From 4622d98e9669af953d6ff5d88a4f5f6212f003f4 Mon Sep 17 00:00:00 2001 From: aliefink Date: Wed, 1 Feb 2023 11:20:39 -0500 Subject: [PATCH 1/4] first commit --- LFPAnalysis/lfp_preprocess_utils.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/LFPAnalysis/lfp_preprocess_utils.py b/LFPAnalysis/lfp_preprocess_utils.py index 469ab92..d9b4c05 100644 --- a/LFPAnalysis/lfp_preprocess_utils.py +++ b/LFPAnalysis/lfp_preprocess_utils.py @@ -504,6 +504,8 @@ def make_mne(load_path=None, save_path=None, elec_data=None, format='edf'): """ Make a mne object from the data and electrode files, and save out the photodiode. Following this step, you can indicate bad electrodes manually. + + to-do: add site specificity """ # 1) load the data: From a416577b2eac4515776b513d04c0e746167f412a Mon Sep 17 00:00:00 2001 From: aliefink Date: Wed, 22 Feb 2023 09:51:35 -0500 Subject: [PATCH 2/4] BLAHHH --- scripts/LFP_preprocessing_step_by_step.ipynb | 1935 +----------------- 1 file changed, 12 insertions(+), 1923 deletions(-) diff --git a/scripts/LFP_preprocessing_step_by_step.ipynb b/scripts/LFP_preprocessing_step_by_step.ipynb index 8de8dc2..dc25bf1 100644 --- a/scripts/LFP_preprocessing_step_by_step.ipynb +++ b/scripts/LFP_preprocessing_step_by_step.ipynb @@ -145,7 +145,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -168,7 +168,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -1292,965 +1292,7 @@ "outputs": [ { "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\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(\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", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_device_pixel_ratio', {\n", - " device_pixel_ratio: fig.ratio,\n", - " });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = 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._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute('tabindex', '0');\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", - " 'z-index: 2;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\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 = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box;' +\n", - " 'pointer-events: none;' +\n", - " 'position: relative;' +\n", - " 'z-index: 0;'\n", - " );\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\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", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box;' +\n", - " 'left: 0;' +\n", - " 'pointer-events: none;' +\n", - " 'position: absolute;' +\n", - " 'top: 0;' +\n", - " 'z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.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", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\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", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " /* This rescales the canvas back to display pixels, so that it\n", - " * appears correct on HiDPI screens. */\n", - " canvas.style.width = width + 'px';\n", - " canvas.style.height = height + 'px';\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 (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " /* User Agent sniffing is bad, but WebKit is busted:\n", - " * https://bugs.webkit.org/show_bug.cgi?id=144526\n", - " * https://bugs.webkit.org/show_bug.cgi?id=181818\n", - " * The worst that happens here is that they get an extra browser\n", - " * selection when dragging, if this check fails to catch them.\n", - " */\n", - " var UA = navigator.userAgent;\n", - " var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n", - " if(isWebKit) {\n", - " return function (event) {\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We\n", - " * want to control all of the cursor setting manually through\n", - " * the 'cursor' event from matplotlib */\n", - " event.preventDefault()\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " } else {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'dblclick',\n", - " on_mouse_event_closure('dblclick')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " canvas_div.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " canvas_div.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\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", - " canvas_div.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\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", - " 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", - " /* 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", - "\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", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\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 = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\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", - " // 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", - "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", - "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], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\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", - " fig.canvas_div.style.cursor = msg['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.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", - " // 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", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\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", - " img.type = 'image/png';\n", - " }\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", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\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", - " 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(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\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(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * https://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", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " // from https://stackoverflow.com/q/1114465\n", - " var boundingRect = this.canvas.getBoundingClientRect();\n", - " var x = (event.clientX - boundingRect.left) * this.ratio;\n", - " var y = (event.clientY - boundingRect.top) * this.ratio;\n", - "\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", - " 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", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, 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", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n", - "\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.binaryType = comm.kernel.ws.binaryType;\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " function updateReadyState(_event) {\n", - " if (comm.kernel.ws) {\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " } else {\n", - " ws.readyState = 3; // Closed state.\n", - " }\n", - " }\n", - " comm.kernel.ws.addEventListener('open', updateReadyState);\n", - " comm.kernel.ws.addEventListener('close', updateReadyState);\n", - " comm.kernel.ws.addEventListener('error', updateReadyState);\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", - " var data = msg['content']['data'];\n", - " if (data['blob'] !== undefined) {\n", - " data = {\n", - " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", - " };\n", - " }\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(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 = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\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;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n", - " '';\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 / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\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 () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\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", - " 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) {\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 = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\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 = 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 = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\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 (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\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", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\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", - "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(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], + "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.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(\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\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.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\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute('tabindex', '0');\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 'z-index: 2;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\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 = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute(\n 'style',\n 'box-sizing: content-box;' +\n 'pointer-events: none;' +\n 'position: relative;' +\n 'z-index: 0;'\n );\n\n this.context = canvas.getContext('2d');\n\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 this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box;' +\n 'left: 0;' +\n 'pointer-events: none;' +\n 'position: absolute;' +\n 'top: 0;' +\n 'z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.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 if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\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 if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n /* This rescales the canvas back to display pixels, so that it\n * appears correct on HiDPI screens. */\n canvas.style.width = width + 'px';\n canvas.style.height = height + 'px';\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 (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n /* User Agent sniffing is bad, but WebKit is busted:\n * https://bugs.webkit.org/show_bug.cgi?id=144526\n * https://bugs.webkit.org/show_bug.cgi?id=181818\n * The worst that happens here is that they get an extra browser\n * selection when dragging, if this check fails to catch them.\n */\n var UA = navigator.userAgent;\n var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n if(isWebKit) {\n return function (event) {\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We\n * want to control all of the cursor setting manually through\n * the 'cursor' event from matplotlib */\n event.preventDefault()\n return fig.mouse_event(event, name);\n };\n } else {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n }\n\n canvas_div.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n canvas_div.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n canvas_div.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n canvas_div.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n canvas_div.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n canvas_div.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\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 canvas_div.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\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 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 /* 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\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 if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\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 = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n fig.canvas_div.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.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\nmpl.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\nmpl.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.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\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 img.type = 'image/png';\n }\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\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\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 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(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\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(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction 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\nmpl.figure.prototype.mouse_event = function (event, name) {\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n // from https://stackoverflow.com/q/1114465\n var boundingRect = this.canvas.getBoundingClientRect();\n var x = (event.clientX - boundingRect.left) * this.ratio;\n var y = (event.clientY - boundingRect.top) * this.ratio;\n\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 return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n // Prevent repeat events\n if (name === 'key_press') {\n if (event.key === this._key) {\n return;\n } else {\n this._key = event.key;\n }\n }\n if (name === 'key_release') {\n this._key = null;\n }\n\n var value = '';\n if (event.ctrlKey && event.key !== 'Control') {\n value += 'ctrl+';\n }\n else if (event.altKey && event.key !== 'Alt') {\n value += 'alt+';\n }\n else if (event.shiftKey && event.key !== 'Shift') {\n value += 'shift+';\n }\n\n value += 'k' + event.key;\n\n this._key_event_extra(event, name);\n\n this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n return false;\n};\n\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar 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.binaryType = comm.kernel.ws.binaryType;\n ws.readyState = comm.kernel.ws.readyState;\n function updateReadyState(_event) {\n if (comm.kernel.ws) {\n ws.readyState = comm.kernel.ws.readyState;\n } else {\n ws.readyState = 3; // Closed state.\n }\n }\n comm.kernel.ws.addEventListener('open', updateReadyState);\n comm.kernel.ws.addEventListener('close', updateReadyState);\n comm.kernel.ws.addEventListener('error', updateReadyState);\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 var data = msg['content']['data'];\n if (data['blob'] !== undefined) {\n data = {\n data: new Blob(msg['buffers'], { type: data['blob'] }),\n };\n }\n // Pass the mpl event to the overridden (by mpl) onmessage function.\n ws.onmessage(data);\n });\n return ws;\n};\n\nmpl.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 = document.getElementById(id);\n var ws_proxy = comm_websocket_adapter(comm);\n\n function ondownload(figure, _format) {\n window.open(figure.canvas.toDataURL());\n }\n\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;\n fig.cell_info = mpl.find_output_cell(\"
\");\n if (!fig.cell_info) {\n console.error('Failed to find cell for figure', id, fig);\n return;\n }\n fig.cell_info[0].output_area.element.on(\n 'cleared',\n { fig: fig },\n fig._remove_fig_handler\n );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n var width = fig.canvas.width / fig.ratio;\n fig.cell_info[0].output_area.element.off(\n 'cleared',\n fig._remove_fig_handler\n );\n fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n '';\n fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n fig.send_message('closing', msg);\n // fig.ws.close()\n};\n\nmpl.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 / this.ratio;\n var dataURL = this.canvas.toDataURL();\n this.cell_info[1]['text/html'] =\n '';\n};\n\nmpl.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 () {\n fig.push_to_output();\n }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'btn-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\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 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) {\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 = '';\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n // Add the status bar.\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 = 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 = '';\n button.addEventListener('click', function (_evt) {\n fig.handle_close(fig, {});\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\nmpl.figure.prototype._remove_fig_handler = function (event) {\n var fig = event.data.fig;\n if (event.target !== this) {\n // Ignore bubbled events from children.\n return;\n }\n fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n // this is important to make the div 'focusable\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 } else {\n // location in version 2\n IPython.keyboard_manager.register_events(el);\n }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\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\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n fig.ondownload(fig, null);\n};\n\nmpl.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.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n", "text/plain": [ "" ] @@ -2272,965 +1314,7 @@ }, { "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\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(\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", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_device_pixel_ratio', {\n", - " device_pixel_ratio: fig.ratio,\n", - " });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = 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._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute('tabindex', '0');\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", - " 'z-index: 2;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\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 = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box;' +\n", - " 'pointer-events: none;' +\n", - " 'position: relative;' +\n", - " 'z-index: 0;'\n", - " );\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\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", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box;' +\n", - " 'left: 0;' +\n", - " 'pointer-events: none;' +\n", - " 'position: absolute;' +\n", - " 'top: 0;' +\n", - " 'z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.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", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\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", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " /* This rescales the canvas back to display pixels, so that it\n", - " * appears correct on HiDPI screens. */\n", - " canvas.style.width = width + 'px';\n", - " canvas.style.height = height + 'px';\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 (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " /* User Agent sniffing is bad, but WebKit is busted:\n", - " * https://bugs.webkit.org/show_bug.cgi?id=144526\n", - " * https://bugs.webkit.org/show_bug.cgi?id=181818\n", - " * The worst that happens here is that they get an extra browser\n", - " * selection when dragging, if this check fails to catch them.\n", - " */\n", - " var UA = navigator.userAgent;\n", - " var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n", - " if(isWebKit) {\n", - " return function (event) {\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We\n", - " * want to control all of the cursor setting manually through\n", - " * the 'cursor' event from matplotlib */\n", - " event.preventDefault()\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " } else {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'dblclick',\n", - " on_mouse_event_closure('dblclick')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " canvas_div.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " canvas_div.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\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", - " canvas_div.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\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", - " 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", - " /* 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", - "\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", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\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 = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\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", - " // 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", - "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", - "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], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\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", - " fig.canvas_div.style.cursor = msg['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.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", - " // 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", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\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", - " img.type = 'image/png';\n", - " }\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", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\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", - " 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(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\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(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * https://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", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " // from https://stackoverflow.com/q/1114465\n", - " var boundingRect = this.canvas.getBoundingClientRect();\n", - " var x = (event.clientX - boundingRect.left) * this.ratio;\n", - " var y = (event.clientY - boundingRect.top) * this.ratio;\n", - "\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", - " 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", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, 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", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n", - "\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.binaryType = comm.kernel.ws.binaryType;\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " function updateReadyState(_event) {\n", - " if (comm.kernel.ws) {\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " } else {\n", - " ws.readyState = 3; // Closed state.\n", - " }\n", - " }\n", - " comm.kernel.ws.addEventListener('open', updateReadyState);\n", - " comm.kernel.ws.addEventListener('close', updateReadyState);\n", - " comm.kernel.ws.addEventListener('error', updateReadyState);\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", - " var data = msg['content']['data'];\n", - " if (data['blob'] !== undefined) {\n", - " data = {\n", - " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", - " };\n", - " }\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(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 = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\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;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n", - " '';\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 / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\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 () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\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", - " 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) {\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 = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\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 = 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 = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\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 (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\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", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\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", - "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(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], + "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.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(\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\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.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\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute('tabindex', '0');\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 'z-index: 2;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\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 = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute(\n 'style',\n 'box-sizing: content-box;' +\n 'pointer-events: none;' +\n 'position: relative;' +\n 'z-index: 0;'\n );\n\n this.context = canvas.getContext('2d');\n\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 this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box;' +\n 'left: 0;' +\n 'pointer-events: none;' +\n 'position: absolute;' +\n 'top: 0;' +\n 'z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.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 if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\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 if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n /* This rescales the canvas back to display pixels, so that it\n * appears correct on HiDPI screens. */\n canvas.style.width = width + 'px';\n canvas.style.height = height + 'px';\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 (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n /* User Agent sniffing is bad, but WebKit is busted:\n * https://bugs.webkit.org/show_bug.cgi?id=144526\n * https://bugs.webkit.org/show_bug.cgi?id=181818\n * The worst that happens here is that they get an extra browser\n * selection when dragging, if this check fails to catch them.\n */\n var UA = navigator.userAgent;\n var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n if(isWebKit) {\n return function (event) {\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We\n * want to control all of the cursor setting manually through\n * the 'cursor' event from matplotlib */\n event.preventDefault()\n return fig.mouse_event(event, name);\n };\n } else {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n }\n\n canvas_div.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n canvas_div.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n canvas_div.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n canvas_div.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n canvas_div.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n canvas_div.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\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 canvas_div.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\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 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 /* 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\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 if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\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 = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n fig.canvas_div.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.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\nmpl.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\nmpl.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.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\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 img.type = 'image/png';\n }\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\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\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 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(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\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(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction 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\nmpl.figure.prototype.mouse_event = function (event, name) {\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n // from https://stackoverflow.com/q/1114465\n var boundingRect = this.canvas.getBoundingClientRect();\n var x = (event.clientX - boundingRect.left) * this.ratio;\n var y = (event.clientY - boundingRect.top) * this.ratio;\n\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 return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n // Prevent repeat events\n if (name === 'key_press') {\n if (event.key === this._key) {\n return;\n } else {\n this._key = event.key;\n }\n }\n if (name === 'key_release') {\n this._key = null;\n }\n\n var value = '';\n if (event.ctrlKey && event.key !== 'Control') {\n value += 'ctrl+';\n }\n else if (event.altKey && event.key !== 'Alt') {\n value += 'alt+';\n }\n else if (event.shiftKey && event.key !== 'Shift') {\n value += 'shift+';\n }\n\n value += 'k' + event.key;\n\n this._key_event_extra(event, name);\n\n this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n return false;\n};\n\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n // Create a \"websocket\"-like object which calls the given IPython comm\n // object with the appropriate methods. 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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.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n", "text/plain": [ "" ] @@ -3936,9 +2020,9 @@ ], "metadata": { "kernelspec": { - "display_name": "lfp_analysis", + "display_name": "Python 3", "language": "python", - "name": "lfpanalysis" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -3950,7 +2034,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.10.5 (v3.10.5:f377153967, Jun 6 2022, 12:36:10) [Clang 13.0.0 (clang-1300.0.29.30)]" + }, + "vscode": { + "interpreter": { + "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" + } } }, "nbformat": 4, From 6ce898021f127b77568ee9328164a56d2ee054e9 Mon Sep 17 00:00:00 2001 From: aliefink Date: Wed, 22 Feb 2023 09:55:12 -0500 Subject: [PATCH 3/4] converging with main --- LFPAnalysis/lfp_preprocess_utils.py | 432 +++++++++++++++++++++++----- 1 file changed, 361 insertions(+), 71 deletions(-) diff --git a/LFPAnalysis/lfp_preprocess_utils.py b/LFPAnalysis/lfp_preprocess_utils.py index d9b4c05..d73e429 100644 --- a/LFPAnalysis/lfp_preprocess_utils.py +++ b/LFPAnalysis/lfp_preprocess_utils.py @@ -16,7 +16,18 @@ def mean_baseline_time(data, baseline, mode='zscore'): """ Meant to mimic the mne baseline for time-series but when the specific baseline period might change across trials, as mne doesn't allow baseline period to vary. - + Parameters + ---------- + data : 2d numpy array + original data + baseline : 2d numpy array + baseline data + mode : str + choice of baseline mode + Returns + ------- + baseline_corrected : 2d numpy array + baselined data """ baseline_mean = baseline.mean(axis=-1) @@ -40,15 +51,25 @@ def mean_baseline_time(data, baseline, mode='zscore'): return baseline_corrected -def zscore_TFR_average(data, baseline): +def zscore_TFR_average(data, baseline, mode='zscore'): """ Meant to mimic the mne baseline (specifically just the zscore for now) for TFR but when the specific baseline period might change across trials. - This presumes you're using trial-averaged data (check dimensions) - TODO: make this more general to any kind of baselining ('mean', etc. ) + Parameters + ---------- + data : 2d numpy array + original data + baseline : 2d numpy array + baseline data + mode : str + choice of baseline mode + Returns + ------- + baseline_corrected : 2d numpy array + baselined data """ @@ -75,15 +96,25 @@ def zscore_TFR_average(data, baseline): return baseline_corrected -def zscore_TFR_across_trials(data, baseline): +def zscore_TFR_across_trials(data, baseline, mode='zscore'): """ Meant to mimic the mne baseline (specifically just the zscore for now) for TFR but when the specific baseline period might change across trials. - This presumes you're using trial-level data (check dimensions) - TODO: make this more general to any kind of baselining ('mean', etc. ) + Parameters + ---------- + data : 2d numpy array + original data + baseline : 2d numpy array + baseline data + mode : str + choice of baseline mode + Returns + ------- + baseline_corrected : 2d numpy array + baselined data """ # Create an array of the mean and standard deviation of the power values across the session @@ -118,22 +149,39 @@ def zscore_TFR_across_trials(data, baseline): def wm_ref(mne_data, elec_data, bad_channels, unmatched_seeg=None, site=None, average=False): """ Define a custom reference using the white matter electrodes. Originated here: https://doi.org/10.1016/j.neuroimage.2015.02.031 - (as in https://www.science.org/doi/10.1126/sciadv.abf4198) Identify all white matter electrodes (based on the electrode names), and make sure they are not bad electrodes (based on the bad channels list). - 1. iterate through each electrode, compute distance to all white matter electrodes 2. find 3 closest wm electrodes, compute amplitude (rms) 3. lowest amplitude electrode = wm reference - Make sure it's the same hemisphere. TODO: implement average reference option, whereby the mean activity across all white matter electrodes is used as a reference [separate per hemi]... see: https://www.sciencedirect.com/science/article/pii/S1053811922005559#bib0349 - TODO: this is SLOW; any vectorization to speed it up or parallelization? - + Parameters + ---------- + mne_data : mne object + non-referenced data stored in an MNE object + elec_data : pandas df + dataframe containing the electrode localization information + bad_channels : list + bad channels + unmatched_seeg : list + list of channels that were not in the edf file + site : str + hospital where the recording took place + average : bool + should we construct an average white matter reference instead of a default? + Returns + ------- + anode_list : list + list of channels to subtract from + cathode_list : list + list of channels to subtract + drop_wm_channels : list + list of white matter channels which were not used for reference and now serve no purpose """ if site == 'MSSM': @@ -239,11 +287,8 @@ def wm_ref(mne_data, elec_data, bad_channels, unmatched_seeg=None, site=None, av def laplacian_ref(mne_data, elec_data, bad_channels, unmatched_seeg=None, site=None): """ Return the cathode list and anode list for mne to use for laplacian referencing. - In this case, the cathode is the average of the surrounding electrodes. If an edge electrode, it's just bipolar. - From here: https://doi.org/10.1016/j.neuroimage.2018.08.020 - """ pass @@ -251,8 +296,22 @@ def laplacian_ref(mne_data, elec_data, bad_channels, unmatched_seeg=None, site=N def bipolar_ref(elec_data, bad_channels, unmatched_seeg=None, site=None): """ Return the cathode list and anode list for mne to use for bipolar referencing. - - TODO: figure out a renaming convention across sites so that this can be generalized. + Parameters + ---------- + elec_data : pandas df + dataframe containing the electrode localization information + bad_channels : list + bad channels + unmatched_seeg : list + list of channels that were not in the edf file + site : str + hospital where the recording took place + Returns + ------- + anode_list : list + list of channels to subtract from + cathode_list : list + list of channels to subtract """ # helper function to perform sort for bipolar electrodes: @@ -303,12 +362,21 @@ def match_elec_names(mne_names, loc_names): The electrode names read out of the edf file do not always match those in the pdf (used for localization). This could be error on the side of the tech who input the labels, or on the side of MNE reading the labels in. Usually there's a mixup between lowercase 'l' and capital 'I'. - This function matches the MNE channel names to those used in the localization. - - params: - mne_names: list of electrode names in the recording data (mne) - loc_names: list of electrode names in the pdf, used for the localization + Parameters + ---------- + mne_names : list + list of electrode names in the recording data (mne) + loc_names : list + list of electrode names in the pdf, used for the localization + Returns + ------- + new_mne_names : list + revised mne names merged across sources + unmatched_names : list + names that do not match (mostly scalp EEG and misc) + unmatched_seeg : list + sEEG channels that do not match (should be rare) """ # strip spaces from mne_names and put in lower case mne_names = [x.replace(" ", "").lower() for x in mne_names] @@ -357,9 +425,18 @@ def detect_bad_elecs(mne_data, sEEG_mapping_dict): https://www-sciencedirect-com.eresources.mssm.edu/science/article/pii/S016502701930278X https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7472198/ https://www.biorxiv.org/content/10.1101/2021.05.14.444176v2.full.pdf - Plot these channels for manual verification. + Parameters + ---------- + mne_data : mne object + mne data to check for bad channels + sEEG_mapping_dict : dict + dict of sEEG channels + Returns + ------- + bad_channels : list + list of bad channels """ # Get the data @@ -373,14 +450,13 @@ def detect_bad_elecs(mne_data, sEEG_mapping_dict): var_chans = np.array([*sEEG_mapping_dict])[var_chans] std_chans = np.array([*sEEG_mapping_dict])[std_chans] + bad_channels = np.unique(kurt_chans.tolist() + var_chans.tolist() + std_chans.tolist()).tolist() # - - return np.unique(kurt_chans.tolist() + var_chans.tolist() + std_chans.tolist()).tolist() + return bad_channels def detect_IEDs(mne_data, peak_thresh=5, closeness_thresh=0.25, width_thresh=0.2): """ This function detects IEDs in the LFP signal automatically. Alternative to manual marking of each ied. - Method 1: 1. Bandpass filter in the [25-80] Hz band. 2. Rectify. @@ -389,14 +465,27 @@ def detect_IEDs(mne_data, peak_thresh=5, closeness_thresh=0.25, width_thresh=0.2 5. Eliminate close IEDs (peaks within 500 ms). 6. Eliminate IEDs that are not present on at least 4 electrodes. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6821283/) - + Parameters + ---------- + mne_data : mne object + mne data to check for bad channels + peak_thresh : float + the peak threshold in amplitude + closeness_thresh : float + the closeness threshold in time + width_thresh : float + the width threshold for IEDs + Returns + ------- + IED_samps_dict : dict + dict with every IED index """ # What type of data is this? Continuous or epoched? if type(mne_data) == mne.epochs.Epochs: data_type = 'epoch' n_times = mne_data._data.shape[-1] - elif type(mne_data) == mne.io.fiff.raw.Raw: + elif type(mne_data) == mne.io.fif.raw.Raw: data_type = 'continuous' n_times = mne_data._data.shape[1] else: @@ -500,37 +589,55 @@ def detect_IEDs(mne_data, peak_thresh=5, closeness_thresh=0.25, width_thresh=0.2 # Below are code that condense the Jupyter notebooks for pre-processing into individual functions. -def make_mne(load_path=None, save_path=None, elec_data=None, format='edf'): +def make_mne(load_path=None, elec_data=None, format='edf', site='MSSM', overwrite=True, **kwargs): """ Make a mne object from the data and electrode files, and save out the photodiode. Following this step, you can indicate bad electrodes manually. - - to-do: add site specificity + + Parameters + ---------- + load_path : str + path to the neural data + elec_data : pandas df + dataframe with all the electrode localization information + format : str + how was this data collected? options: ['edf', 'nlx] + site: str + where was the data collected? options: ['UI', 'MSSM']. + TODO: add site specificity for UC Davis + overwrite: bool + whether to overwrite existing data for this person if it exists + kwargs: dict + dictionary containing lists of different types of channel names + Returns + ------- + mne_data : mne object + mne object """ + # OPTIONAL: Set specific channel names that you might need: + eeg_names = kwargs['eeg_names'] + resp_names = kwargs['resp_names'] + ekg_names = kwargs['ekg_names'] + photodiode_name = kwargs['photodiode_name'] + seeg_names = kwargs['seeg_names'] + + if site == 'MSSM': + if not eeg_names: # If no input, assume the standard EEG montage at MSSM + eeg_names = ['fp1', 'f7', 't3', 't5', 'o1', 'f3', 'c3', 'p3', 'fp2', 'f8', 't4', 't6', 'o2', 'f4', 'c4', 'p4', 'fz', 'cz', 'pz'] + # 1) load the data: if format=='edf': + # MAKE SURE ALL THE EDF CHANNELS HAVE THE SAME SR! See: https://github.com/mne-tools/mne-python/issues/10635 + + if not photodiode_name: + photodiode_name = 'dc1' + # EDF data always comes from MSSM AFAIK. Modify this if that changes. + + # This is a big block of data. Have to load first, then split out the sEEG and photodiode downstream. edf_file = glob(f'{load_path}/*.edf')[0] mne_data = mne.io.read_raw_edf(edf_file, preload=True) - elif format =='nlx': - ncs_files = glob(f'{load_path}/*.ncs') - for chan_path in ncs_files: - # This is leftover from Iowa - let's see how channels are named in MSSM data - chan_name = chan_path.split('/')[-1][:-4] - ch_num = int(chan_name[4:]) - try: - fdata = nlx_utils.load_ncs(chan_path) - except IndexError: - print(f'No data in channel {chan_name}') - continue - lfp.append(fdata['data']) - sr.append(fdata['sampling_rate']) - ch_name.append(str(ch_num)) - unix_time = fdata['time'] - info = mne.create_info(ch_name, np.unique(sr), ch_type) - mne_data = mne.io.RawArray(lfp, info) - - if format=='edf': + # The electrode names read out of the edf file do not always match those # in the pdf (used for localization). This could be error on the side of the tech who input the labels, # or on the side of MNE reading the labels in. Usually there's a mixup between lowercase 'l' and capital 'I'. @@ -541,27 +648,156 @@ def make_mne(load_path=None, save_path=None, elec_data=None, format='edf'): new_name_dict = {x:y for (x,y) in zip(mne_data.ch_names, new_mne_names)} mne_data.rename_channels(new_name_dict) - right_seeg_names = [i for i in mne_data.ch_names if i.startswith('r')] - left_seeg_names = [i for i in mne_data.ch_names if i.startswith('l')] - sEEG_mapping_dict = {f'{x}':'seeg' for x in left_seeg_names+right_seeg_names} + if not seeg_names: + seeg_names = [i for i in mne_data.ch_names if ((i.startswith('l')) | (i.startswith('r')))] + sEEG_mapping_dict = {f'{x}':'seeg' for x in seeg_names} + + mne_data.set_channel_types(sEEG_mapping_dict) + + mne_data.info['line_freq'] = 60 + # Notch out 60 Hz noise and harmonics + mne_data.notch_filter(freqs=(60, 120, 180, 240)) - mne_data.set_channel_types(sEEG_mapping_dict) + # Save out the photodiode channel separately + mne_data.save(f'{load_path}/photodiode.fif', picks=photodiode_name, overwrite=overwrite) + # drop EEG and EKG channels + drop_chans = list(set(mne_data.ch_names)^set(seeg_names)) + mne_data.drop_channels(drop_chans) - # 3) Identify line noise - mne_data.info['line_freq'] = 60 + bads = detect_bad_elecs(mne_data, sEEG_mapping_dict) + mne_data.info['bads'] = bads + + mne_data.save(f'{load_path}/lfp_data.fif', picks=seeg_names, overwrite=overwrite) + + elif format =='nlx': + # This is a pre-split data. Have to specifically load the sEEG and photodiode individually. + signals = [] + srs = [] + ch_name = [] + ch_type = [] + if site == 'MSSM': + # per Shawn, MSSM data seems to sometime have a "_0000.ncs" to "_9999.ncs" appended to the end of real data + pattern = re.compile(r"_\d{4}\.ncs") # regex pattern to match "_0000.ncs" to "_9999.ncs" + ncs_files = [x for x in glob(f'{load_path}/*.ncs') if re.search(pattern, x)] + # just in case this changes in the future: + if len(ncs_files) == 0: + ncs_files = glob(f'{load_path}/*.ncs') + if not seeg_names: + seeg_names = [x.split('/')[-1].replace('.ncs','') for x in glob(f'{load_path}/[R,L]*.ncs')] + else: + if not seeg_names: + seeg_names = [x.split('/')[-1].replace('.ncs','').split('_')[0] for x in glob(f'{load_path}/[R,L]*.ncs') if re.search(pattern, x)] + elif site == 'UI': + # here, the filenames are not informative. We have to get subject-specific information from the experimenter + ncs_files = glob(f'{load_path}/LFP*.ncs') + if not seeg_names: + raise ValueError('no seeg channels specified') + else: + # standardize to lower + seeg_names = [x.lower() for x in seeg_names] + sEEG_mapping_dict = {f'{x}':'seeg' for x in seeg_names} - # Notch out 60 Hz noise and harmonics - mne_data.notch_filter(freqs=(60, 120, 180, 240)) + for chan_path in ncs_files: + chan_name = chan_path.split('/')[-1].replace('.ncs','') + # strip the file type off the end if needed + if '_' in chan_name: + chan_name = chan_name.split('_')[0] + try: + fdata = nlx_utils.load_ncs(chan_path) + except IndexError: + print(f'No data in channel {chan_name}') + continue + # surface eeg + if eeg_names: + eeg_names = [x.lower() for x in eeg_names] + if chan_name.lower() in eeg_names: + ch_type.append('eeg') + if resp_names: + resp_names = [x.lower() for x in resp_names] + if chan_name.lower() in resp_names: + ch_type.append('bio') + if ekg_names: + ekg_names = [x.lower() for x in ekg_names] + if chan_name.lower() in ekg_names: + ch_type.append('ecg') + if seeg_names: + if chan_name.lower() in seeg_names: + ch_type.append('seeg') + elif chan_name.lower()[0] == 'u': + # microwire data + ch_type.append('seeg') + signals.append(fdata['data']) + srs.append(fdata['sampling_rate']) + ch_name.append(chan_name) + if len(ch_type) < len(ch_name): + ch_type.append('misc') + print(f'Unidentified data type in {chan_name}') + + if np.unique(srs).shape[0] == 1: + # all the sampling rates match: + info = mne.create_info(ch_name, np.unique(srs), ch_type) + mne_data = mne.io.RawArray(signals, info) + else: + ## Now we have to account for differing sampling rates. This will only really happen in the case of data where ANALOGUE channels + ## are recorded at a much higher sampling rate, or with micro channels. Find the lowest sample rate, and downsample everything to that. + ## I generally don't like this but it should be OK. Make sure that you identify synchronization times AFTER downsampling the analogue channel, and not before: + ## https://gist.github.com/larsoner/01642cb3789992fbca59 + + target_sr = np.min(srs) + mne_data_resampled = [] + + for sr in np.unique(srs): + ch_ix = np.where(srs==sr)[0].astype(int) + info = mne.create_info([x for ix, x in enumerate(ch_name) if ix in ch_ix], sr, [x for ix, x in enumerate(ch_type) if ix in ch_ix]) + mne_data_temp = mne.io.RawArray([x for ix, x in enumerate(signals) if ix in ch_ix], info) + if sr != target_sr: + # resample down to one sample rate + mne_data_temp.resample(sfreq=target_sr, npad='auto', n_jobs=-1) + mne_data_resampled.append(mne_data_temp) + else: + mne_data = mne_data_temp + + #Because of the resampling, the end timings might not match perfectly:https://github.com/mne-tools/mne-python/issues/8257 + if mne_data_resampled[0].tmax > mne_data.tmax: + mne_data_resampled[0].crop(tmin=0, tmax=mne_data.tmax) + elif mne_data_resampled[0].tmax < mne_data.tmax: + mne_data.crop(tmin=0, tmax=mne_data_resampled[0].tmax) + + mne_data.add_channels(mne_data_resampled) + + mne_data.info['line_freq'] = 60 + # Notch out 60 Hz noise and harmonics + mne_data.notch_filter(freqs=(60, 120, 180, 240)) + + new_name_dict = {x:x.replace(" ", "").lower() for x in mne_data.ch_names} + mne_data.rename_channels(new_name_dict) + + # Save out the photodiode channel separately + if not photodiode_name: + raise ValueError('no photodiode channel specified') + else: + mne_data.save(f'{load_path}/photodiode.fif', picks=photodiode_name, overwrite=overwrite) + + # Save out the respiration channels separately + if resp_names: + mne_data.save(f'{load_path}/respiration_data.fif', picks=resp_names, overwrite=overwrite) + + # Save out the EEG channels separately + if eeg_names: + mne_data.save(f'{load_path}/scalp_eeg_data.fif', picks=eeg_names, overwrite=overwrite) - # 4) Save out the photodiode channel separately - mne_data.save(f'{load_path}/photodiode.fif', picks='dc1', overwrite=True) + # Save out the EEG channels separately + if ekg_names: + mne_data.save(f'{load_path}/ekg_data.fif', picks=ekg_names, overwrite=overwrite) - # 5) Clean up the MNE data + drop_chans = list(set([x.lower() for x in mne_data.ch_names])^set(seeg_names)) + mne_data.drop_channels(drop_chans) - bads = detect_bad_elecs(mne_data, sEEG_mapping_dict) + bads = detect_bad_elecs(mne_data, sEEG_mapping_dict) + mne_data.info['bads'] = bads - mne_data.info['bads'] = bads + mne_data.save(f'{load_path}/lfp_data.fif', picks=seeg_names, overwrite=overwrite) return mne_data @@ -569,6 +805,20 @@ def make_mne(load_path=None, save_path=None, elec_data=None, format='edf'): def ref_mne(mne_data=None, elec_data=None, method='wm', site='MSSM'): """ Following this step, you can indicate IEDs manually. + Parameters + ---------- + mne_data : mne object + mne object + elec_data : pandas df + dataframe with all the electrode localization information + method : str + how should we reference the data ['wm', 'bipolar'] + site : str + where was this data collected? Options: ['MSSM', 'UI', 'Davis'] + Returns + ------- + mne_data_reref : mne object + mne object with re-referenced data """ # Sometimes, there's electrodes on the pdf that are NOT in the MNE data structure... let's identify those as well. @@ -599,15 +849,53 @@ def ref_mne(mne_data=None, elec_data=None, method='wm', site='MSSM'): return mne_data_reref -def make_epochs(load_path=None, save_path=None, elec_data=None, slope=None, offset=None, behav_name=None, -behav_times=None, -baseline_times=None, baseline_dur=0.5, fixed_baseline=[-1.0, 0], +def make_epochs(load_path=None, elec_data=None, slope=None, offset=None, behav_name=None, behav_times=None, +baseline_times=None, baseline_dur=0.5, fixed_baseline=(-1.0, 0), buf_s=1.0, pre_s=-1.0, post_s=1.5, downsamp_factor=2, IED_args=None): """ - + TODO: allow for a dict of pre and post times so they can vary across evs + behav_times: dict with format {'event_name': np.array([times])} baseline_times: dict with format {'event_name': np.array([times])} IED_args: dict with format {'peak_thresh':5, 'closeness_thresh':0.5, 'width_thresh':0.2} + Parameters + ---------- + load_path : str + path to the re-referenced neural data + elec_data : pandas df + dataframe with all the electrode localization information + slope : float + slope used for syncing behavioral and neural data + offset : float + offset used for syncing behavioral and neural data + behav_name : str + what event are we epoching to? + behav_times : dict + format {'event_name': np.array([times])} + baseline_times : dict + format {'event_name': np.array([times])} + method : str + how should we reference the data ['wm', 'bipolar'] + site : str + where was this data collected? Options: ['MSSM', 'UI', 'Davis'] + baseline_dur : float + only to be used if baseline_times is not None + fixed_baseline : tuple + time to use for baselining , only to be used if baseline_times is None + buf_s : float + time to add as buffer in epochs + pre_s : float + time to add before baseline event if baseline_times is not None + post_d : float + time to add after baseline event if baseline_times is not None + downsamp_factor : float + factor by which to downsample the data + IED_args: dict + format {'peak_thresh':5, 'closeness_thresh':0.5, 'width_thresh':0.2} + Returns + ------- + ev_epochs : mne object + mne Epoch object with re-referenced data """ # Load the data mne_data_reref = mne.io.read_raw_fif(load_path, preload=True) @@ -695,11 +983,13 @@ def make_epochs(load_path=None, save_path=None, elec_data=None, slope=None, offs for ch in list(IED_times_s.keys()): for ev, val in IED_times_s[ch].items(): - event_metadata[ch].loc[ev] = val + if len(val) > 1: + event_metadata[ch].loc[ev] = val + else: + if ~np.isnan(val): + event_metadata[ch].loc[ev] = val ev_epochs.metadata = event_metadata # event_metadata - return ev_epochs - - + return ev_epochs \ No newline at end of file From 0c68324b1f8665de0f6882a32e8638478a624fc4 Mon Sep 17 00:00:00 2001 From: aliefink Date: Wed, 22 Feb 2023 10:04:43 -0500 Subject: [PATCH 4/4] commit to converge to main 2 --- LFPAnalysis/EDF_analysis_step_by_step.ipynb | 14228 +++++++++++++ .../EDF_preprocessing_step_by_step.ipynb | 3989 ++++ LFPAnalysis/NLX_analysis_step_by_step.ipynb | 14228 +++++++++++++ .../NLX_preprocessing_step_by_step.ipynb | 4948 +++++ ...LFP_analysis_step_by_step-checkpoint.ipynb | 17613 ---------------- ...reprocessing_step_by_step-checkpoint.ipynb | 4835 ----- 6 files changed, 37393 insertions(+), 22448 deletions(-) create mode 100644 LFPAnalysis/EDF_analysis_step_by_step.ipynb create mode 100644 LFPAnalysis/EDF_preprocessing_step_by_step.ipynb create mode 100644 LFPAnalysis/NLX_analysis_step_by_step.ipynb create mode 100644 LFPAnalysis/NLX_preprocessing_step_by_step.ipynb delete mode 100644 scripts/.ipynb_checkpoints/LFP_analysis_step_by_step-checkpoint.ipynb delete mode 100644 scripts/.ipynb_checkpoints/LFP_preprocessing_step_by_step-checkpoint.ipynb diff --git a/LFPAnalysis/EDF_analysis_step_by_step.ipynb b/LFPAnalysis/EDF_analysis_step_by_step.ipynb new file mode 100644 index 0000000..43d24a4 --- /dev/null +++ b/LFPAnalysis/EDF_analysis_step_by_step.ipynb @@ -0,0 +1,14228 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example analysis notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, we will go through the basics of: \n", + "\n", + "1. Load pre-processed data\n", + "\n", + "2. Synchronize to behavioral data\n", + "\n", + "3. Process the behavioral data \n", + "\n", + "4. Create epochs\n", + "\n", + "5. Downsample the epoched data \n", + "\n", + "6. Look for IEDs (**automatic** or manual) \n", + "\n", + "7. Save the epoched data \n", + "\n", + "8. Compute TFR and drop IED trials if you care about low frequencies. \n", + "\n", + "9. Plot\n", + "\n", + "10. Example of statistical analyses (TODO)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The main point of this notebook is to make epoched data **CORRECTLY** as this will be the crux of many, many analyses in the future, and to provide tools for IED detection in epochs (both automatic and manual)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On IEDS: \n", + "\n", + "- Great images of interictal and ictal spiking: \n", + "\n", + " 1. https://pubmed.ncbi.nlm.nih.gov/32007920/\n", + " \n", + " 2. https://www.sciencedirect.com/science/article/pii/S1059131119304200\n", + "\n", + "- More on IEDs: \n", + "\n", + " 1. https://www.frontiersin.org/articles/10.3389/fnhum.2020.00044/full\n", + "\n", + " 2. Why removing trials with IEDs might be important for generalizable conclusions about behavior: \n", + " \n", + " https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3770206/\n", + " \n", + " https://academic.oup.com/cercorcomms/article/2/2/tgab019/6179205\n", + " \n", + "- Especially for low frequencies (sub-gamma) this is important! \"Low-frequency power remained elevated for several seconds following the IED... Low-frequency power was elevated and high-frequency power suppressed in pre-IED epochs compared with non-IED epochs.\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%reload_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import mne\n", + "from glob import glob\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.backends.backend_pdf import PdfPages\n", + "import seaborn as sns\n", + "import scipy\n", + "from scipy.stats import zscore, linregress\n", + "import pandas as pd\n", + "from mne.preprocessing.bads import _find_outliers\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('/hpc/users/qasims01/resources/LFPAnalysis')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from LFPAnalysis import lfp_preprocess_utils, sync_utils, analysis_utils" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading pre-processed data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's a good idea to setup a sensible directory structure like below. Note that all my data lives on '/sc/arion' which is Minerva. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "base_dir = '/sc/arion' # this is the root directory for most un-archived data and results \n", + "\n", + "load_dir = f'{base_dir}/work/qasims01/MemoryBanditData/EMU/Subjects/MS007' # save intermediate results in the 'work' directory\n", + " \n", + "# I have saved most of my raw data in the 'projects directory'\n", + "behav_dir = f'{base_dir}/projects/guLab/Salman/EMU/MS007/behav/Day1'\n", + "neural_dir = f'{base_dir}/projects/guLab/Salman/EMU/MS007/neural/Day1'\n", + "anat_dir = f'{base_dir}/projects/guLab/Salman/EMU/MS007/anat'\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's load in the re-referenced data, the photodiode data for synchronization, and the electrode dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Opening raw data file /sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/wm_ref_ieeg.fif...\n", + " Range : 0 ... 1867007 = 0.000 ... 1823.249 secs\n", + "Ready.\n", + "Reading 0 ... 1867007 = 0.000 ... 1823.249 secs...\n", + "Opening raw data file /sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/photodiode.fif...\n", + "Isotrak not found\n", + " Range : 0 ... 1867007 = 0.000 ... 1823.249 secs\n", + "Ready.\n", + "Reading 0 ... 1867007 = 0.000 ... 1823.249 secs...\n" + ] + } + ], + "source": [ + "wm_ref_data = mne.io.read_raw_fif(f'{load_dir}/wm_ref_ieeg.fif', preload=True)\n", + "anode_list = [x.split('-')[0] for x in wm_ref_data.ch_names]\n", + "photodiode = mne.io.read_raw_fif(f'{load_dir}/photodiode.fif', preload=True)\n", + "\n", + "csv_files = glob(f'{anat_dir}/*labels.csv')\n", + "elec_locs = pd.read_csv(csv_files[0])\n", + "\n", + "# Sometimes there's extra columns with no entries: \n", + "elec_locs = elec_locs[elec_locs.columns.drop(list(elec_locs.filter(regex='Unnamed')))]\n", + "\n", + "elec_df = elec_locs[elec_locs.label.str.lower().isin(anode_list)]\n", + "elec_df['label'] = wm_ref_data.ch_names\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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labelBN246labelxyzmni_xmni_ymni_zgmNMMAnatAnatMacroBN246YBA_1Manual ExaminationNotes
0lacas1-lmolf1A13_L-6.5496541.77678-7.038720-6.1496934.97941-12.45010GrayLeft ACgG anterior cingulate gyrusArea s32L Mid Orbital GyrusL OrGLeft frontal pole 1 CNaNNaN
1lacas10-lacas9A9l_L-9.7467349.7723337.287610-11.1581045.8798038.08775GrayLeft Cerebral White MatterUnknownL Superior Frontal GyrusL SFGUnknownLeft superior frontal gyrus 2 CNaN
3lacas12-lacas9A9l_L-10.1464050.9716746.871680-11.5214047.7469349.07517GrayLeft SFG superior frontal gyrusUnknownL Superior Frontal GyrusL SFGLeft superior frontal gyrus 3 CNaNNaN
4lacas2-lmolf1A32sg_L-6.9492842.57633-2.246680-6.8262236.24643-7.12713GrayLeft ACgG anterior cingulate gyrusArea s32L ACCL CGLeft cingulate gyrus DNaNNaN
5lacas3-lmolf1A32sg_L-7.3489243.375892.545354-7.4178637.55496-1.76236GrayLeft ACgG anterior cingulate gyrusUnknownL ACCL CGLeft cingulate gyrus ENaNNaN
\n", + "
" + ], + "text/plain": [ + " label BN246label x y z mni_x \\\n", + "0 lacas1-lmolf1 A13_L -6.54965 41.77678 -7.038720 -6.14969 \n", + "1 lacas10-lacas9 A9l_L -9.74673 49.77233 37.287610 -11.15810 \n", + "3 lacas12-lacas9 A9l_L -10.14640 50.97167 46.871680 -11.52140 \n", + "4 lacas2-lmolf1 A32sg_L -6.94928 42.57633 -2.246680 -6.82622 \n", + "5 lacas3-lmolf1 A32sg_L -7.34892 43.37589 2.545354 -7.41786 \n", + "\n", + " mni_y mni_z gm NMM Anat \\\n", + "0 34.97941 -12.45010 Gray Left ACgG anterior cingulate gyrus Area s32 \n", + "1 45.87980 38.08775 Gray Left Cerebral White Matter Unknown \n", + "3 47.74693 49.07517 Gray Left SFG superior frontal gyrus Unknown \n", + "4 36.24643 -7.12713 Gray Left ACgG anterior cingulate gyrus Area s32 \n", + "5 37.55496 -1.76236 Gray Left ACgG anterior cingulate gyrus Unknown \n", + "\n", + " AnatMacro BN246 YBA_1 \\\n", + "0 L Mid Orbital Gyrus L OrG Left frontal pole 1 C \n", + "1 L Superior Frontal Gyrus L SFG Unknown \n", + "3 L Superior Frontal Gyrus L SFG Left superior frontal gyrus 3 C \n", + "4 L ACC L CG Left cingulate gyrus D \n", + "5 L ACC L CG Left cingulate gyrus E \n", + "\n", + " Manual Examination Notes \n", + "0 NaN NaN \n", + "1 Left superior frontal gyrus 2 C NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "elec_df.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following commented code can be used for plotting and removing IEDs from the continuous data if you decide to do detection with the continuous data " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filtering raw data in 1 contiguous segment\n", + "Setting up band-pass filter from 25 - 80 Hz\n", + "\n", + "FIR filter parameters\n", + "---------------------\n", + "Designing a one-pass, zero-phase, non-causal bandpass filter:\n", + "- Windowed time-domain design (firwin) method\n", + "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n", + "- Lower passband edge: 25.00\n", + "- Lower transition bandwidth: 6.25 Hz (-6 dB cutoff frequency: 21.88 Hz)\n", + "- Upper passband edge: 80.00 Hz\n", + "- Upper transition bandwidth: 20.00 Hz (-6 dB cutoff frequency: 90.00 Hz)\n", + "- Filter length: 541 samples (0.528 sec)\n", + "\n" + ] + } + ], + "source": [ + "IED_times_s = lfp_preprocess_utils.detect_IEDs(wm_ref_data, peak_thresh=4, closeness_thresh=0.25, width_thresh=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'lacas1-lmolf1': array([ 52.82617188, 254.41015625, 424.97070312, 470.578125 ,\n", + " 519.04980469, 555.97167969, 569.50488281, 602.07421875,\n", + " 602.33496094, 699.81347656, 838.47363281, 1017.45996094,\n", + " 1026.04003906, 1042.21777344, 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549.20800781, 570.03222656, 570.61328125, 570.87988281,\n", + " 594.24023438, 601.96484375, 602.85644531, 603.33398438,\n", + " 627.13085938, 662.97753906, 697.64257812, 699.41210938,\n", + " 782.22070312, 782.51953125, 874.671875 , 895.59863281,\n", + " 954.70800781, 1024.96972656, 1042.16992188, 1141.60742188,\n", + " 1147.34277344, 1147.74902344, 1148.02832031, 1230.63378906,\n", + " 1236.91796875, 1237.23144531, 1237.50390625, 1248.54394531,\n", + " 1334.75097656, 1335.03515625, 1336.07910156, 1336.50878906,\n", + " 1360.92578125, 1361.2890625 , 1361.60449219, 1442.57226562,\n", + " 1500.95800781, 1501.94921875, 1505.35058594, 1583.18261719,\n", + " 1612.1796875 , 1616.74023438, 1617.00195312, 1617.31152344,\n", + " 1698.28808594, 1699.30175781, 1739.36621094, 1816.76074219]),\n", + " 'lpcip2-lpcip5': array([ 602.87109375, 1237.50976562]),\n", + " 'racas1-rmolf4': array([ 52.41015625, 166.14746094, 280.11914062, 424.81542969,\n", + " 564.48339844, 1218.48339844, 1335.33691406, 1582.76953125]),\n", + " 'racas11-racas10': array([ 601.81542969, 611.53125 , 1583.42578125]),\n", + " 'racas2-rmolf4': array([ 52.4140625 , 377.12011719, 669.61914062, 1016.74121094,\n", + " 1314.32910156, 1582.65820312]),\n", + " 'racas4-racas6': array([ 52.53613281, 166.04394531, 254.00488281, 377.22851562,\n", + " 550.32128906, 700.01464844, 1016.70800781, 1161.14453125,\n", + " 1335.46582031, 1517.140625 ]),\n", + " 'racas7-racas5': array([ 52.70214844, 53.22070312, 71.04980469, 72.29199219,\n", + " 73.43359375, 76.7421875 , 166.078125 , 253.9375 ,\n", + " 254.29101562, 316.80957031, 364.72851562, 424.83203125,\n", + " 432.3203125 , 470.87792969, 519.00097656, 519.27050781,\n", + " 545.21972656, 555.82421875, 569.28222656, 576.26367188,\n", + " 601.55957031, 601.90136719, 669.93164062, 699.68164062,\n", + " 700.04785156, 776.015625 , 857.59570312, 857.93457031,\n", + " 975.35253906, 1016.515625 , 1017.4921875 , 1100.66015625,\n", + " 1141.54296875, 1147.27050781, 1147.64453125, 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669.72949219, 1016.765625 ,\n", + " 1582.76074219, 1697.8984375 ]),\n", + " 'raimm3-rmolf8': array([ 52.63476562, 669.62597656, 874.67578125, 1016.89941406,\n", + " 1236.8515625 , 1582.76367188]),\n", + " 'raimm4-rmolf8': array([ 52.44726562, 377.02148438, 669.61230469, 874.6953125 ,\n", + " 1016.88867188, 1022.30371094, 1236.86132812, 1655.23535156,\n", + " 1817.47753906]),\n", + " 'raimm6-rmolf8': array([ 377.02441406, 523.96191406, 874.65429688, 1016.890625 ,\n", + " 1236.86035156, 1582.72949219]),\n", + " 'raimm7-racas9': array([], dtype=float64),\n", + " 'raimm8-racas10': array([ 52.98046875, 311.42578125, 611.453125 , 1016.68554688,\n", + " 1017.54492188, 1450.20117188, 1655.77929688]),\n", + " 'rcmfo1-rcmfo5': array([], dtype=float64),\n", + " 'rcmfo10-rcmfo5': array([ 164.45996094, 669.65039062, 681.01464844, 1022.31542969]),\n", + " 'rcmfo11-rcmfo5': array([ 6.70117188, 164.46386719, 218.80859375, 426.58007812,\n", + " 562.21972656, 615.81738281, 669.64941406, 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461.60351562,\n", + " 471.17480469, 485.58496094, 502.80566406, 503.78710938,\n", + " 505.10449219, 507.42089844, 518.97949219, 524.0390625 ,\n", + " 544.83007812, 555.4609375 , 555.76074219, 569.15625 ,\n", + " 569.59667969, 579.20507812, 597.43261719, 662.43554688,\n", + " 697.72265625, 699.96484375, 733.00585938, 772.09082031,\n", + " 838.578125 , 842.68066406, 851.71777344, 857.38085938,\n", + " 857.63867188, 874.20117188, 883.52734375, 964.85253906,\n", + " 973.3515625 , 1016.82617188, 1025.67089844, 1056.44238281,\n", + " 1084.73339844, 1135.28222656, 1147.02636719, 1151.18847656,\n", + " 1209.78125 , 1218.53027344, 1264.07226562, 1296.50585938,\n", + " 1338.86328125, 1344.93066406, 1437.90332031, 1442.78613281,\n", + " 1489.19335938, 1492.01464844, 1500.53515625, 1517.91113281,\n", + " 1560.52929688, 1574.47753906, 1582.54199219, 1582.81445312,\n", + " 1696.64941406, 1706.75976562, 1708.47949219, 1738.38769531,\n", + " 1757.27246094, 1757.96484375, 1812.86621094, 1816.125 ,\n", + " 1816.48144531, 1823.05566406])}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IED_times_s" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'lacas1-lmolf1': array([ 52.82617188, 254.41015625, 320.65722656, 424.97070312,\n", + " 470.578125 , 519.04980469, 555.97167969, 564.48046875,\n", + " 569.50488281, 602.07421875, 699.81347656, 838.47363281,\n", + " 1017.45996094, 1026.04003906, 1042.21777344, 1047.37304688,\n", + " 1147.35839844, 1335.74804688, 1360.90527344, 1501.69726562,\n", + " 1577.35058594, 1697.70019531]),\n", + " 'lacas10-lacas9': array([ 490.05566406, 753.46679688, 770.76074219, 1228.51757812,\n", + " 1306.94042969, 1501.10253906, 1586.54296875, 1697.73242188]),\n", + " 'lacas12-lacas9': array([ 268.70898438, 481.61425781, 1142.23828125, 1521.73632812]),\n", + " 'lacas2-lmolf1': array([ 52.83105469, 254.08398438, 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1815.69335938, 1821.08105469]),\n", + " 'lhplt2-laglt5': array([ 602.81054688, 1147.78125 , 1361.35058594, 1442.80859375,\n", + " 1698.21679688]),\n", + " 'lhplt3-laglt4': array([ 602.80957031, 1091.39648438, 1147.77832031, 1262.54003906,\n", + " 1361.35058594, 1678.37402344, 1682.74609375, 1688.43457031,\n", + " 1694.09863281, 1698.21875 , 1754.70117188, 1771.80566406,\n", + " 1777.21386719, 1786.47167969, 1787.31738281]),\n", + " 'lhplt4-lhplt6': array([ 52.84375 , 254.19433594, 424.9140625 , 597.58984375,\n", + " 1026.23730469, 1141.88378906, 1147.27539062, 1230.11230469,\n", + " 1336.46582031, 1361.32617188, 1361.86425781, 1442.54101562,\n", + " 1500.97460938, 1501.50292969, 1502.39550781, 1583.01074219,\n", + " 1616.95410156, 1685.97949219, 1697.33007812, 1698.24316406,\n", + " 1739.32519531, 1821.07519531, 1821.59375 , 1823.24121094]),\n", + " 'lhplt9-lhplt8': array([ 53.81640625, 54.40722656, 227.29589844, 254.21972656,\n", + " 364.68554688, 425.31640625, 470.80273438, 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1739.28515625, 1815.72363281,\n", + " 1816.62792969, 1821.08105469]),\n", + " 'lpcip1-lpcip4': array([ 320.63183594, 481.30175781, 507.42089844, 524.609375 ,\n", + " 531. , 602.08300781, 652.25878906, 760.60351562,\n", + " 1003.70019531, 1147.77636719, 1207.45214844, 1237.24804688,\n", + " 1270.80078125, 1274.46582031, 1281.21484375, 1361.59960938,\n", + " 1442.97070312, 1461.17382812, 1500.99316406, 1583.18847656,\n", + " 1697.46679688, 1698.00390625, 1767.00585938, 1816.57617188]),\n", + " 'lpcip11-lpcip10': array([ 52.80371094, 182.16503906, 183.06738281, 186.49316406,\n", + " 227.20507812, 254.03515625, 258.16699219, 321.1328125 ,\n", + " 364.515625 , 391.1796875 , 470.52539062, 520.73925781,\n", + " 524.72167969, 544.90527344, 549.20800781, 570.03222656,\n", + " 570.87988281, 594.24023438, 601.96484375, 602.85644531,\n", + " 627.13085938, 662.97753906, 697.64257812, 782.51953125,\n", + " 888.328125 , 895.59863281, 954.70800781, 1042.16992188,\n", + " 1141.60742188, 1147.34277344, 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1471.80371094, 1582.65820312]),\n", + " 'racas4-racas6': array([ 52.53613281, 71.04296875, 166.04394531, 254.00488281,\n", + " 377.22851562, 424.79589844, 470.3671875 , 550.32128906,\n", + " 700.01464844, 838.23730469, 1016.70800781, 1042.02148438,\n", + " 1161.14453125, 1230.18359375, 1237.05273438, 1335.55273438,\n", + " 1357.95019531, 1465.44238281, 1501.79394531, 1517.140625 ,\n", + " 1678.37792969]),\n", + " 'racas7-racas5': array([ 53.22070312, 71.04980469, 72.29199219, 73.43359375,\n", + " 76.7421875 , 122.15625 , 166.078125 , 254.29101562,\n", + " 316.80957031, 334.58691406, 364.72851562, 424.83203125,\n", + " 470.87792969, 519.00097656, 545.21972656, 555.82421875,\n", + " 569.28222656, 576.26367188, 601.55957031, 669.93164062,\n", + " 700.04785156, 768.04199219, 776.015625 , 829.17285156,\n", + " 838.37695312, 857.93457031, 872.09960938, 975.35253906,\n", + " 1016.515625 , 1017.4921875 , 1100.66015625, 1141.54296875,\n", + " 1147.27050781, 1148.08203125, 1218.53222656, 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772.09082031, 775.77050781, 801.41699219,\n", + " 803.26074219, 857.38183594, 1084.734375 , 1085.60253906,\n", + " 1208.86230469, 1218.53027344, 1437.83398438, 1500.38476562,\n", + " 1823.05371094]),\n", + " 'rpcip2-rpcip5': array([1419.05957031]),\n", + " 'rpcip7-rpcip6': array([ 555.38574219, 1430.16015625, 1433.45703125, 1529.92871094,\n", + " 1536.38671875, 1769.67773438]),\n", + " 'rpcip9-rpcip8': array([ 2.76953125, 29.24707031, 52.390625 , 81.61230469,\n", + " 98.76953125, 102.1953125 , 114.55078125, 121.9140625 ,\n", + " 123.88378906, 138.21875 , 218.921875 , 226.859375 ,\n", + " 277.92871094, 278.86621094, 302.14746094, 322.03710938,\n", + " 332.32128906, 334.27441406, 353.45410156, 377.234375 ,\n", + " 407.39160156, 412.0234375 , 424.46777344, 432.7578125 ,\n", + " 438.35449219, 447.33789062, 471.17480469, 485.58496094,\n", + " 502.80566406, 503.78710938, 505.10449219, 507.42089844,\n", + " 518.97949219, 524.0390625 , 544.83007812, 555.76074219,\n", + " 569.59667969, 579.20507812, 597.43261719, 662.43554688,\n", + " 669.63769531, 697.72265625, 699.96484375, 733.00585938,\n", + " 772.09082031, 838.578125 , 851.71777344, 857.38085938,\n", + " 883.52734375, 961.71484375, 964.85253906, 978.58300781,\n", + " 979.80078125, 1016.82617188, 1056.44238281, 1084.73339844,\n", + " 1135.28222656, 1147.02636719, 1151.18847656, 1191.18457031,\n", + " 1218.53027344, 1264.07226562, 1296.50585938, 1338.86328125,\n", + " 1343.64453125, 1344.93066406, 1437.90332031, 1442.78613281,\n", + " 1489.19335938, 1492.01464844, 1500.53515625, 1517.91113281,\n", + " 1555.5625 , 1560.52929688, 1574.47753906, 1582.54199219,\n", + " 1673.85351562, 1696.64941406, 1706.75976562, 1708.47949219,\n", + " 1738.38769531, 1757.27246094, 1757.96484375, 1816.125 ,\n", + " 1823.05566406])}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IED_times_s" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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Mf/3rXzvtVDxnIWEqIO3KbtiwAbt27ZJNLJZOq3Llu1x5eTl27twpMtd99913su+lI6HWvPjii4rt8wWUhGk2mxXz0/Iu6+DBgwHYBiLwAAp/h4SpgNIAhlSY0m6YK7PB8XmONTU1YIzZdPfsze6wniXiS0iFOWfOHPFZyWpyYUp9oVICJTyPhKlASEiIXUsojRSyjuvsCNbJl61ThNhzgQDAU089BQB48sknXdYmVyDNQdurVy/R/Vd6z+TXU21JhkARJvkxFdBoNIiOjpYlf5Y+3aU5b1wpTOn70/nz521y67T1Pjt06FBFP6K30Wg0ePrpp2E0GtGtWzeEhoaiqakJxcXFGDlypBh4a2lpEb5jNYvZWVOKOgtZTBWsHfhSYUotm7ss5s6dO1FeXq7ahs5GYmKiCJLnln/v3r1iYM1sNgvRBQUFybrt0tSXNPgT4PAwMg7PUg7IxaiWuMpZmpub2/TV3XfffS45l7eRRlTl5eWhrKwM7777LoqKigDY+msTEhLw4IMPAggcfyZ1ZVXgWQw4JSUlMJvNCA4Olj21+ZO+o2t52BP4Cy+8gPr6egwcOLBD5/AVpA82nU6HHTt2oKGhAfv37weg7K/l/w+ymAHO8OHDERUVhblz54oyPjvC+ua4ePFih8/HhWk9wBMREYHY2FjVzHWdnYiICBsrqOQWUko27c+QMFXQ6XRYuHAh4uPjxdOaR+CozejoCDxoITY2VjYi6e8xos3NzTYDPUq9D35NSJiEgKfQ4MLkNwePUumoMBsaGnD69GkAre+2ShkA/BWTyWTjGrEnzKamJnz99dd+P9uEhOkA3Gpx9wmfUcIF29DQ0KHu7O7du0WUj16vl/lQO+OiRm2xYMECce0qKytt/Jn2hFlXV4cvv/wSq1evdn9DvQgJ0wFiY2MBtKb3MJvNYvCCT0mqqanBf/7zn3alwGhsbMTZs2fFdteuXf1emDExMZg2bZrYtg4aUBJmWzlt/Q0SpgPw2f5Go1GE5gUFBYmg8oqKClRVVTm9PHx1dTVOnDghK9Pr9bLk0/66AKw9odmzmIECCdMBdDqduJG4aPR6vWp0iiNcunQJ69evx5dffikrt55H6UjS5s6IvUEtpesaFBSEmTNnyrb9Gf/+dS6EP7F56ou6uroOPcU/+ugjxXLrTHX+Oiqr0WhUAyasfcicpKQkvPzyywBacwMpzU7xF0iYDsJFKO1mtjeoQGnC7+jRo/Hss88iJiYGCxcuRFJSEpYtW+ZTmQhczbx58xTL7Y1ES/28jkxk76yQMB3E2jo++uij7Ram0hJz48ePF5E9UVFRmDlzps9NeHY1ISEhiI+PR3BwMJYsWSKSTtt7GAUHB4tubGeOHW4LCslzEKkwNRoNRo4cKW4SZ5eJ46tWazQaMMYwduzYgBt15MyZMwd1dXWIiIjA5MmTMXLkyDb30Wq1aGxs9GuLScJ0EOlT/JVXXhEujW7dujmdi4aP7Kanp2PUqFGua2QnRKPRiInUjogSCAxhUlfWQaQ3gdS6cR8nx5EBCf6e6q8DO+6Gv2f6c0B7u4S5bt06xMXFISwsDCkpKTh8+LDd+nl5eUhJSUFYWBgGDBig6O/LyclBYmIidDodEhMTFTNvO3teV6ImOOsEWfZuFsYYtm/fLlvyj3Cebt26AYBYltBkMvmdv9dpYe7YsQOLFy/G66+/jsLCQowbNw5TpkyxmdTLKS0txWOPPYZx48ahsLAQy5Ytw8KFC0XmNwDIz89HRkYG5s2bh5MnT2LevHmYPXu2LEGws+d1NXyS7/Dhw2Xl1hOq7QVZf/jhhzh//rzY9vc4WHfBB8VOnz6N3bt3Y9WqVbL7yR/QMCcfNaNGjUJycjLWr18vygYPHozp06dj5cqVNvVfeeUV/POf/xQrCQNAZmYmTp48ifz8fACtM9SNRiP27Nkj6kyePBlRUVHYtm1bu86rhNFoRGRkJGpraxUzt9mDMYYbN26ge/fuMud2Y2Mj3nrrLfHO+eCDD+KRRx5RPMabb74p2/7973/v1+4Qd3H9+nWsXbvWpnz58uXtPmZZWRlOnz6NtLQ0l4yGd+ReA5y0mCaTCQUFBUhLS5OVp6Wl4ciRI4r75Ofn29RPT0/HsWPHxHC3Wh1+zPacF2jtVhqNRtlfe+F5gKwjTsLCwhAXFye2//e//6l2q6yzIpAo24daAEJH2Lx5M44dO4a9e/farXf79m2PuGmcEub169dhNpttFqXp1auXLHGVlKqqKsX6LS0tYnRSrQ4/ZnvOCwArV65EZGSk+Ovbt69jP9RJJkyYINtWGy2UBqQHqnvEVUyePNmmzBWLQNmbJXT16lWsXr0aH3/8cYfP0xbtGvyxftIzxuw+/ZXqW5c7ckxnz/vaa6+htrZW/HH/oauRWkxA3fEtHRjiOWyI9qHU3XRFYjR7PumSkhJYLBaUlZW53Wo6Jczo6GgEBwfbWKnq6mrVpd1iYmIU64eEhIhRSbU6/JjtOS/QapUiIiJkf+5C6oNTe3LzgaHU1FSMGTPGbW0JBJTW92zvymvSBXXtCVM6Mu/ubPdOCVOr1SIlJQW5ubmy8tzcXNUbbfTo0Tb19+3bhxEjRoinnlodfsz2nNfTTJo0SXxuy2KmpqbapP4nnKNbt26YP3++rMx6XufVq1exZs0au/NkL1y4gL/85S9i295YqPQVxVXZEdVwuiu7dOlSfPDBB9i0aROKi4uxZMkSlJeXIzMzE0Br9/G5554T9TMzM1FWVoalS5eiuLgYmzZtwsaNG/Hb3/5W1Fm0aBH27duHVatW4dy5c1i1ahX279+PxYsXO3xeb6PVasXgjpIwW1paxBOX3i9dQ+/evfHCCy+IbWuL+cknn6CmpkZk31PC2hfe1NSkKk6pK8zdwQ1Oh+RlZGTgxo0bWLFiBa5cuYKkpCR88cUX6NevH4DWAG2pbzEuLg5ffPEFlixZgrVr18JgMGDNmjWyuXVjxozB9u3b8bvf/Q5vvPEGBg4ciB07dsjC1do6ry+gtBK1yWQSIWSctpY6IBwnNjYWQ4cOxalTp2QW02w2i1DJ77//Hrt370bPnj1twv6s535aLBY0NjYqzgmVWkyfEybQurKUdHUpKZs3b7YpGz9+vIjSUGPWrFmYNWtWu8/rC3Bhcot55MgRFBQU4Ne//rX4R2q1Wr+f5Otp+CwfqcWUTs9raWlBQUEBAOBnP/uZbMBQaU5tfX29ojClYnS3MOkOcSHcEtbW1qKpqQm5ubm4efMmiouLRYY9ivZxPdIMehy1xYfq6+thMplQU1MDAIrvn2qDSOfOnROffdJiEsrwUd/6+nrZSl0NDQ0iqslfljnwJZSEqSauhoYGfPzxx6iqqkJWVpZsPIBP4VMStbUrxt0zW8hiuhDe/ZEukAMAZ8+eFf9YGvhxPUrJoNUs5p07d4Tbrbi4WPyfXnzxRQwaNAiAragZY3jnnXdkZdSV7UTwp29eXp7MN1ZWVmZTh3AdSssnqFlM6VhHQ0OD2CcyMlK8ZljvW1hYaHOco0ePYtu2bU5PkncUEqYLkeaHVYu5dNc/MpDhU++qqqrE9VWzmKdOnRKfS0tLxf5arVZVmHyyhTXfffedOIarIWG6EEeWW/fnzG7egl93s9mMNWvWALgbAMCTcivBcy/xrPBcmNbBA/Zccu561yRhuhCpb1aNjuSiJZSRup94qBwXF39vtAcXJBe4dOAOkHeRrWcIuWuFaxKmC7H3dAZaR20ffvhhzzQmwPjFL34BACLUkQszOTkZ6enpmD9/vk22CQ5PD8O/v3XrlqwrLF3p+uc//7lsX7Uuc0chYboRrVaLrKwsREZGYu7cuVi0aFGHF7gllOETIsxmM1paWkQX85577kFqaip69+6Np556CqmpqbJwUADo06cPALk1/NOf/iQG8LjIn332WXTv3h0PPfSQqEfC7CSkpKSIz7/85S/Ro0cPLF68GPHx8RTx40akETx8PRiNRiN7dejbty/S09Oh1+tlPRe+r/X/hw/6cIvJH6oTJkzA7NmzER8fb5M531VQgIGLmTp1Kh5++GHU1NSIQQXC/QQHByMkJAQtLS3497//DaBVSGrzdaX/G6moJ0yYgIMHDwJoXcWNMSZ80FKRDx48GIMHD3b57+DQI9zFaDQadO3a1W3ZEgh1rBOj2XttkI6gS4M+pCK1WCyyDHyeHLgji0n4DQMHDhQxyYD9uOTo6Gg8//zzCA8Pl1nVQYMGifDJuro60Y3lFtlTkMUk/AbrbBZtuTL69+9vk9s3MjJSzPE0Go24fPkygNauryeTp5EwCb+Bj65yrP2RjsLdXo2NjaisrARgK3p3Q8Ik/IZ7770XiYmJYlspk54j8FxRQOska8A2sMDd0Dsm4TdoNBo8/fTTYIzh+vXrHco/q9frYTQaxYRrEiZBdBCNRuNQ3LI9+AgsTxMjzQnsCagrSxAKWLtGPG0xSZgEoYD1aC0JkyB8AOuoLerKEoQPIB040mg0Hk/QTcIkCAWkXVlvLIpLwiQIBUJCQoRPNDk52fPn9/gZCaKTMHPmTCQnJ6N///4ePzcJkyBUCAoKwsCBA71zbq+clSAIu5AwCcIHIWEShA9CwiQIHySgBn+4P8poNHq5JYS/w++x9vpAA0qYPA0h5eMhPEVdXZ1NLiJH0DBvhDV4CYvFgsrKSps8L0DrE65v376oqKgQk2QJui72sHdtGGOoq6uDwWBoV9rSgLKYQUFBNuknrImIiKAbUAG6LuqoXZv2WEoODf4QhA9CwiQIH4SE+SM6nQ7Lly+nFZ+toOuijjuvTUAN/hBEZ4EsJkH4ICRMgvBBSJgE4YOQMAnCByFhEoQPEnDC7N+/PzQajezv1VdfldUpLy/HE088Ab1ej+joaCxcuFAsHc4pKirC+PHj0aVLF/Tu3RsrVqzwStImd7Nu3TrExcUhLCwMKSkpOHz4sLeb5Days7Nt7g3pitGMMWRnZ8NgMKBLly54+OGHcebMGdkxmpqa8Jvf/AbR0dHQ6/WYNm0afvjhB+cbwwKMfv36sRUrVrArV66Iv7q6OvF9S0sLS0pKYhMmTGDHjx9nubm5zGAwsKysLFGntraW9erVi82ZM4cVFRWxnJwcFh4ezv74xz964ye5je3bt7PQ0FD2t7/9jZ09e5YtWrSI6fV6VlZW5u2muYXly5ez+++/X3ZvVFdXi+/ffvttFh4eznJyclhRURHLyMhgsbGxzGg0ijqZmZmsd+/eLDc3lx0/fpxNmDCBDRs2jLW0tDjVloAU5urVq1W//+KLL1hQUBC7fPmyKNu2bRvT6XSstraWMcbYunXrWGRkJGtsbBR1Vq5cyQwGA7NYLG5ru6cZOXIky8zMlJUlJCSwV1991Ustci/Lly9nw4YNU/zOYrGwmJgY9vbbb4uyxsZGFhkZyTZs2MAYY+zWrVssNDSUbd++XdS5fPkyCwoKYnv37nWqLQHXlQWAVatWoUePHhg+fDjeeustWTc1Pz8fSUlJMBgMoiw9PR1NTU0oKCgQdcaPHy+L+EhPT0dlZaVYtq2zYzKZUFBQgLS0NFl5Wloajhw54qVWuZ8LFy7AYDAgLi4Oc+bMQUlJCQCgtLQUVVVVsuuh0+kwfvx4cT0KCgrQ3Nwsq2MwGJCUlOT0NQuo2SUAsGjRIiQnJyMqKgrffvstXnvtNZSWluKDDz4AAFRVVdksUhoVFQWtVouqqipRxzqlId+nqqoKcXFx7v8hbub69eswm80216JXr17iOvgbo0aNwpYtWxAfH4+rV6/iD3/4A8aMGYMzZ86I36x0PcrKygC0/u+1Wi2ioqJs6jh7zfxCmNnZ2XjzzTft1jl69ChGjBiBJUuWiLKhQ4ciKioKs2bNElYUgOKS3owxWbl1HfbjwI8nlwP3BEq/099+I2fKlCni85AhQzB69GgMHDgQH374IVJTUwG073q055r5hTCzsrIwZ84cu3XUkvbyC37x4kX06NEDMTEx+Oabb2R1ampq0NzcLJ6WMTExNk9Avqy4p5cEdxfR0dEIDg5W/J3+8hvbQq/XY8iQIbhw4QKmT58OoNUqxsbGijrS6xETEwOTyYSamhqZ1ayursaYMWOcOrdfvGNGR0cjISHB7l9YWJjivoWFhQAgLvbo0aNx+vRpXLlyRdTZt28fdDodUlJSRJ2vvvpK9m66b98+GAwGr2TtdgdarRYpKSnIzc2Vlefm5jp9k3VWmpqaUFxcjNjYWMTFxSEmJkZ2PUwmE/Ly8sT1SElJQWhoqKzOlStXcPr0aeevmVNDRZ2cI0eOsD//+c+ssLCQlZSUsB07djCDwcCmTZsm6nB3yaRJk9jx48fZ/v37WZ8+fWTuklu3brFevXqxuXPnsqKiIrZz504WERHht+6SjRs3srNnz7LFixczvV7Pvv/+e283zS289NJL7NChQ6ykpIR9/fXX7PHHH2fh4eHi97799tssMjKS7dy5kxUVFbG5c+cqukv69OnD9u/fz44fP84mTpxI7pK2KCgoYKNGjWKRkZEsLCyMDRo0iC1fvpzV19fL6pWVlbGpU6eyLl26sO7du7OsrCyZa4Qxxk6dOsXGjRvHdDodi4mJYdnZ2X7lKuGsXbuW9evXj2m1WpacnMzy8vK83SS3wf2SoaGhzGAwsBkzZrAzZ86I7y0WC1u+fDmLiYlhOp2OPfTQQ6yoqEh2jIaGBpaVlcW6d+/OunTpwh5//HFWXl7udFtoPiZB+CB+8Y5JEP4GCZMgfBASJkH4ICRMgvBBSJgE4YOQMAnCByFhEoQPQsIkCB+EhEkQPggJkyB8EBImQfgg/wcUnkGwxtZ9+gAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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dugQAGDx4sN2PBEd6ejpWrlzJBzCvXbvmdr5ig8TbDeAEwkXvOdRqNQDn4i0pKeE9J0dNTQ0aGxv5LqfTp0/z527cuGGXhjPPy5XF3SBSU1MTvvzyS1y+fBkfffQRrl+/jh9//BEAMGTIkPtez/1g/fTTT27lK0a6VG0mAgtOIJy34+DE6+xFLi8vBwDMmTMHMpkM+fn50Ov1Tr3lrVu37Krf9/O8Op0OjDEcPXoUp06dQq9evfDrX/+aj0jbUlNTw/8gMMawbds2vrvJVfGWlpY6/cHqTpDn7QZwbV5b8XJdZI2NjXYDJgwGA27dugWJRILExERelAaDwWmV01G12ZnnjYmJQXBwMIxGIzZt2oSvv/4aRqMROp3Oqo1tCye6YcOGITo6mhfuwIED0bdvX6fXcQwcOJBPx7ZW0d0g8YqcpqYmvoqrUqmszsnlcl7Qtp6Iq4rGxMRAoVCgV69eCAsLA9AxPtoRnbV5bcUrk8kwcuRIAD/XDHr16gUAuHLlitMIOPfDMWTIEPzhD3/AzJkzkZycfN9gJkdUVBRCQkJgMpkcVvPd5ebNm13qQvMFJF6RU1dXBwDo06cPP6bYEkft3sbGRj4yz/WRAuAjubW1tQA6IviWuCNeAJg1axYf/Y6KisKf//xn3ns68u5ms5kvp1qtRkhICB599FHMmjULSqXS/uYdEBQUxHtfoUGrmpoabNy4EW+//XZA9h2TeEUOJzRbr8vhSLwnTpyAyWRC79698eijj/LHuWAPx7hx46y+OxIv50Ed/XCEhITg97//PWbNmoVnn30WQUFB/A+EIzFUVFTg3r17CA0NdXo/rsDdh1DxFhcX83/v3bs34CY/kHhFDud57yfe6upq/uXjul6mTZtm5V0txatQKDBp0iQ8+uijvHfuTLy2XpojLCwMycnJ/KANzivaitdkMvG1gfHjxyMkpOuxVE9EnJuamnD+/Hn+e319vVXkPRAg8YqYCxcu4MyZMwCci3fAgAEIDQ1Fc3MzTp8+jZs3b6Kurg4SicQuems5Bnz48OGQSqWYOXMm5s+fDwC4d++e3RYm9xOvLQ8++CCADmFxPybl5eX417/+hTt37qBXr15WI+u6AvcDcePGjS6Prf7hhx/AGEP//v35eeRHjhwJqH2HSbwihTGGvXv3AuiYKmlb5eUICQnhh0pWVlbyk/Tj4uL4ABWHQqHAiBEj0KtXL6u1v0JDQ/k2bWlpKUpLS8EYA2OMFy8XjLof/fv3R2hoKFpbW3HhwgU0NDRg9+7duHPnDgBgwoQJDqvg7hAeHo6IiAgwxrrcZcR53UceeQRjx46FVCqFwWDA/v37BZXNk5B4RUpdXR3u3bsHAPjtb3/rtN8U6Oh2AToizNy0Qc472fL000/jL3/5i1W3k0Qi4au9+/fvx+eff47q6mqYTCa+O8ZVzyuRSJCcnAwAOHz4ML777jv+3Pjx463GKQtBSLu3tbWVb1o88sgjUCgUWLRoEQDgzJkzHp+n3FVIvCKlqqoKQMdA/futANK/f39efNykEWfidYatOOvr63mvGxISwk/vdIWUlBRIpVLU1dXxQaHFixdj+vTpgtq6lggRb21tLdra2tC7d29+uOXgwYMRHR0Ns9mMc+fOeaSMQiHxihSu+hsfH39fW4lEYtdP6u6SP7bibWho4D2/q17XMi2NRsN/j4iIcDhbSAhCxMtVtWNiYqxWahk1ahSAjqZDW1sb2tvb8e233/ptKCaJV4QYjUZ+2RdXxAt0BIp+85vfICgoCGPGjHG5jcphK9B79+65HayyxDIolZSUZDe8UihclN1oNLo9p5gTr+0iDo888giAjibLunXrsG7dOhw4cADbtm2zW8XEF5B4Rcju3bsBdASJOlsGyJYhQ4Zg1apVmDdvntt52gq0ublZkHj79OmDadOmISkpyWPtXEtkMhk/y8pdz2jpeS2JjIzE1KlT+e+Wa7Ht2bPH5zOZSLwio7W1le8jnT17ttsey522qSWeFi/QsQgDNynCG3BNA2drmDmiubmZH87paPmkyZMnY82aNYiNjUVkZCSefPJJvh/8q6++8kCpXYdmFYkMbsB97969kZCQ4LN8vSFebzN69GiUlpbizJkzmDp1qksrqnBziPv27WvXlcYhlUrxu9/9jv8eGxuLS5cu8eto2U7N9BbkeUUGVwUcOHBgp8vtehpbgTY1NXU5YOUrHnzwQcTExMBsNlsNdXSG2WxGUVERgJ/bt64QGRnJL8dkudj9jz/+iEOHDnW68L8QSLwig6sy+3qBeNsAV3NzM7+oeqCKF4DVWl6d0dTUhHfeeQd6vR5SqbTTVUkdwVWxueGqQMc608eOHfPasEoSr4hgjPGe19fitRWoyWQShXgHDx4MiUSC+vp6fuqkI/Ly8nDt2jUEBwdj7ty5bkfjucAh52UZY/xAj8TExC6WvnNIvCLi9u3buHv3LoKCgny+FrWtQNvb23nxcsu8BiIKhYLvNuLas7bcunULV69ehUQiwfPPP48RI0a4nQ8X2eZWIbl16xZaWloQHBzstf8rEq+I4KrM3FxXX+LIu3LjkQNZvMDPy+eUlJQ4nNbH9Zk/+OCDbnW9WcIFqe7evWu1FK9KpXK6aJ5QSLwigquGcTNzfImjaqRYxDt27FhIJBJcu3YNW7Zs4WdGnT9/Hnl5efwm7UKqt3K5nI9mczt1AD8PFvEGJF4RwS3H4mwNZm8ikUjsph1yuy8Gunj79OmDp59+GhKJBDdv3sSZM2fQ1taG3bt34/z587h37x4kEgmGDx8uKB9uModer0dFRQUAx4vEewoSr0gwm824desWAPuF5nzFc889hxdeeMGuCu2tQRaeZPjw4XjiiScAdIxNrqystJqbO2XKFKf9uq7CtXuPHz+Ou3fvQiqVurTiZVch8YqEhoYGmM1mhISE+G0nALlcjv79+9uN0gp0z8sxevRoSCQSXL9+nR9iGhsbi9mzZyM1NVVw+lxzhhuhNWrUKK+1dwESr2jgXoi+ffv6dHCGI2yDZWIRb1hYmF3VeP78+dBoNB55praxCG7esreg4ZEigasy+2roXWdYijckJMSr3sXTPPnkkxg/fjyuXr0KlUrl0lrQrhIeHs7vDxUZGSloET1XIM8rEiw9r7+xrDaLxetycEvDTpo0ySvt0V/+8pcAgKeeesrjadtCnlckcOINNM8rNvF6G7VajZycHJ/kRZ5XJJB4CVtIvCKgra2NH5dL4iU4SLwigAtWyeVytwfMewMSb2DQJfFu3rwZ8fHxUCgU0Gg0/PAyZxQVFUGj0UChUGDw4MHYunWrnU1+fj4SExMhl8uRmJhot+mzK/kyxqDVaqFWqxEaGoopU6YEzEp/QuBGVvXv39/v3UQAiTdQcFu8eXl5yM7OxiuvvIKysjKkpqZixowZ/JKitlRVVWHmzJlITU1FWVkZ1qxZg+XLl1tt81hcXIyFCxciMzMTZ86cQWZmJhYsWIATJ064le/rr7+ON998Exs3bsTJkyehUqkwbdo0txcgCzS4e/T1NEBnkHgDBOYm48aNY1lZWVbHEhIS2KpVqxzav/zyyywhIcHq2NKlS9mECRP47wsWLGDTp0+3ssnIyGCLFi1yOV+z2cxUKhXLzc3lzzc3NzOlUsm2bt3q0r0ZDAYGgBkMBpfsfcWmTZuYVqtllZWV/i4KY4yx/fv3M61Wy7RaLSssLPR3cUSJJ941tzyvyWRCSUkJv3cLR3p6Oo4fP+7wmuLiYjv7jIwMnDp1ih9b6syGS9OVfKuqqqDT6axs5HI50tLSnJatpaUFRqPR6hNoNDU18fvMBqLnDYQ2eE/FLfHW19ejvb3dbs5jdHS00w2IdTqdQ/u2tjZ+4rIzGy5NV/Ll/nWnbOvXr4dSqeQ/gSIOS7ipZf369RM8cN5TkHgDgy4FrGyDJoyxTgMpjuxtj7uSpqdsOFavXg2DwcB/AnEDZa696485vM6wFG+g/KD0RNwaYRUVFYXg4GA7T6bX652uQKBSqRzah4SE8H2Wzmy4NF3JlxtHqtPprJYd6axscrk84AMugSheS29Lntd/uOV5ZTIZNBoNCgoKrI4XFBQ4XW0vJSXFzv7gwYNITk7mx8g6s+HSdCXf+Ph4qFQqKxuTyYSioiK3VwIMFMxmM2prawG4vzGYN7Gckkie14+4G+HatWsXk0qlbPv27ayiooJlZ2ezsLAwduXKFcYYY6tWrWKZmZm8/eXLl1mvXr3Yiy++yCoqKtj27duZVCpln3zyCW9z7NgxFhwczHJzc1llZSXLzc1lISEh7Ntvv3U5X8YYy83NZUqlku3evZuVl5ezxYsXswEDBjCj0ejSvQVatLm+vp5ptVq2du1a1t7e7u/i8Ny9e5etW7eObdmyhZnNZn8XR5R44l1zW7yMdXRdxMXFMZlMxpKSklhRURF/bsmSJSwtLc3KvrCwkI0dO5bJZDI2aNAgtmXLFrs0P/74Y/bwww8zqVTKEhISWH5+vlv5MtbRXZSTk8NUKhWTy+Vs8uTJrLy83OX7CjTxVlRUMK1Wy95++21/F8WOe/fusZaWFn8XQ7R44l2TMPb/0SMCRqMRSqUSBoPBb6tVWFJUVITCwkKMHj0a8+fP93dxCA/iiXeNxjYHMFz/7gMPPODnkhCBCIk3QLl9+zY/LrurawkT3RsSbwDS1tZmNXkjkCLNROBA4nWTiooK7Nixgx8dBvw86MRT1NXVwWQyAQCGDh0a8H3RhH+gZXDcoKGhAZ988gkYY7h48SLkcjm/8PgTTzyBSZMmeSQfrq3bv39/LFy40CNpEt0P8rxucPToUSsvywkXAA4dOuR0DLW7cF49Li5OVCszEr6FxOsidXV1KC0tBQA8/vjj/LKhGo2GX9Fx7969DjeychXGGAoKCnDs2DEA/tsZgRAHVG12kW+++QaMMcTGxmLSpEn4xS9+wZ8zGo3YvHkzamtrsXbtWiiVSjz++OMYOXKk0/S++uorXLp0CdOmTeP3Hrp586bV9EXqIiI6gzyvi8yfPx9jx47F4sWL7WYpRUREYNq0afx3g8GATz/91GovHEt0Oh2OHj0KnU6Hzz77jLe7cOECb5OQkIC4uDgv3AnRXSDP6yIKhQJz5851ej4pKQk3btzgl+4xm80oLS3F+PHjAXQEu27evAmdToevvvqKv85oNOLdd9/F3LlzcfjwYQDArFmzvL5VBiF+aHikBZ4aHnny5Ens27cPADBs2DDI5XKcO3cOZrOZt5FIJMjIyMCBAwesgmD9+vXDH//4RwpUdXM88a6R5/UCo0ePxrFjx2AwGPhd1zlkMhni4+ORmpqKmJgYREZGYufOnQA69rpZsmQJCZdwCfK8FnhyYkJjYyOOHTuGoKAgKBQKREVF4aGHHoJUKrVrMx8/fhznz5/H3LlzKcLcQ/DEu0bitSDQZhUR3ReaVUQQPRgSL0GIFBIvQYgUEi9BiBTqKrKAi90F4s4JRPeCe8eExItJvBZwG5IF4s4JRPeksbERSqWyS9dSV5EFZrMZ169fR3h4uMNdFoxGI2JjY1FTU0NdSRbQc3FMZ8+FMYbGxkao1WoEBXWt9Uqe14KgoCCXlpyJiIigl9QB9Fwc4+y5dNXjclDAiiBEComXIEQKidcN5HI5cnJyaEE4G+i5OMbbz4UCVgQhUsjzEoRIIfEShEgh8RKESCHxEoRIIfEShEgh8Tpg0KBBkEgkVp9Vq1ZZ2VRXV2POnDkICwtDVFQUli9fzu8vxFFeXo60tDSEhoYiJiYGr776qsf3NfI3mzdvRnx8PBQKBTQaDY4cOeLvInkVrVZr926oVCr+PGMMWq0WarUaoaGhmDJlCr/bI0dLSwv+9Kc/ISoqCmFhYZg7dy5++ukn9wvT5W25uzFxcXHs1VdfZbW1tfynsbGRP9/W1sZGjBjBpk6dykpLS1lBQQFTq9Vs2bJlvI3BYGDR0dFs0aJFrLy8nOXn57Pw8HD297//3R+35BV27drFpFIp+89//sMqKirYihUrWFhYGLt69aq/i+Y1cnJy2PDhw63eDb1ez5/Pzc1l4eHhLD8/n5WXl7OFCxeyAQMGMKPRyNtkZWWxmJgYVlBQwEpLS9nUqVPZ6NGjWVtbm1tlIfE6IC4ujv3jH/9wen7fvn0sKCiIXbt2jT+2c+dOJpfLmcFgYIwxtnnzZqZUKllzczNvs379eqZWq5nZbPZa2X3JuHHjWFZWltWxhIQEtmrVKj+VyPvk5OSw0aNHOzxnNpuZSqViubm5/LHm5mamVCrZ1q1bGWOMNTQ0MKlUynbt2sXbXLt2jQUFBbEvv/zSrbJQtdkJGzZsQL9+/TBmzBisW7fOqkpcXFyMESNGQK1W88cyMjLQ0tKCkpIS3iYtLc1qdE1GRgauX7+OK1eu+Ow+vIXJZEJJSQnS09Otjqenp1tt2dIduXjxItRqNeLj47Fo0SJcvnwZAFBVVQWdTmf1TORyOdLS0vhnUlJSgtbWVisbtVqNESNGuP3caFaRA1asWIGkpCRERkbiu+++w+rVq1FVVYVt27YB6NiuxHa3+sjISMhkMn6nQJ1Oh0GDBlnZcNfodDrEx8d7/0a8SH19Pdrb2+2eQ3R0tMd2SwxExo8fjw8++ADDhg1DXV0d1q5di4kTJ+LcuXP8fTt6JlevXgXQ8X8vk8kQGRlpZ+Puc+sx4tVqtfjrX//aqc3JkyeRnJyMF198kT82atQoREZG4plnnuG9MQCH830ZY1bHbW3Y/werHF0rVhzdY3e6P1tmzJjB/z1y5EikpKRgyJAheP/99zFhwgQAXXsmXXluPUa8y5Ytw6JFizq1sfWUHNx/yqVLl9CvXz+oVCp+TyKO27dvo7W1lf/VValUdr+ker0egP0vsxiJiopCcHCww3vsDvfnKmFhYRg5ciQuXryI+fPnA+jwrgMGDOBtLJ+JSqWCyWTC7du3rbyvXq/HxIkT3cq7x7R5o6KikJCQ0OlHoVA4vLasrAwA+P+QlJQUnD17FrW1tbzNwYMHIZfLodFoeJvDhw9btZUPHjwItVrt9EdCTMhkMmg0GhQUFFgdLygocPslFDMtLS2orKzEgAEDEB8fD5VKZfVMTCYTioqK+Gei0WgglUqtbGpra3H27Fn3n5tb4a0ewPHjx9mbb77JysrK2OXLl1leXh5Tq9Vs7ty5vA3XVfT444+z0tJSdujQITZw4ECrrqKGhgYWHR3NFi9ezMrLy9nu3btZREREt+wq2r59O6uoqGDZ2dksLCyMXblyxd9F8xp//vOfWWFhIbt8+TL79ttv2ezZs1l4eDh/z7m5uUypVLLdu3ez8vJytnjxYoddRQMHDmSHDh1ipaWl7LHHHqOuIk9QUlLCxo8fz5RKJVMoFOzhhx9mOTk57O7du1Z2V69eZbNmzWKhoaGsb9++bNmyZVbdQowx9v3337PU1FQml8uZSqViWq2223QTcWzatInFxcUxmUzGkpKSWFFRkb+L5FW4flupVMrUajV76qmn2Llz5/jzZrOZ5eTkMJVKxeRyOZs8eTIrLy+3SqOpqYktW7aM9e3bl4WGhrLZs2ez6upqt8tC83kJQqT0mDYvQXQ3SLwEIVJIvAQhUki8BCFSSLwEIVJIvAQhUki8BCFSSLwEIVJIvAQhUki8BCFSSLwEIVL+D9BFPGHKwMJnAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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ANkSOyWSCxWIBuSXcgcRLOKUtxAsAjz/+OCwWi0hGdvLkScnnH330EYYPHy5xq+3skHgJp/i7z8tJTExEXl6eyOHb1NQkqWX37duHqVOnoqqqimrf/0HiJZzSFn1ejkajEf1fmUyGL774AgBw6tQpDBo0CEDLggaaSmqBxEs4pa1qXk5ISAi6d+8OACKCaGVlpcTGm9BHHQkSL+GUturzWjNw4EAAgFarhclkEkEYeGbBa9euiYRlnRkSL+GUQIoXAHbt2iXcJUeMGAGgpUlNIYtIvMRdaMs+LychIQEGgwEAUFVVJWrZWbNmiUTePF53XV0dTpw40WZla0+QeAmnBKLmBe4EZud06dIFCoUCqampAIAzZ84AAPbv3y+JuNKZIPG2U0wmk92kXvYoKSnBBx984Bd/4LYesOLMmjVL8j8PWDds2DAAQG1tLYqLi/Hjjz+ivr7eburRjg6Jtx1y+fJlLF26FMuWLcOVK1fQ3NwsWVdrjV6vR1lZGS5cuIC3337b5wIORLMZaAlQx9f7Ai2DV0BLrG7GGIxGI8rLy8XnFy9ebNPytQdIvO2Q77//Xrzft28fCgoK8Ne//tVuJvnWydSWLVsGk8nks7IEqtkMADNnzkRlZSUaGhqQnp4OoGVdsb2ydMb4zyTedggPcA4Ax48fx61bt2AwGPDzzz/b2Lb2ATabzfjvf//rs7IEUrxRUVE4efIkGGOSZGa8CW1NZ0xVQ+JtZzDGHPbfTp8+bbONNxefe+45sc2XSasD1eflHDp0CAUFBZJt9rIS3rhxo41K1H4g8bYzmpqaYDQaIZPJEB8fL/ns+++/x5o1a0RazObmZjQ2NgIA4uPjkZGRAcC3WQgC1eflaDQahIRIw4vzmM8AhC90ZwydQ+J1gxs3boj5R3/BE29pNBpMmzYNsbGxePHFF8XnDQ0NOHTokMS2S5cu6NKli3Ar9FX/z2w2iwGwQInXHtaDdzw5HImXcEhjYyNWrVqFlStX4ubNm347DhdkdHQ0unXrhtzcXPTp00fMbwItjgnAnRqWi/Zu+X+amprcKov1VFV7Ei/3wIqKihIj0jdu3Oh0q41IvC5y6tQpWCwWNDc34+zZs347DhcvFyTnsccew5gxYwAAP/74I4xGo414+V+dTmcz4nzjxg387W9/Q3NzMxhj+Oijj+5aFi5euVwuPJvaA8OGDYNarcYLL7wgFvEzxuyOxndkSLwuYj2P6MsBodbwJm9r8QLAyJEjRUrT48eP24g3PDxc9A95X5hTWFgIg8GAoqIifPLJJzh58uRdPZO4GPgC+fYCz1AYGRmJkJAQ0SrwR9NZr9ejoKDA7VZLW0DidZGJEyciOTkZgH/Fy0XHs+hZo1arRVbEHTt2iDlenipVJpMJIbduOvPWwg8//ICamhp07doVX375pdPVOVy8arXa09PxGz169BDveZrXHTt2+Pw4n332GXQ6nV/27S0kXheRyWQYMGAAAP8GR3MmXgC47777bLZZ38jcJ5j3i+8GnzvesmULvv32W8ln7Vm81iQmJgJocZnk3Q5rampqbM7NFRoaGkSL69SpU2Lw7vDhw1i1alXAw9OSeN2AC8NfCaENBoOYBnIk3n79+uE3v/mN+F+hUEgSjPft2xcAJEvmHLlWAi2O/fX19Thx4gR27dolGakOFvEOHz5cvP/qq68kn926dQsff/wxvvjiC9TW1jrdj8lkkti0doppbGzE9u3bsXPnTuj1enz55Zc+KL3nkHjdgDsHNDc3u7xowB24t1RkZKRTwTz00EPIzMwEAPGX06tXLwAtzWTeJLb3sPn1r38NoKW22rp1K0JCQqBQKCTNw2ARb3h4uJgTP3HihKS//5///Ee8/+abbxzugzGGpUuX4sMPP8SpU6cA2LawVq9eLXFd5cEBAgWJ1w2USqXwNPKHRw+/GbgAnTFmzBi88cYbQoSc+Ph4yOVymM1mcRPba0oOHTpU5B6yvtnPnj0rkpLzVoBKpXL7XNqamTNnivfr16/HP/7xD2zZskXSfXC2eOH48ePi/f79+wHc/TemZnMQIZPJhHePP8TLfZp79uzpkr1arRajzxyZTCb6wHzNq72aNzo6Wkw9tWbLli24ePGiaEJbr+5pr1g/YAwGA06ePImffvpJMrh47do1h4s2rFsc3E+a/8bjx4+X2PKxD391n1yFxOsmvhCvyWTC+vXrcfDgQbGNMSaiQ7hS8zpj8ODBAICdO3dK5oPT09Nx5swZPPHEE1AoFEhPTxdN60GDBuHJJ58U+9i5c6e48a0HxNozTz/9tNPPHfmNt65Bb9y4AYvFIn7jXr16YcGCBRg4cCDGjh2LqVOnAmjpVgRybpnE6ya+EG9ZWRkuXryI3bt3i6acTqeD2Wy269PsLtYj0tu3bxfHiImJwfz58yUDPK+88goaGhowceJEDB06VDhjnD9/XtzoraNatFeSkpIknmgcmUyGhIQEAJBkJeTU1tZKamTGGJqamsRv3LVrV6hUKjzzzDPIyMiAUqkU4wCt59PbEhKvm3DxeuMQYB3KdO/evQDu9HdjY2O9XsGj1WpFM/LkyZNiICw6Ohr333+/xLZnz56YP38+unbtCplMhj/+8Y/iM5PJJJk7Dgasg9dxIiMj0adPHwCwW/PyPn5SUhLCw8MBtAxW8VYJ32YNd0XV6XS+KbgHkHjdxFnNyxhDXV2d06kZvV4v+cHPnDmD5uZmIV7uaO8NMpkM8+bNkyyikMvlDmtQ62Oq1Wr0799f/N+tW7d25Rp5N+677z507dpVdB2AlpbDPffcA8C+ePn00IABA8SMAg83GxYWZrOqCbgzlUc1bxDhTLw7d+7Ehg0bsHHjRoff59MVPXr0gEKhgMlkQl1dnVhA721/l6NWqyXZ93r27OnyqLH1jW89hxwMyGQy/P73v8fTTz+NjIwMGI1GZGRkiPO4ePGizcowPhrfo0cP8fty8VovP7SGD+JZP4jvNo/sa0i8bsJ/tNYjjWazGRUVFQBabhBHzWo+Ajx27FjRxKupqRE3kK/EC0j7qvaak47gqUWAliZnsMFH4ceOHYu3334bffr0Qffu3REeHg6z2Yz33ntPCNhsNosHcVRUlMfiPXDgANatW9emsbRIvG7Cn+BXr16VOGpwUXLshaxhjAnRx8XFiSmh8vJyMMYgl8vthnjxFGvx8gEbV4iIiECvXr3AGJMMbgUzMpkM/fr1A9Ayf809sbhw5XI5wsLChFi5g4Yj8Vo3mw0GA/bu3YvQ0FCsX7/ej2chhcTrJl27dhUDGNZP2dYR/Pm0jzV6vR4WiwVyuRwajUbUcNwZokePHjbztt4wcOBAWCwWdO/e3e0R7Oeffx5PP/20iNrYEUhKShLv+TQdbyFFRERI5vE59garAGnN2/pB3Vbzvx6Jd+3atUhISIBarUZKSgoOHDjg1L60tBQpKSliMMTe06mwsBBJSUlQqVRISkoSSabcOS5jDPn5+dBqtQgLC8OYMWP8Ek2fj1xWVFSguroa7777rigLb54eOXLEJiga/1G7desGuVyO6Ohoyc3hi8Eqa8LDwzF58mTMnDlTeFO5SmhoqM3IdLBj3Zfn64CtxQvY1rR3q3mbmpps7rG2yuDgtni3bt2KefPmYdGiRaiqqkJ6ejomTJhgt6YBWryGHn30UaSnp6Oqqgpvvvkm5syZg8LCQmFTXl6O6dOnIycnB0ePHkVOTg6mTZsmcWJw5bh/+ctfsHLlSqxevRqHDh1CXFwcxo0b5/N1nqNGjQIAHD16FJs2bZKs9fzVr34l3nMfWQ4Xr/USPr4vAH4RS3JyclD2W/2BTCbDn/70JwDAzZs3YTQabcTbOridI/Gq1WoxCv3jjz9Kvrt37942SYTmtnhXrlyJ3/72t3jppZcwePBgFBQUoHfv3li3bp1d+/Xr16NPnz4oKCjA4MGD8dJLL2HWrFl49913hU1BQQHGjRuHhQsXIjExEQsXLsQjjzwiiRp4t+MyxlBQUIBFixZhypQpGDJkCD799FPcvHkTn332mbun6RRn7ovx8fFiza11CFfgzlPeWkyjR4/GxYsX0dzcLPpkhP8ICwsT8+g6nc5GvK27F/YiVQItDwJe+/LwO1OmTBGf2/Mn9zVuibe5uRkVFRXIysqSbM/KynKYM7W8vNzGPjs7G4cPHxbzoY5s+D5dOW51dTXq6+slNiqVCpmZmQ7LZjAYoNfrJS9XUCgUkpU21vOg4eHhQoStRx55DW3dVJbL5XjllVcwd+5cl45NeIdMJhODgo2NjTbiDQ0NlTxcHdW8gDR+tEwmQ+/evUVzvC1WHLkl3oaGBpjNZpu5v9jYWDG03pr6+nq79iaTSfjOOrLh+3TluPyvO2VbtmwZNBqNeLnT58zMzIRCocBzzz2Hp556CgDEg4P7ArfOI8sD1/EfmPPAAw8IJwLC/3ChFhcXi8UX1uunreeBnY3+Wy/YiI2NhUKhENFW/BnnjGPrOuICrUdEGWNOR0nt2bfe7so+fWXDWbhwIfLy8sT/er3eZQGPGjVKElNq4cKFIpZSZGQk5HI5LBYLdDqduAH4U97Z05zwP7wFdPnyZfFbWIt05MiR2L9/P4YPH+7UVdV6wQb3SuNdKn+GSuK4VfPGxMRAoVDY1GSXLl1y6IkTFxdn1z4kJEQM3Diy4ft05bhxcXEA4FbZVCoVIiMjJS93sH4oWIdGlcvldmNJ3S3EDdE2TJgwQby3dtDgZGRkIDk5GRMnTnS6nwcffBBdu3bF1atXxfJK/tu2uz6vUqlESkoKSkpKJNtLSkrEIE1r0tLSbOyLi4uRmpoqnmqObPg+XTluQkIC4uLiJDbNzc0oLS11WDZ/0lq8ZrNZ1LzBsD62I9OnTx+xJhcAQkJCJBEyFQoFHn/8cbs+zdaEhobi9ddfR0FBgbiXuXgNBoNfoq1Y43azOS8vDzk5OUhNTUVaWho2bNiAmpoa5ObmAmhpPtbW1mLTpk0AgNzcXKxevRp5eXl4+eWXUV5ejo0bN2Lz5s1in3PnzkVGRgZWrFiByZMno6ioCHv27JGEJr3bcbkz/jvvvIMBAwZgwIABeOedd9ClSxc8++yzXl0kT+Di5U9gPhimUCgcTvwTbcfIkSNF7qdu3bp55Rxj/V21Wg2VSgWDwYDa2lq3PNvcxW3xTp8+HVeuXMGSJUtQV1eHIUOGYNeuXSLwWV1dnWTuNSEhAbt27cJrr72GNWvWQKvV4v333xeDPEDLdMmWLVvw5z//GW+99RbuvfdebN26FSNHjnT5uAAwf/583Lp1C7/73e9w7do1jBw5EsXFxQ6H+/0J7xLwmte6yexLLyrCM6xF5eskavfddx9OnDiBHTt2YPjw4XjooYf8kqhNxjpbjggn6PV6aDQa6HQ6rx0bzpw5g7///e+IiorCnDlzcOTIERQVFaF///7IycnxUYkJb9i5cycOHz6MJ598EsOGDfPZfk+dOiVpWY4aNQrZ2dkSG1/cax6NNhN3h9e8jY2NMBqNYvUJ9XfbD+PHj0d6errPPdCs10MDto4fvoIWJviJyMhIhIWFibhJvNlM4m0/KBQKv7iOhoSECP934M5MiK8h8foJmUwmluQ1NjaKmpemiToH06dPB9DyEPdXDDBqNvsRjUaD8+fPQ6fT2fXkITouXbp0wbhx45CYmOj2ii5XIfH6Ed4ku3Llimg2B0skRsJ7/O1fQM1mP8LFy6NFKpVKG79mgvAUEq8fad1Ejo6OpjlewmeQeP2ItQMJcCcVJUH4AhKvH1Gr1XjhhRdgNpthMBgkUTMIwlvIw8oKX3pYWWM2m6HX630aGZIIbnxxr1HN2wYoFAoSLuFzSLwEEaSQeAkiSCHxEkSQQuIliCCF3COt4APvroaAJQhP4feYN5M9JF4reIwpX6cdIQhHXL9+3eNlojTPa4XFYsGFCxdE0qnW8NCw58+fpxQiVtB1sY+z68IYw/Xr16HVaj1edUQ1rxVyudyl/LiehIntDNB1sY+j6+JtYAYasCKIIIXESxBBConXDVQqFRYvXgyVShXoorQr6LrYx9/XhQasCCJIoZqXIIIUEi9BBCkkXoIIUki8BBGkkHgJIkgh8dqhX79+kMlkkteCBQskNjU1NXj88ccRHh6OmJgYzJkzxyYf67Fjx5CZmYmwsDD07NkTS5Ys8coRvT2ydu1aJCQkQK1WIyUlBQcOHAh0kfxKfn6+zb1hnc6EMYb8/HxotVqEhYVhzJgxOHHihGQfBoMBf/jDHxATE4Pw8HBMmjQJv/zyi/uFYYQNffv2ZUuWLGF1dXXidf36dfG5yWRiQ4YMYWPHjmWVlZWspKSEabVaNnv2bGGj0+lYbGwsmzFjBjt27BgrLCxkERER7N133w3EKfmFLVu2sNDQUPbBBx+wH374gc2dO5eFh4ezc+fOBbpofmPx4sXs/vvvl9wbly5dEp8vX76cRUREsMLCQnbs2DE2ffp0Fh8fz/R6vbDJzc1lPXv2ZCUlJayyspKNHTuWDR8+nJlMJrfKQuK1Q9++fdmqVascfr5r1y4ml8tZbW2t2LZ582amUqmYTqdjjDG2du1aptFo2O3bt4XNsmXLmFarZRaLxW9lb0tGjBjBcnNzJdsSExPZggULAlQi/7N48WI2fPhwu59ZLBYWFxfHli9fLrbdvn2baTQatn79esYYY42NjSw0NJRt2bJF2NTW1jK5XM52797tVlmo2eyAFStWIDo6Gg888ACWLl0qaRKXl5djyJAh0Gq1Ylt2djYMBgMqKiqETWZmpsS7Jjs7GxcuXMDZs2fb7Dz8RXNzMyoqKpCVlSXZnpWVhbKysgCVqm04ffo0tFotEhISMGPGDJw5cwYAUF1djfr6esk1UalUyMzMFNekoqICRqNRYqPVajFkyBC3rxutKrLD3LlzkZycjKioKHz33XdYuHAhqqur8eGHHwIA6uvrERsbK/lOVFQUlEol6uvrhU2/fv0kNvw79fX1kszswUhDQwPMZrPNdYiNjRXXoCMycuRIbNq0CQMHDsTFixfx9ttvY/To0Thx4oQ4b3vX5Ny5cwBafnulUmkTTdST69ZpxJufn4//+7//c2pz6NAhpKam4rXXXhPbhg0bhqioKEydOlXUxgDsrvdljEm2t7Zh/xus6kgpT+ydY0c6v9ZMmDBBvB86dCjS0tJw77334tNPPxVB9T25Jp5ct04j3tmzZ2PGjBlObVrXlBz+o/z888+Ijo5GXFwcDh48KLG5du0ajEajeOrGxcXZPEkvXboEwPbJHIzExMRAoVDYPceOcH6uEh4ejqFDh+L06dN44oknALTUrvHx8cLG+prExcWhubkZ165dk9S+ly5dcjurYKfp88bExCAxMdHpS61W2/1uVVUVAIgfJC0tDcePH0ddXZ2wKS4uhkqlQkpKirDZv3+/pK9cXFwMrVbr8CERTCiVSqSkpKCkpESyvaSkxO+pLdsTBoMBJ0+eRHx8PBISEhAXFye5Js3NzSgtLRXXJCUlBaGhoRKburo6HD9+3P3r5tbwViegrKyMrVy5klVVVbEzZ86wrVu3Mq1WyyZNmiRs+FTRI488wiorK9mePXtYr169JFNFjY2NLDY2lj3zzDPs2LFjbNu2bSwyMrJDThVt3LiR/fDDD2zevHksPDycnT17NtBF8xuvv/4627dvHztz5gz79ttv2cSJE1lERIQ45+XLlzONRsO2bdvGjh07xp555hm7U0W9evVie/bsYZWVlezhhx+mqSJfUFFRwUaOHMk0Gg1Tq9Vs0KBBbPHixaypqUlid+7cOfbYY4+xsLAw1r17dzZ79mzJtBBjjH3//fcsPT2dqVQqFhcXx/Lz8zvMNBFnzZo1rG/fvkypVLLk5GRWWloa6CL5FT5vGxoayrRaLZsyZQo7ceKE+NxisbDFixezuLg4plKpWEZGBjt27JhkH7du3WKzZ89m3bt3Z2FhYWzixImspqbG7bLQel6CCFI6TZ+XIDoaJF6CCFJIvAQRpJB4CSJIIfESRJBC4iWIIIXESxBBComXIIIUEi9BBCkkXoIIUki8BBGk/D9WUBKCurv/XgAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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SBQUFSc49//zzNHv2bPFzbGwsLVq0SGITHR1NcXFxA8q3tLSUAFB+fr7k/K1bt0ihUNCBAwfEcxUVFeTi4kIff/yxyfL3hiObWEd1dTXpdDravn27WZtf//rXpNPp6Jtvvhn0/K9evUqPPvoovfPOO4Oetq0ZjHfNqhq8vb0dubm5iIqKkpyPiorCqVOnTH4nJyfHyD46Ohpnz54VHd6bsxHSHEi+psjNzUVHR4ckHX9/f4SEhJhNp62tDQ0NDZKDsZz+muiAbZvp7733Hu677z6UlJTgz3/+M/70pz9JBv6cHasEXltbi66uLqOQvX5+fkahfQWqq6tN2nd2dqK2trZPGyHNgeRrrixKpRI+Pj4Wp7NlyxZ4e3uLB8cGtw5LBO7r6wtg8AXe1NQkWUBz5coVVFRUYP78+ZIuojMzoEG23tMdRGRyCqQv+97nLUnT2nwtpa90UlJSoNfrxePatWvfOb/hhLmNJj0RavCysrJBzfuPf/yj0TmZTIaFCxfiiy++QGlp6aDmNxSxSuBqtRpyudyotqupqTGqXQU0Go1Je1dXV4waNapPGyHNgeRrrizt7e24efOmxemoVCp4eXlJDsZyLKnBJ06cCKA7VntnZ+eg5FtXVyd2p4KCgsQ17wIjRozAW2+9hdbW1kHJb6hilcCVSiW0Wi0yMzMl5zMzMzFnzhyT3wkPDzeyP3bsGMLCwqBQKPq0EdIcSL6m0Gq1UCgUknSqqqpQVFRkVTqM5Vgi8KCgIMhkMnR1dYmx3b8rBw8eFP9+6KGHsG7dOtx9990YNWoUsrKyxDL1fqecDaub6MnJydi3bx/S09NRXFyMtWvXory8HAkJCQC6m7Q/+9nPRPuEhASUlZUhOTkZxcXFSE9PR1paGl588UXRZs2aNTh27Bi2bduGixcvYtu2bcjKykJSUpLF+QJAfX09zp07hwsXLgAAvvrqK5w7d06s+b29vfHss89i3bp1+Ne//oX8/Hw89dRTmDFjBh566CFrHwVjAZY00V1cXPD9738fAFBRUdFvmufPn+/zul6vR01NDQBg8uTJYkvxhRdewKpVq/Dpp5+KU3JFRUXO7U1mIEPvu3btooCAAFIqlRQaGkrZ2dniteXLl1NkZKTE/vjx4zRz5kxSKpU0YcIE2rNnj1Ga77//Pk2dOpUUCgUFBQVRRkaGVfkSEb311lsEwOhITU0VbVpaWmjVqlXk6+tL7u7utHTpUiovL7f43nmazDrOnz9POp2O0tPT+7T75JNPSKfT0ZEjR/q0q6iooJ///Od05swZszaffvop6XQ62rx5MxkMBpM2165do5dffpl0Oh3V1NT0fyMOYDDeNRmRM/98DT6DEZR9OFFYWIhDhw4hMDBQ0rLrzcWLF3Hw4EFoNBo8//zzZu22b9+O5uZmyOVybNy40eTg6GuvvYampibMmTMHDz/8sNm09u3bh4qKCixduhRarda6G7MDg/Gu8Vp0xqZY0gcHIA5y1tTUmF1VWF9fL057dXV14erVq0Y2dXV1aGpqAhFh7ty5feY5adIkAEBxcbHJ68I6jTsZFjhjMVu2bAEANDY2WjzibUkfHADuuusuuLq6wmAwoK6uDgaDQTIlSUR4/fXXJd/55JNPJJ/1ej3S0tIAdM+tC04dzTF16lQAQElJidG93L59G08//fQd74jC1dEFYO4MXn75Zbz33nuorKzERx99BIVCge3bt+PRRx/t83uW1uAymQwajQbXr1/HxYsXcfLkSXz99deYN28enn76aXz44Yfij8T06dNx/vx5lJeXo7W1FW5ubnj55Zdx9epVsVZ+4IEH+r2nMWPGQC6Xo6urCzdu3MDYsWPFa2lpaZgyZQq2b9+O+vp6bNu2DW5ubv2mOdTgGpzpl9u3b6OhoQFPPPEE1Go1nnnmGTz11FM4cuQIvvrqqz6/a6nAASA0NBRAd83c0dGBgIAAlJWVITMzEzk5OQC6WwQ/+tGPoFQqIZPJUFRUhL/85S+Qy+WiuDs7OzF9+vR+85PJZJgwYQKA7ulSgbq6Oty6dUv87Ovri3/+85/9pjcUYYEz/fLee+8ZLe8FgMDAQPz1r3/t87uWNtEB4J577jF5/tSpUxgxYgTa2tqQlJQEmUyGefPmAQD+/ve/4/Lly6JtfX09fvSjH0GlUvWbHwCMHj0aQPdy6M8++wwffvihyX0JFy9eBBHhzJkzd1TfnAXO9AkR9bmEtLm5WVLb9aS1tdWqGlwul0Oj0Yife46ANzc34+c//7n4Q3PfffdJvtvY2AiDwYBXX33V6FpfCHPkQkshPz8feXl5AIBHHnlELHdrayvOnTuHo0eP4tVXX71jNqywwJk+qayshIuLCzo6OhAREYFf/epXOHToEMrKysRlnr19AdTV1SE9PR2LFy82G7bIHLNnzwbQPcI9Z84c+Pr64vr163jsscfE5jTQ7axRqH0B4JlnnsGmTZtEJ46WIqRRXV0NmUwmmXYLCgrC//zP/4gbjHq2VgoKCqzKx1GwwIcply9fRlNTU792whTS6NGjsWDBApw+fRo5OTn4/e9/L+4Cu3HjhuQ7zz33HK5du4b58+cjPz8fAODqatl47r333ouoqCg89dRTAIDVq1cjOTlZFH5PVq5cic7OTixfvtyiPrcpxowZY/K8t7e3OKhmKu07ZaMKC3wY8tvf/hZTp05FWFgYrly50qetMPgUHh4OoHsgbMSIERg5ciQiIiIAQNJE/8c//iFpIgsbPpRKpcXlE/ISmDZtmkk7hUKBzZs3S2p2a1EqlXjggQfQ1NQkaWV873vfE/+eMmWK0fdKSkoGnKc9YYEPM9ra2nDixAmkpqYiLi4O+/bt69NeWNNtared0B+ur68H0D2gdubMGZPpWCNwe7Nw4UI89NBDSElJwdSpU1FfX4+FCxeK1318fMSmv7Arramp6Y4ImMgCH2bs378fM2fOFD+7ubmZ3ePe1dUlBiQQmuM9EQReV1cHALh+/bq4caN3jSjsHByqREVFwdXVFXFxcdi8ebPoK04gOTkZer0ev/jFL8QReuGHbSjDAh9GfP3116KYe+7IMjeXLeybl8vlRvupge7VZ0D3ks7W1lZxJ5iXlxceeOABcZ83MLRr8N6YWvft4uKCHTt2wN3dHWq1GgALnBlivPrqqwC6Bblo0SKx5jU3YCRc9/X1NbmpQ6lUirVZXV2daH/PPfcgJiZGMoB1Jwm8P4QftjvB1TMLfJhw4cIFBAQEAADGjx+PFStWYOPGjQC6p4hMbSoU/MXffffdZtMV5q2rq6vFGl9ozvccqBLCBDsDd1KwBhb4MEHwYgJ8u0579OjRkMlkMBgMogPMnghTXH2NUo8bNw5At6MGwf+4IHBh/lhYZ+4ssMCZIUVbW5so4IKCAnFe18XFRexP9t4yefv2bXGATdh1ZQp/f38A3fPqjY2NAL4dfFOpVJgxY4Y4teYsCPdnjUdfR8ECHwYcOXIEcrkccrkc//d//ye5NnnyZACQRJEBugUvk8ng6uoKT09Ps2kL/Wzhx0ChUEjsw8PDxU0kzoJwz7du3UJLS4uDS9M3LHAnh4jEUfKgoCCjpZzCopTm5mZJk/PkyZMA0K8477rrLskUmL+/v2RAbsyYMWIt7yyMHDlS/BEz5yxiqMACd3Jqa2tBRDAYDHjkkUeMro8ePVocYPv0008BSMXenysjmUwmacKHhYUNVtGHNMLOty+//NLBJekbFriTI+wE8/DwMLvYRBDxqVOnQET46KOPxFq454YOcyxZsgQ3btyAXq9HSEjIIJV8aCO0bMrLy428wZSUlOCpp54aEttKWeBOirDD69KlSwD6rlkXLFiAzs5OuLm5Yd26deJyU8FfeX+4u7tj1qxZ+OlPfzoIJb8z8PHxgVKpBBFJopd2dHTgrbfewuTJk7Fr1y4HlrAbFrgTsHr1aqP9yT/84Q/xz3/+U2xCCnPgpvDw8BBHub29vcX+peBUwRKWL18+bJrnQHfXRBhs6zma/tVXX4k75/R6PY4ePeqQ8gkMSOC7d+9GYGAg3NzcoNVqceLEiT7ts7OzodVq4ebmhokTJ2Lv3r1GNhkZGQgODoZKpUJwcDAOHz5sdb5EBJ1OB39/f7i7u2P+/PlGTvLnz58v7vsVjri4uAE8haHBpUuXoFKpcOTIEfFcTk4OHnnkEfzjH/8QF5gI89XmWLFihWSxS88XmDGN8Ex77ofvvTvvzJkz+M///E9s3rx50MIyWYPVAj948CCSkpKwceNG5OfnIyIiAosXLzba9C9QWlqKJUuWICIiAvn5+diwYQMSExMl0R1zcnKwbNkyxMfHo6CgAPHx8YiNjZVM3ViS729+8xvs2LEDb7zxBs6cOQONRoOHH35YnJ8VWLlyJaqqqsTjzTfftPYxDBnS0tLg6emJwsJCGAwGEBE++eQTyOVycb5WrVb368LIz88PMpkMly5dQlFRUZ++yZluBIH33KwjuI96/PHHxXN33303DAYDPv/8c/sWEAMQ+I4dO/Dss8/iueeew7Rp07Bz506MHz8ee/bsMWm/d+9efO9738POnTsxbdo0PPfcc1ixYgVee+010Wbnzp14+OGHkZKSgqCgIKSkpGDhwoXYuXOnxfkSEXbu3ImNGzfi8ccfR0hICN5++200NzfjL3/5i6RMI0aMgEajEY/eO4fuFI4cOSJZQFJeXo6WlhajwZ2f/OQnFqWXmJiIjo4OrF692qqgjsMVYfqvrq4ObW1tuHnzJpqamiCTyTBlyhSjMYkvvvjC7mW0SuDt7e3Izc1FVFSU5HxUVJRJR3VAd+3c2z46Ohpnz54VX0RzNkKaluRbWlqK6upqiY1KpUJkZKRR2fbv3w+1Wo3p06fjxRdfNKrhe9LW1oaGhgbJMRQgIiO3QX//+99F5wtEhLfffhvnz5/vcy15T3x8fPDee+9Z1fceznh6eorrCsrLy8VADGPGjIFKpcLkyZMRHR0tPs/GxkZJV8oeWCXw2tpadHV1Gf26+/n5mV22V11dbdK+s7NTXD5pzkZI05J8hX/7K9uTTz6Jd999F8ePH8cvf/lLZGRkSJpTvdmyZQu8vb3FQ1hf7Wh6rh2PjY0FAHzzzTfils2xY8ciJiYG6enpDinfcKCn2+Xq6mpxxkIIpAh0+5h78MEHRa8wBQUF4q47ezCgQbbeUydE1Od0iin73uctSXMwbFauXImHHnoIISEhiIuLwwcffICsrCzRk2ZvUlJSoNfrxcOccwR7s3nzZgDd/etp06aho6MDMpkMubm5ALpXmO3cudPkPm5m8BCa6VVVVeIAW2BgoJHdY489Jr73Fy9etFv5rBK4Wq2GXC43qq1ramrM9tk0Go1Je1dXV9FlrTkbIU1L8u25bdHSsgHdCxYUCoXEt3ZPVCoVvLy8JIejWbNmjfjshLXks2bNAvCtA0RTHliYwUeYaSguLkZHRwdcXFxMzli4ubnhBz/4AYDugIz2wiqBK5VKaLVao6DpmZmZmDNnjsnvhIeHG9kfO3YMYWFh4soqczZCmpbkGxgYCI1GI7Fpb29Hdna22bIB3Z5NOjo6htyUkLm+fkFBgUS8wpLJnnuvAYi7xBjb0nsbbHBwsFkPssKS3hs3bvQ57jOYWN1ET05Oxr59+5Ceno7i4mKsXbsW5eXlSEhIANDdpO0ZJjYhIQFlZWVITk5GcXEx0tPTkZaWhhdffFG0WbNmDY4dO4Zt27bh4sWL2LZtG7KyspCUlGRxvjKZDElJSXj11Vdx+PBhFBUV4emnn8aIESPE0cwrV67glVdewdmzZ3H16lUcPXoUP/nJTzBz5sx+I1Hag7NnzwLoDt2TmppqNBpORPjb3/4GoHu9+Pjx48UXTKjRBVjg9sHd3V2yBDg6OtqsrZeXl/j/dO7cOVsXrZuBBBXftWsXBQQEkFKppNDQUMrOzhavLV++nCIjIyX2x48fp5kzZ5JSqaQJEybQnj17jNJ8//33aerUqaRQKCgoKIgyMjKsypeIyGAwUGpqKmk0GlKpVDRv3jwqLCwUr5eXl9O8efPI19eXlEolTZo0iRITE6murs7iex+MoOymqK2tpf/6r/+iv/71r2Jg+uPHj0tsTp48STqdjnQ6ndG9Nzc3i9c2bdpEXV1dg1o+xjzFxcW0du1a+vLLL/u1zcvLI51OR2+++Wa/toPxrsmITPjqYcwyGEHZ6+vr8corr2DHjh2i59GDBw8aDb6MHj0av/jFLwAALS0t+M1vfgMAmDhxIuLj443S3bRpE4DuhRUvvPDCgMrGDIyWlhaL3FLV19fjD3/4A1xcXLBhw4Y+QzoNxrvGa9EHmf6WIxIR0tPT4ePjI1lBZ8pDZ01NDRoaGmAwGPDb3/5WPB8TE2My7fnz56O1tRWPPfbYAEvPDBRLfc75+PhAoVCIcdBtDQt8EKmursbrr78uejcxZyOEDKqpqRH/k3sGmm9uboanpydkMhl+97vf4csvvxRjfPn5+ZldeRcZGYlp06Y5lf8zZ6Onf7rq6mrU19dLQhcPNizwQeTo0aO4ffs2/vd//9esTWVlpeSzsIZcEP3Fixfx7LPPYunSpaLNRx99BKDbh5owqGiOngOczNBEmLa9du0adu3ahbS0NJs5cGSBDyL/8R//AaB7bbIpL6XAt7G+hMB2JSUluH37trggJz09HVOmTMGUKVPEmlrYCvrMM8/Y+hYYOyDU4GfPnoXBYEBXV5fNRtVZ4INIcHCwuFrpgw8+MLpOROJUmBAt89q1a2L/29PTU9KXe/LJJ8W/XV1dMXbsWJuVnbEfvRde+fr62mz9Pwt8EJHL5Vi0aBGA7sUMvf11HT58WFw2GxISIu4EEzaN9B4pHT16tOgyacGCBRZ5V2GGPr03/yxfvtxm/7cs8EFm9uzZYpieDz74QGyqd3V1iUsUvb294evrixkzZgD4dtGDKffEK1aswAMPPGAUUpe5c1EqlWItPm7cOJsuf2aB2wDBIZ9cLsfrr7+O1tZWyRr5F154ATKZzCjuteCgoSdubm6SULaMc/Doo49CJpNZvFd/oLDAbcDcuXPFZaaurq7Ytm2buJll0qRJoneVsWPHSkLsWuLBlHEONBoNXn75ZZtvXmKB24CRI0di2bJlYhRKoNsvHSCN8+Xq6ioZcLHUMQPDWAoL3EbMmDEDiYmJaG1tFc8RkVGkEGGn28iRI4fcjjbmzocFbkNkMhnWr18vfp41a5ZREL6QkBCMHz8ezz77LI+SM4MOC9zGeHt7Y/r06WhubsbDDz9s0mbFihWS5jzDDBa8m8xKBrrDR6/X37HeWxnHwLvJ7iBY3IwjYIEzjBPDAmcYJ4YFzjBODAucYZwY0/5dGbMIkw5DJYQR47wI79h3mehigVuJ4M96qIQwYpyfxsbGAc/C8Dy4lRgMBlRWVoo+03rS0NCA8ePH49q1a0MiAspQgp+Nafp6LkSExsZG+Pv7SzYlWQPX4FZiLjRNT4ZKiKOhCD8b05h7Lt91/QQPsjGME8MCZxgnhgU+iKhUKqSmpooOHZhv4WdjGls/Fx5kYxgnhmtwhnFiWOAM48SwwBnGiWGBM4wTwwJnGCeGBT5AJkyYAJlMJjl6OlgEgPLycvzwhz+Eh4cH1Go1EhMT0d7eLrEpLCxEZGQk3N3dMXbsWLzyyivfaXPBUGT37t0IDAyEm5sbtFotTpw44egi2RSdTmf0bvQM6UxE0Ol08Pf3h7u7O+bPn4/z589L0mhra8Pq1auhVqvh4eGBmJgYXL9+3frCEDMgAgIC6JVXXqGqqirxaGxsFK93dnZSSEgIPfjgg5SXl0eZmZnk7+9Pq1atEm30ej35+flRXFwcFRYWUkZGBnl6etJrr73miFuyCQcOHCCFQkF/+tOf6MKFC7RmzRry8PCgsrIyRxfNZqSmptL06dMl70ZNTY14fevWreTp6UkZGRlUWFhIy5YtozFjxlBDQ4Nok5CQQGPHjqXMzEzKy8ujBx98kO69917q7Oy0qiws8AESEBBAv/vd78xeP3r0KLm4uFBFRYV47t133yWVSkV6vZ6IiHbv3k3e3t7U2toq2mzZsoX8/f3JYDDYrOz25P7776eEhATJuaCgIFq/fr2DSmR7UlNT6d577zV5zWAwkEajoa1bt4rnWltbydvbm/bu3UtERLdu3SKFQkEHDhwQbSoqKsjFxYU+/vhjq8rCTfTvwLZt2zBq1Cjcd999+PWvfy1pfufk5CAkJAT+/v7iuejoaLS1tSE3N1e0iYyMlKxiio6ORmVlJa5evWq3+7AV7e3tyM3NRVRUlOR8VFQUTp065aBS2YfLly/D398fgYGBiIuLQ0lJCQCgtLQU1dXVkmeiUqkQGRkpPpPc3Fx0dHRIbPz9/RESEmL1c+PdZANkzZo1CA0NhY+PD7744gukpKSgtLQU+/btAwBUV1cbxYH28fGBUqkUAxFWV1dLQhkB38aOrq6uRmBgoO1vxIbU1taiq6vL6Dn4+flJgjE6G7NmzcI777yDKVOm4MaNG/jVr36FOXPm4Pz58+J9m3omZWVlALr/75VKpVEwyoE8NxZ4D3Q6HTZt2tSnzZkzZxAWFoa1a9eK5+655x74+Pjgxz/+sVirAzAZqYSIJOd729D/H2Bzpignpu7Rme6vN4sXLxb/njFjBsLDwzFp0iS8/fbbmD17NoCBPZOBPDcWeA9WrVqFuLi4Pm1617gCwn/c119/jVGjRkGj0eD06dMSm5s3b6Kjo0P89dZoNEa/yDU1NQCMf+HvRNRqNeRyucl7dIb7sxQPDw/MmDEDly9fxqOPPgqgu5buGYuu5zPRaDRob2/HzZs3JbV4TU2NGMvOUrgP3gO1Wo2goKA+Dzc3N5Pfzc/PBwDxPy08PBxFRUWoqqoSbY4dOwaVSgWtVivafPrpp5K++7Fjx+Dv72/2h+ROQqlUQqvVIjMzU3I+MzPT6hf1TqatrQ3FxcUYM2YMAgMDodFoJM+kvb0d2dnZ4jPRarVQKBQSm6qqKhQVFVn/3KwakmOIiOjUqVO0Y8cOys/Pp5KSEjp48CD5+/tTTEyMaCNMky1cuJDy8vIoKyuLxo0bJ5kmu3XrFvn5+dETTzxBhYWFdOjQIfLy8nLKabK0tDS6cOECJSUlkYeHB129etXRRbMZ69ato+PHj1NJSQl9/vnntHTpUvL09BTveevWreTt7U2HDh2iwsJCeuKJJ0xOk40bN46ysrIoLy+PFixYwNNk9iI3N5dmzZpF3t7e5ObmRlOnTqXU1FRqamqS2JWVldEPfvADcnd3J19fX1q1apVkSoyI6Msvv6SIiAhSqVSk0WhIp9M5zRSZwK5duyggIICUSiWFhoZSdna2o4tkU4R5bYVCQf7+/vT444/T+fPnxesGg4FSU1NJo9GQSqWiefPmUWFhoSSNlpYWWrVqFfn6+pK7uzstXbqUysvLrS4L7wdnGCeG++AM48SwwBnGiWGBM4wTwwJnGCeGBc4wTgwLnGGcGBY4wzgxLHCGcWJY4AzjxLDAGcaJYYEzjBPz/wAey+agwjL7wwAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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16nHKy8vxj3/8A0FBQXj55Zeduu/2wOsFAAsWLMCQIUPcclxn3Gve1U7oBBQUFMBoNCI7O9vTVQGgbgldYXml4ZEdyV7w4UrAteGhroDE60akOZzKyso8WJP7uFu8zc3NYkiqIyBd+Pvq1aserInjkHjdyI8//ij+v3btWoewQGoOJOnKB87C399fOIQ6ktNKKt7q6mrZ+44OideN8MW3OBUVFR6ph8lkwqeffoqrV6+qWt7Q0FCnH1Oj0XTImUVKsVZVVTllv+54MJN43Uh1dbXsfU1NjUfqce7cOZw5cwbvv/++SIY+bNgw8bmrnHVcvB1p9QTlot/tFW9TUxNef/11vPnmmy5vYVAmDTeSl5cne++p1eKlThreGujevTteeOEF+Pn5wc/PNbcFHx7z1HmrobS8ygeso3z88cdoampCU1MTioqKROZMV0CW102oNaM8dRNL50nzkNPg4GAMGDDAqTOJlPDgj47ksFJOxrDH8paVlWHjxo0Weajr6upw/vx58d7V/WcSr5uQ9vN+9rOfAXCteO3tc3FL46qmspSOKF4uMB6BZ4949+7dCwB47733ZNuV01NJvJ0EPle4W7duQiiuEm9JSQm2bNmCr776SmwzmUw4fPiw1fnP7ggY4UNQHanZzOvCJ7i01mz+4osvrH52+/Zt2XtX93lJvG6CL7QVFBTk8pv47bffhtFoxGeffSa2nT17Ft9++63F5A4OX4rTlXRky8vFW1VVZbXVYjQacfLkSfHe19dXNnbPfQl8neL2zDW3BxKvm+DiDQwMFF5Xdy572doqfe4QL3eEdUTx9urVCxqNBs3NzVZ/F6WXvLm5WYwYMMbE/zze/uLFi7h+/bqrqk7idRe8SRUYGChLQu5srKVWtTURws/PzyVRVUo6crM5ICBAPMCs/S5qfdgTJ06AMYbTp08LKzx+/Hjx+bvvvuvkGt+HxOsmvv/+ewAtIuI5jF0x3qnMBsKt3IkTJ8S2gIAATJgwQbwPCgpqV0YSe+mI2TS4ILVarfBFWBMvrzdPIgHct678+gYGBqJnz54yr73ZbHZJ3Um8boI/lUNCQoR4XbHgtDLwo66uTiaW559/HqtWrZKFQLrLErryodUWmpubxe+i0+nsFq+yFSN9YPJr+eKLL1p8z9mQeN3A7du3hTNjwIABYsHpO3fuOP2HVXpLb968iZ9++km858NUUmsgnTftSjpSeGRxcTGOHTsGoMXxpNfrhXitRb7x30qv12PFihViu7RVM2rUKACAj4+P6OO76uFI4nUDFy9eFP/z5hm3QnzlvKysLOzcudNiuMFRvv76a9n7mpoalJSUALgvXOD+4l8A8PDDD7frmPbSkSzvO++8I65VYGAgNBqNiOluzfJKhS4lKCgIU6ZMEe95H99Z8dJKSLxuQLouLV/ukq9Zy3/Yzz//HJWVlfjkk0/afBzpTcdvxM8++0zMU5X21R566CEkJibi97//Pbp3797mYzpCR7G8ytYO74vza2bN8iqXPVUuwvbkk0/KQkt5KmFlJJazoNhmF8MYE8MFv/zlL4VjiDedlTdyexbi4pE/ANC7d29xE/KbRzocpNFoEBcX1+ZjtQVuefmEfHc4ydRQzqXmYuTivXHjhqifyWRCbW0tevToIbO8gOVvpTyfkSNHIjQ0VNbicSZkeV1McXGxEGjfvn3Fdh6wYDQaZVMD25NDSeoAi4qKsvjcHSGQtuCWlzHmUY/zhQsXZO+5GKUtEz5h49ChQ/j73/+OEydOWIh3/vz5sv08+OCDsvfDhg3D9OnTXZbMn8TrYqT9HelC1dJoI+mK8c6Y0ePr64tHH30UMTExsu3Sh4cn8PPzE+ftyaZzbm6u7D23vNLfh/8mfJHxU6dOWYg3KioKL7zwAsLCwjBr1iyXJDGwBYnXyZw9exb/+c9/xBAE9zQOHz5cVk4qXmnzyxmeST61T5nT2hkLZbeXjuS04qgFsHz44YcWYZJ8xEAq0gEDBmDp0qUYOXKkS+uoBonXiTDGkJGRgUuXLolpd9bW/5GKVzq8Y+9MFMaYbLhH+gDgsbVSj/LEiRM91seUInVaffHFF9i2bZtbF3hTu75SiysNrlDms+ZDbu1ZpcOZkHidiLTPyScAWFv/h79vamqS9f/sES9jDO+88w7+9re/ibzL0gguvm/pTTlixAiHz8cVcMtbW1uLkydPoqamBsePH3fb8dVSD3HnIdCS/pWj7Btz3OWdbw3yNjuR/Px82fuKiopWLa+ymdzQ0NCqJ7ayshI3btwA0NInGzFiBDIyMgBYin/hwoWoq6sTQ1OehlteaVYRtcTvruDw4cP49ttvAbQ4p3i/VjodMjAwEOHh4WJlSjXcEQduD2R5nYg0kgkAdu/eLfIzK39waTSP1PKazeZW+70ffvih+P/QoUMA7ltZZbL0vn37yvJTeRrev5Sm4nFHxsZr164J4QLAkCFD8MADD6BXr14WqWq4r0DqSOS42yllC7K8TsRWJI20CQvcf9rfuXPHInD97t27NmcBKZt+ZrNZNM+dvdKBs+E3v1Sw7ujzKpMQ/PznP4evr69qK8faGDwAt4+N24IsrxOxNdFAOV+W3yB37961+J4tT6zaGsK8iefn5ydzUnVElA8xoKX+rk6VqpxXy8fT1bontuY2q9XfU5B4nQgXndTpweGOGg4XL4/gkZaxJd4jR45YbOPhlz169OjwK91JAyGk/PDDDy49riNDcFIHFiAXbEdZIA4g8TqN+vp6YUH79OmDDRs2YPTo0eJzZV/J39/f4gnPh3hsiVfaD+M3FW+ud6T+mDWsjTUfPHgQQEsAxc6dO50azO9oxhKleKXWmcTbCeE3W2BgoBDlQw89JD5XCkuj0ciauDqdTsTW2hvAwC01HyfuSE06a/Tu3dvqZ2azGf/9739RWVmJw4cPO+2YylBMaaYLNZRRbnPmzBH/d5RhIoAcVk6DB0lIlwoZMGCA+F/5NAfkfStpbqt79+7BaDTiL3/5C3x8fLBixQro9XrZbJdx48aJ4SIu3o5kFayh0WgQEhIiugpz587FBx98AEDuuFLr27cVLt6goCAsWrTI4UyZgwYNwoIFC1BdXd1hAjQAsrxOg4tXemP4+flhyZIlWLx4sepq89KyQUFBwpKeOXMGmzdvhslkgtFoFPNOpV7mxMREIXZvEi8gbyEMHz5c5Ey+dOmS1e98//332Lhxo5ib7Ajcsx0YGIiQkBC7Is34xA4ecTVkyBA8+uijDh/blZB4nQQXr7If27NnT9GXVSJtNt+4cUOIVxm0wPvS/BhDhgyBj4+PEC+3WN4i3hkzZqBbt2549tlnAdxPeK5MS8uvg9lsRlpaGoCWtLaOzkjinmZHgiueffZZPPbYY3j++ecdOpY7IfE6CTXL2xpSaxwREWF1dT5lbiV+DGUf1xv6vEDLJPU//vGPIoOHteEt/tBSpvbhMcd37tzB+++/36qnmnualUE0tggODsbkyZMtRgk6EiReB6mvr8fly5ctxiW5R9MR8UotwZw5czB48GDVcjwAg4uXi1npBPMWy6vE2hxm7rhTZrbgsdB79uzB1atXceDAAZv752GOnpj540rIYeUgBw4cQHFxMWJjYzFjxgyxvS2WNzo6GgUFBRgwYIDqEEpMTAwuXLggLAfv81oTr7dYXiXW0s5w8UpXKeCYTCZZcEtDQ4PV8+d9Xncs6eJOyPI6gMlkEl5Q5XSxtojX19cXs2bNEhkHlfCABi5enpyOD7d0FsubkJAge8+b0Vy8arHPt27dknnwt27dKuK8lfD9dOQmcFtok3h37dqFqKgo6PV6xMbG4tSpUzbLZ2VlITY2Fnq9HoMGDcKePXssyqSnpyM6Oho6nQ7R0dGq43ytHZcxhtTUVBgMBgQEBODxxx+XZW5sL8qbiDfHmpubhRVo79OdJ6gbP368bNqg2WwWsbadrdkcHByM5cuXi/fcgcVFx1scv/71r4Vg1bJs5ufnq46RS5ea6Uw4LN60tDQsX74c69atQ15eHhISEjB9+nQUFRWpli8sLMRTTz2FhIQE5OXl4ZVXXsHSpUuRnp4uymRnZyMpKQnJyck4f/48kpOTMXfuXNk6svYc989//jPeeust7NixA2fPnkVkZCSmTJnSrqRuUpReTn6j8P6uj49Pu5/uCxYswMyZM/Hkk0/KlgfhSdP8/f3FTdhZms1Ay/j4c889h/nz54vzu3fvnmxOrcFgEEM4xcXFqrHk0tlKHBLv//PWW2/hxRdfxMKFCzF8+HBs27YN/fv3x+7du1XL79mzBwMGDMC2bdswfPhwLFy4EL/5zW/wxhtviDLbtm3DlClTsHbtWgwbNgxr167F5MmTsW3bNruPyxjDtm3bsG7dOjz33HOIiYnBe++9h3v37uHf//63o6epirJvphSvM5YNCQoKwqhRo+Dv7y8TLw+LNBgMIn65M4kXaEkYMHToUNmKEtJUuFqtVoy78qmWSpTT+BhjJF6g5SbKyclBYmKibHtiYqLVdV+zs7Mtyk+dOhXffPON8KJaK8P3ac9xCwsLUV5eLiuj0+kwadIkq3VrbGxEbW2t7GULZZOMv+cW2dmxxVLx8vBLaQZI5c3YWW5OLt4rV66Ia/z0008DaL1booxjbmxsFFMuO8v14TgkXj71TBkiFhERYXUt0vLyctXyJpNJNHGsleH7tOe4/K8jddu8eTNCQ0PFq7UUncr5nfy9Mhm3s1CzvNLzU1ra9qSN7UhIU+VweCbMfv36ycrOmTMHv/vd74QTT/mA5VZXp9M5JTNnR6JNDitl07C1tC1q5ZXb7dmns8pw1q5di5qaGvFqLZ7WWrPZHeLlx5YGcnSEhHKuQJl+JjIyUjyounfvLnuA9e3bF7179xYBH8oHbGdtMgMOjvOGh4fD19fXwpJVVFRYDdiOjIxULe/n5yeGQqyV4fu057jcQ1leXi48tq3VTafTOSQ4ZT/LXeJtbGwU525tPqw7Fsd2F9IMjkBL1gspAwcOFEnRuSitzYXuzOJ1yPJqtVrExsZaxKBmZmZa5AjmxMXFWZQ/duwYxowZI8IDrZXh+7TnuFFRUYiMjJSVMRqNyMrKslo3R1F6kvkkeOkar86E76++vh5GoxE+Pj4WDyKe9kZ5g3szymmDyn6uNLqN30NdUbwOdwJWrlyJ5ORkjBkzBnFxcdi7dy+KioqQkpICoKUpWlJSgv379wMAUlJSsGPHDqxcuRKLFi1CdnY29u3bJwtpW7ZsGSZOnIitW7di1qxZOHLkCI4fP47Tp0/bfVyNRoPly5fj9ddfx5AhQzBkyBC8/vrr6Natm2pmi7bA+0wjR47EuXPncPnyZZjNZpdbXk5YWJhFpoxp06Zh/PjxHT79TXtQTuxQ69u3Jt7OFqABtEG8SUlJuHXrFjZt2oSysjLExMQgIyMDAwcOBNCyiJN07DUqKgoZGRlYsWIFdu7cCYPBgO3bt2P27NmiTHx8PA4ePIhXX30V69evx+DBg5GWliZbN7a14wLAqlWrUF9fj8WLF6Oqqgrjxo3DsWPHnBYWx2+EUaNG4dy5cwBaPKKuEq9yf2pNZuWk/s5GcHCwRd9+9OjRyMvLk90f0uElqZ+DLK+CxYsXY/HixaqfvfvuuxbbJk2aZLE+jJI5c+bIMhY4elyg5UZOTU1Famqqzf20BaPRKIa2IiIi0L17d1RXV6OgoECELzpbvL6+vvDz8xNT4zqzSJVotVoYjUbVBdN69eqF1atXy0QdGhoKHx8fGI1GVFdXi2vVmcVLsc12wm8CHx8faLVa0Y8uKSlxmeUF5MNBnl7lz50sXLgQEyZMwLRp01Q/V1pjPz8/4bS8ceMG7t27h8bGxk4t3s418OVCrl69CuB+FNXQoUORkZEhy+zgCvFKnTOdbVaMLXr16uWwE65fv34oLS1FYWEhPvroIxiNRtHV6IziJctrJzyKigcOqE2cV0t146zjAl1LvG2BDzH98MMPoivDg1tIvF0YHiQhnb6mHIKylu6mPUiHTTrjDehM+JxotQkLnfHaUbPZTqZPn47HHntMNkwxZcoUETet1+tdkhZU2hTvjDegM7GWRkij0XhFTmtHIfHaiY+Pj+rN8dJLL+Hy5cuYMGGCS47bu3dvXLt2DYB3JFX3JNaizPz9/Tv8ShJtgcTbTiIjI4WX0xVMnjwZGo0GDz/8cKeNZXYWPj4+0Ol0FkkT3LWEqLsh8XZwtFqt1eESwhK9Xm8hXuUqjJ2FzteWILo0Uh8BDy995JFHPFUdl0KWl+hUSP0Czz//PEpKSjB8+HAP1sh1kHiJToXUadWnTx/ZelGdDWo2E50K6ZRJVwTNdCTI8hKdivj4eNy9exfR0dGerorLIfESnQpfX98u452nZjNBeCkkXoLwUki8BOGlkHgJwkshh5UEPvG9tZUTCKK98HtMuc6zI5B4JfAFyVpbOYEgnEVdXZ3VqYytoWHtkX4nw2w2o7S0VDVjIdDytOzfvz+Ki4u7VD6p1qDroo6t68IYQ11dnWzhOEchyyvBx8fHYi0cNUJCQugmVYGuizrWrktbLS6HHFYE4aWQeAnCSyHxOoBOp8OGDRtckuLVm6Hroo6rrws5rAjCSyHLSxBeComXILwUEi9BeCkkXoLwUki8BOGlkHhVeOCBB6DRaGSvNWvWyMoUFRXhF7/4BQIDAxEeHo6lS5eKxa04+fn5mDRpEgICAtC3b19s2rSpXYHoHZFdu3YhKioKer0esbGxOHXqlKer5FJSU1Mt7g1p0n3GGFJTU2EwGBAQEIDHH38cFy9elO2jsbERf/jDHxAeHo7AwEDMnDkTN27ccLwyjLBg4MCBbNOmTaysrEy86urqxOcmk4nFxMSwJ554guXm5rLMzExmMBjYkiVLRJmamhoWERHB5s2bx/Lz81l6ejoLDg5mb7zxhidOySUcPHiQ+fv7s3/+85/s0qVLbNmyZSwwMJBdv37d01VzGRs2bGAPPfSQ7N6oqKgQn2/ZsoUFBwez9PR0lp+fz5KSklifPn1YbW2tKJOSksL69u3LMjMzWW5uLnviiSfYI488wkwmk0N1IfGqMHDgQPbXv/7V6ucZGRnMx8eHlZSUiG0HDhxgOp2O1dTUMMYY27VrFwsNDWUNDQ2izObNm5nBYGBms9lldXcnY8eOZSkpKbJtw4YNY2vWrPFQjVzPhg0b2COPPKL6mdlsZpGRkWzLli1iW0NDAwsNDWV79uxhjDFWXV3N/P392cGDB0WZkpIS5uPjwz799FOH6kLNZits3boVPXv2xMiRI/Haa6/JmsTZ2dmIiYkR68ECwNSpU9HY2IicnBxRZtKkSbLomqlTp6K0tFQsHObNGI1G5OTkIDExUbY9MTFRrJzYWbly5QoMBgOioqIwb948FBQUAAAKCwtRXl4uuyY6nQ6TJk0S1yQnJwdNTU2yMgaDATExMQ5fN5pVpMKyZcswevRohIWF4euvv8batWtRWFiIt99+GwBQXl4uyw8MAGFhYdBqtSgvLxdlHnjgAVkZ/p3y8nJERUW5/kRcSGVlJZqbmy2uQ0REhLgGnZFx48Zh//79GDp0KG7evIk//elPiI+Px8WLF8V5q12T69evA2j57bVaLcLCwizKOHrduox4U1NTsXHjRptlzp49izFjxmDFihVi28MPP4ywsDDMmTNHWGMAqvN9GWOy7coy7P+dVZ1ptT+1c+xM56dk+vTp4v8RI0YgLi4OgwcPxnvvvYfx48cDaNs1act16zLiXbJkCebNm2ezjNJScviP8uOPP6Jnz56IjIzEmTNnZGWqqqrQ1NQknrqRkZEWT9KKigoAlk9mbyQ8PBy+vr6q59gZzs9eAgMDMWLECFy5cgXPPPMMgBbr2qdPH1FGek0iIyNhNBpRVVUls74VFRWIj4936Nhdps8bHh6OYcOG2Xzp9XrV7+bl5QGA+EHi4uJw4cIFlJWViTLHjh2DTqdDbGysKPPFF1/I+srHjh2DwWCw+pDwJrRaLWJjY5GZmSnbnpmZ6fBN6M00Njbiu+++Q58+fRAVFYXIyEjZNTEajcjKyhLXJDY2Fv7+/rIyZWVluHDhguPXzSH3Vhfgyy+/ZG+99RbLy8tjBQUFLC0tjRkMBjZz5kxRhg8VTZ48meXm5rLjx4+zfv36yYaKqqurWUREBJs/fz7Lz89nhw4dYiEhIZ1yqGjfvn3s0qVLbPny5SwwMJBdu3bN01VzGS+//DL7/PPPWUFBAfvqq6/YjBkzWHBwsDjnLVu2sNDQUHbo0CGWn5/P5s+frzpU1K9fP3b8+HGWm5vLnnzySRoqcgY5OTls3LhxLDQ0lOn1evbggw+yDRs2sLt378rKXb9+nT399NMsICCA9ejRgy1ZskQ2LMQYY99++y1LSEhgOp2ORUZGstTU1E4zTMTZuXMnGzhwINNqtWz06NEsKyvL01VyKXzc1t/fnxkMBvbcc8+xixcvis/NZjPbsGEDi4yMZDqdjk2cOJHl5+fL9lFfX8+WLFnCevTowQICAtiMGTNYUVGRw3Wh+bwE4aV0mT4vQXQ2SLwE4aWQeAnCSyHxEoSXQuIlCC+FxEsQXgqJlyC8FBIvQXgpJF6C8FJIvAThpZB4CcJL+T+8BssC1T8knAAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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e+3vswhMxff755zhz5gzi4+PR2NiIlJQUAL+KhDYuJpNJ1rLSdB988AHq6+tF5++qZaqqqoLVasXo0aM9uk9vcTgcwpDBoUOHAEB4aFv//v3xyy+/iBoFlvnz56vmYWhOTLQQc3JyMG7cONn1NrSSsgXe1NQke76NGzcKr6nwLly4gPfeew/jx4/HFVdc4ausO/H9998D6AwEDBw4EIBz3++rr74SfYf2gahI6OK96Oho2Wuw1ue7775T/Azo3O66T58+TuJhLZPVasV//vMfAEB6errQ1/Ml7IJEvV6PDz/8UHhPLZPceGJSUhKGDh3q8/wIeVHtzAGCmveYmBiYTCYMGzbMKY2cS+PJ7GfaIpeWluLEiRPYsmWLL7KsCO3/tLW1OfVtAMi2vrQ/IA0ssKtvWSZOnKh4/draWmEY4fDhw9i7dy927drlVFFZcbERQF/1pWw2G06dOiVYSrbPK92o1FV/SKkMfIUmxLR3715s374ddrtd1GkH5AtQzjJRMbmabUx/TNYfV2sJAiua6dOnC2JiXSy5sa+IiAgAzlOH5IIPANzuZUgHwPfs2SMco4KREzi7QM9ut6O+vh7vv/++k/AJIR71UwHgww8/xGuvvYa9e/cCEN83td4Uev9ySMvE1wS9mJqamrBjxw58//33+Pbbb4UfiA5ayomJpmFFQX9saWRPDtZlUmsPhJqaGuH1FVdcIdtnkquMNPwtvW+5sLgn0Ptjl8LT6/bp08cpT2zjYrVasXPnTvzwww/4+OOPRef96quvsGbNGqHPo4TD4RDWppWUlABwPYDuSjBcTG74+9//LryuqanpsmWirau7AT1CiKjyqBUWZlv7+Ph4WSvgSvjSbaaVLBMAzJ07V/Se7edQiyQ3/kTFxOaJFZPNZhMaBemMdbrRzb///W/FfAEQ9QnpdVyJydV9Br2bt27dOqSmpiIsLAw5OTmqbsARFRXlkWWyWCxwOByioAOtmNJpOXLfZV0ZX4WF29ra8OOPPwrv6X2MHDkSgPNMBcD1I3ekYnIVCEhPT8eQIUOE96zlraysdHLJ6GvqUrF5UloG4SoS6mrIgQ6YA7+umHZV5q4scFets6eoKqZ33nkHy5Ytw5///Gfs378fkydPRl5enmo7x0RGRgo/DK1Mcg8CqKiowPr164UZDhSLxeK0X7eUixcvdmtqkhIvvfQSXn31VUFQtMJSEciJyVV/zRsxAeKHRksr3dmzZ0ViovdMxUTfE0JQX18vpGOnX7kKR3tahjRfriwTm3fpFtxBbZmee+45LFiwAPfccw+GDx+OtWvXIiUlBUVFRapcr7S0VHjtSkxAZwWRTjNpaWkRKqtSKPnkyZOiWem+EJPNZhPO+eqrrwL4tT8ndVddbRpSUFAgvJbet7uni9x6663C69DQUNGTHTs6OmSn4EjdPOlULtYLceV+eTqJmA46u7NMv//975GUlITbbrtN9JnafSbV7F5HRwfKysrw2GOPiY7PmDHDacEbpStPDgwJCZGNwNGWkG2N+vXr57KfYbVaUV5eDqBTTGwrS9m+fbvovacRKVds2rRJ9L69vd2p7ydnmeh95+XlYcyYMaLWXyoedxWJdfOam5txxx13CI1Te3u7bN9QKiappWe57LLLRO8jIyOFBsRTMdF1aa7EZDQaMWzYMAwbNswpXdAGIBobG2G325GQkCA6npCQoLiKs7CwENHR0cIfHc1XwuFwuN04g61UYWFhslNqKEePHhUCEC0tLViyZInLcwPyYz3ewvaVgM5+CnW7pGJiKx77dHWpG6XX60WVx11FkrpHer1eGCiWrg+jSPtMroIJ7PmPHDkiOueePXsUBcKKHACefvppJwt41VVXOeUJcL7noHbzAGdfmT7iRA5vnhzY1tam+LnZbBZeS8XkqrPLuonjxo1DbGwslixZ4rK/wVomT3bE+fDDD/HGG2+AEILdu3fLbhC5Y8cOoYzotel8sosXL4pWxQLKLhR77960yvRa9DtKHoK0z+QKNgAhHezev38/NmzYIPs9qQAcDgcqKytFx2bOnIkxY8bguuuuE5WFTqfzqkHpLqq5efHx8QgJCXGyQg0NDU7WiuLNkwPfeOMN/PTTT7KfXXvttcJr9sfw5qmEdJJsbGwsli5diq+++krWPaWVpLW1Fa+99hoWLlwouD9S2traBDdy+/btwrIJKenp6UJDER8fD0AcQLBYLMIKWUB+AivQeb90nMiTikRdr1GjRgH4teyULBO9T7vd7nYSsLuGRslb8WToQa/X44YbbpD9zGAwOO3cpBaqWSaj0YicnBwUFxeLjhcXF7ucwuIpno50s5XQm0VhUrdHKhAasrbZbCCEYO3atWhubsbf/vY3xXOyfQolIdFrSxf7sa0srRysm+fuHjwJC99zzz2YM2cOhg8fLrq2O8sEiK1Tfn4+8vLyRGm7so+dJ7smGQwGl5FCtjENWjEBwAMPPIBXXnkFr732Gg4dOoT7778fdXV1WLRoUbfPzVZuaWGyFYcVU319PW6//Xanc02YMMHpmNS1YytObm6usPK1ra0Nzc3NopZZaVn8N998I3uckp+fD6DTdaSViL0XWjFoxWW3RJaDreDSAIAcUVFRGDFihFCe7sQUFhYmpLVarUJeBw8ejLFjx4pm4dO8urJgrBVqaGjA6tWrceLECcX0M2fOxEMPPeTynjQjprlz52Lt2rVYuXIlRo8ejdLSUnz00UdCx7Y7sGKSVhS20NixhgkTJmDw4MGiDUWkaQAgOTkZl19+uegYK9icnBwhdN7c3OxU2ZRWcrqr0FSw7e3tQkvOiklqmdxNYmVh+5Ge4k5MBoNBuDbbAFB3etasWVi4cCGAznKy2WwuI3esmCoqKkSNwbRp05Ceni5KHx0d7fbe2boQ1IO2QOeWUidOnIDFYkFZWZlo/KI7sJaCXQQIiMWh0+nw8MMPY/bs2UJfStpCSS3bXXfd5XRsxIgRSE5OxqRJkxARESHMTu7o6HDqUyhVGFcuy9133y1YQ3a+H5tXqWWi/5Va3O5uW0yvp5TvkJAQIQ2bZ7aCs79FYWEh9u/fr3g9ep2qqiphfRJlwIABuPnmm0XHPBGHZiyTmrCWSRpYkIqrT58+yMrKEgpfWqjsGpfIyEjF5d0LFizA9OnTAfz6I0nHxgDl6JarGQtxcXFCxWPF6SvL1BXcVT6dTidcm1qvkJAQUZ5jYmKE1w6HA59//rnwftCgQaLzUWtM10OxGI1GhIaGin5bT8SkKcukFqxlMhqNWLJkCSIiIvB///d/br8rrSTh4eFYtmwZpk+fjj/+8Y8eXZ8Vk1QkSpbpl19+UTxfeHi4YJmoRdHr9aJIHWuZCCHCdZVC9+7G6dzhagdYCnV3Dxw4AMB9xJTtM0kjcK4GwCMiIqDT6YTFf4DrWRUUf1qmoF1pK7VMsbGxbjujFLlK0qdPH0yaNMnj67OV5ttvvxV9pmSZ5I6PHDkS2dnZ0Ov1TqJQqsxWq1V0TWmfjzJ58mQ0NTUJ1tRbpBavT58+woAptRDU3aXjZZ5ayXnz5jn1S3/55RckJSXJpqe/N7v4zxNL421EsztoxjJ5gyctrjfnkFqmt956Cxs3bnTad05uzGTKlCkYPHiwR/liLdOOHTvc5v/yyy/HggULuhzwkbPglFtuuQUAkJaWJkojdbGVoCug2aAC7XfJVXpqhdg8eGKZ2DS8z6QAa5m8HcOQFmpXWixX05KAzgmxdEHczz//jE2bNslaJrZySM8pbSSUNlVRa4MQV2KieaNjUhTp1CigcxMTlujoaOFeb731VuEctHyUBr0B7y0NWzbcMinA/rDS7ZDdwbpToaGhXa6MSqPuFLq2Z+PGjYrjJa5aciVL1dHRIbhDvhgAV0IqZjav9DOdTicaT5Iry6FDh4qiuNJoK/Uy6EwKGoAZMWKE07nYPpe3lsldA9hdglZM7I/mbZ+A/TG701pJ+zhyLeq5c+cUJ8NKn+QHiH98aWVm3TwaRlZztx1pMIGNzLFlyAZclBoY1p1T2p+C7uFBAzAzZsxA3759MWXKFCEtO+vfk99OKlw1CdoABAA8+uijqK6uFqb2eEp3XEQWaWWXEw27kFCKnA8/dOhQYa8/V30mGhlUsx8gbSzY6CBbSdPS0lBVVQVAOZrnqgFjxUQbCZ1Oh+joaDz44IOitFdffbVg5T2xTGr3k1iC1jIBnT92Zmam1+ab/RG6s7GkVExyLZ/D4UBiYqLwnhWynEVlzyk9P62QTU1NQiPgqn/RXaRiSk5Oxr333oslS5aI7pW1sHSalRT2XqS/F3Xd6uvrZadRSfNA8UQoau3eKkdQWyZf0J1ZAtLKfvvtt+P1118XHWPdFvp+8eLFCA0NlQ1ps7On5ULTgHiXWaUVwb5Abuk72zBQdDodHnnkEVy6dEkxP64qPi2fkydPup3hbTKZsGDBAuh0Oo8s04gRI3D8+HGfTGFzR68XU3dgK7tOp5P9wSwWiyiKFxISIiyrkMOVWyi1Qkaj0aMK1VW8sfjh4eGK412A2NJIG7DBgwcLe+K5s0yA2Dq5Q6/XY9asWR6n7w5B7eZ1B19MwWHdIKPRKFv5PvnkE5GYpJM1pYwZM0Z4LXUbpbuV9oRHuXgKey/SviUdvDUYDB6JqafSa8VEn+0qF371FE/30aaDuikpKcjNzXWZ9rrrrhNes8uxAYim0gBwmkEQLEjFRN1Jq9UqNBBcTEHE+PHjkZWVhdmzZ3f5HOwP7smmIHPnznW7DMNgMOCJJ57AjTfe6OTOSC3VvHnzPM9sF6FBEm+mWrlDGvRhvQQaqfP3s7B8QfDJ30cYDIZuCQnw/gf31LXU6XROVokyduxYYV6e1FKpwcSJEzF8+HDRGFNXGT9+PPbs2eNknUNCQqDX6+FwOISlF0pbEvRkeq2Y1CI/Px/79u3D3LlzRVs3A75xXfLy8pCQkICMjIxun8sTpDO1u8O0adOQnZ0tu/DSaDT6ZNu0QNJr3Txf8Zvf/AbAr5s45uTkYNGiRYiJiRFNs7n++ut95rpkZ2ervm2VGoSGhir286T3w26KEyxwy9RNRo8ejSuvvFI2ksfOrhg/frw/sxV0SMe0aIAomOCWyQcojcfQjVr85ZIFM9LIKA9AcEQkJCTg4YcfdjmYyemEXZ/mj9kKasDFpDJqzp3TEqxlUuvB0mrD3TxOj6CrO+/2JLiYOD0CLiYXnDhxAgsWLEBqairCw8MxZMgQLF++PKjmk3H8hxbEpFqf6fDhw3A4HHjppZcwdOhQHDhwAAsXLkRbWxueeeYZtS7LCVK4mFwwc+ZMzJw5U3g/ePBgHDlyBEVFRVxMHCe4mLykpaXF5dSUrjw5kKMN2LG6YBWT3wIQNTU1+Mc//uHyCRjePjmQox3YBYP+3LfBl3gtphUrVkCn07n8++6770Tfqa+vx8yZMzFnzhzcc889iuf25smBHG2RmpoKnU6H/v37B+XsBwDQES83QWhsbERjY6PLNIMGDRIG4err65Gbm4tx48Zh48aNXi2FPn/+PKKjo9HS0uLXjTE4gaG5uRkREREBsUy+qGte95ni4+Nd7mHAcvr0aeTm5iInJwcbNmxQfRNATnDjyQPZejKqBSDq6+sxdepUDBgwAM8884xooxC5HW44nGBHNTF9+umnOHbsGI4dO+a0/Lq7D+HicHoiqvldd911Fwghsn8cjhbhnRgOx0f06CUY1IrxwVuO2tA61h3PqUeLiT5ahA/ecvxFa2trl7ec9nqcyZ84HA7U19cjMjLSaSDv/PnzSElJwalTp/gYlAReNvK4KhdCCFpbW2E2m7s8hNOjLZNer3e7r3RUVBSvMArwspFHqVy6+xAEHoDgcHwEFxOH4yOCVkwmkwnLly8P2un6asLLRh61y6VHByA4nGAiaC0Th9PT4GLicHwEFxOH4yO4mDgcH8HFxOH4iKAQ06BBg5z2mXjsscdEaerq6jBr1ixEREQgPj4eS5cuddrwsrKyElOmTEF4eDj69++PlStXam5JyLp165CamoqwsDDk5OTgyy+/DHSWVEVuTxJ28SkhBCtWrIDZbEZ4eDimTp2Kqqoq0TksFguWLFmC+Ph4REREYPbs2fjxxx+9zwwJAgYOHEhWrlxJzpw5I/y1trYKn9tsNpKRkUFyc3NJeXk5KS4uJmazmRQUFAhpWlpaSEJCApk3bx6prKwk7733HomMjCTPPPNMIG5JFbZs2UIMBgNZv349OXjwILnvvvtIREQEOXnyZKCzphrLly8nI0eOFNWNhoYG4fPVq1eTyMhI8t5775HKykoyd+5ckpSURM6fPy+kWbRoEenfvz8pLi4m5eXlJDc3l4waNYrYbDav8hI0Ynr++ecVP//oo4+IXq8np0+fFo5t3ryZmEwm0tLSQgghZN26dSQ6OppcunRJSFNYWEjMZjNxOByq5d2fjB07lixatEh0LD09nTz22GMBypH6LF++nIwaNUr2M4fDQRITE8nq1auFY5cuXSLR0dHkX//6FyGEkObmZmIwGMiWLVuENKdPnyZ6vZ7s2LHDq7wEhZsHAGvWrEFcXBxGjx6Np59+WuTCffPNN8jIyIDZbBaOXX/99bBYLCgrKxPSTJkyRTT6ff3116O+vl54wncw09HRgbKyMsyYMUN0fMaMGfj6668DlCv/UF1dDbPZjNTUVMybNw+1tbUAgOPHj+Ps2bOiMjGZTJgyZYpQJmVlZbBaraI0ZrMZGRkZXpdbj541TrnvvvuQnZ2NmJgYfPvtt3j88cdx/PhxvPLKKwCAs2fPIiEhQfSdmJgYGI1GnD17VkgzaNAgURr6nbNnzyI1NVX9G1GRxsZG2O12p3JISEgQykCLjBs3Dq+//jrS0tLw008/4amnnsLEiRNRVVUl3LdcmZw8eRJA529vNBqdnibflXILmJhWrFiBJ5980mWaffv24aqrrsL9998vHLvyyisRExODW265RbBWgPxjGwkhouPSNOT/Bx+CddNDOeTuUUv3JyUvL094nZmZiQkTJmDIkCHYtGmT8BzhrpRJV8otYGIqKCjAvHnzXKaRWhIKLaRjx44hLi4OiYmJ2Lt3ryhNU1MTrFar0ColJiY6tTQNDQ0AnFuuYCQ+Ph4hISGy96iF+/OUiIgIZGZmorq6GjfddBOATuuTlJQkpGHLJDExER0dHWhqahJZp4aGBkycONGraweszxQfH4/09HSXf9KHBlP2798PAEIBTZgwAQcOHMCZM2eENJ9++ilMJhNycnKENKWlpaK+1qeffgqz2awo2mDCaDQiJycHxcXFouPFxcVeV4pgxmKx4NChQ0hKSkJqaioSExNFZdLR0YGSkhKhTHJycmAwGERpzpw5gwMHDnhfbl6FKwLA119/TZ577jmyf/9+UltbS9555x1iNpvJ7NmzhTQ0ND5t2jRSXl5OPvvsM5KcnCwKjTc3N5OEhARy2223kcrKSrJ161YSFRWlydD4q6++Sg4ePEiWLVtGIiIiyIkTJwKdNdV48MEHyRdffEFqa2vJnj17SH5+PomMjBTuefXq1SQ6Opps3bqVVFZWkttuu002NJ6cnEw+++wzUl5eTq699lpthsbLysrIuHHjSHR0NAkLCyNXXHEFWb58OWlraxOlO3nyJLnxxhtJeHg4iY2NJQUFBaIwOCGEVFRUkMmTJxOTyUQSExPJihUrNBMWp/zzn/8kAwcOJEajkWRnZ5OSkpJAZ0lV6LiRwWAgZrOZ3HzzzaSqqkr43OFwkOXLl5PExERiMpnINddcQyorK0XnuHjxIikoKCCxsbEkPDyc5Ofnk7q6Oq/zwtczcTg+ImjGmTicng4XE4fjI7iYOBwfwcXE4fgILiYOx0dwMXE4PoKLicPxEVxMHI6P4GLicHwEFxOH4yO4mDgcH/H/AGkzFdG/brHSAAAAAElFTkSuQmCC\n", 