diff --git a/.ipynb_checkpoints/domain_of_applicability-checkpoint.ipynb b/.ipynb_checkpoints/domain_of_applicability-checkpoint.ipynb
index 89441189d17e1bc753e2f9bb0d24fc7386a98db0..4d087e3d346b7f7bec05f207f7bf01194b30f6fa 100644
--- a/.ipynb_checkpoints/domain_of_applicability-checkpoint.ipynb
+++ b/.ipynb_checkpoints/domain_of_applicability-checkpoint.ipynb
@@ -1,117 +1,5 @@
{
"cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
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- " Jupyter.notebook.execute_cells(c);\n",
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- " ({metadata: {tags}}) => tags && tags.includes(tagName)\n",
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- "window.runCells = function runCells(tagName) {\n",
- " var c = window.findCellIndicesByTag(tagName);\n",
- " Jupyter.notebook.execute_cells(c);\n",
- "};\n",
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- "execution_count": 2,
- "metadata": {},
- "outputs": [
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- " #$( document ).ready(code_toggle);\n",
- " window.runCells(\"startup\");\n",
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- "<IPython.core.display.HTML object>"
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- " else \n",
- " {\n",
- " $('div.input').show();\n",
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- " code_show = !code_show\n",
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- " #$( document ).ready(code_toggle);\n",
- " window.runCells(\"startup\");\n",
- "</script>\n",
- "<!-- The raw code for this notebook is by default hidden for easier reading. -->\n",
- "To toggle on/off the raw code, click <a href=\"javascript:code_toggle()\">here</a>."
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -136,7 +24,7 @@
"<b> This notebook allows to reproduce results from the paper:</b>\n",
"C. Sutton, M. Boley L.M. Ghiringhelli, M. Rupp, J. Vreeken, and M. Scheffler, Identifying domains of applicability of machine learning models for materials science. Nat. Commun. 11, 4428 (2020) [<a href=\"https://th.fhi-berlin.mpg.de/site/uploads/Publications/s41467-020-17112-9.pdf\" target=\"_top\">PDF</a>]\n",
"\n",
- "<span class=\"nomad--last-updated\" data-version=\"v1.0.0\">[Last updated: January 25, 2021]</span>\n",
+ "<span class=\"nomad--last-updated\" data-version=\"v1.0.0\">[Last updated: January 27, 2021]</span>\n",
"\n",
" \n",
"<div> \n",
@@ -189,10 +77,25 @@
"That is, the $y$ values are almost determined by the third degree polynomial for low $x_2$ values but are almost completely random for high $x_2$ values. Discovering applicable domains reveals how different models cope differently with this setting even if they have a comparable average error. To show this, let us examine the average error obtained from three different kernelized regression models."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "## Code initialization"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:24.427984Z",
+ "start_time": "2021-01-27T02:15:24.047224Z"
+ },
+ "hidden": true,
+ "init_cell": true,
"tags": [
"startup"
]
@@ -217,6 +120,12 @@
"cell_type": "code",
"execution_count": 2,
"metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:24.432580Z",
+ "start_time": "2021-01-27T02:15:24.429503Z"
+ },
+ "hidden": true,
+ "init_cell": true,
"tags": [
"startup"
]
@@ -233,17 +142,28 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "heading_collapsed": true
+ },
"source": [
"## Data generation\n",
"\n",
- "First, the data for $n$ points is generated in the form of numpy arrays."
+ "First, the data for $n$ points is generated in the form of numpy arrays.\n",
+ "\n",
+ "Then, we use the sklearn library to fit our data with a linear, a gaussian (radial basis function = rbf) and a polynomial kernel. Our original data as well as the predicted values for each kernel are stored in the ``example_df`` data frame."
]
},
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:24.455791Z",
+ "start_time": "2021-01-27T02:15:24.434096Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [],
"source": [
"np.random.seed(7)\n",
@@ -260,17 +180,17 @@
" y[i] = val + np.random.normal(0, math.exp(x2[i]/2), 1)"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Then, we use the sklearn library to fit our data with a linear, a gaussian (radial basis function = rbf) and a polynomial kernel. Our original data as well as the predicted values for each kernel are stored in the ``example_df`` data frame."
- ]
- },
{
"cell_type": "code",
"execution_count": 4,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:24.814338Z",
+ "start_time": "2021-01-27T02:15:24.457730Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [
{
"data": {
@@ -470,7 +390,13 @@
{
"cell_type": "code",
"execution_count": 5,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:24.828601Z",
+ "start_time": "2021-01-27T02:15:24.815629Z"
+ },
+ "init_cell": true
+ },
"outputs": [],
"source": [
"def prediction_mesh(X, Y, kernel):\n",
@@ -489,42 +415,50 @@
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:24.928314Z",
+ "start_time": "2021-01-27T02:15:24.829820Z"
+ },
+ "init_cell": true
+ },
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
+ "/* global mpl */\n",
"window.mpl = {};\n",
"\n",
- "\n",
- "mpl.get_websocket_type = function() {\n",
- " if (typeof(WebSocket) !== 'undefined') {\n",
+ "mpl.get_websocket_type = function () {\n",
+ " if (typeof WebSocket !== 'undefined') {\n",
" return WebSocket;\n",
- " } else if (typeof(MozWebSocket) !== 'undefined') {\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",
+ " alert(\n",
+ " '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",
"\n",
- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\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",
+ " this.supports_binary = this.ws.binaryType !== undefined;\n",
"\n",
" if (!this.supports_binary) {\n",
- " var warnings = document.getElementById(\"mpl-warnings\");\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",
+ " warnings.textContent =\n",
+ " 'This browser does not support binary websocket messages. ' +\n",
+ " 'Performance may be slow.';\n",
" }\n",
" }\n",
"\n",
@@ -539,11 +473,11 @@
"\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",
+ " this.root = document.createElement('div');\n",
+ " this.root.setAttribute('style', 'display: inline-block');\n",
+ " this._root_extra_style(this.root);\n",
"\n",
- " $(parent_element).append(this.root);\n",
+ " parent_element.appendChild(this.root);\n",
"\n",
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@@ -553,281 +487,325 @@
"\n",
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- " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
- " }\n",
- " fig.send_message(\"refresh\", {});\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",
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- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\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",
+ " this.imageObj.onunload = function () {\n",
" fig.ws.close();\n",
- " }\n",
+ " };\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
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- "\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._init_header = function () {\n",
+ " var titlebar = document.createElement('div');\n",
+ " titlebar.classList =\n",
+ " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
+ " var titletext = document.createElement('div');\n",
+ " titletext.classList = 'ui-dialog-title';\n",
+ " titletext.setAttribute(\n",
+ " 'style',\n",
+ " 'width: 100%; text-align: center; padding: 3px;'\n",
+ " );\n",
+ " titlebar.appendChild(titletext);\n",
+ " this.root.appendChild(titlebar);\n",
+ " this.header = titletext;\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
"\n",
- "}\n",
+ "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
"\n",
- "mpl.figure.prototype._init_canvas = function() {\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",
+ " var canvas_div = (this.canvas_div = document.createElement('div'));\n",
+ " canvas_div.setAttribute(\n",
+ " 'style',\n",
+ " 'border: 1px solid #ddd;' +\n",
+ " 'box-sizing: content-box;' +\n",
+ " 'clear: both;' +\n",
+ " 'min-height: 1px;' +\n",
+ " 'min-width: 1px;' +\n",
+ " 'outline: 0;' +\n",
+ " 'overflow: hidden;' +\n",
+ " 'position: relative;' +\n",
+ " 'resize: both;'\n",
+ " );\n",
"\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
+ " function on_keyboard_event_closure(name) {\n",
+ " return function (event) {\n",
+ " return fig.key_event(event, name);\n",
+ " };\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",
+ " canvas_div.addEventListener(\n",
+ " 'keydown',\n",
+ " on_keyboard_event_closure('key_press')\n",
+ " );\n",
+ " canvas_div.addEventListener(\n",
+ " 'keyup',\n",
+ " on_keyboard_event_closure('key_release')\n",
+ " );\n",
+ "\n",
+ " this._canvas_extra_style(canvas_div);\n",
+ " this.root.appendChild(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",
+ " var canvas = (this.canvas = document.createElement('canvas'));\n",
+ " canvas.classList.add('mpl-canvas');\n",
+ " canvas.setAttribute('style', 'box-sizing: content-box;');\n",
"\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
+ " this.context = canvas.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",
+ " var backingStore =\n",
+ " this.context.backingStorePixelRatio ||\n",
+ " this.context.webkitBackingStorePixelRatio ||\n",
+ " this.context.mozBackingStorePixelRatio ||\n",
+ " this.context.msBackingStorePixelRatio ||\n",
+ " this.context.oBackingStorePixelRatio ||\n",
+ " this.context.backingStorePixelRatio ||\n",
+ " 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",
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- " 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",
+ " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
+ " 'canvas'\n",
+ " ));\n",
+ " rubberband_canvas.setAttribute(\n",
+ " 'style',\n",
+ " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
+ " );\n",
+ "\n",
+ " var resizeObserver = new ResizeObserver(function (entries) {\n",
+ " var nentries = entries.length;\n",
+ " for (var i = 0; i < nentries; i++) {\n",
+ " var entry = entries[i];\n",
+ " var width, height;\n",
+ " if (entry.contentBoxSize) {\n",
+ " width = entry.contentBoxSize.inlineSize;\n",
+ " height = entry.contentBoxSize.blockSize;\n",
+ " } else {\n",
+ " width = entry.contentRect.width;\n",
+ " height = entry.contentRect.height;\n",
+ " }\n",
+ "\n",
+ " // Keep the size of the canvas and rubber band canvas in sync with\n",
+ " // the canvas container.\n",
+ " canvas.setAttribute('width', width * mpl.ratio);\n",
+ " canvas.setAttribute('height', height * mpl.ratio);\n",
+ " canvas.setAttribute(\n",
+ " 'style',\n",
+ " 'width: ' + width + 'px; height: ' + height + 'px;'\n",
+ " );\n",
+ "\n",
+ " rubberband_canvas.setAttribute('width', width);\n",
+ " rubberband_canvas.setAttribute('height', height);\n",
+ "\n",
+ " // And update the size in Python. We ignore the initial 0/0 size\n",
+ " // that occurs as the element is placed into the DOM, which should\n",
+ " // otherwise not happen due to the minimum size styling.\n",
+ " if (width != 0 && height != 0) {\n",
+ " fig.request_resize(width, height);\n",
+ " }\n",
+ " }\n",
" });\n",
+ " resizeObserver.observe(canvas_div);\n",
"\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
+ " function on_mouse_event_closure(name) {\n",
+ " return function (event) {\n",
+ " return fig.mouse_event(event, name);\n",
+ " };\n",
" }\n",
"\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mousedown',\n",
+ " on_mouse_event_closure('button_press')\n",
+ " );\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mouseup',\n",
+ " on_mouse_event_closure('button_release')\n",
+ " );\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mousemove',\n",
+ " on_mouse_event_closure('motion_notify')\n",
+ " );\n",
"\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mouseenter',\n",
+ " on_mouse_event_closure('figure_enter')\n",
+ " );\n",
+ " rubberband_canvas.addEventListener(\n",
+ " 'mouseleave',\n",
+ " on_mouse_event_closure('figure_leave')\n",
+ " );\n",
"\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
+ " canvas_div.addEventListener('wheel', function (event) {\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
- " mouse_event_fn(event);\n",
+ " on_mouse_event_closure('scroll')(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",
+ " canvas_div.appendChild(canvas);\n",
+ " canvas_div.appendChild(rubberband_canvas);\n",
"\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
+ " this.rubberband_context = rubberband_canvas.getContext('2d');\n",
+ " this.rubberband_context.strokeStyle = '#000000';\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",
+ " this._resize_canvas = function (width, height, forward) {\n",
+ " if (forward) {\n",
+ " canvas_div.style.width = width + 'px';\n",
+ " canvas_div.style.height = height + 'px';\n",
+ " }\n",
+ " };\n",
"\n",
" // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
" return false;\n",
" });\n",
"\n",
- " function set_focus () {\n",
+ " function set_focus() {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._init_toolbar = function() {\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",
+ " var toolbar = document.createElement('div');\n",
+ " toolbar.classList = 'mpl-toolbar';\n",
+ " this.root.appendChild(toolbar);\n",
"\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
+ " function on_click_closure(name) {\n",
+ " return function (_event) {\n",
+ " return fig.toolbar_button_onclick(name);\n",
+ " };\n",
" }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
+ "\n",
+ " function on_mouseover_closure(tooltip) {\n",
+ " return function (event) {\n",
+ " if (!event.currentTarget.disabled) {\n",
+ " return fig.toolbar_button_onmouseover(tooltip);\n",
+ " }\n",
+ " };\n",
" }\n",
"\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " fig.buttons = {};\n",
+ " var buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'mpl-button-group';\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",
+ " /* Instead of a spacer, we start a new button group. */\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
+ " }\n",
+ " buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'mpl-button-group';\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",
+ " var button = (fig.buttons[name] = document.createElement('button'));\n",
+ " button.classList = 'mpl-widget';\n",
+ " button.setAttribute('role', 'button');\n",
+ " button.setAttribute('aria-disabled', 'false');\n",
+ " button.addEventListener('click', on_click_closure(method_name));\n",
+ " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
+ "\n",
+ " var icon_img = document.createElement('img');\n",
+ " icon_img.src = '_images/' + image + '.png';\n",
+ " icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
+ " icon_img.alt = tooltip;\n",
+ " button.appendChild(icon_img);\n",
+ "\n",
+ " buttonGroup.appendChild(button);\n",
" }\n",
"\n",
- " var fmt_picker_span = $('<span/>');\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
+ " }\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",
+ " var fmt_picker = document.createElement('select');\n",
+ " fmt_picker.classList = 'mpl-widget';\n",
+ " toolbar.appendChild(fmt_picker);\n",
+ " this.format_dropdown = fmt_picker;\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",
+ " var option = document.createElement('option');\n",
+ " option.selected = fmt === mpl.default_extension;\n",
+ " option.innerHTML = fmt;\n",
+ " fmt_picker.appendChild(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",
+ " var status_bar = document.createElement('span');\n",
+ " status_bar.classList = 'mpl-message';\n",
+ " toolbar.appendChild(status_bar);\n",
+ " this.message = status_bar;\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\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",
+ " this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\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",
"\n",
- "mpl.figure.prototype.send_draw_message = function() {\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",
+ " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
" }\n",
- "}\n",
- "\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\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",
"\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\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",
+ " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1], msg['forward']);\n",
+ " fig.send_message('refresh', {});\n",
+ " }\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\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",
@@ -842,78 +820,108 @@
" 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",
+ " 0,\n",
+ " 0,\n",
+ " fig.canvas.width / mpl.ratio,\n",
+ " fig.canvas.height / mpl.ratio\n",
+ " );\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\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",
+ " switch (cursor) {\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",
"\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\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",
"\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
- "}\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
+ " for (var key in msg) {\n",
+ " if (!(key in fig.buttons)) {\n",
+ " continue;\n",
+ " }\n",
+ " fig.buttons[key].disabled = !msg[key];\n",
+ " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
+ " if (msg['mode'] === 'PAN') {\n",
+ " fig.buttons['Pan'].classList.add('active');\n",
+ " fig.buttons['Zoom'].classList.remove('active');\n",
+ " } else if (msg['mode'] === 'ZOOM') {\n",
+ " fig.buttons['Pan'].classList.remove('active');\n",
+ " fig.buttons['Zoom'].classList.add('active');\n",
+ " } else {\n",
+ " fig.buttons['Pan'].classList.remove('active');\n",
+ " fig.buttons['Zoom'].classList.remove('active');\n",
+ " }\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ "mpl.figure.prototype.updated_canvas_event = function () {\n",
" // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\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",
+ "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",
+ " 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",
+ " fig.imageObj.src\n",
+ " );\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
+ " evt.data\n",
+ " );\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",
+ " } else if (\n",
+ " typeof evt.data === 'string' &&\n",
+ " evt.data.slice(0, 21) === 'data:image/png;base64'\n",
+ " ) {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
@@ -926,9 +934,12 @@
" // 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",
+ " var callback = fig['handle_' + msg_type];\n",
" } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " console.log(\n",
+ " \"No handler for the '\" + msg_type + \"' message type: \",\n",
+ " msg\n",
+ " );\n",
" return;\n",
" }\n",
"\n",
@@ -937,32 +948,40 @@
" // 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",
+ " console.log(\n",
+ " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
+ " e,\n",
+ " e.stack,\n",
+ " msg\n",
+ " );\n",
" }\n",
" }\n",
" };\n",
- "}\n",
+ "};\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\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",
+ " if (!e) {\n",
" e = window.event;\n",
- " if (e.target)\n",
+ " }\n",
+ " if (e.target) {\n",
" targ = e.target;\n",
- " else if (e.srcElement)\n",
+ " } else if (e.srcElement) {\n",
" targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " }\n",
+ " if (targ.nodeType === 3) {\n",
+ " // defeat Safari bug\n",
" targ = targ.parentNode;\n",
+ " }\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",
+ " var boundingRect = targ.getBoundingClientRect();\n",
+ " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
+ " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
"\n",
- " return {\"x\": x, \"y\": y};\n",
+ " return { x: x, y: y };\n",
"};\n",
"\n",
"/*\n",
@@ -970,19 +989,19 @@
" * 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",
+ "function simpleKeys(original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object') {\n",
+ " obj[key] = original[key];\n",
+ " }\n",
+ " return obj;\n",
+ " }, {});\n",
"}\n",
"\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
+ "mpl.figure.prototype.mouse_event = function (event, name) {\n",
+ " var canvas_pos = mpl.findpos(event);\n",
"\n",
- " if (name === 'button_press')\n",
- " {\n",
+ " if (name === 'button_press') {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
@@ -990,9 +1009,13 @@
" 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",
+ " this.send_message(name, {\n",
+ " x: x,\n",
+ " y: y,\n",
+ " button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event),\n",
+ " });\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
@@ -1000,265 +1023,307 @@
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\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",
"\n",
+ "mpl.figure.prototype.key_event = function (event, name) {\n",
" // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
+ " if (name === 'key_press') {\n",
+ " if (event.which === this._key) {\n",
" return;\n",
- " else\n",
+ " } else {\n",
" this._key = event.which;\n",
+ " }\n",
" }\n",
- " if (name == 'key_release')\n",
+ " if (name === 'key_release') {\n",
" this._key = null;\n",
+ " }\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",
+ " if (event.ctrlKey && event.which !== 17) {\n",
+ " value += 'ctrl+';\n",
+ " }\n",
+ " if (event.altKey && event.which !== 18) {\n",
+ " value += 'alt+';\n",
+ " }\n",
+ " if (event.shiftKey && event.which !== 16) {\n",
+ " value += 'shift+';\n",
+ " }\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",
+ " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
" return false;\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\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",
+ " this.send_message('toolbar_button', { name: name });\n",
" }\n",
"};\n",
"\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\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",
+ "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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ "mpl.default_extension = \"png\";/* global mpl */\n",
+ "\n",
+ "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",
+ " ws.close = function () {\n",
+ " comm.close();\n",
" };\n",
- " ws.send = function(m) {\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",
+ " 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",
+ " ws.onmessage(msg['content']['data']);\n",
" });\n",
" return ws;\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\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",
+ " var element = document.getElementById(id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm);\n",
"\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
+ " function ondownload(figure, _format) {\n",
+ " window.open(figure.canvas.toDataURL());\n",
" }\n",
"\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
+ " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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.parent_element = element;\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",
+ " 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",
+ "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
+ " var width = fig.canvas.width / mpl.ratio;\n",
+ " fig.root.removeEventListener('remove', this._remove_fig_handler);\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",
+ " IPython.keyboard_manager.enable();\n",
+ " fig.parent_element.innerHTML =\n",
+ " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
" fig.close_ws(fig, msg);\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\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 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",
+ " this.cell_info[1]['text/html'] =\n",
+ " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\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",
+ " 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",
+ " setTimeout(function () {\n",
+ " fig.push_to_output();\n",
+ " }, 1000);\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._init_toolbar = function() {\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",
+ " var toolbar = document.createElement('div');\n",
+ " toolbar.classList = 'btn-toolbar';\n",
+ " this.root.appendChild(toolbar);\n",
"\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
+ " function on_click_closure(name) {\n",
+ " return function (_event) {\n",
+ " return fig.toolbar_button_onclick(name);\n",
+ " };\n",
" }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
+ "\n",
+ " function on_mouseover_closure(tooltip) {\n",
+ " return function (event) {\n",
+ " if (!event.currentTarget.disabled) {\n",
+ " return fig.toolbar_button_onmouseover(tooltip);\n",
+ " }\n",
+ " };\n",
" }\n",
"\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " fig.buttons = {};\n",
+ " var buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'btn-group';\n",
+ " var button;\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",
+ " if (!name) {\n",
+ " /* Instead of a spacer, we start a new button group. */\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
+ " }\n",
+ " buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'btn-group';\n",
+ " continue;\n",
+ " }\n",
+ "\n",
+ " button = fig.buttons[name] = document.createElement('button');\n",
+ " button.classList = 'btn btn-default';\n",
+ " button.href = '#';\n",
+ " button.title = name;\n",
+ " button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
+ " button.addEventListener('click', on_click_closure(method_name));\n",
+ " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
+ " buttonGroup.appendChild(button);\n",
+ " }\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",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\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",
+ " var status_bar = document.createElement('span');\n",
+ " status_bar.classList = 'mpl-message pull-right';\n",
+ " toolbar.appendChild(status_bar);\n",
+ " this.message = status_bar;\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",
+ " var buttongrp = document.createElement('div');\n",
+ " buttongrp.classList = 'btn-group inline pull-right';\n",
+ " button = document.createElement('button');\n",
+ " button.classList = 'btn btn-mini btn-primary';\n",
+ " button.href = '#';\n",
+ " button.title = 'Stop Interaction';\n",
+ " button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
+ " button.addEventListener('click', function (_evt) {\n",
+ " fig.handle_close(fig, {});\n",
" });\n",
- "}\n",
+ " button.addEventListener(\n",
+ " 'mouseover',\n",
+ " on_mouseover_closure('Stop Interaction')\n",
+ " );\n",
+ " buttongrp.appendChild(button);\n",
+ " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
+ " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype._remove_fig_handler = function () {\n",
+ " this.close_ws(this, {});\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function (el) {\n",
+ " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
+ " el.addEventListener('remove', this._remove_fig_handler);\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
" // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
+ " el.setAttribute('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",
+ " } else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
+ "};\n",
"\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
+ " if (!manager) {\n",
" manager = IPython.keyboard_manager;\n",
+ " }\n",
"\n",
" // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\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",
"\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
+ "};\n",
"\n",
- "mpl.find_output_cell = function(html_output) {\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",
+ " 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",
+ " 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",
+ " if (data['text/html'] === html_output) {\n",
" return [cell, data, j];\n",
" }\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",
+ "if (IPython.notebook.kernel !== null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target(\n",
+ " 'matplotlib',\n",
+ " mpl.mpl_figure_comm\n",
+ " );\n",
"}\n"
],
"text/plain": [
@@ -1271,7 +1336,7 @@
{
"data": {
"text/html": [
