n",
" <td>1.580000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" alcohol chlorides citric_acid density fixed_acidity \\\n",
"count 6497.000000 6497.000000 6497.000000 6497.000000 6497.000000 \n",
"mean 10.491801 0.056034 0.318633 0.994697 7.215307 \n",
"std 1.192712 0.035034 0.145318 0.002999 1.296434 \n",
"min 8.000000 0.009000 0.000000 0.987110 3.800000 \n",
"25% 9.500000 0.038000 0.250000 0.992340 6.400000 \n",
"50% 10.300000 0.047000 0.310000 0.994890 7.000000 \n",
"75% 11.300000 0.065000 0.390000 0.996990 7.700000 \n",
"max 14.900000 0.611000 1.660000 1.038980 15.900000 \n",
"\n",
" free_sulfur_dioxide pH quality residual_sugar \\\n",
"count 6497.000000 6497.000000 6497.000000 6497.000000 \n",
"mean 30.525319 3.218501 5.818378 5.443235 \n",
"std 17.749400 0.160787 0.873255 4.757804 \n",
"min 1.000000 2.720000 3.000000 0.600000 \n",
"25% 17.000000 3.110000 5.000000 1.800000 \n",
"50% 29.000000 3.210000 6.000000 3.000000 \n",
"75% 41.000000 3.320000 6.000000 8.100000 \n",
"max 289.000000 4.010000 9.000000 65.800000 \n",
"\n",
" sulphates total_sulfur_dioxide volatile_acidity \n",
"count 6497.000000 6497.000000 6497.000000 \n",
"mean 0.531268 115.744574 0.339666 \n",
"std 0.148806 56.521855 0.164636 \n",
"min 0.220000 6.000000 0.080000 \n",
"25% 0.430000 77.000000 0.230000 \n",
"50% 0.510000 118.000000 0.290000 \n",
"75% 0.600000 156.000000 0.400000 \n",
"max 2.000000 440.000000 1.580000 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wine_qa.describe()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"y = wine_qa['quality']\n",
"X = wine_qa.drop(columns=['quality'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import LabelEncoder\n",
"\n",
"label_encoder = LabelEncoder()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LabelEncoder()"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"label_encoder.fit(X['type'])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"color = label_encoder.transform(X['type'])\n",
"X = X.drop(columns=['type'])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"StandardScaler()"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"prep = StandardScaler()\n",
"prep.fit(X)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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" <td>-0.862469</td>\n",
" <td>3.282235</td>\n",
" <td>0</td>\n",
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" <th>2</th>\n",
" <td>-0.580068</td>\n",
" <td>1.026697</td>\n",
" <td>-1.917553</td>\n",
" <td>0.768188</td>\n",
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" <td>-1.092486</td>\n",
" <td>2.553300</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.580068</td>\n",
" <td>0.541412</td>\n",
" <td>1.661085</td>\n",
" <td>1.101694</td>\n",
" <td>3.073817</td>\n",
" <td>-0.762074</td>\n",
" <td>-0.363868</td>\n",
" <td>-0.744778</td>\n",
" <td>0.327510</td>\n",
" <td>-0.986324</td>\n",
" <td>-0.362438</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.915464</td>\n",
" <td>0.569958</td>\n",
" <td>-2.192833</td>\n",
" <td>1.034993</td>\n",
" <td>0.142473</td>\n",
" <td>-1.100140</td>\n",
" <td>1.813090</td>\n",
" <td>-0.744778</td>\n",
" <td>0.193097</td>\n",
" <td>-1.446359</td>\n",
" <td>2.188833</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
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" 0 1 2 3 4 5 6 \\\n",
"0 -0.915464 0.569958 -2.192833 1.034993 0.142473 -1.100140 1.813090 \n",
"1 -0.580068 1.197975 -2.192833 0.701486 0.451036 -0.311320 -0.115073 \n",
"2 -0.580068 1.026697 -1.917553 0.768188 0.451036 -0.874763 0.258120 \n",
"3 -0.580068 0.541412 1.661085 1.101694 3.073817 -0.762074 -0.363868 \n",
"4 -0.915464 0.569958 -2.192833 1.034993 0.142473 -1.100140 1.813090 \n",
"\n",
" 7 8 9 10 type \n",
"0 -0.744778 0.193097 -1.446359 2.188833 0 \n",
"1 -0.597640 0.999579 -0.862469 3.282235 0 \n",
"2 -0.660699 0.797958 -1.092486 2.553300 0 \n",
"3 -0.744778 0.327510 -0.986324 -0.362438 0 \n",
"4 -0.744778 0.193097 -1.446359 2.188833 0 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_trans = pd.DataFrame(prep.transform(X))\n",
"X_trans['type'] = color\n",
"X_trans.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X_trans,y)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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" <th>7</th>\n",
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" <th>9</th>\n",
" <th>10</th>\n",
" <th>type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>580</th>\n",
" <td>-0.747766</td>\n",
" <td>0.941059</td>\n",
" <td>1.179346</td>\n",
" <td>1.835408</td>\n",
" <td>3.922363</td>\n",
" <td>-1.438205</td>\n",
" <td>-0.177272</td>\n",
" <td>-0.681719</td>\n",
" <td>-0.613385</td>\n",
" <td>-1.800231</td>\n",
" <td>0.973942</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>5595</th>\n",
" <td>0.426120</td>\n",
" <td>-0.229336</td>\n",
" <td>0.147046</td>\n",
" <td>-0.048903</td>\n",
" <td>-0.397511</td>\n",
" <td>0.083090</td>\n",
" <td>0.195921</td>\n",
" <td>0.726602</td>\n",
" <td>0.058683</td>\n",
" <td>-0.083949</td>\n",
" <td>-0.362438</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2415</th>\n",
" <td>1.013063</td>\n",
" <td>-0.571891</td>\n",
" <td>0.697606</td>\n",
" <td>-0.265683</td>\n",
" <td>0.913879</td>\n",
" <td>-1.325517</td>\n",
" <td>-0.363868</td>\n",
" <td>-0.072147</td>\n",
" <td>-0.075731</td>\n",
" <td>-0.685532</td>\n",
" <td>-0.969884</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2406</th>\n",
" <td>-0.747766</td>\n",
" <td>-0.286429</td>\n",
" <td>-0.403514</td>\n",
" <td>0.918266</td>\n",
" <td>-0.783214</td>\n",
" <td>1.379008</td>\n",
" <td>-0.363868</td>\n",
" <td>1.609430</td>\n",
" <td>-0.815006</td>\n",
" <td>1.561559</td>\n",
" <td>-0.301694</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3775</th>\n",
" <td>-1.083162</td>\n",
" <td>-0.400614</td>\n",
" <td>-1.298173</td>\n",
" <td>0.434681</td>\n",
" <td>-0.243230</td>\n",
" <td>-1.100140</td>\n",
" <td>0.071523</td>\n",
" <td>0.495385</td>\n",
" <td>-1.083833</td>\n",
" <td>0.022213</td>\n",
" <td>-0.119460</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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"580 -0.747766 0.941059 1.179346 1.835408 3.922363 -1.438205 -0.177272 \n",
"5595 0.426120 -0.229336 0.147046 -0.048903 -0.397511 0.083090 0.195921 \n",
"2415 1.013063 -0.571891 0.697606 -0.265683 0.913879 -1.325517 -0.363868 \n",
"2406 -0.747766 -0.286429 -0.403514 0.918266 -0.783214 1.379008 -0.363868 \n",
"3775 -1.083162 -0.400614 -1.298173 0.434681 -0.243230 -1.100140 0.071523 \n",
"\n",
" 7 8 9 10 type \n",
"580 -0.681719 -0.613385 -1.800231 0.973942 0 \n",
"5595 0.726602 0.058683 -0.083949 -0.362438 1 \n",
"2415 -0.072147 -0.075731 -0.685532 -0.969884 1 \n",
"2406 1.609430 -0.815006 1.561559 -0.301694 1 \n",
"3775 0.495385 -1.083833 0.022213 -0.119460 1 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(X_train).head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## MODEL"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.regularizers import l2\n",
"from tensorflow.keras.layers import deserialize as layer_from_config\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense\n",
"model = Sequential()\n",
"model.add(Dense(12, activation='relu', input_shape=(12,), kernel_regularizer='l2'))\n",
"model.add(Dense(7, activation='relu'))\n",
"model.add(Dense(1, activation='relu'))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(None, 1)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.output_shape"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense (Dense) (None, 12) 156 \n",
"_________________________________________________________________\n",
"dense_1 (Dense) (None, 7) 91 \n",
"_________________________________________________________________\n",
"dense_2 (Dense) (None, 1) 8 \n",
"=================================================================\n",
"Total params: 255\n",
"Trainable params: 255\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'name': 'sequential',\n",
" 'layers': [{'class_name': 'Dense',\n",
" 'config': {'name': 'dense',\n",
" 'trainable': True,\n",
" 'batch_input_shape': (None, 12),\n",
" 'dtype': 'float32',\n",
" 'units': 12,\n",
" 'activation': 'relu',\n",
" 'use_bias': True,\n",
" 'kernel_initializer': {'class_name': 'GlorotUniform',\n",
" 'config': {'seed': None}},\n",
" 'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
" 'kernel_regularizer': {'class_name': 'L1L2',\n",
" 'config': {'l1': 0.0, 'l2': 0.009999999776482582}},\n",
" 'bias_regularizer': None,\n",
" 'activity_regularizer': None,\n",
" 'kernel_constraint': None,\n",
" 'bias_constraint': None}},\n",
" {'class_name': 'Dense',\n",
" 'config': {'name': 'dense_1',\n",
" 'trainable': True,\n",
" 'dtype': 'float32',\n",
" 'units': 7,\n",
" 'activation': 'relu',\n",
" 'use_bias': True,\n",
" 'kernel_initializer': {'class_name': 'GlorotUniform',\n",
" 'config': {'seed': None}},\n",
" 'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
" 'kernel_regularizer': None,\n",
" 'bias_regularizer': None,\n",
" 'activity_regularizer': None,\n",
" 'kernel_constraint': None,\n",
" 'bias_constraint': None}},\n",
" {'class_name': 'Dense',\n",
" 'config': {'name': 'dense_2',\n",
" 'trainable': True,\n",
" 'dtype': 'float32',\n",
" 'units': 1,\n",
" 'activation': 'relu',\n",
" 'use_bias': True,\n",
" 'kernel_initializer': {'class_name': 'GlorotUniform',\n",
" 'config': {'seed': None}},\n",
" 'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
" 'kernel_regularizer': None,\n",
" 'bias_regularizer': None,\n",
" 'activity_regularizer': None,\n",
" 'kernel_constraint': None,\n",
" 'bias_constraint': None}}],\n",
" 'build_input_shape': TensorShape([None, 12])}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.get_config()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[array([[-0.23686874, -0.19311607, 0.25608552, 0.45216107, -0.44883013,\n",
" 0.12735248, -0.27799475, -0.34724903, -0.36516488, -0.13880157,\n",
" 0.06434762, -0.08538723],\n",
" [ 0.45619643, 0.3062129 , -0.36823976, 0.09083998, -0.0962466 ,\n",
" 0.28709912, -0.38253808, 0.21384323, 0.4487946 , -0.15352917,\n",
" 0.34897137, -0.16057491],\n",
" [ 0.39637506, 0.30053723, 0.0657289 , -0.32518148, 0.19909 ,\n",
" 0.25109065, -0.04374504, -0.30389845, -0.4958763 , -0.4813373 ,\n",
" 0.14830625, -0.09838951],\n",
" [ 0.18361819, 0.03434992, -0.27300262, -0.46840084, -0.25263703,\n",
" -0.41598642, -0.46077287, -0.04716647, -0.00423789, -0.34675503,\n",
" -0.11975026, -0.14759278],\n",
" [ 0.37515664, -0.39420414, 0.37617946, -0.354519 , 0.02532387,\n",
" 0.44032753, -0.02766562, -0.32615924, -0.06457603, 0.227296 ,\n",
" 0.3299955 , 0.44607723],\n",
" [-0.37208664, -0.30729675, -0.20290005, 0.42685306, -0.44951248,\n",
" -0.4709648 , 0.00506771, -0.3011495 , 0.11663699, 0.3395232 ,\n",
" 0.18600595, 0.32516134],\n",
" [ 0.34303284, -0.29608023, 0.17781258, -0.43846858, -0.48684406,\n",
" 0.17714894, -0.10156894, -0.20272446, -0.1992315 , -0.03481054,\n",
" 0.17789471, 0.37859535],\n",
" [-0.23636913, 0.24654925, 0.22689497, 0.43009663, -0.20585585,\n",
" -0.15588748, 0.20472276, -0.27669346, 0.08950341, 0.32386947,\n",
" -0.33324456, 0.22892666],\n",
" [-0.16400266, -0.36610162, 0.3779608 , 0.471709 , -0.08942688,\n",
" 0.14097822, 0.00488365, 0.00093424, -0.0266453 , 0.30038464,\n",
" 0.20284033, 0.2566185 ],\n",
" [-0.442021 , 0.05212295, 0.32900155, 0.44485962, -0.4558549 ,\n",
" 0.33103597, 0.09660316, -0.41960692, 0.39413798, 0.3106314 ,\n",
" -0.33424056, -0.1626494 ],\n",
" [-0.24115407, 0.4602207 , 0.49076343, -0.23807621, -0.3624016 ,\n",
" -0.13129747, 0.46161783, 0.19630146, 0.48974347, -0.417974 ,\n",
" -0.22493076, -0.2266382 ],\n",
" [ 0.07029271, -0.48934793, 0.47004294, 0.4616301 , 0.45653605,\n",
" -0.01912987, -0.26799023, -0.34723318, -0.4962381 , -0.46157622,\n",
" -0.3368361 , -0.12912607]], dtype=float32),\n",
" array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32),\n",
" array([[ 0.5119241 , -0.50690645, -0.19727984, -0.20443287, 0.22416377,\n",
" 0.2711159 , -0.12710556],\n",
" [ 0.15499598, -0.2636216 , -0.31484902, -0.44804516, -0.45801753,\n",
" 0.07088327, -0.41382098],\n",
" [ 0.20295805, 0.0386349 , -0.25486377, 0.2739662 , 0.299623 ,\n",
" 0.2441482 , 0.26657343],\n",
" [-0.45216933, 0.30255854, 0.40912664, -0.07060167, -0.5582654 ,\n",
" -0.00078917, -0.08921412],\n",
" [-0.05497813, -0.16320038, -0.29954505, -0.39630744, -0.04104292,\n",
" 0.33859825, -0.31044522],\n",
" [-0.20679668, -0.12290451, -0.21695474, -0.29097515, -0.5336252 ,\n",
" 0.3210761 , -0.12361661],\n",
" [-0.09603837, 0.32441342, -0.071116 , -0.15234265, -0.49664885,\n",
" -0.28621715, 0.50200945],\n",
" [ 0.44141227, 0.31548744, 0.07347447, 0.05771083, -0.00281239,\n",
" 0.28850698, 0.45974773],\n",
" [ 0.10413837, 0.16814917, -0.27596703, 0.40743446, -0.37834412,\n",
" -0.34849638, -0.00210375],\n",
" [-0.43215984, -0.1657252 , -0.38901347, -0.48422462, -0.13701686,\n",
" -0.25405252, 0.35204726],\n",
" [ 0.18285197, 0.48634166, -0.35934955, -0.2489196 , 0.5572513 ,\n",
" -0.36976755, 0.23130172],\n",
" [-0.4840423 , 0.17980474, 0.22712177, 0.02914917, -0.2609849 ,\n",
" -0.50372 , -0.07594264]], dtype=float32),\n",
" array([0., 0., 0., 0., 0., 0., 0.], dtype=float32),\n",
" array([[ 0.79572505],\n",
" [-0.17806226],\n",
" [ 0.06281346],\n",
" [ 0.06576443],\n",
" [ 0.3437894 ],\n",
" [-0.19019526],\n",
" [ 0.07703102]], dtype=float32),\n",
" array([0.], dtype=float32)]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.get_weights()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Failed to import pydot. You must install pydot and graphviz for `pydotprint` to work.\n"
]
}
],
"source": [
"from tensorflow.keras.utils import plot_model\n",
"plot_model(model, show_shapes=True)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"wg, bs = model.layers[2].get_weights()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(7, 1)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wg.shape # 1 weight per input per neuron"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.79572505]], dtype=float32)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wg[:1,:5]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1,)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bs.shape # 1 bias per neuron"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.], dtype=float32)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bs[:5]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"model.save_weights('weights.h5')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compile and Fit"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
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" <th>type</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>580</th>\n",
" <td>-0.747766</td>\n",
" <td>0.941059</td>\n",
" <td>1.179346</td>\n",
" <td>1.835408</td>\n",
" <td>3.922363</td>\n",
" <td>-1.438205</td>\n",
" <td>-0.177272</td>\n",
" <td>-0.681719</td>\n",
" <td>-0.613385</td>\n",
" <td>-1.800231</td>\n",
" <td>0.973942</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>5595</th>\n",
" <td>0.426120</td>\n",
" <td>-0.229336</td>\n",
" <td>0.147046</td>\n",
" <td>-0.048903</td>\n",
" <td>-0.397511</td>\n",
" <td>0.083090</td>\n",
" <td>0.195921</td>\n",
" <td>0.726602</td>\n",
" <td>0.058683</td>\n",
" <td>-0.083949</td>\n",
" <td>-0.362438</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2415</th>\n",
" <td>1.013063</td>\n",
" <td>-0.571891</td>\n",
" <td>0.697606</td>\n",
" <td>-0.265683</td>\n",
" <td>0.913879</td>\n",
" <td>-1.325517</td>\n",
" <td>-0.363868</td>\n",
" <td>-0.072147</td>\n",
" <td>-0.075731</td>\n",
" <td>-0.685532</td>\n",
" <td>-0.969884</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2406</th>\n",
" <td>-0.747766</td>\n",
" <td>-0.286429</td>\n",
" <td>-0.403514</td>\n",
" <td>0.918266</td>\n",
" <td>-0.783214</td>\n",
" <td>1.379008</td>\n",
" <td>-0.363868</td>\n",
" <td>1.609430</td>\n",
" <td>-0.815006</td>\n",
" <td>1.561559</td>\n",
" <td>-0.301694</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3775</th>\n",
" <td>-1.083162</td>\n",
" <td>-0.400614</td>\n",
" <td>-1.298173</td>\n",
" <td>0.434681</td>\n",
" <td>-0.243230</td>\n",
" <td>-1.100140</td>\n",
" <td>0.071523</td>\n",
" <td>0.495385</td>\n",
" <td>-1.083833</td>\n",
" <td>0.022213</td>\n",
" <td>-0.119460</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" 0 1 2 3 4 5 6 \\\n",
"580 -0.747766 0.941059 1.179346 1.835408 3.922363 -1.438205 -0.177272 \n",
"5595 0.426120 -0.229336 0.147046 -0.048903 -0.397511 0.083090 0.195921 \n",
"2415 1.013063 -0.571891 0.697606 -0.265683 0.913879 -1.325517 -0.363868 \n",
"2406 -0.747766 -0.286429 -0.403514 0.918266 -0.783214 1.379008 -0.363868 \n",
"3775 -1.083162 -0.400614 -1.298173 0.434681 -0.243230 -1.100140 0.071523 \n",
"\n",
" 7 8 9 10 type \n",
"580 -0.681719 -0.613385 -1.800231 0.973942 0 \n",
"5595 0.726602 0.058683 -0.083949 -0.362438 1 \n",
"2415 -0.072147 -0.075731 -0.685532 -0.969884 1 \n",
"2406 1.609430 -0.815006 1.561559 -0.301694 1 \n",
"3775 0.495385 -1.083833 0.022213 -0.119460 1 "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.head()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
" <th>9</th>\n",
" <th>10</th>\n",
" <th>type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>4872.000000</td>\n",
" <td>4872.000000</td>\n",
" <td>4872.000000</td>\n",
" <td>4872.000000</td>\n",
" <td>4872.000000</td>\n",
" <td>4872.000000</td>\n",
" <td>4872.000000</td>\n",
" <td>4872.000000</td>\n",
" <td>4872.000000</td>\n",
" <td>4872.000000</td>\n",
" <td>4872.000000</td>\n",
" <td>4872.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>-0.016026</td>\n",
" <td>0.006686</td>\n",
" <td>0.002471</td>\n",
" <td>0.002101</td>\n",
" <td>-0.003891</td>\n",
" <td>-0.010268</td>\n",
" <td>0.000643</td>\n",
" <td>-0.004830</td>\n",
" <td>-0.010745</td>\n",
" <td>-0.001289</td>\n",
" <td>0.008001</td>\n",
" <td>0.753900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.995389</td>\n",
" <td>1.000496</td>\n",
" <td>1.007176</td>\n",
" <td>0.977487</td>\n",
" <td>0.982927</td>\n",
" <td>0.977455</td>\n",
" <td>0.996934</td>\n",
" <td>0.989782</td>\n",
" <td>0.997873</td>\n",
" <td>0.998497</td>\n",
" <td>1.010191</td>\n",
" <td>0.430782</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-1.753954</td>\n",
" <td>-1.257000</td>\n",
" <td>-2.192833</td>\n",
" <td>-2.530192</td>\n",
" <td>-2.634589</td>\n",
" <td>-1.663583</td>\n",
" <td>-2.976217</td>\n",
" <td>-1.018034</td>\n",
" <td>-2.091935</td>\n",
" <td>-1.941780</td>\n",
" <td>-1.577330</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>-0.831615</td>\n",
" <td>-0.514799</td>\n",
" <td>-0.472334</td>\n",
" <td>-0.765942</td>\n",
" <td>-0.628933</td>\n",
" <td>-0.762074</td>\n",
" <td>-0.674862</td>\n",
" <td>-0.765798</td>\n",
" <td>-0.680592</td>\n",
" <td>-0.670050</td>\n",
" <td>-0.666161</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>-0.160823</td>\n",
" <td>-0.257883</td>\n",
" <td>-0.059414</td>\n",
" <td>0.054484</td>\n",
" <td>-0.166089</td>\n",
" <td>-0.085943</td>\n",
" <td>-0.052874</td>\n",
" <td>-0.534581</td>\n",
" <td>-0.210144</td>\n",
" <td>0.039907</td>\n",
" <td>-0.301694</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.677667</td>\n",
" <td>0.255949</td>\n",
" <td>0.491146</td>\n",
" <td>0.768188</td>\n",
" <td>0.373895</td>\n",
" <td>0.590188</td>\n",
" <td>0.631312</td>\n",
" <td>0.558444</td>\n",
" <td>0.461924</td>\n",
" <td>0.712265</td>\n",
" <td>0.366496</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2.941590</td>\n",
" <td>15.813640</td>\n",
" <td>9.231281</td>\n",
" <td>5.203824</td>\n",
" <td>6.468004</td>\n",
" <td>6.534509</td>\n",
" <td>4.923029</td>\n",
" <td>5.498078</td>\n",
" <td>9.870879</td>\n",
" <td>4.436775</td>\n",
" <td>7.534354</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 \\\n",
"count 4872.000000 4872.000000 4872.000000 4872.000000 4872.000000 \n",
"mean -0.016026 0.006686 0.002471 0.002101 -0.003891 \n",
"std 0.995389 1.000496 1.007176 0.977487 0.982927 \n",
"min -1.753954 -1.257000 -2.192833 -2.530192 -2.634589 \n",
"25% -0.831615 -0.514799 -0.472334 -0.765942 -0.628933 \n",
"50% -0.160823 -0.257883 -0.059414 0.054484 -0.166089 \n",
"75% 0.677667 0.255949 0.491146 0.768188 0.373895 \n",
"max 2.941590 15.813640 9.231281 5.203824 6.468004 \n",
"\n",
" 5 6 7 8 9 \\\n",
"count 4872.000000 4872.000000 4872.000000 4872.000000 4872.000000 \n",
"mean -0.010268 0.000643 -0.004830 -0.010745 -0.001289 \n",
"std 0.977455 0.996934 0.989782 0.997873 0.998497 \n",
"min -1.663583 -2.976217 -1.018034 -2.091935 -1.941780 \n",
"25% -0.762074 -0.674862 -0.765798 -0.680592 -0.670050 \n",
"50% -0.085943 -0.052874 -0.534581 -0.210144 0.039907 \n",
"75% 0.590188 0.631312 0.558444 0.461924 0.712265 \n",
"max 6.534509 4.923029 5.498078 9.870879 4.436775 \n",
"\n",
" 10 type \n",
"count 4872.000000 4872.000000 \n",
"mean 0.008001 0.753900 \n",
"std 1.010191 0.430782 \n",
"min -1.577330 0.000000 \n",
"25% -0.666161 1.000000 \n",
"50% -0.301694 1.000000 \n",
"75% 0.366496 1.000000 \n",
"max 7.534354 1.000000 "
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.describe()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"model.compile(loss= 'mse',\n",
" optimizer= 'adam',\n",
" metrics=['mae'])"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.callbacks import TensorBoard\n",
"tensorboard_callback = TensorBoard(log_dir=\"./logs\", histogram_freq=2)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/200\n",
"39/39 - 1s - loss: 30.1151 - mae: 5.3200 - val_loss: 28.5518 - val_mae: 5.1323\n",
"Epoch 2/200\n",
"39/39 - 0s - loss: 26.1131 - mae: 4.8185 - val_loss: 24.1813 - val_mae: 4.5552\n",
"Epoch 3/200\n",
"39/39 - 0s - loss: 21.3529 - mae: 4.2008 - val_loss: 18.6717 - val_mae: 3.8795\n",
"Epoch 4/200\n",
"39/39 - 0s - loss: 14.9560 - mae: 3.4191 - val_loss: 11.3318 - val_mae: 2.9452\n",
"Epoch 5/200\n",
"39/39 - 0s - loss: 7.7744 - mae: 2.3636 - val_loss: 4.8249 - val_mae: 1.8100\n",
"Epoch 6/200\n",
"39/39 - 0s - loss: 3.1764 - mae: 1.3951 - val_loss: 2.3708 - val_mae: 1.1400\n",
"Epoch 7/200\n",
"39/39 - 0s - loss: 1.9999 - mae: 1.0546 - val_loss: 2.0377 - val_mae: 1.0359\n",
"Epoch 8/200\n",
"39/39 - 0s - loss: 1.7745 - mae: 0.9917 - val_loss: 1.8771 - val_mae: 0.9905\n",
"Epoch 9/200\n",
"39/39 - 0s - loss: 1.6282 - mae: 0.9490 - val_loss: 1.7408 - val_mae: 0.9486\n",
"Epoch 10/200\n",
"39/39 - 0s - loss: 1.5098 - mae: 0.9099 - val_loss: 1.6276 - val_mae: 0.9144\n",
"Epoch 11/200\n",
"39/39 - 0s - loss: 1.4112 - mae: 0.8806 - val_loss: 1.5280 - val_mae: 0.8826\n",
"Epoch 12/200\n",
"39/39 - 0s - loss: 1.3258 - mae: 0.8487 - val_loss: 1.4459 - val_mae: 0.8551\n",
"Epoch 13/200\n",
"39/39 - 0s - loss: 1.2586 - mae: 0.8245 - val_loss: 1.3779 - val_mae: 0.8336\n",
"Epoch 14/200\n",
"39/39 - 0s - loss: 1.2006 - mae: 0.8038 - val_loss: 1.3176 - val_mae: 0.8132\n",
"Epoch 15/200\n",
"39/39 - 0s - loss: 1.1541 - mae: 0.7858 - val_loss: 1.2651 - val_mae: 0.7958\n",
"Epoch 16/200\n",
"39/39 - 0s - loss: 1.1105 - mae: 0.7711 - val_loss: 1.2209 - val_mae: 0.7811\n",
"Epoch 17/200\n",
"39/39 - 0s - loss: 1.0719 - mae: 0.7553 - val_loss: 1.1767 - val_mae: 0.7665\n",
"Epoch 18/200\n",
"39/39 - 0s - loss: 1.0371 - mae: 0.7426 - val_loss: 1.1390 - val_mae: 0.7533\n",
"Epoch 19/200\n",
"39/39 - 0s - loss: 1.0068 - mae: 0.7298 - val_loss: 1.1026 - val_mae: 0.7417\n",
"Epoch 20/200\n",
"39/39 - 0s - loss: 0.9788 - mae: 0.7187 - val_loss: 1.0702 - val_mae: 0.7312\n",
"Epoch 21/200\n",
"39/39 - 0s - loss: 0.9512 - mae: 0.7080 - val_loss: 1.0426 - val_mae: 0.7210\n",
"Epoch 22/200\n",
"39/39 - 0s - loss: 0.9274 - mae: 0.6985 - val_loss: 1.0138 - val_mae: 0.7115\n",
"Epoch 23/200\n",
"39/39 - 0s - loss: 0.9070 - mae: 0.6900 - val_loss: 0.9910 - val_mae: 0.7030\n",
"Epoch 24/200\n",
"39/39 - 0s - loss: 0.8859 - mae: 0.6812 - val_loss: 0.9678 - val_mae: 0.6946\n",
"Epoch 25/200\n",
"39/39 - 0s - loss: 0.8684 - mae: 0.6741 - val_loss: 0.9488 - val_mae: 0.6869\n",
"Epoch 26/200\n",
"39/39 - 0s - loss: 0.8511 - mae: 0.6677 - val_loss: 0.9312 - val_mae: 0.6794\n",
"Epoch 27/200\n",
"39/39 - 0s - loss: 0.8362 - mae: 0.6612 - val_loss: 0.9141 - val_mae: 0.6735\n",
"Epoch 28/200\n",
"39/39 - 0s - loss: 0.8227 - mae: 0.6567 - val_loss: 0.8985 - val_mae: 0.6672\n",
"Epoch 29/200\n",
"39/39 - 0s - loss: 0.8107 - mae: 0.6528 - val_loss: 0.8833 - val_mae: 0.6623\n",
"Epoch 30/200\n",
"39/39 - 0s - loss: 0.7967 - mae: 0.6462 - val_loss: 0.8700 - val_mae: 0.6579\n",
"Epoch 31/200\n",
"39/39 - 0s - loss: 0.7855 - mae: 0.6422 - val_loss: 0.8588 - val_mae: 0.6530\n",
"Epoch 32/200\n",
"39/39 - 0s - loss: 0.7744 - mae: 0.6372 - val_loss: 0.8447 - val_mae: 0.6484\n",
"Epoch 33/200\n",
"39/39 - 0s - loss: 0.7663 - mae: 0.6348 - val_loss: 0.8352 - val_mae: 0.6427\n",
"Epoch 34/200\n",
"39/39 - 0s - loss: 0.7552 - mae: 0.6303 - val_loss: 0.8249 - val_mae: 0.6402\n",
"Epoch 35/200\n",
"39/39 - 0s - loss: 0.7455 - mae: 0.6260 - val_loss: 0.8139 - val_mae: 0.6379\n",
"Epoch 36/200\n",
"39/39 - 0s - loss: 0.7372 - mae: 0.6239 - val_loss: 0.8022 - val_mae: 0.6329\n",
"Epoch 37/200\n",
"39/39 - 0s - loss: 0.7287 - mae: 0.6190 - val_loss: 0.7935 - val_mae: 0.6294\n",
"Epoch 38/200\n",
"39/39 - 0s - loss: 0.7198 - mae: 0.6159 - val_loss: 0.7846 - val_mae: 0.6268\n",
"Epoch 39/200\n",
"39/39 - 0s - loss: 0.7118 - mae: 0.6121 - val_loss: 0.7776 - val_mae: 0.6238\n",
"Epoch 40/200\n",
"39/39 - 0s - loss: 0.7048 - mae: 0.6091 - val_loss: 0.7685 - val_mae: 0.6205\n",
"Epoch 41/200\n",
"39/39 - 0s - loss: 0.6975 - mae: 0.6072 - val_loss: 0.7603 - val_mae: 0.6186\n",
"Epoch 42/200\n",
"39/39 - 0s - loss: 0.6898 - mae: 0.6033 - val_loss: 0.7516 - val_mae: 0.6144\n",
"Epoch 43/200\n",
"39/39 - 0s - loss: 0.6836 - mae: 0.6014 - val_loss: 0.7433 - val_mae: 0.6110\n",
"Epoch 44/200\n",
"39/39 - 0s - loss: 0.6772 - mae: 0.5978 - val_loss: 0.7396 - val_mae: 0.6112\n",
"Epoch 45/200\n",
"39/39 - 0s - loss: 0.6698 - mae: 0.5944 - val_loss: 0.7339 - val_mae: 0.6078\n",
"Epoch 46/200\n",
"39/39 - 1s - loss: 0.6649 - mae: 0.5932 - val_loss: 0.7253 - val_mae: 0.6050\n",
"Epoch 47/200\n",
"39/39 - 1s - loss: 0.6581 - mae: 0.5895 - val_loss: 0.7174 - val_mae: 0.6019\n",
"Epoch 48/200\n",
"39/39 - 0s - loss: 0.6518 - mae: 0.5870 - val_loss: 0.7151 - val_mae: 0.6014\n",
"Epoch 49/200\n",
"39/39 - 0s - loss: 0.6465 - mae: 0.5850 - val_loss: 0.7075 - val_mae: 0.5994\n",
"Epoch 50/200\n",
"39/39 - 0s - loss: 0.6409 - mae: 0.5824 - val_loss: 0.7052 - val_mae: 0.5990\n",
"Epoch 51/200\n",
"39/39 - 0s - loss: 0.6351 - mae: 0.5807 - val_loss: 0.6996 - val_mae: 0.5974\n",
"Epoch 52/200\n",
"39/39 - 0s - loss: 0.6299 - mae: 0.5779 - val_loss: 0.6957 - val_mae: 0.5964\n",
"Epoch 53/200\n",
"39/39 - 0s - loss: 0.6249 - mae: 0.5769 - val_loss: 0.6852 - val_mae: 0.5910\n",
"Epoch 54/200\n",
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"Epoch 55/200\n",
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"Epoch 56/200\n",
"39/39 - 0s - loss: 0.6101 - mae: 0.5707 - val_loss: 0.6729 - val_mae: 0.5869\n",
"Epoch 57/200\n",
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"Epoch 58/200\n",
"39/39 - 0s - loss: 0.6034 - mae: 0.5688 - val_loss: 0.6649 - val_mae: 0.5841\n",
"Epoch 59/200\n",
"39/39 - 0s - loss: 0.5992 - mae: 0.5662 - val_loss: 0.6654 - val_mae: 0.5858\n",
"Epoch 60/200\n",
"39/39 - 0s - loss: 0.5942 - mae: 0.5640 - val_loss: 0.6536 - val_mae: 0.5804\n",
"Epoch 61/200\n",
"39/39 - 0s - loss: 0.5929 - mae: 0.5646 - val_loss: 0.6576 - val_mae: 0.5830\n",
"Epoch 62/200\n",
"39/39 - 0s - loss: 0.5893 - mae: 0.5624 - val_loss: 0.6485 - val_mae: 0.5794\n",
"Epoch 63/200\n",
"39/39 - 0s - loss: 0.5843 - mae: 0.5605 - val_loss: 0.6436 - val_mae: 0.5772\n",
"Epoch 64/200\n",
"39/39 - 0s - loss: 0.5833 - mae: 0.5602 - val_loss: 0.6489 - val_mae: 0.5810\n",
"Epoch 65/200\n",
"39/39 - 0s - loss: 0.5802 - mae: 0.5579 - val_loss: 0.6387 - val_mae: 0.5765\n",
"Epoch 66/200\n",
"39/39 - 0s - loss: 0.5771 - mae: 0.5582 - val_loss: 0.6442 - val_mae: 0.5798\n",
"Epoch 67/200\n",
"39/39 - 0s - loss: 0.5749 - mae: 0.5574 - val_loss: 0.6314 - val_mae: 0.5744\n",
"Epoch 68/200\n",
"39/39 - 0s - loss: 0.5707 - mae: 0.5557 - val_loss: 0.6330 - val_mae: 0.5759\n",
"Epoch 69/200\n",
"39/39 - 0s - loss: 0.5710 - mae: 0.5564 - val_loss: 0.6313 - val_mae: 0.5757\n",
"Epoch 70/200\n",
"39/39 - 0s - loss: 0.5684 - mae: 0.5555 - val_loss: 0.6230 - val_mae: 0.5717\n",
"Epoch 71/200\n",
"39/39 - 0s - loss: 0.5679 - mae: 0.5546 - val_loss: 0.6233 - val_mae: 0.5724\n",
"Epoch 72/200\n",
"39/39 - 0s - loss: 0.5615 - mae: 0.5529 - val_loss: 0.6209 - val_mae: 0.5717\n",
"Epoch 73/200\n",
"39/39 - 0s - loss: 0.5585 - mae: 0.5518 - val_loss: 0.6187 - val_mae: 0.5708\n",
"Epoch 74/200\n",
"39/39 - 0s - loss: 0.5572 - mae: 0.5509 - val_loss: 0.6169 - val_mae: 0.5706\n",
"Epoch 75/200\n",
"39/39 - 0s - loss: 0.5607 - mae: 0.5529 - val_loss: 0.6158 - val_mae: 0.5710\n",
"Epoch 76/200\n",
"39/39 - 0s - loss: 0.5524 - mae: 0.5496 - val_loss: 0.6186 - val_mae: 0.5731\n",
"Epoch 77/200\n",
"39/39 - 0s - loss: 0.5553 - mae: 0.5526 - val_loss: 0.6109 - val_mae: 0.5689\n",
"Epoch 78/200\n",
"39/39 - 0s - loss: 0.5501 - mae: 0.5490 - val_loss: 0.6090 - val_mae: 0.5679\n",
"Epoch 79/200\n",
"39/39 - 0s - loss: 0.5489 - mae: 0.5487 - val_loss: 0.6064 - val_mae: 0.5673\n",
"Epoch 80/200\n",
"39/39 - 0s - loss: 0.5465 - mae: 0.5480 - val_loss: 0.6043 - val_mae: 0.5674\n",
"Epoch 81/200\n",
"39/39 - 0s - loss: 0.5527 - mae: 0.5518 - val_loss: 0.6070 - val_mae: 0.5696\n",
"Epoch 82/200\n",
"39/39 - 0s - loss: 0.5442 - mae: 0.5481 - val_loss: 0.6022 - val_mae: 0.5671\n",
"Epoch 83/200\n",
"39/39 - 0s - loss: 0.5432 - mae: 0.5477 - val_loss: 0.5998 - val_mae: 0.5663\n",
"Epoch 84/200\n",
"39/39 - 0s - loss: 0.5385 - mae: 0.5454 - val_loss: 0.5957 - val_mae: 0.5641\n",
"Epoch 85/200\n",
"39/39 - 0s - loss: 0.5390 - mae: 0.5449 - val_loss: 0.5956 - val_mae: 0.5646\n",
"Epoch 86/200\n",
"39/39 - 0s - loss: 0.5370 - mae: 0.5451 - val_loss: 0.5916 - val_mae: 0.5620\n",
"Epoch 87/200\n",
"39/39 - 0s - loss: 0.5359 - mae: 0.5443 - val_loss: 0.5871 - val_mae: 0.5609\n",
"Epoch 88/200\n",
"39/39 - 0s - loss: 0.5343 - mae: 0.5435 - val_loss: 0.5910 - val_mae: 0.5635\n",
"Epoch 89/200\n",
"39/39 - 0s - loss: 0.5328 - mae: 0.5436 - val_loss: 0.5899 - val_mae: 0.5628\n",
"Epoch 90/200\n",
"39/39 - 0s - loss: 0.5311 - mae: 0.5424 - val_loss: 0.5867 - val_mae: 0.5613\n",
"Epoch 91/200\n",
"39/39 - 0s - loss: 0.5319 - mae: 0.5428 - val_loss: 0.5861 - val_mae: 0.5612\n",
"Epoch 92/200\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"39/39 - 0s - loss: 0.5292 - mae: 0.5412 - val_loss: 0.5823 - val_mae: 0.5600\n",
"Epoch 93/200\n",
"39/39 - 0s - loss: 0.5269 - mae: 0.5403 - val_loss: 0.5822 - val_mae: 0.5609\n",
"Epoch 94/200\n",
"39/39 - 0s - loss: 0.5297 - mae: 0.5430 - val_loss: 0.5811 - val_mae: 0.5599\n",
"Epoch 95/200\n",
"39/39 - 0s - loss: 0.5262 - mae: 0.5414 - val_loss: 0.5848 - val_mae: 0.5617\n",
"Epoch 96/200\n",
"39/39 - 0s - loss: 0.5239 - mae: 0.5401 - val_loss: 0.5810 - val_mae: 0.5608\n",
"Epoch 97/200\n",
"39/39 - 0s - loss: 0.5231 - mae: 0.5408 - val_loss: 0.5835 - val_mae: 0.5612\n",
"Epoch 98/200\n",
"39/39 - 0s - loss: 0.5236 - mae: 0.5406 - val_loss: 0.5800 - val_mae: 0.5599\n",
"Epoch 99/200\n",
"39/39 - 0s - loss: 0.5226 - mae: 0.5399 - val_loss: 0.5768 - val_mae: 0.5586\n",
"Epoch 100/200\n",
"39/39 - 0s - loss: 0.5218 - mae: 0.5398 - val_loss: 0.5862 - val_mae: 0.5651\n",
"Epoch 101/200\n",
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"Epoch 102/200\n",
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"Epoch 103/200\n",
"39/39 - 0s - loss: 0.5196 - mae: 0.5386 - val_loss: 0.5710 - val_mae: 0.5578\n",
"Epoch 104/200\n",
"39/39 - 0s - loss: 0.5195 - mae: 0.5401 - val_loss: 0.5761 - val_mae: 0.5594\n",
"Epoch 105/200\n",
"39/39 - 0s - loss: 0.5167 - mae: 0.5381 - val_loss: 0.5687 - val_mae: 0.5564\n",
"Epoch 106/200\n",
"39/39 - 0s - loss: 0.5149 - mae: 0.5378 - val_loss: 0.5743 - val_mae: 0.5595\n",
"Epoch 107/200\n",
"39/39 - 0s - loss: 0.5153 - mae: 0.5380 - val_loss: 0.5697 - val_mae: 0.5590\n",
"Epoch 108/200\n",
"39/39 - 0s - loss: 0.5144 - mae: 0.5383 - val_loss: 0.5680 - val_mae: 0.5558\n",
"Epoch 109/200\n",
"39/39 - 0s - loss: 0.5143 - mae: 0.5385 - val_loss: 0.5675 - val_mae: 0.5564\n",
"Epoch 110/200\n",
"39/39 - 0s - loss: 0.5136 - mae: 0.5372 - val_loss: 0.5642 - val_mae: 0.5560\n",
"Epoch 111/200\n",
"39/39 - 0s - loss: 0.5147 - mae: 0.5395 - val_loss: 0.5656 - val_mae: 0.5572\n",
"Epoch 112/200\n",
"39/39 - 0s - loss: 0.5126 - mae: 0.5377 - val_loss: 0.5673 - val_mae: 0.5582\n",
"Epoch 113/200\n",
"39/39 - 0s - loss: 0.5099 - mae: 0.5366 - val_loss: 0.5615 - val_mae: 0.5556\n",
"Epoch 114/200\n",
"39/39 - 0s - loss: 0.5128 - mae: 0.5381 - val_loss: 0.5611 - val_mae: 0.5555\n",
"Epoch 115/200\n",
"39/39 - 0s - loss: 0.5095 - mae: 0.5374 - val_loss: 0.5589 - val_mae: 0.5541\n",
"Epoch 116/200\n",
"39/39 - 0s - loss: 0.5121 - mae: 0.5382 - val_loss: 0.5590 - val_mae: 0.5537\n",
"Epoch 117/200\n",
"39/39 - 0s - loss: 0.5074 - mae: 0.5362 - val_loss: 0.5598 - val_mae: 0.5551\n",
"Epoch 118/200\n",
"39/39 - 0s - loss: 0.5081 - mae: 0.5372 - val_loss: 0.5590 - val_mae: 0.5542\n",
"Epoch 119/200\n",
"39/39 - 0s - loss: 0.5060 - mae: 0.5349 - val_loss: 0.5657 - val_mae: 0.5574\n",
"Epoch 120/200\n",
"39/39 - 0s - loss: 0.5072 - mae: 0.5373 - val_loss: 0.5638 - val_mae: 0.5554\n",
"Epoch 121/200\n",
"39/39 - 0s - loss: 0.5058 - mae: 0.5353 - val_loss: 0.5562 - val_mae: 0.5528\n",
"Epoch 122/200\n",
"39/39 - 0s - loss: 0.5079 - mae: 0.5362 - val_loss: 0.5564 - val_mae: 0.5538\n",
"Epoch 123/200\n",
"39/39 - 0s - loss: 0.5051 - mae: 0.5369 - val_loss: 0.5559 - val_mae: 0.5539\n",
"Epoch 124/200\n",
"39/39 - 0s - loss: 0.5034 - mae: 0.5354 - val_loss: 0.5525 - val_mae: 0.5525\n",
"Epoch 125/200\n",
"39/39 - 0s - loss: 0.5035 - mae: 0.5364 - val_loss: 0.5557 - val_mae: 0.5545\n",
"Epoch 126/200\n",
"39/39 - 0s - loss: 0.5069 - mae: 0.5370 - val_loss: 0.5553 - val_mae: 0.5534\n",
"Epoch 127/200\n",
"39/39 - 0s - loss: 0.5021 - mae: 0.5342 - val_loss: 0.5551 - val_mae: 0.5538\n",
"Epoch 128/200\n",
"39/39 - 0s - loss: 0.5028 - mae: 0.5349 - val_loss: 0.5559 - val_mae: 0.5537\n",
"Epoch 129/200\n",
"39/39 - 0s - loss: 0.5024 - mae: 0.5365 - val_loss: 0.5544 - val_mae: 0.5523\n",
"Epoch 130/200\n",
"39/39 - 0s - loss: 0.5032 - mae: 0.5355 - val_loss: 0.5601 - val_mae: 0.5547\n",
"Epoch 131/200\n",
"39/39 - 0s - loss: 0.5010 - mae: 0.5354 - val_loss: 0.5521 - val_mae: 0.5508\n",
"Epoch 132/200\n",
"39/39 - 0s - loss: 0.5059 - mae: 0.5362 - val_loss: 0.5553 - val_mae: 0.5517\n",
"Epoch 133/200\n",
"39/39 - 0s - loss: 0.5000 - mae: 0.5342 - val_loss: 0.5500 - val_mae: 0.5515\n",
"Epoch 134/200\n",
"39/39 - 0s - loss: 0.4977 - mae: 0.5340 - val_loss: 0.5528 - val_mae: 0.5538\n",
"Epoch 135/200\n",
"39/39 - 0s - loss: 0.4997 - mae: 0.5352 - val_loss: 0.5485 - val_mae: 0.5525\n",
"Epoch 136/200\n",
"39/39 - 0s - loss: 0.4980 - mae: 0.5342 - val_loss: 0.5516 - val_mae: 0.5544\n",
"Epoch 137/200\n",
"39/39 - 0s - loss: 0.4980 - mae: 0.5354 - val_loss: 0.5449 - val_mae: 0.5512\n",
"Epoch 138/200\n",
"39/39 - 0s - loss: 0.4985 - mae: 0.5347 - val_loss: 0.5444 - val_mae: 0.5512\n",
"Epoch 139/200\n",
"39/39 - 0s - loss: 0.4967 - mae: 0.5338 - val_loss: 0.5541 - val_mae: 0.5566\n",
"Epoch 140/200\n",
"39/39 - 0s - loss: 0.4957 - mae: 0.5334 - val_loss: 0.5472 - val_mae: 0.5498\n",
"Epoch 141/200\n",
"39/39 - 0s - loss: 0.4983 - mae: 0.5357 - val_loss: 0.5439 - val_mae: 0.5488\n",
"Epoch 142/200\n",
"39/39 - 0s - loss: 0.4982 - mae: 0.5356 - val_loss: 0.5479 - val_mae: 0.5523\n",
"Epoch 143/200\n",
"39/39 - 1s - loss: 0.4984 - mae: 0.5348 - val_loss: 0.5434 - val_mae: 0.5502\n",
"Epoch 144/200\n",
"39/39 - 0s - loss: 0.4950 - mae: 0.5339 - val_loss: 0.5497 - val_mae: 0.5544\n",
"Epoch 145/200\n",
"39/39 - 0s - loss: 0.4961 - mae: 0.5356 - val_loss: 0.5423 - val_mae: 0.5497\n",
"Epoch 146/200\n",
"39/39 - 0s - loss: 0.4937 - mae: 0.5331 - val_loss: 0.5391 - val_mae: 0.5485\n",
"Epoch 147/200\n",
"39/39 - 0s - loss: 0.4937 - mae: 0.5328 - val_loss: 0.5396 - val_mae: 0.5489\n",
"Epoch 148/200\n",
"39/39 - 0s - loss: 0.4944 - mae: 0.5343 - val_loss: 0.5370 - val_mae: 0.5473\n",
"Epoch 149/200\n",
"39/39 - 0s - loss: 0.4953 - mae: 0.5356 - val_loss: 0.5414 - val_mae: 0.5503\n",
"Epoch 150/200\n",
"39/39 - 0s - loss: 0.4934 - mae: 0.5334 - val_loss: 0.5409 - val_mae: 0.5486\n",
"Epoch 151/200\n",
"39/39 - 0s - loss: 0.4906 - mae: 0.5324 - val_loss: 0.5372 - val_mae: 0.5482\n",
"Epoch 152/200\n",
"39/39 - 0s - loss: 0.4945 - mae: 0.5354 - val_loss: 0.5382 - val_mae: 0.5484\n",
"Epoch 153/200\n",
"39/39 - 0s - loss: 0.4927 - mae: 0.5347 - val_loss: 0.5394 - val_mae: 0.5505\n",
"Epoch 154/200\n",
"39/39 - 0s - loss: 0.4893 - mae: 0.5317 - val_loss: 0.5383 - val_mae: 0.5487\n",
"Epoch 155/200\n",
"39/39 - 0s - loss: 0.4896 - mae: 0.5316 - val_loss: 0.5407 - val_mae: 0.5522\n",
"Epoch 156/200\n",
"39/39 - 0s - loss: 0.4935 - mae: 0.5349 - val_loss: 0.5376 - val_mae: 0.5490\n",
"Epoch 157/200\n",
"39/39 - 0s - loss: 0.4944 - mae: 0.5351 - val_loss: 0.5339 - val_mae: 0.5485\n",
"Epoch 158/200\n",
"39/39 - 0s - loss: 0.4883 - mae: 0.5320 - val_loss: 0.5366 - val_mae: 0.5497\n",
"Epoch 159/200\n",
"39/39 - 0s - loss: 0.4877 - mae: 0.5322 - val_loss: 0.5338 - val_mae: 0.5474\n",
"Epoch 160/200\n",
"39/39 - 0s - loss: 0.4873 - mae: 0.5316 - val_loss: 0.5347 - val_mae: 0.5480\n",
"Epoch 161/200\n",
"39/39 - 0s - loss: 0.4868 - mae: 0.5306 - val_loss: 0.5394 - val_mae: 0.5521\n",
"Epoch 162/200\n",
"39/39 - 0s - loss: 0.4901 - mae: 0.5343 - val_loss: 0.5456 - val_mae: 0.5536\n",
"Epoch 163/200\n",
"39/39 - 0s - loss: 0.4887 - mae: 0.5331 - val_loss: 0.5352 - val_mae: 0.5492\n",
"Epoch 164/200\n",
"39/39 - 0s - loss: 0.4858 - mae: 0.5314 - val_loss: 0.5359 - val_mae: 0.5486\n",
"Epoch 165/200\n",
"39/39 - 0s - loss: 0.4873 - mae: 0.5335 - val_loss: 0.5359 - val_mae: 0.5485\n",
"Epoch 166/200\n",
"39/39 - 0s - loss: 0.4863 - mae: 0.5321 - val_loss: 0.5343 - val_mae: 0.5486\n",
"Epoch 167/200\n",
"39/39 - 0s - loss: 0.4856 - mae: 0.5312 - val_loss: 0.5325 - val_mae: 0.5478\n",
"Epoch 168/200\n",
"39/39 - 0s - loss: 0.4856 - mae: 0.5310 - val_loss: 0.5422 - val_mae: 0.5524\n",
"Epoch 169/200\n",
"39/39 - 0s - loss: 0.4870 - mae: 0.5331 - val_loss: 0.5325 - val_mae: 0.5479\n",
"Epoch 170/200\n",
"39/39 - 0s - loss: 0.4877 - mae: 0.5325 - val_loss: 0.5394 - val_mae: 0.5528\n",
"Epoch 171/200\n",
"39/39 - 0s - loss: 0.4857 - mae: 0.5320 - val_loss: 0.5288 - val_mae: 0.5464\n",
"Epoch 172/200\n",
"39/39 - 0s - loss: 0.4847 - mae: 0.5316 - val_loss: 0.5335 - val_mae: 0.5500\n",
"Epoch 173/200\n",
"39/39 - 0s - loss: 0.4914 - mae: 0.5365 - val_loss: 0.5317 - val_mae: 0.5474\n",
"Epoch 174/200\n",
"39/39 - 0s - loss: 0.4860 - mae: 0.5318 - val_loss: 0.5281 - val_mae: 0.5463\n",
"Epoch 175/200\n",
"39/39 - 0s - loss: 0.4843 - mae: 0.5319 - val_loss: 0.5275 - val_mae: 0.5454\n",
"Epoch 176/200\n",
"39/39 - 0s - loss: 0.4841 - mae: 0.5309 - val_loss: 0.5298 - val_mae: 0.5469\n",
"Epoch 177/200\n",
"39/39 - 0s - loss: 0.4837 - mae: 0.5313 - val_loss: 0.5313 - val_mae: 0.5494\n",
"Epoch 178/200\n",
"39/39 - 0s - loss: 0.4815 - mae: 0.5302 - val_loss: 0.5298 - val_mae: 0.5478\n",
"Epoch 179/200\n",
"39/39 - 0s - loss: 0.4808 - mae: 0.5309 - val_loss: 0.5339 - val_mae: 0.5504\n",
"Epoch 180/200\n",
"39/39 - 0s - loss: 0.4830 - mae: 0.5316 - val_loss: 0.5299 - val_mae: 0.5475\n",
"Epoch 181/200\n",
"39/39 - 0s - loss: 0.4822 - mae: 0.5310 - val_loss: 0.5296 - val_mae: 0.5497\n",
"Epoch 182/200\n",
"39/39 - 0s - loss: 0.4814 - mae: 0.5302 - val_loss: 0.5259 - val_mae: 0.5462\n"
]
},
{
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"output_type": "stream",
"text": [
"Epoch 183/200\n",
"39/39 - 0s - loss: 0.4811 - mae: 0.5309 - val_loss: 0.5266 - val_mae: 0.5467\n",
"Epoch 184/200\n",
"39/39 - 0s - loss: 0.4806 - mae: 0.5305 - val_loss: 0.5265 - val_mae: 0.5461\n",
"Epoch 185/200\n",
"39/39 - 0s - loss: 0.4798 - mae: 0.5300 - val_loss: 0.5292 - val_mae: 0.5472\n",
"Epoch 186/200\n",
"39/39 - 0s - loss: 0.4797 - mae: 0.5297 - val_loss: 0.5278 - val_mae: 0.5471\n",
"Epoch 187/200\n",
"39/39 - 0s - loss: 0.4855 - mae: 0.5311 - val_loss: 0.5243 - val_mae: 0.5455\n",
"Epoch 188/200\n",
"39/39 - 0s - loss: 0.4808 - mae: 0.5300 - val_loss: 0.5298 - val_mae: 0.5475\n",
"Epoch 189/200\n",
"39/39 - 0s - loss: 0.4799 - mae: 0.5306 - val_loss: 0.5340 - val_mae: 0.5503\n",
"Epoch 190/200\n",
"39/39 - 0s - loss: 0.4852 - mae: 0.5333 - val_loss: 0.5328 - val_mae: 0.5485\n",
"Epoch 191/200\n",
"39/39 - 0s - loss: 0.4784 - mae: 0.5288 - val_loss: 0.5290 - val_mae: 0.5477\n",
"Epoch 192/200\n",
"39/39 - 0s - loss: 0.4799 - mae: 0.5301 - val_loss: 0.5266 - val_mae: 0.5468\n",
"Epoch 193/200\n",
"39/39 - 0s - loss: 0.4784 - mae: 0.5301 - val_loss: 0.5384 - val_mae: 0.5521\n",
"Epoch 194/200\n",
"39/39 - 0s - loss: 0.4804 - mae: 0.5317 - val_loss: 0.5237 - val_mae: 0.5457\n",
"Epoch 195/200\n",
"39/39 - 0s - loss: 0.4800 - mae: 0.5299 - val_loss: 0.5234 - val_mae: 0.5452\n",
"Epoch 196/200\n",
"39/39 - 0s - loss: 0.4778 - mae: 0.5304 - val_loss: 0.5270 - val_mae: 0.5476\n",
"Epoch 197/200\n",
"39/39 - 0s - loss: 0.4792 - mae: 0.5300 - val_loss: 0.5264 - val_mae: 0.5458\n",
"Epoch 198/200\n",
"39/39 - 0s - loss: 0.4820 - mae: 0.5312 - val_loss: 0.5267 - val_mae: 0.5461\n",
"Epoch 199/200\n",
"39/39 - 0s - loss: 0.4769 - mae: 0.5297 - val_loss: 0.5251 - val_mae: 0.5457\n",
"Epoch 200/200\n",
"39/39 - 0s - loss: 0.4772 - mae: 0.5291 - val_loss: 0.5244 - val_mae: 0.5457\n"
]
}
],
"source": [
"history = model.fit(X_train, y_train, \n",
" validation_data=(X_test, y_test), \n",
" epochs=200, \n",
" batch_size=128, \n",
" verbose=2, \n",
" callbacks=[tensorboard_callback])"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"204/204 [==============================] - 1s 3ms/step - loss: 0.5030 - mae: 0.5416\n"
]
},
{
"data": {
"text/plain": [
"<tensorflow.python.keras.callbacks.History at 0x7fc480325790>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(X_trans, y)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"39/39 - 0s - loss: 0.5195 - mae: 0.5464\n"
]
},
{
"data": {
"text/plain": [
"[0.5195215344429016, 0.5463820695877075]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"score = model.evaluate(X_test,y_test,batch_size=42,verbose=2)\n",
"score"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from matplotlib import pyplot\n",
"pyplot.title('Loss / Mean Squared Error')\n",
"pyplot.plot(history.history['loss'], label='train_loss')\n",
"pyplot.plot(history.history['val_loss'], label='train_val')\n",
"pyplot.legend()\n",
"pyplot.show()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"y_pred = model.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[6.],\n",
" [6.],\n",
" [5.],\n",
" ...,\n",
" [6.],\n",
" [6.],\n",
" [6.]], dtype=float32)"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_pred.round()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_test"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import explained_variance_score, mean_squared_error, r2_score"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"explained_variance_score(y_test,y_pred.round())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mean_squared_error(y_test,y_pred.round())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"r2_score(y_test,y_pred.round())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_score_nn = pd.DataFrame()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_score_nn['error'] = ('mse', 'r2') "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_score_nn['Full_NN'] = (0.57,0.28) \n",
"df_score_nn['NN_redwine'] = (0.60,0.13) \n",
"df_score_nn['NN_whitewine'] = (0.75,0.09) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_score_nn"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.save(\"model.h5\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| stackv2 | 2024-11-18T18:03:05.338842+00:00 | 2021-10-26T20:34:55 | {
"license": "MIT",
"url": "https://raw.githubusercontent.com/anadiamaq/Wine_Quality_Project/f3d5b4f4c8b92fa0c329a262b3b68f0940c8197b/Neural_Network.ipynb",
"blob_id": "ce0cd210f68b85e0fb01e8e07782fbb675a60d98",
"directory_id": "cc8b770db4dd9fd9348e3cf10008490786d0df5d",
"path": "/Neural_Network.ipynb",
"content_id": "27afdeac62e57d92fbb56d5bc532e45ddf50f650",
"detected_licenses": [
"MIT"
],
"license_type": "permissive",
"repo_name": "anadiamaq/Wine_Quality_Project",
"snapshot_id": "ea49ac20115f33240a4e7cff4b9df17f3dda3090",
"revision_id": "f3d5b4f4c8b92fa0c329a262b3b68f0940c8197b",
"branch_name": "refs/heads/main",
"visit_date": "2023-08-19T14:28:50.719848",
"revision_date": "2021-10-26T20:34:55",
"committer_date": "2021-10-26T20:34:55",
"github_id": 413470801,
"star_events_count": 0,
"fork_events_count": 0,
"gha_license_id": null,
"gha_event_created_at": null,
"gha_created_at": null,
"gha_language": null,
"src_encoding": "UTF-8",
"language": "Jupyter Notebook",
"is_vendor": false,
"is_generated": false,
"length_bytes": 89900,
"extension": "ipynb",
"filename": "Neural_Network.ipynb"
} |
49e7d4fa22c6ef9d4e193d71b15d802e69f939d1 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Make inline plots vector graphics\n",
"from IPython.display import set_matplotlib_formats\n",
"\n",
"set_matplotlib_formats(\"pdf\", \"svg\")\n",
"\n",
"#matplotlib.rc(\"font\", **{\"family\": \"serif\", \"serif\": [\"Computer Modern\"]})\n",
"plt.rcParams[\"text.usetex\"] = True\n",
"plt.rcParams[\"text.latex.preamble\"] = r\"\\usepackage{amsfonts} \\usepackage{amsmath}\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create a plot for the costs and tax revenues of smoking"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"## Numbers in Billion currency (USD, BP and EURO)\n",
"\n",
"### taxes\n",
"USA_tax_2018 = 30\n",
"Ger_tax_2018 = 14\n",
"UK_tax_2018 = 8.8\n",
"India_tax_2018 = 2.5\n",
"\n",
"### direct cost\n",
"USA_direct_cost = 170 #195.9 billion accounting for inflation since 2006 to 2018\n",
"USA_direct_cost_lb = 153.2 #133 billion accounting for inflation since 2006 to 2018\n",
"USA_direct_cost_ub = 219 #252.3 billion accounting for inflation since 2006 to 2018\n",
"Ger_direct_cost = 26.6 #16.6 accounting for inflation since 1996 to 2018\n",
"UK_direct_cost = 2.56 #2.5 accounting for inflation since 2017 to 2018\n",
"India_direct_cost = 6.15 #6 accounting for inflation since 2017 to 2018\n",
"\n",
"### indirect cost\n",
"USA_indirect_cost = 190.4 #156 accounting for inflation since 2006 to 2018\n",
"Ger_indirect_cost = 12 #accounting for inflation since 2003 to 2018\n",
"UK_indirect_cost = 8.8 #8.6 accounting for inflation since 2017 to 2018\n",
"India_indirect_cost = 22 #21.5 accounting for inflation since 2017 to 2018\n",
"\n",
"### Other estimate\n",
"Ger_direct_cost_other = 28 #79*1/3 accounting for inflation since 2015 to 2018\n",
"Ger_indirect_cost_other = 55.8 #79*2/3 accounting for inflation since 2015 to 2018"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1440x360 with 4 Axes>"
]
},
"metadata": {
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"output_type": "display_data"
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],
"source": [
"## plot these numbers in four subplots next to each other\n",
"\n",
"fig, ax = plt.subplots(1,4, figsize=(20,5))\n",
"xlabelsize = 20\n",
"ylabelsize = 20\n",
"titlesize = 26\n",
"legendsize = 18\n",
"yticklabelsize = 18\n",
"\n",
"# plot USA\n",
"x_USA = np.arange(2)\n",
"#USA_direct_y = np.array([USA_direct_cost_lb, USA_direct_cost, USA_direct_cost_ub])\n",
"#USA_indirect_y = np.array(3*[USA_indirect_cost])\n",
"USA_yerr_direct = ([USA_direct_cost - USA_direct_cost_lb], [USA_direct_cost_ub - USA_direct_cost])\n",
"\n",
"ax[0].bar(x_USA[0], USA_tax_2018, color='k', label='tax revenue')\n",
"ax[0].bar(x_USA[1], USA_direct_cost, color='orange', label='direct')\n",
"ax[0].bar(x_USA[1], USA_indirect_cost, bottom=USA_direct_cost, color='blue', \n",
" label='indirect', yerr=USA_yerr_direct)\n",
"ax[0].set_ylabel('Billion USD', size=ylabelsize)\n",
"ax[0].set_title('USA', fontsize=titlesize)\n",
"ax[0].set_xticks(x_USA)\n",
"ax[0].set_xticklabels(['tax', 'cost'], fontsize=xlabelsize)\n",
"ax[0].legend(prop={'size': legendsize}, loc='upper left')\n",
"ax[0].tick_params(axis='y', which='major', labelsize=yticklabelsize)\n",
"ax[0].tick_params(axis='y', which='minor', labelsize=yticklabelsize)\n",
"\n",
"\n",
"# plot Germany\n",
"x_Ger = np.arange(3)\n",
"Ger_indirect_yerr = ([0.25*Ger_indirect_cost], [3*Ger_indirect_cost])\n",
"Ger_indirect_yerr_other = ([0.5*Ger_indirect_cost_other], [0.5*Ger_indirect_cost_other])\n",
"\n",
"ax[1].bar(x_Ger[0], Ger_tax_2018, color='k', label='tax revenue')\n",
"ax[1].bar(x_Ger[1], Ger_direct_cost, color='orange', label='direct')\n",
"ax[1].bar(x_Ger[1], Ger_indirect_cost, bottom=Ger_direct_cost, color='blue', \n",
" label='indirect', yerr=Ger_indirect_yerr)\n",
"ax[1].bar(x_Ger[2], Ger_direct_cost_other, color='orange')#, label='direct other')\n",
"ax[1].bar(x_Ger[2], Ger_indirect_cost_other, bottom=Ger_direct_cost_other,\n",
" color='blue', yerr=Ger_indirect_yerr_other)#, label='indirect other')\n",
"ax[1].set_ylabel('Billion Euro', fontsize=ylabelsize)\n",
"ax[1].set_title('Germany', fontsize=titlesize)\n",
"ax[1].set_xticks(x_Ger)\n",
"ax[1].set_xticklabels(['tax', 'cost', 'new estimate'], fontsize=xlabelsize)\n",
"#ax[1].legend(loc='upper left', prop={'size': legendsize})\n",
"ax[1].tick_params(axis='y', which='major', labelsize=yticklabelsize)\n",
"ax[1].tick_params(axis='y', which='minor', labelsize=yticklabelsize)\n",
"\n",
"\n",
"# plot India\n",
"x_India = np.arange(2)\n",
"India_indirect_yerr = ([0.25*India_indirect_cost], [0.5*India_indirect_cost])\n",
"\n",
"ax[2].bar(x_India[0], India_tax_2018, color='black', label='tax revenue')\n",
"ax[2].bar(x_India[1], India_direct_cost, color='orange', label='direct')\n",
"ax[2].bar(x_India[1], India_indirect_cost, bottom=India_direct_cost, color='blue',\n",
" label='indirect', yerr=India_indirect_yerr)\n",
"ax[2].set_ylabel('Billion USD', fontsize=ylabelsize)\n",
"ax[2].set_title('India', fontsize=titlesize)\n",
"ax[2].set_xticks(x_India)\n",
"ax[2].set_xticklabels(['tax', 'cost'], fontsize=xlabelsize)\n",
"#ax[2].legend(loc='upper left', prop={'size': legendsize})\n",
"ax[2].tick_params(axis='y', which='major', labelsize=yticklabelsize)\n",
"ax[2].tick_params(axis='y', which='minor', labelsize=yticklabelsize)\n",
"\n",
"\n",
"# plot UK\n",
"x_UK = np.arange(2)\n",
"UK_indirect_yerr = ([0.25*UK_indirect_cost], [0.5*UK_indirect_cost])\n",
"\n",
"ax[3].bar(x_UK[0], UK_tax_2018, color='black', label='tax revenue')\n",
"ax[3].bar(x_UK[1], UK_direct_cost, color='orange', label='direct')\n",
"ax[3].bar(x_UK[1], UK_indirect_cost, bottom=UK_direct_cost, color='blue', \n",
" label='indirect', yerr=UK_indirect_yerr)\n",
"ax[3].set_ylabel('Billion BP', fontsize=ylabelsize)\n",
"ax[3].set_title('UK', fontsize=titlesize)\n",
"ax[3].set_xticks(x_UK)\n",
"ax[3].set_xticklabels(['tax', 'cost'], fontsize=xlabelsize)\n",
"#ax[3].legend(loc='upper left', prop={'size': legendsize})\n",
"ax[3].tick_params(axis='y', which='major', labelsize=yticklabelsize)\n",
"ax[3].tick_params(axis='y', which='minor', labelsize=yticklabelsize)\n",
"\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig('cigarettes_tax_cost.pdf')\n",
"plt.show();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Estimate Pensions saved - simple upper bound"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# number of people dying from smoking in the different countries\n",
"\n",
"# USA\n",
"# Source: https://www.cdc.gov/tobacco/data_statistics/fact_sheets/fast_facts/index.htm\n",
"USA_deaths_per_year = 480000\n",
"\n",
"# Ger\n",
"# Source: https://www.dw.com/en/study-finds-berlin-and-bremen-states-top-list-for-smoking-related-deaths/a-18823625\n",
"Ger_deaths_per_year = 121000\n",
"\n",
"# India\n",
"# Source: https://en.wikipedia.org/wiki/Smoking_in_India\n",
"India_deaths_per_year = 10000000\n",
"\n",
"# UK \n",
"# Source: https://www.nhs.uk/common-health-questions/lifestyle/what-are-the-health-risks-of-smoking/\n",
"UK_deaths_per_year = 78000\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Estimate the Pensions \n",
"\n",
"# USA\n",
"# Source: https://www.aarp.org/retirement/social-security/info-2020/biggest-social-security-changes-for-2021.html\n",
"# https://www.nolo.com/legal-updates/social-security-and-ssi-disability-and-benefit-amounts-for-2021.html\n",
"# \n",
"USA_pensions = 1488 * 12 #adapted for inflation from 2021 to 2018\n",
"\n",
"# Ger\n",
"# Source: https://www.iamexpat.de/expat-info/german-expat-news/how-much-average-german-pension-2020\n",
"Ger_pensions = 2907 / 2 * 12 #adapted for inflation from 2020 to 2018\n",
"\n",
"# India\n",
"# https://www.glassdoor.co.in/Salaries/civil-servant-salary-SRCH_KO0,13.htm gives average in 2020\n",
"# https://www.pensionfundsonline.co.uk/content/country-profiles/india states that people get max 50%\n",
"India_pensions = 11147 * 0.00004 # adapted for inflation from 2020 to 2018\n",
"# the 0.00004 are the ratio of civil servants to population in India\n",
"India_pensions += 7 # 500 rupees from https://en.wikipedia.org/wiki/National_Social_Assistance_Scheme#Components'\n",
"\n",
"# UK\n",
"# Source: https://www.unbiased.co.uk/life/pensions-retirement/what-is-the-average-uk-retirement-income\n",
"UK_pensions = 130.32 * 52 #134.25 per week in 2020 -> inflation to 2018"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Years of life lost due to smoking related diseases are estimated to be around 10 years\n",
"# https://www.cdc.gov/tobacco/data_statistics/fact_sheets/health_effects/tobacco_related_mortality/index.htm\n",
"# https://www.blueprintincome.com/tools/life-expectancy-calculator-how-long-will-i-live/info/smoking"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"85.7088\n",
"21.10482\n",
"0.744588\n",
"5.2857792\n"
]
}
],
"source": [
"# compute pensions saved in billion\n",
"YLL_smoking = 10\n",
"\n",
"USA_pensions_saved = (YLL_smoking * USA_pensions * USA_deaths_per_year) / 1000000000\n",
"Ger_pensions_saved = (YLL_smoking * Ger_pensions * Ger_deaths_per_year) / 1000000000\n",
"India_pensions_saved = (YLL_smoking * India_pensions * India_deaths_per_year) / 1000000000\n",
"UK_pensions_saved = (YLL_smoking * UK_pensions * UK_deaths_per_year) / 1000000000\n",
"\n",
"print(USA_pensions_saved)\n",
"print(Ger_pensions_saved)\n",
"print(India_pensions_saved)\n",
"print(UK_pensions_saved)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# plot the individual values for all countries\n",
"\n",
"# TODO plot people dying\n",
"# TODO plot pensions per person\n",
"# TODO plot estimate for pensions saved"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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],
"text/plain": [
"<Figure size 1440x360 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# update plots from above\n",
"\n",
"## plot these numbers in four subplots next to each other\n",
"\n",
"fig, ax = plt.subplots(1,4, figsize=(20,5))\n",
"legendsize -=2\n",
"\n",
"# plot USA\n",
"x_USA = np.arange(2)\n",
"USA_direct_y = np.array([USA_direct_cost_lb, USA_direct_cost, USA_direct_cost_ub])\n",
"USA_indirect_y = np.array(3*[USA_indirect_cost])\n",
"USA_yerr_pensions = ([0.4*USA_pensions_saved], [0.2*USA_pensions_saved]) #Personal estimates as justified in text \n",
"\n",
"\n",
"ax[0].bar(x_USA[0], USA_tax_2018, color='k', label='tax revenue')\n",
"ax[0].bar(x_USA[0], USA_pensions_saved, bottom=USA_tax_2018, color='red', \n",
" label='pensions saved', yerr=USA_yerr_pensions)\n",
"ax[0].bar(x_USA[1], USA_direct_cost, color='orange', label='direct')\n",
"ax[0].bar(x_USA[1], USA_indirect_cost, bottom=USA_direct_cost, color='blue', \n",
" label='indirect', yerr=USA_yerr_direct)\n",
"ax[0].set_ylabel('Billion USD', fontsize=ylabelsize)\n",
"ax[0].set_title('USA', fontsize=titlesize)\n",
"ax[0].set_xticks(x_USA)\n",
"ax[0].set_xticklabels(['tax', 'cost'], fontsize=xlabelsize)\n",
"ax[0].legend(loc='upper left', prop={'size': legendsize})\n",
"ax[0].tick_params(axis='y', which='major', labelsize=yticklabelsize)\n",
"ax[0].tick_params(axis='y', which='minor', labelsize=yticklabelsize)\n",
"\n",
"# plot Germany\n",
"x_Ger = np.arange(3)\n",
"Ger_yerr_pensions = ([0.4*Ger_pensions_saved], [0.2*Ger_pensions_saved]) \n",
"#Personal estimates as justified in text \n",
"\n",
"ax[1].bar(x_Ger[0], Ger_tax_2018, color='k', label='tax revenue')\n",
"ax[1].bar(x_Ger[0], Ger_pensions_saved, bottom=Ger_tax_2018, color='red', \n",
" label='pensions saved', yerr=Ger_yerr_pensions)\n",
"ax[1].bar(x_Ger[1], Ger_direct_cost, color='orange', label='direct')\n",
"ax[1].bar(x_Ger[1], Ger_indirect_cost, bottom=Ger_direct_cost, color='blue', \n",
" label='indirect', yerr=Ger_indirect_yerr)\n",
"ax[1].bar(x_Ger[2], Ger_direct_cost_other, color='orange')#, label='direct other')\n",
"ax[1].bar(x_Ger[2], Ger_indirect_cost_other, bottom=Ger_direct_cost_other,\n",
" color='blue', yerr=Ger_indirect_yerr_other)#, label='indirect other')\n",
"ax[1].set_ylabel('Billion Euro', fontsize=ylabelsize)\n",
"ax[1].set_title('Germany', fontsize=titlesize)\n",
"ax[1].set_xticks(x_Ger)\n",
"ax[1].set_xticklabels(['tax', 'cost', 'new estimate'], fontsize=xlabelsize)\n",
"#ax[1].legend(loc='upper left', prop={'size': legendsize})\n",
"ax[1].tick_params(axis='y', which='major', labelsize=yticklabelsize)\n",
"ax[1].tick_params(axis='y', which='minor', labelsize=yticklabelsize)\n",
"\n",
"\n",
"# plot India\n",
"x_India = np.arange(2)\n",
"India_yerr_pensions = ([0.7*India_pensions_saved], [0.3*India_pensions_saved]) \n",
"#Personal estimates as justified in text \n",
"\n",
"ax[2].bar(x_India[0], India_tax_2018, color='black', label='tax revenue')\n",
"ax[2].bar(x_India[0], India_pensions_saved, bottom=India_tax_2018, color='red',\n",
" label='pensions saved', yerr=India_yerr_pensions)\n",
"ax[2].bar(x_India[1], India_direct_cost, color='orange', label='direct')\n",
"ax[2].bar(x_India[1], India_indirect_cost, bottom=India_direct_cost, color='blue',\n",
" label='indirect', yerr=India_indirect_yerr)\n",
"ax[2].set_ylabel('Billion USD', fontsize=ylabelsize)\n",
"ax[2].set_title('India', fontsize=titlesize)\n",
"ax[2].set_xticks(x_India)\n",
"ax[2].set_xticklabels(['tax', 'cost'], fontsize=xlabelsize)\n",
"#ax[2].legend(loc='upper left', prop={'size': legendsize})\n",
"ax[2].tick_params(axis='y', which='major', labelsize=yticklabelsize)\n",
"ax[2].tick_params(axis='y', which='minor', labelsize=yticklabelsize)\n",
"\n",
"\n",
"# plot UK\n",
"x_UK = np.arange(2)\n",
"UK_yerr_pensions = ([0.1*UK_pensions_saved], [0.1*UK_pensions_saved]) \n",
"\n",
"ax[3].bar(x_UK[0], UK_tax_2018, color='black', label='tax revenue')\n",
"ax[3].bar(x_UK[0], UK_pensions_saved, bottom=UK_tax_2018, color='red', \n",
" label='pensions saved', yerr=UK_yerr_pensions)\n",
"ax[3].bar(x_UK[1], UK_direct_cost, color='orange', label='direct')\n",
"ax[3].bar(x_UK[1], UK_indirect_cost, bottom=UK_direct_cost, color='blue', \n",
" label='indirect', yerr=UK_indirect_yerr)\n",
"ax[3].set_ylabel('Billion BP', fontsize=ylabelsize)\n",
"ax[3].set_title('UK', fontsize=titlesize)\n",
"ax[3].set_xticks(x_UK)\n",
"ax[3].set_xticklabels(['tax', 'cost'], fontsize=xlabelsize)\n",
"#ax[3].legend(loc='lower left')\n",
"ax[3].tick_params(axis='y', which='major', labelsize=yticklabelsize)\n",
"ax[3].tick_params(axis='y', which='minor', labelsize=yticklabelsize)\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig('cigarettes_tax_pensions_simple.pdf')\n",
"plt.show();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Add the average cost of healthcare for each country"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"56.900000000000006\n"
]
}
],
"source": [
"# Use the average spending for healthcare on >= 65 year olds as given in the paper\n",
"# Comparison of Health Care Spending by Age in 8 High-Income Countries\n",
"# see: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2769102\n",
"\n",
"# The numbers for 2015 in USD are: US: 24655; Ger: 12442; UK: 9584;\n",
"# When translated to the respective currency and accounted for inflation from 2015 to 2018 this yields\n",
"USA_cost_health_care_old = 26133\n",
"Ger_cost_health_care_old = 10871\n",
"UK_cost_health_care_old = 7333\n",
"\n",
"# numbers for India???\n",
"# https://www.statista.com/statistics/953150/india-per-capita-public-expenditure-on-health/\n",
"# taking the number in rupees translating them into USD and multiplying with 2.5 as that seems to be\n",
"# the difference between the average and population older than 65\n",
"India_cost_health_care_old = 22.76 * 2.5 \n",
"print(India_cost_health_care_old)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"125.4384\n",
"13.15391\n",
"5.69\n",
"5.71974\n"
]
}
],
"source": [
"# compute pensions saved in billion\n",
"YLL_smoking = 10\n",
"\n",
"USA_hc_saved = (YLL_smoking * USA_cost_health_care_old * USA_deaths_per_year) / 1000000000\n",
"Ger_hc_saved = (YLL_smoking * Ger_cost_health_care_old * Ger_deaths_per_year) / 1000000000\n",
"India_hc_saved = (YLL_smoking * India_cost_health_care_old * India_deaths_per_year) / 1000000000\n",
"UK_hc_saved = (YLL_smoking * UK_cost_health_care_old * UK_deaths_per_year) / 1000000000\n",
"\n",
"print(USA_hc_saved)\n",
"print(Ger_hc_saved)\n",
"print(India_hc_saved)\n",
"print(UK_hc_saved)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
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0OSAyNDkgNDU4IDUxMCA0MDYgNTEwIDQwNiAyNzUgNDU4IDUxMCAyNDkgMjc1IDQ4NCAyNDkgNzcyIDUxMCA0NTggNTEwCjQ4NCAzNTQgMzU5IDM1NCA1MTAgNDg0IDY2NyA0ODQgNDg0IDQwNiA0NTggOTE3IDQ1OCA0NTggNDU4IF0KZW5kb2JqCjE0IDAgb2JqCjw8IC9Bc2NlbnQgNzQ5IC9DYXBIZWlnaHQgMTAwMCAvRGVzY2VudCAtMjUwIC9GbGFncyA0Ci9Gb250QkJveCBbIC0zMyAtMjUwIDk0NSA3NDkgXSAvRm9udEZhbWlseSAoQ01SMTcpIC9Gb250RmlsZSAxNSAwIFIKL0ZvbnROYW1lIC9DTVIxNyAvSXRhbGljQW5nbGUgMCAvU3RlbVYgNTAgL1R5cGUgL0ZvbnREZXNjcmlwdG9yCi9YSGVpZ2h0IDUwMCA+PgplbmRvYmoKMTUgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyOTYzNCAvTGVuZ3RoMSA0Mjg2IC9MZW5ndGgyIDI3NTExCi9MZW5ndGgzIDAgPj4Kc3RyZWFtCnicjLcFVNXb1j4snQLSSG26u5GUDumS3sCm2XQ30t0oISFISLektHSDgHR3I5L/zbnnHvDcd4zvG4wBPHPVM9ea85nzR0WmpMokamJrBJK0BTsxsTGz8gPE3qiw8QBYWTmYWVnZkamo1CycrEF/m5GpNEAOjha2YP4nE8QcQEAniE0c6ASZ98YWDJB1tgawcQDYuPnZePhZWQHsrKx8/51o68APEAe6WJgA3jADZG3BIEdkKjFbO3cHCzNzJ8gx//0XQGtMB2Dj4+Nh/Gs5QNQG5GBhDAQD3gCdzEE2kBONgdYAVVtjC5CT+x9bAGgFzJ2c7PhZWFxdXZmBNo7Mtg5mQnSMAFcLJ3OACsgR5OACMgE8eAxQANqA/uMaMzIVQM3cwvE/dlVbUydXoAMIADFYWxiDwI6QFc5gE5ADAHI6QFVGHqBoBwL/Z7L8fyYwAv6+HAAbM9s/2/29+mEjC/Bfi4HGxrY2dkCwuwXYDGBqYQ0CKErKMzu5OTECgGCTh4lAa0dbyHqgC9DCGmgEmfAXcyBAUlQZAIR4+Ld7jsYOFnZOjsyOFtYPLrI8bAO5ZgmwiZitjQ0I7OSIjPxAUNzCAWQMuXh3lv+8rRXY1hXs+TcwtQCbmD54YeJsx6IOtrB3BsmI/z0FYkJ+tJmBnABcrKysPHxcAJA9AORmbM7ysL2aux3or0G2BzPEBW9PO1s7gCnEC5C3hSkI8gfZ0xHoAgI4OTiDvD2fDvyJkNnYACYWxk4AI5CZBRj5cXeIGWT6Hwx5fgcLN4AOKyT62ACsDz///KcHCTATW7C1++P0vx74b1f/Mb5+besG8GTi4AAwsXOxAvg4uQA8nHwA7z+X/+P4f53+y6oEtPibFOvjhjJgU1sA33+4Qy7tb/4AFpe/A4L272yhA/xxBIBFwRYSxyAA7WPY67JysRpDfrH9/w7+v5b8XyH/sMv/V9T/DyNJZ2vrv8Zp/5rwv+NAGwtr979nQOLY2QmSE29sIZkB/p+5mqD/ZPIbkImFs83/DMs4ASHZIQo2s/7vZQJYLBwlLdxAJkoWTsbm/wmhvwfUH5LP2gIMUrJ1tHiQGwATGyvrvwchKWdsBdEUR8ij/TUGgmTUn88qATa2NXlIPXYubgDQwQHojgw5nQ0CuQCekL+QfAC5/RXdABZmsK0TZBEA4qU3wNTWAfnhcVkBLFJAGxvgg/UvAxuARRxk7fRoYAewqJmDnhg4ACzyQBsjk0cLJ4BFy+IfxAVgUXpE3AAWVQuzJwfwQDy0g2Q5xOn/mnghK8wfl/BBoOMjZINwVLQBmT3hCCFpavoIIRRNn8yHEDS1foScD5OfDHM94CfjEIYmtpCy4Pj0TJ5/jJaPRghPM4eH3P/HAqEKNIZEzeNtQcgaAx2eOMcOIWvkAHqyih3C1wZo/MckCGeHh3f8xwBhbQyJNGvrJy8BYW4GcrCBqJKRteOjGeIA8MnuEOq2TyCEtK2jNdDR/NEEYS0q8fieD/f7BEL4Kv65gANC2NHZzs7hIRb/MUIoQwLLGmjzaIKQtne2dQJB+P1HAP4ZgVAHO9sYPUiI2aPbHH/dPcRHh0cThL4dyAFSZ56shvgAtIFYHR9qyj9Wvr9P+/MoTog7dpBqB7YGmT6xsv1t/ddkiGdAx4ctHK0ejRDP7KydH13lfHgM26dZAhFZFnN3O3PQoy+c3H8Rt7B9ZMgJ8eXPm+SEeOIBcrB9NECcgPQNj8kDYe/k+jgOkU4WJ3MH0JMZD9Fu6/x4Y1wP8W7xJL64IHQdIWXlHwwh6wgJwEeuELFgAf1xEVwQqmCLp0R4H3x+mqVcfA/b2Fj8aeVm/W8YmECq7KMZwhtk7wx8zDNu9ofnAjk+yN2fUzkeBx6NEB+Aj/S4IS6IPiII/dePCEJd7BFBeIs/Igjnx9DmgXCVfEQQilKPCEJP+hFBOMk8IggZ2UcE4SL3iCBc5B8RhMubRwThovCIIFwUHzUPwkXpEUG4KD8iCBeVRwThovqIIFzUHhGEi/ojgnDReEQQLpqPCMJF6xFBuLx9FFwIF+1H9JdgAY2tQE5/pBAf+2N2/znA8c+CP7OL7yFvLByMnW1MrUGP8QjpPh7EFdI6Pk1yPu6/t/9zb4gTj2nHB3HC6BFBnDB+UiYgXpg8gQ8R+AQ+pM0TCOFs9gRCmJo/gRCGTwsQhNqTMsAK4WT1BEJIPSknkA6H5VES2R6qF/gJhLCyfQIhrOyewIdUeAIhrByewIc8fgIhrJyeQAgr5ycQwsrlCYSwcn1SMSGs3J5ACCv3JxDCyuMJfFB6sMlTHWN7qE8gm3/ZHkTRGWwGdHC2sQY6P+H2UKKcLKxNnrzHQ5kysQBBSorFE58eqtX/VBq2B41xtAMaP1n+oC//alweBObP1oXtQVr+bF7YHoTlX+0L24O6PGlg2B70Rekp5vl3E8P2IDP/bmPYHtTmaSPD9iA4f7QyD5rjaOr0r9LB9iA+YKN/ufigQf/qe3g4/2x8HqToaWvzIEZPW5sHOfqj93lQpD+anwdR+t/u50Gc/o/2h+1/+p8HpfqzAWJ7kKs/OyC2B836swViexCuf/VAbA/y9UcTxPYgYf/ugtgelOz/aIPYHjQN+LQ3Y/2jEWJ70LV/dUJsD5r2pBVie5AyxaeY83+aIbYH+frfGH2Qrz/i+Y9G3djZAdJ7OP31TQVJpf/ivz6YQSA3kDHyzJSt8atAy8rApstyUUJXpvUhwXGqdc33dEyeMw7NzldoCEl0ZekBiw4Xokm97Rg/VyVoz0VmSW89d79WI4Q0JCg3XnvdGMSpjK43Iv8Ywe0ezt0VreoiRiJiUhPZ8Lq199Lwt4L5CtUqS5Vl78yLppSDdenaKeVW1VU0NxA8ta68UcYth3JTNMYUpR6p658/QZVtlDGJTw7vxESMSI955IY+cX4xjpk5fE8qG8eA7L0XxZHnqb3EHv170mO+WI3d8RsBJYE2PjHMOZxsQh2JEuhyhmWFJ3FsLsMoySHWpTm6/9tZWwulBfmYNDlHoGXyD7Nam/QTbeJaIDYxd70SGJhF/vswou3kwmn/WFXILD8yJxVIX0gd/UJAv/5NnvX+zXT2CxEhMH1wwBwsd2iYk88zfkyxpL2jnht/siCVAy9bw6ZrUUAYF63Zc7RwUbwdDZc4fa64Ug2VEZiDCzf83JehAcvQpQct/aN6w8bMY5Kf8yIsXf3mbElTV2+L34lr6S3DnjQwHdmYJ2qF5XyqbTe6Vr0xQ+na9dII9W2tK7KjDnyh5TBcDmK0+oCNMMd+IOS8lUwcpkZHnhgZK5Rdrru7SfaM7lWcr7I6Si1JgNdsEg3aNtdn2TRz80iK26DuBjZBNzjU5OimbW4+I58v6RoTo+OSe/SUfShis9SypYWNcceLqSlfWAnlFWXA/bWGu8uL7qHH7XXZBDXsZEHN79hfEZ4p4xbHhiMW/WRV5PisCtsA49RHFlue86L1fd/0mmNEWta3EBU13p3umyXxsIChOEnRNnGdI1H0q2UmzRPQQQ2QeHCVtqsADuUyTRjoy1Pku8AWLQX5CPe4LbPJwPeRuz9nbfaOfOEVgaDn9pu9OAP5t/EPu69FHzJ8ObypEXdF62jIpg0Up3bmv/e2ESwDSIICMwK3UdZJXYwTRq0//dbxe7ETOSz7wTYQlLlN4IaYaqrsSFSeORga4S+RyHUcs4GRXAiD49wez9aU39rTtvfluN6sgG8nZcEUtX/5gkHlaGAE1/gX/jMDRCvcWw8M71NtkmKPQPFCJxG0eN78D9aKv9GJtRO79wUStLq+mL4Wjad1uFX7qPALBobX7KNuWgVLk6uGfLdszPXYFpFa0e8MRZPxLJFyBqzr10YYbZ0EA4RDFk0yU34metdTBSXrE9dt8/fhji80VBjMwyl+cGxUvNAzDFgYOhHNCdcMuu4kpKkT/rKoOIn+c/jAJy3kMCULRK7JxHue+fMyevNbUB6BIiGPbswatPAQEx5CzofTIPMtXhqVwd7CnODB8cZguWsaa3g97pOV9qJT+rzcdR/5McyQN6kWxDBQ+AEOIWxWLzpCzNITz4HzXfKxu4fbZi5YY7TwvttYJId4o5F6yC4ZP182NNheijFOf1OTC2RKnfqNld/cPO4VrF1iITPDpZY/GRCSKL0n1zJ760UVDJ921idHBdI5yVq2cJLP+dRDmRwrpQVCgPnMXAZquSSoST4zw+0lADKZHJqKG8L1pwWmlJukR51vuPwsQryd5qC5Bv9Ifdb7Qboy8GTt/sbFvvUcYFjLjYAhktASftdSss9ynDRsO1aTyfD69+FzEhBS2RuXzzElR6giH+YBzsAk9K/musFIdNkwJZo9mDT0GzYhb7QAae7kyXHXslCHlEp75ObbkylDt5hrXemtpx+VjDuZMhlPGbnVOniaVdZK38XjBc+x5MB/9p7UiMJLaFk8/fren+mgl7xeVhWbrmyal72r2MUzDLxfDsxL+JqMwTe5XRGASJrRm93R0vLTGQHvSCM+gjtoEaNAifC4u/QHydZLrHdQZCH4tvenVdgNO0t+ESChkqFYR9kK6wtR2eugtyl8rgxdx9TLFDKeRt3Vvqq6sEbUCZMmh+Ijvhbm+gn0jV+/8ba9sgXWRAreNYaONWZUbwz0FPWvmVy5MZViaOAYIOLTPaOKd0ntxxL0fWuB2dWg5ZALQmfp1kWAoUTl+x69/jb6xS26fP+7pty5AIXf6e7uhHRiP+XXvoOMfLCmghEG/DNWNqno2rrKxNKYR5LKTDm+33t55mWav3PnrM/pw2qKqyJXRShPqp09cs8lsv29LlGEjKR51ZGpUixNiPLBaRPOm9UfKZqiOG47RBSf5Dw/Uvm1ETtKX9eP4HbGQOokJiGCdn7KlA/snF1qgDfXcsgFFgNhubvwQ0ff638ipC4rAV1ZvQz32HmPJAoFsOEI4fnhaqDLl86xZfzKg7IFfAh39ahS3C4sBeXbd8vmxC5fIYvohW4mU8X6h3/dPu7VZKJva8TOk9fnlIq/+UJ7F/WxlQFJF4caT/S10UJTRYNlBy8HhoT0byzOLIb2/csERePoAr/YvRqnZJFP7FrXMOYFqF7jn91ee9mZvG7lkxisGygiSgCaaH4YOEMKnfQeF9V+hnyBOZq7Rk56539MkFKduDwiq4dWEjp247KqJZ4aeV22qB0Cy41NTEOob88QPrgaS9rCNAEFiwvHTyxte8uQMOMygrezGUXTw31xRcK+ddxyzexoQNbzvU7+o3uMguOvTf7Y4xZjuz6SwHCT48GImVSJtEawdR4R2HLLeTU5cdSBpjdaxt0NkTPvPkkrxsjIAoZVIfggNOOALQx485NOp8YyQUmJLtrDRxjZ5v2KyakrAO9lgo5AnKL3uRgRy0VnJAUahff0S+W6+R8XljKKAmUW/K9z/Lq6zy40D8Jbf3oQ9b8fNguf3DAy++nW/ZPQxo0AgxOF6Pvw1yLmKer3mlfV8Xutjrr9l7BZWZMfT23FSVMWniNlv+Qk37GboRGLeQX1XP1ap8eTL6rjygWkdLBKFkBEmL1rvWy5kNYyFS1CnhuzCaOk62Fw6UAhrSJAiYIzPbH+1XTg9t3RLNKRewJ9ZMPBj32LGRXKpak5Q886KvN0xQS24uhQpinz7+G5F+/E5cngod/b+ofH5BBshKegYowVtOMAN0tC45ZRb2xsqhlQ+3LVSnqRafv9y5Wfq5MGyi5+R1ZnJXqH8XsUpjOsF1n8UlGq9rh+wonb0L4Ph5xMe+h5II3ZF8ZkYqI09sZQM+xE7bKBO2UesfPzzjdtw3t7ebxSVC/CrIMqezGBjm1McgzcnbWMpWhO1tAvDq7DYwaqHZuUrqU/dWwkGuLGRhsoymDf3u0f49tifsrEwsKl+yISU7k0j29N5fVp9pvndr5KpINefaGgSikqxtpYbOkvAd/KPqvf5K+F6l5GvlUBCzouaU7RJLPOb8VauasbnfFZI1rkwCfyoFMnl6Ov/Da2qLTRqJJF3yzhARGM1ufBva+c/PwM9lPd93j8oswbEn2nvc5QXWUveJqaCMMK7gvltokY3pivXKRdnTbjnY393sMjJDbzIsat59qcU/Lqb1MDRF3xdt8KTdDWuiqlMDQEqHTgIDRPi2m3yCnMbRGyMXb9fkkaz3OQThP1ogaf40LFcLUZFsMiAxklEsimohlT/F6J9qDrGC0i7dYctbxRtX6bf+SK3ym9naKZy5jgmtAa3JEyRi59FEbSVosWuPU+mIxluZpYl8FYYqKBQoQVfOOPE7xkWXfgop/QhRIiPBFq55JBTBaOkUT/XqO6BlviOiA/PmrIYNlPh+BrhqrbQLx9pwOzVFZqvNs9pVyl38DnAtpBJKhS5/MG0pBNdtX3bzw2QDjYJJwzaK5I8VmOYEFYBtbX9isIc6S0DEz3YqFr0UXTHEVOtrMcvTM9vbffYqsrQ2zXTHRR6smHRCb9e4wMPn5b0OvOMk3E3LanQsDK7AAxcqnwX91DjTcbZ698z3v1cUlNuAsuljlmqV3Y4QfltnInssm7wXDMvMpML/cEpFHH4Dg3YHG7FwC5iLOfHuNNA8Lum0V9kQ+rmlNUfFK0P98tT5OfDrzcfBuD+zE9lZHEQclerzTYsPRFRjQdu3DpZ6xv40SoxCJwJ60DTO5Yl1nLUcSfYleOx1eiwPMj6QkjBFOIcYRlnkPu6p5YKSn6vNvwtUm1pUTkZbDeTcTeiE6K+AzkxsuyzvVi8z9+xyIEYqQIet1rbKVQapPLEs9d7TMFRu1jJEtPMGVDE1wZazVtOoMkZdfpj82umuDbSMyobygEocTHtWsjHSz2adygPKqLcYwzqEKTWHKolFYcFx1kW5XfkMKYZK/kzwxWr2rIvFktA1KND9gwzWW9/MrETWYJB1f97XrNcsLsTUQNiTPgWhymsVSs2dJizfnwaF/R/gfoYrwVLDTpjIiwgGCNQvoWUVA6fPLuDmO0PyHr7BhjTduvsRzlDLHXqpvTnZyDhR0wLCVnekcAe1W1HvlxpHrAAcdUP5855xcZsztNiO8mht3E7vW2B492SKDJ872Ei6mDOQfKEKGYllMiESMmXgPwmkB3l8NzwV8AEI6e1u4YKWPCKcX20mCHdicH6++Bpt0ZUWXHddY3tXqCA7L+WvUFvEM+KAOKcve7CRy7qbSDX3CG2TVK3aQDd0n30kDs4cjUI9/3b9mxeOW4fb+8p8vNDhAi5xf+IqKv/Jbn2dX8ay4y8eNa6VbPeTJ7hz4xeDA1+/ypax5X+Q/1Un9AoQ9qrLtZBzj066x6spl1WttdokxzafTUPgZRPsL7sq9fDwi5fJsRJcsA42cbstPuXFBZ54xZ9s/VQ7/nx6JnauG+iVZ2ysVyURipNIYL+pZu+pmZv8toyZbK4TrH8fLkgkHp5JoM9jnmBcIc7DMXnMycd7mMIG7svi50m/h0G4u6Bg5ShlKyihVdOp4aEUXbX2F1gnWoI2D2c1BU+8CkoEjxjg7BGlS8MVwkh6D/xu8U0QIbsubC7s/mKFy337hLKdwrwM4yZfn7m0RCFIsOJIVnWs8jlc3ZzXwXqOvSlwvJJe2CVxLKYXp/UZkKH4XNhqp33VJQY97AcrbrOGfsFsGkgsJj6DCB2SRaktBSzcgw4/1Fns/0DJVYJ5hKK+IUemKuoJi1Ra0KX4a4OeSGcdMh9QMGgjJhmFl11pZXlBFhOs6ch2YZBb8fimSWkVBDi8wclJYGCZ6ESZEawRcS+3hQVmlED99zUme0CX8dsA96ljAX1PL5e+z+q7xLchRnXC7+tFSPZcLaNJ4AH0NuQwZVS//XEyqyd55JL6jaKAUFQiU+hV97Nmi9JpD2+hHBShmyDj0ZR+y+UzsAsPsiNdkc74n8TvfnETdjxLe5VxIlQUUmnAsyVQyNOqN+X/B7I6KTeBGTVKoMkVVZxqQsOlT8T6fggjbfhn6CO6aJsaXXXxLRQ2etdUU+4lZ8854RYQrhPndSt85xhiUNDZePcwtqYCbnlVQCsmbfOlLvEtmUlxBiA55wgFNtxc1LVVa93vfdmuAjiS3Fb418JCnEzlPp5wizlxZmrX2+SBEbv49Tl+sYxXipkSShbopLWRZXXF8t3hAALUeWZ7FqNXX0E9MOcM8IIreYfjXXZK2LvhwomJa/IvWtPKpNXuAa90dDQmco5271K3L9sflC5Jk2K/Y09wRHdImOwKYLyjPETvzvcIidch/1I7Rq6+yGmz3ug4yJ+kiK696B/E5/hM3AGVwinqQffmz9dR+rGDYF0pvJR/Khp2lLRZXaxLizF8s5IDFh+lymAc+qSw9pPVO3TeG8MKU0yxNrKsjNoCvyrxQRWWIIWwNfNrZnuqBNM8M6gXvMkyYmp6OYkeqEUKxLGFs9qd611ZZOVSLOa9Ek4XmfpVggatFTCazSlQXO+Y3NVwpAd7/9nY4yjShTjSp3wCja8SJaTbx/c4FEc5Y6Mh38vODkhmOaQBhdof/dYmPaXtSz921QnDe6NM8KkTMREEqcXVjKjS8AtXGdbvehvyKkwb75Dt/AwsFfJdRn/Pfjb0++RB1/bftdnO2HFBUCRtEmFZ/kFxMtQRVA5KnPS0pgvt7b/dx8eH2Q7COlgCf8IdbePP2lDwWVnjuXZha5g6FqTXNTOgcf9TT2fZQRcC1+MT1Wb8pmtG7Zx15WvG3JcaHkIkepDhTC8BsM60nn7NfuMWr/Jpxxm0NypNChv6BhiboJV/LybrL3EzfYMeezweb1vmygVIP8oTDVCtoe1rl41BcGPPMW7Jzju9gXdu4LWqH8wZvOqcIhFLEkdvsui7O1c/RUKaeD+9DLotEchyxdu8EIDIXe5dUFnidYarsxn/2aEa7LhiMrr8QLG0b3ojTiK+hhYteVVhzEkyQLtXcQ1Hm5Mi4dOOFQzmuvgo0VjKKGYs8TXR3qOcd/1jTe/T6/GijW/wTtL5xvieoTJWsB+wLNG1dxPvvk95YZHJ3EuoWvkKewkHj229vGxOeJjGIqKGaGs2NjYHVzQ4WyUJpwt9dsevAM3+soRtO4JuPYTTrYBM0vw0nhBVEJ9ZTprL1o3bqycLtLah0usKNzA6u5TFwTY9Fu2AQ+X8eO42oktSWKo9trOVf4eyq/jFef1vbp4nxVxoajJMTg6axkHlnEaoWnw3Wc7R1AKmq+ftmX90xr3Q6tL0T5u7hlXNuh1saPBcGO6cvM4r8+Au0uUb2d0DujzPJIqmTn5M4yYXHBApFd+w3s5m9cUM8Xol/hXHvFM9QGmxH8MtCX47pDGe6CwtsC5CMUTqi6NjWmKWl2Rb35OCbd0u/tlL5TGIBrqxBTBXRRm7D5bEtjej8iIvuTM6JFty6Y4r2B5x0K3HfoI9qZs0LxUwSfoF2SJG6NI881dEN0PBp+LYDJdqRr7b6iTHnwypZQtHdL5MwsnpwMHj55H9bej33rkhLRdatSbEafAqJNWEK8Iaq9zs167s9j74g/Q9dvikn5o21KDp9ApbYnt/GsvgG4TQ2GjdJgX/lptD+jk/MW++x20Fme/vx6tzcsxbiSlPYlTZSsmR1xfYabZV/zlqxboYmveR8pcTKFZ54MOBCVBpWplx3efPddsDzn8y7AzAKptSHX4QgNJ3z2mwaHkYnA4We26ukvRADFWU0f8ckPF/cQgi2wkmxQfqPqoCfXtrl+wBF6ayhcjdNfzF+ijrmfr7oQnV99v42lHWknav7hvHbURQemuJg0JgXlZIan1Krf4k2z4NEcg+rNbf3BYpNnPbTkL7xXbAnfbtNq2k46qgqLCBWyTdmpDecDI2XIrxQPaBiyS01aDirSvztXmtvT30cpUuqyIJIsKAATyE5FK/EIoj08rfhqsnkR+lOQR9hdb5tb6Sh0DdEAAxMTOPySUJShdJeaSil45IyMSVxLersLC29KNub7OQWY7dUdK/HpT0PUaTFRXi/OugrsKyCRKny4IYrheEdlUVJPIeBGyHbPExxCMaLK6vjseBlrG1FKuIVv37mQrT1287BMPmxikPyCV2WP9R0xcHSBF+fzaEzTqAHtQb72OspIJc9WP6JdX9d8m2BeVDP8c7aEm/iNzqHTplv/IStu99auIM3kwatw6il+UCrLr+z8Vfz8X7aXaIGWTCbevFs092VqzdrxZAfVuLnOO60KRyJpRHnaQUSUOr8AHnnAYfrjDNtjie+BovwiOO76YbmJp0xuZnKJb3vubnlxX3HpNelJ3R1PFcUloqiwrnkcxq9TzzfJMuK6UIteHe+fP4sHvIKr+EiN5GZHUZM5XnYPZo/TLYmHh3bla5/PicFvGiqjH0sPMIWG4aDRSvWB9xfgjkBq06P7FTg8dzBS2FDrv7RZaSUtoz6KYz19NGU/+la2F6fa7BBnx05Mdb3Wli4QtFRRY5u1Q8lyfrUdMYNJqkKTumpYr7D1TEXVoXmF7pjTcxHEwWdDg7wUiz2vn6dUREEyR62NpbtGiKIbQmKg1HcNSyHI0+o3Nd+32BCKem7Y8TymbiOfcc5qoJHO4Qxenifzt+jZ/HcnoZKfd2CcEeNFx/78BS585/uXo7ZCi5UiU+/wbemyrnE/sXque8fyAxpfkoHUpt2N1N6XzeeqoBFN9Fnz6JJRJKy9Zrl22ghX5qab0JOSIjINpqLu051OQfjpl/LuC1u//IJBXvswphv7a0462LBGvthwcb89QJhy3NmLFhavuTF+q+Eo1nlBNzTrmiL7XY5j/n1Rqa2luBh1H6Ya2Kr3rOZSpdROMI6ZZMYkKO4LvLr50owwt+FMh7U82FoOuM+I2Tgw91yUqfSuwm77jGIGjq1FEG1vOfm/1U5wQ00rfsOThiFCP4QC0XBpzjWkVtWXy3tq1jlfhn0dSo9vjXXR6bv4EqOTFDNxDvi93uBxCoU9swRbh7j6UYlC3xi3WBmc4eptDRL4rE7XqaepMaDS93F+w4d7gvcCtWWV2q/hxk8savFG1xq1JBu6KKlhpE8PgMGFdKr11UDPIMeXXN0rUoTrtWP64l7kaJa2eEyaClqezuW09KgJ9CoPPb1W0U4BZW/2JjKTWpyunKyeFyLp2FtmyvDEPboOclmqSrVtKWVbgZ3xqR7eTuLQ+mHC7BS39HRek9pCsAe3rRT0lB+DPpdPRyWg8NDtNBUAMxN5o/qLhLns0g5q8TecF+lS1hvKL2+7AmproTflbWyTljRmM/F6KkD8fUOdERg5EUdp8Ta365L0FzhM7oI45utkHc78/AM1xh4VXWV9W/tIOpfvPa4bZ8q1qrKZHFfZY0f1iMRx3+2elJ2QF8XQaxrDfjiSy0CqTlhjSBHd8pMmvur9WXW7x6GAljKmEZhkKgVdXrjAzEKVeygVUOVtOxRE53cogW23lDQbzCss+e00TcHxpBVKxctg+fPNIH1Rbrj7gpL4lzlnSibZgnK/706k61LyLtgVRvmKBiKZOFRescu5UXa/GJmjh8xtd6YOdDE3GvruoOajS4V+6sQbsfpfn8KBDapUdyiR7/h/FpVxy/BZ3VKYv14d9udW0W+dJgHYFfG/xoXZc2yKIKeRjXuZed8RRmKwSm8RkGLNkXFOun++kah+NVC1Cmd6Ki0nHwd7sx4+nHfo5qC5UOl3oxcFNRtGdTVSJsLTyKjMa69IwfKz6Jj6FxiIaP4seKingsOAQ1+8rsgnq+tycvK0LhWTP9Ky5gxhvIGv0/Pym/+3D4g3yw1HpgwJlUjG6k4lqvxGkSHSFs0meqsIq+MNPjhjU50fS6rgvlu08KvqZleCfx7z+cQ42isVzvO9APZFad8ChrV/z7/3TO6egPqyNMCxTvAdihpvoRw/TswHGG/WTWD0bXeh6bhTa8oOHcgVgXcm9gLOrt7EdOE34v3HpMkOhjVXPLlfVe5vc6vi2Q1CApemtRw4Z6laKIePMkhJuHvLjBOcK8vfHNhcfHmBp5EpJ1ftLveJUip3UruMD86aos+z3GkYxNBTfHd7JcOURQJ84zh/n3RusuuRf+96iTi9SeSAcqT6+xm2CUr0mCmLapqcQOU21h1Fv2pS1ZGygqCEjDSIqp3b67y4P0aMmzm7mCuGZn9akPM1T6IPqdRd7RBuDmNWPvEXl0D9fCh2Jdyescld0dyDBfXhVR/THVkAN/9QmuUPg/GUTk/uuvvypRv/yrT6UJ6w07WjJoPUd4Ony0SCLl14I14YA3Sl6cAeUEs17ko9yYKYcv7tBxKBeIi8zJPSYusRj0TnzaLrUpOOzQp4sGG740fhwso1uJu6eRVCNcQlj1LD1Ir3rVl/V7t+UainqRBa/SYGWEDYBGAgSN+KtQqYJsa7IiUEZOXQmLxqj9SX3t+8hp01e90f7iFECT3NkmwcqNRmc9CtrfVMc7YzGEj/Pe/UXyVOwXYB9buLMfd8CbLTYdkrGlGud5I8spMcd7BLi4NI6pMonWQEt1/2HMIlCDEaPbEjSYVw2iumhaCgjtNkNn1dXg7hJGGUHeJqeWpJWuZx1+B9wAv7wnOjfzdSLpkfJQWVcYcUXDWTilnAXUMayl7LVjShZeIqZpT0ktpzapSJyo/AG3GWEiSQ9DvfBxAxxiPhRYGu+O1bVL+rN/Ml+Qa+X9+nvpASxhEVrFXi+Z3hvsvNQIGb5KJtFKdBYrBAJbWe45KTgToXKa7vQu2hET9dQVf1Z+l+r0wZzdzEaqeG+J0YxgY1KOSMSy83W4ICKsJkrrOQrApFvVuZzs2Iz/eK7pmJvbqrPKBzv43WpBYYHMD9gP+cPLu+lzK+SltQdWi9fWaOAZMS/gjuYOgvcLidnDcj8xr1c4Myg40mcGz0hxKUswKLiOVM4Mb9WidvVbOwW5yvrrdbozkR/6rTkiN0kRpWTYqRTXBUi8w+yurWJyV637G5lDni4RyLyQ4PDqixwu2DWkwYoWwjlSu3T8lUbcer4NUWcz+/PN0hGOkxdbN0rMId6oJjOvUbjqpPhs3wrz1Dv4vC+byuwX2eSFuGmzIfK8y3qgsEzOSUN5mQyAg0Y/n0vckv139NlRkxAJOIEt07gpXETKHaPlvQO1lPoCxxRjlDE+GaXzSHxkWHdPhqBLpfJRWhhwtQ2UCfhBkC78YtdTwhlc+2Fpr3ci6dko58mO5MqNP/GdJtkgJi29DIh4be0ewcQooI7H77i6h6hmTnZKlJ527KXSFmCe2r4AY6wxPT5zlSfDVOB18+OpXECeB8OH/jLCVApFGfPaumKjbpxmesyqssZ9pTA30oG5rq0kiSlaT2hZxGO1SN5XAECpg9uHvyTJQHIbTC5O25HuR7C/+3OYyj/0rZlexrch2XMoI2pslLGWrBAgSypZ4L7Ybfdfep2PQztWw0lufe8sa/Zx3lD1HoIlmmyUcy+PrEy3zP57UwCijnT7QUi0M1exWL+2pOeTLu1fJmA8sQKeikFVAvRvLME9LcujcoZ25LoMxfhCK9xcBckKh4Acs7NXRW9a3+/sytcL+ea6Sk8HDJQknSAdaquAFMesIrultklDDgaoN1pvqzZ88E+Kvrq8pZpE4wf/hKid54jgHZ7UWf8K+vVicfaI1ljCcQ9E1/i1ZEdmzIqmfWLOcG/UTIWaNOoHWRA+DzXu0fY9WpxCoUsyN7h+RTsqkTNx13JUJtGtpFpSGjx7JcVmJrCK0r+s9aivu2+DeUb8v8sHpGBmVAvsFtHzQKNSv2klex83c5DvXM9EsnAdVLbUHircHtkSS3RiIRwsxhk5NLavbLIWC1VpeWrBEddlQrs81qSUcVr8j+OmoIr8J0Mx5Z1rD3/WWqWVdMAy9MyhKD1k/2NWGmeL0ao+wFuTvaQTL4qyuuoe2Yvhbhb+1NBdXNmZ4nRaxudqb1MqmbjlRYSRk1kqVnDbujbd9Mdn/6IkGJF9FfO63XWO7It07DSreejEZiJLxoBHcngS3ZuflWeYyOKr9KFhnY/tosVZHI91Q0tmof6KghwCBvn6kK79FzFdmOl64+3sj17jC+vdJQs+2LBRj6j+Mo6G1UfQ0UpUOyyCs4XiNbU8NzoEFq09GWfI2edrV0ENhsMf7x6Ha/4j7Yl1+wKCmI5UoIMJ20n8SexpSEFlO6+iyu+LNQcjruc5MT/NRf2us17ftuhG7oEnnxxvY5wjR3+jT4mCKJRJe/6LvIa1czA0w/4NouTuutuF+E+ziqKTI27uS/UzreYvkm2JaU1KQlaPXs5CXQscXzoLBlZzq/h2a4RiObXXBz8i6Ed0M5zHtDL+ZUuAkZ6Bhj3/UbDAXARtLcWpXqCFdn5DIJjA8Iy/pc38OMIXyKeJXK378YS9xtOmH1neIWTPHlgyiSyH5+pGKuc2g4K/wqvzF83EgqgySyPfayMDlmxZkhrBnyeBYpD8U8pg2WlPfHwy55OMMOq45YMjp4QDeH+UeYPG3MOadBPoOmSp8mvIhS21LC4gyoonexSkIZFW+UNDaGptLhxO+BAkUtQct9p3GA+PkTTr2lqCb+rTAtOlqJcZR4S2atJFh3DDRLuCsduf3M2HUM2rGhd/c39LpmHMMDHUeA1OiOrPx1VNwPy2dOJdz5Ijkpyu4CXpHjpyG/Tmk+NLCZfB0e44qmTN7mHZ43ps6b5JkrC/vI+kPXkk/r1as9KwvjrqpC5oOlVHyaOzf/8O7caFuKO0d5kRMUA0eSn7uCmfSbc69U7duupj/ECYs4QAlC/+DwmfLBSJVfFli+zsGNT4xaUZQ3a3FTMY+/E1XrrNMSghWLWBkuUPiqqiVfqW496fTzvKQ+0zzhLN/Mu5v/xleEi63y/aJVs6yqiU0PfCSxKvbu0dziwJSwjhTfynPr5+Vkir63i8BlO0kaL4ZIVQRuDkvnYGni1chQJyV0l8C3Z+WeLdVMBogEwmpLuvHxxRMXegojCvzEg0LPt64n4gZmkErv2z+G02TWjzNYfuTMKF2Hl9WiSVHFRkfBvpiurbF/HUXJ20geptxFuF8X0Jl2pub9iiiUT7lIYCFlQjJ6CM3Wi9Gd/EtKqzJzP+tzroRO0ty8Mg8om0IXEf1VJ7pnaYpYQ0e3M1TdummfR8w5T4hvC8x4ZjU8pZrTuDdF9Wl1BewrqFGbhQNgUd+qt5UqjrU2soP0edGxd+heKb4+fnnzgzEjqMN1pq92ky6/tuuWbSx0y+85zRrUT9PyF/coy6kvKEqPaLnaCeFb0fYLJN4r2ARuyKVkdQQRjiPQyOVna6hYRRaX/9pvA5gaqd2B7V67+CRR46EADl1yidikPqEfZjvQUoRZ3SWsT6JiuQ+7vCCsycXWA+rEthm/8DBtZ/iUX7em+MlQIWb5+J7hXAwR/m7/J9nFqQjX1U28N4CC4wW1RCEdOjY1vUFVzjuqfgkjB5pBE92xfOFGFAxfgzR9sNePb4tfAj+/pHsVyNhWdTUYWZl+KfXeMUPbsM1hlWYipKXKLRA4UhECd+OypfMTvu4m2Qd554tlOXeebRxYXE3o8+wB3ZtVerxJRNPkV18z9Jg96nXxCc4l6bFWswl7zigNEtHThb72DIOkbL1xP0tPZrzKBkoYps8J412t20kR7JLrydKXln+Gu/wBPi4HDorszQeO+uyjRWvm1y4vdSiU5lNgQhWwxzYDna5vbT14JbzWmq/jEt5+gFvLxMCSPmh5XWPoGMU4jYqFmKjwauB9vpkvhX5TStuFtzGtoddLmiMZNFisoVGteIQZuogRVsUtDoQ9knQ7m1Fg2as0wEu1/pWqujpOtq3Q8vJXtxFoAUUv0t9zC9kQSge49NbpcOXqOPxwaxhGaeQr4ONeJYZPTU5wMl80jEwhqU6Tpp+ZzipEmqZhS5GYofzavpCksTDppToXv2UnYGo8paF9rTw7cck/BNTcTruvQ+rjdNc3xDXJs1ei04byQGwwMqJ7PruIazVwj0Sa5TSMkWK9mJ75KwqVi3fkgv5smyNRH7htxmblv9/eQmUnvTEu434bdj+0Yk1Dz04QpBcwM3uq/v3MgZAuNoe/+f7tctnQBXwngMMJny+qTdlYI2/TiXe06A1cuP1+jQEysWhOvRrrW8pG+yvnZ7f9OfJ1s0WxexXgadprtYiFVu88j+S8kS43709fxktascpj+KsyAceCqrUwv+upuDq2NtpZe4R/6Vcnv0e58DJjZCOqKqg2IqGna/MBEsJGCH+aAjmScq4iXqYNeMVN2uTOr9TNXPKAqOnNVFFYHA/8SPnC0fokFWwGfwyc9plIBOP1zGy8Pj8Z1aCS854mJNkkV1EapySSQB+bIBvmaac4faYkbj4j6foJfMpPxnKx1/be7l55MoO0UGOyDPCsWfV0Jb68IT/KHgfdUDXoR80ChglXExXnkBgI5m0E7WKpRHyOGRtF4dY1Q4wu5qo8gHb5xjRInQK6PV/gYM2lpEh7GOMb6Gh08rcJJ1FrMGLKInF3aiKwhzB69uZH/we6uONEjy+ybs/vLfzxcd/WMvLsbYI7Iq4SvMmpxybNI6Js1qGv3WwHGHA8CRpZr+VH4E+h7c+t8oV3Fzpx9XHJBXuyvuxq6eZoZQMkx3lP9LC21Kr0LAHd8FpTJJRfFlf1jmoxE5gFHMfechx1/8DzubMI9EmZw1Sca8CPofu69WK0rwFuIS52pqL7U3b+rl7rJhz4t/EOK87iXKeL4Vp/x6fzxYQdqvYJ5TsV5jKbtHgOAipiFkL+oIZqnaW3YqZD8u5WVkL2aGOJmydYg7SlDsmfilFl3NOCna/UmVUs6vZZb64wKHpovZgDza47OKpo2L5k4FfcVkS/ZG4UfZdaSVUsqFwczBtqTcio5c6kmDoYv68dhMrkXeoUPUOUE4CUFs/vgFe5oD5bY4x3EmlIC4P61fL1BC+L2wb2WW7oeZDDpWSTzs31vXGYluW1SxxO3TbR7Xq4HGyaQyS2DyNGOTDJ6gRY+ZLpeYUpzgD+MmrZV3e9XzcWL63bdsuMJipkW+L6ss5qKUviX16OU0t7dPBto19PZ3LHznXM/ljxZ1TOuxFnZWpaNwqQtvS7BM/3f19eSkI2o3jZ3CROEH+RzbwmRnn0lZfBJUi+PY0jXJ+wbX83P3ek4kdD/+XMNpSeZ5vdFZHuMkXm8zs3C9ArF+0KQiwydPiUb6jn2IOh4u5lgi2ueqrb9BVLskTc9HCsC70AE/lgf9jSdxGEDifqHXPl39E3dX4kMIiJHLrm8G2Ide0vN5AUbEGbmrwp4TxrDGoRic7xuqQKYGjS4I5NLYqCDpIOIFMzSWb0NbX8ZBqvaCTRxgLHLu4S0BixvcuvjC7JiYsWs9n1i7YcSoXT3rUnRAUl2gexwcFyazNfppeC5jPSKEHefYDVc4HJT1ZWWtEj+7xu9ksK9vlfXoLwRl/Pc94kiqsbcS8S9vpU08NNZZvxpFdbjtGD7bcWD+gb0KOSjyi5M66la/am3yy/6tUpI1FpzHTLMt6i6U+Op0X0vRNpsX9eJIeGNMQMLeMqly6YCJQLox4OwFyGNYuC3+nhedfCrgj1PXxvXEmbRtBt+ESmusIJzQ3qmQqA3lcSU+2TRWFDYWdvq3zMNlg8/VfU8wKMnzdOawsx5jeHvXwSw6kHXqrJyj0BVCMiwN1zAy3ym8xUJsy9nwg1cvQRz/2y/dQDz6x74/tycCLA/gMs8GTfk1w3v8govMyV31HW4STuZ6ZYSEDsQulOo1lloXyXrF67+f7gOdQ8nA37kA/FQNAvdmSVOxaNSq2jrVYjB7b2qOLilciJbtrfgmrrvAUfF+r5wSXBB5+S1jotLad3xd1hZ4UGT3HqE48ZLWD1C56joaftl7eTj7gbTOfRjezsTpfl3GS//V7ujwYICRXp7URek1jZd6+lQUHv7s6VHJmTiHUKxuC2RWjnolAEMTLZXdoEPMduPDgWRssRl6u0UHh7vBGXKT2DpvB8lfJi12Y9ypDipLEgqVq3n8KsfA/7eTBNaIi2Z3xl8xmvfKZJHBb1uffLu5KUwCrbt+dOhVMOY7iBOnPV8MrlTQ37/WVeWejkUJFfgoQxDL8H3zzjxkINQOc6PZvlW/THNDi3nn4ugOMfU5lac/z6vFpTK03VWx19dXwOFprrSyvn2k10gAkL7Zcfg/Sfc1vz1eGzzFjpI2THL0KRXQrHRisZY3sDZLm2ueN3+77Fs9AoN002zX7+fB3Nl63B0jZ2EM3/fAW/NFoIFYPKduI65eYHaNxZhIiNuMJOWKgiE1sMDqqqyK+S/TQu7mV2h8M4Ui425vj45XUl88IhCpOPR8GzuTh+Y2YOmJxGNAWdYgLa/nH2OEdOReqOHdD3VmjS7OT5xG+uMmgb/bwX9oz4QSPPn6/fStDteFURWb7OvpubaHlLagWv2WHUDFYSLd7tImRd1Q8bNWiYAJ7On5DEUJx8p6yT2ladpzBNkG58V3CnOv51TRvHNoSXe0f9/Nh/0ZW+ySjMSRjRIzQR0GcyMBVygciTVX++OHmZiGcacuh3qp+SYtvickegGTjGFGL9Y7WkcsoTCjg8tQhG6yZ1EzKLzs+Z432vUiTFwElKA0fU59ngYxtr7CKO6DovEALfYISzx2Nothav5VSjlYkNHCZJdy/2v3mLLrvdXVN6LxSr89oJheJWcyMefk97ZJDHcLwSqzqztVDnpYpvxGjCp77sQjfPXmuQASOR4sAny5cJxZX72Lho/JzaLoSLBgmGihlUHIqvt0KDE3c9UPWV5JsKajw/FkWZt4WJv+anhhI4gy1mdl1fdUN0zSAVyQ4JNBzxuDIOG8d2D0iQrzzR5u1qQUeiFvqSzd9BTiEIrbD+TAbyDdiD9AuPtL8Bpr/o8hoQiTaTQmrSeiegMYwlLRianD32OsZ0eZL45vhn10fHVB1YalZdyfoP4Nc58kX+Mu7w+NtehwcOd6U5AfErRtubGxtu6tSSSPg39s3naMM/0jukI3EqFTw71JjS6D6RS6Ahy4BCm15UdEysRo1DFX2OFV+/bii3CqTYuRnt9F2RDDIuEEg9PpojNYn0GGyKMow1nHy30hpOfdeOeOozUtCCikAib+LDAh5jeHeP7l+GBjO/UqxLP4RExsd44ddSZ6dzV5halPDCkCYDve8Dge4cDlv2ZuuuOvqoNF54V+UW/IoRImVV/GK26UrSppYxmXzDGB/7yMGqIzXwl59Exgf2sk0FViNdu8uqwSohwouXzV9xF7jhW7tgpkqFsJMNAhFupd7Kwl6C8xeCXB3Ehr9d5eYqgJguclV921UYkLGvvFgvpjLAIQ3eR1WJUlkChe57qBgg67j6bfn+xTk/uhVd6tF1j6to4ee6QQsXVyryEnCGdza0lvf6INTiSWylWz3noOMeD8nJvduNzQkX+733nNisatDC2gj0TjSnL/sUvLa89sg1U+11RbwzmTHfP4NSto9+I/hTKtr2tiEQfZEj/jPj8enn7pXzrXHdKWR10f4OJ7MLmp6RsNNN6ELxM9vFgMAPfuW0nR2zq+/8OnRGsHeoLmSr1ST27MTsLr7k5uoGVVp9i3jVxhX3bHL0fdl0DS5nKxQ8Wo9+Cpdl0wpcWkdjoI/raa49VHMXXu3d+/WrAkRGvThx5mwE4QkaBU2+1jjebJLezoqetfHZ9BV2lBMUxe0eNz85o29ENDBFW2rMrGA4qMEpJi6qfaOfYmcFCyDCJn3dV6bAAlb3CemsmoIKf0CRcus+rZkcmhq7pwo2LXaDBrH+WcB0ak/MmG8AO69I7k1R9sAaG8LZaIQIAROd1hRWmKoWiCjZ90cVh1E8OhWJAotjxnOieBONu9nl9luls6SSbmaUqaDA7JnWbdBR3nMJWiFFmKtg2lu94CE2MotXHxyemVQMGvKEkX4XJCF38aQ9+iBSj44Rzl34zplAzHYuuLnap8HwVbVk4hoQDxgMwy3uigZTrsVGd/3O4+7j3maeX9xv6U32uAU+BDwc/XHCVtIF3/vypbvydznAPI4ABxzFJWM0gVMjeVU4MikgI5b/bTlc7Yhf0P0U4Cj4WwnLItd11xQZH8mJiTQrmWnxMu9I8vsfOjrXEhifKyOFkXjjxazNbkzxQwNeZaaEvS+2uqj0kyld+fR1LxV2tIWrRg1pVbRO/NdMjmzSt0Mh39fzVgcjMGHwgT0lJau7PFN6iOaJfKfR/hYcXuyutZ4zUJdNmc1WRMxV3m8Yf/Y5GJ8pg4ywq3oF8AVf1SZzYA3ZjYDouGF3xdH52n2fB/TXSLQPjaAM7yR3l166Chmqt0err6HscLm9EP7ld2zz1YEp1XpCtE9iLSOpUg9cFGQTQlIUjHbQFsfIyw/mRd3c4xAIrISq4Yvoc3m1llk4iHzeioauKqiEceEp59IRKUpBhzkLmOXKwJXXw8PlQuu9uM1dpoeVUWraJcx0HdbpOCEcYUlKReYXFMe81Q+W6dzBaUhtCXU1UW6JZKbokdfTKFG244XOP1SA+4WJcYfhbjJM0dIoXkm74TDft3j8WY7Xmd8K24q2gChM3dYTJeJbfWS+S6bzNjbPB5n3+OxxPOYC0O9dfWhk/bmQFasOacop1Zk3BVPCbOPPtDClPAu/XDLg4NpavqN5EbLj5dsVF5bCSFQcO9Im7iCsBiMhpHgk+t5Ln8nk28bXI81X0Sgj9hkLHrKbZktb4qtwnFRTyL/zOE33oZtiNU1LKg8pd/Ze93i9wGqL0+2NF2t+hpnVOcOx0HRawwzWdviAm/epQ6hdvyOTm1XLZnuBmiZ1tNWiYHcwGxz83jBoUpE28ednqa3Q4qyZwGld8owfpcazWNjUr1jx6mKN9ruKqrvHu0PdFSZP3Vd65za29GI1oEMy7SaXg/U/ootu2TIp7H7gPBdpWN0uCBbQwFA22uzQTlhaInnLR7ejDVf+Kf5zAikR5eq8PwmN4Yg+Jkf96vbcQLVblPoLDWGK890awo9HBu926DH7NepMGfqWx/wDJVm5LSmO0aDufk7EnOZjufRdzH7UE/ltxVirfNlofsWEzGFMMO1/s1SMPdrVR3oQvVXRDNuYbWWsFRpUNSPJpyyoVnkHbzQ25APNIbnOyyFSJyoeSFqje+RrGW5BFKqpz4Qub5P88fYEA37P7Ib3LQp1FZaFpFaK2f2tpUWmp5Oi627eCGJc0aTdZ0d2E++zSVxpP3PUZnni6l5euI+xhh8sftUSSnpvJwXCSQQuRsa2LNt40jMyQW/EvCoUOgs/SGZEc6I9oBTYvCJJBQD8YU/qXS1EakiW5y6hBr+AmMwLg/kzrG8pa4fS/XLkttYkbWZY+p8zru6814SVHzzzSiC/2UP2lt/buo3HfM9Su7w1c1g3hJ5wAKe+1Oigw+Y6yl2bh6EflflBVRpgIz2TaKs6P3HZ0PviU8nlJM65STxwhHsfbUKIBnAS+FF+CwtpZlsHJT0UfVm/y4wSW5gdBVuRJ2V/JUO4AV3omm5nan0omLsHNooloYoq+bNasaRjFPw32WBUOBd90Gs05fAIhhxjz8svnFO7DbtjXPgT6a5ifQAbXzmvTd50DcGkvRCvOaPy7QBQkwFpwi455huPDziaPcgIC8cH7BvhCWncvuroaO55A2TkSYk7tLs/A7JRviW/eMGyeq0iSRjQ794c4TTWNFQRLgen2XcKcho0TcOUfcdBayfWOWU/SFlfvtb4WpgK+Wo1nmNFMfCZuY7ncFFIQAe6YtMkVOvPVQYBg+W3SZF7DlGuVRKIz9hKVpsZU5MN+FfAKvU90OraqB/K5hYWqknt09zQXGPBJx5kSEJRBunv+M1fZYz9lHqOTDc/JI+EWYX42yTc8w3c6ZfMZYEFUtKRwPfF4T0mPLS+aCmq3XiwOcWr7/FfdX09VYNlltBgakxJyVP1kPHg7avo3r6UWBs5gd4Ad87BdVuV0YyLvRrPt1IebsonI0ULPXuRHIpLSlwkrIO+nExb6L2Re5qP+EZT4JxjhG7Y5vzb9r0o48v8LpEMmd0ZCjLBuet1C80lxRecEuEvZx3hiEXc8Cd1z39399rWpvHwrMRRYRXrJ2qklXHrvme2s0rgvrKaH2X/fHHaKN9pTUQqNlqwkjMkta0ePDhmHSx76Auje0ywRTqWGG4a+wp/zJxiwGkkq4jrE/vBSQlARZv2Tab4xVfh8Iyi8T6xI8DgeP8sN5bN1C2SsIgKdthNzo+pfVTJDRpnjs54/Jp2kzJaU+PKZfcSfFNSxxN09fVzcrwPRj8JbAvfiUh8t3bBfDFwTBU8c6R4XD3LBhWFdf1GsDKGJJip9N3EK8Uzv07E1WtGRCzHQsWvcNioBhdnqBbfKUCJubpbv+laV+hcc8zidgiM+19FE4VZ90sfBi9Z4YgyttZiU23wtRUOhv8aciZVGPW43Hrxngl6qYPwhMxE/yMNR+DPqSHnToElw1FDsatlBSP2sfvoWbNChtjC6i2iWiFGWs2uhdZBFf7htS7Pt24ZUkeK1CPfd8Bya36Lq17PqCI8ifAHC+Taef2/2l9gJ8JVuHhUFXzfpUvqAa2Of38ZtEyNqnGJpfpyMd4IXg9Y/9MNdBWxcsd+pRyHFQ5maXAS7NWNiDsXufSztAPqJ5Pbc3YYaauQI2B1T4R5KGiBj7iZefVGCvZzT+gNkw47ni1WJF5/CN5EZ6lx3NKEI1zDuVbsJrQLT9SMWY7TWzpsWzV4oVcrBV3wEt2S2+sHnKslxeh9WlsF3XesOnJzbmXaT6GO7y2oD8ISFVzQXk7MxrSTxuYyIGYZFn7mODefrOtOrhDG+pp7nM7DldyejtvSPmbPTh7PuQznOFUSXFd0eysEDUWw2eK+cwhTqqkGeFt7ZJ2VrmPR8xHdGC2CSLHj4zrSd1zq7iNfUxBmg5NtZe0p56LX5p4OyigCOuP52s0lDzLv8uuiyQjks5zvJ1/kgSFmvaPJ/y/PMDDP9vsQ0oVYsy3c1qLctdctbUihm0Un3R6QShIucv0PxKtg9T7er5NCJ3wxcb1q2aBLaJkxNMElePc1aePwGn8uzBekozgeLki6jDPky/He+haUZv4QUS/M6grcdyrjafs93/DOFboFc4vi4VeG04oqdrO5t/JkezoQjqYka0fGp1Vgxzfh6gSI+vSSW4JlIU3ob7vxWRbR7WsPaAMP+fDXrbgsyxg2eH5szbRevUbsyTKIdocUaRSEVqzOidqC2AARB5VCAyBgckCFDrTVyrn3V2cbEBRTsDD4CBMSz/w8UmsPKYIG8WBAfos2AmcFQRXKW9aq1MVi5m6wz6d4t5VDiHiPX9OrctLwHefsHBSsw5UykKOglQ0A9odd6qkgaHE1GJgOZWd1yo8HnpffzezQKrPYhtJwRshMgGsFVbRMkL9m3bfo5jST/pdMYw9JmIA7fAusCGn+PHMlIgTmOeO3aigwbrHEAK14fqvjus+LeGj5Xa1NsQ7W6TsggCgYgJupItygzRat2PXzTvkf9DVgMj14pMv7975L32g78NiQfF8M4R+K6Ct2S8PlapcgS4NnuHCG/Fjc6ys3B81dNvh40YLdC98pZwnpYcq+gfoBoN272IQZW52ShUjShJc05fnd+1kkJsvxrpJyr1L1gmg2t07cN5p9OS2AuvkNKbXmUu2W9/bHnyU3WIs7JEzZUcwby/f/A9jKfmT33FKOSXSTcTQFzxw1fMmwq4DjWj5Jc4jAQe8khnd/2OYhywk4daSghGUBRLc1+2WCglIL+Q525verPceVe4YP0tR7EpiPgYHuMdeeQEowbNIEI21Q+R/usS97IhVXGu3dMuCyqEa6OsWyjeRDg889rZSq7elSE8gPHfUNeuQ17E+6vWa+jK04KNXMB0xHU5VfhRDy3ddkagObE4LhDOww0Imr678hD7tT7ZHw7XqXu+y6977Lu9urll1xH7YNNHA+eDjIYPAE3DPXmKdKqOMyNocjUZo3bjNAhCFVd36iJbSktU34UupH8pER5pZau87bDFm18U+WJAAXuF07f8u9Q/siUMSKACREmvdTpvlLYtdmslfbLp08gikXE8UuJpO7rGQRcAW3bWnyfxzaw/mPY3Deu4UCYJoWPZacaEIDgGT5Vcb5CZXhiY57ErfsR3op94vlTLnXzqS/fpPCGbXfbGc50DME4szUX1qoGmIA0bsV6sJcdmYwoUhZMikucADIfpYMfW3brrZDclMB/pzn6+4K3ubXncwNZkYNXmmDjWO56hLdgdM2VyIy60Is/asYndvGHenFdgAP0l0YLIrG/Jp5TCnBObCBBUhV90zOK/VJ8SUo2UR+UW9SRBA1hWrZBsLtCJO+TDzYte2LfZ/tBaP38bn2fkvLSb2KNFmSr5kicNZWJr1J8zeKwoPogQV8W3EXzTXCJzpvc2nJa7K3QpU+JhxkzcwFYKFDT022kYWGAdx716gv10LDq7GHr03Xew/kUUHEqNdLg2h+FteWN7YGsHyxYpnX5RvIy72R8Smc3fbEI4pxwfNRrWKG/zE0EERjb8C9zPhjgE8VViipXY7hYXwa7svT+FWN3OtB1GVxwWex/oJG5BF1cD6UAuS0nkI+6pJsTSzN6fBQ/OEIgl51J+/STujWx/+zUHLGGw8f2eHVzLIjxyXYaJlJ9dXhpli3/yxq7a4aKCYeSuGwsAj0mEhTetKV9sZE6KjYLYQMX4FI21oodmGIL3mZnQ1fstFDOoAIHoZx5OqZ7zQrfhq2boOF4Izf8DSl3asyCT09mbPysOIBJuLdDvv35kfSxkABBPld4OiaiRsYi8Zag7NrkeIcU+7U5P8fluK1Oea1dzcFiBwnEDO0dm9mOBqxbfjgwX1XEzws8yUm2ooopdbTqWApPY4iHZ1WgwA+TZifQy/ne7ofNoO2MW9NA6t0az5qEZsOzkw9rFCmmRghFhQiqCSZYe6jp+txEWn/Hlw+x5pvwQITu3XiAnAkKep2V16HHnPbMMV/G72c4MIyds8VIAG4yOR1D/ANQSavckkZ8Quo9w9Wr8y6Y6CzbnIKEaMqaUZ25iJBhcDrKOmo2wdRSofrY3TnxsnUrNXfKQmF3aHsWSXZ7kEUk60LxbLxmREkxBeBjn8Xkwp31ZCkA2B5bQQbU1JsoLDUfh/gI6Q7o4YRdqAUTsPR2CtYHY0IyJ7Wl/x8FV5tPSWNhFahtd2GIT4/IQ5ajEKOJrfTyWKk+swPOLynbZ3FP/Ot4ThnhARoitrrOzGC5KUxU+Ei6ex8kvjY3QDSeedjmKIfAB2rFwjx6TW5w26gH89bdmUfoOBLnpKEp+/dYkiNm/LqG/FPuedwFn7jfaehMG8Htu+iicqeHcgUqnc1Ma7LhYqhhd/nO6i1zKOfHRtM6OM2BZGBwBT97/01letu/Fv55fdMEdeWd1JD5kIlMicc8sWvScsjkSS8bU4L8GNEmjiDVy3JT1dwU+nDSvjVmKaEVuhub4VFcKIsTwVpeDxXULlbT7KYRjz39VpplOzRJcxCrjsROTSCU4isXGQchoiE2OAlJYbqPVpMmTKQrowGKtWH9uFuNOryBlW7RiDVHYlqwrnGyop4n4oeXRAi04LOcKLstC3NwvmDKRIK+vawz86E8ykQcDf4tPLCnq4F5flaHjJLCM1PR+qKIOia8mRruwLG1JV6g18NzoSW0baItRBKrhY6D1HIOoEoGUdMdv8unXJrxp+6G8sch9d/U7Dl7atWKdDSauQf+NrxfUXZgk9dMTxp99CcmI4TZsKJB+WjvLDmNTzdaR1KRwYfzw58SqocRloraQq6YB81GBtpkAL5S0ZvDXjB3Fy8wfo+Wz3fDJv7rmGDJMQ9k1KjX0e4gFDAF4a/WNnN/901Cn4M0Cc/MnJy2gyG3x3b+wcibGhJbquYf/kRzunYKQsYkGzmyMsYZ2ZLbDrCU2owTTxi0svYNnZ/8euX/xb2yMNGTCJb3o1zMswRyenUQYz46xVy3wgjiHeSQTlfhInDrBq7eUn4EIj8zgczGL94uCqbLjOp4xRo78XOYccMMCP/5is9u/7kFn32Kk4F1Zo/j+YcZvqf4xr5YHGqC7MXLCT3pO21fziCcMAk1Oj9b4vTJVG2UNyi8OFkF4Qmr35mR5h/DxuFUZyo8c8Uv/3QIMnElGjmKn1qSfxiOTYIi6jzDDJMm/52eOZh0PPJTBqciBGigZuqivTIRMs7+NEPSF2RCK8DyyUZoWJGZhxWGvzj3ohNwE+DaGpGXa337WjzY8Z3bwahb0hopVwhfDXpNiViGOGWFcoxc3IGCqyxH1draDnrGRybzUNCFDIBs6Wo7KohuwndxLQocBTU60wIG/p/3IWBWkhoA+wLyC9HLQLY2E6pLFV9llOsl9wXRKi8atPkXcmiwrKZ1me6MQK1ClxkVmB4+7b+wXq9zkEAJHOC7jYAhbtFnVzlwkLBfxfp8GFDD7FCWqMlhnoHuDsT+8eN09dRAQpvYUnrhHb8B3FK91KSoWZn4O6ErbXY8pePFuzO9zenzsuhjLjhidrWlM0DT8WYqVetEgQE08aM7EnR3VbVogrts/odzv+1hDAL6JrLK+tObyE7N+j0bjsQjXH37W6PCf+q0wRTlNQDysVQkZiNHgHTYDNDTqP1AxPNrkmtdpJ9SK6Gb97ZdUJKvsIGtnxAWFFg7F2P/GWCFUlzdMOcHbEqtLScX0feX4L4/bqtBXMpBuIqmildTXM8GjEeq4hU5eplgjM/BFaf04CEHquikgHe9L6gcsvLejmWRkwSaVSU2Egs+Sm/RcyhPD3gPuYcd0054Ue+GYu3zY4rsLCT8Fv//jc2dmHRzDzblatV1HWMdAAaaqJholUKxNcfEsYQBa5Dd0zYPkcNPjzS4k8Po57Ly1IZdYVD1I18B9r4cstfCJv/q0TpbRgcRyKP4Ue/iYbCYGPfNuCc1uaIJOFje3UE/pzqymsnJiYaGRgP1OWdK5W6mDlsSwKOe93w0iTiIR1AJMk6JoACUxe2oSlFxRMHG22/p46YgoCi//WkmbbQSXYDLwudjDlo7RU83pP88iUSV391VVOd9+27leLSXJBbkAQoKeJXo1CKRbDr3SXvWwbdWUE/TSV5lrffAbGStb0mHcUWKE6k2myyrFv8zrKPMMqfoiII3inc5/mnklfsYJk59UhL9w5qw7KuGPc24dSKOv9YQ5jFurLdBLGnCX3XkLoBCJ7BJLQrakKvMxrPpFOOmBjCyKiLJo0+vniMXANQurjU1FIGs96HdJK5ahb0NoN4TOkDUsZ3DWaRRpxaB7BF0j7eM/1cWr6YC3XSVWRYkgn8mqSFpCUl2wvLwARgRG+eyPYHkOdhFVco4O+6vTylcVTxcEKq7WfkuL/+KQqSK2NL5eAFMO/2Ek99z3UFzrsfNiNHiMtz5LNDrXSwGYbfkvU2qaWiybZ8L5f2fHAHmLeHbgixr7DjdOeDjPiWtfbtpTu3KPWY8EIeFcMjsCFJAORBr/UvdgY/U+rWovzxEtvERohDnb3RWxYH1wrVx9bCntQ/zjHpCPoBKWAGuZVwDOD0sGSEhW4XrK5PV5OLGleqpEEV49PEd/ABVt7dcUsff8kTt5/W/1U6KbdQSjAp9zzJp5Ete8NGxsZKtzoz/E7eqtgy6CbicGP772B4QjprL1OR4+zOVux89K/Nmk1Dw6cie5CrFBmcutEQUA269ioBZjCySfVd16Vjy/g9M12tnEW6iqVIPPyIYMQxYZ7/XYmC06aarXnlFNnkBtsMaODMuUM1RYcuNEBRdIsQMyNv+wcQWuW+wSUo/g29b++Jvp87melTKApic9rIec6Vo/Wk9Gw3VPgyF2ut/99QoYnuwDMuHMPPh/A0A7xY/wD7nLI+n+fNLoltn529sKXPeKLZSPWSsXvo/0NIX+G14qvIjMqpcSmyMyTms8fPDsaS5JoC1LS36Hl+j4KXZhWSeH3r9aBpgAEAHZngcZhpNctMIKax4r+DWbqfPAmYdyY/TdPoKEBoKtZB3vKsYm8GudXx/RrWwm3SavyDEnkRysHdb5Piylctc5RskAiKnS5UMcBtLBzujP2OLMMxD+7xVYPXHGbBrPuRwhoJRGnLR00bLBp4kn99v65RQPKLvgBiJ7QzcmjlqewrKFesexdRxMHyfsOsPSiaySt+1YIvCjun2Q/2yNtwuJbLMcmsAzdu91GC1mc6cz4v9XraiJ/TD/hM4vQoF/y5iAT3DFfLxVItT+N8oSKUAb9jTY//vg0JIf/z8GauTS++h05sQkTttQ0NzAxA7MsfID906swzSMI3wiQCNqPPj4xkeKdjDYNpZQswUYFtRRntAKpzR+YwyicnDqgV3gfIxsjzSoI5Cnt3yhZAAJXlF6vqp3qerWhEA97mb5g73M+l3r2ag1BdYa6mXkvs8IJLDSv/nwVQvxhuCMF+mYTsYbzzlyTlp/Pi4xW0aJqN+8VkWWJziAJ6C2zny2p9FhrAUV6CnpkMhjHzzh3i1FNYBUPUM0pTvShY0WoFimVU5dSJyLr4nMXZdq3+DtV9wkzsG9flsXJlQMD2FOWbOV0dmJasaxG0D0pAO+fJKKR/K0PzRdfLD0E/JSnkceA5PfSFHNp2N9QpbyUmNLC4v/HZse+sHyn/CYoTjDDqdk/bRcmLR3zZ9xir5qiiSIiEi+S0STcyIiTbnIir6lYjRMmeBenr+FbEa+cy7VmeJS1OdxCRBq69Yibylcrf8UddT5rgQCINQzrXSBDS7tdaezJYTRDnO7npXLK98uPYXTMlpV7Ee+b8ZWSJWy7elVd1crCARkc5HfXEv+jjhxHIMlbrTva6tYUri7A76wyuoNF//TcPy/U8GPfZ7bVGNl//WTnS0do/dhYQaueiEXky3UKYmyTmaKoh7eDCAnEbOQ1QI8ix+VaRRi9x0nE3t9Y/rbhYi3OJkzbPuzcusu5szTwoFlqVY6OzAJ83XN+eOrdV17yq24JVUbiMJlB3aEGBbmt/Gc975zu6bHH04NbyZoRmDY4RELnzBNvMW8dcKpf4hcDocdgmbTPk/7ggIosupB3Lk7cFL/YE+qmlebGNoZaeT3sXusIUYDynWfdEgam5GH3yvW+fsqCDqtehY/cA/AEiFrscV/7kkP7YXhKdgAyMGRU050Q/Lq4HBBAUcbJJ6zFBaYGS7a6oCHMWbOAWboQiKoBeCnSlNW/+XOOUAdo2I88Suv6UAkQ5/m/bt6PotqRL1RQ57Gxsm4YuixFHzVdbQIbeRAVP/Tsc0/nGbZG9JFOZGgzMg8LHDLnX7I2BNjDO+HHE8ALXPa9SCJbnLLdaLCKbHELbOGkMibvDv4ehd6bUub54+hlHYm7OD0uC/l7Byvw8RRnDWgo3PTIknEvhSy20DtwFBJB1wks+GjsZWAYKIOri0OVObrnnI+otQw09i6SHJziSkXHx4se3aH5xwdvflD2jb28T5RMeTp2F9ILJx/Pqq9zufusYoV/V8nERP8ASus2yLibR8cwkMSFv43SyH1A9qJWuGlKtGSs83Pl7WTJTBKKSQKC8gbcn5YNI8N8UHcV8FEok0JEsAQ6hr3o3J8bgvbv/oFvdleSqzifeR9S3wMSCTB0560G97teEO6p1MmipOQziC+jbJiqJTKfmYU0QQAxv4ZVJDsnxk5dCyoZM7nk4BSGGXK/rnp6wAiouyRWFazA/iLXbRuzsoYJ1OH0lhjUUo0L5XNF9NZigaFrk9oVrp0WKqtPLVNuPagwO2GsnQC+z0p0M62MPHWaqICxqK7yeavMHPzwAMEQcf1wyQKGnIcmSnBIjn1mm4xVTSvLVnjl5Q2DeOZcBOVXjUkWsgS+7x6mglXAQwLEMimxenRmAp7alrwTHNhraKYM9GPri398iJb9q03pP6C9DCliI6HCr6C4b8jok/6F4/pdMHFJG12EGdSUzfygd8Pp4pW9YfExrjhSy/zfxW+3Z64kuzab64dzxA7MaqDBME8sqo2HUUTlrvEGBjzm3+szJNmI2TMSrc8xS72/Vry+lgTDFYiUp+XdRseqBY1nJxAsb3rUFvxdFMbDq4KcMvkZzdL2kIj/NEVX6MxDlK3deWeuIVhCTst7EBULNqHKHeq1dSRK16sXpE2PEDg5Hrvyne1p38vEPCPAMTXep6DHvJIOuwVCwgOpmxbvOlvhJ83fTxdv9HPxRKNOdITKTK5UKDtvr27AMcoinWQvumcuLsjd/4/s6hoSj+qQVNPGPEZJrCJR9fepRswZgoeUAnWiG3Xd4mnaTmipMLut0h3ND1BaejosI2S63tyuuALeQbnYxuCL/RIDRSvw1shARaWjR8jNgIM5YHdvAFV3pq/2RsQ1HVrT+AdzHgKbiebi62vfhrO+xIfyduEFUrsbngKg5VqJMSIqn+xKy7LMR7FgkAsB6E1CNlyoGcHPQBo15JxXzbWdB38Id63WZmkt9GaLY22KRQNqFw9o4QFl/KK8BGLkI5eql8a6ajQn4KjojtKymQuul05q/86XARQA1uhojnIPU4oz0pTOzJLmDrCpKGnyoes0KcauK0KpvzstjnJ90jM9HRqzuaHMRfnEtDq5+WYU4lnZN02A1L4D3y1G3N01EsPIgyZkBvw5nyITdzZ9qflmUVSJ8Ac+2sXChtKJ+6cAjB7H0IUtSkC6al3Gp+0rgQ7mg9t57wXRY3x1D/M5RBen4NDOlXYyMT4wUYe8zdMPhZ68YULeRnqHaXSq1WiMOeOWrth99l2fip6boNA3QesyvGo8Fr+EOvl1TJfabnFVVfwvYDNs3fNqF4SJGi/qOCLSURozUS9rOIMwoBMaHpXTATFC+6j+KMNfssnOKj4R9PgfyP5TcIRyyy5Q9wEXIqvMau2jAJU8ddFKsuNapJjMhg1gsevi9FC2QWvRHGxFbP6c5mYIHrcg1D2nI5D82WVdEK2QugRzp0kQSUYmDZ4yeQfrYDR5SBYQXMb1xAeLPHJkMmPUMeap/UYzlpHmHxAVE0XZLMpPMVwNlJ3ivgKEuDdOvsTN+mTfgTtcWI29j6/w/AKVdaeB3Nc0W067nuadmoLhPxZhUW9+55Sh+gI+mGotxDCnRVNv2nohkxrbKrzrmTqF+i3c4NuMIW4kLpA9bwtd6LkU4m7pCvSaMwb/XOXq/qLpDTijTlXrZOdCtkRUBezBEY6dRv61jNx1W7L8PzEKbTN56+sDt4Nz02CXfWVQviCJU1P3otPqJRi6bPWCSh5I4I9umAyC68V8uNpcYIF5nw0OmP8q6L6+i2AAGX24VbEoxGgxS7QBBxH490x7tlJupGV1RIbczPXvXwiWMqT9RELJ1kJQ3mJTSBKioQqrQEd6XeO0AEtzHVF9UunJVnRaMx1xoZTUwb0VYqiQgBI9nzDOhWiSobZTOPy12dOpJmGWj1HDdr+RbIaGrQtn0++2WrpluU3ZPBn8BCzuWmHS7qGer+4If/CLoJLU9LBk5jp6MnR8zKUT0eJT1kfEmZqjtmaLRHjo0aNhfgj2M9+Rn0DueX68RLRXQqQAkz0AMt/dk8nlNQcoiQCtGxi/c9yCCOYrWd9Xpw7is0Qm97+Zcm/uPoXLSjisUdWvUhCBy8b4gMd009cXmmf2HGWsFxKY9Ccyto++qf3u9F4LmXQFdym3hklfMEiAskiRq/MjJgDQsdvkfk55n6bkHGOFkrCv6pZX0VJS3VShyOtwMvoncrh5tH9NSIh7Bz2pr473xEgWzVzIY22H4/qAbx3mRVEc4ySlbysoFjNyRIACd33BSDF3M6Y3ClA8j7xlXyDi3sPe+NDf/IfnrD3qLFAhH7lwe53eOHsd6YPWwqzJp79cwarDyPg8ADgn8LUHm7MBkUnwc3YOm/qVm5HuZaqcOXUTCyYmDxYSPcttqb7aoXTw5ihuGJqn3kNSsOVMdMeu2QjroOSwzjQNpTP3a385c9XDRwoUqIyScu6IkENlJ8V22YRRoMM0l2x7g5Lv+ZctIaLG9OsUXD+wl1ksnylAtCheC3t44k99Sv+jmXegfi10Lfs7JIq1sySFpyTGLaPA5PjNnZSZVM/2QSuyHAxyKqQ4p+PJpQdQXw9DrA94+S9dMHKiJqM/T2egkqymGi8NlpvA9UOUGFhl9TTtcHn4ZS5JiY9td3iIQc3L/WPaL2/WxNYu7Pe6pnNUNVER8XuuddWr5g6Mq+QBZqRPUNJi8Zf70F071/tY9KKHBi9+rlWfHOgSFy1V4k1CKQEDh2m3rI4qH/cTkkmustTWy8vpN1otwF3SnWDgy5K2JsB/cX0YDbsx1usB50GoQTCrIZx4Lzuh33sDGR9tvLT4UeQLWcg6Kl5spvE6TzTGWAex8pRv5yMHOfxIDIpegU0YK6eHt0zlSLbaC5mCtMK9rO1HvkdWM1yFWPwvbxv57yBJ/FCM59Wbhv/DY3pERRe7xLCL6Pe/2qRm2wYfjzzk0LHkPDzXQug9sNwzIoqgqHfhzHfsF83apd69FTGUQVNqtt58dUzl3TQC8DiKPFEYW1Y5ekKCEIqOFlWJHspHPd58lhJfqponYw1pPpeqab5rtUMgSXNFLpTB0ZuFTvCVniDA/0REdcuXOGjc6onHyp1oVWqrLIftQA7bFaJESyKUP9hsqQhAfCSMb4QYDy9vMx6sSOgxSqc2CThCF+6DtzDoVoV3TQbRJuqutw5YwE7uI70Qu+Jcc+tm9A4ld94txQ5gXBOgF3oW1b5W2eJk/qUu3ysS3PbvC31TEWErO+Dq6I6VOBsiYyiZbGf4UzL3mxy8P/r8k0qKp/Mli0GSGF+tF7q/aQESgO1GNAPIquAiN8XvRSk2fDm3wcm9HWqoigx+LGkmEgaJkxvHAYMWoO2C+bH4ONnjpc6v3/xJo3vycARR1ZEZA8bUavtv6Z2sbLcIlOwG2zBCSm6IqSectK4l7/en7Eu1PCnet6OMxqJHwMhYNllRr7DK0l70haPbrXkDB1wm3z82PY0Sk5Pnl+ikF+ybZyoNpk/K8+LvKYu49dlZmXXvoibTiax+hagW7xgTkzpvjpcquMjMBORH0XlQzjNy/Z3/XV7+yobsy3ZNXk6sgbZyORmdule8OweyvzwT686l1DUUmkH+mwwO3mCWKbgUsxV05O/c9OG6cR9ZLCCYj5KNDIFh/JHTp/lVxUTvpJd/ojjTsr19uSQA8qDRXNHk4nR0Rrn7gopGiyBNK5LsPzGFGGlMrA56+8Fo4Wk+pTf37Py/B4CELYl+zyt0YCs5keHB3O0UiFdAvDdDfKdh2eX49EXpA7e2KvHvroy2qNa+BGrtxCcbdvHC4Ya284zgJoTR2+rCEUDY7v/b+zgm0o/ORO/X62cZfyB2Qza6wfRWnu2dG7WoNw0/mcWc3j2VDycQpiaa0IjfLb7G4Bt84+QTWzSCRSa6+HjgQ1ijQvKqRMN6Z0cqmm6pyH9QvAB8G7UxZP9cnG+6QtWrpbJKCfeZ3cGtGBuh1IZS4VKGl70i665KCoRIjRmJtFLX7HRDe5vAPUXNcl27Al1uNW3QJhqlmfv6ACga/QvHmyR41iM1a5sww34Os57yyLwbDtMPjlv9Pq84421VhL+wVfkw3Tvv/nEIHPRiYPnJ2EB18qq2h8YXc+qExHmYyvs8HKJpGEYduoMZ+Vdgt5wc3QTlW/jaxriKPBDnKYDXJvj7MkOrZ2rXwGlGhEVxZrCD13guH3F3q9jjAskAtITOuk1CzZasnTQ9TQt2DjO59CO5vt1eKHCC7CtuWuOKTCu6BY1JDH0INYNk+LP//BY9OG4fko53e4MUNRKJ4NIWUWKAuact7vpTlZevnL0TdWA5VjHHYEXPFq4rBFw3dqq82D6ZR5oO6cxtbhvnELkYJhJc47cDPJZho37PeIUDUgWtQz35OcxP70aoTjNbbFwjQlBd4ofUlcu5zbclwkzAX/Z5GEEbNYNlG4cn41+tSPs2l2smLom55EfiWn58WHkKwfnlIVHOBJNd73F03WEy+XCBMClfgasd8hTvVL9QwX1g9TS1iqYhE3U5yhdN5bkU6lS0w/QBMgOFF2JqIXujOJlP8heFaZJuQgYPXr5VkOsmAr4stidG4yDCeZ+HcJTfvEoYhRyAGxIBLFg3eNm8t4nVaAY+1HDi6xIOtCb/nD9mJyrzLxfYv1Vj1tzSYhn2KCtpg2ltXDyoNcWY5bogcxdtWzB1sCnQq9jTdKZFeioWuEPhRTVxjJvIlIeAxwm60qT9pIW+zQJJandF9/pvuK8LmZgHnRhAjjAGowFfR2/ewLPK3lhwmDEmuaCd+hgD295lt2FZKOHlCe3Ve4pFI3/P6sskoeV30UM0ddYhddzt46W+miXIQtA28cBgis8ZuqK2pS6moaz3Ye9MXgW2AxweaKdXG84mTkbJK9CKGz8/G7lZmWDcppWK2kK05p4J1reatbNSCPxwvnM63gjR9/kOc5ViqJ/wKuGDonso4H+Ji8dpWk9cuEJ3qiYeo4T9mfhSOAb1AnHBCF9ywPXsA2emhEZIV+248qXALQuzErjg+I1b0OnzCnMbRpY2UHL+BAzpKTvcu7GtuvC3xhYbmjlZDOQ75LAy2mYCOpc90OCJWm0JH+jmYKafNnM/gfjtNUpGeg8tSmsPxGhYqSJA8FpC0Dljb1izE6jhruZG7bmsYkVShFVaNNyszWRZWrIUbzLIiYWIM1AW6Icsx++oUjA6/WW/Lp53CPzKVUFsfh2j7VrPyqNbkgTccm47O8RY13+m3+2fjD6FNmz4/4m2XXF/Ndf6YNvzafl0J9qRUsckmSdA8rR6b1Dx+9muWPt0jwq+13B3AKpeBi+e/HLyPb+pJXYtIp/2wfp27KWWy8xlqWg+/BsZGYP94YjN7tKJiPAbX1vUuwan6Sw3gUWIYykB0u6cJTn2hMQRjOhynEdwjGKj56dv09H/UpHbvcB2qdLL1ZZLQjBcZoyJ/t++kvPwwhF4DYrsGC0rXbTngqm8UDgj57FE/YPPMUACIxUNtj0UN9uWuQ9lr9eZYL6/G67bDsdC7OD2//X5EnBPxHP6zFTnCLb7ZYTLpDuIA781OitXp+4X6MPz4htsEBXxVPoGf0d/h+nwHV+0YnavZQ/kal3DQl/aUc72zSFTBi/S3ERPyrxgkr+nSvbfU29pPw+pmcuqhAkg6pq/01aNKYay/piQfcmajy1VmpOmq6PtD2EKZ0zZzzfX7BNj2MRPNiegyT17aHotH57jaUoSm/viFWsJ05NqPu8DSo6gvhHUEjmackT1IRtT7ExWy7cpo+HBbC56Vxa0riduvSFoSjdhafMq1hA3LQMO2zZePmNyx46Y9I9jfh7ZAjc3bTTgvWEoty8sMaXGKMkY6E2tKo7lR1OxecWhQtfAUDdrcgOpfDohlKu6TzFgUD4cDG8OizqxSXpM07bZWuVrD9WSuw4JYTGTw56Mg8z2zLsB38ybx8l/nArJxuoyOrULG4cA5CO1582aYc2c7BB/gK3BrQsqigChR9WurSaxx3Zoi/iav6UDhFgwL4/7agn/jmsztZLazwIa7erTjG9gw2MYMtB/Q/i5Dl5X+qzpFVrvFGD05LKpSMeOkHkRdjltP8InHtTtapr2NL/ju6B7uHyFGi5vGHKAfP4HEqSFh3ql/uuw0iOPt3aD8DsLmUbbnUkW07SqR/+30Z1p+kPQ0BdvB3nH+Atvl4nVuZTglgv2WtZJ4q6SssR7Y9I4vMHWIvB01D9dBd5ibik4z7Yzo7J1fXuhBCDqRWYsj48yEtaZP0hylV1ut9Ke0tyqhUU46vY0wYPHlhf6IuLSrDhAqP2gLQ/pOFSjIf+FpkXXZUeq0ZRv48nMCXGCIqVbJ48J7hd1You21+T6lTjQ1DMDSLUmPe+u/HEAE4b7GgCW6fF7CMMc7m51PylXvHUH/EbAFJonRvLmHNWrHDS8qNNueZVniwJteXNCFRl6+3QqMLMuyBEiFSeQ0JfFIgT+Ml9Oq0sB1cqWNr+QyDftNyBxStI6p6QRHb3Yb3Y15HF83xa2dx3/BCv5w7VO0ksyvzvOVUM/sbxINmwTjuAAEIv46Z7eTnnKAPbLJapsasW15hykiO8QltHybErgXTpOnCdLLxsfmtMzyYOOUGl9fHNtO2hyw5Z5esc2nmkSiKTAYe8/nbgdpqf4gVSPaa+8tmq6WwcsxgHyP9N9xwE70CxaQupCKzMuCyI3YzehKhRlmOUdGFGSUk/0jmeS4ez760s7VRDyI7QlRMspUiW9hCTuZWS+4MwRhRT2Rz2SrTe0yO3FfPUZqWuYaASSCAgb/SLJEIGK7QRT3pYijfPkVZJZSWsYpxjeWB09CW+2a/9Sxnf0YDj6tNBvFq/S3ctyg8tMjqSPUOpHQOTikTuPDGOykdzsTiTqX13w1D6sUJUAdO9CLOtVzdwuPb5w0rXq2gCF9hk/Er0AdHhqUgFgkaxRjFYUSg5/jMYCQKCFTydXKFXxzrCRsqdLU6iC3mGw/uJtI28BxzJFawaUEE9iM8ho/CXG0pvKuqiDVpGLxZqriqQTsTYJdPZASoHsFvcgG9WjCwjOjJlKlHqW6Adm1X709WsgNgkzeMJX53q7460HoJVDZC+DDSTXXZo8rYFCEFD5OIonBkZJFEQVvF+Ogfcuj5L50D5Ksd+r/TaxkCE/YWKWACcZJsxAJIrsJnjX6OMIJXczUSiyRl8GxMlfTKLnI3nCQMiPKhU/NWSNUo3VXRYPOjsT2C18lCkVy/VyIS1GZ5btNKAs2d9aAfo81I++90m5tFeO0m54q+WFecC6Fwco9VmesEEbwaqpcypwYldXx0n2XyUzRkx+VL1rq+rEB5vfliwIB602JmjUgbs7DB54OuI1YcLZz38DrJOY8Vgl+VarRdbXs+PlgGfsoIq5N0CvMUEHlISiFrwIokdWDsXulTdL+qUgLvp4Sepmlk1px2jLjXxmRvzjvQhGrePTLVJ8a5Urb8Z6n2aM7tc2zjfowZL/x07TlE+ukb+34QkVuLJfEvHaMRokK5YbP3w9Xr3JHayTryajd9VMqH3aXnKcB+2BxGv3KOg2x047nNWNJGlYqKnizb7d7ouDTHJpeW6dZCEnFhnjqEgq99l8j67gOL9/wM20Kk3a/advli9mwnqq9vRPflArdcyk6ddGE4oAP4KLsPv35HWNkgguaLCly53pP28C6G46MFaHEi4xmJvYis6I0qFlSt0XhcFAv2Fk781Xh9lCX/+b3ehBL1+cRowKJsbUKFPZuCj7TCyh1QqRyOtMCX7K8UC2XSnCMscoKiZwY5vToUcLypH7gpR7BgYar8+mRI29s+5aeordg2qD2KAXZQ0MQuT4wsNuhsgWMk69n/VnfGAovInFhvcH7bsaPF4EoVGGz1Rln1OWjsgqm3hRkZMpPFbX418EWS/p8PW0q26LYMLRhv/YXFHSp2jyjaAfirJXFRSDTzUSGl+1os0fcgK2jZlJT8/FNvLov2miCp6YhgAYEZu3KjSru8ZL2TtfftPPtSiTmascgqLbl094dQ9i11VT8LsunmNuaHAGnnLkRYJf0WBvW7KG2B33aizeb0E9C+qVJxX5mWRjj5UEIQ2YLm7kTlWknXPWr10HWyBPDnX2Q0D0L+9ezSFse6JPpKQPv+qveKlvOsOE4yxwMWHngr+iX8G8kxdT9tf5aFcRPs5Luw3UQimH9hTeKvlf9jwa+Zuvk+riaQ0X7r8fILMn+O/2p2YDBNnSDfnG5LRuowU28USpbSczaxtoR+//IY3fx86xoEEJP1oaTulgWfdz21AT90UVzCQJ/OgyEx4miaCQNCxUvPSGNSbcbyapDjkuFjjSgHry0Qrnpa0bKI2zrXxxYxNAhq+Sm4m+Y4OrYhKqWtsJC3uM6p1Me7GTl0ddrffm3rlf6UVsRQ6N8n7dmx11zz9wFj6+G+lRMXrzXtYQ8CtAbzISmx5I997Juh0UEwFA/u9UhhSFcN/+tnIvJiPoCKXhfJfpEWfs8bI9StPQVHLmf6FgTYhP9CgFH6J0pcnnP1inC2PmxrczKrgk4sxnH3Et9rrj+D7g5sGL4nqnVGQeEz61+yTG4euDdnCyGj4ySzCOwmKmMqfkJpEQ25cyyv6SA2F3JgVJj7mMb4gzBcXYRqFYNvU/MGdW2iUP3aMyRUNQm8ckIAIBAANtoYpONAqvk9hwXVktiRhB3fKtNBQB0We1N4Fk1VYDLiBfr/Unae984QLDqlx8yG0V1oBV0ULilUGGEXbqEtHy4r4qBfWVvymc8bSTtbYaKUm/AuVQ7P5StnrzeznS7gm2ISSCrfLRrAvZeJRk3rjNJ9d8+armoI8v9b513AISFTCtvSfPVqQ8JJM+QewAhUiPIfpaMUuX5efA0/1JZut1XclQ6oPOKuHCBwN2K5LwuR5VBtsKjjp/1tkZYp4YP7IE88AgW1zFKnVWrynxOZZ3oOBFwn6P30JqEiGzP1l++FPAJ8oYxE+3lZWQgmGFR29SF62LumgT8sIEZ3zslEO7OZi7PVqatm+Pi49e1wk4s1UuE4jKJBf5t2/izfqUXFtjJiOlRYPaGXEUj/hB/veV8NiZOT4Xz7tFbWqUGwX5bC8Jfia4a8xRs8g06qI4aqQxrZ96d3ncRaJlUFVrtPPIMkv7rc3rGOSzae3CTKEAwlwx6kGtns98pTzhmYmvkotwW++HyhjZMjUOvqN/2lBO4TCxZ+nOFLWBORNbCLS5vreXzjm52chw+GGGry/WfUTu6kOdBjMcjIh0rTWBfdF5xUiyOinOITgFdgx0n31nSPj8UwNYsx+sD45PLzbBgDqUQJnX7Sdp9URafbqnTbhDEcZRW+VtBXHBQhIthwAtQJ5emVADRPOmvDbIfgxxx9eVSGs+EpjJccoKmhI2p9yzk1MeSpHankL5wYH2u0hVoQGX1hS/q9S2CpJEzo2YAci2zqPZi2zbXHOWAvOsUwtNvfU16iYkd3LEo6cSilzhqWTDxch9CSTg/WZgVUK3zSgso4w5PDE/mjmDfKGRx11lvkzURzaRMWjiWMu7GN13nJ+PkqOcAgvDAC0RbSDMInnDLS+F5n4Qpgb0TPAq+RUXSL9YAKuV5yMxIek4oM249a39Lr2FRb1K80nu74Khh2uXdqoKvqvYtKNmOSMhxrTG/UqHTwqAxW1rB9vkMmkU0TKAAgD/q2eYbWxHVsWxkl/pddVCahemMnuxkZLKs1/Evyf3L3q/bo7RGXYQ6FgaYPXLFWk2+5mkFcrOIaurq6bxt//VnAl227bpbUiQsy8vLFcW9L4WyurwYshfVhwe7XPppvE+amUAD0DB2aIDVgAZT9FHH6Y1kRrALYEMgy9OG9Gj04FGEkoW+0I/4VFqvEBlTqNllG4k67tEnJEDLTRSHUtjDmtCV4L2VMNXzUlK06WljTSPwlh3ucvbUQrJs4KM/LrWbucySzAsS/zCt1c0m762FZPsmitmLaDUvR528arAWtjJq8bZIr2JlWeqFk1OFjzk/tc+jQ3szLpBCZvyy+WBOtFANuXSRpJ8ipvyQRIOBkfw+482fW6cRc3veymLimCMdgyh5XChqYhl61DOkHUwITNd+O6MSUPohZR881MREN7CDwZq5QrFqpCHttnchzm5BNpJSBe3uYXFFk0SRJFPzru7wVuJjLOmW42Z4jDed9CZWjxrTmctYV1UM+itAowplbmRzdHJlYW0KZW5kb2JqCjEzIDAgb2JqCjw8IC9CYXNlRm9udCAvQ01SMTcgL0ZpcnN0Q2hhciAwIC9Gb250RGVzY3JpcHRvciAxNCAwIFIgL0xhc3RDaGFyIDEyNwovU3VidHlwZSAvVHlwZTEgL1R5cGUgL0ZvbnQgL1dpZHRocyAxMiAwIFIgPj4KZW5kb2JqCjE2IDAgb2JqClsgNTI4IDgxNSA3NjEgNTkyIDY1MiA2ODYgNzA3IDc2MSA3MDcgNzYxIDcwNyA1NzEgNTIzIDUyMyA3OTUgNzk1IDIzMCAyNTcKNDg5IDQ4OSA0ODkgNDg5IDQ4OSA2NDYgNDM1IDQ2OCA3MDcgNzYxIDQ4OSA4NDAgOTQ5IDc2MSAyMzAgMzExIDQ4OSA4MTUgNDg5CjgxNSA3NDAgMjcxIDM4MCAzODAgNDg5IDc2MSAyNzEgMzI2IDI3MSA0ODkgNDg5IDQ4OSA0ODkgNDg5IDQ4OSA0ODkgNDg5IDQ4OQo0ODkgNDg5IDI3MSAyNzEgMzExIDc2MSA0NjIgNDYyIDY1MiA2NDYgNjQ5IDYyNSA3MDQgNTgzIDU1NiA2NTIgNjg2IDI2NiA0NTkKNjc0IDUyOCA4NDkgNjg2IDcyMiA2MjIgNzIyIDYzMCA1NDMgNjY3IDY2NiA2NDYgOTE4IDY0NiA2NDYgNTk4IDI4MiA0ODkgMjgyCjQ4OSAyNzEgMjcxIDQ2OCA1MDIgNDM1IDUwMiA0MzUgMjk5IDQ4OSA1MDIgMjMwIDI1NyA0NzUgMjMwIDc3NCA1MDIgNDg5IDUwMgo1MDIgMzMyIDM3NSAzNTMgNTAyIDQ0NyA2NjUgNDQ3IDQ0NyA0MjQgNDg5IDk3OSA0ODkgNDg5IDQ4OSBdCmVuZG9iagoxOCAwIG9iago8PCAvQXNjZW50IDc1OCAvQ2FwSGVpZ2h0IDEwMDAgL0Rlc2NlbnQgLTI1MSAvRmxhZ3MgNAovRm9udEJCb3ggWyAtNjIgLTI1MSA5NzggNzU4IF0gL0ZvbnRGYW1pbHkgKENNU1MxMikgL0ZvbnRGaWxlIDE5IDAgUgovRm9udE5hbWUgL0NNU1MxMiAvSXRhbGljQW5nbGUgMCAvU3RlbVYgNTAgL1R5cGUgL0ZvbnREZXNjcmlwdG9yCi9YSGVpZ2h0IDUwMCA+PgplbmRvYmoKMTkgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyMTY2NiAvTGVuZ3RoMSA0Mjk0IC9MZW5ndGgyIDE5NTM0Ci9MZW5ndGgzIDAgPj4Kc3RyZWFtCnicjLcFVNVL9z5OgwjSSHMo6e6U7k4pyUNzqEN3CAoI0kiLNNJIdzciXdIh3R2/w33ve8H7ftf6/xdrAc+ePTPPntn7mf2hIlNWYxQxtTMGStqBwIysTCx8ADEFNTVWNgALCzsTCwsbMhWVuiXYBvhfOzKVJtDRydIOxPfEQ8wRaASG2MSNwBBHBTsQQNbZBsDKDmDl4mPl5mNhAbCxsPD+19HOkQ8gbuRiaQpQYALI2oGATshUYnb27o6W5hZgyD7//RdAY0ILYOXl5Wb4azpAxBboaGliBAIoGIEtgLaQHU2MbABqdiaWQLD7H0sAaAQswGB7PmZmV1dXJiNbJyY7R3MhWgaAqyXYAqAKdAI6ugBNAQ8xAxSNbIF/x8aETAVQt7B0+s+Amp0Z2NXIEQiAGGwsTYAgJ8gUZ5Ap0BEA2R6gJiMPULIHgv7jLP8fBwbA36cDYGVi/We5v2c/LGQJ+muykYmJna29EcjdEmQOMLO0AQKUJOWZwG5gBoARyPTB0cjGyQ4y38jFyNLGyBji8Bd1I4CkiArACBLi3/E5mTha2oOdmJwsbR5iZH5YBnLOEiBTMTtbWyAI7ISM/EBQ3NIRaAI5eXfmv6/XGmTnCvL8LzKzBJmaPcRh6mzPrAGydHAGyoj/7QMxIT/azIFgACcLCwsPCwcA6AAAuplYMD9soO5uD/xrkPXBDAnC29Pezh5gBokD6G1pBoT8QfZ0MnIBAsCOzkBvz6cDfyJkVlaAqaUJGGAMNLcEIT+uDjEDzf6DIRngaOkG0GWBJCArgOXh55//9CE5ZmoHsnF/dP/rjv8b6z9WUVE7N4AnIxcbgJGNkxXAy80D4ObkAXj/Of+fyP8b9V9WZSPLv1mxPC4oAzKzA/D+hzzk1P4OAMDs8ndO0PxdMbSAP7YAMCvaQXIZCKB5TH09Fk4WE8gv1v/fBfDXlP8r7R9W+f/M/P+hJOlsY/OXA81/PP7XwcjW0sb9bxdIMjuDIYWhYAcpD9D/+GoB/1PPCkBTS2fb/xmWARtBSkQEZG7z3+MEMFs6SVq6AU2VLcEmFv/Jor8HNB4q0MYSBFS2c7J8EB0AIysLy78HIXVnYg1RFifItf01BoSU1Z8XKwEysTN9qD82Ti6AkaOjkTsyZHdWCOQEeEL+QkoC6PZXggOYmUB2YMgkACRKb4CZnSPyw/WyAJiljGxtjR6sfxlYAcziQBvwo4ENwKxuAXxiYAcwyxvZGps+WjgAzG8s/0GcAGblR8QFYFazNH+yATckQntIqUOC/q+JBzLD4nEKLwQ6PUJWCEclW6D5E44QkmZmjxBC0eyJP4Sgmc0j5HhwfjLM+YCfjEMYmtpBXgenp3ty/2O0ejRCeJo7PpT/PxYIVSMTSNY8nhaErImR45Pg2CBkjR2BT2axQfjaGpn84QTh7Phwj/8YIKxNIJlmY/PkJiDMzYGOthBhMrZxejRDAjB6sjqEut0TCCFt52Rj5GTxaIKwFpF4vM+H830CIXyV/pzADiHs5Gxv7/iQi/8YIZQhiWVjZPtogpB2cLYDAyH8/iMB/4xAqIOcbY0fRMT8MWz2v84eEqPjowlC3x7oCHlsnsyGxGBkC7E6PTws/1h5/97tz604IOHYQ548kA3Q7ImV9W/rv5whkRk5PSzhZP1ohERmb+P8GCrHw2XYPa0SDkg8Fu72FsDHWDi4/iJuaffIkAMSy58nyQGJxAPoaPdogAQB6R4eiwfCHuz6OA4RT2awhSPwicdDtts5P54Y50O+Wz7JL04IXSfIy/IPhpB1giTgI1eIWDAD/zgITghVkOVTIjwPMT+tUk7eh2VsLf+0crH8Nw1MIQ/toxnCG+jgbPRYZ5AnCnJdQKcHufvTlf1x4NEIicHokR4XJASRRwShL/qIINTFHhGEt/gjgnB+TG1uCFfJRwShKPWIIPSkHxGEk8wjgpCRfUQQLnKPCMJF/hFBuCg8IggXxUcE4aL0qHkQLsqPCMJF5RFBuKg+IggXtUcE4aL+iCBcNB4RhIvmI4Jw0XpEEC5vHhGEi/aj4EK46DyivwTLyMQaCP6jhHjZHqv7zwH2fyb8WV28D3Vj6WjibGtmA3zMR17Ov8QV0j8+LXJerr+X/3NtSBCPZccLCcL4EUGCMHnyTECiMH0CHzLwCXwomycQwtn8CYQwtXgCIQyfPkAQak+eARYIJ+snEELqyXMC6XGYHyWR9eH1Aj2BEFZ2TyCElf0T+FAKTyCEleMT+FDHTyCEFfgJhLByfgIhrFyeQAgr1ycvJoSV2xMIYeX+BEJYeTyBD0oPMn2qY6wP7xPQ9l+2B1F0BpkbOTrb2hg5P+H28ESBLW1Mn9zHwzNlagmEPCmWT2J6eK3+56VhfdAYJ3sjkyfTH/TlX43Lg8D82bqwPkjLn80L64Ow/Kt9YX1QlycNDOuDvig/xdz/bmJYH2Tm320M64PaPG1kWB8E549W5kFznMzA/3o6WB/EB2T8rxAfNOhffQ83x5+Nz4MUPW1tHsToaWvzIEd/9D4PivRH8/MgSv/b/TyI0//R/rD+T//zoFR/NkCsD3L1ZwfE+qBZf7ZArA/C9a8eiPVBvv5oglgfJOzfXRDrg5L9H20Q64OmGT3tzVj+aIRYH3TtX50Q64OmPWmFWB+kTOkp5vifZoj1Qb7+N0cf5OuPfP6jUTdxdoT0HuC/vqogpfRf/NdXMxDoBjRBnp6wM+EPsioPargoFSF0ZVwbRFhYavrwSbvzPSf41WSGp4U8YpLUTwdRQ9MivP4E5an84JEJL0K6vYshN73WFP9f8dLpYCiJzhXRA8YvvZfHreZAElM7AvNh7oQPKxj7USrEaIa6cLrUMVoTN++429g3itezbpucNnR4twG2gJwzdLJ4QvBHkbSmbZgwbTLKJXCfth56BKajbqKzuozG8C1MmGXEuWhDOP6l+4puxOe++fmelK7OBPajTLn2Eq6iYCx8bA9ZsrT+6B/TZ+RhKvletOr2zwQy7dtrX6pYnB7mtyywamtHMDqacTDsA8omM+9wl6SiKpdD7tlIk0mv+NtNNiZ4ghmISEtjZUziqrZLUY4YbrjOc+qZpaGYo7jx0TmlvgXzs//G0j5wGu4tH3NWT1adSn/Ly7r80ypQqSb9HWe5AtFi4rc8jdISynCSkEgu6T6GK1gHZssFrYjp1p4tyzFT44YMCvvpzNtnk4dK43fxJJnPFMZsLjFiKuiGTb3LaF1r2AraO19DC7lJBmxjviJY2EFy/D2oTREMRGIHa/miLOlI9uYzoxaD7vhjFobmUL5827yBmTO1+UEAfT5jaxipt2Gu3icr+52CmIJReWHdeI3HFI2gNXrzbcVg4VVjAD6fFoaeYgsGP/EPepae46hTCgZE5ivlL/r539Oc1F8kHYRJ+ZGgdz2X0SHFzaBlG53wRSt7w0Qro+NCfZu9ViRrAIwuuc5Ft0mX9tg05lxnaDafHKCrTXl7Hz/utnfFmDn/bqG1R/UDnvtBXYatHUzMqAGvh7L378+4jGog0XzuGNlIHYF2np/DHmNmY2/zu60m1Nf5Mtsz/cp/dknCMhLihrT1HTMZH84WovFMryqom2oBYVV/VBgfb7RNRZ5BZ+XRp2tkdhVkKBX6EKWNJ1mJ8DyDqxNDWgF7uS6b49Y28BCIKtMF8ny/jJozUYDbfic4Xq1Pn/h+PkXQUvuqEqciFF6RDJpymsls9VVvPNWp2xAmhl/PWZNYXH5brba0nKw/qrvN63Ok3P45+5z3Mw3+CwfNo8Jh1oLaX2qDI81mv0xdgN4qovm6fnQdCaK/UI6giBHcjsJsubr/OfST2qKOfCKPMRjt1WUYcoymsR5vJXOeTQ/6yWnvzpBfwZBhd4c8ZwJ3mxCWDCFL+ZB5IQHt+VQKGCDSgQnjLN251BEvNDxopHeKg/RK8eym4LRs3y6LNW43g7Rw1ICNvsqLo9w9XkIhg60KmkMDsT2LdHqje3s8MfMjHWXbHHwua0TKzQX9F7ao69ff3ZcD0WjpOZ7Vle2UNY1oWWLG2oi/yABfXHJSahq8zPhYqDVIod1EbfI9N33MD+eVa9ptbJLsXiMmQ4MMWgSvDJWZSPEKr6ZMGK6iEFi/DlySy01vL8hx3M1dGwjUzwbMgFyHdnOq6kQ/0xQLuxGYvFT4NmAkEjp/N/Zy/lWeaOBRH5xLM3hFQpP5TKbQY/LCgsQhYRtzIRVvFkAdZsHnxKjBD/fWcNUHtRsOlaPN96WXU2CM/HjmjAntZPuODuOMB7v5uJISe5S5ctWvFE8i9N7fTFAuMj9NE866ip3rqE3WX7grkArugb52UL+4JuPE36GrCdZ3AMjMDXbPIyiFZWyqEp3dtiyGWkuMbvvq2WnNX8WMJcc7Mj43aqvUsSqo+DjD3Mhkblx4yVeU6Hc+/gtb/dPdGiUwZKPs8FdtskOT+yxy4wy+gl86J22sr/I7n5u0g47jOtI3ZMGSymWV43yBUDb1vqDj0/3XH+jQuFAZqWg5atnYwelXUHzVqC8rmcbTdFkJS1uKAdHpq86t7WiTI5SI6Be/t51/s+7zocfsW5XutfT1b4Ut/cxjSXbSBNGpPNdeDe/Xl+EKf25XO8eDDh1OG39sIUf5dsPvTfI39KNR4kI06bn3fHFzTrFauL2A2YaOC9WhHGCwaKvX9symic2wYMhAeTNzuSYWVdoPkU/Mh8bdOH4z1WNusJaBfW1vVTuapOhdoj9PJ8bREZ/gtFIOvUUPWVO9qx8RbGtYS1UUW3Z42avspFU6TzqHhCdyEpxeM/wBdrMyJ3HnWq4VX5nFmRyAsuMgF6G8MHdr+qosbDEtUEVBov5ri8/rprL9XPnhFHQWAy33DNQbJcUg2hxPin23ijsP8U0nNhEoxTYVsnZfC/PGxBFa0uvrH9/ghUIKNK6oPCjGatA/6Oz3JV7RXoxXb6P5OfmyDqeJ/2pPzheTqa9Hb1FDxq0fsfbslmPE9s2YL0tgLkUTfYGNvV651dJtA0TMbgLtuHfyn15sPpuUOSMvvWOQUZP8PfJDxpPAy9EGFyEz4gOrPFUEHG8fK4jVtisx8cXNEO8HnJdrY3iSrP4oz3eWzR25xE4zulXSuQFtNa0FFtVsnlB2fVLfnHWg9WQ+C4zSkfkPm+JTf2rpDwxM+XXcG00xuyRmsQoK4SOfJcs+uHD3bIVL+sxlUGDcFbsUorhJwJflxghH0guQjDbsZSAXYgoEBQ69nBUS64kpKnHYNhXJKx5UNuycNY6zrn3OpJWqnYEUOTuVhp+1JO4SZHchrG1fFnjfWolZOjwoPmVOuI4aohVfWdxsQCByRChn4Jokp3skG17M2FgusHjvEKDh6HrQx8K4pggfyI5Gfc8W+3KP8v0vHzHReY40OP2xXFLnL8trJws6vkoUutjJMa8zC4Qx+fxRHdCt/BHL5lg47nCjecu5mbwYxeeqOuwxuspgEy3mCIQyVW5j3RvjN2HLvxxN781WyU7Mx62hIKS+zokpl5NUijUR+jjKagNtyia3Uv9MdOFeTgGWIlzE4ZoLoaNsYlRDuYOxdWBfjr2nh5in4jC2HAGTf4w3vGZQfIeVJmLBcGOKv3eD1GvYesuxWSycrg81pAMFts3lbJf7tYCneRelM2qBowoAsWE9rFCs/iL9B5r3jb6NqM+IiNBRYiFzMC6q+l6MokbfudVYS4sgZ54yzcvVMNy92or0G/kdd1xi+YXZLL3KUZKc+1W0/IbJ9A9aryXz+ZnQZxxAFhsvD4eyUXxWgEHQec4XTdLMwFfuOzayeuvw3M+vkPKyejOqF8Wd5jGHjr86Y2B1zuFowqQeL1MmnpO5615yCjUL/upgDDW8UxsOj3id2zBQdAOIDt56NiNR8SEJ/H1iSLDXg0sg9avc9t7HTFWiPjW34K6xXis9ADrCgqJlWcB7SUpi63M+Rm0duNyTr8NAZpARejPZr2R27j40G6kmTzS6EEzCLyBE7cK5ifljHJFPacHH98Lkjj/myQ3e7hCb3Q2aDCSG1orbV4CBQX0V6ulufNc22INSxQLoFAamxiZY6D6paliaq2/kWLcUO8b5GJnSlRxNPrk4fOj5id6hjyvpy48L8LMMdnfoa3n5EUZ/EdVYZanYmGnV0HXXYnuyiW8Iv0ya8fQTPHLBR8M1Op1lokrU+j509haqmBoGbbDfdYybFuuiEW+jiuzWvWRWzYDzGbV18mvKDJ4cxQbnAJRO9VOHvJRd31lM9189aX2jb6LrndxSYOu4c51dmjvTKDJ22got4IC7/ZzfP5h7l94F5NEPzd3L5wjsNvDtDdS5QCmhMr7wtIKZeStTLQR/n7yfzYUTkzStHoF3XtijXu2UjlDL4Ne7UOe6FwBeHISR7kV1qTNCKfTT7S1kuDaDZUdjnTdioJxxlPHWjMnDiUUkNA589l774qO4hQ8WkiM5wZBGsL8vs4me7HicMrTcMpeeYaS0RJYNuVsM/LdgnR/fatOAN9m7zBXjUz7W3MS6k992zNrMmmdGvnfNBBBHit7eiUeu2yjRm0j5HARQB+CahatyNQLrwbLqnIsGn+HhNaodTvRV5cWnAkiTi0olqNPwhEosFiKqClITSnh1Ja/lLTcJe5NH5hrKGYhOkPy1YvjYasYlb6dwa3xvq9XCRJTfr5GC2AgAUkQKzvFVLPqwefWXz042szeM1p9JdjInVlE9j7dD7m80pkiG5WZi+y2bJutasbn2lkqPiyADlzKqNkAY1iPtU7ZaRu8a2usegHXGUp7sLork7zub69t9LrhxT9VvL1qo1LQuvwuWel0Rhw16SoIBnbQ0qdquJS6ZCblhrJ4SzUh1PIPoSHy5cwLlcRL5H0lWK6BkA34S2Jbp5ANl/GhKR0rrJs06m8vRqVBuiSwM343qHb5dXKEeZ5q4QylZBALg+ea9Ll8M5YF+rZhmzJKPeTbqVCkUw7blvRu88QrKvO1/rhnNgYzT+3ktUjPOAMP/c+tOdbQUfUbRIKIg1qGEpEbiWaEWsnd5AeP2yKsFac9EycHJpToH7tnySz0Kt1QkZqXJ/W85tbmgwtwq2oWoeOSVg9n4Vy2EMNal2jZoAd6WWKS/hE24y/DyPzi2v3ZLO06iQwNgdkQDFwdkx95EZt6BqowsWqyslthLPtFGB9hD6/Imf6nlKsMOnHfVJWAuCenUBiepCq9yeCN+zYMjV+wCHmQOtG5mYftTh37GyZ47ifmEVgiMCzNjtzEbRUjpdwkptswujQ3Tc09r2VoXoY74ZYa+WM9h4uv1GQtqFzFg0dwtFD0KtqfyIqApn0aUzgyB4ytuXgzaC+1RxMHTyY0+BWKoyvbnsHJGqwhfsN/1IHphTdPFg+AdpRmkG+rECI6jqj5Tyy8vpDD4SIJCdQOaM2djapL1yJMPKn64RrHlaTE0k1ZiQrOPc2B/vy0ShxlF8jaZohMtyVfwGpFbObFLRwucmQ254fLUdd7l28lmYu6brtwKgQcZOPe100gDNWW8lToM0QqXeMtLpJ+xzo1Lxnw4h+aWxP2Mx+uQ08+6gvZB/L5OsSMgxXewMK0oXpnOqIOE8lWPpUcWpVj00RshueB8uBgUol7j049HA/gHEdIoTaD1rrvf21LhA4M3IYpFHkRFDrfCAAaPdUO+5x73kmEhV9ctRE2WehgpEgeqo4TtZvpVgfde5+nlhJhJc9WFbFmzpPapw7kWKJw3MT4HYeh8V/S1J0o6L0aDhEivmWMZCquaUcC9PxA09Uqpu0XsMgNWjV9gkMTupIkbV3ylNVSa/oQ0Df0uu3zppjXSwnHMvv8Wa9puLwV/KuBlou2z5fqzYnOf0hpg4Cj/IYaAUibn12u0w24a5Bi/O3bUadUkOoMzyYvOYSpR298plIi2FLSHpTMxPlr6y3TRt4kx+Z6ZHwmRkbOcyN5LLDSbvh1MCydH0QgrGTvQewPXpdBv6VpznJ5TLMv3yvPA0yZfW62/IVdmiAqGK/NevDwnjrpsW8KE2VV8GI+V96BHDvxeiaGKZc5PCkxEvaQHFZVHFohMJqRCFB899GP13fykPvzF+Po3U0oNkKPgmOEO4QKSS8gOS90pzPlgmfL7rwyDn4GE2Z+ygwRZZHGTjZMZtNbenwfEsezFyQoYDD8/XSdHHUZX/UJ8/aO8nkn0C0ejg8knhjrF7/TXaeTGeVkmv4I7qGQUQ0nhb/odygtpMC3TEVSy0NNghF9hTy2T9AaDaLOOxexTctNbkTxr0hRPsX+iVX2CkQ5n+IyvJbvTzrWjo8oTtrJmpOrwWiEm5pWUWc7hmhvxrm0h8qZZo2pNY4GS0xce+RLWwR9VApN5OrSgrYjvpvg0428ctN/ixG57abwwmxRGILxSsX29Tij/zX4C4/yCfF5iUM9YaRRZKuaEUeQNtATOVY+Ya1CbY1vYz7SL9baMxru62t0kAzlRd6eNEuJeFWNCTvXujYqlXNwjJp5m3RDD+Rgtt4DCnuL78qvOUSTYaS6EqfDhBf6yIwUdf4s6/8uD0QHsewzGdtRWgyZDfOovOks1se58r7u0VwS4hy9sVCeH8QvPsMWOXDlamJcY2lHWvw9xbLWhWqrLR5aNVE+Xc9X97lvyJd/7fRKqNoMke7r4WoNZXyLO6FW9r+36bnU+eII+/h1eC079t+hetc3CgCrYBl/eH/wo0nRqW00iZOttmmpfSxJDfsHOQuWLNFzSHulTH93oma/SjVgznn8R9eB1N3VmOfxtgPZjpuFVKc8MR9Nr2xajOPI2zzJW6+SBL58td+y33VIFC6LLmkUzR0xxsaW5xUMGego/z6R+Q0DFeWFqC53u1+jOsFIfFLhEZaZ3726w7BClIW+yIPWGKaXlrKpgInPA//QlNomvnWZJStrnBovwpG38VktSC36JX97WJU0lTT+H4cgsZjVXSeROkZ49V7jQyDUdLN3CfvdmPb+CukZqrzaVTMGIYVsoEcOzPPbeV6OvJF9zzidkaYqaNXqbZXVs6I3Yblq/ghK6UqLPXP/Xfq56XenLYjFa0/DFdp0QH7hkBmseug8yq8smJoEsp1kKe799EZvbOPUuQYRwuXQb4wJcqKkSR65CkTsxxop0DVeuWlEu+930Mf0OWc9mdYBZBENjWxSH7DY72Tll1xmwvPbz9FKCsMRnQpUKo9SEIsSGNHa5cXIHryoM7p3mxFpG2NpNzlBqghk8iwdhv6anxPSxjnJgX207/6LxHc/4BO+OH5m7dlXrvLVnW0n0fL9UWTx5b1kb0Rx3MgU7ff34Ph5FWlKZ54pGvLuMFV1TW/ojp3Qngg3CqoTLOveKCN4euJqInZuMy/xVEnQC6IZti0Aj1D9a6L2NiW/RADzIHXcpDk3lwp++t7E7iuM89hshVI9V33hz+yfS2s2abS1Jnb6Pics13Cj+8uZIoyljYRSIqj6gc1fvdgrPtqLhpn5r/UubidDrOimcDQn1ZfVuHhI/sv6Xpy41GV7OPaK+LQOw3/w9CFG05Yel3CTCXTpCDtYyXngf4BPyGXkdvlBpq0mW91/CC9sjJnbbGGb1a5mk2u9rft3lQEgbPC3mRc+TR8zQZWYtHF9S1KAbQ4xYiQeVNtGcGJzIdGqRRmQ+7XdoiUUzN0JwEesyJZPQ3/ki6/T1q6vKeVppqaC2Z5hcdBZjuIQBr9d5PDjzgk3ExDz2Rd4WKarqtVBIM3UbSec1MMtyx4m2v+dPvW1EpPmxsibSepN+znBdbhDJPXa8+9NuGGpP/bfJ+7TONdj1VW56LNctm3hH39mamnAnXwBMcEbxVg32MAvNbN/u616T0/KsZYLmJX0E9dKTO/rvGs5eAD/6po/G5sjuawfQqRvqRysGGFmx2P7SMmN3XkiKO7hmx+SFuK/N+z2nxu7Y5UkJuCM3zFNSuysSGrZV4QxV5HjKSPUuDvZm4vzMWCxOM8oAfgLF9zxwsbns2jJVO61Ds947mAgy8WYqZuqKiC/lOHHKUD+jrBLFvnA0mKAJf2EVdZFIzcj4fkKIwDVkN7ACrBr2xbOZOeut6Fh8GSMlh0+exwScR4UOG9vUUlYOqebe4rqkeRjefEqnwUsVaTp8uxV9WQW/HX+L8TkbCvyI2CDuUfykJCyn1mih9BhE9fQYz2HxjdI8aJxDHqbE4N1JXM+obdJhmuyYzMxh/RT4mRwKo5cE6sfol4kzgbzyRbuSktgxPhfY6y6nbP7paPdhSxOW6grLQUXk61j7mdBYohFTkveLPhfkDZM5vg4vKidKwn9f2DK3vdK8xrNyLjehHN6Uq05cb/xxhMLwQbOArMUxsVYS5dWzfjDHTgMKj7g0cujBUJF2A20KW8m6DJOYYpF393jid7lDEhxwX1TSYkHZ1/MdYRtzXYSb+KOYXHAnzQyw54N7q4SgTV64FhR2oqHQa8n0ZXLvOJ2Zlsm98N92ngHFvvlhx6ZhYgFDMBo9HZjGIYJ6QtWsgXqLKWlpbVwGjLgxbbcVt/Ii/ffliWzxlf7iyPx0YRvKnzKFd5LRe1C3tnYROrcz2rmpCO2n2btfRr+m0qHYtArhZaya526C/TCyIMWYBbRvamnWSpyOtgjnGS8MMM8rcrKkfbtxA0dwT7V6YIZq6r5+qQyNHc9khOpZWP6SBc8AwZ+AsG+nH8haX7THXn42Kt2xUCJS7YMSpl0LEoJNYZjw4S4sWN2kP+589ktgjwlXrZbI9cx31r7PX5JURU18uW2sbent23blqPKGcSYiZKWZQf7nE40YX/SvsWdCfjkU8fGELyie9Q9YRkVAH0UTcaAbMyBKcXxZ5n5xb6sv8J6/cUnwuBgedP4rbLhibNgjalO0//RuJeW5bkYPBi9aHKV5jlZtxo0h/L1wAqNWIQeNM/KK2XRcU9cESXLRRFKzQU5JxZVUMR8KvwsoqJOkhK6k9433UYLF+jHJpv/wEqYS46cli98Nul/Z3oxkENl0oNbQOTRDuYsDVDPrbJKF7EOQaNa+ChMk8F/L151LIfhUGNvYl1nBc8Sdqp3vW67dlXMrXRQMovpailEw/UrHAGbN5T7f3mbquC7fjf6MaJvo3ghwOpNw5mwdzQ6rkvDGvPDH3/NXHfzeagkwXt6nnpefPgGVPs+JKfwMHrUX2nhVoAWVezpfZGw2zVYi3L2bwyCOED1RuRNVw5oe2XYsx1Az5Xkz8O5d5YJn4THLPRSz2waPLGoGzfbHl7h417S0krUNR1uzJr772XrWVJ/OCIwSf8YBmZY3DU1kVD/hsoD9g1JkxsyGA2+9ffVjr7PZErxNx2DzJvfx7A3GCW6+lnF5/8jjOEodZhchxP96OkvioYtvUGK5WiDMHb98PwPLzVwCYPkBXedN4TwNr3goIUN3OeRaH7b09d2ODtfVi/tYKESORkfqce73iaO8/XW1jQ5qOJlazlft9hgpccFj2xeZxm9NPkaNy/tffj+PktREHpxxGLTrPpl8A/clKEKX/X3s+98A/X5HXuYlMJGx8da7kppgrhgtc2/ovUUkFmJDpNfV5BXv4/KxO6RFlV8S8X6E8uvdvEDpDRVKkp5+Dg65+JJkM0AGVDTI3HrzG/F7XdeE+A/cLIWaBOuIYqgNClBsBzzLnq3ll/PMA5+9yUZxdTx/hlPFlTTDyJ03x21hHq2Vt7I193UVsgKJV78zMhv1Z7NdOdcAuflLBvdHRGdNVp64gLYW6oEI/w7imJDMKAeLgDbBV+1eMRiOp+eRXWrBPUSv5HdaRCo5HfNiP6iyxFX+bIImqRvjz7QbVvNXYGHS+mmMNUQ79prNdOb9iVTzy+bPNzs6JolHzj0f8Rwz9Avjbo/VqNZh7lHSGwtkh5PsUJm36gPhuN+GvHjWOjDbaOWorBQlG0pAOwV/kFHHU9fNl4+hY9dGXYkHMGHOJoRXgw5uSnmRmSueC0OQOWZMfIVOaL0AlQN2cLrgnADgG0jDkIbRccQd0YfO1EXMIG7rjBJ9y+naDvTf72o5k2xlE1H7jM/9E4+hhYSkbvrIIn1oFurMyedclHrCe8jELi0ATuI94Sdkq64zznsScnFrbuJ8gUpkZzhZUGMN2fvRBK/+0++MJptMviueMc3F4jkbPgl9kph7JXfyZ2IOnOtHidscCaRIb42diR1MzT5CPiNacLhJq14Foa24VBCXmYiWEPrzXAT/HhWt7gEx2GW+RdWqNCqi8K7yVOzU832Ff8i5rRSqO7+bxtGI5B8qcCUhRfS8pS3U+a5itK8HC1mkfTerxI6M+Cfy8agshyr1+zIawxGNN3sk0TqW8Ti3DeO3m1fbehbog7hQCcmmCIJxMXF7EwYKr5nF9MlBhJnjgdt3NfNrC6U1wZTmu8HmWu81+lmjo5kUZAQUGvq7X4k1WGMQjOWmyCSN7EeRHcMg/QjxjOrRH1MN/+5+UxQZCrXyafxMaM6uh/BYJt+FqNFIhira/QVFuuYJyOisqd5hhszfJHbpyja6qEtKbCNhrj+B0JpDS4jRcLCBX7r6Y7kv6YglM87gFsnWcR6soOhYj8gHN2isTkeJfe6XbUz0UDfft2+qN1kYJy0YFr31UpdiCL3v1gdiglvzfg06Eoau+nSmMXyJ2XMd2P9CWhdSW+gp8VEHiwALNbsC3Xa+mEBmcskiYfzq+Td2XBFv+U9R6xGo3Mk3Y1vXdqVNK7dd4StyDb+AES6C3Cl+sF+f9e14Jcwh5b92zY5VVFRFlSBgSeq1D87cq+FAC2uozBBwbB5A71JiHSbppWyZzfw2AdtcWonZM4KrPPsuoKQRAW7KMrs/16l8EoF4/BhhYcAav1SlTUwdh1H+06KUm0nhJyE/K5TzN2Js5Enqb3Dz38FVz7ibdySnGwcOf0620YiQOc/6CvyA8HOm0umK0d+gBkwHnLgpIe3vq9jS7e4keaZewnSy83q3X+A8J4AkPPirGpf76kjEayKbHorGxIz4cpsdxmdLRvT1ym5+7KAKjDM0z4w7N7Mxtd/z7+iyaKqGS7zgu+xQPl3HdX4MJIQrMA31TJ8Cnn3b3it+b7NEGd1RQIuZb1GVR1qz3cA2hQlya8GYrrXFqFrYOxgYTfxqmcV6q3ZA7RhC24TMRSrEjyqljKn6zEon0/dVF65kuSQxQD2e2xsn5XiEwmcHSccg+etbK2a1iMhLvzOUjuasQWjXZU9q3HI8D3o7KMsjZNHz8pHOXGBuYhbXBkXMa+sLpSiLsSOvdwwJQ0smWR8KJ0NrX3kyDQ12JrHQKPGTFnPXuKSSdx6hmMpluLu/FxCitfFOBlm5DLXR0myjvN3WY0OfdJyEPQMFoc2suBFQhZQYeHfqE1yt662zHZ6+rKILG3/XYqdSLM+34LFmPWaQ8C5giP69dgsepWPtW7vnL9/ykLVlnN9eySezX7HaYGnZ697WZNV+Ky9dbz3bMif3u1XulnbGMet3UsjaT77eRLP8NdS2s/ipFvdd89L3+sySqazPhSeJos9qo8UEe9z6KcQuoYZovAdPosyXP7t+/mi5IyYeMcZWDyd4oAnM90VB/flti0DIqLveQKGVFXEVras62YXb1V7mja6bHBFpvgYPzX7U1t1qLJVbKm9tH7gbDzqN68NC1DB/oyvV3sbz9iz7q+TbYJ+k8Mj4vRzdjy+Tv2W5/khU9d6sIGrD8aUEp1NsqhPsQmXGUcESdY+LfrWwtiKfIi2oKNQ0PdPfI1wboonMZdayzMmhyuxAN/jhI/mF5wZmj0GuCK7utG6VuNVwoDo1XO2nIhU/3VGTVTbH57OEQCsyKAS1QffdwJioLVmPZIYi2ROdicuiHMO39VT6bfCDkbf+v5/bKkR/vcnEQpkKX4jP2F9Wme27EPPhyDFwCo2kTxuo1rvNsvukfZWydB48ybhdhOssXIZZXbqt3EIT5PDGPeo1l0+yPObIb5Fc9kTVGcYzLp+hHeKNQte2zxtoULk0hKdmQ1B6EoxiAgayxQbwsO+fvagPfkPl3tGdlO7U63GOl1UjURo/dAAckAu/CaVj+j6d+Eb6XXaIE2u9Flu+D1dmQbR4Ttqb/ezvfTQZXuGxK5QOX8NEoCkwnF5fq/iXFKBrQGGQbNp+vKlfCojMHA8+z9HuuDDUpdaUxe54y94nxt9/xi6rPQFDcGuRUgLij0Ic/8jst5doK+YQ29hxt/zWGFr2PqYeZ+yTK33DR5LVLwpqd+swFvzMOtTf412cbjGmAo8jTrBIio6D/Z5JOFRiWM2+GevPPV42mLaodhapc7icL4CnNo1MZYvbOJPiYcszVaSFFx0KbFv0zmPs4F+TWhff9SKIfrGjRuSLmtaTstkUSMMI/iFKPnJhP9+fhRcB0JhQ4I2pIVUYuS/Gp9JEJFA6dxQ3CbgsHr4P5ArlnVLfDMLrw5X/epUUxO+nsyrIY+NIGhRQYWUg9hVWWcI/qCRo8tJ58ZIm2lCJCjkf3Y+HdyPUm4g+lXXSCQp5+cr08Dv8nK6dKiwZiXzDBMlLVXHQsWW/EHNvC4o4zHYx+V363nHNts6xIaXgD63Z9VY6nJgzurWZPadXLlDuJsKsrVFdeB7PkIN1r0eJVjqBsSG9szYaJwuf7zLVhaiul6BnL3LrOmD2Op4JUcbV+HPxq8QLICzaui6R+inkktNnuandPzcQcCxSyOWy6VSAUsOvg3vLbrn8WsjqwE09Vlo6pZ57M8RXXMHK1diQjWxfjh2GnP/KBvF+SoiUy+9IHqjFU9gidkSRiYnl4S6uQPkuMZCrgBy/YsXc0uTY+6b3cC4piMq48+uAePXbfguOAlNLdb/z5VfwUXIWuEpBxGOqY5irNzT91KrsJaQ6BB9GE0+bFoVewhX87PRtSB6rx4Auumv5KJ39wXVlnuqwxT+s+k75N9PnZLu3C5hZ7Z/I/XPm0vWd9cylMZDDKfXtD6Eg36LZpe/GVUZ5JHeY7/D954QYT7ICoFhfV3nqGMmYtyhhib4AlR4Wa+/ROclpFqQt6MyM0Ebb7RZ/JHmBjyvIuBJ/3soD/SwyWAtrVKTd6lPcVYM/gv4tDwtG/+h50wGdxxLT/YssyxvipKypRRKXZk7hayojGOaaqyw4R5eIl5SFRgIo/MbULUkfJce/bUx3j3wkIyLEXu5/mexJPpO9Hqo8HglebMQuAKnWldRHYoJfZqzIaXaJGUC/vR8EgumkuA81PZd3GnB84G1Rm2y5oRzquk4uhPjwdBxPvv80aVq3ApyAaaETe9mhCLzvP55IvaQg8rCT4fai2ny5mmPEVaaTZuHeKvP+kmdTNwKT+vrHC/uBoerWMWODZ3o18s9hrzHu59DvXn5WXWlTYi4Q2nbDw8vNHWnFSFR9hwrfePG+P9WXQyYPPzbimSdj8gGPTwwXYxVlT7ZzCJyUsBs/zzt5ytqPLGP7JX4S7XWEMDpVjCe8ZcR3w1jYn5ak6Kadv12rFpBWiaxuvzVScZPAt7X59o1/9iuL7Er2LvQCy29avGFV2Rkufx8k3e5u5h9XU2WJ83INglu1zj+bxpIzAAntPiLIJzBToq85acKnkJJioKauflHHOHw9/AAuJ61TFF5ceiflJQ4lpaV7w2qUKtJd53gUnthM6OZfO4xKvKkZ3dAy5g9T8CHpHTKCZrRnT1Rrvsjn5yXzrXOLC2Gfgc5BebNxArafV1LPONmqQvdck1YOPzLoo9QL9BLhpN195jqapVGR1ybhTcCuPZa7XDfxGjuElsrX+oUuHp2dvNDCua4I7+MgKEzm6Vq9Tp0LjzNEXsRtfiLv8VGrmQ/zYsL71Hrqlf4wO40zyhcmZOZTkQFvTBi7YX3LOl2VZILJyOyroS/g7os43asyh53poRP9YOrCCCYLUdA8+Ls7J0rh2zOXj0yxdYSpwN0IApYuyub0wdG3zwKTJI8TA8LTyZyJSQ/YjODQ5e/eGTowIYlXRwfzyBghxzo5AIsotRSQTtsWUz4YmDpV2wzYKHUMY8vdjVll0nTYcyQXe+0v/yTC5lUv3+N+p+lIlK739QKrQW8pczjB7pgeHgntW9Mho9clVIr1YApycocnA7BbplOqsFKSPPf3PBVeI+H3qTf5h19UW1ltp9z6MsPsjj709HgCaq9FNmRWRWHF9QLYPuRVH8uwn2L6Ybw1IvGkiEd11oC/8JxrIYOmNlmscmBpgJeeD0Ro02f8FEtrtIzjdjuqdS9ONxE67qmNST7MUTp6XYwct6CWD+W/B1B6tlrUlOvNj+x9YNKVdDSfJ0qQi0PIafJ7lgsOptoSeGlP1fNdD7EYCT6yn7uIJCbQmnyywcR9jMqkk7LXw/HtqyI9HeyIBuSQYCRhz+gWGB/2JoSP36Tg5/eyaDhl6uOj+NhC+y/yT/p77PYZsRNdy8yPSY0CHPudIF+KO1WqCD8nAr0jkwwNr/sdfySvFlM0BGlNedpwQmHBJ+ROiZQ0CS8J4CMroXX1oH4x8vS1TP/WNX1D7/UFQEavro13BOPf1+YvzFqvmuzXBU2MAGclmUtbTUaGvhQntdacNBRsSg5/dYWdMBIAfaJ86qyB+ybTpEKFQK8boGEoqBsRG2vYTBiwnItuX4BB3GZ/FRbqn5MvsBbwrZjGb11yISg2qTo414nvB9wVxaG0BTB91eV3hjo1Qblb77mgCU2OQuZFtgnlSJyT99klsXGexbJQ82Cjgkgv0nmyf8oBnHcrWeICwCi7HtNxYLJeKRBD8dcgdwzZWcB9F8Ew7BL/2SAuvYLNAubP29Vaf61Z6xy91G+CObVObDTNNIusyewMYhohpPQd45TT7LHTKEC45w56X+UwmxRRGV21aQaSDwWBO2lM+hKI5F77ppZwRsl8HzQbCApeVnfbf9HnRhNfbjzkXi4OkG2uW4hOhiZB1BBuWFWLld04SHYVmAKxkcDeu6kLrgJwo59xp61na4BOxr4HmBh/scJTFnLTlWcyYfs2LKoomeB9tfHdswps5ql3vbSSgXDfrIKpQAOC62T6gAPaqahRGjuznxYYP3T36r1pX4xqYSPlUHTAHgiPEn6/Mzv3zhd62dgLTEEaCdOG0Pypgr9tGDSNHljK/ZmKRXCwq9ztxaBQ8Xm/+XviT5bDb+KlRxh9K+uCZ9LZc4l14V2PSpoKesSLYMm37OlkPgh69diwVSG7DSnBMxi1xMEsBWyVbtgpm/qRrSveJ9S4i9LUOt9uoYdtdl1LAttxztcR0FMwtwOocAXUtH7UuyffVyx/fKcd+JWHzcJLVqj+2fts+J92lMvlMi34Q0rDklKzC3MOKzYWXBb4FQHxvwgzqPzYlGm6514jRRDzxdCGQ72pZUz/1hNoonZCsGCSErmLrgkYVKdzyF5s8eYqjkpnAlBOpxH37bb1EVFZlNc6XKi4oDEGUZW83PcL4fUTkKyYcKRaec5G+9GH0qC/cSkKmavFb4Iq7/wwdX9YL7NY66KIJ/Gci4k+zwN4EZu8RdlhwVc81R2/bleVJozb8Vuerj/Dp95JaLH31e+gG2ysEuDFtg1/3CqC/NicNiyNhqWDhxSHJNoZItRzjfgo5jXnAY9lloCwtdpuWRo/0vVXO1BBpyANbxHl4lcGi/OI+TFt5PG2l070EbD74wISDZJwvw6CHbMLvcqm1x18MIbCMDqI1wIv8jTu+zOK71xw3Tl7BGBwY340alsdKJvV0DVwDwmdUJFe9t9TQu06x2EzCnLK2chtCe2dbNX/jsPqciQwBgzjYnRgWeHxNTKnIiS0raO+N1QPYQslcX8tI9x33kQxdHZz5EqBysFtx3kDPdOfjJrtqq15mqoW9UYAB298RLSk9nU1lwZmOPulwEHKtgvPSI7G9DUMs11SHF+ny3t0LSFomve3Y4RK43OFr76hpkWmyCzqzVz5ScCaRPdE+IgdIywh9lK0FXo4uQpgWYZ+8WaGduu7zAnb56Zy6UP4bE+F3IcZimHl76p9+BMTU5GIcLjyoDvhTLsy90TTj7RhOIPfQJWvwLm0ys0kitgXvlNroqaLq4DUvRa1NlLQt2Us2mq+P/oLSYfg8zc/OeBUDrUu03Z8BLliRZI/7h3rjZlEXx5QpTnBHhuQyMLbHTODsjLlnitBpQgeaqPBjX5fnJsoL7DG6c7zrldosLatnpZjOv3aEvT2gPiHUVPadCjsHsM0c5zYZe7wV5yfX0qvSuf40l8ei0et5WgUh9FtLaqBO+Z/FkdRd0+q7IuH0nDjhFy1+rn17q+9RCM0g9u8qBtmj8GXIJmAQu+QIILSNJTXRoRSODffFbL3Ehz2jnqRzxt+73nUf37r4c8niMS/8OFO8rendKZPOzW37qkC5wAZ8K2hYYYduBx+iS6tAIv+UO9t+KyU1xX4Z8Yzj8XzmEX4EHHYdP6rFwMuZ1GGmorZpZ9bYmeKeN72JB0mkll7MAyF3OuZ687yqSa9C33DdZXJUEQi/SKgpg6mtjb9yKw9GAu6SL3bHjCQOvEc+kN6FCfZLzufjeViQLyptB4h7HHCuQ2H0GXzugjKniCUp+ndxmWjR4UlX2A3yH2GYXTp05bvckQTq3BA9hvJsdZxF5bRUic95NArx+ty7IO5z/RKKBVpBljOWS7WFrecKoGFnHso71bOTLmtNXfZ0/U1ArS23Uy122uyAX6c3lkzyYfzGDqo9fGFvV72LKaKpyIFqRfRduJJLM5EH1RSt5/dR1Rn6U1ZmxY4joVl+kQHuCQr7KdymHRH83MC3Iqm54q6EnH0YzsBfdPIJLDowJeOiFuUmw3NZ/u0LB/IOaljpJdRSiO+kizoxp8B4DoDs2acpfv56UobfJbHF1swXDa5RNi806emtmVDIpM4+8+uYQ22X/HIbUuN3bCeUaFaR8v1O15ooJuq8744bDj2HOJcS/DZVD8wmAjnrAygDPi9bT8LNO+E4vx55cdOD1sVWK2D2MnfxjwaCgvrv7korM7t/NpVPngvorlXP9M1qVFFaL+Vn5mb4jho7ndlTGDkUmU7aUvIb3UUZiP1morrfhTksPy6DFSHDiQvSsS2PTb7jHhT9cjTzdUPDMxkVqFVa+eI7dk/P6PXSTC252ZG+Zm7z9pW0vTbWixJgwtglcEnJXuf/dFwI/bxoM4ofg6NqBsGzNX3TEe3Aa7R8XFyfTyUJBeHUM0S5d40/Ra2MnFQNTHaZeBOuv9gO9XisnhzCVeOy8AtksT/1cV0+n6yWxkDkF+FwqlT9d1gv5ZHoYteoP3Ny9Tq7dDAesa5b0FSlAV3igdlk8ZDIeBwubTf1d/bzGtyc0YZe8EouDCg9wSwSLbB+xzzFiDE68tpEsSMQn+/WGAL43L+W7qaiy3JwPGsVatmO5sepSE7cre+8rTrgzz5E2SuM8V5d0Uz8krtwt0dUV/atvKM8B/HNwGKluwS+ponGlpmrmsju52Zpj/M1VB6Twje1DF5RS5m3+8bWbvZ0l1+K5RTeM6z+HaB0LJ2FPjjSHq4OVFm4cKJKXP6RIUj8fPnmTZr6dVMSVfOcan1Q+V6ip9Ti05jv7Zed6nE6x0PWiMcZFzJ22AEcIgIwL5kprNV6xK18D15MY6j8LxqqskCh5FPVGPTp0PycGHAo06UPT7rkzNLOYO0wnu7lSBYU/9+1svwrtR4p+u+qK+lUNcA95dmB5vPtr1BskyWTRam7ec/9L6zS5q8s44abs60pt+5nMMDPQ+zyJt1r68MC1zT2RyEdRuPDNoGbDglAdX4cEf6BJISp21vgY0Un+LHIh3EiMgq4jgNA9+IzasIazNxTSDj3O5NqHwdFTsPmw2V9VfstuC319xDj9zVgYlokgMnCN0Kr5BU4xmrAXuD7EZD3Fd6rIigl6d9K3hZJ7GTCuvntzkuUnuzuiSb2mln5poW7bANsD4taPo1dfrA/7Re7ZfBy8QV0P2xCtZ/PWKvwdrqiGZdzyFcP4jFEiMU/eJk6jWjx3VVCewkS/QPOVzKQ26TQidxQdW7pnPZyxP/Xdnxr6UVPOfCnMDKTBsbGh9Z3Jnud/LOl2y0BYpGeMdQH2odQ0iexX9Z3uADmXwBEeGwIrNqJTpRXll0Trw/zaUpHFcT7vQBLSjBJxMFIAZ8phCg1Yd+JV45zGEa3S9FrzJZaN0Rt8hgHjXsie6O/r1uCIZyvFANijhzHwqa+XQcKsIrOiw0I+Watgj1lNcy1QmVhDNn8tk4IekNy3hKUDl5uTsbBtJecpPL6Cde/S3TkNXMU9B+ek0WllAFTybS+3nTCbgQpwFKC2WP+aVL54JlxZa8bZwYGg7xIG5lBTcA3OCZclLKSJBI9jFXRV2vxw0YyyzQ504hzlP5pWfm++eS0PF+fe7W0MQRScHUudf5ARrvbBL4Xzp6aenJiFvLYTyDwtmuhkr9/vwHc5RzW0NBsDsLmjG3o6PUYDNjJp5K00STVpIffCaY4BSPP9lMWqI6Gt0KrLxvdGDz8Y23mZVgsZN9pl75noXtEVSOhnwWgrLYeEXrATFJlGZ+tP459SboThqd2VFnyN5HMMqJosD2S9gnVBxFtv04kvcvyHH34m2x99cndD7vZMfo3+KctvoT7b8G7y5cIyT6UXUtK+jNMSiJb9a045BQ+pKX4mVKUN6PAcOYgyUWCyPbdfKklFCQm5LK2yLQj+yswco/29GR07upQL11VBTR1RipWLjJDkFzFODbgttDJMtkpx25dfYU8EdKsTc2Xx17tF0tjFXdb9X1pDvcUV6xVP3q/7WJMjzEe5GDbnfo5NfmfY/elHlzKeYUR8uf8Xy7BHHbmvQ3123gFh8iKyPiFdICWsbwjCLjcOAvhp3+/mcnNqiqcB24ukZhJ1FiqkwxYHMou+qZxSu5TOKVVIxAPRYlWlZDH85+rpDfEdiNQwkGJ1ZphAT00MPMDWFXky8+5+vMLfUVhu+1i7pVa73uVMX2s+u4s46Pj3DHQw72rQtF8jS7RAFH3MikQeufn7O3j53KuTgKry2Rl9w8W8Uac92qy0ZCvjreFaWv2gN0czy7M8QOhQ6JtwZ0jrV9yGHuoTh0jhFz+IKsUH9FU2843q9UZTCS1BVnThyNoMvLU/gu0U84XtXOB2rsHPeq5nfUOD7sGw4AOuloIygjPT6tQY2X9cXhybctgl2Y6uL3eZ5kY2/PY+M6g57/HMntWCMmWhtxaAATfeHAQTosrHJxERRbspMPau29ckzmb7MumoKhDHt30C0qDyYR6FN7JmcqJPLz5HNXWjZed0VQavExHr0w774KsWz4YMYBrhbVs7JKMwn/kI/Vbb9+rI+XWvU0dt7zIB3JMbYzGY+jPhs1DVa1ShsUzaTUX/sEpfFSOyJeZyFmjLBlb26nUyYxKlnfEZiAhOfHQGwW/n3GTVltkkE5i5HH/UVbYvjWgFzhEKln+miFMphkl/8oGx1d1B3m2iR9MSFUqC/Hi/okNN6oYfC3auLxuziUFdHn4weIlwG4uIATVF3b5wXT6ev3JrTw9Bf4QWS1ohU6dKNMtoPdAQblX/tTict6WIw+s9kDbSWwrTwIPH+gaYpc7Cvp/5ZE4o5hWkpaT778SXjlWaey5o7dLdRS2pKuLDeDnhDhgLfgjzDqz0ch3IAUi+RZ4+KXonsq50oG2BiYQFCdhIlsUyBaSSh38/Zy6XSC4cIdbu1Cb+FGjWGQFQGguAVrGM0mypRmTp0STdDWuxbpl+AdEwrYOksnXXqbf1AVNTBDRQBIbZhM/safSiFRO0h1ggdvsCy2YjFAZzAi4Rqeeo2cz1LORtlTtpNyByKO9rQY+p1LkEd8S15co8+cdh2ty/QYULGNzRwWxw8oXVR8B2pL83iuE7I+8kbaXWtL5q0e+WrPe/yAtq1uq6+xJ0sBqXpwrF7VvZFYdFNTLLnypvM9cjFIcTPoqAtOrFGF20Ilks5DqvRmbVYsEgZtdtuSvWhWe8VTk/vv3UO+qyppLNBc8ZVUg1ddyCqdYpqB9KJNsNiOB1sKAYjHQ+MBqr22ehfDlnPSKgkx8QGcFzPWown1AZremvyc3eioXdir9m3HXDET5Z/WXuj2vkc/4U80O1uiPsj5xkSn4lQ6Vc+HumHGT5XEMLoHHD//dSgGo8tyPZZElNX3XYp03p36Fdxh0EaNYemSvDtX30/o6tjZMYWbHdlFU4IQuJvnQeG9cQJuPuvzMq/1Cz+UuU05u5bb3fzC2Pshy40YSS/g3rLX0w/1/IhP8/r6jj8mSiP3pOWATM6CsJDfOZkMlUXa1z55glBUSDfd36E2+iC5X8KGi0601ehVcNpoDFQazPPvV/7b7d3oBF+qS2RVc4IXQhq+OwKzZWc5QDLWfT3P3eR3rRtzoN8ThyLlhHvDwF2qogt7uawcrFvmEfGXiqX8+CL89Ysj7TfdIkHtQhCy5Ad0fZ/UiTkbnTVb4nsysIIy9htYdNmkiku9CuPKsfrxduzI+YPOUY09hbmzTZxXi47Q6/sFWp++7ALn06T0kl+tbJJ8dbYU4ut/VVlHNnUaTUk/jYpFh4zyW+otbgWYhhlstsVitoDlihsOcBTO80GQ7FrX9isOGcnbDZ8kF8oKMhfeqTeh863AyVfAHeC4DwSOr9w9D2ZChfsE6FeqN6AwY3wlFNv9W8q27T6Cv7HvwrddlT61Z4NDSn+igp5+rkmst/umGoRWYWHOvdN85BvnlpAw3MC3ji88XNRTsQ2FXrGBBzJhK7FvzE1gtfR+zTe+jJglTxWhglWaHujDZFsPBUu85iw6cmMvy+4m1qWUfYRp0+cTY5KRgYFBGDDrbSm615VFLbU3RaCpWxjJuEjUgn1AqNXAjUZlPK8SVMrErdCnDzusXHAzMArvxB+Gm3MAfpWGb/VCeobsTeDUHf9YU7gOkhDGOX5QmtVRe2h19/rEu1/LGaI/I1XzuBP88MtU5Qps5lqFhfLlk+MHGnr/XwCvEVDuTDE2BWFsSwEbNAodwHse91SOLeBklAj/dxNoyvRbDV3DKP0fI9psQ3lXohPHKGrqUlXzaLoIQsvE7fr9aTMJGhlR26J/gNdUjSNikJU5+LndBn5zxjN4VzynjFXBYfSR7uhO4E8+N2zy1Tc1JDAzIfsiqgmg4DQaObW+r7Qz+etYTA9QvrBmKGeWMsHoxCXcmWxYDDtWaVhSOZdRvnh27jPzqEOHlTnY6SDxssJC0+V/LzJwMrtlJCklZ5gRB8vFysrsc8/ZNIsqyWut+gpRB7+Ckt2EGrnPDGp7KMRAsjP+VpxfFjVuRip+gYAn90HsVMtCbV4FUv5R2ZpzyHsSt58etOxH/X4IiqW8xj1thigAqwXN3RlmxzrAsSsSbe/x7q9L2XZl2S1L8Pd2k0AkMRFuDYwR2fit6y0qKrmVWN2zR6r30QOnm9zbvPj6uBpHZNg1d/+1C8UkEO8u5NcJHI8ZOw51529pw7pK3BABKYLNJ2dM/9c8vAxxw/In09y9LW9zjvr0kLAKUDTA79W1lbkSzUpsOdXJQDsKffsTEiBVSHnCCFh3CfJeuYMdA55YhUVyxKJ795F13srdP1gVjzMgWxy81lLrv3YpnXiBVnO0T8771T8eia8uRd8ONAbLO7xu+qDu3U7Q/oxOsDo8BmyAuJnRn/JSN/w+cqiFcaXwph1PM+Fsg3Puqsd09VhJFVi86CCO64y2b2EwHnJl+ys0rbhIYcJ+ARthIno+Ro/2Mp9VjCOywp7XvT56Yej9PK1Tf15vgedip6DEh1tAaaSw09ilk+3rXxeWLTIu5mSennXpTEDPq+YXRikWh6NRoclGoH8eW0ULKMDnl+rqimt05ZV0K+58VDd3gLD9LGPzbEFOYpvkRy2RsFhy2+rS1kXJ0M7HYwbAC1jtVPVGpM4aZaNQD05pibW3qsWGxU3Uvby8ZbiKIupCsaTJfWUHrYQa0JfbLdcNIqiNyxNzDtzJd2hBFugjAwf1dlQoITXDJ+6PdSznaBwH8oAPNL0rox9BYOLh2B2OKOp4/BCQuih0RCnN2cD4bw9ectaDXIhV2Q2qgq6hRta97brviK8Y1LDzon26N3Y3aD2//cF2qHipQFubDFZrbUwc87W0+FGun+vyhF3hFJeeBAOovG6e4nLflTCjX1RXqy7NVm6y32VQxxV3GSOyFXa1B6FqsYa8INSmYWvP456wOlhiLWSqwq7xAUm77hxqna3u0k+TtRByIyGfotzkUjStJvhjOw4jVjISspnClkCZEnD+z+deEP5HT1p1bGNGL/ze/GaChzbr5XHbjzilGHnE2HLjp3JnhqH1McLhjQrJOZ95M4yv4Q3wrT/Q/9UL3ECbtG7zaFImgrxWScewO1iXdwyUpLHImyI9ugMzOvABMPhPWSfnQECwoGojdSwvMNmtYnOIgsHzzfMnCLJwjVxOnIpdZgFfMouhlc84D5VyJEXEWBd3vlJgyu+4u/yl+96k0nWj/lgy1i7uf8A/fEknQIylAyi7Ge12G+kjTqcXzK2aCrcSO9tNanVNNOr4+4jRwoWkamKygWyU4DaoJiVirB8hyiYyvgF53rrKxVpcwEvV97SBri0rul6SwVDoYG0ppKquB9rZzkJpcOz1ryYEA/3FtsYaPjpj65L4QsodiSD3spOp5KZ+psXsm9DFiWGjM2kazV5R1MVFtGbrJbS5HhAjg5mkyKPVoJ3tX31Ylf4fkENB3exdB3ld/OD18PFzOdsFrQ6SUib2slOVs+48F7aZLaWaQtnMkFtuLQ2LAEU5mShB1hDz3lptsalvZzzgxOOVZo9R+Y16n1N03TEjgszyLiXVpNvCcJESk4rPm7IDnY8TAG/XpClaGWwQybPfpxPp0ZPygNDuPDG6l1R+3nQk0w41GpgP15JtWdDkrnhBkdpqt/BRwLdrsyPpDe+z+u7091a5P81EGEVvfRamSNWdO4i5LevhBSEj9bi/IkoTtyr+vmUutEcZloXJGMQBUukYUA8OajOgvHvu1IX7WM/HmoDEtKQlqLDB4dd4RAjph78blhcnC/khpoulQk5o0Zon79mtEPYpYc32ixCA45n26UYw/2h2cFG584CaRTBraAe+O4mXok6WwjKO5eir35dJoJVnGkak2MTCa0z38X5K8IhRqOwzeLiWhGi7DDw3nedQ/qiS6blyyEvwIpKWiPRgabL2TrYZNRHnEu7+XEE0LA8yWr34P/YuMMvtsSszhh12HCsa537moaq2cmf5YNeDhA7bKSPvhBgwFN+/4xIbMIfqQOltTR09dRCmA8EftcL7bECQwSX1XApLLMDUAG747+/ipvPaf17UMyrdjVBgg5xyRLFn6GW4E1u3HTwj3G4hJNeIJ5ldNM5xqjwVYfcl0zk7dRHDcJWPvZDMKagnV2DVi3DzceDAgBgCa+WYBTfFjbhEIAtW0Nj7D/GjU9yNtvHVXqre/HG1H1ZXSKLHIBHjF5skaTSQJ/W5dMlc5mGsWCx/C/TChc6eMlNoE6uU2Ma6jtqe+Das7jxPTAThTIyMmVEW9vwDtnh2h71qwt+lSbsRliI9EgZBn0uTr1UChdqRGJubRUvL8K2uWF/lh7Y+qir1724quxR6pqxPlMJxkhNXeVXfHUy58NcQ2HaRNUs9fW1RRIxpZic2/Fy2vQV6snoz3IHb+2Igfp2YnKryBCF+li79CvkRL6STTB07wj1Y1+cqETublOUkIvKnfliHZxNLkfrdUJ1YKPuFFjsuLY+rZJ+c2kDPbVM9BXN7AbhNHPFFko0zSmTE1bBlI2peyfINySSnH6YcEwNwN01Z+CHX2wiRQeqQExoHRpJSCHQxKNR8Jy3os+9PMt4qr1C3Oeib2lmQHYuZFSjMHn6rRdoHmMDImDOXv4c8MIv/hz8V/gWvFtUaE1rOjJXZrGblzgH6gWE9Wt2HaFdFqhlzafY2E4Wa9HU7W2fDDg9k2ES/5UnzIef8Tw5i8fjNX0llmTV23STU62LswzAAvrAWkRCGjYXdNym52ghMWp+iWpfPexwzFFKZuPSPhbtruBvjS2rR/dwQURPrexFMMA+BmibZMZlmBJaiJH15EAL6kot7RreincryLrh3qvmT3FXlT7r4ynhJwMv74rdJwXaEkbrRV1BVRFN/Y1dxiKirDkkchxJTzD09CMSkFkjz6A00S1+u+E4PCqpKV9ClefMzTfSmvDBaE7TJYDFT8DJsNjNuPFVo4qSbVSg2SniElJkSWS+rVw7IOjV+iIRotwdhXof6YBALJM9NzJ/bnxhoCoFStz8eTN1JErNBSd5NspSbPuNxs6pOSBmFj3ngmxfJql07cXZ9pQU4WyFPVVxwRlo/2zdGTWLyCyMRrMXNiAwobyEuDj5huJvwCr1IDsLcDEKNFT8qjiZ9XR1in/UQtL4dEnBuLz0eX49HHpNNPkN6BKWockiEkvuPR9d4xP5QqJucbQUqLbHJ+f9qnTuGh9FCMhRsloTDWMlh849m+F53bmE98R0UOoRTRazAmpfmUtwBGMsmvKpJC16vAx9mfp4obl8goqR2f/W8e+XPfewA/XgjA4GwoRUR1tTgli/NYlekKGTQmeMGOPGZTZMTE/LQsEdW13AF8ZSsiByjrOy0lalJWoZo8N9frRPTZUF5CmuD9Q8UiNwzUHhmYfT1oKI/Tp53rEjgIKS+7NN2fzSukl4J/StCzuIcAGybYM4pBqchTEhVUqXzBIESCUDW8M69YgqpP/x3IhwIebjcBUz74rq/dqBbUQgksi87nIDQA+B4KoQFPLFVO3/n3h/J8fZd1SDV01+VZZOpXCx/nTVYUcQvRdgsBGvX/HorHpPZpZsXx7qJcvvBmBFI/oHcUjls/2vbZfmxvScy6HUSS7XQ6O+0SoIFvjdMpEvejF1g55M5JmASThQOMwzqqBnij5i31s+Z09x6S4weU3/sL37cNr4oxQG88sLZuCvpQU6kNmB+J5VBRENU7KWoMl/yWkIM6TIdAjabn8usguAOwdxGDZ3Mn81ifJOTXxF/24s6ZtRGJP+C0Wbdqn4uK8BFj1VtMDxIxs3sLqxaF/qUQbXU6aoIT4vVfrLZ077pIAwbf8d7av4mN1gGFOEJuDqb5cK7E0s8PhIoEjFanO7tb6xZA4G7A/mirXLsloqtZbZM7LZuEDxZA/ZRdihdTs4i/sFYs8MkzTPMr1Bgsa4LaPC1wsKvu53djXRepTplxKT29gZmYaqvUiDW7q/oP5qq+DGG4acFtNY/5WkMwVAHTVr+m9RQDsDzhn8/9DgqNB8DQflk7r5gXxqrBC+LwGG9GUsNSdyDXTPKkHe7A0eRt9fS1lgO2EI1n8UAJnb8MDwr6yAf+KN4Pz1uiDSpVcEj5j7K9YZ96Uc9iXHFHRWOYaYPDOa8ldb6S37h3HN2N9nxqFr7DWM42Gw6If93tJz4HNNiauLX+JnegNgB137heqUWmRfNIb6pRmJB8KD/S92fClXEEahm1G9f2g0mnpeyDJl5LCdtEgRyEveC8IyB/gkuSOVDcoqbXbOOzs7sOQvSPku+3UwQb8IC4eo4HML8bede2Ue3x3DhagvJ4SJSyi4XlnoLVykmZ1gb6X6dovOaXQrXlFdrmSfsI4Wp07al8KE4z2AXjK/B1tuWubGjq6e1UgL389813OFa6tGHrbkgULhuOWmCDvTNuRdGzAMnk+Iu+hQzNdiXyjK4XJSXmiHqXDRksCRe0YiAT0rBaG/VUqtmAHF1RHXv7+73DP7eKffb3vTkbZ+RC8gQm2T7rQTdS11ck8tmanLYFWJuUOHVuZSLdZZ7TxO2hDo5G1qmmdVUKUmWxG5pAp6g1nqDocvCcOcje016GdO8bG9o7OHRAWOpWyOWffM3rw1WjpNHx5dmhfiV8wVKspWA87HgN08pAyajIjLyZeyD3HM3gUHA1eZUtfB+dGJzJ/AukgGi8k36itj9O9BMUx6fZXSTnsF4NGkxSnrZWPX+yIV7TTtWxd+CRR4RBAdJwOq4OWIShu2xobuJf0TEDGIO6rM10+0RCjwoTMtHN8Vq5TfqxqMxjNQMzXMqNBDZno3fy24AsL8jvvB4K8ik49DnXWbhQgb8b/ZPBxP7q1l+KcPOPxYSkHtUgB/1fIDflGDUkIMU8EUBiPdvuNGCFw3tZL7mwGjiIJAZ8CTKKnrrEucuhIVLFZPXOTPI3nzRTooEvD1ZQLC9IOVUadtYULYe4PG5iYUvMp3u7jRki26IeDMj9XiK7PaIIcuK0gjN4l5kFkk7KyhtMMOZWZGZOxaaNTmGQp7/ZuHCyE52yMXxvtGBH+hNGysa68+apmUN0PGEc6j0ouC/CaMZ+uveT3ZW3e7c/czOPzQC8+k/g5bI70XltfRZOO3vC3pQr2gYtoHkK4qcIrVSblChP31o2ytfqwqXDEI39o8q3UcU74d5VXflesaRe4es5X8bwaqjP8+iLApKaq9sVeI5mUfRz+CeG6MOJbA20KZnpiWFkw+Wy0WwwbC4JeZAC403ORm0dK4KkqgOUvydQGoqv5bnJq7D6vbadGuKGWZIC7MB7UVyIgXgBz1Hxed4RHBNpvzuIhZpPqWAWRPu7DWIkMdm/kzSMkXdPZGeESr5sMydwXbxowBIUcD7O4Z/wHGGXrsVOhG+OvrUhD2Xz6la+6dsjtrwBWRVDjIvIVnhcTKExourbwziznU9cs8lE+EalTQZ7cM/rryTBofSpcYqImDPAe2A8phywCaiAZrH/k+B8ljSf+PRPq7U4XuyGFTfd2Qbvm8fA2SRvpuvbNZqeuYcgxSb2xahr+crppNYfP0Q1y6jE80QGoy2OxvEVZ8s0HFJn9C0P72jFsdRM34JT2ocDNh4/JKwjdlPXTZ0jAE7cxCsEU41ije9UyMOs88J5TPPZbNWKARV/PlNHsRkFj8BHJyqbJS1zZEFvnR9YrVudSTcsN1dmoLX67VSEixxA9RTZL6Q+LaINbEjKFU81f2huFTUbEZfzr/r+YDVI6MywXYuA0P/eHuLoOasMTpHovqLUNbcvkkNBj2CvgplbmRzdHJlYW0KZW5kb2JqCjE3IDAgb2JqCjw8IC9CYXNlRm9udCAvQ01TUzEyIC9GaXJzdENoYXIgMCAvRm9udERlc2NyaXB0b3IgMTggMCBSIC9MYXN0Q2hhciAxMjcKL1N1YnR5cGUgL1R5cGUxIC9UeXBlIC9Gb250IC9XaWR0aHMgMTYgMCBSID4+CmVuZG9iagoyMCAwIG9iagpbIDUwOCA3ODMgNzMxIDU3MiA2MjYgNjYzIDY3OSA3MzEgNjc5IDczMSA2NzkgNTQ4IDUwMyA1MDMgNzY0IDc2NCAyMjIgMjQ4CjQ3MCA0NzAgNDcwIDQ3MCA0NzAgNjI0IDQxNyA0NTAgNjc5IDczMSA0NzAgODA4IDkxMyA3MzEgMjIyIDI5OSA0NzAgNzgzIDQ3MAo3ODMgNzEyIDI2MSAzNjUgMzY1IDQ3MCA3MzEgMjYxIDMxMyAyNjEgNDcwIDQ3MCA0NzAgNDcwIDQ3MCA0NzAgNDcwIDQ3MCA0NzAKNDcwIDQ3MCAyNjEgMjYxIDI5OSA3MzEgNDQ0IDQ0NCA2MjYgNjI0IDYyNSA2MDAgNjc3IDU2MSA1MzQgNjI2IDY2MyAyNTggNDQyCjY1MCA1MDggODE5IDY2MyA2OTIgNTk5IDY5MiA2MDYgNTIyIDY0MCA2NDMgNjI0IDg4NSA2MjQgNjI0IDU3NCAyNzIgNDcwIDI3Mgo0NzAgMjYxIDI2MSA0NTAgNDgzIDQxNyA0ODMgNDE3IDI4NyA0NzAgNDgzIDIyMiAyNDggNDU3IDIyMiA3NDUgNDgzIDQ3MCA0ODMKNDgzIDMyMCAzNjAgMzM5IDQ4MyA0MzEgNjQwIDQzMSA0MzEgNDA4IDQ3MCA5NDAgNDcwIDQ3MCA0NzAgXQplbmRvYmoKMjIgMCBvYmoKPDwgL0FzY2VudCA3NTggL0NhcEhlaWdodCAxMDAwIC9EZXNjZW50IC0yNTAgL0ZsYWdzIDQKL0ZvbnRCQm94IFsgLTU4IC0yNTAgOTM5IDc1OCBdIC9Gb250RmFtaWx5IChDTVNTMTcpIC9Gb250RmlsZSAyMyAwIFIKL0ZvbnROYW1lIC9DTVNTMTcgL0l0YWxpY0FuZ2xlIDAgL1N0ZW1WIDUwIC9UeXBlIC9Gb250RGVzY3JpcHRvcgovWEhlaWdodCA1MDAgPj4KZW5kb2JqCjIzIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMjE2OTAgL0xlbmd0aDEgNDI5NCAvTGVuZ3RoMiAxOTU1NwovTGVuZ3RoMyAwID4+CnN0cmVhbQp4nIy3BVTVW/M+ToN0dxykpTtFWrpbGg5wqEM30iWIdJd0SkqDNNIISJfSDYJ0/A73ve8F7/td6/9friU8s2fvzzN7zzwz0FAqqzGLmoFNgFJgO2dmdhY2AYC4gpoaOy+AjY2ThY2NA5mGRh3kbAP8rx2ZRhPo6AQC2wk88RB3BBo7Q2wSxs4QRwWwHUDWxQbAzglg5xFg5xVgYwNwsLHx/9cR7CgAkDB2BZkBFFgAsmA7oBMyjTjY3sMRZGHpDPnOf38F0JsyANj5+XmZ/toOELUFOoJMje0ACsbOlkBbyBdNjW0AamBTENDZ448jAPRCls7O9gKsrG5ubizGtk4sYEcLYQYmgBvI2RKgCnQCOroCzQAPMQMUjW2Bf8fGgkwDULcEOf1nQQ1s7uxm7AgEQAw2IFOgnRNki4udGdARAPk8QE1GHqBkD7T7j7P8fxyYAH/fDoCdhf2f4/7e/XAQyO6vzcampmBbe2M7D5CdBcAcZAMEKEnJszi7OzMBjO3MHhyNbZzAkP3GrsYgG2MTiMNf1I0BUqIqAGNIiH/H52TqCLJ3dmJxAtk8xMj6cAzkniXtzMTBtrZAO2cnZOQHghIgR6Ap5OY9WP9+Xms7sJud13+ROcjOzPwhDjMXe1YNO5CDC1BG4m8fiAn50WYBdAZws7Gx8bFxA4AOAKC7qSXrwwfUPeyBfy2yP5ghQfh42YPtAeaQOIA+IHMg5Aeyl5OxKxDg7OgC9PF6uvAnQmZnB5iBTJ0BJkALkB3y4+kQM9D8PxiSAY4gd8AbNkgCsgPYHv7985s+JMfMwHY2Ho/uf73xf2P9xyomBnYHeDFz8wGYObjZAPyc/ABeCPD5c/8/kf836r+sysagv1mxPR4oY2cOBvD/hzzk1v4OAMDq+ndO0P9dMQyAPz4BYFUEQ3IZCKB/TH09Nm42U8h/7P+/C+CvLf9X2j+c8v+Z+f9DScrFxuYvB/r/ePyvg7EtyMbjbxdIMrs4QwpDAQwpD7v/8dUC/qeeFYBmIBfb/1mWcTaGlIionYXNf68TwApykgK5A82UQc6mlv/Jor8XNB4q0AZkB1QGO4EeRAfAzM7G9u9FSN2ZWkOUxQnybH+tASFl9efDStqZgs0e6o+Dmwdg7Oho7IEM+To7BHIDvCA/ISUBdP8rwQGsLHZgZ8gmACRKH4A52BH54XnZAKyvjW1tjR+sfxnYAawSQBvnRwMHgFXdEvjEwAlglTe2NTF7tHABWLVB/yBuAKvyI+IBsKqBLJ58gBcSoT2k1CFB/9fEB9lh+biFHwKdHiE7hKOSLdDiCUcISXPzRwihaP7EH0LQ3OYRcj04P1nmfsBP1iEMzcCQ7uD09Ju8/xitHo0QnhaOD+X/jwVC1dgUkjWPtwUha2rs+CQ4DghZE0fgk10cEL62xqZ/OEE4Oz684z8GCGtTSKbZ2Dx5CQhzC6CjLUSYTGycHs2QAIyfnA6hDn4CIaTBTjbGTpaPJghrUcnH93y43ycQwlfpzw2cEMJOLvb2jg+5+I8RQhmSWDbGto8mCGkHF7AzEMLvPxLwzwqEup2LrcmDiFg8hs35191DYnR8NEHo2wMdIc3myW5IDMa2EKvTQ2P5x8r/99f+/BQXJBx7SMuzswGaP7Gy/239lzMkMmOnhyOcrB+NkMjsbVweQ+V6eAzw0yrhgsRj6WFvCXyMhYvnL+Ig8CNDLkgsf94kFyQST6Aj+NEACQIyPTwWD4S9s9vjOkQ8WZ0tHYFPPB6yHezyeGPcD/kOepJf3BC6TpDO8g+GkHWCJOAjV4hYsAL/uAhuCFU70FMifA8xP61Sbv6HY2xBf1p52P6bBmaQRvtohvAGOrgYP9YZD8fDcwGdHuTuT1fOx4VHIyQG40d6PJAQRB8RhL7YI4JQF39EEN4SjwjC+TG1eSFcpR4RhOLrRwShJ/2IIJxkHhGEjOwjgnCRe0QQLvKPCMJF4RFBuCg+IggXpUfNg3BRfkQQLiqPCMJF9RFBuKg9IggX9UcE4aLxiCBcNB8RhIvWI4Jw0X5EEC46j4IL4aL7iP4SLGNTa6DzHyXEz/FY3X8ucP6z4c/q4n+oG5CjqYutuQ3wMR/5uf8SV8j8+LTI+Xn+Pv7PsyFBPJYdPyQIk0cECcL0SZuARGH2BD5k4BP4UDZPIISzxRMIYWr5BEIYPm1AEGpP2gAbhJP1Ewgh9aSdQGYc1kdJZH/oXnZPIIQV+AmEsLJ/Ah9K4QmEsHJ8Ah/q+AmEsHJ+AiGsXJ5ACCvXJxDCyu1Jx4Swcn8CIaw8nkAIK88n8EHp7cye6hj7Q38C2v7L9iCKLnYWxo4utjbGLk+4PbQoZ5CN2ZP3eGhTZiAgpKWAnsT00K3+p9OwP2iMk72x6ZPtD/ryr8HlQWD+HF3YH6Tlz+GF/UFY/jW+sD+oy5MBhv1BX5SfYt5/DzHsDzLz7zGG/UFtng4y7A+C88co86A5TubO/2od7A/iY2fyrxAfNOhfcw8v15+Dz4MUPR1tHsTo6WjzIEd/zD4PivTH8PMgSv87/TyI0/8x/rD/z/zzoFR/DkDsD3L15wTE/qBZf45A7A/C9a8ZiP1Bvv4YgtgfJOzfUxD7g5L9H2MQ+4OmGT+dzdj+GITYH3TtX5MQ+4OmPRmF2B+kTOkp5vqfYYj9Qb7+N0cf5OuPfP5jUDd1cYTMHs5//VUFKaX/4r/+agYC3YGmyLNTYFPBIKvqoJaLSlESN+b1YYTlH23h0To9YdzOtNPZXpbyiKmvxx3EjMzKCQeTlGeKQ75NeZO8OLgYcdfrSPdfSpTOcoaS7FkVO2L+2H950mEBJDcDE1uM8iaFr2IdxqiQYRi9gXtDF6c1dRPM28m5+Wkj77bNaVOXfxdgCyg4w6RMJHF+L5rZtgsToUNJ/cN5QEcPMwrb8U2yi7qMxugtTAQo6lysJZLo0mP1TVTKwOLi1/TeniTOXzlyXRU85SE4RLiespSZg7Fjs2fPI1SKvRnU7Z8J5dh37iqStiz66w8laIm2KTaw108PBneMqyNWQw8RqL9xR39vqpqdPVmwvuQBZj0lyDVBabE9ihqUmlflNZ//TFo83n+8uvQBMxDA7d48FPC6FT51u/8zkL5lPUpBTnte9AwYICfXy86AkAXClsb3FBfLB52ok23xsUmzjr+ZcM1mNezM/QBUMfOmjD78nSRZ+h3szIwvl1sx5U42tOcdQ8Dajx0D2v26glS3TY1Sz5cmEBx4MCdIszlzNwvrJspncHOdd7gqZKnz0ktXBC3Nizch45S2Bxb9161NS19iuLwMPvMym4zHEmCknCEF4/oCK8d9lo8BRZOZqGLywk6Eo63T50UK+7XrECZDVn2VkS7xW+tI1C0fjnW2hecWszOJDj2VitGM+uV8A28thX4Gi1JFX6jujbnDfKeqGRmN9au8fr8QYBJAGf+iTyxP4B2dlmq5ojXWB+I+L33Yl1anNHnYrLXSNyf3sMaDq7AMVtuoKDNEGt5ZF+4Ktm9/x4YpwpJXDFp275wYTsD0Yl/+rEwibK4gfsfX/HLA7RsUJchazl+y44ub3U81o2eRAUHOGqGfUMMoaMf84wPvyQlpoV7Qcil/YVcd8XxRyKxe3KdNLwpnOxafFZdXrWRtErSjuKS9M/N7zUXlXUjogdTL2ZhCG2Vpqk9LhH5ULMf7LEGcrOe/3mPhLXHWq3/dmeBMmPdtu5gfoyxi4nmeab+doo/nn+uguSQ/KaZ6ZInD27SMosnb9Jm5y6f7V3bPynNt9eJyA6PxqymCwCh0s7bYPcRkgjYG4tHvU5Vf7th+MSSOrTUhWuob3AA6A73P6jo6xxA/uIeOKI2H5qPe5WNeYiRexBMhry+gX1HTx+7MNszqKrHi+gtd9LGoxHSlkoVFfZPBLo1cq5LjnHOkIiNrPb7aN8jTJKxnhp6qbDFi0GST7eZY70aoVH5VRKmDCI/dOSLLOY/+hY7x+fdhp1amFFf+QDe1ZmvUZzblfEJXaSgBsvlJ8oKbhM8F+1NthJJH1hBckkdLGeJscL/RJRgbUxLv00dKio78nrvwyUEPpDIG4HLtWL46E54cTVUaGBZIH8NcNszFeO3506o+HO7o+ysoQqVeUS7ZO9accrV9Wsqml+lJXwBD8ASMY42Y0pLEyGWIIB7lkAoRKs2mbpafX2XRYiez8Mgo0XaZbvjY8jCDyUgZeyrPDNhPoMxg2LikEJiQuJfsiHiIBFO0PlfdJfwWK/h8iCuScl/cid2DH0yqhic77P4djcl4v3nMXJ8rT1chjCh/9YfgJXdFeFGTEyCOzOSD17aMrA3SDsttEvrmK41Ml9MSSW4h74C5Q8OtyTgG0zIO8ipf26YrH47Bsays9apB9jF7qDaUmGEzevXimJ4hx09OZY1rqPiLjhqSp1lQ5gTLKgXvZHpDbaZkCSSIQtqs5yX3e5jWVvV+jvCT1cd50s1lr36Pan6FrL6NHcFHv1hoL6Q36Y1KZ9LYQtO21awa2mdo8wvH8ICthtJ/CnzqWaAVeDQ0CBLY150VoiYA6lRtJHRJceo0LgCTMuQ74BXMoTlg1HczJq+Ax8SR74AcQu9jEthEQVcUJEnPsuUQt0cHzzqgGV9hox/qQ/ksXr7qUInvwuXEU6XzCVszt1TYGSv74h7UUZRuvIPSWTdI1CwlH+yR1WpD4HM0gQHNH0ynNGdnMK1UW2anKH0/M8syO9DT58re7FTxMtzSBHqEmL+rG5C26sz4bCRvzlIo2k9C8FmLb7RG6dq5dQCYbKoMm+3KE42SAk0s66uCGPqvlNS7Q7jDZ5kib7MKrt8vvbpOw1CJB7mDZQGSAsvOUL/ayIaEw4Nt98v5CKh6LSQS364znoxo9kEvmZKiLWpavjKM8YU7j9RNW7ggGUDLNWA0gK41m3whJm/Z++X3/SljFuWvfI+w6oENk9ZzprLb9p1fZt3qJaoKTSLKPT9WQ620JtpYGerqInpDdnUrV0IJe/PnpJt6fE9QUX9zk09VdYWN5Rx/ioCXrylZV/oSzik2DDd+cjY2iNJEptPqyjDmiN24WdmCOszT2J0YDBeAJmHT8Gzpx2tpHzZ4ZW/LeaU56zctUBy19wpJS2Jk5iEkDDLECDNnt37s+Ky68HX0hEgeuE3rVbqrnCF0a8LjK5Ojy4K9B2cATSwAjgfD0HMyh2LJbY/1AUlk7BfBZQt2MbmBMDjKpzPYMT/g7v2/AXwJJevT3Y8F+1IZHdgODnEKqHZlUoCM3CJiPpoWG8Zc/SQ0TOd3gFN+ndJKaxN0NRWYGHIZcv7Xds5roeukX6qki+n5XT6x/7z6il6yIObaGE5aQziEd0w7GZYU0ZkoczRxx1ri2j90PwcN08rvVNCach29besC8jUKVO2s8ITqCJnh7Dts7lfOPVrLsQ++iWueK+Ex3q9Mxp42U/YF1HLXv3ieLhqsgPUGnrkbXmH5Gm2/5Dj2dhAEi6Sm9QtN5fnS1JlCKY7kxh0BrlvNoY/Cj1i/j6zGvJUmbszqn/3wK3xXPmZxqDJ0/DZybQ43vtn5eilUkqXhmBdrNUt1hbP1wbFVhJHbSCY9xI49XA3X3HX43jUjPryKmbyXo152niSL3Fzn0qqZPFGnpjY9M/w7cU/X76ByfjgJmh6SziEatEqbauUt7G9XngFQq5FjyiIrc36MkZ8MBCeaisZQUlv08+nGZ5EjDb9cFqwkLEQ11n9JQ01TB80oX03gZao4LodG3rKS3uqeCtenK1Ecehutv5TdLwnWvbOzl22VsTj5ztOa9vn9qeJQiLkGFP1RH1RRYTO9mmo6+/U20tosYxNyeR9vZcJP7sLzAKGMMMW2UE14sAftxvM5CoWP+4mkqYxZ8HH8dXK1uO6iK4e1HvGpMOCb6zQ8xh1yFBfvX+hY3Qu/xbodyThtXeLGgbuHwWMOZwNbPYhmMg133/3ymqCZCYgSGSNxImFHRdqGX0JxZGjcLvC2wKFuO72BqpLYEsn/MJXUNYxSlmFMAN4y2AME6/DnQcsgWfWZHWXUYX8pm/La22Oak27wal7yPY/7vp1D9JHtK/6d/ubSwOLesSa7nAyBOeuP3Blxm4n5LoQgj9AVVMyywEL5RZNl0vMDu2qBlzEYtZFgFnWQSOEsnmx46I1tnYE917Pmfbx35OmGY88P+mFFwEZJY+fVY3Lt43WO/dY/Z+KOhLlgoOpZ4pQHcIdJOV3wqtIi33Huzrbf7dR0rWUZ1KiMtVq+fxskvbq1FOws7BcQ/exavlXBtmGUr8bkJA4tsGZP6MWY6Vubmo/0TPRk+f5xq4axyi1oXZ7r4upa09vzR2pRziuyjhcjcSK0qS/z14KJ9yOREb2ZpMWn0U6riyzMPh4hx1ve5vDX//oukYvetJJV1//WA/l5JWUhegT5B5aJJNjjBa2I2y/tc0t3SNmZZJNI2vRkkrdh4nR7SctSzGZiAxX4hpZ2xh16JU6/GI5z8neiJF8RP/8Wj2Nh1SnYslfRL+w9f77a0ku8otK7oPYDONARfqZPK2cDY4ffqLWC5OvJVTHVrEm4n4Uw8RaF+Xf2fZRIZ3PDtg9WGjfoPboCstoAiMcavwCbM/WEDVU9mydBhtKCW1XYQbjzzqT9Zi0WZVhwloIK3WjGaFNVGxysz5rndkRbGxg+e0KL7bKSFdu7pfIRaBPoiBSMXj6Jkt+/+2pj9kgmAAmTsfz597wFem1c0ww33gTDiCwq9q1WB3i2Oe5GKa1jTrLjwG+E45TGmAKf1NJMBCRLvOhVxfEC1DM0fiIUGDaQH+6pBzbZ+3A8m4AyVRp+uQlXVc+X9wP1uOMl1/sSwtT35ESwVVLPjczVvVk0PqlVlKozvpkbpKLprxcPPr6VzcpVXNQNoczSQQ9xUIeztZQmL/gemJERgCRQXJ1+6jUUQAyL+myiV+oZOj3Z6lWv/M3RrTr/IZTQYS6jTZVjJH8tsqKPTuTXhrV5l1rTfGx2Td8e9AzdsZfZ7uoZdjYqqB+3Y3oPHZxUorzj1wnbq+SHr/oilhRtF3DNqcjksjFkwmBjtjOs+ExN1KK6TtTwl+158YocZxkLMie54s+OmRX9CT+aQTVIafQpTQCZ/YPvG2nevXQhSAmgDbgoP/pyTMsFf6t1MUKYNE9LALUGL8XxcZnIWwkpKi+pfjpSHYSh/Dqim+aOA8fyJV2FDOWvwdAsgZ5E9A/qtiDPEPnkiVU/2BBU63lXvlpULtn225VWaPZ2dcIY8g1PxyN+XgOzkZomE2CxYsMxlGXZiWw7nJBO+z2yh9w3L52CalWkpaCDlJWdYnj26MvEMv0YC3el0c6Chndc76naHPTjJD4v8RdLRjWLSfuO4FDpBx68B0uDTzz5DbN8ilCT02icaH54awdiopeX3OOrCoWhYmKPOu5LHdj4WKXT+qIjCDL0DrSraIvn8RMp0DVREWUlo6Lp4cTgOgtlrfmqYWDAc6LQZwCDokA0drCdQmqKxq6/PsIy33pZaO1elMe4Sx0tZRukRtyaW3I+e0YikRzqn8v5HNgjh7/hx3ARJXRPPeMMn6EQNRO8QD5n/PySm0IM65vhB/N1A7TTeMkvqbloaGVQwUXKLLUwnxZgXH11DY5Da/gxTi7rfzBcD4Isl0aWX/Q1CbxWFzJ6G6zydhab9Uvby3zNRVj7btpkF0G5XiEJrctEtZ3+m9yPkYNkt0j8yXliFhEHCwbJknp0BRsUre8mdPrdXMEVEeHxpOhnEc/9+mfMTcou8JIr0QUcrG3LK7EAFgzOc0ySO0qWjfO6xpm7SqF2E0vScanrTY3YBPWc2ZenFD/q69yDiXXATXpbe1GE3xx9yakaEw9241gBxM/47pqL+ILr4Ki3ctLTTWZNu/k+vm114ou+6zi1v7k0+SxNigVE94e6nJn+YI53sjnSlo9R5dRtdbicKmSDJlVv19vu0IzlQn0JwnO4bJJf+F5y25j3/KRuUbRZ6KIZ/Dra9jzeKYpOpobtCiHbaocehzH4/hs0X/sSx3O1ysy2rwUJApUvgjjMpcPeSzim0j4fQ3puOltAe7fvhJXfKu5L+BlqxPUe2WbrPSfcexns8GWpdhVznHHLAqBF1FviASpr+/JIDYdoGzNwta6fENzH1/iIgRsq5a3KdsJXXkkl1HWd94ycyPjfC6Uca532SWxAbhdvSPmsU37+5FYCBGdabBDOhEzyTkuu1/VLXXkfVqQxT7PpVwa6zxg2nlAsEkF9rTs3OIL3LtVl6M6bU+qhOelQKEsNKTeeh35LK57GV5kBa/Xug90KZWuuNI0vesZNAjmt5++kCr78Dyvz9ZzHaw5uyWMvvKdiiRVwKzZrYwfJojPkRVIlTkTxwpRcqiKp0ra9ZRFB6b0kfu7H+CSsHrs9TrbdtW2Vwzqn3fdaI1FynwaHHT/fJ6xhh6h458TkTw1erNXi/Fa1cD+i+AkHNsMEQqdfTDu/8Mgc9vPQ3lXGfwfOfX7D/omZhd+MuW4qtSGwv/flVhSSnbjr9LCTsXa4jMP672RsLKFphRE/3JT0X0bW3AvMd732Ui6qaCJ0Qjxvh11JtSTNG0UU3CuUaK5wUKFQhsR01S/Q31HWjjHuEdflXeYqYPqGplMSEoTqAMddqxkEyEYcwNO1P2bkdbnYwc9sRl9u9SVWhvDGNlG7oNQciuRbmu3uJs5NWJr1NIR7p015Drs8S8Cfn7X3QDwJ2jRDFgnL8xN7cTQs2iXhRIaTbEw1E/QDqtXf9I6f8vdVvCV5LQIyq0GgPeaXENVzYsx0itngVvQ1nNB1YlOt0nPukYa6Isba0+5VJg4ES8e+xXnbgVtZhn1phueLDiFezFpAQwmpEG7bGHj1PL8kUlZKgP3da8PjcBx27m2hBrQJc0cxalrubz06OYv1vxw4wxwF897SnyWYvLXGalYq0xQchM1Jqxuv7hbvFjGx7gZ93hY+ls0R1UTGnsVQzDxUu4cR23aDslkZZJpyvV6XXTJhUYrKvF3wub+93daeWtv3VkOn3iOa2sYL3C684o32UhivkEBwNZZgkLXpAC5IfGUpoPBaW2CHpT0sS0NLR9Hda3pTomf6w8tExPY92ssgsGXPwYQZRtCGZrkzHPYX4dPf5quVnBF9Bin+lLLRPz6nOH0Gjs8hFaRhbPViCZJoKm/M8YUFQxFWczLv1QCY2xp74MfS4ySq30YorIuLn+aEf4r/zTYZEcQBf3S/ECLctEg0i9v9OoQm3FUpUf2kVoMYGbr3XnPmyqER8SuSJ2pHZ91EKLvfYjTx81r6hnYgJ4kbTAoSd4v398a37i/OCRRQjkpfsFpqvjbRERsqWxh4/y0icCtmNRXh/K54Avq20mdaZVSlQueUIEJ1h3TERdjYRLRK7NI24hndUXZcerHhZWRwQ4jraXsrztlAoaGH+L5xyirHpiFx6hxinOlm0/kAkwa7JGUUhnH04e6WvWW7UbRCcGc7cRGbDSOhRI/n5olAMIawExesNAVmXXEKgojkbTjz1JJj3uCYmb1L1POkI3CQahXCxoB+5/1dFQVF++uYAmN85SMnWqE5kmrmnlpZ9ZVRV77lODL5PbPaD+Fj94O0Q51FJb6/q1TWn1MSJDbwERfAYJxQldgdXK55//hl/520RsFJ8FJV0xVPb7wzqvXbqEouRu90BYhCp8My53u3SVoY3WsyqUY/ZsIoeNuakuwLmHhOuo39T22brJ307mKLtStii3xbQS19IF8pHxzy5sCbHTpr/+xFQwp8p68fhfeeF8eFeCB802gSgsv31zkQc9ib+RaNtCJ32MO7MUA46DDwHul2bkOLVHQEFl44oSVVw9FRCDSSnUnZdlCfvcMhHiskQ9eodBZw/8sHifeZ30//GKZiKPTkkp6KU+DwJRGC4ofG/Z5AjCkZAlW5oTMY0eh2bosFPaaM8PWsk191WkvCm4HQYxb1XdHI0Y1vnPVCDBfRvdWwiI+81JJv9d5eD9QraWnWo3rSkShIiMuNSTPUvsedRjuR7B7QRnctgr7/OoowUCuvdwW74F20R7rFVu/OtB1aAcMhg9/TRHREpFWY/cOXPUdQmiY+q5SBsi/SAm2e7cd+pfUwVvOi2HKnVHyrdcHZa/PSguHcgIO6vAnp88EotVMTPQbQ0Piy0WIJfX2WakM8ijscJp4cTLYR8vyFQvz0ydBhR4RakHNb9nCN0pXaBmFjv6+mhxwvHqH2FTofq12+B6O9oJo1tPIa7Bk283zcuqlbtOxlLPy8Wvu+TM91xbuATdZLhy7gCzjRMgqdd9ri3wbU1A61Ej6IDXu1hxZdqdtW5ed8YXcKCZHBnZ4h1dQa0I/VJQHyoxAVb/gFiHk8W6Xar03wTaXLupalgE1VCr/+uXpwE2mvPCZeGWe5JrSrWFtWKilvU6+EriY+4sYaDLOYEB5KvBIKm2FNjqunCyLy9hacsDzdW06aZ55Ge170frYf/Cpv73KchMRTIrvq0nfRFwEhZG+nHBDNBIN7C417svSVZiBdJUBHjm5DHL3WV3hSCTyS6Q5zgLAqjbybULggInGGMh5zybhWYC1u+0HJp6j208Ess5Qo/SQ3vvklakd1qD4/ArfEgW8oCqtSqBnYOu9FD0zT9GRDcKmv/jd5Pk7V8983iQON9aVqLqa8HfVf2qsYwFuWixJJTq0El2SsFs/7lPOc/TxV04LYxIHa5p/EB6Gzxp/ruZj9zp/CLfMbbvtQP42mi6XT9czqd0a3fNlH7EUSgqKN8rKUPtq3HTNELjAptC4664ZMslZSUM+aYSL8DN4EGD6/YNGYRgo7B/Cw//Dc1lQxCtfz2HKPtZ0kXWN/4XmcscfuReSRyEsh/0sinN30diAMPTlWXC59f9RrHAglVIXbw0/Xfe5cKELUOtUB/1rZio07yMkNt+ceH5/r3TYCeFsCVPa73JYHKEwwLx3t0JgY8mFFzTuCshck/PKdnAdM0qCyKFr/F/WoK9rYr2YgaWDO5/eRGJPWWlvg4YAtAwEVRIwTRM1KL/9T/FfNUqZLErNHbwZmXL8oLKC/Tv3lV1a7bfN9X8WccW5uUQGBiZduGB1mojUkoeaWHiM9+7V84j0ZZwU6v+nh4o9r5b6U3TC80lvXavMjSZ89ob0g3KmdH/TEKiovlpTHSdvl1Mg8PPWUEuQDw1N+FyDsIKDp3kyJ8nFMmWW5sI3i6MsKtEdKrUhwpoERkQqPltPEKezruqvQpnRtxtVI0BoMS1NNzM3X2y4lV4pqxppeGtTScOzXO9zmjkZIZxGsUTCV0hPCwlwf+Vn7DDEVnFAOGXcXpLz8OZWmjiY3gmuP0fNzRIVCBXV4NXnQHb+aw9saAebAPe2Okg2KKrW3JabwTq6sMF9Zk/HD3g4cAwaWoqUgsw6zws4Ap7OxpXkBbK5w93k6kAE1Ym5SYSzIMYa29Aav5AL1wsvFW4UpAGehpmDtncdcW6IyFU9ko0AhwWh1OWysroBO4k4N5dxKZnbtYu9a/Jfi6xhlGqZlhslPC/GEiUiKUZLN5JuhFeXTsbF8mRquay/7fqYm94sFH9AqHxlPki/mhNOqcX66qUPcEjv1AW5p+8Wdt9z5mOXIRUF9CHPVZpK+6FVwoqGvPlB1d8ItHW9ryR8Y450oSuey+in59c6o1AuF8B1xapIC3s91mr5FNbO7Q02KmQCGcW7sMkn69l3mnC8ncnauOlYbQtfSw4D4XhaiCGptpRVuXFLmJqLxgu9Lu0W2u2I22aQnjHEz6bN+sI2XmnW/+dUyxpuk/WCEyKwsjdysPaPau9KKPEnV7ouWk+euZZu0NxVhLtF8L7SEqLXemaC9OpqZemfmG93Qc1+DPTlqpB0b3GKIqKFBVKZKe2UXT/IrOZraOn8p1+kaie/7bcT0WFeaegrtkYnymK9KL3PIRcPm7a1Xf87rTs6FlvOG23Me8xIF7z4DHVNKymNMP5E228x1WJLMI80d+HLDA0s2nbKqlrnfsDY18scdunPWb2IHxVa/NC/yldjoWPOXnPUXBY6X7xlfKBJkEX+01jgjqae1YyjdkIS131CuPvUl+NRvE44M5/p7l2TwKr32srn7OA6GR0dNTM+z8k790txKrWTmei5f4Kt9fppgrQgu2CVjsvl7mptlGaMMdqXDptdQIzBKY6tJhtECx1tPtsdJ+aOTTnxEy0W0Q//um91Ej7Oa0Fga4TVmRAVyFEQy71Ln8X0srTd1jAj49shdlK8nqFNWEZgLnfjbqIV9AyROh4+WoFYoMnbfZlKjAyz0j45MUOTLZFH3035V3p9GpiXp48unDYJH31YL9OzUdM1JJnUM2FOb7lZ7Cha9njpnYToO7tBBwrlxY9ono3xzlrGBg9tZn4NfjRnz+UPCsAea7dz4EG9s5stYs+Qi3uF5x5PYDFoon/Za08gOdruR3IVW6aNokgqpb9RIpx+8EpyVayzl8yaHtj93cL9y2OE8CFmeodhwReNS0buhjl5Zh13aqtfUvxb8dDHgMK3fc6uaNYTJ3zeq+5Xxmwz1JGh7MXMptUPx+iBsNGISkaRDhLjcDVVwd+L5Jxz+rrf6/T+AWTz5wiz1+K+MfH/Ke46z3D9vjnZyGVjWOY/ZFE+k9Xz9qoOI0LzNyoluuI2ZkS5ToUuiyBwrf2cNOzxFYKu8pvzT8FE3dIh2q5JZuER/GjT1XaDSHv9PUjqBkzwlwlSCkoR8uPjCFvcPJjcsdYCUz0PrwyLNfiac+TSj6nBNhQsbricV31OyuUTkq+8Q/eZUQn7TZmTf6X6C2ZVL/8QG1yOTm2eQHM9dehAArewSOdKPWVs4sk/JOCGCquPco28lmBA9VPcqBvHI9RWW2qdgI1O5EOAZ3Wty6fXfLYiblzTlODwWZJnvPytyV/W2oKcv3GVgqQkiS1T3VU+Vu7JOJ9SbxLyhNgz1b5UPlK6LkA5MhI6nrJYlcDdW4GWE4x0ZN4/Z2zveVGerzlzQugsorSO8+1BxD2DkJU3IlMQUhsup/omhmyFJ4rEwtecznx64OZSvth6NiZiL/SWutdwIU9pSi6MpT5vlFf2arm8OzSg1zkUi1WWV1nfTSd0UGlVvy7XMChHMKC8JfqG7xWGjRUexdrJa8veM37DPyH4Zj6BLY0bvUqfidnmp98h7VH4tSKvSMAwKWrVsc1JVtT8Sb2qfVMj89TYUlI0ERgl9XjHZGBzSSsJHW0qtV5XQoe/r4el4hzRuPSnPMFTmL+KUEToXScF8ucuCBHfqtyyNpxZBfYCVhP3L3zjdY1ZA4eYK/4jnmu4FlXDm1XdP2pmSVzYV1e/o4SXn1CFa/qwKi9BQM8+COT1ibDtzBVG+3rKnQ3IYTpkwqGNlTKHOnn1CiWIbBycGSNY/qTEgvYqRIciP7WzF173ZLIuOYGYoV6rsKXdvllpMGITMi8vfsP6z5TAgoIzCKqvLJWpB0nMw1QXUbIxkxmLIiN1OMyDqafRigijcy1tQrEv3d4VS8yLgEEVRjwzvdrlQNBKVh8w4cVknE6rEEfTDUNUPDWOR5W1yGsExocNUfT13iqdbiuP57+/TUJEbv1998snyubIBmefyYVi0B01vf/yG6oKewIVNr/K6a75y6J21CjSugxj6M+zG3IoWXNLBodTXjH6f/Abe1E0ylc3noqMxL8ulsiMIQ9tj4R7MJnulLxW8y4UrRWq+xDWe6JyZbLCoTktnQOhSZ7/8lIwwmh7jhWiVIohhPnW9jfhcQjqozXq8afszKT9Ty3yVZ+eVcJ3D9bAEhY1qn0Ffg+5sJ2+vfqtT387bcXsD+7KUuv0cPKZfXC4yaxR0F/zAtcX5oALThgPknQ1H6DK8oKlcXmaNtiq4nKlPFePKlV46CRvSEq7ep07D5GWgSiz/YkLGWxW/c4ND47bsfoxShpMVhJmvYUbb5cLX4ZGqwJMEeMjIM54reozahRpxDqOqttzYGfT7Ju1gZeCko3UiY6VKQspprdgJv8boC+0rsRKTuM8WsI7sL2N6bNMmaDQob3jz6JjPl/mZ4wPDr7RI3hOUhfFZeVinIM4FUKG+vRW2SxSIeL6XLxII38iF5tfPNcZReyDYcSXmmvyiP8bRmGlmsmPFei0Cy/b5aG9ttnM89nxbsIZJwdLmJCeLaY2eOdJrsyI/8eqxn4yegWqnFAmVCxztcTlIvKKRpPKM0lRcLfe5qvC7PGxdIy2jOMkwI3o3N2JvLOXxmAtPWjA2gxDJrYUpoXlRv4dqPPNAsoXnGvxAq96ibXW63TtFMH7kP+eydrnDWb5yubJAyforWejVFXp04zeL9vzYhVCBqDexfNQ0NbG5UW9137ztd4xEQSiRD/LFg6ZsOafFqYsYiZ75+evZ9Is7u7MIa9nWM38lxNu0iTduaY3Bjmo41TshRSxaMOF0tsqFY99DHJfK8731qK3sOlievyheLlPaRD1Bwa+C0a7s1K/FlPwcIFkv2qEi2t9FUWksArbz1zdYo3Snk8Vl3v3g7qwLsyRl5kxbpUsPaqOViwSlMV8u+yQ5T1RkZAL6r0qm3HyJYORQ4YKRw6uIoNS+V4LQY9EFJfc6EV99P4WhOsEsQBXj/kytVl+kkmBPEUtuh5VTGtKIF7Ni7ps3u2NeajaZLAmTnflS6nOQ2T4eidUr/88eDs0KL5XAMm8rUxPZshFEx3XZP8Rx3b/9qsrfD48wzH0+1eywAK49JX07r/Yyjhyst7y6EV42bOuUUMMyuUIlTlANNHJTXlZR+EFE1g/P+DxnyeLDRwGunKgq9nfItcurH7/caGuy2pgo91oCoHLcb2Lh26ZzGtFD5M1vPkuS0SHcLiMdvJocklrlSSiIL7gKc9aktDov3q9W34frKOEysrc0NesW8itVVu5THnytyN3C1PCCNXDR0OA7GuxRXPTsGJkJn/FUmU7QEFLmuxzVAny5rjqJ7VxVq0+v1F9sGsTNy3ptLOmKw2kGt3IJYaOmj8vRTMb6oQvYDC7+2DHJthSe+10slhd9l8WsmCKpzjapM/xSnUj8WoEIK7gAPty5+N0FhP4ZnajNZvCCqhl3nW4mXgqeNd10EXU6MheNnFuTFvwSQ9BsA4JvkJe7EZPDfee1TXRLC0eZWzPVCkNwbsP0wTdG+gH50xOViU/NcT9ewHHbheWoHaHNmt8BBdwiAjlpR/yXdT6jErJ01KjwGizmiIxLt5VhYS7j1qhsa1+bGN6txr5gH+f9hUbTENL/dWNz7WdfB5V/NTWb51HQt2+f+O3sCAjg43MJhIacfyg098SR48WFiSo2vgxsdXHMERzypxwSxPSU63yHE1TKYujhXE6Awu1gFhr5jm52Xv+Axe9Q2CdSP6jDZFAukHRZiOlrzNI5oxoTnDhHbkEDK3FjqORhgIjEa3OLVVecmF1MgklHxWdgNqoRC6KPOM8r3MvvRhHCUaBnVKiPbXUP1S6Hhz2wPVW9N9yqBjIdda3uzauPf2X2f9s5C3f6BWf67sY2U3Iyymjl3lw+gKjbq59h8UaFt0M8vqI8Ddxy52bh7Xh5uIemj/qqbDfDpzSt6/ZLmRYNqt7HocWZSyjb0zQN6139w0GYOFUchSr7nyy5WxiUHlYyH/svFxXuBiiO2htNsAQZ0jkVv4+ZSRVO4TbjO9Aj9h9o40fFD8w8x6lEZeXXIDUNbjMa9RBj/4h2TEGNWHo1C+dmrfs6LQIjqp5F6SNnon6tiaXpNyiwQQj23t01QK6rgA3uR0RBf+gdc552V3M6HyGxZAGCJJVjaO5ur+m16QfFzS7M14PRy2ZVvqEtjb/1qtqbU1EbxWj9+pspE17AVUtssfrwH17hD32+W7mecpYlJTstStxfV7LarX0pxRt9WtjTii9k0tRPAS8sN58XiRS8FJ2FLmEWAKBmaufcviIXqtM98u7+LR8LlvYfPQf6NZoFLnb18L0ocQxKDPXauM0Mh56aCtfO92/cFLVX6Eds/Ux04O9xS1dKgukfFcUDf7mTDohkYsPEkJYQzAt2nI8Rab5OIrYCqanT3VHKXOJ41ZFjkA60JJ0EZEh7s8I6HrDIjC+8rqjCuYlV4mykRmwbTNa8OoX7MpXOQWRQtPK5gdmaihPjDjwEhcZnFl2U78YmlRUbc8YmdfO+C2WG11mkS7wWZ2emk3/9TV2ccTRtfFv/lLcXrz+29FQPu+QvTLThT+mB9Fu3WJfoGsQXi7Dm3zTw0XQreq97K98PvcNunwkjaduPa7jJDKPrJpdXT5UMkWqmHO0PKGnQcn0luVZ1nueNeVpHj/TVwoQ0w6eAskFMteZKt87LxI6zid1+yh3ekPUFD9FVtRaKvzYGucj8DLnBs87SL9+e/Z4gIvlYmh6SnsoZI3yiKpAFUj33cQRZ3VPfiIrxFxLb4VKwXuTa6ybhOnTYe8Io6VwLEWGprID3gi+L6STeg4p2X3M4IJbzDFUc4zepkqzifsYdwrBg/OTDKKDFq0tMdqUZc+Apv5D0xUbmaIIXidBD4kCluTSxI5YatqqEqcfXqhi0fOb58eZrkeGPXSrR6HljAeMiQjihUBF8EjpcLb3qHhz5FN4fXXHG8Cg2PxwIVGXttVGTyI7FjZRxI8NatMNu8o0G3nvAU33P6u4nCMBSHKVZ58zJqen3xRs8luXJwhUeUlUTJE6ysfsYDchNn+esfsWvxSeevFsOK2JhUf0lWnIdWz73bTfMgnT5soPDq0zSr+/9+j4FKn+QVXDXS9WteBlcjD3z8+xMLOX99VC+zBF0kr8spqzNqzPulh2J3OdJAvSD+FiX64JbhCUYOaRaajzLFk3TSNJ2z2yLlTL04T1wD2uoKp1NrNB30jzU7wbi7EVGUNlgoF1D8SNOfeeG3tJdaLYh9mDMdzcSVTAwqO/bFNZ5pmAhRHS5poCzQg2/UkS76c1vfrOLZE9u9uXg9Cwti7DcAKyWKChq+i/MrTwnGZeKNRlT+xVAN6JE9hrBAByJ7fxJhoJAvU8834yBkDdX+cuBkTvZO+jbSTOwGG8Y9agfh6Beof6Md9W8P9Ze5zHmpTiR2K8S6+99yAOcic00lIGjAxjuUSPXwradSJ4TwnTeUiXZUb4ZlE2k+ZPLHKNZP1blXqXn3WvZRyOL7YxyjjK5YQiy1eA+G8X7+A2LRF8UQdHwBwX3UJYDAb6rk0hmz9WhqvCmydIzCVEskSJOjGsHIR95cbCVEW36/SApRUXf+yC2QpwSoFoUjeh3KF09qDPmLvpmOolVkpjBe6lMIpQtJi9e8a+SV2+Ul+NjBrl9oFz9E5qqsOUQFJ2sNVWJR6beEfs5e5GIzXxapSYhnWHGpkltOCf7gb8sdicKdby4uWmP+QWr1uaIhyAmnQEqm5539PvJ5/1NeqcrjfSNTj1BmPfPCjF01MoViRTNPKooNdrFhTt/ZeGph/FvZX5KiGbo48rAthSyNZ4MallZQY0vrkMBmCV43uOYos6FmHq9jwo6vBhpL+sTOcjQZhbsIMkg4mQ/iZQ6J6lJ8lnImiSvZzf74DpvCD0hQEk6VkEWG6lpk0QF78iQaCKf/Fqd0FfHuWoZvZW3w8ALKNu1BGqoAmMcditw/v7E99ZUEmpfH8ONasGC6S2jl92caE7g5a3f3K6OZuwXmpCwXynbMNzIxh+/vdKMaPrUqogJQ3Ybe5hmtdvmtX5ygdmdrGxPnA915O9YGUo37eYNlknT0xzYVN5hI3TMESr4CcxvbZZbylkIZtqveeuSyLBNqhfcV6/HsXjSuIDgTKc9yFiYP9zq2508P1klEewKpdxBzEI9AZrRkGKevke1+XBJ8zM2NHJLuIat09AxteT+ROeV7p1h2UoE79ib97lctImDWQYLsjSvvnLnZKTYBMob0n69vmMnbqYjxmxJu8QO1902wQzhosuwPSOZnomkJg9YsPfeg56tQsYE5woO2/HGyeJtpBM00qxaoQgc2szbL9cl4zqwbmUiNdepvhoeNcDGhsNZm6mOhmWsXfAm3oEl1oSmJ+XSyeBmVzc3i3+tK67xBold62r0Zj3TSSAeVpLv/bvYup3S5FXxzuuqhnv9o59V1K6JEROjLDeEermv5RtJa6mGObaqcmTEcGsvtRd+tRP5cxxyZ72j7X5phjjgbTLZaYyXBLyzASAUwyCC3bUoJxhrheQQdVc/o3wR+xx5/eYbN4OXbENjh8/IZd8P5R9s69aIfmsKZ7p5I7IBwpm4t7Ocsb4yQuv0B+14dMXVA/NgM/1WAb85p4SMve6a8Oq3cSn0hhIp/j9UDBA1b3Tdu5tEXfOo+z9EReuly3NqzcsEesZyeroTaNxuJp/VBxJgqIECd0xKTJZ32mwXWbFfoR2ZiAxDJns7NoJ6eHOjJBQ3CsdphLJ7jN+yd+2DUnrZJHRfIuYDP788fH0ms2tPTCnwpd+cBI+smzhZheobfYbxB2RAV4QW7sb6u2mbesKrSa2+HItM46iZ+1BU7rtSAgSVnWC08Ylx7+32UFxsgh/A9nRzhcOeqCZKX3HzbNVkVfcy8cWXIKEJkVpojjc/vTS+B5rkVmH8stkaWKcn9gh1VKP82UMmVjiYwwb189kFUDs3N4mMNPcUWtC02pVZIErNmO3DdhBU630n2hXMgvPpPaqYccqP5sMeltXvq+Yn/Vw+n5X8V/QrrAF2ddnF7G9dlezfV7XvNoAto6MoQR1amAEMmu9vnMeNyicyXau4FVcQOpCNOpQFdPc209ZdpFrEEht8YQaX0IgMDNJLAXCKLS9eV+pQ8/uo38ALiWJ53guCM4XvHJx4kqROsjjYlpnKalGui6+1Ck+vbzaIW9fHrK8wfAhCclFUAbpyQi9WFjn76BZInOHyAYxWPUzvp35mt9CHFfwU93yRFsk/ifWq4sQ0LIpJSncaKvos6W7ECxQvLG0Jlr9oPGO9dkF1z/QkKxiaScVjvHHf/zrPVrRfTqpD+HMkt1a7eOcEKvlkKK2LRHLfvGmnTdh3VjvN2FJ7RkSNbRQzs0vqS5GsymdmVC7Prm3So28um7KOv4FCz9/An3SkIqXYmvLRsMNDI48KbWNOd73YxSHL80YYcjaQnzPrmhBfB3s6AOgtgue7s2fIq5n7F3GJL94lFy6MQhc/Y2ONQNo8HmnwsgKrF4aZS91I2nEAOD0Rp75PlfLyGoQZ7CQESwzNmHfHYMymLghhJjdv9rKO3Q1klwm5QKNriOWG74S/+LQOZf4GKy+2lBgR72JD5m2YVMFyPMpFpmcPjylj27eZ9+aG6b2mWfmn+cSt+jrjOoTYu3JywhOtKR+SspppVWDXDCsWf/yinErg+SylotRZhpjEbXhkT08Rn/XD8Hfm2l2RlOdmSsGYC5XAHCWc3CvHxSBQpt2XIfKwX5yujTqwjOYHqx7vIuLEUhk2o9SIitDiIX3v1OMY5aM2f/Dgh/7jsMCOLVL4SBflUEdqS9bpajpy9cnPCwJO21l7o5cbvUgTZMT1m8MLKdkf9cOonmGho79fEjHg806qOHvN8U4F63c/z3fbPgxiPqIrzC9ZLXd+n76mFbzqFkKXA7O2wI/aHYJWtyc4d9P2c2lMKbBe5Sa61JynfxTTi2E5PkftCt8IlMg73F0KLfisL/2CnyU0chmxjNOgaC+5TJSoIMsF4aLIVivEQVguHa9oRVlCkstwxiwmp4FbgZFzIndpn7ECpouXARX1AjR/5FYV3IkLl8xgBjOS+HZegl/MBT1+WUwNxqBQ6RgPbB6tYp0U03Hlh/x+vKLt7m0+2y3pMu51nILYnn0G+FX63ZGk9yv4FnPL+oZyaYUaozrWvUky/cia4/riiaN0mmOxdE55kjVGkq++/T7AkOe/jpZueo0HPO81Qsq12+LP3rSSl8peP1uYN9U2yFr8ilGSLw+fS5JQ87rk4MKVXodbWBzfS6defpLV5kLcOeD9Ya2kCdjvJFJ8ReRu9S111A7zeYNQPR6x+OUv4I/uev6bdED/aIx5B9NL4+nlwxl0zsSJkaph6eJjmc7TScoNkx0PNMyVZ9BR0qZM9TehXVAfKuiGt0RcMPUOu39nxKvlFe/SvqWMcpL2nZQ5lTNCYKIS7VwVT7gEhhKCULltK7mXOxrbe8Zjn3GkP3c7/NmegAsXMI8v3wPXX+yx2FRMhSrIxPctUAqrMTbURNnUybikMo/2pf9ZTS2lGPCD3dfN3zDkg9noRVfEM9oOSgx8VwcJiA30aM9z1vluItXcM7q/H8KVz2L7vrMHQofu372+ieuOJ0Zn8yg/shSHa9fV92Agpjry9/5OOPXRr7f1eNJslzT7WU0+goNNkLWRTUds6OuIKmh8JOedeGFFa0wlTrquV7IZbjJCWUjUmbZd3CE4njwp30ZHcqPEKQ2g1CPceiLQET9pyH4eNRRn4R2xGyCZLbOMxM+PvNSCpqmCLayQS2JMCsmOdPQtrPrYi/6CmWcMXlepQWFgAKv6yivmG5L151y3902bDAFGhD83nXmiN7lRe7W9YEK2xbNViP3RXjdPNCQMPOs09mJJ6Aajt+CuE0pJOaNMR6NINbBVDte5ayQWr8fklaK4O1kXvOOZriJErF7x1UurlQu0GUXrzBuKDk/2CnoR89MSE4n6me+qp9ry8d7IOs8XdXEozuDWTh8a6EK3cOqki/HfLizuuiTGjJswfjgvaD5EVSFSoSpl/ir+xEFV/+ui/nYLActRsk8Bo6OWMmet1cjVTySZl6enUGrBb3lihDjEFR3RYSNttRoHjaJKg+30bQlbmuGLLdT9zNavtnbQfYVJjLRZn6YAErZoHZzwiuFKWDXUHXRwPbjMqVQhPsswd5I/ozGXeGa1rtbarSiazbCnZcQ/DXNf6uN9O7IObx7a6JYu0/vOC5g+rx/GYvEgX80f780XeOt1KHctlihvYM6CdVsU8Hmwb+5W3I3JUixqi1o/HjV8GbERb9pQPMH1tDCQqrLvlmWjs+aIXy39WXqSpJkTHSp8dUPnoczHM/00TZym9brzVhfiWmh3JbqVddlMWIquCDw5l1ZK3sMjqNUP6pISOPApcURILsBlgzXqC8Dmed0kew1h1K8pjUttSpxB2KZCpUX9ENHNpm+LeYF28tn8M3nFE1euF0IVdqnyfvW/Mf27SvHYphOlX6pM7wWePXcOy23tpUNS5spc8PHofXHRqv2RfGGKGziYV1LGPLFYklj4bu/DGoVtgWz1GAZjaf3V6cht0PaZhhGKRTV8IMjNemuyzwHPehmZntVd0V2r05nsNonTKt3IH+a1oxuHzatRD1xZpGpwUS4sdpipHFZF9XXKygF+y710tR8KD7VVmoCTXGAkyLl7PYdKehAF1bN77S3miBn9TbfJmKn5kg4BI7nPNZJHTpgSrquq6Yc6KbNYo/ebSZ0C3mRt31tbsoBWWHBE5vpQXTJYDvrN2PoH7z/8ej3JyeaRWZugczmxsy6k2mbI5O6LYV2nOUUV8ub9hD1poFTAu4taG4XNEbphIpf0Z4ap95e6KJWoYHnZUpdx9xfEBGtRPA6FHKCeWJJcQWKpSp7h4jdUoTkz3BUGxJeGJEf1KRnidc5LTVs3O9Js1JWm0Egp50K0XCVDfkolVd3t1Xo8n8Je4NM63sgdyC2HUBxZ7TgSM+B77HnWKCs3BwqixRQ5X77xS3L6Lc0/gq+hKA6QyBTs9YYqpCEfd3P6CkqtCaz4oX7ypXPF0YCawPTiWyzV2dvS6S2RjwR+B3DdNIe311/v7TUtYHy/gF3N6tsvIg01Os30nRNlPge0mzbK2Vi8HPjp9+IgPgPLzaKRSxybtlmAep5XBIVbNKNDYqgAgXhwy1UvgLrIRzPHwQm+3H8pEzbrXeKHoxDWcaEEe28qx5gUaBanTK3stJxzbtNN5iZFMaaeTqLxTNVgJ+7ogChzqYBlJb9FGeAlebALUttit7QwPFVwtWik/NwntBVff2sFC6qBXuNCaldUxwzF+NK+/rmEOWhBcjV5LUYqv3HHqt2oJLpEtY2g7ekITHurpF/lP6c/+jn/pNBISULaihzil78VjEW1J74QObkxfgPIYnmxbruy6DbJXIslG+Yr8cXGMft++TyfONmZkQZbnoseWk+NPJeYo4l7Qxv7Drp6k1cTXn3YrLIOWVlD/dXnlKILt8Qmk9hnr2LfKSDFiya6njKNl3/qgCLjL7Ju83lXaa0ZvqmMyqR7LOaQpCXVOKq/1ScX5EQAYCsoXuLwcyUpn2/2r966GHdTy8WKOE5QLJNizeZqDp6C7UiTYfyAR3CdHaTIq7DkwipVeYJbgIM264g121KkgFrsmbHe3he9NRK1V72zRXAfdBSmheDH+VmAq3BOLRdGs3d7TC4jgWnbajfpYMW4PkXvBybDs4hOsTajYY/Xzb08xGHJ1zM0MJxJhV5tW/zNQPwRpZcUSKH6p6jenFav36dhxjWj0s1XoVWIdQJJLD7OIMq1U/Daw2mYwLuIXvYs7Cd6IoQ3EWr3dOsmnfbGvRSs9/JGSBaVUb1M1LuPnUaTC5pFZcRVBqGnGpT1150a5kRZv+w9DMTpJlCs3stg8ZLZH/iNRKzSa0ViVlhn7XuMollR77S2xYduD8xPi7sT4jlPikHOIjtZn1dq2SG9DiQrThRWW1VGDyvZU6MmLv0m7Ifj82wkvmBUMDjw9CcC7WSJ5Gk0vpK8wLe5odq6umcpVEzkZWxqMDUecDl7Y+6y+pY9s1VfhODEwQllSQCuRK4Tou/KuXkNUhWUE55ownnPDbW/UUzLcUN1fymEL515Q/bCVam2HiS3ji4g/pn+7SfUypI9skZnLkXoXARla6EfijRdeHvdpaKFdJLvn/Us6RZkQaOzrvEfzXlhW6v1nWiwJr7P3kB0QcsAiZEl8kuzy8g2B053UBA0eGECiyW0/18AwRE+7s0tdr6beWjHHMX4MKWPtJQAmZXHlJBh8TaOnqOjVIYyGpvb5BRB6/pdHFVeSt+lqbO1gdqB7JWgaC6ZF8Pg/HVDQ3b5iwSVbsKXS1y4ndPOYTiq9CGlCTIXuzgLh2iujNIuvcmx/c9v9pAUMAjOsQg2PPiD90ZebA3dbO0TafcCGW/p1LTXgAzf2JefiJ7MC1yHaMy4vxiJpGjfaG23Hphy71MQPLKw0j3t55tXhwfDULh6bN9fSTkl9O4xefzDCQ5qrmX1ghkYE+wgWYnn6FGn9oZ/gjQl8jSPPA6jEwkn07FCtLBjZnrbzLvI70feVtmN3yyKuwcvEHi581KE/o6s+XDNzuNu+q2spA0k6IzZtO+6EXv3WQvOU8AV/+FCZA2ruV/qaUcbC9FOOLlm6BprOhCit2/uaKtLvOJOELvKtckCOVm4OO51yqvuELL1dH2FzperbYpjUMMGCAGUx2452BV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"text/plain": [
"<Figure size 1440x360 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# update plots from above\n",
"\n",
"## plot these numbers in four subplots next to each other\n",
"\n",
"fig, ax = plt.subplots(1,4, figsize=(20,5))\n",
"\n",
"# plot USA\n",
"x_USA = np.arange(2)\n",
"USA_yerr_hc = ([0.5*USA_hc_saved], [0.5*USA_hc_saved])\n",
"\n",
"ax[0].bar(x_USA[0], USA_tax_2018, color='k', label='tax revenue')\n",
"ax[0].bar(x_USA[0], USA_pensions_saved, bottom=USA_tax_2018, color='red', \n",
" label='pensions saved', yerr=USA_yerr_pensions)\n",
"ax[0].bar(x_USA[0], USA_hc_saved, bottom=USA_pensions_saved + USA_tax_2018, color='green',\n",
" label='HC saved', yerr=USA_yerr_hc)\n",
"ax[0].bar(x_USA[1], USA_direct_cost, color='orange', label='direct')\n",
"ax[0].bar(x_USA[1], USA_indirect_cost, bottom=USA_direct_cost, color='blue', \n",
" label='indirect', yerr=USA_yerr_direct)\n",
"ax[0].set_ylabel('Billion USD', fontsize=ylabelsize)\n",
"ax[0].set_title('USA', fontsize=titlesize)\n",
"ax[0].set_xticks(x_USA)\n",
"ax[0].set_xticklabels(['tax', 'cost'], fontsize=xlabelsize)\n",
"#ax[0].legend(loc='lower right', prop={'size': legendsize})\n",
"ax[0].tick_params(axis='y', which='major', labelsize=yticklabelsize)\n",
"ax[0].tick_params(axis='y', which='minor', labelsize=yticklabelsize)\n",
"\n",
"\n",
"# plot Germany\n",
"x_Ger = np.arange(3)\n",
"Ger_yerr_hc = ([0.4*Ger_hc_saved], [0.4*Ger_hc_saved])\n",
"\n",
"ax[1].bar(x_Ger[0], Ger_tax_2018, color='k', label='tax revenue')\n",
"ax[1].bar(x_Ger[0], Ger_pensions_saved, bottom=Ger_tax_2018, color='red', \n",
" label='pensions saved', yerr=Ger_yerr_pensions)\n",
"ax[1].bar(x_Ger[0], Ger_hc_saved, bottom=Ger_pensions_saved + Ger_tax_2018, color='green', \n",
" label='HC saved', yerr=Ger_yerr_hc)\n",
"ax[1].bar(x_Ger[1], Ger_direct_cost, color='orange', label='direct')\n",
"ax[1].bar(x_Ger[1], Ger_indirect_cost, bottom=Ger_direct_cost, color='blue', \n",
" label='indirect', yerr=Ger_indirect_yerr)\n",
"ax[1].bar(x_Ger[2], Ger_direct_cost_other, color='orange')#, label='direct other')\n",
"ax[1].bar(x_Ger[2], Ger_indirect_cost_other, bottom=Ger_direct_cost_other,\n",
" color='blue', yerr=Ger_indirect_yerr_other)#, label='indirect other')ax[1].set_ylabel('Billion Euro')\n",
"ax[1].set_ylabel('Billion Euro', fontsize=ylabelsize)\n",
"ax[1].set_title('Germany', fontsize=titlesize)\n",
"ax[1].set_xticks(x_Ger)\n",
"ax[1].set_xticklabels(['tax', 'cost', 'new estimate'], fontsize=xlabelsize)\n",
"#ax[1].legend(loc='upper left', prop={'size': legendsize})\n",
"ax[1].tick_params(axis='y', which='major', labelsize=yticklabelsize)\n",
"ax[1].tick_params(axis='y', which='minor', labelsize=yticklabelsize)\n",
"\n",
"\n",
"# plot India\n",
"x_India = np.arange(2)\n",
"India_yerr_hc = ([0.5*India_hc_saved], [0.5*India_hc_saved])\n",
"\n",
"ax[2].bar(x_India[0], India_tax_2018, color='black', label='tax revenue')\n",
"ax[2].bar(x_India[0], India_pensions_saved, bottom=India_tax_2018, color='red',\n",
" label='pensions saved', yerr=India_yerr_pensions)\n",
"ax[2].bar(x_India[0], India_hc_saved, bottom=India_pensions_saved + India_tax_2018, color='green', \n",
" label='HC saved', yerr=India_yerr_hc)\n",
"ax[2].bar(x_India[1], India_direct_cost, color='orange', label='direct')\n",
"ax[2].bar(x_India[1], India_indirect_cost, bottom=India_direct_cost, color='blue',\n",
" label='indirect', yerr=India_indirect_yerr)\n",
"ax[2].set_ylabel('Billion USD', fontsize=ylabelsize)\n",
"ax[2].set_title('India', fontsize=titlesize)\n",
"ax[2].set_xticks(x_India)\n",
"ax[2].set_xticklabels(['tax', 'cost'], fontsize=xlabelsize)\n",
"ax[2].legend(loc='upper left', prop={'size': legendsize})\n",
"ax[2].tick_params(axis='y', which='major', labelsize=yticklabelsize)\n",
"ax[2].tick_params(axis='y', which='minor', labelsize=yticklabelsize)\n",
"\n",
"\n",
"# plot UK\n",
"x_UK = np.arange(2)\n",
"UK_yerr_hc = ([0.5*UK_hc_saved], [0.5*UK_hc_saved])\n",
"\n",
"ax[3].bar(x_UK[0], UK_tax_2018, color='black', label='tax revenue')\n",
"ax[3].bar(x_UK[0], UK_pensions_saved, bottom=UK_tax_2018, color='red', \n",
" label='pensions saved', yerr=UK_yerr_pensions)\n",
"ax[3].bar(x_UK[0], UK_hc_saved, bottom=UK_pensions_saved + UK_tax_2018, color='green', \n",
" label='HC saved', yerr=UK_yerr_hc)\n",
"ax[3].bar(x_UK[1], UK_direct_cost, color='orange', label='direct')\n",
"ax[3].bar(x_UK[1], UK_indirect_cost, bottom=UK_direct_cost, color='blue', \n",
" label='indirect', yerr=UK_indirect_yerr)\n",
"ax[3].set_ylabel('Billion BP', fontsize=ylabelsize)\n",
"ax[3].set_title('UK', fontsize=titlesize)\n",
"ax[3].set_xticks(x_UK)\n",
"ax[3].set_xticklabels(['tax', 'cost'], fontsize=xlabelsize)\n",
"#ax[3].legend(loc='lower left', prop={'size': legendsize})\n",
"ax[3].tick_params(axis='y', which='major', labelsize=yticklabelsize)\n",
"ax[3].tick_params(axis='y', which='minor', labelsize=yticklabelsize)\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig('cigarettes_tax_pensions_hc_simple.pdf')\n",
"plt.show();"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Estimate the Pensions saved - complex (not featured - too vague)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Entity</th>\n",
" <th>Code</th>\n",
" <th>Year</th>\n",
" <th>Deaths - Smoking - Sex: Both - Age: All Ages (Rate)</th>\n",
" <th>Deaths - Smoking - Sex: Both - Age: 15-49 years (Rate)</th>\n",
" <th>Deaths - Smoking - Sex: Both - Age: 50-69 years (Rate)</th>\n",
" <th>Deaths - Smoking - Sex: Both - Age: 70+ years (Rate)</th>\n",
" <th>Deaths - Smoking - Sex: Both - Age: Under 5 (Rate)</th>\n",
" <th>Deaths - Smoking - Sex: Both - Age: 5-14 years (Rate)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>AFG</td>\n",
" <td>1990</td>\n",
" <td>63.895905</td>\n",
" <td>16.589519</td>\n",
" <td>267.230009</td>\n",
" <td>679.006755</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Afghanistan</td>\n",
" <td>AFG</td>\n",
" <td>1991</td>\n",
" <td>61.846347</td>\n",
" <td>15.456913</td>\n",
" <td>266.975516</td>\n",
" <td>677.617648</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Afghanistan</td>\n",
" <td>AFG</td>\n",
" <td>1992</td>\n",
" <td>53.436511</td>\n",
" <td>12.767999</td>\n",
" <td>266.430053</td>\n",
" <td>679.505810</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Afghanistan</td>\n",
" <td>AFG</td>\n",
" <td>1993</td>\n",
" <td>47.044347</td>\n",
" <td>11.000425</td>\n",
" <td>267.969428</td>\n",
" <td>683.973588</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Afghanistan</td>\n",
" <td>AFG</td>\n",
" <td>1994</td>\n",
" <td>45.799808</td>\n",
" <td>10.738020</td>\n",
" <td>272.403687</td>\n",
" <td>691.007773</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Entity Code Year \\\n",
"0 Afghanistan AFG 1990 \n",
"1 Afghanistan AFG 1991 \n",
"2 Afghanistan AFG 1992 \n",
"3 Afghanistan AFG 1993 \n",
"4 Afghanistan AFG 1994 \n",
"\n",
" Deaths - Smoking - Sex: Both - Age: All Ages (Rate) \\\n",
"0 63.895905 \n",
"1 61.846347 \n",
"2 53.436511 \n",
"3 47.044347 \n",
"4 45.799808 \n",
"\n",
" Deaths - Smoking - Sex: Both - Age: 15-49 years (Rate) \\\n",
"0 16.589519 \n",
"1 15.456913 \n",
"2 12.767999 \n",
"3 11.000425 \n",
"4 10.738020 \n",
"\n",
" Deaths - Smoking - Sex: Both - Age: 50-69 years (Rate) \\\n",
"0 267.230009 \n",
"1 266.975516 \n",
"2 266.430053 \n",
"3 267.969428 \n",
"4 272.403687 \n",
"\n",
" Deaths - Smoking - Sex: Both - Age: 70+ years (Rate) \\\n",
"0 679.006755 \n",
"1 677.617648 \n",
"2 679.505810 \n",
"3 683.973588 \n",
"4 691.007773 \n",
"\n",
" Deaths - Smoking - Sex: Both - Age: Under 5 (Rate) \\\n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"\n",
" Deaths - Smoking - Sex: Both - Age: 5-14 years (Rate) \n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"death_rates_by_age_df = pd.read_csv('death-rates-smoking-age.csv')\n",
"death_rates_by_age_df.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# select year 2017 and our countries\n",
"\n",
"death_rates_by_age_2017_df = death_rates_by_age_df[death_rates_by_age_df['Year'] == 2017]\n",
"USA_death_rates_by_age_2017_df = death_rates_by_age_2017_df[death_rates_by_age_2017_df['Entity'] == 'United States']\n",
"Ger_death_rates_by_age_2017_df = death_rates_by_age_2017_df[death_rates_by_age_2017_df['Entity'] == 'Germany']\n",
"UK_death_rates_by_age_2017_df = death_rates_by_age_2017_df[death_rates_by_age_2017_df['Entity'] == 'United Kingdom']\n",
"India_death_rates_by_age_2017_df = death_rates_by_age_2017_df[death_rates_by_age_2017_df['Entity'] == 'India']"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# import age bracket data for the USA\n",
"# manually transferred from Wikipedia: https://en.wikipedia.org/wiki/Demographics_of_the_United_States\n",
"USA_age_brackets = ['0-14', '15-24', '25-54', '55-64', '65+']\n",
"USA_population_by_age = [61175933, 43351778, 128863172, 42179856, 51055052]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# import age bracket data for Germany\n",
"# manually transferred from https://www.statista.com/statistics/454349/population-by-age-group-germany/\n",
"# how ridiculous is their pricing to download a file??\n",
"Ger_age_brackets = ['0-14', '15-24', '25-59', '60-64', '65+']\n",
"Ger_population_by_age = [10.65, 9.29, 39.46, 5.65, 18.09]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# import age bracket data for the UK\n",
"# manually transferred from https://www.statista.com/statistics/281174/uk-population-by-age/\n",
"# because of their pricing\n",
"UK_age_brackets = ['0-14', '15-24', '25-54', '55-64', '65+']\n",
"UK_population_by_age = [11.96, 7.81, 26.49, 8.17, 12.39]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# import age bracket data for India\n",
"# manually transferred from Wikipedia: https://en.wikipedia.org/wiki/Demographics_of_India\n",
"India_age_brackets = ['0-14', '15-24', '25-54', '55-64', '55-64', '65+']\n",
"India_population_by_age = [372444116, 231950671, 458975293, 76809762, 66185333]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-20-a8ca8f86b8a4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'age_brackets.pdf'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m 270\u001b[0m \"\"\"\n\u001b[1;32m 271\u001b[0m \u001b[0;32mglobal\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 273\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfigure_manager\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m display(\n\u001b[0m\u001b[1;32m 42\u001b[0m \u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 39\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1227\u001b[0m \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1228\u001b[0;31m ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[0m\u001b[1;32m 1229\u001b[0m renderer)\n\u001b[1;32m 1230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_get_tick_bboxes\u001b[0;34m(self, ticks, renderer)\u001b[0m\n\u001b[1;32m 1171\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_tick_bboxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mticks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1172\u001b[0m \u001b[0;34m\"\"\"Return lists of bboxes for ticks' label1's and label2's.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1173\u001b[0;31m return ([tick.label1.get_window_extent(renderer)\n\u001b[0m\u001b[1;32m 1174\u001b[0m for tick in ticks if tick.label1.get_visible()],\n\u001b[1;32m 1175\u001b[0m [tick.label2.get_window_extent(renderer)\n",
"\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 1171\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_tick_bboxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mticks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1172\u001b[0m \u001b[0;34m\"\"\"Return lists of bboxes for ticks' label1's and label2's.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1173\u001b[0;31m return ([tick.label1.get_window_extent(renderer)\n\u001b[0m\u001b[1;32m 1174\u001b[0m for tick in ticks if tick.label1.get_visible()],\n\u001b[1;32m 1175\u001b[0m [tick.label2.get_window_extent(renderer)\n",
"\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36mget_window_extent\u001b[0;34m(self, renderer, dpi)\u001b[0m\n\u001b[1;32m 903\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Cannot get window extent w/o renderer'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 904\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 905\u001b[0;31m \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_renderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 906\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_unitless_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 907\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m_get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 297\u001b[0m \u001b[0mclean_line\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mismath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_preprocess_math\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 298\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mclean_line\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 299\u001b[0;31m w, h, d = renderer.get_text_width_height_descent(\n\u001b[0m\u001b[1;32m 300\u001b[0m clean_line, self._fontproperties, ismath=ismath)\n\u001b[1;32m 301\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_svg.py\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m 1172\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_text_width_height_descent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mismath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1173\u001b[0m \u001b[0;31m# docstring inherited\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1174\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_text2path\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_text_width_height_descent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mismath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/textpath.py\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0mtexmanager\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_texmanager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0mfontsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_size_in_points\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m w, h, d = texmanager.get_text_width_height_descent(s, fontsize,\n\u001b[0m\u001b[1;32m 69\u001b[0m renderer=None)\n\u001b[1;32m 70\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/texmanager.py\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, tex, fontsize, renderer)\u001b[0m\n\u001b[1;32m 458\u001b[0m \u001b[0mdvifile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_dvi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 459\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mdviread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDvi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdvifile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m72\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdpi_fraction\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdvi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 460\u001b[0;31m \u001b[0mpage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdvi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 461\u001b[0m \u001b[0;31m# A total height (including the descent) needs to be returned.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 462\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mpage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheight\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mpage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/dviread.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 241\u001b[0m \u001b[0mintegers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 242\u001b[0m \"\"\"\n\u001b[0;32m--> 243\u001b[0;31m \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 244\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/dviread.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 317\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 318\u001b[0m \u001b[0mbyte\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 319\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dtable\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbyte\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbyte\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 320\u001b[0m if (self._baseline_v is None\n\u001b[1;32m 321\u001b[0m and len(getattr(self, \"stack\", [])) == 3):\n",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"# plot the different age brackets as bar plots\n",
"\n",
"## plot these numbers in four subplots next to each other\n",
"\n",
"fig, ax = plt.subplots(1,4, figsize=(20,5))\n",
"\n",
"# plot USA\n",
"x_USA = np.arange(5)\n",
"\n",
"ax[0].bar(x_USA, USA_population_by_age, color='blue')\n",
"ax[0].set_ylabel('population')\n",
"ax[0].set_title('USA')\n",
"ax[0].set_xticks(x_USA)\n",
"ax[0].set_xticklabels(USA_age_brackets)\n",
"\n",
"# plot Germany\n",
"x_Ger = np.arange(5)\n",
"\n",
"ax[1].bar(x_Ger, Ger_population_by_age, color='gold')\n",
"ax[1].set_ylabel('Population in Mio')\n",
"ax[1].set_title('Ger')\n",
"ax[1].set_xticks(x_Ger)\n",
"ax[1].set_xticklabels(Ger_age_brackets)\n",
"\n",
"# plot India\n",
"x_India = np.arange(5)\n",
"\n",
"ax[2].bar(x_India, India_population_by_age, color='orange')\n",
"ax[2].set_ylabel('population')\n",
"ax[2].set_title('India')\n",
"ax[2].set_xticks(x_India)\n",
"ax[2].set_xticklabels(India_age_brackets)\n",
"\n",
"# plot UK\n",
"x_UK = np.arange(5)\n",
"\n",
"ax[3].bar(x_UK, UK_population_by_age, color='blue')\n",
"ax[3].set_ylabel('population in Mio')\n",
"ax[3].set_title('UK')\n",
"ax[3].set_xticks(x_UK)\n",
"ax[3].set_xticklabels(UK_age_brackets)\n",
"\n",
"plt.savefig('age_brackets.pdf')\n",
"plt.show();"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"USA_death_rates_by_age_2017_df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# plot the different rates of death from cigarettes by age brackets as bar plots\n",
"\n",
"## plot these numbers in four subplots next to each other\n",
"\n",
"fig, ax = plt.subplots(1,4, figsize=(20,5))\n",
"death_rate_brackets = ['0-14', '15-49', '50-69', '70+']\n",
"\n",
"# plot USA\n",
"x_USA = np.arange(4)\n",
"USA_death_rates_0_14 = 0\n",
"USA_death_rates_15_49 = USA_death_rates_by_age_2017_df['Deaths - Smoking - Sex: Both - Age: 15-49 years (Rate)']\n",
"USA_death_rates_50_69 = USA_death_rates_by_age_2017_df['Deaths - Smoking - Sex: Both - Age: 50-69 years (Rate)']\n",
"USA_death_rates_70_plus = USA_death_rates_by_age_2017_df['Deaths - Smoking - Sex: Both - Age: 70+ years (Rate)']\n",
"USA_death_rates_by_age = [USA_death_rates_0_14, USA_death_rates_15_49, USA_death_rates_50_69, USA_death_rates_70_plus]\n",
"\n",
"ax[0].bar(x_USA, USA_death_rates_by_age, color='blue')\n",
"ax[0].set_ylabel('rate of death per 100k')\n",
"ax[0].set_title('USA')\n",
"ax[0].set_xticks(x_USA)\n",
"ax[0].set_xticklabels(death_rate_brackets)\n",
"\n",
"# plot Germany\n",
"x_Ger = np.arange(4)\n",
"Ger_death_rates_0_14 = 0\n",
"Ger_death_rates_15_49 = Ger_death_rates_by_age_2017_df['Deaths - Smoking - Sex: Both - Age: 15-49 years (Rate)']\n",
"Ger_death_rates_50_69 = Ger_death_rates_by_age_2017_df['Deaths - Smoking - Sex: Both - Age: 50-69 years (Rate)']\n",
"Ger_death_rates_70_plus = Ger_death_rates_by_age_2017_df['Deaths - Smoking - Sex: Both - Age: 70+ years (Rate)']\n",
"Ger_death_rates_by_age = [Ger_death_rates_0_14, Ger_death_rates_15_49, Ger_death_rates_50_69, Ger_death_rates_70_plus]\n",
"\n",
"ax[1].bar(x_Ger, Ger_death_rates_by_age, color='gold')\n",
"ax[1].set_ylabel('rate of death per 100k')\n",
"ax[1].set_title('Germany')\n",
"ax[1].set_xticks(x_Ger)\n",
"ax[1].set_xticklabels(death_rate_brackets)\n",
"\n",
"# plot India\n",
"x_India = np.arange(4)\n",
"India_death_rates_0_14 = 0\n",
"India_death_rates_15_49 = India_death_rates_by_age_2017_df['Deaths - Smoking - Sex: Both - Age: 15-49 years (Rate)']\n",
"India_death_rates_50_69 = India_death_rates_by_age_2017_df['Deaths - Smoking - Sex: Both - Age: 50-69 years (Rate)']\n",
"India_death_rates_70_plus = India_death_rates_by_age_2017_df['Deaths - Smoking - Sex: Both - Age: 70+ years (Rate)']\n",
"India_death_rates_by_age = [India_death_rates_0_14, India_death_rates_15_49, India_death_rates_50_69, India_death_rates_70_plus]\n",
"\n",
"ax[2].bar(x_India, India_death_rates_by_age, color='orange')\n",
"ax[2].set_ylabel('rate of death per 100k')\n",
"ax[2].set_title('India')\n",
"ax[2].set_xticks(x_India)\n",
"ax[2].set_xticklabels(death_rate_brackets)\n",
"\n",
"# plot UK\n",
"x_UK = np.arange(4)\n",
"UK_death_rates_0_14 = 0\n",
"UK_death_rates_15_49 = UK_death_rates_by_age_2017_df['Deaths - Smoking - Sex: Both - Age: 15-49 years (Rate)']\n",
"UK_death_rates_50_69 = UK_death_rates_by_age_2017_df['Deaths - Smoking - Sex: Both - Age: 50-69 years (Rate)']\n",
"UK_death_rates_70_plus = UK_death_rates_by_age_2017_df['Deaths - Smoking - Sex: Both - Age: 70+ years (Rate)']\n",
"UK_death_rates_by_age = [UK_death_rates_0_14, UK_death_rates_15_49, UK_death_rates_50_69, UK_death_rates_70_plus]\n",
"\n",
"ax[3].bar(x_UK, UK_death_rates_by_age, color='red')\n",
"ax[3].set_ylabel('rate of death per 100k')\n",
"ax[3].set_title('UK')\n",
"ax[3].set_xticks(x_UK)\n",
"ax[3].set_xticklabels(death_rate_brackets)\n",
"\n",
"plt.savefig('death_rate_age_brackets.pdf')\n",
"plt.show();"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# estimate the number of deaths by age group\n",
"\n",
"# since the age brackets of death rates from smoking and actual population don't really overlap\n",
"# I have to match them in a slightly different way. \n",
"# I will merge the 15-24 and 25-54 age brackets and multiply them with the 15-49 age group. \n",
"# the 54-65 age group will be multiplied with the 50-69 bracket for death rates\n",
"# the 65+ age group will be multiplied with the 70+ bracket for death rates\n",
"\n",
"# USA\n",
"USA_deaths_young_adults = 1/100000 * USA_death_rates_15_49.values * (USA_population_by_age[1] + USA_population_by_age[2])\n",
"USA_deaths_old_adults = 1/100000 * USA_death_rates_50_69.values * USA_population_by_age[3]\n",
"USA_deaths_seniors = 1/100000 * USA_death_rates_70_plus.values * USA_population_by_age[4]\n",
"\n",
"# Ger\n",
"Ger_deaths_young_adults = 10 * Ger_death_rates_15_49.values * (Ger_population_by_age[1] + Ger_population_by_age[2])\n",
"Ger_deaths_old_adults = 10 * Ger_death_rates_50_69.values * Ger_population_by_age[3]\n",
"Ger_deaths_seniors = 10 * Ger_death_rates_70_plus.values * Ger_population_by_age[4]\n",
"\n",
"# India\n",
"India_deaths_young_adults = 1/100000 * India_death_rates_15_49.values * (India_population_by_age[1] + India_population_by_age[2])\n",
"India_deaths_old_adults = 1/100000 * India_death_rates_50_69.values * India_population_by_age[3]\n",
"India_deaths_seniors = 1/100000 * India_death_rates_70_plus.values * India_population_by_age[4]\n",
"\n",
"# UK\n",
"UK_deaths_young_adults = 10 * UK_death_rates_15_49.values * (UK_population_by_age[1] + UK_population_by_age[2])\n",
"UK_deaths_old_adults = 10 * UK_death_rates_50_69.values * UK_population_by_age[3]\n",
"UK_deaths_seniors = 10 * UK_death_rates_70_plus.values * UK_population_by_age[4]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(USA_deaths_young_adults)\n",
"print(USA_deaths_old_adults)\n",
"print(USA_deaths_seniors)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| stackv2 | 2024-11-18T18:03:05.346098+00:00 | 2023-07-15T10:35:53 | {
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04e6b7e5ddc6888c7264fab1cafedce9bf390d0d | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load DataFrame and Buildup JS-useable table"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import pandas as pd\n",
"from tqdm.auto import tqdm"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>geometry</th>\n",
" <th>adcode</th>\n",
" <th>name</th>\n",
" <th>center</th>\n",
" <th>centroid</th>\n",
" <th>childrenNum</th>\n",
" <th>level</th>\n",
" <th>parent</th>\n",
" <th>subFeatureIndex</th>\n",
" <th>acroutes</th>\n",
" <th>adchar</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>--</td>\n",
" <td>110000</td>\n",
" <td>北京市</td>\n",
" <td>[116.405285, 39.904989]</td>\n",
" <td>[116.41995, 40.18994]</td>\n",
" <td>16.0</td>\n",
" <td>province</td>\n",
" <td>100000</td>\n",
" <td>0.0</td>\n",
" <td>[100000]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>--</td>\n",
" <td>120000</td>\n",
" <td>天津市</td>\n",
" <td>[117.190182, 39.125596]</td>\n",
" <td>[117.347019, 39.28803]</td>\n",
" <td>16.0</td>\n",
" <td>province</td>\n",
" <td>100000</td>\n",
" <td>1.0</td>\n",
" <td>[100000]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>--</td>\n",
" <td>130000</td>\n",
" <td>河北省</td>\n",
" <td>[114.502461, 38.045474]</td>\n",
" <td>None</td>\n",
" <td>11.0</td>\n",
" <td>province</td>\n",
" <td>100000</td>\n",
" <td>2.0</td>\n",
" <td>[100000]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>--</td>\n",
" <td>140000</td>\n",
" <td>山西省</td>\n",
" <td>[112.549248, 37.857014]</td>\n",
" <td>[112.304761, 37.618555]</td>\n",
" <td>11.0</td>\n",
" <td>province</td>\n",
" <td>100000</td>\n",
" <td>3.0</td>\n",
" <td>[100000]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>--</td>\n",
" <td>150000</td>\n",
" <td>内蒙古自治区</td>\n",
" <td>[111.670801, 40.818311]</td>\n",
" <td>[114.077404, 44.331072]</td>\n",
" <td>12.0</td>\n",
" <td>province</td>\n",
" <td>100000</td>\n",
" <td>4.0</td>\n",
" <td>[100000]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3236</th>\n",
" <td>--</td>\n",
" <td>654322</td>\n",
" <td>富蕴县</td>\n",
" <td>[89.524993, 46.993106]</td>\n",
" <td>[89.386618, 46.532364]</td>\n",
" <td>0.0</td>\n",
" <td>district</td>\n",
" <td>654300</td>\n",
" <td>2.0</td>\n",
" <td>[100000, 650000, 654300]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3237</th>\n",
" <td>--</td>\n",
" <td>654323</td>\n",
" <td>福海县</td>\n",
" <td>[87.494569, 47.113128]</td>\n",
" <td>[88.046601, 46.362515]</td>\n",
" <td>0.0</td>\n",
" <td>district</td>\n",
" <td>654300</td>\n",
" <td>3.0</td>\n",
" <td>[100000, 650000, 654300]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3238</th>\n",
" <td>--</td>\n",
" <td>654324</td>\n",
" <td>哈巴河县</td>\n",
" <td>[86.418964, 48.059284]</td>\n",
" <td>[86.402485, 48.310203]</td>\n",
" <td>0.0</td>\n",
" <td>district</td>\n",
" <td>654300</td>\n",
" <td>4.0</td>\n",
" <td>[100000, 650000, 654300]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3239</th>\n",
" <td>--</td>\n",
" <td>654325</td>\n",
" <td>青河县</td>\n",
" <td>[90.381561, 46.672446]</td>\n",
" <td>[90.39768, 46.263028]</td>\n",
" <td>0.0</td>\n",
" <td>district</td>\n",
" <td>654300</td>\n",
" <td>5.0</td>\n",
" <td>[100000, 650000, 654300]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3240</th>\n",
" <td>--</td>\n",
" <td>654326</td>\n",
" <td>吉木乃县</td>\n",
" <td>[85.876064, 47.434633]</td>\n",
" <td>[86.200562, 47.399536]</td>\n",
" <td>0.0</td>\n",
" <td>district</td>\n",
" <td>654300</td>\n",
" <td>6.0</td>\n",
" <td>[100000, 650000, 654300]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3241 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" geometry adcode name center \\\n",
"0 -- 110000 北京市 [116.405285, 39.904989] \n",
"1 -- 120000 天津市 [117.190182, 39.125596] \n",
"2 -- 130000 河北省 [114.502461, 38.045474] \n",
"3 -- 140000 山西省 [112.549248, 37.857014] \n",
"4 -- 150000 内蒙古自治区 [111.670801, 40.818311] \n",
"... ... ... ... ... \n",
"3236 -- 654322 富蕴县 [89.524993, 46.993106] \n",
"3237 -- 654323 福海县 [87.494569, 47.113128] \n",
"3238 -- 654324 哈巴河县 [86.418964, 48.059284] \n",
"3239 -- 654325 青河县 [90.381561, 46.672446] \n",
"3240 -- 654326 吉木乃县 [85.876064, 47.434633] \n",
"\n",
" centroid childrenNum level parent subFeatureIndex \\\n",
"0 [116.41995, 40.18994] 16.0 province 100000 0.0 \n",
"1 [117.347019, 39.28803] 16.0 province 100000 1.0 \n",
"2 None 11.0 province 100000 2.0 \n",
"3 [112.304761, 37.618555] 11.0 province 100000 3.0 \n",
"4 [114.077404, 44.331072] 12.0 province 100000 4.0 \n",
"... ... ... ... ... ... \n",
"3236 [89.386618, 46.532364] 0.0 district 654300 2.0 \n",
"3237 [88.046601, 46.362515] 0.0 district 654300 3.0 \n",
"3238 [86.402485, 48.310203] 0.0 district 654300 4.0 \n",
"3239 [90.39768, 46.263028] 0.0 district 654300 5.0 \n",
"3240 [86.200562, 47.399536] 0.0 district 654300 6.0 \n",
"\n",
" acroutes adchar \n",
"0 [100000] None \n",
"1 [100000] None \n",
"2 [100000] None \n",
"3 [100000] None \n",
"4 [100000] None \n",
"... ... ... \n",
"3236 [100000, 650000, 654300] None \n",
"3237 [100000, 650000, 654300] None \n",
"3238 [100000, 650000, 654300] None \n",
"3239 [100000, 650000, 654300] None \n",
"3240 [100000, 650000, 654300] None \n",
"\n",
"[3241 rows x 11 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"local_dir = 'D:\\\\Sync\\\\GeoData'\n",
"\n",
"def get(e, key='adcode'):\n",
" if isinstance(e, dict):\n",
" return e.get(key, 0)\n",
" return 0\n",
"\n",
"main_frame = pd.read_json(os.path.join(local_dir, 'main.json'))\n",
"main_frame.parent = main_frame.parent.map(get)\n",
"main_frame"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"def query(expr, df=main_frame, to_series=True):\n",
" found = df.query(expr)\n",
" print(f'Got {len(found)} records')\n",
" \n",
" if to_series:\n",
" if len(found) == 0:\n",
" print(f'Got ZERO records of \"{expr}\", but series is required, using None for return')\n",
" return None\n",
" if len(found) > 1:\n",
" print(f'Got MUTIPLE({len(found)}) records of \"{expr}\", but series is required, using the first one for return')\n",
" return found.iloc[0]\n",
" if len(found) == 1:\n",
" return found.iloc[0]\n",
" \n",
" return found\n",
"\n",
"def trace(i):\n",
" # Get the trace of the [i],\n",
" # [i] refers the index in the [main_frame]\n",
" se = main_frame.iloc[i]\n",
" adcode = se['adcode']\n",
" acroutes = se['acroutes']\n",
" \n",
" df = pd.DataFrame()\n",
" for j in acroutes:\n",
" df = df.append(query(f'adcode == {j}'))\n",
" \n",
" df = df.append(se)\n",
" \n",
" df = pd.concat([df, query(f'parent == {adcode}', to_series=False)])\n",
" \n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Got 1 records\n",
"Got 1 records\n",
"Got 7 records\n"
]
},
{
"data": {
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" <th></th>\n",
" <th>acroutes</th>\n",
" <th>adchar</th>\n",
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" <th>center</th>\n",
" <th>centroid</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>None</td>\n",
" <td>JD</td>\n",
" <td>100000.0</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>--</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>[100000]</td>\n",
" <td>None</td>\n",
" <td>650000.0</td>\n",
" <td>[87.617733, 43.792818]</td>\n",
" <td>[85.294711, 41.371801]</td>\n",
" <td>24.0</td>\n",
" <td>--</td>\n",
" <td>province</td>\n",
" <td>新疆维吾尔自治区</td>\n",
" <td>100000.0</td>\n",
" <td>30.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>473</th>\n",
" <td>[100000, 650000]</td>\n",
" <td>None</td>\n",
" <td>654300.0</td>\n",
" <td>[88.13963, 47.848393]</td>\n",
" <td>[87.048189, 48.013014]</td>\n",
" <td>7.0</td>\n",
" <td>--</td>\n",
" <td>city</td>\n",
" <td>阿勒泰地区</td>\n",
" <td>650000.0</td>\n",
" <td>13.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3234</th>\n",
" <td>[100000, 650000, 654300]</td>\n",
" <td>None</td>\n",
" <td>654301.0</td>\n",
" <td>[88.138743, 47.848911]</td>\n",
" <td>[87.917843, 47.884663]</td>\n",
" <td>0.0</td>\n",
" <td>--</td>\n",
" <td>district</td>\n",
" <td>阿勒泰市</td>\n",
" <td>654300.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3235</th>\n",
" <td>[100000, 650000, 654300]</td>\n",
" <td>None</td>\n",
" <td>654321.0</td>\n",
" <td>[86.86186000000001, 47.70453]</td>\n",
" <td>[87.230803, 48.313428]</td>\n",
" <td>0.0</td>\n",
" <td>--</td>\n",
" <td>district</td>\n",
" <td>布尔津县</td>\n",
" <td>654300.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3236</th>\n",
" <td>[100000, 650000, 654300]</td>\n",
" <td>None</td>\n",
" <td>654322.0</td>\n",
" <td>[89.524993, 46.993106]</td>\n",
" <td>[89.386618, 46.532364]</td>\n",
" <td>0.0</td>\n",
" <td>--</td>\n",
" <td>district</td>\n",
" <td>富蕴县</td>\n",
" <td>654300.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3237</th>\n",
" <td>[100000, 650000, 654300]</td>\n",
" <td>None</td>\n",
" <td>654323.0</td>\n",
" <td>[87.494569, 47.113128]</td>\n",
" <td>[88.046601, 46.362515]</td>\n",
" <td>0.0</td>\n",
" <td>--</td>\n",
" <td>district</td>\n",
" <td>福海县</td>\n",
" <td>654300.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3238</th>\n",
" <td>[100000, 650000, 654300]</td>\n",
" <td>None</td>\n",
" <td>654324.0</td>\n",
" <td>[86.418964, 48.059284]</td>\n",
" <td>[86.402485, 48.310203]</td>\n",
" <td>0.0</td>\n",
" <td>--</td>\n",
" <td>district</td>\n",
" <td>哈巴河县</td>\n",
" <td>654300.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3239</th>\n",
" <td>[100000, 650000, 654300]</td>\n",
" <td>None</td>\n",
" <td>654325.0</td>\n",
" <td>[90.381561, 46.672446]</td>\n",
" <td>[90.39768, 46.263028]</td>\n",
" <td>0.0</td>\n",
" <td>--</td>\n",
" <td>district</td>\n",
" <td>青河县</td>\n",
" <td>654300.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3240</th>\n",
" <td>[100000, 650000, 654300]</td>\n",
" <td>None</td>\n",
" <td>654326.0</td>\n",
" <td>[85.876064, 47.434633]</td>\n",
" <td>[86.200562, 47.399536]</td>\n",
" <td>0.0</td>\n",
" <td>--</td>\n",
" <td>district</td>\n",
" <td>吉木乃县</td>\n",
" <td>654300.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" acroutes adchar adcode \\\n",
"34 None JD 100000.0 \n",
"30 [100000] None 650000.0 \n",
"473 [100000, 650000] None 654300.0 \n",
"3234 [100000, 650000, 654300] None 654301.0 \n",
"3235 [100000, 650000, 654300] None 654321.0 \n",
"3236 [100000, 650000, 654300] None 654322.0 \n",
"3237 [100000, 650000, 654300] None 654323.0 \n",
"3238 [100000, 650000, 654300] None 654324.0 \n",
"3239 [100000, 650000, 654300] None 654325.0 \n",
"3240 [100000, 650000, 654300] None 654326.0 \n",
"\n",
" center centroid childrenNum \\\n",
"34 None None NaN \n",
"30 [87.617733, 43.792818] [85.294711, 41.371801] 24.0 \n",
"473 [88.13963, 47.848393] [87.048189, 48.013014] 7.0 \n",
"3234 [88.138743, 47.848911] [87.917843, 47.884663] 0.0 \n",
"3235 [86.86186000000001, 47.70453] [87.230803, 48.313428] 0.0 \n",
"3236 [89.524993, 46.993106] [89.386618, 46.532364] 0.0 \n",
"3237 [87.494569, 47.113128] [88.046601, 46.362515] 0.0 \n",
"3238 [86.418964, 48.059284] [86.402485, 48.310203] 0.0 \n",
"3239 [90.381561, 46.672446] [90.39768, 46.263028] 0.0 \n",
"3240 [85.876064, 47.434633] [86.200562, 47.399536] 0.0 \n",
"\n",
" geometry level name parent subFeatureIndex \n",
"34 -- None 0.0 NaN \n",
"30 -- province 新疆维吾尔自治区 100000.0 30.0 \n",
"473 -- city 阿勒泰地区 650000.0 13.0 \n",
"3234 -- district 阿勒泰市 654300.0 0.0 \n",
"3235 -- district 布尔津县 654300.0 1.0 \n",
"3236 -- district 富蕴县 654300.0 2.0 \n",
"3237 -- district 福海县 654300.0 3.0 \n",
"3238 -- district 哈巴河县 654300.0 4.0 \n",
"3239 -- district 青河县 654300.0 5.0 \n",
"3240 -- district 吉木乃县 654300.0 6.0 "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trace(473)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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| stackv2 | 2024-11-18T18:03:05.377480+00:00 | 2021-01-17T06:30:24 | {
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} |
ebcf761f951eb8ee5bd659376ecbdd3c2aeb5e68 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Customer Segmentation Analysis\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"df_orders = pd.read_excel('orders.xlsx', sheet_name='Sheet1')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"df_bikeshops = pd.read_excel('bikeshops.xlsx', sheet_name = 'Sheet1')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df_bikes = pd.read_excel('bikes.xlsx', sheet_name = 'Sheet1')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>order.id</th>\n",
" <th>order.line</th>\n",
" <th>order.date</th>\n",
" <th>customer.id</th>\n",
" <th>product.id</th>\n",
" <th>quantity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2011-01-07</td>\n",
" <td>2</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2011-01-07</td>\n",
" <td>2</td>\n",
" <td>52</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2011-01-10</td>\n",
" <td>10</td>\n",
" <td>76</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2011-01-10</td>\n",
" <td>10</td>\n",
" <td>52</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2011-01-10</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" order.id order.line order.date customer.id product.id quantity\n",
"1 1 1 2011-01-07 2 48 1\n",
"2 1 2 2011-01-07 2 52 1\n",
"3 2 1 2011-01-10 10 76 1\n",
"4 2 2 2011-01-10 10 52 1\n",
"5 3 1 2011-01-10 6 2 1"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_orders.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>bikeshop.id</th>\n",
" <th>bikeshop.name</th>\n",
" <th>bikeshop.city</th>\n",
" <th>bikeshop.state</th>\n",
" <th>latitude</th>\n",
" <th>longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Pittsburgh Mountain Machines</td>\n",
" <td>Pittsburgh</td>\n",
" <td>PA</td>\n",
" <td>40.440625</td>\n",
" <td>-79.995886</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>Ithaca Mountain Climbers</td>\n",
" <td>Ithaca</td>\n",
" <td>NY</td>\n",
" <td>42.443961</td>\n",
" <td>-76.501881</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>Columbus Race Equipment</td>\n",
" <td>Columbus</td>\n",
" <td>OH</td>\n",
" <td>39.961176</td>\n",
" <td>-82.998794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>Detroit Cycles</td>\n",
" <td>Detroit</td>\n",
" <td>MI</td>\n",
" <td>42.331427</td>\n",
" <td>-83.045754</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Cincinnati Speed</td>\n",
" <td>Cincinnati</td>\n",
" <td>OH</td>\n",
" <td>39.103118</td>\n",
" <td>-84.512020</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bikeshop.id bikeshop.name bikeshop.city bikeshop.state \\\n",
"0 1 Pittsburgh Mountain Machines Pittsburgh PA \n",
"1 2 Ithaca Mountain Climbers Ithaca NY \n",
"2 3 Columbus Race Equipment Columbus OH \n",
"3 4 Detroit Cycles Detroit MI \n",
"4 5 Cincinnati Speed Cincinnati OH \n",
"\n",
" latitude longitude \n",
"0 40.440625 -79.995886 \n",
"1 42.443961 -76.501881 \n",
"2 39.961176 -82.998794 \n",
"3 42.331427 -83.045754 \n",
"4 39.103118 -84.512020 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_bikeshops.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" vertical-align: top;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>bike.id</th>\n",
" <th>model</th>\n",
" <th>category1</th>\n",
" <th>category2</th>\n",
" <th>frame</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Supersix Evo Black Inc.</td>\n",
" <td>Road</td>\n",
" <td>Elite Road</td>\n",
" <td>Carbon</td>\n",
" <td>12790</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>Supersix Evo Hi-Mod Team</td>\n",
" <td>Road</td>\n",
" <td>Elite Road</td>\n",
" <td>Carbon</td>\n",
" <td>10660</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>Supersix Evo Hi-Mod Dura Ace 1</td>\n",
" <td>Road</td>\n",
" <td>Elite Road</td>\n",
" <td>Carbon</td>\n",
" <td>7990</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>Supersix Evo Hi-Mod Dura Ace 2</td>\n",
" <td>Road</td>\n",
" <td>Elite Road</td>\n",
" <td>Carbon</td>\n",
" <td>5330</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Supersix Evo Hi-Mod Utegra</td>\n",
" <td>Road</td>\n",
" <td>Elite Road</td>\n",
" <td>Carbon</td>\n",
" <td>4260</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bike.id model category1 category2 frame \\\n",
"0 1 Supersix Evo Black Inc. Road Elite Road Carbon \n",
"1 2 Supersix Evo Hi-Mod Team Road Elite Road Carbon \n",
"2 3 Supersix Evo Hi-Mod Dura Ace 1 Road Elite Road Carbon \n",
"3 4 Supersix Evo Hi-Mod Dura Ace 2 Road Elite Road Carbon \n",
"4 5 Supersix Evo Hi-Mod Utegra Road Elite Road Carbon \n",
"\n",
" price \n",
"0 12790 \n",
"1 10660 \n",
"2 7990 \n",
"3 5330 \n",
"4 4260 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_bikes.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"df_merged = df_orders.merge(df_bikeshops, left_on = 'customer.id', right_on= 'bikeshop.id')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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" </tr>\n",
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" <th>0</th>\n",
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" <td>Ithaca Mountain Climbers</td>\n",
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" <td>52</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Ithaca Mountain Climbers</td>\n",
" <td>Ithaca</td>\n",
" <td>NY</td>\n",
" <td>42.443961</td>\n",
" <td>-76.501881</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>19</td>\n",
" <td>1</td>\n",
" <td>2011-01-28</td>\n",
" <td>2</td>\n",
" <td>66</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>Ithaca Mountain Climbers</td>\n",
" <td>Ithaca</td>\n",
" <td>NY</td>\n",
" <td>42.443961</td>\n",
" <td>-76.501881</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>19</td>\n",
" <td>2</td>\n",
" <td>2011-01-28</td>\n",
" <td>2</td>\n",
" <td>61</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Ithaca Mountain Climbers</td>\n",
" <td>Ithaca</td>\n",
" <td>NY</td>\n",
" <td>42.443961</td>\n",
" <td>-76.501881</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>28</td>\n",
" <td>1</td>\n",
" <td>2011-02-10</td>\n",
" <td>2</td>\n",
" <td>47</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Ithaca Mountain Climbers</td>\n",
" <td>Ithaca</td>\n",
" <td>NY</td>\n",
" <td>42.443961</td>\n",
" <td>-76.501881</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" order.id order.line order.date customer.id product.id quantity \\\n",
"0 1 1 2011-01-07 2 48 1 \n",
"1 1 2 2011-01-07 2 52 1 \n",
"2 19 1 2011-01-28 2 66 2 \n",
"3 19 2 2011-01-28 2 61 1 \n",
"4 28 1 2011-02-10 2 47 1 \n",
"\n",
" bikeshop.id bikeshop.name bikeshop.city bikeshop.state \\\n",
"0 2 Ithaca Mountain Climbers Ithaca NY \n",
"1 2 Ithaca Mountain Climbers Ithaca NY \n",
"2 2 Ithaca Mountain Climbers Ithaca NY \n",
"3 2 Ithaca Mountain Climbers Ithaca NY \n",
"4 2 Ithaca Mountain Climbers Ithaca NY \n",
"\n",
" latitude longitude \n",
"0 42.443961 -76.501881 \n",
"1 42.443961 -76.501881 \n",
"2 42.443961 -76.501881 \n",
"3 42.443961 -76.501881 \n",
"4 42.443961 -76.501881 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_merged.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"df_merged = df_merged.merge(df_bikes, left_on= 'product.id', right_on = 'bike.id')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
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" <th></th>\n",
" <th>order.id</th>\n",
" <th>order.line</th>\n",
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" <th>customer.id</th>\n",
" <th>product.id</th>\n",
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" <th>latitude</th>\n",
" <th>longitude</th>\n",
" <th>bike.id</th>\n",
" <th>model</th>\n",
" <th>category1</th>\n",
" <th>category2</th>\n",
" <th>frame</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
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" <td>2011-01-07</td>\n",
" <td>2</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
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" <td>Ithaca Mountain Climbers</td>\n",
" <td>Ithaca</td>\n",
" <td>NY</td>\n",
" <td>42.443961</td>\n",
" <td>-76.501881</td>\n",
" <td>48</td>\n",
" <td>Jekyll Carbon 2</td>\n",
" <td>Mountain</td>\n",
" <td>Over Mountain</td>\n",
" <td>Carbon</td>\n",
" <td>6070</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>132</td>\n",
" <td>6</td>\n",
" <td>2011-05-13</td>\n",
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" <td>2</td>\n",
" <td>Ithaca Mountain Climbers</td>\n",
" <td>Ithaca</td>\n",
" <td>NY</td>\n",
" <td>42.443961</td>\n",
" <td>-76.501881</td>\n",
" <td>48</td>\n",
" <td>Jekyll Carbon 2</td>\n",
" <td>Mountain</td>\n",
" <td>Over Mountain</td>\n",
" <td>Carbon</td>\n",
" <td>6070</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>507</td>\n",
" <td>2</td>\n",
" <td>2012-06-26</td>\n",
" <td>2</td>\n",
" <td>48</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Ithaca Mountain Climbers</td>\n",
" <td>Ithaca</td>\n",
" <td>NY</td>\n",
" <td>42.443961</td>\n",
" <td>-76.501881</td>\n",
" <td>48</td>\n",
" <td>Jekyll Carbon 2</td>\n",
" <td>Mountain</td>\n",
" <td>Over Mountain</td>\n",
" <td>Carbon</td>\n",
" <td>6070</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>528</td>\n",
" <td>18</td>\n",
" <td>2012-07-16</td>\n",
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" <td>Ithaca Mountain Climbers</td>\n",
" <td>Ithaca</td>\n",
" <td>NY</td>\n",
" <td>42.443961</td>\n",
" <td>-76.501881</td>\n",
" <td>48</td>\n",
" <td>Jekyll Carbon 2</td>\n",
" <td>Mountain</td>\n",
" <td>Over Mountain</td>\n",
" <td>Carbon</td>\n",
" <td>6070</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>691</td>\n",
" <td>13</td>\n",
" <td>2013-02-05</td>\n",
" <td>2</td>\n",
" <td>48</td>\n",
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" <td>Ithaca Mountain Climbers</td>\n",
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" <td>NY</td>\n",
" <td>42.443961</td>\n",
" <td>-76.501881</td>\n",
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" <td>Mountain</td>\n",
" <td>Over Mountain</td>\n",
" <td>Carbon</td>\n",
" <td>6070</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" order.id order.line order.date customer.id product.id quantity \\\n",
"0 1 1 2011-01-07 2 48 1 \n",
"1 132 6 2011-05-13 2 48 1 \n",
"2 507 2 2012-06-26 2 48 1 \n",
"3 528 18 2012-07-16 2 48 1 \n",
"4 691 13 2013-02-05 2 48 1 \n",
"\n",
" bikeshop.id bikeshop.name bikeshop.city bikeshop.state \\\n",
"0 2 Ithaca Mountain Climbers Ithaca NY \n",
"1 2 Ithaca Mountain Climbers Ithaca NY \n",
"2 2 Ithaca Mountain Climbers Ithaca NY \n",
"3 2 Ithaca Mountain Climbers Ithaca NY \n",
"4 2 Ithaca Mountain Climbers Ithaca NY \n",
"\n",
" latitude longitude bike.id model category1 category2 \\\n",
"0 42.443961 -76.501881 48 Jekyll Carbon 2 Mountain Over Mountain \n",
"1 42.443961 -76.501881 48 Jekyll Carbon 2 Mountain Over Mountain \n",
"2 42.443961 -76.501881 48 Jekyll Carbon 2 Mountain Over Mountain \n",
"3 42.443961 -76.501881 48 Jekyll Carbon 2 Mountain Over Mountain \n",
"4 42.443961 -76.501881 48 Jekyll Carbon 2 Mountain Over Mountain \n",
"\n",
" frame price \n",
"0 Carbon 6070 \n",
"1 Carbon 6070 \n",
"2 Carbon 6070 \n",
"3 Carbon 6070 \n",
"4 Carbon 6070 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_merged.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Removing duplicate columns"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"df_merged = df_merged.drop(['customer.id'], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"df_merged = df_merged.drop(['product.id'], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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" <td>48</td>\n",
" <td>Jekyll Carbon 2</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>132</td>\n",
" <td>6</td>\n",
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" <td>1</td>\n",
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" <td>Ithaca Mountain Climbers</td>\n",
" <td>Ithaca</td>\n",
" <td>NY</td>\n",
" <td>42.443961</td>\n",
" <td>-76.501881</td>\n",
" <td>48</td>\n",
" <td>Jekyll Carbon 2</td>\n",
" <td>Mountain</td>\n",
" <td>Over Mountain</td>\n",
" <td>Carbon</td>\n",
" <td>6070</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
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" <td>2</td>\n",
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" <td>Jekyll Carbon 2</td>\n",
" <td>Mountain</td>\n",
" <td>Over Mountain</td>\n",
" <td>Carbon</td>\n",
" <td>6070</td>\n",
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" <th>3</th>\n",
" <td>528</td>\n",
" <td>18</td>\n",
" <td>2012-07-16</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
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" <td>Ithaca</td>\n",
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" <td>42.443961</td>\n",
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" <td>13</td>\n",
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" <td>48</td>\n",
" <td>Jekyll Carbon 2</td>\n",
" <td>Mountain</td>\n",
" <td>Over Mountain</td>\n",
" <td>Carbon</td>\n",
" <td>6070</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" order.id order.line order.date quantity bikeshop.id \\\n",
"0 1 1 2011-01-07 1 2 \n",
"1 132 6 2011-05-13 1 2 \n",
"2 507 2 2012-06-26 1 2 \n",
"3 528 18 2012-07-16 1 2 \n",
"4 691 13 2013-02-05 1 2 \n",
"\n",
" bikeshop.name bikeshop.city bikeshop.state latitude \\\n",
"0 Ithaca Mountain Climbers Ithaca NY 42.443961 \n",
"1 Ithaca Mountain Climbers Ithaca NY 42.443961 \n",
"2 Ithaca Mountain Climbers Ithaca NY 42.443961 \n",
"3 Ithaca Mountain Climbers Ithaca NY 42.443961 \n",
"4 Ithaca Mountain Climbers Ithaca NY 42.443961 \n",
"\n",
" longitude bike.id model category1 category2 frame price \n",
"0 -76.501881 48 Jekyll Carbon 2 Mountain Over Mountain Carbon 6070 \n",
"1 -76.501881 48 Jekyll Carbon 2 Mountain Over Mountain Carbon 6070 \n",
"2 -76.501881 48 Jekyll Carbon 2 Mountain Over Mountain Carbon 6070 \n",
"3 -76.501881 48 Jekyll Carbon 2 Mountain Over Mountain Carbon 6070 \n",
"4 -76.501881 48 Jekyll Carbon 2 Mountain Over Mountain Carbon 6070 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_merged.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"df_merged['price'] = df_merged['price'].apply(lambda x: 'high' if x > df_merged['price'].median() else 'low')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
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" <td>1</td>\n",
" <td>2011-01-07</td>\n",
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" <td>Ithaca Mountain Climbers</td>\n",
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" <tr>\n",
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" <td>42.443961</td>\n",
" <td>-76.501881</td>\n",
" <td>48</td>\n",
" <td>Jekyll Carbon 2</td>\n",
" <td>Mountain</td>\n",
" <td>Over Mountain</td>\n",
" <td>Carbon</td>\n",
" <td>high</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>507</td>\n",
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" <td>2012-06-26</td>\n",
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" <td>2</td>\n",
" <td>Ithaca Mountain Climbers</td>\n",
" <td>Ithaca</td>\n",
" <td>NY</td>\n",
" <td>42.443961</td>\n",
" <td>-76.501881</td>\n",
" <td>48</td>\n",
" <td>Jekyll Carbon 2</td>\n",
" <td>Mountain</td>\n",
" <td>Over Mountain</td>\n",
" <td>Carbon</td>\n",
" <td>high</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>528</td>\n",
" <td>18</td>\n",
" <td>2012-07-16</td>\n",
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" <td>13</td>\n",
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" <td>2</td>\n",
" <td>Ithaca Mountain Climbers</td>\n",
" <td>Ithaca</td>\n",
" <td>NY</td>\n",
" <td>42.443961</td>\n",
" <td>-76.501881</td>\n",
" <td>48</td>\n",
" <td>Jekyll Carbon 2</td>\n",
" <td>Mountain</td>\n",
" <td>Over Mountain</td>\n",
" <td>Carbon</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" order.id order.line order.date quantity bikeshop.id \\\n",
"0 1 1 2011-01-07 1 2 \n",
"1 132 6 2011-05-13 1 2 \n",
"2 507 2 2012-06-26 1 2 \n",
"3 528 18 2012-07-16 1 2 \n",
"4 691 13 2013-02-05 1 2 \n",
"\n",
" bikeshop.name bikeshop.city bikeshop.state latitude \\\n",
"0 Ithaca Mountain Climbers Ithaca NY 42.443961 \n",
"1 Ithaca Mountain Climbers Ithaca NY 42.443961 \n",
"2 Ithaca Mountain Climbers Ithaca NY 42.443961 \n",
"3 Ithaca Mountain Climbers Ithaca NY 42.443961 \n",
"4 Ithaca Mountain Climbers Ithaca NY 42.443961 \n",
"\n",
" longitude bike.id model category1 category2 frame price \n",
"0 -76.501881 48 Jekyll Carbon 2 Mountain Over Mountain Carbon high \n",
"1 -76.501881 48 Jekyll Carbon 2 Mountain Over Mountain Carbon high \n",
"2 -76.501881 48 Jekyll Carbon 2 Mountain Over Mountain Carbon high \n",
"3 -76.501881 48 Jekyll Carbon 2 Mountain Over Mountain Carbon high \n",
"4 -76.501881 48 Jekyll Carbon 2 Mountain Over Mountain Carbon high "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_merged.head()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"df_grouped = df_merged.groupby(by=['bikeshop.name', 'model', 'category1', 'category2', 'frame', 'price'], as_index=False).agg({'quantity': 'sum'})"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"bikeshop_names = df_grouped['bikeshop.name'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
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"text/plain": [
" bikeshop.name model category1 category2 frame \\\n",
"0 Albuquerque Cycles Bad Habit 1 Mountain Trail Aluminum \n",
"1 Albuquerque Cycles Bad Habit 2 Mountain Trail Aluminum \n",
"2 Albuquerque Cycles Beast of the East 1 Mountain Trail Aluminum \n",
"3 Albuquerque Cycles Beast of the East 2 Mountain Trail Aluminum \n",
"4 Albuquerque Cycles Beast of the East 3 Mountain Trail Aluminum \n",
"\n",
" price quantity \n",
"0 high 5 \n",
"1 low 2 \n",
"2 high 3 \n",
"3 low 3 \n",
"4 low 1 "
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_grouped.head()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"model_pivot = pd.pivot_table(df_grouped, index=['model', 'category1', 'category2', 'frame', 'price'], columns = ['bikeshop.name'], values='quantity', fill_value=0)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
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"text/plain": [
"bikeshop.name Albuquerque Cycles \\\n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 5 \n",
"Bad Habit 2 Mountain Trail Aluminum low 2 \n",
"Beast of the East 1 Mountain Trail Aluminum high 3 \n",
"Beast of the East 2 Mountain Trail Aluminum low 3 \n",
"Beast of the East 3 Mountain Trail Aluminum low 1 \n",
"\n",
"bikeshop.name Ann Arbor Speed \\\n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 4 \n",
"Bad Habit 2 Mountain Trail Aluminum low 6 \n",
"Beast of the East 1 Mountain Trail Aluminum high 9 \n",
"Beast of the East 2 Mountain Trail Aluminum low 6 \n",
"Beast of the East 3 Mountain Trail Aluminum low 2 \n",
"\n",
"bikeshop.name Austin Cruisers \\\n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 2 \n",
"Bad Habit 2 Mountain Trail Aluminum low 1 \n",
"Beast of the East 1 Mountain Trail Aluminum high 2 \n",
"Beast of the East 2 Mountain Trail Aluminum low 2 \n",
"Beast of the East 3 Mountain Trail Aluminum low 0 \n",
"\n",
"bikeshop.name Cincinnati Speed \\\n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 2 \n",
"Bad Habit 2 Mountain Trail Aluminum low 0 \n",
"Beast of the East 1 Mountain Trail Aluminum high 0 \n",
"Beast of the East 2 Mountain Trail Aluminum low 0 \n",
"Beast of the East 3 Mountain Trail Aluminum low 0 \n",
"\n",
"bikeshop.name Columbus Race Equipment \\\n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 4 \n",
"Bad Habit 2 Mountain Trail Aluminum low 0 \n",
"Beast of the East 1 Mountain Trail Aluminum high 0 \n",
"Beast of the East 2 Mountain Trail Aluminum low 2 \n",
"Beast of the East 3 Mountain Trail Aluminum low 1 \n",
"\n",
"bikeshop.name Dallas Cycles \\\n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 3 \n",
"Bad Habit 2 Mountain Trail Aluminum low 4 \n",
"Beast of the East 1 Mountain Trail Aluminum high 1 \n",
"Beast of the East 2 Mountain Trail Aluminum low 1 \n",
"Beast of the East 3 Mountain Trail Aluminum low 1 \n",
"\n",
"bikeshop.name Denver Bike Shop \\\n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 27 \n",
"Bad Habit 2 Mountain Trail Aluminum low 32 \n",
"Beast of the East 1 Mountain Trail Aluminum high 42 \n",
"Beast of the East 2 Mountain Trail Aluminum low 35 \n",
"Beast of the East 3 Mountain Trail Aluminum low 39 \n",
"\n",
"bikeshop.name Detroit Cycles \\\n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 5 \n",
"Bad Habit 2 Mountain Trail Aluminum low 8 \n",
"Beast of the East 1 Mountain Trail Aluminum high 6 \n",
"Beast of the East 2 Mountain Trail Aluminum low 3 \n",
"Beast of the East 3 Mountain Trail Aluminum low 6 \n",
"\n",
"bikeshop.name Indianapolis Velocipedes \\\n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 2 \n",
"Bad Habit 2 Mountain Trail Aluminum low 1 \n",
"Beast of the East 1 Mountain Trail Aluminum high 3 \n",
"Beast of the East 2 Mountain Trail Aluminum low 3 \n",
"Beast of the East 3 Mountain Trail Aluminum low 0 \n",
"\n",
"bikeshop.name Ithaca Mountain Climbers \\\n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 23 \n",
"Bad Habit 2 Mountain Trail Aluminum low 14 \n",
"Beast of the East 1 Mountain Trail Aluminum high 27 \n",
"Beast of the East 2 Mountain Trail Aluminum low 23 \n",
"Beast of the East 3 Mountain Trail Aluminum low 13 \n",
"\n",
"bikeshop.name ... \\\n",
"model category1 category2 frame price ... \n",
"Bad Habit 1 Mountain Trail Aluminum high ... \n",
"Bad Habit 2 Mountain Trail Aluminum low ... \n",
"Beast of the East 1 Mountain Trail Aluminum high ... \n",
"Beast of the East 2 Mountain Trail Aluminum low ... \n",
"Beast of the East 3 Mountain Trail Aluminum low ... \n",
"\n",
"bikeshop.name Philadelphia Bike Shop \\\n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 6 \n",
"Bad Habit 2 Mountain Trail Aluminum low 1 \n",
"Beast of the East 1 Mountain Trail Aluminum high 0 \n",
"Beast of the East 2 Mountain Trail Aluminum low 0 \n",
"Beast of the East 3 Mountain Trail Aluminum low 5 \n",
"\n",
"bikeshop.name Phoenix Bi-peds \\\n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 16 \n",
"Bad Habit 2 Mountain Trail Aluminum low 27 \n",
"Beast of the East 1 Mountain Trail Aluminum high 18 \n",
"Beast of the East 2 Mountain Trail Aluminum low 33 \n",
"Beast of the East 3 Mountain Trail Aluminum low 23 \n",
"\n",
"bikeshop.name Pittsburgh Mountain Machines \\\n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 6 \n",
"Bad Habit 2 Mountain Trail Aluminum low 1 \n",
"Beast of the East 1 Mountain Trail Aluminum high 2 \n",
"Beast of the East 2 Mountain Trail Aluminum low 4 \n",
"Beast of the East 3 Mountain Trail Aluminum low 1 \n",
"\n",
"bikeshop.name Portland Bi-peds \\\n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 7 \n",
"Bad Habit 2 Mountain Trail Aluminum low 7 \n",
"Beast of the East 1 Mountain Trail Aluminum high 7 \n",
"Beast of the East 2 Mountain Trail Aluminum low 10 \n",
"Beast of the East 3 Mountain Trail Aluminum low 13 \n",
"\n",
"bikeshop.name Providence Bi-peds \\\n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 5 \n",
"Bad Habit 2 Mountain Trail Aluminum low 13 \n",
"Beast of the East 1 Mountain Trail Aluminum high 5 \n",
"Beast of the East 2 Mountain Trail Aluminum low 8 \n",
"Beast of the East 3 Mountain Trail Aluminum low 4 \n",
"\n",
"bikeshop.name San Antonio Bike Shop \\\n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 4 \n",
"Bad Habit 2 Mountain Trail Aluminum low 0 \n",
"Beast of the East 1 Mountain Trail Aluminum high 1 \n",
"Beast of the East 2 Mountain Trail Aluminum low 2 \n",
"Beast of the East 3 Mountain Trail Aluminum low 6 \n",
"\n",
"bikeshop.name San Francisco Cruisers \\\n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 1 \n",
"Bad Habit 2 Mountain Trail Aluminum low 1 \n",
"Beast of the East 1 Mountain Trail Aluminum high 0 \n",
"Beast of the East 2 Mountain Trail Aluminum low 1 \n",
"Beast of the East 3 Mountain Trail Aluminum low 0 \n",
"\n",
"bikeshop.name Seattle Race Equipment \\\n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 2 \n",
"Bad Habit 2 Mountain Trail Aluminum low 1 \n",
"Beast of the East 1 Mountain Trail Aluminum high 2 \n",
"Beast of the East 2 Mountain Trail Aluminum low 3 \n",
"Beast of the East 3 Mountain Trail Aluminum low 1 \n",
"\n",
"bikeshop.name Tampa 29ers \\\n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 4 \n",
"Bad Habit 2 Mountain Trail Aluminum low 0 \n",
"Beast of the East 1 Mountain Trail Aluminum high 2 \n",
"Beast of the East 2 Mountain Trail Aluminum low 6 \n",
"Beast of the East 3 Mountain Trail Aluminum low 2 \n",
"\n",
"bikeshop.name Wichita Speed \n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 3 \n",
"Bad Habit 2 Mountain Trail Aluminum low 0 \n",
"Beast of the East 1 Mountain Trail Aluminum high 0 \n",
"Beast of the East 2 Mountain Trail Aluminum low 1 \n",
"Beast of the East 3 Mountain Trail Aluminum low 0 \n",
"\n",
"[5 rows x 30 columns]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_pivot.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Normalizing individual columns"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"for i in model_pivot.columns:\n",
" model_pivot.loc[:,i] = model_pivot.loc[:,i].apply(lambda x: x/model_pivot[i].sum())"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr>\n",
" <th>model</th>\n",
" <th>Bad Habit 1</th>\n",
" <th>Bad Habit 2</th>\n",
" <th>Beast of the East 1</th>\n",
" <th>Beast of the East 2</th>\n",
" <th>Beast of the East 3</th>\n",
" <th>CAAD Disc Ultegra</th>\n",
" <th>CAAD12 105</th>\n",
" <th>CAAD12 Black Inc</th>\n",
" <th>CAAD12 Disc 105</th>\n",
" <th>CAAD12 Disc Dura Ace</th>\n",
" <th>...</th>\n",
" <th>Synapse Sora</th>\n",
" <th>Trail 1</th>\n",
" <th>Trail 2</th>\n",
" <th>Trail 3</th>\n",
" <th>Trail 4</th>\n",
" <th>Trail 5</th>\n",
" <th>Trigger Carbon 1</th>\n",
" <th>Trigger Carbon 2</th>\n",
" <th>Trigger Carbon 3</th>\n",
" <th>Trigger Carbon 4</th>\n",
" </tr>\n",
" <tr>\n",
" <th>category1</th>\n",
" <th>Mountain</th>\n",
" <th>Mountain</th>\n",
" <th>Mountain</th>\n",
" <th>Mountain</th>\n",
" <th>Mountain</th>\n",
" <th>Road</th>\n",
" <th>Road</th>\n",
" <th>Road</th>\n",
" <th>Road</th>\n",
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" <th>Mountain</th>\n",
" <th>Mountain</th>\n",
" <th>Mountain</th>\n",
" <th>Mountain</th>\n",
" <th>Mountain</th>\n",
" <th>Mountain</th>\n",
" <th>Mountain</th>\n",
" <th>Mountain</th>\n",
" </tr>\n",
" <tr>\n",
" <th>category2</th>\n",
" <th>Trail</th>\n",
" <th>Trail</th>\n",
" <th>Trail</th>\n",
" <th>Trail</th>\n",
" <th>Trail</th>\n",
" <th>Elite Road</th>\n",
" <th>Elite Road</th>\n",
" <th>Elite Road</th>\n",
" <th>Elite Road</th>\n",
" <th>Elite Road</th>\n",
" <th>...</th>\n",
" <th>Endurance Road</th>\n",
" <th>Sport</th>\n",
" <th>Sport</th>\n",
" <th>Sport</th>\n",
" <th>Sport</th>\n",
" <th>Sport</th>\n",
" <th>Over Mountain</th>\n",
" <th>Over Mountain</th>\n",
" <th>Over Mountain</th>\n",
" <th>Over Mountain</th>\n",
" </tr>\n",
" <tr>\n",
" <th>frame</th>\n",
" <th>Aluminum</th>\n",
" <th>Aluminum</th>\n",
" <th>Aluminum</th>\n",
" <th>Aluminum</th>\n",
" <th>Aluminum</th>\n",
" <th>Aluminum</th>\n",
" <th>Aluminum</th>\n",
" <th>Aluminum</th>\n",
" <th>Aluminum</th>\n",
" <th>Aluminum</th>\n",
" <th>...</th>\n",
" <th>Aluminum</th>\n",
" <th>Aluminum</th>\n",
" <th>Aluminum</th>\n",
" <th>Aluminum</th>\n",
" <th>Aluminum</th>\n",
" <th>Aluminum</th>\n",
" <th>Carbon</th>\n",
" <th>Carbon</th>\n",
" <th>Carbon</th>\n",
" <th>Carbon</th>\n",
" </tr>\n",
" <tr>\n",
" <th>price</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>low</th>\n",
" <th>low</th>\n",
" <th>low</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>high</th>\n",
" <th>...</th>\n",
" <th>low</th>\n",
" <th>low</th>\n",
" <th>low</th>\n",
" <th>low</th>\n",
" <th>low</th>\n",
" <th>low</th>\n",
" <th>high</th>\n",
" <th>high</th>\n",
" <th>high</th>\n",
" <th>high</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bikeshop.name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Albuquerque Cycles</th>\n",
" <td>0.017483</td>\n",
" <td>0.006993</td>\n",
" <td>0.01049</td>\n",
" <td>0.010490</td>\n",
" <td>0.003497</td>\n",
" <td>0.013986</td>\n",
" <td>0.006993</td>\n",
" <td>0.000000</td>\n",
" <td>0.013986</td>\n",
" <td>0.048951</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.003497</td>\n",
" <td>0.006993</td>\n",
" <td>0.017483</td>\n",
" <td>0.010490</td>\n",
" <td>0.006993</td>\n",
" <td>0.003497</td>\n",
" <td>0.006993</td>\n",
" <td>0.006993</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ann Arbor Speed</th>\n",
" <td>0.006645</td>\n",
" <td>0.009967</td>\n",
" <td>0.01495</td>\n",
" <td>0.009967</td>\n",
" <td>0.003322</td>\n",
" <td>0.026578</td>\n",
" <td>0.014950</td>\n",
" <td>0.016611</td>\n",
" <td>0.014950</td>\n",
" <td>0.008306</td>\n",
" <td>...</td>\n",
" <td>0.009967</td>\n",
" <td>0.009967</td>\n",
" <td>0.014950</td>\n",
" <td>0.009967</td>\n",
" <td>0.003322</td>\n",
" <td>0.011628</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.011628</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Austin Cruisers</th>\n",
" <td>0.008130</td>\n",
" <td>0.004065</td>\n",
" <td>0.00813</td>\n",
" <td>0.008130</td>\n",
" <td>0.000000</td>\n",
" <td>0.020325</td>\n",
" <td>0.020325</td>\n",
" <td>0.004065</td>\n",
" <td>0.024390</td>\n",
" <td>0.008130</td>\n",
" <td>...</td>\n",
" <td>0.020325</td>\n",
" <td>0.016260</td>\n",
" <td>0.016260</td>\n",
" <td>0.016260</td>\n",
" <td>0.008130</td>\n",
" <td>0.008130</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.016260</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cincinnati Speed</th>\n",
" <td>0.005115</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.015345</td>\n",
" <td>0.010230</td>\n",
" <td>0.015345</td>\n",
" <td>0.007673</td>\n",
" <td>0.017903</td>\n",
" <td>...</td>\n",
" <td>0.012788</td>\n",
" <td>0.000000</td>\n",
" <td>0.002558</td>\n",
" <td>0.002558</td>\n",
" <td>0.002558</td>\n",
" <td>0.000000</td>\n",
" <td>0.010230</td>\n",
" <td>0.007673</td>\n",
" <td>0.010230</td>\n",
" <td>0.020460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Columbus Race Equipment</th>\n",
" <td>0.010152</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.005076</td>\n",
" <td>0.002538</td>\n",
" <td>0.010152</td>\n",
" <td>0.027919</td>\n",
" <td>0.027919</td>\n",
" <td>0.025381</td>\n",
" <td>0.012690</td>\n",
" <td>...</td>\n",
" <td>0.015228</td>\n",
" <td>0.002538</td>\n",
" <td>0.002538</td>\n",
" <td>0.005076</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010152</td>\n",
" <td>0.005076</td>\n",
" <td>0.017766</td>\n",
" <td>0.005076</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 97 columns</p>\n",
"</div>"
],
"text/plain": [
"model Bad Habit 1 Bad Habit 2 Beast of the East 1 \\\n",
"category1 Mountain Mountain Mountain \n",
"category2 Trail Trail Trail \n",
"frame Aluminum Aluminum Aluminum \n",
"price high low high \n",
"bikeshop.name \n",
"Albuquerque Cycles 0.017483 0.006993 0.01049 \n",
"Ann Arbor Speed 0.006645 0.009967 0.01495 \n",
"Austin Cruisers 0.008130 0.004065 0.00813 \n",
"Cincinnati Speed 0.005115 0.000000 0.00000 \n",
"Columbus Race Equipment 0.010152 0.000000 0.00000 \n",
"\n",
"model Beast of the East 2 Beast of the East 3 \\\n",
"category1 Mountain Mountain \n",
"category2 Trail Trail \n",
"frame Aluminum Aluminum \n",
"price low low \n",
"bikeshop.name \n",
"Albuquerque Cycles 0.010490 0.003497 \n",
"Ann Arbor Speed 0.009967 0.003322 \n",
"Austin Cruisers 0.008130 0.000000 \n",
"Cincinnati Speed 0.000000 0.000000 \n",
"Columbus Race Equipment 0.005076 0.002538 \n",
"\n",
"model CAAD Disc Ultegra CAAD12 105 CAAD12 Black Inc \\\n",
"category1 Road Road Road \n",
"category2 Elite Road Elite Road Elite Road \n",
"frame Aluminum Aluminum Aluminum \n",
"price low low high \n",
"bikeshop.name \n",
"Albuquerque Cycles 0.013986 0.006993 0.000000 \n",
"Ann Arbor Speed 0.026578 0.014950 0.016611 \n",
"Austin Cruisers 0.020325 0.020325 0.004065 \n",
"Cincinnati Speed 0.015345 0.010230 0.015345 \n",
"Columbus Race Equipment 0.010152 0.027919 0.027919 \n",
"\n",
"model CAAD12 Disc 105 CAAD12 Disc Dura Ace ... \\\n",
"category1 Road Road ... \n",
"category2 Elite Road Elite Road ... \n",
"frame Aluminum Aluminum ... \n",
"price low high ... \n",
"bikeshop.name ... \n",
"Albuquerque Cycles 0.013986 0.048951 ... \n",
"Ann Arbor Speed 0.014950 0.008306 ... \n",
"Austin Cruisers 0.024390 0.008130 ... \n",
"Cincinnati Speed 0.007673 0.017903 ... \n",
"Columbus Race Equipment 0.025381 0.012690 ... \n",
"\n",
"model Synapse Sora Trail 1 Trail 2 Trail 3 \\\n",
"category1 Road Mountain Mountain Mountain \n",
"category2 Endurance Road Sport Sport Sport \n",
"frame Aluminum Aluminum Aluminum Aluminum \n",
"price low low low low \n",
"bikeshop.name \n",
"Albuquerque Cycles 0.000000 0.000000 0.003497 0.006993 \n",
"Ann Arbor Speed 0.009967 0.009967 0.014950 0.009967 \n",
"Austin Cruisers 0.020325 0.016260 0.016260 0.016260 \n",
"Cincinnati Speed 0.012788 0.000000 0.002558 0.002558 \n",
"Columbus Race Equipment 0.015228 0.002538 0.002538 0.005076 \n",
"\n",
"model Trail 4 Trail 5 Trigger Carbon 1 Trigger Carbon 2 \\\n",
"category1 Mountain Mountain Mountain Mountain \n",
"category2 Sport Sport Over Mountain Over Mountain \n",
"frame Aluminum Aluminum Carbon Carbon \n",
"price low low high high \n",
"bikeshop.name \n",
"Albuquerque Cycles 0.017483 0.010490 0.006993 0.003497 \n",
"Ann Arbor Speed 0.003322 0.011628 0.000000 0.000000 \n",
"Austin Cruisers 0.008130 0.008130 0.000000 0.000000 \n",
"Cincinnati Speed 0.002558 0.000000 0.010230 0.007673 \n",
"Columbus Race Equipment 0.000000 0.000000 0.010152 0.005076 \n",
"\n",
"model Trigger Carbon 3 Trigger Carbon 4 \n",
"category1 Mountain Mountain \n",
"category2 Over Mountain Over Mountain \n",
"frame Carbon Carbon \n",
"price high high \n",
"bikeshop.name \n",
"Albuquerque Cycles 0.006993 0.006993 \n",
"Ann Arbor Speed 0.000000 0.011628 \n",
"Austin Cruisers 0.000000 0.016260 \n",
"Cincinnati Speed 0.010230 0.020460 \n",
"Columbus Race Equipment 0.017766 0.005076 \n",
"\n",
"[5 rows x 97 columns]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_pivot.T.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we are clustering different bike models based on their quantity purchased (across different bike shops)."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.cluster import KMeans\n",
"from sklearn.metrics import silhouette_samples, silhouette_score\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to obtain a optimal value for the number of clusters, I use silhouette analysis. Silhouette analysis can be used to study the separation distance between the resulting clusters. "
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"cluster_sizes = [i for i in range(4,9)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### k-means"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cluster size: 4, Score: 0.17628084123482804\n",
"Cluster size: 5, Score: 0.17881270878689673\n",
"Cluster size: 6, Score: 0.15308714267240162\n",
"Cluster size: 7, Score: 0.1652600316390975\n",
"Cluster size: 8, Score: 0.12135881839897107\n",
"Optimal Cluster size: 5\n"
]
}
],
"source": [
"best_k = None\n",
"best_score = -np.inf\n",
"for k in cluster_sizes:\n",
" kmeans = KMeans(n_clusters=k, random_state=90)\n",
" labels = kmeans.fit_predict(model_pivot.T)\n",
" silhouette_vals = silhouette_samples(model_pivot.T, labels)\n",
" avg_score = np.mean(silhouette_vals)\n",
" \n",
" print('Cluster size: {}, Score: {}'.format(str(k), str(avg_score)))\n",
" if avg_score > best_score:\n",
" best_k = k\n",
" best_score = avg_score\n",
"print('Optimal Cluster size: ', best_k)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"result = KMeans(n_clusters=best_k, random_state=90)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"labels = result.fit_predict(model_pivot.T)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Printing customer names in each segment"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bikeshop Name : Cluster Number\n"
]
}
],
"source": [
"print('Bikeshop Name', ':', 'Cluster Number')\n",
"clusters = {}\n",
"for idx, i in enumerate(labels):\n",
" if i in clusters:\n",
" clusters[i].append(model_pivot.T.index[idx])\n",
" else:\n",
" clusters[i] = []\n",
" clusters[i].append(model_pivot.T.index[idx])"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{1: ['Albuquerque Cycles',\n",
" 'Dallas Cycles',\n",
" 'Denver Bike Shop',\n",
" 'Detroit Cycles',\n",
" 'Kansas City 29ers',\n",
" 'Los Angeles Cycles',\n",
" 'Minneapolis Bike Shop',\n",
" 'New York Cycles',\n",
" 'Phoenix Bi-peds',\n",
" 'Portland Bi-peds',\n",
" 'Providence Bi-peds'],\n",
" 3: ['Ann Arbor Speed',\n",
" 'Austin Cruisers',\n",
" 'Indianapolis Velocipedes',\n",
" 'Miami Race Equipment',\n",
" 'Nashville Cruisers',\n",
" 'New Orleans Velocipedes',\n",
" 'Oklahoma City Race Equipment',\n",
" 'Seattle Race Equipment'],\n",
" 2: ['Cincinnati Speed',\n",
" 'Columbus Race Equipment',\n",
" 'Las Vegas Cycles',\n",
" 'Louisville Race Equipment',\n",
" 'San Francisco Cruisers',\n",
" 'Wichita Speed'],\n",
" 0: ['Ithaca Mountain Climbers',\n",
" 'Pittsburgh Mountain Machines',\n",
" 'Tampa 29ers'],\n",
" 4: ['Philadelphia Bike Shop', 'San Antonio Bike Shop']}"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clusters"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"custSegmentCntrs = pd.DataFrame(result.cluster_centers_.T)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(97, 5)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"custSegmentCntrs.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"5 cluster center vectors in 97 dimensions"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.017843</td>\n",
" <td>0.012131</td>\n",
" <td>0.005509</td>\n",
" <td>0.010127</td>\n",
" <td>0.022998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.004576</td>\n",
" <td>0.014289</td>\n",
" <td>0.000446</td>\n",
" <td>0.008157</td>\n",
" <td>0.002041</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.012125</td>\n",
" <td>0.014094</td>\n",
" <td>0.000267</td>\n",
" <td>0.012970</td>\n",
" <td>0.002688</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.019311</td>\n",
" <td>0.013655</td>\n",
" <td>0.002464</td>\n",
" <td>0.010810</td>\n",
" <td>0.005376</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.007549</td>\n",
" <td>0.010792</td>\n",
" <td>0.001801</td>\n",
" <td>0.005640</td>\n",
" <td>0.026333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4\n",
"0 0.017843 0.012131 0.005509 0.010127 0.022998\n",
"1 0.004576 0.014289 0.000446 0.008157 0.002041\n",
"2 0.012125 0.014094 0.000267 0.012970 0.002688\n",
"3 0.019311 0.013655 0.002464 0.010810 0.005376\n",
"4 0.007549 0.010792 0.001801 0.005640 0.026333"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"custSegmentCntrs.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cross checking my cluster centroid calculations for cluster 4"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"20"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(model_pivot.T.index).index('Philadelphia Bike Shop')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"25"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(model_pivot.T.index).index('San Antonio Bike Shop')"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"model category1 category2 frame price\n",
"Bad Habit 1 Mountain Trail Aluminum high 0.022998\n",
"Bad Habit 2 Mountain Trail Aluminum low 0.002041\n",
"Beast of the East 1 Mountain Trail Aluminum high 0.002688\n",
"Beast of the East 2 Mountain Trail Aluminum low 0.005376\n",
"Beast of the East 3 Mountain Trail Aluminum low 0.026333\n",
"CAAD Disc Ultegra Road Elite Road Aluminum low 0.010851\n",
"CAAD12 105 Road Elite Road Aluminum low 0.027079\n",
"CAAD12 Black Inc Road Elite Road Aluminum high 0.004082\n",
"CAAD12 Disc 105 Road Elite Road Aluminum low 0.020957\n",
"CAAD12 Disc Dura Ace Road Elite Road Aluminum high 0.002688\n",
"CAAD12 Red Road Elite Road Aluminum high 0.007417\n",
"CAAD12 Ultegra Road Elite Road Aluminum low 0.010204\n",
"CAAD8 105 Road Elite Road Aluminum low 0.004729\n",
"CAAD8 Claris Road Elite Road Aluminum low 0.019563\n",
"CAAD8 Sora Road Elite Road Aluminum low 0.012794\n",
"CAAD8 Tiagra Road Elite Road Aluminum low 0.016974\n",
"Catalyst 1 Mountain Sport Aluminum low 0.014187\n",
"Catalyst 2 Mountain Sport Aluminum low 0.014187\n",
"Catalyst 3 Mountain Sport Aluminum low 0.010105\n",
"Catalyst 4 Mountain Sport Aluminum low 0.009458\n",
"F-Si 1 Mountain Cross Country Race Aluminum low 0.009458\n",
"F-Si 2 Mountain Cross Country Race Aluminum low 0.012794\n",
"F-Si 3 Mountain Cross Country Race Aluminum low 0.011499\n",
"F-Si Black Inc. Mountain Cross Country Race Carbon high 0.002041\n",
"F-Si Carbon 2 Mountain Cross Country Race Carbon high 0.007417\n",
"F-Si Carbon 4 Mountain Cross Country Race Carbon high 0.022449\n",
"F-Si Hi-Mod 1 Mountain Cross Country Race Carbon high 0.002688\n",
"F-Si Hi-Mod Team Mountain Cross Country Race Carbon high 0.002688\n",
"Fat CAAD1 Mountain Fat Bike Aluminum high 0.002041\n",
"Fat CAAD2 Mountain Fat Bike Aluminum low 0.014834\n",
" ... \n",
"Supersix Evo Hi-Mod Utegra Road Elite Road Carbon high 0.009458\n",
"Supersix Evo Red Road Elite Road Carbon high 0.002041\n",
"Supersix Evo Tiagra Road Elite Road Carbon low 0.017522\n",
"Supersix Evo Ultegra 3 Road Elite Road Carbon high 0.010851\n",
"Supersix Evo Ultegra 4 Road Elite Road Carbon low 0.016875\n",
"Syapse Carbon Tiagra Road Endurance Road Carbon low 0.010851\n",
"Synapse Carbon 105 Road Endurance Road Carbon low 0.012794\n",
"Synapse Carbon Disc 105 Road Endurance Road Carbon low 0.019662\n",
"Synapse Carbon Disc Ultegra Road Endurance Road Carbon high 0.012794\n",
"Synapse Carbon Disc Ultegra D12 Road Endurance Road Carbon high 0.000000\n",
"Synapse Carbon Ultegra 3 Road Endurance Road Carbon high 0.011499\n",
"Synapse Carbon Ultegra 4 Road Endurance Road Carbon low 0.011499\n",
"Synapse Claris Road Endurance Road Aluminum low 0.009458\n",
"Synapse Disc 105 Road Endurance Road Aluminum low 0.020957\n",
"Synapse Disc Adventure Road Endurance Road Aluminum low 0.011499\n",
"Synapse Disc Tiagra Road Endurance Road Aluminum low 0.011499\n",
"Synapse Hi-Mod Disc Black Inc. Road Endurance Road Carbon high 0.005376\n",
"Synapse Hi-Mod Disc Red Road Endurance Road Carbon high 0.007417\n",
"Synapse Hi-Mod Disc Ultegra Road Endurance Road Carbon high 0.004082\n",
"Synapse Hi-Mod Dura Ace Road Endurance Road Carbon high 0.000000\n",
"Synapse Sora Road Endurance Road Aluminum low 0.011499\n",
"Trail 1 Mountain Sport Aluminum low 0.024940\n",
"Trail 2 Mountain Sport Aluminum low 0.020309\n",
"Trail 3 Mountain Sport Aluminum low 0.004729\n",
"Trail 4 Mountain Sport Aluminum low 0.008163\n",
"Trail 5 Mountain Sport Aluminum low 0.011499\n",
"Trigger Carbon 1 Mountain Over Mountain Carbon high 0.002688\n",
"Trigger Carbon 2 Mountain Over Mountain Carbon high 0.000000\n",
"Trigger Carbon 3 Mountain Over Mountain Carbon high 0.002688\n",
"Trigger Carbon 4 Mountain Over Mountain Carbon high 0.030415\n",
"Length: 97, dtype: float64"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_pivot.T.iloc[[20,25],:].sum()/2"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 0.022998\n",
"1 0.002041\n",
"2 0.002688\n",
"3 0.005376\n",
"4 0.026333\n",
"5 0.010851\n",
"6 0.027079\n",
"7 0.004082\n",
"8 0.020957\n",
"9 0.002688\n",
"10 0.007417\n",
"11 0.010204\n",
"12 0.004729\n",
"13 0.019563\n",
"14 0.012794\n",
"15 0.016974\n",
"16 0.014187\n",
"17 0.014187\n",
"18 0.010105\n",
"19 0.009458\n",
"20 0.009458\n",
"21 0.012794\n",
"22 0.011499\n",
"23 0.002041\n",
"24 0.007417\n",
"25 0.022449\n",
"26 0.002688\n",
"27 0.002688\n",
"28 0.002041\n",
"29 0.014834\n",
" ... \n",
"67 0.009458\n",
"68 0.002041\n",
"69 0.017522\n",
"70 0.010851\n",
"71 0.016875\n",
"72 0.010851\n",
"73 0.012794\n",
"74 0.019662\n",
"75 0.012794\n",
"76 0.000000\n",
"77 0.011499\n",
"78 0.011499\n",
"79 0.009458\n",
"80 0.020957\n",
"81 0.011499\n",
"82 0.011499\n",
"83 0.005376\n",
"84 0.007417\n",
"85 0.004082\n",
"86 0.000000\n",
"87 0.011499\n",
"88 0.024940\n",
"89 0.020309\n",
"90 0.004729\n",
"91 0.008163\n",
"92 0.011499\n",
"93 0.002688\n",
"94 0.000000\n",
"95 0.002688\n",
"96 0.030415\n",
"Name: 4, Length: 97, dtype: float64"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"custSegmentCntrs[4] # They match"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"# Assign meaningful column names to dataframe\n",
"custSegmentCntrs.columns = ['x1', 'x2', 'x3', 'x4', 'x5']"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>x1</th>\n",
" <th>x2</th>\n",
" <th>x3</th>\n",
" <th>x4</th>\n",
" <th>x5</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.017843</td>\n",
" <td>0.012131</td>\n",
" <td>0.005509</td>\n",
" <td>0.010127</td>\n",
" <td>0.022998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.004576</td>\n",
" <td>0.014289</td>\n",
" <td>0.000446</td>\n",
" <td>0.008157</td>\n",
" <td>0.002041</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.012125</td>\n",
" <td>0.014094</td>\n",
" <td>0.000267</td>\n",
" <td>0.012970</td>\n",
" <td>0.002688</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.019311</td>\n",
" <td>0.013655</td>\n",
" <td>0.002464</td>\n",
" <td>0.010810</td>\n",
" <td>0.005376</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.007549</td>\n",
" <td>0.010792</td>\n",
" <td>0.001801</td>\n",
" <td>0.005640</td>\n",
" <td>0.026333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" x1 x2 x3 x4 x5\n",
"0 0.017843 0.012131 0.005509 0.010127 0.022998\n",
"1 0.004576 0.014289 0.000446 0.008157 0.002041\n",
"2 0.012125 0.014094 0.000267 0.012970 0.002688\n",
"3 0.019311 0.013655 0.002464 0.010810 0.005376\n",
"4 0.007549 0.010792 0.001801 0.005640 0.026333"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"custSegmentCntrs.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since each row corresponds to a particular bike model,cat,subcat,etc combination. We will append it to add clarity\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"custSegmentCntrs.index = model_pivot.index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cluster Inspection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cluster x1: ['Ithaca Mountain Climbers',\n",
" 'Pittsburgh Mountain Machines',\n",
" 'Tampa 29ers']"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>x1</th>\n",
" <th>x2</th>\n",
" <th>x3</th>\n",
" <th>x4</th>\n",
" <th>x5</th>\n",
" </tr>\n",
" <tr>\n",
" <th>model</th>\n",
" <th>category1</th>\n",
" <th>category2</th>\n",
" <th>frame</th>\n",
" <th>price</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Bad Habit 1</th>\n",
" <th>Mountain</th>\n",
" <th>Trail</th>\n",
" <th>Aluminum</th>\n",
" <th>high</th>\n",
" <td>0.017843</td>\n",
" <td>0.012131</td>\n",
" <td>0.005509</td>\n",
" <td>0.010127</td>\n",
" <td>0.022998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bad Habit 2</th>\n",
" <th>Mountain</th>\n",
" <th>Trail</th>\n",
" <th>Aluminum</th>\n",
" <th>low</th>\n",
" <td>0.004576</td>\n",
" <td>0.014289</td>\n",
" <td>0.000446</td>\n",
" <td>0.008157</td>\n",
" <td>0.002041</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Beast of the East 1</th>\n",
" <th>Mountain</th>\n",
" <th>Trail</th>\n",
" <th>Aluminum</th>\n",
" <th>high</th>\n",
" <td>0.012125</td>\n",
" <td>0.014094</td>\n",
" <td>0.000267</td>\n",
" <td>0.012970</td>\n",
" <td>0.002688</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Beast of the East 2</th>\n",
" <th>Mountain</th>\n",
" <th>Trail</th>\n",
" <th>Aluminum</th>\n",
" <th>low</th>\n",
" <td>0.019311</td>\n",
" <td>0.013655</td>\n",
" <td>0.002464</td>\n",
" <td>0.010810</td>\n",
" <td>0.005376</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Beast of the East 3</th>\n",
" <th>Mountain</th>\n",
" <th>Trail</th>\n",
" <th>Aluminum</th>\n",
" <th>low</th>\n",
" <td>0.007549</td>\n",
" <td>0.010792</td>\n",
" <td>0.001801</td>\n",
" <td>0.005640</td>\n",
" <td>0.026333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" x1 x2 \\\n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 0.017843 0.012131 \n",
"Bad Habit 2 Mountain Trail Aluminum low 0.004576 0.014289 \n",
"Beast of the East 1 Mountain Trail Aluminum high 0.012125 0.014094 \n",
"Beast of the East 2 Mountain Trail Aluminum low 0.019311 0.013655 \n",
"Beast of the East 3 Mountain Trail Aluminum low 0.007549 0.010792 \n",
"\n",
" x3 x4 \\\n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 0.005509 0.010127 \n",
"Bad Habit 2 Mountain Trail Aluminum low 0.000446 0.008157 \n",
"Beast of the East 1 Mountain Trail Aluminum high 0.000267 0.012970 \n",
"Beast of the East 2 Mountain Trail Aluminum low 0.002464 0.010810 \n",
"Beast of the East 3 Mountain Trail Aluminum low 0.001801 0.005640 \n",
"\n",
" x5 \n",
"model category1 category2 frame price \n",
"Bad Habit 1 Mountain Trail Aluminum high 0.022998 \n",
"Bad Habit 2 Mountain Trail Aluminum low 0.002041 \n",
"Beast of the East 1 Mountain Trail Aluminum high 0.002688 \n",
"Beast of the East 2 Mountain Trail Aluminum low 0.005376 \n",
"Beast of the East 3 Mountain Trail Aluminum low 0.026333 "
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"custSegmentCntrs.head()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"model category1 category2 frame price\n",
"Scalpel-Si Carbon 3 Mountain Cross Country Race Carbon high 0.034269\n",
"Jekyll Carbon 4 Mountain Over Mountain Carbon high 0.030282\n",
"Scalpel 29 Carbon Race Mountain Cross Country Race Carbon high 0.028039\n",
"Trigger Carbon 3 Mountain Over Mountain Carbon high 0.025935\n",
"Habit Carbon 2 Mountain Trail Carbon high 0.023375\n",
"Trigger Carbon 4 Mountain Over Mountain Carbon high 0.023261\n",
"Catalyst 4 Mountain Sport Aluminum low 0.021564\n",
"Jekyll Carbon 2 Mountain Over Mountain Carbon high 0.021079\n",
"Supersix Evo Hi-Mod Dura Ace 2 Road Elite Road Carbon high 0.021058\n",
"Trigger Carbon 2 Mountain Over Mountain Carbon high 0.021043\n",
"Name: x1, dtype: float64"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"custSegmentCntrs['x1'].sort_values(ascending=False)[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cluster 1 top 10 models purchased are predominantly high-end bikes of mountain category. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cluster 2: ['Albuquerque Cycles',\n",
" 'Dallas Cycles',\n",
" 'Denver Bike Shop',\n",
" 'Detroit Cycles',\n",
" 'Kansas City 29ers',\n",
" 'Los Angeles Cycles',\n",
" 'Minneapolis Bike Shop',\n",
" 'New York Cycles',\n",
" 'Phoenix Bi-peds',\n",
" 'Portland Bi-peds',\n",
" 'Providence Bi-peds']"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"model category1 category2 frame price\n",
"F-Si 2 Mountain Cross Country Race Aluminum low 0.021705\n",
"Catalyst 3 Mountain Sport Aluminum low 0.019743\n",
"F-Si Carbon 4 Mountain Cross Country Race Carbon high 0.017716\n",
"Trail 5 Mountain Sport Aluminum low 0.016265\n",
"CAAD8 Sora Road Elite Road Aluminum low 0.016141\n",
"CAAD12 Disc 105 Road Elite Road Aluminum low 0.015987\n",
"CAAD8 105 Road Elite Road Aluminum low 0.015796\n",
"Habit 4 Mountain Trail Aluminum high 0.015531\n",
"Synapse Carbon Disc 105 Road Endurance Road Carbon low 0.015407\n",
"Trail 3 Mountain Sport Aluminum low 0.014985\n",
"Name: x2, dtype: float64"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"custSegmentCntrs['x2'].sort_values(ascending=False)[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cluster 2 top 10 bikes are primarily low end bikes and this cluster is similar to cluster 5."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Cluster 3: ['Cincinnati Speed',\n",
" 'Columbus Race Equipment',\n",
" 'Las Vegas Cycles',\n",
" 'Louisville Race Equipment',\n",
" 'San Francisco Cruisers',\n",
" 'Wichita Speed']\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"model category1 category2 frame price\n",
"Synapse Hi-Mod Disc Red Road Endurance Road Carbon high 0.024675\n",
"Slice Hi-Mod Black Inc. Road Triathalon Carbon high 0.023494\n",
"Supersix Evo Hi-Mod Dura Ace 1 Road Elite Road Carbon high 0.023053\n",
"Slice Hi-Mod Dura Ace D12 Road Triathalon Carbon high 0.022961\n",
"Synapse Hi-Mod Dura Ace Road Endurance Road Carbon high 0.021672\n",
"CAAD12 Red Road Elite Road Aluminum high 0.021196\n",
"Synapse Carbon Disc Ultegra Road Endurance Road Carbon high 0.020239\n",
"Supersix Evo Ultegra 3 Road Elite Road Carbon high 0.020187\n",
"Supersix Evo Hi-Mod Utegra Road Elite Road Carbon high 0.019819\n",
"Synapse Hi-Mod Disc Black Inc. Road Endurance Road Carbon high 0.019755\n",
"Name: x3, dtype: float64"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"custSegmentCntrs['x3'].sort_values(ascending=False)[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cluster 3 bikes are primarily road bikes that are high end."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cluster 4: ['Ann Arbor Speed',\n",
" 'Austin Cruisers',\n",
" 'Indianapolis Velocipedes',\n",
" 'Miami Race Equipment',\n",
" 'Nashville Cruisers',\n",
" 'New Orleans Velocipedes',\n",
" 'Oklahoma City Race Equipment',\n",
" 'Seattle Race Equipment']"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"model category1 category2 frame price\n",
"Synapse Disc Tiagra Road Endurance Road Aluminum low 0.024608\n",
"CAAD12 Red Road Elite Road Aluminum high 0.022889\n",
"Synapse Sora Road Endurance Road Aluminum low 0.022305\n",
"Slice Ultegra Road Triathalon Carbon low 0.022077\n",
"Supersix Evo Ultegra 3 Road Elite Road Carbon high 0.021857\n",
"Synapse Disc 105 Road Endurance Road Aluminum low 0.021122\n",
"Synapse Carbon Ultegra 4 Road Endurance Road Carbon low 0.020372\n",
"CAAD12 Ultegra Road Elite Road Aluminum low 0.020073\n",
"Slice Ultegra D12 Road Triathalon Carbon high 0.019531\n",
"Supersix Evo Ultegra 4 Road Elite Road Carbon low 0.019435\n",
"Name: x4, dtype: float64"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"custSegmentCntrs['x4'].sort_values(ascending=False)[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cluster 4 top 10 bikes are primarily road bikes that are low end"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"model category1 category2 frame price\n",
"Slice Ultegra Road Triathalon Carbon low 0.055453\n",
"Trigger Carbon 4 Mountain Over Mountain Carbon high 0.030415\n",
"CAAD12 105 Road Elite Road Aluminum low 0.027079\n",
"Beast of the East 3 Mountain Trail Aluminum low 0.026333\n",
"Trail 1 Mountain Sport Aluminum low 0.024940\n",
"Bad Habit 1 Mountain Trail Aluminum high 0.022998\n",
"F-Si Carbon 4 Mountain Cross Country Race Carbon high 0.022449\n",
"CAAD12 Disc 105 Road Elite Road Aluminum low 0.020957\n",
"Synapse Disc 105 Road Endurance Road Aluminum low 0.020957\n",
"Trail 2 Mountain Sport Aluminum low 0.020309\n",
"Name: x5, dtype: float64"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"custSegmentCntrs['x5'].sort_values(ascending=False)[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cluster 5 bikes are primarily low-end affordable bikes and a mix of road and mountain category. A mix of frame material as well."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Agglomerative Clustering"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.cluster import AgglomerativeClustering\n",
"\n",
"clustering = AgglomerativeClustering(n_clusters=5, affinity='euclidean', linkage='complete')\n",
"cluster_assignments = clustering.fit_predict(model_pivot.T)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"\n",
"clusters_new = {}\n",
"for idx, i in enumerate(cluster_assignments):\n",
" if i in clusters_new:\n",
" clusters_new[i].append(model_pivot.T.index[idx])\n",
" else:\n",
" clusters_new[i] = []\n",
" clusters_new[i].append(model_pivot.T.index[idx])"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cluster Number : Bikeshop Names\n"
]
},
{
"data": {
"text/plain": [
"{1: ['Albuquerque Cycles',\n",
" 'Dallas Cycles',\n",
" 'Denver Bike Shop',\n",
" 'Detroit Cycles',\n",
" 'Kansas City 29ers',\n",
" 'Los Angeles Cycles',\n",
" 'Minneapolis Bike Shop',\n",
" 'New York Cycles',\n",
" 'Phoenix Bi-peds',\n",
" 'Portland Bi-peds',\n",
" 'Providence Bi-peds'],\n",
" 4: ['Ann Arbor Speed',\n",
" 'Austin Cruisers',\n",
" 'Indianapolis Velocipedes',\n",
" 'Miami Race Equipment',\n",
" 'Nashville Cruisers',\n",
" 'New Orleans Velocipedes',\n",
" 'Oklahoma City Race Equipment'],\n",
" 2: ['Cincinnati Speed',\n",
" 'Columbus Race Equipment',\n",
" 'Las Vegas Cycles',\n",
" 'Louisville Race Equipment',\n",
" 'San Francisco Cruisers',\n",
" 'Wichita Speed'],\n",
" 3: ['Ithaca Mountain Climbers',\n",
" 'Pittsburgh Mountain Machines',\n",
" 'Tampa 29ers'],\n",
" 0: ['Philadelphia Bike Shop',\n",
" 'San Antonio Bike Shop',\n",
" 'Seattle Race Equipment']}"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print( 'Cluster Number', ':', 'Bikeshop Names')\n",
"clusters_new"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Comparing the results of Heirarchical clustering to k-means, I observe the following:\n",
"Cluster 3 (heirarchical) is identical to cluster 0 (k-means) which have high-end bikes of mountain category.\n",
"Cluster 1 in both cases are also identical. \n",
"Difference between the two methods: \n",
"Cluster 4(heirarchical) is identical to Cluster 3(k-means) with the exception of 'Seattle Race Equipment' now being classified in Cluster 0 using heirarchical."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| stackv2 | 2024-11-18T18:03:05.377628+00:00 | 2019-03-12T06:29:32 | {
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124439ba69a87af862ccde8cff21f37391752fb4 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "scratchpad",
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/margaretmz/CartoonGAN-e2e-tflite-tutorial/blob/master/ml/metadata/Add_Metadata.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "laCUI2hzapKv",
"colab_type": "text"
},
"source": [
"Reference: https://github.com/margaretmz/selfie2anime-with-tflite/blob/master/ml/add-meta-data-Colab/Add%20metadata%20to%20selfie2anime.ipynb. "
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "lIYdn1woOS1n",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 272
},
"outputId": "ac55b1e4-be6f-4f5f-9769-c75e8020508f"
},
"source": [
"!pip install tflite-support"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting tflite-support\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/29/97/7af9b18583d9bb01cdfcd0ab446062ef97720ebf941e33f7b156861ed179/tflite-support-0.1.0a1.tar.gz (390kB)\n",
"\r\u001b[K |▉ | 10kB 19.4MB/s eta 0:00:01\r\u001b[K |█▊ | 20kB 1.7MB/s eta 0:00:01\r\u001b[K |██▌ | 30kB 2.3MB/s eta 0:00:01\r\u001b[K |███▍ | 40kB 2.6MB/s eta 0:00:01\r\u001b[K |████▏ | 51kB 2.0MB/s eta 0:00:01\r\u001b[K |█████ | 61kB 2.3MB/s eta 0:00:01\r\u001b[K |█████▉ | 71kB 2.5MB/s eta 0:00:01\r\u001b[K |██████▊ | 81kB 2.7MB/s eta 0:00:01\r\u001b[K |███████▋ | 92kB 2.9MB/s eta 0:00:01\r\u001b[K |████████▍ | 102kB 2.8MB/s eta 0:00:01\r\u001b[K |█████████▎ | 112kB 2.8MB/s eta 0:00:01\r\u001b[K |██████████ | 122kB 2.8MB/s eta 0:00:01\r\u001b[K |███████████ | 133kB 2.8MB/s eta 0:00:01\r\u001b[K |███████████▊ | 143kB 2.8MB/s eta 0:00:01\r\u001b[K |████████████▋ | 153kB 2.8MB/s eta 0:00:01\r\u001b[K |█████████████▍ | 163kB 2.8MB/s eta 0:00:01\r\u001b[K |██████████████▎ | 174kB 2.8MB/s eta 0:00:01\r\u001b[K |███████████████▏ | 184kB 2.8MB/s eta 0:00:01\r\u001b[K |████████████████ | 194kB 2.8MB/s eta 0:00:01\r\u001b[K |████████████████▉ | 204kB 2.8MB/s eta 0:00:01\r\u001b[K |█████████████████▋ | 215kB 2.8MB/s eta 0:00:01\r\u001b[K |██████████████████▌ | 225kB 2.8MB/s eta 0:00:01\r\u001b[K |███████████████████▎ | 235kB 2.8MB/s eta 0:00:01\r\u001b[K |████████████████████▏ | 245kB 2.8MB/s eta 0:00:01\r\u001b[K |█████████████████████ | 256kB 2.8MB/s eta 0:00:01\r\u001b[K |█████████████████████▉ | 266kB 2.8MB/s eta 0:00:01\r\u001b[K |██████████████████████▊ | 276kB 2.8MB/s eta 0:00:01\r\u001b[K |███████████████████████▌ | 286kB 2.8MB/s eta 0:00:01\r\u001b[K |████████████████████████▍ | 296kB 2.8MB/s eta 0:00:01\r\u001b[K |█████████████████████████▏ | 307kB 2.8MB/s eta 0:00:01\r\u001b[K |██████████████████████████ | 317kB 2.8MB/s eta 0:00:01\r\u001b[K |██████████████████████████▉ | 327kB 2.8MB/s eta 0:00:01\r\u001b[K |███████████████████████████▊ | 337kB 2.8MB/s eta 0:00:01\r\u001b[K |████████████████████████████▌ | 348kB 2.8MB/s eta 0:00:01\r\u001b[K |█████████████████████████████▍ | 358kB 2.8MB/s eta 0:00:01\r\u001b[K |██████████████████████████████▎ | 368kB 2.8MB/s eta 0:00:01\r\u001b[K |███████████████████████████████ | 378kB 2.8MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 389kB 2.8MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 399kB 2.8MB/s \n",
"\u001b[?25hCollecting pybind11>=2.4\n",
" Using cached https://files.pythonhosted.org/packages/89/e3/d576f6f02bc75bacbc3d42494e8f1d063c95617d86648dba243c2cb3963e/pybind11-2.5.0-py2.py3-none-any.whl\n",
"Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tflite-support) (0.9.0)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from tflite-support) (1.18.5)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from absl-py>=0.7.0->tflite-support) (1.15.0)\n",
"Building wheels for collected packages: tflite-support\n",
" Building wheel for tflite-support (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for tflite-support: filename=tflite_support-0.1.0a1-cp36-cp36m-linux_x86_64.whl size=3973687 sha256=5f56ff03c7b502a35e95c2d648db2b329c76623906964880516bf5371b9744bf\n",
" Stored in directory: /root/.cache/pip/wheels/5f/65/21/4fa08c598c3e79a1a5260646eb37204d4859fc3da0bfeb8011\n",
"Successfully built tflite-support\n",
"Installing collected packages: pybind11, tflite-support\n",
"Successfully installed pybind11-2.5.0 tflite-support-0.1.0a1\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "hSBlosV-Wim7",
"colab_type": "code",
"colab": {}
},
"source": [
"import os\n",
"import tensorflow as tf\n",
"from absl import flags"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mayO_BGeWrT1",
"colab_type": "code",
"colab": {}
},
"source": [
"from tflite_support import flatbuffers\n",
"from tflite_support import metadata as _metadata\n",
"from tflite_support import metadata_schema_py_generated as _metadata_fb"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "v_czMsARW1SW",
"colab_type": "code",
"colab": {}
},
"source": [
"!mkdir model_without_metadata\n",
"!mkdir model_with_metadata"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "8_Qa9lo4W6zV",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 765
},
"outputId": "96cb260e-bb55-4667-c163-09c6cc600d1c"
},
"source": [
"!wget https://storage.googleapis.com/cartoon_gan/whitebox_cartoon_gan_dr.tflite\n",
"!wget https://storage.googleapis.com/cartoon_gan/whitebox_cartoon_gan_fp16.tflite\n",
"!wget https://storage.googleapis.com/cartoon_gan/whitebox_cartoon_gan_int8.tflite\n",
"!wget https://storage.googleapis.com/cartoon_gan/whitebox_cartoon_gan_full_int8.tflite\n",
"\n",
"!mv *.tflite model_without_metadata/"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": [
"--2020-07-23 04:45:53-- https://storage.googleapis.com/cartoon_gan/whitebox_cartoon_gan_dr.tflite\n",
"Resolving storage.googleapis.com (storage.googleapis.com)... 108.177.12.128, 172.217.193.128, 172.217.204.128, ...\n",
"Connecting to storage.googleapis.com (storage.googleapis.com)|108.177.12.128|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 1524512 (1.5M) [application/octet-stream]\n",
"Saving to: ‘whitebox_cartoon_gan_dr.tflite’\n",
"\n",
"\r whitebox_ 0%[ ] 0 --.-KB/s \rwhitebox_cartoon_ga 100%[===================>] 1.45M --.-KB/s in 0.01s \n",
"\n",
"2020-07-23 04:45:53 (105 MB/s) - ‘whitebox_cartoon_gan_dr.tflite’ saved [1524512/1524512]\n",
"\n",
"--2020-07-23 04:45:56-- https://storage.googleapis.com/cartoon_gan/whitebox_cartoon_gan_fp16.tflite\n",
"Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.203.128, 142.250.98.128, 173.194.216.128, ...\n",
"Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.203.128|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 3057696 (2.9M) [application/octet-stream]\n",
"Saving to: ‘whitebox_cartoon_gan_fp16.tflite’\n",
"\n",
"whitebox_cartoon_ga 100%[===================>] 2.92M --.-KB/s in 0.02s \n",
"\n",
"2020-07-23 04:45:56 (146 MB/s) - ‘whitebox_cartoon_gan_fp16.tflite’ saved [3057696/3057696]\n",
"\n",
"--2020-07-23 04:45:58-- https://storage.googleapis.com/cartoon_gan/whitebox_cartoon_gan_int8.tflite\n",
"Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.203.128, 142.250.98.128, 173.194.216.128, ...\n",
"Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.203.128|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 1551584 (1.5M) [application/octet-stream]\n",
"Saving to: ‘whitebox_cartoon_gan_int8.tflite’\n",
"\n",
"whitebox_cartoon_ga 100%[===================>] 1.48M --.-KB/s in 0.009s \n",
"\n",
"2020-07-23 04:45:58 (165 MB/s) - ‘whitebox_cartoon_gan_int8.tflite’ saved [1551584/1551584]\n",
"\n",
"--2020-07-23 04:46:00-- https://storage.googleapis.com/cartoon_gan/whitebox_cartoon_gan_full_int8.tflite\n",
"Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.204.128, 172.217.203.128, 74.125.31.128, ...\n",
"Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.204.128|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 1551584 (1.5M) [application/octet-stream]\n",
"Saving to: ‘whitebox_cartoon_gan_full_int8.tflite’\n",
"\n",
"whitebox_cartoon_ga 100%[===================>] 1.48M --.-KB/s in 0.01s \n",
"\n",
"2020-07-23 04:46:00 (129 MB/s) - ‘whitebox_cartoon_gan_full_int8.tflite’ saved [1551584/1551584]\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "InH13DM9cdqP",
"colab_type": "code",
"colab": {}
},
"source": [
"# This is where we will export a new .tflite model file with metadata, and a .json file with metadata info\n",
"EXPORT_DIR = \"model_with_metadata\""
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "E4ta4ByLYST3",
"colab_type": "code",
"colab": {}
},
"source": [
"class MetadataPopulatorForGANModel(object):\n",
" \"\"\"Populates the metadata for the CartoonGAN model.\"\"\"\n",
"\n",
" def __init__(self, model_file):\n",
" self.model_file = model_file\n",
" self.metadata_buf = None\n",
"\n",
" def populate(self):\n",
" \"\"\"Creates metadata and thesn populates it for a style transfer model.\"\"\"\n",
" self._create_metadata()\n",
" self._populate_metadata()\n",
" \n",
" def _create_metadata(self):\n",
" \"\"\"Creates the metadata for the CartoonGAN model.\"\"\"\n",
"\n",
" # Creates model info.\n",
" model_meta = _metadata_fb.ModelMetadataT()\n",
" model_meta.name = \"CartoonGAN\" \n",
" model_meta.description = (\"Cartoonizes an image. Reference: https://bit.ly/cartoon-gan.\")\n",
" model_meta.version = \"v1\"\n",
" model_meta.author = \"TensorFlow\"\n",
" model_meta.license = (\"Apache License. Version 2.0 \"\n",
" \"http://www.apache.org/licenses/LICENSE-2.0.\")\n",
"\n",
" # Creates info for the input, normal image.\n",
" input_image_meta = _metadata_fb.TensorMetadataT()\n",
" input_image_meta.name = \"source_image\"\n",
" input_image_meta.description = (\n",
" \"The expected image can be of any shape but with three channels \"\n",
" \"(red, blue, and green) per pixel. Each value in the tensor is between\"\n",
" \" -1 and 1.\")\n",
" input_image_meta.content = _metadata_fb.ContentT()\n",
" input_image_meta.content.contentProperties = (\n",
" _metadata_fb.ImagePropertiesT())\n",
" input_image_meta.content.contentProperties.colorSpace = (\n",
" _metadata_fb.ColorSpaceType.RGB)\n",
" input_image_meta.content.contentPropertiesType = (\n",
" _metadata_fb.ContentProperties.ImageProperties)\n",
" input_image_normalization = _metadata_fb.ProcessUnitT()\n",
" input_image_normalization.optionsType = (\n",
" _metadata_fb.ProcessUnitOptions.NormalizationOptions)\n",
" input_image_normalization.options = _metadata_fb.NormalizationOptionsT()\n",
" input_image_normalization.options.mean = [127.5]\n",
" input_image_normalization.options.std = [127.5]\n",
" input_image_meta.processUnits = [input_image_normalization]\n",
" input_image_stats = _metadata_fb.StatsT()\n",
" input_image_stats.max = [1.0]\n",
" input_image_stats.min = [-1.0]\n",
" input_image_meta.stats = input_image_stats\n",
"\n",
"\n",
" # Creates output info, cartoonized image\n",
" output_image_meta = _metadata_fb.TensorMetadataT()\n",
" output_image_meta.name = \"cartoonized_image\"\n",
" output_image_meta.description = \"Image cartoonized.\"\n",
" output_image_meta.content = _metadata_fb.ContentT()\n",
" output_image_meta.content.contentProperties = _metadata_fb.ImagePropertiesT()\n",
" output_image_meta.content.contentProperties.colorSpace = (\n",
" _metadata_fb.ColorSpaceType.RGB)\n",
" output_image_meta.content.contentPropertiesType = (\n",
" _metadata_fb.ContentProperties.ImageProperties)\n",
" # output_image_normalization = _metadata_fb.ProcessUnitT()\n",
" # output_image_normalization.optionsType = (\n",
" # _metadata_fb.ProcessUnitOptions.NormalizationOptions)\n",
" # output_image_normalization.options = _metadata_fb.NormalizationOptionsT()\n",
" # output_image_normalization.options.mean = [0.0]\n",
" # output_image_normalization.options.std = [0.003921568627] # 1/255\n",
" # output_image_meta.processUnits = [output_image_normalization]\n",
" output_image_stats = _metadata_fb.StatsT()\n",
" output_image_stats.max = [255.0]\n",
" output_image_stats.min = [0.0]\n",
" output_image_meta.stats = output_image_stats\n",
"\n",
" # Creates subgraph info.\n",
" subgraph = _metadata_fb.SubGraphMetadataT()\n",
" subgraph.inputTensorMetadata = [input_image_meta] \n",
" subgraph.outputTensorMetadata = [output_image_meta] \n",
" model_meta.subgraphMetadata = [subgraph]\n",
"\n",
" b = flatbuffers.Builder(0)\n",
" b.Finish(\n",
" model_meta.Pack(b),\n",
" _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)\n",
" self.metadata_buf = b.Output()\n",
"\n",
" def _populate_metadata(self):\n",
" \"\"\"Populates metadata to the model file.\"\"\"\n",
" populator = _metadata.MetadataPopulator.with_model_file(self.model_file)\n",
" populator.load_metadata_buffer(self.metadata_buf)\n",
" populator.populate()"
],
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "vRlUqyFMcNyy",
"colab_type": "code",
"colab": {}
},
"source": [
"def populate_metadata(model_file):\n",
" \"\"\"Populates the metadata using the populator specified.\n",
" Args:\n",
" model_file: valid path to the model file.\n",
" model_type: a type defined in StyleTransferModelType .\n",
" \"\"\"\n",
"\n",
" # Populates metadata for the model.\n",
" model_file_basename = os.path.basename(model_file)\n",
" export_path = os.path.join(EXPORT_DIR, model_file_basename)\n",
" tf.io.gfile.copy(model_file, export_path, overwrite=True)\n",
"\n",
" populator = MetadataPopulatorForGANModel(export_path) \n",
" populator.populate()\n",
"\n",
" # Displays the metadata that was just populated into the tflite model.\n",
" displayer = _metadata.MetadataDisplayer.with_model_file(export_path)\n",
" export_json_file = os.path.join(\n",
" EXPORT_DIR,\n",
" os.path.splitext(model_file_basename)[0] + \".json\")\n",
" json_file = displayer.get_metadata_json()\n",
" with open(export_json_file, \"w\") as f:\n",
" f.write(json_file)\n",
" print(\"Finished populating metadata and associated file to the model:\")\n",
" print(export_path)\n",
" print(\"The metadata json file has been saved to:\")\n",
" print(os.path.join(EXPORT_DIR,\n",
" os.path.splitext(model_file_basename)[0] + \".json\"))"
],
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "YVSJFR4HcUuI",
"colab_type": "code",
"colab": {}
},
"source": [
"#@title Specify directories (don't do this for fp16)\n",
"tflite_model_path = \"whitebox_cartoon_gan_int8.tflite\" #@param {type:\"string\"}\n",
"# The original .tflite file\n",
"MODEL_FILE = \"/content/model_without_metadata/{}\".format(tflite_model_path)"
],
"execution_count": 11,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "t123hRt1chmQ",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
},
"outputId": "7f2ce7aa-c16f-4b5d-a034-0addc1cbe965"
},
"source": [
"populate_metadata(MODEL_FILE)"
],
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"text": [
"Finished populating metadata and associated file to the model:\n",
"model_with_metadata/whitebox_cartoon_gan_int8.tflite\n",
"The metadata json file has been saved to:\n",
"model_with_metadata/whitebox_cartoon_gan_int8.json\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "K8YarWzv_ZiC",
"colab_type": "text"
},
"source": [
"For the float16 model the description will be slightly different. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "iq1AjgB9dMc1",
"colab_type": "code",
"colab": {}
},
"source": [
"class MetadataPopulatorForGANModel(object):\n",
" \"\"\"Populates the metadata for the CartoonGAN model.\"\"\"\n",
"\n",
" def __init__(self, model_file):\n",
" self.model_file = model_file\n",
" self.metadata_buf = None\n",
"\n",
" def populate(self):\n",
" \"\"\"Creates metadata and thesn populates it for a style transfer model.\"\"\"\n",
" self._create_metadata()\n",
" self._populate_metadata()\n",
" \n",
" def _create_metadata(self):\n",
" \"\"\"Creates the metadata for the CartoonGAN model.\"\"\"\n",
"\n",
" # Creates model info.\n",
" model_meta = _metadata_fb.ModelMetadataT()\n",
" model_meta.name = \"CartoonGAN\" \n",
" model_meta.description = (\"Cartoonizes an image. Reference: https://bit.ly/cartoon-gan.\")\n",
" model_meta.version = \"v1\"\n",
" model_meta.author = \"TensorFlow\"\n",
" model_meta.license = (\"Apache License. Version 2.0 \"\n",
" \"http://www.apache.org/licenses/LICENSE-2.0.\")\n",
"\n",
" # Creates info for the input, normal image.\n",
" input_image_meta = _metadata_fb.TensorMetadataT()\n",
" input_image_meta.name = \"source_image\"\n",
" input_image_meta.description = (\n",
" \"The expected image is 224 x 224, with three channels \"\n",
" \"(red, blue, and green) per pixel. Each value in the tensor is between\"\n",
" \" -1 and 1.\")\n",
" input_image_meta.content = _metadata_fb.ContentT()\n",
" input_image_meta.content.contentProperties = (\n",
" _metadata_fb.ImagePropertiesT())\n",
" input_image_meta.content.contentProperties.colorSpace = (\n",
" _metadata_fb.ColorSpaceType.RGB)\n",
" input_image_meta.content.contentPropertiesType = (\n",
" _metadata_fb.ContentProperties.ImageProperties)\n",
" input_image_normalization = _metadata_fb.ProcessUnitT()\n",
" input_image_normalization.optionsType = (\n",
" _metadata_fb.ProcessUnitOptions.NormalizationOptions)\n",
" input_image_normalization.options = _metadata_fb.NormalizationOptionsT()\n",
" input_image_normalization.options.mean = [127.5]\n",
" input_image_normalization.options.std = [127.5]\n",
" input_image_meta.processUnits = [input_image_normalization]\n",
" input_image_stats = _metadata_fb.StatsT()\n",
" input_image_stats.max = [1.0]\n",
" input_image_stats.min = [-1.0]\n",
" input_image_meta.stats = input_image_stats\n",
"\n",
"\n",
" # Creates output info, cartoonized image\n",
" output_image_meta = _metadata_fb.TensorMetadataT()\n",
" output_image_meta.name = \"cartoonized_image\"\n",
" output_image_meta.description = \"Image cartoonized.\"\n",
" output_image_meta.content = _metadata_fb.ContentT()\n",
" output_image_meta.content.contentProperties = _metadata_fb.ImagePropertiesT()\n",
" output_image_meta.content.contentProperties.colorSpace = (\n",
" _metadata_fb.ColorSpaceType.RGB)\n",
" output_image_meta.content.contentPropertiesType = (\n",
" _metadata_fb.ContentProperties.ImageProperties)\n",
" # output_image_normalization = _metadata_fb.ProcessUnitT()\n",
" # output_image_normalization.optionsType = (\n",
" # _metadata_fb.ProcessUnitOptions.NormalizationOptions)\n",
" # output_image_normalization.options = _metadata_fb.NormalizationOptionsT()\n",
" # output_image_normalization.options.mean = [0.0]\n",
" # output_image_normalization.options.std = [0.003921568627] # 1/255\n",
" # output_image_meta.processUnits = [output_image_normalization]\n",
" output_image_stats = _metadata_fb.StatsT()\n",
" output_image_stats.max = [255.0]\n",
" output_image_stats.min = [0.0]\n",
" output_image_meta.stats = output_image_stats\n",
"\n",
" # Creates subgraph info.\n",
" subgraph = _metadata_fb.SubGraphMetadataT()\n",
" subgraph.inputTensorMetadata = [input_image_meta] \n",
" subgraph.outputTensorMetadata = [output_image_meta] \n",
" model_meta.subgraphMetadata = [subgraph]\n",
"\n",
" b = flatbuffers.Builder(0)\n",
" b.Finish(\n",
" model_meta.Pack(b),\n",
" _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)\n",
" self.metadata_buf = b.Output()\n",
"\n",
" def _populate_metadata(self):\n",
" \"\"\"Populates metadata to the model file.\"\"\"\n",
" populator = _metadata.MetadataPopulator.with_model_file(self.model_file)\n",
" populator.load_metadata_buffer(self.metadata_buf)\n",
" populator.populate()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "k0FVg1kkdN4P",
"colab_type": "code",
"colab": {}
},
"source": [
"def populate_metadata(model_file):\n",
" \"\"\"Populates the metadata using the populator specified.\n",
" Args:\n",
" model_file: valid path to the model file.\n",
" model_type: a type defined in StyleTransferModelType .\n",
" \"\"\"\n",
"\n",
" # Populates metadata for the model.\n",
" model_file_basename = os.path.basename(model_file)\n",
" export_path = os.path.join(EXPORT_DIR, model_file_basename)\n",
" tf.io.gfile.copy(model_file, export_path, overwrite=True)\n",
"\n",
" populator = MetadataPopulatorForGANModel(export_path) \n",
" populator.populate()\n",
"\n",
" # Displays the metadata that was just populated into the tflite model.\n",
" displayer = _metadata.MetadataDisplayer.with_model_file(export_path)\n",
" export_json_file = os.path.join(\n",
" EXPORT_DIR,\n",
" os.path.splitext(model_file_basename)[0] + \".json\")\n",
" json_file = displayer.get_metadata_json()\n",
" with open(export_json_file, \"w\") as f:\n",
" f.write(json_file)\n",
" print(\"Finished populating metadata and associated file to the model:\")\n",
" print(export_path)\n",
" print(\"The metadata json file has been saved to:\")\n",
" print(os.path.join(EXPORT_DIR,\n",
" os.path.splitext(model_file_basename)[0] + \".json\"))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "fhjdWaHOfHzG",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
},
"outputId": "c94668d8-a2c0-4dce-969b-6ac934a8ecc4"
},
"source": [
"populate_metadata('/content/model_without_metadata/whitebox_cartoon_gan_fp16.tflite')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Finished populating metadata and associated file to the model:\n",
"model_with_metadata/whitebox_cartoon_gan_fp16.tflite\n",
"The metadata json file has been saved to:\n",
"model_with_metadata/whitebox_cartoon_gan_fp16.json\n"
],
"name": "stdout"
}
]
}
]
} | stackv2 | 2024-11-18T18:03:05.388468+00:00 | 2020-07-24T04:52:14 | {
"license": "Apache-2.0",
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"committer_date": "2020-07-24T04:52:14",
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fbd6fee0b3fc353ad47fd28297bbfcc59c00802e | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Time delay-cosmography simulations\n",
"\n",
"This notebook requires standard python libraries and the publicly available packages on github:\n",
"\n",
"- lenstronomy (https://github.com/sibirrer/lenstronomy)\n",
"- fastell4py (https://github.com/sibirrer/fastell4py), based on the original fastell fortran code (by Barkana)\n",
"\n",
"The packages are based on Birrer, Amara & Refregier 2015 and an official release is planned with Birrer et al. (in prep)\n",
"Installation for each package can be found on the github page.\n",
"WARNING: a proper installation of fastell4py needs a fortran compiler.\n",
"This notebook has been tested with lenstronomy 0.3.1.\n",
"\n",
"For further information, please get in touch with the author of this notebook, Simon Birrer: [email protected]"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# import of standard python libraries\n",
"import numpy as np\n",
"import os\n",
"import time\n",
"import corner\n",
"import astropy.io.fits as pyfits\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
"%matplotlib inline\n",
"\n",
"from lenstronomy.LensModel.lens_model import LensModel\n",
"from lenstronomy.LensModel.Solver.lens_equation_solver import LensEquationSolver\n",
"from lenstronomy.LightModel.light_model import LightModel\n",
"from lenstronomy.PointSource.point_source import PointSource\n",
"from lenstronomy.ImSim.image_model import ImageModel\n",
"import lenstronomy.Util.param_util as param_util\n",
"import lenstronomy.Util.simulation_util as sim_util\n",
"import lenstronomy.Util.image_util as image_util\n",
"from lenstronomy.Util import kernel_util\n",
"from lenstronomy.Data.imaging_data import ImageData\n",
"from lenstronomy.Data.psf import PSF"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## simulation choices"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/sibirrer/Library/Python/3.6/lib/python/site-packages/ipykernel_launcher.py:16: RuntimeWarning: divide by zero encountered in log10\n",
" app.launch_new_instance()\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"# define lens configuration and cosmology (not for lens modelling)\n",
"z_lens = 0.5\n",
"z_source = 1.5\n",
"from astropy.cosmology import FlatLambdaCDM\n",
"cosmo = FlatLambdaCDM(H0=70, Om0=0.3, Ob0=0.)\n",
"\n",
"\n",
"# import PSF file\n",
"path = os.getcwd()\n",
"dirpath, _ = os.path.split(path)\n",
"module_path, _ = os.path.split(dirpath)\n",
"psf_filename = os.path.join(module_path, 'Data/PSF_TinyTim/psf_example.fits')\n",
"kernel = pyfits.getdata(psf_filename)\n",
"\n",
"plt.matshow(np.log10(kernel))\n",
"plt.show()\n",
"\n",
" \n",
"# data specifics\n",
"sigma_bkg = .05 # background noise per pixel (Gaussian)\n",
"exp_time = 100. # exposure time (arbitrary units, flux per pixel is in units #photons/exp_time unit)\n",
"numPix = 100 # cutout pixel size\n",
"deltaPix = 0.05 # pixel size in arcsec (area per pixel = deltaPix**2)\n",
"fwhm = 0.1 # full width half max of PSF (only valid when psf_type='gaussian')\n",
"psf_type = 'PIXEL' # 'gaussian', 'pixel', 'NONE'\n",
"kernel_size = 91\n",
"\n",
"# initial input simulation\n",
"\n",
"# generate the coordinate grid and image properties\n",
"kwargs_data = sim_util.data_configure_simple(numPix, deltaPix, exp_time, sigma_bkg)\n",
"data_class = ImageData(**kwargs_data)\n",
"# generate the psf variables\n",
"kernel_cut = kernel_util.cut_psf(kernel, kernel_size)\n",
"kwargs_psf = {'psf_type': psf_type, 'pixel_size': deltaPix, 'kernel_point_source': kernel_cut}\n",
"#kwargs_psf = sim_util.psf_configure_simple(psf_type=psf_type, fwhm=fwhm, kernelsize=kernel_size, deltaPix=deltaPix, kernel=kernel)\n",
"psf_class = PSF(**kwargs_psf)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/sibirrer/Library/Python/3.6/lib/python/site-packages/ipykernel_launcher.py:76: RuntimeWarning: invalid value encountered in log10\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# lensing quantities\n",
"kwargs_shear = {'e1': -0.04, 'e2': -0.01} # shear values to the source plane\n",
"kwargs_spemd = {'theta_E': 1.66, 'gamma': 1.98, 'center_x': 0.0, 'center_y': 0.0, 'e1': 0.05, 'e2': 0.05} # parameters of the deflector lens model\n",
"\n",
"# the lens model is a supperposition of an elliptical lens model with external shear\n",
"lens_model_list = ['SPEP', 'SHEAR']\n",
"kwargs_lens = [kwargs_spemd, kwargs_shear]\n",
"lens_model_class = LensModel(lens_model_list=lens_model_list)\n",
"\n",
"# choice of source type\n",
"source_type = 'SERSIC' # 'SERSIC' or 'SHAPELETS'\n",
"\n",
"source_x = 0.\n",
"source_y = 0.1\n",
"\n",
"\n",
"# Sersic parameters in the initial simulation\n",
"phi_G, q = 0.5, 0.8\n",
"e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)\n",
"kwargs_sersic_source = {'amp': 4000, 'R_sersic': 0.2, 'n_sersic': 1, 'e1': e1, 'e2': e2, 'center_x': source_x, 'center_y': source_y}\n",
"#kwargs_else = {'sourcePos_x': source_x, 'sourcePos_y': source_y, 'quasar_amp': 400., 'gamma1_foreground': 0.0, 'gamma2_foreground':-0.0}\n",
"source_model_list = ['SERSIC_ELLIPSE']\n",
"kwargs_source = [kwargs_sersic_source]\n",
"source_model_class = LightModel(light_model_list=source_model_list)\n",
"\n",
"\n",
"# lens light model\n",
"phi_G, q = 0.9, 0.9\n",
"e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)\n",
"kwargs_sersic_lens = {'amp': 8000, 'R_sersic': 0.4, 'n_sersic': 2., 'e1': e1, 'e2': e2, 'center_x': 0.0, 'center_y': 0}\n",
"lens_light_model_list = ['SERSIC_ELLIPSE']\n",
"kwargs_lens_light = [kwargs_sersic_lens]\n",
"lens_light_model_class = LightModel(light_model_list=lens_light_model_list)\n",
"\n",
"lensEquationSolver = LensEquationSolver(lens_model_class)\n",
"x_image, y_image = lensEquationSolver.findBrightImage(source_x, source_y, kwargs_lens, numImages=4,\n",
" min_distance=deltaPix, search_window=numPix * deltaPix)\n",
"mag = lens_model_class.magnification(x_image, y_image, kwargs=kwargs_lens)\n",
"kwargs_ps = [{'ra_image': x_image, 'dec_image': y_image,\n",
" 'point_amp': np.abs(mag)*1000}] # quasar point source position in the source plane and intrinsic brightness\n",
"point_source_list = ['LENSED_POSITION']\n",
"point_source_class = PointSource(point_source_type_list=point_source_list, fixed_magnification_list=[False])\n",
"\n",
"kwargs_numerics = {'supersampling_factor': 1}\n",
"\n",
"imageModel = ImageModel(data_class, psf_class, lens_model_class, source_model_class,\n",
" lens_light_model_class, point_source_class, kwargs_numerics=kwargs_numerics)\n",
"\n",
"# generate image\n",
"image_sim = imageModel.image(kwargs_lens, kwargs_source, kwargs_lens_light, kwargs_ps)\n",
"poisson = image_util.add_poisson(image_sim, exp_time=exp_time)\n",
"bkg = image_util.add_background(image_sim, sigma_bkd=sigma_bkg)\n",
"image_sim = image_sim + bkg + poisson\n",
"\n",
"data_class.update_data(image_sim)\n",
"kwargs_data['image_data'] = image_sim\n",
"\n",
"\n",
"kwargs_model = {'lens_model_list': lens_model_list, \n",
" 'lens_light_model_list': lens_light_model_list,\n",
" 'source_light_model_list': source_model_list,\n",
" 'point_source_model_list': point_source_list\n",
" }\n",
"\n",
"# display the initial simulated image\n",
"cmap_string = 'gray'\n",
"cmap = plt.get_cmap(cmap_string)\n",
"cmap.set_bad(color='k', alpha=1.)\n",
"cmap.set_under('k')\n",
"\n",
"v_min = -4\n",
"v_max = 2\n",
"\n",
"f, axes = plt.subplots(1, 1, figsize=(6, 6), sharex=False, sharey=False)\n",
"ax = axes\n",
"im = ax.matshow(np.log10(image_sim), origin='lower', vmin=v_min, vmax=v_max, cmap=cmap, extent=[0, 1, 0, 1])\n",
"ax.get_xaxis().set_visible(False)\n",
"ax.get_yaxis().set_visible(False)\n",
"ax.autoscale(False)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## time delays\n",
"time delays are defined in **lenstronomy** as the difference in light travel path relative to a straight line. Negative values correspond to earlier arrival times. The units are in days."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the time delays for the images at position [ 0.67714792 -0.5345247 1.48961759 -1.28508121] [-1.62600148 1.89186155 0.26605048 -0.68322604] are: [-133.28616805 -166.17307566 -106.33917043 -97.18921825]\n",
"the measured relative delays are: [-33.09518909 14.35870032 22.87683437]\n"
]
}
],
"source": [
"from lenstronomy.Analysis.lens_properties import LensProp\n",
"lensProp = LensProp(z_lens, z_source, kwargs_model, cosmo=cosmo)\n",
"\n",
"# time delays, the unit [days] is matched when the lensing angles are in arcsec\n",
"t_days = lensProp.time_delays(kwargs_lens, kwargs_ps, kappa_ext=0)\n",
"print(\"the time delays for the images at position \", kwargs_ps[0]['ra_image'], kwargs_ps[0]['dec_image'], \"are: \", t_days)\n",
"\n",
"# relative delays (observable). The convention is relative to the first image\n",
"dt_days = t_days[1:] - t_days[0]\n",
"# and errors can be assigned to the measured relative delays (full covariance matrix not yet implemented)\n",
"dt_sigma = [3, 5, 10] # Gaussian errors\n",
"# and here a realisation of the measurement with the quoted error bars\n",
"dt_measured = np.random.normal(dt_days, dt_sigma)\n",
"print(\"the measured relative delays are: \", dt_measured)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## kinematics\n",
"Kinematics can provide important complementary information about the lens to constrain cosmography"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[301.9413739] velocity dispersion in km/s\n"
]
}
],
"source": [
"# observational conditions of the spectroscopic campagne\n",
"R_slit = 1. # slit length in arcsec\n",
"dR_slit = 1. # slit width in arcsec\n",
"psf_fwhm = 0.7\n",
"\n",
"kwargs_aperture = {'length': R_slit, 'width': dR_slit, 'center_ra': 0.05, 'center_dec': 0, 'angle': 0}\n",
"anisotropy_model = 'OsipkovMerritt'\n",
"aperture_type = 'slit'\n",
"\n",
"kwargs_galkin_numerics = {'sampling_number': 10000, # numerical ray-shooting, should converge -> infinity\n",
" 'interpol_grid_num': 1000, # numerical interpolation, should converge -> infinity\n",
" 'log_integration': True, # log or linear interpolation of surface brightness and mass models\n",
" 'max_integrate': 100, 'min_integrate': 0.001} # lower/upper bound of numerical integrals\n",
"\n",
"r_ani = 1.\n",
"r_eff = 0.2\n",
"kwargs_anisotropy = {'r_ani': r_ani}\n",
"\n",
"vel_disp = lensProp.velocity_dispersion_numerical(kwargs_lens, kwargs_lens_light, kwargs_anisotropy, kwargs_aperture, psf_fwhm, aperture_type, anisotropy_model, MGE_light=True, MGE_mass=True, r_eff=r_eff)\n",
"print(vel_disp, 'velocity dispersion in km/s')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model and parameter choices"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# lens model choicers\n",
"fixed_lens = []\n",
"kwargs_lens_init = []\n",
"kwargs_lens_sigma = []\n",
"kwargs_lower_lens = []\n",
"kwargs_upper_lens = []\n",
"\n",
"fixed_lens.append({}) \n",
"kwargs_lens_init.append({'theta_E': 1.6, 'gamma': 2, 'center_x': 0.0, 'center_y': 0, 'e1': 0, 'e2': 0.})\n",
"#kwargs_lens_init.append(kwargs_spemd)\n",
"kwargs_lens_sigma.append({'theta_E': .2, 'e1': 0.1, 'e2': 0.1, 'gamma': 0.1, 'center_x': 0.01, 'center_y': 0.01})\n",
"kwargs_lower_lens.append({'theta_E': 0.01, 'e1': -0.5, 'e2': -0.5, 'gamma': 1.5, 'center_x': -10, 'center_y': -10})\n",
"kwargs_upper_lens.append({'theta_E': 10, 'e1': 0.5, 'e2': 0.5, 'gamma': 2.5, 'center_x': 10, 'center_y': 10})\n",
"\n",
"fixed_lens.append({'ra_0': 0, 'dec_0': 0})\n",
"#kwargs_lens_init.append({'e1': 0.0, 'e2': 0.0})\n",
"kwargs_lens_init.append(kwargs_shear)\n",
"kwargs_lens_sigma.append({'e1': 0.1, 'e2': 0.1})\n",
"kwargs_lower_lens.append({'e1': -0.2, 'e2': -0.2})\n",
"kwargs_upper_lens.append({'e1': 0.2, 'e2': 0.2})\n",
"\n",
"lens_params = [kwargs_lens_init, kwargs_lens_sigma, fixed_lens, kwargs_lower_lens, kwargs_upper_lens]\n",
"\n",
"# lens light model choices\n",
"fixed_lens_light = []\n",
"kwargs_lens_light_init = []\n",
"kwargs_lens_light_sigma = []\n",
"kwargs_lower_lens_light = []\n",
"kwargs_upper_lens_light = []\n",
"\n",
"fixed_lens_light.append({})\n",
"kwargs_lens_light_init.append({'R_sersic': 0.5, 'n_sersic': 1, 'e1': 0, 'e2': 0., 'center_x': 0, 'center_y': 0})\n",
"#kwargs_lens_light_init.append(kwargs_sersic_lens)\n",
"kwargs_lens_light_sigma.append({'n_sersic': 0.5, 'R_sersic': 0.1, 'e1': 0.1, 'e2': 0.1, 'center_x': 0.1, 'center_y': 0.1})\n",
"kwargs_lower_lens_light.append({'e1': -0.5, 'e2': -0.5, 'R_sersic': 0.01, 'n_sersic': 0.5, 'center_x': -10, 'center_y': -10})\n",
"kwargs_upper_lens_light.append({'e1': 0.5, 'e2': 0.5, 'R_sersic': 10, 'n_sersic': 8, 'center_x': 10, 'center_y': 10})\n",
"\n",
"lens_light_params = [kwargs_lens_light_init, kwargs_lens_light_sigma, fixed_lens_light, kwargs_lower_lens_light, kwargs_upper_lens_light]\n",
"\n",
"\n",
"fixed_source = []\n",
"kwargs_source_init = []\n",
"kwargs_source_sigma = []\n",
"kwargs_lower_source = []\n",
"kwargs_upper_source = []\n",
"\n",
"fixed_source.append({})\n",
"kwargs_source_init.append({'R_sersic': 0.1, 'n_sersic': 1, 'e1': 0, 'e2': 0., 'center_x': 0, 'center_y': 0})\n",
"#kwargs_source_init.append(kwargs_sersic_source)\n",
"kwargs_source_sigma.append({'n_sersic': 0.5, 'R_sersic': 0.05, 'e1': 0.1, 'e2': 0.1, 'center_x': 0.1, 'center_y': 0.1})\n",
"kwargs_lower_source.append({'e1': -0.5, 'e2': -0.5, 'R_sersic': 0.001, 'n_sersic': .5, 'center_x': -10, 'center_y': -10})\n",
"kwargs_upper_source.append({'e1': 0.5, 'e2': 0.5, 'R_sersic': 10, 'n_sersic': 5., 'center_x': 10, 'center_y': 10})\n",
"\n",
"source_params = [kwargs_source_init, kwargs_source_sigma, fixed_source, kwargs_lower_source, kwargs_upper_source]\n",
"\n",
"\n",
"fixed_ps = [{}]\n",
"kwargs_ps_init = kwargs_ps\n",
"kwargs_ps_sigma = [{'ra_image': 0.01 * np.ones(len(x_image)), 'dec_image': 0.01 * np.ones(len(x_image))}]\n",
"kwargs_lower_ps = [{'ra_image': -10 * np.ones(len(x_image)), 'dec_image': -10 * np.ones(len(y_image))}]\n",
"kwargs_upper_ps = [{'ra_image': 10* np.ones(len(x_image)), 'dec_image': 10 * np.ones(len(y_image))}]\n",
"\n",
"fixed_cosmo = {}\n",
"kwargs_cosmo_init = {'D_dt': 5000}\n",
"kwargs_cosmo_sigma = {'D_dt': 10000}\n",
"kwargs_lower_cosmo = {'D_dt': 0}\n",
"kwargs_upper_cosmo = {'D_dt': 10000}\n",
"cosmo_params = [kwargs_cosmo_init, kwargs_cosmo_sigma, fixed_cosmo, kwargs_lower_cosmo, kwargs_upper_cosmo]\n",
"\n",
"ps_params = [kwargs_ps_init, kwargs_ps_sigma, fixed_ps, kwargs_lower_ps, kwargs_upper_ps]\n",
"\n",
"kwargs_params = {'lens_model': lens_params,\n",
" 'source_model': source_params,\n",
" 'lens_light_model': lens_light_params,\n",
" 'point_source_model': ps_params,\n",
" 'cosmography': cosmo_params}\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Computing the PSO ...\n",
"10\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/sibirrer/Library/Python/3.6/lib/python/site-packages/scipy-1.1.0-py3.6-macosx-10.13-x86_64.egg/scipy/optimize/minpack.py:163: RuntimeWarning: The iteration is not making good progress, as measured by the \n",
" improvement from the last five Jacobian evaluations.\n",
" warnings.warn(msg, RuntimeWarning)\n",
"/Users/sibirrer/Library/Python/3.6/lib/python/site-packages/scipy-1.1.0-py3.6-macosx-10.13-x86_64.egg/scipy/optimize/minpack.py:163: RuntimeWarning: The iteration is not making good progress, as measured by the \n",
" improvement from the last ten iterations.\n",
" warnings.warn(msg, RuntimeWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"20\n",
"30\n",
"40\n",
"50\n",
"60\n",
"70\n",
"80\n",
"90\n",
"100\n",
"max iteration reached! stoping\n",
"-0.9946236498162453 reduced X^2 of best position\n",
"-4960.685453458524 logL\n",
"9975 effective number of data points\n",
"[{'theta_E': 1.6603722938914431, 'gamma': 1.9821200420846221, 'e1': 0.050616378958603984, 'e2': 0.05061230225860866, 'center_x': -0.00036403507450744277, 'center_y': -0.0006958294944654635}, {'e1': -0.03976839375759865, 'e2': -0.009594250522078868, 'ra_0': 0, 'dec_0': 0}] lens result\n",
"[{'amp': 1, 'R_sersic': 0.20134568299060338, 'n_sersic': 1.001896948163526, 'e1': 0.06008263928306136, 'e2': 0.09292545031541763, 'center_x': -0.0002634024572058802, 'center_y': 0.100059064142947}] source result\n",
"[{'amp': 1, 'R_sersic': 0.40103822634365055, 'n_sersic': 2.004597486050985, 'e1': -0.010270681881581253, 'e2': 0.051786741103077724, 'center_x': 0.00030306296437844406, 'center_y': -2.025501303271819e-05}] lens light result\n",
"[{'ra_image': array([ 0.67719341, -0.53445342, 1.48914335, -1.28509974]), 'dec_image': array([-1.6260406 , 1.8917727 , 0.2660324 , -0.68344014]), 'point_amp': 1}] point source result\n",
"{'D_dt': 2811.891523850982} cosmo result\n",
"203.69378900527954 time used for PSO PSO\n",
"===================\n",
"Computing the MCMC...\n",
"Number of walkers = 220\n",
"Burn-in iterations: 10\n",
"Sampling iterations: 10\n",
"InMemoryStorageUtil does not support storeRandomState\n",
"97.3131148815155 time taken for MCMC sampling\n",
"301.0533661842346 total time needed for computation\n",
"============ CONGRATULATION, YOUR JOB WAS SUCCESSFUL ================ \n"
]
}
],
"source": [
"\n",
"\n",
"# numerical options and fitting sequences\n",
"\n",
"num_source_model = len(source_model_list)\n",
"\n",
"kwargs_constraints = {'joint_source_with_point_source': [[0, 0]],\n",
" 'num_point_source_list': [4],\n",
" 'solver_type': 'PROFILE_SHEAR', # 'PROFILE', 'PROFILE_SHEAR', 'ELLIPSE', 'CENTER'\n",
" 'Ddt_sampling': True,\n",
" }\n",
"\n",
"kwargs_likelihood = {'check_bounds': True,\n",
" 'force_no_add_image': False,\n",
" 'source_marg': False,\n",
" 'point_source_likelihood': False,\n",
" 'position_uncertainty': 0.004,\n",
" 'check_solver': True,\n",
" 'solver_tolerance': 0.001,\n",
" \n",
" 'time_delay_likelihood': True,\n",
" }\n",
"\n",
"image_band = [kwargs_data, kwargs_psf, kwargs_numerics]\n",
"multi_band_list = [image_band]\n",
"kwargs_data_joint = {'multi_band_list': multi_band_list, 'image_type': 'multi-linear',\n",
" 'time_delays_measured': dt_measured,\n",
" 'time_delays_uncertainties': dt_sigma,}\n",
"\n",
"from lenstronomy.Workflow.fitting_sequence import FittingSequence\n",
"\n",
"mpi = False # MPI possible, but not supported through that notebook.\n",
"\n",
"from lenstronomy.Workflow.fitting_sequence import FittingSequence\n",
"fitting_seq = FittingSequence(kwargs_data_joint, kwargs_model, kwargs_constraints, kwargs_likelihood, kwargs_params)\n",
"\n",
"fitting_kwargs_list = [['PSO', {'sigma_scale': .1, 'n_particles': 100, 'n_iterations': 100}],\n",
" ['MCMC', {'n_burn': 10, 'n_run': 10, 'walkerRatio': 10, 'sigma_scale': .1}]\n",
"]\n",
"\n",
"start_time = time.time()\n",
"chain_list, param_list, samples_mcmc, param_mcmc, dist_mcmc = fitting_seq.fit_sequence(fitting_kwargs_list)\n",
"lens_result, source_result, lens_light_result, ps_result, cosmo_result = fitting_seq.best_fit()\n",
"end_time = time.time()\n",
"print(end_time - start_time, 'total time needed for computation')\n",
"print('============ CONGRATULATION, YOUR JOB WAS SUCCESSFUL ================ ')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## analyse model output"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{'theta_E': 1.6603722938914431, 'gamma': 1.9821200420846221, 'e1': 0.050616378958603984, 'e2': 0.05061230225860866, 'center_x': -0.00036403507450744277, 'center_y': -0.0006958294944654635}, {'e1': -0.03976839375759865, 'e2': -0.009594250522078868, 'ra_0': 0, 'dec_0': 0}]\n",
"reduced chi^2 = 1.013577616963093\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/sibirrer/Software/lenstronomy/lenstronomy/Plots/output_plots.py:320: RuntimeWarning: invalid value encountered in log10\n",
" im = ax.matshow(np.log10(self._data), origin='lower',\n",
"/Users/sibirrer/Library/Python/3.6/lib/python/site-packages/matplotlib/figure.py:459: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
" \"matplotlib is currently using a non-GUI backend, \"\n",
"/Users/sibirrer/Software/lenstronomy/lenstronomy/Plots/output_plots.py:647: RuntimeWarning: invalid value encountered in log10\n",
" im = ax.matshow(np.log10(self._data - model), origin='lower', vmin=v_min, vmax=v_max,\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 12 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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5+fqNvkPVvwNG336lTvYN58iys9q51MqrjfH1FrTlZWL+/Pl48cUX8etf/xojIyNYvHgxPvShD+Gzn/1sS/v1bPOGG26IY445BqeffnptMWLx4sV41atehYULF2YWMjbaaCPsuuuuNdfDt7zlLTjxxBNxyCGH1PaXJAmuueYavP71r8ef/vSn2ravvPIKDjroIKRpil//+teYMWMGTj75ZPzXf/0XXnzxRRx33HEA1j6vwrProYcewu23344PfOADmXHceuutOP744wGsfY7svffemDdvXi2g9YIFC/DUU09hn332wcjIWgt377334k9/+hP23XdfXHPNNbV9XXrppbXPffPNN2O//fbDvHnzsGTJEixbtgwvvPACKpVK5nnw9re/HRtssAGOPvromnr8uuuua/o9rA99bjamlLU6oJgtl+0Ddf2ArC3W+kqHVmmXR5Q67Rk4otTJ+opSJ7F+B2i/nzxrpz1388w6wnfQbB4t+wH+nJ1z8vxMmvJjzz33xNKlS/HII4+gr68PfX1rv6ZbbrkFO+64Y6bv4sWLM/+///778apXvar2///93//FCSecgCOPPBKvec1r2jK+nXbaCUuXLq0tfADAkiVLVPn38uXLawsfYXwAMmMkhFShxK5U/P73v8cb3vAG7LfffvjWt76FJEnwhS98AXfeeWctI8DOO++Mp59+urbwAQCrVq3CokWL8I53vKPQcX/1q19l/v/ud78bzz//vPpmDQB23XVXrLPOOvjJT35Se6b09fXhxhtvxBZbbNGSPd5pp51w3XXX1RY+AOAXv/iF2nfJkiW1hQ9g4nkQXF8ImTbQ7aU0DAwMYN68ebjyyitrP9Yvu+wybLfddth1111b2rdnm9/0pjdhzpw5DZmwLr/8crzuda/LKESWL1+eibkU5tY33nhjQ129Tb366qszqu+f//znmD17Nt70pjcBWPucWrx4cW3hAwDuuOMOPPLIIw3PKe93x5577okrr7wS4+PjtWfNI488guXLlzf9DTM6OoqHHnrIfR49/PDDeOWVV3DJJZdg//33x/rrr9+0PyGF4Zx88hY/NtlkE+y6664YHR3N/Pv4xz+Ov/mbv8n0lRNJABgeHsbMmTNr/z/66KNx1VVX4Qtf+AIefPBBPPjgg/jgBz/Y0vi22GILNfWtVqeND0BmjIQQQY8b2rIxPDyMRYsW4dOf/jR22GEHfOITn8BrX/tafOITnwAAbLnllnjmmWcatnv66aebuso04+mnn878f+ONN27qLhgm0Pfff3/mmXLzzTcDQMNzJQbtebBmzZrMJDrA5wHpKWjHS8E+++yDDTfcENdccw3WX399rL/++rj55psxNDTUsuuLZ5u33HJLAI02PfxfPiMs+ynrLZta/wwK/w/H33LLLRvGEMZR/5zyfndssskmOOmkkxp+w2y//fbRv2E0/vrXv+I973kPBgYGcMUVV+DZZ5/FokWLapl6CGkrPT4nnzS3lxdeeAFLlizJBP4JxEb1f+mll/CZz3wGn/nMZ/DmN78ZJ554In784x/j3nvvxR//+MdC43vqqafwute9rqF+0003LbS/vITANYAtYdbaY9xaPFcWbV/eqpgnsW4nUsrlRXbR5GayThu35qoCTJwDzRVG4rnFyG36lXprfN5nDeOTJinmXHntnpxOG3el7v9rD0SJXdm56KKL8NWvfhWvf/3rAayNZSGDxgU233xzvPDCC7X/Dw0NZYJVA2sl0Rr1cZuef/752iRWIxxn7ty56gT3gQceMLf1eOqppxps/4wZM7DuuusW3qeHPCv91RupX5y6fuHLURVOIhGGuqIYben1k4ryePVUj4nbckzog0P9qNhGyodDWd7Vshwk0oNKnSxr7i1A1m2lT+mruQB5LkKyvaK0W8/ERGmX9JQLDG15aQgLHD/96U8b2g466CAcc8wxhd0CPdscFkY222yzzPMgxPeTda1Q/wwK/w/Hb/acWrp0adSxXnjhBVx55ZW48MILG9qee+65qH1Z3H777dhnn30wc+ZM7Lnnnvj617+OSy65xFTqrL/RhF0fExO2UWGsw+06Im5bzW5r9l22Dxjt/XX96vtqLi7Svmp2WdpvaV+930J5f0tZruLe75rwGa3ttTF51jKv20p933AMzZ0V4JzcY9KUHzfccAP+7u/+Dn/5y1+wdOnSzL8QDb8Iv//973HCCSegr6+vNjEvwpIlS/D2t78dW221Va1up512whZbbBG9L775I0RCiV2Z0BZ8N9lkE6y//vq1RYbbb78dm2++Of75n/+51mfWrFmYO3cufvvb39bqHnvsMbzhDW+o/T9JEvzLv/xLrnHccMMN2HjjjTF37ly1/Xe/+x1WrVqFrbbaquGZsnTp0prfdBGWLFmC97znPRkbvv/++xfaF58HZPpAt5cyMHv2bOy333645JJL8K53vSvz79hjj8UWW2yBd7/73YX379nm++67DytXrsRBBx2UqT/44IPxwAMPtG2x4IADDshkZJk3bx5WrVpV+01x++23Y6+99sI666xT67Pjjjvi1a9+deY5lYcbbrgBO+ywg/qsefTRR6P25SlBhoaGsGjRolqGHkLaC+fkbVV+DA4O4gMf+EBD/S233IIf/OAHOOKII3DzzTfja1/7GpYtW4aNN94YO++8M5566imcffbZuY/zm9/8BldeeSXuu+8+pGmKT37yk1ixYoUanDQv3/ve93DKKadg0aJFOPXUUzFr1iyceuqpeOaZZ6JXx0dGRrBs2TIcfPDBtRSM9957b83vkpDegqvMZeL3v/89rr76aixevBjPPPMMtt12Wxx//PFYtWoVvv/97wNY69N822234fLLL8dJJ52E559/HscffzxmzZpVC0wNAFdeeSWOOuoo3H333Vi2bBn+7d/+Deutt16ucVx33XX49a9/jUsuuQSnnXYa7rrrLmy55ZZ45zvfiSOOOAIvvfQSFi5ciHPOOQfbbrstbr31VlQqFbz2ta/FHnvsgXnz5hU+B2effTaOOuoo/PKXv8Q3vvENbLHFFjjppJOwcuXK6OfBX/7yF6xatQqHHnooXnrpJYyMjES/dSSkO6AtLwMHHHAA5syZg3POOadhXnzbbbfh5JNPxvz583H99dcX2r9nm1988UWcffbZOOWUUzA6Ooo777wT8+bNw9y5cxuyvbTCuuuui5/85Cf4zne+gx122AGf//zncd555+HFF18EsDYz5JFHHolrr70WZ5xxBtZZZx185Stfwb333ouf/exnUcdauHAh7rjjDvzqV7/CRRddhOeeew5bb7013vOe9+Diiy/GLbfckntff/rTn3DAAQfggAMOwGOPPYYnnngCb3vb2/Dxj38cV111Ff7yl79g6623xqc+9alM7BNC2gPteFsXP9Zbbz1VYveud70Lt9xyC/bYYw+cdtppOPXUU7H55pvjmWeewR133GEGkrP43e9+h8MOOwzbbbcdxsbGcPfdd2OfffbB448/Xnjsq1evxt57743zzz8fl19+OZYvX44TTzwRX/3qV/Hyyy9H7++II47A1772NVx//fWYOXMmtttuO3V1WLq9eBGIPVlufbnZviSeC4zVV6NdUiLv54XlsuF9/hi3GC3biuZWYkXZ1uTU7cwGE46ludJYY5E0k8jVUyQydQ1K7ErFaaedhgMOOADnnnsuNtpoIzz11FP4n//5H3zwgx/E8uXLa/3e97734ayzzqplNrnjjjvw7ne/Gw8//HCtz6mnnorNNtsMp59+OoaHh/HNb34Tf/jDH3Knon3/+9+PL37xizjmmGOw6aab4oknnsAll1xSaz/zzDPxxBNP4Nhjj8Vxxx2HoaEhPPjgg5nU40V44oknMHfuXJxzzjn4+c9/jj/+8Y/4+Mc/juuuuy76ebBmzRp88pOfxIIFC3DLLbdgcHAw87YysIFwQR+sZtTtEzd3n7j5Qn3iGNzUcHsJbi1SKj2yprFdyqdl+2hViywlyZoU2nNV0erqtwsfUXOFke3WvrTnp5ftpcgzMc55t6TQlpeC+fPn48EHH1RfCI6OjuKKK67Ahz/8YdUNPS+ebf7CF76A0dFRHHnkkdh8883x5z//GR/5yEdats2Ss846C3/7t3+LSy+9FJVKBd/97nfxuc99rtb+3HPPYY899sBZZ52FSy+9FMPDw7jmmmtw7LHHRr+MfOihh7DLLrvg9NNPxwUXXIBZs2bh8ccfxw033JBJgJCHb33rW3jb296Giy66CBtttBEWLlyISy+9FGma4stf/jI222yzWswP+XnqWW8j3ZZLd8Zgty1bP1L1N7TcXkLZysY1oNR5dt1yewl9pS31srHEuL20ms0llNV5rkC2W5lvvO3yzrU5Jy9GUu9n3bRzkuTvPA3Ybrvt8OCDD+Lwww/HxRdf3JFj7AwufmgUXfzQtrN84rS6VClbY/EWP7T2mHSTqdKuHctrl33GnL4xx9L2FeqGAYynaQIAO775temdV56LWJLX7LM0TdMd/Z4khl6z4+1it912w29/+1vssccetaCq7WTpNFr80GJ6eDE/rHKIBTKi1MmynDDL8uq6frJO9pV1q5T9rzS2D+XpuviRVu04UMyW0453jl625Wma4uijj8Z555031UOZMh76u8lb/NDsOzAxZ81jy0NZ2kptv16799wZNdq1+CZaXystsJc2WGu35vdazL9W5+yck+dn0gKeloGTTjoJTzzxBB599FFss802+OxnP4tnn302Wh4XQwX6aqS2IGEtcngLJfXHy9Mu8RZUOoFczYxZCNG289otZYjWri2OWITtPeWHtUqsGUcNLw+47FM0p7i2kKNdg+o54SozKSFf+cpXcPfdd9eCYX/+85/HPffcEyVvjmEDEZdvsOoOLhc/MgFPnSd38MyRCx6jWkBTUTcqJ8TV+lExy5THH6727RPb9Ilb3FN25G0HJmyLnBB7b/O8gKV5goc3w9tmui6E0JYT0j3M2UC35TJ49eiMtX+l0EUupAe73i9tubT71b9akGhgYsHBUkh7dt8LiFrElmtBUmNo5++bVlUgMXN2zsnzw8UPQZqmWLBgAbbaaiusWbMGv/nNb3D88cer6Q0JIXmhoSXlY8aMGTjzzDOx+eab45VXXsHixYvxn//5nw1ZaQjpHWjLCSGk3NCOc/FDcMYZZ+CMM86Y6mEQMs2goSXl49hjj8Wxxx471cMgpIugLSfdgRY3iRCSB9pxLn5MMf3In5vak4LJ+jzxQbT2Zv2svnm2y4v2TrWoC4w2Fq3d2qZPadfcYiTaebe2yTu+GLx30kVziuftq8kSKbEjxGfDzSfKg1Wp8sCMiToZ36OiGOJxLaaHlD/L+B1VXbD0/ZauNJVqfUXcyNrxh0VdIvaVVPcfE3jOK8e4emrlVttjpNDSDg+bvUoIbTkhXcPsdXVbPipsebDb1vNjeHVje6ZvNSaItIlaQOqK0R5ja4vY+iK/SbznkhUKt9XfN5oLTEwQ1NxuLWh0m+GcPAsXPwghnafHDS0hhEwLaMsJIaTc9Lgd5+LHFDLgdyGk/HCVmZCmbMonMSkDtOWEdAWb8QcEKQrteHsXPzRJTp/RrkX91aIJWxGEtb7esaxoxUmTOrldzFhluV+p8yIY9yvtnlSrvr5ZuyUVa9UtphNo2Vq89LNW3xi3GC1bjHbeR432VKnT8ovL66pIKEW5/3a6tWjnwvoszcdNQ0vys44oryvK6yntWl+rPZRnG+1zmtTJ7WTdzJmivbrh7PVEu+g8q9o+Y5bePijqEdxd5ARXS0slbmIZoT9kYRk1DL2WDSZTrh5X3rbjaWPffrGNdLsZCGW5vT6UGp7biSTG7UWTZVv7yktMyvTA9HB/oS0vEzHzYC8DU79SJ83ToNJX1glTWTNvsm6WUp5ttM9R2j1bPVspZ+qkra6WZ4mHwSzxMJo5O9sPyNrvVm15ZlJZteXDMhuXk05Ks+WWre8XrpXq9qFsGDDNPhex37Ic48ri/b6ZimyVeQhft5cBJq9bi7Wd/tylHe+Ga4AQQgghhBBCCCGkY7RV+VFEuRGjxvBWrL1jecoQT3lhjcUL3uYpQzxlSRHlRxE1iNUu8RQfnQh4qrXnCYKqKS+KKEOGDsxVAAAgAElEQVQs5Yc2Vi24qaXM6K/rV9+uBioSaMoTeV48ZYcXsLVIfnHtLSxXmUkewls4S7nhta9ToH2O0q69IZR954g3gFLlEd4CyrrZom94i5h5KygHo70tlIZY3nDVIHVw3gBqQVCBibd542N633FPpqEgg+iFgHl9om5A7j+Mw9iX9xyY6jwPnu3XkO3ltYa05d2OFyzSUnZ4yo9QlgKGfqM8WPe3vhwUH5raA5iwu5apbFX5Udt+fXF8cYBgt7U6WW/acnngcMI8Wy6VFVIxN579C2SVGVpwa82WpxE2XQY87VOUI9IEpHV/81D0rbuX/KGMyEuhSFBtK4kC5+TNoacxIaSz0L+QEELKD205IYSUG9pxLn4QQjoNDS0hhJQf2nJCCCk3tOMtL35YriR53VaKBikt4iLTqYCoXkDTIm4xnXJ70SVQel9t/83qOoUXXM4KvOm5yHhuMUXcXrRznSc4q4anUvQCJmkuMJZrlxZ8tZlbC6CPf0ypQ4qeN7RER8Za86TMXsBSNYidUtb2ae0/I5uuSqTnSLcWUQ71MgieDJLXFw6Qx+0l3PyrRN1qUV7TWCeD4A1X60eFFHp4qLGvbM+UqxLssbHGOmBCYp0WdJXRJPjSnmgTE82215cnC+2YnguPPD0rcmzXldCWdz1FA5pq7f1Ku6yzXGCCi4sW5FSWZZ3mAjPHaPfcWlRbLk5MsNWW20tuW24NoMO2fGS4sW+mXWwfbtcR0S5dIIMtt9xqxpVJnfb7wppnWwFytb7twvp91Gk6Ycut3x9ekoIA5+Q6VH4QQjoMV5kJIaT80JYTQki5oR3n4gchpMPQ0BJCSPmhLSeEkHJDO1548SOp+wsUy+YSk6HFc1vx3F6sYxVxe4nJ5qK5xWj7sqRaed1irH3F5Nlutk+Ldkbe12RbmpzOkkt7LjKa20qn3F6KuLUUOZdaBhkg6wKjfYfadjHbaNeI+pkZXIkI5DWqqYbdbCtOu5XtZV2lznWL2UC0VyXSmjwamJBIZ+TR8gBB4y21v0KKnJFFB1mzlC8LWfRwtX5Y1GmyZymPzri1VNul/Dkjix5pbJcuMEEWLfc5rmQoGFMyvFh4zz953XRCKm2hZabxsr3I9rG6v/Vl+bV3PbTlXYs3D9Xm3N48WnOBsdxipAtLcHuRpm6mUrayvYSy5YKouUhqdl1zdQF0F0Zp13PbcnkyWrTlo2sa6wDflmu2elRxcbGywQS7LrfPZAEbbdzGs7/WbynNBVBzhfGY6mxflq330H6reG77Fpore15XGNpxKj8IIR2HhpYQQsoPbTkhhJQb2nEufhBCOguDKxFCSPmhLSeEkHJDOx6/+FHvAhKTwcTLsFJke7ldjNuK52IT41bjHV9zRWm13XJ70bJyeNlgoLRbsrK8kZO9fjGyZS2zixdZX25XUepkeTLdXjot15Nyau87iJHgaedd217/fFxlJhNoUfsB3a1Fi/bvSaGt7fO61WQyAAjZc5BFywwAM8SHGayW+zTtKTAhixaSZulnNq7Inj0ptFYHTGRm0eTPgO/WMqpkc5F9Q70l/9VcRWTfMaXOsrPaM6tI1oCiz5y8fVs9F/Ky6F5oy7uN+jlbTDaXmHnyQN1fYMK9BYjL9jKr7m99X61dS6biZYPJ2G/Flkv73SlbHmypdFGULoah3nNR9LK1xLgoSrtes+VWtpdQh8Y6WZ/HZmrXWKsUybDS6rOgaKZGbbsYa+r9FtJc/Dkn16HygxDSYWhoCSGk/NCWE0JIuaEdj178qFc5WGoJL0ho3oCmnlpD1rcaENVToVj7zxvQ1No+b7u3f9nHUoY02399udn2rebO9lZ+rTzhXqA5K7iptn3eN3KdVn7IcynNURGViLciLdtzB0dCfHClzNgZXKnnkW/lrLd12ts+7W2e+7YvYv+ZGHbVHc8UlZm3gdVXlwPiFWaf8gQdM6JZhjdrmkIDyL6ZW7O6sS4T3LS63YjyhlDWy2PJW7AWBE8M1Xub5wV5i3lDFhMwVLMp2vPLCpwX9jtotDfbRuKp3mLateCnsq5rrSVteddRPye35nOeQlmb5w4o7f1G+6BSlnUzlLKmBpFlz5ZrahFgQuUhg5h2wpZngoy2UaWntWvKPEAPSCoDTWu2PK/9blbfbP8elsI4XGNFx5KXImoRSdFxeMeVMXM15UbM76607i/n5Fmo/CCEdBgaWkIIKT+05YQQUm5ox7n4QQjpLAyuRAgh5Ye2nBBCyg3tePGAp5okJyYIad6Appbkx9uXJ+fT5IDyZHhj6XfaiwRUtVxVvHat3nNr8aRUMVKrdgbxDBItea48CbLV15NuewFNw+e2XFE8t5agUvRcfKSyUl5X4bjtCA6ljVUe1wtE5QWPZXAlohGuZy+wnSwXljoX2H6G0GXPrHYYFFJoVxatBAHNtIubLAS0G1GC4QFZt5ZawNNhvT24tWRcXaQEu+4voLu4xATphNJu0apbZMyxNJslt7HcYeqx3Fa89np5cf3xw3egubrIsqxb4Yxl6qAt7yYSNM5viwY81dxaZHuo19xbrLK0tVp5ttGeNyBqxpaL/8zIacsTYahibHnoO+K4ugDAyFD1r+XCOJL9C2TtfnBhsezHuFLn2S9Jp225t38vXIIkry33iAmO7dl6ze5bbvvaca2+efEChXNOrkPlByGkw9DQEkJI+aEtJ4SQckM7zsUPQkhnYXAlQggpP7TlhBBSbmjH4xY/pMTOiyztubV42WBCuyXB8/blueDIspetRXM7iXG7yev2YrnVeBlctGN5bjGWlK25VKozLjAxObM9OZ/mlmKdC02i5rm1WC402vbN9mP11VxgpInyzr+XzUWiye00Vxh53FSpk/X6Z6Wh7VW8DCtF3FYsKfTMJnWAyDAgLtxBsbP+aod+od+Wsmgtwr+MsB+k0hn5shLBP8iggaxUWkqh11T7ZNxexLHCbuVdJcuh3ZJKexkAikSzL/Kc8bKSxWA9E/NKwPNmNajvqz1HtPNutWtuMfK7FJdFF0Bb3m00c0WPmSf3N6kDJlwO+o126fYS7O4MpU7WW9leNFuuZYsZFAZEur0EFxfpqpgoxijjXiLa89ryjH02bHnYLuO2WMBF0XN7KerWEoiZ2+fNChmLNs/sBF42GXmteW4tMZnBZDlcTjGu5tq+5PE5J88PlR+EkM7CVWZCCCk/tOWEEFJuaMfjFz/qVQxFlR1F1BhFA5pqyg1NWeEFgmo14KqXf91TwXjtsr5oQFRt9dYadzOsVeC8K7pewKA8q9xeILpQr62iy7IWxBTwA6LWHyeWcCwtCGqe/WsqDktdFPbrrTLn3X/DeWizoU2S5G8A/ADA5tXhXZCm6TltPQgphPa2b5bRHqP8yK3sMI4VylLZMSAiqGkBTWXgOy0wndruvA203hBKZcdw3V9AV3ZodYAeRFMrx7wtjAmM7T2zYgKae88kDWtfAW3i450L7zki2703s9p3Yb35lWV5PUwZtOVdRf1c2punWvekpvyQ5YG6v4Ad8HRAqdNscYwyRJbDfjPKPUOxF5C2ekS5kVJxgwZ136gT0NS05WL/4W6R9jlGpafZak8NHaPcy6viaPU3h3UsDWsbLSGCRoyKz6v3gpRq8+T+HH09xUjYh7xutN/D1pydc/LmUPlBCOkwHVllHgVwXJqmdyVJsi6ApUmSXJem6f3tPhAhhBCAtpwQQsoO7TgXPwghnaUDErs0TZ8E8GS1/EqSJH8EsDWArjS0hBBSemjLCSGk3NCOxwc8rZfQxLi1eK4inmw2JqCp1q65usi+nXZ78QKaWlJfzQXIkwjHBDyNcXtp1q8daLI2zQXGko9pAUu9ds+txZLQ5ZXIyX0WyeOtjSkPViCkZvv1coZrAWOt7ScobGg3SZLkTvH/C9I0vaC+U5Ik2wF4G4DbixyEtBcpXw6qZC+wnezjua1Ybi+hLKXWmeB81Yu0X+i2NXn0mHGpBtm0DHI6Jm6iIHuWbi9rhhrLUnEty2uUetmuyaYtKbXnStE8GJoebE2iPWe0QIneM3HAaZf7sIKYas8i67hFyOs+aQXB09wrtbLVPqKUOx0Q0KaQLc9lxwHa8lhkEoK87t/ePFILcirrLVcXzcVlptGuBUTV9qW50gDAQHUw0lWxIj54pfohM64u0sBCaZfBTYcbt1FdGJUg1FY5xkVRszUxbi8eMa6EeRMu5HF1KRLoup22PJy3Qafdc2uxXMK17QeUdmDis3gu/tZn9p5LnJM3h8oPQki38lyapjs265AkyToAfgbgmDRNX56cYRFCCMmJa8cB2nJCCOlyps2cnIsfhJDO0qHI0kmSDGCtkf1xmqY/b/sBCCGETEBbTggh5YZ2vHi2F8/VpNWc49r+Pbme54ITky1G25fnFhOT7aWI247lFuNJ0IrIzrwIzTHuFzHERFbW6jw5shaF3/qsY0q7Na68xLjA5JUzWhJw7zsqkjPcazcljO2PLJ0A+C6AP6Zp+vW27pxEE+OqYvXNm83Fk11bUukgkdZcXYAJdxctg4tsHxN1UiodpNBrpNsLGsuae0t9eUipk3fQGqUuxpXCy5oVsOxJ3meqPP9y+1Avx6+5zVj70p6F3jOtqGQ6b+YAL5uL59ZiyeJHlbJILDH50JZ3Fc2yOnjzSO0+6zfatWwvXuYXr91ze7FccIINrxgTjuCOOG4YOM2Wa+6KI8JtUXNRlHdCTGYuzRZbbi/1/dpN3ixe3jzZc7eS+0iddgvtd2OryPMarseY3xQDSrvlFuP1lXbfc/HX3GY4J88PlR+EkM7SmVXm3QD8PwB+nyTJ/1brPpem6TXtPhAhhBDQlhNCSNmhHY9f/Gi2yuwFEY0JvKkpGLRVamssnjIkJv953u1jlCVaQFOr3dt/zBs577xrx5+KgKeSmOByXkBTLWCntgovsT5r2JdlQrxzpK2Ye3hqjRg1SZGc4VZ7fYDE7GfvSGTp3zYchkw64Qvw1Bqe2kPWe0H0rHbNjnr3gwxumio3v2wPbxOHxWv3YdGuKTtkeahJHaCrQGTfEaVd1o0rfeU9Ku/AvKoyT/kRo9bQ3uLKOkuxE/qkSl39GLSxtqoC0YLjac8fS2HoBTwdrfvbrKwFwpX76jy05d1EgkblsDeP9BTO7Qh4mlfZ4dkHMwiyFqhaGMNx5abQbLkMaKqpPGLsb0xAU80+5FWY1VPkxolROGvzaG1uaM0tvYQFEk/ZoSUh8H4LSjzleMALeGoFMdWeBVbZU3Z4yR+0ds7J80PlByGkw3TGv5AQQshkQltOCCHlhnacix+EkM7SoeBKhBBCJhHackIIKTe043GLH5rEznKf8NrDgb2AqJZ8NUbOl1fuZwXpKSIXLCI39FxZYgIKxQSq087xZLq9eLIzzdUiT85zT4LmBXptVld/rEARcyIlkNo1HpPnXB6/SMBSL2e4df6a5xRHzxva6Yomb9bKVrsmhZauMFrAPM3VBfDdFwPSvSXjflDVKo/LgKdC9xxcXCwpdFBNy2CUMW4vsjyi9JXH0gLuaS4wlktf3kCn1vNXO9eahN2StWvfpSULD/vI66pTj+ZCK8+FNhfx0KTQXnBZzdUFmLgf5Pcny5rrl2xf1XSkHYC2vKuodxWICZzvuZrEuKppbmsx7VpA48ycWnN1MXy+QqBTaeuli0sIbqoFMQUm7q9hpU7WewGnZdmzv5bbS97A9a3OyT23Fc1VRva1gpjGuOVpriDa57LOSZFz4LnCxPym0Gy997vUcuHRjqU9w6z73Q1q3uN2nMoPQkhn4SozIYSUH9pyQggpN7TjXPwghHQaGlpCCCk/tOWEEFJuaMc7lu3Fc5/QovrGuJrEZDDJm1mmaDaYvNlaimaL0WS5XuaYom4tRbLBtEqMBFCTcllywiA9s+TOWruUq1WUOoknw9ZMS1HpdkCTG1rfZUy2lph9NWvXvzca2ulKcFHx5M2Wq4vm1uJlG/DsqHW/Bwm0lD9LQjaAMbGRl4FFuqqsVtpXK321bQDdhUY7vizHZBho1e1FO9detgbruw7j1sZcX/ayIGh4tism20veZ50llda+C+1zW5kjtLJ1XXTeytKWdxv188eYDE3ePS3b82ZoASbsvdUe9mW52OgZKiYItnxU3DSyXGsXN4fmViZttWdfNRcYL4MLUMx+ecS4guTd3sNzmbY+Z4wLTF5Xb2v82hwg5ryEzzCo1AET14OV7WVMadeeBYCf7cULx6D9FtLufc7Jdaj8IIR0FkrsCCGk/NCWE0JIuaEdj1/8qF9Z8wKeeqvQVkA1b/+a2iFGmVFEZaLtM097EZWLttrnKVdkfVEVjBfoTtumVbSVyaKBN73845qywzIB3up42FdMEFKNmCCkWtkLziT3G9PeLGc4oK/im9dFjxva6YSm8rCUH54yRCpC8gbJ02wvoN+vmXtfuQS1tzpeQFNL2bG6rp/VLreXwSrXKGXrDX8nAp5qz1/rDZz2XXjBaWU5jFsGt7Vsj0ZMYGjNPmt2ypvLWGhvPrW3gfK7mKm0W8oPLdDiTKWufruOQVveNWhJCLx5ZMw81AtCaqlA+pvUWcfy7rlMoGrlEtQCWWv3jiy3qqzLo0AOePPJmDl3zL46jTeWmHmmN6et3481lhiFtRzfoNKuPUOt712z9fIa0hQhcl/a/Wo9V7TfQt5vuQw9bsep/CCEdBauMhNCSPmhLSeEkHJDO87FD0JIp6GhJYSQ8kNbTggh5YZ2PGrxQ0rsNNmaJkW1pFp5A29aAVWLBPEs6vYS9mXJrb32vG4rMW43nlxQcwuS9Z67kXUutW0kWl8v4JMX8FSTmOWRG44qdVr+cuu4efOTWzm9NTxzU8QcWS5AmgtNTLvW1zqXzaWPNLTTiRi3F8+VRZNQF7WjAet+0GyHJmuW8mfpwhJcTTRXFlle6bR7AVFlH1mnybItV4kiAU+1+916JoXvSHN1kWXpniFdnIqMz3NLsQLrac9XKO0Sb64i0dxetECI2vcDTHyH8lxZgRZnKHWaC0znrC1tebdRP7/15pkxrmxa2XJ10ez+gFInjxvltij7jjdv12y55/ai3XNeEOY87hV5XVhadXvxXF0895RWg/HHYD2j8wY8lcT8VvFsecC6rsbr/ta3a8816xmn3QP9Sl/vfraem/Vz9uxnph2n8oMQ0llSAON5l5IIIYR0JbTlhBBSbmjHufhBCJkEOpHvjRBCyORCW04IIeWmx+149OJHvWzIi7DeaoYVrz2mryXT91xsWs0mo8lyvWwxMW4x2lit78Vza8nrFgOj3cOT1uV1a7GkXhlppNKuCb3k+FtdC9WO3ylXFM2dS+Jla4lp1857s8w0mWsiRc8b2rJj2a5Q1iTPsj5PhoC8bi+eDNaKjh6wIrFrriaai4rM0LLKadfcXjy3GTkGORbp4qJle2mn24v3zPHcmYJ7hhyzli0hj1nwns9aFgovW4Dnoqt97hiRsGZbretuhlKnZUwCJuT4M5Q6uV3HBM205V1H/ZzOc4+Osa+arfYyuMhjxczJi7hMW/YvlGPcXjT7JO/ZGLeQGFfyTruit4rnlt7O/XvH8jJ35c3ilTeDV/3xQ728LqSt1lwc+5V2WY753ajNbSpGe1MXH9pxKj8IIZNAjxtaQgiZFtCWE0JIuelxOx69+FG/cuatVlqrctpqpxe4s+i+WlWZaCtoMTnLtTdTRQKqevuXZavdezvg5YZ2c0fnxLrvPGWHpwyR35G2SiupOO1TQRE1hrVyrq0YWwFL8wY0tcbiBpLqcUNbdqzAdqFsBbbzguRpQfAsO5g3SJl1P4dLUL7hk2/+NOWHF9BUU4HIbbS+ltpD7mtN3d/6cWmBLbXgp57aw8ILxuYFup2ljEkbizUmL6CpF4jRUrzk3b/2HPECDloKRO1t4EynXVNzAHrAU+07kNdN259vtOVdQ4LGZ3NMYGBtTqnZZFlv2XqtbO3Lm0eO1/WTdYCuHNPsuqX8CPeaFTA6b0BTT80h662+ngqmSEDUVtFucW9uafUtQsx59841lPYYW+4pP2S7dl0NKO2A/7szPNcstXXez2qeyx6341R+EEI6CyV2hBBSfmjLCSGk3NCOc/GDEDIJ9LihJYSQaQFtOSGElJset+NRix9SYucFc/SClGmSHU9mbx0rb8DRdrrFaAHXLFeSIgFTY9xmvEBSngvNVAdXinFr0dxipAStYtQH2hncNC+WW4r2WbSATlJCFxOoV/sOvYCleQOaWn3Vc8pV5tITI2/WylaQPC/ws2UTNcK1ZwUA84LgaQFJNbcWy+1lpVLntXvBT63gq3LcnUAL+KedK+m+IYNwavJfy3YEvODplrtUKMcEd5V4Ace1Ou/5pwWm0wLSAhPnSHNvAbLnPdTLa0Fze5F18rpqGdryrqN+zhczD41pL5KEoKh7guZeoLmVWW5/ed1eNPe0PHi/T2Lm3Jr9KRIw1cJ7buZ1W7FsaozbiDamVn9LaNdYv9GuzXM1YtxeYgKNe0F1vd/AMeEemj73aMep/CCETAI9bmgJIWRaQFtOCCHlpsfteDtj5BBCCCGEEEIIIYR0HS1ne/GysbSaDcZzZZF9LYld3mwuXgaVmHzMnpxbk363Q27oRfTWzmuRnOMw2j3yyulisr1Yri7addFpVxcvWwucdk1O2Ge0e9letH15x8otmzMwV1N7fJW5rGgyUi/ThpftxbOTnm2SaK5XmrQU0N1ehpRyjNvKCqXvKxHbey4wnXZvKUqwDXLMmqzcu+2t71q7hqQrh/YdW24lGtpcwbouQ7v1zNFsnibRt6TSwXXIy/Ai+1hZdjS3F3mNt5qFAQBteReRoHHOZ7lXFJnnFnVLbJct9zIoyXvGc3vx3BM8YlzdY/q2mu2lXW7pnqtKjCu65xbTTrwQAtq5slzltevOc3vR7Lo1X9Iyv3i/6zy3GOtaqM+Y1PA99Lgdp9sLIaSz0L+QEELKD205IYSUG9rx+MWP+lUkazWy1YCoYWB5Vjvzqh28Y3krt57awgpI6uVPLxJwVe6ryFiLBl+q309RrPvOC2iqrbxa3+tkqTy8gKWWGqNPqfPefsQEPJV4K9pNA5Yax9JUIlxlnl7kDWgao+bwAjfnuZ4D2ptBLcgpMPEWUL4J19QWVhBSL+DpK0q7LAeViKYGqa8vI9J2rDR7rcV7ZstrLKgYLGWEFrzQe4sp8dRNWrBo7xr1lB8yUGwYvxXk1FN2aGXrbWNblES05V1F/VzcCxzsBen07skYBbI3j7TmRto8RAsSrKk9AD+gad5LOCaIadEkBHm/l6Jzcu+8B2Lm5Jp9teyvFnDUwpw/1uH97sycX2EMk+qGfeLDjIlJQt75hnYtAhP2V847rLJ2DXvXhWzXArx73hUZetyOU/lBCOksXGUmhJDyQ1tOCCHlhnacix+EkEmgxw0tIYRMC2jLCSGk3PS4HY9a/JDBlbwcw0UConquKjGysCJuMVZA0rwSNyu4U96Apq26xcT09eR8Me5KMWiyt5iApvVBfJr19YLqFnHd8VxcpIRNk+BpriZWe70rSX1Zc2WJcZHRzqtWl+dYrotRjxvastJf97e+7NkuTyrt3a8a3qVkSVK1IHiyvKb613J7CWXPbUVzdZFlWbcG05NgG+S50J45mnsLoH9HViDbUPaC1Em84Ix9okOfMktKxQH6Rhvbx5WyFfA0nAPp9mLJ+T23lwGnnW4v049mrujePNhzUdTKltuiVu/N/yXaZeUFlrSCHI8odZ5N0Mbquaq0IwlBGefkmlu6NXe03GG0vtp14X1W1ZaLi7Eiyv3ygVNlVBjbJBhIMSjts1hzjHA9Dhjt2u9Ced+MKn37nHbrXIwp7Rl63I5T+UEI6SyU2BFCSPmhLSeEkHJDO87FD0LIJNDjhpYQQqYFtOWEEFJuetyOF1780CQ3Xk7xGBm+J+mJyebiSfCK5OH2ovLGyAk9aXhet5k8x/JcdLwo3Z4szWO87i/gS+y8CMedzuoi0eyF5QqT1v212mNcUZplWKlH62u5quTN5mKh3e+ZjXvc0JYVLdtLjG2KcT3z7LSGZkcsl4FQlq4msjyk1MW4vWhuLVp5urq6aEi7Ic9luJ6GlDogmw0lfJ+WxN3LNqChXWOZ61r4igxIf5Swf3HhJeELNXxKwrjk+DUXF3ndWu5AmtuLltllwGgPn9s7Pya05V1HvY315m7e3NCbZ3vu47IcY7+1uY/l9qK5tcj7S5vv5HV1kfWtzrNj+pZpTq65mluuLtpc3XMfjxm/eq6E0ZP2u085mKyrhLJ4WKWKC4zn9mLZ8gGlb0z2u5iMqU0zONKOU/lBCJkEetzQEkLItIC2nBBCyk2P2/HoxY/6t8zWW7wiwZe8leOYlVFtv9YKm7bK662YxyhHigRM9ZQjrQZHjRmrtgpb9O1swAtOp63iy+N6iiOrr4c31n6lXlNzAPqKuHePaMFPveCvnrqqvpx3X9qKsbfKbNrTHje0ZcKzF5rtiVGteXbYsyee7bDeBmpv2DXlx5DRvrrub31fTRnSC8FN8yK/i3DeZhjt2ts063v1Ap5qqHMR423hQPWCT8RGmYCnfUq7uDC8IHlBxSHvK3le5HXTr/T1Ap5qbx5bCnxKW941eEkIvMCZ3jzZm+fGBOmU5XAJecHevXveCmiqKQwknjI97zzam4fH7Kvsc3JP7SGxEhZo7V4g2ky52kEGNh0Q5T7Pllcv/or8AELeOVrt6yk/pH3VlHmyT1FFUKi3zrv7+6fH7TiVH4SQzkKJHSGElB/ackIIKTe041z8IIRMAj1uaAkhZFpAW04IIeWmx+14YbeXvLKrmIConkuAty9LHuS5SoST4MnOvMA0njzJc1ux9q8FRPXGIvevjUULgibbM+dP/CfIwWRd4mjsUuUmk3VjQk+nySFjAnPGSOY1NLmfJc3sa1In6y23mD6lznMl8dqtIKVaztTmnZ0AACAASURBVG/PHcjK294MdZ+pfg2Q7kSzJzG2z9u+1XvXCsymBZb0JKkjSllzhZFlzdVFlmUQ1F53dbEYrvsLZL83rWxJ3IvYJvWZJ91exAO0v+qD0mfMlsLzy5JSj1c/pPxMWhBTz9VF9vXcWuQ2Wrmw2wtteddRP1f2XM09V4+YwP7enLvofMxzZdPcXooEOfY+q/Vcm+5z8nE5Px/PjgPw5+Seu1HM9xYTTiHYaBnEtE/a8sHGdsmoYuMz56L6cJfjl7Z4WKmzbLkXLN5zYxtV2rVrXLvWacep/CCETALjPW5oCSFkOkBbTggh5abX7TgXPwghHSXlKjMhhJQe2nJCCCk3tOMFFj/qJTSWdN6T4OXNUZxHIu252HhuLV62Fy/fspfNxZM3tcstJk9fVc4nPkyQjWVkdTIPtiKxqzj+E+NC1xZuOLnqKOXGoV26wiSiryYFU/NYR2C5rWgye609JtuLd41LvAwtqdLPux81NwGrvdl+mu1Lo9dXmctEEbcWT7rp5aqHUyeJkUJrUdmtbC+hPOy0e24vso7ojNb9BbLfi+biYrm6aHbaQ3u+ywwv/aI8qLi9yOffePVDyAwBmlvnDFEnP7cmlZZl6Q4Trj3LrWVQqdPcYlqBtry7aDYn9zKYxGRg9LKOxGQb0dDub8uWa/d8TIYnbc7PObloFye7r8U5eWLU144vx6W0F8n2Im11vzCmNVtuGELtXEvGqg+pUTFJ0DJ3yTmElRGoyO/CmN8PHr1ux6n8IIR0nF5fZSaEkOkAbTkhhJSbXrfjUYsfMqe4FzxJq/NWhpsFa2m2Ly//eN4gmV7O8piAptoqb9G3p9oK4YDSLvuYq43KyqhcBa3luU4a6wB9RVSuQmvIVWRtlTnzlmys8TiV0cZyzJsF6x73AihqAU299pigvmmBdi8nulXvqUi8XOyaikXbl5o/PuUqc5mIefuQN9hznjeE3hsM9dpS2q23hZraQFMeyDqp/BhuUifrPfUUmcD7roCJ77XoeXWDq4dnogySp7w5HJyp7z9VBjauPNNGX5mo094WWsoPTdlhqTn6m9TJsqyT59qDtry7SNA4v/XmwV7AUmse7N1HnrJbw5pbeXOzvDYhbzDI+jLn5BPlTszJJdLua8OOCXjaXz1v8vzJ4NVB3ddvKD/Gq/VW8OoRRfmh2WLPPsuy97vQ+90ac78HaMep/CCETAK9bmgJIWQ6QFtOCCHlptftOBc/CCGdhcGVCCGk/NCWE0JIuaEdL7740WoguyIBUWP69it9iwY09fKf591/qwFRPfmULGcCnon/hOCiUvbVr0nsDAlwkIN5AZUkXnCljMSuKiuTEuQRoRGuydGk3tw6bvWvFeQtr7TSkolq7Vpfuc8Y17CYQFLa8bWApF5AVM8eFg2u1OuGtkx40ktPKu3JVD1XyKIubWNN6mRZuhxoLjDDRvuw0j5klEk+tCCGVtmy00XIXHeK7FxKpYO7y4xZYntDFq0xWvWHGhSRcAfEhRUTpFR7vrfqYhvj9gLQlncb9XbTs8Ux9tebJ+cJtq6NxUO7xIpcdt6cPmbOPV3n5AEZ0LTInDzRfBUR9717bt3qfKLSWO5Tzi8wcV4HhS2Xgao1NYQWvHpE+L7OEA/+cFqsayEmEG5M6If6bfLQ63acyg9CSEdJQYkdIYSUHdpyQggpN7TjxWPUEEIIIYQQQgghhJSCaOVHvdQmJhJtEbeWGLl0jPuAl/Nba4+RJ8Vkc/EkdANKnZVZphbtXUq9Zoh9hWjIRnuolxI3KQurKFdMkcjSUmI3JtprEjpjWa6i1Qsdupf3XQ61SERx2de77rQ6V8IXsb123XnZWDy3GW2b+u28cTVQIv/CJElmpmlqejAkSbJVmqZPTOaYJgPvGvVso5cBwGovguce4WUQSZU6WZbbaC4yVrYYZnmJx7PX7cScayiycvmcG1CyvcjnY3iDZrnCzKheRMNCKj0gMr+E57uV4SUmm4A2V/C2l9mLXGjLu456G1zUFntZDWNc0Vt1YQxYNtWztXndLWPcw6b9nFxs483JZXlU+5INFxjPdGhzcu260twWgYlzJMcnXYhCtpdB5fwCum2T2bxClpcZ4lmwRlgYLduW5zqlhWiQ7db9qJ0L5G2nHafygxDSWYLELvbfFPG/SZLsrDUkSXIogPsmeTyEENIVFLHlUwhtOSGE1ME5ORc/CCGdprrKHPtvingQwG1Jknw5SZIBAEiSZLMkSa4C8F0AF07ZyAghZCopYMunENpyQgiph3PyeLeXZnI2TVZWSJJjbO+5rcS42HjuB1rmFmv7IhHWW203XWRC5GhFVgcA/VVt7YBo1yJL9yl1AJBUP3jFkMAF5I2SyexSlZBl3F6EtjxI76SUTkr4RjyNrpIFRkokYzJRFMlkUcQlYKxgu2aLirqy5N1Oc3/R+tXvryzBldI03T9Jko8B+DqAuUmSXATgFAAvAnhnmqb/M6UD7BAx17Bm+4puH2Oni6C5tHkZRmS7ljnGcosh3UOMm1V4vmRk54pUWj4zZTk86+QzUT7TwjNLSq0HV4p9VbfPI5XWMkdo85YYKXUstOXdSd5sLkXdcD1XdG0sncZ6lnjtXhYzzslF2ZmTe6SGW7pGeLbmucZq7cpnzbgIiXI41/3Cx1Cedw153mq2XGSLGVwl9l/9rJY7lGbXrWsw73zIuwcset2OU/lBCOkonZLYJUlyUZIkzyRJ0lb5cpqm3wOwB4DXYq3BfRTAW6bbZJkQQmLohNtLp+w4QFtOCCH1cE7eQqpbbZW4WT+rb8wqs7XfIivWMfmU+5V2NwhPxPi8VWYtEJWVMzysLsuVZblKWVtlHmjcBphYUbZyjtdyihdcZQ4rypngSmL8YWVV7n9ElEOgpsz+xfi0nNzyvMn7N9RbyopWlSGhbNkMbf+asqJoe5G3L60qS1Q6F1zpYgDfBPCDdu40SZL9AFwA4AkAiwAcCeDyJEk+mabpM+08Vrfg2R7r7YRnO/Paxvp6jbzqo5jAo1ow43GlTpatgKqkNbxnfatvamK2l8+08Ey0lB/aNjJg4LD2tlCUB6oqEC1IIqC/LfTeOFtzBW0uIe9B997pjC2/GB2w40Bv2PL6+6OoUjXvfCbGlnsqlHGlDvDtqqsGUPbpzaljlB+ck4t9RczJx6tzciuJgOcx4D0LtIQIWkDUGeL8981EU6QtD0Gv16yeqMs8F6rKD0sF5F1jnjop5vdJM7vAOTmVH4SQSaATq8xpmt4K4IV2jjNJku8DuLr67y1pmn4GwG4AXgPg/iRJPtjO4xFCSJkogx0HaMsJIcSi1+fkXPwghHSW4sGVNkmS5E7x7/BJGO27AeydpukRaZquBIA0TZcAeBvWrmr/aBLGQAgh3UexgKdTYccB2nJCCGmEc/I4t5cEjXIkT0JXv319ucg2Rft6gZ6KBkRtNaBpXgleRv4qBqMFUtJkdWa7EtxNBlTKlJU82p7ELuOKUh2LDK4kg5iG4E2jIuCSXHHU5MbjQkMn85f3VY8hFyw1F5c+p71V2Zm1vSbtjAn26AU/8upjZKatkKKwxO65NE13bO9oXN6UpulL9ZVpmq4BcHySJD+f5PFMCp7tzCutbFZuV3urwfQsab8n+Q/tnVGL9iZ53FqLoLkred9b5pmWNJZlED2I52f4EAOifUA800Kg08xzVpbr/jYr551rWHOV8Ci33NjEY1mloC2fCjsO9IAtT9D8DWarAU+LuAHDqPPaY+ZeWmBMzYXG+qzafaIdi3PyNs/JxxuPL7837RnrXUMalgtQqM/Ycun2Uv2sGfsrzkVwlxlaMVHXr9hy77qRZcvtJa9LcRE4J28h5gchhOSlLJGlAWydJMnWTdr/OmkjIYSQLoO2nBBCyk2v23EufhBCOkvngit1gvvgiwBayRRJCCHlhLacEELKDe14/OKHGjlWwYvK60n280rwZH1MtHhPXhTTXiQriCZ/strVCO5CNiclcGoeayXKdL8ReXpgsLF9QIk8LWXBUmKmrSbKaMlBWjcqZXWOfkvK5sINK6NJ98n85OIkhbFKOZ/m4tJOmWcMXmRr75hjSns78fab120mpNVqN0mSXArgXVjrh/gYgAVpmn63xd3uodRtCGCv6r//aHH/pcK7Rou4NxZ1P9SOGeNm1myfzeqbHZ+0Roybn4UmlU6VspVhQMtWoFGRs6UZRjlUiWfqULXdkr3ndWUB9LmAVrYyDCRKXcz13Alb3iE7DvSYLc+bFSmPLW5Wl2dOnndOkyp13jbyuJZ7hOeqrt0HMa7qnJMrdeI4ck6einGHbC+J7Cv3Uf3b6jPWslGJ9mNO+rhotnxoohyyvPQbmb9qmXmM3xwx11gRNzQo7Rqck1P5QQjpNGlnDG2apvM7sM9bjKarkiQ5HcDBWJtqixBCeosO2PJO2PHqfmnLCSGkHs7JW1/8iHlLF/MWrkjfmPzkMdt7byS97fPmc3ZXCOVqshE8ra9algF95CplvxJcaVC0h/oBuYqtRV+TV44MKKREl6vIVVAlZ7hEe/uWpo3tMqBSJviTXNGuBmiyApoW+V41Yq67mLebrSo6YlQilbp+7aZEErtm3ASg9EHyNIqq3gIx90Crx/KCocUoQzwFoWan26n+6nViFAhaEFNZNpUdSl1GGRKeKTIgoCeylYMNz1L5NnPlRDk8czPPKfFMDW+EK8OiHXrZC8SotXuBHmP1wrTl3UW93SqqxtD6xQSbjJn7ePvS3mprKhEv4KkXTNKbs3tqbc7JJ9rl3FueF6kyCeo5eXz5vWqKHs0UZ9pl8FQloKpVriHPpWbLV4uu1b7yu5afNXy+vgjlhzcfirmHo1R8PW7HqfwghHScaWJo54JB8gghPQxtOSGElJtet+Nc/CCEdJS0QxK7TpAkyRVK9SCA1wN4DYDPTe6ICCGkO6AtJ4SQckM7XmDxI29QJS94krZ90eB0MUHxmm3fqfznRYIvaX1lwDUpoetT5HYyuJIWSEmT1cn6iqjLBAEK+/X0yiInOIScN3yGwdVQGVckyBkJWfWzSIlwRYylIk58CG6UyX8upX91f+vLRa6rVskbRLRTx+8UJVpl3lSpGwLwGwD/mabpNZM8nkmhqLSyyPPAs+mtBjy1TFPeYJFF3RNJZ/AClo7V/bXKZnv1+TI20lgHTMi2x0VdRQ4gPF/FM1c+M8OzOPPMVlxg+gy3F016HyPnj3GLyQNtebkp6h6e1z7L+pi5lXbNWvd8aB9T6qx9evcR5+TKNsg/J5d1ck4uz0tISJAJqKrMyS0TE+ozQVJF55qtThvrMmXtAQJMnFfxWaC5tRjfe/heKyJIqmVrPbcWb25VxC1f0ut2nMoPQkhnKdEqc5qmWmRpQgghtOWEEFJuaMe5+EEI6SwpSrXKTAghRIG2nBBCyg3teBsWP4pmc2m2jbVdzLFi3G489wcvinZeqWlMVpGMHC/kjjYiDGtlmXtaRokeUHKOS7ldZU61IGV1c0R5QGmXhBtKSuxE/nAo0rp+cROGcY0ZkaMrI411Msq0lNNVFB1kjPRS20ajVVeYGFeXslKWVWaSpcj1HONCE5NtJfT1pNDyth9Q2vuN9v6c7VodKYb8LqXtk99rUCCPKHVyuzGjfbRJHQCMVg82KqTgo+KZFeozWQvkDkJfeTHLZ1b1wrOk0iHLgryWWp1LePdQKxmLaMu7i2Y2NiZbS95953Fl8ea0mi2XhPqMe4PSrm0j91/UVd3N9sI5+cT4qvuV2WIy2V7EcUMf2TeJsCeaC6T8EV9zYRxtrMu0y88i7H7tXGj+spj4vs3vPdh6sXlRW+xlJGqVXrfjnLsRQjpL2t2rzEmSLIE9D2sgTdOdOzgcQgjpTmjLCSGk3NCOF1/8iHkbHvBWsIq8dY8ZS6ur4NYxvcA03iq0m59dC9wpV1nlW6T+xroBJaf4DBE8SQ2ktK6omy3KM6vLwAPriAGIZdCwzLtGZB+qKHeZDHKkrKyOGgFNtXMhkSvK2iqzFkmpU4FD8yqKutgGtYUUXb/KfN9UD6DbafUe8e4BeYxW3xZaQfA0ZYem8pBxK7Wy1a4F4SPNsYKQaioPS/kxXPe3vu9Ik7pMu6jMlKtvSYfFG9IB+eY0lJ1nUkVRewDimSa2cQMtGu15g+jBqbOgLe9eNPupkccWN9u/VafZci/Yo7UvzcZ7yg+NGLV1VBIDzskbyta50PDaPaT9l9dY+KgZ5Z4oB1u+Rtjy2eK01Wy58cvY+941Wx6j0pssaMep/CCEdJouX2XG2mfBF9M0fSRJkncCuDtN01emelCEENJV0JYTQki5oR2fkkUnQgjpJg7BRDqtmwC8YQrHQgghpBi05YQQUm46bsejlR/NJHWtBseTFHGbKerW4krclDrtuDGBa6KClAXZrNhIqtqk7CpI1MzgS6FdasWkdjvI7aSsbs56E+V1t1j7d+aGYrDiAGuqi3OrpW7vyYli0FpJ2bAMHqQET8rIyhKlLkJu1yqdcpGZ7nS5xO4pAO9KkuR+rP2KZyZJMtvqnKbpqkkbWZfj5ZrXbKdnBy2pdKi3JM9aQFJp2kaUuhi3llCWceWklQsmbYUxPtKIdFUZEuUZSv1MY7sRpU4rWwFPw/ZakFMAGB3J/gWAcSGbrqysFuTForlXGs+p8FwvGvxdu5+sfbVDak1b3j0kaP5dFvmei7i/1Ndr15ll1739Bjy3F+2yLBrwlHNyMT5nTt6srr6+yPzc+14zrpNVw266MFbt+qgW5BTQfV8j7F3NxdEYq+fmG+Oi2KoLY6/bcbq9EEI6TpdL7C4A8BUA/4W186qbnP5KHiFCCJn+0JYTQki56XU7zsUPQkhHSdPuXmVO0/S0JEl+hbXSuh8AOB3Aw1M7KkII6S5oywkhpNzQjrdh8SMmsrS3Xd4MMLF9PHmRh5eNJe+YPLccLZo0oEdT1rKayD6ZDClSmhe+cakrljrxUD9T6L6CrA4ANthu7d/1XiUGIC6jV55CA2tEnJqBlxuPKTbX3Fqsz+rhSewmK+DNuFMuaoNy54HKsU2RMcRs0+WrzEjTdCmApUmS/AuA76Vp+shUj6msFLGtgJ7Jos8oNzuWlMFqbi/S9En1a830Ge1aVpGYdtKI/K6k6loqkcN3aLnFhLJ13ked9prbjPCFkZldQnlYDGBYDHZmKEvjKv1qHLTnlCeVRkRdERfiZtCWl5NW3dJjXM29Obdm68eNvnmJcQ/Lm+2Fc/L2zsnzkjplWZe5rqoX0Ziwv5oLo7Tfsjzo2HLN9sW48rQzNESr9Lodp/KDENJZunyVWZKm6cemegyEENKV0JYTQki5oR0vvvhRRPFRJAhpq8eMOVZRZYgXZCyvSiSziq6srJpBQJ2AoDK4Um0f8mADSlnmDJeBlMLq8lY7TdT1i3elj/1u7d/Vz4t9ygTmL2Oy8FY2i9z7ntpCtndaTdHpfRVRllj7KYuhJTrtVAx5wRottUdfXb/69rBfKzBeUBlYgS9HlToRKy1/4ExR95JyfKIj1R5yYhKeLkNKHTChGLGUHV77SN1fQA+Yt0YMcIYoD1RfyPbJC0982cH2ec+jInOOejqtZqQt714mKxh7nmtMC14ty+H+9pIExODZVy3gqqeq4px86tHm1GbA0+pfGbx6RAayXtNYJ1V+ITht5vqTga4L2L522PW85J2z045T+UEImQS6XWJHCCHEh7acEELKTa/bcS5+EEI6S0pDSwghpYe2nBBCyg3teNzih5dTvIhkp1VXlTztnZADeuN2A5oWOGaePNqaHC/T1/PRqekVhe5OBk8KZVk3KgTJ46PZvwAwJnVjyjFFXarotqQ8y5MQF7mhreBKtWM623fKhmjBnZr1mwy8Y4VzUX9Oe11i1+14AcaKbh+DFoROSpUHlHYr8JlGuAQ1mSzgu72Eem976/MHFxi6v/hIF5gg4JbBa6ULTBBwy4CpmlvLGqVO9s0ESVVk0aNCdT4q3WKqG8oghvI5Fh5/4+KLLzrxbNWtpdXtacu7n8kK5A7Yc1rNlbxfKVtjLWIjreCpWnuR+bsG5+Rxc/JU6au5tVgmJvS1ro/wCUdkwFPxUUNw0xmKW6Nsl88azZZrnymWybpPtfkI7TiVH4SQSUB7gBJCCCkXtOWEEFJuet2OT+YiMSGkRxkv8G+qSZJkTpIkpyVJ8ockSV6q/vtDkiRfTJJk3akeHyGETDZls+MAbTkhhEh6fU7eceVHqy4n7VydaXVfkxVNu+G4k7lEFa7wESE8ljnBQ87wJ5aIbYTG7OXH1v4denGibmSFKNf9lcfEhBRrzMitHVYr88jOwr4yEj3R3qo7SZGF01YXWz0DZO0/rzuPtf+ixw1t3WA4C/BjAA8AeD+Av1TrtgHwiWrb/lM0rkln3Ci3ihZtX9Zp2QJkHZR2y1xqbmTa5zKSdqjtMdmdwucT1jTjakEmkK5H4Uk0Q9QNKWWRv0DN9jJqtGvuTsNSNl3tLF1h1igZAizCs8ySGdek4M13s3YfOfp0cnvacmKhuZWYtrza0Gf8AgmZkxJxH3r7l1NKz20mb4bHTLuRzaXjlGROLomZk2faw/b6UFzbE76WjDuq+FzhM2QywIiHQbDxlotS+FyWC6P2uWIy5cX8PmjltwTtON1eCCGTQEkVdm9I0/R9dXUPAvh/kyR5YCoGRAghUwltOSGElJtet+MtL34UXQD1AoLG0IlAq0VyjndKGRITUEdbWVVXaa2ofmFFdKZYGZb5wbU6ucocVpfDajQADIml1bB/eUxlZVauJmurrFrApYbyWHYbwFdAeKuxMe3aKra2XdFgk0XavUBSMUzWKvUUsiJJkr3SNL1WViZJsjeAlVM0po5g3QOeOiht0s/Cs5PW28J+5W2hfAPXXx1EnzA3nu32PkuRtzPWs0NTrggri1XOfnuVoOzQgpjKeuORMhEET6mT9dab4xAQTwbGG1XKFePN8JjyHMo8p5wLKuYazHsfxiiW8h67y+lJWy4pov5s9bu2lBXBhmdieCrGuiLuM2nXk6AMMY4bdmWdC005orW3g16Zk0tliDcnl2jXW8ycWHteZxSbYlxB8SHVHlIF0l+NdGope4Ittz6rNiZJq/Psds65e92OU/lBCOk4JZXYHQLgv5MkuRBAVTuKVwFYDuDQqRoUIYRMFbTlhBBSbnrdjnPxgxDSUcrqX5im6R8A/HOSJJthrYFNADyWpunTUzsyQgiZfGjLCSGk3NCOc/EDgC5xK3JhdNoNwAry6QUElbKsINfq8zTAK+UZeFK0V4MuSb2izBkeAilJWZ3Uc4eYTaJZbh7KUlYmy6NKnu3M9pqLjDi855bi5RyPkQt7ARbzXgMxbgh59pH3+K0GZGrHdt1AmqbPAHhG1iVJckGapodP0ZDajiczLRq0y8oxr+EGyas+rQZE5Ms+0SG4D0jT1CckrSF4nud200pw3/r9Z6Te1b8yPqYsD4pyMJkMiDrxeLLcXrT2UaWs1clyxtVFlMPzRUqlxxQpdSZ441hjX/lsinlOSfLeT624tXjQlpebokHNi7g2Zmy5+E+w0QPSViu/RqR9l4GFK9V5ZMUIiBruX++ekvY5xtWlFqTYCWIMcE6u2boxwxVdm3N7c3btO5S2PDPusexfoM6FsXoOMi6M4gBa8Gov+YJny2OSFHj9mv0+qN9fr9txLn4QQjpOmQ2twd5TPQBCCJlsaMsJIaTc9Lod5+IHIaSjlFVilySJlTEvwbR8dhBCiA1tOSGElBvacS5+AJi4CPqc9laJcTPQoiVbEZQ12dW4IbENEq+MhFBIw3UfIHGAoRfsfoAeOXp1Y/u4TFkuymF8MgKzHP+4IiH23IG8bCyWxM6LLK1lhygyi4rJtGFtpx2/SJaAolkE8ma2KRlPAnhbmqbP1jckSfJ/UzCejuFdg9494rl2xXz/lhQ5qHql7ZIuMLXjS3sn+iZVqW+ljb4kmotLTLYXr6ypk3sVy21lrO6v1TcjhRZlLUOAJpvWpOCyfURmGao09rWeU+PKcyomw4HEy77kuXLmgba8O4m5TvLup9V9ZmyhdHup/vLoF75+qi03XClCNo5EGMVEyfIlp6HaNW+5KGp4WRe9eSjn5PqcXNpqbb4wprTLr0J+bM2+qbZccduRZSujXM3tRX7+tLHsudJbxLgwtvq7tNftOBc/CCEdp4yrzAB+AeB1ABoMLYBfT/JYCCFkyqEtJ4SQctPrdrxnFz+slcNmxKzgxbS7ipDqKqO1mqrlnDaDE1VXx+Ub0UHtVaUcoFwxlhH6agcV5bAiKt+uihXjsLo8LOoyObeHs3+BuhVnZeXWWsWt9RVD0b5Db5U2pj3mrXnet3BF8pw324fXt9k2MdvH7KsbSdP035u0fXIyx9JprHtEe6secw0XDdAbSJS3hfINoXxzGILjZWyjDI5a3b5Pvi0UmeGTur/1xATEq99ns/02Q573YaO+V7CUGcH8e3bYuoZDvam8UJSXahA98exJ+xr7as8mYOL5nkcBmPd+iwlgHDsJpi0vJ3nnKxbatROl6JO2vHp/ZGy5Evw0Y8tl+0B2P0A2uPVw1cYncnsxFk+p6uGpsbUy5+RCOSGGol1PMfbNU2NnypqtVuy6HL+8bsNnsZT32m8O77PE2HqJd67aFVC1m2inHe/ZxQ9CyORQVv9CQgghE9CWE0JIuaEd5+IHIYTUSJLknU2axwG8DOCBNE3XNOlHCCFkCqEtJ4SQctMpOz4tFj88+Y4mD7KCm7Zy/FaDkGXk5qJzX5DYycA6RsCdWk7u/sY6ABitSriGjeh5/dVjVWTwKSmxq9T9BbIfLMjFjOBwQTonjz+syBW13NsAMBI+n5FHfFxK75ThafJ+T6LmtccEgywiS/OuqxhZW8w1GDOWvNuXgJuRHa4WRXooSZILAfxnmqbTwgMhJsCYdg3H3EMS7+1DRZFKy2Bkg9KtwAKb2AAAIABJREFUZaBxG2lHgqy6TwaWk3bsFWcwVYq+MfHsgebiIrx6Mg/raXHRtYlWA0+7UunQzwtoaMjew/NpXHF1kfWW65l2jVjXjSaV1gIKFj1XRfpPITejB215DDEuUUVdbgPSfSDYXem24gU/lfdcf3XOKJ8Fshz2XxHzzBHpiuGMWXNRzNzfiit6Zk6qzL85J/fdaPO2S6yPHdBcGK2wAWm1s/ysFSXgqRbwGtBdGGOCwXv3Y5E5vUWv2/FpsfhBCOluSiSx2xPAdwFcg7XBlZ4FsCmAAwDsC+A4AG8A8DkAKwCcPDXDJISQyYe2nBBCyk2v23EufhBCOkqKUq0yHw3g+2maLqyrvzZJkoUADkvTdL8kSfoBHAZOmAkhPQJtOSGElBva8Slc/AirTtL9xJP0xETbL/LFelk9YqSiRSKwm9JyLVq0Ek0eAEarej0pMUwUDV9inMwg9xoQwqGKsi9Zl4l8nDaOSUaGruUMF7K5YSWn+KiRUzzIzUZGGrepP64modPKlqxOa2/n9+pJ9Ipk0rCuy7x9PRlr0UjvJVplfi+AbxlttwE4vlq+FcBnJ2VEk4x3D4wq7Z4M33OrqT9GM6RUWsqjQ1naphmzJsrB5kh5dMZOBqn0S/pxvetdO1daWSqWZQB+zcVFPh+LZJvpBYpk0YmhZsel26ksK89neQ2qz28lQ5v3HJJ9vIwdntuMJSvPA215dxOTCaPIfCWPe5ZnK4OtlfZX2vLgzihdFKWrQug7IOaJ8rlQ6WvcfyLdSqqG19PHm2684Z4WO+gT49Pm6nIe3Ktzcu0airFf3pzVdUVXjJfmwphxi3HOpXSBGVey2Xh2PSaTnneuNNtc1C2mi+iIHed8ihDSccYL/JsiXgCwv9G2f7UdAGYDMH4mE0LI9KQkdhygLSeEEJVen5O3rPzwlBmdktZ4K4BeQNOib7A1vBW4vIEx5QqgHH9YZazIgEnKyiswsfqbiFXcinMy5CpnnxKcTXs7am2vBX/TVoG1lWdgYsV5xAiuFMpannQgew5HlTrvLZin7Ij5XvOu3HrXBYz2vG8BZZ+iwVHzKp0stUiJJHZfBXBukiTbAfglGv0LP13ttweAJVMwvo6jvamICVBWRD0l661rpaK8LawoKpABIaGQb9gGxxrbzeCnYXziUapd45q9ke0jSlkqPGRZDEVVfnRa4dDtWOdCi/cXc66KnFf5zKsFPzSez+H5lglMLp/fEcoP737T3qzGKLE8aMu7ixT6d+pto6HtxwuWGzO30d66awGtgQlb3m/Y53D/DIt2LaCqhzV31PDmuWPGnDkENLUCcXvHmk5z8hGlLkaNrc0viySvGNd2JOqt9vAZM/ZfKVufr9XfF62oYOq37XU7zpgfhJCOUxZDm6bpN5MkeRxr5XPfwtpn6xiAuwF8IE3TK6td/wtZrwVCCJn20JYTQki56XU7zsUPQkjHKZF/IarG9MokSfoAbALgufr0WWmaPjclgyOEkCmEtpwQQspNr9vxKV/8sFxVtFWpGMm+1u718+RFReVJntS0r65ffXtQtfUZAU9HneBJMlBRTaIrJbhivyEYoCUH9yR2QfaVGhK7UM5I5MT4grRu1Ai+NKIFXxLHkmrCViXCeSX9liyv1TzellytHsuIxQQ09e4Rz0WnGSWT2AEAkiTZCsCuADYC8HySJL9L0/TJKR7WpBBzD2iuZUXuIcC4xgx5aUDapuDOMmP2RJ0Mbhps0oC0bYpm1pIyj61sHPOoUpY2SMTwQzBZWmBTqyzNba8H6JJfVb9SXzH6VprUybJ1rvO6xWiyflkvr6VMQL26v4DvTjBqtI8p7Z6r5zR2ewHQe7Y8j4ths76WTF9z64sJ9q5huW8Ed5dBEbBauisGGy3dYjKBrHPetJl7VgmWL5Fdk3BPKy7nAJDI4Mch4KlhwKf7nLwTc2o4dZKMLVdckDRSY6fh+864LbbowqgFkJf1McHk8waQpx3vgsUPQsj0pyyrzNWV5f8PwCdRF3onSZILAHw6Ta2fOYQQMr0pi/GjLSeEEJ2yGL5O2fFef5lECJkE0gL/pohTAXwcwOcAbAdgVvXv56r1C6doXIQQMuWUxI4DtOWEEKLS63Pytio/YtxKtAi9Wrsnw4/ZV6tZNWLcWmLcYoKUqaLUyXormrQmt5MSNbkm1q9I4KScLkSWzmSLKSKxMyR8Y5rETkjwQsRpOf5MlOlq+4jcXoyliFtLq+5M1nWlydaKZGuJyY5RdF/N9m/RbCz1YyrLKjOAQwCckqbp10TdXwCcmSRJCuA/AHxhSkY2SWj3k3ePae5m1vbWvtRI70rUeosgdc5kc5kpOlRtV7+RISAwLmWsI43lESn/RWPZyuASyprLhlW2XDl6iXC+vPOq1cntLbeXPqVdzSYjKuV1o2VTkIRn3ZgiNQd0qbd3j3jZlzzZteXa4EFbXi48VxRvPlNkPiXLmXmu4vZl2fQw58zYcunOWP074Nhydx4q/BLl/RvcWqy5Ve25ZMzJPXppTi7nBt41lHdO653qom/1w7Nfnt9xZQ6SOX+Ku1TRe8Sb32v3pmXLm7my047T7YUQMgmUyL9wMwD3Gm33VtsJIaQnoS0nhJBy0+t2PGrxQ64WeW+hvH5FTnxMMMY+pd1aDfS211bMiygEYhQGcjUvqfsLwE3ok1lZVgJF9YkD9CnBlTLKEufLlm9Ka3Vin5kAgtW+VnClWs5xZWUZmFhdjnmT3M6Apt7KrVb2gjd5K7cx112eYGRanbYKnHcVWW5vrSZ3YpU5SZK9AZyDtbfrhWmafqUNu30QwIcALFbaPgTggTYco6vx1BgjSrnVgKiybLaHwHA5FSAAAKn8qL5Nqwh7OEd5gz8i7c3QRHm4Wj9D2F7RtaZMGFDqZH2/Ugfoyg8rIGr4iCWawBQmfIXyXGpBYzW1hyx7591U4SjPRI+MYkh55o1HvC3UnnXW/ajty2uPhba8u6ifB3jPYC+AojUf8ZQhri2X88Dqhay9VZf0yRtRM6ZCuTFbseXynhtRgnj2iZtDBkwdU+bXcnjhUBmFhaF80JBz5ukyJy+qXItRtmkkSlnWZWx59bqxAu2qAdClElRR8Y05Kr6in9VTdnjKc+1+h9LeTspkx6n8IIR0nHb/SKsGQToPwHsAPAZgSZIkv0jT9P4Wd306gMuSJNkGwE8BPI21K8sHAdgDa40tIYT0JLTlhBBSbnrdjnPxgxDSUTrkX7gzgD+naboMAJIkuQzAAQBaMrRpml6RJMlfsTbI0jlY+45pBMBSAHunaXpdS6MmhJCSQltOCCHlhna8hcUPTwYf0KRi2n6s7Sz5kuaiUlTmX1HaPcm/59aiua1UlDpZb6Um17bPnEtNojfQWAfogabGpOyrKuHKSMFy5kwHgPFU2b+U22kSOyUQnJToaYGULLcXLdCSFXypVQmeJhOVx/LcmfK6rcRcd17AU88txpPMegFRrfu54CrzJkmS3Cn+f0GaphdUy1sD+D/R9hiAfyx2mCxpmi4GsDhJkgqATQA816spEUfq/gJxAU1DecRpt/pm7q0QcFRxKQCyAd1qSNs1p/rX+CZnVPc7U0h6h0V5cFX1r3hqDooPoLlXeK4s0o4PKPWWq0ZQgEu3m+mE/Nwz6v4CWW+mUG+5xWjuSJpbjOX2Uql2kLJ4LfiplEdLa1GT4Iv2EcXtxbzuRdkLMKw9Hz03tFgK2PJmdhygLe8YXlBzTyZfxGVBlk33h7HsXyDrihHKsi7jkrCOMlhRnlm9QeTcclAYjeDOOCqMgnSLqSUZ0Hevn1fpquK482j2wZqT9yk3a1nn5EUCNnv2RvtdZAavrnaQAWUryg9Tyx0rnEsr4KnnBtxOt/siAYwlvT4np/KDENJxClqp59I03bG9I8lP1bg+M1XHJ4SQbqOALf//2TvzMKmKq/9/u2dnEyIq4AKGRKNo1FchGE1wwSAYQdkmmLxiNBr3JYryonHAHVyjiXtQo1EBIwgEFRdA5aeAaBRM3MIqguzb7Ev9/qCr59yeU33uvTM9M919Ps9zn6mpqlu3+va951bX/Z5TLWrHAbXliqIolGwfk+vkh6IoKSVFa4SvA3Ag+f+AWF5gIpHIEgToojGmT5jjKIqipDNqyxVFUdIbteNNPPkh9ZST5HCrqrhwudBw+wVxH0jWP6BeNiW5J3CuLn7KJbcXToLnJOYC45FqkR1zY3IvGk3a4+ISZfJCRJZ2Suw42Vh1w7o1dH/SLicrC+vW4nclCik6unMt+IR6rnKpfc6VhqaDrPYSRPIaZEWjIPduE7EEwA8jkcjB2GNgfwXgnJBtfYbsWDgjMGHcWjgXF9c9yqVddWtito3aC08Ef1tObqIo/VatgW1H8sj+eTH/iXziX5FL5LH5RbE8Up5HOpuX8NeV9rPai19Xjkxye+E8lACgKOEvwLvA0DzuXLnOZV6SPIA8M0mmtPJLHfP88zzzSF3uOeRyE5Oef9L9yrnYBEVteesi8dndWDfZIK4ski13XdPWbtcQl23O1cJjy7kBMLXlpK2cmFsLtd955Aa3KyB63B+IAY67sjkudm7s5hm/0/2YpV9qiP3I9DG5VFcaU3NwK7wA/MpdeeSBa1cPoq4uki3nzhu9Lul54dwSG7vyTRC3+GTliec02+24Kj8URUk5TW1ojTE1kUjkcgCvY8+zbrIx5rOQbZ3XlH1TFEXJVNSWK4qipDfZbsd18kNRlJSSIokdjDFzAMxJQdOKoihKAmrLFUVR0hu14yEmPxLl7y71lS0XFEXsqi30OEFWj5BWuoiELOdWg+EiFEeYPLp/kHIw5X6IR/gVIk/XOCR0rMROiCxdR06Mbd8TuZuJgu+R2DFyuiCuKlLk+8ZGW5Zka65yTkYqRWOWVhnirrsgq71IqyuFdQ1L/CyJ+2aVDjmDkNxaOFcVThYtyfj9HMvKS+kKLDZqP1Avpa4qr8/LI+VxHxGX30ksHSXlVCptJbN5ZB/6zOJWe5FWFaHl+Uw+zaNuHdYFhC70le4uMNTVpQ2TT91euDS3AgzNl1yIPN8VJ0un8mlm5MSt4ADUS8g97lpkP7+uZXQ/yfUgyP5BUVveOgnjuiqttCG5VLlcHcRVvmKGi7ot1lC7XtmwPI8zdoKxzSOGgLq42DQd53L3NEVaPc/lih9fzYX0nx43E8fk0vg8yAomFHtaokwewD9j6fM87u7EuDhR6Pnh3IGoi1YQtx7JJVhaGUdyyw+ygmO22/Egv6sVRVEURVEURVEURVHSjka7vXCBQQF+VkUKeMq15QqIyrXlOr7fN9iufaT1z7mApFGmblMoOzikN/yet1BVDY9JZ7y5WdAgAYG4WWZP8FXbD7o/SXOzyPTzSTOrUnC3IMoPaUZbUm5IAU25QLjcWxfpuvcT8FRSdvgNiEoJMsucguBKSjMgvZW2L7MkZQjNoy/wuLZoOd3PvuwrIMqOKiIRqCz3/gWA/LL6NFVxxGGMdi7zhhCot5PUXlKFgFXY0WcW91aKqhJomopQrFqBqjmoiIVTfrjsYGvEPgup2qOdI92GqcspP2geVY7Yc0yVIZxKxKMGIY3Zt8fS20LPc5Z5c0iDO3JvA6V7CPDxRt1neWNQW956MJBV2LQu/etKS+MRP8HmbVq09cTA0bS14fnElhcQW57D2XIKp5Zg0tSWUzWFLY86gnxKAeBFlQgzJs9x9LWpxuQUT8BTm0f3J+nGjsmlMbfrvFmiTNoT0JSkc5k8Tv3j+d6ZG8fzm4V0kLPlkopPCn4aVpnOfW9h1N7Zisb8UBQlpdABmqIoipKeqC1XFEVJb9SO6+SHoijNQLb7FyqKomQCassVRVHSm2y346EDnvqt5wpoyrXHte1yq+HcZYK4rXCuCJyrCj0ulxd2f1ddrn2OIIFiqdTKfuGe/tP1xZljBQlaK32HXFAtScoVJkgp4H/9ccktxtVXKUCYJEuT3Fq4e4iTC/oJeCq5tfhtK0jAVEq2G9p0R3Jb4STNdD/X/lwMuwJHeVxKTTJpcFObriitz8unkteYoaSSY88TkPEJoPLjuGSWcYUBgJxYv5gYqp60K0Yf/dz5TB515eCkyBSrEK9ylLcEVKluvUroZ3K5vbQTyrm2JLcY7lznk++aBrWNB8lzSKVtcDzq6kmfqdWxC9clf65i8iS3FZdrGBfQ1FU3LGrLWyd+XVfptUHHjn7HVkHcs1zXIefCyNltmkfvyXa2466Io4wt97jA2GdBhC9ng2AyaZdLMx1Hc98L58pBg4zSZ1Rjx+SJ/Ugs9zsml8bBTeH2Yok40vZrdz1D7SWSR/wa6XWTxwQ1p894a8ONw5bbQKf0uwridii5BEtuMdz1Jv0+0EUIeFT5oShKysl2iZ2iKEomoLZcURQlvcl2O66TH4qipJRUrSmuKIqiNB9qyxVFUdIbteONmPyQXE38urAEcd9wyba4tri6Llkatz/9XJLsy+7viqQeRrZG8Rul21VXctHhVskJ0j9uBlH6LqS15pvC7UVal76x0ZT97h9ktZcapjxIFHFXX8O0FcRtJrFeItk+y5zuuKTzNp+uSsK5rbhcXVi3FpLHrXZCXUnoai52hY5c4l9BI71bKXMhuUipC0udjeTu8A2w8tgI4woDAHmxjkuSXOr+QVcd4c4hddXg7JjrfrNdJKpyz2oxqR742M/NraoC1K/cQj8ft8ILzW8jlHOuLjRN87iVXwrJcjKe1V4YdyePVDr2ZXik0uQaqol9sZLrmGtlDHpdSC4y3OpLLteDsKgtb10kk7dLY0Puee4aj9Qk1HOVA7wtp2lrA6tIY3S1l4qY4colRoPacuu2UFjXMI92stY1KI/hWg3G5kvu565zyf1+cLlyRJPkQchzkYoxuWsc63cc7GqLYs+BZzVKkrZfsWdlLpqO7VhADDy15XZsQFd046hz2fJYWnJlCeLC6LqH/P7WCbIaJJjybEWVH4qipJxsn2VWFEXJBNSWK4qipDfZbscDT35ws5h+6gNulQhXN5LwF+CVITTtmrmUlCHcG3hOGeEql9amdgWlayqk2Xt63riZVe679Pv9JiK9XfAbxLMplR9BAqJKyhFJ+cHNjkuz4K7rUmqfO1dhZ/f9fm9+lFiJ6LJa6Q/9rsMENJWCnNK0K+CpLadveipJhfLde/7SwJT0DY99m0ff0NM3i5Y6csPXMRdujiNYWk7C38S+5jN59AUU7Yp9WeXnbZqFe1bSY3Hn1aWKk55p3Gelgwl7XKqw4AKOErGFR81B89slyaP7uZQjnPLDowKJdZIGx6Vpe43Q751eQzZdS05gNbkhqq2iiByTU2NIyhCg/h6QlFiu+62xA1615a2PxO9UGgNIgf8lZQi93qj94q7DfCaPpqkaLY8JbprnCC4dZWx5HmPLa4UIv5zaA/AGNI7n0XZjf6WxmwtJ+eGqG4YwY/IggTUlZYi0IALFngt6+j1Kz4S/iWmr3vOoPeg1FEtzAW0BoM4GNCUfoKaqYZqOWzhb7rLfnEokyO8XThnu+n3Cfe+0LNvtuCo/FEVJOdk+y6woipIJqC1XFEVJb7Ldjuvkh6IoKSfbZ5kVRVEyAbXliqIo6U222/FGT35w7iU0HXHUlZbplqRa0rEktxguYCkXGJT2i7bJxVFy7e8XaX12Vzl33lznXZLYBQmqxOF3fXGXKwdXHkRiF8atxVXOBfgKE+hJkqUFWV9dCh4VxLVLusbCuM3omuKZDydfboqAp5UJfwGvLDpfKM+NyaY9Mlein+XcXvLJDRGJGUVDLlbDGLQoMZ60fZvkgpwCvGSXuoUECXZmuxgkMBz9XuyxqAzXZZMtnFuNJE+mx+SCn1JXFuqKwgU3pW4tXJq2xaU9LjjkSyqMHaCA7JRHKlu5fYScAM/zJ3Yyq8mFSdNcQNMgrmFScNQgbTUFastbJ9wiBNIzntocey9L4ylq31yuXNx1Su2DzXfZytxYIGvq6kJdFey96AlMSTpg3Vlc5RzU1cXun0NdYYRfi65iyZZyixC46vpFCisguUMFCXgq7S/Zixwm7XJrsc8Njy0ndrugyPsXcLvAxPtKPkw86Dm5cKkLow3Q6woizbkw0vEK5/YiBUQNEjxW+l4p2W7HVfmhKEpKUf9CRVGU9EdtuaIoSnqjdlwnPxRFaQay3dAqiqJkAmrLFUVR0ptst+OBJj8MGrqjBFnNhcKtGiOt9hJ2/XLJBYeTnbmkgRycq0YqcH1+KhvjpI+cC4+0pngQqV1zSuz8Rp6m6SDlVIImrXvfWBcdzq1GclWR3GJc57WGKZdcw4LcY8kiSyfLV9KPICtVWMkn577iyvesAMCk6UOLpnNiF2TO7vo8Lpo/dWWpIR3nJLE1wmoBVJZt03nkBHF9dbm6SG4vFC4qPrXj9liS+4TLtkj4Xe3F5XZjXVlcK7Bwbi+ulWHax/663GLi+5MTVEQqFMUa8EilyZfESeg9cvrYieNWBQD4FVoauyKSur0olmQrMErPcGl1OWn1O9dKFvY6dbm1VDDl9DrPi120OeX1eTmkgfhqL+QD0JVdchhbTsvtfhHHai827Xl+kHs+mvAXkF25JSRbLxHE7SbIai9+XdWD2AXO1QWQnxvWLBcS+1xI7LZ1YXSu9hK74FxjAM6FsYa6M8b+BnFLlFzDXCuuSW4v0u+PxN8H6oruRZUfiqKkFDppqiiKoqQnassVRVHSG7XjISY/7CwSsww2i2sGlAsi6lKRcOVSwFMpYCl3LGnm1XX8VCg+uNl5TuGRmJaUHZEkeU1BY2eZJWWHpIYIMnPKvcmQyrm2gihLuM8dRDkSJJAvd96koLqueyRMQFQw5Upm0dg30TRt3wZygTNpPpdH0zmk0QhRgXDKDy4gHn3bV1vL101sEwCijPKDC25K7QkXhBTg76cok3YpL7jgsdxbWpc94OCeGVLAU9cbPJt2qT24fJfyw+Zzag8AaBfreNsOpP329Wmr+Mhngpx6oNcKeVtoVR5cYDyAf1tI1U3cd0XTUoDhCqa8qdUeFLXlrQdJjc0pp4O84Q+ilOUCoboCntq69DrmVGyRUpInSCCoXc+NdYDaZ1pu0zSPtm8DWUepso8YUO43h6QCCXvf+P194fox63cRgiABS4MENOXOkUsRVJDwF/AqJdvE/mlDlXskbYNW55MGOFteTb5LasutDadqkCpSV1J+BBnvcAFPJeW59PtAGtODKc9WVPmhKErKyfZZZkVRlExAbbmiKEp6k+12XCc/FEVJKRpZWlEUJf1RW64oipLeqB1vxORHmIClXF2pXJL807Sr3G/AUldfUh3I1OIKYmqpc5TT/SS3Fs4tBkx5WPxKrYKsGR7E1YRzAXG5hXABR8O4zUjBWSXZmuSKIrn1+Al4Kt0jfu83vwFRNbhSdmBlnFJAUynIKcC7tZB4d/GHFZXJcoHTPPaOHCwaq+BxeyEXppXKelxZyAFqheCnNrBaDjkmFwSUSnpdQeS4+4XaZtstrn2Al9/S7nO2qbHyZSlwHecCRCXNUvBTztXFWU5OlnV3abMXaZ9Ipe33nke/GIKVQNeRk0UDmtrgeFXkYpVcVaSgwFJaaouWNzVqy1sXia7oLlc5bjwhubJx4x1qR2jdKibf5ZbHlXNpTx5xgYkwA1XqlpgbMzZcEOvEuhzWBYba/xxyzNzY/vRcun4/SIFo431K3iVxf6mtIGPyIOUWydUe4IOWFzLl1JbT4KbW3YULWA3U23CXLbfPcE+gXPIDsYax5ZyLoeS6KwVEBervI1fQYJsO61YvuaJnux1v7G9dRVFakNElJZhvDCa99lqDstunTcNf5s1rgV41pC7EpiiKki0MLCnBg8bgEsaWXz9tGm5LU1uuKIqSLfy8pAQTjcEFjB2/dNo03JCmdjzTbLlOfihKBtBnwAAcetxxLd0NJybEpiiKkm0cNmAADs4gW64oipJtHDJgAA7IIDueabY8sNtLMnm7tJoLVzcqlAeRlUnlzblaSxASZYs0j6Zd50pya5FWe+EIsgJMkGjQfiNLS6u91DB5iXX9ysI4VxKaL62m4uqL39VcwrrFWIO0Y8sWbF63Dr+58UaMPftsJEL9+5K1laxc+t6SSezUvzDzca0+wbmyuFxgOLeWXCYtSaWdzwEim+aw8tc80gFWXk1uAo8sOpbOJR8wj2he7f1MZb5BpMTcyiqS24tLUitFirf50nPE5YLEub3QukHcXoqYPM4Fpg05GJVFW7cXTh4N1K/2Qr9rKoW2blI1zKoAAFBlpdLkBHL3g8uVxdatAbB7yxZsX7cOZ9x4I+6N2fJK1L9549xeOBeYVI1p1Ja3XiRXdGnMLo1X7C1B73NqU6h9sPe6a7UXe8263OY4t5co7ewuNIC6peXH7uk6ukKL4CpDsXXpSiGedOyD0c8UxC1fcvUGU+6iqVxgpONIrvLSswqo/75dq4DZ50ER8Wv0pK3bC7HldGWXPMZ1lbq5VsfS1cRoVjFpusILZ1+TrcZVC6B0yxbsWLcOJ914Iz4lY3JrxyvBr/bC/dYIu4Jjst/qasdV+aEoaY8xBs/efjtOGDwY3z/iiJbuDku2zzIriqKIGINXb78dxw4ejAMzxJYriqJkE8YYvHH77eg1eDD2zxA7nmm2PJDyg84WNTagqaTG4MqltxkuZQQ3Q8bVlcpdM7dhoG3lJMmj/cpl8gBZ2eF6e8eVc3D7BAmiIykMgihDJDVEkOCoYZQhYQKmBnkjEFQ5Yuu8OW0azr/lFpx7443446hR8f1NrJ5flUmQcumzKNmDK8AXF4CRU3sAvJohj0m7lCFswFMuTRQg9G1hQeyGNURikBPgCRl/W0g6yCk/JHWVp02S5lQwnNoDqD/fkqrNZWctrueIFLBQCnhq01TNIalAaDlVfrSJvRmkbwit2gMACmL5hWSnfNJYJPbBaBC8GnIyamMnq5pcuJWlDdOSssOZ/27MAAAgAElEQVSl1rB1a7Dn+1g4bRoG3XILBt94I+4dNQoV2PN91SFYcFQlO/CrxpaUIZx6kwuOSu0IvedpvrVFUSaP7kfvAy6gs3NsajtJFCD0Db9N1xJVALXlUZ/SZlqPqglyY2lPkFWynzQO5p5RrjExRxDlRhhlCEX6zcA9C1zP6IKEvwBQRCpYW+0JaErsekHMhheSvDzyYOFseTUNaBqTWdCA1TS4aQVjy4MEp7bKDwBYMm0aTr/lFpx+4414JGFMXo36+4ULcgr4H5NL44mW9mxorajyQ1EyAGMMnr3zTpwyYgQO/OEPW7o7Dcj24EqKoih+MMbgH3feiZ+OGIFuGWDLFUVRsg1jDObeeSf6jBiB/TLAjmeaLdfJD0XJEF5/7jl8t2YNRv/f/7V0VzyEkdepgkRRlGxl/nPPYfOaNRiWAbZcURQlG1n83HPYsmYNzswAO55ptjxwwFOLJNmXgnQ2lSsKTQeR99Q0spxKvPwel/Y5iPuJzTdMXmJbfgOewlHeWKTgSn4DZ0rlkqsLTbvK/crKaF/odcG1L5U3NuBp4nVP75O62lo8O2kSrn3wQTwxfryznqutxDwpWFqyviYaykybNVbcUCmzlb9yQVAB2e2FKFJFVwvJ7YWzfYZ0zEqlaUDTHBrQNNYxGuS0jjH+USqPJpLc/Jh+1s+9IEmJbbNUkksls0VMnhQsjRvcSM8ZzhWHprkgp0B9/7lgd4Aj4Ck5gEcWHQt+V8gExqP5NJBtlLRlv3ca+I4GN7WyaFpeSa4bLqBpOZOWyq0bYyWA8tpaTJs0CRc/+CD+PH58A7cXlwuNNG5pCtSWtx6CuKJLz3hrH6JMHs13jXckV4hKpq6rr3Y/1m2RpukHoO6Msfx8Uu4JWEoNUxI8ri5kH2szqFuj67cQh+scW7jfPS63GZNQL1ldjhwmT3Kl5567ruDbXLqAGPs2xFZzbi8F5GFgbTkNcsoFN/UEp65smK4kBthj12P702uV2mopeHUF6t0XKwCgthYzJ03C6AcfxNTx41GHercX20XOHZXmu1zNud9C3Phcx+Q8qvxQlAxi1uTJ2LpxI/73hhtauisesn2WWVEUJQhzJ0/G9o0b8Zs0t+WKoijZyvzJk7Fz40YMTnM7nmm2XCc/FCWDqK6qwt/vuQdnnn8+Onft2tLdAVD/diqb/QsVRVGCUF1VhX/ccw9+mea2XFEUJVupqarC7HvuQb/zz0enNLbjmWbLA7u9cO4siWWA/9VcXJGn/Za76gYpl75Uvy4wkvuLa+bMyp+olIxzcclh8hLrct8PJ12k+Ax87QvuMwZxa/HrflHD5AG8RMwlK+PcpYKscGLzOVcX1/5B3F78rCaTeD7rAPzjscdw3rhxOOqEE/Dh/PkewxXEnUiS2EnfK5hyJfOhrhhWEkqlr9wKLjTNreBC0y63F87OS24vnms41nG6akAukdfmxfIjwisDWu5Z+SW2v3E8UCTZOCcrdrm9SLZFul+5PnGyZynCP+eqA9S7uxQyeYn51q3FJYXmyqksOj9Wl7oj0VUaamLfRy35XqhU2kqkK8rq8yqI1llyaylj8mjaHqqalNnylx97DCPHjcOPY7Zcaqs5UFveukgc87ncL+zlLY2Dax3lnNsLtTncyi+u8iiTJ7kwiuNUcmGamAsMdUvMoyu/xDooreZFbQZ1gayLGTb6rMgn9qOxv0+CuIpL7ooc0u8AydZzzyWnqwv5x664VUBdFIXVXAqJrbfP4xzGbRGod1GsJg9GyZaXE3cpa8M5+0zTLrfDStTfH/barwLw6mOPYci4cTjshBPw6fz5qCLlLjcyzu1FcovnfqvomJxHlR+KkmFUlpfj+fvvb+lueMh2iZ2iKEpQKsvL8Vya23JFUZRspqq8HLPT3I5nmi2PGOP/IxVEIsaKdqTZwGjCX4B/o8ftQ+tK5a62pLdoQd4o+v0s3DFpvuuz+A3u5HqLyeW7Zn5bWvnhV9nhUhhIgTe5WVBXW9LMqaT8kJQZYQKahtlfKvdTl1PJNEbFshtAjTERANg7EjEDEJwXgKXGmONC7KokIRKJNNtzzNo28lIe7Um6HZPm8vyUx14agbw08qSLEuoBXoWBTVMFAn2DZYOX5jmCrXHQt1I2CFsNeStVS8vJfpVMHk1LwdK4+1WysxyuIHf2mREkeC09l4VMnuttoH1bWEQuHC4IXgH5YvNIw/aNbS05GfTNYPnuPX9Ld9Tnle2sT+/e7v0LADvJiduV8BcAyO7xfJq3gynf4Sin6Z1MHnmJmRJMzI4D4Wy52vHUEWRMbtN5Qjl3HwP1toDaRynIJXfP0zZcQY4LhfIioTyPKacKBGvDqZqD2nKr+KAKMWrLbVBsqharIRIAmp+oBKB5NC0FtnQpy6Uxt/SG2+/vE9d1EVd+MAoPoF55B9TbbWrLaV1bTvehKj7ue6EBS60tLyfGmNpta9c9tpx8V7sT/gJeu27TRCziqWvzy5g8gA+kStWbXJpeNzRdx+Qlu652QcfklNCrvSiKoviBRqRXFEVR0hO15YqiKOmN2nGd/FAUpRnINMmcoihKNqK2XFEUJb3JdjseePIjUS4bZcpoeZCAplxdV7nfIKS0X65AUFKgUg7u+EFcTbgLz+UKYvd3nQvORcZVzp3XpsSv24urnAvuxMkBJVcWWjdIwFKu3CUd5+SIQaTnYfbn0q7rWnIXklx4pICoycoTv+dsn2XOVuw1SoNB5gppl7xWcpWMJMmDIy+IHc5nAqJGSWdzaWcZorGO5xJ5cLSGT9vP6JK0hgmG5vpcbF9jf6UgeK7gtPYjegKekspW1uxxX6HyZxoEL1anDXV7obLqmCw6SvX4tLOxk1hHTmYViWhXEdMlV9IgeLsbpkvJSeOCm7qCkNp0maO8lMlz1eWO1dyoLW9d+B2Tc66t3JjSFSCfK3eNyTn7IQWnloKbutzCObhr1FB3w1hnqS2m9pu6w3BEbQcdv6CoC03EBlclneJstWscyo2pgthyi+u5yLm9cM/lHNIA55biclUpZGx5IRPkFCDBTV223AanJgaS2nKbpva7kpSXWbcYcrIl+8rZYmr/uTR1X6HfKxegvNpR1++CC67rQgOeJkeVH4qipJRMDJakKIqSbagtVxRFSW/UjutqL4qSMRx70kmY8sknKGzTRq7czGT7muKKoih+mTxvHs767W9buhssascVRVFkjh82DPe//z6ikpSoBcj2MXkg5QcNkmKlUpwrC8CvOc65coSVctFLSXKRkbBtSe4vrpkyK1VyzST5lVa7Iuvbui4JMieFctVNltcUcN+b5PYSZE1zzlVEcmdyucVwrhxcubRCSqpXc3Gdv8TjX3Pfffj+4YdjxOWX46lJkwK5rXDuREFcbCSJXbbPMmc7NMp5niPNub3QtBSVnnN7kQjiphe3TUSbmsusBuBZNYB0Nip0jO5nJdI55FjS6kvS/RzkHuTOpbRCWh51AbJSaCJf5mTRnCS6QX5sLreQW+4B8F5EFqIlroudQyqPplJoK5EuI2H9S8nSLGUxrTKVN9MI/mVJ8mi+JKW29foNHoyjjj8ePzjiCLz897+jvKqqwX4taU/VlrcegozJOVcTbszLeBk493eNM6uZcir55+yL5NYSZszqGq/kx06SITe1ZzWXmE3JcfxCsm4vUfoBSV1q63NibdWRk+2x63XePgFuux7vK98tFskd1Lqz0M+aQ2xqXiydy6yWA9Tbcuqq6HR7aawtjw0knLa81PsXSLDlse+b2mrJRZHa/cqEejSPdrUCQDQaxXn334+O++2HE0eNwhvPPce6sXLurDQ/7O+TxPYTr5lst+Oq/FCUDKDPKafgoB/+ELm5ubho3DgUtSL1hx2gZfMss6Ioih+uuece5BcUIL+wEMMvuKClu+MhjC1XFEXJNn5eXIx2HTsiLz8fF951V6tSf+iYvBEBT7k3S1JgzSABTTmk4KoSUvv00gwTBFV66037yb29DBLw1DVLLgWKSlavKZDepHL1ODVCkMCd0ttPKaApF/iT5odVfgSdmU3si6RMoTPD19xzD9q0awcAyMnNxa8uvxxPTJqU9FjccSVlhxS81XUvZvsss1IPfWsiqQmkwGxBlB9+Z/pF5QfJyyOVc2OvNvOEpyp9K+hSg9i3gVFHW/YtpedtJaNCCUKE6VeEeYMJ1L8lzCN5ueRtn83PK6zPyydpq/Lw5HGB7wDAvjmkQfBo2r5Oo6/ggig7djF55HWffUtIYuixyo5SJo/Wdb1tLCN5pwwejL27dAEAtG3XDlfdcgv+/Ne/oiqm/qBvz1sKteWtC79jcu4Zz9V1BTStTagHeIM1Uux+XBDUxDR3LE5lEiTIqRQYnxub5ZOby9p1qtagAVHrYp2hyg+PCoQ8pCKMssLQthhbTY/baFse6wt91njseqycqjlymEDeroCmNt+l9sjnVB4hbXlFzFg6lR07vX8BoIzULUv460pLwatp97iApzXRKH47cSKK2u+J0t2mQwecNGoUZj33XLxuTcJfgFeB+Bn/I0lax+Q8qvxQlDTHqj4sRW3b4sJWpv7I9llmRVEUievvuQdt29cva1NQWIhzW5n6Q+24oiiKm5OKi9G2Y8f4/23at8fv77oLOa1I/ZHtY3Kd/FCUNGfkJZcgNzcXpTv3THfv3L4dbdq2xc8GDmzhnu3BhNwURVGyhUOOPBLdf/hD7Ny+HQBQXl6OaDSKCy+9tIV7Vo/acUVRlOQMvuwy5OTmorx0j+xk1/bt2LtrVxx5/PEt3LM96Jg8hNtLorsK58oCND6gqcXVvgSd1QnjwiK5wHB9oX2lnzWa8Dex3B5LCmjqap+TKbo+v9/ZLpdbjN8bwCXF4tqR3FokOaOrLufWwpUHkUamwu3F5XbD7c/1744rr8TfH3oIffv3x8V//CMuHzIEALD0/fcbHMN13qQAipILTuL3lnidZNqssRIeet1wLjAutxebL7n8BZFHc/lB3F5Ylz1HQNS4lJhInp1SZEYq3ZRYiTbn6kLzOVcXoF4q7QqCZ6XSBZJUmnNvAQAqWrP70S+2kkkHcGvZvb1hejeRR1MXF5suZfIAXkrNucBI5f9atgzDTjwROTk5mLpgAT796CPcMm4cvvjmG0/9lkZteevC75hcCljKuaJLAVEprsCN3LE4UvEm1mXLxXFm7ANwAa0BYhdpkFPyAT22nOtYEwoAOBfFKON2E2VcXYB6u57L2G8AyIvZeMnthdr/5rTlZYxd3033R8O0y0WRC07NpbkgqEC9W+Kt55yDLj164OzLL8dJI0bg+iFDYIzBhwsXxq856+ET5PeFdA0nK9cxuZcUDa0URWkuNq1fj03r16N9TGa35J13AISb9EsVmTZrrCiK0tR8uHBhPP2fzz7D/3vnHexowf5wqC1XFEVx892aNfhuzRr8+Gc/w0kjRuDj2Ji8NdnO1tSXlkAnPxRFSSl0OT5FURQlPVFbriiKkt6oHQ84+UH9fqRVNywuiR23Jjnn6uHn7TUnp6OyO/shw74Jl1ZQqWHyuFk1SS7tWrVFWu2F65+rrvQd+UWaNZQiDLv297vai+R+4WpLWs2Fu56lY4V1e+FWowniapLo0pLs3uQiRzfGrSUxzZ0rSrYbWoWHrl5hJaWuZwbn9tKUq1b5tdmuZx53D9FI/nmxD0vl01Emqj5QL0t2uaVYXFJntpxxa3FKtWP9crm92L5SqXMe4wJD3V5yyMousGkqiaYuMO1JZ0pjJ8zhd1Ibkz2XO6TSNl26gy+37i6cqwtNu8p3MXnSai9c3UTXltrYvq3tDZ3a8taDNCaXVnMJgt2fc2kB5DGr5AoTxpZL4xlpnEn3IeYrvh+3mhfNd60G43HdZNxOOLsdcfj9iHafcWGkttrm0/7RZ4W125zbIlDv2ui05UUJfxPTAWx5XcyYljH2m6bpyl0etxfrFsM3H0+7XBDLmTxa17q7cK4uNG3LE1d0oasjSSswNrZcWu0l2+24BjxVFCXlNHdwpUgkMiISiXwWiUTqIpHIcY1sTlEURYHacUVRlHQn28fkTRrwNMrUc6kh7AwVnSHlZqJoeZiAqUD9rBv9sNzst+vL5Wakgyg7uHa48yKpYFwBT13H4NpKVi8sQVQgXF1J2REmICrNl5QhTan8CKIskdQY0sxuYvuJn4frS1MqO5IFh20FbyuXAxgK4LGW7ojiD0754Qp+yuU11ZvDIAFPpTct3P2US25M8oKNfTPqEXPQN3uxHV3KDk45EuRtYDwIHvMGkLZPg+BR5UdcMULfANKAeDafngB64jaTf+xrOPIKr4K8miuP5VNlRwWpG1d+0DeIJGId07yo/CAvHtn9uTePXJBUmqZ5wJ5rqwpZj9pxgWRjck55QcfBnH1yjUO5MTO1WWHsb2PfvkrjDNc4kRunSOdCCojqIm5WQwYk5cqpYtC2xQWkpvkuFV9eLD/XoeKLqzyoNCbFtrzcoezgbHkZseWSys6W72Ly6H40yClVeVQk/E0st/Y6cWxfnZBP0zSPG+sHUWtL13gL06psuSo/FEUJxbiSEpQag1JjUBHbqmPbr3/9a0/d5p5lNsb8xxjzRSObURRFyWhKSkpgjGG3RDsOqB1XFEVpjXBj8srYpmNyLxrwVFGU0Gzfvh1nnX56g1noz7/+Ol7HwDsjHYDOkUjkQ/L/48aYx8M1pSiKonBs374dp59+eoP8r4kdB0LbcrXjiqIozUDimNxOWuiY3EvgyQ+/wZW4gKYUq8pySYgT22kKuCCoruNSbF9d7gsWl1uLlTy5XHjsfpKLkKt9ya2F9jvVUh/u+5ZcXaR9JOm55MrhkqFzsrIwbi+cewmty0njadpVzu2frP3ECQiuruTWEqSvAFBTU4PFixY12D+xXkhDu9kY4/QNjEQibwLowhTdaIx5JdwhlZaGSk45O9fYgKdBXOqkeyRIgGOb9gTWI8ZDklBTF5ZIrLEcRxA9m/ZIoRnZMyePBuqD33FBTmm5x22GSqHth2SCsALgI8tVM+UAqmJpTh4NAOWlDfPKmOCmpeT8cm4nktsL5+pC84O4zXAB9eh139zU1NRg0aJFvuqGsOVqx1NIsjE5tT/2VuTG6TQtBSmluMaZXHkqcL2N9use7TpXfsdDNKC1Ib4QNKh1HXUHYYjbZSHgqSs4Nuf2ksvYepcLo81vDba8MlZO3Vo4F5hScmFSuykFNC0VyjlbTNN+3V7s30QXdM5FXlrQQBpPJN4DOib3hyo/FEVJOanwOzTG9E9Bs4qiKIqDprblascVRVGal2wfk+vkh6IojSInp+Ergdra+nnmpvAXVBRFUVKHZMcBteWKoiitHR2TywSa/KB+QpycjXPrcLmUSPtLkpzGyumonI9Tm0luLUHKI0nyaNrlyuLXLSaxXa4tycVHcovxK5Vy1ePkhtx+LrkiVy5JG4O4xQQp9xttOexqMpysLdnxE+85aWUa6bNIq8HUAejcuTN21TQUx/bo0cPzf3OvKR6JRM4G8BCAfQD8MxKJ/MsYM6CZu6GEgF5jVHIaxuZzdiZIhP8wbnKSTNVVnsdIqI3jxnHlW6IRf/UijhUI4uXCSTf0BFCpM7ecG2Nwqsk+1ZV82kqkK8sb5gFkhQCaR9Kc1JmTPbtWY9mVUI/mufbn0mVCeUvSuXNn1Piw40Dz2nK148nhxuRhVxXkxobcmFxydWkOuHEgVx5kpb4g4yHRlpNlmuzqXnWkQh51K2HwuDDWef/6gXOBlGisLafzpNRWU1teFXuuVRBjSN1a7H7UFaaUlNtmqS2lYwTJrYXz0OHacq32Yr9WenqSreCSuNqL5CbLjUek8Upimzom94cqPxRFCc327dsxsH//Bob422+/9dRrbkNrjJkOYHozH1ZRFCXt2L59O/r3b6hYTrTjQPPacrXjiqIo/tExuT8aHfBUCp5EJx25GWfX/hxBFAyNpTmVH1GmnDtXrsClktqDu8hd5zrMeZXkU643pVy53+Cnrja5fFfQKr/KD1dAU6l9v8oN19sDv+0nvpFOFswryJrgft5619TU4MOlS5OuKa4SOyUs9Novc9ZyE8TecG/7pKDBYQKUSeVAvc0pIG8Qa2lw1FgQujrSmTzGeNY5brw6xtB7gvTFymlAvxyyj+1LNMAbRtp/+5awhnw+zxtCmo69hqNvE+mbQasCKSd9pW/u7HXDvQEE6pUXrreBXMBSKaBpkPLWQE1NDZYuXSrWU1ve+kh83gdRW3OLAEiLEDQnkoJY2k8aG9LPL9lqrq40NgPq7XItiZJZR21hTAWSR/I4lYhLxWcDphrHSbH7GfJrj9a1NjjH8WvQ2nV6fJq2trya2PIqYmBriEyCVfERY1tpbbkjoCmn3OBsPacGofmugKY2TdUeXHBT8lE96USFR+IYvynHC67xio7J/ZHqxT8URUkzOu+zDy65+mp07Nixydo0ITZFURQlPKNGjcKJJ57YpG2qHVcURWk+DjjwQFx0+eVo27Ztk7WZ7WNynfxQFMXDSf3745ZJk/D5N99g/B13NMkkSF2ITVEURQnPww8/jLlz5+L9999vskkQteOKoijNx+Bhw3DHfffhy2+/xR/Gjm2SSZBsH5MHdntJPAFckFNAXvPbqppoB/zu42c/CSkQHkeq3F4Mk8e5rUSYvEQkF5lUw/VLcmXh8iW3FknOGKSuuJa7sL/LLSaM24sUQDGZq0qygKd+gyvVxupWlJejfYcOuPTqq3HxlVfi4QcfxL2TJmHL9u2g5Obmos9PftLgHly7dq2n7UwznErzY6+xIO4vjQ14yklO6f3OBT6jsew42TQXIC0xnc/sT91a8mJaXI/bSD4pj3UsL4/0hXTMSpzp/lT2bKXUNI8GP7XuLkFcXajU2rq4UPk35+pC82mQ00rGxYXKk4NIoTm3GC74qcttxe63y1Fu0ztJXnO+RSsqKkLfvn3x2muvYdmyZRgzZgzee+89T53c3Fz85Cc/abAvteOA2vLWSOL40BUYX3JV577XIIsYNCd+x4m0vIYpD+JykC+UVzvq2uMSU+yxi9bdpZbab9KwtdG5pDyHHCyPsdXVJJ0bK6cBrSV3RVpuXR85Vxeg/rPU0IDVpH+cLa8kxraCnIuKhL8A75bC2Xe6n8uthXsWVDJ16fHp91qV8DexvCYhL/E6k1ynXG4tBkBtXR06dOiAG266Cdf+3//h7jvvxMMPPYTdpfTJpWNyv2jAU0XJEL6LBTT6xuX8GYKioiIAwB+uvx7H9emDXyQExevYsSMWfvBBg/1uuukmz/+ZJplTFEVJFevXr8dFF12Eiy66qMnabNu2Lfr27Yt3330Xhx12GD7//PN4WceOHfGBDzsOqC1XFEXxw4ovvgAArGqCMbldqrZNTPUx4fbb0euIIzD6N7/x1NMxuT8iJsCXkhuJmPaxtJ0YpDPDNB3xWZ7L5NG63D6u/WhdbtlY11Kydj/u+LSuS5nB7S8tVcvVde3P7eMKWMrtp8qP5HUzRfkBAO07dUJefr5zPymYYy2As4YPx/g77kD7Dh1QVVWF2tpa/GPaNNzyxz9i5Zo1SfenGGMiANAmEjGHMOUSnwBLjTHHhdhVSUIkEknr5x61zW1Iui2TV8SUFznKbbqQ5BUy5QWO8kKhPD/hr580fVvIpXOJcadvBm2QPKr8yGWUHzk5DfMAVX60tPIjPz8fnTp1EmrJfPXVV2jffs+obdeuXdi0aRPGjBmD6dOnw+/Yz9pxIJwtVzueOuiY3N7K3NiUpl3j3Kban6alcjqO59J5jvI8Ji+I/SxIqOennEvTPFdf8hL+utKc/QbqbbRH+UEa4JQfUVV+sPs3t/IDADrsvTdyc3M95QC/iIDr98HvLrkE14wdi4KCAlRWVqKuthZPT56Mu267DRu++47dX8fkyQm92gvniiGtYEK/DHsfulxZuB+m1HjS/Q7u2RPnjhmDo44/Ht/v1Qsfv/suLj75ZE+/FxmDS086CQDw8Pz5OCHScPrANaFgP6tLTsjtH8btxdUWNzkCphzgvxfJnSgM0rDJVS5NhPgtb4rJD9cKJonpsJMj3OQEN1EiTX64jCO3/5Zt23z3hTOUdQC2bt2KgsJClJeXxyc9VscmPaQJDxfZLrFTZHr27IkxY8bg+OOPR69evfDuu+/i5AQ7bozBSTE7Pn/+fOQydlyarHSVc5OR3D0URCpNB0GFTJ6Udg3O7bMyn3GFAYDqWFoaUHMTHjSfDoyjAR4adpUZz4CZGD87+UFXAvCs9kJXDoj9lSLwSwNeuo80+VHG1OUmRGi+a3LETnoEmXGsqqrCd2RQG5ba2lqUlZVhw4YNgSc9XKgtb10kc0fhbtkgruocdH/XtXBgbEz+49iY/F/vvovLEmz5QmNwZcyWPzh/Pk5mbLk0zpNsvbTSn2s1lzomj7P1kqsLzad1OVueR2xeLknnxSrQCQ1qy6sEW24nMuhEN0WawLY2nE540FXA7KRHtcOWVxPDaz8WtcV0IoGz5dxEiFTumjyx+a5niU1zEx40zU140Hx6rWzesoWtK43v6Xhi2/btyMnJQXlZWYNJD2nCw0W22/GMcHvp2asXThw0CMs++AC59FWXoiiB+X/vvYeH//QnPPznP2MNUXqEJRMjRStNT69evTBo0CB88MEHyFM7riiN5rbbbsPKlSubZNIDUFuu+OP7vXrhhEGDsFzH5IrSaOa++ir27dIFD95/f5NMiqsdz5DJjwWzZmH+zJmIAJg0bRo6du7c0l1SlLTlm7VrceP11zdpYLNsn2VWZGbNmoWZM2cCAKZNm4bOascVpVHce++9Td6m2nJF4p1Zs7Bg5kzkALhLx+SK0ii+/OIL3Dx2rI7Jm5BAkx80Qqzk1sLhkttZpGjTLheY6tgbjSjqZ7Qk9wlODudaQYb7rFz7Yd1e7LFcMUEktxhpZZfGurgEwe/3KrnFhHe7h0gAACAASURBVHGFceVLbidSW66YHpK0UnKraa6YIH766vdYYQ1mts8yKzJh3kzT69W6IgSJJyS5rQRxa+H8fQuZ8nyhHKiX17p8zvMS6tE8oP5ZlkPl03RlF/uXxvQgDw8rm46GDBhlZdG15GRQt5eaWLkfFyBJKm1lzZzvNk1TV5UyoZxzgeHifNB8l9tLptm+TPs86UyYMblrPGhtgmscH2QcWUNsuR2Tc+26bDFXTpFiw9k0tYmSC6O0Ggzn3pDnKM8V6krxS2g6J9ZwHjlADjGGOdatxWHLbcwPyb2FQt0VrS2vo24v1IXRxvwg+7vcQvy6vXCuKDTtcmvh9ufK6TG5vtBy7nO5PqvfFVxoWhpvuFan0zF548gI5YcfjotE4sb9J5FIs04IKEo2o8tqKU1FhPiFRxgfcUVRUofacqWp6EvG5CeQtKIoqUXteIjJj8TZWdcKJdKa4haXsoPDVZc7rtQW1+8aJo+26fqsNUwep/Jw9TmMMoQSpC63TxikWUNpxl6qGyTQlasup/wIEhDV72ovrT0gapC6kgol7Gxxts8yK6nHXq/0rXxjA5ZyAcpcb7VqkuQB/AoBrreFXN08ptzzhpCp6yqPr9xQw5dHY6++ggg/ggSPld6gcUHmuDeEAB/hn3tb6FrtpZzJ41QgXJBTmk/zMhm15a2LoGPyIOPspnirnEz5IY3zJIVw2GD3nH2i9tVvQFO6jyugKafio+XcyjdcOf1ePKtdxj4MVfl5VraMGcAgY37uvEnPzbC2nFNhUPvO2fogAUu54NiuZ0kNUy6NAcKoR111wyy4oGPycGSN8kNRlJYj22eZFUVRMgG15YqiKOlNtttxnfxQFCXlZPsss6IoSiagtlxRFCW9yXY7HjjgaaIcLayrSmPrJpNw+Ql4KknAONmXy60l2T4UyS3GdTH6LadIQVBThd/gpkECWUnlUtArSRoZJCBpUwZElfZvqoCoQer6Oa9BUf9CpTmh12oZky8FEAvr9lLNlFP5bEHsb76jnAtuSh/QBSRt811B9DipNJf2uLqQNPdMoeV+XRVdkl8uOCwXGA+olyW7pMycFJpzgaH7cG4vXBBUWpdzdUmsm+moLW9dpGJM3hSRlGjw1GT9co0rahz5Fr8uzZJbC5dH0y5XDrufy5WFC14qBqd2lEu2PMLkSbZcIojLtGTLueehy+2FC4jK2X3JBVJqvykDmnJjBCmoOk27xuxB3NqDonZclR+KojQD2T7LrCiKkgmoLVcURUlvst2OZ8TkR2FREX4+aBCiAPbdf3+069ABpw0bBgB4d84cVJVn07sZRVGU9KOoqAiDBg0CAOy///7o0KEDhsXs+Jw5c1CudlxRFKXVY8fkQP2YvH/Mlr83Zw6q1ZYritKCRIzxP/8TjUSMlXlZiRWVV4WRYrnKcxLqJat7QPfumLdqFdvn03r0wPrVqxu05YmWnKR/NF/qi0texpW7InJzeZIEmSJJ3JrKBUaSTAVZDUZyi5Ek1kFWhpEiikursbT0ajBSuetYjXWhCYMxJgIABZGI6RJi/zXAUmPMcY3shpJAJBJplZP+3bt3xyqHHe/RowdWx+x4Y6FS5SKSLmDyCpm6BY5yzq2Flucl1Eusy0mouRVeaL602kuqpdKS7XVJfjkXIZfbi9/VXrio/kC9W0o5k+cql1Z7oX3JdKwdB8LZcrXjqaMpx+R+x9x+xuQHde+Otxy2/BdkTO6yX1xfJbc9qVxaYYVLu1Zgkcoltxbuc3P9o2nXZ+W+l+a05Zw7qGTXaR611dUJ9RLTjXVrsflSX7lnFeB/5Rs/bi/SedUxeWrJCOXHutWrcUjCOuGN9XlTFKVpUP9CxQ+rV69GJKLWWlFaK2rLFT+sW70aP4pEnBMlauUVpeVQOx5i8iNoUKXEepsDKE3SjePJwJ0Lbiqtv06hFyan1pDOouvhIn1vjSWI4oPL8xtIVlr/3VVXaoubeZUCqkoBTaWARVK51L6k9ghSN1V3Z+be9dlJEMVgutGZ2HEu8BlVc3ABTV3l9g1tJZPnSktB9KRy1wsB6W2hX4VgENVaDZN2BZaTguRVMmmaV8aUN1b5kblXfDD0PLQuGjMmz+TxOAD8gthy+km5c8EFscxl8mi+S1kSRNnBtcWlwyrX/RIksD83jvQT8JQLKMopN4I8C7hyV1+k4KxcoFvuuRZW+SEFWNcxefOREcoPRVFaN9luaBVFUTIBteWKoijpTbbb8Waf/LBv1fz6D7qkctwsqOTz5mormvDXVe6nL4qieFGJXeaRKe4p1KZzMT8URalHbXlm0ZlRK0vjZFd5kPE7p2bg4o9IyjVXzBCuXFGUPagdD2gb6AmLkjwLdzKlNcWjQjnFZWitRMkl1bX5tUwewLsf0BPDfS4pYCl3XiRXF1ebdUw5/SzNFeTURZjgp5LbilQuuaJw+ySWc8eqYeoGCajKSdhcbjXN5RZD66RizXA/ZLuhVVon9B6wrg6cTBbgJbuS5JZKnbmAqK50LpMnBdGj5dyPD+7HRaqk0n7lwdK5pGkpCB7nCgPUu7NUMHk0TfPo/ooXteWth1SMyV11g7QljZkt0piYc1Wh7bs+azRJHsDbT9p/m8+5utBy7neI61iuiRq/AU1dbol+F1xwIZ1LbszNuQhxeYlp6Rlq811uLX7LaZ7UV65uUwYpldxedEzeMujEqKIoKSfbJXaKoiiZgNpyRVGU9Cbb7bhOfiiKknKy3dAqiqJkAmrLFUVR0ptst+OBJz+SyeQ4WZrLlSOMC4yrXJJ1cautuOR0Fk7CRvdxtcWV+5UjBnGLccGdC78SRD/4vWFckirJLYXL87sCjCtfkvMFWQ2msau5SLK3xpa7ZHOpWDPcL+pfqKQTLkltIVNOV3bJZ8qp20olk+dKWxeWXCaP5rvcXji3liCrvfjFZZtqmHJOakzz6HnjVgMIstpLBVNe7ii3+dk+GPSD2vLWRyrH5BRpzMi5pbtcQSyuMbXFtZqUbd81drNtudxeOFd0ya2Fc4sJ4vYixS+R3F5ccQbD2HLuHpbGuZILo6ucc0txub1I7pB+V2txub1w5dIKLFxaWiFScnXxUzeVqB1X5YeiKM1AthtaRVGUTEBtuaIoSnqT7XY88ORHYhBI1wnk1tGW1tkOU+7qAzeLGTaIKfdZg6g0OOUJhVOGSAFNXTO7tUJ5qpFuKOm74vIkFYik/JCCo6ZKOSIpN6SZ38aWS8FXmxN9q6qkI/S6tQoBV+BNq9zg1CA0LQU5pWlO7UHzXW8L/QbRcz2TuHzJTgcJ/Ca9weOCn0oBT6uZPKD+e6PKEOktt+JGbXnrojFjcgq3MIBU7hqT+x2HSsoP1zhbGtNLam1O+VHH1JXsKxdE2pV2Ldhg83OZPHoMSfkRdswfZpwcRPkRRJnhNyCpK6Aq99ypZsqlzyKVBxlnuwIBc201J9lux1X5oShKSjFQQ6soipLuqC1XFEVJb9SO6+SHoijNQLZL7BRFUTIBteWKoijpTbbb8dCTH1ayE2Qd8FSUA/USsDBBTOl+riCmNl+S4FGk9hGgXAoUxSG5zTQlUl/8Bjn1U1cq5+oGCVjalG4zft1eGiuh8yOxa+lZ3pY+vqI0FfQeKyNp61bhCnhq09SVxZW2dV0BTbkgepLbC32OcM80MOWu+9avSyDNCxPYDpDdXmyaC3JKj6U0DWrLWydhxuScC0uQcmlM7kJyJU+sl9imrUttBu2XX1d12ia1Oda+ckFO6X4utxcu3+X2IgUs5Y7Fnd/GLmIQNrC/5ArC2X0poCjnKkPzpb7Q/aWAplK5FPxVcmXRMXnrRJUfiqKkFI0srSiKkv6oLVcURUlv1I7r5IeiKM1Ats8yK4qiZAJqyxVFUdKbbLfjjZ78cMnCUuHi4nJLsftJEjzJbcVVzkmApTW1pfZddbn2pcjZQeRujV0Fxu9soXRjSW4vrnJp1ZIgq7Vwx0qF24y0WksQtxZJYteaZHWUbJ9lVjIfez9SV5hykuZWe+FcXQDerSWM24tLKu13tRfJzkouf1wkflreFKu92HRrsneZjNry1k1Ljckp0sowkttdsn1ovuSqTsu5lWFc9rGOyQviqsKlg9jiIC6KXJsSYVZNDLuyF1c3iNsKV1dyO5HcWlzHl9xamqq8NZDtdlyVH4qipJzWZPQVRVGUcKgtVxRFSW+y3Y436eQHN+MsrTkeNohpEGWIX+WG6w26tGa4pNzg9qdIAVOTHSfZcTlSHfwtjOKDywv7xpFrVwrKxwUx9bO/X+VHWGVHmFnm1oj6FyrZCr23KxP+At5nWhDlBxfQlHsz6QrIl1gvKH5VaUEC23FqD1qntdu5bEBteXrRnGNy7g26NCZ3ESZgqaQsqRbKJbVFLVOXHl9SgUjKDklF01jVtovGBvaXFM5BVCLcmFbaP4yyozmVH60RteOq/FAUpRnI9llmRVGUTEBtuaIoSnqT7XZcJz8URUk52T7LrCiKkgmoLVcURUlvst2OB5382AxgtZ+KrV32oyhKSulO0q8D6Byijc1N1BfFi287rqQW+pwsd9ZSlBaje8L/YWy52vHUoWNyRVH8oGNyQsSYbBe/KIqiKIqiKIqiKIqSyaQqho6iKIqiKIqiKIqiKEqrQCc/FEVRFEVRFEVRFEXJaHTyQ1EURVEURVEURVGUjEYnPxRFURRFURRFURRFyWh08kNRFEVRFEVRFEVRlIxGJz8URVEURVEURVEURclodPJDURRFURRFURRFUZSMRic/FEVRFEVRFEVRFEXJaHTyQ1EURVEURVEURVGUjEYnPxRFURRFURRFURRFyWh08kNRFEVRFEVRFEVRlIwmN0jlSCRiUtWRpibiMy9ZflPt39j2G1s3zH5h2w9DU15UjW0ryP5hjiXtw5U3RZ8a224YjDERADj99NPN5s2bA++/dOnS140xpzd5x7KcMHY8iO2T8qS2gpQHOVaq20+WF+T4ftryu7+LprJDkr0xTJ5rfykdtlxqP1mei8b2L52wdhwIZ8vVjqeOaCRiEt9gNtb+uZD2l9psrK0OUx6kLxKNbd91/zfVWDuMTaf5YceOjd0/TF+CHEv6rqTPUie0Lz1XXHX9ljf2e/GU65g8TqDJj9YEvXijTF5OI8vpA8Xmc/vQci7P1T5XLu0PH+XSA0o6VrJjJms3GdINXyeUc/u79uHyJeMhGTdXeZ1Qnmwf1/FrA/SvLkmen7aCHKsxbN68GR9++GHg/SKRSOcm6oISkpyEvwBvR3OYvCDlNJ3nKM9l8ri6uY7yKJPHteXqH3f8IJ/Vtu96DnHPF9d5D2KzOTjbIdmWGpKuTahH8wCgmsmrYdJcHt2f5nN5NN+1fy1TTvtVlyQP4AfE3Gel7Vcx5TQvXSdCwthyteOpIwqgXSxt7RO1b9QWcvZVsuW0nLN/0jiTO76rL5zd5uwvTReQvHymPMrk0XZpOb3npd8MNG3Jc6RtW9QmUbi2wsCN52jaZd9qmXIu7SrnbLnrucDV5exqkL64jmXhrlH6/XD9qyR5NG2/wzomz9W/IOP/IOeCe25xaXquLTomT+PJD9dAxMJ94Upmkgfgf2LpIBMlnPGRyqVBMXfcIMaPSzfn5If0UJHuOx4D1OkdmY5wD2BFaa3kAfgpvAMbbvJEGlBLdpa2xQ2upUn4akd+60dteWsjAmAH0u06UpTWQT6AU3zWtRNZ1L5T+8+Nk6Xxu/TckSY3XGNym7aT7t7JN7XjaTv5oSgW14y60kowyHpDqyhK6tFnQYpRW94q0YkPRQlHlVwl81A7rpMfSmaQx+Rxb9/CuKi4ZnGT7ePaP0j7Nt8lEZRkc5ykUyp3+UJKM97JzajOMiuK0jwUwWvbOIk75wLjesPG2Vlq+6oT6rnSdLBF20ovy6i2vLWhqxYoSuNI/CFc5yjjFNKcG5lLjWGYui7XK05tG2XSkirXPv9U+eFFJz8URUkxamgVRVHSH7XliqIo6Y3acZ38UBQltRg1tIqiKGmP2nJFUZT0Ru140ynmSkpKsGnTpqZqrsmZN28epk2bFv8/bH+feuopLFmyJGmdvLw8lJSU4KijjgrcfjrQvXt3GGNwxhlnNMvxzjjjDBhj0L17d2edXGbLi205ZMtjNlrOtcO1mbgft9l9aF6UbNL+XJ+5tvy20xRb1LHZcp6YoQ26KS3G6NGj8eGHH2Lnzp3YunUrPvroI9x7770t3a0mY+XKlbj77rvj//ux6xyJzxWOffbZByUlJUntVzrTr18/GGPQq1evZjneZZddBmPcUQ4KABQG2PKFjXsOcPZZena4bLotTw9C2HIlZUTgb7WQFStWwBiDnj17NigbPXo0jDFo27YtgOYf32UjI0aMwOjRo1u6Gy1G4vM3lbRt2xbGmKTnO9mYmxv/S3bd9ZuisePvCNnC7FuPjsmz1l3wySefxIABA1LSdn5+PsaPH4+jjz46Je0rSlph1NCmE2PHjsWTTz6J119/HUOHDsW5556LV155BYMHD27prqWMW2+9Feedd15K2t53330xfvx49OjRIyXtK0qzEcaWKy1K3759cfDBBwMARo0a1cK9UQBg5MiRKXveKIqIjsnT6IVDE7Nu3TqsW7eupbuhKNlBhhnOTObyyy/HY489hhtvvDGeN3v2bEyYMKHZ+lBQUIDKyspmO96KFSua7ViKktaoLU8rRo0ahd27d2P58uUYNWoUbrvttpbukqIoLU2W2/FmVX506tQJjz32GDZs2IDy8nIsXLgQffr08dQxxuDKK6/E7bffjo0bN+K7777Dn//8Z+Tn58fr7LXXXnjiiSewbt06lJeXY/Xq1Xj88ccD9YVzeznyyCOxcOFClJeXY/ny5Rg4cCCWLFmCp556qsH+/fv3xyeffILdu3fj3XffxeGHHx4v2717NwDg6aefhjFGdNk46KCD8Pzzz2PTpk0oLS3FJ5984pmh33vvvfH0009j8+bNKC0txbx583Dsscd62rAysquvvhpr167F1q1b8cILL2CvvfYCALRp0wa7d+/GpZde2uD4ixcvxrPPPhv//6ijjsKbb76J0tJSbN26Fc899xz23XdfZ/+feuopLF68uEH+pZdeitLSUrRr1w4AEIlEcMMNN+Crr75CRUUFvvjiC5x77rkN9ispKcF3332HnTt34plnnkGHDh2cx7b4dWHhyiUXmLBuK8mOmRuyTU6O11i3mqbYrBSPRWeZ04qOHTtiw4YNYj0/dskYg8suu8yTl2h7rey6d+/emDdvHsrKyjBmzBgAQGFhISZOnIhVq1ahoqICK1aswB133OFp74ILLsDy5ctRUVGBVatWxfcNAuf20q9fP3zyyScoLy/H4sWL0bt3b2zatAklJSUN9h81ahS++uor7NixA3PmzMH+++8PYI+EfPny5QCA+fPnx58HyTjyyCMxc+ZMbNu2Dbt27cKiRYvQv3//eHmPHj0wffp07NixAzt37sTMmTMbyNml52iPHj1gjMGgQYM8+0WjUaxfvx633nprPO/kk0/GBx98gPLycmzYsAF/+ctf4hJ5jnnz5mHq1KkN8idNmoTVq1fH/y8oKMDEiROxZs0aVFRU4F//+hcGDhzo2Sc/Px8PPfQQtm3bhi1btuC+++5DXh63tlc9hdiz4ovdCmIbdWWhdpRzdeHKXVJozjZLG7c/zWu1qPKj1eF87mLP/Txy5EjMnDkTkydPxuGHH44f//jHTXJcyTZHo1GUlJRg9erVqKioiE++UKzdHTRoED777DOUlpZi9uzZ6NSpE3r27Im3334bu3fvxpIlS3DkkUd69jXG4JprrsEDDzyALVu2YNu2bXjwwQcb2AdpPGvdfEaMGIFHH30U27dvx9q1azF+/HhEIt6z26tXL8yePRs7d+7Ezp07MXXqVOy3337xcusC2K9fP0ydOhW7du3Cf//7X1xyySWezzx8+HCcdNJJ8eeBfaaccMIJeOedd7Bjxw7s2LEDH3/8MYYPH95s30Oy3zV+7bqfcQFl9OjRqKysjP9WsRx++OEwxuDUU0+N5w0ePBhLlixBeXk51q9fj4kTJyI312sxhw4dii+++AJlZWVYsGABfvSjHyU9f0Dw8a4f18YwdcO6wCRzhWHH5jomb77Jj/z8fLz55pvo378/xowZg7POOgubNm3Cm2++6TEeAHDttdeiW7du+M1vfoO7774bv//973HVVVfFy++77z6ceOKJuOaaazBgwACMGzdOHFBKFBUV4fXXX0dRUVF8dvz+++/HQQcd1KDuQQcdhLvvvhu33347Ro0ahX333RdTpkyJl5988skA9kip+/bti759+2L9+vXscffZZx+8//776N27N6677jqceeaZ+Otf/4oDDzwwXmfGjBkYMGAArrvuOhQXFyMajWLevHkNBrwjR47Eqaeeiosuugg33HADfvnLX8aNYFlZGWbPno2RI0d69jn44IPRu3dvvPjiiwCAzp07Y/78+WjTpg3OOeccXHHFFejXrx/eeOMN56BzypQp6N27dwNZd3FxMebMmROfDHrooYdw00034fHHH8cZZ5yB6dOnY/LkyR7f0iuvvBI333wzHn/8cQwfPhzl5eWYNGkSe1wlXVBDm0589NFHuOKKK3Duuefie9/7nrOeX7vklxdeeAGzZs3CoEGDMHv2bADAK6+8gksuuQR/+ctfMGjQIJSUlKBz587xfa677jo88sgjmDFjBn75y1/ikUcewa233tpgwiUo3bp1w5w5c7Bx40YMHz4cjz32GP7+97+jqKioQd2f/OQnuPzyy3Httdfioosuwv/8z//EJ+PXr1+Pc845B8CeyWD7PHBx6KGHYuHChejatSsuvvhinH322Zg+fXr8eZCfn4+33noLhx12GC688EKcd955OPjgg7FgwQJ06tTJ01ay5+iqVauwaNGiBs+Dfv36oUuXLvHnweGHH47XXnsNmzdvxrBhw1BSUoJzzjkHL730kvMzTJkyBYMGDUKbNm08+SNHjvQMnl966SWcd955uOOOO3DmmWdiyZIlmDlzpidW1l133YXf/e53uPXWW/HrX/8a3bt3x7XXXus8tpJqdPIjnTj55JPj9/NLL72EqqqqJnN9kWzzLbfcghtvvBGPP/44Bg8ejIULF+L555/Hr371K087Bx10EG655RbcdNNNuOiii/DTn/4Ujz/+OF588UW8+OKLGD58OHJzc+M2iXLttdfigAMOwK9//WvcdtttuOiii3D77bfHy4OMZydNmoTdu3dj+PDheO6551BSUuKZeOjZsycWLlyIwsJC/OY3v8F5552HXr16YdasWQ369cQTT+CTTz7B2Wefjfnz5+Phhx9G7969Aez5XfD222/jo48+ij8PnnzySbRv3x6zZ8/GihUrMGzYMAwfPhzPPvssOnbs2GzfQ7LfNX7tetBxwYwZM2CMwdlnn+3JLy4uxoYNGzBv3jwAe+KkvPzyy1i8eDEGDx6MCRMm4KKLLsKdd94Z3+eYY47BlClT8Mknn2Do0KGYNWsWO2Gj6Jg8PvPoZ8OeZYrZraSkxGzatMlZfv7555vKykrzgx/8IJ6Xk5Njvv76azNp0qR4njHGLFiwwLPv9OnTzfvvvx//f9myZebyyy93Hovb5s2bZ6ZNm+bs76WXXmoqKytNt27d4nm9e/c2xhjz1FNPxfOeeuopU11d7fkcQ4YMMcYYc+ihhxoApm3btsYYY0aPHi3264477jC7d+82Xbp0YcsHDBhgjDHm5z//eTyvTZs2ZuPGjebRRx+N561cudJ8/fXXJicnJ553//33m/Xr18f/P+uss0xNTY3p2rVrPG/s2LFmy5YtJjc31wAwd955p9m2bZtp3759vE6fPn2MMcb86le/MgBM9+7djTHGnHHGGfHvcdOmTeaGG26I79OtWzdTW1trhg0bZgCYnj17mtraWnPuued6Pt8zzzxjFi9ebACYaDRq1q1bZx5++GFPnblz5xpjjOnevbvzPJ7ObL+Ibac5tlNj28lkO4ls/ZjtZ2Q7Mbb9lGx9yfaT2NaHbMeR7djYdjTZfky2I2JbL7IdTrZDY9sPyfYDsvUgW/fYdgDZ9idbl9i2L9n2JlvH2LYX2dqQLT+20e/E2o1jjzrcmO+WBd4AfBjEPunWeDsOwBx55JHmv//9rzHGmNraWrN8+XIzYcIEj03wa5eMMeayyy5L+qwYPXq0McaYK6+80lPvF7/4hTHGmDPPPJPtZ/v27c2uXbvMzTff7MmfMGGCWb9+vYlGo87PuHLlSnP33Xd77PqSJUvi/0+aNMls2rTJFBYWxvNGjBhhjDGmpKTE81zZvn276dixYzzvqquuMsaY+L69evUyxhjTr18/8Xnw/PPPm7Vr13qOS7ff//73prq62hx88MHxvP33399UVlaasWPHes679By9+uqrzbZt20x+fn4879FHHzXLli2L///CCy+YL7/80nMu7Xno27evAWD69etnjDGmV69eBoDp3Lmzqa6uNsXFxfF9+vbta4wx5thjjzUAzCmnnNLg+gFgFixYYKZOnWoAmO9973umrKzMXH/99fHySCRi/vOf/xiz50Jmt/MAM4psw2PbYLINJJt9HlDbfyLZrD2ndvxYsll7/SOyfZ9s1t5S29qRbO1iWwHZcskWIZt0/aRio7YjjC1XO566LRd7ntuu7+7JJ580W7duNXl5eQaAmTVrllm5cqWnjrW/bdu2NUDD8R23Sba5U6dOZvfu3Q1s8z//+U/z+eefe+xudXW1+f73vx/PmzhxojHGmP/93/+N5w0cONAYY8yPfvQjj437z3/+YyKRSDxv3LhxprS01HTq1MkAwcazzzzzjKevH3/8sXnhhRfi///tb38zn3/+efxcAjA/+MEPTE1NjRk0aJDHFk6YMCFeJzc312zcuNHceeed8bxp06aZefPmeY537LHHGmOMadeune97XFDEDgAAIABJREFUs6m/h2S/a/zY9SC/V+jzd8aMGebVV1/19PHzzz83Dz30UPz/VatWmcmTJ3vq/Pa3vzVlZWXme9/7ngFgpkyZYj777DNPnXHjxhljkv8eK45tZ8e2X5LtF2SzvxP6kY2O/+2YnxvnHwuYY8hmx/xHgB/r22cJHd/T50p3NBzTdyVb4rMmB/W2XMfkpvmUH/3798fSpUuxcuVK5OTkICcnBwCwYMECHHfccZ66c+fO9fz/73//GwcccED8/3/9618YM2YMLrnkEvzwhz9skv717t0bS5cuxbfffhvPW7JkCSv/XrVqFb7++mtP/wB4+uiXU045Ba+99ppTZt6nTx989913eOedd+J5VsVx4okneurOmzcPtbW1nn7tu+++cVnYq6++it27d2PEiBHxOsXFxZg+fTpqamrix5s7dy527doVr7N48WKsXLmywfEstbW1ePnll1FcXBzPGzFiBEpLS/HPf/4TAHDqqaeirq4O06dPj3//OTk5eOutt3D00UcjGo3iwAMPRLdu3fDKK6942n/55ZfdJ1Bp/Rijs8xpxLJly3DYYYfhzDPPxMMPP4xIJIKbb74ZH374YdzdIYhd8ou1FZZTTjkFW7ZsYd+sAcDxxx+Pdu3aYdq0aR6b8vbbb6NLly6h7LGld+/eeOONN1BRURHPmzlzJlt3yZIl2L59e/x/+zywri9BOOWUUzBlyhTPcSl9+vTBRx99hJUrV8bz1q1bh4ULFzY479JzdOrUqejQoQNOP/10AEBOTg6GDh3qedvXp08fTJ8+HXV1dfG8f/zjH6iurnZ+z5s3b8bbb7/teR4UFxfj66+/xtKlSwHsGQ+sX78eCxcubPA8sOOBI488EkVFRZ7ngTGmwfNBaUbC2HKlRcjLy8PQoUMxffp0VFdXAwBefPFF9OjRA8cff3yj2pZs8xFHHIG2bds2WAlrypQpOPTQQz3KhFWrVnliLtmx9dtvv90gL9GmvvLKKzDGxP9/+eWX0aZNGxxxxBEAgo1nJXvZv3//uC209mrlypVYtWpV0t8wNTU1+Oqrr8Tn0X//+1/s2rULzz//PAYPHtzADYSjqb+HZL9r/Nj1sOOCKVOm4NRTT40rTY866igceuih8WfRIYccgu7du2Pq1KkNnvVFRUWe7zvxOa2/Hxh0TN58kx+dO3fG8ccfj5qaGs92/vnne1w8AHgGkgBQVVWFwsLC+P+XX345ZsyYgZtvvhlffvklvvzyS88NGYYuXbqwS99yeVz/AHj66Je9997b6RIDAF27dsXGjRsb5H/33XcNJOlcv6LRKAoKCgAAlZWVeOWVV+Ln6pBDDsHRRx/tkRN27doV3333na/jUV588UUcc8wx8cmo4uJizJw5Mz6I79y5M3Jzc7Fz507P9//MM88gLy8PXbt2RZcuXQCgweflPr+SZmS5oU03qqqqMHv2bFxxxRXo1asXLrjgAhxyyCG44IILAASzS35JtDuSbbQDt3//+98emzJ//nwAaPBcCQL3PKisrPQMoi3N/Tzwa5+l5+i3336L9957L/48OPXUU7HPPvuIz4O6ujps2bJFfB4MHDgQ7du3RyQSwYgRIzyTKp07d0bXrl0bjAcmTJgQ/970edBKUTueFgwcOBCdOnXCnDlzsNdee2GvvfbC/PnzUVFR0WjXFz92Cmho0+3/1Ha47CfNd9lUl22wx29Ke9m5c2eMHTu2gc3q2bNn4N8wHNu3b8dpp52GvLw8TJ06FZs2bcLs2bPjK/VwNMf3QPst2fWw44KZM2eiuroaw4YNA7Dn98PatWvx3nvvAah/1r/66quec79q1SoA8Dwz9HnhkywfkzdbbK2tW7diyZIlnsA/lqBR/Xfs2IGrrroKV111FY488khcf/31+Pvf/45PP/0U//nPf0L1b8OGDTj00EMb5O+zzz6h2vPLli1b4gaKY/369Wyw0f322w9bt24NfLwpU6Zg1qxZOPDAA1FcXIyNGzd6ZtiTHc/O7nIsWLAAGzZsQHFxMf72t7/h+OOP9/jibd26FdXV1TjhhBM8bxEtGzdujCtUEo+fLNiqxV7IhuRxgcC4ctcMYF3C38Q2DZNH25L2txqdHEe5bYvuX0vSdj+uTcB7czc8495zwR2fgzu+q636QpNxhjPdsd9dbdJa9UyePBmTJk2KBw/za5cqKio8waoBNIhNYaFv8ADZNtrjnHHGGewA94svvnDuK7Fhw4YGtr+goADt27cP3aYf/DwPevXq1SC/Mc+Du+66C4WFhSguLsZHH33kefvHfc/RaBR777130uNNnz4djzzyCIYMGYLVq1dj//339wySt27dim+++QZnnXWWsw2rhtx3332xbdu2eL70PCgAb0dpHrVdVUw5V7faUW6hNtYw+cZRzuF6Jtn7Vdo/Zagtb1XYIIccdoKDi88zYsQIXH311exYzA9+7BSw516ldsLG9wtjqzhcY0V7/LDjWY6tW7di+vTpePLJJxuUbd68OVBbLhYtWoSBAweisLAQ/fv3x3333Yfnn3/eqdRp7u9Bsuthf69YlXhxcTGeeOIJjBw50qNWsfteeOGF+Pjjjxvsb5WQGzZsCPX7wdpbey/Ru4Ib59Y4yjlcv0k4Gy7djdxzxXV8W5d9lqgdbz7lx1tvvYUf/OAHWLNmDZYuXerZbDT8MCxbtgxjxoxBTk6Or6i+LpYsWYJjjz0W3bp1i+f17t07/vYpCEHe/L311lsYMGCA8wZdtGgR9ttvP/zsZz+L5xUVFeGMM86Iz4oGYe7cudi+fTtGjhyJ4uJivPTSS54H4KJFizBgwID4Ci0AcNxxx+Hggw9Oery6ujpMmzYNxcXFGDlyJLZt24bXXnstXv72228jJycHe+21V4Pvf+nSpaiursbatWuxfv16DBkyxNP20KFDA39OpTWhErt0gpvw7dy5M/baa6/4JINfu/TNN9/gsMMOi/8fiUQ80duT8dZbb2Hvvff2BESmvP/++ygrK0O3bt1Ym2IDLYdhyZIlOO200zw2fPDgwaHaCvo8GDlyZFytl8iiRYtw7LHHeoJLd+vWDT/96U9DPQ+mTZuGoqIinH322Tj77LMbBBVctGgRzj77bESj9UOFoUOHIi8vL+nxtm/fjrlz56K4uBjFxcX497//jWXLlnk+Z5cuXbB79272uwP2PNvLy8s9z4NIJNLg+aA0J+r2kg60adMGZ555Jp5//nmcdNJJnu2aa65Bly5dcMopp4RuX7LNy5cvR2lpqcfFGtgTHPOLL75ossmCIUOGeFZkGTp0KMrKyuK/KcKOZzneeust9OrVi7VXdLUTP0hKkIqKCsyePTu+Qk+yPjXn9yDZ9cb8XnnxxRfRr18//PKXv0TPnj09z6IvvvgC33zzDXr06MGefzs5smTJkgbPaf39wKFj8iZVfuTn58dlS5QFCxbgb3/7Gy6++GLMnz8f99xzD1asWIG9994bffr0wYYNG/DAAw/4Ps67776L6dOnY/ny5TDG4MILL8Tu3bvZ5Vb98tRTT+Gmm27C7NmzMWHCBBQVFWHChAnYuHFj4Nnx6upqrFixAiNHjowvwfjpp5/G/S4p999/P84991y8++67uP3227F27VocdthhaNu2Le6++27MnTsXCxcuxJQpUzB27Fhs2bIF1113HYqKinD33XcH/pw1NTV4+eWX8Yc//AHdunVrsPTtfffdh0suuQSvv/46Jk6ciHbt2uGuu+7Cp59+in/84x9J254yZQquuOIKXHPNNZgxY4bn83755Zd49NFH8eKLL2LSpEn48MMPUVhYiF69euGQQw7BhRdeiLq6OkyaNAn33HMPNm/ejHfffRfDhg3z/HhS0hGdZU4nli1bhldeeQVz587Fxo0b0b17d1x33XUoKyvDM888AwC+7dL06dNx2WWX4eOPP8aKFSvwu9/9ztfS1QDwxhtv4LXXXsPzzz+PW265BR999BG6du2Kn//857j44ouxY8cOjB8/Hn/605/QvXt3vPPOO4hGozjkkENw8sknN2rQ88ADD+Cyyy7DrFmzcP/996NLly4YO3YsSktLAz8P1qxZg7KyMowePRo7duxAdXW1863jhAkTsGTJErzzzju49957sWXLFhxzzDHYsmULnnrqKTz99NO44YYb8Oqrr+Lmm29GbW0tSkpKsHnzZjz22GOBP+emTZviz+ROnTo1iIx/22234eOPP8aMGTPwyCOP4IADDsDEiRPx2muv4YMPPkja9pQpUzB58mTs2LEDf/7znz1lb7zxBl5//XW88cYbmDhxIj777DN06NABRx99NAoLCzFu3Dhs3boVjz/+OCZMmICamhp89tlnuPDCCz0/ZJTmRm15OjBkyBC0bdsWf/rTnxqMixcuXIgbb7wRo0aNwptvvhmqfck2b9u2DQ888ABuuukm1NTU4MMPP8TQoUNxxhlnNFhlpDG0b98e06ZNwxNPPIFevXrhj3/8I/7yl7/ElWKNGc8mMn78eCxevBj//Oc/MXnyZGzevBn7778/TjvtNDz99NNYsGCB77Y+//xzDBkyBEOGDME333yDb7/9FscccwzOP/98zJgxA2vWrMH++++P3//+9x5ldiIt8T0ks+uN+b0yZ84clJWV4bHHHsOKFSs8S88bY3Dttdfi2WefRYcOHfDqq6+iqqoK3//+93HWWWfFV4acOHEiFi1ahKlTp+Kvf/0rjjjiiLirrkJRO96kkx8dOnRgJXYnnXQSFixYgJNPPhm33HILJkyYgP322w8bN27E4sWLnYHkXLz//vs477zz0KNHD9TW1uLjjz/GwIEDsW7dutB9Ly8vx+mnn45HHnkEU6ZMwapVq3D99ddj0qRJ2LlzZ+D2Lr74Ytxzzz148803UVhYiB49erCzw5s3b8YJJ5yASZMm4YEHHkBBQQG++uorj8vIWWf9f/beP1q2s6zzfPapqnPvTSK2GSCCyKJtccbV2msxwzj0ijpGpxscp1F00RClbTsjLB21O1EXStKSGH6E3w2orIFF0rQrbSKZQaUdRmFsoBHtETL4i7CMSGskQQMJ4ebHvfecqrPnj7Pfqu/e9X3q2e+uqnNqn/p+1rrrvPd996/atfez33r39/u832NvfOMb7c1vfrOdPn3a/uAP/sC+/du/3f7iL/6i02e9/fbb7Yd/+Ift3nvvtY985CNzx3PFFVfYG9/4Rrvttttsb2/P3ve+99k111xDB2+Qj370o3bPPffYU5/6VDot2Y/92I/Z3XffbS9+8YvtxhtvtLNnz9pdd91lN99883SZN7/5zXbppZfaj/zIj9jVV19t733ve+2lL32p/cqv/MrCfSfpV0nqPDlyF1vMASl7UrZo/eiYImlzjkSO4VlY2PpsW+y4C9ImNovCZoGf2V5uvPFG++7v/m5761vfapdeeqn9zd/8jf3e7/2eveAFL5h6bM3axaWf//mftyc+8Yn2yle+0vb29uwXf/EX7ZOf/GTrqWif97zn2Ste8Qq7+uqr7QlPeILdd999tVjw+te/3u677z675ppr7Kd+6qfs/Pnzdvfdd9ekuF2477777Lu+67vsLW95i73nPe+xT33qU3bVVVfZBz7wgeznwYULF+zFL36xXX/99fbhD3/Ydnd3a28rkbvvvtu++Zu/2V7zmtdM5dV33XWXXXvttWZ2+MYwyaFvvvlmK4rCPvShD9n3fd/31awhOdx+++32zne+037/939/7hl111132Xd+53faq1/9anvPe95jZ8+etdtuu81e+tKXhtv9jd/4DRuPx3N5RBLf+73fa9dee61dffXV9tSnPtUefPBB+8M//EP7hV/4hekyL33pS200GtnLX/5yOzg4sFtvvdXe9KY32Zve9CZ3v2esHs9SGeMws7B49sUJaWfdxhzbS4RnP0zHhfdtW+uaOHkUZjYi9VdeeaXdfffd9IXgeDy2d7/73fb93//91Ibelig2v/zlL7fxeGw/+qM/apdddpl9+tOfth/4gR9YOjYjb3zjG+1rvuZr7LbbbrOdnR27+eabp7HSbLn+bJM///M/t2c961n2yle+0t7xjnfYmTNn7N5777Xf+Z3fqVkF2/C2t73NnvGMZ9gtt9xil156qd1www122223WVmW9upXv9qe+MQnTnN+4OdhHPX3EMX1rr9Xzp8/b+9973vtRS96Ue33T+Ld7363nT171q699lq76qqrbDKZ2Gc+8xn7zd/8zam68s4777QXvvCFdtNNN9mv//qv28c//nF7wQteUBtIYTDbvJG6qJ0R2eaj9fBZxPrpngWnbNRFlvZto2j6rBcufDil1NbwtKc9ze6++257yUteYu9617uO+3DEApIQOvLRsU6pBe3eOpGXO1qf+bfZsU6cdpZTxPusE9I+Ict6nWq2Pgbd1I04B9sry7IwM3vmN35d+fHfeJvlUvy9f3RnWZbPjJcUOewURZmycORlW9puLr/8cvvd3/1du+KKK6ZJVcXmcbXN8ngYlPFa3yftuA7GthT79p32VM/22Wb7qR63P3GWnZD2dQ9+pDhu1i2WK46vj1NFUT7ZzP7yuA/kGCjL0n78x3/cfumXfum4D0X0nH9W/U0xmMVnLGPMZXHb67Oz3wpeLE/H4PXZJ43lmttP9emYv2hm++qTTzmyhKd94Gd/9mftvvvus7/6q7+ypz71qfayl73MPv/5z2fL48TRs0i54I22tlWGeMoMlpCUjfJ2HRmOEqIyvOSnUVIm9nYzWgfbm8mX5t6MbrnEbpPAt4Ua/PB5zWteY5/4xCemybB/7ud+zv7oj/4oS94sjp7TxuM0vkFjyU09Zci4sdyi8qrwkrHlJKReG4rlG8OOmV183AchRM9p/hZgvxPMuv1g9pTfrH/PyIm2TAXibn/L47gGP4CyLO3666+3Jz/5yXbhwgX7yEc+Yj/90z9NpzcUQrREmaVFDzl16pS9/vWvt8suu8wefvhhe//7328/+ZM/OTcrjRBbg2K5EEL0G8VxDX4gr33ta+21r33tcR+GECcMBVrRP6655hq75pprjvswhNggFMvFZuDlTRJCRCiOa/BDnAiSjJ9ZWLxHZGSLiWwvbH0mcfPsJ0xuzWRr0XvmaH08hkhih+Bxs6SAi6Tl9XOuQLtJYMJTIU4ap4zHpiiJqGeLYeszW4qX+6kM2hl5qRjnWZ/9RbF8kyjMbDdcSgixiEX9oby53ebpOnEBS3jKfot4ffr9xnLqk9dRH1gIsV4ksRNCiP6jWC6EEP1GcVyDH6L/nD7uAxAxWx5oN5XCNC2xODko+eMRoFi+cQwtLzGiEOKQM8d9AMfFlsfxzoMfSUKDkjuU34xIHSt7s0uwdjxYth7Kg1h54LRHx8Jkr5F9Ido+k9h2ld1Gy+Zkvo+ONSKa/rW5nIf3g4ytF82jzWYlwfZV2l66zOyCEuUhWTayzXjrMwtMNAPMQdCOsPNCpeUaZd440nf7+Mb/vXIUp0dQh/Fi2PjbLI9I/Smo2yVlXOc0KbM6s1kn5wypw/LFQfsZ+ABnLpmVT1UrnoENnLoIjqWqPw0b3YXyED7YTtqHdxNWNzLeVhO4ufcvzLfvg5dir5qTeh/m8RtD+/jC/DpjWDZtH9fZvzC/LNo32Awq3pSCOVPRsqlsc55ZC2OXxbaZTcSbwWxpFMs3ijRz139rvG/H+rEsPqdtNddh60fPCtyuN1PGqPG3WU7PgF1SZzaL61h3JqN9GsshVmP54sdV60PdaYjlF3/54d+LngQbfRqc2af8Q1j4ssO/n7tzVve5v5qVv1D9PTerOoBgluL6HrTX4m6KtRec9hSrnVjP2vfZswBuezbVN6vLXXZvyfYxqWPTw5ZOO4P95sjp83vRkj1PvFlmFsHs6bg+3Y7iuJQfQoh1o0ArhBD9R7FcCCH6jeJ458EPprZgb6U9BUI0ypzaI7UHHkOkvIiOdZXKkGWVH5GyxHsLFalQom2xc5Xzxiut1zWJz6I671i8ebRZXY5yo4vygykrvO993FiuuS9GpJiJVCAsCaoZv98wNKbj9kamJ43l6ijQbipRnGZ1LLZEsS+KzWbx28JIGZLqmdoDy1jHVB7YfhG2VztDtceZL4P9n5lvP33xfDu+QdzBneGHiV4LVa/A8FxOyCusAwgY5bKZ2wjFzuLyAPdv82Ws8zojq1Iu5KgZcxKmJjy1xQFpZ+Vofaz3lh2Q9omzbDcUyzeJwub7v8v2qSNlRxvlRyp7Kr/dxl+vndVhOVL51eI33PQpLp+C+EtVfBjLYdmLKmWIfQXs4AnfMCt/7XOg/u9XG4WHxfi3ZuX9v63+wrZAxcHiNovrpRPr28Z9jN87WK6+xMGanyVI9PsjUrZj3Zgs6/XpGeyjepMMsHW8PjXbL67H+tzsuRA9y9jvEMVxKT+EEOtGEjshhOg/iuVCCNFvFMc1+CGEWDcKtEII0X8Uy4UQot8ojnce/Ijkzm2TI3nypiGp82wpqbyuhKhtbTXL2l68/TNpeldby7K2mIgoyeigsVyTyLYSScHYfj1bTCova3vxiGwx6Xr1JMpcrtYeti9mZfHWYdYkT27YlMTWjrm0rQ+0fSJHGh/Z0NhyTIqN9UNn2SSBZvJnLC+b8JRZXcxmEmi0utQsMCmhqZPwdLfy5dSsLujhYdmOMTiQLG+Y5I6V90mSUrNZwjuWpNRsZqGp1YEsO8meJ+P5OiSyL3rXRRs7zKrpmuSbwZ5PUZxHib+XeDqyvaR671xGyf1CFMs3igMzO1+VmW2Q9alZf8qMW9Eiqxri3cttYX1mz/ZyqvG3WabJrzG5aRW3a7YXEtfR6oLJT6d+yZqH8vGz8iVfOSs/7inzdbuwsXTiMKEoSUi658Tq1I62R1w/xWhWZwaxHNaP7JKsHxj9/lklXeMzmyQgsp+w5w86lKKJAyIruvdbLOpzR+eArV9r3PI4LuWHEGLNaJRZCCH6j2K5EEL0G8VxDX4IIdaMAq0QQvQfxXIhhOg3iuNLz/YSSZgjq0lkv4isKGbtLTI5s8Hk2GLWYXthy3pWlC4WmZyZZ6KZIKLZWtquY8az1UeyX4TN7OIdC5uZJmd99hm62GI8VplQe/FsLDNyMkuz88Ildgq0m0ZTwryqGTWaRPGIzebizfbCbC/M4hJJoT3XSao/DTtlM7vUZgWAje1W5RFsdAQHm7Lm16doccrpdiHyZyyj/HmPtTvrp/VQ6oxS6PGF+Tomq8bbGqXSk6rsxdboemPXi2elRGk8a1837B7Cr/JgQZ23PnNAmcXPZ2YLZc/vzve7YvnGsq4Y3qTL7H9m8cwx7FngWRyZrYXF/REssHtqvoxWmNPEorgL8X3EAg3EVPvCn83Kn/q1Wflv/+Tw719/dFZ39rNQPvxz/ixs9rFZOVkXmW0R6/G23CcWF7Qt4nPjoKwv11yWziZj82Wvb8jqvVi+LOz3DZb3G8s195+Oj33VHmiBYbZDhNnKc2z7Off4wtlgFMel/BBCrBsFWiGE6D+K5UII0W8UxzX4IYRYL0quJIQQ/UexXAgh+o3ieP7gR1PG7MnamJWDtUeyuMiK4u0rstiwfbHM2G22v6ztpa0FqI0Vpa1Fpqttpq2FJcooHq3vSSsj2RyTszHZMG7L+6zR+knC5tmVos8Vrb9K2lpwoszcGC7ZeWfXl0aZN4/mTEdePGGxJ4e2lkMso+SUSpmhji3LZnhp1V4dGGb1R9lzkkIPYadocRlUH2AHPhRaQYp0Czi3ArOgjNHKQmTLF85BHbZfmF+ni60Fpc61ZZMUGo6fyYfHpA6XbSPjbTszTI4VJnoOsWWjGVa8/afTyjL9m/H+QdTXyDlWdq66z/qiWL5JlGaWHAysM4/fPfvW8Jpjs9+xa9K7Z7vMkMisLmYzi4tncUz1zNZoZnaqWgCtLBjXkx0GY/kQdjaoDqaAg8ZYvpOm2HkAdjq5d1Z+CMqJR6H80Kz42H3V34dndedh2YM085ZjW0n1GJ/RIpPiOrYfQACIZu5itr0o/nl95iiWL9q+OXXRsTCLjhfB0rF4sx4uWqe537Z4v1/S/RDNLNPmfqRrbXkcl/JDCLFmFGiFEKL/KJYLIUS/URzPHvxoKjJy1A5stC9HrdFVJRIlRC0ay3nL5qhccs4FO74cZUs093Ok/Ig+KxKNgrKEP2w01ButbJvkp00SUvbWmo1Od10/Opa2yo51JSrzEikt2q83/zk7F5FiaLYjBdpNIzfhaU6CSlaOYiOWvbeB6SWd97aPJTxlbw699qTyYInxzGYqjwEc1A4JiJgsDt+wpWovSeiYqDRylB34NjAdg6f8mJC3hRM4lnTY3hs4ptyIkngibdf3YNdQ9NYt2uZu0B5tF/ePcXS0oA7Xw7oo+WlOwvLoOZClAlEs3ziab7u9t+6s79al7xEpmLEcJa92Y3H1l6k9cL3aOnAA01juJCxlsRxJyoeaggI+zE6Kq4/M6ooHZ+UBudEwIfUjoPxIKo+987O6ZZUdExL3mZrDrJuyDYnWZ9eYF5/aKpBzEpJGsOcaU8uZza5hL6ErU0dF5WgSATwW1j/3zlXzWVDrmyuOS/khhFg3CrRCCNF/FMuFEKLfKI5r8EMIsV6UXEkIIfqPYrkQQvQbxfG8wY/C5iU0nqS/beLNyEritUcWmShhKbOdeO3L2l7aWniWtcVgfZQQNZIuRnJJDyZB9iRkCbZsjsTOs62wbUVJ9zAcFKSd2Tq8Y2H7ZKCULZL7RXj7SvuIEk1FiWq9c8Gu8draWx5o+8SyNqxIhh9JoVHSGkqdSdlLkkfXh/8ki0stiSkcYEEu7gm5rNGewpZl9pZmfWRrSbJnXKcmhU77IlYWs1mcw++aJbSLYqc57W1pkyCu7Xa95wyzU3XZvrcOO1fMAsOSoJpxi0DU7/Gk0KydPZ+8/kF8LhTLTxJRgvUolkdxncV3rz2K5WE7sbjULIoYy8kHZ7HV/rq4AAAgAElEQVR8x/GEJbuiZzVBO2Oym6DthSWy9raV1sP4jrdgWg8PlcWfyFYY9bmjupxtee1RfFq2f8xg/W8vvqZ6vFTwGm5rIzaLUwSke8t77rLkrFFy2RmK412T+AshhBBCCCGEEEL0AtlehBBrRqPMQgjRfxTLhRCi3yiOZw9+NC0ekRWE2Te89shqEklBPSsIs5UMg/ZIahrZXnJsK5EFKLKiRLNydLEjed8L2z+TsEVySi8DfZRtmWW797bbdjaXaEaCyNaC5wLDCZOlRbK9LhaYKJu/GT+vTC4XZZ727hFmq5ltSIF2kyht/t7oKiftYm9E+TOTSntS58gWE0ml0/ZxfWZxYfYWs5l8GeXJmEE/yZJR8oyzvSSLCsvkj+vjMvvECmNmtk+kzui2SfVjUmc2u7c9qXQkT25rFcmR1ecQxeTI0pezfSZlZtuKLELebC9sZh3ve2GzDbBnZWQ79WYgCGd+USzfWNi9FM3+x5bN6RtG/XNv5q5OthYoT58FsFG0uAyS7cV5sJXVRY+x/IDE8to6aCFsOcMKrlebeYvYGaN2eGxQix2L78161s7IiZXrIOe5sMrZEtPlwmbjwn2xGbiwHP2mwH15M3ulS8D73ZouMa/PvtCKrjgu5YcQYt0o0AohRP9RLBdCiH6jOJ49+NEcTYpGgaMkolHC1EhtkbMsU3tgu7d+F+VHlNA0UnZE7d55Z4qZaPQeiZJaRW/ZuowoR0l+uiQxNePKjqgdP18aMY+UHW1GeRNsWe+7SPv33sqzbXnJj9K1j8fvjRgv2pf3vSyKC+sYZS6K4qvN7JfN7LLqUN5RluVbVroTMSVHCcbipBd7mYrDS5KXyiDWCN8Wsm0NYQF8M7hDbjSm7PCUIekSxzeITNlRS1waKDvwrkFlR6qPlB8TUodlLzZGyg9G9JY5eo5Ez6yc/bM4H8XRrm+C0vpeQlOmaIoSb3vKD6ZGjJQfkTIweibWUCzfeHLuI3YfRves1w9mKo8o4Wmk4vOUI6mMSUwxlqcYjfG7ptwYz9cxxR5bx2ym/PDaa8mnybLI3rnDv+dx+9Ce4rqXUJnd8zlqj7YxPorDkaIoZx9e4v11w84lXnesT85UIN45Zd+bl/CUxWqW3DqahIAmPlUcl/JDCHEErH6UeWxmP1WW5f9XFMWXmdmdRVF8oCzLu1a9IyGEEBWK5UII0W+2PI5r8EMIsWZWP8pcluXnzOxzVfnhoig+ZWZfZWYbGWiFEKL/KJYLIUS/URzPGvwobN7CkZPojpVzkpB6tpDI6jFsLOdtK7KtdF2/S0JTtq9ofW/ZKCFqzvpt8SR2zLYS2WaY3C9KYmrGJXRsvSh5XiQXXHdC05x1vPPKLDyRHJrZWrxztXBO8TUnVyqK4mlm9gwz+3/XthMxJZJCszgWyZfNZhJoltgO66MkeUxejcfi2VYSNasLeEl2qg92UDrLVlJnlD9HthdMYsdsLawO6ydOO7O9MFl0ZHvJISc5YlurKC6bY5tpU78qmNQ5R5Y+InV43TI7jCe7jmwv7PgWyavn1lUs3zia/c8ce3jUz2UWRS++RraXnFjP9lVLlF0d+BAqd8iNjrEa4+6EXMIsoSlaYVgS0gnctCxhtdnsXvLuybRZPCS2bJSc2mtnLJskNMdOlWP17rKvVRLFT2ZRjBJSM6u8t6+uieeb2zQL7J6K41J+CCHWTedA+/iiKD4O/39HWZbvwAWKorjEzP5PM7u6LMuzSxykEEKIhXSK5WEcN1MsF0KIo0F9cg1+CCHWS/dR5i+UZflMr7EoipEdBtl/X5ble7oenhBCiBZ0i+UL47iZYrkQQhwZ6pPnD340JaiR1cOzrbS1Z0QzsGAZ6yILDdtuJAeM5jT35IZDUsf2FVlRhqTOWzaahafr+pFcre1sL9FMI56Vhc2wEsmNoyz6kUTP+0zsXDC54bpnc/HW92Z2WXQs0YxC3vfG7ucaq88sXZjZzWb2qbIs37TSjW8RbWfo8GIAi61M3uzN8BLNAMDK3gwCLA7XngPVh2CzupjNZM0HLFU+Lkey/ptxW8sYAkJSReOdgLYXZmvxZnuZkHY2m0tke4nsETmxb5USe1aOnp+e1DuKk22l1N652iV1rBx9F95sMbitEaljz8JItu1ZTZvPTHorKJZvFIuevV36dpH9LLKqY71nkWGxnM7m4myfWlyIRQWtLGh3TPUY6zGuT9tL3p5maMH4jvEZy8wenWMxZOuz+LCsbdGLn21nefH6CJHtJef3RdjPXJLoGRida/abI2dbbFnvt07ubC9zbHkcl/JDCLFe1uMvvNzM/pmZ/UlRFH9Y1V1bluX7Vr0jIYQQplguhBB9R3E8P+HpormDozeGOWqLSKEQKSdyVCRM2RFtP3ozlaM8aZv8Lfp8uN9llR2rTB7HEvpEo53e+mw01Pss7C0YLsuSgLLjit4oIrittqElJ6FpWzVI81iibUWKnFSO7nee+HQtmaV/d243ohUYx7FuEVHi6ii25iTJY0lOsRxtK3qTVHvbBwsfkJsL3xymN4tjSGxXextY/cW3fkzZESU5xWUmzrKTBXVmPPaxdk9B0FyuSaTsjJQfLAm5pw5ifQWkJO1d4+AyRApEL6FhTvJZpthh5Uj5YU57k/nlFcs3He9ERv1kds9Gif29Mkt4yuJ2tC3vWFjS6lrC0nH9b7M9xfqaig+CaaqvbRP2td/4a+ar8JiKiimqohsgUn4gy6pAvP0mookBogkJvD53qFYg7Tm/SaLtsvgYPVe7KO+wPue3EJKuPe93ZVqfn1PFcSk/hBBrZr2ZpYUQQhwFiuVCCNFvFMc1+CGEWC9rnlZLCCHEEaBYLoQQ/UZxvHvC08gqwqwcObaYVO8l+Yzkel0sNpFEN0qYmmOLYefF+6yRrSZKchO1d0mIGoGSLSZXjqRe3tzX0TzbTCLsSeyKBXUW1Hn1KJFL31FOiMlJaJqIpOvesuw79trbJtI9qoSnYn1ENrlICs3ioCd/xvKpoJ1tK7InIknCjFLm8b6/nFlDFp1sL7AsOGCorSXH9oL1zNbCZNWeFJpZKXD9VSXJi2ypXe1Q7PnhdVYiqycjRyrdVoodJSE9RerM+HfpJT9Nn9VL4s3OFTsW7/nMkoDPoVi+MTArOhL13aJYn3PP5iSnHpG6qE+NpBiN8RsTnqaEqFiHdsd0Ce9jfLf5shdfx0F7ZC+LlmWfO8fKFtFlHSQ6PoTFGmZ1wfquyamZrcYrt4XFUs9OyuIzwiyKORMesN9F3nPnqCch6BtSfggh1otGmYUQov8olgshRL9RHNfghxBi3SjQCiFE/1EsF0KIfqM4nj340ZQWL2ufyLFnLGtriWwpzOqCx+DtP7LdtLX4dD3+yCLT5XvJkUsikZyOZeaPJIJMIudZVZjsy5Mbpm1hCMj5rGlbnmwtteO1xMJNJDf3ZnNpa4vBfXhyw7bt3mwwfJYXWHLLA+2mEt3vrD2yN0ZZ/b04G1klom0tvgZn1/Z4z1mgwpthJZXZrCxY7mp7YRYXJrXGem+GgbZZ55eVT3vXTfRdpmukjdUDZ/xhtI3Z0axgbSy2DBY72XmPJPLeDA6RnQnPG5sNLfrejbQn5j+7YvmmsWjmrujeiPrRXdqx7FkUo21Fkv2pncWZzWV80FjOZrZFXA2v5Jz4ymZd8u6pqB+aOwOTt44H2380w2FEZNXB64711b39MytHjhWdrd9mvSZe/IxiffRcZRYab1/MIrRoNpfmsuqTL0bKDyHEepHETggh+o9iuRBC9BvF8e4JT9smwstJkBglRM3ZVs4oc7SvtutHSf9yEqJGyhNPEdNWJRKd12iueI9IhdB2lLvrG0uW4M8bRWbJ3dic5jlJRBksIZJZnAg1HV90ziNlCJYj5UZOO1OJuNfPlgfaPpGjFOuS2NJLmDdcUIfbihR03psUlsTOSLuXZDRKaBopP1JyVO9tI9tvpOyIEvKtSgHQpK3y03vzy1RxOSoU3G/6rNEbsDbPz0ROkjx2Dtl59972puNHhYunfGHfa6T4wTLee4zoujhcSLF8E4kSPre9D7r2o6NYHvV5c+6zpPJANUektmJx14uv+6TOU2ZFMOVFFN8iZUWXxJ3ePiO1RBe8Pnc0oUF0DG2V6d7+2bOfrR/Fz5zfJ0yZh+VIPZQzOcSi38Xqk9eR8kMIsV40yiyEEP1HsVwIIfqN4rgGP4QQ60aBVggh+o9iuRBC9BvF8azBD5xTnEnsosSZOUlG27ZjOZL8R8lVuyZUjeSC6bij5K2RnDtqx/oooWmUNNAy2nNoa3HxEqJG82wzuZtnC1lU5x0fA0NIZPthCVdZkiNv+0iUMImVo0SyUbt3rpRcqV804/MqYyNLbDkkdc0yS5LHni9tEvwm8KpjyZKZZQDtJ11sLRegjiVH9awuWG5ra/HkuVFCPhZHjbR7RPLj9B3hZ8XvtZW9orGPyGKD5yeysiDR9cRiWyQ7Z9+LZ3c6Rdbxvve0LJ63IVnWS0B8QNoXybaV8HTzaV6fXp+87X3krd/WFoPlqD3qW3qJ7SP7dJeE0fukvY29OqJtnzmKKdF2okkIvDi3yuSqXehit8mJxdFvKWb1zkl4ymKxd92w3zXeb50oVi+XwkBxvIt1TAghhBBCCCGEEKI3yPYihFgvpZkdeGkmhRBC9ALFciGE6DeK48vP9uJJbo6qHcvLSvByJHrRbC/MohPZXqIs3GwGmDbHEkmhIrtSjsQsIpIrMjk0yxbtHROTDu+QOqyPQgCuHwnFsJ1dlxHMohPNwBLdF96ybLtRe46ktsa6tZOiE23tC9GsXsvOhGU2i29RHPZgVhGW6d2zFyTZczSbywWnPdV7tpl90h7NNhDZWjzLX9tZsZaVcufMkMYsRl3l4+y68M4VIyd2RVJpRnSuo+8yKo+cdnZeI1l2zixAtYXEsYNW9LYzMEYzAXq2Q3ZPR1b0aF/LWo4jCyOLqWbcFhPdRzmXfU4/Oceil4js6dGySFuLY84664J9Ru96Z0T3SES6BnZJnRmfDaarrYVZcNi2otkeWR9u7mC2ECk/hBDrpbStD7RCCNF7FMuFEKLfKI7nD340R5m9N3rRG78oOVPb5E1YXjbRU9Qevb30RsGjhKVd3p52nau9S5Kc40h4mjOPtjf6z5Qd0ZzfDG+Uu226IHZMuN1lk5C2GYWPEjml7Xoqk/QZOl8XWx5oN43mG5Toe43e8HkJFnPUHJECLnpbmK5X735jb9jZ20BUa5yH8h6p2yfLYlxgyU+9xJdMBeKpBdhbn2WVHwxPgZfIUX7gdRGB20rXEJ5r9vbZOxcsPkbqJqYmxH3isTDYufZUPuy79q6LdA5xfZbwNLpu2j5f6b2mWL7RRArlHBUfi/vDoN3bFru/PLUC669EygyW3NSL9W2VcznkqKVz+txsHW+/DKYsiRTY0X6jfnxETt+Rfb6uv18OgvaI6Lpj11UUa9smn25ui/0uZM89qbE5Un4IIdbPlgdaIYQ4ESiWCyFEv9nyOK7BDyHEepHETggh+o9iuRBC9BvF8bzBj8LmbROehK5LO8qPlpVLM7lfZEvpmryJHSv7LDkJT6P2yEITJUT1zjuTyB13wtNIrh3J3LtYXZAoRqxrtuyc5KqMnOSozEKTk4C4uZ05tjzQbhrN79OzDHZJ1hy1R3E4J97gZcWuRyZJxfuGJSf1EpqeD9pZwlNWjuwPWM6RfS9rH2QwmS0SJUjOSYznPYf2Sd2yyQlZHIsk+l5sZOeFnXfvu2Z2qFPOtpjdKUr0GN2PLDmfEp72h0V98ug6z+k7tk3M3+ZYItg9E7UzW0uO1axL4s82Vpd1WM1zEqKyz+Xd/2wdllA6shJ624qWbWurj36/RHZM7IOwSQoQ/Nwp0Wlke/F+n3SJxd4zmF1XuK30udjvz7kNbyFSfggh1otGmYUQov8olgshRL9RHNfghxDiCNjyQCuEECcCxXIhhOg3Wx7HO8/2Es1g0lbqFc0G48n0o1kAlrXgRMfKpNvM6uK1R7aZKAt3VM75XqJ2y2hntLW6YH0k146sLmazY+1idfFg0mdvhpSonWVj7iK3jmZk8JZlZS8e7pD2RRI8Sew2l8Lmv6cotna15EUzVUWxq22meTN+nzN5KlpVWBlnc8HyhcZyzfb9xl8zbrGJrC5mXNa9ylmxcrL9s+Nj3ws+89isIgzcDp4rNoOJNzNDWwl7zvOd2T8R7/nDYN9VNAOLdy6GpA5n0ZmQOhbno+eAZnvpD83rN5rNJZoVsWvfktVHM3chObY1FhOYxYVZYXBfUX/HI8c+zp6hOdtq2yfvStsZGD17BosVOXZHRtffelF/JBHZZSPwWtqFMvt94sXitIxn54x+Y0fP+CGpq7HlcVzKDyHEepHETggh+o9iuRBC9BvF8fzBj+bImzda2XZkNFo/GqXGcteEqG0TpnpvJLuoXJgyZNnjx/1GnyVK7tZ1zvGIZd9Ypno2d3fzuFap+FgnOWoMpiKJ1BhYbqOkWrSvSFmiOcX7wSKlThQvotjL3lRHCj/vWKI3cOxNiZdsLL0Z9JQf5xt/zerJTS+QdrYtL6Eqe1vZVc2wrLIjetsXwb6XcdDOvvcoCajZ7BoaO+3pzVrX5KpR/4F9Fu/5yWDKDpaoMUreiGXvvI1IHZ6r6O09e3M5h2L5xsAmIYj6yTnKEBaLo2T7uJ53LIwouTK7f7yEpuyeylGWREQK6qhPvmxCVHYsXWFxM3rWMJVHG+UHO+/Rs6LL78IBVO6QHdS+KzionQP/mMy4IpM9tz3FJpZZrGaxOFIhtv2tJDV2HSk/hBDrRaPMQgjRfxTLhRCi3yiOa/BDCHEEbHmgFUKIE4FiuRBC9Jstj+NZgx+YKC+S0EW2mEjqxdbPmZ88x4LTVs63rC2FWV1Wuf02y7aV4C0757h3X7VNrhTJFdvYW9pK5HKSt+aQjtVLeMramYUnJ6FqZJHxpO/pHHr7au6zWQ7Z8kC7qaTvEGNTjj0xSmzXNiEqrtdVHs2sIsyCgpYCZlvBdrS9nG8s12xP6+XYXpZN/Oy1Rwn9IotIJE9OnyGKAV4y6gGp8+TBbZN0RiHGe46xaxST1A1IL+kgQyrNrCTMloLXDSbRw2toRJZl0n+WBNWs/XXF7vu5hcRG0PyOchJvLptMMkoiHOHFcma1YLEiSh4dxVeEXes5dtBV2l6i3y9G2qM677ZtOyGBF3/bWtXN2j8vcn5X0t8/ELMHGAwJB3Dh7FTBtBjzZVn8ZMlPveuSrYePl8iaijBbTJQigG5gS5HyQwixXiSxE0KI/qNYLoQQ/UZxXIMfQogjYMsDrRBCnAgUy4UQot9seRxferaXHEtBNENKlIE9kkJF+4psJ8tmuc6ZYWUdtpicY/HOe3Qu2DoRbWV1WPYyRzN7hlfuMttLTjxgx4r7Z7MUMJknfj78rpjcm62PnzNHLoiwY/G221wHy/S60CjzxtHWvtglXkSxNYpNOXaqyDLHLC6ebSXVe7O97JE6LDP7Au6fHd+ythYkknWzW7BLvDPj1wXbFp4LZlX0JLtMHpxzLiLY9Yzq6CH4TpJsGmcNQNvLoPqSC7ywgLa2l31SZ2Z2itSzGZXMuC1mWYttDcXyjaMZN3Ni+ZAs23UGl8g+3XZWJMTr+7SdOSvH6pJjRVm2Tx79fomei6yflfPczHl+RM8iZjHy+u9tjzHHGltrr/6DVhe0wOwU9eXMzEr4YJPqwxTwYC8hrre1W6FtMXqu5cxIxM679wxlv5VqG9ryOC7lhxBi/Wx5oBVCiBOBYrkQQvSbLY/jnZUf0Zv/Lkl8ojnHc0YDo4So0cgrU2HkqFRWmdB0FLRH28p5UxuNMqcRUzZ3tge+JUujrNEoM55/9vbQG03unJCzsX8P3C+7eaKEpEzZwZKcYtn7LlhCoygZJBKdq0jt4R03ZcsD7SZR2GIFH7suct4GRutHcTzCe9MfvQ1kygwss4SnTDnivaFP7WyfZvytUfRmMidhX46yo4taItouiw1RsucoyakFdTmEiRwhoI9AbjGoFiicZ95kSNpBPtRF+eGV0xtF7xpmyhCW/LRt/0EJTzcbnIQgfCve+NssRyq/KNlktC1GpBbwEksyNRgr58QMdvyrnISgq3qSPmPhP6wvzmJV6dy3qX+O7VHybaYK9lTD7DuOvpeca7R2rof1v2ZmI1SBBMlPJ0nF5yhDDqoAG8VyrPOST0eKRnYNY6xv60hwVd9bHsel/BBCrBdJ7IQQov8olgshRL9RHNfghxDiCNjyQCuEECcCxXIhhOg3Wx7Hswc/FknscuYM7yKxi7aVI8GLkhctm5AoRyI3JHUjsixbJ3dfoS2GSHhrEjuSCY3J7g48iV2l9UIpWS3hUKXr8iwVLMnnKomSDLFEcAdOe5SwlF1XuCxLQsqkhZ4tJkqumrOtKLlqs67WVvqSS3E8LEqSF0lyo0S3LInesnY0sziJHbMXoEw01TMrCy7rWQ5SO67D1o8ksd49GMUehNlG2qzXtp0RydKRQWO5ZVg2UWtbW+wOXLhDeAAnC4xre6kukh04QbXnW3WR5Nhe8LoakWW9hKesL8H6DROnPbS9KJZvLG2TV0fP+Kh9lRZGJJL8s3LXxMerSmga3Wfesmxftd8PxNaC8altP9yD9c8PICiwPjnWsX6m1zeMLDKRhTL8XQkLp1g9gPMzRAtjdQ53nAuUWRhrtpdHSR2sn2J4lMLAjNteIpuu9wxjx8Lu1ymK41mTdgghhBBCCCGEEEL0DtlehBBrx1MCCSGE6A+K5UII0W+2PY5nDX6wzNKRRG7dthgzLhuLLDTLzunN9r9KK0pki4kkduGxoISOyOnY3NhmvvR3umzV7t1Y09leQPc2AV1XOpYDrMNy469ZnqQ+miWB2Va8ebTZTBZMdubNwMKsKGz73meKbDPR/YawY2FyRW8GmUWXRSmJ3cbR/D672g+ZLSaKzTkxn+HdL9FsL0mSyqww2O7N5pLqve2z/bPZXtpYVdpaPTyp9yrsJou2uWycZbJ1b9llCWeWqxYYEXm0mdmwmmJlQPXDZmWlZd53ZhJIsQ+fc2yGFrS67EKZ2bCi5zuzyuB6WBfNTIEolm8ei2L5ouWwziyO1dF91HbGOCSKhdFsLl3sb2btP0tOn71z/786GIw52M9OFrya/Zz0yZeegXE4X2c2myFlAoGoGM8vG80uaBbPcMiIflfieUsx2ovlaeaXHeeX76D6XK7tJc2M8+isDuNzirteH+OALIvr47lgFsXoubrod23Ntqo4LuWHEGL9bPsosxBCnAQUy4UQot9sexzPHvxojtJ5o8hsEDKqY+3RKDSWl03UlKNiYduKEhpFo8gsyanXjtuPkqNi8qTpPNij+Tqz2cgpjiKzubGjecYj5QeOIuMbtUk1zDmBulry1TQyC9vvmkAxkfP2IUpoylQYkTIjR9nBrlHv7WmUKDZ6a8NG8nPWQbZ9lHmTQAVflyR5TAEXxdYoYWpXWMI7L4FYpMxgCUvxrcyksZy3fab2wHKk8PDq2X2eo/DIuQXXnQws523fqrbvXoPpzStcxPjmcDcl0RvNr2NmNq4uEk8VmVSOky/N6lhy011SZ8af7yOnnSXJw05euka9vkgb9ZVi+WaxajV2pAL0ro1VxvVE14SmiUjtHfXJo0kMPLVH2CcnymtMsjwg/d+Bk/B0ZcoPeJhNmLID++FQPmBBB2DJT1nybDP+jAoVO+S4mNrDzGz3TLUdT8VXHczOBd6ewN8vuxCsWfJqr2/ErkG8bg5IHVNme187u+6QbY/jUn4IIdZKWWqUWQgh+o5iuRBC9BvFcQ1+CCGOgG0PtEIIcRJQLBdCiH6z7XG88+BHJJdmdV0sLm1sNdGybZOrRvKknERRXSR0kQQvsrpgvZfQlM2DXbO9pIRAaHtxLCjT7WfYXpKtBfeJyyY5GdpeJrDsfiVH22F6dYcoKSBLxGc2O6857XgqUn2UVMu7L9jxsXbPysKOxbPgMDkiEsnUF7YrudLG0YyJUZweBu05SUxzEuYh0SWU2r0EY6nshQ62Pit7tprI1sLuZyQnoWlbjsvq0tYSt6xlsQ3hNZ6soJjkFB6w6ZmJdSwh4dhJeJqeafunZ3W756Fc/UWrSySn33PaI4su62uwZdP+574fxfKNY9E9FPWDu8Ryc+py+jYR0SXWNi5Gvx+84+sySYHXJx8SKwaznUd98mgSgqVtL84kBCl+YZJTjH+pr87s6WZmk+DL9GzlCWoVQds9rMQsjEOwMKZYnewvTdK5wPWZRWYffC1jCMbpK/ISTmOZ9a0imy7C7mfcVjqv7Pen4riUH0KII2DbA60QQpwEFMuFEKLfbHsc1+CHEGKtlCaJnRBC9B3FciGE6DeK4x0GP9pkAzdrP5NFji3Gk7BF60cSt+hYmO2EycDZLAhee5c5w731UWI1rDS0KKsbMYndaH4dMz7byw7JMt1VYpckdDjqiBK7tN2alA1TJ1ewubXNrKYrY7Ox5NhamFWkizXLuwdSu2draTvDinfdenaYRccSrZNjXZvSI4ldURSny7I8v6D9yWVZ3neUx7RO2toPI6tKZElsE+fbWikRdu9693vUzmSmOe3M9hLJs9d9WzDrW7O+LV1neloHUf8gonY9Vg8CbwY0JpXG9hTb9p0ZApLVE+XR+xBhmFTaK6dlvb7CkNThszKa5SJ6ZimWbxaFzfdDcmZrWdbKchS2tS6w63hZWwu759x2tI1XC+MMUjVbBumzsxkYsa422+KSffI0y8sB9J1rMzBW+8V++hhiXbLguPv3pj9L6y9u5r/l8PPjuSYWISynGL4L3wWun86Ld67T75fTF0EdfL69Rw//erZFdg3hZ2a2GO+5y65RNvOk+uSco+yvCCG2kDTKnPvvmPjDoii+iTUURfHPzexPj/h4hBBiI+gSy48Rxb1WxssAACAASURBVHIhhGigPrkGP4QQ66YaZc79d0zcbWYfLYri1UVRjMzMiqJ4YlEUv25mN5vZO4/tyIQQ4jjpEMuPEcVyIYRooj55nu0FJXaRhC7K8h/ZZyLbTCSt7pLluutsMexcdJnNJZJHedmkWWbokWNriWZ7SZmRa7Iv+LBJIlY4srMEyyZtZnZQHdcBaMfHoMsdVLqvfcwcHQzRoax3wLJY43GRco5thUnMvOuqJHWsPee6NdLu0cVC49lmmEUnh774C8uyfG5RFP/CzN5kZt9VFMUtZvavzeyLZvatZVn+3rEe4IpYJo5HUuqcOJ9Dl2swssV4ljfWHtFlNpejpKstZFGdt822lr+cbXm0tee51ziZLWFAZnsZwXMUZ35Jzxl8TmG8SxLrMdhmRiDiPVWV0TXDrCxms+cP9gXYzC85M8ctsuiyc6tYvlk077Uc22DODC2rtDAu25/oEp+ivhWzf0WzIrn3GZlhcejYWlifvGZFJ1YO7JOn+sj24vbJUz/Z6ZOnmRV3nItpGvcwEJ1iS9rM14G2d2hubcN1fh9MZ75xZu5K52roxPIEWhhrsbyK4Wh1wfO2d+7w7wVYp40FJhHZZNk9htvxZoZhbHscl/JDCLFW1iWxK4rilqIo7i+KYqXy5bIs/62ZXWFmX2eHAfevzOwfnJTOshBCdGEdtpd1xXEzxXIhhGiiPvkSCU8TUaK7Res2l4veukdvK7x9RW8nl337yUaBu7wd9c4FS640ImoNs5nig6k9zGajnN7I55AlDIJ2pvyIwFHSNOKMI8tszvCI2ig2eQtnZlZWI9Ge8qPtm2Dv7XDbBGA5CUsRloSUKTOYmsTbh6fsyFGBsPZjSq70LjP7RTP75VVutCiKf2Jm7zCz+8zsN83sR83sV4uieHFZlvevcl/HRfP27ap666Ka6xqHjwrvHorixTrA88OOK2rP2X5Oe9vvPdpWpPxscyxdqB0reaaxhHn4HB2RN5s78LYQ41161mHC09ozuVJ+eElOu6hIo7fz0Rtv975bTyx/l60hjpttVyyPvru2feplFdLN8iJwWwekPnqTnaMKi96aR8kkWZlNNmDG+9y1hKdEmeCtzxKeRn3yHDV26p/X1B7wYVN9Tp8fVSAHRFnhJUFt28+stRNlOvtNYzb7fRTFcpbQ2myW8LSW/BXOW1KGjB6FbcF2o4ksVplgeOHXpT65lB9CiPWzjlHmsiz/k5k9uMrjLIri35nZb1T//kFZlv/KzC43s6eb2V1FUbxglfsTQog+0Yc4bqZYLoQQHtveJ9fghxBivXRPrvT4oig+Dv9ecgRH++1m9pyyLH+kLMtHzczKsvyYmT3DDke1bz2CYxBCiM2jW8LT44jjZorlQggxj/rk+baXRGTVWNZqwuq6SnDbbjf6LJ48KZKSLpvwdNqOsjZnHusknWOyuzbtLLkbJmJiEmGUyB0Q3RrOHz6ZzB8zSvT2W+q68PhZQlWsr+0fttFWkp9jV1rWAhPZWpg0FOXukZUl2ld0j3WRnpfWWWL3hbIsn9lpze58Q1mWX2pWlmV5wcx+uiiK9xzx8aycwuZjZo5kOZLR58TW1tYp4/djToKvZYniRdv7ldU1YbdLjgWmC6v83hfVbSoon2YJBZl8+nDF6g98WHzm7FfPqj2UteO20vZhnZrF1ebrvX4T66t0seiya92scyw/jjhutgWxHIn6E1F8XdbCGN3/Obb4KEE7i6UMb5/sPmL9e++zTu8jx4rCJhQYOvf/kCREHZGEp1375Ansm9f6xMP6cZrVE36muDbOCOa1Pjl5WNXs6Vhuuf0o4am3bPrdNLoIFvhyKKfks+dmVRfBRZB+v+yDrYd9156FkSWnxkPGH+T7QTvrbyyyJDf7D9veJ+88+CGEEG3pS2ZpM/uqoii+akH7Q0d2JEIIsWEolgshRL/Z9jiuwQ8hxHpZX3KldfCnFr+AaJmWVwghThCK5UII0W8Ux/MHPzw5ZLS8WSzBy7HFRHI8dgxt5UFt2ldpj2BSVPxiplJWZ+5qlNAliRyzupiZ7Z6u1smYDYZlns6ZU3wCcrtpZmmQ1UVyPbbd0rH9oMQuZWb2pI1tZWNdYddNlM06yni+7OwMXrzrsq82+zWbTau1aoqiuM3Mvs0OfYifNbPry7K8ecnNXkHqvsLMnl39+5dLbn+jaBtnc+yLXWJns7yoDoksNuwe7jrbQURaL3pKe/c7W8ZrZ+clZ7aXnPO6qC5n+12z1nfBm+Er0WVmHHzOGM4QkGTj8HDZBVn5XvWsqz1noZyk2APH9sLK3v3U1haT01dB1hHL1xTHzbYglhc2/50ua0VfxWwv0bF0iW8sbkYxybOit7a1OOtPz4XTD8Vyuu8xPgzZbDDYTz8D64/m93WUfXKcpYpta2ovL+frvGVd20oQW9KyO8RqYlY/xwk8L6fSeb0UFngSeGAufsLh30c/P1v/1GPT8iXVZ9w7P1vl/COz8m71vZyC7/I8WmTwuKq/0T2Cj53oucWee+wnlfrkUn4IIdZNuZ5AW5bllWvY5oedpl8viuKVZvZP7XCqLSGE2C7WEMvXEcer7SqWCyFEE/XJuys/Et4bhpxRYrZ+9BYwejPVZRS6q0qlbdK/nORKtRHnakU3oRLWk+RFtTmtqxFJNrKM9agc2SFvudwvvrqhduDG2sE3WtXocjhKDSPTJQx3ps+FI8v4+XH+7VSPdXhcq3pTnPNGk522SG0RKZra7CtKaLpoHW+fi+7huUR5/ZHYLeKDZnYikuS1ffPeNklejloi581itC2mlFr2ObCsQrAkdXisXjsSvdmMEqJ2YVllR/RMXxY8bwekntV57bVlyZvLEOw5jUjzaL7sva1M7QN4m+glV89NWNosd+mrNFEs32yiey7qsy/bT8ZlctoZ3j3PlCO4bLT/VPbus6jPzpIge8qOVI9q6lr/nPTJd7HPThKi0j558GsO++FRn5wlDsU++fBgvn6AyhJUc5PtFk57Iooxtd8HTvJUxqBSvtvjoPIJXz8rf/Xlh38f+ks4wA/N1j931szMzjw8a370LGx/VP9rZjZylB9RLO+i5IqeBci2x/FV9k2EEOIk812mJHlCCNF3FMuFEKLfdI7jsr0IIdZOX0aZi6J4N6neNbP/xsyebmbXHu0RCSHE5qBYLoQQ/Wbb43jnwY8uEuMcW0xbCV6bfS2qa67H6tpadKLPEknsWJJTs5lEDuVhbnKllDyJJFTCemZ1MQO5HdTVZL27pA5JN9Q+1KHcrvoMu4HmqGZfCaws3rlISZ1qsju046Q65xi62Fqi9aOEp10sMG2Oqa3FxmMZSX25Jn/hmngCqTtvZh8xs58sy/J9R3w8a6VLkrvofsiJzSwOenGSSZ1x2ZLUMUkpxtkhWdaVOpM6vJ+YvQKJkvwxO0zXbbUlRyLfpb0rntx9Ubu3Tiqz78rMbFw9q/CZgrLqMHaRBzxLhOgl5kvlKMkplr1rtIt1LOrrIIrlm0czxi6bhDSKz22s7Dn994QX1xldEp5m2VpIXe2equ5ltG/g/c366swKZxb3yad9ebS6MNtL1CfHAAhWjPRZds/x1dOPZC+JafosB06ffAK/BXaqE48WwJL0yXNCDMbt1Od3Y1T6/XIReJCS1cXM7BtfePj3wb+Y1e1DRtOH/qOZmZ15YFY1/MKsnL732m8O2D2b1OI01LFn2NhpnzSWa+5r1FgOr1/FcSk/hBBHQF9GmcuyZJmlhRBCmGK5EEL0nW2P4xr8EEKsl36NMgshhGAolgshRL9RHM8b/CisvcSurUQukji3kVsvm+F8HRadKOtutP8ByqaqsjvDC5HboqyMye0wGzVmlraLq79YxywwKLFjUxrAnOE1CwzRKWLm6CRBxjnPUTY3SJmlib3FzOj84bXM0osPJWvWiuh7ZxYXb19dYFYW9lW02WdkX4g+yyJK688o87bQjGmRlaWLDRDLUezEsicTXVRnFtteUsgaO+3MFsOW9bbPpNjssvfa8XOXwbJsW0jOsozjyITuhYjI1pJi05jUYRnrcNkk18bnzBjKSVbtWTGp3Jw8q2szQ8BFlp5PXe+RZWcIYHiWUMXyfpFjz247Q0o085cZt2dFzxhmJ/RifdFYzqPrTBr0uYj9yGRVG8zXzdWTPjmzpXszME7736vsk+Oy5xrLGe+TD3C2FzLDIlr9ajPLwOeexjo4PvwOD4jFBmGz0CBp/QmJ32Y2s/uMwffz+T+dlf/6Px/+ffjeWd2j4GsJYh/7zRFZGLNsh1Bmz0XWzk6Z4riUH0KII2CTR5mLoviY1Z8bCynL8pvWeDhCCLGxKJYLIUS/2fY4vtLBj2XfUHRRSzTLXbYVjXg3l2uzfk4iKTpKDv9Jo6xM1dAsp2XxzRLOL57Ku94ocqq/GOougvLpagOjS4wyPl/t4LFZ3aPQTpIv7cAobTp+HJhFlUtR1ddG4eFk4ihzqvdGi9u+BVtWoZEzsttFgeG157ytnyxYDpeN3uTQc1lu/Cjzn8aLbCdtr9dlrxss57wtRJjyA19wpRdAXsLTtsqQSPnhsQ7lRpQQdZUsGweRKDks1qdlJ047y+eXo/xgyg5M3rd/of7XzGwX36Kmiyg46d4zOz3ra+3wAXOUG22faZ1RLN84mt95FD9XqRrKUXZ7Ku9FtP7147BKlQtLaNpmEoLUp0U1M+ufD5naw2z9ffIEJh4lfXKMiSx+MYWHtywSqjmgvHMwfywHcJGk2DQh8dvM7PzZw7+nQcxho/88KyeVByY5ffAzs3K1/gUnOWyipvyBenbde8+l9LG8+zVdQjUVI9lW+vi1e0lxXMoPIcR6KW2zR5nt8BBfUZblfymK4lvN7BNlWT583AclhBCbhGK5EEL0G8Xx47H2CiG2iWqUOfffEfKDNptO64Nm9vVHunchhOgDHWL5EaNYLoQQi1CfPF/50ZSD5cgvoySlOcmVcqSgkYVlUZ23/S4JT3P2z2RjnqyOJU8bOglRU/0O03ubzeR2KKu7+HFQfvzh39NfMXf8Zma2Vw3OYZIge3BWTDcQJkHFY03JlVBiiPOEE4ldDjWJHbmZ2yaCM2t/XXRNfMrWz0mAGFlkSlKPdauUuW/4KPPfmNm3FUVxlx1+7NNFUVzkLVyW5WNeWx8obP7aXTbZswV1UWI5LOdIpSMrCJaT6tiTmaYyhiZQKk/vDS/xZlvarJM+17IJUXNY95sQPL70vXrJ2iakniU5xXqvfdz422xP3zcmOd0nyU+99lGyaOIJhPaD4AHAkgDuwIfJuZ8iVhHTFcv7QxSrl7WCLJuY1yOtF11qLCY26xM5ySZp3470ydtY0VNf1UuIujF9cmf/qU/u2Vci2wojx+pCnwuOrSWdV/z9gHH7scq2MroP1rkAIeCBP54/GLQIPTi/z5J4s3CgwPssUX+CJTSNYNvytrPtcVy2FyHE2tlwf+E7zOw1ZnaTHT4jPhgs33HoTQgh+o1iuRBC9Jttj+Ma/BBCrJVyw+cUL8vyxqIo/i87lNb9spm90sz+4niPSgghNgvFciGE6DeK42uc7SWxrOwtJ9t/Fzm2t91oWxE5thgqLQ92ijOc1OaUHszX0YzUKKtjcrfTIPhOsjozs8d99eHfU18OO4ANnHtg/mBTtmkzs+FjC/dfRDO0BNmiWXtXi8y6iWT8bJ5uNptLG2sYs7V4M8OwY1lWLr3ho8xWluWdZnZnURTfYWb/tizL/3Lcx9Qn2lyDUTuTInsWmER0WbHrHa0szArhzSqS6jF0MRlrdEyeVSWysTELDLOuIcd92zGrC9YXzrLse4u+l66zvSRVNEqZ9yCb/34lO9/DxxhcBOn5ilL2Cewsxb7jioFdYvciybVi+eZQ2Hz/chUzb3n7aq7j2RLZbC/eeov2FbUze5y3nS62HGZfMZv1v1mdWX2Gwml8iGzrPeqT036285uDzfCyLLVZTWBmyHH1GbzZXtIsLY88NKu7CDY2SnEfjxmfC1UZBw4K+N7TNVCbgcZ4uUsYZet722TPvdq2tjyOS/khhFgvGz7KjJRl+S+O+xiEEGIjUSwXQoh+ozi+vsGPZdUSx0FbNUpz2SjharT96HOzUeZaO6n3EhJNRym95ErTrIOnoR3mD0+jy1/2lbBRchntwTzZQ9jWID+/WE5CJTaaGSWc6xNd1U8szkXqJEaX+7oH02ptHYu+x2WvgS5J9HBZT+0RqflYe/SmhCkEUBXAlBk5l3J0TB6RCmTRcriPdScxZfts004VBVBm591TdiTlxpDUYZnVmUFCVHyDSMr4BnEMj7T9qv0ADgCfQ6l8QNQguXSNv6zcBcXyk0+O6jknOSqL6972I9Ur69JF13bOJAkJT7UQJTzNWTbsk6dy1Cd/3FP4ASRW2CdfFhYfPaJnIa6fkptOMGE1xPIUw/dPzepQ5Rep+NL2a8cPF15SfKxCVdGlv4EsurYVx6X8EEIcAZsusRNCCBGjWC6EEP1m2+O4Bj+EEOulVKAVQojeo1guhBD9RnF8fYMfUTIW5uCIksetO2/lUe5rWbpK7EKmekTMyITZm6p6tLqUIBQ/qMqoOztAIXk7cmRxOTfxKm/4tgkON4EulrJV2dAksdtcjspqyCTHzfLUcUfqsN4LZ0md6m2fwewVq0xQtk/qPBceOxdeQlRma4kSoh4HeExtbUlevWdXYrYYlvgNn0KsXLPFoGy6Ku+B7WUE+QKTbH0I+QjxOZNk014MzHkmrSOM5lwjiuWbz7piemSNZRYXzxYTxfJofZbcNeeyZOtHyV9zEneuI8ln2CdH1tAnzwFjhFdmTJNDR9uHcu0aqD4Wxu8hlpPtBWM5xO2UEBUTWrPjw8+ByVVZe2RN9Z57rC7qj0T3CG5n2+O4lB9CiLVTHvevMCGEEEujWC6EEP1m2+P4UeZDE0JsKQcd/h03RVFcXBTFjUVRfLIoii9V/z5ZFMUriqL4suM+PiGEOGr6FsfNFMuFEALZ9j75kSo/mGQnR6KXk02+j2TZUwKy7B10ygPQ9e49PCunOcM92Vxq34fM0hPYFku9j7Ixki0Z5Vkscz6C9VHmZSY7Y4vmSN+7LrvMOh6RnK4L0fF5528TAmcH/r2Z/ZmZPc/M7qnqnmpm/2vV9txjOq6Vc5QzbUVSZiaPNpslvffkyWxe++hzdbkvcmbPYLLqfaedheFoNgSEWWBw+6uY6SORthvZWjzSevj95sz2kjMbDLsuWBm/F1SIJ4n0+MJ8nZnZIOhFJVk0k0cjRylDjqTYHorlm89R9oMj24g3c9cgzaoR3DsF3DMFXHjsWcHiA4tZzfVY+7RuE14Pb2CfnM5mlWHrY316Mz7DCXsueN/7oFoAYy3G7XHViRjDZ92DU7HomHG9moMIf3MEwTH6fcHw+jBpPfz8Y9LurbvtcVy2FyHE2unpwOTXl2X5PY26u83sZ4qi+LPjOCAhhDhOFMuFEKLfbHscX+ngBxut85KIRm+9FyVraa4fbWuwoM5bP6LLqFn4Vn8FiTtZPZ2TGocIcZ1UfwHm/n70C/MbPfdFfgAXvlSt8/lZ3WMw0XYahcX9wyhqGrH1RoZT8rjaaHHJl6Ujzk55uk6H9px1ooRGCBsFZ+vlvImORtS9bUX32AlVZT1SFMWzy7L8bawsiuI5ZvboMR3TiWWn8des/oBKMXvkPLWSMgTf+rDkqdHLvJx7gCVXjRLnId6xpHovOWrCiw0sYeqyKpBl1UHLPpNxWabcYI8xs9njZULqsL72Bm1vvlx7m0iUIR5pPfRY47bSs469AW2WE9E16sXmtt/7oq6IYvnJhPUncohiYU35UcXwWt5OEgyxfUL6iRO4UFkSY++zsONDlhV8MAWAq5xgfXKmwtg/3j45xiwWv1ABUYtlpN5TS7BrkMVCrMPuQLT99PuhptyDCzNS+kQqPrb/KGGpF+u73I9Rn95btkesLI5L+SGEEJwfNLP/vSiKd5rZZ6u6p5jZX5rZPz+ugxJCCJGFYrkQQvSblcVxDX4IIdZOH/2FZVl+0sy+pSiKJ9phgC3M7LNlWf7t8R6ZEEIcD4rlQgjRb7Y9jh/p4AeTuHryJpTILdvOkuR0SRwZyYe6XkzLyo+YxMubhzqVByirw3KSg9XkXw/OiinpEptb3GyWVOk8HACo9excYz/Gk8tN4DOhxDjJyiIJHpZrEjU4lMi2EiVEjb73LutHEuZIAhjZWrrua1FdRI+TK5mZWVmW95vZ/VhXFMU7yrJ8yTEd0srpmkh3WXKk0snuMjw1q2NSaYwBA4gzO+P5fXrHsk7YZ24S2V1Wtc4mET2/ERbbvISoLDEcrs9sMzVbTZJKw3NoBOVhJc3fYZ0SgyR5+JxyrJrs+HLsh4uW89pz7YuK5dtJl+8d4xsmNx1VMRwtByz5aa0fC+3pXsJklmjbSPuNLMle8u1VwhKC1qzcXfrkB0v2yTHJ54XGX6vnTp3GL6dPnn5/4DoTzwITJE9l/VQW1/FUoFsnXU5DYnUxg+sGbY1grWLXIPYx2O+TWlxntnzj5ci23vZcePdlZF3c9jgu5YcQYu301F+4iOcc9wEIIcRRo1guhBD9ZtvjuAY/hBBrp4+BtigK72V6Yf38SEIIsRR9DHyK5UIIMaOPQW+VcXzpwY9Ikh/NZOHNvZ0za4WjNs2mS1b0Nttqax+oSaKIVKxmb/FmOKnq0UpS7s7KSa42xCzbIAGjOmw8sDQLDJ50NtE0yvbOQZlI7PZh/0yWxrLs12RnxBZjFkvQ2srOlm3vakWJtr9oHW+9KIt218/atM0023oqsfucmT2jLMvPNxuKovjrYzieI+G4Hoop/tek0thePa1Gjkw1ZWqvSYoxzlVS3wFIfqOZWZa1wqxSSo33ELMIRbJutv6y33XO51t6BgWnPoptbDYXVvbap88clHoTiXeBzzzcVprBzJGCp/U9Ww7SReocWSVzZiBTLO8vkU120Tq5sJm1cCaNZHcZQt+UzfziWkWqe20f4z/0M9lsMOuwBbr2DWJFZ/e82TH1ydEuRKzoe1Bms13RPnlgP8dloj55ZBXBj8c+Nv5+iK67MbEwIiXsIJrtpe3na9YvWpZ9lViOnhvqk3PWZXMTQogpZYd/G8B7zey/dtp+6ygPRAghNoEexnEzxXIhhJiy7X3yzsqPLiciJwkXq4sSonlvuVhy1ZzjYtuM3opH+wnfqpMRZTbCaGY2YSOTUIfKijTyWRvthP1OzyEeDI4Yk5HRGmlZHKK8MF+ujSxjOY3okzozPgrtjaint3NRwqAoUV5OwtJIWWJBe9vRYG//3rbYvPfRttqOKCPRG8s+UZbl/7ag7cVHeSzrokvsY/XRdZWD97YwvZVBtQcmP2XJyjBOpPY9iGEFzAy/czC//1WScw+w+3FA2vEexePue/LTiCj2RLEtUkPUyuRtHnsLWjgyIZoQkLwl9fbP4nBXtWGOonaZZTaNbYjly7KOHze15M478+URxO8hUX4gNQVB9eZ+QPq2ZrM+4x6oQXbwnqv+4mf2jpXtf5rE1FFjH5AkmNiPxWcV65PvsofQKvvkRJmd0yc/IP1sT41d65OnWIjnEg6lS38C42P6DvFZOSC/FaLfD/j9H5B27/PR7cOxRL8fjLR7dW377OqTc5TzQwixdjZk1FgIIcQSKJYLIUS/2fY4rsEPIcRa6ZO/sCiKb13QfGBmZ83sz8qyvLBgOSGEOHEolgshRL9RHO8w+NH2hLWV7Ub78ZKZ5ibp8raP+4jsCznkyJuo7JYkDKpJuEHqxhJB1eZEB4nWfvCNp+YdT1YXXTEkuVJt/5VMcewkV7pQSfAiCV2tjiRfwnrP1hJJhFkivByJXrT9LklGc44/uu5ZOdpXVwldj0aZP2Tzatjm4Z8viuKdZvaTZVmeaIdBW2tTZN2K2j12WLIykErvEtsLSlZrsbFadgjxBqXSO49UBZQHE3KuZXY/RjJYrPfai8bfruD6ngS8y7ZYQlaEJUTsajeKrIKsLud7Yc9flLinZxGT6pvxhIAT8nyMngNYzrFa5sT55vo5/awN5EO2RbF8VVb0SIbf1eK4A7E2xeoBqTOb2WF2nB8A6Z7CfiQ+C1KfEp8F+ySJZxk8jHLaa/1zYsHZIfZ0M7P9nfljRYbJjsl8zGZ1WzmD9MnRlpPOldcnT+cN+9Z4LlkSUK88tcjgeYNDbdtnRpjds7ZNYlvxvjdmW0HCFATJ1kOOySyv/7+ork1783dnc7ltj+NSfggh1k5fRpnN7H8ys5vN7H12mFzp82b2BDP7bjP7n83sp8zs683sWjN7xMyuO57DFEKIo0exXAgh+s22x3ENfggh1soGZYpuw4+b2b8ry/KGRv1vF0Vxg5n9UFmW/6QoiqGZ/ZCpwyyE2BIUy4UQot8ojq9g8KNrJvFoBha2jieLbSsViiT73vbbSkW72huonJBYWQaOlQUtLgWRoO2DHtmTEU6PtdrvAIRDaIHxMtonynJ+/2y2Fqy7cG5+WSZRxHLt8zlyvnQKUQMVzfbC2nO+92j9SOIXSZiz5NodtpVlzQq2z9bvAf/YzN7mtH3UzH66Kv8nM3vZkRzREdBWBr+ovGi7ORnNPZIsuDbby+6snKTSAyfGJUlqLdM+xs4kP35kVmcQe9pavyJrWdRuxm0hBWlvY5VoruO151hd0raidbzjP0qi2BNJrRlsNrYDZ0dTKTXOekDWR6W1J5UuSXsXW4zXvqiuTduGceJjOfPu59hoI3KvjUWgLSz1Q9HqgTO/pDLWIVN7N7Rjn5JZSHZIPxitHt79O23H+zdZLMl9bFa/19MsM7j+fmBVwW3RPjnOnFPM19XiS4c++T6xwDCri9ms/+3NgIL16bi8Z1VarY01lLUXpI5Zl/C7wPZ0DrwfxszCiNuatuP+ocx+a3huJtaeE7fbPvd6wFri+Lpm9RNCiCkHHf4dEw+a2XOdtudWlti41QAAIABJREFU7WZmF5nZl47kiIQQYkPoSRw3UywXQgjKtvfJOys/2o4eR2/JvCSoUfuy+4pUKKzd29eyCoGdoH2Q3hzBsCCOYmN9GvEeByPL3sjn9O0ofGg2P7sHSxiEo8BslJmNOO+fhzqSHG5M6pr7TUXvLVnbUeYcRU/01jynve2b4jZvf6M3ftG2IgUAO1ZcrkcSu9eZ2VuLoniamf0Hm/cX/kS13BVm9rFjOL5jIVJu5NwjOeogjDfpzRbGvuFovoxvC9nbMFxnAKozRnkW1q8CSvTWCuMN5o1OD1t8nmCZJf9kao9mPSMt62X+Ys+cRcs1Yfv3jpXVsYStXjlKjhrti63vLbsITHJKE5LvGyU9k1jiPWyPriss5yg7cu7H6HpQLN9cllX9dLk2IgVSRE3FB3H51Jn5duyTHlQxHu+j84+231dBlo36zEi6Z2v9cEd5kpaJ1B7IUfbJU31NbU365DUFNul/7zv98Fo5HQscH1NJRElCkZw3+JG6p62Kr5bwmiV0hXW8e6TtZ41UgDm/L7Bt2+O4cn4IIdZOXwJtWZa/WBTFvXYon3ubHf4+nZjZJ8zs+8qy/LVq0ZusZooQQoiTj2K5EEL0m22P4xr8EEKsnR75C60Kpr9WFMXAzB5vZl9oTp9VluUXjuXghBDiGFEsF0KIfrPtcTxr8IMlV/JgVpNFy3nLtpFIJ+VZZGvBOpRKRclRmVyQ2XG62iOYvAm3n2RVTAJulienY6DEK0kPvX11kdiNiRzOS/6UpHUooUM54j6zvWDyJTgWZnth5a7tkYx0WYlyW6uJOXXRspEVIedYF9EziZ2ZmRVF8WQz+4dmdqmZPVAUxe+XZfm5Yz6sIyHnGuti+cu6H8gBeJLfZHfZBdvLgDzhRpAkFaXWOyS21RLPVRaYCdSxeMPqsMzqzOoP41Rfew5AmdliIisKI5IMe+uzemY1wePvamWJPhez0EREx79sclaWENWzZ0bPmZz7jVk5o75MTlJMxfJ+Ecngc5KeR0nNQ0k+bHiaXBpuOpaImiVBNZvF6n14z4u2kHOYtLpinyQ3rSUhJlZwdh+bQZ/dCaC0/xy8k8bzw+xALDl3G1if/IDYXjwrepQQlU1CgH12fF6m3XrJnVn88qybq+KAXNAHzk7TZ/QsROk7jGyLWPY+K7OxRVbzE2pFN7PVx3EpP4QQa6cvo8zVyPIvmNmLrfHbsyiKd5jZT5Ql+0kuhBAnn74EP8VyIYTg9CXwrSuOa7YXIcTaKTv8OyZ+3syuMrNrzexpZnam+nttVX/DMR2XEEIcOz2J42aK5UIIQdn2PvnSyo8cGXzUzqws3vqRraUgy3pWl0jqGUlNi6C9rdTUk0pNJc5kVpdmeWd/vo6Bsi6Ufk+zWGdYXRCWLZnNg32AVhUiB2ZWFzMn8zSR1WG5q62lrcQ4Jxtz9L13sc1E2aS9Y2Hb7ZKxnW2/ue++jDKb2Q+a2b8uy/INUHePmb2+KIrSzP6lmb38WI5sjXjfTzQDALsGsZxu8zYyUHq/HfByooBAn2TBQ7C1pFkDEJSsDojX0rXUVfWnYaYAZmFBdfOQlL3ZXpgtJJrtJbJvIuw7xu2XTj2D2VZyjn/Q+NtclpVzzhU7x97MOsw2U7PgkFmGECaRZzO7lM4zkdlel7VidrXNNPsqzWtKsXyzKMwsdd/S9R3NhBRZ2aLunhfLoz5vxA7coCkuY/weXAILX1zVwQ5OPwTbIh+C2UpG0PfF+zP1M72+L+vn4rL43JgCth18SKRtDaGd2VK8Pnn0WaM+eaqvWVXIc29CrDBeO7P1YTnqM3vXVSK6RmvxPThXSKQfYBbG2sw8qR3Wifo+XWdzifpmUd22x3HZXoQQa6dH/sInmtkfO21/XLULIcRWolguhBD9ZtvjePbgR+6ILlNo4Ha6qkHYG682iZgW1XlvvQtS1yWJGFNz4Paxjio/YAc7TpJPBkvaNCBzU5vNRkkHMCKORKPMB+QtV21kNCUMIiPbZrMRd2xnyZUitYdZ+zdq3shrW2VI9CYEXwKw7Udv1ZdVFHnHiiyrUom236NR5rvN7IVm9n7S9kIz+7OjPZz1wr6XqC66ByJ1kpfgiymdWByJ3s6gkq0Wx1LiOOdtXYpjtcR4JIkbviEbQvuw8Rd2ebjfxt9F5XRYuK3orU8Ehu4u92P0zOqi/IjUGrhelBA1J7lqljKEJGdksLetWD9xnrltFYresjlvA6NnRpt+mWL5ZsLuOSRSc7Fro4tqqFnPmKqp4KZDNcJOFfhqao/LoHzp4+Y3+tDZafGSKvDWFFgklpdnnPbq+Lx+7HSdqPPtcAAPhpRw00t4WpBzFSkYavtKyg9Ue4zn27HPjcqO9BmZGgTba+vY4nJOfyJHkRi2k/PmKfqm+w9UfDWlaPXX+3zsHhmTOm9bUSzPUfFtexyX8kMIsXbWMcpcFMVzzOwtdvi75Z1lWb5mBZt9pZndXhTFU83s/zCzv7XDkeXnm9kVdhhshRBiK1EsF0KIfrPtcVyDH0KI3lFlgP4lM/tHZvZZM/tYURTvLcvyrmW2W5blu4uieMgOkyy9xQ5f3u+b2Z1m9pyyLD+w3JELIYRIKJYLIUS/6VscX2nCU1YeOO0kz1yYuMVblu0rsrXkJERlUlCUMkUJT9OynuyYScNZwreaOgu0UiitZio8ZktB2R3K3aYSu735ujZMkyuV83VmYHvxkiNVx7LvJF9Kdhc8V/iZx6S+a/K4traYrraVSOK3bPuyyVeRtusfYXKlbzKzT5dl+Rkzs6Iobjez7zazpQKtmVlZlu83s/cXRbFjZo83sy+c9CkRIylzTtLfSLoZJfhyE4RN6n/NzMroQYEPhd35ZoydKc6cxsRuUB6eq/5CYrpdEoeZ1cVr33OWTQ9mjGeRrYSVvWcOe/5GHJfthSVyZO2R3WhI6rDsJqJNz8SMxHn4/JvKxh2r57K2F1YeO+3L2hcVyzeLwuavX7z22T3hXbptLYxI1DWsXZukz+fZRqYWD0xY/XcumpW/+llV3dNmdZ/5f2bl8WfMzOySc7OqvQvz5VryazhZaf8T50pJfeqaVT2wwHgWmmkSTdjYAC1AVZDMSdyJpGNl9g1sj/rktTqS/DQnfnnXGOtTR4TPHTxvVcNO8AD0klevw8Lo/a6MfndGKSjUJ1+MproVQqydssM/M3t8URQfh38vgU1+lZn9Nfz/s1Xd6o65LA/Ksrz/JHWWhRBiGVYcx80Uy4UQ4kjZ9j65bC9CiLXTMVJ9oSzLZ672SOYpiuJjlvGyoSzLb1rj4QghxMbSIZYfSRw3UywXQog2bHufvPPgR9uj8pZLJ96T7Q4ay5n5tpTmNpvbYvtiFpkce0FBlvXk2jukzpMDs/YogzFqXAfkii6JxQVne2Hzh6MsrJPtZTJfZ8Zne0FpeZKQefOEMyuLJ/GNJMBtJWpRZmpP8p/2u0pZW2S7yZEzL8oM7W03J6M7bmcNyZXuNbOvhv8/parrwidtPfmfNpbm9RBdYzlz0efcY/ukvO8sm+JELTaQ8oF3YaZA6kyhslvZWfYgXuLMMaNR/a8Zt6oMSR0uG9k38FC99kjy2lwOt9msZ7BnznHbXjxbCzvvkd2Ibcvbf7oGCvKcRCJZO5sVAMurmO0leiZENrZohgDF8s2itPlrwrPKsXsa69j6eM+k+jbPfWblprP6ObOtTAGLoV36tbPy33vO4d8nPYMfwMO3mpnZzkOPTatGX5w1J4uLd0+nPjEeP+PA61BhdbWRIZnhBbeB/XC0laS++CptL9g/T+fds8WkuHXgPXcbf83iGUyi/kSE91xhz80dMnNOFMuRml3xCC2M7PcDu9wiqzu7LBXHpfwQQhwBa9Aaf8zMnl4Uxd+1wwD7QjP7/i4bKsvyh1Z4XEIIcWJRLBdCiH6z7XFcgx9CiLWz6kBbluW4KIofN7PftsOB/lvKsvzkincjhBACUCwXQoh+s+1xPHvwY5EOJZIv5cibmFXFy1bPZmth+82RBzGLi2fBYfaHWhboxnYWLdvcZxtq5zJJ3M5DFVpcqm98AiewluWa7HiVs70kCR2zwpjNsmtHMnnPisIk86uc7SWSGDM5n7d+ZJuJrCZtZW9YzpmtJbLNLLqHmm3r0K+VZfk+M3vfGjZ9osHvo0tsXHb2Cc/WwrZVm8mpyiqPM0GNoZzqse4AyjtptheMZ0RKjPEQy8Pd+boRLlt9yMi+EbW3WXZC6qJn7ibZXnZInfdZk1ocOys7pH1E6rz1mQWmNkMMfq/VwQxgAXxOpuejN0PA1I7lWDnHjb/N9nXMUOa1L5ohINUrlm8Wze/U6zuyWO/1r5vbxvXwOo1mq6rNpkVsFWNnZq1pDIf4bY/+7az8uTurDUFH98FPw8HOe2gGcIBFmvUDZ1iBgy2q/WLfl6VY9NIujslsibhsbbvEVsfiS47VBaG2F9I/x/NPrepo5YPtt51hCteL7NPeby1Wh9dwuu4GsADajVJcHwS/fDF+H6WFkfW9up7LRYMbiuNSfgghjoCtMWELIcQJRrFcCCH6zbbH8c6DH23eEOByZjy5WZu31ov2j9tt+1baW997M8ZG4Jiyg9XhdqP1kRzlB6M2ok8SonrJlVjC0xxSIiUv+dt0nuwlR5Gjt8e4ja7KEJq0ixxLpAzJSSIatUcJUbsmPG2rIsm535E1+AvFEjTfEjJVgdnsHho67ex+Zfeudz+z9bxtjclblz14M7h7Yb5uCOXT6UMEF2PtDRxJ/FxTAGQoP5gCYVmVSOm0s2dOpBKJ1B5s2Rzlx4C0szoznjQW606R9pHTvtv465Vr+4QNpLeE0ZtXT+2Y6msJH2G9KGFgl2eW98xYNs7jNsTxgwlP9xctCHgK5Lb9GdwPU3uYzRQheJ+x5KcsCaqZ2flzh38veRBW+hwoP/b/w+Hfz/7+rO7sZ2flL1ZHcG5WhW/z073sJRFlCuiamiupKeYXm6NIyxI1iNksVnjPHXZ8te0TZQkDj59NSIDfRU35kdax+TosR2oz3EbUJ/aInivT5yZR7pnNnt34/YZxPUPFt8qEp21/P0S/e72Yvu1xXMoPIcRaKU2BVggh+o5iuRBC9BvFcQ1+CCGOgG2X2AkhxElAsVwIIfrNtsfx7MGP5miRJ1kaLKjDem8ecibr9ZLgsC8xsrWwbXn7YuszC4s3J3iOnLgLTOrkWXiSXG+AEkCyTS/JaZKIHTjDhkx6FyUJZZJ4TzYXyehZOSfBYk57+lzenOZRssjIitIl4VHXhKds+56cbtGykS1GHD846s+uMWaPyJFmYplZzzxJfypjEj0sJ1X0LkiZ907Nl8+TBGdms9i168SulITPkw8nKbInT25rVfGk4qze+14iVyKzpXR505Nja2Ht3voDUsesLlj2Epqeavw147YWz/aStrULlSPYQbLA1BImkudjTSqOCfGSxB+WZVbMrlZOZhPrer+yZ1ITxfLNAW0vrM+9yqTmkf2BXYcYvyE1qY0erY6ZJJQ2Mzv3yOHfLz0wq/tyWN/Onj38e+rsrA5voOoZsf/YrKpc8sKtWbmrv16fH0kxrpZoFrdVnaSoz50z8QBC++TElh71M8ekDteLrC7NZRjsI7Lnnpu8utrACB4GeF2l6w3PJbMgebb91EfwLIzjBXVmebG4yyQF0e8TZNvjuJQfQoi1s+0SOyGEOAkolgshRL/Z9jiuwQ8hxFpZ17RaQgghjg7FciGE6DeK45mDH3jC2s7G4tlT2q7vyWojiw3bbyQfimwtnlwwyYq8Y2XSuC4Kthx7g2d7iWaema7n2WKijNJkn0zWFWWGjmwvkYS4zbJRe1sJcXRdRPN4e+t3yfbcdbaXtnOG56yPbPso86bRnKHKi1dtZ6LwLAu7jeUWldkMASibTvV7cAB7ZIaA2gwsUC6qD8my/mP9xNOJknVqWeUbf83qklwm2fWWjWJP9OBmsSlnBjFqhSRlXI49R7z2YWM5s4Z8mdR7s7kw2wsrn3b2daqDVLo2W1qaAYDMCmBmNq6uUe8eYvdYJJv2ZOXRjB1trZiL4rli+WbRfDbnWNHZdRD1d/B67DoLWLLADB0L44XqBn0UAiTeX2eq9XZhndo9WR0AziCD6xfESuJZuRlt7GFNoj73jrOxgtTn/H5gm2XXSE7MiPrsObO5RGkBmB3Ue1akuD1E6yuWq40NggcoztyFFsY009yyM7hgOZpFJ8dWn2Mx2vY4LuWHEGLtbPsosxBCnAQUy4UQot9sexxfevDDG3WKEpay9i4JGnG7njIkSnjK3lJ5CTmb6+CybZIfJZZNeBq94fc+CzsX0Vu+LiybVCuaa34V82hHyeOWVaG0VVPkjOzm3CM5KpNl97Xo7ZOm1do8mt93zjWecz8yZYj3NpAlyWMqEHiZZ8NHZ2X2VgffAE2PDw4QE1tOlR/QfkCCek0BAIFyUNVjMunorRVTe+CyXpxnMJVFzvretlgde2ZGypCc5K/szZ6n7Djd+OuV2TpmZrtnqr+n5uvM+HXFkgiyxHhmMxWIp3gaN/4uWnafLNvlzWMU571rRbF882jG8BylKMLWx2sv3ct4z+Zch7hsiuV7EOxRxTeolB1eks9pkk64T9myB7BTL5F1YmfJTnlO/58dirf7VfXPo35o1DeMFMqrUP2yayx6hmKi6lNVYEcV34gkQGdJTs24+hPLBySWs7jt3Rc5yvSob9ZWbe313bc9jkv5IYRYO9s+yiyEECcBxXIhhOg32x7HNfghhFg72z7KLIQQJwHFciGE6DfbHsezBz8WSecimTzbjpckNJVR8sTWN4sTkkbtzeW8Y/ES07QFtx+tH1lF8LxECU/ZufBkc8tK7NpKLyMJXZQQdRW2F5ZQiMnOlt1+14SpbZPHdk14Gt0PUbK7nPt920eZN4nSZtdeZF/ocg+xsifpR4vLsPG32T4i7UO0mDxW/YUFdkjwqiWmPDXfXrMvBMlPWRJMz/bCEp6ifBfPS1LyRvcogt9L+tg56yOrSnjqyZej5LC7pD6yvYACvlZmtphT8J/TyfYCK6FUeof0kmpS6eoks8R4ZlwKze4HT0rNno/e/dRWSt01SV7bZcTRgPL1Zkw34/1ET0afrilch/VZu/a98DpN+8D4fgEsjClu1xJKEwsjxvIRBI2U3JrZz7DsWWE8u83c/lvUs/uF9am9hKjse12WnMT5i9bBevY7o1lmtnxmwfcSqE+TW8N3jXGbWRhryauZDRYOfGqngosVr6H96rqJklNHVhcs51jHcvr0i+oW1W8LUn4IIdaK/IVCCNF/FMuFEKLfKI5r8EMIcQRse6AVQoiTgGK5EEL0m22P450HP5gkn7UPSJ0Zt6Is2o6Zb5GJLDRtLTZdrCweTMrlbT+ytTDwvLFz7FlsmO3Fk6AxmBpwWYkcs7jkzC7RdTYWJlGLJMZRZvwoS3ZkAcpp7zLbS2RbyZHY5cwkse0Su02C2V48qXSUtX9I2vdJe2Rlwe2yWQFwWdxWzUpRLZxmCjBzZgBwbC0s23808wuS9hXNYIKfD+0dOZncp/t01m9rqUOiGV7Yfr3ZXNraXjwL0C4p12wrUGa2F1z2olSHVpeLZmUmlUary051sPj9s2sIr6UxXLjM1sKsX5EtButzpNI5ttOoXxe1iaNn0UxreB1EMzAymy+b2cW7p1ncj5at3QdwUReVhdGznyRbQs3Wgvcv6UCjFa1seRHjdgri3/D62dEsldHuc+6xtnaYyP7Q1RId9f3Ybw2vv5HKzPZoNrO7oNWlFsurawCtLiNibT1wDjpdTxjL92EWosjmG8V6Fre72uKj9lVeYyeRZWdcFUIIIYQQQgghhNhospUfzdGiaKTJU24wZUjXt9pM7ZDTzkY+2WhlTkLVnISmbVUeuP2chKZM2RGpPZYdFctJeJozt/WksZzXjmXcFht59bY1btmeo1Jhiexy2rskVMVt5bzxy7kfo0TI2z7KvGk0v2/vfmOqOPyO0/XK3vphu/emBF6qUJVIlCSTlQtInMc4gA+Lb/tTMjRUgOAbogOSJA+XTesP4K0/U4F4ydzwbdei+8ls9r1EiS+7vhWKngmRYoi9+cX1mYrHU34kwQZTe2C9l/A0JTe96BLYPibJS28L8VqAA0vfNyo/aklxqxO/jwlPoT19R6hiYvcD3gvem8NU9uJ8aveeWeyZsqgv1LxOFMs3j+Z35t3n6TuPkk3itcnu30jlh+t5yrBU9uJXUd0sSQHSJN1/XnLqdP8W8AFR7RElPE1xHeN7rZwSY/LVKV4sZ5MUdCGnz942wX1Oe3RdmdPOngE15R/8J6k8MH6fgnJSfKDag14DznUzVfHBF8tUfBjLc5R50e8PFou95NddFDu43LbHceX8EEKsnW33FwohxElAsVwIIfrNtsdxDX4IIdbOto8yCyHESUCxXAgh+s22x/GswQ+cHofJpZmM3rOasHVYEtIoSSmWo22ta6RrHC8yRyRRYwlNPasMs8NEErQcWVoXlk14mpMQNcd20mUe7RzZWjSPd8483W2TjLZJQtr2XETbir5Xzx6z7aPMmwQmPGXxgFkZoqTBzOqCy3ryaJYIFe0PzBbj2V5obCMWGJQ6Y3lYHThLfGpWl8cy0noDWH9E7A/euYwkqzm2k+h+btvx8Z4TbRPXRclfoySnZjNbi5fwNJUvwrqLZ+UzVZlZXbC+cB5605iGUmlMtFh9sWO4WKOEpkw27VnDWDkn4WmXZ46HYvlmUdp8DM7pe0X9DZb81LPFMAuLF6vTsXr3xJTzrBKsaHCwI9hYsj94CVMTXuLLBK5fK1d/SQ7UWrtZ+4SnkYUm+s3QdcKG6FkTTXIQPQvYcwvbWXLTUZCc+jRaXSCWD4l1FUlxO7QwOrGcJTSNEp5GyVGj3xc5EyIwPIvTtsdxJTwVosdcef319r6ytFf81m/Ntb38jjvsjR/84DEc1Txlh39CCLEtfNv119sNZWkvIrH8h++4w/5VT2O5EEJsC//D9dfbz5Sl/VMSx190xx32kp7G8ZMWyzX4IcQJ4L979rPt6c985nEfhstBh39CCLFtfO2zn21POUGxXAghto2/++xn21eeoDh+0mJ5ds6PRVKbaAYVlDpFtpic2V7Yvrz5txmRxYZtn5Fjf+mSWdmT0jG5mbf9dc/2EmWRZtdPzmwvUbb6ManvOlsKy7YcHQuTsHky1C62HHbdn33gAXvg3nvtBdddZzc873m19iRvi2xq0f3WNjN1lNFcbAbN7wuvMRY7maUC66PZJzx5NEqd0zIjUmc2s8B4UmoWx2txjFhgarLpSj6LM32g1JlJrRm1GWDQVpP2A8vmSJ3ZzAmR3Si6XyO8Z2I6lmjmHfyszPbizXZzmtR7s72cqTaCVheUSqfZAND2MoQDG5BeEEqhk0SazQpgZrZXXZh7WAfbYrO9sPLEzB574AE7e++9dsV119ktVSwf2yyGRzOItX2meXaoRXXNbYjNoPns9u75dB149oS2NtrIZmzG7VnMCubNdEj7E44FZroO3N/J3jCAHTALjDfbC1sW65K1sRYnYFk2Y5pH1Gfuss0ueH3DFCpxn9FzK+e5sAvxN8VoZks0i2M5s7sckC8GvzecpSuV2QwvWPau6xSrH3vgAXv43nvtWdddZ59u9MnTYUT3U1ureY5VHdn2OC7lhxA9pyxLu/1Vr7JnPfe59rRv+IbjPpw50gDMNo8yCyFERFmW9qFXvcr+/nOfa086IbFcCCG2irK0j77qVfZ1z32uXXZC4vhJi+WdZ3tho8ReEs7mOmbtk5TmJDxFcGQuSgTF5i/PIdo/Izo/Q1LvzZnOFCE5CU2PO+FpjsIgSjLKRj69ZaO3ZG3fdHjtbZUnkaLJU8Hg6PGH77jDXnTjjXblddfZq6680hBMbuSd1+itT6Qc0Shzv2he217ithTHPOVGqt8ndTntuF18wceUD14ytR1SRwEFCCY+S28LR/AmCd/27Qzm18G3SmnZHQjeAzhZabPRW3ckSiiKb9DwHK9b+cGUJ+z4mNoD670kp6jsGJG6UyQh3mlQfpzC5KZVGd8QYjmB3+s+fG/j6sRiklNMiJcSnbI321hmSU6xPn1Xn7jjDrvixhvtO667zt515ZW2bzMVH0uol6OMZM8cpM11oVi+OaAXP+rvpPt3mf6Gma8CZMoPTFgd9Tmjfl4NogI5gA2kGD5wFF4FU26Qne3AQe/AB08KgwLWYSoas/gZu0PquiRMXbbP7n0/7LcYi/tY5z23dqsgPyJJSs248mMID4bdKu6PoG5AlB+eSm+anBqCcS1RdVLxoTIEjp/F8qYKJH3ff3LHHfatN95o33LddXZ71SdP9+sEllt2QgRz2ru4C7YJKT+EOAGUZWm/etNN9q3Pf7591dOfftyHM8e2jzILIUQbyrK0D9x0kz3j+c+3J5yAWC6EENtGWZb2uzfdZN/4/Ofb409AHD9psVyDH0KcEH7n1lvt/nvusStf9rLjPpQaXbJKb/uotBBie/nYrbfaF++5x/7xCYjlQgixjfzxrbfaQ/fcY//jCYjjJy2WZ9temifAk/AyWwtLbhq1RxI9M25XQdlVNGLVnCe9zfZzYBYYJlHzpG4sial33qKEpm0tLsuOikVSrcgWE1lZIlkv1rMkqLhsZHuJbDM5tpocW0xkRTlobGMymdjtr3ud/cRb32rvuuGG2n4iu1FbW0tki/HsLydt1LjPYOJEZvlj8YIlOcV6TwrNEp6ijBRjbtqXJ5lltpbI/sjAa/E0yKcPqkAxAcmtZ4FJsIR5KI8eEAsMOi68DgX7rMw6hOeaPb+82NMW71wPGn+bZZbQtJbkrvG32Y7JTU9VC2GSuyihKcqmWyeyhZNVwheTrou9c7O6PZBKp0SnKPGPbC/MAjOxmbXl/GRiv/W619kL3/pW+9UbbpjG+jEsa6Tc5pnRpK2sftE4JJxdAAAgAElEQVQ2xPHRfOZGP1IiW4xnX29r48VtYczAa54RSfopEL8xFqcy2g5rsZjY3mrHwuI6WiCrbQ2cmyv6DljyUK/PvWzcjmC/GVg7i+9mZqdJwmk8fxhrz1xSre+c/1GytWDCUxLLa3ZUKDPrEtZNqmDLkpyame1X15NnURyTumY5xerUJ//g615n3/PWt9pvN+J4Wwt/lLy6032TuexJRMoPIU4Q//ctt9gX77/frvyZnznuQ6mx7aPMQgiRw+/dcoudvf9++196HsuFEGJb+YNbbrFH7r/fruh5HD9psVyDH0KcIPb39uzdb3iDfedVV9mlT3rScR+OmR0GzW33FwohRA7jvT37wBveYN9y1VX2d3ocy4UQYluZ7O3Zh97wBvvvr7rKvrzHcfykxfLOtpfIlsLqmC3Gs6fkzPaSRnC62mJS2ZuhhdlOIisMOxe4fTbqxGw/ZrPz4s32wvYVZW729rtouRwiW0tki/GkYOy6i2ZjiZaNZGU5s8FEthh2fFG7Z/Hx7ETvffvb7Qeuvda+8fLL7RMf+pBNbHbtLWtr8Y6FnQsj7WIzaN6HnuUutbMZXrDes7VEs72gvJRZPZitYllLn0dZfRiUyaIVImWlZ/YXMy6VxmWHLK0/LgtlZivBhzWzP6CFJJrtJSKyT3axveDxpXq0t+zCwiihTrJntLqMyGwvI2eGgEF1YCiPxplbJtVJwlkBcDaXZHFBq8sFmDEoKe89W0vUnu4NvNdS3X98+9vtOddea193+eV2VxXLzfjMGlgf9VW8a6FNnFYs30wiS3E06wjrm2E5XWfMstEsp31g3GdEVouo74qUcFOlexnjAMaH1D5wfgEx+wSb+WvAgrJZ7cSxz5IzC8+yffHouRjZXlIsP4XPMjivbLYtPFcDeDAMyWwvBRzMiLTX7Er4kKmgVpcJL6dZXtDCiLE8siim8thpH9t8n2liZh99+9vtO6691r7m8svt7kafPGc2yKjPjsiKvhgpP4Q4YVw4d87u+Df/5rgPo8a2S+yEECKXvXPn7H09j+VCCLHN7J87Zx/ueRw/abG8KMv2H2m3KMrLqnLKLYZ5a1hyM5awDpfNacdy9BaKvT3cCdq9fe0E7SwhULQ+G3n15slmiQAjZYc3qsW2xdo9wtF3Uhe9XWBqikjhECURxWWwjiU/zVF+5KwfqViWVZ4wNUc0ihztCz8fe6OIb3Lwc6XR7zSg/oiZjcuyMDP7r4qifLblc5vZnWVZPrPDqmIBg6Io04tzFqeZ2oAlq8Qyq8PyKacdXuBPl2F1WL7IaU/PJG991r5LlvU+a3oDtQOBeoe8iUJqb6BSQlW4sbz7cZ/UsfsxR/VWOy5Sl5M4mz0zWV/A6x/skiSmmNju/2/vbIPluMo7//TMXMmyJVkOwgHJBfJbSrEgxAkuA4mjAApWtBhsWAzGSm28lTiVUKnii/MCxg5EvNjmAyZkkQlsttgPOBblgG1coRYtNkmwDFaCHBMo4oDAjg2OFku2pPs2d3o/qM+dp3v+zzx9embuvTP9/1WpbuucPt09Pd3/PtPn/zxHly8mND2tt0yXtyKS07aVkM1l1oyZk92yk8e6y9PHT/098ZyqV8N9WbWoAURRm1ost+qn+5SJ5HI6LpZ7I5PF0cjiMrqW9HL4+7SIzGY6LlJNy6njo+OMJEl/PlueKvwVyd9zqE+NnFk5zVPLZd1cel1rX0hrkS5bWu09V6ZAWc4Nlq2gXQUNp3PbUWK6mCQZJNMUKWh8to7nQPbc0FXxnB/e75ug1SFZqQh2Zlj6jXRZt58C7pyY52pbiV1w581oZ97x7nLQ8GlVf1IvF/6K5LUYaTVaVye/9hJho4SqujymD4C0PmznX0TkOPvki0SHvRBCSAwhvpAQQsj4Qi0nhJDxhjrOlx+EkCVg0ixzhBBSR6jlhBAy3tRdxwdOeKpByVisUI0q9RqU8NNKtuiFagTbl5fE1CIcS0yyR2Q7s5IChs9nhbrEJDxdAPUaLxFrWbzEmZbFb9CwGC+sxEsY5IWCoH1524+xpqPtD5qEVJdb+wqf0fteyiZfYnKllYuO30R6gJKfosR3IjihKdqWFZKI2nnhfTNOvYdnKbbut6mQRE8V6iSawdJrJUQN5dpqnXSM5eyvvrf1w7qsNsTcd17YCwoVRbZ6EZFmtrK2P08p+3Mo11ZpncRUh7iERHrIHi3StVXr86rt6MGirpPcoeSmucR3yqs8k7WbVZ5lfQ2G5VlQJoLtzZ792bJCe+GL3vc9jA4vtXzloEdwyz6jrYgC1N/RoJBpq88btqU1AfVDdRmaEEDfJx6urquNTYWwFfUBdKhFo6SW56qn0JoiDRAio7Xe6xNXwZvYIBfCCJ5LTa3VmdauWddbptfVoYinq3Vb4Lzo85oLgQHr5kKLsu8rF2KkvsOg5XNKgGeU7s8BrddaHTTcCyu09LkYaiJih6WUDeGPSXiKrptBkltPMnR+EEJGyiQmSyKEkLpBLSeEkPGGOs7ZXgiZGF7wohfJW971ruU+DEi/ucOtf4QQUkd+5Z3vlE1bty73YUCo44QQ4vOC886TV//2by/3YUDq3iePdn4UrTb6hCC7tDdrhxW+4Z1oZOtCoSIifjhLsCIhW3EMViiJZ0FDYS1onm/L4u3Zla3vCJUhO6J3LqzjRvsvG6rhhV94oSZl9oWs414WbrRuTL03m0tM2EzxHvvvH/iAXPG7vys/+O535Zv797vn1bPJo2XrvuoXDtevnCwvXhgZuga9MDpkyffCYvR2PW2z2g9K2XtA23CbejlYoUEojEaXNdVyA2xXz1Dihf95ITyImOdIbjaA7GHZAln/Rbr2ZSvrPwp78WZ+0WEvTRRHZXzY+TDLjjqBs8oKHWZ5mQFlIt2ZAawZADyrNKpHtunw9+zzzpM/+Oxn5XsPPSQ3XXaZO2tXTHjiMKCWr0yQfqFQcqs/4s0KgvRXX8foOrSeC21Qb4VlDII+Jj3zS5odQFPFIejZYDpBy0EojAgOccxpqdKv8LzQITb6uYFmjqlCwxjKhseqPlfQbR1y0gKhKDos8TQ15VoIS1z3M6rNi9UB6OnZgojqqa+cB1dDXWRhli4dtqhne1kMYVQCrUNcwjIKS9TL3mxZVohisc//1o9/XC7atUsO/9M/yZOPPmr2s/qVWZdFWR227qW66zidH4RMABs3bZI37N4tIiJ/+NGPLvPREEIIqcJb9+yRtNORcy++WC581auW+3AIIYRE8uJt2+TC175W0k5HrrrlluU+HFKgcs6PsiOG1ihdUlivTL3nRvCSo3oJUfUbviouED0Y5b1Rj3HBoFE4a2QHJW/13uhrnEG00m/kvcSbngPBao/cFFUTAg3qzEAJS72Eq1X2745Ei8h1f/ZnkmSv/jdfcIG88vWvlwP79/dsq2pCVO9conq93qRZ5sadouZYOoycYHpdNIKHRvg8h4FIVz+rOkMGfZNf9hrXZTo/WxjB06N6OZeH87S1HCGLZeAmqjpaiEYJc/uf6l1Puy3CKKlOjJcbOczqc8ns1PJqx/mhXSKLo6jW+QvnQGUc1SODIdHpjB4B1C6PrFyPEE4/r+p7Nw+Tm6IkqHpd3QYlP52XU66PX77ySmlOTUnSbMru226TGy67bHHdsjo+Kr2llq88it+51WdG/RlL1wNIf70kp177YjmqR8Q4/sJnybk91HL4DFq/dUJUlARUrwz71MYHQI5ARIyWe+7CRtJb3wD6LtL9jJYWB93W7des7S6vD46P89QBvOSi7vIZL+wuP/fEqb8//n637FnVLoikElB9XhYTmuqE1Nql93z+b7F+JnsuWC698NjQ1zhyeXh9chGRN99yi7RWr5ZGsyk/92u/Juf8wi/I4UcfXaz3nKwoWTyi7O/TokO/7jpO5wchY87GTZvk8t27ZdXqU0+p09euXXHuj7TCP0IIqRNv37NHmq1Tv0YajYace/HFsnWFuT+o44QQYhNcH41sxKC1erW8ZYW5P+reJ+fLD0LGnOtuuklahTnCzn/5y+WS171umY6ol7onVyKEkH6cfe658qqrr5am0vLVp58u19166zIeVS/UcUIIsXnThz8sU6d1k6Q0mk256A1vkM0ve9kyHlWeuvfJo8Je9NufssmVrFAN1B6FtWjLT4ydD+ElRNX1KATG2r5nF/QS2ySgDH1W1EbX63bou7Dw5l9HeLZDLxTFCq/wkoii68ZLwmmt2wZlqH0blOnlUYXFoOND2z/4wAOy7qyz5Lxt2+Tcbdtk/113iYjIk9//fl9LbJWEqGXDZornadLeGo87xevCskKjewiti5IyimDrZhvUi3Ttp1YoS6iPSYYXs64X+rUKlOnPGp4T+jVkLlQl++CWZdlKSIfwLNRom7n2CdgnsEVr23cTJMnTifF0qEpITpqzTzsJT0Uva7962K91EWYW6HnlT57TCe+y5Wkd1nJcLWcW6RPPqTK1r+DA1klOUcJTHeqCrNJekryjR47I1//mb6TZasmrr75aRES+vm+ffPvAgcV1wr1TxnaNytA1juiXJI9avnJIpXtNNFVZICbkGPVvqyT+F8GWfkv3A154dsxIrRV+268+t262QjrXWyYi0gj6lx9zgjTAQ9DSZdjeCVEMzwpTy7N6reUoxFF/ltU6BDGEMKr6NevUwfxs+Lu5W7b5ku7yeTu6y4e/mh2gOpiF73WX//PUn44Ka5kDyzNWiOLJ/F8RkVn1HaIQRi/hKUpUrfUb3WOPfelLMjc9LT9/+eWy5swz5eBdd0na6cixp5+WIl7orhfmayUlLybHLv4mrbuOV875QQhZGXzlzjvlK3feKZe96U1y6xe/KO95+9tFpHrHZRRM2ltjQggZJtPPPy8fv/ZaERF59dVXy5c/9Sn5H7/3e7nO90qAWk4IITb/eMcd8o933CGXv/e98sY9e+Svsj75vNNuKam7jvPlByFk5NT9LTMhhEwC1HJCCBlv6q7j0S8/imELlkXYC4vxbHHhwCyLMcKabQXtH7VbAGX6uKwZYJqF9UTscB90LF5Yi5fBHVmhvNlcNOg7Qtb1qsSEiqCwGM+2a20rZraUftvywmIGDXtBoS7W9vvNPtEvxMWbJccLayk7y45lN637W+aVhP4+kPage98Ki0GWSrSuHvGwrMzh2p8z1kXPDI3nJEY67OmBV49mE9D78WaDsazIAStExgtbgW1A2EoC7NF6WzqsBYXA6BlcWupkBIu0O4NLNyw6/9DVD1uUgl/FnaDZXHJhLSd6y04e611Xh7oo1/TirnSoC5rZBc0Ao8v1PaB1fh6UiZy6jhYkLlSybFhLcT9lykI5tXxlUXzueyHHVki217/2Znvx1kWzvVjXmRemi0IQ0WexPt+UU49CGDvqBg0arkNh0AwqGkuroe6DEBnvWWA9SxZnawH6L9LVZVQm0tXwKaXvTTXbi6wPf8/pll34X7rL297WXT5tw6m/z/+4W/bTx7vLT586oTrURev2iUy3Z47j+tCurToRKNwQ6bMu130QdI15fQ2rb+71Qfpty1q2fr8Uw16Kn6PuOs6Ep4SQkbPUyZWSJHlbkiTfTpKkkyTJKwfcHCGEEKGOE0LIuFP3Pnm086Pf6LLnVkBuCO/Nr8ZKfor2P2hC1BhnR799ipRPiOodv9fe2ldMQlPP5VH2bVmMQ6Cfm8Gq99wQ1ra8hKgxb1Y950dZZ4h1LtAb237OkH6urLKjQlWdId79vAwWu8dE5C0icsfS73rlU9RCa655pBeeM0SPtCAnmqej1rEMCtJG7xr27gF0v2o3iN4+zI1XHO4vYCU2RaOBeuQx1OvRvIY6mdD5AUYu9Qhgbjn7kNotknN+ZKONuUmoUEJT6wsGGek62u0BRgYt50cozyU0VUnyguMDuT1ERE6AMpTw1EqiNw/K9LrI2CLSdX7oS6SsW7GKQ69fefG4lhDquIPl/BTB/USrP+H1ab3+cxWtttzOVdprUI7knAsP1Fdx8Xn6LSKLv7Ia1o+hUGQ4Mxa1HDjzRLq5Q632KOHpFHD0IbeHiNJwfQL0crgw5pUq/vvfCeTwA6f+nvhJt+xE98QsZLp9Umn1SaXVs5lI6+TVs2q3Qeu1iw/pNtJvka5GW8mp26Asxo2NfuPpH+FI162Ep16C46Lzo3jJ1b1PzpwfhJCRs9RCm6bpd0REkmSYP58JIaTeLKWWU8cJIWT41L1PzrAXQkgl/ujmm+VImsqRNJWj2b/Z7N+12awFIt34wgoWu41Jkjyi/l2/dJ+OEEImnz+8+Wb5tzSVf0tT+UH273D27xql4yLVtFyo44QQMnLecfPN8oU0lS+kqdyb/bs/+3c1++Q5opwfOkmKF9aCbD6WHS8QE9ZSBS8hqrevmIR6yN7kJUS1Qln6bbPffvtty0toan3Wsm8LrVASdN14iXJjQlVituWF2KCwFO9YYsJikAUZfRYU6qK3W/xO+lmfPUt/bLjSsaNH5eqdOxePMVgIv/u4SmIl+H4uwZE0Tc3YwCRJviIiLwJV703T9IvVdlkP+iXiQsuWXnra4U3tVkVnY/Cuuxi9QHqAbNUx95iVUC+U55KAq5XTcOKMkxVs0SjURaRrlc4lIQW2aWSPFulaqXWoi97WokUbdQpEBM7fquo7yovczi6iGcvqHKzQz+P6xYSmql6HuIRlK6zFs0pPgzIU4qK/S5TwtHivdLIyL1SyX5K74rIVvvjc0aPy33bu7GnzzYKOF/ddEur4CCk+m60ko2UiNIptUD/RC5EsQzhW6weI91wqbqcICntB/RkUCiPS/SwoukOke9wdtYMpddPrRKidrGELxj0qXW3i+qDhKNRFpKvbSJ9FuhqeqwfJTXNar4+lUfgrkj8ZIUTlJ492y2ZVRukf/UN3+cSRU3+P/bRb9kR38VhW/ZyqbithDeGOM0rAka7rhKbqsQG1GGmlFfaCEqL20/KwvbnC/3U7S6uraP2siBw/elT+eOfOnr7b/2GfPAfDXgghlWm32/LIww8vin34IYBmLhg2aZruGMFmCSGkVrTbbfnWww/3dJj/H1h32FpOHSeEkOGw0G7Ldx5+uOdlaFHL694n58sPQshISWVZkisRQggZItRyQggZb6jjFV5+FK04lgU4xgIXWMo0KCgEJo2o15Sd2Qa10e0sVxmaMcELdbHcxmhdVGZZJ0O5913FzAqCrpuYGVy8fVlhI16ox7DCXqrWo8/ar33xTa8XbuSd17JhM0mz2euMXMgrQEWLXWWSJLlKRP5CRF4oIl9KkuRbaZpevsSHseIpq9MxVmetF55OLEfqq9jQrn71MTbVVaBML+cs2JlotSJuHD1zS1jOlaltDSu0KGfvVsshVCVRH1DbtsO6uv08CHUREZnPPMO5sBdlew4hLtZsL7NZex3qom3RKKxlGqxrhb0gK7Q32wsKgUFuuQXBsw14Vmkv7AU9E5Nms7evstB7hy+lllPH+xOuEVF/vbAVqz/o1Vfp03uzsVj13u8Lb/vhHFhhK1OgDIUjmvoM9p/7/aBusBbQutzMVw6LWq7a62UULpNECHw4rrYRo9rMPliiPlNuFrIgjCr8RFb/sLvc6l13RoW1HPvP7nKYkUtr+ZwOccyWT6p6retBl72ZufRHRVpt1YfltlFfDHcp9sm9mblirnGE1vJimFrKPnkOOj8IIZV5wcaNcqTdG028ZcuW3P+XWmjTNP1bEfnbJd4tIYSMHT+zcaN8H+j4hQUdF1laLaeOE0JIec7cuFH2Ay2/iH3yHNEvP/rNKY6cD94IvTePuJUwNSa5qsego5Mo95w1gt5vm9aIKTpXVZKcWutax9BvWzHOj37zYOsykf6uBb3sjc7qbXluhhjnR4wzpEpCVS8hKvoslhvLO+9V6ovX8rGjR+XNO3b0JDx96qmnctupu8VupeONBsaM+lV1FXjXiDcqMmhSX2/UfFj1q4x6eL+pg02VXSEkR20ZI4ip9wAECUe9zoDeZpqtrAeTGmq4DM1ml6oPGD7XguqjWc6PMPI3p+pn1chfGA3Mlan9otFAz/mB2iO3h1WPRg6tJHr9Ep62BSfnmwdletm7Bov3xbGjR+WaHTt6dP4/lI6HdanlK4t+fXLk3PAS51dxeOj2etnI4Qmp4gL0+rn6XKD7qGrCU5QwFSVEFelqXdNIiBo0vAP0UZNWHKoO++qoA9Tb72Tbbaj6tjrWBnARou0vGNvXBC2fNpwdQeN1ktPgBhERORlcgGqbSJdnjfo5UIZcHLoMtfecH0U3X9hGjFajBMbo90Gx/fNHj8q7d+zo2T775Hno/CCE9HDmWWfJT5991l2v3W7Ltw4eXHwAhIdPUVjrLrSEELLUrF2/Xo6fOCELIHxFs9Buy6MHD/Z0qJETnlpOCCFLy7qzzpJjJfrkC+22fPfgwUUNDy9gilpedx0f5qyChJAJYMcVV8i3jhyRvXfdJedecMFQttmp8I8QQkh1vvTYY/K1w4flzddeK81mzDi8DXWcEEKWjl2/8zvy+WeekRs+8xl54TnnDGWbde+TRzk/UulvrUe2XStRXljW9iF9MMiCpx/dqJ21rmeXDutaFmSvPulTJtJ9wzTv1HvtvVAava6VsBRtX4OSZqF23lszL1RDl6HkazGhKt7yUoa9DBK2YtVbCU+texHdO6jMOhenr10r0ydPys6rrpLXv/GN8pX77pM973mP/ODxx82Eq+hY9XqTJpyThheaNap9xaw7aDIw736cAmUopG2YYTFWCAxKuJerz0RTh40sqI1Nre6tb6n6ZrYDbQjQVudmK7+eSD4spQF+RzfAQyEXtqNObDiuBfVQ1Puf1+XZfnXYSy4hXrYPywrthb3M9inT5bp+HqzrJdHzEqIWo7Q7Wd2Csy66RkL9GevWyfoNG+TPP/lJ+aOPfEQ+9Cd/IvfceafMqy9+QU5d8/PS+5xADj5q+coB9cmtvp/XtwpYfWfUt0T95Bisa2nQUWnUJ0e6b9V7CU+RllshMDCERt3AQcuRPot0w0qa6gcS0nWdZFWvG7alwxIbEWEtxfVECrre6S2zkqeGEBcU1ijS1Xj9rJlWxxryWCP9FOlqvBWiGJYt/fQSSqOE0ah9MfFp8f962Qt76YjIqrVrpdPpyI7du+V111wj+z/3Ofmrm2+WZ558ErZpg30W+3h113GGvRAyIQRxeywdtOsg0p6fl1arJa1WS37zqqvkN664Qv7nJz4h773hhtx6zVZLLrn00p4O/hNPPAGPjRBCiM+V118vV15//VC2dca6dXLGunXy4b175U8/8hH5zUsukWd+/OPF+larJb986aU9AwLfKei4CLWcEELKEGZY+cIQ+uQL7bY0W6d+sv/G7t2y453vlE/fdJN89rbbcus1Wy152aWX9rzIOcE+eQ6+/CBkQvjafffJ/Z/7nGy9+GJzHU/wUhG5YOtWabfb0po6NWQwNzcnnYUF+cHjj/esv2HDBtl/4EBP+Y033hi1X0IIIad43zveIde9733SKISqoBFrVB84f+vWxeVOpyONRkMOP/64zM3O5tY7c8MGuR/o+E0FHe+3b0IIIV3u+9jH5MLXvEY2XXhhT53lkka8ZOtW6XQ6i+6q9tyczM/Py9OHD/esu27DBvkM0PL3s0+eI0kj3kg1kyQ9PVtek/3Vb0/0Y7rl1CegTC83QFlirBvKW8a6jcJfa9naPqpHM7NYs7WUbW/Vo/YC6q11qszw4oW9eOt6oR5VQ1W89jEzy6CwFyuso9/2vbAVL9TEC2sZ5rkokzn6ymuukds+/elT1riFBfnLW26RT95+uxw/fjxn99N2w1Cu7YhpmiYiIqcnSVolc8i/iBxM0/SVFZqSPiRJUlr0m4W/xWVPx1H7KWPdFqjXmj4FylaBel2G1tXbR8tom3pZl60Cy6jM2r51rN5nQecKnWPLSr0Y1tLsLdPraquztkU3Sk7NoGcw0DMcoLAXHeqibc/zmVdZawuaWUXbm9G6MWEtKMO/FbYSOwNAcftothdPZ8vO9vLtZ5+VdWeeKTPT0/LP3/iGfOCGG+TQI4+Yz4Fi+xMispDpuEg1LaeOj47VSZJuCsvZX6vPPQXKUD/T03qrH+3pvlevt4WOFT0XkGbqbVnbR+3RtmKeRTHronrzGZmtrGf2yml1E5SpDQQN11qf03IQ9uLNFmbN5hJoG1oetqv3pWd+OZ4ta/3U4Yonwzb1vtQymu0FPRcsffVCzcs+C3RZzMxb+rh0//5t7363/P6tt8r87KzMz8/LX7///XL3HXfI7MyM+fujGHLz7yIyzT75IhOR8PSc88+XP967V/7XoUPyQLstH//qV3vWeSBN5RXbt8srtm+X/zsECxIhk8pPnnpKZqan5S8+9CH5xU2b5GMf/KAcP37cb0jIALz0/PNlz9698sVDh+Tb7bZ8Fuj4P6ep/NL27fJL27fLN6njhPTlicOH5aEHH5S3bN8u//W1r5VDjzyy3IdEasCLzz9f3rV3r9x+6JDc3W7LHqDl+9JULtq+XS7avl3upJYTYvLMj34kJ44dk0/deKO8edMmuev222V2ZsZvSEwmIuzl3G3b5NW7dsm/HjggzakpvwEhxOShBx+Ul2/cOLRkl5xTnJThwm3b5Nd37ZJDBw4shlwRQqqz8+KLh5q0mFpOyvCSbdvklbt2yffYJydkYB68+2558O672ScfItEvP4r2eW3VQjb7YgbzQGjnzSxghYLodf/+3nvla/fcI4mIfHDfPtlg/HDzwgtaRn1ol4AyXW7N1lK2PcrMrY9PY62L9m+169def37rO+jX3gvF8EJFBp3BJWbdmLCXpfws3mwzZWerKXMsaF9VQoAs6p5ZelxBs3Zpyuq4t30RkS/fe6/83T33SEtEPrFvn5y1cWPOPoq2r+vRNebNEOPdj8imat0jKHu7fv6hGQS05VaHuKDwBmSrtqzW4TWJs+UAAA9SSURBVLxM6Rlc9HL4qxo19HK2QkzYSwc8CLR9WluhO2C2Gv1ZvZA6z2qMwlbQDDB6GYWiiOAZAvS6bVCPjh+10et67XU5smqL4GvQq/dmMCtCLV9ZFJ/N3o8a/f0loNzqZ6NZBy0euvde+fo990hTRP503z5ZX+iTo98H+rjQZCHWzJGIcKxothpr/1Vmc9Fl1syVbVCG2unfUvrzt7IVcrO5qPqg4Vq/W2oHQbe1ZjfBLz9vthet5XC2F3VhoLBFEXyNaf8CCmvxtF4vo7AXpKVWKAqqR9dlzCxC3kx6MTPLoG1Z/ZXic6V439ZdxyfC+RGTt4QQsvTwDiUe1HFCVj68S4kHtZyQlU3d79Colx/aKoMSKKI3s+jNcrE8YM0vrveP1g3HoN9MF9v/SpIs1l+WJDC5qX7DhpwVo3Z+WO09t4XnDNEUz1URL6EpGgn2vqsqSUA9N8UwnR9eElDvs1gJi7y55FHCo5iR6Bg3RlkXijdi6H0WBLofyXjgXcOejle1aYZnTdHl8XNJsuiQeFmSSBVDdYwTDLkBvdFCPdKkctTBEX40QqjLqybcQwkFUUJwPZrYavfWxzwnPAeh50CISaaMRgarOkPC8rxRXzYhqXX8ZZ0dZRKehuUY91GMe6n4TCx+p9TylUfZHzGes6NsP9jSd+u6CFqur+OmiFyRJIvbutLQcuQAKW4bHStyU6A+v9ZEzwGFXHiWliNnh6X16FiikscCZ0hD2SkWtd4Q84bj+GiAX4kdta/g3rP0WX9WT5/mQZn+XsI+dBJUL/l1GyxbffayWm9dF2UTUuvlqvWecx21123rruMT4fwghKxs6v6WmRBCJgFqOSGEjDd113G+/CCEjJy6v2UmhJBJgFpOCCHjTd11PPrlR9EOaVluUHKkKie7zNupYAHrGMdV1s7sJRy16kcd9oKOz0to6lnTve9i0ISqXihGTMK1UYW9DJpQFIWSoM8yzISr3rnyPktMWAta17PgWdT9LfO4YyWO88JagjbHhr8ELUcJs60k2jHbRsvoHkFhGZZ9GSXBQwnzdBuU5FTvQ2/LS3iKbNGWlboBylCopRd+qfG0DWmLZQ/2wkqQxVqXoXWtJHhzfcr0tixbNwqHQvWDhs2IdM+nZ6uOeb7GhL1YZWT5Qd9LTD/PC4lGoFCS4n5T9a+4L+tYEOj3hb5nUP/aulZRfwbpLwoL0u2thNReWIvWkqnCetZyTD1KQdAwLoYElOvvogkeuJ6+ebqu6z09QWExMcmrvbAbFAJj6WMV/Y0Ja4lJIVA2+aqVCLnuOk7nByFkpDC+kBBCxh9qOSGEjDfUcb78IIQsAXV/y0wIIZMAtZwQQsabuut49GwvRau/N4fxoJSZOeC0NWvkV3ftEhGRF27eLGesXy+//ta3iojIP9x/v7Snp3vaoIzW3mwrlr3Ia1827AXZ+nS9dbFa2/XWRftC20Ez+nizyWi8WUeqzGDizQZjbcubk3uY7avM5lIlBKjquehnd449Fo+6v2UedzztKxv+0m/dNWvWyI5du6QhIi/avFnWrl8vOzMd33///TIPdLwK1gwx3mwGKIQH3U9W+MIqUGbZpsvO9mJZoVtOfdmwFzHqEV7IITpvyJIsEhc2UnY2GN0G2aKtsBkvLAV9794MBjEzIKCZBWKs1IOEvSCo5SsHNIJrhTyXnUGx36wtRawQyNVr1silmZa/YPNmOX39enlNpuXfUH1yq89bFqRpIrifqQn7tUJ1ys7sZbUvzmxTbK/bzYGyKmEvVli8XjfgnWv0LPBmFbH0D4XwWyGIAatviUIY9bIXvo2eBUiXrfbeLGVenz9Gq70ZGr3te1pedx2fCOfHWWefLbd9/vO5sluy/1+xZYs888MfLsdhEUKEFjtSjo1nny1/XdDxO7L/v2rLFnmaOk7IskItJ2XYcPbZ8v6Clr8v+/9vbdkiR6jlhCwb1PEKLz+Kb5Ost40JKBMReTJdWrPNvYcPL9m+Xpd0Py1KGOo5NAZ1huh1Yt7sem/fPbcIqvccCNbIVtl6zw2hly1nhZcQddD2obwNyvTyoAlNR+X8iKn3qLvFbpJIRSRdYh0/sEw6jhKGWvdAqEeJ83Q5cnAUyz3nRwuUxYwWeglNPYcfokrCU2sEDY2mocSfutxy1HjODZSEz3N+oGUvUa7lbPH2XyWh3qDOj35Qy1cmyAmqQQ7lQ0us4yIi/3sJtfy6BPvVQql1HyAXDKpHEyfoer2MkqDq5RjnRwPUW24Pr09f9gqo6jBATkrt4dTODfTc0IRzqNvH6F/AcumghKfecwu5AMtMQuBpuff7AblMUPJWK3l13XV8IpwfhJCVTd2FlhBCJgFqOSGEjDd11/Elf/lxTvYmFsWpobeZZeLY0CiV92YUbdeqLztK5sVEE1JHaLGbPJJMx5H2eto8aGyz5ZxA9auceuScqBJ7TkgdoJZPFq9Qzgivz436wXoZaam3ri5DWh6T00hrfd1/2BHSD+p4hZcfRQuNZYWqgjenvPVywUtIitqj7aJQFb0tq75se433ciUmLAat41kfre168697ITgIFMLiJcIbNAmpta6XUKhKe2Q10+UxnwVtywu7iQl7iQk38uyEMdRdaCcVdA17ydIGfUnstdf1KJmaxtN5lNzVszrrMt2hR2EvVogMSpKH6qu+SELPLC/hKSqz7mtPm5De6HOB+hXIXqzbeWEjMWEtMQlLkVV6mAlP0ef2dNp7vsaGumio5SuL4jVhJSFFCZ297xJpodXfQ9tCyTZFute/NWDpgZ4B6D7wtNwKVUH3iVePJlEQ6Wq4NzjrabUV4piAspgQRtSP9epjkp6j8zajynSi6bKhIChhtYD1RLDue2GBXj/ZCrH0JnTwtFrjPVfKhth432tdYdgLIWTkcCSGEELGH2o5IYSMN3XXcb78IISMnLoLLSGETALUckIIGW/qruOVX34Ey01VC7MX1hFA9iprW5ZtN9iStP0Kha1YITZeWA2yfXm2syphMRovBMha1wtr8dqUnUVm0FlHYqxcSxnWstyzwXjnytpX2fnHLTvgPCgrC+MLJx/vHiijfWVzJ1n1Xu6msiEwuj3SeSuEJ9ibLSs0ilP3rNJzoExvw5sBwMq14sXvV8l7UkWnvUz2etkKBemAspjZVhYK61nLMSGBaDkmrCbGlu3NvOA9B2Kglq8sUum916xQurLfmxUqovcZiAmh8frUMWF16Bmg72lvBpJQbv0mQGEzehnlLLHCVtC+qszWYrX3chJ69V74RMALtfd+02imjfLQToe1ePqGwiU7Rj0KcfRCyb2wfAH13rNAL3shNtb3UnZf1qwwdddxOj8IISOn7kJLCCGTALWcEELGm7rreOWXH96ot5cYsWxCT+vNrP7i0FvYGGeHN1tM2eRw3ohhjDMkZkQUvcUt69AoHhfaptce4b0FHmaSUE2V0cdhOkeG6WLx3Bplk5jqcm9E0fssVam7xa5OxLglkCPEenaUTebsOdWs9sj5gZ4ZllsDjfDp5SlQZo3StsG62gVSNuGpNwvPoG4PiyquuJjRPOT8iBlh85KIxiQ0Rc4Oz1UXk/AU6bDX7xqVjg9rG2R4oOs/YGlVAPWDvTbDAGkt6lN6MyyKUV/2h53XJ7fcFsjRaCVHRc4N5PLwnBue8yPm94mmym81hOcc0Whnhz5u5LKrosXW74v5wt/iul7CUjQJAaKM/nq/D5CWe89QtGwdc911nM4PQshI0dZcQggh4wm1nBBCxhvqOF9+EEKWgLpb7AghZBKglhNCyHhTdx0fOOGpZYsrGxahvwAU4mLVo0R2lm0stBs0oanVHp0Lz3a2ANYdVZJThFc/Cgu0yPDCWjw7tbWt5U6IWiVhqdXe27+3Ly+R1DCp+1vmOjFowlNrXS88DyXh9rQRhU9a65ZNHBeT+M5bjqnXx+Ul5PNs44PqP9JWr94LhYxJ8umFvYwioamXsDUmLKbqsXjhjcOAWr5ySKW3/2n17cp+b174tQWq9xJDWpqDJinwwl683w9lQ+11uZWQFR0/0nq9LeuzemEvKAQHrWudH90u4D2jq3yXXr1Gh3Ci78DSapSwFPW5vbCYGC33QjBjQlGsEHVUhp4bXn8oJvSo7jpO5wchZKQwszQhhIw/1HJCCBlvqOOjy2dECCGEEEIIIYQQsiKo7PwIlhmUdV0Eh4qgbMiWxRlZxbxsyp4dueo82N5sL1XCVmIyV/fbp4X1Vqvs2y5v+x6eBc6zIw4ayqKXY2YciJlne1izyQw620uZzNJLYYfuR90tdnUFXaOeTlug8EVkL0ZlVr0180q/4/PCG63tezOw6PIWqEdWZs8W7tXHhFJWwdN5T690uWUZ9sJOvBkCqszmgvZlfZay2/c+n16OeSYNG2r5yqJfCDrSn5jZrrx+qoUXYoJ+H8T0iVF9TEgxIibE0evze78vNGVnexn0WKxzgdqj54alSchB4D1XdRlKB+CFpejfnSgsxXuWeCGKXohl1fBy6xkSiAk9KvtctBwedddxhr0QQkZO3S12hBAyCVDLCSFkvKm7jg/88sNKDINAo1iDjlzpcivJz7CcHZoqb2at9jHzm/crs7ZVpl3Z9mUZNDnSoElIrWMZtTNkmAlLve1784CvJFbqcZGlwUvUJYKdEx5e4mlrBKbf/jWeW8FLPLcA6j03hwhO+IfWtUbrvGeaNxpZJYk2IuY5MKhDznJDVHFuxCQ0Ra66QROyWqN5noNvVAmrNdTylUMqvf1uS/OQWwzpk9X3DT8WdJspY79ePxPtC7lArL5Zvzb6WKxrtUpy7JiEq1W25U3YUPac9ltGeM4P5Jj0NMD73jy3AkpILUbZDNiXdV15ya+RvqLnhucc0Xj995ik7t4Li36OxeL3UHcdp/ODEDJSmFyJEELGH2o5IYSMN9RxvvwghCwBdX/LTAghkwC1nBBCxpu663jsy48jIvLDqjtDlh4vVIYQMpa8VC1/uSOyscI2jgzrYEiOgXR8UGJCJQkhy8pLC/+vouXU8RHRETlyfBm1nBAyNrBPrkjStO7vfwghhBBCCCGEEDLJDGtGO0IIIYQQQgghhJAVCV9+EEIIIYQQQgghZKLhyw9CCCGEEEIIIYRMNHz5QQghhBBCCCGEkImGLz8IIYQQQgghhBAy0fDlByGEEEIIIYQQQiYavvwghBBCCCGEEELIRMOXH4QQQgghhBBCCJlo+PKDEEIIIYQQQgghE83/B/i9A7rL0OpSAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 1152x576 with 12 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 12 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import lenstronomy.Plots.output_plots as out_plot\n",
"\n",
"from lenstronomy.Plots.output_plots import LensModelPlot\n",
"print(lens_result)\n",
"lensPlot = LensModelPlot(kwargs_data, kwargs_psf, kwargs_numerics, kwargs_model, lens_result, source_result,\n",
" lens_light_result, ps_result, arrow_size=0.02, cmap_string=\"gist_heat\")\n",
" \n",
"f, axes = lensPlot.plot_main()\n",
"f.show()\n",
"f, axes = lensPlot.plot_separate()\n",
"f.show()\n",
"f, axes = lensPlot.plot_subtract_from_data_all()\n",
"f.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of non-linear parameters in the MCMC process: 22\n",
"parameters in order: ['gamma_lens', 'e1_lens', 'e2_lens', 'R_sersic_source_light', 'n_sersic_source_light', 'e1_source_light', 'e2_source_light', 'R_sersic_lens_light', 'n_sersic_lens_light', 'e1_lens_light', 'e2_lens_light', 'center_x_lens_light', 'center_y_lens_light', 'ra_image', 'ra_image', 'ra_image', 'ra_image', 'dec_image', 'dec_image', 'dec_image', 'dec_image', 'D_dt']\n",
"number of evaluations in the MCMC process: 2200\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:root:Too few points to create valid contours\n",
"WARNING:root:Too few points to create valid contours\n",
"WARNING:root:Too few points to create valid contours\n",
"WARNING:root:Too few points to create valid contours\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 849.6x849.6 with 25 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"print(\"number of non-linear parameters in the MCMC process: \", len(param_mcmc))\n",
"print(\"parameters in order: \", param_mcmc)\n",
"print(\"number of evaluations in the MCMC process: \", np.shape(samples_mcmc)[0])\n",
"import corner\n",
"\n",
"\n",
"# import the parameter handling class #\n",
"from lenstronomy.Sampling.parameters import Param\n",
"# make instance of parameter class with given model options, constraints and fixed parameters #\n",
"\n",
"param = Param(kwargs_model, fixed_lens, fixed_source, fixed_lens_light, fixed_ps, fixed_cosmo, \n",
" kwargs_lens_init=kwargs_lens, **kwargs_constraints)\n",
"# the number of non-linear parameters and their names #\n",
"num_param, param_list = param.num_param()\n",
"\n",
"from lenstronomy.Analysis.lens_analysis import LensAnalysis\n",
"lensAnalysis = LensAnalysis(kwargs_model)\n",
"\n",
"mcmc_new_list = []\n",
"labels_new = [r\"$\\phi_{Fermat}$\", r\"$\\gamma$\", r\"$\\phi_{ext}$\", r\"$\\gamma_{ext}$\", r\"$D_{\\Delta t}$\"]\n",
"for i in range(len(samples_mcmc)):\n",
" # transform the parameter position of the MCMC chain in a lenstronomy convention with keyword arguments #\n",
" kwargs_lens_out, kwargs_light_source_out, kwargs_light_lens_out, kwargs_ps_out, kwargs_cosmo = param.args2kwargs(samples_mcmc[i])\n",
" D_dt = kwargs_cosmo['D_dt']\n",
" fermat_pot = lensAnalysis.fermat_potential(kwargs_lens_out, kwargs_ps_out)\n",
" delta_fermat_12 = fermat_pot[0] - fermat_pot[2]\n",
" gamma = kwargs_lens_out[0]['gamma']\n",
" phi_ext, gamma_ext = lensAnalysis._lensModelExtensions.external_shear(kwargs_lens_out)\n",
" mcmc_new_list.append([delta_fermat_12, gamma, phi_ext, gamma_ext, D_dt])\n",
"\n",
"\n",
"plot = corner.corner(mcmc_new_list, labels=labels_new, show_titles=True)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3439.633754698765\n"
]
}
],
"source": [
"from lenstronomy.Cosmo.lens_cosmo import LensCosmo\n",
"lensCosmo = LensCosmo(z_lens=z_lens, z_source=z_source)\n",
"print(lensCosmo.D_dt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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}
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| stackv2 | 2024-11-18T18:03:05.390190+00:00 | 2019-07-17T21:09:44 | {
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b5c0a9561169e177f23b65c5f50db1dffd31ed2d | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prepare BOLD5000 data for input into a deep learning model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook takes the BOLD5000 dataset and prepares it for use in a deep learning model. Since the dataset is the direct output of fmriprep, we still need to perform a few steps before it is ready to train on.\n",
"\n",
"First, we must regress out nuisance signals. In fMRI analysis, many nuisance signals are gathering from the processing pipeline and are linearly regressed or detrended from the whole brain timeseries. Some commonly used nuisance signals include motion parameters (rigid body motion involves three translation, three rotation parameters), average CSF signal, average white matter signal, and all their derivatives. The regression of the global signal - or the mean signal across all brain voxels - is highly contended in the fMRI field. Many consider it to be physiological noise, others consider it to contain important neural information. For our purposes, we will not regress out global signal. Our goal is to decode visual stimuli features from fMRI timeseries of visual areas of the brain. A recent study on the global signal showed that the visual cortex contains much of variance in the global signal topography [1]. For this reason, we will only regress out six motion parameters, two biological, and their derivatives. We can pull these signals from one of the fmriprep files.\n",
"\n",
"Second, we must extract the timeseries data of brain regions that are involved in visual processing. Luckily, BOLD5000 has these region masks already created for us for each subject. We will take these binary masks and mutiply them to each subject's fMRI data to extract the information we need.\n",
"\n",
"Finally, we need to label each part of the timeseries data with the image it corresponds to and package the resulting data into neat X and Y matrices.\n",
"\n",
"[1] Li, J., Bolt, T., Bzdok, D., Nomi, J. S., Yeo, B. T. T., Spreng, R. N., & Uddin, L. Q. (2019). Topography and behavioral relevance of the global signal in the human brain. Scientific Reports, 9(1), 1–10. https://doi.org/10.1038/s41598-019-50750-8"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import os\n",
"import sys\n",
"from glob import glob\n",
"import pandas as pd\n",
"import subprocess\n",
"from scipy.io import savemat\n",
"\n",
"\"\"\"\n",
"Takes a subject dataframe loaded from the regressors.tsv file and extracts FD.\n",
"Performs motion scrubbing by marking all volumes with FD greater than threshold with 1.\n",
"Additionally marks one volume before and two after. Converts to a sparse matrix where\n",
"each column has one frame that is censored.\n",
"\"\"\"\n",
"def get_censored_frames(subj_df, threshold):\n",
" # Threshold should be bounded between 0 and 1\n",
" if threshold < 0 or threshold > 1:\n",
" raise ValueError('Threshold should be bounded between 0 and 1.')\n",
" # Extract FD column\n",
" fd = subj_df['FramewiseDisplacement'].values\n",
" fd = np.nan_to_num(fd)\n",
" # Create censor vector\n",
" censor = [0 if m <= threshold else 1 for m in fd]\n",
" # Censor one back, two forward\n",
" censor_fixed = np.zeros_like(censor)\n",
" for ind,c in enumerate(censor):\n",
" if c == 1:\n",
" try:\n",
" censor_fixed[ind-1:ind+3] = 1\n",
" except IndexError:\n",
" censor_fixed[ind-1:] = 1\n",
" # Convert to sparse matrix\n",
" censor_feat = np.zeros((censor_fixed.shape[0], np.count_nonzero(censor_fixed)))\n",
" col = 0\n",
" for ind,c in enumerate(censor_fixed):\n",
" if c == 1:\n",
" censor_feat[ind,col] = 1\n",
" col +=1\n",
"\n",
" return censor_feat, censor_fixed\n",
"\n",
"\"\"\"\n",
"Takes a subject dataframe loaded from the regressors.tsv file and extracts relevant regressors (list)\n",
"\"\"\"\n",
"def get_regressors(subj_df, regressors):\n",
" # Should be of dim TRs x # regressors\n",
" regress_mat = np.array([subj_df[regressor].values for regressor in regressors]).T\n",
" # Calculate derivatives manually\n",
" deriv = np.diff(regress_mat,axis=0)\n",
" deriv = np.insert(deriv, 0, regress_mat[0], axis = 0)\n",
" final = np.hstack((regress_mat,deriv))\n",
" return final\n",
"\n",
"\"\"\"\n",
"Returns subject directories from fmriprep directory\n",
"\"\"\"\n",
"def get_subj_dirs(fmriprep_dir):\n",
" subj_dirs = [f for f in os.listdir(fmri_dir) if os.path.isdir(os.path.join(fmri_dir, f)) and 'sub' in f]\n",
" return subj_dirs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Nuisance signal regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"YOU NEED PYTHON 2 TO RUN THIS. Literally just because of one AFNI command. Sigh.\n",
"\n",
"This takes about 8 hours to run."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"PARAMETERS - change these if you wish\n",
"\n",
"fmri_dir: where the fmriprep output data lives, should contain subject folders\n",
"nuisance_dir: where the confounds_regressors.tsv files are located, should contain subject folders\n",
"regressors_dir: an output directory to hold the nuisance regressors text files\n",
"preproc_dir: an output directory for the fully processed subject data\n",
"\n",
"\"\"\"\n",
"fd_threshold = 0.5 # Threshold of FD for censoring a frame\n",
"# All the nuisance regressors we wish to remove. Do not include derivatives, these are calculated manually\n",
"regressors = ['CSF', 'WhiteMatter','X','Y','Z','RotX','RotY','RotZ'] \n",
"# Set directories\n",
"fmri_dir, nuisance_dir, regressors_dir, preproc_dir = ['dataset/ds001499-download/',\n",
" 'dataset/ds001499-download/derivatives/fmriprep/',\n",
" 'dataset/regressors/',\n",
" 'dataset/preprocessed/']"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4 subjects\n",
"Processing sub-CSI1\n",
"\tSession 1 out of 16\n",
"\t\tRun 1 out of 10\n",
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"\tSession 3 out of 16\n",
"\t\tRun 1 out of 10\n",
"\t\tRun 2 out of 10\n",
"\t\tRun 3 out of 10\n",
"\t\tRun 4 out of 10\n",
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"\tSession 13 out of 16\n",
"\t\tRun 1 out of 10\n",
"\t\tRun 2 out of 10\n",
"\t\tRun 3 out of 10\n",
"\t\tRun 4 out of 10\n",
"\t\tRun 5 out of 10\n",
"\t\tRun 6 out of 10\n",
"\t\tRun 7 out of 10\n",
"\t\tRun 8 out of 10\n",
"\t\tRun 9 out of 10\n",
"\t\tRun 10 out of 10\n",
"\tSession 14 out of 16\n",
"\t\tRun 1 out of 10\n",
"\t\tRun 2 out of 10\n",
"\t\tRun 3 out of 10\n",
"\t\tRun 4 out of 10\n",
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"\t\tRun 6 out of 10\n",
"\t\tRun 7 out of 10\n",
"\t\tRun 8 out of 10\n",
"\t\tRun 9 out of 10\n",
"\t\tRun 10 out of 10\n",
"\tSession 15 out of 16\n",
"\t\tRun 1 out of 10\n",
"\t\tRun 2 out of 10\n",
"\t\tRun 3 out of 10\n",
"\t\tRun 4 out of 10\n",
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"\t\tRun 7 out of 10\n",
"\t\tRun 8 out of 10\n",
"\t\tRun 9 out of 10\n",
"\t\tRun 10 out of 10\n",
"\tSession 16 out of 16\n",
"Processing sub-CSI2\n",
"\tSession 1 out of 16\n",
"\t\tRun 1 out of 10\n",
"\t\tRun 2 out of 10\n",
"\t\tRun 3 out of 10\n",
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"\t\tRun 3 out of 10\n",
"\t\tRun 4 out of 10\n",
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"\t\tRun 7 out of 10\n",
"\t\tRun 8 out of 10\n",
"\t\tRun 9 out of 10\n",
"\t\tRun 10 out of 10\n",
"\tSession 6 out of 16\n",
"\t\tRun 1 out of 10\n",
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"\t\tRun 3 out of 10\n",
"\t\tRun 4 out of 10\n",
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"\t\tRun 7 out of 10\n",
"\t\tRun 8 out of 10\n",
"\t\tRun 9 out of 10\n",
"\t\tRun 10 out of 10\n",
"\tSession 7 out of 16\n",
"\t\tRun 1 out of 10\n",
"\t\tRun 2 out of 10\n",
"\t\tRun 3 out of 10\n",
"\t\tRun 4 out of 10\n",
"\t\tRun 5 out of 10\n",
"\t\tRun 6 out of 10\n",
"\t\tRun 7 out of 10\n",
"\t\tRun 8 out of 10\n",
"\t\tRun 9 out of 10\n",
"\t\tRun 10 out of 10\n",
"\tSession 8 out of 16\n",
"\t\tRun 1 out of 10\n",
"\t\tRun 2 out of 10\n",
"\t\tRun 3 out of 10\n",
"\t\tRun 4 out of 10\n",
"\t\tRun 5 out of 10\n",
"\t\tRun 6 out of 10\n",
"\t\tRun 7 out of 10\n",
"\t\tRun 8 out of 10\n",
"\t\tRun 9 out of 10\n",
"\t\tRun 10 out of 10\n",
"\tSession 9 out of 16\n",
"\t\tRun 1 out of 10\n",
"\t\tRun 2 out of 10\n",
"\t\tRun 3 out of 10\n",
"\t\tRun 4 out of 10\n",
"\t\tRun 5 out of 10\n",
"\t\tRun 6 out of 10\n",
"\t\tRun 7 out of 10\n",
"\t\tRun 8 out of 10\n",
"\t\tRun 9 out of 10\n",
"\t\tRun 10 out of 10\n",
"\tSession 10 out of 16\n",
"\t\tRun 1 out of 10\n",
"\t\tRun 2 out of 10\n",
"\t\tRun 3 out of 10\n",
"\t\tRun 4 out of 10\n",
"\t\tRun 5 out of 10\n",
"\t\tRun 6 out of 10\n",
"\t\tRun 7 out of 10\n",
"\t\tRun 8 out of 10\n",
"\t\tRun 9 out of 10\n",
"\t\tRun 10 out of 10\n",
"\tSession 11 out of 16\n",
"\t\tRun 1 out of 10\n",
"\t\tRun 2 out of 10\n",
"\t\tRun 3 out of 10\n",
"\t\tRun 4 out of 10\n",
"\t\tRun 5 out of 10\n",
"\t\tRun 6 out of 10\n",
"\t\tRun 7 out of 10\n",
"\t\tRun 8 out of 10\n",
"\t\tRun 9 out of 10\n",
"\t\tRun 10 out of 10\n",
"\tSession 12 out of 16\n",
"\t\tRun 1 out of 10\n",
"\t\tRun 2 out of 10\n",
"\t\tRun 3 out of 10\n",
"\t\tRun 4 out of 10\n",
"\t\tRun 5 out of 10\n",
"\t\tRun 6 out of 10\n",
"\t\tRun 7 out of 10\n",
"\t\tRun 8 out of 10\n",
"\t\tRun 9 out of 10\n",
"\t\tRun 10 out of 10\n",
"\tSession 13 out of 16\n",
"\t\tRun 1 out of 10\n",
"\t\tRun 2 out of 10\n",
"\t\tRun 3 out of 10\n",
"\t\tRun 4 out of 10\n",
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"\t\tRun 7 out of 10\n",
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"\t\tRun 10 out of 10\n",
"\tSession 14 out of 16\n",
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"\t\tRun 3 out of 10\n",
"\t\tRun 4 out of 10\n",
"\t\tRun 5 out of 10\n",
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"\t\tRun 7 out of 10\n",
"\t\tRun 8 out of 10\n",
"\t\tRun 9 out of 10\n",
"\t\tRun 10 out of 10\n",
"\tSession 15 out of 16\n",
"\t\tRun 1 out of 10\n",
"\t\tRun 2 out of 10\n",
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"\t\tRun 7 out of 10\n",
"\t\tRun 8 out of 10\n",
"\t\tRun 9 out of 10\n",
"\t\tRun 10 out of 10\n",
"\tSession 16 out of 16\n",
"Processing sub-CSI3\n",
"\tSession 1 out of 16\n",
"\t\tRun 1 out of 10\n",
"\t\tRun 2 out of 10\n",
"\t\tRun 3 out of 10\n",
"\t\tRun 4 out of 10\n",
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"\tSession 3 out of 16\n",
"\t\tRun 1 out of 10\n",
"\t\tRun 2 out of 10\n",
"\t\tRun 3 out of 10\n",
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"\t\tRun 8 out of 10\n",
"\t\tRun 9 out of 10\n",
"\t\tRun 10 out of 10\n",
"\tSession 4 out of 16\n",
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"\t\tRun 2 out of 10\n",
"\t\tRun 3 out of 10\n",
"\t\tRun 4 out of 10\n",
"\t\tRun 5 out of 10\n",
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"\tSession 10 out of 16\n",
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"\t\tRun 7 out of 10\n",
"\t\tRun 8 out of 10\n",
"\t\tRun 9 out of 10\n",
"\t\tRun 10 out of 10\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\tSession 11 out of 16\n",
"\t\tRun 1 out of 10\n",
"\t\tRun 2 out of 10\n",
"\t\tRun 3 out of 10\n",
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"\tSession 12 out of 16\n",
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"\tSession 13 out of 16\n",
"\t\tRun 1 out of 10\n",
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"\tSession 14 out of 16\n",
"\t\tRun 1 out of 10\n",
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"\tSession 15 out of 16\n",
"\t\tRun 1 out of 10\n",
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"\t\tRun 8 out of 10\n",
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"\t\tRun 10 out of 10\n",
"\tSession 16 out of 16\n",
"Processing sub-CSI4\n",
"\tSession 1 out of 10\n",
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"\tSession 10 out of 10\n"
]
}
],
"source": [
"# Get all subject directories\n",
"subj_dirs = get_subj_dirs(fmri_dir)\n",
"\n",
"print('%d subjects' % len(subj_dirs))\n",
"\n",
"# Loop through each subjects and get regressors, perform scrubbing\n",
"for subj in sorted(subj_dirs):\n",
" print('Processing %s' % subj)\n",
" # Absolute path of current subject\n",
" subj_dir_abs = os.path.join(fmri_dir, subj)\n",
" sess_dirs = sorted([f for f in os.listdir(subj_dir_abs) if os.path.isdir(os.path.join(subj_dir_abs, f)) and 'ses-' in f])\n",
" if not sess_dirs: # If there are not multiple sessions, then set to list of empty string to iterate only once in for loop\n",
" sess_dirs = ['']\n",
" for sessnum,sess in enumerate(sess_dirs):\n",
" print('\\tSession %d out of %d' % ((sessnum + 1), len(sess_dirs)))\n",
" # Absolute path of current session\n",
" sess_dir_abs = os.path.join(subj_dir_abs, sess)\n",
" conf_sess_dir_abs = os.path.join(nuisance_dir, subj, 'ses-' + str(sessnum+1).zfill(2))\n",
" bold_files = sorted(glob(sess_dir_abs + '/func/*task-5000scenes*bold.nii.gz'))\n",
" confound_files = sorted(glob(conf_sess_dir_abs + '/func/*task-5000scenes*confounds*.tsv'))\n",
" # For multiple runs\n",
" for runnum, (bold, confound) in enumerate(zip(bold_files, confound_files)):\n",
" print('\\t\\tRun %d out of %d' % ((runnum + 1), len(bold_files)))\n",
" df = pd.read_csv(confound, sep='\\t')\n",
" censor_mat, censor_frames = get_censored_frames(df, fd_threshold)\n",
" regress_mat = get_regressors(df, regressors)\n",
" nuisance_mat = np.hstack((censor_mat,regress_mat))\n",
" prefix = os.path.join(regressors_dir, subj + '_ses-' + str(sessnum+1).zfill(2) + '_run-' + str(runnum+1).zfill(2) + '_')\n",
" outfile = prefix + 'censored.txt'\n",
" np.savetxt(outfile, censor_frames)\n",
" outfile = prefix + 'nuisance_regressors.txt'\n",
" np.savetxt(outfile, nuisance_mat)\n",
" # Use AFNI to perform regression\n",
" outfile = outfile[:-3] + 'mat'\n",
" savemat(outfile, {'nuisance_regressors': regress_mat})\n",
" subprocess.call('read_matlab_files.py -infiles ' + outfile + ' -prefix ' + prefix[:-1], shell = True)\n",
" design = glob(prefix[:-1] + '*.1D')[0]\n",
" prefix = os.path.join(preproc_dir, subj + '_ses-' + str(sessnum+1).zfill(2) + '_run-' + str(runnum+1).zfill(2) + '_')\n",
" outfile = prefix + 'preproc.nii.gz'\n",
" subprocess.call('3dTproject -input ' + bold + ' -prefix ' + outfile + ' -ort ' + design + ' -polort 2 -passband 0.009 0.1 -blur 6 -quiet', shell = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some of the localizer files accidentally got processed as legit files, so I had to manually delete these files..."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['dataset/ds001499-download/sub-CSI1/ses-04/func/sub-CSI1_ses-04_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI1_ses-04_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI1_ses-04_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI1_ses-04_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI1_ses-04_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI1_ses-04_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI1/ses-06/func/sub-CSI1_ses-06_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI1_ses-06_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI1_ses-06_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI1_ses-06_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI1_ses-06_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI1_ses-06_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI1/ses-08/func/sub-CSI1_ses-08_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI1_ses-08_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI1_ses-08_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI1_ses-08_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI1_ses-08_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI1_ses-08_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI1/ses-09/func/sub-CSI1_ses-09_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI1_ses-09_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI1_ses-09_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI1_ses-09_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI1_ses-09_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI1_ses-09_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI1/ses-11/func/sub-CSI1_ses-11_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI1_ses-11_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI1_ses-11_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI1_ses-11_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI1_ses-11_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI1_ses-11_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI1/ses-12/func/sub-CSI1_ses-12_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI1_ses-12_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI1_ses-12_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI1_ses-12_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI1_ses-12_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI1_ses-12_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI1/ses-13/func/sub-CSI1_ses-13_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI1_ses-13_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI1_ses-13_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI1_ses-13_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI1_ses-13_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI1_ses-13_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI1/ses-14/func/sub-CSI1_ses-14_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI1_ses-14_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI1_ses-14_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI1_ses-14_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI1_ses-14_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI1_ses-14_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI2/ses-02/func/sub-CSI2_ses-02_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI2_ses-02_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI2_ses-02_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI2_ses-02_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI2_ses-02_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI2_ses-02_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI2/ses-06/func/sub-CSI2_ses-06_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI2_ses-06_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI2_ses-06_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI2_ses-06_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI2_ses-06_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI2_ses-06_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI2/ses-07/func/sub-CSI2_ses-07_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI2_ses-07_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI2_ses-07_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI2_ses-07_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI2_ses-07_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI2_ses-07_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI2/ses-08/func/sub-CSI2_ses-08_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI2_ses-08_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI2_ses-08_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI2_ses-08_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI2_ses-08_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI2_ses-08_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI2/ses-09/func/sub-CSI2_ses-09_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI2_ses-09_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI2_ses-09_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI2_ses-09_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI2_ses-09_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI2_ses-09_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI2/ses-10/func/sub-CSI2_ses-10_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI2_ses-10_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI2_ses-10_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI2_ses-10_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI2_ses-10_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI2_ses-10_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI2/ses-13/func/sub-CSI2_ses-13_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI2_ses-13_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI2_ses-13_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI2_ses-13_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI2_ses-13_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI2_ses-13_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI2/ses-15/func/sub-CSI2_ses-15_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI2_ses-15_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI2_ses-15_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI2_ses-15_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI2_ses-15_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI2_ses-15_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI3/ses-02/func/sub-CSI3_ses-02_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI3_ses-02_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI3_ses-02_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI3_ses-02_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI3_ses-02_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI3_ses-02_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI3/ses-04/func/sub-CSI3_ses-04_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI3_ses-04_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI3_ses-04_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI3_ses-04_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI3_ses-04_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI3_ses-04_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI3/ses-08/func/sub-CSI3_ses-08_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI3_ses-08_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI3_ses-08_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI3_ses-08_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI3_ses-08_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI3_ses-08_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI3/ses-09/func/sub-CSI3_ses-09_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI3_ses-09_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI3_ses-09_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI3_ses-09_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI3_ses-09_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI3_ses-09_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI3/ses-10/func/sub-CSI3_ses-10_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI3_ses-10_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI3_ses-10_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI3_ses-10_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI3_ses-10_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI3_ses-10_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI3/ses-12/func/sub-CSI3_ses-12_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI3_ses-12_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI3_ses-12_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI3_ses-12_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI3_ses-12_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI3_ses-12_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI3/ses-13/func/sub-CSI3_ses-13_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI3_ses-13_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI3_ses-13_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI3_ses-13_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI3_ses-13_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI3_ses-13_run-10_preproc.nii.gz\n",
"['dataset/ds001499-download/sub-CSI3/ses-14/func/sub-CSI3_ses-14_task-localizer_bold.nii.gz']\n",
"Removing dataset/regressors/sub-CSI3_ses-14_run-10_censored.txt\n",
"Removing dataset/regressors/sub-CSI3_ses-14_run-10_nuisance_regressors.txt\n",
"Removing dataset/regressors/sub-CSI3_ses-14_run-10_nuisance_regressors.mat\n",
"Removing dataset/regressors/sub-CSI3_ses-14_run-10.nuisance_regressors.1D\n",
"Removing dataset/preprocessed/sub-CSI3_ses-14_run-10_preproc.nii.gz\n"
]
}
],
"source": [
"num_subs = 4\n",
"num_ses = 16\n",
"\n",
"for sub in range(num_subs):\n",
" for ses in range(num_ses):\n",
" subname = 'sub-CSI' + str(sub)\n",
" sesname = 'ses-' + str(ses).zfill(2)\n",
" sess_dir_abs = 'dataset/ds001499-download/'+subname+'/'+sesname\n",
" conf_sess_dir_abs = 'dataset/ds001499-download/derivatives/fmriprep/'+subname+'/'+sesname\n",
" bold_files = np.array(sorted(glob(sess_dir_abs + '/func/*bold.nii.gz')))\n",
" fake_files = np.array([True if 'localizer' in f else False for f in bold_files ])\n",
" if np.any(fake_files):\n",
" print(bold_files[fake_files])\n",
" prefix = os.path.join(regressors_dir, subname + '_' + sesname + '_run-10_')\n",
" badfile = prefix + 'censored.txt'\n",
" print('Removing ' + badfile)\n",
" os.remove(badfile)\n",
" badfile = prefix + 'nuisance_regressors.txt'\n",
" print('Removing ' + badfile)\n",
" os.remove(badfile)\n",
" badfile = prefix + 'nuisance_regressors.mat'\n",
" print('Removing ' + badfile)\n",
" os.remove(badfile)\n",
" badfile = prefix[:-1] + '.nuisance_regressors.1D'\n",
" print('Removing ' + badfile)\n",
" os.remove(badfile)\n",
" prefix = os.path.join(preproc_dir, subname + '_' + sesname + '_run-10_')\n",
" badfile = prefix + 'preproc.nii.gz'\n",
" print('Removing ' + badfile)\n",
" os.remove(badfile)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ROI masking and train data preparation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have the fMRI data fully preprocessed, we will need to extract the ROI timeseries of all the visual region masks that BOLD5000 kindly provided, and match the correct labels from the events file. The result will be an X and Y for each ROI mask. The X will be of shape (samples, timepoints, features) and Y will be of shape (samples, classes). Since each stimuli is presented for about 1 seconds with 9 seconds in between each, we will take 10 second windows for each class label. With a TR of 2 seconds, each sample will have 5 TRs of all the voxels in that ROI for each corresponding class label. The datasets will be concatenated for all runs, sessions, and subjects. Thus, the resulting data will be saved in the output directory specified (data_dir), one X and Y for each ROI.\n",
"\n",
"Again, stick with Python 2 for the sake of the AFNI commands used. Also, be sure to specify the directories at the top of the following cell.\n",
"\n",
"This will take about a full 24 hours to run."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from collections import defaultdict\n",
"import nibabel as nib\n",
"\n",
"\"\"\"\n",
"DIRECTORIES - BE SURE TO SET THESE!!\n",
"\n",
"roi_dir: where the roi_masks are located, should contain subject folders\n",
"preproc_dir: where the fully processed data is stored\n",
"events_dir: where the event files are held, should contain subject folders.\n",
" This is probably the same folder as the original dataset/fmriprep folder.\n",
"data_dir: the output of where you want the training data to be saved\n",
"mask_dir: the output of where you want your resampled roi masks to be\n",
" \n",
"\"\"\"\n",
"roi_dir, preproc_dir, events_dir, data_dir, mask_dir = ['dataset/ds001499-download/derivatives/spm/',\n",
" 'dataset/preprocessed/',\n",
" 'dataset/ds001499-download/',\n",
" 'dataset/traindata/',\n",
" 'dataset/masks/']\n",
"\n",
"# Dicts holding training set and labels for each mask\n",
"X = defaultdict(list)\n",
"Y = defaultdict(list)\n",
"Ynames = defaultdict(list)\n",
"\n",
"# Manual one-hot encoding\n",
"onehot = {'imagenet': [1,0,0,0],\n",
" 'rep_imagenet': [1,0,0,0],\n",
" 'coco': [0,1,0,0],\n",
" 'rep_coco': [0,1,0,0],\n",
" 'scenes': [0,0,1,0],\n",
" 'rep_scenes': [0,0,1,0],\n",
" 'none': [0,0,0,1]}"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sub-CSI3\n",
"\tRHPPA\n",
"\t\tPreprocessed file 1 out of 142\n",
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"\t\tPreprocessed file 142 out of 142\n",
"\tLHEarlyVis\n",
"\t\tPreprocessed file 1 out of 142\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"\tLHOPA\n",
"\t\tPreprocessed file 1 out of 142\n",
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"\tLHPPA\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t\tPreprocessed file 54 out of 142\n",
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"\tRHRSC\n",
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"\tRHLOC\n",
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]
},
{
"name": "stdout",
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"text": [
"\t\tPreprocessed file 9 out of 142\n",
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"\tRHEarlyVis\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t\tPreprocessed file 107 out of 142\n",
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"\tLHLOC\n",
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]
},
{
"name": "stdout",
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"text": [
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"\tRHOPA\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t\tPreprocessed file 17 out of 142\n",
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]
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t\tPreprocessed file 70 out of 142\n",
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]
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"name": "stdout",
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"\tRHPPA\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t\tPreprocessed file 123 out of 142\n",
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]
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"sub-CSI1\n",
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]
},
{
"name": "stdout",
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"\t\tPreprocessed file 34 out of 142\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t\tPreprocessed file 131 out of 142\n",
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]
},
{
"name": "stdout",
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"\t\tPreprocessed file 87 out of 142\n",
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]
},
{
"name": "stdout",
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]
},
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"\t\tPreprocessed file 139 out of 142\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t\tPreprocessed file 95 out of 142\n",
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]
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t\tPreprocessed file 48 out of 84\n",
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]
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"\tRHOPA\n",
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"\tLHLOC\n",
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"\tLHPPA\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t\tPreprocessed file 42 out of 84\n",
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]
}
],
"source": [
"# Walk through ROI mask directory\n",
"for root, dirs, files in os.walk(roi_dir):\n",
" # If in a subject folder\n",
" if 'sub' in root:\n",
" subname = root.split('/')[-1]\n",
" print(subname)\n",
" # Gather all mask NIFTIS\n",
" mask_files = glob(root + '/sub-*mask*.nii.gz')\n",
" for mask_file in mask_files:\n",
" maskname = mask_file.split('-')[-1].split('.')[0]\n",
" print('\\t' + maskname)\n",
" # There are many runs and sessions per subject\n",
" preproc_files = glob(preproc_dir + subname + '*_preproc.nii.gz')\n",
" # Resample mask, use first preproc file as representative sample\n",
" mask_resamp_file = mask_dir + mask_file.split('/')[-1][:-7] + '-resamp.nii.gz'\n",
" subprocess.call('3dresample -master ' + preproc_files[0] + ' -prefix ' + mask_resamp_file + ' -input ' + mask_file, shell = True)\n",
" # Load new mask file\n",
" mask = nib.load(mask_resamp_file).get_fdata()\n",
" for pnum, preproc in enumerate(preproc_files):\n",
" print('\\t\\tPreprocessed file %d out of %d' % ((pnum + 1), len(preproc_files)))\n",
" items = preproc.split('_')\n",
" ses = items[-3]\n",
" run = items[-2]\n",
" event_file = glob(os.path.join(events_dir,subname,ses,'func','*' + run + '_events.tsv'))[0]\n",
" # Load events and image\n",
" events = pd.read_csv(event_file, sep = '\\t')\n",
" img = nib.load(preproc).get_fdata()\n",
" # Apply mask\n",
" img = np.reshape(img, (img.shape[0]*img.shape[1]*img.shape[2], -1))\n",
" mask_fixed = mask.astype(bool).flatten()\n",
" roi = img[mask_fixed] # Shape: voxels x TRs\n",
" # Get relevant time intervals and labels from events file\n",
" for index, row in events.iterrows():\n",
" # Beginning TR of trial\n",
" start = int(round(row['onset']) / 2)\n",
" # Ending TR of trial, start + 10 sec, or 5 TRs\n",
" end = start + 5\n",
" x = roi[:,start:end].T\n",
" y = onehot[row['ImgType']]\n",
" X[maskname].append(x) # Big X should be of shape (samples, timepoints, features)\n",
" Y[maskname].append(y)\n",
" Ynames[maskname].append(row['ImgName'])\n",
" # Save last ten TRs as no stimulus, if enough data is left\n",
" if roi.shape[1] - end >= 5:\n",
" x = roi[:,end:end+5].T\n",
" y = onehot['none']\n",
" X[maskname].append(x)\n",
" Y[maskname].append(y)\n",
" Ynames[maskname].append('none')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fix some weird things, like LHLO and LHLOC were on separate keys because they were misnamed, so combine those."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"X['RHLOC'] += X['RHLO']\n",
"X['LHLOC'] += X['LHLO']\n",
"X['RHRSC'] += X['RHRRSC']\n",
"Y['RHLOC'] += Y['RHLO']\n",
"Y['LHLOC'] += Y['LHLO']\n",
"Y['RHRSC'] += Y['RHRRSC']"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"del X['RHLO']\n",
"del X['LHLO']\n",
"del X['RHRRSC']\n",
"del Y['RHLO']\n",
"del Y['LHLO']\n",
"del Y['RHRRSC']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Save the data"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"\n",
"with open(data_dir + 'X_unfixed.p', 'w') as f:\n",
" pickle.dump(X, f)\n",
" \n",
"with open(data_dir + 'Y_unfixed.p', 'w') as f:\n",
" pickle.dump(Y, f)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"with open(data_dir + 'Ylabels_unfixed.p', 'w') as f:\n",
" pickle.dump(Ynames, f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Load data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"\n",
"with open(data_dir + 'X_unfixed.p', 'r') as f:\n",
" X = pickle.load(f)\n",
"\n",
"with open(data_dir + 'Y_unfixed.p', 'r') as f:\n",
" Y = pickle.load(f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I messed up, each subject's ROI mask has a different number of voxels and thus a different number of features, so it will be difficult to concatenate them. Let's take a look at which voxels are highly correlated with the labels, maybe we can choose the voxels based on that."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of significant voxels: 166 out of 172\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of significant voxels: 98 out of 131\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of significant voxels: 88 out of 112\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of significant voxels: 115 out of 157\n"
]
}
],
"source": [
"from scipy.stats import pearsonr\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# A priori known number of voxels extracted from each subject for the LHPPA\n",
"num_voxels = [172, 131, 112, 157]\n",
"for n in num_voxels:\n",
" sub_timeseries, sub_labels = zip(*[(arrx,np.repeat(np.array(arry)[np.newaxis,:], 5, axis=0)) for arrx,arry in zip(X['LHPPA'], Y['LHPPA']) if arrx.shape[1] == n])\n",
" sub_timeseries = np.concatenate(sub_timeseries, axis=0)\n",
" sub_labels = np.concatenate(sub_labels, axis = 0)\n",
" sub_labels = np.array([np.nonzero(label)[0][0] + 1 for label in sub_labels])\n",
" sub_labels[sub_labels == 4] = 0\n",
" corr,p = zip(*[pearsonr(voxel,sub_labels) for voxel in sub_timeseries.T])\n",
" fig, ax = plt.subplots(1,1,figsize=(6,4))\n",
" ax.plot(corr, color = 'r', marker = 'd')\n",
" ax.set_xlabel('Voxel')\n",
" ax.set_ylabel('Correlation with class labels')\n",
" plt.show()\n",
" sig = np.array(p) < 0.05\n",
" print('Number of significant voxels: %d out of %d' % (np.count_nonzero(sig), sub_timeseries.shape[1]))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LHPPA\n",
"\t(5, 112)\n",
"\t(5, 172)\n",
"\t(5, 131)\n",
"\t(5, 157)\n",
"RHLOC\n",
"\t(5, 190)\n",
"\t(5, 417)\n",
"\t(5, 597)\n",
"\t(5, 561)\n",
"LHLOC\n",
"\t(5, 152)\n",
"\t(5, 455)\n",
"\t(5, 430)\n",
"\t(5, 327)\n",
"RHEarlyVis\n",
"\t(5, 696)\n",
"\t(5, 241)\n",
"\t(5, 285)\n",
"\t(5, 356)\n",
"RHRSC\n",
"\t(5, 116)\n",
"\t(5, 143)\n",
"\t(5, 142)\n",
"\t(5, 278)\n",
"RHOPA\n",
"\t(5, 205)\n",
"\t(5, 95)\n",
"\t(5, 187)\n",
"\t(5, 335)\n",
"RHPPA\n",
"\t(5, 161)\n",
"\t(5, 198)\n",
"\t(5, 200)\n",
"\t(5, 187)\n",
"LHEarlyVis\n",
"\t(5, 522)\n",
"\t(5, 254)\n",
"\t(5, 210)\n",
"\t(5, 408)\n",
"LHRSC\n",
"\t(5, 78)\n",
"\t(5, 59)\n",
"\t(5, 86)\n",
"\t(5, 51)\n",
"LHOPA\n",
"\t(5, 187)\n",
"\t(5, 85)\n",
"\t(5, 101)\n",
"\t(5, 279)\n"
]
}
],
"source": [
"for mask in X.keys():\n",
" last = 0\n",
" print(mask)\n",
" for sample in X[mask]:\n",
" if sample.shape[1] != last:\n",
" print('\\t' + str(sample.shape))\n",
" last = sample.shape[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will take the top x most correlated voxels from each subject from each mask, where x is some number less than all the subjects' number of voxels for that specific mask, but not so low that we filter out all the voxels."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"topvoxels = {'LHPPA': 100,\n",
" 'RHLOC': 170,\n",
" 'LHLOC': 130,\n",
" 'RHEarlyVis': 220,\n",
" 'RHRSC': 100,\n",
" 'LHOPA': 70,\n",
" 'RHPPA': 140,\n",
" 'LHEarlyVis': 190,\n",
" 'LHRSC': 30,\n",
" 'RHOPA': 80}"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from scipy.stats import pearsonr\n",
"\n",
"X_new = {}\n",
"\n",
"for mask in X:\n",
" # Get subject specific number of voxels to identify them\n",
" last = 0\n",
" num_voxels = []\n",
" x = []\n",
" for sample in X[mask]:\n",
" if sample.shape[1] != last:\n",
" num_voxels.append(sample.shape[1])\n",
" last = sample.shape[1]\n",
" for n in num_voxels:\n",
" sub_timeseries, sub_labels = zip(*[(arrx,np.repeat(np.array(arry)[np.newaxis,:], 5, axis=0)) for arrx,arry in zip(X[mask], Y[mask]) if arrx.shape[1] == n])\n",
" sub_timeseries2 = np.concatenate(sub_timeseries, axis=0)\n",
" sub_labels = np.concatenate(sub_labels, axis = 0)\n",
" sub_labels = np.array([np.nonzero(label)[0][0] + 1 for label in sub_labels])\n",
" sub_labels[sub_labels == 4] = 0\n",
" corr,p = zip(*[pearsonr(voxel,sub_labels) for voxel in sub_timeseries2.T])\n",
" # Get indices of top x correlated voxels for this mask\n",
" idx = np.argsort(np.abs(corr))[::-1][:topvoxels[mask]]\n",
" x.append(np.array(sub_timeseries)[:,:,idx])\n",
" x = np.concatenate(x,axis=0)\n",
" X_new[mask] = x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or, you could run PCA and see which voxels are most important in the high variance components."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"from sklearn.decomposition import PCA\n",
"\n",
"# A priori known number of voxels extracted from each subject for the LHPPA\n",
"num_voxels = [172, 131, 112, 157]\n",
"for n in num_voxels:\n",
" sub_timeseries = np.array([arrx for arrx in X['LHPPA'] if arrx.shape[1] == n])\n",
" sub_activations = np.mean(sub_timeseries,axis=1)\n",
" pca = PCA().fit(sub_activations)\n",
" explained_variance = np.cumsum(pca.explained_variance_ratio_)\n",
" num_components = np.nonzero(explained_variance > 0.9)[0][0] + 1\n",
" voxel_weights = np.sum(np.abs(pca.components_[:num_components]), axis = 0)\n",
" plt.close('all')\n",
" plt.plot(voxel_weights, color='r')\n",
" plt.xlabel('Voxel')\n",
" plt.ylabel('Sum of component coefficients')\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"topvoxels = {'LHPPA': 100,\n",
" 'RHLOC': 170,\n",
" 'LHLOC': 130,\n",
" 'RHEarlyVis': 220,\n",
" 'RHRSC': 100,\n",
" 'LHOPA': 70,\n",
" 'RHPPA': 140,\n",
" 'LHEarlyVis': 190,\n",
" 'LHRSC': 30,\n",
" 'RHOPA': 80}"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.decomposition import PCA\n",
"\n",
"X_new = {}\n",
"\n",
"for mask in X:\n",
" # Get subject specific number of voxels to identify them\n",
" last = 0\n",
" num_voxels = []\n",
" x = []\n",
" for sample in X[mask]:\n",
" if sample.shape[1] != last:\n",
" num_voxels.append(sample.shape[1])\n",
" last = sample.shape[1]\n",
" for n in num_voxels:\n",
" sub_timeseries = np.array([arrx for arrx in X[mask] if arrx.shape[1] == n])\n",
" sub_activations = np.mean(sub_timeseries,axis=1)\n",
" pca = PCA().fit(sub_activations)\n",
" explained_variance = np.cumsum(pca.explained_variance_ratio_)\n",
" num_components = np.nonzero(explained_variance > 0.9)[0][0] + 1\n",
" voxel_weights = np.sum(np.abs(pca.components_[:num_components]), axis = 0)\n",
" idx = np.argsort(voxel_weights)[::-1][:topvoxels[mask]]\n",
" x.append(np.array(sub_timeseries)[:,:,idx])\n",
" x = np.concatenate(x,axis=0)\n",
" X_new[mask] = x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Save finalized fixed data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ok, finally made it to cleaned data, ready to train on!"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LHPPA: shape of X is (19380, 5, 100)\n",
"LHPPA: shape of Y is (19380,)\n",
"RHLOC: shape of X is (19380, 5, 170)\n",
"RHLOC: shape of Y is (19380,)\n",
"LHLOC: shape of X is (19380, 5, 130)\n",
"LHLOC: shape of Y is (19380,)\n",
"RHEarlyVis: shape of X is (19380, 5, 220)\n",
"RHEarlyVis: shape of Y is (19380,)\n",
"RHRSC: shape of X is (19380, 5, 100)\n",
"RHRSC: shape of Y is (19380,)\n",
"RHOPA: shape of X is (19380, 5, 80)\n",
"RHOPA: shape of Y is (19380,)\n",
"RHPPA: shape of X is (19380, 5, 140)\n",
"RHPPA: shape of Y is (19380,)\n",
"LHEarlyVis: shape of X is (19380, 5, 190)\n",
"LHEarlyVis: shape of Y is (19380,)\n",
"LHRSC: shape of X is (19380, 5, 30)\n",
"LHRSC: shape of Y is (19380,)\n",
"LHOPA: shape of X is (19380, 5, 70)\n",
"LHOPA: shape of Y is (19380,)\n"
]
}
],
"source": [
"for mask in Y:\n",
" Y[mask] = np.array(Y[mask])\n",
" Ynames[mask] = np.array(Ynames[mask])\n",
" \n",
" print(mask + ': shape of X is ' + str(X_new[mask].shape))\n",
" print(mask + ': shape of Y is ' + str(Ynames[mask].shape))\n",
" \n",
" np.save(data_dir + 'X_' + mask + '.npy', X_new[mask])\n",
" np.save(data_dir + 'Y_' + mask + '.npy', Y[mask])\n",
" np.save(data_dir + 'Ylabels_' + mask + '.npy', Ynames[mask])"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"\n",
"with open(data_dir + 'X_fixed.p', 'w') as f:\n",
" pickle.dump(X_new, f)\n",
" \n",
"with open(data_dir + 'Y_fixed.p', 'w') as f:\n",
" pickle.dump(Y, f)\n",
" \n",
"with open(data_dir + 'Ylabels_fixed.p', 'w') as f:\n",
" pickle.dump(Ynames, f)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Environment (conda_pytorch_p27)",
"language": "python",
"name": "conda_pytorch_p27"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.15"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| stackv2 | 2024-11-18T18:03:05.420822+00:00 | 2019-12-13T20:32:30 | {
"license": "MIT",
"url": "https://raw.githubusercontent.com/arashjamalian/fmriNet/a33ce002e46b4236e339ed0b6a4a35b696fa06eb/prep_data.ipynb",
"blob_id": "b5c0a9561169e177f23b65c5f50db1dffd31ed2d",
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"cell_type": "code",
"execution_count": 1,
"metadata": {
"slideshow": {
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"outputs": [],
"source": [
"import numpy as np\n",
"from scipy import stats\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import arviz as az\n",
"import pymc3 as pm"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"az.style.use('arviz-darkgrid')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
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"%%HTML\n",
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{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"from traitlets.config.manager import BaseJSONConfigManager\n",
"path = \"/home/osvaldo/anaconda3/etc/jupyter/nbconfig\"\n",
"cm = BaseJSONConfigManager(config_dir=path)\n",
"cm.update(\"livereveal\", {\n",
" \"theme\": \"serif\",\n",
" \"transition\": \"zoom\",\n",
" \"start_slideshow_at\": \"selected\",\n",
" \"controls\": \"True\",\n",
" \"progress\": \"False\",\n",
" \"shortcut\": \"False\"});"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"##### <center><img src=\"img/logo_inst.png\" width=\"750\">\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<h1 align=\"center\">Introducción a la programación probabilista</h1> \n",
"\n",
"\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Objetivos\n",
"\n",
"<br>\n",
"<br>\n",
"\n",
"* Aprender a construir modelos simples con PyMC3 y a explorarlos con ArviZ\n",
"\n",
"\n",
"* Aprender sobre las pruebas predictivas a posteriori\n",
"\n",
"\n",
"* Comparar groupos en términos de tamaño del efecto (en oposición a la \"significancia estadística\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Programación probabilista\n",
"\n",
"<br>\n",
"\n",
"\n",
"* Aunque conceptualmente simple, los modelos Bayesianos a menudo conducen a expresiones analíticamente intratables\n",
"\n",
"\n",
"* La *promesa de la programación probabilista* es la separación clara entre modelado e inferencia. Les practicantes deben centrarse en el modelado, no en *los detalles matemáticos / computacionales*\n",
"\n",
"\n",
"* Los modelos se escriben en código y luego se compilan para obtener la distribución *a posteriori*\n",
"\n",
"\n",
"* Los motores de inferencia universal pueden resolver cualquier modelo probabilista (!exagerado¡)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## PyMC3: Programación probabilista en Python\n",
"<br>\n",
"\n",
"<center><img src=\"img/PyMC3_banner.svg\" width=300></center>\n",
"\n",
"* Construcción de modelos\n",
" * Una gran colección de distribuciones de probabilidad\n",
" * Una sintaxis simple y poderosa\n",
" * Integración con herramientas del ecosistema Python\n",
"\n",
"* Inferencia\n",
" * Markov Chain Monte Carlo (NUTS, MH)\n",
" * Monte Carlo Secuencial (SMC, SMC-ABC)\n",
" * Inferencia Variacional\n",
"\n",
"\n",
"* backend computacional\n",
" * Theano --> Velocidad, diferenciación automática, optimizaciones matemáticas\n",
" * PyMC4 --> [Tensorflow Probability](https://medium.com/@pymc_devs/theano-tensorflow-and-the-future-of-pymc-6c9987bb19d5) más velocidad (vectorización), diferenciación automática, optimizaciones matemáticas, soporte para GPU y TPU."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 0, 0, 0])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.seed(123)\n",
"n_experimentos = 4\n",
"theta_real = .35 # En casos reales este valor es desconocido\n",
"datos = stats.bernoulli.rvs(theta_real, size=n_experimentos)\n",
"datos"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Auto-assigning NUTS sampler...\n",
"Initializing NUTS using jitter+adapt_diag...\n",
"Multiprocess sampling (2 chains in 2 jobs)\n",
"NUTS: [θ]\n"
]
},
{
"data": {
"text/html": [
"\n",
" <div>\n",
" <style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
" </style>\n",
" <progress value='3000' class='' max='3000', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
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" </div>\n",
" "
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"source": [
"with pm.Model() as nuestro_primer_modelo:\n",
" θ = pm.Beta('θ', alpha=1, beta=1) # a priori\n",
" y = pm.Bernoulli('y', p=θ, observed=datos) # likelihood\n",
" trace = pm.sample(1000) # MCMC, motor de inferencia universal"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## ArviZ: Análisis exploratorio de modelos Bayesianos\n",
"\n",
"<br>\n",
"<br>\n",
"<center><img src=\"img/logo_arviz.png\" width=400></center>\n",
"<br>\n",
"\n",
"* Diagnosticar la calidad de la inferencia\n",
"* Criticar a los modelos, incluyendo la evaluación de los supuestos del modelo y de sus predicciones\n",
"* Comparación de modelos, incluyendo la selección y promediado de modelos\n",
"* Preparar los resultados para una audiencia particular"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
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\n",
"text/plain": [
"<Figure size 1200x200 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"az.plot_trace(trace);"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean</th>\n",
" <th>sd</th>\n",
" <th>hpd_3%</th>\n",
" <th>hpd_97%</th>\n",
" <th>mcse_mean</th>\n",
" <th>mcse_sd</th>\n",
" <th>ess_mean</th>\n",
" <th>ess_sd</th>\n",
" <th>ess_bulk</th>\n",
" <th>ess_tail</th>\n",
" <th>r_hat</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>θ</th>\n",
" <td>0.337</td>\n",
" <td>0.18</td>\n",
" <td>0.043</td>\n",
" <td>0.672</td>\n",
" <td>0.006</td>\n",
" <td>0.004</td>\n",
" <td>828.0</td>\n",
" <td>828.0</td>\n",
" <td>771.0</td>\n",
" <td>804.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean sd hpd_3% hpd_97% mcse_mean mcse_sd ess_mean ess_sd \\\n",
"θ 0.337 0.18 0.043 0.672 0.006 0.004 828.0 828.0 \n",
"\n",
" ess_bulk ess_tail r_hat \n",
"θ 771.0 804.0 1.0 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"az.summary(trace)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## El problema de los tanques alemanes\n",
"\n",
"<br>\n",
"\n",
"* Durante la segunda guerra mundial los aliados lograron recolectar números de series de tanques alemanes capturados\n",
"\n",
"* Cómo estimar el número total de tanques alemanes a partir de esta información? "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Multiprocess sampling (2 chains in 2 jobs)\n",
"Metropolis: [N]\n"
]
},
{
"data": {
"text/html": [
"\n",
" <div>\n",
" <style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
" </style>\n",
" <progress value='5000' class='' max='5000', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" 100.00% [5000/5000 00:01<00:00 Sampling 2 chains, 0 divergences]\n",
" </div>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"The estimated number of effective samples is smaller than 200 for some parameters.\n"
]
}
],
"source": [
"serial_numbers = np.array([15, 20, 28])\n",
"with pm.Model() as model:\n",
" N = pm.DiscreteUniform('N', lower=serial_numbers.max(), upper=serial_numbers.max()*10)\n",
" #N = pm.Deterministic('N', pm.Exponential('N_', 1/10) + serial_numbers.max())\n",
" #N = pm.Exponential('N', 1/10) + serial_numbers.max()\n",
" y_obs = pm.DiscreteUniform('y_obs', lower=0, upper=N, observed=serial_numbers)\n",
"\n",
" trace = pm.sample(2000)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"az.plot_posterior(trace, var_names='N');"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Modelos Multiparamétricos\n",
"\n",
"<br>\n",
"\n",
"Prácticamente todos los modelos de interés en estadística tienen más de un parámetro (multiparamétricos).\n",
"\n",
"* Los parámetros que no son de inmediato interés pero son necesarios para definir un modelo de forma completa se llaman *nuisance parameters* (o parámetro estorbo). (Ej. La varianza cuando queremos estimar la media de una distribución Gaussiana).\n",
"\n",
"\n",
"* Al incorporar estos parámetros permitimos que la incertidumbre que tenemos sobre ellos se propague de forma adecuada a los resultados."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Infiriendo la velocidad de la luz\n",
"\n",
"A finales del siglo XIX Simon Newcomb realizó una serie de experimentos destinados a determinar la velocidad de la luz. En uno de ellos\n",
"Newcomb midió el tiempo que le tomó a la luz recorrer 7,442 métros. Las medidas obtenidas se muestran a continuación."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"datos = np.array([24.828, 24.826, 24.833, 24.824, 24.834, 24.756, 24.827, 24.816,\n",
" 24.84 , 24.798, 24.829, 24.822, 24.824, 24.821, 24.825, 24.83 ,\n",
" 24.823, 24.829, 24.831, 24.819, 24.824, 24.82 , 24.836, 24.832,\n",
" 24.836, 24.828, 24.825, 24.821, 24.828, 24.829, 24.837, 24.825,\n",
" 24.828, 24.826, 24.83 , 24.832, 24.836, 24.826, 24.83 , 24.822,\n",
" 24.836, 24.823, 24.827, 24.827, 24.828, 24.827, 24.831, 24.827,\n",
" 24.826, 24.833, 24.826, 24.832, 24.832, 24.824, 24.839, 24.828,\n",
" 24.824, 24.825, 24.832, 24.825, 24.829, 24.827, 24.828, 24.829,\n",
" 24.816, 24.823])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"az.plot_kde(datos, rug=True)\n",
"plt.yticks([]);"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"\n",
"\n",
"\n",
"\\begin{align} \n",
"\\mu &\\sim U(l, h) \\\\\n",
"\\sigma &\\sim \\text{Half-Normal}(\\sigma_{\\sigma}) \\\\\n",
"y &\\sim \\mathcal{N}(\\mu, \\sigma)\n",
"\\end{align}"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Auto-assigning NUTS sampler...\n",
"Initializing NUTS using jitter+adapt_diag...\n",
"Multiprocess sampling (2 chains in 2 jobs)\n",
"NUTS: [σ, μ]\n"
]
},
{
"data": {
"text/html": [
"\n",
" <div>\n",
" <style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
" </style>\n",
" <progress value='4000' class='' max='4000', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" 100.00% [4000/4000 00:02<00:00 Sampling 2 chains, 0 divergences]\n",
" </div>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"with pm.Model() as modelo_g:\n",
" # Priors\n",
" μ = pm.Uniform('μ', 24, 25)\n",
" #μ = pm.Normal('μ', 24, 10) # alternative prior\n",
" σ = pm.HalfNormal('σ', sd=1)\n",
" #σ = pm.HalfNormal('σ', sd=datos.std() * 100)\n",
" # Likelihood\n",
" y = pm.Normal('y', mu=μ, sd=σ, observed=datos)\n",
" trace_g = pm.sample(1000, tune=1000)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1200x400 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"az.plot_trace(trace_g);"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/osvaldo/proyectos/00_BM/arviz/arviz/plots/jointplot.py:143: UserWarning: plot_joint will be deprecated. Please use plot_pair instead.\n",
" warnings.warn(\"plot_joint will be deprecated. Please use plot_pair instead.\")\n"
]
},
{
"data": {