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\n", 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bbetucPH2UCwWC1vVk5yczOZ3qeNEc3Mzm85x1vIC8taX3hRcnQGgK43Onj3LjkU4+MXFy+mWNDQ0sJHlXr16WTlffPDBB8xqKhUvHZGmZaTYsuhKCQ8PZ2I9evQoAHEcLC5eTrdEuCjAz8+PLXanlle4DloqMhqqpqGhQbbbLPyNnHjd0W2mdUr90YODg9mxcPFyuh2EELz33nsA7oiLClJoTSlSkVHPNbpgBBBbXuFv7FledzjO0EgblKCgoB4jXh6ArgcinFahURmp2OSc/XU6negzXQV07do11m2Viteel5W7LC9g7VrJLS+ny0MIQWlpqSgsq6N8++237P2UKVMAiEeQpYKTxo+66667ALR7aFGBSLvNVFRy01HutLz05iOsl/YibC0X7S5w8fooJ06cwL/+9S98+OGHij3LhFkK6IitsJsrtLxZWVlWv9dqtUycVLxSy0vdF3/66Ser31PL6w7xCldB0bbRtkizMXQ3uHh9FLqcrr6+XnFECRrIfNKkSWwbFe+5c+dYYL/w8HCbAouKihJ9loqXdq3lrJ+7RpuBO0sUhZ+poJ3xmfYluHh9FKFglUaUkHNNpEJqa2vD//73PwDWAdCFSEPXSLvN9Hu5eV53Wl4ALEBA79694e/vj+HDhwNov0koTTXqS/ABKx9FuBROaRYBOh0ktFpyVtCeZZRaPDqvS7HnIulOywu09wIWLlzIbhjBwcHo1asX6uvrcf36ddx9991uqaerwcXrYSoqKthql6lTpyI5OdnlfZpMJtGKIKXJt+hzqnAgSs7K2rO8UvFKrag98bpztJkijaYRHh6O+vp6XLt2rduKl3ebPQghRLRM7csvv3SLs7x0KZ+SOE6EELdYXukUjbTbTC2xJ90j7UFHxLvzYn0uXg8iXaYGwC0xpKXzl0qe61pbW5ljhdDyygnVnuUVTtGo1WqrwaHOtrxS6PO8NBl4d4KL14PQZFhCJwd3hCuVWlol4qVW18/PT2T5lIpXeExyFpRuM5vNVr2NzrC89PmXHm93hD/zehAq3qFDh4IQguPHj7slsLuzlvfs2bMsw19ISIjd0LuAffEKo3LK/Va6ski4Lxps35Pilfpqd0e45fUA9fX1qKqqYvOlUVFR7GJ3h3ilF6Sj4v3HP/7BEoRJB5yA9iwVwmdXe5kMhKPLcuL19/dn26VdZ/pZOkLtTujxdWfxcsvrAf7617+KRoAjIyNZdgB3irdPnz64desWrly5gpdffhnBwcF44YUXZAOvST2d5IQ5YcIEjB8/Htu2bUNzczOio6NttkEoWLn6VCoV1Go1WlpamKUFxN1oT1peoXg7Sgrgq3DL62YIIVZTNwMGDPCI5aUuiNSSNTU12cz6cPz4cdFnqU8wxc/PDwsWLMCiRYvsdpuF2BKG3KCV8H1ndJsB4ODBgx6rx5tw8boZamEpwcHBUKlUbGrFHd04+swrl0DN1s3hzJkzos/2Ap0HBQXJdqul0HjLo0ePlv1eTrzUCvv5+Vm5VLoTYfe/u6ax4eJ1Iw0NDVaJw6nQpN04V6D7lAtWbsvbSmodbVleJfzqV79CcnKyzRCr9iyvJ593KXTFlLNB7ro6TonXXoZ6OYqKihAfH4+goCAMHTrU6gIHgPz8fMTGxkKj0SA2NhaffPKJ4nr37NmDmTNnsvAo0q6iJ7l06ZJsulG6CIBaOrPZ3GEOn46QdpuF2PK2ogsEHnjgATzwwAO49957XWoD0L4of+rUqTa7v3KOGu72a7YHnc7qrv7NisXbUYZ6KZWVlZg9ezaSk5NRVlaG1atXY8mSJcjPz2dliouLkZaWhoyMDJw4cQIZGRlITU3FkSNHFNXb0NCABx98EBs2bFB6WC5RXV2N3NxckXjDw8MxZswYlhkiMDCQXcyuJoW2122WE69wbnny5MmYPXu2Q91iV6ECFQ5YdcY0EcXRVKMXL17Enj17rFZnmc1mVFVVidrflVAs3o4y1EvJycnBoEGDkJ2djdGjR2PBggV45plnsGnTJlYmOzsb06dPR1ZWFmJiYpCVlYVp06YhOztbUb0ZGRn44x//iIcffljpYTkNIQR///vfrbb/8pe/xOOPPy5aueOu6Qv6e+kieVv7Fj7vdoZoKfYsb2d0mx0V74EDB1BeXo5//vOfou3ffPMNtm/fjq+++spjbXQFReJ1JkO9raz3x44dY3c0W2XoPp2p1xFaWlpQV1cneilFmO2O8uijj8omjHaHeFtbW1nQt5CQECQlJSEoKAizZ8+W3XdNTQ327dvndH2u4CuWlyZck649LioqAgAcPnzYA61zHUXidSZDva2s921tbSxfja0ydJ/O1OsI69evh06nYy9pJEJHkI4uA9YL1Sn0udeVbjP9rb+/P9RqNaZPn46VK1eyVTVS8XrTaviC5XVk/KGr5sByasDK0Qz19spLtzuyT6X1dkRWVhaMRiN7OTOlIBfFgg5SSaHODMLUpc7W16dPH3bsKpXKplUXZixYtmyZ0/U6g7cHrITitSXA3bt3y27vqoIVomgMXUmGeoqtrPcBAQEshKitMnSfztTrCBqNxuWVLXQwaNasWQgNDcWwYcNs3lDodlesDhWvdJqIire1tRWtra2sDtpNnTNnjlUUSE8j122mg22OOoC4gvC/FWY/FCKd/7ZYLPDz8xPdBOlN9/Dhw/jiiy8AtN/4vZ3zWZHldTRDvRBbWe8TEhLYBWarDN2nM/V6kqamJhw9ehQ3btxg4u3fvz+b6rIFXYjvSrdZaHmFqNVqdpEJLzz63tlsfK4gJ146Gm7Pb9pdBAQEsHPi6HQR7RkIl3NaLBZUV1cz4QIQzYR4C8Wz1x1lqM/KysLly5exY8cOAEBmZia2bNmCFStWYOHChSguLkZubi527tzJ9rl06VJMmjQJGzduxLx587B3714UFhaKupcd1Qu0r92srq5m0f6///57AO2W3VZX1hlef/119p56CTmS3Z0KqL6+Hg0NDXa9nGxBbxZS8VIvrtu3b6OxsZFZWXqjcKYuV5HrNtP2dIZ4VSoVNBoNmpqa0NLSgrNnzyI4OJhF1pAGlwfal1sGBQWxFWGUc+fOiT67Y2mnqygWb0cZ6mtqakRzr9HR0di3bx+WL1+OrVu3Qq/XY/PmzWz+EwCSkpKQl5eHNWvW4KWXXsKwYcOwa9cuJCYmOlwvAHz66ad4+umn2ef09HQAwNq1a2EwGJQeqizSAQ6z2Qx/f3/REjlbCK3yV199hTlz5iiuny4ul7tZhISEMPHStlGL05lTRBRvW16gvXve1NSEffv2oaqqCgCwatUqfPnllygrKwNwZw6+sbGRna/PPvtMtB/6WwodbPUmTvmN2ctQ//7771ttS0lJQWlpqd19zp8/H/Pnz3e6XgB46qmn8NRTT9ndh6vIxSEODw+XXVkjh1qtZlNfSsXb1tbGnvulMZsA66koah38/f1l54Q9jZzl7WzxhoSE4ObNmyLxXb58WRR4XqPRQK1Wo7Gx0WZIIdqb0+l0MBqNXUK83LdZIXJ/mtycri2ENyi53LX2aGxsZAMqcoNPUvHSbAr9+/f3ypI4Od/mzhav3OPCsWPHRJ9TUlJYr6ipqUnkrEEHROl/NX36dADt59jba4W5eBVy6dIlq21K5oeFibGUOoUIoz7KiVEaPYJaaVdG5F1BmP8IaBcx7UJ3puWVIsykMG3aNCQkJLDR7++//170PQ1kRwkPD2c3Tm9bXy5ehVDL8eCDD7JtdMrLEfz8/NjzsdJEWHIhW4VILS91IHHnYJ0SpMsg6bkLDAzstGkWe8/6SUlJeOihhwDcudEIF7MEBASI0qkEBwejX79+bOBRzkGnM+mea6U8CB0tjYyMxJw5c1BXV2c34oQcjrrtSVEqXmp5vS1eOvdMlyt2ltUFxFE+AgICRI8qwgFRuV5QQECAyPJOmjQJAQEBiIiIwLlz57h4fQWLxYLt27ezkfQ+ffpYJblyFGEuXCUoEe/169fZXKW3us1qtRr+/v4wm82or69n0y+dOecsXMsbFxfHnnfHjRsnmiGQy7bYt29fREREICgoCCqVii2jpN1mpcHu3Q3vNjvI7t27RVNgcmtpHUX6LOgoHYlX2E0VZrbvDG8mOVQqFbvQL168iIqKCgDWmf08SWxsLIB2wdGMiID1IKN00QvQHtNLq9WyWQ56c6Q9B6VpZtwNt7wOkpiYyAYy9Hq9S/OmVEzOiteWGIWWl04TjRgxwslWuofevXvjxo0bouAKSsYIXOWuu+7CmjVr4O/vLxpgkj5KxMTE4D//+Q/7vGzZMnbjkUYdoZ+9bXm5eB1k8ODBeP7553HlyhXExcW5tC9nlwYq6TZ3lTWoclNajji0uBPqBSecNpI6ufTu3RuzZs3C559/jqioKLt+4ELL683IlFy8CnCXmyUVmVIf547ES7cLMxR09mIEKXLzrO6In+UMwcHBiIuLg9lslm1DYmKiaBDLFvS3ZrMZzc3NXnGAAbh4vYKzkSQ7Ei918xO6I44ZM8bJVroHuYUa3hIvAMybN8/lfQQEBCAoKAjNzc2or6/3mnj5gJUX8FS3Wbhvilycq85EKt6xY8d6NORrZ0FvQO6Iw+0sXLxewNmIGs6I19vdZqF4Bw0ahMcee8yLrXEf9LzKZYLsLLh4vQAd8HBHUmwpQvHef//9TrTOvQjF642VTZ6Ci7eHQsVrMpkcjuFsNpvZs6w98QqfJ731LCZE6AbZFdrjLrh4eyhqtZp5/jjadRZ6Y9mL1iH0XvJk8mpHEbaBi9e9cPF6ASW5i0wmEwghTLyBgYF21w5TjyJaj7fpToIV0hXEy6eKvERISAiMRqNdy1tZWYkPP/wQ99xzD4vV1ZE1DQsLw+zZs3Hu3DkkJCS4tc3OIHQj9ebIrLvpCl5W3PJ6CUemiw4ePAiLxYLy8nLm2udIV/iBBx5Aenp6l7B6KpUKEydOhL+/v2gZpa9Dz21rayvy8vK8EiqWW14vQf98e2t6hXf1yspKAF3jOVYpM2bMwJQpUzol0HpnIfwfvv/+e9TW1toMtu8puOX1Eo6IV2iVaUB4XxQv0DkZEjoT6biDNx4JuHi9REfiJYSIxHvt2jUAvive7ohwrbA3lgdy8XqJjsRrMplk4wp7a20ux5rnnnuOveeWtwfRkXhtDWR5I3g6R5677roLU6ZMAeBDlrejDPVSioqKEB8fj6CgIAwdOhQ5OTlWZfLz81m6kNjYWNHibUfrJYTAYDBAr9cjODgYkydPxqlTp5w5RI9DR5ttiZdOIfXu3Vs0atydXAy7A3Rtsk9YXkcy1AuprKzE7NmzkZycjLKyMqxevRpLlixBfn4+K1NcXIy0tDRkZGTgxIkTyMjIQGpqqigfjCP1vv7663jjjTewZcsWHD16FJGRkZg+fbrXw5XI4ajl1Wq1nZosnKMMKl6vXGNEIePHjyeZmZmibTExMWTVqlWy5VeuXEliYmJE255//nkyYcIE9jk1NZXMmjVLVGbmzJkkPT3d4XotFguJjIwkGzZsYN83NzcTnU5HcnJyHDo2o9FIABCj0ehQeVe4fv06MRgM5NVXXyUWi8Xq+7KyMmIwGMgHH3xACCEkOzub/PnPfyb19fUebxvHca5evUoMBgMxGAzEbDY7/Dt3XGuKLK8zGeptZb0/duwYc7S3VYbu05F6KysrUVtbKyqj0WiQkpJis20tLS2oq6sTvToLnU4HlUqF1tZWWS8ranlpN3np0qV44YUX+DNvF0O4XlqaLtTTKBKvMxnqbWW9b2trY15DtsrQfTpSr63sAPbatn79euh0OvZSkvnAVQICAliXSy5BN/Vl5lNDXRthAHl6ndXX13eKx5VTA1ZKM9TLlZdud2Sf7ipDycrKgtFoZC/qCNFZ2POP5eL1HSZPngygXbwHDx7EX/7yFxw9etTj9SoSrzMZ6m1lvQ8ICGAhQG2Voft0pF4aGE5J2zQaDXr37i16dSb2FuVT8fJ53a6P8NorLCwEYJ3MzBMoEq8zGeptZb1PSEhgLnO2ytB9OlJvdHQ0IiMjRWVMJhOKiopsts3bUPHKPWvTmM7c8nZ9qHiFCbeFETw9heKFCR1lqM/KysLly5exY8cOAEBmZia2bNmCFStWYOHChSguLkZubi527tzJ9rl06VJMmjQJGzduxLx587B3714UFhbiwIEDDterUqmwbNkyvPbaaxgxYgRGjBiB1157DSEhIXjyySddOkmegsYOlsuyzi2v7xAcHMzy9lKEETw9hWLxdpShvqamRjT3Gh0djX379mH58uXYunUr9Ho9Nm/ejMcff5yVSUpKQl5eHtasWYOXXnoJw4YNw65du0QxdDuqFwBWrlyJpqYmLFq0CDdv3kRiYiL279/v1VCj9qBzvXKZE7jl9S369+8vEq/SDJDOoCKdMSzmI9TV1bE7aGc8/545cwYfffQRwsLCsGTJEtF3b731Fm7cuIGnn34agwYN8nhbOK6xd+9eUXpQoD3XUUVFBZ566imrDA3uuNa4b7MXoc9KN2/etLpTd5SXiNO1kAt8cPjwYdTV1aG0tNQjdXLxepFevXqxbnFeXh5z1iCEsG5zV4iGwekYe+GMXMkoaQ8uXi+iUqlYd6q6uhqbNm0C0D5YRZ9muOX1DeLj42W39+/f32Pxs7l4vcyNGzdEn2nyKqDdC6u7RaDorgwaNAjDhg2z2u7JdDNcvF7mkUceEX2uq6vjz7s+yq9//WusXbtWlBPZkwOfXLxeJi4uDn/84x/Zc1FdXR1/3vVxhDddT+aK4uLtAqhUKnaHNhqN3PL6OML/jXebewD0Ds0tr+/TWZaXx23uIggtL10FxS2vbyJMrsa7zT0AYSwkanm5eH0T4f9GF594Am55uwhC8VJfbN5t9k3Gjh2Lc+fOISIiwm5SOFfh4u0iCLMG8mde30aj0SAtLc3j9fBucxdBmHiMjzZzHIGLt4tALW9bWxtb38stL8ceXLxdhMDAQPj7+wO44zIpzHLP4Ujh4u0iqFQqq2wInpzg5/g+XLxdCKl4PTlSyfF9+NXRheAB1TlK4OLtQghHl1NTU73YEo4vwMXbhRgwYAB7P3LkSC+2hOMLcCeNLkRiYiIGDBiAqKgoNvLM4diCi7cL4efn16n5kji+De82czg+Chcvh+OjcPFyOD4Kf+YVQMOtdmaSbU7PhF5jriQs4eIVcPv2bQDgg0acTuP27dtOR9vguYoEWCwWXLlyBaGhobIJuevq6jBw4EBcvHix03P5dmX4eZHH3nkhhOD27dvQ6/VOu8FyyyvAz89P5ChhC28k4vYF+HmRx9Z5cTW+FR+w4nB8FC5eDsdH4eJVgEajwdq1a3nCawn8vMjj6fPCB6w4HB+FW14Ox0fh4uVwfBQuXg7HR+Hi5XB8FC5eDsdH4eKVYciQIVCpVKLXqlWrRGWqq6vx6KOPQqvVIjw8HEuWLIHJZBKVKS8vR0pKCoKDg3H33Xdj3bp1Ljmid0W2bduG6OhoBAUFIT4+Ht988423m+RRDAaD1bURGRnJvieEwGAwQK/XIzg4GJMnT8apU6dE+2hpacHvfvc7hIeHQ6vVYu7cubh06ZLyxhCOFYMHDybr1q0jNTU17HX79m32fVtbGxkzZgyZMmUKKS0tJQUFBUSv15PFixezMkajkURERJD09HRSXl5O8vPzSWhoKNm0aZM3Dskj5OXlkcDAQPLuu++SiooKsnTpUqLVasmFCxe83TSPsXbtWnLPPfeIro2rV6+y7zds2EBCQ0NJfn4+KS8vJ2lpaSQqKorU1dWxMpmZmeTuu+8mBQUFpLS0lEyZMoXcd999pK2tTVFbuHhlGDx4MHnzzTdtfr9v3z7i5+dHLl++zLbt3LmTaDQaYjQaCSGEbNu2jeh0OtLc3MzKrF+/nuj1emKxWDzW9s5k/PjxJDMzU7QtJiaGrFq1ykst8jxr164l9913n+x3FouFREZGkg0bNrBtzc3NRKfTkZycHEIIIbdu3SKBgYEkLy+Plbl8+TLx8/Mjn3/+uaK28G6zDTZu3Ih+/fohLi4Or776qqhLXFxcjDFjxkCv17NtM2fOREtLC0pKSliZlJQUkXfNzJkzceXKFVRVVXXacXgKk8mEkpISzJgxQ7R9xowZOHTokJda1TmcOXMGer0e0dHRSE9Px/nz5wEAlZWVqK2tFZ0TjUaDlJQUdk5KSkrQ2toqKqPX6zFmzBjF542vKpJh6dKlGDduHMLCwvDtt98iKysLlZWV+Nvf/gYAqK2tRUREhOg3YWFhUKvVqK2tZWWGDBkiKkN/U1tbi+joaM8fiAe5fv06zGaz1XmIiIhg56A7kpiYiB07dmDkyJH48ccf8corryApKQmnTp1ixy13Ti5cuACg/b9Xq9UICwuzKqP0vPUY8RoMBrz88st2yxw9ehQJCQlYvnw523bvvfciLCwM8+fPZ9YYgOx6X0KIaLu0DPn/wSq53/oqcsfYnY5PyiOPPMLejx07FhMnTsSwYcOwfft2TJgwAYBz58SZ89ZjxLt48WKkp6fbLSO1lBT6p5w9exb9+vVDZGQkjhw5Iipz8+ZNtLa2srtuZGSk1Z306tWrAKzvzL5IeHg4/P39ZY+xOxyfo2i1WowdOxZnzpzBz3/+cwDt1jUqKoqVEZ6TyMhImEwm3Lx5U2R9r169iqSkJEV195hn3vDwcMTExNh92UpmXVZWBgDsD5k4cSJOnjyJmpoaVmb//v3QaDSIj49nZf773/+KnpX3798PvV5v8ybhS6jVasTHx6OgoEC0vaCgQPFF6Mu0tLTg9OnTiIqKQnR0NCIjI0XnxGQyoaioiJ2T+Ph4BAYGisrU1NTg5MmTys+bouGtHsChQ4fIG2+8QcrKysj58+fJrl27iF6vJ3PnzmVl6FTRtGnTSGlpKSksLCQDBgwQTRXdunWLREREkCeeeIKUl5eTPXv2kN69e3fLqaLc3FxSUVFBli1bRrRaLamqqvJ20zzG73//e/L111+T8+fPk8OHD5M5c+aQ0NBQdswbNmwgOp2O7Nmzh5SXl5MnnnhCdqpowIABpLCwkJSWlpKpU6fyqSJ3UFJSQhITE4lOpyNBQUFk1KhRZO3ataShoUFU7sKFC+RnP/sZCQ4OJn379iWLFy8WTQsRQsh3331HkpOTiUajIZGRkcRgMHSbaSLK1q1byeDBg4larSbjxo0jRUVF3m6SR6HztoGBgUSv15PHHnuMnDp1in1vsVjI2rVrSWRkJNFoNGTSpEmkvLxctI+mpiayePFi0rdvXxIcHEzmzJlDqqurFbeFr+flcHyUHvPMy+F0N7h4ORwfhYuXw/FRuHg5HB+Fi5fD8VG4eDkcH4WLl8PxUbh4ORwfhYuXw/FRuHg5HB+Fi5fD8VH+D9Hq3gK4HtjVAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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7VXk+2MayNmALPvPNlthzc3NRUlICAFYLVfoqJHbCY5iK2rQa3xbPbkvsgP12O199prnlxzhBQUEAbIudd99xbocgHom9HfDTTz/hhx9+8PnBH1zUfn5+ZtV4Zz27Xq8Xi5PYC7LZEztfebZz584t5mOvGm8wGMTYe34NfD07X4bEfptz+fJl7N27F/v377fyNr4GFzX36K317BUVFWCMoXPnzjbnogN/LjVluhcAY8wpsdvz7FqtFowx+Pv7IzY2FkDLY/F9ARL7bY7pQ+brm1xYir21nt20Cm9vfbjevXtDIpGgqqpKDJutq6sTebVF7DzoFxYWJmbbkdgJt2M6SszXq5L2xO6sZ7906RIA+1V4oEmovP/9jz/+APBnFV4ul4u8m8Oe2Pn02K5duwqx+/q9B0jstz2mYvd178JFzYXW2n72loJznP79+wMAjh07BsC59jrQsti7dOkimguVlZU+3+dOYr/NMRV7VVUVcnNzxTBSX8MVnl2n00Gn00EikbTYVz5w4EAATRNidDpdq8Xe2NhoNn7f1LN37doVEokEer3e50fakdhvYxobG8WDx4Wzb98+bN682Se9jCsCdNyrR0REtDgCznTXnmPHjokIur2gniWBgYFigk1tbS0OHjyIL7/8UsyF79q1K6RSqXh53LhxA0BTIPC3334TE258BRL7bUxVVRUYY5DL5cKLAU1dU76yA+mtW7eEV3RFgI5PYmmpCs/h9+X06dPinvCqd0tIJBLh3S9fvoydO3fi5MmTYuIOPw/fVIKL/cKFC8jLy8PmzZt9anosid1NMMZQV1fn1jxMH96RI0eaTfPkw0K9SXl5OVauXIl33nkHlZWVLqnG8y23+vbt65A9D9KdOXNGtN3tbVJiC97Xnp+fb5beo0cPIXLTHWQAmG1QcuLECYfzcjckdjfQ2NiIr776CllZWfj666/dlg8Xe3h4OHr06IEFCxbgnnvuAeAbYi8sLBR/r1u3rs0BusbGRnHNza0ca4otYTsjdr4CDo/oc5KTk0W3H6/G85iAaaC0uLjY4bzcDYndDdTX14uH48iRIy7dAJIxhsuXL4MxJkaHde/eHUDTlkp8bfzff//d60M4TZeFYoyJXXT5ohDOevYrV67AaDRCLpc73O728/NDTEyM+K5UKh0O0AHWW0NNnToVr776Ku666y6RZil202640tJSl+1B31bcLva1a9ciJiYGcrkcCQkJ2Lt3b7P2u3fvRkJCAuRyOfr27Yt169ZZ2WRnZ2PgwIGQyWQYOHAgduzY4XS+jDFkZmZCpVIhKCgIY8aMERMb2kpwcDCeffZZ8X3jxo1if/m2YDAYsGzZMqxbtw7//Oc/RX8zFztgvoDi0aNHAQD79+/HJ5984lFvbzAYrHoFeP58gomzATruMSMiIpzabHHKlCl4+OGHMXPmTMyaNcvh4wBrsSuVSlF+Dhf7rVu3xMuYwxjD2bNnfSJg6laxb926FRkZGVi8eDEKCwsxatQoPPDAA3bXBistLcXEiRMxatQoFBYW4rXXXkN6ejqys7OFjVqtxvTp05GamoojR44gNTUV06ZNw4EDB5zK991338V7772H1atX4+DBg1Aqlbj//vtFkKWtREREYMKECeJ7Tk4O3nrrLRQVFQFoWp3lk08+sdqTrjl4JBpoevB5F5RpsKpr166Ij48HABw+fBi1tbX44YcfUF5ejvXr13tso8Ly8nLo9XrI5XK88MILZr9xsTgboOMe09EAGyc0NBSDBg1Cv379RPvaUUzF7u/vL9alN8XUs2u1Wuj1ekilUrESzpYtW/Ddd985la87cKvY33vvPTz99NN45plnMGDAAKxatQpRUVH4+OOPbdqvW7cOvXv3xqpVqzBgwAA888wz+Mc//oGVK1cKm1WrVuH+++/HokWLEBcXh0WLFuG+++7DqlWrHM6XMYZVq1Zh8eLFePjhhxEfH4/PPvsMNTU1+M9//uOy6x8yZIiZ1zUYDNi+fTveeOMNLF++HOXl5di3b5/DWwyZip3Tt29fq22kx48fD4lEgsuXLyMvL8/st2+++aYVV+I8vHsqJiYG3bp1M6s68wi3s9V4LnZn2txtxfTFIpfLbdYoTMXOvXp4eLjZCjqHDx+2OVHp7Nmz+Pbbbz2yx6DbxN7Q0ID8/HykpKSYpaekpIi2myVqtdrKfvz48Th06JBo99iz4ed0JN/S0lJoNBozG5lMhtGjR9stG9DUFueDOvinOWQyGWbPnm0meFvYq+lYwtv+ycnJmDBhAv72t79h5syZVnadO3fGnXfeCQBiKy0eqT958qRbvHtpaSmKiorEA827yPr27QuJRGIWPecruvJqPGPMoWpuaz17WwgICBCDdyzXuuOYit20h2TYsGFmtS7L3WUYY/j3v/+NwsLCFrc8cwVuE3tVVRUMBoPVWzgiIsJq73SORqOxad/Y2Ci8nz0bfk5H8uX/OlM2AFi+fDkUCoX42NtgwJKUlBTExcVZvYA4jgTwGhsbhbeMi4tDYmIiBg8ebHOfMgCiKs9JT0+HQqGAwWDA7t27HSq3I1y+fBkFBQX44osvsH37dvz444/QarWivc6rsjxwCPy5RZPp+PSWvHttba1oYnlS7ADw4IMPYujQoWbNMlP4wpQGg0HEJbp164agoCA8/fTToufg0qVLqKmpEV2ypqvo8JejO3H7xo6W1R7GWLPBFVv2lumOnNNVNqYsWrQI8+fPF991Op1Dgr/jjjvEQx8bG4uQkBDo9XqcOnUK//3vfx3y7OfOnYNer0fnzp0dWmUlPj4e1dXV0Gg0SExMhEKhQHJyMr777juo1Wr069fPrqdyFL1ejw0bNphFm48cOSLa5L179xaLRMTHx2PPnj1mL0nTF1VL7XZePQ4NDbVqtrgblUrV7NDcgIAAyGQy1NfXi5ecads+MjISFRUV+OqrrwA0veQWLFhgFjD1xG6xbvPs4eHh8PPzs/KUlZWVdttcSqXSpj3fnbM5G35OR/LlYnGmbEBTtTw0NNTs4yw9evRAUFAQQkNDxeiua9eutTiu+uTJkwCAfv36ORSJlkgkSE5OxrRp04RXHTp0qBgk8tlnn7W5nciDUabcvHkT+/fvB/DnRBSgaehpRkYGnnrqKdGkkEql4lpa8uy8Cu/Ii84b8Jca/380Fbvl6reNjY0oKysz8+zXrl3Dtm3b3Bq1d5vYAwMDkZCQYBVtzs3NRVJSks1jRowYYWWfk5ODYcOGiTXG7dnwczqSb0xMDJRKpZlNQ0MDdu/ebbds7iAoKEi0521595qaGvGff+rUKQBNYm8LycnJ4m9+ztZQWVlp1VXJq9f8gbfstrJEIpE4PLDGG+11Z7DcINL0+5133onw8HCzGkllZaXVTrPHjx8X3anuwK3R+Pnz5+PTTz/Fxo0bcfz4ccybNw/nz59HWloagKZq8eOPPy7s09LSUFZWhvnz5+P48ePYuHEjNmzYgJdfflnYzJ07Fzk5OcjKysKJEyeQlZWFvLw8ZGRkOJyvRCJBRkYG3nnnHezYsQPFxcV48skn0alTJ6f7YdsKf+tbttvPnj2LlStXYteuXaitrRUDVEzbvq0hMTERQ4YMAdD6UXZ6vR4ff/wxfvnlF5E2ceJEpKammj3QLQUmAccj8r4udtM17WQymahB8e8vvPACFi5cKF62VVVVYrER0/vkzra7W9vs06dPx9WrV7Fs2TJUVFQgPj4eO3fuFA9sRUWFmUeLiYnBzp07MW/ePKxZswYqlQoffvghHnnkEWGTlJSELVu24PXXX8eSJUsQGxuLrVu3IjEx0eF8AWDBggWora3F7NmzUV1djcTEROTk5DjdD9tWoqKikJ+fD7VajZEjRyI4OBiMMWzfvh2MMRw8eFB44C5dulgN6GgNkZGROHz4MA4fPoyRI0eKrjBHsZxkk5CQgL/85S8AmrobDxw4AD8/P4cWdXTEs5sOVPFVsZt68rCwMLtNLV7b4UOJJRIJHn/8cRw8eBB79uzB6dOnzWpfrsTtAbrZs2dj9uzZNn/btGmTVdro0aNb7IaYOnUqpk6d2up8gaabnJmZiczMzGbP425M23N5eXl46KGHUFpaataG517dVf3LvN1769YtbNu2DU888YRTx1sGk0w9U3JyMnQ6HeLi4hyKLTji2XU6HRoaGsyWiPY1TMXO40u2sCz/oEGD0LlzZwwaNAh79uzBhQsXcObMGbG2nSuhsfFepkuXLuJBKSkpAWMMx48ft2nrquCUUqkUc8HPnTuHiooK0X4sLy9HbW0tamtrUVxcbHNct+USTKYPcKdOnTBt2jQMHjzYobI4Inbu1bt37263q9HbmIq9uXH7SqVSdNUplUpMmTIFQNM9HDduHOLj4x2e0ecsbvfsRPNIJBLMnj0bK1asEPPQueecPHkyqqqqxEAfR+dwt4Sfnx/mzZuHlStXwmAwYP369QCAu+66CyUlJQgJCRF92rGxsXjsscfEsY2NjWaz2SIjI62izc6WBWi+Gu/r7XWgKfrO71tzM/IkEglefPFFXLlyBSqVyqz2M2rUqBa7f9sCid0H8Pf3R1RUFM6dO4fvvvtODIsNDw/HkCFDUFtbi4aGBrPZW21FLpdj9OjR+Omnn0Qaj66bzg84c+YMrl27JrqSzp07h5qaGnTq1Anp6elt7vN2xLPfDmKXSqWYNWsWKioqzBYSsYVMJrP74naX0AGqxvsMvCpsOv6dr282efJkTJ061eVV2OHDhze7tBOvjn700UeiX56X74477nDJ4BZH9mi/HcQONFXL77nnHrNFRHwJ8uw+gmXgRiqVmnXfuAOZTIZZs2bh559/xoMPPojQ0FDodDrk5uZi0KBBuHnzJnJycgA0efRDhw6J9rOji0e0hCNi5wFKWzPOCMchsfsIsbGx8PPzE21Xf39/t1bpONHR0XjyySfF9+7du4uxBleuXBFiB5r6/vnkH1dFxVsSe319vZi405oRi8Sf+GZ9owMSHh6O9PR0McR02LBhXi5RU5lM258XL14UUXtXi91em52/XGQymcfHxLc3yLP7EKGhoXj44Ydx8eLFNk9ScQUSiQR///vfYTAYsHz5cuFh/fz8XOZleYDOnmfn00IdXYaKsA95dh8jMDAQffv29akgj5+fn1kbna+X7gpaqsZzz05V+LbjO08U4dOYdhU1t8eas5DYPQeJnXCI4cOHi66/u+++22XnbUnsVI13HdRmJxwiLCwMzz//PBhjLh2f3tKgGvLsroPETjhMcxM8Wgt5ds9B1XjCq/ARfLZWzTEajWY7phJtg8ROeBU+P9/WvnharRZGo9GlXX0dGRI74VWaEzsfJtulSxePjCZs75DYCa/SnNh5cI7a666BxE54FUfE7umlwtorJHbCq3CxGwwGq+43Pq+exO4aSOyEVwkMDBTtcUvvzsVOwTnXQGInvIpEIhGz2SzFTgNqXAuJnfA69trt1GZ3LSR2wuvYErvBYMDNmzcBkGd3FSR2wuvYEjsXulQqFUsvE23DrWKvrq5Gamqq2OI4NTVVDJSwB2MMmZmZUKlUCAoKwpgxY6z2FKuvr8eLL76I8PBwBAcHY/LkyWYLNTqS95EjRzBz5kxERUUhKCgIAwYMwAcffOCqSyecwJbYTSPxNKDGNbhV7LNmzcLhw4exa9cu7Nq1C4cPH0Zqamqzx7z77rt47733sHr1ahw8eBBKpRL333+/2fLGGRkZ2LFjB7Zs2YJff/0VN2/exKRJk8zWHm8p7/z8fHTv3h1ffPEFSkpKsHjxYixatAirV692/Y0gmsWW2Ck45waYmzh27BgDwPbv3y/S1Go1A8BOnDhh8xij0ciUSiVbsWKFSKurq2MKhYKtW7eOMcbY9evXWUBAANuyZYuwuXTpEpNKpWzXrl2tzpsxxmbPns3Gjh3r8DVqtVoGgGm1WoePIaz5/vvvWWZmJsvNzRVparWaZWZmsm3btnmxZL6DK541t3l2tVoNhUJhtuHivffeC4VCIXY4saS0tBQajQYpKSkiTSaTYfTo0eKY/Px86PV6MxuVSoX4+Hhh05q8gaaJF83Nrqqvr4dOpzP7EG3Hlmfne5dbboVMtB63iV2j0dhc1L9Hjx7QaDR2jwGsNzCMiIgQv2k0GgQGBlo9BJY2zuatVquxbds2PPfcc3avafny5SIGoFAoEBUVZdeWcBwu9traWpHGV7F1xxz6jorTYs/MzIREImn2c+jQIQC2t7JhDuxlZfm7I8dY2jiTd0lJCR566CH83//9H+6//367eSxatAharVZ8LPdUJ1oHb5eb1pT4fnckdtfh9Eo1c+bMwYwZM5q16dOnD44ePSp2DzHlypUrdrce5ruUajQas9VMKysrxTFKpRINDQ2orq428+6VlZVISkoSNo7mfezYMYwbNw7PPvssXn/99Wavi9Yudw98VhvvLdHr9S7fjIJohWcPDw9HXFxcsx+5XI4RI0ZAq9Xi999/F8ceOHAAWq1WiNKSmJgYKJVK5ObmirSGhgbs3r1bHJOQkICAgAAzm4qKChQXFwsbR/MuKSnB2LFj8cQTT+Dtt9929lYQLqJLly4AmvrWGxsbRVNLLpcjKCjIiyVrZ7goWGiTCRMmsMGDBzO1Ws3UajUbNGgQmzRpkplN//792fbt28X3FStWMIVCwbZv386KiorYzJkzWc+ePZlOpxM2aWlpLDIykuXl5bGCggI2btw4dvfdd7PGxkaH8y4uLmbdu3dnjz76KKuoqBCfyspKh6+PovGuwWg0sjfffJNlZmayq1evsr1797LMzEy2detWbxfNZ3DFs+ZWsV+9epU9+uijLCQkhIWEhLBHH32UVVdXmxcAYP/617/Ed6PRyJYuXcqUSiWTyWQsOTmZFRUVmR1TW1vL5syZw7p27cqCgoLYpEmT2Pnz553Ke+nSpQyA1Sc6Otrh6yOxu46PPvqIZWZmspMnT7Jt27axzMxMtnfvXm8Xy2dwxbMmYYwxb9Uqbnd0Oh0UCgW0Wi0N/mgj2dnZKC4uxrhx43Do0CHodDo89thjiI2N9XbRfAJXPGs0Np7wCXjgtKysTATnXLUtNNEEiZ3wCXhPzJkzZwAAnTt3dvv+9B0NEjvhE1juHzd48GAvlaT9QmInfIKgoCBER0cDaOpuHTlypJdL1P4gsRM+w6xZs7Bv3z6MHTuWqvBugPZ6I3yGwMBAbN68mUbNuQny7IRPQUJ3HyR2guggkNgJooNAYieIDgKJnSA6CBSNbwN8WgEtT0W4G/6MtWUqC4m9DfAVb2l5KsJT3Lhxo9VbWNOstzZgNBpRXl5uc21znU6HqKgoXLhwgWbEWUD3xjbN3RfGGG7cuAGVSgWptHWtb/LsbUAqlSIyMrJZm9DQUHqg7UD3xjb27ktrPTqHAnQE0UEgsRNEB4HE7iZkMhmWLl1Kq9HagO6Nbdx9XyhARxAdBPLsBNFBILETRAeBxE4QHQQSO0F0EEjsBNFBILG7gD59+ljtZLtw4UIzm/Pnz+Ovf/0rgoODER4ejvT0dDQ0NJjZFBUVYfTo0QgKCkKvXr2wbNmyNk188EXWrl2LmJgYyOVyJCQkYO/evd4ukluxtesxXzYbaBoGm5mZCZVKhaCgIIwZMwYlJSVm56ivr8eLL76I8PBwBAcHY/Lkybh48aLzhWnrtjQEY9HR0WzZsmVme8bduHFD/N7Y2Mji4+PZ2LFjWUFBAcvNzWUqlYrNmTNH2Gi1WhYREcFmzJjBioqKWHZ2NgsJCWErV670xiW5hS1btrCAgAD2ySefsGPHjrG5c+ey4OBgVlZW5u2iuY2lS5eyu+66y+5+gitWrGAhISEsOzubFRUVsenTp9vc27BXr14sNzeXFRQUsLFjx1rtbegIJHYXEB0dzd5//327v+/cuZNJpVJ26dIlkfbll18ymUwm9u5au3YtUygUrK6uTtgsX76cqVQqZjQa3VZ2TzJ8+HCWlpZmlhYXF8cWLlzopRK5n6VLl7K7777b5m9Go5EplUq2YsUKkVZXV8cUCgVbt24dY4yx69evs4CAALZlyxZhc+nSJSaVStmuXbucKgtV411EVlYWunXrhiFDhuDtt982q6Kr1WrEx8dDpVKJtPHjx6O+vh75+fnCZvTo0Wajp8aPH4/y8nKcO3fOY9fhLhoaGpCfn4+UlBSz9JSUFOzbt89LpfIMp06dgkqlQkxMDGbMmIGzZ88CAEpLS6HRaMzuiUwmw+jRo8U9yc/Ph16vN7NRqVSIj493+r7RrDcXMHfuXAwdOhRhYWH4/fffsWjRIpSWluLTTz8FAGg0GrGXGScsLAyBgYFiL3KNRoM+ffqY2fBjNBoNYmJi3H8hbqSqqgoGg8HqPkRERIh70B5JTEzE559/jn79+uHy5ct46623kJSUhJKSEnHdtu5JWVkZgKb/+8DAQISFhVnZOHvfSOx2yMzMxBtvvNGszcGDBzFs2DDMmzdPpA0ePBhhYWGYOnWq8PYArOa7A03BGdN0Sxv2/4Nzto69XbF1je3p+ix54IEHxN+DBg3CiBEjEBsbi88++wz33nsvgNbdk9bcNxK7HebMmYMZM2Y0a2PpiTn8P/H06dPo1q0blEolDhw4YGZTXV0NvV4v3upKpdLqTV1ZWQnA+s1/OxIeHg4/Pz+b19gers9RgoODMWjQIJw6dQpTpkwB0OS9TXesNb0nSqUSDQ0NqK6uNvPulZWVSEpKcipvarPbITw8HHFxcc1+5HK5zWMLCwsB/Lnl8IgRI1BcXIyKigphk5OTA5lMhoSEBGGzZ88es7Z+Tk4OVCqV3ZfK7URgYCASEhKQm5trlp6bm+v0Q3s7U19fj+PHj6Nnz56IiYmBUqk0uycNDQ3YvXu3uCcJCQkICAgws6moqEBxcbHz982pcB5hxb59+9h7773HCgsL2dmzZ9nWrVuZSqVikydPFja86+2+++5jBQUFLC8vj0VGRpp1vV2/fp1FRESwmTNnsqKiIrZ9+3YWGhraLrveNmzYwI4dO8YyMjJYcHAwO3funLeL5jZeeukl9ssvv7CzZ8+y/fv3s0mTJrGQkBBxzStWrGAKhYJt376dFRUVsZkzZ9rseouMjGR5eXmsoKCAjRs3jrrevEF+fj5LTExkCoWCyeVy1r9/f7Z06VJ269YtM7uysjL24IMPsqCgINa1a1c2Z84cs242xhg7evQoGzVqFJPJZEypVLLMzMx20+3GWbNmDYuOjmaBgYFs6NChbPfu3d4uklvh/eYBAQFMpVKxhx9+mJWUlIjfjUYjW7p0KVMqlUwmk7Hk5GRWVFRkdo7a2lo2Z84c1rVrVxYUFMQmTZrEzp8/73RZaD47QXQQqM1OEB0EEjtBdBBI7ATRQSCxE0QHgcROEB0EEjtBdBBI7ATRQSCxE0QHgcROEB0EEjtBdBBI7ATRQfh/1fTXdzhKLhkAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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rid+aUy1vS0sLCgoKMGvWLKvps2bNQl5ent158vPzbcrHxsbi6NGj4oj7XZURlunKeh3R3NyMuro6qxcDamtrAQC33XabOJC6Mzq3vAB32OgtToW3trYWbW1tCA4OtpoeHBzc5YmJ6upqu+UtFov4I+mqjLBMV9briLVr1yIwMFB8jRw50uVl9SeXLl0CAJvv21EymUx89AkAXL9+vUfqxay5dMJKuI4nICKbabcqf/N0R5bp7HpvJTU1FWazWXxVVla6vKz+RPiDeKvLQt3p3JGDW97e4dR1Xq1WC5lMZtPa1dTUdPlXOiQkxG55uVyOoUOHdltGWKYr63WESqUSBwxnPzh16hQAYPjw4S4vIyQkRDyZeOHChR6pF7PmVMurVCphMBjEnjcCk8mEqVOn2p0nOjrapvzevXsxceJEsetdV2WEZbqyXuaaK1euiC3lnXfe6fJyOv+7VFRU8OWi3uDsGa5t27aRQqGgzMxMKi4upiVLlpBGoxHPKKakpFBCQoJYvrS0lAYNGkRLly6l4uJiyszMJIVCQTt37hTLHDx4kGQyGaWlpVFJSQmlpaWRXC6nQ4cOObxeIqLLly9TUVERffrppwSAtm3bRkVFRVRVVeXQtvHZZqK8vDwyGo305z//2e1lHTp0iFavXk1Go5HefvvtHqhd/9ETvzWnw0tEtGHDBgoLCyOlUklRUVGUm5srfrZo0SKKiYmxKr9v3z6aMGECKZVKGjVqFKWnp9ssc8eOHRQREUEKhYL0ej1lZWU5tV4iovfff58A2LxWrVrl0HZxeIkyMjLIaDTSgQMH3F5We3s7vfvuu+Ilo5ycnB6oYf/QE781CRHvzwjq6uoQGBgIs9ks3lQ+kDQ2NuL1118HACxevNhmcDlXXLlyBW+//bb4fsWKFVaDsw9UPfFb477NTHT69GkAHUPZ9ERwAWDIkCEYP368+D43N7dHlss4vOz/Wltb8fHHHwMAIiMje3TZjz76KMaOHQugY0idlpaWHl3+QMXhZSAivPbaa+LN851byp7y8MMPo7W1FXK5HPn5+T2+/IGIwzvAmc1mbNmyRezsolar3bq+2xWlUon7778fAHDw4EG+dNQDOLwDmNlsxvLly8Xb/65fv47ExES3eq11Z+rUqSAitLa2ivcLM9dxeAewzZs3i90YW1tb8eKLLyIwMLDX1qdUKsXjaT5x5T4O7wB15swZ8caQqqoqzJkzxyND1syYMQMAUFlZKT7viLmGwzsAtbe346OPPgLQ0X85PT3dY91MtVqt2J+8rKzMI+vsrzi8A5Dw5IOWlhYsWLCg145x7ZFIJOJlI+G6MnMNh3cAEsb2uvfee+0+sqS3RUREAABKSkr4rLMbOLwDTHNzM86fPw/A/oPCPGHUqFEAOrpjXrp0Cd9++614/N3Y2IgLFy7gb3/7m1fq5kt43OYBpqamBkQEtVot3k/taQqFAsOGDUNNTQ3S09MBdNw2OGjQIDQ0NCAsLAxAx9nwgIAAzJ071yv17Ou45e2n6uvr8d133+HKlStW04UBDbp7ZIknREVFWb2/4447oNVqxeACwNmzZ1FUVISamhpPV88ncHj7ievXr+PAgQN4/PHHsWXLFqSmpmLr1q1ISkrCN998I5YThnT19nhder3e4ePdN954A8APY0kL6uvrcfHixR6vm6/gWwI78ZVbAi9duoS8vDxoNBqo1Wp88sknsFgsXXawaGlpwfLly3H+/HlkZWWhvb0dixYtEo89vcVkMmHPnj146aWXUFlZic2bN0Ov10Mmk2H48OHYs2ePuE2NjY347rvvoFarIZPJIJPJIJFI0NLSgsTERK8dv7uqJ35rHN5OfCG8QhAdvWWvra0NMpkMAGCxWCCXyyGRSJCammrzBMC+pq6uDps3bxZHs+xKU1MTVq5cKT6h0Bfw/bwD0D/+8Y8ug3v58mXcc889OHPmDPz8/PD73/8eM2fOFD8XhmMdP358nw8uAAQEBCAxMRE3btzotpxarca//vUvD9Wq7+CzzT7k9OnTOH36NKRSKa5evQoiglQqxfDhw5GQkICrV69Cr9cjNjYWGo0GUqkU06dPx5kzZ7Bv3z4MGjQIgwYN8qmzt1KpFC+99BKSk5Mxa9YsbNmyBUBHZ497770XOp0OlZWVOHXqFL7++muEhoa6NaKoL+Hd5k768m5zeXk53nrrLQQFBWHw4MFITk52eN729naUl5e79NyhvoY6xl2DVCoV369ZswYWiwUAcPXqVaxfv95q0Pe+iHebBwiLxYL3338fQUFBaGxsREJCglPzS6XSfhFcoKPFFYIrvI+JiRHfBwUF4cMPP/RG1TyOw+sDcnNzIZFI0NTUhLi4OAwZMsTbVepTJk+ejLa2NvF9aWkpPvjgAy/WyDM4vH1cWVkZcnJyAAAPPPCATecG1tFj68knn8Rdd90FIoJcLkdZWZnYDbS/4vD2YVeuXMF7770HtVoNiUSC6dOne7tKfVZERAQWLlyIZ599VpzW389Ac3j7sL/+9a9QKpVob2/HggUL+vxJmL5g+PDhiI2NBdDRFbSurs6qh1l/wr+GPqi+vh4nT55EQ0MDAGD69Om46667vFwr3zFlyhTk5uaiqakJb7zxBi5evAiVSoU//elPYoeV/oBb3j6kra0NeXl5eOWVV/Cf//wHABAWFmbV0YI5RuguKZfLMXz4cGi1WmRnZ3u5Vj2Lw9tHHDlyBC+88AJMJhNuu+02AB3XMBcuXOjlmvmm6OhoNDc3W43WUVJS0q8GfOfw9gFVVVXYs2ePTbfHhQsXQqlUeqlWvk0ikWD16tV4/vnnsWTJEgAdfwyPHTvm3Yr1ID7m9ZC2tjZ8+OGHePzxx+Hv7489e/YgOzsbOp3OqtMBADz11FOQSCRev23P1ykUCkyePBlAx6iVX3zxBXbv3g2DwWDznQMdw9/6Qp9vAXeP7KSnu0d+//33+O9//4u77roLn376qdiKKhQKtLa22pSfO3cudDrdgOmb60k3btxAWloaZDIZpFIpoqOj0djYiH/+85+QSCS47bbbcPbsWaxcuRLR0dG9Xh++JbCH9WR4GxsbsW7dOodGZrx+/Tpmz57NJ6Z62e7du1FQUNBtmfb2diQlJeH222/v1bpw32YPIyJs2LABf/zjH7Fz584uR4L49ttvkZmZecvgPvDAA9BqtTa37rHeMWfOnC7/zerr6wF09AN/5513bMp9++23vV4/Z3HL28mt/hqePHkSO3fuFN9HRkbil7/8JYAfbh64cOGC1Tzjxo3DqVOncOeddyIuLg4WiwUZGRmYN28e7x57QUNDA/7whz9AIpEgKCgId999NwYPHoympiaMHz8emzdvhlQqRVBQEJ5++mmoVCq8/PLLaGlpQUtLC372s58hPj7eZrmfffYZvvzyS6SmpqKkpASTJk2ye1wt4N3mHtbdF9re3o7169ejsbHRavrvfvc7FBYW4vDhwzbLCwoKwvPPP+/RQc2ZY27cuIGWlhabR7zs2rVL7JFVW1sLrVZrM+/169exYsUK8bPPPvsMX331lU25efPmISAgwGpQPQGHt4d194WePXtW/Ku8bNkybNmyxaaVBTqOdWfOnAmZTIb77rvPU1VnPejVV1+1ukvJnoaGBixfvhyNjY34y1/+YvcstcVigUqlQlJSks0fCT7m9aATJ04A6BhCRqPRWN1DKpg3bx7S0tLwwAMPcHB92PLly1FXV4fq6mo0NzcjICAAL7/8MhYtWiQeG2s0Grz++uvIyMiAQqGAxWJBW1sb6urqxOXI5XK0tbXh888/75V6csvbSXd/DS9fvoyjR49i3Lhx4pjHW7ZswenTpzFixAj85je/6Vf9ZlmHzgP4Ce+3bNmCs2fPiodDzc3NWLZsGbRaLcrLyzFs2DC8/vrrOHXqFKZMmYLExESbzja829zD+vIwOKxveffdd1FYWIjr16/jV7/6FWbPnu3U/D3xW+MeVoy54Le//S2uXLmC2tpajBkzxit14PAy5qIhQ4Z4dUgil05Ybdy4EeHh4VCr1TAYDNi/f3+35XNzc2EwGKBWqzF69Ghs2rTJpkxWVhYiIyOhUqkQGRmJXbt2Ob1eIoLRaIROp4Ofnx9+/vOf4+TJk65sImN9Hzlp27ZtpFAoKCMjg4qLiyk5OZk0Gg2Vl5fbLV9aWkqDBg2i5ORkKi4upoyMDFIoFLRz506xTF5eHslkMlqzZg2VlJTQmjVrSC6X06FDh5xab1paGvn7+1NWVhYdP36c5s+fT6GhoVRXV+fQtpnNZgJAZrPZ2a+FMaf0xG/N6fBOmjSJEhMTrabp9XpKSUmxW3758uWk1+utpj333HM0ZcoU8X1cXBzNnj3bqkxsbCzFx8c7vN729nYKCQmhtLQ08fOmpiYKDAykTZs22a1bU1MTmc1m8VVZWcnhZR7RE+F1are5paUFBQUFmDVrltX0WbNmIS8vz+48+fn5NuVjY2Nx9OhR8c6arsoIy3RkvWVlZaiurrYqo1KpEBMT02Xd1q5di8DAQPHFt+AxX+LUCava2lq0tbXZ9MkNDg4Wn/t6s+rqarvlLRYLamtrERoa2mUZYZmOrFf4r70ywmMtb5aamoply5aJ781mM+644w6rC+2M9QbhN0ZuXKl16WzzzX11iajb/rv2yt883ZFl9lQZgUqlgkqlEt8LXyi3wMxT6uvru3w06604FV6tVguZTGbTytbU1HR5h0xISIjd8nK5HEOHDu22jLBMR9YbEhICoKMFDg0NdahuNxMeWuXv72838HV1dRg5ciQqKyu5E0cn/L3Y1933QkSor68Xe+u5wqnwKpVKGAwGmEwmPPbYY+J0k8mERx55xO480dHRNoNf7927FxMnThQ7c0dHR8NkMmHp0qVWZaZOnerwesPDwxESEgKTyYQJEyYA6DhWzs3Nxbp16xzaPqlUihEjRtyyXEBAAP9I7eDvxb6uvhdXW1yRs2e4hEs2mZmZVFxcTEuWLCGNRkPnzp0jIqKUlBRKSEgQywuXipYuXUrFxcWUmZlpc6no4MGDJJPJKC0tjUpKSigtLa3LS0VdrZeo41JRYGAgZWdn0/Hjx2nBggVOXSq6Fb6UZB9/L/b19vfidHiJiDZs2EBhYWGkVCopKiqKcnNzxc8WLVpEMTExVuX37dtHEyZMIKVSSaNGjaL09HSbZe7YsYMiIiJIoVCQXq+nrKwsp9ZL1HG5aNWqVRQSEkIqlYqmT59Ox48fd2UT7eIfqX38vdjXJ8M7UDU1NdGqVauoqanJ21XpU/h7sa+3vxe+q4gxH8U34zPmozi8jPkoDi9jPorDy5iP4vAy5qM4vHaMGjUKEonE6pWSkmJVpqKiAg8//DA0Gg20Wi0WL15s8/jI48ePIyYmBn5+fhg+fDheeeUVtzqi90XODszg64xGo81vQ+iaCzg2IERzczOef/55aLVaaDQazJ07F99//73zlemVC1A+LiwsjF555RWqqqoSX/X19eLnFouFxo4dSzNmzKDCwkIymUyk0+koKSlJLGM2myk4OJji4+Pp+PHjlJWVRf7+/rR+/XpvbFKvcHZghv5g1apVdM8991j9NmpqasTPHRkQIjExkYYPH04mk4kKCwtpxowZNH78eLJYLE7VhcNrR1hYGL311ltdfv7ZZ5+RVCql8+fPi9O2bt1KKpVK7E2zceNGCgwMtLpAv3btWtLpdNTe3t5rdfckZwdm6A9WrVpF48ePt/uZIwNCXLt2jRQKBW3btk0sc/78eZJKpfTvf//bqbrwbnMX1q1bh6FDh+InP/kJXnvtNatd4vz8fIwdO9bqjpDY2Fg0NzeLT6HLz89HTEyM1S2HsbGxuHDhAs6dO+ex7egtrgzM0F+cPn0aOp0O4eHhiI+PR2lpKQDHBoQoKChAa2urVRmdToexY8c6/b3x6JF2JCcnIyoqCkFBQThy5AhSU1NRVlaG9957D4D9AQaCgoKgVCqtBgcYNWqUVRlhnurqaoSHh/f+hvQiVwZm6A8mT56MDz74AGPGjMHFixfx6quvYurUqTh58qRDA0JUV1dDqVQiKCjIpoyz39uACa/RaMTq1au7LfPVV19h4sSJVrcm/vjHP0ZQUBCeeOIJsTUGbG/6B2xv/HdkEAJf5+zADL7uwQcfFP9/3LhxiI6Oxp133om///3vmDJlCgDXvhNXvrcBE96kpCS7j2bs7OaWUiD8o5w5cwZDhw5FSEiIzVMBr169itbWVqvBAewNHgDY/mX2Ra4MzNAfaTQajBs3DqdPn8ajjz4KoPsBIUJCQtDS0oKrV69atb41NTXi/euOGjDHvFqtFnq9vtuXWq22O29RUREAiP8g0dHROHHiBKqqqsQye/fuhUqlgsFgEMt8+eWXVsfKe/fuhU6n6/KPhC/pPEBCZyaTyekfoS9rbm5GSUkJQkNDrQaEEAgDQgjficFggEKhsCpTVVWFEydOOP+9OXV6awDIy8ujN998k4qKiqi0tJS2b99OOp2O5s6dK5YRLhXNnDmTCgsLKScnh0aMGGF1qejatWsUHBxMCxYsoOPHj1N2djYFBAT0y0tF3Q2Q0N+88MILtG/fPiotLaVDhw7RQw89RP7+/uI2OzIgRGJiIo0YMYJycnKosLCQ7rvvPr5U1BMKCgpo8uTJFBgYSGq1miIiImjVqlXU0NBgVa68vJx+8YtfkJ+fHw0ZMoSSkpJs7tv85ptvaNq0aaRSqSgkJISMRmO/uUwkuNUACf2NcN1WoVCQTqejefPm0cmTJ8XPHRkQorGxkZKSkmjIkCHk5+dHDz30EFVUVDhdF76flzEfNWCOeRnrbzi8jPkoDi9jPorDy5iP4vAy5qM4vIz5KA4vYz6Kw8uYj+LwMuajOLyM+SgOL2M+6n9cnPg9LM0uXgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from itertools import repeat\n", + "\n", + "pre = 0.5\n", + "post = 0.5\n", + "\n", + "sr = wm_ref_data.info['sfreq']\n", + "\n", + "evs = [] \n", + "durs = [] \n", + "descriptions = []\n", + "stas = [] \n", + "stes = [] \n", + "data = wm_ref_data.get_data()\n", + "\n", + "for ch in wm_ref_data.ch_names: \n", + " fig, ax = plt.subplots(1, 1, figsize=(2, 2))\n", + "\n", + " ch_ix = wm_ref_data.ch_names.index(ch)\n", + " # Plot the IEDs \n", + " signal = data[ch_ix, :]\n", + " IED_ts = IED_times_s[ch]\n", + " sta, ste = analysis_utils.lfp_sta(IED_ts, signal, sr, pre, post)\n", + " stas.append(sta)\n", + " stes.append(ste)\n", + " \n", + " ts = np.linspace(-500, 500, len(sta))\n", + " ax.plot(ts, sta, 'gray')\n", + " ax.fill_between(ts, sta-ste, \n", + " sta+ste, facecolor='k')\n", + " \n", + " \n", + " \n", + "# # Nan out the IEDs: \n", + "# starts = np.ceil((IED_ts - 0.1) * wm_ref_data.info['sfreq']).astype(int)\n", + "# stops = np.floor((IED_ts + 0.1) * wm_ref_data.info['sfreq']).astype(int)\n", + " \n", + "# for a,b in zip(starts, stops): \n", + "# signal[a:b] = np.nan\n", + " \n", + "# data[ch_ix, :] = signal\n", + " \n", + "# # Make events and annotate \n", + "# evs = evs + (IED_ts - 0.1).tolist()\n", + "\n", + "# durs = durs + (0.2 * np.ones_like(IED_ts)).tolist()\n", + "\n", + "# descriptions = descriptions + ([f'BAD_ied_{ch}']*len(IED_ts))\n", + "\n", + "# # Let's replace the data with the IED's nan-ed out\n", + "# wm_ref_data._data = data\n", + "# # Make mne annotations based on these descriptions\n", + "# annot = mne.Annotations(onset=evs,\n", + "# duration=durs,\n", + "# description=descriptions)\n", + "\n", + "# wm_ref_data.set_annotations(annot)\n", + "\n", + "# events_from_annot, event_dict = mne.events_from_annotations(wm_ref_data)\n", + " \n", + "fig, ax = plt.subplots(1, 1, figsize=(2, 2))\n", + "ts = np.linspace(-500, 500, len(sta))\n", + "ax.plot(ts, np.nanmean(stas, axis=0), 'gray')\n", + "ax.fill_between(ts, np.nanmean(stas, axis=0)-np.nanmean(stes, axis=0), \n", + " np.nanmean(stas, axis=0)+np.nanmean(stes, axis=0), facecolor='k')\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Now process the behavioral data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, one should load in their own functions for behavioral stuff. I'll just write the functions relevant to me here for demonstration purposes. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Utility functions for image memorability ratings. \n", + "import pandas as pd \n", + "import numpy as np \n", + "import os \n", + "from scipy.stats import norm, zscore, linregress\n", + "\n", + "# Note: Much of the following is ported from: https://github.com/cvzoya/memorability-distinctiveness\n", + "\n", + "def dprime(pHit, pFA, PresentT, AbsentT, criteria=False):\n", + " \"\"\"\n", + " Note: from: http://nikos-konstantinou.blogspot.com/2010/02/dprime-function-in-matlab.html\n", + " \n", + " \n", + " Parameters\n", + " ----------\n", + " pHit : float\n", + " The proportion of \"Hits\": P(Yes|Signal)\n", + " pFA : float\n", + " The proportion of \"False Alarms\": P(Yes|Noise)\n", + " PresentT : int\n", + " The number of Signal Present Trials e.g. length(find(signal==1))\n", + " AbsentT : int\n", + " The number of Signal Absent Trials e.g. length(find(signal==0))\n", + "\n", + " \n", + " Returns\n", + " -------\n", + " dPrime: float\n", + " signal detection theory sensitivity measure \n", + " \n", + " beta: float\n", + " optional criterion value\n", + " \n", + " C: float\n", + " optional criterion value\n", + " \n", + " \"\"\"\n", + "\n", + " if pHit == 1: \n", + " # if 100% Hits\n", + " pHit = 1 - (1/(2*PresentT))\n", + " \n", + " if pFA == 0: \n", + " # if 0% FA \n", + " pFA = 1/(2*AbsentT)\n", + " \n", + " # Convert to Z-scores\n", + " \n", + " zHit = norm.ppf(pHit) \n", + " zFA = norm.ppf(pFA) \n", + " \n", + " # calculate d-prime \n", + " \n", + " dPrime = zHit - zFA \n", + " \n", + " if criteria:\n", + " beta = np.exp((zFA**2 - zHit**2)/2)\n", + " C = -0.5 * (zHit + zFA) \n", + " return dPrime, beta, C\n", + " else:\n", + " return dPrime\n", + " \n", + "# def calcMI(pmf):\n", + "# \"\"\"\n", + " \n", + "# Parameters\n", + "# ----------\n", + " \n", + " \n", + "# Returns\n", + "# -------\n", + " \n", + "# \"\"\"\n", + " \n", + "# pmf_1 = np.sum(pmf,axis=1) # marginal over first variable\n", + "# pmf_2 = np.sum(pmf,axis=0) # marginal over second variable\n", + " \n", + "# MI = 0\n", + "# for i in range(np.shape(pmf)[0]):\n", + "# for j in range(np.shape(pmf)[1]):\n", + "# MI += pmf[i, j] * np.log(pmf[i, j] / (pmf_1[i]*pmf_2[j]))\n", + " \n", + "# return MI\n", + "\n", + "def compute_memorability_scores(hits, false_alarms, misses, correct_rejections):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " hits : array-like\n", + " TODO\n", + " false_alarms : array-like\n", + " TODO\n", + " misses : array_like \n", + " TODO\n", + " correct_rejections : array_like \n", + " TODO\n", + " \n", + " Returns\n", + " -------\n", + " memory_ratings : pandas DataFrame \n", + " DataFrame with the following ratings added: HR (hit rate), FAR (false alarm rate), ACC (accuracy), DPRIME (d-prime), MI (mutual information)\n", + " \"\"\"\n", + " \n", + " \n", + "\n", + " len_args = [len(hits), len(false_alarms), len(misses), len(correct_rejections)]\n", + " if not all(len_args[0] == _arg for _arg in len_args[1:]):\n", + " raise ValueError(\"All parameters must be the same length.\")\n", + " \n", + " memory_ratings = pd.DataFrame(columns = ['HR', 'FAR', 'ACC', 'DPRIME'])\n", + " # , 'MI'\n", + " \n", + " reg = 0.1 # regularization for MI calculation\n", + "\n", + " nstimuli = len(hits) \n", + "\n", + " hm = hits+misses\n", + " fc = false_alarms+correct_rejections\n", + "\n", + " hrs = hits/hm\n", + " fars = false_alarms/fc\n", + " accs = (hits+correct_rejections)/(hm+fc)\n", + "\n", + " dp = []\n", + "# mis = []\n", + " for i in range(nstimuli):\n", + " dp.append(dprime(hrs[i], fars[i], hm[i], fc[i]))\n", + "# pmf = np.array([[correct_rejections, misses], \n", + "# [false_alarms, hits]]) + reg\n", + "# pmf = pmf/np.sum(pmf)\n", + "# mis.append(calcMI(pmf))\n", + " \n", + "\n", + " memory_ratings['HR'] = hrs\n", + " memory_ratings['FAR'] = fars\n", + " memory_ratings['ACC'] = accs\n", + " memory_ratings['DPRIME'] = dp\n", + "# memory_ratings['MI'] = mis\n", + " \n", + " return memory_ratings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Synchronize to behavioral data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to analyze the neural data with respect to the behavioral data we need to be able to synchronize the two using the photodiode (or TTLs, eventually?) " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 485 neural syncs detected\n" + ] + } + ], + "source": [ + "# Find the timestamps of ONSET and OFFSET of all the sync signals in the photodiode \n", + "\n", + "# moving average helps us detect the deflections \n", + "sig = np.squeeze(sync_utils.moving_average(photodiode._data, n=11))\n", + "timestamp = np.squeeze(np.arange(len(sig))/wm_ref_data.info['sfreq'])\n", + "# normalize\n", + "sig = zscore(sig)\n", + "# look for z-scores above 1\n", + "trig_ix = np.where((sig[:-1]<=0)*(sig[1:]>0))[0] # rising edge of trigger\n", + "neural_ts = timestamp[trig_ix]\n", + "neural_ts = np.array(neural_ts)\n", + "print(f'There are {len(neural_ts)} neural syncs detected')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the .log file and/or .csv file, depending on how your task logs the behavioral data. Eventually this should be fairly standardized across tasks." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "log_path = glob(f'{behav_dir}/*.log')[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "csv_path = glob(f'{behav_dir}/*MB_MEM*.csv')[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next step pulls relevant timestamps from the behavioral logfile. THIS STEP DIFFERS DEPENDING ON YOUR TASK. \n", + "\n", + "Maybe one day this step will be unified across all tasks (i.e. either we have a programmer make our tasks, or we unify best practices for task design). " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 486 behav syncs detected\n" + ] + }, + { + "data": { + "text/plain": [ + "381.5762" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Now get the relevant timestamps from behavioral logfiles. This will differ depending\n", + "\n", + "MB1_ts = {'trial_start': [], \n", + "'deck_start': [], \n", + "'feedback_start': [],\n", + "'ITI_start': [],\n", + "'ITI_stop': []}\n", + "\n", + "MEM2_ts = {'trial_start': [], \n", + "'face_start': [], \n", + "'slider_start': [],\n", + "'slider_stop': [],\n", + "'ITI_start': [],\n", + "'ITI_stop': []}\n", + "\n", + "beh_ts = []\n", + "\n", + "MB1_FLAG = True \n", + "MEM2_FLAG = False \n", + "\n", + "with open(log_path, 'r') as fobj:\n", + " for ix, line in enumerate(fobj.readlines()):\n", + " line = line.replace('\\r', '')\n", + " tokens = line[:-1].split('\\t')\n", + "\n", + " if tokens[1] == 'EXP ':\n", + " # Determine which task we are looking at \n", + " if tokens[2][0:3] == 'MB1':\n", + " MB1_FLAG = True\n", + " MEM2_FLAG = False \n", + " elif tokens[2][0:3] == 'MEM':\n", + " MEM2_FLAG = True\n", + " MB1_FLAG = False\n", + "\n", + " # Grab photodiode timestamp\n", + " if tokens[2][0:4] =='sync':\n", + " if 'autoDraw = True' in tokens[2]:\n", + " beh_ts.append(float(tokens[0]))\n", + "\n", + " # Get MB1 deck \n", + " if 'MB1_left_draw' in tokens[2]:\n", + " if 'autoDraw = True' in tokens[2]:\n", + " MB1_ts['deck_start'].append(float(tokens[0]))\n", + " \n", + " # Get MB1 feedback\n", + " if 'MB1_face' in tokens[2]:\n", + " if 'autoDraw = True' in tokens[2]:\n", + " MB1_ts['feedback_start'].append(float(tokens[0]))\n", + "\n", + " # Get MB1 ITI cross \n", + " if 'MB1_ITI_cross' in tokens[2]:\n", + " if 'autoDraw = True' in tokens[2]:\n", + " MB1_ts['ITI_start'].append(float(tokens[0]))\n", + " elif 'autoDraw = False' in tokens[2]:\n", + " MB1_ts['ITI_stop'].append(float(tokens[0]))\n", + "\n", + " if 'New trial (rep=0' in tokens[2]:\n", + " if MB1_FLAG: \n", + " # remember to discard the first one later - it's pre-session \n", + " MB1_ts['trial_start'].append(float(tokens[0]))\n", + " elif MEM2_FLAG:\n", + " MEM2_ts['trial_start'].append(float(tokens[0]))\n", + " \n", + " # Get MEM2 ITI\n", + " if 'MEM2_jitter' in tokens[2]:\n", + " if 'autoDraw = True' in tokens[2]:\n", + " MEM2_ts['ITI_start'].append(float(tokens[0])) \n", + " elif 'autoDraw = False' in tokens[2]:\n", + " MEM2_ts['ITI_stop'].append(float(tokens[0])) \n", + "\n", + " # Get MEM2 Face\n", + " if 'MEM2_images' in tokens[2]:\n", + " if 'autoDraw = True' in tokens[2]:\n", + " MEM2_ts['face_start'].append(float(tokens[0])) \n", + "\n", + " # Get MEM2 slider start\n", + " if tokens[2][:16] == 'MEM2_conf_slider':\n", + " if 'autoDraw = True' in tokens[2]:\n", + " MEM2_ts['slider_start'].append(float(tokens[0])) \n", + " \n", + " # Get MEM2 slider stop\n", + " if tokens[2][:16] == 'MEM2_conf_slider':\n", + " if 'autoDraw = False' in tokens[2]:\n", + " MEM2_ts['slider_stop'].append(float(tokens[0])) \n", + " \n", + "beh_ts = np.array(beh_ts)\n", + "print(f'There are {len(beh_ts)} behav syncs detected')\n", + "\n", + "# Note: fixation crosses need fixing on stop time duplicates\n", + "MB1_ts['ITI_stop'] = np.unique(MB1_ts['ITI_stop']).tolist()\n", + "MEM2_ts['ITI_stop'] = np.unique(MEM2_ts['ITI_stop']).tolist()\n", + "\n", + "\n", + "# Get the choice times: \n", + "csv_data = pd.read_csv(csv_path)\n", + "MB1_ts['choice'] = (csv_data['MB1_draw_key.started'].dropna() + csv_data['MB1_draw_key.rt'].dropna()).tolist()\n", + "MEM2_ts['choice'] = (csv_data['MEM2_recall_key.started'].dropna() + csv_data['MEM2_recall_key.rt'].dropna()).tolist()\n", + "\n", + "# Do some corrections: \n", + "# Get rid of first trial start (pre-session)\n", + "MB1_ts['trial_start'].pop(0) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sanity check - the sync pulses are similar in number. That's nice, but not guaranteed. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, compile the behavioral data for each trial. We will use this to add to the mne metadata later, which is very important for analysis. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "subj_count = 0\n", + "\n", + "# Load the database with image DPRIME information\n", + "database_file = f'{behav_dir}/all_mem_data.xlsx' \n", + "all_mem_df = pd.read_excel(database_file, engine='openpyxl')\n", + "all_mem_df = all_mem_df[['img_path', 'DPRIME']]\n", + "\n", + "# Turn into right format for modeling: \n", + "MB1_n = 60\n", + "MEM2_n = 120\n", + "li_mb1 = []\n", + "li_mem2 = [] \n", + "\n", + "act_rew_rate = {}\n", + "act_rew_rate['pids'] = []\n", + "\n", + "r1_chance=30\n", + "\n", + "for elem in ['actions', 'rewards']:\n", + " act_rew_rate[elem] = np.zeros(MB1_n).astype(int) # len(task_files), \n", + "\n", + "# Load the merged task data \n", + "csv_data['trials_2.thisN'] = csv_data['trials_2.thisRepN'].shift(-1)\n", + "\n", + "##### First, process the Bandit task: \n", + "mb_df = csv_data.dropna(subset=['trials_2.thisN'])\n", + "act_rew_rate['pids'].append(mb_df.participant.iloc[0])\n", + "\n", + "# Change Gender so that female = 2\n", + "mb_df.Gender[mb_df.Gender==0] = 2\n", + "\n", + "# add score, reward probability and expected value \n", + "mb_df['choice'] = mb_df.apply(lambda x: x['MB1_draw_key.keys'], axis=1)\n", + "# Make the trials 1-60 \n", + "mb_df['trials_dm'] = mb_df['trials_2.thisN'].shift(+1)\n", + "# mb_df.trials_dm.fillna(60, inplace=True)\n", + "# # get rid of the extra rows in the .csv that populate between trials \n", + "mb_df = mb_df.drop_duplicates(subset='trials_dm', keep='first')\n", + "mb_df['reward'] = mb_df.apply(lambda x: x['reward']/100, axis=1)\n", + "mb_df.reward[mb_df.reward==100] = 0\n", + "# 0 is male, 1 is female \n", + "mb_df['choice'] = mb_df['choice']-1\n", + "mb_df.rename(columns={'MB1_draw_key.rt':'draw_rt'}, inplace=True)\n", + "mb_df.dropna(subset=['img_path'], inplace=True)\n", + "\n", + "# # check chance: \n", + "# if mb_df.reward.sum() < r1_chance:\n", + "# # print(f'Subject {subj_count} performed worse than random')\n", + "# bad_subs.append(subj_count)\n", + "# continue\n", + "\n", + "##### Fit RW model to DM data\n", + "mb_df['bic'] = np.nan\n", + "mb_df['alpha'] = np.nan\n", + "mb_df['beta'] = np.nan\n", + "mb_df['RPE'] = np.nan\n", + "\n", + "# RW_model = RW() \n", + "sub_act_rew_rate = {}\n", + "sub_act_rew_rate['pids'] = np.array([subj_count]) \n", + "\n", + "for elem in ['actions', 'rewards']:\n", + " sub_act_rew_rate[elem] = np.zeros([1, MB1_n]).astype(int)\n", + "c = mb_df.choice.dropna().values.astype(int)\n", + "r = mb_df.reward.dropna().values\n", + "sub_act_rew_rate['actions'][0, :] = c\n", + "sub_act_rew_rate['rewards'][0, :] = r\n", + "# RW_model.fit_all(sub_act_rew_rate) \n", + "\n", + "# mb_df.RPE= RW_model.fit_metrics['prederr'][0].tolist()\n", + "# mb_df.bic= RW_model.fit_metrics['bic'][0]\n", + "# mb_df.alpha= RW_model.fit_metrics['params']['alpha'][0]\n", + "# mb_df.beta= RW_model.fit_metrics['params']['beta'][0]\n", + "\n", + "# Save dict for modeling decision-making performance: \n", + "c = mb_df.choice.dropna().values.astype(int)\n", + "r = mb_df.reward.dropna().values\n", + "act_rew_rate['actions'] = c # [subj_count, :]\n", + "act_rew_rate['rewards']= r # [subj_count, :]\n", + "\n", + "##### Second, process the MEM2 data: \n", + "rm_df = csv_data.dropna(subset=['MEM2_trials.thisN'])\n", + "\n", + "# add coding of memory choice: \n", + "rm_df['hit'] = 0\n", + "rm_df['miss'] = 0\n", + "rm_df['corr_reject'] = 0\n", + "rm_df['false_alarm'] = 0\n", + "\n", + "# add score, reward probability and expected value \n", + "rm_df.rename(columns={'MEM2_recall_key.keys': 'response',\n", + "'MEM2_conf_slider.response': 'confidence'\n", + " }, inplace=True) \n", + "\n", + "rm_df['trials_mem'] = rm_df['MEM2_trials.thisN'].shift(-1)\n", + "rm_df.trials_mem.fillna(120, inplace=True)\n", + "\n", + "# Change Gender so that female = 2\n", + "rm_df.Gender[rm_df.Gender==0] = 2\n", + "\n", + "rm_df = rm_df.merge(mb_df, on='img_path', how='left', indicator=True)\n", + "# Clean up the merge\n", + "rm_df.drop(columns=['participant_y'], inplace=True)\n", + "rm_df.rename(columns={'participant_x': 'participant'}, inplace=True)\n", + "\n", + "\n", + "# compute the hit-rates and false-alarm rates \n", + "### TO do so, use the merge to find image_paths that were in \"both\" dfs ('old') \n", + "# OLD = 2, which is correct in this case \n", + "hit_bool = (rm_df._merge=='both') & (rm_df.response==2)\n", + "hits = hit_bool.sum()\n", + "# NEW = 1, which is false in this case \n", + "miss_bool = (rm_df._merge=='both') & (rm_df.response==1)\n", + "misses = miss_bool.sum()\n", + "\n", + "# or just the \"left\" df ('new')\n", + "false_alarm_bool = (rm_df._merge=='left_only') & (rm_df.response==2)\n", + "false_alarms = false_alarm_bool.sum()\n", + "corr_reject_bool = (rm_df._merge=='left_only') & (rm_df.response==1)\n", + "correct_rejections = corr_reject_bool.sum()\n", + "\n", + "# categorize image by memory choice\n", + "rm_df.hit[hit_bool] = 1\n", + "rm_df.miss[miss_bool] = 1\n", + "rm_df.false_alarm[false_alarm_bool] = 1\n", + "rm_df.corr_reject[corr_reject_bool] = 1\n", + "\n", + "# compute dprime for the subject\n", + "hm = hits+misses\n", + "fc = false_alarms+correct_rejections\n", + "\n", + "hrs = hits/hm\n", + "fars = false_alarms/fc\n", + "\n", + "# Adjust extreme hit-rates or false-alarms\n", + "if hrs == 0: \n", + " hrs = 0.5/hm\n", + "elif hrs ==1: \n", + " hrs = (hm-0.5)/hm\n", + "if fars == 0: \n", + " fars = 0.5/fc\n", + "elif fars ==1: \n", + " fars = (fc-0.5)/fc\n", + "\n", + "# only the extreme values by replacing rates of 0 with 0.5/𝑛 and rates of 1 with (𝑛−0.5)/𝑛 where 𝑛 is the number of signal or noise trials (Macmillan & Kaplan, 1985)\n", + "\n", + "dp = dprime(hrs, fars, hm, fc)\n", + "\n", + "# Add in subject-level memory characteristics (\"rates\")\n", + "rm_df['hit_rate'] = hrs\n", + "rm_df['false_alarm_rate'] = fars\n", + "rm_df['subj_dprime'] = np.nan\n", + "if dp != float(\"-inf\"):\n", + " rm_df['subj_dprime'] = dp \n", + "\n", + "# # Get rid of horrible mmemory performers \n", + "# if rm_df.subj_dprime.mean()<=0:\n", + "# bad_subs.append(subj_count)\n", + "# continue\n", + "\n", + "# # Get patient demographics\n", + "# rm_df['age'] = np.nan\n", + "# rm_df['subj_gender'] = np.nan\n", + "# rm_df['age'] = demographics_df[demographics_df.participant==rm_df.participant.iloc[0]].age.values[0]\n", + "# rm_df['subj_gender'] = demographics_df[demographics_df.participant==rm_df.participant.iloc[0]].Sex.values[0]\n", + "\n", + "# Merge in the image DPRIME \n", + "rm_df['DPRIME'] = rm_df.merge(all_mem_df, on='img_path', how='right')['DPRIME']\n", + "mb_df['DPRIME'] = mb_df.merge(all_mem_df, on='img_path', how='right')['DPRIME']\n", + "mb_df.rename(columns={'DPRIME': 'image_dprime'}, inplace=True) \n", + "\n", + "rm_df.rename(columns={'DPRIME': 'image_dprime',\n", + "'Gender_x': 'image_gender',\n", + "'MEM2_recall_key.rt_x': 'recall_rt',\n", + "'MEM2_conf_slider.rt_x': 'slider_rt'\n", + "}, inplace=True) \n", + "\n", + "# li_mb1.append(mb_df)\n", + "# li_mem2.append(rm_df)\n", + "\n", + "# dm_df = pd.concat(li_mb1, axis=0, ignore_index=True)\n", + "mb_df['male'] = 0\n", + "mb_df['female'] = 0\n", + "mb_df.male = mb_df.apply(lambda x: 1 if x.choice==0 else 0, axis=1)\n", + "mb_df.female = mb_df.apply(lambda x: 1 if x.choice==1 else 0, axis=1)\n", + "\n", + "# col_mask = ((mb_df.columns.str.startswith('MB')) | (mb_df.columns.str.startswith('trials.')))\n", + "\n", + "# mem_df = pd.concat(li_mem2, axis=0, ignore_index=True)\n", + "rm_df['phit'] = rm_df.response - 1\n", + "\n", + "# # Get rid of trash: \n", + "# act_rew_rate['pids'] = np.array(act_rew_rate['pids'])\n", + "# act_rew_rate['pids'] = np.delete(act_rew_rate['pids'], bad_subs)\n", + "# for elem in ['actions', 'rewards']:\n", + "# act_rew_rate[elem] = np.delete(act_rew_rate[elem], bad_subs, 0)\n", + "\n", + "col_mask = ((rm_df.columns.str.startswith('MEM')) | (rm_df.columns.str.startswith('MB')) | (rm_df.columns.str.startswith('trials.')) | (rm_df.columns.str.startswith('trials_2')) | (rm_df.columns.str.endswith('_y')))\n", + "rm_df = rm_df.loc[:,~col_mask]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50 blocks\n", + ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . found matches for 36 of 50 blocks\n", + "sync succeeded\n" + ] + } + ], + "source": [ + "# Do regression to find neural timestamps for each event type\n", + "if len(beh_ts)!=len(neural_ts):\n", + " good_beh_ms, neural_offset = sync_utils.pulsealign(beh_ts, neural_ts, window=50, thresh=0.95)\n", + " slope, offset, rval = sync_utils.sync_matched_pulses(good_beh_ms, neural_offset)\n", + "else:\n", + " slope, offset, rval = sync_utils.sync_matched_pulses(beh_ts, neural_ts)\n", + "\n", + "if rval < 0.99:\n", + " print('sync failed')\n", + "else: \n", + " print('sync succeeded')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# sync_data = pd.DataFrame(columns=['slope', 'offset'])\n", + "# sync_data.slope = [slope]\n", + "# sync_data.offset = [offset]\n", + "# sync_data.to_csv(f'{save_dir}/sync_data.csv', index=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# pd.DataFrame(MB1_ts).to_csv(f'{save_dir}/MB1_ts.csv', index=False)\n", + "# pd.DataFrame(MEM2_ts).to_csv(f'{save_dir}/MEM2_ts.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create epochs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ok, now that we have done some pre-processing, we want to make epochs aligned to specific behavioral events. Need the slope and offsets we determined from photodiode synchronization." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# all behavioral times of interest \n", + "gamble_choice_ts = [(x*slope + offset) for x in MB1_ts['choice']]\n", + "gamble_feedback_ts = [(x*slope + offset) for x in MB1_ts['feedback_start']]\n", + "gamble_fixation_ts = [(x*slope + offset) for x in MB1_ts['ITI_start']]\n", + "\n", + "memory_cue_ts = [(x*slope + offset) for x in MEM2_ts['face_start']]\n", + "memory_choice_ts = [(x*slope + offset) for x in MEM2_ts['choice']]\n", + "memory_fixation_ts = [(x*slope + offset) for x in MEM2_ts['ITI_start']]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# set some windows of interest \n", + "\n", + "buf = 1.0 # this is the buffer before and after that we use to limit edge effects for TFRs\n", + "\n", + "feedback_pre = 1.0 # this is the time before the feedback appears \n", + "feedback_post = 1.5 # this is the time the feedback is present \n", + "\n", + "choice_pre = 1.0 # this is the choice deliberation period\n", + "choice_post = feedback_pre\n", + "\n", + "baseline_pre = 0\n", + "baseline_post = 0.5 " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Used Annotations descriptions: ['gamble_feedback']\n" + ] + } + ], + "source": [ + "# Example - make feedback events \n", + "\n", + "evs = gamble_feedback_ts\n", + "\n", + "durs = np.zeros_like(gamble_feedback_ts).tolist()\n", + "\n", + "descriptions = ['gamble_feedback']*len(gamble_feedback_ts)\n", + "\n", + "# Make mne annotations based on these descriptions\n", + "annot = mne.Annotations(onset=evs,\n", + " duration=durs,\n", + " description=descriptions)\n", + "\n", + "wm_ref_data.set_annotations(annot)\n", + "\n", + "events_from_annot, event_dict = mne.events_from_annotations(wm_ref_data)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the epochs below. **Note that I am setting a fixed baseline period across all trials. By default, this will subtract the mean of the signal during the basline period from my signal during my period of interest.**" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Not setting metadata\n", + "60 matching events found\n", + "Applying baseline correction (mode: mean)\n", + "0 projection items activated\n", + "Using data from preloaded Raw for 60 events and 4609 original time points ...\n", + "0 bad epochs dropped\n" + ] + } + ], + "source": [ + "epochs = mne.Epochs(wm_ref_data, \n", + " events_from_annot, \n", + " event_id=event_dict, \n", + " baseline=(-feedback_pre, 0), \n", + " tmin=-(buf + feedback_pre), \n", + " tmax=buf + feedback_post, \n", + " reject=None, \n", + " reject_by_annotation=False,\n", + " preload=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**As an alternative to setting a baseline parameter in your Epochs code:** If you can't baseline your epochs in a standard way across trials (i.e. against a default time like -0.5 to 0 seconds) then you can MAKE a set of epochs to use as a baseline. The code below makes epochs based on the duration of the fixation cross, and subtracts the mean of these epochs from our epochs of interest. " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# # Make baseline epochs for 2-arm bandit: \n", + "\n", + "# evs = gamble_fixation_ts\n", + "\n", + "# durs = np.zeros_like(gamble_fixation_ts).tolist()\n", + "\n", + "# descriptions = ['gamble_fixation_ts']*len(gamble_fixation_ts)\n", + "\n", + "# # Make mne annotations based on these descriptions\n", + "# annot = mne.Annotations(onset=evs,\n", + "# duration=durs,\n", + "# description=descriptions)\n", + "\n", + "# wm_ref_data.set_annotations(annot)\n", + "\n", + "# events_from_annot, event_dict = mne.events_from_annotations(wm_ref_data)\n", + "\n", + "# baseline_epochs = mne.Epochs(wm_ref_data, \n", + "# events_from_annot, \n", + "# event_id=event_dict, \n", + "# baseline=None, \n", + "# tmin=-(buf + baseline_pre), \n", + "# tmax=buf + baseline_post, \n", + "# reject=None, \n", + "# preload=True)\n", + "\n", + "# # # Make baseline epochs for recognition memory: \n", + "# # my_annot = mne.Annotations(onset=memory_cross_start,\n", + "# # duration=0.5,\n", + "# # description=['memory_cross']*len(memory_cross_start))\n", + "\n", + "# # wm_ref_data.set_annotations(my_annot)\n", + "# # events_from_annot, event_dict = mne.events_from_annotations(wm_ref_data)\n", + "\n", + "# # rm_baseline_epochs = mne.Epochs(wm_ref_data, events_from_annot, event_id=event_dict, baseline=None, tmin=-buf, tmax=0.5+buf, reject=None, preload=True)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# # baseline the LFP data in time: \n", + "\n", + "# buf_ix = int(buf*epochs.info['sfreq'])\n", + "\n", + "# time_baseline = baseline_epochs._data[:, :, buf_ix:-buf_ix]\n", + "\n", + "# epochs._data = lfp_preprocess_utils.mean_baseline_time(epochs._data, time_baseline, mode='mean')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Downsample and annotate the epoched data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Resampling is the two-step process of applying a low-pass FIR filter and subselecting samples from the data.\n", + "\n", + "Using this function to resample data before forming mne.Epochs for final analysis is generally discouraged because doing so effectively loses precision of (and jitters) the event timings (see commented example below to prove this to yourself)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# \"\"\"\n", + "# An example where effecting jittering of triggers occurs when\n", + "# downsampling before epoching.\n", + "# \"\"\"\n", + "# import numpy as np\n", + "# import matplotlib.pyplot as plt\n", + "\n", + "# # 1 sec of data @ 1000 Hz\n", + "# fs = 1000. # Hz\n", + "# decim = 5\n", + "# n_samples = 1000\n", + "# freq = 20. # Hz\n", + "# t = np.arange(n_samples) / fs\n", + "# epoch_dur = 1. / freq # 2 cycles of our sinusoid\n", + "\n", + "# # we have a 10 Hz sinusoid signal\n", + "# raw_data = np.cos(2 * np.pi * freq * t)\n", + "\n", + "# # let's make events that should show the sinusoid moving out of phase\n", + "# # continuously\n", + "# n_events = 40\n", + "# event_times = np.linspace(0, 1. / freq, n_events, endpoint=False)\n", + "# event_samples = np.round(event_times * fs).astype(int)\n", + "# data_epoch = list()\n", + "# epoch_len = int(round(epoch_dur * fs))\n", + "# for event_time in event_times:\n", + "# start_idx = int(np.round(event_time * fs))\n", + "# data_epoch.append(raw_data[start_idx:start_idx + epoch_len])\n", + "# data_epoch = np.array(data_epoch)\n", + "# data_epoch_ds = data_epoch[:, ::decim]\n", + "\n", + "# # now let's try downsampling the raw data instead\n", + "# raw_data_ds = raw_data[::decim]\n", + "# fs_new = fs / decim\n", + "# data_ds_epoch = list()\n", + "# epoch_ds_len = int(round(epoch_dur * fs_new))\n", + "# for event_time in event_times:\n", + "# start_idx = int(np.round(event_time * fs_new))\n", + "# data_ds_epoch.append(raw_data_ds[start_idx:start_idx + epoch_ds_len])\n", + "# data_ds_epoch = np.array(data_ds_epoch)\n", + "\n", + "# # Look at the results\n", + "# assert data_ds_epoch.shape == data_epoch_ds.shape\n", + "# fig, axs = plt.subplots(1, 2)\n", + "# t_ds = np.arange(epoch_ds_len) / fs_new\n", + "# for di, (data_e_d, data_d_e) in enumerate(zip(data_epoch_ds, data_ds_epoch)):\n", + "# color = [di / float(n_events + 10)] * 3\n", + "# axs[0].plot(t_ds, data_e_d, color=color)\n", + "# axs[1].plot(t_ds, data_d_e, color=color)\n", + "# axs[0].set_ylabel('Epoch then downsample')\n", + "# axs[1].set_ylabel('Downsample then epoch')\n", + "# for ax in axs:\n", + "# ax.set_xlim(t_ds[[0, -1]])\n", + "# ax.set_xticks(t_ds[[0, -1]])\n", + "# fig.set_tight_layout(True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given the above, we use the built-in downsample method of mne Epochs, which will do the filtering and sub-sampling for us. Pick a frequency that is a factor of the current sampling rate." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Prelude: Some background on filtering: https://mark-kramer.github.io/Case-Studies-Python/06.html" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Number of events60
Eventsgamble_feedback: 60
Time range-2.000 – 2.498 sec
Baseline-1.000 – 0.000 sec
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Downsample \n", + "epochs.resample(sfreq=wm_ref_data.info['sfreq']/2)\n", + "# mb_baseline_epochs.resample(sfreq=wm_ref_data.info['sfreq']/2)\n", + "# rm_baseline_epochs.resample(sfreq=wm_ref_data.info['sfreq']/2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following functions detects all of the IEDs on each channel, for each event, returning (samples, time(s)) for each IED. " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "if all(np.isnan(np.array([1, 2, 3]))):\n", + " print('1')" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting up band-pass filter from 25 - 80 Hz\n", + "\n", + "FIR filter parameters\n", + "---------------------\n", + "Designing a one-pass, zero-phase, non-causal bandpass filter:\n", + "- Windowed time-domain design (firwin) method\n", + "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n", + "- Lower passband edge: 25.00\n", + "- Lower transition bandwidth: 6.25 Hz (-6 dB cutoff frequency: 21.88 Hz)\n", + "- Upper passband edge: 80.00 Hz\n", + "- Upper transition bandwidth: 20.00 Hz (-6 dB cutoff frequency: 90.00 Hz)\n", + "- Filter length: 271 samples (0.529 sec)\n", + "\n" + ] + } + ], + "source": [ + "IED_samps = lfp_preprocess_utils.detect_IEDs(epochs, \n", + " peak_thresh=4, \n", + " closeness_thresh=0.25, \n", + " width_thresh=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'lacas1-lmolf1': {0: array([nan]),\n", + " 1: array([nan]),\n", + " 2: array([], dtype=int64),\n", + " 3: array([nan]),\n", + " 4: array([], dtype=int64),\n", + " 5: array([nan]),\n", + " 6: array([nan]),\n", + " 7: array([], dtype=int64),\n", + " 8: array([nan]),\n", + " 9: array([], dtype=int64),\n", + " 10: array([nan]),\n", + " 11: array([nan]),\n", + " 12: array([nan]),\n", + " 13: array([nan]),\n", + " 14: array([nan]),\n", + " 15: array([], dtype=int64),\n", + " 16: array([], dtype=int64),\n", + " 17: array([nan]),\n", + " 18: array([], dtype=int64),\n", + " 19: array([nan]),\n", + " 20: array([nan]),\n", + " 21: array([nan]),\n", + " 22: array([], dtype=int64),\n", + " 23: array([nan]),\n", + " 24: array([], dtype=int64),\n", 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47: array([], dtype=int64),\n", + " 48: array([nan]),\n", + " 49: array([], dtype=int64),\n", + " 50: array([nan]),\n", + " 51: array([nan]),\n", + " 52: array([], dtype=int64),\n", + " 53: array([], dtype=int64),\n", + " 54: array([], dtype=int64),\n", + " 55: array([], dtype=int64),\n", + " 56: array([], dtype=int64),\n", + " 57: array([nan]),\n", + " 58: array([], dtype=int64),\n", + " 59: array([], dtype=int64)}}" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IED_samps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Add this into the metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Replacing existing metadata with 120 columns\n" + ] + } + ], + "source": [ + "# Let's make METADATA to assign each event some features\n", + "\n", + "event_metadata = pd.DataFrame(columns=['rt', 'reward', 'rpe', 'dprime']+list(IED_samps.keys()))\n", + "\n", + "event_metadata['rt'] = mb_df['draw_rt'].tolist()\n", + "event_metadata['reward'] = mb_df['reward'].tolist()\n", + "event_metadata['dprime'] = mb_df['image_dprime'].tolist()\n", + "\n", + "for ch in list(IED_samps.keys()):\n", + " for ev, val in IED_samps[ch].items():\n", + " if len(val) > 1:\n", + " event_metadata[ch].loc[ev] = val\n", + " else:\n", + " if ~np.isnan(val):\n", + " event_metadata[ch].loc[ev] = val\n", + " \n", + "epochs.metadata = event_metadata\n", + "# event_metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NaN ... \n", + "5 NaN NaN NaN NaN ... \n", + "6 NaN NaN NaN NaN ... \n", + "7 NaN NaN NaN NaN ... \n", + "8 NaN NaN NaN NaN ... \n", + "9 NaN NaN NaN NaN ... \n", + "10 NaN NaN NaN NaN ... \n", + "11 NaN NaN NaN NaN ... \n", + "12 NaN NaN NaN NaN ... \n", + "13 NaN NaN NaN NaN ... \n", + "14 NaN NaN NaN NaN ... \n", + "15 NaN NaN NaN NaN ... \n", + "16 NaN NaN NaN NaN ... \n", + "17 NaN NaN NaN NaN ... \n", + "18 NaN NaN NaN NaN ... \n", + "19 NaN NaN NaN NaN ... \n", + "20 NaN NaN NaN NaN ... \n", + "21 NaN NaN NaN NaN ... \n", + "22 NaN NaN NaN NaN ... \n", + "23 NaN NaN NaN NaN ... \n", + "24 NaN NaN NaN NaN ... \n", + "25 NaN NaN NaN NaN ... \n", + "26 NaN NaN NaN NaN ... \n", + "27 NaN NaN NaN NaN ... \n", + "28 NaN NaN NaN NaN ... \n", + "29 NaN NaN NaN NaN ... \n", + "30 NaN NaN NaN NaN ... \n", + "31 NaN NaN NaN NaN ... \n", + "32 NaN NaN NaN NaN ... \n", + "33 NaN NaN NaN NaN ... \n", + "34 NaN NaN NaN NaN ... \n", + "35 NaN NaN NaN NaN ... \n", + "36 NaN NaN NaN NaN ... \n", 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"40 NaN NaN NaN NaN NaN \n", + "41 NaN NaN NaN NaN NaN \n", + "42 NaN NaN NaN NaN NaN \n", + "43 NaN NaN NaN NaN NaN \n", + "44 NaN [2085] NaN NaN [2084] \n", + "45 NaN NaN NaN NaN NaN \n", + "46 NaN NaN NaN NaN NaN \n", + "47 NaN NaN NaN NaN NaN \n", + "48 NaN NaN NaN NaN NaN \n", + "49 NaN NaN NaN NaN NaN \n", + "50 NaN [1395] NaN NaN NaN \n", + "51 NaN NaN NaN NaN NaN \n", + "52 NaN NaN NaN [213] NaN \n", + "53 NaN NaN NaN NaN NaN \n", + "54 NaN NaN NaN NaN NaN \n", + "55 NaN NaN NaN NaN NaN \n", + "56 NaN NaN NaN NaN NaN \n", + "57 NaN NaN NaN NaN NaN \n", + "58 NaN [1527] NaN NaN NaN \n", + "59 NaN NaN NaN NaN NaN \n", + "\n", + "[60 rows x 120 columns]" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "event_metadata" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can plot the epochs and annotate them. Specifically, look for evidence of interictal discharges and seizure ramping during your behavioral epochs, as these should be used to exclude epochs (and potentially channels) from analysis. \n", + "\n", + "Resource: https://www.frontiersin.org/articles/10.3389/fnhum.2020.00044/full" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "/* global mpl */\n", + "window.mpl = {};\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(\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", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = this.ws.binaryType !== undefined;\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById('mpl-warnings');\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent =\n", + " 'This browser does not support binary websocket messages. ' +\n", + " 'Performance may be slow.';\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = document.createElement('div');\n", + " this.root.setAttribute('style', 'display: inline-block');\n", + " this._root_extra_style(this.root);\n", + "\n", + " parent_element.appendChild(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message('supports_binary', { value: fig.supports_binary });\n", + " fig.send_message('send_image_mode', {});\n", + " if (fig.ratio !== 1) {\n", + " fig.send_message('set_device_pixel_ratio', {\n", + " device_pixel_ratio: fig.ratio,\n", + " });\n", + " }\n", + " fig.send_message('refresh', {});\n", + " };\n", + "\n", + " this.imageObj.onload = function () {\n", + " if (fig.image_mode === 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function () {\n", + " fig.ws.close();\n", + " };\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "};\n", + "\n", + "mpl.figure.prototype._init_header = function () {\n", + " var titlebar = 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._canvas_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._init_canvas = function () {\n", + " var fig = this;\n", + "\n", + " var canvas_div = (this.canvas_div = document.createElement('div'));\n", + " canvas_div.setAttribute('tabindex', '0');\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", + " 'z-index: 2;'\n", + " );\n", + "\n", + " function on_keyboard_event_closure(name) {\n", + " return function (event) {\n", + " return fig.key_event(event, name);\n", + " };\n", + " }\n", + "\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 = (this.canvas = document.createElement('canvas'));\n", + " canvas.classList.add('mpl-canvas');\n", + " canvas.setAttribute(\n", + " 'style',\n", + " 'box-sizing: content-box;' +\n", + " 'pointer-events: none;' +\n", + " 'position: relative;' +\n", + " 'z-index: 0;'\n", + " );\n", + "\n", + " this.context = canvas.getContext('2d');\n", + "\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", + " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", + " 'canvas'\n", + " ));\n", + " rubberband_canvas.setAttribute(\n", + " 'style',\n", + " 'box-sizing: content-box;' +\n", + " 'left: 0;' +\n", + " 'pointer-events: none;' +\n", + " 'position: absolute;' +\n", + " 'top: 0;' +\n", + " 'z-index: 1;'\n", + " );\n", + "\n", + " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", + " if (this.ResizeObserver === undefined) {\n", + " if (window.ResizeObserver !== undefined) {\n", + " this.ResizeObserver = window.ResizeObserver;\n", + " } else {\n", + " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", + " this.ResizeObserver = obs.ResizeObserver;\n", + " }\n", + " }\n", + "\n", + " this.resizeObserverInstance = new this.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", + " if (entry.contentBoxSize instanceof Array) {\n", + " // Chrome 84 implements new version of spec.\n", + " width = entry.contentBoxSize[0].inlineSize;\n", + " height = entry.contentBoxSize[0].blockSize;\n", + " } else {\n", + " // Firefox implements old version of spec.\n", + " width = entry.contentBoxSize.inlineSize;\n", + " height = entry.contentBoxSize.blockSize;\n", + " }\n", + " } else {\n", + " // Chrome <84 implements even older version of spec.\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", + " if (entry.devicePixelContentBoxSize) {\n", + " // Chrome 84 implements new version of spec.\n", + " canvas.setAttribute(\n", + " 'width',\n", + " entry.devicePixelContentBoxSize[0].inlineSize\n", + " );\n", + " canvas.setAttribute(\n", + " 'height',\n", + " entry.devicePixelContentBoxSize[0].blockSize\n", + " );\n", + " } else {\n", + " canvas.setAttribute('width', width * fig.ratio);\n", + " canvas.setAttribute('height', height * fig.ratio);\n", + " }\n", + " /* This rescales the canvas back to display pixels, so that it\n", + " * appears correct on HiDPI screens. */\n", + " canvas.style.width = width + 'px';\n", + " canvas.style.height = height + 'px';\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 (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", + " fig.request_resize(width, height);\n", + " }\n", + " }\n", + " });\n", + " this.resizeObserverInstance.observe(canvas_div);\n", + "\n", + " function on_mouse_event_closure(name) {\n", + " /* User Agent sniffing is bad, but WebKit is busted:\n", + " * https://bugs.webkit.org/show_bug.cgi?id=144526\n", + " * https://bugs.webkit.org/show_bug.cgi?id=181818\n", + " * The worst that happens here is that they get an extra browser\n", + " * selection when dragging, if this check fails to catch them.\n", + " */\n", + " var UA = navigator.userAgent;\n", + " var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n", + " if(isWebKit) {\n", + " return function (event) {\n", + " /* This prevents the web browser from automatically changing to\n", + " * the text insertion cursor when the button is pressed. We\n", + " * want to control all of the cursor setting manually through\n", + " * the 'cursor' event from matplotlib */\n", + " event.preventDefault()\n", + " return fig.mouse_event(event, name);\n", + " };\n", + " } else {\n", + " return function (event) {\n", + " return fig.mouse_event(event, name);\n", + " };\n", + " }\n", + " }\n", + "\n", + " canvas_div.addEventListener(\n", + " 'mousedown',\n", + " on_mouse_event_closure('button_press')\n", + " );\n", + " canvas_div.addEventListener(\n", + " 'mouseup',\n", + " on_mouse_event_closure('button_release')\n", + " );\n", + " canvas_div.addEventListener(\n", + " 'dblclick',\n", + " on_mouse_event_closure('dblclick')\n", + " );\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " canvas_div.addEventListener(\n", + " 'mousemove',\n", + " on_mouse_event_closure('motion_notify')\n", + " );\n", + "\n", + " canvas_div.addEventListener(\n", + " 'mouseenter',\n", + " on_mouse_event_closure('figure_enter')\n", + " );\n", + " canvas_div.addEventListener(\n", + " 'mouseleave',\n", + " on_mouse_event_closure('figure_leave')\n", + " );\n", + "\n", + " canvas_div.addEventListener('wheel', function (event) {\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " on_mouse_event_closure('scroll')(event);\n", + " });\n", + "\n", + " canvas_div.appendChild(canvas);\n", + " canvas_div.appendChild(rubberband_canvas);\n", + "\n", + " this.rubberband_context = rubberband_canvas.getContext('2d');\n", + " this.rubberband_context.strokeStyle = '#000000';\n", + "\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", + " canvas_div.addEventListener('contextmenu', function (_e) {\n", + " event.preventDefault();\n", + " return false;\n", + " });\n", + "\n", + " function set_focus() {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'mpl-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\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", + " 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", + " /* 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", + "\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", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\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 = document.createElement('option');\n", + " option.selected = fmt === mpl.default_extension;\n", + " option.innerHTML = fmt;\n", + " fmt_picker.appendChild(option);\n", + " }\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", + " // 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", + "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", + "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], msg['forward']);\n", + " fig.send_message('refresh', {});\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", + " var x0 = msg['x0'] / fig.ratio;\n", + " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", + " var x1 = msg['x1'] / fig.ratio;\n", + " var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n", + " 0,\n", + " fig.canvas.width / fig.ratio,\n", + " fig.canvas.height / fig.ratio\n", + " );\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", + " fig.canvas_div.style.cursor = msg['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.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", + " // 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", + " var img = evt.data;\n", + " if (img.type !== 'image/png') {\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", + " img.type = 'image/png';\n", + " }\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", + "\n", + " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", + " img\n", + " );\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\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", + " 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(\n", + " \"No handler for the '\" + msg_type + \"' message type: \",\n", + " msg\n", + " );\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(\n", + " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", + " e,\n", + " e.stack,\n", + " msg\n", + " );\n", + " }\n", + " }\n", + " };\n", + "};\n", + "\n", + "\n", + "/*\n", + " * return a copy of an object with only non-object keys\n", + " * we need this to avoid circular references\n", + " * https://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", + " }\n", + " return obj;\n", + " }, {});\n", + "}\n", + "\n", + "mpl.figure.prototype.mouse_event = function (event, name) {\n", + " if (name === 'button_press') {\n", + " this.canvas.focus();\n", + " this.canvas_div.focus();\n", + " }\n", + "\n", + " // from https://stackoverflow.com/q/1114465\n", + " var boundingRect = this.canvas.getBoundingClientRect();\n", + " var x = (event.clientX - boundingRect.left) * this.ratio;\n", + " var y = (event.clientY - boundingRect.top) * this.ratio;\n", + "\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", + " 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", + " // Prevent repeat events\n", + " if (name === 'key_press') {\n", + " if (event.key === this._key) {\n", + " return;\n", + " } else {\n", + " this._key = event.key;\n", + " }\n", + " }\n", + " if (name === 'key_release') {\n", + " this._key = null;\n", + " }\n", + "\n", + " var value = '';\n", + " if (event.ctrlKey && event.key !== 'Control') {\n", + " value += 'ctrl+';\n", + " }\n", + " else if (event.altKey && event.key !== 'Alt') {\n", + " value += 'alt+';\n", + " }\n", + " else if (event.shiftKey && event.key !== 'Shift') {\n", + " value += 'shift+';\n", + " }\n", + "\n", + " value += 'k' + event.key;\n", + "\n", + " this._key_event_extra(event, name);\n", + "\n", + " this.send_message(name, { key: value, 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", + "\n", + "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", + "// prettier-ignore\n", + "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", + "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n", + "\n", + "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n", + "\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.binaryType = comm.kernel.ws.binaryType;\n", + " ws.readyState = comm.kernel.ws.readyState;\n", + " function updateReadyState(_event) {\n", + " if (comm.kernel.ws) {\n", + " ws.readyState = comm.kernel.ws.readyState;\n", + " } else {\n", + " ws.readyState = 3; // Closed state.\n", + " }\n", + " }\n", + " comm.kernel.ws.addEventListener('open', updateReadyState);\n", + " comm.kernel.ws.addEventListener('close', updateReadyState);\n", + " comm.kernel.ws.addEventListener('error', updateReadyState);\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", + " var data = msg['content']['data'];\n", + " if (data['blob'] !== undefined) {\n", + " data = {\n", + " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", + " };\n", + " }\n", + " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", + " ws.onmessage(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 = document.getElementById(id);\n", + " var ws_proxy = comm_websocket_adapter(comm);\n", + "\n", + " function ondownload(figure, _format) {\n", + " window.open(figure.canvas.toDataURL());\n", + " }\n", + "\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;\n", + " fig.cell_info = mpl.find_output_cell(\"
\");\n", + " if (!fig.cell_info) {\n", + " console.error('Failed to find cell for figure', id, fig);\n", + " return;\n", + " }\n", + " fig.cell_info[0].output_area.element.on(\n", + " 'cleared',\n", + " { fig: fig },\n", + " fig._remove_fig_handler\n", + " );\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_close = function (fig, msg) {\n", + " var width = fig.canvas.width / fig.ratio;\n", + " fig.cell_info[0].output_area.element.off(\n", + " 'cleared',\n", + " fig._remove_fig_handler\n", + " );\n", + " fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n", + " '';\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 / this.ratio;\n", + " var dataURL = this.canvas.toDataURL();\n", + " this.cell_info[1]['text/html'] =\n", + " '';\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 () {\n", + " fig.push_to_output();\n", + " }, 1000);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'btn-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\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", + " 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) {\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 = '';\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " // Add the status bar.\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 = 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 = '';\n", + " button.addEventListener('click', function (_evt) {\n", + " fig.handle_close(fig, {});\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 (event) {\n", + " var fig = event.data.fig;\n", + " if (event.target !== this) {\n", + " // Ignore bubbled events from children.\n", + " return;\n", + " }\n", + " fig.close_ws(fig, {});\n", + "};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (el) {\n", + " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (el) {\n", + " // this is important to make the div 'focusable\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", + " } else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (event, _name) {\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", + "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(\n", + " 'matplotlib',\n", + " mpl.mpl_figure_comm\n", + " );\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib notebook\n", + "fig = epochs.plot(n_epochs=1, n_channels=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dropped 1 epoch: 5\n", + "The following epochs were marked as bad and are dropped:\n", + "[5]\n", + "Channels marked as bad:\n", + "['laimm12-laimm11', 'lmolf4-laimm6']\n", + "Dropped 0 epochs: \n", + "The following epochs were marked as bad and are dropped:\n", + "[5]\n", + "Channels marked as bad:\n", + "['laimm12-laimm11', 'lmolf4-laimm6']\n" + ] + } + ], + "source": [ + "# Need this following line to save the annotations to the epochs object \n", + "fig.fake_keypress('a')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can examine information about the epochs you dropped: " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + ">" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check the epoch annotations \n", + "epochs.get_annotations_per_epoch" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "((),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " ('USER',),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " ())" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "epochs.drop_log" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save the epoched data and use for plots" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because most of your downstream analyses will operate on these epoched data, let's save them. Also, in the interest of reproducability, these will likely be the data you share (see: https://oir.nih.gov/sourcebook/intramural-program-oversight/intramural-data-sharing/2023-nih-data-management-sharing-policy)\n", + "\n", + "**Bad epochs will be dropped before saving the epochs to disk**" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting existing file.\n" + ] + } + ], + "source": [ + "epochs.save(f'{load_dir}/feedback-epo.fif', overwrite=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading /sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/feedback-epo.fif ...\n", + " Found the data of interest:\n", + " t = -2000.00 ... 2498.05 ms\n", + " 0 CTF compensation matrices available\n", + "Adding metadata with 120 columns\n", + "60 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n" + ] + } + ], + "source": [ + "epochs = mne.read_epochs(f'{load_dir}/feedback-epo.fif')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we will plot some basic TFRs of our behaviorally-locked analysis using a wavelet transform. Let's set some parameters first" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "# power parameters \n", + "freqs = np.logspace(*np.log10([4, 128]), num=20)\n", + "n_cycles = 4 \n", + "sr = epochs.info['sfreq']\n", + "buf = 1.0\n", + "buf_ix = int(buf*sr)\n", + "\n", + "good_chans = [x for x in epochs.ch_names if x not in epochs.info['bads']]\n", + "picks = [x for x in good_chans]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the metadata to assign conditions to parse your epochs!" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "0\n", + "0\n", + "1\n", + "0\n", + "0\n", + "1\n", + "0\n", + "0\n", + "0\n", + "1\n", + "0\n", + "0\n", + "0\n", + "6\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "3\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "2\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "1\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "1\n", + "0\n", + "0\n", + "4\n", + "4\n", + "0\n", + "1\n", + "1\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "3\n", + "5\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "2\n", + "1\n", + "1\n", + "1\n", + "1\n", + "0\n", + "0\n", + "3\n", + "Not setting metadata\n", + "Applying baseline correction (mode: zscore)\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "2\n", + "3\n", + "0\n", + "0\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "1\n", + "0\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "2\n", + "0\n", + "1\n", + "1\n", + "Not setting metadata\n", + "Applying baseline correction (mode: zscore)\n" + ] + } + ], + "source": [ + "data_parsing = ['reward==1',\n", + " 'reward==0'\n", + " ]\n", + "\n", + "\n", + "diff_pow_epochs = {f'{x}':np.nan for x in data_parsing}\n", + "\n", + "# baseline_pow = mne.time_frequency.tfr_morlet(baseline_epochs, picks=picks,\n", + "# freqs=freqs, n_cycles=n_cycles, use_fft=True,\n", + "# return_itc=False, n_jobs=-1, average=False)\n", + "\n", + "# Compute power without averaging over events\n", + "for parsing in data_parsing: \n", + " data_struct = np.ones([epochs[parsing]._data.shape[0], \n", + " epochs[parsing]._data.shape[1], len(freqs), \n", + " epochs[parsing]._data.shape[-1]])\n", + " for ch_ix in np.arange(epochs._data.shape[1]): \n", + " ch_data = epochs[parsing]._data[:, ch_ix:ch_ix+1, :]\n", + " bad_epochs = np.where(epochs.metadata.query(parsing)[epochs.ch_names[ch_ix]].notnull())[0]\n", + "# print(len(bad_epochs))\n", + " good_epochs = np.delete(np.arange(ch_data.shape[0]), bad_epochs)\n", + " ch_data = np.delete(ch_data, bad_epochs, axis=0)\n", + " ch_pow = mne.time_frequency.tfr_array_morlet(ch_data, sfreq=epochs.info['sfreq'], \n", + " freqs=freqs, n_cycles=n_cycles, zero_mean=True, \n", + " use_fft=True, output='power', \n", + " n_jobs=1)\n", + " \n", + " data_struct[good_epochs, ch_ix, :, :] = ch_pow[:, 0, :, :]\n", + " temp_pow = mne.time_frequency.EpochsTFR(epochs[parsing].info, data_struct, \n", + " epochs.times, freqs)\n", + "\n", + "# temp_pow = mne.time_frequency.tfr_morlet(epochs[parsing], picks=picks,\n", + "# freqs=freqs, n_cycles=n_cycles, use_fft=True,\n", + "# return_itc=False, n_jobs=-1, average=False)\n", + " # Baseline correct the TFRs\n", + " temp_pow = temp_pow.apply_baseline(baseline=(-feedback_pre, 0), mode='zscore')\n", + "# baseline_pow.crop(tmin=-baseline_pre, tmax=baseline_post)\n", + "# temp_pow.data = lfp_preprocess_utils.zscore_TFR_across_trials(temp_pow.data, baseline_pow.data)\n", + " temp_pow.crop(tmin=-feedback_pre, tmax=feedback_post)\n", + " diff_pow_epochs[parsing] = temp_pow\n" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(25, 116, 20, 1281)" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temp_pow._data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot: \n", + "\n", + "freqs = np.logspace(*np.log10([4, 128]), num=20)\n", + "\n", + "for parsing in data_parsing: \n", + " save_file = f'{load_dir}/gamble_TFR_{parsing}.pdf'\n", + " with PdfPages(save_file) as pdf:\n", + " for label in diff_pow_epochs[parsing].ch_names: \n", + " plot_ix = diff_pow_epochs[parsing].ch_names.index(label)\n", + " region_label = elec_df[elec_df.label==label].YBA_1.values[0]\n", + " if region_label == 'Unknown':\n", + " region_label = elec_df[elec_df.label==label]['Manual Examination'].values[0]\n", + "# plot_data = np.mean(diff_pow_epochs[parsing].data[:, plot_ix, :, :], axis=0)\n", + " plot_data = np.nanmean(diff_pow_epochs[parsing].data[:, plot_ix, :, :], axis=0)\n", + "\n", + "\n", + " f, tfr = plt.subplots(1, 1, figsize=[7, 4], dpi=300)\n", + "\n", + " tfr.imshow(plot_data, aspect='auto', interpolation='bicubic', cmap='RdBu_r', vmin=-3, vmax=3)\n", + " tfr.invert_yaxis()\n", + "\n", + " # neg_plot.vlines(3*buf_ix//4, 0, len(freqs)-1, 'k')\n", + " tfr.set_yticks(np.arange(0, len(freqs), 4))\n", + " tfr.set_yticklabels(np.round(freqs[np.arange(0, len(freqs), 4)]), fontsize=10)\n", + " tfr.set_xticks(np.linspace(0, plot_data.shape[-1], plot_data.shape[-1]//250))\n", + " tfr.set_xticklabels(np.linspace(-(feedback_pre*1000), feedback_post*1000, plot_data.shape[-1]//250))\n", + " tfr.set_xlabel('Time (ms)', fontsize=12)\n", + " tfr.set_ylabel('Frequency (Hz)', fontsize=12)\n", + " tfr.vlines((feedback_pre * diff_pow_epochs[parsing].info['sfreq']), 0, len(freqs)-1, 'k')\n", + "\n", + " f.suptitle(f'{region_label}')\n", + " f.tight_layout()\n", + " pdf.savefig()\n", + " plt.close(f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Statistical Analyses:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At this stage, you should **heavily** brainstorm the statistics you want to do, before you start writing any code. What is the goal of your analysis? What would actually allow you to show what you want to show?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As an example, let's say I want to compare the reward vs. no-reward conditions for every channel, and identify the timepoints and frequencies that exhibit significant differences between conditions. To do so, I would utilize a non-parametric cluster-permutation test." + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [], + "source": [ + "# def stat_fun(*arg):\n", + " return(scipy.stats.ttest_ind(arg[0], arg[1], equal_var=True, nan_policy='omit')[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "for label in diff_pow_epochs[parsing].ch_names[0:1]: \n", + " region_label = elec_df[elec_df.label==label].YBA_1.values[0]\n", + " if region_label == 'Unknown':\n", + " region_label = elec_df[elec_df.label==label]['Manual Examination'].values[0]\n", + "\n", + " plot_ix = diff_pow_epochs[parsing].ch_names.index(label)\n", + " \n", + " rdata = diff_pow_epochs['reward==1'].data[:, plot_ix, :, :]\n", + " nrdata = diff_pow_epochs['reward==0'].data[:, plot_ix, :, :]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for label in diff_pow_epochs[parsing].ch_names: \n", + " region_label = elec_df[elec_df.label==label].YBA_1.values[0]\n", + " if region_label == 'Unknown':\n", + " region_label = elec_df[elec_df.label==label]['Manual Examination'].values[0]\n", + "\n", + " plot_ix = diff_pow_epochs[parsing].ch_names.index(label)\n", + " \n", + " rdata = diff_pow_epochs['reward==1'].data[:, plot_ix, :, :]\n", + " rdata = rdata[np.unique(np.where(~np.isnan(rdata))[0]), :, :]\n", + " nrdata = diff_pow_epochs['reward==0'].data[:, plot_ix, :, :]\n", + " nrdata = nrdata[np.unique(np.where(~np.isnan(nrdata))[0]), :, :]\n", + " \n", + " X = [rdata, \n", + " nrdata]\n", + " \n", + " F_obs, clusters, cluster_p_values, H0 = \\\n", + " mne.stats.permutation_cluster_test(X, n_permutations=1000, out_type='mask', \n", + " verbose=True)\n", + " \n", + " if any(cluster_p_values<=0.05):\n", + " print(region_label)\n", + " # Create new stats image with only significant clusters\n", + " fig, ax = plt.subplots(1, 1, figsize=(6, 4))\n", + "\n", + " times = diff_pow_epochs['reward==0'].times\n", + "\n", + " evoked_power_1 = np.nanmean(X[0], axis=0)\n", + " evoked_power_2 = np.nanmean(X[1], axis=0)\n", + " evoked_power_contrast = evoked_power_1 - evoked_power_2\n", + " signs = np.sign(evoked_power_contrast)\n", + "\n", + " F_obs_plot = np.nan * np.ones_like(F_obs)\n", + " for c, p_val in zip(clusters, cluster_p_values):\n", + " if p_val <= 0.05:\n", + " F_obs_plot[c] = F_obs[c] * signs[c]\n", + "\n", + " ax.imshow(F_obs,\n", + " extent=[times[0], times[-1], freqs[0], freqs[-1]],\n", + " aspect='auto', origin='lower', cmap='gray')\n", + " max_F = np.nanmax(abs(F_obs_plot))\n", + " ax.imshow(F_obs_plot,\n", + " extent=[times[0], times[-1], freqs[0], freqs[-1]],\n", + " aspect='auto', origin='lower', cmap='RdBu_r',\n", + " vmin=-max_F, vmax=max_F)\n", + "\n", + " ax.set_xlabel('Time (ms)')\n", + " ax.set_ylabel('Frequency (Hz)')\n", + " # # ax.set_title(f'Induced power ({ch_name})')\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "# # Create new stats image with only significant clusters\n", + "# fig, (ax, ax2) = plt.subplots(2, 1, figsize=(6, 4))\n", + "\n", + "# times = diff_pow_epochs['reward==0'].times\n", + "\n", + "# evoked_power_1 = X[0].mean(axis=0)\n", + "# evoked_power_2 = X[1].mean(axis=0)\n", + "# evoked_power_contrast = evoked_power_1 - evoked_power_2\n", + "# signs = np.sign(evoked_power_contrast)\n", + "\n", + "# F_obs_plot = np.nan * np.ones_like(F_obs)\n", + "# for c, p_val in zip(clusters, cluster_p_values):\n", + "# if p_val <= 0.05:\n", + "# F_obs_plot[c] = F_obs[c] * signs[c]\n", + "\n", + "# ax.imshow(F_obs,\n", + "# extent=[times[0], times[-1], freqs[0], freqs[-1]],\n", + "# aspect='auto', origin='lower', cmap='gray')\n", + "# max_F = np.nanmax(abs(F_obs_plot))\n", + "# ax.imshow(F_obs_plot,\n", + "# extent=[times[0], times[-1], freqs[0], freqs[-1]],\n", + "# aspect='auto', origin='lower', cmap='RdBu_r',\n", + "# vmin=-max_F, vmax=max_F)\n", + "\n", + "# ax.set_xlabel('Time (ms)')\n", + "# ax.set_ylabel('Frequency (Hz)')\n", + "# # ax.set_title(f'Induced power ({ch_name})')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "lfp_analysis", + "language": "python", + "name": "lfpanalysis" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/LFPAnalysis/EDF_preprocessing_step_by_step.ipynb b/LFPAnalysis/EDF_preprocessing_step_by_step.ipynb new file mode 100644 index 0000000..adf2336 --- /dev/null +++ b/LFPAnalysis/EDF_preprocessing_step_by_step.ipynb @@ -0,0 +1,3989 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example preprocessing notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook we are going to walk through a single patient example. There are probably some patient-specific stuff in here that might change with other patients. Should be able to demonstrate the usage of different functions from the toolbox.\n", + "\n", + "1. Load raw data (.edf in this notebook) using mne\n", + "\n", + "2. Add in electrode information\n", + "\n", + "3. Notch filter line noise and cleaning out bad channels \n", + "\n", + "4. Re-reference the data \n", + "\n", + "5. Annotate data (i.e. artifact and IEDs) \n", + "\n", + "\n", + "Must read guides: \n", + "\n", + "https://www.sciencedirect.com/science/article/pii/S1053811922005559\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%reload_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import mne\n", + "from glob import glob\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.backends.backend_pdf import PdfPages\n", + "import seaborn as sns\n", + "from scipy.stats import zscore, linregress\n", + "import pandas as pd\n", + "from mne.preprocessing.bads import _find_outliers" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('/hpc/users/qasims01/resources/LFPAnalysis')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from LFPAnalysis import lfp_preprocess_utils, sync_utils" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load raw data (.edf in this notebook) using mne" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's a good idea to setup a sensible directory structure like below. Note that all my data lives on '/sc/arion' which is Minerva. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "mne: https://mne.tools/stable/index.html" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "base_dir = '/sc/arion' # this is the root directory for most un-archived data and results \n", + "\n", + "save_dir = f'{base_dir}/work/qasims01/MemoryBanditData/EMU/Subjects/MS007' # save intermediate results in the 'work' directory\n", + " \n", + "# I have saved most of my raw data in the 'projects directory'\n", + "behav_dir = f'{base_dir}/projects/guLab/Salman/EMU/MS007/behav/Day1'\n", + "neural_dir = f'{base_dir}/projects/guLab/Salman/EMU/MS007/neural/Day1'\n", + "anat_dir = f'{base_dir}/projects/guLab/Salman/EMU/MS007/anat'\n", + "edf_files = glob(f'{neural_dir}/*.edf')\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Try loading in the data into memory" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting EDF parameters from /sc/arion/projects/guLab/Salman/EMU/MS007/neural/Day1/MS007_MemBandit.edf...\n", + "EDF file detected\n", + "Setting channel info structure...\n", + "Creating raw.info structure...\n", + "Reading 0 ... 1867007 = 0.000 ... 1823.249 secs...\n" + ] + } + ], + "source": [ + "MS007_data = mne.io.read_raw_edf(edf_files[0], preload=True)\n", + "# If you try to preload, it will kill the kernel (at mem=4000). Probably need to request more memory in Minerva (mem=8000 seems to work)\n", + "\n", + "# # If not preloading: \n", + "# raw_data = MS007_data.get_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Sanity check\n", + "plt.plot(MS007_data._data[0,:4999])\n", + "plt.title(\"Raw iEEG, electrode 0, samples 0-4999\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Sanity check the photodiode\n", + "trig_ix = MS007_data.ch_names.index('DC1')\n", + "plt.plot(MS007_data._data[trig_ix, 10000:50000])\n", + "plt.title(\"Photodiode\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add in electrode information" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the electrode localization data and add it in\n", + "\n", + "csv_files = glob(f'{anat_dir}/*labels.csv')\n", + "elec_locs = pd.read_csv(csv_files[0])\n", + "\n", + "# Sometimes there's extra columns with no entries: \n", + "elec_locs = elec_locs[elec_locs.columns.drop(list(elec_locs.filter(regex='Unnamed')))]\n", + "\n", + "# # identify the bundles for re-referencing:\n", + "# loc_data['bundle'] = np.nan\n", + "# loc_data['bundle'] = loc_data.apply(lambda x: ''.join(i for i in x.label if not i.isdigit()), axis=1)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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labelBN246labelxyzmni_xmni_ymni_zgmNMMAnatAnatMacroBN246YBA_1Manual ExaminationNotes
0LaCaS1A13_L-6.54965041.776780-7.03872-6.14969034.97941-12.45010GrayLeft ACgG anterior cingulate gyrusArea s32L Mid Orbital GyrusL OrGLeft frontal pole 1 CNaNNaN
1LaCaS10A9l_L-9.74673049.77233037.28761-11.15810045.8798038.08775GrayLeft Cerebral White MatterUnknownL Superior Frontal GyrusL SFGUnknownLeft superior frontal gyrus 2 CNaN
2LaCaS11A9l_L-9.74673050.97167042.07965-11.03380047.4579643.55471GrayLeft SFG superior frontal gyrusUnknownL Superior Frontal GyrusL SFGLeft superior frontal gyrus 2 CWMNaN
3LaCaS12A9l_L-10.14640050.97167046.87168-11.52140047.7469349.07517GrayLeft SFG superior frontal gyrusUnknownL Superior Frontal GyrusL SFGLeft superior frontal gyrus 3 CNaNNaN
4LaCaS2A32sg_L-6.94928042.576330-2.24668-6.82622036.24643-7.12713GrayLeft ACgG anterior cingulate gyrusArea s32L ACCL CGLeft cingulate gyrus DNaNNaN
...................................................