- "<img 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0dHRMevj+B6BtLQ0ZGRkoKOjAzU1NcjNzXURuvxa8LZG5HI5Ghoa/K4jo9GI7u5u6PV6lJSUICsrCykpKRCJRJDJZMjPz0dlZSWam5sxMDDg8V55eXnRF7ocB5lEgv7/9/8wde21mN69G5P33YexvLyIv09NTQ2ysrLCzk1U+vDFhoQuETG82fHwzWSjo6MhbSTj4+OzdjZbLBY0NzdDLpdDLpejtbVVOJ0pKSlBcXGxy4bj69SEF7qdnZ1BJ0N+g6mvr/e5wVRUVAifM9onurxVWrQdJDiOQ2FhIYqLiyP+ut4mKlVXV6OkpIRKH4ig8ObyEm4zmdVqhUwmQ1tbm9/c1NnZCaVSCalUivr6ekEQ1dXVIScnJ2Chq1AoZhW6vtZRb28vGhoaUFZWhpycHKSmpkIkEkEqlSI3Nxfl5eVoampCdnb2nAjduXCQ4LhT/QPReG33L+cNDQ0oKCig0ofTEBK6RNj4c1IYGRkJa3oQ3/DhazPq6emBSqVCSkoKamtrPWzIysrKgjJlD1XoBrvBpKSkIDc3F2VlZWhsbPRqdxZO8F8wou0JzHFzZzLPcZxQP+3r8uKzzz6LBx98MNZLgjhNmM3lRSwWh1Rjy4dCoUBra6vX+wYHB5GXlwexWIyKigqPL/t6vR7Z2dkBC91IXgkymUzo7+9HU1MTKioqkJeXJ/QziMViZGdno7S0VHB8iHRD7lwMv+A4R/9AXhROir0FXybhKzf99a9/xcGDB0nsxgASukTIBOKFy3HB19i6h1gs9uhsHh4ehkajgVgsRmlpqc958+Xl5S6m7LMJXbFYjI6OjqglQ5PJBJlMhuLiYo8NJiUlBdnZ2S6WQqFuMIODg3Myzpjj5qiubyaKioqg1Wq9/rsajUb893//N44cORLrpUHEmECbYIOtsXWPjIwMNDc3u9xmMpmEHoHCwkKfua++vt6jRpfjOJ9rfi5KnnJycqBWq1FZWYmCggLI5XKIRCJIJBKoVCoUFxcLjg/hiF+JRBL1ccYcF3z/QDhRVVWF7Oxsr7nJZDLhxRdfxJVXXhnrpTEvIaFLBE0wTgrhmqpbrVakpKSgu7tbEM7l5eUQi8XIz8+f9XUrKipcTNkDEbrt7e1RTYjeLkE6WwrNtsEE4vjAO0gMDg5GPcHn5OSgsrJyTjaT2U6Pv/Wtb+Hhhx+O9RIhYoTdbvfqpOArP6SlpaG9vT3k3JSZmYnGxkZYrd57BPw9t6GhASqVKmChOxdNrLm5uahwq+MfHh5GW1sbampqoNFokJ6eLpz8KpVKFBUVQafToaOjI+CG3LnIsxwXmf6BQMOX+wwfv/jFL3Do0KFYL5F5CQldImCC3UR4YRluHVxqaira29uh1+shlUqhVCoDPoXhTdmdNxN/QlcikUQ9AQfSVMJvMO3t7T43GN5SyNsGwzcBBtptHU5kZWWhpqZmTjaTvLw8j43YOQ4fPownn3wy1kuFiAGzjRP3FnxNf6i5SaVSQa/Xo6WlRegRaGlpCcjBobGxEZmZmUEJ3WgPmsnNzQ3oS6vBYHBxfFAqlUJDbnp6OjQaDaqrq9HW1obh4WGX54bTCxFsFBYWer0CFI0oKSmBRqPxef8PfvAD3HHHHbFeJvMSErpEQISyifDhrfQg0LBYLEhNTYVMJkNaWhoaGxuDsgHiT0gDFbopKSlRv6QWqND1Fu6WQu4bDG8ppNfrIRKJPDaZaERmZiZqAxgpHImYTVR/5StfwQsvvBDr5ULMIXa7PeTclJGRgaamppCFrlKphFwu99kj4C+ampqQkZERsNCdi9Hh4VydcXZ8KC4uhkqlEia9yeVyFBQUCI4PgbjbRCLUajVKSkrmJDfNJqqPHj2KBx54INbLZV5CQpfwi7MdT7CbCB8pKSkh1cH19/cjJycHIpEIGo0mJIuy6upq5OfnByV0o90kIZfLXfwwww1/G4yzpVBLSwsGBwcj2vTGcXNzSZWP2Tb7PXv24E9/+lOslw0xBwTSIxDIiWxDQ0PQecVgMECj0UAkEiErK8tnj4C/aG5uRnp6esBCdy6+UEa6DMloNKKnpwcNDQ0oLS0VpsDxjg+8nVlTUxP6+/sjnpvmsn+goKDAb1nV7bffjh/+8IexXjbzEhK6hFf4TaStrQ0dHR0hG6pbrY46uEC8JvkwGo3QarUQiUTQarXC5cBQ3lun0yEvLy9goTsX3cCRFrreoqurCyKRCPX19cIGwzs+pKWluVgKhbvBhHNCHWz4s1gymUw477zzcOLEiVgvHyKK8Lmpu7sbLS0tYeWm7Oxs1NXVBfz40dFRVFRUCD0C2dnZqKmpCem9W1tboVAoghK6Op0uqusrJycHVVVVUX0P3vqwpqYG5eXlyM3NhVQqhUgkQmpqquD40NDQgN7e3rByU6ClGJGI2d7rq1/9Kn75y1/GevnMS0joEi642/EUFRWhrKwspETOR6B1cGNjY6iuroZEIkFOTo5Q1xvO5cXa2loPU3Z/Qncu/B3dJxxFIzo7OyESiVx+T2dLoUhuMGlpaWhqapqTzUQqlaK5udnrfSaTCRs2bMC//vWvWC8jIgo4N8GOj4+jqqrK5WpNKJGbmwudTjfr4/gpanyPQGdnJ6xWKwoKClBVVRXSe7e1tUEulwcsdFUqVVwJXWfrQ5PJhMHBQbS0tKCyshL5+fmQyWQeDbl6vR7d3d0BOz5kZWWhurp6TnLTbGVVV111FX7/+9/HehnNS0joEgA8NxG+0cx94EIo4c2CxzksFguampogk8mQnp6OtrY2lxOazMzMkC4vWq3Bm7LPldCNtjDs6OiAWCye9XGR2GDmyhOT4/zbEplMJixZsgQKhSLWy4mIIL5cXtzXdihRUFCA6upqv7mpvb0d6enpQo8AP/DBarVCo9GgoqIipPdub2+HTCYLSuhGu+kzOzs76kI3GOvDoaEhtLW1obq6GgUFBTh+/Dj+9re/QSwWIzMzE0VFRaitrUVnZ6dXN5q5OAXnY7ZmwV27duGtt96K9XKal5DQnefMZhVWXl7uYs8VSvgTql1dXcjMzERqairq6upcNhE+srKyoNfrQ3rvYE3Z09LSfJ4YRirmQui2t7dDIpGE/HzeUqi6uhpqtdqrpRC/wcyVVdBsI5qHhobAGENhYWGslxURIby5vPD5yd2eK5TwJ1T7+/uRm5sLiUSCqqoqrz0C4Vzx6ujoQFpaWsBCdy7cTbKzs6N+AhqK9WFHRwd2794NxhgSExNx5MgRfP755ygsLERGRobQkJuRkQGNRoOamhrhC8pc9Q/4K+EymUw466yz8PHHH8d6Sc1LSOjOYwJxUuC/SYezmXgTqkNDQygoKIBYLBaaBXw9PycnJ6g6OufwZcruT+hGW4TOxXu0trYiJSUloq9pMBjQ0dEBnU6HwsJCF8cHuVzussFEw+3BYDD4HWvc2toKxhh0Ol2slxYRJoHkpubmZhfXglBCq9V6CFXnHoGioiKMjIz4fH5xcTFKS0tDeu/Ozk5IpdKAhe5ciNC5eI/+/v6grA9VKhU2bdoExphHXH755eA4hxtNV1cX6urqoNVqkZmZKTTkSqVSocSktbU1apaL/q4GmkwmrF69GiKRKNZLa15CQnceEoxVWG1tbdiXB53r4DiOQ1lZGcRiMdRqNYaHh2d9fl5eXkB1dN4iWFP2uThtnYtT45aWFqSmpkb1PTjuVL1deXk5tFqtV0uhUDaYjo4O3H///fj000+F2/iToIGBAa/PqaqqAmMMnZ2dsV5iRIgE46TQ1tbmcuk/lHAuzXLuEcjOzkZfX9+szy8tLQ25tKurqwspKSlBCd1olxXMRU0r7/E925dho9GI48ePIykpSRC2ycnJHmL3vffe8/l8qVSK4uJilJSUICsrS3B8SEtLEzy5m5ubMTAwELbjgz8PdqPRiOTkZGRlZcV6ic1LSOjOI0Kx46mvrw/78iDvn1hXV4fU1FSoVCph0lkgkZ+f77eOzl80NjZCqVQGvJnMhSPCXAjd5uZmSKVS19v7+jCekoLx//wH4xIJLC+9BMvx4xjLyAAX4jhPb40lvKVQfX09SkpKXCyF3DcYd8eH4uJiHD16VNjcNmzYINzf29srjJP29lkKCgrAGAPHcbFeakSQuDfBBpKbOjs7kZqaGlZu4kuz+B4BhULh0SPgL8rKylxGjAcT3d3dkEgkAeemuWgUm4vyiJ6eHs91PDyMcYUC4//+N8bS0jD03HN4f8cOfJUxJHk5yXWO5cuX+8ynqampLqesJpMJfX19aGxsRFlZGXJyclwacnNyclBWVobGxkb09fUFLH6NRqPfsqre3l4wxlBSUhLrpTYvIaE7DwhlE+HD3dQ82LBYLMjKyhKGPjQ3NwdtBcTPXg/l/d0vbwYidKNtlTUXQpffuPmfzR98gOmdO2Fbtw72RYtgT0qCfckS2NasgW3rVkzcdx84HyUB/oJvLPF1yhrIBiORSPCHP/wB+/fv99jENm7cKJyS8JZp/k7jk5KSMD09HeslRwSIrybYQNZ2T08PxGJxWEK3sLAQqampfnsE/EVFRYXL5MVgore31+XzByJ0o22VNRdCt7u7GyKRSGgeG5dIMH3ZZbCtXw/bkiWwJSbCzBiGGEMnY5AwhjNm8kFSUhIOHTrkkSduu+02r6I0kEmXJpMJAwMDaG5uRkVFBfLy8oSGXH6cc0lJCerr69HT0+P1vw//hd9XWVVDQwMYY2hsbIz1kpuXkNCNY8LZRPhobW11scAJJvr6+pCdnQ2xWIysrKyQBj7wm1Gonc0tLS1BmbL782mNVMyFs0NDQwPkcrljI/n3v2HbvBm2lSthW7UK9sRE2BMSYE9MhG3jRtjOOgu29ethfeaZoN8nnFHDnZ2dOH78OLZt2+axcSUmJuLYsWMoKioSNpi2tja/DXafffYZVq9eDZvNFuulR8zCbE2wgQRf6xmsOLVaT/UI8CU2HOe7R8BfVFVVQa1Wh5wfRSKR8HtbLBaMjo76zE25ubl+x19HIubC2YH/wmoymTCmVsN29tmwr1gB25o1sCcmYpoxTDGGAcbQxBgGGcMJxvDlL39ZGMhw1113eeSMEydOuLxPuKOGh4aG0NraKkzXlMvlwhfzzMxMaLVa1NXVoaurS/hv6avBrrS0FIwxDAwMxHrpzUtI6MYh/CYyPDwMg8EQ0ibCR0dHh0vDRCBhMBhQWFgIkUiE4uJiFBUVoaSkJKT3t1q9N4wEGsGasqenp6O+vj6qiX4uhG59fT0UCgU4jsPkLbc4NpJzz3Wc5iYmwr5woePPRYswvWsXbBs2YHrXLnA9PUG9j9fLkLNEWVkZHnnkEaxYscLrpciLLroIUqlUEBEKhUJwfBCLxS4bjLOl0IkTJ7B161bY7faIrqfPPvsMt956K84880wsW7YMe/bswYkTJzze54MPPsAFF1yARYsW4dJLL0VKSkpEP0e8YLPZYDQaMTg46OGkEKxYFYlEGB0dDfg5/P9/fI9ARUVFWD0INTU1IXv58kI9UKHLl/xEM2/MhVcv7/HNcRwmHn1UyE3TS5dimjFYGMMkY5hgDDrG0D4jeK/ZskV4jdbWVqxbt84lb6xevdrlkIIvJ+ju7o7YZx8eHkZ7eztqamqg0WiQkZEBkUgkhFqthk6nQ0dHh0tOzMrKAmMMFoslomuJclNgkNCNM5wbzfLz81FVVRWyyLVaPevI/MXo6CgqKyshkUiQm5uLgYEBWK3h1bFZrY6GkVCFcrCm7HNhRzMXQlev1yMjIwPc4CBsZ57puCy4fTvsycmOsoWFCx1/T07G9PnnY3rbNtjOPhujQW6kzqcz/h5nMplw4sQJ3HTTTUhISPAqcBMSEvD44497LYMwGAwoLy9HamqqYCnEi9+MjAx8+OGHuOeee3DRRRfBaDRGdE3t378f99xzDz799FNkZmbi6aefRmJiIl544QXhMf/4xz+QkJCAX/3qV1CpVHj44YeRnJwMjUYT0c/yRcY5N5WVlaGwsDCs3MQLGaPROOtjzWaz0G0NHj4AACAASURBVCOQmZkp9Ajo9XoXV5Zgw30gTTDBN1fyJ9KzCd25GGc7F0K3vb3d4fFtMmH68sthX7UKk9u3wzojcC2MwTpzqtvCGPSMoYsxXMcYXn/9deF1/va3v7lcAWKM4fDhw0Iumq2cIFIxMjKCuro6waUjMzNTcKNRKBT4/PPPcezYMZxxxhno7++P6Jqi3BQYJHTjBG9OCoWFhSgvLw85iXs7dfC1iTQ0NCAtLQ0ZGRno6OhweXxlZWXIdWxWq6OzWavVhvTcYE3ZMzIyoi503RskohH68nI0PPQQpnftcgjbpCTY1q8/dZLrLHR37IBt0yZMX3ABuCAv8802mKKnpwcvv/wy1q5d6yFqnX9eunTprC4RdXV1UCqVLhtMZ2cnamtr8fLLL+Occ84RXvcb3/hGxNbW4OCgx20PPfQQ1qxZI/y8Y8cO3HvvvS6P+fKXv4ybb745Yp/ji4rdbsfk5KRLEyx/OTic3DQ6Oip07/t6jMViQVtbGxQKheCo4pybGhsbkZmZGfJnqKurc/HpDib4+nbnkq75IHTbmptR+cMfYnrfPiEfDScmYnRG3FpmTnMnZ05yR1atQitj2MkYli1b5uLWcNlll3l8YX7nnXfAcYH3D0Tkd2prc7FzNBqN6O7uhl6vx0cffYQLL7xQEOO7du2K2FUnyk2BQUI3TpienvZwUgjH+oYP/vKgt/pai8WCzs5OZGRkQCqVor6+3mu9XHV1dVijOsMZWuFeejGb0FUqlX6n20Qioj5JbGQExptvxuSyZbCvWAH74sWwM+aoy120yPFnUpLjtkWLHKe5a9di4vHHg34vX369VVVVePzxx7F8+XK/HdOMMRw6dAgdHR2zvldNTQ2ysrJ83v/UU0/h0KFDKC8vR05OTlTX29tvvw3GGMxmM5qbm8EYg1gsdnnM66+/joULF8JqtUb1s5zu2Gw2jybYSEw1M5vNgpDxdj/fI5CSkoKamhqvOay5udmlhj/YCMeVxlvphT+hW1BQgLKysqjmprmYJGZ8+GFMrFgB2/LlsC1dCjtjmGYM5hmhO8kYbDOnuk0JCbCtWQPdzp1Cvjhw4ABefvllPP300zh8+LBHPklOTkZeXl7Qfr3hRHNzM9LS0nze/9e//hXbt2+HTqeDXC6P6nqj3OQJCd04wWazeZy6hnuSarVaMTIyIlyadr59cHAQeXl5EIvFqKio8Fsnp9PpQr68F+7v4W5BNB+E7vjnn2Nq9WpYV6+Gbft22M46y3FykpBwSvAydurvCxdiav9+jIbwmZxtzEwmE1JTU/H1r399VnHLGMOqVatw8uTJgN+roqICubm5Pu9/9NFHceedd87Jejty5Ai2bdsGAJBKpV47qtPT08EYQ11d3Zx8ptMVu93ukZvCPUnlQywWo7e31yNnFRUVCT0C/kob3Eubgo1wfg9+AArHnWqEm03o8s1Y0YrMzEzU1tZG7fXHioowdcYZDqG7fTtM69djbEbY2mYEL5+bbIxhOjER0xddBK68HEuXLg0orzDGsG/fvlntCCMZzj0R3uKVV17Bnj175mS9UW7yhIRunGC32z0SaU1NDfLy8sLaSDiOc7k8aDKZUFJSApFIhMLCQhgMhllfI9w6uHCms3V3dwdlyh7tRM9xHFJSUqIqdCceewzTy5bBvHmzQ+jOiF3bypWOTSQx0XGacsYZjlizBlP79mE0BP/gxsZGiEQivPnmm8KIzkDi0KFDQTf9lZaWQq1W+7z//vvvx4MPPhj1tZaXl4fExES88cYbAIBPPvkEjDGPy4jFxcVgjKGgoCDqn+l0x31dtrS0uDSJhhqpqano7OyE1eoY+OCtR8BfhNJs6xzhTGfzdojgT+iq1eovvNC1/OEPsC1bhrGNGzG6aRMaZ8oTBpyErokx9M2EbfVqTO/eDenvf++SPxYvXoytW7d69dTlbcja29v92hFGMmpra13KqtzjN7/5DQ4cOBD1dUa5yTskdOMEb0I3XIFptTpsyfiCfp1OJ/gKup+i+IuGhoawTm/CEew9PT0uzXTj4+NobGxEc3Oz10tac3HpLiUlBW1tbVF7/YmHHnII3S1bTgnd7dth27AB9oQE2DZswNQ115yKfftg27oVFqdGj0CipqYG3//+9326J3iLBQsW4NVXXw1pCpFWq0VRUZHP+2+//XY8/vjjUV1nnZ2d2LJlCw4dOiTYmPGbydDQkMtjtVotGGNQq9VR/UxfBCYmJiIqMPmQyWRobW0VegTS09M9egT8hfsX4WAjHMHu3kxnsVjQ2tqKxsZGr5O6NBoNSkpKopqblEpldIXuiy/CtnQpRjdsQCNjQrTPiNxhxpDtFIbdu2E94wy8NDMRzbm2f8GCBWCM4aabbvKaa1588UW//QORjOrqar9lVT/5yU+iXhNLuck3JHTjBG9Ct6mpyWUqWCjB18GlpaVBLpejpaUl6E7pcGfShzOG2NmUvaenB0qlEqmpqcKkLplMJoypbWtriwuha37/fUytXAnrjNOCEMuXw56QgOldu1yF7jXXwLZ5M6xPPDHra5tMJsjlcq9NILPFli1bHE4QIf5earXa70Z/6NAhPPPMM1FbYyMjI7j44otxySWXuDg70OXB2XEXupEY9sALXZlM5rdHIND8EEqEM4aY93k1GAwYHBxETk4OJBKJMEhFKpUKlmItLS3Iz89HcXFxVHNTtEu3xhUKTK1di8GkJBehOzAjdBtnBG5OQgKyGUPxsmXoSkrCPxnD5s2bcfDgQZecsnHjRjQ1NbmMCeZjx44dXvsHohH8oAlf93/ve9/D3XffHbX1RbnJPyR04whvSTic+rOenh6oVCqh1m18fDyk1wln6ITVGt7JNG/kXVhYCLFYjLKyMgwPD2NkZAS9vb1oaGhAaWkpsrKyIJFIhFGQarUaNTU1aG9vj3iNV7SFLtfXh9G9ezG1dClsa9fCtmmTw4x98WLYFy/G1BVXuArdq692CN3f/tbnaw4MDOCdd97Brl27gha4iYmJeOqpp2adbT9b5OXl+e06v/LKK/GHP/whKmvLbDbjmmuuwVlnnYWuri6X+/iGD4lE4nL7G2+8gYULF2JiYiIqn+mLhLvQdbfWCjYGBweRn58PkUiE3NzcoLx0nSOcoRNWa3gn0xznKAvTarUQi8UoLCzE4OAgDAYD+vv70dTUJEwRTE1NFYYV8LaRra2tYa8p94h6j4LRiKbLLoOROSafdc2I3DHmcFsonxG6rWefLZzqdjKGvyYmQqVS4S9/+YtLbvnBD34AjuO8lk0lJCTg3//+d/R+F6eYrazq7rvvxkMPPRSVtUW5aXZI6MYR7ptJV1dXSLPgh4eHodFoIBaLUVpaCqlUivb29pCFakdHB9LS0kJ+fkNDQ0idzbxfp0gkQnZ2NgYGBvzW6BqNRiiVSqjVahQXF0OlUgl+iOnp6dBoNNDpdOjs7HQZVBBsSCSS6ApdjkOVQoH+w4cdInf9etjOPBMT992H6UsvhW3rVkzt3+8QuV/+Mqa3b8f0zp0Y89LoVV9fj5///OdYv3590AKXMYbzzjsPKpUqIr9TdnY2qqqqfN6/c+dOvP322xFfV1NTU/jGN76BtWvXQqfTeX3Mjh07cP/997vcds0118wrCx9/TE5OuqxNb41YgQTfIyAWi6HRaJCZmYm6urqQc4s/V5lAItQca7FYUF9fL+QW3td3bGzMa24xmUzIz89Hdna28MWcvyoll8uhVqtRXV0d9hfzaAvdnp4ebFm+HK8xhjbG0M8YuhnD/zKG3BlRW7xwIaauuQaFCxdCxxjqGYPkoYfAcY7R5s755cYbbwTHcfjRj37kNf/ce++9Uc2zfBQVFfktq7rlllvws5/9LOLrinJTYJDQjSPcha77iMnZYnR0FOXl5RCLxcjPz8fg4CCsVivS09PR0tIS8mbS1dUVVh1cKCUYnZ2dSE9PR1pamstGNpspe3Z2Nqqrq4WfeT/Euro6FBUVQalUChO6lEolioqKUFtbi66uroCbHgKZvx5u8CcMo3o9xnJzMdrcDI7jYP7kE0xffLFjJPCWLQ7/3IsuguX3vwfnVBOYmZmJu+66C8kztXGBhLs37ve+972ImrX7a5QxmUzYunUrPvnkk4ivq4ceegiMMbz66qvQaDQuYbU67Hl4U/bf/OY3yMrKwiOPPDLvTNn94S50OY4TLtsHsp7Hx8e99gjk5+ejpqYm5NzCC253V5lAw70HIJDo6+tDVlaWcErb398v3Dc2NgaDwYC33noL77zzDi699FJ85zvfwZtvvolPPvkEBQUFLv/P9/T0oL6+HiUlJVCpVMJVKecv5h0dHQF/MY+2j/iBAweE/LCOMXyJMZw5c9XnGsagYQwDCxfCuHw5uphjWMTvGcOvnn5a+HzOOSY5ORltbW349NNPhducv5SvWrUqqnmWj9kaBQ8cOIDnn38+4uuKclNgkNCNI9yFrjdDcm9hNpuh1+shlUqhVCqFLmY+MjMz0djYGPJmEm4dXDBelwaDAWq1GmKxGJWVlYLYD0bo+js15DjXQQXuU7oyMzNRXFwMvV6Pnp4er+8zF0K3uLgYGo3G631jGg2sv/gFJr79bVh/8hOMp6eDM5kwNDSE999/H5dffnlIp7fOTSIfffRRxH8nf+OZTSYTVq9e7eEXGQm2bdvm83dtbW0VHvfBBx/g/PPPx8KFC3HJJZfMuzGb/nAXurN54PJhsVjQ0tICuVzutUdAo9GgsrIy5NzC18mOjIyEnNsCPUzgOE5wrCkuLhZcF5z/DcrLy/GVr3zFIQTdRtzya+vqq6/G448/jpMnT6Kmpsalac35i7lWq3WZ0pWRkYHCwkK/X8yjKXTffffdWXPHOYzhKcZwkjG8yhjuXbcOicwxUEav13v1zX3rrbfQ2trqctuqVauEv0fbe5jjZh/P/KUvfQmvvvpqxNcV5abAIKEbR7hvJnwi9eUjabFY0N7eLpx8NjY2eq1Vy87Ohl6vD3kzCbcOLpAa3/HxcVRXV0MikSAvLw9DQ0OwWq3CGMhATdlzcnJQWVkZdKIzGAzo6OgQZqArFAqhpk6lUqGkpAT19fXCxhhtoavValFYWOhx+2hDA6zPPoup/fsxfdllmLzrLrS89x6+853vYOHChWEJXMYYrr/++qhZp6WlpaGpqcnrfUajEUlJScjOzo71MiS8MDU15bFmxWIxenp6/IpI/hK9Tqfz2iOg1WpRVlYWcm7ip6vx+SIauc1sNqO+vh6pqalQqVTo6+sT8i9/omuxWPDqq68KTgKMMVx00UW49957cf3112Px4sU+19yqVatwyy234LnnnoNMJkNfX5/L2hgZGUFXV5fPL+ZarRZ6vR7d3d1RG4He1tY2qw/uSsbw1x07oEpIQBFj+D/G8Pkdd2Dd6tVgjOHmm2/2+rwbbrgBHMfh/PPPF2578MEHhb8fOHAg6kI3KyvL5Uqgc5hMJpx//vl4//33Y70M5y0kdOMId6HrL4n39/cLXb5VVVV+T33z8vKg0+lC3kzCrYNzH+PrHPyIT7lcDoVCgfb2dpfTlWBN2XNzc0MSut5ieHgYbW1tgg+wXC6HSCSCSCSCUqlEaWkpGhsb0dfXF5Ldlr8oKiqCVqt1iNvWVpjfegsTDzyA6XPPhW39ekxfcAGKzj0X9y1diqQwxS1jDCtWrMC7774b8d/DOfw18fX09IAxhtLS0lgvQ8IL3oSuVCpFR0eHx+0Gg0HoESgpKfFbVhDOeHBehAZysuwr+Ktmvhp1eacXqVSKxsZGj5NfkUiE+vp63HHHHT7XFj86ljGHT+xtt92GK664wmdZUXJyMnbv3o2HH34YJ0+eRKMXf+yRkRF0dHRAp9NBo9EgPT1dyE1yudzli3m4a9pkMrn8fs4lTssZw5GEBPyZMVQxhoHERDQwhgrG0MwY6hISIHVzWvA2bfGdd97BkSNHhJ+d/32SkpKiPjTCny2byWTCxo0b8dlnn8V6Gc5bSOjGEe6bifOJAX+b0WiEVquFSCRCUVFRQJfs1Gp1WJcHQ2084aOzs9NrZ/PQ0BDy8vIgkUhQU1PjdbPxdqrtT+jOdgkq3OBFf1FREfLz84UaYqlYjDy5HOWlpWhqavLqoxlIjGm1sD7xBEyXXgrT5ZfD+vjjmLr2WtjOOQe2NWswkZiITxcswDUROL3l4/LLL4+6JRt/ibmzs9Pr/fX19WCMoampKdbLkPCCN6GrUChcav9HR0dRUVHh0SPgLyoqKsKa/sjnyGB8wZ3D2xUjPs8WFRUJTi++ct+bb76JLVu2uFwR+dGPfoSDBw9iyZIlPoXs1q1bBQGckJCAvXv34pxzzvG5Rs877zwcPXoU77zzjs+1ajAYIJPJoFaroVarhS/mKWIxctPSUFpcjIaGhqC/mH/wwQdgjGErY/gVY8hijsazVxiDhDmazfoYwwRzjAHuZQxV69Yha8EC1DCG2qVLsc9LGYd7uP97XXzxxcLf33777ajmJ4VCgYaGBp+5a9myZZDJZLFehvMWErpxhLfNJCUlBd3d3RgbGxMu7efk5LiI39miqKgorMuDvDF6qHVw7qbuY2Njwoao0Wj8NrR4e+9YCl2OczThdHR0OH7u7QX3l7+Au/12GK+/Ht23347i3/4Wov/8x8NHc0ivh/nkSZjfew+jXmy2xrKyHI1mq1djYs0aTKxfD/uSJbAvWoTePXtwfNkybI2QuOVPmn74wx9G3cSe34RFIhF6enq83l9SUgLGPCcAEacH09PTHmuTr/3nL+376hHwF9XV1cjPzw85NznnyFCe6z7dzGw2o7a2FikpKcjJyfF7Utza2urhZrJlyxYcOXIER44cEbxhly1bhkOHDnmdBMavwyuuuAKPPvoo9uzZI9y+bNkyn6e+27dvx4MPPoiPPvoIzTONqhznVgc/PAzuo4/A3X03jAcPou+WW1D+i19A8q9/ITU1FTk5OSgrK3N8MW9pwfjf/w7zu+9iLD9faGzV6XRYuXIltjOGPMYwyBxOC52MgWMMVsZQNnN6a2YMhpk/J7ZuRe8llyBz5r4jbv9GvlwWnOPWW28V/n7WWWdFNT+lpaW5/Du65y7G5s9whtMRErpxhDehm5aWhrKyMshkMigUCrS1tQU98KGkpATFxcUhbyTh1sHx5vIWiwXNzc2QyWTIyMhAV1fXrM/lhaWzGPYndPPz8/16tUZK6HZXVMD84YeYOngQtu3bMbV3LyZvuMHhcfuVr8D06qsuPprV3/8+zGvXYmrJEkwvWYKp1athuu8+DHd3O153ZATTu3bBnpgIe1ISbMnJmF6yBBVJSfhvxrA4ggKXMYdRu1wuR25ubtS/GHAcJ1wi7u/v93q/SqUCYwxWqzXWy5Dwgjehm52djeLiYqFHoKGhIeg6/nCGyfDhq4QikHD+Is07vchkslkH6xgMBqHpMzk52ed0wUWLFuHo0aP4z3/+A6VSiauuukq4b/VM7aq32LRpE55//nnk5+fj3Xff9drY5hy7d+/GY489hhdeeAHlOTkY/+c/MfmNb8B27rmYvvhiR2668kpMXXUVxp59Fi1NTcKQhNJnnsHYxo2YXLrUkZtWrsTo176Ggbo6XHvttWCMQcoYJmdigjGMModv7hRzTEMbTEiAdUYIjzIGy+LFGN67F5mMoYUx3O/0WX/605/CZDLhrLPOEm5LSEjAk08+6TI44vzzz3cpkwhnWM1s4c8ysq2tDYwxVFdXx3oZzltI6MYR7ptJd3c3xGIxJBIJ6urqQm4GKy8vR2FhYcgbCT9GONQ6ON45ITs7GykpKUH9LrzIHh4eDljoRrpLd7S1FeP/+Q/M//gHxjQaZP3xjxj/5jcxvWMH7MuWwbZyJWxnnomp66/HxNGjmDpwAJOHD2N0pubL/MknjoEPixbBlpzsGOObkABbUhJaDh+GXC5H/+HDsDMG+8zm8TFj2BRhcctvKD/+8Y8xODgIjvO0Y4tW8P8P+DLIl0gkWLJkiTD6kji9sNlsLutyYGAAUqkUYrF41h4Bf1FfXx+Sx7ZzyGQytLW1hfRc/otrbm6u4PQSiMvNnXfeKayn9957D+Pj41Cr1fjhD3/oUpPrvvb4v99///0wGo1oa2vDm2++iZUrV/pds4w5alWPHz8OhUKBZ555BpdeeimWM4aDjOFWxnAFYziPMXyYkID6JUtgXrgQk0uWYHrTJkzt34+J++/H5Ne+hqnrr8dYTg44znEVybZ+vSM3LVwIe0KCIz8lJqJ6piTjZ8wx9czOGGwzYXeLqZnHTDDHSa+JMeQmJqKSMZQyhp1Ov8e2bdtgNBrx2GOPufx+f/7zn3H77bd7CH7+7zfffHNUchNfVtXV1eX1/pqaGjDG0N7eHutlOG8hoRtH8EJ3aGgIBQUFEIvFSEtLC6uRzGq1oqqqCgUFBSE/n6+D47uNg91INBqNUNfqy0HCV4yNjXmcJvsTugUFBREVuuP//Ccm77gDU1df7TitPXgQxm3bMLFrl2N4w5o1jvrZdetgO+MMTO3d6zg52bkT4//4BziOw9R118HutInYExJObRKJiTAdPgxbUhKGGcPLjGFDhIUt//dt27ZBLpe7/H4qlSrq9bkcx6G7u1uotfZ2/9///nds2LABdrs91suQ8AIvdI1GI4qLi4Wmp3CuFFmtDo/tcMaLW62h+4SPj4+jsrJS+BIe6BWr559/3kV88cN4jEajUFe6efNmfPLJJ3jiiSdcyhGcY+fOnfje974niLlFixbh/fffx1//+lfcddddXsXv4sWL8bWvfQ1Hjx7FtcnJ+BtjyE1KQsPatdAsX45S5mgK0zDHxLIGxtDDGIaTk9G5bh0GduyA9ZxzYP7Tn8BxHCYefBD2RYu85iYbY/g3c0w943/mBa97TM+IXV70jjBH7W5LUhL+uGiRx++hUCiEMbZ8XHfddThx4oTLbe6lG9H4Us6XVfnyDNdoNGCMuYzmJeYWErpxxPj4OMrKyiAWi6FWqzE8PIzc3NywhW5tbS1yc3PDeg2JRBJUHZzFYkFDQwOkUqlghxPKqQ9/muzc2OJr+hDHzW78HUyMyeWYuuwy2LZuxfSuXZg6cABTX/oSJhcvxsSFF2L6ssscI3q3b4dt9WrYk5MdJ7xr18K2YgUmv/UtcL29jhMTfhNJSnKUJzhtKLrkZDzMGJZEUOC6nyodPXrU64lF1EeGzkR7ezvEYrHP+9955x2cd955JHRPUyYmJlBdXY2UlBRkZ2ejr68v7JIoqzX8MedWqxVKpTIon3Bnpxe+YSvQnoeBgQEPAXrOOefgueeew7Fjx4Qvl1Kp1GWN8etx27ZtPksWDh8+jJSUFBgMBmi1WqxduxaMOWp1d+/e7fKl9TzmaArrYAztK1ag98ILYd63D2PJyWhijtG7gzNis5+dKjcYYI7TVmVSEg7v34/W9eth43NRYqJHbppgrie53k5zbcxR0jDNGKYTEjDNHA1pWYxB8fWvY5HT78g7Lnz3u9+F0WiE84ltYmIitFqtx+Aa53j00Ucjnpv4sqqBgQGv9ysUCiQkJGB6ejrWy3DeQkI3jujp6YFKpXIRlOE6JlitjsuDWVlZYb2GVCoNuMmkt7cXKpUKUqkUDQ0NglMBxwXv2sDbB7lPH4q60O3owNRVV8G+dKnj1HbdOkds2oTJxYsxtWULpvbvh+2MM4SyBPuCBQ6Ru2kTbOvWYfrSS2F+/33YNm8+tZHMCN1p5uhYvjGC4tZbJCcn44MPPvD5eyoUCp9DHCIZLS0tSE1N9Xn/yy+/jL1798Z6CUYdsVgMxhgaGhpcbu/v78eCBQtw8uTJGH0y/5hMJqSnp7v0CITrmGC1+nZkCSaysrJQX18f0GPdnV74K0aBXq16+eWXhXW1a9cur2vuhhtuECzB+vv7sWPHDjDmaCBTKpUYHh6GVCrFZZdd5vX5q1evFhwIVq9eLQyOaW5uxiOPPIJFjOE/M4KVbw4bYI4GMQNznOAWLV4My9q1mFq1CpPJybDO3Nc683gdY/gJc7gn8Ce1U+zUqSwvaKd9iFv3soWpmcdxzDES+GHGsJQxbNiwQfjdnX/HJUuW4L333sM3v/lNlytPr732moufrnssWrQI3XxfQ4SC90b3VVb1+eefY+XKlXFfVnU65yYSunHE9PS0RwNEuIbqVmtoI3jdI5A6OJPJJFif8Z38Vuvsgy/8hbeyCX9CV6PRRMRFwPL66w7RunYtbOeeC9vGjbAtXQp7UhKmExMxvWIFpg4dwvT55zvKEhYscAjdlSsdJQyXX+6oiztyBJO33ebYFBISYExMxJ+Y40QmmgKXMYbLLrsMFRUVfq2EZDKZzyEOkYzGxkaPsgnn+PWvf40DBw7EeglGnenpaWzZsgXPPPOMy+1//OMfsXLlSoyPj8fok/nHbrd75KaampqwHRP4RtVwXiMnJwe1tbV+H+PP6WW2wRfOr3H22WeDMYbvfOc7sFqtePvtt/HAAw+4DIrghds111yD6667Tjit/Pjjj5GXlweOcwwo4E95jx07hmeffRaXXHKJxxpOSkrCddddhxdffBF/+ctfsHDhQtzKGLqTkjCxdCkG16xBz4IFMDLH6StfI5vBHG4I5pnbrIzByBiMCxeib9Mm1G7ZgpTkZPyBnTqp5ets3U9rbV7+7lyywDGGceZoTqtiDHkJCfj25s0h5awrr7wSy5YtA2OOKXKPPPKIx2OeeuqpiOamrq4uv2VVH330Ec4888y4v9p0OucmErpxhHvDh9VqRVlZWViG6lZrYJPJZgt/dXBmsxl1dXXCHHv3y4B8sb8/GzF/IRaLXXwyx8fH/Qrd4uLi8JKfwYDJO+6AbcsWx2nu4sWeJxkJCZi68EJMHj4M24oVjpPaxYth27oVU1/+Mibuuw+TN96IyVtuwahKBX1yMm5mDMlzIHAXL16Mp556CjKZzOGjOXO52duAC6lUipaWMHVcJAAAIABJREFUlqgLXb1e77dr+oknnsAtt9wS6yU4JzzzzDPYunWrywnR7t278dBDD8XwU/nHbrd7rEu9Xo/s7Oyw8kq4UxetVivy8/NRU1Pj9T5+BLE/p5eUlJSAHGA++eQTYY2Vl5fDarUiIyMDRUVFQi3plVde6XUgwqZNm/DjH/8Yf//73zE4OIidO3eCMYYdO3a4XDJ3tt3ydQn/9aQkDC5divElSzCxaJGLEOX/bGMMnzCGoRmRa2YMXYwhnznG837OGOSMYc+MAHZ+vr9wF7v8STDfiNbAGP6HMZdyBV/h7LDgK9LS0rB3716P02Bf+T+UaG9vh0Qi8Xn/G2+8gYsuuijuhS5w+uYmErpxhLfNpLKyEmq1OqzNpKOjI+zLg0qlEk1NTR63d3d3IyMjQxjv6s2Sx5tzQjAhkUhcTlz8negWFhYGJXRHW1pg/p//geWll2B+6y2Hv21vLyYPH8b0JZc4Tmt9Jf2lSzF9wQUOMbxmDaYOHMDEvfdi4tgxR1x1FVIOHsTB66+PqrB1rsfdt2+fi73a0NAQWltbUVlZ6TLggvfRFIvFqKioCHnARaBRU1MDlUrl8/7vfve7uOeee2K9BOeElpYWJCQkQC6XAwCKiorAGINGo4nxJ/OP+7qMxJUivqzJfWBDMOGrvGtgYCAgp5dAyrIsFgv27dsHxhhuuukml7z461//Gow5amkHBgbQ29uL//3f/8WFF17odb3yVmEJCQlIT093yV38GO8HH3wQ7dXVyHj8cXx06aV4NCEBl848/yPmOK01MdfaWWehO84Y9MxRK2tgDPkJCTjJGN6fiXTmaDRbwxgemnkt59IFZyHrfMrrTeTypQ+TzFE+sS+I3HXHHXfg0KFDHrfzIv/JJ59EVlaWx/2/+93vIpabWlpaXGqq3eP48ePYt29frJffnHC65iYSunGEN6Gr0+nCbiTr7u6GRCIJ6zVUKpVLHdzIyIgw5rO8vNzvRuWtoSyYcDeE9yd0nUfnzhZjubmYvPVWTF94oaPWdsUK2DZuxMQDD2Di6FFMb9/u6pDAXE907UuXYuLYMVgfecThtrB/P6auuQYjV1+NN7Zvx0V+5tsHGwmz3J+UlIRf/epXAY3KHBwcREtLC8rLywXRKxKJPAZc8BZkkYjKykrkzFgaeYtvf/vb+P73vx/rJThnfPWrX8Xdd98NAHj44Yexc+fOGH+i2ZmYmHBZl5FoJAunrImPwsJC4YTVanU4vZSWlgbs9CKTyQTnBH/5z/mUkb89MzNTcFq4//77hatNRqNRmJh2++2344EHHsCaNWs81u2GDRtw7NgxfPzxx0Lpwtlnn40+jQaTR45gavduDC9cCCNzTB+ruuACKPbsQfOCBbAy11NW5xIEM2P4F2N4kTEUMMdJrmomshhDEWP4EWNInImfM8eJr/Nr8uJ10um1J5ijRGGcOWp9Tczho9vKHGN/qxnDzRHKeYwxrF+/Hv/61788hm1cffXVEfti3tDQ4Les6umnn8YNN9wQ6+U3Z5yOuYmEbpzhnmAj4TPJe5gGO2jCOXJyclBXV4fx8XHodDpIJBLk5uYGJF69NZQFE6mpqS6XFmcTukVFRbMnuL4+TP7Xf8G2caPj1JbvOE5Ohm3JEkxv2wbbkiX+L+UlJGDiv/4LXF0dpq65Bo2rV+MnCxZgVQQTfSCxevVqfPrpp0EneN4sv6urC/39/WhqakJ5eTlycnIE8SuTyZCfn4/Kykq0trZiaGgopM2krKwMBQUFPu+/+eab8eSTT8Z6+c0Zn376qdBYs2rVKrzyyiux/kiz4i50Ozs7kZqaGlZu4jgurKs9VqsVxcXFKCkpcXF6yczMDKju1mq1Qi6Xo7W11e9jeN/cdevWueQ8ZzssmUwmCN3U1FThdv6Ld0VFheAT689Z4OD110N71VUYX7cO1qQkYUjDZGIiphYswMCKFRhh3mtn+b9PM0ej2UrmOLntnRGlphmBWsgcVoYKxtA+I1zHZgSy+0mt+4ku77LQxhyDJP7PKfSM4XsRyGnOV6l27dqFH//4xx6PeeONN4SBN83NzSF/Ma+rq4NSqfR5/2OPPYbbb7891stvzjgdcxMJ3TjDPcE2NzcjPT09rM2Et08J1dTdarUiNzcXRUVFUCgUkMvlQU9oC2cevfulRX9CV6vVBiR0x1NTYTv7bIclmB8hO2vNWlISFIsW4dbExFlPXaMR3/3ud31OG5st/PlHmkwm9PX1oaGhAaWlpcjKykJKSorgn6pWq1FdXY329vaATpGLi4tRWFjo8/6vfOUreP7552O9/OaMiYkJrF+/HgcOHEBycjL6+vpi/ZFmxV3o8t3q4XyB5r8EhzqMxmq1orS0FPn5+VCpVEhNTQ16Qlt6ejqam5t93j82NiY0SDHmsAjjS8GOHDkCxhi2bt0Ks9kMs9mMkZERwWpsz549wv/jVVVVwjSwBx54AFqtFs8//zyuuOIKlzW9kzFUMsfJqbuIdT5t9WX1xd8+OSNsR5nDnaGGOZrUcpmjdrefnTrB9fYa/ImuL9/cUcZQ5yRyZcxxonvf4sWCUP3Sl74k/F4rVqzwK/D9xSIvXrx33HGHxxfztLS0oL+Y19TUICsry+f9x44dw9GjR2O9/OaM0zE3kdCNM9w3k/b2dshksrCErvs892BjeHhYqO0MdQqSe51tMJGWluZyadGf0J1NUHEch9HmZlgffti/yJ0lxhjDH5njxGSuxS1jDFs2bxYs20INvj4yUKFsMpnQ29uL+vp6lJSUQKVSQSKRQCQSIT09HRqNBjqdDh0dHR7/fWZrEty7dy9ee+21WC+/OeUnP/kJGGO47bbbYv1RAmJyctJlXfJfoMfHx0POTRaLxaPZNJgwmUyCT7ez00sw4av/gI/8/Hxh3TkPMHjggQewfv16MOaoJbVaHcK9t7dX8No9fvy48P/4hx9+KDzXuYznt7/9LRhzuAz89LvfxYcXXgizDyEbSvClDAbGUM4YdEuWYGrxYkwnJgb9Ws5i28ocgjmNMaQyhqbkZExcfz0+ef11MOYop2pqahJKO44cOQKTyQS1Wo1nn302ZNHrfOrL53r+i3ljYyNKS0uF2mz+i3lBQQGqq6vR1tbmYSNWUVGB3Nxcn7npzjvvxA9+8INYL7855XTLTSR04wx3odvd3Y2UlJSwhC7HcSG5HoyNjaGyshJisRgymSws9wf38oNgwt3abDahy/tOCmEyYVwkwsSDD2J6507YVq92jOINYdNoY456tmVzKGoT3X7+1o03or29PSyRy3Gc0PEeajkCxznKH7q7u6HX66HVapGZmQmxWAyRSISMjAwUFhaitrYWWVlZPifWmUwmnHfeefjggw+ivr7q6urw1a9+FUuXLsXGjRvx85//HBMTE1F/X2/k5uaCMQaxWByT9w8Wd6Eb7hdo59wQqEc3H2azGXq9HikpKZDJZGG5P6hUKjQ0NPi8/6WXXgJjDGeeeSZKSko8TmAZY8L7m81mnDx5Eow5yhOcPapvueUWMMawd+9e4bbe3l58deVK/JExtK1b52hs9dMAG0o4e+TypQihimi+Bpj/czIhAaPr16OOMSgZQ+pPf4qbbroJjDHceOON4DgOr7zyChhzuME45y3+1Dvc2LFjB44cOYKTJ0+6/Hv7+2KuUCig0WhQU1MDtVqN/Px8nznuxhtvxNNPPx319UW5yTckdOMM982EFyPhXB4MthnMYrGgtbUVMpkM6enp6OjogFarRWlpacifIS0tDR0dHSE9V6FQuNTQ+bMXKykpcRW6JhOsP/4xbBs2OIY6hJjccxjDf7HZm8LCCffXTnL7eVFyMj7885/DFrjOmyz/BShSr8lxHEZGRtDV1YXa2loUFRUJJ24ikQhKpRJarRZ1dXXo7u6G0WiEyWTCxo0b8dlnn0V1bRkMBmzevBnXXnst5HI5Tpw4gVWrVuGxxx6L6vv64pe//CU2b96MqampmLx/sLjnpnDdVHx9kZ0turu7oVQqBaeXmpoa5OXlhfz+2dnZ0Ov1Pu8/fPgwGGO45557YLU6vmi/9NJLLvZYCxcuxJ133onPP/9cELQHDx4U1kRra6vgtfv6668Lt6d//etCjWwkxW20wqWUIiEBtqVLMXH77Xj94otxFmM444wzhH+X999/HxznsO9aPNOY+8orrwi/u3Md88qVK1FUVIRnn31WcKUINbZt24Zvf/vbeO2116BWq132CqPRiJ6eHuj1ehQXFyMzM1PITc5fzDs7O4Xn7d+/Hy+99FJU1xblJv+Q0I0z3DeTSNjv8EMXArk8ODg4iJycHKSkpECn0wmXJUtLS8M60Q12M3MOdw9ff0K3tLQUarVa+Nn85z87xvMuWBB0UjczhhOMYW8UxW2gsXP5clT68XoMJbq7u/0apUcylEolSkpKoNPpoNFokJ6eDpFIBLFYjKuuugrbt2/Hz372M9TU1ERt1OZLL72E5cuXY3h4WLjt3XffRVJSErq7u6Pynt7Q6/X4v//7P6xcuRK/+93v5ux9w2VqasplXYbbZOq8vv3VyPLhy+mlrq4urBNdfwMnxsbGhDKEt99+2+W+PXv2OL6Q+vCD/eY3v4mTJ09CrVbjueeeA2MOD1h+stfIP/+J/oQEWFnkyhTmSuzaGYM9KQm2jRsxdfAgGj/+2KWOdunSpejp6RHWP1/LfPHFFwtuCRUVFUIt7wsvvOAiRt977z1s3LgxqBzp67/D8uXLce211+KnP/0p/vGPfwhT6/hQq9VQq9Woq6tDUVERlEolxGIxxGIxbr75ZuzevRv33HMPSktLo3bCSrnJPyR04wx3oct3xo+MjIS1mcxmij46Oory8nKIxWIUFhZ6vF95eTkKCwtDfn/F/2fvu+ObrLf/T9pC2aOlBdlDEJS9BREZCigqQ4WvAipSrziuCg64frnoT0TxqveioiKO73Wg4GradKV7pnu36d57JmmStmnS9++Pp89jdtKmabXmvF7nJX1Gksae83k/5/M+7xMUZHLghCUPDg7WWQitBrrl5ehavbrXIFdMDD1h0h8A4A4nwgdLlkB1yy1of/fdfgWflZWV3NazvYGuUChEXl6ezrGWlhYUFxfjxRdfxKRJkzg5prS0NLvE1qZNm7B3716dY62treDxePj666/t8p7GbPPmzXB1dcW+ffugVCoH7H1tNX2g29HBcO+1pf/64qGhoWapA5aUXgoKCmxSpomOjjY5cCI2NpaLxZycHB0AzI7pff311/HJJ59g48aNFuN59OjRWLVqFe664w6Ejx2LdjLUrv0zuIYI6unTodqzB+rFi9Hx4os4deoU93vu3LlTJ9aDgoK4c6ye9qFDh0BEmDx5Murq6nSuZws8WVlZePLJJ60aLsHj8TB//nzMnDnTIv9Xe3hNTEyMjva4TMbsSlVWVuLs2bOYM2cOpk6dCh6Ph59++skuseXITebNAXSHmOkvJuws9r5q0LKu39DFent7O4qKiuDv74+QkBCTi1ZmZiZiY2P7/P76YLU3rt8sYg7osjJWbZmZ6HzsMeNTzUwk7jgi3DHIwFZ7ctqKkSORvmMHVAcOQL1yJTqPHu1X8FlWVgZfX1+7g1yZzPyo4bKyMhARsrOz0dTUZLftMg8PD7z22msGx6dOnYpXX33VLu85lEytVludV3rjEREREIvFRs9VVFRwSi+lpaVGKVxFRUUIDg7u8/uzjUrGzr399tsgYvi52u+dnJzMxam2Rvmjjz4KIsLEiRNxyy23cFv22u5BhDeJaQ4bbMDaF+8iQtvMmVA9+CBUBw6ga8MGdO3ahfSUFO53vO2223RiXCqVcgM0Hn30UaSmpnLg9cKFCwY5gaXssZJhra2tOHHihMGoZUs+e/Zs7Ny5Ezt27NCpEH/33Xfce0VERCAzM9NobpJKpXBzc8Nvv/0GqVRqN/DnyE3mzQF0h5jpA12WdlBXV2fTYmKsolpfX4/w8HAIBALk5eWZleSxlQcXGhqKwsLCPt+rXfGpr69HYWGh0Wle6bGxKHr+eWimTLGqkttBhG+IsHIAwawxnq82wHUiwktjxqBlzx6oDhxggO6SJeh4/vl+BZ+WJgL1pwsEApSWlho9l5WVBSJCRUWFXWPLxcXFqCbkH2HE5Z/BjAFdc6PBrXVjFdXm5mbExsaCz+dbVHopKSlBUFBQn99fJBIZnazW0dHB8W0PHDigc/zzzz/nKrSpqanc8Q0bNoCIkf2TSqWQSCQ4ffo0iAjjRo7EF1u3onH8eHTyeDr6tH8WVxOhmseD9L77fs9Nq1ZB9cAD+OD993Wqq/rNp2xT3+jRozld4mnTpumMQGbdVP+AVCrFpUuXdOTeeuuTJk3SUawJDQ1Fbm6u0dwkkUgwbNgwhIWF2TW2HLnJvDmA7hAzY9uDtigWsB4SEsIBTZmMadry9vZGUlKSVV3TYrEYkZGRfX5//clqfblXLpcjPT0dfD7fQDcxMzMTFUlJaN6yBRpnZ4sJu5YIZ4kwZQABrb56grFrxhIzxUgyYwYU+/YxFZPt26FevRqKa9f6FXwWFRUhICDA7iBXKpWCz+ejoqLC6Pm4uDgQEaRSqV1jy8XFBe+9957B8ZtvvvkvNZWtr2YM6FpSLOgt0JTL5cjMzASfz0dsbKxVjW62SjDqT1ZjXaFQcPzcS5cu6Zx76qmnQERYuXIl0tLSoFQqkZ2dzdEZnn/+eQgEAkRFRWHLli0YSQTB1KlQ97OiwkCDXDURal1dIb3/fgbo3n031EuXov2jj7BlyxYQEfcdHDp0SCfOS0tLuRHHLLXg3//+t9GcYKl/oKmpCQ8//LBZSsP48eMxYcIEnWNHjhwxyENCoVBHrUHb6+rqQERISkqya2w5cpN5cwDdIWbGFhN2QIMti0l4eDjEYjHy8/MhEAgQFhbWqyqxrRPazG1PWvPZk5KSEBgYyKlAtLS0MJXdnBzkffklqu65Bx2jR1tM1olEOEwM93WgKrjW+l5nZ0hdXdHt5IT28ePRuXw51EuXQr16NTpeegkyI5UPWzw/Px9BQUF2B7qsDBXbhKPvgYGBcHJyslsTGmuO7UHbTKPRGMRmVFSUyUYua51VdCktLdWJcWvvt3VCmylFGfYBjIgMKs7r1q3jwJxIJEJISAg+++wz7vrY2FgUFxQg57vv8IGLC6rpz9Vwpu9tRGglhrYgcXJC++LFUC9bBvWKFeg8dgyVmZmcxvDDDz/M7FK5uCArK0sn1vfv3899R7NmzTIpbWht/0BdXR0OHz6sM0nNmK9cudLkmF9WvcPYucLCQhARCgoK7Bpbjtxk3hxAd4iZMaBrSdDcGg8LC4O/vz/8/PxQWFjYa7myoqIihISE9Pn9o6KidJo5rHWJRAI/Pz+uMUGhUHDTh9pSUtB58CDUbm5mk3QnEd4iwoo/AJg15h5E8HFxgWbMGGgmTYLa0xONCxdC8cgj6HjuOSh+/LHfQa5MZnn0ZX+5pcEUP/30E8aPHw+NRmPX2Nq0aRP27dunc0wikQx4w8ef1YwB3bi4OGRmZtqUm0QiEfz9/eHj46Oj9GKt19TU6PBke+vJyclISkoyOH7hwgUQEaZOnaqTL+VyOce9PX36NDesggW6Y8eOhTQvD51eXlB6ePwpKQo67uKCzhEj0ECESmKmtqWuW4fOp56C8uuvIauqwpUrV0DEDL0oKiqCp6cniAhH9foKPv74Yy7vffzxxyZzRllZGXx6oTJTXl6O3bt36zShubi4YNWqVfj+++/NAmZfX1+TtKrU1FQQEerr6+0aW47cZN4cQHeImbHFJDw83KzOoyWgmJCQAG9vb4SGhkIm6/3koI4O23lwMTExJjubjbm2ILxAINDZWlQqlWitrIRq/35oxo0zmaDrifA6MXSAwQaz2q5dTd5DhLoePUr1zJlQbdsG9Y03Iu+BBww6kfvbc3JyuA5oe7qlwRRff/01pk+fju7ubrvG1vnz5zF27Fi0trZyx65cuQIXF5cBlfD5s5qx3JSQkKDDUe2Ns0ov3t7eCAgI6LOyTF1dnU1a46mpqUalE3fv3g0iZoyv9vHExEQufi9fvsw16R4/fhxEhNs3bkTHsWNQu7v/qau43dSjlTtyJJpHjUIQEXJ5PPyXmJG80dHRXAzv2bMHRITt27dDJpPhrbfeYnLd8OEQi8XcdQcPHgQR0yRmTr+7pKQEAoGg17mmqKgI169fh0gkskofXCqVwtvbG5WVlUbPR0ZGgojsrkDgyE3mzQF0h5gZW0zMyd+YA4q5ubnw9fVFZGQkYmJi+rwgdXTYzoPrTeWnvr4eYWFh8PPzQ1FRkUE1WFlZifZDh9BtYoRlGhEeJ8KwQQSz+sMenLQ+zx3EgO8vqWc709WVmdbm6Ymu5cuhWr4cMW+8YfVo3r56VlYWIiIi7A50Wb6dKaWMixcvYtGiRXYHuqwo++bNmxEUFISvvvoKEyZMGDRR9j+bdXd3G8RqX/S19ZVeEhISEBMT0+fcYuso4vT0dKPSiR4eHlz8fv755+joYJrkWBmtsWPHIjExkfv9165dy3BxV62Cpg+63X9IHzYMmgkT0OjkhHgiVLu743gPb3n27NkoLy9HfX091xzGDsOora3lBj/87W9/4/INy6n94IMPLALWgegfYGlV2pq/2i4QCODq6mr33SZHbjJvDqA7xMwY0DXXFWzMKysrIRQKERAQgJKSErS3t5vcnuvNa/r5+fX5fpFIhPT0dLPXyOVypKamgs/nIykpCTIZU32OiYlh5H/kcnQ99RS6jXBxVUT4mQib7QherZmKZgzgav88jAgniFDCfnYnJ0Z4ffRodLu6Qj1vHtr+8Q94//ILJ61jL7c0472/vKKiAnw+3+T24blz57B27doBia/c3Fxs27YNI0eOhKenJ1566aVBG7P5ZzNjQDcjIwMikcjqPGBM6SU3N9emRtfm5maui74v92dkZCAuLk7nWG1trU7curi44Msvv4SPjw+nGLB582akpqYiISEBMqkU/+vsjIbBBqb96Tweup2c0DViBNqJUEqE8v378Z+33+aaynbs2IEff/yRyY88ns4whrNnz4KIGf1bWFgILy8vEBE8PT2NKi1oe0FBwYD0D1iiVV29ehWTJk2y+0M44MhN5swBdIeYGVtMkpKSrBq/29LSgri4OPD5fGRkZOhI8tg68KG6utomHpy5Lc729naUlZUhICAAwcHBqKmp0TkfGxuLgh9/hHrtWoNk3EyEC0SYZUeA2x8+nRjtzFrtzz9sGMPLHT8e6jlzoJk8Ge3//Cfqa2rMbvX3l7Oaw/ZeTEpLS81uQ7766qvYtm3bYIeewyyYsdyUk5NjleygTGZa6cXWRld2qI5EIunT/VlZWQYV5ZCQEC52Z8+eDSJGFiskJARr1qwBEeHFF19EWloa0n7+Gc233fbn5+Jqu7MzQwsbOxbNbm6oI8Knw4ejtYcmwvKXiQjLly8HEWHdunU6cV1VVcWpHjzxxBMcr/n111+3mDPEYrHOUAd7OUt7MZVrL1++jDlz5gwI0HWYaXMA3SFmxhaTtLQ0JCQkmEzUCoUCWVlZ8PHxQXR0NJqamgyusXXgg608uKSkJCQnJxsF57GxsVwjijEt39SffoJszRqdRKwhZut/3x8AxJrzrcRUmlX6CwmPB42bG7rHj0fXmjXo2rgRXVu3oi0ri9OQbG5utmuST05OhkgksvtiYmkb8umnnzaYCuSwP6bpx2Z+fj7Cw8NNxr1SqdRRejE2htzWgQ8ymYyLl77cn5OTg6ioKJ1jFy9eBBGjufrZZ59h4sSJICLMnDmTG3X77bffIjsqCk133AENjzf44LQfXTNxIjRjx0K9eDES3d0RQYRX77hDh9P6yCOP6OS6c+fOGcQ2qyHMKjKMGzfOJB9W23NycrieEnu6JRmz9957D0uXLh3ssPvLmwPoDjEzBnSzsrKMglS2EhoYGIjAwECUl5ebBKLGknlvvKGhwSYeXEpKig51gt2yZMG5uUWq6tAhaLT4uOVE2KmVYK2hFAykjybC00TINrWQ8HjMMIsxY6CZNQtdK1dCvXIl2t96CzKZDDU9FV1rmils8cTERGbb1c6LSV5eHoRCocnzhw8fxpEjRwY79BxmhXV2durEZnFxsUmQWlNTg9DQUItKL2wO62tusnV6pL5GeHt7OyeRtXr1atTX1yMyMtJgyllOTg4qX3sNGheXQQemNgNb7f8OG4bu0aOhnjYNnStWII3HwztE+OzSJR2pwLq6OsyZM4f7PowpuJSXl2PMmDHcNSdPnrQqZwxU/wBLqzJ1/uzZs9i4ceNgh91f3hxAdwiafiI2xmFrampCdHQ0fHx8ONktc8k8Ly/PbOXFkrM8uLa2tj7dr12Vrq2tRUhICPz9/TkOsan7VN99B3XPQqIhwuc9QHKgwas+15ZHhlq8M4nwEREkRhaSzpEj0TlhAjpuuAFtDz6Izu3b0bV6Nbpuuw2q++6D8tIlyHq2zyxVGfrLRSIRx4W2p1uqzuzduxfPPvvsYIedw6wwfaDLUo60j2krvbDTsczlBlv5/+z0yPr6+j7dr12VbmpqQmRkJBYtWgQiZvADe93333//ez5wcsJHBw6g09V10EGqrd5GhGpiCgjxc+eiY9cubpepZvVq/J0IY4jRkm1padFp3nrwwQe57+Suu+4yysPfvHkziBgFBlN6tfqekZExIP0DltQdTpw4gZ07dw522P3lzQF0h6DpLyYFBQUIDQ3lqhfsdLC4uDi0tLRYlcxt1cFln+T7yoNLT09HbGwsx9NLSUmxCJo7k5KgmT0b3UTIJ8LiPwDANQZ4lxDBm0wLwmucnNA5cyZab7sNaW+/DT6fDz6fj8iff0bGr78iPyUFNTU13CJRVVVllVi6rR4bG2swptMenpmZicjISJPnt2/fjtOnTw922DnMCtPPTVVVVfD19UVHh67SS0REBBrB2xdnAAAgAElEQVQaGqzKDTU1NeDz+dxrZGRk4KuvvsIrr7yCBx54AKtXr8b06dOxZcsWfPvtt0apWXw+34Dbb60XFBQgJCQEWVlZXF5lJ6JduXJF59pNmzaBiHADEVLMxPyfxbuIUEaEQGIG6fCIcOedd+LnK1eQ9vPPON5T2V68eDFkMhlX8Kirq0NzczPc3Nx08uH777+vE9vNzc2YNm0aiAw1dc15WloaYmJi7J6bCgsLzdKqvLy88NBDDw122P3lzQF0h6DpLyashm1JSQkCAgIgFApRWVnZq2TOTh3qK9CVyWTcdnpv721vb+ea5EJCQozy9HRcLkdHezu6XnwRGh4Pn5OhmsFguycR/kFMFcTsluDo0ei69VYorl6FrLwcMhkjaVNZWYmcnByIRCIEBgbC29ubk4ITiUTcVqw9k3x0dDTS09PtvphYWrTWrVuHt99+e7DDzmFWmEql0olVlrtvTOnFUl5QKBRIT0/HpUuXcN9992HTpk2cTJU5HzlyJI4cOaJDVbBlTDpbOAgMDERFRQUKCgq499JXlNhz773gEeHcDTeg4w8AVPvqGiKohg9H8+zZOHfTTZhCzLAH9vceNWoUvLy8MHPmTBAR9u3bh/DwcCQmJsLb2xvl5eX4+eefdaq5RIzCgvYu0Zdffsldk5KSYnXOSE5ORlxcnN1zU15entmmt4MHD+LYsWODHXZ/eXMA3SFo+kA3Ly8PfD4fvr6+EIvFRhu2LHlFRYVN24N95cE1NzcjOjoafD4fQqHQ7Gfv/PlnqLduhWbOHGgWL0apmxvu/AOAWm2/lQjfEqHd3ELC46F75Eiob7wRncePo83EHHVtb2pqQklJCdLT0xEaGgpvb29OSD8uLg7Z2dmoqKgwqUXbF4+MjERmZqbdF5OkpCSzTW8333wzLl26NNhh5zArTB/osrsPfD4f6enpOkovxjw/Px+XLl3Cvn37uAYvY842Lzk5OeHQoUNc9762r169mntdf39/lJeX9yo3SaVSjmIhEAi4z/7bb79x780OseiMiID63ntRPmwYcojQPGHCoIPVvriKCCpnZ6hnzkTnI4+gNT6e07s9ffo0zp8/b/S7vnr1Krczw+amrVu3gogZqJGeno4bbrgBRIwKQ1NTE6RSKVasWAEiwq5du3qVMwaqf8DS0Jx77rkHJ06cGOyw+8ubA+gOQWMXE5mMkYBiE0tfaQMdHbbLgymVyl7x4JRKJbKzs+Hj44PY2FiOc2XqetXZs+geO5aRtXFywmVieGGDDWyJCK7EDKBItmIh6Vq6FHJfX8hDQtCWkQFZH+gH7PjLuro65OfnIykpCaGhoRzlISQkBElJScjPz0dtbW2fKQ5hYWHIzs62+2ISHx9vkgsslUoxY8YMfPPNN4Mddg6zwrq6utDRoav0wjYnmYrtpqYmfPbZZ1i/fr3JGJsyZQrGjx/PVRMFAgFXTZw7dy5+/fVXbrzrpEmTuPvefPNNdHR0ICgoCKWlpVblpvb2dhQWFkIgECA8PBxisVhnt+vNN98EEWHBggVMbvr6a3RPnIhuZ2eoiNnuH2zA2hcvJ8KdRCj74QfIk5Igk0rx008/cd8l+9BbVlaGZ599Fk5OTty5kydPQiqVclMOCwsLuer7c889Bx8fH7zxxhvc9V5eXjoVX39//37LGf3pGRkZZmlVt99+O86ePTvYYfeXNwfQHYLW2dmJgoIC+Pn5ITQ0FEVFRfD29u5TJZd1W+XBOjqs58HV1NQgODgYAQEBKCsrQ3t7O8RiMSIiIgyvVyrR+cMP6B41Ct08HkqcnLB6AMCrMaUG/eayqUR4lwiN1iwkLi5QL1/OgFsbk6+pBgltykNcXBxHeRAIBIiMjERaWhqKi4ut1t8NCQnRGc9pLzfHBZZKpZg4cSK8vb0HO+wcZoWpVCqUl5dzSi8lJSWcDqmxXBAeHs7JcWm7k5MTNm3ahBkzZnDHHnvsMa4q6OHhgR9++IHbTr///vvx97//3WjFNyYmBsHBwSgqKrKYmxobGxEREQGBQICCggK0t7ejoqIC/v7+3DUHDhwAEbNd3xEdzYBcHg8aFxd00Z+Ql+vkhPIRI7CaDCurbDOZvgauTCbjGvJY379/P8rKyuDt7Y2vv/6a+/4LCwshkUhQXV2Nxx9/nMmvPB4WLlwIIsL8+fORnJyMoqIiNDQ0WPVgHhcX1yuqQ189NTXVLK1q5cqVeO+99wY77P7y5gC6Q9ASEhIgEAiQn58PpVJp8+QfNsF7e3tb3Fo0576+vmZ5cDIZs03N5/ORlpam814GovDt7VBduwbNwoXodnGBhginyLDZq78BrbFmMv17ZhGjfWtN5UYzfDjUs2ah/Z130GZlR7ElLy4uhp+fn1XXalMeIiMjIRAI4O3tjcDAQIuUB6FQiHwraBW2emRkJDJMPABIJBK4uLggPDx8sMPOYVYYW8VllV5YxYO6ujqjOSE/P5+LK09PT+zduxeenp7csdmzZ2PJkiXcz3fccQdX2Z01axZef/117txbb72Fm2++GUSk0wTl7u6OX3/9FQUFBSZzk0KhQEZGBvh8PuLj43V2xyorKyEQCLifFy9ejA1EKJ82Dd3Dh+vG+2CDViu9iwgaFxdoJk9G9d/+htk939W1a9e42KutrcWoUaNARHjvvfcMgCb7/bIUBSKGLvLf//4Xu3fv5v5/ad/X2NiIW265RSefvv3224iJiYGfnx+8vb3h7++PmJgYZGZmoqyszKiMYnR0NNLS0uyem5KSkhAfH2/0nFQqxfz58/H5558Pdtj95c0BdIegsRU5NvGyIt19aQRjnZWF0Z5I1Fv39/dHRUWFwXF2dr2fnx/CwsKM0hsKCwt1VB9UX3yB7ilT0O3sjFIeD9v7GeD21icQ4UUiFFi7kMyZg85HHoHym28g6+fBDrbMeZdKpaitrTVKeQgNDdWhPAQEBOiM7LSXm6NIsKNWk5OTBzvsHGaFSaVSgzxk7gE4MjISHh4eOrE2b948LF++nKMiEBE3UpaIoSawldxbbrkFe/bs4aq4ly9f5s5pUxgWLFgAsVhs9DNUVlYiKCgIQUFBRpt4tWldbW1t2OnsjCIiaJyd0e3sPOigtTeuIUIlEbIWL4by008hq63F888/DyLCDTfcoAMqv/jiCxARnJ2dUVJSohOXTz/9NPew0draijNnznDftYeHB/f/6+OPPzaI6fj4eI72MHnyZG7wDUt9KCwsREpKCsLDwznqS3BwMBISEiAWi1FdXY2IiIgB6R8wJ7EolUoxZcoUXLt2bbDD7i9vDqA7BI3lwWlXI2wRRGerrbaC5cDAQAMeHKs76evry82uN3avjrB8fT00a9dC4+qKT11cBpWLu4wIl4nRkrQK4E6eDNGpU3adWlZYWIjAwMB+ez2W8pCdna1DefD29kZISAjS09NRUlJit5HDwcHBJikSbId7YWHhYIedw6wwtVptENssRclU3pDL5fjhhx+wYcOGPsWop6cnB2o9PT3x3HPP/b4jowWWP/zwQ533lUgkEIlE4PP5yMzMNKk1Xltby8mbpSQkgN+TDzQ9gxMGG7xa6y1EON4DXGfOnIk5c+Zg7ty5cHZ2BhHh1ltvha+vL+rq6iCTybBjxw4QMXJi2jHZ1NSk06DGHv/qq690aCguLi4o71GS0fasrCzuPc+fP282N0gkElRVVSE3Nxfx8fEQCoVcbgoMDERqamqvKA+99ZiYGLO0qtGjR8Pf33+ww+4vbw6gOwRNH+i2t7eDz+dbluUy4/0BloODg1FcXMy9Hqs7KRKJLDbKlZaWIigoCB0dHegUCFDi4YGNgwRunYlwkAjRZP1WpMbTE8pPPkHtAEwty8/PR1BQkN1en13MfHx8EBcXxz2oaFMecnJyUFlZ2S8qD4GBgSYrxykpKSAi1NfXD3bYOcwKMwZ0reXHdnR0IC4uDv/zP/+jA1BZzVoi0mmAMufs/drXe3p6cvkyPz+fk+uzlPPYBqv29nZ4v/028ojQScSop/wJRvu2ECN1aG3+GzZsGFauXMl9d1euXNGJSe3BGFlZWTrnfv31V+4+Ho9ntKL76KOPcv8/WFDdG29ubkZQUBCio6MRHR1tlPJQXl7eLznYHK2qtbUVRISYmJjBDru/vDmA7hA0Y4uJn59fr7Vz9cGyt7e3TWA5NDQUBQUFqKqqglAo5HQnrbmXnaCkVCrxwqZNcBkAQDtM72d3IrxOzBQgi8B2/Hho3N3RPW4cVPfeC1nPlLLa2lq7A11LI3P7y/l8PioqKiCTGVIeQkJCjFIe6urqel1Z8fPzM9gaZT0iIgJEBKVSOdhh5zArTKPRGMR2eHg48vLyepVLUlNTdbifPB7PaNOa9vhYa0Dwzz//jLCwMPj5+aGoqMiq5lu2f0GhUODKvn026eNqelyld7yLCPKe4woitFLf+b5SItQQM4ExhghLb7wRI0eOBBFDKzh37hw++OADvPnmmxwHd/78+ViwYIHR72z9+vW4evUq91C7a9cuEBE2b95sEK9Xr141uP/FF1/kpjhmZmZyjYJvv/12n3OTUChEXl4el5u0KQ9hYWFGKQ/aQ3es9bCwMOTk5Bg9V1FRASJGjcJhg2sOoDsEzRjQ7Y18jim31ExmycPCwhAWFma1Zqa2V1RU4KuvvsIdd9xhE3jVbx4z1kym33A2jRjt205rF6tRo6CePh1dCxdCceoUmior0dzcjNbWVm48rz2BrlgsNjo3vj+d5X1XVVWZvKa1tRUVFRUc5SEgIIBTeYiKirKa8sDn841uccpkMvj6+mLEiBHQaDSDHXYOs8KMAd3o6Gjk5OT06eH7+vXrmD17ttmYZ/V2x48fz9EfxowZg3vuuccoME5MTOxVL0JTUxM33jzQ3R3qXoJQjRFX9ABRGRE+I8JyImwlwgdEKBk2DG09Dbi9BbkKYmTCilxcUPHQQ2gQi+Hl5cVVai9evIjq6mrIZDKOmztq1CgONBYXF+PTTz/V4USzPmfOHJw5c4ajHXz++ecG8bpx40YQEVasWIHbb7+du3fPnj2or6/H4cOHQcRwc+vr6/ucn8ztAslkpikP7FQ+lvJgaehOcHAw993oe05ODogIZWVlgx12f3lzAN0haMYWk5CQEBQWFtoEdPsiqs4uSAUFBdz0IGtHe7KuVCpx/vx5jBgxwu5VXNZHubriscWLkWwNx47douTxoPH0hDwuDvLkZEh6tu5bWlrQ3NyMpqYm5Ofnw9vbGw0NDWhpaUFraytXzegvz83NRWhoqF2BLjvSmZ1Zb603NjaiuLgYaWlpBpQHkUjEUR7Y70QikXA6q8Ze7/vvv4eHhwe6u7sHO+wcZoUZy00ikQgZGRl9zksSiQReXl5m8wPbgLZy5UqMHTsWRIzc1bJlywyu/eGHH3r1/lyjbnU1Knk8dBJTeTUGRNXEVGdZMKwmgrIHgKp6flb3/FxFhGwirNf7fEuJ8DIRiq0Az2qtf8t7XmvTpEnICA9Ha2sr/P39udc9ceIEvL29UVZWhvT0dA7MvvLKKzox98ILL3AA+MqVK9iyZYvBdzh8+HAU6anIhIaGcud//PFHNDc348iRI9yxxYsXcyD5nXfesSk/+fv7o7i4uFf3NDc3o7S0FBkZGTqUh4CAAMTGxiIrK8uA8mCuITc+Ph5EhNbW1sEOu7+8OYDuEDRji0lERITJrmJrnR0j3Jt7tHUnQ0JCkJmZ2av7xWKxzpO/vX3+/Pl49913UV1dDYVCAWVQkIFEELeY8HjoGjYM7Z6eULm5QT1mDJRvvgmFQmHgEomEG96RlJTEAV9tZ6u+tgLf7Oxss9N6+sPZKlZfOHTazlIe8vLykJiYaEB5YCdPlZaWGt1W/OyzzzB37ly7A12pVIqzZ89izZo1GD9+PDw9PbF7926j25ISiQRHjx7FxIkTMWbMGOzfvx81NTV2/Xx/Fuvu7jaI8cTERKSkpNiUm6Kjo+Hj44NVq1ZZjHG2iYqIGVagT2kYMWJEr4G2t7c3ihITUd0DXFuI0NEDNLXBZ0MPgG3vAbydPQBU2XNMSozutpQIYiKcJcIUd3c8/vjjOHfuHI4fP85Jo3lST9OblmtTHTqIUEYM1UpGhP/ofQ+urq4csBw3bhyOHDmCd999FykpKdi3bx/zHp6eKC0tRXNzM1paWpCTk8M9UGgD4Li4ODzwwAM6rz9p0iS8/fbbaGhogEwm4yros2bN4nKcVCrVGRRBxAz/sKWaK5PJIBAIUFpaanNuqqurQ0FBAZKTk7ndSG3Kg4+PD3Jzc43mJqFQCB6Ph66uLrvGlCM3WTYH0B2CZgzoxsTEICsry6bFpDdVYblcrqM7KZVKIRKJkJ6ebtX9SqUS/+///T+d+en2ch6Ph3vuuQc+Pj5oa2vTBalyObruuovpnp44kfGxY9Ht5IRuHg/qsWOhcnODaswYNC9aBP///hfBwcFITEzkOKnV1dUIDg7mOMkKhQJtbW1oa2uDRCIxqPpqA9++VH2zsrIQHh5uV6Db0NDANSf292trUx6ioqK4LmptykNpaSmamprwr3/9C8uWLbN7TGVlZWHKlCl47bXXEBQUBD6fj02bNmHUqFEQi8U61+7YsQPTp0/HtWvXwOfzsXjxYixbtszuC96fwYwB3dTUVCQkJNiUm9iqcHt7O65evYopU6aYjXmWbzpp0iQcPXrU4Pw//vEPq99bKpVygxNEPeC1mZiqrD7dSdkDQFU959qIUNoDgEt7wG0hEWqJcIkIY838DgsXLkTs8uXocHWF1MUFTT0Am60my4lQx+OhhQiZRFjQh9y4e/duREVFobGxEU1NTXjkkUdAxGgPl5WV6eSmJ554AkSMmoI2tWH69Ol47bXXuJ9ffvllg5g/deoUd/7dd9+1OYeYozvZ4tqUB5FIxOUmbcpDcXExGhoa8Msvv2Ds2LF2p1U5cpNlcwDdIWjGFpP4+HikpaXZtJhY2zRSUVGBwMBAA93JhIQEpKamWrxfLBZz4zv70/Wby8Y5O+Olw4chFouNVmFZV8bFQbNkCQd0NRMmMGB34kRopk2DZu5cqI4fh6IH2Obl5SEhIUFH6iYgIABpaWkoKyuDRCIx+j5tbW1cMjUGfLXBrzngy86UtyfQZSfl2VMmTSaToaZHpaK2tpajPERERMDX1xdPPvkkJk+ejKlTp+LixYvIyMiwW0zJ5XIoFAqdY21tbXBzc8MLL7zAHWOF8oVCIXcsLy8PPB7PoafZY/rxnpmZidjYWJtyU2Jiok5uqa2tNQpgtau2LBg7fPgwJk+ebPDwa2lceXFxMfbs2YNpPB4OE+EVYqQG5aRLUdCu6LJUAhboqojR3s4hQhERfiLCd0QIIcK+8eNx8eJF3H///UYb7YgIM4gQ1QOUm1mA7eKC9pEjIZswAaVOTviCCKtHjsSPP/6I2NhYXL16FatXr+ZeY+XKlbjjjjswe/ZsHTULbffw8MCOHTu48+fPn9fJSyEhIdy5N954A9nZ2Thy5IhBtdzd3R18Pl8nxqurq7l8v3z5cptzijX9A/3hLH2rvLxch/IgEAjwz3/+E+7u7nB3d8eFCxcQGxtrt3hy5CbL5gC6Q9CMAd3k5GQkJyfbtJhYahqxpDtp6TMolUr85z//4Wag95frN5d5DB+OT+bNg+z4cShMgE4DsJuXh87XX0fX7t3oeuABdFy+DEVdHRSlpVA0NBhcrz3GOCsrCxkZGYiKiuI4qUKhEAkJCVzVl01W+sDXUtW3qanJoOqbnp6OqKioAQGg/SEfZs4rKyu5QSX6i1liYiL279+PGTNmYOnSpXjiiScGPNbWrl2Lhx56iPv5zJkzcHNzM6BSrFixAo8++ugAf7o/punHfW5uLiIjI23KTSkpKUhMTDQ4LhQKTeYTbfDINl5p+7Zt27jXkcmY7fmPPvoIDz30ENfgdjsRhETIJaYam9sDNrtIdzJiF+lycDuJqeyyVd0cIlQQIbAH5F4hwjj9PObkhDvvvBPvvvsujh8/jkWLFoHH48GdCM8SM43xOhGeJsJEYlRixvaA9pUrV+KZZ57BJ598wo0oJiIcO3YMWVlZiImJwa+//op58+aBiOHYbtmyxeABgPWtW7fi448/5mgNLGVk/vz5qKmp4XJTQkIC1qxZo3PvtGnT8Omnn3KAlq0EDxs2DCKRqN8AaG/7B3rrpuhbUqkUWVlZ8PLygoeHB9auXYtt27YNeJw5ctPv5gC6Q9CMAd309HTEx8fbtJjExcUZbRrR1500Nbfe3BZlbm4uNm3a1O9VXG5RI8LhYcMQN3o01MuXQ3X0KJSpqVaB3N54W1sbR9lISkqCVCrVOS+Xy1FXV4f8/HwkJibqdPtGRUUhIyOD2xLsa9U3JSUFUVFRkEgk/d7oZgmA9reXlZXB19fX5Pljx47hwIED3N/9QFpraytGjRqFs2fPcscefPBBbNy40eDahx9+GOvWrRvAT/fHtc7OTp3YLygo0B3v3QdPT0+HSCQympsKCwtx//33m6xWshVeY7tIt912GxYvXmz0HvceYComgoAIvxHBnxg+bAsx/Fh5z79ZTq52pVfeA25ZuTCWYvA1EdaZyWU8Ho8bc0xEGD16NE6cOIF//etfeOKJJzBjxgyrcuLEiRPh5eWFzz//HKmpqRzHlsfj4b333kNISAh+++03fPjhh5g/f77R13BxccGiRYu4n/l8Pvdg3tzcjNTUVE7nmH04YH3WrFl45plnuJ/PnDnTLzmDHXlva/+AJWf1k02pxpw/fx6rV68G4MhNg20OoDtETT/hs0/ttiwmxqgH9fX1VutOZmRkGCxGSqUS77//vsmtOVt9prMz3po8GXUTJ0Lj6Qn12rXo+PprKHq4sv3ptbW1CAkJQUBAAMrLy62+TyKRoLy83GTVNy8vD3V1dYb8YS3wK5VK0draioKCAggEAiQmJpqt+trq5eXl8PHxsetCIpMx44z9/f1Nnj9w4AC8vLwGJca8vLwwZswYVFdXc8e2b9+Oe+65x+DaZ555BvPnzx/Ij/eHNX2gW1JSAqFQaFNuMpbfWlpaEBMTAx8fH+Tk5KCxsdGqZjVr/UFiVBH8e0Dub0TwJoZfqyBCpbMzGomhJEjo92ouC3TriJBGTHNaHRGSiPAMEabqAclt27bhvvvuw/Tp001+luHDh2Pp0qU616xYsQI//vgjPvzwQxw5cgQTJkyw6vfasWMHRCIRl1dOnjzJnTtw4ABOnjyJDRs2GEiM8Xg87Nq1C19++SXq6uogkUi4aq6bmxvi4uLwySefYPPmzQbvuXz58n6brGjP/gFtt7Srdfr0aWzZsmVQYsyRm3TNAXSHqOkvJmKxGBERETYtJsnJyUhKSkJHB9NslpaWBj6fj8TERMhksl4vRrGxsRY1MPvqdxLBm8dD14gR0Hh4oHvMGKhXrEB7j7C7vaq4iYmJJjm41rpcLkd9fT1X9Q0ODuaqvuwkHv2qL7uVz+fzkZWVBalUajXXty/gt7S0FAKBwO5A19KUt7vvvhsnT57sU4xIJBKIxWKLbqxR46uvvgIR4f/+7/90jm/fvh27d+82uP7pp5/GggUL+vQ5h5qpVCqdvFBRUQF/f3+bcpM2/UGpVCInJwc+Pj6IiYkxGFv+/fffm63umnNWpYCI8AyPB3EPwA0ggl/Pv7OIqeBWurigjpgmszL6na6gdnJCVw8YriOmmptLhP8x8Z4eHh64++67dXi1zs7OWLBgATdq15iPHj0at912Gx5++GFMmzaNO7537158++23OHHiBG677TZuQIO+u7m56TwYPPTQQ2hra0NjYyMKCwvxzTffcAMl9H3EiBE6AyY+/PBD8Pl8JCcno7W1FQEBAVyl19XVFTExMTY34bI+UP0Dlna1nn32Wdx///19ihFHbupfcwDdIWr6QLewsBAhISE2LSZpaWkQiUQoLy9HQEAAhEIhqqure7UYRUVFQalU4oMPPrBZUUF/OtpIYrhquUTocnFB5+jR6Bo1CmpXV3RNm4b2r7/+w1Rxe+ts1TczM5NrePD29uZGXQoEAgQGBqKmpsZi1ddarq85Ly4uNltp7S+3pAl8++234/XXX+9TjHz99ddW/Z1VVlbq3Ofv7w8XFxecOXPG4DX/ytuD1po+0K2uroaPj49NuSk/Px9hYWE68VhaWmpyhykrK0tnalpffC8RSohQSUwzWD0R8olRXlAMG4ZyYiaQtfF4kPUAXVb2i6UqyImpAD9PusNrbr75ZrO6wPPnz8exY8fw0Ucf4b777uOOOzk5cVPOjPm4ceOwf/9+vPfeewgKCtLRwF21ahX27t0LT09Po/fecMMNeOSRR/DFF18gMjKSA88TJkyAt7c3zp8/j1tvvVXnYYD9TGvWrMGFCxdQVlaG2tpaDkA7Ozvjhx9+gEwmM5ubeiO9OFD9A6WlpWZpVY899hgOHTrUpxhx5Kb+NQfQHaKmD3RLS0sRGBho02KSkpICf39/8Pl8ZGdnQ6lU9ur+vLw8fPfdd9x0nL66fnPZWGdnnHdzg3TSJGhGjYJm9Ghoxo2DytMTne7u6Bg7Fo0LFyL0008RHRrKzTq3pfLa1taGzMzMfqvi9qXqW1tbi+joaHh7e3Pi5mzVl5Xgsobry/LpelP1LSgoQGBgoN2BbmZmJiIiIkyeX7FiBd5///0BiyuRSIRRo0bh6NGjRs+fOXMG7u7uBsdXrlw55Bs+rDV9oMtyHXubT/RzC/vwl5qaira2Nov3NDQ0mOTfWuMPEqN520GEpp5/y3o8gQhniCB0cUHF6NGo6jku6QG4VfQ7ZSGNCKuJMKLndZcsWcKBupiYGPz73//GwYMHLe5+jRs3Ds888wz4fD6uXbuG9evXW/273HrrreDz+aivr4dUKuVkxFgwarLY4OKCc+fOcQ/Ycrlch3er7zwej3vA4PF4+PLLL43mJlukF6uqqgakf6CoqAgBAQEmzxkRluoAACAASURBVO/fvx/Hjx8fsLhy5CbT5gC6Q9T0F5PKykr4+fn1aRFRKpUQi8Xg8/kQCARobm7u02v87//+r9HRkX1xZyLcv2ABAgMDIZdKoWhtRde+fVBv3oyugwfRtXUr1GvXQr1yJdTz5kE9Ywbku3ahzssL6R9+CN+eWechISFISkpCYWEhGhoajKof6HtdXR1XNSorKxtQgMt6fX099xlYbV5zVd/4+Hjk5eWhtrbWKq6vpcqKWCxGcHCw3YFuWloaoqOjjZ6TSqW48cYbceXKlQGJqZycHLi5uWH37t0mdSdZCZ+QkBDuWH5+/l9Cwsda6+rq0skNbPOQTGaZ/qTv7e3tKC4uhq+vL/h8Purq6np9/+nTp3udf8a6uuI7Yiq4VcRUaKX0+9jeT7Qeyp2J8G8iRBPh6vDh8CNCLDGV3yxiRvL6EOFdIuwiwmOHD3Nxq6/MUlZWhp9//hmvvPIKNm/ebLIqrU3NmDZtGn766ScEBwfj3LlzWL58uekigpMTRykgImzevBnV1dWIiYnBm2++iaVLlxrPx87OWLNmjc5rr1mzBtHR0bh8+TJ2795tkPufeuopBAQEIC4uDjk5Odz0Q0sP5pZyU1lZ2YD0D+Tn50MoFJo8f9ddd+HVV18dkJhy5Cbz5gC6Q9T0gW5tbS34fL7ZZjFjXldXh9DQUPj7+yMhIaFP3dHZ2dlYsmRJvwBcT2dnvDp5MsrmzYPq6FEo2IqlXA7V0aPo2rEDqmeeYfyRR6BevBiaKVOgmT0bqocfRte996Lr0CEogoK4iVzx8fEIDAzkKqOxsbHIzs7mdBiNVXETEhIGvIrLVkxycnK4QRzmPgPL9S0oKEBSUhLH9fXx8UFERARX9W1paelV1bexsZGbYmYrn86SJycnIy4uziTQnTx5Mq5fv273eKqvr8f06dMxbdo0hIaGQiQScZ6Tk6Nz7Y4dOzBjxgxcv34dPj4+WLJkCZYtWwa1Wm33z/lnMH2gy+qe6nNpLXlzczPXvJmcnAyBQNDnivDFixd7xdtdSIRIYlQXfiJGyzaRCInDhyOpB7g+cPfdHDXgDBGCifBBj3/Wc305EaqcnXGVCD8S4SoRSv7zHy5utTn6AoEA0dHRyMzMREVFBVexjI+Px8WLFxlNXy0urkHu9PTE3LlzuZ89PDzw0ksv4ejRozrKCfo+f/58HDlyBHv37uW+o3HjxuGhhx7CqlWrDLRyWaC9bt06nDp1Cn5+fuDz+RyfmMfj4f3330djYyOKioqQkpKC0NBQbiJiWFgYN3ihqamp19KLmZmZ8PX1tXtuysnJMUuruvXWW/HWW2/ZPZ4cucmyOYDuEDV9oNvY2AjvnkYsaxJ/W1sbUlJS4O3tjeTkZG6rpjc8X6VSiaeeeqrPjR/avmbcOHyyaBFa77kHXZs3Q+PuDvW8eeg6eBCdFy9CUVqKjsuXod60CaqjR6F65hnmupkzoZk+HeoNG6A6dQqqU6fQde+96Hz1VQMN3ebmZpSUlCA1NRXh4eE6iZcFw35+foNWxW1qakJkZCQEAgGKi4v79BoSiQQVFRUmq75isRg1NTUmq74tLS2IjY2Fj48P8vPz7TrKWCaTISEhgWt2NAZ0R48ejYCAALvHU3h4uMm/zc2bN+tcy47ZnDBhAsaMGYN9+/bpdD//1U0f6CoUCq5L3pq8olAokJWVBT6fj7i4OLS2tqKmpsYmnm99fT3Onz+PW265xap8tIAIEUQIIsI1Jydc7QGpAcRwdptHjoRq/36E7t6NWUS4mwiJEyfi6ogR+IAIISNHoogIxUTIGTUK54jwOhG8R42C6qmnoNDj2rOVyoyMDERGRsJHa0cqPj4ewcHB8PX1RX5+PsrLy/HLL7/gxIkTuP322002jA0bNgzr16/H3//+dxw4cICjKDg7O2PWrFkmKQsuLi7YuXMn/vWvfyEmJgbXr1+3qOjArgETJkwAn883mltkMoZ2kJOTg9jYWPj7+8Pb2xv+/v5c1Ve/+KCf25KTk7mG3L4O3LHWLdGqFi9ejI8++sju8eTITZbNAXSHqOkvJi0tLRxvydJWXmlpKQICAhASEoLa2lruXG94vtnZ2diwYUOvwKx+YnVxdsbDnp6InD4dLXfdBdX+/VBv3AiNhwc0o0ZBvWwZurZtg3rdOmYyWXo6VM88A/WmTejavh2aWbOYa5csgeqJJ6A6fZrxY8fQdfgwlAUFZkGhTMZ01rJjaNnFJTAwECKRCGKx2CwVoD+ruIWFhVxFp7m5uV9fu6Ghgav6hoSEmKz6VlZWIiAgAKGhoWjoGZJhz1HGMhkj0p+SkmL0XEtLC4jIrlOHHNb/plarDXIOn8/XyTWmvLq6GkKhEIGBgSgvL9cBqrbwfFnx/7a2NuTn5+PBXbvgQQztwCjYI8L/EUHs4oLcZctwlZjqbiURlE5OUC9YANX27UgePhxfElMBjt66FcE9YLjE1RV1xOjmXh81Cq/3AN2IVavQ9eCDUMbHm43btrY21NTUcAN62Nzk7++P2NhYHSqAVCpFTEwM3n//fTzwwAMmm81Y8Hv33Xfjo48+QnR0NN544w2rB/i4uLjgueeeQ2ZmJr755hscPnxYh1qxYMECpKen9yo3NTU1cVXfsLAwneJDSkoKioqK0NTUxNHJgoKCUF1dbXVuskV6MS0tDTExMSYfwmfNmmWgfOCwwTEH0B2ipg90ZTIZJ7liKtlr607m5uYaLBoVFRUWeb4KhQLvvvuu2Y5hg6d9vZ8nTJiAxx9/HNeuXUPW6dPoXLsW6ltugXr9emjc3dE9ahTU8+dDdeQIVF5eUD38MNQbNqDj22+hyMhA5wsvoOv225lK7qJFUD355O8g9/RpqB57DF2PPQaFhaqoNm2jrKwMcrmck9ZJTk7WAYWmZL9s9dbWVsTFxcHHxwd5eXlWcYhtdalUioqKCk4Ojq36stq+ubm5Zqu+veHTWVpcoqKikJ6ebvRceXk5iAhZWVmDHW4O64XpA92Ojg4IBAKdceH6LpPJOPm8tLQ0yOVyk0C1L0CXLQS0iMXo+Mc/0LVuHSTz54NPhIdJtwHW1dUVM2fOxBZi6Ai5Li6IIIarq+DxoJoxA10HD0K8dSuuEMPHPU6EfzzxBP5JhBBnZ5Q6O6OACJ8T4Y0ekPs6EeoOHEDXgQNQJiebjdGmpiaOtpGfnw+5XK6zI6UNCsPDw5GamoqSkhLuIbmgoADffPMNnn76aaxcudIo/UDbhw8fjhdffBFCoRAXLlzAXXfdZVI1x8nJCXPmzNGp8u7evdukIkxvXLvqGxcXx1V92THrmZmZZqu+vR2zbi4/JScnm5zkJpVK4e7ujl9//XWww81hcADdIWv6i4lSqYS3t7fR+e2WdCe1qynmtgezsrKwdu3aPlETeETYtXw5rl27hpiYGG77qU0mQ/svv0D16KNQr17NUBaWLYPq0CEG5Pa4euNGqB57DKoXXmDG9O7cCfXSpdBMn46u++9naAunT0P18svo2rULnefOQWECNLa1tXFbo5a4uCwo1KcCBAcHIzExEfn5+aivr+8TQC0vL4e/vz/CwsK4CupAe1NTEzcQJC0tzWjVNy0tDSUlJWa5vn2t+oaHhyM7O9voYpKdnQ0iQnl5+WCHm8N6YRqNxiB3BAYGorS01OgOU2FhIfz8/BAWFmY0f3V0dHBjXyUSSa9Bbnt7OyQSCQTffYeGzZvROXUqOm+8EV1Ll6KImKEQfycyGGVLRNhIhP8QQ2OoI0LT5MnoOnAAqmPHcM3dHZfodx7v/82YgZ+IkDR5MhKI0dYNd3XlgO47w4YxtKqTJ6EwEUtyuZybQhkVFYWmpiaLoDA7OxuxsbGcMktgYCBHU2J3pBoaGhAQEICzZ89ix44dJqkIzs7OuOmmm3Qay9avX49HHnkECxcuNLh+5MiR+OSTT+zygN7a2soVZpKTky1WfU19hr5KL8bHx5ulVQ0fPhyhoaGDHW4OgwPoDlkzVjXx8fEx0L2tqamxSneyo6ODE+LWv0ahUODkyZNmZWhM+QRnZzw7eTJyp0yBdP16+P38MyIiIgyBXVsb2v38oL7jDqgOHtQBuSovL3Rt2AD1mjXo2rULqhdeYIDt009DvXAhA3bvugtdBw6ga+dOqI4fhzItzWjS067ilpaW9jr5ao/4TUhIQFBQkE4jSVZWFiorKw1GA+svUCzXLDMz0+7UCFNeXFwMgUCAmJgYgyq1ftVXexFlaR39UfUNDg5GTk6O0cqKSCQCEUEikQx2uDmsF2YM6IaEhKCwsFDnWGNjIyIjI7mqpbncZM2OlSmQK5fLIZPJ0PTBB+iYMQOtixejfsUK1C9fjoQJE5BDjELCmunTuRx3yy23YNKkSVweW0wEXyLETp0KlZcXyu6+G5eIcImYyWmpPB78ifA+EUR33om3iZmEVuvighAi/JcIce7uUD3+ONrDw43GTHNzM6Kjo3WquL3NTQ0NDSgsLNR5YNUfRCORSNDW1obk5GR89NFHePjhh02OACZi9HX37t2LkydP4qabbuKOL1y4ECkpKXbJTSyNKiwsDI2NjQb5s7q6mqv6BgQE6NA6jDUa9yU3xcTEIDk52Whuqq+vBxEhMTFxsMPNYXAA3SFrxoCuv78/KioquIWBBVPW6k6yDW3a24ZZWVk6nbzW+vIxY/D5woVo3rgR0vXr0XLTTZDOnImI996DUChEYmIiCgoKdCW/amoYsLp5M1THjv0OdA8fhnrpUqjXroXqqae4pjPVqVNQPfss1MuXM2oMzz6Ljk8/hTInx2hyY6u48fHx/U4/KC0tRXp6OiIiIjg+XWhoqEHFoaamBkKhEEKhsF+2+vri2lPWxGKxVQuq9iKqT+sIDw/nqr6m+MXGqr75+flc05uxykpQUBB4PN6Q7xgeamYM6EZEREAsFnMPzqy6CassYik3sTtWDQ0NVgNc9u9WJpNBLpdD5eUFzcyZUG/bhq5t29CxeTPqli9HODEyYvf2VC+JCBs3bkRtbS0ef/xxptLZU9mNIYLskUfgM306LhHhm5EjkUyE1OHD8TkR3uTxkLhrF94gYsCuszOCiZEfS3zsMSiTkozGFjva21IVt7euLUmoPX6c3ZHSzsG5ubk4d+4cHnjgASxbtszo2HZ2ytrzzz9vcofHFteeQpmenm5VEYDl+hYXFxs0Gmvn4MbGRqurviUlJfDz80NGRobR3FRUVAQiQn5+/mCHm8PgALpD1owtJkKhEMXFxSguLoafnx9CQ0N7pTup3dCmUCjwzjvvmOXi6qst8Hg87PHwQMy0aejauhVd27ahbcMG1K9YAenNN6Nr4UI0BwYaVENZya+cnBw0X76Mru3bod64EV33389QFNauRdfdd6PrrrugeuklXaB76hS67rkHne+9Z5KqUF9fj7CwsD5XcfuSrGtqapCbm6vDM2MXmfDwcFRWVg5KJbe2thZCoRDBwcGoq6uz6bWkUikqKyuRnZ1ttOpriuvLKn6wg0lMbSlevnwZI0aMgEajGexwc1gvrLu72yC3xMTEIDs7G5WVlQgKCkJQUJBZzq4x4GpNQ1t7ezuUSiUHcNva2jhwow90FbfdhoaVKxHp5IR8ItxHhBUrVnA81C+//BJubm4ch/V2IvxKhOIbbsAvRPAmQtaYMeATQdijtvB/M2fi1yVL8AYx3NyrRDhPBNdhw4yCLO0q7kBw9Nkdqby8PJ0czD6cC4VClJaWQiZjJplFRkbiwoUL2LdvHxYtWoSLFy9CIBDY5bM1NjYiPDycK9jY8lps1ZfNwcaqvpWVlQZVX7lcjuzsbPD5fKSkpHDgVj83RUREgIhQU1Mz2OHmMDiA7pA1Y0A3ODgYQqEQvr6+EIvFve5QZrcH4+PjsW7dOqurtzfccAP279+P4uJidL7+OjQzZ6JjwwY0r16NhpUrodi4EZrZs6HeuhUKvWoF+/TMNVj89hvi//lP1N1xB9qnT0fXpEnouvlmpgHtgQeYSq820H35ZXTt3Mk0qhkBnGzS6u8qbm+cHf7g6+uLmJgYTlNSmwNrTu+2vxY4Vp83KSnJ5Laere+h3cyn/XuyDTNisRghISEIDAzkuqf1/59JpVL4+Phg4sSJmDhxIjo6OgY73BzWCzMGdGNjYyEUCsHn85GRkQGFwjoZRG0XCASoqqqySFNgt6b1QWPH5cvQzJmDznXr0LJmDepXrEDbxo2oc3dHPBGm9uQz9uHey8uLy3GHDh0CEeE2InzF4yGXCPU8HkqGD8f7RPiWx8NXRIjZuhWXJk3CG0R4y8kJPsQ0qt11110GsWKvKm5vvKWlhZMzi46ORnh4OHx8fHSqoeb0bvvLi4qKuPxorzxtSl4yNDQUycnJyMvL46g0xgoibG6KjY3F7NmzMWLECFRUVAx2uDkMDqA7ZE0b6CoUjO4kuyXV2tra60WEBbpHjhwx2W2r75s3b8bVq1d1+KhtWVmQrV8PhYcH5PPmoWvpUkYGbMkSdPz3vxaTkUwmQ+OXX6J97lx0jh2LjrFj0T52LNrd3SG/6SbIN22C8uhRdL7yCtOYdu+9UB07BqVYrPM62lXckpKSQVlE5HI5xGIxfHx8OD1Q9hxbDdVXPmCnJeXl5XHTkvpjMYuKioJAIBjw70K76hsWFqbTQc1Wfaurq7mqr0wmw9mzZ+Hi4oKTJ09CpVINdqg5rJemDXTb29tRUFAAPp8Pf39/q7V0jXlAQICO5Jg1VVydeKyshOzOO6Hw9IR89myoli6FZs4ctM+dixe08hrbiOXh4QEiwrJlyyCVSjFnzhysIWb8byMRZK6uaCBCNTFjfgOIULdnD847O+M8Ea47OeErIiwjZlqVNuCKiYkZsCquKWe35yMiInSAdltbm041lN2R0o5Zc/z83uaHhIQE+Pj4WE2j6i/X/j0jIyO53OTn56ezA8Gub3K5HJ999hlGjhyJgwcPQiqVDnaoOazHHEB3iBq7mGjrToaFhSEzM7NPi0hERARmzJhhEdyOGTMGTz75JJKNyONUVVUhODgYUV98AekTT0C9ejXUK1aga88edFy/bpJaoONNTVCvWgXNxIlQL14M9apV6Fy6FJ033IBONzfUrl+Pyk2bULd2LZpuvx3Nhw6h0ceHS7raVVyRSDRoVVx2S1IgEKCwsNBiAteecqY9LYntvmanJfV2WltZWRm3mPWnPm9vF5TU1FSOqtDQ0GAwMen69eu4+eabsXTpUowePRpXrlxBd3f3YIeZw/poHR0daGhoQHh4OFe1FIlEfQa5HR0MNaukpKRXVVzW6+vrER4ejuBvvkHzM89AvW4d1MuXQ71jBzo+/xyzzeS+8+fPQ6FQ4Or338OHCE3E6OMqFy9GAhEKiFFkiHB1Rf66deAT4TdiGtV2EmH4sGEc+Gb1siMjIwetiiuRSJCQkAA+n4+cnByrcpO23m1oaKhRLe7e5tqamhoEBQX1C42qr66905WamorGxkaUlJQgLS2Nq257e3tj7dq1WL16NVxcXHD27FlH38AfzBxAd4haR0eHge5kQkICUlNTe7V4KBQKnD9/3qKiwrx58/D++++jtrbWIFlIpVIkJyfD29sbKSkpv1d4q6qgKCqCohfb5O0//ADN1KlQ33gj1KtW/e4rV0IzcSK6du+GJDwcNV99hfwrVxDJ53NJl63g+vr6orCwcFASp0Lxu5qBrYuZRCIxOS0pKSkJhYWFJhssZDIZx4PNysoatKoRK19miqrAftbffvsNq1atwtSpU+Hu7g4XFxcolcrBDjOH9cHUajXXUJSQkACpVMrtXNgCdENDQ1FQUNCrKq52E6pIJPqdHlRby+SmngfHM2fOmMx9BQUF+O6777B5wgQUEaGECPFEaJw7F/E9/64jQumUKfju9GnsIMIWInj03L9ixQqEhoYiMDCQe9gbrHisrKxEYGAgQkJCbAKX2jtS2rKLQqEQCQkJZmUXtXmw7FTOwfguWPkygUBgsnejra0NIpEIt956K6ZPn44pU6aAiCAWiwc7zBymZQ6gO0RNpVIhLi5Opws5JSUFiYmJVi8c6enpFnVx16xZA4FAYHKbqqysDAEBAQgODjYJZHrjHR9/DI2HB1fN1XbNpEno2rzZ4B6JRIKkpCRue5Rt+mIBoSWdxf5yiUSC+Ph48Pl85Obm9vv7tbW1oba2FmKxmBtZrN/MV1VVxUnKmQOXA+GlpaUm5cu0f6cLFy5g2LBh+Nvf/ob29nZ0d3ejqqpqsEPMYX207u5uJCQk6PBpxWIxIiIibAK6rHKDtmSYKYCrUCi4OLCmCTUvL89kDty5cyeICNuIUEGE3B5wm+7qygHdSiK0zZ6NPbt3G9z/ySefwMfHBwKBwICiZIsOd2+cnfTFFkb6G1yyO1L5+flITEyEUCjUkV1kd6Tq6+sRFRUFPz+/AWkMNuXV1dXcLqipYoRcLse3336LsWPH4t5770VzczMAoLq62tEg+wczB9Adwqa/EGRkZFi1PSiXy/HWW28ZlY8hIri5ueHEiROIi4szmYBbW1s5UJeRkdFviVMZHAzNzJlMd7Q20F2+HJqJE6F6/HGd69ntUT8/P45/qg0IRSKRTsdtXFycRQ3YvnhFRQU3Vrm+vn7AErb+tCRtnllycrJZyS97uTZVwdzWaE1NDfbs2YMxY8bgu+++c1AVhpB1dnbq5JzCwkKEhobaBHRZnWpLNAWZTMb9/SUlJVlN99GmbrFqC9p+YP16tM+di3pnZw7gxvdwdpt5PHTt3Mnxell3dnbGb7/9xvFPWZk+fUCozQs1pwHbF2fH5wYGBqKysnLA8kBrayvKysqQnp6OyMhI8Ht233x9fZGQkGBR8sserk1VSElJMbkGtLS04Omnn4aLiwsuXLjgALZ/cHMA3SFs+otJTk4OoqOjzS4WaWlpuOWWW4wC3LVr1+LUqVPw8fFBYmIiCgsLDSqhcrkcRUVF3CSjfudWyWTo2r0bGjc3BuwuXgz1TTdB4+4Ozbx5aA8I0ElYxhq9jLk2x8zYaN/y8vJe81/1F1VrdR/t4RKJhBslnJ6ebtW0JHt8jqamJoSHhyMgIMBsNVkkEmHevHm4+eabkZOTM9ih5LB+NpVKpZN3SktLERQU1CeAy9IURCIR/P39ER8fj/z8fF0N7h6vqKjQkS/rzd/uyy+/zOXCRYsW6cgmfvrpp4wW7xNPoGP8eJQTIb2Hq1tLhMbRo1H1zjsGOfXGG280GHhgDFSVlpZyvFDt0b6sIktfeg20QZ2lCZD2dJZG5e3tjeTkZOTm5kIkEnE7UqzkV05ODqr/P3tnHhdV9f7xw6aAuGGCgkuLu6hZ2eYPKbW+mktpbrmkJUhafbVcMre+mUulRllqarhkpWkWICDDpqAIiBuy7y4ssgwzwAw78/n9gfc2l1mAYWAQnvfrdV7lufcOd4a5z/lwznOeT1ZWs6UySKVSPlVB26bchIQEjBkzBnZ2dggJCaE/wB8BSOi2YeoK3cTERFy4cEFjLu4777yjUvu2Q4cOePfdd3Hp0iWUlJQgNzdXZSb0/PnziIiIQExMDB+Im2NpnmulMTGomjwZNX37ouaxx1Bja4vqESNQ/tNPkMuFs7hpaWk6/Qxla1/lQuqBgYG4du1ag9IdcnJyEBAQAD8/P2RmZhpkEJHL/3URCgoKUnGcUy75Vdfet65bUlPv486dO/xSpaaBWSaTYd++fTA3N8fChQtRUlJi6MeIaAbqCt179+7Bx8dHJ5HLbTYrKCjgN2vWrcHN5QB7eHjg1q1bOomllJQUQWx87rnn+P+/e/du7Xn37qFyzhzcMTFBzkORG8sYbs+ejZ/379e4ka0xTdnaV7kii0ajHTWNq/WqvNJliMaVVhSJRGr/8BWLxbzRg7K9L1eOUJv1eGNadnY2/Pz8EBQUpPEPD5lMhr/++gvW1taYOHEiHjx4YOjHiGggJHTbMHWFbmpqKgICAjQK3e7du/MB2MbGBtu3b8f9+/c15rtxu21TUlJw8eJFflmcq3cYGxsrKA2l15afj7J//kHFzp0o37cPpcnJKrO4+qw7qyndgSupozwTqryZ4urVq1rtfpuzKbsI3bx5s8G/B6lUqlbkK7slNSZvsKSkhM//07bRJi8vDwsWLIC5uTkOHjxIMyVtmLpCNzs7G56enlptftXN4mrLxS0sLERaWhouXbrEL4t7eXkJqpQ09tnkNhsxxvDJJ5/w/79hw4Z/z5NI8JurK1YyBlfGMIAx2NrYoH///gKRa25urpfYoJz/qsloh4vDXH3ec+fO4dKlS81am7u+e05MTISXlxfvfteQ6+qK/LorUomJiY1akeIc3+pLVSgqKsKaNWtgYmKCzZs3o6qqytCPENEISOi2YeoOJtzGME2Dx+HDh9GnTx+cOHGCzxfjvLu1iZOLFy/C29sbycnJapfZuDIzGlMASkpQGh6O8oMHUb5/P8qCgvjdzg1tdWdxW8JBiEt3qGt56+3tDS8vL9y+fdtgIpdLEdCHi5AmtyRuI1lMTIxaFyHl+zh//rzWWe3r169j+PDhGDBgAK5du2boR4doZqqqqgSxJy8vDx4eHpDL6zeKaGjJMLFYjLCwMHh5efEOe/fu3RNUKfH09ERwcDBu3LihPgVAJkPprVsod3dH+b59cJs6FRbs3zrhFhYWYIzhiSeeEFzn4eFRbylGZ2fnZnv+VYx2Hs6EcrO/UVFRBhO5EokEYWFhfPWbpsRqZevxqKgolbKL2tLOpFIpfx/aZrXT0tLg6OiInj17ws/Pj/4AfwQhoduGqSt0MzMzce7cuQbNlNQ3iCiX5dE2e1pcXIz79++rzA7yrjqJiZB/9RWqXnsN1WPG1NbWffVVVK5eDXkDKgJwf5E3xyxuYwMudx8ikUhQY1HZQaglNn5x5cua00VI+Q+aixcvqrglpaam8s5O9aUqHDt2DFZWVpgxYwYkEomhHxuiBagrdJXtgotUxwAAIABJREFUxZsyi8t9p5KSkuDt7Y2LFy+qpOsox7CsrCzExcWppABERUUhJTkZxT/+iKo33kD188+jeswYyJ5/Hj8yBvuHG8kmTJigmr4glwvSGkaOHKnWZEckErVYfEpPT4e3tzfOnz/PT0xwqzRcKcL60h300bg0quDg4Hpzk3VtUqkUd+/eRXR0tMqKVFRUFJKTk5GWlsanc2lLVTh//jxsbW0xduxYcjl7hCGh24apO5g8ePAAHh4eapcHlQeRkpISvZXlUTe4ZGdn86461zZuRP7w4ch9/nnkvvkmJHPmoGzKFFSPGYOKPXu0vlZ+fj4ftFNTUw1We7KwsJB3MkpKSuLvo+575RyElJfZ9OVuJpfXLq9xtZMN6SLEzZJwMytXrlxR65YkFouxbNkymJmZYc+ePbRzuR1RNzZx9uKFhYVaZ3HrW2HKz8/nbVob6yrGpQAkJiYiIiICEXv24MHo0cgfPRoPpk5F4Zw5KH3rLVwzMcH2h2L1o48+4oXrxo0bIZfL4efnx/f169cP8fHxWL58uUDk9urVq0U2phYVFfGlFbmKFNx75VZplEsRcpVnlNMd9BUfuDSqlt6UW1JSInivXLoDZ2vMOZwpr0gVFxfjf//7H0xNTfHpp5+SA+MjDgndNkzdwaSgoAAeHh6QyWQ6LQXqWpZHY5PJUOnsjIrnn4d07lzkzpiBe2+8gYzXX0fe6NHIf/VVJIaHqwgkZevcsLAwg83iyuW1tWB9fHxw4cKFemco6m784pbZuFlPbSkA9bXs7Gz4+/sb1EVILq8Vr1wKSWpqqlq3JDc3N7zzzjsYNmwYevfujcuXLxv6USFamOrqakEMKi0thYeHh6Dud2NXmLj8/MuXL+tl5aTiiy9Q+cwzkM6di7wZM5A5ZQoyXn8dkXZ2OPcw99bCwgLm5uZgjOHxxx+HVCrF4MGDeUG7ZcsWSKVSlbJiH374YbM/i1lZWRCJRPD390d2dnaDnt20tDSBI6Gyu5mum1Lz8/P1lkbVlMZVnuH+CFJ2OONSO44ePYrZs2fjpZdeQpcuXXD27FlKVWgDkNBtw9QVuhKJBB4eHpBKpY1aCpTLm1aWR2MrLETVjBmoeu01VL7/Pt/KFy9GyaRJkI4di7ATJwQ5V9evX0dAQADOnTtn0Flc5dnTpjgZce5mt27dUkkB4HYVaxu0lUsERUVFGcxFSC6X4+7du/Dx8UFoaKjaVAWuksWmTZswcOBAfjn33XffNfSjQrQwdYVueXk5PD09kZ2d3egVpgcPHiAoKEjv+fmVLi6ocnQUxqYlSyCdNg3BRkZ44aFoVa437uLiIhC0YWFhOH36tErawoULF5rtOVSePb1+/brOMUG58sylS5dUUgC0OS9yLTU1ld+cbCi7dbn8XzthdZVnuM8sKysLP/30E4YOHYoOHTqAMYaxY8ca+lEh9AAJ3TZM3cGkpKQEHh4eKCgoaPAsrlQqFVgJ61VIyWSodHVF9YsvCgaTyvffR9X48aiaORPy7Gw+5yo0NJSv7MBtImlKHUldW2ZmJvz8/BAQEKDW8ripgxSXN3jlyhXBruLIyEhBukNhYSFCQ0PrrfvY3K2kpAS3bt2q105YKpVi1apVMDU1xdatW1FZWYn4+Hhcv37d0I8K0cLU1NSoCF0fHx/cu3evUStM3PcuMjJS7zGg4ssvUf3cc6h87z1hbHrjDdywt8eTajaYKefidu3aFSkpKXjjjTcE59ja2jbbH+i5ubkICgrC+fPn9T57Wl+6g3J6UlFRESIjI+Hl5dXiaVR17zkhIaFeO+GSkhLs2rWLd2AsLS1Feno6rTa1EUjotmHqCt2ysjJ4eHggJyenQbO46enp8PX1RWBgYIOWvnRp5WfOoHrcuNpZ3cWLUfnee6iaOhXVzz+Piu+/h1z+b96dt7c3UlJSUFxcrHYTifJMQ3NY+ioLuhs3brTI7KmmXcXczK+fnx/S0tIMNpMrFotx8eJF+Pr6ap3pT01NxdixY2FjY4OAgABaDmznqBO6IpGIn5GtLzZlZmbC398ffn5+gk1g+mxlFy6g6j//QZWTEyrffbdW8M6YgernnsP999/XWE3BxMQEjDG88sor+OOPP/h/c23FihV6j03K6Vzh4eEtZv5QN92Bi01eXl7w8fFBYmKiwYwolFMVUlNTNZ6Xk5ODGTNmoFOnTjhx4gTFpjYICd02jDqhy9krZmRkaAxAYrGYd9GKjY1t3o0DRUWo+O47VE2aVFt1YcwYVE+YgIrPP4fsYd3a+vLu6m4i4WYauBq3+tj0xRU2b46Zksa04uJiXL16FR4eHrh48aIgv4wrk6SvIur1NeVUBU0/TyaTwdvbGz179sS4ceOQmZlp6MeCaAXUFbplZWUIDAzE5cuXkZaWpvH7VFRUhGvXrvHL8s1avk8mQ/mxY6iaNo2vulD9yiuo/OgjyNPT0bt3b4Gw5RqXyvD9999j586dYIzB2NiYP/7dd98Jatw21W5cLBbzKzvaBF1zN5lMhtu3b/OVZrhNgY012tFHy8nJgUgkQmBgoMaqG3J5rQPjgAEDMHToUMTGxhr6sSCaCRK6bRhuMFHe0JGQkIDQ0FBBDUnl5f+GlOVpjgGl9MYNlB87hnJ3d5Revoz8Bw8Es7iNDYxcjdu6m76U/eIbMrgoFxRvTGHz5mic2Pbz8xO4CCmnOyjb+opEIkRGRiIpKalRBg/1NeUcwNu3b2tdWt60aRNMTEywbt062rlM8CgUCpVc3NTUVMEKDSeOuLJ8XB3wgIAArfbR+m6lCQkoP3EC5YcPoywgAJIHDxAeHo6FCxfy4nXGjBkqM7s3btzAsGHDwBiDqakpGGPo0aMHxGIxX+NWeRY0JCSk0UYWqamp8Pb2RmhoaIuULtTUOLHt4+MjqMTTGKMdfdwHZ0RR354FmUyG/fv3w9zcHAsWLEBxcbGhHwmiGSGh24aprq4W5LspLwVypa+45X/uL29uplDbrEpzNi5QcRsY9BW8JRKJRiMLTTuKCwoKeLGtq5Wwvj6TpKSkBi9LcukOnB2qv7+/WqGvS7pDQ1MV7ty5g4kTJ8La2hqenp60HEgIUCgUgpJhyrGJywVNSEgQ5KlzM4XJycktMiuornHpXMHBwUhPT+dnal966SVBZYXevXsjNDRURfwuXrxY5TW5TV+ajCzS09NV8o+lUinCw8MNngPLPes+Pj4ICQmpN15rMtrhNhvr6ljHfY7h4eH1pirk5eVh4cKFMDc3x88//0xlDdsBJHTbMNu3b8dnn30GT09PjTN6XFkeT09PXLx4Ebdv3xbUfOX805sr71W5KQvLpjrm1Nc4I4uYmBjBjmJuFunatWu8TaYhZ0qU88ya8pmoE/qcZ3xDN/Tdu3ePH9C0pSoEBgbC3t4eY8aMQXp6uqEfA6IV4u7ujpUrV+L06dPIzMzUaPyQkpICb29vBAYG4vbt24iMjBSkJkVEROh9xULT8xMeHs5XWeFmIJ9++mk+NYGbveXq506fPh2MMYG1enh4eL0/S9MKDReLb968ye+dyM3NNVhsKi4uxvXr1/VSeYYzeOCEvrLRDpfuoO01Hjx40KBUhRs3bmD48OF46qmnyIGxHUFCtw3j5uaGsWPHomPHjjAzM8MLL7yATz/9FH///TdycnIQGBgIT09PtWV5uFlBzj+dG1w4s4OkpCS9Oekoz+IaSlhyS2wxMTH88lrd5f+WcA5SbpyLkKaSOE0dpNR5xnODaXJyMv9+ZTJZg1IVSkpKsHPnTpiZmWHFihUoLy9v8e/80aNH1W4Q2rlzp9brKioqsGbNGtja2sLS0hITJ05EYmJiC911++OPP/7AxIkTYWVlBWNjY4wcORIrVqzA77//joyMDNy4cQNnzpyBl5cX4uPjVb5zBQUF/AZNbsXCx8cHV65c4ZfD9fWsZmRkwNfXF0FBQSrC8tixY/Va/XKzvlZWVjrHx9zcXCQkJPDvlat2oLz835KxiUujEolEek8j0Wa0U/f9Kq92Xb16VWuqwvHjx9G5c2e89dZbKCwsbPHvPMUmw0FCt42jUCggl8tx4cIFfPHFF5gwYQIsLCxgbGwMExMTLFq0CKdOncK9e/fqDZT1DS66bPgqKCjgN1IkJycbdAnu7t27/LJkbm6uYPlfJBLx7zcsLEyt05c+Az0nLG/evNkiLkLcYMr9YcO9X29vb/j4+ODcuXOIj4/XOJBkZWVh2rRp6Ny5M/744w+DpSpwg0lAQADCw8P5lpWVpfU6V1dXdO3aFe7u7vDz84OjoyPs7e0hlUpb6M7bJxUVFbhy5Qq+/vprTJkyBd26dQNjDB06dMAbb7yB48ePN2glo7CwEOnp6bh+/Tq/HK6cqqOLy5dEIkFERITKLG7dNnv2bD4HV1v7z3/+o/PzyRnCcMJSLBarXf7njGd0TU1qSJxITEyEl5dXi+1Z4NIduLFH+f36+vrycVJTukNhYSFcXV1hZmaG3bt3GyxVgWKT4SCh2w5ZuHAhbG1t8f7772PSpEno2rUrjIyM4ODggGXLluHXX39tUOH1wsJCvrRM3cGlPgtJ7i/x1pAeUFxczO/kvn37tsZ7Vh5MlTeRNDW3TLkVFBS0ChchuVyOtLQ0eHt787PKyukOt27dQmJiIlJTUxEWFoYnnngCDg4OiI+PN+h3mxtMJBJJg6+5f/8+TExMcOjQIb5PLBajU6dO+Oabb5rjNgkNfPnll+jSpQvmz5+Pt956i897feqpp7Bo0SIcPHgQcXFx9cYmzojl5s2bCA4O5nPyuWe1PgdCbbO46lpqairee+89QXUF5TZs2DCdZj5LSkoQExPDb67SFF+UjWfqpiY1xdVMuUkkEt7eu7lTy+prmZmZOH/+PHx8fBAcHCww2uHSHWJjYxEXF4dnn30Wffr0waVLlwy6V4Bik+EgodsO4YI8R2VlJa5evYrdu3dj+vTpsLa2BmMMAwcOxJIlS3D48OEG+cZLpVI+D1Tb4MLN4p47dw5JSUkGDZjcTElDbTKVW32bSBprZMEJS0O7CHFlgjw9PREdHc0Lfy6vOTY2FpcuXcK2bdvAHtqgDhgwAD///LPBN53pMpi4u7vDyMhI5ZoZM2bAyclJz3dIaEMsFuPBgwf8v2tqahAbG4t9+/Zh7ty5sLOz43Ng582bh59++qlBqx7Kz6qyAyEnBO/evQupVAqpVMrP4sbExDR6FjgyMhJOTk6CTWleXl46PYd5eXn8H7137txp1LXKqUmXLl1SqWSRmpraqMkFLo0qODi4Xqvz5o5N6lIV6qY7cHGgU6dO6NWrF77++muUlpYa8JtNscmQkNAlVKiurkZ0dDT27t2L2bNno1evXmCMoX///liwYAH279+vdeZT0+Di6enJ55f5+fkhJSWleetgamklJSW8mNPmmNPY11RnZOHv76/VMlPZTtjQO6g5tzXOpUrTebm5uZg7dy769euHRYsWYdGiRZg0aZKhv7r8YGJjYwMTExMMGDAAbm5uWgX42rVrYW9vr9K/YcMG2NraNuftEo2kpqYGycnJOHz4MN5991088cQTYIyhV69emDlzJvbs2aM1V5NrnBCsuxmV27MQHx/fpD82Dxw4gP/+9786Va5RFnNhYWF6+aNXuZJFeHi4IO9V24Y+ZZOcW7dutUgalaZWVFSEiIgIeHl5ISUlReN5UqkUn3zyCWxtbfH222/D2dkZzzzzjMHLG1JsMhwkdIl6qampQWJiIg4ePIgFCxagf//+YIzBzs4Os2bNwvfff4+oqCitQZCbxeWCNzejy+2u1WUGVNeWl5eH4OBg+Pr6NpurEje4aDOySEhIQEpKCkQiEQICAvDgwQODDSJyee2sja+vLy5evKh1ticqKgpDhw7FwIEDcePGDUN/PQX4+flh69atEIlE8PPzw7Jly2BkZIRNmzZpvMbZ2RnDhw9X6ecsQYnWi0KhwJ07d3D8+HE4Oztj0KBBYIzB2toaU6dOxc6dO3H58mWtf1Bzs7geHh4IDQ1VcVvUZQZU11ZYWMiXe2zOPQsymQz5+flISUkRlCBUNrJIT0/n46Sh06gePHgAf39/BAQEaE0lSU1Nxf/93//BxsYG/v7+Bl9hUoZik+EgoUs0GoVCgfT0dBw9ehTvvfceBg4cCMYYHnvsMUyfPh3ffvstrly5wtfH5PK6QkNDBWVi6tbyrTu4cIXi9RncOae1K1euGCQ9QNnIgptV8fT0xKVLl5pU37apn4u6VAV157m7u6NTp06YNWvWI7MZYtmyZTA3N4dMJlN73NnZGQ4ODir93377LTp06NDct0foEYVCgezsbJw6dQrLly/HiBEjYGRkhC5duuD111/H1q1bERQUBIlEAplMxj+HQUFBgj80uRnQxMREwQyoSCTiq5KoW51pSktPT4ePjw8uXrxYbzmt5mgSiYQ3suA2o3p4eODChQuIjo7Wyx4EXWITN7sdGRmptaqCj48PbGxs4OjoiPv37xv6q9ggKDa1DCR0iSajUChw//59/P7773B1deXrSXbu3BlPPvkknnrqKfj5+dUrLDUtr3GDi6al/4Y0sVjM56oZeiMFlx7AVZpQt4lEm5FFc9yLj4+P1tltsViMpUuXokOHDvj+++8fqSLr/v7+YIwhMjJS7XFaHmy7KBQK5OXl4e+//8bKlSvx7LPPwsTEBBYWFhg0aBC6deuGs2fP1ls6UNmEpW4tX85mXNdavlKpFJGRkfD09GzQRrvmbEVFRYiMjISXlxfi4uJw9+5d3L59m1+NU16Ba26rceV7SU5O1niesgPj2rVrDZ6i0BgoNrUMJHQJvaNQKBAQEAALCwv069cPL7zwAoyNjWFlZYUJEyZgy5Yt8Pf3h1gsbtDgwpW8CjxzBqHbt+P6li2I37sXqSEhtYOLRILS6GiUhoWhNDoa8jrCkNvkFRISYpCZEuXGuQhpSg+oz8hCn0uoDU1ViImJwdNPP41+/frhypUrhv56NRpuMLl69ara49yGj7oz1DNnzqQNH20MhUKBhIQEWFtbo2fPnnBycoKZmRk6duyIsWPHYu3atfDy8mqQaFVenQn28kLIN98gavNmxO3ZgxSRCDnZ2SgpKkJpXFxtbLp+HfI6z9n9+/fh5+eHwMBAg6cuZWdna02jUl6BU2dkoVx7u6n3kpubi4CAgHpTFe7evYvXXnsN3bt3h4eHR6tKVWgIFJtaBhK6RLNQUVGB3377DTU1NVAoFCgsLISXlxfWrFmDF154AaampjA3N4ejoyPWr18Pb29vrUGy9PZtVHzxBcoWLkTRrFnImz4dae+8g/DPP0f0f/+L+4sXo+Ddd1GyciXKfvoJ8tRUwQ5qdUXnW7IpuwjFxMQ0+F6UveLrFk/njCwaO5Mkk8n4kkXaNpjIZDKcOnUK3bp1w+TJk5Gfn2/or5VOuLi4aF0e5Er4/PLLL3xfYWEhrKysqIRPG0ShUOC3335DRUUFFAoFZDIZAgICsGnTJjg5OcHCwgKmpqYYM2YMVq5ciTNnziArK0vzM5aaioqdO1G+eDGKZ81C/rRpuDNnDiLXrsX1Tz7BnSVLkP/uuyj+6COU7dmD0pgYFBcX4+bNm3wN2JZOV6r7nMfGxjZ6Y27d2tvcLLevr2+TjDuSk5P5VAVtqRJBQUHo06cPnnvuOaSlpRn6a6UTFJtaBhK6RIujUChQXFwMPz8/bNiwAY6Ojhrd22QyGeRiMSq2bUPl4sWo2L4dFbt3o+Kbb1D5/vuomDABRbNn497q1YhbvRq3338fadOnI+GTT+Bz9ixEIlGjy4bpu3EuQn5+fk12EWrIJhJtRhYSiYRPVdBWskgikeDjjz+Gqakptm3bhurqakN/bRrEjBkzsGPHDvj4+MDHxwdLly4FYwxbtmzhzxk/fjzGjx8vuM7V1RXdunXDkSNHIBKJ4OTkREXZ2yEKhQJlZWUICQnBV199hddee02je5tMJoO8pATle/eicuFCVGzdWhubvv0WlR99hMqJE1Hy1lvIXLUKCWvXItrFBanTpyN5+XL4nzxZ7zPYEk0sFvPxICMjQy+vV7e2urKRhbb6xcXFxYJUBW0OjF9//TXMzMywfPlygzgw6gLFJsNBQpcwOAqFAqWlpbhw4QL+97//YeLEiejUqROMjY0xevRo7Jg3D8mvvw7J2rUo37ABlatXo2L9elSuXInqYcNQ6eKC8gMHUH7gAEr27kXaJ58gacYMhO7bx9fyDQkJaVCheH025Y0U4eHhzZZryxlZ3LhxQ2Bkwb1nbhMJV2T9woULWlMVkpKS8NJLL6FXr14ICgoyyHLg6dOnMX36dNjb26NTp04YNWoU3N3d670XdcX66+ayOTk5qSz7lZeXY/Xq1bCxsYGFhQUmTpyIhIQEfb8t4hGkoqIC4eHh+OabbzBlyhR0794djDEMHToU6+bOxe1Jk1Dw4Yco37y5NjZ99hkq1q1D9YgRqJwzh49Npfv2If3zz5EybRqu7NqlUsu3ufPx67aMjAz4+PggJCSk2SpKKBtZcCUmufd88+ZN/j1zqQr+/v5aUziysrIwffp0dO7cGb///rvBUhV0iU8bNmxQG58GDx7Mn0OxqXkgoUu0SsrLyxEWFoYdO3bgkxdfxClTU5xgDGGdOiHJ1hZZQ4dC/uKLqB4wAJWurig/cABSNzfErl6N259+imJnZ5SJRBpNHZp7cJFKpbhy5YpBXITUvWduB3VQUBDS0tLUbgyUyWTw9PTEY489hldeeaVea8rm5MUXX8S8efNw6tQpBAUFYf369TA2NsbWrVu1XscYw6pVqwQWm7du3WqhuybaA9XV1bh+/Trc3Nzw0YQJONqxI35lDBctLJBgY4PMQYNQ/MILqBo4kBe6JXv3IuGzzxC9ahWk772Hsr/+4utuc6YOyuUWr1+/jvT09GapDKOcRhUbG9uisUmTkQW36TgpKUnjPoorV67gySefxPDhww3uwEjx6dGChC7R+omNRc2rr6JkyBCkDR6MsF694GdqimjG8MDEBKJBg7B/zBjsHT0a6Zs3o9TNDRUff4yy0FCVYFlSUiIItHVr+epjcOFchIKCgpCXl9dig4i6xqUqnDt3DlevXlW7iSQoKAgRERFYv349TExM8Pnnn6Oqqsqgv3J1+cAuLi7o3r271usYY3Bzc2uu2yIIIdnZqHnzTciHDMHdIUMQYW8PUceOuMYYHhgZIbRvX/wydix2DhqEpPXrIfvhB1SuWIEyb2+1sSknJwfx8fGC5zQgIABRUVF62YjKpVGJRKImp1HpQ/Ry1SY40Xf+/HmBkUVoaCguXryIvXv3wtzcHIsWLUJJSYmhf+sUnx4xSOgSrZ+4OGD8eGD0aGDKFGD2bNRMn46SoUMh6dIF/5iZYZmxMT6zscE6a2ucGzoUl2fMQMylS/U6+ShbRzZ1cCkpKUF0dDS/wcSQLkJyuVyQqqA8S1J3E8msWbPAGIOJiQnGjh2L06dPG/o3rpb9+/eDMabVypMGEqJFefAAmD4dGDECmDwZmDULNW+9BfnTT6Ooa1dc6NgRyxjDBjs7rOrcGX8OHoxLkybhlpdXvSlUyoYzdd3MIiMjG1XlQCaTITExEV5eXoiIiGjRFAlNgltdqoJMJkNBQQFSUlIQFRWFlStX8rFp5MiR2L9/v6F/4xqh+NR6IaFLtH4uXwYWLABmzQJeeQV46SXA0RGYPh3FL72EoP79UeLsDPGiRYieMAF/jB6N13v3brR7m7bBRSQS8YOLulq+BQUFvC+9oV2ElHdRaxPcMpkM/v7+sLOzw8svv4wffvgBy5cvx3fffWfo37ha3nnnHfTv31/rOYwx9OjRAyYmJujevTsWLFiAnJyclrlBov0REwMsWwbMmVP7x/hLLwH/93/AG2+gfPx4XLC3h3jJEkjfew9xr7+Os88+i3l9+6q4t126dKleMwZuI2pycjKuXr3Kmzr4+vpqreUrkUh40x5D1xCXy+VISUnh9y1oe8/Xrl3D0KFD4eDggF27duHTTz/F2rVrDf0b1wjFp9YLCV2i9XPtGrBiBfDtt8DmzcCnnwLr1wN79gAuLsDp00BICODjU/vf3FyBe9v777+v1b2toYNLZGSk2sElNjYW586dw+XLlw3itlZ3UONc5rTtoi4pKcH27dthamqKjz/+GBUVFYb+LWvl0qVLMDY2xt69e7Wet3jxYpw5cwYhISHYu3cvevbsiUGDBkEul7fQnRLtipQUYPVqYNs24H//q41N69YBu3cDzs7AkSPAlSu1sSk4GLh3T+DetmLFinrd27Q972KxGKmpqbh27RoCAgIEFVji4+ORmJgIX19fBAcHIz8/36Cxqbi4GFevXoWXlxeSkpI0vjeZTIYjR47AysoKb7/9NiQSiaF/y/VC8al1Q0KXaP0UFdUOJCtWAD/+COzbB/z0U+0As3EjcO9evS+hUCiQmZmJP/74A66urhg+fDgYY+jatSsmT56Mbdu24eLFiw1a0uMGl6tXr/KbKby8vPjBRVt5r+ZsWVlZOH/+PIKDg7UaY2RmZmLKlCno0qUL/vzzz1ZfZP3+/fuws7PDhAkTGu3IduXKFTDGBHUoCUJvVFYCP/xQ+wf399/XxqZ9+2rj0qef1s741oNCoUB+fj7+/vtvfPLJJ7x7m6WlJV555RVs3LgR58+fR0FBQb3CVyKRICMjA9evXxdYjIeGhvIW44aITXl5eXxucE5OjtbY6uzsDDMzM7i5uT0SDowUn1o/JHSJR4MbN4AtW2pnSZYvr/3v+vW1syQ6oFAokJubi7/++gsff/wxRo8ezbu3jR8/Hlu2bIFIJNLo3qbsInT//n2V8l5c7ciWGFxkMhni4uLg6emJGzduaP1ZoaGh6N+/P0aOHInExEQ9/5L0j0QigYODA0aMGKFz3Ug7OzssX75cz3dGEA9JTQW2b68Vu1xs+vRT4J9/AB2EmkKhgFQqhY+PD9bb58gtAAAgAElEQVStW4eXX34ZHTp0aLB7W35+PoKDg+Hr64uMjAzcvXtXYDHOlR6Mjo7GvXv3mr3cYmpqKs6dO1dvqkJMTAxGjx6Nvn37IiwsrBl+UfqH4tOjAQld4tHh/n3A1xc4dqx2EElOBvQ0G6lQKCCRSOp1b8vJycHevXvh4eGBqKgotYOEcu3I5h5cGpOq4Obmhg4dOmDp0qUanXhaE6WlpRg7diz69u2LzMxMnV/Hzs4OK1as0OOdEUQdcnOBgADg+HHgr7+A2FidRK46OPe2wMBAbN68Wa172+nTp5GVlYVDhw7hn3/+0ZhGxVmM3759G6GhoYJyi8p1bfURm4qLixEVFQVPT08kJiZqTVX4888/0a1bN0yaNOmRcWCk+PToQEKXINSgUChQUlICkUiEDRs2YNy4cejYsSMsLS3RqVMnrFmzBmfPnkV2dna9y4nKg0vdWr5NGVyysrLg5+dXb6rCgwcPMHv2bFhaWuLo0aOtPlUBAKqqqjB16lRYW1sjLi5O59cJCwsDYwzu7u56vDuCMBzK7m3btm3j3ds6dOgAMzMzfPDBB0L3Ni0xRLmWL/cHs4eHBwIDA/lavoWFhY2OTQ1NVZBKpVi5ciVMTU3x1VdfPTIOjBSfHi1I6BJEA6iursawYcMwcuRIrF69WsW97aOPPsKpU6dw7969Rg0uyrV8Gzq4NCZVISoqCoMHD8agQYMeqcLkLi4uYIxhz549guLq4eHhvOVnXbvMgwcPYunSpTh16hSCg4Ph5uaGHj16YMiQIVpL/hDEo86UKVPwxBNP4JNPPsHUqVMF7m1Lly7F0aNHG1RxQSaTIScnBwkJCbhy5Yqg5jZXblHbH9VyuRxpaWk4d+4crly5ovUP+OTkZLz88suwtbVFYGDgI/EHOAfFp0cLEroE0UASExMFMw6ce9vOnTsxefJkdO3aFUZGRnBwcMCyZctw/PhxpKWlNUj4coXi6xtcpFIpP/OSnp6udcA6dOgQLC0tMXfuXBQVFRnwkwMSEhIwceJEWFpawtbWFmvXrtVa6aF///5q7TIZYwgMDASgapcZGBiIl19+GdbW1jA1NYWdnR1cXFyQl5fX3G+PIAwKl7bEUV1djRs3buD777/HzJkzYWNjA8YYnnrqKSxatAgHDx5EXFxcg4QvV24xIiJCYOgQGRmJpKQkvpZvcXExrl27Bk9PTyQkJGhNVeAcGJ2cnJq07K8PGhubAO3xKSMjAwDFp9YECd1HGCcnJ7UPWkPq8unycBPaqaqqQlRUFPbs2YM333wTPXr0AGMMAwcOxJIlS3D48GGtA0B9g4uvry+8vLxw/vx5rTPHBQUFWLJkCTp06IAff/zR4DuXCwsL0bt3b4wbNw5+fn5wd3dH165d8eGHH9Z7raurK7p27Qp3d3f4+fnB0dER9vb2Om/8IFoOik+th5qaGsTFxWH//v2YN28e7O3twRhD3759MW/ePPz4448NMrnhyi2mpKQIavn6+PjA29sbPj4+SE1N1RibiouLsWHDBpiYmGD9+vUGd2Ck2NQ+IKH7COPk5IRx48apLJ1UVlZqva4pDzfRcGpqanD79m38+OOPmDNnDno/NLHo378/5s+fj/379yM6OrpB7m23bt3iHdvq1vJNSEhAZmYmiouLER0djZEjR+Lxxx9HRESEoT8CAMCOHTtgZWUFsVjM9x08eBAmJibIysrSeN39+/dhYmKCQ4cO8X1isRidOnXCN99806z3TDQdik+tl5qaGqSkpOCXX37B4sWL8eSTT4Ixhl69emHmzJnYs2cPrl692qBNs4mJifD09IRIJIK/vz88PDzg7e2Ny5cvIy4uDpmZmSgsLERGRgbGjx8Pa2trnDt3rlWkKlBsah+Q0H2EcXJywptvvtno63R9uImmUVNTg6SkJBw6dAgLFy7E448/Xq97m1QqRVhYmEqqglgsRlpaGl8ofs2aNbCyskK3bt0wdOhQ+Pv7G/rt8jg6OmLGjBmCPolEAiMjIxw9elTjde7u7jAyMlIpGD9jxgzBkiDROqH49OigUChw9+5d/Prrr3BxccHgwYPrdW/TlKrA1fLlyi3u2bMH5ubm6NmzJ+zt7XHq1CmDz+RyUGxqH5DQfYTRdSDR9eEm9ItCoUBGRgaOHTum1r1t1apVGDlyJDw9PbW6GkkkEixfvhyDBw+Go6Mjnn32WQwZMsTQb4+nZ8+e2Lhxo0q/nZ0dPvvsM43XrV27Fvb29ir9GzZsgK2trV7vkdA/FJ8eXTj3tj///BMrVqzAyJEjBe5tq1evxsiRI3H06FFkZWVpXY3aunUrBg4ciBdffBEvvfQSunTpwm/YMjQUm9oHJHQfYZycnNC5c2dYWFjA3Nwc48ePR2RkZL3X6fpwE82LQqFAVlYWfv/9d7z88sswMjLCwIED0aNHD43ubQkJCXj++efRu3dvXLhwgV8OrG95uCUxNTXFrl27VPqHDx8OFxcXjdc5Oztj+PDhKv27du2CmZmZXu+R0D8Un9oOnHvbP//8g6lTp8LExAR9+/ZFjx49NLq3ZWZmYurUqejSpQtOnTpFsYkwGCR0H2G2bNkCd3d3hISE4OTJkxg1ahQsLCwQU4/tpK4PN9Ey1NTUYPbs2Th58iTy8vJw9uxZ/Pe//8UzzzwjcG+bP38+unbtivHjxzdog4+hMDU1xe7du1X6hw0bhmXLlmm8ztnZGQ4ODir93377LTp06KDXeyT0D8WntsnKlSuxZ88e3r3ts88+U3Fve/fdd9G7d2+MGDGiVTswUmxqH5DQbUVIpVIkJCTU2zTlN0kkEtja2mLhwoVaf05jHu6ioiJ88cUXGDNmDLp27QobGxtMnToVt2/frvf9HD16VO2u6507d9Z7LaEK59527tw5rFmzBt27d8eUKVNaTb6bJmh5sG3Q2uITxabWA+feFhQUhM2bN6NPnz4YOXJkq3dgpNjUPiCh24rQFHzrtvv372t8jfnz52Po0KFaf05jHu6YmBj06tULGzduhEgkgqenJxwdHWFpaYmEhIQGvZ+AgADBrmvaUKIfqqurW8XO5fpwdHTEzJkzBX1SqbTBGz7qluuZOXMmbfgwAK0tPlFsar1QbCJaEyR02xjz58/HsGHDtJ7TmIeby7dSpqSkBNbW1li1apXWn8MNJnV3phLtix07dqBz586C78Hhw4dhamraoBI+v/zyC99XWFgIKysrKuHziKLP+ESxiWgqFJvaByR02xCFhYWwsbHBokWLtJ6n68OtzPPPP485c+ZoPYcGEwL4ty6qk5MTRCIRjhw5gm7duqnURa1rmQnUFmXv1q0bjhw5ApFIBCcnJyrK/ojSUvGJYhPRUCg2tQ9I6D6iREdHY9KkSXB3d0dwcDBOnDgBBwcHWFpaIjY2lj/vzp07MDExwfHjx/m+hj7cmpBIJLC0tMQXX3yh9TxuMLGxsYGJiQkGDBgANzc3/S5plZcDRUVAK89Tbas0NE8yPj4eEyZMgIWFBWxsbLBmzRocPny43jzJ8vJyrF69GjY2NrCwsMDEiRPrXZYmDI+h4lOrik2VlbWxiRzdDALFJoKDhO4jSmZmJiZPnoxevXrBzMwM3bp1w7Rp03Djxg3BeRkZGWCMqaQkqHu4G2qx6eLiAisrq3pnV/z8/LB161aIRCL4+flh2bJlMDIywqZNmxr1XtUilwMREcCvvwKHDwOnTwMxMUB1ddNfm2gwlCdJqMNQ8alVxKbKSuDGDeDkydrY9PvvwPXrJHhbGIpNBAcJ3XZIU3ZPHzlyBIwxHDt2TKefvWzZMpibmzdtN25lJeDjA3zzDbB/P3D0KODmVttaie1te+FRyZM8ffo0pk+fDnt7e3Tq1AmjRo2Cu7v7I7Fhpj3xyMemmhrgwgVg1y5g797a2PTjj7X/DgqqPU60CBSbCA4Suu0QXXdP+/r6wtTUFJs3b9b5Z/v7+4Mxhueffx6WlpawtbXF2rVrGzRbU1FRgTVr1uCFHj2w1swM24cPx/39+wEvr9p24ADg7g5Q3p3BaW15ki+++CLmzZuHU6dOISgoCOvXr4exsTG2bt3a7D+baDitITb9+eefmDhxok7x6csPPsDazp2xzMwMm0aN+jc+HT0K7NsHZGbqfH+EfqDY1P4goUs0iPDwcFhaWuL9999v0uucPXsWjDGMHj0afn5+cHd3R9euXRuUf+fq6oquXbvC6/PPkfjee/hs2DAstraG7OTJ2sHk779rZ06Skpp0j0TTaFV5kg/Jz89X6XNxcUH37t31/rOIlkVfsYkTuo899hjGjRunU3waa2WFq2+/jetffCGMT56ewNdfAzdvNukeiaZBsal9QkKXqJe4uDhYW1tj6tSpTTYnGDNmDBhjuHfvHt938OBBmJiYNKicy6FDh4DwcGDHDhT//jtmdeyIvxYvrhW6Z88Cu3cDyclNukeiabR0nqREIkGfPn0wb948Qf+CBQtgZ2cHsVis9rr9+/eDMYbS0tJG/Tyi9aDP2OTi4gJTU1NYWVkJvjONiU9/bdlSm1Ll4SGMTx4eJHRbARSb2ickdAmt5Obmok+fPrC3t0dQUJAgMT8uLo4/T93u6RkzZmDHjh3w8fGBj48Pli5dCsYYhgwZIvgZEomkwQW6JRIJkJYG/PADcPw4tr/4ItYPH14rdH/6qXaJsKhI3x9Du+JRzJP09/eHkZERTp48CeDflQNfX1+N17zzzjvo37+/TvdJGJ7miE39+vXDjBkzBD+nMfFJmpQE/PwzcOgQ4OX1b3z65Zfa1KpWbNX9KECxidAFErqEVi5cuKAxT07ZAUbd7ukNGzZg8ODBsLS0RMeOHTFq1ChYWVmR5WIrpzXkSUZGRjb62hUrVsDa2ho3b95Ez5494erqqvHcS5cuwdjYGHv37tX5XgnDou/YdODAAbKEbeVQbCJ0gYQu0aKYmppi165dKv3Dhw+Hi4uLxuucnZ0xfPhwlf5du3bBzMxMr/dINB5950levXq10dfK5XIMHDgQHTt2xJNPPomSkhK1592/fx92dnaYMGECamgXPKEExae2B8UmgoQu0aKYmppi9+7dKv3Dhg3DsmXLNF7n7OwMBwcHlf5vv/0WHTp00Os9Eo1D33mSTSnxtGbNGjDG8OWXX6o9LpFI4ODggBEjRpCDEaECxae2BcUmAiChS7QwLbk0qGt9QnVLYYMHD67nnbVPmiNPcsuWLTrdy82bN2FmZobRo0fDysoK6enpguOlpaUYO3Ys+vbti0wq80SooaXiE8Wm5odiE8FBQpdoURwdHTFz5kxBn1Qqbfhmjzp/6c6cOVOQj6eMrvUJGWNYtWqVIDDeunWrQe/vUcbJyUntQJqjZQONtjzJcePG8ec1Jk9SF8rLy+Hg4IBXX30VZWVlGDlyJJycnHjhUFVVhalTp8La2lowyBGEMi0Vnyg2NQ6KTURTIKFLtCg7duxA586dBYW4Dx8+DFNT0waV7/nll1/4vsLCQlhZWeGbb75Re42u9QkZY3Bzc6vvrbQ5nJycMG7cOMEgGh4ejsrKSq3XFRYWonfv3jrVHtUX69atQ+fOnXHnzh0AwK1bt2BmZobvvvsOQO3vnTGGPXv2qLy/8vLyFrtPonXTUvGJYlPjoNhENAUSukSLwgUeJycniEQiHDlyBN26dVMJPOPHj8f48eMFfa6urujWrRuOHDkCkUgEJycn2NvbNyqfqSH1CdvzYPLmm282+rodO3boVHtUX1y+fBnGxsYCkQEA27Ztg7m5OeLj49G/f3+NszsZGRnNfo/Eo4Eh4xPFJs1QbCKaAgldosWJj4/HhAkTYGFhARsbG6xZs0bFYtPJyUllya+8vByrV6+GjY0NLCwsMHHiRCQkJDTqZzekPiFjDD169ICJiQm6d++OBQsWaF0iayvoOpg4OjrqVHuUIFojhopPFJs0Q7GJaAokdIl2Q0PrEy5evBhnzpxBSEgI9u7di549e2LQoEGQy+UtdKeGwcnJCZ07d4aFhQXMzc0xfvz4BtWM1HUDD0EQtVBs0g7FJqIpkNAl2gVNqU945coVMMZUlp+U0VTIfOfOnVpfu6KiAmvWrIGtrS0sLS0xceJEJCYmNur+9MWWLVvg7u6OkJAQnDx5EqNGjYKFhQViYmK0Xqdr7VGCICg2NQSKTURTIKFLtHn0UZ/Qzs4Oy5cv13icG0wCAgIEmwnqywNzdXVF165d4e7uDj8/Pzg6OjY671gTTbHLBGo/N1tbWyxcuFDrz9G19ihBtHcoNlFsIpofErpEm0Zf9Qnt7OywYsUKjce5wUR5t3Z9cDu1Dx06xPeJxWJ06tRJYyWJxqCrXaYy8+fPx9ChQ7X+HFoeJIjGQ7GJYhPRMpDQJdos+qpPGBYWBsYY3N3dNZ6jy2DC1d6se82MGTM01gZuaebPn49hw4ZpPUfX2qME0V6h2NR0KDYRDYWELtFmaUh9wrplgg4ePIilS5fi1KlTCA4OhpubG3r06IEhQ4ZoLfvDDSY2NjYwMTHBgAED4ObmptXpSBe3t5aksLAQNjY2WLRokdbzdK09ShDtFYpNTYNiE9EYSOgSLUJKSgqWLVuGESNGwNjYuEVmBRpSn7BumaDAwEC8/PLLsLa2hqmpKezs7ODi4oK8vDytP8vPzw9bt26FSCSCn58fli1bBiMjI2zatEnjNc7Ozhg+fLhK/65du2BmZqbTe9aV6OhoTJo0Ce7u7ggODsaJEyfg4OAAS0tLxMbG8ueps8tsaO1RgmiNUGxShWIT0ZYgoUu0CB4eHujTpw9mz56NQYMGtZrlr+Zk2bJlMDc3h0wmU3vc2dkZDg4OKv3ffvstOnTo0Ny3JyAzMxOTJ09Gr169YGZmhm7dumHatGm4ceOG4Dx1dplAw2qPEkRrhGKTKhSbiLYECV2iRVAum/Pmm2+2i8HE398fjDGN9R5b+/IgQbQHKDapQrGJaEuQ0CWajEQiQZ8+fTBv3jxB/4IFC2BnZyewXwTa32By9epVtce5DR91y/XMnDmzXXw+BNHcUGxSD8Umoj1BQpfQC/7+/jAyMsLJkycBAGfPngVjDL6+virntpfBxMXFRevyIFfCR7nYe2FhIaysrPRSwocgCIpN6qDYRLQnSOgSemPFihWwtrbGzZs30bNnT7i6uqo9ry0OJjNmzMCOHTvg4+MDHx8fLF26FIwxbNmyhT+n7i5qoLYoe7du3XDkyBGIRCI4OTnprSg7QRC1UGyi2ES0X0joEnpDLpdj4MCB6NixI5588kmUlJSoPa8tDiYbNmzA4MGDYWlpiY4dO2LUqFE4cOCA4Jy6u6gBoLy8HKtXr4aNjQ0sLCwwceJEJCQktOCdE0Tbh2ITxSai/UJCl9Ara9asAWMMX375pcZz2uJgQhBE64ZiE0G0T0joEnrj5s2bMDMzw+jRo2FlZYX09HS159FgQhBES0KxiSDaLyR0Cb1QXl4OBwcHvPrqqygrK8PIkSPh5OSk1n2HBhOCIFoKik0E0b4hoUvohXXr1qFz5864c+cOAODWrVswMzPDd999B6A2R+7MmTM4c+YMxowZg2HDhvH/rs/ZhyAIQlcoNhFE+4aELtFkLl++DGNjY0EpGgDYtm0bzM3NER8fz7vWqGsXLlwwzI0TBNGmodhEEAQJXYIgCIIgCKJNQkKXIAiCIAiCaJOQ0CUIgiAIgiDaJCR0CYIgCIIgiDYJCV2CIAiCIAiiTUJClyAIgiAIgmiTkNAlCIIgCIIg2iQkdAmCIAiCIIg2CQldgiAIgiAIok1CQpcgCIIgCIJok5DQJQiCIAiCINokJHQJohWxcOFCMMYwZ84clWMfffQRGGN47bXXVI5NmDABjDGsXLlS0J+eng7GGBhjuH37tuDY+PHjwRjDf//7X0H/xx9+CMYYxjMGKLWlD19n2tSpUCgU+Omnn2BmZsa//nvvvce/hrOzs8q9bty4EYwxjBo1SqfPhiAIgiAaCwldgmhFPP/882CMYePGjSrHXn/9dTDG8NFHH6kcs7e3B2MMBw4cEPT7+PiAMQZjY2OUl5cLjvXq1QuMMfz888+C/vHDh4Mxho8ZA4yM+PbCQ0G75u23eUGu3KKiovjXGP7wNbZs2cL3vfrqq2CM4YMPPtDpsyEIgiCIxkJClyBaEd27dwdjDMeOHVM51r9/fzDGsHfvXkF/cXExLzaDg4MFx3bv3g3GGAYMGCDoF4vF/DWhoaGCY7bm5rUCWEno1jAGq4fn97Wy4q997rnnwBiDubk5KioqAABSqRRGRkZgjOH8+fMAgKqqKnTq1AmMMRw/frzJnxNBEARBNAQSugTRSigoKOAFZFhYmOBYaWkpLx5FIpHg2LVr1/jrsrOzBceWLl1am24wbZqg//Lly/w1BQUFau/hkpLQzagze2tsbIydO3di5cqVYIxh7Nix/GuIRCL+vMLCQgDAzZs3+b7k5GS9fF4EQRAEUR8kdAmilRAeHs6Lwby8PMGxmJgY/lhGRobg2G+//QbGGLp06QKFQiE4NnbsWDDGsG7dOkH/oUOHwBiDjY2NoD8kJORfkfpQ6MoZw1QlkftYly4IDAwEALzwwgtgjGH16tX8a3z55ZdgjGHo0KF83759+2qvfewxlXskCIIgiOaChC5BtBJ+/fVXMMbQtWtXFTH4119/gTGGjh07orq6WnBs06ZNYIxhzJgxgn6FQgFra2swxnDkyBHBsVWrVoExhldeeUXQf+DAATDG0NvUFDAywlXGMFhJ5FoYGeHeQ6FdXl6ODh06gDGGM2fO8K8xadIklc1pXE5v3ZllgiAIgmhOSOgSRCth8+bNfN5rXXbs2AHGGBwcHFSOzZ49G4wxLFq0SNCfm5vLC9Tw8HDBMW5j24oVKwT9XGWH8Y6O+N+IETB5eL3Rw/++v3Ahf67yDHRmZiYAoKamBt26dQNjDIcOHeLPfeqpp8AYw44dOxr/wRAEQRCEjpDQJYhWwrx588AYwzvvvKNybMmSJWCMYebMmSrHRowYAcYYtm3bJui/ePEiL0SlUqngWJ8+fcAYw08//STo5yoj2Nra8tcO7N8fVg83kh0+fJg/183NDYwx9OnTh++Lj4/nr4uJiQEgFNwXLlxo9OdCEARBELpCQpcgWgnPPvusSkkujpdeegmMMaxfv17QX11djY4dO4Ixhr/++ktwjE9D6N1b0F9UVKS2SkN1dTWslCoqcDO+N27cUFuLd+7cuWCMYdasWXyfu7s7ny9cU1MDAPDw8ABjDCYmJpDJZLp/QARBEATRSEjoEkQrQKFQoEuXLmCM4cSJEyrHe/ToAcYYjh49KuhXNoSIjY0VHOMqIrz66quC/oiICP6aBw8eAACuXr2Kp59+mu/v0aMH/Pz8AADHjh0DYwxWVlaC/OB+/fqBMYbdu3fzfS4uLipGEZ999hkYY3jmmWd0+3AIgiAIQkdI6BJEK0B5eT8iIkJwTFvZMV9fX42GEJrycI8ePQrGGKytrSEWi/HBBx/wpcsYYzAyMsK9e/f485cvX64imLOysvjzL1++zPc7ODiAMYbNmzfzfePGjQNjDB9++KHuHxBBEARB6AAJXaLdoFAoUF1djfLyclRUVKC6urrVlLpSrmsrFosFx65cucIfy8/PFxzj8mSfeuopldfs27cvGGP48ccfBf1r166tzb0dOBA9e/b8t2zYY4+BMVWL3tGjR4Mxhs8//5zv+/vvv8EYg5mZGUpLSwEIjSJ8fX0BAJWVlbCwsABjDL/99pvuHxBBEARB6AAJXaLNoyxw5XI58vPzUVhYCIlEAqlUipKSEpSWlqKiogJVVVV8bmlLojzLWhcudUDdMVdXVzDG8MYbbwj6S0pKeAHL1bzlcHR0FOThWlpa4uuvv8bEiRPBGIOLiwt/rlwuh4mJCRhj8PDw4PvXrVunUtLM399fxSgiKiqK70tLS9PtwyEIgiAIHSGhS7RpampqUFFRgdLSUsjlcsjlckgkEhQVFUEqlaKwsBBisRhisRg5OTm4fv06CgsLUVxcjNLSUpSXl6OyshI1NTXNOvu7YcMGMMbwwgsvqBz7/PPPwRjDiy++qHLMyckJjDF8+umngn5ltzSu9Fdqairmz58vELlvvfUW7ty5IygL9ssvv/CvExoayp+bk5PD93Ni+eOPP+b7OKOIIUOG8H179+7lqzi0ltlzgiAIov1AQpdok3ACNz4+Hrm5uSgtLUVZWRlKS0shlUpRXFys0rKysuDh4YGCggKBAOZmf4uKiiCTyVBWVobKykq9pj5wtXAXKtWp5Xj77bfBGMO7776rcqxXr14qNWsB4MSJE2CMoXPnzsjMzMQHH3wAU1NTgcjduXMnf35SUpLaygq7du0CYwz9+/fn+5TTEX7//Xe+f/LkySpGEe+88w4vqAmCIAiipSGhS7QpFAoFKisrUVZWBrlcjvPnzyMjIwPl5eUoLy/XKnSzs7Ph4eHBz+gWFxfzM78SiUSt+NVX6gNX8eDLL79UOcbVyd2+fbugXyKR8OI0JCREcIybIe7duzcvSrlqCtz/FxcX8+dzrmydOnUSVFaYNWsWGGOYO3cu33f9+nWVdISamhp0794djDEcPHiQP/fxxx8HYwzffPNNoz8TgiAIgmgqJHSJNoFCoUBVVRU/a8vN4IpEIhWhK5FI1ArdnJwcFaGrrhUVFaGoqEhF/HICmBPSDU19UCgU6PTQkOGPP/4QHKupqYG5uTkYE9rsAsIyYbm5uXx/bm4uhgwZIpi9tba2xrfffouNGzeCMYbhw4cLXotzRHNychL0c8YS3333Hd+3b98+MMZgY2PDv6eEhASVGeHs7Gy+LzQ0tHG/UIIgCILQAyR0iUcaTQKXE7b+/v5IT09vlNAVi8Vaha42Aaxt9ldT6oOyIIyKihK8vzt37vDHoqOjBceOHz8Oxhi6desGhUKBhIQEuLi48AYSjDF06NABmzZt4p3RpkyZopJeAABjxhfymeMAACAASURBVIwBYwzr1q3j+zIzM9WWNVu0aBEYY5g+fTrfd+TIET5VgpsRPnv2LBhjMDU15SszEARBEERLQkKXeCRRrqTAbTRTFrhcCwgIQFpaWoNSFx48eNAkodtQ8RsZGYmkpCQ+9cHPz48XlAUFBYKZX5FIxB+rKxa5TWpDhw7lBWzddvr0acFnxpUT279/P99fVlYGMzMzMMZw9uxZvp8TqsolxABgwIABYIxhx44dfB9nFDFx4kS+j8stVq7MQBAEQRAtCQld4pFCoVCoVFJQJ3C5FhgYiNTU1AYJ3dzcXH4zmr6ErroWHByM2NhYXvz+8MMPfB3buqkPXJ3cfv36CQRwSUkJnnnmGRVh269fP3zwwQcqZb4AICMjg++/du0a36+cAnH//n2+nysh9txzz/F9+fn5/LnK9sFcHvGmTZsAAN9//z1/nnJlBoIgCIJoSUjoEo8MdQVu3TQFdS0oKAjJycmNErr5+fnNLnTj4uL4f3/yySd8abG6s7/cTOkrr7zCz/4uWrSIz+nl2rPPPouTJ0+iqqoKO3fuBGMMAwYMEHx+p0+f5tMZKioq+H5OaNvZ2QnO5xzNPvroI77P29sbjNU6sZWUlAAAioqKeKMIHx8fbN++XXBv//zzTzN+KwiCIAhCMyR0iVYPV0mhMQKXa8HBwUhKSmqQ0M3Ly2sRoRsUFCQQutOnTwdjDPPnz1c5d/z48byQ5SoY1G1fffUV5HI5X/WBSxmYN2+e4HNcs2aN2lq9XG1d5RJgVVVVsLS0BGMMJ06c4Ps3bdoExoTuaQEBAfy9rFq1SnBvlpaWKCsra6ZvBkEQBEFoh4Qu0WpRKBQoLy+HWCxutMDl2oULF5CYmKgidIuKilREZX5+Pjw8PJCXl9fsQjc+Pp7/9/Dhw8EYw+bNm/m+W7duYceOHYKNZVwbMWIEli1bxv87KSlJsPGNq5Tw9ddfC6o+cOYSdVMJuJxb5bq6N2/e5F8/JSWF7+fc01xdXfm+rVu38pviuGtsbW3BGMObb77Z/F8UgiAIgtAACV2i1aFcSeHevXvw9vZutMDlWkhICBISEholdHNzc1tM6EqlUr7O7ZYtW7BmzRoMHTpURdx27twZH3zwAS5duoTi4mLs2LEDjDH07duXf92ioiKkpKTw17i5uSE5ORmFhYUoKCjg0x3c3d35qg+acm5//vlnMFZbd5fLDa6urkbnzp3BGMPRo0f5cydNmiS417fffpufDT58+HBLf30IgiAIgoeELtFqUK6kwNn1ZmZm4ty5czqJ3PLycoSGhiI+Pr5BQregoKDFha5y/Vl14pb7f+UZ4OLiYsyZMweMMUybNk3Qf+bMGTDGYGRkhL///ht3RCKUbd6M64sX868VERHBlzzjKisYGRkhPz8fVVVVUCgUWLJkCRhjeOONN/jfT0xMDP8aCQkJAIDy8nK+YgNjDEuWLIGPjw//76ysLEN9nQiCIAiChC5heLSVCsvJyYGnp6fOQvfSpUuIi4trkNAVi8Xw8PDAgwcPmlXoBgYGIiEhAcXFxfDy8hKI28cffxwffvghfH19+Vlbe3t7ldcYNGiQSrpDcXEx74g2ZMgQpL/1FsAYwBiOPHz9LsbGkKam8oYXn332GX++suHF4MGD+dfnUh8OHTrEpyjU1NRAIpHw9XcZq3VPq6mpwccffwzGGJ555hlDf7UIgiCIdg4JXcKgqCsVppymwFVC0DV14fLly4iJiWmU0M3JyWkxoctVKOjRowciIiIE98VtEps8ebLg+qysLL7KwdmzZwXHuDSChS+8wItcMIYPHorRCYyh6j//4c9/7bXXas9fuJCv+atsUnHmzBl+9pczinj99deRkpKCYcOG8edZWVnx7m9PPPEEL5IJgiAIwpCQ0CUMQkNLhXF5s3K5XCehGxYWhtu3b/P/lkgkSEpKQmZmpkr1hcLCwhYXunPnzgVjtS5jdc/jNqmtX79e0H/+/HleYKanp/P9RUVF/CawH3r1gsLIiBe6zz48f/3Df5dER6OoqAjW1tZgjOH777/nX8fDw4N//Tt37vAlz7hZ3sWLF/M/hxPc7733HioqKnD79m1BigRBEARBGBISukSLorzRrCGVFLi82ZKSEp2Ebnh4OKKjoyGXyxEbG4tz587Bx8cHHh4e8PT0RGBgICIjIxEfH4+MjAx4eHggOzu7WYVuQEAAL3S5WdGNGzcKzsnLy4OJiQkYYzh16pTgmLqNaMXFwnzfcBMTXuSWMQbTh/1/P+yTnzmDW7du8edzm9yKi4uxceNGMMYwcOBAvu/evXv8uVwlCCsrK17obt++HWKxGF9++SUYqzW/kMlkKC8vR1VVFT/bSxAEQRAtCQldokVQFrilpaUNLhXGzbIWFxfrLHQvX76M8+fPw8/PD2lpaZBIJBCLxbh37x7i4uIQERGBwMBAeHp6wsPDA35+fggPD0dsbCzu3LmjV0tgTugmJiYiNzeXF7N//vmn4JwLFy4INn4pH9O0Ee2PP/4AYwympqaQ9+4NxUNRG6GUA5z5sE926RLc3d3BGIOFhQUKCwv51/nPf/4DxoR1fZVneRmrNZf44osvwFitRfD58+dRVFSEsWPHgrHaGr7KJc8kEgmKioogk8lQVlbGV30g8UsQBEE0JyR0iWZFXSWFxuTbSqVSeHh4QCqVNlrkZmdnw8fHB56enoiPj+dnkOvL0b158yaioqIQHBwMLy8vXvyGhYUhOjoa6enpTTKV4ITuxYsXNYpZzkL3scceU7lXTRvROEOIUaNGoXz9en5Gd+/Dn9GbMSiMjVE9bBiKi4qwfPlyMMb+n73zDm+rPPv/4xEncZy9yCYkhJAGsgiEkQGkFF52oKyXXymBhAAtG8oI5W1pA7SMMtMywuwLCbQcDduSLNmWbMtLlmx5SLZlyZYtb1vDS/H6/v6wzkHbkmzFft3nc133Vfs5Q8em57o+uX0/941LL73Uo/yBLWd4++23ud/LRRddxD3r5s2bodPpuMEUl156KbKzs2E2mzlx/+qrr7j7eU97CyS/7MALKr8UCoVCGS+o6FKiAiu4NpsNWq2Wk5lwZZXNJnZ2doZ8TUdHB5RKJXg8HiQSCQoLC0PajMZKdUNDg8daY2MjKisroVKpkJmZCYFAAIZhkJKSgqysLGg0GhgMBrS0tPi9r3dIJBLo9Xq8//77IIRgwYIFPtfdf//9IITgqquu8lgPthGNnaL261//GrbGRrRt2gQQgntdgnozIRiePx/dSiUcDgfXMeE3v/kNdw/vcgaDwcBladlMbkNDAzo7O7kBEc8++yyUSiU+//xzLqNcX18f8OcPJL8dHR2wWq1c7XRvby8tfaBQKBTKmKCiSxl33DeaNTc3g2EYrl4z3Oju7gbDMGhvbw9JijUaDXg8HrKzs9HW1obCwkKoVKqQRNdut/uIbiBRa25uRnV1NdRqNRQKBVf3KxQKIZfLoVarUVVVhaamJp/PkkgkqKysxKFDh0AIwb59+3w+Y9u2bSCE4KmnnvJYF4lEATeizZ8/H4QQvPfee7BareB9/z2s772H81yDIv5n7150VVfD4RjpGczW2n7xxRfcfT755BOunEEsFmPZsmUeJQtsptb9OX788Ufk5eXhrrvuAiEEe/fuDTvLbbfbPQTYXX5p6QOFQqFQIoWKLmXc8NdJYaybyXp6esAwDNra2gKe09vbC71ej+TkZEilUtTX13PHVCqVT0aXlapAohssGxlM1FpbW1FTU4Pi4mKuLphhGAgEAmRkZEClUkGv10MkEkGv1+OSSy4BISMjeb1LKFgJ/fLLLz2OBdqIVlJS4pGJZcsw9Ho9t87WOjscDo+yidLSUm79oYceAiEE55xzDhISEkAI4f532rRpsFgscDgceOKJJ0AIwYYNG5CXl4e8vDwsXLgQhBAcO3Ys7N9fqNnfkpIS5Ofn09IHCoVCoYQEFV3KmBkeHkZ/f7/fTgpWqzXiGlun04m+vj5uWpm/Y3V1dZBIJEhJSUF1dTX352421Go1CgoKQhJdtkwiEtENFO3t7TCZTNBqtVAqlRCLxWAYBj/++CNmzJgBQgjeeOMN1NfXw2q1wuFwQKlUchJaXFzscb9AG9G++OILriNCR0cH9w8MdlIaIQR1dXXc+W+++SYIIVi8eLHH72Lr1q0eGdy1a9fimmuuASEEV155JXceO6b4scce8yhbIISgqKho3H5/3qFSqaBUKgOWPjzxxBO4/fbbJ/qVoFAoFMokgYouJWJC6aTAymM4NbbewePx0NTU5LHW0tICuVwOPp/P1QD7u1aj0SA/Pz9k0eXxeDCbzVETNYdj5M/+7l0MPvnkEzAMA4ZhkJaWhqNHj4KQkRHA7e3tHtcG2oj22GOPgRCCiy66CA6Hg+s/zLYKW7duncf5d999N7yHUeTn53P1v+xgCJPJxJVEvPbaa3A4HCgrK+POSU5ORnZ2Nh588EEuExxKnXKkkZ+fj4KCAr+ZX7vdjvvvvx/33HPPRL8aFAqFQpkkUNGlhI13J4VgrcLYGttgpQejhUAggMVigdM50oWhoKAADMMgPz8fVqs16LXFxcXIy8sLWXT5fL5H5jMaIRaL8e6773IZWPbP8PX19aioqOCytps3bwbDMBCLxVAqlcjNzQ24EW337t0ghODw4cNwOBzcRLnrr78ehBDccccdHuefe+65IITg6NGjsNvt+Pvf/46ZM2dyAvvII4/AZrNBIpFwa2q1Gg6HA2+99RYIIZg7dy46Ojogl8u5YRIPP/xwVH93OTk5QTPGt99+O44cOTLRrwiFQqFQJglUdCkh4y647iN7Iy09CDWEQiFqa2tRWloKPp8PuVwe8v1KSkqQm5vr8TwTLboikYirhd22bZvP8UsvvZSTTYvFAr1eD5VKxckxIQTffvstsrOzUVxcjOrqaiQlJYEQguPHj8PhcHCbANnNZK+//jp3f/fhD9988w0OHDjgUa6wevVq7tynn37aJyPMjg0+cOAAHA4HTp06xV3L4/Gi+rtTKBQ+5Rzuce211+LZZ5+d6FeFQqFQKJMEKrqUkPDeaNbX1xdyuzAej4fGxsaIa3QFAgGEQiHEYjFqa2vDalOm1WqRk5MTsugKBALU1tZGXXT37NnDtQJzP2az2TDL1SXh448/9jjGbkRbsWIFqqqqoFarIZfLcfz4cU40v/zyS6jVapSVleHEiRPcelpaGncf97KJFStWcF8nJiaCEIInn3ySO3fz5s2cdLMCzdYW//3vf4fD8dPGtKSkJJ9Si/GOjIwMlJWVBTx++eWX49VXX53o14VCoVAokwQqupSg+OukEG4/XIFAgIaGhrAlt6GhATKZDAzDoKCgwGejWShRWlqK7OzssETXZDJFXXSXLFkCQgjeeustj2NFRUWceObn53scC7QR7R//+AcnqkVFRcjKykJycjJeeOEFEEIQFxeH7OxsVFZWorGxEc8//7xHBnf69On43e9+x30vEongcDhQUVHhk6k9efIkCCGIiYnh2pvt2rULhBDcdNNNUf29ORwOSKVSn+Ea7nHhhRfib3/720S/NhQKhUKZJFDRpfiF7aRgMBhgMpkiElw2UlJSYDabQz6/vb0dOTk54PF40Gg0EIvFMBqNEX12eXk5srKyQhZdoVAYddH99ttv/WZaHQ5HwLG8DkfgjWhHjhwBIQSXX345t2axWHD77beDEIKNGzciJycHIpEIb7/9Npe5JYRg/fr1SEtLwxtvvAFCCObNm8d9LjudzT1Te/DgQRBCsHPnTjgcDhgMBkybNg2EEHz44YdRF12RSISqqiq/x+x2O8455xycOHFiol8fCoVCoUwSqOhSPHDvpNDT04P8/HwUFRVFJJlsiEQimEymUc9zOBxQq9Xg8XhQKpXo6OiA0+mEVCqFwWCI6LMrKiqgUChCFt3k5GSPQQzRiD/96U9cVrSxsdHjGNs9gRVJd3ENtBHt4osvBiGeE87q6+uxZcsWEEJw8OBBtLS04KmnnuJG9BJCsH37dm7K2/bt20EIwf79+1FeXg6z2Yxrr73WI4Nst9u5UoejR4/CaDRi9erV3P2qXcMoohnJycmoqakJKLqLFy/GDz/8MNGvEYVCoVAmCVR0KQACtworKiryGLgQSaSlpQUV1Z6eHlRUVEAoFEImk3EdFtiQyWSoqqqK6LN1Oh3kcrmH6LJSFK5IjVew433Xr1/vc2zfvn0ghODBBx/0yWSyQuku4p2dnVzN7IkTJ7j1uro6rtb38ccfx7p16zzKFQghqKiogMPhQENDAzeg4pVXXoFUKsX333/Prb3wwgsoLS1Famoqd+2//vUvLsNMCMGmTZuiLrkOR/DNgna7HTNmzIBEIpno14lCoVAokwQquv/hjNZJwbs9VyQRSFT7+vpgMpkgFouRmpoKg8HgtzwiIyMDer0+os/W6/XIzMwMWXRTUlKiLrp79+716FrgLmrz5s0DIQQffPCBx7HXXnsNhPhORMvJyeFkU6PRcOvuk8/YiIuLw44dO3zE1L3ulv3Zf/jhB+661NRUpKen49577+XKG5YuXepxb+8JbtEIm80WdERza2srV9tMoVAoFApARfc/luHhYQwNDQUUXDZKS0s9uhZEEpmZmT6i2tTUhMzMTAgEApSVlQUc+OB0OiGXy6HT6SL67MrKSqSnp4clugaDIarCtmrVKi576r5eWlrKiaNCofA4Fmgj2gcffMDJJ/sz2e12TkrZ2LJlCxQKBTZu3AhCCJ544gnuHg888AAIIdixYwe3dvjwYRBCsHXrVm6NHVnMZnpjY2NBCMGyZcvA5/OhUCig0WhgMBjQ0tIy7oMj2LHGTU1Nfo8bjUYuU02hUCgUCkBF9z+ScDopVFRUePzpP5JQKBQoLy+H0+lEZ2cn8vLywDAMCgsLQxoNnJWVxV0fblRVVUEmk/mIrs1m8ytLbGY5WpLb1NTE1dr+8MMPHse++eYbEEIwbdo0tLW1eRwLtBGN3RzGjueVyWTYuXMnJ7ixsbH44x//iM7OTg+RTklJ4aSYFe8XX3yRWzv77LNBCMFzzz0Hh8MBk8nkMTVt9uzZSEhI4GqAy8rKoNFooFAokJycDIZhIBQKkZmZiaKiIlRVVaGpqWlM8tva2soNH/F3vKSkBIQQNDQ0TPQrRqFQKJRJAhXd/yCGh4fDbhXmnRGNJHJyclBcXIySkhIu89fa2hrW9aWlpRF9tsFggFQqDVl0RSJRVDdVSaXSgJu3nn32WS776r4ebCPatm3bQAjBoUOHcOedd/qUKzz66KPcuW+/+ioIIZgzcybs//gHnL/7HdSuemFCCDIzM+FwOFBYWMityWQyOBwO/Pa3v+XWFi5ciN/85jdcd4hvvvkGzc3N3OfY7Xa0tbXBaDSipKQE2dnZSE1NBcMw4PP5SE9PR2FhIfR6PSwWS8D/Fv7+kcCOk/Z3PCsrC4QQdHV1TfSrRqFQKJRJAhXd/wC8OymE0yrMYDAgLS0tYsnt7e2FTCYDn89HWloazGZz2G3KlEolSkpKIvr8mpoaj+cPRXQDta8aj3jnnXdACMGiRYt8jl1zzTUghOBXv/qVzzP524jW1taG+Ph4j3ICQgjOOecczxIIux19b72FG1xtxW6LicFwbCyGZ8zA666xv0unT4fNtcmL7QqxaNEi2Gw2buQvIQQzZsxAYWEh1q5dC0II7r33XjAMg9bW1lF/9o6ODm7CnVKphEQiAcMw4PF4kEqlyM/PR0VFBerr62G1Wn2ub2hoAMMwQctO4uPjMTg4ONGvHIVCoVAmCVR0pzCBOimEI4omkwkikShswezr60N9fT3S0tK4sb2RDHxwOp3Iy8uDRqOJ6Fqj0QiJRBKy6IrF4qiKLltqsGfPHp9j7AavN99802Pd30a0jo4ObjwvGwsWLMBbb72FP/zhD1zdrs1mQ+8336BnyRIkus77lBXd2FjsdfXAvW/6dDifegoOhwO7d+8GIQR33nknl7ll47333sO//vUv7nupVAqGYSKeiNbZ2Qmz2Yzy8nLk5eUhLS0NPB4PDMNAIpEgNzcXZWVlqKurg8FgAJ/PD3ivkydPYv78+RgaGproV49CoVAokwQqulMQtpNCS0sLWltbA240CyXMZjOSk5PDuqatrQ1ZWVng8XgoLi5GQUEBVCpVxFnhgoICqNXqiK5luzqEKroSiQSVlZVRE12268GRI0c81quqqjh59B4i4b4RrbOzEx999BFXQ8vGI488wrXdYrs67N+/Hw6HAwNXXQWRK3NLCEFDTAyGExLQGRODONfayblzMXjeeaivqOCyxOyzsrF8+XJ0dnZy/XV37dqFxsZGMAzjNwMbaVitVjQ0NECn06GgoID7iwDDMGAYBjk5OdBqtTCZTB71up9++ilWr16N4eHhcX2fTp06hZtuugkrVqzArFmzsGXLFnz22Wc+n/Ppp5/i3HPPxfTp03HhhRdCIBCM63NQKBQKJXyo6E4hvFuFZWdnQ6vVRiyYTqcTFosFfD4/pHPtdjtUKhUYhkFeXh46OzvhdDqhVquRn58f8TOoVKqIRbm2ttYjIx2K6Or1+qhIrnvP2/fee8/j2KlTp7jNY95dBdiNaDfffLNHWQIb119/PXeuxWLhJpW9+OKLcDQ0YGjZMjzm+tythGA4Lg7DCQk46eqaEE8IOlatwtDq1fjqL3/xuT97v1deeQVarZarF/7ss89GLScYr7DZbNBqtUhOToZKpUJGRgY37CIlJQUffvghbrzxRmzYsAEmk2lcZXfXrl2466678N1330Emk+H555/nNvmxfPvtt4iJicHRo0eRnp6Ohx56CPHx8cjNzR2356BQKBRK+FDRnSJ4d1Lo6+tDfn5+xH/yZ6OlpQUMwwTNCPf09KC8vBwCgQAZGRloamryOF5SUoLc3NyIn6GoqAgFBQURXVtXV4eUlJSQRTctLS1qouu+yUsqlXocO3r0KAgZGdfrvl5bW+sjnmx2l828fvrpp9z53333HSfMmgMHMLR2LYbj4rDBdd0LrpKF4YQE3OcS1r0zZ2Jo6VIMbtyI63/xC4/PueGGG0AIQUJCAoxGI5544gkQQrBkyRK0t7fDbDaDx+NFVXLZ0Ol0Hr83u92O5uZmVFdX4/3338fmzZs5Kd+7d++4vVttbW0+a4cOHcL8+fO57zds2IB77rnH45zLLrsM11133bg9B4VCoVDCh4ruFGFwcNBno9l4TDVrb28HwzB++9z29fWhpqYGIpEIIpEIRqMxYC/e7OzsiJ9Bo9FEPLTCu/RiNNGVSqXQ6XRREbXPPvuM2zjm3cLsxhtv5OpiHQ4HzGYz/vSnP2Hx4sU+gqtUKvH999+DkJEhD+4b1NieuBfPmYOBxEQMz52LKlc2lxCCrGnTMBwbi8GYGCxxrb0+dy6GFi7E/954I5etjY+Px1dffYXzzjsPhBDcfffdaGlpwfz580EIwbPPPguHY6TtmEAgOCOiW1paioyMjIDHn332Wfz85z+H2WyGRqOJ6vv20UcfgRCC3t5e1NTUgBACHo/ncc67776LhIQEOJ3OqD4LhUKhUAJDRXeKMDQ05COZY82kOp1OWK1WMAzj0++2sbER6enpEAgEqKioQE9PT8B7VFRUQKFQRPwMY/k5GhoaIBQKJ4XoPv7441zW1mQyeRxbvXo1CCF45pln8PDDDyMpKclDcGfNmuUxROKhhx4CIQTbtm3zyHCy93ll+nQ458/H0Nq1eNclp/MIQT8hGCYEBW73LomLwwuuXrpsfP311xAIBNz3GRkZOH78OAgZmbDG/o5qamq4nrzRjuLiYmRlZQU8fuTIEdx2221n5H27++67sWbNGgBAcnIy1y7OHYlEAkIIdDrdGXkmCoVCofhCRXeKMDw87CN55eXlyMrKGpPoOhwOMAyDjo4OOJ1OdHR0IDc3FwzDoKioCHa7fdR76PV6ZGRkRPwMY5nOZrFYIBAIQhZdmUyGioqKqIjalVdeCUIIrrvuOg/RZSd6sSUH7q28Fi1aBEIIHnroIY97rV+/nhNjds29NCJ3xgz0LluGobVrca1rI9od06Zh2FWj+4ory7syNha/cJVAsMEOn2CzzOzENLZn70033cR9ZnV1NUQi0RkR3aKiIiiVyoDH//u//xu//vWvo/6uZWVlITY2Fu+99x4AcIM+vEsc2P8eOTk5UX8mCoVCofiHiu4UwZ/ojlUwnc6R+luGYWCxWKDRaMDj8ZCdnY22traQ7+E9nSzcGIuwNzY2emym6+7uRkVFBfR6vd9JXTKZDOXl5eMuaXa7nZPWRx99FLW1tTAajTh27Bg3mYyNJUuW4OjRo1Cr1VwpwcmTJ7l7abVa7lyxWMytHzt2DIQQLJ4xA/0zZ6J3+XJ0rVmDGa57fD57NoZjYjB01lnY6coYz4mL86n//fHHH1FWVsZJ98cff4yHH36YO+5eqlBZWQmJRHJGRDc/Px/5+fkBj99888147LHHovqe1dfXY/ny5bj66qu5Nmas6La3t3ucW1BQAEIIlEplVJ+JQqFQKIGhojtF8Ce6Yx324C66AoEAUqkU9fX1Yd+jpqbGo5dtuDGWMcRNTU3g8Xjo6+tDXV0dxGIxUlNTIRKJuJ+LHVNbXV0NqVSKsrKycZe0yspKThQffPBBXHfdddzGKTYSEhJw/PhxrmXWl19+yXU9aGxs5O7FDp2YM2cOOjo6fDLGd192GQbmzIFz4UIIXL15CSFoSkzEcEwMGl11t9znxsTgUteznHvuubDZbHjqqadAyMgUNHYDGiEEZ599tsc/DioqKnw21kUrlEolVCpVwONXXXUVXnrppai9Y1arFZs3b8YFF1wAm83GrdPSBQqFQpm8UNGdQnhLXm1tLVJTUyMSRFYM2elVGo0m4oEP3i2+wo2xZKabm5vBMAyysrLA5/NRWloKq9UKm82G9vZ2mEwmbkxtSkoKN6ZWLpdD3F3a+wAAIABJREFUo9HAYDCgtbV1zO2z2PZh3jFr1iwsXLjQb3nCfffdB0IIrrjiCo/166+/3qeEoKmpCQkJCSCE4JOPPoJ91y4MJCbiEdfaRXFxGJ45Ez0zZ2KfW/3vioQEJG/ciOmu7//yl7+gtbUVCxYsACEj44jZc2NiYnyyt2VlZUE3iI1nZGVlobi4OODxnTt34o033ojKu9Xb24vLL78cq1atQkNDg8cxdjMan8/3WH/vvfeQkJCA06dPR+WZKBQKhTI6VHSnEKdPn/aQPO+NWKFGS0sL5HI5+Hw+tFotUlJSUFdXF7Goms1mjxZf4UZVVRXS09PDvq67uxsFBQVgGAZKpRKdnZ0h1ejm5eVxG5+Sk5PBMAySk5M50TIajR6DCkKJF1980UNwt2/fjg8++ADl5eV+yxPsdjtX0vD73/+eW29vb8fs2bNBCMG7777LrZ88eZKTUZPJhJKMDDTcfjvOcZUmvDxvHtR33YXNbh0YFsbHw7JjB/48dy4IIUhKTERDQwO36cw7nnvuOZ+fS6vVQi6XnxHRzczMRGlpqd9jdrsdGzduxPHjx8f9vRoYGMANN9yABQsWoLy83O85GzZswL333uuxdvnll9P2YhQKhTLBUNGdQniLLpvNDHUqms1m48QwPz8fVqsVTqcTEokERqMxYlFtaGjw2BAWbhgMBkil0pDP7+vrg9Fo9ChRYLtC9PX1oaurK6DoesuU3W5HS0sLDAYD1Go15HI5hEIhGIZBamoqsrOzUVJSApPJFHQMLjtW92c/+xk++OADbooZW98ZFxeHhoYG7vyioiJOMDMzM7n1lJQUbt29xOLQoUMeG8eKiorw7bffcuc+8eij3LAKNlLnz8fps87CKlfZwqFDh2C323HhhRf6SO6FF17oUSbBxmidEMYzpFJpwI2CdrsdK1aswD//+c9xf6/Y3+1bb72F3Nxcj3A6R1qHsQMjfv/73yMjIwNHjhyhAyMoFAplEkBFdwrhLbrBeuB6Zz5LS0vB5/ORmZmJ5uZmj+MymQxVVVURiypbJxvp9UajMeQa39bWVsjlcq7tWWtrq8fvIBTR1Wq1QYWLHVRQVVWFoqIijyldIpEISqUSpaWlqKurQ2dnJ1paWriyghMnToDP53Oiy/a93bVrl8dn/MU1oWzBggUe43WffvppEEKwYcMGj+dhRwI///zzcDhGOjA89thjXI0vK6yJiYkghGDjokVw3ncfTrrGCxNCoFKpuP687hEfHx+w20FRURFycnLOiOiKxeKA45ntdjvmzp3rUz4wHqxZs8ZvhpsQApPJxJ336aefYv369UhISMAFF1xARwBTKBTKJICK7hSiv7/fJ0Prrweue+bTYDAgNTUVYrEYtbW1frO/mZmZ0Ol0EYsqO10t0hpfk8k0ao2vw+GAWq0Gj8dDfn4+9zN3dHSAYRh0dXVx5wYTXblcjpKSkrAlzG63o6mpCZWVlVCpVEhPTwefzwfDMHj99dc5MSoqKgLDMJzosiN9WUFl49prrwUhBAcOHPBY37p1KwgheOSRR/xmf9mNYQUFBdi4caOHlF1zzTWY5mol9v6qVRjcsgX7XLW4V+7bh6ysLEyfPt1H5l566aWAP3dhYSHy8vLOiOimpKSgpqbG7zGbzYbY2FjI5fKJfg0pFAqFMomgojuF8Bbd7u5uMAyD9vZ2HzFsaGiATCaDUCiETqcLKqFZWVkoKyuLWHRDzSwHirq6uoCb6vr6+lBdXY3k5GTIZDI0NjZ6HO/s7ATDMHA4HCGJrkKhiEh0A8mXxWLB4cOHQQjB2rVrwePxuMzvv//9b04m3YcutLe3Y9asWSCE4MMPP+TWDQYDd/6//vUvbp0VaTb729zcjFtvuYU7d1p8PN6/5Rb8wdVtYTYhsJ57Lko2bODOef6aa7isM9vRgd2M5q9kgY38/HwUFBScEdEVCASora31e8xisYAQArVaPdGvIYVCoVAmEVR0pxADAwM+EsgwjEcpQnt7O3JycsDj8aBWqz0EMFAolUqUlJRELLqsbIYyXMJf1NfXe4zxZaO5uRnp6ekQCoWorKz0K+v+JrsFE93RdvZHEhdddBEIIThy5Aj3PEVFRXjppZdAyMhgCOZ//xeK779HYU4O11aMEOJRk/rxxx+DkJERws3Nzdz6/v37QQjB7bffDvG772Kda7MaIQQxhCBr40acXr0aK119cX8bF4ehs87C4bPOAiEE82NjEeOWwV22bBlX8hBsQIPD4UBubm7Qll/jFXa7HQzDoL6+3u9xvV4PQghqamom+jWkUCgUyiSCiu4Uwlt0nU4nBAIBLBYLHI6f/rSvVCr9ZnkDRX5+PtRqdcSiy0oKu7kt3PCebma321FYWAiGYTjJCnQtW77h/tlnUnTr6+u5wQvfffcdHA4HJ2wHDhwAIQTXnX8+eq6/Ho49e9By3XV44LLLQAjBqlWrkJ6eDpVKhcrKStzmKmfYv2EDujMz4XBtlGM3mV29bZuHsBJC8GBsLIYTEnBy3TpuTTdrFjqnT8dMPzWncW4DJIKVLLCRnZ3N/YMpmsH+Y8m9n7B7sFPIvIc2UCgUCuU/Gyq6Uwh/opuSkoKCggIIhULIZDJYLJawRVOlUqGwsDBi0e3q6gpYQhFKNDY2gsfjobe3F3q9HkKhEBkZGWhpaRn1WlYsOzs7QxLd7OxsaDSacRM0tu1XbGwszGYz9zwNublY5CpPeHvePAxs24b+a67BwCWXYLtr/eD996O0tBTKnBzo770Xi11tyP4aF4eB2bPhuO46/O9nn/nI6lq3r7UxMRiOicFel8D+IiYGQ/Pm4U9+JqK5xznnnBO0ZIGN8Sz1CBZtbW1gGAatra1+j8tkMhBC4HQ6J/o1pFAoFMokgoruFGJwcJCTub6+PphMJvB4PAiFQhgMhpDbjHmHRqNBXl5exKLb29vLSUok17Nt0qRSKVJSUsL6WVjJ7ujoCFl0xzNDyY7O3XHOOej97DN0S6VQ/PnPULqytoQQaJOSMLR8OQYuvxwNt9/OZWV/+OCDkfscP45v3FqDlRKC4ZgYtMXGYoOriwIhBPGE4BVCsN/1/T5CMEwIStwElk8ICuPiEB9Ecrdv3x5yn+BgvW3HM9j/DwSSbx6Ph8TERG4sL4VCoVAoABXdKQUrus3NzcjMzIRAIEBqauqYOiY4nU6UlpYiJycn4uvZWuGmpqawr7VarcjKygLDMFCr1R7dE0IJfxvygoluTk7O+Imu3Y7NK1eCEILnzjoLgzt2YGD3bjhWrMCxJUtG6mFjYjC4di2GFi3C0MKF+Kfr/DhC8PKBA9izZw/iXZlcbnMZIZjvOoddm08IlIRA57b2vUt0D7FZWkLwT9f17DkzvCT3V7/6VVhT4NLT01FeXh510W1oaOBqrf0d/+abb7B06VIMDw9P9GtIoVAolEkEFd0phMPhQF5eHhiGQWFhIWw2GxQKBcrLy8ckuhUVFZDL5WO6B5/PD6tsore3F+Xl5RAIBMjIyIi4a0NPTw8YhkFbW5uH/Lr3pnUPpVKJoqKicZEz44kTnECmLl+Ogcsuw8COHeifMQP7XZnYexMSMLRmDYbnzkVnbCyWBMm0jhbxhGCB6+u5hOArQiBwk9mdQa6NiYnBm2++GfbPmJaWBp1OF3XRraurA5/PD3j8o48+wvr166noUigUCsUDKrpTiJaWFigUCo8SgbF2THA6naisrIxoBK97JCcno76+PqRzzWYzJBIJUlNTYTKZuF64DsfoHSL8CTPDMB71vGdCdLuMRvzTNcQhgRB0LVyIoYULMbR0KRxuG8E+nz0bg3Pm4ERcHKYHkNCEUQTX36aycCI2NhaffPIJamtrQ6rLdQ+xWIyqqqqoi67RaERycnLA42+88Qa2bds20a9g1OHxeCCEoKqqymO9paUF06ZNw5dffjlBT0ahUCiTEyq6U4ihoSGf2tWCgoIxdUxwOkdG8KalpY3pHqmpqaitrQ16TkdHB9f6TKvVchlcfy3CQg1/LdaCie54tcvqe/NNHHINX9g7YwaGFi3C8IwZGI6NRZqbZMpXrcIVXqUJ7rF38WLcs2qVz/pct6+FhOA+r+Prvc4JJrnffPONx4ALsViM3NxclJWVwWw2o7OzM+DPmZqaCoPBEHXRraqqgkgkCnj86NGj2LNnz0S/glFncHAQy5cvx4svvuix/uabb2LOnDno6emZoCejUCiUyQkV3SnE8PCwj+ip1WoUFBSMSVJDmUw2WkgkEhiNRr/Huru7odVqwePxkJ2d7bFxzOn8qT2Ze+eEcILH43nUB/f09AQV3cLCwrGJWUcH+m++GetdU8j+Jy4Ow6562WFC8LxLMhfExCAugOTGEoLzZ83CUrchDmzcRgjuJj91WNjmdfwAIXASgvNHkdykpCTk5+dzz80OuNDr9SgoKIBMJuMGXKSlpSEvLw8VFRWor6/nfn/JyckwGo1RF12dToe0tLSAxx9//HFcf/31E/0KnhFefPFFrFy50mPj3c9+9jMcOnRoAp+KQqFQJidUdKcQ/kRXq9VCqVSOSVLNZrPfgQ3hhFQqRXV1tU+2tba2FiKRCGKxGGaz2W83BX+dE8IJPp/vMTEtWEY3Ly8vLNHtKi9H74cfwvnSS+g7dgzdcjkcjY0w7N3LCaXCTXKHCcGaMZQZ/JyM1N6yZQ7s/7r3z/0nIZjnJc3e99myaVPAVl3uYbPZ0NDQgIqKCuTn5yMtLQ0Mw4DH40EqlXK9jC0WS8CNYuMRZWVlSE9PD3j84MGDuPvuuyf6FTwjGI1GxMTEQCQSAQDy8/NBCEFubu4EPxmFQqFMPqjoTiH8iW5FRQUUCsWYJNVisYDP54/pHhkZGaisrOS+b2trg0KhAJ/PR3l5OXp6egJe629DWTjBDs0IRXTDGWnbI5Gg/7rrMHjOORiaPx/DiYkYWrAA/bfeik927QIhBLNc2dVhQtDgJ/s6HjGLEGx0fb0qgNi6x+OLFqEriDSOFlarFWazGWVlZWAYBqmpqWAYBnw+H+np6SgsLERlZSUaGxvD6uAQLEpKSqBQKAIe/+Uvf4mHHnpool/BM8b+/ftx5513AgAeeughnH/++RP8RBQKhTI5oaI7xfCWvPHYSNbS0gKGYSLuw+t0OiGXy6HT6dDV1QWNRgMej4fc3NyQpqWxdbahDIjwF0Kh0GMj3Gii6/7n/IBhsaD/pptGam+nTcNwbOxIxMdjaPp0/PfMmSCE4FpC0E8I/kpI0N61y6dNCyqnt8XF4cWYGGwegxDHEIJ/bd2KwY0b0XPy5Jjlk506Z7FY0NnZibq6upEBF0olRCIRJ78ZGRkoKipCVVUVmpubI5JftVqNnJycgMevvfZaPPPMMxP9+p0xvvvuO0yfPh0WiwVz587FX//614l+JAqFQpmUUNGdYnhLXk1NDSQSyZhEt729PeL2XmwoFAoolUqkpKQgLS0t7AltkfbhdTpHOj6YzeaQRLegoCAk0e3h8TC0YgWGvepvhwnBECFY7pLLI4Tg7CDyOY149sN1j7Vu9buVhOA94tkDdzshuJWEtuksISYG5fv2YeCyyzC4dSu6ZbIxiy47lrepqcnv8Y6ODphMJmi1WuTk5HCZX6FQCLlcDrVaDYPBgJaWllHlt6CgAHl5eQGPX3HFFfjjH/840a/fGeP06dNYtGgRdu/ejfj4eDQ3N0/0I1EoFMqkhIruFOP06dMekldXV4fU1NQxie5Yuh6wGWGhUAgejwe9Xo/e3t6w7xFuH173SE1NRV1dXUiiW1hYGFio7HY4mprQVVwM569+NZLB9ZLcYeI5tCGSiCMEL86bh30u0d1HCG7wOudCl1A/47XuL2u8JCYGpn370L9vH4bWrcPpO+6AI8DPH06wY3lbWlrCusZoNKK4uBhZWVlITk4GwzBITk6GQqFAcXExampqfCazjdYNY8uWLXjnnXcm+vU7ozz55JMghODmm2+e6EehUCiUSQsV3SmGt+haLBYIBIIxia7D4YhoM5jD4YBKpQLDMBCJRFCpVBE/g1AoRENDQ0TXikQij9Zmo4lubm6u57rNht6vvkL/zTeP1OPOmoWhAJI7REa6HoRTTuBvfb7b1/P8HP+QEFzgtbbez3m/JARDCQkYXLsWg5s2of+mm9A1TgMx2JKW9vb2iO9ht9vR2tqKmpoaaDQayOVyCIVCMAyDlJQUZGdno6SkBOnp6QFF1263Y926dfjss8+i/n7pdDrs378fiYmJWLp0KZ599lmcPn066p/rD4VCAUIIeDzehHw+hUKh/F+Aiu4Uo7+/3yebyjBMRFlUNtjNYO6DKIJFX18fqqqqkJycjPT0dDQ1NaGwsBBFRUURP0NKSopH+UE4IRaLYTKZPH6eQKKrUqk8Rddmw+n778fQggUYTkjwK7dsVBKCDSEK7mxCsN+rbGEhITg3iBCzU9MSiWcJAyEEc/xkhU8RguGYGAzNmYPTBw+i99tv4bBYxkVyHQ4HmpqauLZv43VPVlybm5tRXV0NtVqNzMxMMAzDbXzLycmBVqvlBlzY7XYsWbIE33//fVTfrc7OTixbtgx79uyBSCTCZ599hrlz5+LRRx+N6ucG4oUXXsCyZcswMDAwIZ9PoVAo/xegojvF8BZddqpYV1dXxJLJbgYLpUa2qakJMpkMycnJqKqq4jawFRUVjamfbygDJwKFdw/fYKJbVFQEpVLJfd977BiG5szBcHx8QMHtIwRPBMnOekeSq/TAe73Qdb/vwriXvziLjHR4GGazuatWYeDnP0d3iN0kQg2LxcKVtIznff2FVCqFRqNBZWUlVCoVMjIywOfz8eOPP+L888/H6tWrcfjwYSgUCvT19UXl3Tp27BiSkpLQ0dHBrf3jH/9AXFwcLBZLVD7TH3q9Hj/++CPmzJmDV1999Yx9LoVCofxfhIruFMNbdNmd8aF0NwgWAoEgaOmAzWZDfn4+eDweN0LX/bhGo0FeXl7Eny8WiwMOnBgt0tLSUFNTE7bodtXUYGDbtqCS+4Gf7Opo4d7+iy03uIgQDBKCN4lnne2DhCCFEFwc4r33k5EuD8OEYCg2dqRc4bbbMLh5M/qOHRtX+TSbzeDxeFGXXIfDAYlEAr1e77Fms9lgNpvxxz/+EYsXL8b555+PadOmQavVRuXd2r17N2699VaPNavVipiYGHz++edR+Ux/7N27F9OnT8eBAwfQ29t7xj6XQqFQ/i9CRXeKMTAw4CF53d3dY+pB655Rdd/QxUZvby90Oh0EAgEyMzMDljewO+8j/fy0tDQYDIaIrvUeVhFMdNk2Vt0FBei/4w4MTZ/uV3AbyU+9a0OVWu/1q92Ov0EIrvI650pCUE0ILvdanxHgns+7Pd9gbCz6duxA/513ov/OOzG4fTtOHzw4rvJZW1sLgUBwRkQ32Kjh2tpaEEJQXl4Op9PpMTFsPFm8eDFeeukln/Xly5fjd7/7XVQ+k0KhUChjg4ruFMNbdMMpOwgW/kb4NjQ0IC0tDampqaipqQnaZ7e8vBxZWVkRf75MJvOZrBbOtVVVVdz3FosFFRUVfqd5lWRmwvTAAyMDIAK0DvtDEIF1j/OJ56ayYJHgZ+1BP+veG9AIGWkvVur+nDExsK9Yge4DB34S3QsugPOpp8ZVPo1GI5KTk8+I6AqFQphMJr/HtFotCCGor6+P6rsVHx/vt18tHb9LoVAokxcqulMMb9F1On0HJoxVFjs7O5Gbmwsej4fi4uKQ+uvqdDpkZmZG/Pnek9UiudbhGOmqwOPxIBaLPQYaqNVq1GZkwLZzZ8COCuWEYFmI4hqoxvbfhMDoyuAGK3nwd72/TO5NhGDAz7PaV65Ez4ED6L/jDgzs24eBSy5Bz7//Pa7yaTAYkJqaGnXJtdvtYBgGZrPZ7/GcnBwQQuBwOKL6bsXHx+PNN9/0Wd+0aRMOHz4c1c+mUCgUSmRQ0Z1iDA4O+oied3utSCIzMxPl5eUoKysDn89HVlYW2tvbQ75+rBPaMjMzodPpIhbdvLw8rgtEY2MjrFYrOjo6UFdejpr330fbnj3oD1Cm0EUILg1BbqePcvwWt3v+3uvYQjIyyjfUOt84QsDzes6hOXMwNHMmhmNj0TdnDk5v3ozBzZsxsGsXnC+/DMc4d0eorKyERCKJuuiyfZwbGxv9Hk9NTUVcXBwGBwej+m7R0gUKhUL5vwcV3SmGP9GVSqUR17c6nSPlDzKZDEKhkJPmcMcBGwwGSKXSiJ9BoVBwNZjhRHt7u8+wit7eXlitVnTn5KD/lltGuir4EdxhQvAZCT66NxQhZb/OIASdhOA+t7VYMrKh7YMQRJmNFYTA6v2sCQkYnj0bQ0uWYHDZMrRu3oyuRx6B86WX0CMQjLvkOhwO6HQ6SKXSqIsuO5kv0GCKU6dOYd68eVGrzWXZvXs3Dhw44LFms9nO+GY0CoVCoYQOFd0pxtDQkN+Mpl6vj0gw29vbkZ2dDYZhkJmZiZ6enojuYzQaIRaLIxbd7OxslJaWhnx+T08PSktLwePxkJycjJKSEu5Yb28vbLW16L/xRgzNmuVXcDuI/81m3rW5ocppIiF4nRCs9Fo/QvzX3SYGuM+vyUh3Bo9MblwchhITMbhmDfqvuQYD69dDd8cdaG5ujqqAlpeXIz09Peqi29zcHHQwxYkTJ7By5UoMDw9H9d06duwYZs+eDavVyq198skniI+PP6PtxSgUCoUSOlR0pxj+RDcrKwtlZWVhiWV3dzdKSkrA4/GgVCqRk5MDtVodsaiOdRSxUqmEVqsN6dyGhgZIJBKIRCKYzWZkZWV5ZIN7Kyrg/K//CpjF/bMfoQ2l/paNcwjB04Rg+SjXxwU4vtnP2mxCoA7wvMPTp2No/nwMLV6Mgc2b0b9jBxR//nNYo3kjCa1Wi8zMzKiLLtuvN1CnjL/97W/YtGlT1EWXHRixd+9eiMVinDhxAvPmzZuwgREUCoVCGR0qulMMf6Kbm5uL4uLikCSxr68PRqMRqampkEgk3CY2lUqFwsLCiEW1vr4eQqEw4uvz8vJG/RnsdjvXy9d9kxyXDe7sxMAvf4nhALW4KkKweBSJne31vfcGsW2EoIcQOP2cG2n8kox0e/ArubGxGI6LG8lMT5+OwQ0b0PXqq2B+/BFtbW1RFdDi4mJkZWVFXXTZfr12u93v8VdffRWXXHLJGXm/KioqcPXVV2PmzJlYsmQJnnnmmQkbAUyhUCiU0aGiO8UYHh72EcDCwkKoVKpRZbK1tRVyuRwCgQAVFRUeY4PHOvChsbERfD4/4uvz8/MDZpTZkcNCoRAZGRk+vXxzcnJg+OILDG3e7FcWBwnBrREIqL/a3VbXPb/2Wp9GRvrmhlrqwJZF5AQSXDJSlzs0ezaG5s/H4IYNGFq+HH2vvYZm12jeQH/qH69gew5HW3RNJhOEQmHA48899xx+/vOfT/SrR6FQKJRJCBXdKYY/0R1NUru6uqBWq8Hj8ZCfnw+bzeZzTmlp6ZgGPrB1luFuYhtN1tva2pCZmQmhUOgxctjj5//2W3Rt2+ZXFjMJwUw/khnutDNCCNaQkalkYi+hXUYIVoV5r33EtxbXO5M7NH8+hufNw8All2Bg1y4M7N+PLr0eTS7R7ejoiKqAqlQq5ObmRl10q6urg7Yxe/jhh302iVEoFAqFAlDRnXL4E91AktrX14fq6mokJydDKpWisbExoGhWVFRALpdHLLptbW1gGCbizWxFRUUepRPuNcR5eXmw2+0Br2269VYMx8R4iOIAGRmXG458evfQnePnHG9pDlbr66+dWALxbRvmt1xh2jQMJyVhcO1aDG7dioGLL0bf22/D4XCgsbExaE3reEV+fj7y8/OjLrp6vT5oG7N7770X991330S/ehQKhUKZhFDRnYJ4i55Op/OR1ObmZqSnp0MoFKKystKjTMFfjLUPbkdHBxiGgcPhiOh6jUaD/Px8OJ0j9b4ikQhisXjUQRj9H3yAIa8JZ3I/JQTeQuq+4WwRIdjpde4bruzt6hAlOdHr3v4k+cogWVznrFlwzpuH3tWr0fnAA+i79Vb0X3EFBq68Ev133oneL76AwyW27OYt76lv4x25ublQqVRRF93Rujvccsst+O1vfzvRrx2FQqFQJiFUdKcgp0+f9pC9qqoqyGQyOJ0jG7YKCwvBMAxUKlXQTKh7GAwGpKWlRSy6bNN/f2URoURJSQmys7O5iWxarXbU7PDp7GwMrVrFyWIP8d/KK1hsIAQLvNb+5LqfKcx7BYoZhEAaLIMbF4fT55wD69VXo/j99yEUCsEwDCT//jcKBAKUaTQwm81cBrehoQEMwwTcvDVewXbiiLbolpSUQC6XBzx+9dVX48UXX5zo145CoVAokxAqulMQb9E1Go0QiUTQ6/Xchq3m5uawRNNkMkEkEkUsug6HAwzDoLOzM+xr+/r6kJWVBR6PB7lcHnwiW18fnM3NcFqtGHjkEa5k4XMSuJ1XKMFmfGcRAgcZ6YJw+SjXLAzhvjcGyeKy0876r74aPTweHK6+uHa7Hc3NzaiqqoJKpUJ6ejp4PB54PB6kUinX97ixsTGqspuVlYXi4uKoi65arUZ2dnbA4xdffDFef/31iX7tKBQKhTIJoaI7BfEW3fLycjAMg5SUFBgMhog2hJnNZiQnJ0csut3d3WAYBm1tbWFd19LSgvT0dPD5fKSlpQV99tP/+AeGdu7E0MqVGFq/HsPz56MvQBY3kPSeRYJPQkskBIcJwZ4xZnHnEwJtIMGNjR2pv920Cc7nn4fDZBpVBm02G+rr61FeXg65XA6GYcAwDAQCAeRyOTQaDWpqasa15ZhcLodWq4266BYWFgbd9Hb++efjo48+mujXjkKhUCiTECq6U5D+/n44nU7YbDbk5eVx0tPV1RWxqFosljG1B+vr6+PGuIYqxhqNBjweDwUFBdBqtVAoFAHPH3jySQzPmoXhuLgRUSQEX4bpj19tAAAgAElEQVSRxY0nBA8Rz2lo8wjBO4Tg4TAyvqHI9GNBMrj9l16Krrw8dOfno6umJiIxrK2thUAgQHt7O4xGI4qLi6FQKLiSh9TUVCiVSpSVlXmUPIQb6enpKCsri7ro5uXlobCw0O8xu92OlStX4uuvv57o145CoVAokxAqulOQvr4+lJeXc9k8k8kEhmFG3XA2WmZ1LO3BnE4neDxe0M4ObNTV1UEkEkEikcBiscDpdEKv1yMzM9P3fIcDp996C8MzZowIbmwseojn5jF2A1ggST2LEDxHPDsmLCUETS75vJ/8VEs7lizuCkJQF0hyExIwcOml6NLrxyyGRqPRb99ZtuShsrIShYWFkMlkHiUPhYWFqKysRFNTU0glD1KpFDqdLuqiG6wW2G63Y968eeDxeBP92lEoFAplEkJFdwqSm5uL1NRUmEwm9PX1obOzc0wdD5xOJ9rb28EwDDdtLJIQCoVoaGgIeNxqtUKpVILH46GsrMxDzH26PvT2ov/jjzG0Zs1IFpf8NKgh3FrcRD9rp1z36yC+PXWXhHn/OPLTBjaPiInB0MyZGNy8Gb3Hj8NhNo+LGNbU1CA5OTmkc61WK1fyoFQqIRKJwDAMhEIhV/JgNBr9Dp+QSCSorKyMuujK5XKUlJT4PWaz2RAXF4fMzMyJfu0oFAqFMgmhojsFsVqtHkLqcES+EcxdQtmd/JHeIyUlBXV1dT7rvb290Ol0EAgEUCgU6Ojo8DmnuroaUqmU+77/vfcwvHgxhuPjMRwbi34/WVxCAk8iG62/rZ6MjAR2bx82l4y0GgulPIGN28hItwefEoUtW3D64YfR8+OPcIxzGzCDwRB0wMJo4V7yIJfLuZIHkUjkUfKQmpqK6urqqItueno6ysvL/R5rbGwEIQRFRUUT/dpRKBQKZRJCRXcKMjAw4COSDMP4jMYNJ1hZ9iehoYZIJILJZPJYa25uhkwmQ0pKCoxGY8DSiJqaGkgkkpHvLRYM7dgxUq4wYwZSEhL8TjLzJ7MbCcG7xLOUIYkQzB5FWBPCzOIuJARqP4J7et06yF9/PapTy6qqqiASicbtfna7HU1NTT4lD6z8hlvyEG6kpaVBH6Cko7KyEoQQGAyGiX7tKBQKhTIJoaI7BfEWXacz9PrYQNHT0zNmWU5LS4PBYIDT+dPYYbafr8MRvKzCZDJBLBbD6XTitECA4aVLMRQTg+u9BDNYLe6ThOApr3MeJCPtwoQhZntDifvISPsxd8EdXLsW3d9/z43n7ezsjJroVlZWQiwWRzXLarVawefzUVBQ4Lfkobi4OGDJQ7gRLHOsUqlACEFbW9tEv3YUCoVCmYRQ0Z2CDA4O+ohicnIyzGZzxJLKdk1oamqK+B4ymQyVlZUwmUxITU2FVCoN+X51dXVITU0dKVv4wx9QQPxPFwsUewnBuV5r+9xE9MYwhDlYzCEjfXEH583D0IIFGJo/H6fvu48TszMhunq9HmlpaVEVXYfDAR6PB7NbXTFb8qDRaCCXyyEQCDxKHsrLy1FfXx92lwehUAij0ej3WEZGBgghcDqdE/3aUSgUCmUSQkV3CuJPdMVisU/ZQLghEAiCbiYLRXTT0tLA5/NRUVERVhcIro9vby8OLVgQcbbVXWBTpk3DwMyZ+HD69KAlD951ud6xnBBc4fr6ACEYmj0bg2vWoH/bNnQfO4b2piZ0dHSgs7OTG88bTdHV6XSQSqVRlVybzQaGYdDQ0BDwHO+SB6lUynV5kMlkXMlDc3Nz0JIHHo+Huro6v8f4fD5mzpyJoaGhiX7tKBQKhTIJoaI7BRkaGvIrmdXV1WMS3UCbyUaL3t5ebmiFRCKJaFNcQ0MDvvnmGyxduNBHNP0NeNhAgndHiCUEt8bEYGtcnN/jc5OSRs3qxhOCNwnBafLTmOD3585Fj06H7upq2NvaYLVa0dnZiY6ODrS3t0On03H9hDs7O2G1WmEb581oFRUVkMlkURVdtpNHY2NjWNdZrVaYzWaUlZX5lDwoFAqfkgdWqC0Wi9/7/fOf/8SSJUswPDw80a8dhUKhUCYhVHSnIP5EVy6XQ6fTjUl0JRIJjEZjWNc0NTVBKpUiNTUVMpkMWq02os8+duwYYmJiRs3YTiMEf501C//tlqWNIwSH4uLwNBm9S0JsbCwuvvhiLPQj1O6xhIz02R1ISIDSLcNc+OST6O7uRk9Pj0dYrVYUFBSAYRhoNBpOfN2DzfrabLYxyW9ZWRnS09OjKrpsu7mWlpYx36utrQ01NTV+Sx5ycnLAMAyqq6v9/k6OHz+OdevWRV107XY7XnnlFezcuRNz587FkiVLcMMNN0Cr1fqca7PZcPDgQcyfPx9JSUm47bbb0NjYGNXno1AoFIp/qOhOQfyJbk5ODkpLS8ckuuFkhR2OkY1CDMNArVajq6sLubm5KC4uDusze3p6sHPnzqClBWzMIASfEIKVXudmJySge9Ei/O+sWRGXPHjHZzExI31wk5LwlxkzQAjB0vh4CL7+GikpKcjJyUFZWRnq6+thMpkgEomQlpaGxsZGTn67urpgt9t9sr7e8htu1re0tBSZmZlRFd3W1lZupPN435stedDr9cjNzeUm+7ElDyqVClVVVTCbzXjjjTewdevWqL9TpaWlOOuss/DSSy9BLBaDx+Nh9+7dSExMhE6n8zj3F7/4BVauXImTJ0+Cx+Nh8+bN2LJlCwYGBqL+nBQKhULxhIruFGR4eNhHGPPy8qDRaMYkupmZmdDr9UHP6evr4wYWyGQyNDc3c8cKCgqgVqtD/rysrCwkJiaGLJ/ugsuWHdxHCIbi4mBbswZr4uMDXhsXF4fpXrW67pEUG4v/l5DAfW+aMwdDy5ZhaPVqXJeUBEII7rzlFnR0dMBoNEKtViM9PZ2TtJSUFBQWFsJgMKCtrc1v1rerq4v7c30g+WWzvsHkV6vVQi6XR1V0m5ubuXZz0fycxsZGMAyD9vZ2ruQhJycHqampOHjwIJKSkrB48WK88soryMrKito7xf73cqerqwsLFizAE088wa0plUoQQiCRSLg1vV6PmJgYnDx5MmrPR6FQKBT/UNGdgvgTXZVKhcLCwjGJblZWFsrLywMe7+jogEKhgEAggE6n89lsFs4zvPXWWyGVKgQqX2C/TomNRUpcHFb4k9dp03Duued6XjttmsfnxsTE4Le//S0atVr8z3XXgRCCdXPmwPndd+ixWmFrbUWSS3Q/+ugjTlrNZjM3xriqqgoVFRXIzc3lalKTk5ORnZ3NZX3tdruP+LrLbzhZ3+LiYigUiqgKKLupLtwOCuFGfX09N6jE+5hOp8M999yDdevW4corr8Tjjz9+xt+1iy++GHfccQf3/csvv4wFCxb4lFJs27YN99133xl+OgqFQqFQ0Z2C+BPd4uJi5OXljUl0lUolSkpK/JYXlJaWgsfjQalUwmq1+r1erVYjPz8/6Gd0dXXhqquu8pHSA7NmYcm0aR4lCf9v2jRscm0mm0f8txsLtKHs1rVrccf113uKr0tY3UMul3PSuWfPHhBCcP/993NrMpmMO7eiogJ2ux0qlQo8Hg/FxcVwOBw+8spmfTUaDTIyMsDn88EwDPdn+erqarS2tgbM+nZ1dQXN+hYVFUGhUERlo1soAjqeYTKZIBAIAh5/4IEHcNddd03Ie2a1WpGYmIhXXnmFW/vlL3+Jyy+/3Ofce+65B5dccskZfDoKhUKhAFR0pyzeAllWVobs7OwxiW5+fr5P6YHFYoFEIoFIJBq1I0NJSQlyc3MDHi8qKsICP63D7k5KwrY5c7jvF8XHQ56YiLqEBK5e96sFC+CcPh3pJPDYXw8BDpItjnPJ844dOzjBbG9vR4KrdOGLL77g1n//+9+DEIKzzz4b9fX1XC2uxWLxm6ENlLVtbGyETqfzm/UtLS0NOetbXl4OgUAAlUo15lrfYFFXVwc+nx9VyXU4RsYZp6SkBDx+xx134PDhwxPyjh06dAhJSUmwWCzc2v79+3H99df7nPvoo4/i3HPPPZOPR6FQKBRQ0Z2ynD592kMi9Xo9MjMzxyS67qUHDocDBQUF4PF40Gg06O7uHvX60tLSgLL9l7/8BbGxsZxszpw5E4QQzJ8+HfPd1ufGx6Nz/370r1mD11zrcwlB2+LF6Js3D5+7ZXz3hii4MTExuOmmm3D06FF8//33+NnPfgZCCJ566ilOJoVCIXe+0Wj0yfLecsst3O/CXxY33HDP+mZmZnJZX6lUisLCQp+sr9VqRU5ODvh8Pjcud6y1vqNlWoVCYdRFt7KyEhKJJODx6667Ds8880xE74jNZoNOpxs1/G0iO3HiBPePHnf279+PG264wef8Rx55BBs2bIjoOSkUCoUSOVR0pyjeoltdXQ2pVDom0dVoNMjNzUV1dTWSk5ORkZER1kjgiooKKBQKn/W8vDwui0oIwe7duwOWILyWmIihc85B/6ZN2OCS1kMJCRicMQODM2ZgN5uVdbt2m+u8mJgY1NbWoqioCKdOncIVV1wBQghuvPFGTjBra2u563g8Hrf+7LPPghCC888/3yPLy25ge+GFF8LK4oYbwbK+MpkMAoEAaWlpaGpqCpr1tdlsQWt9Q21vNlqmdbxitJ7Au3fvxh/+8IeI3pHPP/88pJrv+vp6j+tSUlIQHx+Pl19+2eeetHSBQqFQJhdUdKco3qLLtrgaa0ZXKBRCIBCgsrISfX19YV2v1+uRkZHhsdbT04MdO3aM1NnOm4cTJ05gjluZAiGEE9pYQmCeNw9DiYmQu3VAyLn0Ugxu2QLe3Lke1y2OicGXSUl43FXbu37FCmQIhSgqKkJVVRXXK/evf/0rJ4Rff/01tymttbWVW7/44otBCMHDDz+Mnp4e2O12HD9+nPus6urqqEluoGhtbYVCoeB6zo6W9XWP7u7uUWt929vbA2Z9q6qqIBaLoy66Wq02aKu0rVu34u233z5j71Vubi4SExNx8OBBv8dffvllLFy40Gd9+/btdDMahUKhTABUdKco/f39HkJZX18PoVAYkeD29PRAq9VybbJsNltE96muroZMJvNYe+eddzhZFAgEnPSyGdgXzz4b17hajF0bF4ehWbPQP2sW7ne1Ctu0aRNsNhteefllxLmVJfxq+XK07NqFgYsuwg5X1vWxNWvQft99qHjnHXz04YfcuV9//TUqKirQ2NiIBx54AIQQXHbZZZwUNjU1cRnnkydPor6+HqmpqVzZgnuW90yFxWKBWCz2yOK6Z33z8vIgFou5qWNZWVkoLS2F2WwetdY3lKyvTqdDWlpa1EVXo9EgKyvL7zG73Y5169bh008/PSPvVHl5ORYsWIAbbrghYE9ctr2YVCrl1iorK2l7MQqFQpkgqOhOUbxFt6mpCQzDhJ2Fra+vh1gshlgsRlFRkU9GNpyoqamBRCLhvjcajZg9ezYIIbj99ttxzTXX/FSLO3cuRK+9BvPmzdyGsxNnn42O885D0/btSHLV5z5x5Ai2b9/ukcm9euVK9D/6KPrvugvt553HXf/jVVdh4MABDNx1F948dAiEECxYsAAqlQoymQw8Hg8rVqwAIQSHDx+GyWSC1WrFDz/8MJJRjo2FTCbD008/jXXr1nGfd+TIkTMmuF1dXSguLgaPx0NRUREcjuD1wJ2dnTCZTCguLvZb61tVVYWWlha/WV/287yHWrS1tSEvLw9SqTRo1nc8QqVSITc3N6DoLl26FKdOnYr6+9TS0oKVK1dixYoVkMlkyM3N5aK8vNzj3F/84hdYtWoVTp06BT6fjwsuuABbtmzB4OBg1J+TQqFQKJ5Q0Z2iDAwMeEhmW1sbGIZBT09PSFJqs9mQl5cHHo+HkpIS9PT0wGAwjKnOt7a21qN84rbbbgMhBLNnz8Z5553HieOsWbNgMpnQJ5HgNZd4zomLg2nPHvTefDNOuHrfxhKCBLeNavGurz++7DL0P/ooBnfvxo+LFnHntj3xBPqffx4DN9+MG9evByEEt912Gyd1er2eu9f7778PoVAIhmFw4MABEEKwZMkSToTd4+OPPz4jktvS0gKZTIbU1FSYzeaIRbmpqSlg1ler1cJsNsNms/m9vq2tDXK5HEKhEAaDIaqjjB0OB/Lz81FQUBBQdBMTEyESiaL+PmVkZASs4d27d6/HuewI4Hnz5iEpKQkHDhzw6MxAoVAolDPH/2/vzMOiLtc3/rKvKmKCglsuuWGmplmGuJaWe1lu5XEBytTKPcutFFMrS8+x1NCs00+PZjLgIMOObIKKICAgyC6ybzO4oHD//oDv23yZYZVFx+dzXe9VPHw3Rua6bp653/shoauh1BS6RUVFPPe0LjF679493Lx5E+fPn4e/vz/y8vL49x7X55ueng6pVIp79+7h3LlzXCjUnEi2fft2lJWVIT8lBS9Upy8s7d4d5e++i0ejR+NlpY1rjDH00NfHj6++yr9OeOUVPBw3DhXdu+PT6vNHdO6M8i++QPkXX+DesmUwq/b4/vTTT1zE/frrr2CMwdDQEIWFhVAoFEhLS0Pnzp1VxI1yFzkoKKhFBa5CocCNGzfg6uqKS5cu1SpCm7qUu74BAQG86+vl5SXq+iYnJ0MqlSIgIAAFBQV1dn0b6vWtbwUHB/POdc1VWFgIxhhCQkLa+u1GEARBPKGQ0NVQagrd0tJSPrK1NiGam5sLPz8/nD9/HomJiSo2B0GoNlXoZmZmws3NDXn+/uhpYiISjh2qBamWlhYSEhKQkJCA77//nn8/pFcvPBwxAlsNDUXnLRswAPnvvYfdPXqAMQZrS0s8+OwzPBw/Ho969MBL1ceveeUVLnQvTZvGz7927RoXbB988AEYYxg/fjxKS0uxf/9+WFhYiDzDs2bNglQq5c+mq6uLM2fOiDaA1TbitykrPz8fFy9e5B3U1ugcC13f+Ph4hIWF8a6vi4sLPD09ERUVhbS0tFoFd81RxnV1fesTvxcvXuSDN2ouISEjJiamrd9uBEEQxBMKCV0N5dGjRyKReffuXbi4uCAnJ0dFgCoUCu77DA8Pr7Xre/v2bbi6ujZJ5N67dw9ZWVnw+vln2FenHQjrdQMDvFEtfO3s7BAQEAA3NzfMnz8fjDH0794d6UuWYKqZ2T+ikzG4vPkmyu3tUW5vj6kdO4IxhgUvvICHb72Fh1OnInfwYB5L5jp3bpXQXbsWe6ptEl27duWCVKFQoHv37twv3KtXL5Uurq+vLxdzmzZtAmMML774osgKoBz7FRwcjNjYWGRmZtbrpVXXxU1KSsL58+dx8eJFUQe1NVdOTg68vb3h4eGB2NhYtV3f8PBwJCQk1Ov1LS1t3Cjj0tJS+Pn5ISYmRq3QjY6OBmMM6enpbf12IwiCIJ5QSOhqKDWF7v379+Hq6orbt2+rdGk9PDzg6emJzMzMOsVqdnZ2oze03bt3D3fv3oVCoUBJSQnC//Uv0Vjejzt3RrSNDXSrv16zYgUCAwNx+/ZtmFUL20WLFqnYByZ368ZF7t1ly9Cu2s5weMgQlK9Zg/JNm3C6esSvDmMotLWt2og2dSqm9OwJxhjef/99LsQE0VRzDRkyBIwxdOvWTSTc3nrrLf5sNUWdMOwhIiICfn5+kEgkkEgk8PPzQ0REBJKTk+sUrsXFxQgNDYWrqyvi4uKarTvcWKEdHx/P7RI1kxrkcjmys7NVur6CML9+/Xqjur61DbXw8vJCbGys2q6vkHBQUlLS1m83giAI4gmFhK6Gok7ouru78zG9RUVFCAkJgUQiQXR0NMrK6t+kJmxoa8gUNEHkCnmtpaWlUCgUWF3dNdVhDL8PHoz748Zhb3X31ERLC+7ffAOpVIodO3aoiE5TExOYVWfifjtqFBe6IW++yY+JW7AA5Zs2oXzTJnxSHVU2ytgYD2fMwIONGyF3doZpdff40KFDKCsrQ2BgIPpWb07jQnryZAQFBeHDDz8EYwxTp04VCbVu3bqBMYa9e/fWKxpLS0uRmZmJ2NhYBAcHQyqV8uzbS5cuIS4uDnfu3IFcLkdaWhrc3d3h4+ODnJycNuniClPW3NzcGpUPXFRUhNTUVERFRfGufFO7vgUFBYiJiYFEIkFiYqLarq+Hhwe0tbUpzYAgCIKoFRK6GkpFRYWK8PT09MStW7cQHx8PNzc3XLx4Efn5+Q3uzhYWFjZ4Q5vQxS0tLYVcLucdXcvqTWCbe/XCwwkTUPrqq3ip2p+7qEMHFPv4IDk5GUOHDhUJz4EDB+LIkSP869AhQ6osChMm4Ntq8dzN0BAPqru55Zs2YXB1F3hDnz548P33KCsrE+2e9/Lywvvvvy+6T/v27eHl5cXF17Bhw6qusWEDr2VkZPDjPTw8mtQtzcvLQ2JiIi5fvgxvb2+4uLhAIpHAxcUFfn5+SElJafZNZw1ZQkawr68v8vLyHrsrrNz19fT0FHV9a/P6lpSU4NKlS3B1dcXNmzdr7foeOHAABgYGqKioaOu3G0EQBPGEQkJXQ1EndL28vODh4QGpVIpbt241OlNX2NBWWFhYbxdX+HhZuXvn4uLCBWL00KHIGzECQQMH8ppsxAgoCgrg5OQk2gD26aef4urVq9wXa6Svj/SxY3Gva1c8NDfH1OoO7YIePVC+dCnKN23C7dWr+TXcX34Z9//v/1BWVobt27dzQWuotLFNSH5YtWqVSHAJ9T/++IPX3d3d+XmZmZmPLS6zsrIgk8ng7u6O8PBwXLx4UaUbevPmzVqnnDXHUs7njYyMhFwub5H7KHd9lX9OT09PhIeHIyoqiltp1HW0ha7vn3/+CWNjY1hbW+P+/ftt/XYjCIIgnlBI6GooykJXoVAgIiICLi4u8PX1RWlpaZM2lAliVTlyrGYXV9mmUFOkzJs3D4wxvGxqCoWFBe726oUN1RvTuunpIee33/gxwnJzc+PnL1iwAIwxTBw4EPd79UJ5u3ZQtG+PdtXH/qdzZ5SOGQPFokU4OX16VSqClhaKli7F3cRElJaWivJ6GasaTLFz506YmpqCMXEm7pUrV/hxkZGRvP7tt9+CMYYePXo8tri8fv06JBIJLl++LPLB1uyGCh5YqVSKoKAgREdHIyMjo9Gb3NStvLw8+Pr6wt3dvcn5vE1dyj+nn58fT3cQPnGIiopCamoqioqKuFBeuXIldHV1sWvXLrItEARBEHVCQldDqaysxL1795CamooLFy7Ay8sLfn5+uH79epNEriBmXVxccOfOnXptCjUFTW5uLoyrR/l++uGHKFmzBg9eeQXdqj23/3rjDQwYMEAkQkeNGiW6Ro/qCLGdXbuiwtwcj4YORajSOTc6dED2mDFIGzcOi6tjwUZ27owsd3d4e3tzGwJjDDo6OliyZAkyMjIQGxv7jyUiNJTf79ixY2CMwdjYWCQoBcE9bdq0Jgu83NxcLi5TU1Mb3A2tOeVMIpHAx8cHV69eRVJSEvLz8xvV9U1KSoKbmxuCgoK4mGztVVJSgrCwMG5VUNf1PXnyJLp3745+/fqhffv2OHr0KIlcgiAIol5I6Goo9+7dQ3BwMFxdXREbG4u7d+8iLCwMERERTRa69+/fh5ubG09nULfZrDaBJlgGdHR0kJycjLKyMpxXsjIINgIdHR2eXavsi42Li+PHBnbqhEf9+uHRiBHYUz2prJueHh6ZmeHh229DfuUKXqiuvzdnDiZPnqyyse3333/nz3vq1Kmq7q+uLgoLC/k9P//8c7WCW0hi+OKLL5rUwRTSDEJCQh5LXMrlcmRlZeHGjRsICQmBu7s7XFxccOHCBYSEhODGjRvIyspSa0NQFpfx8fFtkuxQVlYVX+bl5QUvL69aN9/J5XKcPHkSL730Enr37o2+ffvCwMCALAsEQRBEvZDQ1VAePXqEK1euiPy0V69eRXh4+GMJXaED2ZAurkKhQGJiIqRSKUaNGgXGGN58803+/ffee08kPq2srPD333/zr4WRxWVlZTh69GiVP1dPD/eeew6PBg/GoxEj8FaHDmCMYaG5OSo6dcLDceOQnJz8T1JDtSWBMYZO1TYJPT09nD17Fu7u7ggJCcHKlSvBGMPgwYNFzz9p0iQwxrBs2TKRaNer7kKfPHmyUaKusLAQgYGBPM2gucWlQqFAfn4+kpKScPXqVfj4+EAikcDV1RX+/v6IjIxESkoK0tLSIJPJ4OXlhezs7DYRuGVlZbh58yZcXV0RFhamEl+mLMi/+OIL6Ojo4IsvvsDDhw8BAHK5vI3fYQRBEMTTAAldDaamSI2KikJoaOhjCV2ZTIbExMR6u7j5+fkIDAyEq6srAgMDoVOdc3v8+HGUlZUhPDwc2traXIROnDgRqampOH36NBhj0NbWFomwxYsXgzGG8cOHo6JXL1RYW+P+8OFoX32NI926obJjR5Q7OmLbtm0iAW1sbAx7e3suaIcOHcoF4ZUrV/D666+DMYZx48YhICCApwFYWlqCMYYff/yRP4eQ3coYQ2xsbINFnTA+19/fH/n5+a0mJktKSpCRkYHo6Gj+7yEkH1y6dAkJCQnIzs5u1Y5uaWmpyKpQ272Tk5Mxfvx4mJub4/z586isrGzrtxRBEATxlEFCV4N58OCBSKTGxsYiMDCwyf7cu3fvwtfXl0/9unHjBs9/Ve4qxsXFieLLvvvuu6qcXBMT5ObmwtXVVdRpXbt2Lb/Gp59+CsYYhg0bJhI9ffr0AWMMX335JR6+8w4qOnVCmNIQiZsdOqCkXz+snjsXWlpavG5nZ4cLFy6gpKQE06s3qC1cuFB07d69e3MrgpABe+LECX4NZ2dn7n89fPgwT21oiDgsLi5GWFgYJBIJYmNj28wiUFBQwEcJx8XF4ebNmwgPD4eXlxcXvoGBgbh+/TrS09Nr7bA+7hKsCp6enrV2kxUKBWQyGbp27YrRo0cjJSWlrd9KBEEQxFMKCV0NpqbQFXa2N0XkCjYF5Y/GhfxXNzc3BAYG4urVq/Dy8lL5aH7kyJFgjGHevPXFcnMAACAASURBVHnYtWuXqJP7/PPPi0TOyy+/DMYYVq5cyWtJSUni3NrERJS/+y72mpuDMQZrbW1c6NcPvWpMTxszZowoReD5558HYwy7d+8WCS/heIlEwuvKUWheXl7c/zpr1iwwxjBixAgVkV9zZWRkwMPDA97e3m1qEUhJSYFUKkVAQIDaiWw1Bz0IXV9vb29cuXIFiYmJyMvLe2yRnpiYWOukNWHJ5XLs3LkTurq6WL16NR48eNDWbyOCIAjiKYaErgZTXl4uEqxJSUnw8vJqdBe3LptCcXExUlJS4O/vz6OhhHG3165dg6+vLxeMY8eOFdkJGGNwdHQUiU7B4qDsfz1+/DjPuuVCrbgYU155BYwxkcDV1dWFfvVQigMHDvBrZGdnq40sU36+W7du8frOnTvBGEPv3r15lzE/Px+vVN9z1qxZohgs5U5oaWkpIiIiIJFIcO3atRbLpK1vlZaW4sqVK5BIJIiJiWmwUJXL5bhz5w7i4uIQGhoKDw8PuLi4wN3dHcHBwYiNjUVmZmaDo81KS0sRHh4OV1dXJCQk1PocmZmZePvtt9G+fXucPn2arAoEQRDEY0NCV4OpKXSFqLGGClwhUaG2zWZlZVXDDry8vHDhwgWkpKTwcbcxMTEICgrC/PnzeZqCICinT5/Oxehvv/2GsuJi3PP2hvtHH/FjUuPi+D2WL18Oxhhef/11XispKYFR9UQ1YQ0YMAC7d+/mX/v7+6sVtCkpKbx+4MABMMbQuXNn0c8lTEybNWsWrykUCpiZmYGxqvHBxcXFSEtLU+mEurq6ws3NDdevX1fbQW2NlZOTA29vb8hkMmRlZT329QoKCpCcnIyIiAj4+vpCIpHwP2giIiKQnJys9mdtiFWhrKwMFy9eRM+ePTF06FAkJCS09VuHIAiC0BBI6GowNYVuZmYm3NzcGmRTqG+zmXLX8vLly2rH1SoUCnTt2pULTC0tLSxZskQkRgM8PSHftAkPx4/Hl9XHDjI2Rvknn6AsLQ1lZWU8X3fTpk0oKytDeno6JkyYwK9hYGCAZcuWITQ0FGfPnuV15biq2gStvb09GGOYMGGCqD5o0CAwxrBlyxZei4+P59cOCgpS+Vmjo6P5UI7g4GBud/Dw8MClS5cQHx/f4hu/lOPL6kozeNyl/AdNcHAwpFKp6GeNi4vjwzBCQ0PrtCr88MMP0NfX5+kWBEEQBNFckNDVYB4+fCgSsdnZ2XBxcVE7+rehgx8Eoenh4QGZTIaMjIw6u3TKMV/nzp2DQqHA1q1bwRhD165dEfXVV8gbOBBZo0fjter4r2U9e+LRyy/jwe7dSElJEVkO/v77b3Tp0oXXdHR0cOzYMaRVi+Jdu3ap9f46OjpWpTaMHy+qv/rqq2CMYfXq1aLupa6uLhhjOH36NK8LiRA6Ojqi7mVeXh78/f0hlUp5RrAgOvPy8pCYmIjLly+r3fjVXNPNysqqvLbBwcFwc3NDUlJSi4np2gR2bm4uEhMTER4ejvPnz3Mbi5BkkZqaKvqDKDs7G3PnzoWxsTGOHz9OVgWCIAii2SGhq8HUFLr5+flwcXGBQqFoUhdXOUEgMjKyXoH2zTffcJEbFRXF62+99RYYY3j//fdR/q9/oXz0aOS99x4Mqzep/ThkCHKGDUPu2LH4YccOLi4//PBDkcAVosKUxVNtU8vGjBkDxhhWrVol6ia2a9cOjDEcPXqU14OCgvh94pQsFF999VVVx3nQIC7ubt68ySeLKQ+bqG0VFxcjNTW11ulmtVkA6lsZGRm4cOECfH19kZeX16oiV3nl5uZyy0RGRgbS0tJw/fp1PuHMxcUFO3fuxJQpU9CrVy88//zziIqKauu3CkEQBKGhkNDVYGoK3eLiYri4uKC4uLhRXVyFQoHk5GS4u7vDx8cHd+7caZDoefvtt7mgVRaX5tVpCT99/z0ezp6Nh5MnI6D6WMYYkubORdlbb6HE1hZz33yTD3lQ9uIOHjwYjDHY29uL7jl06FAwxrBx40bR83eoHixx+PBhXo+JieHXVB79+/PPP4MxBjMzM9HrMXPmTJ4eIXRP69tgVd+Sy+W4ffs2YmNjVSwAYWFh9dod5HI5IiMj+R8fbbXxrazsn3HCtVkVFAoF7ty5g82bN2PgwIE8Yu7dd99t67cKQRAEoaGQ0NVgHj16JBK6CoUCLi4uyM/PF0WG1WVTKCgo4IIuJiamwUJKoVDgueeeA2MM+/fv5/UrV65wcRkeFobylSvxaNQo7BwxoipBwdQU5UuX4uG4cbg7Zw46V19D6OIuXLgQ586d41PONmzYgJs3byI3NxfFxcUwMDAAY1UjfoV7Kntrg4ODeV0Y/aunpycaxbtixQqVzW9lZf/Ek33xxRdwd3eHn59fs3dPBQuAkHPr6ekpsjtER0dzu0NeXh58fX3h7u5ep4WkpVdpaSkuX74MiURS5zjh/Px8LF68GPr6+jh48CAqKiqQnZ1Nm88IgiCIFoOErgZTU+jeu3cPLi4uyMrKqtemoFAokJCQgPPnz8Pf3x+5ubmNEj9RUVFcXIaEhPD6wYMHebdULpfjnosLHk2YgCnVXd6FvXvj4dSpuDVsGEb26sWvYWFhgYMHDyI1NRUZGRm8/ssvv0Amk8HFxYUPc2CMISAggIvyv/76S6239ssvvwRjDDY2NqJnF2LQPv74Y17Lysri196xYweio6NbrXtaVFSElJQUREZGws/PDxKJhEe5yWQy3Lx5s0G2iZZYylaFujr9UVFRGDJkCHr16oWwsLC2fmsQBEEQzwgkdDUYdULXzc0NwcHBSExMrHUUbW5uLgICAnD+/Pkmfyz/yy+/cH+uspdXiBubMmVKVU0uR9mhQ2hfvfnrcK9eODNsGMwMDUXRYWfPnuVeXC8vL14XBkIUFBRwEa2np4ezZ8/yjNtVq1Zxy4PyMypbEZQFfseOHXmEmFD/+++/+T2jo6PbRFSWlVXFql26dAkSiQTBwcEICgridgeZTIawsDAkJCQgJyenxaew3bp1C25ubggJCVGbuiG8nv/973/Rvn17TJ8+HQUFBW39tiAIgiCeIUjoajAVFRUqm80SExMRGhrKo69kMhnCw8Nx8+ZN5OTkIDo6Gq6urggODn6sDNglS5aoTTnoVd2l3bFjB68FBwdzETmvetMYYwyG1WK3Xbt2ItH2008/gTGGLl26iK69adMmMMbw4osvijJuhSiy119/nQ+ySElJ4VaEXbt28WvUjBCTy+WIiorCsmXLeFJEW4ncrKwsyGQyeHl5iaLT6rI7BAUFISYmplEDHupbyoMo4uLiahXUhYWF+OSTT6Crq4tvv/0WFRUVbf2WIAiCIJ4xSOhqMI8ePap1s5myOAoLC8OFCxf4x+F+fn6Ij49/rK7gwIEDwRjD5s2beS0xMZGLSC8vL17ft28fGGM80ktINhDG7Y4ePVp0bSEqrGb27fTp08EYw/z589U+y8aNG/kgizNnzvB7HTx4EElJScjPzxdFiKWlpeH8+fNwcHDgwynefPPNVhe4CoUCMTExkEgkuHLlSoMEa2FhIVJSUnDt2jVud1CeWJeSkiLyJTd05ebmwsfHp95BFHFxcRg1ahSsrKzg7+9P0WEEQRBEm0BCV4PZuHEjHBwccOLECSQnJ6sVraWlpXzXfkhICOLj40WZr1KpFCEhIbhx4wbu3LnTIOGbmZnJRaREIuH133//HYwx6Ovri7rFI6o3ojFWNVRi3rx5SExMxBtvvAHGGJYsWSK6vq2tLRhjWLlypajeu3dvMMawc+dOXisoKOBRZH/99Rev+/j48HtKpVIu9IUIsx49emDevHlo3769yELx6aeftqrILSgowMWLFyGVSkUT3Rq7ak6sE3JuPT09eUc/Nze3zn/fhloVzp49C3Nzc0ycOBHZ2dlt/TYgCIIgnmFI6Gowzs7OmDFjBk8oeOGFF7BkyRL8+uuviI+Px5kzZ3D69Gl4eHjwgQvKq6ioCMnJybh69Sp8fHzg4uICNzc3BAYGIiYmBrdv31a7IUvws2ppaYm6fh9//DEYY3j11VdRVlaVKSvUGGMwMjLCv//9by6iunfvDsYY9u3bJxJSQjyZsoc2NzcXWlpaYIzh3LlzvK5si4iPj+d1wf4gTEpTKBTIz8/nG9G0qzN9Bc+vYKP46aefWk3kpqSkQCqVIiAgoNlHCSsUCuTk5CAhIQFhYWF8Q59UKkVQUBBiY2Nx+/ZtlJaWNtiqUFJSgg0bNkBHRwdbtmzBw4cPW/13/vjx46I/TIS1e/fuOs978OAB1q1bB0tLSxgbG2PSpEmIj49vpacmCIIgWgoSus8Ajx49QlRUFA4cOIC5c+fCwsICWlpa0NbWxrx58/DLL7/g+vXr9aYIKA87ED4Od3V1RUBAgGjK14YNG9SmGQgZt2vWrMGtW7fw2muvicTIL7/8wo/Nzs7m9fPnz/N6UlISr1+8eJHXAwICeD0xMZHXhSSGDh06iARazdG/SUlJooEUwkY6BwcHSCQSXvvmm2/g7++PqKgopKWl1drZfJylLCxjYmJafFOZsAoLC5GcnIyIiAj4+vpyu4Orqyvc3NwQGxtbq93h1q1bsLOzw3PPPQd3d/c2syoIQtfLywuhoaF83b59u87zHB0d0aFDBzg7O8PDwwO2trawtrZGcXFxKz05QRAE0RKQ0H0GWbRoEfr164eNGzdi4cKF6NmzJxhjsLa2xty5c/HTTz/hypUr9Qrf0tJSpKen4/r16wgICOBTvoYNG1YVFbZwIReCd+7c4V3SXbt2icb4Ckv5o3l/f39ev3XrFq+7urryunLk2aFDh8AYQ8eOHUXCUEhcGDNmjOjZR48eDcaqBk6sWrWK5+/yTXHz5iEzMxNlZWUICwvj9dDQUJVJX8JUs1u3bj12zFdOTg6P66rLA9saKykpCa6urvD09MTFixdV7A5RUVEIDQ2FVCpFly5d8OqrryItLa1Nf7cFoVtUVNTgczIyMqCjo4MjR47wWkFBAUxMTLBnz56WeEyCIAiilSCh+wySl5eHBw8e8K8rKyuRnJyM48ePY8mSJejXrx//WH/mzJnYu3cvQkND690EJZfLkZaWxjduff7551wI/uc//xFZAYRUhYULF4Ixhk6dOokEqiBczc3NRfVvv/0WjDE8//zzonvXNuRBSFxwcHAQPacwlUtZ4ArjgBljuHLlCj/+5MmT3Fus/BrI5XJkZWXhxo0bCAkJUYn5aojvVVgKhQLx8fFwdXVFWFiY2slirbXkcjmuXr0KiUSCGzduiDYwZmdnIz4+HpcuXcLu3bv5v2efPn2wZ8+eNt901hSh6+zsDC0tLZVzZs+eDTs7u2Z+QoIgCKI1IaFLqFBZWYmMjAz8+eefcHR0xKBBg8BY1ZCHt956C7t27YK/v7/aj+2DgoK4WLx+/Tru3LmDGzduYNGiRaKOqbW1Ndzc3PDJJ5+o7bjW1okV7AVvv/22qG5nZwfGGBwdHUV1CwsLMMZw4MABlJVVfTy/bt060bOYmJhg06ZN+L//+z9eU84Y3rlzJxhj6N+/f71iVTnmS9n3KmzoE4Z1KJ8njBN2c3NDUlJSm3Zx8/Ly4OPjAw8Pjzo7yunp6ZgyZQq6deuGNWvWYPXq1Zg/f35b/+pyoWthYQEdHR307dsX+/fvr1OAr1+/HtbW1ir1zZs3w9LSsiUflyAIgmhhSOgS9VJZWYmcnBycOXMGq1atwksvvQRtbW2Ymppi4sSJ2LZtGzw9PVFQUMCjwiwsLHgnMCkpiXdQGWMYO3YsvLy8cOHCBQwfPhyMMcyePRs3b95EXl4eFAoFJk2aBMYYli9fLhJYL7/8MhhjWL9+vagujBs+ePAgryUnJ/N7+vj44I8//uDZucL66KOP+NAJYYOalZWV6NpLly4FYwxTp05ttHAsKChQu6Hv4sWLuH79OmJjY+Hu7g5fX99mHyfc2JWcnMyzd+vyHvv7+6NHjx4YNmwYEhMT2/rXU4SHhwe+/vpryGQyeHh4wMHBAVpaWvjqq69qPWf58uUYPHiwSn3fvn3Q09NrycclCIIgWhgSukSjqaysRGFhIVxdXbFu3Tq88sor0NXVhaGhIRecr732GnJzc3Hx4kVeEzqxQkdToVCgR48efIOa0AF1d3fnnVgnJycumOVyOUxMTMAYw/Hjx9UKWl9fX153c3Pj9ZEjR/L/F9IZrK2tRQJu9erVau0PQrd4xYoVjy0mhUEWkZGRouxiX1/fx8q3fZylbFWIjY2t1Wohl8vx3XffQV9fn1tBngYcHBxgaGgIhUKh9vvLly+HjY2NSn3v3r3Q19dv6ccjCIIgWhASusRjU1lZidLSUnh4ePDcWR0dHWhra3NRqdxZFYRTbm4ur7u6uvIO6LVr10QpB1KpFMHBwfD09OT1sLAwtYI2Ozub1zdu3Kiy4W3EiBGYM2cO7ywrCzlh4MQHH3wgqgti/Pvvv28WYZmXlwdfX19cuHABaWlpuH37tkq+rZeXFy5fvsxHNbdU8kJ+fj58fX3h4eGB27dv13rcnTt3MGfOHJiYmODEiRNt7sVtDMLvTVhYmNrvk3WBIAhCcyGhSzQb6enpXFC+8847KpvPGGMYMmQIVq5ciVOnTkEqlaqNBPP19eX1mzdvIiUlBREREfi2WrjqaGvj2s6dSHB3R2ZmJnZ/8w0YY+jZpQvuhoejKCsLX3/9NR8UwRhDz5498cMPP6CoqAjTpk0DYwyLFy8WibnBgweDMYZt27bxWmFhodp83qaupKQkuLm5ITg4WG3ntuaGL6Hre+HCBYSGhiI+Ph7Z2dnNInxTUlK4VaGuLvKlS5fQt29fDBw4ENHR0W39a9ZoBKEbHh6u9vvCZrSaUWJz5syhzWgEQRBPOSR0iWbj1KlTYEw8bGH48OE8KcHc3BxOTk6YMmUKOnToIBLCJ06cwK1bt6BQKHhCw3PPPcfF1t3Ll/HNuHFgjGFA+/YomDEDafPm4fLatZjZvz8YY3jT2hpuM2ZggKWlqIs7bNgw0aADQdBu375dJDAFW8Rvv/3G6xEREaLNdU0VlSUlJQgLC4Orqyvi4+MbJVTz8/ORlJQkmlgnCNSYmBhkZmbWGwWnvORyOSIiIuq1KigUCvz8888wMjLCggULUFpa2ta/Yk3C3t6+TuuCEC/266+/8lphYSFMTU0pXowgCOIph4Qu0Ww4ODiIBObs2bOhUCjw+eefgzGGcePG8WPLy8uxePFiMFaVfas8vU1IeRg5cmSVCMvLw4Pt2zGvesTvu0OH4sHevSi3t0f5+PEY3rEjGGPoq+QF1mIMhtXRYd9++61aQXvixAlev3XrFj9XeRDFX3/9xa0YTR0OkZWVBZlMBi8vL+Tk5Dx2J7aoqAgpKSm4du2aaHCHv78/IiMj6xxkkZ+fDz8/P1y4cKFOq0JeXh4++OADGBgY4NChQ6ioqGjD36yGM3v2bDg5OUEqlUIqlWLZsmVgjGHr1q38mAkTJmDChAmi8xwdHWFmZoZjx45BJpPBzs6OBkYQBEFoACR0iWbDxsaGi8WNGzdycTR16lSecKDMzJkzwRjDkiVLUFFRwae3WSp1ZHv27Ikvpk7FdVtbDKwWw9tGj8aDdetQvnIl7g4YAL0aPmAbCwuctLXlloNt27bB29sbV69eFQ1/UBa03t7evJ6RkcHre/fuBWOqub0NWQqFAjExMZBIJLh69Wq9OcRNXaWlpcjIyEB0dLRokIXwM9+6dQsFBQXcqhAYGFinVeHatWuwsbFB7969a/24v6U5ffo0ZsyYAWtra5iYmGDo0KFwdnau1xtc05PNGFPx2drZ2alYEu7fv4+1a9fCwsICRkZGmDRpEuLi4pr7xyIIgiBaGRK6RLNQUVHBB0XUFLS9evUCYww//fSTqC4Mpti3b5+obmVlxbtwhw8fxrY33sBpIyPoVAuXA6amSO/TB1cGDsQwfX0uaEwNDPDlxIm4vmYNIt97j9eDgoIQFxeHkJAQfPfdd7zu5eWFpKQk5Ofn48iRI2CsKitYWfR99NFHYIxh4sSJjRKfBQUFCAgIgFQqFU18a40ll8t5frHyIAtheEdCQoLaQRYKhQK///472rVrh5kzZ6KwsLDFf29qY/To0Zg3bx5OnToFHx8fbNq0Cdra2vj666/rPI8xhs8++0w0/jcyMrKVnpogCIJ40iChSzQLaWlpXEAqC4uysjLeWZXJZLx+79497uWVSqW8XlRUxK/j7+9fVYyJQcyoUbx+qmtX2OvpwaBG5+6Lvn2Rum0b7u3fj/PV3WItLS3RWF5nZ2cwVjUFLTQ0lG/2Eia02djYiETg5MmTwZh4slp9KyUlBVKpFBcvXkRBQUGrityaS0hVkEqliIyMVBlkERwcDIlEAolEAgcHB+jp6WHfvn1tblXIy8tTqdnb26Njx451nscYw/79+1vqsQiCIIinDBK6RLOgHP1VVvZPvqpyVFh6ejqvR0VF8XpKSgqvK09W42InOhr/q7ZF6GtpYWS1J5cxhg7V/zVmDOs6dcKnhob4q1cvrKrecFYzK3fLli1gjGHo0KG8i5mfn48ZM2aAMQY7Ozue5RsaGsqjxZR9vnVZCK5cuQKJRIKYmJgWiwRr6EpNTa3VqlBYWMgHWSxatIhHwQ0fPhzOzs6t9WvTKISx0Hfv3q31GBK6BEEQhDIkdIlm4eDBg2CMoUePHqK6MFbXxMRE5K88efJklUA1NhZ1Dw8fPgzGqiarcYKCsHnIEL7JTBC5y3r1wspu3cAYw+guXaCwt0fCtGk4+/rr6GtuzhMgJk6ciK1bt8LT0xPz5s0DYwyzZs0SCb9R1R3j9evXo6CgALdu3cKlS5d4RNmWLVsQFBSE2NhYtWN8c3Jy4O3tDZlMVufo3NZYcrkc165dq1dwKxQKnDlzBh07dsSkSZPwv//9D1999RWOHDnS4r8vTWH+/Pno2bNnnccwxtCpUyfo6OigY8eOWLhwIe7cudM6D0gQBEE8cZDQJZqFlStXgjGGyZMni+pbt24FY1WDGtTVhw8fLqoL08nGjx/Pa4kuLuhYnaDAGEOP9u0hW7AA+OEHzO/TB4wxzH/jDcDbGwgLA4qK8O6774IxhgkTJoimtwk2ildeeQXnz5/nNgVhetuhQ4e4EIyJiRH5eSMiIuDr66syxleI6goLC0NJSUmbityCggL4+fnBvTpjuLbjiouLsWbNGujo6GDbtm149OhRy/1yNAOBgYHQ1tbGgQMH6jxu8eLFOHPmDAICAnDgwAF07twZL7zwguhTBoIgCOLZgYQu0SwIXtaVK1eK6nPnzgVjDAsXLhTVBSG6aNEiUX3ixIn8OpWVlTh27BiPA2OMYVSPHij57jvg4EHg00/xanVHd/PmzaLrjBgxgmflAv9MbxMEbd++fWFgYAA9PT28/PLL/Ppnz57lHVCJRMJ9vspe25KSEqSlpSEiIoJPMpNIJPD390dUVBTS09PbRPCmpqZyb3BdqQq3bt2Cra0tOnfuDJlM9sRPOcvIyICVlRUmTpzYaO9wSEgIGGOijFyCIAji2YGELtEsdO/eHYwxHDx4UFQfUm052Llzp6guZOU6OTmJ6l26dAFjDN999x0XyYLYZIzhzwkTAHt7YPlyYMsWdKmOHKv5cXvHah/v77//zmt3797l15PJZLh79y78/Pzg6Ogous+wYcOwcuVKLF26FIwxdOvWTUUsZmRk4MKFC/D19UV2drYo3svV1RUSiQS+vr64du0aUlJSmpzB21CrQmRkJCQSCaKjo+u0Kri7u8PS0hKvv/66yDP9pFJUVAQbGxsMGTKkyZm2VlZW+Pjjj5v5yQiCIIinARK6xGOjUCi4UPT09OT1R48ewaDacvDXX3/xenl5OXR1dcEYg4uLC68XFBTw63Tu3Jn//5tvvvlPVNiBA8DffwMyGe7evCmyFggoJzcEBgby+o0bN3g9KSmJ14WhEAYGBggMDISTkxOmTp0K/eroMhMTEzg4OODEiROIi4vD9u3b4eLigsjISLUTyeRyObKyshAbG4ugoCDe9VXOtVVOgnhcq4K/vz/c3d1F+b81V2lpKbZu3QodHR2sXbsW5eXlLfXr0GzcvXsXY8aMQffu3ZGZmdnk61hZWWHFihXN+GQEQRDE0wIJXeKxUU5WSEtL4/WkpCRej42N5XVlwZmQkMDrvr6+orgwQ0NDHDp0SDSGV1nwxMXF8XpiYiKv13b8+fPnwVjVlDNloSeMKB44cKDo55o2bRr3886cORNmZmbQ19eHmZkZHBwccPToUdFo4dqWQqFAdnY2z/J1d3eHi4sLPD09ER4ejsTEROTn5zc6pSEtLY1bFeoSzmlpaXjjjTfQsWNHnDt37om3KgDAw4cPMW3aNJibm4t+dxpLcHAwGGNPbJIEQRAE0bKQ0CUem1OnToExBiMjI5GH0s3NjQvLBw8e8PrZs2erosL09fHw4UMAQGZmJvpUbywT4r8EgePi4gLGGPT09ETXv3DhArcbqLu+gYGB6PiffvoJjFVNOVPG3t4ejDFMmzZNVBfsFXv27EFFRQUGDRqEcePGYffu3Zg7dy63WfTs2RMLFizAoUOHEBUVpbbLW1P45ubmIiEhAWFhYfDw8ICLiws8PDxw6dKlWgc6KHeMo6KiIJFIcP369ToFsq+vL7p3744RI0aIutitTVxcHCZNmgRjY2NYWlpi/fr1on+zmgj/Jnv27MGCBQvQsWNHGBgYYOTIkbh+/ToA1VG+hw8fxrJly3Dq1Cn4+vpi//796NSpEwYMGFBnJBlBEAShuZDQJR6bHTt2cHGqzL59+8AYwwsvvCCqf/PNN2CMYciQIQAAHx8fWFhYcJFrZWWF+/fv8+N//PFHMMbQp08f0XWEXNVu3bqpvW///v1F9U8//RSMVU05U2bChAlgjOHTTz/ltYqKChXbRWZmpqgbWlFRgfj4eBw+fBgLFy5Ez549oFyQuAAAF11JREFU+fO/++67+PHHH3H58uV6hW9ZWdVgh8TERFy+fBmenp58oENISAji4uKQnZ0NhULRYKuCXC7Hnj17oKenh48++gj37t1r0L9lS1BYWIiuXbti7Nix8PDwgLOzMzp06IBPPvmk1nOE11LdsrS0RHFxscooX29vb7z22mswNzeHrq4urKysYG9vj9zc3Fb4KQmCIIgnERK6TzF2dnZqhUBDckMb22GriwULFoAxhvfff19UFzZzzZgxQ1SfP38+GGN47733sGvXLj4hTU9PD4wxLFiwQHT8559/zqPClNmwYQMYY7C1tRXVV6xYAcYYpk6dKqpPnz4djFVNOVNGEFXK0VXp6en89WzoCNnKykokJyfj+PHjWLp0KR9x/Nxzz2HGjBnYs2cPQkJCUFpaWq/wVR7o4O3tzSPNJBIJZDIZUlNTaxXQWVlZmDVrFkxNTfHf//63za0KTk5OMDU1RUFBAa8dPnwYOjo6uH37dq3nZWRkQEdHR7TRsKCgACYmJtizZ0+LPjNBEAShGZDQfYqxs7PD2LFjERoaKlr1bTRqSoetLoQor61bt4rqr776Khhj2Lhxo6g+dOhQMMa4EGSM4cUXX+TX2bRpk+j42bNngzGGpUuXiupCKsMHH3wgqk+ZMgWMMZUNSIOrp6V9++23vPbgwQO1o4j9/Pz4s8nl8sa/KKgSvpmZmfjzzz/h6OjI79+hQwdMnToVO3fuhJ+fX72JDAqFAhEREXBxcYGfnx98fX0hkUjg6urKs3xv3ryJnJwchISEoHfv3hg0aNBjeVubE1tbW8yePVtUKyoqgpaWFo4fP17rec7OztDS0kJRUZGoPnv2bFEnlyAIgiBqg4TuU4ydnR1mzpzZ6POa2mFTR2VlJUxNTauiv/78U1QXIr6UxcyjR49451ZY//rXv1BWVsYjyn7++WfRPYYPHw7GGHbs2CGqjxw5Uq3A7t+/P48oU34eIyMjMMZw+vRpXk9ISODPER8fz+tHjx4FYwxdunRp1OtRF5WVlcjJycFff/2FVatWYdiwYdDW1oapqSkmTJiArVu3QiaToaCggPtuCwoKEBAQAHd3d6Snp3PxW1JSgvT0dERFRcHf3x9r166Frq4uTE1NMXjwYJw7d67Znvtx6dy5M7788kuVupWVlcofQcqsX78e1tbWKvXNmzfD0tKyWZ+RIAiC0ExI6D7FNFXoNrXDpo7bt29zoXjlyhVez87O5vVLly7x+nfffcfr+vr6OHr0KCorK/Hw4UO1nVUAMK8e53vixAlRXRj+cOzYMV5T9taePXuW17Oysvh9r169yuvu7u58Q5uyL3jjxo1gjOH1119v1OvRGCorK1FYWAhXV1fR9DZDQ0PY2triww8/RO/evXHu3DnRwIqaKzc3FwsWLEDfvn0xc+ZMTJ48GSNHjmyx524surq62Ldvn0p98ODBsLe3r/W85cuXY/DgwSr1ffv2QU9Pr1mfkSAIgtBMSOg+xdjZ2aFdu3YwMjKCoaEhJkyYgLCwsHrPa2qHTR3KkWClpaW87u/vz+vFxcWoqKjg4lFYISEh/PjU1FRej46O5vXS0lJe9/f353W5XM7rfn5+vJ6ZmcnrERERvB4UFMTryh+FHzx4kCcnKPPOO++AMYbFixc36vV4HITpbe7u7hgzZgy0tLQwYMAA6Ovr45VXXsHnn3+Ov//+G1lZWbzje/XqVQwaNAh9+/YV/aHR1r5cZXR1dUXddYFBgwap+KWVWb58OWxsbFTqe/fuhb6+frM+I0EQBKGZkNB9itm6dSucnZ0REBCAkydPYujQoTAyMhIJRXU0tcOmjp9//pknDaird+3aFaWlpXwjmLCee+450fEXL17k3yspKeH16OhoXk9NTVVbT0lJ4fXAwECRwBY4ceIEGGPo2LGj6L6fffaZ2o1ugo/4m2++adTr0RxUVlbio48+goeHB5/etn37dkyaNAkmJibQ1tbGSy+9hDfffBNGRkaYPXu2io/1SYKsCwRBEERbQUL3CaK4uBhxcXH1LiF7tiZFRUWwtLTEokWL6rxPYzpsJSUl2LZtG0aOHIkOHTrAwsIC06ZN41mmQmTX+PHjRecJ9drWiBEjRMf/8ccfYIzBzMxMVFce8qD8cytn9CrXaxO027ZtU3tfQYArC3xl3/HJkydrfR3bgvv37yM4OBhOTk6wsrLCokWLRFnBTyK2traYM2eOqFZcXNzgzWg1R//OmTOHNqMRBEEQDYKE7hPE8ePH6xSHwsrIyKj1GgsWLFCZ8FWTxnTYoqOj0aVLF3z55ZeQyWSQSCSwtbWFsbEx4uLieMLBRx99JDrvjTfeED2ztrY21qxZw6eNzZo1S3T8rl27wJhqFu+///1vtdaCAwcOgDGGXr16ierbt29XK2g/+OADHmmmjDAUQjmJQdlffPnyZTWv4JPBk2RPqAsnJye0a9dO1HU+evQodHV1GxQv9uuvv/JaYWEhTE1NKV6MIAiCaBAkdDWMBQsWYNCgQXUe05gOm+AFVUYul8Pc3ByfffYZnn/+eTDGsH//ftExwgYyxhjat28PmUwGAJg8eTIYY1i9erXoeAcHBzDGMH36dFF9/fr1YIxh7NixovqaNWvUdpI//PBDMMbw7rvviupjxoxRiS6rqKiAoaEhGGM4c+YMAKC8vBxffvklf/bCwkKV149oHEKcnZ2dHWQyGY4dOwYzMzOVOLuak84AwNHREWZmZjh27BhkMhns7OxgbW2t0uUlCIIgCHWQ0NUgCgsLYWFhoZIrW5OmdtiUGTVqFN555x1oaWmBMQZ3d3cAVV3GL774QtTNDQ8P5+cNGDAAjDEVj7DQGV65cqWoLmTlfvjhh6K6kK27ZMkSUd3W1haMMWzYsEFUF8b1Kg8fUN64FhQUhP379/OIM3U2CqLp3LhxAxMnToSRkREsLCywbt06lQElNSedAVVWjbVr18LCwgJGRkaYNGkS4uLiWvHJCYIgiKcZErpPKVFRUZgyZQqcnZ3h6+uLP/74AzY2NjA2NkZMTAw/LjU1FTo6OqJoroZ22GqjqKgIxsbG+Pjjj7koTE5ORnl5OZ+GprwsLCygo6ODPn36QF9fH4wxnDp1SnTNgQMHgjGGvXv3iuqjRo0CYwxbtmwR1YcNGwbGGL7+6isgPx+oHnHbrVs3MCbO4i0rK+PP4uPjw+sBAQG8LmT+ClFjjDEMGDCgQa/Hs0x9Hu66qM2qs3v37lZ4coIgCOJZgITuU0pmZiamTp2KLl26QE9PD2ZmZpg+fbooUgsAUlJSwBhTsSQ0pMNWG/b29jA1NcWRI0fAGIOBgQFKSkrw1ltvqYiWLVu2QCaTwcPDg9sKakaLVVZWwsTEBIwx/O9//xPdy8LCAowxODs7i+pmZmZgjOF3e3vgl1+A//4X90NDuUj18PDgx8bExKgkNOTl5fEusrD09fWxfPlyLFy4EIypjhAmVKnPw10XgtD18vISTfZr7NASgiAIgqgNErrPII+T7nDs2DEwxvDbb7/xDWQDBgzgU8oEgcgYQ6dOnUTnRkZG8mMSEhJ4PT8/n9dDQ0N5/e7du7zu7e3N60VKm8UC168Hfv8d+Pe/kaCU03vz5k1+vEQiAWMMurq6yMvLw5YtW3iqgrBRbsOGDVxgzZo1C4wxLF++vNlec02lPg93XQhCtzWi0U6fPo0ZM2bA2toaJiYmGDp0KJydnZ+aDX0EQRBE0yCh+wzS1HQHd3d36OrqchuB0KEVurFaWlr48ccf4eTkpDZBQYgEY4xh5MiRMDY2hqWlpajTm5WVxY+Pi4vj9aSkJDx48ADr1q3Dix06/OP/3bULcHUFXF1xYfVq/hzKU872798PxhjMzc1FFgVhFPFbb70lek5BtG/fvr25X/pnhlGjRqkkXNSkNYXu6NGjMW/ePJw6dQo+Pj7YtGkTtLW18fXXX7f4vQmCIIi2g4Qu0SBCQ0NhbGyMpUuX8pqNjY3oY3/BdrBixQowxvD222+LrvGf//yHHz9s2DB4eHjA2dkZxsbG/BrKmbAeHh5cuD548ACOjo7o0KEDNldPLdNmDB927AjFyZOAqysOOTqCMYbuXbvyazx8+BB2dnYiAW9kZIQNGzZg4sSJYIypeJO7du0KxhiOHj3aEi+lxiN4uLdt21bncYLQFTzcffv2xf79+1uky5qXl6dSs7e3V8lbJgiCIDQLErpEvcTGxsLc3BzTpk0T2Rnat28PxhiMjY1F43lnzJgBxhgcHR1F19m0aRMXm+np6bz+/vvvgzHVTNxffvkFjDFYW1vzTNUjR47gh+rObW9LS7xrYIC/Fi8GXF2xrnr4w9iRIwFUCWUhJ1ewKKxatYp3jV988UUwxuDk5MTvWV5ezn2+Fy5caLbX8FlC8HDX57X18PDA119/zT3cDg4O0NLSwldffdWo+xUVFaFbt26YN2+eqL5w4UJYWVmhoKBA7XmHDh0CYwx3795t1P0IgiCIpwcSukSd5OTkoFu3brC2toaPjw/fMOTj48MF5PHjx0XpDsOHDwdjDAMHDoSTkxOkUimkUin69OkDxhjatWsnusfKlSvVphwIMWWvvfYan5JVVFSE1f/6FxhjmDx4MHaNHo1NgwcDrq54p3ps76y331a7Me7zzz8XXf+5554DY0yUSJGens6Pb0hygCbSXB7upuDg4ABDQ0MoFIpGnefp6QktLS0+ye7s2bNg7J/YO3XMnz9fZRAJQRAEoVmQ0CXqxM/Pr1YPr3Lmqbp0h82bN6N///4wNjaGgYEBhg4dClNT0wZPZVNm/fr1sLa2Vqlv3rwZlpaWj/UzEmKay8PdFDw9PcEYQ1hYWKPPXbFiBczNzXHt2jV07txZ5RMFZQIDA6GtrY0DBw40+VkJgiCIJx8SukSroqurqzIsAgAGDx4Me3v7Ws9bvnw5Bg8erFLft28f9PT0mvUZicajzsPdFAShqzxkpKGUlZWhX79+MDAwQO/evSGXy9Uel5GRASsrK0ycOFHkCScIgiA0DxK6RKuiq6uL7777TqU+aNAgODg41Hre8uXLYWNjo1Lfu3cv9PX11Z7T1Egpdd3L/v371/OTPbvU5uFuCvb29k2yLgisW7cOjDHs2LFD7feLiopgY2ODIUOG0BhhgiCIZwASukSr0rlz51azLjQ1Uooxhs8++0w0xCAyMrKen+zZpDYPd2hoKGJjY/lx6ib0zZ49W+ThXrZsGRhj2Lp1a5Oe5dq1a9DT08OwYcNgamqK5ORk0ffv3r2LMWPGoHv37sjMzGzaD0wQBEE8VZDQJVoVW1tbzJkzR1QrLi6GlpaWyvQ2ZYTNaDW7cHPmzBF5hZVpaqQUYwz79++v8xhNpGYMm7Du3LlT6zl1ebjHjh3Lj2uoh1t5dHNjuH//PmxsbDB+/Hjcu3cPL774Iuzs7Hj3/uHDh5g2bRrMzc1FApwgCILQbEjoEq2Kk5MT2rVrJxoScPToUejq6tYZRyXEi/3666+8VlhYCFNTU+zZs6fB929IpNSzLHTHjh0r6sqGhoaivLy8zvMKCwvRtWtXjB07lmcjd+jQQSWfuCXZsGED2rVrh9TUVABVU/j09PTwww8/AKj6A4cxhu+//17l51MeLkIQBEFoFiR0iVZFEEV2dnaQyWQ4duwYzMzMVETRhAkTMGHCBFHN0dERZmZmOHbsGGQyGezs7GBtbd0or2VDIqUYqxpfrKOjg44dO2LhwoV1djU1BTs7O8ycObPR5zk5OcHU1FSUV3v48GHo6OjUm6XbHAQFBUFbW1v0RxAA7Ny5E4aGhrhx4wZ69uxZa+c5JSWlxZ+RIAiCaBtI6BKtzo0bNzBx4kQYGRnBwsIC69atw4MHD0TH2NnZqVgS7t+/j7Vr18LCwgJGRkaYNGkS4uLiGnzfhkZKLV68GGfOnEFAQAAOHDiAzp0744UXXkBZWVmD7/U00lSha2tri9mzZ4tqRUVF9dpRCIIgCKKlIaFLPBM8TqRUSEgIGGMqHUNNw87ODu3atYORkREMDQ0xYcKEBuXZNnWDIUEQBEG0NCR0CY2nOSKlrKys8PHHH9f6/dqGLOzevbvO6z548ADr1q2DpaUljI2NMWnSJMTHxzfpGR+XrVu3wtnZGQEBATh58iSGDh0KIyMjREdH13leU7ORCYIgCKKlIaFLaDTNFSllZWWFFStW1Pp9Qeh6eXmJNjrV51F1dHREhw4d4OzsDA8PD9ja2jbad1wbjzPKF6j6A8HS0hKLFi2q8z5NzUYmCIIgiJaGhC6hsTRXpFRwcDAYY3B2dq71GEHoKqdJ1IeQJHHkyBFeKygogImJSaOSJOp7psaO8lVmwYIFGDhwYJ33IesCQRAE8aRCQpfQWBoSKVUz3eHw4cNYtmwZTp06BV9fX+zfvx+dOnXCgAED6owka4rQFbKBa54ze/bsWrOBW5sFCxZg0KBBdR7T1GxkgiAIgmhpSOgSGktDIqVqpjt4e3vjtddeg7m5OXR1dWFlZQV7e3vk5ubWeS9B6FpYWEBHRwd9+/bF/v376xw33JRpb61JYWEhLCws8MEHH9R5XFOzkQmCIAiipSGhS7QKiYmJcHBwwJAhQ6Ctrf3EdCybCw8PD3z99deQyWTw8PCAg4MDtLS08NVXX9V6zvLlyzF48GCV+r59+6Cnp9eSj6tCVFQUpkyZAmdnZ/j6+uKPP/6AjY0NjI2NERMTw49TN8q3odnIBEEQBNHakNAlWgUXFxd069YNc+fOxQsvvKBxQlcdDg4OMDQ0hEKhUPv95cuXw8bGRqW+d+9e6Ovrt/TjicjMzMTUqVPRpUsX6OnpwczMDNOnT0dERIToOHWjfIGGZSMTBEEQRGtDQpdoFZSza2fOnPlMCF1PT08wxmrNon3SrQsEQRAE8bRDQpd4bIqKitCtWzfMmzdPVF+4cCGsrKxEo2GBZ0/ohoeHq/2+sBmtZpTYnDlznonXhyAIgiBaGhK6RLPg6ekJLS0tnDx5EgBw9uxZMMbg7u6ucuyzInTt7e3rtC4I8WLKE9cKCwthamraLPFiBEEQBPGsQ0KXaDZWrFgBc3NzXLt2DZ07d4ajo6Pa4zRR6M6ePRtOTk6QSqWQSqVYtmwZGGPYunUrP6ZmlBlQNTDCzMwMx44dg0wmg52dXbMNjCAIgiCIZx0SukSzUVZWhn79+sHAwAC9e/eGXC5Xe5wmCt3Nmzejf//+MDY2hoGBAYYOHYqff/5ZdEzNKDMAuH//PtauXQsLCwsYGRlh0qRJiIuLa8UnJwiCIAjNhYQu0aysW7cOjDHs2LGj1mM0UegSBEEQBPHkQUKXaDauXbsGPT09DBs2DKampkhOTlZ7HAldgiAIgiBaAxK6RLNw//592NjYYPz48bh37x5efPFF2NnZqZ0MRkKXIAiCIIjWgIQu0Sxs2LAB7dq1Q2pqKgAgMjISenp6+OGHHwBU+XfPnDmDM2fOYOTIkRg0aBD/ur7xugRBEARBEE2BhC7x2AQFBUFbW1sUkwUAO3fuhKGhIW7cuMEnaqlbfn5+bfPgBEEQBEFoNCR0CYIgCIIgCI2EhC5BEARBEAShkZDQJQiCIAiCIDQSEroEQRAEQRCERkJClyAIgiAIgtBISOgSBEEQBEEQGgkJXYIgCIIgCEIjIaFLEARBEARBaCQkdAmCIAiCIAiNhIQuQRAEQRAEoZGQ0CUIgiAIgiA0EhK6BEEQBEEQhEZCQpcgCIIgCILQSEjoEgRBEARBEBoJCV2CIAiCIAhCIyGhSxAEQRAEQWgkJHQJgiAIgiAIjYSELkEQBEEQBKGRkNAlCIIgCIIgNBISugRBEARBEIRGQkKXIAiCIAiC0EhI6BIEQRAEQRAaCQldgiAIgiAIQiMhoUsQBEEQBEFoJCR0CYIgCIIgCI2EhC5BEARBEAShkZDQJQiCIAiCIDQSEroEQRAEQRCERkJClyAIgiAIgtBISOgSBEEQBEEQGgkJXYIgCIIgCEIjIaFLEARBEARBaCT/D13H1MPIVuxAAAAAAElFTkSuQmCC\" 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\" width=\"639.8333333333334\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
@@ -1326,7 +1391,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "heading_collapsed": true
+ },
"source": [
"## Setting the DA\n",
"\n",
@@ -1345,8 +1412,15 @@
{
"cell_type": "code",
"execution_count": 7,
- "metadata": {},
- "outputs": [],
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:24.935741Z",
+ "start_time": "2021-01-27T02:15:24.929469Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
+ "outputs": [],
"source": [
"doa = {}\n",
"doa['lin'] = (example_df['x1'] > -0.3) & (example_df['x1'] < 0.3)\n",
@@ -1367,7 +1441,13 @@
{
"cell_type": "code",
"execution_count": 8,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:24.956573Z",
+ "start_time": "2021-01-27T02:15:24.937636Z"
+ },
+ "init_cell": true
+ },
"outputs": [
{
"data": {
@@ -1462,7 +1542,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "heading_collapsed": true
+ },
"source": [
"## Settings\n",
"\n",
@@ -1472,7 +1554,14 @@
{
"cell_type": "code",
"execution_count": 9,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:24.962371Z",
+ "start_time": "2021-01-27T02:15:24.958180Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [],
"source": [
"root_path_dir = \".\"\n",
@@ -1491,7 +1580,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "heading_collapsed": true
+ },
"source": [
"## Functions\n",
"\n",
@@ -1501,7 +1592,14 @@
{
"cell_type": "code",
"execution_count": 10,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:24.988206Z",
+ "start_time": "2021-01-27T02:15:24.963503Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [],
"source": [
"def gen_sgd_inputs(target, model=None, random_state=None, end_label=\"_predE\", filename = 'data.csv', prop = \"Ef\"):\n",