"image/png": 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y8puXxzX1YF95ioDdktrUedSmzhPuNvV6DTt3wcZNdgDftTt09ti4OHtc7wnjLSZOsCe7UsnJqYW7Tc+WwrbzKGyHmcJ25AiUiHy0yvDRqtATmwIBe8bldsB2u9v/Jqc2dR61qfNEWptWVRs2b4YNmwwbNsCRo6Hr09NhwniYON5iwgTormnkW4m0Nm0vhW3n0XTt0qkZY9i7zw7Yq1aFnuQYF2d/hRvowT6TgC0ici5SUyyuuByuuNzCGMOhItiwETZsNGz+HCoq4O8fwN8/sPvLBg8ydvieYHHxGOjSRa9XIpFCYVs6HWPsr2tXfWL3YBcXN6+Lj4MJE+wa7OkK2CISASzLIrsvZPeFm2+08HjsqeM3bDRs3AS7v7RPttx/AN74iyE+Di6+2DBhvMW4S2DoEIiJ0WuZSLiojCTMVEZyYTQ22r1Baz41rFkLJSXN67p0gSmT4PLLLaZOPn0N9plSmzqP2tR5orlNKysNmzbDxo12vXdJaej65GS73nvsxRaXjLUn1omNdX74jtY2VRmJ86hnWxyrvMKwbh2sWWuPc9tyfNvERJg6Ba74mj2+rc7yF5FolZZmMXMGzJxhl5zkF9glJ5s2GbZus0c5+XQdfLrO7ltLTITRowyXjLUYe7E9vndcnF4DRTqKwrY4ht9v2LMXNm6CtZ/aX7O2PJu/e3c7YE+bYjH+UtU0iojzWJbFgP72sIG3zrXw+Qz79sOWrfD5Fnt2y+rq5vpvsL/dGznCMGI4jBhuMWKETrgUOZ8UtiWqHS62axY3fWb4bDNUVYWuzxkK06bCtKkWOUM1VJaIdC4xMRbDcmBYDsy7xcLvN+w/YIfvLVsNW7dCRSVs/ty+gB3Ae/Sww/fIERYjhtuvpfoGUOTsKGxLVCkpsWdg2/y5PQnE4cOh65OTYdxYmDjRYuoU6KkZ2EREglwui6FD7JMmb7nZDt95ebBjJ+zYaZ88fjDPPq+lpAQ+XmWHb8uCPn0MQwbDkMH2PoYMgR6Zdm+6iJycwrZELI/HcDAPtu+AL7bZIfvoV8aajYmBkSPsiR7GXwrDL+ocJ/6IiJwPLpfFoEEwaBBcN8d+7aytNez+0g7gO3fZAfzYMTh0yL4EAjhASgoMGWwYMAD697Po3w/69VMIF2lJYVsiQl2dIT8fvtwLX+4xfPklHDgIHk/odi6X3ZsyZjRMuNQ+sz4pSS/oIiLnS1KSPWTguEsA7NfX8nK79nvvPti3z/45P9+u//58i30JlKAAJCZAdrahXz/ol23Ruzf0yrIv3bppKELpXBS25YIxxlBeDoeL4VARHDxo91wfzIMjR6CtQSjdbhgxHEaPshg9yv5Z4VpE5MLKyLCYMN6etTIQwBsbDXn5sG8fFBTao6AUFNiv73X1sGevfWkZwgFiYyGrpyErC3r1gqyeFpndITPTPpE9s7tdEqiecXEKhW05L3w+Q0UFHDtuf9147BiUHYNjxw2lpXZt9eFiqK8/+T7S02HIYPtEnmHD7JN6evfSC66ISCSKj7dPPM8ZCoEADuD1Gg4ftmfkzS+AwkJD8REoPmKXAnq9diA/VBS4ReuelsQE6NbdkNnd7glPT4P0dKvpusUlDVJTdfK7RDaF7QhijMHvN/h8pun3luuafz+b5cFVLZb7/dDQCI0tLg0Nob8Hlp2ohZoaQ3WNPWZrTY19qa6xv0asKAdfi2H2TsayoEcPO0QPGAAD+lsMHAADB0JGul4sRUSiXWysZZeP9IPp0+CrQbyszA7excVQfMRw9KjdOVNaCqVl9ntLXX1zjXiztufgc7kgNdWQnmZ/G+p22z3j3bvVEBvrx+22gsvdyfZ1YiIkdIGEhOaLzveRjqKwHWbTr/hqQj0eluM4HywLMtLtXojgpStkZlr07gW9e0NWT02eICLSWcXGWmRlQVYWXDIWWgbxgPp6Q2kZlJXZ4fv4cXuWzIoK7EsllDf9XFNjdxwF1oVqaLpu30TZMTHGDt4tQnh8vF32EhNjXwI/x8a2+LlpXeBL2JCOrxb7b1Uq+dUOsqb/xtO/a9fhShRR2O7k4uPsF5MuXezrk13cyZDshhQ3uN0WycmBn+1Lt672V3rqGRARkXORkGCR3Rey+7Zc2vZ7i9drqKi0g3ZlJdScgBNN37z6/AmUltbZ38QGvpFtuq6vg/oGu7QxMPmZz2d/c3viRIf/iaeksO08ljFtnZYmF0pJiRdXjAuL5k/FltX8M00/B3+1Wm7XvMJqte4r+/jK7ZsvCsciItI5GWPweKCu3lBXZ6ivs3+uD/zeAD6vweeza829PoPXCz6vHfS9TcsD5Z8tne799aurLcsuifnOPyWezz9RIoB6tsMsLq465PeMjAzKy8ubv24y7f0CTCJVoE3FOdSmzqM2dZ4zbdMu8fYlLa0DD6pdFLadxhXuAxARERERcSqVkYiIiIiIdBD1bIuIiIiIdBCFbRERERGRDqKwLSIiIiLSQRS2RUREREQ6iMK2iIiIiEgH0TjbIiIicl4YY/jkk09YuXIlmzdv5vDhw3i9Xvr378+sWbP4zne+Q5cuXc56/8uWLeOBBx4AYOHChcyePbvVNkePHuVPf/oTa9eu5fDhwyQlJdG/f3/mzZvHddddR0xMTHDbvLw83n//fVavXk1+fj4VFRV069aNSZMm8b3vfY/Bgwef9bEGbNy4kZUrV7J9+3Z27txJTU0NN954I7/97W/Ped8SHRS2RURE5LxobGzku9/9LvHx8UycOJHp06fT2NhIbm4u//qv/8oHH3zAf//3f5OYeOYTt5SWlvKLX/yCpKQkamtr29ymsLCQW265hYqKCqZPn86MGTOoqanhww8/5OGHH2b9+vU8+eSTwe2fffZZli1bRk5ODjNnzsTtdrNnzx7eeustli9fzssvv8yECRPO+v8B8Ne//pWlS5eSmJhIr169qKmpOaf9SfRR2BYREZHTeuSRR1i6dClffvnlSbdxuVwsWLCAb33rW6S1mIrR4/Hwwx/+kI8++ohFixZx9913n/H9P/744yQnJ3PTTTfx6quvtrnNK6+8Qnl5OT/96U/5p3/6p+DyH/3oR3zzm9/kzTff5Ac/+AF9+vQB4LLLLuOee+5hxIgRIft57733ePDBB3niiSd47733zvhYW7r99tu56667GDRoENu2bWPevHnntD+JPqrZFhERkfMiLi6O++67LyRoB5Z/73vfA+yyijP15ptv8tFHH/HrX/+apKSkk25XWFgIwOWXXx6yPDU1lXHjxgGETOF+0003tQraALNnz2bAgAHs27eP48ePt3k/P/vZz7jiiisYNWoU06dP55FHHqGoqKjVtqNHj2bo0KEh5SvSuShsi4iISIeLjbW/TD/T0FlcXMxvfvMb5s2bx5QpU065bU5ODgCrVq0KWV5VVcXnn39OZmYmQ4YMadf9xsXFhRx3wNatW7nxxhv5v//7P0aOHMkdd9zBpZdeyjvvvMMtt9wSDPwiASojERERkQ7317/+FYBp06a1+zbGGH72s5/hdrv5yU9+ctrt77rrLlauXMmTTz7J6tWrGTZsWLBmOyEhgRdeeIGEhITT7ueLL75g7969jB49mtTU1OByj8fDAw88gN/vZ8mSJSG94ps2beKOO+7g17/+NS+++GK7/0ZxPoVtERER6VCrVq3iz3/+M4MHD+aWW25p9+0WL17MmjVrePnll3G73afdvnv37vz5z3/mxz/+MZ988gmrV68GICEhgdtuu42LLrrotPuorq7m4YcfxuVy8eMf/zhk3ccff0xRURH/7//9v1blJ+PHj2fmzJl88MEH1NTUtOt4pXNQ2BYREZEQV155ZZv1xwDDhg1rtezJJ5/kpptuanP7L774ggceeICUlBSeffZZ4uPj23UMhYWF/P73v+fmm2/msssua9dt8vPzuffee0lKSmLRokUMHz6c6upq3n77bZ555hlyc3NZtGjRSUtZ6uvr+f73v8+BAwd44IEHmDRpUsj6LVu2AHDw4EGef/75VrcvLS3F7/dz8OBBRo8e3a5jFudT2BYREZEQd9xxB9XV1SHLPvjgA3bv3s0PfvCDVtsPHz68zf1s27aNu+66C5fLxcsvv8zQoUPbfQw//elPSUlJ4dFHH233bR555BEOHz7MBx98QGZmJgDJycl897vfpaysjP/8z//kvffe4/rrr29124aGBu6//37Wr1/P9773Pe69995W21RWVgLwzjvvnPI46urq2n3M4nwK2yIiIhLizjvvbLWsqKiI3bt388Mf/rBd+9i2bRvz58/H7/fz6quvMmbMmDM6hl27dlFdXc348ePbXP/ggw/y4IMP8uijj3LnnXdSU1PD5s2bGTlyZDBotzRp0iT+8z//k127drUK2/X19dx///2sWbOGu+++mwcffLDN+wyUhrz44ovMmDHjjP4e6bwUtkVEROS8CgRtn8/HK6+8wsUXX3zG+7jhhhva7CHeuXMnO3fuZNKkSWRnZwdHIPF4PEDo0H4tBYbw+2oZS8ugPX/+/FZ12i0FPjBs2bJFYVvaTWFbREREzpvt27czf/58vF4vL7/8Mpdccslpb7N//36AkOnRH3vssTa3ff7559m5cyfz5s0Lma49IyODgQMHcvDgQZYsWRJyImZVVVVwIpyWddiB0pE1a9bwne98h4cffviUx3nVVVfRu3dv/uM//oPp06e3ml3S4/GwdevWk/bGS+eksC0iIiLnRUVFBfPnz6eqqorLLruMtWvXsnbt2pBtUlJSWpWpzJo1C+CUs1O2x6OPPsr999/PY489xnvvvcfw4cOpqqpi5cqVHD9+nGuuuYapU6cGt//5z3/OmjVryMzMJDk5uc2THm+88Ub69u0L2L3izz77LPfccw//+I//yOTJk8nJycGyLA4fPsymTZtIT0/n/fffD95+06ZN/O///i/Q3Lv+2Wef8cgjjwD2h4TThXyJbgrbIiIicl7U1NQETyJcvXp1cOi9lvr06dNmTfj5cPnll7N48WJeeeUVPvvsMzZu3Eh8fDyDBw/m+9//Pv/wD/8Qsn1gxJXS0lJeeOGFNvc5ceLEYNgGu5Tk7bff5uWXX+aTTz5h8+bNxMfH07NnT6666qqQ3naAgoICli5d2mpZQUEBYP8/FLadzTLGmHAfhIiIiIiIE2m6dhERERGRDqKwLSIiIiLSQRS2RUREREQ6iMK2iIiIiEgHUdgWEREREekgCtsiIiIiIh1EYVtEREREpIMobIuIiIiIdBCFbRERERGRDqKwLSIiIiLSQRS2RUREREQ6iMK2iIiIiEgHUdgWEREREekgseE+gM6uvLw83IcgIiIiESIjI+OMtm+ZI377Oz/vLoN77rL4p29b5/vQwi4tLY3KyspwH0aI9rSXerZFREREHMDvt69dDk13rij9w6LzqEVEREQkhK8pbMfEhPc4JJTCtoiIiIgDOL1nO1qpOUREREQcwO+zrxW2I4uaQ0RERMQBgmUkSncRRc0hIiIi4gDBMhLVbEcUhW0RERERB1DNdmRSc4iIiIg4QKBmW2UkkUXNISIiIuIAPvVsRyQ1h4iIiIgDqGY7Milsi4iIiDiAarYjk5pDRERExAF8qtmOSGoOEREREQdQz3ZkUnOIiIiIOIBqtiNTbLgP4FS++OILnn/+eT7//HO8Xi85OTnceeedzJo1q937aGxs5KWXXuLtt9+muLiYtLQ0ZsyYwYIFC+jWrVvItnV1dSxevJgdO3awY8cO8vLyMMbw4Ycf0rdv3zb3/+1vf5sNGza0ua5Pnz6sXLmy/X+wiIiIyFnSDJKRKWLD9rp167j77ruJj49n9uzZJCcns2LFCh544AGOHDnC/PnzT7sPv9/PfffdR25uLmPHjuXqq68mPz+fJUuW8Omnn/KXv/yFrl27Brc/duwYTz31FGAH5bS0NCoqKtp1vD/4wQ9aLUtJSWnnXysiIiJybgLjbKuMJLJEZNj2er08/vjjWJbFokWLGD58OADf//73mTt3LgsXLuSaa66hT58+p9zP0qVLyc3NZc6cOTz99NNYlgXA4sWLeeKJJ3jmmWf45S9/Gdw+IyODV199lZEjR5Kens5dd91Fbm5uu475hz/84Vn+tSIiIiLnzhj7WmE7sm/QifcAACAASURBVERkc6xbt46CggLmzJkTDNpg9xTfe++9eDweli5detr9LFmyBIAHH3wwGLQBbrvtNrKzs3nnnXeor68PLk9OTmbatGmkp6efx79GREREpOMpbEemiGyOQA309OnTW60LLNu4ceMp99HQ0MDWrVsZOHBgqx5wy7KYOnUqtbW1bN++/bwc8zvvvMOLL77Ia6+9xvr16/EHzlIQERERkU4rIstI8vLyAOjfv3+rdZmZmSQlJZGfn3/KfRQUFOD3+xkwYECb6wPL8/LyGD9+/LkcLgAPPfRQq/0//fTTjB49+pz3LSIiIiLRKSLDdk1NDXDyEwzdbjfV1dWn3EdgvdvtPuk+Wt7X2Zo5cyZ33XUXw4cPJy0tjaKiIt544w0WLVrE/Pnzeeutt+jdu/c53YeIiIhIW9LS0nA11Y3ExFQAPtxuNxkZ8eE9sA6SkZER7kM4YxEZtqPJnXfeGfL74MGD+dnPfobb7ebf//3fefXVV3nsscfCc3AiIiLiaJWVlcGffU1j/9XU1FBebp3sJlErIyOD8vLycB9GiPaE/4is2Q70Op+s97qmpua0w+oF1p+s5zqw/GQ93+fqtttuA2Dz5s0dsn8RERERiXwRGbYD9dRt1WWXlpZSW1vbZj13S9nZ2bhcrmD991cFlp+spvtcpaenY1kWtbW1HbJ/EREREYl8ERm2J0yYANDmGNeBZYFtTiYhIYExY8Zw8OBBioqKQtYZY1i7di1JSUmMGjXqPB11qC+++AJjzGnHAhcRERER54rIsD1lyhSys7N599132bVrV3B5dXU1L774InFxcdxwww3B5SUlJezfv79V2cmtt94KwMKFCzGBwSeBN954g8LCQq677joSEhLO+jgLCwvbnGHy6NGj/OIXvwDguuuuO+v9i4iIiEh0i8gTJGNjY/nVr37F3Xffze233x4yXXtRUREPP/wwffv2DW6/cOFCli5dypNPPslNN90UXH7jjTeybNky3n33XQ4dOsSECRMoKChgxYoV9O3blwULFrS676eeeipYfL9nzx4Afve735GUlATA3Llzg0MFbty4kSeeeILx48fTt29fUlNTOXToEKtWraK2tpbrrruOb37zmx32fxIRERGRyBaRYRtg8uTJvP766zz33HMsW7YMr9dLTk4ODz30ELNmzWrXPlwuF3/4wx946aWXeOutt3jttddIT09n7ty5LFiwgK5du7a6zfLly1uVnSxfvjz488SJE4Nhe8SIEVx77bXs2LGDbdu2UVtbS0pKCuPGjePmm29u93GKiIiIiDNZpmV9hVxwkTaEjYiIiITPmY4j3TJHfOduP3v3wb/8zmLSRA39dyFE7dB/IiIiInJmYmLsa58vvMchoRS2RURERBwgtqk42OsN73FIKIVtEREREQcIhm31bEcUhW0RERERBwiUkahnO7IobIuIiIg4QKBn26ewHVEUtkVEREQcQDXbkUlhW0RERMQB4prCtkdhO6IobIuIiIg4QIx6tiOSwraIiIiIA6iMJDIpbIuIiIg4QPAESQ39F1EUtkVEREQcIFZD/0UkhW0RERERB4iLt68bG014D0RCKGyLiIiIOEBign1dVx/e45BQCtsiIiIiDpCQYAHQoLAdURS2RURERBxAPduRSWFbRERExAG6NIXteoXtiKKwLSIiIuIAwZ7tuvAeh4RS2BYRERFxgAT1bEckhW0RERERB0hMtK/rG8J7HBJKYVtERETEAbp0sa/rVUYSURS2RURERBwgUWUkEUlhW0RERMQBEprKSDT0X2RR2BYRERFxgJY928ZoyvZIobAtIiIi4gCBmm2/Hzye8B6LNFPYFhEREXGAwGgkALW14TsOCaWwLSIiIuIAsbFWsJTkxInwHos0U9gWERERcYiUFAuAGoXtiKGwLSIiIuIQ7hQ72qlnO3IobIuIiIg4RIq7qWe7JswHIkERHba/+OIL7rnnHsaPH8/YsWO59dZbWbZs2Rnto7GxkRdeeIGrr76a0aNHM336dB5//HGOHTvWatu6ujpeffVVfvSjH3Httddy0UUXMWzYMA4dOtTu+1u2bBnDhg1j2LBhvPfee2d0rCIiIiLnwq2wHXFiw30AJ7Nu3Truvvtu4uPjmT17NsnJyaxYsYIHHniAI0eOMH/+/NPuw+/3c99995Gbm8vYsWO5+uqryc/PZ8mSJXz66af85S9/oWvXrsHtjx07xlNPPQVAnz59SEtLo6Kiot3HXFpayi9+8QuSkpKo1WnAIiIicoGlqmY74kRkz7bX6+Xxxx/HsiwWLVrEP//zP/PII4/w1ltvMWDAABYuXEhRUdFp97N06VJyc3OZM2cOb7zxBg899BDPP/88P//5zyksLOSZZ54J2T4jI4NXX32V9evXs3LlSkaNGnVGx/3444+TnJzMbbfddka3ExERETkfAj3bqtmOHBEZttetW0dBQQFz5sxh+PDhweUpKSnce++9eDweli5detr9LFmyBIAHH3wQy7KCy2+77Tays7N55513qK9vntM0OTmZadOmkZ6efsbH/Oabb/LRRx/x61//mqSkpDO+vYiIiMi5CoxGUl2tGSQjRUSG7Q0bNgAwffr0VusCyzZu3HjKfTQ0NLB161YGDhxInz59QtZZlsXUqVOpra1l+/bt53y8xcXF/OY3v2HevHlMmTLlnPcnIiIicja6drWjXWVlmA9EgiIybOfl5QHQv3//VusyMzNJSkoiPz//lPsoKCjA7/czYMCANtcHlgfu62wZY/jZz36G2+3mJz/5yTntS0RERORcBMJ2eftPOZMOFpEnSNY0nUKbkpLS5nq32011dfUp9xFY73a7T7qPlvd1thYvXsyaNWt4+eWXT3pfIiIiIh0hLS0Nl6u577Rr10YAqqpiyMg487LYSJeRkRHuQzhjERm2o0VhYSG///3vufnmm7nsssvCfTgiIiLSyVR+pV6kW1e7o7LsmI/y8vJwHFKHycjIiLi/qT3hPyLLSAI9xCfrva6pqTlpr3dAYP3Jeq4Dy8+lN/qnP/0pKSkpPProo2e9DxEREZHzpWtX+wTJ8nK71FXCLyLDdqCeuq267NLSUmpra9us524pOzsbl8t10prswPKT1XS3x65duzh69Cjjx48PTmQzbNgwXnjhBcAeBWXYsGG89tprZ30fIiIiIu3VNcOOdj4fnKbiVi6QiCwjmTBhAn/84x/Jzc1l9uzZIetyc3OD25xKQkICY8aMYcuWLRQVFYWMSGKMYe3atSQlJZ3xWNot3XDDDdTV1bVavnPnTnbu3MmkSZPIzs4mJyfnrO9DREREpL3i4y1SU6GqCkrLIDU13EckERm2p0yZQnZ2Nu+++y533HFHcKzt6upqXnzxReLi4rjhhhuC25eUlFBdXU2PHj1CyktuvfVWtmzZwsKFC3n66aeDY22/8cYbFBYWMm/ePBISEs76OB977LE2lz///PPs3LmTefPmtfqwICIiItKRsrLssH3kCAweFO6jkYgM27GxsfzqV7/i7rvv5vbbbw+Zrr2oqIiHH36Yvn37BrdfuHAhS5cu5cknn+Smm24KLr/xxhtZtmwZ7777LocOHWLChAkUFBSwYsUK+vbty4IFC1rd91NPPRUsvt+zZw8Av/vd74IT1cydO5fx48d35J8vIiIictayesKePXbYlvCLyLANMHnyZF5//XWee+45li1bhtfrJScnh4ceeohZs2a1ax8ul4s//OEPvPTSS7z11lu89tprpKenM3fuXBYsWEDXrl1b3Wb58uWtpoJfvnx58OeJEycqbIuIiEjE6pVlXxcfMYB1ym2l41lGp6qGVaQNYSMiIiLhc6bjSH81R2RkZPDHPx3nuRcMV3wNfvXLiBwL46xo6D8RERERCbu+TWNCFB4K73GITWFbRERExEECoxoXFILXqwKGcFPYFhEREXGQrJ6QkAAeD3zlNDQJA4VtEREREQdxuSwGNM39dzAvrIciKGyLiIiIOE6glERhO/wUtkVEREQcJmeIPeTfzl2q2Q43hW0RERERhxk92r7eth38fgXucFLYFhEREXGYoUPskyRraiAvP9xH07kpbIuIiIg4TGysxfCL7J+3bQ/vsXR2CtsiIiIiDjR6lH29bbvKSMJJYVtERETEgcaMtk+S3LYtzAfSySlsi4iIiDjQqJFgWVB0GI4dU+92uChsi4iIiDiQ220xaKD9s+q2w0dhW0RERMShVLcdfgrbIiIiIg41epRdt/2FerbDRmFbRERExKHGNE1us2cP1NerdzscFLZFREREHCorC7p3B58Pdu4K99F0TgrbIiIiIg5lWVaLuu3wHktnpbAtIiIi4mCBum2dJBkeCtsiIiIiDjamqWd7+w7w+xW4LzSFbREREREHGzIEEhOgpgby8sJ9NJ2PwraIiIiIg8XGWowYYf+sIQAvPIVtEREREYcLniS5TWUkF5rCtoiIiIjDaXKb8FHYFhEREXG4kSPAsqC4GMrK1Lt9ISlsi4iIiDic220xdIj985at4T2WzkZhW0RERKQTGHuxff35FvVsX0gK2yIiIiKdwCVj7bpt9WxfWLHhPoBT+eKLL3j++ef5/PPP8Xq95OTkcOeddzJr1qx276OxsZGXXnqJt99+m+LiYtLS0pgxYwYLFiygW7duIdvW1dWxePFiduzYwY4dO8jLy8MYw4cffkjfvn3b3P+zzz7L559/zsGDBykvLycxMZE+ffpw/fXXM2/ePBITE8/pfyAiIiJyPlw8xq7bzi+A48cNXbta4T6kTiFiw/a6deu4++67iY+PZ/bs2SQnJ7NixQoeeOABjhw5wvz580+7D7/fz3333Udubi5jx47l6quvJj8/nyVLlvDpp5/yl7/8ha5duwa3P3bsGE899RQAffr0IS0tjYqKilPex6JFi+jfvz/Tpk2jW7dunDhxgg0bNvDkk0+ydOlS3njjDQVuERERCbvUVIvBgwz79sPnW2HmjHAfUecQkWHb6/Xy+OOPY1kWixYtYvjw4QB8//vfZ+7cuSxcuJBrrrmGPn36nHI/S5cuJTc3lzlz5vD0009jWfYnuMWLF/PEE0/wzDPP8Mtf/jK4fUZGBq+++iojR44kPT2du+66i9zc3FPex+rVq+nSpUur5T/+8Y95++23efPNN7n99tvP9F8gIiIict6NHQv79sOWrYaZM9SzfSFEZM32unXrKCgoYM6cOcGgDZCSksK9996Lx+Nh6dKlp93PkiVLAHjwwQeDQRvgtttuIzs7m3feeYf6+vrg8uTkZKZNm0Z6enq7j7WtoA1w7bXXApCfn9/ufYmIiIh0pHFNddsbNoAxOlHyQojIsL1hwwYApk+f3mpdYNnGjRtPuY+Ghga2bt3KwIEDW/WAW5bF1KlTqa2tZfv2jhndfdWqVQAMHTq0Q/YvIiIicqbGXwpxcVB02K7dlo4XkWUkeXl5APTv37/VuszMTJKSkk7bY1xQUIDf72fAgAFtrg8sz8vLY/z48edyuAD86U9/or6+nqqqKjZv3sz27duZPn06N9xwwznvW0REROR8SEqyGHeJYf0GWLMWBrSOWnKeRWTYrqmpAeyykba43W6qq6tPuY/AerfbfdJ9tLyvc/Xyyy+HnEx5/fXX88QTTxAXF3de9i8icjoej4eqqiqqq6uD1zU1NTQ0NFBfX09DQwONjY3B7S3LwuVykZiYSHJyMsnJybjdbrp160ZWVhZJSUlh/GtEpD3S0tJwuUILFTIyMk55m6u/Xs/6DSdYneviB/e3v3Q2Epzub4tEERm2o9H69esBKC0tZd26dTz99NPceuutvPLKK2RlZYX56ETEKRoaGqiqqqKiooKKigoqKyuD1ydOnDirfVZVVbW5PCUlhZ49e9KvXz8GDRqk8C0SgSorK0N+z8jIoLy8/JS3mTjeEBMD23f42Pz5cQYOiI4TJdvzt11o7Qn/ERm2A73OJ+u9rqmpIS0t7ZT7CPSKn6znOrD8ZD3fZyszM5PrrruO/v37c8stt/Db3/6WZ5555rzeh4h0HL/fz4kTJ6iqqgr2Cgd6hBsbG/H7/RhjgpeYmBhiY2OD17GxscTFxREfH09cXFyrS6AHKnDStt/vx+v1Bi+NjY3U1dVRX19PXV0dtbW11NTUUFNTw4kTJ0J6ptvi9UJ9vUV9PTTUWzQ0WHg94PFaeL32eoDAW6vlgvg4iO9iiI83JCSAO8WQlGSorq6murqaffv2sXLlSnr27MnAgQPJyck5oxPJRSSyZGRYTJlsyF0Df3vfcP+90RG2o1VEhu1APXV+fj6jRo0KWVdaWkptbS1jxow55T6ys7NxuVzB+u+vCiw/WU33uRozZgxpaWnBkz1FJLIYY6ipqaGkpITS0lJKSko4fvw41dXVEX+GfmMj1NdZ1AUvzb97vefnTTMmxuBOMaSmGrp195Oaajh69ChHjx5l3bp1DBgwgDFjxtC/f/+Q0Z5EJDp841qL3DWG95fD3fMN8fF6HneUiAzbEyZM4I9//CO5ubnMnj07ZF1g3OsJEyacch8JCQmMGTOGLVu2UFRUFDIiiTGGtWvXkpSU1CrMny8nTpygurqazMzMDtm/iJwZYwxVVVUUFRVRVFREYWHhSb/58vuhocHuGfZ4wdfUK+zzgTEWxkAgj7tc4HIZLBfEuOzfY2Ltr2iDF5fB1fRzIJcGro2x78/vA58ffD4Ljwc8HrtHurHRoqEBGhvsXuqGBvD7O/5N0eezqKywqKyAwoIY4uMNXbv56Z7pp2tXQ15eHnl5eaSnpzN27FhGjBhBbGxEvqWISBumTobu3aGsDJavgOvmhPuInCsiXxmnTJlCdnY27777LnfccUdwrO3q6mpefPFF4uLiQkb5KCkpobq6mh49eoScVHnrrbeyZcsWFi5cGDKpzRtvvEFhYSHz5s0jISHhrI+zsLCQlJSUVl+nejwefvOb3+D3+7n88svPev8icm78fj+HDx/m4MGDHDhwoFVto98PtbUWNdUWNTUWJ2rs3mG7UkO9PC01NlocKY7hSHEMCYmG3r19ZPXyU1FRwccff8zGjRu59NJLGTlypE4MF4kCcXEWt90KL/y7YdFiw6xvQEyMXvc6gmUi9PvSk03XXlRUxMMPPxwyXfsjjzzC0qVLefLJJ7npppuCy/1+P/fcc09wuvYJEyZQUFDAihUr6NOnD0uWLAmZrh3gqaeeChbfr1mzhpKSEq655prgiUFz584NDhX45ptv8vOf/5xLL72U7Oxs0tPTKSsrY+3atRw5coTBgwfzP//zP63uo6VIK/QXiXZ+v59Dhw7x5ZdfcuDAARoaGlqsg+pqi8pKi8pyF5WV1gXpJXYqV4yhZ08/2f18BPotEhMTGT9+PGPGjCEmJia8BygShc50tI2v5ogzOYmwttYw9zZDVRX8/HGLr8+M7NdDnSB5nk2ePJnXX3+d5557jmXLluH1esnJyeGhhx5i1qxZ7dqHy+XiD3/4Ay+99BJvvfUWr732Gunp6cydO5cFCxa0GYKXL19OUVFRq2UBEydODIbtcePGMW/ePDZt2sSuXbuorq4mOTmZwYMH8+1vf5vbb7+dxMTEc/gviEh7GGM4cuQIe/bsYe/evdTW1gbXeTxw7JiLY2UuKsotfL7IfjOJJn6fRfHhGI4Uu+iZ5adfPx9Qx+rVq9m2bRvTpk1j0KBBqukWiVBJSRa33Ayv/IfhpT8ZvjYdunTR8/V8i9ie7c4i0j6hiUST6upqdu/eza5du0LGufd4oLTERWmJ3XutkpALw7IMPbP8DBjoIz7eXtarVy+mT59Or169wntwIlHiQvZsA9TVGb71bUNpGdxzl8U/fTtyXy+jtWdbYTvMIu1BIxLpPB4P+/fvZ9euXRQWFgaX+3xQVuai9KiL8nILYyL3DcPpYmIM2f189OnrJ1BJctFFFzFt2jSSk5PDe3AiEe5Ch22AFR8Yfvkre+jP1//LokePyHz9VNiWsxJpDxqRSOT3+yksLGT37t0cOHAAj8cTXFdRYXH0iIuyUpdKRCJMfLyh/0AfWVl+LAtiY2MZN24c48aNIz7Q9S0iIcIRto0x3P9Dw7btMGUy/O5JKyLLvxS25axE2oNGJFIYYyguLubLL79k37591NXVBdfV1UHJURdHj8RQXx95bwgSKiXFz6AhPtLS7LebxMREJk2axKhRo1pNMy3S2YUjbAMcOGi4+7uGRg/85CGL6+dE3murwraclUh70IiEU0NDA4cOHSI/P5+8vLyQcbAbG6Gs1MXRoy6qq1SHHX0M3bsbBgzyEpj1PSMjg0mTJjF06NCI7EUTCYdwhW2AxX82/NsfDIkJ8NorFn36RNbzUmFbzkqkPWhELqTGxkaOHDnC4cOHKSoqori4GL/fH1zv9baow66wQHXYUc+yDL16++nXv/kkyq5duzJx4kSGDBminm7p9MIZtn0+w//3oGHLVhh+Efzbc1ZEzSypsC1nJdIeNCIdxePxcOzYMcrKyigpKQlOk/7Vl6DaWig/7uL4cRcVFRZG42A7UkyMoU9fP336+gjMgZORkcH48ePJycnRGN3SaYUzbAMUFxvmf9dQXQ3XXwc/+VHkfABW2JazEmkPGpFz4fV6qaqqorKyksrKSsrLy6moqKC8vPykU6PX10NlhT1EX0WFi/o6hevOpK3Q7Xa7GT16NCNHjgxOKCbSWYQ7bAOsW2/48SMGY+Dhhyyui5D6bYVtOSuR9qAROR1jDFVVVRw/fpzy8vKQUH2yQB3Q2AgnaiyqayxOVFtUVbloaIiMF3EJr5gYQ+8+dugOlJe4XC6GDh3K6NGj6dWrl+q6pVOIhLAN8F//Y3jpZUNcHDz/jMWokeF//ilsy1mJtAeNSEsNDQ2UlpZSWlrKsWPHghev13vS23i9UF9vUVcHdbUWdXUWtbUWdbUWXm/4X6wlslkuQ2amn959/KSmNr89paenk5OTQ05OTpuz/4o4RaSEbb/f8NjPDZ+shvR0+OO/W/TpHd7XcIVtOSuR9qCRzssYw/HjxykqKuLw4cMcOXKEqqqqNrf1+6G21g7R9XX2z3V1FvV1FvYQ2ArVcu7cKX569/aT2aN5chyA7t27079/f/r160evXr2IjY0N30GKnGeRErYBamsNP1hg2LMH+veDP/ybRWpK+F7fFbblrETag0Y6l5qaGvLz88nPz+fQoUPU19e32qa+HmqqLWpqXNTWWpyosairRyODyAXjijF0726H7owMQ8sBS+Li4ujduzc9e/YkMzOTHj164Ha7VXIiUSuSwjZAWZnhu/cZSkph3CXw9FPhG6FEYVvOSqQ9aMTZ/H4/R48e5cCBA+Tn51NWVhay3ueDqiqLygoXVZUWNTUq/TgVy2UHP5cFVtM1Vut+fWPAAJimn0/ze9PeL9SfEVViYw1du9mhO6Orn7YmouzSpQtpaWmkpKSQmppKSkoKCQkJxMfH06VLF+Lj44mJicGy7FnyXC4Xfr8fn88XvPj9fvx+P8aY4HCULd8uXS4X8fHxxMXFERcXF9y/Qr6cq0gL2wB799kzTNbVwRVfg1/83CIm5sI/1hW25axE2oNGnMfr9VJQUMCBAwc4ePBgyEyMxti91sePW5Qfd1FdbWHUY43LZUhKMiQmGbp0gfguhi5dDPHxEBdriI2D2FjoyCGhTYvgbczJL36/hd8HPj/2tQ+8PguvF3xeu6ynsdGisQEaGiwaPTjoWwlDcrIhLd3gTjG43Xa7hWuo7ri4ONxuN8nJyaSmppKenk5GRgYZGRmkpaVpOENpl0gM2wAbNxl+8qjB44E5s+DhH1/4Kd2jNWyr0E3EgbxeL/n5+ezbt48DBw7gsQupm9bB8WMujh+zKC934fE4JXidndhYQ0qqITXVjzvFDmsJCXCm7yG+lj3UNPdUW0093RYtfm7Hvi2rvcfQVn/JyftQjLGnu689YQXr7qurXdTVQvT1plucOGFx4kSLJZbdhl0SICHBkJBgf2CKjTXExDZdx9gflAL/X8sKfHAJvTbGsj/wtPzmoel+XC57BJWYGIIXj8dDeXl5m2HA5XLRo0cPsrKy6NWrF7169cLtdnfw/0fk/Jkw3uKJx+HxJwzvLoOUFMP996Jvc9pBPdthFmmf0CR6BQL23r17OXjwYEjAbmiwpzo/VmaPZ92Ze6/j4gzpGX7S0w1p6X5ONoxzo8+i2uuizmtR53NR77Oo91l4/KEXXzCAnen/1LQO4U27sQLrWpSkNG9j7LIVC1yAyzLEWBDTdB3rMsRZxr52QbzLkBDjJyHG0CXGvm1bvF6orraorrKorLQfJ35f532cnCmXyxDfBbo0fQuSkEDw25GkJENbndrdu3dnwIABDBgwgKysLM2eKUDk9mwHvLvM8Nvf2a9637vH4tu3X7jXCfVsi8gF5/V6ycvLY9++fa0Cdn29HbDLSl1UVQWjXKdjuQxpaYauXf2kZ9ilBl9V47Eob4yhojGGao+Lao+LRn9HBx+Lpk7T0I5oY6/rGIaEGIM71k9KnB93nH2dFucjNha7DjrDAH78fqiusqiosDh+zC4x6qyPofbw++2RedqelMmQkAipKX5S0+xvUtxuQ1lZGWVlZWzatImEhASGDh3KRRddRFZWlnoLJWLNmWVRXQ3/9gfDH/9kSEmBG67X4/VU1LMdZpH2CU0iX0NDAwUFBezbt4+8vLxWAbu01EVZSWcOR3Ydb3rTCXRpaa17FSsbXZQ1xFBWH0t5YwyeTj4lvIXBHecnI95HRryf7glekmJD3xoaGuBYmYtjx1xUlHfub0fOh9g4+wNg16724zQweyZAWloaI0aMYNSoUSQmJobvICUsIr1nO+Cll/381//Y37I98bjFzCs7/jUhWnu2FbbDLNIeNBKZKisrOXjwIAcPHqSoqCg4OgIoYIMhMQnS0uzSkPSM1iNU1HktSutjKK2Ppawh5gL0Wke/UmZByAAAIABJREFUpBg7dGcm+OiR4CW2xb/M47GDd2mJi/IKy0EnXIaJZUhPN/Ts6ad7ZvOY4rGxsYwaNYpx48apvrsTiZawbYzhX54x/N9b9jkLT/3GYvKkjn0tUNiWsxJpDxqJDF6vl8OHD5OXl0d+fn6rx0ldLZSVuSgtdVHTqQK2fbJbcrIh2W2f1JiaZkJ6BQG8fjjW1HNd2mCXhnSe/9H558LQPcFHz0QvWYleEmKa3zYaG6G0xMWRIy5O1HSmx2LHcMU0z6CZkmL/n10uFyNGjGDChAmkpKSE+Qilo0VL2AZ7lslf/trwwYfQpQv869MWY0Z33GuAwraclUh70Eh4+P1+SkpKOHToEIcOHaK4uDikPMQYqKywOHbMxfFjLurarAt1BpfLDtT2SWaGhES73jUxwT7ZrK3JAn1+KG+M4XhDDKUNMZQ3xDSdYijnn6FbFx+9k7z0SvTSpUXwPnHC4ugRFyVHXTQ26v9/bgwZXQ39+vlIS7f/x3FxcUyePJmLL75YJ1M6WDSFbQCv1/DoY4ZP10FyMjz7LxYXXdQxz3+FbTkrkfagkQsjEK6LioqCl5bhGuwa2fLjLo4fd1FebuGL6sll7N7n+PimsaqbruPjDXHxzcvj41v3Un+V30CNx0WVx0VlYwzHG2OobHQpXIeB1dTjnZ3sISvRS2COC2Pg+DGLI8UxHD+u+u5zlZrmZ+AgH2lp9tt1ZmYmM2bMICsrK8xHJh0h2sI2QH294aGHDVu2QkoKPPevFkOHnP/nvcK2nJVIe9BIx/D5fCHh+vDhw63Ctcdj915XVLiorLDHD46er+TtsJyQaEhsuiQkNA+DFt/lzCaA8fqhzmcPu3fC66LW66LWZ1HjcXHCq2AdiWItQ+8kD32TvHRL8AWXNzRAyVEXR47EUFerdjt7hqxedugOfCC99NJLmTJlinq5HSYawzZAba3hwR8btu+A9DR4/lmLgQPO73NeYVvOSqQ9aOT8qaioID8/n/z8fA4dOoTX6w1Z7/FAZaU9NXpFhRVV9a5duhhS0/y43c0z952uR9oYaPBbNPgsGpuu630WDX4XDb7m3+t9LrzqCY1qybF++iV7yE72hJSZVFbaZSalJS58GsP7rMTFGQYN9tEzyz5Jul+/fv8/e28eJsdV33t/Ti29z75v0miXLGuxLcmbDBgItoVJjGNsJ+GF+waz3ctiwAlLIGZJMOYSBzA3Nn5uHMKTYMAQB+z4tRQvgIUtvEmyNNqX0SyafaZ7eu9azvtH9WzWYmmme7pnVJ/nqad6qnvqVHXXqfqe3/ktXH/99fh8vgIfmUuumKtiGyAWk9z5OcmBg1BV6QjuBS256+uu2HaZFsV20bhMHymdvLmHDh3i6NGjhMPhKe+PWa4jkbknrj2ebBq9ckl5uc3pnutSQsISJExl3BrtFIRxBHTKEq5F+gJDIKnzm7QEDWp91nhBHctygip7ehSiF3AO+JlQU2OxfKWFqkJ5eTk33ngjlZWVhT4slxwwl8U2wOio5JOfkRw9CjXV8IPvC5oac9PHC31up8MV23OAYrtoXM6foaEhDh48yOHDh4lEIuPbbRtGR52CICPDc80tBPx+SXWNTVW1TWnp1NuELZ1c1eGMOu47HTUU7Dl0fi6zi1exaQ4aLAiahPSJ1JWJBPT1qvT1ukGV50swZLP6YjPrsuXlpptuoq6urtCH5TJD5rrYBhgJSz55p6S9HWpr4fv3CZqbZ96/i+Hc3ogrtucAxXbRuJwbpmly+PBh9u7dS09Pz/h2y4LhYeHkHx6ee1PlmiaprbOpb7BPqbQ4klYYTGsMpZygRMt19XCZFpJKj8WCkEGDfyJ/t5QwNCmo0s3dfW7ouuSii03KyiRer5f3ve99roV7jjMfxDbA4JDk05+RnOhwLNzf+8eZu5QUy7lNxhXbc4Biu2hczk48Hmfnzp20tbWRTqeBrEgYdAT20LCCPccENkBJiZPXd3JBDVvCYEqlN6nRm9RIu4Vg3hRNSLyqxKvaeBXntSYkqgAluxZi6vyGBCwJli2wAdMWGLbAlGBkX2dsQcYS827mQBOShoDBgqBBpXfC2p1OQ1+vE1R5+vLnLpNRVMnadSalpZJQKMT73vc+Nx/3HGa+iG2A4WHJpz7rWLirqhwL98KF0+/TxXRuY7hiew5QbBeNy+mJRCK8+uqr7Nu3b7x6YyoFvT0qvT1zdfrbKWm+YIFFecXEbWA0o3AirtOd0C/4MuanIvEqTmnzkGYT0m0CmsSv2gQ0Gz3P4xHTdoJMM28ILE1PDji1FNK2mHMzDyHNsXY3B6bm7g6HJ4Iqbfd6PCOaJll/iUEgCPX19fzpn/4p6tjI2WVOMZ/ENjguJXd+VnL0GFRWwHfvEyxeNL2+XGznBq7YnhMU20XjMpVoNMqLL77IwYMHGesqkYigs0NleGhu+WBPIKmqlixYaI1XqLMldCc02mMewhm32uKYqC7R7UmLRYn+5oLaAJIopBCkEBgILMDMrqUQ2RYcFClRIbtI9Ozigexa4kVyvjp+TJinJwWojq0TpkLCdER6sf3WAkm932RB0KDGZ5H9ujDNbKXKHoXoBVU19dzx+SSXXGag67Bu3Tre+ta3FvqQXKbBfBPbAOGw5DN3SQ4fgfJyp9LkdPJwF+O5zXmx/frrr3P//fezc+dOTNNk+fLl/I//8T/YsmXLOe8jk8nw0EMP8etf/5qenh7Kysq49tprufPOO6mqqpry2WQyySOPPEJbWxttbW20t7cjpeSZZ56hubn5lH2PjIywbds2nnvuOQ4fPkxfXx/BYJA1a9bwwQ9+kGuuueZNj6/YLhoXh3Q6zSuvvMKuXbuwLCdn8PCQI7Ijkbn6oJdUVEhaF0+IbNOGjrjO0aiHlHWhuYlIfKokoDrW6YBmE8wuobOIahuIozCKQgSFmFBIIIijEEfBFHm4NqREB3zYeJH4Ji/SxofEj8SHjR/JaYpsnhZLQtKcyCATNxVipkLMUEhahb/OfapNS8BJIRjUJ1eqhP4+lf4+hXR6LvbF/FFZZXPxGifN6JYtW1i6dGmBj8jlfJmPYhucLCWfuUty8BCEgnDvPYJ1a8+v/xbjuc1psb1jxw7uuOMOPB4P7373uwkGg2zbto3u7m4+//nP85d/+Zdvug/btvnwhz/M9u3bWb9+PRs3buTEiRP893//N83Nzfz85z+fEkjS1dXFO97xDgCampqIx+OEw+Eziu1HHnmEr371q9TW1nLllVdSV1dHb28v27ZtI5VK8Vd/9VfccccdZz3GYrtoLnRs22bPnj384Q9/IJVKAc409rGjKrHo3BWjoRKnGEZF1l3EsOF41MPxmE5mXvhiO5ZfTZHoSnYtJB5F4lGz66wPtU+18avOa+Us93kJxFAIZ0V1RKiEUYiiYOdDUOcK6YhtR4A7QjyAjV86gjyATRCbwJtYy02bceEdMZwqnZGMWqAc6E6J+JagQWNgolIlOP2zv09hYECZ41VWc0frYpMFC2z8fj/vf//78fv9hT4kl/NgvoptgGhU8tdflOzZCx4PfP2rgs1XnXu/LcZzm7Ni2zRNbrjhBnp7e/n5z3/OqlWrAGdK/5ZbbqG7u5utW7fS1NR01v388pe/5Etf+hI33ngj3/nOdxDZB+SYSL7tttv4+te/Pv75eDzOrl27WL16NeXl5XzoQx9i+/btZxTbL774Islkkre97W1TKngdO3aMW2+9lVQqxTPPPHPWVEzFdtFcyPT39/Pss8/S398PQCIOx45pc9hdBLw+yaLFFrW1jp+5LeF4TOfIqKeAItsRxF7FxpMVx7oi0ZVsMKECqpgcWDgRXKicZps2KfDwfHGs1I5VOpa1UkezVusYClYxi+oZIrLiO4RNCTYlMrvOLmfy9o0ZgpGMykhaZSSb8nE286c7QZUmzQGDKu+Em4ltO7NP/f2Oi9eF7N8thOTSy0yCIcmKFSu47rrrCn1ILufBfBbb4JR2/9uvSV54EVQFPv9Xgi03nFt/LcZzO5ff61xnG2eVHTt20NHRwc033zwutAFKSkr42Mc+xhe+8AUee+wxPvGJT5x1P48++igAn/3sZ8eFNsDtt9/OP//zP/P444/zpS99abzyVjAY5Oqrrz7n47zyyitPu33x4sVs2bKFn/3sZ+zcuZPrr7/+nPfpMvuk02lefPFF9uzZg5QS04Tjx1R6epQ5m35MUSQtCyxaFtgoipMxpSuhcTDiJTkr7iISv+oEEpZk3TJCmo1Ps/GpcoplMtcYQAYxvqQnLSkhSKCQxHH9SCHGfagvNKQQJHC+j36YMp4U0hHhZdmlUlpUYhFEEtIlId2kJei4Kpg2jGRUhlIqg2mNcCa/4tuUgs64Tmdcx6faNGVLxJd6bKprJNU1JqYJw0MK/f1Ojns5R/vxdJFScOigyvpLTQ4ePMjatWtpaGgo9GG5uADg8wm++Q349nckTz4F37xX0tMr+X8/KFDONt04hylKsf3SSy8BsHnz5lPeG9v28ssvn3Uf6XSa3bt3s2jRolMs4EIIrrrqKn72s5+xd+9eNmzYkKMjn0DTtClrl+Lk+PHjPPPMMyQSCQD6+xSOHlUx5mR2EQAnT/aixRZer7NlMKXSFvYyauQzM4GkTLep9Frji089+6TZmADO4IhjI7uYCEzAFE5AoZUNLLSZ+vfY6/HPvyEA0WX6SCGIohJFpQvGhbhX2lRiUSMtqrCoxkJXoMZnUeOzgAymDUNplf6UxkBKI27mb2YoZSkcjXo5GvVSols0BUyaAgaBbL742job04ShQSebycjIhSO8o1GFvl6F+gabHTt28N73vrfQh+TiMo6mCb74eaiokPz7I/Av/wrtJyR/8wVHjM83ilIJtre3A7Bw4cJT3qupqSEQCHDixImz7qOjowPbtmltbT3t+2Pb29vbcy62Y7EYW7duxev1ctlll+V03y65wTAMnn/+efbu3Qs4VeyOHNIIh+eu/3Jpqc2SpRYl2WqPCVPQFvbSm9TIj9hx/GgbAyYN/qnp2sBx0RidFEg4KlTiCJIoJBHF7ffsclrSQqEHhR6hA44FvBSbWizqpEktFj5FUue3qPNbQJq4KRhIafQlNYbS+SuGFDVUDkRUDkQ8lHsci3eD38SvSerqberqLzzhfaJdpbbOprOzk87OTlpaWgp9SC4u4wgh+PhHBQsWSP73P0ie+w309Ei+9fdQXT2/+mZRiu1YLAZwxqT8oVCIaDR61n2MvR8Khc64j8lt5ZK7776bwcFBPvWpT52375VL/unv72fr1q3jfl9dnQrHj6vIOerj6fU6GUbq6hy/bMOGw6Mejkc9eSmC4lNtWkMGzQEDvzYhsDPAABoDQmUAlWHUee3z7OJYwCOoRFA5LDwgJeXYNGDSIE1qsAhqkmDIoDVkYNkwmFbpy2uhJEE4oxLOqLSFnWqVjQGThoCJb5LwNgwYzArvcHh+VqxMpwW9PQqNTTavvPKKK7bnKWVlZVPixuD8/b4Lyfv/HFauMPj0Z6McOCj5yMfh/9xfwkWrTi9R59K5jVGUYnsu8w//8A888cQTXHPNNXzsYx8r9OG4vIG9e/fym9/8Btu2Safh4AGN8MjctGarmlOQpql5wi+7I65zIJKf4McKj8WikgwNfnM8i0cG6ESnQ+j0orouHBc6QhBGJYzKfuFFk5JaTBqlSRMmwUlW7zUyzXBGpSfhCO/8xBIIhjMawxmNvVnh3RAwaQyY+HRJQ4NNQ4NNJgODAwqDA1nhPUcDok9HV6dKQ6Nj3e7v76e2trbQh+SSYyKRyJS/izGI8M1Yshh++E/w+S867iTv/2CEr3xJ8La3Tu2LxXhuczZAcszqfCbrdSwWo6ys7Kz7GLOKn8lyPbb9TJbv6fDd736Xhx56iCuuuIIf/OAHbvWuIsK2bbZv386uXbsAGBwUHDqoYRpz76EqhKS+waZ1kYXuzOYzmFLZF/YSyYNfdqlucVF5OuuT69CHyiHhoRvNdQdxOSOmEJxE56TQeUVKyrBpwqRZGlQLmyqvRZXX4uKKNMNphZMJnZ6klqec7xPCuy084QLV6DfxeCSNTTaNTY7wHuh3giujo3NfeKdSgoF+hdo6m507d7qZSVyKlqZGwYP/B/72a5KXXoYv3y35iz+TfPhDAk2b2/2wKMX2mD/1iRMnuPjii6e8NzAwQCKRYO3atWfdR0tLC4qijPt/v5Gx7Wfy6T5fvvvd7/LAAw+wadMmHnzwwfEMJy6FJ5PJ8NRTT43/5u3HVTpOzMUqiZLqGkdkBwLOlqihsC/spT+lkuvz8ak2K8rStARMhHB8sI+hc0h4CAt3IOlynkxyOdknvASkTTMGLVlf70qvTaU3zeryNMNpla6ExsmEnqe83oKhtMZQWmPviKTaZ9HgN2nwG3g80NRs09Rsk4hDb69KX58yh4OmoavLEduHDx/mmmuuITB2A3FxKTJCIcG374EHfij52aPw749A2z7JV/8Wqqvmbh8syvnzjRs3ArB9+/ZT3hvbNvaZM+Hz+Vi7di3Hjx+nu7t7yntSSl544QUCgcApYn46TBbaDz30kFtAoIhIJBL84he/oL29HcuCfW0aHSdyL0zzTWmZzSWXmly02hHaaUuwZ8TLb3sD9KdyGwApkCwrTfP2+jgLgo7QbkfjcRHiJcXvCm0pUaREkxJdSjxS4pH2pMXZrmc/I4qvlEFRkBAKh4SXZ5Qgj4kQrwgf/agIAVU+i3WVad7VFOPSqiS1PhNBfr5HiRPA+fqIj20nQ/xhwE9nXMO0IRCExUssrrjSYPXFBlVVNoi593vGogqjowLbtseDwl1cihVNE3zyfyl8/auCQAB27Ya/vEPy2s651/fGKErL9pVXXklLSwtPPPEEH/jAB6YUtXnwwQfRdZ2bbrpp/PP9/f1Eo1Fqa2unBFXeeuut7Nq1i/vuu29KUZuf/vSndHZ2ctttt83YAv29732PBx54gA0bNvDDH/7QFdpFRCqV4rHHHmNoaIhMBtr2aETnWBVIn0+yaIlJTc1EefWjUQ9Ho568ZHUo0S0uqUxR5nGCLftR2Sl8DBVaYGerIurISQtoSFRAnbRWAFU667FFZLeL8W1yyuvJn1Wy+5q8XX3DZ84XS5JNa+isJ+cBT2XzfydRSGXzXicusGwtKaFwCA+HhIeAtFmIwSJpUC7sbDo/k5Q1kV87buanH0sE/SmN/pTGXiFpzJaKr/TaVFVLqqpN0mno7VHo7VHnVKn4k90KpaUWbW1tbNy4cUrtCReXYuTtbxMsXQJfuVty9Bjc+TnJpz6R5Oab5JzLx12UFSTh/Mq1jxW5ueeee7j55pvHt5+uXHtHRwfbtm2jqamJRx99dEq5doB777133Pn+97//Pf39/Vx33XXj02633HLLeKrA//iP/+CLX/wimqbxgQ984LRTc5s2beLyyy8/43kWm6P/fCGdTvOf//mf9PX1kU7D7l06qeTc6ZyaJlmw0KKxaXaCHwWSpSUZlpdlUISTA/sV4eME2vRKM54nataftyRbSjwobULZUuPe7HKh2dOTiFOqW8ZQiF8oYlxKKrBZJA1aMfBNsmwPpVU6YjonE1peMu68kZDmlIpvCU6kuJTSif3o7lQZHS3+QbyiSK64ykDT4L3vfa+bmaSIme8VJM+XVEryD/8o+f+2On9fdQV8+UuC0tLiuAfO2QBJgCuuuIKf/OQnfP/73+fJJ5/ENE2WL1/OXXfdxZYtW85pH4qi8MADD/DQQw/xq1/9ih/96EeUl5dzyy23cOedd54itAG2bt16itvJ1q1bx19v2rRpXGyPfc40TR5++OHTHsMnPvGJs4ptl9xj2zZPPvkkfX19GAbs2T2XhPZE8KPH42zpT6rsi3iJ5qkoTUC1ubQqSYXXsWZ3ofGS8JESeRIQWWFdj0mltKnAohT7nCzGNpBRwFAEhgBTyRa/EWAKsATY2b/trJ/52FoKMfG3ADn+Ols0J7vdGv8/Zz9T93Xqvk+HYMLbQJPOotsS3QZdSrwWeG2JxwafJfFnl4AFAUuiS/DjlFOvxs5+b5O+QiApBbGs8I6jEBfKeEXIZLZg0GwMlPKGEIygMiJUdkkvjZgskQaNmOOBlReVO9bu9pie18qoMVNlf0TlYERS5zdZGDKcQj41kpoak3BYcKJdJVLEefpt2wmUbGi02bdvnyu2XeYMPp/gS1+AdWvhvu9JXtgBf/lhyd99DVaunBv3uGlbtu+55x6EEPz1X//1KfkdXc6d+TT6LBZeeOEFXnnlFSwLdu/UiMXmxvVZUmKzdNlEUZqoodAW9jKQyteYWNIcMFlTkUJTnDR+rwgf7eg5F2malDRlcy/XYxI4jf9tUoGILohpgqjmrBOaIK0IUqogrTiCek4LyHNBSrw2hExJ0JSETEnJpCVkSrRzuGtb4LioIEijTJStFyJbsXOicudY9U0r6+pikR2YQNF9335psxiDpTJDkAkrc29S41hMZzg9OzEZJbrFopDjZjI2oz00JDh+VCORKK7vbIySUif2Q9d17rjjDvSxdEYuRYVr2T4zvb0lfPqzEbpPgq7Dpz4huOmPKahb1Ln8XtMW2ytXrkQIwe7duzl48CA7duygr68PRVGoq6tj48aNb5oxxMUV27mmvb2dX//61wDs36cy0F/8zgeaJlm8xKK+YaIozaGIl+MxPetZnIc2hWRtZYqmgAk4qfxeFH4SubRmS0k1FkulwQKMKdNopoBer0K/T2HIIxjyKCTneGqnWUNKfFkxPraMifKg5bz22blrzgTMSf7m5iRf8zHxnkQhhmNpT82SRV1ISSMmy2WGBibSUoYzCkejHnoSWt76z2R8qs3SkgwLQ47oltLx6T5+TMU0i+2almy83MDvh+uvv57ly5cX+oBcToMrts9MRUUFHR3DfPNeyfPZHBrX/RHc9VmB31+Y/jYrYnvdunXs3r0bcLJ8wMQIY/369Xz72992p6vOwnztEIXANE1+/OMfE4vF6O5WOHq4aL2kximvsFmx0sTrdf7uiGvsD3vz4pc9RoXH4pKqJEFNYgOvCy/78eSuII2UNGOyWqapYkL1RTRBR0Ch26/Q71Ww5liAy1xCkRK/xbh7itdyBLjPcqzmnqxLiyfryqJJiSonXF5mggmMohBBJSwUIiiMoJLMl1sSUCotVsgMiyYN6mKG4NCol5OzJLqDms2qsjQN2QFsJgOHD2kMDRbXzFrrIpMFC21aW1v54z/+40IfjstpcMX2mRk7Nyklj/wMfviQxLJhUSv8/dcFCxbM/nNlVsS2lJL6+nquu+46WlpaME2Trq4unnrqKQYHB6mvr+cXv/gF1dXV02lm3jNfO0QhePnll3nxxRdJp+Dll3TsIi6/riiSRYud6o/gCINdw35GMvm0xEuWlWZYXuoEQcYQ/F4EcppppFaarJdpqrOWRkvAsaDKwZDKgHeO+xBfKGSFtyqdzCuaLbM+546/+ZhI91ngsyU+SxK0oMSwCVpnduCIIxhCZVCoDKIylIeKo15psxSDFTIzHlA5Jrq7E7lNkXkmKj0mayvTlOjZjD59CkcOF4+V2x+QbNxkoCgKH/rQh9wMWkWIK7bPzBvPbdduyd1fkwwNQyAAX/xrwbVvm92+Niti+yMf+Qif/OQn0bSpVsRMJsN3vvMdfvzjH/P+97+fL3/5y9NpZt4zXzvEbJNMJvnRj36EYRgc2K/S31e87iNen+Tii02CIafrHY/q7I9485LKbwyfanNpZYqqbBXIdjReFn6MHImdoLTZIFM04Vj1DAH7SlXaSjXSanGIDJf8o0jHnaXckJRnJBWGTUVGUmbIUwJg00APGieFxkk0Mjm0fGtSspwMq2QGb1Z0j2YU9oa9DKXzP+OlIFlelmFpSQYhIJ2G/fs0RiPFYeW+dINBKCS59tprWbNmTaEPx+UNuGL7zJzu3IaGJHd/XbLLcbLgtvfBxz86e1Un8y62ly1bxuOPP37Gz0gpueGGGzAMg2eeeWY6zcx75muHmG127drF7373O2IxwWuvzI4FazqUlNisXmPi8UDKEuwe9mWL0uSPer/BusoUHgUM4GXho114crNzKVmMwWUyhY4TlHeoRGV3meb6YLuMo9mSqoykNm1Tk7apT9l4J/mV20AvKieETic6Zo4GgROiO03WU4vuuMa+iDdPJeGnUu6xWF+ZokS3sW04fEilr7fwhoDmFovFSywaGxu55ZZbCn04Lm/AFdtn5kznZpqSh/5Z8pNHnL/XXOy4lVRW5v85lNfUf0KINw2uEEKwevVqtm3bNt1mXFzOif379wNOYFKxCu3qapsVq0xUFSIZhZcG/Xl94KtCsro8zcKQAcAQCr8XAWI5siD6pM3lk6zZvV7B76t1RvXCWO9UWxKwJnyVA5bjj6zbTio9LbtWsstY4RvkxBUjnD+Rk9IFjqX/c5aJlIOGEJgKpBUnU0paEaRVSCmCjILrMjMJUxH0+QR9PufaEFJSk5Y0Jy1aEjaVhqQRi0ZpsZEU3VLjiPDQhzqj79EUgn14OYLOWplmGQZNQZM6v8mhUS9Hozr5vF+EMyrP9wVYlw1GXrHSwu+XtB8vbBXbgX6FxUssTp48STQanVIMzsVlLqJpgv/5UcGa1ZK/v0eyZy/c8THJvX8Py5YV/l48bbEdCoXo7+9/08/19va6Hdklr4TDYQYGBrBtxz+yGKmsslm12il93pdUeXXIn1e3kVLd4tIqx6ImgX142CO8OSuEUi9NrpRJ/Egs4LUKjbbS3Pvgng5FSiozkpq0TZkhKTUkZW/iLzx9phctaAMpBVKqk74wrjqpDOPaRGrDhMoFK8ilEPT7BP0+hdcqoNSwWRy3WRSzKDclCzFZKE2GUTiAlxNoM7q2MkLhFeHnqPSwQaaoUSwuKk9T5zN5bdiX10GvJQWvDfmIG07RqAULbTQNjhwunOBOpwXhsKAmQkvOAAAgAElEQVS8XHLgwAE2btxYkONwcck112wWPPQgfP5Lks5O+PgnJV/5G3jrNYW9105bbC9dupRXX32VF154gauuuuq0n3nuued47bXXeOc73zntA3RxeTPGBn2xmCiaIKTJlJTarLrIEdqdcY1dwz7y95CVLA4ZrCpPowhIIHhR+OkTOXJVkZKLybBGphHAsC74bY1O2JM/saLZkvqUTVPScUGozJy5mqSlSNIeScZjk9Elhi6xVLBUiaU4r20hkQrYimPBllnT9pisFhIEwllLEDYoUqBaoNgC1QbVcv5WLYFuCjRToBvZ15ZAAQI2BGxJpXF6wW6CI7x1QVgXRHRBRFcI64LMBebnPqor7CpX2FWmUpmRLI9ZLItZVEqbq2SSdQja8HIMfUYDxhGh8t8Exl2fqnwWb62Ls2vYT19e3bkEB0e9JC3B2or0eGXYQwcLJ7h7exTKy53y7Rs2bHDLt7vMGxa0CH74T3D31yQvvwJ/8xXJR+6A/+cvCpePe9p3l1tuuYWdO3fykY98hPe85z1s2bKFpqYmLMuiu7ubxx9/nK1bt6KqKh/96EdzecwuLlMYGBgAHLFdbPh8kovXOK4jfUmV3XkU2h7F5pLKFLV+JwiyC40/CB/pHLmNeKTkKpmkMes2cjCk8odKLS8p/HRbsjhmsShhU5uyTxHXGc0mWmITC1ok/DZJvyThtzE0WXAvImGDbgg8Y0ta4Esr2cV57U0JNATlpqTclLQkp+4jqgmGs/nHhzyCAa9yYQSaCsGwV7DDq7CzXGNl1GLlqEnQlmySKS4izS58dKBNf1ZACI7hYQCVq2SSKtVmU02So1GdfWEv+byAOuIeLClYX5mivsFGSsePuxAX7eCAgrnMYnR0lI6ODhYuXDjrx+Diki9KSwT/+1vwg3+S/OI/4KH/Kxkehk99ApQCpJ2ddoAkwLe+9S3+9V//FSnlKaMFKSXBYJBvfvObXHfddTM+0PnKfA1imE2eeuopDh06xNEjKt1dhQ8+mkCydp1JeYUknFF4oT+QN9eRSo/JpVUp/JrEBF4TPo7ksBJkubR4i0wQQmIKeLFK50go9991VdpmRdRicdxCn3RnSnpthitMwmUWoyUWKW/hRfVMEBK8aYE/peBPKgQSCsHs2pc5/eBoVBMMeB3h3edVGPGIWXHbKTSq7Vi610ZMAtnaNb2ovCx8RGeYtlKRknUyzSoyAPQkNHYO+/Lq4gXQ4De4rCqFENB+XKXjRGHuW0uWmjQ12yxZsoR3v/vdBTkGl1NxAyTPzHTO7Zf/IfnH7zsPlOve5aQHzGWmkrwGSAJ84Qtf4E/+5E947LHH2LNnDyMjI6iqSn19PZs2beLmm2+mpqZmJk24uLwpY+PF6Q8b80NDo015hZNw/9XBfPloSxaFDC7Kuo1EUNgu/ERymDt7kcywUabQcCyuz9TqjOTSbURKmpM26yImtemJHzEWsOitNRisMkn6i+zHnSFSQMonSfksRsqtKe9pJoRiKiVxxVnHFIJJlVJTUmpKlsSdNB4ZAf0+R3j3+hQGvPNTfFuKYH+pxqGQypqIyZpRi3ppcYOMsxsvB/FMe1BpC8FO4WNIqlwpkzQETHxqgpcG/XktLNWT1NkzIllbmaZ1kUU6TUGylPScVGlqtjl27JgbKOkyb/nTmwWhEHzzW5Kt2yARl3z1b8Hrnb375Yyd1FatWsWqVatycSwuLtNiBpMzeUPTnKI1APsjXhJ5CMASSNZUTGQbaUfjJeHPWdo0ISWXyhQrcPbf5Vf4bbWeU3/iiozN5cMmDSlHQNpCMlBl0t1gECnNS8Rj0WNqEC63CJdbkP3uNRNKoiplUZXSUWftsQTNSZvm5IT47vUp9PgVun0KEX1+FRGyFMGuCmdG5aohk6aUzWUyTT0WLzCznPEdQieJ4C0ySYXX5qraJC/051dwn4h78GtOoallyy1SSUFklvNwJxKC8IigvELS1tbGFVdcMavtu7jMFte9SxAMwd/eLXn+9/A3fyu55+9A12fnHql+9atf/eqstORyWlKpVKEPYc7T3d1NX18fsbggPFIc2UiaW2wqqySRjMLukdz7aatCsqE6SVPQRAKvCS+7hC9n2UZ80uZtMsnCrH/2zjKVF6r0nPln67Zk47DJ1UMmpaYTvNjZmGHfyhS9dSZp39x2E8k1tgIpvyRcZtFXa9LRnGGwyiQesLEUicdQ8NiCMtOZJVgVtVgas5xiMhLimsjZtVFoMqrgaFAhoQkaUzbl2DRj0os6o8I4CaHQhUYzJiFVUuOz6E7o2Hm8EAfTKiHdpsxjU1llM9CvYFmz+ztZNtTUSsLhMOvWrUNRiuMeeiFzvlU936gj/H7/vNUWMzm3BS2CNWvg2d/AiRPQ1Q1v2TxzH+5z+b1csV1g5muHmE3C4TAnTpwgk4HBgcL7bCuKZOVFTlBkW9hL1MjtMemK5IqaBNU+Gwt4Xvg5LqY/lf5GqqTFO2SCcmwyAp6r1TlUOoOAtDfQkLS4ri9DU0oigP4qgz0XJRmssbAK//PNDQRkPJJoic1AjUlHkyO+kz7Hyu1NC3y2oDojWZywuThiUZO2UaQkpom8BLXOKkIw5FXo9is0Jy1KpJMusA+N5AwEd0YonERjYVZwV3lNuhM6Mm+CW9Cf0qj1mQQ9kpJSmU1fOnu/TzIpaGi0sW2D6upqqqqqZq1tl9Pjiu0zM9Nza2gQLF8Ozz4HR49BOAJXXjGzLCWu2J4DzNcOMZuk02kOHjyIqkB3d+GL2lTX2NTXSxKmYE+OrdqqcIR2hdcmheA3IkCf0HO2/0UywzUyiRcI64Kn6j0M+HKjgIWUXBp2rNkeCQmfTdvKJB0tBlb+K2jPb7Lie7TUpq/WpLMpQ6TUwtSkkxnFcqzeC5M2q0ctKjM2lnB88Oeyq0lSExwNqdSnbMosSTMmHegzcinJCIUeNBZiENIkftWmN5m/qrQSwUBKoyVoEPCDojLLM3QCTZOUlUtSqZTrFloEuGL7zOTi3FqaBS3N8Nvn4cABR2hfsj6/Ytt9xLnMeRobG1EUBZ/fxh+AZKKwx1Nb51gXu+K5tYgpSDZWOz6laQTPiEDOAiEd/+w0K7JZGU74FX5Xo2PmyALqNyVvG8hQnw2A7K7PcGRRGruILNnCBm8mmzM7mztbNZ0c2+M5t3HycY/l6LaFk7vb1OT4Mpbbu5DYKgxXWgxXWhxenCaQUKgd0qgd0AgmVVoTNq0Jm5QCx4IqR0Mqg565KbxTqjMovKE3Q3VGcq1MsI3AjFxKwkLldwR4h0zQEjQJZwzaY54cHvVUEpbCrmEfG6tTtLTYRMI2w0OzJ7h7Tqq0LLDp6upiaGjItW67zHve+Q5BLAbf+UfJwz+SLF8Om6/K3/3PFdsucx5d12lqaqKzs5OqKpuuROGUjqZJKiuzgjKRy+4lWVeZosZnYQC/yaHQ1qVks0zQgBPQubNMZVd57txGqtI27+zPELDAVCUHl6borzFzsu/p4kkLyqJOpg8n7Z6KLylQcjQ4MhVJJltcJ+WVJH02SZ9NaiwfuD6LQb0CEkGb9mCG9pYMobhCXb9O3YCGz1C4KGpxUdRiWBfsL1U5GlTnnJuJqQiervVwY0+aUstms0zyLIEZXcP9QmMnXi6VaVaXpxnJqEQy+bu39CZ1jkYtlpQYrFhp8tqrOunU7PwO6bRgcFBQUyPZvXs3b3/722elXReXQnLTnwiOt0t++Rh84+8l//eHjtU7H7hi22VesGTJEjo7O6mts+nqLJwrSUWljRAQySjEzNw9mBcGDZqDJjbwOxFgKEdCOyht3iYTlGFjCPhdtU5HMHfH3ZywuHbAQJNOKr+9q5IFSeOnZwRVwyqVEY3SURV/+gxWQ8UGrwm6CbqF0C1QpLOMmbcdk3Z2UZCGCpMXS0WzBVpKEEidvh1Dc0R3PGARD9jEQjbRoJV/dxoBsZBNLJTm2KI0FSMq9QM61UMalQZcPWSyYcTkYEhlf6lGIoe5aPNNUhNsq/Pwnp4M9dJiCQZHmZk1+gAeqrFYIEwurUzyu75gXnNw7w97qfRYVHhtVq0y2b1LQ+Y55/cYJ7tUampMDhw4wNVXX43X652Vdl1cCskn/qfg0GHJnr3wpS9LHnoA/P7c9zlXbLvMC1asWMHzzz9PKGQRKpHEooURCWNW7f4cln4u0S1WV6QB2C28OSu9Xi1N3iKT+JDEVXi61sOwN3dT1ytGTa4cNp2y7uUme1cmZ9U3WzWhoU+nZkijbFRFTBmASShPICrjiNIklCQRpSnwGTM26EtTgZQOKR2Z1CHuRca9EPM564QH3XQs62XRqQObpM9mNGQRLnOWhN/O27hRiglXE82Ehl6dph4P/rTC2lGLi0ctjgcVdpVrjOpzI0NFxKPwWrnG5SMm62WKLrSZVVAVgpfwUy1jhHTJmooUu4bPz5/2fJAIXh3y89b6OKVlkoWtFu3HZ6fTRCKCeFwQDJrs37+f9evXz0q7Li6FRNcF3/gafOjDkuPt8OBDks98Ovc3XTdAssDM1yCG2UbTNEZHRxkYGEBVYXCwEOJAsnSZhabBwYiHZE5ya0s2VScJaJJuNF4Rvpy4dzRKg7fKJB5g0CN4qt5LNFeFaqRkfdhkU9hCACfrMuxbkZo1/2xhQ1OPzpr9fmqGdXxpxRHa5XHEogGUVT0ol5xAXdaP0hBxBHcog9DtnHjOCEUiPBYikEGUpRDVMZTGCErrEMqyPsTyXpTmYURNFEqTCI8FlgKGhm4KQgmV6hGN5h4PTb06JXEVxXYCIPP1HdoKjJbadDUaxEI2nowgkFaoNCQroxZ+SzLoVXLmw59PBr2ClqRNSTZNe+8MB6eWEAyhshiDMo/NqJHbWas3YkpB3FRoDJiUlknCI4J0eja+d6eNqirJ6Ogoa9eunVGGBpfp4wZInpl8nFsgIFi6BLZug/0HYP06J2vJ+RzTm+GK7QIzXztEIQgGg7S1teEPSPp6Zz9frc8HC1ttbAl7wr6cBEcuCBq0lphkgOdEAHMmVrosC6XBZplEBTr9Ck/XeXJXqEZKNg2brB11/L+PL0hzdFEGZmPsI6FqWGXNgQD1AzqqLaAkiXLRSZRLT6Cu6EWpjSJCaYRauEJIQpEIn4koS6LURlFahh0RvrQPUTuKCDmzGKR0VEshlFCpGdJp6dapHNHQDUHGY2PmLgnNpIODRMCmt85ksNLEm3HKyNdkHNEtgCFPkefsFoK4KlgStynD4hCeGR9vQigoUlKLRbXXoiOuY+fRvSNmqgQ0J/92WblNb68yK+4kyYSgqdkmnU6xYMECt6JkgXDF9pnJ17k1NQoGhyQHD8Gu3XDjlnMveOOK7TnAfO0QhSAUCtHV1UUsFgVgZJYL3FRW2lTXSMIZhRPxmWcu0IVj1VYV2CW8OUnxt1hmuFKmUIAjQYXf1ujYObJWCim5eshkVcwR2ocWp+hsNmbFfV6xYPUBH4s7feimAK+Bsq4T5bJ2lKq443td5AhVOgOBmihK65BjAa+LgM8AU0WkPfgyCpURx+pdNayhWpD05cfinfFI+mtMwqUWwYRCIKPQkLJZGrMI64JoEbuWjGqO2A7YEBcKwzmIcRhAdQreKBKfatObzMdoZ4LBlEZTwCDgBU2D4eH8f99SCvx+SahEIqVkyZIleW/T5VRcsX1m8nlul6yH/34aevvAMGDTxtyJ7eK9W7q4TIPLLrsMgPoGG3WWrZelZU57wznKWLC4JINHhTAKh2YY6AXQIg0ulykEsL9E5flqHZnD0u6bBw2Wxywkkn3Lk3Q3GjnZ95uhmrCuzXEZQbERK3pQr38dZfEAOZgIKBhClYiaGOqabrQ/akPdshvl0nZEXQSEpDSmsuy4j6teDrLqoI9QLD8nGy63eHVdgrYVSZJem6AF7+o32Dxo4LEKN0NwVoRgf4nTDxfK3FyHthD8QfiRQHPQpNqb34w6phTsGvYB0NhkU1Zm57W9MXp7nOvoyJEjGMbs9GEXl2IgEBB87jPOM/GXj0H3ydzd3+bwo8jF5VQWLlxIRUUFmuYI7tmkbExsp2cutnVFsrjEyXn9uvDOWBTXS5OrZRIBHAyp7KjMXWo/ISXXDBosjdtIJG0rU/TVzk5qP90QXLInQPmoBrqJ+paDqGu6EPrs/vazgQhkUBYPoF5zCPXGXSjrT0BFDEUK6gd0Nu4Ksv51P1VDqpMMPKeNQ3+NyUuXxulszCCRLItZvPdkmuZEcc4anMhm1anBwitzcz0MCXV84Lu2IoUq8jvYGEprnIg5FvRlK0wUJf+Dm9FRQTIJhmFw7NixvLfn4lJMXHE5bNzgWLYffMgV2y4up0UIMR5F39hkkXvVcXo0TRIM5U5sLw5l0BQYRqFrhkmDSqTFNTKBAhwPKLxYlTuhTdZ1ZEncxhaO0B6onh2hLWxYs89PSVwFr4H61gOI6tistF1ohNdEWdqP9o79qG9vQ7QMgZBUjGqs3R/gkj3+vFi6bRWOLE6zc22ShN8mYMEf9RusDZsgi8vKHdcEQ7pAARrI3TW5W3hJIAjqkuWl6Zzt90zsC3tJmoJAAFoWzMbARmRLxsPBgwdnoT0Xl+JBCMH/+rhACHjuN9C2Lzf3NVdsu8w7Vq5cicfjwe+HisrZEQBl5U47UUMhY8+sW6lC0hpyrNptwjsjYaxIydUyiQ70egW/q8md6whScvmwybKs60jbitkT2gDLjnmd1Hm66Qjt8uSstV1MiMoE6uXHUG94HbG8B1Sb8lGNjbuCrDzkw5OHTBaRUouX18fpanCu08vCJtcMGihFJrhP+p2+WCNzJ1JNIXhJOO4dS0oMyvIcD2BKQVvYyXnd3GLj9eb/Ox7odwwGHR0dpNP5H1C4uBQTS5cIbrjOef1vP3HFtovLadF1nVWrVgHQ0Dg7U9zl5c409WBq5lbt5qCBR4UoYsZW7XUyTSVOWe7f1sw8K8Nk1kecyoMA+5enGJxFoV3br9HU6wEkyqZjTo7sCxwRyKCu7UK97nXEgkEAGvp1rng1SGOPnvNJHluFw0vSHFySQiJZGre5rjeDaheP4O73ZcU2ub0PnBQ67WgIAesqU4g8z6D1JDWGUiqqCq2L8n9PSyQgEQfbtjl+/Hje23NxKTb+/M+cZ+X230NHx8z7tyu2XeYlF198MeAUmdG0/D/8KyodsT00YxcSyeKQE5R0cIa+2uXSYiWO5XF7tZ7TaoAL4xaXhB1xfXDJ7Plog+M+sqTdsfSJVSdRGiKz1vZcQAQM1E3HUd++DypjqLZgxVEfqw75UPKg0042GOxencRUJfVpyeYho2hcSvqyRZrKsdFyfEyvCh9pBGUem0Ul+Q4knLBu19bZ+PNehVWM1ypwxbbLhUjrQsHmq51b2SM/m+di+/XXX+fDH/4wGzZsYP369dx66608+eST57WPTCbDD37wA971rnexZs0aNm/ezFe+8hWGhoZO+WwymeThhx/mc5/7HNdffz0rV65kxYoVdHV1nXH/zz77LN/4xje4/fbbWb9+PStWrOD+++8/73N1yS1VVVVUVVWhKFBVnd9gOZ9fEgiALWFghpUja7wWId3GAI4xg9RiUrIhm3nkeEChM5C73HBlGZtrBh1x0dGU4WTD7GYsqO/X8WUU8GVQVvbMattzCVEZR712P8qaThCS+gGdy3YH8Cdz71YyUmHx+kVJbCFZHLe5eLQ4gibTqiCRfcqVkdv7QFoo7BSOAF5Rmsav5vc+EzFUepMqQsyO7/bwkPPFdXZ2YtvzL+DYxeXN+LPbnHvltqchFpuZ4C5asb1jxw7+/M//nFdffZUbbriB22+/ncHBQT7zmc/w8MMPn9M+bNvm4x//OPfffz8VFRV88IMf5JJLLuHRRx/ltttuY3h4eMrnh4aGuPfee3niiSfIZDKUlZW9aRv/8i//wr/9279x+PBhamtrp3WuLvlh6dKlAFTnWWxXVU1Ytc0ZFp5ozWYgOYYHcwZW7RZMarEwBLxUmbt8wIqUXDtgoEsYKTM51jrL/pwSWrqd81GW9xa0OM1cQAhQVvSivuUgeA1CCZUNu4KUjub+1h8psziyyLkeNoyYNBVJlpJwtjJqWY5dScAZEPejoimwujz/feHI6IR1W/fk99ofjQpM08nhPDAwkNe2XFyKkbVroHUhpNPwzHMz21dRim3TNPnKV76CEIJ///d/5xvf+AZf+MIX+NWvfkVrayv33Xcf3d3db7qfxx57jO3bt3PjjTfy05/+lLvuuov777+fu+++m87OTr773e9O+XxFRQUPP/wwf/jDH3j22WfHXRHOxqc//Wm2bt3KK6+8wqc//elpn7NL7lm0aBEwFryYvwdTTa0jtnuTM7Nq+1WbOl+2IMxMCthIycXSefDvLVVz6j6yNmxSYUgyuk3bihSzUNRuCv6UIJhUQdiIRa4AOFdETRT1nW1QFUWzBOvaAnkR3N0NBifrMgjgLYMGQbPwg6FItgpcKEfp/6YgBC8LHzbQEDCpynPu7ZGMykhaQVGgpibP1mYpiESc7663tze/bbm4FCFCCLbc4PSB/3pyHlq2d+zYQUdHBzfeeON4oBtASUkJH/vYxzAMg8cee+xN9/Poo48C8NnPfhYxyUp4++2309LSwuOPPz6lElEwGOTqq6+mvLz8nI91w4YNtLa2Ttm/S3FQXV2NrutoGgSD+Xno+/yS0lKJlHAyMTOx3RoyEAJ6UInOoOJdPRYV2BgC9pXO7JgmU5axWRsZqw6ZxsizZe10VA075yNqovMyl3Y+EX4D9ZpDiJrR/AluAYeWpBkNWfhsuLY/U/AMJbHsYDOUYzeSMSJC5UjW5eviinTegyVPJpy28i62gWj2+nDFtsuFyvXvAlWFffuhs2v6fbsoxfZLL70EwObNm095b2zbyy+/fNZ9pNNpdu/ezaJFi2hqaprynhCCq666ikQiwd69e3N01C7FhqIo1NfXA1BSmp8HYF2dIz4HUuqMUv4pQrIg6LiQHBYzqxa5TDr7ORRSyai5GwReFjZRgcEKc1ZT/E2mNOoMQkTdaEHan+sIzUa5+vC44F6z3483x6kBpQJtK5MYmqQmI1kTKaw7SXRcbOdPBL+eDZYs1W2aAvntGyezM2hl5fkP/o5Gne+uv78/r+24uBQrlZWCSy9xXj+/ffr7KUqx3d7eDjjVAN9ITU0NgUCAEydOnHUfHR0d2LZNa2vrad8f2z7Wlsv8pKqqCoBAIA8PJSHHq1R2xmfmF90UcNL9xRB0zyDdn0dKGrMFPA6X5DYocmHCOdeji9JQoIkc3cw27HPLSE+XMcFNWQKPoXDxfj9Kjo2kKZ/k0BJn1nBd2KQsU7hZiHhWbAfyZNkGyAjBvuwgeVlphny6raUshbjhnFNJSX7FdiLutBOJRLCs4vDBd3GZbTZf7fSD57dPv7/lbo45h8RiThW4kpKS074fCoWIRqNn3cfY+6FQ6Iz7mNyWy/xkzCXInwexXVkp8XohY4kZ+mtLFmXT/R0Snhml+2vBQAWGdcGIJ3dj6bHsEgOVBolA4YSTamW/G8198M8EodmoVx3GemY1pTGNpce8HFqa2wC//mqT+n6TqhGNq4cMnqz35K5y6XkQz87u+JEIKXNX1OkNHMbDRaQJ6TYNfpOeZO4Ck9/ISEYlqJuUlEpGRvLWDOk0mCZomk0kEqGysjJ/jblMm7KyMhRl6v2+oqKiQEeTf2b73N69xeIfvxdmbxtIWUZl5fk/W4tSbLu45IqxAZtnZp4Zp2WsYE5nQsOegam3ymtR5rExcbKQzOiYpGPVbg/mzqqNlDRnM0t0NRbWomyOTZsn8vCDFhApQQ6UII/VIFM6wmuCz3DWpUlEQzjnmVdEMINy+VHs51fQ1Ouhr8YkUpbDQYxwcrBv2hmkLg0XRa2cxhCcK0kVLEDFEdyJPE3LmEJwRHpYTYamQH7Fdsx0Hvb5ryYpSKUEoZAkGo26YrtIiUSm1hqoqKhgJJ+jsAJSiHPzeaG1Fdrb4fcvhtl81dR7yLmI/6J0IxmzOp/Jeh2Lxc5o9R5j7P0zWa7Htp/J8u0yP/B6nVRZWo6FitcnqcyWgj8Rm5nwGyticxydzEysblJSm01v1uPLXdcuMyQBGyxFEiktrEV5rH05ePb+P5ewuyqwnl6N/buVyK4qGCxFdlcij9Zh72vC3rEU67/WY+1qQcZzO8hQ6kYRrU5WlxVHvYgcT1qkfZKj2fSQl42YlBoFmBURgmR27OnPoysJwIlsFqFav4kq8ieELdu5T6g5HFOfCTM7vp6cTMDF5ULjomyujn37ptevi1Jsj/lTn84ve2BggEQicVp/7sm0tLSgKMoZfbLHtp/Jp9tlfuDJmrTVHBvUGhoshHACI+Pm9LtRQLWp82crMc4wMLIEGx8SU8CgN3fWu9q0I1BGSyxkge8Y4TGx3V+KzMyC0sgzdm8p9o6lEAmgagZLV7Zx5due5tIrtrN6/assXr6fQDAKGQ15pB7rqTVYuxYg7dz9vsraTvAaBBMqjb25t8aerDcYLjPRJFwzaCAKkJ0kMcmVJJ+EUYiioAqo9uZvYGplT0OZhTzzRlZsJ5PJvLfl4lKsXLTKuYfs2z+9/y9Ksb1x40YAtm8/NfRzbNvYZ86Ez+dj7dq1HD9+/JSc3FJKXnjhBQKBwDnl0naZu4z5seXSTVMISV29I0BPxGYmThaVZBACTqIyOoN0fwClWatdWBfYOTxhb9YYmMr7lPWbEym1iPstMDTsQ/WFPpwZIS2B/VorAK1LD/Int/0bG67azsLFR1l+URtrLn2FTZt/x43ve4S3/NGT1DV2gVSQR+qwX1iKtHLzGwuPhbLauUcu7PLkPFgSAQeWpTBVSW1ajqePnE1SWbHtzbPYRgiGs4/VoJY/K7onK7INI/8+8DKbTN+tIulyIbNiubNuP3tujjNSlGL7yiuvpKWlhSeeeIL9+yeGEUKJLqYAACAASURBVNFolAcffBBd17npppvGt/f393P06NFT3E5uvfVWAO677z7kJGvKT3/6Uzo7O3nPe96Dz+fL89m4FJJ85D+vqnICI1MzDIzUhGRB0DEbHciWfZ4JY2J7rIhHrvDYTt+xiqFao4BjC53UhvJAY87dKmaV4RAkvHh9CS67cjseb+a0H1MUSWNLJ9de/19c886nUDUD2VuO/ftl5MpILFoHwZ/Gm1FoyIN1Oz0pO8n6sEl1enaFWzr7pPPkW2wDsexjNZBHse3P9sXMLBRwLXCadBeXoqAha9sZHIRM5vw7RVEGSGqaxt/93d9xxx138Bd/8Re8+93vJhgMsm3bNrq7u/n85z9Pc3Pz+Ofvu+8+HnvsMe655x5uvvnm8e3vfe97efLJJ3niiSfo6upi48aNdHR0sG3bNpqbm7nzzjtPafvee+8dd74/dOgQAN/+9rcJBAIA3HLLLWzYsGH8808//TRPP/00AF1dXePbxqzpixcv5iMf+Uguvx6X80Dm4UlR15ANjIzryBkEW7UEDTTFmXruZeYuEf7sucZzWDESJpKY5dEF9bwYrDIZqDKoGdKxdixBfcvBOVngRloCRbMBga6fW27mpgUneNu7nuS327Zg9pchT1QhWodmfCxCkSgrerF3LaSpR6e7wch5ese+GpOqYYO6QZ23Dhj8qtGDqcxOdhIj245HyrynrcwIARI0JX8dplR37kHJZP6/P5EdqLiF21wuZMrKwO+DZAr6+qGl+c3/ZzJFKbYBrrjiCn7yk5/w/e9/nyeffBLTNFm+fDl33XUXW7ZsOad9KIrCAw88wEMPPcSvfvUrfvSjH1FeXs4tt9zCnXfeedrI6q1bt57idrJ169bx15s2bZoitvfv339KNcsDBw5w4MCB8c+7YrtwjE195kpz656JwMiZ5daWtIayxWdEblKiqVlZbOb4oThWgc+XLpKJMAFHFqUpj6joIyGs7cudyoh5tCTmBQG2qeANnV/gWU19L6sveZXdL1+BvbcZ0TySk3MXCwdhTzPBpPr/s/feYZbd5Z3n53fCjZVjV+qcJXWrkRrlhLKEbRawERh7wWm8uzDgCMMMa4wZAx6GAXufWYPtx/uMGQvDYq1NEsIISUhCrdTdUudc1aG68q2qG0/4vfvHuVVdnSvcW3WrdD7Pc56bqk649/zO+Z73vO/3pWbcYKymxN+ngkNr89SMm9QUDN4+7PFiU/kcO6YykXEzH3twpHiwcUqYVz8VSwl1kWLKWKr8W2QXHYDi8XjZlxUSUqkopWhpEbp7YGBgCYltgC1btvC3f/u3V/27L3zhC3zhC1+45GeRSISPfOQjfOQjH5nWMp9++ukZreNHP/pRPvrRj87of0Lmj1KL7eZmjVIwXDDmVBjZFPWpsgUHOEFpBMdEbLxEqbyTjNoGrqmoT5lBmLsCAlz5mLDr2hzb3kxgDVXjP7sB87bDqNjCdLacDSoZ5ACMj9aSz8WJxadfgLZ+85scObCJzHgxur1mYO7rY2tU5wjS3URbn81YTelzFDwLDqzPc/2bcTakfU7FDXpKaVN5GSbGhDkPaSSJ4jLcUg/EIi0xD6UgmwWnxN0/L4VdzNQKUy5D3upEi0OgcOmMvytSIaGqkJDyMNH1rFS1PU3NwYzOZOcmkDuLudrd2CWLRBeKKjjul1ZQDEUUSguGKKoylXPISFdpdl2bxbU0jFThP70ZGV1E0bdkAeoziBjsfvWmGf2raWpalp0NXrilE6tq+SAATUNW2dKGUrU+JzuC/f/2IZekV34BPDHC5uKHPy1EWFbs4Joqk1vOyqJV6ED/fIxFIR4Pfp/a2tp5WF5ISOViF8PT3izaTVTOmTMkpAx4XnDi0yW4pWvbQm1tcOKZS2GkqYS2ot3fcVW62+jpYnJltVta8eIbipPxYN6t/fNz23+6jFdrXtuaJRvTkI3i/3QT+sziEAVKgXl9UNp+/PAG+s/OzF3F94piroSFq6p5HCIuEc+gbrR8EedjKwqMVflENdw14JTdDnDiKyp3olEjPnEEV8NgofTfX43t0xjz0Rp6z5T/jkAsFnh5m6YZiu2Qtzx28fTnzuIGaii2Q5Y0k2K7BG5jdfVBCsmoY5DzZz90mmMelgFpFIMlKIycYKw4nJsdXXLxcrQqWM/2PpuIUwF5JFPIxYXXtmYYqfHAM9E/X4c+0bjQqzUtVGOGiaYyz//kQUZTddP6v/GxGs6cWh68iM3inubl1scA1Z4CoHGofFmGYsDejTk8U2gtCFtT5U3/KaY4l7ye4UI2SvBb9OesORVPXxphc12Q2jM4YODMwzisLubtNzQ0XNQOPCTkrcZEj8TELG6ghqMnZElTyjSSmppAwA7NMWI10eziDFZJDcAHMCkACR+W5UsbwzsZNxiIKCxfsaq78uz2PBt2X5ujt8UFUehXV6MPLg4fbmNrD9SncQoxnvnRo2TSV+5q63kmLzx9P54bgcZxVEeqpOujlgWtn+vLGNmGIO/+4NqgOHTrqE9DGe0Aq4upKukyppE0iM8KPETgyHjpx0hHwqM55uP7cOL4/DR0qi8Wg3d1dc3L8kJCKplUcGikbnoxkfMIxXbIkmYysl2CQG9NMYVkeI5iu7EotvtUaSOHWilOFostSx4pVIqXG4J5t/XZ1JZZiM0GMYLmKT0dQXRRv9mF3te+wGt1dZStMW8/DNU5cpkqnnzivbz03N2c7lmOX7yDIgKFfIyBvlaefeoRUsNNEHExbz6KKrHFnGoeB4SqrFn2uxj9zR79TS4GcNOwWzZT5+pim/h0mU55SoQbJLhwOJW1GCthHj1A1NBcU4xq93Sb5PPzcXdJqK8PvrerdWwOCVnqiAipYlxjNmK7ot1IQkLmyqQbyZyDZkIyGQiB0TkVPgnJoif0cAlTSCbYryKsFJe2gtCa1/TFSicu+mMGR5IGazOaaw7EeHVbFidSIebbEyg4uqqAawtrTkTR+zqgOofRNbLQa3ZFVNTDvOMg/nMbcNNxThzZwIkjG7Bsh6rqMTLj1bjulMZHpo9x01FUfBaVOtNYF2pzMJqgZtxksLG8KR5HVhVoHLZYVoDlOU1PorTjIuYLVX5gpDNahjEHcJ0UaMbH1XBwdO4NqqaiEG5syhM1hUxacerk/MTIGhqC5l2xWIy2trZ5WWZISKVy9ix4HlgWNF7sGn1Vwsh2yJJmoqnNXCVhNAqGEUTIs3Ow9IoagqmCQq1sGW5pjyuT48Xo9q1DLmYpQvpT+HmjzYitiLoG1xyIoSrU2rqn06G7M4gE6tdWIenSCqByoBIu5oN7MO/aj1rbB3EHz42QGm46J7TjDqp9BPP+vRit41ee4VzWpSEDQM14+U8Rhahwsj24G3HDsFfyeoOWYkrVKAZuGXK2l4nHNQTrv3s4Nqd6jkuxua5AQ9TH82DvXmuyfXq5WdYe3IHbuHEjlhXG5ULe2hw+EjyuWgn2LLo0hyMo5K3BHM/fsaL9VdZTzMVoOlq0RSigkDIVa+1WMTrFo84V7hlwebrFRpdoWZ6heLrF5hfOONSNWVxzIMbejXmkAi/bj69wqB0zqRuz0DtXYN5xaKFX6aooBTSnMZvTyNYeGE4iBQtVVYBkAVVC55ErrkdDGjneTHV6ftKFejod2s/a1HkGqzKaY1WlW25rMRe8lMXIE9SJz+0SXDafSNv05krr1rOmusDq6uDuxcEDFvl56BgJEI8LjY3BvnbttdfOyzJDQiqZI0eDx3VrZ/f/FXiKDAkpHRMV9HPVmmZxpHhzjCq5RQvCCFK2/FRHKV5QcTygK6e5e8AtabRwzDb4aYuNr6B52K7YCLco2L8uj1aC9NWi+2oWepVmhFKBW4nRPoqqyc+b0AZQtUGDneQ8+ar7FpxqD0Tl5jGvpGOjMxvsnL0lrpGoEs09kiUCDOVN9o6U9u7JyiqHzXVBxPz4MZOhwfk7XS9f6aMUrFy58pKdlkNC3mrs2h0ck9avn50GCMV2yJLGNINolprjnj7heuXPUWwXimLbBKJl7GbXryyeUwl8YEVWc1+/S7SEzW7OxE3+bYrgvnZ/HLME9oqlJh8XTrcFIk6/2Vmu65ulR00gtqOuge3OTzT1TJuLVkKzIzQXSvNDVbuaOk/QwNkS3shNiuYdkiGOMOoYvDwYL2nDnJVVDtfVB2lQ3ScMTvbMX0FyIqlpaQkuUG6++eZ5W25ISKWSSgm7dgfPb71ldvMIxXbIkmYi19Cc47lqQqQZcxTIWhSD+WBlmimvOj07RXB35jS/dKYwmb9aCqYK7qYRi21vJIjMQ/vomdLd5eCZAqkkciqM0k0HZWmIB1HV2Lw4X4BrC/1NQTHm8mxpxsaaTLC/92GWLF+7WnzukwxVCBlPsWMgPuc7XucQNtUWJoX2yZMG3Sfm0/lHWLc+iGqvWbOGlpaWeVx2SEhl8sLPA/vgdWuhvS2MbIeEXEQkEvjdWtbcRLJTNH2IluBWfsoJht0aKb2TxIX0KosfqSSjGCR9ePisw3WjpStCOxM3+WFrhJwB1RmTG3clqBmrrMOKa8s5O8C9HUgJuom+JSiK7Whh/n7P4fpAbLeV4qJQhLXjwfyOqdL4XleLz72SJYkw7hq82J+goEvz/RgI2xryrK05lzpy/KjJXGpEZkpbu6a2VrBtmzvvvHPelhsSUsk89ePgfHnnHbMfi5V1VgwJKTHRaJBHOddi+olubVFTUHOMbvdkghN/Ox4tUl5bNYCUMvmRSnICCwO4ccTjF3odmksU5R6IGXyvLTLpUrLtzQRdp+y5W8CUkFMdDo6tIR1DjobRuumgYsHFYHQeO4aO1AYR7SZHsObopNOZ01T74ACnSpBC0iA+90uWBMKYY/Bif5x8iZxHYqbm1pYsnUkPreHgfrOYOjJ/330iIaxeE3z/t956K9XV1fO27JCQSuXAAeG114O74w8/NPv5hGI7ZEmTTCYBsG1QavYn70IeXBdMBTX23ERqxjPoSVso4DbJEZu7CfhV8ZTiRRXnJRXDARod4dGzDjcPudglsAdM24HgPp4wMESx9kSMLfvi2BXS2t034fjyYnR7TweSqbwumJXKPDnNAeBEBd8IGp3H5lhjcO1YIByPEsGfYwrJMvG4VzLEEFKOwc8H4jglimg3RT3ubM1SH9W4Lux506Kvb36bRpmmsOkaF9MMukVu2bJlXpcfElKpfOPx4Dh0/32wrDWMbIeEXJJ4PI5hGCgFkTnpK8XYWDDQGqJzzyfdk4ox7hrEEW6THOZ8VO4pxTEV4buqiqPYKGDTuM+7TxdYOz731BLPUDzTbPNio4WnoHHEYvvOBA3DldFt8swyl1SNB76JfnVVmE5yNYpRW38eXVAgSPsBiM3hGrSpoGnLazRwcI4pJF3icrdksYGBvMmL/YkSCW1hXXWBm5tzRE1hfFyx8zWb1Mh8n5aF9Rs9kskgOPHggw+iymRLGhKymDh2XHj2ueD5Bx6b25gIxXbIkkYpRU1NYPkWj89NNIymguHSlph76ocvilcHY3gaWvGDqNk8RLgBCspghxHn31SCMQwSPtwx5PFLZxzac3O8kFCKg9XWeWklW/cl2HA4iln+jJmrrFvQzt03BBmoQb+8ugSdRZcuE9F/d471DjNlQsfOReNvGwl2thPYZOdgRbRGHG6XHAZwOmuxYyA+Z0ciCNJGbmnOsbHOQSk422uwe6c1T23YpyKsWevT3CwYhsEjjzxCIpGY53UICak8tBa+9GVBBO6+E1avCsV2SMgVmfCJTSTnJhr6+wxEoDHqU2XNPbqd9kxeGkjg+NCE5mHJ0DYPOdwT9CuLH6gkr6soBaDeFR7sc7m3z6HGnZsKHYkYfLctwt6aIKrd3hdh+84kdamFjXLn4sKejbnAe/tUQxDhrqDc8kpBcjak4wjCWM38ejpaxbrh2dZltuY1ncWo9h41S+9rETZLgZskjwK60zavD8WQEuRQt8Y87mrN0BTz8X04eMDk0EETvQB3Wpav0HR0BmP9gQceCNuyh4QU+cEP4Y03IR6Dj35k7mMzFNshS54JsZ2co9h2HMXwUDDo1hUdA+bKiGPyfH+SsWJKyT2S5Q6dpUbmR+BopTigovyrquYAETSwPKf5X0473DroEvdm/535huLlBpsfttqMW4p4wWDbngTrjyxslHu4wWfvhjyCID1N6J+tD8RlyCRyvBmAdFLjzWOfYaXB9oIxVjBmcYIT4W0jgVo/ik16NlFtEW6QPNdLYL93aDTCGyNR5lqsaCphS32etzfniJgwPq547VWbvrPzWwg5QUenz8pVwXHmrrvuYv369fO+DiEhlcjwsPDfvxac+37zNxStLaHYDgm5Kq2trQDU1M49hNndbSIC7QmvJNFtCAomf9aX4Ni4jQh04fGoZLhVZ2mcJ9HtKsXrRowfqCSni64lG9I+7zld4PoRd07OEGfjJv9fe4SDxRbcHWcjvH1nckFzuQebPPZuKKaU9NfiP3Ut+mT9gq1PJaFP1aP3dQBMNgSaL2rGTRSKvAG5Weweq7KaZQXBA/bOIqqtRLhZ8mwg2O49I1EOjs1daDdEPO5almFFlYtI4J+96/X5a79+PsKq1R5r1gbHlre//e1s3bp1AdYjJKTy8H3hs/9ZGBuDtWvgve8uzXxDsR2y5GlvbweCyLZtz01wp8cNhgYVhoItDQVK5W+nRbE3FeOZswnOZgOnkpV4PCgZHtAZVouDPQ/5DmPK5FkjwVMqwQAmtsC20UB0bxibfRGlZyhebLJ5stVmzFLECkEu96aDsQVzLBlo9nj1+ixjVT64FnrHWvyXViNjsQVZn4VGpCi0X14NwKk2h95l8yu2G4ppRmfiRtCvfgZYWrhxOFjffSo641xtJcItkmM1LlrgtaEYx9NzK65UCBtqCtzakiNpCfk8vLHb4vhRC5lPm5cihiFs3OTTtTxIHbnlllvCLpEhIVP4m78TXn0NYjH4k08rLKs049T8zGc+85mSzClkVuTz+YVehSWPbdscPXqUbDZLNqPIZOZ2jTk2ZtDWrknagqlgsFC6++yONjiTszmbszCVUGVrqpTQicdGHJrEx0TIoOZsZ3YlssrgGDYpZdKAT5VAV06zOqPJm4qUrWYshiCwCDxcZWIKNBeE6qxJ+1kbzxLGq/S83013beFsi4sAdWMmjCWQo63ISBIVdyHhzGYzFx26rxq9Yw1yZBmIYrDeY//6/Pz+HgIbjsSwPcWeGpPh6MzG6Y0jHp15IU1gcykz+OEMEW6XHCvw0AKvD8XonWNqUZXlc3Nzjo6kFxRBnjXY+6ZFLrcwMa5oVLhuq0dDg6CUwf333xdGtCuUeDw+o7+/UEfE4/Elqy3KuW1P/Ivwtb8Jnn/yjxXbb5jeMWQ6v9c8ZuOFhCwca9asYXBwkOYWPWcP20JBceigyabNPmtrHMZdg1PZ0ub8jrkmu4bj7E9pupIunUmPalvTgUeHeGhgQExOK4szWIwx80jgVVGKk9icxmItDteKQ40n3D3gcm1E8Wq9RW985t+lZyheabA5njS5ZcilyYENR2O09dkcWpNnvHp+LULEgBMrHAYbPVb2RGgatqC3Dr+3DuozGGv6UF3DqHm2wCs3IiB9NcihZUh/LQC+IZzscOjudOb9wqd5yCKRNygYcCI5s/2qJa/ZXPTVfkXFZnQhaopwh2Rpx8cXeG0wTl9+LqdGYXWVy8a6AqYK/PkPH7IYHFi4G8n19ZoNmzwiEYjFYjz88MN0dXUt2PqEhFQaP3te+G9fLeZpf1jx0AOlPQAqkbAWfyEZGRlZ6FV4SzAyMsI//MM/IAIv/dzGLUHqwspVHstXaLTAK4Nx+ud0gr46VZZPW8KjLe5RGzlfkKZR9GJxRln0YeGVIRxribARh01SYOLS4lTc4JV6i1RkdkJCibBh3OeGEY+IgCCcWeZybEUBb4FqFuM5RdfpCMv6bcwJhwjTR7WOodpTqGUpVGyhfQxnh2QjgcDuq0H6a8AJvmStgu/9RJeDG1mAU4LADbsT1KRNdteavF4//R/f1sIvngkuBI9is8OYflTQEOFOydGOh6eDcTyXO1VRQ7OtMU9zLBD+Q0OKwwetyQ60841hCCtX+XR0apSC5uZmHn300Uk71JDKpL5+ZvUjF+qI+vr6JastyrFtO3cJf/gJoVCAX3gU/vgP1Yy85qfze4Vie4FZqgOiEvnWt77F2bNn6ek2OHG8FMJY2LTZp7klENy7h2Mlj3BfjripaY17tMY8GmM+5pTjgg8MEES9T8/WkeEKREVzrTisw8EANHCoymRnvUXenJ2oiPnC9mGXtZngIsKxNMdWOvS2ugth1ACA7Sraztq0n7WJX+hDV5/GaBtFtaWgLluxqSaSt5DBaqS/KK7T5+eju5ZwttnlVIdDPrZwp4KOMzbrj8VwFHynMzr9/UiCOy2rspoMih+oKtxp/hiGCHdIjg48fA0vDcYZnoPQXhZ32VpfIGIKvg/Hjpr0njFYqB24pkazfqPHhG32ddddxx133IFlhTe0K51QbF+eUm/bSzuET31acBy45Wb4/Odmnqcdiu1FwFIdEJXI0aNH+f73v4/rwssv2fj+3E+CSgkbNvq0tAYi8chYhAOjkZL48U4XUwmNUZ/mWCC+kxcUgaYwOI3FKWUxhFmydJNq8bleCnQRRHkdBW/UWuyrMfFnY9tG4JF8y5BLvRtsw1iVz5FVBUZr59fr+TwEqjIGTcMWjcMWNekLUhwiLqplDNU8jmpMQ00eZcz/YVW0gtE4kkogw1XIYBWMnx/lFYSxas1wncdIvc9YlY8scJl8tKB4++tJLF/x8waLAzXTF4Prxz1uGwrSqn6sEgyp6f3vRI52Jx6+wI6BOEOzFNqGEq6tK7CiKijOHB9XHNhvkcsujMi27SCavawtiGYnk0nuvfdeVq5cuSDrEzJzQrF9eUq5bc8+J/zJZwXPg1tvhj/7U0U0OvNxG4rtRcBSHRCViIjwjW98g5GREbpPGHSfKFWER1ix0mfFykBwDxdMXh+KkfMXRsUkLU1LzKM17tEY9Zmqe9MoerDpUTbDJcrzbhaPt0meRoLtT5vwWr3NseTs5q9E2DTus62YWgIwWO9xbGWBTHLhWz5GHEXDsEnTiEV9ysK68KLN0FCbRdVnUXUZVG0OqvOoSGkuGESAvI2MxQNxPRpHUkkYi3Ep5ZxO+IzU+ozUeaRqffwKCmyaHly/J0gf6YsqfrAsMu19piWveeisgwnsVFH2T9Pq77yItsDLA7NPHamyfG5ozFMT0ZOWft3HzQVzGuno1HQt95kIXm/evJk77riDaHSWzX1CFoRQbF+eUm3bD58UvvAXgq/hnrvh//yPCtue3bgNxfYiYKkOiErl8OHD/PCHP8T34bVX7JK2R25u9lm3ITjRuRr2j0bpTtssWB4EYCuhOe6xrJhyYk3RYuMojqsIx+bY0hoAEVbicr0USBTtEAciQVOb/tjs5h3zhW0jHuvTPgZBVLav2ePE8gK5eGUctpQOvKHrUya14yY14+bF4nuCqAvVOVTSgZgTuJ3EXLC9oPjSEDA1aAWegXgmeCbkLSQbhWwEyUaCVBD30uLQNYV0VRCxHq0JpoXKfb8ahoYte+PUj1rkDfh+W4Qxe3r7SrWreWevQ0xDDxbPq/i0RPrUHG1fw8tzyNHuSLhsqc9jGeA4cGCfRSq1ABfYSli2TLNipc+Epm5tbeWOO+6YtD0NWVyEYvvyzHXbfF/4+t8K//Px4PUjD8En/khhzjIFcmKdrkYotheYpTogKhUR4YknnuDUqVMMDSr27rEopRiOxYQNmzxqiw10Uo7BnpEoI87ChxMNJbTEPNoTFwvvPkyOKZse7DlZCprFIsrNU4oojyYNXq23yc7Sr7TG1bxtxGNVNohqC0J/s0d3h0OmauEj3echEM8rqtIm1RmD6rRJImsQc0orwgQhG9dkEsGUrtKkkz75qCzktd20MX245kCcxhELR8GTyyIMTdPqL+YLD/c61HnCEAb/ppLT2mcvzNHeMTi71BED4Zr6AiuLaSOpEcX+/VZJiq5nhtDSolm+0p/My66urubWW29l/fr1MyrwCqksQrF9eeaybem08NnPCS++FLz+4Afgd35LYcwy7XHqOl2NUGwvMEt1QFQyQ0NDPP7442itOXrE5PSpUncyFNraNatWn7udeyZrcXA0QtpbuK6JUzGV0Bb36Ey6NEX9yaBgAcVRbI6oyJwKK2Oi2SIF1uCiALeYz713DvncjQXN9SmP5blzAnu4zuNUm8NQg1/RItP0IJEzSOQMogWDqKOIOIqoY2D6YGiFIUG0VxT4Jnim4JuCawn5qFCIavJRIRfT5BIavUhbkkUKii374lRnTDwFT7VG6Jvm3Y+oLzx01qHBDbzmf6SS5Kexn5oi3ClZ2or2frNNHYkamhubcjREg7SRnm6D7hPz3W5daG7RrFjhk0gG78RiMbZv3851110XFkAuAUKxfXlmu22nTgmf/I/CiW6IROA/fEJx/72lGbeLXmy/8cYb/NVf/RU7d+7E8zzWr1/Phz70IR555JFpz8NxHL7+9a/zr//6r/T29lJbW8s999zDxz/+cRobG8/721wux+OPP87evXvZu3cvJ06cQET4yU9+Qmdn52WXcfz4cb7yla/w0ksvkcvlWLlyJY899hjvf//7rxpdWKoDotLZvXs3zz77LFrD7l0W42OlVy4XFiqJwNmcxeGxCKNuZYhuCJxNOhIuK6pcEta5w8EZLParCH1zKKqsF58bJU8zQb7ymKV4ucHi5Cw6BE7QUNBcN+axMqMnW+DmopozbS69Le7CWNeFTIu6lMnmQzGijkHOgJ+0RBiYptCO+MJDfQ6NjpBD8W8qwbi6+jiyRLhbsrTg4xVTR2YT0a6L+NzYmCNuCa4LB/ZbjAzP5xWP0NQkrFjlk0wG+3gsFmPbtm1sI7Gb4QAAIABJREFU3bqVSGRu3S5DKodQbF+e2WzbT54W/uK/CpkMNDcFjiMbN5buAnlRi+2XXnqJ3/qt3yISifDoo4+STCZ56qmnOH36NJ/4xCf4jd/4javOQ2vNb//2b/P8889z/fXXs337drq7u/nxj39MZ2cn3/rWt2hoaJj8+1OnTnHvvfcC0NHRQSaTIZVKXVFsHzlyhMcee4x8Ps/DDz9MS0sLzz77LIcPH+aDH/wgn/70p6+4jkt1QFQ6IsKTTz7J4cOHcRzYtdMmnytPdCqZDPIpm5rPDbWBvMnxcZv+vDWvziVXRmiJ+ayscmiJnYt2D2GwX0U5iTWjrnznZiusxON6yU/mc5+OGexosBidpT83QJWr2Tjusy7tE9MTWxC4bQw2egw2eGQTFZZm8hbFdhRrTkRp6w+Si1K24sctNulp5mjHPeGBviCinUPxE5VgbBpCOyKaeyRLIxpXw46BBCPOzC90OxMuWxrymAoyGdi7p3zHi4sRGhqDC/eqqmD8RKPRSZEdFj8uPUKxfXlmsm35vPDVvxK++/3g9Zbr4LOfUTQ1lnbsLlqx7XkeDz/8MGfPnuVb3/oWmzZtAmB8fJz3vve9nD59mh/96Ed0dHRccT7f+c53+NSnPsU73/lOvvSlL01GmR9//HE+85nP8L73vY/Pfvazk3+fyWTYtWsX11xzDXV1dfzmb/4mzz///BXF9gc/+EFeeeUVvv71r3PXXXcBQTT9wx/+MK+++irf/OY32bZt22XXcakOiMVAoVDgO9/5DoODg+TzsHuXTaGEBZMXkkhoupZrWlr1pJDN+4qetE1Pxl4w95JLkTA1q6sdliddzOJqjaPYq6KcwEbPQnRbIlwjBTYSOEho4EC1ya46i8IcilNMLazK+GwY92lxzj+cZWOaVK3HSK1PqtbHiVbc4W5JY2hoO2uzqjuKXSwcPVBl8kqDhTfNdKIaV/PAWYdqH3IonlYJRqchtGOieYdkqUNT8BU7BuKzuKMkbKx1WFfjADA4qDi43yqJbeh0qK7RrF7tU1sX7LeRSITrr7+ebdu2hSJ7CROK7csz3W07eiyw9TtxIriJ+r/+Gnzo12fuoT3ddboalXN2n8JLL71ET08P73znOyeFNgTFH7/7u7+L67o88cQTV53Pt7/9bQB+//d//7x0jscee4yuri6++93vks/nJ99PJpPcdttt1NXVTWs9jx8/ziuvvMJNN900KbQhOCB+7GMfA4JGKiGVSTQa5V3vehd1dXXEYrD1epdYGV0uslmDgwcsXt5hc7LHwHEgZgrrax3ua89wa0uWFVUOEWPho7FZ32BPKsa/9SY5OBqh4CuqEW6WPO+UNGvEwZjhdbqnFLuNGN9XVZzEwgA2j/u891SB60Y9TD277943FEeqLb7fHuWfOqP8vMHiVMzABxJ5g/a+CNccinPbK1Xc9GqSzQdiLD8ZoWHEJOIoCPV3yYkWFCt7Itz8SpL1x2LYvmIoovjesgg/b7KnLbTbcj7v7A2E9hgGT6nktIR2UjT3F4V23le82D9zoW0q4YbG/KTQ7uk22LdnfoR2LC5susZl29s8ausE0zS54YYb+NCHPsTNN98cCu2QkMugtfCt/1f47d8NhHZjI3z1y4rf+g2jLEJ7ulRkJcXLL78MwO23337RZxPvvfLKK1ecR6FQYPfu3axateqiCLhSiltvvZV/+qd/Ys+ePdx4440lX88bbriBRCJx1fUMWVgSiQTvfve7+ed//mdSqRTb3uayb4/F6Gj5rkMLecXxYxYnjguNTcKyNp+GhqAxTWPU59q6AgN5k96cRX/OorCAlXCONjg0FuXoeIQVSZc1NQ5VpnCT5LmWAm8QRLpnkl6SVgY/UwlaxWOb5GkQzY0jHpvGPHbWWRypMmeXrgJkLcWBGosDNUEb79a8pi2vWZbXNDhCIm+QyBu0Dp77H9cUsglNJuGTjWuycU0uHhQiLnTDl0WDBAWgDSMmDSMWDSkTVUyPSpvwZq3Fwerp/65KhK2jPtenPBQwiMmzKk5hGsWQjeJzp2SJI2Q8xUv9CbIzvGsUMzXbm3LURTRaw6GDJv195a+zME1h+Uqfjg6NYQTnqk2bNnHTTTdRXV1d9uWHhCxmzvQKf/4FYdfu4PUtN8OnPqmor1v4VM2KFNsnTpwAYMWKFRd91tzcTCKRoLu7+4rz6OnpQWt92a5ZE++fOHFi1mL7SutpmiadnZ0cOXIEz/PCCvEKpqqqive85z1873vfo6+vj+u2ehw+ZNJ3trwnVxHF4IBicMAgEhWam4MUk+pqoTXu0xr3gQIpx6A/ZzGQNxlxzAXJ8fZFcSwd4UTGZkXSZW2NQ9IUbpE8m3DYTZTTWDMqeuxTFk+SZBUuW6RA0hduH/K4btRnZ53F8Vk2xZnANRSnEianEsHvGPGFJkfT6AgNTiC+a13B9hW144FP9lQEIRcTcnFNNqaDx+JUWCQWe2VBIFZQJLIGVVmTZMagbtS8yN6wN6o4VG1xImnMKO0o5gt3Drh05ItdWbF5TcWmZe/XIS63SQ4LGHUMdgzEZ3yxWmP7vL0pKIR0HNi312KsjBffAcKyNs3KVT4TdY4rVqzg9ttvv6iQPyQk5HxEhH/9Hvxf/13I5SAeg//jf1f80i9QMRaYFakA0+k0wGWv5KuqqhgfH7/iPCY+r6qquuw8pi5rNlxtPZPJJFprMpkMtbW1s15OSPlJJpO85z3v4cc//jGHDx9mw0af2lrhyBETPQ+3jZ2C4vSpwIYwngiEd0NjILzrIpq6iMP6WvA0DBVMBvMWgwWTMddgPlWfFsXxdITujM3KKpd11QXqTM1dkqMfk51Ep90yGwClOE6EHmzW4bBZHGo94e5Bl62jijdqA9E920j3VBxTcSZucmZKB3NTCzWeUOcKtY6mzg1e17iCLYpEXpHIG1wod3wjiHxnpwjwXFyTTWi8CjmqKh10u7S9YLI8hemB5avAalBTtBtUKGHKFLwOPg/+zvQVtquwPLA8hXGJfc5T0Bc1OBM36EkY025QM4kIqzOatw+7xDV4wMsqxgk1DZcNETbjsFUKKKA/Z/LqUBx/hp0cW2Meb2vMYRmQySj2vmmVtPHVpUgmNes2+NTUBPlM9fX13HHHHWF79ZBpU1tbi2GcP95mmve9mJi6bWd6ff70zzI8/0Lge3/jDRaf+2wVXZ2V4/gFFSq2Q0IWAsuyeOihh2hoaGDHjh0sa9PU1GoO7LNIp+cvnyCXVfR0m/R0m9i20NCgqW8U6uo0kQhTot5Bp8qRgslwwWTYMUk55owFxmzQojg2HqEnbbO2xmF1tUOL8nlQsnSLxW4Vm5FPt68UB4hyhAgbcNgoBepd4a5BlxtHYH+1xaFqc06FlJdcrqEYiShGIkByysFZhLgPta6eFN81rlDrCdWuYGpFVdakKnvxAd2xgjSUbFyTi2nyscAfOx/TFCIliIhLIKJjjiJS9OyOFgLP7gn/7ohjTBYklgMfGLMVI7YiFTEYiCr6osasPdSrXM0twx6dRQ/1FAYvqPi08rMtEW6RHF14AHSnbd4cic7wDpCwptphU62DUjAyoti318L3yvcdGkaQMtLVFRRMRyIRbrrpJrZs2YJpVpZQCKlsRkdHz3v9ViiQ9H3hO0/A3/ytkMtDxIZ/9zuKX36Pj2GMMZ+bP50Lm4oU2xNR58tFr9Pp9FUjxRPR5stFrifev1zkezpcbT0zmQxKKZLJ5KyXETK/KKW46aab6Ojo4Ec/+hGQ4fq3eZw4bnLq5PxGkQFcV9HXZ9LXByAkk0J9g1BXr6mpEWwLWuI+LUXxrQXGXYPhgslQcXLKmPPtieLAaJQTaZsNNQ5dSZcVyqNL0hySCHtUFGcGUWlPKfYS5RAR1uOwXhySvnBjyuP6UY9jSZPDVSb9UTWnFJOrohQ5C3KWydkLPxKhygtSUCYEePBck/Qh4hlExrkoLQVAK8GxBXfK5JmCKNBG8LniXLTZ0EEk2XaLEWo3mC4VWb4UPpA3wTEUjnHu0VcKrcBXgSuMr0CUQlNsqgPnPlcK14CCoSgUH/MmJbnbYGlh05jP1lEPW4Ll7lFR9hOZVupJrfjcLjlq0WiBN0ei9GRm5jdtIFzXkGd5MhDrZ04bHD1iImW8aK2r06zb4BEv3mVZu3Ytd95555zORyEhbxWOHhO+8F+E/fuD11u3wB//gWLFispIGbkUFSm2J26fdXd3c+2115732cDAANlsli1btlxxHl1dXRiGMZlXfSET78/lVt3U9bwQ3/c5deoUnZ2dYb72IqSzs5Nf/dVf5Sc/+QlHjx5l9RqfpibNwYMWuexCDWhFJqPIZODUSRNUIL5ra4OppkYTjUFtRFMb0ayqDm6rjbtGMfXEZLBg4erSr3/eN9g9EuNY2mZzbYGWuM9GHFaJwx6iHJ6meJrALYru/URYgcsGcWgQzfq0z/q0z5ilOJo0OFZlzjxdYY6IUozbinH74s8sXYyCT0bDNVWeUOVD0hMMCSLSMWdu66CBnAkZS5E1g+ncc8iZipwZCOuyXpTMElMLG8d9rhv1iBfNd/oweVnFptWoBhE24HC9FDCBnKd4dShOaoYe2hEjKISc6Ah59IjJmdPlu6hWSli5OohmQxCwufvuu1m9enVZlhcSspQoFISv/lWWv/t7wfchmYT/7d8pfvGdzLnlermpSBW4fft2vva1r/H888/z6KOPnvfZ888/P/k3VyIWi7FlyxZ27drF6dOnz3MkERFefPFFEonERWJ+pus5sU6/8zu/c95nr732GtlsloceemjW8w9ZWGKxGI888gj79u3jZz/7GTW1Djfc6NJ9wuTkSSMIAS4kosikFZk0nDkdvBWNCtU1elKAJ6uEaltTbWtWVrmIQMoxGMhb9OctRpzSCotx12THYILmqMfmugI1Ec0NUmA9LjuJcmqGRZS6mNN9HJtmfFaLy3Jcajxh26jPtlGfUUtxMmFwMm7QFytNfvds8QzFcFQxfAlnNiVCwg8KAGO+ENPBc0uCduKGgCmBE6GvJiaFZ0C+GE3Om2ry+UJu52yJ+ML6tM81Yx6J4GYMaRRvqijHsae1b8REc7PkaC92Je3Lmewajs34Dk51sRAyYQmeB/v3WoyMlO/CLZHUbNx0rjHNtddey+233x52fgwJmQY7dwlf/JJw6lQOgLvuhN/794qmpsVxHKxIsX3LLbfQ1dXF9773PX7913/9vKY2f/3Xf41t27zrXe+a/Pv+/n7Gx8dpaWk5r1jxV37lV9i1axdf/vKXz2tq881vfpOTJ0/yvve9j1gsNuv1XL16Ndu3b2fHjh08++yz5zW1+epXvwrAL//yL896/iELj1KKa665huXLl/P000/T3d3NqtUTUW6TbKayvOEKBUVhwGRwIHhtWcXId72mvr6YhhLV1Ecd1tc65H1Fb9aiN2cxXCid08lAweLZPpPlSZcNtQ7VpubOySLKGEPTiV5ORSkGsBhQFq9KjE48VolDK36QxjHmc+2Yj6tgMGrQH1UMRA0Gogb5ConuilJkrCAa/ZZChLa8Zl3aZ0VWYxV9zdMo9hRF9rQuHERYhcvbpEAUwRfYlwpSmGZ6wdgWd7m+IY9lQDYLe9+0yZWtI6TQ3qFZvcbHMIKL+Pvuuy+MZoeETIOxceH//utzXSCbmxUf//dw1x2L6zhakR0kYWbt2j/5yU/yxBNP8PnPf553v/vdk+9fql17T08PTz31FB0dHXz7298+r107wBe/+MXJwoIXXniB/v5+HnzwQRKJBADvfe97z7MKPHz4MO9///vJ5/M88sgjNDc3h+3alygiwoEDB3juuecoFApoDT3dJid7jLLmd5aSSESob9DUNwSFl1MznAq+4nTW4mTGZmzGnfYuj6mEtdUOa2ocJuobu7HYpWJkZlBEeSksEdrw6BCPdjxil+hQkzeC9uCpiEHKVoxbirQVPM62oC/kylhaaM9pOnOazpxP0j/32TAGh1Vwt2K6qUVVonm75FhWjGaPOgavD8VIezPbTxXCproCa4opViMjiv17LbwyFUJGIsL6jR4NDcF+uWLFCu67776wjifkioQdJIPz7TPPwn/7qjBc3JR3/SJ88o/r8bzRK//zPLNo27VP8MYbb/CXf/mX7Ny5E8/zWL9+PR/+8Id55JFHzvu7y4ltCKLMX//61/mXf/kXent7qaur4+677+bjH/84TU1NFy3zHe94B6dPn77sOl1qGceOHeMrX/kKO3bsIJvNsnLlSh577DE+8IEPXNXjcbEPiLci6XSan/70pxw/fjx4Pa44eNAkM4+OJaVAKaGuXmhq0jQ2aabezR51DE5mbE5mbLwSXUjETM3G2gKdCQ9VLMw7TIS9KkJ+jqIbgjSNGjRN+DSJTxM+NegrxjxzRhBpzliKzAV5z9nie6EgvzJKggLRBkdodDRNBU1zQZgqgx2gG5ujKsIw0/dPN0XYiMM1UsACfIGDoxGOjUdmfBcmZmpuaAzyswFO9hgcP26WLR2soVGzfoNHJBL0Xbjjjju47rrrKsb3N6RyeauL7f5+4ctfFZ5/IXi9Yjn88R8qtm5RFblti15svxWotJ0mZHqICIcOHeKZZ56lUMijNUEud8/8O5aUBCXU1wvLlgXCe8Ky1dNwMmNzPB0h45XmYqLG9tlcV6A5FkQpPQLRvb9EonsqZlGA1+JTK5pqNFXFabqZsvkJQV4U4xNR8XRxKlRImspVEcEUsIu54UYxT9wg8NtW5z0Wc8iL71laiAjYGiJaiPuBI0vSC/LQL/WrjaM4g80ZZdGPOa2mNBOoYsrIFimQKN6tGMibvDESIzuL/bAt7rKlIU/EAM+Dg/sthobKc3FsGMLqtT7t7YGob2pq4sEHHwyb04RMm7eq2NZaeOJf4Gt/I2SzYFnwa78Kv/arikgkOH5U4raFYnsRUGk7TcjMyGaz/PSnP+Xo0aMAjI4qDu4vfyOMcmJZQnOLpr1dk6w6d3joy5kcHY8wVDApxQVFU9RjQ21hMtLoAUeIcEBFyJZYdF+ECBEgiSaBJoGQkOAxPvEaPa2iFkdxToDbRRE+RZiXS4yrohd43JcpU/A6qoWYDzEtRPxzQrlc36oLjGAygsGwMhnEZHwGEexJROjAY6sUqCPYL7JeYC95Omsx0/3OVMK1dQWWVwVpI2NjigP7yjc+Ewlh02Zvctxs27aNW265JXSkCpkRb0WxffyE8BdfEt7cE7y+ZjN84o8Uq1edP1YrcdtCsb0IqLSdJmTmTORyP/PMM7iui+/DkcMmfWcXaZR7EqGuTujo9GlolEndNFwwODIWpS9fCtEtNMd8NtQUqC+Kbg30YLFfRRmZaSFlKRHBBhLooigXkhI8TyJUoYlfIkf8Qjw1YcUXPBYu8rwOHEgmfK6NYgTaKk62FqIaokXhHNOBoI7p2W+aT9FfG4UUn8vkFKxHMAXPXRTu5KMirxQZDLIoshjkmJvvuRKhC4/NUqChKLIdHw6PRzkxbl8lGejSNEY9tjbkSVqCSJA20n2iXP7ZQbv1NWt9TBMSiQQPPPAAy5cvL8OyQpY6byWx7brCP/xP+B/fCFyB4nH43d9WvOuXwLxEE7NK3LZQbC8CKm2nCZk9Y2NjPPXUU5w5cwaA/j6Dw4dM/Hlo915uYnGhs9NnWdu5FJMx1+DwaIQzuZlHHC8mEN1rqp3J9BKAAUyOKZtubLwKTNUwRYriW1OFUCV6UpwnkWmJ8bmggTyKPIocxuTzvFIUCCanKJAnJg8qJu3FEmElLhvFoaYosj0Nx9MRjo5FcGchjE0lbKotTPrM5/NB2sjoaHni+pYVFEE2NQW/dVdX13lF9SEhM+WtIrYPHhL+/ItC8cYwt90Kv/9xRWvL5cd9JW5bKLYXAZW204TMDa01r732Gi+99BIiQi4H+/dZpMcXV/Hk5YhEhPbOIB914s74mGNwaCxCb0lEd5DTvabaoT3hMVGb6AEnsTmhbPowZ9QgZyExJBDccTSxoviOINhSfCQoJFQEThmKILrsofBR+MXnBXVOOBcmBHXxeaUI52kjQiOaNeKwApeJ3kCOH4js4+nIrBsvtcY8rqvPEy/6C/aeMTh2tHwXvLW1mo2bPaJRMAyDW2+9lW3btoVFkCFzYqmLbccR/v5/CP/4j+BrqK2B3/uY4t53cNWxU4nbFortRUCl7TQhpaG3t5cnn3yS8fFxtIZjR8vbmW6+MS2ho0PT2eVPiu7Roug+WyLRHTU0nUmXrqRHtX0uZ8IBzmBxUtn0YlVkxDvkAooCu1NcOvGo5dzvOe4adKdtejI2/ixTPOKm5tq6AssSQcv1XA4OH7JIlalJjVLCilVBJ0iloK6ujoceeoiWlpayLC/krcVSFtv79gfR7Inm3vfcDb//MUV9/fTGfiVuWyi2FwGVttOElI58Pj/Z7h1gcEBx6GD5PH0XAssKcro7Os9FulOOwcHRKP0lyekGEOoimq6ky7K4R8w8d8jSwBAm/Zj0KYtBzFB8VwIiVCE049EsPu14k64iEFj49WYtutM2w87s9xNTCWuqHdYWPdy1htOngtxsPcvo+NVIJISNm73JTpCbN2/mzjvvDDtBhpSMpSi2CwXh7/5e+Oa3gnFaXw9/8HHF3XfNbJxW4raFYnsRUGk7TUhpERF27drFCy+8gNaaQgEO7Ctf/uhCYVlCZ5dPe8c50T1cMDgwGmWoUEonBqE+olkWd2mLeyRtueBTSGMwgkFKmaQwGMcggxGK8HIgQhShGk0NmhrR1KJpwL8oX93T0J+3OJuz6MtZc/RvFzoTHhtrC5MpI6kRxZHDFtls+TpBtrUHnSBNM+gEee+997JmzZoyLS/krcpSE9v7Dwh/9p+FnpPB6wfug499VFFbO/OxWmnbBqHYXhRU2k4TUh76+/t58sknSaVSiEBPt0F3d/kaaiwUli10FUW3WTQSGcibHBiNknJK7ywSNzWNUZ+mmEdj1CdhXf5wlkORKRYS5lDkVfA4tZBw4nGx5IRfkaKbykRu+MRkAQYS+GsDZjFXfOpkSPDe1L+xAAvBIhDYseJ0uctGX4LUouGCyVDBYjBvzspV5IKNojnms6m2QG0kSEXJ5+DYMZPBgfKlaUWjwvoNHvXFTpDLly/n/vvvDztBhpSFpSK2PU/4xj/C3/8/gq+hsRH+6A8Ut986+3FaKds2lVBsLwIqbacJKR+O4/Dcc8+xb98+AMbHFIcOmmQySyvKDWBHhOXLfdraz7mXnM1aHByLlLQV/IVEDE2NramJaGptnypbkzA1kRku0gOcovC+cCpMcfqYKtIL8yDSVTGSPLXoMoYQFyFWfB2dMs3XnpXzFGnPYNw1yHgGo47BqFMKcT3BxRaRngc93SanTxtImVJGQGhp1axdF9QmmKbJbbfdxtatW8MiyJCysRTE9unTwp/9ubBnb/D6HffAH/6eoqZmbuOmErbtQkKxvQiotJ0mpPwcPHiQZ555hkKhgNZw6qRBT3f5ckwXkmhMWLHCp3WZnjTN6M1aHB6LMFpG0X0hlhISliZhaaKGEDOFqClETU3EkMnJnkUflqm4cF6EfMJ2zyNwFfHU+R7WEwffIIIciGNLggh0BIqP5yLJ0VlYCXoaPFFTHhVait7aooqPRY9tUZN+23ryPfBF4YvCE/C1wtGKgq/I+8HzmbZOnz5Ca8xjXY0zKbJ9P3AZ6ekx8dzyjRnbFtau92huDr7z1tZWHnjggRkLoZCQmbLYxfYPnwzaredykEwGBZAP3H91p5HpsNDbdilCsb0IqLSdJmR+yGQyPPPMM5PFk7kcHDtqMTQ4cSN/aRFPCCtW+jQ3nxPdZ3MmR8aijJQhvWT2CJYC2xDsSQEuF7yGqCHYppwn1Ocr0CkChSliN3huUPCD9wpFMez4ClerEkaX5w9LCV1Jl1XVDsliatCEyD7ZY+KWUWSD0NysWbvex7ZBKYObbno7N954I4ax9O5ChVQei1Vs5/PCl78i/ODJ4PX1W+E//QfFsmWlG6+h2A6ZFZW204TML0ePHuWZZ54hk8kAMD6u6D5hMjy0dEX38hU+LS36vI6Ux8ZL59O9MAi2goh5sTg3VSDgLUMwFRiqmA+tzm3tRHRZF6PInihcDd6EcC6K64nni/d7ujLVts/ypEtX0sUu6lrXhd5eg9Mnyy2yi9HsdR7NLcFpsampifvvv5/m5uayLjckZCqLUWx3dwuf/oxw7DgYBvzmhxUf/MClu0DOhVBsh8yKSttpQuYfx3F4/fXX2blzJ64bdL0bH1ecOW0wOGAsiQ6UFxKPC13LfVpaz+V0Zz3FiXSEkxkLR4cRxLcKlhLaEy7Lk+5kqghANgunT5n0nTXmIcVKaG4JcrMnotnbt9/I9u3bMc1KuvMS8lZgsYntH/9E+Iv/IuTy0NgAf/Jpxdu2lWfMhmI7ZFZU2k4TsnDkcjlef/11du9+A88LRLfvw9CgQd9Zg1RKIUvMvcSOCO3tgXuJXWwlqAX6chY9GZuBvFnGfOCQhSJqaFrjHm1xj6aYP9kpVGsYGlSc7TUZGZmfCH4kEuRmT7Rbb2pq4r777gsb1IQsGItFbPu+8NdfFx7/p+D1DW+DP/lPioaG8o3bUGyHzIpK22lCFp5cLseePXs4cODAefuH78PYmGJsVDGaMkin1ZJpkGMYQkuLZlmbpqb23CGp4Ct6cxa9WYuhQii8FyuWEhqiPo3RwKKxLqLPy3HPZBRnew36+4yyp4qcQ1jWFvhmW1bQbn379u3ceOONYTQ7ZEFZDGI7kxH+9HPCiz8PXv/6B4PUkVKnjVxIKLZDZkWl7TQhlYOI0N/fz/79+zl8+DC5XO6iv3FdyGYVuZyikAfXVTiOwnGC574HrkcZrdFKTyKpWbZM07rsXLQbwPEVfXmTgbzFQN4MU00qEFMFTi9Vlqba9qm2AyvGaltfVEA6NqYYGjQYHDT7pK1mAAAgAElEQVTIla0RzaWJxYX16z3q6s85jdx77700NTXN63qEhFyKShfbZ3qFT34qyM+OROBTn1Dcd+/8jOFQbIfMikrbaUIqExFheHiY06dPc+bMGXp7exkfH5/2/2sdCHPXVcFjUZDncop8LhDr+TxUUuGdUkJdndDUrGls0lzYDXvUMRgqmAwXTEYck7wfiu8JFEWXlKJjykSBpqWCIk1VLNJU6sq/uEzOj8m/NxBMI4hWW8XHqCnETD1Z1HgpslkYTRmMphSplIHjzP++ppTQtVyzfIWPYYBlWdxyyy1s3bo1dBoJqRgqWWwfOCD84SeFVCpoUvP5zyk2b5q/sRyK7ZBZUWk7TcjiwXEcRkdHSaVSpFIp0uk02Wx2csrn8xQKhWnP77w0lVGD8TFVOcWZSqirFeobNPX1QlX1xYetrKdIOSZjbtBcZdw1yXhLzbkjEM/xoriNW0EkOWbq4qMQNWbexKeU+H5wEZfJKLKZ4DE9rhZEXE+lrl6zdp1HIhG8Xr58Offccw+1tbULul4hIRdSqWL7tdeFT/7HwD97/Tr44p8rmpvnd1wvVrFtzcN6hISElIFIJEJzc/MVbclEBMdxKBQK5PN5crnc5JROp0mlUoyOjjI6Ogr41NcL9fUCaLSG1IhicNBgaHA+c2kvtSGKVDEiepzAoq2uXlNbK1TXCFVVQsISEpZH+5R/0xJ0N8z6BjnPIOefs9CbeJxo9OIJzL8wDyLNdjHqbBvFZjvFqPREA54JMR01ZbKY8KpzluBuhueB5yl8PxDC2g+a2khxumKfnGLkW+T8Sevz5+k6ioKjcApUzgVakUhEWL0mcL4BSCQS3Hnnnaxbty7sAhkSMk2efU74zJ8JrhsUQn7+c4pEIhw/0yUU2yEhSxilFNFo9P9v787Doqz6/4G/Z2BYBggVMQ1IlBpFkExETDHXryVipY8LmZeSqWmZ26NJlk9W3yftaflq2iOZueRV7pBrruUC/FBUcsUUZFFcQFFhQGCG+/79cTPTjMOq4Cy8X9c1F+OZM+c+9xwZPnPmc58DR0dHPPHEE1XW06WpXLt2TX8rLCxEMw8RzTzKIarKcfeuDNev2eH2LfOviqLRyJCXa4e8XOnfdnYi3NxEuLiKcHGRbkoXEXZ2gItChIuiHEB5je1qBUAQZSiv2GFREGV/764IKXgHIF2oKVYdp+pSLipqQi7TrastBde69ballI66nbsoAmVlQGmpFNyWlkqzxqWlQFnFfSllSNeTxkkuF+HtI8Dn6XLY2Um/C0FBQejWrRscHR3N3T0iq/HbbhEL/iNCEIBeLwL/+lAGR8fG+97yMBhsExFkMhk8PDzg4eGBjh07AgDy8/ORnp6OtLQ05OXlVcx6a1FaClzLscON62ae7TZQXq6b+TYsFeHoKG0Z7+QkwskJcHQUoVCIUDhIs+MKBWBv/3fAay+XnmfYxuMgzRSj4oLWigtcK/LqpQteKwLrMhk0ZTD7hx3LJqK5p4i2flo4OUklrVq1Qq9evbicH1Ed7d0v4vMvRIgiEBEOzP5nw684YouYs21mlpZ7RFSZe/fuITU1FWfOnNGviiIIwM0bcly9Yof79635zVeEXA7Y2Uk3uRyQyaUyecXMs0xuMFtdcaqyiplq09akCrp3VlH4e3dIXQqGUA4IggzlghRgCwLQmGeh64u7uwDftuVwr1g+0tXVFWFhYUwZIatiKTnbhw6L+Nd8EeUC8NorwD9nyMz+e2StOdsMts3M0v7TEFVHq9UiLS0Np06dws2bNwFIAWRenhxXsuQoKuKKDvT4ubgKaNO2HM2aSX/O7O3t0blzZwQHB0NhuH4kkRWwhGD7/yWJ+OAjEVotEP4yEP2+DPLaXjDSgKw12GYaCRHVmr29Pdq3b4/27dvj2rVrOH78ODIzM9GihYAWLQTcvi3D1Ww73Ltna6uAkCVyc5Nyspt7SkG2XC5HQEAAunbtChcXFzP3jsg6XbwkYt58KdDu1weYM9syAm1rxmCbiB7KU089hVdeeQW3bt1CcnIyLl26BA8PER4eWhTck+HKFeliSgbdVL9ENGkqwufp8oqVcyTt2rVDaGgomjRpYsa+EVm3/Hxpeb+SEqBrCDDvQ+Zo1wemkZiZpX0dQvSw7t69i5MnTyI1NRXl5dLKH/fvA9evSRdT2srW8mQe9vYinmwpoGUrAS4uf89kt2vXDsHBwWjWrJmZe0hUP8yVRlJWJmLqDBFnzwFP+wDf/1cGNzfLet+21jQSBttmZmn/aYgeVVFREU6dOoUzZ87oN9URBCA3V46bN6QdBDnbTbWh20W0RUsBnp4CdJs82tvbIyAgAJ07d4abm5t5O0lUz8wVbH/5tYCt2wFXV2D5Mhme9rG892lrDbaZRkJE9crFxQXdu3dHSEgILl68iNOnTyMvLw8tWwpo2VJAaSlw65Yct3LluFcgA7iMHRmQy6U0EU9PAc08BBhe39i8eXMEBgaiXbt2XCubqB4diRexdbu0ytIn/7LMQNuaWXSwffr0aSxZsgQpKSnQarVQqVSIiopCeHh4rdsoKyvD8uXLsW3bNly/fh3u7u7o06cPpk+fDg8Pj0qfs23bNvz0009IS0uDQqFA586dMXXqVAQEBJjUFQQBv/zyC7Zs2YLLly/Dzs4O/v7+GDduHPr16/fQ505k7RQKBQICAtChQwfcuHED586dQ3p6OoBSeHkJ8PISoNUC9yp2hrx7R9ram7PejYvcTsQTbiLcmwho0kTaEVRusKiNs7MznnnmGXTo0AEtWrQw+9JjRLbm1m0RC/8jJTm8PhII7crfsfpmsWkkSUlJGD9+PBwcHDBo0CC4uLhg7969yMnJwZw5czBu3Lga2xAEARMmTEB8fDw6deqEkJAQZGVlYd++ffD29sbGjRtN8vyWLVuGRYsWwcvLCwMGDEBRURF27twJjUaD1atXIzg4WF9XFEVMmzYNe/bswdNPP40XX3wRZWVlOHDgAG7fvo158+Zh9OjR1fbR0r4OIWpI5eXluHLlCtLS0pCenq5PM9HRagG1WobCQhnUhTIUqeW4f5+buFg/EQ4OFRsMOUq7e7q4SDt+Ojub1nZzc4Ofnx/8/PzQqlUryOVcUpIaj8eZRiKKIv75vohjyYDqWSDmOxkcHCz3/dZa00gsMtjWarUYOHAgbty4gY0bN8Lf3x8AUFhYiGHDhiEnJwd79uyBl5dXte1s2bIFc+fORUREBL766iv9jMi6deswf/58jBw5Ep9++qm+fmZmJgYNGgRvb29s3rxZnwuYmpqKESNGwMfHBzt27NC/8e/evRvTpk1D586dsWrVKjhVbFeWn5+PYcOGIS8vD7/99hu8vb2r7KOl/achelwEQcCtW7dw5coVXL16FdeuXYNG2mP8gXrA/fsyFBfJUFws3b9/X4aS+7JGvyV5ZWSyvwNbR0dpF02jXTPtAbmdtF28TI6/Z5ENtp8XK7atFwTp9RcrfpYLgCjI9Bv0QPx78x6ZXPoKWi4D7OxE2NtLu3PaV+zUWV287OrqCi8vL3h7e8PLywvu7u6cwaZG63EG2/sOiPjkM+k9Y+UPMvi2tuzfO2sNti0yjSQpKQnZ2dkYOnSoPtAGpNmOSZMmITo6GnFxcZgyZUq17WzatAkAMHPmTKM37sjISPz444/Yvn075s6dqw+SY2NjodVqMXnyZKOLbvz9/REREYHY2FicOHECISEhAIADBw4AACZNmqRvAwCaNWuGsWPH4vPPP0dsbCymTp36iK8Ike2Ry+Vo0aIFWrRogeDgYAiCgPz8fOTm5uLmzZvIzc1Ffn4+NBqNNAvqYjovoNUCJSUylJQApSUylJTIUFoqQ2mp9O+yMsAWg3G5XISTszQr7Oxccd9JhLOzCEenv7effzRVzcM83PyMXC6Hq6srXF1d4e7ujubNm6N58+bw8PCAUql8+G4S0UNRq0Us/U76fR4z2vIDbWtmkcH2sWPHAABhYWEmj+nKkpOTq22jtLQUp06dQps2bUxmwGUyGbp3744NGzbg7Nmz6NKli9Fxe/ToUelxY2NjcezYMX2wfevWLQCodOZaV5aUlMRgm6gW5HK5PgDr0KEDAOkrTrVajdu3byM/Px93797V39RqNeztAVdXEa6uQGVBoCgCZWVAWakMpRU/y8qkGfGyMhk0ZYBGK4NWA2i0sJiLNfWz044iHJ2koNrJSRdgS7PV1ZHL5XBzc9MHt0qlEs7OznB2doaTkxPs7e1hZ2env+mIoghRFCEIAsrLy41uWq1W/1MQBAiCoK8LAHZ2dpDL5ZDL5VAoFHB0dISjoyOcnJzg7OwMFxcXpoMQWZAfV4m4nQ94e0u52tRwLDLYzszMBAC0bt3a5DFPT08olUpkZWVV20Z2djYEQYCvr2+lj+vKMzMz9cF2ZmYmlEolPD09Terr+mJ4XN1XB1evXoWfn59R/atXrxqdCxHVnUwmg5ubG9zc3Ex+l7VaLQoKCoxuhYWFUKvVKCwsRFFREQApMHV0FCF9V1X9rKxWC5SXA+VaoLxcJt0vB8oFGYRy6NMqBFGXWiGDaJBK8eBPiABkf880y2RSOoVcLqVw2MkBOztpDWl7hZR24eAgpV3UNDvt6OiIJk2awN3dHe7u7kb3lUol0zCIqEpXr4qIjZPuz5gqg6Mj3y8akkUG22q1GgCqXD/V1dUVhYWF1bahe9xVmvKqtA3DY+nuV7Uxgq6+4XFffPFF7Ny5E8uXL0e3bt30S1HduXMHa9asAQAUFBRU208iejj29vZo1qxZlb+zgiCguLgYRUVFKCoqglqtRlFREYqLi/W3+/fvo6SkBGVSvok+zxiOgHFg/vgvbTFMu2jSpAmeeOIJo8DaMHWNiBovd3d3k2+NasojXvCfQpQLZXixpwIvv/REQ3av3tU1p90SWGSwbS10edxHjx7F4MGD0bNnT2g0Ghw4cEC/rCC/NiUyD8NgtSaCIKCkpASlpaXQaDQoKyvT/9RqtdBqtdBoNPpUCsObLpVCl4Khu+Zcd18mk0Emk0Eul0Mmkxmlb9jb20OhUMDJyUmfduHs7AxXV1c4OztzdpqIanTv3j2jf9d0EWFauohdv0nvU1FjtBZ3wWF1eIFkPapsFtmQWq2Gu7t7tW3oZsUNZ64fbMPwWLr71R3TsF1AmllbsWIFli9fju3bt2PDhg1wc3PD//zP/2DcuHF46aWXqlzLm4gsh1wuh1Kp5IV6RGTzVq4WIYpA3z6A6ll+oH8cLDLY1uVmZmVlITAw0OixvLw8FBcXIygoqNo2fHx8IJfLq8yZ1pUb5oH6+voiJSUFeXl5JnnbulztB/PIHRwcMGXKFJOVUY4ePQoAJv0nIiIiMofsbBFH4qX7b45loP24WGSOg261j/j4eJPHdGW6OlVxcnJCUFAQMjIykJOTY/SYKIpITEyEUqk0CoZ1bSYkJFR53K5du9bqHLZv3w4AddrtkoiIiKihrNsozWqH9QDa+DLYflwsMth+4YUX9BvIpKam6ssLCwsRExMDhUKB1157TV+em5uL9PR0kxSQESNGAAC++eYbGO7ds379ely5cgWDBw82usho6NChsLe3x7Jly4zaSk1NxY4dO+Dn52e0gyRQeZrK7t27sWXLFnTs2BEDBgx4yFeBiIiIqH7k54vYvUe6PyqSgfbjZJFpJPb29vjf//1fjB8/Hm+88Ual27Ubrm39zTffIC4uDgsWLMDQoUP15UOGDMGuXbuwY8cOXL16FSEhIcjOzsbevXvh7e2N6dOnGx23TZs2mDJlChYtWoRXX33VaLt2APjss89MLngcPnw4WrVqhbZt28LR0RGnT5/GsWPH4OPjg8WLFxutYUtERERkDrt2AxoN4O8PBHVksP04WWSwDQDdunXDL7/8gm+//Ra7du2CVquFSqXCrFmzap2aIZfLsWzZMixfvhxbt27F6tWr0aRJEwwbNgzTp0+vdMmwyZMnw8vLC2vWrMG6deugUCjQpUsXTJs2DQEBASb1w8PDsXfvXvz555/QarXw9vbG5MmTMX78+FqtgkBERETUkERRxI6d0jf8rw5moP24yUTD/Ap67CxtCRsiIiIyn7quI/1gHFHZ8ngnU0RMnSFCqQR+3SyDUmmdAbe1Lv1nkTnbRERERFQ/dLPa/fvBagNta8Zgm4iIiMhGFRaKOHhIuj94EANtc2CwTURERGSj4hOAMg3Qxhdo387cvWmcGGwTERER2ag/DkkpJH37yCCTcWbbHBhsExEREdkgtVpE8nHpfu9e5u1LY8Zgm4iIiMgGJSRKa2v7tuaOkebEYJuIiIjIBh2sSCHhrLZ5MdgmIiIisjEajYjjJ6T7PcM4q21ODLaJiIiIbMyZs8D9EqBpU+DZZ8zdm8aNwTYRERGRjTmWLKWQdO0CyOWc2TYnBttERERENubPU9LPLl0YaJsbg20iIiIiG1JaKuKvi9L9oI7m7Qsx2CYiIiKyKX9dlJb8a9YUeKqVuXtDDLaJiIiIbMjZc9LPwEBw10gLwGCbiIiIyIacOStdHBkYwEDbEjDYJiIiIrIhly5JPzv4m7cfJGGwTURERGQjiopF3Lgp3W/ja86ekA6DbSIiIiIbkZlZDgBo0gRwd2caiSVgsE1ERERkI9LTpWDbt7WZO0J6DLaJiIiIbMTlDAbblobBNhEREZGNuJojBdve3kwhsRQMtomIiIhsRF6eAADwbG7mjpAeg20iIiIiG5FbEWw3Z7BtMezN3QEiIiIienSiKCIvV9rQprmHmTtDepzZJiIiIrIBRUXA/RLpvgeDbYvBYJuIiIjIBty9K/10dgKcnHiBpKVgsE1ERERkA9RF0k9XV/P2g4wx2CYiIiKyAUUVwbYLg22LwmCbiIiIyAboZ7ZdzNsPMmbRq5GcPn0aS5YsQUpKCrRaLVQqFaKiohAeHl7rNsrKyrB8+XJs27YN169fh7u7O/r06YPp06fDo4qrB7Zt24affvoJaWlpUCgU6Ny5M6ZOnYqAgACTuqIoYt++fVi7di0yMjJQWFiIli1bIjQ0FBMmTICPj89Dnz8RERFRbRWppZ8uDLYtisXObCclJWHUqFE4ceIEBg4ciMjISNy6dQszZszAypUra9WGIAiYPHkylixZgqZNm2Ls2LF4/vnnsWnTJowcORL5+fkmz1m2bBlmz56N/Px8REZG4uWXX0ZycjIiIyNx4sQJk/pffPEF3nvvPWRkZKBfv34YPXo0vL29sXHjRrz66qu4ePHiI78WRERERDUpLZN+Ojqatx9kTCaKomjuTjxIq9Vi4MCBuHHjBjZu3Ah/f38AQGFhIYYNG4acnBzs2bMHXl5e1bazZcsWzJ07FxEREfjqq68gk0lX5q5btw7z58/HyJEj8emnn+rrZ2ZmYtCgQfD29sbmzZvh5uYGAEhNTcWIESPg4+ODHTt2QC6XPqPk5eXhxRdfRKtWrbB161Z9fQBYvXo1FixYgKFDh2LBggVV9vHOnTsP9yIRERGRzWnatGmd6hvGEZtjRSz6VkTfPsCnH1vsfOpDa9q0qcXFTbUZL4sciaSkJGRnZyMiIkIfaAOAm5sbJk2aBI1Gg7i4uBrb2bRpEwBg5syZ+kAbACIjI+Hj44Pt27ejpKREXx4bGwutVovJkycbBc7+/v6IiIhAenq60ex2Tk4OBEHA888/b1QfAHr37g2AwTQRERE9HuXl0k87O/P2g4xZZLB97NgxAEBYWJjJY7qy5OTkatsoLS3FqVOn0KZNG5MZcJlMhu7du6O4uBhnz541OW6PHj2qPK6uDgC0bt0aCoUCKSkpUKvVRvUPHjwIAOjWrVu1/SQiIiKqDwy2LZNFXiCZmZkJQApmH+Tp6QmlUomsrKxq28jOzoYgCPD19a30cV15ZmYmunTpor+vVCrh6elpUl/XF8PjNm3aFLNmzcLChQvx8ssvo1+/fnB1dcWFCxdw9OhRjBo1CqNHj67pdImIiIgeGYNty2SRwbZulvjB1AwdV1dXFBYWVtuG7nHXKlZ215Ubzkir1Wo0a9as2voPHjcqKgotWrTARx99hPXr1+vLg4ODERERAXt7i3yJiYiIyAa4u7vrryVzcCgGcB9KZ0c0bWqbi23XNafdEjASfERLly5FTEwMpk6dildeeQVubm5ITU3FggULMGbMGHz77bfo16+fubtJRERENujevXv6+xqNtOaF3K4Ud+5ozNWlBmOtF0haZLBd1Syyjlqthru7e7Vt6GbFH8ylNmzD8Fi6+9Ud07BdAEhMTMSSJUsQFRWFiRMn6su7dOmCmJgY9O/fH1988UW1wbY1fkIjIiIiy2AYR7z3rnSzZdYYN1nkBZK6fOrK8rLz8vJQXFxcaT63IR8fH8jlcn3+94N05YY53b6+viguLkZeXp5JfV1fDI97+PBhAEBoaKhJfU9PT7Rt2xZZWVko0u2fSkRERESNikUG2yEhIQCA+Ph4k8d0Zbo6VXFyckJQUBAyMjKQk5Nj9JgoikhMTIRSqURgYKDJcRMSEqo8bteuXfVlGo30FU1lm+PoyuVyORQKRbV9JSIiIiLbZJHB9gsvvKDfQCY1NVVfXlhYiJiYGCgUCrz22mv68tzcXKSnp5ukgIwYMQIA8M0338Bw757169fjypUrGDx4MJycnPTlQ4cOhb29PZYtW2bUVmpqKnbs2AE/Pz8EBwfryzt37gxA2sDmwWOvW7cON27cQKdOneDg4PAoLwcRERERWSmL3EESkDa2GT9+PBwcHDBo0CC4uLhg7969yMnJwZw5czBu3Dh93ejoaMTFxel3bNQRBAETJkxAfHw8OnXqhJCQEGRnZ2Pv3r3w8vLCpk2bTFYfWbZsGRYtWgQvLy8MGDAARUVF2LlzJzQaDVavXm0UbJeXl2Ps2LFITk6Gh4cH+vbtCzc3N5w/fx5JSUlwcnLC2rVrERQU1PAvGBERERFZHIsNtgHg9OnT+Pbbb5GSkgKtVguVSoU333wT4eHhRvWqCrYBoKysDMuXL8fWrVtx/fp1NGnSBL1798b06dPRvHnzSo+7bds2rFmzBmlpaVAoFOjcuTOmTZuGgIAAk7plZWVYvXo1fvvtN2RkZECj0cDDwwOhoaGYNGkS/Pz86u8FISIiIiKrYtHBtqU5ffo0lixZYhT8R0VFmQT/1dEF/9u2bcP169fh7u6OPn36YPr06fDw8Kj0Odu2bcNPP/1kFPxPnTrVJPi/efMmfvvtNxw+fBiXL1/GrVu34O7ujs6dO2P8+PF47rnnHun8bZGlj2llli9fjq+//hoAsGHDBnTq1KnWfW0MrGlM9+3bh19++QXnz59HcXExPD090alTJ8yePRutWrWq87nbKmsYU1EUsW/fPqxduxYZGRkoLCxEy5YtERoaigkTJsDHx+ehz98WPe4xvX//PtatW4dz587h3LlzyMzMhCiKOHDgALy9vas8RkZGBhYtWoSkpCTcv38fvr6+iIyMxOuvvw6ZTPbQ50+NC4PtWqpLWktVKktrycrKwr59++Dt7Y2NGzc+UlrLV199hR9++AFPP/00unbtimbNmiErKwv79++HKIr4+uuv6/RGZuusYUwfdPHiRfzjH/+Avb09iouLGWw/wFrGVBRFfPzxx9iwYQOefvpphIWFwcXFBbm5uUhOTsaXX36p39m2sbOWMV24cCFWrVoFT09Po92EExISoFQqsX79eqhUqnp9bayVOcb06tWr+mV4vby8UFRUhLt371YbbKelpSEyMhIlJSUYOHAgWrRogUOHDuHSpUsYPXo05s2bVz8vCNk+kWqk0WjE/v37i4GBgeL58+f15QUFBeKAAQPEgIAA8erVqzW2s3nzZlGlUokzZ84UBUHQl//yyy+iSqUS582bZ1Q/IyND7NChgzhgwACxoKBAX37+/HkxMDBQHDhwoFheXq4v37Nnj3j06FGT4yYnJ4sBAQFiSEiIWFpaWqdzt1XWMqaGysrKxCFDhojDhw8XZ82aJapUKjElJaWup26zrGlMV69eLapUKnH+/PmiVqut9FzIesY0NzdXbN++vdinTx+j+qIoiqtWrRJVKpUYHR1d5/O3ReYaU7VaLcbHx4t37twRRVEUx40bJ6pUKvHKlStVHuONN94QVSqVePDgQX1ZaWmpOGrUKFGlUoknT56s9XlT42aRq5FYmqSkJGRnZyMiIgL+/v76cjc3N0yaNAkajQZxcXE1trNp0yYAwMyZM42+foqMjISPjw+2b9+OkpISfXlsbCy0Wi0mT55stJmOv78/IiIikJ6ejhMnTujLBwwYYLQ0oU6XLl0QGhqKe/fu4a+//qrbydsoaxlTQzExMbh06RI+//xz2NnZ1fmcbZ21jGlJSQm+++47+Pj44MMPP6x0LO3tLXK/scfOWsY0JycHgiDg+eefN6oPAL179wYAi9v1zlzMNaYuLi7o0aMHmjRpUqt+ZmRkIDk5GaGhoejVq5e+3MHBAdOmTQMAbNy4sVZtETHYroVjx44BAMLCwkwe05UlJydX20ZpaSlOnTqFNm3awMvLy+gxmUyG7t27o7i4GGfPnjU5bo8ePao8rq5OTXR/vPlHXGJtY3ru3DnExMRgypQpeOaZZ6rtV2NlLWMaHx+Pe/fuoX///hAEAXv37sXy5cuxbt26SjfyasysZUxbt24NhUKBlJQUk12LDx48CADo1q1btf1sLMw1pvXZz+DgYCiVyhr7SaTDyKsWdLtNVrZrpaenJ5RKZY1/JLOzsyEIgtGOlYZ05ZmZmfpczczMTCiVSnh6eprU1/WlNn+cr127hsTERHh6ejJnsII1jWlZWRnmzJmD9u3bY/z48dX2qTGzljE9d+4cAEAul2Pw4MFGu9zK5XJERUVhzpw51fazsbCWMW3atClmzZqFhQsX4uWXXzbK2T569ChGjRqF0aNH13S6jYK5xrQ++2lnZwdvb2+kpaVBq9VyEotqxO8xgegAAA7qSURBVP8htaCbqXjw60EdV1dXk01tHqR73NXVtco2DI+lu//gRTsP1q/puBqNBu+//z7Kysowa9Ysph9UsKYxXbx4MTIzMxEbG8vxq4a1jOnt27cBSJthdejQAZs2bYKfnx9SU1Mxb948rFy5Ej4+Phg1alS1fW0MrGVMASAqKgotWrTARx99hPXr1+vLg4ODERERwYCsgrnGtK5q6qeLiwsEQUBRURHc3d0f+jjUODCNxIYJgoDo6GgkJydjxIgRRrtuknVISUnBypUrMXnyZH4rYSPEigWgFAoFvvvuOwQFBcHFxQVdunTB4sWLIZfLsWrVKjP3kupq6dKleP/99zFp0iQcOnQIJ0+exM8//4zS0lKMGTMGBw4cMHcXichMGGzXQk2zyGq1uspPvzq6x6v6pK0rN/ykXt0n/Jo+dQuCgLlz52LHjh145ZVX8Mknn1Tbv8bGGsZUq9UiOjoa7dq1w8SJE6vtC1nHmBo+NzAwEE8++aRRfZVKBR8fH2RnZ6OgoKDavjYG1jKmiYmJWLJkCd544w1MnDgRLVu21H+AiomJgb29Pb744otq+9lYmGtM66qmfhYVFUEmk8HFxeWhj0GNB4PtWtDlf1WWR5aXl4fi4uJK87oM+fj4QC6XG+VnGtKVG+ag+fr6ori4GHl5eSb1dX2p7LiCIOCDDz5AXFwcIiIisHDhQsjlHGpD1jCmxcXFyMzMRGpqKgIDA9GuXTv9TXe1/siRI9GuXTvs37+/2r42BtYwpgDQtm1bAFV/UNaVG66k0FhZy5gePnwYABAaGmpS39PTE23btkVWVhaKioqq7WtjYK4xrc9+lpeX4+rVq/D29mZ6ENUKI7BaCAkJASCtIvAgXZmuTlWcnJwQFBSEjIwM5OTkGD0miiISExOhVCoRGBhoctyEhIQqj/vgUn+6QPvXX39FeHg4/vOf/zDPtxLWMKYODg4YNmxYpTfdH4K+ffti2LBhJlfkN0bWMKbA3wHZ5cuXTeprNBpkZ2dDqVRWmTPcmFjLmGo0GgBAfn5+pX3Iz8+HXC6HQqGotq+NgbnGtD77eeLECRQXF9fYTyI9cy7ybS00Go3Yr1+/ahfhN1wY/+bNm2JaWprJ5gZ1XYT/8uXLddpYoby8XJwzZ46oUqnEqVOncmOMaljLmFZFN87c1OZv1jSmug01Nm7caFS+dOlSUaVSibNmzXr4F8KGWMuY7tixQ1SpVOKgQYNMjq07RmRk5KO9GDbCXGP6oPrY1ObEiRO1Pm9q3Lhdey3VZXvZ6OhoxMXFYcGCBRg6dKi+vLLtZbOzs7F37154eXlh06ZNj7Rl8JIlS7B06VIolUqMGTOm0q+3+vfvb7SRQGNmDWNaFV1/uF27MWsZ0+zsbERGRuL27dvo3bs32rZti/PnzyMpKQleXl7YsGFDpcvONUbWMKbl5eUYO3YskpOT4eHhgb59+8LNzU0/pk5OTli7di2CgoIa/gWzAuYa0y+++EK/uVBCQgJyc3Px0ksvQalUAgCGDRtmtFTgpUuX8Prrr6OkpATh4eHw9PTkdu30UOzmz58/39ydsAbe3t4ICwtDdnY2jhw5gtOnT8PHxwfR0dEYMWKEUd39+/fjwoULJoGtTCbDwIEDYW9vjz///BPx8fG4c+cOwsPD8fXXX8PDw8PkuCEhIWjdujX++usvHD58GJcvX0ZwcDC++uorPPfcc0Z14+LicOHCBWg0Ghw/fhzHjh0zuT333HMMtitYw5hWRdef4cOHo2XLlo/2QtgQaxlTd3d3hIeHo6CgQP+7qtFo8Oqrr+LLL79E8+bN6//FsVLWMKZyuRwRERFQKpW4fv06jh07hpSUFJSWlqJfv3748ssv+b5rwFxj+q9//QvHjh3DhQsX9Pnz6enpuHDhAi5cuICQkBCjY3h4eKB///64efMmEhIScPz4cTRt2hTvvvsu3n33XaOdK4mqw5ltIiIiIqIGwgskiYiIiIgaCINtIiIiIqIGwmCbiIiIiKiBMNgmIiIiImogDLaJiIiIiBoIg20iIiIiogbCYJuIiIiIqIEw2CYiIiIiaiCm+3kTERERPQRRFHH48GH8/vvvOHnyJK5duwatVovWrVsjPDwcb775JhwdHR+6/V27dmHGjBkAgG+++QaDBg0yqXPz5k388MMPSExMxLVr16BUKtG6dWuMHDkSgwcPhp2dnb5uZmYmdu/ejSNHjiArKwt3796Fh4cHQkND8fbbb8PPz++h+6qTnJyM33//HWfPnsX58+ehVqsxZMgQLFy48JHbJuvAYJuIiIjqRVlZGSZOnAgHBwd07doVYWFhKCsrQ3x8PP7v//4P+/fvx9q1a+Hs7FzntvPy8vDJJ59AqVSiuLi40jpXrlzB8OHDcffuXYSFhaFPnz5Qq9U4cOAA5syZg6NHj2LBggX6+osXL8auXbugUqnQr18/uLq64uLFi9i6dSv27NmDFStWICQk5KFfDwDYsmUL4uLi4OzsjFatWkGtVj9Se2R9GGwTERFRjaKjoxEXF4e//vqryjpyuRzTp0/HqFGj4O7uri/XaDR477338Mcff+Dnn3/G+PHj63z8efPmwcXFBUOHDsXKlSsrrfPjjz/izp07mDt3LsaOHasv/+c//4lXX30VsbGxmDJlCry8vAAAPXv2xIQJE9ChQwejdnbu3ImZM2di/vz52LlzZ537auiNN97AW2+9hbZt2+LMmTMYOXLkI7VH1oc520RERFQvFAoFJk+ebBRo68rffvttAFJaRV3Fxsbijz/+wL///W8olcoq6125cgUA0KtXL6PyJ554Ap07dwYA3LlzR18+dOhQk0AbAAYNGgRfX1+kpaUhPz+/0uN8+OGH6N27NwIDAxEWFobo6Gjk5OSY1O3YsSOeffZZo/QValwYbBMREVGDs7eXvkyva9B5/fp1fP755xg5ciReeOGFauuqVCoAwKFDh4zKCwoKkJKSAk9PTzzzzDO1Oq5CoTDqt86pU6cwZMgQ/PrrrwgICMCYMWMQHByM7du3Y/jw4fqAn0iHaSRERI/Z0aNHMWbMmCovktJ9Xf/TTz8hNDTUDD0kqn9btmwBAPTo0aPWzxFFER9++CFcXV3x/vvv11j/rbfewu+//44FCxbgyJEjaNeunT5n28nJCUuXLoWTk1ON7Zw+fRqXLl1Cx44d8cQTT+jLNRoNZsyYAUEQsGnTJqNZ8ePHj2PMmDH497//jZiYmFqfI9k+BttERETUoA4dOoQNGzbAz88Pw4cPr/Xz1q1bh4SEBKxYsQKurq411m/evDk2bNiA2bNn4/Dhwzhy5AgAwMnJCZGRkWjfvn2NbRQWFmLOnDmQy+WYPXu20WMHDx5ETk4Opk6dapJ+0qVLF/Tr1w/79++HWq2uVX+pcWCwTUREREb69u1baf4xALRr186kbMGCBRg6dGil9U+fPo0ZM2bAzc0NixcvhoODQ636cOXKFXz55Zf4xz/+gZ49e9bqOVlZWZg0aRKUSiV+/vln+Pv7o7CwENu2bcOiRYsQHx+Pn3/+ucpUlpKSErz77ru4fPkyZsyYYfLN0p9//gkAyMjIwJIlS0yen5eXB0EQkJGRgY4dO9aqz2T7GGwTERGRkTFjxqCwsNCobP/+/bhw4QKmTJliUt/f37/Sds6cOYO33noLcrkcK1aswLPPPlvrPsydOxdubm744IMPav2c6OhoXLt2Dfv374enpycAwMXFBRMnTsStW7ewZs0a7Ny5E6+88orJc0tLS/HOO+/g6NGjePvttzFp0iSTOvfu3QMAbN++vdp+3L9/v9Z9JtvHYJuIiIiMREVFmZTl5OTgwoULeO+992rVxpkzZzBu3DgIgoCVK1ciKCioTn1ITU1FYWEhunTpUunjM2fOxMyZM/HBBx8gKioKarUaJ0+eREBAgD7QNhQaGoo1a9YgNTXVJNguKSnBO++8g4SEBIwfPx4zZ86s9Ji61JCYmBj06dOnTudDjReDbSIiIqpXukC7vLwcP/74I5577rk6t/Haa69VOkN8/vx5nD9/HqGhofDx8dGvQKLRaAAYL+1nSLeE34NpLIaB9rhx40zytA3pPjD8+eefDLap1hhsExERUb05e/Ysxo0bB61WixUrVuD555+v8Tnp6ekAYLQ9+kcffVRp3SVLluD8+fMYOXKk0XbtTZs2RZs2bZCRkYFNmzYZXYhZUFCg3wjHMA9blzqSkJCAN998E3PmzKm2n/3798dTTz2FVatWISwszGR3SY1Gg1OnTlU5G0+NE4NtIiIzKS0trbS8qpk5Ikt39+5djBs3DgUFBejZsycSExORmJhoVMfNzc0kTSU8PBwAqt2dsjY++OADvPPOO/joo4+wc+dO+Pv7o6CgAL///jvy8/Px0ksvoXv37vr6H3/8MRISEuDp6QkXF5dKL3ocMmQIvL29AUiz4osXL8aECRMwevRodOvWDSqVCjKZDNeuXcPx48fRpEkT7N69W//848ePY/PmzQD+nl0/ceIEoqOjAUgfEmoK8sm6MdgmIjKTrKwsk7Ly8nJcvHjRDL0henRqtVp/EeGRI0f0S+8Z8vLyqjQnvD706tUL69atw48//ogTJ04gOTkZDg4O8PPzw7vvvovXX3/dqL5uxZW8vDwsXbq00ja7du2qD7YBKZVk27ZtWLFiBQ4fPoyTJ0/CwcEBTz75JPr372802w4A2dnZiIuLMynLzs4GIL0eDLZtm0wURdHcnSAiakx0m9oAwLfffouXXnpJ/9h///tfLF68GADw/fffo3fv3uboIhER1RMG20REj5ku2H7qqadw/fp1dOrUCU8++STS0tKQlpaGwMBAnD17Fm3atEGPHj0wb948c3eZiIgektzcHSAiaqxCQ0Px2Wef4c6dOzhw4AA0Gg0+/fRTfP/991CpVLh58yaUSqW5u0lERI+AOdtERGY0fPjwSrevrmnTDCIisg5MIyEiIiIiaiBMIyEiIiIiaiAMtomIiIiIGgiDbSIiIiKiBsJgm4iIiIiogTDYJiIiIiJqIAy2iYiIiIgaCINtIiIiIqIGwmCbiIiIiKiBMNgmIiIiImogDLaJiIiIiBoIg20iIiIiogby/wFTkruy7RBLjgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 720x480 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"az.plot_joint(trace_g, kind='kde', fill_last=False);"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## La distribución a posteriori como único estimador\n",
"\n",
"\n",
"* La distribución a posteriori representa todo lo que sabemos sobre un problema (dado un modelo y un conjunto de datos).\n",
"\n",
"* Como regla general las cantidades de interés se calculan como valores esperados:\n",
"\n",
"\n",
"$$\n",
"J = \\int \\varphi(\\theta) \\ \\ p(\\theta \\mid y) d\\theta\n",
"$$\n",
"\n",
"* Por ejemplo, para calcular la media de $\\theta$ reemplazamos $\\varphi(\\theta)$ con $\\theta$:\n",
"\n",
"$$\n",
"\\bar \\theta = \\int \\theta \\ \\ p(\\theta \\mid y) d\\theta\n",
"$$\n",
"\n",
"* Esto es un promedio ponderado, cada valor de $\\theta$ es *pesado* de acuerdo al *posterior*.\n",
"\n",
"* En la práctica y al usar métodos numéricos lo que tenemos son muestras (arrays) del posterior, por lo que estas integrales son solo sumas!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Distribución predictiva a posteriori\n",
"\n",
"Como ya vimos los modelos Bayesianos son generativos en el sentido que es posible generar datos a partir de ellos $\\tilde{y}$:\n",
"\n",
"\\begin{equation}\n",
"p(\\tilde{y} \\,|\\, y) = \\int p(\\tilde{y} \\,|\\, \\theta) \\, p(\\theta \\,|\\, y) \\, d\\theta\n",
"\\end{equation}\n",
"\n",
"\n",
"* $y$ --> datos observados\n",
"* $\\theta$ --> parámetros\n",
"\n",
"Usando PyMC3 obtener muestras es muy simple"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/osvaldo/proyectos/00_BM/pymc3/pymc3/sampling.py:1511: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n",
" \"samples parameter is smaller than nchains times ndraws, some draws \"\n"
]
},
{
"data": {
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" <div>\n",
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" "
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"text/plain": [
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"metadata": {},
"output_type": "display_data"
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],
"source": [
"pp_samples = pm.sample_posterior_predictive(trace_g, 100, modelo_g)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Distribuciones predictivas a posteriori\n",
"\n",
"* Los datos simulados pueden ser usado en si mismo como predicciones\n",
"* Los datos simulados pueden ser usados para evaluar las predicciones del modelo"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"arviz.data.io_pymc3 - WARNING - posterior predictive variable y's shape not compatible with number of chains and draws. This can mean that some draws or even whole chains are not represented.\n"
]
},
{
"data": {
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U2cF0U6XZ6TSItpUfbP/jH/9wJz2GDx/OvHnzMAyr0Pt3v/sd48aN4+OPP+a5556jsLCQP/zhD7XyOoQQdZPSHq6yUVkt7bUsODi4Tr++yzV9+nROnDjBO++8c6WnUiVXy/V86KGHiI2N5emnn77SU7mmXS3XU1RfbVxLh4Yv7QZ3eZv4lVEqYrqev8PbJLCM5/fs2UN8fDymafLkk08yd+7cUjeja62ZM2cOc+fOxd/fn02bNkl3IeTfZl0j1/PyeLKSt9yjLUQNO3DgAAkJCZw4ccKjXuZCiMuXrqGBosxAG8BQ0MTQpJplbzB9+nRM02TIkCFlBtpg/abq+eefJyYmhgsXLvA///M/NfkShBB1nATbQtSw4cOHs2rVKsaPH0+rVq2u9HSEqNPSTUUTo+Jf0DY1NOllBNvffvsta9euxcvLi5dffrnC9qpKKebOnYthGHz22Wds2LChulMXQlwnpGZblOvSGwaFZzZv3nylpyDEdeO0VtxqmBVu00jBdg1Obd00CVZpyCuvvALA73//e49uIo+OjmbEiBG88847PP/88yQlJdVIz38hRN0mmW0hhBDXpPMa7BoaVtLWr56yvrIvSoAnJiayfft26tevz8SJEz0+5pQpUwgLCyMlJYW33377MmcuhLieSLAthBDimnTaVIQZVl12ZRopTaa2NnQ6ncyYMQOA0aNH07hxY4+P2aBBA15++WUAFi5cSGFhYZXnLYS4vkiwLYQQ4pp0WivCy2n5d6lQhTvY/uyzzzh48CDBwcGX1S3ogQceoFmzZmRmZvL5559XeX8hxPVFgm0hhBDXHK3hjAnhldwcWayRockwrf1WrlwJwBNPPEFQUFCVj+3l5eVesOzdd9+t8v5CiOuLBNtCCCGuOecBpcDTNXb9Xdv/lJ7uXqn24Ycfvuzj/+53v8PLy4stW7awd+/eyx5HCFH3SbAthBDimnNWK4KVFUB7QikIVvDh/32MaZp0796ddu3aXfbxmzRpwqBBgwBYtmzZZY8jhKj7JNh2MU2TTz75hMcee4y4uDjuuusuJk6cSHJycqltY2Ji+Oyzz67ALGvX8ePHiYmJYfv27Vd6KkIIUaEsE0I8rNcuFqw0n636EKheVrvYk08+CcBHH31ETk5OtccTQtRNEmy7zJw5k/nz59OzZ09ef/11XnjhBYqKinjqqaekb7IQQlxlsrQiuIrB9ukD+zi0dw/e3t7ce++91Z5Dnz59uPHGG7lw4QIfffRRtccTQtRNEmwDq1evJjExkalTpzJmzBi6d+9OXFwcb775Jl26dGH69Onk5+df6WlSUFBwpacghBBXnFPDOQ0hHpaQFFvzkZXVvuuu3xASElLteSileOKJJwDrRkmtqxb8CyGuDxJsA//4xz9o3bo1AwYMKPG4YRiMHDmSs2fPsnbt2hLPFRUV8eqrr9K3b1/uvvtu/v73v5d4fufOnYwcOZK+ffvSv39/hg8fzpYtW9zPFxQU8OabbzJkyBBuu+02nnjiCXbt2lVijJiYGFatWsXs2bO56667mDBhAi+99BJjxowp9Rrmzp3rvjseIDU1lSlTptC/f3/i4+OZNGkSp0+fLrHP999/z0MPPURcXBxjx47l1KlTFZ6no0ePEhMTww8//FDicSk/EUL8mrI11FfgU4Vg2+l08ulH/wfAbx98sMbm8vDDD+Pr60tycjIHDhyosXGFEHXHdR9sOxwOdu3aRZ8+fVBl3GnTqVMnGjRoUCrA/Otf/4rD4eDVV19l4MCBLFq0iK+//hqACxcuMHHiRFq2bMns2bOZMWMGd9xxh7umT2vN5MmT+c9//sOIESOYN28ezZs3Z/z48aSnp5c4zvLly7Hb7cyYMYMnnniCfv36sWvXLrKystzbmKbJhg0b6NevHwDZ2dmMGjWK06dP88ILL/DKK6+QlpbGhAkTME1rWeO0tDQmTZpEixYt+N///V969OjBrFmzKjxXbdq0ITo6mtWrV5d4fPXq1TRp0oRu3bp5csqFEKJasi+jhGTjxo2kpZ0iMDiYbv3vqrG5BAUFER8fD8AXX3xRY+MKIeoOr9oaWGtNXl5ebQ1frvr165cZNJfn3Llz2O32ClcQa9y4MWfOnCnxWKNGjXjppZcAKwOdlpbG3/72N+68806OHTvGhQsXeO655/D3txpT9erVy73v1q1b2bx5M++99x5RUVEA9OzZk2HDhrFixQqeffZZ97YtWrTgf/7nf9zfFxYW4uvry4YNG7jvvvsAK4t+9uxZd7D9wQcfoLVmwYIF7uNHRkZy//33k5SURFxcHB9++CEBAQHMnj0bb29vevfuTXZ2trv/bHkGDx7MW2+9xYQJE/Dx8UFrzVdffcXdd99dpfMuhBCXK9uDJdov9cknnwDQ7977yPPxBWqu5GPQoEF89dVXfPnll0yaNKnGxhVC1A21EmxrrRk4cGCJsolfS8+ePUlMTKz1wC82NrbU9zNmzMBut9O8eXPq16/PtGnTuPfee+natas76AXYtm0bzZo1IyIiAofD4X68W7dupbqfXBykA/j6+hIbG8vatWvdwfa6devo0KEDzZs3d4/fs2dPfH193eOHhITQunVrkpOTiYuLIzk5mZiYGLy9vUu8hsqC7f79+zN//nySkpLcWfbU1FQGDhzo6akTQohqOacVLQ3T4+211qxbtw6AgYMGc04rajLYHjBgAIZhsGfPHn7++Wdat25dY2MLIa59tVZGcq1kORs0aIC3t3ep8o2LpaenExYWVuKxhg0blvg+ODgY0zTJysoiKCiIN998k8LCQiZPnsyAAQOYOnUqmZmZgFXmkZqaSmxsbImvjz76qNQ8goODS82nX79+7Ny5k6ysrFIlJMXjf/nll6XGP3TokHv8s2fPlvkaKhMQEMAdd9xBYmIiAImJiXTq1ImWLVtWuq8QQlSXqSFHQ4MqvMXs37+fU6dOUb9+feJ6xZBTw/cxhoaG0qdPH0BKSYQQpdVKZlspRWJi4jVRRuLl5UWXLl3YtGkTTz/9dKl99+7dy7lz57j55ptLPJ6dnV3i+6ysLAzDcAesnTt3ZuHCheTn57Np0ybmz5/PvHnzmD17NkFBQTRv3pyZM2eWOZ+LlfVaYmJi8PX15ZtvvqFVq1ZkZmaWCLaDgoLo168fw4YNK7VvgwYNACvTXdZr8MTgwYN55plnSEtLY/369YwdO9aj/YQQorrOA77K+vJU8Q3uXfrEss1Wnx+dihAUt3hpvGooLzRo0CCSkpL44osvyryJXQhx/aq1mm2lVInSiavZ//t//49nnnmGNWvW8Jvf/Mb9uGmaJCQkEBISUiKYBfj2228ZMWJEie/bt29foiwDwM/Pj759+7Jnzx42bdoEQPfu3fnwww9p0KABzZo1q/J8i0tJ1q1bR6tWrUqUkBSPn5SURERERKngvVhkZCRr1qzBbre75/ztt996dPzu3bsTFhbGtGnTsNvtpc6NEELUlmytqpTVBljzn/8AEN23Pz29TLTDIBfFeofidi+zSoF7ee6++24mT57Mli1bSE9Pr/A+ICHE9cX28ssvv+zJhnW5x3OnTp04fPgwy5cvp7CwEMMwOHToEPPnz2fnzp3MnDmTtm3burdPSEgA4NChQ/j6+vLpp5/y2Wef8cwzz9CuXTv++9//smTJEux2O7m5uezYsYMVK1YQGxtL7969ad68Obt372blypX4+PiQl5fHwYMHSUxM5ODBg3Tu3Nl9nNtuu43IyMhSczYMg7///e+kpqZyzz33lMi8t2/fng8//JD//ve/+Pr6cu7cOfbs2cMHH3yAn58fzZs3p3Xr1vzjH//gwIEDBAYGsm7dOlavXs358+cZNGhQhR8ClFLk5uayevVq7rjjjquuXtvPz69O/7xeb+R61h01cS2PmQo/BeEeFkGey8nl+UmT0KbJ27P/l+ahwWRpaGGADTimDVoo7fGy7+UJCgri66+/5tSpU7Rr144uXbpUb8BrgPzbrFvkel4ePz+/Sreptcz2tebFF18kOjqaTz/9lA8++AAfHx86d+7MO++8U2aw+9hjj3Hw4EGmTp2Kr68vf/zjH7nzzjsBq4OI1prFixeTnZ1NaGgoAwcO5KmnngKsYHXOnDksW7aM999/nzNnzhAcHExUVBSPPvqoR/MtLiXJysoqlVkODg4mISGBJUuW8Prrr5OXl0dYWBi33HKLu7a6adOmzJkzh/nz5zN58mQ6duzIlClTGD9+vEfHj42N5d1332XQoEEebS+EEDUhWytuqMLNkX//5lucDgdt27alXbsbAAhSkKuhs02zzqE4bCra2apfyD148GB27NjBF198weOPP17t8YQQdYPSHi555Wk977UoODi4Tr++2vD+++/z4Ycf8vnnn2Oz2a70dEqQ61m3yPWsO2riWn5hN7jDyyTAg0x0nobfPTuR9e8vY+TIkcyZMweAkyYcNg1u8zLJ1rDRbjDA26zSIjllSUlJoWfPnnh5eZGSkuK+R6aukn+bdYtcz8vjSXOJ635RG1E1qampfPfdd6xcuZLf/va3V12gLYSouwo1ODTU93D7gw7Yv95abKzETeQKd0eShgoaG5qfzOoXbkdERLhbum7YsKHa4wkh6gYJtkWVJCQk8PzzzxMVFVVmtxMhhKgt57QVKBsexMWFGjYfPET68eP4+Pi4W/MB+AN2DUWugPtGV7DtqIGWgH379gVg/fr11R9MCFEnSLAtquSll14iKSmJN954g/r1Pc0vCSFE9eVoRaCHy7QfMRVHN1hdSHr16lWiO5ahwN9Vtw0QbFh9u3+ugez2xcG2h1WaQog6ToJtIYQQ1wRPF7PRGo6biv3rrWC7f//+pbYJVJCrfxmsvWFypAaC7d69e+Pt7c3x48c5fPhwtccTQlz7JNgWQghxTcjRiiAPMtvngHyHg53ffw/AHXfcUWqbQKU5f9H3jRXka6q9uqS/vz89e/YEkLptIQQgwbYQQohrgHYFwkEeJJ9PmIqC/Xu4cOECQUFBREVFldomgJKZbUNBC0NzrAay2/Hx8YDUbQshLBJsCyGEuOrlu/6sbPmI4hKS41usFXt79uyJYZR+qwtQmvOXZLFbGZrjpqK6pdbFmfSkpCTsdnv1BhNCXPMk2BZCCHHVK85qV7bS41kNCtjz/WbAWgCsLIEKzmswLwqsQ5S1b2Y1g+3OnTsTEhJCbm4u27dvr95gQohrngTbQgghrnqediJJ04rGmGzeXHGw7aPAW0HeRY8pBc0MTZquXimJzWbj9ttvB6RuWwghwbYQQohrwHltZaMrk2Yqin4+zOnTp/Hx8aFr167lbmvVbZd8rInSnJK6bSFEDZJgG5g8eTIxMTHExMTQu3dvhg4dymuvvcb58+cr39lDqampLF26lHPnztXYmF988QUxMTE4HI4aG/NKO378ODExMSV+9RoTE8Nnn33m8RjLly9n27ZtJR5LTU0lJiaGLVu21NhchRC/nvNaEVBJZjtfW0F5iquEpGvXrtSrV6/c7QOV5vwlWexQZS3znlfNUpLiYHvHjh01+v++EOLaI8G2S/v27UlISGDJkiU89NBDJCYmMmPGjBob/9SpU7z77rvk5OTU2Jh9+vQhISEBLy+vGhvzapSQkOD+lawn3n///VLBdqNGjUhISKBjx441PT0hxK8gl8oz22mmIsyALZXUaxcLcNVtX8ymINywxqqOFi1aEBERgdPpJCkpqVpjCSGubXU7SquC+vXrEx0dDcDNN99Mfn4+b7/9NmfPniUkJOQKz64k0zRxOp0EBwcTHBxcrbG01hQVFeHr61sjc6vp8QD3dakOHx+fGhlHCPHrK15a3b+S7dK0oonSfO/qr11ZsB2oNOmmAZSMuJsoq277BqqX3o6LiyMlJYVvv/2WwYMHV2ssIcS1SzLb5bjxxhsBSEtLAyAvL485c+YwYMAA4uLiGD16NMnJySX2+fTTT3nooYeIi4tj4MCBjBs3jvT0dLZv386YMWMAeOCBB4iJiWH06NHu/bZv387IkSPd+82fP5+ioiL380uXLmXIkCFs2bKFYcOGcdttt7F79+4yy0j27dvHH/7wB+Li4hgwYABz584lPz/f/XzxPvv372fEiBHExcXx9ddflwklYukAACAASURBVHkOpk+fzlNPPcWaNWsYOnQocXFxTJo0iaysLI/GS01NZcqUKfTv35/4+HgmTZrE6dOnSxzj+++/d5+zsWPHcurUqVLzuLSMxOFwsHTpUu677z5uu+02hg4dyrJlywC49957OXnyJMuXL3eXBqWmppYqIxk5ciQzZ84sdaxnn32WyZMnu79PSUlh/PjxxMfH079/f6ZPn05ubm6Z56vY6NGjmTZtGv/3f//HkCFD6Nu3LwsXLkRrzbp163jggQfo378/M2fOLHGdofKfhcOHDzNlyhQGDx5MfHw8w4cPd98IVqz45+WHH37gscce44477uCpp57i559/LnfOmzZtonfv3qSnp5d4fOvWrfTq1YvU1NQKX7MQtSlXW8urGxUkm00NZ0zwOnuGlJQUAG699dYKxw1QUFaxYGNDc9oEZzVLSYqD/Uv/jQohri+1mtnOzy//fyrDAF9fVePb+vlV/8YW+CXIDg0NBWDGjBls27aNMWPGEBoayooVKxgzZgwfffQRISEh7Nixg3nz5jFq1Ciio6PJzc1l586d5OXlERkZycSJE3nttdd49dVXCQ8Px9/fytHs2rWLcePG8Zvf/IYnnniCM2fOsHjxYpxOJxMnTnTP58KFC8ydO5fHH3+c8PBwWrZsWSowzczM5OmnnyYyMpKZM2eSmZnJokWLyMnJKRVYTps2jQcffJDRo0fTqFGjcs/D8ePHWbZsGWPHjqWwsJBFixbxyiuv8Oabb1Y4XnZ2NqNGjSI8PJwXXngBm81GQkICEyZM4P3338cwDNLS0pg0aRLdu3dn/PjxpKSkMGvWrEqvzaxZs1i7di3Dhw8nOjqa9PR0Dh48CMCcOXOYOHEit956K/fddx9glZBkZGSUGKNfv34sW7YMh8PhLsPJyclh69atTJs2DYBjx44xatQounTpwvTp0ykoKGDJkiW8/PLLvP766xXOcceOHWRnZzNlyhQOHTrkvqZ79uzh6aef5uzZs7zxxhvccMMNPProo4BnPwtnzpzhxhtvZMiQIXh7e7N582aee+45li5dyk033eQ+/oULF5g3bx7Dhg3D39+fBQsWMG3aNJYvX17mfG+99VZCQ0P56quv+P3vf+9+PDExkS5dutCsWbNKr4sQteU8ioBK/mvP0VZ3kT1brKx2ZGRkpb/58wcKtZU5975o/PoK/BRka6uG+3L16tULgL1795KTk0NQUNDlDyaEuGbVarB958Dyg+JeMTBv9i//iw25T1NQUPa2XW6GRX/+ZdsHHtZkl3O/ybcbLv9/RofDgWmaHDhwgL/+9a906NCB8PBwfvrpJ9avX8+rr75K3759AejevTv33nsvK1asYOzYsRw4cID27dvz2GOPuceLi4tz/71t27YARERE0LJlS/fjixcvJiYmhpdeesn9WEBAANOmTWP48OHuYD8vL4/Zs2dXmKlZsWIFPj4+vPHGG+6bgvz9/XnppZd48skn3XMAGDZsGL/97W8rPSdZWVksXLiQ9u3bA9CgQQMmTJhASkoKERER5Y739ttvo7VmwYIF7g8WkZGR3H///SQlJREXF8eHH35IQEAAs2fPxtvbm969e5Odnc3KlSvLnc+RI0dYvXo1U6dO5Z577in1fIcOHfDx8SEsLKzCspG+ffvy5z//mW3btrmzTxs3bsRmsxEbGwvAu+++S/PmzZk3bx42mw2w6jCHDx/Ojz/+SIcOHcod3263M2fOHOrVq0fv3r3ZsGEDH3/8MZ988glhYWGAlTXeuHGjO9j25GehZ8+e7qWgTdOka9euHDx4kMTExBLBdl5eHpMnT3afA7vdztSpU0lPT6dx48al5muz2bj77rtZvXq1O9jOz89nw4YNTJgwodzXKcSvIVdT6c2RmVoRqjSfb7IWsykOdCtiU1ZgfV5D8CVvHY2U5oxWhFajlKRp06a0adOGo0ePsmXLFvr373/ZYwkhrl1SRuKye/duYmNjuf322xk1ahRhYWFMnz4dpRTJyckl+qYC+Pn50bt3bw4cOABYQfTBgwd588032bVrl0cdQgoKCti7dy/x8fE4HA73V7du3bDb7Rw+fNi9rbe3Nz169KhwvAMHDtC7d+8Sd9/Hx8ejlHLPs5gnb0RgvVkUB9pg/VrU29ub/fv3Vzjetm3b6NmzJ76+vu7XFRISQuvWrd3lN8nJye7xihUHuuXZsWMHhmEwYMAAj+ZfnvDwcKKjo1m7dq37sXXr1tGrVy/8/PzcryEuLg6ttfs1REREEBAQwI8//ljh+B07dixxHVq0aEGrVq3cgXbxY8UZd09/FvLz81mwYAH33nsvsbGxxMbGsnXrVk6cOFHi+H5+fiU+bBR/0Dpz5ky5cx40aBBHjx51X9v169djmqb7A6YQV8p5DYGVbJPpykJX1l/7UoHKypxfqpFhBfDFTA2Oy4i7i/9vlFISIa5ftZrZ/np1+VnmS1fP/dc/Pd/2ow9qplTkYhEREUydOhXDMGjSpAkNGjRwP5eZmUlgYGCprh8hISHuwOTWW2/lhRdeYNWqVXzwwQcEBgZyzz33MHr06HK7heTk5GCaJjNnziyzfvji+tmGDRuiKlk6LTMzk6ioqBKPeXl5ERQUVKqMwtObPi/9NaxhGAQFBZGZmVnheNnZ2ezdu5cvv/yy1JjF9fBnz54tkY0t63iXOnfuHIGBgfj4+Hg0/4r069eP9957j+eff578/Hy2bt1aIqucnZ1NQkICCQkJpfa9tLb5UgEBASW+9/Lycmf4L36ssLAQ8PxnYeHChXz99deMHDmSdu3aUb9+fZYsWeIep6LjA6W2u1irVq3o3LmzO0uemJhIXFxcqXkL8WvL1Yp2hlnhNpla0arwArt37wZw/waoMgFKl+q1DVZme6epKDAh2VSkaoVdw21eJiFVSFPFxMSwcuVKNrky7kKI60+tBttVqZ+urW09H9OvVKBaLDQ0lNzc3BL1vWAFixfXOw8ePJjBgweTmZnJmjVrWLRoEY0bN+bBBx8sc9zAwECUUowePbrMrHXTpk2r9BpCQ0NL3LwIVmlMTk5OhXXZFbl0PNM0ycnJcZe3lCcoKIh+/foxbNiwUs8Vf5AJCQkhOzu7wuOVtW9ubi5FRUXVDriLS0m2b99ORkYGhmGUyKwHBQVx5513MnDgwFL7Xu75LI+nPwvffPMNDz/8cImfqaKioko/iHlq8ODBLF68mEcffZQdO3Ywf/78GhlXiMulXb2zK6rZznPVXf+8bw8Oh8N9X4snAoAzZQTb9RX4KtjkVNRTil42k/MovnMa3K5Mgjz8J1ec2d6xYweFhYU12qlJCHFtkNZ/HoiKisLpdLJx40b3r9QLCgr47rvvyqwbDg0N5ZFHHuGrr77i6NGjAO5SCbvd7t7Oz8+Pjh07cuLEiRK13tWZZ2JiYon/0Dds2IDWutwPEpU5deoUhw4dcpeSbN68GbvdXul43bt3JykpiYiIiHIz+5GRkaxZswa73e4+P99++22F43br1g3TNFmzZk25rbS8vb1LnOfyFJeSrFu3joyMjBIlJMWv4ciRI5d97qrC05+FwsLCEmU36enp7N27l06dOtXIPPr168cbb7zBK6+8QmhoaKWlS0LUtjyseseKQtRMrQgx4IdduwDo0qWLxx9AA5XmSBnt/wB80Jw0DR7yceKtIBhNkYbvHAZ3epnYPDhEu3btCAsL48yZM+zcudPj8hYhRN0hwbYHbrjhBuLj45k9eza5ubnubiQOh4NHHnkEsNqt5eTk0LVrVxo2bMju3bs5dOiQ+2azli1bYhgGn3/+OXfeeScBAQG0bt2aMWPGMG7cOLTWxMXF4evry8mTJ0lKSmL69OlV+hX+o48+yj//+U+ee+45Hn74YTIyMnjrrbfo169fiZsjqyI4OJgXX3yRp556iqKiIhYuXEhMTIy7FKQ8jzzyCKtXr+bpp59m6NChhIaGkpGRwdatW7nrrrvo0aMHDz74IB9//DGTJ0/m/vvvJyUlpdKljdu2bcugQYOYN28emZmZdOzYkTNnzpCcnMyzzz7r3mbz5s306dMHPz+/EjXnlyruSpKfn1+ihARgxIgRDB8+nOeff56BAwcSGBhIWloa3333HSNGjLjsc1oeT34WbrnlFlatWkWzZs0wDIOEhIQazbL7+/vTt29fEhMTGTZsGMalNVxC/MqKl2mvKHbONCFUaXa5gu2Klmi/VPHCNlqXPIZTQ4ZpEKx0iU4l7Wya46bihKlobau8iFspRUxMDP/617/47rvvJNgW4jokwbaHXnzxRRYuXMjixYspKCggKiqKRYsWuWuVo6KiWLlyJWvWrKGgoIDmzZvzpz/9yZ0JDw4OZvz48axYsYJVq1Zx8803s2TJErp27cqiRYtYunSpO9hr2rQpsbGxVf51Y2hoKAsXLmTBggVMnToVPz8/+vfvz7hx4y77dbds2ZKhQ4eyYMECsrKy6NGjB1OnTq10v+DgYPeKnK+//jp5eXmEhYVxyy23uH+927RpU+bMmcP8+fOZPHkyHTt2ZMqUKYwfP77CsadMmUJ4eDiffPIJCQkJhIeHM2TIEPfzzzzzDFOnTuXZZ5+lsLCQTz75pNyxiktJfHx8St2c2bp1axISEvjLX/7CrFmzKCoqokmTJsTExFRaRnM5PPlZmDhxIq+++iqzZs2iYcOGPP744+zYsaNU//LqiI2NJTExkUGDBtXYmEJcLk+Wac/SiijDZMeOHUDVgu16gALygfoXPX7UVAQbmoIyAvEIm8kBp0ErQ1f4IaBYr169+Ne//iV120Jcp5TW2qP7qyurpb2WBQcH1+nXd7mmT5/OiRMneOedd670VKpErmf1zJkzh+TkZN57770rPRVArmddcjnXcpdDUU9BZDlZZFPD53aD2wrOcWPbNmitSU5OJjw83ONjrLMbRNtMwi/6Rc4Gu0E7Q7PTqejnbeJ/UVBtaljjMOhqM2nswS9/fvjhB+Lj4wkMDOTw4cPuVqLXOvm3WbfI9bw8nqzkLb8jFkIA8PPPP/Of//yHxMREhg4deqWnIwQAuSgCK8hs52DdyJi8Zzdaa5o3b16lQBusjiTnL2rzl6fhnIamhibYsDLnFzMUtDc0h0zP3kKjo6MJCAggNzeXffv2VWluQohrnwTbQggAZs+ezYwZM+jXrx933333lZ6OEIBVT+1fQalGtqloqGDnzp1A1UpIwOpikq8Vu5yKZKciV8MJU9HU0HgpCFGas2XE+q0NTYYJFSxo7Gaz2dwLkkkpiRDXH6nZFuW69IZBUbctWbLkSk9BiBIcGgoqWdAmW0PDy7w50qHhG4fBBQ1Kw3En7HAY+Cro5WX19Q5WmoNOAy4pY/FWEGZAmqlo68GNkr169WLdunVs3ryZUaNGeTxHIcS1TzLbQgghrkrnAT9FhS32srUiWGl3ZrtLly4eja017HAqAhR0MUzStMJbKeor2Oc0OOFUmBpClBXQm2XE002UJk171mKweJGdbdu2ebS9EKLukGBbCCHEVcnqRFL+86a2AmHOZXPkyBHA88z2MVOR7VqZcqdp4Kc0t9lMomyaaJvJPtNgs9PAB6inIKesYNvQnDatNoGV6dKlC4ZhcPLkSVJTUz2aoxCibpBgWwghxFUpV0NAGYvNFDsP+ChI/sEqIWnbti0NGzasdFyHhn2morNhsttp0MmmCVWQryBDK7rZNGFKc9aE/aaVOT9bRga7vrK+MjwItgMCArjpppsA2L59e+U7CCHqDAm2hRBCXJWKF7QpT7YueXOkpyUkB103VZ5DYQNuMDSBCnJNyDAh3NBE2TR+wM9OhaM4g16GpoYm3cNSku7duwNSSiLE9UaCbSGEEFel3EoWtDmnocFF9dqelJAUaTjkVLQ3TH50Krp5mSgF/kqTZipMwAdoY2gKFUQYJie14qxZdkDdxLWfJyTYFuL6JMG2EEKIq47WlWe2c7QiqIpt/342FWGGVS7S3NAEucZXGrY4DTJNxTqHQa622vtdQNFUwbFybpIMUVbHlAselJIUB9u7du3C4XBUvoMQok6QYFsIIcRVpxDQgF8F25zTUHQmnZMnT6KUonPnzhWOqTUcNhVtlMlRU9HGsCJku4aftJXV7uttEmFoNjoMwpTmhKnoZDM5rxWnywioDQWhBmXWdF+qffv2NGjQgPz8fPbv31/p9kKIukGCbSGEEFedXNdiNqqcGLZIW18/7f4BsALZwMCKOnJDuitYdmKVioS4xt7tVISjcWDVa7e1adrYND+bCn8FeUATA/Y4y37LDFGaTLPy12QYBrfccgsgpSRCXE8k2BZCCHHVOa8rXqb9nIYABftdy5936tSp0jGPmgY3GJqftfWnUnDGhDStiLJpnEA91yEjDasWO0RpjpkG7ZTJQaciyW6w3aH4yancLf9CyulWUhap2xbi+iPBthBCiF+d1tZS52XVQQPkUnm9dgOl2bt3L1B5sF2kId2ERspaZr2lq4TkkGlwo6s2O1BBnuuYPgraG5oDToPvHYqtToN8DU40DZS1pPs6h0GWaWXIc7TVUrAyEmwLcf2RYFsIIcSvpkDDZofBv+wG/7Yb/NthkOxU2C8JVCtb0OachiAFe/bsAaBjx44VHjfVVIQakKUV4Ya13HqehtOmdSPkWQ3BaM675uHQcEIrMrT1wSBQaXyAC6airaG53cuknaH51mFwAavkJcuDYLu4jOTQoUOcPXu28h2EENc8CbaFEEL8KtIcJmsdBn5o+nub/NbbpIfN5KxWfOMwyLsoWM3VEFjBgjY5WuFbkMdPP/0EQHR0dIXHPq4VLVzLqzdxlaccNRUtDI2Psnp2NzI0513lID+ZCi+s2u5WNpNIpTGA8wpOaoVScINN08mm+c5hEIAm04NSkuDgYCIiIgBZ3EaI64UE20IIIWpdjoZvLtjpajO52UtT33XzYyMDetlMmhma9Q6DHFdpSZ6rJrssWlvjpSYfwDRNGjVqROPGjcs9doGGs6bVE/uMCQFKs9uh2OQwqAcUmVabwUbK+tOu4aBT4cBa8CYARYZStLdZwfghxy8Ta2PTtDY0p7UiU/ptCyHKIMG2EEKIWuXQ8L3DoFM9G83KeNdRCm6yaSINK0ucpa0yD+9yYtd8158pe60SkujoaFR5bUuAk6aisQHZru83OQyyNdTHynSvcRh4AQ1dme0UU1E8WqyXyQUg37SeD0CT5gr2i0UaVtb7mKvkpDISbAtxfZFgWwghRK3a7bRuPozysVW4XTubpqnS/NdhUL+CoDXXtdjNPlcnkspKSFK1opmhOehUZGlFLy+TBgo6eWnivUwCFJzWCm/X2IecinwNt9hM/AwIN8BHaRyudoQ+Cg47fwnubQp62EwyTMW5KtRtb9++He1JdC6EuKZJsC2EEKLW5Girc0dXm1lh9rlYJ5vGxOpGUp5cV1vA4k4kxcF2nobCS7LLRRoyTQjVmj1Ogx42k0YKTmlFU6UxFAQpTXOl2e80OGtaN1+2MjQhrndIq8ZbkY/CwFps56hTleg+EmpAQwV7y+nFfbGoqCjq1atHTk4OR44cqXR7IcS1TYJtIYQQteaAU9HOpvH1rJwZQ0FTpcnSitzy2gJq8NemO9gOiYrma7vB13aD1XaDL+wGOxyKCxrStSJEwX5t3fAYZWjOYS1sU7yoTbZWdLGZOLFWgiwEor1+OXio0tiBC04oAJoowNX+z6mtzP2/7AZ5WrPFqcirZIEbb29v9weE4qXmhRB1lwTbQgghakW2CadNRYRRtVIJh1K0MzQ7HUaZNdC5WpFz/GfOnz+Pt48P+e060MlmMsTV4STO28RLwTcOg5+cEKw0PzoNIm0aw4BTppXVVgqcrvrrEAOaK5MzKOq5eoAXC3IF5coAG9BQaWxY3UzWOwxytWKgt0lfb40XsNpuVNpzu2vXroAE20JcDyTYFkIIUSuSTYMIm9VaryrOa7jJMCkAUssKtoFj+6ysdosOkfT3s9HEsLLiSlnBcWebJkJpfnAaZGirA0kzV9B/ylQ0df09V1vt/fY5rYVrbGjyUGx2Ghx11WUbrq4pvq5WhN5YmfGDDoUv0Ntm4qegsWHVnGej2OKo+O21S5cuAOzatatqJ0cIcc2RYFsIIUSNK3Ct2Ni2ilnt4rZ/gQbcZDPZ7zRKrDJZpK2v7XusYLtHdEf8ygnmgwxNAzQ/mgZe2lo9ssiVyQ67qITknKseO0BBG1fGu4vNZK+pOOIKuBspjULhBAqxPhCcx6oRL64aCQC8gOZKc9xVK16e4sz2Dz/8gNPprNI5EkJcWyTYFkIIUeN+dmWPPa3VLpaH1fLPR0FzZb1JHb9osZjz2sosb3PVa99cTicSu4Zkp8JE4dSadCAIOKMhWIGXa8gjptWasJsyOW4qbraZBKE5aSpu9zLZ47Rqxxu56rYLNPzsVBw3FaGG5pi2lm3P01YGvIEBoYamHrDfLP8tNiIiAn9/fy5cuEBKSkrVTpIQ4poiwbYQQogapbVVz1zVrDZYwXSA6+9W/22TA07rRkSAc6YiUytO7beC7U6dOpXYP8+1HPyXdoMDToNCoBmadNNgtcNgi8PAB42prSA7xWnQ2ebksCuI72CzykpOmwoTqx3hPqdBQwUOwNBwwFS0NjQRBoSiaaA0O51WfXkDpfHDynZnmeVnt202G507dwaklESIuk6CbSGEEDXqjCswblTFrDbABa3wV78E6U0U+ALHXaszHtIKZ04Wp44dA0r22D5hwlq7QX2lifMyqac0t9hM2nhZNeBNlCZPK85oxVcOg40OAwW0s1n15W1tv6xs2crQ/OhU3GhoMjRka2iItTCOA0U3m0mmhuaGxh+4oOGYqWiorEV3mhjQQMHBCrLbcpOkENcHCbaFEELUqKOmoo1h1T5X1fmLlmlPNSHJYWCi2eo0KHTCEVPhk2xltZs3b07Dhg0Ba3n1XU6D3l4mnW2aDFNhx1piPUMrbvYyOaYVfmgGeZt0NUz2OxXnNWQ4rVUmbzJMlLKOH6Y06aaiAGuFyL1OgwINThTeaEKxykZCDM0xUxGCZqND4a812VrRyjApAk6bVo15WSTYFuL64HWlJyCEEKLucGir20e0dyXNpl3b7nGt6liE1c/6gla0VZrdrrrojobGruGgEz7T1o2OZ/aXXMzmhKk4ZCrucK0GCZCiFSFKE6CsrHMrA/Y7oUhZKz5ewGpJmAf812mQr5U7Ex+oNIUo2tqsVSe72DT7nIqzWL26/RScV4pQwNSKn0yFjw2KUOwwraA8zGbVdwe5+nHfYCsdcRd3JNm7dy92ux1vb+9qnn0hxNVIMttCCCFqTKpWBCuoX0lWO0fDeodBIYpONpMYm4kPVvCd4rRqpvt5mbSxaSK8rLKQn0xFgYYjyQcA6NixIzkadjoUPW2/BNpODceciptsmmxXwGtT4K80+VrhMOGQqWhkaJobVulII2WyzmlwyrQy27lAe0NzwlQ4gHoKbGgKAT80Z1312YdMRbjShBma33g5OWpaHUsuAE0Mjbf6pQTmUjfccANBQUEUFBSQnJxcMxdACHHVkWBbCCFEjTluKlpWcmNkvoaNDoO2hqanzSTMgIaGtVR7iLKWVW+lTOpdFKMqrOXQC4B9+61gOzIyiu0Ogw42TehF72bHTasPdnvDWomyoasGvEArApRmn2ktTmNoqx78jKno42XVd+90GqQ6rRsx/RQ0NTSHnQo7kKcVvtpaeTJdK/zRHDUVPbxMTpiKpsrqWnIOxTmtaGZYrQazXe0ML6WUKrOUxKHhB4fiO4fBQedl1OIIIa4qEmwLIYSoEYUaMkzrpsHymBq2OQ1aGZr2tpJ13flAHopbvEx+NA2yXJUoTg1HtOJGm0k9tDsL3DAyCgdWUH2x/U6DFq6scpYr016grUD9Jptml9NGe0NzXllLuhsKmto0TQzo52ViU4rdTkWmCe0MTbKp0K65BBmaQEOTaSpytUIBrZUVvJ8FbrZpcrR1k2hjZS2aE6qsDHlZiktJioNtrWG7U3EeRXPDypz/XM6+QohrgwTbQgghasRJUxFmUGrFyBwNmxwG/8wp5FO7QbYJHcsIyFOdVnu9Xl6aTjbNFqeBU1vZ8vpASwXnU0+Sn5uDzcuLvLY30tlmYlx0vOK2gzcZVnScra3l2jNdGemmyioBCVZWUJzhynYHuvb3VXC7zSRQwbd2g1SzOCC3WvwpwM/1mtJMRZDS5CvrA0aqadV5ewNHnAovBY0Nq2/4qUqC7eL2fz+ZihxtlcW0NjQxNpPdDqvXtxDi2iTBthBCiBpxUitaXBJEnzHhG7tBsNL08PPivLYC6qNlBJ+HTIOmyspItzasmxuTTesGxAibSaGCooP7AQi7oT0hPj40vuRd7Khp9bi+wbAy7cU3KWa4FqFJ14omSnPcVOSbkKWhtSqZYfcyrHrr7l4mZ03FGVORqhUdbZoiFA5tjX3alb3O1lYQf0orvLHmftK1EE8zw6oTz9JldyXp1q0bAPv37+dCfgHJpqK7zXQvuhNiQGub5icpJxHimiXBthBCiGqzu5Ynb3xRj+xC1wIzt3iZRNo0J+1WW77+3ibJFy2FDlYgmqqhjStYL14y/YBDuYJZqwd3/o9WsN2oQxQhZWTH97lKVGyGFUg3UFaZSKZWhCorCI60aX5y3cjojaJJGe+EQQrsKDraTMIVnDINckzwRbPbNMhFkaYVRzT84DQIxKoTP4eVtT+vFRdMaKJcN2nyS//xi7Vo0YLQ0FDsdjsb9+3HHwi+ZD43uNoL2iW7LcQ1SYJtIYQQ1XZGWzcwXrw8+z6nVXfczLDa7x2xm9xo0wQq6ONaCv28K4A8air8UIRc9K7kryBQWYG4wurBnXbQqtduExlJ+iXZ8SINJ7QiyhWEW/XaGoe2yj78gbMm3ORq+ZerraA+rIygPUhpzmk4haKVzaSVMtllGhwzFfkagtC0MzSxz5hjdAAAIABJREFUXppsE/7jNGiA5pSpaG3TGGh+clolNcEKfJQmrYxsvlLKXUryzY5dZa66GaCsDHd5XU2EEFc3CbaFEEJUW5qpaHJRoJhtWjXcHV39pX90Km7wMdwtARsqaG+zljk3TThsKuorTQC/jOHUUIjVRjDdtILtQwesTiRtI6PI01bwXOwn5/9n78166zyzLM1nv985nOdR1DySGuxQ2OExIjJjyMzI6qjMRqIKhaqLArr7ooH+Ef1bGujqm0ZldWdWFbKiYsqY7fQoy7I1ixopzvPM871798V+D0nJEiVH2Ikw+n0AIzWQ1OFhJLjO4tprCWJwKP2bC+k4ci4N5UyZMBCgMUCLwaIJnfgC5ON0irFowkMVzLxbuwD+phpRhCWEySj0i9Ed4EyKjtxMWe1use31yMFgGN5gYk9wp+uNJFc+uvCZGE6d40G5ncV2JvOVJIvtTCaTyfxBmMFEykLXuaG+3tgoHie5r8ILjY/uqI0EY93gchK0xs56JMC4ubP9tcLrAC1Grl+/BsDRM2fpCcZoEqBq8Kl63V69MnDeoDsYc/UIicp2U8omnu0eeMrSZYfAjHk85o4KbQIvFUqJ0C5GnygLCG+X3ppyMBhvFP54phUOBBhPue19YiyboAZLT3j+6mL7/sUPt7PajzMo/mLjSRWCmUzmj5sstjOZTCbzB7GYBGBnEopbBg/TZDvAPRUGA7SER5VkIfBC6rbuFaPCo00mDzRwKBiHg7EMTN+7y+b6Og2NjXzz+FE2kvMczYX5hrEdw9hMB4lteJykHXe46y8Ilk0oxJtFnkRr+rwM783+04pyqjBum9Bk0AIUYrwYlDkTLkTvFz8SjF+WgZOiLJqwpel5EY+mPB59gZ1GknvXrrG6uvrEx1MIDASe+P6ZTOaPmyy2M5lMJvMHMWHC4K5Gj/upArBZdqr4joYnz7fvA9bwoZvdrnbNPDpyIBhFquy7XB+zGRnheDUwbbCgwn+uBX5aK1hJndbgGe32dBw5b2BidImL+S31v28Bak/5nIJANOF+DJwJSnvw6sAmIIpR4s0jlQAvFO6a/6YMnE3tI3MqKMaoei7cD0eFGfusWO7fN0TXviFUlUuXLj31ed4nxsQT3j+Tyfxxk8V2JpPJZP4gppJzXeeOCkeSuJ43F7QDT9GIyynfPGaB5pTXVnM3vFtcsIML8XvXXGwPnz7DaBRWVBCBKj6lXpjwXgzcV19w7BSPqdTMneyB5HrfNe++HgjG7FPEa2k+srOJi2lw0Xw4GOv4kWQ77uD3BT+W7A3Gp1FYVHhPAwr8tCy4UHq2exNvbHk8tz1jcPL8o+M2T2IwGFPqWfZMJvPVIYvtTCaTyfzeRPN4Rv92PMNzxUNJw95V4XCwR4ZntgxGo1f/XY/CyWCEJIxvReEnZeAnpYvmq9Hz3EsKU1e99m91+Cw/KwMnC6U5NYD0Cny9qnyrolyM3s3dkVztboHp5L6DO+/NYhwN9tQc9JT6i4SKQP+u75SNGA14vrxRjGkV2gVWEY4FQ0XYwJcn23AnfA3htgaW1VtVHs9tT6pwPuW26+M2T6JFvKFlJovtTOYrRRbbmUwmk/m9mTMXgPXKv4eplcRjGC5sj+xq2Lirwo9qgQkTpgzeLQMLBk0Gn6RqvReDsg/jL6qRMRV+WQbejoHp5GyPjJzm3zVEKiLMm68rrpgPyPQG+Fah3IrClhnz5sJ6M4luM3iogSruFD+egzbzJpVfl4HSjC2Dn5SBD2rCssIiwtE0My/4AmU7xnx6n7OF8r2KMqGyPeH+alCq4u/bgDHzWO56yoQ3Xnq2s016zE+KomQymT9estjOZDKZzO/NjMm2qw0+S74/ietp2+nKBnhYi3xc+rHhNys+cLNP3Cm+rIEGjOHg9m938IPAbwTl3VKY26oxe+sGAEdOn6Y3wJuF0i/GMsJtFSZVuBX9GLJLjJsamFJJrSOew17B8+Eg9Kcs9WQSr7MKvyoDvy0DD0zowWMkKwqXNPAftgrerQV6UQw/yFwyrxG8Y8IgxpnCOFsxugqjirFmwrzC64VSpN/P7nKmN807yL+djiRv3rzJ0tKTOkucHjFm85FkJvOVIovtTCaTyfzeTKtsj8Jsmk+X1/PZ4yoMpb/bMvjNWslrFd1eSJwxP6SMJrRitIjwqYbtzu6awt+XBfsxVm/fQsuS5tY25vcf4Xpalhw3oRFjFQFz5/znZaBZvI7vZhQ2TBhILwimklDtEqNBdnLQV0rh7ejtJ2eC0ghsIewT+H5F+Z8aIi+kMZxZC5h5NKVmcMW83/tY4bGUSRUMOCBGBD7WQCFwrjDG8fn3em571rxzfLC/j0OHDgFw8eLFpz7fPSkaozlKksl8ZchiO5PJZDK/F/W8dl8S1xOphaQqO93bQ0nkXonCwWp45JByWv0IsSY+0/5Gmme/FYU+jP+35o0jqyLMXf0UgJEzpzlXwO/KwP+9VbA/uJvcjo/K/GmhtKaKvf6UA59QF9XgkY0tbNt9bwGWTLiqwvcqyvHgc+ydGCpwujCW8EPMsxV3tF8plPMVZRlhBeFCDF4LqMIvaoGpVDk4bYEAXIjesf1C8DXLefUGFvAZ+b70WOp923tFSZrFn9+ne9+ZTOaPjSy2M5lMJvN7UV9mrOe1x3cN2yymkZquNMZyV4WvNz06auOtITAclLYApyrGkUK5rYFflIGbKvygiHRjLKa8dnX4LINiLJgfY75ZKGrQK+4S3zSffD9aGB/EwFAwFkxoTm7whEJEOJAe5x0VIt7PXT8+XDShRDgejJ7g/xbAqnn+/LYJLxZGtxg1fK591Xye/psV5fWK8WZFGQpGCy7WL9eExkDqDHd3G2BWhR55frENyd3OUZJM5itDFtuZTCaT+b2YNqEvCUUzd6rrDnJdeIvA9ehHha27KklW1cX2S4WhCG3p4/TiLSDXVRgR41QFJoDV695EcvT0aX5ZBlYRXiiUixroDXC2ULbM201mVBgJxoZBxJ3gKXbiF4I3jCwYfBKFN4rIQspt39RAFR/ReaFQusS2/24ujeZMqNAANJp3Z7dgXIteVdibvqt2pyGboWBUgR+nWfrjwVDgfqrwW0gvFGBn3GavRhLw3PZcjpFkMl8ZstjOZDKZzO+FRyD814vmK4et6e/qee2t1EhyIjyqDi+r0CJwMhgruEOuBjct0IlSACHAr2uBTQvM3rgOwLmRERaSwK0ZfFoKDxXmTJgybwy5Hr1ju0c8F35ElCvRs+Dgi44Bb0I5VxjHCxe9Swrj0asAm4ChNGSzaC6M582z5L0BZlTYQqhiTBN4s1Dum/BpqioMKQ9eBfrSEecFhf5gtIpxR72FpWXXTwbqYvvu3bvMzs4+9Xl3sZ2d7Uzmq0IW25lMJpP53GjKHtcjENOplUTE2z6W06Hk7TR407pLG5p5hnskKCKwYkIr/mcrBgHvxP7LqnJbhfWtLR7cHgVg4dhpmjAqGO9rwQLCtAlng3IkKCUwZsI7tbC9IgnCJh4ZqeGC94YKzeJOdVVcVH+iQkvww8j96c8b0rDOfFqd7BI4FIxLKrQGn3uPGKdSs8q4Cu9Hn5DfF8AQd9LF+G1Z0IO/z7TCeJTtxUuAzs5OTpw4AewdJalHc2rZ3c5kvhJksZ3JZDKZz80KPvjSnH7vYnvn173Bv8GMqnDysan2cfP880hyu1cMwAVsD97R3QzU1Kv32u7eRGOkubWNnv0HOBCMFlzcD6EMivG7WHAoGCsIR0VZxDu/i/Tx+zEemLCKC9zrUThf6PbEfB/GR2VgLsIWPOLE+8KlbB8n7sO4o8IgiuAvDuoz6t8slA2Ed2KgD6M0byQZwlgy4ZZ61KRJ4Frq/t5N3d3eq5GkIv6TgKUstjOZrwRZbGcymUzmc+OHfT5hrgYzKSIB7tr2p1yxsJNJrnM5BtrE6Anucq8ajKn3Y68hDAbjaAG/jYF2gbmb1wDoOXmK/YXHQLoL4wjKqgjfqCgNGA+j1wE24EeQg0FZw53pOYOtdMi4rH6o2JEeVzS4psKCCXMmrJlXA9bpFj+srL/9HRO6BJbwJpEqLt5bxF8cfLPQ7cq/nuDPT03gEMYvy4I+MTrFGNNApzyqmJ9HbAN0iLGYoySZzFeCLLYzmUwm87mZs50IyYJ53KJ1l7PdL8Z4qv6TXZpwyXzs5qB4rnkT7+CeMWHdoBDjRDCOFspVFQ6Lbue1+4dP0yTwvaoykMRuhxk/qwXuaGAOYQPhhgkLCpMWeDUJ7qsa6BBjE6/6O1vsiNwbKmwiDIoSBTqAB7vaPnrEVxs70xLl9SicCMqCCQMCjSnKUuCz7YV4S8qm7QjtBoHzlci0CRWDRmDZhJbHnte62H5WI0nn53S2o8HFUvhVLXArVRFmMpl/HrLYzmQymcznZs52KuvmbCd7vGounrvk0VGbOqNRaBPoL3YiJBupim8iZbcPBKNisG7e8PEwie0DJ4f5dlVpFWgWj4wsImnFUlGgA2MrZbSXTbiuwhsVZdWEDfxjngo+aAN+DHmtFBrE6BVfeDxfKJMmzKX0iwtbF8ZXoue5a3hHtwAVMQqMJatHYjzq8UbFM+SbafSmKQgdYlxW2AAEY+Gx5/XFF19ERBgbG2N6evqpz3/n53C2o8Evy8AKwvHCGFXhRq4OzGT+2chiO5PJZDKfi60U/ajnjd3l9l9Pq+e11/A5875dmq5mxj0VGoxtcT6nwjouPAUo0p/9t7KginFVhdvXPUbyndPD21GO2QiT5s53K/W1x3re26v7fC4d3qsF+lAeWsCw7REZcJe6KxjtAoJgGIcLH7O5ov4tspL+zeX0+I8EY0mFjmDpaNKw1M89vyue3ijwpxWlxFg2f1HyYlDuaEAQqjzqoAN0dHRw8uRJYO8KwI7UkmLP4VDfUKFBPN5yKBivV5RrUXLmO5P5ZyKL7Uwmk8l8LubNxV6xLbZlVyuJ57Ufpsn1XdXa3KspDfj4TP2w8m7q6r6pATAWEOZSm8mpoEyVxuTNGwCcHjkFwFSED7RgQLzrY6Dw2Em9as8bQlzQryEsYUTxVpAq8DA5wmrwwISAN4wspffrE+NIMGbUm1W2zA8jr6elyHsqHKsYQ7gzXuDufCvK7fioeO4voA9vSJlTeK0wanguvE38Yz3O8/Rt1+Mn68/4Wq2l2MvuY9AOgeHC+CRmCZDJ/HOQ/z8tk8lkMp+L3eJ609zB7kgT7TMprz2d8sx11gx+vLzFuMEKwq/KwO1SGFNoxbihgSaE7xbK/sLbOr5VMRYe3KPcWKfa0MDCoeO8UxP+S62gDeNoULZEeDUYjSlOYubNIlXcaT5XRO5rQDDAaAY+jQE1mDJoSP3ZQ6m7uh3PkTekQZp7aeWyGWPWhENi3FehG2MMz4VvmoveNYSbFrhcevVfneEksCdNqATYF5Rpc8G8YrIdPalz/vx5YO8jSZEdd3svrkd55Bi0zvFgTKt/XTKZzJdLFtuZTCaT+VzMpUl0cJe7K7nca0Bp0A7MKttxjU2DX9UCNeCVYHyjUL5TVT5Qny2/EQPdonSLsb8wbkcfi+kTuJ/y2ieOH+e7jYGLMTBhwkuFsmFCoxgH0qR6N0ajwJp4NjoCszFQwQd4moGqGIsGd9KCZau4EF3ZftFg/K4MfBqFZozRKMynAZsW8ce2iTeNnAlGnxhnC6MB4VwwujE+jcI/loHVJGTr1YebCtcjvBTccSdFSSYfc7efd7b9WbntWB8UKj6rqBsE9ocnO+uZTOaLJYvtTCaTyTw3ZjCnuzLXJnSnX8+kvPYyO+0kZvB26RPoLzQWrIpXBnYJNJmxhB8nDgm0Bo95eC7aneepJLZPDQ9zSQNdYjRi3DPhVOFCeRUYSAMyAY+g1EwYCv539ZaQboyaCX0Y75aBh+p+d4cY/1gGNvD89aK5AJ7SwA0VflsGmjE6DX4ZA90Cf1FRbzQRCAaKMavCgcKoAAfE+FUZWFDoLaCCO9oXY2AgeD59BX+/6ccEc/1Icnx8nMnJyad+LTqe0UgyZkKH1Id9PsvR4H3hz5P7zmQyvz9ZbGcymUzmuVnBIwz1zPD8Lpd71nZq8upifMx8JKZRhCPVwKxBbzBm1afZ2zAUP+AbEOOh+ZniweBidSl1bLeeHGHRPHZxPihLJmzhk+hTJhwJyrgJB5OQ3UJ4KUSmUia7CWMej1Rs4IeJpi7s76iwYT41rybMmzCrnidvNLhrfrQ5au5E/1mhNCQ3v0uMBXFRO2HeKV6Iz7CfDcZvykA0d9TBn7ePNdCRjje3DCb10UPHtrY2Tp3yfPpeUZIOMZb3cLbvROFoeLqSrh+vzmWxncl8qWSxnclkMpnnZlaF3jRmU3e5u3e53L1JbPensZtPY2A4qIvsIlCmnPQ/loEeMVrEvxEtGvQHeJAODHvwirqVGy62OXmaAdxJrgZ4s6JMm2ejJ1XYlxzuNjR9YzMaRUC8Zq8XqCEE8xq/Kj5OM2/CtwtlLvVkH6gYBdAR4MXgS5TgtYFLeL76HQ3btYB9Yv4cBNgU7wkfEONmai05WxjvxEBraitpSdPv+8TYSB0sW/hPA3ZTj5LsdSTZiL/AUf3s362bR3wO7CG2RWBQ/MVKJpP58shiO5PJZDLPzbztiOs1wHBHuJY6prvYWZO8r0IDXqnXG2DFjE7gw1Sp1wj0C7SLMa1Cqxn31SMmo+rDKzPJ2T5w8hQ3VejE3dyThfFqodyLwnxyz5uAGQv0BKNZ4NMotKSDzO9Voh9N4nnuVWAeYST4x1hOmexGXMgPBV+4PIBRgh9uBne0O81z3R+W/qJi0YQeFDXBTGjEjywXDE4UxpAYpbgrv473dqt4NeFSeh5mHstO148kHxfbZnBXhR/XAj+vBcZV+C9l4OP46FHmhAr9Yae28GkMBGMq57YzmS+VLLYzmUwm89zM7oqILKTjSElz6B0C6/UYBXBbXRSPm7BfjJnSMDEW1XPUyyYuYPHYxWX19cUDYnysgs5MsbqwgIhw5tQpptPHaRdoE+gNcLJirJowFoUmMSbVh3G6xbilggIFxiLCmUKZw0XvWjpOXFXh5zFQAD+sKi9XPMIyGoVPSqFa+OHlhyacS078IvCXVWUdj6CsmwvoGhAxNsTjG7eTiH2xMFrE2MK/6W4aDLKzImkKM48Z0E+q/9syeCsGrkXh64XyPzYoXyuMFwtlxYRflGG72aS+3vks+sVfQNVylCST+dLIYjuTyWQyz0Ut9V/Xx2wWzAdhwEV4j/iRYI94tnvZYBBjUr1Gb6qMTFrgSFDWDZrEmDOPZ7SL8W4MrCrU0rFheeMqAAePHIHmZjbNoyA9YoypZ50PidGEi/MuQPE4yeuFT7MvAEeCcUmFQ8E4H5QZwOdtjEsWEPzg82D6jmgGd9TFexU4LcaiBioBvl4ol9WbVd4slP7U5V1Vd8DXTVg1/zcfqFCaz9KfDcY6LsyXgNcqSlUAjFnYri2s8+KLLxJCYGJigvHxcUqDt8pAA8afVZTB4C9qOsQwhDcL5WAwflsG1hWmFfbtESGp0yD+QuFxsZ/JZL44stjOZDKZzHMxb95sUdkltndWJD0qMpeOJO+qHyvO4i50M3B1UzkgyqgGxlRYNGHVfLWxT1zMz5nwflmwgjCamkiGTw1vxyzej8IDFUY1cDkGflW6K30tBgLQLBBNGC588GYd4bVUkVcC+4M7yjWEDQRBicB+MWoG/1QK/7UsaMSYsMCgGO3BaBRjPLqz3oBxNQpB4OWK0STGDbxicNlIgzXu+o+lPPTBYFQxxlUQE3oKOJzE8IwJNfNYTp3W1laGh4cBuPDRRd4qA81ifKOw7TEh0nO7bP7ThZFgdIvxVvSDzebnTIcMBPtM/WAmk/niyGI7k8lkMs/F3K4IiVm9Y9vQdCjZk4ZhutiZNR9XYX8wxgwWVNlQ4XIMnC+Uw8HoDR4/2V8Yh4LRETzL3C/G1E0X27UTIzw02e6G/pcNyp9UlO9VlR9WldcqHtG4EoWu1K+9YWBp3GbN7yR5UArX1YdpijRyUyOwjLBs8H/UCi7FwNeCosmFvqmBTYQ2MQaDL2O24s73cnKtj6ajUIAl9TjJlvkqZX2OvVe81nArrUcCvFQoJNHfgE/d76Z+JPmzjy5iwKvFo4uc8GgjiQi8VBj31asWn5e+9HXLZDJfDllsZzKZTOa52O1kb+CjKW14k0ZFoGKwan40WeDHkpMm7BPvtRagEoxmMV4vjC2DZfWe6gUVOpNwX0lHjIupiaT11Aj7xVtGJky4t6sbuhA4XHjOepGdrPLHUdDUrf1J9BaSKybcjcJtC2hqAhG8faRicALjuxXloQnDQRnCO7dHVTgo7gCPmdAfjE6Bq6k5ZSB4JrtmEMWrBtdSE8iMeka7WTyy0Y43nNQUDgZow18cLKu/eNlN/UjywoWPeLWinxHa4D9pWNr1+4b6wan5genz0JP6uuPz6/NMJvM5yGI7k8lkMs/FvLGd0X7kODLltOdxl3oSYSgYq+KLkgsKN2Lga40FcyYcLwxLcZSQ4hbTBlsmVMyPGTcMZpKz3XdyhH9XVfaLO94fl8Ivy8Dsrsq7Joxu6iMvxoVSaMDow6vtlvBqv8FgtGIcQqniLxoMmEXYELgWvQ5w04T7CE0GjQJVgWU8GqOAGDxMx5G9IY3lhJ2RmVVc+A4E7/SWdNRZAUC4pf7cDQcX2zPmneW7eTEdST74+ALNT3GqW/DneCv99br5v9EBz70OWU294AtZbGcyXwpZbGcymUzmmWya/9eRfr9gHtmAnTrA+XQkOa6ebZ5S//1PY8GgKAcaCqZMeDkoK7gQDOZCc8KE2ybMItQw5peWWBh/CMArI6fYEmgJcLYwjgT/7+0YeKcU1tUFcwSaUmZ7mkAXxloQ+sSPIZvx2rxOoCnAiHiMo8Td9CN4M8qfVZUfNihdYowjjITIhHmd3onCWDBhCe+oHlVhMHV8D4s3lGworO3Kat9Porcdj7u0YlzSgBkMF7bdljKtj7rLTWdeJBQFs1NTjI+PP/HrEsSXOpfT+00l0X+uolxVeW63uv71y2QyXzxZbGcymUzmmcynert6lGHBhM5dx5E9Saw1pgq8HoEpFe6bUDHoMLi6GekUYyB43GTV4L55x/WQGCfFaMeoINy9eROAloFBpju6ebsUBsVz3WMmHAvGDyoujn9SunCNAvuALYESmEPowTiRGjtW8c9hHj9OPFHYtl/cjfJPWlAVbzgJAp1itGDMWKA/5dBbcSe+wJ3q2yq0pY/RHFxIT9pOLnsozb+vmrvHEY/LLJkwZd5H3oY77Osm2874usH9xlaGR0aAvcdt2lOHN8CUQr8Yg+K948/rbvdIXpLMZL4sstjOZDKZzDPxvPaOGls0F6NlqgPsEv+zNWO73/mmCksqvFpR5sXHXo6lyrqHCg810I7RjdGa+qsfEuhCWbzutX9Dp4b5fhG5FAsuR6ErPYQ585jGSxXjcDCm8ax0tygPVagAijAkygMTWpKrbGasIAyLsZRiKBWMEmHShEYM2X4REWgTeKEwlnEHfUzhRKFs4kuUbcAEQqv4UWZncrmnk/itiNcePlD/2JaOQcEY1UBnitEY7pbPpve7HH1a/uUn9G0/TqsYK/jR6rQJA+Kfw6lCGdVHKwWfRna2M5kvjyy2M5lMJvNM5k3oSlqsZu68diSB3Sbeb71psKzCvmBMqU+J9wRjy7yDurviVXrzCldioEOUZjwTfbv0DLQBhwXW03LkqeERzlbghCirCP85BvbLTjQDPEYxKMaGwX28tQSMN0LkUysYS2L7iBhjBFowVhE+Uq8NHMFYwHPfN2NgQf3zWzA4GowGgeOFURG4WAb2CzQAD8y7rMdU6MBYw+fam4DRCNej8JsyMGvCu6XHVWL6/DZNmFRYx/8Nf159CGfFYEyFM4U9cdzmcdrw9c41PIZS/4nDkLhj/jxZ7E7xz3kzu9uZzBdOFtuZTCaTeSYLu44jl5LALqQuwo2F9GcL+CrhBQ004OMun2jB6UKZjUaPeNa6RYzu4LGUzdRAUgEaMV6vKhOpY/vM8CmmTBgq4F9VIuMqfBq9VaTetrFs9fYTY0IDywadeL57U30053Qwvl9RSqAjHVAupLaS/cEn5ZvTfPo7ZeBaKTQLHA/GQ/VRmm7xyr/19HkVuJs8qd4Asqzu3leAMQvMRBfS51KV4I0YWE+Rm4hX7t1W4UDw7u4awl0VrkbhWGE0ys6S5MWLF7GnWNRt4iuaM6nrvO7MB/HHefs5oiQ+kJOPJDOZL4MstjOZTCazJxuPHUcumlfzQX3Exv+swOgQ2AJGo7eBPExHkp2p8eKqhnSw6OMySyn60CXuDA+JdwdOptq/A6dGuB1h0uDXMaQZ9MAnGrhU7jyeNYVVhE48JnIuKLcJtNb/rZSXLoB5hDsmVDEOpOGd7lQEWE1Vf2+X7qAfKIyJJFbfrEQ2EK5HX6OMuAPdJt5OsogvRq4gtGG0Be/aPlzAC4XSI4YA/1T6C5FGgdvRM9zN+AuGaRXulMJwemFz7tw5KpUK09PTjI2NPfHr0yrubM9EtnvQ6xzdtWT5LOpZ8kwm88WSxXYmk8lk9qRe81c/jlzcFVWYT1nuBYMSYSAYV0p3fxXh6xWPX9Tw9180OJ4OFtfNc9GD5qMqJfBqoby/tsX8vTsAbJ48zUcxcDIYf1VV/qpB+etqpFWMn5UFH9WEOxEW8D7vTbwObwPhbvRcdQNwQwM31Y8GO/Eav0Y8fjJpQl8wquJuc68YE/iLh9bU9jFtcCj4iMy7ZWAiClMqXFBftLyugTGF5fR8tIpn1uvu+6B440ivGA3pcU6r0C7+WBvxo84SKIILcYDm5mZOnz4NPD1K0ozHeCZ3jQ7VaRPoTvWDz6JDdqoLM5nMF0cW25lMJpPZk3pUpM6iuRu7lbLbneIHlOsGwYx3So9L/KAaXQAbLKowWTNs9b2xAAAgAElEQVReKZTN1L99T4XDQZkT2ERoBioIk7dvYaq0dHQQ+vdRwdgnxodR+HEtsGowiFEDfhoD4+YfpyV4Z/RA8LXHCRUQ6MeYM1+Y3C8eJQHYxCv6GoC+AEeDMoXPyO9LB4xX00T7mAqKR1UmTbhl8I2KclKM80FpF2MTIaaPWxqouUgHF7Ib+IhNd3K8r6RJ+3HzesItBE1DOrsTI7ujJE9CxCsPF43t0aHdHBTj4XM41h3Z2c5kvhSy2M5kMpnMnizuOo40c/ezU7wOsEO843pK4UEU3okFFfFjxKNFOqAE7phwsjHQG2BWvYbPgAN4b3UFo1dgFGEzHUcOnBrha4Uya8Lf1Qrupt7oUQ08MKGG0GHeJCKpNq/A6MTddPCDwQMpkjFF4BjKPF7RZ8AnaVa+AgyIC+WFVMnXiXErCjMGl6Lw32uBisDxoCxa4HQwVOChSYqoGFWMFYNpYFzhQqol3GCnOnHe4I1C2TLhYSnMqsdnSlzML5k8UsNXF9sXLlzY8+vUnHL0jzOUDlZrz3Ct687287SXZDKZ5yeL7Uwmk8nsyUKq+QNvvBA8ujCnQsD4aS0wZYGBYPRgVEUYqWh6Xx+p2TR4pbkKwP0k/FqBFTxr3YHRJEafwJ3rfhx55NQwH2pBIz4O852K8o2K8sNq5H9piLSJEfBRmMsW6Fdjw9yBfmh+ZGiAif9bCsxaoILQgWfQt3DxP29efVePYcylaMmywUcxsGbCyUJ5vWocTJ3U6+lzWDAf5gEYrnivdzNsR0l+UQvMqVfyGe7yDwo0Y8yLfyOuiU/Ht4k79nd2xT6e50gyQmph+SxN4o735DNc6yb8sazu+VaZTObzksV2JpPJZJ5KPSrSkXTaUvr1nMG7MTCpgU4x9ovSF3bWDM8mN3nRfB3yeGG0Bu98Hk392i3Bs9QVDEWoGZwrlAvXXGwfHx7GDH5YjRRJlA4G6ArQV8CfVyL3KWjDHelZ8UaTcRO+Ji72B8UPBCtJlN/EDzebkvvdBzQEZVIDF1WYV2EtfQwQasC/qUbaBO6nY89l4LAY78VAb6oh7EsCW82jK12pXvBwMCLGhzEk99odeBHoDdAjxprBXfPHXpofcV6PO0eN586do1qtMjs7y4MHD574daolV/9p7E+tKnsh4u57zm1nMl8sWWxnMplM5qksmS8UFttiW1g2eKsMNIrxF5VIazqAXDGo4RV57em7y6QKSya8EFzpTRqsmh8ENmMsIBwWYyN1WU8pjKXav4OnRmgOxqkCzgXjYgyPzI+/UHhV4AIwgHHPwnZcY128Bu9w4U0qi9RdXWELd7R7Bb5TVRY00IZXAh4ujCGMCsZrFWW48Mf4nUrkcgzEJFiHUyb8kygUCH1AFXe4mwARH7FZNaEFP3ycTJ/3pvkB6UBwN/21wphUoQk/9OxNAzn1nHVjYyNnzpwBnhwlUfO3N57uXO9Pj/dZ8+05t53JfPFksZ3JZDKZp7KQjiHr3EoNH9+pKI1AZ4AZ88NAM//10SSso3mOu1VgX/D1xo9joMDYQJg1aMAd4U2Dk2L8aBPmR28A0HBqhIPi8YjDwXun34phO3ss4lGQGtAl7iAH/AjxbspBbwEnxJhHqAKSDiuXEY4E5URhdKRJ9SWEA2KcrHhU5qoKJ1JP9aHggvUX0VcfW0J9MEbYF5Tewj+PWRM68c9rw3zg55r6cxjwFwdlquobTBPwowqKN5QsITSZH5+OxidHSR5nBc9rb+6Rt25N1YvTzxTb/tOITCbzxZHFdiaTyWSeymKq/QOYUbijgdcrkU12HO9JFRoNOoNX+h3YdrU943041erdrSkbaYBmBViyQC/u4A6I8ZYG7P5d4uYm1cZGjh0+zECKowSBNwulCeMfy8CEwv0oLOLHidcsUEMYFGNJhWsxMICxanAoKIoLaMVz1iVeAdgksL+AvqCsmfdmV9Pn9nEZ6DZ3rMfTUeOUCaUZDxQqBjWEITGaxMX/lkFDsO1oy/ngy5eLCq8Uyhr+Ntdj4KZ688mSCa8VSpGc6Sm8q3tUhdUkfOti+8MPP/zM12hOhf50qLq1x9dyXzCmnuFad4ixnJ3tTOYLJYvtTCaTyTyVhVT7t2Xwbpny2cH/vDv9+aK5eDV8PbEnOeFXNNAWjH3BMINLm5FuUQr87QNGsxgrePRizYTKzasA7DtxktaieKTKrhB4pTDOBuPHtYL/tBVYx93iNozm5Jg3i7JuwpVU1zehgQp+ROhbkf5/6xWAB8VYMB/MuW/CjSicFG8VuQ2cCMatGDgQvIf7ngmfxsBQYfQEZQNhXb26cCN9zKb03XVDYDB4zruvgCE80nKzFL5bUTrSv/OnFWN/2HHm9wWvK/yg9A/08ssvAx4jUX00fD1v0BOMFmFbnD+JATGmntG33ZZc99xIksl8cWSxnclkMpknEs2PHTsFPopCV/CFyCbqDSUwry4we8VbPRrSWmQ0uKtCG75qOG4eI4kmbOLCvBtjXTzTvJlGaW6nvPaxUyOs4CLycabM3fI/r0QagSPi4r0Z+EYRuW/+omDW/ODxrvlRpCFs4C8IquwcDDZhbABVhUNpcGY2HTG+UwYOppjJMnC+cOd3zdwpbzcX8eMGhwv1sZ4k6KvilYiHxFi2wFjpHyNgtIrRV0BF/L+mACeCUsF4aIEOgT6Mi1EYj3D27Fmam5tZWlrixo0bjzwf9WGhVvFml6fRm4T05h5CuhEXBut7/i8jk8l8HrLYzmQymcwTWTaPWazhUZFDYrSnuETd8R5PVXudeOd0BV8rvBxl+5iwF7gaAy80VZgxYS3lp9sEVtSjGG1ijBTGjVT7d3rkFDF1dO/mkgoLJnyrotyyQAdGf/CO6h4xrsSCTWAsjeysGUwgdOECu8Q4VXj++64F1OC2BY4EQwuoiNEj8NcNShMem/lZDPSIMRqFfjFq5nGNBqCr8Cz3Ji54FWNRXYAXeNVgk3hTyH+PgdeqSj9wz9K6pHm0BeBkMKoIGwjzJhwSj7P8Nga2isp2lOT999/ffj5i6j3vFu8O38vZLlIDyl5REhH/uixnZzuT+cLIYjuTyWQyT8SPI+FGFE4UxjpChxjR3CHtFLiXKuouWmBGPZoxbsLbZWAOD20sJHF6tBqSODc68GNApR4n8VXFO9d90Obk8MgjE/EAY+pC/lsVpYr/ujsYbebT8NMmFGK0480is+oObQ2YwefkFaHBjE680vBSCbMKXyv886ohIEZncBe7B2VRfVr9d2XgR1uBzuDu90Y6clxPPwG4VAYCXnUYk/M9q14ZWE3PxZDA0WAsIdwrvb98Nb39YIAWDAUWFZqC0SlGIx7h+cYrrwDwwQcfbD8ni+bHkQ3iR5B7iW2AfvHGl71oF2Ml57YzmS+MLLYzmUwm80QWzfPQD1MrR71je9G82cKAT9XnznuTM/1mRXmxULrF6BZjw+AnZcHxYETzaIeP4uy4sE0CI4VRM2MixUgGhkceiZCsGnxYBl4tlKZdozLtBrfT42wWGA5KIS6sJxBIWW5JlX8F8DsNzJhHLv6hrAAuekvEnfb0zx4pjEYRGgReqShDwVgCXhXvBb+iwtsauBQDh8WYwev7JlNUphl3/u+o8O2qYngbSH8wqrhjPVz4TwQWzV9YHAi2PW5zX4VS3fmOBodffhV41NmeN9nOyLcm4b4XA2JMm+yZya7ntjOZzBdDFtuZTCaTeSJLeKd2vXZv2YR28Xn1doxf1wKbCGdFORQ8ztAjntXuCtAiwvmKUsMrAN9dLynxXHCRxHoHRiFwLBjXxifYXF4ihEDr0ZN0JxFp6VDwRGH0p+9ao+rjMcvic/JngtGAxz62gGYxWnBB3Y/Ri+IzNR7xOC3ebLKFO7lvl4FWMebwUZk1g31iHqvAuKKBBvMYRkcBx0XZRDiYavqOBV+HPJoq/mZT08icuZt8Lvja5T0VmsVbP+6ZcBw/Hp1ONX8H05FkAEyEmvjjGQhGw0vubF++fJnVVd95nEsREnBne+0ZX9Mu8RcTe61Etou3oWQymS+GLLYzmUwm8xnMYD66c3ostYnUne0F9dzvlhkFUA1CRxLkTcA9FVowIv7+f1IojWL8ZKWG4mK77v6qeBtIVeD9a374d/joMVYbGrdF5Ki6Kz2SnO5o/mfBvNe6ERgulH4xrmrg5aC0i3nNXpLYqwitGPswIsI8gT7xyMaYumO+mgRyt3jDRzVFW+bMlyQXUjTkWnR3vgVjPbnyl9Qz7LMIrXg0xg9BhRaD5gCDArfSTwIaUrTmxxpYNuGnMfCbMtCSViAXzfPhEdhM4z+n9u9nYP9+VJWPPvoI2DmOBK80XDP2HK4JKbc9s0crSRuWM9uZzBdIFtuZTCaT+QwbeBd2q3g2ew0Xmk3A/TRiU8Od7iWgan6gOJdE2kaKM6yZL0M2CtTMxe06Poke8APCOfVceH2m/eTwMBXxdpFVg8tReKXQ7RXLSYNSYQNhIDWJHBRYTVV/5yrGgfTvNKeFyVXgTwrlWxVNj89YwYXvPYW3y8A3KpEasKkwlx7/yeBOvpjXk/SYcSkKA8FfNFzVwO0IC2nqfcaggovuc1JfzfRjyMOFf/5TCjP48mQv8HKh9IhxQIxxCxi+JLlu8GaIjMXApHpbyeGXdnLbNfPnp96DXknZ7Wc1ifTu+jo9iTbxiM6z1iYzmczzkcV2JpPJZD6Dd2cLR4NHKZaTq71lcEeFVwvlgQmdeLRjCY+Q3FdfYZzAxfnJwl3S2yqsqX/TicldDkCTGGcL40EUPk1NJD0nh4lJSNbjI127vlvd0+CrifiYTLvApxYYrHg0ZEqF44UP2XRgLOMd2G9UvLmkFc9Xfz0oR8RbR26r8POyoAnjtgljKdaxP7hAnojCUDBGTVgBWvADyuPBGAz+yb6cBmzm8Wx4EVzMbwlcUXeqNxU+0kATxj4xps3F7zowFIwfVNUrEfGfIOwvvP5wUYVpFV5OR5Lvv/8+8+lrUuwyqdt4dt66N9UiPo2KQKPsHTXJZDLPTxbbmUwmk/kMM+oxj0MpurFk3kTykXpMoh+PVbQKHEjirRvjgQkDwViO7jIfF+O9GFhTj01UUk66Ce/j7sDzzaMqzKTjyHjyNJsGf1cruKXC0V1z8TWDe9FjFr1irCV3fcXc3e4RuKnCmHrUYwHB0uOoBp9pHxJvVllBOFLA2UJpF593j/gLh49iYKL09cgm8SPGQ6KMWqAX43xFOVB4W0h98v0eQiW9kNhAuBf9hUWr+eT7VHLMxbzzuipe9eeHm57vDgIvFP5CZdKEcYOzwcd/3omB7yex/d577zOnOwNCdVrEWHvGkWRXai3Z2svdJh9JZjJfFFlsZzKZTOYzPFA/7GtKum3ZvFf6lnqG+5a5extxp3TDfJ68ET98XAVOBWUK4U6U7Sq5Kj50U+Ji+VhhNAQQjPmbXvt39NQIm0A046Qov0gxCvBavWWEFjEaxHPkm8D5QllAOBGUGRM+iT5ks4ZQAXrTi4YF9S7wVozrUWhKznyRxmz+tFAUwTD+viz4h63AisKKCb+tFbRgHAg+Md+VjiHHTRgJPthzOijNGBFjIlUNPkQQhZ+XBYfFKMUz3Y0Ghm1HVqaisGneGFLBX5i8Wxb0BWMAF8fFi+cJRcHk5AQ3xh4+srAJqf7vGV/birgjvleU5HmaTTKZzPORxXYmk8lkPsOkBQ7uqt5bMmEiNV/0BeNmDBQC+4KvInaLC9BDwRiL4i0lwfhtzfPZjbg4jkAFocDYNO+hrgg0LS2wNjUJQPepkwwmMb0pwglR3ouBS1G4Vrpj3o2wbsKiwTHx48NZ89jKqsEafjgpuLBdT3V39zTQGdzdbRbv6l5HeKkw7qtHVrrFe7i3rD5yI8wgTABngrElMBr9kHLL/Jj0tgoVMb5d8Q7xKkINF/xt7LSEzAItZmwhdInX+s1EF76/KQM/qgUuqVcWruKLnLc1sIxnu+83tnHs3AuAu9vdjy1stsJziWTPbT/97Z6nszuTyTwfWWxnMplM5hFK9TaO42Gnem9aXXC34QeTD1KrxuHU1tEtxrgKB4JxWd21vhaF+xo4V3h0xCvtjCpKr/hozgrweqFcThPk/UNDbLZ1Mm3Cv64qx4NxQwP9GBNR+CAKXRiIsW7uLL9SVdbwg74edqrttlJ9XTswj/AgwpjBG4WyLxgtKfYyrTAsBhg3ojAY3F3uEuO9WNAmSgNeLbiA0A2MRY9/NIrHaQpc6HamSsM+DEuNJBv4C4p/W4nMWKDE3fgqxpIJKyK0YfSI8ddV5X+oqjeLIMxFYcOgTNn0DjGOpCPJKx++T/tjX7s22XtFsk5PYM/c9rOm3zOZzPOTxXYmk8lkHuGBeVa5HlFYxzPSw8FYxXPIa/jbDAVjVgVJx4pNwLgK+1A+0MCApLo98aVJn5DxKIqKt5jMKNxLy5FHTo3QhC9C1sSbTP6iqlQFHkRvGLmmwj0VFhEGxRDcaTbglzHQhbGB0Ij/XScQFX5eBroEBgs4LP6YmoAJFRaAIwGuxADqR5LzyYH/QAO1lPE+EDzPMo/wQemCuwS6g3/cWRM6goviSlrIXMarEOcQOlGmCGyZZ8sbxTgdlAYRQhLu3eIT9/Ve8DWFeRXupx7zb73qYvvmB+8/srAJPja0mtz2vegVY17xyfgn8Kzp90wm8/xksZ3JZDKZR7irgb4AUq/aU588Pxp8mnw8ertHexLX8+ZVf/uDO8MlMI3XA36vErlvXmNXAl0pHjGfPuZAgH8oA4s3XGz3nBqmNxjnC+VS9G9RjfhB4TzuajfitYSLaaL9v20FLkZhxeChCku7HFkBosCmGDctcCD1WPcX7krvE2MF7+3uEXfIZ5JT3WDwohi9Aj0YNWC5FCZV6BDl12XBRBROiK9r9mJMpMNNBRrwpckSuBoDoyoMFf6CYwYftzkYvMKwilEzmFJBxFtQBEPFeL2qtIlxofQXL0Mvu9geu/QRNzdqj3ztmvFoytYzvsbNuNu+/JS/bxOvbXyWaM9kMs8mi+1MJpPJPMK4wf7UEQ1wLQb2BY9EtAncIxDEXW2fdHfxOIDxSRRKhJoJR9LQzabBZmoFWUzHhwsI/UlUzqowlppImk+d5nyhnC68C3pa4aoKn5QuWs8UyqFgdOGCsU92hP2iCe3BaEqO9iZCL8Y3glJJgvaiChPRnd1gvlJTNXi3DMyY8P2KYuKPdQW4a8KqwVyafr+Surur5ln0KeCTNBAjGFPqonsDd/7rz+Im/g33tULZnwT/AxOWFcZMmFPhgQV+XQbeLgNtwQeDVk2oCPxN1RcwP4pCw5ETtHZ1UW5s8LcfX9k+HgV/gVR3t/dCxB30+aeM21TT431WZ3cmk3k2WWxnMplMZpvSfJXwUPrusGUuCk8GY9GEVjMmo4vIw6LMmdAsPlW+nJzmAqNV4KWKT5ILj4q2g2IcD8qJwnix4s5xvYmk69QwB8Rd17PB+EUZuFZ6d3XEXeAldZe7B+N/boy8LModDXRj/E1FWUyxjRqwH2MJrzE8jtErxn+qFTxUb+TYUFLdn/BaobxcMfrFWAYWgTm8laUbozU9/psWmAT6iQSEWQssKbwVC5+jFyiTGK+mrPcycDcK78TAkcI4hLKGcEkDFTOOBKWCMSCe3b6ngQY8e75sQktwoT5hwmYIHEq57akL7/FuGZjdJbifN2/dLcbCU0S5SD6SzGS+KLLYzmQymcw2s+YHfvWqvHvq1XkDycVep567hqHgbx8RhjA+jO4CNwFdwRjEuBO9Q9twUW0IFYSuAJ3isZTl9XUW7t0FoOfkad4phfkITWaMqbCIO7yvF0aXQF9IozHAXRWag1fV9QhMGymv7Z9HTzDuqI/zlEHYLzBSKP/PVsGNdbg0BoszHvu4ot54EtWz4RGhCvSLt5cMiAvn0xJpFBACNWAj1feVBlOpmSTi9YZtuOAuER4Ca+b58OXklG/hgnoj1QTeVKEd46UiEvFWl7noz/fRAk4F44EK/Uls3/jgfc4WyltlYD4J7ufNW3eJMf/MRpJ8JJnJ/KFUnv0mmUwmk/n/C+MqNJg3a5jBreiHhh0CV82HV1rTcWGrsC0ya2JMqvs3PWIcLYybJsykCXVwd7YWjcOFV9/tE2NWYfrWDcyMpq5uGnr7uKXCmHm8pMTz4KvAuUJ5Owam0guAP6m4yNzEOJ8iKz/aChgungV/DM0YS/eN3/7CWL6tyIyxMBVZnffP+cS/Cgz9r4H3Y+DjLWP0/4w0vwldp4UmgW9XlA81MBKM/7AVWEP499XIW2XglnrEZApf3Rwx4wZhOzai6b+A1wHuE+WYeBylwHu2KwaNAY6JMgf8ugz8oKp0YEwi3DHhNYyBYGgUDgej5+VXAbj74fsowvmK8bsY+LYoral//Fl0iz/maI+uUNZpFcsrkpnMF0AW25lMJpPZZkyFvmAEgam0+tgCNJkLuFnzIZgucVd2xSBG+EAKTgTl3ejDLwNi/NdawbFC+TAGCnzZsIJwpoi8FwOdYnwQhbspr3341DAHK3A+KO+WgarAG0F5P3oTyJgKFYN53C0eV9g0Y8ZgQ4RGgXvJMfbYijBuwuZd4+3/rXzi51s0Qij94x0To/Gu8uO/VfhbpXEAjn43cO1fQ3+v928HjDWBwQDdwYgaOIYfWc4iTANFcrLrSW53tj1n/lEUVgL04VGYWmpOmTE4GpQ5DfQG42IMDKRZ+VH1jvCW1A3ebsaRl172r9foLa7OzPPXg11Eg7fKwJmgrFoA9lbcjQJN4oNFXU8S2/ixaCaT+cPIYjuTyWQygDucMyaMpHq72yr0Bx+E2RBYN2+oaEwjMnPmndZz4gMvpbqw7BC4VAYCRrd49KQBbyPpFR992TJYUfhoVxPJweERXiqMi6UwZe4Ij6bFyAL4sPRfLwEgXNCC3mBUDdSMIYH7eHyjnFU6ez3SMnREuXUM6BNOvyIc2QdFv/Dng8p/bC4w8UXKFmCmUdj/3cDMO8rmFFz7j8r//vfw/b8SDv2bgkNdxh0N/N2msSaBkaDcT53jx4NyWwNVSJLfRfwyfnDYJv7iZFI9L10xmEQo8TaTqvghZSW9fwjQqO4uP1Q4UHiU5bII/7Kvk//r+EnmR29y4cMP+Mt/8WccK/xtr2h4ls7epks8o9/1hHdoFeOuPlu0ZzKZvcmZ7Uwmk8nA/8femwfZmV7nfb9zvu/e3vcFaDT2HYMZzILBrJwhRc6QWkxtkZ1IdhzFcWLLKceJ7IpcqrgqKklVsi05dlWsMI4ciXbJsmxtlm0tJIciB7Pvg1mwN9Zu9L6v937ve/LHeRsAQQwpkkNW2PieKlZh0LgX9359wX7e53vO7+AIP/BOdM2cP92GM6PnzZeztIjRkjjS7wTlWlpocyCLjOLVEtR7yAcUzhaKIShuzO/KIvP+R3itUAKwkoYjO/bu50QQTkdlEaEDmI9ONwnAefT6CvTdBP7nxoK/Xg3sz4x7ck+8ixHjvZ+r8+JPFWxbjFQF8kz4m7+q/He/kHHgh5WZhzI+vh9GOzK61PnWFRMWTCi2Kg/+TMa//F3h2M9mtN8lFDX43O8Zn/3JgsopX3BzxjJ2a6QrvdcpQKPj/sAIQDPGLN5pz/HKxoDABEqfGGsC2ySyiJAZXDQ37cNBuD+L11nmILxZKGZe0ZmJwv057DzqVZJrb7zKtdStPqy+/fJKFMKfq0pi17/vt6pZShpJqVIfhkqzXapUqVKlAE+1M3xw8WoUetUNdps4Vm/RhDYRNC1kuRqFdvNlNl4RMQrzBLxDnOIxjNcqIkI0OJL5psmxKOzMIjWEqZRsV/cepBahA+OIRnrUmMdpIT3iie/6CvaIMp0OAAE4tBb4/P8befZvFEy+ZBTLcPmEE0kuRWFYlV1ZJCaG9vEgrEbfXtmYBj8LfAi0Q2CiotzzpHDfL+cc/sWcLYdg3y74kUNGJV2jFoGPVSL3q6f1V9NBoI7/cC3wgwKpKtImTkNpxjgbhRmThEX013QxKs3myMFcfDDTZVyI8HrwOonbefieo14lOf3G61xbxw8KHMsjNeDCTYSSD1KnGLMfMATZBKymTnepUqW+eZVmu1SpUqVKATCVqCHt4hSS7WosmNAuxnTwbu+iGcMmbBGjCUNF2KqOkFs2T6CPZpF583R8nQyiGN2596rfCsrezPF2WVHn6tB5AAb37QfxJLg1+b9a6jyvm9ROopt3MZ6pK+8Vwux7xn//3xrnfztiBXTeLzzyqzktjytbJVIYjEXlZFDmTNiEv69+NZrVyAV61ZhAMIRBMZ4tlDbx6kfXA8r3/0rOL/4jYa7qr605GienvNe+JTe2iFFFEk3ch0J7iBh+GAhAkzndY4sa0ygR2KxGa1rrHvCEfM1gIghVLFFL/GvvBOXLIaMJp65830NOJDn1xmsM1+N1U1wVX0D0RlDWvo5R7krDlLcz1Ln4c5XpdqlS35pKs12qVKlSpTCDMfMUu8AN2GaMuejp5ltRWUXAhAExdqqxZgLibO4dYkwAW8QICH0K70W53l2OwK6q8kKhLJmb1HeCMHbpIqFep9LUTPvgNq5F4eOVwPfmkX7xZDrDhzQ7BZYTTq9fITfjX/+u8S//XmB6Aho2wcF/kHP3L+b0bhdWTBiKylxKm8ejUEmEjcMaOReF5cQBP6qRlbSwZjjROe5TT4jbMGZFeKcx471COZwZr3428K9+KvCfTjvvew7YLJGA0I6RA2NoWmzjFnwExxjuUx8gbcaYjAKpZrNbIrMIixifDz5M2oovt8nE+/MdaTB1KAhHDt9FtbGR1bk5zp0f+ophxk0pbX83fG10XzUNSX4QvaQZ7+mXKlXqm1dptkuVKlWqFMt4Ct2bUu1OMd6Mjp07E5QFg0GJHM4jzQqLCAv4YpsJEzIx6ggfySNXozCI860zDMXT6m4V3gneS17Xel+7e89eujJhQOG+zDhtwn2Pb5sAACAASURBVGjC9vUKnE/98QLnUD+SRxS4dtaIAfo/qhz7TM7gY0KbwF4xfrQSuVeN9rTRcRZHFQrCiggH1XgnuDmeS/WMAq9P9Am8Edws94r/sDwfYdRgTz1w+RVjfhp+86cDf/IyzJtQEx8AXULoJFLgh4wGPJmeAdqAhyrG0SySCai4mV8CZs3T7HmUq1G4lKohBT7EuMXcnB/LIm8Hpcgr3HPffQCceuNVzt1krFvE2egjUZj9OnWSjlQTup2aU0WoVKlS37xKs12qVKlSpZhORi/DeLFQ31RosF+Nh/NIhFSrMNqAq8G72d3iVYg3gyMBe9UHLVfMWEsUkRoQMJ5fLrhXfWhxNRE8JlNfu3f/QcYRjuWRGYNzwdPoVuDTefAtkGYIhiLUI1xDOfi3cx76aeXQz2RkjUoHPvS3KsKuzIgKrcnwb8PT3msI1wpPfxdNaFHjxZCRQapo+LVYFejETfjm1G2uITQ2Cn/pVzK23y+srcBn/7fI+HORyaj0i6+Ln08/XhuuN6y9tz5q8H7hf2eD+TKcPvWaTA0/8Chen2nDaIC0OAfeD37AOaJGI/BaITxy9CjgQ5JvByWmFLolPceBzDgRfLjyg9Qhfti4nZrFWP6GP02lSpW6WaXZLlWqVKlSXI0wFn2DYY7xQ3lkk7ohHYtCPRE1qgitYpyMyiZxdF2XGMNRGVDjmgmbFN6ISjO+vKaGVyW684wJE6Yi1DEKuD4c2bfvAGsmbMV4NSj71BiNyr1ZpEWhCeMawtIEnPiNgt+vKdsxOhthx9MZUdwg1/BUehXvmN+lxkyqcXRk8NcaAl0Yl1CeryktYowFZQLhbvGOdQ2uM7K71WscltLtKsbrQdnZKvzAz2d89BNgEV76R4Gp9wP94oztDFKi72aZxN2eQRgpvEO+irCIs82nTVjB6SMZAMYFEzpSBzzgA5jzBq9EYXe61nuOem979I3XWDA4ldLtVvEtknvUmePXvq7Z/oBkGz9UlSpV6ptXabZLlSpV6g7XSIQ3C6UBN6cHM6NBHVXXLnAqutntVR+WEzwJ3y6RHLgUlICwQ4yhIPRY5HKik6znuo0Yf72zSpYWtczg5nLqzCkA2vcdYLdGvhiUjtQhNox7cmMoOsd79ZLx2k/Xefe3jYv/JvJwHtkqxngysh3p/UxE75WfCEoRvTMN3pFuU3iqEunEeNWUpQiT+A/Dj+cxUUkcYdginog3iTFqwk4xWvG+eV2gqQrf+78qex8VYh1e/7nAq5d9fXvAU/h6IpRkiVWdY5w25clK5NEsUgG6MOYQlvBrVsVoSv8d1b8Ofu3bxXihUK6YUDWjev9DAFw9+R75yjKvBWU1LcBZMn9fBzPj9NdItzvTJsnbfb1ZrFzZXqrUt6jSbJcqVarUHazzQXi98G2OXWLMmzCo7rrWSSRXo5M5Nic6yZUADWKY+mr3yeCd75EgnIzCc4WyaG6oGxKRYzeRE2uBk9GX3dSBLEYmzp0FoHXfITqTWd8pkXej0iNGp8BQFNbejzz/9wpqk9C1DT7yfd4nn4/ei25LQ4mNiSwyF+FiFJ5NVI8m4EpaJz+gxjZxMzyShicruNmsIWyTyHDa2lgAIbpxvTePqHjV5FIUaganLeP7/77Su0+oL8Dpk34Y6QR6gGpaVh9Sul7Dt1z+XyvKhSjU8GRZ8bqLpD8DxmYiC+Y0lzw9xy78+duIjERFBgbpH9hCDIGlt19jzXzwtAm/LjVgmzjtZPxrDEEat6eONENZIylV6ltUabZLlSpV6g7V2SCcjsJ9eaRZoEVhHt8OGdN69jY8xW4SY5M44m/YlDYzxiOcCMKaeDf58YpxLDcycTMYEeaSyTSV6xi6zbhxrFy7wuryElqpcGjnTqZMaRFfOT4WhcPJ9L/yvPHv/36gvgidh4Qf/z8y2vvgvaBcw6sWniC74a4KfKoawYxzpmwWY0AcWWjmuLuGtGSxY52BjfHHQWkC2vHqx4oJO9XXsDcADZqGLzPP60ejd7CvNih/9eeVB/73jM1PKY0IHQItGDWUxkQU6cCo4lWUqyjBvFv9tvmPYsGNvCZ04DhCrxjzCA1pUc5gZnQIjJnQIf58g8ceBuDKKy+xZHAt+vr36+m2OAHlTLz9j3wRv4Nxu952c9oc+rU636VKlfraKs12qVKlSt2BuhbhTBSezCOFeR87WkLqiW9FzMX/XAHJPPrwoCYDN27C1syXu/QIjCL04KZWkoElsae7BWbqgTWEw5mTRGbPeqrdtXsvuxqUXjU+lQfORMUwdubG73zBOP7zgVCDvoeFv/xLSlu7EKIwbN4l78Co4JzrOqDmiWxMr2EJPzQsA+OFv/bC3NSuJbzeKsIlU/YQmRM3svPAHoks48OcEwH2ZD7UeEyNKdyw92Nc6VR6H8poBrqJjK7BdBp2dAO9XiPx19KAMYQwmV7DgwT2iS8I6sAHINfJK+sJNfgyoUdzxxQuIRzTyODDjwFw6uUXqQq0iTO2m4Cl1D7fqT4EOf0BZJLOD+htV3CjsPpNfcpKlSoFpdkuVapUqTtONXOs3dEs0poSTcEN1RZxUzifNim+mwYdt6gxac5cXsZRd73iCL5REwY0MhFh1CSZMzeDhrELo1+Nl1YDFZwA0kNkLPW1u/cdpI5wODN25N4PX0VYmzb++S8bRNj1SeGBf5Bzd7NTUSpyw4AKwjY1VgQwQwVORDfehzNf0nN3Ssl/s1B+t6ZcjP46F/FOtCQ84CjGdBoWnTDhP9eVAmMReD0ok8E77qejUAFWEEZMmDXhCQ2AsHrNePHv1Bn5XKSCXTe8y2lY1BGD/usc37A5jQ9pbsfIU82mBWMWZVNKtSO+vr5DLJljuGbCjz7myfb5V1+hLdQZj0ILMGuebINzw3ercSHevn/dIdx2k6TIOv7vm/usfStaMj8UlSr13a7SbJcqVarUHaa3grBZjM3pJ8CsuTFdNe8zg5vpNozLUWgS2KTrXWBPitsV+gV6zBnNzSlNPhnk+sbEAIBwlxq1NKyXAxfMBx5XzrrZ7tu3nxaBQTHmI9fT6tfaMo78bM72Two/9r8I1Rz61NitkRPJ7BqwgFM3nBYCMxFeL/wwEXGTuaTOCa8jiMHBLBJwcsgISht+4Bgno8D7y1VgAqGTdda1d9zPR+VE1ERn8eHGFoxHcx8IPXXcmLsIJ/7PwOYzkTwh/NaZ4wCdWMICkvjh3pefwK/DWhr6bDWjSTyFB1+MMxSVferDi29F5fsOH6KxrZ3VpUWuvfcecybs1sisCeM3Jdk71BiOclsD+7Xxf35Q+E7JDM4E4Yt1ZbE026U2gEqzXapUqVJ3kMajV0GOZO5izDwBnY/CZvW+M7iBtrSCvYrTRK6YsJh6xlWDHZnxx4WyClyKytngWyhX8BXrghvWgQzeikJPrmxRY9l8Kc5owv5t2neAg5kh4tWWTIzDYpyLQttDyn/5dzNi5gtm2hRWTZjHBwqbUgpcS6YsIoylWsye9B6PZk4TGRRjEad4gA8nZsBWjP1ZZAe+Or1Ir/spDRTAx/JAFa9UFAIPaKAJo5v1wUU3/eejE1QO/Ziy7TEnlPzRPw50191YR5w3HvDtnBluwJuBBfwgs0U9zV6/0zBiwj2ZD5lW8S75q4VQFWOXem+8Jhn3PuRUklOvvIDhWMADWeRsvIm9LdClcPU26XZ7Sq9vZ8Sbse9osv12EC5H4WOVSGfpUkptAJUf41KlSpW6QxQN3g7K3WpUkt9awo3iGp58rmveYBo3j31qnI1KMKEi3uU2MU4Vbno7MTapG9iYEtAAKdH1JHwxCrsq3k/eJMZ8NM6dcbPdv+/A9frKuwvw6i8WjFxzc5qTmNnRu88NBq8WSjW97hxoBC4GoTBP4ZeAAfHEuVt9GPIeNabMEYBXTTideNQZbvJPRwXx51rDk+0gkONr0zvT+2gB7srcnHbK+lCjV1D+sMhoBaIKD/7djJYemL4Kw7/lXfU6cp38MYywTXygs4ZXRlYQmgX6xEkkawhTgET/c5KuaQa8UhdWUqL+bFA++uijAAy98hJmwlAUdosfHM7cZK53qm/2vFW5QJM4m/xWNcl3jrV9Mfjm0CfySFtJHCy1QVSa7VKlSpW6Q3QhVS+232Sq58zT3QBsSb8fDBYNrgV1ioYYw2lQ0tJmSQwerkRqiYoxhqezzfh69PU+dQO+aKUqYMkctin0T4+xPDeHqLJlz14AZuvGf/yFyJXjxn/+uUAeoR0fUJxKHfFLJowBXXh3fFCMZoEOhTWMCfOtkzW8W92VTPxAWqPeiFdDFvAUvAe4T7xu8mQW2KVGJQ0tPh+UPZlXVtrF6y2tZpw1X2c/kQYZd4in3HOsb3+ESrPw1P/oP2Jf+HfGzAVP+gVP/ptwTGED3sX2zZHGhSBUFPZmbsDrCM/GjLbUKy8QDmhkGeVKEAYlciYKjz7yCACXXn6RRQwxmE91nFNBrpvoLWIs2O1Ndbv49/BWtXyHaiRL5nSbR7JIQ2m0S20glWa7VKlSpe4ABYNT0SsJy8B7QTheKF+oK5ciiBjr9d4Fc/NdE+8gz0ehbr7kZQlPmh/NjV6BqVQ9mIlKNzFVJTwRLpK5vmrKbjGGi0ivOq5udH2ZzY5ddDU1MBqNX/onMPa6kTfAJ386o0G9q6zmSfMm4IW610ma0kbHJjG2qddEOtIw4pEskouvfO9OZvtkFPamLnSBUzo2p0T+Mt4hL0QwgW4xthCYQmi1yHh0Zngnxkn8sJEB06bs0EhFoFW85lHDKzI1YO9HlD2PCzHA2X8asOA1lmaMBXzQc5/6dY/4c46YG/o2MbKE/FtMXydd23kT9mWROt63LwxajzxApVplbmKc2QtDLBtciUKrepr9euF1kkz8UDV82yqJ3daEN2PXBy2/nToZhJ2ZldWRUhtO5Ue6VKlSpe4AXYxyfRnLM3WlZrBLja3qJq8deKauvBeEGRNqdmOQbhVhszj1YwnYLsb+3JgInnguIPRLJBPvd6+xvnbcGdE5Rr8YqxGq5ijAU6dvVEgC8Jl/Izz3xz5F+Zd+Vjl4yOsWmUCLBCLCCp6gHxBj2rxWMm/KoHiNYwZhS6KdLJoTRZrwgcnLUehQu475U4xBNXaqMWqwSyNXzf98hhFF6cN4Oeas4ei+TWKMmUKEKRN2qLGMEKJ31TvF2K+Gpvd9Mghbfyqj0gxkEBf8ejaka1ozY19uVPBlOoqn7dMJryhpoHIJoS/92oBzJhwQY5tG5lAK4Gylmfvuvx+A+qsvMGbCnEHFoD8dpIaSwd6ixshtzHabuJG/Vd+JGsmCwUgUDuh3wNWXKvUdVmm2S5UqVWqDKxi8G4TZ5GOerkTuz42taqwZ1Ex4PI98vBKZM+G5Qpg3IyQkoIqxVSOzafX5J/JIMPhycHp0hhM1FsyrJk4d8W5xnTSEidCTe31jzYSLZ84AsH3/ATa/E/jir3t2++DfVLY+6ul1izjxZMaUKsbJ6F3tvVlkETdmQZzksWaeKj+QRR6ouOEWfBjwzaBsSwYzpGtSxWsLm9WNb3cihkQ8UZ4z4WhmdBFZQJhPxBYwzps/z9NZYCoKc3jyXCQUXw9+3SpAdw983z/J+ZFfyejsdLPsGyuNa9GrMV3ixrotDXvOJoRijl/P9WvRm671IsKpKOQm9EhkFWHC4Mgj3tseeflFVhAaMZbx9/dAHnk/+CGkP3Wzbx16bMeYv43XbSTd6fg2+uBTQdiTWVkfKbUhVZrtUqVKldrgOhmFa+YbGR/IHSUHUE8kEgMGUxXi0SxSICyasIwPRG4GQJhCaMeRgS8HZcI8te4TWErPWeAIwPWUtgF//kmDTZkb1skoTKbhyG2D+/lnv+gs7c1PK/t/MONgZoyY0CJOCZkyN44zwD0aGYpupPeoUTHjUlTW8KS7UX3Qc7v6ivJX6oqZD2hOmpDhybIAqyJMBijMKy+71JhNJlcwqhiGsC3h/S4mhvgM8EQWmWF906PSntjXs+nAETH6MPZn8Pgeo5KRkmlP4TsSW9vMu9IdeF86ptd21oTWtCEyw/vXa/gCHfCUegGhG9gs3lXvPubLbd58+UX6xIkkC+Z97U6BfZnXSRRHOV67Jd1uS0SScIup1jQ8+e1a276aUu29ZapdaoOqNNulSpUqtYG1YnC8UB7QyIH8K83MvHl9oUeNXL/yMZsxZhIm72DuQ3gFcBfGC8F3na+ZV0u6iSwnUsn639CMp6GdYnSKp7m5+IDiPDB1zs12364DtG2Gvl1w4G9lLJvQbsZYcNO7JQ0FzuFbKRuAV4MScZzgjAnvpcS6l3XiiA9G9ggM413zE9GXyTRgNOPklB1Eno1ulK+YsFWNBYRFM/rEeM+EPoWPVQPtiRayjGAIuRgvFEqLOH5QzK/PGsLTeWSLGFcQZiJEEQ5mxt21wNCvBa58KaaBQ+NMwvi13rTI5gpe4Rkwf+85RjRPqdtTnSTDqyxT+OGiAag98AgiwsiFITZPXuNaVBrNTTfgdwKA81G8SnJLZeQ6keQ2n6Nmvn2LbS5GYUDLVLvUxlVptkuVKlVqgyoYfKmuNANH8692SvMmaZHNjd8bjW6M63i6GxBaDc4GN6uVDBrE2K6R+ZR0T4kP8n1lBcH/oxFoUqMWhaVo1A2mp6dZnBgHYODBA3zmnwnf//MZ0ig8lBX8WXB2d2PaEql4N7oN40R0o71dIo9lxq7M6yqGd5snIsxHmELYLZ4THy+UJYNH8sBqInS0itGvbm6XzZPiunnqvYLQhTETlQMSmYz+mHVFjN+q54yYcM2UAIzhPfcBjD258dHM6SOXTJguPDU+9cfG8O9Ghj4TWJn3rvZL0V/bzsxoSt3qFYRBjIvJkNcQRlEKhPHU7Z4GnpDAsCmL0aksWUcHg4fuAmD4lReJGBWx6wm2CtfrJK0YU/GrqyFtAgu37W0bK7f5/W9V0ZySs7tMtUttYJVmu1SpUqU2qN4Ongjfn0Wy2/ikafMUe6vcMDrvR6EDmE54ugNZ5E+DXh90bFe4S40v1zMUZ2xPpIqGr2f3ysMkXvVQYECgOzNGishYhNlz3tduG9zG051NNFaEuR6lFaNTvT6xitdaTgZhBTfTP5gFVvF6yqHMmMCHH6t4haRPfPjxS4X64hwTdkhkGmGnGKNBASETX3BzPgq9Cn0Yayb8h7oyHp18ctUUwXjXhBcLZSaZXAcYCluJ9OOJdDt+IDCBuyuRq1HYkRsd+Gs7ZUKDGUd/UGndAfU5eP/XAsckEhCuJaNfXO+Fu3mfQmnA18nnOHu7GaeD1BGOm1dC3jFlzWCbGL2PfASAl55/ni4xJtOgZC3hTNbrJO8EpQsYv8VAt8vte9st36YayZg5K72nTLVLbWCVZrtUqVKlNqDGIgxH7zrvzm6fGo5E51/3plRxxfwxJPPdADyReaVjFWdbH9LIc0HJxcgwJhFUoC0NGBrei3ZTbEyY4/dyYDZERhFm33DsX3PTfvqiMR7cqB/RyBUT5oLQBZwP8HJULGH6LorSjtdT9qlxOrgZbRCnqbQpPJ0HzgRhLAqqPpxZAa6YMBKdzLEKHJDIZRPagXtzT4AHxQ3yOvu6T4xNyQS2CWwl4hbcqIkPRj6aR45kkQJPibsxrkahRaBbja6UTL8TlNVceOjvZH7tP29cO21UMFoFTPwaFOnvXk6/ruPXfn1QcjldX8ErMTsTnnECZQFh/+NPAvDmc8+yW2DWFDG4fJOpXq+TRPHPyc3yQdfbJNt8e2okV6JTXaQ026U2sEqzXapUqVIbTMHgraAMqJMuWj/AyIxFR/V1pK8PRd9wOBt9UG+rGmMprTZgj0SeK5QD6oa1jpvqg+rEDsM3Rvan9LcNN4un6558twBLdXjn374PQEX2QYS3EiLk0cxN+TBCENhhjhEUjEGNnAlCh7g7rEWYim4Yu8VoVyeQbFHolsiICY0RVN1Yz+PLbPaoP36rOj0kE+OuhN87HYVcPf1tSgtrTqc+eAU34RUAhBlzysdBMaL5upoBjHNBmU1LY3apsSpCr0QupyHV/kPKwNN+wX//VyMt0dnlFZybXaQ7Ann6+9sxmvA0fxRhFWeFN2LMo3w6DzRiFKkO8/Bjj4MI186eYXl8hBYxEOPCTcOQ63WSSXNai91koj8o2W4WY/lDrpEEg9EoDJYVklIbXKXZLlWqVKkNplNRaBVH+m37ACOzGn1QsVeMqrjxHLqpslEg7BfjuUKYTo8ZRnkkj2xPS1EM39Z4ISizqb/dCHi+7Wl3nxhbK8acwVKAod8IzF47C8D3/PhB8opwOirdYnRmEM2HFHeo0Zr5IOMmYCQRR0aiL8r5clQ61GgUrzg4YcT4XF0YsYxNREbwXvp9mdcvFhAq+AFkRXy1+6wJDQL3amTYhBiNDjGi+NrwaD5sWTNA3FBXE6u7ivF+FC4YtGIUAnflxpIJl4KwPfMEuorQnxmT0a/twZ/MyZpg9DRMPRNZNHg25HRibCGmBBuqGJ1p+LGaBiOLlHg77cUYBu5VI+KLdNY6uhi4514AXjl+nHYx1kwZDfIV/exOgbsTDvHmRTZt4kjEW4kkzeL1mg9TY+YHwZYy1S61wVWa7VKlSpXaQFo0OB+EwxoZNz4wNbwauY6AA1/60mR+W98HJI1XozAUfQCwHeNoFhmOwqW0zMYQKmJM4wn5LD68OIeQYSDCRyqBc8EJI0NDkeE/iKytOYnkkYcOci56TWI/kdNROJNWxN8rkRdTTaRNnAm+S4wxb0czHIW1lLTORDfhawiXoq+P/ysVN88CjJmyNVVZ3ggZXQmV1ybeNzeDB3Mnf8ynRH4hYQ1bxFP6RWCHRvbmMbFMvEv9elQmTTicebWkE2NAjdeD0iduzP2xfgApgGq3sPfH/cI/91uRejBaMA5lRrfCAG6eA1BL16cJv4vQjNGRyCUROBkzjuRORCkQlgz2pSrJy8ePU4n+54yvXmRzIDOaxXgr3LACFfHB1KVbPi9NOKIvfogh9NUy1S51h6g026VKlSq1gXQyCLsyY86EHuUDcWqXzSklnWosR3ihEE4n5rZvlHTG9CNZoEDYIca9uXE5CH9W17SS3VPiDKMHr2JU8MS8gvOj96qnlyMGQ58pCPUFivowAMf27+dE4Ui/NRFGoveROzGOx4yI00Km8aU72zSiGJ14hUOAGRPqCF1i/GgWmE9pflvmg3eLBgvRDwcHNDINLEYYDp7c1/DXtl6lmML53vs0MofXQ2rp/fSo87DXcXwFnhYvIfSJkZt3w5/MIvMGlyNsEVgyX9neKJ5QF8C+H1IGflj51D/MaMz8LkEbfpfB8IPBGjDJDaNt6VDTLQn1hzAUhF6MFqARvytw4ImPAjD03JdZE6Mz1XyGb6mBqMDRzDgZfdnNutpvs0kyF78Gq3/eD+LX0XqFZGtptkvdASrNdqlSpUptEC2YLyrZr8ZVk6+gjNyq0Sg0irFi8IeFUjOhRYQ+3ORZws61mQ8k3pVFGgU2iXEprWfZldLSDDeEFXwgMOKd770aEXEje/m4MfeOEaJXSFr6N9HY1clw4nfvzIyjGlnA0/hLaXFNBaNHjE0Kk1GJCHepIRj3Z4EucW72DoUR8cFEBc5EoT39ehRPar9X43Vs3mUTtmSwRYwX68rvFz762I0j7qYTlWTFYMx8kPTtQjld+Lr5KnBP5vjD1oQmDMA7wWspd2eRFwplMHOSyKU0rFoTv3OwWhX2/o0c61NyPFGexUHlBZJ44J6Ez3MDgbjeH6/ixr2G8VoUBtP2xXYi7cceRfOc+eErnL10iT7zw9d48EVGN2t35un7q4VeP3C0fUBv+8Nc2z5pXk0pKySl7gSVZrtUqVKlNohOBmF34jtPRNjyAanh+ubIZfP6SK/A1szcZKUBPO/nCi9Hx8vtVU8jz5oPQoIgAhVxM7wsTrIYvyn17Ulm/lwUKKDaBoeOOPavZ+9+jhfKHMI2iRzJjaG0yXHRnHM9D/QJbBMQgbNRqODrypsSD7pRABHa1BfE9CTSxmuF0i/GIXUiSgvGSgaH1KibMG7CfPSDxdmoWHSiSQGciJqqMX7g6MQ4LJFlYFaEiL/OIroBruGVkgeyyOWo/FHdqzBLJlwIXjk5h698XzM3yesDlxF/DTNDRnviimcpRW7G+94r+H8LnizX8KFWA3qSUd6E35aoILS1tLDlgWMAnD7+LNMmxIQmHL0lsW4WT98XzZfdgF+HW5NtWN8i+eG443ETNpWpdqk7RKXZLlWqVKkNoPl0W36/GuMGXfLBFZIJ8/7tigmPZZH56ENxaxEWUBowtorxRB6YwWslreJp8WwyYYqbxGBu1hfNu8bzqd7RhXE6Ks/VfEHNjo9nfOrXq2zqcbPdsf8g06kXfSShCV8rBE096XbxhPzuzEkjUwEmEXZJ5LQJ+9S4ako1+bWZCG1qdAvsFuNKMo735ZEGvG4yVAgH1J8/AH9WKI9JpBnjUupqLyM041svtxKZRnkgi0yK0GAwSNoYifG8ZbTjiMDngpKZV0G2Z5Fu8RrLNVMMJ7dsl4iKr2rP8fpNsMiJfxj4wt8qWHgtMGHCVm70u+uJRpJxYzjymgnL5qZ7GaGGMB4kLcSBXjMGUm/7yvNfZgT1x0VxtOMt6lNjixrvB6+TtIl9xdDkupqwDy3ZHotC/9e481Kq1EZSabZLlSpVagPobEq1q+JGZvPXSA3fL4QVhINqjOMViU1iXDGvh/SJ+ep1W1+Rblw24YVC6TdfxNKKMWveNZ40N31XUzJbIPSqEVOHeQ1hixg7u3PeSAttevYdoNncUB/MjNEAV0zZLMZgqsE0puHCVuDzhafTPepr4nembYurZqyaU0XaBDarsYD3njtTqt2hzuV+MSjvRAFZ76TDiUTOXkjVF8XoVuO/qAQWgAIfzJyKwv48cgVfdd6dEu1GgT71w8s0Xrk5USg7MuOhPLInM9qSu2NRSAAAIABJREFU8b5iygCeJtdxzOKSKJ1d/r36F79qLNahUY3edC0b8DXtTojxa1vDjWoVv7aCcTUlzgJcRHjoI08AMPz8szRYpB1f736qkK8ijfSod+D3ZcbrhdKKH55uHYZs/pBqJKvmz99bVkhK3SEqzXapUqU2lJYNTgRhPH645IT/P2st8ZJ3q2HmVYEPMturBu9FpQejPzOGCh8ADMAqRoaQpyHDCwnvt0cdAbgUfTuk4evBl3FDOG2O1COZ74j3x88GeOYfF4y9FNktvvjl4hknkezcf4BhhDZxQ/2FQhHc0K2vF9+txoWoLAMjpmwRmEu4uIAnw9MmLOOLWiZN2IRxMijtOP/67UI5oJF+9V51zUCiL9rZpsbl6AthDO9vP5FFIm5AK4m48kJQtqonzAsmbMc3OAIc08g9mR9OhqLyUB4ZN+HPaspQFC4G3+C4jLAGzOF1nGX8TkAd4aGfUKodMH0FRv5j4M2gFOJ1mSW8490isC0xxgOOLmzBWAN2JXzjMv6YHPiJY0fJG5tYnJxg/MxpIk4ymU7/Pm5Wr/jq9n3iw5tXEw5x8ZbPThO+Sv5b1UQa3r3dVtNSpTaiSrNdqlSpDaUqniK+GZQXg35VircRdSEl2c3iXWzFe7e3ygxeKbxP3JYSYhOnT1xOg4qbMRYEdohxJXWSD2RuatvFl9yse6SY2NqLKaWt4wa4iveNx58xLj1jnP6lgvemhYFijdnLlwB46uB+VhHU4I2gxOipbMS3PGbAfZkxZnCyLoiBinO1mzGm0pKcMfOv9akn1WZu5p7MfMhvwnwh5kj0dP2e3MgTn/v9KCylmkcrsC837suNGRPOF35tmjHOmnJvFjkXlSpGTMbW++OwP/MkfMISUcXgI3nke3I3+esp+uMaqYqxOx2E5vHrO9aq7P9J3yx59jcjsmjcr4GG1O2u4l1vXyrjhngxOrUl4JWPcfM+uw+3wmJjI/seegTwKkklkU6axXgj+Hr3dTXjA5qLwANZ5P0gNMJXVUmaxD6ULZLjaZNnqVJ3ikqzXapUqQ2lXOCuzHgq94TytSAbOuEO5psf9yYDN2rCZrn9+uvxZDo3iRExptN2xAaM1QggDGgkBwqRtLgFMjPq5kNzK+n3Rs3T8YjXGx7UmOoOTifJ5ow3/oWvhtz+VzKaO6H9yhAxRho6Oznc2+sJOMIDGjmH0oTxF/LIjAmVVG0pgJpAI16D2C6RLQrXDHpx7N42Na6ZsF2NF5IhPpIbS+YDhRURdqlxUCNvpcFJX/YjbCZSxdgskTeD0G6e4B5Pi3Y6xBGH7wdl1NwkDkX/0dmAMRqd4jGYwfflgXPRO+qXotClnho7SQQQT+BH0ntdw1GCdaDzaaVtJywvwYXfCaymhDrg5nkZR/y1pu9lDbgnMxRPidvF8X9VjFngzbrwxJNeJbnwpWdYQcgNFk2wCG8WNz4gItAjfoDpUtiuxox99ZBk04ew2MYS3WVTabZL3UEqzXapUqU2pDKBR7PIkglnbzMUtlE0YkIT0J3e4li8PeXBDK9XSKSKm7V581RzJgqTaTCwhveeL6blNi04rWOTespax3nOzThqzhJne9m8YlCkv++l/zuysgAtu4WDPwLnUZ458R4APfsO8icxJ/dXxh/UnNv9Y3nA1GspbXja3mIwg29rzIBGhH51kzmbBgnbMa5GXyhzOvjGyFGDi6kD3irGu1EZMWE8kVhOBSXDOJ2GBydNuBiVf1tXlg0uRWXFhGPiHfXB1FFXc4xgAzf415Ppct+XG3s1sga8GJSV6ImziHfcT0fhnrSSXnCmdxU/oDRksOu/8Sty8g+MNybkurHtF6MR37DZiA9LzgJLidt9KVFaWsXowe84XERpfvKTAFx+/lmmVteuL76pig9ZjsUbn49e8TsC4IfVmglXbvo66f2u2Vdvl/xGtIDfvejYuP8kS5X6KpVmu1SpUhtWucCDeeR0kNvSFTaCzgdhT+ZJ9jrSr+82RmbCfLgtINQF1HxQsFP9scsI7cCMKVsxLidkXUOqmDQLVHH31Zd4zw0C9VRfeMP0+oBhPBF454veZznyPyl/serm/EvvuNnu2refUTwZ7hFjAu9Y78q9e74+wPhO8INDzRwp2C/GVEpynV2ttOBDns14HaVJYGsGv1fPGMDYlRnPFEqB8XQe6RVjJCptKbVuwegCuvEKUo9AS0qjKxgL6qjDEfPXeymxsHN8tTkYF1IHWgUeq0S68VXk/66WMWh+IOjHGDelETfFnamvneF0ki6g92Gh+5BQbYPpYedsNwFmvqp9Jgq5OBUkIJwNSge+9CczY9iUnvTcAjx+9120D2yhWF1h9KXnGMURgPPmZvdMvGEBetWYMj+UVcQZ4kO31LA+jMU244lCcrs7L6VKbVSVZrtUqVIbWu3indo3blrasVE0Z96rHUy35KeSiarcxsicCr5gRXH+82oyVcvBMXIBrzwg+LKbm7rZuUCnwNVE7qglCsYWIlMIrXjaakAs4M1/nuojP6AMHFAaxTc8Tp45BcCmfQepIdwrkS7xnvHOZBCHCl8oc9F8OHAyDWQWePL6RhDOFMKY+RrzTrzuEDDGog8Nvl0IK+YowHaBAxppAoZNmEqHjL9Q8YPDNolUBJ6qBKoYr0ThmimdGB+pGPdk/tiXEm+8JV2TJZyWUsGJK+vLYgYF9qbrPGJwLZFY2hQCxhUTWlNPvgL0q9EkRoGBCAd+JuMHfy1j/xGhAT/QCG6wZ4BNKeE3/HDVjN+laMKRfZvU0mp444ooH/vEUwBc/uLnqOEUlzGcMT5njkwEZ6RHu7GmfW/ilZ+/ZZjygxbbLBkUf45/X+Mm9JdGu9QdptJslypVasNrvxor3Ljdv1F0IQg7MrtOdZg0R+6Z+RDam4XwhbryuUTGWIrQmljJIfGoJ/A0vAG7nlwupDrI+rrzKrCNyEpKkGcAxKsmjTitJJIoJC9Hpi9DQwcc+6+9svB7QVk1Ye6ck0j69x8gAudNk5l0msdbQThnwhK+sfI+NVpSiny/+gbLXWJ0i7FosIZQ4Mb2ShAQY8mECROvEeXGg7nRqz7894W60qPwRBY5l5jSnSLszyKnTTmWRdYS/aNNvJ4xoHB/5in0em2kCd8KOZKwhlVSIo/3n+/PvHrSgA+vThrMpgHUM4VXStbg+sFn0YQlPKXv2yRUG4SrUdmi63s8vRpSN3/s+sd4Fq/YgJv2AfFNkR3iNZeLUfmhp58G4PIzn2MF4YnUyT8ZlV7sesVKBLrVeeTgJr9fjHejE1zW1Sw3iCTBjHNB+NO68sW6MvV1/n0F82VL/eUym1J3mEqzXapUqQ0vFTeEZ+PG+b+8wrzesesm4zIRhUYzni2U14PSKHAki9e3PJ6Oyplk/gyoynpC6jWRZfOKw5XoHekqxhKwS43XglM7mvHKSae5Aa1gzET/vQxj8HHlf/gF4eDfzqi3KTMm9IrxKVtjeugcAFv27Qd82G40CBWE8yacCkKTQZMIT+XGJJ7Wb1WjVT21Xk3d4t2ZV1Mm8YrQmgjzUQgJC/jRPLIjLcu5HAVN2MIns+B3OoLShqfqu9UPG28Fr3lUUrf5XHAKSDOOLdwsxl1qmPihwwc84WqE1wvhUhBerCv/vp5h+BKeOYQ1E2ZxRva56FjKakrsx8zvCnTjhJQm8UPEcjCGPx8Yv2h0p2tewUkpGV7XWWX91z4kuzvzSkk0T6oDkD3+MfJKhbmLQ8ycP8ufBmWrRAwf9LwahKX0EeoSYzol3SLOD+8Q4+RN6XZT6udPR/jDhRojUbg/i/xAJbLp6/zzmjF/f81lsl3qDtPG+clTqlSpUl9DO9WYjD4UuBF0NdEu2pJxWYtwMQrvB2VQjU/mkUOZ0SNOoPhIHukS4z416ng14c204GQ+kSraxE3aZFpms4qgwL4sMmTOwZ7HDXqOm8kC4QI+4Kh48nvxaIXBx5U68IQGehROXbpALAqqLa3olm00JoTdRHo/EZhL6e8u9WrH1URLOaLeM94kRjB4LwoHxd+H4mnvSErEm4GtGIeT0b4W4HIQOhX6NTJsSjOeCg9mkZmUji+lwdI8VUMazY3ta4XwdsjIUjf8aB6pA5/KvV4SzdPp96Py2zXl9US/+aQGWjAGibSlQ43gJvmdqFwxN/X94ncUesW76YU3eRj6fwK/88vGyc8GVsWX9LSJUVOhUwzBrw04nnAMYQdOjVnCDwYZxvtN7ex55DH/zHzxcwyhiJE+A37HZyiZ6S6x68k2+MFrkzhZZR0V2Jj+/POFcm9jzhN5pF/9QPv1NGbl1shSd6ZKs12qVKk