195RpCiP8A39rd_R32.614580-40.97720038.4856234.704870-57.3846039.36843WhiteRight Cerebral White MatterUnknownR Angular GyrusR IPLRight supramarginal gyrus 4 AWMNaN
196RpCiP9A39rd_R37.010560-42.97610040.4823039.443400-59.4205041.61339GrayRight AnG angular gyrusUnknownR Angular GyrusR IPLRight supramarginal gyrus 5 BNaNNaN
197uLmOlFA11l_L-14.14270046.174330-15.82410-14.13340039.07736-22.87590WhiteLeft MOrG medial orbital gyrusArea Fo1L Superior Orbital GyrusL OrGLeft frontal orbital 3 BWMNaN
198uRHplTrHipp_R10.6346604.597446-17.8208013.547580-8.16845-23.89940GrayRight PHG parahippocampal gyrusSubiculumUnknownR HippRight parahippocampal gyrus CNaNNaN
199uRmOlFA11m_R1.44305246.174330-21.414803.62845339.04309-28.86140GrayRight GRe gyrus rectusArea Fo1UnknownR OrGRight frontal orbital 4 ANaNborderline WM
\n", + "

200 rows × 16 columns

\n", + "
" + ], + "text/plain": [ + " label BN246label x y z mni_x mni_y \\\n", + "0 LaCaS1 A13_L -6.549650 41.776780 -7.03872 -6.149690 34.97941 \n", + "1 LaCaS10 A9l_L -9.746730 49.772330 37.28761 -11.158100 45.87980 \n", + "2 LaCaS11 A9l_L -9.746730 50.971670 42.07965 -11.033800 47.45796 \n", + "3 LaCaS12 A9l_L -10.146400 50.971670 46.87168 -11.521400 47.74693 \n", + "4 LaCaS2 A32sg_L -6.949280 42.576330 -2.24668 -6.826220 36.24643 \n", + ".. ... ... ... ... ... ... ... \n", + "195 RpCiP8 A39rd_R 32.614580 -40.977200 38.48562 34.704870 -57.38460 \n", + "196 RpCiP9 A39rd_R 37.010560 -42.976100 40.48230 39.443400 -59.42050 \n", + "197 uLmOlF A11l_L -14.142700 46.174330 -15.82410 -14.133400 39.07736 \n", + "198 uRHplT rHipp_R 10.634660 4.597446 -17.82080 13.547580 -8.16845 \n", + "199 uRmOlF A11m_R 1.443052 46.174330 -21.41480 3.628453 39.04309 \n", + "\n", + " mni_z gm NMM Anat \\\n", + "0 -12.45010 Gray Left ACgG anterior cingulate gyrus Area s32 \n", + "1 38.08775 Gray Left Cerebral White Matter Unknown \n", + "2 43.55471 Gray Left SFG superior frontal gyrus Unknown \n", + "3 49.07517 Gray Left SFG superior frontal gyrus Unknown \n", + "4 -7.12713 Gray Left ACgG anterior cingulate gyrus Area s32 \n", + ".. ... ... ... ... \n", + "195 39.36843 White Right Cerebral White Matter Unknown \n", + "196 41.61339 Gray Right AnG angular gyrus Unknown \n", + "197 -22.87590 White Left MOrG medial orbital gyrus Area Fo1 \n", + "198 -23.89940 Gray Right PHG parahippocampal gyrus Subiculum \n", + "199 -28.86140 Gray Right GRe gyrus rectus Area Fo1 \n", + "\n", + " AnatMacro BN246 YBA_1 \\\n", + "0 L Mid Orbital Gyrus L OrG Left frontal pole 1 C \n", + "1 L Superior Frontal Gyrus L SFG Unknown \n", + "2 L Superior Frontal Gyrus L SFG Left superior frontal gyrus 2 C \n", + "3 L Superior Frontal Gyrus L SFG Left superior frontal gyrus 3 C \n", + "4 L ACC L CG Left cingulate gyrus D \n", + ".. ... ... ... \n", + "195 R Angular Gyrus R IPL Right supramarginal gyrus 4 A \n", + "196 R Angular Gyrus R IPL Right supramarginal gyrus 5 B \n", + "197 L Superior Orbital Gyrus L OrG Left frontal orbital 3 B \n", + "198 Unknown R Hipp Right parahippocampal gyrus C \n", + "199 Unknown R OrG Right frontal orbital 4 A \n", + "\n", + " Manual Examination Notes \n", + "0 NaN NaN \n", + "1 Left superior frontal gyrus 2 C NaN \n", + "2 WM NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + ".. ... ... \n", + "195 WM NaN \n", + "196 NaN NaN \n", + "197 WM NaN \n", + "198 NaN NaN \n", + "199 NaN borderline WM \n", + "\n", + "[200 rows x 16 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "elec_locs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The electrode names read out of the edf file do not always match those \n", + "in the pdf (used for localization). This could be error on the side of the tech who input the labels, \n", + "or on the side of MNE reading the labels in. Usually there's a mixup between lowercase 'l' and capital 'I'.\n", + "\n", + "Sometimes, there's electrodes on the pdf that are NOT in the MNE data structure... let's identify those as well. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Could not find a match for rhplt9.\n" + ] + } + ], + "source": [ + "new_mne_names, unmatched_names, unmatched_seeg = lfp_preprocess_utils.match_elec_names(MS007_data.ch_names, elec_locs.label)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'unmatched_names' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43munmatched_names\u001b[49m\n", + "\u001b[0;31mNameError\u001b[0m: name 'unmatched_names' is not defined" + ] + } + ], + "source": [ + "unmatched_names" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So we retun a new list of channel names for the mne data structure as well as a list of channels in the localization csv which are not found in the mne structure. Make sure that unmatched_seeg does not factor into any referencing schemes later - it's not in the MNE data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized pointsNot available
Good channels276 EEG
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Rename the mne data according to the localization data\n", + "new_name_dict = {x:y for (x,y) in zip(MS007_data.ch_names, new_mne_names)}\n", + "MS007_data.rename_channels(new_name_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['rhplt9']" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unmatched_seeg" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We have a total of 196 sEEG electrodes\n", + "We have a total of 19 EEG electrodes\n" + ] + } + ], + "source": [ + "# Note, there is surface EEG data that we should separately indicate from the sEEG:\n", + "right_seeg_names = [i for i in MS007_data.ch_names if i.startswith('r')]\n", + "left_seeg_names = [i for i in MS007_data.ch_names if i.startswith('l')]\n", + "# This is optional. I might want to look at scalp EEG at some point (lol) so might as well tag them here. \n", + "eeg_names = [\n", + " 'fp1',\n", + " 'f7',\n", + " 't3',\n", + " 't5',\n", + " 'o1',\n", + " 'f3',\n", + " 'c3',\n", + " 'p3',\n", + " 'fp2',\n", + " 'f8',\n", + " 't4',\n", + " 't6',\n", + " 'o2',\n", + " 'f4',\n", + " 'c4',\n", + " 'p4',\n", + " 'fz',\n", + " 'cz',\n", + " 'pz']\n", + "print(f'We have a total of {len(left_seeg_names) + len(right_seeg_names)} sEEG electrodes')\n", + "print(f'We have a total of {len(eeg_names)} EEG electrodes')\n", + "# MS007_data.set_channel_types()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized pointsNot available
Good channels216 EEG
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sEEG_mapping_dict = {f'{x}':'seeg' for x in left_seeg_names+right_seeg_names}\n", + "EEG_mapping_dict = {f'{x}':'eeg' for x in eeg_names}\n", + "trig_mapping_dict = {'dc1':'stim'}\n", + "# Drop random chans? \n", + "drop_chans = list(set(MS007_data.ch_names)^set(eeg_names+left_seeg_names+right_seeg_names+['dc1']))\n", + "MS007_data.drop_channels(drop_chans)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_182879/1830004009.py:4: RuntimeWarning: The unit for channel(s) dc1 has changed from V to NA.\n", + " MS007_data.set_channel_types(trig_mapping_dict)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized pointsNot available
Good channels196 sEEG, 19 EEG, 1 Stimulus
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Set channel types:\n", + "MS007_data.set_channel_types(sEEG_mapping_dict)\n", + "MS007_data.set_channel_types(EEG_mapping_dict)\n", + "MS007_data.set_channel_types(trig_mapping_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_182879/3983129385.py:7: RuntimeWarning: Fiducial point nasion not found, assuming identity unknown to head transformation\n", + " MS007_data.set_montage(montage, match_case=False, on_missing='warn')\n", + "/tmp/ipykernel_182879/3983129385.py:7: RuntimeWarning: DigMontage is only a subset of info. There are 19 channel positions not present in the DigMontage. The required channels are:\n", + "\n", + "['fp1', 'f7', 't3', 't5', 'o1', 'f3', 'c3', 'p3', 'fp2', 'f8', 't4', 't6', 'o2', 'f4', 'c4', 'p4', 'fz', 'cz', 'pz'].\n", + "\n", + "Consider using inst.set_channel_types if these are not EEG channels, or use the on_missing parameter if the channel positions are allowed to be unknown in your analyses.\n", + " MS007_data.set_montage(montage, match_case=False, on_missing='warn')\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized points196 points
Good channels196 sEEG, 19 EEG, 1 Stimulus
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# make montage (convert mm to m)!! I'm not sure if we will ever use or need this step, but why not just do it\n", + "\n", + "montage = mne.channels.make_dig_montage(ch_pos=dict(zip(elec_locs.label, \n", + " elec_locs[['mni_x', 'mni_y', 'mni_z']].to_numpy(dtype=float)/1000)),\n", + " coord_frame='mni_tal')\n", + "\n", + "MS007_data.set_montage(montage, match_case=False, on_missing='warn')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notch filter line noise and cleaning out bad channels \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We want to remove the line noise (60 Hz and harmonics in US data, 50 Hz and harmonics in EU data). \n", + "\n", + "To do so, we use a band-stop filter that removes a narrow band of frequencies. \n", + "\n", + "Maybe eventually we don't want to use filters, especially if interested in ERPs: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6456018/" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting up band-stop filter\n", + "\n", + "FIR filter parameters\n", + "---------------------\n", + "Designing a one-pass, zero-phase, non-causal bandstop filter:\n", + "- Windowed time-domain design (firwin) method\n", + "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n", + "- Lower transition bandwidth: 0.50 Hz\n", + "- Upper transition bandwidth: 0.50 Hz\n", + "- Filter length: 6759 samples (6.601 sec)\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", + "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s\n", + "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.1s remaining: 0.0s\n", + "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.2s remaining: 0.0s\n", + "[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 0.3s remaining: 0.0s\n", + "[Parallel(n_jobs=1)]: Done 215 out of 215 | elapsed: 13.5s finished\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized points196 points
Good channels196 sEEG, 19 EEG, 1 Stimulus
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Identify line noise\n", + "MS007_data.info['line_freq'] = 60\n", + "\n", + "# Notch out 60 Hz noise and harmonics \n", + "MS007_data.notch_filter(freqs=(60, 120, 180, 240))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting existing file.\n", + "Writing /sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/photodiode.fif\n", + "Closing /sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/photodiode.fif\n", + "[done]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_182879/2725322655.py:2: RuntimeWarning: This filename (/sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/photodiode.fif) does not conform to MNE naming conventions. All raw files should end with raw.fif, raw_sss.fif, raw_tsss.fif, _meg.fif, _eeg.fif, _ieeg.fif, raw.fif.gz, raw_sss.fif.gz, raw_tsss.fif.gz, _meg.fif.gz, _eeg.fif.gz or _ieeg.fif.gz\n", + " MS007_data.save(f'{save_dir}/photodiode.fif', picks='dc1', overwrite=True)\n" + ] + } + ], + "source": [ + "# Save out the photodiode channel separately\n", + "MS007_data.save(f'{save_dir}/photodiode.fif', picks='dc1', overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Denote bad channels" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Clean up the MNE data \n", + "\n", + "bads = lfp_preprocess_utils.detect_bad_elecs(MS007_data, \n", + " sEEG_mapping_dict)\n", + "\n", + "MS007_data.info['bads'] = bads" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['laglt10',\n", + " 'laglt8',\n", + " 'laglt9',\n", + " 'lcmfo7',\n", + " 'lhplt1',\n", + " 'lhplt2',\n", + " 'lmcms1',\n", + " 'lmcms2',\n", + " 'lmcms9',\n", + " 'lmolf4',\n", + " 'lmolf5',\n", + " 'racas8',\n", + " 'rpcip7']" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bads" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's pick out any bad channels missed by automatic screening, or restore channels that were erroneously deemed bad" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When we plot the data using the interactive plotting, bad channels are grayed out. 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'_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", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\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 = document.createElement('option');\n", + " option.selected = fmt === mpl.default_extension;\n", + " option.innerHTML = fmt;\n", + " fmt_picker.appendChild(option);\n", + " }\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", + " // 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", + "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", + "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], msg['forward']);\n", + " fig.send_message('refresh', {});\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", + " var x0 = msg['x0'] / fig.ratio;\n", + " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", + " var x1 = msg['x1'] / fig.ratio;\n", + " var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n", + " 0,\n", + " fig.canvas.width / fig.ratio,\n", + " fig.canvas.height / fig.ratio\n", + " );\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", + " fig.canvas_div.style.cursor = msg['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.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", + " // 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", + " var img = evt.data;\n", + " if (img.type !== 'image/png') {\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", + " img.type = 'image/png';\n", + " }\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", + "\n", + " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", + " img\n", + " );\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\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", + " 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(\n", + " \"No handler for the '\" + msg_type + \"' message type: \",\n", + " msg\n", + " );\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(\n", + " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", + " e,\n", + " e.stack,\n", + " msg\n", + " );\n", + " }\n", + " }\n", + " };\n", + "};\n", + "\n", + "\n", + "/*\n", + " * return a copy of an object with only non-object keys\n", + " * we need this to avoid circular references\n", + " * https://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", + " }\n", + " return obj;\n", + " }, {});\n", + "}\n", + "\n", + "mpl.figure.prototype.mouse_event = function (event, name) {\n", + " if (name === 'button_press') {\n", + " this.canvas.focus();\n", + " this.canvas_div.focus();\n", + " }\n", + "\n", + " // from https://stackoverflow.com/q/1114465\n", + " var boundingRect = this.canvas.getBoundingClientRect();\n", + " var x = (event.clientX - boundingRect.left) * this.ratio;\n", + " var y = (event.clientY - boundingRect.top) * this.ratio;\n", + "\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", + " 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", + " // Prevent repeat events\n", + " if (name === 'key_press') {\n", + " if (event.key === this._key) {\n", + " return;\n", + " } else {\n", + " this._key = event.key;\n", + " }\n", + " }\n", + " if (name === 'key_release') {\n", + " this._key = null;\n", + " }\n", + "\n", + " var value = '';\n", + " if (event.ctrlKey && event.key !== 'Control') {\n", + " value += 'ctrl+';\n", + " }\n", + " else if (event.altKey && event.key !== 'Alt') {\n", + " value += 'alt+';\n", + " }\n", + " else if (event.shiftKey && event.key !== 'Shift') {\n", + " value += 'shift+';\n", + " }\n", + "\n", + " value += 'k' + event.key;\n", + "\n", + " this._key_event_extra(event, name);\n", + "\n", + " this.send_message(name, { key: value, 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", + "\n", + "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", + "// prettier-ignore\n", + "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", + "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n", + "\n", + "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n", + "\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.binaryType = comm.kernel.ws.binaryType;\n", + " ws.readyState = comm.kernel.ws.readyState;\n", + " function updateReadyState(_event) {\n", + " if (comm.kernel.ws) {\n", + " ws.readyState = comm.kernel.ws.readyState;\n", + " } else {\n", + " ws.readyState = 3; // Closed state.\n", + " }\n", + " }\n", + " comm.kernel.ws.addEventListener('open', updateReadyState);\n", + " comm.kernel.ws.addEventListener('close', updateReadyState);\n", + " comm.kernel.ws.addEventListener('error', updateReadyState);\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", + " var data = msg['content']['data'];\n", + " if (data['blob'] !== undefined) {\n", + " data = {\n", + " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", + " };\n", + " }\n", + " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", + " ws.onmessage(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 = document.getElementById(id);\n", + " var ws_proxy = comm_websocket_adapter(comm);\n", + "\n", + " function ondownload(figure, _format) {\n", + " window.open(figure.canvas.toDataURL());\n", + " }\n", + "\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;\n", + " fig.cell_info = mpl.find_output_cell(\"
\");\n", + " if (!fig.cell_info) {\n", + " console.error('Failed to find cell for figure', id, fig);\n", + " return;\n", + " }\n", + " fig.cell_info[0].output_area.element.on(\n", + " 'cleared',\n", + " { fig: fig },\n", + " fig._remove_fig_handler\n", + " );\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_close = function (fig, msg) {\n", + " var width = fig.canvas.width / fig.ratio;\n", + " fig.cell_info[0].output_area.element.off(\n", + " 'cleared',\n", + " fig._remove_fig_handler\n", + " );\n", + " fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n", + " '';\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 / this.ratio;\n", + " var dataURL = this.canvas.toDataURL();\n", + " this.cell_info[1]['text/html'] =\n", + " '';\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 () {\n", + " fig.push_to_output();\n", + " }, 1000);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'btn-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\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", + " 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) {\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 = '';\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " // Add the status bar.\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 = 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 = '';\n", + " button.addEventListener('click', function (_evt) {\n", + " fig.handle_close(fig, {});\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 (event) {\n", + " var fig = event.data.fig;\n", + " if (event.target !== this) {\n", + " // Ignore bubbled events from children.\n", + " return;\n", + " }\n", + " fig.close_ws(fig, {});\n", + "};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (el) {\n", + " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (el) {\n", + " // this is important to make the div 'focusable\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", + " } else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (event, _name) {\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", + "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(\n", + " 'matplotlib',\n", + " mpl.mpl_figure_comm\n", + " );\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "/* global mpl */\n", + "window.mpl = {};\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(\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", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = this.ws.binaryType !== undefined;\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById('mpl-warnings');\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent =\n", + " 'This browser does not support binary websocket messages. ' +\n", + " 'Performance may be slow.';\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = document.createElement('div');\n", + " this.root.setAttribute('style', 'display: inline-block');\n", + " this._root_extra_style(this.root);\n", + "\n", + " parent_element.appendChild(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message('supports_binary', { value: fig.supports_binary });\n", + " fig.send_message('send_image_mode', {});\n", + " if (fig.ratio !== 1) {\n", + " fig.send_message('set_device_pixel_ratio', {\n", + " device_pixel_ratio: fig.ratio,\n", + " });\n", + " }\n", + " fig.send_message('refresh', {});\n", + " };\n", + "\n", + " this.imageObj.onload = function () {\n", + " if (fig.image_mode === 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function () {\n", + " fig.ws.close();\n", + " };\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "};\n", + "\n", + "mpl.figure.prototype._init_header = function () {\n", + " var titlebar = 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._canvas_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._init_canvas = function () {\n", + " var fig = this;\n", + "\n", + " var canvas_div = (this.canvas_div = document.createElement('div'));\n", + " canvas_div.setAttribute('tabindex', '0');\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", + " 'z-index: 2;'\n", + " );\n", + "\n", + " function on_keyboard_event_closure(name) {\n", + " return function (event) {\n", + " return fig.key_event(event, name);\n", + " };\n", + " }\n", + "\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 = (this.canvas = document.createElement('canvas'));\n", + " canvas.classList.add('mpl-canvas');\n", + " canvas.setAttribute(\n", + " 'style',\n", + " 'box-sizing: content-box;' +\n", + " 'pointer-events: none;' +\n", + " 'position: relative;' +\n", + " 'z-index: 0;'\n", + " );\n", + "\n", + " this.context = canvas.getContext('2d');\n", + "\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", + " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", + " 'canvas'\n", + " ));\n", + " rubberband_canvas.setAttribute(\n", + " 'style',\n", + " 'box-sizing: content-box;' +\n", + " 'left: 0;' +\n", + " 'pointer-events: none;' +\n", + " 'position: absolute;' +\n", + " 'top: 0;' +\n", + " 'z-index: 1;'\n", + " );\n", + "\n", + " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", + " if (this.ResizeObserver === undefined) {\n", + " if (window.ResizeObserver !== undefined) {\n", + " this.ResizeObserver = window.ResizeObserver;\n", + " } else {\n", + " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", + " this.ResizeObserver = obs.ResizeObserver;\n", + " }\n", + " }\n", + "\n", + " this.resizeObserverInstance = new this.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", + " if (entry.contentBoxSize instanceof Array) {\n", + " // Chrome 84 implements new version of spec.\n", + " width = entry.contentBoxSize[0].inlineSize;\n", + " height = entry.contentBoxSize[0].blockSize;\n", + " } else {\n", + " // Firefox implements old version of spec.\n", + " width = entry.contentBoxSize.inlineSize;\n", + " height = entry.contentBoxSize.blockSize;\n", + " }\n", + " } else {\n", + " // Chrome <84 implements even older version of spec.\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", + " if (entry.devicePixelContentBoxSize) {\n", + " // Chrome 84 implements new version of spec.\n", + " canvas.setAttribute(\n", + " 'width',\n", + " entry.devicePixelContentBoxSize[0].inlineSize\n", + " );\n", + " canvas.setAttribute(\n", + " 'height',\n", + " entry.devicePixelContentBoxSize[0].blockSize\n", + " );\n", + " } else {\n", + " canvas.setAttribute('width', width * fig.ratio);\n", + " canvas.setAttribute('height', height * fig.ratio);\n", + " }\n", + " /* This rescales the canvas back to display pixels, so that it\n", + " * appears correct on HiDPI screens. */\n", + " canvas.style.width = width + 'px';\n", + " canvas.style.height = height + 'px';\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 (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", + " fig.request_resize(width, height);\n", + " }\n", + " }\n", + " });\n", + " this.resizeObserverInstance.observe(canvas_div);\n", + "\n", + " function on_mouse_event_closure(name) {\n", + " /* User Agent sniffing is bad, but WebKit is busted:\n", + " * https://bugs.webkit.org/show_bug.cgi?id=144526\n", + " * https://bugs.webkit.org/show_bug.cgi?id=181818\n", + " * The worst that happens here is that they get an extra browser\n", + " * selection when dragging, if this check fails to catch them.\n", + " */\n", + " var UA = navigator.userAgent;\n", + " var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n", + " if(isWebKit) {\n", + " return function (event) {\n", + " /* This prevents the web browser from automatically changing to\n", + " * the text insertion cursor when the button is pressed. We\n", + " * want to control all of the cursor setting manually through\n", + " * the 'cursor' event from matplotlib */\n", + " event.preventDefault()\n", + " return fig.mouse_event(event, name);\n", + " };\n", + " } else {\n", + " return function (event) {\n", + " return fig.mouse_event(event, name);\n", + " };\n", + " }\n", + " }\n", + "\n", + " canvas_div.addEventListener(\n", + " 'mousedown',\n", + " on_mouse_event_closure('button_press')\n", + " );\n", + " canvas_div.addEventListener(\n", + " 'mouseup',\n", + " on_mouse_event_closure('button_release')\n", + " );\n", + " canvas_div.addEventListener(\n", + " 'dblclick',\n", + " on_mouse_event_closure('dblclick')\n", + " );\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " canvas_div.addEventListener(\n", + " 'mousemove',\n", + " on_mouse_event_closure('motion_notify')\n", + " );\n", + "\n", + " canvas_div.addEventListener(\n", + " 'mouseenter',\n", + " on_mouse_event_closure('figure_enter')\n", + " );\n", + " canvas_div.addEventListener(\n", + " 'mouseleave',\n", + " on_mouse_event_closure('figure_leave')\n", + " );\n", + "\n", + " canvas_div.addEventListener('wheel', function (event) {\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " on_mouse_event_closure('scroll')(event);\n", + " });\n", + "\n", + " canvas_div.appendChild(canvas);\n", + " canvas_div.appendChild(rubberband_canvas);\n", + "\n", + " this.rubberband_context = rubberband_canvas.getContext('2d');\n", + " this.rubberband_context.strokeStyle = '#000000';\n", + "\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", + " canvas_div.addEventListener('contextmenu', function (_e) {\n", + " event.preventDefault();\n", + " return false;\n", + " });\n", + "\n", + " function set_focus() {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'mpl-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\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", + " 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", + " /* 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", + "\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", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\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 = document.createElement('option');\n", + " option.selected = fmt === mpl.default_extension;\n", + " option.innerHTML = fmt;\n", + " fmt_picker.appendChild(option);\n", + " }\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", + " // 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", + "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", + "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], msg['forward']);\n", + " fig.send_message('refresh', {});\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", + " var x0 = msg['x0'] / fig.ratio;\n", + " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", + " var x1 = msg['x1'] / fig.ratio;\n", + " var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n", + " 0,\n", + " fig.canvas.width / fig.ratio,\n", + " fig.canvas.height / fig.ratio\n", + " );\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", + " fig.canvas_div.style.cursor = msg['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.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", + " // 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", + " var img = evt.data;\n", + " if (img.type !== 'image/png') {\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", + " img.type = 'image/png';\n", + " }\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", + "\n", + " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", + " img\n", + " );\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\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", + " 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(\n", + " \"No handler for the '\" + msg_type + \"' message type: \",\n", + " msg\n", + " );\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(\n", + " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", + " e,\n", + " e.stack,\n", + " msg\n", + " );\n", + " }\n", + " }\n", + " };\n", + "};\n", + "\n", + "\n", + "/*\n", + " * return a copy of an object with only non-object keys\n", + " * we need this to avoid circular references\n", + " * https://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", + " }\n", + " return obj;\n", + " }, {});\n", + "}\n", + "\n", + "mpl.figure.prototype.mouse_event = function (event, name) {\n", + " if (name === 'button_press') {\n", + " this.canvas.focus();\n", + " this.canvas_div.focus();\n", + " }\n", + "\n", + " // from https://stackoverflow.com/q/1114465\n", + " var boundingRect = this.canvas.getBoundingClientRect();\n", + " var x = (event.clientX - boundingRect.left) * this.ratio;\n", + " var y = (event.clientY - boundingRect.top) * this.ratio;\n", + "\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", + " 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", + " // Prevent repeat events\n", + " if (name === 'key_press') {\n", + " if (event.key === this._key) {\n", + " return;\n", + " } else {\n", + " this._key = event.key;\n", + " }\n", + " }\n", + " if (name === 'key_release') {\n", + " this._key = null;\n", + " }\n", + "\n", + " var value = '';\n", + " if (event.ctrlKey && event.key !== 'Control') {\n", + " value += 'ctrl+';\n", + " }\n", + " else if (event.altKey && event.key !== 'Alt') {\n", + " value += 'alt+';\n", + " }\n", + " else if (event.shiftKey && event.key !== 'Shift') {\n", + " value += 'shift+';\n", + " }\n", + "\n", + " value += 'k' + event.key;\n", + "\n", + " this._key_event_extra(event, name);\n", + "\n", + " this.send_message(name, { key: value, 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", + "\n", + "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", + "// prettier-ignore\n", + "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", + "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n", + "\n", + "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n", + "\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.binaryType = comm.kernel.ws.binaryType;\n", + " ws.readyState = comm.kernel.ws.readyState;\n", + " function updateReadyState(_event) {\n", + " if (comm.kernel.ws) {\n", + " ws.readyState = comm.kernel.ws.readyState;\n", + " } else {\n", + " ws.readyState = 3; // Closed state.\n", + " }\n", + " }\n", + " comm.kernel.ws.addEventListener('open', updateReadyState);\n", + " comm.kernel.ws.addEventListener('close', updateReadyState);\n", + " comm.kernel.ws.addEventListener('error', updateReadyState);\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", + " var data = msg['content']['data'];\n", + " if (data['blob'] !== undefined) {\n", + " data = {\n", + " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", + " };\n", + " }\n", + " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", + " ws.onmessage(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 = document.getElementById(id);\n", + " var ws_proxy = comm_websocket_adapter(comm);\n", + "\n", + " function ondownload(figure, _format) {\n", + " window.open(figure.canvas.toDataURL());\n", + " }\n", + "\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;\n", + " fig.cell_info = mpl.find_output_cell(\"
\");\n", + " if (!fig.cell_info) {\n", + " console.error('Failed to find cell for figure', id, fig);\n", + " return;\n", + " }\n", + " fig.cell_info[0].output_area.element.on(\n", + " 'cleared',\n", + " { fig: fig },\n", + " fig._remove_fig_handler\n", + " );\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_close = function (fig, msg) {\n", + " var width = fig.canvas.width / fig.ratio;\n", + " fig.cell_info[0].output_area.element.off(\n", + " 'cleared',\n", + " fig._remove_fig_handler\n", + " );\n", + " fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n", + " '';\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 / this.ratio;\n", + " var dataURL = this.canvas.toDataURL();\n", + " this.cell_info[1]['text/html'] =\n", + " '';\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 () {\n", + " fig.push_to_output();\n", + " }, 1000);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'btn-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\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", + " 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) {\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 = '';\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " // Add the status bar.\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 = 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 = '';\n", + " button.addEventListener('click', function (_evt) {\n", + " fig.handle_close(fig, {});\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 (event) {\n", + " var fig = event.data.fig;\n", + " if (event.target !== this) {\n", + " // Ignore bubbled events from children.\n", + " return;\n", + " }\n", + " fig.close_ws(fig, {});\n", + "};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (el) {\n", + " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (el) {\n", + " // this is important to make the div 'focusable\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", + " } else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (event, _name) {\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", + "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(\n", + " 'matplotlib',\n", + " mpl.mpl_figure_comm\n", + " );\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib notebook\n", + "fig = MS007_data.plot(start=0, duration=120, n_channels=8, scalings=MS007_data._data.max()/20)\n", + "fig.fake_keypress('a')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare your revised list of 'bad' electrodes to thoe originally detected above." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['laglt10',\n", + " 'laglt8',\n", + " 'laglt9',\n", + " 'lcmfo7',\n", + " 'lmcms1',\n", + " 'lmcms2',\n", + " 'lmcms9',\n", + " 'lmolf4',\n", + " 'lmolf5',\n", + " 'racas8',\n", + " 'rpcip7',\n", + " 'laimm12',\n", + " 'lcmfo14',\n", + " 'lmolf7']" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "MS007_data.info['bads']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Re-reference the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you're like me, you find the concept of re-referencing somewhat confusing. Isn't the data recorded relative to a ground and reference in the EMU (https://ahleighton.github.io/OE-ephys-course/EEA/theoryday3.html)? \n", + "\n", + "It is, but we do digital re-referencing of the recorded signal to clean up any remaining shared noise. \n", + "\n", + "**Re-referencing should be an EXTREMELY conscious choice as it changes the LFP signal dramatically!** In our case, we choose to do local white-matter re-referencing because electrodes in white matter should be fairly stable (low-variance) and not contain local, slow oscillations of interest. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's use the localization data to determine the gray vs. white matter electrodes. \n", + "Then, let's re-reference each gray matter electrode to the closest and most low-amplitude white matter electrode. \n", + "\n", + "Make sure 'bad' electrodes are not used in the re-referencing. Same with unmatched seeg electrodes (not present in the mne data structure)." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "anode_list, cathode_list, drop_wm_channels, oob_channels = lfp_preprocess_utils.wm_ref(mne_data=MS007_data, \n", + " loc_data=elec_locs, \n", + " bad_channels=MS007_data.info['bads'], \n", + " unmatched_seeg=unmatched_seeg,\n", + " site='MSSM')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['lmolf1',\n", + " 'lacas9',\n", + " 'lacas9',\n", + " 'lmolf1',\n", + " 'lmolf1',\n", + " 'lacas8',\n", + " 'lacas8',\n", + " 'lacas8',\n", + " 'lacas8',\n", + " 'lhplt5',\n", + " 'laglt6',\n", + " 'lhplt5',\n", + " 'lhplt5',\n", + " 'lhplt6',\n", + " 'laglt6',\n", + " 'laglt6',\n", + " 'laglt5',\n", + " 'laimm11',\n", + " 'laimm11',\n", + " 'laimm6',\n", + " 'lmolf6',\n", + " 'laimm8',\n", + " 'laimm6',\n", + " 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'racas9',\n", + " 'racas10',\n", + " 'rcmfo5',\n", + " 'rcmfo5',\n", + " 'rcmfo5',\n", + " 'raimm5',\n", + " 'raimm5',\n", + " 'rcmfo5',\n", + " 'rcmfo5',\n", + " 'rcmfo5',\n", + " 'rcmfo5',\n", + " 'rcmfo5',\n", + " 'rcmfo5',\n", + " 'raglt4',\n", + " 'rhplt4',\n", + " 'rhplt4',\n", + " 'rmcms7',\n", + " 'rmcms7',\n", + " 'rmcms1',\n", + " 'rmcms8',\n", + " 'rmcms7',\n", + " 'rmcms7',\n", + " 'racas3',\n", + " 'racas3',\n", + " 'rmolf8',\n", + " 'rmolf3',\n", + " 'rmolf3',\n", + " 'rmolf3',\n", + " 'rmtpt3',\n", + " 'rmtpt3',\n", + " 'rmtpt4',\n", + " 'rmtpt4',\n", + " 'rmtpt4',\n", + " 'rpcip5',\n", + " 'rpcip8',\n", + " 'rpcip5',\n", + " 'rpcip6',\n", + " 'rpcip8']" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cathode_list" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sEEG channel type selected for re-referencing\n", + "Creating RawArray with float64 data, n_channels=116, n_times=1867008\n", + " Range : 0 ... 1867007 = 0.000 ... 1823.249 secs\n", + "Ready.\n", + "Added the following bipolar channels:\n", + "lacas1-lmolf1, lacas10-lacas9, lacas12-lacas9, lacas2-lmolf1, lacas3-lmolf1, lacas4-lacas8, lacas5-lacas8, lacas6-lacas8, lacas7-lacas8, laglt1-lhplt5, laglt10-laglt6, laglt2-lhplt5, laglt3-lhplt5, laglt7-lhplt6, laglt8-laglt6, laglt9-laglt6, laimm1-laglt5, laimm12-laimm11, laimm13-laimm11, laimm2-laimm6, laimm3-lmolf6, laimm4-laimm8, laimm5-laimm6, laimm7-laimm8, lcmfo1-lcmfo4, lcmfo12-lcmfo10, lcmfo13-lcmfo10, lcmfo2-lcmfo4, lcmfo3-lcmfo4, lcmfo7-lcmfo6, lcmfo8-lcmfo6, lhplt1-laglt5, lhplt10-lhplt8, lhplt2-laglt5, lhplt3-laglt4, lhplt4-lhplt6, lhplt9-lhplt8, lmcms1-lmcms5, lmcms2-lmcms5, lmcms3-lmcms5, lmcms4-lmcms5, lmcms9-lmcms8, lmolf2-lmolf6, lmolf3-laimm6, lmolf4-laimm6, lmolf5-laimm6, lmolf7-lmolf1, lmolf8-laimm8, lmtpt1-lhplt5, lmtpt2-lhplt5, lmtpt3-lhplt5, lmtpt4-lhplt5, lmtpt5-lhplt7, lmtpt6-lhplt8, lmtpt7-lhplt8, lmtpt8-lhplt8, lpcip1-lpcip4, lpcip11-lpcip10, lpcip2-lpcip5, racas1-rmolf4, racas11-racas10, racas2-rmolf4, racas4-racas6, racas7-racas5, racas8-racas10, raglt1-raglt4, raglt2-raglt4, raglt3-raglt4, raglt6-raglt5, raglt7-raglt8, raglt9-rhplt8, raimm1-raglt5, raimm11-racas12, raimm12-racas12, raimm2-raimm5, raimm3-rmolf8, raimm4-rmolf8, raimm6-rmolf8, raimm7-racas9, raimm8-racas10, rcmfo1-rcmfo5, rcmfo10-rcmfo5, rcmfo11-rcmfo5, rcmfo12-raimm5, rcmfo13-raimm5, rcmfo2-rcmfo5, rcmfo3-rcmfo5, rcmfo4-rcmfo5, rcmfo7-rcmfo5, rcmfo8-rcmfo5, rcmfo9-rcmfo5, rhplt1-raglt4, rhplt2-rhplt4, rhplt3-rhplt4, rmcms2-rmcms7, rmcms3-rmcms7, rmcms4-rmcms1, rmcms5-rmcms8, rmcms6-rmcms7, rmcms9-rmcms7, rmolf1-racas3, rmolf2-racas3, rmolf5-rmolf8, rmolf6-rmolf3, rmolf7-rmolf3, rmolf9-rmolf3, rmtpt1-rmtpt3, rmtpt2-rmtpt3, rmtpt6-rmtpt4, rmtpt7-rmtpt4, rmtpt8-rmtpt4, rpcip1-rpcip5, rpcip11-rpcip8, rpcip2-rpcip5, rpcip7-rpcip6, rpcip9-rpcip8\n" + ] + } + ], + "source": [ + "MS007_data_reref = mne.set_bipolar_reference(MS007_data, \n", + " anode=anode_list, \n", + " cathode=cathode_list,\n", + " copy=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized points196 points
Good channels127 sEEG
Bad channelslcmfo14
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "MS007_data_reref.drop_channels(drop_wm_channels)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['lacas11',\n", + " 'laimm10',\n", + " 'laimm9',\n", + " 'lcmfo11',\n", + " 'lcmfo5',\n", + " 'lcmfo9',\n", + " 'lmcms6',\n", + " 'lmcms7',\n", + " 'lpcip3',\n", + " 'lpcip6',\n", + " 'lpcip7',\n", + " 'lpcip8',\n", + " 'lpcip9',\n", + " 'raimm10',\n", + " 'raimm9',\n", + " 'rcmfo6',\n", + " 'rhplt5',\n", + " 'rhplt6',\n", + " 'rhplt7',\n", + " 'rmtpt5',\n", + " 'rpcip10',\n", + " 'rpcip3',\n", + " 'rpcip4']" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drop_wm_channels" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized points196 points
Good channels116 sEEG
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "MS007_data_reref.drop_channels(oob_channels)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized points196 points
Good channels116 sEEG
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "right_seeg_names = [i for i in MS007_data_reref.ch_names if i.startswith('r')]\n", + "left_seeg_names = [i for i in MS007_data_reref.ch_names if i.startswith('l')]\n", + "sEEG_mapping_dict = {f'{x}':'seeg' for x in left_seeg_names+right_seeg_names}\n", + "MS007_data_reref.set_channel_types(sEEG_mapping_dict)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Annotate data? Optional" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# We COULD annotate the bad timepoints post-referencing. However, my personal preference is to do so after EPOCHING so I am not sending \n", + "# too much subjective stuff to all my downstream analyses... \n", + "\n", + "# Furthermore, we only filter after epoching which definitely helps visualize bad epochs \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Channels marked as bad:\n", + "['laglt10', 'laglt8', 'laglt9', 'lcmfo7', 'lmcms1', 'lmcms2', 'lmcms9', 'lmolf4', 'lmolf5', 'racas8', 'rpcip7', 'laimm12', 'lcmfo14', 'lmolf7']\n" + ] + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[36], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnotebook\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m fig \u001b[38;5;241m=\u001b[39m MS007_data_reref\u001b[38;5;241m.\u001b[39mplot(start\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, duration\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m120\u001b[39m, n_channels\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m8\u001b[39m, scalings\u001b[38;5;241m=\u001b[39mMS007_data\u001b[38;5;241m.\u001b[39m_data\u001b[38;5;241m.\u001b[39mmax()\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m20\u001b[39m)\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# fig.fake_keypress('a')\u001b[39;00m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/IPython/core/interactiveshell.py:2364\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2362\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2363\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2364\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2365\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/IPython/core/interactiveshell.py:3528\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3524\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mWarning: Cannot change to a different GUI toolkit: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 3525\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m Using \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m instead.\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m (gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select))\n\u001b[1;32m 3526\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n\u001b[0;32m-> 3528\u001b[0m \u001b[43mpt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mactivate_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3529\u001b[0m configure_inline_support(\u001b[38;5;28mself\u001b[39m, backend)\n\u001b[1;32m 3531\u001b[0m \u001b[38;5;66;03m# Now we must activate the gui pylab wants to use, and fix %run to take\u001b[39;00m\n\u001b[1;32m 3532\u001b[0m \u001b[38;5;66;03m# plot updates into account\u001b[39;00m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/IPython/core/pylabtools.py:360\u001b[0m, in \u001b[0;36mactivate_matplotlib\u001b[0;34m(backend)\u001b[0m\n\u001b[1;32m 355\u001b[0m \u001b[38;5;66;03m# Due to circular imports, pyplot may be only partially initialised\u001b[39;00m\n\u001b[1;32m 356\u001b[0m \u001b[38;5;66;03m# when this function runs.\u001b[39;00m\n\u001b[1;32m 357\u001b[0m \u001b[38;5;66;03m# So avoid needing matplotlib attribute-lookup to access pyplot.\u001b[39;00m\n\u001b[1;32m 358\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pyplot \u001b[38;5;28;01mas\u001b[39;00m plt\n\u001b[0;32m--> 360\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mswitch_backend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 362\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow\u001b[38;5;241m.\u001b[39m_needmain \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 363\u001b[0m \u001b[38;5;66;03m# We need to detect at runtime whether show() is called by the user.\u001b[39;00m\n\u001b[1;32m 364\u001b[0m \u001b[38;5;66;03m# For this, we wrap it into a decorator which adds a 'called' flag.\u001b[39;00m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/pyplot.py:227\u001b[0m, in \u001b[0;36mswitch_backend\u001b[0;34m(newbackend)\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[38;5;66;03m# make sure the init is pulled up so we can assign to it later\u001b[39;00m\n\u001b[1;32m 226\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m\n\u001b[0;32m--> 227\u001b[0m \u001b[43mclose\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mall\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 229\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m newbackend \u001b[38;5;129;01mis\u001b[39;00m rcsetup\u001b[38;5;241m.\u001b[39m_auto_backend_sentinel:\n\u001b[1;32m 230\u001b[0m current_framework \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39m_get_running_interactive_framework()\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/pyplot.py:912\u001b[0m, in \u001b[0;36mclose\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m 910\u001b[0m _pylab_helpers\u001b[38;5;241m.\u001b[39mGcf\u001b[38;5;241m.\u001b[39mdestroy(manager)\n\u001b[1;32m 911\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m fig \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mall\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m--> 912\u001b[0m \u001b[43m_pylab_helpers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mGcf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdestroy_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 913\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fig, \u001b[38;5;28mint\u001b[39m):\n\u001b[1;32m 914\u001b[0m _pylab_helpers\u001b[38;5;241m.\u001b[39mGcf\u001b[38;5;241m.\u001b[39mdestroy(fig)\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/_pylab_helpers.py:82\u001b[0m, in \u001b[0;36mGcf.destroy_all\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m manager \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mfigs\u001b[38;5;241m.\u001b[39mvalues()):\n\u001b[1;32m 81\u001b[0m manager\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mmpl_disconnect(manager\u001b[38;5;241m.\u001b[39m_cidgcf)\n\u001b[0;32m---> 82\u001b[0m \u001b[43mmanager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdestroy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mfigs\u001b[38;5;241m.\u001b[39mclear()\n", + "File 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\u001b[38;5;241m=\u001b[39m {socket \u001b[38;5;28;01mfor\u001b[39;00m socket \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweb_sockets\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m socket\u001b[38;5;241m.\u001b[39mis_open()}\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweb_sockets) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 152\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclose_event\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/_api/deprecation.py:200\u001b[0m, in \u001b[0;36mdeprecated..deprecate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 199\u001b[0m emit_warning()\n\u001b[0;32m--> 200\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/backend_bases.py:1771\u001b[0m, in \u001b[0;36mFigureCanvasBase.close_event\u001b[0;34m(self, guiEvent)\u001b[0m\n\u001b[1;32m 1769\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1770\u001b[0m event \u001b[38;5;241m=\u001b[39m CloseEvent(s, \u001b[38;5;28mself\u001b[39m, guiEvent\u001b[38;5;241m=\u001b[39mguiEvent)\n\u001b[0;32m-> 1771\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprocess\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevent\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1772\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mAttributeError\u001b[39;00m):\n\u001b[1;32m 1773\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/cbook/__init__.py:312\u001b[0m, in \u001b[0;36mCallbackRegistry.process\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 311\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexception_handler \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 312\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexception_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 313\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 314\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/cbook/__init__.py:96\u001b[0m, in \u001b[0;36m_exception_printer\u001b[0;34m(exc)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_exception_printer\u001b[39m(exc):\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _get_running_interactive_framework() \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mheadless\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m]:\n\u001b[0;32m---> 96\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 98\u001b[0m traceback\u001b[38;5;241m.\u001b[39mprint_exc()\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/cbook/__init__.py:307\u001b[0m, in \u001b[0;36mCallbackRegistry.process\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m func \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 306\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 307\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 308\u001b[0m \u001b[38;5;66;03m# this does not capture KeyboardInterrupt, SystemExit,\u001b[39;00m\n\u001b[1;32m 309\u001b[0m \u001b[38;5;66;03m# and GeneratorExit\u001b[39;00m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/backends/backend_nbagg.py:81\u001b[0m, in \u001b[0;36mFigureManagerNbAgg.create_with_canvas..destroy\u001b[0;34m(event)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdestroy\u001b[39m(event):\n\u001b[1;32m 80\u001b[0m canvas\u001b[38;5;241m.\u001b[39mmpl_disconnect(cid)\n\u001b[0;32m---> 81\u001b[0m \u001b[43mGcf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdestroy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmanager\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/_pylab_helpers.py:66\u001b[0m, in \u001b[0;36mGcf.destroy\u001b[0;34m(cls, num)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(manager, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_cidgcf\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 65\u001b[0m manager\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mmpl_disconnect(manager\u001b[38;5;241m.\u001b[39m_cidgcf)\n\u001b[0;32m---> 66\u001b[0m \u001b[43mmanager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdestroy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m manager, num\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/backends/backend_nbagg.py:144\u001b[0m, in \u001b[0;36mFigureManagerNbAgg.destroy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m comm \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweb_sockets):\n\u001b[1;32m 143\u001b[0m comm\u001b[38;5;241m.\u001b[39mon_close()\n\u001b[0;32m--> 144\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclearup_closed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/backends/backend_nbagg.py:152\u001b[0m, in \u001b[0;36mFigureManagerNbAgg.clearup_closed\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweb_sockets \u001b[38;5;241m=\u001b[39m {socket \u001b[38;5;28;01mfor\u001b[39;00m socket \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweb_sockets\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m socket\u001b[38;5;241m.\u001b[39mis_open()}\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweb_sockets) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 152\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclose_event\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/_api/deprecation.py:200\u001b[0m, in \u001b[0;36mdeprecated..deprecate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 199\u001b[0m emit_warning()\n\u001b[0;32m--> 200\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/backend_bases.py:1771\u001b[0m, in \u001b[0;36mFigureCanvasBase.close_event\u001b[0;34m(self, guiEvent)\u001b[0m\n\u001b[1;32m 1769\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1770\u001b[0m event \u001b[38;5;241m=\u001b[39m CloseEvent(s, \u001b[38;5;28mself\u001b[39m, guiEvent\u001b[38;5;241m=\u001b[39mguiEvent)\n\u001b[0;32m-> 1771\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprocess\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevent\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1772\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mAttributeError\u001b[39;00m):\n\u001b[1;32m 1773\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/cbook/__init__.py:312\u001b[0m, in \u001b[0;36mCallbackRegistry.process\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 311\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexception_handler \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 312\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexception_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 313\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 314\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/cbook/__init__.py:96\u001b[0m, in \u001b[0;36m_exception_printer\u001b[0;34m(exc)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_exception_printer\u001b[39m(exc):\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _get_running_interactive_framework() \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mheadless\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m]:\n\u001b[0;32m---> 96\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 98\u001b[0m traceback\u001b[38;5;241m.\u001b[39mprint_exc()\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/cbook/__init__.py:307\u001b[0m, in \u001b[0;36mCallbackRegistry.process\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m func \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 306\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 307\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 308\u001b[0m \u001b[38;5;66;03m# this does not capture KeyboardInterrupt, SystemExit,\u001b[39;00m\n\u001b[1;32m 309\u001b[0m \u001b[38;5;66;03m# and GeneratorExit\u001b[39;00m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/mne/viz/_figure.py:427\u001b[0m, in \u001b[0;36mBrowserBase._close\u001b[0;34m(self, event)\u001b[0m\n\u001b[1;32m 425\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmne\u001b[38;5;241m.\u001b[39mchild_figs):\n\u001b[1;32m 426\u001b[0m fig \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmne\u001b[38;5;241m.\u001b[39mchild_figs[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m--> 427\u001b[0m \u001b[43mclose\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 428\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_close_event(fig)\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/pyplot.py:925\u001b[0m, in \u001b[0;36mclose\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m 923\u001b[0m _pylab_helpers\u001b[38;5;241m.\u001b[39mGcf\u001b[38;5;241m.\u001b[39mdestroy(num)\n\u001b[1;32m 924\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fig, Figure):\n\u001b[0;32m--> 925\u001b[0m \u001b[43m_pylab_helpers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mGcf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdestroy_fig\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 926\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 927\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclose() argument must be a Figure, an int, a string, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 928\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mor None, not \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mtype\u001b[39m(fig))\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/_pylab_helpers.py:75\u001b[0m, in \u001b[0;36mGcf.destroy_fig\u001b[0;34m(cls, fig)\u001b[0m\n\u001b[1;32m 72\u001b[0m num \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m((manager\u001b[38;5;241m.\u001b[39mnum \u001b[38;5;28;01mfor\u001b[39;00m manager \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mfigs\u001b[38;5;241m.\u001b[39mvalues()\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m manager\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mfigure \u001b[38;5;241m==\u001b[39m fig), \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 75\u001b[0m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdestroy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/_pylab_helpers.py:66\u001b[0m, in \u001b[0;36mGcf.destroy\u001b[0;34m(cls, num)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(manager, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_cidgcf\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 65\u001b[0m manager\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mmpl_disconnect(manager\u001b[38;5;241m.\u001b[39m_cidgcf)\n\u001b[0;32m---> 66\u001b[0m \u001b[43mmanager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdestroy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m manager, num\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/backends/backend_nbagg.py:144\u001b[0m, in \u001b[0;36mFigureManagerNbAgg.destroy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m comm \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweb_sockets):\n\u001b[1;32m 143\u001b[0m comm\u001b[38;5;241m.\u001b[39mon_close()\n\u001b[0;32m--> 144\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclearup_closed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/backends/backend_nbagg.py:152\u001b[0m, in \u001b[0;36mFigureManagerNbAgg.clearup_closed\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweb_sockets \u001b[38;5;241m=\u001b[39m {socket \u001b[38;5;28;01mfor\u001b[39;00m socket \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweb_sockets\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m socket\u001b[38;5;241m.\u001b[39mis_open()}\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweb_sockets) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 152\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclose_event\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/_api/deprecation.py:200\u001b[0m, in \u001b[0;36mdeprecated..deprecate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 199\u001b[0m emit_warning()\n\u001b[0;32m--> 200\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/backend_bases.py:1771\u001b[0m, in \u001b[0;36mFigureCanvasBase.close_event\u001b[0;34m(self, guiEvent)\u001b[0m\n\u001b[1;32m 1769\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1770\u001b[0m event \u001b[38;5;241m=\u001b[39m CloseEvent(s, \u001b[38;5;28mself\u001b[39m, guiEvent\u001b[38;5;241m=\u001b[39mguiEvent)\n\u001b[0;32m-> 1771\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprocess\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevent\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1772\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mAttributeError\u001b[39;00m):\n\u001b[1;32m 1773\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/cbook/__init__.py:312\u001b[0m, in \u001b[0;36mCallbackRegistry.process\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 311\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexception_handler \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 312\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexception_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 313\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 314\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/cbook/__init__.py:96\u001b[0m, in \u001b[0;36m_exception_printer\u001b[0;34m(exc)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_exception_printer\u001b[39m(exc):\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _get_running_interactive_framework() \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mheadless\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m]:\n\u001b[0;32m---> 96\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 98\u001b[0m traceback\u001b[38;5;241m.\u001b[39mprint_exc()\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/cbook/__init__.py:307\u001b[0m, in \u001b[0;36mCallbackRegistry.process\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m func \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 306\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 307\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 308\u001b[0m \u001b[38;5;66;03m# this does not capture KeyboardInterrupt, SystemExit,\u001b[39;00m\n\u001b[1;32m 309\u001b[0m \u001b[38;5;66;03m# and GeneratorExit\u001b[39;00m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/mne/viz/_mpl_figure.py:190\u001b[0m, in \u001b[0;36mMNEAnnotationFigure._close\u001b[0;34m(self, event)\u001b[0m\n\u001b[1;32m 188\u001b[0m parent\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mcallbacks\u001b[38;5;241m.\u001b[39mdisconnect(callback_id)\n\u001b[1;32m 189\u001b[0m \u001b[38;5;66;03m# do all the other cleanup activities\u001b[39;00m\n\u001b[0;32m--> 190\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_close\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevent\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/mne/viz/_mpl_figure.py:114\u001b[0m, in \u001b[0;36mMNEFigure._close\u001b[0;34m(self, event)\u001b[0m\n\u001b[1;32m 112\u001b[0m is_named \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmne, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfig_name\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_child:\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmne\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparent_fig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmne\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchild_figs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mremove\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_named:\n\u001b[1;32m 116\u001b[0m \u001b[38;5;28msetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmne\u001b[38;5;241m.\u001b[39mparent_fig\u001b[38;5;241m.\u001b[39mmne, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmne\u001b[38;5;241m.\u001b[39mfig_name, \u001b[38;5;28;01mNone\u001b[39;00m)\n", + "\u001b[0;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + } + ], + "source": [ + "%matplotlib notebook\n", + "fig = MS007_data_reref.plot(start=0, duration=120, n_channels=8, scalings=MS007_data._data.max()/20)\n", + "# fig.fake_keypress('a')" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting existing file.\n", + "Writing /sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/wm_ref_ieeg.fif\n", + "Closing /sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/wm_ref_ieeg.fif\n", + "[done]\n" + ] + } + ], + "source": [ + "# Save out the re-referenced data:\n", + "\n", + "MS007_data_reref.save(f'{save_dir}/wm_ref_ieeg.fif', overwrite=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "lfp_analysis", + "language": "python", + "name": "lfpanalysis" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/LFPAnalysis/NLX_analysis_step_by_step.ipynb b/LFPAnalysis/NLX_analysis_step_by_step.ipynb new file mode 100644 index 0000000..43d24a4 --- /dev/null +++ b/LFPAnalysis/NLX_analysis_step_by_step.ipynb @@ -0,0 +1,14228 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example analysis notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, we will go through the basics of: \n", + "\n", + "1. Load pre-processed data\n", + "\n", + "2. Synchronize to behavioral data\n", + "\n", + "3. Process the behavioral data \n", + "\n", + "4. Create epochs\n", + "\n", + "5. Downsample the epoched data \n", + "\n", + "6. Look for IEDs (**automatic** or manual) \n", + "\n", + "7. Save the epoched data \n", + "\n", + "8. Compute TFR and drop IED trials if you care about low frequencies. \n", + "\n", + "9. Plot\n", + "\n", + "10. Example of statistical analyses (TODO)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The main point of this notebook is to make epoched data **CORRECTLY** as this will be the crux of many, many analyses in the future, and to provide tools for IED detection in epochs (both automatic and manual)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On IEDS: \n", + "\n", + "- Great images of interictal and ictal spiking: \n", + "\n", + " 1. https://pubmed.ncbi.nlm.nih.gov/32007920/\n", + " \n", + " 2. https://www.sciencedirect.com/science/article/pii/S1059131119304200\n", + "\n", + "- More on IEDs: \n", + "\n", + " 1. https://www.frontiersin.org/articles/10.3389/fnhum.2020.00044/full\n", + "\n", + " 2. Why removing trials with IEDs might be important for generalizable conclusions about behavior: \n", + " \n", + " https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3770206/\n", + " \n", + " https://academic.oup.com/cercorcomms/article/2/2/tgab019/6179205\n", + " \n", + "- Especially for low frequencies (sub-gamma) this is important! \"Low-frequency power remained elevated for several seconds following the IED... Low-frequency power was elevated and high-frequency power suppressed in pre-IED epochs compared with non-IED epochs.\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%reload_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import mne\n", + "from glob import glob\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.backends.backend_pdf import PdfPages\n", + "import seaborn as sns\n", + "import scipy\n", + "from scipy.stats import zscore, linregress\n", + "import pandas as pd\n", + "from mne.preprocessing.bads import _find_outliers\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('/hpc/users/qasims01/resources/LFPAnalysis')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from LFPAnalysis import lfp_preprocess_utils, sync_utils, analysis_utils" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading pre-processed data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's a good idea to setup a sensible directory structure like below. Note that all my data lives on '/sc/arion' which is Minerva. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "base_dir = '/sc/arion' # this is the root directory for most un-archived data and results \n", + "\n", + "load_dir = f'{base_dir}/work/qasims01/MemoryBanditData/EMU/Subjects/MS007' # save intermediate results in the 'work' directory\n", + " \n", + "# I have saved most of my raw data in the 'projects directory'\n", + "behav_dir = f'{base_dir}/projects/guLab/Salman/EMU/MS007/behav/Day1'\n", + "neural_dir = f'{base_dir}/projects/guLab/Salman/EMU/MS007/neural/Day1'\n", + "anat_dir = f'{base_dir}/projects/guLab/Salman/EMU/MS007/anat'\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's load in the re-referenced data, the photodiode data for synchronization, and the electrode dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Opening raw data file /sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/wm_ref_ieeg.fif...\n", + " Range : 0 ... 1867007 = 0.000 ... 1823.249 secs\n", + "Ready.\n", + "Reading 0 ... 1867007 = 0.000 ... 1823.249 secs...\n", + "Opening raw data file /sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/photodiode.fif...\n", + "Isotrak not found\n", + " Range : 0 ... 1867007 = 0.000 ... 1823.249 secs\n", + "Ready.\n", + "Reading 0 ... 1867007 = 0.000 ... 1823.249 secs...\n" + ] + } + ], + "source": [ + "wm_ref_data = mne.io.read_raw_fif(f'{load_dir}/wm_ref_ieeg.fif', preload=True)\n", + "anode_list = [x.split('-')[0] for x in wm_ref_data.ch_names]\n", + "photodiode = mne.io.read_raw_fif(f'{load_dir}/photodiode.fif', preload=True)\n", + "\n", + "csv_files = glob(f'{anat_dir}/*labels.csv')\n", + "elec_locs = pd.read_csv(csv_files[0])\n", + "\n", + "# Sometimes there's extra columns with no entries: \n", + "elec_locs = elec_locs[elec_locs.columns.drop(list(elec_locs.filter(regex='Unnamed')))]\n", + "\n", + "elec_df = elec_locs[elec_locs.label.str.lower().isin(anode_list)]\n", + "elec_df['label'] = wm_ref_data.ch_names\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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labelBN246labelxyzmni_xmni_ymni_zgmNMMAnatAnatMacroBN246YBA_1Manual ExaminationNotes
0lacas1-lmolf1A13_L-6.5496541.77678-7.038720-6.1496934.97941-12.45010GrayLeft ACgG anterior cingulate gyrusArea s32L Mid Orbital GyrusL OrGLeft frontal pole 1 CNaNNaN
1lacas10-lacas9A9l_L-9.7467349.7723337.287610-11.1581045.8798038.08775GrayLeft Cerebral White MatterUnknownL Superior Frontal GyrusL SFGUnknownLeft superior frontal gyrus 2 CNaN
3lacas12-lacas9A9l_L-10.1464050.9716746.871680-11.5214047.7469349.07517GrayLeft SFG superior frontal gyrusUnknownL Superior Frontal GyrusL SFGLeft superior frontal gyrus 3 CNaNNaN
4lacas2-lmolf1A32sg_L-6.9492842.57633-2.246680-6.8262236.24643-7.12713GrayLeft ACgG anterior cingulate gyrusArea s32L ACCL CGLeft cingulate gyrus DNaNNaN
5lacas3-lmolf1A32sg_L-7.3489243.375892.545354-7.4178637.55496-1.76236GrayLeft ACgG anterior cingulate gyrusUnknownL ACCL CGLeft cingulate gyrus ENaNNaN
\n", + "
" + ], + "text/plain": [ + " label BN246label x y z mni_x \\\n", + "0 lacas1-lmolf1 A13_L -6.54965 41.77678 -7.038720 -6.14969 \n", + "1 lacas10-lacas9 A9l_L -9.74673 49.77233 37.287610 -11.15810 \n", + "3 lacas12-lacas9 A9l_L -10.14640 50.97167 46.871680 -11.52140 \n", + "4 lacas2-lmolf1 A32sg_L -6.94928 42.57633 -2.246680 -6.82622 \n", + "5 lacas3-lmolf1 A32sg_L -7.34892 43.37589 2.545354 -7.41786 \n", + "\n", + " mni_y mni_z gm NMM Anat \\\n", + "0 34.97941 -12.45010 Gray Left ACgG anterior cingulate gyrus Area s32 \n", + "1 45.87980 38.08775 Gray Left Cerebral White Matter Unknown \n", + "3 47.74693 49.07517 Gray Left SFG superior frontal gyrus Unknown \n", + "4 36.24643 -7.12713 Gray Left ACgG anterior cingulate gyrus Area s32 \n", + "5 37.55496 -1.76236 Gray Left ACgG anterior cingulate gyrus Unknown \n", + "\n", + " AnatMacro BN246 YBA_1 \\\n", + "0 L Mid Orbital Gyrus L OrG Left frontal pole 1 C \n", + "1 L Superior Frontal Gyrus L SFG Unknown \n", + "3 L Superior Frontal Gyrus L SFG Left superior frontal gyrus 3 C \n", + "4 L ACC L CG Left cingulate gyrus D \n", + "5 L ACC L CG Left cingulate gyrus E \n", + "\n", + " Manual Examination Notes \n", + "0 NaN NaN \n", + "1 Left superior frontal gyrus 2 C NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "elec_df.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following commented code can be used for plotting and removing IEDs from the continuous data if you decide to do detection with the continuous data " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filtering raw data in 1 contiguous segment\n", + "Setting up band-pass filter from 25 - 80 Hz\n", + "\n", + "FIR filter parameters\n", + "---------------------\n", + "Designing a one-pass, zero-phase, non-causal bandpass filter:\n", + "- Windowed time-domain design (firwin) method\n", + "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n", + "- Lower passband edge: 25.00\n", + "- Lower transition bandwidth: 6.25 Hz (-6 dB cutoff frequency: 21.88 Hz)\n", + "- Upper passband edge: 80.00 Hz\n", + "- Upper transition bandwidth: 20.00 Hz (-6 dB cutoff frequency: 90.00 Hz)\n", + "- Filter length: 541 samples (0.528 sec)\n", + "\n" + ] + } + ], + "source": [ + "IED_times_s = lfp_preprocess_utils.detect_IEDs(wm_ref_data, peak_thresh=4, closeness_thresh=0.25, width_thresh=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'lacas1-lmolf1': array([ 52.82617188, 254.41015625, 424.97070312, 470.578125 ,\n", + " 519.04980469, 555.97167969, 569.50488281, 602.07421875,\n", + " 602.33496094, 699.81347656, 838.47363281, 1017.45996094,\n", + " 1026.04003906, 1042.21777344, 1141.69628906, 1147.35839844,\n", + " 1237.56542969, 1335.74804688, 1360.90527344, 1362.01269531,\n", + " 1501.69726562, 1697.70019531]),\n", + " 'lacas10-lacas9': array([ 753.46679688, 1228.51757812, 1501.10253906, 1697.73242188]),\n", + " 'lacas12-lacas9': array([ 254.04980469, 1142.23828125, 1147.74511719, 1237.25 ]),\n", + " 'lacas2-lmolf1': array([ 52.83105469, 254.08398438, 254.40917969, 470.57910156,\n", + " 555.97265625, 569.50292969, 602.07324219, 699.81347656,\n", + " 838.46875 , 1017.46582031, 1026.06542969, 1147.32714844,\n", + " 1148.35839844, 1237.5625 , 1335.78417969, 1360.90722656,\n", + " 1617.08496094]),\n", + " 'lacas3-lmolf1': array([ 254.41210938, 432.55175781, 470.57617188, 519.05273438,\n", + " 555.97167969, 569.50097656, 602.07226562, 602.40234375,\n", + " 782.25390625, 1080.34375 , 1147.3515625 , 1148.06738281,\n", + " 1237.56445312, 1238.01757812, 1360.90527344, 1361.65917969,\n", + " 1442.56542969, 1501.04492188, 1697.36035156, 1697.72851562,\n", + " 1698.01953125]),\n", + " 'lacas4-lacas8': array([ 603.03222656, 767.95019531, 1698.29101562, 1815.89257812]),\n", + " 'lacas5-lacas8': array([ 52.70898438, 71.06640625, 424.86230469, 470.43457031,\n", + " 754.20800781, 1062.67089844, 1147.34960938, 1237.32910156,\n", + " 1360.92675781, 1543.01660156, 1697.36035156]),\n", + " 'lacas6-lacas8': array([ 52.70898438, 316.74609375, 372.52441406, 1279.17871094,\n", + " 1337.71582031]),\n", + " 'lacas7-lacas8': array([ 254.06347656, 1535.70703125, 1697.49414062]),\n", + " 'laglt1-lhplt5': array([ 53.07226562, 54.40917969, 1237.23339844, 1365.04394531,\n", + " 1616.96875 , 1698.00488281, 1698.2578125 , 1816.62597656,\n", + " 1821.07714844]),\n", + " 'laglt10-laglt6': array([ 52.83886719, 53.98632812, 54.39257812, 254.2265625 ,\n", + " 364.6953125 , 432.55371094, 545.32128906, 601.97460938,\n", + " 602.77636719, 603.0625 , 1016.72753906, 1026.02441406,\n", + " 1141.67285156, 1147.27050781, 1236.94335938, 1237.46582031,\n", + " 1360.89941406, 1361.55273438, 1442.55273438, 1504.13574219,\n", + " 1616.95410156, 1698.25097656, 1701.00292969, 1815.72070312,\n", + " 1820.89453125]),\n", + " 'laglt2-lhplt5': array([ 53.0625 , 254.20605469, 764.83496094, 1018.28417969,\n", + " 1147.29296875, 1237.23730469, 1365.05273438, 1442.68359375,\n", + " 1501.52539062, 1616.97070312, 1697.32714844, 1698.2265625 ,\n", + " 1816.62109375, 1823.24121094]),\n", + " 'laglt3-lhplt5': array([ 53.06640625, 254.20703125, 602.10058594, 1018.29199219,\n", + " 1147.30175781, 1236.9609375 , 1237.67089844, 1336.23242188,\n", + " 1361.01953125, 1361.57519531, 1365.05761719, 1442.53515625,\n", + " 1501.05761719, 1616.9765625 , 1697.32519531, 1698.26074219,\n", + " 1816.62304688, 1816.99804688, 1823.24121094]),\n", + " 'laglt7-lhplt6': array([ 53.11230469, 137.36816406, 254.20507812, 311.62597656,\n", + " 321.07324219, 364.72949219, 368.24707031, 519.46386719,\n", + " 602.76367188, 622.85058594, 782.46484375, 799.06152344,\n", + " 807.23730469, 843.41503906, 881.45214844, 970.75878906,\n", + " 1017.65332031, 1017.98046875, 1141.92382812, 1147.63476562,\n", + " 1336.01953125, 1336.44628906, 1360.375 , 1360.88574219,\n", + " 1361.55175781, 1442.52832031, 1442.91601562, 1501.01464844,\n", + " 1505.31347656, 1583.01269531, 1617.22265625, 1685.97558594,\n", + " 1698.234375 , 1739.31445312, 1815.72265625, 1821.55175781]),\n", + " 'laglt8-laglt6': array([ 52.83789062, 53.98828125, 54.40332031, 74.30761719,\n", + " 227.36816406, 253.984375 , 311.59179688, 320.765625 ,\n", + " 321.078125 , 364.70996094, 471.36035156, 509.7421875 ,\n", + " 601.984375 , 602.78613281, 603.0703125 , 604.52539062,\n", + " 782.46386719, 782.72167969, 1026.02539062, 1141.921875 ,\n", + " 1147.27246094, 1236.87792969, 1237.20898438, 1237.47753906,\n", + " 1336.23535156, 1336.62695312, 1360.89550781, 1361.54589844,\n", + " 1616.95800781, 1697.32324219, 1698.26757812, 1699.91210938,\n", + " 1739.3046875 , 1815.7109375 , 1817.50292969, 1821.25976562,\n", + " 1823.24121094]),\n", + " 'laglt9-laglt6': array([ 52.8125 , 53.99121094, 227.32421875, 253.94335938,\n", + " 320.77539062, 471.40332031, 519.00097656, 603.47460938,\n", + " 604.51464844, 606.61914062, 1017.5859375 , 1147.28125 ,\n", + " 1238.00292969, 1335.74316406, 1337.13964844, 1442.52050781,\n", + " 1502.921875 , 1616.70410156, 1697.29882812]),\n", + " 'laimm1-laglt5': array([ 74.05175781, 74.34765625, 570.22851562, 602.26757812,\n", + " 603.05957031, 1018.29199219, 1141.92382812, 1237.66894531,\n", + " 1336.23925781, 1360.95898438, 1361.56054688, 1616.96289062,\n", + " 1697.6328125 , 1698.24023438, 1816.72167969, 1820.89160156,\n", + " 1821.23632812, 1823.24121094]),\n", + " 'laimm12-laimm11': array([ 779.5390625 , 918.64550781, 1002.96875 , 1086.25488281,\n", + " 1099.98242188, 1143.34277344, 1153.3359375 ]),\n", + " 'laimm13-laimm11': array([ 569.06738281, 680.359375 , 914.22753906, 984.60351562,\n", + " 1090.59960938, 1697.50390625, 1698.31152344, 1821.27832031]),\n", + " 'laimm2-laimm6': array([ 602.11621094, 1236.96484375, 1360.98925781, 1501.09863281]),\n", + " 'laimm3-lmolf6': array([ 52.8984375 , 602.28222656, 603.4921875 , 1148.39941406,\n", + " 1360.90332031, 1361.64355469, 1739.37792969]),\n", + " 'laimm4-laimm8': array([ 73.86230469, 254.0625 , 519.03417969, 570.90820312,\n", + " 602.08496094, 602.88964844, 782.23632812, 782.52636719,\n", + " 1026.05371094, 1141.68554688, 1147.31933594, 1236.91308594,\n", + " 1237.27441406, 1238.08886719, 1360.98730469, 1361.62597656,\n", + " 1442.57910156, 1501.0234375 , 1617.02441406, 1697.45507812]),\n", + " 'laimm5-laimm6': array([ 254.09765625, 570.62695312, 570.90527344, 602.0859375 ,\n", + " 602.88671875, 753.45410156, 1026.05664062, 1141.6640625 ,\n", + " 1147.27929688, 1236.90820312, 1237.26074219, 1360.90527344,\n", + " 1361.328125 , 1361.61523438, 1442.59960938, 1501.03515625,\n", + " 1617.05078125, 1697.38964844, 1739.35644531]),\n", + " 'laimm7-laimm8': array([602.12988281]),\n", + " 'lcmfo1-lcmfo4': array([ 602.29882812, 1697.6328125 ]),\n", + " 'lcmfo12-lcmfo10': array([ 222.24902344, 474.42675781, 475.24511719, 476.02539062,\n", + " 575.06640625, 757.26171875, 981.26464844, 1016.70214844,\n", + " 1093.78027344, 1162.43359375, 1273.71191406, 1757.98242188]),\n", + " 'lcmfo13-lcmfo10': array([ 474.42773438, 474.98730469, 475.24609375, 550.41894531,\n", + " 575.06738281, 981.26660156, 1053.56738281]),\n", + " 'lcmfo2-lcmfo4': array([ 87.43945312, 1147.33789062, 1616.96386719]),\n", + " 'lcmfo3-lcmfo4': array([], dtype=float64),\n", + " 'lcmfo7-lcmfo6': array([], dtype=float64),\n", + " 'lcmfo8-lcmfo6': array([], dtype=float64),\n", + " 'lhplt1-laglt5': array([ 53.05664062, 59.56640625, 62.45605469, 88.91210938,\n", + " 230.49511719, 254.19921875, 385.7421875 , 424.89746094,\n", + " 602.56347656, 603.17285156, 799.02539062, 935.20898438,\n", + " 1002.87792969, 1026.04785156, 1029.50683594, 1058.98632812,\n", + " 1059.82617188, 1218.56542969, 1361.42089844, 1365.03710938,\n", + " 1390.62402344, 1478.203125 , 1501.5078125 , 1504.71875 ,\n", + " 1554.72949219, 1698.03515625]),\n", + " 'lhplt10-lhplt8': array([ 52.84082031, 53.09960938, 54.20996094, 73.79003906,\n", + " 227.34863281, 253.96679688, 320.73046875, 321.1015625 ,\n", + " 432.54296875, 470.7734375 , 509.71191406, 545.27539062,\n", + " 555.77734375, 570.24804688, 570.88183594, 597.58203125,\n", + " 601.97949219, 602.78613281, 603.05859375, 603.31152344,\n", + " 699.76171875, 782.74804688, 874.64453125, 953.86914062,\n", + " 1017.65332031, 1026.03125 , 1147.30371094, 1148.33984375,\n", + " 1230.09667969, 1230.70507812, 1236.91113281, 1237.2421875 ,\n", + " 1237.50390625, 1335.0234375 , 1336.26855469, 1360.91796875,\n", + " 1361.29394531, 1361.57421875, 1442.55566406, 1501.00585938,\n", + " 1504.1015625 , 1583.0078125 , 1617.23925781, 1685.97265625,\n", + " 1697.328125 , 1739.28320312, 1815.69335938, 1816.94042969,\n", + " 1820.65527344, 1821.08105469, 1822.94433594]),\n", + " 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670.07714844, 1237.484375 ,\n", + " 1442.91210938, 1582.5859375 , 1697.78808594, 1757.96289062,\n", + " 1813.12304688, 1816.11132812]),\n", + " 'rpcip11-rpcip8': array([ 52.57714844, 53.33203125, 261.57617188, 264.09375 ,\n", + " 264.45996094, 278.8671875 , 322.03417969, 334.27636719,\n", + " 353.45605469, 377.23339844, 450.6875 , 505.10253906,\n", + " 571.81347656, 699.97167969, 772.09082031, 857.38183594,\n", + " 1084.734375 , 1148.00976562, 1208.86230469, 1218.53027344,\n", + " 1437.83398438, 1500.38476562, 1582.33203125, 1766.90820312,\n", + " 1823.05371094]),\n", + " 'rpcip2-rpcip5': array([], dtype=float64),\n", + " 'rpcip7-rpcip6': array([], dtype=float64),\n", + " 'rpcip9-rpcip8': array([ 2.76953125, 29.24707031, 52.390625 , 98.76953125,\n", + " 226.859375 , 264.45898438, 278.86621094, 302.14746094,\n", + " 321.76953125, 322.03710938, 332.32128906, 334.27441406,\n", + " 353.45410156, 377.234375 , 407.39160156, 412.0234375 ,\n", + " 432.453125 , 432.7578125 , 438.35449219, 461.60351562,\n", + " 471.17480469, 485.58496094, 502.80566406, 503.78710938,\n", + " 505.10449219, 507.42089844, 518.97949219, 524.0390625 ,\n", + " 544.83007812, 555.4609375 , 555.76074219, 569.15625 ,\n", + " 569.59667969, 579.20507812, 597.43261719, 662.43554688,\n", + " 697.72265625, 699.96484375, 733.00585938, 772.09082031,\n", + " 838.578125 , 842.68066406, 851.71777344, 857.38085938,\n", + " 857.63867188, 874.20117188, 883.52734375, 964.85253906,\n", + " 973.3515625 , 1016.82617188, 1025.67089844, 1056.44238281,\n", + " 1084.73339844, 1135.28222656, 1147.02636719, 1151.18847656,\n", + " 1209.78125 , 1218.53027344, 1264.07226562, 1296.50585938,\n", + " 1338.86328125, 1344.93066406, 1437.90332031, 1442.78613281,\n", + " 1489.19335938, 1492.01464844, 1500.53515625, 1517.91113281,\n", + " 1560.52929688, 1574.47753906, 1582.54199219, 1582.81445312,\n", + " 1696.64941406, 1706.75976562, 1708.47949219, 1738.38769531,\n", + " 1757.27246094, 1757.96484375, 1812.86621094, 1816.125 ,\n", + " 1816.48144531, 1823.05566406])}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IED_times_s" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'lacas1-lmolf1': array([ 52.82617188, 254.41015625, 320.65722656, 424.97070312,\n", + " 470.578125 , 519.04980469, 555.97167969, 564.48046875,\n", + " 569.50488281, 602.07421875, 699.81347656, 838.47363281,\n", + " 1017.45996094, 1026.04003906, 1042.21777344, 1047.37304688,\n", + " 1147.35839844, 1335.74804688, 1360.90527344, 1501.69726562,\n", + " 1577.35058594, 1697.70019531]),\n", + " 'lacas10-lacas9': array([ 490.05566406, 753.46679688, 770.76074219, 1228.51757812,\n", + " 1306.94042969, 1501.10253906, 1586.54296875, 1697.73242188]),\n", + " 'lacas12-lacas9': array([ 268.70898438, 481.61425781, 1142.23828125, 1521.73632812]),\n", + " 'lacas2-lmolf1': array([ 52.83105469, 254.08398438, 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1815.69335938, 1821.08105469]),\n", + " 'lhplt2-laglt5': array([ 602.81054688, 1147.78125 , 1361.35058594, 1442.80859375,\n", + " 1698.21679688]),\n", + " 'lhplt3-laglt4': array([ 602.80957031, 1091.39648438, 1147.77832031, 1262.54003906,\n", + " 1361.35058594, 1678.37402344, 1682.74609375, 1688.43457031,\n", + " 1694.09863281, 1698.21875 , 1754.70117188, 1771.80566406,\n", + " 1777.21386719, 1786.47167969, 1787.31738281]),\n", + " 'lhplt4-lhplt6': array([ 52.84375 , 254.19433594, 424.9140625 , 597.58984375,\n", + " 1026.23730469, 1141.88378906, 1147.27539062, 1230.11230469,\n", + " 1336.46582031, 1361.32617188, 1361.86425781, 1442.54101562,\n", + " 1500.97460938, 1501.50292969, 1502.39550781, 