@@ -1608,7 +1706,14 @@
{
"cell_type": "code",
"execution_count": 11,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:25.050348Z",
+ "start_time": "2021-01-27T02:15:24.990204Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [],
"source": [
"def get_all_values(rep, target, target_label, skip=None, end_label=\"_predE\", prop=\"Ef\", filename=\"data.csv\"):\n",
@@ -1831,15 +1936,35 @@
"## DA Analysis"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "### Gamma value\n",
+ "\n",
+ "As a reminder, the impact function, which the SGD maximizes to find DA is given by: \n",
+ "$\\mathrm{impact}(\\sigma) = \\left( \\frac{s}{k} \\right)^\\gamma \\left( \\frac{1}{k} \\sum\\limits^k_{i=1} l_i(f) - \\frac{1}{s} \\sum\\limits_{i \\in I(\\sigma)} l_i(f) \\right)^{1-\\gamma}$ \n",
+ "where $\\gamma$ determines the weight between the coverage and the error reduction terms. This value is 0.5 by default and can be changed with the slider above and calling ``update_gamma()`` or directly by setting the value as a function parameter, i.e. ``update_gamma(0.4)``. This sets the corresponding value in the file ``neg_mean_shift_abs_norm_error.json``, which serves as a settings file for the SGD."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 12,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:25.063230Z",
+ "start_time": "2021-01-27T02:15:25.051647Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "08a02c20c8f44a179a55268b32f171a6",
+ "model_id": "216ccaeca8c44683b32965a944e661cd",
"version_major": 2,
"version_minor": 0
},
@@ -1864,21 +1989,17 @@
"gamma_slider"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Gamma value\n",
- "\n",
- "As a reminder, the impact function, which the SGD maximizes to find DA is given by: \n",
- "$\\mathrm{impact}(\\sigma) = \\left( \\frac{s}{k} \\right)^\\gamma \\left( \\frac{1}{k} \\sum\\limits^k_{i=1} l_i(f) - \\frac{1}{s} \\sum\\limits_{i \\in I(\\sigma)} l_i(f) \\right)^{1-\\gamma}$ \n",
- "where $\\gamma$ determines the weight between the coverage and the error reduction terms. This value is 0.5 by default and can be changed with the slider above and calling ``update_gamma()`` or directly by setting the value as a function parameter, i.e. ``update_gamma(0.4)``. This sets the corresponding value in the file ``neg_mean_shift_abs_norm_error.json``, which serves as a settings file for the SGD."
- ]
- },
{
"cell_type": "code",
"execution_count": 13,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:25.068641Z",
+ "start_time": "2021-01-27T02:15:25.064693Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [],
"source": [
"def update_gamma(g = None):\n",
@@ -1904,7 +2025,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "heading_collapsed": true
+ },
"source": [
"### Feature selection\n",
"\n",
@@ -1914,7 +2037,14 @@
{
"cell_type": "code",
"execution_count": 14,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:25.075479Z",
+ "start_time": "2021-01-27T02:15:25.069642Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [],
"source": [
"def list2str(List):\n",
@@ -1951,7 +2081,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "heading_collapsed": true
+ },
"source": [
"### Removing old files\n",
"\n",
@@ -1961,7 +2093,14 @@
{
"cell_type": "code",
"execution_count": 15,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:25.082275Z",
+ "start_time": "2021-01-27T02:15:25.076688Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [],
"source": [
"def rm_old_files(model):\n",
@@ -1980,7 +2119,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "heading_collapsed": true
+ },
"source": [
"### Splitting data into folds\n",
"\n",
@@ -1990,7 +2131,14 @@
{
"cell_type": "code",
"execution_count": 16,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:25.086863Z",
+ "start_time": "2021-01-27T02:15:25.083508Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [],
"source": [
"def split_data(model):\n",
@@ -2006,7 +2154,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "heading_collapsed": true
+ },
"source": [
"### Running the analysis\n",
"\n",
@@ -2017,6 +2167,12 @@
"cell_type": "code",
"execution_count": 17,
"metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:25.092167Z",
+ "start_time": "2021-01-27T02:15:25.087902Z"
+ },
+ "hidden": true,
+ "init_cell": true,
"scrolled": false
},
"outputs": [],
@@ -2036,7 +2192,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "heading_collapsed": true
+ },
"source": [
"### Summarizing data\n",
"\n",
@@ -2046,7 +2204,14 @@
{
"cell_type": "code",
"execution_count": 18,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:25.099832Z",
+ "start_time": "2021-01-27T02:15:25.094021Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [],
"source": [
"def summarize_data():\n",
@@ -2068,17 +2233,26 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "heading_collapsed": true
+ },
"source": [
"### Displaying data\n",
"\n",
- "``generate_table`` will compile the summarized data into a table while writing it into a csv file."
+ "``generate_table(data_summary, gamma)`` will compile the summarized data into a table while writing it into a csv file."
]
},
{
"cell_type": "code",
"execution_count": 19,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:25.116050Z",
+ "start_time": "2021-01-27T02:15:25.101666Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [],
"source": [
"def generate_table(data_summary, gamma):\n",
@@ -2113,174 +2287,13 @@
},
{
"cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Calculating: mbtr split 1\n",
- "Calculating: mbtr split 2\n",
- "Calculating: mbtr split 3\n",
- "Calculating: mbtr split 4\n",
- "Calculating: mbtr split 5\n",
- "Calculating: mbtr split 6\n",
- "Calculating: soap split 1\n",
- "Calculating: soap split 2\n",
- "Calculating: soap split 3\n",
- "Calculating: soap split 4\n",
- "Calculating: soap split 5\n",
- "Calculating: soap split 6\n",
- "Calculating: ngram split 1\n",
- "Calculating: ngram split 2\n",
- "Calculating: ngram split 3\n",
- "Calculating: ngram split 4\n",
- "Calculating: ngram split 5\n",
- "Calculating: ngram split 6\n",
- "Calculating: atomic split 1\n",
- "Calculating: atomic split 2\n",
- "Calculating: atomic split 3\n",
- "Calculating: atomic split 4\n",
- "Calculating: atomic split 5\n",
- "Calculating: atomic split 6\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 tr th {\n",
- " text-align: left;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr>\n",
- " <th></th>\n",
- " <th>Model</th>\n",
- " <th colspan=\"3\" halign=\"left\">Global</th>\n",
- " <th colspan=\"4\" halign=\"left\">DA validation</th>\n",
- " <th colspan=\"4\" halign=\"left\">DA identification</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th>MAE</th>\n",
- " <th>95AE</th>\n",
- " <th>R</th>\n",
- " <th>cov</th>\n",
- " <th>MAE</th>\n",
- " <th>95AE</th>\n",
- " <th>R</th>\n",
- " <th>cov</th>\n",
- " <th>MAE</th>\n",
- " <th>95AE</th>\n",
- " <th>R</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>mbtr</th>\n",
- " <td>mbtr</td>\n",
- " <td>14.22</td>\n",
- " <td>54.10</td>\n",
- " <td>0.83</td>\n",
- " <td>0.43 (0.02)</td>\n",
- " <td>7.57 (0.70)</td>\n",
- " <td>20.85 (3.31)</td>\n",
- " <td>0.89 (0.01)</td>\n",
- " <td>0.45 (0.00)</td>\n",
- " <td>7.66 (0.14)</td>\n",
- " <td>20.91 (0.65)</td>\n",
- " <td>0.88 (0.00)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>soap</th>\n",
- " <td>soap</td>\n",
- " <td>14.06</td>\n",
- " <td>51.02</td>\n",
- " <td>0.84</td>\n",
- " <td>0.78 (0.01)</td>\n",
- " <td>12.26 (1.35)</td>\n",
- " <td>34.41 (9.47)</td>\n",
- " <td>0.85 (0.01)</td>\n",
- " <td>0.76 (0.01)</td>\n",
- " <td>11.80 (0.32)</td>\n",
- " <td>37.93 (1.90)</td>\n",
- " <td>0.85 (0.00)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>ngram</th>\n",
- " <td>ngram</td>\n",
- " <td>14.67</td>\n",
- " <td>51.10</td>\n",
- " <td>0.83</td>\n",
- " <td>0.54 (0.02)</td>\n",
- " <td>9.29 (0.96)</td>\n",
- " <td>28.42 (2.13)</td>\n",
- " <td>0.87 (0.01)</td>\n",
- " <td>0.54 (0.01)</td>\n",
- " <td>10.45 (0.18)</td>\n",
- " <td>37.75 (0.45)</td>\n",
- " <td>0.86 (0.00)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>atomic</th>\n",
- " <td>atomic</td>\n",
- " <td>65.52</td>\n",
- " <td>154.49</td>\n",
- " <td>0.24</td>\n",
- " <td>0.85 (0.01)</td>\n",
- " <td>62.45 (6.47)</td>\n",
- " <td>144.37 (10.10)</td>\n",
- " <td>0.25 (0.07)</td>\n",
- " <td>0.85 (0.00)</td>\n",
- " <td>62.91 (1.30)</td>\n",
- " <td>155.19 (2.88)</td>\n",
- " <td>0.26 (0.01)</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Model Global DA validation \\\n",
- " MAE 95AE R cov MAE \n",
- "mbtr mbtr 14.22 54.10 0.83 0.43 (0.02) 7.57 (0.70) \n",
- "soap soap 14.06 51.02 0.84 0.78 (0.01) 12.26 (1.35) \n",
- "ngram ngram 14.67 51.10 0.83 0.54 (0.02) 9.29 (0.96) \n",
- "atomic atomic 65.52 154.49 0.24 0.85 (0.01) 62.45 (6.47) \n",
- "\n",
- " DA identification \\\n",
- " 95AE R cov MAE \n",
- "mbtr 20.85 (3.31) 0.89 (0.01) 0.45 (0.00) 7.66 (0.14) \n",
- "soap 34.41 (9.47) 0.85 (0.01) 0.76 (0.01) 11.80 (0.32) \n",
- "ngram 28.42 (2.13) 0.87 (0.01) 0.54 (0.01) 10.45 (0.18) \n",
- "atomic 144.37 (10.10) 0.25 (0.07) 0.85 (0.00) 62.91 (1.30) \n",
- "\n",
- " \n",
- " 95AE R \n",
- "mbtr 20.91 (0.65) 0.88 (0.00) \n",
- "soap 37.93 (1.90) 0.85 (0.00) \n",
- "ngram 37.75 (0.45) 0.86 (0.00) \n",
- "atomic 155.19 (2.88) 0.26 (0.01) "
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2021-01-27T01:26:29.607Z"
}
- ],
+ },
+ "outputs": [],
"source": [
"gamma = 0.5\n",
"update_gamma(gamma)\n",
@@ -2330,806 +2343,14 @@
},
{
"cell_type": "code",
- "execution_count": 21,
- "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",
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- " 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",
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- " this.rubberband_context = undefined;\n",
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- "\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",
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- "\n",
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- " }\n",
- "\n",
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- " var button = $('<button/>');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
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- " var icon_img = $('<span/>');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('<span/>');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('<span/>');\n",
- "\n",
- " var fmt_picker = $('<select/>');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option);\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('<span class=\"mpl-message\"/>');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'] / mpl.ratio;\n",
- " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
- " var x1 = msg['x1'] / mpl.ratio;\n",
- " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x * mpl.ratio;\n",
- " var y = canvas_pos.y * mpl.ratio;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " var width = fig.canvas.width/mpl.ratio\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var width = this.canvas.width/mpl.ratio\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('<div/>');\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
- " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " // 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"
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vogaNWpAoVCI49atWwAeBN0VK1YYHOvQoUNQKBQ4e/asWXWbikHXMgy6EnGUpjPl5rbX/3rdpKD7+l+vy/1xKuVLdQSUKg3emLcX9/MKK34DEZGDc6agCwCXLl1C//798fjjj0OhUKBt27Y4fPgwDh8+DIVCgePHjxu8/tatW1AoFPDy8gIALFmyBC4uLpg2bRr27NmD8PBwLFiwAAqFAnFxcQAeBN2tW7eWOrdCoUBQUJDZdZuCQdcyDLoScaSmq+jmto5bOpq8OkPknUiZP43l4u7ex/NTi9YCXhR0Ue5yiIgkZ7WpCxv+Y3x+bsl5uht6STZ1oaSCggIcOHAAb731FurXr48LFy6UOa3h4Su6HTp0QP/+/Q1es2zZsjKDLq/oOgYGXYk4U9PNDZtr1lzdL4O+xImbjrm17s8BF6FUafD8tADEp2TLXQ4RkaSsdjPa2b9Nu0p7bnvlz2UGX19fKBQKxMTEoFGjRvj8888Nnp86dSoee+wxpKSkAADc3Nzw2WefGbymQ4cOZQbdsubo1q9fn3N07QyDrkScqemy8rMqXKHBdaMrPvD+wOCxwf6DcTDxoEMF3qy8Qrw+dy+UKg1GeUbIXQ6RLCpaM5uchzOtunD27Fl0794d69atw/79+7Fr1y688cYbaNmyJXQ6nbjqwjfffIM9e/Zg1qxZeOSRRwxWXfj2229Ro0YNrFq1CkFBQRg4cCCaN29eZtB9eNWF6tWrY/bs2ZJ9PgZdyzDoSsTZms6UdXR1eh32xe/DQL+BBs8P8B2Avdf3Qqd3jG12t59MFG9MO3K19J24RM7MlD/r5Dysvo7uitfKXkd3xWuSr6N7584dDB48GC1btkTNmjXRuHFj9O/fH1evXhVfs3r1ajz//PN49NFH8dxzz5VaRzczMxNDhgxB/fr10aBBA4wZMwabN28uM+h6enpi0KBBqFOnDho0aIDvv/8eWq1Wss/HoGsZBl2JOGPTZeVnYV7YPHTa2gmv//U6Om3thHlh80pd5dHr9TiadBRDAoYY/CP54a4P4Rfrh0Kdfd/opdPp8cHKI1CqNHj314Mo1DpGQCeqLFN+elO8ZjY5B0l2Rju3HfAaAKz1KPp6brtT7YxWHHR37dpl0/My6FqGQVcibLoiEbciMGrPKIN/KN/b+R52XN6BgkrcaCC1U/H3xKu6G4/FyV0OkU2YOh+/eM1scnxWD7pVAIOuY2HQlQibztD5u+cxYd8Eg38su2/vjk3Rm5BbmCt3eWX6ZmsklCoN2swMxr37+XKXQyQ5U1dYceQ1s8kQg675GHQdC4OuRNh0ZbuUegnfHfgOrdWtxX80PbZ6YP359bhfcF/u8gzczsjFyz8GQqnS4Eef83KXQyS5qrJmNj3AoOs4GHQtw6ArETZd+a6lX8O0w9PgutFV/Mfz7S1vY/WZ1cjIz5C7PNHK/VehVGnQYooGF2/ZT11EUuAV3aqHQddxMOhahkFXImw60yRlJWFO2By4e7obbDm87NQypOamyl0ecgu06LhwP5QqDT5ZG+ZQS6URmYtzdKseBl3HwaBrGQZdibDpzHMn+w4Whi9EO692Bj8eXXBiAW7fvy1rbUFRt8Qb0wLP35S1FiIpcdWFqodB13Ew6FqGQVcibDrLpOamYvmp5Wi/qb3B1sKzj81GUpY830u9Xo9B645DqdLg7QX7kFsg3TqJRHLjOrpVC4Ou42DQtQyDrkTYdJWTkZ+BNWfW4O0tbxvsvjb18FRcS79m83ou385Eyx/8oVRp8L99V2x+fiJbysrPwns734OgFtBmYxuja2aT42PQdRwMupZh0JUIm846sguyseH8Bnhs9RADb2t1a3x74FtcSr1k01pm7I6CUqXBS9MDcSvdPpdEI7KWYYHDIKgF/HLyF7lLIQkx6DoOBl3LMOhKhE1nXbmFudh8cTO6b+9u8KPU8fvG41zyOZvUkJadD7dZwVCqNPh6C398S87t/V3vQ1ALUEep5S6FJMSg6zgYdC3DoCsRNp00CrQF8L7ijf/s/I9B4B0ZPBIRtyIkP79n2HXxxrST1+VfFYJIKm9tfguCWoBvjK/cpZCEGHQdB4OuZRh0JcKmk1ahrhCaWA36+PQxCLxDAobgSNIRyZYBK9Tq0GPpQShVGrz/v8PQ6bjcGDmfAm2B+Gfq6I2jcpdDEmLQdRwMupZh0JUIm842dHodQq6H4GO/jw0C78d+HyMkPgQ6vc7q5zwac1e8qrstIsHqxyeS2+37t8U/S7aeC0+2Ze2gm6/NhyZWgzF7x+Bjv48xZu8YaGI1yNc69jbqOTk5cpfAoGshBl2JsOlsS6/X43DSYXwe8LlB4O3j0wf+sf7Q6qy7JNjov05CqdKg7Zy9yMwtsOqxieR2IeWC+Gfobs5ducshCVkz6N7IuoFe3r2KVutQtzH42su7F25k3bDKeYwZOnQoXF1dsX//fri5uaFWrVpo27Ytjh07Jr4mLy8PY8aMQb169fDkk09iwoQJWLNmDRQKBdLS0gAAoaGhUCgUCAgIQN++fVGnTh18/PHHAIAlS5agbdu2eOKJJ9CkSRP06dOn1PfPw8MDH374ITZu3IiWLVuidu3a+Oijj5CVlYXo6Gh06tQJtWvXRtu2bXH27FmTPx+DrmUYdCXCppOHXq9H+K1wjAgeYRB4e3n3gvcVbxTorBNKE1Kz8cK0AChVGsz3j7bKMYnsxaHEQ+IKJ9b+TyLZF2sF3XxtPnp590Jrdesy12BurW6N3t69Jb2yO3ToUDRs2BCCIMDLywsBAQF444030KhRI2RnZwMAvvrqK9SoUQOLFy9GUFAQBg0ahGeffbbMoPuvf/0LP/zwA0JCQnDw4EEAwDfffIONGzciNDQUu3btQufOnfHss88aXPH18PDAM888g3feeQe+vr74448/8Pjjj2Pw4MFwdXXFunXrEBAQgDZt2uCll14yeaodg65lGHQlwqaT39nksxgfMt7gL9v/2/5/2HJxC/K0eZU+/i/Bl6BUadBqqj9ik7m+KDmPXVd3QVAL6LS1k9ylkMTKCk8FugIkZCSYNTZGbTRp+2jPKE+Tj2nuhYmhQ4eiWrVquHDhgvhYZGQkFAoFNBoNUlJSULNmTcyfP9/gfW3bti0z6I4fP77c82m1WqSlpeGRRx7Bzp07xcc9PDzwxBNP4N69e+JjI0eOhEKhwPbt28XHAgMDoVAocP78eZM+H4OuZRh0JcKmsx8XUy9iUugkgysNnbd1hjpKjeyCbIuPm51fiPbzQ6BUafDFhnArVkwkrz/P/ylO/SHnVlZ4SshIMCm0Sj0SMsy7B2Lo0KF47rnnDB7Lz8+HQqHA6tWrxQAbFRVl8JrZs2eXGXQDAwNLnePo0aPo2rUrnnzySSgUCnH8/PPP4ms8PDzg4eFh8L6FCxcanAMAYmNjjZ6nLAy6lmHQlQibzv7Epsdi6uGpcN3oKv5F+s6Wd7D27Fpk5GdYdEyfyCTxxrTQS3esXDGRPBaFL4KgFvBl8Jdyl0ISc7ag6+rqWupxhUKBpUuXYsuWLVAoFLhzx/DvamNzdE+fNlwv/fr166hTpw66deuGHTt24OjRo4iIiECtWrUwY8YM8XXFc3RLWrp0KRQKw8iVmJgIhUKBXbt2mfT5GHQtw6ArETad/UrITMCsY7Pg7uku/oXaflN7LD+1HPdyH/yoyZS7h/V6PT767SiUKg26LAlFgdb6qzwQ2ZrqkAqCWsDkg5PlLoUkZq2pC8MDhxudn1tynu7wwOGSTl0oL+iae0U3MjLS4HVr166Fi4sLMjMzxccyMjKgUCgYdO0Yg65E2HT279b9W1hwYgFe/+t18S/idl7tsDh8Mc4lnzP57uFzieloPqXoqu66Q7EyfiIi6yi+mXPBiQVyl0ISs9bNaJpYjUlXaf1j/a1QddkqCrrFc3TnzZtn8LyxOboPB91ly5ahevXqBjeeFV8NZtC1Xwy6EmHTOY6UnBQsPbkUb25606S/qMu6e3jy9rNQqjQQZgThblblb3QjklO/3f0gqAWsO7dO7lJIYs626kJ5QRcAJkyYYHTVhYyMoilsxoLu2bNn4eLigk8++QQhISH45Zdf0KJFC9SsWZNB144x6EqETed40vPS8duZ39Dur3ZmX5lIzsyD8FMQlCoNpuw0fV1EInvksdUDglqA9xVvuUshiVl7Hd3e3r3L/ElYb+/eNltH92Elg25ubi5Gjx6NunXrol69ehg1ahQWLFiA6tWrQ6crmnpmLOgCwPr168Vw+/bbb+PMmTOoV68eg64dc+qge/HiRXTv3h21a9dGkyZN8P333yM/37z/TX711VdQKBT4+uuvzXofm85xjdozqsK5Zm3UbTA2ZKzB+34/GAulSoPmUzQ4n5QuU/VElaPVadFmY1E4OZh4UO5ySGJS7IzmH+uPsSFjMdBvIMaGjIV/rL9d74z24Ycfws3NTe4yKsSgaxmnDbr37t1D06ZN0alTJwQFBeHPP/9EvXr1MG7cOJOPce7cOdSpUwd169Zl0K1CHt5O2Nj4aPdHBu/LL9Shy+JQKFUa9F991ORFwInsSUpOitjjUSlRFb+BHJq1g669279/P+bPn4/g4GD4+/tjxIgRUCgU2LBhg9ylVYhB1zJOG3Tnz5+POnXqIDU1VXxs7dq1qF69Om7cMO3HJ506dcJPP/0EpVLJoFuFjNk7RvxxW0VzdaccmoLIO5FiqN1/8Y643JjvGWl/TEckhcv3Los9fuv+LbnLIYlVtaAbHh6O9u3bo27dunj00Ufx8ssvY+3atXKXZRIGXcs4bdDt2LEj+vbta/BYWloaqlWrZtL/3Ly8vNCsWTNkZ2cz6FYxpt49XHL09+2P7Ze3I7sgG8PWn4BSpUGH+SHIyef2qeRYwm6GiX1tzz9uJuuoakHXkTHoWsZpg26jRo0wbdq0Uo8/88wzUKlU5b43MzMTTZs2xdatWwGAQbeKMeXu4f/s/A+8Lnihj08fg+c6bOqAKQdmo9WMDVCqNPh1z2W5Pw6RWYr/o9dhcwe5SyEbYNB1HAy6lnHaoPvII49g8eLFpR5/9dVXMXLkyHLfO2nSJHTs2FH8tSlBNyMjA0lJSeKIiIhg0zkwU+8e1uv1OHn7JL4/8D3cPN0MQu9Lqz7Eiz8vRFyKZbuuEclhY9RGsc/J+THoOg4GXcs4ddBdsmRJqcdfeeUVjBo1yuj7Lly4gBo1ahgsK2JK0J0xY4bBvtfFg03nuMy9e/huzl2sPbsW3bd3Nwi8r6nfwW+Rv+FONrcIJvv368lfIagFDA0cKncpZAMMuo6DQdcyTht0LZ260KNHDwwePBhpaWniaNasGUaPHo20tDRxnb2H8YouFSvUFWJf/D58sH2IQeB13eiKb0K/wYmbJ7giA9mt6UemQ1AL+Cb0G7lLIRtg0HUcDLqWcdqg27FjR/Tr18/gsfT09ApvRlMqlWVemS0eV69eNen8bDrS6vR4d+UOvPDLaLRe/4ZB6H1/1/vwivZCZn5mxQcisqExe8dAUAuYGzZX7lLIBhh0HQeDrmWcNujOnz8fTzzxhLh3NQCsW7cOjzzySLnLi4WFhSE0NNRgNGnSBB999BFCQ0MN9rguD5uOACAiLrVoubEp3pgctA4D/QYaBN52Xu0w89hMXEq9JHepRAAerCO9+sxquUshG2DQdRwMupZx2qBbvGGEh4cHgoODsX79etSvX7/UhhFdu3ZF165dyz0WV12gypiw+TSUKg3cZgUjPbsA5++ex7TD09D2r7YGoXew/2D4xfpxSSeSVbe/u0FQC/j78t9yl0I2wKDrOBh0LeO0QRcAoqOj0a1bN9SqVQuNGzfGd999V2oLYA8PD3h4eJR7HAZdqoyb6Tl4aXoglCoNZvo+2GkqLTcN6ig13tv5nkHg7bilI5aeXIqkLPaOpfK1+dDEajBm7xh87PcxxuwdA02shv+JqIBer4e7pzsEtYCQ+BC5yyEbqGpBNywsDDNmzLD5eTds2ACFQmHwU2ZzMehaxqmDrsBGT2sAACAASURBVJzYdFTS8pArUKo0aD5FgwFrjqH3isMYtv4EfCKTkFNQgCNJRzB+33i02fhgR7bW6tYYFzIOhxIPQacv+yZIKu1G1g308u5V5tJwvbx7iUvDUWkZ+Rli/0Xeiaz4DeTwrB10dfn5SPf1Q/yoUbjW7yPEjxqFdF8/6PLt4z+ZS5cuhUJh++iTnJyMsLAwaLWWbyLEoGsZBl2JsOmopJjkLLT8wV/cHlip0qDFlKKvnReHIvFeNgDgZtZNLD+1HJ22djK4yttjRw+sP78e93LvyfxJ7Jspm3309u7NK7tGXEu/Jn6vEjIT5C6HbMCaQbcgKQkxPXoi+sWXEP3yKwZfY3r0RIEd/HsoV9C1BgZdyzjm77YDYNNRsbxCLTovDkXzEiH34cDbZXEo8gof/E+/eA3fIQGGS5S95vkaph6eirPJZ7lEWRlM3b7ZP9Zf7lLtUsStCPF7lF2QLXc5ZAPWCrq6/PyikFsccB8eL7+CmJ49Jb+ye+zYMfTu3RtPP/00Hn/8cbi7u2PHjh0AHkwfKDlKTl08ePAgOnTogJo1a+Kpp57C8OHDkZqaKj4fFxcHhUIBLy8vfPHFF6hbty6aNGmC1auLbtz8/fffoVQqUb9+fXzxxRfIzc0V31vW1IXc3FxMmTIFzZs3x2OPPYbnnnsO48ePN/rZGHQtw6ArETYdFfOJTCoz4D48fCLL7pVLqZcw+9hstPNqZxDWBvgOwM4rO5FTaNpKIFXBmL1jxGkKxkYbdRuMDRkrd6l2KSguSFwNhKqGssKTvqAA+fHxZo2UDRvKDrgPjRS12uRj6gsKzP48W7ZswcKFCxEQEICQkBBMmzYNLi4u8PX1RXJyMiZOnAiFQoGwsDCEhYXhwoULAICTJ0/isccew7vvvgs/Pz/88ccfaNiwId544w1xukFx0G3WrBkmT56MPXv24IsvvoBCoYBKpUL37t2h0WiwatUqPPbYY5g/f75Y18NBV6/Xo0ePHqhduzbmz5+PkJAQeHl5YfDgwWb9XhVj5jCOQVcibDoqNmz9CXGagrHRYooGwzeEl3ucrPwsbL64GR/u+tAguHXY1AELTizAtfRrNvpE9qt4aayKxkC/gXKXapc2X9wsTpWhqqGs8JQfH29SaJV65MfHV+qz6XQ6FBYW4uOPP8b7778PwPjUhb59++K5555DQYlwHRwcDIVCAV9fXwAPgu6gQYPE1+Tl5eGJJ55Ao0aNkJ394KcgAwcORNu2bcVfPxx0i4/999+mr27CoGsZBl2JsOmoWO8Vh026ott7xWGTjqfX6xFxKwLfHvgWbhvdDALciOARCLkegkJdofj61JxUjAgeATdPN7RWt4abpxtGBI9Aak5qOWdxTGP2jjE6P5dXdCv2v9P/g6AW8Jn/Z3KXQjbibEE3NTUV48aNQ7NmzVC9enVxisKLL74IwHjQbdiwISZOnFjq8fr162Py5MkAHgTdhzedevnll/Hhhx8aPPbjjz+iSZMm4q8fDrqTJ09G3bp1zfpsDLqWYdCVCJuOilnrim5ZkrOTsfrManT9u6tBmOv6d1esPrMa2y9vLzf07YnbI8Enls+KUys4R7cSZh6bCUEtYMK+CXKXQjZirakL1z8fguiXXi4/vL70Mq4PGSLp1IXevXujcePGWLlyJfbv34+IiAgMGDAASqUSgPGgW716dSxYsKDU4y+++CJGjBgB4EHQ3bVrl8FrXF1dMXToUIPH5syZg3r16om/fjjofvnll2L4NhWDrmUYdCXCpqNilZ2ja4pCXSFCrodgZPBIk4JeyeEsV3YPJh7Ea56vVfh5e+7oyVUXjJiwbwIEtYCZx2bKXQrZiLVuRkv39TPpKm26n8YKVZctNzcXLi4uWLlypcHjffr0qTDoNmrUCJMmTSr1eFlXdK0RdHlF13YYdCXCpqNixasulHdVt9PC/QarLlRGXHocFoYvhKva1aSgOzJ4pFXOK6fAuEBxGke3v7uhx44eBuvnlpzO0HNHTy7TZsRn/p9BUAtYcXqF3KWQjTjTqgvp6elQKBT4/fffxcdSUlJQp04dMeiuWrUKCoXCYEUEoGiObosWLQzWud2zZ0+Zc3StEXSL5+gWrwhhCgZdyzDoSoRNRyUl3stGl8WhBuvnlgy+//WMsPo53TzdTAq67p7uVj+3LXlf8RY32vhg1we4ff+2uDzb2JCxGOg3EGNDxmLpyaVw3VgU/j/x+wT3C+7LXbrd6bmjJwS1gE3Rm+QuhWzE6uvo9jSyjm5P26yj27ZtWzRv3hw7duzAzp074e7ujubNm4tBd9++fVAoFFiwYAHCw8Nx6dIlAA9WXXjvvfeg0Wjw559/olGjRmWuumCNoFu86sLjjz+On3/+GSEhIdiyZQuGDBli9LMx6FqGQVcibDp6WF6hFj6RSRi+IRy9VxzG8A3hmLQtUgy7h6/cter5Kropq+Twi/VzyOD314W/DJZbS80tfxpGwLUA8fsyIngEpzA8pHgJu6C4ILlLIRuRZGc0Pw0SRv0X1z76CAmj/ot0P43Ndka7fPkyOnfujNq1a0OpVGL58uX4+uuvxaCr1+sxadIkNG3aFNWqVTNYR/fAgQPo0KEDatSogQYNGmDYsGFlrqNrjaALADk5Ofj+++/x7LPPiuvofvXVV0Y/G4OuZRh0JcKmI1NodXp8sPJI0fSFRfuRW2Cd6QuA6Vd0i0fbv9pi4v6JCIwLtPvNAvR6PdacWSPW/nnA58jIzzDpvcVLaAlqAd8e+BZanfW+544suyBb/L5E3LL+TxjIPlk76JJ0GHQtw6ArETYdmerCjQxxe+CFgRetdtwRwSNMCrjvbHmn1DJl7bza4bsD3yHkegjytHlWq8ka9Ho9fon4xWBJNXOD+arIVeL754TN4S5zABIyE8TvCddkrjoYdB0Hg65lGHQlwqYjc8wPiIZSpcG/f/DHxVumXZmsSGpOqsmrLqTnpWPnlZ0YGTxSnMdaPN7c9CamHJqCAwkHUKA1f7kfa9LpdZh9bLZY24R9EywK4nq9HnPC5ojHWRm5suI3ObkzyWfE70d6Xrrc5ZCNMOg6DgZdyzDoSoRNR+bIydfinYX7oFRp0GfVEeh01rnCuCduT7kht6x1dFNzU7Ht0jYMDxpeap5vh80dMP3IdBxJOoICnW1Db6GuEFMOTRFrUR1SVaoGrU6L7w58Jx7PK9rLitU6nn3x+8SbE3mFu+pg0HUcDLqWYdCVCJuOzHXgcrJ4Y5rnsTirHTc1JxUjg0fC3dMdrdWt4e7pjpHBI01aPzc5Oxle0V74PODzMqc8zDo2CydunpB8nmu+Nh9f7ftKPPesY7Og0+sqfdwCbYHB2sNVeSOJvy//LS7PRlUHg67jYNC1DIOuRNh0ZImvtpyGUqWB8FMQbqXnVvwGG7p1/xbUUWp84vdJqdDbeVtnzDs+D6dun7JKAC0puyDbIIwuiVhi1SuO2QXZ+FTzKQS1ALeNbjicZNpWzM5m9ZnVENQCPvb7WO5SyIauX7+OuLg4ucsgE8TFxTHoWoBBVyJsOrLE3aw8tJkZ/M/auiflLseohMwErDu3Dv19+5cKvd3+7oaF4QtxNvlspQNpZn6mwdXkNWfWSPJj9Xu59/D+rvfFG/HOJJ+x+jns3dywuRDUAsbsHSN3KWRDCQkJiImJkbsMMkFMTAwSEhLKfI6ZwzgGXYmw6chS28ITxCkMey7clrucCsWlx2H1mdXo49OnVOjtsaMHfjn5Cy6kXCg3oJ66carUTm6uale8t+098deeFzwl/Ry37t9C9+3dIagFvL3lbcSkVa1//CeFToKgFjDt8DS5SyEbSk5ORnR0NFJSUuQuhcqRkpKC6OhoJCcnl/k8M4dxDLoSYdORpfR6PT5ecwxKlQbt54cgK69Q7pJMdvXeVfzv9P/Q27t3qdDby7sXVpxegSv3rhi8Z9zecRWuDLHzyk6b1B+bHot3trwDQS2g699dcSPrhk3Oaw+GBg6FoBbw68lf5S6FbEiv1yMxMRHR0dGIiYkRfzzOYR8jLi4OMTExiI6ORmJiotELBswcxjHoSoRNR5URk5yF56cGQKnSYMbuKLnLMZter0d0SjR+PfkreuzoUSq4frjrQ/x25jf4XfUzaQm0UzdO2az2c8nnxB3Cenv3rnC3NWdR/J+TjVEb5S6FbEyv1yM5ORkJCQmyBzuO0iMhIQHJycnl/lSMmcM4Bl2JsOmospbtvQKlSoPmUzQ4k5BW8RvslF6vx7nkc1gUvgjd/u5mUrB9eLiqXW1a89EbR8Wd5Qb6DXTI7ZHN1WFzBwhqAZpYjdylEJGZmDmMY9CVCJuOKiuvUIuuS0KhVGnQc9khFGitu5qBHHR6HU7fOY35x+ej87bOZoVdWwuMCxTXEf4y6Evka/NtXoOtFGgLxO/zsRvH5C6HiMzEzGEcg65E2HRkDSeupYo3pq054Fw3R2l1WrsOugCw9eJW8fzfhH4j+XrBcrl1/5b4OS/fuyx3OURkJmYO4xh0JcKmI2uZsvMslCoNXpwegITUbLnLsSp7D7oA8NuZ3ww2qnDGXcOiUqLEz5iSw7vviRwNM4dxDLoSYdORtaRnF6DtnL1QqjT4/M8TThW0Hl5SzF7m6Jak1+sx7/g8sZYVp1fIVotUDiYehKAW0GZjG6e9ak3kzJg5jGPQlQibjqzJ98wNcQqDT6Tz9NSpG6fsbtWFsuj0Onx/8HuxHq9oL1nrsTbvK94Q1AI8tnrIXQoRWYCZwzgGXYmw6cia9Ho9hq4/AaVKg7Zz9iAt23lujKpoHd1xe8fJXSKAohu2/rvnv2JdfrF+cpdkNX+c+wOCWkDf3X3lLoWILMDMYRyDrkTYdGRtCanZeGl6IJQqDSZvPyt3OVZlbGc0ua/kPiy7IBufaT6DoBbgttENhxIPyV2SVSwMXwhBLWBE8Ai5SyEiCzBzGMegKxE2HUnh94Ox4hSG47G8aUgOablp+GDXBxDUAl7/63VE3omUu6RKm3xwMgS1ANUhldylEJEFmDmMY9CVCJuOpFCo1eE/yw9BqdKgy5JQ5BXyxiE53Lp/C/+3/f8gqAV02Nyh1LbGjubL4C8hqAUsCl8kdylEZAFmDuPsMujm5eUhIyOj1HAkbDqSytnENLSYUnRV99c9XPNULtfSr6Hjlo4Q1AK6buuKpCzH/bPed3dfCGoBf5z7Q+5SiMgCzBzG2U3QzcrKwvjx49G4cWO4uLiUORwJm46kNMv3ApQqDZ6fGoCrdzLlLqfKirobhTe83oCgFtDLu5fDrkHbaWsnCGoBu67ukrsUIrIAM4dxdhN0Bw8ejDp16mDixIn4/fffoVarSw1HwqYjKd3PK0SH+SFQqjQYsPoYdDrnWVvX0YTdDIO7pzsEtYABvgOQlZ8ld0lm0eq0aLOxDQS14DQ31xFVNcwcxtlN0H3qqaewdu1aucuwGjYdSW3vhdvijWlbTsTLXU6VFhwXjNbq1hDUAr4I+gJ52jy5SzLZ3Zy74koXF1IuyF0OEVmAmcM4uwm6Tz/9NIKCguQuw2rYdGQLY7xOQqnSoPWMICRnOk64ckbbLm0TA+PE/RMdZoexS6mXxLpv378tdzlEZAFmDuPsJujOmDEDgwYNkrsMq2HTkS3czsiF8FMQlCoNxm8+LXc5Vd6aM2vE0Djj6AyH2K752I1jYs0F2gK5yyEiCzBzGGc3QffXX39F8+bN8dZbb2HmzJlYvny5wVixwrH2l2fTka38FXZdnMIQeumO3OVUaXq9Hj+f+FkMjstOLZO7pAr5xfpBUAt4a/NbcpdCRBZi5jDOboJutWrVyh1cdYGobDqdHv1+OwqlSoO3F+xDdn6h3CVVaTq9DqpDKjHsbozaKHdJ5VJHqSGoBby/6325SyEiCzFzGGc3QdfZsOnIli7fzkSrqf5QqjSY5x8tdzlVXoGuAKP3jhbDrm+Mr9wlGfXLyV8gqAUMCxwmdylEZCFmDuMYdCXCpiNbWxx0CUqVBi1/8Mf5pHS5y6nysguyMch/EAS1ANeNrjiYeFDukso07fA0CGoBk0InyV0KEVmImcM4uwq69+/fx8qVK/HJJ5/g3XffxSeffIJVq1bh/v37cpdmNjYd2VpugRYei/ZDqdLg/f8dhpZr68ouPS8dfXz6QFALeP2v13H6jv3dMFh85Xne8Xlyl0JEFmLmMM5ugm5CQgJatmwJFxcXuLu7o2fPnnB3d0f16tXx73//GwkJCXKXaBY2Hcnh6NW74o1pfx6+Jnc5BOD2/dt4d/u7ENQCOmzugMv37Gvb5gG+AyCoBaw5s0buUojIQswcxtlN0O3fvz9atmyJS5cuGTx+6dIltGrVCgMGDJCpMsuw6Uguk7adgVKlwcs/BiIpLUfucghAXHqcuM1ul21dkJiZKHdJoq5/d4WgFrD98na5SyEiCzFzGGc3QbdevXrYunVrmc9t2bIF9erVs3FFlcOmI7mk3s+H++w9UKo0+FId7hBruVYFUSlReHPTmxDUAt7b+R7u5tyVuyTo9Xq4ebpBUAvYH79f7nKIyELMHMbZTdB9/PHHsXv37jKf8/HxQZ06dWxcUeWw6UhOO08lilMYAs7dlLsc+seJmyfg7ukOQS2gv29/ZOZnylpPel66uDLEmeQzstZCRJZj5jDOboLuu+++i3bt2iE93fBu8czMTLz55pvo0aOHTJVZhk1HctLr9fhsXRiUKg3azd2LjFzueGUv9l7fizYb24hLeuVp5du6OTY9Vgy69jSdgojMw8xhnN0E3fPnz+PJJ59E3bp10a9fP4wdOxb9+/dHvXr10KBBA5w/f17uEs3CpiO5xd29jxemBUCp0mDarnNyl0MlbL+8XQyYX+37CoU6eTb5CL8VLtaRU8j53ESOipnDOLsJugCQmJiIb775Bu3atUOrVq3wxhtvYNKkSUhMdLwrDWw6sgcr918VpzD0XXUEvVccxrD1J+ATmYS8Qq3c5VVp686tE0PmT0d/kmUudWBcIAS1gDe83rD5uYnIepg5jLOroOtM2HRkD+Lu3hd3TCseLaYUfe28OBSJ97KlLaAwDzj7N+DVH1jTqejr2b+LHq/i9Ho9FoYvFMPuryd/tXkNm6I3iTfHEZHjYuYwjkFXImw6klteoRadF4eieYmQ+3Dg7bI4VLoru2nxwHJ3YEZdYGZ9w6/L3Yuer+J0eh1+OPSDGHbVUWqbnn/F6RUQ1AIG+Q+y6XmJyLqYOYyTNei6u7sjKioKAODm5gZ3d3ej47XXXpOzVLOx6UhuPpFJZQbch4dPpAQ9WphXFGaLg+3DY2Z9YMVrvLILoEBXgLEhY8Ww63PVx2bnnnF0hjhPmIgcFzOHcbIG3WHDhuHataLdm4YOHYphw4aVOxwJm47kNmz9CXGagrHRYooGwzeEW//kZ/8uO+A+PM5xkwIAyCnMwecBn0NQC3Dd6IrQhFCbnHf8vvEQ1AJmHZtlk/MRkTSYOYzj1AWJsOlIbr1XHDbpim7vFYetc0KdFrh1DjjxO7D4+YpD7sz6gJdj7XgopfS8dPTd3ReCWkDbv9ri5O2Tkp/zM81nENQCVkaulPxcRCQdZg7j7CboDh8+XLy6+7Dr169j+PDhNq6octh0JDfJr+gW5ALXjwGHlgB/fQTMb2baVdySY62HNT+yw7uTfQc9dvSAoBbQYVMHXEq9VPGbKqH4XJsvbpb0PEQkLWYO4+wm6FarVg0nTpwo87mTJ0/CxcXFxhVVDpuO5Gb1Obo5acDlYGDvDODPHsDshmWH14UtgUWtgBn1eEXXAvEZ8ei0tRMEtYDO2zojITNBsnO182oHQS0gOC5YsnMQkfSYOYyzq6AbHl72lSW1Wo3GjRvbuKLKYdOR3IpXXSjvqq7brGDjqy6kJxXNodVMAn57y3hwXdYG8B4NnFQDd68Aej3n6FZSdEo02m9qD0EtoOeOnribc9fq58guyBZvgLPFNAkikg4zh3GyBt1ly5ahRYsWaNGiBVxcXPDMM8+Ivy4eTZs2hYuLC29GI7JA4r1sdFkcarB+bsng2+oHf1y6lVkUTpMvARHrgZ2jgKWCkXBaD1j9NuD/HXB+J5Bxs+wTV7Tqwoy6wMwngVuOteOhLYXfCsdrnq9BUAv4aPdHyMjPsOrxEzISxKAblx5n1WMTkW0xcxgna9ANCgrCxIkTMXHiRFSrVg2ffPKJ+OviMXnyZKxfvx65ublylmo2Nh3Zi7xCLXwikzB8Qzh6rziM4RvC8feJWHw59zfMnToax+a+C/2C5mWH0dmNgD97AiGzgCt7gdx000+cFl+0hFhZ6+jO+OfrolZA8mXpPryDC4kPQZuNbSCoBQwJGILcQuv9PRh5J1IMupn5mVY7LhHZHjOHcXYzdWHmzJm4ceOG3GVYDZuO7EpeFhCzD9g/D9jQC5jTpOxg+3Ozonmzh38F4sMqv85tYV7R9ASvAUU3nnkNKPp13BFg/rNF51z8PMNuOXZe2SkG0vH7xqNQV2iV44bEh0BQC3D3dJdl+2Eish5mDuPsJugmJyfj8uWy/7G7fPky7t61/hw1KbHpSFZZd4ALu4HAKUVb7858ssxgmz63FXym98RP077GlXPHAZ3OdjUmhAPz/vUg7N69YrtzO5g/zv0hht1ph6dZJZhuu7QNglpA9+3drVAhEcmJmcM4uwm6ffv2xbhx48p8bsKECejXr5+NK6ocNh3ZjF4PpMQAp70An7EPpguUNVa+Afh+DZzZCqTFIzuvAB6L9kOp0uDdXw9Ktx2wMQZh9wWGXSP0ej0Why8Ww+4vEb9U+pi/nfkNglrAQL+BVqiQiOTEzGGc3QTdxo0bY/fu3WU+5+vriyZNmti4osph05FkdFrgRiQQthrY9rnxzRlmNQDWdQOCpwEX/YH7KWUeLiIuFc3/uUFtYeBFG38YAAknGHZNoNfrMfXwVDHsrj+/vlLHmxM2B4JawNiQsVaqkIjkwsxhnN0E3Zo1ayIwMLDM5wIDA1GzZk0bV1Q5bLoqJC0RWNW+xPJb9Yp+nZZo+LrCvKJlt7z6F00n8Opf9OuK5sEW5ABxh4EDiwDPvg9C4cNj3jPAxg+BAwuBa4eA/GyTP8JczQVxRYbT8fcs+CZUUvzxh8LuVdvX4AAKdYUYHzJeDLveV7wtPtY3od9AUAuYfmS6FSskIjkwcxhnN0HX1dUVY8aMKfO5MWPGoHXr1jauqHLYdFVE+B/lrxMb/kfR69Lii5bbKmsFguXuRc8Xy04FLgUAwdOBdd2BWU+VfexF/wa2DgbCfgNunAa0lt+klFugRdclRcuQdVkSitwCG09hAP4Ju88UfbYlLxZNx6BScgtzMSRgCAS1gDYb22Bf/D6LjlN8jKUnl1q5QiKyNWYO4+wm6K5fvx7VqlXDhAkTEBkZiTt37iAyMhJfffUVXFxc8Mcff8hdolnYdFVAWqJpmyLcjS1/TdmZ9YumH+yeAKx80/hxlrsBu8YCpzyLQqCV75SPTEgT19id43fBqsc2GcOuSTLyM9Bvdz8IagGveb6G8Fvmb+Pc27s3BLUAzwueElRIRLbEzGGc3QRdAJg/fz5q164NFxcXcdSuXRs///yz3KWZjU1XBaxqb1rQNbZGbUXb467pCASogKhdQOZtm3ykRUEXoVRp0HyKBieupdrknKXEhzHsmiA5Oxk9d/SEoBbQflN7RKdEm/X+Dps6QFAL8I/1l6hCIrIVZg7j7CroAkB6ejoCAwPh5eWFwMBApKebsUC9HWHTVQHGtsS1dCxoDuybA1wNAfLkWcA/r1CLHksPQqnSoNOi/cjOt86arWa7fqxE2H2JYdeIhIwEeGz1gKAW0GlrJ8RnxFf8JgD52nxxnu/xm8clrpKIpMbMYZzdBV1nwaarAqwZcmfULdpQwQ5E3UjHv3/wh1KlwY8+Mm7Re/0YMLcpw24FLqZeRPtN7SGoBfTY0QPJ2ckVvufW/Vti0L1yj6tcEDk6Zg7j7CboLl++vNyxYsUKuUs0C5uuCjDniq6x+bkln/caIPcnEi3bewVKVdF83SNXZdyshWHXJBG3ItD2r7YQ1AL67e6HjPyMcl8fdTdKDLqpuTJNUSEiq2HmMM5ugm61atWMjuL5uo6ETecE7sYCv7xsGEh/ebno8XPbTQ+6Dx/D2Di3Xe5PLCrQ6tB7xWEoVRq89fM+ZOYWyFfM9aMPwu4vLwOpsfLVYsf2x++H60ZXCGoBQwKGIKcwx+hrDyYehKAW4LrRFTq9DXfDIyJJMHMYZzdBtyxpaWnYvHkzXF1dceWKY/14jU3n4A4sst6UBFNWXVjxWsXr6drY5duZeH5qAJQqDVQ7zspbDMOuSXZd3SVeqR0XMg4FurL/g+J9xRuCWkDnbZ1tXCERSYGZwzi7DrrFVq1ahS5dushdhlnYdA7sbqxpAXZdV+DQr+W/puQ6usVb8z68ju6K1wzX0bUjv4XGiFMY9l+6I28xcUdKhN1XGHaN2HB+gxh2px6eWuYV23Xn1kFQC/ho90cyVEhE1sbMYZxDBN2QkBA8/vjjcpdhFjadAzN1qsEvLxe93pyd0c5tL5qLu9aj6Ou57XZ3JbckrU6PPquOQKnS4I15e5GeLeMUBuCfsPt0ibB7Td567NQvEb+IYXdR+CLoH1pzecGJBRDUAkYGj5SpQiKyJmYO4+w+6Obk5ODjjz/Giy++KHcpZmHTOTBzpiVUATHJWXhhWtEUhm+2RspdTtF2yAy75dLr9Zh+ZLoYdtedW2fw/PcHv4egFjDl0BSZKiQia2LmMM5ugq6bmxvc3d0Nxquvvoo6derAxcUFmzdvlrtEs7DpHBiDbil/HL4mTmEIjroldzmGYffXV4F7cXJXZHcKdYWYsG+CGHZ3XN4hPvdl0JcQ1AIWhy+WsUIishZmDuPsJugOHToUw4YNMxijR4/G3LlzER1t3o4/xS5evIju3bujdu3aaNKkCb7//nvkidnrKwAAIABJREFU5+eX+55bt27hgw8+QLNmzVCjRg08/fTT+Oijj3Dp0iWzzs2mc2AMuqXodHoMWHMMSpUGbefsQer98v8c2cS1Qwy7FcgtzMXQwKEQ1ALabGyDoGtB0MRq8IbXGxDUAt73fh+aWA3ytXbw+0lEFmPmMM5ugq613bt3D02bNkWnTp0QFBSEP//8E/Xq1cO4cePKfV9sbCyGDRuGjRs3IjQ0FFu2bEHr1q3xzDPPIDXV9PUm2XQOzNw5ulVEfEo2Xv4xEEqVBmM3nZK7nCLXDgFzmjDsliMzPxP9ffuLV3ZLjtbq1hDUAnp598KNrBtyl0pEFmLmMM5pg+78+fNRp04dg3C6du1aVK9eHTdumPcX+pUrV6BQKLBt2zaT38Omc2Cmrrpwt+rd9e8Zdl2cwuB31k6C0bWDJcKuANy7LndFdudG1g24bXQrM+wWB97e3r15ZZfIQTFzGCdr0C1rXm55wxwdO3ZE3759DR5LS0tDtWrVsGHDBrOOlZqaCoVCAS8vL5Pfw6ZzcBWto3tgkdwVykKv12PQuuNQqjRwmxWM5Ew7WTGCYbdcmliN0ZBbcvjH+stdKhFZgJnDOFmD7sPzcp999lnUrFkT3bp1w6effopu3bqhZs2aePbZZzF8+HCzjt2oUSNMmzat1OPPPPMMVCpVhe/X6XQoKCjA9evXMXjwYCiVSmRklL+tZklsOidQ3s5oVVhSWg5e/SkISpUGIzZGlFq6SjaxBx6E3aUMuyWN2TsGbdRtyg25bdRtMDZkrNylEpEFmDmMs5upC//73//Qtm1bJCcnGzx+584dtG3bFsuXLzfreI888ggWLy59R/Grr76KkSMrXjvyv//9LxQKBRQKBVq2bInLly+X+/qMjAwkJSWJIyIigk1HTmtreLw4hWHnqcSK32ArsaHAnMYPwq6dbsRhax/7fWzSFd2BfgPlLpWILMCga5zdBF2lUondu3eX+dyuXbvQrFkzs473yCOPYMmSJaUef+WVVzBq1KgK3x8fH4/w8HB4e3vDw8MDzz33HBITjf+DPmPGDDEYlxxsOnJGer0ew9afgFKlgTAjCLfSc+Uu6YHYUIbdh/CKLpFzY9A1zm6Cbq1atbBp06Yyn/Py8kLt2rXNOl5lpy6UlJOTg3/9618YP3680dfwii5VNbczctF6RtEUhiF/nrCfKQzAQ2G3NZCWIHdFsuIcXSLnxqBrnN0E3V69euHZZ5/FkSNHDB4/fPgw/vWvf6FXr15mHa9jx47o16+fwWPp6ekW3YwGAN26dUOPHj1Mfj2bjqoC79OJ4hSGLSfs7MppzH6G3X/ka/PRy7uXuJwYV10gci7MHMbZTdC9ceMGXn/9dbi4uKBhw4Z45ZVX0KhRI7i4uKBt27Zm/+bNnz8fTzzxBNLS0sTH1q1bh0ceecTs5cUyMzPRuHFjjB492uT3sOmoKtDr9Ri5MQJKlQav/hSExHvZcpdkKGYfw+4/bmTdQG/v3uI0hZJfe3v35jq6RA6MmcM4uwm6xQIDAzFr1iyMHj0as2bNQmBgoEXHKd4wwsPDA8HBwVi/fj3q169fasOIrl27omvXruKvlyxZgnHjxmHbtm04cOAAPD098frrr+OJJ54wa3c0Nh1VFcmZeXCbFQylSoNPfw+DTmdHUxgA4GrIg7C7rA2Qbkc3z9lYvjYf/rH+GBsyFgP9BmJsyFj4x/rzSi6Rg2PmMM7ugq41RUdHo1u3bqhVqxYaN26M7777rtQWwB4eHvDw8BB/vXfvXnTt2hUNGzZEjRo10LJlS3z++ee4evWqWedm01FVojl7U5zCsPFYnNzllHY1BJjdiGGXiJwSM4dxdhV09Xo9tmzZgkmTJmHAgAG4du0agKKrvHFxcfIWZyY2HVU14zadglKlwUvTAxF3977c5ZTGsEtEToqZwzi7CbqJiYkQBAE1atSAq6srXFxccOrUKQDAiBEjTFr71p6w6aiquXc/H23n7IVSpUH/1UehtbcpDABwdW+JsOsKpPPPJxE5PmYO4+wm6Pbp0weurq5ITExEYWEhqlWrJgbdzZs3o1WrVjJXaB42HVVFey7cFqcwrDtkpzvIXWHYJSLnwsxhnN0E3Tp16sDHxwcAoNVqDYLuwYMHUatWLTnLMxubjqqqb7ZFQqnS4IVpAYhJzpK7nLKVDLvL3Rh2icihMXMYZzdBt27duvD19QVQOuhu374dDRs2lLM8s7HpqKpKzynAm/NCoFRp8OHKIyjU6uQuqWxX9gKzGz4IuxlcXouIHBMzh3F2E3R79+6NLl26oLCwUAy6p0+fBgD06NEDAwc61h7sbDqqykIv3RGnMKwKNW/FEpu6sodhl4gcHjOHcXYTdKOiolCvXj288MILmDx5MlxcXDBq1Ci89dZbaNCggdnLe8mNTUdV3ZSdZ6FUafD81ABcupUpdznGGYRdd4ZdInI4zBzG2U3QBYCYmBgMHjwYTZs2xaOPPoomTZpg0KBBiImJkbs0s7HpqKrLyivEWz/vg1KlwX+WH0KBvU5hAIDLwQ+F3ZtyV0REZDJmDuPsIuhqtVpcunQJ9+7dk7sUq2HTEQFHY+6KUxiW7r0sdznluxzEsEtEDomZwzi7CbqPPvoogoOD5S7Fath0REV+8jkPpUqDf//gj/NJ6XKXU75LgQ/C7orXGHaJyCEwcxhnF0EXAJ5//nns2LFD7jKshk1HVCQ7vxAei/ZDqdLg3V8PIq9QK3dJ5bsUCMx66kHYzbwld0VEROVi5jDOboLu+vXr4ebmhtu3b8tdilWw6YgeiIhLRfMpRVMYFgZelLucihmE3bYMu0Rk15g5jLOboNunTx80bdoUNWvWRIcOHdCnTx/07dtXHP369ZO7RLOw6YgMzdVcgFKlQYspGpyOd4D5+JcCGHaJyCEwcxhnN0G3c+fOFQ5HwqYjMpRboEXXJaFQqjTosiQUuQV2PoUBAC76Pwi7/3sdyHSOnzgRkXNh5jDOboKus2HTEZUWmZCGFv9MYZjjd0HuckzDsEtEdo6ZwzgGXYmw6YjKtijoIpQqDZpP0eDEtVS5yzHNRU2JsNuOYZeI7Aozh3EMuhJh0xGVLa9Qix5LD0Kp0qDTov3Izi+UuyTTXNQAsxo8CLtZd+SuiIgIADNHeRh0JcKmIzIu6kY6/v2DP5QqDX70OS93OaaL9mPYJSK7w8xhHIOuRNh0ROVbtveKuGvakat35S7HdCXD7so3GHaJSHbMHMbZTdDNy8uDVusAd2GbiE1HVL4CrQ69VxyGUqVBh/kh2HIiHsPWn0DvFYcxbP0J+EQm2e/mEtG+DLtEZDeYOYyzi6Cbl5eH6tWrY/fu3XKXYjVsOqKKXb6diVb/TGEoXmO35NfOi0OReC9b7jLLdmH3Q2E3We6KiKiKYuYwzi6CLgA0a9YMvr6+cpdhNWw6oorlFWrhNitYDLoPjxZTNOiyONR+r+xe8CkRdt9k2CUiWTBzGGc3QXfBggXo0qUL8vPz5S7FKth0RBXziUwyGnJLDp9IO/5zdMEHmPlkUdhd1Z5hl4hsjpnDOLsJuhMnTkTTpk3RpEkTfPrpp5g4cSK++eYbcUyaNEnuEs3CpiOq2LD1J8RpCuVd1R2+IVzuUsv3cNi970A31xGRw2PmMM5ugm7z5s3LHS1atJC7RLOw6YgqVnwzWkWj94rDcpdasahdDLtEJAtmDuPsJug6GzYdUcWc5opusSjvEmG3A8Pu/7d352FRle0fwAekXHIrX/XV3kJNE/cNNc0lca3UFK205VdamWVvmq+FO2rmkpZbmWaKmVouhSko4oK4hIo7yqKCsorIvg+zfH9/jJxhhAMzMIdzZvh+rutc5QAzt5wbr6+Pz9wPEVUKZg5xDLoSYdMRlc0u9ug+qljYTZa7IiKyc8wc4hQVdOPi4jBjxgy88MILeP755/HCCy/gyy+/tMkbx6YjKlu+RouXVgSUuaq78+xduUu1TMifDLtEVGmYOcQpJuiGhITgySefRO3atTF69GhMnjwZo0ePRp06dfDUU0/h+vXrcpdoETYdkXliU3MwYEVAiXN0W842zthddSQCer1e7nLNF7LXGHbX92bYJSLJMHOIU0zQHTp0KFxdXZGammryeGpqKlxdXTFs2DCZKisfNh2R+fI1Wuy7HIcJXucxfO0pTPA6j32X45CUkY83NvwjhN3/7b6CAq1O7nLNZxJ2XwRyUuSuiIjsEDOHOMUE3SeeeAL79u0r8WPe3t6oXbt2JVdUMWw6IuvI12jx352XhLD7zi9nkZlXIHdZ5gvZCyyoz7BLRJJh5hCnmKBbr1497Nq1q8SP7dq1C/Xq1avkiiqGTUdkPTqdHssPhQlhd+iqQCSk58pdlvmu7WHYJSLJMHOIU0zQHTNmDFq2bImbN2+aPB4ZGYnWrVtj7NixMlVWPmw6IuvbfvausH+35zdHcSM+Q+6SzFc07P7EsEtE1sPMIU4xQTc6OhrNmjWDk5MTunbtildeeQWurq5wcnJC8+bNERMTI3eJFmHTEUnjeNh9tJl3CM4ePmg33w8nb9rQkbsmYbcPwy4RWQUzhzjFBF0AyMrKwpo1a/D6669j0KBBeOONN7B27VpkZWXJXZrF2HRE0rkWmw7XxUfg7OGD52b5YlewDf1F+Opuhl0isipmDnGKCLr5+fnYsGEDbty4IXcpVsOmI5JWbGoOBn53Qti3+72/DY0fu7qLYZeIrIaZQ5wigi4A1KhRAydOnJC7DKth0xFJLz2nwGT82PRdV6DW2Mj4sSt/GMPuhr4Mu0RUbswc4hQTdHv27IkNGzbIXYbVsOmIKke+RovPfzeOH3t701lk2Mr4sUfDbm5q2V9DRPQIZg5xigm6Z8+eRatWreDt7Y38/Hy5y6kwNh1R5dHp9PjWz0bHj135A/Csx7BLROXGzCFOMUG3fv36qFGjBhwdHeHo6Ij69evjySefNLlsCZuOqPLtOBuNFrMMxwb3+OaI7Ywfu/J7kbDbj2GXiCzCzCFOMUHX09MTCxYsKPWyJWw6Ink8On4sMMJGxo9d3mkMuxv7A7lpcldERDaCmUOcIoKuVqtFeHg4UlPtZxWDTUckn5A4Gx0/xrBLROXAzCFOMUH3sccew+HDh+UuxWrYdETyik3NwaAi48e+s5XxY5d3MOwSkUWYOcQpIugCQKtWrbB37165y7AaNh2R/NJzC/DmRuP4sS92XbaN8WOXthcJuy8x7BJRqZg5xCkm6G7ZsgWdO3dGYmKi3KVYBZuOSBnyNVpMLTJ+7K1NQbYxfuzSb8aw+/MAIC9d7oqISKGYOcQpJuiOGjUKTZo0QY0aNdCrVy+MGjUKo0ePFi53d3e5S7QIm45IOfR6PVb4hQthd8j3gYhPy0VmXgHmeoegyyJ/uMw9iC6L/DHXOwSZSgnCDLtEZAZmDnGKCbovvfRSmZctYdMRKc/Oc8bxY50XHhb+/9HruVm+CL6jkJPKLm5j2CWiUjFziFNM0LU3bDoiZToefh8uc0oOuEWvlrN8lbOye3GbIeh61gV+dmPYJSITzBziFBt0c3JybOMd0iLYdETK9en2C2UGXWcPH8zbFyJ3qUYXfzWG3U0DgTwbOQyDiCTHzCFOUUE3KCgIQ4cOxVNPPYVq1arh4sWLAIDp06fD29tb5uosw6YjUq4ui/zNCrpdF/nLXaophl0iKgEzhzjFBF1vb29Uq1YNr7zyClauXAkHBwch6C5evBhDhw6VuULLsOmIlMtl7kGzgq7L3INyl1rcha0Mu0RkgplDnGKCbvv27TFlyhQAgEajMQm6+/fvx7///W85y7MYm45IuWx2RbfQBa8iYXcQwy5RFcfMIU4xQbd69eo4evQoAMNJaUWDbkBAAKpXry5neRZj0xEp11zvENvbo/uoomH3l8FAfqbcFRGRTJg5xCkm6D777LNYv349gOJBd+3atXj++eflLM9ibDoi5crMK8BzIqPFil7rj99U9ptig7cw7BIRM0cpFBN0v/rqKzRs2BAnTpwQgu6lS5cQGhqK//znP1i0aJHcJVqETUekbMF3UtDSjLD71Z6ryNdo5S5XXPBmhl2iKo6ZQ5xigm5+fj5effVVODg4oHHjxnBwcEDTpk1RrVo1jBw5EhqNRu4SLcKmI1K+zLwCzNsXgq4PT0brusgf8/aF4F56Hj78NVgIu+7rz+B+Zp7c5Yo7/0uRsDuEYZeoimHmEKeYoFvo6NGjmDVrFj766CN4eHjgyJEjcpdULmw6Itum0+nx3WHjscEvLDmKa7EKPqihaNjdPJRhl6gKYeYQp7igay/YdET2wedqAlzmHoKzhw+en3MQ+y4r+Gf6/KZHwm6W3BURUSVg5hDHoCsRNh2R/bgen47eS48Jq7vLDoVBq1Pom9TO/cywS1TFMHOIY9CVCJuOyL48yMrH6z/9I4TdCV7nkZFXIHdZJTMJu8MYdonsHDOHOAZdibDpiOyPWqPDrL+uCWHXbWUAoh5ky11WyYqG3S0vM+wS2TFmDnEMuhJh0xHZr21Bd4U5vB08/RAYkSR3SSVj2CWqEpg5xDHoSoRNR2TfgiKThaOEm8/0waaTkco8XOLsRtOwq1boCjQRlRszhzhFBd2srCwsXboU7u7u6N69O8LCwgAAXl5euHr1qszVWYZNR2T/YlJyMHRVoLCVYfquK8grUODhEmc3FAm7rzDsEtkZZg5xigm6169fR+PGjdGoUSOMHDkSjo6OwhHA06ZNw1tvvSVzhZZh0xFVDdn5Gkz+7YIQdl/74TQSMxR4uETRsOv1KsMukR1h5hCnmKA7cOBAuLm5IScnBxqNBg4ODkLQ3bNnD5o1ayZzhZZh0xFVHTqdHquP3BTCbo9vjuByTJrcZRUX9BPDLpEdYuYQp5igW7NmTRw8eBAAoNVqTYJuYGAgatSoIWd5FmPTEVU9h0Luoc08w+ESreYcxJ8XY+Uuqbig9Y+E3Ry5KyKiCmLmEKeYoNugQQPs3r0bQPGgu337djRp0kTO8izGpiOqmsLuZaDPcuPhEt/4hirvcIl/fmTYJbIjzBziFBN0x48fj86dOyM1NVUIupcuXUJeXh569uyJDz/8UO4SLcKmI6q6UrLVGLcxSAi7/7f5HNJzFXa4RNGwu3U4wy6RDWPmEKeYoBsbG4tnnnkGDRo0wBtvvAFHR0eMGDECLVq0QPPmzZGYmCh3iRZh0xFVbQVaHebvCxHC7oAVAbidpLA5tv/8wLBLZAeYOcQpJugCQGpqKubOnYtevXqhVatW6NmzJ+bMmYOUlBS5S7MYm46IAGDnuWi0nG04XKL9fD8cD7svd0mmzqwrEnZHMOwS2SBmDnGKCrr2hE1HRIXO30lB14eHSzSb6YOfTtxW1uESZ9Yaw+6vI4GCXLkrIiILMHOIU0zQ/fTTT3Hq1Cm5y7AaNh0RFRWXlotX1pwUtjJM/f2Ssg6XYNglslnMHOIUE3RbtmwJR0dHPPPMM5gxYwYuXLggd0kVwqYjokflqrWYsuOiEHZHrDuFhHQFBcrTaxh2iWwQM4c4xQRdALhw4QJmzJgBZ2dnODo6olWrVpg/fz5u3Lghd2kWY9MRUUn0ej1+OH4LzWYawq7r4iO4cDdV7rKMTq8uEnZfY9glsgHMHOIUFXSLOnPmDD7//HM0bdoUjo6O6Nixo8XPERYWhkGDBqFWrVpo3LgxvvzyS6jV6lK/Jjw8HFOmTEGbNm1Qs2ZNNGvWDJMnT8aDBw8sem02HRGV5siNRLQtPFxi9kHsCo6RuySjomF32yiGXTuTmVeAud4h6LLIHy5zD6LLIn/M9Q5BZp7CRuCR2Zg5xCk26AJAZmYmvLy88PTTT8PR0dGir01NTUWTJk3Qr18/+Pn5YfPmzahXrx6mTJlS6tetW7cOHTt2xOrVqxEQEICtW7fi2WefRZs2bcoMyUWx6YioLBGJmej37XFhK8PC/Teg0erkLsvg1KoiYXc0UJAnd0VkBcF3UvDcLF+h54pez83yRfAd25tyRMwcpVFc0M3Ly8Pu3bvh7u6OmjVronr16nj11Vexfft2i55nyZIlqF27tsloso0bN6JatWqIj48X/brk5ORi74Y+c+YMVCoV9u3bZ/brs+mIyBxpOWq8vemsEDbe+eUs0nLM/0u1pE59z7BrRzLzCkRDbuHVcpYvV3ZtEDOHOMUE3f379+Ott95CnTp1UK1aNbi5ueHnn39Gamr59q717dsXo0ePNnksLS0NDg4O8PLysui5cnNzoVKpsH79erO/hk1HRObSaHVYuP+GEDb6fXscNxMz5S7L4OR3DLt2Yq53SKkht/Caty9E7lLJQswc4hQTdB0cHPDCCy9gzZo1uHfvXoWfr2HDhpgzZ06xx5s2bQoPDw+Lnuvw4cNQqVQICAgw+2vYdERkqV3BMWg1+yCcPXzQbr4fjtxQyImQRcPub+4Muzaqy8NZzmVdXRf5y10qWYiZQ5xigu6dO3es+nxOTk5YsWJFscfbtWuHjz76yOznycvLQ8eOHdGtW7dSB7xnZGQgLi5OuIKDg9l0RGSxC3dT0e3rI8LhEj8cv6WMwyVOrmTYtXEucw+aFXRd5h6Uu1SyEIOuOMUEXWtzcnLCypUriz3etm1bTJo0yeznee+991CnTh1cv3691M/z9PSESqUqdrHpiMhSCem5GL72lBA8puy4iFy1Ag6XCFxRJOyOAXLTgau7ge1jgQ39DP+9uhvQ5MtdKZWAK7r2i0FXnKxBt0uXLkKA7Ny5M7p06SJ6de3a1aLntsbWhTlz5sDJyQl+fn5lfi5XdInImvIKtPj890tC+HhlzUnEpSlgzFfgt8awu/jfhv8uqG/63zVdgLRouSulR3CPrv1i0BUna9B9//33ERUVBcCwcvr++++Xelmib9++cHd3N3ksPT3d7DejrV27Fg4ODti6datFr1uITUdEFaXX6/HTidvC4RLdvvbHeSWMfwpYYgy7JV0L6gNru3JlV2Ei72eVGXI5dcE2MXOIs9utC0uWLEGdOnWQlpYmPLZp0yY4OTmVOl4MAHbu3AkHBwcsW7as3K/PpiMiazkedh/t5/sZgshsX+w8J/Nq6dXdpQfdwuvaHnnrJEGBVoexP50pM+Ryjq5tYuYQp5igO2HCBGF191F3797FhAkTLHq+wgMj+vfvj8OHD2PLli2oX79+sQMj3Nzc4ObmJvz6xIkTeOyxx+Dm5oagoCCTKzY21uzXZ9MRkTXdup+FASsChFAyf18ICuQ6XGL7WOM2hdJWdbe/Lk99VMyiA8bxdXuCYzBvXwi6PjwZresif8zbx5PRbBkzhzjFBF0HBwecO3euxI9duHDB4pPRACA0NBQDBw5EzZo10ahRI8yYMaPY6Wb9+/dH//79hV+LvalMpVLB09PT7Ndm0xGRtaXnFuDdzeeEwDJuYxBSs2U4XGJDP/NWdDf2r/zaqJj9V+KFnlnsc0PuckgCzBziFBV0z58/X+LHtm7dikaNGlVyRRXDpiMiKWh1enzjGyoElz7LjyHsXkblFsEVXZsRkZiJNvMOwdnDB69v+Ec5R0yTVTFziJM16K5evRrNmzdH8+bN4ejoiKZNmwq/LryaNGkCR0dHi9+MJjc2HRFJ6c+LsWg1xzAXtc28QzgUUvGDdsxm7h7dbe5AXiWHcBJk5hUI2126Lz6C+5mcfWyvmDnEyRp0/fz8MG3aNEybNg0ODg4YN26c8OvC66uvvsKWLVuQl2dbP6BsOiKS2uWYNHRffERY3V195CZ0uko4XEKTbxghVtaqrmddYGVr4Lo3oIRDL6oQvV6Pj7ddgLOHD57jm8zsHjOHOMVsXViwYIFd3SA2HRFVhsSMPIz84bQQdif/dgHZ+RrpXzgt2jBCTGyOrt9sYFFD09PUUkp+wzFZ34YTt4We8DrN77u9Y+YQp5iga2/YdERUWfIKtPhi12Uh2AxdFYiYlBzpX1iTbxghtv11wxvPtr9u+HXh/Nzk28CvrxnD7teNDAdOcL6upM7ceoDmD2cvf/77JWUcIU2SYuYQp6ige+XKFUydOhUvv/wyBgwYUOyyJWw6IqpMer0em05GCgGnyyJ/BEUmy12WYctCyF5gRStj4F3nCtw5JXdldikhPRddHx71O+T7QOSoK2F1n2THzCFOMUG3cH5tz5494ejoiL59+6JLly6oVq0amjVrhtGjR8tdokXYdEQkhxMRSejg6Sfszfwt6K7cJRnkpQO+MwDPesbA+9dkIPuB3JXZjXyNFq893MbSfr4fIpOy5C6JKgkzhzjFBN3evXvj008/hVarhYODAy5evAjAMAu3ZcuW2L17t8wVWoZNR0RyiUzKgttK4+ESs/+6BrVGIWOl4i4AG/oaw+7SZ4ELWwGdQuqzYXO9Q4R7fvh6JU7hINkxc4hTTNCtV68ejhw5Ar1eD0dHR5w8eVL42I4dO9CmTRsZq7Mcm46I5JSRV4AJXueF4PP6hn+QnKWQvbE6LXB2A/DN08bAu2kQcC9E7sps1p8XY4V7vfxQmNzlUCVj5hCnmKDbsGFDHDlyBADQtGlTbN26VfiYr68vatWqJVdp5cKmIyK5aXV6LDsUJgSg3kuP4Ua8gubaZiQAu98rcsjEk8DhOUA+/8ndEjfiM9B6rmGm8tubzkJbGSPmSFGYOcQpJugOGjQIP/zwAwDg7bffRqtWrXDo0CEcPXoUXbt2Rffu3WWu0DJsOiJSin2X4/D8w8MlXOYegu+1BLlLMnXzCLC6ozHwftcWCPORuyqbkJ5TgL7Lj8PZwwe9lhxVzqo9VSpmDnGKCbq+vr5C0I2NjUWnTp3g4OAABwcHPP3006LHAysVm46IlORabDpeWHJUWN397nB45RwuYa6CXODY18DCBsbAu3OcYV4vlUin02Piw+0prWYfxOWYNLlLIpkwc4hTTNB9lF6vx82bN3H16lWo1Wq5y7EYm46IlOZ+Zh7c15/S0M67AAAgAElEQVQRwu5HvwYjqzIOl7BEUjjg9aox7C7+N3B6NaAtkLsyxVl37KZwL7efVch0DZIFM4c4xQZdW8emIyIlytdo8eWeK0JAGvJ9IKKTK+FwCUvo9cCV34HlLYyB98cXgOgguStTjMCIJDR7ODP5f7uv8FCIKo6ZQ5ysQXfNmjVmX2vXrpWzVIux6YhIqfR6PbacjkKLWb5w9vBBp4WHceaWAufZ5qQA+z83hl3PusDfnxker8JiUnLQaeFhOHv44OXVJ5FXoJW7JJIZM4c4WYNu4R5ccy5HR0c5S7UYm47IfDq1Gun7DyB60iREuY9B9KRJSN9/ADob3LZkS07feoCOCwyBqcUsX2w9c0eZK4PRZ4EfexnD7vLmwOUdhpXfKiavQIvha0/B2cMHHTz9lLcaT7Jg5hDHrQsSYdMRmacgLg63hw5DaGsXhLZpa/Lf20OHoYA/Q5K6m5yNwd+fELYyeOy9qpzDJYrSFgBn1gKLmxgD75ZXDHt6qxCPvVeFe3U87L7c5ZBCMHOIY9CVCJuOqGw6tdoQcgsD7qNXm7a4PWwYV3YllpWvwQdbg4UANWb9GSRlKnRMVVoM8PtbxrC7sAFwdCGgtv+VzT/ORwv36Hv/CLnLIQVh5hCnqKCblZWFpUuXwt3dHd27d0dYmOF0Fy8vL1y9elXm6izDpiMqW/r+AyUH3Eeu9AOcqSo1nU6PlYfDhSD1wpKjCIlLl7sscWG+wPftjIF3VQfgpr/cVUnmWmw6Wj2chfzelnPKGg1HsmPmEKeYoHv9+nU0btwYjRo1wsiRI+Ho6IiLFy8CAKZNm4a33npL5gotw6YjKlv0pEniq7lFVnVjJn0sd6lVhs/VBOGUrdZzD+LvK/FylyROnQ34zwMWPmUMvLveBTIUXHM5pGar0XvpMTh7+ODFZceQlsN/4SBTzBziFBN0Bw4cCDc3N+Tk5ECj0cDBwUEIunv27EGzZs1krtAybDqiskW5jzFrRTdqzBi5S61SrsenC8HK2cMHyw+FKXsFMfE68MsQY9j9pikQtB7QKmxGcDlodXq8u/mc4VCIOQeVvcpOsmHmEKeYoFuzZk0cPHgQAKDVak2CbmBgIGrUqCFneRZj0xGVzawVXZc2XNGVwYOsfLz+0z9C2J3odR6ZeQo+tEGnAy7+CixzNgben/oAsRfkrqxCvvOPEO7BruAYucshhWLmEKeYoNugQQPs3r0bQPGgu337djRp0kTO8izGpiMqm7l7dGM+nQJddrbc5VY5ao0OM/+8JgStgd+dQNQDhd+H7AeA9ydFZu/WA3ymA7m2dzzu0dBE4Xs/889rcpdDCsbMIU4xQXf8+PHo3LkzUlNThaB76dIl5OXloWfPnvjwww/lLtEibDqispU5daHIdbNff6Qf8FHmnFc7ptfrse2fO3ju4eESHTz9cPJmktxlle3OKWBdd2Pg/bYlcG2PzczevZucjQ6efnD28MHIdaeQr+GhECSOmUOcYoJubGwsnnnmGTRo0ABvvPEGHB0dMWLECLRo0QLNmzdHYmKi3CVahE1HZJ6CuDjcHjas2BvQQlu74NaQoUjwXICw9h2Ej915623khYbKXXaV88/tZHR+eBpX85k+2HQyUvl/6dCogZMrga8bGQPvryOB5NtyV1aqXLUWw1afhLOHDzovPIy4tFy5SyKFY+YQp5igCwCpqamYO3cuevXqhVatWqFnz56YM2cOUlJs77hHNh2R+XRqNaInfoDQ1i4I7+aKmEkfI/2AjzA/V33nDmI+nmwShBM8PaFJTZW58qolJiUHQ1cFCv+c/r/dV2xjpTElCvhtjDHsLmoIBCwFCvLkrqwYvV6PL3ZdhrOHD5rN9LGN1XOSHTOHOEUEXbVaDT8/P0RHR8tditWw6cheSXVc772vFxv245byxrOsEydwe8hQIfCG9+iJlO3bodfY/rvrbUV2vgYfb7sghN1RP57G/QzlBcZi9Hrgxj5gZWtj4F3TBbh9XO7KTPwWdFf43v5w/Jbc5ZCNYOYQp4igq9fr8fjjj+P4cWX9gVMRbDpSCk1WFhIWLkJEr94I69QZEb16I2HhImiysix+LimP6zUn6AKAXq1G8qZNCO/SVQi8kSNfQ/a5c+V+bbKMTqfHqiPGaQA9vjmCKzE28mav/Ezg0ExgQX1j4N37AZAp//a4S9GpaDnbsBf6g63Byh7pRorCzCFOEUEXADp06IDffvtN7jKshk1HSpBz8SJC27Uv+Q1e7doj5+FkE3NIeVyvJisLkSNGCs9jThgvSLyP+K++Mqkh7osvUJCQYPHrU/kcCklAm3mHhBmv3pds6M+7hCvAzwOMYXfJM8D5TYBOnq0YD7Ly8cKSo3D28EH/b48jPVfBo9xIcZg5xCkm6Pr4+OD555/HpUuX5C7FKth0JDdNVpZ4yC0Sdsta2dXrdNBmZCB566+SHNdb0TCec/ESoka7C18T1qkzHqxfD11+vkV1UPmEJmTgxWXGwyWW+IZCaysrkTqtIdwuecYYeH8eYAjBlUij1WH8z0HCaXShCRmV+vpk+5g5xCkm6Hbu3BkNGjSAo6MjmjRpgs6dO6NLly7C1bVrV7lLtAibjuSWsHCReaeOvTkO91euRMK8+YidOg3REyYgyn0Mbg0ajPAePc0a/SV2XK86JQV3J0xEaPsOCHVpg9D2HXB3wkSoH77B1GphXKtF6u7diHihl/B1twYOQuaRI8qfDGAHUrLVeHOj8XCJ97acs60VycxEw/aFwrC7oL5he0N+ZqW8/LJDYcL37q9LsZXymmRfmDnEKSbovv/++2VetoRNR3KL6NXb/IBqxeuW20Do1Gpk+PmV+nkZfn5mh/GERV+b9XvWpqfj3uJvENq2nfC10RMmIv+2ssdJ2YMCrQ5zvUOEwDZgZQBuJ1m+D1xWt48b3qBWGHhXuhjewCbhX5YOhdwTvmfz94VI9jpk35g5xCkm6NobNh3JLaxTZ/PCqUsb3H3nXcRMmYL4WbORuHQZHqxfj5Tt25G+/wCyAgORe/ky7rzzrmFV1pznLLKyWtoV1rGTWZ8X0au3Rb/3vIgI3H3vfZNV4cQlS6HNrJwVuqpsx9lo4XCJ9p5+OB5+X+6SLFOQZxg9tuhfxsC7fSyQesfqLxWZlIV28w2HQoz+8TTUGp3VX4OqBmYOcQy6EmHTkdzMXdE1N0Sae1yv2WHYgiusU2eLf/96vR4Zfodxc8AA4++194tI2/sn9DoGCimdi0pB10X+wuESG07ctr0tJMm3DYdLFIbdrxsZDp/QVGyMXqHsfA0Gf38Czh4+6Pa1P+6l28CINlIsZg5xDLoSYdOR3Ky9LcDcqQv5kZGW7euVYEXXpO7cXCSt+8Fk9Thq7OvIvVK5bziqamJTc/Dyw9O9nD18MO2Py8grsIHDJYrS6w3HBn/b0hh413UH7pyu4NPq8dnOS3D28EGLWb7453aylQqmqoqZQxyDrkTYdCQ3a73RqyiT43ofnaM7zDhH19qruuaG8bJqj/3v5ybPGz9zFjRJPHlKKjlqDT7dflEIuyPXnbLNlcvcNMBnOuBZzxh4vT8Bsh+U6+m2nI4SvicbA7l/nCqOmUMcg65E2HSkBNaco1tIp1Yj/YAPYiZ9jKgxY4od1wvAMGXBnBDbtp3Vw3hZsv/5B5HDhwvPH961G5I3b4G+gie7Ucn0ej3WHbspBDvXxUdwMdpGj26OvQD81McYdpc5Axd/BSzYCnP+Toqwh3nybxdsb0sHKRIzhzgGXYmw6UgpNFlZSFj0tenJaIu+tmp4fNTdCRPNCrp3J34gSRgvi76gACm/bkO4a3fhtW6//AqyTlXsn6RJnP+NRLQtPFxi9kHsuWCjY7S0GiBoPfDN08bA+8sQIPF6mV96PyMProuPwNnDB24rA5CVz6OryTqYOcQx6EqETUdVmTolxaygW3SebmWHcQDQpKQgYe48k60WMZ9OgTomRtLXraoiEjPRd/lxYXV30YEb0Ght9I2BGfHArv8zht2FTwH+8wB1domfXqDV4fWfDLOG28w7hFv3OQGErIeZQxyDrkTYdFTVmTNHVylyQ67jzpvjjFMeOnTE/VWroMvJkbs0u5OarcZbm4KEsPvOL2eRnmNDh0s86qY/sKqDMfB+3w4I8y32aYsO3BB+zweuxstQKNkzZg5xDLoSYdMRPTwZbeIHpiejTfxAWMlVEr1Oh/R9+xDRp48QeG/2fwnpPj7cR2llGq0Onn9fF4Jf/2+P2/YKpzoHOLoIWNjAGHh3jgfSDP8ycOBqvPB7/frADZmLJXvEzCGOQVcibDoi26TNysb9FStM3lB39+13kBcWJndpdmfX+Ri0nG14Y1a7+X44Gpood0kVkxQObHnFGHYXN0GS3wp0mHcAzh4+eH3DPyiw1a0apGjMHOIYdCXCpiOybflRUYj+6COTOcH3Fi6EJtVGJwYo1IW7Kej2teENWs1m+uCH47dsewVdrwcu7wCWNxcCb+i89pi46Afcz7TB0WpkE5g5xDHoSoRNR2QfMgMCcGvIEOPhFT16InXnTui1Nnb4gYLFp+Vi+NpTwj/vf7bzEnLVtv391Wcn4+TK8cbVXc+6wN//BXKUt22HbB8zhzgGXYmw6Yjsh06txoOff0ZYl65C4I18bRRyzp+XuzS7kVegxX8fnhbm7OGDV9eeRHxartxlldvGwNtw9vCB+8zvkLKimzHsLm8BXN5pWPklshJmDnEMuhJh0xHZn4LERMTN+NJkekTc9P+h4N49uUuzC3q9HusDbqPZTEPY7fb1EQTfsb0V0DO3H6D5w9/Df3degl6jBk6vARb/2xh4vV4FkiLkLpXsBDOHOAZdibDpiOxXzsWLiBw92jiOrHMXPPhpA3T5+XKXZheOhSWi/Xw/OHv4oOVsX/xxPlruksyWkJ6Lrov84ezhgyHfByJHXeRQiLQYwzQGYfZuA+DY10CB7a5ckzIwc4hj0JUIm47Ivum1WqT+sQsRPV8QAu+tQYOReeyYbb+ZSiFu3c9E/2+Nh0t4/n1d8RML1BodRv14Gs4ePmg/3w+RSSIHnoT5AN+1NQbe1R2Bm0cqt1iyK8wc4hh0JcKmI6oatOnpuPf1YoS2aSsE3ugPPkR+ZKTcpdm89JwCvPPLWSHsjv85CKnZarnLEjVvX4hQ66GQMraz5GcBh+cCC540Bt5d/wdkJFROsebQ5ANXdwPbxwIb+hn+e3W34XFSFGYOcQy6EmHTEVUteeERuPvu/xn377Zrj8Tl30Ir8THG9k6j1WGxj/FUsb7LjyP8nvIOl/jrUqxQ47JDFsxcvhcC/DLYGHa/eRoI+gnQyTx1Ii0aWNPFUNOC+qb/XdPF8HFSDGYOcQy6EmHTEVU9er0eGYcO4eZLA4zjyF7sg7S/vKHXKfuf3ZVu74VYtJpzEM4ePmg77xAOX1fOGwBDEzLQeq6htrc2BUFj6RYLnQ64sBVY+qwx8G7oC8RdkKbgsmjyDWG2MNg+ei2oD6ztypVdBWHmEMegKxE2HVHVpcvNRdKatQjr0FEIvFFvvIHca9fkLs2mXYpORffFR4SV07VHb8q+Hzo9twD9Hu4lfmHJUSRnVSD8ZT8A/ppcJFTWA3z+B+SlW69gc1zdXXLAffS6tsfw+VlJwK+vPTwCuZ7hv7++Znj8UZZ8LpmNmUMcg65E2HREpI6NRexn/zUZRxY/ezY0Dx7IXZrNSszIw8gfTgth99PtF00nG1QinU6PD7aeF6ZDXIq20ql5USeBda7GQLmilSFUVlao3z5WfDW36Kru9teBG/tK/7wb+4zPa8nnkkWYOcQx6EqETUdEhbLPnMHtV14Vwm54N1ekbN0KfUGB3KXZpLwCLb7447IQdoetPonY1JxKr+OH47eEGn4LumvdJ9eogcAVwNeNjEHw19eA5NvWfZ1H5aYB37mYt6L7dRPzPi8ryXCZ+7lkMWYOcQy6EmHTEVFR+oICpGzdivBurkLgvf3Kq8g+c0bu0mySXq/Hz4GRwsEMXRf542xkcqW9fmBEknCwxfRdV6TbQpESBfzmbgyCixoCAcusvz82KQLwmQ4sNjO8WnJtG2UI6eZ+LlmMmUMcg65E2HREVBJNcjLi58xBqEsbIfDGfvYZ1LGxcpdmkwLC76O9p+Fwiedm+WL7WSuvrJYgNjUHnRcehrOHD15efRJ5BRJPSNDrgevewMrWxkC4tisQGVCx59XpgIjDwLbRpmFzYQPrBt2F/zL/ORc2sMZ3rMph5hDHoCsRNh0RlSb32jVEvfGG8XS1Dh2RtGYNdLk8JctSkUlZGLAyQNhGMMf7mmSHS+QVaDFi3Sk4e/igg6cfopMrcctEXgZw0MN0/+zeD4Gs+4aPmzv3Nj8TOLvBOD6s8PquLXBqlWGWrzlTFzzrWX/117Nu5X0/7QgzhzgGXYmw6YioLHqdDmne3oh4sY8QeG++NAAZhw5Br9dDp1Yjff8BRE+ahCj3MYieNAnp+w9Ap1buoQlyycgrwPtbzglh940N/1RsAoKImX9eFV7jWFii1Z/fLPGXgY0vGYPhkmeAE9+WPfc2JdIQlL952jRYbh5mWDHWFj2uOPphmC3h+dZ2NXzc2iu/DLrlxswhjkFXImw6IjKXNisLicu/RWi79kXGkb2JmwPcDL8uPHXt4X9vDx2GAv7ZUoxWp8fSg2FCEO299BhuxGdY7fl3nY8Rnvs7/wirPW+56LTAuZ+BJf8xIzzWK773dtG/DKPM4i+Lv4Ym3zDtYfvrwMb+hv9e22NcITZ33+2jK8cMulbHzCGOQVcibDoislR+ZCSiP/jQZBxZiVebtrg9bBhXdkV4X4rD8w8Pl3CZewgHr1X8WN2QuHThwIr/23wOWp2883sFmfeATYPMD5ErWgEnlltnuoElkxQ8yxhXJlxPVryuKoiZQxyDrkTYdERUHnq9HonLlpcddlu7IP2Aj9zlKtbV2DT0/OaosAL7vX8EdOUMp6nZary47BicPXzw4rJjSMtR2F8wto81b7/sT30MY8usydzZuJy6IClmDnEMuhJh0xFReUVPmmTcrlDKFTliJPLCw6HXSvyufxt1PyMPo340Hi4xaVswsvMtO1xCq9Pj3c2Gvb+t5hxESFwln1Jmjg39zAuRG/tL8/pZSYaAuvBfhsC98F+GXxddNeYcXUkxc4hj0JUIm46IyivKfYxZK7rCxIYuXXH3/97D/e++R+axYzx5rYh8jRYzdl8Rwu7QVYGISTF/UsJ3/hHC1+46HyNhpRVgyUlmcuLJaJJh5hDHoCsRNh0RlZe5K7pF37z26HVr4CDETf8fUn7dhtwrV6r0fl69Xo/Np6KEwyU6LzyMM7fL/svAsbBEIeTO/PNqJVRaTld3m7daem2P3JUWX/0trO3eDbkrs2nMHOIYdCXCpiOi8krff8DMPboHkB8VhTRvbyQsWIDI0aMR2rZdyau+7TvgzhtvInHJEmT4+qIgLk6607wU6tTNB+i4wHDQQ4tZvvj1nzui34Po5Bx0eHgQxYh1p6Q/FKIiNPnmzb219mlqFVWQZ5wGccFL7mpsGjOHOAZdibDpiKi8dGo1bg8dJr6qW8rUBV1ODnLOn0fyL78g9rP/4mafvqJBOaJPH8RMmYIHP/+M7HPnoMux7PADW5zze+dBNgZ9d8JkpTYrrwD7Lsfh/S3nMHztKbz7yzn0WnpMWP2NTa3EQyHKy5y5t0r0x9uGGuXeVmHjmDnEMehKhE1HRBVREBeH28OGlTxHd5j5c3T1ej0KEhKQcegQEpcuw51x4xHWoWPJ4bdtO0SOGo0ET0+k/eWN/Mgo6HUlnzBWEBdnCOM2OOc3M68AH2w9L4TdwlFkhVsbil5/XrSho5nLmnurRJd3GoLuooaAOlvuamwWM4c4Bl2JsOmIqKJ0ajXSD/ggZtLHiBozBjGTPkb6AZ8Kr5jq1WrkXruGlG2/Ie5/M3Br0GDRVd/wHj0R/eFHSFr3A7JOnoI2Pb1CK85KodPpsazI4RIlXc08fDBgRQDyNQretmDrspONK8+hB+SuxmYxc4hj0JUIm46IbIkmORmZx47j/vercPf99xHepav4loe+/exizu++y3GlBt3Ca99l/jkuqc3DDEHX+xO5K7FZzBziGHQlwqYjIlum12qRFxGB1N27ET9nDiKHD0eoSxvzx561aYuYSR/L/dso1ftbzpW4XaHo1XymDyZ4nZe7VPt2Zq0h6C5vbjjamCzGzCGOQVcibDoisjfazExknzmDm2au6EaNGSN3yaUavvaUWSu6w9eekrtU+5Z82zghIjpI7mpsEjOHOAZdibDpiMhemTXnlyu6ZIl1roag6z9P7kpsEjOHOAZdibDpiMhemT/nl3t0yUz+8x6OQusmdyU2iZlDHIOuRNh0RGSvypy60NoFN/u/pOipC4DheOCXVgSIruo2n8mpC5Um+qxx+8KDm3JXY3OYOcQx6EqETUdE9qy0Ob+hrV0Q7tod6rt35S6zTLGpORiwIsBkjm7hfwesCLCNwyLsgU4LLG9hCLqn18hdjc1h5hBn10E3LCwMgwYNQq1atdC4cWN8+eWXUJuxwrBw4UK4ubmhTp06UKlUuHPnjsWvzaYjIntX0pzf5C1eCO/eQzg4QpuWJneZZcrXaLHvchwmeJ3H8LWnMMHrPPZdjuNKbmXz/tQQdDcPk7sSm8PMIc5ug25qaiqaNGmCfv36wc/PD5s3b0a9evUwZcqUMr/26aefxksvvYQRI0Yw6BIRWSjn/HmEtu+A0NYuuPvOu9ArfAsDKUToAePRxdnJcldjU5g5xNlt0F2yZAlq166NlJQU4bGNGzeiWrVqiI+PL/VrdQ+PvPT29mbQJSIqhzRvb2EbQ7zHTOj1erlLIqVTZwNfNzKE3cs75K7GpjBziLPboNu3b1+MHj3a5LG0tDQ4ODjAy8vLrOdg0CUiKr/7q1cLYffBTxvkLodswY43DEH3j7flrsSmMHOIs9ug27BhQ8yZM6fY402bNoWHh4dZz8GgS0RUfnqdDnFffCGE3QxfX7lLIqW74GUIuoubAAV5cldjM5g5xNlt0HVycsKKFSuKPd6uXTt89NFHZj2HJUE3IyMDcXFxwhUcHMymI6IqT5efjztvjkNoaxeEdeiInEuX5C6JlCwz0ThmLOKw3NXYDAZdcXYddFeuXFns8bZt22LSpElmPYclQdfT0xMqlarYxaYjoqpOk5yMWwMHIbS1CyJ69YY6NlbukkjJfnYzBN39U+WuxGYw6Iqz26Bb2VsXuKJLRCQu/9YthLt2N4wde+VVaDMy5C6JlCpwhSHormwNPHxzOJWOQVec3Qbdvn37wt3d3eSx9PR0vhmNiEgm2WfOILRde4S2dkH0hInQFxTIXRIpUeIN4/aFuAtyV2MTmDnE2W3QXbJkCerUqYO0IsPKN23aBCcnpzLHixVi0CUisq7UXbuEN6clzJvPsWNUnF4PfN/+4ZvSGhuu5S0An/8BefyXgJIwc4iz26BbeGBE//79cfjwYWzZsgX169cvdmCEm5sb3NzcTB47ceIE9uzZg6+++goqlQrr16/Hnj17EBwcbPbrs+mIiEqW+O23QthN3rxF7nJIaaKDDIdGFK7qFr0WNjB8nEwwc4iz26ALAKGhoRg4cCBq1qyJRo0aYcaMGcWOAO7fvz/69+9f7LGS3lj23nvvmf3abDoiopLpdTrEfvZfQ9h1aYPMI0fkLomUIi/DEGZLCrlFwy5Xdk0wc4iz66ArJzYdEZE4XW4uosaMNYwd69QZuSHX5S6JlMDnf6WH3MLLd4bclSoKM4c4Bl2JsOmIiEpXcP8+br40AKGtXXCzT18UJCTIXRLJbXkL84Lu8ufkrlRRmDnEMehKhE1HRFS2vPBwhHfpahg7NnwEUvfsRfSkSYhyH4PoSZOQvv8AdI9sOSM7trixeUF3cWO5K1UUZg5xDLoSYdMREZkn68QJhLq0Ed6gFtqmrcl/bw8dhgL+WVo1cEW3XJg5xDHoSoRNR0RkHp1ajYhevY1B99GrTVvcHjaMK7tVAffolgszhzgGXYmw6YiIzJO+/4B4yC1ypR/wkbtUkhqnLpQLM4c4Bl2JsOmIiMwTPWmScbtCaVe79rg1aDCixoxF9ISJiPviCyR4euL+96uQvHkL0vbuReaRI8g5fx554REoSEyELi9P7t8eWSo6SDzsco5uiZg5xDHoSoRNR0Rknij3MWat6Jb3CuvQETf79MXtV1/FnbfeRswnnyLeYyYSlyzFg/XrkbJ9O9IP+CDr5CnkXr0K9d270KSmQq/Vyv2tqbryMgzbE5Y/9/BktOcMv+ZKbomYOcQx6EqETUdEZB6zVnRd2iBy1Gik7NiBB+vXI3HJUsTPnIWYTz7FnbfeRuTw4bjZpy/COnS0akgO794DtwYOMkyBmDARsdOmGVaRv/seyb9sRtrevcjw90f2uXOGVeR796DLzeXRxlSpmDnEMehKhE1HRGQea+/R1eXloSAxEXkREcg5fx6ZR44gbe+fSN68Bfe/X4WEBQsQ98UXiJ4wEVFjxuLWoMEI79HTdPKDFVaRI/r0Mawij38LMZM/ebiKvARJP/5oWEXefwBZJ08aVpHv3FHcKrJOrUb6/gMc92YDmDnEMehKhE1HRGQenVqN20OHia/qVtLUBb1WC21aGtTR0ci9dg1ZJ08h3cfHsIr8009IXLrMsIr86RTcefvhKnLffgjr2Mm6q8iu3Q2ryKPdET1hgmEVeX6RVeQ9ewyryGfPIS883LCKnJNj1VXkgrg4wz3huDebwMwhjkFXImw6IiLzFcTF4fYwkWA1TPnByrCKfN+wihwcjMyjRw2ryFu8cH9V4SrydERP/ABRY1/HrcFDrL+K3L6DYRX5lYeryB9PRvxXHoZV5B9+QMpvD1eRAwORe+WKcRVZozH9vSjkLx5kPmYOcQy6EmHTERFZRqdWI/2AD3+cMtIAABPmSURBVGImfYyoMWMQM+ljpB/wsetApdfpoE1Pf7iKHIKsU6eR7uOD1J07DavIy5YjftZsxHw6BXfffgeRw0cYVpE7dbbuKnI3V9xyG4io0e64/epwq24lIekxc4hj0JUIm46IiKRUuIqcf/OmYRX52DGk/fmXYRV59WrcW7jQsIr8wYfCKnKENVeR27TFrUGDceeNNxHzyadImDsP91etQsqv25Dh64vsoLPIv3nTsGqs08n7vbLz/cbMHOIYdCXCpiMiIiXS63TQZmRAHRNjWEU+fRoZvr4PV5E3IKL3i1ZdLQ5t7YLQtu0Q0acPIke+hugJExA340vDeLeNPyNt75/IDAhA7rUQFMTHQ5efb9Xfb1XYb8zMIY5BVyJsOiIiskVmj3sb7Y6U7duRtGYtEuZ7Ivazz3Bn/Fu4NWQIwru5VnwrxZAhuDNuPGI/+wwJ8z2RtGYNUrZvR8ahQ8g5fx75kZHQpqWV+ia8qrLfmJlDHIOuRNh0RERki6w17k2Xl4eC+HjkXgtBZkAA0vb+iQc//4zEJUsRN+NLRE+YgMiRryGiTx+Etm1X/mDcrj1u9u2HyFGjEf3Bh4j/6iskLv8Wyb/8goT5nlVivzEzhzgGXYmw6YiIyBbJsQqq1+mgSU1F/s2byA46iwxfX6T8us0wsWLuPMPBIG+8iVsDByGsS1frbqto0xYxkz622u9FDswc4hh0JcKmIyIiW6X0cW+6nByoY2ORe+UKMo8dR+ru3Xjw0wbcW/wN4r6YjrvvvY/I4cPNXimOGjNG1t9PRTFziGPQlQibjoiIbJk9jHsza78xV3TtGoOuRNh0RERE8jJ3v3HKjh1yl1ohzBziGHQlwqYjIiKSV5n7jQunPLzQC1mBgXKXW27MHOIYdCXCpiMiIpJfafuNI/r0RVjnLkLgTVyy1Ka2ZhRi5hDHoCsRNh0REZEylLbfWB0Tg6jX3zC+MW20O/KjouQu2SLMHOIYdCXCpiMiIrIN+oIC3F+5Ugi7YV26Is3bW+6yzMbMIY5BVyJsOiIiItuSdfo0Il7sIwTeuC+/hDYrS+6yysTMIY5BVyJsOiIiItujSU5G9IcfCWH31uAhyL12Te6ySsXMIY5BVyJsOiIiItuk1+mQvMULoe07CMcMJ/+yGXqdTu7SSsTMIY5BVyJsOiIiItuWG3Idt4YMEVZ3oz/4EJoHD+QuqxhmDnEMuhJh0xEREdk+bVY24r/yEMJuRO8XkXXylNxlmWDmEMegKxE2HRERkf1I//tvhHfpapy5u/xb6BUyc5eZQxyDrkTYdERERPZFffcuosaMNc7cHTMW6rt35S6LmaMUDLoSYdMRERHZH71ajcTl3xqPD+7SFen798taEzOHOAZdibDpiIiI7FfWyVOI6P2iEHjjv/KALjtbllqYOcQx6EqETUdERGTfNElJiJ4wUQi7t4cMRe7165VeBzOHOAZdibDpiIiI7J9ep0PyL78gtF17Q+Bt3wHJXl7Q6/XIOHlSCMFFr4yTJ61aAzOHOAZdibDpiIiIqo7ca9dwa9BgY6Dt5lpiyC28IsePt9prM3OIY9CVCJuOiIioatFmZSHufzNKDbhSrOwyc4hj0JUIm46IiKjq0ev1Zgfd0NYuVnlNZg5xDLoSYdMRERFVTQy6ysGgKxE2HRERUdXEoKscDLoSYdMRERFVTQy6ysGgKxE2HRERUdXEoKscDLoSYdMRERFVTWLzczl1ofIx6EqETUdERFR1RY4fzzm6CsCgKxE2HRERUdXGk9Hkx6ArETYdERERVQZmDnEMuhJh0xEREVFlYOYQx6ArETYdERERVQZmDnEMuhJh0xEREVFlYOYQx6ArETYdERERVQZmDnEMuhJh0xEREVFlYOYQx6ArETYdERERVQZmDnEMuhJh0xEREVFlYOYQx6ArETYdERERVQZmDnEMuhJh0xEREVFlYOYQx6ArETYdERERVQZmDnEMuhKJjo6GSqVCcHAw4uLiePHixYsXL168JLmCg4OhUqkQHR0td/xRHAZdiRQ2HS9evHjx4sWLV2VcwcHBcscfxWHQlYharUZwcDCio6Nl/5ue3H/D5Kq2Mi7eD+VdvCfKung/lHfxnph3RUdHIzg4GGq1Wu74ozgMuiSZuDjuGVIS3g/l4T1RFt4P5eE9oYpi0CXJ8A8oZeH9UB7eE2Xh/VAe3hOqKAZdkgz/gFIW3g/l4T1RFt4P5eE9oYpi0CXJZGRkwNPTExkZGXKXQuD9UCLeE2Xh/VAe3hOqKAZdIiIiIrJLDLpEREREZJcYdImIiIjILjHoEhEREZFdYtAlIiIiIrvEoEvlEhYWhkGDBqFWrVpo3LgxvvzyS4tPZPn888+hUqkwdepUiaqsOsp7P0o6QrJ169aVULH9q8jPyKlTpzBgwAA88cQTqFu3Ll588UVERERIXLF9K8/98PLyEj1qdejQoZVUuf0q78/I3bt3MW7cOPz73/9G7dq14erqir/++qsSKiZbxKBLFktNTUWTJk3Qr18/+Pn5YfPmzahXrx6mTJli9nNcu3YNtWvXRt26dRl0K6gi90OlUmHatGkICgoSritXrlRC1fatIvfE398fjz/+OKZMmYIjR47Ax8cHs2fP5n2pgPLej6SkJJOfjaCgIGzbtg0qlQqrV6+upOrtU3nvSX5+PlxcXODi4oJdu3bB398f7777LhwcHHD06NFKqp5sCYMuWWzJkiWoXbs2UlJShMc2btyIatWqIT4+3qzn6NevH+bPnw9nZ2cG3QqqyP1QqVRYtWqV1CVWOeW9JxqNBs8++yxmzZpVGWVWGdb4M6uQp6cnqlWrhnv37lm7zCqlvPckKCgIKpUKAQEBwmM6nQ7NmjXDpEmTpCyZbBSDLlmsb9++GD16tMljaWlpcHBwgJeXV5lfv337djzzzDPIyclh0LWCitwPBl1plPeeHDx4ECqVyuLwRaWr6J9ZRT3//PMYPHiwFaurmsp7T06ePAmVSoVLly6ZPN6hQwd89NFHUpRKNo5BlyzWsGFDzJkzp9jjTZs2hYeHR6lfm5mZiSZNmuCPP/4AAAZdK6jI/VCpVGjQoAGqVauGJ598Em+//TZXqqygvPdk/vz5aNCgAby9vdGqVStUq1YNLi4uws8LlU9FfkaKCg4OhkqlsjgcU3HlvScajQbt2rXDyy+/jKioKKSlpWHt2rWoXr06zp07J2XJZKMYdMliTk5OWLFiRbHH27VrV+bfqKdPn46+ffsKv2bQrbiK3I/33nsPe/bsQWBgINauXYuGDRvi+eefR05OjlTlVgnlvSeTJk1CjRo18NRTT2H9+vU4evQo3nnnHahUKpw+fVrKku1aRX5Givriiy9Qo0YNHkdrBRW5J/fv30f37t2FNwbWrFkTf//9t1Slko1j0CWLOTk5YeXKlcUeb9u2bal7pG7cuIHq1avj8uXLwmMMuhVX3vtRkn/++QcqlQq//PKLtcqrksp7Tz788EOoVCr8+OOPwmN6vR4dO3bE8OHDJam1KrDGz4hOp0PTpk0xZswYa5dXJZX3nuTm5qJv377o0aMHvL29cezYMUyePBm1atXCqVOnpCyZbBSDLlmsvP/kNHToULzzzjtIS0sTrmeeeQaTJ09GWloadDqdlGXbLWv9s2zRr/vkk0+sUVqVVd578tVXX0GlUiEsLMzk8alTp6JFixZWr7OqsMbPyNGjR6FSqTjGykrKe0/WrVuHGjVqIDk52eTxQYMGmfxrIVEhBl2yWN++feHu7m7yWHp6eplvInB2dhadSalSqXDr1i2JK7dP5b0fYpo2bYpPP/3UStVVTeW9J4VzWx8Nup9//jlatmwpRalVgjV+RiZOnIh69eohPz9fggqrnvLek08++QStWrUq9viXX36Jpk2bWrtMsgMMumSxJUuWoE6dOkhLSxMe27RpE5ycnMocCxMQEGByNW7cGGPGjEFAQAByc3Mro3y7U977UZIzZ85ApVJh8+bN1i6zSinvPUlISICTkxPWrVsnPKbX69GhQwf+k3kFVPRnJD8/H/Xr18fEiROlLLNKKe89WbZsGapXr44HDx6YPO7m5obevXtLVi/ZLgZdsljhoO/+/fvj8OHD2LJlC+rXr19s0Lebmxvc3NxKfS7u0a248t6PjRs34oMPPsAff/yB48ePY9WqVWjQoAFcXFz4l44KqsjPyNSpU1G7dm2sXbsWfn5+GDduHKpVq8YDIyqgon9m/fXXX1CpVDyQwIrKe09iYmJQt25duLq6Yu/evTh8+LCwt/3333+v7N8G2QAGXSqX0NBQDBw4EDVr1kSjRo0wY8aMYkc39u/fH/379y/1eRh0raM89+Po0aPo3bs3nnrqKTg5OaFp06b46KOPkJSUVMnV26fy/oxoNBrMnTsXTZo0weOPP45u3brBz8+vEiu3TxX5M2vs2LFo0qQJ30dgZeW9JxcvXsTLL7+MRo0aoU6dOujWrRt27NhRiZWTLWHQJSIiIiK7xKBLRERERHaJQZeIiIiI7BKDLhERERHZJQZdIiIiIrJLDLpEREREZJcYdImIiIjILjHoEhEREZFdYtAlIiIiIrvEoEtEREREdolBl4iIiIjsEoMuEZGNyMvLk7sEIiKbwqBLRFTE6dOnMXDgQNSuXRt16tTBiy++iNOnTwMA7t69izFjxqBu3bqoVasWhgwZgmvXrglf+95776Fz587FnvO3336Do6MjEhIShMc2b96Mdu3a4fHHH8d//vMfLFq0CHq9Xvi4p6cn6tWrh6CgIPTo0QOPP/441q9fj+zsbEyZMgXPP/88atasiebNm2Pq1KnIzs42ec3U1FSMGzcOTzzxBBo3bowFCxZg/vz5qFevnsnnxcTEYPz48XjqqadQo0YNuLm5ISQkxCrfSyIiuTHoEhE9FBgYiMceewwvvfQSdu/eDT8/PyxcuBB//PEHMjMz0axZM7Ro0QI7d+7EX3/9hW7duqF+/fqIiYkBABw6dAgqlQrh4eEmzzt8+HC89NJLwq+//fZbODk5wcPDA/7+/li5ciVq1qyJpUuXCp/j6emJ6tWr47nnnsP69etx/PhxhISEICkpCZ988gn27t2LEydOwMvLC82bN4e7u7vJa44cORL169fHhg0b4OPjg6FDh+I///mPSdBNSUnBM888g44dO+L333+Hj48PBgwYgIYNGyIjI0OKbzERUaVi0CUieqhXr17o0KEDtFptsY+tWbMGjo6OCAsLEx5LSUnBE088genTpwMANBoNGjZsiAULFgifk5qaiscffxwbN24EAGRkZOCJJ57AvHnzTJ5/+fLlqF+/PnJycgAYgq5KpcLevXtLrVmj0cDHxwcODg548OABACAkJAQqlQo7d+4UPk+tVqNJkyYmQXfu3Ll48sknha8DgOzsbDRs2BDffPNN6d8sIiIbwKBLRARDwHN0dMR3331X4sfHjh1b4raEUaNGoUePHsKvJ0+eDBcXF+HXmzdvxmOPPYbk5GQAgJ+fH1QqFa5duwaNRiNcwcHBUKlUuHLlCgBj0M3NzS32mlu3bkWnTp1Qq1YtqFQq4QoKCgIAeHl5QaVSISsry+TrJk6caBJ0X3jhBYwdO9akDo1GgxEjRmDUqFHmfuuIiBSLQZeICEBsbCxUKhV+//33Ej8+cOBADBs2rNjjH3/8MVq2bCn8OjAw0CSwDh48GK+88orw8e3bt5uE00evAwcOADAE3bp16xZ7vT///BMqlQqTJ0/GwYMHce7cOWzbtg0qlQoBAQEAgKVLl6JmzZrFvnbmzJkmQbdly5aidXTr1s2M7xoRkbIx6BIRoewV3ddffx1du3Yt9vijK7p6vR5PP/00Zs6ciaSkJDg5OWHbtm3Cxw8ePAiVSoW///4bwcHBxa60tDQAxjejPWr8+PFwdXU1eWzfvn0mQdfcFd0ePXrg1VdfLbGOGzdulPEdIyJSPgZdIqKHevXqhU6dOkGn0xX7WOEe3Vu3bgmPpaamonbt2sIe3ULTp09H8+bNsX79etSoUQOZmZnCx9LT01GrVi2sX7++1FrEgu6oUaPQu3dvk8fGjRtnEnQL9+ju2LFD+JyS9ujOnj0bzZo1E/YFExHZGwZdIqKHCqcuuLm5Yc+ePfD398c333xjMnWhVatW+OOPP+Dt7Q1XV1eTqQuFzp8/D5VKhaZNm2LMmDHFXqdwysKsWbPg5+eHw4cP44cffsDLL78sfI5Y0F23bh1UKhUWL14Mf39/TJkyBc2bNzcJugAwYsQI1K9fHxs3boSvr68wdeHJJ58UPic5ORnOzs7o0aMHfvvtN5w4cQK7d+/G1KlTsXnzZit8R4mI5MWgS0RURGBgIPr164eaNWuibt266NOnD/755x8Ahjm67u7uqFOnDmrVqoXBgwebzNEtqnD/6549e0r8+M6dO+Hq6ooaNWqgXr16cHV1xZIlS4SPiwVdjUaDadOmoWHDhqhbty7GjRuHo0ePFgu6KSkpePPNN1GrVi3861//wqxZs/D555+jWbNmJs+XkJCAiRMnonHjxnj88cfx7LPPYty4cbh8+bKl3zoiIsVh0CUiqgL0ej26dOnCaQpEVKUw6BIR2aHdu3dj1apVOHr0KPbt24dRo0bBwcEBx44dk7s0IqJKw6BLRGSHfH190alTJzzxxBOoXr06unbtir/++kvusoiIKhWDLhERERHZJQZdIiIiIrJLDLpEREREZJcYdImIiIjILjHoEhEREZFdYtAlIiIiIrvEoEtEREREdolBl4iIiIjsEoMuEREREdklBl0iIiIisksMukRERERkl/4ffZVUGCmk+w8AAAAASUVORK5CYII=\" width=\"639.8333333333334\">"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n"
- ]
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:06:01.992636Z",
+ "start_time": "2021-01-27T02:06:01.886944Z"
}
- ],
+ },
+ "outputs": [],
"source": [
"cov_error_data = {\n",
" 'mbtr': {'x': [], 'y': [], 'x_err': [], 'y_err': []},\n",
@@ -3172,6 +2393,7 @@
}
],
"metadata": {
+ "celltoolbar": "Initialization Cell",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
diff --git a/domain_of_applicability.ipynb b/domain_of_applicability.ipynb
index 89441189d17e1bc753e2f9bb0d24fc7386a98db0..4d087e3d346b7f7bec05f207f7bf01194b30f6fa 100644
--- a/domain_of_applicability.ipynb
+++ b/domain_of_applicability.ipynb
@@ -1,117 +1,5 @@
{
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{
"cell_type": "markdown",
"metadata": {},
@@ -136,7 +24,7 @@
"<b> This notebook allows to reproduce results from the paper:</b>\n",
"C. Sutton, M. Boley L.M. Ghiringhelli, M. Rupp, J. Vreeken, and M. Scheffler, Identifying domains of applicability of machine learning models for materials science. Nat. Commun. 11, 4428 (2020) [<a href=\"https://th.fhi-berlin.mpg.de/site/uploads/Publications/s41467-020-17112-9.pdf\" target=\"_top\">PDF</a>]\n",
"\n",
- "<span class=\"nomad--last-updated\" data-version=\"v1.0.0\">[Last updated: January 25, 2021]</span>\n",
+ "<span class=\"nomad--last-updated\" data-version=\"v1.0.0\">[Last updated: January 27, 2021]</span>\n",
"\n",
" \n",
"<div> \n",
@@ -189,10 +77,25 @@
"That is, the $y$ values are almost determined by the third degree polynomial for low $x_2$ values but are almost completely random for high $x_2$ values. Discovering applicable domains reveals how different models cope differently with this setting even if they have a comparable average error. To show this, let us examine the average error obtained from three different kernelized regression models."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "## Code initialization"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:24.427984Z",
+ "start_time": "2021-01-27T02:15:24.047224Z"
+ },
+ "hidden": true,
+ "init_cell": true,
"tags": [
"startup"
]
@@ -217,6 +120,12 @@
"cell_type": "code",
"execution_count": 2,
"metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:24.432580Z",
+ "start_time": "2021-01-27T02:15:24.429503Z"
+ },
+ "hidden": true,
+ "init_cell": true,
"tags": [
"startup"
]
@@ -233,17 +142,28 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "heading_collapsed": true
+ },
"source": [
"## Data generation\n",
"\n",
- "First, the data for $n$ points is generated in the form of numpy arrays."
+ "First, the data for $n$ points is generated in the form of numpy arrays.\n",
+ "\n",
+ "Then, we use the sklearn library to fit our data with a linear, a gaussian (radial basis function = rbf) and a polynomial kernel. Our original data as well as the predicted values for each kernel are stored in the ``example_df`` data frame."
]
},
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:24.455791Z",
+ "start_time": "2021-01-27T02:15:24.434096Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [],
"source": [
"np.random.seed(7)\n",
@@ -260,17 +180,17 @@
" y[i] = val + np.random.normal(0, math.exp(x2[i]/2), 1)"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Then, we use the sklearn library to fit our data with a linear, a gaussian (radial basis function = rbf) and a polynomial kernel. Our original data as well as the predicted values for each kernel are stored in the ``example_df`` data frame."
- ]
- },
{
"cell_type": "code",
"execution_count": 4,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:24.814338Z",
+ "start_time": "2021-01-27T02:15:24.457730Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [
{
"data": {
@@ -470,7 +390,13 @@
{
"cell_type": "code",
"execution_count": 5,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:24.828601Z",
+ "start_time": "2021-01-27T02:15:24.815629Z"
+ },
+ "init_cell": true
+ },
"outputs": [],
"source": [
"def prediction_mesh(X, Y, kernel):\n",
@@ -489,42 +415,50 @@
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:24.928314Z",
+ "start_time": "2021-01-27T02:15:24.829820Z"
+ },
+ "init_cell": true
+ },
"outputs": [
{
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"window.mpl = {};\n",
"\n",
- "\n",
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- " if (typeof(WebSocket) !== 'undefined') {\n",