7QhWBnZl9VQf1u1UXbkq1zeD54ObrE9XI3puqJVdNaBfvPDcLjOFG+RNZYCItTanh/OUO9Y2MGW765hF2qnExKIusG0ZhW6qcdBLJMCoYa8OR+qwbxRV8w2JruuarEU6c8gpJ19591ET4hEYE7z53EROdww3eNjVmo6P+ehR6Mq9OLJvQKRBNOBM8Ac7SsKYBNYR7JLIl9wS1ZnA8KD3qC3ieVCeo/Enhq9UbUyp+qq48nEcyfPgzx+sZhvFMkTEPNKQUeS76psq29FzN+LVtSen3nHnXfUkc3beAsCeLtOIHilGUAmeEV3CzvmZCxUDFDzcLwO7vV1AYfdF496RvnJw0YcaglpYHWXqOzWnYciz69zHg3HFFWEa4/xNOJbn6xc8hwDS+rGcyCt0Crwc/dHWLV4rWUZmt6XMzoMa56L30c9H78h+rRHZXs29o0HH8A0g5pUptdJVmu1SpUneMdqtXJL7bF90smHepB8T72e9E4VoUjmaR1lvMz+Uo7FBLg2lunJuADMfrtaaEec28OjKN0JMSzzpwRCKvBF9lnuPkjG1qXDAhJrO7ZHD2nwZe/mt1Zl+J5CkVD6RFNQKjaXPkwL4DgKDqveJ1hrQbZjeHW8V4Pvjr6U3vsUl8HXo02CqRC+bJeQGMo2yRyDYx3jClk4gZvBoUzPvOzcCO3Hgoi1yKPtQ5arBqwv7cqIsn449W/K6AcsNwLyEcJGLA6SjsVGeB5+JDiznwYG5sFu+WP5pHpqJvcyyAs0F5OA1aNqTOtQE9uPnO8cPAmhmz+LXevEN46Cn/Zp75bGAPAQEyg4lUKQFH/1XwH+YvRN+yuWDCdKqbVIHB7/kUACMvPU+xtEBPeuwEwmGNLJvwfvCedqP44CR4b30B4UA6pD5XV5rF2Cp2/Y7Kn1c18+ctV7SXuhNVmu1SpUrdMVqnQFyz7+6f+FeiMKieXl+OvrWwXz2BvFmrqVu7RY3xKPSId5z3qHHGnFe9lBjQBXClEGZMaFG7Xsl41TKWIDWMvQc8Yz4ktwjMo8y8bMy9a8QADbuEh7JIA8aaCW/XldEoXDhzBoCB/Qcx4ExQtooPNwpeP1nCqwavB+VEUDrFaBdj3gQzY9GEbnUCyiJen9iCb2g8lOoJm8S7+W8EYTnCLMI2iXSkys1C6qvv1ciVKOQYzWKcCt71aBNjHl9X3p/IHxmewNfMGDKhiPBHhXItqi/+SQZ3AdiaGUcrxqeqkUGxlGgbb0Wlh0gdYSeBOYTVm8giF9KBoyX9XhfGI39VyXKYect4902/5kFgMzExyp3V3aF+DZcMlqIPwA5HoZ9IBWjZs5f+HTsJtRpXnz/OJMI9EqgDLxTKg1nkpZBR2FdWSdrED3WteBVpFjiWeW/+G9WEQVci5cSb0vNSpe4ElWa7VKlSd5S2q3H5u3jJjaXByO3qa7pPBN/AuGTOiL5Zw1GuD63NGaxGN8v3JqO5R7zHnuMLgC4lwzcVhBpuREej/9oQ2jB2izFpvkAGoAjGhV/39vCWH1a29cPRPNKlnrqeRFgwYfbMSQDa9h2gEWM1YfCagS1p+2GGp+mzwVPvgDBiypeT+V4yeDdkacBTuEuNJytGo95IkfdkxuEscrxQKuId5g78wAHwZnAMYYu6+VPgc3XljUJZRDgTlX71DaSDaqwl8sYwwgTKnAkno9NVHpVAE0YH/pnqwDheKG/VhcvR+eeb1bdG5mJMpQU6V8noIjKPsoKnzwU+KFrFjXOHGvN9yqc/7d+/d38jUjNj1Rx3WCCpQ+/1kgDMoFw2ZRJhCscm1vED1Z6Pe5Xk0jOfIwBB/PW+Z0IfTgo5EbxWMpOMcAuwEuGVQugWr9c04Cn1N3p36FIQZg3+U135/bryH+rK84VyJW7sDa+lSkFptkuVKnWHaasaE9FT3+9GTa+TI4BXC2V/ZkS5PV/7ShS2amTSnJP9bkpyTUhr1h0tlwlsyx2/VzG4mIxcHTfMJLzeZjwpnkc4IJECYeqZyNJlI2+DnX9RGVDoNaeIrNclNoUal86dBaDlwCG2SWTclBWEo1lARHg4M3pSVeMcSiNGE8Yn80CXefLeiTGRUIDbJfJIHhlF2CqRMUir5eFKVA5r5LWQsUOMMREGxSgMTkelLyXRW8Wccw0MaOQHKoGn8kiPGLN2Y0CzFeMHs4JmfJNjtxqP55G9FT/gLKQk+C9XI1vVH/t84SvZr0V4LyofzYxjmVdBcoxJlLXUNw8YjXgCXqT3sWjOP//0Twh5I8ycMRZedmTglEgaSPUPQ25GQ7raLeLf01aMAXG0SB3Y/Amvklz8/B9RC5HpKPTgh5Y/K4RtGjkR/DVPp/fj/XFhGuETFT9AXTFP8r+Rte1Dwe9WDIjxZB754Urk+yue/J8Nwp8WyqUgG46DX6rUukqzXapUqTtKVYFN6qnvd6OuRGGbGpeSIdqnxoQJfbdUSJbMySsD4oNpfWKcM6ULOGNOLLkU3a51irEUHQG4T+P1DnWV9R8SPuy3jKee7RgPZcZazRj6155qb/uvMppahd0Yz0Y30o9nXpvoujREsbZG3tRM57adTJqmhTDGE7mbREm4vY5kICdRrpryr9Yy3jKva0Q8WT2A0a7eMZ6M/h6XTNijkVPR6yOC0E1kwtzEdQi8n1B+BXBfHlGBbWJcTevhDeFLhfJqULoVDmSWeta+nKaCD2E2ADvUGDU3+lMIHWLUxR8TxGkg7wantHRjbM6M3SntN4S7CKymcs5cSqAFPwS1pu9zJ3ChI+OxHxI6DgsNnUZEWLUbKbOk/1XxgdEVE3ala7hVhU04L3vg8Y/S0NHB8tgoo6+8wCqGCjSmTvpC9NdwKSqLBrUI7yc6zEH1g9gBjZyNQoPdfrHNB31e3w7CZnHmebv44e7/Y+/Ng+w8rzO/3znfd3tHo7uBbjTQ2PeN4L6IFDdJ1L5Ysh07HntSU65UZpJUMlPxLKk4UxWXU/kji1OeSeaPcWo8Hns08tiakbXSlkRSFCUu4g6S2Hc00Pu+3u87J3+c9zZAENRmyQOS31NFotF9+97b73cb93mf9znP05yGZx/OjduyuN8ninjsChXebajIdoUKFd5zGFB/R5Jt8yAv68R5rRRuzoIwjlpkWV+Ny8nHnUm09glhQdiUbDR71DhjUeCSAX9VZCwlb7ETqqkQ9gxNxHiCSL+4IzOOIVz6y5LFUWjuhY2fVBThFY/c5vU4i0CvOM8cCQtJz559lBqV6r2S0jwkyOJJE+oI+8XpSI/3YFbyvtxYg3MQozuDzZmjGhF0wxYk95yF1/xcKRwx5bLDERMmPTLFj5jwrbryV0XE3r0vi6HAZiLxZBZhwoSzyfrxkdzYm0Vle6s4XeK8XEYd/VYpOWuhAA8b5AgQJHLEIyHmZKksuHB3qoefQhgshcsu9BDK9oQoHenjJYLEzxBZ4j3qTBI17edN2PDrGYf+95xdeyUV6ghrMZaTxeUkwq50/ReA9zVZatwEVegT2N6cs/NjnwbgtS/9OctECk0XzgxBqOdNOGnxenjOhAsmHFJjKb2m1ko0VS5xpdjmh2HC4KVC2K7Opuz68YCSNr8fyI1+dR6rKycrlbvCuwwV2a5QocJ7Dv3ijDsr2cHvFIx4pEVcMGG9Oms0KrunruPXvuTCeokCkgWHC6Y0EdFwMwanS12JgNubMp7bcGZpWEdiCHGR8Ac7sCb5dndnzqlSqI+D5LDxlzO6mkJpbcQGZgKXXNmqzsWjQbZ79+xDUmxgkwdhPm+Aw6BrZDBrPI+MsCwcK4VudaYliKwSfuZlh8cLZdiFoTQEuTaLjO4WhY/nJXsyZ684B9UYtqh17xLnK0XGV+rKYPpcDbgrL3mgZmzWUHyXPcj8TeLMEtXwTcAuhSaJUpwacJnIJl/0UNnFIz5vlTq3ZM778niRfb9QxpPCvkTYNu7MStqAxvhpCVxCGTGhRDhNDLXW82iFbCcU+0mE92eRQuIEUT8kkaAyjdDkQf7PW9TYj3lEBx74zOcAOP7VLzFfhN9cJLTx4wi3ZcaURUHOhVJ4IDd6lBW1WQT2ZMa4RxvpD0Pp8INSOZA5C/zofG0V2Js59+fG6aRyj/+Ix6hQ4Z2CimxXqFDhPYeaQJ/C4DtM3R5MCvY5k5XSljF/q1+7nohff0oh6VU4m/Kfz5ig0siGdlaLs0phmUgWaSYq2JthpSURIj5vFdCXRVqFAVv/ds7t/1/OwIejtny7RpPgOQu/cWOQ7uQbDWV7PznhMZ6QUNe/WyiFxPBlnhTi/kRICwv1dcyE3RqRfWfKaGpcJLKkP6rGksNduXHK4jk2AXUJlbhU2Clw3EOh7xHYkUpz6kQs4Xpxzl2TUDNYCi5wT81WSoCUUPp7cU5aDPytltjATLkwYsLjpbJa4jk40J85a3BGHWYtEle6ccZd2Z0FgW4iIgEbfu5ZYICwjAxZzBk0CUzMOMf+sGD4+0ZLihDMkmXkUc9oSycC3ywyVnvYU2aS73ytOu+7735a16xlcXyMU089wbTHJsGBCRNaxSkRpj2uR6tE7vnsVWvTL3G6MfQjNqpHTWgR2CL+E1W0dyWVe5M6TxXKc0W8lipUeCejItsVKlR4T2KDROvgOwXmMOjCPJGo0pqeehS/vJmNDKeByGaJj5s9SmLmEOZceF9mzCVf9gDOy6UwBSv5y074sq8ewluDcdaVNTinTJhKa9fcp2TNSjPOrVlYItZrJJYYznETRpKy3bN3Py1Aq8cAYAtwxpT3aRkNki7szoxmdQynW4PYLxKRgOdM2JcZB7IgzQcy5/VUdDNvkSs96sKYCY/VlVdLZcrh5aQUH1BnU+bckzm9OGvTyYDjnDRl6Sol9ZgL3YSy30oUxHTiHCvDFtFCpMLUCHvFHHDJYxjwg7nRLPH3dqA9C0J9ycP20iHOhMdmp01jxTuJRsk60Al4stcMoqxXpwvnlS87J79gnPrDkseWlVXiK3XtQx4Rjk4Q4bF0DWtAljZaA005d3ziMwAc/4svcglNpxFx0vNsmbFOnQNqvOKhLK/EJV6lbm/OnAv29vRh2eFEKdySGVPERvDa/PcfBpVIlflIzWgCvllXXruqVr5ChXcaKrJdoUKF9yQ2pFSS5XfIG/h4ivYYNWFX5ld9PqwGV+NSspm4h1/7QrIRdBCDbjvFuZxiADPxpPALezPDcQze9F8roXh2SwxjvvSKc/50PKE8pWD0JRI5ZFeezxlXepbmmTh9EoDevfv5aF4yiLCEc9KFTnEWklIOEdM3bGHRuOihuBfAukT6780iLWOW2By8aIriPF0q0y4047RLENmN6nw0D2/6fjU+2WRcduElC9V60YVDybqwCHylUCbSa+K8wVY1ni+VgQwG1DAJj/sUoYp3ERugboKMjpjQIs62zNmhzilTRKDHw4O9WUNGniU2CBcsIvUKhB6cGoIijAObxNCUTnKhjK9v/rSSd8D8OefVJ52DYkiyoBhxGrCEMEBksHcQw5rzhNe6TZyHfuGzAJz9+l9QX1pilFhjITZn29XDruPCt5aVOWvc9xVsF2PKYeZt8v9OmNCvUXzTKFP6adAkcHPuPFwz5j1I9+N15VgpXDaiOOgd8vtb4b2NimxXqFDhPYkmiZzl4XfIm/Vgkjo3apBJCLV73OLnaMA9VM516kwROclHyow+MVol0jVmCbJYIpxLA5GrU+RbQdgE2iRUakfoT4ppTWC1OU/+fsnz/3XB5SeiNKVRJf54XfluoRwuo+DFgRMnTmBlSfPq1QxsWE9JtET2SWwKWnFetCCMezPjSCkMphzvQVc6BO4S5wVTVktYLI5a2CO+WigLHj7pDZnzvqxkbRbDly6wWqO0RQXuz40WgffnxtFSGbNQtPdmxpYMNkis53cL5fFlpXRhiUgSWXZ4OIuhQyOe94KHd1wcptKQY0l45zOJQdQJg0mD0x5pLmsFbs6cSRc2pIKcXrliT+lOqSxNwIQrklJgJoBxFO9Qtn0u3rZP/knJ5Xr8DA2y3Z82JEsIfTiTKO7R0NmIeNx81720969ncWqK8Se+RZ1Qr0siNrEOjCKsV6NNYxMjvJnUdqYB1ePL5Vtep3WHk6WwN20Ih36KivbZNAjcwCqBO3PnEzVjZxZE/0ipvFAqL5YVjalw46N6lVaoUOE9i34NJfZGh3uom3POm1TtGYIAt1512wbJXQ2cLoUxj2rvDmLQcWPmnExV546zJUX9bRfngsXAZAsRpVdHkk0hElD6Mb7xlDBzDrJ26LpDVyICd6vzSM3YIM4skbRxE8aJVNPevWc/Sw7PlBq51bWSreqYw+mUuW0GXy+UZYLw92DMepDNRSJh5Zl6POZdWUkXMbSpEnF578tjgHMmPX/HOW3CVnXWp3e7doG1Yoy40kIQ4HZic5EDd+fGRSLdpJHo0iWwJYNtGj7qcY/6+FxgoxqnXFeq6Yt0HXKBjeI8Wo8HXpNSP3ZoWD8KicKgItlJxhE2J890u8AiUfTTRlhydqixDAx8WmlaBfMX4NUn4tooMdQ6JaHsTxJKfI6zQPjBhyzWpKOmHPxkqNvnv/xFSuJ1VRAtnUMmLFpjiFi5JTOGTThz1e9JS1qvY8vFW8ptzpnQo9HUWveIiuz9CX7FLhk8VtdFK6f6AAAgAElEQVTrnjjFegfxfqhmPFIz7s+rKcoKNz4qsl2hQoX3LPpTVvKNHjM26TBJ5El3XkVcxi0a/+Sqzw1bHNsvA8+Xyhp1VksUr5QOW8U5Woa63AWMuNJOWECilh3WqDNOZGQ0E9Jxi4byeeLzQSfXf1pZ1w4QBK85WQYmkjp6jxo96owfeR2I4chf6GiizUN1fraujLvQJkH6+8U44pqaFKFdY0BzBmHWhS0aBH2GIHuZCB0KC2mDcHduNGkQ3KFSmHI4XCjbM2dP5m+KnbtkSrc4SwKjHuvXJdCtacPhMTi6DBwzYUAdkSDirUkx3iXGZRPmTFj2sFlskDgdWEiDoRNE/GA7sDFLueBE5nnjdXfBhE5izVo0CP+Sw7Ard2Yl7cTm6XCpMVzarmz+pXjrfu1PSrz0lWHJxmNlwEnP2JQ84M34yrXpE+eOzwTZfvXRr1Gbn2WR8G0vpvXuUEdFaMGZRziYGa+WEQUIoZB3KrSIvGnuwR1Om7BNgwA3ypSafkyyPW7wg0K5Kzd2ZDf4L2WFCj8BKrJdoUKF9yw6iX8EJ2/w9/WLJpQu7MzerOKNO/Rc44cdSfXbTxcKHtaIfnWmidjA2WQzgSCHThDsBQ+/7xqcSROKZGFQguD34Dz2LEyeBG2BjZ9Ruokovgxh2CNTeciVHowhhBdNmUjDkRv37uNAWw1TuCNz7qwZvRp16ssIZ13pkRgEbCaaBtvT8ODBzGgT2JsIa92dH5RKbs6MwK2ZsSa9m7UQ9pllImVEgK1X2RhmLTYFW8R5MDOeLpQRg25x2oA3SmWeINiPZMaxUhixiDPsSoN+NZwhVzKPts02CYLrhLJ9uRSeLZVmidKcWWKzIB7Diz0apTwlYelZq/Ezj5qQEdeqOVlTejWGJy8TJxT9OBs/lVHrhNlBGPq20YknA0r8f5lINVmVhSVlijjJWPRQwHfceiddW7exPDfL+Fe/hBGe+XEUM6d0mE/q/SkTNihsUnixiDQYiNOU9bm+SfGeSB77/vSpIZMfO4Wk9NgcHsycdRUzqfAuQ/WSrlChwnsWIrAuqYw3Mk6Y0C7Oumue5rjLm8i2OYxY2C5mPawA0whd7itDcC8VShOhoI64sD+LfOZTaQ0OZcZIsibIVYT7TAlH/m2Q/Q2fUDpWCyNpPG+rlIyYcMQFEuWb9riPsaOhbG/eu5fnF+rUiOHUQRfUI1e6BWcVQbQn0sDf8VLJENbjvJoq1ksisWR7Fs//e6a0Ev7oWYchg6eKjDpXkj76NIpzGjhcCohzW83YkMEdeRBuIUj/lMUpwP25sajCwSyk728XypGkcncSxHKJyOlecthAqNEt5jxRCnWHezJjlUaqy7qklA9ZtGQm8Zd5j/SWWroekq5TOzGMumxhFcoIj/2swME2Y8uvKus/qqw7FHafPA22FmlIssWjEbSDK5uAaRPmPe7s1l/5WwD84E//hBxfKblpvC6WLfLZc2DB477vrxmHTThZCq0C3ZkwYaykhJyx2FQ0ThGGPOxH7qHWL3pYS66H4xYlQ1t/Qn93hQrvBFRku0KFCu9p3Oi+7emUcrFf32yFqHsQ6q6rPjfhMSjXKL3JE0mrE0Nz7tAiQazaiXztGnGb6fQnyQbSTpDENTiLCCdfhvEjjtZg0+f0TXXhI65ME4OSTcD+LFI0bG6W8XNnAfjFvXv4/nydJYdBg+eK8Dk7zjacDeqcQlkvzq15RPQVOIeyiKWDyBc3gkTeriVLCHvEeKJQHi+UVwplBughfMIXrrI0QGxGXi6VXljZuKxXuD03TphwyiJppFucLoWzKebvnszYIc636hG3t1fDdrPEFUU7fObOMJG3fW9u5AKLCM0SZFWJ69MsMIywQcPmkafP19Ofq3AWJYpspgky303kdU+5ULiw87MZ+/77jI51wjRCLanTWbqPQmB75lhKJSkQcnGOmbBG4LZf+jVEhOPf+y6cOZWuRXo8hLYsiHufOGOuzHmcBD2QG0dNmLQg0APqnDHBPE5gNieyPJfI9ZLDE4Xy9bryaF35Sl15utA3nSYtOxwrhVtze5MlqkKFdwsqsl2hQoX3NHolbCRvp7j9p8a5MlJItlzjYR33sHfkV5GTIRNmPMjujAtzhB3hpIXF4FBmDKb8a4iEjgsWySEQJO8lV0j2kAYxXDaYmhZaumDLR5WWHqGNUII3QGqajGSRFgklfsRh6ngMR7b3raO1dy0zFop2C1ETPpT81h9pMgZSMUwLzh41TpbCuAu7U/rK8UJ4I1kYtqjzRJmxXpxHmp1PNRmfrBm7stggDKQc7AVi+LGB0yZMArdlbyZ1GxQeyIwLpisbjXGL18XG5NduUtiWxQDj5TQ42qfwkbyMDG2ibn29xlBiQ/FdcDigzglXBpIn2yw2RndkUeJztIz1XCIIbjRFwlAZteg1wmqyQcMWchLFEJqT8l/SiO8Lwi3p8WPINEh0RpyEXLZIeuncMMCuhz4IwPF/H+q2E3nhi+4UDkbE9416fP8c8Zp7MDcmHI4sxZDrWYvynVaJ5BCIAqY5F161aBH9VM34TFOUHq0W58m6ciRdz5MpsWRVRbQrvEtRke0KFSq8p1FLw3EjNyjZPmrCgFwpsWlg4hoLCcARE1bJlda+ORdWOVxMw5CzBHnz5EneRBTUNNTZtSn+Lydi71oIG8oMsPd+4XP/Oufgb0QkXXMabJwhkkK6cAYk4ucmPYju6JHXAOjfs49jJmzKM9YrTFk8px5xuiUI+lSKAZx0YbNcsRzUJMj1EMKgCZlHOUyBc9M1HvaXyhhWfDA3mtOA5Uiyx7jDU3WlHdiZv/Vid0qQyxrgIjxdJHIs8b1HUknL/bmxTY1Bv/L22UV4wxeJ4c124NFCuVgGidyZOcPGytDl5XQNetTZr84IqZad8FpPp7U5j3JrFkOhG8TpUNgskUrixOnB2bNw7HfrnPtaSQvGTLL+jCGME2rxIrBGInUmlyD6BXBLspI8/YXPo2WJE9dNU5TjokU6iBLfM5GWuz1F8V2ol4ynYp83TNiYVO1lj3r6VeJ8KDe2ZpH7DTEsuS9zPlCLWvY3CuGkxaaqQoV3KyqyXaFChfc8etUZuQGtJHMeavWB6yQzhF/7yt+nLWLX3pcbYwAS7Y+XPRTOZpwTKVM7w7k3c573KGlpJerYGznSHXj4foE8eZMH1JFmoX1VKlHxsJcsE2prD9AlTksWRG4tMJSGI+/dtzc8vrmwSY2nLKMT6FVYp86LpbIhc7aoM4fEgKBE6sUowiZ1uiQaGWsa998LK3F+APMWCvFNanSk4cMmfCVB40gZhHFjGoS8Fj8og6TuVWe7Gi+WSt2jPfGSx2Zkg5DUX2EAY4MYf1UosyLcIkFWp1Lr4mkTvlso61Max7ZUxlNa2FvakiVkIHPWEI9TEi2Vk8RzruFsllhngD0a6nNLOploxjn7kjH0lHP+8yULy7FhaEopImMWHuhmgZZUV7/owqW0ATn4kY/T0d3N1KWLjD35GBlhNxKP194kwiul0pJSVp6o68p6douzuZZxzIWaw8kyNielw5P1iG98KLcVkn0t2iUsKS9Z+Nu7KzZS4V2M6uVdoUKF9zz6JKLRbjScKiVlC7+ZbLunJJKrPv9cqawRp09Cza17/Nmn8Q99gWAe6mkPMRx6uoymxsVE3kZTOXs9KbUGDJ5x5p8qmbcYdFtInx9L5HyBGC6cJgjmS0UM7H2uVjJ49AgA6/bsZdbg5FKJlZGqsVacix7ecQhi3aewVpzHC2UxbRLOlEFMmwkiWPMwptc0rBUNPFbE9XugFraGAY3Iv5MmLJfwZJmxMzP6Mt7iC56wSCFpxNTtVKedKLN5rK58tZ5ROLxSCt+oK5dNmBQBEW7LjHXinHOljVCjH9GSFuCCC98phAslbBfnYiksSqjorRqnKb3idKbEkjxtcpaBW9RoFThhSjNhv9mYBl7XraTAwM0fF1rWwNIoXHw0TCQFsdmYKePjXolTjSZgNcaEx2lGa3Mzt3/ulwF47d/9MZqu+xLOmMftOwQ+mDtrJSIhXy6Fi8kyUgD3ZcZFi/tvcXihjHbOTRIzAT8MbcRaLKZNTYUK71ZUZLtChQrvefTIlYGuGwlHTNim/hZ1cI4g3B3p75csFPCdyV98qQxvbrNfGbrLJRWXAAc1cpO7knvbCNXT8DRQGSkjOc7RPy759u8a3/wDZwwYR1aKbAC2irFWnINpCHCZKGJ53ZTBpGwP7TrIBYPdNaWWQZ+E8n3RhFkTDqlxyYUmnJuzyK++6MI2Mc64MFgqI0TL5YhHDnRNYFV6DjMlvFhm3JcbzdLIenYO5M6cwzeKIJfr5a1RiebwQqkITq8GwT5jwt484vnuzoxuCeJeEgT5vrzkpiwGJR+uhcVnT2a0Eev9l5ZFtjZGu8BfFRnfLJQxIn1kNY56DH52RTgKZbpO02nIdNCEbomc7404g6acTo2TS0S84zzCYk255z+Pt/KzXzB82VlISvh5QkXfpJasQvF9RXoNLbtw96/+BgCvPvpVFsfHacIZRGlL/u9Ji+e2WY1pF+7OjBeKUP3r7nQIDGShuD9VRBnQOvEfqzVyKt3klsx5vlDsBvv9q1DhZ4WKbFeoUOE9j0xgjV7x994IWLaoBT+o9pavjXu09IlEPvErpdKjRp8GOXqhFBYIknTEYnCvcGcqRfotirA/i9bChtc4FNEg2ZG7LAyfhcGnggFtf0RTU6UnBTYG8QY9BhkXNZJOMuAudcbGxpi7fAmA/Xv30qUxRHemFPZmxhqN+vM2jZKbJiJlZKs6Pcm2sE6ibv5ZU3pxdkukapy3yANvKNRfKpQOCWvMcCJsvQKrJTYkr5rygbxkEqH7GrJ91KJBsYawJwvifMZDsT5vwjFTbsmcbepBNnNndxbxheMesYJ7MieTeE5NwJjDZSLZ5COZsVdjEHXWo+Tmssfw4GghzAOjEuvfTKjVGc4pU7aLMU2syRLO04XySF5GY6Y4qzGmELZ8WGnrg+UxmP5Gmai2M08kgUyYUJe432EiYWUZGAN2HryJrYduplxe5tV/+f/QRJD+ASlYICxEowbbNTY6Pclz/XypNEu8zpYRtqjx/VK5KzPG/MfL1z5rYRFqbGRO3YBWrgoVfhaoyHaFChUqEMf5IzdQ8/NRi2G7/uv8Kz2eSlggkhxagdLDfvDFeiRqHMqMmyVsBR2EF3qZ8PmuEdgsznAajlxeUbZJSjeAM/anJQAb3y8sbVEWCXW0hVC/d2M0A7vUeb8a465kwHmHl1+PfO2+LVuRjlU0J9lyARh14bBFbfsywuEyhkCngS6NTGoDnknpIIJza+aowv7MGEcZs4gyPFbAKVM+VStRjY93JIV/zCIjOoY/I0ax+yo+N25wooz0D5fwN2sakuzT2ACcS77nYxanAo1BvjXijKXN2S4NW0dv+tq9YuQI4yj/sp4x6MIdufH+3OIUhWiXfAPlXy9lTJqwSUo6CUJcEl76ULuDiBbEUOgajeezSAwyZsBYTbj5l+OFcuLPjLxuSPqehg992UOpvoiwQZyNEsr4vMPH//5vAfCDP/gXjI2OkuOc9WwlkvCsC+uTX/1yIt4OLLmnNYZOi6HZI6Uw6T+6or10OO9Xcrlvz4w30oBrhQrvNlRku0KFChW48XzbR03Zlvl1c4cbSSR1j6HAHZkx4sLxQlg2SaquMCokO0hjCDIUXCdU2QVYKUNpSukWTiJ5g86xx4P5HPxVZSCFytW4woamJSwlLcAfFRlZIqq3qjH4RpDtbfsP0CfOOZQ7WzMG1NkozjFT2sRZL84LhbLgEZl3phReT4OcIx5vUm1EY+R5i8QUdeeCw6NLwp/XM3aJsSMLi8awwWaN6L7v1pUuDYL9bCm0y5Xq8MLD574zcwY9Ek86EgGtpZ/PCLvKjMPxUrgzt5Ws826JnPO6s0IWlyQaIF9H2KNGU3qcoTLU+CWHHZnRSeSXt+NsVmO7OgvEUGGZPNdjCM+VUS30nGV041iqXN+gTq/EoGY/xgLChg8LzT0wPwKj345r5IS3v12cfnVuEcOAN0qhzcOffc6F2z/6SbbcfCvL83O88M//L1qIKMdVadDyfCHkGhuMEyaoxGZutDTOm6KAZhFl+EpKJ6n9iF+lIQ/P9up0uy6FPo37r1Dh3YaKbFeoUKEC4Z1d9itteP8psWzhZ7422g5CEZzy8JkfN6FX4WJSBFcna8kmdWY8iNQqnHoicBnwyVoBInyzriwTw4wO5MmjWxIpJhf/tASDdXcKTTt0Rf0ukxWlOUX8zRCti83AXglif86UwTci9m/dvgMsAtvUOLJszBIbG4CHMmOJIMDfLDLOm/BMoZEhnkj9EnB77gwBJGLbJuEzf8aV2WR/+W5d+V6hdIrzehFJIN0Km7NI8TieWjgh/O4vllEas1iGHWZXZkxafFx6pIosexDHJwvlQBZkvIE82VTG0uulW4Pkh7If/uM+MUqE2/Lwbp81pYNQwBcIYn+iVA6ps1ad23Kjh1DXm9OwIoSFpC4wDjxeKMseyS81cZoSwZ9uyjjwX2Tc9F8qex+Ule+eQ3i6rqwVZ15SFjrOnMZw6nxav1/4x78NwOE/+gOWBy8mN78AzmmP0poNChdcKT2U/y4VjpTCtEd2+faUmjPp/EiF+kKykFyNfZlzolK3K7wLUZHtChUqVCBIXK/C8A2grB0zoUV4Sz07hBWiJR3pnyiFXjEOl8qmLCrLIcj2mMG8B5Gq4UwQZS17sqgRnyaU21mCXE/ReDCBYefsN+PO/t5vhB1hjiC+TqjgywjrxVmPcRplvxp1jTbEWWD4jcMAbNl3gHGED+fG/qaMeRceLZTVOM0qXHalXaKwpRXYnhkDEhXmSpTmbJBQz9dpeMR7hJWNwa9owYbMOV1GK+ULhfIDU9oIT/BONTZmcV+XTCg9NikTHoOZxzyyyfslynh2ZpGi8VIpbE1NmHVg23U8yL0aVpxBC0J+oRR6CIvFgDr35U6vOEdNeSQ3HqqVjCKsUWd1uo8+dS4jkSBjpPODsH/0pOi/dmCrOK0e13/MhVdM6UopJT3E9237sLLncxmtrVdSXgrgNYTTpXDU4n7PEhae7em5nnDof+gDbL/7XsqlJZ76/f8jMmYkruc0wkgZaSQZzlBail1NGRdd2KVOt0IvzryHJejV8u1/j8xjoHf9NWvaKdHoevwG+B2sUOFniYpsV6hQoUJCr/gNUW5zzIStcn0LyaQLXSmlok+cI6Z0qTPtMCAxbNiaMpyVUE+zNPR4UMIWIcCSh/q5lIhdke4/B5ZmYPUOYest0LIvJXUQec9O+LbbcAShRNisxqgLC8nXXLeC0SORRNK57wAbxelRmDdnuxinPbzYl0woHD5TM3Zn8HAeqSSjHspnF87W3HiyDIX5F2pGM84czpLDIXUkFz5YM+6uhTL895tLfqWpZJGwTzxTKKeSj7jNIzHjaBl55BddKEXYncXA4EWL4cHV4pwqw+O+GlL+9FvRmuwth0tlozifaDIerBkK/CBFMa6VeK6PF0qfwIeSb7sxwNoEPFgzbsmMYQ9ffBOhfC+JcH9uzCLMp1SQbRrRhP0SA6N1YILYBDU2QyJOtxluQY7rRDZ3O/B+KVkmKuVrWUQBzqIMesbH/sn/DMArn/8jJs+eZtFhuxgl8J0iVPnWtE4AMxavhrWJSYwT9pp1GsVDo28zAzGWNowd13l978uck5W6XeFdhopsV6hQoUJCX0pc8L/BN3pPtpDTZWRUP10XjpVCh/h1LS2THgr16TL80mtTHN528UR2nPMeX8sJgjaf1O1dtbjDGYdZhOakbDaIZJ7U0DU7lEf+74zf/V+EF0tlHc7ZlKe9mihdWYVzOcX1fTKPTOhzLuxV541TZykWF6i1tHLL9q1s0SiXeXqh4FiptOKsT8UzGyWG/qaSF3mth0q7APSLM+jKuEUc3kkTTplSehDsTzQZZ5OyfMmEvZmTaXipVeBTtagH365h57jkoUC34jQ7vF7GRmSHRpPmBo00kkWPNRt34YGaMaCR191A6eF7fjVFBj6QGdsyT+2MoTjPITxbV0TgQGaMGTxTxgDprbkzkIYMGyUxO9VZo852DTW9ICxNu/NonhxLkYsdOGdd6FLYmjubMfwqC9A0cP4p42v/bcnE98JM0oJwjtjgdGaxcenAGTWhk8gzL3A67rqXux7+AFYUfP93f5sZoDmlpLzmyqKHJ/+yCUsGry+V9EqsnTtcaJy0WBTj/Md6xpN15bkirlEj2u+Sv1XVbmCVhEXlzI+hbi97DGT+Tf6+Vqjw06Ai2xUqVKiQsJLb/Df0eJMO3y2VJwtlyCPNoU6onRnwrbryVKEMGSuEYtLDI9suzgThQy4dbs6cc6ZslWiKjMHFUGwLgnDNeWRyH08K9NLV1hFCsVZgDUZfDrQLYx6NjlGi4nQQzYZzRGxdhwhPWRDi1eIUDueSX3vT3n2Ma85pE141oVOD4PcIfLRWcqQUSoG6xXBjE5F2sQjsEOfW3DEPC8ykw1mL2+zKnEMppu/uzPheXbmYSl+WHV4slVszoyYR69gskf0swIOZcdyVP1gOj/iuFFd4KuWULxscK4OY3pEZLSna71QZRUFjBt8ulBELwr9VYTKt4oLDsEdayDpxTqS0j7Z0TRcdni2VoRI6FLqJqvYTRfjHC2BfZrQSCvUUwrEiElCM2BRNJ4U7x1lKEZCrJVoj5xAWES4fg5nTMPhnJXWPYcyGlegNE7rEGSXWZCHFPXYQG7Bf+O3fQbKME1/7Mm889k3OpKHhEucb9WzFn/9MGbGRawSWHJ4thKdLpUSYdmFHUt+VaP98o1S+USgnCmGwDAvS22GnGidNfmju9lw6LVjwt5YUVahwo6Ei2xUqVKiQIBJWkr8J3/axUniyrvSJ87HcuCc39mXRELg9c+6pOR+vGf0SdeZPFsq0RdvhkEdm803qvFoquzNjmeTnJgYsZxBmE5GqEwq0OBwtle/UlQWC2K/87EB9yrnwhYK5eTgozlfrujIU2ZxsIx0SMXfLCJtxPtFU8kBubFFnLc6cwEjya2/af4BhE+7IjD3irMqEMqVO1NJaO/BkKbQRfvmoKhceqhm7cmeLQjvCfcn2kQF3X5UK0qvh811256gJLxZBIhtV7tMeirLj9Cm8v8n5tVqZWgujjvyLy8qihWL7Z/WMXCJZYzIRzU6JgdTHixjC3KXO/bmxSsK3PZpudyap4+E/j03FJRNeM2FTFvnhpcNfFBk71bk9DcA+VwjH0rBrU9r4aFrzx+sKRIOmAC+Y0uLOrMUmqEugVSL7vEHC13xayWowddSZPhyWlAFxMoQjJqz2yOCeI4h44/XRhHNh7yHu+s2/B8BTv/1btC4usAanOSnrRzwGWN8olN48ym1GPeIbt6nzyZpxIIvr+/7cGE/Z6R/Ijbsz44gJF1xWNrbXwxqJzcng25Btc3i6UDaqc0tWydoVbnxUZLtChQoVrkKj7vznBXd4rRROmfBwzVIhSnyt7uGH3ZWO2HOBHZnzSG70qvNoPRTwzINkLXkML27TUKvbCTvKLKGSzhOZ2DlwW+bcUXMeqhlNacDyapriwKUvlxz/Q+N7v1NywSILep1EgU3jvjKPWLg+MQ7kMRi3Chgh4tvAGU3K9tb9B7g3MzZmEeM3WRrm4Uc/XQo7cufB3JhJPu3DpTAF9GFs1CDKowa9akyaMGrCOnWar7o8dY+Sn4/XjBOl8EypFA4vFMLjdeXxeqRnfDyPEp35tMa9Cg/USraoc8aicfOZQilwHs5LNmcxfAihWE95DKTenxtbr4pkXCvOSFJhTyfP90Z1ZoiPt6ZynMkyBja7xemQSN3Yqo7giMRzWHaYJHLTc1JLpwhNHpuQhkK9kEjz9wrlhMfFzBFaUgZJa7ew+ZF4e7/4ZyXLRJZ1J1EH30zkpM+Z0KpxYrGQIg+bgQf/h39Ce/96ps+c5hv/4p/Rll4vu9RoIyw14whzpTGJcHtWsoSwL63Leo2169LYDJ0wQSSKo3aos0qcJ0p928ZWkbDVnCivT1HOpZOZvXr9uYYKFW40VGS7QoUKFa5Cr0a5zc+rOvoNEwZNeDC3twyIDSWLwKZr1LpMYnBsX+bMuHDGhXViHLVQmteoczrZAy4ScW+dKUJOCdtHIfBiIbxcCCPeKK65Al9yLnw5Prv148prFh7tOQ9rQgZ04ZxAyYF96nSnTcFiul2nOa9bxnhStvv3HWBT5rjDaVNaJFJW1qpzLhHTXGC9RBLJCxZRhHfnQaKOlkKPOgMCp9Ng6JJHGU0D50xYk/zfAvxiU0mnxvDd3sx4KDfaxenLYnDvnEmK/XPuzpwNmXNvzflvWkruyoM0vlQq4xaZ3SMWivbGzLk1My5csxFbI0HEzyeLS4/AgMSa9IvTLkK/wElXpi02Ax/JjYO5c9iUZqKI577MGHc4XgbhJfnpTZz1WQxtjgMmwq7MuUWdVQIfzGK4tD0lh4RPX9j+OQWBiWedkbORcLJFGtcrSPvFtH45YVsaIMh/R8cq7vun/ysA3//n/ydy/jSGM26y8jiLOBeLyExvk/DIN6xO/RJpOHUPW8zVcX5jCPdkUef+VKp9vx42qkf5zzUv1MLhdRMOZVdONypUuNFRke0KFSpUuAqN4pOpnwPZHjY4WYYlovU6ROGUxeDh2x2xX0rNkdvU+V6ZhV8V6Ei53NMGkx4DgE3JHrCQiPJqghSdNUn17Nc8t28ZxTS09MHG+2CZ8PDWiVKZLPmLM5xuHJUg9xAK9BLwAsryzDQz587Gne45SCcxkDnu0KIRMzjjoVA3NhsTLix6vCEpsCZz5h0Gk51nyKM0ZoHwpj9dhlfXPVTTbowfFMo9uTGgsTHZnTn9GjGE3cm7vT6lkywj3JNHkvSxUtivhghMoTyUGw/UbCWd5M+WlT0SHvGb8grfqOwAACAASURBVPBuXz24WkuK7Sulsj0prTUN60ndY9jx5iyaOKeIjZwSJToP5NHAOUtsLB7Ko5yoRZwmIg3FTDheCrvV6BSYcuHVQtiVR8pILZHSfg2bSI3YXLUOCP33xvqd+3NL6nekiZwmYxVByicJkt1EtGi2CHSps+PTv8jm+x6gXFzkn/3d36ScnmIMST+TkCPsalJGEV5O1prGRqRZ4uRl2COLvC/F+ZXppGKdOjep0ynO94s4ebgWmcQanb3G0nXWhM40RFmhwjsF1cu1QoUKFa5B78+hTXLJIw7uttxou85dm8MZ07AWXOfr7nDalQznjtzowmmR8G8/a0FWLxFxefNpUE6JeLoBhbtyZ08WnmFPXu4GamYMfjEI4YbPZmRZKK6bU7XJDGE76CQG9bapM5WIlDu8XiijFqrj9JFojuxav4F1Pd08a8rRFN03b9APnHFZqT2vW3idFz3aLTeK8VIR9o8OnMOlcjBz1ims14iX25RI2oUyVO6TptyZG73XeUcb88i1hrDWXErZ230aaSTrUkZ0o31yS8rA7pIYPHQin/pMGfaObZlz+JoM6S6cS/7mkpa14lxC2aPOFOGzdoQ14nyxHmuySuCABtH9VpHxekpqmXGhlgqCloG2VJZjRH72KYtYPXeYSfGJ6yR84uF4j/jALb+YATDymHFu1NmbG91ElN9k2jjV0mtlAWGZaKQ8b8omgfv+t9+jpaubsy8+z3/4W7/I6MwMdYR+CaJ8oW5sEeOYKdvVmHZWst7Xq3M5EeVGnN9lixOHFgmryO3JQvXy22Ryb1bnfCLpjd+BE+n6VajwTkJFtitUqFDhGvTqz963/VJKYBh4m391xz0I+Za3+fqIwaQJOzQU3525s1Wdm9RWvM5NwHDy/GawUq0e0XfweiG8YPEAjYZBcKaedeYvQtYO2z4cde4F0E54tfNEzXsixZltKbau1ePnOp4qutsERo6EX3v9vgPJkwzfL5VunFYVVIOkd6ef89VEFi+h7M1KtmShYh9O0XotOAfUGXa4WY1JFzokbDD/vshQgYdqRv/brNuYBcGdd/heEZ7pboFJCwvFTcmyc9qEjRoE96VSOG3C52rGNnW2qHHahL8slBaH8US+G1ggvOzZVY/br7Fh25wy0OdFuCULkl8CZ0vh63WlLpFWokTyylJKeVnwuN0UUDMYLGWFyGY4x0wg+adXp9OYXo30mWgDFbbvF7Z+Rtn72zlld3jbm4gUkZJYQyeU5yZxFhwWPDZia8Tp2rGTz37hy7R0dXHxhef40q99ltaZKdpT0Y6KcNaUPjHeMKVXghxDkO1LqXmyU+J36rAp667akKjAnVlkq1++Dn/ulCDnl9K3DKWTnOuVPVWocCOjItsVKlSocA16k+f0esfbPw3GLMj7wR+SnHDBBBV/Exm5GuGhjoG7QRP2qDPuwhZ1Jl2ppbSIKGJxZhNBbviuv1bX8M6m+2so203A+T8PVXvgY8rqNmEYjZi5lIjRjNNFNB024TRLWF2eNmXaQyHNiQzmyTQcuX3/ARYQ9okx4zCCUJgx4bAnKZNjBocLxQWaxBkAJjyq23eoc29uqATJfblQpok4w6/VleFUrd5PkNXrofRGLjl8ra4YwkdqJRdNeKFQ9qX4wDhViIHF50ph0oWHc2OVws7MV+wltyTP9hLwVKGMlmGRuWxCv/pKdTuE+j5usYGad1glzqwLH8iNDoQJh/szY5WH3WcG4flS6cfZJE4nQYQXEJ60jCER5ogmzymUERdGXXihFNqTWbomkUdeJAvRZpzb/m5Gz93KkgpPFhnvzyNaMGIhBcWZcNguTrs4wyirgQsurAY6Dh7iU1/4Ms1dXQy/8By//6lHeOprX+FmLVFi8LVDo7V03IWzKfd6FWFbmUhrsluNUxZDnlejSeCWzHixvL5/O6wkQVVOmrKjGoqs8A5ERbYrVKhQ4Rq0Svw38TMg2+7waqnsS3F3b4dTJqxLCuW1mE/H52sEBk3ZmYUCO+mheM4QkW/9OG3Jr11c9f1b1Lk/xa41MkgayvbaZafWq2gz3P5pYTrZC4qryHorUT4znzzBSy4MJ5XxDjUuu9IhwpTDVFK27zqwnzrwSimsJYjXieXwKPdK5CR/t1CWCJLWh/OSKatwVuN8MKWm3JMZBzKjU50WSJYYY8mFBzOjpkF8569zrSaST/lrhTLjwqdqJZsFLnuox9vTxmbQIzLxqClLLrw/t5XrsCWR6BGHfoWHUkxjmzifX874+rKyU50tWSi5DazTsNV8s1D2Z06fROHMAvDpWsmEC8+bckfN6dEop5kHTOJ0Yh5Su6ezCuNv10p6cbqIfO714rxfjSWEvywzhorw9HQTlh/FmZTYOGZ4Gn10agrb1GihMSwpzHgo/jVi0zCVXk9OnI50HbyZX/93/5HWnjWMHj/Kn/zmr/MPPvwwI9/7DgoMl8IeCQ/+ZY+NhEgMSl5Oa9KeNjUL1zkxGlBY8zYV7xvVGbU4iRgx3mTVqVDhnYKKbFeoUKHCddAn/jOxklz0UJO3/RCSMONRVvN2t2nUVw9kzqjDrmRNyIDnUpPkTRrNjm0EgWpJ2cw71NmaOY+WyizQKLBpfNTVBLv+Uc6df1xjqVfZIk4PAM5wulUNIBWUCJH13C5RKPNKimHrFKfuzuDrkURy/0376ZZUoqKh1rZoPNev1ZV/s5RxyYQhjzSM7ZnTIr5SovNyqTxfKoMOh0ulldgArRJnW8qrHkW4Q40udb5ZV14shBFLA5kGz9SV46VQuPDZppIeheNJie9VX0mzOF4qM2np78vtTZuiJoFbM+P5UllukEiN6vgdWRQIvVJGnfr5q9oMJzxiGQvgUObckhvLHsOO7QqfrJUcsWgLFQvbRQFMJ0V5mxgdOIYwjvJqGV7lBSIRZB64s8nZjdOC84xH5GEmcRKhhD99EkFmnXP/puCp/7HglaUgrCpCa/LgTxBkeAp4XxZ185a83I1N2cBNt/J3nnyeu/+736LW3sHrr7zM7/1nnyFfmGeR2CzenRsCvFRcZSVJtpKRlLd98m0aWm9OUYtj19hJmiQsOa+UQp9efzNaocKNjopsV6hQocJ10Kt//XIb9xjCO/gjYsouWfho+69DtksnKdLg7uxIhSHjLsyYJI92DPXVCRW6ThDpjNg0fLOuXEqNfxCeX4ja91HCu9vaEV7vj9dKagIdRHFNE04pMGFRNd4uMOpByhaAYxbk1R0WLp5nbmaGvFbjwK5dgFN3oSA85bubMz6ShZVEgNUe6mkHzoQL8x7+7QdzY1NSX3do/Dxd6aTh9VL5D8mrPWTCF+sZTcChzJhzeKZQvl5XvlJXjrowkDm/1FTSSeSbnzThkZoxnAbvxiyq1/vFuSezlczzqzGgsY7fK5SxZA05aYIL/FqtoCaxIThcRnrJY3XlmVLZqEYtEfQ+De/8aylfemMWpwKvWxTuDCef9TJR796m4SHvwnHCCz2dLCwz7iiRDb49N3rF6cM56sq0x1DsPI3MbmcPxsUvGpMvOS++7MyVsdbraQxhCmfSUUgHDT92rHujKGcEYXNXN3f+43/K33n6FT75D/4hv/lb/4gD7S3UCZ+7Ag/kJa+aMmmwVuJUZs4j1nJn8vqPXIdsN0vESb5a6lvI+GaNTc3AD2mdrFDhRkZFtitUqFDhOuhN5K74a7y/X24kM/wIzn7WwhvdfZ3bDbqw4JHgMOdRKQ6R63zJo91xV1Kh64RJxAiiXUcY8VDFw8+dSHv6c/BJZ+RcPE4LcHuKnvP0/TkRLzfnjWbHaBxs8rBDvFYKpBSROWD68CsA7NizhzyvMW1hh6jhDJmwp6Z8vshYdmGHGpdFaBO4KTfuyowOcQ7mztYMQNiRhQorwD25cWfu3JUbfTifaip5KC9Zrc6ThfJyqSwQlff9AgezUNM/Vosym++XyrBFvN46hVUSPu2v1zM2qHNX7j90Q3RL5gxoxA5+va6csVRwk8Nna8anmkr2Zs40MGjR2LgrCxLdII93pPzw55Pye1vudOCMI5xLZHJtKrhZspR9rWHmGXXhg3mo3aMEmT1bCusVRCIKcIcYIyhTV1mAhl1Y7FD6PhRv9298KYZN68AtanFqAbxGRgtwEaEvtWJuwMiINssS0JROoz1r+eA//J/4f3/3d9iax0ZoyoWjpbBVIxnlsULT8CVcKoXhFPe4NWXCXw9bNQqUrm2OXE3ES3ZWZLvCOxQV2a5QoUKF66BJgpCN/TXe309Y+Hl/2EDXkoeyvUW5Ltk7XoYCW3oM6zVdFbc3R0TFtWVRDhPH/5GfXBIEe7uGb9Z4c5FNMWe8/nsF3/2vCqaPGj0YN+eRbpF7NAr2As04S0kpVUJ13Zw5PyiVIYvBvg6JQcbx114G4PZDhxhzqKefZ8xiPb8zXycjSnjqhLK6hiDGl1xYdGFvGiK97NFeOexB5hqtkcdKYUvm9CpsyuBjNePTtXKl5XCrOvuzaNysC7xSKN+uK6skcq0bsYt96ny7rtSBD+Q/uiAlE9iVOR/Ljc/UjA/VIvca4rr1KjxcCyL/iUTwT5bhX55Mr6FmgfuzkudLZaqMNJS6RIlNAWxQY6PADLKiODcl3/kS8ANTbtMYRC0JC9GCR0HNiEHpkjZIEePXiGo8mMOWT8fb/fAzzsXBiAY85spaDE2bqimHV4pQzB3nztzZQvwsDpxZoQxhU1ER+iQ+Xq+20t55S+YsObxYCutwzqbX32qJ63M5RT1eb40PZsbh8s3Z28MeJw9DP8dm1woVfp6oyHaFChUqvA169af3bU96DHVt/hEDXZctjt83XOd2Mx7FKt0S5Gp3IqIXSxhKZKoGFCbMmqxEuUEohJvFOGahjC8Rw5FCkO6hrzvlArRtFjp3w/4sUjsulFeGJNdrKOehkof6LSJsEmfagwiOu9DmsWk4n5TtQ4cOcdqEluRbnkaQlLqxRuC2zMLyotCqzloJe8iOLMp+So9huHXJ89uw18w5nDdZWYcG1il8KDduyoxJh5dK5Tv1DPWw5nysZhxKmc6NdT1VRmHPZnE6f4J3QhXedvPUKWHjmQfuzJ1HalFP/9XlbGXTtCuDtTj/qp5x1ISP5kZ/IqyjHokra8UZRag7rM0iXcaJDcikxN+VSCx5tlDOWGx8lgXaiWIkJ57LHMJZgz2boOv22JEc/pLRRswJKPGfEwOcZ1zZKkY3cNaFvTmsSo9XpJxvQbiYfi+6JdZkU9oEPlcKm7PIDh9Jm4kLKYVE0qap/zplNQ1sSJuPq9Xvyy7szK7EClao8E5DRbYrVKhQ4W3QJz+9b/tEKWxPivMPw2BSEfuuQ7ZPlUGClgk7QWsiNI8WmmrYI/auO3MWaOQmB6ltIVTpS6b0QmqNDMJthXPpS6nE5nPKKhF258ZlizZDITzbWzQUTyOsIEuE9aJNoFlCBVfCJjPncO7VINv7bjrExVI47vF4JTCDckdrzkdqxqALm1Nx0G51LltYdhqZ16Memd0twCWXlY3Ia6WwLfPrlgKJROnNnbnzoZqxPXPuzq943BuYdPhOEWUznQL5z9iasFmdcymqrkfj791qXLLI1X6iUFQieq+0KIFpXKtVGMdLYZeGmjxFpHS0S1hKZg2aPJo9JxAuE/X2npTpOnBQS9aK044zkzZKJ0zZLc7A5yIJ/OKjxvJkxEPen1nKT48TkQ6cORU2inMuZZSvF2hKhHwOoRtnGmG2LFGJzcGIw4O1khdShN8ahc2ZczQN9zZdtUbbkpXkeoOSktTtY3blRGfYYE+ymExXTpIK70BUZLtChQoV3gZrJd7cl3/CN/jlpEhv/xGqdulwzqFHYvDw2q8dtSA/4ymppHQ4U8J5V7aIsSxQk1CfF9PQ4TTRvFgCQ640JwtIQ/POgNHvGEuj0NQFvQ8rm3H6BY6UQd6a8ZWovXmEdoLY9wgcLoULFjnSc0QZS1NmLI6NMHFpEBGhdd9BJi18ursx5hDuyQseaG9i1KPVcb1G0cxOcV4slW5lxbM+5KFmN6L7VgETFgORe36M6Df3qAXvvea2YwZP1pUDGlaWNnGmXK6b7/zTYrM6w8ZKpXt/KuLpF2eW8Od3CGzUeH6PFRnLAl0Eie4S56VS2S7GrDc88UGEJ4jNR3OK8suSgn0gczaI///svVmQndd1pfntff6bM5AzcsQMECAJEAAHcBBFStRgyyVZll22OyrcVREV0f3ULxX12C9+6oiO7n7piHrpiI7qcneHo11VsmXZ1mBJJEWKM0iCJAYS85gjckDO9/7/2f2wz00kwcRAiaJI6V8RGQRzuHnvf8/Nu846a6/FMr6OLKXSBIyNGEsY75vQeUjYsFuIK3Dxe4XHC6qxIZ14FPgq2WBuwZk3z2NvXdNOWcU92QY8N1/jSvTN2PtRqZqv15/V3H8+m2xBM8njX0ev+Fpdb1ASoFt8E3kxClOWUmjUffCXS3W7xOcQJdkuUaJEiVsgEyeAN5OC52peJ36sEFbWIQyXotCrThJuhwlzcjO0DoG8kgYb29Mx/YQJ368q/18t0Ih7txeSQrxsMIVQIHQlBTpPHx3qarXr2E7Yr37X3duDf6iEBuFw5nfkvcKTMRYRFkx4MXflugkne/eKD8y9VSgzifQBXCuU8ffcr71lx05eaWpnEmjBuIoyRGSDCCvReLNQDgQvOOkSt8NUcTtH3Tc9GmU1o3kgKc/vFsre5Fm/E2bNn7vWNZ8bj/BSrhzKItuCq/J7g9GtrtZ/UmgU2BaM4ykzugv35J8x4fcrkX/dUHAgcyX6SKGEaNwbjKrAvHlxzaQJF00pMH5cKMtAxKMcGzAOBqNTjD4xwNiqxq7gpPxUIczj6nHdJ6347YoIw/8qsPnrwr6vejvnczVdtYfUV+HZqDwVIjkeTajiyrSmj4vmyTY/W8x5v1A6xKiar7cmEc6bMJo2PM1mdIiT8ZiGBkT81ORWthAR2JvU7ZEoq0VPw+pkez1FvESJzzJKsl2iRIkSt8GmdSIAH80ie0Jk0eBHNeVM8WEC4G2E6/RP34SRKKh9VIEFt5BE3PbQLsaXk7d3BWFD+h2G0ZzKU1aArfig3xKCpGSObUSmEer52jPvGAtnjNAI/f9CacCYE/jbqjKR7CeV9N2zeNnJAlBgnDBPmFhCnHCnPO/3TJhIFpKBfQ+Q4wq64FnLD2XGlSi8slijT4xB8TbAITVOFE6mutI1uJ7831346cCAGleND5XQ3AkTJvTIjcHUK9EjAR/JIsPqt381+mnBPRo5lSwLnxT2JK/5iUI4aUKDwD3ipFPEE1b+sBJpFOOaeJ71/RqpiPF4FqkatInH/l034elQeBwf7pn3incvkZk3zwi/DtynERFh3oQeidSS5UNwn3cjRtdjyp5/l9E77Pnps3gyTN+quu0lSRMIWyRyPHq0Yl0pF9xKAk6w75HIo2kTtEWNP6oUtOMDtFXgfVMe1MiywbE18w+b1dfErdJ++pJ15lTaeIEr3lVg7pN7qkqU+FRQku0SJUqUuA02JW/xWjSv8Qc/VXGV9tWUoDAT/ci8/w5iqSWrieHH6mtRHwTsFE9o6Bcnm69EZbdEWlIiheJ+3LMpOtDUs7hXcAV0t0bejLraBpghVKeNykbY8lUhbNRVcp2pN09uxFXTJjyD+YBGSIr5RqAbGJJIA1733YeX6FSPOdke2n9g1S+8RXxocIe6Wn4x90HFKXNSvWxeZrNi7g0GH6YbUmMGV0o34gU3B2+Rgb0eJkxWr+mZwjcGj2eR/vSOdy4NXTaLW4U2iOdmf1II+InFS7krxBvXOR25Zv67h8XIzNfYlLm3ek8weoAGcVL7ZqG0pbu3bDBduNJcRWjBOJG7T3qbupWkS4yT0TdSY+aDtJdR7kl5NFWEd02TWi2MJWuLYqvtoe/mwt5UtDMRBUnDuL6O/N8R+EWhyXPuG5gmhX2ZsT9ELkTheC4MZca+EHkrV6bTHrQ1rY1RW/+6i/jjGYmSSpb8hGdAnKSXKPF5Qkm2S5QoUeI26BRXQhduocB1CHw584G2I4VwrhC26O0zm4FVH2uPfLQV72IiE3UbQIcYL9a8DOXhLLJkroZ2p8i3FbyMpAlvVRScpFbwNsaQbjcCvV8KPPNXGf3/OgPgW6FgfzDOFEoLnloya/4YYlK2CzwlYtRgSeDxzOhNCRrXEBqBs++6jaTp/gdYThF0j1WKZC2ABTP6M6UicLJwhVyB3eKe3q5VFdrJ9sUobFXjWBR6xVaJ8p0Qk32hR4xjhfBBFJ7KIj164+vn4o28chHYp3F1kO9XxUyEn+VKr3il+gK+kbi8pjnxurnSfjiLPJAZswhfzyJm8INcKYAeNQ6EiCFcNZ+MVXzQdYO4v7sZYzBtTMA3gSqeR90GCMISfrqxwo3uUAOq5yOv/c85l/+p4Fry/DcgLOOke96ExUSyBzQyibCyajdxe5Hhm6MxoENtNaFke4p2fFgjY3i75o7gJzSvpiZOqA+T3vqFUsHXztSazw2pcbUk2yU+ZyjJdokSJUrcBvUM5dulkmTi1eWzJhyNXkt9J/igG/TdFGNn5lnSgvuoM3xI8WwUdogrxgWePLGEcFgj8wgbEGaiq8wCHAiRy8kTXtTvZ31IslFo2OhJFfdnXpAzB3SmVItWvDa8grGUIgU3iRO9gHGP+NBmv0QmECpzs1w4dxaAwX0HWMFtAoYT5TcLZX8wpgpjNsLppG4+mkXm8OSRBrmhdnfhJK4Z920fCHfPgqdT8sWJ5Pf9UnYjD9uvu3+9a83nOtXtQnWf9S8DM1fRX8g9W/3xzHiyEjleuPq8aO6dnzNXvO8NxqDCTvV0lQvmw5H3BKNDvDzH8DSS5eS5nkOI6VLk+MYhim/IPNHFU0Sq6fP9GqkCy+LDlMcJZHgSyvg7xsXnjMvfLZiNxljKbI94a+gCMJsGFacKz+1WjA340GQGq+kkr9eU7rTJyZNiH/HXzUaMH+aBToxo/nheSkr8sKbN4i2e3knzU5Gz8QZV6RXf+N5q81uixGcRJdkuUaJEiTugLw3r3Q4VgV0aWTS5KyIwkkhs703Rc5PmRLcV2B6MSYNLBSwZPJosKyvmg3FfqPgQGziRG8EJ2kYiU3gLYUzxcNUZY/zViMU6UTK6MFThzcKjBJfwN4UtIaa8Z7iYyNcUBgg9wLvpWmxMnu2J4+/5/w8O09HdRYvAPnULwzL+OA8FoycIL+Y+XHlfiHSqJ5N03WQhGUve8RNRORTiXQ1F1nElCrMISyY8ncWPDKmeKZSd6xQNPRBcmb16Z6v9R1A1eLVQzkThqRQ7KMkm8UjmUYfTJvy85lXu29XYFW4o6w+HyKQJlbSJeTJzVbwd3yg14tGOK3gF+0gU2nErSn1ocsEkKfaeYjJjwu50XU+ZkuEbtHrRzc6vCVkrLFyBhTeMRrHV8pqF9Jwtmavcp1Fa8fvjCTQpcx0A45JBLu7bnjR/TENinI7CYyFy3bzgpl3dNtMqxou5049NyroJI2ZeZnO/Rsai3xdwW9WAllaSEp8vlGS7RIkSJe6AgRTndqchukkTHswib+R6W8K9mDzLllTAtThXCDEpzIPintUMV1/71X2xM7iPdZ8aH0RXKpdxMgXQjjf7zYmT4QiM/n3Bsb8sOP2/5CylzORdakxHJzs+LKcMq1GIEBC61P3VCpyLyn0a2ZkZr+ZKqzgBHBBYfO9tAHr2PcCSeelNrxoXCrfLHE4NjQOZ8lah9CRfMiR/td7wsA+J8UHu9e+b1dXfu8WcwSuF0iWRL2TxQ/na4Ckls0lRvRnNAo+EyJHcBw7vFteSbaSC8UwWab/pd/art1y24icDX6/E1cdeR4PAE1mkasL5mHz6AdrVhy378axtb4c0LiAUFpnF01o0eeo/KIRKGiadMuFkykSvYKsDlg24LaivBYZ+zy/uB38X6UhqdRNuAaml21hGWAEGMXaIN4s24Ccu4AOVKyacS97t+nzDYIrpuz8Yw+qnGw24N/2hYGxMhLvvFh7sRVwl71W3jqwtuRksrSQlPmcoyXaJEiVK3AHNaYjuVrnA4ER8JAr7grEjeHb0rSLKRqL4cF7gQ0N/NYMzJmQmDKr/uwG3sDwYIucLYdY8R/kblYKrJiyu8c8WuD97aypRWUrEpzZnXP1eivv7gkfKBYQ9wThdyKqC2ZZsJtWUbtImxpLJquL9cBZpx9XXRfOmwiDG+ZRE0rX/AG0YubgSOWXCY1mkNf3/s/NVNqZEi2654a/uFSf1tfQYLpt74ffdZfqIX1P4aVVpBJ5e0xa5FmcLYdttioY2KdwbjBdyvWN5yorBW7nwUq7cFzxx5Va32yXuaRb7cLnLWmwUeDQrGInKlcIHAUfNBxcHM+OJEGnEFe0GjAso8wiv5cpVEyYRlhCeKwIb8Ofz/mBsEtguxnWcdC8iGMKyCXv+UEHh2ltGfsEfsCvWRnvawBluCRkHEF8HBTe849V03+dxv3rdbtUo/n0qfmrQhnEqqifwCDyY7DKnozAe+Uh9+3gUetR/fkci23ULTZ/4pmmxtJKU+JygJNslSpQocRcYSMkIt8KYQZv4x95U2HKr4a8RE9TsIxaSKylDWMXo18iJpNJGhfvEeKNwpbFdjN0BTuZOsKqJQGlKHdmuxoi553kOuPrdgmIRWrYLPU+4qt2ERw6eMx98W0TZGzwubqsY180JzzxCB57cMaxwOlkSclyNrppw+di7AGza9wDLCDMpnWVH8MHGeYOf1JQowuEsMmeu1k+bb2RaxKMMtya1s1GER5MafieYeRnPkVT3vk2NhnXe2armCS93ig/cFYy9ajyfIh3XnmYU5jGCL+XKD2s+yPi1SmTLHW5Txb3GUfwx3wp7gyvLL+fKknk2eFNSpXMRtqZmyQ6gHyfd4whbJdIhRh/GBjG2JuV8Pjrpbk0bNEl+f8Uj/x7oh4HH/CL/4nu+KariWZ3eqAAAIABJREFUpxyT6fsf1UgFnxsYS5u3nNRqWvG1tGR+knHJlNlEnCdS8+dV8/r29rThGk1+axG3Fg2oMY3nea/FhHkSEPiQcjP4oCg+I9Gn3Pb1WKLEZwkl2S5RokSJu8CAOIG9lVpdT9EAV6sfCpF31km4qCU1t5asHmtxLnoahLfneVnINRP2SGQUj/mrYOxKZPiKedmJ4IOLAW8OPJoqsw1YnGVV1d7yF4GonjrSKsZUlFRG4lF/G3ESZeIe4OupuEbxdJIZgzMFqwo4AtPLy4y9fwKAL+7bjwhMputxv3gF/LM1RYDHmzPa0ynBidQw2ZsG+i5HYSI6SftGVtyxEAic1L1SKJejpEQYWbf2Hnzj06MfbepcDzuC8UQWuRSFf6wpP6kp/1xTvl9TPijc+vCNSuThzGi6S77XpV5Kc3OM5Fo0i1tHtqpx0TwVZiV9fjLCl0Okmgiz4Yp1BV+XI6kttEHcTpMBl1C6BabxnG3FE00iUMPoUWPbtz2r5txPjY65SI7fzxWEDnwAdmMamOxVYwuRDB/EvZIbWSL8zTixn8Kf1zHzxJfLyV/epbBZI9eicDURaxHYH4z9GnmpUE4Vrl6b1e1FN3zt22+ykgyVvu0SnyOUZLtEiRIl7gLt4gNhM+twubqFZK0XuDslXJy6iRCMmRO+ArdK1DGXVL9gQpu6+jdWOImaNOH/qQaquA1hSI3z0clwFWETMSmSxk4tOF1PEgGu/JeCuARtu4Tux12JrODWjYsmXIuuiu8R44Po6vgbhdItxoa0s1jBvcPv5sKcuQKe4U2CK+8fJxYFLV3d/MttA6wYVJLP+8d54Gc1ZadGENjflDGF8HAWOVe4mtkjxoVkjzkalW81FGy8wzuTJXL+k9wTNL6URVrwmvc++egTZOY52jvvomho7fP3dBb5aiVyMEQeDJFvVCJfrkR23mWT5VrUh0Bvl2oDdUuN8EwWaRMv/Zkx36Ag0JesIOCEtwnPQm9IXu7LxY187KnoVqBC/bp4E6gjx9NL7jsAg48KO/5VYFoF5YZtKMftItvVdfHxQhgMvrlrwcjTgGYNYT768OW1CO/nMBFhd7ofs3hm9kYRMoE3b1KxH8yMTnxNP5crp6JbjDas+Z5hNabiDetIf7Ie3SrJpESJzxJKsl2iRIkSdwERf4MfWUeZHE8WkptV0/uCceamSveR6JF/vcmPWsfFKGCeNHIqKeKzwFYxtokXyGzEKBAuFcKbNR+UFDxTO092g2hOwJYRZqeNke8nVfu/Dah4DF2Ok7rRwuvSwT29582TOrrwnOdMIU+2go0pwq8vRfoJniFdfdeHI/fsf4D5NEy3WVzxXcTJ0At5YIcaFfGBwiE1DqfSk7dz5W9zZdqEP6kUbLnNu5KZe7OfzZVjhXA4RA4mr/QcTg471+Gyo+YbpU0fkyBLsrj0qJPvxl9BSO0S96RPRT/duBUGxVNcGgS+FOJqLvmSwYUo3Jd5E+QMvmmqAVMI36wUVBDGEKain44s4BvB5WTJUFzxrhfXXDbhcGY8+JcVDvyZUmnx+5DjJxrXUFbS5mAjxgSkxke3AVWSvUPx1JpplGGMI0Wg0aBVPZXkShQ2qzFrcFAjp6OyvGbf0yAwHGCXGDvUeLtQxk04Wgij0S1AFfHByLo1q5KsOecL4XoaOJ4x941Pp49YEvESnxFkd/6WEiVKlCgB7ts+VvhA3FqMpFrxm7FBnFieLIQDmecMj0b3XK8lftF8eG8qelnJITX2SORkzOiRgpcKZVAiCwjtGHuzyH+pZRRAL8YoQkPybI+b53wfjcLSDDRvFirB6HjEf+EQkfMop6JSmCeVZJCyuo3JKFwyYRu2ajEZUuP5XGkUeEgjv8h1tUjnp0e9zObBAwd4Nlc6xKPmvhgKHsiMZ5NS+XxN+WB6hQXzRsKr0YnsBEK3wH+TFbSGm6+go5r872dT4+beYGyWDxcHjURP5rg50g/cfrNrnbi/TxN1b3rE4/EGbnFftgTjpQKK6Ak0PQIrGO1ivFYoO9TYjHESZd4igtKAJ5n0qdEYjVdjoEOMWiLognu+e8RzxusNlxMmbBGjWYxoxjWENjxxpCltpiaBHjyy74wJp6PSlGxEfZlwuuqe/utpbc7jJH8+vRyG1IeF71NfRw1AVhi/yIWvNNx4zQyK+7u/ECJXo9Cm3hh6slBmEtmOBkdMOFX4up0x4R2EbWbUL6es+Xg4xHU3XyVKfNooyXaJEiVK3CV6xUnEojlxgqS2JpKwHvYG46c1ZbcZC+nY/TrCwTWWhgmDORMmEPZq5GuVyN9VhQLDksrZiltLhoJxOldyPAquiiuRHUSW8Yr35ZTs0bpdOfS/e3SDiavRU3hNd7N5okXAeFwjF1EeC5FBNY5VM86bp1eQVOyJKGwKRnfKwe4VH5y7ePRNAJb3P8iiCbvEOGmwNXiahQD/tqHgmgmvmqekvJALuXmDYAZsC/Yhoh0NrgNTURgx93JvUnggRPqEdUnzSBR2r/McXE9q5+OV37zM2S1OSkdvsTkD6MWv2ThOyDeL8aYpfxwiM+bE+Fx63maoD8EKlwzu0cgRU9rNDfsFnmgzkEj+9hC5kge6iCzh8wEXEQ6EyAsryqWXDJ7N6fkfKywFV7eXk01pUkDNi3V6iMwA7eqbnw68BTMCG0RoNOM9E56MfppQxZ/PbeobhnsTAX8qFlTSScaAGkdrQjV6fvgDmXlFfTAK8wz4hej18AdDpFddtf9xTflK9vGy2EuU+LRR2khKlChR4i4RUgrC6Brf7VRSCttv8TOt4qTyZOHEsV39OH+tH/V8FC5GLy/5dkPkTIQ3Y6AJOG4ezTeeIvg6LPKuSYrqqw+7kXKchV2hYCod9dfvs3X4n/oenOyDp1EUwABGp3pSxN7gZHCjGINiTOIxgXN45vWwwtHcf25IjWtLK0yePA7AM4cOslWNLVlkSI3TUXklVx7KvLxmVzB2NmT8eUPBl7JIIU6q3ovu3f5ZTflZzVM+/r6mvJy7lWAoDSM+kUX6dX2ivZIsBOvZRM4UwtbbxP19mugWj9Ubu82grap/3/nka94cjGVz7/Sw+onIZjXuS0R4GU9Z2SRu09kgxorA0xqp4Ok1c+anEJ34qqjbSQrgWO4Ri8s1OPUfco6/DPnzvnEzfH3NA4W5XtyeyL3gkX2dRGYQsjREeR3oxv3h360GrlpSraPQJf57d2gkCry55nXUJD7DcMY8baR1zTUJ4jatvgB7gjFhQpO4TaUncMfCqRIlftMoyXaJEiVKfAzc7NsesVvbF+rYG4xLUbhQuNe1d8335xGO5MoSwpMS+WlN+V7VZd5HJNKhcG86Dl9BeCEGcqAJLx7pT0f6biUxQvRBtpF/KCgWjLWc7jEKRnGiUv98Nx6p1qdGa2rzaxSjSywN0wkhur98m0Rej94meKxQLp84RsxzNnT38KWtQyAwF4WdarxbeMxevZSmajBdeBRgsxib1RgSeCaLPJpF9gf/eDzzQcRvVCKPZZFtwe7olR41Txq5ucSmHve382Pkdf860aXGsgk57jG/FQbVuJzWWLf4tR83V39HTdgU4GCD0ZoKauYQZqKfBHSmNI938er2gHv/L9RjHMUr2RsxwDgZlbejsrnZ2Podf7JO/U2BxMgiTtLfi8q2zFsmW/DCpRxhujB2im/yGnHyPmcgIgyLe9NfqSmLyT5VTxWZRenC132+5jCiX42zhbLpNq+nrSnhpB7JOHiLUpwSJT5LKMl2iRIlSnwMDKgxmYa2wBsdB+9A5poktVCal4n0ruEGbxXiedPJh3vNnKS2p0i1BoPzhTKSYv7mEW/tS0NuG9Vj3ZZSMsXrURl72TjzHwre+h9qFInMZBinJNDMjWP5jRhdmXtfd6W0jBEDMTgfPUd6B5FZERYN/rnmm4J7iETgytG3ANh58BAnzEnSlShcMY9BXGvBHjehJxMq4uS+FeO6+EakU31gtFc99eXjDiLW/do343z0dsq7ifv7NOCDrNCJfeh05GZs18h4KnFRgR7xwcBe8ROINjyGcV+yzVQw3jXlWhTa1OhLhTiFGRtxq87VNHC4U93735oiHRcRamZ8M0SGvxnIWmDiAlx/GSJCb/JjN+Exg7MI/eIJNdXC6FT/vCvlrrRXgChGl/ipz4IJZ6KwHF2VnzQvqsmBd25qhrxs8pFW1bVoE5+FGLUbPzMe64ObJUp8NlGS7RIlSpT4GGhM6RRXox/Pr5irj3dCE34kPxJZzYKeN3it0FWlcIsafx4KLqZ2xnPpeH4gWU8UJ8htiWgrTigbUtb2F0LBFMLlv3VNuvdLNyJPmoFOiQhuC2gAOsVYSUS/P/i/JwthxoTllMnckO73hShcMB+GO4cSEVbeOQLA4YMHOV4oYr5BGFbjyRA5v8YuMRa9rt0MLhXCNRP2fwL2jtzSbd+04Ykp7m/Xx4j7+3VDxYceG4Xbku0hdZV4LN31unqr4upvxIcbv6zuqTeEzJzgno1KJp4LP4ai+HPfiA9m1qIT5fqbvwFXC6E/wEAbDH/Lv3Lxb3KieeFME/ByLbBZfPixLSWb1HDV3ENM3NtfRWhIOe3bNDJiwj0aUeDnuSfbDKjHw/SlhJuYHmdLej1V+PBzWaQB2bdz4XTh6+tyun7N4hu0sfSzC3b7tJcSJX4TKMl2iRIlSnxMDKtxyYSx6CUqd9N0eM2EQYksIrSIH/W/XlOmze0hXwwFv9cQOYf7c68h9OJZxZvFFcVG3BtuQCvGViLXEKaSl/a0KdffL5g7bkgG/d+8oS134XXZ8whbkxLeaD68FvDUi7dzYQbYqZH5ROanETYCPfjAI/hw6AYxxt9xZfvxgwe5bvBqHtisxoFgHm2IkyAzz8AezJQJg1kTOoAt66jRHxejJrTLjYHVOkbMFdbbqaS/CXSnxzx1G1JYEbecnE1WkiEllQzBoMJs9AFTCbBFI5r80hvxU4nL0e1KLel5dkruzZPvWCACSyk2MgOuIBw34fEsMvBHAa3A9Acwf8xvd5NELiPswIuDokEfkRpwpVA2iNGUHpdhtIoT8gsmDIrxSqE8GiKjprxdCDvFlfkmfJj3bLoO1/A88uk0b1A1bwf9Qa6cK4RG8es3pH4yUFezN4jxXE35QU15Nlf+saa8kCuTn519VonfcZRku0SJEiU+JgbFCzauRKHvLsjcknkqxoC4GrhkXghzrPAa7EGMp1MM2iuFkqeosy9mkTGE96OsRvQFjBXcu9qXPLlVvL3vZFQu/53fTu9TSuMayb2GsID/0e8SowFDxRscuxTeKYR3C6UVVwrnU5TgtmDco5FzKM04MS8MGhbnOff++wCs7D/kSqfAYY1ISgzZpsb5qMwmUtQVhA+iUMV4sBI/kSi+yzeVCdVxuvjNx/2th14xps1TY8Zvs9cYFuNSGpLckOL5LkehX4xZ/FRiLAqPB6fSyyn7ejAYG4ATUdmrnsndlIYaqwDYautoBT/VWEb4oOZDlhs6YOgr/ntHv1usFudkyarShDFpwqD6WhjFM8RbzPPXDWGp8LU6Z74ZnY7CsnlW+2QURnCryALCVvUSJXC1fqt6ydE7hfDDmjJjwhMh8nQlcm+yHLUkNXskwqlCuBB9aPcbWeSblcgfVCJDieQfzW89jFqixKeFkmyXKFGixMdERbwk5PwaS8jtMBKFPvVBtq0aOVkIL1eFMZy8PFNx28fZHC5Fr1PvEh8kzIEzpjRgbA6uZDfh5S3n8JKagHAdYXbUmPi5y3kD39HV7OFO3NZRxe0pIyhtGEVKh5iMPuTWkuwBRwon2p34huLVwgfq+nFLyTKQH3+XGCMd/QNk/YM8ETx+bZ4b7HarGmPRrS4DYqyY8XauHArmjYi/IuoWkqGbnoOZyGqCymcNXeJWhy65vW97s/oJwLy557wicLEQAp6IE/Dh3J3B6BDDMMbNN3MV/P/ncULdJK5yPxW8aXN78mFnadhRgRXxOL57iPT9cUbX/cK9X61bkNyTPWlerDSNr5sW9XXjkX/1/G6Po+wRV9mnTehR4/VcuW4+1Fsfwq0Cgxgj0TPmrxRCzeCD6DMCz6Qh2a51mMpQyh0/F4WvVSLDClPpaw0CO4LxtSwyacKxcoCyxG8YJdkuUaJEiV8CG8UVweY7fN+5Qni58ESGCxG2ifF6TTlpQgN+tL9HvfHuJ0VgUyJJj4XImUK5FIWI+e8pfKBtlxhLEcZMaUkqZQFc+a8FROh6UNiwSyFpjQMKI8lT25Ci5wC2SOSdQulR45DE9LvcYhDxNsi9GplDeVgjkwjdwAzCW295vvYjBw/SIB7Xtkejq/CJ4zamwdAThedK/3C+xgaBB8InQ4JHTOgQ9+2uxftR2K5G+AxyrCBOuBtSHN6tWg43pft/ufDNVSN+gjFjvrlYMuFadHvIferDjhdN2SXGisiq374BmDf3So8ibAtue2pNRHslbdguRm8PbVWhebPw1P8a6H8ioMAF89i+AtJJijCNDysG4FwUGtUJPcnWVMM96v3i1fYjKTFlkxhPZpGxwmcNLpqfrny/GjgehXaFQ8HoS3F/t8IyPjfxRIir5VGXbyLVjQJPZJHLUThXfAYXQ4nfGZRku0SJEiV+CcSkME/dgTe2i1GYofgx+6moXECoIlQwNgVXCl/KlZpBq0QaELaJcSQKTebDkIMYp1G6MQbVGEt+66WULIFB4X4Shv9MU7Cbk6xhjZxOsYNd4jaUGsK1KDQLPKnGi4Uwnz5fTdFwe1KEX4bxTlSqGG0aacMYfcdr2u8/eJAMmInC3tQQeHEN6ekXz0WeKoSTKwXPZMUnZu24tI6FZDrCeBR2fwZV7Tp61Vg09yBP3uJutuFr53SKzNsg/txdMreSzOFkdNzg3syHZgs8YlITCQboUSfVEb9eu8RV6lZc9a5naS8iWAF9wdNFruO/txVjGmV/KFhC6Ew/Mx2FFvW1N4swY+4Zz3Cfvxr0pSZTMTigkWmEI4VyshCaFMai8E70odnLJtyP8VBmDOuNDeF6uBLdhrVXb/i7h9UV8ptTSZoFHssi7xU+0FyixG8CJdkuUaJEiV8CE+ZJF+fucES9gLA9QLfCQ1lkJjoR2pgykruAv6sFzkdP6LhoSqdEvp/rahMkuJK3jHu0343KOEojRg+epR1F2P3vMp78jxntD9z4054BnWZcxwncSsrjnkNoU9iqkb8vlDcKTxgZpEDwLObRCGO4Wtoo8FBmtIhwXzBGUuzfwIFDdCby15cywU+uyUFeMVdZX4nCvY2B4U/oXWfRYCJ+1CpyPCr3BPtMNwr2pg3IkN46I1oEtooxbTCbrCTt4raeDG/UDHiqyYAYneJv6O9EoT06ob5kwn71gpkFfFYgYHSIE+xm/GeW039fMuGQGh34OmmbjZz/64JLf1+wHL2tchqvdJ9EaBY/2THgclT6xeggkuNWltzgSlTuyyLfaYhsJzJvXuh0jxpPZJFGjO3i3nJLz2WfGhORdVX/GYM3c2873RFuqNmtAhtl/YKbDvEynNdzXb3N3Lw46Oc1ZbwcpCzxa0ZJtkuUKFHiY2IlDTw+EJwsVW+jmF2NPlA5HoXZwvjAhB0pgq8DY3cwtqqxTYwe/F2/E3g/+kDidYSAcQVhA65utiU7SBuwJJ5tXODe2qZNiq2RjjPEM5eBfvG85QJhrxS8Uwgv54FzhX+uGeMiCgjdKdN7pxgL4paXutVkaHGGi6dPAVDb9xAWnWhnAn3iKRPnEuF+s3B1vWrC4ebKJ6ZqX0j55mtJ9WR0MvZZKbG5FTrFiW+3GFftdlYSt49cjrIatyd48+aQGlVzchmA+zRSwecCGpJVZyYlnmwVS9nswpgpMdl7FGhKm74Kfl8umTeeKvD6EePEX0Uu/nXB28vC3uA17Z3i3uyJ3Ijiud9LuLWqXjd/PAqXo9AtkQtREYGvN0aWgWoavD2c+dp/IyqtYoyaU5I2cd/19E3XpWbwaq7cH4xu9WswHm+kumxWV/bXw+70mM5HYSLCP+fKxei2mruJ7ixR4ldBSbZLlChR4mNi3IRuhQ3qhOjCTW/wMxFey4Vna8p7hZPYawW8WgQaEe4N7rNuFOHdXDgkkfPRIzx6MKYS8d21hhApwkE1uoJ7puvlKHMGI0eMlctGC+6brXOUDXgm89GoNABgLCb7Sod6ZN4AXq8e0+3VEAaIFLg3twlDzehVz1COwNQ7RwEY3ryZoruH01EZFt8oiMADIXI0F35U85KeezQS8HrtTwLRnDTtWEOqzeC9whM4PgvV7LdDEOhS34Bk3NqK1JvyqC8UQguuTm9O3uQBMZbMr8WMwe7gCrbhivYXgl/zt6MiCveIN0JeiK6QdyWffUOyNzWn5/5HVWWTwHaJtH4x0NoDtRk4+ayxCbcm5eaq+rR5QVIjvk5HESzd5lWULepDtpPmBLdffWj2+TwQzdfKl7PIEl72NGVwPanMffJhK4mZF0B1irE9Pe9N4sS/3ug6nMj3eptfES8BejFXXqop+4PxVCWy5TPq7S/x24WSbJcoUaLEx8R4dDIAnkl9Zs2g29lCeCFXNgh0S6QR4flcuWrCIvBIKDgflQqAQJsaswKLQGawIMKSuUd3b3AisoLSI04ip6Ng5kplg8D1FXj/f8t5/b+vMXM0rhnYdMvJkPht5MBx82zl/RoZlMiswcbgVhbFuI6uDrnNAgMamUO4P/iQ5Qep3e/Y224h2XPgEHvVLSpno7KSMrWvmzCNMB09wm5FhIeyyPGV4hO5/mNG8p/f+NyouR1i22dc1a7DrSTc1krSglsuDD9JmbdU6JLU7P7gvutR86bMDD/dmMEJ996UdV0zeDJzFlv3iBd4dKSmvO0ZlFaMEZz0PpVFKpkw8Efu/j7/3cj53O93jpPuDF+3y+n0ZdH3BjTiNpUl8wKdNiLHCvdmP5NFZk04kgYW+9QbMhfxzeGb6fOb1E+D6rhkwpQJh8KH4xzXFtysLZxaDzPm8Yebg60bF1mixK8LJdkuUaJEiY+BekFLPfKvN8XnjZjn/h4rZDUTOEd4KESWzUtqmoH9alw1Jy3e4CgcqXnrn+ApE43AvcEYL5wcNCSV+6K513sxkZsLpoz9sKA2DS29sHmfp0SA9/lV8UzskFRLS3FvBcL38kC7GP0C75kr1oMaaRePUMsQZqPwtUpBTVxRHImwLUTefNOTSAYPHqJJjAdDZKMY36sp/7WmvF4ID4TIJfNIwkdDZG8wztciS58AxzkdlR1rMrTN4Fih3Bc+Pyplf1JuBzGu2PpZ0CJOOrtSxvaswQZzMjthsFkjVdy3XQMqYrTjRPfFXPlCFjE8erIKbMKYSV78hSi04mklkn6mnl/j1ij/+sDvK1kzLF40XnvD73cNV7az9DOLCM0IC+Y/Vz8RuWqgIkwXyjK+SRpQb8F8M1euRn+Mreq2ljkTThTuq+4VV+yr5ratdwrh4RCp3PT8DiV/d13NHlb70IBuHZei59X/QaXgyjqDlCVK/DpRku0SJUqU+BiYw5XG9vT/Iu4HPV4ob+TKY1lko/jx/lVzP/WMOcntFOOEpeFBEZ4OETU4aUJfGjrrwH2wj4XIa9Hb/9qTSr5g4kkWuLJZVI1L/9kVy91/qrRnPpAJTshqCFdQqghd6ai/FycjTfjQWC/u/W3FifmCef5xxHimEtmiruSvmBOzTRivv/46AAOHHuFa9Mc4miwRbcCmFBO3UaAPW2143FpRTv2KmcdT0QcG1yrYl82v0/An0Ej5aaFdfB1pGmy8VSpJv3rE5EZc2V6SG2puf1pnkwZv5cIedf91J670r4jQLp5L83pU9mtEES5Ef+tfAHYGI+BpOWFNjORFE7rEqLQKW77hz9np7/pAsOLWkSXz7xV8Q+eDup7x3YOxgNKJcRnYgr9GBNij/rteypWZCIUZ28RfU9dMOJP7erpuwt/VlP9UDYxF4a1UUnNtzUBjY7Lk1K0kQ6mdcmHN9Zw3b0d9PEQG1b//ZutXiRK/TpRku0SJEiXugA8K4WThvtPRwpv21jvK3iheUw4+xFYzt5Us4iUfjwTj1VwpgPtDpCfANB4jOJqykIPA3mBcN5hEacboFTgVld1qXEgq5kbgwk8KqtegpRsO/Z5yLd0fv2vuwzaM1vRvxX3Ym9W4JxhPZ8Y1nFz3YVwxTyTJEQ5r5IHMuGY+8DiaIuUWr1xiZGQEDYGmAw9xOWUwPxIi36lEfr/B2/42ieclB4Wzidjc3xg4X8ivpG6/n65DXcEuDI4Xwv0hop8j/iTiVqTx1Jq4nhoLvnGZNbgneJLHSEpguZI2YpuDb5ZOR+GLwajgg5eubgt7NLKAE2RS/naO+VpJ96GZelmR+/mX8IbKZvGc9h3fDojC1NvG8hlPG4mQIgXrDZO+EV0wQdMpThXYIV529HrhdfNXIwyq0ZNsLz/LlU7zIpuA18P/VZ7x91UlYuQRujH+pFJwMEQygVcLr2OvN5MOyw0rSSaudtfJdDR4PVd2B1stx9mlkVO3yTj/dcMMRiO8kitH8s/Roi3xS6Mk2yVKlChxBxiwbPBGofyi0I+0Hy7gddYr3LADnCtcmasY5AhPZpFu9ci9FjwtYrzwTOpWInO4/7ZXjW1q/ChXIq4m94sTpn3isXoCzOXGhb9xiW/fnyl9jawZKDMa0+3lCF3ALN4K2ZVi0PrUmDR4uQi04EU6IdkM2jGeqvgDGTFPwpgzWDLh/33FVe3N+x5gb1sTTwQn5d3KhzYgl6KwORgPZpEThbBgsDH40NyxX7JgZMY8cWTtYOSp6MRu4HPIWfrUNzFbEnlez9pQEY+NrIoX9byW+7Brh3hKyQa8ibJdoCk4+Q7iXu8LUdkmvi41SdAN+GnGmClLhfv+e5K1aIkbkZRv58rXNCIIxSZl+MtC31eF5WZhKJH5iCvbhtGTBmgVH7QdTxGBr5jysPp9WAS+nwcosh4WAAAgAElEQVSuFnAtCpOF8H4hHDFl3lyl3pSiA4cxDqpxyZQV/LVXxQt8fi+LbBLj5zXlcpSPWEnqmxczOJmU+HvWrJlNa6xfnzaqBi8VytFC6RJj3ydU8FTis42SbJcoUaLEHbAnGAcz46vBye2JXBhbc5R9rFAeTgkjl03II7wVlV0a+cCcEO0PxhuFEjC61KgZvJB7+cwKPjDZIF5xfd2chGfY6tH6Rnwwbhlv/Dv7M2NlDJo64YFvCOdSFbYf6acGv0S6x/GBuAx4plJ4VbfBD6qBORNaEWq4HaQR2CVGs0AR4UShvF8IE+YFJotHXgVg9+FHyRG2rkMW5tNj6BfPdN4ZjCO5Es24N3j5yPTHzDau+7J3Blv17c6bnzocyuInFin4aaJPjKlkz+kQuHKLIpcBMUajRxo2Cvw0VyoCL+fCyUIZVFjCs8d3J+9zlzoJPhIDXWJcQDgcIq3iWdkbkr1jKcU2Kq5SK0Kf+JDlZRO2JmL98L8P3P/vM2TQE2baueH1XobVIc8qPui4iLAZb2+8RyM14OsaGRDfDDQm1TxHuE8jGxVWBP5FxU9irgLXgU41/iCL7AnGsUJ5Nlcm7EZO99FCOBX9dKVuJekWv2/nI5wufDh37amHCOxS43Tx6VKg6+ZKfjPGV7PIPcGfzxK//SjJdokSJUrcJabwI/zHK5HX0oDXgrmneWcw9gVvqvtF7urzhsJzj5/IIkWEdwulB2NA4Ce5shhTeQhOSsEJ95lUy24IGxJh7VF4OaoPUSLEHLI2eOBPlIWKMmqCpAHIBtwu0pY8uIv417ZjdANv5sLPcz+mb8dYTo9NgT0S6Q4eR/dPuTIVXSHfFyIHM+Po668BsO+Rw1Tkw4kgdVxIzY71CL66l/jdlYJG8eHPo4V+rGP8yybM2w2F0qPg3B5w80nD5wUN4iR7wjzb+lZWkn41xlLWdpfAQyGSp5OGByuRfVlEccK7ORgmvtFpw9XvwVQ6tISnjMwh7JSCFZyoDgajKQVGzgPdqQjn5ahsDZFWjBlRunAy3Yinh1Tw05AGYD6p2vN4Xbvhpyo5wtXonv2T5sR3zoQKQkWhPf1eMR/O3BVc7Z0x92gPEJlI1qevZpGdwXi7UH6eu//7y1lkLHqD5aV0YiICW8T4Ra7sCbZu7ftm9dOcqU+p0GYuba53q/Fg9vkZ5C3xyaAk2yVKlChxl5gwoU+NfvUK6CO5crxwItAgXhqTm+daP6SRl3Ef9sPBeKHwYpnHgtEmxrkoXMTVYgEe1si8eVLEnLkq6ZFvftT+Ti5Mm6uPEdj8B8oT/yljz7c8pq+ZG6UnpIE3/14fRiMp2/9nNbAC/HFWsMiNuu4cVyuH1H//y4XSkoY/N4eUOLEwx3vvvQfAfY8cZqvaRxTlaE621w4wBoHDWeSDlYLRNTaQk3c5pLZicLQQHkyeXfCc7SX7sD3g84j+VDM+pMZUdHX6ZrSlAdMVXB3tFfhiJfJgFplKnu+a+TVpMy+XqULyKFs6RRDezoV96pGCF83tQ+dM6cBtFVnaeI0XymYxFsxj9FrFCfY8kF+O/Ox/Kpg4EcmAAkmdo25H2UykET9VOYef5LxWKNvSZmKDuX2jwDO6G8TXx180FCwZPJsrj0pkEWEHkXlR3i7cX63iFpGvZZ4N/1KhvJtSSrrEeLsQ5hJ5FvHB5K23GJoN4icFpz+FQcmFRLT3qrGztI38TqIk2yVKlChxlxiPQm968+5VJ6Kv5crWRPiupqHIApiLXuaxOxTUcM9ppxjNChPJxrGIl9hsD0ZNoIZbAM6a0+ZOjD9tKNgoxgYsDaTdaP1raFEaG+VDiRAF9Up2V9WLRM4NYUqEHcF4PEReKQKzpqtFKC0Y7WKMpCSHLwX3ztZwlXTF4Mzbb1EUBd1Dw1T6hz9SlQ4wbq54dt7EYVoFvtCS8UahLACPZJEzaej0dojJKz+4Zvh0OsJ7hfBIiJ97hXBIjavJVzy4ZrDvZgyrMZH8+kvpc9uCJc+/e9YnzTde28WYTmu1CR98zPCTlSAwpJGInwhUgbOFr4EMXyeTuNrdK3A9Civm1e45wrn/XDDygnHyP9ZXlRNnxTcDktZYBSfvTcBlPIN9o8Bzhbq/HLhoykMh0iQ+9/BAMFbM+HEMdIhxFeFLoWAiCs/lylxabkFgVzC+nkVaBZ7LfR23i/HDXJmPcCYKe4Jx6RbWHIDtaaNz8wZnxbxI6O1cmPkVle/c4KXcoypLov27i5JslyhRosRdoJp8yGurnVuS4jhhflT+Zq7sz9yX+npUDOGwwvO5p3xsEuNMFHYTGU8pDgsIh9RVcsXzi3OcOO9WV7dnEOaSZj3zdmTs1UjNYC+Rqya0pZ+xNeR6EUktkEDyz27CuJqG0q6Yk/YBcVtKa/q+BYQvZpFcXGWt2Y14tTeShWTvw4fpC06gb8b56Crmeh7qwUpgrxov5v7W82AWebXQWx7l11sDc4MDiahUzdMo9gej87fgHWyDQLP4JmW7GudvkZIxrMaICRtSOgm4BaUFj/nbnnnT57koDKincmwQ2CSe8NFBZAphMro6nAnsUL/w76SSpYg3SebAVHQLU47HQs6lpJJdfxHQCsy8a1x7I2J4hntz2ujVs7GH8U1fF76mzxR+4nG28PV+yXz2YCgYe9Q4EZUBtWSFglZzK8nFKOxWowXj2ZrPDxTp8TcI7AvG1yqRprQWRqPw17VAHuEejZyN62eYg6/rYfVrBj4EfSQXflhTLpvQKN5S+avgaCG0iVupSvzu4rfgT1WJEiVK/PoxaU5u1pZqXDUvrXk1F17Jlcczz8LuElYHFGsYZwqhEaNqwr4QeTYqTSmST8W/fg3hfvFjfKfpwr0a+XGuXDcYxwcvz/8fOUf/smD0HyLXgSWEWYSVNBQJrlJKivsLOKmOIpwwZRyhT2CjGk+GyBVzUt4iRpMYHeKxbBcKz/LelnlucbcYv3jVhyO3P3KYXfpRhlz3r2+9DbHYFYwdaryQKx3iJPoXuTJy080tmsfFTZnweOYKdtXg5VzZJMa23yKVsB4d2Z1SMsZuYSVpE39eZ9aotdvUOB+VYXE1+WwUulO29pJ5YUzASXkEnq95qokhLJmfnsziz7VgtKV1cyqR9k41RJzsFkBTrzD4LacOZ/6vghDd8NSA57rX03a2qQ9AmriNqYowm4Zsn09JHBsERgq3HM0Z1Mz4IApfzQq+0xCpIbycK5YGdp+uRMZN+HGuHCt845Cbb1b2BePblUgHxvUI0wh5uk7rXc86dgbjbCFcKXyOIgBfq0S+kHkx1a9Cti9GHyx+KHw+B3hLfHIoyXaJEiVK3AUmohfK1JGbZx5Pm5CJk9Fu8SG1kegEZwD4QR7Ikxf24SxyslDmU6nMgERmTHknKm14bXsBaFIR/zEPnEntfwJMvWosnIPQDFu+5JXo/SkqzeEkqyGlkNS92wB7NfJEKBhIpKldBBVjFn8jyE04FCLNifCdT/aBHWpMmNBtBW+kMpt9jzz6IYW/jjPxhn/9dtgTPN7wudzr4Q9nkbcL5fmacrIQXs2Fn9SURuDpzHOa583tAm1iqyr3bws2JztDxK/32bj+W/OQOoGeXkO2N6sxGd3CsSM4Ub5mXnhzDehTY7MYo/gG77gFAqymYGxNJTQrAAiIFytdR8jN0zJacaW8DU8e2fHnStYC02eMa8/51u4a9U2iMQUsinu4R83J9zWEHom0qcdChuiP55SJ+6fFeLEI9CYPd19wT3WHGqcK5aVcmYie//5wGhB9s1C+X1N+VFNezl2NXhFXtLeq348dcuvrCV6ksww8XygPpyHglk+AGK8YHM3d6nSn10OJ336UZLtEiRIl7gITJvSsGbY6XcC4Ca1ifCeLXDfhfHSrySw+SNmmRjAvpxnCmIueqtGE2yymTSkwOiSC+DDkCk6ZOwWGiQTSH2qLXP7rAoCBbyldGzz5YQmhIXlnSQplhXpBiXtjd6vx5cyoidAfjKsmtJhxOnrBThNGrxqNInSkCLUcGAi+aVg0uHzqA+ZnZ2lsbuGrD9z/EaWuZnC+8DSJu8HeYBwIkZdyZc6Er4TIFjUW0wDgVyqRA5mrtecL4dma21Me/BxVst8tWpNqPZZSSa7FDzcg1rFFjXlzRbdujajIjRKX7cGHDs9GYSjcyFpvUs/XbsDtST/OA52kplBj1bqR45uabRpRvPGxCcjMy4NqQBNC40Zlx586fTj1fxdktUhMK7CGcC4K4wV04z5vt6j4OqoCfQITIjykkfEozBak4V/YLHE1L/7eEKmZp+g04Or/P+XKm4UylV6Pf1iJHA5u3Vow95iPmifdzJkwA0wU/rhuhs8DuE1J0v36pPBu4Yk83SXLKkFJtkuUKFFiXeQGYxFOFMKLNeWDKLyXKy/WlO9XlR/ngW1iHFKjRd0e8XoemE8KpUXPCV4EWjFMhBejEuMN1XAmZQyft0CLeRNgDaEXY4cZFyXQhtd1z79nzJ8ytBEGvxOIeFbyLG5RqbswBEnlIkafGIqwV4x5cbXNzJXrRbxYZAlhn0aGgqeQdIhxLiWn7NDomwyFH77qqvbOQw+yrSH7yPU6H4Ve5WPF8A2rp2pcicI/F15eskWN4eRVvxiF53OveH8i5RL/th7Hb1bjYvT87LU+4rVoFr9m0yarQ5JQt5IIG/BEnLG0CSzwE5kuge0aWU5zArPRq99XgEKdkHdgZBjzKSayCY8BVCAL0CteyNSE0Yxx3x8pDZ2wPApXf+yrr77xWwHGEC6iZLjC3ogxjiefbEqWl1MmdAr8IFeWEJ4KkesijKXHvkf9RGgwi5gIm9eQ6/tCZEdqEu1UHxZV4PEs0q/G01nkySwyY0JQz8ZfCzP4RS68WygtQGHCtU/owGQyet38/b9lJzAlfnmUZLtEiRIl1qBqTrB/UFOOFZ6i0IgxLH6kfcWEqnn5yzLwD7lXR69EOBNhQaDFjCsIYp7k0aM+vKYGV9MQYhWvd9+a/grP4L7tSkodeQNlManim4hc+J4Tmt5nlA0dTrAq1Bv8hPrbekMi3i24NaQZYzjzxAvD2wjbUwzbBL4RaFdhQNwL3IBx2dw60C/uwW7EeO01H4587PDhj9SiF+Z14bvX8XHfCR3Ji/tYqiN/u1D+qaY8W1NvoVTjK1n8rVcIt6gxFn1Ib0ciz+s1Su4MkRX4UClQ3es9arA7M8SM8UJWbR/dEmlMCvNSWitN6fleInn6U/pNDpw3uEcihjehViNMpXKmpRQRWWtSDv93Ffb8G2XoK2HNPRRAaIR0SnMjIUcxzqYowa9mkROF0kDkfFQOh8iuYFjy/S8bNKu/fsaTD/1YlFVy3afQsWZNXDe3cB3K3PoyjQ9QfjGLqHkSy/V0zczgZ4loPxoi32yIPJJFTt/GbnK3iGkN77sLO1WJ3x38lv/5KlGiRIm7x2iEH9WUWRO+WIk8U4nsDsa4eWnGnAnPZAVPVSLbg/HthsjXKpHtGpk2JxST5oqeALMmXDBlPApzInSp0Yo3B345i3wxi7QkJXAZj3UbFC8YyRLxacEYGxMmX3bmNfhtZbO4baNerz2djvAr6WcCbsVYEc9c7hH+f/bePEbPK73y+z33vl9trGKt3IprifsmSqL2pbV0W5Lt3sYwbLfHCZxknAwMTxLkn8k4yQyCBAiCAAYGyMBIxojjsac9ntjuXd2t1tLaKVEiJYq7uO/FquJaVayq773Pkz+eW8ViiVJT3Wq3mnwPUJBI1vLV9933e8899zzncDhHnPUEJ/NtucZ7HV7bPk98EPKceovg6tyIeUaFEyoc3urDkZ+/954PPW/H1H25PT/DHaU7wN2F8YWa8uWa8sU8pLY82ofI/c2IRnECeUyFjuA2okPXUbfniGeqH5n2b9MbEXvFPccHLUf/iZffFAHmiub8dW+ObDA4pYEOPHqyQ3ztDJiwPHiFe8h/TkCP+MZrIltQlj1Z8JWvBaTJKbbhNpVZeBZ9ga/fyQxv8A3eHHFi3SzGUY20irmPW9xeNME0dTsaRy1wZ1QOq3DlI/Zzu1NgRR5oXBhsqtGySeCJmtIcPBawVHgtCXtS4DdqifW5YKYvb3aul3P+STAZNfhxQ8IVbj1UZLtChQoV8NbGt+qBO6KyJigD6j7h5+uBARMeiMpjNWVO8KKP3nwzHTG3YxxWTzIwYDHK5mD0iTtZ7w/KsmAMqudfP1h4aUcTxrfKiODqYg1XlM9mst6N17WPXlBalgoddwpdS71J8LRJHoecDlcVaxizMcZMWJ2H6g4nYVPw6MEJ9Y1DgQ+ytQKTp+xHVCjMBxgvmSeuNPSf5sTBA4gIDz5w/zU/UQ32qbAmfnpVfMHn9G45LAvKkRxVtyYo+6+jbk8R6xkq7OJgXDS3Lq2ObsOp46/PSROW5CbSOr4Gp9ZYjvcbMpiNk20QtqTALIxLuDrekv87R4zzBAJGaUZvNGYDZkaZ3MoxCkyYbxidsLs1qpYV9BfKyAulD8B2ipPxHUmo5/jDiHEgL6dleejTTGgX91jPxJB6WtDKfE32is8lTPramwS+XCjnVPjLemB7PfDrtcT8aYJ8g/hzeD37zvUwYXwoojHlU7ENVfpIhRmoyHaFChVuKZxTH17ak4Rdubjibyc80WBM4J0UPPvZYHVUPl8oHWLclm/MZj5AuCAPSwa8anrQXHFsxyjEq8VPE+gRY1hkKjFiaTCWBmMgwYtl5Arul23A49WOZhVxjbjiPYTQsjrywL+JrP/jSEIYU1e1J1VscJpdAm15qHI4k/llhbE9BZrFa7hPZLKSgI78dZeA/uw1V2B14ar2j0tvkTzy5usAbNiwgfb29muez+PqloG5Fbn4mTE3p9GcM1f6O/KJxExsiMZ5u7ZqvBDoix4j2RcMyeu0NPfRF/gwpeCRezWMflzBXhIAhBMmOb3GODb1f7BMjAlxa8ck+Y95GHefBpZsL3nvvyk58Q3fREaE0+Ze6Hq2lKS8EWzJaSVHk2/22iUPZiIczOr27dHYnwJJoQ2fI9inPt9wUD0KcxJmsCt5O+NkLGeH5PSeaZ/XIp68ciLbWIJ8OH/7thwDmGb8/fSfdUyF5+uB7+WP7aWn9oC/Vi18uoOWFW4OVGS7QoUKtxRiPiqv40TiYia3v9GQ+GpN+UpNeaqm3FcYvQHOIXQHphIwLubUhK785+7gdo3JY/S7o3LehGAewbY4KIPqfuzbg9IWPDHhaCbDCa+lHkUYBi4DnRgH8ci/ybSQIIGiNdCMN+sp7sm9wvQjfBgjMB/jnEEr3uC3Mwn3RmW7emaxV2z7AGSzeDzcj+uBkypcNh+k9Hxvj1p7/tXXAHjwwQeveS6nq9qVkvezI4h78SftI2uzuj2T/LUFt5O8N0PlvS3ndRtOvA0g206OmbAmGC344OPi4Ir26SRI8KHFAHTixLgt2z/AVd9OjNE8b9CEt40Oq0dLnhgShj8wTn87MZE8W/4Knt1uGMtIDCEkjBKhjrBOlF9tUBZFZdR8s7cr+TzE2uBq9gHLSr54hGBXLrd5P12lLv3mSnrfNNuGyFV1G5wk786Z1wsENkZlXxJeLsM1DaYd4uT/+HU2OMm8TGl3EtZG5Us15dGa+9qfKwMnEuxVYX11LVS4DiqyXaFChVsK7QLronF7NHrErRJP1ZT5wUnFzBvlgDJV0Q5uq1gg16ZivF0P1HHS2gVMYBzDFbutKTJsHtk2YIHBBH9dD9SNKRX7fSJjCKMIhtCFURqMK4z9qGR81ElNhKkPsve2DlPjka0oCiwQTzDpC/BmVqe7xfggCYUJDeKtf0uCtzA+WLgqaFkFjcBdQWkTY8jg4BYn2w8//PA1z81Jc0/v/IpcfGroy/XtV7K6PVs8v3wmVkblaN4cTaJFvPL9QK4qxzxh5rJ6lGNHLpK5aMJ584z2YwiFekRkKbA2r/UyWz8s+6fbcb/2UP77ZtyecUwDax8XWjtgYgAGX9M8f2D0m6/ng3g7agOuqBfAFg00mK+9xwuljlu5tpXuL18VjHfLgBkszEkjV7L16bz6fIXmwcd114mDXJifR1Un2nuS0B6MrzQkTptwX1QWBWNrCny/Htha+qZmeVQOzGidtBwRmPDBzt7c0Dlb4K7C2ByVF0pPXrnZB3kr/HSolkWFChVuSYwYbC0D9xRKx8eQxYE8aDaJUyosmKai7SjhDMJ8MVYFY6/6oOM5ArMxvlIkZgXh/pBozv5YAVaKN+QtxLJf21XozpyZfQlh4CXl9T9RtvzTkvHk5LpPnAAF3JaSssrtNhK/4V9Acmufk/FVoryRAo14UkOBq47jeNrHZfUj9/nBeKrmxR6l+A3i6MAgx/bvA+CBBx6Y+r3NYG9W+Sol79NDUybMk/aRDcFV2IkZ6vbCYMwS2JGuvY2vyVaIJmBRNNrFN3ULxNXtjdFIuL97feEE+KQKI3ie9mi2mExgzIm+Ni/iJzut+KnOxbzem4Kr1RM1uP2L/nhPfcOl4noe2g24wj4X43YxOnDSPgq8npX5zYWxVDwX/MdlYGvd21PPIfSbq/gNeJZ4V35+3k+Bw+pWlsUy48nBT57q5j9j0rJyf+EzF6uj8VYKLA7G04XyQKEsCH5dLhC/ZqbHAO5WYSQT9OI6a71H/HW7rF5XX6HCTFRku0KFCrcczOCdMnBb9NSEj8KouU+1M99gx8zLMeZkFXh/Ep4vI91izA7QbMZuc4+q4Mf6zeIDiqujZwZfQOjG2G+Rblxdv5yH1rpwcnQMwSaMw3/hJTa9vxqpR29bnJ+ruEPWsydPwVuzw7aGD08C/pjEY96OZgLWIsaACovEGDAnbe8k95rOj1fj1I4mT2Cpv/UqAOvWraOrq2vquTmVSUVvRbQ/dazIVpJkkzF37lmejjn5BOJCVnkn0Sa+afogq9ulC9xcMd8oLg8+tDuBk9GeTKwbMYZx21J7PjU5qIE12Y5yKDeZzsrk20tohKViHLbA574cCAVc3muM7PHUk7XBmI9HE6rAeHBS2oCv2+2lx/EFgXtq3rTYLm5NelcDhcG2uqvii4Nng7cEjwfE/NRm40ds9uq4D/ykCk0CK6MxL6/tVcGvu1dLL3XqEM82D+KP5bZpA6iDCoeS8EBxfaINPlS8IKfpbCnDhzZGFSpUZLtChQq3HCYr0Nf8hHiuyUKXyei5syZ0i/u2ny8D75Z+3C+49/XFFGkEevG2vgXiCtrGqOxVYciEuRhDBFrxSul9OVVkmRjzg+VIP2HwGWWkHxq6YN4/EgqM5QI7LXh6CNe+gRd4qYnghL4duD0qYwbva6A9eCzaHIER3BozL3jCxCEVmsRYkXOyxxS2p8DaaOx8w4cjH3rooamfpeZRa2tu4pKZXyQ6gw8GTnqH10cvGpoeS9cgThJ7g7EjhWuSMSbV7ckN1Fwxdmtgrni0YwNuEzlvsKpQRkVYmTO0zyk05ASRUyZsEqXAZxc6xOiUHAlpQmueVRCDC21C7+P+eE980xslL6tvFFryHMCQQo/4CU6JZ8C/UPoq7stKfQfGORNWBf+5H5hwQGFBMBrEGDc4i5f2jHB1IzwdAwovlIH5olPXydoZnu47otEmxitlYGhGkM5kDOAl9YHp2wuj+SPWeTLYr67E90Vjjrg1ZebwZYVbGxXZrlChwi2Fywa7k3B3oT+x9ntwhl97QKGO8dcTkRPqA42C+6GX5oa+9UE5K17wctCEiyqsFePd5Mkkx03oEqVbvLr9CsJilIsIUYwLgIwoh3I1+5Lfi8Qmr81uFeWyuWrXKTrVIhizMlkACbcJPFooJ/Km4qIJDeZZzmeTNwZeMaFPlLdLVw5ni3uv6wY/KgNtApuD8dprHx6OPJaP7xdd5/i+wqeDVVHZp4Ka17kvDcbOGQORvcGV6wauTS2ZLTA3q9trgoK5bSqI8q4KcwXaRTmbPdBN+GnKYjHOEGjK1g8DdppHTTZhnLVAl12ND2wJniM/G7eWrPqqTxMMvqpMDLq/e1FQRsTz4C+af928HO9nCMdU+CD5CUxvMLoiNIvxTgo8USTaBX5Yj/Srx+0NmTCafHZiefanT2LE4K1SeDMFNkRjTTAGTVgX9UNZ7SKwORqLg7ElBVe583KejAF8PQVaBZZ8zDo/psIsrmbM3xGNUeOaHPQKFSqyXaFChVsGZvBeCqyM9rE+7cnPPTvNr30pq70XTPhyg/J7tUTdhLkBWgUGzZNCRkyoA49F5aQJS4PykoZc6eHH9LPNc67Pmmdi9+VkiKMpoMCpv1XGL0HLYpj7pJPhVmBvzvIeA6Jd9cVONvZdykkPNdxXvicJZ9X9tY3ZjnA+K5SNwX8/J2LCHbka++XSa9M3R+XChXPs3r0buEq2y5zsUGUJ/3wxX3xTdCJbgtZGJ46np6mwi4JbhjYEZfcMX/e6HAPYLLCgMOaJsaseGTRhQVA2BleQD2lgYR6YBbeQTOA2j1aMt1OgMZ+aNGCMBG8ZvYhwX3OkAaMfuACsWwG9vx5Y+68Kmnv879SEZjFm57VyMle0FwhDwApRXqxHrpir1z1izANOW6DffK0uE7+u/PcXTgLBfEOyoxQOJGFr6ZF8jcCThTJHjHc0sC6nA10PIdtLni6U24JeQ4iWBWNfElbKR69zzar26mkZ81HgrqjsTH7qVaECVGS7QoUKtxDOZM/1qhtodxvFVd56VsuerQdqwBcLZXEwjuekh8WSUw/M/dAXTFgajIsCGIyZD2i1AM04MdfgSmAEZiEM5fSJOsB540geMuv7zyIh+nF7J+7tnsCVyMFpA2hN+DF/iVe392BsVycrnXkosxE4n2Phavm4/oh6MkkL0CjGj8vAHHELzJJovP66W0hWrVrFnDlzAPhAvVxkbnX3+LlCBNZEZW/ytdUgcEdUtidPsgFfS60CE1NDg1dZYVv2Ie9NwvrcBnoRV8EnEJqCME+Ms+aDtvODt0g2CtTE04s7iCQAACAASURBVETquKWpS5Q2gXa8Fr0J3/AdqCt9edB2PG/4vvRHga57AxMIJcJuFeZkz3ZT/vrTCkuDJ+dcwT3Vz9UD3fiG4qFCaTflpTJwUYUJ8Qr5FjM6UEZMOI+wMwVG8QSTNoEna8qmwsuh3km+iVgfPQ7xvPrPnVkSBE6Qe8O1SURD2TJ2no9g2vjcQuTaXO1kV/PvXy4rO0kFR/V2WaFChZsee5OwpS78oB5pAo6qXLeaedRgaym8VQrP1QNnVNimbqlYH401UWnI75rby0CjOFEBr6UexQsz1gfjjTKyXJT95pF+DxeJS/hR+DnztBAweiVxGKE1p5CM14V1Dwid64Se+wXDWIRxFmEublUZzjGB4OpnY/5eDXiu8dqobC09DaVLrqqUs/DBtkbzwpONQdmRAjUxdmhgYzTmZO9smzBlIZmM/Bs2+CAJGz/FtsgKH40FeQhysgK8N7jneTqpXhS86nxDdJV7cNpLsyYax7KdYZm4Yt2GMWDCGXVPf5O5en5RodOMEQPDk0EuZwJ9RANNYhRitJqn2RjGW1dK+s3Xbhf+OAieZgIwPqjs6neS3heccM/GN4YrREkIR1XYLIkBE3aWYcqT3RR8YzlkxlmFCyaMiHDCAnNwW8qvFMpvN6Spivam/LTsUfe394hxLAk7kvBaGTigYep6/Tgk803l/UXiwHVyzmFyQDqwOs8tDBtsK4Xv1QM7U2B+bm49VZHtClRku0KFCrcAOsVVug7xY+rBPOD3o7p/7EhOvkNOGpkj7ht9oFCeKpS10biATLUkDpsT9h4xjiQ/6h4RHxRrC0aJMaTQj6cdLBPlAoEh88GuMfM334Q37dWAVWIkoHMubP4XkUf/94hJAITLOX97FKEbZZSrNe0h/38Xnq2teKvfBK6yv5f86H1bCuxPrqifNGHQXHm7ZMK90YnLouDkbFFW/ieV7QcffBAzeDcFVsSrloAKP1+IwIao7MpV5gCbohe8TA5PLglGf87R3hC8LXSSHLYILI/Geymg4n/uDFBgnFYvuZnAPcmDJozjBPu0BRpwUj0ZVTmC0BGgKViOABQGkivuS4PRItCXB2wjwvm3lHf+q5Jdf5IYKpVZYnTi0YJtYhzWQHseyjxhgZXB4wCPqvBGPfBkLfFoTbmC59P/VpFoNfeSJ4F+82u2Of+Ou5KngLxSD7xWBi4o7NdAR4C7o7EmGo8UyqwbWLtnsnq/Ivqg6vVaPM+aJ7r0YhxIwov1QE3giZry+ZpyV+HvH++mKp2kQkW2K1SocAugU1wZe6xQNhTGvYXxeE35Yk25I7pX8/0UeLYM9GflbsKExVm1UvPhyLmZhO5KTmotH+83qt/810alXeDNeiRlVXIBfky9uxTGTeg0j1W7gCvSAU8/OJyj/Gq4z7WpcEV6do5aKzAuIZzLGdtXybaT/gmEMZzAd2B05dSH9mwx+M2sADYCDxXK7+QoszsLZVXhpSCjBmdUWBaM8+fPs3PnTsDJ9nETRgxW34AFp8Knh3nBM6P3ZsLXKHB/VN4tPTmkKZPdfcntS424KjuJ1cGV4SMmPBKVIyo8EJSL5hnWS3O036wADxVGX1DmobTjCrXirab9CsmMZHAWcpm7WzucaBuD+CBvN0bLIsESXHzP+PYzHjv4uSJxyYSauj1jXvDB3n0mLAvKEDALH3TsN6E3l+cUGC+nQF18FuGuqEzgmdwD6tfh9rrw5+OBPSo8FJUvNyhP1JQ10VgW3ep1ozikYaqRcn1OEqrPWPb7NdAnxhYNHFXhkZqyMRqt037MvADzZpxEVLg1UZHtChUq3PQ4qMKc4KredESBOQE2ROMLWZHqFj+GPqDC7jJwJKvejeKpEMlgRxkosj2jGRiSq4UfxxMcR+jGSUeTGAdzaUgjxh5cyTY8xWB5dIJ07grs/NNE0ynPQZ70ijYAjfggZcAH2TbiSSUBT0c5a07CI7AZ5STCZjEu4d7zewsnLfstsCYk7iuMkyYMm3BXuOo7OKhectMo8NJLL2FmrF69mtnz5vNeKWyOPznBpcKnj9ujcjgJlzLh6wqwqfAUjSsGq7IveQTfPO1PwnD+3MRVdXptdGJ8Crc5vJQCG4JyVoVZGAfUr4Wm4Cc7jfiaHQPGEN7SyHHz4qROfP0dx20W88S4bJ7MA9DdC32/7+kkB/9Mefm4p+K0iXEZt12N5xOeURO2lIEC3+j1RmMgwXfKyLqgU2krCbionm/fmm0rX5+InFboCEYQ4XdriXWFr+FJzBfjXI4f/EkYMW+onDzd6Q6eEz499eSc+ub7oPo8xWMfU4x1e/QmzTOV8+qWRkW2K1SocFNjwuBAEtbcgM94lsCKaKwIxuZoLAhOSn9QRs4pvFMKP87qt5kT2WEzTlqgBfhAA+dNpkj2Kby9L2R7RwdGEz60aPnndQmcVDj4Q+P4t5Xv/k+JCVUmcitkGx4/OIiTjaZM2MF92GUeRAs5/q81l37sNf/7Ljy7+1v1SAF8seaWmneSMD/Y1KBjaXAkCSuik4wXXngBgMefeIKtuQCop7pj/ELQIk6o35uW37wkGMuC50SD//ubZaAZ38DtzHaSt1JgRTCWBN9EPlQkdiThHklctsChFIjiFqsDGugElgWfK2jNnn8f7vWTknWSWBt8CLFF3F5y2oSzmSifzf7yFowFXw70bBR0DN79k8TrE0KjGecQGhH6AlwB2lD2aOD+kJjINpMrCHdHZVnh180YPkQ8gXDAAh3kJtag7NJAu8BCfINpxjW547W8qb4RdftItlHV5Or3WR+VA8lPdsDtVMPmRVV3FR+uip+O6w22Vrj1UL11VqhQ4abGpKr9k6L+pmPA3HrRF42HCmWp+HBku8Dh0gs5OsSj00ZwVfCBIlHgnuiWPIRmJtwZlWW5ta8OtItNRf7dHpWDKlysw5G/c7X6jt8M1ENgAo9auwKcMBj3+hCa8veRnIXchbEKJ+cNwBaNdAMnLbA2JGoivFAGJsxYLEZzgB2lD59tnFZKc0CFruAZzWbGiy++CMCqxz5P4tpSkAr/8FgZPL/55LQYu7XB6M2Ee4m4av1mCvSKcSq5zSLg6uqdUTmRi2g6xTiJ0MZVongsuT1pwJwArys8ZUTxcibJ63oQ4TKwOioJX4tqsKsUbsu2k9ZgFAgShDv/u0hshku7jN3fUMqcXHLcoNGMLuAcgVn4kO4dURlQ4aAKmwuPxZwT/TGsjMrqYLSL0ShGFKFA+J2GRATqYjw7EXkmz2FMx0IxTn1EBOAkLM9iLAvGOfXnb7cKs/Mm/O0ycKyE95PwUGEsjzd2TfQGV/wrO8mti4psV6hQ4abFJ1G1J2HmzZFzMrksDS4B66OXuAwS6BSjN/ufW8Qzed9IkX6DBaIUIswVJ0ITJoj6Mfx49lMPI3TgRPmUwulnlLEBaO6ExV+4agEocL/sWE4racGyBcX94IFc251b/eYDq4OyMSoNGMc10CfuSR8TYWFQjibYmrPGl+XfccT8aH4yZWT//v2cPHmShsZG2u59kPuKD5eCVPiHRRTYFJUdSabi6yQn3/QG45UUWBeU2bkV8RLCgAn3Z+tPk3h2+rYUuCcqhzTQile0Px4TQwQw2JkH+haJMT9HWQJTxTaXTIgIl8wjIA1XvUcRTuHXhOSs+WYM5gt3/IHbSd7/C6XjtK/HCwhvq0dNGkYzPpw7bD63kIAx87W6JBj3RKXfAn3RaBFjwPz3vWz+uSJwVgMX8JOgTTOI8IJgXkr1Mfx4MEf5nTdPL1kSjHX5GlkdvLHyG2Xk9njjRHsSm6KT/f7KTnJLoiLbFSpUuGlx+KdQtS/DlH0D/AY8W/w4eFsSxjAeFmWnuRLYjHHR/AbdgLh3W2BTMFqDW00axYfNSjxyD7wq/vUUGL8Mu/+934Ef+z1hqBYZzn7tlIchQ27bG0fYhFdht+PDkTErdgFjCJiH8VwKLAtOCLZp4JGglDly8Pv1yIpo3JdVbTMfDu2bljIyaSFZef+DPNrWdEMJDhV+/pgffNh39zQ7xCThXhqMV1NgkRi/XlO+VkvMymR6EguCK8TnLNAjcMmcuDfI5HCl5273q1DHiHjJzaAJhRkJYSxHUJ41YWktIkA/woqgbEuRAPTmOYBG/ERmw9MeY9n7pcBjHcrqnLF9Djhongs/LkJ3VqYnr78f1d16sTEad0ZvyxwG7onZrmLGPhXeKwPro/KfNyTuj8qWFPhA5ZqM68Zs2Tr9Mer28exd352ER2vK8mhTm8wxYERh3IQlP8UpT6PAnVF5JwXGqkOiWw4V2a5QocJNiWRuIVkZPpmUdFa9NXLSXnHWhLnZB/1GrjEfFRjOVdd1kxy758rfrACPRKUfb/QrDQZw32vCVfIGoBPlkAXG/qZk7DLMWirUn4pcxjOMC4wulBEERZjInuzX8tt2C17LPlvhLAHN5SAns9WkTTzZYWNUdiLMxuvXVwbj89OU6g9UuGhO/ifxXLaQ/Mrjj1c+7c8YNkXlSBIuTFvWXoBjbAjGq2XguAoNORJvT7r2BdwYfUBxQfYlXwE+MGFdUEzcrzwKvJ0C50wYxXOyz2Wi3Y6yXQP3B2VA1dVuXI2eJcYRE9pzok/Cle8gcNe/jCz/g4IfNRfcU3gu95VsTRlHEINB80SdEXPyvk8DdwVX5huCr+W92SbTibFFnZj3irEwQAxwf81YF413kvBcHnCeHIxcGD46lSQZHE5Cvwn3F3pNvGWZr/1GgUeKxO4kHEnyiQtrFga3s7xVhms85RVuflRvoxUqVLgpcdKEZlzN+iQYMG+8m8RZdUvIziRcQrhDlNdSoMCP7xsw3jOvib6/UBol16Kbf+1sfIisV3zIS/DBxlc0will67f8rnvHHwhlDMxBkUyOi/z5EViEcVfwjG3B6Ee4grADz1gOGB1mHNHAyqjcXyiP1ZSHC+OyCqXAuyrclYm2GhxMwn71xr5a/p0vXRmbKrP5zS888TO8AhV+HmgRTxXZnj5M2JZG48FC+UCFV8rAfHHrxKVpn1cI3BOVc+qJI+dze2ljcFLcgqd3tAAP1JxMm3hhjK+7wAhu72jET1cSnt7TYz5EucVcYZ9cv4MIjeJpOReAHXWomRIwLhAIeSiyDowiXDDYm9xi8u40crwiGs1m/F09ch6hDd9xbJuhYt9XKC0GK4Jy3ITv1wNvlh4b2P8RLZL96taWDdGYM40ZmfkwsRf5wF2F8XDhcYBvJWEoRw/eKDbmqMV3cjNohVsDFdmuUKHCTQfLXu2V0wYAbwRqMKhM+bXHzAtsZgMv1f2I+ZQ5yW0XjxI7RaABYUVW6FYH/5wCL70YxJXs87k1chxPVIgCd88xHv7HQs/DQtfmyPzs53ZCDscQPCTQj9l3asDw6vc2YAFGa847/lpMLCx8A/ClmtGd66cT/jjbMUYQ3kte+vGD0vOBH4w6lQ1cN/jz17cyceUK8+bNZ+3atZ/Gy1HhU8by4ITtemUr3QGeKJQ5WeVW4N3y2s/rCrCmMBRfpyMGR5NvKi+ar70zJswXY5Z4ckiPeDnMnGwv6c8nLs15/V0AjiBsMPX4PITF4oR6GKEzr+Ohfcq3/nni8FY/gfG6Jr8+ZokXQl1EaBFjrhgfpMDZrOLPNjiKcNk8hWRzhPkYJ/Xagcg2gSXR/eaPFMqXasodOfawXby0ZiZ2Jr+uVsywiOxV4bL573lb9EjBzvwct4qnvZz+BKQ5CDxYKKMmvFkllNwyqMh2hQoVbjoMmteo98onu5NdNFf+ZuU/D5jQHfyGexGhBhw2T2hYnNXrBoxFQZlf8+P3ZcEzj4ds0qctLBBjkKtRfV1iNBp0NIH9To11f1xAJtZj+BvzWSaLa4RHQ2K2QMzEpDP/W7soAznJYWH0VswHCr1mg3FSYVychP1OLbEiur/3gag8Xihd+S5QN3i9DOz+8XMAPPHE48gn2alU+AdDyP7f3clV6ZmIWf1+qqYsCca2FHiu7vaSSXK3OhjLgjKM0G1e9d4uRrMI5/Ma3avC7OBrcoEox8yJogAbJHFBXY1uySkkIwg/ItKJnyxdNK97d8XaWyePvWIceQ+O/L8li5JXtm+URA2mmis7UE6b0JDjNd+sB4YT/Mcy0gI8GpUWgePmGfKNGFvKwP5phHtt9BzuS+bPx2Tu9qJgnNBrqc+4wl4NPFCka2L8TuYs7dujcsaEVdOIeIPA+mj8ak3p/YRMqiZeLBUwni8D/Z9QHa/wy4eKbFeoUOGmwwENLA9Xh5vMvPVxX/JyiY86vh0wb2Oc8murl3q8XPeq9NGcdb0hKOtFGSIPSxlcMa+/HsFJ+4B6acxtwRgy16cv4vaTprHEYB1eS17nPl+UMYyzBFIejhzLw5FzMZZEYVzhIh7lVmZSfiET/zvFh8LaxK4hBAC7k0dG3FcoTcHJxqJgdGblG1y9/3EZaBZj949zvvbjj396L0iFTx2dwbO2Z0bcTUejwJ2F8UShjBscSsL36oHn64H3krAkuM/6mAXa8E2i4IOInXgM3lzxSvd2gTHzjWOj4Ik7RWAuxizcs92FkfDTnIRxGqbaTi/ktb/wtyPFLBg6DDue87X8vgZKPEM+AUtzesqhnNpzTIX/p+7Dl1+rJc4i3Fcol01oEKNbvLhnXxJf7/jXr8w17tOxMFtJpivK76lHIi6LV//ugsK2MnB/VI6qTKnaH4VkfKJa9kLg3uj+8m0p8MNcMV/h5kRFtitUqHBTYdScJC+bQTpbgCs5eeOHZeDgdTyTg9mvXTc4XArvlIEt9cC5PHTYkInFfVF5JkXm4mkLncFv9kuDx5eNqJPgvmhcMa+eruMEZC7GM/8OtvzXdS7tz7F8RPpEKbBr3pQbgKYAPygDY5lgC8ZlvMVvBKHAWBSN4ybcMy09AaBU2KWePnK9QUczL7J5sR5YGozFA6fZtWsXAI899tjP8CpU+IfAumgMmXD6J5C0NdFJ7J2F8sWaR0M2Chw3YTbGcYRBFQ6qD0WOmCdzmAlDWQ0fNqFF3Nq0MRofqPBYS42L+NruxIcie3GryaRfexxy86lbn2KbsPprvhh3/nmidtk4Q2AMuDMoXQLvW+DeoFwxL426hF+ba0XpjH5Kc8k80vONFNkUlTGDLjGOqnAwE+6VwZsjB6c9P83iiS6TmdvJYFcKrJsWDzpu8EYKbMgE+7QKKz8igSQZ7EnCD8rAnk+Yoy3iG6anC+XerNZXuDlRke0KFSrcVDiiwsJgNEy7cYk48b2jML5QKPdE5ZgKL5VhanhMDU4mOG3wTD1wMN+MFR8Cm40PES4JxlspYMCaqJT4MfWK4BaPg0k4Zf7m+mBQ9qtH9jXh9ddH9sKJbyqXjsDwBZiF0SpGizhpMSbTSGCxGE+HxJh5kY7gcWqtmdBczsrfQXMryczs321JqJvxSPFhNjZs8GoK7FfhwUJZFY0XXngegE2bNtHT0/OpvSYVfj6oibcTvpfCdYf+JjGZTLI/CYXA3OA2i0cK5WsNykI88WNxUJ9RELdvXDE/7RnB11ILxntl4M7g9o9j9cSawon8ZMFNazBquPUiwFQufImn8ghG91cibYth7CKc/qtEke1RpyywOBjzUF5JkTvEOJtPlD4nJa9p4I26e8EPqrAxX3MXc+Tm+zlDfI8KJ9XV47XBeH9a8ya4BexE9rsfUS94mkzjSTl5pFe81Gpv+mhVe9zg1TIwkGcfNhU/nRdExH30DRXZvmlRke0KFSrcNNAc3zWowgv1wAv5yPy56R9lYHvyY+vzBv/feOSHdeF744F+87a4L9SULnFP9mx8GE1x/ynmedXzxQe/angd+rJgnDYYyAU2D0XlpTJwMavPipDqsO9fl6DQ+0Rgyb1e8z5XjEMq1PFjfBDaMeYGj1JrEx++DLjaDT6ANplsUjfYEG0qUQTgvMK2FFgQ7Jqc8QmDvcmfnx7xGMDufCf44Q9/CMBTTz31832hKnxq6A1u8djzE6rIbwvGSb0agzeJWoCVEVrE6DJPvrlo0AM0B+PzRaIT33QuEeO8Cc/VA62ivHWl5HZRmvB8+gJoMDgPLAvKPIH1QSmY3Lh6ckkohE1/mNXt7xrjB/0a6xAjGpQiNGBsy4PGDRj7iTwanGRvqwdOqDBgvv4NaA7+GPcl4YGobCsDA/mEq861zZu9ueBmTGFHcutYu/hJz7vJh5c3RuOSuao905oFfvr1chloF08n6azYVIWPQbU8KlSocNPgjDkZvbfwo/Lbo7IpKndE5c78cVdUNkdlU1CWiBHEeLcM7DVhiRgrxaZU77nBj7AjRqs4UR1CuCsqcwKcMI/o68tEd1cKnDZPLTljwq58g2/BhyUPfz1x8QjU2uHB/9L/LQo04erc5EBkLccKLgjGAQ2UeJKJq+yuEk4AbRhJnJCsmKZqj2d1TnHFTvFhry1l4Pv1wJAJj9WUtfFqXffY2NhURfvTTz/9832hKnyqmMre/hhhtVlgfrDrJpjMj0anQFc0VkXjXF6D51QYN2F1VK4APcFPc9oCrAwwZsbfl5GHQmIif03Kw4iHykAUuC3AElzlLvIKN6C4o2Dh5wRTOPbdhOIq+pqo2SLl6nrAIzwHTNijwkKBxYWxOBhbU6TfPH7vkoGZ8WYZCAZ3F8qWfHK1MSo7k5Dy89MoMCc40R7lagLJniQcV+HO4PGYe5OwPNqHFGePA3SivWmGdevjYMbUY6hwa6Ei2xUqVLhpcFgDt0WjK/jNtGfaR/e0jwS8p15p/as15b9t9LSPYyb824nIrtJze5fkHOBZGMncM/1woZQiBPNBsrrAPDH2lfBm6cS4DaM/Dy8GfHBsYI9y4D+6neNL/yww0R6pAzUzdumknm3ZKiK04aR5LA9ejeGqY8r/nUCYhecT3xaNZvH84AvqzXsj5oUlgyZ8tx7Yn1zJfqqmPDSjtAPglVdeYWRkhAULFrBp06Z/kNerwqeDFoFV1xkGnInlmWzPnFXoEaMGDJmfyPSKciDnt+9W4bHoJzPb1Yn9sZzQsaExMm6wn0BTzn4fUGGlKB/kZshjJsyLrk634Op3iRPu2/9JZOMfBm7/I59MHDKZ2iTW89UwG8/4bsTYY8JOFXaXgdXip08PZFX53qiMiJfi/PlEZFvpw8Q/LANtBrPEC5wmsSj481U3GDL4u3rg2dJnIUZx8n7mI7zaH+Ra+btuMFo0mSvuPyoDW3/Ca1Th5kT1qleoUOEXhok8oDekP7viM5IHoT6uSnnC4J3S821XRePxQpkfnMgaroq3i3FWXS3eq+7NjghDufBiTTDOqg89topH+b1dBp4tIwGjGaNL4EJOIJmLcf4KvPt/JFBY8nnhNx51ZfoKguDqWsLD/wqMpcHj+46VwjDQLTbl5fYKbMcV4IIKZ9UJ9bfrgW/WIwPmRKkZ4+6oPFlTHq8pK6ITl+thuoWkivz75cPyYDkF56M/p1vcQz0zZ7o7e7SXBWOfBf5RTWnG19leDQiwRJR9KTJP/Do5pEJzCNxZKMMm9GFMIFzBPd+CMGxuXRk2oQe3kbRi1PIpzeicwLwvFTRGV6+bJ5Sz5j70HjyaMAosMrd5dOD2jfPAt+uRHvH0n7FMfNcH47Zg1BEWYD4YavCXZWRYYUspnMsxeyPqQ8UXVejOz8Nv1RJfbFDmBo/RXB49U3x6vOKlbMO6t1CKG7hMLhm8WAbOmrApKncH5bT6+9BwpXLfMqjIdoUKFX5hqOPlGe8kL1k5OO2o95PiqHrVdO0jboAX1G96CjxZKEuzvWJvEp6p+5Hwf9qQWBqMYZz8nsUtJCNAh8CvFJ632waczhnCvxIT83P7XRc+SHUot1d6zrYxfEVomiO0zIHf+qPArhRIueSjQJnIfmxBaMeVeMGLQhrFFe7x7HeNwGXc0xrxYa/NhWdm3x6M+cH4rVqijrAhwoLoFoKPg5lNke3KQvLLiclhwJ0zhgGnQwT6greMzvzanuAK+ZC5tem2CMvES5j+w0RgVfBByC0psC4ohzTQHoT5Aq0CLcHtUsMIb6qwKSSOW2TAoC94+kmP2NQMxKz83xJ/Hxirw6v/S2LLv1Yu12FNoSwSCAaXA6wOCuIKebOBCrxTunc74AR8UfDNcE8wfpwCrWL8doOyQowOMdoEvj4R+XcTkZdKJ+tLgyvyt0ejN0f/fVD6+8KRJHyrHvhxGZgwnwl5uwysifahk6Hr4bzCSznp5+GoNAu8mPz6bxVo/ule6gq/hCh+0Q+gQoUKty5miVecg6vSO1PglPmA042oRpMw8yzeu+P1Zb0jSXg/uTI9GQl4Uj29YJbAsujKWaO4Gu0tc55SEPCUhrXBaAiuNk/kQbJ54iU3R02I5rXUNXMFTvAhxlGEost44H+L9AwZC1o9yq8BowvjMH6HF4yQlfBxg9PJfdztBkdxhX0exiwMQbgjKEct8HDN/eMD6kNyj9aUIXNbysep/NOxc+dOTp48SXNzM4888siNP/EVPlNYGjyS75TBwo+4fhYHY2fdy3Cmb8Lmi+fBrwrGHg3cHxPf0MgcjKMITclPVs4ptAYny6eTUlPh6Vri6+ORWVm1HrDAmCVSVrBr+OnMZYwJCzQi1HHrSomv87FdiXPbDFPj/x4N/OE/Fy4Wwh1F4o0UaQQ+FxPPlgVzUBoMziFcSEKHwHgZ6BDjuAZuE+VtIn81HvnVWmJTVJ6tR2piqMFcUQ7iCS5HEB4VZWUwhtTnLnaqsCZ46+RsYeq96GDyoeSZLZPXwyXzHP1NhbEkGCdzbveaaJ5cVB0e3VKolO0KFSp8JtAT4JFCacBrpj9JQcSAObntnnEDU/MhqD0qPFwofdG4kFMEdqbA7VF5OHo5xlwx+g0u558rmSi0YDTjhPyKwTl1It4MXMA4nIR+FS7CVMRfVzDO40SiEaMJmBV8KmtP8nSSxvxTfHvgju0OXH07rEI/VwcjySr2o1E5mv2xs8VrqXvELTRvU3Ho8gAAIABJREFUpsDm7MU+mjzVZMENku3vf//7gBfZNDdXetsvK4LA+qjsTOEji5saBBYEz6OejnnB6Fdhmfg10oTnUXeLr9CUrSGDKpxRL3fqKwIH1JtVH6gpo3jl+WyMrcRMMPz6WxqNUgPjAsvRbJvyBxmBpjsiG//7iBRw+mXlb/7nRBj3HO1gxkEVtpaRHlGOEXLspW88z5kni/QFt2F1B09DqQHfqEf+YsIV9gETVgfjAsLKrKyPGZxL8FYS3siRnmuD8Xjhsx+TRHvCfDN7e9SfOBA52ca6NjjRPpGJ9gOFsvIGfd4Vbi5UZLtChQqfCZxQeD+5RWPMjGfqgeGfUNYxOUB4OAlLZ6hFpXkxxTkVHi+UWQLbSuHV0uPwvlB4zfIo/j06cKV7rhgD5n7qGtCHccqEBlzZuqI+SHnGhAENdOXkkQmu1rH3q1deH/16yY4/TYyMGCUwOxgT5oONl6aI9GSsoDEb2G+eKtGN0oYxmgfFmjEuqB+53x2UQxZYG520vFEGVgSjN/hzckyhV/jYxrvpqCL/bh70imexH/mYKMBlmWxPt5u04rndw3iCzW4N3BmVUQs04oO281DO4UOIF1WYFWB+gGfqkbkC80QZzKcw4Os6YBzL18mwQK/4tVDD8+cNWIjHB7Y/ErnvX0VCI+x8C/72f0jEUaNH3NY1hFsv7hQl5u/fm2M191ugWZ3cXjShOxgq0I4PMK+OxpKgDCAcVuFt84i/lWK8q4EdZWC5KJeyZ3wmId6TfEPefQOs6e3SBzuXR+OMwvYy8GCh1y2WqnBroHrpK1So8JlAK36sXccVtSvmqQLPXp7gTB5q2pOE7+fs7LdK4Tt5KPC1FDiswjult8edVy+biMDDUek34dkycFyFbnHFeiz/3H4V5gY4iVBmT+Zozrv+QkjMLUDMvZ4v1gPncR/32qj8fkNitwYCRgf+hnpn9Bv6lVPGsf+gHP620r/dM7mHFU4gjONH2f25zDrgOdoncx5xK3CewMaYSHh+8EaU183TEvrE/65PjLdzTfvqKXuMe1iXxhtTtU+fPs327dsBePLJJ3/2F7LCLxQisCEXu3xU0c0c8bOUQbv26+aJp+gsC8YonnTTHHwgsiMPAxtuo7hi8IORks1B6crkfW5O+mnET3TC1NCk8U4KNJqv2zPI1Oc1Y5wiUMOLnNbdLdzzvxbUWuDE+3D+L0p6Ivx+Q6IFv27L/HgLcS9sH8oE8Gdl5JQKB1VYFGBzVO4ulA5xS1WBDyX60DOsFmVQ/Hdqw/hOGblkrkxPf+5GzGdCNsy4ppKR7SHu7z6l8K0JTxyZE7w4amsZuHdaln2FWxOVZ7tChQqfCXQE6ODqzexeM7aWwtmknCsDIrAmKA9GYwKva5Zg7EuBdmCpeHnGaYXvp0ibwEIx/l4jllM5usULac4bPF8PzBPjkgkdYrxQD9TMOJmHEGfhA1c/SoEFucluGKhnZfrJoPygLpzK8WSXgQXiNddjZhz90xKtw9w7hYcehiTGQK5tv4Rw0ZxwkIn95A1/IkcHzguGEriEW2Q0+7/7UD7gapb3iMGjxVVV/1iuiu+9QQvJs88+C8DmzZuZN2/ez/5CVviFoydAp8IBFdZcZ9Ml4v7uIyrMmbZOFgRjdwqszak7hzTQJ65IX8bLX/pNOKrC7zYk/qwMvG7eSvn5BqVB4MxYTsIxmBC3cl1GeE99zQYTOgVK85jLHozDeUB4Fr4Bfeh2pf7HBdv+x5JXnoX7/okx0SLcUSh7UqBBjF8JyvdSYASYD6zGfdhmRiHGfDEWFn6dr4zGARWWi3LQIglvZP3AAl+tJfYnf39BjVEVvl6PNODX0NwA/erDnxfNiX4AjirsKT2ppRnjIEJJ4DLGfVEpDb5TDywWmPdTEm0zP3lrhhvO8q7w2URFtitUqPCZhAjcXRjvFIFiwpibc4Qb8JKK5dFYDgyr0RaN4+Z3tBGDh6LSLMZ7GmjDGxQ/0MCOaUNhCXhXA/0q9AWlFWM0q1413A/9ngbmiPHVQtlSCs9ZpARaUP6vMjBoIWdee/lGJ8oREy6+bgy9bYQCfvefCZeDH1EfSpENolw04TjC5P2zEfeGh/y9W4Or1rtTYAKhFeVoJiSLolGIE/Vj5haZSV/pqHnd/Dzx4dMbwbe//W0Afu3Xfu1nfMUqfJawPiov1wN94fpV40uD8aN6oG5XE3zmiKuxo+b/vl+FucHoMeGQCvcXyuHS2KvCY8Djs2psHVZE4Y1SeKQw1gXlXQ0kgfkYw8EYV7dCpRwHuDZbPQQgQIP6ZjLiNpYR4L7NxsjvBR57VFjZ4raWx6NyQn2T2YvxT2uJv6xHDpsr4wE4YIEWM/5qInJvdLvJ62Vg1GA3kflitJufMMU86zBmcDwJfcGYE4zu/DysFeUiwkn1jPqdZeCK+XU3gl+30WCEQHdOWunIPu8DOT70RoeUp2PEYH8STudSrMcLrZJLfslRke0KFSp8ZhEEPtdS4+9HPaf3C4VyRH2QqVthoSjDwOeiMWHG98rAiAm7DBpFeDTnaE+inpUiw2+U59V93SdUWCPKjpwMUmCsi8agepTZ35SBAxrQqYEu4ZI5OZiLx/5tjokTKjRMGAf/bQnA6t8MLFssbE2wVQPNGLMM9ufHY8ASEpcJLMixa8ME5qDUBfrz53TiySbNGGMI87IP9qsNiZZpROpYvsEvukELydDQEC+//DIAX/nKVz75C1ThM4v2PAi5Nwmbig+vh5Yc93dchdvyeonig5Kn1ZsT10ZjX+kRlzWMQ8lbVvebW7m+Mrvg3ZFx5oivxxdLz8bertCLcdiEjcEYBCbyoPA5hH0KXWKcssCICktQjuWMbsNQhcsSuPsfQ1dUdiUvqDmowkOFsaWE18rA2aj0iOVoTLegKUYPxoTBj8pIkU+LejDminEe8QhEURYEY0SFk/k0CHHlukN8A/3d0lNQFooPG08gHDShTeDpQmnE2J4HrTuC8Uo98IWaMmpwSoUv1nRqc6/mufjj5t//ekp1mYcwDydhWTQeDEq7UA1U3gSoXEQVKlT4TKMpCPdG5d3Sy19ui8ZThdIpxgtlZDj7Jl/Ndo+5WcluBE7P8K3WxP3QE+aJH6+UgUHzz3lJIxM4uW0FxtUb5AqzPGzmnlVwf3cTxnz8Zt+I21rGRdjzDWWsH5q64de/5v7RYXOv6bLgzXwT2TpSAF04QZ6FMS6wKSTOmbClDFN52rVsNJmF+2X3aGBdvNYHqnlQ1AwW3qCa9t3vfpeUEps2beK22277WV+qCp8xrIvupR75iOWwJBjHZwxS9gY4lRXVReIxP23ByeYhc1tKgWfRbx0r6cvFMgFjkXjOd8LXaRS/hmbBlEGsGU/qOWWByaqmxcGvIcmnPWcQWgRaxDicAvdGRc14M7nVawyhQWBvCnQHuLcwNkdjWb5GTiJcRFgniVoerryA/16DBg1mPFZ4xf07GuiLylOFsj663WxA/T2iUzyDe68F3k+BvUnownikUGoY23IC0LJovJ+LshrwKveN0TcXZxS2ll469UI9sCMFyuu8FpPlN8MmfL6m3B5dJa+I9s2BStmuUKHCZx7dAVZG460y8Gih1MTzgD8Qo1eMZ+ruw2wX4cFC6Qvu696WAs+Xnkk9hnDKhLPqFotufMjrfI4NbMiq8QpJ1EQ4kf3V/TZJAWzKy61MFnIIR8z9nBPA6jLx9e94hMpj/0VgTjM8nzyqLwJDFrJ657aTZRinceXwNEIr7u8UMw4kt4204YpYwJMVhtQfzfoZauXZTOg7ciTgjeCb3/wmAF/96ld/ylemwmcZLdmbvTcJm6+jbi8QY3v2/U/ajuaJ8Y56lnyDwLqobMve5DZxdbk7xwSuwYl2t3j6iYjxRINyajyyRQMbRDluQpMIpQnn88Yx4ZvLWRhnEF7VQEf+Xo3ABYQ7SVwW4cgJ41/+lTHLjNv+ReCv65EHglez18SHN+vmjysBo9luNQa8Z5ENQbmzSLxcBj4w/znHCHynDv0WWCiJCTyO8/l6oBlPDZoNDItwjynJ4DI+V7I0GKdU2JEz+reVgW14rGBnNF7LOfwjCs9mL/jSYKyr6Udau06pF/SsjcbyG8zgnkySqcj4Lwcqsl2hQoVfCqwKPmC4S73trd/cW+216T5c2CSwK7l61Q10mtJvgX9fj/SKsSoYq3M9+xEVTlqgFWNRMA4oJITLFmgwJ88A66MxpD4YNoExR4zz5vFnl/CvKXAFcTwId/yfNYZ/kJj7mPB6CtRxYr0QH5oawe0kCWFpULbnNr8Jg6ejctCE0QRDCC34ENkYnp4wJyrjeKJK24zn57AGmrhxC8ng4CCvvPIKUFlIbmasjsaz9cBqM1pnELMiW02OTxukbPA4eE6q0BeNXoF9OR2oSbx+fa74KczJulEYnDBhhRivlYH/pCHxaFS+nwIjCJdN+FqR+MvST46u5J/dgF8LCzPhvpQjBSOev/22Bpb//+y9d4xe53Xu+1vr3d/04TT2IpJibxJFSZRlNcsq7iXJ8cmxfWzjBMeBESA3QXDjm4vzRxzjIHFuzo0RBLnJBZybxHDiGsuxZdmyZFm9s4lNEimKpNg7h0NO+fa71v1jbRbJkkyJSixZ+wEGoobDr8z+9t7rfd6nqNOZYeO9jih87LOZzeMTB+X0wjR2nW5tGFMz/KCZ6BdjXKXJHqsShjaacsLDeNwgJDE7XJmAcxKlNNhT7VpdkiwWshXT/J4WY32O4bqbuIasqfTry1P4PO4plQVVQsvqHD6OXQgd4nQRO2z7qh2El87Ggw67LBJg9piwq5K0ZGJRfvrPViW5nP6CWOS8q/EL8lFrvClQD9s1atR4S0AFrkjGPWWYFreWyh4H92CzFyRHBU5YRAQ+bMKQR2XzVJwjVSLCfI3SmYbHTf3GInN3mRgCejEywgHi4vjrjWC9DmbhGMIUjPlqPJQTzSqFZACjVePGuNESLb1w3cdjkG8jhooVKXPQhBc8tKcFURpy1CK5oYXI+W5PMFZyphCnnYhQO4bSUw3oC5Oz1+VFjNawR2KCcv4pJLfffjtmxvLly5k1a9Ybc5BqvOnQJiG92pyFK1+G3Z6hIYFYcA6jOlNjZ2V2VcCyNBl3myIIU9Q5bMEqj0uClzEwHiP0yE+W8RkdlyPC8zngblM6iFQSiM91AbRL7BDNl8yIC/tdOIFgREnUMya8e1bm4eXCgbXO/d80/tvvw4/KRIkzYsJUce5vCiMIH2zJ3NdMFBLeiHEYO1yY5E6bODM1Sq5mi7MkGZtcOGHCYRcGMK5rxIK+W5xV1fvfbcIhFz7eyGxx4bvNYLQ/1IjrzfHKdD1HjR+ViSnivLsw9iJsK4X+KuP73N/86T8fNNhjyvVFpq+ShOlLvpL8/PdOq8dqHfBbB/WwXaNGjbcM2iWycx8cU7a4MEng1xqZ/hTbqrstdJwlcF0RJRanjUjZYXUWjrnQZbDWhD5xfpILThDmKiGMiFppP7ebsi0LeysZSYcIqy3RJZDcmSROp0Bb6Qw+ZtjVIRk5iLJIjNWeWCTGAQtZSq5MYk2EhRjbK/ZtEs7swtiahWMGx9AqnUQ4QehcE3BNYeyyiGA7F9tM6JEwcPWc5+/y3/7t34BaQvJ2wHx17mwqgx7No+diooS58Dix4IOz8pITHtKRiQrTFdbl8AMclmCMj1v4J7qycF+Z6KjytE+6MUoMy93ibDVlmWTUlQPVTtAIIZdqFThmSimRib8qK4cJCdcIsM2FT/6m8OW1zg9+AIsXOkvfY+wzCUOwhoTkoy2ZixRWl5FWcnUyHq7Si16omOMZ4mwToVVhfuGMlvCwC31ilAhPZaFVnN05mP2rCuPxUrmuMjrOcmejBPO80YTFVWKQuPNAVtokdqdWm9IQuLnFfm434TSOGeww5aaGMaGemn/lUR/iGjVqvKXQdNhSxYZ9opHp1UjhuLtUNlUmpVsq09K5jv8ksESdAwa3lRHhNxenzaPRrpMwN03FWZGcS5LTjZOJdJK54lyd7AwLPVudYYIVW/OVzF9/0Xnu70rmEvXuaysWe7Y6e10rQYicyRIfkkhGGY/TFJiGsz0LJ1CMkMgYoT3vxJmbnPEKB6pq+dMoHbbl0H1PO0+958GDB2sJydsILRW7/XT++Q+HShghd51jlEwSGfPnVrovL4yGwwuuzBOvMufjZ64tnEuT0UbE3+10ZSIRKfibhdGPs8sjDvBijOEq5i8hHKnaWc3hhSqDu68yD7fgbHalvExZ+V/jtfzFlx1bbwhhYFyTlcvV2J6V4wYNryRXGoksxxDmSLzOB7NyVTLmqfO9prLPhRsbRp/CeAlJyD6H25uJCeKsy8qSFIZr89BVX56c9zWMEy58pxmmyQ7ida9Q43FTBtR5Z3rlQXvUIwVpWfJ60H6boD7MNWrUeEugdHiiKdzRTPRJ1CzfZ8pPSmVLFhYn4+bCmKH+srFauw2+VyZGXZgnRgew2ZVDhJykA+jX0DzPTc7VDeOJrJwAQLilMB6uWOUTAlcno8TZ+Zhxx23xHBOuEK5riegvQ+jCua/SbTeJofkIQqNKHmkBOsUZJ85zlXkyxvLQlg4TSQ7dErnJpzwkI33nvL8dJvRIRKpNf40SkhUrVjBz5szXeURqvJUwT519Fmz1SzGj0m2fW98+sxq2c/W9cQKXFbFLM0MttMhNY1sZUqgVhTExwWEXrkyZplTGXxGWJYvmRw+NuOK04OQqau8QMAtnuym9GD1E4kkYhJ1Nrlz8cWXudYKV8D/+2GnuMw65cLEYO1w5aqGxvrQRJsNHc7DqBbDPhT6cAy5MltBWN11ok0hsWZk8ZGJqtFq8h7VlZPBnD5nM1ny2PbMF6CHkXC0CgzhDBoeqHaZlr3ANghjaHy0jOWnWeforarz1UQ/bNWrUeNPjsMFdTWVzVvrFz5Rh7DJhQqWRnPYKMVnu8HSOmL/CnRsbmSGEgSr9YJhg1i5Rox1nFJhTDR+jRLrBTInYs+zCSY/yi1ERhg/Do38ZPZBTP6rcshLuayaGEaaJ0Y1Xxq+4qWaEEuFScdodnNCaXiTOhhxDy2lW+xQxeE8Rp1MjpeSACRP0bEavOWwxYUCMNiKJ5Hzw7W9/G4APf/jDr++A1HjLoUVgziuw2/2VLvjc+vZ+jeSdcxnvKwqnVZyNpsxSxxGOS+wsTZEYyCep81SVPX3AYEsWJlRD9QQcRBhHtTjEWanBdK+vjMPrPdGEqiwqzolejEFVlvxBYsI80N7Isb6qMHoTJJxDLuyx0GPPUudE1Wx5iWQOV6GC0zD+pZk45sJvNjJ94vysVLolJGGP5cSRapHcoc41hXGUaKH8YVWMc6Qa6ne5cLE6nywyh13PmLfbgd0unHqFOXqDnTZi1oP22wn1sF2jRo03JdzhuMFPhsb4/piy3aK1rQTmJONDDeNjLZk9Juz2l58yzWFNFp4qI2HkpobxfFXxXEi01bXjFETD3WEX5mlsJz9cht6zANrcWVcqs9U4CbwzGY+Xwpq/yZw6Dp0XC/P+W+KECXsd+jCOeJgqIytASDiG04PTonAcoRVDPTTXYwj9RHteInTdBcIVhdEnMSwddF4kIdnjkdE95HLeTXXPPvssjz76KKrKxz72sdd5dGq8FTG3Kqx5KbstcpbdPhfzkrHlHMY7VTs6T1sM0+AMmrA+h6xrgYaHoXThkAlTxHk+hzF5ljgnRZifjCUSEqwmzoDCZRJnSQdOA+dwFYPplYzrOEqJkNuEd/xJwfV/WTA0Sen2YJlHgZLI9X7ahOMe520fcFwiCnAU5wTCVI3XNOhwRYrF7DfGosxqhRinkGqhG7+TlYUzTZ3rkjFVI3Fltjp9hPFxH7HbNRHnlkbGiN2mbfbz16RdFouXqwqr69ffZqiH7Ro1arxpcMjgzqZy+5jytbHEP40ltoxmWkWYmZxPNTJdEiUWLRWTdk1hrCnl5waFshqYt5rQXuX/jngYm9octnpoqOfjXJ+MEYms3DGHB5vKmAfzfVFVbNEmYZico8YTpfLg4/D8w44kmPeHidwi7CAWBMeRMzFnBXEjnkgsFIQYro85FAitONs9YsX2Exflzupnl2tE/U1Ux73Sa1dD9WnGfq6GIex8JSRf+9rXALj11luZMmXKhR+0Gm8ZtL6KdnuGRvJGPudjNKVipA+c870rU0ic1mZlShH+gj1Z2GXxGA0iZaSUMDQXlXxjcYrzabcLN7WGVGSDK9tcWFA4s6ss/G6Jz3+DOD86CeZ6CvH3Y33CB8YZk9T517Kg3JE56QIuLFTjZ03loEVqylQ1tpkyCecIynXJ6AVa1bm9VLZbVK9fUxiTJTLvrZJxHTVhSynsy7A1R/nVoAs3tcTQvc+FaRIpL6PA0oYzO8GlRTDiS1/CXJ9wWFNGQU9bPWi/7VAP2zVq1HjTIIyHhhIM7oeLzKyWdKY18phAVzVkn0a/xsC9thTWVYUSIw73VdGAPQSjjcMdZaLbo8GuCUzBmVrAzCK2nS/RzEZTXqhiyBpAt8Rgbx7M8rALz47Bs38XPXDTPqJ0zlIahCY7cnGjHrokauanqnMM6ESYL5G37QgHEbajVc106LyX4hxC6ACubhgHXJgkzrGqfOd0vva+Knc3e5T+dJzHDXxsbIxvfOMbAHz605++0MNV4y2IeRW7PfSStVm3RLHN/nO+rxJJJpuznmG3C4VlydjtQk9SRoABce4vEyqR6+0S5sWjHjs0E8XZZkLyeKwk8IEikxGey3HOtku8hraqnAmC6T5RldQcqczFCjyYEzcWzvD3mvzP33Y2/dgYIoqqRoh/P02de8pIByorf8RAcm5qMWZplEN9Y6ygF+eywllSRDzfbHV6JQbo75XKt5qJDnGWFcb1RZget1ksbrd7vLZxAhe/ymK3rHTaCyuT84WirLK5nyjlRSbWl8JqpcqbBvWwXaNGjV86mh5V4z/LyvOmLCmc5cnY6Mq0IhIEkkRU1syXuan1K9zcMMaAO8aUb40l9pjQ53BpMp7Nwo9Kpctii/oUwZr950aOLWd39huM0xhG2jyG5ibCMxYmrH1EPu4+E3YmZc7nCvqWChd/ItEFvFcyh1G6ie3vIwjtxITc6lFMMU6cJgISxRqp0nIvFqMQ6MXZL7G9Pk9Dhz3ioand45EpLHKa1VbmVxXac/T8ii1+9KMfcejQIaZMmcLNN9/8Rhy6Gm8xtArMSs4zr8Buv3SHaFbVxrrnnNNuZWGR4COR5KNEE+v6UphZsdvrSj0TJThZQorRIVHasreERUWYHxXnJ2VitjgdEgNxvzhHEWYSz5OBEYSPFJlugQMIP2wKXSOAwYb/L1OOOt8tC1aoscuFh5vKQVeurcycU9R4uEy0A9cXxjSN8+/hUvnemPLtUWUMmCMx0PdqLMhPOixSZ4rE4iM7PF9JZLZmYdSjCOeVUoC8ihztlpDxXCj2GdxVKs/lMEafKytzj+zu1aVwd1P5flN/blFV45eDetiuUaPGBWG0cuuPOS9KM3g1uMNQNWA/Uio/bIbhaEkybk7GCYfHs7I8GcvbC4aJoprtOQbWl3uedoHlKYbb49XrGRZYaxHBNw5Ao3wjI9yaMkMiTFJYlZUejbr2yWLsR+glBuKF6qyzuBGfRNhJ6FOvvxJu/V8FkzqhC+NxV6hSTSYTxkslkhG2eUSdTRdnTGKAPlmZtiaJYxIpI7Mx9nuUflxeRMHGaUPknmprHGCvh061rdK0TjrPbemvfvWrAHziE5+gKOqahbcr5leSkZcOYtOrxJLmS9jtpclYn/WMxGSgisprTyHR2OjKLIwHS2WfhefgILCkYSwqnF0u7HXhvUUm4Xy3TOyuDIaKIjhPZMVwksOAR0LQBhLdeOVfgHty4sONTB/GoAttH0u0T4LmIGz9qTEMPGSKeAzk3ThTC6dPon590GBDKfy0KsEZr5E7fjjDHleuTMaUBDc3MhM8Buw2YGcW7i2VAxaZ3d1Ea2YBzEhOfzVJnb6u7bGzrHJIxoTLX2UgPx9khzWlsCorlybjhoYxPznt1eJ7RxbuKpVVWWmXIBk+0Hjl+MEa/7Gor7Y1atS4IOw0YasJI5XMoVViWByvUW0+IGFGBLi/qVFx7rHSH68wSZzLGmF02mnC3ZWj/6aGnZFGlESqAcQQnj22ieemsyUdpcGPy9Br3lrEjej0zW17DtPUdovCm4niXFNELN8EMZ60RI/AO1Lm22OJFqgaJWG/CccRJhLylr0nYVYnjAq4RQlOOzBapRiMEQx4O04TpbDMIKHJNoMDEsy6VXncfUQV/ADOFpREtM7NVuexHBFhQ5U0ZkLFrK3PyrJkbDeNgeU8bqg7d+7k3nvvBeCTn/zk6z/gNd7yeKVWyXaJXaLdJi+KpZsisBV41oRF1fcvTc4To5lrCueH2dmDctzhtmbi6mQMirDdoplyxGPgPIlwkcTifF0plC4M41xUMepHXEgIk9XoMThSRWAKIfHY7/BUVlaos8qFjkK4/NeUB//O2HWbcdl7M8c1zvGEMwysLpXZ1UD8/THhjjLRI877G5lpAvdn5aks9GFMUZhZvb9NGbrdaYigEgz/k6Wy24XZYiEzc1iSnJMO203ORCWOV5iYjKMGm7JwQ8NoXMDQO+zwWBnym1sKo+WcxzpssC6Hdn5ZMibLy6cy1fjloh62a9SocUGYl5x5Kdjm061vJz3MhhuycqKSQUxQZ64a3dXw3UYM0euzsK1ijXoELktx0zv3htFNDPFXFcbEKkVkhwn3NZV+hYkYj5SJMYGPNDIT09l/mz0qpHdZ3NgV4TdSZrvDjgxbSRyphvfvNxPbKh1mkzA4GnGhPIVwZIPx5B9nDn9Kmf/RuGkXlUSkDecSdZ41YXc1eC/XzCpTCpxeor1OHMYIDWmfQCmQPAag3R7xaHOreueDBisazk4TJldD9dYstFW/kwPwTQIzAAAgAElEQVTV358Pvva1r+Hu3HDDDXU9e40zrZLH3F8UGTlDnBdcmMXZz5VI5Gjf01SmaPz8vOQ8USUEDRDtpTcl49mqvOaqZNxZKivUUEIC9VBWFoixAaUdWFpkDjaVQyZMF+eEw0GEZPDuwrirVAarJJ9xREHNIXNKFTrM2Ydy63vgsa/CiV1wfJXTcmV4H1qqhe8aU7owBiuqeQxnMs4MDQZ+1GHUhQ8XmfUWQ+tkdZrENecSzdxVJi4pjCuSM1gm9rhwuMrcv7sZu14Xq/OOFMlBIvG4j2VlefHzrZ2vBacc7i+VqeosPWdhbQ6bTdiWhaXJmXmei+4avxzUw3aNGjXeEEhlQmohDENTcEjOqEd+7wETdroyUjE/XThjHjnRBXGDG3PhyayM92CfTydsHKnMgBOqG1k30QY5AeexUnmgqlD/eJEZSC9+XVtKYZsLVEzzVJyHTDELNvqEh7xjZTK+NZaq2L1or5tAFG5ME7iYzF//rVGeAtsZw/Bej8i+2RLD9BUNY+NoolENKkerrO4S4ShQOJwghvPxRDHGbhP6cXag9FRmrmUacpIegVaC8V+cIk1lcxauLSKSbXbyF7Fcr4SyLPnnf/5nAD71qU9dyGGu8SuClmpg3pSVdxZnNf/T1FnXFIYr0+JpdEkUwDxZKu8qIlFjSauy+aQwS+Fpi2F4XPWZ7tUoa9pgQofAYjVeKBMnRRi2WGi7CAsUNnnEDB7x8FXscEFLGACOEjtmxytZyHEXetwZFqFwZ01b4or3O498x1n7XWfxlcICMvtF6XTYB6w3oUWEwqGBMIjzZBl53/s8zqfnUN6ZjEdytELOVKenSiCap86dzcRcNaZpFPuURBQixHm914XjWegQp43YBegApsr5LYZfDqcH7YvUWXzOTsOp00w3L94BrPHmRa3ZrlGjxr8rWgWmCixMUek8TZ19FnKQdVnpqBIPFiTnhobx7sKYKlFScWdT+dnJMdbnKH4RQhP5TBZ+WipPZqVFQ6v9rsJ4KCub8tn4sqbDj8oYHI5Vus/3FZnpEvKQAx6Ndlcm58GmcAChJJ7nCozDhAnp+sJ46ofOoW3Q0gWLP5OAyAruw1mSjGkFbMzCUBVX1iPwAsIoQlGV5RihtW4FeiTMmr0CRyX03dPV6SQKbE5rtI9WTPhkibi1Geq0Smz1n6/h6o477mDv3r2MHz+eD3zgA2/0Ia7xFsXc6vN16Bx/bUPi87f7ZVIu5qjTLc6jlZTrkrYCq5jnVmIwnpSitfHpaoG4PivjCf/BLUXmmWoILXA2ZWFqciaKs8WUieLMTUanwCkRLi+Mruo8yzinCK9Di4N4nEeDCDM/oqjCgc3O3GMlG0lMrvwMbdW5d5HEv+/FeN6Vu8rEv5aJUYcWj4X/qqxME2OTCcmpinvifR/3MH3usdglm5KcX2sxPlx9XV8Yi5MxSWLwzkSyyevFmMODZZzvi855nIMG95TKJHWuLX5+0B7xaMvdmoXNVdJLjV8+ama7Ro0aF4zsYQRqEqxtrqQVY8Sge7JipsdJaLmvLYzxEcrBYY/86Oey8mSl5e6WkFVMFedQaTxRJgbEWZdjGJ2SYsA9ZsJOD01km8Asd9Zk5aelsEyNnzaVQ1UznQDXamZeA8pmxPLNl4jQ2mvC0SpiTBCWiLHelQaR7T006Nz+D3HXWvpppa03BvpuQhf+nAkzxLkvR/tdgTCAVekkUWZzEiXhCGGWLDzMkqU7J1BmYAwhzE0x+ewzYWHD2FKxbHs9Eh9uLqJtcob6i5jHV8NXvvIVAD7zmc/Q2tr6xh34Gm9pFAIL1dmQlRvkrIFvhhrPZGXuS7KiRaII5uEsPJaVm5LQJ1Fb3iaOOuzPwWR3iLPPlXESWdYthM57XnKernLxl6SQjrQ77EYYJ85zLixVY50pO1z4VCPz/zbjzImFqrPBhauTscqUHjeK8cpl/wOWXwK3Djj7Rp3dlUY83lKw7R9tGH3qfHtMOOgRv2nuPGNKr0SU3zYSkyWeo8vCaPhoVmbgrEYYMmFOct7bOPv70mq3rVtgj8W15cMXwDhnj6jAAXEW61nvyfNZ2JBjETL1HKr0lMfu167K9Nov0CXnt+tV4z8G9bBdo0aNC4ZxNhs3EVvUCWiI005EenXAy2oKJ0hV41zpvk8RBRAjLowJoMoiNS5KkZRQQOi2TXjehRuKsyURXQLXqLHahK+PJQ5W29oOJIQGwo9HhEdcaAV2eGxPG6e12bEFvNnjTrYA4wdl4s7/p2R0CLpmC+PfH2zYMQQHprqx3ZUThM6zDeeDhbHVhc4cde0ZoRPnZFV2MwPDNLbB96JM19Cp7zOYnUJ2c9pousuidOPRrFyeInHhBRNuaZxf3N+mTZt48MEHSSnxmc985rUf3Bq/0pilzhYT9nkYISF2UVZ7GBlfqjdOEg2qq7Nw51CTGepsyUKXwBEXJmjEWB6shtLjFubBaVVW/DWF8XxOHHGhgxhwk8J0jFVZubnIPFIq/RLnw0aU69W42xL955xD20x5T8N4rKkcdbj0mjBcfqfpjBtx7vuWccVHlSv6nR+ViVGi/fHiZDRwDGGmRsnNRI0Smu0Z7s2JjzQMU3igVBZpGCUf8ViKlzhXvUKyyKDDqlK56mUY51dD6eFDGXI4ZcLTJjQErk1noz43m7DdhOsadkZjf8zgGRP2WTRjLk3GBDkrb6nx5kE9bNeoUeOC0RBY9hIW7PVAJNrjBh3W5Ris942WDEhsH/cJnMS5s1QGLTSkO3IM320apqQNWTlVmTW1MkkZMJOMA0+40iRu9E5sRY9WZRktFRPWTrQ+tirs+kHJrrvivV32O8q8hjML4y4ruFSNBYVzqMz8zCL6b0CC4TqYYyu5rJ7jYoy1JBSYKc5mF4Y9ogZ7iMSBAg9WLUdpxnMm9AtsrOQjkxUeKpV56fxZ7b//+78H4P3vfz/Tp0+/4GNU41cLSSJRY0NWJknUiCeBi6pa80uLnz+vU8VwH2xNPHQqGGRzJ3ucR0eq4e+ACe9Izr+Vwn6PheOyIuRkD5bK4zn0349lYZrAXuI8X1Q460o4jOBulBJpPcegyqYXXnD4Lzj9jcxXxgpaPIpyflAmNn8vs/3rxsH7jN6/TXS3RKznSTdWlyFdSwQT3K3GBlMeLkOuVQD/2ExMk5ChfKeM60XGmYFxkGCXJ5/DOEPINx4ulcXJmfgqAl3z8KAccuGwR238SKWP75IYoE843JzsTK732hxJLe+qhvhTHpK1fSbMTc7yhtFaD9hvatTDdo0aNd5wrCmF3R7azAnqTJdIMDjfSKrJAu9tGPsNKJTLMbJEasAzWehwZ24RZqmHysQYIVsZ9mhxPIAySrDrp//uBZTnrdJSYgwIPOtRz+wIU3AOV8P5OGBlI3Tkf+MgCvM/k1i4TPhgw/jHsUgiWVbEQDxEbEkrweTfUSrD7hxF6cS5COcpEkIsAB7wxECVyb04OcMVwz69SkjYbcI7C+PhUpmozrALSyut+3GHq85zYTM4OMi3vvUtAD772c+e9/Gr8fbCdHGeRdh5TuTf7OTc11SWur8sUyoCC1oL2luMH44lDlWNq89ZSDx2uaAOTYXr1fhxVlaXsKxwVhTOqjKGyn0uDAhsd+UiCcPzf2pkTikcyMIuhMvUGTA44XImYvMU8IMxZVIBVybjORN+nBPd4sg8Zdx4Y3AP/OyrzuX/3RlC2InSBrg7CyoT8pm8b4Gj1TXrVDUQj0hIvoaI4XwfkZrytClzzZidHPM4Jx8qlYuSM+dlzs2THh6R/S4ctJDITRBnhjq9EtK4VEna1nrIXToldNuPVe2dNxSGABtyJJDMVOc9DaulIm8R1MN2jRo13nBckpx5RP7sPhMeMaWTKMcYqFifUx5fStx82jgrM5EqhWO/C8vaCiYPx88+noUuhXefYXKcQYsb3UmHCQmeK0M73iA01cMIEyuz1CDOeDy2uSvz4ijQjXGgqk1vABdLpluczaUw9sGCmxcb0y+GqxrOrioPeAFGtzhHqnxdwZkmcJU43zZhmEgL6CCyuFuqob4bGCJqpX+tkXk6K4VEVfY0dQ54VK/vqSIITzNaRhgkL012Jrf8F+HrX/86J0+eZOHChVxzzTVvxKGt8SsIkWhBfDwr09RpSMhHxkmUt8x8lUSNboEPNTK3NSPj/rCHMbhQ6K4kJlckY47HkPpMaSwoYFFhbM3K6lL4YCOzrUzM1jBSPmPC8sJBjB83E9swLhFnq8c5NUJUt69DWZCNj7cY5ZjylCujCO+4wuj53cQ9f5zZdptx1U3C1NnOEKGnPgEcM6Gten9FdlpVuLEwJomzMwuPZuXaZEzF+W5OiEObOFcWEe95Xym8YMHi70PoAZaKYZUU7qgLhy38KCMOE6tOgUsaTufLnL+HLEiKdxYxaA9VTPmAOMuLuC6syfH/767Lat5yqIftGjVqvCEwj8zsUcL42CHQRTBkSzy0mY9kZZo5y5Kzx4JJK4lt1ExspXYK9IrTRbSivaMQtmRhYxYuTuHM10rH+LzF9+cnZ54668vIuG7BWYgzJELpximEVnFaXDlVpYM0PeQjkVkdFemTgFZ1ntknbC0E64+khWlzhAnqzBDnq83Qjs5uhIZ8X9UO6QhzteQ7OWEEQ92DYWdMWjAOZ6JEVNgwUQbUrU5GOGkwpTI/9oizPoex86bCaJdgtMZVyS7ndTzMzkhIPvvZzyJ100WNV8EEhX6LyLol57Dbz51OAnqVj0+nwiWFszdHlOB6Ey7G2SPKcs08WcUL7h4Tbm8meiSzMEVO/HGCvV2SjM05Ekk2WJzrlxVO6Zm7S2V3VR5l1e7TCLFQ3+LK7c1gl08Si+WjCO+52tlynfDCA84Pv2zc+mXBUvgsegRWJuOShrO9hH9pJiZ7aMqfzImTHtK4zaas8tM+lLju3FMKXcALHjKOJPGcCbi7VE5Vf+5X6BNnhRr98vJ+ldMYdHikVFYUQUYcNngkR+TgdIl2zSN+toMATjdVxsJ/yMPIOaHOl3vToh62a9So8YZAiJuYEQ2KwwQ7u6GM5I4+jfitfR51ye9s2IvSDsYqRuikByu0qpKi/M3RUVqyskyNiRImRLMoqzjpcF1h9Ag8VAoPlVVDJI5JJBI0UXqJ7d6pYnyoMB7LyoMVSzYNY3sl9zBg6n7jts8brQ1Y+X8VzOx3tnpigjk/bipDDpPUmSXO98qIQBsCSpx7cnHGaJmBEwgLxDnhcZN3QMWZo9CHcU9OzBTn0iJTqNIC7MqhgU1EFOI4iQbM7RbM2/nOzLfffjtbt26lu7ubj33sY2/UYa7xK4xlKYprZmmwr9PE2YBwxGHgF3zuFqqzIysXJ2e7CX3qlA4PlImVyXjawui8wRI/LWP4bsdpUThq0OexKDUi1m9dVt6RjCsK5/kc6SDTq/i+Qx7SrQInETnaTYRWnPniHEd4ICsLPifsXV1yfIuz+XZn+keUiZWE7IGsdEnGqwX+DleOlc5kdZYqjBNnTRaOeiw2JqUYgne4cKQyXh8DejwMo+MVetTpEl6Tfnq4kqAsTs40DfPz2lK4JBnDRMTprORcniJR6fnqWnDCI7Z0sjjd6vTVa+k3Nephu0aNGm8IRHhRxXMgEkaGgY2lsMaUEYKVWTuSuEidqQr9Ejd3JwbuYybscWGOOFeOa2HkxBhHqxvwfot2yn6B6ep8b0zZ6XGzPf3sI8AmV9pw+nHminNQQgt+W06csLj4zcXYLcJ8yQyjrDiY+dIfGoP7oXMaTDLnmCg3pUxD4LEyGvNGTFhfCntNaSXMVzNwjlXmrVGgA2dFci5T45+aiRGEWRILgysLY5cJizzzrCknmoklybm/qTzvwgRxPthidBM19c9YFG+83PbzyyHnzJe+9CUAPve5z9HV1XWBR7fG2wFdAnOSszZHwUuSyJjeYsqAvnr6TYtElv72HC2pW0z4eMO4u1SeyDFoHyXkVmPA2jKaJi9X4w5LDOL0iXPShKbAfnN2a+TYH67kV+4RnTkB5whRKOWEGbod5yMps9YVdWcEZVo/XP9byj1/bRy5PzP7w0JWYZeBCXyrmegjIhCnizHowjJ1lhaxWH7clNZK1nUgC4PADclYm5UCuKxqmjwlQq+G1KvxGn7fzWrQnqHOxepszsI2E+apsdmUToF3NYxOQgv/rEXu/7xkTKlTR95SqIftGjVqvOE44RH91SpRVdwFrCiceWSyB3u9xaJ0YdSE54jyhXaJrdcOnIsEPtBi9LcWHD3lTHN4KsOoCDcUmTYJR/4JVwZwhoFDVYb1sSptpB2YqcYLHpKMY8BMnFUEE5+BXgdHGbff+LM/NA4cgAnT4bIvFQwPRMvckuTcW2qwVg4D4txr6UyWeAfOIFFYMx5jBKGfKK75TpnOJJLM12DyugR2Z2Gcwm+ocWdWns3RMnmROh9qGKcqjWaTKNV5LZXPt912G08//TQ9PT38zu/8zht2XGv86mOhOneVwh6P5tTZ6jzTDKnCL9IJz1FnhwmLC2NNmXi8dN7fMH7QVHYi9Ct0GQy6MF6d4yZsFeXGhvGzpjJenKzBWI84rG4KR1xRcaZLMNqjDuPFuUWNx7PwNFpFe8LjnnCLuM0bUmYEYcEHYMvTwvW/qdza4vywFN5RGIdM2OTCfoRPpMycwlmVhYezsr/pZLwqhHE2mrJYjcvU2eLR+LoX5XD1XC8YPG+xS3VVNRz/ImSHR7PSI848cR7PwkETOgWed+WSZEyVMGr+NCvtAlef43mp8dZCPWzXqFHjDceYR1nNiCvHLJijaerMUadXo4b5ouRcliL7dnnhTBJn0OGwC09WcpA7S2XC0BjPjCrHXOgWmImxrhS2eUg62ogimiMIbcSwux/FgFMIx13oFqcwmJRgjUXL42WS2YOyUDOrdimPfj5z6BBMuwiu+rOCyQOwC1gpxsNZwWGXRdHN4UoWMo642YbR0mlDOEJUQveK002wVw1i6D8qwq3JWJdDq20I7VK120lki/cS2s/e6nc2V18+DeKVUJYlf/7nfw7A7/7u79LT0/NGHtoav+JIAsuTsTorEyTSLmYl59ksrHiZGMBzoRK64ofKiBFcbcrFlrmxMO4tI1VjchVpudOEeRImyR7CD3GMGOpn4TznyjDQRLhBncsbcd4/XCqDLpzCGUXoxTiKMohQWiymp6gzI8GYx6L4Q/+7c9CEQ24MiPOPX3c6+2HuTSHX2o0wV0KqUXokm+wyZehMFGgkBB0iSnyWFs7FZFZlZWUyDnnElPZrJAz9IpQemmwFZklEmY4S14lJ6szXiFDcZJE8cklyLtJX183XeHOjHrZr1KjxhmNAYUBDpewezYc7TPhpM9IOlqVoN+tXuLaqWV+WYKY6ne5sxFmcnCGHbaOZPRamxj0ubEHJFYPdUaV6DFY3xQbOIZTpGC3AQeA5V9o9tl4POQwhTK7Mk7PEWfOC8sDnS44fhhkz4eY/S5zqE5JHjN/dFauk7ghR/TxCJIUME4zaAE6n+Jn87C4x3tcwNmU5s7WciFr5UYLVRmCqxuP3izNFYKUa86s4sVczVL0avvnNb/Lcc88xMDDAb//2b1/ooazxNsRkhQnmrMvClUWYj+9qKgvdf2FZy4CGFGUbwklzbm8mPtmSWZqcrSa0e7QtisNqT0zEGCFkHQ0LA/Fa0+r8EqYSXo1MLMQvTcbGrNyflePEkHsVmXVVmtDTKAvNeLgp9CgsUuOIx6L2kVI5stlY/w8GDke2OL//OefeXDAgwtIiNND3WhiphRj8xyfnmAvvaWQmnhNhOqChcV+ejGMu7LZYhF+a/BXP39HKDNkCCM73y0SrhsFxvjqtEjrux8qgsG+6gCbKGm8e1MN2jRo1/l0hEvXB/eos9kjb+EmpXJKCzepTuIYYuE/ruyeos8dhpyl9DWFFiuKJNmC+GEs0iiOOGvxzmShw+omBVght8zYPZu5GjYzunVnY6sJ4jDYRSgTF6GgVulqhdybc+KXEgV6lA9iHkMTBhfFu7EQYqZJMUvVcGWEWEUM4oHDQgrW6SKPMZnWpjCB0EyU0QwYPWSQL4FHS04VzXTIeyWFMg9c/aI+NjfEXf/EXAPze7/1erdWu8bqxPDl3l8ouC2/ERRrs9vJfwG4DLFLnkAkDwDF3/rWZ+HCR6RYhpah3nybORHFWZ6VZGosLZ6Mr2YOxzsQu1lSBO0qlM8cC3jwWqmMOV2tmqysnUPqJmL2JGM+gqENbjtfeVUnKBgWuXeQc+C/Khq8bz/6b8Wfb4Lf+MHPXhMS6prOVKKdqApdp5iOFM6pxLj9UKnPFMYnze0Fyri+ioGeaxA7UQROeJEp/XnoeH7QYtEuP1yoirCiivfJ0SdXRKolkhjpL9JWH9hpvLYi7n1c7wtGjR/+9X8svDX19fb/S7+/thvp4vvlx0EKP3C6wIsVwfMzhgaYyaML0FGaluclptnewY2iYuVW8nwIHPBI67i+Vk8TW8YBGhJaIcMRCpvKBhtGQ0Ij/w2jiqMc2sJ905nc6L6AsTcbgAWdtUnKP0FsxzAcldOSXJuNfxlK1pey0VEkh43EOESUYSDB1o0SJzoLkjLqw1aCFYDWuKpwGztastIjz3oZx0IWjHvrxTuFM5NrrxVe+8hU+//nPM2nSJFatWkVHx/lsav/HoT4331o4aPBoqdzUCHPk3U3l5nOY1lc7nmMO95bKjhwL1FYVZotx1OM82paV9zeMrQZP5lhIHyWq2DuqRtdRolG2CZQIKzVzsUZF+faq0vxDjczDTeWww95Kv91XeSjagUnEkJ4UjlrIvY6Y8PzDzqb/VZKHQVtgxicSU39d6W2E/KyrWlQrQRYAHPKIALwixa7UdI0duvWl8GgZ53Ubwvhq8F5ZGO3ALoc1pfKCxU5XW3Xdu1Sd4hwN9pkkksKZqRd2LXg9qM/P14e+vr5f+DPpC1/4whfO58FGRkYu9PW8adHe3v4r/f7ebqiP52vHMYc7m8p2i63QkwRz2875tz6+FnQKzFLnJMEYdUvUnO82Yb0pA8B4jQKZWe2tXJJHKSRMlauycsCF7Vk4jrJInIaGeaoUYbFGPN57GkabRFnEj5rKURfeocbqXcK9f1ByFKFrUWi793UIqTUq0q9tGM9a5OWOw7m/TBwhbpKTcQaAqWrs9UQPMD85DWBpcnaZsjw5H2wYT5bKMKHBvq4wrmk4L3hsj89OziyNBcdUdbabsrJ4bdrsl2JwcJBPf/rTDA8P8yd/8iesXLnyQg/TG4763HxrobNicLdkZY46YwJ7TZhWDYivdjyTwAx1DrhwwIUud7o14jD3m3KckJZ1SUSEFgJz1blSMmMI4zU8DE1guTr94mwyZb3FUHvUo5RmsykHXShEuKUwdpvQKc4kgSMIh4ks/9ni9FTxhAp0zBB6r1NO7nSGd8Pxtc6xx4y+m5VGEUU+BfG6Jmsw2PPU2e/KbHUWnnO+TpTwpZxyYbiSzJ1weCgrj2bl6TKkb53iXF5E8+P0dHYHyz302c+YcHVxNkv7Pxr1+fn60N7e/gt/ppaR1KhRg16B9zWMYaKpcb8JT1hc8RdUW8hvdMxUqpjc8Tg/a4YZ6oQJMzWz0ZRZRDLH3qaxrlRaJGqlL1Znaxkmx2li7CVyvPtxPtowdrgw051WIq3k2Swc8sitXfM8PPlHTUaPwrN3GvM+pHgjUhYWFcYJF55qKrsQkjl7KuNVC1Gz3ifRIrfHFMNZmIztrixQZ78LKs5VydhSRjXzAAYiXJacgwb7LRYxi5JzxIOxO2DKnErDfiH4q7/6Kw4fPsy8efP41Kc+dYFHp0aNwGlJyEYTFqpzZ1M5Yk7/eQyELdV15SdN5WkTThlcm4xWDRPwURd2eCT3tIqzMDmXFtCXne2lcEKc3a7sMufdReimezCOeeRgl1XLanv1BbGwfbRUuqqB+6jHwP/jHHKzsUrnPR6nZaqw8E+VDT+DTX+fmfUbCWlVCuJ8nJsik3uLCXeUiR6caRhbsjBHnSxxvRz02AUbBE4S5VMniAjPQYvnW1QYl6Sfb48sPZj9kw43Fuenz7aqBOw0lDoG8M2OetiuUaMGEDfGFqKYZoo67s5+h6dz3CgvTcbUN4hxcY/Gt90eqQRKMGYjCL2uLE3RzqjiLG1LWNMQh6cs0k0GK/3zKJE724dzY0uYIrdl4coU6QcFMGJQuLP6OeHOPyppHoeeWXDLlwp6CzhkwpVF5klL4FFA0wLR/CjQ5VGkMbnSah6uWu8W4Oz0iCubr8ZDzcQyNcYpfKupZ1K/l1Z5xeubSia0sG0C67LQQxRwrLzALeNdu3bxt3/7twB84QtfoNF4LWm/NWq8MrSSQ/ysVNqBxclZl5Ub5NVzt08jCby3YXSXwqas3FkmugW6xDlKpPXMVqNDIlu/J8NMcR5BmaDO5VbyfS/4Zgnv1syzhKdiJcbPEOZqZOlvNuXHZWKmGF1EMdbsZPS68oI5p87xXEBkZgtwQBJL320MXF/QKJQZGIMi9FfD+qYMmy3kLTtRFMcRNo0q44hq+xZisdAOeCVpG+ew14V5yekjrmfbBebp2YX1ycos2S3ODYVTnMN0jxERqic86uVPeLDlY9Ui/dzZ+nT0YYtEOlOHQKc4nUBH1WHQQT2Q/zJRD9s1atR4WYhECcwkMfY5rM1x01qe/DU1pJ1GrlJJ9lau/dOyjEniZBGmK4yXjImwTI0lKXSOi8zYb8JTWWgROJrjpjcTY0yEhkSpzEOlstOEUw7bLDEgzpAJBxF0v3Hv/1HSHISJc+G3vpTo6oHtFozYT8tEv/qZm1ybQDehu96PMCDGMnUeycoRhIswxjRueLc2jHuaAg7XF86GMpj0TpweFRYkY6/DHocZGrnFQx6LiwZwWWFnbrKvF3/6p3/KyMgI73znO3nve997YQ9Wo8ZL0C7BGD9QKvMkmgy3VAbI84EIXMnS/okAACAASURBVFM4nRIyD6Fa3LszCedxU9rceV/DeDwrgnClGttN2CCJOTiHHe62RGul5z4lytVquApXF87VZJ4qhcey0qPOaI74vh5xpoiw343OKkXoVMVwj8cYAnYj9BfQgSEizDxa8k/fEJb9dyUXyoDE7l6HGPtM6RXDHSYmZ2lysoePZIcph3JkY89T5z0p2mpLEa5RY6sJdzZDOtaGs82UeclZoE4J7LHYVdzvwkiVa95ddRXMrIyerXI23ei0xC97MOxHHI64MOgRedovzk4LX8to1WPQSTWISzx+J/HnC91Zq/HqqIftGjVqvCpEYIrAeDE2ZOHuUrkyGRNfhuUe8qhbNmDEQ8M46HC8MgJ2VPrHpWocr7aQu4HFydhkynsK54A7328mWqobyP99ZJSGR+70FJxDIkxz4wWEXo+LWI86xyui7X2FMVudx7PyEMJcz/zgLzNjg9A3F/63vxCealeshBWF0S/OU6Wyo8rfHiDysbskDFGZqGTeRzBMrVW+rzl8siVTAutNuTQ5LcD9OVjtCRKykwHgB6WiIlxRZERgaxkDR5/G7/ZCsH79er75zW8C8MUvfhGpw3hr/Duguxq4HyyVXpyns7Agnx+7DXEdWZ5icNxYCi7OKReecmWuOhuz8P2xBASbngXUoQVnxIUksAhjsgRzvNaUR03B4KQH616I0yvOgRzn15DDEQ/JV0ZJxHA5TZ1hi2jBTOxiHQEOILSVxkN/5AztcDzBZz/n3Ng4m3FdeuZpE7ZkYW2pHMjGiIQuvF/g/S3hGbm3VA44vKswVmfhySqTe1F1bXomB6O9IQvrymidHZDI2V6sRq9UTHRFAAw7HAGGDZouHCeGbhHhuMffd1fDea/4mTZQqh220uEkwaafdOFk1Wkw5EFQFMD4E2NoKdUQDu0Sw30rwd7XzPjrRz1s16hR47zQELiscCaa81ipzE3OwnOKFprVluhpLWFsZ8bNdaIavYT84nlTnrcwI74jGf0KD5fKHHGOORy2qGje4pGlvbJNOTiSmaPOJo/iiucJXeUpoJe4GQ0htOOsNeGurJxwmIjx7COw+ylI7XDL/5lY1a704fxaq9Eu8M2xxB4PScoUjGkaA/YGi6KJPpxFDWdbhlBHwhjOf2pkuhS+Pxb11DcWxiNlbPW2EuawSQm2mrDXhA82Mp0SN8VNpnTiXJ7sggyozWaT3//938fd+fVf/3VWrFjx+h+sRo1fgG6BdxfBPmeHH58Y40bnvHe6pBoAZ1TFNtlhn8WOVwnsrWIyy8pU2UkMjaWEWfKgBHPbYjBJYojcYcqqEiYppKqJ9iSRhZ+A3qp4ar4ac9XYkpUOgflV3OY0dfow1liiHWcsKUs/pTz6P40Xvmv8eHFi/LuEZSmudUY04rYQUridnugTZ644EzUkYirwzmTc3VQGE/QQrbP3NRUXGHNhXrXD1UXE+w27cMhi568grj2j1WJfODusldX3lWC7VxbOInW65dWH4ULidfQInB7AT//XqsjV1FawZxROAftcGDY58zpKYrhvlXju019y+r/ykv8nyIdXyxx/O6EetmvUqPGaME2htxEmpEGPPNkkMYzf0ngx02WVNnufC+sqo9AMcS5OTinCERc2NGFDjtSOUpQxIlbrkylzHOGQJq5vjHJ3UznuQjswIEa3QI/GjaBTnBGJVICjLozDGQdkEfqvdhb9QaKhMDZVaTpclODZpvCQaxVFBovFuLXFGDK4rZloIVJGbiqMw1lYa6HnTBI/nyQycTdk5drCaAKP5YTjLFfjGMJSjG/lxLJkzAjSjtVZGDa4tcVelxznXHz5y19mzZo19PT08MUvfvHCHqxGjfNAq8A1ydgiwmPZ+dem8r6G0fca/BwtEibhBeI0EHa6sESdbQbvKIyri2iTfbhUJmss6ncZ/KRMdHnIxg57RGxOJ/NY5eXoFGeqwFXJGQJ2mrDLhPc3Mgdc6FDhw2o8YtFE2UoMuZMUFmuYE8eReeCagrm/UbL1X40n/jLTNrtg3bT/v703D4+ruvK1371PlebBsixZtjxKtjwbbMCGhoQxZozDECA3SScdktCEJHC7Lx0gX4cmSecm6XTS3Zkw8GWAQAhjEuIMzPNosPE8T8iWZXmQrFlVdfa6f+wjYdmyLduSLcnrfZ56Sqpz6pxdWqp91ll7rd/y++0SHxHPwDecyUZ4O2VJt7AuNKyJGtu0K6m8nDKMt45MhM3OUo931lPWR9SrnY+KB/io9EjrZf/yjHegQ4HK0LBJDC346PfgKD98fNAzhes2imQXxC0ZQWdHvJ2wXdo0ugFoj5m3r2RKx7Nf9XP4OVL9bI/qbKPakgONE82ejeIdzgDv8GZwbJb7UoLPRxQ4JXBY4wt3WqLo9CspSwNgxUQRbq8QYo2/2MQNpKILUICX/htthfLAR7Paqc/OY/6uRjZFy8Fx4MxAmB1paG8I4Y/JAMTnWjsRQgytCaE43Ue8Ea8+UGsMp9mQjWJY6gKSeHmvMcZH2PcIbHI2is545ZPxVlgQGpJiGALkBD5vfZUz7AxhB4avpIX8JWVZGnolgotijhoxVIuP+F+b5v8+VSE8mgy4KOaY0I3mIAfjvffeY86cOaRSKe655x4+/vGPH9XxjgUn2ndzoBPPy+fhnQ1UOUOFFUYGQoHx0d321AOLd84S0SMZpUXUOMM659MYWkQ4NxA2in/tmjTfqTGJXy2zwCkxx2Zn2BG1eV8ilhrnayLqxM97cQw14qO3SRHWOUtrdDM+xQqrIoWltKiwMQcfsd0ghotjIQUG/pD09R7NSeHJW0NqlkPuWDjjRzHIsGTgI8njA2GI9XPaSmdYFBU6DjI+NzododH53PZtGCyGjMiBzTE+EDHKCicFjiLrnd2E8XPQLmfY7GCb+BW6TLxW+BAg0woxX+PJZCtk9qBMoH4/j4zu6GxrZFtR+jlrQ8Mu8bmHiehClm4gDy+PNdj6JcwselYzO2bgjMDxRmj4dSKg1PiJPwN/MRtlHenRvq34HMF68REfxwc63kngwzFHmaVTg4d2CoMPPl8KQy6+YHNRykfENjtLeiT11yTQ0gwrHgnZ+lfHh38eI14ISQyt+BzQV8KAFvzFcDBwRVqKURbeSBrWOEshvhApJ4CPxRxvh4Y6sZxkQ6rEcknMO+aLQ1gnAR+LhWwTWBn6hhoXxlJUOksrQo2z/H1aiDWwLYT5yYCpVo7a0W5tbeVLX/oSqVSKuXPnctVVVx3V8RTlSMgJLFfEHatCwyJnCUOowpA0H6hmWHyUMx49UvjVpyQwyjjGB8Kq0PK++IhtEsMTiYBhUWpEPkILhmeSlslWaBNos4aLYo6tzuv0D4s0+S+OuygIYNghlpNjXsVoQWh5z/nC6w1iQAwfj4cY4MXQkp6Cp5MBuVFAYJcYmmOGWbcbnv9qioaNsPTnjov/j48s7xbDWyE0pSxtAoMtFFlHrbMU4dvDV0aFiSF+NS4DYXrgGG6FHONrWVY7w8thwOAoFNxo/P5tQLHxK2SDjDDU+kDCNjFUOsP2KKw8yED5PhFoidJoaqO87HS8rv+xIhHN81kGbTO/F+psK0o/Z0bsgyIYiCrT8bJRe6LJeXHUba3E+OKgQnPkLcHBN21oiU6Zh89P3imGUSIkje/AlgBa8RfYtCjFoyKKcKcZwTh401kGI6wXy8qUv3gUWa9QUuAD0jy8p5XaKCp0lg3ZKAFrnY8mjbBCMUJx4JVEGiodd/+LozUKzmx6zlF+TUAOgoAvHBLYjCHd+ALHhMC9bQE7BYYZId9CnoHhRljjDAtDy3AcG8RyYdw72s0Cm0NLZtRl7plUQBteVaDIwKvOkMRwXqSbuyI0vBcaCq3w4Xj3i8oOxHe+8x1Wr15NcXExP/zhD7UoUjluGAOTYsJwCXkv6ug6xvoivVjkcLfhncsa8f+npweOMvuB1F2RDVkR2qjLqmON+Ej5UCvsEp921SbwZuhlOV9JGnKjlJGCuGNB0rLDGf6Q8PnYw61wZlrYkaY1Mwj5c8qyWQyNziAGfpUIyEaowTDGCtVi2CKWITjKDWQHwvohhsm3xnjn6ykaVznWNATk5hgKjYD4nPM47dFo36hnnQRe9xpfjDk0kiJsxLBJLIET6q0PSFQg7DDCemdJAmMRCgN/g7AbQ6UYasWwAz+/5uB7DYwOhKao6PyFpM/vbhGfx16PIRndAGQj5B2DTpS1DrZEKlPtKiqTg+5phh8JCfF5/g3iFapGHYdum4eLOtuKMsAIDOTii5mGR5l1LpqcqsU7jyF+ghpjO6dsdJdW8Q/w6SCTA2Gbg2qB2dY7pOn4CeZATn0VkC3wkZjXoW4RL121wxnedZamEDaJYReOAnzDizZjGS7CFCNUR9G0OJBwkAiFP/2H0FoL2cNg9hctn/uw8HZSOlqv14thU6S1WyLCbxIB9WIoNr5YsxXD9Jjv/liDYYfzn6EJX9A0IRASAk8nLCkDFwaO10KvOpCNdyKeTllCA+MjVYDnUtanuABnx3zqy9Hw1FNP8bOf/QzwOduFhd0VYFOU3iPfwNlxxy7nc6XXOV9/YfERznwjzLSOwV3c6A83sAKvuz8hJmxM+NznkZEs3oRAaBXYHPrmOtVi+FVbwFgrZFtf3JdpYKMzTA+iNup7HT/d+huA11KWy9NChhifD77OGT4acwyxwsspy9xYyNKUb7C10xlaMJSfJPCNgLHTDYksqAPaxM8hAQaHd7zjCCUGSoyjWWCE9frk8b3yll9NWZpFCB1Ui6VW/I39qYFjW5RCUxfC9EAYZx2tAoudpVUMY4zQAqwSQxDNWYMM5BpHVRRFzzQwMVIyORZFiTXO92Gox9finBY4Cnrp3Cn5IAd/d5QqlGeko1C2r6POtqKcAFjjlz8LESZbr/qxyRmeT1oKrZ+gCw8j929CF8uS4wNhfejbrZ9pHLkHOV4osCS0TIsavoC/UJQav3yanYInxWtaZ+EjY9sdjIyUDFL47o7DjSPEL5muf8xRv0ZIy4bP/jDg4hLh6WRAZVTMFMOroaQBIxFarb8ZuSoIqcZQh+HswDfvqI+i3M34CP5QCxfEHaHAG0nvXJcav7TbJH58I4zjPWdpcF4ySywscr618/rQcHIgh/U37ootW7Zw4403AnD99ddz8cUXH90BFaWHKbQ+v3jfAruDYaKCyZWh4YKYMCYQdovhnZTlnJivd8gwMCEmVIjvvvpUMmCXGIpwzAy88s87KZ/T/V7Ukr3UCsOMsCPSrr4oHrLEWc4OHNlGGGJ9TvUq5ws0M4EYQqWzJDpypaH476BODDOCkPpWw4ZmQ3aeod4IBcBoGzLaQoEVH80VWO4Mf01ZSqMc9k3OUBka6vErXCdbn1LSIIb1zjA0yt9+31k2OcMG58+Zb3xhYmAMJUaYEDhy8bKky0NLM4axRjgpEOqjJmGr8BHm07vZkfJwaRJYHFr2CFRY4cxudBhOCdRFAY9GfECoO+ktCfE22hAaCqx/z+nm6DvuHmvU2VaUEwxjoMD4C8NUETY5w5uhJc/BJOsYchQOYXngl4ZfTVnOiB34WKvCqL9iFK1qVyypcV7+qi7Svx2MMCI94NQwQZuFraHhmSjqHCAUYSg2jp2bhWW/8ekZM/7REhYaftHmL0Q5CGU41mHIwDIGx5i4j+rn4Ys8jYGTrePF0LIpNMwMHCtCy5ZoSXSkdSwPfeRpj/PjLTKOP6f8DUEOvnXzjtBQA5QZodT45htrQsPkwCuwHA3JZJLPf/7z1NbWMmPGDL75zW8e1fEUpS9RaoSVGKoEJgbCcwlDloU1zjBxr++OiQIHV6WFPJ20hBjecYZM59Pk2oC/sw5nfOOov6QCdglUBL7duxX4TcKSZ2CSERaKYZczVDpIYBli4IyYY1iUP74nSsdLF2FZGLDlkRRv/0G44AuW/32JUB8Ydoh35teG3slNRWNNCLzuLM3inelTYo7RVngv5RvdLHWGAgN/F3MYfC54mwhrQksSy2jrmB44Rlo6Um6aosL0nc4XlBfgVwDXpQxDLBQYiWp0pKOFfU8h4qVMV4aG8kCYFcgBG3K5aKWyfV6vF8iOVjhyoqj0wXDiVypWOh/F/3DcRbKF/RN1thXlBCZufES6zAobneHt0JIT+hbjg4/Q6R5thVhMeCNlOS3myDM+hSUh7ZJY8ErKkmGEZ11Am/gLyWB82kVblFKShl9iHZ0WsKbFsDtlqHd+SbbcOorwS7prXcCSx5NICgpPM2RfELDV+VzKbIQShJXi003mBCmGWq/b+34Iy50lA+9Qvx5aGjGcG4QsEMt28WoFZcbnuK+IGmU04/PJqyIZryyEkdbvuxafvpOGL34qMHBW7PBk0Q7Et771LRYsWEBeXh6/+MUvSE9PP/SbFKWf0B7dXhFazo85xsaE3c6wJvQR3UH7fIfSo7SVV1KWiUbItL5bYqMYnkwFnB04AnyB5Zw0h8M3c2lESGBJOGGjNTRHnRYFw4Uxx8ROxcvS6XlHSvjyAqG1Aeb/l2P53+Dvr4IzZgu5uR94ggkHy0NDZXTMkUYidSLDgtA386pP+pqT0AjVzueaG4TtYrgw7hhvhFUYloWWFMIo43sPrEsZcq2QZvxxRhphVuDTc3qzdKNF4J3QR/zPift5vSsSAuudj9an4fPIJ0fj6240eo/AuymvCnVGcHirrn0Vlf5D5W4GGmrPIydsjyaEhmHWtyLOOMIJfLuDt1OWYiMkjCENwQhscCZqKiHkGiHfQJODJc5GsoGQwFBg/AWmMT2DVFsb+UbYFBUZtmCodl7lpE0gIylsf8LxmYuEsmLD6ynDK6GfoV2UMVmAgIlkyMSQwp870whExTZt+OXqhPgCJ2tgrBG2YsgGmh3sihpvNOIjWBOscGHcsS70NyufSwuJm87tlI+WefPm8fWvfx2A++67j49+9KM9c+BjjH43BxY9bU8ReD7lO0qWWuHZlGWw8RHp82KuyyhqvcDL0XsmWCEp8JeUZXekxjHU+pvjNHxL9R1RvnON8w2oPhJzTI0JDVHEeJT1qXYHyjtuSAn/87jh+V87Ei3+tSCAGSfDjTcYsssNi0Pf4n16IGRGxwkFVjnD+tBQHulov52yzIw5Rls/L65yZj/nstb5bpRVoWGw9R0dh1oYYx3Dejg/+kD23OXgzb3+Nl2ljDSK1xl/P0qJGW/lsAM2LkoZWR0aJgVC+UHs0JfojvRfcOedd97ZnYO1trYe7Xj6LJmZmQP6851oqD2PHGt8FfvYSF3kvdBHF46k4CbHwBAjrBaftzwtEMQYGqILZ16k6rEwtKwILUaExS5gB4YGfAvhanx+X1XoNXJbjZfG2rXQ8dy/h5T/nWVkJszJdFw+AwqzDdXAC0nfQSYX7xAXAUOifMpWMaRbr6AyKLoIZxtIGcOHgxBjDIOMXzqeFjgaMMwMhOFG2CSGIUZIGi9fOMwKl6X59JW/pgLOjjtKA/+36ilH+8EHH+SWW24B4NZbb+W6667rmQMfB/S7ObDoaXsa4zWxlzjvPOcbYXXKkmZ8tHN4F85buvG52StCS40Y8iPnvNoZ5sZCTon5lIqt0craOTHH5EBocr5JTcL4CHOehRFWWBtatoiPpnfl3Kdbw5lToPwCQ0MMWhqguRaqtsGaHYbCcwJmBI6JAR3F0NUO3ggtDsMZMceIwDfkKrTCwtCyE59u8aGY6xTB3+lgUcrSKP6P04If62kx78j2dCS7K3tujlrNnxzz2uL7Xgd2O5+/vSz08+apMWFsQMdNRndpiW526sRwZtwxrBc+X2+RmXnohB2NbKPRloGG2rMzDeKLU9rb6e7dUtdF0lwxfCqH4YOHxUeNloReWmpmcPj53AtShjpn2OgM6fgLZqERksAefOe0IiPki1ciaARG4AukYsaPvS6WRjKRZHrMUZgQfn2P8Kc/+OPPvtbw5esNgYnyvp1hZcoQRBX+dRjODBxbxPBuyrJLfF75ZITCKGov4rV2J1thu0CTMywMDYOsMM7CyTFHHHi0zbILQ1wgjhAaw7kxx5hA+FPSd468Ou56NBLzxz/+kc9//vM45/jSl77Ev//7v/drmT/9bg4sesuer6QsQ41QEQhbHSxIeoWfmYEcsKguJbAg9N/zwUYYZoVKZyizvtiyPFI2CfBRWotwmhVWREWFs6OIciiwNDRsFcOpgWPoQea87Q5eS1qWvQ81z4Rc9w+G6Zmd86uXhpbd4hVGSo10ciBDgWdThvWh5WPxkNKoy2ydwMqUYYMYjHgHe0rgUzdWhIbNzjAhEMZ1ozDxcNjbnk5gmfNyfqcHnVPhRGCbwNrQ0giUW5+KeKRFizscvB1aRlrffOhYNGXrSbSpjaIoLEx5Z7m9pa5EP7uoOnxXFPEx7O9sB8ZPEimBxamAXAPFVnzjGhPJ+xkIkA6HPY5/TxyfL1kQCGOBp12ANV4GzOGd7sEIi5xlfdS0ZihCaUxIRZ3gGsXQ7HwqyJPLDa/8wLFni/9ck+Zazvxf3pHPMpCL0CJCuvEFjrswzAgcK5wv0Dk15hgV5U5uFcPSlG82sVt8dGxNCO87L8cVGJgT+Oh7nTO8lPTtkofiC5C245c5R1vhpaSlVYQ5Pexo/+1vf+P666/HOcff//3f93tHW1G6yzTrc7FHWaHUgo07Xk9ZXkhZ0vjAKW2nWWB1aNjpDB+JOa8ShHd2t4vhgpgjP3IWl4deYejDMR+lnRrViryesh0O7MkxodgJC0LLCCdMCaRLyc72upJRIyH7uhgNgaMZIVt8Yefa0DA2EE7p4v0J8d0xcwxcFg95J2WpFWGXM2wR3/Z8mBWmx0KK9nJ0T4oJY0RYElo2pAwTovbuPemgJsU7v0mBc6O/J/ibg/edYa3z9SvjrVeHOtJzy15pIzNjjtIBkJt9IDSyjUZbBhpqz8OnyflGNQ4QAwikorbnKXwqRotAZWhowlAQRYiS+NzkAC9TZaMmL9EhcMZr0jZEHePS8Qodw43PPdwmPm+yDUM+jpjxF8JMA9MDx8lGqE3P5t55jbzykCAOMgrhilssl832xUHW+IvAaynD0tAy1TpqMOTh5baGRI1mgsjJT0XR/ITAFgcNGDLFPwcIrcBU6yNQcQMp57Vti/FNNDZGS9UXxx0LQosDsoAPxVyPLXv+5S9/4XOf+xzJZJIrrriCe+65hyAIDv3GPo5+NwcWvWnP91K+hmJ2VLDYIvByyrAyWoUqsn4uacZQ43zfgEn71Jg48ekbFpgdODZEjuI5MbdfmkN9VJQHvjV8XlSovSTq0DstEEZEkemk+Ajz+84rDY21wnpneC1lEScseMCRkRJuvR6GduGJNgm8lrIMwkfgq8UXgtY4Q6H1aTFTAsfQQ6Sj1ThYEVpagYlWGHWUOc4FBQW8v7uWN1KWAiPMDLwj3RbV2qx3fl6t6MbYDkVK4N3QUC+G02OO3B68WTjWdCeyrc42egEYaKg9D59G8W3fHb6A0EXSe8nIMU1FP6eifWujbmZDrWCibfWRJnUrEPV5IPS1hzRjyEOIG99gp13az0fRDSNxjA284kAWwubQskr8RLz2gZB1D4YADD/Hcu6NliH5gjWGdPENLWqcoQFfBNlsvO6sj0YJgTHRefxU1942eocYVoWG0wLvNGcibHSWMwLHmJiP3jcKPJCwWAyTjLAJQ0rggljISrEMN0JlaDg7redkqebPn891111HKpXiyiuvZN68ecRiA2MRUr+bA4vetGdS4JmUZUbg83fbWRN1Y62w3rFOx9dOHKiQO4wc23pfH82H4wd27FwkbbcqNJRYn8YyyPic6yVR/UpBpBhSaOGkwJEB1ANVzrAwZVj9rvD81/18ddJlhsu+YimI+QLxAO/Ur3KWLHxxdgFgozxz8A7+BTHHkG7eW4vAdoGVoW8iVB453UeS0tGSk8+zuxuYEAjlRtiNL5jf5nyUfbyVHlFWahB4M2XJMz7qfyD5wP6CppEoitJBe3MASySLF+md5uCLGWd0KXnVmVoHjyW8/NM2jK+QN0I+QhKhGBgceA3VPAM5CG+GlpHGERdYIvC+BKQAh+AwpEctk2tCgw0h1SK4GigbIwyzjvhVMba/EXLStQGzz/ER8p1i2O0MrfiCSQMU4kgYQ7kRnIH1YigACoGSfTplbgz90uWMwPFu1FGzHq8SMCVaXm7t+KyWmUHIFjE0OR/1WuYsJwdeo3xcTHrM0X7ooYe4+eabSaVSXHXVVdx1110DxtFWlMMhbuDkwPFeaCk0rsN5rAi807rSGc6ybj9JwH0xQK4RVjpLebQidyCs8ccfHUWqX0laMozX7y5EWOEMq8QrjQwRYWnKsDta/RtmhIvijtNmGdJutjz1Y8fi+UJOY8j1t1nCuO8hsMkZplnHqCiX/H0x5ESFk0UGqqL0klNN55uMA34+AyUGhhrHdoGNzrI86Ys/hxqh2ArZh5ifJJLrW9ecYIx1NIvhb84Sw0u5To+7I1al2petzqc2Tgi8836iZMZpZBuNtgw01J5dk4wm1ET0c6P4aPBO5x3eQiMUW+94rw8hiSFmfJvxbIRMG6WHCJgocr3DCWudJSEGZ7yGdobBR7EFqvCpJVkGmsWQb2Gy8RJeVaFhj/Ht2cHf+W9f7nj3ByGxXDjtx3HEQMxY0sIQsYZsA7kiZBhDiFdM8ZF4LwM40TouiJof1EcR+B0C253xklnGS/btcYYZMcdrKUureGWSPc5wbpqPerU4eDARsFsMJwUhO8VHd0YHwhADp8YcNVHh57kxd9T5kmEYcuedd3a0Yb/66qv52c9+NuAcbf1uDiyOhT3fSRkSeEm8vR2zTaFhSWioiJy2rr6DdeLVPAxwWuDYKIaNUZOp0fbQEdXQwVrnW6Y3iqHQCDkCzcbXumQbYbTxEnUFlo4xrA0Nf3xBmP89RyoFkybBp/7ZsmuUZWQgtIqhVrz6yRi7f2fZ0anyiAAAIABJREFUHc5HfqccYTOsFvGR9hrxKTYx49ub5+DT99pb2Yf4OXuls9Q6KMlIIz/ZRrH1xZz5R5kqsjftBZdbnGHWERTb92U0jaSb6AVgYNGf7NkcpW8Exuc9pwMZxkcicuCwnbhm8fmAMT4obmwvVtx30mwQWJsy7I7SOhrFy+1lGyEXPyEnjXdiW6Ml2BgQjzojWuhIF9kdadbmAKER9oilOdonTlRQiXfcjfHLvjMCR3qUF7mm1fDYr4WljzkQyCmG//VTr4+VFo/TlEzQ5gzN+FSWQcarmJwaOLaKZbPzNwGNxquJTAqE0/dqJuME1oW+6KcVQ4YINVhyjOPswLHWWWZEBTo7HDyeCGgSKMdRhY9ejbXCGTF/cawVeD1l+VDcMegoL0Z79uzhi1/8Is8++ywAt9xyC7fddhvWDqCrUUR/+m4qh+ZY2DMUeCllGWp9atje1Au8l/LNpUZFUp4xhGYxbBNDrcN3b90rl3m3g2VRq/GRUe53nvGt2QP8jfsugRrxjiFAmfUO9d7zsROv210dtYJvFh+oyInGUOsMbywQXvpOSKIZbAzOucFy2eWG4VHUuauiy3b2RBHukkiv+0hzsZ34YtE9YmgUSOBzsC0+6LJBfB72KYFjfOEgGuvqjuxEB6FFfM8FgNmxnouS9xXU2e4megEYWPQne7aJX1YM8U5km0CLGJrwEYdsA4OjDoZDrByyiGR9JAuVwkctUlGetcEfK9fAICMUGN/Kd7t0PneDGKrFR7sNvlCnImowEQfa8DnZrWJoicbYGuUxtwBbnW+HbIFYNO5WoNgIexy8j48yhQ7yrBDsFJb9xfHOX4WG3f4zzLzQcNYNlqwc30gmTM+gpqWVGjEkxWAQ7zAjOHzr+VMCx2DrG1WsCw0Nxo9ptPUFSO0XmiLj9XWXi2FoNL41zpBhfNHmHoENzo8xLTpPgHBezHFKzF9smwVeSFlOCoQR9vCjTu0453jkkUe48847qampISMjg5/+9KdceeWVR3zMvk5/+m4qh+ZY2bP9Ozc98OoX+1LnoDIqxE6Jz4UuMj5yfKDodX2krFErvpV4q3wgi5oT3cyXWj9HdCe6mxB/zKZoPkzhdbJfrjYs/1nIpjeFn/y3YcbJ3fc026LcZmt8ZL4nndRq5ztCtjcDMqZ37LnN+V4KoyJZv/7QpOZwGTDOdn19PS+88ALOOeLxOGlpaeTm5pKXl0d+fj5DhgwhIyPjiI+vF4CBxUCxZzKavHeLr4bf4bzcXokRhu6lsHEoRLyT3CRQL4a6KL2iPlL9KDA+IlQQLRumGe+kVzpYFVoqxWAEiqyvuj/YuXc3NvPI9t1s39NAeVMtWU2NbGxLsrEtBWHI2LhhTFoMG4+zaHM2f/l1DtbkEcSGUFycyy3/ZPjQWcZHqaMx5+fl0VBf7zs4itfjXhEaNjpLDKEIwRlf/pjA32Q0i1c4CfGyhJnAEOM/Q2PUrKYiEF5LWbJFyLewKDRsj24yQrz84BDjZbmKokTPFvEawKVdRNkOh/fee4/bbruNt99+G4Dy8nLuueceZsyYccTH7A8MlO+m4jmW9tztfKHj6THXSQqvp3B7BSYOFnHuLnsE3kpZMhDqHch64dJJHzQH+/X9Qm4OXP4xCA4ykYcCy0LDFjGcEjhKjvKzO4HVkbLIqfscryftmRSv5LJdzH5FrgONAeNs33rrrdx7770H3WfIkCGUlpZ2eowcOZIJEyYwbtw40tLSDvhevQAMLAaqPZ1ArfhOY9XOR2qHRjJRJYdYkuyKUPwFoTZywOsiBzwrin4Pip6zgEpnWOEMrW1t1G3exM6NG2jetJ49G9ezfeMGtlVVsa26msaGhiP+fFm5uZSWljJqr+/wiBEjOOWUUxg2bBi5ubmdWh6XWcdO8XJ/OQbGW0ex8RF4AXYBm1O+62RbpIpSj2GY8dGxDaF3rAPjL7C7nJcaS+C1cyuscEbcdRRWNgm82u5oH2Fhz6ZNm/jOd77D448/DkB2dja33HILN9xwA+np6Uf8t+svDNTv5onKsbbnNgfvpOx+nRb7EhKtVi6LcsPLrNCGj1AHxksQNtUJH/+E0Nbm87lv+xdDednBJ5T2SPRII0wNjkzbulH83w/gtJjbr3Cyp+y5w8G7oZcPnBEcebOb/sKAcbbffPNNfvSjH9Hc3EwymSSZTNLQ0EB9fT11dXUkk8mDvj8Wi1FWVsbEiRM7HlOnTqWsrAxrrV4ABhgnij3bi2C2Ol9sU2xhuBWGmSOf3ML2aLqD9du28d7iJaxauoTKpYvZsmI5NVsqEecOeoxYWho5efnk5+eTnZNDKp5GEI+RGwS01guxtATNYYr6xibaGupprt9DS3PzIcc2fMQIiidMYsyEiXxo0gRmTJnMpEmTIJ7O26FhVWhJN34JOc/4m4ZMfJOI5aEvDM3Ga3/Xi/+50AhtYqjng06aI6xwepSb3e5Qb3ewYJ8l1+4iIixdupT77ruPBx54oGO+uvrqq/m3f/s3hg8f3v2D9XNOlO/micLxsOdmZ1iS8k2q+lq0tEVgUWhpEJi1T9fFUGBx6IsWp7qQt/8Kd98rNDVBEMCFc2DKZMOE8VBWBmldTOKt4p3YFvHOcndVkFKREtXa0DA+6qTZVTrH0dozIV5/fIsYTj7KNLv+xIBxtg+GiFBbW8vWrVv3e2zYsIHVq1fT2NjY5XtzcnKYMmUKp512GhUVFUybNo2JEyeeEBGmgcyJeEFvbXe8xbDLQZGFUiMM74beqnOOTZs2sXjxYpYuXdrxvHPnzi73z8rOYVhZGQVjy8kdU0bh2DLyR4xk9NAS8kuGMi4vj1IrZOwRanfB9u3w4rvw+stC42446wL4v183JIxhnTNsCA05zY1kVFfStHUrS7dUsWLrVuq2bqVxy/u8v3YN1dXVXY4lFosxYcIEpk2bRtmEiWSPn0AwbhJjR4+i0EI9lg2hYbAVMsQ3ZmjGy3RZ41NzkgaSYigxwt/FHWV75Xk2iu9MV+UO/+K+ZcsWnnzySR566CGWL1/e8fp5553HHXfcwfTp07t/sAHCifjdHMgcL3vWOJ+iURH4mpLjLR/nxN8ELAuNz00+gHa0CGwRw9LQMNjA0N0h9/5EePmVzvv9138aTjvVH6C6WmhphTGjwRjTIdO3IjrXpMArjBxsXCudYZCBqVHL9wNxpPZsP89y52thpgayX9OggcwJ4WwfChFh69atrFq1qtNjxYoVtLa27rd/PB7vuHhPnz6d6dOnM2XKFPLy8o7D6JUj4US/oLdFjndVlOddZH3x33ArBGGKNWvWsHjxYpYsWcLSpUtZsmRJlzekQRBQUVHBSSedxLRp05g2bRoVFRUUFRXRgC9EXB8aMo3PdRwepbPs2A3f+JKjvgtfPTsXppxv+Mj1lonpXkEgxE/UG6LK/3LrO7VVieGtlGFPECd7Vw1D161g65pVrF69mpUrV7Js2bID2jktM5Mh5eMZUjGRsnHjGT6+gvi4CopHl5GemUFSIIHQIoZMI5weCOMD6Sj+bMY3ytkjUBZJix2qOKmxsZHly5fz1ltvMX/+fN55552Obenp6VxyySV87nOf46yzzuqeIQcgJ/p3c6BxPO3ZEOVEZ0YF0sdL4WKHg6WhRfC64PvK+HVFUnxh9vrQMNQKzYscqxcJa9bCmjXw4P2G/ChsPe8exwO/hYICKBsLySS0tfm5dPY1lrwZlvKozXx7YCUUqIqadgXAtKB7ee6Ha0+3Vw1NGr7rb3c+/0BDne2DkEqlWLt2LUuXLmXt2rUsWLCAJUuWUHcA2ZuysrIOB7z9ubi4+BiPWukOekH31NbWsnj5Ct5ctoL3Vixn7bJlbFm5nGRb2377pqenM2XKFKZNm9bhXE+ePJnMzEyAjoLFmkgntV5gdOSEZhqorhX25Fk2O9905qGbU2xeCXmDIacQCsYYZp9tOOdUKI37fOq1oZfsGmO95m0Ovn36Mmd5P/S63cMRynOyqGtuYqcY8g0UW6HYCAUI1VVbWbJkCcuWr2DZ6tWsXL2a99et7fIzAlhrGTpqNPnl48kbPZbRJUMZPXwYGUOHk19SQklJCYPz8siKiimLDZ0iVIlEgj179rBnzx42btzIsmXLWLp0KUuXLmXDhg3sPZ0aY5g9ezZXXXUVV111FYMGDeox2/ZX9Ls5sDje9gwFloaGSmd818MD6G33NCKwQ2C18ykjk6I57HCVNhJRlHqDM2Th58JhxpGx14H++8eOJ+dDIrH/+42B//83hroSS3W0ghczXk0q10D5Xi3mu0N37Vkf5aVXOkMGMClwDOtBTe7+hjrb3aT9H0xE2LJlC0uWLOkU9auqquryfSUlJUydOpUpU6ZQXl7OuHHjKC8vZ8iQIZgT9b+uD3C8LwDHEhGhpqaG9evXs27dOtavX8+qVatYvnz5Af9vs3NzGTt1GqVTpzNy2klMmDad0eMryEyLE8cXGHpFDh+xbo6kCEW8ozssSlFpaRLeWwxP/EFYuhQef8SQm2vYA6zeBm2DDHtiXkM8C58TXe+gGd9goSDS6651hmoxhJHW+DDjL1xpeAc/kZFFdXNLR+e3VoFGDG3ilVMMvqjR63gLsTDF7vc3U7dmFXVrV7Fj3VrWrlvL+rVraa6vP+TfNCMjg4yMDNLS0ojFYqRSKVpbW0kkEl2uhu3NsGHDmDZtGhdccAGXXnopw4YNO+T5TiROpO/miUBfsWdtdJPeKF4Te+wRtis/FG0CWyLnOATGRec66qZWUST6fedXIwuioMKQqMuvSQirV0N1NaSnQ1oavPmu0NQGn/zfATuj1cxXHnYMmWgYUwwTBwujsoTB1nT7JuBg9myMNMUro+L8kdENxqAT2MluR53tbnKoCWPnzp0d0at2J3zdunUc6E+Xl5fHuHHjGDt2LCNGjOikkDJ8+HAKCwvVGe9F+soFoCcQEXbu3MnWrVupqqrqqEfYsmULGzZsYN26dQesSQAYPXo0kydPZvLkyUyZMoXp06czZsyYjoYpLeIfSaCxFbbXQHMCUkmQEIYNg2GFXqN79xbh1degslJYsRI2bPQOOPjJ9tt3Gs45u/P/tRNowEsO1ou/WDVFnSsbHDRjcOId7xzjHeyWqInOYAsFVhiZl4ttqEfwmt2J6D1JoE2EEEMmkG4gHX/hcw5q8BeG98WQEBhhHCN3VdO6fi3r1q1ly5YtVFdXU1VVRXV1NdXV1ezZs6dbdsnNze1wrNsfU6dOpaio6PAMfIIxkL6bSt+yp4hvRrPOWbY7r9Q0wvoW6AfKae7OMZvwHWirxbdbL7IwxjpKDL2iGZ0U3yxnhzPsFEOT+EY7Mfw8K9HcZ/GdffMtFBpo2+q4/jOdfZK0LJj5UcNHP20ZmgX5UcO0TLqWNywoKGD37lra8M71HvFNz3aJIRkFW0ZYoaSbsrMnCupsd5MjmTAaGxtZsWIFS5cuZc2aNR1RxcrKygM64e1kZGRQVFREYWFhx2Pw4MEMGTKEgoICsrOzD/rIyMhQZ/0g9K0LgNDS0kJTUxPNzc00NTXR2Ni43++1tbXs2rWr47F792527tzJjh07aDtASkQ71lpGjRpFeXk55eXllJWNZ8SIKYwZO4n09DySCUgkfa5fIgljR0NJif//WbhIuPcXQlUV7Nq9/7Fv+SfD5R/z+778ivD1b3T+3y4dDmedCVdeYSgdfmT/ky3RZL5bfJS7MXqt/UxBPI36RALBXyTSjI9ixxECfFQ7EXXAbO+QBv4GocgI461jvIVYN3IJm5ub2blzJ4lEouMRj8dJT08nPT2dnJwc8vLyCILg0AdT9qMvfTeVo6ev2rNFfIOtdqWmTPNBc7DMaEUtiB4GP9eE+EBAE17ms0l8zYbFO9glxjfIOlLH/Uhpd65TeKfbd+X9wPluZ9Nm4df3C8uWQW2dz+tuZ1AhfOpfLMNP8Z19q6uElrro8zsfWQ8dBEGctkSCkVMN+WmGPCM0bRCaq4VMB+IgDP2+YeiDGhecB1lZH1xPlq/wr6enw+jR/nozdCj7+SyplBCGPkrf3/2Z7jjbsWMwjgFJTk4Os2bNYtasWZ1eb21tZePGjaxfv54NGzZ0RCPbn2tqamhtbaWyspLKysojOre1lvT0dOLx+EEfsViMIAgwxmCt7fTc/tj39b2f28+19+/t7H1Dcbg/70t339Pd88TjcZLJ5BG//2Dnb5eeTKVSpFKpjp/3fS2ZTBKGIa2trYe8+ToUxhgKCooxZjhZ2aVkZQ8nO2s46ZlliIyjrW00/3JLBhfN8RPWK68Kt/9r+zn3P/f/+SfDFR9r/0ywdNkH27KyIDMT4jEvR5W7V11weRmcfy6MGAEV4w1Tp0Bh4dFPkpkGRhhhBEDUKCYVRZSaBGx2nFrXRlMUhfda2Ia2qPtlHN/xbZhxDLFezi9nn1zr7pKVlcWoUaOO+jMpinL8yDQwLhDGBUIoUCe+OdgOgTZncXjn2kUPg3dicwxkGWGwgZHWdbRxP56+oImCCwfuFOIZM9pw5zc+GGhLi7DgHfjZXcK2aji1UBgf97Kt//5bx9+e6uooIQBP/t4wOBLh/s8/O/7wxwOf97RTDFlZ/uc33hQeenj/fbz7IDzyW9MR6Jl3j/C7RyAtDrm5Qk6O/6zO+avWf37PUFrq9/3dI8JjjwtOAPHXrYoK+P7/7T/VmOps9zAZGRlMmjTJ6/92QVtbG9u2bWPnzp1dRjLr6upoamrqeLRHP9t/Bi/V1tLSQktLy7H8aMpRkp2dTVZWVscKRTKZzdaqLKzNJggGEcQKicUKCYJCgthgbri+kEsuLmbYsGG88Wac/++ODxzn1qR/tJPcq3gmPd1PYPE0iMejn6Pf0+KQl/vBvuPHwTfvMJQOh+HDIe8gulClpYZv/tuxuerEDOQT5Xanx6ht3vem4cTQb1UU5egIjE+zKOTAAYiBSGam4cMfgtNnw8JFMH78B3N3wSAYVgLG+lSY9udYLMC5kGAvH7Z0uGHqFCEIvNMcBNEj+nnvfoETJxouuVgILDQ2waZNULkFUim/3e31p2+P3yWSflV135XVxF7Xt8ZGoXp75+39LWNP00jou0th+xKGYYfz3b7E3R5R3ffn9mcRwTnX6bmr1w61j3Ou01LPgX4+2Lbuvv9oj5Odnd1xY3I45z3UfsYYYrFYx8pBEASdVhHat+39nJ6e3uFk77s6UL1dWL2Gjrlf9nmeNAlKhvpz19QIS5fT6TqRlubloAblw5AhkHG8tK96mf7y/VQOjdpyYKH2HFj0hj1TKaG21jv0g/IhFi05JhK+g2ZTEzQ0eOccfHTbGJhQ8cE1raZG2LkrypGPtmdmwqiRfeOap2kkA4wgCMjNzSU3N/fQO5/A9JcLQMlQQ8nQ7u1bXGw4X5UmFUVRlH5ELGa6jEKnpRnS0iA3F0pKDn6M4mJDf1da7j8JL4qiKIqiKIrSz1BnW1EURVEURVF6CXW2FUVRFEVRFKWXUGdbURRFURRFUXoJdbYVRVEURVEUpZdQZ1tRFEVRFEVRegl1thVFURRFURSll1BnW1EURVEURVF6CXW2FUVRFEVRFKWXUGdbURRFURRFUXoJdbYVRVEURVEUpZdQZ1tRFEVRFEVRegl1thVFURRFURSll1BnW1EURVEURVF6CXW2FUVRFEVRFKWXUGdbURRFURRFUXoJdbYVRVEURVEUpZcwIiLHexCKoiiKoiiKMhDRyLaiKIqiKIqi9BLqbCuKoiiKoihKL6HOtqIoiqIoiqL0EupsK4qiKIqiKEovoc62oiiKoiiKovQSseM9gIOxfft2/vrXv/Lyyy+zYcMGdu7cSX5+PjNnzuQLX/gCJ5100kHfX1lZydy5c2lububaa6/lW9/6VrfO+8QTT3D77bcfdJ/TTz+d++67r8tz3n333bz22mvs2LGDvLw8ysvL+eQnP8nFF1/crfMPVPqbPTdt2sTdd9/Nu+++S3V1Nfn5+YwbN45Pf/rTnH/++d0690DleNkSQER45pln+M1vfsPGjRtpaGigpKSE2bNn88UvfpGRI0fu957GxkZ+8pOf8PTTT7Njxw6Ki4u58MIL+cpXvkJ2dvZhf/6BRn+xZzKZ5Pnnn+f5559nyZIlVFdXAzBu3DiuuOIKrr32WoIgOLI/wgCiv9izp889EOmPtlQ/aH/6tLP9m9/8hnvvvZdRo0Zx5plnMnjwYDZv3syzzz7Ls88+yw9/+EMuueSSLt/rnOO22247ovNOmjSJr3zlK11ue+qpp1i7di1nnXXWfttee+01vvzlLwNw7rnnMnLkSPbs2cPq1at54403Tth/snb6kz0XL17MZz7zGVKpFOeddx5z5sxh165dPPPMM9x444189atfPeAxTwSOly0Bvv/97/OrX/2KoqIizj//fHJycli1ahWPPPII8+fP53e/+x0VFRUd+zc3N/PpT3+alStXctZZZ3HppZeycuVKfvnLX7JgwQIefPBB0tPTj3g8A4H+Ys/333+fm266iaysLM444wzOO+88GhoaeOGFF/jmN7/Jyy+/zF133YUx5ojHMxDoL/bs6XMPRPqbLdUPOgDSh3nqqafkrbfe2u/1BQsWyJQpU+S0006Ttra2Lt/7i1/8QiZPniy/+tWvpKKiQr7xjW8c9Xja2tpk1qxZMnnyZNmxY0enbVu3bpWZM2fKnDlzZOvWrfu9N5lMHvX5+zv9yZ5f+MIXpKKiQp555plOr2/ZskVmzJgh06dPP+BYTwSOly1rampk4sSJcu6550p9fX2nbe3Hu+222zq9/j//8z9SUVEhP/jBDzq9/oMf/EAqKipk3rx53T7/QKW/2LO6uloeeOABaWpq6rRvU1OTXHnllVJRUSF/+ctfun3+gUp/sWdPnnug0p9sqX7QgenTOdtz5sxh1qxZ+71+6qmnMnv27I67pX1Zv349//3f/83111/PpEmTemw8zz77LHV1dZxzzjkMGTKk07Z58+bR2NjInXfeyfDhw/d7byzWpxcRjgn9yZ6VlZUYY/jwhz/c6fXS0lIqKipobW2lqampx8bS3zhetty6dSvOOWbMmEFubm6nbeeccw4AtbW1Ha+JCI8++ihZWVnceOONnfa/8cYbycrK4tFHHz3scQw0+os9hw4dyqc+9SmysrI67ZuVlcXnPvc5ABYsWHDY4xho9Bd79uS5Byr9yZbqBx2YPu1sH4x2o+1rvDAMue222xg9ejRf+tKXevScjz32GABXX311p9dFhKeeeopBgwZxxhlnsGzZMn71q1/xi1/8gtdffx3nXI+OYyDSl+wJUFFRgYjw8ssvd3q9qqqKNWvWMHHiRAoKCnp0PAOF3rTl6NGjicfjLFq0iMbGxk7bXnzxRcDn37ezadMmampqmDlzZpcO2syZM6msrGTbtm1HNJ4Tgb5kz+6MU3O2D05ftGdvzvMDmb5kS/WDDk6/vM2oqqri9ddfp6ioaL98obvvvpsVK1bw8MMPk5aW1mPn3Lp1K2+88QYlJSV86EMf6rRty5Yt1NXVMXXqVO644w4efvjhTtsnT57MXXfdRUlJSY+NZyDR1+wJcPPNN7Nw4UJuvvlmzjvvPMaMGdORsz1q1Cj+67/+q8fGMpDobVsWFBRwyy238L3vfY+LLrqoUx7hW2+9xSc/+Uk+/elPd+y/efNmAMaMGdPl8caMGcOrr77Kpk2bGDZs2BGNaSDT1+x5MB5//HGALutpFE9ftWdvzfMDmb5mS/WDDk6/c7aTySRf+9rXSCQS3HLLLZ2iGKtWreLnP/85n//855k6dWqPnveJJ57AOccVV1yxX+Rk165dAKxcuZINGzbw3e9+l/PPP5+GhgbuvvtuHnnkEW666SYeeeSRHh3TQKAv2hOgvLychx9+mJtvvpmnn3664/VBgwZx5ZVXMmrUqB4dz0DgWNnyH/7hHyguLuZf//Vf+d3vftfx+imnnMJll13WKcrT0NAAQE5OTpfHan9938iN0jfteSAefvhhXn75ZU4//XTOPvvsoxrPQKWv2rM35/mBSl+0pfpBB6dfpZG0V9YuWLCAa665hssvv7xjWyKR4NZbb2XUqFE9rhLhnOOJJ57AGMNVV13V5XbwSzc333wzV155Jfn5+YwYMYJvf/vbnHTSSSxevJh33nmnR8fV3+mr9gRYsmQJ1157Lfn5+TzxxBO89957PPvss1x++eV85zvf4Z//+Z97dEz9nWNpy5/+9Kd87Wtf44YbbuCll15i4cKFPPjgg7S1tfGZz3yG55577qjPcaLTn+z5wgsv8O1vf5vS0lJ+8IMfHPV4BiJ91Z69Oc8PVPqqLdUPOjj9xtl2zvH1r3+d+fPnM3fuXL75zW922n7PPfewZs0avvvd7/b4MtTrr79OVVUVp59+epe6knsXD3Slv3zuuecCsGzZsh4dV3+mL9szmUzyT//0T1hr+elPf8qUKVPIzMxk5MiR3H777VxwwQX87W9/49133+3RcfVXjqUtX3/9dX7yk5/wqU99iuuvv56SkhKys7M59dRTmTdvHrFYjO9///sd+7d/Nw8UuW5//UCR7xORvmzPfXnppZe46aabKCws5L777qO4uPioxjMQ6cv27M15fiDSl22pftDB6RdpJM45br/9dv7whz9w2WWX8b3vfQ9rO98nrFixAucc11xzTZfHePjhh3n44Yc5//zz+fnPf35Y529XK+iqkA5g1KhRBEFAGIb7Ve0C5OXlAdDW1nZY5x2o9HV7btiwgS1btjBnzhwyMzP32z579myeffZZVq5cySmnnHJY5x5oHGtbtheszp49e79tRUVFlJWVsWLFCpqamsjOzmb06NGAL5TsivbXD5TTfaLR1+25Ny+++CJf/epXKSgo4P777z9ks5QTkb5uz96c5wcafd2W6gcdnD7vbO/9D3bJJZfwH//xH13m2J555pldqkPs2LGDl156ibKyMmbRm5bIAAAGqElEQVTOnMnkyZMP6/y1tbU899xzDBo0iI985CNd7pOens6MGTN45513WLduHaeeemqn7evWrQO8bNyJTn+wZzKZBGD37t1dbm9//USPxBwPW3bHNtZa4vE44J3o4uJiFi5cSHNzcydFkubmZhYuXMiIESO0OJL+Yc922h3t/Px87r///o6bKuUD+oM9e2ueH2j0B1uqH3QIjq/M98EJw1BuvfVWqaiokJtuuumIBNHffPPNA4q5JxIJWbdunWzevPmA728Xb//2t7990PP86U9/koqKCvnsZz/bSWB+3bp1ctJJJ8mMGTOkrq7usMc/kOgv9mxra5OZM2fKxIkT5ZVXXum0raqqSk4//XSZMGGCbNiw4bDHP1A4XracP3++VFRUyKWXXrpfo4Xf/va3UlFRIZ/4xCc6va5NbQ5Nf7Lniy++KFOnTpUzzzxT1q9ff9jjPBHoT/Y83HOfaPQnW6ofdGD6dGT7Zz/7Gb///e/JyspizJgx3HXXXfvtc8EFFxyxAP727du55JJLKC0t5fnnn+9yn3Y5qQOlHLRz6aWX8vTTT/PUU08xd+5czjrrLBobG3nqqadoa2vj+9//Pvn5+Uc0zoFCf7FnWloaX/va17jjjjv44he/yDnnnENZWRk7d+7k6aefprm5meuuu46xY8ce0TgHAsfLlhdddBEPPfQQCxYs4MILL+S8884jNzeXFStW8Oabb5KRkcHtt9/e6Vhf+MIXeO6557j33ntZuXIlkydPZsWKFbz66qtMmzaNz372s0c0xoFEf7Hn+vXr+cpXvkIikWDWrFn8+c9/3u9cpaWlXHnllUc0zoFCf7Gncmj6ky3VDzowfdrZ3rp1K+CXe+fNm9flPqWlpb3WbWrJkiWsWbOG6dOnM2HChIPua4zhRz/6EQ888ACPPfZYh77lzJkz+cd//McuO0CdaPQne1577bWMGDGC+++/n0WLFvHSSy+RlZXFlClTuOaaa5g7d26vjLG/cLxsGQQBv/zlL/n1r3/NX//6V+bPn08ymaSwsJC5c+dyww03UF5e3uk9WVlZPPDAA/zkJz/h6aef5q233qKoqIjrrruOL3/5y2RkZPToGPsj/cWeO3fuJJFIAHTpaAPMmjXrhHe2+4s9lUPTn2ypftCBMSIix3sQiqIoiqIoijIQ6TfSf4qiKIqiKIrS31BnW1EURVEURVF6CXW2FUVRFEVRFKWXUGdbURRFURRFUXoJdbYVRVEURVEUpZdQZ1tRFEVRFEVRegl1thVFURRFURSll1BnW1EURVEURVF6CXW2FUVRFEVRFKWXUGdbURRFURRFUXoJdbYVRVEURVEUpZdQZ1tRFEVRFEVRegl1thVFURRFURSll1BnW1EUpY+ydOlSJkyYwCc+8YkD7jNv3jwmTJjAj3/842M4MkVRFKW7qLOtKIrSR5k2bRpTpkxh0aJFrF27dr/tIsJjjz2GtZaPf/zjx2GEiqIoyqFQZ1tRFKUP0x7VfuSRR/bb9sYbb1BZWclZZ53F8OHDj/XQFEVRlG6gzraiKEof5rLLLiMnJ4cnn3ySRCLRadujjz4KwDXXXHM8hqYoiqJ0A3W2FUVR+jBZWVnMnTuXuro6nnrqqY7Xd+/ezTPPPENRURHnnnvucRyhoiiKcjDU2VYURenjdJVK8sc//pFkMsmVV15JLBY7XkNTFEVRDoE624qiKH2cCRMmMGPGDN5++202bdoEwGOPPYYxhquvvvr4Dk5RFEU5KOpsK4qi9APao9uPPvoo7777LuvWreOMM85g5MiRx3lkiqIoysFQZ1tRFKUfcPHFFzNo0CB+//vf89vf/hZAo9qKoij9AHW2FUVR+gHp6elcccUV7Nq1i/nz5zN48GAuuOCC4z0sRVEU5RCos60oitJPuPbaazHGAHD55ZeTlpZ2nEekKIqiHAp1thVFUfoJY8eOpaSkBNAUEkVRlP6COtuKoij9hEWLFrFt2zZmzZpFWVnZ8R6OoiiK0g3U2VYUReknzJs3D4BPfepTx3kkiqIoSnfRTgiKoih9mIULF/LYY4+xdu1alixZwpQpU5gzZ87xHpaiKIrSTdTZVhRF6cNs2rSJxx9/nOzsbM455xzuuOMOrNVFSUVRlP6CERE53oNQFEVRFEVRlIGIhkcURVEURVEUpZdQZ1tRFEVRFEVRegl1thVFURRFURSll1BnW1EURVEURVF6CXW2FUVRFEVRFKWXUGdbURRFURRFUXoJdbYVRVEURVEUpZdQZ1tRFEVRFEVReon/BwH1jukCER8fAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 720x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pp_data = az.from_pymc3(trace=trace_g, posterior_predictive=pp_samples)\n",
"az.plot_ppc(pp_data, alpha=0.6);"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<center>\n",
"<img src=\"img/bayesian_workflow.png\" width=700>\n",
"</center>\n",
"\n",
"[Mathematical Theory of Bayesian Statistics](https://www.crcpress.com/Mathematical-Foundations-of-Bayesian-Statistics/Watanabe/p/book/9781482238068)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Accidentes Mineros\n",
"\n",
"<br>\n",
"\n",
"* Tenemos registro del número de accidentes en minas de carbón en el Reino Unido entre 1851 y 1962 ([Jarrett, 1979](http://biomet.oxfordjournals.org/content/66/1/191.abstract)).\n",
"\n",
"\n",
"* Queremos estudiar el efecto de las regulaciones en seguridad sobre el número de accidentes. Además estamos interesados averiguar el año en que ocurrió un cambio en la tasa de los accidentes y los valores de ambas tasas.\n",
"\n",
"\n",
"* Para algunos años no tenemos datos\n",
" * Los datos faltantes $\\tilde{y}$ pueden ser estimados automáticamente usando la distribución predictiva a posteriori $\\int p(\\tilde{y} \\,|\\, \\theta) \\, p(\\theta \\,|\\, y) \\, d\\theta$:\n",
"\n",
"<br>\n",
"<br>\n",
"\n",
"[Documentación de PyMC3](https://docs.pymc.io/notebooks/getting_started.html#Case-study-2:-Coal-mining-disasters)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"accidentes = np.ma.masked_values([4, 5, 4, 0, 1, 4, 3, 4, 0, 6, 3, 3, 4, 0, 2, 6,\n",
" 3, 3, 5, 4, 5, 3, 1, 4, 4, 1, 5, 5, 3, 4, 2, 5,\n",
" 2, 2, 3, 4, 2, 1, 3, -999, 2, 1, 1, 1, 1, 3, 0, 0,\n",
" 1, 0, 1, 1, 0, 0, 3, 1, 0, 3, 2, 2, 0, 1, 1, 1,\n",
" 0, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2,\n",
" 3, 3, 1, -999, 2, 1, 1, 1, 1, 2, 4, 2, 0, 0, 1, 4,\n",
" 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1], value=-999)\n",
"años = np.arange(1851, 1962)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(años, accidentes, '.')\n",
"plt.ylabel(\"# de accidentes\")\n",
"plt.xlabel(\"años\");"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<img src=\"img/modelo_mineros.png\" width=350>\n",
"\n",
"\\begin{equation}\n",
"y \\sim Poisson(tasa)\n",
"\\end{equation}\n",
"\n",
"\\begin{equation}\n",
"tasa = \\begin{cases}\n",
"t_0, \\text{if } t \\ge cp,\\\\\n",
"t_1, \\text{if } t \\lt cp\n",
"\\end{cases}\n",
"\\end{equation}\n",
"\n",
"\\begin{align}\n",
"t_0 \\sim Exp(\\lambda) \\\\\n",
"t_1 \\sim Exp(\\lambda) \\\\\n",
"cp \\sim U(L, H)\n",
"\\end{align}\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/osvaldo/proyectos/00_BM/pymc3/pymc3/model.py:1444: ImputationWarning: Data in acc contains missing values and will be automatically imputed from the sampling distribution.\n",
" warnings.warn(impute_message, ImputationWarning)\n",
"Multiprocess sampling (2 chains in 2 jobs)\n",
"CompoundStep\n",
">CompoundStep\n",
">>Metropolis: [acc_missing]\n",
">>Metropolis: [pc]\n",
">NUTS: [t_1, t_0]\n"
]
},
{
"data": {
"text/html": [
"\n",
" <div>\n",
" <style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
" </style>\n",
" <progress value='11000' class='' max='11000', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" 100.00% [11000/11000 00:08<00:00 Sampling 2 chains, 0 divergences]\n",
" </div>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"The number of effective samples is smaller than 10% for some parameters.\n"
]
}
],
"source": [
"with pm.Model() as model_cat:\n",
"\n",
" pc = pm.DiscreteUniform('pc', lower=años.min(), upper=años.max())\n",
" t_0 = pm.Exponential('t_0', 1/10)\n",
" t_1 = pm.Exponential('t_1', 1/10)\n",
" \n",
" tasa = pm.math.switch(pc >= años, t_0, t_1)\n",
"\n",
" acc = pm.Poisson('acc', tasa, observed=accidentes)\n",
" trace_cat = pm.sample(5000)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1200x1000 with 10 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = az.plot_trace(trace_cat, combined=True);\n",
"[ticks.set_rotation(45) for ticks in ax[0, 0].get_xticklabels()];"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean</th>\n",
" <th>sd</th>\n",
" <th>hpd_3%</th>\n",
" <th>hpd_97%</th>\n",
" <th>mcse_mean</th>\n",
" <th>mcse_sd</th>\n",
" <th>ess_mean</th>\n",
" <th>ess_sd</th>\n",
" <th>ess_bulk</th>\n",
" <th>ess_tail</th>\n",
" <th>r_hat</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>pc</th>\n",
" <td>1889.617</td>\n",
" <td>2.481</td>\n",
" <td>1886.000</td>\n",
" <td>1894.000</td>\n",
" <td>0.165</td>\n",
" <td>0.117</td>\n",
" <td>227.0</td>\n",
" <td>227.0</td>\n",
" <td>228.0</td>\n",
" <td>507.0</td>\n",
" <td>1.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>acc_missing[0]</th>\n",
" <td>2.124</td>\n",
" <td>1.811</td>\n",
" <td>0.000</td>\n",
" <td>5.000</td>\n",
" <td>0.101</td>\n",
" <td>0.071</td>\n",
" <td>323.0</td>\n",
" <td>323.0</td>\n",
" <td>351.0</td>\n",
" <td>889.0</td>\n",
" <td>1.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>acc_missing[1]</th>\n",
" <td>0.926</td>\n",
" <td>0.956</td>\n",
" <td>0.000</td>\n",
" <td>3.000</td>\n",
" <td>0.028</td>\n",
" <td>0.020</td>\n",
" <td>1192.0</td>\n",
" <td>1114.0</td>\n",
" <td>1227.0</td>\n",
" <td>1608.0</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>t_0</th>\n",
" <td>3.164</td>\n",
" <td>0.301</td>\n",
" <td>2.612</td>\n",
" <td>3.724</td>\n",
" <td>0.004</td>\n",
" <td>0.003</td>\n",
" <td>4710.0</td>\n",
" <td>4710.0</td>\n",
" <td>4678.0</td>\n",
" <td>5471.0</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>t_1</th>\n",
" <td>0.946</td>\n",
" <td>0.120</td>\n",
" <td>0.734</td>\n",
" <td>1.178</td>\n",
" <td>0.002</td>\n",
" <td>0.001</td>\n",
" <td>4008.0</td>\n",
" <td>3967.0</td>\n",
" <td>4058.0</td>\n",
" <td>5202.0</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean sd hpd_3% hpd_97% mcse_mean mcse_sd \\\n",
"pc 1889.617 2.481 1886.000 1894.000 0.165 0.117 \n",
"acc_missing[0] 2.124 1.811 0.000 5.000 0.101 0.071 \n",
"acc_missing[1] 0.926 0.956 0.000 3.000 0.028 0.020 \n",
"t_0 3.164 0.301 2.612 3.724 0.004 0.003 \n",
"t_1 0.946 0.120 0.734 1.178 0.002 0.001 \n",
"\n",
" ess_mean ess_sd ess_bulk ess_tail r_hat \n",
"pc 227.0 227.0 228.0 507.0 1.01 \n",
"acc_missing[0] 323.0 323.0 351.0 889.0 1.01 \n",
"acc_missing[1] 1192.0 1114.0 1227.0 1608.0 1.00 \n",
"t_0 4710.0 4710.0 4678.0 5471.0 1.00 \n",
"t_1 4008.0 3967.0 4058.0 5202.0 1.00 "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"az.summary(trace_cat)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"scrolled": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