1583.01074219,\n", + " 1616.95410156, 1685.97949219, 1697.33007812, 1698.24316406,\n", + " 1739.32519531, 1821.07519531, 1821.59375 , 1823.24121094]),\n", + " 'lhplt9-lhplt8': array([ 53.81640625, 54.40722656, 227.29589844, 254.21972656,\n", + " 364.68554688, 425.31640625, 470.80273438, 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1739.28515625, 1815.72363281,\n", + " 1816.62792969, 1821.08105469]),\n", + " 'lpcip1-lpcip4': array([ 320.63183594, 481.30175781, 507.42089844, 524.609375 ,\n", + " 531. , 602.08300781, 652.25878906, 760.60351562,\n", + " 1003.70019531, 1147.77636719, 1207.45214844, 1237.24804688,\n", + " 1270.80078125, 1274.46582031, 1281.21484375, 1361.59960938,\n", + " 1442.97070312, 1461.17382812, 1500.99316406, 1583.18847656,\n", + " 1697.46679688, 1698.00390625, 1767.00585938, 1816.57617188]),\n", + " 'lpcip11-lpcip10': array([ 52.80371094, 182.16503906, 183.06738281, 186.49316406,\n", + " 227.20507812, 254.03515625, 258.16699219, 321.1328125 ,\n", + " 364.515625 , 391.1796875 , 470.52539062, 520.73925781,\n", + " 524.72167969, 544.90527344, 549.20800781, 570.03222656,\n", + " 570.87988281, 594.24023438, 601.96484375, 602.85644531,\n", + " 627.13085938, 662.97753906, 697.64257812, 782.51953125,\n", + " 888.328125 , 895.59863281, 954.70800781, 1042.16992188,\n", + " 1141.60742188, 1147.34277344, 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1064.37207031,\n", + " 1100.30859375, 1116.56640625, 1147.61523438, 1151.47949219,\n", + " 1164.03027344, 1218.52832031, 1230.06445312, 1266.91894531,\n", + " 1300.22949219, 1339.12304688, 1426.53320312, 1442.31835938,\n", + " 1471.70410156, 1482.82910156, 1501.56835938, 1511.28027344,\n", + " 1616.015625 , 1655.3046875 , 1655.88085938, 1713.94824219,\n", + " 1757.42773438, 1791.57128906, 1805.87988281]),\n", + " 'rpcip1-rpcip5': array([ 471.07324219, 606.81835938, 670.07714844, 857.83984375,\n", + " 1161.39257812, 1237.484375 , 1442.91210938, 1582.64941406,\n", + " 1697.78808594, 1757.96289062, 1813.12304688, 1816.11132812]),\n", + " 'rpcip11-rpcip8': array([ 38.26074219, 41.86328125, 261.57617188, 264.45996094,\n", + " 278.8671875 , 322.03417969, 324.34765625, 334.27636719,\n", + " 353.45605469, 377.23339844, 383.8125 , 427.45117188,\n", + " 433.72558594, 446.86523438, 450.6875 , 505.10253906,\n", + " 523.93359375, 528.01953125, 571.81347656, 602.01074219,\n", + " 602.93945312, 772.09082031, 775.77050781, 801.41699219,\n", + " 803.26074219, 857.38183594, 1084.734375 , 1085.60253906,\n", + " 1208.86230469, 1218.53027344, 1437.83398438, 1500.38476562,\n", + " 1823.05371094]),\n", + " 'rpcip2-rpcip5': array([1419.05957031]),\n", + " 'rpcip7-rpcip6': array([ 555.38574219, 1430.16015625, 1433.45703125, 1529.92871094,\n", + " 1536.38671875, 1769.67773438]),\n", + " 'rpcip9-rpcip8': array([ 2.76953125, 29.24707031, 52.390625 , 81.61230469,\n", + " 98.76953125, 102.1953125 , 114.55078125, 121.9140625 ,\n", + " 123.88378906, 138.21875 , 218.921875 , 226.859375 ,\n", + " 277.92871094, 278.86621094, 302.14746094, 322.03710938,\n", + " 332.32128906, 334.27441406, 353.45410156, 377.234375 ,\n", + " 407.39160156, 412.0234375 , 424.46777344, 432.7578125 ,\n", + " 438.35449219, 447.33789062, 471.17480469, 485.58496094,\n", + " 502.80566406, 503.78710938, 505.10449219, 507.42089844,\n", + " 518.97949219, 524.0390625 , 544.83007812, 555.76074219,\n", + " 569.59667969, 579.20507812, 597.43261719, 662.43554688,\n", + " 669.63769531, 697.72265625, 699.96484375, 733.00585938,\n", + " 772.09082031, 838.578125 , 851.71777344, 857.38085938,\n", + " 883.52734375, 961.71484375, 964.85253906, 978.58300781,\n", + " 979.80078125, 1016.82617188, 1056.44238281, 1084.73339844,\n", + " 1135.28222656, 1147.02636719, 1151.18847656, 1191.18457031,\n", + " 1218.53027344, 1264.07226562, 1296.50585938, 1338.86328125,\n", + " 1343.64453125, 1344.93066406, 1437.90332031, 1442.78613281,\n", + " 1489.19335938, 1492.01464844, 1500.53515625, 1517.91113281,\n", + " 1555.5625 , 1560.52929688, 1574.47753906, 1582.54199219,\n", + " 1673.85351562, 1696.64941406, 1706.75976562, 1708.47949219,\n", + " 1738.38769531, 1757.27246094, 1757.96484375, 1816.125 ,\n", + " 1823.05566406])}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IED_times_s" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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B5+NVxbWZcM+ePYuMjAz+5eAOVFZW8iTord2Dy4G/vz/fnYjV18uQauFy1+La9fX1NGHCBIqMjKQTJ05QSUkJf9TW1tp0bXJam1nt3DfeeMPhfbfH8uXLSa/XU3FxsWxjGI1G+te//kUmk0m2MbwJUVzbbNyCggICYPWxd+9em65LTvEeP36c9Ho9vfPOOw7vuz3WrVtHer2ezp49K0v/JpOJ1qxZQ3q9njIzM2UZw9sQxbUdjJzFtffs2YODBw8iISEB48ePd2jf7fHhhx8iPz8fEydORFxcnMP7Ly8vx9q1awE0ZQiZN2+ew8fwNkRxbQ+CGducaaxiMKOVXPe85kcODQaDyFjpJIR4nQQrPeLMAA2G3OJt3m9BQYEs4wgsEeJ1AiaTibuJ7rjjDqeP72zxsmAUgbwI8ToBo9HIA1PkzhppDWeLV2TucA5CvE6AxTRrtVqnHQU0x1niZV9MQrzOQYjXCTDxhoeHu2R8ucXLkgzodDoAQrzOQojXCTDxuirii4m3srIScngG2ZcCE++NGzdkGUdgiRCvE2AH4V3hJgJ+E6/JZEJtba3D+2fiZTuLxsZGi5Q/AnkQ4nUC7Ohjp06dXDK+Wq3m99pybJ1Zn0FBQfyLQmyd5UeIV2aIyOUrL9B0QAFwvHgbGxt5QTOtVssPXQjxyo8Qr8xUVlaisbERgHNPEzWHWYIdLV62PVYoFPD39xcWZycixCsz7AB+YGCgS8/SspKijhYv6y8gIAAKhUKI14kI8coMy84RFRXl0nnI5S5qnhFTiNd5CPHKDIvz7datm0vnIcTrfQjxyggRoaSkBMBvPlBXIZd42T0vq8zAxCvKq8iPEK+MVFZW8vS2rNymq5BLvMxvzHKBMaNcVVWVKG4mM0K8MsKOAXbs2JHn63IV5lFWjqS5eP38/Pi1iq2zvAjxygg7gB8REeHimfyWAtZgMDg0dJH5eP38/ABAWJydiBCvjDA3UWtpcZ0Ji+5qaGiQvPpu2rQJr732Gi9Pag57zjyFrhCvcxDilZFLly4BcA/xqlQq7utl4Zq2kJWVhaKiIgBN6Xibw1Zec/Gy+15htJIXIV6ZqKurc/lRwOaEhYUBkJbpYv/+/fznrKwsnnua0fyeFxArr7MQ4pWJixcvAmhyobAVz9WwHQDbzrfHL7/8wtP3MI4cOWLxOxMvu+cFxMrrLLymuDYA7NixA2PGjEFoaCgUCgVOnDhhz+U5BLZljomJcdkcmsPcVbasvIcOHcInn3wCwDJp3k8//WTRztrKy8TrqJrAdXV12L59O3bu3Imff/7ZIX16A15TXBto8mHec889SE1NlXpZDodZmt1lywz8lvzuypUr7Vqcs7Ky+M/R0dGYNWsWgKYvJfOts7V7XmYcMxqNkn29RIT9+/fj0qVLOHjwIIxGI958802cPn0ap06dwpYtW/gX4+2OZPGuWrUKzz33HKZPn46+fftizZo16Nq1K95++22r7Tds2IBu3bphzZo16Nu3L6ZPn45nn30WK1eu5G3WrFmDhx56CIsXL0ZsbCwWL16MBx54AGvWrJE0bnJyMv7xj3/gwQcflHpZDscdxctW0Nra2jYPy9fX11usmnfffTdCQkKgUqnQ0NCAU6dO8XbM2swirIAmn7Kvry+A37KI2MqePXuwb98+vP/++9izZw9Wr17dos3777+PwsJCSf16I15dXLs95CquXVtbyz+0zEjkDphbnNsS1bVr1wA03ccuWLAAOp0OSqWSH67Iy8sD8FvAh0qlsrjnVSgU/Oyy1Pve77//3urzQUFBFv7yDz74AGfPnm1hQLud8Nri2rYgV3FtNqeAgACLFckdYHm02hIv+38JDQ21MLbdd999AIBz586hoaGBG7MCAwNb1ElmW2cp971ttZ07dy6mT59u8SXx8ccf45///KfsdZXdFUn1eRnuXlzbVhYvXowFCxbw341Go0MEzO7DIyMjb7kvRxMSEoLCwsI2jVZs5W1e3SEiIgJarRY3b97EuXPn+M7JmjVdqsXZYDDwekchISEYM2YMevbsifLycosMJC+//DJu3rxpcdu1Z88ePP300zaN401IWnk9obi2FDQaDYKCgiwejoBtK6Ojox3SnyOxxV3ExNs826VCoUD//v0BNF2j+crbHKkW52PHjvGfk5KS0KtXL4vttzlarRYvvvgiH7ewsPC2PAThVcW13QX2ge3Ro4eLZ9ISJl52aMIa5tvm5vTp0wcAkJubyyOuWH4sc9i22daV95dffgEA9OrVCwkJCe2279ChA+bPn89jtm/HrbPkbfOCBQuQnJyMxMREJCUl4d1338X58+cxc+ZMAE1b0UuXLmHz5s0AgJkzZyItLQ0LFizA888/j8zMTGzcuJH7EIGm+5kRI0Zg+fLl+P3vf4/PP/8cGRkZFsaL9sYFmu7jzp8/j+LiYgDgPsGwsDCnGY5qamq4+8SVOatagwnSaDSivr6+xWknImp15QV+M8CxLTPQ/srb3u1NbW0t3wk8+uijNl+LQqFATEwMcnJyUFhY6FY+dWcgWbxTpkzBtWvXsHTpUpSUlGDAgAFIT0/nlsiSkhIL32tMTAzS09Mxf/58rFu3DjqdDmvXrsXjjz/O2wwbNgxbtmzBq6++iiVLlqBHjx7YunUrhgwZYvO4APC///0Pf/7zn/nvU6dOBQCkpKRAr9dLvVS7YKtuQEAAd5e4E/7+/tzlYzQaWwjUaDSirq6u1S2rNQOctZWSibeurg7V1dVtGu6Y3zYoKEhyNFrXrl2Rk5PDkx7cTthlsJo1axZ32jdn06ZNLZ4bOXIksrOz2+xz0qRJmDRpkt3jAsAzzzyDZ555ps0+5IZtE12Vo7k9FAoFOnbsiLKyMqvi3bt3L4CmaKzWEuYlJibi2LFjiI2NxZQpU6y2UavVCAgIQFVVFW7cuNGmeNmqa8/RSfOosYqKCvj4+LidhV8u7BKvoHWYeN1xy8wIDg5GWVlZi4MDRMRvNdoS0tixYxEfH99udpCQkBBUVVWhvLy8zTRAzBBpT+1iFjV248YNrFq1CgEBAZg7d65b7nocjTiY4GA8QbzMqt78aOCZM2f4/fpDDz3U6usVCgXCw8PbTWVrq7vo3LlzAOyLAw8ICLBw71VVVfEjjN6OEK+D8QTxMv+zuYW2vLwc3377LYAm6795MIS9sHvmttxSNTU1PFLL3iR98fHxFr+3ZUn3JsS22cEwg5W73vMC4CtVcXExTCYTcnNzLQ6K/O53v3PoOKdOnYLBYMCgQYNaFVpgYKDF4QYpxMbG4quvvuK+3ttFvGLldSBE5BErr/khg8uXL+Ozzz6z+LujvnjMI8yKiorwxRdftAimYKv/rUSj+fn54dlnn8Xdd98NQIhXYAdVVVXc/8mySbgjSqUSPXv2BNDkHWC1lABpftb2YBFs5pw9e9bid5a04FZDScPDw3nsdVlZmdV8W96GEK8DYatuYGAgVCr3viOJi4sDYBlsMWfOnBbb2luludU3JyeH/0xEfOV1REx5YGAggoKCQEQ4cODALffn7gjxOhB2v+vOW2ZG8/IrM2bMkOU+/d577wXw23tSUFDAj/GVlpaiqqoKSqXSIeeeFQoFX8EPHTp0y/25O+69PHgY7lCH11bMAxnUarVsGS7j4uLQv39/KBQKvPXWW6ipqUFJSQkiIiKQkZEBoCle2lE7lXvvvRe5ubkAmsIu7TWCeQJi5XUg7IysO1uazXnmmWcQHh6OWbNm3dLRyvZQqVTw8fHhftz8/Hw0NDTwImxsC+8IwsPDeYilrYn2PBUhXgfCzsh6inijoqLwwgsvOG2bz8RbUFCAoqIiNDY2QqvVonfv3g4dh+0ibiVRgycgts0Owmg08gB7d0p94050794dQFOyAhYEws7tOpIuXbrg119/FSuvwDZYLq0777zT5RUB3ZWQkBD4+fmhsbGRJyxgvllH0l6KW6PRiOzs7FYTBVy5cgWrVq1qkaPaVsrKypCZmYmioiKYTCYLV5wjESvvLXLz5k1s2bKF+ysdlY3DG1EoFNDpdMjPzwcA+Pj4yFKEjYm3tLTU6lnibdu24eLFi9BoNJg3b16LUNAdO3agoqICX3/9NQYPHixp7LKyMqxbt87iucceewwDBw6040raRqy8t0hubi4XLgC3SDvrzvTq1Yv/3KNHj3YPN9gDS7pfXV3doqhaXV0d//+qra21mkKWHc4A0CJ7S3uwHYU5zQNTHIUQ7y1AREhPT+e/d+jQgR9RE1hnyJAhGDVqFLp27erQaC5zVCoVT3L4zTffWPyteegks3ibY55dJDMzU9LYLAsJC05RqVQYP368pD5sRWybbwF2lA1ocnckJiZCqRTfh22hUCgwYsQIjBgxQtZxOnbsiBs3biA3Nxfr1q1DWVkZxo4d2yLXVXPxEpFF/m4iQlFRkUXGlrZgZ6THjx+PkJAQWTOqiE/aLWBejmXixIlumer1dsV8VWfnltPT03l4ZkJCAhQKBa5evWqRlICltPX19eXJATZt2oSjR4/aNC4LkQ0ODoZOp5PVDSfEaydExLdgo0aNcvFsBM1pL1b63nvv5bc47D7VZDLho48+AtBkBb///vt5+/T09DZLxLDXsy8CZ/jOhXjtoL6+HkuXLuV5i5OSklw8I0Fz1Gq1RbBMdHQ0tFotAGDatGno2LEjz9/FVmbzDBzDhg1D3759MW3aNP7cmTNn2hyzoqICRASlUumUsq7intcOvvvuO/5zZGRki/SpAvfgnnvuwZdffgkAGDduXIscWbGxscjLy+PiZSVh4+LiuMuvd+/eGDZsGA4fPtwibVBzzLfMcoabMoR4JUJEPEE40JQZU+CeJCQkIDIyEn5+flbPVzMxl5WVoaioiFc/bO7bZSs0syS3Bottd1a4qRCvRMrLy1FeXg6lUomFCxd69akVb6Ct01JMlDdv3uTRVD4+Pi1yabFTYu2Vbjl+/DgAOC3Czq573vYq1Ddn//79SEhIgJ+fH7p3744NGza0aLN9+3b069cPGo0G/fr1w86dOyWPS0TQ6/XQ6XTw9/fHfffd1+59ilSYqyEiIkII18PRaDS8XAo7Rmh+j8swL1faWknRvXv3Wnw2nIFk8dpSod6cgoICjB07FsOHD8fx48fxyiuvYM6cORYJzzIzMzFlyhQkJyfj5MmTSE5OxuTJky1cMbaMu2LFCqxatQppaWk4evQowsLC8NBDD3HDkiNwx6LZAvtp/v9oLf0sMz6ZTCaraWWvX79ukbnDPIpMVkgigwcPppkzZ1o8FxsbS4sWLbLafuHChRQbG2vx3IwZM2jo0KH898mTJ9PDDz9s0WbMmDE0depUm8c1mUwUFhZGqamp/O81NTUUHBxMGzZssDq3mpoaMhgM/HHhwgUCQAaDobXLpw0bNpBer6fs7OxW2wg8h8rKStLr9aTX6+nzzz9vtR1r8/XXX7f6N71eT9XV1TaNazAY2v2stYekldeeCvWtVb0/duwYz5/UWhvWpy3jFhQU4PLlyxZtNBoNRo4c2ercpBbXJiJucXRUIW6Ba9FqtUhJScE//vEPTJgwodV2rG5W8yoT5ieXnnjiCYfku7YVSeK1p0J9a1XvGxoauBBaa8P6tGVc9q+UuS1evBgGg4E/2isTWV1dzVOXekKeKoHttOfa6du3LwDwCpQMduggODjYedvl/8cua7PUCvXW2jd/3pY+HdWGodFoJBmd2LeuVqt1++yQAscSHh4OhUIBo9GI69evcyMWs7lIPTroCCStvPZUqG+t6r1KpeKm+tbasD5tGZdlr5AyN6kw8bpzTmaBPJjHOjMPBhFx8TbPxukMJInXngr1rVW9T0xM5JFJrbVhfdoybkxMDMLCwiza1NXVYf/+/a3OTSq//vorAM/JUSVwLImJiQB+i4W+du0aqquroVKpXON9kGrh2rJlC6nVatq4cSPl5ubSvHnzSKvVUmFhIRERLVq0iJKTk3n7/Px8CggIoPnz51Nubi5t3LiR1Go1ffrpp7zNoUOHyMfHh1JTUykvL49SU1NJpVLRDz/8YPO4RESpqakUHBxMO3bsoJycHJo2bRqFh4eT0Wi06drasgB+99133KL43XffSX3bBF5AeXk56fV6Wrp0KTU2NtKuXbtIr9fTf/7zH8l9OcLaLPnGrb0K9SUlJRa+15iYGKSnp2P+/PlYt24ddDod1q5di8cff5y3GTZsGLZs2YJXX30VS5YsQY8ePbB161Zu4bNlXABYuHAhqqurMWvWLJSXl2PIkCHYtWuXQ4LEWVA74Bl5mQWOJygoCD4+PmhsbERRURE/JihHihtbUBD9v/VIAKPRiODgYBgMhha5qOrr67FixQo0NjZi3rx5IlfVbcpHH33Eb5+AphDLv/71r5IPIrT1WbMVYTK1EbVajb/85S+or68Xwr2NueuuuyzEm5SU5JQTRNYQ53kl0LlzZ9nKggg8gz59+vDY5cjISIcXZpOCWHkFAgmoVCpMnz7d1dMAIFZegcBjEeIVCDwUIV6BwEMR4hUIPBRhsDKDubzNk24LBHLAPmO3EmYhxGsGy7ghzuoKnEVFRYXdB11EhJUZJpMJxcXFCAwMtOp4NxqN6Nq1Ky5cuCACNcwQ74t12npfiAgVFRXQ6XR2l8gRK68ZSqXSppIlQUFB4kNqBfG+WKe19+VWj5YKg5VA4KEI8QoEHooQrwQ0Gg1SUlJEvuZmiPfFOnK/L8JgJRB4KGLlFQg8FCFegcBDEeIVCDwUIV6BwEMR4hUIPBQhXitER0dDoVBYPBYtWmTR5vz583j00Ueh1WoRGhqKOXPmoK6uzqJNTk4ORo4cCX9/f0RERGDp0qW3FIjujkgt9+rp6PX6Fp8NlvAfsK3MbG1tLf72t78hNDQUWq0WEyZMwMWLF6VPxu6ksV5MVFQULV26lEpKSvijoqKC/72hoYEGDBhAo0aNouzsbNq9ezfpdDqaPXs2b2MwGKhLly40depUysnJoe3bt1NgYCCtXLnSFZckCyyX9nvvvUe5ubk0d+5c0mq1VFRU5OqpyUZKSgr179/f4rNRWlrK/56amkqBgYG0fft2ysnJoSlTprTIHT5z5kyKiIig3bt3U3Z2No0aNYri4uKooaFB0lyEeK0QFRVFq1evbvXv6enppFQq6dKlS/y5Tz75hDQaDU+ivX79egoODqaamhre5s033ySdTkcmk0m2uTsTqeVevYGUlBSKi4uz+jdbyszeuHGD1Go1bdmyhbe5dOkSKZVK+uabbyTNRWybW2H58uUICQnBXXfdhTfeeMNiS5yZmYkBAwZAp9Px58aMGYPa2lpkZWXxNiNHjrSIrhkzZgyKi4tRWFjotOuQC3vKvXoLZ8+ehU6nQ0xMDKZOnYr8/HwAtpWZzcrKQn19vUUbnU6HAQMGSH7fxKkiK8ydOxfx8fHo1KkTjhw5gsWLF6OgoADvv/8+AOslSTt16gRfX1+LkqPR0dEWbdhrLl++bLUCuydhT7lXb2DIkCHYvHkzevfujStXruD111/HsGHDcObMmTbLzBYVFQFo+r/39fVtUe/KnvftthGvXq/Ha6+91mabo0ePIjExEfPnz+fPDRo0CJ06dcKkSZP4agxYr+dKzcqJ2lLa1NORWu7V03nkkUf4zwMHDkRSUhJ69OiBDz74AEOHDgVg33tiz/t224h39uzZmDp1apttmq+UDPaf8uuvvyIkJARhYWH48ccfLdqUl5ejvr7eouSotXKjQMtvZk/EnnKv3ohWq8XAgQNx9uxZTJw4EUDT6mpeNbB5Kdq6ujqUl5dbrL6lpaWSq1neNve8oaGhiI2NbfPh5+dn9bXHjx8HAP4fkpSUhNOnT6OkpIS32bVrFzQaDRISEnibAwcOWNwr79q1CzqdrtUvCU/CnnKv3khtbS3y8vIQHh5uU5nZhIQEqNVqizYlJSU4ffq09PdNknnrNuDw4cO0atUqOn78OOXn59PWrVtJp9PRhAkTeBvmKnrggQcoOzubMjIyKDIy0sJVdOPGDerSpQtNmzaNcnJyaMeOHRQUFOSVrqK2yq56Gy+++CLt27eP8vPz6YcffqDx48dTYGAgv2ZbyszOnDmTIiMjKSMjg7Kzs+n+++8XriJHkJWVRUOGDKHg4GDy8/OjPn36UEpKCt28edOiXVFREY0bN478/f2pc+fONHv2bAu3EBHRqVOnaPjw4aTRaCgsLIz0er3XuIkY69ato6ioKPL19aX4+Hjav3+/q6ckK8xvq1arSafT0WOPPUZnzpzhfzeZTJSSkkJhYWGk0WhoxIgRlJOTY9FHdXU1zZ49mzp37kz+/v40fvx4On/+vOS5iPO8AoGHctvc8woE3oYQr0DgoQjxCgQeihCvQOChCPEKBB6KEK9A4KEI8QoEHooQr0DgoQjxCgQeihCvQOChCPEKBB7K/wEmc76eupeAYAAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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dMWXKFNlrVSqV4KknTJigqAOwoaFBGA/39/eXTMnlveve3t6SJgFv/wOQtPE7AiRwQjF8nnbv3r2dNoNNjjFjxsDPzw8jR44Ujnl4eAjisrQE9G9/+xsCAwMRGhqqaKeUhoYGoXbi4+OD1NRU4XreKWecnr+/v/Cgk+uBb09I4IRieEeTsxaYmCMuLg7Tp0+XVLltXdet1+vx4IMPQq1WmwhS7HGNqaurE6ri3bp1g8FggMFgAPBHZ5xcjeCee+4BQAIn3ABnR3Exh0ajwYgRIySzz5QEbuCe31jg4jYzh3tgcQcbbybo9XoAf2yqIFcjULpsNDs7G6mpqYpjwCuFBE4oho/72rsXuFLEolaSJ7c1FmTPnj1NbAMCAgBIl6fyB1ivXr0ktnIenNtYW4bKSU9Px/Xr13Hs2DGb7O2FBE4opq08uBz2hF4yDiIhJ1Du1fliF3Gcd+NdUuU8OO/1t3WxDK/K2xJdxhFI4IRieBvV1W1wOewRuJh+/frJpmE8GiBu9xs/IOQEzocLlQZ+MA4o4WxI4IRijJdMtiX27hjKvfZ9992Hvn37QqPRSIQqHncHTB9eYq8vJ/COGpvNLoFv3rwZQUFB8PLyQnh4uMXoGgCQmZmJ8PBweHl5YciQIUhNTTWxSUtLQ0hICHQ6HUJCQrBv3z7F+TLGkJKSAoPBAG9vb0yaNEkY2uBMmjQJKpVK8oqPj7fjLnRNamtrhWAJ7bHxvXg1mBJefPFFVFdX44EHHoCXlxcefvhhhIeHo0+fPlCr1RgyZIjEng8FcsTtcL7FkhgucKUe3NXNHMUC37VrF5KTk7Fy5Urk5eUhOjoa06dPN7v/U1FREeLi4hAdHY28vDy8/vrrWLRoEdLS0gSbrKwszJ07FwkJCTh79iwSEhIwZ84cnD59WlG+77zzDtavX4+NGzfizJkzCAwMxNSpU03aOYmJiSgtLRVe//nPf5Tehi7LV199JbxvS4FPnjwZADBv3jy7rvf398fkyZOFdnX37t0RFhaG559/Hvfee6/VsXJx55lcJ52SKrrcBomuQrHA169fj/nz5+Oll17CyJEjsWHDBgwYMAAffvihrH1qaioGDhyIDRs2YOTIkXjppZfw4osv4t133xVsNmzYgKlTp2LFihUIDg7GihUrMGXKFGzYsMHmfBlj2LBhA1auXImnnnoKYWFh+PTTT1FTU4PPPvtMUqZu3bohMDBQeMk9kQl5xLHM2rINPnHiRERERKBPnz52pzFq1Cjhfb9+/XDPPffAx8cHYWFhUKlUkrZ2RESE5Fpx1du4TQ5IPXhOTo7FOenGY+Wu3PZIkcAbGhqQk5ODadOmSY5PmzZNEpBeTFZWlol9bGwssrOzhS9qzoanaUu+RUVFKCsrk9jodDrExMSYlG379u3Q6/UIDQ3Fq6++arEns76+HlVVVZJXV0Y8RXP48OFtmndcXJxD1/fv3194Lx4L59+Di9T4PAA89NBDwnu5arW4NnPw4EGLe4YbC/ynn36yVnS7USTwiooKNDc3C2OGnICAALN7MJeVlcnaNzU1CXN7zdnwNG3Jl/+1VrZnn30WO3bswNGjR/Hmm28iLS0NTz31lNnvvGbNGvj5+Qkv42CDnYGWlhanrHJijEm8jbnACq7CVRFTuEcWi9RYxDExMaiqqkJMTIxsOTw9PSXXWOodNxb4nj177Cq3LdjVwjf+gowxizdfzt74uC1pOsMmMTFReB8WFobhw4cjIiICubm5GDt2rEnZV6xYgaVLlwqfq6qqOpXIW1pakJqaCpVKhaSkJIdEYtw5KldV7cxYErhGo0FISAgmTZpk8Xo+E05JFd2VKPLger0eHh4eJt66vLzcxHNyAgMDZe01Gg38/f0t2vA0bcmXR7dUUjYAGDt2LDw9Pc0+cXU6HXr06CF5dSaqq6tx48YNlJeXo7q6GowxbN26Fdu2bbM5RPGdO3eQmpoqTNUEWj2ao2PSHQ2xwOX6F8TLVuUQd5hZurfGQ32udBiK/kNarRbh4eEm27lkZGQgKipK9prIyEgT+/T0dEncLXM2PE1b8g0KCkJgYKDEpqGhAZmZmWbLBrSuEGpsbETfvn0tffVOi/jHVFdXh+rqaly5cgVFRUVmN+0z5uTJkybrnC3d086KuA0uF/Rh4MCBFq8Xd8RZ6oA09uD2Dv3ZguIq+tKlS5GQkICIiAhERkbio48+QnFxMZKSkgC0VmlLSkqwbds2AEBSUhI2btyIpUuXIjExEVlZWdiyZQt27NghpLl48WJMnDgR69atw+OPP479+/fjyJEjOHHihM35qlQqJCcn4+2338bw4cMxfPhwvP322+jWrRueeeYZAMAvv/yC7du3Iy4uDnq9HgUFBVi2bBnGjBmDBx980P672IERL364ceOGpAZSXV0tu/DCGLkfoLtVzwGpKC2tOLMFS9Vw7unVajVaWlpcuu2RYoHPnTsXN2/exOrVq1FaWoqwsDAcOnQIgwYNAtAaflY8Nh0UFIRDhw5hyZIl2LRpEwwGAz744AM8/fTTgk1UVBR27tyJN954A2+++SaGDh2KXbt2Yfz48TbnCwCvvfYaamtr8fLLL+PWrVsYP3480tPThc4grVaLb7/9Fu+//z7u3r2LAQMG4LHHHsOqVavabOFEWyP2Krt378aTTz4pfLbVc7THnPP2QDy+bc8Y/7x58wTHZWlGGxd/9+7dUVVVZffsPFtQMXt2Ru/CVFVVwc/PD5WVlZ2iPV5QUIDPP/9c9tzjjz9uk6f68ssvkZubKzm2atUqZxSvQ9HQ0IC33noLo0aNwrPPPmtXGhcuXMDu3bvh5+eH5ORkWZuzZ8/iiy++QEBAgND0eeutt0w6QJ3xW3OvXhLCBEuepLa2FleuXMHXX39t0YsYT8RYuHCh08rXkdBqtejbt6+klqMUPhHH0n3nVXTx/HZX9ax3jbpXF8bSD62hoQFbt24F0Ppji4mJkbUzrsrz0Q93ZPLkyQ5t4sCr9vX19WaHj7mYjQXuin4N8uBujjWBc8Tb9Rjj6iWNHQkeQ91exCvszEV34QIXT45xlQcngbs5XODjxo0zOSceCjLXyVhQUCD57O4dbo52tmo0GuEemXu4imPamdso0VmQwN0cHoLIz8/PZKKGeBdNc/z888+Sz+44POZseDXdnMC5t9ZoNML9JA9OKCYrK0vwwF5eXiaTL8QB/8SxyMQYtyGtTfYg/hC28ZpyjriKzv8nJHBCMXxHTKDVgxt7X3G10NyKOm4TGhqKoUOHYubMmS4oqXvBq+DGwUaMz5PACbu5ceOG5POQIUMsVq+vX79ucg3wR0fRsGHD8Nxzzzl1m2B3RbwsVQ45D05t8C5EcXExsrKybF4MIod4nvn8+fOhUqmsLu/cvHmzyTG57XIJy/CeeHP/P35PtVotfH190aNHD5ct3HHvLtFORF1dHWpra1FZWYlPP/0UwB9RR8SLIACgpKQELS0tFlchcc8bFBQkeBSDwSC7cs8SchveE5axFp9NLPDZs2e7tCwk8A5AS0sLNm3ahJqaGsk64m3btqG5uRkvv/yysDVOU1MTPvnkEwBAcnKy2XBTcpFP1Wo1EhMTcePGDdnAlwBw+vRpyRqA9oyg2lnhveiFhYWy58UCdzVURW9nampq8M9//hN37941CRLAp4iKq87intnvv//ebLq8J9dYmGq12uSHJe4ZP3z4sORcW/4Y3QXxvZLz4iTwLsTBgwcV2YuHts6cOWPWzpLnNT723HPPmU2HBK4c8QpHuRh+JPAuhK0dadybi8erLc264gKXW/ZofMzT01O2k6e5uVnIl6rotqNWq022QhJDAu9C2Lomm9uJ7S1NOrHkweXEPH/+fADSXTvEY7PkwZUhXnQihjFGAu9K8ID6c+fOtWjHPbdY4Ja2quVtP1sDF/DxbbHH4T9EtVrttgExXAV/sBp78ObmZqHWRgJ3cxhjgnCtTY6QE7ilyRHcztzElNmzZ0OtVgsho/mDoLm5WZhpRe1v+zHnwcWf2+K+0jBZO2JcBfbw8EBzczNmzZqFAwcOSGx555pSD248hs4JDQ1FcHCw4Jl1Oh1UKhUYY6itrYWvry8J3AG4Bzf+H4l3ZnVVnHcx5MHbEbHAPT098X//93+Ij4+XDaP0+++/A7Bd4NY8OCDtpFOpVCbVSj4kRwJXjrkqelvPKyCBtyPGT3OdTocRI0bIPtl5tdm4ii7XC9/S0iL8sJTMHRcvc2xoaBBiuZHAlWPNg7fVPSWBtwE3b94UguuJJz6IFx1Y4/bt2/j2229NFoTItcPFXsNcFV0O/qPje8FxqINNOdba4G0lcGqDuxjGGDZu3AigdVjrxo0b+Otf/4qePXtafJqHhoZKlhtevHgRFy9eNLGrr683qe5xL6/T6RQtYjAXfMBSU4CQx1wVnf9vlDx4HYEE7mLEVWoeL/7999/HmDFjhO1s5Tz4jBkzMHDgQGi1Wuzfv9/kvFarRUNDg6z4eC1B6dJO8dJF8TY8JHDlcA9+9epVyXEe+86WDSecAVXRXYzcFjgAkJeXZ7G65uXlhXHjxkGv18tezz2AnPjs9RJiDy4WOM1iUw6vOdXV1Un6Sfjvoa32pCeBuxhLe4/zeGeW2uDmRMpFJ9cG50Nq3bt3t7mc4nIYC9zSjpqEPOKlvOL/EfWiuwmMMVy6dAnHjh0za5OXlwfAcoeL3Ey00aNHm+2lBVrXiwOme6VbQ1xF5+3w0aNHY+TIkYrSIVqn/PLoqnJDm20lcGqDu4jVq1fbbKvEg8+ePRuhoaHYvn07AHmBnzt3DoDyDQrkPHhbtRXdER8fH1RWVqKmpga9evUC0PYRcsiDuwCloZYsCVytViM6Olr4zL292IOLe73FGxgojWEuHiYTBwYk7IN3csp5cBoH78SY61h74IEHkJiYiIkTJ0qOW/tnDx482MSWV90LCgqwZs0anDp1ChUVFfjll18EW6U7ZMp5cHff6MCVcIGLl/jyPhkaJuvEyMXDXrlypSAWPumFI16iKYe4OmcscD709s033+Cbb76RXDdkyBBF5eZp3r59WwjaSLPY7Md4A4TKykrU1dVBpVK12f5uJHAXwJeAihF7QuPxaWtbw+r1emg0GqhUKmEPa2veeejQoYoXMxgMBgBAUVGRcIwEbj/GHaHi9QE0k60TY2loDDCt9lp7mut0OixYsAAqlUqo2lmrOtsTSD8wMNDkGLXB7cd4uirvYFPadHIEaoO7AOOxaeNtefv16yf5bG0tONDqxcUPgrFjx1q0f+SRR6ymaYxGozGZgGEtljphHuPpqu2x/JY8uAvg/8ioqChMnDjRZEjEy8sLXl5eqKurw8yZM+1aF6zVavHss88Kw2XGWIqZbol77rlHEthR6Vg68QfcU/Opw+0hcPLgTqayslKoHnt5eZkd70xMTERQUBDGjBljd17Dhg3Dm2++ieHDh0uOv/DCC3anKe7dfeKJJ+xOh4AwzZh3qpIH78QwxpCZmYnjx48L8c0ttZN79+6N559/3uF81Wq1ZErqM888IwnbqxTxA4n2IXOMPn36AGgdlWhubiYP3tkpLy+XbF4g15vuCsRe19GZZ+Ix27bsDHJHfHx8hDBYNTU1JPDOjEqlMgm1NGHChDbJW/xQ4cNo9iIeO2+ryRjuijgMVn19PQm8syPucfb29ja71NPZiJsCjkZfEcdaJw/uOGKBt8dGjtQGdyLi9qu5nSVdwYQJE1BYWIiIiAiH0xK358mDO45Y4NYi3boCErgTaatF/Mb4+PggMTHRKWl169YNTzzxBLy9vSkWmxMQC9yWSLfOhgTuRMSCsHccuiMwevTo9i6C29DeAqc2uIsw3gqY6JrIVdFJ4J0YPp+bB1Qkuja8Q62urq7NI6oCVEV3Os8//zyKi4tNZpcRXRM+snLr1i1hhiO1wTsx3t7eGDFiRHsXg+ggcDHzSU9qtbpNo9RSFZ0gXAgXMw+l5evr2yabDnJI4AThQsRtcMB6cA9nQwInCBdiXB1v67kSJHCCcCHG01I7hQffvHkzgoKC4OXlhfDwcBw/ftyifWZmJsLDw+Hl5YUhQ4YgNTXVxCYtLQ0hISHQ6XQICQnBvn37FOfLGENKSgoMBgO8vb0xadIkyQZ+QOt45CuvvAK9Xg8fHx/MmjULv/32mx13gSCsYzyfv8N78F27diE5ORkrV65EXl4eoqOjMX36dCG6pzFFRUWIi4tDdHQ08vLy8Prrr2PRokVIS0sTbLKysjB37lwkJCTg7NmzSEhIwJw5c3D69GlF+b7zzjtYv349Nm7ciDNnziAwMBBTp06VxEhLTk7Gvn37sHPnTpw4cQJ3797FjBkz0NzcrPRWEIRV+IYHnLb24GAKGTduHEtKSpIcCw4OZsuXL5e1f+2111hwcLDk2IIFC9iECROEz3PmzGGPPvqoxCY2NpbFx8fbnG9LSwsLDAxka9euFc7X1dUxPz8/lpqayhhj7Pbt28zT05Pt3LlTsCkpKWFqtZodPnxYtvx1dXWssrJSeF29epUBYJWVlbL2BGFMSkqK8Pr1119tvq6ystLh35oiD843hp82bZrk+LRp03Dy5EnZa7KyskzsY2NjkZ2dLQz8m7PhadqSb1FREcrKyiQ2Op0OMTExgk1OTg4aGxslNgaDAWFhYWbLv2bNGvj5+QmvzjzHnGgfxMNitgTYdCaKBF5RUYHm5maTQHwBAQEoKyuTvaasrEzWvqmpSRgbNGfD07QlX/7Xmo1WqzWpNlkq/4oVK1BZWSm8jPd7JghrLFiwACNHjsSiRYvafIWeXTPZjAfqGWMWB+/l7I2P25Kms2yMsWSj0+lof2zCIQICAjB79mxhz/C2RFGOer0eHh4eJt6uvLzcbHjdwMBAWXuNRiPE+TZnw9O0JV++yMOaTUNDg7Atjy3lJwhn0B7iBhQKXKvVIjw8HBkZGZLjGRkZiIqKkr0mMjLSxD49PR0RERHCrhnmbHiatuQbFBSEwMBAiU1DQwMyMzMFm/DwcHh6ekpsSktL8eOPP5otP0F0apT2yu3cuZN5enqyLVu2sIKCApacnMx8fHyE3sHly5ezhIQEwb6wsJB169aNLVmyhBUUFLAtW7YwT09PtmfPHsHm+++/Zx4eHmzt2rXswoULbO3atUyj0bBTp07ZnC9jjK1du5b5+fmxvXv3svz8fDZv3jzWt29fVlVVJdgkJSWx/v37syNHjrDc3Fz28MMPs9GjR7Ompiabvr8zejYJwhac8VtTLHDGGNu0aRMbNGgQ02q1bOzYsSwzM1M49+c//5nFxMRI7I8ePcrGjBnDtFotGzx4MPvwww9N0vz888/ZiBEjmKenJwsODmZpaWmK8mWsdahs1apVLDAwkOl0OjZx4kSWn58vsamtrWULFy5kvXv3Zt7e3mzGjBmsuLjY5u9OAifaCmf81lSMKdytvotTVVUFPz8/VFZWtv2kBaJL4YzfGq0HVwh/HlZVVbVzSQh3h//GHPHBJHCF8GmvNOGFaCvu3Llj9xx2qqIrpKWlBdeuXZNduF9VVYUBAwbg6tWrVH03gu6NPJbuC2MMd+7cgcFgsHuYjTy4QtRqtdXphj169KAfsRno3shj7r44uvqM1oMThBtDAicIN4YE7kR0Oh1WrVpFc9dloHsjj6vvC3WyEYQbQx6cINwYEjhBuDEkcIJwY0jgBOHGkMAJwo0hgdvJ4MGDoVKpJK/ly5dLbIqLizFz5kz4+PhAr9dj0aJFaGhokNjk5+cjJiYG3t7e6NevH1avXu3Q4oKOiNI4+p2dlJQUk98GjzgEtHH8fkfXrHZVBg0axFavXs1KS0uF1507d4TzTU1NLCwsjE2ePJnl5uayjIwMZjAY2MKFCwWbyspKFhAQwOLj41l+fj5LS0tjvr6+7N13322Pr+QSeKCOjz/+mBUUFLDFixczHx8fduXKlfYumstYtWoVCw0Nlfw2ysvLhfNr165lvr6+LC0tjeXn57O5c+fKBibp168fy8jIYLm5uWzy5MmKApNwSOB2MmjQIPbee++ZPX/o0CGmVqtZSUmJcGzHjh1Mp9MJC/g3b97M/Pz8WF1dnWCzZs0aZjAYWEtLi8vK3pYojaPvDqxatYqNHj1a9pyr4vebg6roDrBu3Tr4+/vj/vvvx7///W9J9TsrKwthYWEwGAzCsdjYWNTX1yMnJ0ewiYmJkcxiio2NxbVr1/Drr7+22fdwFfbE0XcXLl26BIPBgKCgIMTHx6OwsBCA6+L3m4NWk9nJ4sWLMXbsWPTq1Qs//PADVqxYgaKiInzyyScA5GO99+rVC1qtVhKnffDgwRIbfk1ZWRmCgoJc/0VciD1x9N2B8ePHY9u2bbj33ntx/fp1/Otf/0JUVBTOnz9vMX7/lStXANgXv98cJHARKSkp+Mc//mHR5syZM4iIiMCSJUuEY/fddx969eqF2bNnC14dMI3RDpjGYLclZnxnx55Y9Z2Z6dOnC+9HjRqFyMhIDB06FJ9++ikmTJgAwPnx+81BAhexcOFCxMfHW7Qx9rgc/o+7fPky/P39ERgYKNk8EQBu3bqFxsZGSZx2uTjugOkTvjNiTxx9d8THxwejRo3CpUuX8MQTTwBo9dJ9+/YVbMzF7xd78fLycsXhvakNLkKv1yM4ONjiy3g7WE5eXh4ACP+0yMhI/PjjjygtLRVs0tPTodPpEB4eLtgcO3ZM0nZPT0+HwWAw+yDpTNgTR98dqa+vx4ULF9C3b9+2j9+vqEuOYIwxdvLkSbZ+/XqWl5fHCgsL2a5du5jBYGCzZs0SbPgw2ZQpU1hubi47cuQI69+/v2SY7Pbt2ywgIIDNmzeP5efns71797IePXq45TCZpXj27sayZcvY0aNHWWFhITt16hSbMWMG8/X1Fb5zW8Tv55DA7SAnJ4eNHz+e+fn5MS8vLzZixAi2atUqVl1dLbG7cuUKe+yxx5i3tzfr3bs3W7hwoWRIjDHGzp07x6Kjo5lOp2OBgYEsJSXFbYbIONbi2bsbfFzb09OTGQwG9tRTT7Hz588L59sifj+H1oMThBtDbXCCcGNI4AThxpDACcKNIYEThBtDAicIN4YEThBuDAmcINwYEjhBuDEkcIJwY0jgBOHGkMAJwo35f2zHTvupmU7qAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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v+vDhwxEeHo4tW7aI2/r164eZM2cavD9avnw5vvvuO+Tk5IjbYmJicP78eaSnpwMA5syZg7KyMvz4449imWnTpsHHx0d0QLHkvPn5+QgODkZmZiYGDx4sbtdoNHjkkUfwzTffYM6cOQAax1wDAgJw8OBBg/m7m+JIedHXrVuH6upqLFiwoM3uw2/evCn5H/ft2xeRkZHYuXMngMYe9k6dOuGFF17AgAED2iQmW9HmedE7uj/42bNnUVdXJzmOSqVCWFiY0eM4qj94XV0dqqurAbR9Da5rd3T58mUsXrwYAODm5ibmgnOU/8PDYpHAreEPbqpMa/uDl5aWQqlU6s3KMnWctWvXwtvbW3w4inWwICAnJyeDhgfW5IknnkCPHj3E8w8bNgxAY0YZIUuMcPvAmKZFnWwd2R/cEKaOs3LlSmg0GvFRWFj40OfrCAgC8vDwaJVrbAnh4eH485//rNeR1q9fP7E1wTW4eTiUP7i/vz9qa2v1kvyZOo6j+oMLAmouK4s1mTZtmvi8vLwc/fv3FwXOiRrNw6H8wSMiIqBQKCTHKSkpQVZW1kP7jNsbQg1urUUm5jBixAicPn0avXr1Qnh4OBQKhfgDy010M7HUjjQpKYkUCgUlJiZSdnY2xcXFkYeHB+Xn5xMR0YoVK2ju3Lli+dzcXHJ3d6clS5ZQdnY2JSYmkkKhkNjKHj9+nORyOSUkJFBOTg4lJCSQs7MznThxwuzzEhHduXOHMjMz6YcffiAAlJSURJmZmVRSUiKWiYmJoZ49e1JqaiplZGTQxIkTadCgQVRfX2/W53cU++Cvv/6a1Go1/fTTTzaN47fffpO8fvDggWhfXFdXZ6Oo2obW+K45lD84EVFVVRXFxsaSr68vubm5UVRUFBUUFJj92R1B4FqtVhRRRkaGrcORoNVq6f333ye1Wm2Rr3tHhP3BbYAjjIOfO3cOBw4cAADMmzcPQUFBtg2oCX//+99x9+5dvPrqqwgODrZ1OFaD/cEZq5CSkiI+b2p20B4QVrdxT3rzsMAZCRqNRlyS2adPHzg5tb+vCHe0mU/7++8xNkV3jvfTTz9tw0iMIwyVscCbhwXuwBjqfhEW7yiVyjadomoJQg3OTfTmYYE7KBcuXMAf/vAHve2nT58GAMkinfYGT3YxH87J5qCsXr0alZWVkmm6DQ0N4rrr9tZzrgsL3Hy4BndATp48iYEDB2L48OESw79//etf4vPevXvbIDLzEJroWq1WXC/OGIYF7oAcOnRIfC44l1y5ckWcwtu5c2colUqbxGYOSqVSFPn169dtHE37hgXOoKysDBs2bBDTJQkGBO0Zwcq4pKTExpG0b1jgDoauza/Qi378+HFR3EDjssz2jrD6j3vSTcMCdzB0EzkIIjl16pS439nZWUxd3J4ROtrYBcU0LHAH47vvvgPQmLVUyJqiywsvvNDWIbUIQeBCHwJjGBa4A1FZWSl2SgUEBOglUvTy8kJISIgtQrMYYT56ZWUltFqtbYNpx7DAHYisrCzxeY8ePdC/f3/x9YMHD/Dyyy/bIqwW0alTJ8hkMokDKqMPC9yByM/PF58PGTIEnTp1gkKhwIEDB/Daa69ZlP7K1gjiBoCkpCQbR9N+4ZlsDgIR4erVqwCAsLAwcZXYhAkTcOvWLdG6tyPh5uaGqqoqlJWVoba2tl2P3dsKrsEdhOLiYtTX10Mmk2HGjBni9lGjRmHDhg02jKzlzJ49W3wudB4yUljgDoLQPO/atauY7LKjExQUJM5HP3PmDH744QcbR9T+YIE7AESE1NRUAB1jEoslzJ8/H0Bjc/3MmTO4deuWjSNqX7DAHQDdVVehoaE2jKT1aTqWv3nzZrPep+tWas+wwB0AYTKIXC7vUD3l5tC5c2e98fyamhpUVlbil19+MfgeIsLChQsdwqHUrvzBiQhqtRoqlQpubm6YMGECLl26JCkzYcIEyGQyySM6OroFV6HjIAi8o0xisZRp06bhzJkz4uu0tDR8//33RjvePvnkE+zevRt79uwRDTDtFbvyB1+/fj0+/fRTfPbZZzh9+jT8/f3x5JNPory8XBLT/PnzUVJSIj62bt1q6WXoUAj3pba0IbImgYGB2L17t/hdyM3NRVZWFm7fvi06pOpSWlqKt99+G9euXcOaNWvaOtw2xW78wYkIKpUKcXFxWL58OYDGplq3bt2wbt06LFiwAEBjDT548GD89a9/teRji3TEvOhfffUVCgsL8fzzz2PgwIG2DsdqXL58Gbt375Zse+655/D444+Lr69evYpvv/1WfH369Gn8+9//brMYLYH9wXXOm5eXh9LSUkkZFxcXjB8/Xi+2nTt3ws/PDwMGDMBbb72lV8Pr0tH9wYkIxcXFAIBHHnnExtFYlz59+uglkmx6H56WliZ5HRkZaddJI+zGH1z421xsr7zyCnbt2oWjR49i1apVSE5OxvPPP2/0M3d0f/AbN26goaEBcrlc9Na2V+Ryud5S16Z2z8K8dSG5JABJy9HesDt/8ObKzJ8/H5MnT0ZYWBiio6Oxd+9epKamIiMjw2DsHd0fPC8vD0Bj7ebsbP8zkwcMGCB5ff/+fVy5cgVAY7IL4Z583LhxyMzMBNBYgZizIo2IOtzyVLvxBxeGSiz1EBdsaYV52k3p6P7gwucSUhzZO0899RRSU1OhVCohk8ng5OSE2NhYAI2jOcKP/dtvvy0mlmxoaMC5c+eaPfZ3332HpKQkNDQ0WC3+1sZu/MGDg4Ph7+8vKVNbW4tjx46Z9P6+dOkS6urqJCmL7IX6+noUFRUBQIdcTNIS5HI5pkyZglmzZsHV1RUAMGbMGOzduxfHjh0DAHTv3h0ymQxvvfWW+L7mmumVlZU4d+4cbt++Ldb8HQGLm+hLly7Fl19+ia+++go5OTlYsmQJCgoKEBMTA6CxSfvqq6+K5WNiYnD9+nUsXboUOTk5+Oqrr5CYmCi5uIsXL0ZKSgrWrVuHy5cvY926dUhNTUVcXJzZ55XJZIiLi8OHH36I/fv3IysrC3/84x/h7u4urnO+du0aVq9ejTNnziA/Px8HDx7Eiy++iCFDhmD06NEtuoC2ROjDEETclOvXr4OI4OrqardDZIZYtWoV+vbtK2muX7p0CXK5HMDvpg7du3eHr68vgMYfQ1MpmHWTO/7www8dpha3+KZszpw5uHPnDlavXo2SkhKEhYXh4MGDkiyXumPTwcHBOHjwIJYsWYJNmzZBpVJh48aNmDVrllhm1KhRSEpKwv/93/9h1apV6N27N3bv3o3hw4ebfV4AWLZsGaqqqrBw4ULcu3cPw4cPR0pKCjw9PQE0tgSOHDmCv/3tb6ioqEBAQACefvppxMfHi//8jsTChQsxZMgQVFRUYPXq1XqfQegv6Nu3r8k+EntlypQpkgkwArrDZv369cPx48cBABs2bMAzzzyDiooKaDQaTJ8+HT///DNGjRqll701NzcXffr0se4HaAXYH9xC2ss4+OLFi8XaB2i89xw2bJikzNatW1FaWmpwn6MQFxcHHx8f8XVNTQ0+/PBDSZm5c+fiscceQ3Z2Nh48eIDHH38cxcXFWLZsGbZv34533nkHKSkpuHz5svieyZMnW73Vx/7gDsonn3wiETcA/Oc//5G8rqmpETscO9rQXmuybNkyScvmueee0yszdOhQAED//v0RGRkJIkJAQAD27NmDTp06YePGjeJyW+Fa3r171/rBtwIs8A5GcXExKioq9LZrtVpcuXIFtbW1AH6/PwegtxjDkVCpVHjttdeQkJCAkydPIjIyUq/MwoULJa9dXV31hhSF4TWhed9RfNHsf2DUjmhoaMC2bdvE12FhYbhy5Yo4I3Dr1q1wd3fHmjVrxNo7MDDQIe+/denatSt27dpl1FBRoVDAxcWl2SWkfn5+4pBrR6nBWeAdiH379onPx48fjwkTJgAANm7ciHv37om5wnXvz1UqVZvH2R6ZOXOmyf3R0dEYOnQooqKi0KtXL4OdriqVSrzG5eXlzU7wag9wE72D0NDQIC7YkcvlorgBSEYbAEjuz7t27dom8XV0goKC8OOPP8LFxQWFhYVwd3fHtWvX8Prrr4tlBg0ahE6dOgFovCWqrKy0VbhmwzV4ByE/Px9EhIaGBqxcuVKyLzIyEtu2bYOrq6veghLdISHGNJGRkfD19UVmZiYGDx6M0tJSdO3aFbNmzUJ2djYeffRRAIC7uzsqKytRXl4ODw8PG0dtGhZ4B0EY0+7fv79eemAnJyesW7cOt2/fxuXLl5GRkQEnJycMGjRITI/MmEfv3r3FKawff/wxgMa+jrCwMLGMl5cXKisrUVZW1u47MPm/384RJrEIzfOePXsaLNelSxeEhITg2Wefxbx581BdXY3p06e3Zah2h7H7a2FVXkewLmaBt3M++eQTEJGYP0zw5DLFo48+ivfff5+NAKyE0HFpbIl0e4IF3o4xNMlQd1aWKRxhaaitEJrlQiKNphCRuIjJ1rDA2zGGvK/tPWlDR0DoyCwrK0N9fb3e/gsXLmDv3r166aNsAQu8HfPrr7/qbXNxcbFBJIwuHh4e4v15dna23v6LFy8CaFy9WF1djWvXrmHHjh1tGqMAC7wdUVdXh+vXr4tLEYVMJH379kV9fT2efPJJW4bH/H90nU3379+vl6dPd5pwWloatm/fjry8PGzfvr1N4wR4mKxd8eOPP4rJBISxVgCYNGkSxowZ49CLRtobgwYNwvnz5wEA33zzDf7yl7+AiHDr1i1oNBqx3PHjx8XEJgUFBaisrIS7u3ubxckCbydUVVVJMoUI4nZxceHZaO2QmTNn4saNG7hz5w5u376NO3fuICMjw2h2YYHCwsI2NaDgJno7wVjqXt3EGEz7IjY2Vhy2/OyzzyTiHjx4sMTFVZgo09ZJO7kGbycIHWr9+vUTJ7UsWrRIb903074IDg42mKNtzJgxmD59OoqKitCrVy+cP38eWVlZuHbtGiZPntxm8bHA2wnCL/vAgQMlxvZM+2bKlCkSgYeEhGDIkCFiDjxhiWqvXr0ANE6OuXr1Kh577LE2WYnGArchtbW1+Oabb1BbWyvOVOOOtI6Fq6sr4uPjQUQoKChAr169DApXtyUmWCfNnz8fKpUKWq3WamsGWOA25MCBA7hx44b42sfHR1yOyHQsZDKZydzzMpkMEREROHv2rLht27ZtkMvl6Nq1KyZPniyuVmtNuJPNimRnZ4splJpSWVmpN0lCaMYx9klUVJReJtaGhgaUlJSgqqrKKud0OH/wmpoaLFq0CH5+fvDw8MCMGTMktWhrkZeXhz179hic3HDy5El89NFHetu5eW7/vPzyy1i+fLle56m1/vcO5w8eFxeH/fv3IykpCWlpaaioqEBUVFSrJ7IXZqGVlpZKcn0dOnQIhw4dEl/36dNHnPrYluOjjO1wdXXFokWLxJmJHh4e1kvBTRYybNgwiomJkWwLDQ2lFStWGCy/bNkyCg0NlWxbsGABjRgxQnw9e/ZsmjZtmqTM1KlTKTo62uzzarVa8vf3p4SEBHF/dXU1eXt70+eff05ERPfv3yeFQkFJSUlimaKiInJycqJDhw41+9mJiDQaDQEgjUZjtExFRQVt376d1Go1qdVqys3NFbcL24RHVVUVaTQaunv3rlnnZ+yLzMxMamhoMLjPnO9acziUP/jZs2dRV1cnKaNSqRAWFmY0fkv8wUtKSvDee+/h448/lkxcKSoqgkajkXhVu7u74/XXX4erqyu8vLzMXgbK2BeDBw+2atYdi3rRreEP3r17d6v7gwtiKy0thVKp1BOTqfjXrl2L9957z+A+XYgI33//vcF9R44cwZEjRyTbXn/9de4xZ6yOw/mDG8JUGXP9wWUyGaZNmybZZuyeetasWSxupk2wqAbvKP7gulbATcvU1tbi3r17klr85s2bRi2GXVxczF6D3atXL0RHR8PPzw9eXl6Qy+VYv369XkJ9U37lDNOaOJQ/eEREBBQKhaRMSUkJsrKyTHqIW0JISAi6dOkChUIBJycnvPjii5L9Pj4+eqmNGcZqWNorl5SURAqFghITEyk7O5vi4uLIw8OD8vPziYhoxYoVNHfuXLF8bm4uubu705IlSyg7O5sSExNJoVDQ3r17xTLHjx8nuVxOCQkJlJOTQwkJCeTs7EwnTpww+7xERAkJCeTt7U379u2jixcv0ksvvUTdu3ensrIysUxMTAz17NmTUlNTKSMjgyZOnEiDBg2i+vp6sz5/S3o2a2trSavVUk1NjdnvYZjW6EW3WOBERJs2baLAwEBSKpUUHh5Ox44dE/fNmzePxo8fLyl/9OhRGjJkCCmVSgoKCqItW7boHXPPnj0UEhJCCoWCQkNDKTk52aLzEjUOlcXHx5O/vz+5uLjQuHHj6OLFi5IyVVVVFBsbS76+vuTm5kZRUVFUUFBg9mdvjYvOMObQGt819ge3kPbiD87YP+wPzjCMSVjgDGPHsMAZxo7h9eAWInRZmJqyyjCtgfAde5huMha4hQgr03hpJ9NWlJeXw9vbu0Xv5V50C9FqtSguLoanp6fe9NaysjIEBASgsLCQe9ibwNfGMKauCxGhvLwcKpWqxQtSuAa3ECcnJ6MWvgJeXl78JTYCXxvDGLsuLa25BbiTjWHsGBY4w9gxLPBWxMXFBfHx8ewAagC+Noax9nXhTjaGsWO4BmcYO4YFzjB2DAucYewYFjjD2DEscIaxY1jgLSQoKAgymUzyWLFihaRMQUEBnnnmGXh4eMDPzw9vvPGGnlfZxYsXMX78eLi5uaFHjx5YvXr1Qy0uaI9YanXV0VGr1XrfDSEpKNDGFlsPm1bGUQkMDKTVq1dTSUmJ+CgvLxf319fXU1hYGD3xxBOUkZFBhw8fJpVKRbGxsWIZjUZD3bp1o+joaLp48SIlJyeTp6cnffzxx7b4SFZByKW3bds2ys7OpsWLF5OHhwddv37d1qFZjfj4eBowYIDku3Hz5k1xf0JCAnl6elJycjJdvHiR5syZYzB3YI8ePejw4cOUkZFBTzzxhEW5AwVY4C0kMDCQNmzYYHT/wYMHycnJiYqKisRtu3btIhcXFzHH1ubNm8nb25uqq6vFMmvXriWVSkVardZqsbclllpd2QPx8fE0aNAgg/vaymJLgJvoD8G6devQpUsXDB48GB988IGk+Z2eno6wsDCoVCpx29SpU1FTUyN6RKenp2P8+PGSWUxTp05FcXEx8vPz2+xzWIuWWF3ZC1evXoVKpUJwcDCio6ORm5sLwHoWW8bg1WQtZPHixQgPD4ePjw9OnTqFlStXIi8vD19++SUAw5ZNPj4+UCqVEruloKAgSRnhPaWlpQgODrb+B7EiLbG6sgeGDx+OHTt2oG/fvvjtt9+wZs0ajBo1CpcuXbKaxZYxWOA6qNXqZn3ITp8+jaFDh2LJkiXitscffxw+Pj544YUXxFod0LdRAvRtksyxderotMROqiPz1FNPic8HDhyIkSNHonfv3vj6668xYsQIAK1vsWUMFrgOsbGxiI6ONlmmaY0rIPzjfv31V3Tp0gX+/v4Sf3MAuHfvHurq6iRWSobsmAD7sDdqidWVPeLh4YGBAwfi6tWrmDlzJoDWt9gyBt+D6+Dn54fQ0FCTD1dXV4PvzczMBADxnzZy5EhkZWWhpKRELJOSkgIXFxdERESIZf773/9K7t1TUlKgUqmM/pB0JFpidWWP1NTUICcnB927d297iy2LuuQYIiL6+eef6dNPP6XMzEzKzc2l3bt3k0qlohkzZohlhGGySZMmUUZGBqWmplLPnj0lw2T379+nbt260UsvvUQXL16kffv2kZeXl10Ok5mynLI33nzzTTp69Cjl5ubSiRMnKCoqijw9PcXP3BYWWwIs8BZw9uxZGj58OHl7e5OrqyuFhIRQfHw8PXjwQFLu+vXr9PTTT5Obmxv5+vpSbGysZEiMiOjChQs0duxYcnFxIX9/f1Kr1XYzRCbQnOWUvSGMaysUClKpVPT888/TpUuXxP1tYbElwOvBGcaO4XtwhrFjWOAMY8ewwBnGjmGBM4wdwwJnGDuGBc4wdgwLnGHsGBY4w9gxLHCGsWNY4Axjx7DAGcaO+X9kfDsE5Ops4AAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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/t5xTo9GI997vv//eLefsrrB4GYdIWS/cJVyJ22+/HUDTAnQ8ZOQ6LF7GIZJ4nV3O01liYmJEWpx33nlHdM0ZebB4GYdIE+elubjuQqPRYPz48eJz8xUjGedh8TIOkVrewMBAt587Pj4eMTExAICzZ8+KmUuM87B4GYd4UrwKhcJm7eTdu3cjKyuLPdAyYPEyDvGkeAFAqVTi0UcfBQDk5+cjNzcX77//Pr8DOwmLl3GIp8ULAIMHD7ZxiFmtVhQUFHisvFsJFi/jEGmc19V0r86gUCjw8MMP20RbnTx50mPleZLOnqfM4mXs0nwSvru9zS3p3bs35s2bh3HjxgFoiqeuq6vzaJnu5vjx45g8eTIuX77caWWyeBm71NbWikn4nhYv0BS4cd9990Gv14OIcO7cOY+X6S6ICB999BH+8Ic/4KOPPuq0clm8jF2kVler1XZ4Er4chg8fDgAoLCzstDI7yvfffy9eLa5fv95pualZvIxdpNlEnb1a4qhRowAAp06dQn19faeW7Sr/+9//bD7/8MMPnVIui5exy86dOwG4J4OGHMLCwhAUFASr1YozZ850atmu0NjYKBxV0pznY8eOobGxET/99BNKS0s9VjaLl2mF2WwWXb/bbrutU8tWKBQYPXo0AGDbtm1YtWoV0tPTYbFYOrUezrJ7924ATfWOj48HAHz33XcYPXo0pk+fjm3btnmsbBYv0wopewYA3HHHHZ1evhQ2KXHlyhVs27aty03er6urw969ewE0OdwGDhwIoGkWVkJCAubOnYuamhoUFRV5pHwWL9MKaSHtAQMGdKqzSqJv376Iioqy2Xf69OkulzonJydH3J97770XarVarAzRHE8FnbB4GRvq6+tFZkdp3q03mD17NsLDw/Hb3/5WdN09+f4oF6vVisOHDwNo8gtIvoH7778fZ8+eRUlJCY4dO4aQkBDMmjXLI3Vg8TICIsIHH3wgvKVSmlZvEBgYiN/97ne47bbbMGDAAABda6WFsrIyNDQ0wGq14sknnxT7Q0NDERYWhh49eiAhIQELFiwQc5fdTef3iZguy+XLl21m9UgrHHgbqSsqDV91BaTeSd++fVtlGklLS+uUOnDLywiat2y33357p0RWOYPUJf3pp5+6jNNK+pHz5qsFi5cRSI6qiIgIzJ0718u1+ZlevXqJjJPSTCdvI0WAGY1Gr9WBxetDeHpdW0m8I0aMgFLZdR4NtVoNvV4PANixYwfWrVvXaSGI9jh06BBu3rwJgMXLtAMRISMjAytWrEBlZaXHypHO7U1HlSOkrnNhYSEqKyuxb98+r9Tj5s2b+PLLL6FQKBARESF+VLwBi9cHKCsrw4ULF6DVapGVleWRMhobG0Ueqa4o3paRXkeOHBGtX2eyZ88eqFQqXLt2DQkJCZ1efnNYvD5A80D3oqIij3Sfq6urQURQq9UenXzvKuPGjUNwcDCUSiVUKhUaGxtx4sQJt5djtVodOsW++OILHDp0CACQkJDg9nzWcuGhIh/g9OnT4m+LxYLLly/bjeTpCNL7bp8+fTw2LtkRNBoNnn76aRARDh48iKysLJw+fRoTJkxwWxkXLlxAeno6+vbti0WLFkGlUonvNm7cKIaHlEolxowZ47ZyXYVb3i7O5cuXcfHiRQA/r0zviQyLzcXbVVEoFFAqlRgyZAgAx4uV/fe//8WmTZtk9VCuXLmCjIwM6HQ61NTU2PR2SktLbdYTfuyxx6DRaDpwJe6BW94ujtRNGzZsGEJCQrB//37s3bsXY8eOdWs5viBeiT59+kCpVKKhoQFmsxm9evUCEWHbtm02ccQ//PADhg4d6tQ5Dx48aPO5sLAQw4YNA/BzbDIR4U9/+hP69u3rpivpGNzydnGkYPyYmBgR8dR8yp678CXxqlQqkXFSuj9nz55tNQHg9OnTsFgsePPNN8X8ZEdIM6mio6MBNCUDkKYhSvOKH3rooS4jXIBb3i4NEYmQwL59+9oI64033sCQIUPQ0NCAs2fP4sknnxSr77lCVx4mskdYWBgqKipQXFyMqKgouzmvjh8/jtDQUNTU1ODAgQPQ6XS46667WtnV1taKH68ZM2bg7NmzqK+vx8GDBzFmzBjxXUfuryfglrcLc/XqVTQ0NECpVKJ3795QKpU2D98PP/yAkpISaLVa7Nixw+Vy6uvrxbpEviLewYMHA/h5plFJSQkA4MEHH8Ty5cuhVqthsVhs7svevXvtJnSXfAp6vR5BQUH4xS9+AQDYtWsXPv74YwBNi60FBAR47oJcgMXbhTh58iRSU1Oxfv16WCwW0er27t1beD7HjBljd47tjz/+iIaGBpfKlZbZDAgI8Prwh7NI3vbLly+jvLxcOJQGDhwItVqNyZMntzqGiJCbm4sbN26I1tRqtYo80dJk+tGjR0Or1QL4eQLCnXfe6dkLcgGXxJueno7IyEj4+fkhNja23WiXnJwcxMbGws/PD4MHD8aGDRta2WRmZiI6Oho6nQ7R0dHYvn277HKJCCaTCUajEf7+/rjrrrtw6tQpVy7RK3z99ddQKpWoqKjAnj17bLrMEnq9HsuXL0dSUhICAwPx0EMPISAgAESEiooKu+e1Wq147733sHLlSqxatQqvvPKKeHgB33rflQgODhat63vvvQcAGDJkiPDIjxgxwsb+nnvuAdDU+v71r3/FO++8g9dffx0vv/wyjh07BuDnDB46nc4mAKNPnz63hni3bt2K5ORkLF++HEePHsXUqVMxc+ZM0W1pSVFREWbNmoWpU6fi6NGjeOGFF7Bo0SKbZR1zc3Mxb948JCYm4vjx40hMTERCQoKNB9CZcl999VW88cYbePfdd/Htt9/CYDDgl7/8pVfjYJ2lurraZqHpb775Rvw42VsfNzQ0FM899xyio6NFfG3zWUE3b94U2Rfz8/NRXl4uWm+LxSLStwC+KV6lUtnqvsyePVv83a9fP5EQLi4uDlOmTLGxVygU4lUBaLp2aQgKaPohWLlyJWbNmoWkpKQuOfatIJlzrCZMmICxY8di/fr1Yt/tt9+OBx54AGvWrGllv3TpUnz++ec2eXiTkpJw/Phx5ObmAgDmzZuHmpoa/Oc//xE2M2bMQHBwMDZv3uxUuUQEo9GI5ORkLF26FEBTjqHQ0FCsXbsWCxYsaPfaampqoNfrYTabOz3laWFhIf71r38hNDQUtbW1MJvN4ru5c+e2akmas3fvXnz99dcAgNTUVJw4cUL0XIYOHYpr166hrKwMWq1WCFqn02Hp0qVQKBTYvn07Tpw4gXvuuQdTp0714FW6lx07duDbb78FAPz+978X3d7mNDY2ih8t6R63pEePHli4cCF0Op1nK9wMdzxrslre+vp65OXlIS4uzmZ/XFwc9u/fb/eY3NzcVvbx8fE4fPiweEdzZCOd05lyi4qKUF5ebmOj0+kwffp0h3Wrq6tDTU2NzeYtpGUy+vfv3+qHpr2ZK4MGDRJ/b9q0yeaV48yZMyKo449//CNefPFFqNVq1NXViW65NNzSlYZBnGHKlCkYNGgQHn30UbvCBWATJTVs2DARXDF06FCMHz8eK1euxLPPPtupwnUXssRbWVmJxsZG0R2RCA0NdZgcrLy83K69xWIRwxOObKRzOlOu9K+cuq1ZswZ6vV5sjh4AT0FE2Llzp41zSsrMIHXVpk2bJt7jHBEeHi7+bp75saWNXq+HSqUSYi8uLkZVVZV4V+4qmTOcpWfPnnjiiSdsurttoVKp8Pjjj2P06NGYN28eZs6c2SW7w87i0jhvywsmojZvgj37lvudOae7bCSWLVuGZ555RnyuqanpNAGfP38emzZtAtA0JNTSORUaGoqVK1c6dS6FQoEZM2bgq6++EvuefPJJhIWF4cCBAygoKMD8+fPFd4MGDcK5c+dQXFwshlJCQkK65IQEdxMWFoYHH3zQ29VwC7LEGxISApVK1aolq6ioaNXiSRgMBrv2arVaOEgc2UjndKZcg8EAoKkFbh6031bddDqdV7pLjY2N+Oyzz8Tn5h7xfv36uXTO8ePHIyYmBqWlpVCr1aIVnThxIiZOnGhjK7XUzcudMWOGS+Uy3kNWt1mr1SI2NhbZ2dk2+7Ozs+2OqwHApEmTWtlnZWVh3Lhx4v3DkY10TmfKjYyMhMFgsLGpr69HTk6Ow7p5A6vVis2bN4u5s83RarUuOy8UCgX8/PwwdOjQdiOBwsLCbHojUVFRTnc9ma6D7G7zM888g8TERIwbNw6TJk3C3//+d5SUlCApKQlAU1f04sWL+PDDDwE0eZbfffddPPPMM3jqqaeQm5uLjIwM4UUGgMWLF2PatGlYu3Yt7r//fnz22WfYtWuXzQJO7ZWrUCiQnJyM1atXY+jQoRg6dChWr16NgIAAPPLIIx26Se4kLy9PzFhJSEhAcXGxGBIbPXp0p7yDaTQaDBkyBGfPngUAEYDP+BjkAuvWraPw8HDSarU0duxYysnJEd89/vjjNH36dBv7PXv20B133EFarZYiIiJo/fr1rc75ySef0PDhw0mj0VBUVBRlZmbKKpeIyGq1UmpqKhkMBtLpdDRt2jTKz893+rrMZjMBILPZ7PQxcvjiiy/IZDKRyWSiPXv2iDJXrVpFa9eupbq6Oo+Ua4/KykoymUz08ssv07Vr1zqtXKYJdzxrssd5b2U8Oc5rNpvx1ltvAWjqHi9ZsgR+fn5uLUMuVVVVUCgU6NWrl1fr0R1xx7PGs4o6ASISq8kBQHJysteFC6DdISima8MTEzqBQ4cOiXxLDz30kM8E/zNdG255ZUDtjGfbsz9w4IDI+Hj33XeLyd4M01FYvE5SXV2NzZs3IyYmBuPHj0dGRoYI9l+yZAkCAgJshF1VVYV169ahsbERQFPY45QpU7xVfeYWhB1WzWjLifDxxx+LdChKpdJucrOoqCiUl5fj6tWrQrQSL7zwQpdIWsZ0DTp9YkJ3pvl0M0dZCb/77jtUV1fbCNdoNGLJkiUsXMbtsHidpGfPnkhJSRGhlr169cKKFSuwcOHCVulR/P39RTaHp556qtOnFzLdA+42N8OZrkx1dTWysrIwY8aMVjbV1dXo2bNnl1qki+mauKPbzOJthjcn4zPdC37nZZhuDIuXYXwUFi/D+CgsXobxUTjCqhmS786bieiY7oH0jHXEX8zibYaU37mzE9Ex3ZerV69Cr9e7dCwPFTXDarXi0qVLCAoKsjsBQUpQV1paykNJzeD7Yp+27gsR4erVqzAajS7HBXDL2wylUokBAwa0a9ezZ09+SO3A98U+ju6Lqy2uBDusGMZHYfEyjI/C4pWBTqdDamqqTy6N4Un4vtjH0/eFHVYM46Nwy8swPgqLl2F8FBYvw/goLF6G8VFYvAzjo7B47RAREQGFQmGzpaSk2NiUlJTgV7/6FQIDAxESEoJFixahvr7exiY/Px/Tp0+Hv78/wsLC8NJLL3UoEL0rkp6ejsjISPj5+SE2Nhb79u3zdpU8islkavVsSMvLAk1hjyaTCUajEf7+/rjrrrtsllIFgLq6Ojz99NMICQlBYGAg5syZgwsXLsivjMurHN3ChIeH00svvURlZWViu3r1qvjeYrHQyJEj6e6776YjR45QdnY2GY1GWrhwobAxm80UGhpK8+fPp/z8fMrMzKSgoCB67bXXvHFJHmHLli2k0Who48aNVFBQQIsXL6bAwEAqLi72dtU8RmpqKo0YMcLm2aioqBDfp6WlUVBQEGVmZlJ+fj7NmzeP+vfvTzU1NcImKSmJwsLCKDs7m44cOUJ33303xcTEkMVikVUXFq8dwsPD6c0333T4/Y4dO0ipVNLFixfFvs2bN5NOpxOrvqWnp5Ner6fa2lphs2bNGjIajWS1Wj1W985k/PjxlJSUZLMvKiqKUlJSvFQjz5OamkoxMTF2v7NarWQwGCgtLU3sq62tJb1eTxs2bCAiourqatJoNLRlyxZhc/HiRVIqlfTVV1/Jqgt3mx2wdu1a9OnTB2PGjMErr7xi0yXOzc3FyJEjYTQaxb74+HjU1dUhLy9P2EyfPt0muiY+Ph6XLl3C+fPnO+06PEV9fT3y8vIQFxdnsz8uLg779+/3Uq06hzNnzsBoNCIyMhLz58/HuXPnAABFRUUoLy+3uSc6nQ7Tp08X9yQvLw8NDQ02NkajESNHjpR933hWkR0WL16MsWPHIjg4GIcOHcKyZctQVFSE999/HwBQXl4u8jdLBAcHQ6vVory8XNhERETY2EjHlJeXt7t6fVensrISjY2Nre5DaGiouAe3IhMmTMCHH36IYcOG4fLly/jLX/6CyZMn49SpU+K67d2T4uJiAE3/91qtttUKja7ct24jXpPJhFWrVrVp8+2332LcuHFYsmSJ2Dd69GgEBwdj7ty5ojUGYHe+L7VYiKylDf2/s0rOYmVdHXvXeCtdX0tmzpwp/h41ahQmTZqEIUOGYNOmTZg4cSIA1+6JK/et24h34cKFmD9/fps2LVtKCek/5ezZs+jTpw8MBgMOHjxoY1NVVYWGhgbxq2swGFr9klZUVABo/cvsi4SEhEClUtm9xlvh+pwlMDAQo0aNwpkzZ/DAAw8AaGpd+/fvL2ya3xODwYD6+npUVVXZtL4VFRWYPHmyrLK7zTtvSEgIoqKi2twcLXh99OhRABD/IZMmTcLJkydRVlYmbLKysqDT6RAbGyts9u7da/OunJWVBaPR6PBHwpfQarWIjY1Fdna2zf7s7GzZD6EvU1dXh8LCQvTv3x+RkZEwGAw296S+vh45OTninsTGxkKj0djYlJWV4eTJk/Lvmyz3Vjdg//799MYbb9DRo0fp3LlztHXrVjIajTRnzhxhIw0V3XvvvXTkyBHatWsXDRgwwGaoqLq6mkJDQ+nhhx+m/Px8+vTTT6lnz5635FBRRkYGFRQUUHJyMgUGBtL58+e9XTWP8eyzz9KePXvo3LlzdODAAZo9ezYFBQWJa05LSyO9Xk+ffvop5efn08MPP2x3qGjAgAG0a9cuOnLkCN1zzz08VOQO8vLyaMKECaTX68nPz4+GDx9OqampdP36dRu74uJiuu+++8jf35969+5NCxcutBkWIiI6ceIETZ06lXQ6HRkMBjKZTLfMMJHEunXrKDw8nLRaLY0dO5ZycnK8XSWPIo3bajQaMhqN9Otf/5pOnTolvrdarZSamkoGg4F0Oh1NmzaN8vPzbc5x8+ZNWrhwIfXu3Zv8/f1p9uzZVFJSIrsuPJ+XYXyUbvPOyzC3GixehvFRWLwM46OweBnGR2HxMoyPwuJlGB+FxcswPgqLl2F8FBYvw/goLF6G8VFYvAzjo/wfywSCl8ZLzjkAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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OUdmpaD502RtwqSBs374dK1euxKpVqzB27FhkZWUhMjISb7zxhmr5Xbt2YejQocjKysLYsWOxatUqPP7443jppZdEmaysLMydOxdpaWkYM2YM0tLSMGfOHIWJZe+6jDFkZWVh8+bNWLJkCWJiYrB3717cvHkTf/zjHxV1GjBgAPR6vfi4YoVfeTiNUOJoolJzHwJjTOHd542e+w749GQZ3kDlVGm2pi/Lz4J55CLwYyAU8GMXRbYQ7HUb3dGtdJkgmEwmlJSUYN68eYr98+bNw2effaZ6THFxsUX5+fPn4+jRo6I/aK0MP6cj1y0vL0dNTY2ijFarRUJCgkXd3n//fYSHh2P8+PHYuHGj3cy+LS0taGxsVHyI7sMbhSwMao1ZthAOHz6MyspK1RRm5qMLaufgaDQam2/xBx54ALfddhumTZsmFoOViYqKEtvnz59Ha2urRZfGVpi2O1Z+clkcwtWrV9He3i6GhTgREREWYaecmpoa1fJtbW24evUqBg0aZLUMP6cj1+X/qpWRs+M8+uijiI6Ohl6vx8mTJ5GWloYTJ07YNPG3bduGLVu2WP1eZvz48Th16hSGDh3qUPn+CG8kx44dE/vkqEEOb8y1tbX46KOPrJ7P1qiB+RTnAQMG2EzWotPpsGHDBqvf85iFjo4OREZGqvoPTCaTEB1za6i5uVnV8nAlLncqmt9QxpjNm6xW3ny/I+d0RpknnngC9913H2JiYrB8+XJ88MEHKCwsVDyc5qSlpaGhoUF8+GQcNXikHY04WIffm7/97W9in1q3Tc2pp4bc6PjS7XICFBnzGY3dYe7cuWKb+w+Cg4PFc2ZrPUlH08o7E5cJQnh4OLy9vS2sgdraWos3M0ev16uWl4M8rJXh53Tkunyqa1fqBgCTJ0+Gr68vzp07Z7WMVqtFUFCQ4mMNbmbK4+WEEr5sm2ySq5n9tpx/1sotWrQIS5cuxU9/+lPVss4QBP72v3z5Mj7++GMAnfXnw59yozd/rvqUIPj5+cFoNFqY1wUFBYiPj1c9Ji4uzqJ8fn4+YmNjhdPIWhl+Tkeuy7sBchmTyYSioiKrdQM68+21traK8epbhT8svTGmvbfAHa7cQTdlyhQL0x5Q9ysAwMCBAxV//+QnPxHbOp0OMTExim7E1KlTxbYz8iFyi6ShoUE0+ICAAGENyHkei4qKFMf2KUEAgPXr1+Ptt9/GO++8gzNnzmDdunWoqKhASkoKgE7z+uc//7kon5KSgkuXLmH9+vU4c+YM3nnnHezevRsbN24UZdauXYv8/HxkZmbi7NmzyMzMRGFhIVJTUx2+rkajQWpqKp5//nkcOHAAJ0+exGOPPYYBAwbgkUceAdCZRGPr1q04evQoLl68iLy8PDz88MOYNGmSxUy77iJ7xqnb8CODBw8W27x7wBuHeV5EmZUrV2LUqFEKcVixYoWijFpEoYzBYBDbjlodtpBHOjjyEKVsHY4ePVpRzh3rOrh0clNSUhKuXbuGrVu3orq6GjExMcjLyxPe1+rqakVsQHR0NPLy8rBu3Tq8/vrrMBgMePXVV7F06VJRJj4+HtnZ2Xj22Wfx3HPPYcSIEcjJyVEou73rAsDTTz+NpqYmrF69GnV1dZg6dSry8/PFA+Pn54ePPvoIr7zyCm7cuIHIyEgsXLgQ6enpVtN3dRVuITDGUFdXB51O5xQz1dOR7wHvY8vTlK0xZMgQrFixAnv37hWjO/7+/oiOjkZ5eblCaKwhn593U2+FuLg4i5GryspK3HvvvfjHP/6h2G/uB3FHMhWXz3ZcvXo1Vq9erfrdnj17LPYlJCTYdNoBnV5mNU+zo9cFOq2EjIwMZGRkqH4fGRlpYcI5G61Wi8DAQFy/fh2vvfYaAGDTpk29MmDFXfzjH//APffcIwJ7HPG6y45hPz8/LF26FMePH8fdd99t91j5/JGRkd2osRI1f8esWbNEV0K2AsyHGd1hIdBcBjdj/sDwvH/9GfPukxwRaGvYkCNbcBqNBgEBAZg+fbrNGASOLMauGPK78847MXHiROGfkCMXefeCX5cEoR9i/pDW19e7pyK9GHlo7o477rBb3pGGbw25f++KRVYeffRRaDQa1fUfuSBwP4Y7nM2UIMXNmD90fFZef8ZaxF5ISIjNGBbOPffcgytXrmDatGldvnZISAiioqIQGBjoFKeiNfj/u9rq1dyP5Y48GSQIbsZ8UgzFJFgKAs+G5KhvJSwszKb/yBZeXl547LHHunWsNXx9fdHa2qqwbriFwJeT9/HxsVhq3h0JVKjL4GbMg1HMPcuMMeTn51ud/9Ef+Oc//wnAc8Xysccew6hRoxSOcJ1OJ+IpuJVAgkBg4cKFir/Ng1Fqa2tRXFyMgoICj4pVMJlM3Z7Fae13umOyjzMwGAxYsWKFIkhKo9EI52FzczMYY6LLwAXBHV0GEgQ3c+edd2L27Nnib3NBkJ2MvTEHnzXee+897Ny50+Zcjq4iZzLuC/Duopz2HfixO0EWQj9l5syZos9r3mWQnYye8oZkjOHKlSsAOldY7s7xajz00EO3Uq1eBx9CbW1txaeffir2c8vBHYJATsVeAn8IWlpaFLMu5fwLnrK0uBxLcfHixS4fb00Q5PUX+wLcQti7d6/qfuoy9GN4v5ExJiwBxpgiNZynWAiyVVNXV4eOjg5UVFTc0mSdwYMHOy1kvLdgLV+jtYViewKyEHoJPj4+8PHxQVtbm0iMcf78eUUZT7EQzOv5X//1XwA6Re/pp5+2e7yahTBq1CjnVK4XYS3q0tpCsT0BWQi9CNnrDMAiv6OnCIK1CLtbmazTF+d32LMQqMvQz+GCYK3heEqX4VaFS81CcDQjkiehZiHcf//91GUgOuF+BGuC4OkWgqP0F0EwtxDWrFmDsLAwkXuxvb3dbspBZ0MWQi+Cx7A3Njaqph/vCxZCd4Or+mKXwdxC4PEHaovI9BQkCL0IecKLHNI8ZMgQAH3DQnDEeuivFgIXPRIEAoCyy1BWVib28zRinmYhqDnNHBE1tUbQFwVBbvg+Pj5iboM8vNrTfgQShF4EF4Tm5mbFOpPccvAUC4HXUy0vgSOipiYIfbHLIAum/Ps0Go0QBxKEfgwXBPNMOXz5c08RBN4tUEswQhbCj8gWgrnguWvokQShF8EfCr4WAQAMHz5cdSFTDmOs182CtLUuoiMp4tQcqq5MVuIuZAvBXPDcNfRIgtCLkP/z+VDTPffcIx6Or776SlG+o6MDu3btwt69e3uVKPBGr1YnR9a6VHsrqq3F4OlY6zIA7otW7Ht32YORVyfijSkwMFAxB0B+QK5evYra2lpcunTJLVFtasj1UMuFeOHCBbvn4L+RL4gzZcoUZ1ax10BdBsImausGhISEYNKkSeLv3h7FKIsXXyItMjJSJEu5fPmyzeMZY+IcDz/8MH7xi19YrOTdV6AuA2EXeSHTqKgoeHt7Kx4WucHJ4tBb0rdzYfLz80NERAQ2bNiAX/ziFw4fL69iddttt2HYsGF9bpYjx5aFwH8zWQj9HFv9SuDHhCPV1dWKhW7+/Oc/u7xujmC+wpK8sKkjcMHz9vZ2aA0GT4YsBMIuciSf2lAbdza+9dZbiv29ZUiSN2hzMZs4caJDx3OrR6fT9WgMvzuwJf5yerWehAShlyGvUiQLAm9QWq1WpCWX8fX1xSuvvKJIxeUOuIVgvurRggULxLa8WpE5XFD64jCjOba6DCQIBAAgNjZWbMtDbXziS1NTE9577z2L41pbW1FfX4/CwkLVcfyewtqirH5+fkLgbDlAZQuhr0MWAmGXuLg4sS03CntTo2XM13roSWyt0sy7Nba6NzxOwd6y7X0B2Qoyj9kgQSAAAKGhoWJbXo6ci4MjS711dz2EW+HTTz/Fm2++KdYntDX34JNPPlH8zRtDRUWF+H39octg6x5xQehp31DfduN6KD//+c9x8eJF3HXXXWIfbyA1NTV2j//oo49wzz33uKx+5rS1taGwsBAA8O233wKw/bCfPn1abF+6dAn79+/HmDFjcOLECbHfWnqxvsbixYtx7tw5TJgwQbG/z1oIO3fuRHR0NHQ6HYxGo8XbwZyioiIYjUbodDoMHz4cu3btsiiTm5uLcePGQavVYty4cThw4ECXr8sYQ0ZGBgwGA/z9/TFr1izFDEOg0/x98sknER4ejoCAACxevNhuYI0ziI6OxuzZsxU+BGur+cTExLh94o+8mAx/26sJAo/E5JO1gM7h0paWFoUYAI4t+94XmDRpEhITEy0EsE8KQk5ODlJTU7F582aUlpZixowZWLBgASoqKlTLl5eX4/7778eMGTNQWlqKZ555Bk899RRyc3NFmeLiYiQlJSE5ORknTpxAcnIyEhMTFenKHbnuiy++iO3bt2PHjh344osvoNfrMXfuXMU6CKmpqThw4ACys7Nx+PBh3LhxA4sWLXJLmLCaCf3www9j6dKlqmblmTNnVOcN/O1vf8Mf//hHp859UBvZUBOEJUuWWOyzVo/+IgjW4CLfpwRh+/btWLlyJVatWoWxY8ciKysLkZGReOONN1TL79q1C0OHDkVWVhbGjh2LVatW4fHHH8dLL70kymRlZWHu3LlIS0vDmDFjkJaWhjlz5iArK8vh6zLGkJWVhc2bN2PJkiWIiYnB3r17cfPmTZHpuKGhAbt378bvfvc73HfffZg0aRL27duHsrIyYR73JAMHDlQ0slmzZmHcuHFWy//pT3/Cm2++qdjX0tKCf//73zh37pxTl51XW2JNTRBszdo0p78LQp+zEEwmE0pKSizi0OfNm2d1JePi4mKL8vPnz8fRo0fFjbFWhp/TkeuWl5ejpqZGUUar1SIhIUGUKSkpQWtrq6KMwWBATEyMzZWYW1pa0NjYqPg4A29vb0VYc1BQkNjmcyDME5KY51WQ/Q/OHJpUm+as1o1Re8itBR+RIPQxQbh69Sra29sRERGh2B8REWHVMVZTU6Navq2tTbzRrJXh53Tkuvxfe2X8/PwUMxDt1R8Atm3bhuDgYPGJjIy0WraryMOQcoNLTEzE7NmzkZKSYvN4efTBmd5rNXFRcwrKZrC9Lkt/cSpao88JAsf8DWAvrbRaefP9jpzTWWXMsVcmLS0NDQ0N4uPM1Y9lP4IsCEFBQZg5c6bqm1p+oGRrxZkPWlcFAbA/DZosBPcMO7pMEMLDw+Ht7W3xNq2trbV4M3P0er1qeR8fHzEmb60MP6cj1+WLhtorYzKZUFdX53D9gc6uR1BQkOLjLOQugaMjC3KYsCwIJpMJOTk52LJlC65du3ZL9XJUEORGbn5fbZXtj6g5FZuamrBz507885//dNl1XSYIfn5+MBqNKCgoUOwvKChAfHy86jFxcXEW5fPz8xEbGyseMGtl+DkduW50dDT0er2ijMlkQlFRkShjNBrh6+urKFNdXY2TJ09arb+r4UlGAMdN6ldeeUXMj5DnSdTV1eHs2bMAoJg12R0cFQSNRiNSyvMGTz4EddS6DMePH8d3332Hjz/+2GXXdeldX79+PZKTkxEbG4u4uDi89dZbqKioEH3dtLQ0XLlyRcTmp6SkYMeOHVi/fj2eeOIJFBcXY/fu3di/f78459q1azFz5kxkZmbiwQcfxF/+8hcUFhbi8OHDDl9Xo9EgNTUVzz//PEaOHImRI0fi+eefx4ABA/DII48A6MxLsHLlSmzYsAFhYWEIDQ3Fxo0bMWHCBNx3332uvG1WkbsEXYk9yMrKwqZNmxRhz7LlwFcK6g6MMfz973+32G9NsLiVY2/olgTBtgPWVSs6ufSuJyUl4dq1a9i6dSuqq6sRExODvLw8REVFAeh848qxAdHR0cjLy8O6devw+uuvw2Aw4NVXX8XSpUtFmfj4eGRnZ+PZZ5/Fc889hxEjRiAnJwdTp051+LoA8PTTT6OpqQmrV69GXV0dpk6divz8fEUM/csvvwwfHx8kJiaiqakJc+bMwZ49e9yWsEMeylNLcQ50iqpaMFd+fr6iP3orIiDDQ5XNsSYIPNiKWxXW6kFORUtBkLt8HR0dLnkOXS7Dq1evxurVq1W/UzNVExIScOzYMZvnXLZsGZYtW9bt6wKdapuRkYGMjAyrZXQ6HV577TW89tprNq/VUxgMBvj5+cFgMFidDRgREYH09HRs2bJFsf/y5cuKh0v2n/A4gpKSEpw6dQqJiYkOzza0lsDDmgUjZwKy5dgkC+FHQeDWQHFxsfjeVS+l/n3XPYyAgABs3LixWxmIa2trFX/L4cb8LX3w4EEAwJEjRzBr1iyHzmtt+NCeIHR0dNicudnfBUG+f21tbQqLaezYsS67Ls129DB8fX2d8naQcxI0Nzcr3vRffvklzp8/bzORCUcWBPlBtSZafP/Vq1dJEGwg/37zlbBmzJjhsuuSIPRRfvnLX3apvNw46+rq8P777+Oll16y6/yTBWHgwIFYtGgRVqxYYbU8d4R9/fXXNgWhv/sQvLy8hCjwbgPPJuXK5DEkCH2UoUOHYvPmzVi5cqVD5bub3l0WBG9vbxiNRowaNcpqeYPBAKDTJDYPrZbp74IAKP0ITU1NwopzZmyLOSQIfRgfHx+x2Ik9rE04q62tRX5+vtU5GXIMgiPDYDzArL6+3mam6P6QQs0ecrQiF2YfHx+XjnL1745aP+BWH569e/cC6OxGJCUlWXwvWwiOODvpze84crQiH5Fxde4LshD6AfaGaB1BLU/jmTNnFDM/HREfEgTHkS0E7lh09f0jC6EfYCtvgqOY+wXq6+vxpz/9SbGPLATnQhYC4RI0Go3Djii+HqM55gFIahGKjgiCu9O9eRJyQpnz588DcH0CXRKEfo65mW9tJqe8piSgPqFp9OjRdq9HFoLjyF0GW0l5nAkJQj9h+PDhACyzG/3617/G+PHjxd8RERGKlGh8/oS5IJiHHYeGhlqdXyGjJggRERH41a9+halTpyI+Ph6rVq2ye57+AL9XbW1tYpaoWro6Z0I+hH7C3LlzERISgrvuuguvvPKK2H/HHXdg2bJlmDJlCjQaDfz9/ZGcnIyysjJMnjwZdXV1+P3vf28Rj2CeuMNcMLoCYwyDBg1yeIi0vyAHJnGxlSfxueSaLj070WsYMGAAZs6cafX7oUOHiu2goCBMnz4dwI8BS/YsBFtBRvagboQ6soXAfTiuDummLkM/xpHRB95lMJlMCr+BM1N7LVq0yGnn6kvIFgIJAuEyFi5cCK1WC6PRaLesnINBFgFn5mS0lZKuPyOHLvP1Qly9xB11GfohsbGxMBqNDoUa81DZ9vZ2tLS0iJBic0HozpRsjisy//QFuCA0NzeLvJeuFk+yEPopXWmE3EqQHYvmXYbJkyc7p2KEgHcPuHWg0WhUM2s7ExIEwi5qgiBbCEFBQZgzZ47D5/vVr34lFpd58MEHnVTLvgdv/HwBXZ1O53JriroMhF1sCcLgwYOxcuXKLj2ogwYNolgDB+CLBPF73RMzQMlCIOyiFpzEuwyTJ08mH4CLGDBggOJvEgSiV8BNVzmlWk9NtunPmPsL1BbQdTYkCIRd+JtKDj7igkBBRa7Dx8dHIbhkIRC9Ajk4idNT8/P7O3LcQU8IAjkVCbvITsUjR44gMDCQugw9hL+/v1iCjwSB6BVwQSgtLbXYRxaCa5EtBPIhEL0CtfUZ+BAkWQiupae7DCQIhF3kVafNIQvBtcgiQIJA9AqGDRtm9TsSBNcix3i4cj0GDgkCYRdbfVcSBNdSVVUltl2dLQkgQSAcwNYc/FuZ5UjYJy4uTmz3hIVAowyEXSg02X3ExMQgNDQUAwcO7JHrkSAQRC9Go9GImaE9gUvtvbq6OiQnJyM4OBjBwcFITk5GfX29zWMYY8jIyIDBYIC/vz9mzZqFU6dOKcq0tLTgySefRHh4OAICArB48WJcvny5y9euqKjAAw88gICAAISHh+Opp55SRONdvHgRGo3G4nPo0KFbui+eyJ133unuKhA9gEsF4ZFHHsHx48dx6NAhHDp0CMePH0dycrLNY1588UVs374dO3bswBdffAG9Xo+5c+eKJBEAkJqaigMHDiA7OxuHDx/GjRs3sGjRIsXS5fau3d7ejoULF+KHH37A4cOHkZ2djdzcXGzYsMGiToWFhaiurhafe++91wl3x7NISEgAoHQizpo1y021IVwGcxGnT59mANiRI0fEvuLiYgaAnT17VvWYjo4Optfr2QsvvCD2NTc3s+DgYLZr1y7GGGP19fXM19eXZWdnizJXrlxhXl5e7NChQw5fOy8vj3l5ebErV66IMvv372darZY1NDQwxhgrLy9nAFhpaanDv7u5uZk1NDSIT2VlJQMgzunJNDU1sbq6OpaRkcEyMjLcXR3CjIaGhlt+1lxmIRQXFyM4OFiRR37atGkIDg62ugpNeXk5ampqMG/ePLFPq9UiISFBHFNSUoLW1lZFGYPBgJiYGFHGkWsXFxcjJiYGBoNBlJk/fz5aWlpQUlKiqNfixYsxcOBATJ8+HR988IHN371t2zbRTQkODkZkZKTN8p6ETqfD7bffjtWrV6taUoTn4zJBqKmpUfWMDhw4EDU1NVaPASwTSUZERIjvampq4OfnJ7LJWCtj79o1NTUW1wkJCYGfn58oc9ttt2H79u344IMPkJeXhzlz5iApKQn79u2z+rvT0tLQ0NAgPpWVlVbLeip33HGHQ6s0EZ5Hl0cZMjIysGXLFptlvvjiCwDqw1WMMbvDWObfO3KMeRlHrm2vTHh4ONatWye+i42NRV1dHV588UX8x3/8h2o9tFptj0xCIQhX0GVBWLNmDZYvX26zzLBhw/Dll1+K5JAy3333ndVU0nq9HkDn21te1qu2tlYco9frYTKZUFdXp7ASamtrER8fL8rYu7Zer8fnn3+u+L6urg6tra02U11PmzYNb7/9ttXvCcKjcY47wxLu2Pv888/FviNHjjjkVMzMzBT7WlpaVJ2KOTk5okxVVZWqU9HWtblTsaqqSpTJzs5WOBXV2LBhA4uOjnb4PjjD0UMQjuCMZ03DGGOuEpsFCxagqqoKb775JoDO9NtRUVH461//KsqMGTMG27Ztw89+9jMAQGZmJrZt24Z3330XI0eOxPPPP49//etf+OqrrxAYGAgA+M///E8cPHgQe/bsQWhoKDZu3Ihr166hpKRELG9u79rt7e2YOHEiIiIi8Nvf/hbff/89HnvsMTz00EN47bXXAAB79+6Fr68vJk2aBC8vL/z1r3/FM888g8zMTEVXwhYNDQ24/fbbUVlZ2SOhp0T/pbGxEZGRkaivr0dwcHD3TuI0eVLh2rVr7NFHH2WBgYEsMDCQPfroo6yurk5RBgB79913xd8dHR0sPT2d6fV6ptVq2cyZM1lZWZnimKamJrZmzRoWGhrK/P392aJFi1hFRUWXr33p0iW2cOFC5u/vz0JDQ9maNWtYc3Oz+H7Pnj1s7NixbMCAASwwMJAZjUb2hz/8oUv3gA870oc+PfWprKzs0jMq41ILgQA6OjpQVVWFwMBACycmV3SyHpTQfbGOrXvDGMP169dhMBi6PemM5jK4GC8vLwwZMsRmmaCgIHrwVaD7Yh1r96bbXYX/h+auEgQhIEEgCEJAguBGtFot0tPTKZDJDLov1nH1vSGnIkEQArIQCIIQkCAQBCEgQSAIQkCCQBCEgASBIAgBCUIPMGzYMItErZs2bVKUsZfwFQDKysqQkJAAf39/DB48GFu3bkVfHCTauXMnoqOjodPpYDQa8cknn7i7Si4lIyPD4vngqQAAOC3xsEN0exYE4TBRUVFs69atrLq6WnyuX78uvm9ra2MxMTFs9uzZ7NixY6ygoIAZDAa2Zs0aUaahoYFFRESw5cuXs7KyMpabm8sCAwPZSy+95I6f5DKys7OZr68v+/3vf89Onz7N1q5dywICAtilS5fcXTWXkZ6ezsaPH694Pmpra8X3L7zwAgsMDGS5ubmsrKyMJSUlsUGDBrHGxkZRJiUlhQ0ePJgVFBSwY8eOsdmzZ7O7776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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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0prpeA4mXsEh9fT0AuGQ8lYvX0WfegoICiMViiEQiBAYGWly69cKFCwCANWvW4JVXXnG8sl4EiZewCBevK6KZnF2ziAvzwQcfBAA8+uijAIDGxkYMHDgQQHt9v/76a4SFhSE6OloYn76TIfESFnGH53VEvAaDAVevXgUAjBo1CgAQEhKCiooKtLa2IikpCRKJBADw3XffCb/79ttvna32bQ+FRxIWsWfCfVc4I96amhoYDAZIJBKT5vKWLVuE96GhoSguLoZKpRK26XQ6XL16Fb/61a+cqPntDXlewiK1tbUAXCteR555+RDRgAEDrAaK3HPPPcL769evC+VcvnzZ7vK8CRIvYUZzc7MQDeXJZjNjTGgKDxkyxKqdRqOBTqdDXV0dZs6cibFjxwIAqqurHayxd0Di9SLy8vLwzTffuL0cHobYu3dvk/BGR3G0w+rChQuQy+VgjCEiIsKqnY+PD4KCglBfX48pU6Zg6NChAIDKykrHK+0FOCTejRs3Ijw8HEqlEpGRkThy5Ein9tnZ2YiMjIRSqcSgQYOwefNmM5uMjAxERERAoVAgIiICe/bssbvc3bt3IyEhAQEBARCJRDh58qQjh3dbotPpsH//fhw9etTtQfkVFRUA2juGXIGjnveLL74AAKhUqi6b70899RTeffddAEBQUBAA4OrVq2aLepeXl9tVh9sZu8W7Y8cOLFmyBG+88QYKCwsRFxeHadOmoayszKJ9SUkJpk+fjri4OBQWFmLFihVYtGgRMjIyBJvc3FzMmTMHycnJOHXqFJKTk5GYmGjS3W9LuTdu3MC4ceOwZs0aew/rtuOtt94yufBKS0uh1+shlUrdflPi0Ur9+/d3yf64eHmidluorq4W7JOSkrq09/f3R0BAgPAeaA/xNC6zsbER8+fPv2NmJNkt3vfeew/PPfcc5s+fjxEjRmDdunUICQnBpk2bLNpv3rwZAwcOxLp16zBixAjMnz8fzz77rHCXBIB169ZhypQpSE1NhUajQWpqKiZNmoR169bZVW5ycjL+9Kc/YfLkyTYdS0tLCxoaGkxetwPHjx9HeXk5Dh48KGzj3hCAW0MB29racPHiRQDtnUSugItXr9fbPK+XH6NCobC7BSCRSIQyeYJ4ADhy5AiuXLmCr776Cnq9Hh988IHT0xQ9iV3ibW1tRX5+PuLj4022x8fHIycnx+JvcnNzzewTEhKQl5cnhLxZs+H7dKRcW1i9ejXUarXwclUz0Rn0ej127dqFkJAQ5OTkCBcXD1cEgOLiYrPmoKv4/PPPIZPJAADBwcEu2adCoRD2aXwcncHHdnnnk71w72t8Q87Ly0NiYiJ++OEH7N+/H2+++aYQM+2N2CXe2tpa6PV6BAYGmmwPDAy02rNXXV1t0b6trU0YjrBmw/fpSLm2kJqaCq1WK7xuh+ehPXv2CBMBDAYDrl27BgDCDB++nV/crqS5uRmlpaUA2ntwueCcRSQSCWO0xnmfrXH9+nXh0cDRprufnx+AX8RbX18v3PAkEgkOHjyIl19+GQcPHnTbjdDdONRh1TFQnTHWafC6JfuO223Zp73ldoVCoYCfn5/Jy5O0tLSYBdXzm1PHJn1xcbHLyz9//rxwPh9//HGX7psHS/CbUWd8+umnwpju3Xff7VB5HcX7v//9T/hOJpOZ/K+9dUjJLvEGBARAIpGYHWxNTY2ZV+QEBQVZtJdKpcLd2JoN36cj5Xoj/Pj0ej1+/etfA4DQIccvQo1GAwBu8bw8c0Z4eLhLhoiM4b3FXXnepqYm4VgVCoXDc4k7ijcvL8+qbce5wt6CXeKVy+WIjIxEZmamyfbMzEzExsZa/E1MTIyZ/YEDBxAVFSU0y6zZ8H06Uq43wsU7ePBg9OvXD0B7lJBOpxM6eniuZOOUMK6AMSZ4cz7tzpVw8XbleY1bHk8//bTDLSteXlVVFRhjwrk13h+PxPLWIUW7m83Lli3Dxx9/jK1bt+LcuXNYunQpysrKkJKSAqD9OXLu3LmCfUpKCkpLS7Fs2TKcO3cOW7duRXp6usm0rcWLF+PAgQNYu3YtfvrpJ6xduxZZWVlYsmSJzeUCvzwrnT17FkB7M/DkyZNe0yw6f/48ACAsLExoLtbV1aGpqQlA+7Ma71RztectKysTslHwG4cr4a2srupdUFAAAIiNjXWqt5uP9V67dk1ovRgMBixYsEDoKH3ppZcAtA8xOpMowFPYPTFhzpw5uHbtGt5++21UVVVh5MiR2Ldvn5AWtKqqymTsNTw8HPv27cPSpUuxYcMGBAcH44MPPsBvf/tbwSY2Nhbbt2/Hm2++iT/+8Y8YPHgwduzYgQceeMDmcoH2Qf3f//73wmc+Prhy5UqkpaXZe6jdisFgwKVLlyCRSBAaGiqMWer1eiGXlJ+fnyCC1tZW3Lx502QlA71ej8OHD+PMmTPChWkr//3vfwG0hyHynFOuhB9Pc3OzWb05xscaGRnpVHl33XUXJBIJ9Hq90GT29fVFYGAgbty4Ab1ej+DgYMEmKysLjzzyiFNldjcO/ZdefPFFvPjiixa/++STT8y2TZgwQbijWmP27NmYPXu2w+UCwDPPPINnnnmm033cruTn5wtT2/r37w+ZTAYfHx/cvHlTyBLBwxV79+6NpqYm1NbWCvNZa2tr8corr0AqlaJ///4oLy+3eejr2rVruHbtGgwGA6ZPn+6W41MoFFCpVLhx44ZJvY3hN32pVOp0TDXv4a6pqRGeafmwU2xsLHx9fQEA9957LwoLC1FQUICZM2c6nTWkO6HY5tsAxpgQkGE8RMObr7ynlF9wvJOOD7UBwD/+8Q9cuXIFISEhEIvFXYasGsOfn1UqlVtXIjAOW7TE6dOnAbRnq3SFiDomiefnMyEhAVOmTAHQ7lg43vJ4xSHx3gZcvXoVt27dgsFgELJEAL8Mk3CR8qwWvAnKRdDa2gqxWIy4uDjht8bC7greVOXJ3dwFHy6yJl7+zM8n3TsLv1lw+E3Pz89PaOWo1WrhvbdNISTx3gb89NNPANojmvgMHMB8Li0fNuHi5QI1HgbhF2B9fT30er1N5XMxuXviOr8ZWfJwN2/eFDqNHFlWxRId11TiLZeOjB8/HoBpCKo3QOK9DTh16hQACGO7nI5BI9zzcpHx5i7vXc/Pz0dycjJkMhkYY52OqRrHGPOL1tGACFvh9a6srDSLauI3EB8fH5eNMffp00fofBs2bJjVpjjvG+DRZd4CidfD3LhxQxBZx2ZrR0/Rsdnc2NiItrY2QcSTJ0/Gww8/bOaZO1JZWYkVK1YAaB/r5MH77hYvb7bqdDqzuvHProqn5owbNw4HDx7stDOUPws3NTXhxo0bLi3fnZB4PQx/3lSpVCY5mADrntfHx0fwKCUlJcL47B/+8AcAv4jbUkAEYwxbtmyBr68vTp8+Lcwg6o7wUJlMZjVY48qVKwB+qburePDBBzF9+vRO47SVSqUwC8mbms4kXg/Dm4uWAvA7joXy52HjQP/CwkIA7bNoeMcL/86SeMvKyoTm43fffYddu3YBAEaOHOn0sdgCP05jz8sYE6KcXB0gIpVK8frrr3dpx4eueJpZb4DE62F4k9dSZ5FIJBKGbkJDQ02e2biH4vHIxmO6XLyW0sAYj7fX19cLEwB4zLS76dhTDsAkqMdT0zL5zYu3RLriypUreO+999xZpS4h8XoQvV4veBxroYCTJ09GUVGRyRASALMJGca/5wKoqalBdXU1du/ejZUrVwKw3CkjEolcNvG+Kyz1OGdnZwNoD0Lx1Ir3vIe7oaHBZPqlJRhj2LZtm8enkZJ4PciFCxeEyfZ8wkFHIiIiIBaLzS5qnmSNYzw2qlarBZF8+OGHKCoqglgsxhdffCHMshk3bhyA9rDMp556qtsii/jYa01NDU6fPo2ioiIhCKWrCDt34uvrKzwXP/3000IPviV+/vln3Lx5E2KxWAgs8QQkXg/C15gNCQmBQqGward27VqzbYGBgZDJZLh58yaGDBliMj4MAGPGjDH7TWFhoTAH+uGHH8bJkycRFRWFwYMHO3kktqNWq4UbxZ49e5CRkSGsQ2QpZLI74TfQMWPG4N///rfVSfrGzXze4egJSLwehD/vdtVZZMkri0QizJ49G1qtFk899ZTZ953t08/PD2KxGNu3b+/2YHyRSISYmBiTzwAwY8YMj8cVG59nmUxmNVeYcZPf0ph1d0HitRNXzqPlHUqOJhQYNmwY/va3v1n8TqVSYe7cuejTpw9mzZplsmAY7zTqzNu7kylTppj0Kg8aNMjpWUSuYPTo0Sa94NYm6fOIOKA9YZ8tqX3cAYnXDmpra/H3v//dJCOhozQ3NwvzdJ3JBmIt5A9o9yQvvfQSRo8ejXnz5gnbO0ZyeYInn3xS8FiPPfaYZyvz/ygUCkyYMEFY9/f8+fNmXrWurk4I4+TPyJ6a0EDitYNPPvkEKpXKrhk7HWloaMBf//pXHD58GEB7vHLH51V34O/vD6VSiYsXL7p9AoIt9O7dGzNmzMDjjz/e6Q2ou5k9ezZmzpwJoD2nWMeeZ/6826tXL2EVh6qqKvznP//p3oqCVgm0mebmZiF0ztHhgcLCQmEVgGPHjgFw/0weY15//XWHF7h2B7dDC8ASMpkMarUaWq0WlZWVFpPVDR8+HP3798epU6eQk5MjDOd156MIeV4b4T3DABzKuN/W1oavvvrKZBtjDNHR0U7XzR66w8vfCfBx3469yTx8cuDAgcJzO2MMI0eORFZWVrfWkcRrI8bRSg0NDXZn2q+oqBCm6PXr1w+VlZWYNGmSw9kRCffCI974HGOgXaS82dyvXz+zUM6ioiKTzy0tLW4N4qBms43odDoh3xFjDA0NDSgsLMSoUaNsCqbnd2yNRoM5c+a4u7qEkxjnZDMYDBCLxdi0aRNEIhFEIhHuvvtuiMViRERECAEdLS0taG1thU6ng0qlwo4dO1BfX49Fixa5pY7keW1k4sSJePXVV4Xnn8uXL2Pnzp1C2GFX8Fkzrlq8i3Av/fv3h1gsBmMM5eXlaG1tFeKx+/XrJ8SE/+53v8OpU6eEnucjR45g2rRpOH/+PC5duoS6ujqXp+nlkHjtQKFQCOLbu3cvAgMDcffddyM3N7fT3zHGhIB3T0cREbYhFosxbNgwAO1NZ+Opgh1bTjt37hSG+44ePYpJkyZh+/btwiyvjqtguKyObtnrHYyl2T9dDR2dOHECbW1tkEgk5Hm9iHvuuQdAe6YS3mEZFhZmNu9ZIpF0OlbvrhEFEq+ddPwnGQwGNDc3dxrjyqfhDRo0SLgbE7c/Q4YMAdC+TCgPlRw0aJBFW2tZSCZPnuy2GzaJ106M/xFDhw4V/mnWvG9LS4sgbG9L6t3TUSqVgpflubOtJQvQaDQwGAzQ6/UIDQ1FbW0t5HK5SRy3qyHx2olarUZUVBQYY3jkkUeEhbzPnDljcckMPsTk6+t7W0USEbbR0Wtay7Hl5+eHoUOHIi4uDs888wwGDhyIxx57TOjYcgc0VOQAM2bMwIwZMwC0N61EIhEYY9i8eTOqqqqg0Wjw7LPPAvill/l2WLibsJ/Ro0cL2Ur69u1rcZkWztNPPy28f/XVV91eN/K8TiKRSPDQQw8BaA/eUKlUKC8vF2ae8JxIHXMIE97BkCFDIJFIUFdXJ9ywbxdIvC4gLi7ObHGur776Ci0tLcIQQ3dOeCdch0gkwtKlSxEXF2c124mnIPG6AJFIhOXLlyM8PFxYcrSpqQnffPMNGGPo3bu32eoHhPegUqlMVrW8XSDxugiJRIK5c+ciMDBQSEHD07KOGDHCk1Uj7lBIvG6ALyXJccdi1QRB4nUDgYGBQgZ+4JfBfoJwJSReNxEfHw/GGEJDQ2l8l3ALDol348aNCA8Ph1KpRGRkZJexvdnZ2YiMjIRSqcSgQYOwefNmM5uMjAxERERAoVAgIiICe/bssbtcxhjS0tIQHByMXr164aGHHnJbUHhX3H///XjhhReQnJzskfKJHgCzk+3btzOZTMY++ugjdvbsWbZ48WKmUqlYaWmpRfvi4mLm4+PDFi9ezM6ePcs++ugjJpPJ2K5duwSbnJwcJpFI2KpVq9i5c+fYqlWrmFQqZceOHbOr3DVr1jBfX1+WkZHBioqK2Jw5c1i/fv1YQ0ODTcem1WoZAKbVau09LQRhF6641uwW79ixY1lKSorJNo1Gw5YvX27R/rXXXmMajcZk24IFC1h0dLTwOTExkU2dOtXEJiEhgSUlJdlcrsFgYEFBQWzNmjXC97du3WJqtZpt3rzZpmMj8RLdhSuuNbuaza2trcjPz0d8fLzJ9vj4eOTk5Fj8TW5urpl9QkIC8vLyoNPpOrXh+7Sl3JKSElRXV5vY8FSe1urW0tKChoYGkxdBeAt2ibe2thZ6vd5sWlxgYKDV3LXV1dUW7dva2oQE19Zs+D5tKZf/taduq1evhlqtFl4Uf0x4Ew51WHVcloL9//o39th33G7LPl1lw0lNTYVWqxVenlzxjSDsxa5ZRQEBAZBIJGaerKamxmomgaCgIIv2UqlUWEfWmg3fpy3l8tXnqqurTYIiOqubQqEwybPLbyrUfCbcDb/GmDPrHNn7kDx27Fj2wgsvmGwbMWJEpx1WI0aMMNmWkpJi1mE1bdo0E5upU6eadVh1Vi7vsFq7dq3wfUtLi10dVuXl5QwAvejVba/y8nKbrk1L2D2fd9myZUhOTkZUVBRiYmKwZcsWlJWVCQH5qampqKiowGeffQYASElJwfr167Fs2TI8//zzyM3NRXp6OrZt2ybsc/HixRg/fjzWrl2LRx99FHv37kVWVhaOHj1qc7kikQhLlizBqlWrMHToUAwdOhSrVq2Cj48PnnzySZuOLTg4GOXl5fD19bXY1G5oaEBISAjKy8vN8hj1ZOi8WKaz88IYQ2Njo9XJ/TbhiOI3bNjAQkNDmVwuZ2PGjGHZ2dnCd/PmzWMTJkwwsT906BAbPXo0k8vlLCwsjG3atMlsnzt37mTDhw9nMpmMaTQalpGRYVe5jLV735UrV7KgoCCmUCjY+PHjWVFRkSOHaBEaSrIMnRfLuPu8iBjz0OKiXkhDQ4Owhg15mF+g82IZd58Xim0mCC+FxGsHCoUCK1eu9Nii1LcrdF4s4+7zQs1mgvBSyPMShJdC4iUIL4XESxBeComXILwUEi9BeCkkXguEhYUJK6Dz1/Lly01sysrK8Mgjj0ClUiEgIACLFi1Ca2uriU1RUREmTJiAXr16oX///nj77bedC0S/DbE3JZK3k5aWZnZt8EkxAGxKxdTS0oKXXnoJAQEBUKlUmDVrlrAsjl24JW7LywkNDWVvv/02q6qqEl6NjY3C921tbWzkyJFs4sSJrKCggGVmZrLg4GC2cOFCwUar1bLAwECWlJTEioqKWEZGBvP19WXvvvuuJw7JLdibEulOYOXKleyee+4xuTZqamqE721JxZSSksL69+/PMjMzWUFBAZs4cSK77777WFtbm111IfFaIDQ0lL3//vtWv9+3bx8Ti8WsoqJC2LZt2zamUCiEONaNGzcytVrNbt26JdisXr2aBQcHM4PB4La6dyf2pkS6E1i5ciW77777LH5nSyqm+vp6JpPJ2Pbt2wWbiooKJhaL2f79++2qCzWbrbB27Vr07dsX999/P9555x2TJnFubi5GjhxpMiMkISEBLS0tyM/PF2wmTJhgEl2TkJCAyspKXL58uduOw104khLpTuHixYsIDg5GeHg4kpKSUFxcDMC2VEz5+fnQ6XQmNsHBwRg5cqTd542W+LTA4sWLMWbMGPj7++PEiRNITU1FSUkJPv74YwCW0/b4+/tDLpebpOXpuDIg/011dfVtt2iVvTiSEulO4IEHHsBnn32GYcOG4eeff8Zf/vIXxMbG4scff+w0FVNpaSmA9v+9XC6Hv7+/mY29563HiDctLQ1vvfVWpzY//PADoqKisHTpUmHbvffeC39/f8yePVvwxoB5uh0AZil3bEn/4+3YmxLJ25k2bZrwftSoUYiJicHgwYPx6aefIjo6GoBj58SR89ZjxLtw4UIkJSV1amNtDV3+T7l06RL69u2LoKAgHD9+3MSmrq4OOp3OJC2PpbQ9gPmd2RtxJCXSnYhKpcKoUaNw8eJFPPbYYwA6T8UUFBSE1tZW1NXVmXjfmpoaxMbG2lV2j3nmDQgIgEaj6fSlVCot/pav9sf/ITExMThz5gyqqqoEmwMHDkChUCAyMlKwOXz4sMmz8oEDBxAcHHxHLLQtl8sRGRmJzMxMk+2ZmZl2X4TeTEtLC86dO4d+/fohPDwcQUFBJuektbUV2dnZwjmJjIyETCYzsamqqsKZM2fsP292dW/1AHJycth7773HCgsLWXFxMduxYwcLDg5ms2bNEmz4UNGkSZNYQUEBy8rKYgMGDDAZKqqvr2eBgYHsiSeeYEVFRWz37t3Mz8/vjhwqSk9PZ2fPnmVLlixhKpWKXb582dNVcxsvv/wyO3ToECsuLmbHjh1jM2fOZL6+vsIxr1mzhqnVarZ7925WVFTEnnjiCYtDRQMGDGBZWVmsoKCAPfzwwzRU5Ary8/PZAw88wNRqNVMqlWz48OFs5cqV7MaNGyZ2paWlbMaMGaxXr16sT58+bOHChSbDQowxdvr0aRYXF8cUCgULCgpiaWlpd8wwEaer1ER3GnzcViaTseDgYPab3/yG/fjjj8L3tqRiam5uZgsXLmR9+vRhvXr1YjNnzmRlZWV214Xm8xKEl9JjnnkJ4k6DxEsQXgqJlyC8FBIvQXgpJF6C8FJIvAThpZB4CcJLIfEShJdC4iUIL4XESxBeComXILyU/wMIKbN62O1H0gAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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dwo4dO1BZWYna2lp88cUXqKqqwg8//GD3aZjbt2/j1q1bAIDw8HC75mEN067zUGp5iQhlZWX45JNPzEbb/Oyzz7B//36n3UXC5pZ33759SE9PR3Z2NuLi4vDBBx9g8eLFKC8vt/iNXlVVhcceewwrVqzAnj17cOrUKaxatQqjRo3C008/DQBQq9VITk7Gm2++iWXLluHQoUNISkrCyZMnERMTY/Vy3377bbzzzjv429/+hokTJ+J3v/sdFi5ciIqKin5pBW7duoWRI0fi1q1byMvLw82bN4XwdveHP/zB7LmHhwfS09PN6mA0GtHR0QEfHx8YDAZ88skn+O9//yusKwD84x//EMqbWs3x48cjNTXV5rpXVlYC6LqfrlKptPn91vL19cWtW7cGfaeVXq/Hhx9+iPr6+h6vjRw5Es3Nzejs7ER5eTmmT5+OiRMn9nsdJGTjjnlMTAwiIyOxfft2YdrkyZPx1FNPYfPmzT3Kv/LKKzh8+DC+++47YVpaWhq+/vpr4fK85ORkNDc3Iy8vTyizaNEiBAQEICcnx6rlEhFUKhXS09PxyiuvAOga0iU4OBhbtmzBypUr77luzc3NUCgU0Gq1GD58uNlrp06dQmFhoTWbqFcZGRnIy8vDxYsXHerpXbNmDQICAqwubzQa8e677+L27duYO3cuHn30UbuXfS979+5FRUUFlixZgujoaKctR0w3b95Edna2xdc8PDzw8ssvo62tDTt37kRHRweio6OxZMkSs3J9fdasZVPLq9PpUFJSgoyMDLPpCQkJZhe4d6dWq5GQkGA2LTExEbt27YJer4dUKoVarca6det6lHnvvfesXm5VVRU0Go3ZsuRyOeLj43H69GmL4e3o6BDGaQK6NqgtiAhSqRT33Xcfrl+/DgCIjY01u2bY29tbOO+ZlZUFAL0GNzw8HHK5HF9//TV8fHygUCjw8MMPY9iwYbh06RK+/fZbAF2t6KxZs6yqX0tLC/7+97/j9u3bAOCUFqC7oXC66NixY2bPH3jgASxcuBBSqVT4UvXx8cGLL76IpqamXu/v5CibwtvQ0ACDwYDg4GCz6cHBwdBoNBbfo9FoLJbv7OxEQ0MDRo8e3WsZ0zytWa7pX0tlTL2sd9u8eTNef/31vlZZEBcXB4VCgerqagQFBWHatGmQy+XCDbaampoglUrh5+eHhIQEGI1GGI1GeHl54fLly9izZ4/F0BoMBgwfPhzJyckIDQ1Fe3s7PD09MW/ePLPd7KlTpyI4OBhffvkl8vLyMG3aNCEoluj1emzdutXsCykkJOSeA6c7ys/PD8DgDW9bWxsqKioAABMmTMDzzz/f65fx6NGjMXr0aKfVxa7e5rsrS0R97gZaKn/3dGvm2V9lTDZu3Ij169cLz5ubmxEaGtrrekRERCAiIsLiayNGjDB77uHhIQT7gQcewMqVK1FWVgYiwvz584VbYt7N29sbTzzxhMXXpk2bhqNHj4KIkJOTg5/97GcAunaLf/zxRwQGBkKn04GIsHv3brPgPvjgg0hKSup13fqLr68vgMEZ3itXruDw4cMwGo0IDAzsM7iuYFN4g4KC4Onp2aOVra+v79HimSiVSovlvby8EBgY2GcZ0zytWa6pE0aj0Zh92/VVN7lcbtMYTY7oj29hhUKBRYsWIS8vDzU1NaiurkZAQAD27duH2tpaxMfH48yZM2aXJ44YMQILFizo9Uunv5la3jt37rhkea7S2NiIPXv2CM9jYmJEDS5g46kimUyGqKgoFBQUmE0vKCjA7NmzLb4nNja2R/n8/HxER0cLF7L3VsY0T2uWGxYWBqVSaVZGp9OhqKio17q5o1mzZglfeh999BHeeecd1NbWAug6Jdc9uBMmTMDatWtdFlxg8LW87e3t+PLLL/H++++bTXflNu2NzbvN69evR2pqKqKjoxEbG4udO3eiuroaaWlpALp2RWtra7F7924AXT3LW7duxfr167FixQqo1Wrs2rVL6EUGgLVr12Lu3LnYsmULnnzySXz66acoLCzEyZMnrV6uRCJBeno63nrrLTz44IN48MEH8dZbb8HX1xfPPfecQxtpoJk4caJZp1hv4uLiXFAbc+7Y8n7++ecoKSmBXC5HR0cHJk2ahJSUFFy5csWstQWAuXPnIjo6us/+BlexObzJycm4desW3njjDdTV1SEiIgJHjhzB2LFjAQB1dXWorq4WyoeFheHIkSNYt24dtm3bBpVKhffff184xwsAs2fPxt69e/Gb3/wGr732GiZMmIB9+/YJ53itWS4AbNiwAW1tbVi1ahUaGxsRExOD/Pz8QXelz5w5c6BWqyGRSPDkk0/Cy8sLd+7cQV5eHoKDg7Fy5Uq0t7eL8gHr3vLeqy9kIKioqEBJSQkACGceKioqenRkyuVypKWl9ejbEJPN53kHs/449+YqRASj0dhrx5dYdDqdcL4/IyPDZX0K9jhx4kSP0z6WxMXFYf78+UIHZH9w+XleNnBIJJIBF1ygq3/C09MTBoMBra2tAza8ly5dMgvumjVr4O/vj8bGRuzcuROdnZ2YOnUqFixYYNMFMa7E4WX9ztfXFy0tLbhz586A/OCXlZXhwIEDwvNVq1YJ9Rw1ahReffVVsapmE/5JIOt3w4YNAzAwe5xv376N3Nxc4fmGDRswatQoEWtkPw4v63cDuce5tLRUuEjoF7/4xYDoNbYXh5f1u4F8rvfKlSsAuu457MxLF12Bw8v6nSm8A63l7ezsRFVVFYCuS1bdHXdYsX43EH+c8O677wrXent4eLjtcW53HF7W7+7ebdbpdPjzn/8MnU6Hl156SejQcpWCggKzH2lER0cP+ItHrMG7zazf3d3yHj58GLdv34ZOp8OJEydcWhej0Wg2NM2ECRMwf/58l9bBWbjlZf2u+zEvEeHy5cvCa+fOnUN8fLwQcGdqa2tDcXEx2tvbIZfL8fLLLzt0I7WBhlte1u+6h7exsREdHR3w8PCAr68viAhHjhxxeh2MRiO2b98u/IAjJiZmUAUX4PAyJzC1qnq9HpcuXQLQNfCdaaD38vJy1NTUOLUO1dXVZoMDzpgxw6nLEwOHl/U7uVwu3DHQdLw5ceJEREVFCWVMo1k6S/dd9WeeeWZA/Rqov3B4Wb+TSCQICgoCAGGs6DFjxiAkJASxsbEAgOLiYmFQPGvcvn0bBoPB6vKm8C5fvhxTp061+n3uZHAdBLABIzAwUBhR09PTUxhbe968eTh79iwMBgP++te/4qWXXrrnaZuWlha89957kMvlePjhh7FgwQKzX1QZjUacOnUKTU1NOHr0KBQKhTDPcePGOWcFBwAOL3MK01A9QNdA8aawyWQyLFmyBIcPH0ZjYyOuXbt2z4BVVlbCaDSira0NarUaZ8+ehVQqxYgRI+Dl5YUffvhBKNt99zggIGDQDcTQHe82M6fovqu6cOFCs9dmzJghdCB9/PHHeO+999DU1NTrvLoP2A90DZfb3t4OjUZjFty7LV++3I6auw9ueZlTBAUFYe7cuQgJCbF4KeJPfvITXLhwAQCg1Wrx8ccfY82aNWa70J2dndiyZYtwvycvLy8oFArhONrE19cXv/zlL3Hjxg1MmDDBiWs1sHB4mdP0dVuVkSNHIioqShg/qqmpyWyYXiLC22+/LQQ3NDRUGKdar9ejtbUVN27cwL///W88++yzGDZsmMsvuxQbh5eJ5vHHH8eSJUuQk5ODyspKVFRUCOHNz8+HXq8H0DXm9QsvvCC8TyqVQqFQQKFQOP32LQMZh5eJSiKRIDw8HJWVlTh27BhCQ0Ph7e2Nr776CgDw0EMPYdmyZSLXcmDiDismOtOVVwCwZ88e/POf/xSeO/OOhu6Ow8tE5+fnh6eeegpA1znb+vp6EBF+/vOfD8oro/oLh5cNCNOnT8czzzwjPA8JCcGYMWNErNHAx+FlA8aUKVOEcZ5nzpwpcm0GPr5jQjfudMeEwYqIoNFooFQqB8VoF73hOyawQUcikbj9qI6uwrvNjLkpDi9jborDy5ib4vAy5qa4w6obU8d79zF+GXMG02fMkZM9HN5uTAOWhYaGilwTNlS0tLRAoVDY9V4+z9uN0WjE9evX4e/vb/EcY3NzM0JDQ1FTU8Pngbvh7WJZX9uFiNDS0gKVSgUPD/uOXrnl7cbDw8OqS/KGDx/OH1ILeLtY1tt2sbfFNeEOK8bcFIeXMTfF4bWBXC7Hpk2bhIvnWRfeLpY5e7twhxVjbopbXsbcFIeXMTfF4WXMTXF4GXNTHF7G3BSH14Jx48ZBIpGYPTIyMszKVFdX44knnoCfnx+CgoKwZs0a6HQ6szKlpaWIj4+Hj48PQkJC8MYbbzh0IfpAlJ2djbCwMHh7eyMqKgrFxcViV8mpMjMze3w2lEql8DoRITMzEyqVCj4+Ppg3bx7KysrM5tHR0YGXXnoJQUFB8PPzw9KlS/u851KviPUwduxYeuONN6iurk54tLS0CK93dnZSREQEPfroo3T+/HkqKCgglUpFq1evFspotVoKDg6mlJQUKi0tpdzcXPL396c//vGPYqySU+zdu5ekUin95S9/ofLyclq7di35+fnRtWvXxK6a02zatImmTp1q9tmor68XXs/KyiJ/f3/Kzc2l0tJSSk5OptGjR1Nzc7NQJi0tjUJCQqigoIDOnz9Pjz76KE2fPp06OzttqguH14KxY8fSu+++2+vrR44cIQ8PD6qtrRWm5eTkkFwuJ61WS0RE2dnZpFAoqL29XSizefNmUqlUZDQanVZ3V5o1axalpaWZTQsPD6eMjAyRauR8mzZtounTp1t8zWg0klKppKysLGFae3s7KRQK2rFjBxERNTU1kVQqpb179wplamtrycPDg7744gub6sK7zb3YsmULAgMD8fDDD+P3v/+92S6xWq1GREQEVCqVMC0xMREdHR3CjbPUajXi4+PNrq5JTEzE9evX8f3337tsPZxFp9OhpKQECQkJZtMTEhJw+vRpkWrlGpWVlVCpVAgLC0NKSgquXr0KAKiqqoJGozHbJnK5HPHx8cI2KSkpgV6vNyujUqkQERFh83bjXxVZsHbtWkRGRiIgIABnz57Fxo0bUVVVhQ8//BAAoNFohBtimQQEBEAmk0Gj0Qhl7r5ptOk9Go0GYWFhzl8RJ2poaIDBYOixHYKDg4VtMBjFxMRg9+7dmDhxIm7cuIHf/e53mD17NsrKyoT1trRNrl27BqDr/14mkyEgIKBHGVu325AJb2ZmJl5//fU+y/znP/9BdHQ01q1bJ0x76KGHEBAQgGeeeUZojQFY/L0vEZlNv7sM/a+zajCNR2xpHQfT+t1t8eLFwt/Tpk1DbGwsJkyYgI8//hiPPPIIAPu2iT3bbciEd/Xq1UhJSemzzN0tpYnpP+Xy5csIDAyEUqnEmTNnzMo0NjZCr9cL37pKpbLHN2l9fT2Ant/M7igoKAienp4W13EwrJ+1/Pz8MG3aNFRWVgr3W9JoNGZjT3ffJkqlEjqdDo2NjWatb319PWbPnm3TsofMMW9QUBDCw8P7fHh7e1t8r+kO7qb/kNjYWHz77beoq6sTyuTn50MulyMqKkooc+LECbNj5fz8fKhUql6/JNyJTCZDVFQUCgoKzKYXFBTY/CF0Zx0dHfjuu+8wevRohIWFQalUmm0TnU6HoqIiYZtERUVBKpWalamrq8O3335r+3azqXtrCDh9+jS98847dOHCBbp69Srt27ePVCoVLV26VChjOlW0YMECOn/+PBUWFtKYMWPMThU1NTVRcHAwPfvss1RaWkoHDx6k4cOHD8pTRbt27aLy8nJKT08nPz8/+v7778WumtP86le/ouPHj9PVq1fpq6++oscff5z8/f2Fdc7KyiKFQkEHDx6k0tJSevbZZy2eKhozZgwVFhbS+fPnaf78+XyqqD+UlJRQTEwMKRQK8vb2pkmTJtGmTZvozp07ZuWuXbtGS5YsIR8fHxo5ciStXr3a7LQQEdE333xDc+bMIblcTkqlkjIzMwfNaSKTbdu20dixY0kmk1FkZCQVFRWJXSWnMp23lUqlpFKpaPny5VRWVia8bjQaadOmTaRUKkkul9PcuXOptLTUbB5tbW20evVqGjlyJPn4+NDjjz9O1dXVNteFf8/LmJsaMse8jA02HF7G3BSHlzE3xeFlzE1xeBlzUxxextwUh5cxN8XhZcxNcXgZc1McXsbcFIeXMTf1fxYBmCzMVJHtAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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06FFoNBrccsstvZq3GmR5CQe44Lh4uZWVilfaf+WoWV6eV2RkpGzGj7vi3bNnj+px6Vi0dKjIWX68r86prKx0Wa43HD16FIDzOvc2ZHkJB7gAuCCk/UlPm81KTzOHW2llGCaH767Q2dmJCRMmiCErZb68ad9Ts5kPdXE++eQTTJo0STWtN5w/f168769N2snyBildXV344x//iD/96U8OUU7KZrPUSrpqNnPxSgXpjXibm5vFtihhYWGqlozXURn4oRZ1VVhYiG3btjnkoSybMYaamhqXrYEjR46oLlLwwQcfiPfKsM2+gsQbpJw+fRrNzc1oaGhwsEpK8fa35eX9V87JkycdJjvwZjCvh7Nm84cffoiTJ0+qru6htJD//ve/8c477+DNN99UDds8d+4c/vnPf6KgoMDhe2W5p06dcvh9b0PiDVK2b98u3is3LXfH8qqJVypIfnN7I15pKCZHae2U0xJ5XZVbqhw5ckT2efny5aIJrozG4oJrbW2V7QihVi/pNWttbRWTNYYOHQrgav+3LyHxBiHKoRKleJUOK3ctL9+nlzEmLKUyL44r8fIyXB3jkyOU4pU2m5XWNiUlBQaDQYR1Ki2v1LpL+7Ac5Qwmzu7du8X76dOnA+gelurrGU4k3iBEaXGk82AZY04tb2trq8x7rEQaccVvdGfeaVfi5fUbPnw4EhISRNlS9u/fr1pHm80mRKvsDvDjanHQjDE0NzeLz2pzg6Uea+lvpdZ9zJgx0Gg06OjocPBw9zYk3iDh3LlzYgaPUrxSYXR2dgqLobS8fFfAkJAQ1THSkJAQIUplWcq5v67Ey2/6e++9V4wNSy3vd999J97zwA+pZecPDmmLQqPRIDk5GQBULe+lS5dkllcqZI60Dvy8lC0CvV4vHlTKB05vQ+INAqqqqlBQUICioiIAjuKV3oD8xtdoNEJgXBj8ZoyLi3O6SiNvOivFq/ROuxIvF9WgQYOEhZcKgU+SAIA77rgDQPdMJZ4nbzlw8ZrNZvz2t78Vkyy4eKXXQdl1UFptZR2++OILXLlyRfYA+NWvfgXAef+7tyHxBgEHDx4EcHWBNn7TcqslFS+/8fV6vRAob5Jy+AR9Nbh4+UNAbVsUwLl4Ozs7xW+joqJUrRhv0t5xxx2qgR/8HLiFNhqNsmY7F++JEydEK4M/EHi6+vp6hz4z72dzDh8+LOoSEREhJnH0V5w1iXeAo+zL2Ww2ccMZjUYA3cLgNzEXjlSwynm7I0aMcFqeck1mT73NvMkcGhoKg8EgLLa0/8jPh3t2OUrR1NXVyc6Twy0wcNXicmHeeeedol7KZq+yKW2z2cTveN9crR59BUVYDVCsViveeOMNh+MNDQ3iho2Pj8eJEyfQ1dWFjo4O6PV6VfFGREQgLCxMeKmVK2hIkTabbTab6Ed6Kt5BgwZBo9GoipeHNkonOgBy0Vy5ckWIj1tEzvXXX4+IiAi0trbi7bffRlhYmFh/S3puf/3rX7Fw4UJxPvza3HnnnTh48KBs8Xlp3DavR21tLaKjoxEbG+vQ5+8NyPIOUD766CPV41arVSyROmLECBFYwZvOauLVaDSym1PN08yRNpu51eXxx1KciVfa35X+5eKtra0VD4T4+HjZb6Xi5RYxMjLSoWwASEpKEu+lTXVpl+DixYviuvCuhk6nk3U3uDWWPkh4eeXl5di2bZuYd9zbkHgHKLzJqKSpqUlYpKFDhwpxuBKv8rMr8UqbzdIms9LB5Uy8XHS8Sczr19bWBpvNJjsvpRNMGkzCd3lQxkRzxo0b53AsPDwcgwcPFk1n4Krjios3KipKNlGDd0Gki/Epr52r6+ULJN5rlDNnzvRKPvHx8UhPTxdB+J9//rn4zmAwODiEnIlXOoarXEFDLZ1SvEq4dZJGYwFXHzpcdAaDQdSlqalJiCk1NdUhT6l4ubVT9os5Y8eOxRNPPCE7Nm7cOGg0Gtx7771ijS1e3ldffQWg28L2JF6lpSfxBhEfffQRtmzZ4nSXvp7o7OwUYpwzZw6mTZsmc9IA3QEQgHy6H+BcvLyZedNNN7ks29NQSl5fDn9ocQeQRqMRQrZYLKrNVGXZFotFjEmPHj3aaV3Hjh2L5557TnxOT08H0C0+bn25Befx1rGxseJ8mpubxfWS9mmV105tEkdvQOK9BikrKwMAFBQU9Ji2tbUVn376qcwTeuHCBTDGoNfrxY3Dx0M5P/3pTwFcdbRwCyMdKpIyceJEJCcnY86cOS7rI3VY8Tq5msQAXG06Sx86UicTt2pWq1XUU028vM/b2NgIxhhCQkIwfvx4l/UdNGgQ5s+fj+zsbFm/ftSoUQC6wyTtdrtoHaSkpIjz4R51nU4nu179JV7yNl9jeLpES0lJCQ4cOICqqioRJMAD6IcMGSL6msqmLm8uc2HwPp0zyxsaGor777+/x/pIxcvHl9XEGxISgtDQUHR1dQnxSodW1IaqPvnkE3FMrTnMf8Pjkl0Fk0i57rrrHDzo3No3NzeLaxMSEoKoqCiH8V+lJ5ks7zXI999/j127djksFN7V1YWjR4/2SkSNNBBAGcyvxoEDBwDIN9mqqakB4HhTce/syJEjxTGlN9eZeN1F6rDieSmHajhKp5Vy7SyOmpVV7tbAfyflhhtu8LT6Dvm3tbWJJnh0dDQ0Go1sW1PAsU8v7fPqdDpVb3dvQOL1gI8//hgHDx7Exx9/LDv+5ZdfYufOnXj11Vd9nkkibf5yD6szlIHvfNyR71OrvOkff/xxpKamIjs7Wxzj4uXWxVk4o7vwG7W1tVWI0VnT1ZV4pUyYMEH2eezYscKhJEX5O2U/3xP0er14EPF+r/SBcd999zktV3rtXDn3fIXE6yZ8lQXg6pKjnEOHDon3atPZPEEZgse9ry0tLQ7NNb6kKqeyshJbt24Vc1GVwyRarRYzZsyQhQpygV+8eFEWjaXcAdBd+A3Pz0O6H68SZ+JVG6Z65plnAHQ7sJz1u5Xn68zT7C78GnAnmrQlM2HCBPEAMZvNst9JWxq+3g+u8Eq8mzZtQkJCAgwGA8xmM/bu3esyfUlJCcxmMwwGA0aPHo3Nmzc7pCkqKkJSUhL0ej2SkpKwc+dOj8tljCE/Px8mkwnh4eGYNm2a2OHdV6QWsKOjQ2ZhpeF3agHtnqAUb01NDTo6OrBhwwa8+OKLIkChq6tLNOc4J06cEA8Y4KrTxRXcSnR2dqKlpUVYXrVmqTtw4fFgi4iIiB4nMfDmtTPLC3R7x5955hlkZWU5XfVx2LBhMkvnq3j5NeAPSWUgxurVq7Fq1SqHMeOwsDDcdtttABxbDb2Jx+ItLCzE4sWLsWrVKlRUVCAtLQ0zZ850sAKc6upqzJo1C2lpaaioqMDzzz+PhQsXihkuAFBaWop58+YhJycHR44cQU5ODrKyssScTXfLffXVV/H6669jw4YNOHjwIIxGI2bMmNEr+9dI+7OMMaf7wirF5ylKy8fDGXk/+6WXXgJjDJs3bxZjtjfeeCMAyIQ7fPhwp31NKdKb/eTJk8JL622z2ZMABeXSNa7EC3SfU2JiotP8NBoNxo4dKz77Kl5lt0OtGS71mkt5+OGHkZ2djYyMDJ/q4AqPxfv666/jl7/8JZ566incfPPNWL9+PUaOHIm33npLNf3mzZvxox/9COvXr8fNN9+Mp556Cr/4xS/w2muviTTr16/HjBkzsHLlSiQmJmLlypWYPn061q9f73a5jDGsX78eq1atwpw5czB+/Hhs2bIFra2t+Pvf/+7paTqgDFJX268WUBdvV1cX3nvvPZf9Vw4XL3+aNzY2oqSkRHzPGMO2bdtky7SMGTPGIZ+nn366x7KA7huee1q508uVtewJTzytygB+V+PC7sLHajMzM70+B46y5aIMx3SFRqPBuHHj3HI6eotH4u3o6EBZWZnD0yQjIwP79u1T/U1paalD+szMTBw6dEjczM7S8DzdKbe6uhoNDQ2yNHq9Hunp6U7r1t7eDqvVKns5Q+lJPnz4sMhD6n3mziIpBw8exPHjx/H+++87zR/oFiafNMCt6YULFxzyrK6uln3mTTTO1KlTPbpxf/SjHwG4uk6UL+JReladhScCjuKVTkrwlri4OOTl5WHKlCle5yHNixMSEtJnkVLe4pF4m5qa0NXVJTspoPsklZOZOQ0NDarpOzs7hfVwlobn6U65/K8ndVu7di1iYmLESzqEokTpeOBiVgq+trbWQegnT54EgB7739KtMkaOHKnqUVWSnJzs0Afk1sddeJ+dz39VW1DdXZSWNy0tzWla5aR13oLpq3FRT5GK1W63u/X/6E+8qo3yqc4Yc/mkV0uvPO5Onr2VhrNy5UpYLBbx4kMCaiibzVzMatZaGdYoLV/pZJIibR7rdDoHjy+3kJzhw4eLwAl+Y0VERHh8kynL8cXCSPvQYWFhHjWb+bVxp6/eH0gXJLgW8ei/HBsbi9DQUAdL1tjY6GDxOEajUTV9WFiYaFI5S8PzdKdcbj08qZter0d0dLTs5QwuVh4TzEXL/0pjaJUzeqSCdRWvzB0i3EMpFVVSUpKDw2TWrFniPR93nDhxotP8naE8b18sr/Rm7yk4QeqwkoZT+jI+29vcc889AIDZs2f7uSaOeCRenU4Hs9mM4uJi2fHi4mLVWR5AdyyoMv3u3buRnJwsntLO0vA83Sk3ISEBRqNRlqajowMlJSVO6+YJgwcPRnx8vPBmtrS0OIyLzpgxA4BcrNI5oYBjn1g6dsuXqeGTAKTivfnmm8WNNHr0aLzwwguyFS3uvPNOLFu2TKTxBKVYe6tv11OUlrTZzB94gwYN8unh0dvcddddWL58eZ8O+XiLx7HNS5cuRU5ODpKTk5GSkoK3334bZ8+eRW5uLoDupuj58+exdetWAEBubi42bNiApUuX4umnn0ZpaSkKCgrw7rvvijwXLVqEqVOn4pVXXsFDDz2E999/H3v27MGXX37pdrkajQaLFy/Gyy+/jBtvvBE33ngjXn75ZURERODxxx/36SIB3RZt4sSJsNvt2Lt3L+x2Oy5fviybf8pvemmf9/Tp07J8uLAZY/jb3/6GpqYm/PrXv8ann34quhN8iEM5AT4yMhJ5eXlO6+jtrnd8vi0v31fLd/vtt+PIkSP48Y9/7DKd2uR5d+OR+wuNRtOnHmNf8Fi88+bNw4ULF7BmzRrU19dj/Pjx2LVrl3Cr19fXy8ZeExISsGvXLixZsgQbN26EyWTCm2++iUcffVSkSU1Nxfbt2/HCCy9g9erVGDNmDAoLCzF58mS3ywW6t228cuUK5s+fj4sXL2Ly5MnYvXu3wxpMvsDHQC9duoSWlhYRlDF06FAR8yoNsOer8I8ePRpVVVWoq6sTwfh8mtnp06fFTCLg6viiVLx96cQJDQ2VBZ24WqPKHWbPno0rV644zGRSIhUvb8F4GxwSjHg1q2j+/PmYP3++6nfSPVA56enpPS4FMnfuXMydO9frcoHup2R+fj7y8/Nd5uMrUVFRuHTpEpqbm8Wqg9ddd51wakktL+/jTps2TTjEKisrZcutSL3MP/nJT4TlGTNmDLRaLWJiYlwOufQ2vj7sQkJCMHfu3B4dZ7yl0NbWJjzN3oZlBiPXlu87QOA397fffgu73Q6tVothw4aJvtqlS5fAGENnZ6cQ8rBhw8TYbWVlpcxLffz4cQDds2CkaytFRERg+fLlmD9/fp8PU0ybNg1A99BTbzRb3QnIV7O8JF73ofm8XsCdKzxQY+jQodBoNBgyZAhCQkJgs9nQ3NwsRBAaGorw8HCkpKTg+PHjqKurUx1iUrN4yulnfcXUqVNhNpv7dYyVi9dut4voLhKv+5Dl9QJlzCz/HBoaKsYoGxsbZYuWaTQaMZzV1tamGjjiz+AE6TKr/YVWqxWxwbyFojZ3l1CHxOsFjzzyiOyztP/Kb77m5maHcL+wsDDRz5Nu2aGWTzCg0WhkE+a1Wu01F4J4LUPi9YKYmBhZc1Y6JZAHPFgsFtWJ7dybysUrteLKCKpgQBqSKl22h+gZ6vN6AW9icg+pNEJJulgabxJKxTt48GA0NDSIebnTp09HfX19v3uUrxUSEhLEeH4wPrx8gcTrJdJpgNKxSf7+2LFjIipH6ohS9uliYmJkHuZgIyEhARMnTkR9fT3uvvtuf1cnoCDx9gLSMEBpHDUPwpBa3rFjx6K0tFR85rHSwYpGo8GDDz7o72oEJNTn9ZKlS5dCq9U6TL+T9mGV++4A3ZFW3CJnZWVdc9PMiMBBw3xd7nAAYbVaERMTA4vF4tOuboWFhWIOL9Adu03jl4SU3rjX6LHfB0j7uNHR0SRcok8g8fYB0ifp9OnT/VgTYiBD4u0DpP3eYBz+IfoH8jb3ASNGjIBGo4FWq71mlnQhBh4k3j4gOjoazzzzDAwGQ59ud0EENyTePkIaMkkQfQH1eQkiQCHxEkSAQuIliACFxEsQAQo5rCTwSFFXexYRRG/A7zFfopNJvBL45HlXexYRRG/S0tLi9XK3NDFBgt1uR11dnVhzSonVasXIkSNRW1vr08SFgQZdF3VcXRfGGFpaWmAymbyeWUaWV0JISIhbC473tK9RsELXRR1n18XXBebJYUUQAQqJlyACFBKvB+j1euTl5fW4+12wQddFnb6+LuSwIogAhSwvQQQoJF6CCFBIvAQRoJB4CSJAIfESRIBC4lUhPj4eGo1G9lqxYoUszdmzZ/Hggw8iMjISsbGxWLhwITo6OmRpKisrkZ6ejvDwcNxwww1Ys2aNT4Ho1yKbNm1CQkICDAYDzGYz9u7d6+8q9Sn5+fkO94Z01RTGGPLz82EymRAeHo5p06bh2LFjsjza29vxm9/8BrGxsYiMjMTs2bNx7tw5zyvDCAdGjRrF1qxZw+rr68WrpaVFfN/Z2cnGjx/P7r77blZeXs6Ki4uZyWRiCxYsEGksFguLi4tj2dnZrLKykhUVFbGoqCj22muv+eOU+oTt27czrVbL/vznP7Pjx4+zRYsWscjISFZTU+PvqvUZeXl57JZbbpHdG42NjeL7devWsaioKFZUVMQqKyvZvHnz2PDhw5nVahVpcnNz2Q033MCKi4tZeXk5u/vuu9ntt9/OOjs7PaoLiVeFUaNGsTfeeMPp97t27WIhISHs/Pnz4ti7777L9Ho9s1gsjDHGNm3axGJiYlhbW5tIs3btWmYymZjdbu+zuvcnkyZNYrm5ubJjiYmJbMWKFX6qUd+Tl5fHbr/9dtXv7HY7MxqNbN26deJYW1sbi4mJYZs3b2aMMdbc3My0Wi3bvn27SHP+/HkWEhLC/vWvf3lUF2o2O+GVV17BsGHDcMcdd+Cll16SNYlLS0sxfvx42WbYmZmZaG9vR1lZmUiTnp4ui67JzMxEXV0dzpw502/n0Vd0dHSgrKwMGRkZsuMZGRnYt2+fn2rVP5w6dQomkwkJCQnIzs5GVVUVAKC6uhoNDQ2ya6LX65Geni6uSVlZGWw2myyNyWTC+PHjPb5uNKtIhUWLFmHixIkYMmQIDhw4gJUrV6K6uhp/+ctfAAANDQ2y3QCB7q07dTodGhoaRJr4+HhZGv6bhoYGJCQk9P2J9CFNTU3o6upyuA5xcXHiGgxEJk+ejK1bt+Kmm27Cd999hz/84Q9ITU3FsWPHxHmrXZOamhoA3f97nU7nsNWrN9ctaMSbn5+P3//+9y7THDx4EMnJyViyZIk4dtttt2HIkCGYO3eusMYAVOf7MsZkx5Vp2P87qwbS7u9q5ziQzk/JzJkzxftbb70VKSkpGDNmDLZs2YIpU6YA8O6aeHPdgka8CxYsQHZ2tss0SkvJ4f+U06dPY9iwYTAajdi/f78szcWLF2Gz2cRT12g0OjxJGxsbATg+mQOR2NhYhIaGqp7jQDg/d4mMjMStt96KU6dO4eGHHwbQbV2l+y5Lr4nRaERHRwcuXrwos76NjY1ITU31qOyg6fPGxsYiMTHR5ctgMKj+tqKiAsDVjbBTUlLwzTffoL6+XqTZvXs39Ho9zGazSPPFF1/I+sq7d++GyWRy+pAIJHQ6HcxmM4qLi2XHi4uLPb4JA5n29nacOHECw4cPR0JCAoxGo+yadHR0oKSkRFwTs9kMrVYrS1NfX49vvvnG8+vmkXsrCNi3bx97/fXXWUVFBauqqmKFhYXMZDKx2bNnizR8qGj69OmsvLyc7dmzh40YMUI2VNTc3Mzi4uLYY489xiorK9mOHTtYdHT0gBwqKigoYMePH2eLFy9mkZGR7MyZM/6uWp/x3HPPsc8//5xVVVWxr7/+mj3wwAMsKipKnPO6detYTEwM27FjB6usrGSPPfaY6lDRiBEj2J49e1h5eTm75557aKioNygrK2OTJ09mMTExzGAwsHHjxrG8vDx2+fJlWbqamhp2//33s/DwcDZ06FC2YMEC2bAQY4wdPXqUpaWlMb1ez4xGI8vPzx8ww0ScjRs3slGjRjGdTscmTpzISkpK/F2lPoWP22q1WmYymdicOXPYsWPHxPd2u53l5eUxo9HI9Ho9mzp1KqusrJTlceXKFbZgwQI2dOhQFh4ezh544AF29uxZj+tC83kJIkAJmj4vQQw0SLwEEaCQeAkiQCHxEkSAQuIliACFxEsQAQqJlyACFBIvQQQoJF6CCFBIvAQRoJB4CSJA+T+dEB3lxm/6jQAAAABJRU5ErkJggg==\n", 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\n", 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UjrS9DCiLlzFmJ16lNu+PP/4IoLXioGx0KyTeDoAL9OrVqzJvdvv2bbtZNtevX+/QsnHOnTuHf//737JssXR0FQBZP69Sptm2u8iVsNlsNosQ3JnnlT4L6+LFix6d490GibedsX0A288//yyWb9y4AQAICwtDWFgYAPs+z47AZDLhk08+wYULF/CXv/xFiI6XnXtOqeeVjq7i2IrXFc/LvWxgYCDCw8MB2ItXKnAAis2P7giJt52xFSMXrHS5b9++GDhwoKJ9R3D16lXZ93379gGw97xKbV5nnteVNi8Xb0hICHr06CF+l9rY5gJKSkroKZQg8bY7UrEC8qF/1dXVAICIiAj06dMHgPvivXXrFj7++GPhjSwWi9ueyVa8ZWVlABx7XpPJJMTjbdgsFa9OpxNtZ6n3VXot6IEDB9w6x7sREm87YyvGxsZGscxFM3DgQCFed5MxH330Ec6fP49NmzahuLgYubm52LRpE06dOuXyPvgxhw8fDqBVtC0tLXbPr+LiBeThLsd2Qr47YTOfcsi9r5J4+TUCgKKiIpfP726FxNvOcPHq9XoAcs9rMBgAAOHh4R55XovFIutq2r17t/B6e/bscZi5tg1D+XDE6OhoBAYGgjGG+vp6u7BZrVYLIXLRST2v7ZxeVzwvFykXLZ+0ryTeHj16ID09HQBwzz33OLgq3QcSrw9hjKGurk4mGh42x8bGArjjea1Wq1ju1auXEK/BYLAb+3v16lX8+OOPuHbtmmy9s/DYaDQqZm3PnTuH1atX49ChQ2IdF2/v3r3Rt29fUW7bsBm44335eGR32rzcSzvyvMAdEUu7i7h4g4OD0bt3b9nxuzMkXh9y+PBhrF27FidOnADQ6hkvX74M4I54b968CavVKv6qVCqEhoYiJCRE5vU427Ztw8aNG7F161Zs2LBBduNXVFQAAIYMGaJYHqmXv3jxIqqrq/Hf//4XALB//37xG69EQkNDERERAaC1e8s2bAbuiFfJ87qasJJWTo7Eq+R5g4ODZZ65M/WJ+wMSr4+oqqoSgvjf//4HAPjmm2/E74MGDUJAQAAYY2hsbBThcXh4uHhdCO/n5H29jY2N+OGHH2THOX36NACgsrISX331FQBg2LBh6N+/v12ZeFh+4cIFfPjhh3j33XdlHs1oNIIxJoQSEhIiC9+55+VhM2AvXmmb13bWkK34XQmb2xIv/50xppjI6k6QeH3E2bNnxTKfWcND5vDwcOh0OtGXW1dXJwTKPR0AREZGAriTyLIVLgB8/vnn+Pnnn/Hee++JdYmJiZg5c6b4zvfZ0NAAxpjdyC7O9evXYTKZhKfs0aOHLHFmm3AC7gzUUAqbbcXpblcRLwPgWLxqtVpUJlKbxsZGvzyVsqKiAseOHevw4wI0JdBnVFVViWWz2Yzvv/8e586dAwAhrD59+qCurg51dXXiN+5tgTtJGN625RMVHnnkEQwaNAgffPABgNYMM4c/4SI4OBgjR46EWq1GYGAgamtr0dDQgNu3bzucH3v9+nUhFo1GA61WK9qUdXV1QiSutnkdidfVbDPQtni5rdFoxK1btxAREYGvvvoKBw8eBNCadMvIyBDHak+am5vx/vvvA2itaJ9//nnZ7+Xl5bhy5Qruu+++dkmwkef1AVarVbRteej4ySefgDEGjUaDmJgYABDCKC8vR3l5OQAgPj5e7If/g3n/L68QYmNjxT5seeqpp8Tyk08+iccff1w8prWhoUE2iis7OxvZ2dkYPXo0gNZEFW8X89eR8DLW19eLrLSzsFkqXi5SHi635Xml45q5eJ1lm6XilZaBCxdobU58+umnitfKU2pqahTb19Joq7y8HIWFhbLfy8rKsH//fpw5c8an5eGQeH1AbW0tzGYzAgMD8fTTT8t+mzx5suhC4cLggyBUKhUGDBggbHnYXF9fj19++QU3b96ESqUS619//XVhO3z4cGRnZ+O+++6zK49UvLxtLe0j5cMQDQaDaBfzkD40NFS0zXlor+R5uUClbV5bcdp2FUnFzRiD0WgUoa5tm9dRtllqc/v2bcWZWLaDTrzh3Llz+Oc//ynyC1JsI5oDBw7I2uE8gmqvbi0Srw/gXTYDBgzAsGHDMGLECPHbhAkTxDLvhuGMHTtWNhunZ8+eQhxHjhwB0PqP5ze9Wq3GwoUL8eCDD+KZZ55xWB4lzys9NhdvfX29nXhVKpUQOg+NlTwvR1p+R+Ll5ef7YYzBbDYLgUqfsuEsbObHlgq8srISQGvFOG7cOHFcX2WieXb+0KFDdl14vFdBWoF9//334hx5JULi7cRw8fKBGLNmzcK8efOwfPly2T928ODBsu1+/etf2+2LZ42Li4sVtwkPD8fMmTPFA9CV4EJ05Hl5Quvq1avCe/BtbG0B5YQVR9q25MtctNwr8m2ktiaTyS5kBu4Ik79WhTEmKhH+yhRp2Pz5558DaK04p02bBpVKhebmZp89gVJ6vjwS+f777/Hmm2+KLrZ58+YhMTERwB2vX19fD4vFgoCAALvr6SsoYeUDuECkNaw0EcXRaDTIyspCUVERhg4dqphU6devn2gPA61ht7vw9mtLS4vwTFLPe88990CtVqO5uVmE8FLxcs/MceZ5lcTLPS8XL99GpVJBp9PBaDSKhBOgLF7eFcQYE9lwfl48spA+Nig2NhaBgYHo0aMHbt26hcuXLyMsLAw3btzAunXrAAAvv/yynRc0mUxYu3Yt9Ho9nn32WVmlaLVaZSPeampqEB4eju3bt4t1ffv2Re/evREXF4fTp0+L6Z985Fu/fv1keQFf4pHnzcvLQ1xcHIKCgpCUlCRLGChRWFiIpKQkBAUFYfDgwdi4caOdTX5+PhISEqDT6ZCQkICdO3e6fVzGGHJycqDX6xEcHIwpU6a0W7JACh8bzNu0ztDpdJgyZYqYRWSLVPSjR4+2E4srqNVqIQjuHaTiVavVQqw8FJSK1za85x4PsBev1CvzZbPZjJaWFiFi6Ta8InAkXukQzNu3bwsPGhISIkRgW7kAwMiRI8X2APDZZ5+hpaVFJrQNGzbYbXfmzBncunUL58+ftxvBZtv9VF1dbdf25c0i/n/jycZLly4BgGL/u69wW7zbt2/HokWLsGLFCpSWlmLSpEmYMWOGGO1jS3l5OR599FFMmjQJpaWlWL58ORYsWID8/HxhU1RUhNmzZyMjIwMnT55ERkYG0tPTZf1nrhz3rbfewjvvvIN169bhu+++Q1RUFKZNmyabDOBrzGazCD19ER7xCqxnz55ITU31eD9SMapUKrsbftq0abLv0kpDmgEH5OKzDZul4pV6XmkiSeq5lcTLvS1HmnGWDiHl2J7L008/LZon0hzDl19+afdwAx6JcKT3j+0MMNtx5idPnhRtWqD1mo0ZM0YsS8vMX98SFxeH9sJt8b7zzjv4/e9/jxdeeAHDhw/HmjVrEB0drVirAcDGjRsxaNAgrFmzBsOHD8cLL7yA3/3ud3j77beFzZo1azBt2jQsW7YM8fHxWLZsGaZOnYo1a9a4fFzGGNasWYMVK1Zg1qxZSExMxIcffojbt2/j448/dvc07bh58yZOnTqFkydPyp7fxP/BXHDe0qNHD2RlZWHp0qWym95dbG9229AtPj5ehOShoaGysvfq1Utsz0d/ScsnpS3xarVaWVKrLc8r/X7r1i27rix+PpyxY8fi/vvvF9+Tk5PF8vHjxwHcyeIDdxJQHOlD/xy9uSEmJgZqtRomk0m0v5cuXYp58+YJ26CgICHgNWvWiPPnM7XaA7fEazKZUFxcbOcRUlNTRXbUlqKiIjv7tLQ0HD9+XIRVjmz4Pl05bnl5OWpqamQ2Op0OKSkpDstmNBrR0NAg+zhi//792LlzJ/7zn/9g27ZtMBqNqKurE7V1RESE0yRSRyO92W3DYM7DDz+MP/zhD3j55Zftyj516lQwxpCSkiJbL31bIOC4zWubrOIoide20pNmnPn/RFoZaTQa/OpXv0JAQIBidGKbiR8yZAheeOEFAK0JJR4eFxcXy7qkeOadw4UaFhYmwnLAvrLj8OQiD7X79+/froNF3EpY1dbWoqWlRVaTAa01m3RqmpSamhpFe4vFgtraWvTv39+hDd+nK8flf5VsePvDllWrVuHPf/6zs1MWREdHi66Bn376Cbm5uQDuTDhwJBB/4Sx7LMVRm+yBBx7AgAED7M5LpVKhZ8+eIpyVhtTSflzbZBVHKl6+D2filU6akDJ16lRMnTpVsexRUVF258i78X744QeUlpZi3Lhx2L17t8zOdqy0NDIYOnQoSkpKAMBhvmLgwIGypt6sWbMU7XyFRwkr21qaPyHfHXvb9a7s01c2nGXLlomBCgaDwa49JCUxMVFW+3P4w9CkY5Q7A9KyelqxONpOmpiTVhLSriJH4uUCNxqNdl1AHKl4+fhupWvviN69e8tCdV6hP/DAAwBao7S1a9eK3x966CEArUkmaf+wtHKJjo62258tCQkJCAoKgsViwdKlS9v9nnBLvBEREVCr1XZe9tq1aw5PKCoqStE+MDBQ3ByObPg+XTkur23dKZtOpxPtO2k7TwmtVovFixc7/L2ziVc6nHLQoEE+3bc0FJZeM6WRT448r7T96Ei8V69eFd7Q1ps6Q6VSySoY/r+R9m9zEhISMHnyZKhUKty8eVP2JBMesoeFhUGj0SAtLQ06nU4kqWwJCAjAa6+9hpUrV/ok/9EWbolXq9UiKSkJBQUFsvUFBQWYOHGi4jbJycl29nv37sWYMWNETe3Ihu/TlePGxcUhKipKZmMymVBYWOiwbJ7wpz/9CUOGDBGjeTidLWzu1asX0tPT8dRTT7l147u6b45UnDzR1NTUJEZIORJvY2OjyHnYhsR8P9L+bkcVsCOmT58OABg3bpyIvJQq52eeeQZarVYMU5U+3dN29NmECRPwyiuv2CXYbOmo3IfbgzSWLFmCjIwMjBkzBsnJyXj33XdRUVGBzMxMAK2h6JUrV7BlyxYAQGZmJtatW4clS5bgxRdfRFFRETZv3oytW7eKfS5cuBCTJ0/G6tWr8cQTT2DXrl3Yt2+f7GkPbR1XpVJh0aJFePPNN3Hvvffi3nvvxZtvvokePXrgueee8+oiSVGpVPjtb38LoLUG5+3pziZeoP0ynWPHjsWxY8cwdOhQ2Y0q9cg8kedIvHwwA5/NJMW2sklKSnJbEEOHDsXixYtlFYNtWV555RWxfO+99+Ly5cvYs2cPxo4di6amJjvxAuhUL4JzuySzZ8/GjRs3sHLlSlRXVyMxMRF79uwRYVp1dbWs7ywuLg579uzB4sWLsX79euj1eqxdu1Y2G2bixInYtm0bXn/9dbzxxhsYMmQItm/fjvHjx7t8XAB49dVX0dTUhHnz5qGurg7jx4/H3r177Wp2X5GRkYFdu3Zh1KhR7TaKpjPSr18/aLVau0qRv1WhublZiNPWS/Fwks+YUgovIyMjERYWJsQzatQoj8qp5GmXLl2K3bt3Iy0tTVa2IUOG4OuvvwbQGi7z8Llnz57tdv94i4p192eJSGhoaBA3jTsJku5ITU2NYjiel5eH69evQ61Wo6WlBY899hiSkpLE75WVlbIHCcTGxmLu3Ll2+6mtrcWGDRswd+5cn7fZlWCMYeXKlaJMVqsVFRUViIuLs5un6wt8ca/RxATCIxy1o/nYYenTOaTYdltJQ1IpEREReOONNzpEuEBrc4iPLLt48aKIHpWGYnYWSLyET7FNLEm7WAB7MTsSrz9IS0uzW9eZ38xA4iV8inTQh9JIJNvEU1uZ244kPDwc6enpsmGptr0KnYnOkzoj7gqknpYPirBl/PjxYiRSew7c94Thw4dj+PDhsFqtaGpq6lSViy0kXsKn6HQ6TJgwAVu2bEF2draizfTp0zF48GBoNBrFec+dgYCAgE4tXICyzTIo2+wbWlpaUF9f3yn7vjsLlG0mOiVqtZqE2wGQeAmii0LiJYguComXILooJF6C6KJQV5EEnnj31TN/CcIR/B7zprOHxCuBPznBdkgfQbQXjY2NHg8RpX5eCVarFVVVVQgNDVWcP9rQ0IDo6GhUVlZSP7AEui7KOLsu/D3Ner1e9sgedyDPKyEgIMDhw8WktPXInO4KXRdlHF0XbydlUMKKILooJF6C6KKQeN1Ap9MhOzvbqzcZ3I3QdVGmva8LJawIootCnpcguigkXoLoopB4CaKLQuIliC4KiZcguigkXgViY2OhUqlkn6ysLJlNRUUFHn/8cYSEhCAiIgILFiyAyWSS2ZSVlSElJQXBwcEYMGAAVq5c6dVA9M5IXl4e4uLiEBQUhKSkJBw8eNDfRWpXcnJy7O4N6TOsGWPIycmBXq9HcHAwpkyZgjNnzsj2YTQa8cc//hEREREICQnBzJkzcfnyZfcLwwg7YmJi2MqVK1l1dbX4NDY2it8tFgtLTExkDz/8MCspKWEFBQVMr9ez+fPnCxuDwcAiIyPZnDlzWFlZGcvPz2ehoaHs7bff9scptQvbtm1jGo2G/etf/2Jnz55lCxcuZCEhIezSpUv+Llq7kZ2dze6//37ZvXHt2jXxe25uLgsNDWX5+fmsrKyMzZ49m/Xv3581NDQIm8zMTDZgwABWUFDASkpK2MMPP8xGjhzJLBaLW2Uh8SoQExPD/v73vzv8fc+ePSwgIIBduXJFrNu6dSvT6XTMYDAwxhjLy8tjYWFhrLm5WdisWrWK6fV6ZrVa263sHcm4ceNYZmambF18fDzLysryU4nan+zsbDZy5EjF36xWK4uKimK5ubliXXNzMwsLC2MbN25kjDFWX1/PNBoN27Ztm7C5cuUKCwgIYF9++aVbZaGw2QGrV69G3759MWrUKPztb3+ThcRFRUVITEyEXq8X69LS0mA0GlFcXCxsUlJSZKNr0tLSUFVVJV7I3ZUxmUwoLi5GamqqbH1qaiqOHDnip1J1DOfPn4der0dcXBzmzJmDCxcuAGh9JWlNTY3smuh0OqSkpIhrUlxcDLPZLLPR6/VITEx0+7rRrCIFFi5ciNGjR6N379749ttvsWzZMpSXl2PTpk0AWl+yZftaj969e0Or1YqXe9fU1CA2NlZmw7epqanpdA8bd5fa2lq0tLTYXYfIyEi7F5zfTYwfPx5btmzBfffdh6tXr+Kvf/0rJk6ciDNnzojzVrom/FWwNTU10Gq1spd/cxt3r1u3EW9OTg7+/Oc/O7X57rvvMGbMGCxevFise+CBB9C7d288/fTTwhsDyi9QZozJ1tvasP9PVnXUy5c7AqVzvJvOz5YZM2aI5REjRiA5ORlDhgzBhx9+iAkTJgDw7Jp4ct26jXjnz5+POXPmOLWx9ZQc/k/56aef0LdvX0RFRYnXdXDq6upgNptFrRsVFWVXk167dg2A+29574xERERArVYrnuPdcH6uEhISghEjRuD8+fN48sknAbR6V+k7m6TXJCoqCiaTCXV1dTLve+3aNUycONGtY3ebNm9ERATi4+OdfmzfnM4pLS0FcOclWsnJyTh9+jSqq6uFzd69e6HT6cS7aJOTk/HNN9/I2sp79+6FXq93WEl0JbRaLZKSklBQUCBbX1BQ4PZN2JUxGo04d+4c+vfvj7i4OERFRcmuiclkQmFhobgmSUlJ0Gg0Mpvq6mqcPn3a/evmVnqrG3DkyBH2zjvvsNLSUnbhwgW2fft2ptfr2cyZM4UN7yqaOnUqKykpYfv27WMDBw6UdRXV19ezyMhI9uyzz7KysjK2Y8cO1qtXr7uyq2jz5s3s7NmzbNGiRSwkJIRdvHjR30VrN5YuXcoOHDjALly4wI4ePcoee+wxFhoaKs45NzeXhYWFsR07drCysjL27LPPKnYVDRw4kO3bt4+VlJSwRx55hLqKfEFxcTEbP348CwsLY0FBQWzYsGEsOzub3bp1S2Z36dIl9pvf/IYFBwezPn36sPnz58u6hRhj7NSpU2zSpElMp9OxqKgolpOTc9d0E3HWr1/PYmJimFarZaNHj2aFhYX+LlK7wvttNRoN0+v1bNasWezMmTPid6vVyrKzs1lUVBTT6XRs8uTJrKysTLaPpqYmNn/+fNanTx8WHBzMHnvsMVZRUeF2WWg+L0F0UbpNm5cg7jZIvATRRSHxEkQXhcRLEF0UEi9BdFFIvATRRSHxEkQXhcRLEF0UEi9BdFFIvATRRSHxEkQX5f8AUb6/tlX5oO8AAAAASUVORK5CYII=\n", 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\n", 