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" return MozWebSocket;\n",
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- " alert('Your browser does not have WebSocket support. ' +\n",
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- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
+ " alert(\n",
+ " 'Your browser does not have WebSocket support. ' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
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+ " 'have to enable WebSockets in about:config.'\n",
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+ " }\n",
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@@ -539,11 +473,11 @@
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+ " // that occurs as the element is placed into the DOM, which should\n",
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"\n",
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- " rubberband.attr('height', height);\n",
- " }\n",
+ " this.rubberband_context = rubberband_canvas.getContext('2d');\n",
+ " this.rubberband_context.strokeStyle = '#000000';\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",
+ " this._resize_canvas = function (width, height, forward) {\n",
+ " if (forward) {\n",
+ " canvas_div.style.width = width + 'px';\n",
+ " canvas_div.style.height = height + 'px';\n",
+ " }\n",
+ " };\n",
"\n",
" // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
" return false;\n",
" });\n",
"\n",
- " function set_focus () {\n",
+ " function set_focus() {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._init_toolbar = function() {\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",
+ " var toolbar = document.createElement('div');\n",
+ " toolbar.classList = 'mpl-toolbar';\n",
+ " this.root.appendChild(toolbar);\n",
"\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
+ " function on_click_closure(name) {\n",
+ " return function (_event) {\n",
+ " return fig.toolbar_button_onclick(name);\n",
+ " };\n",
" }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
+ "\n",
+ " function on_mouseover_closure(tooltip) {\n",
+ " return function (event) {\n",
+ " if (!event.currentTarget.disabled) {\n",
+ " return fig.toolbar_button_onmouseover(tooltip);\n",
+ " }\n",
+ " };\n",
" }\n",
"\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " fig.buttons = {};\n",
+ " var buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'mpl-button-group';\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",
+ " /* Instead of a spacer, we start a new button group. */\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
+ " }\n",
+ " buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'mpl-button-group';\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",
+ " var button = (fig.buttons[name] = document.createElement('button'));\n",
+ " button.classList = 'mpl-widget';\n",
+ " button.setAttribute('role', 'button');\n",
+ " button.setAttribute('aria-disabled', 'false');\n",
+ " button.addEventListener('click', on_click_closure(method_name));\n",
+ " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
+ "\n",
+ " var icon_img = document.createElement('img');\n",
+ " icon_img.src = '_images/' + image + '.png';\n",
+ " icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
+ " icon_img.alt = tooltip;\n",
+ " button.appendChild(icon_img);\n",
+ "\n",
+ " buttonGroup.appendChild(button);\n",
" }\n",
"\n",
- " var fmt_picker_span = $('<span/>');\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
+ " }\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",
+ " var fmt_picker = document.createElement('select');\n",
+ " fmt_picker.classList = 'mpl-widget';\n",
+ " toolbar.appendChild(fmt_picker);\n",
+ " this.format_dropdown = fmt_picker;\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",
+ " var option = document.createElement('option');\n",
+ " option.selected = fmt === mpl.default_extension;\n",
+ " option.innerHTML = fmt;\n",
+ " fmt_picker.appendChild(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",
+ " var status_bar = document.createElement('span');\n",
+ " status_bar.classList = 'mpl-message';\n",
+ " toolbar.appendChild(status_bar);\n",
+ " this.message = status_bar;\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\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",
+ " this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\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",
"\n",
- "mpl.figure.prototype.send_draw_message = function() {\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",
+ " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
" }\n",
- "}\n",
- "\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\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",
"\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\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",
+ " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1], msg['forward']);\n",
+ " fig.send_message('refresh', {});\n",
+ " }\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\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",
@@ -842,78 +820,108 @@
" 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",
+ " 0,\n",
+ " 0,\n",
+ " fig.canvas.width / mpl.ratio,\n",
+ " fig.canvas.height / mpl.ratio\n",
+ " );\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\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",
+ " switch (cursor) {\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",
"\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\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",
"\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
- "}\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
+ " for (var key in msg) {\n",
+ " if (!(key in fig.buttons)) {\n",
+ " continue;\n",
+ " }\n",
+ " fig.buttons[key].disabled = !msg[key];\n",
+ " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
+ " if (msg['mode'] === 'PAN') {\n",
+ " fig.buttons['Pan'].classList.add('active');\n",
+ " fig.buttons['Zoom'].classList.remove('active');\n",
+ " } else if (msg['mode'] === 'ZOOM') {\n",
+ " fig.buttons['Pan'].classList.remove('active');\n",
+ " fig.buttons['Zoom'].classList.add('active');\n",
+ " } else {\n",
+ " fig.buttons['Pan'].classList.remove('active');\n",
+ " fig.buttons['Zoom'].classList.remove('active');\n",
+ " }\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ "mpl.figure.prototype.updated_canvas_event = function () {\n",
" // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\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",
+ "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",
+ " 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",
+ " fig.imageObj.src\n",
+ " );\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
+ " evt.data\n",
+ " );\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",
+ " } else if (\n",
+ " typeof evt.data === 'string' &&\n",
+ " evt.data.slice(0, 21) === 'data:image/png;base64'\n",
+ " ) {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
@@ -926,9 +934,12 @@
" // 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",
+ " var callback = fig['handle_' + msg_type];\n",
" } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " console.log(\n",
+ " \"No handler for the '\" + msg_type + \"' message type: \",\n",
+ " msg\n",
+ " );\n",
" return;\n",
" }\n",
"\n",
@@ -937,32 +948,40 @@
" // 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",
+ " console.log(\n",
+ " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
+ " e,\n",
+ " e.stack,\n",
+ " msg\n",
+ " );\n",
" }\n",
" }\n",
" };\n",
- "}\n",
+ "};\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\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",
+ " if (!e) {\n",
" e = window.event;\n",
- " if (e.target)\n",
+ " }\n",
+ " if (e.target) {\n",
" targ = e.target;\n",
- " else if (e.srcElement)\n",
+ " } else if (e.srcElement) {\n",
" targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " }\n",
+ " if (targ.nodeType === 3) {\n",
+ " // defeat Safari bug\n",
" targ = targ.parentNode;\n",
+ " }\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",
+ " var boundingRect = targ.getBoundingClientRect();\n",
+ " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
+ " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
"\n",
- " return {\"x\": x, \"y\": y};\n",
+ " return { x: x, y: y };\n",
"};\n",
"\n",
"/*\n",
@@ -970,19 +989,19 @@
" * 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",
+ "function simpleKeys(original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object') {\n",
+ " obj[key] = original[key];\n",
+ " }\n",
+ " return obj;\n",
+ " }, {});\n",
"}\n",
"\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
+ "mpl.figure.prototype.mouse_event = function (event, name) {\n",
+ " var canvas_pos = mpl.findpos(event);\n",
"\n",
- " if (name === 'button_press')\n",
- " {\n",
+ " if (name === 'button_press') {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
@@ -990,9 +1009,13 @@
" 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",
+ " this.send_message(name, {\n",
+ " x: x,\n",
+ " y: y,\n",
+ " button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event),\n",
+ " });\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
@@ -1000,265 +1023,307 @@
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\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",
"\n",
+ "mpl.figure.prototype.key_event = function (event, name) {\n",
" // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
+ " if (name === 'key_press') {\n",
+ " if (event.which === this._key) {\n",
" return;\n",
- " else\n",
+ " } else {\n",
" this._key = event.which;\n",
+ " }\n",
" }\n",
- " if (name == 'key_release')\n",
+ " if (name === 'key_release') {\n",
" this._key = null;\n",
+ " }\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",
+ " if (event.ctrlKey && event.which !== 17) {\n",
+ " value += 'ctrl+';\n",
+ " }\n",
+ " if (event.altKey && event.which !== 18) {\n",
+ " value += 'alt+';\n",
+ " }\n",
+ " if (event.shiftKey && event.which !== 16) {\n",
+ " value += 'shift+';\n",
+ " }\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",
+ " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
" return false;\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\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",
+ " this.send_message('toolbar_button', { name: name });\n",
" }\n",
"};\n",
"\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\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",
+ "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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ "mpl.default_extension = \"png\";/* global mpl */\n",
+ "\n",
+ "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",
+ " ws.close = function () {\n",
+ " comm.close();\n",
" };\n",
- " ws.send = function(m) {\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",
+ " 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",
+ " ws.onmessage(msg['content']['data']);\n",
" });\n",
" return ws;\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\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",
+ " var element = document.getElementById(id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm);\n",
"\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
+ " function ondownload(figure, _format) {\n",
+ " window.open(figure.canvas.toDataURL());\n",
" }\n",
"\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
+ " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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.parent_element = element;\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",
+ " 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",
+ "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
+ " var width = fig.canvas.width / mpl.ratio;\n",
+ " fig.root.removeEventListener('remove', this._remove_fig_handler);\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",
+ " IPython.keyboard_manager.enable();\n",
+ " fig.parent_element.innerHTML =\n",
+ " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
" fig.close_ws(fig, msg);\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
- "}\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\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 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",
+ " this.cell_info[1]['text/html'] =\n",
+ " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "};\n",
"\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\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",
+ " 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",
+ " setTimeout(function () {\n",
+ " fig.push_to_output();\n",
+ " }, 1000);\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._init_toolbar = function() {\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",
+ " var toolbar = document.createElement('div');\n",
+ " toolbar.classList = 'btn-toolbar';\n",
+ " this.root.appendChild(toolbar);\n",
"\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
+ " function on_click_closure(name) {\n",
+ " return function (_event) {\n",
+ " return fig.toolbar_button_onclick(name);\n",
+ " };\n",
" }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
+ "\n",
+ " function on_mouseover_closure(tooltip) {\n",
+ " return function (event) {\n",
+ " if (!event.currentTarget.disabled) {\n",
+ " return fig.toolbar_button_onmouseover(tooltip);\n",
+ " }\n",
+ " };\n",
" }\n",
"\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " fig.buttons = {};\n",
+ " var buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'btn-group';\n",
+ " var button;\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",
+ " if (!name) {\n",
+ " /* Instead of a spacer, we start a new button group. */\n",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\n",
+ " }\n",
+ " buttonGroup = document.createElement('div');\n",
+ " buttonGroup.classList = 'btn-group';\n",
+ " continue;\n",
+ " }\n",
+ "\n",
+ " button = fig.buttons[name] = document.createElement('button');\n",
+ " button.classList = 'btn btn-default';\n",
+ " button.href = '#';\n",
+ " button.title = name;\n",
+ " button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
+ " button.addEventListener('click', on_click_closure(method_name));\n",
+ " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
+ " buttonGroup.appendChild(button);\n",
+ " }\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",
+ " if (buttonGroup.hasChildNodes()) {\n",
+ " toolbar.appendChild(buttonGroup);\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",
+ " var status_bar = document.createElement('span');\n",
+ " status_bar.classList = 'mpl-message pull-right';\n",
+ " toolbar.appendChild(status_bar);\n",
+ " this.message = status_bar;\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",
+ " var buttongrp = document.createElement('div');\n",
+ " buttongrp.classList = 'btn-group inline pull-right';\n",
+ " button = document.createElement('button');\n",
+ " button.classList = 'btn btn-mini btn-primary';\n",
+ " button.href = '#';\n",
+ " button.title = 'Stop Interaction';\n",
+ " button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
+ " button.addEventListener('click', function (_evt) {\n",
+ " fig.handle_close(fig, {});\n",
" });\n",
- "}\n",
+ " button.addEventListener(\n",
+ " 'mouseover',\n",
+ " on_mouseover_closure('Stop Interaction')\n",
+ " );\n",
+ " buttongrp.appendChild(button);\n",
+ " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
+ " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype._remove_fig_handler = function () {\n",
+ " this.close_ws(this, {});\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function (el) {\n",
+ " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
+ " el.addEventListener('remove', this._remove_fig_handler);\n",
+ "};\n",
"\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
" // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
+ " el.setAttribute('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",
+ " } else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
+ "};\n",
"\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
+ " if (!manager) {\n",
" manager = IPython.keyboard_manager;\n",
+ " }\n",
"\n",
" // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\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",
"\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
+ "};\n",
"\n",
- "mpl.find_output_cell = function(html_output) {\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",
+ " 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",
+ " 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",
+ " if (data['text/html'] === html_output) {\n",
" return [cell, data, j];\n",
" }\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",
+ "if (IPython.notebook.kernel !== null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target(\n",
+ " 'matplotlib',\n",
+ " mpl.mpl_figure_comm\n",
+ " );\n",
"}\n"
],
"text/plain": [
@@ -1271,7 +1336,7 @@
{
"data": {
"text/html": [
- "<img 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\" 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\" width=\"639.8333333333334\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
@@ -1326,7 +1391,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "heading_collapsed": true
+ },
"source": [
"## Setting the DA\n",
"\n",
@@ -1345,8 +1412,15 @@
{
"cell_type": "code",
"execution_count": 7,
- "metadata": {},
- "outputs": [],
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:24.935741Z",
+ "start_time": "2021-01-27T02:15:24.929469Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
+ "outputs": [],
"source": [
"doa = {}\n",
"doa['lin'] = (example_df['x1'] > -0.3) & (example_df['x1'] < 0.3)\n",
@@ -1367,7 +1441,13 @@
{
"cell_type": "code",
"execution_count": 8,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:24.956573Z",
+ "start_time": "2021-01-27T02:15:24.937636Z"
+ },
+ "init_cell": true
+ },
"outputs": [
{
"data": {
@@ -1462,7 +1542,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "heading_collapsed": true
+ },
"source": [
"## Settings\n",
"\n",
@@ -1472,7 +1554,14 @@
{
"cell_type": "code",
"execution_count": 9,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:24.962371Z",
+ "start_time": "2021-01-27T02:15:24.958180Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [],
"source": [
"root_path_dir = \".\"\n",
@@ -1491,7 +1580,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "heading_collapsed": true
+ },
"source": [
"## Functions\n",
"\n",
@@ -1501,7 +1592,14 @@
{
"cell_type": "code",
"execution_count": 10,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:24.988206Z",
+ "start_time": "2021-01-27T02:15:24.963503Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [],
"source": [
"def gen_sgd_inputs(target, model=None, random_state=None, end_label=\"_predE\", filename = 'data.csv', prop = \"Ef\"):\n",
@@ -1608,7 +1706,14 @@
{
"cell_type": "code",
"execution_count": 11,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:25.050348Z",
+ "start_time": "2021-01-27T02:15:24.990204Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [],
"source": [
"def get_all_values(rep, target, target_label, skip=None, end_label=\"_predE\", prop=\"Ef\", filename=\"data.csv\"):\n",
@@ -1831,15 +1936,35 @@
"## DA Analysis"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "heading_collapsed": true
+ },
+ "source": [
+ "### Gamma value\n",
+ "\n",
+ "As a reminder, the impact function, which the SGD maximizes to find DA is given by: \n",
+ "$\\mathrm{impact}(\\sigma) = \\left( \\frac{s}{k} \\right)^\\gamma \\left( \\frac{1}{k} \\sum\\limits^k_{i=1} l_i(f) - \\frac{1}{s} \\sum\\limits_{i \\in I(\\sigma)} l_i(f) \\right)^{1-\\gamma}$ \n",
+ "where $\\gamma$ determines the weight between the coverage and the error reduction terms. This value is 0.5 by default and can be changed with the slider above and calling ``update_gamma()`` or directly by setting the value as a function parameter, i.e. ``update_gamma(0.4)``. This sets the corresponding value in the file ``neg_mean_shift_abs_norm_error.json``, which serves as a settings file for the SGD."
+ ]
+ },
{
"cell_type": "code",
"execution_count": 12,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:25.063230Z",
+ "start_time": "2021-01-27T02:15:25.051647Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "08a02c20c8f44a179a55268b32f171a6",
+ "model_id": "216ccaeca8c44683b32965a944e661cd",
"version_major": 2,
"version_minor": 0
},
@@ -1864,21 +1989,17 @@
"gamma_slider"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Gamma value\n",
- "\n",
- "As a reminder, the impact function, which the SGD maximizes to find DA is given by: \n",
- "$\\mathrm{impact}(\\sigma) = \\left( \\frac{s}{k} \\right)^\\gamma \\left( \\frac{1}{k} \\sum\\limits^k_{i=1} l_i(f) - \\frac{1}{s} \\sum\\limits_{i \\in I(\\sigma)} l_i(f) \\right)^{1-\\gamma}$ \n",
- "where $\\gamma$ determines the weight between the coverage and the error reduction terms. This value is 0.5 by default and can be changed with the slider above and calling ``update_gamma()`` or directly by setting the value as a function parameter, i.e. ``update_gamma(0.4)``. This sets the corresponding value in the file ``neg_mean_shift_abs_norm_error.json``, which serves as a settings file for the SGD."
- ]
- },
{
"cell_type": "code",
"execution_count": 13,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:25.068641Z",
+ "start_time": "2021-01-27T02:15:25.064693Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [],
"source": [
"def update_gamma(g = None):\n",
@@ -1904,7 +2025,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "heading_collapsed": true
+ },
"source": [
"### Feature selection\n",
"\n",
@@ -1914,7 +2037,14 @@
{
"cell_type": "code",
"execution_count": 14,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:25.075479Z",
+ "start_time": "2021-01-27T02:15:25.069642Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [],
"source": [
"def list2str(List):\n",
@@ -1951,7 +2081,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "heading_collapsed": true
+ },
"source": [
"### Removing old files\n",
"\n",
@@ -1961,7 +2093,14 @@
{
"cell_type": "code",
"execution_count": 15,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:25.082275Z",
+ "start_time": "2021-01-27T02:15:25.076688Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [],
"source": [
"def rm_old_files(model):\n",
@@ -1980,7 +2119,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "heading_collapsed": true
+ },
"source": [
"### Splitting data into folds\n",
"\n",
@@ -1990,7 +2131,14 @@
{
"cell_type": "code",
"execution_count": 16,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:25.086863Z",
+ "start_time": "2021-01-27T02:15:25.083508Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [],
"source": [
"def split_data(model):\n",
@@ -2006,7 +2154,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "heading_collapsed": true
+ },
"source": [
"### Running the analysis\n",
"\n",
@@ -2017,6 +2167,12 @@
"cell_type": "code",
"execution_count": 17,
"metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:25.092167Z",
+ "start_time": "2021-01-27T02:15:25.087902Z"
+ },
+ "hidden": true,
+ "init_cell": true,
"scrolled": false
},
"outputs": [],
@@ -2036,7 +2192,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "heading_collapsed": true
+ },
"source": [
"### Summarizing data\n",
"\n",
@@ -2046,7 +2204,14 @@
{
"cell_type": "code",
"execution_count": 18,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:25.099832Z",
+ "start_time": "2021-01-27T02:15:25.094021Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [],
"source": [
"def summarize_data():\n",
@@ -2068,17 +2233,26 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "heading_collapsed": true
+ },
"source": [
"### Displaying data\n",
"\n",
- "``generate_table`` will compile the summarized data into a table while writing it into a csv file."
+ "``generate_table(data_summary, gamma)`` will compile the summarized data into a table while writing it into a csv file."
]
},
{
"cell_type": "code",
"execution_count": 19,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:15:25.116050Z",
+ "start_time": "2021-01-27T02:15:25.101666Z"
+ },
+ "hidden": true,
+ "init_cell": true
+ },
"outputs": [],
"source": [
"def generate_table(data_summary, gamma):\n",
@@ -2113,174 +2287,13 @@
},
{
"cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Calculating: mbtr split 1\n",
- "Calculating: mbtr split 2\n",
- "Calculating: mbtr split 3\n",
- "Calculating: mbtr split 4\n",
- "Calculating: mbtr split 5\n",
- "Calculating: mbtr split 6\n",
- "Calculating: soap split 1\n",
- "Calculating: soap split 2\n",
- "Calculating: soap split 3\n",
- "Calculating: soap split 4\n",
- "Calculating: soap split 5\n",
- "Calculating: soap split 6\n",
- "Calculating: ngram split 1\n",
- "Calculating: ngram split 2\n",
- "Calculating: ngram split 3\n",
- "Calculating: ngram split 4\n",
- "Calculating: ngram split 5\n",
- "Calculating: ngram split 6\n",
- "Calculating: atomic split 1\n",
- "Calculating: atomic split 2\n",
- "Calculating: atomic split 3\n",
- "Calculating: atomic split 4\n",
- "Calculating: atomic split 5\n",
- "Calculating: atomic split 6\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 tr th {\n",
- " text-align: left;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr>\n",
- " <th></th>\n",
- " <th>Model</th>\n",
- " <th colspan=\"3\" halign=\"left\">Global</th>\n",
- " <th colspan=\"4\" halign=\"left\">DA validation</th>\n",
- " <th colspan=\"4\" halign=\"left\">DA identification</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th>MAE</th>\n",
- " <th>95AE</th>\n",
- " <th>R</th>\n",
- " <th>cov</th>\n",
- " <th>MAE</th>\n",
- " <th>95AE</th>\n",
- " <th>R</th>\n",
- " <th>cov</th>\n",
- " <th>MAE</th>\n",
- " <th>95AE</th>\n",
- " <th>R</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>mbtr</th>\n",
- " <td>mbtr</td>\n",
- " <td>14.22</td>\n",
- " <td>54.10</td>\n",
- " <td>0.83</td>\n",
- " <td>0.43 (0.02)</td>\n",
- " <td>7.57 (0.70)</td>\n",
- " <td>20.85 (3.31)</td>\n",
- " <td>0.89 (0.01)</td>\n",
- " <td>0.45 (0.00)</td>\n",
- " <td>7.66 (0.14)</td>\n",
- " <td>20.91 (0.65)</td>\n",
- " <td>0.88 (0.00)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>soap</th>\n",
- " <td>soap</td>\n",
- " <td>14.06</td>\n",
- " <td>51.02</td>\n",
- " <td>0.84</td>\n",
- " <td>0.78 (0.01)</td>\n",
- " <td>12.26 (1.35)</td>\n",
- " <td>34.41 (9.47)</td>\n",
- " <td>0.85 (0.01)</td>\n",
- " <td>0.76 (0.01)</td>\n",
- " <td>11.80 (0.32)</td>\n",
- " <td>37.93 (1.90)</td>\n",
- " <td>0.85 (0.00)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>ngram</th>\n",
- " <td>ngram</td>\n",
- " <td>14.67</td>\n",
- " <td>51.10</td>\n",
- " <td>0.83</td>\n",
- " <td>0.54 (0.02)</td>\n",
- " <td>9.29 (0.96)</td>\n",
- " <td>28.42 (2.13)</td>\n",
- " <td>0.87 (0.01)</td>\n",
- " <td>0.54 (0.01)</td>\n",
- " <td>10.45 (0.18)</td>\n",
- " <td>37.75 (0.45)</td>\n",
- " <td>0.86 (0.00)</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>atomic</th>\n",
- " <td>atomic</td>\n",
- " <td>65.52</td>\n",
- " <td>154.49</td>\n",
- " <td>0.24</td>\n",
- " <td>0.85 (0.01)</td>\n",
- " <td>62.45 (6.47)</td>\n",
- " <td>144.37 (10.10)</td>\n",
- " <td>0.25 (0.07)</td>\n",
- " <td>0.85 (0.00)</td>\n",
- " <td>62.91 (1.30)</td>\n",
- " <td>155.19 (2.88)</td>\n",
- " <td>0.26 (0.01)</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Model Global DA validation \\\n",
- " MAE 95AE R cov MAE \n",
- "mbtr mbtr 14.22 54.10 0.83 0.43 (0.02) 7.57 (0.70) \n",
- "soap soap 14.06 51.02 0.84 0.78 (0.01) 12.26 (1.35) \n",
- "ngram ngram 14.67 51.10 0.83 0.54 (0.02) 9.29 (0.96) \n",
- "atomic atomic 65.52 154.49 0.24 0.85 (0.01) 62.45 (6.47) \n",
- "\n",
- " DA identification \\\n",
- " 95AE R cov MAE \n",
- "mbtr 20.85 (3.31) 0.89 (0.01) 0.45 (0.00) 7.66 (0.14) \n",
- "soap 34.41 (9.47) 0.85 (0.01) 0.76 (0.01) 11.80 (0.32) \n",
- "ngram 28.42 (2.13) 0.87 (0.01) 0.54 (0.01) 10.45 (0.18) \n",
- "atomic 144.37 (10.10) 0.25 (0.07) 0.85 (0.00) 62.91 (1.30) \n",
- "\n",
- " \n",
- " 95AE R \n",
- "mbtr 20.91 (0.65) 0.88 (0.00) \n",
- "soap 37.93 (1.90) 0.85 (0.00) \n",
- "ngram 37.75 (0.45) 0.86 (0.00) \n",
- "atomic 155.19 (2.88) 0.26 (0.01) "
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2021-01-27T01:26:29.607Z"
}
- ],
+ },
+ "outputs": [],
"source": [
"gamma = 0.5\n",
"update_gamma(gamma)\n",
@@ -2330,806 +2343,14 @@
},
{
"cell_type": "code",
- "execution_count": 21,
- "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",
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- " 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",
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- " this.rubberband_context = undefined;\n",
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- "\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",
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- "\n",
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- " }\n",
- "\n",
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- " var button = $('<button/>');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
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- " var icon_img = $('<span/>');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('<span/>');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('<span/>');\n",
- "\n",
- " var fmt_picker = $('<select/>');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option);\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('<span class=\"mpl-message\"/>');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'] / mpl.ratio;\n",
- " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
- " var x1 = msg['x1'] / mpl.ratio;\n",
- " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x * mpl.ratio;\n",
- " var y = canvas_pos.y * mpl.ratio;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " var width = fig.canvas.width/mpl.ratio\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var width = this.canvas.width/mpl.ratio\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('<div/>');\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
- " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " // 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"
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\" width=\"639.8333333333334\">"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n"
- ]
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-27T02:06:01.992636Z",
+ "start_time": "2021-01-27T02:06:01.886944Z"
}
- ],
+ },
+ "outputs": [],
"source": [
"cov_error_data = {\n",
" 'mbtr': {'x': [], 'y': [], 'x_err': [], 'y_err': []},\n",
@@ -3172,6 +2393,7 @@
}
],
"metadata": {
+ "celltoolbar": "Initialization Cell",
"kernelspec": {
"display_name": "Python 3",
"language": "python",