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bzyFDa7a1kScvPqLtAmg6muLfKATH0lX/Xq9XcXFx9U536NChgKary8aNGzVz5kzddddd6tGjR1DLCrUPPjT15HRTXq/kckmT75UuutCwPJ3d7YS6nkgIRy3+likpwtu2MKLb9li7CfJ6z5XLMDXlhq26ePiBsLcLoOloin+jEDxLI6rt27fXli1b6pymvLxc27ZtU+fOnS0VJkllZWWaMmWKevXqpVtuucXycsLB4zn+QZAkr/dYkPnpt7dAp7O7nUBFqh27avG3zCf+ZOqJPzXebXu83WM7cq9p6InZPeXJDe5LJgBUaYp/oxAalkZUhw8frtmzZ+vNN9/UVVdd5XeaV199Vbm5ubr66qstF/fMM89o9+7deueddxp0yD8lJUUul+U7bwVk67Zyeb2FPs95vVJBYZIyM2MbPJ1d7aSmptZbg5V6IiEctfhbpuln/xaOdbZr2/pv19DhI2nqllFkaZmVxrHQaxiGEhMTg66xilnmklzlcrVqJaN5csiW2xAN/cwgcugbZ0pNTY3Y/s1Jf6OiQTR8ZiwF1Ztvvlnz5s3TH/7wB23fvl0XXHCBJKm4uFgbN27U/Pnz9fLLLys1NVXXXnutpcLWrVunl156Sb/85S/Vs2fPBs1bUFBgqc2GaJViyuVS9bc26dghhpTkI8rLMxo8nR3tpKamKi8vr/6VtVBPJISjFn/L/DFz+QTWcKyzXdvWf7umTkrKVVFRmaVlNjdNGZJM01RRkbWw61d5sYzSYlXk50vxlaFbboCsfGYQGfSNM1X1S6T2b076G+V0TvjMBBKULQ07ut1uPffcc0pKStKcOXN07bXXyjAMffzxx7riiiv04osvqkWLFnr22WeVlpbW4OVXVFTo/vvvV2Zmpv7nf/7HSolh53YbmnyvoaqB22PnwRhyuw1L09ndTqAi1Y5dtfhb5pT7DE25r/Fu2+Ptmj+2a2rKxK1yp1kLqQDwU03xbxRCwzB/eqf+Bvj+++/18ssva/Hixdq3b5+8Xq/atWuns88+W7fcckudt66qS2FhobKysgKa9rnnnqtxy6pIfkPweEzty5Y6dlCdH4RAp4tkO8F8mwp2fUIpHLX4W2ak1tnjMVVQmKSU5CMR3baefUXav3zFj+sX3KkzzT+eKqMkX2bzVio5/7EQVagfR1QLVdHrQik+KXTLDZATRiDgH33jTD/tl0juR53yN8qpnPCZCWRENaigGi4lJSV6+OGH/b62evVq7d69WyNGjFBaWpquu+469e7d22cauzd8tHDCmxT+2dI3pUfUbPOHMuOTpdiEoBZFUEWk0TfORL84lxP6JpCgaukc1VWrVql169bq2rVrndPt3r1bhw4dCnh0tErz5s316KOP+n3t/vvv1+7du3XbbbdpwIABDVouAAAAooel43vXX3+9XnjhhXqne/HFFzVx4kQrTQAAAKCJs3wimgPPGAAAAEAjEtabjRYWFio+PrQ/xzht2jRt2bKFw/4AAACNXMDnqObk5Pg8Pnr0aI3nqlRWVmrbtm1asmSJOnXqFFyFAAAAaJICDqojRoyQYRy/xcOCBQu0YMGCOucxTVMXX3yx9eoAAADQZAUcVNu3b1/9//3796t58+a13lYgNjZW7dq10+jRozVhwoTgqwQAAECTE3BQ/fTTT6v/36tXL40ZM0aPP/54WIoCAAAALN1H9fHHH1dGRkaoawEAAACqWQqql112WajrAAAAAHxYCqonqqysVH5+vkpLS2ud5sTzWwEAAIBAWA6q33zzjZ599lmtWrVKZWVltU5nGIY2bdpktRkAAAA0UZaC6vr163XDDTdUj6KmpKSoZcuWIS0MAAAATZuloPrXv/5VpaWl+vnPf6577rlHrVu3DnVdAAAAaOIsBdWvv/5aXbt21SOPPOLzIwAAAABAqLiszFRZWanevXsTUgEAABA2loJq165dlZeXF+paAAAAgGqWgupVV12l1atXa+/evaGuBwAAAJAURFC96KKLNGnSJC1evFiVlZWhrgsAAABNnKWLqUaOHClJys7O1u23366YmBi53W6/56wahqGFCxcGVyUAAACaHEtBNTs7u/r/pmmqoqJCOTk5fqflgisAAABYYSmoLlq0KNR1AAAAAD4sBdUOHTqEug4AAADAh6WLqQAAAIBwszSiWqWoqEjvvfee1q1bp7y8PA0bNky33nqrJGnXrl3Kzs5WVlaW4uPjQ1IsAAAAmg7LQXXJkiW69957VVhYKNM0ZRiG3G539eu7du3SL37xC02fPl1jx44NSbEAAABoOiwd+t+xY4d++ctfqqioSNdcc43+8pe/yDRNn2nOOussNW/enAuvAAAAYImlEdWZM2eqtLRUzzzzjEaPHi1Juueee3ymiYuLU+/evbVly5bgqwQAAECTY2lEdcWKFerVq1d1SK1Nu3btdOjQIUuFAQAAoGmzFFRzc3PVpUuXeqerqKjQ0aNHrTQBAACAJs5SUE1KStLBgwfrnW7fvn066aSTrDQBAACAJs5SUO3Tp482btxY68+mStLWrVu1efNm9evXz3JxAAAAaLosBdXx48ertLRUv/71r/2eg5qbm6sHH3xQpmlq/PjxQRcJAACApsfSVf9jxozRmDFj9NFHH+m8887TwIEDJUlr167V7bffrpUrV+ro0aMaN26chg8fHtKCAQAA0DRYvuH/9OnT1blzZ73yyitaunSpJGnPnj3as2ePYmNjNWnSJN13330hKxQAAABNi+WgGhMTo3vuuUc33XSTVqxYoe+++05er1fp6ek6/fTTuYgKAAAAQbEcVKukpKTUez9VAAAAoKEsXUwFAAAAhFtAI6qrVq0KqpGsrKyg5gcAAEDTE1BQvf7662UYhqUGDMPQpk2bLM0LAACApiugoOpvRLS8vFzr16+XdOyXqjp06CBJysnJUWFhoQzDUP/+/RUbGxvCcgEAANBUBBRU58yZ4/O4tLRUN9xwgzIyMjR58mSNGjXK5/VFixbpySeflCS9+OKLISoVAAAATYmli6lmzJihLVu2aPbs2TVCqiSNHDlSL7/8srZs2aIZM2YEXSQAAACaHktB9aOPPtLQoUPVrl27WqdJT0/X0KFDNX/+fMvFAQAAoOmyFFT379+vhISEeqdLSEjQgQMHrDQBAACAJs5SUE1OTtaaNWtUVlZW6zRlZWVas2aNkpOTLRcHAACApstSUB0+fLgOHTqkBx54QAUFBTVeLyws1AMPPKBDhw7p7LPPDrpIAAAAND2WfkL1V7/6lRYvXqz//Oc/+uyzzzR8+HB17NhRkpSdna0vv/xSR48eVVpamu66666QFgwAAICmwVJQbdeunebMmaMpU6Zow4YN+vjjj6t/EMA0TUlSnz599NRTT9V5wRUAAABQG0tBVZK6d++ut99+W2vXrtXKlSurL5pq27atsrKyNHjw4JAVCQAAgKbHclCtMnDgQA0cODAUtQAAAADVgg6qkB599FEdOXKk3umuuOIKn5HmnJwcPfvsswG1MXXqVJ87KCxevDige9Smp6frV7/6lc9zL774orZv367mzZurpKSk1nnPPvtsjR07tvqx1+vV1KlTA6r35ptv1sknn1z9ePPmzXr55Zfrnc8wDD3++OM+z73//vtaunRpvfNmZmZq0qRJPs9Nnz5dhw4dqnfecePG6cwzz6x+nJubW/3ravX59a9/LbfbXf14+fLlevfdd+udLzU1VVOmTPF57t///re2b9+uli1bqri4uPqUGklq1aqV0tPTlZ6ero4dO6pVq1YB1QcAQLQiqIbAq6++qoMHD9Y7Xb9+/XyCal5enp5//vmA2rj77rt9gurXX38d0Lx9+vSpEVQ//PBDLV68uN554+LifIKqaZoB1ztmzBifoPrdd98FNK/L5aoRVJcuXRrQvKNGjaoRVN98801t37693nm7dOniE1SLiooCXtebb77ZJ6hu3rw5oHk7depUI6jOnTtXH3zwQb3zXnLJJZo1a5bPc7feeqtiYmLUqlUrnXTSSWrdurXS0tJ00kknqX379uratatP8AUAwOkCCqq9e/eWYRj68MMP1bVrV/Xu3TvgBgzD0KZNmywXCDQlFRUVAU2Xnp7u89g0Tb377ruqrKysdR63260zzzxTt99+u7KysoKqEwCASAgoqJqmWX01f9XjQDVk2mj1xhtvqLy8vN7punTp4vO4a9euWrBgQUBtpKWl+TweP368zjjjjHrn8/cLYtOmTdORI0eUnJyswsLCWuf96R0bXC5XwPX27NnT5/GQIUMCnven/vd//1dXXHFFvdP5+3GJl156qc7TG6p06tTJ53Hbtm0Drrfq1mxVxo4dq1NPPbXe+eLi4mo8d9999+m6665Ty5Ytq08nMU1TXq9XeXl5ysnJ0f79+zV06FCf+X744Yc6Q6okeTwezZs3T1dffXW9tQEA4ASG2QiTZF5ent0lRIXU1FS2lUM1tG+8Xq8OHz6s/Px85efn6/Dhwz7/Nm/erGXLlqm4uFg7duzw/4txpUfUbPOHMuOTpdj6fyK5Ls0/niqjJF9m81YqOf+xoJblo7xYRmmhKnpdKMUnhW65AeIz41z0jTPRL87lhL5JTU2tdxrOUQUaAZfLpTZt2qhNmza1TlNRUaFt27bxs8YAgKhBUAWaiGbNmlWfX15eXq6VK1dqwYIFOvPMMzV69GibqwMAoCZLQXXbtm36+OOPNWLECPXp08fvNBs3btRnn32mCy64QN27d2/Q8ktLS/XnP/9ZGzZs0J49e1RQUKDk5GR16tRJ48eP18UXX6zY2FgrpQOQtGrVKo0bN06StG/fPoIqAMCRXFZmeu211zRjxow6zy1ITU3Vc889pzfeeKPBy//hhx/0+uuvyzAMnXPOOZo0aZJGjRolj8ejqVOn6vbbb5fX67VSOgAdu7gtJSVFkvTpp58GdDEgAACRZmlEdeXKlcrMzKxxi5wTtW/fXr169dLy5csbvPxWrVpp9erVNa6Krqio0KRJk7RkyRJ98cUXOueccxq8bADHTgMYOXKk3nnnHRUUFGjlypU6c3A/u8sCAMCHpRHVAwcO1Lidjz+dOnUK6Eb4NYpyufzeuqdZs2Y677zzJEl79uxp8HIbG4/H1Np1pjye0Ny4wd/yQt2G3W1Han0CZVc9Ho+pzMzjh/s//vjj4JeZG6c1m1vJk1vzs2sHp/V1oIKpO1rXGXWzc98M2M3SiGplZWVAv3BjGIZKS0utNOGX1+vVl19+KanmfTqbmg8+NPXkdFNer+RySZPvlS660PqvDvlbnqSQtmF326HeZsGyq56qdsvKRujYd1WvFixYoD/+v99YXub7X7bTE6/0lNc05DJMPTjoIl3e/tWQ1dxQTuvrQAVTd7SuM+pm574ZcAJL91EdM2aMysrK9Omnn9Y53YgRIxQTE6NPPvnEUnFlZWX6xz/+IdM0lZ+fr2XLlmnnzp26/PLLa/zM5onsvi9YuHk8pq64+thOqorLJb39hiG3O/CdVdU91Pwtr+p7yInvDitt1CdSbYdqm4VKffWE6/52P2131/YLVXx0hSRpzbIvdHLJhgbfR9WTG6fLJg+T1zy+HV1GpeaPOk/u1NKI30c1nH0dzvsOBlO3097fdnDCPSFDzc59c6g0xn5pLJzQN2G7j+qwYcP05ptv6p///KeuvfZav9O89tprysnJ0fjx4600IenYLXT+9re/VT82DEM33XST7r333jrnS0lJkctl6ayGqLB1W7m8Xt9flPJ6pYLCJGVmNuxuCKmpqX6X5+/ri9U26hKptkO5zUIhkHoC+QAH225S8ujqoPrlV8vU/yy3lNBSRlyLgJe5aXeiT0iVJK8Zo70/ZKht2nYlJiaGpnhJZplLcpXL1aqVjOb+7wcb7r4OR79IwdXttPe3XcLVN3axc98cSo2tXxqTaOgbS0H1xhtv1DvvvKNHH31Ue/bs0ZVXXqmuXbtKknbt2qW33npL//znPxUbG6sbb7zRcnEtW7bUli1b5PV65fF49Omnn+ovf/mL1q9frxdeeKHWP4AFBQWW24wGrVJMuVyqMXqSknxEeXkNH1H1t7zavrU3tI36RKrtUG2zUKmvnnB90/1pu4nJo+U58LAk6b1/f6ibBl0t09VTTsYAACAASURBVBsrlQV+V43WyWVyGWaNEdWMlntlmqaKiopCtwLlxTJKi1WRny/F+//J2HD2dThHIIKp22nvbzs4YXQo1OzcN4dKY+yXxsIJfRNIULY07NilSxc9+uijcrlcmjNnjsaNG6dTTz1Vp556qsaNG6fZs2fLMAw9/PDDDb6Hqt8iXS61a9dOEyZM0B//+EetXbtWf//734NebrRyuw1NvtdQ1aDxsXOUrB/28be8KfcZmnJf6Nqwu+1Qb7Ng2VXPT9tNSOilk07qKEn6atlyHTla0vBlppVpyg1b5XId+8vpcpl6cNCTapvQ8AspQ8FpfR2oYOqO1nVG3ezcNwNOYekc1SobN27UjBkztHTpUhUXF0uSEhISdMYZZ+j2229X3759Q1ZolSNHjmjw4MHq16+f5s6d63cau78hRIrHY2pfttSxgyztpH76bcrf8oJtI1CRajtS6xNsPeH+pntiuy+//JQOHjyo80cM16i2RYpNTGvQOarVy8yN0z5Pgjq6i5Wx6j4ZJfkym7eK+Dmq1fWEoa8jMQIRTN1Oe39HkhNGh8LFzn1zsBpzv0Q7J/RNICOqQQXVKl6vV/n5+ZKO3QM1nOeH7tixQ2PHjtXAgQP1+uuv+53G7g0fLZzwJoV/tvRN6RE12/xhgy+m8qf5x1NtD6rhwGfGuegbZ6JfnMsJfRO2Q/81FuJyKS0tTWlpaSEJqdu3b68eoT1RcXFx9dX+P/vZz4JuBwAAAM5l6WKqgoICbd26VRkZGWrbtq3faQ4ePKi9e/cqMzNTycn+r86tzfz58zVr1iwNGjRIHTp0UGJiog4ePKgvvvhC+fn5Gjx4cFAXaQEAAMD5LAXV2bNna8aMGXrrrbdqDaoej0cTJ07UXXfdpTvuuKNByz/nnHPk8Xi0bt06rV+/XkePHlViYqIyMzN14YUX6uc//7maNbNUOoBaHPr+e3360XK1SGqlS8/niAUAwH6W0t7ixYvVqVOnOi+W6tu3rzp27KjPP/+8wUG1b9++YbkQC4B/hw4dUu/+WfJ6vTqtV1eCKgDAESydUJqdnV1939S6dOvWTfv27bPSBIAIatOmjXr3ypQkrd+yW4fzC+uZAwCA8LMUVIuKigL6tZnExEQVFvIHD4gGI845W5JkmqY+W/G1zdUAAGAxqKalpWnXrl31Trdr1y6lpKRYaQJAhFUFVUlatGy9jZUAAHCMpaDav39/ffvtt1q1alWt06xevVqbNm3SgAEDLBcHIHKGDclSi+ZxkqRFy9crBLdYBgAgKJaC6jXXXCPTNHXXXXdp4cKFNV5fuHCh7rrrLhmGoauvvjroIgGEX3x8vIYP6ClJOng4Xxu27bG5IgBAU2fpqv/TTz9d1113nV599VXdeeedSk1Nrb64avfu3crNzZVpmrrmmmt01llnhbRgAOEzMqu3Pl6+QdKxUdW+PbvYWxAAoEmzfDPSBx98UJ07d9aMGTOUm5ur3Nzc6tdSU1N1++2364YbbghJkQAiY2RWn+r/L1q2XndPvNTGagAATV1Qd82//vrrNWHCBG3cuFHZ2dmSpPbt2+vUU09VTExMSAoEEDk9OrqVkd5Ge/cf0vKvv9UPxSVqmdDc7rIAAE1U0D/vFBMTo379+qlfv36hqAeAjQzD0AVnnqaNO7M1YtgAlVdU2l0SAKAJ43dIAfh44p7rZcS1sLsMAACCD6o7duzQrl27VFRUVOs0l17KeW5AtDAMw+4SAACQFERQXb9+vX77299q+/bttU5jmqYMwyCoAgAAoMEsBdVdu3Zp0qRJKi4u1oABA3T48GHt27dPY8eO1Z49e7R582ZVVlbqvPPOC+inVgE4j9fr1ddbdskwDA3o1c3ucgAATZClG/6/8MILKi4u1kMPPaQ33nhDgwcPliRNnz5db7/9tubNm6fevXtr9+7d+u1vfxvSggGE3979Hp085madM3GynnzxLbvLAQA0UZaC6ooVK5SRkaFrrrnG7+snn3yyZs6cqe+++04zZ84MqkAAkdexbevq/3+xeoPKKypsrAYA0FRZCqqHDh3SySeffHwhrmOLKSsrq37O7XYrKytLCxYsCLJEAJHmcrl07pD+kqQjPxRr5TdbbK4IANAUWQqqzZs397mhf8uWLSVJhw8f9pkuMTFRBw4cCKI8AHYZefqA6v8vWr7exkoAAE2VpaDatm1b7d+/v/px586dJUnr1q2rfs40TW3atEnJyclBlgjADiOG9q/+/38Wr1IFN/8HAESYpaDar18/bd++XSUlJZKk4cOHS5Ief/xxff7559qyZYseeugh7d27V3379g1dtQAipm3rVJ3Wu7sk6dud3+n3z71qc0UAgKbGUlD92c9+prKyMn322WeSpIyMDF155ZU6dOiQ7rjjDl166aV66623FBsbq7vvvjukBQOInGn33qRmP57m89dX39c7n3xlc0UAgKbE0n1UR48erY0bN/o899BDD6lLly766KOPlJ+fr+7du+v222/3uegKQHQZ1r+Xpv16ku576kVJ0i8fnqFeXTupT48MmysDADQFQf+EahWXy6VJkyZp0qRJoVokAAe4ZfwYrdm0Xa9/+LmSE1uouLSs/pkAAAiBkAVVAI2TYRj6y/3/o4TmcXrg1qvkPqmV3SUBAJoIgiqAeiU0j9df7r/N7jIAAE2MpYupAAAAgHBjRBVAgxQU/aBV32zVmo3bNObsweqf2c3ukgAAjRRBFUCDzP9ilW576K+SpJhmMQRVAEDYcOgfQIMM6nP8lnNrN26zsRIAQGNHUAXQIN0z0pWS2EKStHrDNpmmaXNFAIDGiqAKoEFcLpdO69NDknTwcL5yPLk2VwQAaKyCPkd1+/btWrdunXJzc9WjRw+NHDlSkuT1elVRUaG4uLigiwTgLINO6aHPV34jSVqzcZs6tD3J5ooAAI2R5RHV/fv368Ybb9S4ceP0u9/9Tk8//bQWLlxY/fpbb72l/v37a9myZSEpFIBzDDrl+HmqazhPFQAQJpaCan5+vq677jotX75cPXr00DXXXFPjPLULLrhALpdLixYtCkmhAJzDJ6hu2m5jJQCAxsxSUH3hhReUnZ2tm266Se+//75+97vf1ZgmJSVFPXv21Nq1a4MuEoCztGudqg7uY4f713+7Q16v1+aKAACNkaWgumjRInXo0EH33XefDMOodbpOnTrJ4/FYLg6Ac1VdUHXkh2Jt25NjczUAgMbI0sVUOTk5Ouecc+Ry1Z1zY2NjlZ+fb6kwAM42rH8vfXfgkAb26aFmMdxABAAQepaCanx8vH744Yd6p8vJyVFSUpKVJgA43J3XXaw7r7vY7jIAAI2YpWGQbt26aePGjTp69Git0+Tm5mrz5s3KzMy0XBwAAACaLktB9fzzz1d+fr6mTZtW60UUTz31lEpKSjR27NigCgQAAEDTZOnQ/7XXXqt58+Zp7ty52rBhg0aPHi1J+u677zRr1ix99NFH+uabb9S7d29ddtllIS0YgPN8n1egmJgYpSYn2l0KAKARsTSiGh8fr//7v//TgAEDtGnTJj3zzDOSpDVr1ujJJ5/U119/rVNPPVUzZ85UbGxsSAsG4ByLlq9Xv0vuUPfRN+mt+V/YXQ4AoJGx/BOqbrdbr7/+ur788kstXrxY3333nbxer9LT03X22Wdr5MiRdd66CkD0S01O1J6cY7eg4xeqAAChZjmoVhk+fLiGDx8eiloARJlTT+6suNhmKiuv0JqN/EIVACC0uPkhAMviYmPVL7OrJGn73hzlFRbZXBEAoDEhqAIIyqBTTq7+/7pvd9hYCQCgsQno0H/v3r0tN2AYhjZt2mR5fgDONujHn1KVjp2nOmJofxurAQA0JgEFVdM0LTcQzLwAnG/gCUF13SbOUwUAhE5AQXXz5s01nnviiSf02muv6aqrrtJll12mjh07SpKys7M1b948vfnmm7rmmms0ZcqU0FYMwFG6Z6QrJbGFCoqOavWGbXw5BQCEjKWr/v/1r3/plVde0UsvvaRhw4b5vNarVy898MADGjFihCZNmqRu3bpp/PjxISkWgPO4XC6d1qeHPl/5jQ4ezlf2wcPqUf9sAADUy9LFVK+99poGDhxYI6SeaOjQoRo0aJBef/11y8UBiA6DTjkWTZvFxGjbnmybqwEANBaWgurOnTvVrl27eqdzu93atWuXlSYARJHrLh6pRbMeV/biV3UuF1MBAELE0qH/Zs2aaevWrfVOt23bNjVrFvRvCgBwuG4d26lbx/q/vAIA0BCWRlT79++vbdu2afbs2bVOM2fOHG3dulUDBgywXBwAAACaLkvDnb/4xS+0bNkyPf7445o/f74uuugin6v+//3vf2v9+vVq1qyZ7rjjjpAWDAAAgKbBUlA97bTT9Kc//UkPPvig1q1bp/Xr1/u8bpqmWrRooUceeUQDBw5s8PIPHjyo+fPn64svvtDOnTv1/fffKyUlRQMHDtQtt9yi/v05Bw5wmj05Hr23aJnWbNqma9se0SVd7a4IABDtLJ9AesEFFygrK0tz587VqlWrdODAAUlS27ZtlZWVpSuuuEJut9vSsufMmaMXXnhBGRkZOvPMM5WWlqY9e/Zo4cKFWrhwoaZPn66xY8daLR1AGOz8br9+++yx04HSh7XSJV3jba4IABDtgrrSqXXr1rrjjjtCfni/X79+mjNnjoYMGeLz/OrVq3XjjTfq97//vUaNGqW4uLiQtgvAutNO/CnVfSWSCKoAgOBYupgq3EaPHl0jpErS4MGDNXToUBUUFGjLli02VIaG8nhMrV1nyuPh14qCEeh29DddsH3gyY3Tms2t5Mmt+4thq6SW6pHRXpK0fn+pyir9txfo8hAa4XhPRKtoWO9I1RiJdjweUytWllvabwFVou7eUVW3u+K2V873wYemnpxuyuuVXC5p8r3SRRcadpcVdQLdjv6mkxRUH7z/ZTs98UpPeU1DLsPUlBu26uLhB2qdfmCfHtq+N0dllaY2eCp02k/OU23o8hCccLwnolU07I8iVWMk2jneRmGD91tO6xfYy5EjqrXJycnR0qVL1aZNG/Xs2dPuclAHj+f4zkeSvN5jfxz5xtwwgW5Hf9M98SdTT/zJeh94cuOqQ6UkeU1DT8zuWedIaNUvVEnSqpzyoJcH68LxnohW0bA/ilSNkWgnmP2W0/oF9ouaYcny8nJNnjxZZWVluu+++xQTE1PrtCkpKXK5oiqD2yY1NTUsy926rVxeb6HPc16vVFCYpMzM2LC02dikpqYGvB39TWf62dfX1QdmSYy8CQlSQksZcS20aXdidag8Pr+hw0fS1C2jyG/NZw7qW/3/1Tnlus0wlJiYKEmWllejxjKX5CqXq1UrGc2TA5on1ML1mQm1ULwnok1tfRMN+6NI1RiJdoLZbzmtXxq7aNifRUVQ9Xq9uv/++7Vq1SpdeeWVuvTSS+ucvqCgIEKVRbfU1FTl5eWFZdmtUky5XKr+piwdO6yTknxEeXkc1qlPVd8Euh39TWf8+PKJ4aTOPig9ombFxTK9sVKZV62Ty+QyTJ9w6XKZOikpV0VFZX7r7tGprZrFxKiislKrcsplmqaKio6FUCvLq6G8WEZpsSry86X4ysDmCaFwfmZCLSTviShSV99Ew/4oUjVGop1g9ltO65fGzAn7s0CCsuOHHb1er6ZOnaoPPvhAF198sf7whz/YXRIC4HYbmnyvoaqB7WPnHhlyu9n5NESg29HfdFPuMzTlPut94E4r05QbtsrlMn+c39SUiVvlTqs9VCY0j1efHhmSpG8PVepI6fG/QFaWB+vC8Z6IVtGwP4pUjZFoJ5j9ltP6BfYzTNPfwSBn8Hq9euCBB/Tuu+/qoosu0pNPPlnnIf8qdn9DiBaR+Dbl8Zjaly117CB2Pg3w074JdDv6my7gPig9omabP5QZnyzFJhxfZm6c9nkS1NFdHFCovPvxf2jWOwskSZ/c3ElDbn/at8YGLs9HebGM0kJV9LpQik9q2Lwh4IQRiIYK6j0RRQLpm2hY70jVGIl2PB5TBYVJSkk+0uD9FsLPCfuzQEZUHXvo/8SQOnbs2IBDKpzF7TZk8XcfcIJAt6O/6YLtA3daWYMC5ajTB6h853INbmuq+0k1zzNr6PIQnHC8J6JVNKx3pGqMRDtut6HMzNh6D+NHQ7/APo4MqlWH+999912NGTNGTz31FCEViBIXnTNUV5S+J6MkX2bzWJXYXRAAIGpZDqq9e/fW+PHj9cc//jGU9UiSnnvuOc2bN08tWrRQly5d9Pe//73GNKNGjVLv3r1D3jYAAACcwXJQNU1TPz29deLEiRo+fLhuvfXWoIrKzs6WJB09elQzZ870O02HDh0IqgAAAI1YQEF16tSpGjhwoE477TR179691ulWrlypDh06BF3UtGnTNG3atKCXA8A+R0q9WpNzVG32HVC3ju3sLgcAEIUCCqrvvPOO5s2bJ0lKSkpS//79JUk//PCDTNOUYXCVHoDjPtxcpJ+/ekimpAeafaH7b73S7pIAAFEooKC6cOFCrV27VuvWrdPatWv11VdfyTAMzZ8/X0uWLNHQoUM1bNiwcNcKIEr0ahOnqhOD1m7abmstAIDoFVBQ7dixozp27KiLL75YklRUVKTBgwerS5cuatmypRYtWqRPPvlEkvTpp59q8uTJGjZsmE4//XSlp6eHr3oAjtQtLVZpCYZyi02t2biNIy8AAEss/TJV1W93Z2Vl6V//+peWL1+uZ599VtKx0wHef/99TZ06VSNGjND5558fumoBRAXDMJTV/tg9VL/PK9R3Bw7ZXBEAIBoFFFTvvPNOzZo1S+vWrVNZWc0bdScnJ2v06NGSpHHjxunzzz/XtGnTdOmll6q8vDy0FQOICoPSj9/sf/23O22sBAAQrQI69P/JJ59o4cKFkqTY2Fj16dNHkpSbm6vi4mIlJCT4TN+uXTtdeumluvTSS0NcLoBocYr7+O5l254cGysBAESrgILq0qVL9fXXX2v9+vVat26d/vvf/8owDH366acaMmSIBgwYoNNPPz3ctQKIIienHf81ue17sm2sBAAQrQIKqmlpaTr33HN17rnnSpIqKyt1yimnqGfPnkpLS9PatWu1atUqSdKCBQtUWFioIUOGaOjQoerVq1f4qgfgWCenHd+9bN+738ZKAADRytIvU8XEHBsp6d+/vx5++GGVlpZq1apVuuWWWyRJixcv1qJFi2QYhlJSUrR8+fLQVQwgKrSMM9QxuZn2FVZo215GVAEADWfpqv+fio+P11lnnSVJGj16tFasWKG///3vmjhxIrenApqwk1vHyTAMJbVsoaKjxXaXAwCIMpZGVOvTsmVLn1MFADRNL49PV/Nxj6h5fJzdpQAAopDloLp58+ZQ1gGgEWqX1EwlhFQAgEUhHVGdPXu2WrduHcpFAgAAoIkKaVAdMmRIKBcHAACAJiws56gCQJWnZ7+rTdv3qNLr1f89co/d5QAAoghBFUBYvfbBZ9qya5/i42JVWVlZfXs7AADqE5LbUwFAbU7u3F6SVFpWrn0HD9tcDQAgmhBUAYRV94z21f/fxk+pAgAagKAKIKx6ZBz/0Y8d/JQqAKABCKoAwurkzh2q/8+IKgCgIQiqAMKqxwmH/rczogoAaACCKoCwap2arJSklpKk7YyoAgAagKAKIKwMw6geVf3uwPcqLim1uSIAQLQgqAIIO58Lqr7j8D8AIDDc8B9A2I0Y1l/xcXHq0TldJ7VKtrscAECUIKgCCLurx56jq8eeY3cZAIAow6F/AAAAOBJBFQAAAI5EUAUQMYVFR7Xu2x0yTdPuUgAAUYBzVAFExMT7/6T3Fi2TJO38ZBYXVQEA6sWIKoCIaH1CMN22J8fGSgAA0YKgCiAiTu58wk+pElQBAAEgqAKIiKpfp5KkbfyUKgAgAARVABHR48QR1b38OhUAoH4EVQARkZHeRnGxx67f3M6IKgAgAARVABERExOjbh3bSZJ27jugyspKmysCADgdQRVAxFQd/i8rr9De/YdsrgYA4HQEVQARc+IFVTs4TxUAUA+CKoCIqRpRTUtJkiuG3Q8AoG78MhWAiDlnSD/N+N0vdPl5Zyqhebzd5QAAHI6gCiBiOrVro2vHjbC7DABAlODYGwAAAByJoArANge/z1OO57DdZQAAHIqgCiDicjyHdfODf9Ep427XEy/OtbscAIBDEVQBRFxiiwR99OVqlVdU6K35XyivsMjukgAADkRQBRBxyYktNOGicyVJR0tK9doHn9lcEQDAiQiqAGxx6/gx1f9/Ye5H8nq9NlYDAHAigioAW/Ts0lHnDu0vSdq174AWLltnc0UAAKchqAKwzf+Mv6D6//94c76NlQAAnIigCsA25581UBnt3ZKkhcvWacfeHJsrAgA4CUEVgG1iYmJ0yxXnVz++9XfPKv/IDzZWBABwEoIqAFtNvGSU3GmtJElfb96pdZu221wRAMApCKoAbJWanKj3nntI6W3S9Mq0e6svsAIAoJndBdTmvffe05o1a7RhwwZt3bpV5eXlevzxx3X55ZfbXRqAEOvTI0Pr3vmbEprH210KAMBBHBtUn3nmGWVnZys1NVVut1vZ2dl2lwQgjPyF1Lfmf6H+vbops2tHGyoCANjNsUH1kUceUefOndWhQwc9//zzmj59ut0lAYigmW98qCnTX5Ik9cvsqsvPO1OX/mygurVubnNlAIBIcWxQPeOMM+wuwTYej6l92VLHDpLbbdhdTqMXzPZuqn3lyY3Td54W6uQ+KndaWciXl1dYpKdeeqf69W+27NI3W3bp9397VZmdu+usc7/WWT8bpkGDBqlDhw46dEgh7YdA+9XfdOF4T9j5Pgt0HYPZZsFMF6xI9Vck1ifYNoLpa6e9R6OxjXC03ZB5nfr3zLFBtan64ENTT0435fVKLpc0+V7pogud84ZpbILZ3k21r97/sp2eeKWnvKYhl2Fqyg1bdfHwAyFdniS1br9MMc3fUmH+uyouPv6rVVv27NCWl3fo/17+hyRp9Pm3KefgoyHrh0D71d90kkL+nrDzfRboOvp7LtBtFsx04Vi/cPSXFPr3RSDtNqSNYPo6EuvXkLqdtm3tarsh8zr575lhmqZpdxH1qTr0H+jFVHl5eRGoKvQ8HlNXXH3sjVLF5ZLefsMIy7eb1NTUqN1WoRDM9g53X9nSN6VH1GzzhzLjk6XYBL+TeHLjdNnkYfKax9fR5TI174nlPiOrzT+eKqMkX2bzVio5/7Fam/S3PMM4tksyT3iuvGyXCgreV2Heuyop+a/PMtp3nK5WaTecUI/1fqivX6v6xd90xo/NnbhHDfY9Eel9Qn1t+1vHQNc70HWxus4N/cyEY9tG6n0RSLsNaSOYvq5v/cK5L4vE58Npn8FQ/o2qa38WqXVMTU2td5pGOaKakpIilyv67ry1dVu5vN5Cn+e8XqmgMEmZmbFhaTOQN0ljFcz2jkRfRbpvzJIYeRMSpISWMuJa+J1m0+5En1ApSV6vocNH0tQto6j6ucof/3oZhqHExMRa2/S3PNOsuWOMjeuq1m1+pdZtfqWKco+Kj65RcfFauVuvV0nFEJ9py8vytOnbSmVmZta9wn4E0q+pqal+p/P3lT/Y94Qd+4S62va3joGud6DrEsw6N+QzE45tG6n3RSDtNqSNYPo6kPUL174sEp8Pp30GQ/03qrb9WaTWMRCNMqgWFBTYXYIlrVJMuVyq8a0mJfmI8vIYUQ21YLZ3uPvKthHV4mKZ3lipzOt3ktbJZXIZZo0R1ZOSclVUdMKIqmnKkGSapoqKivwsqfbl+RtRPfG5ZrFuJaVcoJTUCzTzOen2XxzvB9M0lbPvLk28fomefvovDb6dXX39WtUv/qarbWQpmPdEpPcJ9bXdkBG1n9YY6LpYXeeGfmbCsW0j9b4IpN2GtBFMX9e3fuHcl0Xi8+G0z2Ao/0bVtT+L1Do22RHVaOV2G5p870/P9Qn/0HtTFcz2bqp95U4r05QbtuqJ2T3l9RpyuUxNmbjV8gVVtS1PUr3PTb7XpT69DU2+9/i5VYX5r+lI4XxJ0i233KKnn35aHTt2VHp6evW/Fi1aKDk5WSNHjvSpZcWKFTp8+LBGnmNq3numTK9kuKTLLjG0evWxfk1MTFTr1q3Vp0+f6v6vrDT1Q9F8XXbJsWlqm1eSsrKy1KZNm+rHHo9Hq1evrnMbVdWTmDTW5322adMm7d69u95t3Lp1aw0Z4jvqvHjxYv3wQ/0/VXvDdb30yqtdqt/jd99Zov/+97Ma6+hvvRMSzpaUVL2skpLvNPKc/9a5fSQpPj5ek+8d4fPZ+vmla7V6tafOWhMTE5Wamqq+ffv6PL9gwQJVVFT4nefEvm6ReJru/0376s9wXl6eli1bVu82kqTzzjtPsbGx1fuFhx/dqpLibXVun9WrDbVq1arGhcNLly5Vfn5+vW326NFDPXv2lHRsf/TrX5Xr939cUOe2rXL66af7BISKiv0aec66evv1istjNWDAaJ++ufLn/9Xhw/vq/MwUFRWpXbt2GjhwoE8dixYtUmlpab3r2rdvX3Xq1Kn6cWFhoZYsWSJJdX5eJencc89VQsLxU5l27typzZs319tmYmKizj77bJ99fdGRlfJ6v69z20pSly5d1KdPn+rHpmlq/vz59bYp+e4j3G5Dt93i0VPTVwXUr2PHjq3+v9tt6JorN+kfL+yqdd6qvmndurUm35vl2L9nnKPqQJG68q6pj6hWceJV/049R7WKJzdO+zwJ6ugu9htSAz1Hta7l+X3O49W+bCl92FC5Ox4/paCqH1qlHNETT/xGc+fOrbO9zMzMGiHk8ssv1+eff15vrf/7v/+rRx55pLrd7/aZOm9U63rnk6S3335bI0aMqH68cOFCXXnllQHNu3DRYZ/32dSpUzVz5sx65xsxYoTefvttn+eGDRumrVu31jvvI488IkXQoAAAHgRJREFUoiuuuKP6PV5Wlq1+/foFVO/SpUvVq1ev6sevvPKK7rnnnnrna9++vTZs2ODz2frNb27Uv//973rnvf766/XMM8/4PJeRkVHnqH6Vp5+epYkTL6l+vGLFCl1wwQX1zidJe/bsUVLS8VD+4IOPa8aMp+qdLysrSx9//LHPc+edd57WrFlT77xTpkzRlClTqh8XFhaqS5cuAdX78ccfKysrq/rxvHnzdPPNN9c7X1JSkvbs2ePTNw8/fJf++c9/1jvvJZdcolmzZvk8d8opp2j//v31zvvMM8/o+uuvr368adMmnXXWWfXOJ0kbNmxQ+/btqx//7W9/0+9+97t65+vVq5eWLl1a/djjMXXttT/XmjWf1zvvifsI6VhQPemkkwKqN5h9RG5urs/jhu4j7LjqnxHVKOV2G3K77a6i6QhmezfVvnKnlYXktlR1Lc/vc6mlatuiUBVtfL9fH++HZM2cOVM/+9nP9Oc//1m7d++W98TjWT8K1TnsbrehEwZIw2rgafaMbpz4Ht+3z552IyElJXTLatkydMtyoqa433O7DZ3wXaRRcmq/ElQBNCqGYWjChAmaMGGCKioq5PF4dODAAe3fv18HDhxQSUmJ0tLSasx37bXX6uyzz65z2QkJCT6jhFUCGaGRpO7du/s87tGjR8Dz/tTo0aPlDuCvSkZGRo3n7rjjjoBG7IcNG+bzODk5OeB62/wkwQ8cODCgef1dfDd+/Hiddtppdc6XkJCgbt261Xh+ypQpKi8vr7fd3r17+zzu2LFjwOsaFxfn8/icc87xOdxcm/T09BrP3XzzzbrwwgvrnfenfRMXFxdwvR06dPB53KdPn4Dm/el6StLFF19c4319ooSEBBUXF+vkk0+u8drdd98d0CkoP+17t9sd8Lom/SRdnn766QHNa3UfIdWsV3LmPqKqb/ztI5zEsYf+586dW334Y+vWrdq4caMGDhyozp07S5IGDRqk8ePH+52Xw9mB4dC/czn90H99GnroP2DlxTJKC1XR60IpPvLDG3xmnIu+cSb6xbmc0DdRfeh/zZo1mjdvns9za9eu1dq1a6sf1xZUAQAAEP0cG1SnTZumadOm2V0GAAAAbBJ9d8UHAABAk0BQBQAAgCMRVAEAAOBIBFUAAAA4EkEVAAAAjkRQBQAAgCMRVAEAAOBIBFUAAAA4EkEVAAAAjkRQBQAAgCMRVAEAAOBIBFUAAAA4EkEVAAAAjkRQBQAAgCMRVAEAAOBIBFUAAAA4EkEVAAAAjkRQBQAAgCMRVAEAAOBIBFUAAAA4EkEVAAAAjkRQBQAAgCMRVAEAAOBIBFUAAAA4EkEVAAD8//buPCiqK20D+NPNEmWVZNCKYEIwNqKUE0DjRoIiLkFHhTJqFNwGNO6j46jJRKLJMDqT+hgj+Qaw4lQgMROISjRoGYQMbgEhgLiBQgQDSNRRZBVs4H5/8HVLpxvtnQs+vyqq9PS559x73ru8fbcmEiUmqkREREQkSkxUiYiIiEiUmKgSERERkSgxUSUiIiIiUWKiSkRERESixESViIiIiESJiSoRERERiRITVSIiIiISJSaqRERERCRKTFSJiIiISJSYqBIRERGRKDFRJSIiIiJRYqJKRERERKLERJWIiIiIRImJKhERERGJEhNVIiIiIhIlJqpEREREJEpMVImIiIhIlJioEhEREZEoMVElIiIiIlFiokpEREREosRElYiIiIhEiYkqEREREYkSE1UiIiIiEiUmqkREREQkSkxUiYiIiEiUmKgSERERkSgxUSUiIiIiUWKiSkRERESiJOpE9cKFC4iIiMDIkSPxyiuvYO7cuTh27Fh3zxYRERERmYFld89AV7KzsxEeHg5ra2tMnz4dtra2SEtLw4YNG/DLL79g2bJl3T2LRERERGRCokxUW1tbsW3bNkgkEuzfvx+enp4AgNWrV2POnDmIjo7G1KlT4eLi0s1zSkRERESmIspL/9nZ2fj5558xY8YMZZIKAPb29nj77bchl8uRkpLSjXNoHLdvC8gvEHD7tsB+iMxA0zqq7Xp7+7aAczlyvddvQ/rWtj1DGbtNXcZW2367Gsdfx8bYY2vs+Blj+t7OHNuMofsETfUM6ceQvnXdjgzZn5mTKM+o5uTkAAD8/PzUPlOU5ebmmnWejC31qIC//4+A9nZAKgU2/xGYMV3CfohMRNM6CkCr9fbRtHV6rd+G9K1te4Zub8ZuU9v2dOn38eNYZ7Kx1dSeIX3outxPI3NsM5ra07aPruJnSD+G9K3L2Bi6PzM3iSAIokun161bh++++w4HDx6El5eX2ufe3t5wdHREZmamxulrampMPIeGuX1bwJz5HSuUglQKHPhKgv79jbeyPKkfJycno4yVuZbnaWKs2OikpR6WxUchWDwDWD5jUFN9vv8AkuZaCH0c0RwQaaQZBNDaAklbC1qHTgeesdd6Mk3rqOT/V83Oe0BN662h67chfWvbnqHbm7Hb1LY9XfrVdhyNPbbG7qOrfnrrPlOffZk5thlD4tpV/OL+F3h7NfTqx5C+dRkbsa17Tk5OT6wjyjOqDQ0NADou9WtiZ2eH+vr6Lqd3dHSEVCrKuxoAANdK5Ghvr1Mpa28Hauvs4eFhZdZ+tFlJjNEP6c4YsdGF8NAa7U79geZ6AHLD2nL3gyBvhtSqD2ylhrWlwloK9OkP6bPPQWJtq/VkmtZRTV/RNa23hq7fhvStbXuGbm/GblPb9nTpV9txNPbYGruPrvrpzftMXfdl5thmDIlrV/Er/ckG7e1NevVjSN+6jE1PXPdEmagaqra2trtn4bH6OQqQSlW/dUmlgKNDPWpqjPeN5kn9GOusnbmW52nSLWdUAeD5MUB7m+HtvBhgeBtdkVoAjQ87/rSkaR3t6izEr9dbQ9dvQ/rWtj1Dtzdjt6lte7r0q+04Gntsjd1HV/301n2mPvsyc2wzhsS1q/i9PLhJ734M6VuXsRHbuqfNlxhRnna0s7MDgC7PmjY0NHR5trUn6N9fgs1/lEBx0rfjHhHjn3bvbf2QGVjZdFxSF/OflY3Oi6VpHd2ySYItm5683hq6fhvSt7btGbq9GbtNbdvTpV9tx9HYY2vsPnRd7qeRObYZQ+LaVfyGeUr17seQvnUZm5647onyHtXo6GjEx8cjOjoa06dPV/nszp078PPzw5gxY5CQkKBxerHfo6pw+7aAyirA1QUmXUm66sfYZ+3MtTxPg247o9rLaVpHtV1vb98WUFtnD0eHer3v29S3b23bM5Sx29RlbLXtt6tx/HVsjD22xo6fMabvCQzZl5ljmzF0n6CpniH9GNK3rtuRIfszY9HmjKooE9XTp08jPDwcISEh2Llzp8pnKSkp2Lp1K9auXYs1a9ZonJ4HeO0wGRIvxkacGBfxYmzEiXERLzHEpsde+h87diwGDRqE1NRUFBUVKcvr6+sRFxcHKysrzJ49uxvnkIiIiIhMTZQPU1laWuIvf/kLwsPDsXDhQpWfUK2qqsKWLVvg6ura3bNJRERERCYkykQVAMaMGYMvv/wSe/bswbFjx9Da2gqZTIZNmzYhKCiou2ePiIiIiExMtIkqAIwYMQKffvppd88GEREREXUDUd6jSkRERETERJWIiIiIRImJKhERERGJEhNVIiIiIhIlJqpEREREJEpMVImIiIhIlJioEhEREZEoMVElIiIiIlFiokpEREREosRElYiIiIhEiYkqEREREYkSE1UiIiIiEiWJIAhCd88EEREREdGv8YwqEREREYkSE1UiIiIiEiUmqkREREQkSkxUiYiIiEiUmKgSERERkShZdvcM0JMdPnwYeXl5uHTpEq5duwa5XI6dO3ciJCREY/3y8nLEx8cjLy8Pv/zyCxwdHfHyyy8jNDQUkyZN0jjNw4cPsX//fnz77bcoKysDALi4uGDUqFF4//331eqXlZVh9+7dyM7OxoMHD+Dm5ob58+fjrbfegkQiMd7Ci5ip49Lc3Ix///vfOHLkCCorKyEIAgYOHIg33ngDoaGhsLe3V5uGcemga2wKCwsRFxeH/Px8NDY2YuDAgZg+fTpWrFiBPn36aJzmyJEjSExMRGlpKaysrODj44N169Zh+PDhGutfuHABMTExKCgoQGtrK2QyGZYsWYKgoCCjLbfYmTIuRUVF+O677/DDDz+goqIC9fX1GDBgAF577TWsXLkSAwYM0NgHt5kO5thmOouIiMCpU6dgbW2NixcvaqzD2JgnLmI//lts3759u9FbJaNatWoVsrKy0Nrain79+qG+vh6BgYHw9PRUq1tYWIh58+ahqKgIo0aNgr+/P5ydnZGVlYWUlBRIJBK8+uqrKtPU1tZiyZIlOHjwIFxdXTF58mR4eXnBwsICmZmZiIiIUKlfWlqKefPmoaSkBIGBgRg3bhzKy8tx6NAh3L9/H/7+/iYdD7EwZVzkcjnCwsJw8OBB/OY3v1G2W1VVhdTUVPznP/9BcHAwrKyslNMwLo/oEpu0tDQsX74cFRUVmDhxIsaOHYuamhqkpqYiJycHM2fOhIWFhco0sbGx+PDDD2FlZYVZs2bhxRdfRGZmJpKTkzF69GgMHDhQpX52djYWL16M27dvIygoCD4+Prh48SK+/vpr2Nrawtvb26TjIRamjMvq1auRmpqqTE69vb3R2NiItLQ0fPPNN5g4cSKeffZZlT64zTxi6m2ms+TkZCQmJsLa2lrZ968xNh1MHZcecfwXSPTOnj0rVFZWCoIgCPHx8YJMJhMOHjyosW54eLggk8mEEydOqJRXVlYK3t7ewogRI4SWlhaVz1atWiV4eHgIR44cUWtPLperlS1cuFCQyWRCZmamsqylpUVYsGCBIJPJhPz8fJ2XsScyZVyOHj0qyGQyYfXq1WptrVy5UpDJZEJKSopKOePyiLaxefDggTBmzBhh+PDhwsWLF5Xl7e3two4dOwSZTCbEx8erTFNWViYMGzZMmDJlilBXV6csv3LliuDl5SW88cYbQltbm7JcLpcLgYGBgpeXl3DlyhVleV1dnTBlyhRh+PDhynnt7UwZl8TERKG8vFytLUU/ERERap9xm3nElLHprKKiQvD29hZ27dolTJw4UfDy8tJYj7HpYOq49ITjP+9R7QHGjRsHFxcXrepWVFRAIpHg9ddfVyl3cXGBTCZDc3MzGhsbleXnz59Heno6Zs6cid/97ndq7Vlaqt4dUlZWhtzcXIwePVrlm5O1tTXWr18PoOPb8tPAlHGprKwEALX6ADBhwgQAwL1795RljIsqbWNTUFCAe/fuYdKkSfDy8lKWSyQS/OEPfwAAfPXVVxA6/S7KoUOH0NraipUrV6rcfuHp6YkZM2bgp59+Ql5enrI8OzsbP//8M2bMmKFyFsTe3h5vv/025HI5UlJSDFrensKUcQkLC8OLL76o1tbvf/979OnTB7m5uSrl3GZUmTI2CoIg4N1334Wzs7NyjDVhbB4xZVx6yvGfiWovI5PJIAgCTp06pVJ+8+ZNXLt2DUOHDoWTk5Oy/NixYwCAadOm4d69ezhw4ADi4+Nx+PBh1NTUqLWfk5MDAPDz81P7zNfXFzY2NmoHBNI9LkOGDAEAtfoAkJmZCYlEgtGjRyvLGBf93LlzBwDg6uqq9pmDgwMcHR1RVVWFiooKZblirMePH682jWL8FXU6/1tTbBRljI0qfeLSFYlEAktLS7VLntxm9GNIbD7//HPk5ubir3/962PvY2VsdKdPXHrK8Z8PU/Uy69evR35+PtavX4+AgAC4ubnh7t27OHHiBF544QX84x//UKl/+fJlAMCNGzfwpz/9CQ0NDcrPbGxsEBUVpfKwR3l5OQBoPHNhYWEBV1dXlJaWorW1Ve3b2NNM17hMmDABgYGBOHHiBGbPnq28f/XcuXOorKzEhx9+qPLQDuOiH8WXA8UZ7M7q6+tRW1sLoONMwgsvvACgY6xtbGzg7OysNo1i/G/cuKEse1xsnJ2dYWNjo1Kf9ItLV44fP46GhgZMmzZNpZzbjH70jU15eTmio6MRFhYGX1/fx/bB2OhOn7j0lOM/z6j2MoMHD0ZSUhI8PT2RlpaGvXv34uDBg5BKpQgJCVHbqd+9excA8NFHHyEwMBDp6enIzc3FRx99BKlUis2bN6O4uFhZX7Eia3riHABsbW3R3t6uchmbdI+LRCJBTEwMIiIiUFxcjISEBCQkJKC4uBiTJ0/GuHHjVOozLvrx8fGBnZ0dMjIycOXKFZXPPv74Y+W/6+vrlf9uaGjocpzt7Ow01ge6jo2dnZ1KfdIvLppUV1cjKioKffr0UbvUzG1GP/rEpr29HVu3boWzszM2bNjwxD4YG93pE5eecvznV5Fe5sKFC1i1ahU8PDxw6NAhuLu747///S+++OILREVFIS8vT2WlVdyvIpPJsGvXLuWrJWbOnImGhgbs2LEDn3/+OaKiorpleXoLXePy4MEDbNy4ERcuXEB0dDTGjh0LAMjKykJUVBROnTqF5ORkjZd5SHu2trbYunUr3nvvPcybNw9Tp06Fs7MzCgoKcOnSJbi7u+P69euQSvmd3pyMEZeamhosX74cd+/exd/+9je4u7ubcQl6L31i8+mnn+L8+fNITExE3759u3Huey994tJTjv/c+/YicrkcGzZsgFQqxSeffILhw4ejb9++GDRoEN555x0EBgbi+PHjKg96KM4ATZw4Ue39Z4p3e166dEmtfldnMhobGyGRSGBra2vUZevJ9IlLXFwcvv/+e3zwwQcICgqCk5MTnJycEBQUhB07duDu3buIi4tT1mdc9Pfmm29i7969eOWVV5CRkYEvv/wSlpaW+Oyzz5SXuDq/1uhxZ0A1nXF4Umwed4b2aaZrXDqrqanBkiVLUFJSgu3bt2PWrFlqdbjN6E+X2JSVlSEmJgYLFixQezViVxgb/eizLwPEf/znGdVe5Pr166isrMSUKVM0fmsdPXo00tPTUVRUpLxH6KWXXsKlS5fg4OCgVl9x8GxublaWubm5AYDGe+ra2tpQWVkJV1dX3jfUiT5xOX36tPKzXxszZgwAqFzeYVwM4+/vr/H9f5s3b4ZUKlW5H9jNzQ0FBQW4c+eO2n2qivHvfA9X59h0fhoX6HgAoqmpCSNGjDDWovQqusRFQZGkFhcXIzIyEvPnz9fYNrcZw2gbm59++kn5Qvn9+/drbMvDwwNAx0OFDg4OjI0BdNlmesrxn2dUexG5XA5A9bVFnSnKFS9ZBh4lPaWlpWr1FWWdLy+PGjUKAHDmzBm1+nl5eWhqalLWoQ76xEUxjaYnLzXVZ1yMLy8vD1VVVXjttddUzngqxvHs2bNq0yjGv/OZo8fFRlHG2Givq7gAqknqtm3bsHDhwi7b4TZjfJpi4+Ligjlz5mj8s7GxgYWFhfL/in0aY2NcXW0zPeX4z0S1F5HJZLCzs0N+fr7ailRdXY2kpCRIJBKVFWnatGlwcnLCt99+i6tXryrLHz58iJiYGGUdBXd3d4waNQrnzp3DyZMnVeor7rF88803TbJ8PZU+cVH8UtEnn3yC9vZ2ZXlbWxv27NkDQPVsK+Oiv85PuircunUL7733HiwtLdUewgkJCYGlpSViY2NVLoEVFRUhNTUVgwcPVnmqeezYsRg0aBBSU1NRVFSkLK+vr0dcXBysrKwwe/ZsEyxZz6ZrXO7fv4+lS5eiuLgYf/7znxEaGvrY9rnN6E+X2Hh6eiIqKkrjn5OTEywsLJT/V7yyirHRj67bTE85/ksETW/lJVH5+uuvlfcvXrt2DZcvX4aPj4/y8qKvr69y5UhKSkJkZCSkUikmTJigfGgnLS0NTU1NWLZsGbZs2aLSfnp6OtatWwdra2tMnToVDg4OyMrKQklJCfz9/REbG6vyDsKSkhK89dZbaG5uRlBQEJydnXHy5EmUlJQgNDQU27ZtM9PIdC9TxuXmzZuYO3cu7ty5gyFDhii/+WZlZaG0tBRubm5ITk6Go6OjchrG5RFdYvPPf/4TR44cga+vL5577jlUV1cjIyMDzc3NiIqKQnBwsFr7sbGx2L17N1xcXDBlyhQ0Njbi6NGjkMvl+Oyzz9Rev5OdnY3w8HBYW1tj+vTpsLW1RVpaGqqqqrBlyxYsW7bMxCMiDqaMS1hYGHJycuDu7q7ySp3OFi9erHKZk9vMI6beZjQJCAjAnTt3cPHiRbXPGJsOpo5LTzj+M1HtAbZu3frYX64JDg7Grl27lP8/e/YsEhMTUVhYiLq6OtjY2GDo0KGYO3cuZs6cqbGNvLw8xMbGorCwEA8ePICbmxtmzZqFpUuXarzf5Pr169i9ezfOnTuHpqYmuLm5Yf78+ViwYIHaTdm9lanjcuvWLezduxenT5/GzZs3IZFI4OLigoCAAKxYsUIlSVVgXDroEpusrCzs3bsXV69eRV1dHfr164dRo0YhIiICw4YN67KNI0eOICEhAaWlpbCysoKPjw/Wr1+v8b5JoOPND3v27EFBQQFaW1shk8mwdOnSLpOq3siUcQkICEBVVdVj+8/IyFB7Uwa3mQ7m2GZ+7XGJKsDYAOaJi9iP/0xUiYiIiEiUeI8qEREREYkSE1UiIiIiEiUmqkREREQkSkxUiYiIiEiUmKgSERERkSgxUSUiIiIiUWKiSkRERESixESViIiIiESJiSoRkQjduHEDISEh+O1vf4uwsDCcPHkS58+fh4eHB77//vvunj0iIrNQ/20sIiLqdnFxcbhx4wZef/11FBYWYvny5QAAmUyGcePGdfPcERGZB39ClYhIhIqLi2FhYYEhQ4ZAEASUlJSgpaUFHh4esLa27u7ZIyIyCyaqRERERCRKvPRPRGRCFy5cwPHjx5GTk4Pq6mrU1tbCwcEBI0aMwKJFi9Qu4x86dAjvvPMOgoODERkZidjYWBw/fhzV1dVwdHSEn58fNm7ciAEDBnTZ3759+5CXl4f79+/D3t5eeZ/r+PHjzbHIRERGwzOqREQmtGTJEpw7dw4vv/wynn/+efTt2xcVFRW4fPkyAODdd9/F4sWLlfUViWpgYCAqKipQXV0NX19fSKVSnD9/Hnfv3oWLiwsOHz4Me3t7lb6Sk5Px/vvvo729HcOGDYO7uzuqqqpQUFAAAFi7di3WrFljvoUnIjIQE1UiIhM6efIkPD090b9/f5XygoIChIeHo6WlBRkZGcozpIpEFQD8/Pzw8ccfw87ODgBQW1uLxYsXo6ioCBs3bsSKFSuU7V29ehUhISFoa2vDrl27MHv2bJV5WL16NeRyOf71r3/xzCoR9Rh8PRURkQn5+/urJakA4O3tjYULF0IulyM9PV3tcxsbG+zcuVOZpAKAo6Oj8un/H374QaV+YmIiWltbMXnyZJUkVTEP8+bNAwDs27fP4GUiIjIX3qNKRGRiNTU1OHnyJK5du4a6ujq0trYCAMrLywEAZWVlatN4eXlpTHDd3d0BALdu3VIpz8nJAQAEBwdrnIc5c+bgiy++wI8//oi2tjZYWFjovTxERObCRJWIyISSk5Oxc+dONDU1dVmnsbFRrez555/XWFdxhvXhw4cq5YrE1dXVVeN0gwYNAgC0tLTg/v37eO65554880RE3YyJKhGRiVy6dAmRkZGwsLDApk2bEBAQoHygSiKRICkpCZGRkdD0qIBUyjuziIi4JyQiMpHjx49DEASEhoYiIiICgwcPho2NDSQSCYBHl/6NQfEwVkVFhcbPKysrAQDPPPMMHB0djdYvEZEpMVElIjKR2tpaAMDAgQPVPmtpaUFaWprR+nr11VcBACkpKRo/P3DgAABg5MiRsLTkxTQi6hmYqBIRmcjgwYMBAN988w0aGhqU5S0tLdi+fbvyLKcxLFq0CJaWlkhPT8fhw4dVPjtz5gySkpIAAMuWLTNan0REpsav1UREJhISEoLExERcuXIFkyZNwsiRI2FhYYEff/wRzc3NWLRoERITE43Sl4eHByIjI7F9+3Zs3rwZCQkJeOmll3Dz5k0UFBRAEASsXbsWfn5+RumPiMgcmKgSEZmIg4MDDhw4gJiYGJw5cwanTp1Cv379MH78eKxZswZ5eXlG7W/evHkYOnQo9u3bh/z8fFy9ehV2dnbw9/fHokWL+KJ/Iupx+MtURERERCRKvEeViIiIiESJiSoRERERiRITVSIiIiISJSaqRERERCRKTFSJiIiISJSYqBIRERGRKDFRJSIiIiJRYqJKRERERKLERJWIiIiIRImJKhERERGJEhNVIiIiIhIlJqpEREREJEpMVImIiIhIlP4PzJTkQKNkG9kAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8, 6))\n",
"plt.plot(años, accidentes, '.')\n",
"plt.ylabel(\"# de accidentes\", fontsize=16)\n",
"plt.xlabel(\"año\", fontsize=16)\n",
"\n",
"plt.vlines(trace_cat['pc'].mean(), accidentes.min(), accidentes.max(), color='C1', lw=2)\n",
"accidentes_media = np.zeros_like(accidentes, dtype='float')\n",
"for i, año in enumerate(años):\n",
" idx = año < trace_cat['pc']\n",
" accidentes_media[i] = ((trace_cat['t_0'][idx].sum() + trace_cat['t_1'][~idx].sum()) \n",
" / (len(trace_cat) * trace_cat.nchains))\n",
"\n",
"pc_hpd = az.hpd(trace_cat['pc'])\n",
"plt.fill_betweenx([accidentes.min(), accidentes.max()], pc_hpd[0], pc_hpd[1], alpha=0.3, color='C1');\n",
"plt.plot(años, accidentes_media, 'k--', lw=2);"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Comparación de grupos\n",
"\n",
"<br>\n",
"\n",
"\n",
"* Un problema muy común en inferencia estadística es la comparación de dos (o más) grupos\n",
"\n",
"\n",
"* Podemos estar interesados en saber si algún valor de un grupo es más grande que el del otro grupo\n",
"\n",
"\n",
"* Requerimos de un modelo estadístico porque los datos provienen de mediciones con errores que nos impiden sacar conclusiones simplemente calculando las diferencias a partir de los datos observados."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### El conjunto de datos de las propinas \n",
"\n",
"<br>\n",
"\n",
"\n",
"* Queremos estudiar el efecto del día de la semana sobre el monto de propina en un restaurante determinado\n",
"\n",
"<br>\n",
"\n",
"\n",
"[Bryant, P. G. and Smith, M](https://www.amazon.com/Practical-Data-Analysis-Peter-Bryant/dp/0256238715/ref=dp_ob_title_bk)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>total_bill</th>\n",
" <th>tip</th>\n",
" <th>sex</th>\n",
" <th>smoker</th>\n",
" <th>day</th>\n",
" <th>time</th>\n",
" <th>size</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>239</th>\n",
" <td>29.03</td>\n",
" <td>5.92</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>Sat</td>\n",
" <td>Dinner</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>240</th>\n",
" <td>27.18</td>\n",
" <td>2.00</td>\n",
" <td>Female</td>\n",
" <td>Yes</td>\n",
" <td>Sat</td>\n",
" <td>Dinner</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>241</th>\n",
" <td>22.67</td>\n",
" <td>2.00</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>Sat</td>\n",
" <td>Dinner</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>242</th>\n",
" <td>17.82</td>\n",
" <td>1.75</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>Sat</td>\n",
" <td>Dinner</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>243</th>\n",
" <td>18.78</td>\n",
" <td>3.00</td>\n",
" <td>Female</td>\n",
" <td>No</td>\n",
" <td>Thur</td>\n",
" <td>Dinner</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_bill tip sex smoker day time size\n",
"239 29.03 5.92 Male No Sat Dinner 3\n",
"240 27.18 2.00 Female Yes Sat Dinner 2\n",
"241 22.67 2.00 Male Yes Sat Dinner 2\n",
"242 17.82 1.75 Male No Sat Dinner 2\n",
"243 18.78 3.00 Female No Thur Dinner 2"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tips = pd.read_csv('../datos/propinas.csv')\n",
"tips.tail()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1200x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Usamos arviz para graficar datos, no una distribución a posteriori ¯\\_(ツ)_/¯\n",
"az.plot_forest(tips.pivot(columns='day', values='tip').to_dict('list'),\n",
" kind='ridgeplot',\n",
" figsize=(12, 4));"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"categorias = ['Thur', 'Fri', 'Sat', 'Sun']\n",
"\n",
"tip = tips['tip'].values # propina en dolares\n",
"idx = pd.Categorical(\n",
" tips['day'],\n",
" categories=categorias).codes # ['Thur', 'Fri', 'Sat', 'Sun'] --> [0, 1, 2, 3]\n",
"grupos = len(np.unique(idx))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Auto-assigning NUTS sampler...\n",
"Initializing NUTS using jitter+adapt_diag...\n",
"Multiprocess sampling (2 chains in 2 jobs)\n",
"NUTS: [σ, μ]\n"
]
},
{
"data": {
"text/html": [
"\n",
" <div>\n",
" <style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
" </style>\n",
" <progress value='11000' class='' max='11000', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" 100.00% [11000/11000 00:06<00:00 Sampling 2 chains, 0 divergences]\n",
" </div>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"with pm.Model() as comparación_grupos:\n",
" # μ y σ son vectores con shape \"groups\"\n",
" μ = pm.Normal('μ', mu=0, sd=10, shape=grupos) \n",
" σ = pm.HalfNormal('σ', sd=10, shape=grupos)\n",
"\n",
" y = pm.Normal('y', mu=μ[idx], sd=σ[idx], observed=tip) # usamos idx para indexar μ y σ\n",
"\n",
" trace_cg = pm.sample(5000)\n",
"#az.plot_trace(trace_cg);"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"hide_input": false,
"scrolled": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1400x800 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dist = stats.norm()\n",
"\n",
"_, ax = plt.subplots(3, 2, figsize=(14, 8), constrained_layout=True)\n",
"\n",
"comparisons = [(i,j) for i in range(4) for j in range(i+1, 4)]\n",
"pos = [(k,l) for k in range(3) for l in (0, 1)]\n",
"\n",
"for (i, j), (k,l) in zip(comparisons, pos):\n",
" means_diff = trace_cg['μ'][:,i] - trace_cg['μ'][:,j]\n",
" d_cohen = (means_diff / ((trace_cg['σ'][:,i]**2 + trace_cg['σ'][:,j]**2) / 2)**0.5).mean()\n",
" ps = dist.cdf(d_cohen/(2**0.5))\n",
" az.plot_posterior(means_diff, ref_val=0, ax=ax[k, l])\n",
" ax[k, l].set_title(\"%s/%s\" % (categorias[i], categorias[j]))\n",
" ax[k, l].plot(0, label=\"Cohen's d = {:.2f}\\nProb sup = {:.2f}\".format(d_cohen, ps), alpha=0)\n",
" ax[k,l ].legend()"
]
},
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"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
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},
"source": [
"### Tamaño del efecto\n",
"\n",
"\n",
"**Cohen's d**\n",
"\n",
"$$\n",
"\\frac{\\mu_2 - \\mu_1}{\\sqrt{\\frac{\\sigma_1^2 + \\sigma_2^2}{2}}}\n",
"$$\n",
"\n",
"\n",
"* Se puede interpretar como un *z-score*. Cuántas desviaciones estándar una media de un grupo está por encima (o por debajo) de la media del otro grupo\n",
"* [Ejemplo interactivo](http://rpsychologist.com/d3/cohend)\n",
"\n",
"**Probabilidad de superioridad**\n",
"\n",
"* La probabilidad que un dato tomado de un grupo sea mayor que la de un dato tomado del otro grupo.\n",
"* Si suponemos que los datos se distribuyen normalmente, entonces:\n",
"\n",
"\n",
"\\begin{equation} \\label{eq_ps}\n",
"ps = \\Phi \\left ( \\frac{\\delta}{\\sqrt{2}} \\right)\n",
"\\end{equation}\n",
"\n",
"\n",
"$\\Phi$ es la `cdf` de una distribución normal $\\delta$ es el valor del Cohen's d."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
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},
"source": [
"## Ejercicios\n",
"\n",
"\n",
"1. Usar PyMC3 para reproducir el modelo beta-binomial mostrado en el notebook anterior (usando los 3 priors).\n",
"\n",
"\n",
"2. Reemplazar la distribución beta con una distribución uniforme en el intervalo [0,1]. ¿Cuán similar es el resultado comparado con el \n",
"prior $beta(\\alpha=1, \\beta=1)$? Qué sucede al usar el intervalo [-1, 2]?\n",
"\n",
"3. En la siguiente definición de un modelo probabilistico, indentificar el prior, el likelihood y el posterior:\n",
"\n",
"$$y_i \\sim Normal(\\mu, \\sigma)$$\n",
"$$\\mu \\sim Normal(0, 10)$$\n",
"$$\\sigma \\sim HalfNormal(25)$$\n",
"\n",
"4. En el modelo anterior ¿Cuantas dimensiones tendrá el posterior? Comparar con el modelo de la moneda.\n",
"\n",
"5. Correr el modelo de los tanques alemanes usando los siguientes priors, `N = pm.Deterministic('N', pm.Exponential('N_', 1/10) + serial_numbers.max())`, `N = pm.Exponential('N', 1/10) + serial_numbers.max()`. Cómo se ven afectados los resultados?\n",
"\n",
"6. Modificá el `modelo_g` cambiando el prior para la media del likelihood gaussiano, probá usar la media empírica. Probá modificar la escala del prior para la desviación estándar ¿Cuán robusta es la inferencia a estos cambios? \n",
"\n",
"7. Cuán razonable es usar como likelihood una Gaussiana, que es un distribución *no acotada* (está definida de $-\\infty$ to $\\infty$), para modelar datos que si están acotados como los del ejemplo?\n",
"\n",
"8. Ajustá los datos usados en el ejemplo de la velocidad de la luz usando un modelo similar al `modelo_g` pero usando como likelihood una distribución t de Student. Esta distribución tiene 3 parámetros: \"mu\", \"sigma\" y \"nu\", como prior de nu probá usar una distribución exponencial con media 30. Comparar los valores estimados para mu y sigma entre el `modelo_g` y este modelo. Qué se observa?\n",
"\n",
"5. Discutir los resultados del ejemplo de las propinas\n",
"\n",
"6. Aplica al menos uno de los modelos visto en este capítulo a datos propios o de tu interés."
]
}
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| stackv2 | 2024-11-18T18:03:05.436453+00:00 | 2020-03-11T23:18:58 | {
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03fda98d02119034c6ddd0e080b6cea61109b013 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
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"source": [
"# U.S. College Majors - Data Analysis and Visualizations"
]
},
{
"cell_type": "markdown",
"metadata": {
"toc": true
},
"source": [
"<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
"<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Introduction\" data-toc-modified-id=\"Introduction-1\">Introduction</a></span></li><li><span><a href=\"#Libraries-and-configuration\" data-toc-modified-id=\"Libraries-and-configuration-2\">Libraries and configuration</a></span></li><li><span><a href=\"#Recent-Graduates-Data-Analysis\" data-toc-modified-id=\"Recent-Graduates-Data-Analysis-3\">Recent Graduates Data Analysis</a></span><ul class=\"toc-item\"><li><span><a href=\"#Data\" data-toc-modified-id=\"Data-3.1\">Data</a></span></li><li><span><a href=\"#Median-Earnings\" data-toc-modified-id=\"Median-Earnings-3.2\">Median Earnings</a></span></li><li><span><a href=\"#Unemployment-and-Underemployment-Rates\" data-toc-modified-id=\"Unemployment-and-Underemployment-Rates-3.3\">Unemployment and Underemployment Rates</a></span></li><li><span><a href=\"#Outcomes-for-Female-Graduates\" data-toc-modified-id=\"Outcomes-for-Female-Graduates-3.4\">Outcomes for Female Graduates</a></span></li></ul></li><li><span><a href=\"#Women-Graduates-Time-Series-Data-Analysis\" data-toc-modified-id=\"Women-Graduates-Time-Series-Data-Analysis-4\">Women Graduates Time Series Data Analysis</a></span><ul class=\"toc-item\"><li><span><a href=\"#Data\" data-toc-modified-id=\"Data-4.1\">Data</a></span></li><li><span><a href=\"#Gender-Gap-in-Bachelor's-Degrees\" data-toc-modified-id=\"Gender-Gap-in-Bachelor's-Degrees-4.2\">Gender Gap in Bachelor's Degrees</a></span></li></ul></li><li><span><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-5\">Conclusion</a></span></li></ul></div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Background:**\n",
"\n",
"Student loan debt is a major issue in the U.S., reaching __[<span>$</span>1.5 trillion in 2018](https://www.forbes.com/sites/zackfriedman/2018/06/13/student-loan-debt-statistics-2018/#22d8fef07310)__ and becoming the second highest consumer debt category, only behind mortgage debt. Given that the average graduate has more than <span>$</span>37,000 in student loan debt, choosing the right college major is increasingly critical. \n",
"\n",
"This project aims to analyze outcomes for U.S. graduates based on their major in terms of earnings and unemployment. Special attention is also given to exploring the gender gap in bachelor's degrees. \n",
"\n",
"**Goals:**\n",
"<ul>\n",
" <li>Examine graduates' yearly earnings based on their majors;</li>\n",
" <li>Determine whether majors impact graduates' unemployment and underemployment rates;</li>\n",
" <li>Find out if majors' outcomes are correlated with the share of women in them;</li>\n",
" <li>Explore the gender gap's evolution in bachelor's degrees from 1970 to 2011.</li>\n",
"</ul>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Libraries and configuration"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<style>\n",
".output_png {\n",
" display: table-cell;\n",
" text-align: center;\n",
" vertical-align: middle;\n",
"}\n",
"</style>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Seaborn styling options\n",
"sns.set_style(\"white\")\n",
"sns.set_context(rc={\"font.size\":11, \"axes.titlesize\":14, \"axes.labelsize\":12})\n",
"sns.set_palette(\"muted\")\n",
"\n",
"# Center plots\n",
"from IPython.core.display import HTML\n",
"HTML(\"\"\"\n",
"<style>\n",
".output_png {\n",
" display: table-cell;\n",
" text-align: center;\n",
" vertical-align: middle;\n",
"}\n",
"</style>\n",
"\"\"\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Recent Graduates Data Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Rank</th>\n",
" <th>Major_code</th>\n",
" <th>Major</th>\n",
" <th>Total</th>\n",
" <th>Men</th>\n",
" <th>Women</th>\n",
" <th>Major_category</th>\n",
" <th>ShareWomen</th>\n",
" <th>Sample_size</th>\n",
" <th>Employed</th>\n",
" <th>...</th>\n",
" <th>Part_time</th>\n",
" <th>Full_time_year_round</th>\n",
" <th>Unemployed</th>\n",
" <th>Unemployment_rate</th>\n",
" <th>Median</th>\n",
" <th>P25th</th>\n",
" <th>P75th</th>\n",
" <th>College_jobs</th>\n",
" <th>Non_college_jobs</th>\n",
" <th>Low_wage_jobs</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2419</td>\n",
" <td>PETROLEUM ENGINEERING</td>\n",
" <td>2339.0</td>\n",
" <td>2057.0</td>\n",
" <td>282.0</td>\n",
" <td>Engineering</td>\n",
" <td>0.120564</td>\n",
" <td>36</td>\n",
" <td>1976</td>\n",
" <td>...</td>\n",
" <td>270</td>\n",
" <td>1207</td>\n",
" <td>37</td>\n",
" <td>0.018381</td>\n",
" <td>110000</td>\n",
" <td>95000</td>\n",
" <td>125000</td>\n",
" <td>1534</td>\n",
" <td>364</td>\n",
" <td>193</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>2416</td>\n",
" <td>MINING AND MINERAL ENGINEERING</td>\n",
" <td>756.0</td>\n",
" <td>679.0</td>\n",
" <td>77.0</td>\n",
" <td>Engineering</td>\n",
" <td>0.101852</td>\n",
" <td>7</td>\n",
" <td>640</td>\n",
" <td>...</td>\n",
" <td>170</td>\n",
" <td>388</td>\n",
" <td>85</td>\n",
" <td>0.117241</td>\n",
" <td>75000</td>\n",
" <td>55000</td>\n",
" <td>90000</td>\n",
" <td>350</td>\n",
" <td>257</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>2415</td>\n",
" <td>METALLURGICAL ENGINEERING</td>\n",
" <td>856.0</td>\n",
" <td>725.0</td>\n",
" <td>131.0</td>\n",
" <td>Engineering</td>\n",
" <td>0.153037</td>\n",
" <td>3</td>\n",
" <td>648</td>\n",
" <td>...</td>\n",
" <td>133</td>\n",
" <td>340</td>\n",
" <td>16</td>\n",
" <td>0.024096</td>\n",
" <td>73000</td>\n",
" <td>50000</td>\n",
" <td>105000</td>\n",
" <td>456</td>\n",
" <td>176</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>2417</td>\n",
" <td>NAVAL ARCHITECTURE AND MARINE ENGINEERING</td>\n",
" <td>1258.0</td>\n",
" <td>1123.0</td>\n",
" <td>135.0</td>\n",
" <td>Engineering</td>\n",
" <td>0.107313</td>\n",
" <td>16</td>\n",
" <td>758</td>\n",
" <td>...</td>\n",
" <td>150</td>\n",
" <td>692</td>\n",
" <td>40</td>\n",
" <td>0.050125</td>\n",
" <td>70000</td>\n",
" <td>43000</td>\n",
" <td>80000</td>\n",
" <td>529</td>\n",
" <td>102</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>2405</td>\n",
" <td>CHEMICAL ENGINEERING</td>\n",
" <td>32260.0</td>\n",
" <td>21239.0</td>\n",
" <td>11021.0</td>\n",
" <td>Engineering</td>\n",
" <td>0.341631</td>\n",
" <td>289</td>\n",
" <td>25694</td>\n",
" <td>...</td>\n",
" <td>5180</td>\n",
" <td>16697</td>\n",
" <td>1672</td>\n",
" <td>0.061098</td>\n",
" <td>65000</td>\n",
" <td>50000</td>\n",
" <td>75000</td>\n",
" <td>18314</td>\n",
" <td>4440</td>\n",
" <td>972</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" Rank Major_code Major Total \\\n",
"0 1 2419 PETROLEUM ENGINEERING 2339.0 \n",
"1 2 2416 MINING AND MINERAL ENGINEERING 756.0 \n",
"2 3 2415 METALLURGICAL ENGINEERING 856.0 \n",
"3 4 2417 NAVAL ARCHITECTURE AND MARINE ENGINEERING 1258.0 \n",
"4 5 2405 CHEMICAL ENGINEERING 32260.0 \n",
"\n",
" Men Women Major_category ShareWomen Sample_size Employed \\\n",
"0 2057.0 282.0 Engineering 0.120564 36 1976 \n",
"1 679.0 77.0 Engineering 0.101852 7 640 \n",
"2 725.0 131.0 Engineering 0.153037 3 648 \n",
"3 1123.0 135.0 Engineering 0.107313 16 758 \n",
"4 21239.0 11021.0 Engineering 0.341631 289 25694 \n",
"\n",
" ... Part_time Full_time_year_round Unemployed \\\n",
"0 ... 270 1207 37 \n",
"1 ... 170 388 85 \n",
"2 ... 133 340 16 \n",
"3 ... 150 692 40 \n",
"4 ... 5180 16697 1672 \n",
"\n",
" Unemployment_rate Median P25th P75th College_jobs Non_college_jobs \\\n",
"0 0.018381 110000 95000 125000 1534 364 \n",
"1 0.117241 75000 55000 90000 350 257 \n",
"2 0.024096 73000 50000 105000 456 176 \n",
"3 0.050125 70000 43000 80000 529 102 \n",
"4 0.061098 65000 50000 75000 18314 4440 \n",
"\n",
" Low_wage_jobs \n",
"0 193 \n",
"1 50 \n",
"2 0 \n",
"3 0 \n",
"4 972 \n",
"\n",
"[5 rows x 21 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grads_df = pd.read_csv(\"data/recent-grads.csv\")\n",
"\n",
"# Preview of the dataset\n",
"grads_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Rank</th>\n",
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" <th>Major</th>\n",
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" <th>Men</th>\n",
" <th>Women</th>\n",
" <th>Major_category</th>\n",
" <th>ShareWomen</th>\n",
" <th>Sample_size</th>\n",
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" <th>Full_time_year_round</th>\n",
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" <th>Unemployment_rate</th>\n",
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" <th>P25th</th>\n",
" <th>P75th</th>\n",
" <th>College_jobs</th>\n",
" <th>Non_college_jobs</th>\n",
" <th>Low_wage_jobs</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>168</th>\n",
" <td>169</td>\n",
" <td>3609</td>\n",
" <td>ZOOLOGY</td>\n",
" <td>8409.0</td>\n",
" <td>3050.0</td>\n",
" <td>5359.0</td>\n",
" <td>Biology & Life Science</td>\n",
" <td>0.637293</td>\n",
" <td>47</td>\n",
" <td>6259</td>\n",
" <td>...</td>\n",
" <td>2190</td>\n",
" <td>3602</td>\n",
" <td>304</td>\n",
" <td>0.046320</td>\n",
" <td>26000</td>\n",
" <td>20000</td>\n",
" <td>39000</td>\n",
" <td>2771</td>\n",
" <td>2947</td>\n",
" <td>743</td>\n",
" </tr>\n",
" <tr>\n",
" <th>169</th>\n",
" <td>170</td>\n",
" <td>5201</td>\n",
" <td>EDUCATIONAL PSYCHOLOGY</td>\n",
" <td>2854.0</td>\n",
" <td>522.0</td>\n",
" <td>2332.0</td>\n",
" <td>Psychology & Social Work</td>\n",
" <td>0.817099</td>\n",
" <td>7</td>\n",
" <td>2125</td>\n",
" <td>...</td>\n",
" <td>572</td>\n",
" <td>1211</td>\n",
" <td>148</td>\n",
" <td>0.065112</td>\n",
" <td>25000</td>\n",
" <td>24000</td>\n",
" <td>34000</td>\n",
" <td>1488</td>\n",
" <td>615</td>\n",
" <td>82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>170</th>\n",
" <td>171</td>\n",
" <td>5202</td>\n",
" <td>CLINICAL PSYCHOLOGY</td>\n",
" <td>2838.0</td>\n",
" <td>568.0</td>\n",
" <td>2270.0</td>\n",
" <td>Psychology & Social Work</td>\n",
" <td>0.799859</td>\n",
" <td>13</td>\n",
" <td>2101</td>\n",
" <td>...</td>\n",
" <td>648</td>\n",
" <td>1293</td>\n",
" <td>368</td>\n",
" <td>0.149048</td>\n",
" <td>25000</td>\n",
" <td>25000</td>\n",
" <td>40000</td>\n",
" <td>986</td>\n",
" <td>870</td>\n",
" <td>622</td>\n",
" </tr>\n",
" <tr>\n",
" <th>171</th>\n",
" <td>172</td>\n",
" <td>5203</td>\n",
" <td>COUNSELING PSYCHOLOGY</td>\n",
" <td>4626.0</td>\n",
" <td>931.0</td>\n",
" <td>3695.0</td>\n",
" <td>Psychology & Social Work</td>\n",
" <td>0.798746</td>\n",
" <td>21</td>\n",
" <td>3777</td>\n",
" <td>...</td>\n",
" <td>965</td>\n",
" <td>2738</td>\n",
" <td>214</td>\n",
" <td>0.053621</td>\n",
" <td>23400</td>\n",
" <td>19200</td>\n",
" <td>26000</td>\n",
" <td>2403</td>\n",
" <td>1245</td>\n",
" <td>308</td>\n",
" </tr>\n",
" <tr>\n",
" <th>172</th>\n",
" <td>173</td>\n",
" <td>3501</td>\n",
" <td>LIBRARY SCIENCE</td>\n",
" <td>1098.0</td>\n",
" <td>134.0</td>\n",
" <td>964.0</td>\n",
" <td>Education</td>\n",
" <td>0.877960</td>\n",
" <td>2</td>\n",
" <td>742</td>\n",
" <td>...</td>\n",
" <td>237</td>\n",
" <td>410</td>\n",
" <td>87</td>\n",
" <td>0.104946</td>\n",
" <td>22000</td>\n",
" <td>20000</td>\n",
" <td>22000</td>\n",
" <td>288</td>\n",
" <td>338</td>\n",
" <td>192</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" Rank Major_code Major Total Men Women \\\n",
"168 169 3609 ZOOLOGY 8409.0 3050.0 5359.0 \n",
"169 170 5201 EDUCATIONAL PSYCHOLOGY 2854.0 522.0 2332.0 \n",
"170 171 5202 CLINICAL PSYCHOLOGY 2838.0 568.0 2270.0 \n",
"171 172 5203 COUNSELING PSYCHOLOGY 4626.0 931.0 3695.0 \n",
"172 173 3501 LIBRARY SCIENCE 1098.0 134.0 964.0 \n",
"\n",
" Major_category ShareWomen Sample_size Employed \\\n",
"168 Biology & Life Science 0.637293 47 6259 \n",
"169 Psychology & Social Work 0.817099 7 2125 \n",
"170 Psychology & Social Work 0.799859 13 2101 \n",
"171 Psychology & Social Work 0.798746 21 3777 \n",
"172 Education 0.877960 2 742 \n",
"\n",
" ... Part_time Full_time_year_round Unemployed \\\n",
"168 ... 2190 3602 304 \n",
"169 ... 572 1211 148 \n",
"170 ... 648 1293 368 \n",
"171 ... 965 2738 214 \n",
"172 ... 237 410 87 \n",
"\n",
" Unemployment_rate Median P25th P75th College_jobs Non_college_jobs \\\n",
"168 0.046320 26000 20000 39000 2771 2947 \n",
"169 0.065112 25000 24000 34000 1488 615 \n",
"170 0.149048 25000 25000 40000 986 870 \n",
"171 0.053621 23400 19200 26000 2403 1245 \n",
"172 0.104946 22000 20000 22000 288 338 \n",
"\n",
" Low_wage_jobs \n",
"168 743 \n",
"169 82 \n",
"170 622 \n",
"171 308 \n",
"172 192 \n",
"\n",
"[5 rows x 21 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grads_df.tail()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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" <th></th>\n",
" <th>Rank</th>\n",
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" <th>Total</th>\n",
" <th>Men</th>\n",
" <th>Women</th>\n",
" <th>ShareWomen</th>\n",
" <th>Sample_size</th>\n",
" <th>Employed</th>\n",
" <th>Full_time</th>\n",
" <th>Part_time</th>\n",
" <th>Full_time_year_round</th>\n",
" <th>Unemployed</th>\n",
" <th>Unemployment_rate</th>\n",
" <th>Median</th>\n",
" <th>P25th</th>\n",
" <th>P75th</th>\n",
" <th>College_jobs</th>\n",
" <th>Non_college_jobs</th>\n",
" <th>Low_wage_jobs</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>173.000000</td>\n",
" <td>173.000000</td>\n",
" <td>172.000000</td>\n",
" <td>172.000000</td>\n",
" <td>172.000000</td>\n",
" <td>172.000000</td>\n",
" <td>173.000000</td>\n",
" <td>173.000000</td>\n",
" <td>173.000000</td>\n",
" <td>173.000000</td>\n",
" <td>173.000000</td>\n",
" <td>173.000000</td>\n",
" <td>173.000000</td>\n",
" <td>173.000000</td>\n",
" <td>173.000000</td>\n",
" <td>173.000000</td>\n",
" <td>173.000000</td>\n",
" <td>173.000000</td>\n",
" <td>173.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>87.000000</td>\n",
" <td>3879.815029</td>\n",
" <td>39370.081395</td>\n",
" <td>16723.406977</td>\n",
" <td>22646.674419</td>\n",
" <td>0.522223</td>\n",
" <td>356.080925</td>\n",
" <td>31192.763006</td>\n",
" <td>26029.306358</td>\n",
" <td>8832.398844</td>\n",
" <td>19694.427746</td>\n",
" <td>2416.329480</td>\n",
" <td>0.068191</td>\n",
" <td>40151.445087</td>\n",
" <td>29501.445087</td>\n",
" <td>51494.219653</td>\n",
" <td>12322.635838</td>\n",
" <td>13284.497110</td>\n",
" <td>3859.017341</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>50.084928</td>\n",
" <td>1687.753140</td>\n",
" <td>63483.491009</td>\n",
" <td>28122.433474</td>\n",
" <td>41057.330740</td>\n",
" <td>0.231205</td>\n",
" <td>618.361022</td>\n",
" <td>50675.002241</td>\n",
" <td>42869.655092</td>\n",
" <td>14648.179473</td>\n",
" <td>33160.941514</td>\n",
" <td>4112.803148</td>\n",
" <td>0.030331</td>\n",
" <td>11470.181802</td>\n",
" <td>9166.005235</td>\n",
" <td>14906.279740</td>\n",
" <td>21299.868863</td>\n",
" <td>23789.655363</td>\n",
" <td>6944.998579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>1100.000000</td>\n",
" <td>124.000000</td>\n",
" <td>119.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>111.000000</td>\n",
" <td>0.000000</td>\n",
" <td>111.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>22000.000000</td>\n",
" <td>18500.000000</td>\n",
" <td>22000.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>44.000000</td>\n",
" <td>2403.000000</td>\n",
" <td>4549.750000</td>\n",
" <td>2177.500000</td>\n",
" <td>1778.250000</td>\n",
" <td>0.336026</td>\n",
" <td>39.000000</td>\n",
" <td>3608.000000</td>\n",
" <td>3154.000000</td>\n",
" <td>1030.000000</td>\n",
" <td>2453.000000</td>\n",
" <td>304.000000</td>\n",
" <td>0.050306</td>\n",
" <td>33000.000000</td>\n",
" <td>24000.000000</td>\n",
" <td>42000.000000</td>\n",
" <td>1675.000000</td>\n",
" <td>1591.000000</td>\n",
" <td>340.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>87.000000</td>\n",
" <td>3608.000000</td>\n",
" <td>15104.000000</td>\n",
" <td>5434.000000</td>\n",
" <td>8386.500000</td>\n",
" <td>0.534024</td>\n",
" <td>130.000000</td>\n",
" <td>11797.000000</td>\n",
" <td>10048.000000</td>\n",
" <td>3299.000000</td>\n",
" <td>7413.000000</td>\n",
" <td>893.000000</td>\n",
" <td>0.067961</td>\n",
" <td>36000.000000</td>\n",
" <td>27000.000000</td>\n",
" <td>47000.000000</td>\n",
" <td>4390.000000</td>\n",
" <td>4595.000000</td>\n",
" <td>1231.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>130.000000</td>\n",
" <td>5503.000000</td>\n",
" <td>38909.750000</td>\n",
" <td>14631.000000</td>\n",
" <td>22553.750000</td>\n",
" <td>0.703299</td>\n",
" <td>338.000000</td>\n",
" <td>31433.000000</td>\n",
" <td>25147.000000</td>\n",
" <td>9948.000000</td>\n",
" <td>16891.000000</td>\n",
" <td>2393.000000</td>\n",
" <td>0.087557</td>\n",
" <td>45000.000000</td>\n",
" <td>33000.000000</td>\n",
" <td>60000.000000</td>\n",
" <td>14444.000000</td>\n",
" <td>11783.000000</td>\n",
" <td>3466.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>173.000000</td>\n",
" <td>6403.000000</td>\n",
" <td>393735.000000</td>\n",
" <td>173809.000000</td>\n",
" <td>307087.000000</td>\n",
" <td>0.968954</td>\n",
" <td>4212.000000</td>\n",
" <td>307933.000000</td>\n",
" <td>251540.000000</td>\n",
" <td>115172.000000</td>\n",
" <td>199897.000000</td>\n",
" <td>28169.000000</td>\n",
" <td>0.177226</td>\n",
" <td>110000.000000</td>\n",
" <td>95000.000000</td>\n",
" <td>125000.000000</td>\n",
" <td>151643.000000</td>\n",
" <td>148395.000000</td>\n",
" <td>48207.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Rank Major_code Total Men Women \\\n",
"count 173.000000 173.000000 172.000000 172.000000 172.000000 \n",
"mean 87.000000 3879.815029 39370.081395 16723.406977 22646.674419 \n",
"std 50.084928 1687.753140 63483.491009 28122.433474 41057.330740 \n",
"min 1.000000 1100.000000 124.000000 119.000000 0.000000 \n",
"25% 44.000000 2403.000000 4549.750000 2177.500000 1778.250000 \n",
"50% 87.000000 3608.000000 15104.000000 5434.000000 8386.500000 \n",
"75% 130.000000 5503.000000 38909.750000 14631.000000 22553.750000 \n",
"max 173.000000 6403.000000 393735.000000 173809.000000 307087.000000 \n",
"\n",
" ShareWomen Sample_size Employed Full_time Part_time \\\n",
"count 172.000000 173.000000 173.000000 173.000000 173.000000 \n",
"mean 0.522223 356.080925 31192.763006 26029.306358 8832.398844 \n",
"std 0.231205 618.361022 50675.002241 42869.655092 14648.179473 \n",
"min 0.000000 2.000000 0.000000 111.000000 0.000000 \n",
"25% 0.336026 39.000000 3608.000000 3154.000000 1030.000000 \n",
"50% 0.534024 130.000000 11797.000000 10048.000000 3299.000000 \n",
"75% 0.703299 338.000000 31433.000000 25147.000000 9948.000000 \n",
"max 0.968954 4212.000000 307933.000000 251540.000000 115172.000000 \n",
"\n",
" Full_time_year_round Unemployed Unemployment_rate Median \\\n",
"count 173.000000 173.000000 173.000000 173.000000 \n",
"mean 19694.427746 2416.329480 0.068191 40151.445087 \n",
"std 33160.941514 4112.803148 0.030331 11470.181802 \n",
"min 111.000000 0.000000 0.000000 22000.000000 \n",
"25% 2453.000000 304.000000 0.050306 33000.000000 \n",
"50% 7413.000000 893.000000 0.067961 36000.000000 \n",
"75% 16891.000000 2393.000000 0.087557 45000.000000 \n",
"max 199897.000000 28169.000000 0.177226 110000.000000 \n",
"\n",
" P25th P75th College_jobs Non_college_jobs \\\n",
"count 173.000000 173.000000 173.000000 173.000000 \n",
"mean 29501.445087 51494.219653 12322.635838 13284.497110 \n",
"std 9166.005235 14906.279740 21299.868863 23789.655363 \n",
"min 18500.000000 22000.000000 0.000000 0.000000 \n",
"25% 24000.000000 42000.000000 1675.000000 1591.000000 \n",
"50% 27000.000000 47000.000000 4390.000000 4595.000000 \n",
"75% 33000.000000 60000.000000 14444.000000 11783.000000 \n",
"max 95000.000000 125000.000000 151643.000000 148395.000000 \n",
"\n",
" Low_wage_jobs \n",
"count 173.000000 \n",
"mean 3859.017341 \n",
"std 6944.998579 \n",
"min 0.000000 \n",
"25% 340.000000 \n",
"50% 1231.000000 \n",
"75% 3466.000000 \n",
"max 48207.000000 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Summary statistics\n",
"grads_df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 row(s) removed.\n"
]
}
],
"source": [
"# Remove rows with missing values (to avoid matplotlib errors)\n",
"raw_data_count = grads_df.shape[0]\n",
"grads_df = grads_df.dropna() # drop rows with missing values\n",
"cleaned_data_count = grads_df.shape[0]\n",
"print(str(raw_data_count - cleaned_data_count) + \" row(s) removed.\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# Capitalize majors names instead of uppercase\n",
"grads_df[\"Major\"] = grads_df[\"Major\"].apply(lambda x: x.capitalize())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Median Earnings"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Median earnings histogram\n",
"fig, ax = plt.subplots(figsize = (8,6))\n",
"sns.distplot(grads_df[\"Median\"], kde = True, bins = 10)\n",
"ax.set(xlabel = \"Median yearly earnings (in $)\", title = \"Distribution of median yearly earnings\")\n",
"plt.xticks(rotation=40)\n",
"sns.despine()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"**Findings:** The most common median yearly salary range for college graduates is \\$30,000-40,000."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Median earnings boxplot\n",
"with sns.axes_style(\"whitegrid\"):\n",
" fig, ax = plt.subplots(figsize = (8,4))\n",
" sns.boxplot(x = grads_df[\"Median\"])\n",
" ax.set(xlabel = \"Median yearly earnings (in $)\", title = \"Median Yearly Earnings per Major Distribution\")\n",
" sns.despine(left=True)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Major</th>\n",
" <th>Median</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Petroleum engineering</td>\n",
" <td>110000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Mining and mineral engineering</td>\n",
" <td>75000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Metallurgical engineering</td>\n",
" <td>73000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Naval architecture and marine engineering</td>\n",
" <td>70000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Chemical engineering</td>\n",
" <td>65000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Nuclear engineering</td>\n",
" <td>65000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Major Median\n",
"0 Petroleum engineering 110000\n",
"1 Mining and mineral engineering 75000\n",
"2 Metallurgical engineering 73000\n",
"3 Naval architecture and marine engineering 70000\n",
"4 Chemical engineering 65000\n",
"5 Nuclear engineering 65000"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check outlier majors\n",
"grads_df[grads_df[\"Median\"] > 62000][[\"Major\", \"Median\"]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Findings:** 75% of graduates earn between <span>$</span>22,000-45,000. There are 5 outlier engineering majors that pay more than <span>$</span>62,000, with one major paying more than <span>$</span>100,000."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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tb3/jhRde4OjRo/zf//0f+/bto1OnTuUez2q10r9/f1588UVuvfVWX8tGREQEy5YtY8WKFfzwww+kpaXx448/+o6Tk5Pj90Z5oR588EEWL17Mxx9/zP79+5k2bRoul4tBgwYB3k+zhw4d4uTJkzRq1Ihjx46xfft2jh49yksvvcSmTZuCfq3nz5/PF198wZgxY9i+fTvp6els376dRx55hJSUFK6//vpyH1f6c/7f//1f0tPT2bBhA0uXLgW8P+ebb76ZuLg4pk6dyv79+1m1apVfMOvcuTM5OTm8/vrrHD16lHnz5vm9Zo0aNeLjjz/m6NGj7Ny5k0mTJvmOXfoapKenk5GRQXx8PDfddBMTJkxg165d7Nmzh0mTJpGdnU3Tpk1xu93Mnz+fDz74gB9++IH33nsPl8tVYWtQ6eDS/fv3s2LFCj788EPfp/rf/va3/OUvf2H9+vUcOXKERYsWsX79eq644oqgXu9z9enThxdeeKHc7oiIiAgiIyN9dW/ZssX3+3zuzzc6Opq77rqLWbNm8dlnn7F3714mTpxIq1atfEFX6h4FBqnx5s2bx80333zef0uXLqV58+Y8/fTTvPHGGwwePJiXXnqJadOmYbfb/T5hhsof//hHOnTowIMPPshjjz3G0KFDAXzrHbzwwgt89NFHDB48mIkTJzJy5EhGjBhR4fEGDx5MQUEBgwcP9m37xS9+wdy5c/nLX/7CwIEDmTt3Ln/84x+58cYbAXjsscd8f9gvxu9+9zvuuusupk2bRlJSEpmZmbz11lu+fvW7776bLVu2MHbsWIYOHcqQIUMYP348d955J5999hmTJk1i//79QYWGq666inXr1tG2bVumT5/OgAEDmDBhAuHh4bzzzjs89thj5T6uVatWTJ8+nVdeeYUhQ4bw6quvMn36dGw2G9988w1hYWG88sor5OTkcMcdd7BmzRrfFE2AK6+8kgkTJvDaa68xfPhwTNOkf//+vvvnzZvHnj17GDx4MJMnT2bw4MF07tzZ9zuUlJTEwYMHSUpKArxdGK1bt+bBBx/k/vvvp2XLlixZsgSAW2+9lUcffZR58+YxYMAA/vrXv7Jo0aIKu9VatGhBbGwsI0aM4I033uBPf/qTLzjdfvvtPP744zz//PO+mTQvvvgi8fHxAV/r8vTt25f8/PxyV34MDw9n/vz5pKamMnjwYJ599ll+//vf07Rp03L/LZUu6PXYY48xcuRI6tWrx/Llyyvs5pLazzBr+4okItVo06ZN9OjRg6ioKAB27drFqFGj+PLLL+vMapESOqtWreLFF18kLS2tuksJyOFw0KVLF959992gBrlK3aNBjyI/wZIlS0hLS2PMmDHk5+ezYMEC+vTpo7AgdcrJkyd9M07i4uKquRqpLuqSEPkJkpOTSU9PZ/jw4Tz44IO0bt36J3UPiNRE7733HlOmTOE3v/nNeWs3yM+HuiREREQkILUwiIiISEAKDCIiIhKQAkOJ3/3ud9VdgoiISI2lwFAiOzu7uksQERGpsRQYREREJCAFBhEREQlIgUFEREQCUmAQERGRgBQYREREJCAFBhEREQlIgUFEREQCUmAQERGRgBQYREREJCAFBhEREQnIVt0F1GU79p1h9ZZMMrIdNGsczojEWLrHN6juskRERC6YWhhCZMe+MyxLSSc710n9SCvZuU6WpaSzY9+Z6i5NRETkgikwhMjqLZnYbQYRYRYMw3trtxms3pJZ3aWJiIhcMAWGEMnIdhBuN/y2hdsNMrId1VSRiIjIxVNgCJFmjcNxOE2/bQ6nSbPG4dVUkYiIyMVTYAiREYmxOF0mRcUeTNN763SZjEiMre7SRERELpgCQ4h0j2/AuGGtaFzfTm6hm8b17Ywb1kqzJEREpFbStMoQ6h7fQAFBRETqBLUwiIiISEAKDCIiIhKQAoOIiIgEpMAgIiIiASkwiIiISEAKDCIiIhKQAoOIiIgEpMAgIiIiASkwiIiISEAKDCIiIhKQAoOIiIgEpMAgIiIiASkwiIiISEAKDCIiIhKQAoOIiIgEpMAgIiIiASkwiIiISEAKDCIiIhKQAoOIiIgEpMAgIiIiASkwiIiISEAKDCIiIhKQAoOIiIgEpMAgIiIiASkwiIiISEAhDwx5eXkMGTKEH374AYBt27YxdOhQbrvtNhYuXOjbb8+ePSQlJdG/f3+mTp2Ky+UC4NixY9xzzz0MGDCARx55hPz8fADOnDnD6NGjGThwIPfccw+ZmZkAFBcXM3HiRAYOHMgdd9zB/v37Q/0URURE6ryQBoavv/6akSNHcujQIQCKioqYMmUKy5YtY8OGDezevZt//vOfAEycOJEZM2bwj3/8A9M0WblyJQDPPPMMo0aNIjU1lU6dOrFs2TIAFi1aREJCAhs3buSuu+5izpw5ALz55ptERkayceNGpkyZwuTJk0P5FEVERH4WQhoYVq5cycyZM4mLiwNg165dtG3bljZt2mCz2Rg6dCipqamkp6dTVFRE165dAUhKSiI1NRWn08mOHTvo37+/33aAzZs3M3ToUACGDBnCli1bcDqdbN68mdtvvx2A7t27c+rUKY4dOxbKpykiIlLn2UJ58NJP/aVOnDhBbGys7/u4uDgyMjLO2x4bG0tGRgbZ2dlER0djs9n8tp97LJvNRnR0NKdOnSr3WMePH6dly5Yhe54iIiJ1XZUOevR4PBiG4fveNE0Mw6hwe+ltWed+X/YxFovlvMeUbhcREZGLV6XvpM2bN/cNTgTIzMwkLi7uvO1ZWVnExcURExNDbm4ubrfbb3/wtk5kZWUB4HK5yM/Pp1GjRjRr1owTJ06cdywRERG5eFUaGK699loOHjzI4cOHcbvdvP/++yQmJtKqVSvCw8P5/PPPAUhJSSExMRG73U5CQgIbNmwAYP369SQmJgLQs2dP1q9fD8CGDRtISEjAbrfTs2dPUlJSANi5cyfh4eHqjhAREfmJDNM0zVCfpE+fPvz1r3+ldevWbN++nblz5+JwOOjZsyeTJ0/GMAz27t3LtGnTyMvLo2PHjsydO5ewsDDS09OZNGkSJ0+epEWLFjz33HM0bNiQnJwcJk2axNGjR6lfvz7Jycm0bt0ah8PBjBkz2L17N2FhYcyePZuOHTsGrDEpKYm1a9eG+qUQERGplaokMNQGCgwiIiIV02hAERERCUiBQURERAJSYBAREZGAFBhEREQkIAUGERERCUiBQURERAJSYBAREZGAFBhEREQkIAUGERERCUiBQURERAJSYBAREZGAFBhEREQkIAUGERERCUiBQURERAJSYBAREZGAFBhEREQkIAUGERERCUiBQURERAJSYBAREZGAFBhEREQkIAUGERERCUiBQURERAJSYBAREZGAFBhEREQkIAUGERERCUiBQURERAJSYBAREZGAFBhEREQkIAUGERERCUiBQURERAJSYBAREZGAFBhEREQkIAUGERERCcgW7I7FxcWkp6djtVpp0aIFdrs9lHWJiIhIDRIwMOzdu5clS5awZcsWwsPDsVqtFBcX07t3b8aMGUP79u2rok4RERGpRpUGhmXLlrFz505GjBjBnDlzaNiwIQB5eXl8+umnzJkzh+7du/Poo49WSbEiIiJSPSoNDO3bt2fcuHHnbY+OjmbAgAEMGDCATZs2haw4ERERqRkqHfTYr1+/gAcIZh8RERGp3SoNDCkpKaxduxaAwsJCxo8fz3XXXceoUaM4dOhQVdQnIiIiNUCFgWHHjh0sW7aMdu3aAfDqq6/i8Xh4//33GTp0KFOnTq2qGkVERKSaVRgYli5dSps2bdi2bRtLliwhJSWF6Oho1q5dy8mTJzlw4ABLliypylpFRESkmlQ46LFbt244nU6SkpI4duwY69atY/z48ZimSVFREStXriQpKakqaxUREZFqUmFgGDFiBL/5zW/Iysriyy+/5P7776dly5b83//9H3/605/o1asXLVu2rMpaRUREpJpUGBhatWrFihUr2LRpE7169WLAgAEAOBwOBg0axH333VdlRYqIiEj1MkzTNKu7iJogKSnJNyNERERE/OniUyIiIhKQAoOIiIgEpMAgIiIiAQV1eesDBw7wyiuvkJOTQ9khD3/+859DVpiIiIjUHEEFhkmTJtGlSxe6d++OYRihrklERERqmKACQ2FhIdOmTQt1LSIiIlJDBTWGoW3btpw4cSLUtYiIiEgNFVQLg8fjYciQIXTs2JHw8HDfdo1hEBER+XkIKjDceuut3HrrraGuRURERGqoSgNDXl4e0dHR9O7du6rqERERkRqo0sBw3333sW7dOn71q1/5zY4wTRPDMNizZ0/ICxQREZHqV2lgWLduHQB79+6tkmJERESkZqp0lsSiRYtwOBwV3u9wOFi4cOElL0pERERqlkoDw3XXXceIESOYP38+O3bsICMjg+PHj/PZZ5+RnJzMnXfeybXXXntRJ05JSWHw4MEMHjyYZ599FoA9e/aQlJRE//79mTp1Ki6XC4Bjx45xzz33MGDAAB555BHy8/MBOHPmDKNHj2bgwIHcc889ZGZmAlBcXMzEiRMZOHAgd9xxB/v377+oGkVERMSr0sCQmJjIihUraNSoEfPmzWPAgAEMGTKE+fPn07BhQ1asWEGfPn0u+KSFhYXMmTOHN998k5SUFHbu3Mm2bduYOHEiM2bM4B//+AemabJy5UoAnnnmGUaNGkVqaiqdOnVi2bJlgLcFJCEhgY0bN3LXXXcxZ84cAN58800iIyPZuHEjU6ZMYfLkyRdco4iIiJwVcOGmqKgoRo8ezZo1a/jyyy/ZuXMnq1ev5uGHHyY6OvqiTup2u/F4PBQWFuJyuXC5XNhsNoqKiujatSsASUlJpKam4nQ62bFjB/379/fbDrB582aGDh0KwJAhQ9iyZQtOp5PNmzdz++23A9C9e3dOnTrFsWPHLqpWERERCXIdhkstOjqaxx9/nIEDBxIZGUn37t2x2+3Exsb69omNjSUjI4Ps7Gyio6Ox2Wx+2wFOnDjhe4zNZiM6OppTp075bS99zPHjx2nZsmUVPksREZG6o1oub713717WrFnDxx9/zCeffILFYmHr1q3lTt0svS2rogtgmaaJxWI57zGl20VEROTiVMu76KeffkqPHj1o0qQJYWFhJCUl8a9//cs3aBEgKyuLuLg4YmJiyM3Nxe12A5CZmUlcXBwAcXFxZGVlAeByucjPz6dRo0Y0a9bM79oXpccSERGRixN0YMjLywPgm2++Yf369Tidzos+aYcOHdi2bRsFBQWYpklaWho33HAD4eHhfP7554B3FkViYiJ2u52EhAQ2bNgAwPr160lMTASgZ8+erF+/HoANGzaQkJCA3W6nZ8+epKSkALBz507Cw8PVHSEiIvITGKZpmoF2Wrx4MUeOHOG///u/GTFiBFdddRVt2rTxzUq4GC+//DJr167FbrfTuXNnZs6cycGDB5k2bRp5eXl07NiRuXPnEhYWRnp6OpMmTeLkyZO0aNGC5557joYNG5KTk8OkSZM4evQo9evXJzk5mdatW+NwOJgxYwa7d+8mLCyM2bNn07Fjx0rrSUpKYu3atRf9fEREROqyoAJDUlISb7/9Nn/7299IT09n6tSp3HnnnaxZs6YqaqwSCgwiIiIVC7pLIjIykm3btvGrX/0K8C6OJCIiIj8PQQWGxo0b8/TTT7N7925uvPFGkpOTNYhQRETkZySowPDss88SFxfHSy+9RGRkJIZh+JZzFhERkbovqIWbiouLGT58OOC9rsOoUaOwWq0hLUxERERqjqACw8iRIzlx4gTR0dEYhkFubi5Wq5XGjRuzePFirrvuulDXKSIiItUoqMBw44038stf/tLXyvCPf/yDrVu3cvfddzNz5kxWrVoV0iJFRESkegU1hmHv3r2+sADQv39/du/ezTXXXPOTFnASERGR2iGowOByufj2229933/77bd4PB4cDgculytkxYmIiEjNEFSXxIQJE7jvvvu4+uqr8Xg8HD58mOTkZJ5//nn69esX6hprnR37zrB6SyYZ2Q6aNQ5nRGIs3eMbVHdZIiIiFy2olR4BcnJy2LlzJ1arleuuu46GDRuSl5dHdHR0qGusEpdqpccd+84e3xeQAAAgAElEQVSwLCUdu80g3G7gcJo4XSbjhrVSaBARkVorqBaGwsJCPv74Y06fPo1pmhw6dAiABx98MJS11Uqrt2RitxlEhHl7eyLCDMDD6i2ZCgwiIlJrBRUYnnrqKdLT02nfvj2GYYS6plotI9tB/Uj/NSrC7QYZ2Y5qqkhEROSnCyow7Nu3jw0bNmCzBbX7z1qzxuFk5zpLWha8HE6TZo3Dq7EqERGRnyaoWRLNmzcPdR11xojEWJwuk6JiD6bpvXW6TEYkxlZ3aSIiIhctqCaD9u3bc//993PLLbcQERHh264xDOfrHt+AccPQLAkREalTggoM+fn5tG3bliNHjoS6HhEREamBgp5WWddpWqWIiEjFKm1hePzxx1m8eDFDhw4t9/6///3vISmqNtO0ShERqYsqDQwPP/wwANOnT6+SYuoCTasUEZG6qNLA0KlTJwBuuOEG0tPTfQs3ScU0rVJEROqioAY9Ll68mNdff50mTZr4thmGwUcffRSywmqrEYmxLEtJBzx+Yxg0rVJERGqzoAJDSkoKH3zwAc2aNQt1PbWeplWKiEhdFFRgaNGihcLCBege30ABQURE6pSgAkOPHj2YP38+ffv29Vu4qWPHjiErTERERGqOoAJD6foEqampvm0awyAiIvLzEVRgmDJlCv369Qt1LXXGjn1nNIZBRETqlKAuPrVw4cJQ11FnlK70mJ3rpH6klexcJ8tS0tmx70x1lyYiInLRgr741IsvvkhCQgL16tXzbdcYhvNppUcREamLggoMX3/9NV9//TWrVq3ybdMYhvJppUcREamLggoMaWlpoa6jztBKjyIiUhcFFRhOnTrFe++9R35+PqZp4vF4OHz4MH/6059CXV+to5UeRUSkLgoqMDzxxBNERETw/fffc+ONN7Jt2zauv/76UNdWK2mlRxERqYuCCgzHjh1j06ZNPP3009x999089thjjBs3LtS11Vpa6VFEROqaoKZVNm3aFIB27drx7bff0qxZM1wuV0gLExERkZojqBaGJk2a8Oqrr9K1a1deeOEFoqOjKSoqCnVtIiIiUkME1cIwa9YswsLCSEhIoFOnTjz//PNMmDAh1LWJiIhIDWGYpmkGs2NRURGHDx/m6quvxuFwEBkZGeraqlRSUpLvmhkiIiLiL6gWhq+++op+/foxZswYTpw4Qa9evfjiiy9CXZuIiIjUEEEFhvnz57N8+XIaNWpE8+bNmT9/PnPmzAl1bSIiIlJDBBUYioqKuOqqq3zf9+zZE7fbHbKiREREpGYJKjDYbDZOnz6NYXiXOz5w4EBIixIREZGaJahplY888gj33nsvWVlZ/OEPf2Dr1q3MmjUr1LWJiIhIDRFUYOjduzdXXHEFW7duxePx8Pvf/54rr7wy1LXVejv2namWJaKr67wiIlJ3BT2tsq671NMqd+w7w7KUdOw2w+8iVOOGtQrpm3d1nVdEROq2oMYwyIVbvSUTu80gIsyCYXhv7TaD1Vsy6+R5RUSkblNgCJGMbAfhdsNvW7jdICPbUSfPKyIidVtQgSEtLQ31XFyYZo3DcTj9XzOH06RZ4/A6eV4REanbggoMb775Jn379mXZsmVkZqppOxgjEmNxukyKij2YpvfW6TIZkRhbJ88rIiJ1W1CB4Y033mD58uUUFBTw61//mscff5zt27eHurZarXt8A8YNa0Xj+nZyC900rm+vkoGH1XVeERGp2y5oloTb7WbTpk3Mnz+fgoICmjZtypw5c+jSpUsoa6wSuviUiIhIxYJah+Hw4cOsXLmSlJQU4uPjmTJlCr179+brr7/miSeeIC0tLdR1ioiISDUKKjDcdddd3HHHHbz11lu0a9fOt71bt27ccMMNoapNREREaoiguiQKCwuJjIysinqqTSgWblq9JZMjJ4pwukxsVpO2zepp1UUREamVKm1hGDp0aKUP/vvf/35Ji6krSldbdHk85Ba4wADDCceyiliWks64YSg0iIhIrVJpYJg+fToAxcXFhIWFVUlBdUHpaounT3uwWAwsBng8JvlFHpo2tLJ6S6YCg4iI1CqVBobS8QnDhg0jJSWlSgqqCzKyHdSPtOJ0m1hLJq4aBjjdplZdFBGRWimodRgiIyM5fvx4qGupM0pXW7RbDTwlI0RME+xWQ6suiohIrRTULInCwkL69u1L8+bNqVevnm+7xjCUb0RiLMtS0omKtJCd68JjgAFERVi06qKIiNRKQQWGqVOnXvITp6WlsWTJEgoLC7npppuYNm0a27ZtY+7cuTgcDgYOHMiTTz4JwJ49e5g6dSr5+fkkJCTwzDPPYLPZOHbsGBMnTuTkyZNcfvnlJCcnExUVxZkzZ5gwYQJHjx4lJiaGRYsWERtbdW/S3tUWvWMZXO6zsyRaNo3QLAkREamVgl7pMScnh8LCQkzTxO12c+TIEW666aaLOunRo0cZNWoUq1atokmTJjzwwAOMGTOGmTNn8uabb9KiRQvGjBnD/fffT8+ePRkyZAizZ8+ma9euTJkyhU6dOjFq1CjGjBnD7bffzuDBg1m6dCkFBQVMnDiRWbNm0bx5c0aPHs369evZvHkzixYtqrSmUK70WDrFMiPbQbPG4ZWGhrc/Os66T7MoLHYTGWbljpubck/f5tVeV3UJtsba8FxERGqzoMYwLF68mJtuuol+/foxYMAAbrvtNubNm3fRJ/3www8ZNGgQzZs3x263s3DhQiIjI2nbti1t2rTBZrMxdOhQUlNTSU9Pp6ioiK5duwLeN/bU1FScTic7duygf//+ftsBNm/e7JsSOmTIELZs2YLT6bzoen+K0imW2blO6kdayc51siwlnR37zpy379sfHefdtAyKnG5sFihyunk3LYO3P7r040cupK7qEmyNteG5iIjUdkEFhpSUFD7++GP69+/PBx98wNy5c7nqqqsu+qSHDx/G7XYzduxYhg0bxjvvvMOJEyf8ug3i4uLIyMg4b3tsbCwZGRlkZ2cTHR2NzWbz2w74PcZmsxEdHc2pU6cuut6fonSKZUSYBcPw3tptBqu3nH/Vz3WfZoEBNouBYRjYLN7BD+s+zarWuqpLsDXWhuciIlLbBRUYYmJiiIuL44orrmDv3r0MHz6cb7/99qJP6na72b59O//v//0//va3v7Fr1y6OHj2KYRi+fUzTxDAMPB5PudtLb8s69/uyj7FYgnqql1xGtoNwu39dFU2tLCx2Yz3nKVgN7/bqrKu6BFtjbXguIiK1XVDvojabjSNHjnDFFVewc+dOXC4XDsfF/zFu2rQpPXr0ICYmhoiICPr168e2bdvIzDz7iTAzM5O4uDiaN2/utz0rK4u4uDhiYmLIzc3F7Xb77Q/e1omsLO+ncpfLRX5+Po0aNbroen+K0imWZVU0tTIyzIr7nBElbtO7vTrrqi7B1lgbnouISG0XVGAYM2YM06dPp1evXnz44Yf06tWLX/3qVxd90t69e/Ppp59y5swZ3G43n3zyCQMGDODgwYO+7or333+fxMREWrVqRXh4OJ9//jng7R5JTEzEbreTkJDAhg0bAFi/fj2JiYkA9OzZk/Xr1wOwYcMGEhISsNvtF13vTzEiMRany6So2INpem8rmlp5x81NwQSXx8Q0TVweE8yS7dVYV3UJtsba8FxERGq7oGdJlCosLOTw4cPEx8dX2AUQjNWrV7N8+XKcTqdvWuW//vUv37TKnj17MnnyZAzDYO/evUybNo28vDw6duzI3LlzCQsLIz09nUmTJnHy5ElatGjBc889R8OGDcnJyWHSpEkcPXqU+vXrk5ycTOvWrSutR7MkaubMAs2SEBGpGYIKDPn5+SQnJ3PgwAEWL17Mc889xx//+EeioqKqosYqUVMCg4iISE0UVJfE7NmzadCgASdPniQ8PJy8vDxmzJgR6trqBE35ExGRuiCowLBnzx6efPJJbDYbkZGRJCcns2fPnlDXVidoyp+IiNQFQS0Nfe6URLfbXW3TFGub0itXluVye9hzJJ/fzv8mYBeFujNERKQmCOpdv3v37ixYsICioiI++eQTHnvsMX75y1+GurY64dwpf3mFLk7kOLFAwC4KdWeIiEhNEVRgmDBhAvXq1aN+/fosXLiQ+Ph4nnrqqVDXViecO+XvVK4LgCYN7QG7KNSdISIiNUVQXRKffPIJ48aN4/e//32o66lzyl65MiPbgceEuEZ2oiLOdlNUtCphed0ZNXkFQ3WfiIjUXUG1MLz55pv07duXF1980beCogSve3wDnn34SpY/dQ2/uCwKm9X/Za9oVcLatIKhuk9EROq2oALDG2+8wfLly8nPz2fEiBGMHz+e7du3h7q2OulCViWsTSsYqvtERKRuC3qqw2WXXcaTTz7J5MmT+c9//sMf/vAHhg4dyq5du0JZX53j7aJoReP6dnIL3TSub2fcsFblNt1fyL7VTReAEhGp24Iaw3D48GFWrlxJSkoK8fHxTJkyhd69e/P111/zxBNPkJaWFuo665Tu8Q2CftO/kH2rU7PG4WTnOokIOxsaamr3iYiIXLigWhjuuusuXC4Xb731Fq+99hp9+/bFYrHQrVs3brjhhlDXKLVAbeo+ERGRCxfUtSQKCwuJjIysinqqTSivJfFzoVkSIiJ1V1BdEnU9LFxqpW+cR04U4XSZ2KwmbZvVq/NvoLWl+0RERC6c1ne+xEqnFx47WURugYsip5v8Ig/Hsoo0zVBERGotBYZLrHR6YX6hB4vFwGYxMID8Io+mGYqISK0VVJdEYWEhqampnD59mrJDHh588MGQFVZbla7O6HSblK7PZBjgdJuaZvgzpHEdIlJXBBUYnnrqKdLT02nfvj2GYQR+wM9Y6fRCu9XA5TGxGGCaYLcammb4M1PaPWW3GX6rX44bhkKDiNQ6QQWGffv2sWHDBmy2oHb/WRuRGMuylHSiIi1k57rwGGAAUREWTTP8mSm7+iVQskaFh9VbMhUYRKTWCWoMQ/PmzUNdR51RujpjyyYR1K9nI8JuJSrCQsumETV2lUYJDa1+KSJ1SVBNBu3bt+f+++/nlltuISIiwrddYxjKVzq9sGz/dUXUx113afVLEalLgmphyM/Pp23bthw5coRvv/3W959ULJirN+oKj3WbVr8UkbokqBaGuXPnhrqOOieY/mv1cddt3u4p1IIkInVCUIHhyy+/5OWXX6agoADTNPF4PPzwww9s3rw5xOXVXqXTK8s6t/860D7qrqj9tPqliNQVQXVJTJs2jW7dupGXl8fQoUOJjo7mtttuC3VttVqzxuE4nP6X6Ti3/7qyfdRdISIiNUlQgcEwDEaPHs0NN9zAFVdcwaJFi9i6dWuoa6vVgum/rmyfst0VhuG91UqRIiJSXYIKDFFRUQBcdtllfPfdd0RERGCxaFXpypROr2xc305uoZvG9e3nTausbB9NyRMRkZokqDEMnTt35oknnuDxxx9nzJgxHDp0SIs4VaLs2AOH00NOnpuMbCe7DuQRZoMOl0XT5Yoodh3I97uiZVnNGodzLKuI/CIPTreJ3Wr41nOoLhpTISLy82WYZS8OUQHTNPn666/p2rUrmzdvZtu2bdx9991cccUVVVFjlUhKSmLt2rU/+ThllwPOznWSV+Q5bx+7FTwm1IuwUFDkgZLVIBtF2bBZLYwb1opvfyjg3bQMMMBqgNsETBjZpxn39K36hbTKPq9wu3eZa6fL1GJUIiI/E5X2K+zfvx+Ab775Brvdzn/+8x9iY2MZNmwYhYWFVVJgbVN27EF5YQHA6QYMKr2i5a4D+TSubyPMasFjGoRZLTSub2PXgfwqfT6lNKZCROTnrdJ+hWeffZaXX36Zxx577Lz7DMPgo48+CllhtVV5UyXLYzWg2ITSYQrlXdGyUZSNxtFnxzGYplltYxiCmSYqIiJ1V6WB4eWXXwYgLS2tSoqpC8pbDrg8bhMshrdroqIrWtakZYW1zLGIyM9bpYFhyZIllT740UcfvaTF1AWlV6sED9ER5XdLlI5hiIr0jmGo6IqWpccpO2agupYVLvu8akI9IiJStSoNDNnZ2QAcOHCAgwcP0q9fP2w2Gx999BHx8fFVUmBtU3Y54GKXB5vNICfP7bu/slkSLZtG+M08qEnLCmuZY6kJNFNHpPoENUvi/vvvZ9GiRcTExABw+vRpxo0bx9tvvx3yAqvKpZolISKhoZk6ItUrqNWXMjMzfWEBoEGDBpw8eTJkRYmInEszdUSqV1CrL8XHxzN58mSGDRuGaZqsXr2aa6+9NtS1iYj4aKaOSPUKKjDMnj2b559/njlz5gCQmJhY7lRLEflpaloffU2qRzN1RKpXUGMYAIqKijh06BDt27fH4XAQGRkZ6tqqlMYwSHWraX30qkdEygpqDMNXX31Fv379GDt2LCdOnKBXr1588cUXoa5N5GelpvXR17R6grmgm4iETlBdEvPnz2f58uVMmDCB5s2bM3/+fObMmcOaNWtCXV+tVbYpt164FdM0KSz2VNisW5OafqV61LQ++ppWD3hDg/5diFSPoFoYioqKuOqqq3zf9+zZE7fbXckjft5Km06zc51YDDhyooijmQ4shnf1xmUp6ezYd6bc/etHWsvdR+q+Zo3DcTj9ewire3XPmlSPiFSvoAKDzWbj9OnTGIZ3sNGBAwdCWlRtV7YpNyfPjcViYDEgJ89dbrNuTWv6leoxIjEWp8ukqNiDaXpvq3t1z5pUj4hUr6C6JB555BHuvfdesrKy+MMf/sDWrVuZNWtWqGurtTKyva0JP2Q6KSz2YAAm4HR72H/Me5XP49nFvhaEPUfyMU0Tu9VCo2hvE3BOnpv0kw7++Mr+C+6eKK97A2reKo3qhvFX01bTrKp69HsgUjsEPUvi8OHDbN26FY/HQ48ePbjyyitDXVuVupSzJB5ZtJejmQ5M03vNiPLYLFC/ns07tsHhwW2aGHj3N00wLGC3GMQ2CrugkeDljSTPK3Rjmib169lqzOhyjXgX0O+BSG1SaZdETk6O77+GDRsyaNAghgwZQpMmTcjJyamqGmsdwzDweCoOCwAWi0FBkZvCYg9NGtoBAwwDd8njDAxiGtgvuHuivO6N0vPUpC4PdcMI6PdApDaptEviV7/6lW/cAoBpmhiG4bvds2dPyAusjQocbuw2g2JX+YnBZiltSTAxDIiKsBLXCLJzXTjxPiaukZ2oCG/3xIWMTC9vZLvL4z1PWdU92r0mjsCXqqffA5Hao9LAMHz4cL788kv69OnDnXfe6TdTQirWrHE4WaedWC3gPv/q1hgG2KzeVghK3sijIqxERVg5klEEJSGi1IWMTC9vNTybxfCd52KOGQpatU9AvwcitUmlXRLz5s1j/fr1dOjQgTlz5vCb3/yGt99+mzNnNN2vMqWDDMsLC+AdABkVYaFehJXIMIvfKPTytl3IyPTyRrb/1GOGgkbgC+j3QKQ2CXrQI8Dx48dJSUlh48aNtGvXjkWLFoWytip1qZeG/u38b8jIdgL4ZkmAt3WhQT0LbZvVq3D2QnnbNEtC6ir9HojUDkFNqyx16tQpTp06RXZ2Nk2aNAlVTXVGixg7OXlunG4Tu9WgUbQVjwnLn7rGb7/y/jj+lD+YFa2GV9P+CGvVPgH9HojUFgEDw48//sh7771HSkoKVquV22+/nZUrV9KsWbOqqK/KeEw4nefEajWwWgwsFu+t1YLfwM9glfbNto49+xIXFXtoXN9+KcsWERGpEpUGhvvuu4+DBw8yaNAgkpOTueaaayrbvXYzIbfw7KADw/c/sBpgtXpXazQMA4sFLIb3e0vJdqvFwGY1fOHirp6xLFufThEev/nl6psVEZHaqNIxDB06dCA8PByLxVLu9Mq6dMXK4cOTWPLKuxf9+NKXx2qBb38o4J9f55B5xonbbWLgoXlMBAN/2YTrrqrv91qq/1ZERGqDSlsYPvroo6qqo9YrjV1ffp/HOx9lYLNCmN3A6TIxTYPuHRrQvHE4x04W+1osvk0vIOXTLKwWaNM0DIfTw1sfHsdiwPXtFRpERKTmqDQwtGrVqqrqqDP+seMUNiuE270zVsNsBg6nh43/OkXHttEAuEzvYkqrNmdyOt9JZJjFd4Eqp9tk9T8zaRETjtXq7fooHUtROr7CajF8X4uIiFSFC5olIYFlnSkmKuLs8hYFDjdnCtxk5DhJXnmE/t1j6Hx5tN++HhM8bm8ThYnJ0awi7zaXd8vZSZlepeMrSlsqfIHCYpSMqfCOp7jYAZsiIiLnUmAoYRL0chSVatogjBPZDgpKFqApez2JA8cLeCO1iAcHtKTz5dE0bRDG6Xwn4fazb+rFTpOmDcIC1Or9X2lLRUWBwgCsVnytEqWDMw2LgdXAe1uy3aLWChERqYQCQ4mDx4t48sXvqBduITLcSr1wC/VKbiPDrdSLOPt9vXArkREWosKtvn3tNu8Mifg2kXyXXgCG/8WnDMD0QH6RhzVbMuh8eTT9u8fwzkcZgIcwu0Gx08Tlhv7dY37ScykNFCbeVgpnOa0UpTWVzgTxzgDBFx6MknBhKZ0VYjGwUHKrkCEi8rOjwFDCNCGv0E1eoRtwXvDjbVaDyHDv8suGcf6VKk2gdNLmj9lO9h8rpGlDO8NuasqWXTmcynXStEGYX5dFqJm+/4HbPO8eggkZ1pIwUdpSYRjesGEY3v18008tBtaS8KFuEhGR2ueCloa+1J599lmys7OZN28ee/bsYerUqeTn55OQkMAzzzyDzWbj2LFjTJw4kZMnT3L55ZeTnJxMVFQUZ86cYcKECRw9epSYmBgWLVpEbGwsxcXFTJ06ld27dxMREUFycjJXXnllwFpuGziMR6a+SqHDQ4HDTUGRm4KSrwsdHvJLLhFdut3lvrQvm4F3VoVpgsc0CbdZiAizkJPvwu3xBpJr2tajZZNwdh/M50yhi8gwCwYmeUUe3G6wWk1aNYn8SaHj3wfz+MeOU2SdKb6kAaZsN4nFAKPMWhZ+a1uUBAp1mUh10nRnkfNVW2DYvn07Tz75JL169WLevHkMGTKE2bNn07VrV6ZMmUKnTp0YNWoUY8aM4fbbb2fw4MEsXbqUgoICJk6cyKxZs2jevDmjR49m/fr1bN68mUWLFvHaa69x+PBhZs2axY4dO1iwYAErV64MWM+FrsPgdHnIL/JQWOymoMhDocPN3qMFfPj5KUyzvM/mVaP0zbV103DiGodV2LUSGV7SpRJhITLMis1q8O+D/lNCS7tIRvVtVmWtHmWV12VitZxtwbCW7S4p6TKhZJu1pKVDQUMu1I59Z1iWko7dZvgtujZuWCuFBvlZq5YuiZycHBYuXMjYsWPZu3cv6enpFBUV0bVrV8B7Iajnn3+eu+66ix07drB06VLf9nvvvZeJEyeyefNm3n77bQCGDBnCrFmzcDqdbN68mccffxyA7t27c+rUKY4dO0bLli0v6XOw2yw0irbQqMxLmLrjFA2jrGTnuYM6hncWg3dsg9ssfROE6EgrOflnj2GUXL0qmBBSOuPiUEYRhzKKgn4+4XbDe3VN0yxZ1dL7yd4E3t50nD7dGp8d2xFh9RvPERFuCckUz/K6TPxbdiroMinzRdmxGUaZ7hHfeA3DgJLWDcAXOkpDiUGZbhZKtqlLpU5bvSUTu80gIsw728l76W0Pq7dkKjDIz1q1BIYZM2bw5JNP8uOPPwJw4sQJYmPPLpkcGxtLRkYG2dnZREdHY7PZ/Laf+xibzUZ0dDSnTp0q91jHjx+/5IGhPFlniqlfz8rpfPd5YxjKYymZfWlYwO0Gm8X7ht8wyuYXGGxW763T5b29LDaMH7KKsVjAVbKb1QKY3nESDepZcTg9XH91AwqLva0fBQ5vd0pp18q57UoO59kN5868KHB4WLUls9LnEhFmOW/AaGRp60bEOa0cJaGj9P7SdSguBfOcL4Idm3Guc4PHufdZSsZolA0V4B9MODdw+LYZvpVBS0MIGL5wYxiGXxeOQkrVysh2UD/S6rct3G6Qke2opopEaoYqDwyrVq2iRYsW9OjRw3c5aY/HU+7S06W3ZVX0h9M0TSwWy3mPKd1eFUqnSdptht8bcHkMvAMtDaPkFu+bm91qnL2/oscaBnabgbtMKrFYvC0VYVaDyDALzRuH89v+Lcp9vGmaOJweX4gocHhDxeotmeQXubBYDDwe71gKl9vEajFo0sDu26/Q4TmvtqJiD0XFHsh1BfVanSsyzEK9iPMDR1SEtdJZK5Hh3rEelkv8hnpu8Dj3vvMDYeUtH4FUFlBKN5eGk9KgYikTUsp9nOn/ZeljLaXdNWVbUkq3nVOM3zn5eYSZ0gvHeVsWvBxOk2aNw6uxKpHqV+WBYcOGDWRmZjJs2DBOnz5NQUEBhmGQmXn2E2xWVhZxcXHExMSQm5uL2+3GarWSmZlJXFwcAHFxcWRlZdG8eXNcLhf5+fk0atSIZs2aceLECS677DK/Y1WF0mmS9cItOJyVd0vUizAodJi+P7yl39cLs2BiUi/CQn5RybwKs6TLAgi3g8PpoUE9KyfPuHzBwu0pOU6YJeDUTMMwiAizEhFmJabM1TMtFiOoMQwe06TI4fEOAnWcHcORXxImzgYRNxnZxRw54QBM74BOT/lvp4XF3uPBhQcOA7ytFRHWils5wi1ERvgHj9L9w+2Wan/Tqyyg+DZXGlIu6mwBGRV+U/7msi9jMI8tLaXCcFzyP78ZOCXbjLKBpuQYpu945XVXGb4ay46PKdvKA5B0c1PWbMnEWrq8u9Mk3GYw4pZYChzus48J6klW9lpXHMjKBjmRmqLKA8Mbb7zh+3rt2rV89tlnzJ07lyFDhvD5559z/fXXk5KSQmJiIna7nYSEBDZs2MDQoUNZv349iYmJAPTs2ZP169czduxYNmzYQEJCAna7nZ49e5KSkkJCQgI7d+4kPDy8SrojADpfHs2ovt7loV0eB3kF7vP+XMREW7m5cyP2HXfAklAAACAASURBVC3k2CkHLpfpm90Q3yaSfUcLyTpTTOumETSKtrDrQAFFTg8Rdgu3Xt+Yds0jfbMYWjYJwzRNzhR6fMeJaxx+0TMbytZf2SwJi2F432wjrDSh8st1J688QtMGNt9S2QBFxW7q17Pzu4EtzmvlKP3eF0ZKv3ec/b6o2ON3DhNKZrR4uBiGQfkBI9xa0sLhHzqizulaCbMZdfYPe7k9Ohexz6Vx7sEv9GTB7d+qaQS9uzXmHztO8UOmg6YNwritewwtmoRz6szFtaBVJFDeKK9l6ez28x9QbmDz22YEtV955Zx/usrPX+Fe5TSfmhXtV3JbXt2BlLdrEE+h3I3nnjeYh/nKN4zz7w7mdSrnvGWPVx2qdVplaWCYN28ee/fuZdq0aeTl5dGxY0fmzp1LWFgY6enpTJo0iZMnT9KiRQuee+45GjZsSE5ODpMmTeLo0aPUr1+f5ORkWrdujcPhYMaMGezevZuwsDBmz55Nx44dA9Zyxx1JvPm2/2wKX493JR//TJOSqZDeTzWl3//ncB6rNmdisxnYrN4VHJ0ukzsT47imbdR5Jyk98u5DeXyw8xSZp4uJtHu7WApLVn+syjUaLpVJr35PVITF9w8evK9TfpGHef911UUd0+0xKSwbMMpMg/XfVjKLxeGhsMhT0gLiDthddKGsFnwB4tzuk3rnBI7SRb/KhpLaHDhCNQ23qo4vNdu/D+bxwY5TZOWW/PwTYuhUxT//i/qXGcJ/zpUFuXPDX9mA6f3e/9E2q0Hj+pV/6PM7V3UGhpokKSnJN6biUpix/ACn81zekdYlnxAcTg+Nom1Mu/dy7wBF0/Q1o5om/PtgLivSTpSMT/CQnevGMCCmvg2r1YLLZXJnYizXtI3yCzG+aZzm2WZZs0wS8WuqLT2npzTklGGW++VPkrzySMny12dbGBxODw2j7Ez49WWX6CwXxuU2KSx2lwkR/q0bBcXntHoUeSgoM3222HXpA4d3cGg5oSOi/JaPsgNJ7baqGaNzrlBPw61p03ylaunnH3rhdoPYRpVfiqAsrfQYIkdOFFE/0lrSL+9lmiZ7juQz5bUD5S4Is3pLFrmFbiLCLPyQ6fDOVjBNTp5x0To2nP/f3plHR1Gm+/9bVd3p7CRAAogwXryojAsygAxOCANIICQBQRhQBuQyB7c51wUHAXWc4QB6QY44DModr3dk5oKDgIQIF7dxAVl+glxHRIFBICwBs0jWTrq7luf3R6Uq3Z3udHfSSxqezzkautJV9b71dqqfepbv43Bp2La3CsMGdGn3uA6frMM7+35AZa1+/sLh3XB7vzTTePE0MKjlNQzDhpqNnWbjgwCVWjwr5GYEgYCiO7th6+5KiALBahXgdOlJlPl3dEz+uiNYJAFpSRakJbVvf1kx8jc0NHkJfLmHUby3GcaH7CX6pWpAfZOK+qbgynF9zcdXZUqSzS180kp/o8UosUjtexzy7syq90TR8P6hy2G5oUf6+Eznhte/88EGQ4RIShBxvtIJVdMrHzJSJcgKoclJqK6XkZYkobpexqslZXhkEjD0xnSPci5ZJUii/kVtfMF0tLTLW5CmrMqJtcVlERWk6dnNhsxUK3YcqERVrQu9utkw4Q7dragZXhZNNzQ0M7xDbqEeMnUqAAStRxFJrBb9qT49uX37y4qmq4e6JYw2ehsWzT/t3r93qLpehhuKSqhr1LuitocEixBcOaxXP5WKGidSkzy9GwlWAVV1rvZdGC+8O7+G+/hM54bXv/PBBkMEOHSiDrV2FYpKEAT9hl5Ro9/lM9MsfgVh3Mu5rJLQ7GFoKbXsaGlXrARpftI/DT/pn9ahYxARVE3PX9A0gkoETYX+b003NlSVOpVh4Q/T4EgJ/c+PiOBSWnI4fBkdTW6hFTOk4maEeBscLoXgUhTU2kOfS3WD6imCBf2m/vqui371N9yNkqQ2RL/a282VuTLg9e98sMEQAbbuqURqkn4zrK5XIKsEi9iis3Ch0glZJdPzYHgNpuZm4dWSMgAaMlIlVNToTbAyUiU4mttlT83NauPMbRPPgjSCoCePBnKfG4aFZhoWLUaFqjYbF53MYxEKgqB7h2xWERmp7Tc42kwSdXhVqLhaQiyNztaiX9Rc9mvUBMkq4fPjdUGPyWYVkeJDgyPRJuJchQqLpMJqMbRBgJ/fnorKWlfYRb+YzkWkuvky7YcNhghgfDELgi48BOg36tPfO1BRIzc/jekeg0uXNUgisPC/TmFqbhYemdTbbHrTNztRr5JwachMs3a4Ac7VIEhjGBYIYFhohldCI5BG0IxtzYJVqkpQNIKqeiaUxjvuBkdmO5w+3qJfR0vt+H/f1qLWriA5UUK/XklITZJa5264lcd6X0enrMEpBy/6temTCmz6pMJ8nZQgBtTbaPl95EW/mPAQbJk3Ez3YYIgAPTJtOHOpEXaHBo104yDBAlOASRMAgVraXYsCcOZSI373lzMA9Iz5yTndMXNMT5/Hb28nPXcPhntTnY54LeIVo1lVsB4L91AINb82vRVxEgoJB96iX9dmJWL80G5B768RwdmcMNoYIIfDU3/D+F1rrQ1D9OtyO1RGDdGvVjkcXkZHZxb9upK59V9S2UDoRLDBEAG6pUs4crrlxqYR4NCjC7CIgOKmdmiVBMgqQW5qeb9DVvG3j/WeGd5Gg3vioigAJ87b8fu/NuBH2Yn4t/G92jQcht6Yjkcmgdv2hkCwoRD3XApvw0IzftKV57EIFVHQEyyTbIFFv3yhaQSHm6iX3aH60ORw199QYXf7fVuiXz+0Yz6hin55v9dmjV8NDubqgw2GCHDweAMkEWbWvzuCKMAm6aEAoxmR+TtBf79F1BMei/dWtTIYjMRFRdVQWSubjZAuVjk9Ki78MfTGdDYQIoDRYjsYWnIpvHIszDyLll4VrJLiiSi2qIy2B1XTZc1bZMz1n3avXA33bU2OFi+Ht+gXEWB3aC0y7iHSluiXdzmsr8qVeBb9YuIPNhgigN2hml/+3sgKwWrR/8DJy6Aw/m3clOyqionPfgXvthTu30vGsWSFUN0gY8lfzyAxQYJFIvyoR3JYPQjBhEJCCZe0N7QSLmJ1fkkSIAXwWLSEPNwqQFSC3PxT9TIormZFxFDmLokCUpIkpCS13+AwwyR+EkfdQytG+axhdHiLfqka0NCkoqFJBSCHPB5D9Ku1/oZnaMWXGFgsRb+Y+ISVHpsJl9LjoRN1+N36M226m40vfAKQmiSivrF9TydAc0ts6E2dJFG/AUmS7rjISLHAIolh0Vnw1nAw8h/cjx3Me0I5XiSJ9fk7SotXAvj6dAO27K6AIOqfgSan7rafMsJbFfTKI97UABWVPEtf3XI0jMZtuv6GEWppkTS3OzQoangX05/ol2/9DeM9Le9ngyO+YaXHGLN1TyXSkqU2RXQIQI+MBNw1OBPFe6s6dD61WVHRCIEI0EMamqb3a+jeRQqLzkIwGg6h6DzEShOis5y/oxghECuArZ9VosauILlZ08AiAbYEAfu/qcXPB2ZAbRbG0rUr9C9UM/zR/PmJV5si3tQALZKA9GRLRES/PMIoXt6OSIl+WSXBI0E0kNHhnefRXpVRJjawwRBmyqudSEkU0ehs/TRghCmIYD7Jfnj4MuyO9v2xAu49IvT/DJEnQdDr4durs+Dtrj9b3oisLp6WqPexQ9F5iLUmRKzPHwjj+p+rcEBWqM0QkzEXjQBNF0QAEeHM9026SFQb5zFDHypB8RLDMjwYWic2Kq42NcCOin7JCnl4M/zqbXhpchhhGG+DQ1YJtfb2iX4B+t9caO3ogxP9YiIDGwxhxpCE9vU5TrDoyYyJVsm86ffItKGiWm7XzVgS9VAEQb+pSyLMJErDeGiPzoK7u96QsG5yEmoaFI/OZt7HDkXnIdaaELE+f1sY11/RNNQ3KnrzMhm4WOXwmdjakblIoqDfdNu4ExjeCE0zjArdkFDUFqMCiE2CJqsBBo8gCEiwCkiwishoh/OlRWVUD480eiWO+iuTdS+P1bw+I06Z4JQV1DS0b042q1uIJFF0MzACa3Kw6FfosMEQZsyMZUGAJJKHRW5IPU/O6Q5A/2Kob1Ta/eQmCAJ6ddO/wMurXRAFPRShNXfHTEkU26Wz4Mtdn54ioc6uIqm5FMyXhkMoOg+x1oSI9fnbwrj+tbWaHnoQ0GaIKdJzMY0KP7h7JMwETa21+FUkDApWA/RNJJJgPVVGQ99fF/2iVgmjdrcEUffEUe9mbm2JflU3hK7BAYQq+uWZPJpou/pEv9hgCDONThXZGVbUNKiQCbBIMEMTidYWQSb3p0hRQCvLOxiyM6ymkmR2hhVVdQrSkiymC/ua7ontyvz35a7PSLFAUYHMNKvfqoJQdB5irQkR6/O3hXH9jQZkQNshpljPxT2fwheG+JWikqmiaRgTitoxDwWrAbbGPRE0JVFErV3Gmx+V474xiOl10UW/9AeRrmnt0OBoJfrVOrTiLfplJIzGQvTLu0zWe3tiQvyJfrHBEGYM9/C1WS2X1tEs7bxi3vXmtrXF51FZ29q7IAgt0gyGESGKLbkJmkZQND28keJWi26RRAzom4KpuVnmF0eweOcrJCWIqGlQYG9uxWyVBKQkishICVyKForOQ6w1IUI9f7TKMI3PkNGATBQCh5hifS3bIpD4FRGZ4Q33ElKl2WuhGCk+fvIoWA3Qk3hLBA2WcIh+mQmh3saFq0UErEV/wzO0Ek3Rr9b6G51D9IsNhjATjHt440ffo6LWt0VLPm6KmgY4NYJV0o2I5AQRVosAh8vzHLf1S2mVexBIzMlXvkJ1gwJ7kwpR0lsyuFSCs1630FUNQR/7SsLXdYrU/I3PUEqS3rysoyGmzo4gCLBagvdQaGao48rs99FRrrZE0GARRf0hK6Wdol+GwREobGJWrLjpbzQ6m/uluNFR0S9RgE+vRooPoyPFzPHwFP3yUA4MAjYYwkww7uFgSyklUV9OpfnzpBLQN8uGufnXAGh9Du/cA0VVUd2gYOmGUtP7EEx5oyxrsEgCLM2y1QmSABmanrCZIKKhSUFNgwqXomHFpnNYOKPvFW80RLMM0/0zpKiODoeY4p1gPBT+DArDQ3E1GRScCBoZPAyOLqHvr2rNFSpe5a4ejdocnjkd7uWz3qJfGgENDhUNDhWobZ/o1xsLBoS0DxsMEcCfe/jQiTq88d6loMooJbHlBik150HcfF2qR1jD+xyvlFwwcw8amhSUV8umx+LEeTte2tKEwuHdceS0vc1ySUXTZauvzUo0t52+1ARVIzQ0KaYktSQCDpfariftWKs8hkooZZjhmFusQgzRWJdwnyOYfh8tFR1GlYe7UdG5S0dDhRNBOyeSKCAtyYK0pPbtr6hkJol6ezM8cjjcpMzdxcB8qYyGChsMUeLQiTqs3noedY2hJ9dozfHrQHkJ7uV1VbWyRyKlphFq7So2fVKOXl1tbZZLWkShlafK2FbToOr9K0QBGum5FFaLENKTdjTd++Ei2NLFeJybQTTGHqvrY3jM/OGeM6F5hDtaDIp4UczkRNArE4skIC3ZgrQOin65h1BCHkP7Ts2EytY9lWh0qM11v+TTuhMFICPVgsv1ClRNr7AwblSpKVLAunr3/AnF7bNglQSIIoBmlb9A5ZLJiRKIyCNHwthW36R6NNbKTLOELHgUjyqLwZYuxuPcDKIx9s56fQL191BU8qzy6OQ5FJwIemURjjLZjoh+GbDBECXKq51QNIJFEiBKrY0GiwRkZyQgpfmLubpBhdJcoZCaIkFRCPWNCuas/LaVG9fdxZts0/d3b58tNuc/+bqZ+SqXnFfQOkfC2LZi0zk4XCoSLAIy0yxISZTgcGnokWkL2tXc2VUWfRFs6WI8zs0gGmOP1+vTlofCbw7FFRryYKJLZyqTZYMhSvTItKGmXoFGuifBIgkQhZYSyb7ZLfkCqUkWZKRakZZsQXm1E0kJImrtugHh7cYF4OHiNZ58e2RYUVUnm6EFIzxh8UoQdsqEvtmJHrkRBr6+7BfO6OvRtMnh0kKu0OjMKottEUxeQbzODYjO2OP5+vgjmBwK1S2HQm2W4FY18lTLZIOC8UFnKpPlVmNRYmpuFpITJVMVz2hbnJwgIilBhMOlmWEAWSH82/heWDHveqx/6sdIT7EiNUkyhT4Sm8sqt+6p9HDxuv8uOVFCerIFIvQnHxFAik1EWpKl1blCKdPTn7R7IzPNivomFZlpVjwyqTeOnLb7HMfWPZU+r4WskDmO6noZ5dUunKtwYOF/ncKhE3Xhu/BRxntu7bnGsSIaY4/n69MRJEmXZE6ySUhNtCA9xYLMNCuyMhLQs6sNvbomICvTiu7pFmSmWZCeLCElUYTNKsAieuqzMFcXVXUuJFg9Vz9WZbLsYYgSQ29MxxNT++CN9y7hQpXufu3TPcFviaT7k2wgN66v39U3qXhiap9Wxw10rmDn0laFhq8xeu9vuPfPljeiyUlIT5GQkWKJqyRBX8RadbEjRGPs8Xx9IokoCkhoo6dHS3iDWjwTplqm/p54ScpkQqMzlckKRPwxA4ApU6Zg27ZtET2He4wfAGoaFMgqISlBl4y+4dpknx0K6+wyGprUVsqL13TXwxi6i7fFWeRLWTLc4/c2QI6ds0MUgK5pFqQmWYIex8L/OhW18TPMlQgRQVbdO47qr1W1RYOC7/Lxi3sOg3uZ7H1jenQ4JGGzCsjKCN7wYA9DlHAvJ3PJGqob9McCSQQcsoo3PypHYoIeSvDuUOiQqZXyoqtBw/g7UnDDtclRaaLkqxxu9dbzICKkJVvQPd2CihoZFTW6gIhFCk6VMF6T4BimsyAIgq7a5+Nu7p6Q6e6lMDwUKidjdno6U5ksGwxRwj3XoKyqpeZRIyBBEuBUCU0uAlHrDoVac3WFu/JiSpKII6ftmDmmZ1RcvL7K4SqqXYAAZGWIAPTchR9qZVTVKX6VJb25EpPgGKazECgh0yPUYVR4uCVkcnVH56CzlMmywRAhvN337oqKGukJTIar0Cnrf45EMDsUGta/3Fx7KQrAdT1bKimIyHwKN3IKjHO+UnIh7IaDL0+AoQhpkNKsZ17fpAYdTujMbaYZ5konUOty79wJI29CM8tIwQbFVQQbDBHAl/veXVGxrXbWAgiyqjecckcjXe7ZyA/wfgqPtIKeL0+AL0XIUL0DnATHMJ2XgAaFu0Im609c8bDBEAF8ue/dFRW7pEhmDoM7QvP/NTdrwiK2eCIu1ytISZR8PoVHWkHPlyfAlyJke7wDnbk1M8Mw/glFIdNfl1FOyIwf2GCIAL7c9+6Kii5FgyiqphfBkIS2WQVU1Sm6m58AUdTlPDPTLCAiVNUpqG9S21V62VF8eQJ8KUKyd4BhGINQFDJVt1JRlctFOyVsMEQAf4l87oqK/soJB/RNAeC7VHJA3xS/uQHBJA8eOlGHV0ouoKJaBkE3KKaNzMbMMT2Dmpc/TwAbCAzDhEowbcvdlTBVVRe8U0yFTON9URz0VQ4rPUaAYNTsjPdU18u4UOnE6UtNKK924bZ+Ke1Swwu0z6ETdVj51jm95XXzPk6ZsPHv5dj40feRvBwMwzAhIwgCrBYRiQkSUhIlpKdYkJFmRfcubuqYGVZ078LqmNGCDYYI4E8+2f1JfOiN6bhrcCbqGlW4FA1WSc9z+PvhagAIuH+o59y6pxL2Jt3HJwgwqxsIQPHeqshcCIZhmAghigISvAwKU267m6fcdkaahPRkEck2Nig6Ais9NhMNpUdvglE5DLYDZCDmrPwW5dWy/gdiGAtuK39bv1TOQ2AY5qqBNShCV3pkD0MMKa92euiDA56JikapZHW97FEq2Z7mTD0ybab2gzcCEJZzMAzDxAuS6N0QzIqu6bqHolc3G3pm6iGPbukWZKRKSEsSkWQTkGDRFXqvRg8FJz3GkECJiuEslZyam4XTl5rQ0KS2ShJKS5YiVo7JMAwTjwQqGW1Lg+JK7eHBBkMMMMIM5yocaHSoSE+WkJFqaaVjEM5SyaE3puOp6X1bVUlYJAHZGdawnINhGOZqoS2Dwl/JqNEULF57eLDBEGXcFRm7p1tQIwF1dhWyquFHPZI98gfC3Wdh6I3pWP/Ujz22teRRcC8HhmGYcBBSDw8PMavO7Z1ggyHKeIcZMlOtSEqQfLZzjkafBe7lwDAME13aktz2pT/RWTqMssEQZUIJM0SjzwL3cmAYhuk86PoTAqx+fm8aD1prue1Iq2OywRBBNn70PYr3VqHJpSIpQcLknO4hhxmi0WeBezkwDMPEB0F5J1SCEoFSUTYYIsTGj77H3z4uBwS9gZRDVvG3j8uRe1sXVFS7wCEAhmEYJpyY3gk/3+yqVzVHqJ4I1mGIEMV7q5qNBUFPgGluBX3weEPIKo4MwzAM01EkSYDNKiLZJiEt2YL0lNB8BuxhiBBNLhUWL3NMEvTtHAJgGIZh4g32MESIpARJz2Z1QyV9O8MwDMPEG2wwRIjJOd0BgtmK1SkTVBVITRJZdplhGIaJO9hgiBAzx/TEvaN7wCIKUDVdczwzVUKCReReDQzDMEzcwTkMEWTmmJ44ctreqiMl92pgGIZh4g32MESYQB0pGYZhGCYeYIMhwvTItMEpe2Y/cq8GhmEYJt7gkEQEMLpRllc7kWyTUN+oALCwUBPDMAwTt7DBEGbcu1GmJUlwygRBECCJQH2Tyr0aGIZhmLiEDYYw492N0ugZkZ5ixbrHr29rV4ZhGIbptHAOQ5jhJEeGYRjmSoQNhjDDSY4MwzDMlQgbDGFmam4WZIXgcGkg0n9ykiPDMAwT78TMYFi7di0KCgpQUFCAlStXAgD279+PoqIi5OXlYfXq1eZ7jx07hilTpmDcuHF45plnoCgKAODixYuYOXMmxo8fj4cffhh2ux0AUFdXhwceeAD5+fmYOXMmKisrozavoTemczdKhmEY5oojJgbD/v37sXfvXhQXF2P79u345ptvsHPnTjz99NN49dVXsWvXLhw9ehS7d+8GACxYsADPPfcc3n//fRARNm/eDABYsmQJ7rvvPrz33nu45ZZb8OqrrwIAXn75ZQwZMgTvvvsupk2bhuXLl0d1fkNvTMeKeddj/VM/xop517OxwDAMw8Q9MTEYsrKysGjRIiQkJMBqteL6669HaWkpfvSjH6FPnz6wWCwoKirCe++9h7KyMjgcDtx+++0AgClTpuC9996DLMs4dOgQxo0b57EdAD799FMUFRUBAAoLC7Fnzx7IshyLqTIMwzDMFUFMDIb+/fubBkBpaSneffddCIKArKyWOH92djbKy8tRUVHhsT0rKwvl5eWorq5GamoqLBaLx3YAHvtYLBakpqbi8uXL0ZoewzAMw1xxxDTp8eTJk5g7dy6eeuop9OnTB4LQUo5IpAseaZrmc7vx0x3v1+77iCLndzIMwzBMe4nZt+jhw4cxZ84cPPnkk5g8eTJ69uzpkZxYWVmJ7OzsVturqqqQnZ2Nrl27or6+Hqqqerwf0L0TVVVVAABFUWC325GRkRHF2TEMwzDMlUVMDIZLly7h17/+NVatWoWCggIAwMCBA3HmzBmcPXsWqqpi586dyM3NRe/evWGz2XD48GEAQElJCXJzc2G1WjFkyBDs2rULALB9+3bk5uYCAEaOHInt27cDAHbt2oUhQ4bAarXGYKYMwzAMc2UgEBEFflt4WbZsGd5++2307dvX3DZjxgxcd911eOGFF+B0OjFy5EgsXrwYgiDg+PHjePbZZ9HQ0ICbb74ZL7zwAhISElBWVoZFixbhhx9+QK9evfDSSy+hS5cuqKmpwaJFi3D+/HmkpaVh1apVuPbaa9sc05QpU7Bt27ZIT51hGIZh4pKYGAydETYYGIZhGMY/nAnIMAzDMExA2GBgGIZhGCYgbDAwDMMwDBMQzmFoZtiwYejdu3esh8EwDMMwUSEzMxP//d//HfT72WBgGIZhGCYgHJJgGIZhGCYgbDAwDMMwDBMQNhgYhmEYhgkIGwwMwzAMwwSEDQaGYRiGYQLCBkME2LFjByZMmIC8vDxs3Lgx1sPxYNasWSgoKMCkSZMwadIkfPXVV37Hu3//fhQVFSEvLw+rV682tx87dgxTpkzBuHHj8Mwzz0BRFADAxYsXMXPmTIwfPx4PP/ww7HZ7RObQ0NCAwsJCXLhwIazjrKurwwMPPID8/HzMnDnT7JLqcrmwYMEC5OfnY/LkyTh16lRE57N48WLk5eWZa/Thhx9GZZ4dZe3atSgoKEBBQQFWrlwZlTFHcm18zSde1+YPf/gDJkyYgIKCArzxxhtRGXMk18bXfOJ1bQxWrFiBRYsWhXVs/taAiLBixQqMHz8eEyZMMJs7BoSYsPL999/TqFGjqLq6mux2OxUVFdHJkydjPSwiItI0jXJyckiWZXObv/E2NTXRyJEj6dy5cyTLMs2dO5c+/fRTIiIqKCigL7/8koiIFi9eTBs3biQiogceeIB27txJRERr166llStXhn0O//jHP6iwsJBuvvlmOn/+fFjHuWTJEvrTn/5ERETFxcX02GOPERHR66+/Tr/97W+JiOjgwYM0bdq0iM2HiKiwsJDKy8s93heNeXaEffv20fTp08npdJLL5aLZs2fTjh074nZtfM3ngw8+iMu1+fzzz2nGjBkkyzI1NTXRqFGj6NixY3G7Nr7mc+rUqbhcG4P9+/fTsGHDaOHChWEdm781ePfdd2nevHmkqiqdPn2axo4d6/G94A82GMLMtm3baPHixebrtWvX0h//+McYjqiF7777jnJycmjWrFlUVFRE//M//+N3vJ9//jnNnj3b3F5cXEyLFi2iCxcu0JgxY8zthw4dolmzZpHL5aJBgwaZH7qLFy/S6NGjwz6Hp59+mg4dOkSjRo2i8+fPh3Wco0aNoosXLxIRkSzLNGjQIHK5XPTLX/6SDh06ZB5rzJgxVFZWJhw4wAAADbFJREFUFpH5NDY20k9+8hP61a9+RYWFhfSHP/yBVFWNyjw7wj//+U/zBkek38D++Mc/xu3a+JrP+vXr43JtiMg8xoULFyg3Nzfu/26851NWVha3a1NdXU3Tpk2jN954gxYuXBiVNVi0aBEVFxeb22fPnk0HDx4MOFYOSYSZiooKZGVlma+zs7NRXl4ewxG1UFdXh+HDh+OVV17B+vXrsWnTJly8eNHneP3Nw3t7VlYWysvLUV1djdTUVFgsFo/t4Wb58uUYMmSI+Tqc43Tfx2KxIDU1FZcvX/Z5rO+//z4i86mqqsJPf/pTPP/889i8eTO++OILbN26NSrz7Aj9+/fH7bffDgAoLS3Fu+++C0EQ4nZtfM1nxIgRcbk2AGC1WrFmzRoUFBRg+PDhcf934z0fRVHidm2ee+45PPHEE0hPT291no6Ozd8aVFRUIDs7u9X2QLDBEGY0TYMgCOZrIvJ4HUsGDRqElStXIi0tDV27dsXUqVOxZs0an+P1Nw9/233NMxrzjuQ4iQiiKLbax9geCfr06YNXXnkF2dnZSEpKwqxZs7B79+6ozDMcnDx5EnPnzsVTTz2FPn36xP3auM+nX79+cb02jz76KA4cOIBLly6htLQ07tfGfT4HDhyIy7XZsmULevXqheHDh5vborEGvs4RzFzYYAgzPXv29EiGqays9LDkYskXX3yBAwcOmK+JCL179/Y5Xn/z8N5eVVWF7OxsdO3aFfX19VBV1eP9kSac48zOzkZVVRUAQFEU2O12ZGRkoEePHqioqGh1rEhw4sQJvP/+++ZrIoLFYonKPDvK4cOHMWfOHDz55JOYPHly3K+N93zidW1OnTqFY8eOAQCSkpKQl5eHzz//PG7Xxtd8du3aFZdrs2vXLuzbtw+TJk3CmjVr8PHHH2Pr1q0RX4OePXu2a23YYAgzd955Jw4cOIDLly+jqakJH3zwAXJzc2M9LABAfX09Vq5cCafTiYaGBhQXF+PFF1/0Od6BAwfizJkzOHv2LFRVxc6dO5Gbm4vevXvDZrOZWbUlJSXIzc2F1WrFkCFDsGvXLgDA9u3bozLvcI5z5MiR2L59OwD9D3nIkCGwWq0YOXIkSkpKAOhGl81mwzXXXBOR+RARnn/+edTW1kKWZbz11lsYO3ZsVObZES5duoRf//rXWLVqFQoKCgDE99r4mk+8rs2FCxfw7LPPwuVyweVy4aOPPsKMGTPidm18zWfo0KFxuTZvvPEGdu7ciZKSEjz66KMYPXo0XnjhhYivQW5uLnbs2AFVVXH27FmUlpbi1ltvDTzggFkOTMi88847VFBQQHl5efTaa6/FejgerF69msaPH095eXm0fv16IvI/3v3791NRURHl5eXR8uXLSdM0IiI6duwY3XPPPTRu3DiaP38+OZ1OItITkH75y19Sfn4+zZ07l2pqaiI2DyNJMJzjrK6upgcffJAmTJhA06dPN4/vcDjoqaeeogkTJtDdd99NR48ejeh8NmzYQPn5+TR27Fh68cUXzfdEep4dYenSpXT77bfTxIkTzf/efPPNuF0bf/OJx7UhIlqzZg3l5+dTYWEhrVmzJipjjuTfja/5xOvaGLz99ttmlUSk10DTNPqP//gPmjBhAk2YMIE+++yzoMbI3SoZhmEYhgkIhyQYhmEYhgkIGwwMwzAMwwSEDQaGYRiGYQLCBgPDMAzDMAFhg4FhGIZhmICwwcAwUWbZsmVmR71bbrkF48aNM187HA6/+9XW1mLOnDkBj79lyxY88sgjYRyxf/7xj39g7NixHTrG4sWLcfz48TCNqPPx1Vdf4fHHH4/1MBimw1hiPQCGudp49tlnzX+PHj0aq1atCko0paamBkePHo3k0GLCvn37MHv27FgPI2IMHDgQL7/8cqyHwTAdhg0GhulkHDx4EC+++CKcTiesViueeOIJ5OTkYPHixbDb7Zg0aRJKSkqwefNmbNmyBbIso7a2Fg899BCmT5/u97iKomDFihX49NNPkZaWhltvvRVnz57F+vXrce+996J79+44deoUZs6ciZtuugkvvfQSnE4nKisrkZubi6VLlwIANmzYgL/+9a9IS0vDDTfcYB5/9erVaGxsxDPPPNPq9eHDh30eb9WqVbh8+TIef/xxrFq1Cn379sXy5cvx3XffQZZl/OxnP8OCBQsgSRJWr16Njz/+GFarFZmZmVixYgW6d+/uMcff/OY3SE1NxbfffovLly8jNzcXTz/9NCwWC06ePInly5ejrq4Oqqpizpw5mDx5Mvbv34+VK1fCZrPB4XBgy5YtSEhIMI+Zm5uLyZMn48CBA6irq8O8efPwxRdf4OjRo7DZbFi3bh2ysrLw97//Ha+//jpcLhcuX76Me+65B//+7/+O/fv3Y8WKFSgpKUFdXR2WLFmCEydOAABGjRqFxx57DAAwePBgjBw5EidOnMDLL7+MAQMGhOcDxTDhIqxSVQzDhMSoUaPoyJEj5usffviBhg8fbm47fvw43XHHHVRWVkalpaU0ePBgIiKqr6+nX/ziF1RdXU1EegvcIUOGEBHR5s2b6eGHH251rg0bNtCsWbPI6XSS0+mk+++/n+6//34iIpoxYwb99re/Nd/76KOPmm1x6+vraejQoXTs2DE6cuQI/exnP6OqqirSNI2efvppuuuuu4iI6KWXXqJly5aZx3B/7e94REQjRoygb7/9loiIFixYQBs3biQiIkVRaP78+fTnP/+Zzp07R0OHDjUV71577TX66KOPWs3xySefpClTppDdbieHw0EzZsygN998k1wuF+Xn55vnrK2tpXHjxtGRI0do3759NGDAALp06ZLPNRoxYgStWLGCiIhKSkpowIABdOLECSIievDBB+m1114jVVXpvvvuo3PnzhGR3nr4pptuopqaGtq3bx9NnDiRiIjmz59PL7zwAhHpKnz3338/vf766yTLMt1www20Y8cOn2NgmM4AexgYphPx5Zdfol+/fmaI4sYbb8TAgQNx8OBBDBo0yHxfamoq1q1bh08++QSlpaU4duwYGhsb2zz27t27MXnyZPPp+Re/+AU2b95s/n7w4MHmv1988UXs3r0b69atw+nTp+F0OmG323H48GGMGDEC3bp1AwBMnz4dBw8eDDgvf8fzNcZvv/0Wb731FgDA4XAgISEBs2bNwvXXX48pU6YgNzcXubm5+OlPf+rzXFOmTEFycjIAYNKkSfjss88waNAgnD9/HgsXLjTf53K5cOzYMVx77bXo3bs3evbs6Xf848aNAwD07dsXPXr0MD0rffr0QW1tLURRxJ/+9Cd8+umnKCkpwXfffQciapWTsnfvXmzduhUAYLPZMH36dGzatAn3338/AHi0OmeYzgYbDAzTifBuO2tsUxTFY1tZWRnuu+8+zJgxA0OGDEFeXh727t3b5rElSQK5KcFLkuTx+5SUFAB6k6UZM2bglltuwYgRI1BQUIAvv/zS3NffMYwWvAayLAd1PHcURcHatWtx3XXXAYD5ZWyxWPDmm2/iyJEjOHDgAJYtW4bRo0dj/vz5rY5hsbTc1jRNgyRJ0DQNGRkZZiMeQO/2l56ejsOHD5sGhj/cQxTuxzdoaGjA5MmTMW7cOAwePBj33HMPPvzww1ZzVFW1VVth97UNNA6GiSVcJcEwnYhBgwbhn//8J77++msAervr//u//8OwYcMgSRJUVQUR4euvv0ZWVhYeeugh5OTk4JNPPoGmaW0e++c//zneeecduFwuKIqC4uLiVsYJAFRXV+P48eNYsGABxo4di7KyMly4cAGapmHEiBHYs2cPysvLAQDFxcXmfl27dsU333wDIkJDQwP27NkT8HiA/gVsGBc5OTlYv349iAhOpxMPPvgg/va3v+Gbb77BxIkT0b9/fzz00EOYPXu2eY282bVrF1wuFxwOB0pKSjBq1Cj867/+K0RRxP/+7/8C0A2uwsLCsFVnnDlzBk1NTXjssccwatQoHDhwAIqimK2IDXJycrBhwwYAgNPpxObNm3HnnXeGZQwME2nYw8AwnYju3bvj5Zdfxu9//3u4XC6IooiVK1eiT58+kGUZAwYMQGFhITZt2oRt27Zh/PjxEAQBw4YNQ5cuXXDu3Dm/x546dSpKS0tx9913IyUlBddcc41Pg6Fr16741a9+hUmTJiEpKQm9evXCoEGDcPbsWUybNg3z58/H7NmzkZKSgttuu83cb9KkSdi7dy/y8vLQs2dPDB06NODx7rjjDowdOxbz58/H0qVL8bvf/Q7Lli1DUVERZFlGTk4O5s6dC4vFgrvuussMNyQmJuK5557zOc+EhATce++9aGhowPjx43H33XdDEASsW7cOzz//PP7zP/8TiqLgySefxMCBA7F///4Orhrw4x//GDk5OcjPz4fVasVNN92Efv36tVqP5557DkuXLkVhYSFkWUZubi7mzZvX4fMzTDTgbpUMc5WwZ88e1NbWoqioCACwZMkSpKen44knnojxyMLHb37zG9xyyy1B6VVEiw8++AB//vOfsWnTplgPhWE6BIckGOYqoX///nj77bcxceJEFBQUoL6+np9uI8xf/vIXLFmyBPfcc0+sh8IwHYY9DAzDMAzDBIQ9DAzDMAzDBIQNBoZhGIZhAsIGA8MwDMMwAWGDgWEYhmGYgLDBwDAMwzBMQNhgYBiGYRgmIP8fQjGGqiMDDzgAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Compare total number of students to earnings\n",
"fig, ax = plt.subplots(figsize = (8,6))\n",
"sns.regplot(x = grads_df[\"Total\"], y = grads_df[\"Median\"])\n",
"ax.set(xlabel = \"Total graduates per major\", ylabel = \"Median yearly earnings (in $)\", \n",
" title = \"Earnings vs. Total Graduates per Major\")\n",
"sns.despine()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Findings:** Slight negative correlation: graduates in more popular majors (those with higher numbers of students) seem to have lower earnings."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Unemployment and Underemployment Rates"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Unemployment boxplot\n",
"with sns.axes_style(\"whitegrid\"):\n",
" fig, ax = plt.subplots(figsize = (8,4))\n",
" sns.boxplot(x = grads_df[\"Unemployment_rate\"])\n",
" ax.set(xlabel = \"Unemployment rate\", title = \"Unemployment Rate Distribution\")\n",
" sns.despine(left=True)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Major</th>\n",
" <th>Unemployment_rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Nuclear engineering</td>\n",
" <td>0.177226</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>Computer networking and telecommunications</td>\n",
" <td>0.151850</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>Public administration</td>\n",
" <td>0.159491</td>\n",
" </tr>\n",
" <tr>\n",
" <th>170</th>\n",
" <td>Clinical psychology</td>\n",
" <td>0.149048</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Major Unemployment_rate\n",
"5 Nuclear engineering 0.177226\n",
"84 Computer networking and telecommunications 0.151850\n",
"89 Public administration 0.159491\n",
"170 Clinical psychology 0.149048"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check outlier majors\n",
"grads_df[grads_df[\"Unemployment_rate\"] > 0.13][[\"Major\", \"Unemployment_rate\"]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Findings:** 75% of majors have an unemployment rate of less than 8.8%. 4 of them have an unemployment rate greater than 14%."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Compare share of women to unemployment rate\n",
"fig, ax = plt.subplots(figsize = (8,6))\n",
"sns.regplot(x = grads_df[\"Median\"], y = grads_df[\"Unemployment_rate\"])\n",
"ax.set(xlabel = \"Median yearly earnings (in $)\", ylabel = \"Unemployment rate\",\n",
" title = \"Earnings vs. Unemployment Rate\")\n",
"sns.despine()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Findinds:** No clear correlation between majors' median yearly earnings and unemployment rate."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"#Add underemployment rates\n",
"grads_df[\"Underemployment\"] = grads_df[\"Non_college_jobs\"] / grads_df[\"Total\"] \n",
"\n",
"# Create new dataframe with 5 highest and 5 lowest paying majors\n",
"majors_top_bottom_df = pd.concat([grads_df.head(), grads_df.tail()])\n",
"majors_top_bottom_df.loc[:5,\"Majors_ranking\"] = \"Highest paying\"\n",
"majors_top_bottom_df.loc[5:,\"Majors_ranking\"] = \"Lowest paying\""
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Compare the highest and lowest paying majors in terms of unemployment and underemployment rates\n",
"fig, (ax1, ax2) = plt.subplots(1, 2, figsize = (10,4))\n",
"fig.suptitle(\"Unemployment and Underemployment Rates for Highest and Lowest Earning Majors\", fontsize = 14)\n",
"\n",
"# Unemployment bar chart\n",
"sns.barplot(x = majors_top_bottom_df[\"Unemployment_rate\"], y = majors_top_bottom_df[\"Major\"], \n",
" hue = majors_top_bottom_df[\"Majors_ranking\"], ax = ax1)\n",
"ax1.set(xlabel = \"Unemployment rate\", ylabel = \"Majors\",\n",
" )\n",
"ax1.legend_.remove()\n",
"\n",
"# Underemployment bar chart\n",
"sns.barplot(x = majors_top_bottom_df[\"Underemployment\"], y = majors_top_bottom_df[\"Major\"],\n",
" hue = majors_top_bottom_df[\"Majors_ranking\"], ax = ax2)\n",
"ax2.set(xlabel = \"Underemployment rate\", ylabel = \"\")\n",
"ax2.set_yticklabels([])\n",
"ax2.legend(title = \"Majors ranking\", bbox_to_anchor = (1, .62))\n",
"\n",
"sns.despine()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Findings:** There appears to be no marked difference in unemployment rates between the highest and lowest majors.\n",
"\n",
"Similarly to unemployment rates, there is no striking difference in underemployment rates between the highest and lowest paying majors. However, graduates from lowest paying majors seem to be slightly more underemployed."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x576 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Compare share of women to unemployment and underemployment rates\n",
"fig, (ax1, ax2) = plt.subplots(2, 1, figsize = (6,8))\n",
"fig.suptitle(\"Share of Women per Major vs. Unemployment and Underemployment Rates\", fontsize = 14, y = .95)\n",
"\n",
"# Unemployment scatter plot\n",
"sns.scatterplot(x = grads_df[\"ShareWomen\"], y = grads_df[\"Unemployment_rate\"], ax = ax1)\n",
"ax1.set(xlabel = \"Share of female graduates\", ylabel = \"Unemployment rate\")\n",
"\n",
"# Underemployment scatter plot\n",
"sns.scatterplot(x = grads_df[\"ShareWomen\"], y = grads_df[\"Underemployment\"], ax = ax2)\n",
"ax2.set(xlabel = \"Share of female graduates\", ylabel = \"Underemployment rate\")\n",
"\n",
"sns.despine()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Findings:** There appears to be no correlation between unemployment and underemployment rates per major and share of women graduates."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Outcomes for Female Graduates"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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ZM5Wdna26devqqaeeuuDU/u+NGTNGo0aNUmxsrPz9/fXggw/Ky8tL27dvV1RU1Hm39/X11dSpU/Xyyy+rS5cuqlixorp06aKnn3660PXfc889SktL04QJE5SWlqaGDRvqvffeu+BOd4Vp1qyZMjMzz/s/4qZNm8rNze286y/naxQeHq6xY8dq0qRJGjt2rGrWrKkxY8ZctgPMoOxyM3yZAQCAVUxTAwBgGWUMAIBllDEAAJZRxgAAWEYZAwBg2VUr4379+l2thwIA4LpSrDIeP368YmJiFBsbq/fee0+StHLlSsXHxys6Olrjxo0rch3Hjx//Y0kBACilijzox9q1a7V69Wp9+eWXysvLU0xMjFq1aqXhw4dr+vTpuvnmmzVw4EAtW7ZM7dq1uxqZAQAoVYrcMm7evLk++OADeXp66ujRo3I4HDp16pRq166tgIAAeXp6Kj4+XgsWLLgaeQEAKHWKNU3t5eWlCRMmKDY2Vq1atdLhw4cLnMrO39+f83UCAFBCxd6B64knntCqVat04MABpaamFjiTjTHmks+iAwAA8hVZxsnJydqxY4ckqUKFCoqOjtaaNWuUlpbmuk1aWpr8/f2vXEoAAEqxIst43759GjFihHJycpSTk6PFixere/fuSklJ0Z49e+RwODRv3jy1bdv2auQFAKDUKXJv6nbt2mnLli3q3LmzPDw8FB0drdjYWFWpUkWDBw9Wdna22rVrp3vvvfdq5AUAoNS5aqdQ7Nq1q2bNmnU1HgoAgOsKh8MEAMAyyhgAAMsoYwAALKOMAQCwjDIGAMAyyhgAAMuK/D9jACgt0jPzlJntsB3Dxaech/x8+BgGZQygDMnMdmj9rgzbMVxub+BLGUMS09QAAFhHGQMAYBllDACAZZQxAACWUcYAAFhGGQMAYBllDACAZZQxAACWUcYAAFhGGQMAYBllDACAZZQxAACWUcYAAFhGGQMAYBllDACAZZQxAACWUcYAAFhGGQMAYBllDACAZZQxAACWUcYAAFhGGQMAYBllDACAZZQxAACWUcYAAFhGGQMAYBllDACAZZQxAACWUcYAAFhGGQMAYBllDACAZZQxAACWUcYAAFhGGQMAYBllDACAZZQxAACWUcYAAFjmWZwbTZo0Sd98840kqV27dnr22Wf1j3/8Q+vXr1eFChUkSY8//rg6dux45ZICAFBKFVnGK1eu1PLlyzV79my5ubmpf//+WrRokbZt26YPP/xQ/v7+VyMnAAClVpHT1NWqVdOwYcPk7e0tLy8v1atXT7/99pt+++03DR8+XPHx8ZowYYKcTufVyAsAQKlT5JZxgwYNXD+npqbqm2++0UcffaS1a9dq5MiR8vPz08CBA/XFF1/owQcfvKJhAVxf0jPzlJntsB3DJSfX2I4AFKpY3xlL0q5duzRw4EA9++yzqlu3riZPnuxalpCQoDlz5lDGAArIzHZo/a4M2zFcQgIq2I4AFKpYe1OvX79effr00dChQ9WlSxf9/PPPWrhwoWu5MUaensXudQAAcI4iy/jAgQMaNGiQxo4dq9jYWEn55TtmzBidPHlSubm5+vTTT9mTGgCAEipyc3batGnKzs5WUlKS67ru3bvr0UcfVY8ePZSXl6fo6GjFxcVd0aAAAJRWbsaYq7JHQ9euXTVr1qyr8VAArhGHjmdfc98Z/7Q3y3YMl9sb+Kp65XK2Y+AawBG4AACwjDIGAMAyyhgAAMsoYwAALKOMAQCwjDIGAMAyyhgAAMsoYwAALKOMAQCwjDIGAMAyyhgAAMsoYwAALKOMAQCwjDIGAMAyyhgAAMsoYwAALKOMAQCwjDIGAMAyyhgAAMsoYwAALKOMAQCwjDIGAMAyyhgAAMsoYwAALKOMAQCwjDIGAMAyyhgAAMs8bQcALkV6Zp4ysx22Y7j4lPOQnw+/RgD+GD5FcF3JzHZo/a4M2zFcbm/gSxkD+MOYpgYAwDLKGAAAyyhjAAAso4wBALCMMgYAwDLKGAAAyyhjAAAso4wBALCMoxUApci1doSynFxjOwJwXaCMgVLkWjtCWUhABdsRgOsC09QAAFhGGQMAYBllDACAZZQxAACWUcYAAFhWrDKeNGmSYmNjFRsbq1dffVWStHLlSsXHxys6Olrjxo27oiEBACjNiizjlStXavny5Zo9e7bmzJmjH3/8UfPmzdPw4cP11ltvaf78+dq2bZuWLVt2NfICAFDqFFnG1apV07Bhw+Tt7S0vLy/Vq1dPqampql27tgICAuTp6an4+HgtWLDgauQFAKDUKbKMGzRooPDwcElSamqqvvnmG7m5ualatWqu2/j7++vQoUNXLiUAAKVYsXfg2rVrl/r27atnn31WAQEBcnNzcy0zxhS4DAAAiq9YZbx+/Xr16dNHQ4cOVZcuXVSjRg2lpaW5lqelpcnf3/+KhQQAoDQrsowPHDigQYMGaezYsYqNjZUkhYWFKSUlRXv27JHD4dC8efPUtm3bKx4WAIDSqMgTRUybNk3Z2dlKSkpyXde9e3clJSVp8ODBys7OVrt27XTvvfde0aAAAJRWRZbxiBEjNGLEiEKXffnll5c9EAAAZQ1H4AIAwDLKGAAAyyhjAAAso4wBALCMMgYAwDLKGAAAy4r81yYAF+ZwGB06nm07hktOrrEdAUAJUMbAH3Am16mfdmfZjuESElDBdgQAJcA0NQAAllHGAABYRhkDAGAZZQwAgGWUMQAAllHGAABYRhkDAGAZZQwAgGWUMQAAllHGAABYRhkDAGAZZQwAgGWUMQAAllHGAABYRhkDAGAZZQwAgGWUMQAAllHGAABYRhkDAGAZZQwAgGWUMQAAllHGAABYRhkDAGAZZQwAgGWUMQAAllHGAABYRhkDAGAZZQwAgGWUMQAAllHGAABY5mk7AK5t6Zl5ysx22I7hkpNrbEcAgMuOMsZFZWY7tH5Xhu0YLiEBFWxHAIDLjmlqAAAso4wBALCMMgYAwDLKGAAAyyhjAAAsK3YZZ2RkKC4uTvv27ZMk/eMf/1B0dLTuv/9+3X///Vq0aNEVCwkAQGlWrH9t2rx5s0aMGKHU1FTXddu2bdOHH34of3//K5UNAIAyoVhbxp999plGjhzpKt6srCz99ttvGj58uOLj4zVhwgQ5nc4rGhQAgNKqWGWcmJioyMhI1+UjR46oZcuWGjNmjD777DP98MMP+uKLL65YSAAASrMS7cAVEBCgyZMny9/fXxUqVFBCQoKWLVt2ubMBAFAmlKiMf/75Zy1cuNB12RgjT0+OrAkAQEmUqIyNMRozZoxOnjyp3Nxcffrpp+rYsePlzgYAQJlQos3ZkJAQPfroo+rRo4fy8vIUHR2tuLi4y50NAIAy4ZLKeMmSJa6fe/bsqZ49e172QAAAlDUcgQsAAMsoYwAALKOMAQCwjDIGAMAyyhgAAMs4Usc1Jj0zT5nZDtsxXHJyje0IAFDqUcbXmMxsh9bvyrAdwyUkoILtCABQ6jFNDQCAZZQxAACWUcYAAFhGGQMAYBllDACAZZQxAACWUcYAAFhGGQMAYBllDACAZZQxAACWUcYAAFhGGQMAYBllDACAZZQxAACWUcYAAFhGGQMAYBllDACAZZQxAACWUcYAAFhGGQMAYJmn7QAAgGtDemaeMrMdtmO4+JTzkJ9P2aipsvEsAQBFysx2aP2uDNsxXG5v4FtmyphpagAALKOMAQCwjDIGAMAyyhgAAMsoYwAALKOMAQCwjDIGAMAyyhgAAMvKxn9TA8A1yOEwOnQ823YMl5xcYztCmUUZA4AlZ3Kd+ml3lu0YLiEBFWxHKLOYpgYAwDLKGAAAyyhjAAAso4wBALCMMgYAwLJilXFGRobi4uK0b98+SdLKlSsVHx+v6OhojRs37ooGBACgtCuyjDdv3qwePXooNTVVknTmzBkNHz5cb731lubPn69t27Zp2bJlVzonAAClVpFl/Nlnn2nkyJHy9/eXJG3ZskW1a9dWQECAPD09FR8frwULFlzxoAAAlFZFHvQjMTGxwOXDhw+rWrVqrsv+/v46dOjQ5U8GAEAZcck7cDmdTrm5ubkuG2MKXAYAAJfmksu4Ro0aSktLc11OS0tzTWEDAIBLd8llHBYWppSUFO3Zs0cOh0Pz5s1T27Ztr0Q2AADKhEs+UUS5cuWUlJSkwYMHKzs7W+3atdO99957JbIBAFAmFLuMlyxZ4vq5VatW+vLLL69IIAAAyhqOwAUAgGWUMQAAllHGAABYRhkDAGAZZQwAgGWUMQAAllHGAABYRhkDAGAZZQwAgGWUMQAAllHGAABYRhkDAGAZZQwAgGWUMQAAllHGAABYRhkDAGAZZQwAgGWUMQAAllHGAABYRhkDAGAZZQwAgGWUMQAAllHGAABYRhkDAGAZZQwAgGWUMQAAllHGAABYRhkDAGAZZQwAgGWUMQAAllHGAABYRhkDAGAZZQwAgGWUMQAAllHGAABYRhkDAGAZZQwAgGWUMQAAllHGAABYRhkDAGAZZQwAgGWUMQAAllHGAABYRhkDAGAZZQwAgGWef+TOCQkJOnbsmDw981fz4osvKiws7LIEAwCgrChxGRtjlJqaqqVLl7rKGAAAXLoST1Pv3r1bktS3b1916tRJH3744WULBQBAWVLiTdpTp06pVatWeu6555Sbm6vevXsrMDBQrVu3vpz5AAAo9UpcxhEREYqIiHBd7tatm5YtW0YZAwBwiUo8Tf3DDz9o1apVrsvGGL47BgCgBEpcxunp6Xr11VeVnZ2tjIwMzZ49Wx07dryc2QAAKBNKvCkbFRWlzZs3q3PnznI6nXr44YcLTFsDAIDi+UPzyk899ZSeeuqpy5UFAIAyiSNwAQBgGWUMAIBllDEAAJZRxgAAWEYZAwBgGWUMAIBllDEAAJZRxgAAWEYZAwBgGWUMAIBllDEAAJZRxgAAWEYZAwBgGWUMAIBllDEAAJZRxgAAWOZpO4Bt6Zl5ysx22I7hkpNrbEcAAFxlZb6MM7MdWr8rw3YMl5CACrYjAACuMqapAQCwjDIGAMAyyhgAAMsoYwAALKOMAQCwjDIGAMAyyhgAAMsoYwAALKOMAQCwjDIGAMAyyhgAAMsoYwAALKOMAQCwjDIGAMAyyhgAAMsoYwAALKOMAQCwjDIGAMAyyhgAAMsoYwAALKOMAQCwjDIGAMAyyhgAAMsoYwAALKOMAQCwjDIGAMCyP1TGX331lWJiYhQdHa2PPvrocmUCAKBM8SzpHQ8dOqRx48Zp1qxZ8vb2Vvfu3dWiRQvVr1//cuYDAKDUK/GW8cqVK9WyZUtVqlRJPj4+uueee7RgwYLLmQ0AgDKhxGV8+PBhVatWzXXZ399fhw4duiyhAAAoS0o8Te10OuXm5ua6bIwpcPn39u/fr65du5b04QAAZcxU2wEug8qVK2vatGlF3q7EZVyjRg398MMPrstpaWny9/e/4O3XrFlT0ocCAKBUK/E09R133KFVq1bp2LFjysrK0rfffqu2bdtezmwAAJQJJd4yrl69up5++mn17t1bubm56tatm0JDQy9nNgAAygQ3Y4yxHQIAgLKMI3ABAGAZZQwAgGWUMXCZ8c1P8TFWQD7K+Bx8MFzcuePjdDotJrm25ebmSpIcDoflJNe+ix2bAP9z7u8en1OlU5negSsxMVEeHh5q1KiRWrRooerVq8vpdMrdnb9Rfm/UqFHKzc1V1apV9eCDD6pmzZpFHuilLPrwww81f/58zZgxQ5J4P13AiBEjlJ6errp166pfv37y9fW1Hema9fzzzysvL0+1a9fWAw88oKpVq9qOdM1KSUlRrVq15OHhIanog1FdS8rkp0Rubq769eunnJwcBQcHa9u2bXr22Wd1+vRpubu7s9V3juzsbD3yyCPy9vbWPffco5ycHM2YMeO8I7Ah3+nTp7VhwwaNHj1akuTu7s4W8jny8vL0xBNPyNvbW926dVNUVFSBIi7D2wbnycnJ0YABA+Tu7q4OHTpo+/bt2rdvn2s5n1P/c/jwYfXu3VujR4/W6NGj9e9//1vS9TXzUibL2Ol0qmrVqnrhhRfUpUsXPfHEE6pVq5aGDBkiSWzJnOPYsWOqUKGChg8frrZt26pu3brav38/Y3QB9evX17Bhw7QVtvnVAAAQQUlEQVRt2za99dZbkuT6Kx35v1teXl567rnn1KZNG6WkpGjWrFmaP3++pPwPTwo5n8PhkL+/v5555hl16NBB6enpWrdunWbPni0pfywZq3xvv/22WrRooSlTpigqKkqLFi3S22+/bTvWJSmTn6je3t768ccfNX36dEmSr6+vhg4dKi8vL/3rX/+ynO7a4ufnp1OnTmnXrl2SpNq1a+vEiRM6c+aM5WTXpv379+vEiRN69dVXNXfuXD322GP6+OOPbce6Zri7u2vHjh2aNWuWPvroI82ZM0d79+7V559/rpdeeknS9bU1cyV5e3srOTlZL730knr27Kljx47Jzc1Nn376qR5//HFJjJWUv3F18uRJRUREyNvbW3feeaeeeeYZffnll1q0aJGk62PGpcyVscPhkJubm/7v//5PX375pev42j4+PurUqZMOHjyonJwcpoCU/yb39fXVuHHjFBAQICm/bKpUqaLy5ctLknbu3Gkz4jXj7FR0cHCwatasqVq1asnf319r165VYGCgJKYVz47RE088oSVLlmjjxo1677339OSTT2rYsGHat2+f0tLSrosPzivN4XDIw8ND48ePV5cuXeTn56c5c+aof//+mjZtmtLS0rRnzx7bMa8J7u7uaty4sd544w1lZ2fL3d1dYWFhGjRokObOnev6I+ZaVybKeN68eVq6dKmys7NdU4ZhYWGKi4vT+PHjtXfvXnl7e6t69eo6cuSIPD09y+w07LljdXYMqlWr5irfTZs2qV69epLyP1TnzZtnLatN546T9L+p6AoVKujzzz9X7969FRwcrCeffFJDhgzR8ePHy+R7qrDfvUaNGqlOnTpaunSpUlJSJEleXl4yxuiGG264Lj44r4TCxqp69eqqW7eukpOT9euvv0qSMjIyVLlyZVWqVMlm3GvC2T/cevbsqYYNGyoxMdG1rGHDhvL29rYV7ZJ5jBo1apTtEFdKVlaWevTooePHj2v58uXasWOHjh49qoYNG8rLy0t16tTRkSNH9Nprr8nX11cTJ05UZGSkWrRoYTv6VXexsXJzc5PD4ZC7u7s2bdqkzMxMvf/++6pTp46eeeYZ29GvqouNk5S/g9LKlSsVHR2tQYMGKTw8XAEBAWrSpInl5FdXYeN05MgRNWrUSDfeeKNq1KihzMxMTZ06VZUrV9brr7+uiIiIMnmymaLeUzfccIOOHDmiyZMny9/fX2PGjFFoaKiioqIsJ7+6jDHKy8vTK6+8oqCgIPn6+rr+cPPw8FDNmjW1dOlSbdiwQe3atVNmZqa++OILtWnTRpUrV7acvmiluoy3b9+u3377Ta+99po6duyojIwMrVmzRnl5eWrQoIHKly+vFi1aqGLFisrLy1NYWJj69OljO7YVFxorh8Oh+vXru7bqvvzyS33yyScaNGiQ+vXrZzn11VfUe6pixYqKiIjQnXfeKSl/arp+/fqWU199hY3T2rVrlZubqwYNGqhKlSqKioqS0+nUmTNnFB4err59+9qObUVRv3uS1Lx5c50+fVqHDx9WZGSk+vfvbzn11efm5iYPDw898cQTOnDggNq1aydPz/+d66hq1aqKiIjQtGnTtGHDBr399tvq0aOH2rRpYzF18ZX4rE3XAw8PD23cuFF79+5VQECA2rVrp6ysLK1atUohISEKDAxURkaGOnfubDuqdRcaq5UrVyokJER16tRRenq6unTpogcffFARERG2I1txsfdUUFCQ6tWr5/or3BhTJqempQuP0+rVq9WwYUMFBgbq9OnTSkhIsB3Vuov97gUHByswMFBZWVl69NFHbUe1bteuXWrYsKGSk5P1+uuva/jw4ZL+9x37Lbfcoo8//lgnT55URkaG6yu160Gp3jKuVKmS0tLStHfvXjVq1EgVK1bUDTfcoHXr1umWW27Rli1btHDhQrVo0aLMfmiedbGxuvnmm7Vp0yYtXbpUnTp1Us2aNW3HteZi41SzZk1t2bJFixYtUvPmzcv0e4rfveIraqw2b96sb7/9lrGSlJ6eLofDoWeeeUbjx4+Xw+FQeHi4a1xWrVqlm2++WZUqVVKVKlUsp700pbqMPTw85OXlpQ0bNujYsWNq0qSJqlSpoo0bNyovL09du3ZVs2bN5OXlZTuqdUWN1QMPPKDbb7/9utoh4kooznsqMjKyzL+n+N0rvuL87jFW+SpVqqTAwEBVrVpVTZo00auvvqpbb71VdevW1U8//aS5c+eqffv21+VOgKX+z6zmzZurWbNm+vHHH5WYmKgDBw5o7dq18vPzk7u7e5kvl3MxVsXDOBUP41R8jFXxValSxbVFPGTIECUmJmrTpk0KCQnRqFGjrtvZgzJxbGqn06ndu3dr0qRJ8vLyUv369TVw4EDbsa5JjFXxME7FwzgVH2NVMuPGjVP9+vUVHx9vO8ofUibK+Cyn06mcnBzX/8ziwhir4mGciodxKj7Gqniup5NAFEeZKmMAAK5F1+fkOgAApQhlDACAZZQxAACWUcYAAFhGGQMAYBlljFJl06ZNSkhIUHx8vOLi4tS/f3/t2rVLkrRmzRrFxcVZTphv5MiRat++vcaNG2c7Sqlx6NAhde/e3XYMoERK9YkiULbk5ORo4MCBevfdd9W4cWNJ0ty5czVgwAAtXrzYcrqCPv30U/3nP/9RjRo1bEcpNapXr65PPvnEdgygRChjlBpZWVlKT09XZmam67pOnTrJ19dXDodDkpSZmamnn35au3fvVnZ2tkaPHq3IyEilpKToxRdf1OnTp5WWlqaQkBC9+eabKleunJo0aaIOHTrop59+0tixY+Xj46PExESdOHFCDodDCQkJ6tat23l5du3apRdffFEnTpyQm5ub+vbtq86dO+vhhx+WMUYDBgzQyJEjFRkZKUk6fvy42rdvrxUrVsjHx0fPP/+8du/erQ8//FCSFB0drSlTpsjpdBa63jVr1uiNN97QzTffrJSUFFWoUEGPPvqopk+frpSUFEVHR7vOcrNkyRJNmTJFubm5Kl++vP7+978rIiJCEydO1P79+5WWlqb9+/erevXqeu211+Tv71/guU2cOFF79uzRwYMHXeOVmJgoX19fHTp0SC+++KIOHDig3NxcxcbG6rHHHtO+ffvUs2dP1atXT/v379f06dMLrDchIUGNGzfWpk2bdOzYMT344IM6cuSI1q5dq6ysLL355psKDg7Wpk2b9NprryknJ0dpaWm64447NGbMGO3bt0/x8fHauHGjcnNzlZSUpFWrVsnDw0OhoaH6xz/+IV9fX7Vv316hoaH6+eefNWTIEHXs2PHyvhGBkjBAKfLuu++a0NBQ0759e/O3v/3NfP755yYzM9MYY8zq1atNw4YNzaZNm4wxxrz33numd+/exhhjkpKSzJw5c4wxxuTk5Ji4uDizYMECY4wxQUFBZvbs2cYYY3Jzc01MTIzZtm2bMcaYU6dOmfvuu89s3LixQI7c3FzToUMHs3DhQmOMMQcPHjRt2rQxGzZscK3z6NGj5+VPSEgwS5YsMcYYEx0dbe644w6TkZFhdu3aZe67776Lrvfs8/vxxx+NMcb069fPPPTQQyY7O9scPXrUNG7c2Bw8eNCkpKSYuLg4c+zYMWOMMTt37jStW7c2p0+fNhMmTDAdOnQw6enpxhhjBg4caMaPH39ezgkTJpi2bduatLQ043A4zJAhQ0xSUpLrOSxevNgYY8yZM2dMQkKC+frrr83evXtNUFCQWbduXaGvXa9evczjjz9ujDFm06ZNJigoyLWexMREM2LECGOMMU8//bRZvXq1McaYjIwM06JFC7N161azd+9eEx4ebowxZvz48ebxxx83OTk5xuFwmGHDhpnnnnvOGGNMVFSUmTRpUqEZAFvYMkap8uc//1l/+tOftG7dOq1bt07vvPOO3nnnHX3xxReSpICAAIWFhUmSQkJCNHPmTEnSM888oxUrVuidd95RamqqDh8+XGAL++zWa2pqqn799VfXFqYknTlzRtu3b1d4eLjrutTUVGVnZys6OlpS/hRqdHS0/vvf/170XNAdO3bU999/r1q1aql69eoKCgrSunXr9PPPPys6Ovqi623RooVuvfVWNWrUSJJUq1Yt+fn5ydvbW1WqVNENN9ygkydPat26dTp8+LD69Onjelw3Nzf9+uuvkvJPWuDr6ytJatSokU6ePFlo1nvvvVdVq1aVJHXr1k1jxozR4MGDtW7dOp08eVLjx4+XlD8b8dNPPyk0NFSenp4Fxqmw53/2dZLkOjF8rVq1tHbtWklSUlKSvv/+e/3zn/90zXBkZmaqUqVKrvV8//33evrpp11nOkpISNCgQYNcy8++nsC1gjJGqbF+/Xpt3LhR/fv3V1RUlKKiojRkyBDFxcVpxYoVqly5coHT0Lm5ucn8/6PBDhkyRA6HQ/fdd5/uuusuHThwwLVMknx8fCTln8Tcz89Pc+fOdS07cuSI/Pz8CmRxOBznHTfXGKO8vLyLPoeOHTuqZ8+eqlOnjlq3bq2KFStq+fLl2rp1q1544YUi1/v7s/t4ep7/K+50OtWqVSu9+eabrusOHDggf39/LVq0qMAxkc8do9/z8PAosE53d3c5nU4ZY/TJJ5+oQoUKkqRjx46pXLlyOn78uLy9vQvNdNbv8xd22sBevXopODhYbdq00X333afNmzefl9HpdBYYJ6fTqdzcXNfls68ncK1gb2qUGlWqVNGUKVP0ww8/uK5LS0tTRkaGgoKCLnrf5cuXa9CgQYqJiZEkbd682fU987kCAwNVvnx5VxkfOHBAcXFx2rZtW4Hb1a1bV56envr2228l5e/pu3DhQt1xxx0XzVGjRg1VrlxZn3zyiVq3bq0777xT3377rU6cOKGQkJASr/dcrVq10ooVK5ScnCxJWrZsmTp16qQzZ84Uex2StHjxYqWnp8vpdOqzzz5TVFSUfH19FR4ervfee0+SdOrUKfXo0eOy7UB36tQpbd26VX/7298UHR2tgwcP6tdff5XT6SxwuzZt2ujjjz9Wbm6unE6nPvroI7Vu3fqyZACuBLaMUWoEBgZq8uTJGjdunA4ePKhy5crJz89PY8aMUd26dZWWlnbB+z799NMaNGiQfHx85Ovrq2bNmrmmbc/l7e2tt956S4mJiZo6dary8vL05JNP6vbbby9wOy8vL7311lsaPXq0Jk6cKIfDoUGDBqlly5ZFPo+OHTvq3XffVaNGjeTu7q7y5cvr7rvvLnK9a9asKdY41a9fXy+++KKGDBkiY4w8PT01ZcoU3XDDDcW6/1lVq1bVgAEDdPz4cTVr1kyPPfaYJGns2LF66aWXFB8fr5ycHMXFxalTp07at2/fJa2/MBUrVtSjjz6qLl26yMfHR9WrV9dtt92mPXv2uKa2Jekvf/mLXnnlFXXu3Fl5eXkKDQ3Vc88994cfH7hSOGsTgEs2ceJEHT9+XM8//7ztKC4//fST+vbtq5UrV9qOAlwypqkBXPcWLVqk/v3766GHHrIdBSgRtowBALCMLWMAACyjjAEAsIwyBgDAMsoYAADLKGMAACyjjAEAsOz/AfMJwmy+nd0FAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Share of women histogram\n",
"fig, ax = plt.subplots(figsize = (8,6))\n",
"sns.distplot(grads_df[\"ShareWomen\"], kde = False, bins = 10)\n",
"ax.set(xlabel = \"Share of women per major\", title = \"Distribution of Share of Women\", xlim = (-0.1, 1.1))\n",
"plt.xticks(rotation=40)\n",
"sns.despine()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Compare share of women to earnings\n",
"fig, ax = plt.subplots(figsize = (8,6))\n",
"sns.regplot(x = grads_df[\"ShareWomen\"], y = grads_df[\"Median\"])\n",
"ax.set(xlabel = \"Share of female graduates\", ylabel = \"Median yearly earnings (in $)\", \\\n",
" title = \"Earnings vs. Share of Women per Major\")\n",
"sns.despine()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Findings:** Negative correlation: graduates in majority female majors seem to have lower earnings."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Compare highest and lowest paying majors in terms of share of women\n",
"fig, ax = plt.subplots(figsize = (8,6))\n",
"sns.barplot(majors_top_bottom_df[\"ShareWomen\"], majors_top_bottom_df[\"Major\"], hue = majors_top_bottom_df[\"Majors_ranking\"])\n",
"plt.axvline(.5, linestyle = \"dashed\", color = \"black\", linewidth = .9, alpha = .3)\n",
"ax.set(xlabel = \"Share of female graduates\", ylabel = \"Majors\",\n",
" title = \"Share of Women in Highest and Lowest Earning Majors\")\n",
"ax.legend(title = \"Majors ranking\")\n",
"sns.despine()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Findings:** The highest paying majors are engineering majors that are majority male. On the other hand, the lowest paying majors are majority female."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# Find median income and share of women per majors category (sorted by income)\n",
"major_cat_df = (grads_df.groupby(\"Major_category\")\n",
" .agg({\"Median\": \"median\", \"ShareWomen\" : \"median\"})\n",
" .sort_values(\"Median\", ascending = False))\n",
"major_cat_df.reset_index(inplace=True)\n",
"\n",
"# Keep only 5 highest and 5 lowest paying majors categories\n",
"major_cat_df.loc[major_cat_df.index[:5], \"Majors_ranking\"] = \"Highest paying\"\n",
"major_cat_df.loc[major_cat_df.index[-5:], \"Majors_ranking\"] = \"Lowest paying\"\n",
"cat_top_bottom_df = pd.concat([major_cat_df.head(), major_cat_df.tail()])"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Compare highest and lowest paying majors categories in terms of share of women\n",
"fig, ax = plt.subplots(figsize = (8,6))\n",
"sns.barplot(cat_top_bottom_df[\"ShareWomen\"], cat_top_bottom_df[\"Major_category\"], \n",
" hue = cat_top_bottom_df[\"Majors_ranking\"])\n",
"plt.axvline(.5, linestyle = \"dashed\", color = \"black\", linewidth = .9, alpha = .3)\n",
"ax.set(xlabel = \"Share of female graduates\", ylabel = \"Majors categories\",\n",
" title = \"Share of Women in Highest and Lowest Earning Majors Categories\")\n",
"ax.legend(title = \"Majors ranking\")\n",
"sns.despine()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Findings:** The highest paying majors categories tend to be male dominated, while the lowest paying ones are all majority female."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Women Graduates Time Series Data Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Year</th>\n",
" <th>Agriculture</th>\n",
" <th>Architecture</th>\n",
" <th>Art and Performance</th>\n",
" <th>Biology</th>\n",
" <th>Business</th>\n",
" <th>Communications and Journalism</th>\n",
" <th>Computer Science</th>\n",
" <th>Education</th>\n",
" <th>Engineering</th>\n",
" <th>English</th>\n",
" <th>Foreign Languages</th>\n",
" <th>Health Professions</th>\n",
" <th>Math and Statistics</th>\n",
" <th>Physical Sciences</th>\n",
" <th>Psychology</th>\n",
" <th>Public Administration</th>\n",
" <th>Social Sciences and History</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1970</td>\n",
" <td>4.229798</td>\n",
" <td>11.921005</td>\n",
" <td>59.7</td>\n",
" <td>29.088363</td>\n",
" <td>9.064439</td>\n",
" <td>35.3</td>\n",
" <td>13.6</td>\n",
" <td>74.535328</td>\n",
" <td>0.8</td>\n",
" <td>65.570923</td>\n",
" <td>73.8</td>\n",
" <td>77.1</td>\n",
" <td>38.0</td>\n",
" <td>13.8</td>\n",
" <td>44.4</td>\n",
" <td>68.4</td>\n",
" <td>36.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1971</td>\n",
" <td>5.452797</td>\n",
" <td>12.003106</td>\n",
" <td>59.9</td>\n",
" <td>29.394403</td>\n",
" <td>9.503187</td>\n",
" <td>35.5</td>\n",
" <td>13.6</td>\n",
" <td>74.149204</td>\n",
" <td>1.0</td>\n",
" <td>64.556485</td>\n",
" <td>73.9</td>\n",
" <td>75.5</td>\n",
" <td>39.0</td>\n",
" <td>14.9</td>\n",
" <td>46.2</td>\n",
" <td>65.5</td>\n",
" <td>36.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1972</td>\n",
" <td>7.420710</td>\n",
" <td>13.214594</td>\n",
" <td>60.4</td>\n",
" <td>29.810221</td>\n",
" <td>10.558962</td>\n",
" <td>36.6</td>\n",
" <td>14.9</td>\n",
" <td>73.554520</td>\n",
" <td>1.2</td>\n",
" <td>63.664263</td>\n",
" <td>74.6</td>\n",
" <td>76.9</td>\n",
" <td>40.2</td>\n",
" <td>14.8</td>\n",
" <td>47.6</td>\n",
" <td>62.6</td>\n",
" <td>36.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1973</td>\n",
" <td>9.653602</td>\n",
" <td>14.791613</td>\n",
" <td>60.2</td>\n",
" <td>31.147915</td>\n",
" <td>12.804602</td>\n",
" <td>38.4</td>\n",
" <td>16.4</td>\n",
" <td>73.501814</td>\n",
" <td>1.6</td>\n",
" <td>62.941502</td>\n",
" <td>74.9</td>\n",
" <td>77.4</td>\n",
" <td>40.9</td>\n",
" <td>16.5</td>\n",
" <td>50.4</td>\n",
" <td>64.3</td>\n",
" <td>36.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1974</td>\n",
" <td>14.074623</td>\n",
" <td>17.444688</td>\n",
" <td>61.9</td>\n",
" <td>32.996183</td>\n",
" <td>16.204850</td>\n",
" <td>40.5</td>\n",
" <td>18.9</td>\n",
" <td>73.336811</td>\n",
" <td>2.2</td>\n",
" <td>62.413412</td>\n",
" <td>75.3</td>\n",
" <td>77.9</td>\n",
" <td>41.8</td>\n",
" <td>18.2</td>\n",
" <td>52.6</td>\n",
" <td>66.1</td>\n",
" <td>37.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year Agriculture Architecture Art and Performance Biology Business \\\n",
"0 1970 4.229798 11.921005 59.7 29.088363 9.064439 \n",
"1 1971 5.452797 12.003106 59.9 29.394403 9.503187 \n",
"2 1972 7.420710 13.214594 60.4 29.810221 10.558962 \n",
"3 1973 9.653602 14.791613 60.2 31.147915 12.804602 \n",
"4 1974 14.074623 17.444688 61.9 32.996183 16.204850 \n",
"\n",
" Communications and Journalism Computer Science Education Engineering \\\n",
"0 35.3 13.6 74.535328 0.8 \n",
"1 35.5 13.6 74.149204 1.0 \n",
"2 36.6 14.9 73.554520 1.2 \n",
"3 38.4 16.4 73.501814 1.6 \n",
"4 40.5 18.9 73.336811 2.2 \n",
"\n",
" English Foreign Languages Health Professions Math and Statistics \\\n",
"0 65.570923 73.8 77.1 38.0 \n",
"1 64.556485 73.9 75.5 39.0 \n",
"2 63.664263 74.6 76.9 40.2 \n",
"3 62.941502 74.9 77.4 40.9 \n",
"4 62.413412 75.3 77.9 41.8 \n",
"\n",
" Physical Sciences Psychology Public Administration \\\n",
"0 13.8 44.4 68.4 \n",
"1 14.9 46.2 65.5 \n",
"2 14.8 47.6 62.6 \n",
"3 16.5 50.4 64.3 \n",
"4 18.2 52.6 66.1 \n",
"\n",
" Social Sciences and History \n",
"0 36.8 \n",
"1 36.2 \n",
"2 36.1 \n",
"3 36.4 \n",
"4 37.3 "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Add dataframe of percentage of bachelor's degrees granted to women from 1970 to 2011\n",
"women_degrees_df = pd.read_csv(\"data/bachelors-degrees-women-usa.csv\")\n",
"\n",
"# Preview of the dataset\n",
"women_degrees_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
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" <th>Architecture</th>\n",
" <th>Art and Performance</th>\n",
" <th>Biology</th>\n",
" <th>Business</th>\n",
" <th>Communications and Journalism</th>\n",
" <th>Computer Science</th>\n",
" <th>Education</th>\n",
" <th>Engineering</th>\n",
" <th>English</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>2007</td>\n",
" <td>47.605026</td>\n",
" <td>43.100459</td>\n",
" <td>61.4</td>\n",
" <td>59.411993</td>\n",
" <td>49.000459</td>\n",
" <td>62.5</td>\n",
" <td>17.6</td>\n",
" <td>78.721413</td>\n",
" <td>16.8</td>\n",
" <td>67.874923</td>\n",
" <td>70.2</td>\n",
" <td>85.4</td>\n",
" <td>44.1</td>\n",
" <td>40.7</td>\n",
" <td>77.1</td>\n",
" <td>82.1</td>\n",
" <td>49.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>2008</td>\n",
" <td>47.570834</td>\n",
" <td>42.711730</td>\n",
" <td>60.7</td>\n",
" <td>59.305765</td>\n",
" <td>48.888027</td>\n",
" <td>62.4</td>\n",
" <td>17.8</td>\n",
" <td>79.196327</td>\n",
" <td>16.5</td>\n",
" <td>67.594028</td>\n",
" <td>70.2</td>\n",
" <td>85.2</td>\n",
" <td>43.3</td>\n",
" <td>40.7</td>\n",
" <td>77.2</td>\n",
" <td>81.7</td>\n",
" <td>49.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>2009</td>\n",
" <td>48.667224</td>\n",
" <td>43.348921</td>\n",
" <td>61.0</td>\n",
" <td>58.489583</td>\n",
" <td>48.840474</td>\n",
" <td>62.8</td>\n",
" <td>18.1</td>\n",
" <td>79.532909</td>\n",
" <td>16.8</td>\n",
" <td>67.969792</td>\n",
" <td>69.3</td>\n",
" <td>85.1</td>\n",
" <td>43.3</td>\n",
" <td>40.7</td>\n",
" <td>77.1</td>\n",
" <td>82.0</td>\n",
" <td>49.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>2010</td>\n",
" <td>48.730042</td>\n",
" <td>42.066721</td>\n",
" <td>61.3</td>\n",
" <td>59.010255</td>\n",
" <td>48.757988</td>\n",
" <td>62.5</td>\n",
" <td>17.6</td>\n",
" <td>79.618625</td>\n",
" <td>17.2</td>\n",
" <td>67.928106</td>\n",
" <td>69.0</td>\n",
" <td>85.0</td>\n",
" <td>43.1</td>\n",
" <td>40.2</td>\n",
" <td>77.0</td>\n",
" <td>81.7</td>\n",
" <td>49.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>2011</td>\n",
" <td>50.037182</td>\n",
" <td>42.773438</td>\n",
" <td>61.2</td>\n",
" <td>58.742397</td>\n",
" <td>48.180418</td>\n",
" <td>62.2</td>\n",
" <td>18.2</td>\n",
" <td>79.432812</td>\n",
" <td>17.5</td>\n",
" <td>68.426730</td>\n",
" <td>69.5</td>\n",
" <td>84.8</td>\n",
" <td>43.1</td>\n",
" <td>40.1</td>\n",
" <td>76.7</td>\n",
" <td>81.9</td>\n",
" <td>49.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year Agriculture Architecture Art and Performance Biology \\\n",
"37 2007 47.605026 43.100459 61.4 59.411993 \n",
"38 2008 47.570834 42.711730 60.7 59.305765 \n",
"39 2009 48.667224 43.348921 61.0 58.489583 \n",
"40 2010 48.730042 42.066721 61.3 59.010255 \n",
"41 2011 50.037182 42.773438 61.2 58.742397 \n",
"\n",
" Business Communications and Journalism Computer Science Education \\\n",
"37 49.000459 62.5 17.6 78.721413 \n",
"38 48.888027 62.4 17.8 79.196327 \n",
"39 48.840474 62.8 18.1 79.532909 \n",
"40 48.757988 62.5 17.6 79.618625 \n",
"41 48.180418 62.2 18.2 79.432812 \n",
"\n",
" Engineering English Foreign Languages Health Professions \\\n",
"37 16.8 67.874923 70.2 85.4 \n",
"38 16.5 67.594028 70.2 85.2 \n",
"39 16.8 67.969792 69.3 85.1 \n",
"40 17.2 67.928106 69.0 85.0 \n",
"41 17.5 68.426730 69.5 84.8 \n",
"\n",
" Math and Statistics Physical Sciences Psychology Public Administration \\\n",
"37 44.1 40.7 77.1 82.1 \n",
"38 43.3 40.7 77.2 81.7 \n",
"39 43.3 40.7 77.1 82.0 \n",
"40 43.1 40.2 77.0 81.7 \n",
"41 43.1 40.1 76.7 81.9 \n",
"\n",
" Social Sciences and History \n",
"37 49.3 \n",
"38 49.4 \n",
"39 49.4 \n",
"40 49.3 \n",
"41 49.2 "
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"women_degrees_df.tail()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <th></th>\n",
" <th>Engineering</th>\n",
" <th>Computer Science</th>\n",
" <th>Physical Sciences</th>\n",
" <th>Architecture</th>\n",
" <th>Math and Statistics</th>\n",
" <th>Business</th>\n",
" <th>Social Sciences and History</th>\n",
" <th>Agriculture</th>\n",
" <th>Biology</th>\n",
" <th>Art and Performance</th>\n",
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" <th>English</th>\n",
" <th>Foreign Languages</th>\n",
" <th>Psychology</th>\n",
" <th>Education</th>\n",
" <th>Public Administration</th>\n",
" <th>Health Professions</th>\n",
" <th>Year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>17.5</td>\n",
" <td>18.2</td>\n",
" <td>40.1</td>\n",
" <td>42.773438</td>\n",
" <td>43.1</td>\n",
" <td>48.180418</td>\n",
" <td>49.2</td>\n",
" <td>50.037182</td>\n",
" <td>58.742397</td>\n",
" <td>61.2</td>\n",
" <td>62.2</td>\n",
" <td>68.42673</td>\n",
" <td>69.5</td>\n",
" <td>76.7</td>\n",
" <td>79.432812</td>\n",
" <td>81.9</td>\n",
" <td>84.8</td>\n",
" <td>2011</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Engineering Computer Science Physical Sciences Architecture \\\n",
"41 17.5 18.2 40.1 42.773438 \n",
"\n",
" Math and Statistics Business Social Sciences and History Agriculture \\\n",
"41 43.1 48.180418 49.2 50.037182 \n",
"\n",
" Biology Art and Performance Communications and Journalism English \\\n",
"41 58.742397 61.2 62.2 68.42673 \n",
"\n",
" Foreign Languages Psychology Education Public Administration \\\n",
"41 69.5 76.7 79.432812 81.9 \n",
"\n",
" Health Professions Year \n",
"41 84.8 2011 "
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check degrees sorted in ascending order by share of women graduates in the last year (2011)\n",
"women_degrees_df.sort_values(by = 41, ascending = True, axis = 1)[women_degrees_df[\"Year\"] == 2011]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Gender Gap in Bachelor's Degrees"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1296x216 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# List of STEM majors categories sorted in ascending order by share of women graduates in the last year (2011)\n",
"stem_cat = [\"Engineering\", \"Computer Science\", \"Physical Sciences\", \"Math and Statistics\", \"Biology\", \"Psychology\"]\n",
"\n",
"# Add figure for STEM majors line charts\n",
"fig = plt.figure(figsize=(18, 3))\n",
"fig.suptitle(\"Percentage of Men and Women Graduates in STEM Majors from 1970 to 2011\", fontsize = 14, y = 1.2)\n",
"\n",
"# Plot line chart for each STEM major\n",
"for i in range(0,6):\n",
" ax = fig.add_subplot(1, 6, i+1)\n",
" ax.plot(women_degrees_df[\"Year\"], 100 - women_degrees_df[stem_cat[i]], label = \"Men\", linewidth = 2) # Men graduates\n",
" ax.plot(women_degrees_df[\"Year\"], women_degrees_df[stem_cat[i]], label = \"Women\" , linewidth = 2) # Women graduates\n",
" ax.set_xlim(1970, 2011)\n",
" ax.set_ylim(0,100)\n",
" ax.set_title(stem_cat[i])\n",
" plt.axhline(50, linestyle = \"dashed\", color = \"black\", linewidth = .8, alpha = .5)\n",
" ax.locator_params(nbins = 5, axis = \"x\")\n",
"\n",
" # Add legend for first and last subplots \n",
" if i == 0:\n",
" ax.text(2005, 87, \"Men\")\n",
" ax.text(2002, 8, \"Women\")\n",
" elif i == 5:\n",
" ax.text(2005, 12, \"Men\")\n",
" ax.text(2001, 82, \"Women\")\n",
" \n",
"sns.despine(left=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Findings:** The share of women graduates in STEM majors has increased from 1970 to 2011 in most categories.\n",
"\n",
"However, 4 out of the 6 STEM majors categories are still majority male. The other two majors, biology and psychology, are currently majority female."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1296x504 with 12 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Compare STEM time series to liberal arts time series\n",
"lib_arts_cat = [\"Social Sciences and History\", \"Art and Performance\", \"Communications and Journalism\", \"English\",\n",
" \"Foreign Languages\", \"Education\"]\n",
"\n",
"# Add figure for STEM majors line charts\n",
"fig = plt.figure(figsize=(18, 7))\n",
"fig.suptitle(\"Percentage of Men and Women Graduates in STEM and Liberal Arts Majors from 1970 to 2011\", fontsize = 14)\n",
"plt.subplots_adjust(hspace = .4)\n",
"\n",
"for i in range(0,6):\n",
" # Plot line chart for each STEM major\n",
" ax = fig.add_subplot(2, 6, i+1)\n",
" ax.plot(women_degrees_df[\"Year\"], 100 - women_degrees_df[stem_cat[i]], label = \"Men\", linewidth = 2) # Men graduates\n",
" ax.plot(women_degrees_df[\"Year\"], women_degrees_df[stem_cat[i]], label = \"Women\" , linewidth = 2) # Women graduates\n",
" ax.set_xlim(1970, 2011)\n",
" ax.set_ylim(0,100)\n",
" ax.set_title(stem_cat[i])\n",
" plt.axhline(50, linestyle = \"dashed\", color = \"black\", linewidth = .8, alpha = .5)\n",
" ax.locator_params(nbins = 5, axis = \"x\")\n",
"\n",
" # Plot line chart for each liberal arts major\n",
" ax = fig.add_subplot(2, 6, i+7)\n",
" ax.plot(women_degrees_df[\"Year\"], 100 - women_degrees_df[lib_arts_cat[i]], label = \"Men\", linewidth = 2) # Men graduates\n",
" ax.plot(women_degrees_df[\"Year\"], women_degrees_df[lib_arts_cat[i]], label = \"Women\" , linewidth = 2) # Women graduates\n",
" ax.set_xlim(1970, 2011)\n",
" ax.set_ylim(0,100)\n",
" ax.set_title(lib_arts_cat[i])\n",
" plt.axhline(50, linestyle = \"dashed\", color = \"black\", linewidth = .8, alpha = .5)\n",
" ax.locator_params(nbins = 5, axis = \"x\")\n",
" \n",
" # Add legend for first and last subplots \n",
" if i == 0:\n",
" ax.text(2005, 228, \"Men\")\n",
" ax.text(2002, 148, \"Women\")\n",
" ax.text(2005, 55, \"Men\")\n",
" ax.text(2002, 40, \"Women\")\n",
" elif i == 5:\n",
" ax.text(2005, 155, \"Men\")\n",
" ax.text(2002, 222, \"Women\")\n",
" ax.text(2005, 12, \"Men\")\n",
" ax.text(2001, 83, \"Women\")\n",
" \n",
"sns.despine(left=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Findings:** Most liberal arts majors were majority female even in 1970, contrary to STEM majors."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conclusion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Insights:**\n",
"\n",
"Most college graduates earn between <span>$</span>30,000 and <span>$</span>40,000 out of college. However, engineering majors tend to earn much more, reaching more than <span>$</span>100,000.\n",
"\n",
"Majors' earning potential does not seem to have an impact on graduates' unemployment rate, but it appears to affect underemployment. \n",
"\n",
"Graduates from majority female majors appear to have lower earnings. This is in part due to the gender gap in bachelor's degrees that impacts female graduates' earnings. Indeed, the highest paying majors, namely STEM ones, are majority male. On the other hand, lower paying liberal arts majors tend to be majority female. For instance, computer science majors were 80% male as of 2011 while education majors were 80% female."