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JuHbtGlJSUnDhwgXs2rULO3fuxNq1a1mbVatWITc3F5mZmfjll1+QmZmJY8eOITk5udvXBbrabmfPnmW9kvz66684e/YsW/IHBgbimWeewZo1a3D8+HEUFxfjySefxMiRI9ledXfi3nvvBQAsWbIEkZGRbLpQpFBbwB0e4zYLmMimrkZ1dTW2bNnCixxja0S3wRMSElBbW4v169dDo9EgIiICOTk57JtYo9HwxqbDw8ORk5OD1atX48MPP4RKpcL777/PDpEBXT2wBw4cwLp16/Dqq69i0KBByMrK4vXAmrsuABw+fBhLly5l9xcvXgygKxZ1eno6AODdd9+Fp6cnFi1ahLt37yIuLg67d+8WNVHDVWBKaplMxitBHTEW7Q6OGU+fPo27d+/i66+/5r1QbYmE0B4NUVgjKLuz8P7776Ourg7Lli3DhQsXUFBQAKCrLb1u3Tq75qWpqQkbN25k99PS0ux6fXuQlZWFX375BUD3fp81njU6m8CNYUpqT09P3vRRU8NhtoJ7/UGDBtn9+vbAEYtqqMDdGGZ6roeHB8aPH88u20xISLB7XqRSKebNmwcA+O2331xyqMwRzUC6Ts+N4ZbgMpkMzz77LFpaWngTUewJd134r7/+anb+uzNBCMHNmzehVCqN9idwBW6tORzmoCW4G8MInHnwAgMDoVKpHJYfrjC4Pth7A+fOncOuXbuwd+9eozbcKnpbWxv++OMPm+eLCtxN0Wq17DRRZpKJh4eHQ0cT9Eu+3rR0tLi4GABMLj3meqzNzMzE1q1b0dLSYtN8UYG7KcyD5enpaTaIgb3QFzg30IKzoT8ZpzvVba63WaDrBcb0qtsKKnA3hZnM4uXl5TReVPR7mZ11RtvVq1fxzjvvoKSkpNvfMTa3wNadiVTgbgq3g81Z0J8664zOH7RaLfbs2QOtVot//etfbLq5l6Sx2oit/QtQgbspTAnuTDPI9BecOKPAmclAYjl8+LBg+o8//ohr1671JEsmoQJ3UxiBO1MJ7uvri9mzZ7P7zibw9vZ2fP/994LHzJXgv/32m9Fju3fvtllVnQrcDejs7ERBQQHq6+vZNO48dGdCrVazbpkvX77s4NzwOX36tEGakDCZjrOrV69iw4YN+Pzzz82e21ZVdSpwN+D06dPIzc3FBx98wKY5YxWdgfH042xhjITa0UeOHMGZM2d4JXhWVhYIIdizZw/a2tpw6dIle2aTh/PUzyg2ob29HUePHgXAH1d2xiq6EPaa8WUpP//8MwDD+fOfffaZqPPYqjlCS3AXR99JBoMzl+BcnKkdbupFo19VFwr4YAoqcIpFGBurPXLkCACw0USdCe7SSKF45c6IWEEDfD90VOAUi9AP6KDvrcUZvcRyPflwfdK7Cv3798eKFSvw/PPPs2m28qJDBe7i6Av80KFDvOrkxIkT7Z0ls0gkEnZNujOV4NbypzZ69Gj06dMH3t7erNssWoJTRFNbW2uQVlZWhqamJvj6+gIwHjTQ0TCTXpxB4IQQXLlyRTDWnVgeeughjB07lt1n1gHYSuDO3YXqZrS3t1u102v79u2C6UePHnX6TjZnEnh5eTm+/PJLq5zrgQce4HXWMaMYVOAuTl1dHXJychAcHIzo6GiDCJyWYKz9ynidBZx3mIwp2ZyhDW7NFV/6y3FHjRqF++67z2ZONpzzv+uGZGdn49atWwC6ZnCtXLnSLtd11hKcmezyzTffQK1WOywfWq22R2vkQ0JCEBERgePHjwse51bXbYFLxQcnhCA9PR0qlQo+Pj6IjY1FWVkZzyY2NhYSiYT3YdwrO5Lq6mp2256xuZxV4M5Qcl+7dg0ZGRmiloVymTJlCpYuXcoLqmFvJxYuFR/8rbfewsaNG7F582acPn0aSqUSjzzyCJqamnh5Wr58OTQaDfv5+OOPxd4GqyNUVRYTWNESHO3BxRRTp051dBaQm5tr0fdiYmKwdOlSPPzww5DL5bxlsPZ2Jila4Nw43cOGDcOmTZsQFhaGjz76SNCeGx982LBhePbZZ7Fs2TK8/fbbrA03PvjQoUORmpqKuLg4NqZ4d65LCMGmTZvwyiuvYMGCBYiIiMCePXtw584dg8n+vr6+UCqV7CcwMFDsbbA6+sNZVVVV2L17t9FY52Jx1ra2MZg45FKp1Kai+P3337F9+3Zs3rzZwN2SJTHKfXx8EBMTg/79+/PSIyIiEBwczAvUYQ9cJj54RUUFqqqqeDZyuRwxMTEGedu3bx8UCgVGjBiBtWvXGpTwXNra2tDY2Mj7WJuamhoD7yUff/wx2trasGfPnh6fPzo6GikpKQbp+i8VZ4IZxtPpdDatrn/yySeorKxEbW0tdu7cyTtmicAHDBggOKX18ccfx/PPP2/3F62oq9kiPnhoaKhV4oMzf4VsuAvqn3jiCYSHh0OpVKK0tBSpqakoKSkxOmc7IyMDr732muAxa6HfT9Ad7ty5w4rAGBKJBIQQREVF8VwSMzClpDMik8kgk8nQ3t6OO3fu2Cz6qNDL486dO9BoNKIXufj6+mLGjBlGj1vywugpLhcf3JzN8uXL2e2IiAgMHjwYUVFRKCoqEowXlZqayiv9GhsbERYWZvgje4Al//jc3FzMnDlT0GFiR0cHrly5wt5nY+1sZ5zFxsXX1xcNDQ344IMPMH78eJPisSbbtm1DQ0OD6O8tXLjQ6cJZuUx8cKVSCcCwY8pc7O/IyEjIZDKja3blcjkCAgJ4H2tjrtqmP0Xy+vXrKCkpQUZGhqD9zp07sX//fnafEbj+y8DZ2+XcGgqzLNMeWCJu4H/PoDPhMvHBmWo310ar1SI/P99k7O+ysjK0t7cjNDTU1E+3KeaGqrijCYQQfPLJJ+y+0Ewv/ZccI/BZs2YJpjsrGo2Gt2+PHmhm/F0sffv2FWwGORrRr/CUlBQkJiYiKioKEydOxLZt2wzig9+6dYuN8JCUlITNmzcjJSUFy5cvR0FBgUEJs2rVKkRHRyMzMxPz5s3DoUOHcOzYMZw4caLb15VIJEhOTsabb76JwYMHY/DgwXjzzTfh6+uLP//5zwC6/GLt27cPM2fOhEKhQHl5OdasWYOxY8di8uTJlt/FHmKuis5daaQv6JaWFrPtU+b8o0aN4s0vcPYSXB97OH/gvkxNIZVKeWPaQ4YMsVWWeoRLxQf/xz/+gbt37+K5555DXV0dJkyYgNzcXPj7+wPoqgkcP34c7733HpqbmxEWFoZZs2YhLS3NoaWZuckP3JJL37a5uZlt6hiD+wKZM2cOvv76awDOO8mFYfLkyfjvf//L7nd2dtq8o6o767pfffVV6HQ6vPHGGwAAf39/REdH2zRflmLRK/y5557Dc889J3hs9+7dBmkxMTEoKioyec6FCxdi4cKFFl8X6CrF09PTkZ6eLng8LCwM+fn5Jq9hb9rb280KvKamBjqdDlKplOc4ERC/hHHw4MHstrMLnFlKydDZ2WnzPHfnRS+VSnkvmv/7v/9z2nvZu+poLsbNmzfxzTffsF5EjXHlyhUUFRXB19cXX3zxBe9YeXk5RowY0e1rcqvlzhKyyBj6+WPG7XU6HVpaWtiaWXdpb29HfX29yYUd5gICCnWy2spZgzWgAncgn376KbRaLX7//Xeztv/+978F07krw4SYO3cub9/HxwfTpk2Dh4cH5HJ59zPrAPR7s2/cuIEBAwbgrbfeAgA8+eSTBs4OTbF9+3b88ccfWLp0Kfr372+Rm6WnnnrKIE2/puFMUIE7EKHYW8uWLcOuXbtEnae2ttZoO1xotZIjOxTFoD+yoR9S+LvvvjMr8LNnz6J///4IDg5mS+fS0lL07dsXn376qaj8DB06lHefn3/+eVRUVDj1hCHq0cWJiIyMRL9+/diqaUxMjMkxfAZLZsL1BswNXZoLTlhRUYFDhw7x/MEzWMP9kkKhwLhx45zarTMtwR2I/lCLr68vJBIJ1q5di6qqKqhUKgQEBLC93sbgtqu555s2bZr1M21HzHV4cQXe3NyMzs5O3sIhYw4lT58+bdEac2ftSDMFLcEdiP4DzIxny2QyhIWFwcPDo1vVv7y8PLZE4ka8HD58uPUy6wDMlYxM55ZOp8M777yDTZs28Tq8TI1OiFnjHRsbi4CAADz88MPd/o6zQEtwB+Lh4cF7IIVKiO66bnrnnXcQGhrKm/3liMUN1sScwJkSnOvPrKWlhfXIyp0/oD9Mqz9LzhQPPfQQYmJium3vTPTuJ6CXo1+CCwncVDV1zpw5vH39h7a3C9wczLAZV8jGtvWbOdx19hKJxOSsvt58H3tvznsZQssS9aOKGHvIjPV6mxvmcva55tbC2Bz17q53NzfH3Zk70cxBBW4HDh8+jA0bNkCj0eDy5cu8CRtcjHXixMXFCaabE3BvLnnEwL2PjBjPnj1r1AmJEKZETgVOMUlxcTGArnXG+/btw7fffgvAcIGCMYFLJBI8/fTTvLRRo0bREhxdNSPukBcj1EOHDlntGr35Rdl7c96LKSwsBGAoaFPDMAMHDkR8fDy7HxcXZ9a/V29+MIXo37+/gf+85uZmXoAHS72W2tsZor1wrSegF3H37l0DAZpbT8z1OOPl5QWpVIqoqCij9q4k8ODgYCxduhRLly7lpe/Zs4fXi67T6XDnzh3R56dVdIpVqaur4z1UHh4eZpd9enl54bHHHsO8efPYMfNZs2bx3FBx6c0PpjH0177rO8zU6XS8JabdZeDAgT3JltNCx8EdhKenJyvwsWPHIjY2tlsl7qhRowxKG2f0JGJtmDFvc/dIp9NZ5C12wYIFOHXqFNRqNYKCgmzuaNNe0BLcQXR0dLBCDQkJEeXrTb9kdga/7raG6UjrjsAt8VRzzz33YNq0aQgKCgLgHIEXrAEVuIPYvn07u0ikp1VpqVRqdCjN1bCFwPv162eQxqyxdzYvqWKhVXQnwBXbytaCmX7LrKozd690Op2oRSFjx44VnGPep08frF69utc3f6jAnQBr9HZzq+lz5851She+lrBkyRKcOHHCpGdcLuZKcK6L7mXLlpn0cd/bS2+ACtwpsEYJPmLECGg0GgwaNEiUlxNnx9/f3yDgwerVq1FYWIgffvjBwF6r1Rod8vrLX/6CgIAA7N+/H9OmTbN6AAtnhLbBbUx34mpZQ+BSqRTTp093KXEbIyAgAOPGjRM8duDAAaOTXfz9/REUFISkpCQ8+OCDtsyi0+B28cHb2tqwcuVKKBQK+Pn5Ye7cuQZRJa3JwYMH2W1jY620DS6ee+65x2hc94qKCsF0pinkShOAzOF28cGTk5Px1Vdf4cCBAzhx4gSam5sxe/Zsm0XavHjxIrv9+OOPC3pQpQK3jCFDhmDdunUG3lVra2sF7V11OqopJETkr54wYQIiIyN58cCHDRuG+fPnC8bKevHFF3H48GFcuHCBTUtKSkJJSQkKCgoAdAU1aGxsZBdhAMCjjz6KoKAgNgKKuesSQqBSqZCcnIwXX3wRQFdpHRISgszMTPztb39DQ0MD62wvISEBAFBZWYmwsDDk5OTw5nobo7GxEYGBgWhoaDDohCGE4MKFCxgyZAg8PDxACMH69et59yIrK8sg5vdjjz2GUaNGmb02RZjdu3fzIsgaY9WqVawziN6AqWetu7hVfPDCwkK0t7fzbFQqFSIiIozmX0x88PLycnzxxRds80I/RphUKjVwYwzQErynmKtyR0VFQa1W9ypxWwtRArdFfHBTNtaKD8618fLyYmcrdSf/GRkZCAwMZD+mel7/85//APifl1N9h/gSiQRBQUEGq8CowHtGeHi4YHpkZCReeuklzJo1y26hh50Ni3obenN8cCFM2aSmpqKhoYH9GPPUCRguVdTvQWdKmgULFpjMD0Ucxjze+Pn5sWvm3WFtvBBuFR9cqVRCq9Wirq6u2/kXEx9cv6qoH42EeYk4e0SR3oZUKhUcNjPVnHIX3Co+uFqthkwm49loNBqUlpZ2e6aUKfRrAfqhd5jj+lMp3bF319rExsY6OgtOiVvFBw8MDMQzzzyDNWvWoE+fPggODsbatWsxcuRIqwQJMNfZwwjcncZh7YWvr69BmqXeXVwJt4oPDgDvvvsuPD09sWjRIty9exdxcXHYvXu3VdpoXOHqD4VRbM+8efNw9OhRtLa2AqACBywYB3d3TI1Nbtu2zaRD/bS0NHb70qVL+PzzzwHQcXBr0tnZiddffx1AV7BAZr5Db8Tu4+AU04ipenNDEtFhMuvBrYnZanZib4IK3IqIESrXlrbJbYMlnl1cDfpkWRFjQg0MDER0dLTo71EsY86cOQgODsYjjzzi6Kw4HPqKsyJCQu3Tpw9WrFgh+nsUy4mMjMSYMWPofQUtwa2K0APVnWo7fRCtD72nXdC7YEWE2nz689GFcBX3ShTng1bRrYhQ4AL92Wxc1q5di7a2NoP1zBSKtaAluBXRX84KGI8MCnQthggODrZllihuDhW4lfn73//ObisUCqMrnSgUe0AFbmXuvfdedrtv3750EgvFoVCB24AZM2bA39/fKgtYKJSeQDvZbMD48eMxbtw4WnpTHA4twW0EFTfFGaACp1BcGCpwCsWFoQKnUFwYKnAKxYWhvegiYRzgUI+dFFvDPGM9cbpEBS4SJs6ZO4SepTgHTU1NvPjvYqA+2USi0+lQWVkJf39/g6GwxsZGhIWF4caNGy4RPN6a0HsjjKn7QghBU1MTVCqVxctfaQkuEqlUin79+pm0MRcgwZ2h90YYY/fF0pKbgXayUSguDBU4heLCUIFbEblcjrS0NBp7TAB6b4Sx9X2hnWwUigtDS3AKxYWhAqdQXBgqcArFhaECp1BcGCpwCsWFoQK3kIEDB0IikfA+L730Es/m+vXrmDNnDvz8/KBQKPDCCy9Aq9XybM6fP4+YmBj4+Pjgvvvuw/r163u0uMAZ2bJlC8LDw+Ht7Q21Wo0ff/zR0VmyKenp6QbPBje4BSEE6enpUKlU8PHxQWxsLMrKynjnaGtrw8qVK6FQKODn54e5c+fi5s2b4jNDKBYxYMAAsn79eqLRaNhPU1MTe7yjo4NERESQqVOnkqKiIpKXl0dUKhVZsWIFa9PQ0EBCQkLI4sWLyfnz50l2djbx9/cnb7/9tiN+kk04cOAAkclkZPv27aS8vJysWrWK+Pn5kWvXrjk6azYjLS2NjBgxgvdsVFdXs8c3bNhA/P39SXZ2Njl//jxJSEggoaGhpLGxkbVJSkoi9913H8nLyyNFRUVk6tSpZPTo0aSjo0NUXqjALWTAgAHk3XffNXo8JyeHSKVScuvWLTZt//79RC6Xk4aGBkIIIVu2bCGBgYGktbWVtcnIyCAqlYrodDqb5d2ejB8/niQlJfHShg4dSl566SUH5cj2pKWlkdGjRwse0+l0RKlUkg0bNrBpra2tJDAwkGzdupUQQkh9fT2RyWTkwIEDrM2tW7eIVColR44cEZUXWkXvAZmZmejTpw/GjBmDN954g1f9LigoQEREBFQqFZsWHx+PtrY2FBYWsjYxMTG8WUzx8fGorKzE1atX7fY7bIVWq0VhYaFBxJfp06fj5MmTDsqVfbh06RJUKhXCw8OxePFiXLlyBQBQUVGBqqoq3j2Ry+WIiYlh70lhYSHa29t5NiqVChEREaLvG11NZiGrVq1CZGQkgoKC8PPPPyM1NRUVFRXYsWMHAKCqqgohISG87wQFBcHLywtVVVWszcCBA3k2zHeqqqoQHh5u+x9iQ2pqatDZ2WlwH0JCQth74IpMmDABe/fuxYMPPojff/8dr7/+OiZNmoSysjL2dwvdk2vXrgHo+t97eXkhKCjIwEbsfaMC55Ceno7XXnvNpM3p06cRFRWF1atXs2mjRo1CUFAQFi5cyJbqgLDrZEIIL13fhvz/DjZXcrss9Btd6ffpM2PGDHZ75MiRmDhxIgYNGoQ9e/bgoYceAmDZPbHkvlGBc1ixYgUWL15s0ka/xGVg/nGXL19Gnz59oFQq8dNPP/Fs6urq0N7ezr69lUqlwRu5uroagOEbvjeiUCjg4eEh+Btd4fd1Fz8/P4wcORKXLl3C/PnzAXSV0qGhoawN954olUpotVrU1dXxSvHq6mpMmjRJ1LVpG5yDQqHA0KFDTX68vb0Fv1tcXAwA7D9t4sSJKC0thUajYW1yc3Mhl8uhVqtZmx9++IHXds/NzYVKpTL6IulNeHl5Qa1WIy8vj5eel5cn+kHtzbS1teHChQsIDQ1FeHg4lEol755otVrk5+ez90StVkMmk/FsNBoNSktLxd83UV1yFEIIISdPniQbN24kxcXF5MqVKyQrK4uoVCoyd+5c1oYZJouLiyNFRUXk2LFjpF+/frxhsvr6ehISEkKWLFlCzp8/Tw4ePEgCAgJccphs586dpLy8nCQnJxM/Pz9y9epVR2fNZqxZs4Z8//335MqVK+TUqVNk9uzZxN/fn/3NGzZsIIGBgeTgwYPk/PnzZMmSJYLDZP369SPHjh0jRUVF5OGHH6bDZPaisLCQTJgwgQQGBhJvb28yZMgQkpaWRlpaWnh2165dI7NmzSI+Pj4kODiYrFixgjckRggh586dI1OmTCFyuZwolUqSnp7uMkNkDB9++CEZMGAA8fLyIpGRkSQ/P9/RWbIpzLi2TCYjKpWKLFiwgJSVlbHHdTodSUtLI0qlksjlchIdHU3Onz/PO8fdu3fJihUrSHBwMPHx8SGzZ88m169fF50Xuh6cQnFhaBucQnFhqMApFBeGCpxCcWGowCkUF4YKnEJxYajAKRQXhgqcQnFhqMApFBeGCpxCcWGowCkUF4YKnEJxYf4fPd2Y3we2X5cAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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hYRhbYWCHDh1Cbm4ucnJy8Nlnn43osh2hvb0dOTk5IhkAwFdffYU///nP6OjogL+/P1atWjXsRHezVwhXr17F3r17UVpaCoPBgL/97W/iFG97sGtC2L9/P9LS0vD888+jrKwMixYtQkJCAurr663G19bWYsWKFVi0aBHKysrw3HPPYdOmTcjJyRExxcXFSE5ORmpqKsrLy5GamorVq1fj5MmTNq13586dePHFF/HnP/8Zp0+fhlKpxLJly9DW1iZi0tLScPDgQWRnZ+OTTz5Be3s7EhMTR+zXqqmpCWVlZeL9f/7zH/z3v/8dkWU7QkNDA1555RWRSO+9916sWbNG/BGPGTMGa9eutbiDsq1uxgrBZDLh888/x86dO/H73/9eHIoBPbep772vjzS7Pux1/vz5iI6Oxu7du8W0mTNnYuXKldi+fXu/+GeffRbvv/8+ampqxLQNGzagvLwcxcXFAIDk5GTo9Xr8+9//FjHLly+Hn58f9u3bN6T1EhFUKhXS0tLw7LPPAuipBoKCgrBjxw6sX78eOp0O48ePx1tvvYXk5GQAQGNjI0JCQpCbm4v4+PghbYOBHsDZ2tqKt956C62trYiIiIBer0d9fT0mT56MRx55xOKkHGdqaWnB6dOnceXKFahUKoSHh8Pf3x9dXV3Ys2cPLl26BKVSiRUrViAkJARAz3e+ePGiqNC+jzfeeAP19fW4//77MXv27JH4Si7LZDIhPz8fp06dskiAY8eORUpKClpbW/Huu+9CoVAgLS2t37YdiYe92q0PobOzEyUlJcjIyLCYHhcXh6KiIqvzFBcX93vKb3x8PLKysmA0GsXTgNPT0/vFvPzyy0Neb21tLbRarcW6FAoFYmNjUVRUhPXr16OkpARGo9EiRqVSISIiAkVFRQMmBIPBYHGjD/MFO30pFAoAPb+iy5YtQ3d3N3bv3o36+nocPnwYd999tyiziQhXrlyBp6cnjEYjLl++jGPHjuHixYuYPn06YmNj4evra3U9turu7kZ5eTlOnjyJK1euWJSnVVVV+PDDDxEZGYmWlhZcunQJXl5eeOSRR+Dp6Snixo4dO2JPur5ZKgStVovc3FxRDUilUtxxxx2YNm0agoODIZVKoVQqoVKpEBISYreLveyWEFpaWtDd3Y2goCCL6UFBQdBqtVbn0Wq1VuO7urrQ0tKC4ODgAWPMyxzKes3/tRZz/vx5ESOXy+Hn5zfk9gPA9u3bsW3btgE/N/Py8sKaNWsshtvuv/9+7Nu3D2VlZZBKpZg4cSIaGhpw8eLFAddZVlaGsrIyhIWF4dZbb0VISAgCAgLEdzh9+jQ8PDwwbdo0TJw4Ee7u7jh37hz++9//wt3dHRMmTICfnx+ICHq9HgcOHEBLS4vFOoKDgzF+/HhotVo0NzejvLwcQM+ZhElJSRbJYKTdDH0IpaWl+OCDDwAAbm5uSEpKQmRkZL9zMiQSCR599NF+00eS3UcZ+nYmEdGgHUzW4vtOH8oyRyqmr+vFbNmyBZs3bxbv9Xq9KKX7Mv/hmk2fPh0rVqxAbm4uPvvss0E7GQMCAhAeHo4vvvgCbW1tqK2tFRcHTZ48GR0dHeLqQQAoKiqCRCKBXC63qGCsUSgUWLBgAdzc3DBmzBjMmTMHEokERISysjLU1NRg8uTJiIqKGrFKYCA3eoVQUlKCQ4cOAfjf6Ezf/aI3eyYDwI4JITAwEFKptN8vW3Nzc79fZjOlUmk13t3dXWykgWLMyxzKes1j4VqtVlxZZy2ms7MTra2tFlVCc3MzFi5cOOD3VigU4nBgOG6//XbI5XJ8+OGHkEgkmDlzJgICAjBt2jTodDr4+/vDx8dHxP/oRz9CS0uLOIS4fPmyReep+UKh+vp6GI1GkQyUSiWICM3NzRbl58yZM/HjH/9Y3KSkN4lEgujoaERHRw/7+9nqRr1rUktLCz766CPRXzZv3jwsX77c6cPOdksIcrkcarUaeXl5uPfee8X0vLw83HPPPVbniYmJEaWT2dGjRzF37lxxwUtMTAzy8vIs+hGOHj0q/kiHst6wsDAolUrk5eVhzpw5AHr6HgoLC7Fjxw4AgFqthkwmQ15eHlavXg2gZ1SgsrISO3fu/F7b5nqioqIQFRXVb/q4ceOsxgcGBuKBBx4A0NPxeejQIfj7+2PJkiXi3gJEhNbWVrS0tGDixIniD95kMsFkMqG7uxvd3d3w8vKyz5caphvtrkkmkwkFBQUWw+CLFy/GkiVLnJ4MAABkR9nZ2SSTySgrK4uqq6spLS2NvL29qa6ujoiIMjIyKDU1VcSfO3eOvLy8KD09naqrqykrK4tkMhm99957IubTTz8lqVRKmZmZVFNTQ5mZmeTu7k4nTpwY8nqJiDIzM8nX15cOHDhAFRUV9OCDD1JwcDDp9XoRs2HDBpo0aRLl5+dTaWkpLV26lKKioqirq2vI20Cn0xEA0ul0w9qGN7sPPviANBoNFRQUOLspw9bd3U2NjY20e/du2rZtG2k0GtJoNPTmm29SY2PjiK1nJPY1u/YhJCcn49KlS3jhhRfQ1NSEiIgI5ObmIjQ0FEDPL27v8jYsLAy5ublIT0/Hq6++CpVKhT/96U+47777RMzChQuRnZ2NX/7yl/jVr36FqVOnYv/+/Zg/f/6Q1wsAzzzzDDo6OvD444+jtbUV8+fPx9GjRy3K8Zdeegnu7u5YvXo1Ojo6cNddd+HNN990mSHBm8Fo71SsqKjAv/71L4tDHqlUipUrVyIiIsKJLbPOruchsJEZG76Z5eXloaioCDExMf2GpF3dN998g7///e/ifXBwMOLj4xEUFPS9z8+wxqXPQ2BsJIzGCoH+fzTGPHoQGRmJu+++W/SDuTJOCMyljbZhRyLCkSNHcOrUKQA9VcFoSQYAJwTm4kbTsKPJZML+/ftx5swZAEBsbCwWL15s93MHRhInBObSRtOwY0FBAc6cOQMiwj333COGtEcTTgjMpY2WCqGmpkacW3DfffchMjLSyS0aHk4IzKW5eoVw7do1HD16VFzGfuutt47aZABwQmAuzpUrhJqaGrz33nswmUwAgNtuuw2JiYlObtX3wwmBuTRXrRC++eYb/OMf/wDQc7r8Aw88gB/84AdObtX3xwmBuTTzcJ3RaHRyS/6nvb0dBw4cAABERERg5cqVN8zZq6NnPITdlFwtIZhMJmRnZ+Pq1asYN24c7r777hsmGQCcEJiLc7VDhrNnz+LixYtwc3PDww8//L3uF+mKOCEwl+ZqFYL5NnwxMTGD3shktOKEwFyauUJwhYRQX1+P+vp6SCQSzJs3z9nNsQtOCMylmSuErq4uu91YdCiICPn5+QCAOXPm3LBXrnJCYC7NnBCISIz3O8Pp06dx4cIFSCQSLFq0yGntsDcedmQuzXzIAPQcNji6R7+9vR3/+te/xJOr4+PjB7yV3Y2AEwJzaVKpVNzx2Wg02uXGIgPp6OjAX/7yF/E0r9DQ0Bu278CMEwJzaRKJBO7u7jAajQ4dejSZTHj33XfR1tYGmUyGhIQE3Hrrra5xI1Q74oTAXJ5MJoPRaHTYSENXVxf27t2LhoYGSCQS/OxnP7P5MfajFXcqMpfnyKFHIkJOTo54yvd999130yQDgCsENgr0Hnq0t5MnT+LLL78EADz88MPiQTc3C64QmMtz1NmK58+fx3/+8x8AQEJCwk2XDABOCGwUcMQhw9WrV7Fv3z4APc9YvP322+22LlfGhwzM5V3vkKGyshKHDh2C0WhESkoKpk2bZtPyu7u7sW/fPhgMBowbNw6rVq264UcTBsIVAnN5gx0ylJWVIScnBwaDASaTCW+//Ta++eabIS/baDRajCgkJyffcFcw2oITAnN5Ax0ydHV14d///jcAYOzYsfDz84NEIsE777yD9vb26y63ubkZr732Gi5cuAAAWLNmzU01omCNXRNCa2srUlNT4evrC19fX6SmpuLy5cuDzkNE0Gg0UKlU8PT0xJIlS1BVVWURYzAY8MQTTyAwMBDe3t5ISkoSw0RDXXd5eTkefPBBhISEwNPTEzNnzsQrr7xisYy6ujpIJJJ+ryNHjnyv7cJsM9AhQ01NDYxGI7y8vPDkk0/i8ccfh5+fH0wmE44dOzbg8oxGI44fP45du3bh0qVLkEgk+OlPf3pTdiL2Zdc+hIceeggNDQ3iD+ixxx5Dampqv0e+97Zz5068+OKLePPNNzF9+nT85je/wbJly/DVV1+JB7GmpaXhgw8+QHZ2NgICAvDUU08hMTERJSUl4lz36627pKQE48ePx9///neEhISgqKgIjz32GKRSKTZu3GjRpvz8fMyePVu89/f3H7mNxK5roAqhpKQEADBv3jy4ubnBzc0NK1euxBtvvIHS0lLMnz8fEyZMAAC0tLTg448/RmVlpbhqUiKRYOzYsXjooYcQFBTkwG/kuuyWEGpqanDkyBGcOHFCPJn59ddfR0xMDL766ivMmDGj3zxEhJdffhnPP/88Vq1aBQDYu3cvgoKC8M4772D9+vXQ6XTIysrCW2+9hR/96EcAIP6o8/PzER8fP6R1/+xnP7NY9y233ILi4mIcOHCgX0IICAi46UtJZ7LWh2AwGMSTw6OiosT0yZMnY/r06Thz5gxef/11REVFQalU4tChQxYdhSaTCcuWLcMdd9xx03YgWmO3hFBcXAxfX1+Lx7QvWLAAvr6+KCoqspoQamtrodVqLZ7yq1AoEBsbi6KiIqxfvx4lJSUwGo0WMSqVChERESgqKkJ8fPyw1g0AOp3O6q9/UlISrl27hmnTpiE9PR3333//gN/bYDDAYDCI93q9fsBYNjTWDhnOnz8PIoKfn1+/qw8TEhJw7tw5dHV1iSpCIpHAw8MDS5YswaxZs+Dt7T2qHrHmKHZLCFqtVpRrvU2YMAFarXbAeQD0K9+CgoJw/vx5ESOXy+Hn59cvxjz/cNZdXFyMf/zjHzh8+LCYNmbMGLz44ou444474Obmhvfffx/JycnYu3cvHn74YavL2b59O7Zt22b1MzY85oTQ2dkppl28eBFAzxWIfY0bNw7p6ekoLCxEZWUlrl69ihkzZuCBBx64oW6Iag82JwSNRnPdHf706dMAYLUUI6Lrlmh9Px/KPH1jbFl3VVUV7rnnHvz617/GsmXLxPTAwECkp6eL93PnzkVrayt27tw5YELYsmULNm/eLN7r9XqEhIQM2nY2OIVCAcAyIXz33XcAYDXxA4CXlxcSEhKQkJCAzs7Om3oo0RY2J4SNGzciJSVl0JgpU6bgiy++wLffftvvs++++27ADhzzcbpWq0VwcLCY3tzcLOZRKpXo7OxEa2urRZXQ3NyMhQsXipihrru6uhpLly7FunXr8Mtf/nLQ7wX0HHr89a9/HfBzhUIhdmA2Msx/zL0Tgvn/71A6AzkZDJ3NB1GBgYEIDw8f9OXh4YGYmBjodDqcOnVKzHvy5EnodDrxh9tXWFgYlEol8vLyxLTOzk4UFhaKedRqNWQymUVMU1MTKisrRcxQ111VVYU777wTa9euxW9/+9shff+ysjKLZMXsz/wHbe6bMZlMaG1tBdCzP7KRY7c+hJkzZ2L58uVYt24dXnvtNQA9Q3+JiYkWnXrh4eHYvn077r33XkgkEqSlpeF3v/sdpk2bhmnTpuF3v/sdvLy88NBDDwEAfH198eijj+Kpp55CQEAA/P398fTTTyMyMlKMOgxl3eZkEBcXh82bN4u+BalUivHjxwPoGeGQyWSYM2cO3Nzc8MEHH+BPf/oTduzYYa/Nxqzoe8hw5coVcfg3ZswYZzbtxkN2dOnSJVqzZg35+PiQj48PrVmzhlpbWy1iANAbb7wh3ptMJtq6dSsplUpSKBS0ePFiqqiosJino6ODNm7cSP7+/uTp6UmJiYlUX19v07q3bt1KAPq9QkNDRcybb75JM2fOJC8vL/Lx8SG1Wk1vvfWWTdtAp9MRANLpdDbNx/6nvr6eNBoNvfzyy0RE1NDQQBqNhv74xz86uWWuZST2NQmRE+9tfRPQ6/Xw9fWFTqe7YW/dbW/ffvst9uzZAy8vL/ziF7/Al19+if3792PixIn4+c9/7uzmuYyR2Nd4IJa5vL6HDOZzO8xnrrKRwwmBuTxzp2JXVxdMJpO4CzInhJHHCYG5vN7DhgaDgROCHXFCYC7P3d1dnGbc2dkpEgL3yYw8TghsVOjdj8B9CPbDCYGNCr1PTuIKwX44IbBRwVwhtLe3izMWuUIYeZwQ2Kjg6ekJ4H9XxPI1I/bBCYGNCt7e3gD+lxD4cME+OCGwUcHLywtAz4VsACcEe+GEwEYFc4XAIwz2xQmBjQrmhGDGFYJ9cEJgo4L5kMGME4J9cEJgo0LfQ4S+N1ZlI4MTAhsV+t4Zie+UZB+cENiowIcMjsEJgY0ac+bMAQBERkbyw1XshB8Hz0aN5cuXY8KECbjtttuc3ZQbFicENmrI5XIsWLDA2c24ofEhA2NM4ITAGBM4ITDGBE4IjDGBOxXtzPzYC34sPLM38z72fR61wgnBzsy3++InQDNHaWtrg6+v77Dm5Sc32ZnJZEJjYyN8fHz6nUxjflT8hQsX+My7Xni7DGywbUNEaGtrg0qlEnepthVXCHbm5uaGSZMmDRozduxY3vGt4O0ysIG2zXArAzPuVGSMCZwQGGMCJwQnUigU2Lp1K989uA/eLgOz97bhTkXGmMAVAmNM4ITAGBM4ITDGBE4IjDGBEwJjTOCE4ABTpkyBRCKxeGVkZFjE1NfX4+6774a3tzcCAwOxadMmdHZ2WsRUVFQgNjYWnp6emDhxIl544YXvdSGLq9q1axfCwsLg4eEBtVqN48ePO7tJdqXRaPrtH0qlUnxORNBoNFCpVPD09MSSJUtQVVVlsQyDwYAnnngCgYGB8Pb2RlJSEhoaGmxvDDG7Cw0NpRdeeIGamprEq62tTXze1dVFERERdOedd1JpaSnl5eWRSqWijRs3ihidTkdBQUGUkpJCFRUVlJOTQz4+PvSHP/zBGV/JbrKzs0kmk9Hrr79O1dXV9OSTT5K3tzedP3/e2U2zm61bt9Ls2bMt9o/m5mbxeWZmJvn4+FBOTg5VVFRQcnIyBQcHk16vFzEbNmygiRMnUl5eHpWWltKdd95JUVFR1NXVZVNbOCE4QGhoKL300ksDfp6bm0tubm508eJFMW3fvn2kUChIp9MREdGuXbvI19eXrl27JmK2b99OKpWKTCaT3druaPPmzaMNGzZYTAsPD6eMjAwntcj+tm7dSlFRUVY/M5lMpFQqKTMzU0y7du0a+fr60p49e4iI6PLlyySTySg7O1vEXLx4kdzc3OjIkSM2tYUPGRxkx44dCAgIwG233Ybf/va3FocDxcXFiIiIgEqlEtPi4+NhMBhQUlIiYmJjYy3OUIuPj0djYyPq6uoc9j3sqbOzEyUlJYiLi7OYHhcXh6KiIie1yjHOnj0LlUqFsLAwpKSk4Ny5cwCA2tpaaLVai22iUCgQGxsrtklJSQmMRqNFjEqlQkREhM3bja92dIAnn3wS0dHR8PPzw6lTp7BlyxbU1tbir3/9KwBAq9UiKCjIYh4/Pz/I5XJotVoRM2XKFIsY8zxarRZhYWH2/yJ21tLSgu7u7n7bIigoSGyHG9H8+fPxt7/9DdOnT8e3336L3/zmN1i4cCGqqqrE97a2Tc6fPw+g5/+/XC6Hn59fvxhbtxsnhGHSaDTYtm3boDGnT5/G3LlzkZ6eLqbdeuut8PPzw/333y+qBgBWHzxCRBbT+8bQ/3co3mgPLbH2PW+079hbQkKC+HdkZCRiYmIwdepU7N27V9x2fjjbZDjbjRPCMG3cuBEpKSmDxvT9RTcz/0/++uuvERAQAKVSiZMnT1rEtLa2wmg0il8GpVLZL9s3NzcD6P/rMVoFBgZCKpVa/Z43ynccCm9vb0RGRuLs2bNYuXIlgJ4qIDg4WMT03iZKpRKdnZ1obW21qBKam5uxcOFCm9bNfQjDFBgYiPDw8EFfHh4eVuctKysDAPE/OCYmBpWVlWhqahIxR48ehUKhgFqtFjEff/yxRd/D0aNHoVKpBkw8o41cLodarUZeXp7F9Ly8PJt37NHMYDCgpqYGwcHBCAsLg1KptNgmnZ2dKCwsFNtErVZDJpNZxDQ1NaGystL27WZTFySzWVFREb344otUVlZG586do/3795NKpaKkpCQRYx52vOuuu6i0tJTy8/Np0qRJFsOOly9fpqCgIHrwwQepoqKCDhw4QGPHjr1hhx2zsrKourqa0tLSyNvbm+rq6pzdNLt56qmnqKCggM6dO0cnTpygxMRE8vHxEd85MzOTfH196cCBA1RRUUEPPvig1WHHSZMmUX5+PpWWltLSpUt52NEVlZSU0Pz588nX15c8PDxoxowZtHXrVrpy5YpF3Pnz5+nHP/4xeXp6kr+/P23cuNFiiJGI6IsvvqBFixaRQqEgpVJJGo3mhhpyNHv11VcpNDSU5HI5RUdHU2FhobObZFfm8wpkMhmpVCpatWoVVVVVic9NJhNt3bqVlEolKRQKWrx4MVVUVFgso6OjgzZu3Ej+/v7k6elJiYmJVF9fb3Nb+H4IjDGB+xAYYwInBMaYwAmBMSZwQmCMCZwQGGMCJwTGmMAJgTEmcEJgjAmcEBhjAicExpjACYExJvwf/3ndkeXmzpMAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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JOf5o83p6uLlQ9CRZcM9gyJAhsuO7Eht/+MeMGYPIyEjRTm5paRG/kYpXizfgT7dZDS5eAKiqqgrIOUINiVcH0uCHp4fOaDS6Tc/jARVXgxO0BnT4dLjbtm3TvIoAv47ExERZN42rFwCPDPO68KCZzWYTAtWbYRVot1k6FJNPRNjfIPHqQGollYEQ5YPv6aGUDk5QS+HjD7daP6Yr6urqNJWTRsOl/ayuxKt8kUjL91W3WdrMcLUIXrhD4tWBNDLc09Mje9iVgRFP6YpRUVFCDGrRXH4uT+J9+OGHxbY34gXgMWgljU4ry6tZXi1zWAVavADE2laUYUU49Rlyl9dmszkFRbQsKOYuC0iaxOAOs9mMGTNmAOiNdmvhzJkzAC5bf0/i5dP18EEWvHxnZ6dw1dXavKEWL69vf12UjLqKdLBnzx7Z50uXLuG///2vSBWUosyMUiMuLg4//PCDT+IFgGuuuQb79u2TzSKhxG63O/UH89RIrZaXT1PLy0s9EW/FG8jlZrQuxGa323HgwAFkZGS4XWKlr0GW1wfa29tVhQv0zhTpCS4etYnouJVXmwNLCbcwnZ2dTu3n77//HjU1Ndi6dStefPFFWfuPzw+l1fLyuqiJty+6zVqXQH3rrbewd+9ebNq0KWB1CQRkeTUitY58EnC1BICMjAwMHTpUuLLu4N0ZyonG7Xa7OJ+0y8MVRqMRcXFxaG9vx8WLF2VDEbds2SJz93fu3Ang8vQxANwGrP73v/+JbT6DIxecNIAnbSZIu4pcreQY6GgzcLlNz917ZT2OHj2K7777TjQjwg2yvBo5efKk2OZdJUo3GgAmTpyIm266SeZGuoK3xZSrSfBIdmRkpKb+ZOCyFVWuLOAqt1fqrrpblvOzzz4D0Bu9VbaRpdPRSoWhHKqoRjDdZsB5iCVjDO+//z6qq6vDdsA+iVcj0oeQW0O1P/rIkSM1H1M6r/Czzz4rAk5cvAkJCZpH3LgSr6vAmfTBduc28zplZWU5leeWV/mi0jJUMRhuc1RUlKib8m914sSJgJ03WJB4NcLXjR01apTTw5qZmYmnn34aTzzxhC5LwrsyOG+99RYYY8Id1xL04rgSrytxSOeM1pLffM011ziVV+smAnpfGJ6GKgbDbQZct3ulaaxSwikyTeLVyMKFC3H11Vfj+uuvd+pPHTZsGCIiInQPKldbGLy9vV2IV0t7l+NqFI0rQaakpIhtV+JljAkRSufiUgpOrYngKcsqGJYXcB1xdhXFD+XqE3oh8WokJSUFixYtwpgxY3DjjTfKvnOXw+wJ5bxNra2tIrqrZwVB/jBK23Z82Q/Aef0ftfmfle3C7u5ukUElbXtrES/3QNSaFg6HQ4gkkG1eQB60kuLqpRbKdZ/0QuL1AqnVArQNUnDFXXfdhbvvvlt0w7S1tQnrKY0Ie0LNPfzqq6/EttVqlb0M1MSoDJxxaxUZGSnzKpTiVcvh5i6/2nhaqXACbXn5y0H5YvI0ICQcIPF6gfJh9aVj32g0IiMjQ7wQ2travIrEqon3/fffl9VZKkCp5eVdQLzdzOEPOM+B5ijrpWZ5tYhX2jYOFK6aBK6mxyHLOwDgk6lPmzbNLws784e9paVF9Dv6Kl4lUpFJxTtixAgAvcEuacDm22+/BQCnleeVbXs18bob8igNVvkyf5UWXInXVdZVOFleStLwkqlTp2Lq1Kl+Ox4X73fffSf2uZqwXQ2peHlCQmxsLDo6OnD99dcDkE9LIxWvNKpdU1ODiRMnArgsPDVrNHr0aNHdomY93aUmBqOPl8PFS24zETDUxKt0Y93BxWK320UwiLejx44dC0CeySUVnLQvmFtb4LLwJk+e7HQ+qfVVs7yuAkVA8CLN0nMcOHAA77zzjtjvyvKG07KgJN4+AndNuZVLT0/X9XupC8qtjHIiPHdWhZ9/+PDhYh9/kNUCcp7E686ND4V4gd5sMT7dD783SusfTlPFknj7CMruJj3dREBv+qK0e4YxJgs4eYJnhkld5ECJV0+9fEWZ6NLa2oquri7Rtld6N+E0VSyJt4+gfJD1JGhwpIKRPqBcfL/61a8AAHfffbfTb9Vmv/BFvPx61NxTd8f1NxkZGbJkmJaWFhEBN5lMTqO2yPISulFm/Ojp41Ueo7OzUwiELycK9LriRUVFyMjIcPqtWjqjVvGqZZa5s7zcuvuyzIlWjEYjCgoKMHr0aAC9EXWeQjpo0CDcdNNNAC5Pm0PiJXSjtLx68po5UsFw0Wh1TZUzPkqXx1QTr7St6C7DSq2drTbjZKDh7nFzc7MQb1JSElJSUvDYY48Jb4TES+gmMjJSJlhvgjlq4tXaHaN0m7m7azAYVPOApX2/ahZUmtmkTPYPpXjPnj0r3GbeNImNjRXXQ21ewiukXTZ6uok40r5VPohe6+yTSvFylzkmJkZ1WKFUvO4sL2PM5ULhwRQvDwDW1taqeiXhuKogJWn0IaSJBFqmv1HChXrs2DExuMFby+spqOTJ8kr3dXV1yT6rTRcbaPgaRsDlmT6l90baVffNN98gNjbWKYedMYbDhw/DbDZj1KhRQai1e8jy9iF8ndGBi1c6J5Zey8uDSZ7EK325qJWRdl0ps5tCYXkTExPFveAT9SlnE+EvmH/84x947bXXZLOnAL0Lku/evRt///vfg1Rr95Dl7UMkJCT4tKqdmpXVasGVAStP4pW6nK4GF0RHR6uuYBgK8QK9TZHGxkY0NzcDcH6xxcfHyyYz+Nvf/gYA+M1vfoNXXnlFVtbV3FzBhCxvH2LRokWYOHEiVqxY4dXv1ays1hFPet3miIgIMSDD1dQ/rmaRVFtZMBgo+86V16acCJCjFC7gu5fkD7wS76ZNm5Ceno6YmBhkZWVh//79bstXVFQgKysLMTExuOqqq1BaWupUZseOHcjIyIDJZEJGRgbefvtt3edljKG4uBhWqxWxsbG44YYbcPz4cW8uMSQkJyfjjjvu8CpYBTiP/gEg+jc9oRSalkSKgoIC/O53v3NZxpV4+edgW16leKXtYEDfFDjSHPRQoVu827dvx8qVK/Hkk0+iuroa06dPx5w5c1yuB3Pq1CncfPPNmD59Oqqrq/HEE09g+fLl2LFjhyhTWVmJBQsWID8/H5999hny8/Nxzz334NChQ7rO++c//xnr1q3Dhg0bcOTIEVgsFsyaNatfL7AsRSn6Bx54QHOmlquuInfijY6OduuWuxJvMHObpUibFcpF1gB947K3bt0a8vmudIt33bp1eOCBB7BkyRKMGzcOJSUlSEtLw8svv6xavrS0FMOHD0dJSQnGjRuHJUuWYPHixXjppZdEmZKSEsyaNQuFhYUYO3YsCgsLMXPmTJSUlGg+L2MMJSUlePLJJ3HnnXciMzMTr7/+Otrb27F161a9lxmWKPOh9azR443l1XtMTjCHBEqRvizUvJT77rsPZrMZEyZMwM9//nOn7/nQSg5vO4cKXX6LzWZDVVUVHn/8cdn+2bNn4+DBg6q/qaysdJolMS8vD5s3b0Z3dzeio6NRWVmJVatWOZXh4tVy3lOnTqGxsVF2LpPJhNzcXBw8eBC//e1vnerW1dUli4SG+yrqSkuiZb0kjt42r576KMWrdflSf+NJvIMGDZIt3DZz5kwwxnDx4kUx26bVahVDC8vKyrBw4cLAVtoNuixvU1MT7Ha706yHycnJYp1YJY2Njarle3p6RJeGqzL8mFrOy//XU7fnn38eSUlJ4p+yDRSO3H777QDUBx+4Q29XkRb6mnil59MahTcYDLJpcidNmiS29Sy/Ggi8ClgpQ+SewuZq5ZX7tRzTX2U4hYWFaGlpEf/Onj3r8hrChUmTJuGZZ55RHXzgDr1dRd4cE4BYVREIfpvXk+XVCnepg11/JbrcZrPZjMjISCdLdv78edU5iAHAYrGolo+KihIjZ1yV4cfUcl6+Pk9jY6NYjsRT3UwmU9Df/sHAm/5HqdssXXvYF/Gqzd0sHajQ19xmrXCr7Wn1wUCjy/IajUZkZWWhvLxctr+8vBw5OTmqv8nOznYqX1ZWhilTpog3s6sy/Jhazpueng6LxSIrY7PZUFFR4bJuxGX430K6yJk0S8qXY0rFy11mg8EQ9K4if4mXv9BCnQet++6tXr0a+fn5mDJlCrKzs/HKK6+gtrYWBQUFAHpd0bq6OrzxxhsAevsCN2zYgNWrV+PBBx9EZWUlNm/ejDfffFMcc8WKFZgxYwZeeOEFzJs3D++++y727NmDAwcOaD6vwWDAypUr8dxzz2H06NEYPXo0nnvuOcTFxeG+++7z6SYNBKRC4oG7uLg4n7KI3InXZDIFPUPJ3+IN9XxXusW7YMECfP/991izZg0aGhqQmZmJXbt2ielDGxoaZH2v6enp2LVrF1atWoWNGzfCarVi/fr1uOuuu0SZnJwcbNu2DU899RSefvppjBo1Ctu3b5fNzujpvADw2GOPoaOjA0uXLkVzczOmTp2KsrIyr8bGDjRcidcX1MQbqm4iQH493gz8UB7n3LlzaG1t9WrWE3/gld+ydOlSLF26VPW7LVu2OO3Lzc11mo1fyfz58zF//nyvzwv0Wt/i4mIUFxe7PQ7hTEREBCIiIuBwOESaYCDEG6pIMyC3tr680KXC37JlC5YvX+5TvbyFBiYQAj6QIJCWN5TijYyMREFBARwOh0+RYul9CWWiBomXEERFRcnE6+vsju7EG6puFlc9D3pQJsOEaoQRjSoiBPyh7K9tXn/yhz/8QWyHaoQRiZcQ8KBVMNq8oU5w8JWYmBhxDaGKOpN4CQEXG5+Erb+1ef0ND4Bt2LABDQ0NQT8/iZcQKPs+Sbzu+eGHH8T2sWPHgn5+Ei8hUA4hpDavdkLR7iXxEgLlYHR/iZcv7uVwOPqV5eXLxwDAp59+GvTzk3gJwZVXXin77O10PByezGC329HU1IR169bh66+/BhD+ASugN3tw0aJF4rPD4Qjq+Um8hEC5PpKvfZfS/tC//vWvskT+/mB5Afnke8GezIHESwgiIiLETBJ8QW5/oZwpMtQD2f2FNCf89OnTAHonj9i3b5/TfNX+hsRLyDCbzfj973+veyYOV0yfPh2A8/A5X13yvgQfS867iz788EN89NFH+PDDDwN6XhIv4URcXJyu+a/c4WoAQH8a6cWnczp8+DCAyxb41KlTAT0viZcIKK7GzYZ6tQF/MmXKFNX9gV5/mMRLBBS12TKkXSz9gfHjxwPonXpXOpdzc3Oz0wqJ/oRGFREBZeTIkRg6dCjMZjNycnKQmprqN5e8ryBd21cZpHrnnXc8jlP3FhIvEVCio6Px8MMP9ys3WQkXr91ul6VMAsDx48cDJt7+9Qok+iT9WbhAb9OAd3199dVXQTsviZcg/AC3vso0yUA2EUi8BOEHuHj5onZz584F0JsyqVwxwl+QeAnCDygzxqxWq9h34cKFgJyTxEsQfkDZpxsbGyvWOOKTG/gbEi9B+AGleI1Go7C8gcpxJvEShB9QDnE0Go0uV0n0FyRegvADUssbFRWFiIgIEi9BhBvcCisXLPc3lGFFEH6GTwVrsVjQ3t6OpKSkgJyHxEsQfuDqq6/G/v37ZfumTZuGadOmBeyc5DYThB9ISUkJ+jlJvAThByIjIwM+flcJiZcg/ERubi4AYOLEiUE5H7V5CcJPZGdnY/jw4Rg2bFhQzkfiJQg/ERERgbS0tOCdL2hnIgjCr5B4CSJMIfESRJhCbV4JfOa/YC9bQQw8+DMmnW1SLyReCXwWhGAGHYiBTVtbm9fpkwbmi/T7GQ6HA/X19UhISFCdNK21tRVpaWk4e/YsEhMTQ1DDvgndF3Xc3RfGGNra2mC1Wr2e54osr4SIiAikpqZ6LJeYmEgPqQp0X9RxdV98HbBAASuCCFNIvAQRppB4dWAymVBUVNRvFob2F3Rf1An0faGAFUGEKWR5CSJMIfESRJhC4iWIMIXESxBhComXIMIUEq8KI0eOhMFgkP17/PHHZWVqa2tx2223IT4+HmazGcuXL4fNZpOVqampQW5uLmJjY5GSkoI1a9b4lIjeF9m0aRPS09MRExODrKwspxkU+xvFxcVOz4bFYhHfM8ZQXFwMq9WK2NhY3HDDDTh+/LjsGF1dXXjkkUdgNpsRHx+PuXPn4rvvvtNfGUY4MWLECLZmzRrW0NAg/rW1tYnve3p6WGZmJrvxxhvZ0aNHWXl5ObNarWzZsmWiTEtLC0tOTmYLFy5kNTU1bMeOHSwhIYG99NJLobikgLBt2zYWHR3NXn31VfbFF1+wFStWsPj4eHbmzJlQVy1gFBUVsfHjx8uejfPnz4vv165dyxISEtiOHTtYTU0NW7BgARs2bBhrbW0VZQoKClhKSgorLy9nR48eZTfeeCObNGkS6+np0VUXEq8KI0aMYH/5y19cfr9r1y4WERHB6urqxL4333yTmUwm1tLSwhhjbNOmTSwpKYl1dnaKMs8//zyzWq3M4XAErO7B5Prrr2cFBQWyfWPHjmWPP/54iGoUeIqKitikSZNUv3M4HMxisbC1a9eKfZ2dnSwpKYmVlpYyxhi7ePEii46OZtu2bRNl6urqWEREBNu9e7euupDb7IIXXngBQ4cOxeTJk/GnP/1J5hJXVlYiMzMTVqtV7MvLy0NXVxeqqqpEmdzcXFl2TV5eHurr63H69OmgXUegsNlsqKqqwuzZs2X7Z8+ejYMHD4aoVsHhxIkTsFqtSE9Px8KFC3Hy5EkAwKlTp9DY2Ci7JyaTCbm5ueKeVFVVobu7W1bGarUiMzNT932jUUUqrFixAtdeey0GDx6Mw4cPo7CwEKdOncJrr70GAGhsbERycrLsN4MHD4bRaERjY6MoM3LkSFkZ/pvGxkakp6cH/kICSFNTE+x2u9N9SE5OFvegPzJ16lS88cYbGDNmDM6dO4c//vGPyMnJwfHjx8V1q92TM2fOAOj92xuNRrF2r7SM3vs2YMRbXFyMZ5991m2ZI0eOYMqUKVi1apXYN3HiRAwePBjz588X1hiA6nhfxphsv7IM+/9gldpvwxW1a+xP16dkzpw5YnvChAnIzs7GqFGj8Prrr4ulTby5J97ctwEj3mXLlmHhwoVuyygtJYf/Ub755hsMHToUFosFhw4dkpVpbm5Gd3e3eOtaLBanN+n58+cBOL+ZwxGz2YzIyEjVa+wP16eV+Ph4TJgwASdOnMDtt98OoNe6Sudult4Ti8UCm82G5uZmmfU9f/48cnJydJ17wLR5zWYzxo4d6/YfX8lcSXV1NQCIP0h2djY+//xzNDQ0iDJlZWUwmUzIysoSZfbt2ydrK5eVlcFqtbp8SYQTRqMRWVlZKC8vl+0vLy/X/RCGM11dXfjyyy8xbNgwpKenw2KxyO6JzWZDRUWFuCdZWVmIjo6WlWloaMDnn3+u/77pCm8NAA4ePMjWrVvHqqur2cmTJ9n27duZ1Wplc+fOFWV4V9HMmTPZ0aNH2Z49e1hqaqqsq+jixYssOTmZ3Xvvvaympobt3LmTJSYm9suuos2bN7MvvviCrVy5ksXHx7PTp0+HumoB49FHH2V79+5lJ0+eZB9//DG79dZbWUJCgrjmtWvXsqSkJLZz505WU1PD7r33XtWuotTUVLZnzx529OhRdtNNN1FXkT+oqqpiU6dOZUlJSSwmJoZdc801rKioiF26dElW7syZM+yWW25hsbGxbMiQIWzZsmWybiHGGDt27BibPn06M5lMzGKxsOLi4n7TTcTZuHEjGzFiBDMajezaa69lFRUVoa5SQOH9ttHR0cxqtbI777yTHT9+XHzvcDhYUVERs1gszGQysRkzZrCamhrZMTo6OtiyZcvYkCFDWGxsLLv11ltZbW2t7rrQeF6CCFMGTJuXIPobJF6CCFNIvAQRppB4CSJMIfESRJhC4iWIMIXESxBhComXIMIUEi9BhCkkXoIIU0i8BBGm/B/Xx1sB/gGULQAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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aJ92Q2EOI/IO+dOkSvvjiC5EYo0bLssqTWzg8f1weWsvKyhLbU6dO1Ty/vOYajw3o9XrhQvOilZ7gw3yexsi9JT4+XkTwe/fuLfYHIvazZ8/if//7n9tgqBxDaY1iJzc+hMg/qL1793p9nNlsRl1dnUs0HrjqAchBrHbt2uGhhx5SzCZTI7vE/EdvMBhEJN2bJaO4ZQ9GAC0mJgYzZ86E0+lUeCmBiP2tt94SXYDJkye7vE9iJ0KCvB65ryQnJ6Ourk7TsvN+/qlTpxT7s7OzPZ4zJiZG9If5j16v1wuXXJ1DrwW37MGKlmv1yfn9yML0Fv59yzP9ZOTP2BrH8smNDxFy/1CL+++/3+173bp1A+A6zi7TtWtXn++Ju/KyZZcLYKxbt05MtlFjt9vFkN+1koMCgd+PNw8fd2hV5jl69Kiiq8Ifona7HZWVla0iH5/EHiKu1QfOyMjQTDHt3r07+vXrB8A1QCczcuRIn++Ji5277Hq9HiaTSfTBKyoqsH37ds08d97P1+l0QY+Sy/grdvmetRae+OCDDxSv+fe6ZcsWrFmzBl988YWvtxp1kNhDBO9z9ujRAzfddJPL+/Hx8Xj44YdFkgnnN7/5jSJy7k7s/lhXtYtsMBgUEXCOVoCLD/kZjUZNyxks/BW7r318nrlYWloKAPj66699Oj4aIbGHAKfTiY8++ghASz9Xq5BibGwsYmJiXHLKZcvpdDqFRVVHwP0Ru7o4BD+Hun+sJTTexw2lVQeujjL4WstevmfZsssZhwAU8/8PHToUyK1GHRSgCwFylVbGmMhe00Lu23PXXA6AcSublJSkaOtPyqrBYFBYbfmhItPQ0IC0tDTFPnlsPpT4kuQjI48mcG/oxx9/xHvvvYf09HTx3qBBg1BSUoJjx44pxO5PVmC0QZY9REycOBHdunXD1KlTkZiYiClTpmDSpEno168f7rnnHtFOduO59ZbFzrPWUlJSFOf3R+xq95u77/I4N6Bt2bmAIiX2qqoqRcahGlns3EV/7733AADV1dXiPZ6ToD5/MHIHrnfIsoeIQYMGKVY0zc3NBQAMHDhQ0U62oLxvzwsyOhwO8SNOSUlRVF/1x51WD5lxsRcUFODEiRPYuXMnfvzxR1y5csWl3ny43HgtsTc2NmL16tUAgGeeeUbzQSc/oBhjbqPr7ub5NzY24sqVK4pEqBsNsuzXAXPmzMGjjz6qcDfVwpTz0XU6nV8WdtKkSYrXctpr9+7d0bFjRwDAZ599hqVLlyqKSITbjXc4HLDZbLh8+TIOHz4s3ncXuFPHHWw2m8vwZOfOnd2KHXA/DflGgSz7dYDZbNZcH00eY5eH6RISEvyKiHfu3BmZmZkiIUctXHWW3ZYtW0RwMVxil+/hnXfecVla2p0gtWoBqIN8DzzwgOaxycnJqK+v96paTzRDlv06Rbbser0eqamp4jW3wP5QUFAAo9GoORyolfG3efNmxRBgqMUu95211pB3N8SmFrtcWpuTkJDgEogbM2aM6M6Q2ANk5cqVyMrKgslkQl5enssyRGp27NiBvLw8mEwm9OjRA6tWrXJps379euTk5MBoNCInJwcbN270+bqMMSxYsAAZGRmIj4/HLbfcojnzLFLIYjeZTKIoBXC1EIQ/JCYm4qmnnsK0adNc3uvTp4/Lvm+++QZffvll2Cy7TqdTzM9X46nij0xTU5NCvBMnTgQAl/XsBgwYIFx7EnsAFBUVYc6cOXjuuedQVlaGkSNH4o477nCbu1xZWYnx48dj5MiRKCsrw7PPPosnnngC69evF21KSkpQUFCAwsJCHDhwAIWFhZg6daqif+nNdV955RUsXboUy5cvx969e5GWlobbb789oDTNYKK12sr06dNxzz33KFZM9ffcWt2Azp07a1r8r7/+OmxiB5QlqtV468bzuQUA8OyzzyqCpTLx8fEk9mCwdOlS/O53v8P06dPRp08fLFu2DJmZmXj99dc1269atQpdu3bFsmXL0KdPH0yfPh2PPvooXn31VdFm2bJluP322zFv3jz07t0b8+bNw+jRo7Fs2TKvr8sYw7Jly/Dcc89h8uTJyM3NxbvvvosrV664pFVGCrnvysXesWNH9O/fP6QZbO4m1IRT7J7GvN3NFVBbdj5kKdfZ00Kn05HYA8VqtaK0tBRjxoxR7B8zZoxL+SVOSUmJS/uxY8fi22+/Ff+Z7trwc3pz3crKStTU1CjaGI1GjBo1yu29AS0/tPr6esVfqJADduFM+PBUlRYI/dAboD08xuvgu0sfVhfD5P833gyl8c9MYveTCxcuwOFwuGSPderUSVGhRaampkazvd1uF6me7trwc3pzXf6vL/cGAIsWLRKRc7PZ7LFWeqDIY8nhHPt1J3aebRYpy85de/7Qr6ysxOrVq0WpKXmxS+Cq2NV5/wDw8MMPw2g04q677lJc70YvVRXyAJ3a5VQna3jTXr3fm3MGq43MvHnzUFdXJ/7Uc8qDiVwQ0Z9sOX+5lhcRDrFrWXaeQcgF+f7776OqqkoEcLll58dyN17rQdmtWzc8/fTTGDx4MAD4VGE3mgmZ2Pnif2pLee7cObe54mlpaZrt4+LihBvnrg0/pzfX5Vlrvtwb0CK65ORkxV+oGDVqlNiWy06FGk9JJ968Hwy0Hji8og4XNc+QY4wpFqpUW3Z3XpH8UPelwm40EzKxGwwG5OXlobi4WLG/uLhYsX6ZzPDhw13ab926FUOGDBH/Ie7a8HN6c92srCykpaUp2litVuzYscPtvYWbXr16YezYsRgxYoTmkFiouJblDkeXwpNl54KUF6aQLbJ6OStv7lcrRddms4kVb28UQppBN3fuXBQWFmLIkCEYPnw43njjDVRVVWHGjBkAWtzi06dPY82aNQCAGTNmYPny5Zg7dy4ee+wxlJSU4O2338aHH34ozjl79mzcfPPNWLJkCSZNmoSPP/4Y27Ztw86dO72+rk6nw5w5c/Dyyy+jZ8+e6NmzJ15++WUkJCS4zbIKNzqdDsOGDQv7dc1mM3r16oUjR44AaJmPf/z4cfG+p2GxYKEldm7tudjlPAQ50UbtFXgjdrUbzxjDyy+/DADIycnBfffd58vtX7eEVOwFBQW4ePEiFi5ciOrqauTm5mLTpk2i7FJ1dbVi7DsrKwubNm3Ck08+iRUrViAjIwOvvfYapkyZItrk5+dj7dq1eP755/HCCy8gOzsbRUVFijnj17ouAPz5z39GU1MTZs6cidraWgwdOhRbt24Ny4/5ekan02HatGlwOBw4c+YM2rVrpxj6DEYZ6WuhFuyQIUM8lrzmU38NBoOLuH2x7Pzcu3fvFu9VVFR4f+PXOSHPjZ85cyZmzpyp+d4777zjsm/UqFHYt2+fx3Pee++9uPfee/2+LtDyo16wYAEWLFjg8TytldjYWDHaMHnyZGzYsAFAeIKFsmXv27cv7rzzThF15wE6WfRc7Hq9Hm3btlWcyx83fuvWrX7fu6/U1tYiMTExLEOaNBGGuCb9+vVDdnZ2yNZgU9OhQwekp6ejuroa48ePB3DV2jc3N4MxphA7r/dnMBhcZrr548abTCZFGa5QUVVVhdWrV6Nv377XNF7BgMROeEU4x/pjYmLw+OOPK/ZxsTudTthsNsWkHT7MJpfG5miNs6tRu/Gpqali6DOUw3G8u3Dw4EFMmTIFVVVVSEtLC9kDhma9EVGBnM/f3NysEKFs2Q0Gg2Kyiy9uvMPhgNPpVHgNTqdTs1ptIDQ0NODo0aOK4b+ysjK88847+Ne//hXUa8mQZSeiAp7DfuXKFTQ2NiqGxPiSWFo19byxknJ/Wat8t81mC6q1/fvf/47GxkbFQ+nTTz8FABw/fjxkFXPIshNRA3fl1TMT+Uw4rRwBbyYNybEId2IPJnyo0J3HEKqKOSR2ImpwJ3aOvxFtucyXltj9XcZLi2utPNO+fXtFoZJgQmInooZriZ0L9te//jUA15p7nuDHNjU1uYg7mJb9WvUSHnzwwaBdSw312YmowVux5+fno3///j4lSPFj+exKvV6P+Ph41NfXh1XsoUxaIstORA1c7PLyVTL8tU6n8zkTkh/Lh/ESExNdkm0YYwFH5tUJY3379lW8DuWsQhI7ETWoLXtSUpIiABdIFhoXGT+3yWRyEfvOnTvx4osvahbC9Jb9+/crXg8YMMBF8KGC3HgialCL3WAwiOE4IDCryI/lU2NNJpMIptntdjidTnz++ecAgF27dvmV8aYVnDMYDBg/fjzS09MDri14LUjsRNSgduPj4uKQkJAgxB6IZefHypadR+VtNptiCW5/+9Xuip0kJCRgxIgRfp3TF0jsRNSgng2nrpIbDMsui533z202m2LFGX/LV8n9/b59++LSpUvo0qWLv7fsMyR2ImrQErucEBPsPjvvq9tsNkUU3de14Dm8oEbXrl3DMvFFDQXoiKhBXdRCr9fjhx9+ULz2F/Wx6gCdvJSUnOGmnoHnCX5cpBaPJMtORA1qsav7znKpKl9RewUmk0l4DTabTdFdkC37Bx98gGPHjnm1eEekxU6WnYga1GvUJycnKyoUqRfH9AW12J1Op8Kyy/10LlrGGI4dOwYA2LhxI77++muP1+BufDiKdmpBYieihpiYGIXgk5OTMXr0aOTk5ODuu+8O6NzqJBy+TiDQMqVWdtWtVivsdrtLoG7btm0eJ7GoF7IINyR2IqoYO3as2E5JSYFer8d9990X0GKXAFzKgvMxfEB7RdimpiaXJaGBqyWytOABv0iJnfrsRFTRuXNn6PV6OJ1OsZZAMFB3AYxGo2INOPVUWbVrz/G0hFQ418vTgsRORBVt2rTBY489htjY2KAWlFC78erVXeV14wHXCD1Ha598DBA5sZMbT0QdHTp0CCjyroX6wRETE6NY8FE9vGa1WoWwTSaTSI7xJHZ+DhI7QUQQrYo2cp9d7Z7Llj0zM1N0Kbxx4ylARxAR5pFHHkGHDh0wefJkAFfFbrVaFfn4QItwubBNJpMicu+OSFt26rMTxP/TtWtXxcIismvPRZyUlITa2lqFZZfF7ilvnrencXaCuM6Q++0cnrUniz0+Pl5Ya3f16hhjiodDJCCxE4QH1FaYp7o6HA6FeGX3Xgur1SrKX0dK7OTGE4QHZGEaDAaFBZf77FzI7sTO28bGxlI0niCuR2TLbjQahQW32+2abjwXe0VFBT7++GPxOtIuPECWnSA8IovdZDKJ5Bq1G8/hUfp169YBaMkJyM/Pj3hwDiCxE4RHZCGrLbvsxvP6cjabDQcOHBDHXLp0CQAUbSMFufEE4QF5lp3ZbBaWXXbj1YUu5H4778tfD248iZ0gPNChQwexnZycLCx7c3OzsObqPrscgOMWndx4grjO6dixo9g2GAzCsvOMOp1Op4jS22w2RSUbvugEF32o1l73BrLsBOEBeeprU1OTsOxc7CaTyWVhSLmABd++Hiw7iZ0gPCBPkElMTHRZJopbai52h8OBI0eOiGN4+iy39tRnJ4jrmIceegiDBg3C0KFDhbi5W87FL/fTZTfeYrHAbrejoqICAI2zE8R1TXZ2NrKzswG49rm5my7Xr5dxOp146aWXxGtvy06HArLsBOEDarHzZZt0Op1C8ImJiZrH5+TkhO7mrgGJnSB8QC12uQa87MpruevZ2dkuhS3DCYmdIHxALXZ5oQpZ7FrVaHxdMz7YkNgJwgfUIpYr3F5L7P6u/hosSOwE4QPeWna9Xo+JEycq2kbShQdI7AThE+qS0vJrtWUfNGgQHnroIbEv2BVxfYXEThBBQitAJ7vzwVzUwh9I7AThIz179gQA/Pa3v1Xsl1Nh+Xh6+/btodfrkZqaGtDCk8GAkmoIwkceeOABMMZcas3L02G5y56QkIA5c+bAaDRq1qYPJyR2gvADLeHyKa8AMHz4cLEdqfXY1ZAbTxBBonfv3gBaZspFMgfeHWTZCSJIdO3aFY8//jjatm0b6VvRJKSWvba2FoWFhTCbzTCbzSgsLPS4fjXQUsZnwYIFyMjIQHx8PG655RYcPHhQ0cZiseCPf/wjUlNTkZiYiIkTJ+Knn37y+dqzZ89GXl4ejEYjBg4cGIRPTLR20tPTIzpn3RMhFfsDDzyA/fv3Y8uWLdiyZQv279+PwsJCj8e88sorWLp0KZYvX469e/ciLS0Nt99+OxoaGkSbOXPmYOPGjVi7di127tyJn3/+GRMmTFD0mby5NmMMjz76KAoKCoL7wQnieoSFiIqKCgaA7d69W+wrKSlhANjhw4c1j3E6nSwtLY0tXrxY7GtubmZms5mtWrWKMcbY5cuXmV6vZ2vXrhVtTp8+zWJiYtiWLVv8uvb8+fPZgAEDfP6MdXV1DACrq6vz+ViC8IVg/NZCZtlLSkpgNpsxdOhQsW/YsGEwm83YtWuX5jGVlZWoqanBmDFjxD6j0YhRo0aJY0pLS2Gz2RRtMjIykJubK9r4c21vsFgsqK+vV/wRRLQQsgBdTU2Nolgfp2PHjqipqXF7DAB06tRJsb9Tp044efKkaGMwGBRjmrwNP96fa3vDokWL8Je//MVlP4meCDX8N8b+vzS1P/gs9gULFmj+4GX27t0LQHsskmkkI6hRv+/NMeo2/l7bE/PmzcPcuXPF69OnTyMnJweZmZl+n5MgfKGhocHvTDyfxT5r1ixMmzbNY5vu3bvju+++w9mzZ13eO3/+vIvl5qSlpQFosczp6eli/7lz58QxaWlpsFqtqK2tVVj3c+fOIT8/X7Tx9dreYDQaFbOe2rRpg1OnTiEpKcnlIVJfX4/MzEycOnUq4rOdrjfou9HG0/fCGENDQwMyMjL8v0BQogca8CDZnj17xL7du3d7FaBbsmSJ2GexWDQDdEVFRaLNmTNnNAN03l7b3wCdJyh45x76brQJ9fcSMrEzxti4ceNY//79WUlJCSspKWH9+vVjEyZMULTp1asX27Bhg3i9ePFiZjab2YYNG1h5eTm7//77WXp6OquvrxdtZsyYwbp06cK2bdvG9u3bx2677TY2YMAAZrfbfbr20aNHWVlZGfv973/PbrrpJlZWVsbKysqYxWIJ+LPTD9o99N1oE9Viv3jxInvwwQdZUlISS0pKYg8++CCrra1V3gDAVq9eLV47nU42f/58lpaWxoxGI7v55ptZeXm54pimpiY2a9Ys1q5dOxYfH88mTJjAqqqqfL72qFGjGACXv8rKyoA/O/2g3UPfjTZRLfbWTHNzM5s/fz5rbm6O9K1cd9B3o02ovxcdYwHE8gmCiBpo1htBtBJI7ATRSiCxE0QrgcROEK0EEjtBtBJI7EGge/fu0Ol0ir9nnnlG0aaqqgp33XUXEhMTkZqaiieeeMJlRc/y8nKMGjUK8fHx6Ny5MxYuXBjQxIfrkZUrVyIrKwsmkwl5eXn46quvIn1LIWXBggUuvw2eFg4Er1iLV4RkQK+V0a1bN7Zw4UJWXV0t/hoaGsT7drud5ebmsltvvZXt27ePFRcXs4yMDDZr1izRpq6ujnXq1IlNmzaNlZeXs/Xr17OkpCT26quvRuIjhYS1a9cyvV7P3nzzTVZRUcFmz57NEhMT2cmTJyN9ayFj/vz5rG/fvorfxrlz58T7ixcvZklJSWz9+vWsvLycFRQUaGaMdu7cmRUXF7N9+/axW2+91SVj1BtI7EGgW7du7K9//avb9zdt2sRiYmLY6dOnxb4PP/yQGY1GkS21cuVKZjabFQkVixYtYhkZGczpdIbs3sPJL3/5SzZjxgzFvt69e7NnnnkmQncUejzNuwhWsRZvITc+SCxZsgTt27fHwIED8dJLLylc9JKSEuTm5ipmLI0dOxYWiwWlpaWizahRoxSz6saOHYszZ87gxIkTYfscocJqtaK0tFRRdAQAxowZE1BBkWjg6NGjyMjIQFZWFqZNm4bjx48DCF6xFm+h6rJBYPbs2Rg8eDBSUlLwzTffYN68eaisrMRbb70FoGXKrnpqbUpKCgwGg6LgRvfu3RVt+DE1NTXIysoK/QcJIRcuXIDD4dAsTBJIQZHrnaFDh2LNmjW46aabcPbsWbz44ovIz8/HwYMHg1asxVtI7G7wtkjHkCFD8OSTT4p9/fv3R0pKCu69915h7QHvimloFe1wd2y04k9hkmjmjjvuENv9+vXD8OHDkZ2djXfffRfDhg0DEJxiLd5AYneDt0U6tOD/iceOHUP79u2RlpaGPXv2KNrU1tbCZrMpinKon9Tnzp0D4Prkj0ZSU1MRGxur+RlvhM/nLYmJiejXrx+OHj2Ku+++G0DgxVq8hfrsbkhNTUXv3r09/rlb9aOsrAwAxH/g8OHD8f3336O6ulq02bp1K4xGI/Ly8kSbL7/8UtHX37p1KzIyMtw+VKIJg8GAvLw8FBcXK/YXFxf7/KONZiwWCw4dOoT09HRkZWUhLS1N8Z1YrVbs2LFDfCd5eXnQ6/WKNtXV1fj+++99/958CucRLuzatYstXbqUlZWVsePHj7OioiKWkZHBJk6cKNrwobfRo0ezffv2sW3btrEuXbooht4uX77MOnXqxO6//35WXl7ONmzYwJKTk2/Iobe3336bVVRUsDlz5rDExER24sSJSN9ayHjqqafY9u3b2fHjx9nu3bvZhAkTWFJSkvjMwSrW4g0k9gApLS1lQ4cOZWazmZlMJtarVy82f/581tjYqGh38uRJduedd7L4+HjWrl07NmvWLJd5y9999x0bOXIkMxqNLC0tjS1YsOCGGXbjrFixgnXr1o0ZDAY2ePBgtmPHjkjfUkjh4+Z6vZ5lZGSwyZMns4MHD4r3g1WsxRtoPjtBtBKoz04QrQQSO0G0EkjsBNFKILETRCuBxE4QrQQSO0G0EkjsBNFKILETRCuBxE4QrQQSO0G0EkjsBNFK+D+Rsv62H15unwAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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8GQMHDgQgH/wvDVv5JPz2PK+rbV6pnVIozhiTnZ+L13aUldVqFcX70EMPQafTgTEmvgepO0Hi7SacPXsWQFsfqslkQkxMDABlz8tDZqD9riIlz9ueeJUyzs3NzaLHlrZ56+vrZV7/1q1bsFqtUKvViIiIwODBgwEA165dc+k+dCVIvN2E48ePAwDGjh0LAIiOjgbQ9moTHq46E69t2Mwn7Cu1eZ0lrKTfpaEuP7dKpYJGo4FerxfDa2noXFFRAQDo27cvBEFAv379AMjnAdty5MiRLumZSbw/ce7cuYOXX34Zn332md0kd1e5e/euOE+WvyRbo9EgPDwcAFBZWSnaAXLxOgqbuXj5Uq6ActisJHIlz2sbsguCoBg6c5Fy0fIfIdskFqeqqgp79uzBtm3butxQSxLvTxjGGLZu3QoAOHnypCzEdQeeYQ4NDZWtCMnfkWsrXj4pAfjRS1qtVkVRSm1dSVhJy5R6XtthmQAUk1ZcvFy0/Brq6uoUX6Ny/PhxqFQqMMZw5swZu/2BDInXTRwtQN4RXL9+XeZ1jh07Jtt/5MgRvPTSS3j++efFfbYDFqqqqnDo0CEAgMlkku3r06cPAOee11HXjrueV2qn1I5WOret52WMiQk2LlqdTicuDq/kfaUjsLrae4HV/q5AoGCxWLB+/XoAwPjx48Vuio6EP6harRZNTU0oLi5GdXU1evXqhaqqKnzxxRdQq9UICwvD559/joMHD6K8vByvvvoqrly5gn/9619oampCaGgoGGMYMWKErHzuvXg7UklA/A2AVqsVzc3NoqflnlIqSmee1xvxVldXA2j7P2hsbIQgCKJ4AWDAgAGoqqpCeXk5Bg0aJG6vra2Vee0LFy7AarXK5h4HMl3jKjoB7r0A4ODBg3YhWmtrK3bs2CGz85YbN24AAJKTk9G3b18AwH/+8x8AQFFRkZ19fX09+vTpg7fffhuffPIJdDqdmLVNTU0Vv3O4eCsrK2XT7/h8Wo5Su1cpbHa3zauUsJKWxz1qVVUVgB8z5kajUawTAMTFxQEACgsLZXkB7mmjoqKgVqvR2tqK5cuX48CBA+gKkHhd5KGHHhLDTAD4/vvvZfuPHTuGc+fO4cCBA+JD5gir1Ypt27bh7bffdhqG8yRTnz59kJKSAgAoLS3FjRs3cO7cOQDA1KlTsWjRIoerMZ4/fx5Tp07FxIkT7fZFRkYiKCgILS0tqK6udihepYxzZ3heLl7+elAeFvOkGychIQGCIKChoUEWOu/fvx8AMHjwYAwdOlS8tq+++ko2sixQIfG6yIABA7BgwQIxXJa2n5qampCbmyv+bds2taW4uBgXLlxAdXW1bLiiLbwt2qdPHwwdOhS9e/eGIAj45JNPRK+cmJiIiIgI/PnPf8aQIUMwbNgwccTRr3/9a7z33nt4+OGHFcsPCgoSPXplZWW74uVCZIz53PMqJawiIyMBtIXLLS0tYrKK11laHhf0yZMnAQA//PCDeD2DBw/G5MmTxfMJgoCDBw8q3hNvaWpq8jix6C4kXjfhwwrPnz8vepji4mLZg3jx4kU0NDSgqakJGzZswKZNm8T9VqsVOTk5ou3hw4cVu4Dq6+vFhy8qKgqCIOCxxx4D8KOoDQaDKDSdTocnnngCM2fORFZWFlavXo3Y2Fixr9QRPPt8+/Ztu2VoOLZdOy0tLaJAPfG8tu1mQDlsDgkJEetfVVUlemBpBMRJSkoC0BY6W61Wcbxzr1690K9fP6jVarz22msYN24cAKCkpMTnQypv376NV199FVlZWTh9+rRPy1aCxOsmRqMRer0eTU1NYojGf2nHjh2LXr16gTGGc+fOYdu2bbh16xZu3LiBv/zlL9i8eTO++eYb1NTUyN4B9MUXX9idhws0PDxcFE///v3Rv39/0eb+++/3+np4Xy+/hqCgIJn3A+w9JfemgiC0O8JKKdvMBSpdcE4pbJYOwjhw4ACsVitUKpVYZyl8uKfVasX58+dRUlICAJg0aZKsvPHjx4vn93XX0eHDh8UE3//+9z+flq0EiddNgoKCEB8fD6DNwwI/PvgxMTEYNmwYAOCzzz6TjRtubW1FZWUl9u7dCwAYOnQoMjIyAABHjx7Fu+++K2uL/fDDDwAgy6oCwG9/+1sAbQ8iP5c39OrVC8CP0+x69+5tl421Fa+0vSt9YbZt2Gy1WhVnH3GBSj2vUtgMAA888AAAiG18g8GgmC3W6XSibWFhoRi18P8rjiAIYv7AVryMMWzcuBGvvvqq4kIFSkjf6iBtSlkslg5f/dIj8W7atAnx8fHQ6/UwmUztZlhzc3NhMpmg1+sxcOBAbNmyxc4mOzsbiYmJ0Ol0SExMxK5du9w+L2MMZrMZRqMRwcHBGD9+fId0zA8YMABAW1dOfX29GM7FxMTY9aWOGjUKY8eOtVvXKSkpCaNGjZKNEPryyy+RlZWF2tpacThfbGys7DhBELBq1SqsXLlSTOh4AxcvT0YphaSOPK80xAXsh0dKw1JnYbPtpAQpDz/8sMyTS7uCbOFtey70yMhIuzoCEMdDl5WViXUsKSnBunXrcPPmTbS2tuKll15yOGqLs337dvzjH/9AaWkpGhoaxIkfvClSVlbm9HhvcVu8O3fuxJIlS7By5UoUFRUhNTUVU6ZMcdhILysrw9SpU5GamoqioiK8+OKLWLRoEbKzs0Wb/Px8zJkzB/PmzcPJkycxb948zJ49G0ePHnXrvK+//jrefPNNbNiwAd988w0MBgMmTZrk80XM+KD+y5cvi941MjJSXAFi/PjxsFqt6NWrFyZMmIBHH30UZrMZ8+fPh9VqxYgRIzBw4EAIgoDMzEykpqaKZVutVuzbt08Ur63n4EjfUOANtmKNioqys7FNWCllmgH7sJnbS9e5AuSel/cfc09nKzadToenn34aZ8+eRXNzM5KTkx1eS1xcnCwS4KG0LVFRUejZsycYY7hw4QJqa2uxbds2ux+b9957D3V1dWCMiX3NnNu3b+P7779HUFAQ9uzZI06MCAsLQ2JiIoAfI7OOwu1BGm+++SaefvppPPPMMwCA9evX44svvsDmzZuxZs0aO/stW7ZgwIAB4gCHwYMH4/jx43jjjTfwxBNPiGVMmjQJK1asAACsWLECubm5WL9+PbZv3+7SeRljWL9+PVauXInHH38cQNs6R9HR0fjoo4/w3HPP2dWtsbFR9h/maphjMBigUqnQ0NAgJia4oIG2eabDhw9HaGio+DAJgoC4uDi8/PLLduU98sgjGDx4MMrLy7F3715xVFBcXJxd2OxrIiIixEEggG89r1J71/a4xsZG0U6tVsva0JzY2Fhs2bIF1dXVip6Uo1arER0dLQ46keYHbElKSkJ+fj7OnTuHr776SvzhiYyMxKBBg5CXl4fW1lb87W9/Q3l5OcLDw6HX61FXV4dp06bhyy+/FMu6deuWmISMiYlBfHw8vv76a5w9exYzZsyQ/aD4Erc8b1NTEwoKCpCeni7bnp6ejry8PMVj8vPz7ewzMjJw/PhxMVRzZMPLdOW8ZWVlqKiokNnodDqkpaU5rNuaNWsQHh4ufqQCdIZKpRIfcqnQpISFhbn1n9avXz+YTCbx4RUEATNnznT5eE8RBEHWlWR7HYB9ttmRKB15Xls7PnMIaEtU8QEvPXv2dHjPwsPD7ZoQSkyfPh21tbVQqVRieKwED79PnToldkENHz4cCxcuxKRJkzB9+nTxGqKjo8VrCAkJkQmX/5hwz5ucnIzY2Fjxx70jX5bmlnh5e4C30zjSXztbKioqFO1bWlrEQQiObHiZrpyX/+tO3VasWCFOUrdYLG7daKlHFATBaVvMVdRqNZ555hn87Gc/wx/+8AexPdrRTJw4EWq1GhMnTrQbhQU4T1hJcdXzAvLQWSpebzEajZg+fTp+85vfyEZh2RITEyNLfA0YMAAzZswQ/05KShJ/SOrq6qDRaOxC5+HDh4sJRKCt/5l3z/F+58OHD3t9TY7waGyz7a8jY8ypl1Gyt93uSpm+suFI353jLv369cOJEycAQEyi+YI+ffoojobqSARBwMqVKx3udzVstu3nVRqgwQkODobFYrHzvL7AlXHnQUFBGDZsGAoLC6FWqzF//nzZfo1GgxdffBE3btwQu6sYY7hz5w7WrFmDBQsWiGH5448/jpycHPzud78Tn7WHH34YZ86cQUlJCfLy8jBmzBifXJsUt8QbFRUFlUpl58kqKyvtPB7HYDAo2qvVanEEjSMbXqYr5+WesKKiQrzZ7dXNG+69917xu+2A/66GI/E68rzthc1Ax3led5g2bRpUKhUyMjIUu5/UarXsWRIEAaGhoVi9erXMLikpSRwkwomNjcXAgQNRWlpql0H3FW6FzVqtFiaTSTZCCABycnIc/rKkpKTY2e/btw/JycliWOPIhpfpynnj4+NhMBhkNnzYYkf86kVGRmLOnDl47LHH8OCDD/q8/J8SrobN7npeXpa/xCsIAqZOneqzzL0tv/zlLzF79mxxXLWvcTtsXrZsGebNm4fk5GSkpKTgnXfeQXl5OTIzMwG0tSOvXLmCDz74AACQmZmJDRs2YNmyZXj22WeRn5+PrKwsMYsMAIsXL8a4ceOwbt06zJw5E7t378b+/ftl7YX2zisIApYsWYLVq1fj/vvvx/3334/Vq1ejR48eeOqpp7y6SY6wHSDfVXEkXluP4sjzOhNvR4TNPxUEQXCaNPMWt8U7Z84c3Lp1C6+88gquXbuGIUOGYM+ePWIm8Nq1a7K+1/j4eOzZswdLly7Fxo0bYTQa8de//lXsJgKAMWPGYMeOHfjTn/6El156Cffeey927tyJUaNGuXxeAHjhhRdQX1+PBQsW4Pbt2xg1ahT27dunmIQhXMdWvI4mMNhmm11JWHVl8XY0HiWsFixYgAULFiju++c//2m3LS0tDYWFhU7LnDVrFmbNmuXxeYG2Xzqz2Qyz2ey0HMI9pF1Fzub9OhphpSRefqx0+RoSr3vQ2GaiXaRh7927d0VRthc2O2vz8mjozp07JF4PIfES7cJnygBtwwKBthC5vYSVM8/LhVpRUQGr1QpBEEi8bkLiJdpFEATRe/IlaaRvVeC443m5UPmY5rCwsA7L+nZVSLyES/DsMJ9nbNveBTzzvBze50+4DomXcAkuVj6EVGk6Il/1gntTZ56XvxWBozSbiXAOiZdwCS5e3g2oJF4+6Ka5uRmtra3ixBNHQ0elo5eczQAilCHxEi5hGyYrhblS8UqnWjoaP240GsXvrswYIuSQeAmXsG2jSr0mR7qkKxevWq12mIgaO3YsGhsbcd9994kLrBOuQ29MIFxCGiYLgqA4aZ97XqvVKi5r42y2VXBwMJYsWaJYFtE+JF7CJaRrJfft21fRm0rnz/Klh9qbKmm7BjPhOhQ2Ey7Bp2UCbW+PUEKlUol9v1y8ns6XJtqHxEu4hFqtxqxZs9DY2IjRo0cr2giCIHpfVz0v4TkkXsJlEhISsHr1aqdv2eNJKz5emcTbcZB4CZ9i63kpbO44SLyET+Hi5Z6XxNtxkHgJn0Jt3s6DxEv4FN7m5RP2nS2STngHiZfwKbZrJSvNPiJ8A4mX8Cm2nrajlj0lSLyEj7EVL3nejoPES/gUW09L4u04SLyET5GKV6VSUcKqAyHxEj5FKtaIiIgOe70lQeIlfIz0zYb8DfFEx0DiJXxK3759RW9L83Q7FhIv4VPUajUmTJiA+vr6DnnBG/EjNBmf8DmpqakYPHgwZZo7GPK8RIdAS7l2PCRegghQSLwEEaCQeAkiQCHxEkSAQtlmCYwxAEBNTY2fa0J0dfgzxp85TyDxSuCrP8TExPi5JkR3oba2FuHh4R4dKzBvpN/FsFqtuHr1KkJDQxXH5NbU1CAmJgaXL1+m13NIoPuijLP7whhDbW0tjEaj09U4nUGeV0JQUJBLb6sLCwujh1QBui/KOLovnnpcDiWsCCJAIfESRIBC4nUDnU6HVatW0VrENtB9Uaaj7wslrAgiQCHPSxABComXIAIUEi9BBCgkXoIIUEi8BBGgkHgViIuLgyAIss/y5ctlNuXl5Zg+fTpCQkIQFRWFRYsWoampSWZz+vRppKWlITg4GPfccw9eeeUVrwai/xTZtGkT4uPjodfrYTKZcOjQIX9XqUMxm812z4bBYBD3M8ZgNpthNBoRHByM8ePH48yZM7IyGhsb8fvf/x5RUVEICQnBjBkz8MMPP7hfGUbYERsby1555RV27do18VNbWyvub2lpYUOGDGETJkxghYWFLCcnhxmNRrZw4ULRxmKxsOjoaDZ37lx2+vRplp2dzUJDQ9kbb7zhj0vqEHbs2ME0Gg3bunUrKy4uZosXL2YhISHs0qVL/q5ah7Fq1Sr24IMPyp6NyspKcf/atWtZaGgoy87OZqdPn2Zz5sxh/fr1YzU1NaJNZmYmu+eee1hOTg4rLCxkEyZMYEOHDmUtLS1u1YXEq0BsbCx76623HO7fs2cPCwoKYleuXBG3bd++nel0OmaxWBhjjG3atImFh4ezhoYG0WbNmjXMaDQyq9XaYXXvTEaOHMkyMzNl2xISEtjy5cv9VKOOZ9WqVWzo0KGK+6xWKzMYDGzt2rXitoaGBhYeHs62bNnCGGOsurqaaTQatmPHDtHmypUrLCgoiO3du9etulDY7IB169YhMjISw4YNw2uvvSYLifPz8zFkyBAYjUZxW0ZGBhobG1FQUCDapKWlyUbXZGRk4OrVq7h48WKnXUdH0dTUhIKCAqSnp8u2p6enIy8vz0+16hxKSkpgNBoRHx+PuXPnorS0FABQVlaGiooK2T3R6XRIS0sT70lBQQGam5tlNkajEUOGDHH7vtGsIgUWL16MESNGICIiAseOHcOKFStQVlaGd999FwBQUVGB6Oho2TERERHQarWoqKgQbeLi4mQ2/JiKigrEx8d3/IV0IDdv3kRra6vdfYiOjhbvQVdk1KhR+OCDD/DAAw/g+vXrePXVVzFmzBicOXNGvG6le3Lp0iUAbf/3Wq3W7m0Snty3biNes9mMl19+2anNN998g+TkZCxdulTc9tBDDyEiIgKzZs0SvTEAxfm+jDHZdlsb9v/Jqq70/h6la+xK12fLlClTxO9JSUlISUnBvffei/fffx+jR48G4Nk98eS+dRvxLly4EHPnznVqY+spOfw/5fz584iMjITBYMDRo0dlNrdv30Zzc7P4q2swGOx+SSsrKwHY/zIHIlFRUVCpVIrX2BWuz1VCQkKQlJSEkpIS/OIXvwDQ5l379esn2kjvicFgQFNTE27fvi3zvpWVlW6/YaLbtHmjoqKQkJDg9KPX6xWPLSoqAgDxPyQlJQXffvstrl27Jtrs27cPOp0OJpNJtPnqq69kbeV9+/bBaDQ6/JEIJLRaLUwmE3JycmTbc3JyutVrThobG/Hdd9+hX79+iI+Ph8FgkN2TpqYm5ObmivfEZDJBo9HIbK5du4Zvv/3W/fvmVnqrG5CXl8fefPNNVlRUxEpLS9nOnTuZ0WhkM2bMEG14V9HEiRNZYWEh279/P+vfv7+sq6i6uppFR0ezJ598kp0+fZp9+umnLCwsrEt2FWVlZbHi4mK2ZMkSFhISwi5evOjvqnUYzz//PDt48CArLS1lR44cYdOmTWOhoaHiNa9du5aFh4ezTz/9lJ0+fZo9+eSTil1F/fv3Z/v372eFhYXskUceoa4iX1BQUMBGjRrFwsPDmV6vZ4MGDWKrVq1idXV1MrtLly6xn//85yw4OJj17t2bLVy4UNYtxBhjp06dYqmpqUyn0zGDwcDMZnOX6SbibNy4kcXGxjKtVstGjBjBcnNz/V2lDoX322o0GmY0Gtnjjz/Ozpw5I+63Wq1s1apVzGAwMJ1Ox8aNG8dOnz4tK6O+vp4tXLiQ9e7dmwUHB7Np06ax8vJyt+tC83kJIkDpNm1eguhqkHgJIkAh8RJEgELiJYgAhcRLEAEKiZcgAhQSL0EEKCRegghQSLwEEaCQeAkiQCHxEkSA8n8Re/vIqBFdDAAAAABJRU5ErkJggg==\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from itertools import repeat\n", + "\n", + "pre = 0.5\n", + "post = 0.5\n", + "\n", + "sr = wm_ref_data.info['sfreq']\n", + "\n", + "evs = [] \n", + "durs = [] \n", + "descriptions = []\n", + "stas = [] \n", + "stes = [] \n", + "data = wm_ref_data.get_data()\n", + "\n", + "for ch in wm_ref_data.ch_names: \n", + " fig, ax = plt.subplots(1, 1, figsize=(2, 2))\n", + "\n", + " ch_ix = wm_ref_data.ch_names.index(ch)\n", + " # Plot the IEDs \n", + " signal = data[ch_ix, :]\n", + " IED_ts = IED_times_s[ch]\n", + " sta, ste = analysis_utils.lfp_sta(IED_ts, signal, sr, pre, post)\n", + " stas.append(sta)\n", + " stes.append(ste)\n", + " \n", + " ts = np.linspace(-500, 500, len(sta))\n", + " ax.plot(ts, sta, 'gray')\n", + " ax.fill_between(ts, sta-ste, \n", + " sta+ste, facecolor='k')\n", + " \n", + " \n", + " \n", + "# # Nan out the IEDs: \n", + "# starts = np.ceil((IED_ts - 0.1) * wm_ref_data.info['sfreq']).astype(int)\n", + "# stops = np.floor((IED_ts + 0.1) * wm_ref_data.info['sfreq']).astype(int)\n", + " \n", + "# for a,b in zip(starts, stops): \n", + "# signal[a:b] = np.nan\n", + " \n", + "# data[ch_ix, :] = signal\n", + " \n", + "# # Make events and annotate \n", + "# evs = evs + (IED_ts - 0.1).tolist()\n", + "\n", + "# durs = durs + (0.2 * np.ones_like(IED_ts)).tolist()\n", + "\n", + "# descriptions = descriptions + ([f'BAD_ied_{ch}']*len(IED_ts))\n", + "\n", + "# # Let's replace the data with the IED's nan-ed out\n", + "# wm_ref_data._data = data\n", + "# # Make mne annotations based on these descriptions\n", + "# annot = mne.Annotations(onset=evs,\n", + "# duration=durs,\n", + "# description=descriptions)\n", + "\n", + "# wm_ref_data.set_annotations(annot)\n", + "\n", + "# events_from_annot, event_dict = mne.events_from_annotations(wm_ref_data)\n", + " \n", + "fig, ax = plt.subplots(1, 1, figsize=(2, 2))\n", + "ts = np.linspace(-500, 500, len(sta))\n", + "ax.plot(ts, np.nanmean(stas, axis=0), 'gray')\n", + "ax.fill_between(ts, np.nanmean(stas, axis=0)-np.nanmean(stes, axis=0), \n", + " np.nanmean(stas, axis=0)+np.nanmean(stes, axis=0), facecolor='k')\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Now process the behavioral data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, one should load in their own functions for behavioral stuff. I'll just write the functions relevant to me here for demonstration purposes. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Utility functions for image memorability ratings. \n", + "import pandas as pd \n", + "import numpy as np \n", + "import os \n", + "from scipy.stats import norm, zscore, linregress\n", + "\n", + "# Note: Much of the following is ported from: https://github.com/cvzoya/memorability-distinctiveness\n", + "\n", + "def dprime(pHit, pFA, PresentT, AbsentT, criteria=False):\n", + " \"\"\"\n", + " Note: from: http://nikos-konstantinou.blogspot.com/2010/02/dprime-function-in-matlab.html\n", + " \n", + " \n", + " Parameters\n", + " ----------\n", + " pHit : float\n", + " The proportion of \"Hits\": P(Yes|Signal)\n", + " pFA : float\n", + " The proportion of \"False Alarms\": P(Yes|Noise)\n", + " PresentT : int\n", + " The number of Signal Present Trials e.g. length(find(signal==1))\n", + " AbsentT : int\n", + " The number of Signal Absent Trials e.g. length(find(signal==0))\n", + "\n", + " \n", + " Returns\n", + " -------\n", + " dPrime: float\n", + " signal detection theory sensitivity measure \n", + " \n", + " beta: float\n", + " optional criterion value\n", + " \n", + " C: float\n", + " optional criterion value\n", + " \n", + " \"\"\"\n", + "\n", + " if pHit == 1: \n", + " # if 100% Hits\n", + " pHit = 1 - (1/(2*PresentT))\n", + " \n", + " if pFA == 0: \n", + " # if 0% FA \n", + " pFA = 1/(2*AbsentT)\n", + " \n", + " # Convert to Z-scores\n", + " \n", + " zHit = norm.ppf(pHit) \n", + " zFA = norm.ppf(pFA) \n", + " \n", + " # calculate d-prime \n", + " \n", + " dPrime = zHit - zFA \n", + " \n", + " if criteria:\n", + " beta = np.exp((zFA**2 - zHit**2)/2)\n", + " C = -0.5 * (zHit + zFA) \n", + " return dPrime, beta, C\n", + " else:\n", + " return dPrime\n", + " \n", + "# def calcMI(pmf):\n", + "# \"\"\"\n", + " \n", + "# Parameters\n", + "# ----------\n", + " \n", + " \n", + "# Returns\n", + "# -------\n", + " \n", + "# \"\"\"\n", + " \n", + "# pmf_1 = np.sum(pmf,axis=1) # marginal over first variable\n", + "# pmf_2 = np.sum(pmf,axis=0) # marginal over second variable\n", + " \n", + "# MI = 0\n", + "# for i in range(np.shape(pmf)[0]):\n", + "# for j in range(np.shape(pmf)[1]):\n", + "# MI += pmf[i, j] * np.log(pmf[i, j] / (pmf_1[i]*pmf_2[j]))\n", + " \n", + "# return MI\n", + "\n", + "def compute_memorability_scores(hits, false_alarms, misses, correct_rejections):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " hits : array-like\n", + " TODO\n", + " false_alarms : array-like\n", + " TODO\n", + " misses : array_like \n", + " TODO\n", + " correct_rejections : array_like \n", + " TODO\n", + " \n", + " Returns\n", + " -------\n", + " memory_ratings : pandas DataFrame \n", + " DataFrame with the following ratings added: HR (hit rate), FAR (false alarm rate), ACC (accuracy), DPRIME (d-prime), MI (mutual information)\n", + " \"\"\"\n", + " \n", + " \n", + "\n", + " len_args = [len(hits), len(false_alarms), len(misses), len(correct_rejections)]\n", + " if not all(len_args[0] == _arg for _arg in len_args[1:]):\n", + " raise ValueError(\"All parameters must be the same length.\")\n", + " \n", + " memory_ratings = pd.DataFrame(columns = ['HR', 'FAR', 'ACC', 'DPRIME'])\n", + " # , 'MI'\n", + " \n", + " reg = 0.1 # regularization for MI calculation\n", + "\n", + " nstimuli = len(hits) \n", + "\n", + " hm = hits+misses\n", + " fc = false_alarms+correct_rejections\n", + "\n", + " hrs = hits/hm\n", + " fars = false_alarms/fc\n", + " accs = (hits+correct_rejections)/(hm+fc)\n", + "\n", + " dp = []\n", + "# mis = []\n", + " for i in range(nstimuli):\n", + " dp.append(dprime(hrs[i], fars[i], hm[i], fc[i]))\n", + "# pmf = np.array([[correct_rejections, misses], \n", + "# [false_alarms, hits]]) + reg\n", + "# pmf = pmf/np.sum(pmf)\n", + "# mis.append(calcMI(pmf))\n", + " \n", + "\n", + " memory_ratings['HR'] = hrs\n", + " memory_ratings['FAR'] = fars\n", + " memory_ratings['ACC'] = accs\n", + " memory_ratings['DPRIME'] = dp\n", + "# memory_ratings['MI'] = mis\n", + " \n", + " return memory_ratings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Synchronize to behavioral data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to analyze the neural data with respect to the behavioral data we need to be able to synchronize the two using the photodiode (or TTLs, eventually?) " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 485 neural syncs detected\n" + ] + } + ], + "source": [ + "# Find the timestamps of ONSET and OFFSET of all the sync signals in the photodiode \n", + "\n", + "# moving average helps us detect the deflections \n", + "sig = np.squeeze(sync_utils.moving_average(photodiode._data, n=11))\n", + "timestamp = np.squeeze(np.arange(len(sig))/wm_ref_data.info['sfreq'])\n", + "# normalize\n", + "sig = zscore(sig)\n", + "# look for z-scores above 1\n", + "trig_ix = np.where((sig[:-1]<=0)*(sig[1:]>0))[0] # rising edge of trigger\n", + "neural_ts = timestamp[trig_ix]\n", + "neural_ts = np.array(neural_ts)\n", + "print(f'There are {len(neural_ts)} neural syncs detected')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the .log file and/or .csv file, depending on how your task logs the behavioral data. Eventually this should be fairly standardized across tasks." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "log_path = glob(f'{behav_dir}/*.log')[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "csv_path = glob(f'{behav_dir}/*MB_MEM*.csv')[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next step pulls relevant timestamps from the behavioral logfile. THIS STEP DIFFERS DEPENDING ON YOUR TASK. \n", + "\n", + "Maybe one day this step will be unified across all tasks (i.e. either we have a programmer make our tasks, or we unify best practices for task design). " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 486 behav syncs detected\n" + ] + }, + { + "data": { + "text/plain": [ + "381.5762" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Now get the relevant timestamps from behavioral logfiles. This will differ depending\n", + "\n", + "MB1_ts = {'trial_start': [], \n", + "'deck_start': [], \n", + "'feedback_start': [],\n", + "'ITI_start': [],\n", + "'ITI_stop': []}\n", + "\n", + "MEM2_ts = {'trial_start': [], \n", + "'face_start': [], \n", + "'slider_start': [],\n", + "'slider_stop': [],\n", + "'ITI_start': [],\n", + "'ITI_stop': []}\n", + "\n", + "beh_ts = []\n", + "\n", + "MB1_FLAG = True \n", + "MEM2_FLAG = False \n", + "\n", + "with open(log_path, 'r') as fobj:\n", + " for ix, line in enumerate(fobj.readlines()):\n", + " line = line.replace('\\r', '')\n", + " tokens = line[:-1].split('\\t')\n", + "\n", + " if tokens[1] == 'EXP ':\n", + " # Determine which task we are looking at \n", + " if tokens[2][0:3] == 'MB1':\n", + " MB1_FLAG = True\n", + " MEM2_FLAG = False \n", + " elif tokens[2][0:3] == 'MEM':\n", + " MEM2_FLAG = True\n", + " MB1_FLAG = False\n", + "\n", + " # Grab photodiode timestamp\n", + " if tokens[2][0:4] =='sync':\n", + " if 'autoDraw = True' in tokens[2]:\n", + " beh_ts.append(float(tokens[0]))\n", + "\n", + " # Get MB1 deck \n", + " if 'MB1_left_draw' in tokens[2]:\n", + " if 'autoDraw = True' in tokens[2]:\n", + " MB1_ts['deck_start'].append(float(tokens[0]))\n", + " \n", + " # Get MB1 feedback\n", + " if 'MB1_face' in tokens[2]:\n", + " if 'autoDraw = True' in tokens[2]:\n", + " MB1_ts['feedback_start'].append(float(tokens[0]))\n", + "\n", + " # Get MB1 ITI cross \n", + " if 'MB1_ITI_cross' in tokens[2]:\n", + " if 'autoDraw = True' in tokens[2]:\n", + " MB1_ts['ITI_start'].append(float(tokens[0]))\n", + " elif 'autoDraw = False' in tokens[2]:\n", + " MB1_ts['ITI_stop'].append(float(tokens[0]))\n", + "\n", + " if 'New trial (rep=0' in tokens[2]:\n", + " if MB1_FLAG: \n", + " # remember to discard the first one later - it's pre-session \n", + " MB1_ts['trial_start'].append(float(tokens[0]))\n", + " elif MEM2_FLAG:\n", + " MEM2_ts['trial_start'].append(float(tokens[0]))\n", + " \n", + " # Get MEM2 ITI\n", + " if 'MEM2_jitter' in tokens[2]:\n", + " if 'autoDraw = True' in tokens[2]:\n", + " MEM2_ts['ITI_start'].append(float(tokens[0])) \n", + " elif 'autoDraw = False' in tokens[2]:\n", + " MEM2_ts['ITI_stop'].append(float(tokens[0])) \n", + "\n", + " # Get MEM2 Face\n", + " if 'MEM2_images' in tokens[2]:\n", + " if 'autoDraw = True' in tokens[2]:\n", + " MEM2_ts['face_start'].append(float(tokens[0])) \n", + "\n", + " # Get MEM2 slider start\n", + " if tokens[2][:16] == 'MEM2_conf_slider':\n", + " if 'autoDraw = True' in tokens[2]:\n", + " MEM2_ts['slider_start'].append(float(tokens[0])) \n", + " \n", + " # Get MEM2 slider stop\n", + " if tokens[2][:16] == 'MEM2_conf_slider':\n", + " if 'autoDraw = False' in tokens[2]:\n", + " MEM2_ts['slider_stop'].append(float(tokens[0])) \n", + " \n", + "beh_ts = np.array(beh_ts)\n", + "print(f'There are {len(beh_ts)} behav syncs detected')\n", + "\n", + "# Note: fixation crosses need fixing on stop time duplicates\n", + "MB1_ts['ITI_stop'] = np.unique(MB1_ts['ITI_stop']).tolist()\n", + "MEM2_ts['ITI_stop'] = np.unique(MEM2_ts['ITI_stop']).tolist()\n", + "\n", + "\n", + "# Get the choice times: \n", + "csv_data = pd.read_csv(csv_path)\n", + "MB1_ts['choice'] = (csv_data['MB1_draw_key.started'].dropna() + csv_data['MB1_draw_key.rt'].dropna()).tolist()\n", + "MEM2_ts['choice'] = (csv_data['MEM2_recall_key.started'].dropna() + csv_data['MEM2_recall_key.rt'].dropna()).tolist()\n", + "\n", + "# Do some corrections: \n", + "# Get rid of first trial start (pre-session)\n", + "MB1_ts['trial_start'].pop(0) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sanity check - the sync pulses are similar in number. That's nice, but not guaranteed. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, compile the behavioral data for each trial. We will use this to add to the mne metadata later, which is very important for analysis. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "subj_count = 0\n", + "\n", + "# Load the database with image DPRIME information\n", + "database_file = f'{behav_dir}/all_mem_data.xlsx' \n", + "all_mem_df = pd.read_excel(database_file, engine='openpyxl')\n", + "all_mem_df = all_mem_df[['img_path', 'DPRIME']]\n", + "\n", + "# Turn into right format for modeling: \n", + "MB1_n = 60\n", + "MEM2_n = 120\n", + "li_mb1 = []\n", + "li_mem2 = [] \n", + "\n", + "act_rew_rate = {}\n", + "act_rew_rate['pids'] = []\n", + "\n", + "r1_chance=30\n", + "\n", + "for elem in ['actions', 'rewards']:\n", + " act_rew_rate[elem] = np.zeros(MB1_n).astype(int) # len(task_files), \n", + "\n", + "# Load the merged task data \n", + "csv_data['trials_2.thisN'] = csv_data['trials_2.thisRepN'].shift(-1)\n", + "\n", + "##### First, process the Bandit task: \n", + "mb_df = csv_data.dropna(subset=['trials_2.thisN'])\n", + "act_rew_rate['pids'].append(mb_df.participant.iloc[0])\n", + "\n", + "# Change Gender so that female = 2\n", + "mb_df.Gender[mb_df.Gender==0] = 2\n", + "\n", + "# add score, reward probability and expected value \n", + "mb_df['choice'] = mb_df.apply(lambda x: x['MB1_draw_key.keys'], axis=1)\n", + "# Make the trials 1-60 \n", + "mb_df['trials_dm'] = mb_df['trials_2.thisN'].shift(+1)\n", + "# mb_df.trials_dm.fillna(60, inplace=True)\n", + "# # get rid of the extra rows in the .csv that populate between trials \n", + "mb_df = mb_df.drop_duplicates(subset='trials_dm', keep='first')\n", + "mb_df['reward'] = mb_df.apply(lambda x: x['reward']/100, axis=1)\n", + "mb_df.reward[mb_df.reward==100] = 0\n", + "# 0 is male, 1 is female \n", + "mb_df['choice'] = mb_df['choice']-1\n", + "mb_df.rename(columns={'MB1_draw_key.rt':'draw_rt'}, inplace=True)\n", + "mb_df.dropna(subset=['img_path'], inplace=True)\n", + "\n", + "# # check chance: \n", + "# if mb_df.reward.sum() < r1_chance:\n", + "# # print(f'Subject {subj_count} performed worse than random')\n", + "# bad_subs.append(subj_count)\n", + "# continue\n", + "\n", + "##### Fit RW model to DM data\n", + "mb_df['bic'] = np.nan\n", + "mb_df['alpha'] = np.nan\n", + "mb_df['beta'] = np.nan\n", + "mb_df['RPE'] = np.nan\n", + "\n", + "# RW_model = RW() \n", + "sub_act_rew_rate = {}\n", + "sub_act_rew_rate['pids'] = np.array([subj_count]) \n", + "\n", + "for elem in ['actions', 'rewards']:\n", + " sub_act_rew_rate[elem] = np.zeros([1, MB1_n]).astype(int)\n", + "c = mb_df.choice.dropna().values.astype(int)\n", + "r = mb_df.reward.dropna().values\n", + "sub_act_rew_rate['actions'][0, :] = c\n", + "sub_act_rew_rate['rewards'][0, :] = r\n", + "# RW_model.fit_all(sub_act_rew_rate) \n", + "\n", + "# mb_df.RPE= RW_model.fit_metrics['prederr'][0].tolist()\n", + "# mb_df.bic= RW_model.fit_metrics['bic'][0]\n", + "# mb_df.alpha= RW_model.fit_metrics['params']['alpha'][0]\n", + "# mb_df.beta= RW_model.fit_metrics['params']['beta'][0]\n", + "\n", + "# Save dict for modeling decision-making performance: \n", + "c = mb_df.choice.dropna().values.astype(int)\n", + "r = mb_df.reward.dropna().values\n", + "act_rew_rate['actions'] = c # [subj_count, :]\n", + "act_rew_rate['rewards']= r # [subj_count, :]\n", + "\n", + "##### Second, process the MEM2 data: \n", + "rm_df = csv_data.dropna(subset=['MEM2_trials.thisN'])\n", + "\n", + "# add coding of memory choice: \n", + "rm_df['hit'] = 0\n", + "rm_df['miss'] = 0\n", + "rm_df['corr_reject'] = 0\n", + "rm_df['false_alarm'] = 0\n", + "\n", + "# add score, reward probability and expected value \n", + "rm_df.rename(columns={'MEM2_recall_key.keys': 'response',\n", + "'MEM2_conf_slider.response': 'confidence'\n", + " }, inplace=True) \n", + "\n", + "rm_df['trials_mem'] = rm_df['MEM2_trials.thisN'].shift(-1)\n", + "rm_df.trials_mem.fillna(120, inplace=True)\n", + "\n", + "# Change Gender so that female = 2\n", + "rm_df.Gender[rm_df.Gender==0] = 2\n", + "\n", + "rm_df = rm_df.merge(mb_df, on='img_path', how='left', indicator=True)\n", + "# Clean up the merge\n", + "rm_df.drop(columns=['participant_y'], inplace=True)\n", + "rm_df.rename(columns={'participant_x': 'participant'}, inplace=True)\n", + "\n", + "\n", + "# compute the hit-rates and false-alarm rates \n", + "### TO do so, use the merge to find image_paths that were in \"both\" dfs ('old') \n", + "# OLD = 2, which is correct in this case \n", + "hit_bool = (rm_df._merge=='both') & (rm_df.response==2)\n", + "hits = hit_bool.sum()\n", + "# NEW = 1, which is false in this case \n", + "miss_bool = (rm_df._merge=='both') & (rm_df.response==1)\n", + "misses = miss_bool.sum()\n", + "\n", + "# or just the \"left\" df ('new')\n", + "false_alarm_bool = (rm_df._merge=='left_only') & (rm_df.response==2)\n", + "false_alarms = false_alarm_bool.sum()\n", + "corr_reject_bool = (rm_df._merge=='left_only') & (rm_df.response==1)\n", + "correct_rejections = corr_reject_bool.sum()\n", + "\n", + "# categorize image by memory choice\n", + "rm_df.hit[hit_bool] = 1\n", + "rm_df.miss[miss_bool] = 1\n", + "rm_df.false_alarm[false_alarm_bool] = 1\n", + "rm_df.corr_reject[corr_reject_bool] = 1\n", + "\n", + "# compute dprime for the subject\n", + "hm = hits+misses\n", + "fc = false_alarms+correct_rejections\n", + "\n", + "hrs = hits/hm\n", + "fars = false_alarms/fc\n", + "\n", + "# Adjust extreme hit-rates or false-alarms\n", + "if hrs == 0: \n", + " hrs = 0.5/hm\n", + "elif hrs ==1: \n", + " hrs = (hm-0.5)/hm\n", + "if fars == 0: \n", + " fars = 0.5/fc\n", + "elif fars ==1: \n", + " fars = (fc-0.5)/fc\n", + "\n", + "# only the extreme values by replacing rates of 0 with 0.5/𝑛 and rates of 1 with (𝑛−0.5)/𝑛 where 𝑛 is the number of signal or noise trials (Macmillan & Kaplan, 1985)\n", + "\n", + "dp = dprime(hrs, fars, hm, fc)\n", + "\n", + "# Add in subject-level memory characteristics (\"rates\")\n", + "rm_df['hit_rate'] = hrs\n", + "rm_df['false_alarm_rate'] = fars\n", + "rm_df['subj_dprime'] = np.nan\n", + "if dp != float(\"-inf\"):\n", + " rm_df['subj_dprime'] = dp \n", + "\n", + "# # Get rid of horrible mmemory performers \n", + "# if rm_df.subj_dprime.mean()<=0:\n", + "# bad_subs.append(subj_count)\n", + "# continue\n", + "\n", + "# # Get patient demographics\n", + "# rm_df['age'] = np.nan\n", + "# rm_df['subj_gender'] = np.nan\n", + "# rm_df['age'] = demographics_df[demographics_df.participant==rm_df.participant.iloc[0]].age.values[0]\n", + "# rm_df['subj_gender'] = demographics_df[demographics_df.participant==rm_df.participant.iloc[0]].Sex.values[0]\n", + "\n", + "# Merge in the image DPRIME \n", + "rm_df['DPRIME'] = rm_df.merge(all_mem_df, on='img_path', how='right')['DPRIME']\n", + "mb_df['DPRIME'] = mb_df.merge(all_mem_df, on='img_path', how='right')['DPRIME']\n", + "mb_df.rename(columns={'DPRIME': 'image_dprime'}, inplace=True) \n", + "\n", + "rm_df.rename(columns={'DPRIME': 'image_dprime',\n", + "'Gender_x': 'image_gender',\n", + "'MEM2_recall_key.rt_x': 'recall_rt',\n", + "'MEM2_conf_slider.rt_x': 'slider_rt'\n", + "}, inplace=True) \n", + "\n", + "# li_mb1.append(mb_df)\n", + "# li_mem2.append(rm_df)\n", + "\n", + "# dm_df = pd.concat(li_mb1, axis=0, ignore_index=True)\n", + "mb_df['male'] = 0\n", + "mb_df['female'] = 0\n", + "mb_df.male = mb_df.apply(lambda x: 1 if x.choice==0 else 0, axis=1)\n", + "mb_df.female = mb_df.apply(lambda x: 1 if x.choice==1 else 0, axis=1)\n", + "\n", + "# col_mask = ((mb_df.columns.str.startswith('MB')) | (mb_df.columns.str.startswith('trials.')))\n", + "\n", + "# mem_df = pd.concat(li_mem2, axis=0, ignore_index=True)\n", + "rm_df['phit'] = rm_df.response - 1\n", + "\n", + "# # Get rid of trash: \n", + "# act_rew_rate['pids'] = np.array(act_rew_rate['pids'])\n", + "# act_rew_rate['pids'] = np.delete(act_rew_rate['pids'], bad_subs)\n", + "# for elem in ['actions', 'rewards']:\n", + "# act_rew_rate[elem] = np.delete(act_rew_rate[elem], bad_subs, 0)\n", + "\n", + "col_mask = ((rm_df.columns.str.startswith('MEM')) | (rm_df.columns.str.startswith('MB')) | (rm_df.columns.str.startswith('trials.')) | (rm_df.columns.str.startswith('trials_2')) | (rm_df.columns.str.endswith('_y')))\n", + "rm_df = rm_df.loc[:,~col_mask]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50 blocks\n", + ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . found matches for 36 of 50 blocks\n", + "sync succeeded\n" + ] + } + ], + "source": [ + "# Do regression to find neural timestamps for each event type\n", + "if len(beh_ts)!=len(neural_ts):\n", + " good_beh_ms, neural_offset = sync_utils.pulsealign(beh_ts, neural_ts, window=50, thresh=0.95)\n", + " slope, offset, rval = sync_utils.sync_matched_pulses(good_beh_ms, neural_offset)\n", + "else:\n", + " slope, offset, rval = sync_utils.sync_matched_pulses(beh_ts, neural_ts)\n", + "\n", + "if rval < 0.99:\n", + " print('sync failed')\n", + "else: \n", + " print('sync succeeded')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# sync_data = pd.DataFrame(columns=['slope', 'offset'])\n", + "# sync_data.slope = [slope]\n", + "# sync_data.offset = [offset]\n", + "# sync_data.to_csv(f'{save_dir}/sync_data.csv', index=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# pd.DataFrame(MB1_ts).to_csv(f'{save_dir}/MB1_ts.csv', index=False)\n", + "# pd.DataFrame(MEM2_ts).to_csv(f'{save_dir}/MEM2_ts.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create epochs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ok, now that we have done some pre-processing, we want to make epochs aligned to specific behavioral events. Need the slope and offsets we determined from photodiode synchronization." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# all behavioral times of interest \n", + "gamble_choice_ts = [(x*slope + offset) for x in MB1_ts['choice']]\n", + "gamble_feedback_ts = [(x*slope + offset) for x in MB1_ts['feedback_start']]\n", + "gamble_fixation_ts = [(x*slope + offset) for x in MB1_ts['ITI_start']]\n", + "\n", + "memory_cue_ts = [(x*slope + offset) for x in MEM2_ts['face_start']]\n", + "memory_choice_ts = [(x*slope + offset) for x in MEM2_ts['choice']]\n", + "memory_fixation_ts = [(x*slope + offset) for x in MEM2_ts['ITI_start']]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# set some windows of interest \n", + "\n", + "buf = 1.0 # this is the buffer before and after that we use to limit edge effects for TFRs\n", + "\n", + "feedback_pre = 1.0 # this is the time before the feedback appears \n", + "feedback_post = 1.5 # this is the time the feedback is present \n", + "\n", + "choice_pre = 1.0 # this is the choice deliberation period\n", + "choice_post = feedback_pre\n", + "\n", + "baseline_pre = 0\n", + "baseline_post = 0.5 " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Used Annotations descriptions: ['gamble_feedback']\n" + ] + } + ], + "source": [ + "# Example - make feedback events \n", + "\n", + "evs = gamble_feedback_ts\n", + "\n", + "durs = np.zeros_like(gamble_feedback_ts).tolist()\n", + "\n", + "descriptions = ['gamble_feedback']*len(gamble_feedback_ts)\n", + "\n", + "# Make mne annotations based on these descriptions\n", + "annot = mne.Annotations(onset=evs,\n", + " duration=durs,\n", + " description=descriptions)\n", + "\n", + "wm_ref_data.set_annotations(annot)\n", + "\n", + "events_from_annot, event_dict = mne.events_from_annotations(wm_ref_data)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the epochs below. **Note that I am setting a fixed baseline period across all trials. By default, this will subtract the mean of the signal during the basline period from my signal during my period of interest.**" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Not setting metadata\n", + "60 matching events found\n", + "Applying baseline correction (mode: mean)\n", + "0 projection items activated\n", + "Using data from preloaded Raw for 60 events and 4609 original time points ...\n", + "0 bad epochs dropped\n" + ] + } + ], + "source": [ + "epochs = mne.Epochs(wm_ref_data, \n", + " events_from_annot, \n", + " event_id=event_dict, \n", + " baseline=(-feedback_pre, 0), \n", + " tmin=-(buf + feedback_pre), \n", + " tmax=buf + feedback_post, \n", + " reject=None, \n", + " reject_by_annotation=False,\n", + " preload=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**As an alternative to setting a baseline parameter in your Epochs code:** If you can't baseline your epochs in a standard way across trials (i.e. against a default time like -0.5 to 0 seconds) then you can MAKE a set of epochs to use as a baseline. The code below makes epochs based on the duration of the fixation cross, and subtracts the mean of these epochs from our epochs of interest. " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# # Make baseline epochs for 2-arm bandit: \n", + "\n", + "# evs = gamble_fixation_ts\n", + "\n", + "# durs = np.zeros_like(gamble_fixation_ts).tolist()\n", + "\n", + "# descriptions = ['gamble_fixation_ts']*len(gamble_fixation_ts)\n", + "\n", + "# # Make mne annotations based on these descriptions\n", + "# annot = mne.Annotations(onset=evs,\n", + "# duration=durs,\n", + "# description=descriptions)\n", + "\n", + "# wm_ref_data.set_annotations(annot)\n", + "\n", + "# events_from_annot, event_dict = mne.events_from_annotations(wm_ref_data)\n", + "\n", + "# baseline_epochs = mne.Epochs(wm_ref_data, \n", + "# events_from_annot, \n", + "# event_id=event_dict, \n", + "# baseline=None, \n", + "# tmin=-(buf + baseline_pre), \n", + "# tmax=buf + baseline_post, \n", + "# reject=None, \n", + "# preload=True)\n", + "\n", + "# # # Make baseline epochs for recognition memory: \n", + "# # my_annot = mne.Annotations(onset=memory_cross_start,\n", + "# # duration=0.5,\n", + "# # description=['memory_cross']*len(memory_cross_start))\n", + "\n", + "# # wm_ref_data.set_annotations(my_annot)\n", + "# # events_from_annot, event_dict = mne.events_from_annotations(wm_ref_data)\n", + "\n", + "# # rm_baseline_epochs = mne.Epochs(wm_ref_data, events_from_annot, event_id=event_dict, baseline=None, tmin=-buf, tmax=0.5+buf, reject=None, preload=True)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# # baseline the LFP data in time: \n", + "\n", + "# buf_ix = int(buf*epochs.info['sfreq'])\n", + "\n", + "# time_baseline = baseline_epochs._data[:, :, buf_ix:-buf_ix]\n", + "\n", + "# epochs._data = lfp_preprocess_utils.mean_baseline_time(epochs._data, time_baseline, mode='mean')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Downsample and annotate the epoched data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Resampling is the two-step process of applying a low-pass FIR filter and subselecting samples from the data.\n", + "\n", + "Using this function to resample data before forming mne.Epochs for final analysis is generally discouraged because doing so effectively loses precision of (and jitters) the event timings (see commented example below to prove this to yourself)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# \"\"\"\n", + "# An example where effecting jittering of triggers occurs when\n", + "# downsampling before epoching.\n", + "# \"\"\"\n", + "# import numpy as np\n", + "# import matplotlib.pyplot as plt\n", + "\n", + "# # 1 sec of data @ 1000 Hz\n", + "# fs = 1000. # Hz\n", + "# decim = 5\n", + "# n_samples = 1000\n", + "# freq = 20. # Hz\n", + "# t = np.arange(n_samples) / fs\n", + "# epoch_dur = 1. / freq # 2 cycles of our sinusoid\n", + "\n", + "# # we have a 10 Hz sinusoid signal\n", + "# raw_data = np.cos(2 * np.pi * freq * t)\n", + "\n", + "# # let's make events that should show the sinusoid moving out of phase\n", + "# # continuously\n", + "# n_events = 40\n", + "# event_times = np.linspace(0, 1. / freq, n_events, endpoint=False)\n", + "# event_samples = np.round(event_times * fs).astype(int)\n", + "# data_epoch = list()\n", + "# epoch_len = int(round(epoch_dur * fs))\n", + "# for event_time in event_times:\n", + "# start_idx = int(np.round(event_time * fs))\n", + "# data_epoch.append(raw_data[start_idx:start_idx + epoch_len])\n", + "# data_epoch = np.array(data_epoch)\n", + "# data_epoch_ds = data_epoch[:, ::decim]\n", + "\n", + "# # now let's try downsampling the raw data instead\n", + "# raw_data_ds = raw_data[::decim]\n", + "# fs_new = fs / decim\n", + "# data_ds_epoch = list()\n", + "# epoch_ds_len = int(round(epoch_dur * fs_new))\n", + "# for event_time in event_times:\n", + "# start_idx = int(np.round(event_time * fs_new))\n", + "# data_ds_epoch.append(raw_data_ds[start_idx:start_idx + epoch_ds_len])\n", + "# data_ds_epoch = np.array(data_ds_epoch)\n", + "\n", + "# # Look at the results\n", + "# assert data_ds_epoch.shape == data_epoch_ds.shape\n", + "# fig, axs = plt.subplots(1, 2)\n", + "# t_ds = np.arange(epoch_ds_len) / fs_new\n", + "# for di, (data_e_d, data_d_e) in enumerate(zip(data_epoch_ds, data_ds_epoch)):\n", + "# color = [di / float(n_events + 10)] * 3\n", + "# axs[0].plot(t_ds, data_e_d, color=color)\n", + "# axs[1].plot(t_ds, data_d_e, color=color)\n", + "# axs[0].set_ylabel('Epoch then downsample')\n", + "# axs[1].set_ylabel('Downsample then epoch')\n", + "# for ax in axs:\n", + "# ax.set_xlim(t_ds[[0, -1]])\n", + "# ax.set_xticks(t_ds[[0, -1]])\n", + "# fig.set_tight_layout(True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given the above, we use the built-in downsample method of mne Epochs, which will do the filtering and sub-sampling for us. Pick a frequency that is a factor of the current sampling rate." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Prelude: Some background on filtering: https://mark-kramer.github.io/Case-Studies-Python/06.html" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Number of events60
Eventsgamble_feedback: 60
Time range-2.000 – 2.498 sec
Baseline-1.000 – 0.000 sec
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Downsample \n", + "epochs.resample(sfreq=wm_ref_data.info['sfreq']/2)\n", + "# mb_baseline_epochs.resample(sfreq=wm_ref_data.info['sfreq']/2)\n", + "# rm_baseline_epochs.resample(sfreq=wm_ref_data.info['sfreq']/2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following functions detects all of the IEDs on each channel, for each event, returning (samples, time(s)) for each IED. " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "if all(np.isnan(np.array([1, 2, 3]))):\n", + " print('1')" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting up band-pass filter from 25 - 80 Hz\n", + "\n", + "FIR filter parameters\n", + "---------------------\n", + "Designing a one-pass, zero-phase, non-causal bandpass filter:\n", + "- Windowed time-domain design (firwin) method\n", + "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n", + "- Lower passband edge: 25.00\n", + "- Lower transition bandwidth: 6.25 Hz (-6 dB cutoff frequency: 21.88 Hz)\n", + "- Upper passband edge: 80.00 Hz\n", + "- Upper transition bandwidth: 20.00 Hz (-6 dB cutoff frequency: 90.00 Hz)\n", + "- Filter length: 271 samples (0.529 sec)\n", + "\n" + ] + } + ], + "source": [ + "IED_samps = lfp_preprocess_utils.detect_IEDs(epochs, \n", + " peak_thresh=4, \n", + " closeness_thresh=0.25, \n", + " width_thresh=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'lacas1-lmolf1': {0: array([nan]),\n", + " 1: array([nan]),\n", + " 2: array([], dtype=int64),\n", + " 3: array([nan]),\n", + " 4: array([], dtype=int64),\n", + " 5: array([nan]),\n", + " 6: array([nan]),\n", + " 7: array([], dtype=int64),\n", + " 8: array([nan]),\n", + " 9: array([], dtype=int64),\n", + " 10: array([nan]),\n", + " 11: array([nan]),\n", + " 12: array([nan]),\n", + " 13: array([nan]),\n", + " 14: array([nan]),\n", + " 15: array([], dtype=int64),\n", + " 16: array([], dtype=int64),\n", + " 17: array([nan]),\n", + " 18: array([], dtype=int64),\n", + " 19: array([nan]),\n", + " 20: array([nan]),\n", + " 21: array([nan]),\n", + " 22: array([], dtype=int64),\n", + " 23: array([nan]),\n", + " 24: array([], dtype=int64),\n", 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47: array([], dtype=int64),\n", + " 48: array([nan]),\n", + " 49: array([], dtype=int64),\n", + " 50: array([nan]),\n", + " 51: array([nan]),\n", + " 52: array([], dtype=int64),\n", + " 53: array([], dtype=int64),\n", + " 54: array([], dtype=int64),\n", + " 55: array([], dtype=int64),\n", + " 56: array([], dtype=int64),\n", + " 57: array([nan]),\n", + " 58: array([], dtype=int64),\n", + " 59: array([], dtype=int64)}}" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IED_samps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Add this into the metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Replacing existing metadata with 120 columns\n" + ] + } + ], + "source": [ + "# Let's make METADATA to assign each event some features\n", + "\n", + "event_metadata = pd.DataFrame(columns=['rt', 'reward', 'rpe', 'dprime']+list(IED_samps.keys()))\n", + "\n", + "event_metadata['rt'] = mb_df['draw_rt'].tolist()\n", + "event_metadata['reward'] = mb_df['reward'].tolist()\n", + "event_metadata['dprime'] = mb_df['image_dprime'].tolist()\n", + "\n", + "for ch in list(IED_samps.keys()):\n", + " for ev, val in IED_samps[ch].items():\n", + " if len(val) > 1:\n", + " event_metadata[ch].loc[ev] = val\n", + " else:\n", + " if ~np.isnan(val):\n", + " event_metadata[ch].loc[ev] = val\n", + " \n", + "epochs.metadata = event_metadata\n", + "# event_metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NaN ... \n", + "5 NaN NaN NaN NaN ... \n", + "6 NaN NaN NaN NaN ... \n", + "7 NaN NaN NaN NaN ... \n", + "8 NaN NaN NaN NaN ... \n", + "9 NaN NaN NaN NaN ... \n", + "10 NaN NaN NaN NaN ... \n", + "11 NaN NaN NaN NaN ... \n", + "12 NaN NaN NaN NaN ... \n", + "13 NaN NaN NaN NaN ... \n", + "14 NaN NaN NaN NaN ... \n", + "15 NaN NaN NaN NaN ... \n", + "16 NaN NaN NaN NaN ... \n", + "17 NaN NaN NaN NaN ... \n", + "18 NaN NaN NaN NaN ... \n", + "19 NaN NaN NaN NaN ... \n", + "20 NaN NaN NaN NaN ... \n", + "21 NaN NaN NaN NaN ... \n", + "22 NaN NaN NaN NaN ... \n", + "23 NaN NaN NaN NaN ... \n", + "24 NaN NaN NaN NaN ... \n", + "25 NaN NaN NaN NaN ... \n", + "26 NaN NaN NaN NaN ... \n", + "27 NaN NaN NaN NaN ... \n", + "28 NaN NaN NaN NaN ... \n", + "29 NaN NaN NaN NaN ... \n", + "30 NaN NaN NaN NaN ... \n", + "31 NaN NaN NaN NaN ... \n", + "32 NaN NaN NaN NaN ... \n", + "33 NaN NaN NaN NaN ... \n", + "34 NaN NaN NaN NaN ... \n", + "35 NaN NaN NaN NaN ... \n", + "36 NaN NaN NaN NaN ... \n", 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"40 NaN NaN NaN NaN NaN \n", + "41 NaN NaN NaN NaN NaN \n", + "42 NaN NaN NaN NaN NaN \n", + "43 NaN NaN NaN NaN NaN \n", + "44 NaN [2085] NaN NaN [2084] \n", + "45 NaN NaN NaN NaN NaN \n", + "46 NaN NaN NaN NaN NaN \n", + "47 NaN NaN NaN NaN NaN \n", + "48 NaN NaN NaN NaN NaN \n", + "49 NaN NaN NaN NaN NaN \n", + "50 NaN [1395] NaN NaN NaN \n", + "51 NaN NaN NaN NaN NaN \n", + "52 NaN NaN NaN [213] NaN \n", + "53 NaN NaN NaN NaN NaN \n", + "54 NaN NaN NaN NaN NaN \n", + "55 NaN NaN NaN NaN NaN \n", + "56 NaN NaN NaN NaN NaN \n", + "57 NaN NaN NaN NaN NaN \n", + "58 NaN [1527] NaN NaN NaN \n", + "59 NaN NaN NaN NaN NaN \n", + "\n", + "[60 rows x 120 columns]" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "event_metadata" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can plot the epochs and annotate them. Specifically, look for evidence of interictal discharges and seizure ramping during your behavioral epochs, as these should be used to exclude epochs (and potentially channels) from analysis. \n", + "\n", + "Resource: https://www.frontiersin.org/articles/10.3389/fnhum.2020.00044/full" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "/* global mpl */\n", + "window.mpl = {};\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(\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", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = this.ws.binaryType !== undefined;\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById('mpl-warnings');\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent =\n", + " 'This browser does not support binary websocket messages. ' +\n", + " 'Performance may be slow.';\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = document.createElement('div');\n", + " this.root.setAttribute('style', 'display: inline-block');\n", + " this._root_extra_style(this.root);\n", + "\n", + " parent_element.appendChild(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message('supports_binary', { value: fig.supports_binary });\n", + " fig.send_message('send_image_mode', {});\n", + " if (fig.ratio !== 1) {\n", + " fig.send_message('set_device_pixel_ratio', {\n", + " device_pixel_ratio: fig.ratio,\n", + " });\n", + " }\n", + " fig.send_message('refresh', {});\n", + " };\n", + "\n", + " this.imageObj.onload = function () {\n", + " if (fig.image_mode === 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function () {\n", + " fig.ws.close();\n", + " };\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "};\n", + "\n", + "mpl.figure.prototype._init_header = function () {\n", + " var titlebar = 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._canvas_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._init_canvas = function () {\n", + " var fig = this;\n", + "\n", + " var canvas_div = (this.canvas_div = document.createElement('div'));\n", + " canvas_div.setAttribute('tabindex', '0');\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", + " 'z-index: 2;'\n", + " );\n", + "\n", + " function on_keyboard_event_closure(name) {\n", + " return function (event) {\n", + " return fig.key_event(event, name);\n", + " };\n", + " }\n", + "\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 = (this.canvas = document.createElement('canvas'));\n", + " canvas.classList.add('mpl-canvas');\n", + " canvas.setAttribute(\n", + " 'style',\n", + " 'box-sizing: content-box;' +\n", + " 'pointer-events: none;' +\n", + " 'position: relative;' +\n", + " 'z-index: 0;'\n", + " );\n", + "\n", + " this.context = canvas.getContext('2d');\n", + "\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", + " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", + " 'canvas'\n", + " ));\n", + " rubberband_canvas.setAttribute(\n", + " 'style',\n", + " 'box-sizing: content-box;' +\n", + " 'left: 0;' +\n", + " 'pointer-events: none;' +\n", + " 'position: absolute;' +\n", + " 'top: 0;' +\n", + " 'z-index: 1;'\n", + " );\n", + "\n", + " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", + " if (this.ResizeObserver === undefined) {\n", + " if (window.ResizeObserver !== undefined) {\n", + " this.ResizeObserver = window.ResizeObserver;\n", + " } else {\n", + " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", + " this.ResizeObserver = obs.ResizeObserver;\n", + " }\n", + " }\n", + "\n", + " this.resizeObserverInstance = new this.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", + " if (entry.contentBoxSize instanceof Array) {\n", + " // Chrome 84 implements new version of spec.\n", + " width = entry.contentBoxSize[0].inlineSize;\n", + " height = entry.contentBoxSize[0].blockSize;\n", + " } else {\n", + " // Firefox implements old version of spec.\n", + " width = entry.contentBoxSize.inlineSize;\n", + " height = entry.contentBoxSize.blockSize;\n", + " }\n", + " } else {\n", + " // Chrome <84 implements even older version of spec.\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", + " if (entry.devicePixelContentBoxSize) {\n", + " // Chrome 84 implements new version of spec.\n", + " canvas.setAttribute(\n", + " 'width',\n", + " entry.devicePixelContentBoxSize[0].inlineSize\n", + " );\n", + " canvas.setAttribute(\n", + " 'height',\n", + " entry.devicePixelContentBoxSize[0].blockSize\n", + " );\n", + " } else {\n", + " canvas.setAttribute('width', width * fig.ratio);\n", + " canvas.setAttribute('height', height * fig.ratio);\n", + " }\n", + " /* This rescales the canvas back to display pixels, so that it\n", + " * appears correct on HiDPI screens. */\n", + " canvas.style.width = width + 'px';\n", + " canvas.style.height = height + 'px';\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 (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", + " fig.request_resize(width, height);\n", + " }\n", + " }\n", + " });\n", + " this.resizeObserverInstance.observe(canvas_div);\n", + "\n", + " function on_mouse_event_closure(name) {\n", + " /* User Agent sniffing is bad, but WebKit is busted:\n", + " * https://bugs.webkit.org/show_bug.cgi?id=144526\n", + " * https://bugs.webkit.org/show_bug.cgi?id=181818\n", + " * The worst that happens here is that they get an extra browser\n", + " * selection when dragging, if this check fails to catch them.\n", + " */\n", + " var UA = navigator.userAgent;\n", + " var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n", + " if(isWebKit) {\n", + " return function (event) {\n", + " /* This prevents the web browser from automatically changing to\n", + " * the text insertion cursor when the button is pressed. We\n", + " * want to control all of the cursor setting manually through\n", + " * the 'cursor' event from matplotlib */\n", + " event.preventDefault()\n", + " return fig.mouse_event(event, name);\n", + " };\n", + " } else {\n", + " return function (event) {\n", + " return fig.mouse_event(event, name);\n", + " };\n", + " }\n", + " }\n", + "\n", + " canvas_div.addEventListener(\n", + " 'mousedown',\n", + " on_mouse_event_closure('button_press')\n", + " );\n", + " canvas_div.addEventListener(\n", + " 'mouseup',\n", + " on_mouse_event_closure('button_release')\n", + " );\n", + " canvas_div.addEventListener(\n", + " 'dblclick',\n", + " on_mouse_event_closure('dblclick')\n", + " );\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " canvas_div.addEventListener(\n", + " 'mousemove',\n", + " on_mouse_event_closure('motion_notify')\n", + " );\n", + "\n", + " canvas_div.addEventListener(\n", + " 'mouseenter',\n", + " on_mouse_event_closure('figure_enter')\n", + " );\n", + " canvas_div.addEventListener(\n", + " 'mouseleave',\n", + " on_mouse_event_closure('figure_leave')\n", + " );\n", + "\n", + " canvas_div.addEventListener('wheel', function (event) {\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " on_mouse_event_closure('scroll')(event);\n", + " });\n", + "\n", + " canvas_div.appendChild(canvas);\n", + " canvas_div.appendChild(rubberband_canvas);\n", + "\n", + " this.rubberband_context = rubberband_canvas.getContext('2d');\n", + " this.rubberband_context.strokeStyle = '#000000';\n", + "\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", + " canvas_div.addEventListener('contextmenu', function (_e) {\n", + " event.preventDefault();\n", + " return false;\n", + " });\n", + "\n", + " function set_focus() {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'mpl-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\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", + " 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", + " /* 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", + "\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", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\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 = document.createElement('option');\n", + " option.selected = fmt === mpl.default_extension;\n", + " option.innerHTML = fmt;\n", + " fmt_picker.appendChild(option);\n", + " }\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", + " // 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", + "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", + "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], msg['forward']);\n", + " fig.send_message('refresh', {});\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", + " var x0 = msg['x0'] / fig.ratio;\n", + " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", + " var x1 = msg['x1'] / fig.ratio;\n", + " var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n", + " 0,\n", + " fig.canvas.width / fig.ratio,\n", + " fig.canvas.height / fig.ratio\n", + " );\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", + " fig.canvas_div.style.cursor = msg['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.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", + " // 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", + " var img = evt.data;\n", + " if (img.type !== 'image/png') {\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", + " img.type = 'image/png';\n", + " }\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", + "\n", + " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", + " img\n", + " );\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\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", + " 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(\n", + " \"No handler for the '\" + msg_type + \"' message type: \",\n", + " msg\n", + " );\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(\n", + " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", + " e,\n", + " e.stack,\n", + " msg\n", + " );\n", + " }\n", + " }\n", + " };\n", + "};\n", + "\n", + "\n", + "/*\n", + " * return a copy of an object with only non-object keys\n", + " * we need this to avoid circular references\n", + " * https://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", + " }\n", + " return obj;\n", + " }, {});\n", + "}\n", + "\n", + "mpl.figure.prototype.mouse_event = function (event, name) {\n", + " if (name === 'button_press') {\n", + " this.canvas.focus();\n", + " this.canvas_div.focus();\n", + " }\n", + "\n", + " // from https://stackoverflow.com/q/1114465\n", + " var boundingRect = this.canvas.getBoundingClientRect();\n", + " var x = (event.clientX - boundingRect.left) * this.ratio;\n", + " var y = (event.clientY - boundingRect.top) * this.ratio;\n", + "\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", + " 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", + " // Prevent repeat events\n", + " if (name === 'key_press') {\n", + " if (event.key === this._key) {\n", + " return;\n", + " } else {\n", + " this._key = event.key;\n", + " }\n", + " }\n", + " if (name === 'key_release') {\n", + " this._key = null;\n", + " }\n", + "\n", + " var value = '';\n", + " if (event.ctrlKey && event.key !== 'Control') {\n", + " value += 'ctrl+';\n", + " }\n", + " else if (event.altKey && event.key !== 'Alt') {\n", + " value += 'alt+';\n", + " }\n", + " else if (event.shiftKey && event.key !== 'Shift') {\n", + " value += 'shift+';\n", + " }\n", + "\n", + " value += 'k' + event.key;\n", + "\n", + " this._key_event_extra(event, name);\n", + "\n", + " this.send_message(name, { key: value, 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", + "\n", + "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", + "// prettier-ignore\n", + "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", + "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n", + "\n", + "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n", + "\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.binaryType = comm.kernel.ws.binaryType;\n", + " ws.readyState = comm.kernel.ws.readyState;\n", + " function updateReadyState(_event) {\n", + " if (comm.kernel.ws) {\n", + " ws.readyState = comm.kernel.ws.readyState;\n", + " } else {\n", + " ws.readyState = 3; // Closed state.\n", + " }\n", + " }\n", + " comm.kernel.ws.addEventListener('open', updateReadyState);\n", + " comm.kernel.ws.addEventListener('close', updateReadyState);\n", + " comm.kernel.ws.addEventListener('error', updateReadyState);\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", + " var data = msg['content']['data'];\n", + " if (data['blob'] !== undefined) {\n", + " data = {\n", + " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", + " };\n", + " }\n", + " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", + " ws.onmessage(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 = document.getElementById(id);\n", + " var ws_proxy = comm_websocket_adapter(comm);\n", + "\n", + " function ondownload(figure, _format) {\n", + " window.open(figure.canvas.toDataURL());\n", + " }\n", + "\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;\n", + " fig.cell_info = mpl.find_output_cell(\"
\");\n", + " if (!fig.cell_info) {\n", + " console.error('Failed to find cell for figure', id, fig);\n", + " return;\n", + " }\n", + " fig.cell_info[0].output_area.element.on(\n", + " 'cleared',\n", + " { fig: fig },\n", + " fig._remove_fig_handler\n", + " );\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_close = function (fig, msg) {\n", + " var width = fig.canvas.width / fig.ratio;\n", + " fig.cell_info[0].output_area.element.off(\n", + " 'cleared',\n", + " fig._remove_fig_handler\n", + " );\n", + " fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n", + " '';\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 / this.ratio;\n", + " var dataURL = this.canvas.toDataURL();\n", + " this.cell_info[1]['text/html'] =\n", + " '';\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 () {\n", + " fig.push_to_output();\n", + " }, 1000);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'btn-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\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", + " 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) {\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 = '';\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " // Add the status bar.\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 = 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 = '';\n", + " button.addEventListener('click', function (_evt) {\n", + " fig.handle_close(fig, {});\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 (event) {\n", + " var fig = event.data.fig;\n", + " if (event.target !== this) {\n", + " // Ignore bubbled events from children.\n", + " return;\n", + " }\n", + " fig.close_ws(fig, {});\n", + "};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (el) {\n", + " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (el) {\n", + " // this is important to make the div 'focusable\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", + " } else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (event, _name) {\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", + "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(\n", + " 'matplotlib',\n", + " mpl.mpl_figure_comm\n", + " );\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib notebook\n", + "fig = epochs.plot(n_epochs=1, n_channels=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dropped 1 epoch: 5\n", + "The following epochs were marked as bad and are dropped:\n", + "[5]\n", + "Channels marked as bad:\n", + "['laimm12-laimm11', 'lmolf4-laimm6']\n", + "Dropped 0 epochs: \n", + "The following epochs were marked as bad and are dropped:\n", + "[5]\n", + "Channels marked as bad:\n", + "['laimm12-laimm11', 'lmolf4-laimm6']\n" + ] + } + ], + "source": [ + "# Need this following line to save the annotations to the epochs object \n", + "fig.fake_keypress('a')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can examine information about the epochs you dropped: " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + ">" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check the epoch annotations \n", + "epochs.get_annotations_per_epoch" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "((),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " ('USER',),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " (),\n", + " ())" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "epochs.drop_log" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save the epoched data and use for plots" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because most of your downstream analyses will operate on these epoched data, let's save them. Also, in the interest of reproducability, these will likely be the data you share (see: https://oir.nih.gov/sourcebook/intramural-program-oversight/intramural-data-sharing/2023-nih-data-management-sharing-policy)\n", + "\n", + "**Bad epochs will be dropped before saving the epochs to disk**" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting existing file.\n" + ] + } + ], + "source": [ + "epochs.save(f'{load_dir}/feedback-epo.fif', overwrite=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading /sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/feedback-epo.fif ...\n", + " Found the data of interest:\n", + " t = -2000.00 ... 2498.05 ms\n", + " 0 CTF compensation matrices available\n", + "Adding metadata with 120 columns\n", + "60 matching events found\n", + "No baseline correction applied\n", + "0 projection items activated\n" + ] + } + ], + "source": [ + "epochs = mne.read_epochs(f'{load_dir}/feedback-epo.fif')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we will plot some basic TFRs of our behaviorally-locked analysis using a wavelet transform. Let's set some parameters first" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "# power parameters \n", + "freqs = np.logspace(*np.log10([4, 128]), num=20)\n", + "n_cycles = 4 \n", + "sr = epochs.info['sfreq']\n", + "buf = 1.0\n", + "buf_ix = int(buf*sr)\n", + "\n", + "good_chans = [x for x in epochs.ch_names if x not in epochs.info['bads']]\n", + "picks = [x for x in good_chans]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the metadata to assign conditions to parse your epochs!" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "0\n", + "0\n", + "1\n", + "0\n", + "0\n", + "1\n", + "0\n", + "0\n", + "0\n", + "1\n", + "0\n", + "0\n", + "0\n", + "6\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "3\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "2\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "1\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "1\n", + "0\n", + "0\n", + "4\n", + "4\n", + "0\n", + "1\n", + "1\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "3\n", + "5\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "2\n", + "1\n", + "1\n", + "1\n", + "1\n", + "0\n", + "0\n", + "3\n", + "Not setting metadata\n", + "Applying baseline correction (mode: zscore)\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "2\n", + "3\n", + "0\n", + "0\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "1\n", + "0\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "1\n", + "0\n", + "0\n", + "0\n", + "0\n", + "0\n", + "2\n", + "0\n", + "1\n", + "1\n", + "Not setting metadata\n", + "Applying baseline correction (mode: zscore)\n" + ] + } + ], + "source": [ + "data_parsing = ['reward==1',\n", + " 'reward==0'\n", + " ]\n", + "\n", + "\n", + "diff_pow_epochs = {f'{x}':np.nan for x in data_parsing}\n", + "\n", + "# baseline_pow = mne.time_frequency.tfr_morlet(baseline_epochs, picks=picks,\n", + "# freqs=freqs, n_cycles=n_cycles, use_fft=True,\n", + "# return_itc=False, n_jobs=-1, average=False)\n", + "\n", + "# Compute power without averaging over events\n", + "for parsing in data_parsing: \n", + " data_struct = np.ones([epochs[parsing]._data.shape[0], \n", + " epochs[parsing]._data.shape[1], len(freqs), \n", + " epochs[parsing]._data.shape[-1]])\n", + " for ch_ix in np.arange(epochs._data.shape[1]): \n", + " ch_data = epochs[parsing]._data[:, ch_ix:ch_ix+1, :]\n", + " bad_epochs = np.where(epochs.metadata.query(parsing)[epochs.ch_names[ch_ix]].notnull())[0]\n", + "# print(len(bad_epochs))\n", + " good_epochs = np.delete(np.arange(ch_data.shape[0]), bad_epochs)\n", + " ch_data = np.delete(ch_data, bad_epochs, axis=0)\n", + " ch_pow = mne.time_frequency.tfr_array_morlet(ch_data, sfreq=epochs.info['sfreq'], \n", + " freqs=freqs, n_cycles=n_cycles, zero_mean=True, \n", + " use_fft=True, output='power', \n", + " n_jobs=1)\n", + " \n", + " data_struct[good_epochs, ch_ix, :, :] = ch_pow[:, 0, :, :]\n", + " temp_pow = mne.time_frequency.EpochsTFR(epochs[parsing].info, data_struct, \n", + " epochs.times, freqs)\n", + "\n", + "# temp_pow = mne.time_frequency.tfr_morlet(epochs[parsing], picks=picks,\n", + "# freqs=freqs, n_cycles=n_cycles, use_fft=True,\n", + "# return_itc=False, n_jobs=-1, average=False)\n", + " # Baseline correct the TFRs\n", + " temp_pow = temp_pow.apply_baseline(baseline=(-feedback_pre, 0), mode='zscore')\n", + "# baseline_pow.crop(tmin=-baseline_pre, tmax=baseline_post)\n", + "# temp_pow.data = lfp_preprocess_utils.zscore_TFR_across_trials(temp_pow.data, baseline_pow.data)\n", + " temp_pow.crop(tmin=-feedback_pre, tmax=feedback_post)\n", + " diff_pow_epochs[parsing] = temp_pow\n" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(25, 116, 20, 1281)" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temp_pow._data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot: \n", + "\n", + "freqs = np.logspace(*np.log10([4, 128]), num=20)\n", + "\n", + "for parsing in data_parsing: \n", + " save_file = f'{load_dir}/gamble_TFR_{parsing}.pdf'\n", + " with PdfPages(save_file) as pdf:\n", + " for label in diff_pow_epochs[parsing].ch_names: \n", + " plot_ix = diff_pow_epochs[parsing].ch_names.index(label)\n", + " region_label = elec_df[elec_df.label==label].YBA_1.values[0]\n", + " if region_label == 'Unknown':\n", + " region_label = elec_df[elec_df.label==label]['Manual Examination'].values[0]\n", + "# plot_data = np.mean(diff_pow_epochs[parsing].data[:, plot_ix, :, :], axis=0)\n", + " plot_data = np.nanmean(diff_pow_epochs[parsing].data[:, plot_ix, :, :], axis=0)\n", + "\n", + "\n", + " f, tfr = plt.subplots(1, 1, figsize=[7, 4], dpi=300)\n", + "\n", + " tfr.imshow(plot_data, aspect='auto', interpolation='bicubic', cmap='RdBu_r', vmin=-3, vmax=3)\n", + " tfr.invert_yaxis()\n", + "\n", + " # neg_plot.vlines(3*buf_ix//4, 0, len(freqs)-1, 'k')\n", + " tfr.set_yticks(np.arange(0, len(freqs), 4))\n", + " tfr.set_yticklabels(np.round(freqs[np.arange(0, len(freqs), 4)]), fontsize=10)\n", + " tfr.set_xticks(np.linspace(0, plot_data.shape[-1], plot_data.shape[-1]//250))\n", + " tfr.set_xticklabels(np.linspace(-(feedback_pre*1000), feedback_post*1000, plot_data.shape[-1]//250))\n", + " tfr.set_xlabel('Time (ms)', fontsize=12)\n", + " tfr.set_ylabel('Frequency (Hz)', fontsize=12)\n", + " tfr.vlines((feedback_pre * diff_pow_epochs[parsing].info['sfreq']), 0, len(freqs)-1, 'k')\n", + "\n", + " f.suptitle(f'{region_label}')\n", + " f.tight_layout()\n", + " pdf.savefig()\n", + " plt.close(f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Statistical Analyses:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At this stage, you should **heavily** brainstorm the statistics you want to do, before you start writing any code. What is the goal of your analysis? What would actually allow you to show what you want to show?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As an example, let's say I want to compare the reward vs. no-reward conditions for every channel, and identify the timepoints and frequencies that exhibit significant differences between conditions. To do so, I would utilize a non-parametric cluster-permutation test." + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [], + "source": [ + "# def stat_fun(*arg):\n", + " return(scipy.stats.ttest_ind(arg[0], arg[1], equal_var=True, nan_policy='omit')[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "for label in diff_pow_epochs[parsing].ch_names[0:1]: \n", + " region_label = elec_df[elec_df.label==label].YBA_1.values[0]\n", + " if region_label == 'Unknown':\n", + " region_label = elec_df[elec_df.label==label]['Manual Examination'].values[0]\n", + "\n", + " plot_ix = diff_pow_epochs[parsing].ch_names.index(label)\n", + " \n", + " rdata = diff_pow_epochs['reward==1'].data[:, plot_ix, :, :]\n", + " nrdata = diff_pow_epochs['reward==0'].data[:, plot_ix, :, :]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for label in diff_pow_epochs[parsing].ch_names: \n", + " region_label = elec_df[elec_df.label==label].YBA_1.values[0]\n", + " if region_label == 'Unknown':\n", + " region_label = elec_df[elec_df.label==label]['Manual Examination'].values[0]\n", + "\n", + " plot_ix = diff_pow_epochs[parsing].ch_names.index(label)\n", + " \n", + " rdata = diff_pow_epochs['reward==1'].data[:, plot_ix, :, :]\n", + " rdata = rdata[np.unique(np.where(~np.isnan(rdata))[0]), :, :]\n", + " nrdata = diff_pow_epochs['reward==0'].data[:, plot_ix, :, :]\n", + " nrdata = nrdata[np.unique(np.where(~np.isnan(nrdata))[0]), :, :]\n", + " \n", + " X = [rdata, \n", + " nrdata]\n", + " \n", + " F_obs, clusters, cluster_p_values, H0 = \\\n", + " mne.stats.permutation_cluster_test(X, n_permutations=1000, out_type='mask', \n", + " verbose=True)\n", + " \n", + " if any(cluster_p_values<=0.05):\n", + " print(region_label)\n", + " # Create new stats image with only significant clusters\n", + " fig, ax = plt.subplots(1, 1, figsize=(6, 4))\n", + "\n", + " times = diff_pow_epochs['reward==0'].times\n", + "\n", + " evoked_power_1 = np.nanmean(X[0], axis=0)\n", + " evoked_power_2 = np.nanmean(X[1], axis=0)\n", + " evoked_power_contrast = evoked_power_1 - evoked_power_2\n", + " signs = np.sign(evoked_power_contrast)\n", + "\n", + " F_obs_plot = np.nan * np.ones_like(F_obs)\n", + " for c, p_val in zip(clusters, cluster_p_values):\n", + " if p_val <= 0.05:\n", + " F_obs_plot[c] = F_obs[c] * signs[c]\n", + "\n", + " ax.imshow(F_obs,\n", + " extent=[times[0], times[-1], freqs[0], freqs[-1]],\n", + " aspect='auto', origin='lower', cmap='gray')\n", + " max_F = np.nanmax(abs(F_obs_plot))\n", + " ax.imshow(F_obs_plot,\n", + " extent=[times[0], times[-1], freqs[0], freqs[-1]],\n", + " aspect='auto', origin='lower', cmap='RdBu_r',\n", + " vmin=-max_F, vmax=max_F)\n", + "\n", + " ax.set_xlabel('Time (ms)')\n", + " ax.set_ylabel('Frequency (Hz)')\n", + " # # ax.set_title(f'Induced power ({ch_name})')\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "# # Create new stats image with only significant clusters\n", + "# fig, (ax, ax2) = plt.subplots(2, 1, figsize=(6, 4))\n", + "\n", + "# times = diff_pow_epochs['reward==0'].times\n", + "\n", + "# evoked_power_1 = X[0].mean(axis=0)\n", + "# evoked_power_2 = X[1].mean(axis=0)\n", + "# evoked_power_contrast = evoked_power_1 - evoked_power_2\n", + "# signs = np.sign(evoked_power_contrast)\n", + "\n", + "# F_obs_plot = np.nan * np.ones_like(F_obs)\n", + "# for c, p_val in zip(clusters, cluster_p_values):\n", + "# if p_val <= 0.05:\n", + "# F_obs_plot[c] = F_obs[c] * signs[c]\n", + "\n", + "# ax.imshow(F_obs,\n", + "# extent=[times[0], times[-1], freqs[0], freqs[-1]],\n", + "# aspect='auto', origin='lower', cmap='gray')\n", + "# max_F = np.nanmax(abs(F_obs_plot))\n", + "# ax.imshow(F_obs_plot,\n", + "# extent=[times[0], times[-1], freqs[0], freqs[-1]],\n", + "# aspect='auto', origin='lower', cmap='RdBu_r',\n", + "# vmin=-max_F, vmax=max_F)\n", + "\n", + "# ax.set_xlabel('Time (ms)')\n", + "# ax.set_ylabel('Frequency (Hz)')\n", + "# # ax.set_title(f'Induced power ({ch_name})')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "lfp_analysis", + "language": "python", + "name": "lfpanalysis" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/LFPAnalysis/NLX_preprocessing_step_by_step.ipynb b/LFPAnalysis/NLX_preprocessing_step_by_step.ipynb new file mode 100644 index 0000000..0e0785a --- /dev/null +++ b/LFPAnalysis/NLX_preprocessing_step_by_step.ipynb @@ -0,0 +1,4948 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example preprocessing notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook we are going to walk through a single patient example. There are probably some patient-specific stuff in here that might change with other patients. Should be able to demonstrate the usage of different functions from the toolbox.\n", + "\n", + "1. Load raw data (.edf in this notebook) using mne\n", + "\n", + "2. Add in electrode information\n", + "\n", + "3. Notch filter line noise and cleaning out bad channels \n", + "\n", + "4. Re-reference the data \n", + "\n", + "5. Annotate data (i.e. artifact and IEDs) \n", + "\n", + "\n", + "Must read guides: \n", + "\n", + "https://www.sciencedirect.com/science/article/pii/S1053811922005559\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%reload_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import mne\n", + "from glob import glob\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.backends.backend_pdf import PdfPages\n", + "import seaborn as sns\n", + "from scipy.stats import zscore, linregress\n", + "import pandas as pd\n", + "from mne.preprocessing.bads import _find_outliers\n", + "import re" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('/hpc/users/qasims01/resources/LFPAnalysis')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from LFPAnalysis import lfp_preprocess_utils, sync_utils, nlx_utils" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load raw data (.nlx in this notebook) using mne" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's a good idea to setup a sensible directory structure like below. Note that all my data lives on '/sc/arion' which is Minerva. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "mne: https://mne.tools/stable/index.html" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "base_dir = '/sc/arion' # this is the root directory for most un-archived data and results \n", + "\n", + "save_dir = f'{base_dir}/work/qasims01/MemoryBanditData/EMU/Subjects/MS022' # save intermediate results in the 'work' directory\n", + " \n", + "# I have saved most of my raw data in the 'projects directory'\n", + "behav_dir = f'{base_dir}/projects/guLab/Salman/EMU/MS022/behav/Day1'\n", + "neural_dir = f'{base_dir}/projects/guLab/Salman/EMU/MS022/neural/Day1'\n", + "anat_dir = f'{base_dir}/projects/guLab/Salman/EMU/MS022/anat'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "load_path = neural_dir" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "eeg_names = ['fp1', 'f7', 't3', 't5', 'o1', 'f3', 'c3', 'p3', 'fp2', 'f8', 't4', 't6', 'o2', 'f4', 'c4', 'p4', 'fz', 'cz', 'pz']\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "seeg_names = None\n", + "resp_names = None\n", + "ekg_names = None" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# per Shawn, MSSM data seems to sometime have a \"_0000.ncs\" to \"_9999.ncs\" appended to the end of real data\n", + "pattern = re.compile(r\"_\\d{4}\\.ncs\") # regex pattern to match \"_0000.ncs\" to \"_9999.ncs\"\n", + "ncs_files = [x for x in glob(f'{load_path}/*.ncs') if re.search(pattern, x)]\n", + "# just in case this changes in the future: \n", + "if len(ncs_files) == 0: \n", + " ncs_files = glob(f'{load_path}/*.ncs')\n", + " if not seeg_names:\n", + " seeg_names = [x.split('/')[-1].replace('.ncs','') for x in glob(f'{load_path}/[R,L]*.ncs')]\n", + "else:\n", + " if not seeg_names:\n", + " seeg_names = [x.split('/')[-1].replace('.ncs','').split('_')[0] for x in glob(f'{load_path}/[R,L]*.ncs') if re.search(pattern, x)]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "seeg_names = [x.lower() for x in seeg_names]\n", + "sEEG_mapping_dict = {f'{x}':'seeg' for x in seeg_names}" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "signals = [] \n", + "srs = [] \n", + "ch_name = [] \n", + "ch_type = []" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:82: UserWarning: Unable to parse original file path from Neuralynx header: -FileType NCS\n", + " warnings.warn('Unable to parse original file path from Neuralynx header: ' + hdr_lines[1])\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileVersion 3.4\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 2623c1e9-e532-42cc-9e7a-fe23bec5609a\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:96: UserWarning: Unable to parse parameter line from Neuralynx header: -ProbeName\n", + " warnings.warn('Unable to parse parameter line from Neuralynx header: ' + line)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:96: UserWarning: Unable to parse parameter line from Neuralynx header: -TimeCreated 2023/02/05 10:15:26\n", + " warnings.warn('Unable to parse parameter line from Neuralynx header: ' + line)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:96: UserWarning: Unable to parse parameter line from Neuralynx header: -TimeClosed 2023/02/05 10:56:18\n", + " warnings.warn('Unable to parse parameter line from Neuralynx header: ' + line)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:96: UserWarning: Unable to parse parameter line from Neuralynx header: -ApplicationName Pegasus \"2.2.2 \"\n", + " warnings.warn('Unable to parse parameter line from Neuralynx header: ' + line)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:96: UserWarning: Unable to parse parameter line from Neuralynx header: -AcquisitionSystem AcqSystem1 ATLAS\n", + " warnings.warn('Unable to parse parameter line from Neuralynx header: ' + line)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:96: UserWarning: Unable to parse parameter line from Neuralynx header: -ReferenceChannel \"Source 08 Subject Ground\"\n", + " warnings.warn('Unable to parse parameter line from Neuralynx header: ' + line)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:153: UserWarning: Invalid samples in one or more records\n", + " warnings.warn('Invalid samples in one or more records')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unidentified data type in Analog4\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 959cb163-d218-4c33-9620-dc15f647560f\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:96: UserWarning: Unable to parse parameter line from Neuralynx header: -TimeClosed 2023/02/05 10:56:17\n", + " warnings.warn('Unable to parse parameter line from Neuralynx header: ' + line)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:96: UserWarning: Unable to parse parameter line from Neuralynx header: -ReferenceChannel \"Source 03 Subject Ground\"\n", + " warnings.warn('Unable to parse parameter line from Neuralynx header: ' + line)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 71544c0d-e6d8-4b5a-a8f4-c3b722541447\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:96: UserWarning: Unable to parse parameter line from Neuralynx header: -ReferenceChannel \"Source 07 Reference 2\"\n", + " warnings.warn('Unable to parse parameter line from Neuralynx header: ' + line)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 66aceaf9-de74-47a4-8471-e15e2f254eaa\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:96: UserWarning: Unable to parse parameter line from Neuralynx header: -TimeClosed 2023/02/05 10:56:16\n", + " warnings.warn('Unable to parse parameter line from Neuralynx header: ' + line)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:96: UserWarning: Unable to parse parameter line from Neuralynx header: -ReferenceChannel \"AD Channel 0038\"\n", + " warnings.warn('Unable to parse parameter line from Neuralynx header: ' + line)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID c07dc2b0-9228-4f0b-89f3-cc82d8560528\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:96: UserWarning: Unable to parse parameter line from Neuralynx header: -TimeClosed 2023/02/05 10:56:15\n", + " warnings.warn('Unable to parse parameter line from Neuralynx header: ' + line)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID b8bd04b0-bbbb-4d03-9cc5-1795cafa552c\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 84698a65-bfe5-4576-aab0-0edb243aa4f5\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID af20270b-bd78-4d94-a09e-ddd4165c0544\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 51713b09-4db7-4fc9-9e68-28ace7db1a17\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 330ec1c2-c966-489c-bef9-f40cb2081eaf\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 80fb2719-7ef1-480e-a93d-166e280e7fa5\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 2c08f35b-80f4-4854-9952-1332576e7933\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID d6d7203d-5cd7-4590-af47-4d658aac4642\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 7f407302-0e85-43b7-b3ac-92c9172b2aa3\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 3c5d8fe3-0bba-4088-a088-48ba88fe8cfd\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unidentified data type in ABS1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 393740ff-230e-433d-b780-178e3ef14ec8\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID fcb8da22-488c-495a-a823-592319966658\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 2ffa7a06-84ed-4d2d-8dc0-f76c0a05f3a0\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID c165f11b-2178-417d-8651-dd0fab5b2f0b\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID e887013b-9728-49f5-8d6e-443e2404238a\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 2e8a80a0-4ca8-4487-8e71-424040a6c26b\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 5f0bba14-95cd-45ac-80eb-30dedb932e13\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID b3b2ac2a-1cfe-4b5e-82ce-de7336bbd63b\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 7f59a8cc-c829-4005-ad61-b0bb49622fd3\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:96: UserWarning: Unable to parse parameter line from Neuralynx header: -ReferenceChannel \"AD Channel 0192\"\n", + " warnings.warn('Unable to parse parameter line from Neuralynx header: ' + line)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 04ac83cc-0555-4f63-87c3-0b1e8f6ca997\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 9e8a24fa-7f90-4d7b-96eb-c28b45f2fe94\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 9ff49142-33c3-49d2-aaf5-d14e7d1f63c4\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unidentified data type in EKG1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID afbbc14b-91c9-4359-8c2c-b59b32ca9514\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 9de02852-1dc2-4de2-8b81-19db8dc3fcc8\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unidentified data type in Mic\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 979d433b-d012-4a25-82dc-c054811c867f\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 92067a0d-8c2e-4ed7-b9c2-7025efb7a0c6\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 48fd226d-5a37-451d-9100-612e5cf5de41\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unidentified data type in Photodiode\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 16ef7377-5f8d-4652-ab25-1f527a13116c\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID c2584992-90bb-4624-85cf-53ad7ecc99ca\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 512a44f5-6bfe-45d9-8c8a-55ec4d4a7b6b\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 80bf0833-33cf-4605-a8c5-6595a9c59c22\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 14208120-dd9f-47e5-916a-2cb563e91899\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 4915bd5b-712f-4ca8-b07a-62da2885d318\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID a94b26d2-95bd-4453-a620-6ca0c89298e3\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 74198149-3970-49a8-9c1b-82c6b430cfc2\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 624d0305-c094-4b02-b49d-a6db2fa20e31\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 5faa8437-61a0-449c-bde6-6d57c7a02f2d\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 3c18243b-8252-495d-958a-b763f0aa20ad\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID acee17d4-6103-4a6b-b66f-0659ecb69548\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unidentified data type in Audio\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 024987bd-15fc-417f-95fb-558c1edec501\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:96: UserWarning: Unable to parse parameter line from Neuralynx header: -ReferenceChannel \"Source 04 Subject Ground\"\n", + " warnings.warn('Unable to parse parameter line from Neuralynx header: ' + line)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unidentified data type in CHEST2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 581c6652-e14a-4977-b241-17390fb824a0\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:96: UserWarning: Unable to parse parameter line from Neuralynx header: -ReferenceChannel \"AD Channel 0195\"\n", + " warnings.warn('Unable to parse parameter line from Neuralynx header: ' + line)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID acfb5007-1bd7-41f2-871b-41e15bab49a0\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 692e3221-f54f-43c6-ab02-db1699727727\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 29f24039-71da-4eb1-a9df-c18038a8f141\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID b32c8251-0506-48ad-b8f4-4883cd547c20\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID a66da283-1436-4052-a24c-da83604b2d6f\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 72bfe6d4-0197-4afd-a699-5a4098c6e783\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 1466ed01-78cf-4d5b-a564-f9eca1b9ec42\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID b133db27-2d25-498e-b4d6-5689728e7aa4\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID e5a4830c-6c29-4976-adf6-87fd136ed500\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 2f565e20-1852-4ba8-8fa3-df367d848964\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 39f60a89-a285-4863-8fa5-132834d54796\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 95a77411-4903-4f50-ac5f-c96ab6ab06e6\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 752ca1ef-b457-479e-a313-0a6ba44a384e\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 43858b00-5fdc-4a5c-93ce-07b19a12327f\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID c936e11b-22b9-4982-87ad-38f1f496f36c\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 79ee7246-67fa-4573-b703-36ec17f3d70c\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID eda7ff8c-1a9e-43c4-b130-2d921ddacb7a\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 1b256b55-4c02-4efb-85d3-70b4baf72ad4\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 83acf0ab-637f-4234-9a55-73ff3c1d05fc\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID a0cd6975-39f5-4608-a217-21401cb0a4d8\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:96: UserWarning: Unable to parse parameter line from Neuralynx header: -ReferenceChannel \"Source 02 Subject Ground\"\n", + " warnings.warn('Unable to parse parameter line from Neuralynx header: ' + line)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unidentified data type in BLANK2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 715dcb1d-926f-413c-ade9-af4bf4dbfe79\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 2f1c0985-8418-4f31-bb00-064cbbc7ee3c\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID ee4aba97-ede6-469d-964c-f539fb5c0fc7\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 2662efdc-4a60-4ccd-9985-fef5774ba588\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unidentified data type in EKG2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 1080f445-82f7-4164-89af-9ef14fd972f8\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 83c95854-b0a4-4fb7-add2-d46d14c7c5d2\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID bf8cf8a9-b734-462a-a836-af2964f2be22\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 8db8a6db-3665-4146-978a-0a84257390b9\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 6cbc508f-ccc5-419f-b3a0-494362fdbb6e\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 785bcecd-e984-46f4-ab13-b34b638dac11\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unidentified data type in CHEST1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID ce87ad17-bd13-408d-b40a-a4d436a5710a\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 02170735-2453-460f-8a34-b5d1d18ac56b\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 7ae1c7fe-8928-4ef8-9be8-a7c3812e6d5e\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 3ce34952-f769-45a0-a3f1-d76654d67e8f\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID a7502311-b0f5-4cbc-8c1c-ef87b6bc36e5\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 69f58978-0b37-4f1a-aaf2-bde29689d613\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID b12545e2-82d3-4325-867a-bd0ddab3f213\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 5b346c2e-d814-4c50-ac9a-ac3156d766df\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID cc46a51f-7b89-4939-9206-4a073dadeb1b\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 53cc6d97-592c-4f92-8fe4-f7f891e840d2\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID d8d06f78-ca07-491a-9566-cbcaf24c241b\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 744ecad7-290a-49cc-9f63-e31784049fb3\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 72d45b3e-8276-4a48-b5af-c16ad3ab4448\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 3018195f-d1fe-4fb5-abe3-b446758c98a1\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 5901ddb4-e5b5-48b1-94b2-c00a5c86e4d2\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 5be3e66d-d18c-4c94-b131-36e206a9a3e0\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 69f2f6f1-861f-4cfc-a0bc-c1a2a9f1ece8\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID de252544-883c-42f3-8fc5-efd235ef345b\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:96: UserWarning: Unable to parse parameter line from Neuralynx header: -ReferenceChannel \"Source 01 Reference 1\"\n", + " warnings.warn('Unable to parse parameter line from Neuralynx header: ' + line)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID b2fc1e65-ab45-4337-adc0-78dfceaab440\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 890d6aec-5c2d-45ea-b77a-883c3681c92b\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID a8737ea7-2e68-4a26-97fd-4e9ecfe32fcc\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 964b108a-0488-488e-89ec-ef628940ec85\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 72d62758-5e0c-4eae-8632-7e13784c9f12\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 728676d5-092e-4718-bc26-7e0304d4f0af\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 22ae4624-2d63-4c9a-bcf6-44875539457f\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 21137933-c8d0-49e4-bf7f-e8b39651e19c\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 789c08dc-7681-43e1-990c-ef09da5299aa\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 7456706e-b4b0-42e2-984a-26624228920e\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 499d5937-8e65-4326-9638-a5bc1a74d9db\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 98b52f0e-1bb8-4fc8-a4b8-20ef4fa70ee6\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID d456e6c6-3ab3-40a6-a3b6-8afc8bf82a99\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 3cdcd468-9c7b-40c0-b5f1-c65e5e4f48ae\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unidentified data type in ABS2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID df738148-7a8a-4a3d-8f2e-68955c57613a\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unidentified data type in BLANK1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 42430b23-d3ed-4cea-9175-05bcd2dba695\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID f86046c4-bb91-4230-8761-a36a015a9e6f\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID e3528fe5-b798-43a4-a353-573654b37141\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 4e4b595f-13ca-4196-8718-d4a78e101282\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID f7d09658-db34-46e9-b4bb-b5025c1e3395\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID c832fcc3-ced6-41a6-b379-837a8a39a94f\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 89e1529f-ff4d-4987-a3cf-f455e0705d33\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n", + "/hpc/users/qasims01/resources/LFPAnalysis/LFPAnalysis/nlx_utils.py:134: UserWarning: Unable to parse time string from Neuralynx header: -FileUUID 665ad3dc-100e-48f3-bcd9-e65e8b29ec36\n", + " warnings.warn('Unable to parse time string from Neuralynx header: ' + time_string)\n" + ] + } + ], + "source": [ + "for chan_path in ncs_files:\n", + " chan_name = chan_path.split('/')[-1].replace('.ncs','')\n", + " # strip the file type off the end if needed \n", + " if '_' in chan_name:\n", + " chan_name = chan_name.split('_')[0]\n", + " try:\n", + " fdata = nlx_utils.load_ncs(chan_path)\n", + " except IndexError: \n", + " print(f'No data in channel {chan_name}')\n", + " continue\n", + " if eeg_names:\n", + " if chan_name.lower() in eeg_names:\n", + " ch_type.append('eeg')\n", + " if resp_names:\n", + " if chan_name.lower() in resp_names:\n", + " ch_type.append('bio')\n", + " if ekg_names:\n", + " if chan_name.lower() in ekg_names: \n", + " ch_type.append('ecg') \n", + " if seeg_names: \n", + " if chan_name.lower() in seeg_names:\n", + " ch_type.append('seeg') \n", + " elif chan_name.lower()[0] == 'u':\n", + " # microwire data\n", + " ch_type.append('seeg') \n", + " signals.append(fdata['data'])\n", + " srs.append(fdata['sampling_rate'])\n", + " ch_name.append(chan_name)\n", + " if len(ch_type) < len(ch_name):\n", + " ch_type.append('misc')\n", + " print(f'Unidentified data type in {chan_name}')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "target_sr = np.min(srs)\n", + "mne_data_resampled = []" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating RawArray with float64 data, n_channels=95, n_times=4866048\n", + " Range : 0 ... 4866047 = 0.000 ... 2433.023 secs\n", + "Ready.\n", + "Creating RawArray with float64 data, n_channels=22, n_times=77853696\n", + " Range : 0 ... 77853695 = 0.000 ... 2432.928 secs\n", + "Ready.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 14 tasks | elapsed: 1.2min\n", + "[Parallel(n_jobs=-1)]: Done 22 out of 22 | elapsed: 1.7min finished\n" + ] + } + ], + "source": [ + "for sr in np.unique(srs):\n", + " ch_ix = np.where(srs==sr)[0].astype(int)\n", + " info = mne.create_info([x for ix, x in enumerate(ch_name) if ix in ch_ix], sr, [x for ix, x in enumerate(ch_type) if ix in ch_ix])\n", + " mne_data_temp = mne.io.RawArray([x for ix, x in enumerate(signals) if ix in ch_ix], info)\n", + " if sr != target_sr:\n", + " # resample down to one sample rate \n", + " mne_data_temp.resample(sfreq=target_sr, npad='auto', n_jobs=-1)\n", + " mne_data_resampled.append(mne_data_temp)\n", + " else: \n", + " mne_data = mne_data_temp\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "#Because of the resampling, the end timings might not match perfectly:https://github.com/mne-tools/mne-python/issues/8257\n", + "\n", + "if mne_data_resampled[0].tmax > mne_data.tmax:\n", + " mne_data_resampled[0].crop(tmin=0, tmax=mne_data.tmax)\n", + "elif mne_data_resampled[0].tmax < mne_data.tmax:\n", + " mne_data.crop(tmin=0, tmax=mne_data_resampled[0].tmax)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Measurement dateUnknown
ExperimenterUnknown
ParticipantUnknown
Digitized points0 points
Good channels19 EEG, 86 sEEG, 12 misc
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Highpass0.00 Hz
Lowpass1000.00 Hz
Duration00:40:33 (HH:MM:SS)
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mne_data.add_channels(mne_data_resampled)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Try loading in the data into memory" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting up band-stop filter\n", + "\n", + "FIR filter parameters\n", + "---------------------\n", + "Designing a one-pass, zero-phase, non-causal bandstop filter:\n", + "- Windowed time-domain design (firwin) method\n", + "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n", + "- Lower transition bandwidth: 0.50 Hz\n", + "- Upper transition bandwidth: 0.50 Hz\n", + "- Filter length: 13201 samples (6.601 sec)\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", + "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.2s remaining: 0.0s\n", + "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.5s remaining: 0.0s\n", + "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.7s remaining: 0.0s\n", + "[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 1.0s remaining: 0.0s\n", + "[Parallel(n_jobs=1)]: Done 105 out of 105 | elapsed: 25.0s finished\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Measurement dateUnknown
ExperimenterUnknown
ParticipantUnknown
Digitized points0 points
Good channels19 EEG, 86 sEEG, 12 misc
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
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Lowpass1000.00 Hz
Duration00:40:33 (HH:MM:SS)
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mne_data.ch_names\n", + "mne_data.info['line_freq'] = 60\n", + "# Notch out 60 Hz noise and harmonics \n", + "mne_data.notch_filter(freqs=(60, 120, 180, 240))" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "drop_chans = list(set([x.lower() for x in mne_data.ch_names])^set(seeg_names))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Measurement dateUnknown
ExperimenterUnknown
ParticipantUnknown
Digitized points0 points
Good channels19 EEG, 86 sEEG, 12 misc
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency2000.00 Hz
Highpass0.00 Hz
Lowpass1000.00 Hz
Duration00:40:33 (HH:MM:SS)
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_name_dict = {x:x.replace(\" \", \"\").lower() for x in mne_data.ch_names}\n", + "mne_data.rename_channels(new_name_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Measurement dateUnknown
ExperimenterUnknown
ParticipantUnknown
Digitized points0 points
Good channels70 sEEG
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency2000.00 Hz
Highpass0.00 Hz
Lowpass1000.00 Hz
Duration00:40:33 (HH:MM:SS)
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mne_data.drop_channels(drop_chans)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "bads = lfp_preprocess_utils.detect_bad_elecs(mne_data, sEEG_mapping_dict)\n", + "mne_data.info['bads'] = bads" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "mne_data.info['bads'] = bads" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Sanity check the photodiode\n", + "trig_ix = MS007_data.ch_names.index('DC1')\n", + "plt.plot(MS007_data._data[trig_ix, 10000:50000])\n", + "plt.title(\"Photodiode\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add in electrode information" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the electrode localization data and add it in\n", + "\n", + "csv_files = glob(f'{anat_dir}/*labels.csv')\n", + "elec_locs = pd.read_csv(csv_files[0])\n", + "\n", + "# Sometimes there's extra columns with no entries: \n", + "elec_locs = elec_locs[elec_locs.columns.drop(list(elec_locs.filter(regex='Unnamed')))]\n", + "\n", + "# # identify the bundles for re-referencing:\n", + "# loc_data['bundle'] = np.nan\n", + "# loc_data['bundle'] = loc_data.apply(lambda x: ''.join(i for i in x.label if not i.isdigit()), axis=1)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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labelBN246labelxyzmni_xmni_ymni_zgmNMMAnatAnatMacroBN246YBA_1Manual ExaminationNotes
0LaCaS1A32sg_L-7.99839233.75301411.599995-7.30310629.507935-4.544081GrayLeft ACgG anterior cingulate gyrusArea s24L ACCL CGLeft cingulate gyrus DNaNNaN
1LaCaS2Unknown-11.99759133.75301415.199995-11.26367331.336920-0.649451WhiteLeft Cerebral White MatterUnknownUnknownUnknownUnknownNaNNaN
2LaCaS3Unknown-15.59687033.75301419.199995-14.82688033.3532213.587480WhiteLeft Cerebral White MatterUnknownUnknownUnknownUnknownNaNNaN
3LaCaS4Unknown-19.19614834.55190221.599995-18.47526735.2171195.812331WhiteLeft Cerebral White MatterUnknownUnknownUnknownUnknownNaNNaN
4LaCaS5Unknown-22.39550734.55190224.799995-21.68368936.7617979.288654WhiteLeft Cerebral White MatterUnknownUnknownUnknownUnknownNaNNaN
...................................................
67LplfO4Unknown-49.590058-4.19417019.999995-47.937431-2.05269225.197714GrayLeft Cerebral White MatterUnknownL Precentral GyrusUnknownUnknownLeft posterior motor INaN
68LplfO5A4hf_L-54.389097-3.79472621.599995-52.836611-1.11805426.762258GrayLeft Cerebral White MatterUnknownL Precentral GyrusL PrGLeft posterior motor INaNNaN
69LplfO6A4hf_L-59.987975-2.99583823.599995-58.5697050.13888828.441652GrayLeft PrG precentral gyrusUnknownL Precentral GyrusL PrGLeft posterior motor INaNNaN
70uLaCaSA32sg_L-5.59887334.1524589.999995-5.00033329.004945-6.416785GrayLeft ACgG anterior cingulate gyrusArea s24L ACCL CGLeft cingulate gyrus DNaNNaN
71uLmOlFA32sg_L-5.19895338.5463437.599996-4.86334831.539595-11.019659GrayLeft ACgG anterior cingulate gyrusArea s32L Mid Orbital GyrusL CGLeft cingulate gyrus DNaNNaN
\n", + "

72 rows × 16 columns

\n", + "
" + ], + "text/plain": [ + " label BN246label x y z mni_x mni_y \\\n", + "0 LaCaS1 A32sg_L -7.998392 33.753014 11.599995 -7.303106 29.507935 \n", + "1 LaCaS2 Unknown -11.997591 33.753014 15.199995 -11.263673 31.336920 \n", + "2 LaCaS3 Unknown -15.596870 33.753014 19.199995 -14.826880 33.353221 \n", + "3 LaCaS4 Unknown -19.196148 34.551902 21.599995 -18.475267 35.217119 \n", + "4 LaCaS5 Unknown -22.395507 34.551902 24.799995 -21.683689 36.761797 \n", + ".. ... ... ... ... ... ... ... \n", + "67 LplfO4 Unknown -49.590058 -4.194170 19.999995 -47.937431 -2.052692 \n", + "68 LplfO5 A4hf_L -54.389097 -3.794726 21.599995 -52.836611 -1.118054 \n", + "69 LplfO6 A4hf_L -59.987975 -2.995838 23.599995 -58.569705 0.138888 \n", + "70 uLaCaS A32sg_L -5.598873 34.152458 9.999995 -5.000333 29.004945 \n", + "71 uLmOlF A32sg_L -5.198953 38.546343 7.599996 -4.863348 31.539595 \n", + "\n", + " mni_z gm NMM Anat \\\n", + "0 -4.544081 Gray Left ACgG anterior cingulate gyrus Area s24 \n", + "1 -0.649451 White Left Cerebral White Matter Unknown \n", + "2 3.587480 White Left Cerebral White Matter Unknown \n", + "3 5.812331 White Left Cerebral White Matter Unknown \n", + "4 9.288654 White Left Cerebral White Matter Unknown \n", + ".. ... ... ... ... \n", + "67 25.197714 Gray Left Cerebral White Matter Unknown \n", + "68 26.762258 Gray Left Cerebral White Matter Unknown \n", + "69 28.441652 Gray Left PrG precentral gyrus Unknown \n", + "70 -6.416785 Gray Left ACgG anterior cingulate gyrus Area s24 \n", + "71 -11.019659 Gray Left ACgG anterior cingulate gyrus Area s32 \n", + "\n", + " AnatMacro BN246 YBA_1 \\\n", + "0 L ACC L CG Left cingulate gyrus D \n", + "1 Unknown Unknown Unknown \n", + "2 Unknown Unknown Unknown \n", + "3 Unknown Unknown Unknown \n", + "4 Unknown Unknown Unknown \n", + ".. ... ... ... \n", + "67 L Precentral Gyrus Unknown Unknown \n", + "68 L Precentral Gyrus L PrG Left posterior motor I \n", + "69 L Precentral Gyrus L PrG Left posterior motor I \n", + "70 L ACC L CG Left cingulate gyrus D \n", + "71 L Mid Orbital Gyrus L CG Left cingulate gyrus D \n", + "\n", + " Manual Examination Notes \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + ".. ... ... \n", + "67 Left posterior motor I NaN \n", + "68 NaN NaN \n", + "69 NaN NaN \n", + "70 NaN NaN \n", + "71 NaN NaN \n", + "\n", + "[72 rows x 16 columns]" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "elec_locs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The electrode names read out of the edf file do not always match those \n", + "in the pdf (used for localization). This could be error on the side of the tech who input the labels, \n", + "or on the side of MNE reading the labels in. Usually there's a mixup between lowercase 'l' and capital 'I'.\n", + "\n", + "Sometimes, there's electrodes on the pdf that are NOT in the MNE data structure... let's identify those as well. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Could not find a match for rhplt9.\n" + ] + } + ], + "source": [ + "new_mne_names, unmatched_names, unmatched_seeg = lfp_preprocess_utils.match_elec_names(MS007_data.ch_names, elec_locs.label)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So we retun a new list of channel names for the mne data structure as well as a list of channels in the localization csv which are not found in the mne structure. Make sure that unmatched_seeg does not factor into any referencing schemes later - it's not in the MNE data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized pointsNot available
Good channels276 EEG
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Rename the mne data according to the localization data\n", + "new_name_dict = {x:y for (x,y) in zip(MS007_data.ch_names, new_mne_names)}\n", + "MS007_data.rename_channels(new_name_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['rhplt9']" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unmatched_seeg" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We have a total of 196 sEEG electrodes\n", + "We have a total of 19 EEG electrodes\n" + ] + } + ], + "source": [ + "# Note, there is surface EEG data that we should separately indicate from the sEEG:\n", + "right_seeg_names = [i for i in MS007_data.ch_names if i.startswith('r')]\n", + "left_seeg_names = [i for i in MS007_data.ch_names if i.startswith('l')]\n", + "# This is optional. I might want to look at scalp EEG at some point (lol) so might as well tag them here. \n", + "eeg_names = [\n", + " 'fp1',\n", + " 'f7',\n", + " 't3',\n", + " 't5',\n", + " 'o1',\n", + " 'f3',\n", + " 'c3',\n", + " 'p3',\n", + " 'fp2',\n", + " 'f8',\n", + " 't4',\n", + " 't6',\n", + " 'o2',\n", + " 'f4',\n", + " 'c4',\n", + " 'p4',\n", + " 'fz',\n", + " 'cz',\n", + " 'pz']\n", + "print(f'We have a total of {len(left_seeg_names) + len(right_seeg_names)} sEEG electrodes')\n", + "print(f'We have a total of {len(eeg_names)} EEG electrodes')\n", + "# MS007_data.set_channel_types()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized pointsNot available
Good channels216 EEG
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sEEG_mapping_dict = {f'{x}':'seeg' for x in left_seeg_names+right_seeg_names}\n", + "EEG_mapping_dict = {f'{x}':'eeg' for x in eeg_names}\n", + "trig_mapping_dict = {'dc1':'stim'}\n", + "# Drop random chans? \n", + "drop_chans = list(set(MS007_data.ch_names)^set(eeg_names+left_seeg_names+right_seeg_names+['dc1']))\n", + "MS007_data.drop_channels(drop_chans)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_182879/1830004009.py:4: RuntimeWarning: The unit for channel(s) dc1 has changed from V to NA.\n", + " MS007_data.set_channel_types(trig_mapping_dict)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized pointsNot available
Good channels196 sEEG, 19 EEG, 1 Stimulus
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Set channel types:\n", + "MS007_data.set_channel_types(sEEG_mapping_dict)\n", + "MS007_data.set_channel_types(EEG_mapping_dict)\n", + "MS007_data.set_channel_types(trig_mapping_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_182879/3983129385.py:7: RuntimeWarning: Fiducial point nasion not found, assuming identity unknown to head transformation\n", + " MS007_data.set_montage(montage, match_case=False, on_missing='warn')\n", + "/tmp/ipykernel_182879/3983129385.py:7: RuntimeWarning: DigMontage is only a subset of info. There are 19 channel positions not present in the DigMontage. The required channels are:\n", + "\n", + "['fp1', 'f7', 't3', 't5', 'o1', 'f3', 'c3', 'p3', 'fp2', 'f8', 't4', 't6', 'o2', 'f4', 'c4', 'p4', 'fz', 'cz', 'pz'].\n", + "\n", + "Consider using inst.set_channel_types if these are not EEG channels, or use the on_missing parameter if the channel positions are allowed to be unknown in your analyses.\n", + " MS007_data.set_montage(montage, match_case=False, on_missing='warn')\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized points196 points
Good channels196 sEEG, 19 EEG, 1 Stimulus
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# make montage (convert mm to m)!! I'm not sure if we will ever use or need this step, but why not just do it\n", + "\n", + "montage = mne.channels.make_dig_montage(ch_pos=dict(zip(elec_locs.label, \n", + " elec_locs[['mni_x', 'mni_y', 'mni_z']].to_numpy(dtype=float)/1000)),\n", + " coord_frame='mni_tal')\n", + "\n", + "MS007_data.set_montage(montage, match_case=False, on_missing='warn')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notch filter line noise and cleaning out bad channels \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We want to remove the line noise (60 Hz and harmonics in US data, 50 Hz and harmonics in EU data). \n", + "\n", + "To do so, we use a band-stop filter that removes a narrow band of frequencies. \n", + "\n", + "Maybe eventually we don't want to use filters, especially if interested in ERPs: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6456018/" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting up band-stop filter\n", + "\n", + "FIR filter parameters\n", + "---------------------\n", + "Designing a one-pass, zero-phase, non-causal bandstop filter:\n", + "- Windowed time-domain design (firwin) method\n", + "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n", + "- Lower transition bandwidth: 0.50 Hz\n", + "- Upper transition bandwidth: 0.50 Hz\n", + "- Filter length: 6759 samples (6.601 sec)\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", + "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s\n", + "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.1s remaining: 0.0s\n", + "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.2s remaining: 0.0s\n", + "[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 0.3s remaining: 0.0s\n", + "[Parallel(n_jobs=1)]: Done 215 out of 215 | elapsed: 13.5s finished\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized points196 points
Good channels196 sEEG, 19 EEG, 1 Stimulus
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Identify line noise\n", + "MS007_data.info['line_freq'] = 60\n", + "\n", + "# Notch out 60 Hz noise and harmonics \n", + "MS007_data.notch_filter(freqs=(60, 120, 180, 240))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting existing file.\n", + "Writing /sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/photodiode.fif\n", + "Closing /sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/photodiode.fif\n", + "[done]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_182879/2725322655.py:2: RuntimeWarning: This filename (/sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/photodiode.fif) does not conform to MNE naming conventions. All raw files should end with raw.fif, raw_sss.fif, raw_tsss.fif, _meg.fif, _eeg.fif, _ieeg.fif, raw.fif.gz, raw_sss.fif.gz, raw_tsss.fif.gz, _meg.fif.gz, _eeg.fif.gz or _ieeg.fif.gz\n", + " MS007_data.save(f'{save_dir}/photodiode.fif', picks='dc1', overwrite=True)\n" + ] + } + ], + "source": [ + "# Save out the photodiode channel separately\n", + "MS007_data.save(f'{save_dir}/photodiode.fif', picks='dc1', overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Denote bad channels" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Clean up the MNE data \n", + "\n", + "bads = lfp_preprocess_utils.detect_bad_elecs(MS007_data, \n", + " sEEG_mapping_dict)\n", + "\n", + "MS007_data.info['bads'] = bads" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['laglt10',\n", + " 'laglt8',\n", + " 'laglt9',\n", + " 'lcmfo7',\n", + " 'lhplt1',\n", + " 'lhplt2',\n", + " 'lmcms1',\n", + " 'lmcms2',\n", + " 'lmcms9',\n", + " 'lmolf4',\n", + " 'lmolf5',\n", + " 'racas8',\n", + " 'rpcip7']" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bads" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's pick out any bad channels missed by automatic screening, or restore channels that were erroneously deemed bad" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When we plot the data using the interactive plotting, bad channels are grayed out. 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'_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", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\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 = document.createElement('option');\n", + " option.selected = fmt === mpl.default_extension;\n", + " option.innerHTML = fmt;\n", + " fmt_picker.appendChild(option);\n", + " }\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", + " // 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", + "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", + "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], msg['forward']);\n", + " fig.send_message('refresh', {});\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", + " var x0 = msg['x0'] / fig.ratio;\n", + " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", + " var x1 = msg['x1'] / fig.ratio;\n", + " var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n", + " 0,\n", + " fig.canvas.width / fig.ratio,\n", + " fig.canvas.height / fig.ratio\n", + " );\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", + " fig.canvas_div.style.cursor = msg['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.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", + " // 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", + " var img = evt.data;\n", + " if (img.type !== 'image/png') {\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", + " img.type = 'image/png';\n", + " }\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", + "\n", + " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", + " img\n", + " );\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\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", + " 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(\n", + " \"No handler for the '\" + msg_type + \"' message type: \",\n", + " msg\n", + " );\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(\n", + " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", + " e,\n", + " e.stack,\n", + " msg\n", + " );\n", + " }\n", + " }\n", + " };\n", + "};\n", + "\n", + "\n", + "/*\n", + " * return a copy of an object with only non-object keys\n", + " * we need this to avoid circular references\n", + " * https://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", + " }\n", + " return obj;\n", + " }, {});\n", + "}\n", + "\n", + "mpl.figure.prototype.mouse_event = function (event, name) {\n", + " if (name === 'button_press') {\n", + " this.canvas.focus();\n", + " this.canvas_div.focus();\n", + " }\n", + "\n", + " // from https://stackoverflow.com/q/1114465\n", + " var boundingRect = this.canvas.getBoundingClientRect();\n", + " var x = (event.clientX - boundingRect.left) * this.ratio;\n", + " var y = (event.clientY - boundingRect.top) * this.ratio;\n", + "\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", + " 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", + " // Prevent repeat events\n", + " if (name === 'key_press') {\n", + " if (event.key === this._key) {\n", + " return;\n", + " } else {\n", + " this._key = event.key;\n", + " }\n", + " }\n", + " if (name === 'key_release') {\n", + " this._key = null;\n", + " }\n", + "\n", + " var value = '';\n", + " if (event.ctrlKey && event.key !== 'Control') {\n", + " value += 'ctrl+';\n", + " }\n", + " else if (event.altKey && event.key !== 'Alt') {\n", + " value += 'alt+';\n", + " }\n", + " else if (event.shiftKey && event.key !== 'Shift') {\n", + " value += 'shift+';\n", + " }\n", + "\n", + " value += 'k' + event.key;\n", + "\n", + " this._key_event_extra(event, name);\n", + "\n", + " this.send_message(name, { key: value, 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", + "\n", + "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", + "// prettier-ignore\n", + "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", + "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n", + "\n", + "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n", + "\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.binaryType = comm.kernel.ws.binaryType;\n", + " ws.readyState = comm.kernel.ws.readyState;\n", + " function updateReadyState(_event) {\n", + " if (comm.kernel.ws) {\n", + " ws.readyState = comm.kernel.ws.readyState;\n", + " } else {\n", + " ws.readyState = 3; // Closed state.\n", + " }\n", + " }\n", + " comm.kernel.ws.addEventListener('open', updateReadyState);\n", + " comm.kernel.ws.addEventListener('close', updateReadyState);\n", + " comm.kernel.ws.addEventListener('error', updateReadyState);\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", + " var data = msg['content']['data'];\n", + " if (data['blob'] !== undefined) {\n", + " data = {\n", + " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", + " };\n", + " }\n", + " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", + " ws.onmessage(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 = document.getElementById(id);\n", + " var ws_proxy = comm_websocket_adapter(comm);\n", + "\n", + " function ondownload(figure, _format) {\n", + " window.open(figure.canvas.toDataURL());\n", + " }\n", + "\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;\n", + " fig.cell_info = mpl.find_output_cell(\"
\");\n", + " if (!fig.cell_info) {\n", + " console.error('Failed to find cell for figure', id, fig);\n", + " return;\n", + " }\n", + " fig.cell_info[0].output_area.element.on(\n", + " 'cleared',\n", + " { fig: fig },\n", + " fig._remove_fig_handler\n", + " );\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_close = function (fig, msg) {\n", + " var width = fig.canvas.width / fig.ratio;\n", + " fig.cell_info[0].output_area.element.off(\n", + " 'cleared',\n", + " fig._remove_fig_handler\n", + " );\n", + " fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n", + " '';\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 / this.ratio;\n", + " var dataURL = this.canvas.toDataURL();\n", + " this.cell_info[1]['text/html'] =\n", + " '';\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 () {\n", + " fig.push_to_output();\n", + " }, 1000);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'btn-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\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", + " 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) {\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 = '';\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " // Add the status bar.\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 = 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 = '';\n", + " button.addEventListener('click', function (_evt) {\n", + " fig.handle_close(fig, {});\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 (event) {\n", + " var fig = event.data.fig;\n", + " if (event.target !== this) {\n", + " // Ignore bubbled events from children.\n", + " return;\n", + " }\n", + " fig.close_ws(fig, {});\n", + "};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (el) {\n", + " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (el) {\n", + " // this is important to make the div 'focusable\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", + " } else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (event, _name) {\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", + "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(\n", + " 'matplotlib',\n", + " mpl.mpl_figure_comm\n", + " );\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "/* global mpl */\n", + "window.mpl = {};\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(\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", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = this.ws.binaryType !== undefined;\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById('mpl-warnings');\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent =\n", + " 'This browser does not support binary websocket messages. ' +\n", + " 'Performance may be slow.';\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = document.createElement('div');\n", + " this.root.setAttribute('style', 'display: inline-block');\n", + " this._root_extra_style(this.root);\n", + "\n", + " parent_element.appendChild(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message('supports_binary', { value: fig.supports_binary });\n", + " fig.send_message('send_image_mode', {});\n", + " if (fig.ratio !== 1) {\n", + " fig.send_message('set_device_pixel_ratio', {\n", + " device_pixel_ratio: fig.ratio,\n", + " });\n", + " }\n", + " fig.send_message('refresh', {});\n", + " };\n", + "\n", + " this.imageObj.onload = function () {\n", + " if (fig.image_mode === 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function () {\n", + " fig.ws.close();\n", + " };\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "};\n", + "\n", + "mpl.figure.prototype._init_header = function () {\n", + " var titlebar = 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._canvas_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._init_canvas = function () {\n", + " var fig = this;\n", + "\n", + " var canvas_div = (this.canvas_div = document.createElement('div'));\n", + " canvas_div.setAttribute('tabindex', '0');\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", + " 'z-index: 2;'\n", + " );\n", + "\n", + " function on_keyboard_event_closure(name) {\n", + " return function (event) {\n", + " return fig.key_event(event, name);\n", + " };\n", + " }\n", + "\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 = (this.canvas = document.createElement('canvas'));\n", + " canvas.classList.add('mpl-canvas');\n", + " canvas.setAttribute(\n", + " 'style',\n", + " 'box-sizing: content-box;' +\n", + " 'pointer-events: none;' +\n", + " 'position: relative;' +\n", + " 'z-index: 0;'\n", + " );\n", + "\n", + " this.context = canvas.getContext('2d');\n", + "\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", + " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", + " 'canvas'\n", + " ));\n", + " rubberband_canvas.setAttribute(\n", + " 'style',\n", + " 'box-sizing: content-box;' +\n", + " 'left: 0;' +\n", + " 'pointer-events: none;' +\n", + " 'position: absolute;' +\n", + " 'top: 0;' +\n", + " 'z-index: 1;'\n", + " );\n", + "\n", + " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", + " if (this.ResizeObserver === undefined) {\n", + " if (window.ResizeObserver !== undefined) {\n", + " this.ResizeObserver = window.ResizeObserver;\n", + " } else {\n", + " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", + " this.ResizeObserver = obs.ResizeObserver;\n", + " }\n", + " }\n", + "\n", + " this.resizeObserverInstance = new this.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", + " if (entry.contentBoxSize instanceof Array) {\n", + " // Chrome 84 implements new version of spec.\n", + " width = entry.contentBoxSize[0].inlineSize;\n", + " height = entry.contentBoxSize[0].blockSize;\n", + " } else {\n", + " // Firefox implements old version of spec.\n", + " width = entry.contentBoxSize.inlineSize;\n", + " height = entry.contentBoxSize.blockSize;\n", + " }\n", + " } else {\n", + " // Chrome <84 implements even older version of spec.\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", + " if (entry.devicePixelContentBoxSize) {\n", + " // Chrome 84 implements new version of spec.\n", + " canvas.setAttribute(\n", + " 'width',\n", + " entry.devicePixelContentBoxSize[0].inlineSize\n", + " );\n", + " canvas.setAttribute(\n", + " 'height',\n", + " entry.devicePixelContentBoxSize[0].blockSize\n", + " );\n", + " } else {\n", + " canvas.setAttribute('width', width * fig.ratio);\n", + " canvas.setAttribute('height', height * fig.ratio);\n", + " }\n", + " /* This rescales the canvas back to display pixels, so that it\n", + " * appears correct on HiDPI screens. */\n", + " canvas.style.width = width + 'px';\n", + " canvas.style.height = height + 'px';\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 (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", + " fig.request_resize(width, height);\n", + " }\n", + " }\n", + " });\n", + " this.resizeObserverInstance.observe(canvas_div);\n", + "\n", + " function on_mouse_event_closure(name) {\n", + " /* User Agent sniffing is bad, but WebKit is busted:\n", + " * https://bugs.webkit.org/show_bug.cgi?id=144526\n", + " * https://bugs.webkit.org/show_bug.cgi?id=181818\n", + " * The worst that happens here is that they get an extra browser\n", + " * selection when dragging, if this check fails to catch them.\n", + " */\n", + " var UA = navigator.userAgent;\n", + " var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n", + " if(isWebKit) {\n", + " return function (event) {\n", + " /* This prevents the web browser from automatically changing to\n", + " * the text insertion cursor when the button is pressed. We\n", + " * want to control all of the cursor setting manually through\n", + " * the 'cursor' event from matplotlib */\n", + " event.preventDefault()\n", + " return fig.mouse_event(event, name);\n", + " };\n", + " } else {\n", + " return function (event) {\n", + " return fig.mouse_event(event, name);\n", + " };\n", + " }\n", + " }\n", + "\n", + " canvas_div.addEventListener(\n", + " 'mousedown',\n", + " on_mouse_event_closure('button_press')\n", + " );\n", + " canvas_div.addEventListener(\n", + " 'mouseup',\n", + " on_mouse_event_closure('button_release')\n", + " );\n", + " canvas_div.addEventListener(\n", + " 'dblclick',\n", + " on_mouse_event_closure('dblclick')\n", + " );\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " canvas_div.addEventListener(\n", + " 'mousemove',\n", + " on_mouse_event_closure('motion_notify')\n", + " );\n", + "\n", + " canvas_div.addEventListener(\n", + " 'mouseenter',\n", + " on_mouse_event_closure('figure_enter')\n", + " );\n", + " canvas_div.addEventListener(\n", + " 'mouseleave',\n", + " on_mouse_event_closure('figure_leave')\n", + " );\n", + "\n", + " canvas_div.addEventListener('wheel', function (event) {\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " on_mouse_event_closure('scroll')(event);\n", + " });\n", + "\n", + " canvas_div.appendChild(canvas);\n", + " canvas_div.appendChild(rubberband_canvas);\n", + "\n", + " this.rubberband_context = rubberband_canvas.getContext('2d');\n", + " this.rubberband_context.strokeStyle = '#000000';\n", + "\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", + " canvas_div.addEventListener('contextmenu', function (_e) {\n", + " event.preventDefault();\n", + " return false;\n", + " });\n", + "\n", + " function set_focus() {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'mpl-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\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", + " 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", + " /* 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", + "\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", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\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 = document.createElement('option');\n", + " option.selected = fmt === mpl.default_extension;\n", + " option.innerHTML = fmt;\n", + " fmt_picker.appendChild(option);\n", + " }\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", + " // 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", + "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", + "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], msg['forward']);\n", + " fig.send_message('refresh', {});\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", + " var x0 = msg['x0'] / fig.ratio;\n", + " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", + " var x1 = msg['x1'] / fig.ratio;\n", + " var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n", + " 0,\n", + " fig.canvas.width / fig.ratio,\n", + " fig.canvas.height / fig.ratio\n", + " );\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", + " fig.canvas_div.style.cursor = msg['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.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", + " // 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", + " var img = evt.data;\n", + " if (img.type !== 'image/png') {\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", + " img.type = 'image/png';\n", + " }\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", + "\n", + " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", + " img\n", + " );\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\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", + " 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(\n", + " \"No handler for the '\" + msg_type + \"' message type: \",\n", + " msg\n", + " );\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(\n", + " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", + " e,\n", + " e.stack,\n", + " msg\n", + " );\n", + " }\n", + " }\n", + " };\n", + "};\n", + "\n", + "\n", + "/*\n", + " * return a copy of an object with only non-object keys\n", + " * we need this to avoid circular references\n", + " * https://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", + " }\n", + " return obj;\n", + " }, {});\n", + "}\n", + "\n", + "mpl.figure.prototype.mouse_event = function (event, name) {\n", + " if (name === 'button_press') {\n", + " this.canvas.focus();\n", + " this.canvas_div.focus();\n", + " }\n", + "\n", + " // from https://stackoverflow.com/q/1114465\n", + " var boundingRect = this.canvas.getBoundingClientRect();\n", + " var x = (event.clientX - boundingRect.left) * this.ratio;\n", + " var y = (event.clientY - boundingRect.top) * this.ratio;\n", + "\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", + " 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", + " // Prevent repeat events\n", + " if (name === 'key_press') {\n", + " if (event.key === this._key) {\n", + " return;\n", + " } else {\n", + " this._key = event.key;\n", + " }\n", + " }\n", + " if (name === 'key_release') {\n", + " this._key = null;\n", + " }\n", + "\n", + " var value = '';\n", + " if (event.ctrlKey && event.key !== 'Control') {\n", + " value += 'ctrl+';\n", + " }\n", + " else if (event.altKey && event.key !== 'Alt') {\n", + " value += 'alt+';\n", + " }\n", + " else if (event.shiftKey && event.key !== 'Shift') {\n", + " value += 'shift+';\n", + " }\n", + "\n", + " value += 'k' + event.key;\n", + "\n", + " this._key_event_extra(event, name);\n", + "\n", + " this.send_message(name, { key: value, 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", + "\n", + "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", + "// prettier-ignore\n", + "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", + "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n", + "\n", + "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n", + "\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.binaryType = comm.kernel.ws.binaryType;\n", + " ws.readyState = comm.kernel.ws.readyState;\n", + " function updateReadyState(_event) {\n", + " if (comm.kernel.ws) {\n", + " ws.readyState = comm.kernel.ws.readyState;\n", + " } else {\n", + " ws.readyState = 3; // Closed state.\n", + " }\n", + " }\n", + " comm.kernel.ws.addEventListener('open', updateReadyState);\n", + " comm.kernel.ws.addEventListener('close', updateReadyState);\n", + " comm.kernel.ws.addEventListener('error', updateReadyState);\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", + " var data = msg['content']['data'];\n", + " if (data['blob'] !== undefined) {\n", + " data = {\n", + " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", + " };\n", + " }\n", + " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", + " ws.onmessage(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 = document.getElementById(id);\n", + " var ws_proxy = comm_websocket_adapter(comm);\n", + "\n", + " function ondownload(figure, _format) {\n", + " window.open(figure.canvas.toDataURL());\n", + " }\n", + "\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;\n", + " fig.cell_info = mpl.find_output_cell(\"
\");\n", + " if (!fig.cell_info) {\n", + " console.error('Failed to find cell for figure', id, fig);\n", + " return;\n", + " }\n", + " fig.cell_info[0].output_area.element.on(\n", + " 'cleared',\n", + " { fig: fig },\n", + " fig._remove_fig_handler\n", + " );\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_close = function (fig, msg) {\n", + " var width = fig.canvas.width / fig.ratio;\n", + " fig.cell_info[0].output_area.element.off(\n", + " 'cleared',\n", + " fig._remove_fig_handler\n", + " );\n", + " fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n", + " '';\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 / this.ratio;\n", + " var dataURL = this.canvas.toDataURL();\n", + " this.cell_info[1]['text/html'] =\n", + " '';\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 () {\n", + " fig.push_to_output();\n", + " }, 1000);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'btn-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\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", + " 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) {\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 = '';\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " // Add the status bar.\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 = 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 = '';\n", + " button.addEventListener('click', function (_evt) {\n", + " fig.handle_close(fig, {});\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 (event) {\n", + " var fig = event.data.fig;\n", + " if (event.target !== this) {\n", + " // Ignore bubbled events from children.\n", + " return;\n", + " }\n", + " fig.close_ws(fig, {});\n", + "};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (el) {\n", + " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (el) {\n", + " // this is important to make the div 'focusable\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", + " } else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (event, _name) {\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", + "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(\n", + " 'matplotlib',\n", + " mpl.mpl_figure_comm\n", + " );\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib notebook\n", + "fig = MS007_data.plot(start=0, duration=120, n_channels=8, scalings=MS007_data._data.max()/20)\n", + "fig.fake_keypress('a')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare your revised list of 'bad' electrodes to thoe originally detected above." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['laglt10',\n", + " 'laglt8',\n", + " 'laglt9',\n", + " 'lcmfo7',\n", + " 'lmcms1',\n", + " 'lmcms2',\n", + " 'lmcms9',\n", + " 'lmolf4',\n", + " 'lmolf5',\n", + " 'racas8',\n", + " 'rpcip7',\n", + " 'laimm12',\n", + " 'lcmfo14',\n", + " 'lmolf7']" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "MS007_data.info['bads']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Re-reference the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you're like me, you find the concept of re-referencing somewhat confusing. Isn't the data recorded relative to a ground and reference in the EMU (https://ahleighton.github.io/OE-ephys-course/EEA/theoryday3.html)? \n", + "\n", + "It is, but we do digital re-referencing of the recorded signal to clean up any remaining shared noise. \n", + "\n", + "**Re-referencing should be an EXTREMELY conscious choice as it changes the LFP signal dramatically!** In our case, we choose to do local white-matter re-referencing because electrodes in white matter should be fairly stable (low-variance) and not contain local, slow oscillations of interest. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's use the localization data to determine the gray vs. white matter electrodes. \n", + "Then, let's re-reference each gray matter electrode to the closest and most low-amplitude white matter electrode. \n", + "\n", + "Make sure 'bad' electrodes are not used in the re-referencing. Same with unmatched seeg electrodes (not present in the mne data structure)." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "anode_list, cathode_list, drop_wm_channels, oob_channels = lfp_preprocess_utils.wm_ref(mne_data=MS007_data, \n", + " loc_data=elec_locs, \n", + " bad_channels=MS007_data.info['bads'], \n", + " unmatched_seeg=unmatched_seeg,\n", + " site='MSSM')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['lmolf1',\n", + " 'lacas9',\n", + " 'lacas9',\n", + " 'lmolf1',\n", + " 'lmolf1',\n", + " 'lacas8',\n", + " 'lacas8',\n", + " 'lacas8',\n", + " 'lacas8',\n", + " 'lhplt5',\n", + " 'laglt6',\n", + " 'lhplt5',\n", + " 'lhplt5',\n", + " 'lhplt6',\n", + " 'laglt6',\n", + " 'laglt6',\n", + " 'laglt5',\n", + " 'laimm11',\n", + " 'laimm11',\n", + " 'laimm6',\n", + " 'lmolf6',\n", + " 'laimm8',\n", + " 'laimm6',\n", + " 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'racas9',\n", + " 'racas10',\n", + " 'rcmfo5',\n", + " 'rcmfo5',\n", + " 'rcmfo5',\n", + " 'raimm5',\n", + " 'raimm5',\n", + " 'rcmfo5',\n", + " 'rcmfo5',\n", + " 'rcmfo5',\n", + " 'rcmfo5',\n", + " 'rcmfo5',\n", + " 'rcmfo5',\n", + " 'raglt4',\n", + " 'rhplt4',\n", + " 'rhplt4',\n", + " 'rmcms7',\n", + " 'rmcms7',\n", + " 'rmcms1',\n", + " 'rmcms8',\n", + " 'rmcms7',\n", + " 'rmcms7',\n", + " 'racas3',\n", + " 'racas3',\n", + " 'rmolf8',\n", + " 'rmolf3',\n", + " 'rmolf3',\n", + " 'rmolf3',\n", + " 'rmtpt3',\n", + " 'rmtpt3',\n", + " 'rmtpt4',\n", + " 'rmtpt4',\n", + " 'rmtpt4',\n", + " 'rpcip5',\n", + " 'rpcip8',\n", + " 'rpcip5',\n", + " 'rpcip6',\n", + " 'rpcip8']" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cathode_list" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sEEG channel type selected for re-referencing\n", + "Creating RawArray with float64 data, n_channels=116, n_times=1867008\n", + " Range : 0 ... 1867007 = 0.000 ... 1823.249 secs\n", + "Ready.\n", + "Added the following bipolar channels:\n", + "lacas1-lmolf1, lacas10-lacas9, lacas12-lacas9, lacas2-lmolf1, lacas3-lmolf1, lacas4-lacas8, lacas5-lacas8, lacas6-lacas8, lacas7-lacas8, laglt1-lhplt5, laglt10-laglt6, laglt2-lhplt5, laglt3-lhplt5, laglt7-lhplt6, laglt8-laglt6, laglt9-laglt6, laimm1-laglt5, laimm12-laimm11, laimm13-laimm11, laimm2-laimm6, laimm3-lmolf6, laimm4-laimm8, laimm5-laimm6, laimm7-laimm8, lcmfo1-lcmfo4, lcmfo12-lcmfo10, lcmfo13-lcmfo10, lcmfo2-lcmfo4, lcmfo3-lcmfo4, lcmfo7-lcmfo6, lcmfo8-lcmfo6, lhplt1-laglt5, lhplt10-lhplt8, lhplt2-laglt5, lhplt3-laglt4, lhplt4-lhplt6, lhplt9-lhplt8, lmcms1-lmcms5, lmcms2-lmcms5, lmcms3-lmcms5, lmcms4-lmcms5, lmcms9-lmcms8, lmolf2-lmolf6, lmolf3-laimm6, lmolf4-laimm6, lmolf5-laimm6, lmolf7-lmolf1, lmolf8-laimm8, lmtpt1-lhplt5, lmtpt2-lhplt5, lmtpt3-lhplt5, lmtpt4-lhplt5, lmtpt5-lhplt7, lmtpt6-lhplt8, lmtpt7-lhplt8, lmtpt8-lhplt8, lpcip1-lpcip4, lpcip11-lpcip10, lpcip2-lpcip5, racas1-rmolf4, racas11-racas10, racas2-rmolf4, racas4-racas6, racas7-racas5, racas8-racas10, raglt1-raglt4, raglt2-raglt4, raglt3-raglt4, raglt6-raglt5, raglt7-raglt8, raglt9-rhplt8, raimm1-raglt5, raimm11-racas12, raimm12-racas12, raimm2-raimm5, raimm3-rmolf8, raimm4-rmolf8, raimm6-rmolf8, raimm7-racas9, raimm8-racas10, rcmfo1-rcmfo5, rcmfo10-rcmfo5, rcmfo11-rcmfo5, rcmfo12-raimm5, rcmfo13-raimm5, rcmfo2-rcmfo5, rcmfo3-rcmfo5, rcmfo4-rcmfo5, rcmfo7-rcmfo5, rcmfo8-rcmfo5, rcmfo9-rcmfo5, rhplt1-raglt4, rhplt2-rhplt4, rhplt3-rhplt4, rmcms2-rmcms7, rmcms3-rmcms7, rmcms4-rmcms1, rmcms5-rmcms8, rmcms6-rmcms7, rmcms9-rmcms7, rmolf1-racas3, rmolf2-racas3, rmolf5-rmolf8, rmolf6-rmolf3, rmolf7-rmolf3, rmolf9-rmolf3, rmtpt1-rmtpt3, rmtpt2-rmtpt3, rmtpt6-rmtpt4, rmtpt7-rmtpt4, rmtpt8-rmtpt4, rpcip1-rpcip5, rpcip11-rpcip8, rpcip2-rpcip5, rpcip7-rpcip6, rpcip9-rpcip8\n" + ] + } + ], + "source": [ + "MS007_data_reref = mne.set_bipolar_reference(MS007_data, \n", + " anode=anode_list, \n", + " cathode=cathode_list,\n", + " copy=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized points196 points