]
}
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"<a href=\"https://colab.research.google.com/github/cschlick/colabfolds/blob/main/AlphaFold2_with_ManualTemplates.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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"#ColabFold: AlphaFold2 w/ MMseqs2\n",
"-----------------\n",
"- <b><font color='green'>21Aug2021: The MSA/Templates issues should now be resolved! Please report any errors you see.</font></b>\n",
"-----------------\n",
"<img src=\"https://raw.githubusercontent.com/sokrypton/ColabFold/main/.github/ColabFold_Marv_Logo_Small.png\" height=\"256\" align=\"right\" style=\"height:256px\">\n",
"\n",
"Easy to use AlphaFold2 [(Jumper et al. 2021)](https://www.nature.com/articles/s41586-021-03819-2) protein structure prediction using multiple sequence alignments generated through an MMseqs2 API. For details, refer to our manuscript:\n",
"\n",
"[Mirdita M, Ovchinnikov S, Steinegger M. ColabFold - Making protein folding accessible to all.\n",
"*bioRxiv*, 2021](https://www.biorxiv.org/content/10.1101/2021.08.15.456425v1) \n",
"\n",
"- This notebook provides basic functionality, for more advanced options (such as modeling heterocomplexes, increasing recycles, sampling, etc.) see our [advanced notebook](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb).\n",
"- This notebook replaces the homology detection of AlphaFold2 with MMseqs2. For a comparision against the [Deepmind Colab](https://colab.research.google.com/github/deepmind/alphafold/blob/main/notebooks/AlphaFold.ipynb) and the full [AlphaFold2](https://github.com/deepmind/alphafold) system read our [preprint](https://www.biorxiv.org/content/10.1101/2021.08.15.456425v1). \n",
"\n",
"\n",
"<strong>For more details, see <a href=\"#Instructions\">bottom</a> of the notebook and checkout the [ColabFold GitHub](https://github.com/sokrypton/ColabFold). </strong>\n",
"\n",
"\n",
"\n"
]
},
{
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"id": "kOblAo-xetgx",
"cellView": "form"
},
"source": [
"#@title Input protein sequence, then hit `Runtime` -> `Run all`\n",
"from google.colab import files\n",
"import os\n",
"import os.path\n",
"import re\n",
"import hashlib\n",
"\n",
"def add_hash(x,y):\n",
" return x+\"_\"+hashlib.sha1(y.encode()).hexdigest()[:5]\n",
"\n",
"query_sequence = 'EVQLVESGGGLVNPGGSLRLSCAASGFTFSDYTIHWVRQAPGKGLEWVSSISSSSNYIYYADSVKGRFTISRDNAKNSLS LQMNSLRAEDTAVYYCARDGNAYKWLLAENVRFDYWGQGTLVTVSS' #@param {type:\"string\"}\n",
"# remove whitespaces\n",
"query_sequence = \"\".join(query_sequence.split())\n",
"query_sequence = re.sub(r'[^a-zA-Z]','', query_sequence).upper()\n",
"\n",
"jobname = 'manual_template' #@param {type:\"string\"}\n",
"# remove whitespaces\n",
"jobname = \"\".join(jobname.split())\n",
"jobname = re.sub(r'\\W+', '', jobname)\n",
"jobname = add_hash(jobname, query_sequence)\n",
"\n",
"\n",
"with open(f\"{jobname}.fasta\", \"w\") as text_file:\n",
" text_file.write(\">1\\n%s\" % query_sequence)\n",
"\n",
"# number of models to use\n",
"#@markdown ---\n",
"#@markdown ### Advanced settings\n",
"msa_mode = \"MMseqs2 (UniRef+Environmental)\" #@param [\"MMseqs2 (UniRef+Environmental)\", \"MMseqs2 (UniRef only)\",\"single_sequence\",\"custom\"]\n",
"num_models = 5 #@param [1,2,3,4,5] {type:\"raw\"}\n",
"use_msa = True if msa_mode.startswith(\"MMseqs2\") else False\n",
"use_env = True if msa_mode == \"MMseqs2 (UniRef+Environmental)\" else False\n",
"use_custom_msa = True if msa_mode == \"custom\" else False\n",
"use_amber = False #@param {type:\"boolean\"}\n",
"use_templates = True #@param {type:\"boolean\"}\n",
"#@markdown ---\n",
"#@markdown ### Experimental options\n",
"homooligomer = 1 #@param [1,2,3,4,5,6,7,8] {type:\"raw\"}\n",
"save_to_google_drive = False #@param {type:\"boolean\"}\n",
"#@markdown ---\n",
"#@markdown Don't forget to hit `Runtime` -> `Run all` after updating the form.\n",
"\n",
"\n",
"if homooligomer > 1:\n",
" if use_amber:\n",
" print(\"amber disabled: amber is not currently supported for homooligomers\")\n",
" use_amber = False\n",
" if use_templates:\n",
" print(\"templates disabled: templates are not currently supported for homooligomers\")\n",
" use_templates = False\n",
"\n",
"with open(f\"{jobname}.log\", \"w\") as text_file:\n",
" text_file.write(\"num_models=%s\\n\" % num_models)\n",
" text_file.write(\"use_amber=%s\\n\" % use_amber)\n",
" text_file.write(\"use_msa=%s\\n\" % use_msa)\n",
" text_file.write(\"msa_mode=%s\\n\" % msa_mode)\n",
" text_file.write(\"use_templates=%s\\n\" % use_templates)\n",
" text_file.write(\"homooligomer=%s\\n\" % homooligomer)\n",
"\n",
"# decide which a3m to use\n",
"if use_msa:\n",
" a3m_file = f\"{jobname}.a3m\"\n",
"elif use_custom_msa:\n",
" a3m_file = f\"{jobname}.custom.a3m\"\n",
" if not os.path.isfile(a3m_file):\n",
" custom_msa_dict = files.upload()\n",
" custom_msa = list(custom_msa_dict.keys())[0]\n",
" header = 0\n",
" import fileinput\n",
" for line in fileinput.FileInput(custom_msa,inplace=1):\n",
" if line.startswith(\">\"):\n",
" header = header + 1 \n",
" if line.startswith(\"#\"):\n",
" continue\n",
" if line.rstrip() == False:\n",
" continue\n",
" if line.startswith(\">\") == False and header == 1:\n",
" query_sequence = line.rstrip() \n",
" print(line, end='')\n",
"\n",
" os.rename(custom_msa, a3m_file)\n",
" print(f\"moving {custom_msa} to {a3m_file}\")\n",
"else:\n",
" a3m_file = f\"{jobname}.single_sequence.a3m\"\n",
" with open(a3m_file, \"w\") as text_file:\n",
" text_file.write(\">1\\n%s\" % query_sequence)\n",
"\n",
"if save_to_google_drive == True:\n",
" from pydrive.drive import GoogleDrive\n",
" from pydrive.auth import GoogleAuth\n",
" from google.colab import auth\n",
" from oauth2client.client import GoogleCredentials\n",
" auth.authenticate_user()\n",
" gauth = GoogleAuth()\n",
" gauth.credentials = GoogleCredentials.get_application_default()\n",
" drive = GoogleDrive(gauth)\n",
" print(\"You are logged into Google Drive and are good to go!\")"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "iccGdbe_Pmt9",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ceb96c0a-3eb0-49d4-d9ec-79428cd14478"
},
"source": [
"#@title Install dependencies\n",
"%%bash -s $use_amber $use_msa $use_templates\n",
"\n",
"USE_AMBER=$1\n",
"USE_MSA=$2\n",
"USE_TEMPLATES=$3\n",
"\n",
"if [ ! -f AF2_READY ]; then\n",
" # install dependencies\n",
" pip -q install biopython dm-haiku ml-collections py3Dmol\n",
" wget -qnc https://raw.githubusercontent.com/sokrypton/ColabFold/main/beta/colabfold.py\n",
"\n",
" # download model\n",
" if [ ! -d \"alphafold/\" ]; then\n",
" git clone https://github.com/deepmind/alphafold.git --quiet\n",
" (cd alphafold; git checkout 0bab1bf84d9d887aba5cfb6d09af1e8c3ecbc408 --quiet)\n",
" mv alphafold alphafold_\n",
" mv alphafold_/alphafold .\n",
" # remove \"END\" from PDBs, otherwise biopython complains\n",
" sed -i \"s/pdb_lines.append('END')//\" /content/alphafold/common/protein.py\n",
" sed -i \"s/pdb_lines.append('ENDMDL')//\" /content/alphafold/common/protein.py\n",
" fi\n",
"\n",
" # download model params (~1 min)\n",
" if [ ! -d \"params/\" ]; then\n",
" mkdir params\n",
" curl -fsSL https://storage.googleapis.com/alphafold/alphafold_params_2021-07-14.tar \\\n",
" | tar x -C params\n",
" fi\n",
" touch AF2_READY\n",
"fi\n",
"# download libraries for interfacing with MMseqs2 API\n",
"if [ ${USE_MSA} == \"True\" ] || [ ${USE_TEMPLATES} == \"True\" ]; then\n",
" if [ ! -f MMSEQ2_READY ]; then\n",
" apt-get -qq -y update 2>&1 1>/dev/null\n",
" apt-get -qq -y install jq curl zlib1g gawk 2>&1 1>/dev/null\n",
" touch MMSEQ2_READY\n",
" fi\n",
"fi\n",
"# setup conda\n",
"if [ ${USE_AMBER} == \"True\" ] || [ ${USE_TEMPLATES} == \"True\" ]; then\n",
" if [ ! -f CONDA_READY ]; then\n",
" wget -qnc https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
" bash Miniconda3-latest-Linux-x86_64.sh -bfp /usr/local 2>&1 1>/dev/null\n",
" rm Miniconda3-latest-Linux-x86_64.sh\n",
" touch CONDA_READY\n",
" fi\n",
"fi\n",
"# setup template search\n",
"if [ ${USE_TEMPLATES} == \"True\" ] && [ ! -f HH_READY ]; then\n",
" conda install -y -q -c conda-forge -c bioconda kalign3=3.2.2 hhsuite=3.3.0 python=3.7 2>&1 1>/dev/null\n",
" touch HH_READY\n",
"fi\n",
"# setup openmm for amber refinement\n",
"if [ ${USE_AMBER} == \"True\" ] && [ ! -f AMBER_READY ]; then\n",
" conda install -y -q -c conda-forge openmm=7.5.1 python=3.7 pdbfixer 2>&1 1>/dev/null\n",
" (cd /usr/local/lib/python3.7/site-packages; patch -s -p0 < /content/alphafold_/docker/openmm.patch)\n",
" wget -qnc https://git.scicore.unibas.ch/schwede/openstructure/-/raw/7102c63615b64735c4941278d92b554ec94415f8/modules/mol/alg/src/stereo_chemical_props.txt\n",
" mv stereo_chemical_props.txt alphafold/common/\n",
" touch AMBER_READY\n",
"fi"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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]
}
]
},
{
"cell_type": "code",
"metadata": {
"cellView": "form",
"id": "jFNCnjI9_DUG"
},
"source": [
"#@title Import libraries\n",
"# setup the model\n",
"if \"model\" not in dir():\n",
"\n",
" # hiding warning messages\n",
" import warnings\n",
" from absl import logging\n",
" import os\n",
" import tensorflow as tf\n",
" warnings.filterwarnings('ignore')\n",
" logging.set_verbosity(\"error\")\n",
" os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
" tf.get_logger().setLevel('ERROR')\n",
"\n",
" import sys\n",
" import numpy as np\n",
" import pickle\n",
" from alphafold.common import protein\n",
" from alphafold.data import pipeline\n",
" from alphafold.data import templates\n",
" from alphafold.model import data\n",
" from alphafold.model import config\n",
" from alphafold.model import model\n",
" from alphafold.data.tools import hhsearch\n",
" import colabfold as cf\n",
"\n",
" # plotting libraries\n",
" import py3Dmol\n",
" import matplotlib.pyplot as plt\n",
" import ipywidgets\n",
" from ipywidgets import interact, fixed, GridspecLayout, Output\n",
"\n",
"\n",
"if use_amber and \"relax\" not in dir():\n",
" sys.path.insert(0, '/usr/local/lib/python3.7/site-packages/')\n",
" from alphafold.relax import relax\n",
"\n",
"def mk_mock_template(query_sequence):\n",
" # since alphafold's model requires a template input\n",
" # we create a blank example w/ zero input, confidence -1\n",
" ln = len(query_sequence)\n",
" output_templates_sequence = \"-\"*ln\n",
" output_confidence_scores = np.full(ln,-1)\n",
" templates_all_atom_positions = np.zeros((ln, templates.residue_constants.atom_type_num, 3))\n",
" templates_all_atom_masks = np.zeros((ln, templates.residue_constants.atom_type_num))\n",
" templates_aatype = templates.residue_constants.sequence_to_onehot(output_templates_sequence,\n",
" templates.residue_constants.HHBLITS_AA_TO_ID)\n",
" template_features = {'template_all_atom_positions': templates_all_atom_positions[None],\n",
" 'template_all_atom_masks': templates_all_atom_masks[None],\n",
" 'template_sequence': [f'none'.encode()],\n",
" 'template_aatype': np.array(templates_aatype)[None],\n",
" 'template_confidence_scores': output_confidence_scores[None],\n",
" 'template_domain_names': [f'none'.encode()],\n",
" 'template_release_date': [f'none'.encode()]}\n",
" return template_features\n",
"\n",
"def mk_template(a3m_lines, template_paths):\n",
" template_featurizer = templates.TemplateHitFeaturizer(\n",
" mmcif_dir=template_paths,\n",
" max_template_date=\"2100-01-01\",\n",
" max_hits=20,\n",
" kalign_binary_path=\"kalign\",\n",
" release_dates_path=None,\n",
" obsolete_pdbs_path=None)\n",
"\n",
" hhsearch_pdb70_runner = hhsearch.HHSearch(binary_path=\"hhsearch\", databases=[f\"{template_paths}/pdb70\"])\n",
"\n",
" hhsearch_result = hhsearch_pdb70_runner.query(a3m_lines)\n",
" hhsearch_hits = pipeline.parsers.parse_hhr(hhsearch_result)\n",
" templates_result = template_featurizer.get_templates(query_sequence=query_sequence,\n",
" query_pdb_code=None,\n",
" query_release_date=None,\n",
" hits=hhsearch_hits)\n",
" return templates_result.features\n",
"\n",
"def set_bfactor(pdb_filename, bfac, idx_res, chains):\n",
" I = open(pdb_filename,\"r\").readlines()\n",
" O = open(pdb_filename,\"w\")\n",
" for line in I:\n",
" if line[0:6] == \"ATOM \":\n",
" seq_id = int(line[22:26].strip()) - 1\n",
" seq_id = np.where(idx_res == seq_id)[0][0]\n",
" O.write(f\"{line[:21]}{chains[seq_id]}{line[22:60]}{bfac[seq_id]:6.2f}{line[66:]}\")\n",
" O.close()\n",
"\n",
"def predict_structure(prefix, feature_dict, Ls, model_params, use_model, do_relax=False, random_seed=0): \n",
" \"\"\"Predicts structure using AlphaFold for the given sequence.\"\"\"\n",
"\n",
" # Minkyung's code\n",
" # add big enough number to residue index to indicate chain breaks\n",
" idx_res = feature_dict['residue_index']\n",
" L_prev = 0\n",
" # Ls: number of residues in each chain\n",
" for L_i in Ls[:-1]:\n",
" idx_res[L_prev+L_i:] += 200\n",
" L_prev += L_i \n",
" chains = list(\"\".join([ascii_uppercase[n]*L for n,L in enumerate(Ls)]))\n",
" feature_dict['residue_index'] = idx_res\n",
"\n",
" # Run the models.\n",
" plddts,paes = [],[]\n",
" unrelaxed_pdb_lines = []\n",
" relaxed_pdb_lines = []\n",
"\n",
" for model_name, params in model_params.items():\n",
" if model_name in use_model:\n",
" print(f\"running {model_name}\")\n",
" # swap params to avoid recompiling\n",
" # note: models 1,2 have diff number of params compared to models 3,4,5\n",
" if any(str(m) in model_name for m in [1,2]): model_runner = model_runner_1\n",
" if any(str(m) in model_name for m in [3,4,5]): model_runner = model_runner_3\n",
" model_runner.params = params\n",
" \n",
" processed_feature_dict = model_runner.process_features(feature_dict, random_seed=random_seed)\n",
" prediction_result = model_runner.predict(processed_feature_dict)\n",
" unrelaxed_protein = protein.from_prediction(processed_feature_dict,prediction_result)\n",
" unrelaxed_pdb_lines.append(protein.to_pdb(unrelaxed_protein))\n",
" plddts.append(prediction_result['plddt'])\n",
" paes.append(prediction_result['predicted_aligned_error'])\n",
"\n",
" if do_relax:\n",
" # Relax the prediction.\n",
" amber_relaxer = relax.AmberRelaxation(max_iterations=0,tolerance=2.39,\n",
" stiffness=10.0,exclude_residues=[],\n",
" max_outer_iterations=20) \n",
" relaxed_pdb_str, _, _ = amber_relaxer.process(prot=unrelaxed_protein)\n",
" relaxed_pdb_lines.append(relaxed_pdb_str)\n",
"\n",
" # rerank models based on predicted lddt\n",
" lddt_rank = np.mean(plddts,-1).argsort()[::-1]\n",
" out = {}\n",
" print(\"reranking models based on avg. predicted lDDT\")\n",
" for n,r in enumerate(lddt_rank):\n",
" print(f\"model_{n+1} {np.mean(plddts[r])}\")\n",
"\n",
" unrelaxed_pdb_path = f'{prefix}_unrelaxed_model_{n+1}.pdb' \n",
" with open(unrelaxed_pdb_path, 'w') as f: f.write(unrelaxed_pdb_lines[r])\n",
" set_bfactor(unrelaxed_pdb_path, plddts[r], idx_res, chains)\n",
"\n",
" if do_relax:\n",
" relaxed_pdb_path = f'{prefix}_relaxed_model_{n+1}.pdb'\n",
" with open(relaxed_pdb_path, 'w') as f: f.write(relaxed_pdb_lines[r])\n",
" set_bfactor(relaxed_pdb_path, plddts[r], idx_res, chains)\n",
"\n",
" out[f\"model_{n+1}\"] = {\"plddt\":plddts[r], \"pae\":paes[r]}\n",
" return out"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "_sztQyz29DIC",
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 414,
"referenced_widgets": [
"be3e224c70094eab9282889aed846dfc",
"959a63e5881f4e87ba4dff27835ad1c4",
"616fb16d0cc74228b8a69234d17cb01a",
"0955ecb3b4774e73b6db1c02c81cac4f",
"a6d613eed1bc4bb5b4839048661c0a57",
"13a5de0e3ec848c2b1be19e9a4497a8c",
"e8b3b3ca0b824c25995e53b70d32a382",
"c72dcec4187b43b588a787ee08bcb831",
"5fc8306d06564a12bb2e89d0e81f92b5",
"b402cb9c0378463f8983dd1a750f61d3",
"28e30264566e47b5bd861940ac4c4325"
]
},
"outputId": "1a5bbdbd-6ba0-4fe7-a6c2-d6b54b931380"
},
"source": [
"#@title Call MMseqs2 to get MSA/templates\n",
"if use_templates:\n",
" a3m_lines, template_paths = cf.run_mmseqs2(query_sequence, jobname, use_env, use_templates=True)\n",
" if template_paths is None:\n",
" template_features = mk_mock_template(query_sequence * homooligomer)\n",
" else:\n",
" template_features = mk_template(a3m_lines, template_paths)\n",
"elif use_msa:\n",
" a3m_lines = cf.run_mmseqs2(query_sequence, jobname, use_env)\n",
" template_features = mk_mock_template(query_sequence * homooligomer)\n",
"else:\n",
" template_features = mk_mock_template(query_sequence * homooligomer)\n",
"\n",
"if use_msa:\n",
" with open(a3m_file, \"w\") as text_file:\n",
" text_file.write(a3m_lines)\n",
"else:\n",
" a3m_lines = \"\".join(open(a3m_file,\"r\").read())\n",
"\n",
"# parse MSA\n",
"msa, deletion_matrix = pipeline.parsers.parse_a3m(a3m_lines)"
],
"execution_count": 4,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "be3e224c70094eab9282889aed846dfc",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/150 [elapsed: 00:00 remaining: ?]"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"seq\tpdb\tcid\tevalue\n",
"0\t6QD6_D\t0.355\t8.108E-43\n",
"0\t6QD6_G\t0.355\t8.108E-43\n",
"0\t5WTS_A\t0.320\t2.869E-42\n",
"0\t4PFE_A\t0.282\t8.835E-37\n",
"0\t4UT7_H\t0.272\t1.106E-35\n",
"0\t5OVW_J\t0.273\t2.605E-34\n",
"0\t5VM6_A\t0.263\t4.900E-34\n",
"0\t6RUM_A\t0.297\t4.900E-34\n",
"0\t4OB5_H\t0.273\t9.217E-34\n",
"0\t3JBE_7\t0.307\t9.217E-34\n",
"0\t4JVP_A\t0.282\t1.264E-33\n",
"0\t5FOJ_A\t0.302\t1.734E-33\n",
"0\t5U64_B\t0.258\t1.734E-33\n",
"0\t5H30_I\t0.257\t2.378E-33\n",
"0\t5VM4_B\t0.265\t3.261E-33\n",
"0\t1Y18_F\t0.288\t3.261E-33\n",
"0\t5X08_H\t0.279\t4.472E-33\n",
"0\t1XIW_H\t0.253\t8.412E-33\n",
"0\t6VLR_F\t0.269\t1.154E-32\n",
"0\t4M62_H\t0.253\t1.154E-32\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
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"source": [
"#@markdown ---\n",
"#@markdown ### Optional: Supply templates manually\n",
"#@markdown If selected, you will be prompted to upload model files to use as templates. \\\n",
"supply_manual_templates = True #@param {type:\"boolean\"}\n",
"\n",
"#@markdown **Note:** Before having installed dependencies, you must have also previously selected\n",
"#@markdown - use_templates\n",
"\n",
"#@markdown **Warning:** This is not part of the intended way to run AlphaFold, so care must be taken in supplying templates and interpreting results.\n",
"#@markdown - Templates provided here will override any templates found by the original mechanisms (homology search)\n",
"#@markdown - Templates are split into chains, aligned to the query with hhsearch, and passed to the AlphaFold template featurizer\n",
"#@markdown - No QA occurs on the templates you provide. If they are really bad, they might be rejected by the template featurizer\n",
"#@markdown - PDB files are not supported. If converting from PDB to CIF, it needs to be a good conversion that builds fields not built by many available tools. Try this one: https://mmcif.pdbj.org/converter/index.php?l=en\n",
"#@markdown - The filestem needs to be a simple four characters. Filenames with _ separators will definitely break.\n",
"import os\n",
"from io import StringIO\n",
"import shutil\n",
"from pathlib import Path\n",
"from contextlib import redirect_stderr, redirect_stdout\n",
"from dataclasses import dataclass, replace\n",
"\n",
"from alphafold.data import mmcif_parsing\n",
"from alphafold.data.templates import (_get_pdb_id_and_chain,\n",
" _process_single_hit,\n",
" _assess_hhsearch_hit,\n",
" _build_query_to_hit_index_mapping,\n",
" _extract_template_features,\n",
" SingleHitResult,\n",
" TEMPLATE_FEATURES)\n",
"from Bio.Seq import Seq\n",
"from Bio.SeqRecord import SeqRecord\n",
"from Bio import SeqIO\n",
"\n",
"from google.colab import files\n",
"\n",
"def hh_process_seq(query_seq,template_seq,hhDB_dir,db_prefix=\"DB\"):\n",
" \"\"\"\n",
" This is a hack to get hhsuite output strings to pass on\n",
" to the AlphaFold template featurizer. \n",
" \n",
" Note: that in the case of multiple templates, this would be faster to build one database for\n",
" all the templates. Currently it builds a database with only one template at a time. Even \n",
" better would be to get an hhsuite alignment without using a database at all, just between\n",
" pairs of sequence files. However, I have not figured out how to do this.\n",
"\n",
" Update: I think the hhsearch can be replaced completely, and we can just do a pairwise \n",
" alignment with biopython, or skip alignment if the seqs match. TODO\n",
" \"\"\"\n",
" # set up directory for hhsuite DB. Place one template fasta file to be the DB contents\n",
" if hhDB_dir.exists():\n",
" shutil.rmtree(hhDB_dir)\n",
" \n",
" msa_dir = Path(hhDB_dir,\"msa\")\n",
" msa_dir.mkdir(parents=True)\n",
" template_seq_path = Path(msa_dir,\"template.fasta\")\n",
" with template_seq_path.open(\"w\") as fh:\n",
" SeqIO.write([template_seq], fh, \"fasta\")\n",
"\n",
" # make hhsuite DB\n",
" with redirect_stdout(StringIO()) as out:\n",
" os.chdir(msa_dir)\n",
" %shell ffindex_build -s ../DB_msa.ff{data,index} .\n",
" os.chdir(hhDB_dir)\n",
" %shell ffindex_apply DB_msa.ff{data,index} -i DB_a3m.ffindex -d DB_a3m.ffdata -- hhconsensus -M 50 -maxres 65535 -i stdin -oa3m stdout -v 0\n",
" %shell rm DB_msa.ff{data,index}\n",
" %shell ffindex_apply DB_a3m.ff{data,index} -i DB_hhm.ffindex -d DB_hhm.ffdata -- hhmake -i stdin -o stdout -v 0\n",
" %shell cstranslate -f -x 0.3 -c 4 -I a3m -i DB_a3m -o DB_cs219 \n",
" %shell sort -k3 -n -r DB_cs219.ffindex | cut -f1 > sorting.dat\n",
"\n",
" %shell ffindex_order sorting.dat DB_hhm.ff{data,index} DB_hhm_ordered.ff{data,index}\n",
" %shell mv DB_hhm_ordered.ffindex DB_hhm.ffindex\n",
" %shell mv DB_hhm_ordered.ffdata DB_hhm.ffdata\n",
"\n",
" %shell ffindex_order sorting.dat DB_a3m.ff{data,index} DB_a3m_ordered.ff{data,index}\n",
" %shell mv DB_a3m_ordered.ffindex DB_a3m.ffindex\n",
" %shell mv DB_a3m_ordered.ffdata DB_a3m.ffdata\n",
"\n",
" # run hhsearch\n",
" hhsearch_runner = hhsearch.HHSearch(binary_path=\"hhsearch\", databases=[hhDB_dir.as_posix()+\"/\"+db_prefix])\n",
" with StringIO() as fh:\n",
" SeqIO.write([query_seq], fh, \"fasta\")\n",
" seq_fasta = fh.getvalue()\n",
" hhsearch_result = hhsearch_runner.query(seq_fasta)\n",
"\n",
" # process hits\n",
" hhsearch_hits = pipeline.parsers.parse_hhr(hhsearch_result)\n",
" if len(hhsearch_hits) >0:\n",
" hit = hhsearch_hits[0]\n",
" hit = replace(hit,**{\"name\":template_seq.id})\n",
" else:\n",
" hit = None\n",
" print(\"ERROR: Rejected template: \",template_seq.id)\n",
" return hit\n",
"\n",
"\n",
"os.chdir(\"/content/\")\n",
"if not supply_manual_templates:\n",
" print(\"Not using manual templates.\")\n",
"else:\n",
"\n",
" parent_dir = Path(\"/content/manual_templates\")\n",
" cif_dir = Path(parent_dir,\"mmcif\")\n",
" fasta_dir = Path(parent_dir,\"fasta\")\n",
" hhDB_dir = Path(parent_dir,\"hhDB\")\n",
" msa_dir = Path(hhDB_dir,\"msa\")\n",
" all_dirs = [parent_dir,cif_dir,fasta_dir,hhDB_dir,msa_dir]\n",
" for d in all_dirs:\n",
" if d.exists():\n",
" shutil.rmtree(d)\n",
" d.mkdir(parents=True)\n",
" \n",
" with redirect_stdout(StringIO()) as out:\n",
" uploaded = files.upload()\n",
" for filename,contents in uploaded.items():\n",
" filepath = Path(cif_dir,filename)\n",
" with filepath.open(\"w\") as fh:\n",
" fh.write(contents.decode(\"UTF-8\"))\n",
"\n",
"\n",
"\n",
" cif_files = list(cif_dir.glob(\"*\"))\n",
" query_seq = SeqRecord(Seq(query_sequence),id=\"query\",name=\"\",description=\"\")\n",
" query_seq_path = Path(fasta_dir,\"query.fasta\")\n",
" with query_seq_path.open(\"w\") as fh:\n",
" SeqIO.write([query_seq], fh, \"fasta\")\n",
"\n",
" shutil.copyfile(query_seq_path,Path(msa_dir,\"query.fasta\"))\n",
" seqs = []\n",
" template_hit_list = []\n",
"\n",
" print(\"\\nProcessing templates...\")\n",
" for i,filepath in enumerate(cif_files):\n",
" with filepath.open(\"r\") as fh:\n",
" filestr = fh.read()\n",
" mmcif_obj = mmcif_parsing.parse(file_id=filepath.stem,mmcif_string=filestr)\n",
" mmcif = mmcif_obj.mmcif_object\n",
"\n",
" for chain_id,template_sequence in mmcif.chain_to_seqres.items():\n",
" template_sequence = mmcif.chain_to_seqres[chain_id]\n",
" seq_name = filepath.stem.upper()+\"_\"+chain_id\n",
" seq = SeqRecord(Seq(template_sequence),id=seq_name,name=\"\",description=\"\")\n",
" seqs.append(seq)\n",
"\n",
" with Path(fasta_dir,seq.id+\".fasta\").open(\"w\") as fh:\n",
" SeqIO.write([seq], fh, \"fasta\")\n",
"\n",
" \"\"\"\n",
" At this stage, we have a template sequence.\n",
" and a query sequence. \n",
" There are two options to generate template features:\n",
" 1. Write new code to manually generate template features\n",
" 2. Get an hhr alignment string, and pass that\n",
" to the existing template featurizer. \n",
" \n",
" I chose the second, implemented in hh_process_seq()\n",
" \"\"\"\n",
"\n",
" hit = hh_process_seq(query_seq,seq,hhDB_dir)\n",
" if hit is not None:\n",
" template_hit_list.append(hit)\n",
"\n",
" #process hits into template features\n",
" template_hit_list = [replace(hit,**{\"index\":i+1}) for i,hit in enumerate(template_hit_list)]\n",
" template_features = {}\n",
" for template_feature_name in TEMPLATE_FEATURES:\n",
" template_features[template_feature_name] = []\n",
"\n",
" for i,hit in enumerate(sorted(template_hit_list, key=lambda x: x.sum_probs, reverse=True)):\n",
" # modifications to alphafold/data/templates.py _process_single_hit\n",
" hit_pdb_code, hit_chain_id = _get_pdb_id_and_chain(hit)\n",
" mapping = _build_query_to_hit_index_mapping(\n",
" hit.query, hit.hit_sequence, hit.indices_hit, hit.indices_query,\n",
" query_sequence)\n",
" template_sequence = hit.hit_sequence.replace('-', '')\n",
"\n",
" features, realign_warning = _extract_template_features(\n",
" mmcif_object=mmcif,\n",
" pdb_id=hit_pdb_code,\n",
" mapping=mapping,\n",
" template_sequence=template_sequence,\n",
" query_sequence=query_sequence,\n",
" template_chain_id=hit_chain_id,\n",
" kalign_binary_path=\"kalign\")\n",
" features['template_sum_probs'] = [hit.sum_probs]\n",
"\n",
" single_hit_result = SingleHitResult(features=features, error=None, warning=None)\n",
" for k in template_features:\n",
" template_features[k].append(features[k])\n",
"\n",
"\n",
" for name in template_features:\n",
" template_features[name] = np.stack(\n",
" template_features[name], axis=0).astype(TEMPLATE_FEATURES[name])\n",
" \n",
" \n",
" \n",
" #overwrite template data\n",
" template_paths = cif_dir.as_posix()\n",
" template_hits = template_hit_list\n",
" print(\"\\nIncluding templates:\")\n",
" for hit in template_hit_list:\n",
" print(\"\\t\",hit.name.split()[0])\n",
" os.chdir(\"/content/\")\n",
"\n",
" for key,value in template_features.items():\n",
" if np.all(value==0):\n",
" print(\"ERROR: Some template features are empty\")\n"
],
"execution_count": 8,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <input type=\"file\" id=\"files-3ee1d667-a09b-4426-96ad-d571a7bda744\" name=\"files[]\" multiple disabled\n",
" style=\"border:none\" />\n",
" <output id=\"result-3ee1d667-a09b-4426-96ad-d571a7bda744\">\n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" </output>\n",
" <script src=\"/nbextensions/google.colab/files.js\"></script> "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"Processing templates...\n",
"\n",
"Including templates:\n",
"\t 7LXX_H\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "hUYApPElB30u",
"colab": {
"base_uri": "https://localhost:8080/"
},
"cellView": "form",
"outputId": "54fbe8db-f674-4b52-d625-cba84b18cd55"
},
"source": [
"#@title Gather input features, predict structure\n",
"from string import ascii_uppercase\n",
"\n",
"# collect model weights\n",
"use_model = {}\n",
"if \"model_params\" not in dir(): model_params = {}\n",
"for model_name in [\"model_1\",\"model_2\",\"model_3\",\"model_4\",\"model_5\"][:num_models]:\n",
" use_model[model_name] = True\n",
" if model_name not in model_params:\n",
" model_params[model_name] = data.get_model_haiku_params(model_name=model_name+\"_ptm\", data_dir=\".\")\n",
" if model_name == \"model_1\":\n",
" model_config = config.model_config(model_name+\"_ptm\")\n",
" model_config.data.eval.num_ensemble = 1\n",
" model_runner_1 = model.RunModel(model_config, model_params[model_name])\n",
" if model_name == \"model_3\":\n",
" model_config = config.model_config(model_name+\"_ptm\")\n",
" model_config.data.eval.num_ensemble = 1\n",
" model_runner_3 = model.RunModel(model_config, model_params[model_name])\n",
"\n",
"if homooligomer == 1:\n",
" msas = [msa]\n",
" deletion_matrices = [deletion_matrix]\n",
"else:\n",
" # make multiple copies of msa for each copy\n",
" # AAA------\n",
" # ---AAA---\n",
" # ------AAA\n",
" #\n",
" # note: if you concat the sequences (as below), it does NOT work\n",
" # AAAAAAAAA\n",
" msas = []\n",
" deletion_matrices = []\n",
" Ln = len(query_sequence)\n",
" for o in range(homooligomer):\n",
" L = Ln * o\n",
" R = Ln * (homooligomer-(o+1))\n",
" msas.append([\"-\"*L+seq+\"-\"*R for seq in msa])\n",
" deletion_matrices.append([[0]*L+mtx+[0]*R for mtx in deletion_matrix])\n",
"\n",
"# gather features\n",
"feature_dict = {\n",
" **pipeline.make_sequence_features(sequence=query_sequence*homooligomer,\n",
" description=\"none\",\n",
" num_res=len(query_sequence)*homooligomer),\n",
" **pipeline.make_msa_features(msas=msas,deletion_matrices=deletion_matrices),\n",
" **template_features\n",
"}\n",
"outs = predict_structure(jobname, feature_dict,\n",
" Ls=[len(query_sequence)]*homooligomer,\n",
" model_params=model_params, use_model=use_model,\n",
" do_relax=use_amber)"
],
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"running model_1\n",
"running model_2\n",
"running model_3\n",
"running model_4\n",
"running model_5\n",
"reranking models based on avg. predicted lDDT\n",
"model_1 96.78102640654376\n",
"model_2 96.62027751903045\n",
"model_3 93.02879251267596\n",
"model_4 92.55305876513862\n",
"model_5 91.88665396488939\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "6xbvRNrwnJqj",
"cellView": "form"
},
"source": [
"#@title Make plots\n",
"\n",
"# gather MSA info\n",
"deduped_full_msa = list(dict.fromkeys(msa))\n",
"msa_arr = np.array([list(seq) for seq in deduped_full_msa])\n",
"seqid = (np.array(list(query_sequence)) == msa_arr).mean(-1)\n",
"seqid_sort = seqid.argsort() #[::-1]\n",
"non_gaps = (msa_arr != \"-\").astype(float)\n",
"non_gaps[non_gaps == 0] = np.nan\n",
"\n",
"##################################################################\n",
"plt.figure(figsize=(14,4),dpi=100)\n",
"##################################################################\n",
"plt.subplot(1,2,1); plt.title(\"Sequence coverage\")\n",
"plt.imshow(non_gaps[seqid_sort]*seqid[seqid_sort,None],\n",
" interpolation='nearest', aspect='auto',\n",
" cmap=\"rainbow_r\", vmin=0, vmax=1, origin='lower')\n",
"plt.plot((msa_arr != \"-\").sum(0), color='black')\n",
"plt.xlim(-0.5,msa_arr.shape[1]-0.5)\n",
"plt.ylim(-0.5,msa_arr.shape[0]-0.5)\n",
"plt.colorbar(label=\"Sequence identity to query\",)\n",
"plt.xlabel(\"Positions\")\n",
"plt.ylabel(\"Sequences\")\n",
"\n",
"##################################################################\n",
"plt.subplot(1,2,2); plt.title(\"Predicted lDDT per position\")\n",
"for model_name,value in outs.items():\n",
" plt.plot(value[\"plddt\"],label=model_name)\n",
"if homooligomer > 0:\n",
" for n in range(homooligomer+1):\n",
" x = n*(len(query_sequence)-1)\n",
" plt.plot([x,x],[0,100],color=\"black\")\n",
"plt.legend()\n",
"plt.ylim(0,100)\n",
"plt.ylabel(\"Predicted lDDT\")\n",
"plt.xlabel(\"Positions\")\n",
"plt.savefig(jobname+\"_coverage_lDDT.png\")\n",
"##################################################################\n",
"plt.show()\n",
"\n",
"print(\"Predicted Alignment Error\")\n",
"##################################################################\n",
"plt.figure(figsize=(3*num_models,2), dpi=100)\n",
"for n,(model_name,value) in enumerate(outs.items()):\n",
" plt.subplot(1,num_models,n+1)\n",
" plt.title(model_name)\n",
" plt.imshow(value[\"pae\"],label=model_name,cmap=\"bwr\",vmin=0,vmax=30)\n",
" plt.colorbar()\n",
"plt.savefig(jobname+\"_PAE.png\")\n",
"plt.show()\n",
"##################################################################"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"cellView": "form",
"id": "KK7X9T44pWb7"
},
"source": [
"#@title Display 3D structure {run: \"auto\"}\n",
"model_num = 1 #@param [\"1\", \"2\", \"3\", \"4\", \"5\"] {type:\"raw\"}\n",
"color = \"lDDT\" #@param [\"chain\", \"lDDT\", \"rainbow\"]\n",
"show_sidechains = False #@param {type:\"boolean\"}\n",
"show_mainchains = False #@param {type:\"boolean\"}\n",
"\n",
"def plot_plddt_legend():\n",
" thresh = ['plDDT:','Very low (<50)','Low (60)','OK (70)','Confident (80)','Very high (>90)']\n",
" plt.figure(figsize=(1,0.1),dpi=100)\n",
" ########################################\n",
" for c in [\"#FFFFFF\",\"#FF0000\",\"#FFFF00\",\"#00FF00\",\"#00FFFF\",\"#0000FF\"]:\n",
" plt.bar(0, 0, color=c)\n",
" plt.legend(thresh, frameon=False,\n",
" loc='center', ncol=6,\n",
" handletextpad=1,\n",
" columnspacing=1,\n",
" markerscale=0.5,)\n",
" plt.axis(False)\n",
" return plt\n",
"\n",
"def plot_confidence(model_num=1):\n",
" model_name = f\"model_{model_num}\"\n",
" plt.figure(figsize=(10,3),dpi=100)\n",
" \"\"\"Plots the legend for plDDT.\"\"\"\n",
" #########################################\n",
" plt.subplot(1,2,1); plt.title('Predicted lDDT')\n",
" plt.plot(outs[model_name][\"plddt\"])\n",
" for n in range(homooligomer+1):\n",
" x = n*(len(query_sequence))\n",
" plt.plot([x,x],[0,100],color=\"black\")\n",
" plt.ylabel('plDDT')\n",
" plt.xlabel('position')\n",
" #########################################\n",
" plt.subplot(1,2,2);plt.title('Predicted Aligned Error')\n",
" plt.imshow(outs[model_name][\"pae\"], cmap=\"bwr\",vmin=0,vmax=30)\n",
" plt.colorbar()\n",
" plt.xlabel('Scored residue')\n",
" plt.ylabel('Aligned residue')\n",
" #########################################\n",
" return plt\n",
"\n",
"def show_pdb(model_num=1, show_sidechains=False, show_mainchains=False, color=\"lDDT\"):\n",
" model_name = f\"model_{model_num}\"\n",
" if use_amber:\n",
" pdb_filename = f\"{jobname}_relaxed_{model_name}.pdb\"\n",
" else:\n",
" pdb_filename = f\"{jobname}_unrelaxed_{model_name}.pdb\"\n",
"\n",
" view = py3Dmol.view(js='https://3dmol.org/build/3Dmol.js',)\n",
" view.addModel(open(pdb_filename,'r').read(),'pdb')\n",
"\n",
" if color == \"lDDT\":\n",
" view.setStyle({'cartoon': {'colorscheme': {'prop':'b','gradient': 'roygb','min':50,'max':90}}})\n",
" elif color == \"rainbow\":\n",
" view.setStyle({'cartoon': {'color':'spectrum'}})\n",
" elif color == \"chain\":\n",
" for n,chain,color in zip(range(homooligomer),list(\"ABCDEFGH\"),\n",
" [\"lime\",\"cyan\",\"magenta\",\"yellow\",\"salmon\",\"white\",\"blue\",\"orange\"]):\n",
" view.setStyle({'chain':chain},{'cartoon': {'color':color}})\n",
" if show_sidechains:\n",
" BB = ['C','O','N']\n",
" view.addStyle({'and':[{'resn':[\"GLY\",\"PRO\"],'invert':True},{'atom':BB,'invert':True}]},\n",
" {'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n",
" view.addStyle({'and':[{'resn':\"GLY\"},{'atom':'CA'}]},\n",
" {'sphere':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n",
" view.addStyle({'and':[{'resn':\"PRO\"},{'atom':['C','O'],'invert':True}]},\n",
" {'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}}) \n",
" if show_mainchains:\n",
" BB = ['C','O','N','CA']\n",
" view.addStyle({'atom':BB},{'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n",
"\n",
" view.zoomTo()\n",
" return view\n",
"\n",
"show_pdb(model_num,show_sidechains, show_mainchains, color).show()\n",
"if color == \"lDDT\": plot_plddt_legend().show() \n",
"plot_confidence(model_num).show()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "33g5IIegij5R",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 208
},
"cellView": "form",
"outputId": "505e9243-bd77-43dc-e03e-4a8921aa1399"
},
"source": [
"#@title Package and download results\n",
"#@markdown If you are having issues downloading the result archive, try disabling your adblocker and run this cell again. If that fails click on the little folder icon to the left, navigate to file: `jobname.result.zip`, right-click and select \\\"Download\\\" (see [screenshot](https://pbs.twimg.com/media/E6wRW2lWUAEOuoe?format=jpg&name=small)).\n",
"\n",
"citations = {\n",
"\"Mirdita2021\": \"\"\"@article{Mirdita2021,\n",
"author = {Mirdita, Milot and Ovchinnikov, Sergey and Steinegger, Martin},\n",
"doi = {10.1101/2021.08.15.456425},\n",
"journal = {bioRxiv},\n",
"title = {{ColabFold - Making Protein folding accessible to all}},\n",
"year = {2021},\n",
"comment = {ColabFold including MMseqs2 MSA server}\n",
"}\"\"\",\n",
" \"Mitchell2019\": \"\"\"@article{Mitchell2019,\n",
"author = {Mitchell, Alex L and Almeida, Alexandre and Beracochea, Martin and Boland, Miguel and Burgin, Josephine and Cochrane, Guy and Crusoe, Michael R and Kale, Varsha and Potter, Simon C and Richardson, Lorna J and Sakharova, Ekaterina and Scheremetjew, Maxim and Korobeynikov, Anton and Shlemov, Alex and Kunyavskaya, Olga and Lapidus, Alla and Finn, Robert D},\n",
"doi = {10.1093/nar/gkz1035},\n",
"journal = {Nucleic Acids Res.},\n",
"title = {{MGnify: the microbiome analysis resource in 2020}},\n",
"year = {2019},\n",
"comment = {MGnify database}\n",
"}\"\"\",\n",
" \"Eastman2017\": \"\"\"@article{Eastman2017,\n",
"author = {Eastman, Peter and Swails, Jason and Chodera, John D. and McGibbon, Robert T. and Zhao, Yutong and Beauchamp, Kyle A. and Wang, Lee-Ping and Simmonett, Andrew C. and Harrigan, Matthew P. and Stern, Chaya D. and Wiewiora, Rafal P. and Brooks, Bernard R. and Pande, Vijay S.},\n",
"doi = {10.1371/journal.pcbi.1005659},\n",
"journal = {PLOS Comput. Biol.},\n",
"number = {7},\n",
"title = {{OpenMM 7: Rapid development of high performance algorithms for molecular dynamics}},\n",
"volume = {13},\n",
"year = {2017},\n",
"comment = {Amber relaxation}\n",
"}\"\"\",\n",
" \"Jumper2021\": \"\"\"@article{Jumper2021,\n",
"author = {Jumper, John and Evans, Richard and Pritzel, Alexander and Green, Tim and Figurnov, Michael and Ronneberger, Olaf and Tunyasuvunakool, Kathryn and Bates, Russ and {\\v{Z}}{\\'{i}}dek, Augustin and Potapenko, Anna and Bridgland, Alex and Meyer, Clemens and Kohl, Simon A. A. and Ballard, Andrew J. and Cowie, Andrew and Romera-Paredes, Bernardino and Nikolov, Stanislav and Jain, Rishub and Adler, Jonas and Back, Trevor and Petersen, Stig and Reiman, David and Clancy, Ellen and Zielinski, Michal and Steinegger, Martin and Pacholska, Michalina and Berghammer, Tamas and Bodenstein, Sebastian and Silver, David and Vinyals, Oriol and Senior, Andrew W. and Kavukcuoglu, Koray and Kohli, Pushmeet and Hassabis, Demis},\n",
"doi = {10.1038/s41586-021-03819-2},\n",
"journal = {Nature},\n",
"pmid = {34265844},\n",
"title = {{Highly accurate protein structure prediction with AlphaFold.}},\n",
"year = {2021},\n",
"comment = {AlphaFold2 + BFD Database}\n",
"}\"\"\",\n",
" \"Mirdita2019\": \"\"\"@article{Mirdita2019,\n",
"author = {Mirdita, Milot and Steinegger, Martin and S{\\\"{o}}ding, Johannes},\n",
"doi = {10.1093/bioinformatics/bty1057},\n",
"journal = {Bioinformatics},\n",
"number = {16},\n",
"pages = {2856--2858},\n",
"pmid = {30615063},\n",
"title = {{MMseqs2 desktop and local web server app for fast, interactive sequence searches}},\n",
"volume = {35},\n",
"year = {2019},\n",
"comment = {MMseqs2 search server}\n",
"}\"\"\",\n",
" \"Steinegger2019\": \"\"\"@article{Steinegger2019,\n",
"author = {Steinegger, Martin and Meier, Markus and Mirdita, Milot and V{\\\"{o}}hringer, Harald and Haunsberger, Stephan J. and S{\\\"{o}}ding, Johannes},\n",
"doi = {10.1186/s12859-019-3019-7},\n",
"journal = {BMC Bioinform.},\n",
"number = {1},\n",
"pages = {473},\n",
"pmid = {31521110},\n",
"title = {{HH-suite3 for fast remote homology detection and deep protein annotation}},\n",
"volume = {20},\n",
"year = {2019},\n",
"comment = {PDB70 database}\n",
"}\"\"\",\n",
" \"Mirdita2017\": \"\"\"@article{Mirdita2017,\n",
"author = {Mirdita, Milot and von den Driesch, Lars and Galiez, Clovis and Martin, Maria J. and S{\\\"{o}}ding, Johannes and Steinegger, Martin},\n",
"doi = {10.1093/nar/gkw1081},\n",
"journal = {Nucleic Acids Res.},\n",
"number = {D1},\n",
"pages = {D170--D176},\n",
"pmid = {27899574},\n",
"title = {{Uniclust databases of clustered and deeply annotated protein sequences and alignments}},\n",
"volume = {45},\n",
"year = {2017},\n",
"comment = {Uniclust30/UniRef30 database},\n",
"}\"\"\",\n",
" \"Berman2003\": \"\"\"@misc{Berman2003,\n",
"author = {Berman, Helen and Henrick, Kim and Nakamura, Haruki},\n",
"booktitle = {Nat. Struct. Biol.},\n",
"doi = {10.1038/nsb1203-980},\n",
"number = {12},\n",
"pages = {980},\n",
"pmid = {14634627},\n",
"title = {{Announcing the worldwide Protein Data Bank}},\n",
"volume = {10},\n",
"year = {2003},\n",
"comment = {templates downloaded from wwPDB server}\n",
"}\"\"\",\n",
"}\n",
"\n",
"to_cite = [ \"Mirdita2021\", \"Jumper2021\" ]\n",
"if use_msa: to_cite += [\"Mirdita2019\"]\n",
"if use_msa: to_cite += [\"Mirdita2017\"]\n",
"if use_env: to_cite += [\"Mitchell2019\"]\n",
"if use_templates: to_cite += [\"Steinegger2019\"]\n",
"if use_templates: to_cite += [\"Berman2003\"]\n",
"if use_amber: to_cite += [\"Eastman2017\"]\n",
"\n",
"with open(f\"{jobname}.bibtex\", 'w') as writer:\n",
" for i in to_cite:\n",
" writer.write(citations[i])\n",
" writer.write(\"\\n\")\n",
"\n",
"print(f\"Found {len(to_cite)} citation{'s' if len(to_cite) > 1 else ''} for tools or databases.\")\n",
"if use_custom_msa:\n",
" print(\"Don't forget to cite your custom MSA generation method.\")\n",
"\n",
"!zip -FSr $jobname\".result.zip\" $jobname\".log\" $a3m_file $jobname\"_\"*\"relaxed_model_\"*\".pdb\" $jobname\"_coverage_lDDT.png\" $jobname\".bibtex\" $jobname\"_PAE.png\"\n",
"files.download(f\"{jobname}.result.zip\")\n",
"\n",
"if save_to_google_drive == True and drive != None:\n",
" uploaded = drive.CreateFile({'title': f\"{jobname}.result.zip\"})\n",
" uploaded.SetContentFile(f\"{jobname}.result.zip\")\n",
" uploaded.Upload()\n",
" print(f\"Uploaded {jobname}.result.zip to Google Drive with ID {uploaded.get('id')}\")"
],
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Found 7 citations for tools or databases.\n",
"\tzip warning: name not matched: manual_template_45662_coverage_lDDT.png\n",
"\tzip warning: name not matched: manual_template_45662_PAE.png\n",
" adding: manual_template_45662.log (deflated 13%)\n",
" adding: manual_template_45662.a3m (deflated 67%)\n",
" adding: manual_template_45662_unrelaxed_model_1.pdb (deflated 78%)\n",
" adding: manual_template_45662_unrelaxed_model_2.pdb (deflated 78%)\n",
" adding: manual_template_45662_unrelaxed_model_3.pdb (deflated 78%)\n",
" adding: manual_template_45662_unrelaxed_model_4.pdb (deflated 78%)\n",
" adding: manual_template_45662_unrelaxed_model_5.pdb (deflated 78%)\n",
" adding: manual_template_45662.bibtex (deflated 55%)\n"
]
},
{
"output_type": "display_data",
"data": {
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" const label = document.createElement('label');\n",
" label.textContent = `Downloading \"${filename}\": `;\n",
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" progress.max = size;\n",
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" const blob = new Blob(buffers, {type: 'application/binary'});\n",
" const a = document.createElement('a');\n",
" a.href = window.URL.createObjectURL(blob);\n",
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" div.remove();\n",
" }\n",
" "
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"<IPython.core.display.Javascript object>"
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{
"output_type": "display_data",
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{
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"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"cellView": "form",
"id": "fq2HJBe6vdBU",
"outputId": "4d1b4ff8-fe11-4266-b7af-553f28d001cc"
},
"source": [
"#@markdown ---\n",
"#@markdown #### Optional: Package and download any templates used\n",
"from alphafold.data.templates import _get_pdb_id_and_chain\n",
"from pathlib import Path\n",
"\n",
"if not use_templates:\n",
" print(\"No templates used\")\n",
"else:\n",
" cif_files = [f for f in Path(template_paths).glob(\"*\") if f.suffix in [\".cif\",\".mmcif\"]]\n",
" cif_files_used = []\n",
" for hit in template_hits:\n",
" code,chain_id = _get_pdb_id_and_chain(hit)\n",
" for f in cif_files:\n",
" if code in f.name or code.upper() in f.name:\n",
" cif_files_used.append(f)\n",
"\n",
"\n",
" zip_string = \" \".join([cif_file.as_posix() for cif_file in cif_files_used])\n",
"\n",
" !zip -FSrj $jobname\".templates.zip\" $zip_string\n",
" files.download(f\"{jobname}.templates.zip\")\n"
],
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"No templates used\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UGUBLzB3C6WN"
},
"source": [
"# Instructions <a name=\"Instructions\"></a>\n",
"**Quick start**\n",
"1. Paste your protein sequence in the input field.\n",
"2. Press \"Runtime\" -> \"Run all\".\n",
"3. The pipeline consists of 8 steps. The currently running steps is indicated by a circle with a stop sign next to it.\n",
"\n",
"**Result zip file contents**\n",
"\n",
"1. PDB formatted structures sorted by avg. pIDDT. (unrelaxed and relaxed if `use_amber` is enabled).\n",
"2. Plots of the model quality.\n",
"3. Plots of the MSA coverage.\n",
"4. Parameter log file.\n",
"5. A3M formatted input MSA.\n",
"6. BibTeX file with citations for all used tools and databases.\n",
"\n",
"At the end of the job a download modal box will pop up with a `jobname.result.zip` file. Additionally, if the `save_to_google_drive` option was selected, the `jobname.result.zip` will be uploaded to your Google Drive.\n",
"\n",
"**Using a custom MSA as input**\n",
"\n",
"To predict the structure with a custom MSA (A3M formatted): (1) Change the msa_mode: to \"custom\", (2) Wait for an upload box to appear at the end of the \"Input Protein ...\" box. Upload your A3M. The first fasta entry of the A3M must be the query sequence without gaps.\n",
"\n",
"As an alternative for MSA generation the [HHblits Toolkit server](https://toolkit.tuebingen.mpg.de/tools/hhblits) can be used. After submitting your query, click \"Query Template MSA\" -> \"Download Full A3M\". Download the A3M file and upload it in this notebook.\n",
"\n",
"**Troubleshooting**\n",
"* Check that the runtime type is set to GPU at \"Runtime\" -> \"Change runtime type\".\n",
"* Try to restart the session \"Runtime\" -> \"Factory reset runtime\".\n",
"* Check your input sequence.\n",
"\n",
"**Known issues**\n",
"* Google Colab assigns different types of GPUs with varying amount of memory. Some might not have enough memory to predict the structure for a long sequence.\n",
"* Your browser can block the pop-up for downloading the result file. You can choose the `save_to_google_drive` option to upload to Google Drive instead or manually download the result file: Click on the little folder icon to the left, navigate to file: `jobname.result.zip`, right-click and select \\\"Download\\\" (see [screenshot](https://pbs.twimg.com/media/E6wRW2lWUAEOuoe?format=jpg&name=small)).\n",
"\n",
"**Limitations**\n",
"* Computing resources: Our MMseqs2 API can handle ~20-50k requests per day.\n",
"* MSAs: MMseqs2 is very precise and sensitive but might find less hits compared to HHblits/HMMer searched against BFD or Mgnify.\n",
"* We recommend to additionally use the full [AlphaFold2 pipeline](https://github.com/deepmind/alphafold).\n",
"\n",
"**Description of the plots**\n",
"* **Number of sequences per position** - We want to see at least 30 sequences per position, for best performance, ideally 100 sequences.\n",
"* **Predicted lDDT per position** - model confidence (out of 100) at each position. The higher the better.\n",
"* **Predicted Alignment Error** - For homooligomers, this could be a useful metric to assess how confident the model is about the interface. The lower the better.\n",
"\n",
"**Bugs**\n",
"- If you encounter any bugs, please report the issue to https://github.com/sokrypton/ColabFold/issues\n",
"\n",
"\n",
"**Acknowledgments**\n",
"- We thank the AlphaFold team for developing an excellent model and open sourcing the software. \n",
"\n",
"- [Söding Lab](https://www.mpibpc.mpg.de/soeding) for providing the computational resources for the MMseqs2 server\n",
"\n",
"- Minkyung Baek ([@minkbaek](https://twitter.com/minkbaek)) and Yoshitaka Moriwaki ([@Ag_smith](https://twitter.com/Ag_smith)) for protein-complex prediction proof-of-concept in AlphaFold2.\n",
"\n",
"- [David Koes](https://github.com/dkoes) for his awesome [py3Dmol](https://3dmol.csb.pitt.edu/) plugin, without whom these notebooks would be quite boring!\n",
"\n",
"- Do-Yoon Kim for creating the ColabFold logo.\n",
"\n",
"- A colab by Sergey Ovchinnikov ([@sokrypton](https://twitter.com/sokrypton)), Milot Mirdita ([@milot_mirdita](https://twitter.com/milot_mirdita)) and Martin Steinegger ([@thesteinegger](https://twitter.com/thesteinegger)).\n"
]
}
]
}
| stackv2 | 2024-11-18T18:03:05.519281+00:00 | 2021-09-12T17:32:14 | {
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b2a9364323134c6a05f5a55b1fcd688ad27356b9 | {
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"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "TPflashBenchmarkJava.ipynb",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true,
"authorship_tag": "ABX9TyPwdx1uFKziJk1MvTRk9Imz",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/EvenSol/NeqSim-Colab/blob/master/TPflashBenchmarkJava.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "x2BW3Cxro8Rs"
},
"source": [
"# NeqSim Java Benchmark in Colab\n",
"We start by downloading the NeqSim library from GitHub.\n",
"We will run the class TPflash_benchmark. The contents of this file is downloaded (TPflash_benchamrk.java)."
]
},
{
"cell_type": "code",
"metadata": {
"id": "u2mKshViiGJL",
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"base_uri": "https://localhost:8080/"
},
"outputId": "d68576a3-5c47-4e0a-d2b9-fdc7df71ada8"
},
"source": [
"!# Get a release of NeqSim-x-x-.jar\n",
"!wget https://github.com/equinor/neqsim/releases/download/v2.2.1/NeqSim.jar\n",
"!wget https://raw.githubusercontent.com/equinor/neqsim/master/src/main/java/neqsim/thermo/util/benchmark/TPflash_benchmark.java"
],
"execution_count": 1,
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{
"output_type": "stream",
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"text": [
"--2021-12-15 10:50:44-- https://github.com/equinor/neqsim/releases/download/v2.2.1/NeqSim.jar\n",
"Resolving github.com (github.com)... 140.82.114.3\n",
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"Saving to: ‘NeqSim.jar’\n",
"\n",
"NeqSim.jar 100%[===================>] 70.40M 45.6MB/s in 1.5s \n",
"\n",
"2021-12-15 10:50:46 (45.6 MB/s) - ‘NeqSim.jar’ saved [73818810/73818810]\n",
"\n",
"--2021-12-15 10:50:46-- https://raw.githubusercontent.com/equinor/neqsim/master/src/main/java/neqsim/thermo/util/benchmark/TPflash_benchmark.java\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.109.133, ...\n",
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"Length: 3452 (3.4K) [text/plain]\n",
"Saving to: ‘TPflash_benchmark.java’\n",
"\n",
"TPflash_benchmark.j 100%[===================>] 3.37K --.-KB/s in 0s \n",
"\n",
"2021-12-15 10:50:46 (46.1 MB/s) - ‘TPflash_benchmark.java’ saved [3452/3452]\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "i_RgdkfXpav0"
},
"source": [
"# Check Java version\n",
"The Java version is checked by the command java -version"
]
},
{
"cell_type": "code",
"metadata": {
"id": "5oL7A8MRiUSp",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "c8a776a8-1ba9-42a1-edf1-40a2a6b2ac80"
},
"source": [
"!java -version"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"openjdk version \"11.0.11\" 2021-04-20\n",
"OpenJDK Runtime Environment (build 11.0.11+9-Ubuntu-0ubuntu2.18.04)\n",
"OpenJDK 64-Bit Server VM (build 11.0.11+9-Ubuntu-0ubuntu2.18.04, mixed mode, sharing)\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SxnuPUlUpjGO"
},
"source": [
"# Print the benchmark java file\n",
"The benchmark file is printed below. We see that 5000 multiphase flash calculations will run."
]
},
{
"cell_type": "markdown",
"source": [
""
],
"metadata": {
"id": "BlCIiUytszN5"
}
},
{
"cell_type": "code",
"metadata": {
"id": "a3KJkDd_mZbq",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "907fed24-bab3-4e64-cf33-5916d03306ad"
},
"source": [
"f= open(\"TPflash_benchmark.java\",\"r\")\n",
"contents =f.read()\n",
"print(contents)"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"package neqsim.thermo.util.benchmark;\n",
"\n",
"import org.apache.logging.log4j.LogManager;\n",
"import org.apache.logging.log4j.Logger;\n",
"import neqsim.thermo.system.SystemInterface;\n",
"import neqsim.thermo.system.SystemSrkEos;\n",
"import neqsim.thermodynamicOperations.ThermodynamicOperations;\n",
"\n",
"public class TPflash_benchmark {\n",
" static Logger logger = LogManager.getLogger(TPflash_benchmark.class);\n",
"\n",
" /**\n",
" * This method is just meant to test the thermo package.\n",
" */\n",
" @SuppressWarnings(\"unused\")\n",
" public static void main(String args[]) {\n",
" double[][] points;\n",
"\n",
" SystemInterface testSystem = new SystemSrkEos(303.15, 35.01325);\n",
" // SystemInterface testSystem = new SystemSrkCPAstatoil(303.15, 10.0);\n",
" // SystemInterface testSystem = new SystemUMRPRUMCEos(303.0, 10.0);\n",
" // SystemInterface testSystem = new SystemSrkSchwartzentruberEos(298.15,\n",
" // 1.01325);\n",
" ThermodynamicOperations testOps = new ThermodynamicOperations(testSystem);\n",
"\n",
" testSystem.addComponent(\"nitrogen\", 0.0028941);\n",
" testSystem.addComponent(\"CO2\", 0.054069291);\n",
" testSystem.addComponent(\"methane\", 0.730570915);\n",
" testSystem.addComponent(\"ethane\", 0.109004002);\n",
" testSystem.addComponent(\"propane\", 0.061518891);\n",
" testSystem.addComponent(\"n-butane\", 0.0164998);\n",
" testSystem.addComponent(\"i-butane\", 0.006585);\n",
" testSystem.addComponent(\"n-pentane\", 0.005953);\n",
" testSystem.addComponent(\"i-pentane\", 0.0040184);\n",
" testSystem.addTBPfraction(\"C6\", 0.6178399, 86.17801 / 1000.0, 0.6639999);\n",
" testSystem.addComponent(\"water\", 0.27082);\n",
" // testSystem.addComponent(\"TEG\", 1.0);\n",
" // testSystem.addTBPfraction(\"C7\",1.0,250.0/1000.0,0.9);\n",
"\n",
" testSystem.createDatabase(true);\n",
" testSystem.setMixingRule(2);\n",
" testSystem.setMultiPhaseCheck(true);\n",
"\n",
" // testSystem.autoSelectMixingRule();\n",
" // testSystem.setMixingRule(\"HV\", \"UNIFAC_UMRPRU\");\n",
" logger.info(\"start benchmark TPflash......\");\n",
"\n",
" testSystem.init(0);\n",
" long time = System.currentTimeMillis();\n",
"\n",
" for (int i = 0; i < 5000; i++) {\n",
" // testSystem.init(3, 0);\n",
" testOps.TPflash();\n",
" // testSystem.initPhysicalProperties();\n",
" // testSystem.init(0);\n",
" // testSystem.init(1);\n",
" }\n",
"\n",
" System.out\n",
" .println(\"Time taken for benchmark flash = \" + (System.currentTimeMillis() - time));\n",
" // testSystem.display();\n",
" // SystemInterface testSystem2 =\n",
" // testSystem.readObjectFromFile(\"c:/temp/test2.neqsim\", \"test2.neqsim\");\n",
" /// testSystem2.init(3);\n",
" // testSystem2.display();\n",
" // testSystem2.init(0);\n",
" // testSystem2.init(3);\n",
" // time for 5000 flash calculations\n",
" // Results Dell Portable PIII 750 MHz - JDK 1.3.1:\n",
" // mixrule 1 (Classic - no interaction): 6.719 sec\n",
" // mixrule 2 (Classic): 6.029 sec ny PC 1.108 sec\n",
" // mixrule 4 (Huron-Vidal2): 17.545 sec\n",
" // mixrule 6 (Wong-Sandler): 12.859 sec\n",
" // test of ijAlgo matrix - before 4134 ms / 3962 ms\n",
" // // system:\n",
" // SystemSrkEos testSystem = new SystemSrkEos(303.15, 10.01325);\n",
" // ThermodynamicOperations testOps = new ThermodynamicOperations(testSystem);\n",
" // testSystem.addComponent(\"methane\", 100.0);\n",
" // testSystem.addComponent(\"water\", 100.0);\n",
" // testSystem.setMixingRule(1);\n",
" }\n",
"}\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_A0xbEUbp-I1"
},
"source": [
"# Run the benchmark\n",
"The benchmark is run using the following script:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "rLXNemTqighl",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "53c22017-5859-4f54-bf94-688f2e0c2e6e"
},
"source": [
"import time\n",
"start = time.time()\n",
"print(\"start benchmark...\")\n",
"!java -cp NeqSim.jar neqsim.thermo.util.benchmark.TPflash_benchmark\n",
"end = time.time()\n",
"print(\"time \", (end - start), \" sec\")"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"start benchmark...\n",
"Time taken for benchmark flash = 5650\n",
"time 9.475523710250854 sec\n"
]
}
]
}
]
} | stackv2 | 2024-11-18T18:03:05.524592+00:00 | 2023-08-24T14:23:50 | {
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62393f294ad3f5e8e28effdad277319a805c31d5 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Base text classification"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Version 1\n",
"# original: https://www.tensorflow.org/tutorials/keras/text_classification\n",
"# "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import os\n",
"import re\n",
"import shutil\n",
"import string\n",
"import tarfile"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"print(\"tf_version=\" + tf.__version__)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras import layers\n",
"from tensorflow.keras import losses\n",
"from tensorflow.keras import preprocessing\n",
"from tensorflow.keras.layers.experimental.preprocessing import TextVectorization\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# uncomment next 2 lines to start on GPU\n",
"# physical_devices = tf.config.list_physical_devices('GPU') \n",
"# tf.config.experimental.set_memory_growth(physical_devices[0], True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds_tar = \"aclImdb_v1.tar.gz\"\n",
"ds_tar_filename = os.path.join(os.getcwd(), ds_tar)\n",
"ds_folder = os.path.join(os.getcwd(), \"aclImdb\")\n",
"url = f\"https://ai.stanford.edu/~amaas/data/sentiment/{ds_tar}\"\n",
"\n",
"if not os.path.exists(ds_tar_filename):\n",
" print(f\"Get {ds_tar} from {url}...\")\n",
" dataset = tf.keras.utils.get_file(ds_tar_filename, \n",
" url,\n",
" untar=True,\n",
" cache_dir='.',\n",
" cache_subdir='')\n",
"else:\n",
" dataset = ds_folder\n",
" if not os.path.exists(ds_folder):\n",
" with tarfile.open(ds_tar_filename) as t:\n",
" print(f\"Extract from {ds_tar_filename}\")\n",
" t.extractall()\n",
"\n",
"print(f\"Dataset: {dataset}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset_dir = os.path.join(os.path.dirname(dataset), 'aclImdb')\n",
"print(os.listdir(dataset_dir))\n",
"train_dir = os.path.join(dataset_dir, 'train')\n",
"print(os.listdir(train_dir))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sample_file = os.path.join(train_dir, 'pos/1181_9.txt')\n",
"with open(sample_file) as f:\n",
" print(f.read())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# remove unused folder\n",
"remove_dir = os.path.join(train_dir, 'unsup')\n",
"if os.path.exists(remove_dir):\n",
" shutil.rmtree(remove_dir)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preprocessing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Make train, validation and test datasets"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# prepare raw data, here train dataset separated 80/20 with validation ds\n",
"batch_size = 32\n",
"seed = 42\n",
"\n",
"raw_train_ds = tf.keras.preprocessing.text_dataset_from_directory(\n",
" 'aclImdb/train', \n",
" batch_size=batch_size, \n",
" validation_split=0.2, \n",
" subset='training', \n",
" seed=seed)\n",
"\n",
"raw_val_ds = tf.keras.preprocessing.text_dataset_from_directory(\n",
" 'aclImdb/train', \n",
" batch_size=batch_size, \n",
" validation_split=0.2, \n",
" subset='validation', \n",
" seed=seed)\n",
"\n",
"raw_test_ds = tf.keras.preprocessing.text_dataset_from_directory(\n",
" 'aclImdb/test', \n",
" batch_size=batch_size)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# prewiew for raw\n",
"for text_batch, label_batch in raw_train_ds.take(1):\n",
" for i in range(3):\n",
" print(\"Review\", text_batch.numpy()[i])\n",
" print(\"Label\", label_batch.numpy()[i]) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Label 0 corresponds to\", raw_train_ds.class_names[0])\n",
"print(\"Label 1 corresponds to\", raw_train_ds.class_names[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prepare dataset to train"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Standartization"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def custom_standardization(input_data):\n",
" lowercase = tf.strings.lower(input_data)\n",
" stripped_html = tf.strings.regex_replace(lowercase, \n",
" '<br />', \n",
" ' ')\n",
" \n",
" return tf.strings.regex_replace(stripped_html,\n",
" '[%s]' % re.escape(string.punctuation),\n",
" '')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"max_features = 10000\n",
"sequence_length = 250\n",
"\n",
"vectorize_layer = TextVectorization(\n",
" standardize=custom_standardization,\n",
" max_tokens=max_features,\n",
" output_mode='int',\n",
" output_sequence_length=sequence_length)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Make a text-only dataset (without labels), then call adapt\n",
"train_text = raw_train_ds.map(lambda x, y: x)\n",
"vectorize_layer.adapt(train_text)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Vectorization"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def vectorize_text(text, label):\n",
" text = tf.expand_dims(text, -1)\n",
"\n",
" return vectorize_layer(text), label"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# retrieve a batch (of 32 reviews and labels) from the dataset\n",
"text_batch, label_batch = next(iter(raw_train_ds))\n",
"first_review, first_label = text_batch[0], label_batch[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# review for prepared text and vector\n",
"print(\"Review\", first_review)\n",
"print(\"Label\", raw_train_ds.class_names[first_label])\n",
"print(\"Vectorized review\", vectorize_text(first_review, first_label))\n",
"print(\"\\n1287 ---> \",vectorize_layer.get_vocabulary()[1287])\n",
"print(\" 313 ---> \",vectorize_layer.get_vocabulary()[313])\n",
"print('Vocabulary size: {}'.format(len(vectorize_layer.get_vocabulary())))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# prepare: text to vectors\n",
"train_ds = raw_train_ds.map(vectorize_text)\n",
"val_ds = raw_val_ds.map(vectorize_text)\n",
"test_ds = raw_test_ds.map(vectorize_text)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"embedding_dim = 16\n",
"\n",
"model = tf.keras.Sequential([\n",
" layers.Embedding(max_features + 1, embedding_dim),\n",
" layers.Dropout(0.2),\n",
" layers.GlobalAveragePooling1D(),\n",
" layers.Dropout(0.2),\n",
" layers.Dense(1)])\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.compile(loss=losses.BinaryCrossentropy(from_logits=True),\n",
" optimizer='adam',\n",
" metrics=tf.metrics.BinaryAccuracy(threshold=0.0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"epochs = 15\n",
"history = model.fit(\n",
" train_ds,\n",
" validation_data=val_ds,\n",
" epochs=epochs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluate"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"loss, accuracy = model.evaluate(test_ds)\n",
"\n",
"print(\"Loss: \", loss)\n",
"print(\"Accuracy: \", accuracy)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"history_dict = history.history\n",
"history_dict.keys()\n",
"\n",
"acc = history_dict['binary_accuracy']\n",
"val_acc = history_dict['val_binary_accuracy']\n",
"loss = history_dict['loss']\n",
"val_loss = history_dict['val_loss']\n",
"\n",
"epochs = range(1, len(acc) + 1)\n",
"\n",
"# \"bo\" is for \"blue dot\"\n",
"plt.plot(epochs, loss, 'bo', label='Training loss')\n",
"# b is for \"solid blue line\"\n",
"plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
"plt.title('Training and validation loss')\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Loss')\n",
"plt.legend()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.plot(epochs, acc, 'bo', label='Training acc')\n",
"plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
"plt.title('Training and validation accuracy')\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Accuracy')\n",
"plt.legend(loc='lower right')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# test on any raw data\n",
"def test_raw_data(test_model):\n",
" examples = [\n",
" \"The movie was great!\",\n",
" \"The movie was okay.\",\n",
" \"The movie was terrible...\",\n",
" \"Loosers cinema\",\n",
" \"Very good emotions\",\n",
" \"Movie zero\"\n",
" \n",
" ]\n",
" predict = test_model.predict(examples)\n",
" print(\"Test exapmples:\")\n",
" for i in range(len(examples)):\n",
" print(f\"{examples[i]}\\n\\t-> {predict[i]}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Export"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# export to work not prepared data\n",
"export_model = tf.keras.Sequential([\n",
" vectorize_layer,\n",
" model,\n",
" layers.Activation('sigmoid')\n",
"])\n",
"\n",
"export_model.compile(\n",
" loss=losses.BinaryCrossentropy(from_logits=False), optimizer=\"adam\", metrics=['accuracy']\n",
")\n",
"\n",
"# Test it with `raw_test_ds`, which yields raw strings\n",
"loss, accuracy = export_model.evaluate(raw_test_ds)\n",
"print(f\"Accuracy: {accuracy}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(f\"--- Test export model ---\")\n",
"test_raw_data(export_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Save model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# run second cell abowe and this cell before save or load\n",
"text_model_folder = os.path.join(os.getcwd(), \"training_text_model\")\n",
"filepath = os.path.join(text_model_folder, f\"text_base_ex_{int(accuracy*10000)}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if not os.path.exists(text_model_folder):\n",
" os.mkdir(text_model_folder)\n",
"\n",
"export_model.save(filepath)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load and test saved model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# warning! you need run cell with custom_standardization fn before\n",
"# if you only load model\n",
"with tf.keras.utils.custom_object_scope(\n",
" {'custom_standardization': custom_standardization}\n",
"):\n",
" load_model = tf.keras.models.load_model(filepath)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(f\"--- Test loaded model ---\")\n",
"test_raw_data(load_model)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "gputest",
"language": "python",
"name": "gputest"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| stackv2 | 2024-11-18T18:03:05.524704+00:00 | 2020-12-14T22:30:38 | {
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"content_id": "91bf444863ad4b6cd4464f51360c64e4d695d260",
"detected_licenses": [
"MIT"
],
"license_type": "permissive",
"repo_name": "dimishpatriot/neural",
"snapshot_id": "c6e4b8ac64d15b34921649664136ce5798bdbc5b",
"revision_id": "671ee73e75d727a2432609ddff5b9ff334acabbf",
"branch_name": "refs/heads/main",
"visit_date": "2023-01-31T21:37:34.775674",
"revision_date": "2020-12-14T22:30:38",
"committer_date": "2020-12-14T22:30:38",
"github_id": 318010166,
"star_events_count": 0,
"fork_events_count": 0,
"gha_license_id": null,
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} |
f44623c0f732d96ae61df91c68e7c5bb029ed1a2 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pandas Visualization"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib notebook"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['bmh',\n",
" 'classic',\n",
" 'dark_background',\n",
" 'fast',\n",
" 'fivethirtyeight',\n",
" 'ggplot',\n",
" 'grayscale',\n",
" 'seaborn-bright',\n",
" 'seaborn-colorblind',\n",
" 'seaborn-dark-palette',\n",
" 'seaborn-dark',\n",
" 'seaborn-darkgrid',\n",
" 'seaborn-deep',\n",
" 'seaborn-muted',\n",
" 'seaborn-notebook',\n",
" 'seaborn-paper',\n",
" 'seaborn-pastel',\n",
" 'seaborn-poster',\n",
" 'seaborn-talk',\n",
" 'seaborn-ticks',\n",
" 'seaborn-white',\n",
" 'seaborn-whitegrid',\n",
" 'seaborn',\n",
" 'Solarize_Light2',\n",
" 'tableau-colorblind10',\n",
" '_classic_test']"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# see the pre-defined styles provided.\n",
"plt.style.available"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# use the 'seaborn-colorblind' style\n",
"plt.style.use('seaborn-colorblind')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### DataFrame.plot"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>E</th>\n",
" <th>F</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-01-01</th>\n",
" <td>-1.085631</td>\n",
" <td>20.059291</td>\n",
" <td>-20.230904</td>\n",
" <td>-1.725890</td>\n",
" <td>20.232037</td>\n",
" <td>-19.806906</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-02</th>\n",
" <td>-0.088285</td>\n",
" <td>21.803332</td>\n",
" <td>-16.659325</td>\n",
" <td>0.611238</td>\n",
" <td>21.500923</td>\n",
" <td>-19.560530</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-03</th>\n",
" <td>0.194693</td>\n",
" <td>20.835588</td>\n",
" <td>-17.055481</td>\n",
" <td>1.309351</td>\n",
" <td>20.491957</td>\n",
" <td>-18.617827</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-04</th>\n",
" <td>-1.311601</td>\n",
" <td>21.255156</td>\n",
" <td>-17.093802</td>\n",
" <td>0.695880</td>\n",
" <td>19.810618</td>\n",
" <td>-20.131865</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-05</th>\n",
" <td>-1.890202</td>\n",
" <td>21.462083</td>\n",
" <td>-19.518638</td>\n",
" <td>0.320308</td>\n",
" <td>20.059943</td>\n",
" <td>-21.394709</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D E F\n",
"2017-01-01 -1.085631 20.059291 -20.230904 -1.725890 20.232037 -19.806906\n",
"2017-01-02 -0.088285 21.803332 -16.659325 0.611238 21.500923 -19.560530\n",
"2017-01-03 0.194693 20.835588 -17.055481 1.309351 20.491957 -18.617827\n",
"2017-01-04 -1.311601 21.255156 -17.093802 0.695880 19.810618 -20.131865\n",
"2017-01-05 -1.890202 21.462083 -19.518638 0.320308 20.059943 -21.394709"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.seed(123)\n",
"\n",
"df = pd.DataFrame({'A': np.random.randn(365).cumsum(0), \n",
" 'B': np.random.randn(365).cumsum(0) + 20,\n",
" 'C': np.random.randn(365).cumsum(0) - 20,\n",
" 'D': np.random.randn(365),\n",
" 'E': np.random.randn(365) + 20,\n",
" 'F': np.random.randn(365) - 20,}, \n",
" index=pd.date_range('1/1/2017', periods=365))\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
"[1, 3, 6, 10, 15, 21, 28, 36, 45, 55]\n"
]
}
],
"source": [
"a = np.linspace(1, 10, 10).astype(int).tolist()\n",
"b = np.linspace(1, 10, 10).cumsum(0, np.int32).tolist()\n",
"print(a, b, sep=\"\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"scrolled": false
},
"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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot(); # add a semi-colon to the end of the plotting call to suppress unwanted output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can select which plot we want to use by passing it into the 'kind' parameter."
]
},
{
"cell_type": "code",
"execution_count": 63,
"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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot('A','B', kind = 'scatter');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also choose the plot kind by using the `DataFrame.plot.kind` methods instead of providing the `kind` keyword argument.\n",
"\n",
"`kind` :\n",
"- `'line'` : line plot (default)\n",
"- `'bar'` : vertical bar plot\n",
"- `'barh'` : horizontal bar plot\n",
"- `'hist'` : histogram\n",
"- `'box'` : boxplot\n",
"- `'kde'` : Kernel Density Estimation plot\n",
"- `'density'` : same as 'kde'\n",
"- `'area'` : area plot\n",
"- `'pie'` : pie plot\n",
"- `'scatter'` : scatter plot\n",
"- `'hexbin'` : hexbin plot"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"scrolled": false
},
"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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1f39d6dc0d0>"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create a scatter plot of columns 'A' and 'C', with changing color (c) and size (s) based on column 'B'\n",
"df.plot.scatter('A', 'C', c='B', s=df['B'], colormap='viridis')"
]
},
{
"cell_type": "code",
"execution_count": 69,
"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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = df.plot.scatter('A', 'C', c='B', s=df['B'], colormap='viridis')\n",
"ax.set_aspect('equal')"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"scrolled": false
},
"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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot.box();"
]
},
{
"cell_type": "code",
"execution_count": 71,
"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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot.hist(alpha=0.7);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Kernel density estimation plots](https://en.wikipedia.org/wiki/Kernel_density_estimation) are useful for deriving a smooth continuous function from a given sample."
]
},
{
"cell_type": "code",
"execution_count": 72,
"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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<img 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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot.kde();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### pandas.tools.plotting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Iris flower data set](https://en.wikipedia.org/wiki/Iris_flower_data_set)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] File iris.csv does not exist: 'iris.csv'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-73-4a0156a3bc13>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0miris\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'iris.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0miris\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\buono\\01_data_science\\99_venv\\coursera_env\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m 674\u001b[0m )\n\u001b[0;32m 675\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 676\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 678\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\buono\\01_data_science\\99_venv\\coursera_env\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 446\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 447\u001b[0m \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 448\u001b[1;33m \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 449\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 450\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\buono\\01_data_science\\99_venv\\coursera_env\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 878\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 879\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 880\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 881\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 882\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\buono\\01_data_science\\99_venv\\coursera_env\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m 1112\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"c\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1113\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"c\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1114\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1115\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1116\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"python\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\buono\\01_data_science\\99_venv\\coursera_env\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m 1889\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"usecols\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1890\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1891\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1892\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1893\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] File iris.csv does not exist: 'iris.csv'"
]
}
],
"source": [
"iris = pd.read_csv('iris.csv')\n",
"iris.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"pd.tools.plotting.scatter_matrix(iris);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure()\n",
"pd.tools.plotting.parallel_coordinates(iris, 'Name');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Seaborn"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"%matplotlib notebook"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(1234)\n",
"\n",
"v1 = pd.Series(np.random.normal(0,10,1000), name='v1')\n",
"v2 = pd.Series(2*v1 + np.random.normal(60,15,1000), name='v2')"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
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"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
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" 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",
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"\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",
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"\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",
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" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
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" 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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.hist(v1, alpha=0.7, bins=np.arange(-50,150,5), label='v1');\n",
"plt.hist(v2, alpha=0.7, bins=np.arange(-50,150,5), label='v2');\n",
"plt.legend();"
]
},
{
"cell_type": "code",
"execution_count": 78,
"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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-78-aab272ed0abc>:3: MatplotlibDeprecationWarning: \n",
"The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
" plt.hist([v1, v2], histtype='barstacked', normed=True);\n"
]
}
],
"source": [
"# plot a kernel density estimation over a stacked barchart\n",
"plt.figure()\n",
"plt.hist([v1, v2], histtype='barstacked', normed=True);\n",
"v3 = np.concatenate((v1,v2))\n",
"sns.kdeplot(v3);"
]
},
{
"cell_type": "code",
"execution_count": 79,
"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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"# we can pass keyword arguments for each individual component of the plot\n",
"sns.distplot(v3, hist_kws={'color': 'Teal'}, kde_kws={'color': 'Navy'});"
]
},
{
"cell_type": "code",
"execution_count": 80,
"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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"600\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.jointplot(v1, v2, alpha=0.4);"
]
},
{
"cell_type": "code",
"execution_count": 81,
"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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"600\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"grid = sns.jointplot(v1, v2, alpha=0.4);\n",
"grid.ax_joint.set_aspect('equal')"
]
},
{
"cell_type": "code",
"execution_count": 82,
"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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"600\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.jointplot(v1, v2, kind='hex');"
]
},
{
"cell_type": "code",
"execution_count": 83,
"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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"600\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# set the seaborn style for all the following plots\n",
"sns.set_style('white')\n",
"\n",
"sns.jointplot(v1, v2, kind='kde', space=0);"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": true
},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] File iris.csv does not exist: 'iris.csv'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-84-4a0156a3bc13>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0miris\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'iris.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0miris\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\buono\\01_data_science\\99_venv\\coursera_env\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m 674\u001b[0m )\n\u001b[0;32m 675\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 676\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 678\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\buono\\01_data_science\\99_venv\\coursera_env\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 446\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 447\u001b[0m \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 448\u001b[1;33m \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 449\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 450\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\buono\\01_data_science\\99_venv\\coursera_env\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 878\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 879\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 880\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 881\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 882\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\buono\\01_data_science\\99_venv\\coursera_env\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m 1112\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"c\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1113\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"c\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1114\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1115\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1116\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"python\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\buono\\01_data_science\\99_venv\\coursera_env\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m 1889\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"usecols\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1890\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1891\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1892\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1893\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
"\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] File iris.csv does not exist: 'iris.csv'"
]
}
],
"source": [
"iris = pd.read_csv('iris.csv')\n",
"iris.head()"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'iris' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-85-c4313d5a99b4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpairplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miris\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Name'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdiag_kind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'kde'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'iris' is not defined"
]
}
],
"source": [
"sns.pairplot(iris, hue='Name', diag_kind='kde', size=2);"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"scrolled": true
},
"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",
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" 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",
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" 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",
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" resize: function(event, ui) {\n",
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" },\n",
" stop: function(event, ui) {\n",
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" 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",
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" 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",
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" // upon first draw.\n",
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"\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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width=\"800\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"ename": "NameError",
"evalue": "name 'iris' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;31mNameError\u001b[0m: name 'iris' is not defined"
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"plt.figure(figsize=(8,6))\n",
"plt.subplot(121)\n",
"sns.swarmplot('Name', 'PetalLength', data=iris);\n",
"plt.subplot(122)\n",
"sns.violinplot('Name', 'PetalLength', data=iris);"
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e91ddee0e4fbbdda00c14991e490dd96d91610f1 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analysis of Crowdfunding Projects\n",
"\n",
"### Data Description\n",
"\n",
"Kickstarter.com is a crowd funding website where creators post descriptions of projects they want to create, and individuals contribute some amount of personal money to finance the project. In return for funding, contributors typically get a wide range of “Backer Rewards”. The rewards differ depending on the type of project. For example, a book/video project will typically issue a digital copy of the final product to backers that donate above a given amount. The scope of projects can vary widely, from the initial factory runs of complex hardware products. One creator simply requested ten dollars so he could make some potato salad and instead raised $55,000 and threw a crazy party\n",
"\n",
"### Data Source\n",
"\n",
"Webrobots.io runs a monthly scrape of all Kickstarter projects available on the Kickstarter website. The resulting data are available as both csv and json files, and each package covers a number of projects on the website from the start date (April 21st 2009) until the scrape date. From April 2015, Kickstarter began limiting the number of search results for each category. As a result, the numbers of historical results displayed are limited. Webrobots started running scrapes in multiple sub-categories to capture more instances, and as a result, there are multiple entries for several projects.\n",
"\n",
"### Dates of Study\n",
"\n",
"The scrape data that I'm working with was taken from Kickstarter.com in March of 2019. The scrape data includes both historical and current projects, so there's a wide variety of dates captured here.\n",
"\n",
"# Data issues\n",
"\n",
"### Inconsistent Dates\n",
"\n",
"Kickstarter.com limits the number of search results shown for each category of project (ex. 'Film & Video', or 'Publishing'). Because of this limit and because the number of projects in each category varies based on their relative popularity, the active dates for projects across different categories are not all the same. As we'll see later, the popularity of the Kickstarter.com website fluctuates over time, so different launch dates can affect a project's chances of success\n",
"\n",
"### Missing, incorrect or duplicate data\n",
"\n",
"The overall quality of the dataset is very good, with very few pieces of missing data.\n",
"\n",
"# Scope and Goals\n",
"\n",
"### Audience and Business Need\n",
"\n",
"This project is aimed at creators that are interested launching projects on Kickstarter's platform. Kickstarter campaigns are \"all or nothing\", meaning if a campaign doesn't reach the funding goal, all of the funds are returned to backers. For this reason, it's important for campaigns to carefully consider how their project is presented\n",
"\n",
"### Previous Work \n",
"\n",
"There's been a lot of work published using Kickstarter datasets in the past. Most of the past work has focused on trying to predict whether a project will achieve their funding goal based on characteristics such as funding goal, project category, and number of backers. While that's interesting from a technical/model optimization view, actionable information for creators is limited so far. \n",
"\n",
"Citations for previous work is given in Appendix B at the end of this notebook\n",
"\n",
"### Project Goal\n",
"\n",
"In light of the previous work done and the popularity of the dataset, this project attempts to approach this dataset from an original angle. The main question I'm trying to answer here is:\n",
"\n",
"* *Can we create a way for new creators to make their campaigns \"look\" like successful projects* \n",
"\n",
"This project aims to achieve that by running a segmentation analysis on attributes of successful and unsuccesful projects and try to uncover their differences\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Cleaning and Manipulation\n",
"\n",
"### JSON Formatting\n",
"\n",
"Many of columns in the original dataset were returned as JSON objects during the webrobots scrape. When exporting to a .csv file, and then importing into Pandas, the JSON formatting was lost. The cleaning script made for this project re-applied the JSON formatting, and then pulled the relevant information back out\n",
"\n",
"### Interpreted Data\n",
"Some columns were made with interpreted data. Time deltas were calculated between:\n",
"* Campaign launch date and campaign creation date\n",
"* Campaign launch date and campaign deadline\n",
"* Campaign launch date and date funding goal was reached\n",
"\n",
"Natural Language Processing was run on project titles and descriptions to better define project differences. The data from the NLP analysis was used to score project descriptions on their active phrasing and whatever other criteria I make up \n",
"\n",
"### Pruning and other cleaning\n",
"The remaining cleaning work has been selecting the data relevant to the project. The original dataset contained contained image url's, and duplicates of data available in other columns. These items were removed from the dataset used for analysis\n",
"\n",
"### Date formats \n",
"\n",
"All of the columns that are time/date related are in epoch time, and I've left it that way to make things easier to do math\n",
"\n",
"### External Data\n",
"\n",
"Data pulled from IndieGoGo has been merged in with the Kickstarter dataset for comparison"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"181302"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Import libraries needed for analysis\n",
"import pandas as pd\n",
"import seaborn as sb\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import datetime\n",
"import pdb\n",
"import time\n",
"import re\n",
"import os\n",
"import math as mt\n",
"\n",
"url = 'https://tufts.box.com/shared/static/939cuad4z6ieo8njf3ya58a603q4enyh.csv'\n",
"if not os.path.exists('2019KickDataCleaned.csv'):\n",
" df = pd.read_csv(url)\n",
"else:\n",
" df = pd.read_csv('2019KickDataCleaned.csv', index_col = 0)\n",
"\n",
"df.drop(columns = ['subcats'], inplace = True)\n",
"df = df.drop_duplicates(subset = ['id'])\n",
"len(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Project states\n",
"\n",
"There are four different states listed for each project in the dataset:\n",
"\n",
"| State | Description|\n",
"|:---:|:---:|\n",
"|Live | Project is currently actively fundraising|\n",
"|Cancelled | Project was cancelled by the creators before the deadline | \n",
"|Suspended | The project was suspended by Kickstarter for not meeting some criteria|\n",
"|Succesful| The project has met or exceeded the funding goal |\n",
"|Failed| The project failed to meet their funding goal| \n",
"\n",
"Since this project aims to analyze factors that make projects successful, I've filtered the dataset to include only projects that are either successful or failed"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Merge in External Data\n",
"\n",
"### Alexa Site Traffic Ranking data\n",
"\n",
"I snagged the Alexa global traffic ranking for Kickstarter.com for the last three years. Let's take a look and see if there's any correlation between the site's traffic ranking and the success rate of projects. I only had access to the last three years worth of ranking data, so some projects will be missing information\n",
"\n",
"I'm using the launch date as a foreign key to match with Kickstarter's Alexa ranking, so I have to convert the timestamp to M/D/YYYY format\n",
"\n",
"Once the timestamps are converted, merge the Alexa ranking data in with the Kickstarter data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"url = 'https://tufts.box.com/shared/static/1xr291wybxfardn6cteqdmq08khic84t.csv' #Read the Alexa data\n",
"alexa = pd.read_csv(url, skiprows = 6) \n",
"\n",
"#Conver the launch date timestamp (in epoch time) to M/D/YYYY format\n",
"df['launch_date'] = df.apply(lambda x: time.strftime('%-m/%-d/%Y', time.localtime(x['launched_at'])), axis = 1)\n",
"\n",
"#Merge the two dataframes together on the launch date column with a left join\n",
"df = df.merge(alexa, how = 'left', left_on = 'launch_date', right_on = 'Date')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Convert Foreign Currencies to USD terms\n",
"\n",
"There's a number of projects that originated outside of the US. For these projects, the funding goals are in a different currency. In order to clean up the dataframe for analysis, convert these into USD terms.\n",
"\n",
"Kickstarter.com was kind enough to include the prevailing currency exchange rate at the time of the project, so I'll use that for the conversion"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Multiply the goal by the fx rate\n",
"df['goal'] = df.apply(lambda x: (x['goal'] * x['fx_rate']), axis = 1) "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>backers_count</th>\n",
" <th>blurb</th>\n",
" <th>country</th>\n",
" <th>created_at</th>\n",
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" <th>currency</th>\n",
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" <th>...</th>\n",
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" <th>Date</th>\n",
" <th>Metric</th>\n",
" <th>kickstarter.com</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>A comedy web series about the inherently funny...</td>\n",
" <td>US</td>\n",
" <td>1489891481</td>\n",
" <td>915430313</td>\n",
" <td>USD</td>\n",
" <td>1494132705</td>\n",
" <td>1.000000</td>\n",
" <td>5000.0000</td>\n",
" <td>...</td>\n",
" <td>Austin, TX</td>\n",
" <td>19</td>\n",
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" <td>Kickstarter</td>\n",
" <td>10.160000</td>\n",
" <td>4/7/2017</td>\n",
" <td>4/7/2017</td>\n",
" <td>Global Rank</td>\n",
" <td>548.0</td>\n",
" </tr>\n",
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" <td>36</td>\n",
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" <td>1508479978</td>\n",
" <td>1011928721</td>\n",
" <td>MXN</td>\n",
" <td>1512917074</td>\n",
" <td>0.052034</td>\n",
" <td>3382.2282</td>\n",
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" <td>Mexico City, Mexico</td>\n",
" <td>16</td>\n",
" <td>35</td>\n",
" <td>35</td>\n",
" <td>Kickstarter</td>\n",
" <td>5.226900</td>\n",
" <td>11/5/2017</td>\n",
" <td>11/5/2017</td>\n",
" <td>Global Rank</td>\n",
" <td>528.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>61</td>\n",
" <td>We're building the new home of Māori Theatre a...</td>\n",
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" <td>7</td>\n",
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" <td>1482813229</td>\n",
" <td>121124061</td>\n",
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" <td>1489666144</td>\n",
" <td>1.000000</td>\n",
" <td>40000.0000</td>\n",
" <td>...</td>\n",
" <td>Scottsdale, AZ</td>\n",
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" <td>2/14/2017</td>\n",
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" <td>Global Rank</td>\n",
" <td>589.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>183</td>\n",
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" <td>16</td>\n",
" <td>32</td>\n",
" <td>32</td>\n",
" <td>Kickstarter</td>\n",
" <td>195.550000</td>\n",
" <td>10/10/2017</td>\n",
" <td>10/10/2017</td>\n",
" <td>Global Rank</td>\n",
" <td>503.0</td>\n",
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" Unnamed: 0 backers_count \\\n",
"0 0 9 \n",
"1 1 36 \n",
"2 2 61 \n",
"3 3 1730 \n",
"4 4 183 \n",
"\n",
" blurb country created_at \\\n",
"0 A comedy web series about the inherently funny... US 1489891481 \n",
"1 Cabo y un Capitán, dos soldados villistas, tra... MX 1508479978 \n",
"2 We're building the new home of Māori Theatre a... NZ 1426499822 \n",
"3 Comic-book stories celebrate women who crack c... US 1482813229 \n",
"4 A Victorian “gaslamp” lesbian romance, set in ... US 1506228739 \n",
"\n",
" creator currency deadline fx_rate goal ... \\\n",
"0 915430313 USD 1494132705 1.000000 5000.0000 ... \n",
"1 1011928721 MXN 1512917074 0.052034 3382.2282 ... \n",
"2 1902194044 NZD 1429596000 0.685030 6850.2957 ... \n",
"3 121124061 USD 1489666144 1.000000 40000.0000 ... \n",
"4 484654302 USD 1510473540 1.000000 2000.0000 ... \n",
"\n",
" city creLauDelta lauDeadDelta staLauDelta source \\\n",
"0 Austin, TX 19 30 30 Kickstarter \n",
"1 Mexico City, Mexico 16 35 35 Kickstarter \n",
"2 Auckland, NZ 7 28 28 Kickstarter \n",
"3 Scottsdale, AZ 49 29 29 Kickstarter \n",
"4 San Francisco, CA 16 32 32 Kickstarter \n",
"\n",
" funds_raised_percent launch_date Date Metric kickstarter.com \n",
"0 10.160000 4/7/2017 4/7/2017 Global Rank 548.0 \n",
"1 5.226900 11/5/2017 11/5/2017 Global Rank 528.0 \n",
"2 78.343402 3/23/2015 NaN NaN NaN \n",
"3 243.617500 2/14/2017 2/14/2017 Global Rank 589.0 \n",
"4 195.550000 10/10/2017 10/10/2017 Global Rank 503.0 \n",
"\n",
"[5 rows x 31 columns]"
]
},
"execution_count": 4,
"metadata": {},
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],
"source": [
"# Check that the merge was successful and did what I wanted\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### IndieGoGo Data\n",
"\n",
"Webrobots.io also runs a monthly scrape of the indiegogo website. Since there's already been a large amount of work done on Kickstarter data in the past, I want to try and make some unique analyses using a new dataset.\n",
"\n",
"I grabbed the most recent version of the indiegogo dataset, and now I'm going to merge it with my Kickstarter dataset"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"#Start by taking a look at the indiegogo dataset\n",
"url = 'https://tufts.box.com/shared/static/on2cmdxc4vqpm2dutaaf7l8764ktq0u5.csv'\n",
"indie = pd.read_csv('https://tufts.box.com/shared/static/on2cmdxc4vqpm2dutaaf7l8764ktq0u5.csv')\n",
"#Drop columns that are unrelatd to the original kickstarter dataset\n",
"indie.drop(labels = ['bullet_point', 'category_url', 'clickthrough_url', 'image_url', 'is_pre_launch', 'offered_by', \n",
" 'price_offered', 'product_id', 'product_stage', 'project_type', 'price_retail', \n",
" 'perk_goal_percentage', 'tags'], axis = 1, inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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" <th></th>\n",
" <th>category</th>\n",
" <th>close_date</th>\n",
" <th>currency</th>\n",
" <th>funds_raised_amount</th>\n",
" <th>funds_raised_percent</th>\n",
" <th>is_indemand</th>\n",
" <th>open_date</th>\n",
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" </tr>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>Home</td>\n",
" <td>2018-07-16T23:59:59-07:00</td>\n",
" <td>GBP</td>\n",
" <td>10694.0</td>\n",
" <td>1.065059</td>\n",
" <td>True</td>\n",
" <td>2018-07-15T23:59:59-07:00</td>\n",
" <td>58.0</td>\n",
" <td>2397370</td>\n",
" <td>https://www.indiegogo.com/explore/all?project_...</td>\n",
" <td>Make a brew for two with one press using the A...</td>\n",
" <td>2POUR An Accessory For Aeropress Coffee Maker</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Web Series & TV Shows</td>\n",
" <td>2017-09-12T23:59:59-07:00</td>\n",
" <td>AUD</td>\n",
" <td>30.0</td>\n",
" <td>0.003000</td>\n",
" <td>False</td>\n",
" <td>2017-07-14T06:19:44-07:00</td>\n",
" <td>0.0</td>\n",
" <td>2152925</td>\n",
" <td>https://www.indiegogo.com/explore/all?project_...</td>\n",
" <td>This street is the stage where the theatre mee...</td>\n",
" <td>Hustle Street - A musical</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Photography</td>\n",
" <td>2013-02-13T23:59:59-08:00</td>\n",
" <td>USD</td>\n",
" <td>7060.0</td>\n",
" <td>0.415294</td>\n",
" <td>False</td>\n",
" <td>2012-12-10T09:05:14-08:00</td>\n",
" <td>65.0</td>\n",
" <td>291347</td>\n",
" <td>https://www.indiegogo.com/explore/all?project_...</td>\n",
" <td>Creating a photo-narrative book that captures ...</td>\n",
" <td>The Thru-Project</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Home</td>\n",
" <td>2017-01-07T23:59:59-08:00</td>\n",
" <td>EUR</td>\n",
" <td>20.0</td>\n",
" <td>0.000020</td>\n",
" <td>False</td>\n",
" <td>2016-11-24T01:51:38-08:00</td>\n",
" <td>2.0</td>\n",
" <td>1951140</td>\n",
" <td>https://www.indiegogo.com/explore/all?project_...</td>\n",
" <td>World 1st Charity Contest of religious communi...</td>\n",
" <td>Way2goodness</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Health & Fitness</td>\n",
" <td>2018-07-11T23:59:59-07:00</td>\n",
" <td>USD</td>\n",
" <td>250036.0</td>\n",
" <td>6.750033</td>\n",
" <td>True</td>\n",
" <td>2018-07-10T23:59:59-07:00</td>\n",
" <td>532.0</td>\n",
" <td>2393782</td>\n",
" <td>https://www.indiegogo.com/explore/all?project_...</td>\n",
" <td>Wake up gently every morning by stimulating th...</td>\n",
" <td>Sensorwake Trio: The scent-based alarm clock</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" category close_date currency \\\n",
"0 Home 2018-07-16T23:59:59-07:00 GBP \n",
"1 Web Series & TV Shows 2017-09-12T23:59:59-07:00 AUD \n",
"2 Photography 2013-02-13T23:59:59-08:00 USD \n",
"3 Home 2017-01-07T23:59:59-08:00 EUR \n",
"4 Health & Fitness 2018-07-11T23:59:59-07:00 USD \n",
"\n",
" funds_raised_amount funds_raised_percent is_indemand \\\n",
"0 10694.0 1.065059 True \n",
"1 30.0 0.003000 False \n",
"2 7060.0 0.415294 False \n",
"3 20.0 0.000020 False \n",
"4 250036.0 6.750033 True \n",
"\n",
" open_date perks_claimed project_id \\\n",
"0 2018-07-15T23:59:59-07:00 58.0 2397370 \n",
"1 2017-07-14T06:19:44-07:00 0.0 2152925 \n",
"2 2012-12-10T09:05:14-08:00 65.0 291347 \n",
"3 2016-11-24T01:51:38-08:00 2.0 1951140 \n",
"4 2018-07-10T23:59:59-07:00 532.0 2393782 \n",
"\n",
" source_url \\\n",
"0 https://www.indiegogo.com/explore/all?project_... \n",
"1 https://www.indiegogo.com/explore/all?project_... \n",
"2 https://www.indiegogo.com/explore/all?project_... \n",
"3 https://www.indiegogo.com/explore/all?project_... \n",
"4 https://www.indiegogo.com/explore/all?project_... \n",
"\n",
" tagline \\\n",
"0 Make a brew for two with one press using the A... \n",
"1 This street is the stage where the theatre mee... \n",
"2 Creating a photo-narrative book that captures ... \n",
"3 World 1st Charity Contest of religious communi... \n",
"4 Wake up gently every morning by stimulating th... \n",
"\n",
" title \n",
"0 2POUR An Accessory For Aeropress Coffee Maker \n",
"1 Hustle Street - A musical \n",
"2 The Thru-Project \n",
"3 Way2goodness \n",
"4 Sensorwake Trio: The scent-based alarm clock "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"indie.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Making the Indiegogo data look like the Kickstarter data\n",
"\n",
"Since the two websites are unique, there's a lot of differences in how the datasets are structured\n",
"\n",
"The following few steps are manipulating the indiegogo data to look like the Kickstarter data\n",
"\n",
"### Date Conversion\n",
"\n",
"First up, change the dates that are in a YY/MM/DD format and convert that into epoch time (the standard for the Kickstarter dataset)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import datetime # Library for working with datetime information\n",
"indie.dropna(inplace = True) # Drop rows that are missing important information\n",
"\n",
"# Define a function to strip out the extra information on the indiegogo timestamps, and then convert to epoch time\n",
"def epoch(target): \n",
" '''\n",
" This function takes a date from the indiegogo dataset (read originally as a string), converts to a datetime object\n",
" and then converts that date to epoch time\n",
" '''\n",
" target = target.split('T')\n",
" target = datetime.datetime.strptime(target[0], '%Y-%m-%d')\n",
" target = target.strftime('%s')\n",
" return(target)\n",
"\n",
"#Run the epoch function on the project close date and project open date columns\n",
"indie.close_date = indie.close_date.apply(epoch) \n",
"indie.open_date = indie.open_date.apply(epoch)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Renaming Categories\n",
"\n",
"The category labels for indiegogo don't quite match up to Kickstarter categories. I've made a dictionary to convert the indiegogo categories to match Kickstarter categories\n",
"\n",
"Some of the categories in the indiegogo dataset didn't have an obvious counterpart in the kickstarter data, so I've left those in their original categories"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"indieDictionary = { 'Home' : 'crafts',\n",
" 'Web Series & TV Shows' : 'film & video',\n",
" 'Photography' : 'photography',\n",
" 'Health & Fitness' : 'health&fitness',\n",
" 'Comics' : 'comics',\n",
" 'Food & Beverages' : 'food',\n",
" 'Music' : 'music',\n",
" 'Dance & Theater' : 'dance',\n",
" 'Podcasts, Blogs & Vlogs' : 'journalism' ,\n",
" 'Video Games' : 'games',\n",
" 'Art' : 'art',\n",
" 'Fashion & Wearables' : 'fashion',\n",
" 'Phones & Accessories' : 'technology',\n",
" 'Wellness' : 'health&fitness',\n",
" 'Camera Gear': 'cameragear',\n",
" 'Travel & Outdoors': 'travel&outdoors',\n",
" 'Film' : 'film & video',\n",
" 'Productivity': 'productivity',\n",
" 'Tabletop Games': 'games',\n",
" 'Audio' : 'music',\n",
" 'Culture' : 'culture',\n",
" 'Human Rights' : 'human_rights',\n",
" 'Environment' : 'environment',\n",
" 'Writing & Publishing' : 'publishing',\n",
" 'Transportation' : 'technology',\n",
" 'Local Businesses' : 'local_businesses',\n",
" 'Energy & Green Tech' : 'energy&green tech'}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Column Names, Epoch Time Conversion, and some Math\n",
"\n",
"Rename the columns of the indiegogo data to match the kickstarter column names. When the dates were converted to epoch time in the step above, the data was read as a string type. Convert those to ints\n",
"\n",
"Finally, calculate how long the fundraising campaigns were for each project"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# Use the dictionary to re-categorize the indiegogo projects\n",
"indie.category = indie.category.map(indieDictionary) \n",
"\n",
"# Add an indiegogo tag column to separate kickstarter and indiegogo data\n",
"indie['source'] = 'indiegogo' \n",
"\n",
"# Rename each column header to match kickstarter\n",
"indie = indie.rename(index=str, columns={\"close_date\": \"deadline\", \n",
" \"funds_raised_amount\": \"usd_pledged\", \n",
" \"open_date\" : \"launched_at\",\n",
" \"is_indemand\":\"spotlight\", \n",
" \"tagline\" : \"blurb\", \n",
" \"title\":\"name\", \n",
" \"perks_claimed\" : \"backers_count\",\n",
" \"project_id\": \"id\", \n",
" \"category\" : \"fullcats\",\n",
" \"source_url\" : \"urls\"})\n",
"\n",
"# Cast the epoch timestamps as ints instead of strings\n",
"indie.deadline = indie.deadline.astype(int) \n",
"indie.launched_at = indie.launched_at.astype(int)\n",
"\n",
"#Calculate how long each fundraising campaign was run for\n",
"indie['lauDeadDelta'] = indie.apply(lambda x: (x[\"deadline\"] - x[\"launched_at\"])/60/60/24, axis = 1)\n",
"indie['goal'] = indie.apply(lambda x: (mt.ceil(x['usd_pledged'] / (x['funds_raised_percent'] + 0.00000000001))), \n",
" axis = 1)\n",
"indie['funds_raised_percent'] = indie.apply(lambda x: (x['funds_raised_percent'] * 100), axis = 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tagging IndieGoGo campaigns as successful or failed\n",
"\n",
"Since indiegogo creatores receive their funds regardless of how much the projects raise, the indiegogo dataset doesn't have a tag for the status of a project\n",
"\n",
"To fix that, I'll tag any project that reaches it's fundraising goal as successful and any that don't as failed"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/chrismay/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" import sys\n",
"/Users/chrismay/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" # This is added back by InteractiveShellApp.init_path()\n"
]
}
],
"source": [
"inSuccess = pd.DataFrame()\n",
"inFailed = pd.DataFrame()\n",
"\n",
"# First filter out all projects that raised 100% of their goal or more, then add a 'state' column and set that to\n",
"# successful\n",
"inSuccess = indie[indie['funds_raised_percent'] >= 100]\n",
"inSuccess['state'] = 'successful'\n",
"\n",
"# Now do the same for projects that didn't raise 100% of their funding goals, and tag them as failed\n",
"inFailed = indie[indie['funds_raised_percent'] < 100]\n",
"inFailed['state'] = 'failed'\n",
"\n",
"# Merge the two dataframes back together \n",
"indie = inSuccess.merge(inFailed, how = 'outer', on = None)\n",
"del inSuccess\n",
"del inFailed"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>fullcats</th>\n",
" <th>deadline</th>\n",
" <th>currency</th>\n",
" <th>usd_pledged</th>\n",
" <th>funds_raised_percent</th>\n",
" <th>spotlight</th>\n",
" <th>launched_at</th>\n",
" <th>backers_count</th>\n",
" <th>id</th>\n",
" <th>urls</th>\n",
" <th>blurb</th>\n",
" <th>name</th>\n",
" <th>source</th>\n",
" <th>lauDeadDelta</th>\n",
" <th>goal</th>\n",
" <th>state</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>crafts</td>\n",
" <td>1531713600</td>\n",
" <td>GBP</td>\n",
" <td>10694.0</td>\n",
" <td>106.505882</td>\n",
" <td>True</td>\n",
" <td>1531627200</td>\n",
" <td>58.0</td>\n",
" <td>2397370</td>\n",
" <td>https://www.indiegogo.com/explore/all?project_...</td>\n",
" <td>Make a brew for two with one press using the A...</td>\n",
" <td>2POUR An Accessory For Aeropress Coffee Maker</td>\n",
" <td>indiegogo</td>\n",
" <td>1.0</td>\n",
" <td>10041</td>\n",
" <td>successful</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>health&fitness</td>\n",
" <td>1531281600</td>\n",
" <td>USD</td>\n",
" <td>250036.0</td>\n",
" <td>675.003333</td>\n",
" <td>True</td>\n",
" <td>1531195200</td>\n",
" <td>532.0</td>\n",
" <td>2393782</td>\n",
" <td>https://www.indiegogo.com/explore/all?project_...</td>\n",
" <td>Wake up gently every morning by stimulating th...</td>\n",
" <td>Sensorwake Trio: The scent-based alarm clock</td>\n",
" <td>indiegogo</td>\n",
" <td>1.0</td>\n",
" <td>37043</td>\n",
" <td>successful</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>comics</td>\n",
" <td>1373860800</td>\n",
" <td>USD</td>\n",
" <td>12904.0</td>\n",
" <td>430.133333</td>\n",
" <td>False</td>\n",
" <td>1369800000</td>\n",
" <td>151.0</td>\n",
" <td>413752</td>\n",
" <td>https://www.indiegogo.com/explore/all?project_...</td>\n",
" <td>Support independent comics by helping Antarcti...</td>\n",
" <td>Antarctic Press...Keeping it Cold in the Antar...</td>\n",
" <td>indiegogo</td>\n",
" <td>47.0</td>\n",
" <td>3000</td>\n",
" <td>successful</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>crafts</td>\n",
" <td>1513227600</td>\n",
" <td>USD</td>\n",
" <td>78718.0</td>\n",
" <td>272.035000</td>\n",
" <td>False</td>\n",
" <td>1513141200</td>\n",
" <td>179.0</td>\n",
" <td>2276585</td>\n",
" <td>https://www.indiegogo.com/explore/all?project_...</td>\n",
" <td>The first smart padlock designed to protect yo...</td>\n",
" <td>BoxLock Home - Smart Padlock to Protect Delive...</td>\n",
" <td>indiegogo</td>\n",
" <td>1.0</td>\n",
" <td>28937</td>\n",
" <td>successful</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>crafts</td>\n",
" <td>1524456000</td>\n",
" <td>USD</td>\n",
" <td>6135.0</td>\n",
" <td>232.300000</td>\n",
" <td>True</td>\n",
" <td>1521086400</td>\n",
" <td>44.0</td>\n",
" <td>2352230</td>\n",
" <td>https://www.indiegogo.com/explore/all?project_...</td>\n",
" <td>Get 5 star results with our solid Titanium rol...</td>\n",
" <td>SolidTi: World's first solid titanium rolling pin</td>\n",
" <td>indiegogo</td>\n",
" <td>39.0</td>\n",
" <td>2641</td>\n",
" <td>successful</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" fullcats deadline currency usd_pledged funds_raised_percent \\\n",
"0 crafts 1531713600 GBP 10694.0 106.505882 \n",
"1 health&fitness 1531281600 USD 250036.0 675.003333 \n",
"2 comics 1373860800 USD 12904.0 430.133333 \n",
"3 crafts 1513227600 USD 78718.0 272.035000 \n",
"4 crafts 1524456000 USD 6135.0 232.300000 \n",
"\n",
" spotlight launched_at backers_count id \\\n",
"0 True 1531627200 58.0 2397370 \n",
"1 True 1531195200 532.0 2393782 \n",
"2 False 1369800000 151.0 413752 \n",
"3 False 1513141200 179.0 2276585 \n",
"4 True 1521086400 44.0 2352230 \n",
"\n",
" urls \\\n",
"0 https://www.indiegogo.com/explore/all?project_... \n",
"1 https://www.indiegogo.com/explore/all?project_... \n",
"2 https://www.indiegogo.com/explore/all?project_... \n",
"3 https://www.indiegogo.com/explore/all?project_... \n",
"4 https://www.indiegogo.com/explore/all?project_... \n",
"\n",
" blurb \\\n",
"0 Make a brew for two with one press using the A... \n",
"1 Wake up gently every morning by stimulating th... \n",
"2 Support independent comics by helping Antarcti... \n",
"3 The first smart padlock designed to protect yo... \n",
"4 Get 5 star results with our solid Titanium rol... \n",
"\n",
" name source lauDeadDelta \\\n",
"0 2POUR An Accessory For Aeropress Coffee Maker indiegogo 1.0 \n",
"1 Sensorwake Trio: The scent-based alarm clock indiegogo 1.0 \n",
"2 Antarctic Press...Keeping it Cold in the Antar... indiegogo 47.0 \n",
"3 BoxLock Home - Smart Padlock to Protect Delive... indiegogo 1.0 \n",
"4 SolidTi: World's first solid titanium rolling pin indiegogo 39.0 \n",
"\n",
" goal state \n",
"0 10041 successful \n",
"1 37043 successful \n",
"2 3000 successful \n",
"3 28937 successful \n",
"4 2641 successful "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check that the above operations were successful\n",
"indie.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Merge the two DataFrames together and deduplicate\n",
"\n",
"Now that the two datasets look alike, merge them together with an outer join (include only entries that in either dataset, but not both)\n",
"\n",
"Since the webscrape by webrobots searched by subcategory, projects can be listed multiple times if they had multiple subcategry tags. Use the drop_duplicates method from pandas to get rid of the extras"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/chrismay/anaconda3/lib/python3.7/site-packages/pandas/core/reshape/merge.py:938: UserWarning: You are merging on int and float columns where the float values are not equal to their int representation\n",
" 'representation', UserWarning)\n",
"/Users/chrismay/anaconda3/lib/python3.7/site-packages/pandas/core/reshape/merge.py:946: UserWarning: You are merging on int and float columns where the float values are not equal to their int representation\n",
" 'representation', UserWarning)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>backers_count</th>\n",
" <th>blurb</th>\n",
" <th>country</th>\n",
" <th>created_at</th>\n",
" <th>creator</th>\n",
" <th>currency</th>\n",
" <th>deadline</th>\n",
" <th>fx_rate</th>\n",
" <th>goal</th>\n",
" <th>id</th>\n",
" <th>...</th>\n",
" <th>city</th>\n",
" <th>creLauDelta</th>\n",
" <th>lauDeadDelta</th>\n",
" <th>staLauDelta</th>\n",
" <th>source</th>\n",
" <th>funds_raised_percent</th>\n",
" <th>launch_date</th>\n",
" <th>Date</th>\n",
" <th>Metric</th>\n",
" <th>kickstarter.com</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9</td>\n",
" <td>A comedy web series about the inherently funny...</td>\n",
" <td>US</td>\n",
" <td>1.489891e+09</td>\n",
" <td>9.154303e+08</td>\n",
" <td>USD</td>\n",
" <td>1494132705</td>\n",
" <td>1.000000</td>\n",
" <td>5000.0000</td>\n",
" <td>1203770415</td>\n",
" <td>...</td>\n",
" <td>Austin, TX</td>\n",
" <td>19.0</td>\n",
" <td>30.0</td>\n",
" <td>30.0</td>\n",
" <td>Kickstarter</td>\n",
" <td>10.160000</td>\n",
" <td>4/7/2017</td>\n",
" <td>4/7/2017</td>\n",
" <td>Global Rank</td>\n",
" <td>548.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>36</td>\n",
" <td>Cabo y un Capitán, dos soldados villistas, tra...</td>\n",
" <td>MX</td>\n",
" <td>1.508480e+09</td>\n",
" <td>1.011929e+09</td>\n",
" <td>MXN</td>\n",
" <td>1512917074</td>\n",
" <td>0.052034</td>\n",
" <td>3382.2282</td>\n",
" <td>878861613</td>\n",
" <td>...</td>\n",
" <td>Mexico City, Mexico</td>\n",
" <td>16.0</td>\n",
" <td>35.0</td>\n",
" <td>35.0</td>\n",
" <td>Kickstarter</td>\n",
" <td>5.226900</td>\n",
" <td>11/5/2017</td>\n",
" <td>11/5/2017</td>\n",
" <td>Global Rank</td>\n",
" <td>528.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>61</td>\n",
" <td>We're building the new home of Māori Theatre a...</td>\n",
" <td>NZ</td>\n",
" <td>1.426500e+09</td>\n",
" <td>1.902194e+09</td>\n",
" <td>NZD</td>\n",
" <td>1429596000</td>\n",
" <td>0.685030</td>\n",
" <td>6850.2957</td>\n",
" <td>917345297</td>\n",
" <td>...</td>\n",
" <td>Auckland, NZ</td>\n",
" <td>7.0</td>\n",
" <td>28.0</td>\n",
" <td>28.0</td>\n",
" <td>Kickstarter</td>\n",
" <td>78.343402</td>\n",
" <td>3/23/2015</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1730</td>\n",
" <td>Comic-book stories celebrate women who crack c...</td>\n",
" <td>US</td>\n",
" <td>1.482813e+09</td>\n",
" <td>1.211241e+08</td>\n",
" <td>USD</td>\n",
" <td>1489666144</td>\n",
" <td>1.000000</td>\n",
" <td>40000.0000</td>\n",
" <td>1702164653</td>\n",
" <td>...</td>\n",
" <td>Scottsdale, AZ</td>\n",
" <td>49.0</td>\n",
" <td>29.0</td>\n",
" <td>29.0</td>\n",
" <td>Kickstarter</td>\n",
" <td>243.617500</td>\n",
" <td>2/14/2017</td>\n",
" <td>2/14/2017</td>\n",
" <td>Global Rank</td>\n",
" <td>589.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>183</td>\n",
" <td>A Victorian “gaslamp” lesbian romance, set in ...</td>\n",
" <td>US</td>\n",
" <td>1.506229e+09</td>\n",
" <td>4.846543e+08</td>\n",
" <td>USD</td>\n",
" <td>1510473540</td>\n",
" <td>1.000000</td>\n",
" <td>2000.0000</td>\n",
" <td>1042930184</td>\n",
" <td>...</td>\n",
" <td>San Francisco, CA</td>\n",
" <td>16.0</td>\n",
" <td>32.0</td>\n",
" <td>32.0</td>\n",
" <td>Kickstarter</td>\n",
" <td>195.550000</td>\n",
" <td>10/10/2017</td>\n",
" <td>10/10/2017</td>\n",
" <td>Global Rank</td>\n",
" <td>503.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 30 columns</p>\n",
"</div>"
],
"text/plain": [
" backers_count blurb country \\\n",
"0 9 A comedy web series about the inherently funny... US \n",
"1 36 Cabo y un Capitán, dos soldados villistas, tra... MX \n",
"2 61 We're building the new home of Māori Theatre a... NZ \n",
"3 1730 Comic-book stories celebrate women who crack c... US \n",
"4 183 A Victorian “gaslamp” lesbian romance, set in ... US \n",
"\n",
" created_at creator currency deadline fx_rate goal \\\n",
"0 1.489891e+09 9.154303e+08 USD 1494132705 1.000000 5000.0000 \n",
"1 1.508480e+09 1.011929e+09 MXN 1512917074 0.052034 3382.2282 \n",
"2 1.426500e+09 1.902194e+09 NZD 1429596000 0.685030 6850.2957 \n",
"3 1.482813e+09 1.211241e+08 USD 1489666144 1.000000 40000.0000 \n",
"4 1.506229e+09 4.846543e+08 USD 1510473540 1.000000 2000.0000 \n",
"\n",
" id ... city creLauDelta lauDeadDelta staLauDelta \\\n",
"0 1203770415 ... Austin, TX 19.0 30.0 30.0 \n",
"1 878861613 ... Mexico City, Mexico 16.0 35.0 35.0 \n",
"2 917345297 ... Auckland, NZ 7.0 28.0 28.0 \n",
"3 1702164653 ... Scottsdale, AZ 49.0 29.0 29.0 \n",
"4 1042930184 ... San Francisco, CA 16.0 32.0 32.0 \n",
"\n",
" source funds_raised_percent launch_date Date Metric \\\n",
"0 Kickstarter 10.160000 4/7/2017 4/7/2017 Global Rank \n",
"1 Kickstarter 5.226900 11/5/2017 11/5/2017 Global Rank \n",
"2 Kickstarter 78.343402 3/23/2015 NaN NaN \n",
"3 Kickstarter 243.617500 2/14/2017 2/14/2017 Global Rank \n",
"4 Kickstarter 195.550000 10/10/2017 10/10/2017 Global Rank \n",
"\n",
" kickstarter.com \n",
"0 548.0 \n",
"1 528.0 \n",
"2 NaN \n",
"3 589.0 \n",
"4 503.0 \n",
"\n",
"[5 rows x 30 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged = df.merge(indie, how = 'outer', on = None)\n",
"merged.reset_index(inplace = True)\n",
"merged.drop(labels = ['index', 'Unnamed: 0'], axis = 1, inplace = True)\n",
"\n",
"del indie \n",
"del df\n",
"# Drop duplicates from the dataset, use the 'id' column since its known to be unique only to each project\n",
"merged.drop_duplicates(subset = 'id', inplace = True)\n",
"merged.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculate the Funds Raised over Goal Percent\n",
"\n",
"Kickstarter and Indiegogo give a different gauge of percent of funds raised. Standardize on giving the percent of goal for each project"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"merged['funds_raised_percent'] = merged.apply(lambda x: (((x['usd_pledged']/ (x['goal'] + 0.0000001))*100)), axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>backers_count</th>\n",
" <th>blurb</th>\n",
" <th>country</th>\n",
" <th>created_at</th>\n",
" <th>creator</th>\n",
" <th>currency</th>\n",
" <th>deadline</th>\n",
" <th>fx_rate</th>\n",
" <th>goal</th>\n",
" <th>id</th>\n",
" <th>...</th>\n",
" <th>city</th>\n",
" <th>creLauDelta</th>\n",
" <th>lauDeadDelta</th>\n",
" <th>staLauDelta</th>\n",
" <th>source</th>\n",
" <th>funds_raised_percent</th>\n",
" <th>launch_date</th>\n",
" <th>Date</th>\n",
" <th>Metric</th>\n",
" <th>kickstarter.com</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>201554</th>\n",
" <td>6</td>\n",
" <td>The Manipulation :: of Light \\r\\nan Exhibitio...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CAD</td>\n",
" <td>1374292800</td>\n",
" <td>NaN</td>\n",
" <td>7095.0</td>\n",
" <td>334244</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>30.000000</td>\n",
" <td>NaN</td>\n",
" <td>indiegogo</td>\n",
" <td>4.510218</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>201555</th>\n",
" <td>2</td>\n",
" <td>Wine tasting set shipped to your home. Amazon ...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>EUR</td>\n",
" <td>1546405200</td>\n",
" <td>NaN</td>\n",
" <td>35000.0</td>\n",
" <td>2441088</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>45.000000</td>\n",
" <td>NaN</td>\n",
" <td>indiegogo</td>\n",
" <td>0.280000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>201556</th>\n",
" <td>0</td>\n",
" <td>The chain pod for slow shutter speed, low ligh...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>USD</td>\n",
" <td>1396584000</td>\n",
" <td>NaN</td>\n",
" <td>5000.0</td>\n",
" <td>683726</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>44.958333</td>\n",
" <td>NaN</td>\n",
" <td>indiegogo</td>\n",
" <td>1.200000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>201557</th>\n",
" <td>7</td>\n",
" <td>Help us showcase our incredible community incl...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CAD</td>\n",
" <td>1514523600</td>\n",
" <td>NaN</td>\n",
" <td>25000.0</td>\n",
" <td>2218847</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>60.041667</td>\n",
" <td>NaN</td>\n",
" <td>indiegogo</td>\n",
" <td>3.280000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>201558</th>\n",
" <td>21</td>\n",
" <td>Help Creep Cuts LEVEL UP!</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>USD</td>\n",
" <td>1551502800</td>\n",
" <td>NaN</td>\n",
" <td>6000.0</td>\n",
" <td>2454347</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>60.000000</td>\n",
" <td>NaN</td>\n",
" <td>indiegogo</td>\n",
" <td>35.433333</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 30 columns</p>\n",
"</div>"
],
"text/plain": [
" backers_count blurb \\\n",
"201554 6 The Manipulation :: of Light \\r\\nan Exhibitio... \n",
"201555 2 Wine tasting set shipped to your home. Amazon ... \n",
"201556 0 The chain pod for slow shutter speed, low ligh... \n",
"201557 7 Help us showcase our incredible community incl... \n",
"201558 21 Help Creep Cuts LEVEL UP! \n",
"\n",
" country created_at creator currency deadline fx_rate goal \\\n",
"201554 NaN NaN NaN CAD 1374292800 NaN 7095.0 \n",
"201555 NaN NaN NaN EUR 1546405200 NaN 35000.0 \n",
"201556 NaN NaN NaN USD 1396584000 NaN 5000.0 \n",
"201557 NaN NaN NaN CAD 1514523600 NaN 25000.0 \n",
"201558 NaN NaN NaN USD 1551502800 NaN 6000.0 \n",
"\n",
" id ... city creLauDelta lauDeadDelta staLauDelta source \\\n",
"201554 334244 ... NaN NaN 30.000000 NaN indiegogo \n",
"201555 2441088 ... NaN NaN 45.000000 NaN indiegogo \n",
"201556 683726 ... NaN NaN 44.958333 NaN indiegogo \n",
"201557 2218847 ... NaN NaN 60.041667 NaN indiegogo \n",
"201558 2454347 ... NaN NaN 60.000000 NaN indiegogo \n",
"\n",
" funds_raised_percent launch_date Date Metric kickstarter.com \n",
"201554 4.510218 NaN NaN NaN NaN \n",
"201555 0.280000 NaN NaN NaN NaN \n",
"201556 1.200000 NaN NaN NaN NaN \n",
"201557 3.280000 NaN NaN NaN NaN \n",
"201558 35.433333 NaN NaN NaN NaN \n",
"\n",
"[5 rows x 30 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check the indiegogo data at the end of the dataset to make sure it was done right\n",
"merged.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reducing Memory Usage of the DataFrame\n",
"\n",
"I've occassionally run into kernel issues with this notebook, which is most likely due to running out of memory. Optimize some of the datatypes to reduce working \n",
"\n",
"### Re-casting integer variables\n",
"Ints have been automatically given a float or int64 datatype, downcast these to smaller variables to use less memory"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"backers_count uint8\n",
"blurb object\n",
"country object\n",
"created_at float64\n",
"creator float64\n",
"currency object\n",
"deadline uint32\n",
"fx_rate float64\n",
"goal uint32\n",
"id uint32\n",
"launched_at uint32\n",
"name object\n",
"slug object\n",
"spotlight object\n",
"staff_pick object\n",
"state object\n",
"state_changed_at float64\n",
"urls object\n",
"usd_pledged uint32\n",
"fullcats object\n",
"city object\n",
"creLauDelta float64\n",
"lauDeadDelta uint8\n",
"staLauDelta float64\n",
"source object\n",
"funds_raised_percent float16\n",
"launch_date object\n",
"Date object\n",
"Metric object\n",
"kickstarter.com float64\n",
"dtype: object"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged['backers_count'] = merged['backers_count'].astype(np.uint8)\n",
"merged['deadline'] = merged['deadline'].astype(np.uint32)\n",
"merged['goal'] = merged['goal'].astype(np.uint32)\n",
"merged['id'] = merged['id'].astype(np.uint32)\n",
"merged['launched_at'] = merged['launched_at'].astype(np.uint32)\n",
"merged['usd_pledged'] = merged['usd_pledged'].astype(np.uint32)\n",
"merged['lauDeadDelta'] = merged['lauDeadDelta'].astype(np.uint8)\n",
"merged['funds_raised_percent'] = merged['funds_raised_percent'].astype(np.float16)\n",
"\n",
"merged.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Changing object types to categories\n",
"\n",
"A few of the columns listed as object types are actually categorical variables. Columns that have a low number of unique values can be changed to categorical variables, where each category will be given an 8 bit integer value and then mapped to a much shorter array."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"backers_count uint8\n",
"blurb object\n",
"country object\n",
"created_at float64\n",
"creator float64\n",
"currency category\n",
"deadline uint32\n",
"fx_rate float64\n",
"goal uint32\n",
"id uint32\n",
"launched_at uint32\n",
"name object\n",
"slug object\n",
"spotlight category\n",
"staff_pick category\n",
"state object\n",
"state_changed_at float64\n",
"urls object\n",
"usd_pledged uint32\n",
"fullcats category\n",
"city object\n",
"creLauDelta float64\n",
"lauDeadDelta uint8\n",
"staLauDelta float64\n",
"source category\n",
"funds_raised_percent float16\n",
"launch_date object\n",
"Date object\n",
"kickstarter.com float64\n",
"dtype: object"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged['fullcats'] = merged['fullcats'].astype('category')\n",
"merged['staff_pick'] = merged['staff_pick'].astype('category')\n",
"merged['spotlight'] = merged['spotlight'].astype('category')\n",
"merged['source'] = merged['source'].astype('category')\n",
"merged['currency'] = merged['currency'].astype('category')\n",
"try:\n",
" merged.drop(labels = ['Metric'], axis = 1, inplace = True)\n",
"except:\n",
" pass\n",
"merged.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploratory Data Analysis"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>backers_count</th>\n",
" <th>created_at</th>\n",
" <th>creator</th>\n",
" <th>deadline</th>\n",
" <th>fx_rate</th>\n",
" <th>goal</th>\n",
" <th>id</th>\n",
" <th>launched_at</th>\n",
" <th>state_changed_at</th>\n",
" <th>usd_pledged</th>\n",
" <th>creLauDelta</th>\n",
" <th>lauDeadDelta</th>\n",
" <th>staLauDelta</th>\n",
" <th>funds_raised_percent</th>\n",
" <th>kickstarter.com</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>199618.000000</td>\n",
" <td>1.813020e+05</td>\n",
" <td>1.813020e+05</td>\n",
" <td>1.996180e+05</td>\n",
" <td>181302.000000</td>\n",
" <td>1.996180e+05</td>\n",
" <td>1.996180e+05</td>\n",
" <td>1.996180e+05</td>\n",
" <td>1.813020e+05</td>\n",
" <td>1.996180e+05</td>\n",
" <td>181302.000000</td>\n",
" <td>199618.000000</td>\n",
" <td>181302.000000</td>\n",
" <td>199618.000</td>\n",
" <td>81048.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>45.285666</td>\n",
" <td>1.446903e+09</td>\n",
" <td>1.074589e+09</td>\n",
" <td>1.455799e+09</td>\n",
" <td>0.999337</td>\n",
" <td>8.219191e+04</td>\n",
" <td>9.750394e+08</td>\n",
" <td>1.452897e+09</td>\n",
" <td>1.453600e+09</td>\n",
" <td>1.485695e+04</td>\n",
" <td>45.970756</td>\n",
" <td>33.381253</td>\n",
" <td>30.847713</td>\n",
" <td>NaN</td>\n",
" <td>546.971474</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>58.656426</td>\n",
" <td>6.137708e+07</td>\n",
" <td>6.201507e+08</td>\n",
" <td>6.182117e+07</td>\n",
" <td>0.286660</td>\n",
" <td>8.025752e+06</td>\n",
" <td>6.664825e+08</td>\n",
" <td>6.184694e+07</td>\n",
" <td>6.091841e+07</td>\n",
" <td>1.170977e+05</td>\n",
" <td>127.614911</td>\n",
" <td>13.008452</td>\n",
" <td>13.090062</td>\n",
" <td>NaN</td>\n",
" <td>73.028021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>1.240366e+09</td>\n",
" <td>3.000000e+00</td>\n",
" <td>1.242468e+09</td>\n",
" <td>0.009007</td>\n",
" <td>0.000000e+00</td>\n",
" <td>3.271000e+03</td>\n",
" <td>1.240920e+09</td>\n",
" <td>1.242468e+09</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000</td>\n",
" <td>428.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>3.000000</td>\n",
" <td>1.407594e+09</td>\n",
" <td>5.377061e+08</td>\n",
" <td>1.415194e+09</td>\n",
" <td>1.000000</td>\n",
" <td>1.514000e+03</td>\n",
" <td>3.718836e+08</td>\n",
" <td>1.412280e+09</td>\n",
" <td>1.414758e+09</td>\n",
" <td>8.700000e+01</td>\n",
" <td>2.000000</td>\n",
" <td>29.000000</td>\n",
" <td>28.000000</td>\n",
" <td>1.500</td>\n",
" <td>476.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>20.000000</td>\n",
" <td>1.446477e+09</td>\n",
" <td>1.073699e+09</td>\n",
" <td>1.456439e+09</td>\n",
" <td>1.000000</td>\n",
" <td>5.000000e+03</td>\n",
" <td>9.693086e+08</td>\n",
" <td>1.453501e+09</td>\n",
" <td>1.453254e+09</td>\n",
" <td>1.248000e+03</td>\n",
" <td>10.000000</td>\n",
" <td>30.000000</td>\n",
" <td>30.000000</td>\n",
" <td>100.000</td>\n",
" <td>550.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>66.000000</td>\n",
" <td>1.497145e+09</td>\n",
" <td>1.612593e+09</td>\n",
" <td>1.507996e+09</td>\n",
" <td>1.000000</td>\n",
" <td>1.500000e+04</td>\n",
" <td>1.554152e+09</td>\n",
" <td>1.505141e+09</td>\n",
" <td>1.504017e+09</td>\n",
" <td>5.940000e+03</td>\n",
" <td>35.000000</td>\n",
" <td>36.000000</td>\n",
" <td>33.000000</td>\n",
" <td>122.125</td>\n",
" <td>607.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>255.000000</td>\n",
" <td>1.550094e+09</td>\n",
" <td>2.147483e+09</td>\n",
" <td>1.555301e+09</td>\n",
" <td>10.126885</td>\n",
" <td>1.999812e+09</td>\n",
" <td>2.147476e+09</td>\n",
" <td>1.550207e+09</td>\n",
" <td>1.550119e+09</td>\n",
" <td>1.571227e+07</td>\n",
" <td>3303.000000</td>\n",
" <td>255.000000</td>\n",
" <td>93.000000</td>\n",
" <td>inf</td>\n",
" <td>702.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" backers_count created_at creator deadline fx_rate \\\n",
"count 199618.000000 1.813020e+05 1.813020e+05 1.996180e+05 181302.000000 \n",
"mean 45.285666 1.446903e+09 1.074589e+09 1.455799e+09 0.999337 \n",
"std 58.656426 6.137708e+07 6.201507e+08 6.182117e+07 0.286660 \n",
"min 0.000000 1.240366e+09 3.000000e+00 1.242468e+09 0.009007 \n",
"25% 3.000000 1.407594e+09 5.377061e+08 1.415194e+09 1.000000 \n",
"50% 20.000000 1.446477e+09 1.073699e+09 1.456439e+09 1.000000 \n",
"75% 66.000000 1.497145e+09 1.612593e+09 1.507996e+09 1.000000 \n",
"max 255.000000 1.550094e+09 2.147483e+09 1.555301e+09 10.126885 \n",
"\n",
" goal id launched_at state_changed_at \\\n",
"count 1.996180e+05 1.996180e+05 1.996180e+05 1.813020e+05 \n",
"mean 8.219191e+04 9.750394e+08 1.452897e+09 1.453600e+09 \n",
"std 8.025752e+06 6.664825e+08 6.184694e+07 6.091841e+07 \n",
"min 0.000000e+00 3.271000e+03 1.240920e+09 1.242468e+09 \n",
"25% 1.514000e+03 3.718836e+08 1.412280e+09 1.414758e+09 \n",
"50% 5.000000e+03 9.693086e+08 1.453501e+09 1.453254e+09 \n",
"75% 1.500000e+04 1.554152e+09 1.505141e+09 1.504017e+09 \n",
"max 1.999812e+09 2.147476e+09 1.550207e+09 1.550119e+09 \n",
"\n",
" usd_pledged creLauDelta lauDeadDelta staLauDelta \\\n",
"count 1.996180e+05 181302.000000 199618.000000 181302.000000 \n",
"mean 1.485695e+04 45.970756 33.381253 30.847713 \n",
"std 1.170977e+05 127.614911 13.008452 13.090062 \n",
"min 0.000000e+00 0.000000 0.000000 0.000000 \n",
"25% 8.700000e+01 2.000000 29.000000 28.000000 \n",
"50% 1.248000e+03 10.000000 30.000000 30.000000 \n",
"75% 5.940000e+03 35.000000 36.000000 33.000000 \n",
"max 1.571227e+07 3303.000000 255.000000 93.000000 \n",
"\n",
" funds_raised_percent kickstarter.com \n",
"count 199618.000 81048.000000 \n",
"mean NaN 546.971474 \n",
"std NaN 73.028021 \n",
"min 0.000 428.000000 \n",
"25% 1.500 476.000000 \n",
"50% 100.000 550.000000 \n",
"75% 122.125 607.000000 \n",
"max inf 702.000000 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data Skew\n",
"\n",
"From a first look at the descriptive statistics on the dataset, the data seems to be heavily skewed.\n",
"\n",
"The means and medians (50%) for \"goal\", and \"usd_pledged\", are off by an order of magnitude.\n",
"\n",
"I know there's a lot of different categories of projects, and some of the skew is probably due to different funding goals for different categories of projects (ex. A video publishing project needs a lot less money than the first run of a new hardware device)\n",
"\n",
"Knowing some of the skew comes from differences in project categories, let's take a closer look grouping by project category"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Drop projects that raised no money at all\n",
"\n",
"Let's consider only projects that had at least one backer in the analysis\n",
"\n",
"The indiegogo dataset has a few projects with fundraising goals over several million that didn't raise any money at all. Filter out those projects to make the remaining data easier to read"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"merged = merged[(merged['backers_count'] > 0) & (merged['goal'] < 2000000)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Frequency of Success\n",
"\n",
"Start analyzing the data from the two datasets by taking a look at how success rates and funding goals differ across each category"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The relative frequency of projects meeting their funding goal is 0.5887230220078954 or about 58 %\n"
]
}
],
"source": [
"#Filter out projects that are live, canceled, or suspended\n",
"merged = merged[(merged['state'] == 'successful')|(merged['state'] =='failed')] \n",
"suc = merged[(merged['state'] == 'successful')]\n",
"fSuc = len(suc)/len(merged)\n",
"print('The relative frequency of projects meeting their funding goal is', fSuc, 'or about', mt.floor(fSuc * 100), '%')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comparing the Money Raised\n",
"The chart below compares average money pledged to projects in different categories against average raised in succesfully funded projects. It also gives a comparison of the medians for each category\n",
"\n",
"As was seen in the descriptive statistics, there's a heavy skew here. Based on the median pledge totals, it seems like a lot of projects must raise almost no money at all\n",
"\n",
"There's also a strange phenomenom with Indiegogo projects. A few categories (camera gear, environment and green tech, travel and outdoor, health and fitness in particular) are unique to indiegogo. It looks like there's some projects that raised incredible amounts of money that are skewing the averages"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 43200x21600 with 0 Axes>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 43200x21600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Compare the money raised by succesfull projects against all projects in a certain category\n",
"money = pd.DataFrame()\n",
"grp = merged.groupby(['fullcats'])\n",
"sucGrp = suc.groupby(['fullcats'])\n",
"money['AvgRaised'] = grp.usd_pledged.agg(np.mean)\n",
"money['SuccessAvgRaised'] = sucGrp.usd_pledged.agg(np.mean)\n",
"money['MedianRaised'] = grp.usd_pledged.agg(np.median)\n",
"money['MedianSuccess'] = sucGrp.usd_pledged.agg(np.median)\n",
"#money.reset_index(inplace = True)\n",
"\n",
"ax = money.plot(kind = 'bar', title='Crowdfunding Funds Raised by Project Category')\n",
"ax.set_xlabel(\"Category\")\n",
"ax.set_ylabel(\"Funds Raised\")\n",
"plt.figure(figsize=(600,300))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Comparing pledge goals\n",
"\n",
"The same skew shows up in the fundraising goals for each project, there seems to be a few projects that have very high funding goals.\n",
"\n",
"What's interesting here is that most projects have significantly lower funding goals than the top projects. It's difficult to see, but it also seems there's a relatively small difference in the median funding goals between successful projects and all projects"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 43200x21600 with 0 Axes>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 43200x21600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Delete old dataframes to reduce working memory\n",
"del money\n",
"\n",
"# Compare the funding goals for successful projects in each category vs. the funding goals of all projects\n",
"goal = pd.DataFrame()\n",
"goal['avgGoal'] = grp.goal.agg(np.mean)\n",
"goal['sucAvgGoal'] = sucGrp.goal.agg(np.mean)\n",
"goal['medGoal'] = grp.goal.agg(np.median)\n",
"goal['sucMedGoal'] = sucGrp.goal.agg(np.median)\n",
"\n",
"plt.close()\n",
"ax = goal.plot(kind = 'bar', title='Fundraising Goals by Category')\n",
"ax.set_xlabel(\"Category\")\n",
"ax.set_ylabel(\"Fundraising Goal\")\n",
"plt.figure(figsize=(600,300))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Rates of Success across Categories\n",
"\n",
"Certain categories have much higher rates of success than others.\n",
"\n",
"Comics, publishing, dance, music, and theater all have success rates of 70% or more\n",
"\n",
"An interesting point is that these categories are tied to an individual creator, that have a dedicated fanbase they can draw from\n",
"\n",
"Additionally, projects that require a good to be produced (ie technology products, health and fitness equipment, productivity tools) generally score lower\n",
"\n",
"There's no binding rule for Kickstarter or indiegogo projects to produce the final product, all they require is that creators make a best faith effort. Since there's some inherent risks to these products being produced, it seems like consumers may be weighing that in their backing decisions"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 43200x21600 with 0 Axes>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 43200x21600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"del goal\n",
"\n",
"plt.close()\n",
"catCounts = pd.DataFrame()\n",
"catCountAll = merged.groupby('fullcats').count()\n",
"catCountSuc = suc.groupby('fullcats').count()\n",
"catCounts['total_counts'] = catCountAll['source']\n",
"catCounts['success_counts'] = catCountSuc['source']\n",
"catCounts['successRate'] = catCounts['success_counts']/catCounts['total_counts']\n",
"catCounts = catCounts.sort_values(by = ['successRate'], axis = 0, ascending = False)\n",
"\n",
"ax = catCounts.successRate.plot(kind = 'bar', title='Rates of Success vs. Project Category')\n",
"ax.set_xlabel(\"Category\")\n",
"ax.set_ylabel(\"Success Rate\")\n",
"plt.figure(figsize=(600,300))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Funding success across categories\n",
"\n",
"The most popular categories generally have the lowest success rates, while things like comics, design photography, and games seem to perform better than average.\n",
"\n",
"These categories would likely typically have fanbases that closely follow the creators. Due to this, they have a reliable audience to draw from when try to source funds for their campaigns"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 43200x21600 with 0 Axes>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 43200x21600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Look at the number of successfully funded projects in each category vs. the total number of projects\n",
"del catCounts\n",
"\n",
"counts = pd.DataFrame()\n",
"counts['Total'] = grp.size()\n",
"counts['Successful'] = sucGrp.size()\n",
"\n",
"plt.close()\n",
"ax = counts.plot(kind = 'bar', title='Comparison of Total # of Project and # of Successful Projects by Category')\n",
"ax.set_xlabel(\"Category\")\n",
"ax.set_ylabel(\"Number of Projects\")\n",
"plt.figure(figsize=(600,300))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Success rate of projects above a certain amount raised\n",
"\n",
"Because of the large differences between the average funds raised and the median funds raised, it seems like there would be a large number of projects that raise hardly any money at all. \n",
"\n",
"Let's take a closer look project success rate versus the amount of money raised"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Funding Goals and Success Rates')"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"del counts\n",
"\n",
"minRaised = [1, 25, 50, 100, 200, 300, 400, 500, 750, 1000, 2000, 5000]\n",
"lowGoals = [1, 25, 50, 100, 200, 300, 400, 500, 750, 1000, 2000, 5000]\n",
"succRate = []\n",
"goalRate = []\n",
"lowGoalSuccess = []\n",
"for raised in range(len(minRaised)):\n",
" gotMoney = merged[(merged['usd_pledged'] >= minRaised[raised - 1]) & (merged['usd_pledged'] <= minRaised[raised])]\n",
" gotMoneySuccess = gotMoney[(gotMoney['state'] == 'successful') | (gotMoney['state'] == 'finished')]\n",
" if len(gotMoney) == 0:\n",
" tempRate = 0\n",
" else:\n",
" tempRate = len(gotMoneySuccess) / len(gotMoney)\n",
" succRate.append(tempRate)\n",
"\n",
"for goals in range(len(lowGoals)):\n",
" goalFilter = merged[(merged['goal'] <= lowGoals[goals]) & (merged['goal'] >= lowGoals[goals - 1])]\n",
" lowSuccess = goalFilter[(goalFilter['state'] == 'successful') | (goalFilter['state'] == 'finished')]\n",
" if len(lowSuccess) == 0:\n",
" tempLowSuccess = 0\n",
" else:\n",
" tempLowSuccess = len(lowSuccess) / len(goalFilter)\n",
" tempGoals = len(goalFilter) / len(merged)\n",
" goalRate.append(tempGoals)\n",
" lowGoalSuccess.append(tempLowSuccess)\n",
" \n",
"graph = pd.DataFrame({'Dollars' : minRaised,\n",
" 'Minimum_Raised' : succRate,\n",
" '%_of_Projects' : goalRate,\n",
" 'Success_by_Goal' : lowGoalSuccess})\n",
"x = graph['Dollars']\n",
"y1 = graph['Minimum_Raised']\n",
"y2 = graph['%_of_Projects']\n",
"y3 = graph['Success_by_Goal']\n",
"\n",
"plt.close()\n",
"plt.plot(x, y1, x, y2, x, y3)\n",
"plt.legend(('By Minimum Raised', 'Cumulative % of Project Goals < X', 'By Funding Goal'),\n",
" loc='lower right')\n",
"plt.xlabel('Dollars')\n",
"plt.ylabel('Relative Frequency')\n",
"plt.title('Funding Goals and Success Rates')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Funding Goals, Minimum Amount Raised, and Success Rates\n",
"\n",
"In the plot above:\n",
"'By Minimum Raised' Shows relative frequency of success in projects that raised at least the amounts on the x axis\n",
"\n",
"'Cumulative % of Project Goals' shows the % of projects that have funding goals less than the amounts on the x axis\n",
"\n",
"'By Funding Goal' shows relative frequency of successful projects that have goals lower than amounts on the x axis\n",
"\n",
"While a small % of projects have funding goals less than $1,000, projects that raised up to that amount have an outsized chance of meeting their funding goal. \n",
"\n",
"This idea seems to stress the importance of finding ambitious \"first movers\" from the general public to pledge support to projects and share with their friends. This supports the idea that project categories which have fanbases that closely follow project creators have a much better chance at reaching their funding goals. \n",
"\n",
"Let's take a closer look at the number of backers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Time between Project Creation and Project Launch\n",
"\n",
"Kickstarter allows creators to create a landing page for their projects before the project is officially launched. This time is for creators to raise awareness about the funding campaign, field questions from potential donors and tweak the campaign before making it live\n",
"\n",
"There's a clear relationship showing that a delay of between 20 and 30 days gives the most benefit "
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Success Rates vs. Number of Days between Launch and Creation')"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"del gotMoney\n",
"del gotMoneySuccess\n",
"del goalFilter\n",
"del lowSuccess\n",
"del graph\n",
"\n",
"# Plot the delta between launch and creation date vs. success rate\n",
"# Plot the campaign length against success rate\n",
"launchCreate = [1, 5, 10, 15, 20, 25, 30, 40, 45, 50, 60]\n",
"launchSuccess = []\n",
"for i in range(len(launchCreate)):\n",
" longTemp = merged[(merged['creLauDelta'] <= launchCreate[i]) & (merged['creLauDelta'] >= launchCreate[i - 1])]\n",
" longTempSuccess = longTemp[(longTemp['state'] == 'successful') | (longTemp['state'] == 'finished')]\n",
" if len(longTemp) == 0:\n",
" tempRate = 0\n",
" else:\n",
" tempRate = len(longTempSuccess) / len(longTemp)\n",
" launchSuccess.append(tempRate)\n",
"\n",
"graph = pd.DataFrame({'creLauDelta' : launchCreate,\n",
" 'Success_by_Delta' : launchSuccess})\n",
"x = graph['creLauDelta']\n",
"y1 = graph['Success_by_Delta']\n",
"\n",
"plt.close()\n",
"plt.plot(x, y1)\n",
"plt.ylim(bottom=0)\n",
"plt.xlabel('Number of Days')\n",
"plt.ylabel('Relative Frequency of Success')\n",
"plt.title('Success Rates vs. Number of Days between Launch and Creation')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Project Launch Date and Success Rate \n",
"\n",
"Along with that, projects being successfully funded seems to have dropped off slightly in the last few years"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Launch Date vs. Funds Raised Percent')"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"del longTemp\n",
"del longTempSuccess\n",
"\n",
"noOutliers = merged[merged['funds_raised_percent'] < 200]\n",
"x = noOutliers['launched_at']\n",
"y = noOutliers['funds_raised_percent']\n",
"\n",
"plt.close()\n",
"plt.hexbin(x, y, gridsize=10)\n",
"plt.xlabel('Launch Date (in Epoch time)')\n",
"plt.ylabel('Percent Raised of Goal')\n",
"plt.title('Launch Date vs. Funds Raised Percent')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Alexa Site Rank and Success Rate\n",
"\n",
"Look at the success rate of campaign funding and the Alexa site ranking\n",
"\n",
"There seems to be a slight increase in projects reaching their funding goals when Kickstarter is more popular than other websites in general"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/chrismay/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n"
]
},
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Alexa Site Ranking vs. Funds Raised Percent')"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"noOutliers = merged[(merged['funds_raised_percent'] < 200)]\n",
"noOutliers.dropna(axis = 0, inplace = True)\n",
"\n",
"x = noOutliers['kickstarter.com']\n",
"y = noOutliers['funds_raised_percent']\n",
"\n",
"plt.close()\n",
"plt.figure()\n",
"\n",
"plt.hexbin(x, y, gridsize=10)\n",
"plt.xlabel('Alexa Site Traffic Ranking')\n",
"plt.ylabel('Percent Raised of Goal')\n",
"plt.title('Alexa Site Ranking vs. Funds Raised Percent')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Importance of Creators in Project Funding Success\n",
"\n",
"In the previous section, there seemed to be a relationship between categories that have dedicated fanbases and an increaesd success rate in reaching funding goals.\n",
"\n",
"To further explore that relationship, make some comparisons grouping projects by the number of other projects a certain creator has launched\n",
"\n",
"Count the number of projects that a certain creator ID has launched, and then add that as a column to the dataframe"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"creaGroup = pd.DataFrame()\n",
"creators = pd.DataFrame()\n",
"\n",
"plt.close()\n",
"# Group all projects by creator id and then merge that into the dataframe\n",
"creaGroup = merged.groupby('creator').count()\n",
"grouped = creaGroup.sort_values(by = ['backers_count'])\n",
"grouped = grouped.reset_index()\n",
"creators['creator'] = grouped['creator']\n",
"creators['no_projects'] = grouped['backers_count']\n",
"creators.head()\n",
"merged = merged.merge(creators, how = 'left', on = 'creator')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Relationship between number of projects launched and success rate\n",
"\n",
"There's a small subset of creators that have launched multiple projects. From the chart below, there's an increased rate of success as the number of projects launched by a particular creator increases. Note that this plot is shown by total number of projects, and does not consider the order in which projects were launched by a particular creator"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Number of Creator Projects and Success Rate')"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"noProjects = [1, 2, 4, 8, 16, 32, 60, 72]\n",
"suc = merged[(merged['state'] == ('successful')) | (merged['state'] == ('finished'))]\n",
"multi = []\n",
"for i in noProjects:\n",
" tempProj = merged[(merged['no_projects'] <= (i)) & (merged['no_projects'] >= (i - 1))]\n",
" tempSucProj = suc[(suc['no_projects'] <= (i)) & (suc['no_projects'] >= (i - 1))]\n",
" if len(tempProj) != 0:\n",
" sucRate = len(tempSucProj) / len(tempProj)\n",
" else:\n",
" sucRate = 0\n",
" multi.append(sucRate)\n",
"\n",
"multiGraph = pd.DataFrame()\n",
"multiGraph['no_projects'] = noProjects\n",
"multiGraph['success_rate'] = multi\n",
" \n",
"x = multiGraph['no_projects']\n",
"y1 = multiGraph['success_rate']\n",
"\n",
"plt.close()\n",
"plt.plot(x, y1)\n",
"plt.ylim(bottom=0)\n",
"plt.xlabel('Number of Projects')\n",
"plt.ylabel('Relative Frequency')\n",
"plt.title('Number of Creator Projects and Success Rate')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# This is where I left off with the memory reduction\n",
"\n",
"### Number of projects launched and percent of goal raised\n",
"\n",
"There also seems to be an increase in average % of goal raised as the number of projects increases. Above 15, the sample size for each category drastically decreases (ie the number of creators that launched more than 15 projects is very small. "
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 43200x21600 with 0 Axes>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 43200x21600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Compare the money raised by succesfull projects against all projects in a certain category\n",
"projects= pd.DataFrame()\n",
"proGroup = merged.groupby(['no_projects'])\n",
"sucProGrp = suc.groupby(['no_projects'])\n",
"#projects['Avg%'] = proGroup.funds_raised_percent.agg(np.mean)\n",
"#projects['SucAvg%'] = sucProGrp.funds_raised_percent.agg(np.mean)\n",
"projects['Median%'] = proGroup.funds_raised_percent.agg(np.median)\n",
"projects['SucMed%'] = sucProGrp.funds_raised_percent.agg(np.median)\n",
"#money.reset_index(inplace = True)\n",
"\n",
"plt.close()\n",
"ax = projects.plot(kind = 'bar', title='Percent of Funds Raised by Number of Creator Projects')\n",
"ax.set_xlabel(\"Total Number of Projects by Creator\")\n",
"ax.set_ylabel(\"Percent of Funds Raised\")\n",
"plt.figure(figsize=(600,300))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Number of Projects launched by each creator by category\n",
"\n",
"Visual and performing arts projects are most frequently have creators with a high number of projects. The median number of projects for each creator in each category is 1, so creators with multiple project history is a minority segment of the overall sample population"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 43200x21600 with 0 Axes>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 43200x21600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"create = pd.DataFrame()\n",
"createGrp = merged.groupby(['fullcats'])\n",
"create['no_projects'] = createGrp.no_projects.agg(np.mean)\n",
"create['no_projects_median'] = createGrp.no_projects.agg(np.median)\n",
"create.dropna(axis = 0, inplace = True)\n",
"\n",
"plt.close()\n",
"ax = create.plot(kind = 'bar', title='Number of Projects per Creator by Category')\n",
"ax.set_xlabel(\"Category\")\n",
"ax.set_ylabel(\"Avg. Number of Projects per Creator\")\n",
"plt.figure(figsize=(600,300))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The total number of projects vs. creators with multiple projects\n",
"\n",
"The next plot shows the number of projects in each category and compares that with the number of projects with creators who have launhed multiple proejcts.\n",
"\n",
"It's easy to see in this plot that most creators in this dataset have only launched one project"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 43200x21600 with 0 Axes>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 43200x21600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"createNumber = pd.DataFrame()\n",
"multi = merged[merged['no_projects'] > 1]\n",
"createGrp = merged.groupby(['fullcats'])\n",
"multiGrp = multi.groupby(['fullcats'])\n",
"createNumber['all_projects'] = createGrp.no_projects.size()\n",
"createNumber['multi_projects'] = multiGrp.no_projects.size()\n",
"createNumber.dropna(axis = 0, inplace = True)\n",
"\n",
"plt.close()\n",
"ax = createNumber.plot(kind = 'bar', title='Number of Projects per Creator by Category')\n",
"ax.set_xlabel(\"Category\")\n",
"ax.set_ylabel(\"Avg. Number of Projects per Creator\")\n",
"plt.figure(figsize=(600,300))"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, '% of Goal Raised and Number of Projects by Creator')"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"createNoProjects = pd.DataFrame()\n",
"noProjects = merged.groupby(['no_projects'])\n",
"createNoProjects['funding_percent_avg'] = noProjects.funds_raised_percent.agg(np.mean)\n",
"createNoProjects['funding_percent_med'] = noProjects.funds_raised_percent.agg(np.median)\n",
"createNoProjects['staLauDelta_avg'] = noProjects.staLauDelta.agg(np.mean)\n",
"createNoProjects['staLauDelta_med'] = noProjects.staLauDelta.agg(np.median)\n",
"createNoProjects.reset_index(inplace = True)\n",
"\n",
"x = createNoProjects['no_projects']\n",
"y1 = createNoProjects['funding_percent_avg'] = noProjects.funds_raised_percent.agg(np.mean)\n",
"y2 = createNoProjects['funding_percent_med'] = noProjects.funds_raised_percent.agg(np.median)\n",
"\n",
"plt.close()\n",
"plt.plot(x,y2)\n",
"plt.legend(('Median Funding Percent', 'Median Funding Percent'), loc='upper right')\n",
"plt.xlabel('Number of Projects by Creator')\n",
"plt.ylabel('% of Goal Raised')\n",
"plt.ylim(bottom=0)\n",
"plt.ylim(top = 400)\n",
"plt.xlim(right=15)\n",
"plt.xlim(left=1)\n",
"plt.title('% of Goal Raised and Number of Projects by Creator')"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Dollars Raised and Number of Projects by Creator')"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"createNoProjects['usd_pledged_avg'] = noProjects.usd_pledged.agg(np.mean)\n",
"createNoProjects['usd_pledged_med'] = noProjects.usd_pledged.agg(np.median)\n",
"\n",
"x = createNoProjects['no_projects']\n",
"y1 = createNoProjects['usd_pledged_avg']\n",
"y2 = createNoProjects['usd_pledged_med'] \n",
"\n",
"plt.close()\n",
"plt.plot(x,y1,x,y2)\n",
"plt.legend(('Average Dollars Raised', 'Median Dollars Raised'), loc='upper right')\n",
"plt.xlabel('Number of Projects by Creator')\n",
"plt.ylabel('Dollars Raised')\n",
"plt.ylim(bottom=0)\n",
"plt.xlim(right=15)\n",
"plt.xlim(left=1)\n",
"plt.title('Dollars Raised and Number of Projects by Creator')"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Days to Goal and Number of Projects by Creator')"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"createNoProjects['staLauDelta_avg'] = noProjects.staLauDelta.agg(np.mean)\n",
"createNoProjects['staLauDelta_med'] = noProjects.staLauDelta.agg(np.median)\n",
"\n",
"x = createNoProjects['no_projects']\n",
"y1 = createNoProjects['staLauDelta_avg']\n",
"y2 = createNoProjects['staLauDelta_med']\n",
"\n",
"plt.close()\n",
"plt.plot(x,y1,x,y2)\n",
"plt.legend(('Average Days to Goal', 'Median Days to Goal'), loc='upper right')\n",
"plt.xlabel('Number of Projects by Creator')\n",
"plt.ylabel('Days to Goal')\n",
"plt.ylim(bottom=0)\n",
"plt.xlim(left=2)\n",
"plt.xlim(right=15)\n",
"#plt.ylim(ymax = 2000)\n",
"plt.title('Days to Goal and Number of Projects by Creator')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Limiting the Results shown for Number of Projects by Creator\n",
"\n",
"Above 14 projects per creator, the number of unique sample points drops off sharply to just a few creators. Rather than view results for sample sizes of 1 or 2, the above graphics were limited to creators that made 15 projects or fewer to show a better representation of the population"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0 120551\n",
"2.0 17190\n",
"3.0 5724\n",
"4.0 2744\n",
"5.0 1730\n",
"6.0 1200\n",
"7.0 700\n",
"8.0 448\n",
"11.0 374\n",
"10.0 370\n",
"9.0 360\n",
"12.0 264\n",
"13.0 182\n",
"17.0 153\n",
"14.0 140\n",
"16.0 112\n",
"18.0 108\n",
"15.0 105\n",
"26.0 104\n",
"19.0 95\n",
"20.0 80\n",
"25.0 75\n",
"70.0 70\n",
"32.0 64\n",
"59.0 59\n",
"52.0 52\n",
"23.0 46\n",
"35.0 35\n",
"34.0 34\n",
"33.0 33\n",
"30.0 30\n",
"29.0 29\n",
"27.0 27\n",
"24.0 24\n",
"21.0 21\n",
"Name: no_projects, dtype: int64"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged['no_projects'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, '% of Goal Raised and Days to Reach Goal')"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"fundRate = pd.DataFrame()\n",
"rateProjects = merged.groupby(['staLauDelta'])\n",
"fundRate['funding_percent_avg'] = rateProjects.funds_raised_percent.agg(np.mean)\n",
"fundRate['funding_percent_med'] = rateProjects.funds_raised_percent.agg(np.median)\n",
"\n",
"fundRate.reset_index(inplace = True)\n",
"\n",
"x = fundRate['staLauDelta']\n",
"y2 = fundRate['funding_percent_med']\n",
"\n",
"plt.close()\n",
"plt.plot(x,y2)\n",
"plt.legend(('Median Funding Percent', 'Median Funding Percent'), loc='upper right')\n",
"plt.xlabel('Days to Reach Goal')\n",
"plt.ylabel('% of Goal Raised')\n",
"plt.ylim(bottom=0)\n",
"\n",
"plt.xlim(right=40)\n",
"plt.xlim(left=1)\n",
"plt.title('% of Goal Raised and Days to Reach Goal')"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'USD Pledged and Days to Reach Goal')"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"fundRate['usd_pledged_avg'] = rateProjects.usd_pledged.agg(np.mean)\n",
"fundRate['usd_pledged_med'] = rateProjects.usd_pledged.agg(np.median)\n",
"\n",
"x = fundRate['staLauDelta']\n",
"y1 = fundRate['usd_pledged_avg']\n",
"y2 = fundRate['usd_pledged_med']\n",
"\n",
"plt.close()\n",
"plt.plot(x,y1,x,y2)\n",
"plt.legend(('Average USD Pledged', 'Median USD Pledged'), loc='upper right')\n",
"plt.xlabel('Days to Reach Goal')\n",
"plt.ylabel('USD Pledged')\n",
"plt.ylim(bottom=0)\n",
"plt.xlim(left=0)\n",
"plt.xlim(right=40)\n",
"plt.title('USD Pledged and Days to Reach Goal')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### What's Happening here?\n",
"\n",
"There's an odd abundance of projects that reach their funding goal either very close to, or on the deadline date.\n",
"\n",
"Kickstarter.com has a section of their homepage dedicated to projects whose deadlines are within a certain time limit\n",
"\n",
"We don't have data for funds raised per day, but it seems plausible that the presence on the homepage plus the urgency of a project being close to its goal and end date motivates backers on the fence to contribute\n",
"\n",
"It's also plausible that towards the end of a fundraising campaing, projects push more incentive messages to their target audience\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Effects of Staff Picks and Project Spotlights on Campaign Success\n",
"\n",
"As mentioned above, Kickstarter highlights certain projects on their homepage\n",
"\n",
"The dataset that I have includes \"Staff Pick\" tags, for projects that Kickstarter staff favors\n",
"\n",
"The spotlight tag is a little more ambiguous, but based on the success rate of spotlight projects, it seems like all successfully funded projects are spotlighted at some point\n",
"\n",
"The success rate for staff pick projects is almost 90%, and the rate for spotlighted projects is 100%"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"staff = merged[(merged['staff_pick'] == True) & (merged['source'] == 'Kickstarter')]\n",
"staffSucc = staff[(staff['state'] == 'successful')]\n",
"spot = merged[(merged['spotlight'] == True) & (merged['source'] == 'Kickstarter')]\n",
"spotSucc = spot[(spot['state'] == 'successful')]\n",
"unstaff = merged[(merged['staff_pick'] == False) & (merged['source'] == 'Kickstarter')]\n",
"unstaffSucc = unstaff[unstaff['state'] == 'successful']\n",
"mergeKick = merged[merged['source'] == 'Kickstarter']"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8954918032786885 1.0\n"
]
}
],
"source": [
"staffRate = len(staffSucc)/ len(staff)\n",
"spotRate = len(spotSucc)/len(spot)\n",
"\n",
"print(staffRate, spotRate)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 43200x21600 with 0 Axes>"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 43200x21600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"staffMoney = pd.DataFrame()\n",
"staffGrp = staff.groupby(['fullcats'])\n",
"noStaff = unstaff.groupby(['fullcats'])\n",
"staffMoney['avg_raised_staff_picks'] = staffGrp.usd_pledged.agg(np.mean)\n",
"staffMoney['no_staff_avg'] = noStaff.usd_pledged.agg(np.mean)\n",
"staffMoney['med_staff_picks'] = staffGrp.usd_pledged.agg(np.median)\n",
"staffMoney['no_staff_med'] = noStaff.usd_pledged.agg(np.median)\n",
"\n",
"plt.close()\n",
"ax = staffMoney.plot(kind = 'bar', title='Crowdfunding Funds Raised by Project Category')\n",
"ax.set_xlabel(\"Category\")\n",
"ax.set_ylabel(\"Funds Raised\")\n",
"plt.figure(figsize=(600,300))"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the number of successful projects that were not staff picks is 74943\n",
"the number of successful projects that are staff picks is 19228\n",
"the total number of projects is 153333\n"
]
}
],
"source": [
"unstaff = merged[merged['staff_pick'] == False]\n",
"unstaffSucc = unstaff[unstaff['state'] == 'successful']\n",
"print('the number of successful projects that were not staff picks is ', len(unstaffSucc))\n",
"print('the number of successful projects that are staff picks is ', len(staffSucc))\n",
"kickOnly = merged[merged['source'] == 'Kickstarter']\n",
"print('the total number of projects is ', len(kickOnly))"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 43200x21600 with 0 Axes>"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 43200x21600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"staffMoney = pd.DataFrame()\n",
"staffMoney['StaffCounts'] = staffGrp.size()\n",
"staffMoney['NoStaffCount'] = noStaff.size()\n",
"\n",
"plt.close()\n",
"ax = staffMoney.plot(kind = 'bar', title='# of Projects that are Staff Picks by Category')\n",
"ax.set_xlabel(\"Category\")\n",
"ax.set_ylabel(\"Number of Projects\")\n",
"plt.figure(figsize=(600,300))"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 43200x21600 with 0 Axes>"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 43200x21600 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"allKick = mergeKick.groupby(['fullcats'])\n",
"noStaffCont = allKick.usd_pledged.agg(np.sum)\n",
"staffCont = staffGrp.usd_pledged.agg(np.sum)\n",
"contributions = pd.DataFrame()\n",
"contributions['staff_picks'] = staffCont / noStaffCont\n",
"\n",
"plt.close()\n",
"ax = contributions.plot(kind = 'bar', title='Contributions of Staff Picks to Total Funds Raised (By Category)')\n",
"ax.get_legend().remove()\n",
"ax.set_xlabel(\"Category\")\n",
"ax.set_ylabel(\"% of Total Funds Raised\")\n",
"plt.figure(figsize=(600,300))"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Kickstarter staff picks accounts for staff_picks 0.451525\n",
"dtype: float64 % of all funds raised on average\n"
]
}
],
"source": [
"plt.close()\n",
"total = contributions.mean(axis = 0)\n",
"print('Kickstarter staff picks accounts for ', total, '% of all funds raised on average')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Correlation between Variables\n",
"\n",
"Pandas has a method to do a pairwise correlation calculation for all pairs of columns that have numerical data\n",
"\n",
"The only semi-strong correlated pairs are between the total number of backers and the total USD pledged. There's some other weaker correlations here, but in reality there's probably too many confounding factors to give a strong correlation.\n",
"\n",
"Factors that are probably affecting the correlations\n",
"- Differences in required funding goals to bring projects to fruition across categories\n",
"- Varying scales of projects\n",
"- Prior creator history\n",
"- Individual backer tastes"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Correlation Heatmap')"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"select = pd.DataFrame()\n",
"select['no_projects'] = merged['no_projects']\n",
"select['funds_raised_percent'] = merged['funds_raised_percent']\n",
"select['fullcats'] = merged['fullcats']\n",
"select['kickstarter.com'] = merged['kickstarter.com']\n",
"select['goal'] = merged['goal']\n",
"select['backers_count'] = merged['backers_count']\n",
"select['staLauDelta'] = merged['staLauDelta']\n",
"select['usd_pledged'] = merged['usd_pledged']\n",
"corr = select.corr()\n",
"\n",
"plt.close()\n",
"sb.heatmap(corr, annot=True)\n",
"plt.title('Correlation Heatmap')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Appendix A: Data Descriptions\n",
"\n",
"| Columns | Data Type | Description | \n",
"|---|---|---|\n",
"|Backers Count| int64 | Total number of individual backers of a project\n",
"|Blurb|object| The short description of the project\n",
"|Category| list | A list of categories that the project has been tagged under\n",
"|Country| string| Two character country code of the campaign’s headquarters\n",
"|Created_at|int64| The creation date of the campaign in epoch time\n",
"|Creator| string |A list of the creator’s name and their creator ID on Kickstarter.com\n",
"|Currency| string| A two character string of the domestic currency of the project\n",
"|Deadline| int64|The end date/time of the campaign in epoch time\n",
"|Goal| int64|The funding goal of the project in native currency\n",
"|Id| int64|The unique project ID\n",
"|Launched_at| int64|Launch date of the campaign in epoch time\n",
"|Location| string|The main location of the project\n",
"|Name| object|The name of the project\n",
"|Pledged| int64|Total amount pledged (in the project’s home currency)\n",
"|Slug| string|An abbreviated description of the project\n",
"|Spotlight|string|True/False categorical variable of whether the project was featured by Kickstarter\n",
"|Staff_pick|string|True/False categorical variable of whether the project was a staff pick or not\n",
"|State|string|Successful/Failed/Canceled/Active categorical variable of the projects current status\n",
"|State_changed_at| int64|Time/date that the project changed states, in epoch time\n",
"|Static_usd_rate| int64|Static conversion rate between home currency and USD\n",
"|Urls| string|Various URL’s associated with the project\n",
"|Usd_pledged| int64|Total amount pledged, converted to USD\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Appendix B: Previous Work and Citations\n",
"\n",
"[Good Audience](https://blog.goodaudience.com/kickstarter-projects-prediction-of-state-steps-for-a-beginner-analysis-f4630a50b7fe)<br>\n",
"•\tRuns logistic regression, XGBoost, Random forests, LightGBM and an Ensemble method to try and predict whether a project will be successfully funded or not. Based on the Kaggle dataset (limited to 49k entries). Mainly focused on feature selection\n",
"\n",
"[Towards Data Science](https://towardsdatascience.com/predicting-the-success-of-kickstarter-campaigns-3f4a976419b9)<br>\n",
"•\tAgain predictions on successful funding, based on funding goals, category, rewards levels, and location data. Uses random forests, logistic regression, and kNearest Neighbors\n",
"\n",
"[Bentley University](https://www.bentley.edu/prepared/what-secret-perfect-kickstarter-campaign)<br>\n",
"•\tFinds length of description and title are important\n",
"•\tAlso the date of the launch important (winter is the best month)\n",
"•\tHigher average donations, shorter campaign durations\n"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Kickstarter.com Alexa Ranking over Time')"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"merged.head()\n",
"alexa = merged.copy()\n",
"alexa.dropna(axis = 0, inplace = True)\n",
"\n",
"x = alexa['launched_at']\n",
"y = alexa['kickstarter.com']\n",
"\n",
"plt.close()\n",
"plt.scatter(x, y)\n",
"plt.legend(('Alexa Ranking', 'Alexa ranking'), loc='lower right')\n",
"plt.xlabel('Date (Epoch time)')\n",
"plt.ylabel('Alexa Ranking')\n",
"plt.ylim(bottom=1)\n",
"plt.title('Kickstarter.com Alexa Ranking over Time')"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['backers_count', 'blurb', 'country', 'created_at', 'creator',\n",
" 'currency', 'deadline', 'fx_rate', 'goal', 'id', 'launched_at', 'name',\n",
" 'slug', 'spotlight', 'staff_pick', 'state', 'state_changed_at', 'urls',\n",
" 'usd_pledged', 'fullcats', 'city', 'creLauDelta', 'lauDeadDelta',\n",
" 'staLauDelta', 'source', 'funds_raised_percent', 'launch_date', 'Date',\n",
" 'kickstarter.com', 'no_projects'],\n",
" dtype='object')"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.close()\n",
"merged.columns"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Distribution of % of Goal Raised')"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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hSFJ5KSJuaHokZmbW8YoklVskfQ/4NfBmqTAi7m1aVGZm1pGKJJU989+usrIA9m18OGZm1snqXvwYEftUeNRNKJK2k3SLpEckTZf0tVy+maTJkmbkvwNzuSRdIGmmpAcl7VZW1+g8/wxJo8vKPyDpobzMBeUXaZqZWeut1J0fJR1ToO7FwEkR8W5gBDA23zVyHHBzRAwDbs7PAQ4EhuXHGHIXZkmbAaeTjpj2AE4vJaI8z5iy5UYWiMvMzJqkaXd+jIh5pXaXiHgFeAQYDIwCJuTZJgCH5ulRwGWRTAEGSNoaOACYHBEL87hjk4GR+bVNIuLOiAjgsrK6zMysDVpy50dJQ4D3A3cBW0XEvFzXPGDLPNtg4JmyxebkslrlcyqUm5lZmzT9zo+SNgJ+BZwQES/XmrVCWaxEeaUYxkjqltS9YMGCeiGbmdlKKpJUet758TLg+CKVS1qblFAuj4hf5+L5+dQV+e+zuXwOsF3Z4tsCc+uUb1uhfAURcXFEdEVE16BBg4qEbmZmK6FmUpG0FrAe8FHgQ6SBJXeJiAfrVZx7Yl0CPBIR55e9NAko9eAaDVxbVn5k7gU2gnTR5TxSe87+kgbmBvr9gRvza69IGpHXdWRZXWZm1gY1r1OJiLclnRcRHwSm97LuvYAjgIck3Z/LvgGcQxpK/xjgaeCz+bXrgYOAmcBfgaNzDAslnQVMzfOdGREL8/SXSR0J1gduyA8zM2uTIhc/3iTp74Bf515WhUTEHVRu9wDYr8L8AYytUtd4YHyF8m5g16IxmZlZcxVJKicCGwKLJb3BslGKN2lqZGZm1nFqJpXcVrFLRDzdonjMzKyD1Wyoz6ekftOiWMzMrMMV6VI8RdLuTY/EzMw6XpE2lX2AYyU9BbzGsjaV9zY1MjMz6zhFksqBTY/CzMxWC0WSSuFuxGZmtmYrklSuY9lYW+sBQ4HHgF2aGJeZmXWgukklIt5T/jzfPOvYpkVkZmYdq0jvr+Xke6S4N5iZma2g7pGKpBPLnq4F7AZ4/HgzM1tBkTaVjcumF5PaWH7VnHDMzKyTFWlT+VYrAjEzs85Xt01F0mRJA8qeD5R0Y3PDMjOzTlSkoX5QRLxYehIRL7DsvvJmZmZLFUkqSyRtX3oiaQd8QaSZmVVQpKH+VOAOSbfl5x8BxjQvJDMz61RFGur/kC94HEG6qv7rEfFc0yMzM7OOU6Sh/lPAWxHx+4j4HekOkIc2PzQzM+s0RdpUTo+Il0pPcqP96c0LyczMOlWRpFJpniJtMWZmtoYpklS6JZ0vaSdJO0r6AXBPswMzM7POUySpHA8sAq4ErgbeAMY2MygzM+tMRXp/vSbp28BZEfFaC2IyM7MOVfNIRdJXJD0NPAU8LekpSV9pTWhmZtZpqiYVSacBnwD2jojNI2JzYB/gwPyamZnZcmodqRwBfDoiZpUK8vRhwJH1KpY0XtKzkqaVlZ0h6c+S7s+Pg8peO0XSTEmPSTqgrHxkLpspaVxZ+VBJd0maIelKSesUf9tmZtYMNU9/RcQbFcpeB94uUPelwMgK5T+IiOH5cT2ApJ2Bw0n3vR8JXCSpn6R+wI+AA4Gdgc/neQHOzXUNA14AjikQk5mZNVGtpDJH0n49CyXtC8yrV3FE3A4sLBjHKGBiRLwZEU8CM4E98mNmRMyKiEXARGCUJAH7Atfk5ScAvsrfzKzNavX++ipwraQ7SNelBOne9HuRksDKOk7SkUA3cFIeSn8wMKVsnjm5DOCZHuV7ApsDL0bE4grzm5lZm1Q9UomI6cCuwO3AEGDHPL1rfm1l/BjYCRhOOto5L5erUggrUV6RpDGSuiV1L1iwoHcRm5lZYTWvU8ltKuMbtbKImF+alvQz4Pf56Rxgu7JZtwXm5ulK5c8BAyT1z0cr5fNXWu/FwMUAXV1dvheMmVmTFLmivmEkbV329FNAqWfYJOBwSetKGgoMA+4GpgLDck+vdUiN+ZMiIoBbgM/k5UcD17biPZiZWXVNGxhS0i+BvYEtJM0hjWy8t6ThpFNVs4FjIZ1qk3QV8DCwGBgbEUtyPccBNwL9gPFlp95OBibmq/3vAy5p1nsxM7NiqiYVSTdHxH6Szo2Ik3tbcUR8vkJx1S/+iDgbOLtC+fXA9RXKZ5F6h5mZWR9R60hla0kfBT4paSI9Gscj4t6mRmZmZh2nVlL5JjCO1Ah+fo/XgnSdiJmZ2VJVk0pEXANcI+nfIuKsFsZkZmYdqsjQ92dJ+iTwkVx0a0T8vtYyZma2ZqrbpVjSd4CvkXpmPQx8LZeZmZktp0iX4oOB4RHxNoCkCaQuvKc0MzAzM+s8RS9+HFA2vWkzAjEzs85X5EjlO8B9km4hdSv+CD5KMTOzCoo01P9S0q2kEYoFnBwRf2l2YGZm1nkKDdMSEfNI43OZmZlV1dIBJc3MbPXmpGJmZg1TM6lIWkvStFrzmJmZldRMKvnalAckbd+ieMzMrIMVaajfGpgu6W7gtVJhRHyyaVGZmVlHKpJUvtX0KMzMbLVQ5DqV2yTtAAyLiD9K2oB0F0YzM7PlFBlQ8ovANcBPc9Fg4LfNDMrMzDpTkS7FY4G9gJcBImIGsGUzgzIzs85UJKm8GRGLSk8k9Sfd+dHMzGw5RZLKbZK+Aawv6ePA1cDvmhuWmZl1oiJJZRywAHgIOBa4HjitmUGZmVlnKtL76+18Y667SKe9HosIn/4yM7MV1E0qkg4GfgI8QRr6fqikYyPihmYHZ7YmGTLuuqXTs885uI2RmK28Ihc/ngfsExEzASTtBFwHOKmYmdlyirSpPFtKKNks4NkmxWNmZh2s6pGKpE/nyemSrgeuIrWpfBaY2oLYzMysw9Q6UjkkP9YD5gMfBfYm9QQbWK9iSeMlPVs+dL6kzSRNljQj/x2YyyXpAkkzJT0oabeyZUbn+WdIGl1W/gFJD+VlLpCkXr53MzNrsKpHKhFx9CrWfSnwQ+CysrJxwM0RcY6kcfn5ycCBwLD82BP4MbCnpM2A04Eu0lHSPZImRcQLeZ4xwBRSN+eRuJ3HzKytivT+GgocDwwpn7/e0PcRcbukIT2KR5GOdgAmALeSksoo4LLcVXmKpAGSts7zTo6IhTmWycBISbcCm0TEnbn8MuBQnFTMzNqqSO+v3wKXkK6if3sV17dVRMwDiIh5kkpjiA0Gnimbb04uq1U+p0J5RZLGkI5q2H5732/MzKxZiiSVNyLigibHUak9JFaivKKIuBi4GKCrq8sXbpqZNUmRLsX/Kel0SR+UtFvpsZLrm59Pa5H/lromzwG2K5tvW2BunfJtK5SbmVkbFUkq7wG+CJxDuhDyPOD7K7m+SUCpB9do4Nqy8iNzL7ARwEv5NNmNwP6SBuaeYvsDN+bXXpE0Ivf6OrKsLjMza5Mip78+BexYPvx9EZJ+SWpo30LSHFIvrnOAqyQdAzxNuuYFUu+tg4CZwF+BowEiYqGks1h2XcyZpUZ74MukHmbrkxro3UhvZtZmRZLKA8AAenkVfUR8vspL+1WYN0g3A6tUz3hgfIXybmDX3sRkZmbNVSSpbAU8Kmkq8GapsF6XYjMzW/MUSSqnNz0KMzNbLRS5n8ptrQjEzMw6X5Er6l9h2TUg6wBrA69FxCbNDMzMzDpPkSOVjcufSzoU2KNpEZmZWccqcp3KciLit8C+TYjFzMw6XJHTX58ue7oWy0YMNjMzW06R3l+HlE0vBmaTRhU2MzNbTpE2lVW9r4qZma0hat1O+Js1louIOKsJ8ZiZWQerdaTyWoWyDYFjgM0BJxUzM1tOrdsJn1ealrQx8DXSQI8TSSMVm5mZLadmm0q+R/yJwBdIt//dLd8f3szMbAW12lS+B3yadMfE90TEqy2LyszMOlKtI5WTSKMSnwacmu6FBaRb+YaHaTFrniHjrls6Pfucg9sYiVnv1GpT6fXV9mZmtmZz4jAzs4YpckW9VeDTE2ZmK/KRipmZNYyTipmZNYyTipmZNYyTipmZNYyTipmZNYx7f5n1ce5paJ3ERypmZtYwTipmZtYwbUkqkmZLekjS/ZK6c9lmkiZLmpH/DszlknSBpJmSHpS0W1k9o/P8MySNbsd7MTOzZdp5pLJPRAyPiK78fBxwc0QMA27OzwEOBIblxxjgx7B0WP7TgT2BPYDTS4nIzMzaoy+d/hpFumcL+e+hZeWXRTIFGCBpa+AAYHJELMz3eJkMjGx10GZmtky7kkoAN0m6R9KYXLZVRMwDyH+3zOWDgWfKlp2Ty6qVr0DSGEndkroXLFjQwLdhZmbl2tWleK+ImCtpS2CypEdrzKsKZVGjfMXCiItJNxujq6ur4jxmncDdi62va8uRSkTMzX+fBX5DahOZn09rkf8+m2efA2xXtvi2wNwa5WZm1iYtTyqSNpS0cWka2B+YBkwCSj24RgPX5ulJwJG5F9gI4KV8euxGYH9JA3MD/f65zMzM2qQdp7+2An6Tb0/cH7giIv4gaSpwlaRjgKeBz+b5rwcOAmYCfwWOBoiIhZLOAqbm+c6MiIWtexvL+JSEmVnS8qQSEbOA91Uofx7Yr0J5AGOr1DUeGN/oGM3MbOV47C+zDuUjZOuL+tJ1KmZm1uF8pGK2GvBRi/UVPlIxM7OGcVIxM7OGcVIxM7OGcZtKg/nctpmtyZxUzFYz/mFj7eTTX2Zm1jA+UjFbjfmoxVrNRypmZtYwPlIxW0OUH7WAj1ysOZxUmqjnh7jEH2YzW105qZitofyjx5rBbSpmZtYwPlIxs+W4x5itCieVNvCH1jqF91XrLSeVNvOH1jqF91UrwknFzFaJk42Vc1LpQ/zhtE5R2ld77qfeh81JpY9yd0/rdE4wayYnlQ7jD6qZ9WVOKh3MCcY6RbUj72q8P3cuJ5XVRNEPrT+s1gmKnP71j6q+yUllDVPtg+gPqHWCasnG+3Xf4aSyBivyAa3GH1Drqxq5X/c2WTmJgSKi3TGsEkkjgf8E+gE/j4hzas3f1dUV3d3dK7Wu3p4XtsrW1A9bJZ2+T80+5+COfw99RV//XEi6JyK66s3X0UcqkvoBPwI+DswBpkqaFBEPtzcyq6UZX0J9/deiv3itnmbvI636LHR0UgH2AGZGxCwASROBUYCTyhpmVU55mFnjdHpSGQw8U/Z8DrBnz5kkjQHG5KevSnpsJde3BfDcSi7bTI6rdxxX71SNS+e2OJLlddz2aiedu8px7VBkpk5PKqpQtkIjUURcDFy8yiuTuoucU2w1x9U7jqt3HFfvrOlxdfpNuuYA25U93xaY26ZYzMzWeJ2eVKYCwyQNlbQOcDgwqc0xmZmtsTr69FdELJZ0HHAjqUvx+IiY3sRVrvIptCZxXL3juHrHcfXOGh1Xx1+nYmZmfUenn/4yM7M+xEnFzMwaxkmlAEkjJT0maaakcW2MYztJt0h6RNJ0SV/L5WdI+rOk+/PjoDbFN1vSQzmG7ly2maTJkmbkvwNbHNO7yrbL/ZJelnRCO7aZpPGSnpU0rays4vZRckHe5x6UtFuL4/qepEfzun8jaUAuHyLp9bLt9pMWx1X1/ybplLy9HpN0QIvjurIsptmS7s/lrdxe1b4fWruPRYQfNR6kDgBPADsC6wAPADu3KZatgd3y9MbA48DOwBnAP/eBbTUb2KJH2XeBcXl6HHBum/+XfyFdxNXybQZ8BNgNmFZv+wAHATeQrsUaAdzV4rj2B/rn6XPL4hpSPl8btlfF/1v+HDwArAsMzZ/Zfq2Kq8fr5wHfbMP2qvb90NJ9zEcq9S0dCiYiFgGloWBaLiLmRcS9efoV4BHSqAJ92ShgQp6eABzaxlj2A56IiKfasfKIuB1Y2KO42vYZBVwWyRRggKStWxVXRNwUEYvz0ymka8Baqsr2qmYUMDEi3oyIJ4GZpM9uS+OSJOAw4JfNWHctNb4fWrqPOanUV2komLZ/kUsaArwfuCsXHZcPYce3+hRTmQBuknSP0tA4AFtFxDxIOz2wZZtig3QdU/mHvS9ss2rbpy/td/9I+kVbMlTSfZJuk/ThNsRT6f/WV7bXh4H5ETGjrKzl26vH90NL9zEnlfoKDQXTSpI2An4FnBARLwM/BnYChgPzSIff7bBXROwGHAiMlfSRNsWxAqWLYz8JXJ2L+so2q6ZORzl3AAAGQUlEQVRP7HeSTgUWA5fnonnA9hHxfuBE4ApJm7QwpGr/tz6xvYDPs/wPl5ZvrwrfD1VnrVC2ytvMSaW+PjUUjKS1STvM5RHxa4CImB8RSyLibeBnNOmwv56ImJv/Pgv8Jscxv3RInf8+247YSInu3oiYn2PsE9uM6tun7fudpNHAJ4AvRD4Jn08vPZ+n7yG1XbyzVTHV+L/1he3VH/g0cGWprNXbq9L3Ay3ex5xU6uszQ8Hk87WXAI9ExPll5eXnQT8FTOu5bAti21DSxqVpUkPvNNK2Gp1nGw1c2+rYsuV+QfaFbZZV2z6TgCNzD50RwEulUxitoHTzu5OBT0bEX8vKByndxwhJOwLDgFktjKva/20ScLikdSUNzXHd3aq4so8Bj0bEnFJBK7dXte8HWr2PtaJXQqc/SL0kHif9yji1jXH8Lenw9EHg/vw4CPgF8FAunwRs3YbYdiT1vnkAmF7aTsDmwM3AjPx3szbEtgHwPLBpWVnLtxkpqc0D3iL9Sjym2vYhnZr4Ud7nHgK6WhzXTNL59tJ+9pM879/l/+8DwL3AIS2Oq+r/DTg1b6/HgANbGVcuvxT4Uo95W7m9qn0/tHQf8zAtZmbWMD79ZWZmDeOkYmZmDeOkYmZmDeOkYmZmDeOkYmZmDeOkYh1D0pI80us0SVdL2qBNcZzQ23VL+nAeOfZ+Sev3eG0rSVdImpWHuLlT0qdWMrYh5aPn9igvjZb7sKTL8oVyteraRtI1KxNHj3qOkvTDVa3HOoOTinWS1yNieETsCiwCvlR0wdIFaA1yAunal974AvD9HP/rZXEJ+C1we0TsGBEfIF1g24wBHJ+IiOHAe3L9h9WaOSLmRsRnmhCHrcacVKxT/Ql4B4Ckf5B0d/4V/tOyK5hflXSmpLuAD0raXdL/SXogz7+xpH5K9w6ZmgcpPDYvu7ekWyVdo3RfkcvzlcdfBbYBbpF0S8+gJO2XBw98KA94uK6kfyJ9gX9T0uU9FtkXWBQRS++zERFPRcSFub71JP1Xru8+Sfvk8iGS/iTp3vz4UNENFxFLSFebD65VV/lRj6Rdyrbxg5KG1dn2R0t6XNJtwF5FY7PVQLOu7vTDj0Y/gFfz3/6koSa+DLwb+B2wdn7tIuDIPB3AYXl6HdLwGLvn55vkesYAp+WydYFu0v049gZeIv2iXwu4E/jbPN9setw3JpevR7oK/Z35+WWkQf0gXW39mQrLfBX4QY33fBLwX3n6b4Cn83o2ANbL5cOA7jw9hAr37ygvz8vfArw3P69bF3AhaQyw0rZcv9q2J93X42lgUJ73f4Eftnv/8aM1j/5Vs41Z37O+8h31SEcql5CSwgeAqelMEuuzbMC8JaTB9QDeBcyLiKkAkUdvlbQ/8F5JpdM8m5K+WBcBd0cexymvdwhwR4343gU8GRGP5+cTgLHAfxR9g5J+RBpuY1FE7J6nL8wxPyrpKdKAhE8BP5Q0PL/PIoMU7pTfxzDgmoh4MJevXaCuO4FTJW0L/DoiZkjaj8rbfk/g1ohYkN/TlQXjs9WAk4p1ktcjtQksldskJkTEKRXmfyPSqR5I4xxVGpNIwPERcWOPevcG3iwrWkL9z0ulocTrmU4aHwqAiBgraQvSEVOtOr8OzAfeRzqSeqPAup6IiOFKgzLeKumTETGpSF0RcUU+jXgwcGM+pVdx20s6lDbfHsLax20q1uluBj4jaUtYej/uHSrM9yiwjaTd83wbKw1VfiPw5VJPKEnvVBpluZZXSLdrrbSOIZLekZ8fAdxWp67/AdaT9OWysvJOALeTGvmR9E5ge9KAiZuSjrzezusp3BEh0ki044BSMqhbl9IIu7Mi4gLSQI7vpfq2vwvYW9Lmebt+tmhs1vmcVKyjRcTDwGmkO04+CEwmndPvOd8i4HPAhZIeyPOtB/wceBi4NzdK/5T6RyQXAzf0bKiPiDeAo4GrJT0EvA38pMLy5csE6fauH5X0pKS7SafNTs6zXAT0y/VdCRwVEW/m8tGSppBOLb1WJ+aefgtsoHQnwiJ1fQ6Ylk+f/Q3pNrQVt31OWmeQTpn9kTQ6r60hPEqxmZk1jI9UzMysYZxUzMysYZxUzMysYZxUzMysYZxUzMysYZxUzMysYZxUzMysYf4/da/QC/1n4qwAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"x = merged[(merged['funds_raised_percent'] < 200)]\n",
"y = x['funds_raised_percent']\n",
"\n",
"plt.close()\n",
"plt.hist(y, bins = 100)\n",
"plt.xlabel('Percent of Goal Raised')\n",
"plt.ylabel('Number of Occurrences')\n",
"plt.title('Distribution of % of Goal Raised')"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/chrismay/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:824: RuntimeWarning: invalid value encountered in greater_equal\n",
" keep = (tmp_a >= first_edge)\n",
"/Users/chrismay/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:825: RuntimeWarning: invalid value encountered in less_equal\n",
" keep &= (tmp_a <= last_edge)\n"
]
},
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Distribution of # of Days to Reach Goal')"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"x = merged[merged['state'] == 'successful']\n",
"y = x['staLauDelta']\n",
"\n",
"plt.close()\n",
"plt.hist(y, bins = 100)\n",
"plt.xlabel('Days to Reach Goal')\n",
"plt.ylabel('Number of Occurrences')\n",
"plt.xlim(left=0)\n",
"plt.xlim(right=60)\n",
"plt.title('Distribution of # of Days to Reach Goal')"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import preprocessing as pre\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn import metrics\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"from sklearn.metrics import roc_auc_score\n",
"from sklearn.metrics import roc_curve\n",
"from sklearn.model_selection import GridSearchCV"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['backers_count', 'blurb', 'country', 'created_at', 'creator',\n",
" 'currency', 'deadline', 'fx_rate', 'goal', 'id', 'launched_at', 'name',\n",
" 'slug', 'spotlight', 'staff_pick', 'state', 'state_changed_at', 'urls',\n",
" 'usd_pledged', 'fullcats', 'city', 'creLauDelta', 'lauDeadDelta',\n",
" 'staLauDelta', 'source', 'funds_raised_percent', 'launch_date', 'Date',\n",
" 'kickstarter.com', 'no_projects'],\n",
" dtype='object')"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mergeKick.columns"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>goal</th>\n",
" <th>id</th>\n",
" <th>launched_at</th>\n",
" <th>staff_pick</th>\n",
" <th>alexa_rank</th>\n",
" <th>creator_projects</th>\n",
" <th>state</th>\n",
" <th>creLauDelta</th>\n",
" <th>category</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>US</td>\n",
" <td>5000</td>\n",
" <td>1203770415</td>\n",
" <td>1491540705</td>\n",
" <td>0</td>\n",
" <td>548.0</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>19.0</td>\n",
" <td>film & video</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>MX</td>\n",
" <td>3382</td>\n",
" <td>878861613</td>\n",
" <td>1509893074</td>\n",
" <td>0</td>\n",
" <td>528.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>16.0</td>\n",
" <td>film & video</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NZ</td>\n",
" <td>6850</td>\n",
" <td>917345297</td>\n",
" <td>1427147079</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>7.0</td>\n",
" <td>theater</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>US</td>\n",
" <td>40000</td>\n",
" <td>1702164653</td>\n",
" <td>1487077744</td>\n",
" <td>1</td>\n",
" <td>589.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>49.0</td>\n",
" <td>comics</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>US</td>\n",
" <td>2000</td>\n",
" <td>1042930184</td>\n",
" <td>1507662001</td>\n",
" <td>0</td>\n",
" <td>503.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>16.0</td>\n",
" <td>games</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" country goal id launched_at staff_pick alexa_rank \\\n",
"0 US 5000 1203770415 1491540705 0 548.0 \n",
"1 MX 3382 878861613 1509893074 0 528.0 \n",
"2 NZ 6850 917345297 1427147079 0 NaN \n",
"3 US 40000 1702164653 1487077744 1 589.0 \n",
"4 US 2000 1042930184 1507662001 0 503.0 \n",
"\n",
" creator_projects state creLauDelta category \n",
"0 1.0 0 19.0 film & video \n",
"1 1.0 1 16.0 film & video \n",
"2 1.0 1 7.0 theater \n",
"3 1.0 1 49.0 comics \n",
"4 1.0 1 16.0 games "
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Make a new dataframe with only the columns that we'll use for modeling\n",
"kickModel = pd.DataFrame()\n",
"kickModel['country'] = mergeKick['country']\n",
"kickModel['goal'] = mergeKick['goal']\n",
"kickModel['id'] = mergeKick['id']\n",
"kickModel['launched_at'] = mergeKick['launched_at']\n",
"kickModel['staff_pick'] = mergeKick['staff_pick']\n",
"kickModel['alexa_rank'] = mergeKick['kickstarter.com']\n",
"kickModel['creator_projects'] = mergeKick['no_projects']\n",
"kickModel['state'] = mergeKick['state']\n",
"kickModel['creLauDelta'] = mergeKick['creLauDelta']\n",
"kickModel['category'] = mergeKick['fullcats']\n",
"\n",
"kickModel['staff_pick'] = kickModel.apply(lambda x: int(x['staff_pick'] == True), axis = 1)\n",
"kickModel['state'] = kickModel.apply(lambda x: int(x['state'] == 'successful'), axis = 1)\n",
"kickModel.head()"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>goal</th>\n",
" <th>id</th>\n",
" <th>launched_at</th>\n",
" <th>staff_pick</th>\n",
" <th>alexa_rank</th>\n",
" <th>creator_projects</th>\n",
" <th>state</th>\n",
" <th>creLauDelta</th>\n",
" <th>art</th>\n",
" <th>cameragear</th>\n",
" <th>...</th>\n",
" <th>VE</th>\n",
" <th>VI</th>\n",
" <th>VN</th>\n",
" <th>VU</th>\n",
" <th>WS</th>\n",
" <th>XK</th>\n",
" <th>YE</th>\n",
" <th>ZA</th>\n",
" <th>ZM</th>\n",
" <th>ZW</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5000</td>\n",
" <td>1203770415</td>\n",
" <td>1491540705</td>\n",
" <td>0</td>\n",
" <td>548.0</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>19.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3382</td>\n",
" <td>878861613</td>\n",
" <td>1509893074</td>\n",
" <td>0</td>\n",
" <td>528.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>16.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>6850</td>\n",
" <td>917345297</td>\n",
" <td>1427147079</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>7.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>40000</td>\n",
" <td>1702164653</td>\n",
" <td>1487077744</td>\n",
" <td>1</td>\n",
" <td>589.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>49.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2000</td>\n",
" <td>1042930184</td>\n",
" <td>1507662001</td>\n",
" <td>0</td>\n",
" <td>503.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>16.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 223 columns</p>\n",
"</div>"
],
"text/plain": [
" goal id launched_at staff_pick alexa_rank creator_projects \\\n",
"0 5000 1203770415 1491540705 0 548.0 1.0 \n",
"1 3382 878861613 1509893074 0 528.0 1.0 \n",
"2 6850 917345297 1427147079 0 NaN 1.0 \n",
"3 40000 1702164653 1487077744 1 589.0 1.0 \n",
"4 2000 1042930184 1507662001 0 503.0 1.0 \n",
"\n",
" state creLauDelta art cameragear ... VE VI VN VU WS XK YE ZA \\\n",
"0 0 19.0 0 0 ... 0 0 0 0 0 0 0 0 \n",
"1 1 16.0 0 0 ... 0 0 0 0 0 0 0 0 \n",
"2 1 7.0 0 0 ... 0 0 0 0 0 0 0 0 \n",
"3 1 49.0 0 0 ... 0 0 0 0 0 0 0 0 \n",
"4 1 16.0 0 0 ... 0 0 0 0 0 0 0 0 \n",
"\n",
" ZM ZW \n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
"[5 rows x 223 columns]"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dummies1 = pd.get_dummies(kickModel['category'], columns = 'category')\n",
"dummies2 = pd.get_dummies(kickModel['country'], columns = 'country')\n",
"kickModel = pd.concat([kickModel, dummies1], axis = 1)\n",
"kickModel = pd.concat([kickModel, dummies2], axis = 1)\n",
"kickModel.drop(labels = ['category', 'country'], axis = 1, inplace = True)\n",
"kickModel.head()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"kickModel = kickModel.fillna(value = 1000, axis = 0)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/chrismay/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:334: DataConversionWarning: Data with input dtype uint8, uint32, int64, float64 were all converted to float64 by MinMaxScaler.\n",
" return self.partial_fit(X, y)\n",
"/Users/chrismay/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/Users/chrismay/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n"
]
},
{
"data": {
"text/plain": [
"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='warn',\n",
" n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
" tol=0.0001, verbose=0, warm_start=False)"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = kickModel.loc[:, kickModel.columns != 'state']\n",
"y = kickModel.loc[:, kickModel.columns == 'state']\n",
"\n",
"#Use a MinMax scaler to standardize features\n",
"scaler = MinMaxScaler(feature_range = (0,1))\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
"logreg = LogisticRegression()\n",
"\n",
"scaler.fit(X_train)\n",
"\n",
"X_train = scaler.transform(X_train)\n",
"X_test = scaler.transform(X_test)\n",
"\n",
"logreg.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy of logistic regression classifier on test set: 0.71\n"
]
}
],
"source": [
"y_pred = logreg.predict(X_test)\n",
"print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(logreg.score(X_test, y_test)))"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "'numpy.ndarray' object is not callable",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-88-4ab5e3b86095>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: 'numpy.ndarray' object is not callable"
]
}
],
"source": [
"cm = confusion_matrix(y_test, y_pred)\n",
"print(confusion_matrix)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"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": [
"logit_roc_auc = roc_auc_score(y_test, logreg.predict(X_test))\n",
"fpr, tpr, thresholds = roc_curve(y_test, logreg.predict_proba(X_test)[:,1])\n",
"plt.figure()\n",
"plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n",
"plt.plot([0, 1], [0, 1],'r--')\n",
"plt.xlim([0.0, 1.0])\n",
"plt.ylim([0.0, 1.05])\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('Receiver operating characteristic')\n",
"plt.legend(loc=\"lower right\")\n",
"plt.savefig('Log_ROC')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### See if model performance can be improved using grid search"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| stackv2 | 2024-11-18T18:03:05.588009+00:00 | 2019-05-10T03:28:06 | {
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"gha_license_id": "BSD-3-Clause",
"gha_event_created_at": "2019-03-30T22:24:52",
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"language": "Jupyter Notebook",
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"length_bytes": 855295,
"extension": "ipynb",
"filename": "ChrisMay_ProjectUpdate2_kickstarterAnalysis.ipynb"
} |
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