Good channels127 sEEG
Bad channelslcmfo14
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "MS007_data_reref.drop_channels(drop_wm_channels)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['lacas11',\n", + " 'laimm10',\n", + " 'laimm9',\n", + " 'lcmfo11',\n", + " 'lcmfo5',\n", + " 'lcmfo9',\n", + " 'lmcms6',\n", + " 'lmcms7',\n", + " 'lpcip3',\n", + " 'lpcip6',\n", + " 'lpcip7',\n", + " 'lpcip8',\n", + " 'lpcip9',\n", + " 'raimm10',\n", + " 'raimm9',\n", + " 'rcmfo6',\n", + " 'rhplt5',\n", + " 'rhplt6',\n", + " 'rhplt7',\n", + " 'rmtpt5',\n", + " 'rpcip10',\n", + " 'rpcip3',\n", + " 'rpcip4']" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drop_wm_channels" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized points196 points
Good channels116 sEEG
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "MS007_data_reref.drop_channels(oob_channels)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized points196 points
Good channels116 sEEG
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "right_seeg_names = [i for i in MS007_data_reref.ch_names if i.startswith('r')]\n", + "left_seeg_names = [i for i in MS007_data_reref.ch_names if i.startswith('l')]\n", + "sEEG_mapping_dict = {f'{x}':'seeg' for x in left_seeg_names+right_seeg_names}\n", + "MS007_data_reref.set_channel_types(sEEG_mapping_dict)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Annotate data? Optional" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# We COULD annotate the bad timepoints post-referencing. However, my personal preference is to do so after EPOCHING so I am not sending \n", + "# too much subjective stuff to all my downstream analyses... \n", + "\n", + "# Furthermore, we only filter after epoching which definitely helps visualize bad epochs \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Channels marked as bad:\n", + "['laglt10', 'laglt8', 'laglt9', 'lcmfo7', 'lmcms1', 'lmcms2', 'lmcms9', 'lmolf4', 'lmolf5', 'racas8', 'rpcip7', 'laimm12', 'lcmfo14', 'lmolf7']\n" + ] + }, + { + "ename": "ValueError", + "evalue": "list.remove(x): x not in list", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[36], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnotebook\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m fig \u001b[38;5;241m=\u001b[39m MS007_data_reref\u001b[38;5;241m.\u001b[39mplot(start\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, duration\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m120\u001b[39m, n_channels\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m8\u001b[39m, scalings\u001b[38;5;241m=\u001b[39mMS007_data\u001b[38;5;241m.\u001b[39m_data\u001b[38;5;241m.\u001b[39mmax()\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m20\u001b[39m)\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# fig.fake_keypress('a')\u001b[39;00m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/IPython/core/interactiveshell.py:2364\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2362\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2363\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2364\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2365\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/IPython/core/interactiveshell.py:3528\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3524\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mWarning: Cannot change to a different GUI toolkit: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 3525\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m Using \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m instead.\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m (gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select))\n\u001b[1;32m 3526\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n\u001b[0;32m-> 3528\u001b[0m \u001b[43mpt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mactivate_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3529\u001b[0m configure_inline_support(\u001b[38;5;28mself\u001b[39m, backend)\n\u001b[1;32m 3531\u001b[0m \u001b[38;5;66;03m# Now we must activate the gui pylab wants to use, and fix %run to take\u001b[39;00m\n\u001b[1;32m 3532\u001b[0m \u001b[38;5;66;03m# plot updates into account\u001b[39;00m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/IPython/core/pylabtools.py:360\u001b[0m, in \u001b[0;36mactivate_matplotlib\u001b[0;34m(backend)\u001b[0m\n\u001b[1;32m 355\u001b[0m \u001b[38;5;66;03m# Due to circular imports, pyplot may be only partially initialised\u001b[39;00m\n\u001b[1;32m 356\u001b[0m \u001b[38;5;66;03m# when this function runs.\u001b[39;00m\n\u001b[1;32m 357\u001b[0m \u001b[38;5;66;03m# So avoid needing matplotlib attribute-lookup to access pyplot.\u001b[39;00m\n\u001b[1;32m 358\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pyplot \u001b[38;5;28;01mas\u001b[39;00m plt\n\u001b[0;32m--> 360\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mswitch_backend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 362\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow\u001b[38;5;241m.\u001b[39m_needmain \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 363\u001b[0m \u001b[38;5;66;03m# We need to detect at runtime whether show() is called by the user.\u001b[39;00m\n\u001b[1;32m 364\u001b[0m \u001b[38;5;66;03m# For this, we wrap it into a decorator which adds a 'called' flag.\u001b[39;00m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/pyplot.py:227\u001b[0m, in \u001b[0;36mswitch_backend\u001b[0;34m(newbackend)\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[38;5;66;03m# make sure the init is pulled up so we can assign to it later\u001b[39;00m\n\u001b[1;32m 226\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m\n\u001b[0;32m--> 227\u001b[0m \u001b[43mclose\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mall\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 229\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m newbackend \u001b[38;5;129;01mis\u001b[39;00m rcsetup\u001b[38;5;241m.\u001b[39m_auto_backend_sentinel:\n\u001b[1;32m 230\u001b[0m current_framework \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39m_get_running_interactive_framework()\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/pyplot.py:912\u001b[0m, in \u001b[0;36mclose\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m 910\u001b[0m _pylab_helpers\u001b[38;5;241m.\u001b[39mGcf\u001b[38;5;241m.\u001b[39mdestroy(manager)\n\u001b[1;32m 911\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m fig \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mall\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m--> 912\u001b[0m \u001b[43m_pylab_helpers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mGcf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdestroy_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 913\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fig, \u001b[38;5;28mint\u001b[39m):\n\u001b[1;32m 914\u001b[0m _pylab_helpers\u001b[38;5;241m.\u001b[39mGcf\u001b[38;5;241m.\u001b[39mdestroy(fig)\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/_pylab_helpers.py:82\u001b[0m, in \u001b[0;36mGcf.destroy_all\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m manager \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mfigs\u001b[38;5;241m.\u001b[39mvalues()):\n\u001b[1;32m 81\u001b[0m manager\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mmpl_disconnect(manager\u001b[38;5;241m.\u001b[39m_cidgcf)\n\u001b[0;32m---> 82\u001b[0m \u001b[43mmanager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdestroy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mfigs\u001b[38;5;241m.\u001b[39mclear()\n", + "File 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\u001b[38;5;241m=\u001b[39m {socket \u001b[38;5;28;01mfor\u001b[39;00m socket \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweb_sockets\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m socket\u001b[38;5;241m.\u001b[39mis_open()}\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweb_sockets) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 152\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclose_event\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/_api/deprecation.py:200\u001b[0m, in \u001b[0;36mdeprecated..deprecate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 199\u001b[0m emit_warning()\n\u001b[0;32m--> 200\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/backend_bases.py:1771\u001b[0m, in \u001b[0;36mFigureCanvasBase.close_event\u001b[0;34m(self, guiEvent)\u001b[0m\n\u001b[1;32m 1769\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1770\u001b[0m event \u001b[38;5;241m=\u001b[39m CloseEvent(s, \u001b[38;5;28mself\u001b[39m, guiEvent\u001b[38;5;241m=\u001b[39mguiEvent)\n\u001b[0;32m-> 1771\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprocess\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevent\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1772\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mAttributeError\u001b[39;00m):\n\u001b[1;32m 1773\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/cbook/__init__.py:312\u001b[0m, in \u001b[0;36mCallbackRegistry.process\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 311\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexception_handler \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 312\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexception_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 313\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 314\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/cbook/__init__.py:96\u001b[0m, in \u001b[0;36m_exception_printer\u001b[0;34m(exc)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_exception_printer\u001b[39m(exc):\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _get_running_interactive_framework() \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mheadless\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m]:\n\u001b[0;32m---> 96\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 98\u001b[0m traceback\u001b[38;5;241m.\u001b[39mprint_exc()\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/cbook/__init__.py:307\u001b[0m, in \u001b[0;36mCallbackRegistry.process\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m func \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 306\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 307\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 308\u001b[0m \u001b[38;5;66;03m# this does not capture KeyboardInterrupt, SystemExit,\u001b[39;00m\n\u001b[1;32m 309\u001b[0m \u001b[38;5;66;03m# and GeneratorExit\u001b[39;00m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/backends/backend_nbagg.py:81\u001b[0m, in \u001b[0;36mFigureManagerNbAgg.create_with_canvas..destroy\u001b[0;34m(event)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdestroy\u001b[39m(event):\n\u001b[1;32m 80\u001b[0m canvas\u001b[38;5;241m.\u001b[39mmpl_disconnect(cid)\n\u001b[0;32m---> 81\u001b[0m \u001b[43mGcf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdestroy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmanager\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/_pylab_helpers.py:66\u001b[0m, in \u001b[0;36mGcf.destroy\u001b[0;34m(cls, num)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(manager, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_cidgcf\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 65\u001b[0m manager\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mmpl_disconnect(manager\u001b[38;5;241m.\u001b[39m_cidgcf)\n\u001b[0;32m---> 66\u001b[0m \u001b[43mmanager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdestroy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m manager, num\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/backends/backend_nbagg.py:144\u001b[0m, in \u001b[0;36mFigureManagerNbAgg.destroy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m comm \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweb_sockets):\n\u001b[1;32m 143\u001b[0m comm\u001b[38;5;241m.\u001b[39mon_close()\n\u001b[0;32m--> 144\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclearup_closed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/backends/backend_nbagg.py:152\u001b[0m, in \u001b[0;36mFigureManagerNbAgg.clearup_closed\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweb_sockets \u001b[38;5;241m=\u001b[39m {socket \u001b[38;5;28;01mfor\u001b[39;00m socket \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweb_sockets\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m socket\u001b[38;5;241m.\u001b[39mis_open()}\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweb_sockets) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 152\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclose_event\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/_api/deprecation.py:200\u001b[0m, in \u001b[0;36mdeprecated..deprecate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 199\u001b[0m emit_warning()\n\u001b[0;32m--> 200\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/backend_bases.py:1771\u001b[0m, in \u001b[0;36mFigureCanvasBase.close_event\u001b[0;34m(self, guiEvent)\u001b[0m\n\u001b[1;32m 1769\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1770\u001b[0m event \u001b[38;5;241m=\u001b[39m CloseEvent(s, \u001b[38;5;28mself\u001b[39m, guiEvent\u001b[38;5;241m=\u001b[39mguiEvent)\n\u001b[0;32m-> 1771\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprocess\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevent\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1772\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mAttributeError\u001b[39;00m):\n\u001b[1;32m 1773\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/cbook/__init__.py:312\u001b[0m, in \u001b[0;36mCallbackRegistry.process\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 311\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexception_handler \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 312\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexception_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 313\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 314\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/cbook/__init__.py:96\u001b[0m, in \u001b[0;36m_exception_printer\u001b[0;34m(exc)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_exception_printer\u001b[39m(exc):\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _get_running_interactive_framework() \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mheadless\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m]:\n\u001b[0;32m---> 96\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 98\u001b[0m traceback\u001b[38;5;241m.\u001b[39mprint_exc()\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/cbook/__init__.py:307\u001b[0m, in \u001b[0;36mCallbackRegistry.process\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m func \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 306\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 307\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 308\u001b[0m \u001b[38;5;66;03m# this does not capture KeyboardInterrupt, SystemExit,\u001b[39;00m\n\u001b[1;32m 309\u001b[0m \u001b[38;5;66;03m# and GeneratorExit\u001b[39;00m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/mne/viz/_figure.py:427\u001b[0m, in \u001b[0;36mBrowserBase._close\u001b[0;34m(self, event)\u001b[0m\n\u001b[1;32m 425\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmne\u001b[38;5;241m.\u001b[39mchild_figs):\n\u001b[1;32m 426\u001b[0m fig \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmne\u001b[38;5;241m.\u001b[39mchild_figs[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m--> 427\u001b[0m \u001b[43mclose\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 428\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_close_event(fig)\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/pyplot.py:925\u001b[0m, in \u001b[0;36mclose\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m 923\u001b[0m _pylab_helpers\u001b[38;5;241m.\u001b[39mGcf\u001b[38;5;241m.\u001b[39mdestroy(num)\n\u001b[1;32m 924\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fig, Figure):\n\u001b[0;32m--> 925\u001b[0m \u001b[43m_pylab_helpers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mGcf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdestroy_fig\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 926\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 927\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclose() argument must be a Figure, an int, a string, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 928\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mor None, not \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mtype\u001b[39m(fig))\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/_pylab_helpers.py:75\u001b[0m, in \u001b[0;36mGcf.destroy_fig\u001b[0;34m(cls, fig)\u001b[0m\n\u001b[1;32m 72\u001b[0m num \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m((manager\u001b[38;5;241m.\u001b[39mnum \u001b[38;5;28;01mfor\u001b[39;00m manager \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mfigs\u001b[38;5;241m.\u001b[39mvalues()\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m manager\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mfigure \u001b[38;5;241m==\u001b[39m fig), \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 75\u001b[0m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdestroy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/_pylab_helpers.py:66\u001b[0m, in \u001b[0;36mGcf.destroy\u001b[0;34m(cls, num)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(manager, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_cidgcf\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 65\u001b[0m manager\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mmpl_disconnect(manager\u001b[38;5;241m.\u001b[39m_cidgcf)\n\u001b[0;32m---> 66\u001b[0m \u001b[43mmanager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdestroy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m manager, num\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/backends/backend_nbagg.py:144\u001b[0m, in \u001b[0;36mFigureManagerNbAgg.destroy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m comm \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweb_sockets):\n\u001b[1;32m 143\u001b[0m comm\u001b[38;5;241m.\u001b[39mon_close()\n\u001b[0;32m--> 144\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclearup_closed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/backends/backend_nbagg.py:152\u001b[0m, in \u001b[0;36mFigureManagerNbAgg.clearup_closed\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweb_sockets \u001b[38;5;241m=\u001b[39m {socket \u001b[38;5;28;01mfor\u001b[39;00m socket \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweb_sockets\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m socket\u001b[38;5;241m.\u001b[39mis_open()}\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweb_sockets) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 152\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclose_event\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/_api/deprecation.py:200\u001b[0m, in \u001b[0;36mdeprecated..deprecate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 199\u001b[0m emit_warning()\n\u001b[0;32m--> 200\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/backend_bases.py:1771\u001b[0m, in \u001b[0;36mFigureCanvasBase.close_event\u001b[0;34m(self, guiEvent)\u001b[0m\n\u001b[1;32m 1769\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1770\u001b[0m event \u001b[38;5;241m=\u001b[39m CloseEvent(s, \u001b[38;5;28mself\u001b[39m, guiEvent\u001b[38;5;241m=\u001b[39mguiEvent)\n\u001b[0;32m-> 1771\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprocess\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevent\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1772\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mAttributeError\u001b[39;00m):\n\u001b[1;32m 1773\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/cbook/__init__.py:312\u001b[0m, in \u001b[0;36mCallbackRegistry.process\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 311\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexception_handler \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 312\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexception_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 313\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 314\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/cbook/__init__.py:96\u001b[0m, in \u001b[0;36m_exception_printer\u001b[0;34m(exc)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_exception_printer\u001b[39m(exc):\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _get_running_interactive_framework() \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mheadless\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m]:\n\u001b[0;32m---> 96\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 98\u001b[0m traceback\u001b[38;5;241m.\u001b[39mprint_exc()\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/matplotlib/cbook/__init__.py:307\u001b[0m, in \u001b[0;36mCallbackRegistry.process\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m func \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 306\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 307\u001b[0m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 308\u001b[0m \u001b[38;5;66;03m# this does not capture KeyboardInterrupt, SystemExit,\u001b[39;00m\n\u001b[1;32m 309\u001b[0m \u001b[38;5;66;03m# and GeneratorExit\u001b[39;00m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/mne/viz/_mpl_figure.py:190\u001b[0m, in \u001b[0;36mMNEAnnotationFigure._close\u001b[0;34m(self, event)\u001b[0m\n\u001b[1;32m 188\u001b[0m parent\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mcallbacks\u001b[38;5;241m.\u001b[39mdisconnect(callback_id)\n\u001b[1;32m 189\u001b[0m \u001b[38;5;66;03m# do all the other cleanup activities\u001b[39;00m\n\u001b[0;32m--> 190\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_close\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevent\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/sc/arion/work/qasims01/test-env/envs/LFPAnalysis/lib/python3.11/site-packages/mne/viz/_mpl_figure.py:114\u001b[0m, in \u001b[0;36mMNEFigure._close\u001b[0;34m(self, event)\u001b[0m\n\u001b[1;32m 112\u001b[0m is_named \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmne, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfig_name\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_child:\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmne\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparent_fig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmne\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchild_figs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mremove\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_named:\n\u001b[1;32m 116\u001b[0m \u001b[38;5;28msetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmne\u001b[38;5;241m.\u001b[39mparent_fig\u001b[38;5;241m.\u001b[39mmne, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmne\u001b[38;5;241m.\u001b[39mfig_name, \u001b[38;5;28;01mNone\u001b[39;00m)\n", + "\u001b[0;31mValueError\u001b[0m: list.remove(x): x not in list" + ] + } + ], + "source": [ + "%matplotlib notebook\n", + "fig = MS007_data_reref.plot(start=0, duration=120, n_channels=8, scalings=MS007_data._data.max()/20)\n", + "# fig.fake_keypress('a')" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting existing file.\n", + "Writing /sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/wm_ref_ieeg.fif\n", + "Closing /sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/wm_ref_ieeg.fif\n", + "[done]\n" + ] + } + ], + "source": [ + "# Save out the re-referenced data:\n", + "\n", + "MS007_data_reref.save(f'{save_dir}/wm_ref_ieeg.fif', overwrite=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "lfp_analysis", + "language": "python", + "name": "lfpanalysis" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/scripts/.ipynb_checkpoints/LFP_analysis_step_by_step-checkpoint.ipynb b/scripts/.ipynb_checkpoints/LFP_analysis_step_by_step-checkpoint.ipynb deleted file mode 100644 index bf9a702..0000000 --- a/scripts/.ipynb_checkpoints/LFP_analysis_step_by_step-checkpoint.ipynb +++ /dev/null @@ -1,17613 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0fa7627d", - "metadata": {}, - "source": [ - "# Example analysis notebook" - ] - }, - { - "cell_type": "markdown", - "id": "b83943cd", - "metadata": {}, - "source": [ - "In this notebook, we will go through the basics of: \n", - "\n", - "1. Load pre-processed data\n", - "\n", - "2. Synchronize to behavioral data\n", - "\n", - "3. Process the behavioral data \n", - "\n", - "4. Create epochs\n", - "\n", - "5. Downsample the epoched data \n", - "\n", - "6. Look for IEDs (**automatic** or manual) \n", - "\n", - "7. Save the epoched data \n", - "\n", - "8. Compute TFR and drop IED trials if you care about low frequencies. \n", - "\n", - "9. Plot\n", - "\n", - "10. Example of statistical analyses (TODO)" - ] - }, - { - "cell_type": "markdown", - "id": "73a62ccc", - "metadata": {}, - "source": [ - "The main point of this notebook is to make epoched data **CORRECTLY** as this will be the crux of many, many analyses in the future, and to provide tools for IED detection in epochs (both automatic and manual)." - ] - }, - { - "cell_type": "markdown", - "id": "061f89be", - "metadata": {}, - "source": [ - "On IEDS: \n", - "\n", - "- Great images of interictal and ictal spiking: \n", - "\n", - " 1. https://pubmed.ncbi.nlm.nih.gov/32007920/\n", - " \n", - " 2. https://www.sciencedirect.com/science/article/pii/S1059131119304200\n", - "\n", - "- More on IEDs: \n", - "\n", - " 1. https://www.frontiersin.org/articles/10.3389/fnhum.2020.00044/full\n", - "\n", - " 2. Why removing trials with IEDs might be important for generalizable conclusions about behavior: \n", - " \n", - " https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3770206/\n", - " \n", - " https://academic.oup.com/cercorcomms/article/2/2/tgab019/6179205\n", - " \n", - "- Especially for low frequencies (sub-gamma) this is important! \"Low-frequency power remained elevated for several seconds following the IED... Low-frequency power was elevated and high-frequency power suppressed in pre-IED epochs compared with non-IED epochs.\"\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "f3519ee4", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%reload_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "6356dd80", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import mne\n", - "from glob import glob\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.backends.backend_pdf import PdfPages\n", - "import seaborn as sns\n", - "import scipy\n", - "from scipy.stats import zscore, linregress\n", - "import pandas as pd\n", - "from mne.preprocessing.bads import _find_outliers\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "498fd315", - "metadata": {}, - "outputs": [], - "source": [ - "from LFPAnalysis import lfp_preprocess_utils, sync_utils, analysis_utils" - ] - }, - { - "cell_type": "markdown", - "id": "5f14ed87", - "metadata": {}, - "source": [ - "## Loading pre-processed data" - ] - }, - { - "cell_type": "markdown", - "id": "e25279ef", - "metadata": {}, - "source": [ - "It's a good idea to setup a sensible directory structure like below. Note that all my data lives on '/sc/arion' which is Minerva. \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "59c63a36", - "metadata": {}, - "outputs": [], - "source": [ - "base_dir = '/sc/arion' # this is the root directory for most un-archived data and results \n", - "\n", - "load_dir = f'{base_dir}/work/qasims01/MemoryBanditData/EMU/Subjects/MS007' # save intermediate results in the 'work' directory\n", - " \n", - "# I have saved most of my raw data in the 'projects directory'\n", - "behav_dir = f'{base_dir}/projects/guLab/Salman/EMU/MS007/behav/Day1'\n", - "neural_dir = f'{base_dir}/projects/guLab/Salman/EMU/MS007/neural/Day1'\n", - "anat_dir = f'{base_dir}/projects/guLab/Salman/EMU/MS007/anat'\n" - ] - }, - { - "cell_type": "markdown", - "id": "e02fe85c", - "metadata": {}, - "source": [ - "Let's load in the re-referenced data, the photodiode data for synchronization, and the electrode dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "57e7a2b0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Opening raw data file /sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/wm_ref_ieeg.fif...\n", - " Range : 0 ... 1867007 = 0.000 ... 1823.249 secs\n", - "Ready.\n", - "Reading 0 ... 1867007 = 0.000 ... 1823.249 secs...\n", - "Opening raw data file /sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/photodiode.fif...\n", - " Range : 0 ... 1867007 = 0.000 ... 1823.249 secs\n", - "Ready.\n", - "Reading 0 ... 1867007 = 0.000 ... 1823.249 secs...\n" - ] - } - ], - "source": [ - "wm_ref_data = mne.io.read_raw_fif(f'{load_dir}/wm_ref_ieeg.fif', preload=True)\n", - "anode_list = [x.split('-')[0] for x in wm_ref_data.ch_names]\n", - "photodiode = mne.io.read_raw_fif(f'{load_dir}/photodiode.fif', preload=True)\n", - "\n", - "csv_files = glob(f'{anat_dir}/*labels.csv')\n", - "elec_locs = pd.read_csv(csv_files[0])\n", - "\n", - "# Sometimes there's extra columns with no entries: \n", - "elec_locs = elec_locs[elec_locs.columns.drop(list(elec_locs.filter(regex='Unnamed')))]\n", - "\n", - "elec_df = elec_locs[elec_locs.label.str.lower().isin(anode_list)]\n", - "elec_df['label'] = wm_ref_data.ch_names\n" - ] - }, - { - "cell_type": "markdown", - "id": "8a3d7d31", - "metadata": {}, - "source": [ - "The following commented code can be used for plotting and removing IEDs from the continuous data if you decide to do detection with the continuous data " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "dcb0335e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filtering raw data in 1 contiguous segment\n", - "Setting up band-pass filter from 25 - 80 Hz\n", - "\n", - "FIR filter parameters\n", - "---------------------\n", - "Designing a one-pass, zero-phase, non-causal bandpass filter:\n", - "- Windowed time-domain design (firwin) method\n", - "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n", - "- Lower passband edge: 25.00\n", - "- Lower transition bandwidth: 6.25 Hz (-6 dB cutoff frequency: 21.88 Hz)\n", - "- Upper passband edge: 80.00 Hz\n", - "- Upper transition bandwidth: 20.00 Hz (-6 dB cutoff frequency: 90.00 Hz)\n", - "- Filter length: 541 samples (0.528 sec)\n", - "\n", - "[ 0 44 48 140 145]\n", - "[ 0 30 110 113 116]\n", - "[ 0 65 87 104 120]\n", - "[ 0 112 114 183 190]\n", - "[ 0 84 113 171 212]\n", - "[ 0 9 52 120 173]\n", - "[ 0 39 187 201 206]\n", - "[ 0 31 34 72 156]\n", - "[ 0 10 50 122 155]\n", - "[ 0 157 184 227 330]\n", - "[ 0 2 16 83 140]\n", - "[ 0 96 109 110 138]\n", - "[ 0 32 85 124 155]\n", - "[ 0 30 61 96 139]\n", - "[ 0 20 77 94 109]\n", - "[ 0 22 48 109 129]\n", - "[ 0 10 60 96 121]\n", - "[ 0 25 85 95 122]\n", - "[ 0 56 63 140 141]\n", - "[ 0 19 56 56 125]\n", - "[ 0 36 120 122 126]\n", - "[ 0 35 41 118 121]\n", - "[ 0 34 39 119 125]\n", - "[ 0 21 74 92 109]\n", - "[ 0 0 7 68 135]\n", 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550.32128906,\n", - " 700.01464844, 838.23730469, 1016.70800781, 1042.02148438,\n", - " 1161.14453125, 1230.18359375, 1237.05273438, 1335.55273438,\n", - " 1357.95019531, 1465.44238281, 1501.79394531, 1517.140625 ,\n", - " 1678.37792969]),\n", - " 'racas7-racas5': array([ 53.22070312, 71.04980469, 72.29199219, 73.43359375,\n", - " 76.7421875 , 122.15625 , 166.078125 , 254.29101562,\n", - " 316.80957031, 334.58691406, 364.72851562, 424.83203125,\n", - " 470.87792969, 519.00097656, 545.21972656, 555.82421875,\n", - " 569.28222656, 576.26367188, 601.55957031, 669.93164062,\n", - " 700.04785156, 768.04199219, 776.015625 , 829.17285156,\n", - " 838.37695312, 857.93457031, 872.09960938, 975.35253906,\n", - " 1016.515625 , 1017.4921875 , 1100.66015625, 1141.54296875,\n", - " 1147.27050781, 1148.08203125, 1218.53222656, 1237.16894531,\n", - " 1267.28320312, 1335.58007812, 1339.20996094, 1344.66796875,\n", - " 1361.58007812, 1441.97460938, 1453.8359375 , 1471.76660156,\n", - " 1500.99023438, 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1738.45019531]),\n", - " 'rmtpt7-rmtpt4': array([ 253.83007812, 669.83398438, 697.7109375 , 699.93554688,\n", - " 753.06445312, 857.51757812, 935.18457031, 1016.56835938,\n", - " 1017.62207031, 1022.26171875, 1159.45996094, 1237.5 ,\n", - " 1442.56933594, 1450.11816406, 1471.70800781, 1655.52636719]),\n", - " 'rmtpt8-rmtpt4': array([ 52.35839844, 53.45800781, 62.53808594, 73.22558594,\n", - " 223.57714844, 253.84667969, 336.27050781, 377.95214844,\n", - " 412.06054688, 432.95117188, 436.078125 , 470.60644531,\n", - " 491.09082031, 518.96582031, 523.95507812, 540.21582031,\n", - " 552.59863281, 602.5625 , 617.48242188, 621.45019531,\n", - " 669.8359375 , 670.609375 , 699.93652344, 734.67285156,\n", - " 775.68457031, 782.20214844, 956.74511719, 958.76464844,\n", - " 963.34863281, 1017.35546875, 1040.73632812, 1064.37207031,\n", - " 1100.30859375, 1116.56640625, 1147.61523438, 1151.47949219,\n", - " 1164.03027344, 1218.52832031, 1230.06445312, 1266.91894531,\n", - " 1300.22949219, 1339.12304688, 1426.53320312, 1442.31835938,\n", - " 1471.70410156, 1482.82910156, 1501.56835938, 1511.28027344,\n", - " 1616.015625 , 1655.3046875 , 1655.88085938, 1713.94824219,\n", - " 1757.42773438, 1791.57128906, 1805.87988281]),\n", - " 'rpcip1-rpcip5': array([ 471.07324219, 606.81835938, 670.07714844, 857.83984375,\n", - " 1161.39257812, 1237.484375 , 1442.91210938, 1582.64941406,\n", - " 1697.78808594, 1757.96289062, 1813.12304688, 1816.11132812]),\n", - " 'rpcip11-rpcip8': array([ 38.26074219, 41.86328125, 261.57617188, 264.45996094,\n", - " 278.8671875 , 322.03417969, 324.34765625, 334.27636719,\n", - " 353.45605469, 377.23339844, 383.8125 , 427.45117188,\n", - " 433.72558594, 446.86523438, 450.6875 , 505.10253906,\n", - " 523.93359375, 528.01953125, 571.81347656, 602.01074219,\n", - " 602.93945312, 772.09082031, 775.77050781, 801.41699219,\n", - " 803.26074219, 857.38183594, 1084.734375 , 1085.60253906,\n", - " 1208.86230469, 1218.53027344, 1437.83398438, 1500.38476562,\n", - " 1823.05371094]),\n", - " 'rpcip2-rpcip5': array([1419.05957031]),\n", - " 'rpcip7-rpcip6': array([ 555.38574219, 1430.16015625, 1433.45703125, 1529.92871094,\n", - " 1536.38671875, 1769.67773438]),\n", - " 'rpcip9-rpcip8': array([ 2.76953125, 29.24707031, 52.390625 , 81.61230469,\n", - " 98.76953125, 102.1953125 , 114.55078125, 121.9140625 ,\n", - " 123.88378906, 138.21875 , 218.921875 , 226.859375 ,\n", - " 277.92871094, 278.86621094, 302.14746094, 322.03710938,\n", - " 332.32128906, 334.27441406, 353.45410156, 377.234375 ,\n", - " 407.39160156, 412.0234375 , 424.46777344, 432.7578125 ,\n", - " 438.35449219, 447.33789062, 471.17480469, 485.58496094,\n", - " 502.80566406, 503.78710938, 505.10449219, 507.42089844,\n", - " 518.97949219, 524.0390625 , 544.83007812, 555.76074219,\n", - " 569.59667969, 579.20507812, 597.43261719, 662.43554688,\n", - " 669.63769531, 697.72265625, 699.96484375, 733.00585938,\n", - " 772.09082031, 838.578125 , 851.71777344, 857.38085938,\n", - " 883.52734375, 961.71484375, 964.85253906, 978.58300781,\n", - " 979.80078125, 1016.82617188, 1056.44238281, 1084.73339844,\n", - " 1135.28222656, 1147.02636719, 1151.18847656, 1191.18457031,\n", - " 1218.53027344, 1264.07226562, 1296.50585938, 1338.86328125,\n", - " 1343.64453125, 1344.93066406, 1437.90332031, 1442.78613281,\n", - " 1489.19335938, 1492.01464844, 1500.53515625, 1517.91113281,\n", - " 1555.5625 , 1560.52929688, 1574.47753906, 1582.54199219,\n", - " 1673.85351562, 1696.64941406, 1706.75976562, 1708.47949219,\n", - " 1738.38769531, 1757.27246094, 1757.96484375, 1816.125 ,\n", - " 1823.05566406])}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "IED_times_s" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "5a6562c2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { 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\n", 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Is2nhVFRU4N69e7h9+zYuXbqEzs5OJCYmoqysTG9/xRg3qtD0+OCDD6BWq1lLJpfL4e3tzfbTh5W2Op988olJ96QPYWsyatQo1lqkp6ezzDa6xnGEv+/du8f+Lqa6+HUdl5iYyP4uvr6+rL8nZXPNpoUzYsQITJkyBUBX/+rcuXMoKytDUlKSSCzC77oGKIUthxCh6XHixAlcu3aNPSBOTk4IDg6Gm5sbJk6cyEySwMBAAF1vdENRFcZChTNw4EC4uroiPj4eQFdnv7KyEmq1mr3xheYZ8FtfRtji6BvD6Q47OzuDopszZw5CQkIAANnZ2SZdwxawaeEAvwUmlpWVsf4GIUTkMRMKR5ebVNj/8fHxYR1qagZVVVUhODgYJ0+eZOaci4sLFAoFNm/eLFpFmwoZ6DKlzAUVPH3gR40ahZEjRwIArly5IjKTNIWjq8Ux1VQDgAcffFDn9rFjxyIgIADjxo0D0BVlLdWBUpsXDu3AV1VViTr3QoRmG+1Qu7u7o66uDsuWLRP1CZycnNjbHOgSWlZWFhwcHFBTU8M8avQtrjke5eLiwvoautzWusjPz8eBAwe0tqvVahadoavjT+/97NmzTNB2dnZaXj1dLY4prmhKZGQkCgoKMGHCBERHR2PRokXYtm0bFi5cCKDLXJXJZOjs7JTspDibF46Xl5fOmCshwhaHulAfeOABLFiwAGPGjIGbmxvbT+cN0TdleXm5yNFA7XZDHrMVK1YA6D7gs7m5Ge3t7cjMzBR5+CifffYZMjIyoFKpmHCEZlJAQAD7TgdydUU4C1scXQ4EY3FxcYGnpydiYmIwffp0PPTQQ6JWTi6Xs/EeWi+pYfPCAXRH+QppampCe3s7mpubWSvg6emJmTNnAhA/RFRE1E6vqKgQjenQgFN9IfjAb+MsDQ0NTIBXrlzRKrd161acP38elZWVmDx5sig8qbOzEwUFBXB1dUVmZiYTvLDFEfbNrl69CkC3CUbr2tLS0qNwm57w9ttvG4z+CA4OBqB7oFQKSEI4QptbOLGOPlgNDQ3Yu3cv3n//fWY6CFspYYtDt9M5NVevXhWZXPRBMNTi0HEltVqN+vp6lJaWYt++fVr2fnFxMXJyciCTyWBvb4+srCzRPuE1aR9H2OLIZDLWp6KC1iUcen9NTU3sXnornO4GealnTarJDSUhHOF8HeF3GtHQ3t6Ouro6qFQq1g8SCkc4fZiOU1DR1dTU6AzZMSQchUIBLy8vAF2eueTkZHh5eeGbb75hYfpFRUWYPHmyaHoF9eJ1dnayFgToMhfpOJWmR4veLz2vLuHQh7iurs4splpPoPXsaT/P1pCEcIQPEzWxgC4TTt/UbmFsmaenJxYsWICIiAjW4XZ3dzf4Vu0uKoC6pTMzM5kLOy0tjXn+dHncqDl28uRJfPzxx2y7MK2tpsdMM8pcV72E4yq01euNV60ncOHYADKZDI8//jji4uIQERHBtvv5+el8s8rlcq039/jx4zFv3jw2HiOTyUQDnMLvQPfCoR13Yf/I09OTTVnQNW+Hhups375d59RkXU4Q2rJRDLU4tOVUKBRaAjQ39O/b1tYmySTukhAOAIwcORKTJk2CnZ0d/vSnPyEuLg6DBg3SOdA3fPjwHsVQUVMP6DLdhC1Qd8LRN6haWlpqcAZsRkaGaPqy8AEXmpQUe3t70UtAV70GDBggul83NzeLx5A5OjqyGDYp9nMkIxwh/v7+mDRpEoCu/gElNjYWlZWVWLx4cY/OQ2c+Al0tjrDV6U44AwcOZA+OZhTz7du3tbZRz5fQQRAYGCgShb7Jf0JBCU1QilwuFyUeHDt2rMG6mwtaF2tLtmIOJCkcIXTWYkhICKZMmYK//OUvIi+aIQICApgpFB4eLsruY8gdDXSZevPnz8f333+PqKgoUaT1zZs3WV+jqqoK48aN00pIkpeXh8WLF4vqKhy3ESI04YQCEbJo0SI0NTWhrKwMUVFRButuLqhw6HhWU1MT3nrrLVy6dKlPrm9JJC+c6dOnIyIiAkuXLgUAFqbSE+RyORISErB582YMGDBA9BD3ZMpAWFgY9u3bh3nz5mHGjBlsijkdRHVxcYGXlxciIiK0RDFr1iy4u7uLrqkvRmzKlClobGxESEiIaI6QECcnJ0yYMIG5v/sC2kLTbKCZmZlQqVRISUnpt7zb5kLywnFwcBB1+o1FJpNh4MCBAMSu7p7OtaFjTIGBgcxEom5nT09PJCQkYOjQoaJB3Hv37mHDhg1a19HXUnp7e2Pq1Kns5aCPyZMn4+uvv+5Rvc0B/XvR6AGhh627vA7WjuSFY05GjRoFb29vrFu3ziQhTpgwAcBvHjVvb28mmCFDhrDpAY8++ig7RigoQ2JdvHhxt3UaPnw4i2roC6hzpbGxEa2traLQpe6SqFg7XDhGIJPJ8Oc//1krV1xP0ewXCTvyTk5OaG1tRUZGBmbMmMG2h4eHw97eHk899ZTNzaZ0dXVlYi8sLBR518rLy23aacCFYyS9ySegOXaiOS4TGhoKtVotEoidnR2eeuopvY4Ba+ehhx4CAFy/fp0Jh4416YrfsxW4cPoQzRZHUziTJ0/GtGnTtI7rLojVmqHmWnFxMQsLCg0NBWDZdMGWhgunD9GMPtAccxkzZgxeeeWVvqySxaFjT8IVEui4ky171owSzs6dOzFx4kS4u7vDx8cHCxcu7DYpRXp6utaaOjKZzKabaVPRHPnXN1gpJahHkqJUKtm2+6bFycjIwMaNG/Hjjz8iLS0NHR0diImJ6VE2k4KCAlRWVrKPvoE6KSOTyURBp5bIv2Zt2Nvbi+IFBw8eLBIOHQhub2+3qdAco/5zJ06cEP1OSkqCj48PsrOzRZ4gXfj4+HQ7U5Ni7evj9Aa5XH7frWjm7e0tyq5DW9rOzk40NzfD1dUV2dnZ6OjowPTp0/uzqj2mV3YBHdDSjNDVxfjx4+Hv74/o6GicPn3aYFlrXx+nN2iaLvcDwudj8ODBUCgULAqCLqt45swZZGZmWnQpSHNisnAIIdiyZQumT5+OsLAwveX8/f1x4MABpKSkIDU1FcHBwYiOjkZmZqbeY/p6fZy+ZNGiRVAoFCxd7v3AQw89BEIInJycWMgTfYHU1taiuroajY2NaG9vN5h62Jow2chOSEhAbm6uztWPhQQHB7P550DXCgZlZWXYvXu3XvPO0dHR4vNF+gs/Pz/ExsYaFTNn6wQGBrJ1X6nzY+DAgSgpKUFtba0oocedO3dMHmDuS0xqcTZt2oSvvvoKp0+f1jvvxBBTpkzRu6bO/UBfhfVbExs3bhRFl9MW58KFC1rCsQWManEIIdi0aRM+//xzpKen60xK3hMuXLhg04N6vcXSawPZAlQ4mmM5tpIVxyjhbNy4ER9//DG+/PJLuLu7s3RGHh4eLCbpxRdfRHl5OQ4fPgwA2LNnDwIDAxEaGoq2tjYcOXIEKSkpSElJMfOtcGwJfU4SzRX8rBWjhLNv3z4AwMMPPyzanpSUhCeffBKA9ho5bW1t2Lp1K8rLy+Hs7IzQ0FAcO3ZMlDaWc/8hHJpwcXHBgAEDUFVVZTOZP2XEBpL7qlQqeHh4oL6+3uQs+xzrghCCI0eO4Nq1a/j73/8OtVqNXbt2AeiyWvRNyLM0PX3WpD90zbFKZDIZnnjiCbS3t7M+n52dHTo7O9HQ0GDxxZV7i7QCozg2h9BRQiMKbMGzxoXDsRpoFtVbt271c026hwuHYzVQ88wWphtw4XCsBmqq2cJYDhcOx2qgWVdtIZEHFw7HaqC5CHSt9m1tcOFwrAY64U0ul7OlGa0VLhyO1eDm5sai4s258DDFnHN9uHA4VoNcLmdZccydk6KmpsasObO5cDhWBY2aN/eEtjfeeAO5ublmOx8XDseqoMJpbGzUWjO1N5w/fx4qlYqtbtdbuHA4VoVSqQQhBIQQsw6E0sWIzTVGxIXDsSqEa6+aM+8aXSHi119/Ncv5eHQ0x+pwdXWFSqUy29yc27dvY8mSJRg5ciS+/vprzJo1q9fn5C0Ox+rw8fEBYL4o6e+++44lRzFXEhguHI7VQVfcLi4uNksUQWFhIfvu4OBgFqcDFw7H6qDTqgsLC7FhwwaWv+Ly5csmnY8u2AV0jRVdv36913XkwuFYHTSRh0KhwKFDh3D06FEAwHvvvScSQU/RPMYci/dy4XCsDiocJycnODk5scw3v/zyC4qKiow6l1qtZkkQGxsbAYC3OBxpYm9vz1Z1ePbZZ3H37l0QQnD16lWjzbX6+nrI5XJ0dHTgp59+AvDbKti9wSTh7N27F0FBQXByckJERAR++OEHg+UzMjIQEREBJycnjBgxAvv37zepspz7B5r108nJCWvXrsXhw4dx584do8dh6DTsmpoaLFmyBIB51iAy+gyffvopnn32Wfz1r3/FhQsXEBkZibi4OFEuNSFFRUWYM2cOIiMjceHCBbz00kvYvHkzT0jIMcikSZNEv8+cOYOZM2eiqKgIiYmJPT4PHfh0d3dHfHw8gK55P7311hktnDfffBNPP/001qxZg5CQEOzZswfDhg1jyQo12b9/PwICArBnzx6EhIRgzZo1WL16NXbv3t2rinOkTVhYGD788EP2e8iQIYiKisLQoUPx+eef9/g85eXlALrWIvXx8WHr9PQ29MYo4bS1tSE7OxsxMTGi7TExMTh79qzOY86dO6dVPjY2FufPn9cbcNfa2gqVSiX6cO4v5HI57O3tkZSUpLVPmKS9O2iLQ4NH6bPU2+hro4Rz584ddHZ2ai3D4Ovrqzfnb1VVlc7yHR0dekeGpbywFKfnPPHEEzqXN2xubhaJx9CAJj2eLndP87j11kFgUi9JJpOJfhNCtLZ1V17XdoqUF5bi9Jx58+YhMjJSa/ugQYPw5ptvst9Xr17V2QpdvHiRLRtJX950EbSCgoJeRRAYJRxvb2/Y2dlptS7V1dV6FwPy8/PTWV6hUOhNc+ro6IgBAwaIPpz7j/Hjx2PPnj1a2xctWoTk5GScO3cOAJCbm4sVK1ZoCSExMZElAKHP0NSpU9HY2IjRo0f3ai1Wo4Tj4OCAiIgIpKWlibanpaVh2rRpOo+ZOnWqVvmTJ09iwoQJfJ0YTrcEBgay77RPbGdnhxkzZiA1NRWXLl3Ctm3bcOHCBfzyyy+iY2/dugW5XA5CCNzc3AAAISEhOHPmDB555JFerfpttKm2ZcsWfPDBB0hMTER+fj6ee+45lJaWYt26dQC6zKyVK1ey8uvWrUNJSQm2bNmC/Px8JCYm4tChQ9i6davJlebcXwQHB6OoqAg///wz2zZ8+HDs27cPcXFxuHHjBgAgKytLFF4j7B/RboG9vT0efPDBXtfJaMktW7YMd+/excsvv4zKykqEhYXh+PHjLMmC5vo4QUFBOH78OJ577jm89957UCqVePvtt9lgFIfTHRMmTMDy5cvxwQcf4MaNG3BwcICDgwOampqYexkADh8+DHd3dzz22GMAwEwxJycn0flCQkJ6XSeT2qoNGzZgw4YNOvcJfe+UqKgo5OTkmHIpDof1n6dNm4Z//vOfWLVqFQYOHAh3d3c0NDTA19cX7e3tOHv2LMaNG4fHHnsMxcXFrPsgXHsUgFlaHB6rxrF66Kh/cHAwhg0bxlzJkZGRcHJywvr167F582a4uroiJSUFHR0d+M9//sOOp540iuaKgqbAhcOxCbZt2wa5XI6QkBBkZmYC6ArLEbYezz//PG7duoWTJ0+Kxgg1hUPXq+0NXDgcm4AucT969GhRssIFCxaw73K5HM7Ozli0aBELqTl16pRWH8cccOFwbIrQ0FCsXbsW3377rc79a9euRXt7O3M10xUQzA0XDsemiI6Oxty5c5Gdnc08ajdu3EB2djYAwMvLC6NHj2aT4caMGWORevD0UBybQqFQICQkBG1tbXjrrbdACEFnZyeCg4MREREBAJg4cSIcHR3R2dnJTDxzw1scjs0RFBQELy8vdHR0sLGagoICZr7RLDmVlZWIjo62SB24cDg2h0wmwzPPPMN+u7i4QKFQICcnR7SUR0BAAIYOHWqROnDhcGySNWvWsKkCISEhWLFiBVpbW0VRKzSxoSXgwuHYJEqlEvPnz4dCocDChQuRmJiIwYMH4+jRo6itrUV1dTUz2SyBjJhzLQULoVKp4OHhgfr6ej7FgMPIy8vDiRMnsHHjRri4uCA+Ph6fffYZm15w48YNBAUFGXXOnj5r3KvGsVnCw8MRHh7Ofv/xj39Ee3s7UlNT4ezszAKPLQE31TiSYfHixSy/RUxMjFnSQOmDC4cjKVauXAkXFxeMHz/eotfhphpHUjg7O+PVV19lHjdLwYXDkRzPPPOMWZYHMQQ31TiSxMHBwaLn58LhcEyAC4fDMQEuHA7HBLhwOBwTsAmvGg2h4MnXOZaGPmPdRaLZhHBoYjmefJ3TVzQ0NMDDw0PvfpsI8lSr1aioqIC7u7vB5O7GoFKpMGzYMJSVlUkycFTq9wdY5h4JIWhoaIBSqTQYsmMTLY5cLrfYhCSpJ3WX+v0B5r9HQy0NhTsHOBwT4MLhcEzgvhWOo6Mjtm/fDkdHx/6uikWQ+v0B/XuPNuEc4HCsjfu2xeFwegMXDodjAlw4HI4JcOFwOCbAhcPhmICkhVNcXIynn34aQUFBcHZ2xsiRI7F9+3atabWlpaWYP38+XF1d4e3tjc2bN2uVycvLQ1RUFJydnTFkyBC8/PLL3QYC9id79+5FUFAQnJycEBERgR9++KG/q9QtO3fuxMSJE+Hu7g4fHx8sXLgQBQUFojKEEOzYsQNKpRLOzs54+OGHcfnyZVGZ1tZWbNq0Cd7e3nB1dcWCBQtw8+ZN81aWSJhvvvmGPPnkk+Tbb78lhYWF5MsvvyQ+Pj7k+eefZ2U6OjpIWFgYmTlzJsnJySFpaWlEqVSShIQEVqa+vp74+vqS+Ph4kpeXR1JSUoi7uzvZvXt3f9xWtyQnJxN7e3ty8OBB8uuvv5JnnnmGuLq6kpKSkv6umkFiY2NJUlISuXTpErl48SKZO3cuCQgIII2NjazMrl27iLu7O0lJSSF5eXlk2bJlxN/fn6hUKlZm3bp1ZMiQISQtLY3k5OSQmTNnkrFjx5KOjg6z1VXSwtHF66+/ToKCgtjv48ePE7lcTsrLy9m2Tz75hDg6OpL6+npCCCF79+4lHh4e5N69e6zMzp07iVKpJGq1uu8q30MmTZpE1q1bJ9o2ZswYsm3btn6qkWlUV1cTACQjI4MQQoharSZ+fn5k165drMy9e/eIh4cH2b9/PyGEkLq6OmJvb0+Sk5NZmfLyciKXy8mJEyfMVjdJm2q6qK+vh5eXF/t97tw5hIWFQalUsm2xsbFobW1lixWdO3cOUVFRohHq2NhYVFRUoLi4uM/q3hPa2tqQnZ3NEvNRYmJicPbs2X6qlWnU19cDAPt/FRUVoaqqSnRvjo6OiIqKYveWnZ2N9vZ2URmlUomwsDCz3v99JZzCwkK88847WLduHdtWVVXFlgOnDBw4EA4ODqiqqtJbhv6mZayFO3fuoLOzU2d9ra2uhiCEYMuWLZg+fTpb/JbW39C9VVVVwcHBga3IpquMObBJ4ezYsQMymczg5/z586JjKioq8Oijj2Lp0qVYs2aNaJ+uOT6EENF2zTLk/zsGzDU/yNzoqq+11lUXCQkJyM3NxSeffKK1z5R7M/f928R8HE0SEhIQHx9vsExgYCD7XlFRgZkzZ2Lq1Kk4cOCAqJyfnx+ysrJE22pra9He3s7ebH5+flpvq+rqagDab7/+xtvbG3Z2djrra2111cemTZvw1VdfITMzUzQPy8/PD0BXq+Lv78+2C+/Nz88PbW1tqK2tFbU61dXVmDZtmvkqabbekpVy8+ZNMmrUKBIfH6/Tq0KdAxUVFWxbcnKylnPA09OTtLa2sjK7du2yaufA+vXrRdtCQkKs3jmgVqvJxo0biVKpJFevXtW538/Pj7z22mtsW2trq07nwKeffsrKVFRUmN05IGnhlJeXkwceeIDMmjWL3Lx5k1RWVrIPhbqjo6OjSU5ODjl16hQZOnSoyB1dV1dHfH19yfLly0leXh5JTU0lAwYMsHp39KFDh8ivv/5Knn32WeLq6kqKi4v7u2oGWb9+PfHw8CDp6emi/1VzczMrs2vXLuLh4UFSU1NJXl4eWb58uU539NChQ8mpU6dITk4OmTVrFndHG0NSUhIBoPMjpKSkhMydO5c4OzsTLy8vkpCQIHI9E0JIbm4uiYyMJI6OjsTPz4/s2LHDKlsbynvvvUeGDx9OHBwcyO9+9zvm0rVm9P2vkpKSWBm1Wk22b99O/Pz8iKOjI5kxYwbJy8sTnaelpYUkJCQQLy8v4uzsTObNm0dKS0vNWlc+H4fDMQGb9KpxOP0NFw6HYwJcOByOCXDhcDgmwIXD4ZgAFw6HYwJcOByOCXDhcDgmwIXD4ZgAFw6HYwJcOByOCfw//g+AUM1l8wgAAAAASUVORK5CYII=\n", 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rKiowadIkpKSkoLS0FG+++SbmzZvH5usAoLi4GNOmTUNGRgaOHz+OjIwMTJ06FYcPH5bV7vLly/Hhhx9i5cqVOHLkCHQ6HcaPH283Bzh79my280VNTY1Hk9d9ASpkOvVCkZsGSV1mMSELr0t1r51ZZOEY2Z2dOelDQphoAtxNNhG7x4WfJ4SwbK1HH31UUrtCr8ddZAv5ww8/xKxZs/Diiy9iyJAhWLFiBaKjo7FmzRqH5deuXctWewwZMgQvvvgiXnjhBbz//vuszIoVKzB+/HgsWrQI8fHxWLRoEcaNG4cVK1ZIbpcQghUrVmDx4sV49tlnkZiYiE8++QQtLS348ssvrfoUGBgInU7HXrY37L2OcBsdIcIxqBShOEqlFELXEQPShUzF5sy1NpvNbm0w4MgiA22rlQDHQt69eze++uorlJaWYseOHaivr4fRaITFYmm3uywHWUI2Go0oKSnBhAkTrK5PmDABhw4dcviZ4uJiu/Lp6ek4evQoc6fEytA6pbRbUVGB2tpaqzJarRapqal2ffviiy8QERGBoUOH4rXXXnOatWMwGNDY2Gj18nWErrUQKhSLxSJpM3gqJmcL/eVGrqlFduRaq9Vq9mCQ616bzWb2MLF98PTr1w+A/S6dhBBs2bIFCxcuxKpVq1BeXo6DBw8CaBtbSxlPewpZQq6vr4fZbGaRPEpkZCTLUbWltrbWYXmTycRC7mJlaJ1S2qX/uurbn/70J2zatAn79+/H22+/jfz8fDz77LOi33np0qUICwtjL2EgxFcRc61VKhW7OaWkLFKxOxMyFY0nXGvg7nJGuZFr4YPJVsh0Cun69etWnshPP/2E6OhozJgxA9HR0VAqlWx1U2xsrKz224tbUWvbAbyriKOj8rbXpdTpiTKzZ89mPycmJmLw4MEYOXIkjh07hhEjRtj1fdGiRcjOzma/NzY2+ryYxVxroM1K19fXQ6/XW6VIOsKVaw3IX8roSsjBwcFoaGiQbZGFDx3bxRE9evSAQqFg00rUU8nPz3eYmALA5d/G08iyyBEREfDz87OzvnV1dXaWkKLT6RyWV6lULCFfrAytU0q7dOJeTt8AYMSIEVCr1aKbymm1WoSGhlq9fB2a1eXou8rZQ1qOay3FIhNCXArZ3aQQsUAXAKtppOLiYgBtKZi0LUe53cOHD5fVfnuRJWSNRoOkpCQUFhZaXS8sLBSN0CUnJ9uV37NnD0aOHMnGM2JlaJ1S2o2NjYVOp7MqYzQaUVRU5DR6eOrUKbS2tt7TR3IKMRqNzDo5ssg0QURKrECKay3HIre2trKcZLFllO4KmYpRLPBJr9Nhx+rVqwGADfnKy8uhUqlQXl6ORx55xKkX4g1ku9bZ2dnIyMjAyJEjkZycjHXr1qGyshKZmZkA2lzRqqoqfPrppwCAzMxMrFy5EtnZ2Zg9ezaKi4uxfv16q2Mw58+fjzFjxmDZsmV45plnsH37duzduxcHDhyQ3K5CoUBWVhbee+89DB48GIMHD8Z7772HwMBA/PGPfwTQtsrkiy++wKRJkxAREYHy8nIsWLAAw4cPZ0ny9zpUoBqNxuHNSAXkqWAXbUNKfdRqKpVK0UCSu9lddNkh9exsSUlJwZdffonr16/DbDazB8qgQYMwc+ZM3Lx5E8HBwWwz/45GtpCnTZuG69ev45133kFNTQ0SExOxc+dODBw4EEDbH0QYpo+NjcXOnTvx6quvYtWqVYiKisK//vUv/OEPf2BlHn30UeTl5eGtt97C22+/jUGDBmHz5s14+OGHJbcLAK+//jpu376Nl19+GTdu3MDDDz+MPXv2MLdIo9Hgv//9L/75z3+iubkZ0dHRePLJJ7FkyRL4+fnJ/+v5IGKBLgoVMhWVGCaTid3sUsbIUoRMHzIhISGiMRl3hUxTMMWELLTI1NpbLBZMmTIFwF1PpbPiJ24Fu15++WW8/PLLDt/buHGj3bXU1FQcO3bMaZ3PPfccnnvuObfbBdqsck5ODnJychy+Hx0djaKiIqdt3Ou4EjIVpSuhCF1lZ9MwciyysyAcxV3XmlpksXgKNQYGgwEnT55k17pKDgJfNMGxQmwOmUKF4mqMTIWsUqmcbpFDhSxljCzcDFAMdyxyc3Mza793796i/aQ547t27QLQtuChq8CF7EFu376NsrIyt3N9uwI0Gi22/E7qPK2U8TEgzyLTv6uzlEZhvrXUNE2aUtmjRw/RdFKFQsGsNS0zcuRISfV3BFzIHmTbtm3Ytm2bx3ZG7AwuX74M4G5+sS3CTficWVGxdEdbPC1k6jHQTQKl8M033wAQ/84U2+/iqnxHwoXsQYQZQN2RlpYW5r6KTcdpNBrmYrpKbQWcB7qE73tKyPQ8KEDaFBmdlwZcz/0K+zhq1Ci3d+r0BlzIHoQmuJw4caKTe+IedHwcFBTkVIBSTnWQ6lrLiVrT9sSyqSg0gixlM30auNJoNC4XOYwdOxZA21z7xIkTXdbdkfCNBTxITEwMgDbL0dTU5HJReVeDjntdLasLCwvDjRs3PCJkd6LWrvoXGhqK2tpaSRaZTpVKWXIYFxeHMWPGICkpqUtZY4BbZI/Sp08faLVaKBQK7N69G6mpqZ3dJVlQYboSCrWIQrfUFilZXcBdIbe2tsJisYiWa21tZXW6SpOVc7wNPR5GamZfWlpal0zT5UL2MMnJyQDaUj/j4+O9cqi1t3AVsabQPGdnkWu5rrXwM46gcQeNRiOaZ02hc7uuLHJ9fT0bd7vaW4vS1SwxhQvZwwwbNoz9HBUVhc2bN7PfP/nkk87okmToeltXK3eokJxZZKlC9vPzY8EzZ+41ta7CM5HFkCrkHTt2sJ+lCrmrwoXsYXr27Gk1F3n79m1YLBa0tLRYbSkkzDXvKkgVshTXWmrUGpAW8KKWU0omlRTX2mAwsKm2gQMHim4Q2F3gQvYC06ZNY26nUqnE1atX8cknnyA1NRUnTpzA7t27sWXLFpf5yh2J8HwjV9bJkxYZkLYQQ+r4HZCWtLJ//3728+9//3uXdXZ1uJC9wKBBg5CWlsbGmkeOHEFdXR38/f1RUFCAffv24cEHH8SGDRs6uad3uXPnDsuEcjUGlZLPLDXYBUgTspQ5ZNv+GY1G0d05f/nlF/ZzVwxeyYUL2UtMmjSJ7Tgi3A0UuHvjXrt2zWmktiOh1lWr1bpcCebJYJewjLNgl5Q8a4pGo2HfwVEfm5qa2OYJzz77bJcNYMmBC9mL0P2Kna3+kXvgGN0z2dPQG95VsoWwjMFgEH0QyRkjS5lLphF1KRZZoVA43Uz/woUL7OchQ4a4rK87wIXsRXr16uUycV/OmT96vV502+H24moLHSHCnGOxcbIc19qVkAkhzILSEwxd4Wyz+itXrgAA7rvvvm4f5KJwIXsRhUJhFWUdNmwYLly4gKtXr+L+++8HcPewLyns2rULERERqK6u9nhf5QhZuEOHIyETQtgeXHJcazEhNzU1Mcvvao6b4swi//rrrwCA3/72t5Lq6g5wIXuZiRMn4vTp0/j1118xadIkfPHFFxg6dCjbK1koZJPJhHXr1jmsh55gEBwcjH379nm8n/SGlyJkYTlHQm5tbWWeiBzXWmyMTOeDAwICnK5tFkItsm0aaUtLC7Pu9P/AF+BC9jJDhgzB3r178cwzz0Cr1WLRokUYP348W2AhHCOXlZVhzpw5dsExwPqGFDu6pLW1FZ999pnddXoGkzOcnTvsCGcL+KllVSgUklxXV6611GkxIWJzyXR8HBwc3GV29/AEXMgdwFtvvYVJkyYBAN59913cf//9LPoqzD763//+hwULFmDr1q12dQjH0kaj0aEl3Lp1K3788Uc0NDSAEIJvvvkG48ePx5gxY1xGx+W41sBdIdMN2YUIA11SIsKuhCxlix9b6Aoo2yWl9Pe4uDifiFZTuJA7gKysLDuBUCHTnSxaWlrQ3NyMkJAQBAYG2gVpbMfStseXGAwGnD17Fn369EFBQQF27tyJvLw8hIeHIzExEe+++67TPsoVMhWVo3OD5Uw9AXeFLJYgQ4UsZ76XLoKoqqqyeog1NDQAkB406y5wIXcSwgPHjEYjSxcE2oJJdNcKCj2Em2IrIOEa6CtXruDo0aMYPHgwEhIS0K9fP3YDiyEncwq4u/ba0VJGuUKm5cQi4DS4J0fIvXv3hp+fH8xms9VDkHo2tP++AhdyJ6FWq1nkt7m52WpuE7DfnODixYsA7gZobHcldbWZQXh4uNUCDiGEECZ0qVHh/v37A2hL7bT1HuRMPQGuLTJ9aDk7McQWpVLJ+kj/tmazmZ1Ewi0yx2MId9qgyx3pNa1WywTR0NDA9oimq6vq6uqs0g/FthcqLCxkN6+j8Sxt32QyQaFQsLGlK3Q6HfuMbfBNTjKIsJzJZLKbdzebzUzgYjtcikF3/KioqMCPP/7I3Gy1Wi27rq4OF3InIjw4nLp8M2fOZC7m999/DwD46KOPoFAooFQqERcXxz7/9ddfA2gLftGbXbip/+jRo/H999/jqaeeAtD2cHBk9ag1Dg0NlTy9o1AomMWle0JT5LrWwgQT2/7RqLNCoZC94wrdHO/cuXPYvXs3y22PiYnxqUAXwIXcqVAh19bWghACQgjCw8OZe3vp0iVmiYE2ay10fc+fPw8AKCkpAdCWCjpu3DjU1NSgrq4O48aNg1KpxMSJE5n7+8MPP9j1g1pzuVaKuqe2Flmua+3suFbaN2enS4gRExPjcHtb4ekkvgIXcidC52yp60una55//nkAbWNDoTv8+OOPQ6lUsl1IWlpaUFVVhT179gBoE5ZarcYbb7yBf/zjH+xzSqWSWWqa1SRE7viYQvOUbd16alWlRsABa+/EUd/EjnJxhlKpxKhRo+yuDx06VHZdXR0u5E6E3rw0KkuFRG9as9lsJeT4+HgAbbs50vHxkSNH2PvCkyltRZSQkAAAbI5ZiLtCpv1saWmxqpMK2dWe1kLodJaYRXY3ODV27Fg0NTUhMTERt27dwrhx4yTHAboTvpEx3k2hQqZjSnozBwYGsqkT6rb26NGDuZ9qtRrx8fE4f/48E7rBYHBqae6//34QQmA2m9HQ0GA1/SJ1ry5boqOjmYD1ej0TiDtC7tmzJyoqKuwsMp2Wk9s3ip+fH5YvXw6lUml1cKCvwS1yJ2I7Zyv83daVFAaxHP0+a9Ysp22pVCoWHRbOKRNCWJqo3LlVodUXutfuCFlsr2xPJHBIDeB1Z3z/G3ZhbJP2hfOktitzbIUrDNgQQjBo0CCX7dFgljC/u6mpCWaz2W6lllQcPRzaI2ThnLTRaGSrqHxt3tfTcCF3IoGBgVaLCoRHlgiFqlar7SK2Go0GaWlp2LFjB+bMmSOpPWrlhWKhAuzRo4dbZ0RTgQktMp0+kyNk6o3QlUnCOv38/CQv5rhX4ULuZITjNqGQevXqBaPRiDNnzuDFF190+NnRo0dj586dkjdXd7RiiQrZ3ZTFxMREAHcj7xaLhY353RGy0LWmnkN3O7GjM+BC7mTi4+Oh1Wrxt7/9zeq6UqnE2LFjceHCBdHtaf38/GSlLTraNaO9uce0b7Qe4QomqZldwr4JNwGkova1LCxvwIXcBXjhhRcczrkOGzYM69ev91g7juZq2ytkGk2+desWLBYLGx+r1WpZrjp1nQkhrA662MHdiPW9BJ9+6gKIWdw+ffq43CxeDlTIwsX2NL3S3WBSWFgYCCFQKBTQ6/VuBbqAtqi6SqWCyWRCc3MzAgMDce7cOQC+sV2tt+FCvocQuq8Wi8Vq5ZK7Qqa7gJjNZly/fl3y3tiOCAgIQFNTE5qamrB9+3bU1tYiMDCQj5El4JZrvXr1asTGxsLf3x9JSUkO83eFFBUVISkpCf7+/oiLi8PatWvtyuTn5yMhIQFarRYJCQkoKCiQ3S4hBDk5OYiKikJAQADS0tJw6tQpqzIGgwGvvPIKIiIiEBQUhKeffprtqujr0EURdFdKGuhSqVSS1yE7gk5bNTQ0uG2Raf8AoKCgAP/5z39YRN/X1g57A9lC3rx5M7KysrB48WKUlpYiJSUFTzzxhOg+UhUVFZg0aRJSUlJQWlqKN998E/PmzUN+fj4rU1xcjGnTpiEjIwPHjx9HRkYGpk6darV3lZR2ly9fjg8//BArV67EkSNHoNPpMH78eKtIaFZWFgoKCpCXl4cDBw6gubkZkydPtlqc4KsoFAomlvr6ejY+7t27d7tWA9H58MuXL7dLyHQYsW/fPhBCoNFooFQqJUfl72UUxNXGyzY8/PDDGDFihNX+ykOGDMGUKVOwdOlSu/JvvPEGduzYgdOnT7NrmZmZOH78OIqLiwG0nZXU2NiIXbt2sTITJ05Ez5492WFnrtolhCAqKgpZWVl44403ALRZ38jISCxbtgxz5syBXq9H79698dlnn2HatGkA2vKco6OjsXPnTqSnp7v8/o2NjQgLC4Ner++WY7e8vDycOXMGEyZMQGtrKzu+ZsqUKW7XeeTIEezcuRPBwcFISkpiHtjkyZNl1VNaWmp1QiLQNr89f/58t/vWnZFzr8myyEajESUlJZgwYYLV9QkTJuDQoUMOP1NcXGxXPj09HUePHmWJ/2JlaJ1S2q2oqEBtba1VGa1Wi9TUVFampKQEra2tVmWioqKQmJgo2n+DwYDGxkarV3eGTld99dVXbDOD9rqudHx969Ytt5JBKNHR0XbXYmJi2tW3ewVZQq6vr4fZbLabu4yMjGQJAbbU1tY6LG8ymZhrJ1aG1imlXfqvqzIajcZuW1Vn/V+6dCnCwsLYy9HN1p2gogsMDERpaanVNXehDwKLxcK2JHLnvOFevXpZbd17/vx52Vb9XsWtYJfteIpOP8gpb3tdSp2eKmOLszKLFi2CXq9nL+Emed0Ruo8VcFdsAwYMaFedISEhLF+bzv26Y+UVCgW7N65fvw6LxeJW2ui9iCwhR0REwM/Pz8561dXViWYY6XQ6h+VVKhX7zxYrQ+uU0i7NI3ZVxmg02h2c5qz/Wq0WoaGhVq/uTM+ePa0OlQsODm53HrNCobCbbnLXXU9OTsbf//53fP3112x9Ncc1soSs0WiQlJSEwsJCq+uFhYWif/Tk5GS78nv27MHIkSPZNixiZWidUtqNjY2FTqezKmM0GlFUVMTKJCUlQa1WW5WpqanByZMn76mbJjU1Fbt378bly5c95roKl12qVCq3Hw6PPfYYFAoF3nnnHdEcc44DiEzy8vKIWq0m69evJ+Xl5SQrK4sEBQWRixcvEkIIWbhwIcnIyGDlL1y4QAIDA8mrr75KysvLyfr164larSZbt25lZQ4ePEj8/PxIbm4uOX36NMnNzSUqlYr8+OOPktslhJDc3FwSFhZGtm3bRsrKysiMGTNI3759SWNjIyuTmZlJ+vfvT/bu3UuOHTtGHn/8cfLggw8Sk8kk6fvr9XoCgOj1erl/ui6D2Wwms2bNIqNHj/ZYnQcPHiQ5OTkkJyeHbNy4sV11rVmzhly4cMFDPeu+yLnXZAuZEEJWrVpFBg4cSDQaDRkxYgQpKipi782cOZOkpqZald+/fz8ZPnw40Wg0JCYmhqxZs8auzi1btpAHHniAqNVqEh8fT/Lz82W1SwghFouFLFmyhOh0OqLVasmYMWNIWVmZVZnbt2+TuXPnkvDwcBIQEEAmT55MKisrJX93XxAy5cqVKx6r65dffmFCPnfunMfqvZeRc6/Jnke+1+nu88jeoqmpCR988AEMBgNycnIk76DJEUfOvcZzrTkeISQkBP3790draysXcSfAhczxGAMHDuR50Z0EFzLHYyQnJ3Nr3ElwIXM8RntWUHHaB98hhMPxAbiQORwfgAuZw/EBuJA5HB+AB7tkQvNnuvu6ZE7Xh95jUnK2uJBlQrcN6u7rkjndh6amJpfH+fAUTZlYLBZUV1e7dfC2GI2NjYiOjsbly5d9Nu3T17+jN74fIQRNTU2IiopyeRAdt8gyUSqVVovzPYkvrHd2ha9/R09/P6kH6/FgF4fjA3Ahczg+ABdyF0Cr1WLJkiU+nafs69+xs78fD3ZxOD4At8gcjg/Ahczh+ABcyByOD8CFzOH4AFzIHI4PwIXcgVy8eBGzZs1CbGwsAgICMGjQICxZsgRGo9GqXGVlJZ566ikEBQUhIiIC8+bNsytTVlaG1NRUBAQEoF+/fnjnnXckJdd3BnLP0+4qLF26FA899BBCQkLQp08fTJkyBWfOnLEqQ7rKmdze2pOXY8+uXbvIX/7yF7J7925y/vx5sn37dtKnTx+yYMECVsZkMpHExEQyduxYcuzYMVJYWEiioqLI3LlzWRm9Xk8iIyPJ9OnTSVlZGcnPzychISHk/fff74yv5RR6sMC///1vUl5eTubPn0+CgoLIpUuXOrtrLklPTycbNmwgJ0+eJD///DN58sknyYABA0hzczMrk5ubS0JCQkh+fj4pKysj06ZNc3goQr9+/UhhYSE5duwYGTt2rKxDEaTAhdzJLF++nMTGxrLfd+7cSZRKJamqqmLXNm3aRLRaLduofPXq1SQsLIzcuXOHlVm6dCmJiooiFoul4zovgVGjRpHMzEyra/Hx8WThwoWd1CP3qaurIwDYwQgWi4XodDqSm5vLyty5c4eEhYWRtWvXEkIIuXnzJlGr1SQvL4+VqaqqIkqlknz33Xce6xt3rTsZvV6P8PBw9ntxcTESExMRFRXFrqWnp8NgMKCkpISVSU1NtcoiSk9PR3V1NTvWtCvgznnaXRm9Xg8A7P/LW2dyuwMXcidy/vx5fPTRR8jMzGTXHJ0VTU9QFJ7z7OgcaPpeV8Gd87S7KoQQZGdnY/To0UhMTATgvTO53YEL2QPk5ORAoVA4fR09etTqM9XV1Zg4cSKef/55u1MHHa1zJjZnOEs5c7qr4M6Z1V2NuXPn4sSJE9i0aZPde54+k9sd+HpkDzB37lxMnz7daZmYmBj2c3V1NcaOHYvk5GSsW7fOqpxOp8Phw4etrt24cQOtra1W5zw7OgcasLcOnYk752l3RV555RXs2LED33//vdVadOGZ3H379mXXxc7kFlrluro6zx7l67HRNkcSV65cIYMHDybTp093GLWkwa7q6mp2LS8vzy7Y1aNHD2IwGFiZ3NzcLhvseumll6yuDRkypFsEuywWC/nrX/9KoqKiyNmzZx2+r9PpyLJly9g1g8HgMNi1efNmVqa6utrjwS4u5A6kqqqK3HfffeTxxx8nV65cITU1NexFodNP48aNI8eOHSN79+4l/fv3t5p+unnzJomMjCQzZswgZWVlZNu2bSQ0NLRLTz85O9e6q/LSSy+RsLAwsn//fqv/q5aWFlamI87klgIXcgeyYcMGAsDhS8ilS5fIk08+SQICAkh4eDiZO3eu1VQTIYScOHGCpKSkEK1WS3Q6HcnJyely1pji6lzrrorY/9WGDRtYmY44k1sKfD0yh+MD8Kg1h+MDcCFzOD4AFzKH4wNwIXM4PgAXMofjA3Ahczg+ABcyh+MDcCFzOD4AFzKH4wNwIXM4PgAXMofjA/w//DuT9/U5Ck0AAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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YIVx/J/n5+Rg3bhyCg4ORlZWFxYsXY+7cuUhOThZs0tLSEBUVhZiYGJw4cQIxMTGYPHkyjhw5Iqre6upqjB49GvHx8WIv666G4zjMnz8fK1euRGhoKICG57OhQ4darE5rbYP4yy+/GHRT09PTsXnzZvzzn/9Er1698PbbbyMkJMSorTQiIyOxfv167N+/H0DDUNWkSZOQm5trVp8lRERiPjBixAgMGzYMn376qXBs4MCBmDRpElatWtXE/u2330ZKSooQDQOA2NhYnDhxQghbR0VFoaKiAr/88otgExkZia5duwrDAGLqvXTpEnx9fZGVlSXMGjGGiooKKBQKaDQa4Zf+bmDnzp3Ytm0bBg8eDKDhOXHp0qVCZj9LkJ6ejj179mDQoEF47rnnLFbPnURHRwuBsLy8PGi1WuzYsQMAUFRUJHo5HsdxeOqpp0BEeOSRR5CRkYEHH3zQYGy1OcTca6JaUp1Oh4yMDISHhxscDw8Px+HDh5v9TFpaWhP7iIgIHDt2TAjxt2TDl2lKvcag1WpRUVFh8LrbICKcPHlSECgA+Pn5WVSgwF+pPTsy19HNmzfh6ekJACgsLMTRo0fx+eefC+dNWS8rlUrx448/CkK777772nVPNluHGOPS0lLo9fom6wq9vLxQXFzc7GeKi4ubta+vrxe20mvJhi/TlHqNYdWqVVAoFMLLnCs9OgPp6ekYMWKEwXOnVqvFtGnTLF43393tyB/Gbdu24d5774Ver0dOTg7Wrl1rth+jGTNmAGhYiMBxnDAR3xyYFDi6M5hARK0GGJqzv/O4MWWKrbctFi1aBI1GI7yuXLliclmdjdLSUjzzzDMGM2p+++03vPvuu5DJZBavnxdHTU0NRD5xmURubi5Onz4NADhy5AjeffddTJgwwWzlh4aGCutjw8LCEB8fb7YgkqghGE9PT8hksiatV0lJSYur9pVKZbP2Dg4OQva5lmz4Mk2p1xjkcrkwqH43QESor6/H0qVL4ebmhrCwMGGKW1VVFVasWAFHR8cO8YUXKcdx0Gq1QkTZUvz444/w8vICEUEqlRpMYjAHEokEvXr1QkZGBvz9/ZGbm4uXX34ZKSkp7S5bVEvq5OSEgIAApKamGhxPTU3FqFGjmv1MUFBQE/u9e/ciMDBQuCFasuHLNKVeRlOmTp2KLl26YM2aNcjNzRUEqtFosHr16manwFkKR0dHocW29DAMEQl1FBcXNzu8Zw6CgoKE1trHxwc//fST8EjXHkRPZli4cCFiYmIQGBiIoKAgbN68GQUFBcLA8KJFi3Dt2jV89dVXABoiuRs2bMDChQsxY8YMpKWlYcuWLULUFgDmzZuHMWPGYPXq1Xjqqaewa9cu7Nu3D7///rvR9QINgYGCggIUFhYCgBAKVyqVZl203NkoKytDQkICdu3aBblcjilTpsDX1xdAw3NpaGhoh6ao5HFxcUFVVRUqKyuFgI4lOHXqlPBYNGTIEIu12g899JCQWVGlUsHb2xuZmZlNAp5iES3SqKgolJWVYcWKFSgqKsLgwYPx888/C9OrioqKDMYufX198fPPP2PBggXYuHEjVCoV1q9fj2effVawGTVqFLZv346lS5di2bJl6Nu3L5KSkjBixAij6wWAlJQUvPzyy8L76OhoAMDy5cuhVqvFXmqnR6/XIyEhAWlpaUhPT8fs2bMNNvLVaDQ4d+4cPvvsM6v4p1AoBJFaksY9sBdffNFi9Xh4eODgwYNYu3Yt+vbtixkzZuDs2bPtFqnocVJ7xt7GSceOHYvdu3ejS5cueOONN5qcP3PmDL755hurtKJAw/hsdnY2Hn/8cYt2tWfNmoUePXqgrKwM69evt1g9PIsXLxZiHQ4ODliyZEkTGzH3Gpu7a4cQEeLi4rBnzx4EBgbiySefFM6lp6dj//79cHV1xalTp6wmUOCv4JElW9IrV64IAcqnn37aYvU0JigoCDExMViwYAHq6+tRXV1t0IMRCxOpHfLVV18hISEBMTEx6NOnj3A8NzcXu3fvRkREBFQqFVQqlRW9/Gv+riUnNCQnJ0Mmk0Gv1yMsLMxi9TQmODgYOp0ON27cwL333ouioiL069fP5PLYUjU75Ouvv0ZsbKyBQDdv3gydTod169bhhx9+sNpzaGP4lpTfa8bc3Lp1C0VFRQDav0O5GO655x68+uqrwrhpe/MLs5bUzigtLYWnp6ewuVJSUhIKCgoglUqxZs0aA+FaG16klhqC2bt3L1xdXVFXV4dnnnnGInW0xIcffoj3338fMpms3dFk1pLaGe+99x4GDBgAoCHDwpkzZxASEoILFy7YlECBv0RaW1trkVlHp06dAtAQ5e7Vq5fZy28NZ2dnzJo1C127dm33aiLWkookMzMTeXl5iIqKsrYrTaiursaNGzfQrVs3HDp0CFevXsX+/fuhUqnMmpLTXDTeYa2mpqbJnivt4eLFi8JkCXPnaDIWpVJpltQzTKQiWbFiBXbv3o2ePXt26AwdY1i1apXQWubk5GD//v1Cq2qLODg4wMnJCTqdDpWVlWYVaeOxUT4HkTUwh0hZd1cEv/32G1JSUqDVahEaGmowI8ra3LhxAydPnoSjoyPKysrw5Zdf2rRAeSw1DMPP/KmqquqQBQMtYY7tIplIRXDo0CFIpVJIJBLU19cbTG20Njt27MCgQYMANEy3e+KJJ6zskXHwGRDMmR+ouLgYMpkMHMcZzEDrrDCRioCIMH/+fMTGxqJLly5IT0+3me37Tp48CWdnZ1RWVnZ4JLM98IP85ozwXr16FUBDQMrf399s5VoLJlIRcBwHd3d3eHl5YeHChTh//nyz0+06Gq1WK8yqOX78OIYPH25lj4zHEps38XPH77vvPrOVaU2YSI0kLy/PYJhAKpVi4cKF+Pbbb63emu7evRtOTk4oLy/H7NmzLZbhzxLwEV5z5t/ll4uNGTPGbGVaEyZSI+HTg5aXlxs8iz788MNWTx3KJ2UuKCgwmKfbGeC7uxqNxizlcRwn5M6yl+WJTKRG8uyzz0IqleKrr75Cbm6uIIzIyEiMHz8eFy9etIpf169fFxbPjx49Gg4OnWtUjRepuaYGnjt3DjKZDBKJxKx7qloTJlIjkUqlkEqlqKmpgYODg8Ga2aFDh2L16tVW8evHH3+EQqGAVqvFrFmzrOJDezB34IifZeTt7W3VFT7mxD6uogOZOnUqEhIShAgi0JB46vPPP0deXl6H+3PhwgUADd08S+cJsgT8HGNzbYPI92iGDBnS7rJshc7VN7IyTk5OWLlypbCR7MqVK/Hmm2/CxcUFDz/8MA4cONDsfpaWIi0tTejeNs6b25lwdXWFRCIBEaGioqJdq1X0ej1u374NiUTSoX8HS8NaUhHMmTNHmEGyYsUKvPrqq0IGw3HjxuGjjz5CTU1Nh/hy+vRpTJs2DQ4ODqisrERkZGSH1GtuJBKJMAzT3uDR9evXIZFIIJFIOnRpmqVhIhXBnd3J8PBwIYAklUrRvXt3/PDDDx3iS3R0tLBo29KJvCwN3+Vtr0j5oZeuXbt2qmGotmAibQejR4/GkCFDsGvXLgBASEgI1q5da/F6f/31V8hkMiF3bEBAgMXrtCR8FLa9IuWHwvhUpfYCeyZtB15eXvjss8/Qp08fjBkzBl27dhWWi1liaVh9fT2eeuopuLi4GOTrmTRpktnr6ki6du0KAO3K+M5xHKqqqiCVStuVqsQWYS1pO/H29ka/fv2EqGJgYCC+++47i9SVkpKCn3/+2WBz27Kysk4/Hsh3d0tKSkwu4+rVq5BKpeA4TsgpbC8wkZqBdevWITs7GxzHoX///vjggw/adcO1xHvvvYfBgwcLS9A+++wzu7ghzfFMyg9Fubm52c34KI99XY2VCA8Px4gRI4SM+UFBQUhPTzdb+USEb775xmAvzwsXLkCv1xskEO+s8MvV2pNG5fLlywAgaj/azgITqZlITEzEsWPHADQELj755BOz7Kql1+uxePFizJkzx2DsLy8vDxkZGRg5cmS767A2jbcfNHU1zKVLlwCgw3MZdQRMpGaiW7duGDRoECorK+Ho6IjS0lJhP5z2sHDhQsTHxwsbU3Ech19++QWLFi2ym6lvMplMmH9sSpe3vr4eHMcBQKd/Pm+Ozv8XtiGmTJmC8+fPA2jYqXz9+vXCs5Ip3Lx5Exs2bMCDDz6IwMBAAMC///1vaDQas+6taQvwrakpIk1PT4dMJoODg4MQKbYnmEjNSHR0tJBbx83NDRzHYdmyZSaXt2HDBgwYMEDItHDz5k0EBgbi5ZdftqvBeuCvFtCUbPb8Y0bv3r3t7nsBmEjNzoIFC4SZL1FRUUhKSsLHH38supzY2FikpKQYpA49cuQIvv32W8ybN89s/toK/Liy2EUKRCS0vkFBQWb3yxZgIjUzkyZNEm40Nzc3DBo0CEuWLBG1wuP8+fP4z3/+g9DQUOHY6tWrMX36dACwy93J+Zb0zt3c26K8vBxAw7N6420w7QkmUjPj6uoKV1dXHDlyBAAQGhoKIjJ6SKa+vh5PPPEE6uvrhee0jRs3gogsuremtfHy8gLQkK9JzDAM39V1d3fvdAvejYWJ1AIkJiYiLS0NNTU16N69Ox577DEsX74cer2+zc/u2LEDADBz5kwADZPGtVotvvvuO4OhCnuje/fuICJIJBJRWRr4+bp8OlN7hInUAjz00EMYOHAgdu/eDaBhm4OioiJ8/vnnbX728OHDeOmll4T36enpSEpKwvjx4y3lrk0glUqFlvD69etGfUav1wtJtW1tnxtzwkRqISZNmoTTp08jJycHADB+/Hhs2rRJOJ+ZmWlgr9VqwXGcQXRz7969WLFiRaddKyoWPi3pmTNnjLK/cOECHBwcoNfr7W5SfWNMEmliYiJ8fX3h7OyMgIAAHDp0qFX7gwcPIiAgAM7OzujTp4/BzcqTnJwMf39/yOVy+Pv74/vvvxddLxFBrVZDpVLBxcUFoaGhQqS1o3nrrbfwzDPP4Pvvv4der0eXLl1QWFiIY8eO4YMPPsC4cePw5ZdfCovEZ8+ejWnTphksszpz5gymTZtmFf+tAd8a8tMr2yItLQ1Awzpfa24lYWlEizQpKQnz58/HkiVLkJWVheDgYIwdO9YgMVdj8vPzMW7cOAQHByMrKwuLFy/G3LlzkZycLNikpaUhKioKMTExOHHiBGJiYjB58mQh+GJsvWvWrMFHH32EDRs24OjRo1AqlXjiiScsut17a8ybNw8ODg7ChIaAgAAEBwdjyZIluH79Ol566SVs2LABX3zxBZKTk5GRkQEAqKmpwfvvv48ZM2bYxYwiY+Fbw4qKijZtOY4Tvq+HHnrIon5ZHRLJ8OHDKTY21uCYn58fxcXFNWv/1ltvkZ+fn8Gx119/nUaOHCm8nzx5MkVGRhrYREREUHR0tNH1chxHSqWS4uPjhfO1tbWkUCho06ZNRl2bRqMhAKTRaIyyN4aPP/6Y+vTpQ2q1mtRqNfn6+hKAZl8TJ04ktVpNkZGRFBcXR2VlZWbzozNQWVlJarWali9fTjqdrlXb48ePk1qtpnfeeYcqKys7yEPzIeZeE/UzrdPpkJGRgfDwcIPj4eHhOHz4cLOfSUtLa2IfERGBY8eOCUmMW7LhyzSm3vz8fBQXFxvYyOVyhISEtOibVqtFRUWFwcvcvPLKK+jbt68wVDBy5Eg8+eSTeOedd/D888/Dy8sLHh4eePHFFzFs2DAADUmup0+fbld5eoyhS5cu4DgOEokE+fn5rdr++uuvABpaVD4Lvr0iSqSlpaXQ6/XCmBaPl5dXi4PQxcXFzdrX19ejtLS0VRu+TGPq5f8V49uqVaugUCiEl4+PT4vXbioeHh7Ytm2b0HUfMGAAAgMDIZVKMWjQIMycORNRUVEGgY81a9bAz8/P7L50Bvg8vF988UWLNvyPK2A/+720hkkPPHfOj6T/jW+Jsb/zuDFlmsuGZ9GiRdBoNMLrypUrLV5De+jevTtUKlWLQwuNb7Tc3Fy7H25pDf7HqqSkpMWezfnz5yGRSFBbW4tXXnmlI92zCqJE6unpCZlM1qRlKikpadKC8SiVymbtHRwchJB7SzZ8mcbUy+/7IcY3uVwODw8Pg5eleOutt/DTTz8hLy8PO3fuxOrVq1FUVCScP3fuHFasWGF3SbTE8uCDDwJo6IEcPXq0WZusrCwADStmGqeSsVdEidTJyQkBAQEGW50DDVuf8+sd7yQoKKiJ/d69exEYGCisIWzJhi/TmHp9fX2hVCoNbHQ6HQ4ePNiibx3J1KlTMWLECHz99dc4efIkampqsHnzZiQkJOD999/HN998A47jbMJXa8LnNVYoFMJz/J3wC7wjIiI6yi3rIjYqtX37dnJ0dKQtW7ZQTk4OzZ8/n9zc3OjSpUtERBQXF0cxMTGC/cWLF8nV1ZUWLFhAOTk5tGXLFnJ0dKTvvvtOsPnjjz9IJpNRfHw8nTlzhuLj48nBwYHS09ONrpeIKD4+nhQKBe3cuZOys7NpypQp5O3tTRUVFUZdmyWiu405fPiwEMkdNWqUQWR3xIgRNGzYMKqvr7dI3Z2JxYsXk1qtpjfeeKPJubKyMnrnnXdIrVbTjRs3rOCdeRBzr4kWKRHRxo0bqXfv3uTk5ETDhg2jgwcPCuemT59OISEhBvYHDhygoUOHkpOTE91///306aefNilzx44dNGDAAHJ0dCQ/Pz9KTk4WVS9RwzDM8uXLSalUklwupzFjxlB2drbR12VpkXIcR35+fvTmm29SeXk5ffDBB9S9e3cCQL/++iuVlJRYpN7Oxvbt24Uhq/z8fINz0dHRpFaracmSJcRxnHUcNANi7jUJkYmZn+yQiooKKBQKaDQaiz2flpeXG2QP+Prrr9GlSxdMnDjRLhcsm0JOTg4++eQTqFQqlJSUYOPGjQAaJnmMHTsWYWFhkMvliIuLs7KnpiPmXrPPtT02zJ3pPV544QUreWK79OnTB7du3YJKpUJeXh62bduG69evQyaTISwsDACEMeW7ASZShs3h7OyM0aNHo7y8HA888ABeeOEFEBFefvllYWE3HwW+G7h7JoYyOhXTp0+HTqfDvffei6CgILi4uAjZG0pLS4Uht7sB1pIybBKFQoHy8nJ4eXkhPDxcmO5ZU1Njl7l1W4O1pAybxdPTU5jfzXPx4kU8/vjjVvLIOrCWlGGzRERE4LnnnsOTTz6JmpoaFBUVYeXKlRg6dKi1XetQ2BBMIzpiCIYhjgcffBDZ2dkAGlbJlJeX20XCMTYEw7AbduzYAT8/P4SEhMDf398uBCqWu++KGZ2KAQMGoLi4GJ6enkZlW7RHmEgZNg+/isme8xi1BovuMhg2DhMpg2HjMJEyGDYOEymDYeOwwFEj+CFjS2QNZDAaw99jxkxTYCJtBJ9E2xJZAxmM5qisrIRCoWjVhs04agTHcSgsLIS7u7tZF2BXVFTAx8cHV65cscuZTOz6xENEqKyshEqlanOXAtaSNkIqlaJnz54WK9/SGQmtDbs+cbTVgvKwwBGDYeMwkTIYNg4TaQcgl8uxfPlyyOVya7tiEdj1WRYWOGIwbBzWkjIYNg4TKYNh4zCRMhg2DhMpg2HjMJEyGDYOE6mZuHTpEl599VX4+vrCxcUFffv2xfLly6HT6QzsCgoKMGHCBLi5ucHT0xNz585tYpOdnY2QkBC4uLjgvvvuw4oVK4yaiG0NEhMT4evrC2dnZwQEBODQoUPWdskoVq1ahUceeQTu7u7o0aMHJk2ahNzcXAMbIoJarYZKpYKLiwtCQ0Nx+vRpAxutVos5c+bA09MTbm5umDhxIq5evWpeZy2yZdRdyC+//EIvvfQS7dmzhy5cuEC7du2iHj160P/93/8JNvX19TR48GAKCwujzMxMSk1NJZVKRbNnzxZsNBoNeXl5UXR0NGVnZ1NycjK5u7vT2rVrrXFZrcJvR/mvf/2LcnJyaN68eeTm5kaXL1+2tmttEhERQVu3bqVTp07R8ePHafz48dSrVy+qqqoSbOLj48nd3Z2Sk5MpOzuboqKimmylGRsbS/fddx+lpqZSZmYmhYWF0UMPPWTWLSyZSC3ImjVryNfXV3j/888/k1QqpWvXrgnHtm3bRnK5XNgCLzExkRQKBdXW1go2q1atIpVKZXNb/Q0fPpxiY2MNjvn5+VFcXJyVPDKdkpISAiBsp8lxHCmVSoqPjxdsamtrSaFQ0KZNm4iI6NatW+To6Ejbt28XbK5du0ZSqZR2795tNt9Yd9eCaDQadOvWTXiflpaGwYMHQ6VSCcciIiKg1WqRkZEh2ISEhBjMbomIiEBhYaGww7UtoNPpkJGRIWz/wBMeHo7Dhw9bySvT0Wg0ACD8vfLz81FcXGxwfXK5HCEhIcL1ZWRkoK6uzsBGpVJh8ODBZv0OmEgtxIULF5CQkIDY2FjhWHFxsZD5jqdr165wcnJCcXFxizb8e97GFigtLYVer2/WV1vy0xiICAsXLsTf/vY3DB48GMBf33Vr11dcXAwnJ6cm21ma+ztgIm0DtVoNiUTS6uvYsWMGnyksLERkZCSef/55vPbaawbnmlunSkQGx++0of8FjWxxk+HmfLVFP1tj9uzZOHnyJLZt29bknCnXZ+7vgK0nbYPZs2cjOjq6VZv7779f+H9hYSHCwsIQFBSEzZs3G9gplUocOXLE4Fh5eTnq6uqEX2ylUtnkV7ikpARA0191a+Lp6QmZTNasr7bkZ1vMmTMHKSkp+O233wzWEvNbKxYXF8Pb21s43vj6lEoldDpdk93bS0pKMGrUKPM5abanWwZdvXqV+vfvT9HR0c1G9/jAUWFhoXBs+/btTQJH99xzD2m1WsEmPj7eZgNHM2fONDg2cODAThE44jiOZs2aRSqVis6dO9fseaVSSatXrxaOabXaZgNHSUlJgk1hYaHZA0dMpGbi2rVr1K9fP3r00Ufp6tWrVFRUJLx4+CGYxx57jDIzM2nfvn3Us2dPgyGYW7dukZeXF02ZMoWys7Np586d5OHhYdNDMFu2bKGcnByaP38+ubm50aVLl6ztWpvMnDmTFAoFHThwwOBvdfv2bcEmPj6eFAoF7dy5k7Kzs2nKlCnNDsH07NmT9u3bR5mZmfToo4+yIRhbZevWrQSg2VdjLl++TOPHjycXFxfq1q0bzZ4922C4hYjo5MmTFBwcTHK5nJRKJanVaptrRXk2btxIvXv3JicnJxo2bJgwhGHrtPS32rp1q2DDcRwtX76clEolyeVyGjNmDGVnZxuUU1NTQ7Nnz6Zu3bqRi4sLPfnkk1RQUGBWX9l6UgbDxmHRXQbDxmEiZTBsHCZSBsPGYSJlMGwcJlIGw8ZhImUwbBwmUgbDxmEiZTBsHCZSBsPGYSJlMGwcJlIGw8b5fwuiF73TOfBZAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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eesli+vayJGV4t27dAlDdGmotXn75ZUilUhw7dgwA0LNnTwDV90+1Wq2AkRmPJSkDADhx4gScnZ0BwGC4l6Xr0aMHDh8+jNTUVHAcBy8vL2i1Wrz44os4fvy4wTQxYsWSlAEAfjHmkpIS+Pn5CRyNefXp0wdz5szBxYsX+W3u7u6YOHEi3nrrLeTn5+PBgwcCRtgwlqQMysrKUFJSAqB6ChJLH9pVl2XLluHq1as4ffo0P8Ngr169cPz4cfTv31/UnR1YkjLIycmBRCLBgwcPrHZxXicnJyxZsgT79u3Dhx9+yM8yOHjwYNy8eRM//vgjqqqqBI6ybixJGX7EiKOjo1XceqnP5MmT+eF3Z86cAcdxcHJyQmBgIGxtbflbNWLDZmZgcO3aNQBAYGCgwJE0L0dHR6xfvx43b97Etm3bcO3aNXh7e2Ps2LGorKzEwYMHRfkZsCRl+NXTPDw8hA2kBcybNw8A8Nprr2HEiBFQq9VQqVSwtbVFXl4eOI4T3TzD4oqGaXEPHjzgJ5K2pEHeTdWhQwe8/fbb2LhxI2JjYwFUN5p9/vnnAkdWG0vSVk4/C0ObNm1EswZpSwkJCYFCocDFixfx559/QiqVYvPmzaK7d8qStJXTN5Z4enoKHEnLk0qlWLduHTp16oSMjAwA1fdP9S2/YsGStJXTn5y+vr4CRyKMBQsW4OLFixgxYgSA6o4Pv/76q8BRGWpUkkZHR8PT0xN2dnYICAjA8ePHGyyfkJCAgIAA2NnZwcvLC5s2bapVJiYmBn5+flAoFPDz8+OHGBlbb0VFBd544w088cQTsLe3h0ajwYwZM/ivc0xtFRUVKC8vB2B5U6WYk0ajwUsvvYSMjAzIZDJIJBLcuXNH6LB4Jifpjh07EBERgZUrV+LcuXMYMmQIxowZw0+h+KiMjAyMHTsWQ4YMwblz5/Dmm29i8eLFBou+JiYmIiwsDOHh4bhw4QLCw8MRGhqK06dPG13vw4cPkZycjLfeegvJycmIjY3F1atXMXHiRFMPsdW4c+cOJBIJbGxs4ODgIHQ4gnJwcOAXTXZzc8MXX3whcEQ1kIkCAwNp/vz5Btt8fHwoMjKyzvLLly8nHx8fg23z5s2jAQMG8M9DQ0Np9OjRBmVCQkJo6tSpja6XiOjMmTMEgLKysup8vbS0lHQ6Hf/Izs4mAKTT6erdpzU5ffo0abVa2rp1q9ChiMK6deto5syZpNVqKTQ0tFnr0ul0Rp9rJl1Jy8vLkZSUhODgYIPtwcHBfAftRyUmJtYqHxISgrNnz6KioqLBMvp9NqZeoHohXIlEgvbt29f5+rp166BSqfhHa/vKp5/PyN3dXeBIxCEsLIxfRtHJyYmfyV9oJiVpQUEBqqqqai2H5+Ligry8vDrfk5eXV2f5yspKfg3M+sro99mYektLSxEZGYnp06fXu1L1ihUroNPp+If+pG0t0tPTAcBgHZXWzNPTE6GhoSgtLYWTkxP27dsndEgAGtlw9OgoCXrMojh1lX90uzH7NLbeiooKTJ06FRzHITo6ut64FAoF2rVrZ/BoLSoqKviRL62pE8PjLFq0iF/X9NKlSwJHU82kboGOjo6QSqW1rl75+fn1LjarVqvrLG9ra8uv2FVfGf0+Tam3oqICoaGhyMjIwOHDh1tV4pkiOzsbEokEcrm81Tca1SSRSPiZ+21sbJCYmAiZTAZPT0/BVpgz6Uoql8sREBCAuLg4g+1xcXEYNGhQne8ZOHBgrfIHDx5Ev379IJPJGiyj36ex9eoT9Nq1a4iPj7eIZfuEov+q27VrV6scP9oUL7zwAh48eAC5XI7p06ejf//+mDlzpnABmdoqtX37dpLJZLRlyxZKTU2liIgIsre3p8zMTCIiioyMpPDwcL58eno6tWnThpYuXUqpqam0ZcsWkslktGvXLr7MiRMnSCqVUlRUFKWlpVFUVBTZ2trSqVOnjK63oqKCJk6cSK6urnT+/Hm6ffs2/ygrKzPq2ExpcbN0GzZsIK1WS2fOnBE6FFFatGgRabVaGjlyJAEgGxsbSkpKMtv+TTnXTE5Soup/YHd3d5LL5eTv708JCQn8azNnzqSgoCCD8kePHqW+ffuSXC4nDw8P2rhxY6197ty5k7y9vUkmk5GPjw/FxMSYVG9GRgYBqPNx5MgRo46rtSQpx3G0evVq0mq1lJOTI3Q4ovTVV1+RVqslrVZLc+bMofHjx9PcuXPNtn9TzjUJ0f+34jAoLi6GSqWCTqez6t+y9+7dw+effw6JRIKVK1da/HISzSE/Px8ff/yxwaCDM2fOwN/fH2vXrm3y/k0519h40lZIP3Wnk5MTS9B6ODs74/fffwcAdOvWDZ07d4a/vz82bNiA4uJifPLJJy0WC+tg3wrpk7Q1jnwxxeDBgxEfH48vvvgCmZmZsLW1xZgxYxAdHY1ffvmlxeJgSdoK6XvVWNMk2M0hLCwMSqUSdnZ2fM+2nj17YsWKFVi+fDnfY665sSRtZSorK1FUVATA8lbybmm+vr7Q6XS4ceMGFixYgJ9++gkcx0EqlWL48OHYunWrQflLly7hvffew+bNm/khgObAkrSV0XcIkclk9fZpZv5HJpNBo9Hg5ZdfBsdx2LlzJ4Dq3/N79+7l+/cWFRXhxRdfxD/+8Q+sWrUKAQEBiI+PN0sMrOGoldHP4u7q6so6MZhALpfjzTffxLRp0xAbG4vJkyfjqaeewquvvoq+ffvio48+woQJEzB+/Hj+Pdu2bcOIESOa/DmzK2krQkR8i2WfPn2EDcYChYWF4ezZs5BIJMjMzIRMJkNRURGWLl2K/v371+rh1rVrV7N00mdX0lZE/1WX4zj4+PgIHI1l8vf3R1JSEkaOHAkPDw/06dMH3t7eUCqV4DgOBw4cwMOHD9G7d294e3sjOTkZY8aMadLVlF1JW5Ht27cDqP49JZfLBY7GckmlUkRHRyMlJQUAoFQqUVlZiQMHDqBfv35YtGgRnJ2dUV5ejqqqqiY3IrEraStRVVWFvLw8tGnTBkOGDBE6HIvn6+sLtVqNuLg4KJVKnDx5EuvXr8fChQv5Mm+99RYAIDU1FV5eXo2ui11JW4nDhw+jTZs2qKqqsrqlDYUSFRWFW7du4ejRo1iyZIlBggLVs+W7uLhg3LhxTaqHXUlbibNnzwKovgLohwgyTdO2bVv89NNPKCkpweDBg2u97urqipkzZza5dZclaStw//59lJeXg4jw3HPPCR2OVXlcK7lSqWxyHezrbiug76urVCrRpk0bgaNhTMWStBXQzyfblMYLRjgsSVsBfde1+uahYsSNJWkroF8ygc35ZJlYklo5IuLnN2ZJaplYklq5oqIiVFZWQiKRwNHRUehwmEZgSWrlbt++DaB6ZWtbW3bHzRKxJLVy+t+jbL0Xy8WS1Mrpl5bv2LGjwJEwjcWS1MoVFhYCAJuFwYKxJLVy+nukLEktF0tSK1ZVVYUHDx4AYElqyViSWrHi4mIA1auD2dvbCxwN01gsSa2YfurO9u3bs0nHLBhLUiumT9IOHToIGwjTJCxJrVjNKyljuViSNgHHcfjrr7+EDqNeLEmtA+sn1kilpaX49ttvcfv2bfTv37/J0zY2B5ak1oFdSRvpxIkTfL/Y33//HT/++COqqqoEjup/iIgf7M2S1LKxJG2EtLQ0nDhxAsD/5rD5448/sH//fohlTeaioiL89ddfkEgkbLC3hWNfd43EcRzu3r2Ly5cv4/jx4wCAXr164W9/+xvOnDmD/fv34+zZs3B2dkb//v0FjhbIyckBUD2GlM0OaNlYkhrpwoUL2LNnD//c1dUVkyZNgkQiQWBgIO7du4fff/8dJ06cQEBAAGxshP2Sok9SDw8PQeNgmq5RZ1J0dDQ8PT1hZ2eHgIAA/spSn4SEBAQEBMDOzg5eXl7YtGlTrTIxMTHw8/ODQqGAn58fdu/ebXK9RAStVguNRgOlUomhQ4fi8uXLjTnEWvTrqEilUowcORLPP/88Pz5TIpFg1KhRUCqV0Ol0SE5ONkudTZGZmQmALRRsFchE27dvJ5lMRps3b6bU1FRasmQJ2dvbU1ZWVp3l09PTqU2bNrRkyRJKTU2lzZs3k0wmo127dvFlTp48SVKplN59911KS0ujd999l2xtbenUqVMm1RsVFUUODg4UExNDKSkpFBYWRp07d6bi4mKjjk2n0xEA0ul0db7+559/0oMHD+p9f2JiImm1Wlq7di0VFRUZVWdzePjwIa1evZq0Wi0VFBQIFgdTv8edazWZnKSBgYE0f/58g20+Pj4UGRlZZ/nly5eTj4+PwbZ58+bRgAED+OehoaE0evRogzIhISE0depUo+vlOI7UajVFRUXxr5eWlpJKpaJNmzbVGVtpaSnpdDr+kZ2dbfQHV5eqqiravHkzabVa+vnnnxu1D3NIS0sjrVZL77//PnEcJ1gcTP1MSVKTvu6Wl5cjKSkJwcHBBtuDg4Nx8uTJOt+TmJhYq3xISAjOnj2LioqKBsvo92lMvRkZGcjLyzMoo1AoEBQUVG9s69atg0ql4h9ubm6P+wgaZGNjg1GjRgEAzp07h/Pnzzdpf42l/2revXt30d27ZUxnUpIWFBSgqqqqVpO+i4sLf2I8Ki8vr87ylZWV/Cx29ZXR79OYevX/NSW2FStWQKfT8Y/s7Ox6j91Y7u7u6NmzJwDg559/xldffcUPF6uJmvFWjb4TA5uNwTo0qnX30b/ORNTgX+y6yj+63Zh9mquMnkKhgEKhqDfuxpoyZQqio6NRWFiInJwcfPDBB7CxscGgQYNgY2ODP//8E5cuXYKzszNeeukls8fAehpZF5OupI6OjpBKpbWuTPn5+fXeMFer1XWWt7W15eeBra+Mfp/G1KtWqwHApNiai62tLebOnYsePXrw2ziOw2+//YZjx47h0qVLfGx79+4Fx3FmrZ8lqXUxKUnlcjkCAgIQFxdnsD0uLg6DBg2q8z0DBw6sVf7gwYPo168ff5O9vjL6fRpTr6enJ7+oq155eTkSEhLqja052dnZYfr06Vi8eDHGjh1r8FqXLl34r8SXL1/Gtm3bzJaoHMfxg71ZkloJU1ul9LdCtmzZQqmpqRQREUH29vaUmZlJRESRkZEUHh7Ol9ffglm6dCmlpqbSli1bat2COXHiBEmlUoqKiqK0tDSKioqq9xZMffUSVd+CUalUFBsbSykpKTRt2jSz3oJpCo7jqLi4mCorK/ltx48fJ61WS1qtlo4cOWKWegoLC0mr1dKaNWtYy66INestGCKiDRs2kLu7O8nlcvL396eEhAT+tZkzZ1JQUJBB+aNHj1Lfvn1JLpeTh4cHbdy4sdY+d+7cSd7e3iSTycjHx4diYmJMqpeoOhFWrVpFarWaFAoFPfvss5SSkmL0cTVnktbn4MGDpNVqKSoqisrKypq8v4yMDNJqtfTpp5+aITqmuZhyrkmIRNIjXASKi4uhUqmg0+nQrl27FqmT4zh8/vnnKCwsRHBwMAYOHGjyPh4+fIjY2Fj07dsXJSUl2LdvH7p3747nn3++GSJmzMGUc42NghGYjY0Nn5hHjhxBWVmZyfv47rvvcOPGDezatQvx8fEAqn/3MtaBJakIBAQEoH379qioqEBSUpJJ7y0qKjJo0dZ3EOnatatZY2SEw5JUBPT3UIHqcammSE9PBwA4ODjwI28UCgVLUivCklQk9Ldkbt26Ve+8SURUq/fSlStXAAD+/v5Yvnw5+vTpg9mzZ7MV1KwIS1KRUKlUcHZ2BhHhxo0btV4nInz33Xf44IMP8Ntvv4GIUFZWhuvXrwMAfH19oVAo8Nxzz7F1SK0M+3MrIt27d0d+fj6uXbuG3r17G7x2+fJlZGRkAAAOHTrE93fmOA4dO3aEs7OzECEzLYBdSUVE343w+vXrBh3wKysrcfjwYYOyly9f5rf5+fmx0S5WjCWpiLi5uUEul+Phw4fIzc3ltx8+fBiFhYWwt7fHsmXLoNFoDN736FWXsS7s666ISKVSdOvWDWlpaTh27BimTJmCkpISnDp1CgAwceJEODg4YM6cOSgtLcXp06fRuXNnNhuglWM9jmoQosfRo7Kzs/H111+D4zjY2NjwHe89PT0xY8YMQWJizI/1OLJgbm5umD59ukGCSiQSBAUFCRwZIxSWpCLUrVs3TJ06FS4uLmjbti1CQ0Ph7u4udFiMQNhvUpHq0aOHwaBxpvViV1KGETmWpAwjcixJGUbkWJIyjMixhqMa9LeM9RN5MUxz0Z9jxnRTYElaw/379wGgyTPZM4yx7t+/D5VK1WAZ1uOoBo7jkJubCwcHB7N2WC8uLoabmxuys7MF68nUnNjxmY6IcP/+fWg0mscuk8mupDXY2Ng061KB7dq1s8qTWI8dn2kedwXVYw1HDCNyLEkZRuRYkrYAhUKBVatWNcviUGLAjq95sYYjhhE5diVlGJFjScowIseSlGFEjiUpw4gcS1KGETmWpGaSmZmJv//97/D09IRSqUS3bt2watUqlJeXG5S7efMmJkyYAHt7ezg6OmLx4sW1yqSkpCAoKAhKpRJdunTBmjVrjOqILYTo6Gh4enrCzs4OAQEBOH78uNAhGWXdunXo378/HBwc4OzsjEmTJvFLdugREbRaLTQaDZRKJYYOHYrLly8blCkrK8OiRYvg6OgIe3t7TJw4Ebdu3TJvsGZfHbWV2rdvH82aNYsOHDhAN27coJ9//pmcnZ3p1Vdf5ctUVlZS7969adiwYZScnExxcXGk0Who4cKFfBmdTkcuLi40depUSklJoZiYGHJwcKD3339fiMNqkH719c2bN1NqaiotWbKE7O3tKSsrS+jQHiskJIS2bt1Kly5dovPnz9O4ceOoa9eu9ODBA75MVFQUOTg4UExMDKWkpFBYWFitlePnz59PXbp0obi4OEpOTqZhw4bRU089ZbCie1OxJG1G//rXv8jT05N//t///pdsbGwoJyeH37Zt2zZSKBT8is/R0dGkUqmotLSUL7Nu3TrSaDTEcVzLBW+EwMBAmj9/vsE2Hx8fioyMFCiixsvPzycA/OrxHMeRWq2mqKgovkxpaSmpVCratGkTEREVFRWRTCaj7du382VycnLIxsaG9u/fb7bY2NfdZqTT6dCxY0f+eWJiInr37m0wA31ISAjKysr4dUkTExMRFBRk0LslJCQEubm5yMzMbLHYH6e8vBxJSUkIDg422B4cHIyTJ08KFFXj6XQ6AOD/vTIyMpCXl2dwfAqFAkFBQfzxJSUloaKiwqCMRqNB7969zfoZsCRtJjdu3MBnn32G+fPn89v0iyzV1KFDB8jlcn4h4LrK6J/XXCxYaAUFBaiqqqozVjHFaQwiwrJly/DMM8/wS3boj6Gh48vLy4NcLkeHDh3qLWMOLEkfQ6vVQiKRNPg4e/aswXtyc3MxevRoTJkyBbNnzzZ4ra5xqkRksP3RMvT/jUZiXJSprljFGGdDFi5ciIsXL2Lbtm21XmvM8Zn7M2DjSR9j4cKFmDp1aoNlPDw8+P/Pzc3FsGHDMHDgQHz55ZcG5dRqNU6fPm2wrbCwEBUVFfxfbLVaXeuvcH5+PoDaf9WF5OjoCKlUWmesYorzcRYtWoQ9e/bg2LFjBmOJ1Wo1gOqrZefOnfntNY9PrVajvLwchYWFBlfT/Px8fuV2szDbr1uGbt26RT169KCpU6fW2bqnbzjKzc3lt23fvr1Ww1H79u2prKyMLxMVFSXahqMFCxYYbPP19bWIhiOO4+iVV14hjUZDV69erfN1tVpN69ev57eVlZXV2XC0Y8cOvkxubq7ZG45YkppJTk4Ode/enYYPH063bt2i27dv8w89/S2YESNGUHJyMsXHx5Orq6vBLZiioiJycXGhadOmUUpKCsXGxlK7du1EfQtmy5YtlJqaShEREWRvb0+ZmZlCh/ZYCxYsIJVKRUePHjX4t3r48CFfJioqilQqFcXGxlJKSgpNmzatzlswrq6uFB8fT8nJyTR8+HB2C0astm7dSgDqfNSUlZVF48aNI6VSSR07dqSFCxca3G4hIrp48SINGTKEFAoFqdVq0mq1oruK6m3YsIHc3d1JLpeTv78/fwtD7Or7t9q6dStfhuM4WrVqFanValIoFPTss89SSkqKwX7++usvWrhwIXXs2JGUSiWNHz+ebt68adZY2XhShhE51rrLMCLHkpRhRI4lKcOIHEtShhE5lqQMI3IsSRlG5FiSMozIsSRlGJFjScowIseSlGFEjiUpw4jc/wHe8ciVDXik+QAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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EoEuXLtDpdCIBw/nz5+0ORmlhjU6nw8SJE6FWq1FVVeWQHnUOai8htdTO3FLWkwUEBIhe76aX4CaTCefOnQMAjB8/XhyfPHky1Go1SktLRYZWWzWeCNShQwfxd3HEIiEOai9QU1Mj5jVLC2OYJWn8vmmAnjp1CvX19YiIiJBNIe7YsaNYfpufn2/XuaROMimtlJSqua3LgwEOaq8gfUi1Wq3FdrPsZ9Il+O7du8XltNlsFh1k0dHRFst3panHe/bswfvvv99sqq6mCgsLAUD8pyDt1umIxBsc1F5A2iuLW+mWSdlH6+rqRJLC4uJisRPJ0KFDLZ7TeD1BSUkJtmzZgnfffbfZDjegoTOuaVAHBQUB4KBmNjp58iQAy43xmFxkZKT4WdpcT/rd9enTR7SmjVnbbujmzZv4z3/+Y/UcxcXFSE5OBhGhV69e4pJfuqwvLCwUcwpai4Na4RrvPT1u3Dg31qT98/HxwYwZMwAAp0+fxqFDh0RQN7dcVa1W49VXX7UI7ry8PJjNZhiNRpw+fVpczp89e1aUabyysEuXLujZsyeio6NbPe4t4bnfCldRUSF+ljJ/sOZJe1sDkLW2EydObPY5gYGBYhmv2WzGunXrUFNTg6ysLOzbtw9VVVXIzs6WpSju1asXBg4cKHud559/3iGz/bilVjhp2mN4eDhPD7VBYGAgXn/9ddmxGTNmIDAw0Kbn+/j4iFb78OHDYlpp05zj8+fPt+h0c9Tfh//KCicNtTT+vshaFhAQIFZfhYeH2716SmrVm5tyOmrUKKfmiOPLb4WTemGlcVBmm8GDB2PgwIFQqVRQq9V2PbdpOqx+/fqJjrennnrK4rLb0TioFcpsNqOwsFCMm/JCDvt16NC68FCpVEhMTMSePXswZswYDBw4UHRY2vsfRGtwUCvQuXPnsG3bNtkxa8MxzHnCw8PxwgsviPuuCGYJf6dWmLq6OouAjoqKEpMbmPJxS60wjYewtFotHnroIYwePdqNNWKuxkGtMNIqn86dOytya1p2b3z5rSBGoxE7d+4EAI/cvI85Bge1gmzfvl38PHjwYDfWhLkTB7WCNM4fzSuyvBcHtYI0nmbIvd3ei4NaQaSVRL1793bpuChrXzioFaS2thYAMGnSJDfXhLkTB7WCSOtwNRqNm2vC3InHqT3c7du38dFHH6G2tlak3eE8ZN6Ng9rDpaenWyzx404y78aX3x6spqYGx44dkx3TarWcDMHL8V/fgzVduGE2mzFz5kw31Ya1F3z57aHMZrNsl0RH7m/MPBu31B6qcV5pPz8/REdHu7E2rD3hltpDSduzREREICEhgXu8mcAttYcyGo0AGvZV5oBmjXFQeygpqKWdGhmTOD2ok5OTERkZCX9/f8TFxeHgwYMtlk9PT0dcXBz8/f0RFRWFjRs3WpRJSUlBbGwsNBoNYmNjxRpie85LRDAYDIiIiEBAQAAefvhhnDlzpm1v1oWkDCecUJBZICfaunUr+fr60qZNmyg3N5eWLl1KWq2Wrly5YrX8//73P+rYsSMtXbqUcnNzadOmTeTr60tffvmlKHPkyBFSq9X0xhtv0NmzZ+mNN96gDh060I8//mjXeZOSkigoKIhSUlIoJyeH5syZQ926dSOTyWTz+zMajQSAjEZjK347bZOcnEwGg4EuXrzo8nMz17Pns+bUoB45ciQlJibKjsXExNCKFSusln/99dcpJiZGdmzhwoU0evRocX/27Nk0depUWZn4+HiaO3euzec1m82k1+spKSlJPF5dXU06nY42btxo8/tzZ1AnJSWRwWCgn376yeXnZq5nz2fNaZfftbW1OHHiBKZMmSI7PmXKFBw5csTqczIyMizKx8fH4/jx47h7926LZaTXtOW8eXl5KCkpkZXRaDSYMGFCs3UDGmZwmUwm2c0damtrxSbyfPnNmnJaUJeVlaG+vh7h4eGy4+Hh4WI4pqmSkhKr5evq6lBWVtZiGek1bTmv9K89dQOAtWvXQqfTiZu78oBJnWQajYZXZDELTu8oa7oJGBFZHLtX+abHbXlNR5VpbOXKlTAajeImZe50NekKgXu+mTVOm3wSGhoKtVpt0fKVlpZatJASvV5vtXyHDh1Ezq3mykivact5pb2OSkpK0K1bN5vqBrSflpGDmrXEaS21n58f4uLikJaWJjuelpaGsWPHWn3OmDFjLMrv3bsXw4cPh6+vb4tlpNe05byRkZHQ6/WyMrW1tUhPT2+2bu0Jj1GzFjmzx04aWtq8eTPl5ubSsmXLSKvVUn5+PhERrVixghISEkR5aUjrlVdeodzcXNq8ebPFkNbhw4dJrVZTUlISnT17lpKSkpod0mruvEQNvcc6nY527NhBOTk5NG/ePI8Z0tq1axcZDAbav3+/S8/L3KfdDGkREW3YsIF69+5Nfn5+NGzYMEpPTxePPffcczRhwgRZ+f3799MDDzxAfn5+1KdPH3rvvfcsXvOLL76gAQMGkK+vL8XExFBKSopd5yVqGNZatWoV6fV60mg0NH78eMrJybHrvbkrqD/++GMyGAx08uRJl56XuY89nzUV0f/3RDG7mUwm6HQ6GI1Gl14K//Wvf4XJZMKCBQvQq1cvl52XuY89nzWe++1h7ty5IzrKunbt6ubasPaIg9rDSMNooaGhCAgIcHNtWHvEQe1hCgoKAPAGeKx5HNQepL6+HtnZ2QDA36VZszioPUhxcTEqKysBAP3793dzbVh7xUHtQc6dOwcA6NevHzp27Ojm2rD2ioPaQxw5cgSHDx8GANnUVsaa4qD2AESEH374Qdy/77773Fgb1t5xNlEPUFRUhLq6OgDAokWLEBYW5uYasfaMW2oPIH2XHjRoEAc0uycOag8gJYjgsWlmCw5qDyAtteTURcwWHNTtXG1tLUpLSwFAJIpgrCUc1O3c5cuXUV9fj06dOnFQM5twULdzFy5cAAAMGDCgxfxpjEk4qNs5aVVWVFSUm2vCPAUHdTtWXV2NGzduAGjY3ZIxW3BQtyMVFRUiiAHg0qVLABp6vbVarbuqxTwMzyhrJ4gIH330EW7duoWYmBjMmjULly9fBgAMHDjQzbVjnoRb6naivLwct27dAtAwgywzMxNZWVkAgB49erivYszjcFC3E9euXZPd3717t/i5pQ0GGGuKg7qdkHq5rbXKnTt3dnV1mAfjoHaj+vp6HDhwALdu3RIt9ejRo2UpYB999FH4+PCfidmOPy1u9OOPP+KHH37AO++8I/b+6tGjB6ZNmwYAUKvViI2NdWcVmQfi3m83Onv2LICfd/bUarVim9yXXnoJvr6+CAwMdGcVmQfioHaTyspKFBYWyo41TlMUGhrq6ioxheDLbzeRLrdDQkIQFBQEAIiOjnZnlZhCcEvtJj/99BOAhumf48aNQ1ZWFkaMGOHmWjEl4KB2E2mNdFhYGPR6PaZOnermGjGl4MtvNzCbzWKnDV6owRyNW2oXIiLs27cPvr6+4lj37t3dWCOmRBzULvT999/jyJEjsmO80wZzNL78dqG8vDzZ/Xnz5rmpJkzJOKhdSFqFBTSMQ/MQFnMGvvx2EZPJhNu3bwMAXnvtNU56wJyGW2oXkRIedO3alQOaORUHtYtcvHgRAGcxYc7HQe1kd+/eBRGJlrpv375urhFTOqcGdXl5ORISEsTKo4SEBFlnkTVEBIPBgIiICAQEBODhhx/GmTNnZGVqamqwZMkShIaGQqvVYubMmRaZQ2w599KlSxEXFweNRoOhQ4c64B3/rK6uDq+99hrmz5+PzMxM1NbWAuBxaeZ8Tg3qX/7yl8jKysJ3332H7777DllZWUhISGjxOevWrcPbb7+N9evX49ixY9Dr9XjkkUdQUVEhyixbtgw7d+7E1q1bcejQIVRWVmL69Omor6+369xEhBdeeAFz5sxx7BsHsHHjRgQFBeG+++7DN998AwAIDAyEWq12+LkYkyEnyc3NJQD0448/imMZGRkEgM6dO2f1OWazmfR6PSUlJYlj1dXVpNPpaOPGjUREdOvWLfL19aWtW7eKMoWFheTj40Pfffddq869atUqGjJkyD3fU3V1NRmNRnG7evUqASCj0SgrV1lZSWvWrCGDwWBxY6w1jEaj1c+aNU5rqTMyMqDT6TBq1ChxbPTo0dDpdBazqiR5eXkoKSnBlClTxDGNRoMJEyaI55w4cQJ3796VlYmIiMCgQYNEmdac2xZr164Vl/M6na7ZrWW1Wi0WL14s8o5JnnjiiVafmzFbOS2oS0pKrG6QHhYWJtYSW3sOYJk9Mzw8XDxWUlICPz8/dOrUqcUy9p7bFitXroTRaBS3pkHbWEhICN5//30sXLgQx44dQ1hYGIYMGdLqczNmK7snnxgMBvzlL39pscyxY8cAwOqGbkR0z43emj5uy3OalmntuVui0Wig0WhsLq9Wq6HX67F+/Xr06dOn1edlzB52B/XixYsxd+7cFsv06dMH2dnZIhFAY9evX282j7VerwfQ0NI2Tu1TWloqnqPX61FbW4vy8nJZa11aWoqxY8eKMvae25k4oJkr2X35HRoaipiYmBZv/v7+GDNmDIxGI/773/+K5x49ehRGo1EEX1ORkZHQ6/VIS0sTx2pra5Geni6eExcXB19fX1mZ4uJinD59WpRpzbkZUwxn9thNnTqV7r//fsrIyKCMjAwaPHgwTZ8+XVZmwIABtGPHDnE/KSmJdDod7dixg3JycmjevHnUrVs3MplMokxiYiL16NGD9u3bRydPnqRJkybRkCFDqK6uzq5zX7x4kTIzM2nhwoXUv39/yszMpMzMTKqpqbHp/dnTI8lYW9jzWXNqUN+4cYPmz59PQUFBFBQURPPnz6fy8nJ5BQDasmWLuG82m2nVqlWk1+tJo9HQ+PHjKScnR/acqqoqWrx4MXXu3JkCAgJo+vTpVFBQYPe5J0yYQAAsbnl5eTa9Pw5q5ir2fNZURP+fdJrZzWQyQafTwWg0ynbVYMzR7Pms8dLLNpD+PzSZTG6uCVM66TNmSxvMQd0G0tTV5iahMOZoFRUV0Ol0LZbhy+82MJvNKCoqQlBQUJvGvxszmUzo2bMnrl69qthLeqW/R2e8PyJCRUUFIiIi7rlhIrfUbeDj4+O0DeGDg4MV+YFvTOnv0dHv714ttITXUzOmMBzUjCkMB3U7o9FosGrVKrvmmHsapb9Hd78/7ihjTGG4pWZMYTioGVMYDmrGFIaDmjGF4aBmTGE4qN0kPz8fv/rVrxAZGYmAgAD07dsXq1atEvnBJQUFBZgxYwa0Wi1CQ0Px8ssvW5TJycnBhAkTEBAQgO7du2P16tU2Tfx3h+TkZERGRsLf3x9xcXE4ePCgu6tkk7Vr12LEiBEICgpCWFgYnnjiCZw/f15WhhyUs77NnLX+k7Xs22+/peeff57+/e9/0+XLl2nXrl0UFhZGr776qihTV1dHgwYNookTJ9LJkycpLS2NIiIiaPHixaKM0Wik8PBwmjt3LuXk5FBKSgoFBQXRW2+95Y631aKtW7eSr68vbdq0iXJzc2np0qWk1WrpypUr7q7aPcXHx9OWLVvo9OnTlJWVRdOmTaNevXpRZWWlKJOUlERBQUGUkpJCOTk5NGfOHKsJPrp3705paWl08uRJmjhxokWCj7bioG5H1q1bR5GRkeJ+amoq+fj4UGFhoTj2+eefk0ajEYvlk5OTSafTUXV1tSizdu1aioiIILPZ7LrK22DkyJGUmJgoOxYTE0MrVqxwU41ar7S0lABQeno6ETkuZ70j8OV3O2I0GtG5c2dxPyMjA4MGDUJERIQ4Fh8fj5qaGpw4cUKUmTBhgmz2Unx8PIqKipCfn++yut9LbW0tTpw4IcvXDgBTpkxpUy52dzEajQAg/l6OylnvCBzU7cTly5fx7rvvIjExURwrKSmxyH7aqVMn+Pn5yXKcW8uTLj3WXpSVlaG+vr7FnO6egoiwfPlyPPTQQxg0aBAAx+WsdwQOagczGAxQqVQt3o4fPy57TlFREaZOnYpZs2bh17/+tewxW/KXW8uT3txz3a01Od3bm8WLFyM7Oxuff/65xWOOyFnfVrye2sFszYsuKSoqwsSJEzFmzBh88MEHsnJ6vR5Hjx6VHSsvL8fdu3dledCb/i9fWloKwLLVcKfQ0FCo1WqrdW1P9byXJUuW4Ouvv8aBAwdka+kdlbPeIRz27ZzZ7dq1axQdHU1z58612vspdZQVFRWJY1u3brXoKAsJCZGlNU5KSmq3HWWLFi2SHRs4cKBHdJSZzWZ66aWXKCIigi5cuGD1cb1eT2+++aY4VlNTY7WjbNu2baJMUVGRwzvKOKjdpLCwkPr160eTJk2ia9euUXFxsbhJpCGtyZMn08mTJ2nfvn3Uo0cP2ZDWrVu3KDw8nObNm0c5OTm0Y8cOCg4ObtdDWps3b6bc3FxatmwZabVays/Pd3fV7mnRokWk0+lo//79sr/VnTt3RBlH5axvKw5qN9myZYvVnONNL56uXLlC06ZNo4CAAOrcuTMtXrxYNnxFRJSdnU3jxo0jjUZDer2eDAZDu2ulJRs2bKDevXuTn58fDRs2TAwJtXfN/a2ckbO+rXg9NWMKw73fjCkMBzVjCsNBzZjCcFAzpjAc1IwpDAc1YwrDQc2YwnBQM6YwHNSMKQwHNWMKw0HNmML8H+f2ViIWwmD1AAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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eBAC4u7sb5fxWVlb8tokU4W0bEimhlRMnTvClaZycnPjEemNWVOSGvCTStiGRElq5fPmyxnuuHhWXHG8MaK5UN0ikRAvu3r3LPzNWVFTwx3v27GlQhcDWoAivbpBIiRbExMRAJBKhoKAA3t7eyMvLg0qlwqxZs4zaDveHoLa21qjn7WqQSAkNGGP4448/ADQNR5csWYKTJ0/ixIkTfGFrY8GVBaWso7ahnb4JDS5cuAA7OzsolUps3LgRIpEIGzZsaHU7j45APalukEgJnrKyMhw4cAADBgyAUqnkF2evWrXKJO1xPSmJtG1ouEsAaBrmrlu3jt+2Y8qUKSZvk0SqGyRSAgDw/fffQ61WQyKRoKKiAo899pjJ2+SGuzQF0zYkUgIAEB8fj2HDhkGlUqFnz54QCk1/a3A9aUNDA5+8T7SEnkkfUu7cuQM7OztYWVnh4sWL/JxlXFwcTp8+3Sk+2NjY8D/X1tYapSxLV4R60oeUjRs3wsvLCzKZDB9++CEEAgFyc3Px/PPPa1TzMyUCgYBfUUPPpa1DPelDSF1dHaqqqjB+/HjU1tby+7HIZDK8/PLLneqLra0t6uvraa60DUikDyHHjx9vsZqluroa77//fqf7Ym9vj7t371LwqA1ouPsQwmUUcWRmZqJXr15GWcytL9zQmltlY0kwxpCenm5uN6gnfdiorq7mt3bYtm0bqqqq4Orqik8++cQs/nArYSyx1tHKlStx8OBBXLlyBcOHDzebH9STPmRwFRZEIhGOHTuGtLQ0XLx40ajrRPXBUnvSoqIi7N27F1VVVfDw8MD69evN5gv1pA8ZFy5cAAAMHz4cM2fONLM3f/WkllbJft++ffDy8oK3tzdOnz6Njz76CB4eHggKCup0X6gnfYhQqVS4d+8eAGDixIlm9qYJridtvm7V3KjVaiQkJCAgIAAuLi5YunQpZDIZ1q5di+3bt/N2t2/fRk5Ojsn9IZE+RKSnp8PKygpqtRqurq7mdgfAXyJVKpV8+VBzc+rUKY3ot1AoxMsvvwxPT0+sW7cO0dHRUKlUePrppzFz5kzU1dWZ1B8S6UPEjz/+CKCpuHVnpP3pgkQigZVV01OXJQSPbt++jc2bN6N3795QKBTYtWsXMjIyAABTp07FggULsHr1asycORO///47FAoFnn32WZP6ZND/VEREBNzc3CCVSuHt7Y2EhIQ27ePj4+Ht7Q2pVIrBgwdj7969LWxiYmIwatQoSCQSjBo1CkePHtW73SNHjsDf3x9OTk4QCARIS0sz5PK6JGq1mn/ue/TRR83sjSZcb2ru59Lr169j6tSpGDx4MADg6tWrqKysxOHDh3Hx4kUAgKenJx555BHcuXMHy5cvx0svvYTff/+9XQ10BL1FGh0djTVr1mDDhg1ITU2Fr68v5s6di9zcXK322dnZmDdvHnx9fZGamoq3334bq1atQkxMDG+TlJSERYsWISgoCFevXkVQUBACAwNx6dIlvdqtqanBlClTEB4eru9ldXnS0tIgFouhUqkwbNgwc7ujAVcR35zPpQcPHsTo0aNRXV2NESNGAGgKap0/fx5WVlaIi4vDrVu3AAAjR47EkiVLIBQKIRAI8NRTTyEyMtJkvgmYnuXDJ0yYAC8vL+zZs4c/NnLkSCxcuBBbtmxpYb9+/XqcOHGCD/0DQHBwMK5evYqkpCQAwKJFi1BZWamR2D1nzhw4Ojri22+/1bvdnJwcuLm5ITU1FePGjWv1WhQKBRQKBf++srIS/fv3h1wu79BO1pZEUVERMjIysH//fowYMQKOjo4mW8RtKD/88AMuXryIiRMnwt/fv9PbVygUcHV1hUQiweLFi2Fvb4+srCy88847GDZsGK5du4ZTp04hNDQUEyZMwNy5cwH8NTx3cHBARUUFPv74Y37o3h6VlZVwcHDQ6V7TqydtaGhAcnIy/Pz8NI77+fkhMTFR63eSkpJa2Pv7++PKlSv81u6t2XDnNKRdXdiyZQscHBz4lynmCs0ZDImMjIRMJkNAQAAGDhwIAJg+fbrZ/GkNR0dHAObrSaOiojBv3jy89NJLsLe3R01NDe7du8ePOMaMGYOQkBBs3boVly9fxv/+9z9cunQJBw4cQHR0NACgR48eOHPmjEn800ukZWVlUKlU/Op9Dmdn51Zr4BQXF2u1b2xs5Pe7bM2GO6ch7epCaGgo5HI5/8rLyzP4XNr49ddf4eHhgZKSEqOeVxcqKyuxceNGjB07FkuWLIFUKkVjYyNGjRrV6b60ByfSznomPXDgAM6cOcP/AU1ISOCfQ4uKivD5559j6dKlLb63bt06pKWlITc3F6dPn8bHH3+MDz/8kE8d/Omnn0zir0HJDPfXXmWMtVmPVZv9/cd1Oae+7baHRCKBRCIx+PvtsXPnTri6umLHjh344IMPTNaONr744gssW7aMH36p1Wr07t2bL3JtSfTo0QMAcO/evQ7/n7aHWq3GW2+9hdLSUkyZMgVjxozhdx0/fPgwMjMz4eLiwm9U1RwrKyuMGTMGX375JeLj4/H8889DJBIhLi4OQNP9uW3bNgwYMAD9+/fHhg0bsG3btjYfuXRBL5E6OTlBJBK16L1KSkpa9HIcLi4uWu2trKz4bd1bs+HOaUi75qa+vh49e/bEkCFDUF1djfr6+k5LYFer1SgtLYWNjQ3kcjmys7Px66+/8hFKS4MTqVKpRH19vcZicGPzww8/YOLEiSgrK+Oj/7Nnz0ZBQQEyMjLg7u6OyMjINv+YPfbYYxrlZfbt24f3338f9vb22LVrl0aCQ0JCQodFqtdw19raGt7e3oiNjdU4Hhsby+8bcj+TJk1qYX/27Fn4+Pjwv4jWbLhzGtKuuTlz5gw/jLOzs2uxbYMp+fXXX2FjY4Pa2lrs3r0bx44dw/r16y0mgeF+xGIxv3mTqYe8CQkJ8Pb2hr+/P958803Mnj0bQNPjQVxcHK5fv673PdWtWzd+xDJu3Dj06NEDY8aMwbJly1BVVYWCgoIO+az3cHfdunUICgqCj48PJk2ahH379iE3NxfBwcEAmp7zCgoKcPDgQQBNkdxdu3Zh3bp1eOmll5CUlITIyEg+agsAq1evxrRp07B161YsWLAAx48fR1xcHJ9nqku7QNNwKTc3F4WFhQCA33//HUBTT+3i4mLAr8dwMjMzATRFAB0cHJCamopp06Z1Stvc7+3GjRtYv349Zs2a1WltG0qPHj1QUlKC8vJyyGQyo547Ojoan376KT777DOtPWReXh62bt3KB9cMYdq0aUhMTMS4ceM0ek6lUomsrCz069fP4HPrLdJFixbh7t272Lx5M4qKiuDh4YFTp07xF1hUVKQxd+nm5oZTp05h7dq12L17N2QyGXbs2IEnn3ySt5k8eTKioqLwzjvvYOPGjRgyZAiio6MxYcIEndsFmnYCe+GFF/j3ixcvBgBs2rQJYWFh+l6qwajVatTV1cHKygqenp7IyspCRUUFlEqlyZ8JGxsbIZfLIRKJsGnTJnh7e5u0PWPRp08flJSUGD3Cq1Kp8O677+LmzZtYsWIF5s+fzweHPD094ezsDFtb2w4JFGhKEImIiNAQaGlpKfr169fhDkLvedKujD5zV22RmZmJQ4cOQa1WY9OmTfjggw/AGMOLL75o8iVh165dw9GjR6FSqfDee++ZNAhjTM6dO4eEhAR4enpi/vz5RjvvyZMncfjwYQwYMIAfklZVVeGPP/5AXFwcamtrce7cOcyYMaPDbb3wwgv8/7tQKERNTQ127NiBlStXtrA12TwpoRvnzp0DAL40JjfU6eiziS6cPHkSADBo0KAHRqDAX9MwxlxVolKp8J///AeDBw/WSDJYvnw5jh8/jl9++QWff/65UQQKNIm0pqYGdXV1UCqV+OKLL+Dj49Ph89J6UhOQl5eHbt268UOfoUOHIj8/n39WNhWNjY1QKBQQiUSYN2+eSdsyNlxq4N27d9HY2Khz5k5bHDt2jK+ocO/ePdjY2ODu3btwc3MDgBbPjx1l2rRp+PLLL+Hq6orBgwcbLf2SRGpkMjMz+blXrgof15MaO1nifk6dOgWRSASlUmmxU1Ot0a9fP35lzp9//tmiUJq+3LhxA9999x1GjRqFkpISREREAIBGwNIULFu2zOjnpOGuEVGr1QgNDeWFwg3huGhlRUWFSdceJicnAwCGDRv2QA11gaZEAC5405FUTwD4+eef4eXlxfdkSqUSFy9eREBAQKdsn2FsSKRG5PLly3yRr+bV2Lt168bPA5pqyFtQUAChUAiVSoWFCxeapA1Twy2hy8/PN3jbCbVajQ8//BABAQEQi8W4c+cO3nzzTUyYMAHff/89X6n/QYKGu0bk/PnzGDt2LABg/PjxGp/JZDLk5OSgsLAQQ4YMMXrbXKKHUCjstAr0xkYmk/EpowUFBRgwYIDO31WpVPDz84OdnR08PT35rKW6ujqLW5qnL9STGgnGGMrKyiAWi1FSUoJHHnlE43PuRsnKyjJJ+9nZ2QAsc5WLrggEAl6Y3NpNXdi/fz+8vLxQUVGB3r17w8bGBkqlEufOncOmTZtM5W6nQT2pkSgsLOSHuEqlssUzIZeSl52dDZVKBZFIZLS27927B6FQCMaYxRQYM5SxY8ciLy8P6enp7U6NXL58GZ9//jlfNICDMYavv/4a48aNw6BBg0zssemhntRIXL9+HQDw22+/tehFgSaRCgQCCAQCpKam6nXu/fv3Y8aMGa1uxcAtnpdKpSZNTu8MuCVj5eXlGgvytfHOO++goqJCQ6BA05KxuXPnmq3gt7EhkRqJ3377DUDT6pfAwMAWnwuFQowePRpAUz0nXQMjN27cwGuvvYa8vLxWS3SkpKQAgMXn5+qCo6MjP0d6+/btVu3i4+P5pWZA05rjPXv24NNPP8U///lP7Nu3jy+D8qBDw10jwBjjV2+8/vrrrU7Ez5s3DykpKZBKpZg3bx4KCgoQFxfXZm7n3r17ERISAgC4efNmi6Hy3bt3+Yiyh4eHsS7JrLi7uyMjIwM//vhjq9s7xMfHA2iqa3XmzBncuXMHcXFxOH78OF577bXOdNfkUE9qBLhaN4wxjUUB9yORSPi//JMnT4aPjw/WrFnTqn15eTm/1hJoSkK/f7neoUOHADQtSO4qdZm4RQEFBQU4cuRIi8/v3LkDxhgYYzh48CBWr16NP//8E6NHj8Y777xj1Od9S4BEagS4TCJra+t2V7k8+eSTuHv3LoCmFUJDhgzB119/rdX2+PHjEAqFKCsr45efcQkLDQ0N+O6773Dz5k0ATUXZugpcUoNYLEZ6errGyphDhw7xVS64jY+XLVvWpXcJJ5EaAW7d6tChQ9u1FQqFWL58OQ4fPozS0lJYW1sjNjZWayYSl2yuUCj4BIWGhgaUl5fj4MGDCA0NhYODAxobG81SZc9UCAQCjZUwJ06cANBUCCAqKgpOTk4AmiokcEkiXRkSqRHg5j51LfI1YsQInDx5Ej169ABjDMOGDcPKlStx7tw5fgXFtm3bIBAIoFarsXPnTixfvhwKhQJCoRALFy7E0aNH8dxzzwFoEr6tra3Jrs8ceHp68s/2WVlZkMvlOHfuHP+4UFdXB09PT3O62GmQSDtIQ0MDamtrAUCvtaKDBg3CBx98wA99JRIJZs+eDV9fXyxZsgRRUVEAABsbGz5hn3tWmzlzJp/RVFdXh2eeecZo12NJbNiwgS9A/dlnn/HTS4WFhZgwYYLFbJVhah6OqzQS2oakeXl5/PynIel4wcHBqK2tRZ8+fRASEgIbGxtcvnwZc+bMAaAZsQ0ICEBjYyP/Pj8/HwUFBWbd4NbUTJkyReN9TU0NHB0dH9j8ZEOgKRgd4TbbnThxosYaRG6oa2iJjL/97W/o1q0bgKbA06xZs/jPlEolfH19+fdSqRTl5eWoqakBYwyFhYX8EqyuipeXF+Lj4yEUCqFUKvHvf//bKGtNHySoJ9WRe/fu4c6dOy3WhHJBIy6x3hBef/11fPPNN0hOTuaTHKqqqlBaWtqid16wYAEOHDgAuVyOs2fP6hSsepDp0aMHxo8fj6NHj+KZZ5556AQKUE+qM1xEsbS0lD+mUqn4Z0ounc0Q7Ozs8O6772LVqlVITExEv379kJmZie+++66F7ezZsxEdHc0HUB4GAgICMGvWLJMWMrdkqCfVEa68BzeRzv3MwYnYUIKCgnDv3j0EBgYiPz8fO3fubPW5KzAwsMOVCx40HlaBAiRSneFE2Dyay+Xr9u3b1yiVEAQCASIiIpCZmYng4OAHrroCYRpIpDoiFov5uUhuyMutZjH2fN2DVp+IMC0kUj3gNvYpLS1FfX09qqqqAOCBX/lPWDYkUj3o06cPgKbJ9Fu3bkEgEEAkEmkkwROEsaHorh5ww9CLFy/ySd8PWwCH6HyoJ9UDrhSHVCrlk9+9vLzM5xDxUEAi1YPu3btr7PjFGNOroh1BGAKJVE+CgoL46K5MJnsoM2CIzoXuMD2RSqV44okn8NprryEjI8Pc7hAPAdSTGsDMmTNx4cIF6kWJToFEaiAdTQMkCF0hkRKEhUMiJQgLh0RKEBYOiZQgLBwKTzaDWydaWVlpZk+Irg53j3H3XFuQSJvBrWrRp+ofQXSEqqqqdgvYCZguUn5IUKvV/BaGxlxwXVlZif79+yMvL6/LbAXRHLo+/WGMoaqqCjKZrN3SpNSTNkMoFPL7iJqC7t27d8mbmIOuTz90LQFLgSOCsHBIpARh4ZBIOwGJRIJNmzZ12Yp3dH2mhQJHBGHhUE9KEBYOiZQgLBwSKUFYOCRSgrBwSKQEYeGQSI1ETk4Oli1bBjc3N9jY2GDIkCHYtGkTGhoaNOxyc3Pxj3/8A7a2tnBycsKqVata2KSnp2P69OmwsbFBv379sHnzZp0Ssc1BREQE3NzcIJVK4e3tjYSEBHO7pBNbtmzB3//+d9jb26NPnz5YuHAhv40lB2MMYWFhkMlksLGxwSOPPILr169r2CgUCqxcuRJOTk6wtbXF/PnzkZ+fb1xnGWEUTp8+zZ5//nn2ww8/sFu3brHjx4+zPn36sNdff523aWxsZB4eHmzGjBksJSWFxcbGMplMxlasWMHbyOVy5uzszBYvXszS09NZTEwMs7e3Z5988ok5LqtNoqKimFgsZl988QXLzMxkq1evZra2tuz27dvmdq1d/P392f79+1lGRgZLS0tjAQEBbMCAAay6upq3CQ8PZ/b29iwmJoalp6ezRYsWsb59+7LKykreJjg4mPXr14/FxsaylJQUNmPGDDZ27FjW2NhoNF9JpCbko48+Ym5ubvz7U6dOMaFQyAoKCvhj3377LZNIJEwulzPGGIuIiGAODg6svr6et9myZQuTyWRMrVZ3nvM6MH78eBYcHKxxzN3dnYWEhJjJI8MpKSlhAFh8fDxjjDG1Ws1cXFxYeHg4b1NfX88cHBzY3r17GWOMVVRUMLFYzKKionibgoICJhQK2ZkzZ4zmGw13TYhcLkfPnj3590lJSfDw8NAosO3v7w+FQoHk5GTeZvr06RrZLf7+/igsLOSr5lsCDQ0NSE5Ohp+fn8ZxPz8/JCYmmskrw5HL5QDA/39lZ2ejuLhY4/okEgmmT5/OX19ycjKUSqWGjUwmg4eHh1F/ByRSE3Hr1i3s3LkTwcHB/LHi4uIW2xo6OjrC2toaxcXFrdpw7zkbS6CsrAwqlUqrr5bkpy4wxrBu3TpMnToVHh4eAP76Xbd1fcXFxbC2toajo2OrNsaARNoOYWFhEAgEbb6uXLmi8Z3CwkLMmTMHTz31FP71r39pfKZtnSpjTOP4/Tbs/4NGlripsDZfLdHPtlixYgWuXbuGb7/9tsVnhlyfsX8HtJ60HVasWIHFixe3acNt5AQ0CXTGjBmYNGkS9u3bp2Hn4uKCS5cuaRwrLy+HUqnk/2K7uLi0+CtcUlICwLI2F3ZycoJIJNLqqyX52R4rV67EiRMn8PPPP2usJXZxcQHQ1Fty+9ICmtfn4uKChoYGlJeXa/SmJSUlmDx5svGcNNrTLcHy8/PZsGHD2OLFi7VG97jAUWFhIX8sKiqqReCoR48eTKFQ8Dbh4eEWGzh69dVXNY6NHDnygQgcqdVqtnz5ciaTydjNmze1fu7i4sK2bt3KH1MoFFoDR9HR0bxNYWGh0QNHJFIjUVBQwIYOHcpmzpzJ8vPzWVFREf/i4KZgHn30UZaSksLi4uKYq6urxhRMRUUFc3Z2ZkuWLGHp6ensyJEjrHv37hY9BRMZGckyMzPZmjVrmK2tLcvJyTG3a+3y6quvMgcHB3b+/HmN/6va2lreJjw8nDk4OLAjR46w9PR0tmTJEq1TMK6uriwuLo6lpKSwmTNn0hSMpbJ//34GQOurObdv32YBAQHMxsaG9ezZk61YsUJjuoUxxq5du8Z8fX2ZRCJhLi4uLCwszOJ6UY7du3ezgQMHMmtra+bl5cVPYVg6rf1f7d+/n7dRq9Vs06ZNzMXFhUkkEjZt2jSWnp6ucZ66ujq2YsUK1rNnT2ZjY8Mee+wxlpuba1RfaT0pQVg4FN0lCAuHREoQFg6JlCAsHBIpQVg4JFKCsHBIpARh4ZBICcLCIZEShIVDIiUIC4dEShAWDomUICyc/wOaQ8i8nwMzVQAAAABJRU5ErkJggg==\n", 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hvLwco0aNwrJly6zey3ls7WXo0KGoqKjgF0EwHIOJtJ9RXl4OoMP7yj0zcmRmZmLOnDkICAiAv78/zGYz/vvf/6KoqAgFBQWC2+Kma7oKu8qwDybSfgZ3Sztx4kSr1wYNGoRDhw4hISEB/v7++N///oe7d+8iISEBTzzxhOC2uOkddrvbO5hI+xFExN96RkVF2bQZMmQINm7ciPnz5yMkJAT/+te/EBkZ6VB7nEhbW1t7jM3M6Bom0n7EnTt3AHSINSgoqEu7efPm4dSpU4iJibFrCWBXdI7dc+/ePcHPtIwOmHe3H8Etovf19e0ysDXHlClTeiVQoGOulFupxG55HYeJ1A1w+T37muLiYgDAqFGj+qxNbvRkInUcJtI+5tq1a4iJiXFLCoavv/4aQNfPo65gyJAhAJhIewMTaR9z8OBBXL16FX/6059QXl6O8+fP90m79+7dA3UEQ8fw4cP7pE2gY64UYKuOegMTaR9CRKiursaqVauwbds2TJo0CdOnT+fnLl0JlzFOLpf3+llTCNxcKee0YgiHibQPuXXrFoYMGQI/Pz88//zzADqmKTZv3uzytv/73/8CAEJDQ13eVme4aRi2+dtx2BRMH8ItJAA6tnJlZGQAAK5cuYLW1lZ4eXm5rO3r168D6Ng32pdwQbTb2tpgMpkglbJLTihsJO1DuJCYt2/ftigfPXo0/vWvf7msXZPJxHuUR4wY4bJ2bNE50TB7LnUMJtI+or29nb9IjUYjjh07hv/85z84efIkgG8F7ApqamogEokgEom6TQ/hCkQiEZ+o+VESaXl5OeLi4nDw4EF3d4WJtK+ora2FSCSC2WzG9u3bUVVVhSNHjuDSpUswmUyQyWT8FImz+d///gcAGDx4sFuW5nHJndw9DdPU1IQpU6agrKzM5us6nQ5Ah4Nv0aJFKCoqwvz587F48WKYzea+7KoFDol027ZtCA8Ph1KpRGxsLL744otu7QsLCxEbGwulUomIiAjs2LHDykaj0SA6OhoKhQLR0dE2v8F6avfAgQNITk5GQEAARCIRvvzyS0dOzyVwQvH19YVcLkdGRgYWLlyI2tpaPiN2UVGRS9ouLS0FAIwbN84l9fcEN1daX1/vlvY5Vq9eDa1WC7VabVH+4MEDJCcnY+jQoYiMjMTUqVNx9uxZ/vUPP/zQpY8jPSFYpHv37kVGRgbefPNNFBcXIyEhAbNmzUJVVZVN+4qKCsyePRsJCQkoLi7G+vXrsXLlSmg0Gt5Gq9UiNTUV6enpKCkpQXp6OlJSUviwHfa2e//+fXz/+99Hdna20NNyOZzTiFtIkJGRgQ8++AAymQzTpk2zsHEmRMTfZgqNrOAshg0bBgA9xvZ1Jbdu3YJGo0F8fDxKSkosRtNNmzYhLy8Pra2tKC8vx6lTpzBu3DgsX74cM2fOhFKpxPbt293Wd8FJhCdNmoSYmBiLTo8ePRrz5s1DVlaWlf2aNWtw+PBhXLlyhS9bunQpSkpKoNVqAQCpqalobm7mw0YCwMyZM+Hn54fdu3cLbreyshLh4eEoLi7GhAkT7D43VyURJiJs2LABAPDKK69YBZfW6XTYvHkziAhr1qxxqpf366+/xo4dO0BE+PWvf93jml1XUF5ezv8fMzMz+7x9AFi3bh1aW1vh5+cHk8mEY8eO4dixY6ioqEBMTIxFLtWIiAgsXLiQ/7ulpQX79u1Dfn6+05ZUuiyJsMFgQFFREZKSkizKk5KScObMGZvv0Wq1VvbJycm4cOECn0avKxuuTkfatQe9Xo/m5maLwxVwt3nt7e02d5+oVCresXPt2jWntn3hwgUAHV5WdwgU+HautL293S3hPdvb21FdXc33QyqVIiwsDIsXL8azzz4LPz8/zJs3DytXrsTLL79sIVC9Xg8fHx+kp6djzZo1bnk2FfRfu3v3Ltrb2xEYGGhRHhgY2OXu+7q6Opv2JpOJj7XTlQ1XpyPt2kNWVhZUKhV/hISEOFxXd1RUVADoWO3TlVC4TdWffvqp09r95ptv8PHHHwPo20X1D+Pv7w8igkQi4Z0zfclnn32GJ598EgB4X8eoUaNw/vx5PHjwAC+99BImTJgAf39/fslkS0sLsrKy8Lvf/Q5VVVWQyWRoa2vD008/jczMTItpNFdHQ3Toq/VhD2FPG3pt2T9cbk+dQtvtiXXr1kGn0/EHt3TO2Tz8PGqLKVOmAOi49e0ukry93Lp1C5MnT+aDgdkbL9cVdN6ydvPmzT5v/9SpUxCJRCgvL+fDwYhEIixYsABpaWmQyWSorq7G2bNncefOHVy7dg1//etfodfrYTAYUFhYCACYMGECzpw5gw0bNmDkyJEYN24cQkNDMWLECD7jnCsQJNKAgABIJBKr0au+vt5qlOMICgqyaS+VSvkLqCsbrk5H2rUHhUIBX19fi8PZEBE/tRIXF9elXWhoKNrb2zFgwAC8/fbbFs9IjvC73/0OISEhkMvlMJlMvfqcnAEXlbCr6Q9XcfHiRf7u5fbt26iqqsLAgQPR2NgIqVQKf39//pnz888/x9atW7F79260tbXhz3/+MxISEnDjxg00NDRAqVRi2rRpEIlEuH//Pi5fvoyqqirodDrExMTgwIEDLjkHQSKVy+WIjY1Ffn6+RXl+fj4/EjxMfHy8lX1eXh7i4uL4b9eubLg6HWnXU2hsbITZbAYR8V5OW4jFYv55NS4uDvv373e4TaPRiOrqakydOhWA++ZHOzNy5EgAHSN8X6HX6/Gb3/wGCoUCTU1N2LZtG4YOHYoNGzbg6NGjMBqN0Ol0+PDDD61uWd955x289NJLOHnyJE6dOsXvxZ0yZQrWr1+PtLQ0jBkzBt/97ncRHx8PIsKCBQvwhz/8wem3v4IXUr7++utIT09HXFwc4uPjsXPnTlRVVWHp0qUAOm4ha2pq8Le//Q1Ahyd3y5YteP311/HKK69Aq9UiJyeH9/YBwKpVqzB16lRs3LgRzz33HA4dOoSCggKcOnXK7naBjmewqqoq1NbWAgDvhAkKCuo2XIgr4W7vvL29+S+lrnjhhReQmZmJQYMGQavV4mc/+5lDbe7btw/jxo0DEeH48ePYu3evQ/U4k1GjRuHkyZO4f/++S+MdGQwGfPXVV/j666+RmpqK5557DgAgk8n4x42wsDAsXLgQb731FogIUqkUP/3pTzFo0CBcvXoVcrkcy5cv5+ucMmUK5syZgyNHjmDq1Kl8XZ0fXyZMmICcnBysWrUK+/btw6efform5uZuv5jthhxg69atFBoaSnK5nGJiYqiwsJB/bdGiRZSYmGhhf+LECZo4cSLJ5XIKCwuj7du3W9W5b98+ioyMJJlMRlFRUaTRaAS1S0S0a9cuAmB1ZGZm2nVeOp2OAJBOp7PL3h5ycnJIrVbTkSNH7LJfsGABqdVqeuutt+jYsWMOtfnGG2+QWq2mpKQkq/+FuzAajZSZmUlqtdqpny8R0a9+9Sv65S9/SYcPH6YVK1aQSqWiQYMG0fjx40mtVtP69eupqKjI4j0Gg4EiIiJIKpXSnj17emzjzp07pFAoSCwW09ChQ2n+/Pm0bNkyeu2110itVpNaraYlS5aQUqnkrzupVEoffvihzfqEXGuC50kfZ5w9T2o2m/H2229DLBZjwYIFdoXFLC8vx3vvvYdhw4bhzJkzeOedd/DUU0/Z3WZlZSV27doFsViM48eP46OPPuKnHtzNO++8A7PZjJ/85CcYM2aMU+osKyvj6xKLxRg1ahSGDh0KX19fPmzp9evX+Tu7zhw/fhxmsxk//OEP7WprxYoV2LJli1X5sGHD8OKLL8Lb25tfNMEtgfzDH/5gM/erkGuN7RtyIWVlZXy0+LCwMLveM2rUKN7JExERgWnTpqGwsBDf+9737Hr/gQMHIBaLodPpkJ+fb3eCpb4gICAA9fX1OH78uNNE+o9//AOrVq2CwWCAv7+/1SPFtWvXuvRsz5gxQ1BbmzdvxsCBA1FWVoZPPvkEBoMBQMcGhtzcXLzyyisYPnw4VqxYgYKCAphMJphMJsdOrBNMpC6Em6MMCgqyihbfHcuWLcOOHTsQFBSEMWPG4Omnn8bWrVuRnp7e7fvo/z3JAwYMwHe/+12PEijQ8aVTX1/vtEgULS0taGtr6/JOgZsTtTWKOoJEIsE777wDAKiqqsK5c+dw9+5dKBQKvPrqq8jNzeXXAHMLb5qbm3HixAl+6acjMJG6iMbGRrS1tUEsFlutlOqJ4OBgfPPNNxg8eDBmz54NiUSCxYsXY8KECd0ukj937hwGDBgAo9GIlJSU3p6C03niiSdw9uxZiMVi6PV6fgubo2zevJm/VTx69ChGjx4NvV6Po0ePQiKRoKGhAQUFBS7ZaD5ixAiLvbkmkwlLlizBzp07MWfOHERGRvI5WrkNBo7Ctqq5iCNHjkAsFsNoNDqUomHKlCn8HN+sWbPw3HPP2Xy26Qw3RUVEFputPYVhw4bBbDbD39+/1ws2Ghsb+cUnZ8+ehVarxV/+8hecOXMGM2bMwJgxY3Dz5k0kJiY6o+s98uqrr2L16tUgInz88cd47733oFar4evri9GjR/eqbiZSF2AymfhN3PY6JR7mRz/6EU6ePIm8vDwQEcaPH4+IiIgu508NBgO/ACI5OdmxjrsYLy8vvo8lJSW9quvtt9/GsGHDYDQa8e6776KxsRGHDh3C/v37kZubC41GY7WRwdX85je/wYIFCywcQVKptNfTTUykLoBzGLW3t/MLCoTi4+OD3NxcnD17Fn//+99hNBoRGhqKv/3tb7zDojNHjhyBTCZDa2srEhISensKLoNbedSbuMPl5eW8Q6aurg4xMTEYOHAg5s6di5iYGMjl8j6PQAF0bGLIzc3FsWPH8IMf/ADvvvuuU/bwMpG6AG4z+pgxY3q182TOnDk4f/482tra8O9//xtAx/rf3//+91a2XPxebsO7pxIfHw+z2QypVOrwYvs9e/Zg8ODBePDgAZ555hkn97D3xMXF4YsvvsD69et75TDiYCJ1Mvfv3+djzAp1GNkiNjYW58+fR3BwMO7cuQNvb2+cOnXKYu/ttWvXeE/uj3/841636UomTZrEfz6ObGgwmUxoa2sD0DGieqKDzNkwkTpAQ0MDHjx4YPO1y5cvQyQSwdvb22mLCIKDg7F3715+p0VsbCyWLFmCXbt2obW1FZs3b4ZYLEZra6tF9m5PRCKR8PPAjgRfu3btGhQKBVpaWrBgwQKPvmtwFkykAikqKsKIESMQFRVlM3AYty3t+9//vlPbFYvF2LRpE0pLSyESifD8889j9erViIyM5B0VvfUi9hWxsbEAvt1nKwTutv7OnTuYP3++U/vlqTCRCuDQoUNYtWoVVq1ahcWLFyMzM9Mi0oDBYOAX97tik3VMTAz8/PzQ2NgIf39/vPzyy5g7dy4GDBiA5uZmvPjii05v0xWMHz8egGObpbnPd+HChf1iFAWYSO2mvb0dJSUleOaZZ6BQKCCRSDB06FDk5eXxNtyOF6VSyedAcTa//vWv8dFHH6GhoQE+Pj68t1Qqlfa4y8ZT4Cb3uX2Z9tLa2sp7dT19i6IzYSK1E41Gw4+aWq2WF6dWq0V7ezsA4MaNGwA6PLCu+pYPCQnBxo0b8cEHH+Dq1aswmUwoLCy02LLn6XReaSQk1nBNTQ2Ajm1nrkzJ4WkwkdrJpEmTAHTs1VywYAE/XykSifgYrVxERC6ejqtITU3Frl278PHHH+O3v/0tRo4c6fEOo4fhnqOFOI/clXTK3TCR2kloaCgGDx6Mt956i992xs2BFRQUoKmpiZ/36wvBpKWl4fTp01i1ahXef/99l7fnbDihCYmOyAU7d/WXoKfBRCqAn//850hLS+P/TkxM5EM8cukLhwwZ0mfrZsePH4/3338f3t7efdKeM+E80famRGxpaeE/a0fWQj/KMJH2Arlczi/B455BudtiRvdwGcAB2LXnMi8vD2KxGCKRCAEBAa7smsfBRNpLZsyYAbFYDCKCWCwWFDG/P6NSqfhYR1z85a4gIv55nwto1p9gIu0lYrEY69atw8iRI7F27Vq3RYl/1BCJRPxjQU8RBBsaGtDe3g6TyYS5c+f2Rfc8CnZFOQGpVIqf/exnj8w8pafAZQzoyXnELWCQyWSP5PN3b2EiZbiNyMhIAN+KsCu4+VF3pW50N0ykDLfBxULmYvF2BbdI5FGbC3YWTKQMt9F5eWBLS4tNGyJCQ0MDAPR5pAVPgYmU4TakUimUSiUAdJkdr6GhgfcC97epFw4mUoZb4faWdvVcymUHHzx4cL/1nPfPs2Z4DNwSv+vXr9t8nUsS3V+fRwEmUoab4ZxHXDb0zhgMBn6hg7Mi3j+KMJEy3Ap3u6vX662WB54+fRoSiQTt7e18Bu7+CBMpw61wUd5FIhHvxeU4duwYgI4dM/0lCoMtmEgZbkUkEvFCPXnyJF9eXV3N58/pj0sBO+OQSLdt24bw8HAolUrExsbycWa7orCwELGxsVAqlYiIiMCOHTusbDQaDaKjo6FQKBAdHc0n2xHSLhFBrVYjODgYXl5emDZtGr9RmOG5cHtLz507x5ft37+fzxnDhYjprwgW6d69e5GRkYE333wTxcXFSEhIwKxZs1BVVWXTvqKiArNnz0ZCQgKKi4uxfv16rFy5EhqNhrfRarVITU1Feno6SkpKkJ6ejpSUFIt/mj3tbtq0Ce+//z62bNmC//znPwgKCsIzzzzT5UQ5wzPgApMRESorK2Eymfhb35iYGHd2zTPoMc3wQzz11FO0dOlSi7KoqChau3atTfs33niDoqKiLMqWLFlCkydP5v9OSUmhmTNnWtgkJydTWlqa3e2azWYKCgqi7Oxs/vW2tjZSqVS0Y8cOu87NFZm+GT1jMBho/fr1pFarafHixbRmzRpSq9X0xhtvUHt7u7u75xKEXGuCRlKDwYCioiKryOxJSUn8fNbDaLVaK/vk5GRcuHABRqOxWxuuTnvaraioQF1dnYWNQqFAYmJil33T6/Vobm62OBh9j0wm44O5SSQSfs5UpVL12wUMnRH0Cdy9exft7e2825wjMDCwy2VddXV1Nu1NJhM/B9aVDVenPe1yP4X0LSsrCyqVij+4rVOMvmfSpEkwGAwICQnB2LFjAQAvvPCCm3vlGTj0NfWwO5z+f22lEPuHy+2p01k2HOvWrYNOp+MPR3KTMJzD3LlzLZx8jY2N/XqVUWcEpUAOCAiARCKxGpnq6+utRjCOoKAgm/ZSqRSDBg3q1oar0552uZUrdXV1FvFzuuubQqHodbZphnOQSCSYN28etmzZArPZjDVr1ri7Sx6DoJFULpcjNjaWzyjNkZ+f32VE8fj4eCv7vLw8xMXF8ZEMurLh6rSn3fDwcAQFBVnYGAwGFBYW9qto548ynRMw9Zc8L3Yh1Cu1Z88ekslklJOTQ2VlZZSRkUHe3t5UWVlJRERr166l9PR03v7GjRs0YMAAWr16NZWVlVFOTg7JZDLav38/b3P69GmSSCSUnZ1NV65coezsbJJKpXT27Fm72yUiys7OJpVKRQcOHKDS0lJ64YUXaOjQodTc3GzXuTHvrvupqamhpqYmd3fD5Qi51gSLlIho69atFBoaSnK5nGJiYqiwsJB/bdGiRZSYmGhhf+LECZo4cSLJ5XIKCwuj7du3W9W5b98+ioyMJJlMRlFRUaTRaAS1S9QxDZOZmUlBQUGkUCho6tSpVFpaavd5MZEy+goh15qIqJu4Ff2M5uZmqFQq6HQ6Pg0Cg+EKhFxrbBKKwfBwmEgZDA+HiZTB8HAEzZM+7nCP52x5IMPVcNeYPS4hJtJOcLtl2PJARl/R0tIClUrVrQ3z7nbCbDajtrYWPj4+To0E0NzcjJCQEFRXVz+WXmN2fsIhIrS0tCA4OLjHTQRsJO2EWCx2aSwdX1/fx/Ii5mDnJ4yeRlAO5jhiMDwcJlIGw8NhIu0DFAoFMjMzH9sdN+z8XAtzHDEYHg4bSRkMD4eJlMHwcJhIGQwPh4mUwfBwmEgZDA+HidRJVFZW4qWXXkJ4eDi8vLzwxBNPIDMzEwaDwcKuqqoKP/rRj+Dt7Y2AgACsXLnSyqa0tBSJiYnw8vLCsGHDsGHDBrsWYrsDoSlHPIWsrCx873vfg4+PD4YMGYJ58+bh2rVrFjZkR9oSvV6PFStWICAgAN7e3pg7dy5u3brl3M66KjxEf+Ozzz6jxYsX09GjR+n69et06NAhGjJkCP3iF7/gbUwmE40dO5amT59OFy9epPz8fAoODqbly5fzNjqdjgIDAyktLY1KS0tJo9GQj48Pvffee+44rW7h4k598MEHVFZWRqtWrSJvb2+6efOmu7vWI8nJybRr1y66fPkyffnll/Tss8/SiBEj6N69e7xNdnY2+fj4kEajodLSUkpNTbWKmbV06VIaNmwY5efn08WLF2n69Ok0fvx4MplMTusrE6kL2bRpE4WHh/N/f/rppyQWi6mmpoYv2717NykUCj7WzbZt20ilUlFbWxtvk5WVRcHBwWQ2m/uu83YgNOWIJ1NfX08A+LhZ9qQtaWpqIplMRnv27OFtampqSCwW0+eff+60vrHbXRei0+ng7+/P/63VajF27FgEBwfzZcnJydDr9SgqKuJtEhMTLVa3JCcno7a2FpWVlX3W955wJOWIJ6PT6QCA/3/Zk7akqKgIRqPRwiY4OBhjx4516mfAROoirl+/jj/+8Y9YunQpX2YrnYafnx/kcrlFugxbqTK41zwFR1KOeCpEhNdffx0/+MEP+BQX9qQtqaurg1wuh5+fX5c2zoCJtAfUajVEIlG3x4ULFyzeU1tbi5kzZ+KnP/0pXn75ZYvXbO1TpYdSYdiTlsNTEJpyxBNZvnw5Ll26hN27d1u95sj5OfszYPtJe2D58uVIS0vr1iYsLIz/vba2FtOnT0d8fDx27txpYRcUFGSRcxXoyHliNBot0mXYSqcBWH+ruxNHUo54IitWrMDhw4dx8uRJi73E9qQtCQoKgsFgQGNjo8VoWl9f79ysCU57umXQrVu3aOTIkZSWlmbTu8c5jmpra/myPXv2WDmOBg4cSHq9nrfJzs72WMfRsmXLLMpGjx79SDiOzGYzvfbaaxQcHEzl5eU2Xw8KCqKNGzfyZXq93qbjaO/evbxNbW2t0x1HTKROoqamhp588kmaMWMG3bp1i27fvs0fHNwUzNNPP00XL16kgoICGj58uMUUTFNTEwUGBtILL7xApaWldODAAfL19fXoKZjuUn94KsuWLSOVSkUnTpyw+F89ePCAt7EnbcnSpUtp+PDhVFBQQBcvXqQZM2awKRhPZdeuXQTA5tGZmzdv0rPPPkteXl7k7+9Py5cvt5huISK6dOkSJSQkkEKhoKCgIFKr1R43inL0lPrDU+nqf7Vr1y7exp60Ja2trbR8+XLy9/cnLy8vmjNnDlVVVTm1r2w/KYPh4TDvLoPh4TCRMhgeDhMpg+HhMJEyGB4OEymD4eEwkTIYHg4TKYPh4TCRMhgeDhMpg+HhMJEyGB4OEymD4eH8H1JDzAAMQsc0AAAAAElFTkSuQmCC\n", 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\n", 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\n", 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cTHJzc+2403tpqFQqejQ3N7ONzu2kqqqKFBQUkC+++MLss6tXr5KCggKLm97bS2FhIcnKyiJ6vX4oxXxgEbLRuSCPrNFoUF1djZSUFN75lJQUq1WcqqoqM/vU1FScOXMGWq3Wpg2Xpj35NjQ0oL29nWcjk8mQnJxsVrY9e/YgKCgIEydOxPLly808tjFr166FXC6nR1hYmFVbd2bHjh209vPxxx/bdY0r28h9fX3o7+/HqFGjcOnSJYfScCfM6zw26OzshF6vR3BwMO98cHAw2tvbLV7T3t5u0V6n06GzsxMhISFWbbg07cmX+2vJ5vr16/T9woULoVQqoVAoUF9fj5UrV6K2thZlZWUWy79y5Urk5+fT993d3T9IMX/wwQe4dOkSfHx88Nvf/hbPPfccxo8fb/MarmrtCiEbP2/Xrl1DVFSUQ+m4C4KEzMEFvHMQQszODWZvet6eNJ1hk5WVRV9HR0cjIiIC8fHxOHv2LGJjY83KLpPJePsV/RA5ceIEJkyYgC+//BJNTU0YN24cNmzYgE8++cTmdfZ4ZK1WC71eb9HGFsZCttY/80NCkJCDgoIgFovNvG9HR4eZJ+RQKBQW7T09PREYGGjThkvTnnwVCgWAgX9wSEiIXWUDgNjYWEgkEly9etWikBnAV199hSlTpmDKlCn0XFtbG27fvo2AgACr19kSsvGPY19fH430spfvvvuOvu7q6hJ0rTsiqI0slUoRFxdnVg0tKytDYmKixWsSEhLM7A8fPoz4+Hi6mZc1Gy5Ne/LlqsvGNhqNBhUVFVbLBgDnz5+HVqvliZ9xj//7v/+zOPk/JCQEO3bssHkt1wfC/Z+N8fDwoAI3rV5v2LBh0HLduHGDvtbr9T/48WjBVev8/HxkZmYiPj4eCQkJ2L59O5qampCdnQ1goE3Z0tKC3bt3AwCys7OxefNm5OfnIysrC1VVVdi5cyf27t1L08zNzcX06dOxbt06zJ8/HwcPHkR5eTlOnDhhd74ikQh5eXlYs2YNIiIiEBERgTVr1sDHxwf/+q//CmCgLbVnzx6kpaUhKCgIFy5cwLJlyxATE4OpU6c6/i26IZcvX8bx48dx/PhxPPbYYxZt6urqbKZha/gJGBC4qQgJISgsLMSSJUusXqfX66FWq3lNpsuXL+Opp56yWR53RrCQ09PTcevWLbz//vtoa2tDdHQ0Dh06hLFjxwIYqHIZt1mUSiUOHTqE119/HVu2bEFoaCg2bdqEf/7nf6Y2iYmJ2LdvH9555x28++67CA8PR3FxMSZPnmx3vgCwYsUK9PX14bXXXkNXVxcmT56Mw4cPw8/PD8CAZz9y5Ag+/vhj3LlzB2FhYZg7dy5Wr14tuI3m7vzHf/wHNmzYgHfffRcAcOHCBVRUVKC/vx+jR4/Gz3/+c4wZMwadnZ0ICgqymAYnZEseGRgYS+7v7+cFhVy9ehUdHR04f/48nnzySYvXdXZ2UhH/6Ec/ws2bN7Fhwwb84Q9/cPh+H3ZExFK9iWGV7u5uyOVyqFQq+Pv7D3dxXIJWq0VOTg5CQ0PpuaSkJHzwwQc4evQo3nvvPfT09EAul+Opp57C/PnzLaZTXFyMS5cuIS0tDT/5yU/MPt+xYwdaW1vxwgsvUNEWFhbis88+w5tvvolFixZZTLe2thZffvklfH19MWHCBJw5cwZnzpzBF1984VYdk0KeNRZrzTDj8OHDPBHX1tbi6aefxuuvv45NmzbhN7/5De14vHr1qtV0BvPIxlMZ1Wo1VqxYgSNHjmDhwoW4fPmy1XQ7OjoADLTTR48eDQCIj4/nRQs6k4dhXTEmZIYZxvHpXV1dmDhxIgBg3rx5WLJkCQDghRdeAAD09vZSwZrCdXZZa+tyXqa3txeVlZX43e9+h2nTpgEAfH196fWmtLW1ARhotv34xz+m5//3f//XrvsTwrp16zB37lzcvHnT6Wk7EyZkBo/e3l663nRRURH+9re/oaCgwMxu7ty5dHjJWmTcYB6Zm5NcV1eHP//5z/D19eV9bk2YnZ2dAIDAwEB4e3vTNvaIESPQ0tJi6/YEcfr0aXz33XeYNm0aNm7c6LR0XQETMoPH9evXIRKJ0N/fj9zcXLz33nsWOwJ9fX3povPd3d0W0xrMI3PCvXLlCkpLSxETE8P7/Ny5c2bXEEJw584dAKBj2E8++SS6u7shkUhQVVVlx10OjsFgwP/8z//QqrtUKsWhQ4eckrYrYEJm8OCqkEqlEm+++SZvdMEULqDHUY/MtZHVajWmT5+OZ555xmJZjLl79y4d1x4xYgSAgdqBSqUCwA8UGQrXrl0zixI8efLkA7ugPhMyg8f3338PAIiMjLQZdguAdog56pEfffRRAMCYMWPw+OOPAxjwzmfPngUAi21vLopLKpXSdP38/LBw4UIYDAao1Wqbk2Ds5S9/+QsAoLW1lTY1JBIJjh8/PuS0XQETMoMHJwJ7hta48XlHPbJp6KxGo8H+/fuxfPlyGAwG+Pj48MIv7969izfeeAPAvdoAR3JyMvXKQ+2Y6u7upmm98847WLduHf2xcvZCBmq12ikdaUzIDB6cR+ZEagtO7IN5ZGtCFovFtMMLGBj2CgkJQVJSEu3Qam1tpZ+vXbuWRoGZzrzy8PCg5bl9+/agZbcGIQRffPEFRCIRdDodnemWmZkJg8EAjUYzpPRNWbdunVPa9UzIDB6OeGRLDzYhZNAQTQAICwuDVqvF3bt3cfHiRfz0pz8FcG+ihbG3Ki8vp8IaM2aMWVpPPPGE1fLYw9GjR/Hkk0+iubkZwMCyUBxPPfUUvZ/6+nqH0jdFrVZDpVKhrq4OFy5cGFJaTMgMilarpcEPQ/XIxnsjW/PIABAREYHNmzfjk08+wZQpU7B+/XoA96rO3JixRqOBv78/DQcdNWqUWVrcDLiamppBy25Kf38/KioqaOceIcSsFz0pKQkA8Le//U1w+pb45ptv4O/vD51OR7fQcRQmZAaF88ZisdiuUEfjyCzT3lzjYA5bHvmJJ56ATqfD+PHjUV5eToe0JkyYAOCeR66srKSx91qt1mzMGbgn/lu3bgEYGEIy5fDhwxbL8d133/H2cB49erTZFE0uzFSv1zules1Fr/n7+1udmGIvTMgMCidkPz+/QXusgXtCJoRArVbzPuOELBKJbE5IkUqlSE9Pp1VqDm5uONdmP3XqFMRiMVpaWngTZYwZN24cgIHx6c8//5xO+OD46quv8PLLL1vsDeeq083NzRCLxfjlL39pZhMYGEgXQcjOzsZ7771n9b44rIV3EkJoqOns2bMHTWcwmJAZFK6KzA0LDYZEIqEi5YI0ODgh21Nl/NWvfmW2JDFXBp1OB0IILdvp06dpFdcUHx8f6lU///xzfPnllwCAixcvYuPGjfj000+RlZVFh5aMuXjxIgBg6dKleOedd+gqn6ZwPxYTJ07EpUuXcPToUauhpAaDAVOnTrU4+UOlUtHmR2RkpMXrhcCE7KaUlpait7dX0DXGHtleuCV7TIXMeSJ7hJyUlETjuTnkcjkIIRCLxejt7aXtbKlUSsecLcFFYs2cORPjx4/HkiVLMGPGDFy8eJHOV66treU1Bfr7++kPhTVvz/Ev//Iv1JNOnDgRR48exbRp0zBnzhyzlUp2796N2bNnQ61W4/Tp07zPuBpAYGCgzaaHvTAhuyGnTp1CXV0dtm3bJigSiXuYhQiZsx2KkC0hkUjoA15RUQFPT09oNBqUlpbarPYbe+v4+Hi0tLTgtdde483mAu7N2tLpdPjTn/4EkUgEb29vyOVym+Xy9/dHdHQ0DVqRSCRIS0tDQkICdu3aRe10Oh2uX78OmUyGiIgIfP3117z/BRdYEh4ebs/XMShMyG6GwWCgHTq9vb2CJhE44pG5MElrQrbVYz0YXBv866+/BjBwb5Z6q40JDw/n1URMFyfg2scrVqxAc3Mz5s6dS1c64YavBmPRokUYOXIkDRrh6OnpoYtqNDQ08D4z7iAzGAz4xz/+AcA51WqACdnt+O6773i9tUJWmOSqhkIWTLAW3SWkjWwNbqiJ643mqs22EIlEiImJwaZNm3grj1RXV6OsrIx2LMXExCA9PR2JiYl49NFHodForLa9TZFKpdixYwc++ugjNDY2or6+nvaul5aWAgBdpkqv19NedO5/0djYCLFYDIPBMGhV3l6YkN0MU0/AtcXsgROyEI/MeU1nV60B82pndHS0XddNnjwZt2/fRllZGY4ePYqioiIolUpUVlZi0qRJ1C41NZW+9vX1FbySp0gkwq5du7B//37827/9G4CB77C7u5suDvjiiy/iueeeA3AvvJPbCik0NJQ35DUUmJDdjL///e8A7o15Xrt2za52sl6vpx5ssHaiMdzDbzqu6gwhmwrXtEPMGuPHj8f27duxZs0aHD9+HB988AE+/vhjiEQieHl5wdvbmxew0tnZiVdffdXhcgIDw2UajQYeHh4oKSmBwWAAIQRKpZL2dLe1tUGn0+HKlSsABuLDnQUT8jDhiulwer2eVvEmTZoEkUgErVZrV/Waq/6JxWKHPDI33svhjDayn58fr3psOlHCFllZWXjmmWfw17/+FS+//LLZZ42NjfjLX/6Cc+fOISYmhrb1HUUkEtHVTbjve+TIkRCLxTSsVCKR4LPPPgMw8P8fbKcOITAhDxOffvopFi9ezOvpHCo3b94EIYQu/s/FI+/evZsK1da1wMD4rT3BIBzG0V3GOMMjAwM/EDqdDh4eHoLKBQxMpJgzZ47Z+REjRuCzzz7DwoULsX37djOhO4rpksrccJeXlxcdMuNCTv39/Yf83RjDhDwMqFQq3LhxA2FhYTh//rxT5s8CoD2hwcHBEIlE9CE2GAzIycmhVTpLcGOj9nQoGcNVw/V6PS8wYrCZT/YSHh6O3/72t3a3j+3F09MTeXl5+NGPfuS0NP39/Xm1GeOdS9LS0ni2xrt2OAMmZCdj2tlkiXPnztEH3M/PDwcPHnRK3tzKltwQTXBwMJ0mGBkZiTVr1gx6ra3tdSzh5eVFPaXxcIyzPPL06dMBwGYQyIPEL3/5S9TX1yMkJIQXHebv70+bBgaDAf/0T//k1HyZkJ1IZ2cntm3bhjfffNNmG7ixsZH3/u9//7vVlSiFYOyRgYF224svvkirvUqlEtu2bbN4LTfvd+TIkYLy5AIpAH472VlCjo2NRXBw8EMj5JEjR+KnP/0pfv3rX5t9lpWVhT179iA8PFxwD/lgMCE7CUII9u/fD19fX/j4+KCystKqHTc00dfXR2OJubbTUPLnxGi8j1VwcDCdSQRY/tHQarV0+EioRwbuBYUYe2Suk4oTuaN4enpiz549D9VOINwWRabIZDJ8/fXXVhf0HwpMyE5CJBJh5syZ9H1lZaVFr6xSqaiQPvjgAzpWarzPlSP09PTQGUim7b6XXnoJPj4+UKvVeOSRR7Bp0ybe51z7WCwWO+QpOC9uvAgAt87VUIUMALNmzRpyGg8KSqVS0PCevTAhO5HIyEhMnz4dOp0OOp3O4rAP53kDAgIgFovx9NNPAxhYdI57+B2Ba+P6+flZDMJ/44036Iyi27dv87wyVyUXMrxjDHed8f1yYZLOEDJjcJiQnczMmTNpe662ttbsc05w3NAQN8YIAO+//77D+XLp2up1fvHFF6HRaCCRSHhl44JIlEqlQ3lzPxDcOlvAveEoJuT7g0NC3rp1K5RKJby8vBAXF2e1PchRUVGBuLg4eHl5Yfz48SgqKjKzKSkpQVRUFGQyGaKionDgwAHB+RJCUFBQgNDQUHh7e2PGjBm87U+AgXWSlixZgqCgIPj6+uL555/n7bXrDLioKksemRMctyyNcZXc29vbbLqbvXCTI4x/GExRKBS0DWwsZG7SgKN7RHMx0Wq1GoQQEEJoZxcT8v1BsJCLi4uRl5eHVatWoaamBklJSXj22WetRg81NDQgLS0NSUlJqKmpwdtvv42lS5fyNtyqqqpCeno6MjMzUVtbi8zMTCxYsIDGpNqb74cffoj169dj8+bNOH36NBQKBWbPns0bp83Ly8OBAwewb98+nDhxAnfu3MG8efN4IXtDhRPErVu3zCadcz8anJCBgSEWzoMdOXLEoagvbtjLOF1LcIvANzc3gxAClUpF83NUyIGBgSCEwMPDA729vbxoLGsT9BnORbCQ169fj0WLFmHx4sWYMGECNm7ciLCwMKvDGkVFRRgzZgw2btyICRMmYPHixXj55Zfx+9//ntps3LgRs2fPxsqVKxEZGYmVK1di1qxZvP12BsuXEIKNGzdi1apV+NnPfobo6Gj84Q9/wN27d/GnP/0JwEBH086dO/HRRx/hmWeeQUxMDP74xz+irq4O5eXlQr8Kq/j5+dE1r7j2JzDQAcS1g017hwsKCugC68bX2ENPTw/9wRis19l4GKepqQmlpaUQi8WQSCSCh544PD09aa/yq6++ig8//BDAQOeZMybNMwZHkJA1Gg2qq6uRkpLCO5+SkmJ14e6qqioz+9TUVJw5c4Y+fNZsuDTtybehoQHt7e08G5lMhuTkZGpTXV0NrVbLswkNDUV0dLTV8qvVanR3d/OOwRCJRLSKazysxAn0kUceMfNUXl5e1CMO1lQx5dKlSwAG2qrG60RbQiwW00XlFi9eTJsexlFIjsBFNF25coXGE7Nq9f1DkJA7Ozuh1+vNfvWDg4Np28+U9vZ2i/Y6nY52jliz4dK0J19rkUmmNlKp1CxA3lb5165dC7lcTg9bbVBjOFEaC5lrl3KzYUzhqr1XrlzhVa+XL19uM/qLW2fZ3onx8fHxAIBp06bBz88PBoOBN3TmCFyVfuTIkfS1M8MfGbZxqLPLNHidEGIzoN2Svel5e9J0lo0ptmxWrlwJlUpFD3vn93JC5trEWq2WCtl01QoOrtdYp9PRsd36+nr4+fmhurqa1yvModfraZnsjX56/PHHeYsPjBs3zq7lb23BrXQRGxtL44qt/WAxnI8gIQcFBUEsFpt5r46ODqttM4VCYdGem6Fjy4ZL0558OS8wmI1GozFbJM1W+WUyGfz9/XmHPXBCvnnzJrRaLV2l0db0NbFYTK/jhoS4ZW7EYjGOHj1qdk1DQwNdpM50XSprBAQEYNSoUTh06BC++eYbpKen23WdLX784x9Dr9fzJsrbO3+YMXQECVkqlSIuLg5lZWW882VlZUhMTLR4TUJCgpn94cOHER8fTycOWLPh0rQnX6VSCYVCwbPRaDSoqKigNnFxcZBIJDybtrY21NfXWy2/o8jlctoO/vbbb3HkyBEAA9VfW6tCcOPLX375Jbq6ungrb1y8eNFsuiC3jWh4eLigaX7z58/Hm2++iWXLlg3arrYHHx8f3jDe9evXHQ4wYQhHcJdifn4+MjMzER8fj4SEBGzfvh1NTU3Izs4GMFAVbWlpwe7duwEA2dnZ2Lx5M/Lz85GVlYWqqirs3LkTe/fupWnm5uZi+vTpWLduHebPn4+DBw+ivLycF7Y4WL4ikQh5eXlYs2YNIiIiEBERgTVr1sDHx4fGvsrlcixatAjLli1DYGAgAgICsHz5cjzxxBNme/MOFZFIhClTpuDYsWO8dZTj4uJsXjdhwgScOnUKPj4++M///E9a7u7ubhBC8O2339IdF4B76zELnU0THByMn/3sZ4KuGQyFQoGPPvoIQUFBSEhIcGraDNsIFnJ6ejpu3bqF999/H21tbYiOjsahQ4foImJtbW28sV2lUolDhw7h9ddfx5YtWxAaGopNmzbxNtBOTEzEvn378M477+Ddd99FeHg4iouLeQ/sYPkCAysj9vX14bXXXkNXVxcmT56Mw4cP8+aIbtiwAZ6enliwYAH6+vowa9YsfP755y4Jyk9MTMSxY8foe19f30EXWxs7diy8vLzQ399PmwCTJ0+GXq/HkSNHcObMGfq99PT00F50R6OynMlTTz2Fnp4e9PT04PXXXx/u4vygEJEHdQv2B5Tu7m7I5XKoVCq72suVlZU4evQobt++jcLCQruGZDo6Onjj8kuWLIFEIsFHH30EkUiE3NxcPProoygpKUF9fT0CAwORk5MzpPtyBv/4xz8watQohIaG4ptvvrG7zc6wjJBnjY3Wu5ikpCRER0cLWhNq5MiRGDNmDJqamuDv70/HfYODg9HR0YELFy7A29ubDjtxEy+Gm+DgYJw4cQKNjY1MxPcZ5pEFItQjO4per0dGRgZ27NhBJyWcPHnSrMPPy8sLK1asELyeFePBh3lkN0AsFmPv3r28EEfTYSsvLy+88cYbTMQMNo3xQcY0Tjk4OJh2aun1erz88stOW+Cc8XDDPPJDhPEaXBqNxiUrTTAeTpiQH0K8vb3ZhAQGD1YvYzDcACZkBsMNYEJmMNwAJmQGww1gnV0C4eJn7FkphMEYCtwzZk/MFhOyQLiF/OxdKYTBGCo9PT2DDjWyEE2BGAwGtLa2ws/Pz2kRVd3d3QgLC0Nzc7NLwz6HE3e/R1fcHyEEPT09CA0NHTTwh3lkgXh4eAjeetRehKxA8rDi7vfo7PuzN+iHdXYxGG4AEzKD4QYwIT8AyGQyrF69esgrWT7IuPs9Dvf9sc4uBsMNYB6ZwXADmJAZDDeACZnBcAOYkBkMN4AJmcFwA5iQ7yONjY1YtGgRlEolvL29ER4ejtWrV0Oj0fDsmpqa8Nxzz8HX1xdBQUFYunSpmU1dXR2Sk5Ph7e2NUaNG4f3333dog/T7wdatW6FUKuHl5YW4uDjB28YOF2vXrsVPfvIT+Pn5YeTIkXjhhRdw+fJlng0hBAUFBQgNDYW3tzdmzJhBt6rlUKvVWLJkCYKCguDr64vnn3+et72OUyCM+8Zf//pX8qtf/Yp8/fXX5Nq1a+TgwYNk5MiRZNmyZdRGp9OR6OhoMnPmTHL27FlSVlZGQkNDSU5ODrVRqVQkODiYZGRkkLq6OlJSUkL8/PzI73//++G4LZvs27ePSCQSsmPHDnLhwgWSm5tLfH19yfXr14e7aIOSmppKdu3aRerr68m5c+fI3LlzyZgxY8idO3eoTWFhIfHz8yMlJSWkrq6OpKenk5CQENLd3U1tsrOzyahRo0hZWRk5e/YsmTlzJnnyySeJTqdzWlmZkIeZDz/8kCiVSvr+0KFDxMPDg7S0tNBze/fuJTKZjKhUKkIIIVu3biVyuZz09/dTm7Vr15LQ0FBiMBjuX+HtYNKkSSQ7O5t3LjIykrz11lvDVCLH6ejoIABIRUUFIYQQg8FAFAoFKSwspDb9/f1ELpeToqIiQggh33//PZFIJGTfvn3UpqWlhXh4eJCvvvrKaWVjVethRqVS0Z0kAKCqqgrR0dG8nRpSU1OhVqtRXV1NbZKTk3lRRKmpqWhtbUVjY+N9K/tgaDQaVFdXIyUlhXc+JSUFJ0+eHKZSOY5KpQIA+v9qaGhAe3s77/5kMhmSk5Pp/VVXV0Or1fJsQkNDER0d7dTvgAl5GLl27Ro++eQTuqMkMLC/s+lezSNGjIBUKqV7P1uy4d6b7g89nHR2dkKv11ss64NUTnsghCA/Px/Tpk1DdHQ0gHvfta37a29vh1QqNdsyyNnfAROyEygoKIBIJLJ5nDlzhndNa2sr5syZg5///OdYvHgx7zNL85wJIbzzpjbk/3d0PYi7Tlgq64NYTlvk5OTg22+/5W0HzOHI/Tn7O2DzkZ1ATk4OMjIybNqMGzeOvm5tbcXMmTPpPs/GKBQKnDp1ineuq6sLWq2W/vIrFAqzX/OOjg4A5t5hOAkKCoJYLLZY1gepnIOxZMkS/PnPf8bx48d5c9EVCgWAAa8bEhJCzxvfn0KhgEajQVdXF88rd3R0IDEx0XmFdFprm2EXN27cIBERESQjI8NiryXX2dXa2krP7du3z6yz69FHHyVqtZraFBYWPrCdXa+++irv3IQJEx6Kzi6DwUD+/d//nYSGhpIrV65Y/FyhUJB169bRc2q12mJnV3FxMbVpbW11emcXE/J9pKWlhTz22GPk6aefJjdu3CBtbW304OCGn2bNmkXOnj1LysvLyejRo3nDT99//z0JDg4mv/jFL0hdXR0pLS0l/v7+D/Tw086dO8mFCxdIXl4e8fX1JY2NjcNdtEF59dVXiVwuJ8eOHeP9r+7evUttCgsLiVwuJ6WlpaSuro784he/sDj8NHr0aFJeXk7Onj1Lnn76aTb89DCza9cuAsDiYcz169fJ3Llzibe3NwkICCA5OTm8oSZCCPn2229JUlISkclkRKFQkIKCggfOG3Ns2bKFjB07lkilUhIbG0uHbx50rP2vdu3aRW0MBgNZvXo1USgURCaTkenTp5O6ujpeOn19fSQnJ4cEBAQQb29vMm/ePNLU1OTUsrL5yAyGG8B6rRkMN4AJmcFwA5iQGQw3gAmZwXADmJAZDDeACZnBcAOYkBkMN4AJmcFwA5iQGQw3gAmZwXADmJAZDDfg/wEg+9nU6dZHeQAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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1Y9TW1iI3Nxd5eXk4evQo8vLysHz5cnzyySeqjvv000/j2WefxfPPP49PP/0UZrMZd9xxh1smc/Xq1WhtbWWPF198Ue3PcFVx7tw5AMDcuXOxZMkS0XvvvPOO7KR33jyyr6G1sBiEFprQ1wAXsiKeffZZrFy5EqtWrUJSUhJKS0sRFxeHF154QdJ+x44dmDp1KkpLS5GUlIRVq1bhwQcfxDPPPMNsSktLcccdd6C4uBiJiYkoLi7GwoULUVpaqvi4hBCUlpZi48aNWLp0KVJSUvDqq6+ir68Pb7zxhqhN4eHhMJvN7GEymWS/r9VqRXd3t+hxNWG329Ha2goAmDp1KtLS0lBQUMDedzgcskUiWntk6o0pVMhU6FcTqoRss9lQV1eH7Oxs0fbs7GwcPnxY8jO1tbVu9jk5OThy5Ai7IsvZ0H0qOW5jYyPa2tpENkajEVlZWW5te/311xETE4MbbrgBjzzyiMe+xy1btsBkMrFHXFycrG0g0traCkIIQkNDMWHCBADAxIkTsWnTJjYzh9ykd1p5ZPo5YZUYcPk+mXtkL3R0dMDhcLAZGSixsbFoa2uT/ExbW5ukvd1uR0dHh0cbuk8lx6V/vbXt/vvvx65du3DgwAE88cQTqKysxNKlS2W/c3FxMbq6utiDhplXC++88w4AYNq0aaIwFgATttzk8Fp5ZCpkuh/K1Rxah3g3ccf1H0oIcdvmzd51u5J9+sNm9erV7HlKSgoSEhKQnp6O+vp6pKamurXdaDS6hXBXC06nk11sqfcVQoXsLbTWyiNzIV9GlUeOiYlBcHCwm/dtb29384QUs9ksaR8SEsJmdpCzoftUclw63YuatgFAamoq9Ho9vv76a1mbqxVhldTs2bPd3qdjgeVmr5QLremF0VfByYXWXMgKMRgMSEtLQ3V1tWh7dXU15syZI/mZjIwMN/uqqiqkp6ezK6qcDd2nkuPGx8fDbDaLbGw2G2pqamTbBgAnTpzA4OAgJk+e7OmrX5XQ3EFkZCQTrRB6TyqXXJILrcPDwwH4Pn0tDcm5R76M6tC6qKgIeXl5SE9PR0ZGBl566SU0NTUhPz8fwNA9ZXNzM1577TUAQH5+Pp5//nkUFRVh9erVqK2tRXl5OXbt2sX2uW7dOsybNw/btm3Dvffei7fffhv79+/HoUOHFB9Xp9OhsLAQTz31FBISEpCQkICnnnoK4eHh+OlPfwoA+Oabb/D6669j0aJFiImJwcmTJ/Hwww9j1qxZmDt3ru+/YoBCJ7ijdcyuUEHKCVnOIw9XyDy0dke1kHNzc3HhwgVs3rwZra2tSElJwb59+zBt2jQAQ1lOYd9ufHw89u3bh/Xr12P79u2wWCz4/e9/j2XLljGbOXPmoKKiAo8//jieeOIJzJw5E7t37xaFc96OCwCPPvoo+vv7sWbNGnR2dmL27NmoqqpiI2MMBgPef/99/Nd//Rd6enoQFxeHxYsXY9OmTW4nG0fskaWgHllOkFp5ZC5kd3xKdq1ZswZr1qyRfO+VV15x25aVlYX6+nqP+7zvvvtw3333+XxcYMgrl5SUoKSkRPL9uLg41NTUeDwG5zLePLK30FpqYgHAXcgOhwNffvkl4uLi2LhiT3Ahu8NrrTmy0G4luYIZYWgtNVBBLmtN77ftdjtsNhuqq6uxZ88e/PnPf1bUrrEi5LE0/S4X8lWAw+FAVVWVbBmtHLTrSVhjLUQ4UEHqhJbzyHq9ns0S0tfXx0pxlfYcjAUhDwwMoLS0FC+++OKYGG3FhTxK/PWvf8VLL700IotzHz58GLW1tdi5c6eqz9F7ZDmPHBISwsQkdb8rd4+s0+mYN3e9uLiOtJLCW/fTSJRonjlzBjabDZ2dnfj22281P543uJBHge+++w4ff/wxWltbcfr0ac2PR2ulAeUT3gkXSZO7RwY83yfLZa2By+E1nUKIsm/fPq9tk+t+EhaaaO0lhdVscpVtIwkX8ghjt9tRVlbGXo/ESSA8qZubmxV9pr+/n01XK9WHTPGUgZbzyMLPuZa8nj592qtHlQut6WvhUjVaIZw1xXUGldGAC3mE+dvf/iZ6PRJh2ZdffsmeHzlyRNFnqDcODQ2VFCLFk0f2JGTq5el9+IIFC1jG+h//+IfHtskJWRhqD2f2ESUIB9pwIV9lDA4O4u9//7to24kTJzT1HlLT9CjBW9cTxdN80p5C6+uuu070esaMGZgxYwYA+dFUFDkhBwUFsWP5c5YQYWESRRiBjIWhrVzII8jx48fhcDgQHByMNWvWICQkBDabTdMRVWfPnhW97unpUXThUCpkT0u1ePLIFotF9Praa69VPKWtXLILuCxuf3nkr776Cu+8847bvbywDp175KuMb775BgCQmZmJSZMmMa8kNwTUH1CBzZo1i3X5UJF6gp6cniZdAC4LWWqfcv3IANiAGWBo7DmgfJJ5uWSXcJu/hLxr1y5ERETggw8+EG0XemQ1q29oBRfyCEKv4tHR0QCASZMmAfBtge5z584pSpQJM89qJnH3VgxC8VRvLdePDAx1Qf3bv/0bpkyZgltvvRWA8oXY5EJrwH+LxAEQrU0lnJjf6XSKhNzX1zfqE/5xIY8QTqeThWc0QUSF3N7ermpfFRUVePnll7F9+3avtsJ1hKnHU3JPR8N9OjxUDl+TXQAwefJkrFq1ir1WcqEhhHgUsi8e+d1338WvfvUrvP/+++jo6GC/mTB81ul0LPsv/K60zZ9++qni42mBT7XWHPUI5zSjXozeJ7a0tMBmszFv8v333yMiIkJyQoPGxkacOnUKwNDFYWBggBVCSEE9R3h4uCqPTENrT2O5hd9FbT+yFPRC09vbC6fTyW4FhAin4PWHkB0OB0tAHjp0iCW2br/9dlF+wOFwYGBgAGFhYew3DQ0NRUZGBqqqqhR362kFF/II0NXVxbpZgMuhdXR0NMLDw9HX14dvv/0WcXFxaGlpwR/+8AcAQ0NAXYV08uRJ0euOjg5ce+21sscWCllpCaPD4WBC8HSRAMRZa5rIo3jzyK6Eh4czz9fT0yM5gEJY0CKV7FIbWssJcP/+/W4X0u7ubthsNhZBCaMcJXkHLeGh9QjQ0tLCnt90001MHDqdjk1oQE+OhoYGZuva5wy4e9Py8nKPC6sJV2TwNlqJIhSLt2mOoqKiYDAY4HQ6ReWVhBB2j6lUyEFBQV7vk2nbjEaj5PRSaj0yTUB6OhalpaUFzz33HPbs2QMAGD9+PLuQjXbCiwtZIwgh+Oqrr9DT0yMS2vz580V2dC4sWhgi9CQtLS3svuzs2bM4c+YMu/ILs77l5eWyJ66UkL15ZPq+Xq/3GhbrdDr2HYT3+sIQWM1Yb28T+tG2yUUKaoVMf/fs7Gw8/vjj+MEPfiBrW19fL+q6ozOrAkMX2OEs3D5ceGitEadOncLu3btF2zIyMtiJSqHJJBriCT1uT08POjs7UVtbyyqy6Ik6e/ZsUV1ya2srpk6dKtr34OAgE1R4eLhij+xNLK7ExMTg/PnzIo8sPOGVemRg6Hbj7NmzskIWemQplM7Qef78ebz22mtM8BMmTEBwcDAWLVqEuLg4bNu2DQkJCW6fETJ9+nSYTCYEBwfD4XCgs7NTdIEdSbhH1giakBKSnJzsto2Kr62tDQ6Hwy2kfPPNN0VllfTES0hIwOLFi9l24cAIChVsUFAQDAaD4tFBVCxKhUwTXkJPL/TIUkkrObz1JXsTstKL1d/+9jeR1xZ+1xtvvBGbNm0CMJRQpN1jrkyZMgU6nY6JV0k34tGjR/HrX//arcBkuHAha4Rr9dTkyZMlk1Imkwl6vR5OpxOdnZ0sW+ytWGTcuHFIT0/HvHnzZO1ooissLAw6nU51aK1UyNROeE9JnxsMBo9TJbtC7znlkkfehOxtHjFgyFu7Dp+kvw3luuuug8FgQFJSktt47OjoaMyYMYNFVzQhKUxoyvE///M/cDgcePfdd73aqoGH1hrh+k91XTOJQhNeTU1NOHLkCOx2O3Q6HZKTk0WDB+bMmSNaMYOGq/RkkjrxXZce1Sq0lsqG09BW7ZzgnirFAOVC9jQfWGNjo9uF1vW76nQ6rFq1CpMmTcJXX33Ftuv1ehQUFIguTtQjexOyMNz396g37pE1wOl0isKsqKgoVvwhxU033QQAbKaMqKgo0f1ubm6uaCLCWbNmseeeJsATdj252nqqt5ZbW0kOqXmqhR5ZDd48sre20e8qLOZwhUYvVLzjx4+XnGCQ/s+E3WDjx493izCox/YmTuE5Ybfb/dplxT2yBnR2dsJutyMkJAQbNmwA4Dlz6zqn9vjx4xEdHY3MzEwQQnD99ddDp9Ph8ccfR0tLi6jaylMo6eqRo6KiEBYWhv7+frS1tWHKlCmS7RGG5EqQ8sjePKccQiFLrSTiD49Mb19uvfVWdmviKfwXlqlKrf2ltGLOtYKvtbXVLaHmK1zIGkC7NK655hpFXS+u9cz0qn/bbbeJtgcHB7udSJ48squQdTodoqOj0dzc7LG6i3oKuWlwXZG6R/Y1tKZCdjqd6O3tdRt95S0Rp0TIVHAmk0nR/XtYWBgiIyNx6dIlpKSkuL0vTNB5Wj6psbFR9FpJhZ1SuJA1gGaQvZU3UsLDw1mFFzAkZKXQE9dqtbqVNVLPIxQDne2jt7dX9qQT1mcrwZNHVhtaBwcHM9F0dXXJCtmbRx4cHBSVvQqhv4uSqXcpq1atQkdHB+Lj493eE158+vr6ZH83GlpHRESgt7fXr6E1v0fWAFrJJRe6uqLT6USZUaWfA8SeyTW8llqhkp7of/7zn/Gf//mfom4iitKxyK5tGBgYYAUsvnpk4PKFTGqcrzchG41G1m7XTP5///d/gxDCqrC8jewSEhUVxSY+cCU4OJj9rnLhNSGETZhAoyou5DEOvRdS6pEBsJUgb7jhBrfZMzwRFBQk2T/sdDpZiC+8Bxd6oYiICBw4cMBtn74K2el0MqH56pEBz9l1b0LW6XQsh/Dqq6+Kqq0effRRfP/996xnQI1H9gbtPZCbBXRgYIC1nXZDekrIqYUL2c/09/czIXjKVLty0003oaCgAMuWLVPV7wpI3yf39PTAbrcjKCiIDdIA4FZ55Do/ltLZM4UIi02oR/I12QV4XrBcSdcYvbd3Op347LPPAAAff/wxli5dij/+8Y8Ahn4HqdFTvkIvllKFOcDl++Nx48Z57DL0FS5kP0O9sclkUnUS0wohtSIGpDPXrsUglISEBJFAXU88WjOs0+kUCxm4HKbScJiG1r54ZE/Lriq5QCQlJbHndNL7qqoqmEwmts8bbrhBdbs8QS/acl1QH330EYAhb+yti80XuJD9DE1oSC0MrhVSoahrH7LQtqioCP/xH/8BYCg0F87rRe/jxo8fr6q0kp6c1Jv7wyP7EloDQxer5cuXAxi6sBBC3Oa5Fi7+5w9o1CMc6Uax2Wxs+/z581kybNSFXFZWhvj4eISGhiItLQ0ffvihR/uamhqkpaUhNDQUM2bMwI4dO9xsKisrkZycDKPRiOTkZOzdu1f1cQkhKCkpgcViQVhYGObPn48TJ06IbKxWKx566CHExMQgIiIC99xzj8dhgGqholATVg8XqdDatetJCF3pgYbZx48fZ++p7XqiuHb7+CPZ5Xq/KVyaxtt+aYTw7bffSibNpk+frrpdnqDhcnd3t9tFg3YzGY1GxMbGsouezWbz2xRBqoW8e/duFBYWYuPGjWhoaEBmZibuuusu2XWFGhsbsWjRImRmZqKhoQGPPfYY1q5di8rKSmZTW1uL3Nxc5OXl4ejRo8jLy8Py5ctZpZPS4z799NN49tln8fzzz+PTTz+F2WzGHXfcIeqvKywsxN69e1FRUYFDhw6hp6cHS5Ys8cuUtFarlYlirHpkIbRCTOgZPF0APOEq5OEku+hv5zoIQXjSKxWy0+lk44eBoRB30aJFPt3CeEJY8eW6eohrd57BYGAltv7yyqqF/Oyzz2LlypVYtWoVkpKSUFpairi4ONFUNkJ27NiBqVOnorS0FElJSVi1ahUefPBBPPPMM8ymtLQUd9xxB4qLi5GYmIji4mIsXLgQpaWlio9LCEFpaSk2btyIpUuXIiUlBa+++ir6+vrwxhtvABgKs8rLy/Hb3/4Wt99+O2bNmoU//elPOHbsGPbv36/2p3BDeFHx9xXfE1JFEEoEST2y8EI3XCHTk3Y4HpmKsLe3V9Q9Ri8OQUFBXodGCmfvoENEo6OjkZ6e7nHMsa8Ii3V+97vfid5zFbIw/+CvzLUqIdtsNtTV1SE7O1u0PTs7W1TQL6S2ttbNPicnB0eOHGHDyORs6D6VHLexsRFtbW0iG6PRiKysLGZTV1eHwcFBkY3FYkFKSops+61WK7q7u0UPOWhYnZKSoqqPcrhIZXmVlFnS8Fk4u4XaARMUepK6emRfhBweHs4q4oQXGW+zg7iybNky0WuHw4Ff//rXqtujFDpMddKkSaJbOhraC29X/J3wUiXkjo4OOBwOt/7R2NhY2eF2bW1tkvZ2u52NFpGzoftUclyp4gcpG4PB4Da431P7t2zZwmaCMJlMkrW2FHqvLVc4oBVUrMKrO/WsnkJremL19fWx+zolIbkU/gytdToduxAK75PVXmRcJ1q49957VSXw1CLMlgtvC2kmW9gNSKMFf81p7tO3cr0aeqovlbN33a5kn/6yccWTTXFxMbq6utjD06oQ999/P372s5/hn/7pnzwez9/QE+S7775zm7LVk0emXlTYd0w9hNLyTNd9XbhwAU6nc1ihNXA5vH7//ffZNl+8vDDpKFVe6U+ioqLY/0KYr6BCFjoQWvTjWn/tK6qEHBMTg+DgYLerSHt7u2wVk9lslrQPCQlh92hyNnSfSo5Lq3m82dA1bZW232g0IioqSvSQQ6/XIz4+XrUIhgtNDvX19TGvpcSzBgcHM6HT8FptMQjFYrFAr9ejv78fNTU1LHnoi0cGLlc/tba2DitcX7ZsGaxWq8hbagldNUN4myPlken3a2lp8ctcX6qEbDAYkJaWhurqatH26upqzJkzR/IzGRkZbvZVVVVIT09nlTVyNnSfSo4bHx8Ps9kssrHZbKipqWE2aWlp0Ov1IpvW1lYcP35ctv1XAnq9np3c9KRXmrSiF1N6cfPVI4eEhLCT8+DBg2y7rx553rx5sFqt0Ol0LAvsi5BjY2OxYMEC1q+sNfSiKhQy/U2FTmDixIlsri8li7t7Q3VoXVRUhD/+8Y94+eWX8cUXX2D9+vVoampCfn4+gKFQ9Gc/+xmzz8/Px9mzZ1FUVIQvvvgCL7/8MsrLy/HII48wm3Xr1qGqqgrbtm3Dl19+iW3btmH//v0oLCxUfFydTofCwkI89dRT2Lt3L44fP46f//znCA8Px09/+lMAQ+HaypUr8fDDD+P9999HQ0MD/vVf/xU33ngjbr/9dp9+wLGCa9ZY6Qgm1/5PXz0yIN3l5us9aUhICBITEwFczj2onfCAsmDBAp/a4Av0/t1ut8Nut2NwcJAldYX/i6CgIPZ7+WO2ENXDGHNzc3HhwgVs3rwZra2tSElJwb59+1ilTGtrq6gbJj4+Hvv27cP69euxfft2WCwW/P73vxdlFOfMmYOKigo8/vjjeOKJJzBz5kzs3r1bNCuGt+MCQ0Xx/f39WLNmDTo7OzF79mxUVVWJsoW/+93vEBISguXLl6O/vx8LFy7EK6+8omrK1rFIREQEOjs72TpE1Ht5K+wQTq0zMDCgaHFzOWbOnClK8gyXm2++GY2NjawqytdMuJpZPIeLsG0DAwPsFoNOgCjEZDKhtbXVL6s5+vQN16xZgzVr1ki+98orr7hty8rKQn19vcd93nfffbjvvvt8Pi4w5JVLSkpQUlIiaxMaGornnnsOzz33nMdjXWkIPTLtsjEYDF5PeuFKEbTuWliwoIaEhAQsXboUJ06cwKlTp4ZdFEMrvPxR9jlS6HQ6GI1GWK1WDAwMsKQfXUVDiGt9+nDgEwsECMLuHypkJWWWwvWgysrKEBkZqbo8U8iNN96IxMREvPfee1i4cKHP+wEuC9Z1aKTaPu6RJjQ0FFarFVarlSURXbs8Af8KmQ+aCBCEhfi+CLmpqYk9H27WXa/X4+6771bdF+2Kq5B9vUceaYQTLQgHobhCt7n2ovgCF3KAQLPPp06dYl5AjZCFjGRVmieoYO12O1vJAVA3Rc9oQC+EXV1d7H8hJWRhl6nUTC1q4EIOEOj0QF1dXfjggw8AeF/bGJDOTquZ2URLhMkhm80mWVgxFqHTNnV0dLCLj1Sbx48fj9DQUDidzmFXeHEhBwjCKz6t7lJSYWYwGNwy9jfffLM/m+YzwcHBrG0XL14UrdM0lqHtq62tZX3gUh5Zp9OxMtLhDqXlya4AwbVrIzIy0m2pEzlCQ0NZZnjmzJkjXpnmCdo2mlEfN26cX6fo0QKp3IDczKhZWVmYO3euqgkXpeAeOYB48MEH2XM1XksojBUrVvi1TcOF3ifT0FNY5jhWUZN3sFgsmDp16rDrGLiQAwjhVV3NRAnCPuORLJ5QgquQx3pYDQyNRae13cHBwXjggQc0HXUF8NA6oPD1ZBk3bpyilQRHA3rLQGcLGSsZdU/odLoRq+2mcI8cYNx9990AgLlz5yr+zKJFizyuAzyauK697EsN+NUA98gBRmpqKpKTk1VVP02aNAkTJkxgQ/DGEq7CHU7VWSDDPXIA4ksJ49KlSzVoyfDhQlYGFzIHgPu0OGMFV+Hy0FoaLmTOmIYLWRlcyJwxjVDIERERV/y4ca3gQuaMaYST5431iq7RhAuZM6YJCgrCzTffDKfTecVPx6QlXMicMc/dd9+N06dP+30FxUCCC5kz5gkKCsKLL7442s0Y03Ahc64IhjvbSKDDhczhBABcyBxOAMCFzOEEAFzIHE4AwEc/qYTOh+VpnWQOxx/Qc4yec57gQlYJnTPa0zrJHI4/uXTpktcJFXREidw5DKfTiZaWFkRGRnpdd1kp3d3diIuLw7lz58b8nM2+EujfUYvvRwjBpUuXYLFYvM7+wj2ySoKCgtjyof7G2/rLgUCgf0d/fz+lUxvxZBeHEwBwIXM4AQAX8hjAaDRi06ZNY35xsuEQ6N9xtL8fT3ZxOAEA98gcTgDAhczhBABcyBxOAMCFzOEEAFzIHE4AwIU8gpw5cwYrV65EfHw8wsLCMHPmTGzatAk2m01k19TUhLvvvhsRERGIiYnB2rVr3WyOHTuGrKwshIWFYcqUKdi8ebOi4vrRoKysDPHx8QgNDUVaWho+/PDD0W6SIrZs2YIf/OAHiIyMxDXXXIMf/vCHOHXqlMiGEIKSkhJYLBaEhYVh/vz5OHHihMjGarXioYceQkxMDCIiInDPPfcMe2FzNwhnxHj33XfJz3/+c/LXv/6VfPPNN+Ttt98m11xzDXn44YeZjd1uJykpKWTBggWkvr6eVFdXE4vFQgoKCphNV1cXiY2NJStWrCDHjh0jlZWVJDIykjzzzDOj8bU8UlFRQfR6PfnDH/5ATp48SdatW0ciIiLI2bNnR7tpXsnJySE7d+4kx48fJ5999hlZvHgxmTp1Kunp6WE2W7duJZGRkaSyspIcO3aM5ObmksmTJ5Pu7m5mk5+fT6ZMmUKqq6tJfX09WbBgAfnnf/5nYrfb/dZWLuRR5umnnybx8fHs9b59+0hQUBBpbm5m23bt2kWMRiPp6uoihBBSVlZGTCYTGRgYYDZbtmwhFouFOJ3OkWu8Am655RaSn58v2paYmEg2bNgwSi3ynfb2dgKA1NTUEEIIcTqdxGw2k61btzKbgYEBYjKZyI4dOwghhFy8eJHo9XpSUVHBbJqbm0lQUBB57733/NY2HlqPMl1dXYiOjmava2trkZKSAovFwrbl5OTAarWirq6O2WRlZYmqiHJyctDS0oIzZ86MWNu9YbPZUFdXh+zsbNH27OxsHD58eJRa5TtdXV0AwP5fjY2NaGtrE30/o9GIrKws9v3q6uowODgosrFYLEhJSfHrb8CFPIp88803eO6555Cfn8+2tbW1ITY2VmQ3YcIEGAwGtLW1ydrQ19RmLNDR0QGHwyHZ1rHUTiUQQlBUVIR/+Zd/QUpKCoDLv7Wn79fW1gaDwYAJEybI2vgDLmQ/UFJSAp1O5/Fx5MgR0WdaWlpw55134sc//jFWrVolek9qnDMhRLTd1Yb8f6LLX2Ok/YlUW8diOz1RUFCAzz//HLt27XJ7z5fv5+/fgI9H9gMFBQVYsWKFR5vp06ez5y0tLViwYAEyMjLw0ksviezMZjM++eQT0bbOzk4MDg6yK7/ZbHa7mre3twNw9w6jSUxMDIKDgyXbOpba6Y2HHnoI77zzDg4ePCgai242mwEMed3Jkyez7cLvZzabYbPZ0NnZKfLK7e3tmDNnjv8a6be7bY4izp8/TxISEsiKFSsks5Y02dXS0sK2VVRUuCW7xo8fT6xWK7PZunXrmE12/fKXvxRtS0pKuiKSXU6nk/z7v/87sVgs5KuvvpJ832w2k23btrFtVqtVMtm1e/duZtPS0uL3ZBcX8gjS3NxMrrvuOnLbbbeR8+fPk9bWVvag0O6nhQsXkvr6erJ//35y7bXXirqfLl68SGJjY8lPfvITcuzYMfLWW2+RqKioMd39VF5eTk6ePEkKCwtJREQEOXPmzGg3zSu//OUviclkIgcOHBD9r/r6+pjN1q1biclkIm+99RY5duwY+clPfiLZ/XTttdeS/fv3k/r6enLbbbfx7qcrmZ07dxIAkg8hZ8+eJYsXLyZhYWEkOjqaFBQUiLqaCCHk888/J5mZmcRoNBKz2UxKSkrGnDembN++nUybNo0YDAaSmprKum/GOnL/q507dzIbp9NJNm3aRMxmMzEajWTevHnk2LFjov309/eTgoICEh0dTcLCwsiSJUtIU1OTX9vKxyNzOAEAz1pzOAEAFzKHEwBwIXM4AQAXMocTAHAhczgBABcyhxMAcCFzOAEAFzKHEwBwIXM4AQAXMocTAHAhczgBwP8BOMfkPTfpm+EAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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dZ+nSpQgKCpIbp1St/WEVc6DFDCwAGDhwIIDaOdJi2mZDNJYSVzjESktL5Y8+Y58mObtac35kezR0nQULFqC0tFRuFy5cuOX7tXbEJA21Wm3lmX7ggQewcuVKVFRU2ITBbQrBwcHw8fGBUqnE6tWrb/l63kybyo+s0+lQXV1tM8mgoetwfmRbhPOp7vxorVaLiIgI9O/f3yX3USgUGD58OAAgPz/fJgoJc5M2lR85MjISarXa6jqFhYU4duzYLedZbkuIVU72fvzeeustPPzwwy6719ixY1FcXAyNRoOdO3dixYoVLru2V+GsJy05OZnUajUlJSVRdnY2JSQkUEBAAOXm5hIR0fz58ykmJkbanz17lvz9/Wn27NmUnZ1NSUlJpFaracOGDdJm7969pFKpKDExkXJycigxMZF8fHxo3759Dt+XiOjq1auUmZlJ3333HQGg5ORkyszMpMLCQmkTGxtLYWFhlJaWRhkZGTR27FgaPHgw1dTUOPT87LUmWrVqFen1ejp48GCz3G/79u1WHvGzZ882y309jTPftTaVH5mI6MaNGxQXF0fBwcHk5+dHkyZNory8PIefva0L2Ww2y7zH+fn5zXbf9957Twr5k08+abb7ehLOj+xG2np+5KKiIqxcuRIKhQILFiyQ3aPm4NChQ/j222+hVCoxb948q3St3gjnR2bchojo0alTp2YVMQAMGTIEGo0GZrPZKqg9w0JmnESM/YeHhzf7vZVKJXr37g0AyMnJafb7t2RYyIxTiJrQciJIcyLCCh0+fFj+qDAsZMYJiEgOPXlKyIMGDZJNensr5NoqLGTGYYqKimA2m6FQKByaS+0OFAoF/vSnPwGoDWAvslS0dVjIjMOI6amdO3eGSqXyWDl69Ogh57zzgopaWMiMw4gF/r169fJoOQDIaaCnT5/2cElaBixkxmFEM9ZeGpjmRnivc3NzeWUUWMiME7QkIXfq1An+/v48pvz/sJAZh2ksFUxzolAo5FCUyDnVlmEhMxIiwsaNG3HgwAGbc0ajUeY/bgk1MnAzX9SpU6c8XBLPw0JmJDk5OcjKysKmTZtkXieBaFb7+PjI9KeeRjjdrly54lSkGG+EhcxIRChbAMjKyrI6Z5k9whVRWVxBSEgIevToAYCb1yxkRnL27Fn52lLUwM0auaHsEZ5AeK/beggmFjIDoDYOl2WE0MuXL6OmpkbutyRHlyVCyL/88gvMZrOHS+M5WMgMgJt5inv27Ak/Pz8QkVU/WSxQaChVqifo2rUrVCoVampq2nTmRhYyA7PZjD179gCoDWnbtWtXAJDpUS1fe2qxRH0olUo571uslW6LsJAZnD59GlVVVVCpVBg0aJCNkA0GA65fvw6g5QkZAAsZLGQGNxceiMimIsxtQUEBAODYsWMAalOoNpRh0VOwkAEfTxeA8TwiEd7tt98O4OZEi8LCQrzxxhvSbuDAgS1m6MkSIeRLly7BZDJ5dGWWp+AauY1jGbBfNKk7duxod/ZWv379mrNoDhMUFCRjebVVh5dX5UcmIuj1eoSGhsLPzw+jR4/G8ePHrWxGjx4NhUJhtU2fPr0Jn4J3IGZEBQYGIjAwEEDtPOY///nPCA8Pl+l9unfv3mLzXikUCpnWtc2mYHU21q4IFL9q1SrKzs6m+Ph4CggIoPPnz9u1FwHq4+PjKTs7m1atWmUToP6nn34ilUpFS5YsoZycHFqyZEm9Aeobum9iYiIFBgZSSkoKZWVl0bRp06hr165UVlYmbaKjo2nGjBlUWFgot5KSEoef39viWqenp5Ner6e1a9fWa2M0GpuxRE3j66+/Jr1eTzt27PB0UVyGWwPUt9T8yGazmXQ6HSUmJsrzVVVVFBQURB9++KE8Fh0dTfHx8Q486c1rlJaWyu3ChQteJeSNGzeSXq+nnTt3eroot8TevXtJr9fTV1995emiuIw2mR/53LlzKCoqsrLRarWIjo62KdsXX3yBkJAQDBgwAK+88oocWrGHt6dVFZ5p4eBqrYimtbOLJ8rLy91RnGbHa/Iji7+Nle3JJ5/E2rVrsXPnTrz++utISUnBH//4x3qf2ZvTqhoMBvk/EI6u1ooY3y4uLobJZHLoPZ9//jmWLFniFVM7mzT81JLzIzdmM2PGDPk6IiICffv2xbBhw5CRkYGhQ4falF2r1XptahLxA9euXTuX5DP2JO3bt4dGo0F1dTWuXbvW6MSVkydP4syZM9BqtUhLS7Np7bU2vCY/spjE4GwO5aFDh0KtVtus9nEHxcXFqKqqcvt9HEU0q8PCwjxckltHoVDIeeCNea6JCF999ZXcT09Pb/Vxv7wmP3J4eDh0Op2VTXV1NXbt2tVg7uPjx4/DaDS6vWlJRFi8eLHdoTdPkZubCwAei1HtakQt3JiQi4uLbZrTrT0wgdPjyHPmzMHq1avx8ccfIycnB7Nnz0ZeXh5iY2MB1PYpn3rqKWkfGxuL8+fPY86cOcjJycHHH3+MpKQkvPLKK9ImPj4eW7duxbJly3DixAksW7YMaWlpSEhIcPi+CoUCCQkJWLJkCVJTU3Hs2DE888wz8Pf3xxNPPAEAOHPmDBYvXoyDBw8iNzcXmzZtwmOPPYYhQ4bgvvvua9IH6CiZmZno0KEDSktLGx13dzVEZBO+x2Qy4cyZMwBuLgVs7TgqZPHcPj4+srXW2oXsdB952rRpuHr1KhYvXozCwkJERERg06ZN6NmzJ4DaaX1iyh9QW1Nu2rQJs2fPxvvvv4/Q0FC8++67eOSRR6TNvffei+TkZPz1r3/F66+/jt69e2PdunW45557HL4vAMybNw83btzAX/7yFxQXF+Oee+7B1q1b5UQHjUaD7du349///jfKy8vRvXt3TJw4EYsWLXL7tL7du3fL11u3bsXQoUOdmrdsNpuxdOlSlJeXY968eU4t8P/yyy9x+vRpbNq0CYMHD8aUKVOQl5cHk8kEX19f2S1p7YimdWM5ocRKr6ioKBiNRly6dAk5OTm4++673V5Gd8H5kZ2kKfmRq6ursXTpUqtj/fv3x9SpUx2+7/79+7Flyxa5v3DhQvj41P87vHfvXuTm5mLEiBH4/PPPrc499dRTyMnJwYEDB6SwvYGrV69i+fLlUCqVeO211+z+OFv+L2bNmoXKykqsXr0aQOOfaXPD+ZFbGMKppNVqZYaEQ4cOoaioCBUVFQ5dY//+/Vb7GRkZ9dqePHkSaWlpOH36tI2IAeCzzz6TTe277rrLofu3BoKDg6HVamE2m+VnXhfRWvTx8UHHjh0RGhoqBd+ahxZZyM2A5eqiyZMnQ6FQwN/fHytXrsRbb70lnU71UVZWJhc2iF/mHTt21Dv+efDgQZtjAwcOtGkB+Pj4eE3/GKj1k4hgfJZBESw5fPgwAOCOO+6Q7xGLQSxjltXFYDDUe64ljEOzkN3M/v37pSe9R48e8PX1xYQJE2Q8LIVCgU8//bTBtbQnT54EUDvEFhcXB4VCgaqqKrtfvLy8PJkPKSQkBNeuXUOHDh0wefJk9O/fHyNGjJC2AwYM8Lolf/UNQwK1Tj/xIydaRgDQp08fAPaFvGPHDjz22GOYPXu23f9RaWkpXnzxRY8nXmchu5GSkhL873//k/0u4ZgbNmyYrDkElv3fuhw9ehRA7QQWtVqNIUOGAAC+/vprK7tTp04hKSkJAKBSqRAbG4u3334b8fHxsgzjxo3D2LFjoVar8bvf/e7WH7KFIYYR7bVydu/eLeORiRoZuDn8JtLGCioqKpCamoqIiAh06dIFq1evtgpICACffvopwsLCkJqaCk+6m1jIbiQ9PV2Olbdr187KOzxjxgw8++yzVv2zS5cu2XwZqqqqZN9NpEgR3tWKigqkpaXhxx9/REJCAtauXQulUgkiwsyZM6FSqeT9LYmKisJrr73W6mdz2UMErS8uLraaQ09E2LlzJ4DaeeUajUaeu+2226BSqWzWM3/55Zdy0pJg/fr18nVJSYns8hiNRo8uoWQhuxHhoLp06RKee+45m+mjPXr0wMKFCxEYGAiFQoEPP/wQn3zyCYBar/OGDRvw1ltvQaFQQKvVyuEVnU4nZ2P9+OOP+OGHH6yGox599NEWF7a2ufDz85Pis0y5Kn4MjUajzfpzpVIpf2RF39poNEqHWUBAgPRNnDp1CpWVlQBsa31POstYyG5i3759UrjLli2rN1+SQqHAH/7wB7mfl5eHrVu3IjU1FcePH5dNvbpjnE8++SRMJhOUSut/4YMPPoiIiAgXPknrY9CgQQBqJ+EIRPdEp9PZHcoRzWshZNFfViqVmDt3rtXkJBEmuG52i8bGr90JC9nFHDt2DCdOnMB3330HoNbhJCak1Ed4eLjVCqz09HSr99TU1CA6OtrqPb6+vhg5cqTcv3btGqKioqyOtVUGDBgAoLaGvH79OohIeqtHjRpl9z2iby0cWocOHQIAREZGykgywikmhCxqfNHfFml16tIcSyVZyC6koqICa9aswbp166Rz6emnn3bovQMHDsSzzz4r981mM86ePYuLFy/ihRdesDtRYfz48bh8+TK6deuGf/3rXxg7dqxrHqSVc9ttt8n+/zvvvIPNmzfDZDKBiOqNOyaEXFBQAKPRaCNS4OYS2V9//RUlJSWorKyEQqGQLQCRjcOSCxcuYM6cOW6flttyprG0cogI//nPf6yabZGRkU45lHr06IHBgwcjLS0N//jHP6DValFaWtrg6q13333X64aQXEFUVBQ2b94MAHLyS1hYmJWTy5JOnTpBqVTCZDJh//79ICIolUqEh4db2QC1QhapXHU6nfz/XLt2zWrZrNFoxOrVq9GtWzf88MMPiIyMhL+/v1uel2tkF6FQKPDb3/5W7hsMBowfP97p60yZMgXLly9HUFAQfH19GxQxABZxPURGRtr4D+p2TyxRKpUyysj27dsB1I4SWH6+QshFRUWy/z1o0CDp/6ipqZGOMAD4+eefrcog+unugIXsQgYMGIBRo0ahpKQEzz33HIvMg6hUKixcuBAREREwGAzQaDSNzmITQ1eC3/zmN1b7YtTAYDDICSd33HEHfHx8ZEvMsnktAvuLfNLuTMjOQnYhCoUCY8aMwT//+U+vmvrYWlEqlXjkkUcwZcoUvPjiizY1dF1Gjx4tx/FVKpV0bgk0Go3VsJ6vr68c9hO1snB4WYpdODLPnTvntsASLGTG6xk+fDiCg4MbtdNqtYiMjMSRI0fw+OOP2xW+8IgDtVM7RX9YCFnkkRZjzAEBAejTp4/sG4tmu6thITOMBQ8//DBeffXVeltUAwcOlK8tm+KiaS2ELMaj+/btC4VCgfvvvx9A7aq1GzduyPeVlZXZBH1oCixkhqmDZUCLunTp0kVO6bSceCNqZJEsXow1CwfakCFDEBAQALPZbDVHfv369diyZYvV5JWmwEJmGCdQKBR44YUX8Pzzz1tFVxV9Z9FHFtM7xaiDQqGQi11OnTqFDz74ACaTCfn5+TCbzU5FfLEHC5lhnESj0diER7IUstFolE1sUSMDwIgRI+TEnsuXL8tAjCqVyipkVVNgITOMCxBCrq6uRn5+PgDA39/fakJQQEAAZs+eLffF3OwuXbrccrpaFjLDuACNRiM90ydOnABgXRsL/P39MXfuXKtjYornrdDm0qoaDAbMnDkTISEhCAgIwOTJk+UvKMPcCmLCSHZ2NgD7QgZq16ZPnDgRQO13tm/fvrd8b6eFvG7dOiQkJGDhwoXIzMxEVFQUxo8fbxUC15Jz585hwoQJiIqKQmZmJl577TXMmjULKSkp0iY9PR3Tpk1DTEwMjhw5gpiYGEydOtUq4Jwj933zzTfxzjvvYPny5Thw4AB0Oh0eeOABqwXmCQkJSE1NRXJyMvbs2YPy8nJMmjTJ4XxBDFMfYh20WO3U0PTaYcOG4cKFCwgPD3dojLtRnE312JrTqpaUlJBarabk5GRpc/HiRVIqlbRly5ZGn53I+/IjM65DpHYV27Vr1xq0v3r1KlVUVNR7ntOq/j9106oeOnQIRqPRyiY0NBQRERH1lt9gMKCsrMxqYxh7WKamvf322xsdUgoODnbZaqg2lVa1qKgIGo3G5gNuqPzenh+ZcR3du3dHx44dUVFRgUcffbRZ793m0qraoyGbBQsWYM6cOXK/rKyMxczYRaVSYcaMGbhx4wb8/Pya9d5tKq2qTqdDdXW1jHzoSPm1Wi3at29vtTFMffj5+bnGeeUkbSqtamRkJNRqtZVNYWEhjh071mDqVYZp8TjhlCMiouTkZFKr1ZSUlETZ2dmUkJBAAQEBlJubS0RE8+fPp5iYGGl/9uxZ8vf3p9mzZ1N2djYlJSWRWq2mDRs2SJu9e/eSSqWixMREysnJocTERPLx8aF9+/Y5fF8iosTERAoKCqKNGzdSVlYWPf7449S1a1cqKyuTNrGxsRQWFkZpaWmUkZFBY8eOpcGDB1NNTY1Dz89ea6a5cOa75rSQiYjef/996tmzJ2k0Gho6dCjt2rVLnnv66acpOjrayn7nzp00ZMgQ0mg01KtXL/rggw9srrl+/Xq68847Sa1WU79+/SglJcWp+xLVDkEtWrSIdDodabVaGjVqFGVlZVnZ3Lhxg+Li4ig4OJj8/Pxo0qRJlJeX5/Czs5CZ5sKZ7xqnVXWSpqRVZZim4Mx3jaNoOon43ePxZMbdiO+YI3UtC9lJxHRPHoJimovr1683mgKIm9ZOIpJoi3xNrkCMTV+4cMFrm+ve/ozueD4iwvXr1xEaGtpo4ECukZ1EqVTKBGqupi2MU3v7M7r6+RxNxsfrkRnGC2AhM4wXwEJuAWi1WixatMgqmJu34e3P6OnnY2cXw3gBXCMzjBfAQmYYL4CFzDBeAAuZYbwAFjLDeAEs5GYkNzcXzz33HMLDw+Hn54fevXtj0aJFqK6utrLLy8vDww8/jICAAISEhGDWrFk2NllZWYiOjoafnx+6deuGxYsXOzS53hM4Gwe9pbB06VLcfffdCAwMROfOnTFlyhScPHnSyoZaSix1d62lZGzZvHkzPfPMM/T999/TmTNn6JtvvqHOnTvT3LlzpU1NTQ1FRETQmDFjKCMjg7Zt20ahoaEUFxcnbUpLS6lLly40ffp0ysrKopSUFAoMDKS3337bE4/VICIgxKpVqyg7O5vi4+MpICCAzp8/7+miNcq4ceNozZo1dOzYMTp8+DBNnDiRevToQeXl5dImMTGRAgMDKSUlhbKysmjatGl2g1l069aNtm3bRhkZGTRmzBinglk4AgvZw7z55psUHh4u9zdt2kRKpZIuXrwoj61du5a0Wq1cYL5ixQoKCgqiqqoqabN06VIKDQ0ls9ncfIV3AGfjoLdkLl++TABkQIvmiqXuCNy09jClpaVWwdrS09MRERFhFSN53LhxMBgMOHTokLSJjo62mkU0btw4FBQUIDc3t9nK3hhNiYPekhEZFsX/y12x1JsCC9mDnDlzBu+99x5iY2PlMXsxvjt27AiNRmMVn9te/G5xrqXQlDjoLRUiwpw5c/Db3/5WJjh3Vyz1psBCdgF6vR4KhaLB7eDBg1bvKSgowEMPPYTHHnsMzz//vNU5e+ucqU7sbUdihbcUmhJrvKURFxeHo0ePYu3atTbnXB1LvSnwemQXEBcXh+nTpzdo06tXL/m6oKAAY8aMwciRI/HRRx9Z2el0OqvkdQBQXFwMo9FoFZ/bXvxuoOHEYc1NU+Kgt0RmzpyJ//73v9i9e7fVWnTLWOpdu3aVx+uLpW5ZK1++fNm1IZhd1ttmHCI/P5/69u1L06dPt+u1FM6ugoICeSw5OdnG2dWhQwcyGAzSJjExscU6u1566SWrY/37928Vzi6z2Uwvv/wyhYaG0qlTp+ye1+l0tGzZMnnMYDDYdXatW7dO2hQUFLjc2cVCbkYuXrxIffr0obFjx1J+fj4VFhbKTSCGn+6//37KyMigtLQ0CgsLsxp+KikpoS5dutDjjz9OWVlZtHHjRmrfvn2LHn5qKB55S+Wll16ioKAg2rlzp9X/qrKyUto0Ryx1R2AhNyNr1qwhAHY3S86fP08TJ04kPz8/Cg4Opri4OKuhJiKio0ePUlRUFGm1WtLpdKTX61tcbSxoLB55S6W+/9WaNWukTXPEUncEXo/MMF4Ae60ZxgtgITOMF8BCZhgvgIXMMF4AC5lhvAAWMsN4ASxkhvECWMgM4wWwkBnGC2AhM4wXwEJmGC/g/wALNMTJvAOQtgAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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ceoKjqbVA7/vpbT099dX3QKInvMr//u//4r333uvVctO+7KcHqK+eQ6InvIbVasW+fftgNptRWlrq8fm+btPTUNweSPSE12hsbBTvq6qqPDrXarUKT+6t6L2rNn0g99WT6AmvId06ytNtpBytec/xRj890CN6lUoFi8US0BtfkOgJryEVuqdVaOle8tJ18QDPPL3VanUayAsJCRHddtJaSaBBoie8xs2bN8V7vjSVUqTtedtVij3x9FzwgH31Hrg3r548PUF4AduNQDxZsMJZEE96TImn5+15tVrtcFkzPiovkIfikugJh5hMJnz22WdCREroi+id9dEDnnl6Z+15Dhe9Ek/f0dHhUW3lQcHnm10Qyujs7MTBgwfR2NiIRYsWiQU6+4OioiJ88cUXAICIiAg8++yzbs9hjAnRR0VF4ebNmx61673t6d2J3p2n7+rqwqZNmwAAGRkZeOyxx9zm/aBAnt5PKC0txTfffIObN28KwfWFpqYmfPDBB/jss8886p66evWqLP8rV64oOq+jo0OI8tFHHwUAfPfdd4rzdSV6Tzw99+ADBw50+D0/7k700qDk7373O7f5epNTp07hX/7lX3D48GGfpE+i9xOkG29cvHgRTU1NfUrvzJkzaG5uRllZGZqbmxWfV15eDgBiufDGxkZFHpt7+bCwMLEZiCcRclfVe088vTvRK/X0N27ckH32dC+93mI2m1FYWIjg4GBcuHAB//d//+f1PEj0fsDdu3ftxqtv3769T2lKHxrffPON4vPq6+sB9OwkxJsYu3fvdnseF31ERIQYBOOJ6JV4+q6uLre1Fnei5xF93vZ3hq3ov/zyS5f23uLMmTOyz8XFxV7Pg0TvB3CBDhw4UPQjA+j1qLG2tjZUVlaKz+fOnVOUVmdnp6iSGwwGUZa7d++6PV8qeukUVqWTb5S06QH33t6d6HlbX9q15whevecPCVf7F3oT/n978sknAfTMZXD3gPIUn4t+586diI+PR2hoKJKSknDy5EmX9idOnEBSUhJCQ0Px2GOPYdeuXXY2+fn5GDduHLRaLcaNG4eCggKP82WMwWg0wmAwICwsDDNmzMDly5dlNmazGcuWLUN0dDTCw8Mxb9481NTU9OIquIaL/pFHHpHtlCvdNpuXWUk188KFC+J9UFAQbt68qah9/fXXX4MxhoEDByIiIgI/+MEPxHfubjyp6KOioqBWq2G1WhWPzHO2gAbg2SaW7kSv1WoB9MQgXD3IeC0lJSUFQE9s435U8Xm+kydPFmMKbGsdfcWnoj9w4ACys7Oxbt06lJWVISUlBS+88IJsKysplZWVePHFF5GSkoKysjKsXbsWy5cvR35+vrApLi7GggULkJGRgYsXLyIjIwPz58/H2bNnPcp38+bN2LJlC7Zv345z585Br9dj1qxZsm6n7OxsFBQUIC8vD0VFRWhra8PcuXN7PXXUGdyLREVFISwsDN/73vcAwO46ffrpp9iwYQMqKipcpscF/tRTT8FgMACA24eV1WoV15mfM3jwYCEed3EBqehVKpXYOkrpNFbeprcdjQf0rHrDB+y4Ex4vhztPLx3rbwtjTDw8nnjiCYSGhsJqtdo9hL1NZ2eniDUMHjxY/B9sd1fuKz4V/ZYtW7B48WIsWbIEY8eOxdatWzF8+HD8+te/dmi/a9cujBgxAlu3bsXYsWOxZMkS/NM//RPef/99YbN161bMmjULa9asQUJCAtasWYOZM2di69ativNljGHr1q1Yt24dXnnlFSQmJuLjjz/G3bt38Yc//AFAT9V0z549+NWvfoXnn38eEyZMwO9//3uUl5ejsLDQ6W82m81obW2VvVzBGBPBmoSEBAAQ+9lLhVZVVSXalf/1X//l8sHDvWtCQoIIXP3pT39y6dmuXr0q3k+ZMkW8j4qKsiuLI6Sil56ntK/elacHlAfzuFiddXlK03c2BkHanAkPD8e4ceMA9FS1fQkPmGq1WoSGhopt0x8YT2+xWFBSUoK0tDTZ8bS0NJw+fdrhOcXFxXb26enpOH/+vLgpnNnwNJXkW1lZCZPJJLPRarVITU0VNiUlJejs7JTZGAwGJCYmOi0/AGzcuBE6nU68uICd0dDQgI6ODgQFBYmuLh4IkwpN2kbv7u526rmtVquoIsbExMi+czXQhqf39NNPy/Z64+1zT0XPq6ZK++pdtekBZd12XV1d4jc68/Qqlcptu55727CwMAQHB4ua16VLl3q1ToBSeK2ITwzi+/Y9MJ6+sbER3d3d4mnFiY2NdfojTCaTQ/uuri5xIzuz4WkqyZf/dWej0WhkgTV35Qd6tt9uaWkRr+vXrzu1BSDmnY8ZM0bc2DxPadXYVjzOunKam5vR1dWFkJAQDBo0SPbQciVALlr+wOHwskjH1bs631b0nnp6Z6JX4um5lw8ODnY6OAeQt+tdpcNrSY8//jiCgoLQ3t7u9jo4orm5GQUFBW5HAUpFD0CI/saNG14dGejzQJ7t5AnGmN0xd/a2x5Wk6S0bW9zZaLVaREZGyl7OaGtrQ0lJCQBgwoQJ4rij6DcX7LBhwwD0BPm2bduG999/X9Y1xuMAsbGxCAoKQlRUlGgbuhI9jwPYltfRA8gWaRuYi57fuN4I5AHKPL00iOfqf6TU03PRBwcHCwfhyYAjzh//+EdcunQJH3/8scsmFr/GfLEPnU6HgQMHgjHm1S5Dn4k+OjoawcHBdl6xoaHBzsNy9Hq9Q/uQkBARGHJmw9NUkq+zapOtjcVisavWuiq/p1RUVKC7uxtDhgwRVUig52bjno0Lld8Q3O7atWu4efMm7ty5g7/85S8AesRXVFQEABgxYoRIz92KMc3NzaJ6z9viHP7Z1WChu3fvimovFwp/0Hz33XeKBtU4W/Oeo8TTuwvicdx5ekfpPPLIIwA8Fz1vSgI9tdBPPvnEqS2vRfAHpkqlEvEEb1bxfSZ6jUaDpKQk2UgzoGfk2bRp0xyeM3XqVDv7Y8eOITk5WfzTndnwNJXkGx8fD71eL7OxWCw4ceKEsElKSoJarZbZ1NfXo6Kiwmn5PeHGjRtiuOvEiRPtajLSRRy7u7uFYJ988klx03KuXLkCxhj+9re/iRuHnw/cu4nq6uoclkUaxOM3N4d7+tu3bzsVHBdJeHi4mNkWGRmJoKAg2feucDUiD/DM07ubt8A9vTPRc2FHR0eLY9xRlJaWKm7XM8bsRF5VVeVwNCBjTNTS+IhGAH2qYTjDp9X7nJwc/Pa3v8WHH36IL7/8EitXrkR1dTUyMzMB9LR/f/KTnwj7zMxMXLt2DTk5Ofjyyy/x4YcfYs+ePVi9erWwWbFiBY4dO4ZNmzbhq6++wqZNm1BYWIjs7GzF+apUKmRnZ2PDhg0oKChARUUFFi1ahAEDBuD1118H0COaxYsXY9WqVfjiiy9QVlaGH//4xxg/fjyef/75Pl+bIUOG4IknnsCIESNk0XKOdIz4jRs3wBhDaGgoBg8eLLspOBs3bsTRo0fFZ2k1nU8WcRb8u3v3rshzwIABsu8GDBggHrjOqvhc1NI8VSqV+KxE9M6WreZ40qbntQ1nuKvec6/KhQ70NL9CQkLQ0tKieISjVNw8SAs4bvLU1tbizp070Gg0ogkH3AvGmkwmry3x5dNZdgsWLEBTUxPeffdd1NfXIzExEUeOHBHjuuvr62V90fHx8Thy5AhWrlyJHTt2wGAw4IMPPsAPf/hDYTNt2jTk5eXh5z//OX7xi19g1KhROHDgACZPnqw4XwB4++230d7ejqVLl6K5uRmTJ0/GsWPHZF7i3//93xESEoL58+ejvb0dM2fOxEcffeRwnranhISE4OWXX0Z3d7fwiFK46Nva2sT3MTExUKlUslrBiBEjUF1dLfOAYWFhsllh3NM7G2/OxZyUlGT3nUqlQkxMDGpra/HNN9/IvB/HNojHiYyMxK1bt7wiem96elfV++7ubiFKaTMuNDQUkydPxqlTp3D69GnRveoK6UPyjTfewB//+Ed8++23qKmpkfWQABDjTOLi4mRNnNjYWAQHB+POnTtoampyeP09xedTa5cuXYqlS5c6/O6jjz6yO5aamup2JdVXX30Vr776aq/zBXpuZqPRCKPR6NQmNDQU27Ztw7Zt21zm1VtUKpXDwSjAvep5Y2OjED1/EMyYMQOVlZVITU3F9OnTsWHDBnHe008/jXnz5skeDPy8jo4OdHZ22rWbedNB2iSQMmbMGNTW1jptVzprS3PxuRM9Y8yrnr4vbfrvvvsOVqsVGo3GricjKSkJp06dwvXr13Hz5k27+IctPB40cuRIhISE4PHHH8e3336Ly5cvi5F+QE+Ngw+4su1mVavV0Ol0uHnzJk6dOoWXX37ZZZ5KoLH3fgoPhFVWVtqJasSIEVi7di1SU1OhVquxZMkSREZGiqHCtpFrPtgDcNw2tO0qsoW3851NoOFp2p6vVPQWi0X0UnjD07sTvavqPR8IM3ToULvrOHjwYFFNVxJNt72uo0ePFnn85je/EQ8waZB0+vTpdunwOM2FCxe8Mk6ARO+njBo1CiqVCs3NzaJPnvdgAPK15IYNG4aVK1di9erVDruqVCqVGCRkO26AMebW0/N8GxoawBiDxWLBrl27xHm1tbUAeqqmUpSKnjc7NBqN20CeN6L30qaTLc7GcHDGjh0LQL7OQEdHB86cOYOSkhJZu9tW9NLBWnV1daLX5eOPPwbQUyNwVPbZs2eL96dPn+7zMHASvZ+i1WrFzcKnu/alq5AL0jaCL53W62xMQVRUlNjm+fr16ygoKMCNGzdw4MABWK1WIX7pQwlQLnolHppX7515esaYeHi4a9PzfByVi19raRBPypgxYwD0XEdezd+0aRM+//xz/PnPfxZdpsC96r20BiR9MFZXV4uHKHCvdmfLpEmTxMPwiy++wHvvvdenNf5I9H6MbZuxL6LnadlG4LlgBw4c6DS+EBwcLHoM9u7di6+++gpAj0CqqqrAGENwcLCd2Phnd0NxlYjenad3NFbAGbxctp6eMeZW9NLa0IcffmgX7zl16hTa29thNpvFBB3p/y0xMVG8v3PnjiyuMGPGDId5qlQq2chQs9ls18viCSR6P0b6jw4PD3c5tNQd/Gatr6+XCcdde54zatQoh8cvXbok0rdtWkg9vavuJm94ep4GHy/vCl6u9vZ2cS0sFguMRiMsFgtUKpXdeAUpzsZpqFQqmM1m/OUvf4HJZILVakV4eLisi9VgMOCll14C0FMTOHfuHICeWpazpg0A0TtlMBjw1ltvuR056goSvR8jjeTazgHwFL1eD41Gg87OTlk3qbv2PGf69OlCIHfv3hVVeb4GgaOHBhdXV1eXy7HjSvrX3Xl6pd11QE8gj6fHq/h/+tOfRC/JI4884rTWAwCzZs2y+72ZmZn40Y9+BKBn0RLeM+WoyTRhwgTxv+Xt+scff9xlmSdMmICf/exnWLRokdNaiFJI9H6M9Eboa/+sWq0W7VFpEEqp6LVaLX72s58hLi4Oubm5YoVcLkJH50sDc67a9Z54eneidxfEA3o8Mn84tLa2wmKxyNYoUNIHv3jxYuF9hw8fjpiYGNk4EI6j3hKVSiUeEBz+v3GFdKBUX6AlsP0YnU6HsWPH4sqVK5g4cWKf0xs5ciTKy8tx/vx5JCQkID4+XnTDuaveAz1ea/HixQDsg07OHhqRkZFobGxEa2ur0weXJ216Z9V7HthSInperubmZjQ2NsrGi7S1tTltW0sZOHAgZs+eLYusO2pnv/LKKw7Ptw162g7W8SUkej9n/vz5DgfU9IannnoKhYWF6OjowL59+zBhwgTRhWfb3eaOQYMGISQkxKWnB3qq242NjffN07sL4nF4tfvPf/6z7PiaNWv61F6ePHkySktLMWbMGIwZM0Z08dkizWPmzJleGeWpFBL9A4A3BA/0eMvFixdjx44dAICysjIA8qmjSuERZWdTcjlKxt97w9N7Ur0H7Ee+AT2jGd2NsnOHrfd3xaJFi3Du3DmvTODyBGrTBxjR0dFYsmSJ7Njw4cMdjv93h7Tt60wsXITOZvhJ+9fvp6e3FX14eLhXhrh6wsiRI/Hqq6/26tr3BfL0AciwYcMQHR0t2vOOJtooYcaMGbh16xbi4uKcxgR4wOzLL790uABJe3u7ov51d56eT5JR2sth2xzhsYpAgDx9gMIjz+PHj5cNGPGEoKAgvPLKK5g0aZJTG+kagVVVVXbfcw89YMAAl+1aV/30ra2torZgGyBzhu1DSkkg82GBPH2AkpycjPHjx7scEOINpANTTCaTXZTa05lxjibJ8LkJjzzyiOLqvVarxfe//30UFRXhqaee6lPw7kGDRB/A2K7A4wtUKhWefvppXLhwweGKukpFz2ffmc1mWK1WWTuYV+097faaOXMmIiIikJyc7NF5DzpUvSd8Dl8Jho/Z53R3d4vRgUqnwwL28+C56B1F5N0xadKk+x5I628C69cS/QIPrvFBOpz8/HyxGrC7anlwcLBoitiKXrotGOEeEj3hc0aMGIGgoCBotVoxRsBsNssWolDSv+5ox1nGmMM1+gjnkOgJn6NWq0W72WKxoKamRrZjD6Csas6r+FLRd3R0iPUAlA7MCXQokEfcF2bPno3jx49Dp9Ph4MGDdnPsHU1WscWRp+deXjpzjnANeXrivqBSqbBixQoA9otqpKSkKBpqzEUvbdN7MqWW6IEejcR9w9abBwUFYfXq1U4Xw7SFz2KTLhXlbPltwjkkeqLfWLdunUfdZY7WtiPRe45Pq/fNzc3IyMgQ2zZnZGS43AgR6InGGo1GGAwGhIWFYcaMGWJ1Fo7ZbMayZcsQHR0tln223b1FSd7V1dV46aWXEB4ejujoaCxfvly2wktVVZXYXEL6ku4kQ3jGwoULoVKpMH/+fI/7xx2tYuvp7DrCx6J//fXXceHCBRw9ehRHjx7FhQsXkJGR4fKczZs3Y8uWLdi+fTvOnTsHvV6PWbNmyZ7u2dnZKCgoQF5eHoqKitDW1oa5c+fKlgZ2l3d3dzfmzJmDO3fuoKioCHl5ecjPz8eqVavsylRYWIj6+nrxeu6557xwdQKTMWPGYM2aNU7nmbvC0YKWJPpewHzElStXGAB25swZcay4uJgBYF999ZXDc6xWK9Pr9Sw3N1cc6+joYDqdju3atYsxxtitW7eYWq1meXl5wqa2tpYFBQWxo0ePKs77yJEjLCgoiNXW1gqb/fv3M61Wy1paWhhjjFVWVjIArKysrNfXoaWlhQEQaRK9p6amhhmNRrZlyxZx7MMPP2RGo5FVVFT0Y8n8A6X3ms88fXFxMXQ6nWyPuSlTpkCn0+H06dMOz+Hb+qalpYljWq0Wqamp4pySkhJ0dnbKbAwGAxITE4WNkryLi4uRmJgoW/YpPT0dZrNZjBLjzJs3DzExMZg+fToOHjzo8nebzWa0trbKXoR3kFbv2d9X16U2vef4TPQmk8nhgIuYmBine6I5210kNjZWfGcymaDRaOzmTdvauMvbZDLZ5TN48GBoNBphM3DgQGzZsgUHDx7EkSNHMHPmTCxYsAC///3vnf7ujRs3ijiCdEcTou9w0VutVrS3t4MxJh6qVL1XjseiNxqNDoNb0tf58+cBwOF0ReZgIQVbbL9Xco6tjZK83dlER0dj5cqVmDRpEpKTk/Huu+9i6dKl2Lx5s9NyrFmzBi0tLeJlu40U0XuCg4NF997t27fR0tIidv0NpPnwfcXjLrusrCwsXLjQpc2jjz6KS5cuic0ApXz33XdO12Pj63mbTCbZPOyGhgZxjl6vh8ViQXNzs8zbNzQ0iLXG9Hq927z1er3YHpjT3NyMzs5Ol+vFTZkyBb/97W+dfq/Vau/LlNVAJSIiAu3t7WhraxM1spiYmICbKdcXPL5S0dHRSEhIcPkKDQ3F1KlT0dLSgr/97W/i3LNnz6KlpcXpQoDx8fHQ6/U4fvy4OGaxWHDixAlxTlJSEtRqtcymvr4eFRUVwkZJ3lOnTkVFRYXYxggAjh07Bq1W63L5qLKyMtkDibi/SNv1/H/Hd5IlFOLLaOLs2bPZk08+yYqLi1lxcTEbP348mzt3rsxmzJgx7NChQ+Jzbm4u0+l07NChQ6y8vJy99tprbOjQoay1tVXYZGZmsri4OFZYWMhKS0vZc889x5566inW1dWlOO+uri6WmJjIZs6cyUpLS1lhYSGLi4tjWVlZwuajjz5i+/btY1euXGFfffUVe++995harZZFj91B0XvvUlBQwIxGIzt58iT7wx/+wIxGIysuLu7vYvkFSu81n4q+qamJvfHGGywiIoJFRESwN954gzU3N8sLALC9e/eKz1arla1fv57p9Xqm1WrZM888w8rLy2XntLe3s6ysLBYVFcXCwsLY3LlzWXV1tcd5X7t2jc2ZM4eFhYWxqKgolpWVxTo6OsT3H330ERs7diwbMGAAi4iIYElJSex3v/udR9eARO9djh07xoxGIzt48CAzGo3MaDSyy5cv93ex/AKl95qKMRc7CxJ9prW1FTqdDi0tLTTf2wucOXMGn3/+ufis0WiQk5NDcRQov9co+kE8UNh2zY0ePZoE7yEkeuKBwna9+r7s0x6okOiJBwrb7lQSveeQ6IkHCo1GI5uXr3RHG+IeJHrigePHP/6xWGmHhjl7Di2iQTxwhISEICsrC83NzX3eZTYQIdETDySRkZHUBdpLqHpPEAEGiZ4gAgwSPUEEGCR6gggwKJDnY/jUBlo2i/A1/B5zN52GRO9j+Bpu1J9M3C9u375tN1xZCs2y8zFWqxV1dXWIiIhwu+SXUlpbWzF8+HBcv379oey2eth/H+Cb38j+voOvwWBwuZIQeXofExQUhLi4OJ+k/bD3VT/svw/w/m905eE5FMgjiACDRE8QAQaJ/gFEq9Vi/fr1D+3iEQ/77wP69zdSII8gAgzy9AQRYJDoCSLAINETRIBBoieIAINETxABBoneT6mqqsLixYsRHx+PsLAwjBo1CuvXr4fFYpHZVVdX46WXXkJ4eDiio6OxfPlyO5vy8nKkpqYiLCwMw4YNw7vvvut2UkZ/snPnTsTHxyM0NBRJSUk4efJkfxdJERs3bsQ//MM/ICIiAjExMfjBD36Ar7/+WmbDGIPRaITBYEBYWBhmzJiBy5cvy2zMZjOWLVuG6OhohIeHY968eaipqfFeQX25zQ7Re/77v/+bLVq0iH3++efs6tWr7PDhwywmJoatWrVK2PD9+J599llWWlrKjh8/zgwGg2w/vpaWFhYbG8sWLlzIysvLWX5+PouIiGDvv/9+f/wst+Tl5TG1Ws1+85vfsCtXrrAVK1aw8PBwdu3atf4umlvS09PZ3r17WUVFBbtw4QKbM2cOGzFiBGtraxM2ubm5LCIiguXn57Py8nK2YMECh3s1Dhs2jB0/fpyVlpayZ5991m6vxr5Aon+A2Lx5M4uPjxefjxw5woKCglhtba04tn//fqbVasV+Zjt37mQ6nU62R9/GjRuZwWBgVqv1/hVeIZMmTWKZmZmyYwkJCeydd97ppxL1noaGBgaAnThxgjHWs0+jXq9nubm5wqajo4PpdDq2a9cuxhhjt27dYmq1muXl5Qmb2tpaFhQUxI4ePeqVclH1/gGipaVFtvprcXExEhMTYTAYxLH09HSYzWaUlJQIm9TUVNnIr/T0dNTV1aGqquq+lV0JFosFJSUlSEtLkx1PS0vD6dOn+6lUvaelpQUAxP+ssrISJpNJ9vu0Wi1SU1PF7yspKUFnZ6fMxmAwIDEx0WvXgET/gHD16lVs27YNmZmZ4pjJZLLb8WXw4MHQaDQwmUxObfhnbuMvNDY2oru722F5/a2s7mCMIScnB9///veRmJgI4N71dvX7TCYTNBqN3SYe3rwGJPr7jNFohEqlcvk6f/687Jy6ujrMnj0bP/rRj7BkyRLZd47m6DPGZMdtbdjfg3jemt/vbRyV11/L6oysrCxcunQJ+/fvt/uuN7/Pm9eA5tPfZ7KysrBw4UKXNo8++qh4X1dXh2effRZTp07F7t27ZXZ6vR5nz56VHWtubkZnZ6fwJnq93s5DNDQ0ALD3OP1NdHQ0goODHZbX38rqimXLluGzzz7D//zP/8jWUtDr9QB6vPnQoUPFcenv0+v1sFgsaG5ulnn7hoYGTJs2zTsF9EpkgPAJNTU1bPTo0WzhwoUOI7c8kFdXVyeO5eXl2QXyBg0axMxms7DJzc3160DeT3/6U9mxsWPHPhCBPKvVyv75n/+ZGQwG9s033zj8Xq/Xs02bNoljZrPZYSDvwIEDwqaurs6rgTwSvZ9SW1vLvve977HnnnuO1dTUsPr6evHi8C67mTNnstLSUlZYWMji4uJkXXa3bt1isbGx7LXXXmPl5eXs0KFDLDIy0u+77Pbs2cOuXLnCsrOzWXh4OKuqqurvornlpz/9KdPpdOyvf/2r7P919+5dYZObm8t0Oh07dOgQKy8vZ6+99prDLru4uDhWWFjISktL2XPPPUdddoHA3r17GQCHLynXrl1jc+bMYWFhYSwqKoplZWXJuucYY+zSpUssJSWFabVaptfrmdFo9Esvz9mxYwcbOXIk02g0bOLEiaLLy99x9v/au3evsLFarWz9+vVMr9czrVbLnnnmGVZeXi5Lp729nWVlZbGoqCgWFhbG5s6dy6qrq71WTppPTxABBkXvCSLAINETRIBBoieIAINETxABBomeIAIMEj1BBBgkeoIIMEj0BBFgkOgJIsAg0RNEgEGiJ4gA4/8BjzatM4KEnuIAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from itertools import repeat\n", - "\n", - "pre = 0.25\n", - "post = 0.25\n", - "\n", - "sr = wm_ref_data.info['sfreq']\n", - "\n", - "evs = [] \n", - "durs = [] \n", - "descriptions = []\n", - "stas = [] \n", - "stes = [] \n", - "data = wm_ref_data.get_data()\n", - "\n", - "for ch in wm_ref_data.ch_names: \n", - " fig, ax = plt.subplots(1, 1, figsize=(2, 2))\n", - "\n", - " ch_ix = wm_ref_data.ch_names.index(ch)\n", - " # Plot the IEDs \n", - " signal = data[ch_ix, :]\n", - " IED_ts = IED_times_s[ch]\n", - " sta, ste = analysis_utils.lfp_sta(IED_ts, signal, sr, pre, post)\n", - " stas.append(sta)\n", - " stes.append(ste)\n", - " \n", - " ts = np.linspace(-250, 250, len(sta))\n", - " ax.plot(ts, sta, 'gray')\n", - " ax.fill_between(ts, sta-ste, \n", - " sta+ste, facecolor='k')\n", - " \n", - " \n", - " \n", - "# # Nan out the IEDs: \n", - "# starts = np.ceil((IED_ts - 0.1) * wm_ref_data.info['sfreq']).astype(int)\n", - "# stops = np.floor((IED_ts + 0.1) * wm_ref_data.info['sfreq']).astype(int)\n", - " \n", - "# for a,b in zip(starts, stops): \n", - "# signal[a:b] = np.nan\n", - " \n", - "# data[ch_ix, :] = signal\n", - " \n", - "# # Make events and annotate \n", - "# evs = evs + (IED_ts - 0.1).tolist()\n", - "\n", - "# durs = durs + (0.2 * np.ones_like(IED_ts)).tolist()\n", - "\n", - "# descriptions = descriptions + ([f'BAD_ied_{ch}']*len(IED_ts))\n", - "\n", - "# # Let's replace the data with the IED's nan-ed out\n", - "# wm_ref_data._data = data\n", - "# # Make mne annotations based on these descriptions\n", - "# annot = mne.Annotations(onset=evs,\n", - "# duration=durs,\n", - "# description=descriptions)\n", - "\n", - "# wm_ref_data.set_annotations(annot)\n", - "\n", - "# events_from_annot, event_dict = mne.events_from_annotations(wm_ref_data)\n", - " \n", - "fig, ax = plt.subplots(1, 1, figsize=(2, 2))\n", - "ts = np.linspace(-250, 250, len(sta))\n", - "ax.plot(ts, np.nanmean(stas, axis=0), 'gray')\n", - "ax.fill_between(ts, np.nanmean(stas, axis=0)-np.nanmean(stes, axis=0), \n", - " np.nanmean(stas, axis=0)+np.nanmean(stes, axis=0), facecolor='k')\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "f714803e", - "metadata": {}, - "source": [ - "## Now process the behavioral data" - ] - }, - { - "cell_type": "markdown", - "id": "28afc276", - "metadata": {}, - "source": [ - "Here, one should load in their own functions for behavioral stuff. I'll just write the functions relevant to me here for demonstration purposes. " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9dc3af7c", - "metadata": {}, - "outputs": [], - "source": [ - "# Utility functions for image memorability ratings. \n", - "import pandas as pd \n", - "import numpy as np \n", - "import os \n", - "from scipy.stats import norm, zscore, linregress\n", - "\n", - "# Note: Much of the following is ported from: https://github.com/cvzoya/memorability-distinctiveness\n", - "\n", - "def dprime(pHit, pFA, PresentT, AbsentT, criteria=False):\n", - " \"\"\"\n", - " Note: from: http://nikos-konstantinou.blogspot.com/2010/02/dprime-function-in-matlab.html\n", - " \n", - " \n", - " Parameters\n", - " ----------\n", - " pHit : float\n", - " The proportion of \"Hits\": P(Yes|Signal)\n", - " pFA : float\n", - " The proportion of \"False Alarms\": P(Yes|Noise)\n", - " PresentT : int\n", - " The number of Signal Present Trials e.g. length(find(signal==1))\n", - " AbsentT : int\n", - " The number of Signal Absent Trials e.g. length(find(signal==0))\n", - "\n", - " \n", - " Returns\n", - " -------\n", - " dPrime: float\n", - " signal detection theory sensitivity measure \n", - " \n", - " beta: float\n", - " optional criterion value\n", - " \n", - " C: float\n", - " optional criterion value\n", - " \n", - " \"\"\"\n", - "\n", - " if pHit == 1: \n", - " # if 100% Hits\n", - " pHit = 1 - (1/(2*PresentT))\n", - " \n", - " if pFA == 0: \n", - " # if 0% FA \n", - " pFA = 1/(2*AbsentT)\n", - " \n", - " # Convert to Z-scores\n", - " \n", - " zHit = norm.ppf(pHit) \n", - " zFA = norm.ppf(pFA) \n", - " \n", - " # calculate d-prime \n", - " \n", - " dPrime = zHit - zFA \n", - " \n", - " if criteria:\n", - " beta = np.exp((zFA**2 - zHit**2)/2)\n", - " C = -0.5 * (zHit + zFA) \n", - " return dPrime, beta, C\n", - " else:\n", - " return dPrime\n", - " \n", - "# def calcMI(pmf):\n", - "# \"\"\"\n", - " \n", - "# Parameters\n", - "# ----------\n", - " \n", - " \n", - "# Returns\n", - "# -------\n", - " \n", - "# \"\"\"\n", - " \n", - "# pmf_1 = np.sum(pmf,axis=1) # marginal over first variable\n", - "# pmf_2 = np.sum(pmf,axis=0) # marginal over second variable\n", - " \n", - "# MI = 0\n", - "# for i in range(np.shape(pmf)[0]):\n", - "# for j in range(np.shape(pmf)[1]):\n", - "# MI += pmf[i, j] * np.log(pmf[i, j] / (pmf_1[i]*pmf_2[j]))\n", - " \n", - "# return MI\n", - "\n", - "def compute_memorability_scores(hits, false_alarms, misses, correct_rejections):\n", - " \"\"\"\n", - " Parameters\n", - " ----------\n", - " hits : array-like\n", - " TODO\n", - " false_alarms : array-like\n", - " TODO\n", - " misses : array_like \n", - " TODO\n", - " correct_rejections : array_like \n", - " TODO\n", - " \n", - " Returns\n", - " -------\n", - " memory_ratings : pandas DataFrame \n", - " DataFrame with the following ratings added: HR (hit rate), FAR (false alarm rate), ACC (accuracy), DPRIME (d-prime), MI (mutual information)\n", - " \"\"\"\n", - " \n", - " \n", - "\n", - " len_args = [len(hits), len(false_alarms), len(misses), len(correct_rejections)]\n", - " if not all(len_args[0] == _arg for _arg in len_args[1:]):\n", - " raise ValueError(\"All parameters must be the same length.\")\n", - " \n", - " memory_ratings = pd.DataFrame(columns = ['HR', 'FAR', 'ACC', 'DPRIME'])\n", - " # , 'MI'\n", - " \n", - " reg = 0.1 # regularization for MI calculation\n", - "\n", - " nstimuli = len(hits) \n", - "\n", - " hm = hits+misses\n", - " fc = false_alarms+correct_rejections\n", - "\n", - " hrs = hits/hm\n", - " fars = false_alarms/fc\n", - " accs = (hits+correct_rejections)/(hm+fc)\n", - "\n", - " dp = []\n", - "# mis = []\n", - " for i in range(nstimuli):\n", - " dp.append(dprime(hrs[i], fars[i], hm[i], fc[i]))\n", - "# pmf = np.array([[correct_rejections, misses], \n", - "# [false_alarms, hits]]) + reg\n", - "# pmf = pmf/np.sum(pmf)\n", - "# mis.append(calcMI(pmf))\n", - " \n", - "\n", - " memory_ratings['HR'] = hrs\n", - " memory_ratings['FAR'] = fars\n", - " memory_ratings['ACC'] = accs\n", - " memory_ratings['DPRIME'] = dp\n", - "# memory_ratings['MI'] = mis\n", - " \n", - " return memory_ratings" - ] - }, - { - "cell_type": "markdown", - "id": "717df578", - "metadata": {}, - "source": [ - "## Synchronize to behavioral data" - ] - }, - { - "cell_type": "markdown", - "id": "e9cedc2a", - "metadata": {}, - "source": [ - "In order to analyze the neural data with respect to the behavioral data we need to be able to synchronize the two using the photodiode (or TTLs, eventually?) " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "a494bca2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "There are 485 neural syncs detected\n" - ] - } - ], - "source": [ - "# Find the timestamps of ONSET and OFFSET of all the sync signals in the photodiode \n", - "\n", - "# moving average helps us detect the deflections \n", - "sig = np.squeeze(sync_utils.moving_average(photodiode._data, n=11))\n", - "timestamp = np.squeeze(np.arange(len(sig))/wm_ref_data.info['sfreq'])\n", - "# normalize\n", - "sig = zscore(sig)\n", - "# look for z-scores above 1\n", - "trig_ix = np.where((sig[:-1]<=0)*(sig[1:]>0))[0] # rising edge of trigger\n", - "neural_ts = timestamp[trig_ix]\n", - "neural_ts = np.array(neural_ts)\n", - "print(f'There are {len(neural_ts)} neural syncs detected')" - ] - }, - { - "cell_type": "markdown", - "id": "797769f7", - "metadata": {}, - "source": [ - "Get the .log file and/or .csv file, depending on how your task logs the behavioral data. Eventually this should be fairly standardized across tasks." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "e91add5a", - "metadata": {}, - "outputs": [], - "source": [ - "log_path = glob(f'{behav_dir}/*.log')[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "e7d4a130", - "metadata": {}, - "outputs": [], - "source": [ - "csv_path = glob(f'{behav_dir}/*MB_MEM*.csv')[0]" - ] - }, - { - "cell_type": "markdown", - "id": "a72b67f0", - "metadata": {}, - "source": [ - "The next step pulls relevant timestamps from the behavioral logfile. THIS STEP DIFFERS DEPENDING ON YOUR TASK. \n", - "\n", - "Maybe one day this step will be unified across all tasks (i.e. either we have a programmer make our tasks, or we unify best practices for task design). " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "7400fda7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "There are 486 behav syncs detected\n" - ] - }, - { - "data": { - "text/plain": [ - "381.5762" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Now get the relevant timestamps from behavioral logfiles. This will differ depending\n", - "\n", - "MB1_ts = {'trial_start': [], \n", - "'deck_start': [], \n", - "'feedback_start': [],\n", - "'ITI_start': [],\n", - "'ITI_stop': []}\n", - "\n", - "MEM2_ts = {'trial_start': [], \n", - "'face_start': [], \n", - "'slider_start': [],\n", - "'slider_stop': [],\n", - "'ITI_start': [],\n", - "'ITI_stop': []}\n", - "\n", - "beh_ts = []\n", - "\n", - "MB1_FLAG = True \n", - "MEM2_FLAG = False \n", - "\n", - "with open(log_path, 'r') as fobj:\n", - " for ix, line in enumerate(fobj.readlines()):\n", - " line = line.replace('\\r', '')\n", - " tokens = line[:-1].split('\\t')\n", - "\n", - " if tokens[1] == 'EXP ':\n", - " # Determine which task we are looking at \n", - " if tokens[2][0:3] == 'MB1':\n", - " MB1_FLAG = True\n", - " MEM2_FLAG = False \n", - " elif tokens[2][0:3] == 'MEM':\n", - " MEM2_FLAG = True\n", - " MB1_FLAG = False\n", - "\n", - " # Grab photodiode timestamp\n", - " if tokens[2][0:4] =='sync':\n", - " if 'autoDraw = True' in tokens[2]:\n", - " beh_ts.append(float(tokens[0]))\n", - "\n", - " # Get MB1 deck \n", - " if 'MB1_left_draw' in tokens[2]:\n", - " if 'autoDraw = True' in tokens[2]:\n", - " MB1_ts['deck_start'].append(float(tokens[0]))\n", - " \n", - " # Get MB1 feedback\n", - " if 'MB1_face' in tokens[2]:\n", - " if 'autoDraw = True' in tokens[2]:\n", - " MB1_ts['feedback_start'].append(float(tokens[0]))\n", - "\n", - " # Get MB1 ITI cross \n", - " if 'MB1_ITI_cross' in tokens[2]:\n", - " if 'autoDraw = True' in tokens[2]:\n", - " MB1_ts['ITI_start'].append(float(tokens[0]))\n", - " elif 'autoDraw = False' in tokens[2]:\n", - " MB1_ts['ITI_stop'].append(float(tokens[0]))\n", - "\n", - " if 'New trial (rep=0' in tokens[2]:\n", - " if MB1_FLAG: \n", - " # remember to discard the first one later - it's pre-session \n", - " MB1_ts['trial_start'].append(float(tokens[0]))\n", - " elif MEM2_FLAG:\n", - " MEM2_ts['trial_start'].append(float(tokens[0]))\n", - " \n", - " # Get MEM2 ITI\n", - " if 'MEM2_jitter' in tokens[2]:\n", - " if 'autoDraw = True' in tokens[2]:\n", - " MEM2_ts['ITI_start'].append(float(tokens[0])) \n", - " elif 'autoDraw = False' in tokens[2]:\n", - " MEM2_ts['ITI_stop'].append(float(tokens[0])) \n", - "\n", - " # Get MEM2 Face\n", - " if 'MEM2_images' in tokens[2]:\n", - " if 'autoDraw = True' in tokens[2]:\n", - " MEM2_ts['face_start'].append(float(tokens[0])) \n", - "\n", - " # Get MEM2 slider start\n", - " if tokens[2][:16] == 'MEM2_conf_slider':\n", - " if 'autoDraw = True' in tokens[2]:\n", - " MEM2_ts['slider_start'].append(float(tokens[0])) \n", - " \n", - " # Get MEM2 slider stop\n", - " if tokens[2][:16] == 'MEM2_conf_slider':\n", - " if 'autoDraw = False' in tokens[2]:\n", - " MEM2_ts['slider_stop'].append(float(tokens[0])) \n", - " \n", - "beh_ts = np.array(beh_ts)\n", - "print(f'There are {len(beh_ts)} behav syncs detected')\n", - "\n", - "# Note: fixation crosses need fixing on stop time duplicates\n", - "MB1_ts['ITI_stop'] = np.unique(MB1_ts['ITI_stop']).tolist()\n", - "MEM2_ts['ITI_stop'] = np.unique(MEM2_ts['ITI_stop']).tolist()\n", - "\n", - "\n", - "# Get the choice times: \n", - "csv_data = pd.read_csv(csv_path)\n", - "MB1_ts['choice'] = (csv_data['MB1_draw_key.started'].dropna() + csv_data['MB1_draw_key.rt'].dropna()).tolist()\n", - "MEM2_ts['choice'] = (csv_data['MEM2_recall_key.started'].dropna() + csv_data['MEM2_recall_key.rt'].dropna()).tolist()\n", - "\n", - "# Do some corrections: \n", - "# Get rid of first trial start (pre-session)\n", - "MB1_ts['trial_start'].pop(0) " - ] - }, - { - "cell_type": "markdown", - "id": "65ae7982", - "metadata": {}, - "source": [ - "Sanity check - the sync pulses are similar in number. That's nice, but not guaranteed. " - ] - }, - { - "cell_type": "markdown", - "id": "a31e9436", - "metadata": {}, - "source": [ - "Now, compile the behavioral data for each trial. We will use this to add to the mne metadata later, which is very important for analysis. " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "e0f0fee9", - "metadata": {}, - "outputs": [], - "source": [ - "subj_count = 0\n", - "\n", - "# Load the database with image DPRIME information\n", - "database_file = f'{behav_dir}/all_mem_data.xlsx' \n", - "all_mem_df = pd.read_excel(database_file, engine='openpyxl')\n", - "all_mem_df = all_mem_df[['img_path', 'DPRIME']]\n", - "\n", - "# Turn into right format for modeling: \n", - "MB1_n = 60\n", - "MEM2_n = 120\n", - "li_mb1 = []\n", - "li_mem2 = [] \n", - "\n", - "act_rew_rate = {}\n", - "act_rew_rate['pids'] = []\n", - "\n", - "r1_chance=30\n", - "\n", - "for elem in ['actions', 'rewards']:\n", - " act_rew_rate[elem] = np.zeros(MB1_n).astype(int) # len(task_files), \n", - "\n", - "# Load the merged task data \n", - "csv_data['trials_2.thisN'] = csv_data['trials_2.thisRepN'].shift(-1)\n", - "\n", - "##### First, process the Bandit task: \n", - "mb_df = csv_data.dropna(subset=['trials_2.thisN'])\n", - "act_rew_rate['pids'].append(mb_df.participant.iloc[0])\n", - "\n", - "# Change Gender so that female = 2\n", - "mb_df.Gender[mb_df.Gender==0] = 2\n", - "\n", - "# add score, reward probability and expected value \n", - "mb_df['choice'] = mb_df.apply(lambda x: x['MB1_draw_key.keys'], axis=1)\n", - "# Make the trials 1-60 \n", - "mb_df['trials_dm'] = mb_df['trials_2.thisN'].shift(+1)\n", - "# mb_df.trials_dm.fillna(60, inplace=True)\n", - "# # get rid of the extra rows in the .csv that populate between trials \n", - "mb_df = mb_df.drop_duplicates(subset='trials_dm', keep='first')\n", - "mb_df['reward'] = mb_df.apply(lambda x: x['reward']/100, axis=1)\n", - "mb_df.reward[mb_df.reward==100] = 0\n", - "# 0 is male, 1 is female \n", - "mb_df['choice'] = mb_df['choice']-1\n", - "mb_df.rename(columns={'MB1_draw_key.rt':'draw_rt'}, inplace=True)\n", - "mb_df.dropna(subset=['img_path'], inplace=True)\n", - "\n", - "# # check chance: \n", - "# if mb_df.reward.sum() < r1_chance:\n", - "# # print(f'Subject {subj_count} performed worse than random')\n", - "# bad_subs.append(subj_count)\n", - "# continue\n", - "\n", - "##### Fit RW model to DM data\n", - "mb_df['bic'] = np.nan\n", - "mb_df['alpha'] = np.nan\n", - "mb_df['beta'] = np.nan\n", - "mb_df['RPE'] = np.nan\n", - "\n", - "# RW_model = RW() \n", - "sub_act_rew_rate = {}\n", - "sub_act_rew_rate['pids'] = np.array([subj_count]) \n", - "\n", - "for elem in ['actions', 'rewards']:\n", - " sub_act_rew_rate[elem] = np.zeros([1, MB1_n]).astype(int)\n", - "c = mb_df.choice.dropna().values.astype(int)\n", - "r = mb_df.reward.dropna().values\n", - "sub_act_rew_rate['actions'][0, :] = c\n", - "sub_act_rew_rate['rewards'][0, :] = r\n", - "# RW_model.fit_all(sub_act_rew_rate) \n", - "\n", - "# mb_df.RPE= RW_model.fit_metrics['prederr'][0].tolist()\n", - "# mb_df.bic= RW_model.fit_metrics['bic'][0]\n", - "# mb_df.alpha= RW_model.fit_metrics['params']['alpha'][0]\n", - "# mb_df.beta= RW_model.fit_metrics['params']['beta'][0]\n", - "\n", - "# Save dict for modeling decision-making performance: \n", - "c = mb_df.choice.dropna().values.astype(int)\n", - "r = mb_df.reward.dropna().values\n", - "act_rew_rate['actions'] = c # [subj_count, :]\n", - "act_rew_rate['rewards']= r # [subj_count, :]\n", - "\n", - "##### Second, process the MEM2 data: \n", - "rm_df = csv_data.dropna(subset=['MEM2_trials.thisN'])\n", - "\n", - "# add coding of memory choice: \n", - "rm_df['hit'] = 0\n", - "rm_df['miss'] = 0\n", - "rm_df['corr_reject'] = 0\n", - "rm_df['false_alarm'] = 0\n", - "\n", - "# add score, reward probability and expected value \n", - "rm_df.rename(columns={'MEM2_recall_key.keys': 'response',\n", - "'MEM2_conf_slider.response': 'confidence'\n", - " }, inplace=True) \n", - "\n", - "rm_df['trials_mem'] = rm_df['MEM2_trials.thisN'].shift(-1)\n", - "rm_df.trials_mem.fillna(120, inplace=True)\n", - "\n", - "# Change Gender so that female = 2\n", - "rm_df.Gender[rm_df.Gender==0] = 2\n", - "\n", - "rm_df = rm_df.merge(mb_df, on='img_path', how='left', indicator=True)\n", - "# Clean up the merge\n", - "rm_df.drop(columns=['participant_y'], inplace=True)\n", - "rm_df.rename(columns={'participant_x': 'participant'}, inplace=True)\n", - "\n", - "\n", - "# compute the hit-rates and false-alarm rates \n", - "### TO do so, use the merge to find image_paths that were in \"both\" dfs ('old') \n", - "# OLD = 2, which is correct in this case \n", - "hit_bool = (rm_df._merge=='both') & (rm_df.response==2)\n", - "hits = hit_bool.sum()\n", - "# NEW = 1, which is false in this case \n", - "miss_bool = (rm_df._merge=='both') & (rm_df.response==1)\n", - "misses = miss_bool.sum()\n", - "\n", - "# or just the \"left\" df ('new')\n", - "false_alarm_bool = (rm_df._merge=='left_only') & (rm_df.response==2)\n", - "false_alarms = false_alarm_bool.sum()\n", - "corr_reject_bool = (rm_df._merge=='left_only') & (rm_df.response==1)\n", - "correct_rejections = corr_reject_bool.sum()\n", - "\n", - "# categorize image by memory choice\n", - "rm_df.hit[hit_bool] = 1\n", - "rm_df.miss[miss_bool] = 1\n", - "rm_df.false_alarm[false_alarm_bool] = 1\n", - "rm_df.corr_reject[corr_reject_bool] = 1\n", - "\n", - "# compute dprime for the subject\n", - "hm = hits+misses\n", - "fc = false_alarms+correct_rejections\n", - "\n", - "hrs = hits/hm\n", - "fars = false_alarms/fc\n", - "\n", - "# Adjust extreme hit-rates or false-alarms\n", - "if hrs == 0: \n", - " hrs = 0.5/hm\n", - "elif hrs ==1: \n", - " hrs = (hm-0.5)/hm\n", - "if fars == 0: \n", - " fars = 0.5/fc\n", - "elif fars ==1: \n", - " fars = (fc-0.5)/fc\n", - "\n", - "# only the extreme values by replacing rates of 0 with 0.5/𝑛 and rates of 1 with (𝑛−0.5)/𝑛 where 𝑛 is the number of signal or noise trials (Macmillan & Kaplan, 1985)\n", - "\n", - "dp = dprime(hrs, fars, hm, fc)\n", - "\n", - "# Add in subject-level memory characteristics (\"rates\")\n", - "rm_df['hit_rate'] = hrs\n", - "rm_df['false_alarm_rate'] = fars\n", - "rm_df['subj_dprime'] = np.nan\n", - "if dp != float(\"-inf\"):\n", - " rm_df['subj_dprime'] = dp \n", - "\n", - "# # Get rid of horrible mmemory performers \n", - "# if rm_df.subj_dprime.mean()<=0:\n", - "# bad_subs.append(subj_count)\n", - "# continue\n", - "\n", - "# # Get patient demographics\n", - "# rm_df['age'] = np.nan\n", - "# rm_df['subj_gender'] = np.nan\n", - "# rm_df['age'] = demographics_df[demographics_df.participant==rm_df.participant.iloc[0]].age.values[0]\n", - "# rm_df['subj_gender'] = demographics_df[demographics_df.participant==rm_df.participant.iloc[0]].Sex.values[0]\n", - "\n", - "# Merge in the image DPRIME \n", - "rm_df['DPRIME'] = rm_df.merge(all_mem_df, on='img_path', how='right')['DPRIME']\n", - "mb_df['DPRIME'] = mb_df.merge(all_mem_df, on='img_path', how='right')['DPRIME']\n", - "mb_df.rename(columns={'DPRIME': 'image_dprime'}, inplace=True) \n", - "\n", - "rm_df.rename(columns={'DPRIME': 'image_dprime',\n", - "'Gender_x': 'image_gender',\n", - "'MEM2_recall_key.rt_x': 'recall_rt',\n", - "'MEM2_conf_slider.rt_x': 'slider_rt'\n", - "}, inplace=True) \n", - "\n", - "# li_mb1.append(mb_df)\n", - "# li_mem2.append(rm_df)\n", - "\n", - "# dm_df = pd.concat(li_mb1, axis=0, ignore_index=True)\n", - "mb_df['male'] = 0\n", - "mb_df['female'] = 0\n", - "mb_df.male = mb_df.apply(lambda x: 1 if x.choice==0 else 0, axis=1)\n", - "mb_df.female = mb_df.apply(lambda x: 1 if x.choice==1 else 0, axis=1)\n", - "\n", - "# col_mask = ((mb_df.columns.str.startswith('MB')) | (mb_df.columns.str.startswith('trials.')))\n", - "\n", - "# mem_df = pd.concat(li_mem2, axis=0, ignore_index=True)\n", - "rm_df['phit'] = rm_df.response - 1\n", - "\n", - "# # Get rid of trash: \n", - "# act_rew_rate['pids'] = np.array(act_rew_rate['pids'])\n", - "# act_rew_rate['pids'] = np.delete(act_rew_rate['pids'], bad_subs)\n", - "# for elem in ['actions', 'rewards']:\n", - "# act_rew_rate[elem] = np.delete(act_rew_rate[elem], bad_subs, 0)\n", - "\n", - "col_mask = ((rm_df.columns.str.startswith('MEM')) | (rm_df.columns.str.startswith('MB')) | (rm_df.columns.str.startswith('trials.')) | (rm_df.columns.str.startswith('trials_2')) | (rm_df.columns.str.endswith('_y')))\n", - "rm_df = rm_df.loc[:,~col_mask]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "04751e96", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "50 blocks\n", - ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . found matches for 36 of 50 blocks\n", - "sync succeeded\n" - ] - } - ], - "source": [ - "# Do regression to find neural timestamps for each event type\n", - "if len(beh_ts)!=len(neural_ts):\n", - " good_beh_ms, neural_offset = sync_utils.pulsealign(beh_ts, neural_ts, window=50, thresh=0.95)\n", - " slope, offset, rval = sync_utils.sync_matched_pulses(good_beh_ms, neural_offset)\n", - "else:\n", - " slope, offset, rval = sync_utils.sync_matched_pulses(beh_ts, neural_ts)\n", - "\n", - "if rval < 0.99:\n", - " print('sync failed')\n", - "else: \n", - " print('sync succeeded')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "d292a7b0", - "metadata": {}, - "outputs": [], - "source": [ - "# sync_data = pd.DataFrame(columns=['slope', 'offset'])\n", - "# sync_data.slope = [slope]\n", - "# sync_data.offset = [offset]\n", - "# sync_data.to_csv(f'{save_dir}/sync_data.csv', index=False)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "bbb2e135", - "metadata": {}, - "outputs": [], - "source": [ - "# pd.DataFrame(MB1_ts).to_csv(f'{save_dir}/MB1_ts.csv', index=False)\n", - "# pd.DataFrame(MEM2_ts).to_csv(f'{save_dir}/MEM2_ts.csv', index=False)" - ] - }, - { - "cell_type": "markdown", - "id": "f32ddcbe", - "metadata": {}, - "source": [ - "## Create epochs" - ] - }, - { - "cell_type": "markdown", - "id": "4622636b", - "metadata": {}, - "source": [ - "Ok, now that we have done some pre-processing, we want to make epochs aligned to specific behavioral events. Need the slope and offsets we determined from photodiode synchronization." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "80c3344d", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'MB1_ts' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[12], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# all behavioral times of interest \u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m gamble_choice_ts \u001b[38;5;241m=\u001b[39m [(x\u001b[38;5;241m*\u001b[39mslope \u001b[38;5;241m+\u001b[39m offset) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m \u001b[43mMB1_ts\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mchoice\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n\u001b[1;32m 3\u001b[0m gamble_feedback_ts \u001b[38;5;241m=\u001b[39m [(x\u001b[38;5;241m*\u001b[39mslope \u001b[38;5;241m+\u001b[39m offset) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m MB1_ts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfeedback_start\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n\u001b[1;32m 4\u001b[0m gamble_fixation_ts \u001b[38;5;241m=\u001b[39m [(x\u001b[38;5;241m*\u001b[39mslope \u001b[38;5;241m+\u001b[39m offset) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m MB1_ts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mITI_start\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n", - "\u001b[0;31mNameError\u001b[0m: name 'MB1_ts' is not defined" - ] - } - ], - "source": [ - "# all behavioral times of interest \n", - "gamble_choice_ts = [(x*slope + offset) for x in MB1_ts['choice']]\n", - "gamble_feedback_ts = [(x*slope + offset) for x in MB1_ts['feedback_start']]\n", - "gamble_fixation_ts = [(x*slope + offset) for x in MB1_ts['ITI_start']]\n", - "\n", - "memory_cue_ts = [(x*slope + offset) for x in MEM2_ts['face_start']]\n", - "memory_choice_ts = [(x*slope + offset) for x in MEM2_ts['choice']]\n", - "memory_fixation_ts = [(x*slope + offset) for x in MEM2_ts['ITI_start']]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "3e915bb1", - "metadata": {}, - "outputs": [], - "source": [ - "# set some windows of interest \n", - "\n", - "buf = 1.0 # this is the buffer before and after that we use to limit edge effects for TFRs\n", - "\n", - "feedback_pre = 1.0 # this is the time before the feedback appears \n", - "feedback_post = 1.5 # this is the time the feedback is present \n", - "\n", - "choice_pre = 1.0 # this is the choice deliberation period\n", - "choice_post = feedback_pre\n", - "\n", - "baseline_pre = 0\n", - "baseline_post = 0.5 " - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "7fe0166b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Used Annotations descriptions: ['gamble_feedback']\n" - ] - } - ], - "source": [ - "# Example - make feedback events \n", - "\n", - "evs = gamble_feedback_ts\n", - "\n", - "durs = np.zeros_like(gamble_feedback_ts).tolist()\n", - "\n", - "descriptions = ['gamble_feedback']*len(gamble_feedback_ts)\n", - "\n", - "# Make mne annotations based on these descriptions\n", - "annot = mne.Annotations(onset=evs,\n", - " duration=durs,\n", - " description=descriptions)\n", - "\n", - "wm_ref_data.set_annotations(annot)\n", - "\n", - "events_from_annot, event_dict = mne.events_from_annotations(wm_ref_data)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "41fad477", - "metadata": {}, - "outputs": [], - "source": [ - "behav = {'example': {'0':np.array([1,2,3,4])},\n", - " 'example_2': {'1':np.array([5,6,7,8])}}" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "3327aeb9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 2, 3, 4, 5, 6, 7, 8])" - ] - }, - "execution_count": 78, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.sort(np.concatenate([list(x.values())[0] for x in list(behav.values())]).ravel())" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "9f3e61c0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 2, 3, 4])" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "behav['example']['0']" - ] - }, - { - "cell_type": "markdown", - "id": "ac35ef0b", - "metadata": {}, - "source": [ - "Create the epochs below. **Note that I am setting a fixed baseline period across all trials. By default, this will subtract the mean of the signal during the basline period from my signal during my period of interest.**" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "8cbc4d49", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not setting metadata\n", - "60 matching events found\n", - "Applying baseline correction (mode: mean)\n", - "0 projection items activated\n", - "Using data from preloaded Raw for 60 events and 4609 original time points ...\n", - "0 bad epochs dropped\n" - ] - } - ], - "source": [ - "epochs = mne.Epochs(wm_ref_data, \n", - " events_from_annot, \n", - " event_id=event_dict, \n", - " baseline=(-feedback_pre, 0), \n", - " tmin=-(buf + feedback_pre), \n", - " tmax=buf + feedback_post, \n", - " reject=None, \n", - " reject_by_annotation=False,\n", - " preload=True)" - ] - }, - { - "cell_type": "markdown", - "id": "10eeaa65", - "metadata": {}, - "source": [ - "**As an alternative to setting a baseline parameter in your Epochs code:** If you can't baseline your epochs in a standard way across trials (i.e. against a default time like -0.5 to 0 seconds) then you can MAKE a set of epochs to use as a baseline. The code below makes epochs based on the duration of the fixation cross, and subtracts the mean of these epochs from our epochs of interest. " - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "c6417569", - "metadata": {}, - "outputs": [], - "source": [ - "# # Make baseline epochs for 2-arm bandit: \n", - "\n", - "# evs = gamble_fixation_ts\n", - "\n", - "# durs = np.zeros_like(gamble_fixation_ts).tolist()\n", - "\n", - "# descriptions = ['gamble_fixation_ts']*len(gamble_fixation_ts)\n", - "\n", - "# # Make mne annotations based on these descriptions\n", - "# annot = mne.Annotations(onset=evs,\n", - "# duration=durs,\n", - "# description=descriptions)\n", - "\n", - "# wm_ref_data.set_annotations(annot)\n", - "\n", - "# events_from_annot, event_dict = mne.events_from_annotations(wm_ref_data)\n", - "\n", - "# baseline_epochs = mne.Epochs(wm_ref_data, \n", - "# events_from_annot, \n", - "# event_id=event_dict, \n", - "# baseline=None, \n", - "# tmin=-(buf + baseline_pre), \n", - "# tmax=buf + baseline_post, \n", - "# reject=None, \n", - "# preload=True)\n", - "\n", - "# # # Make baseline epochs for recognition memory: \n", - "# # my_annot = mne.Annotations(onset=memory_cross_start,\n", - "# # duration=0.5,\n", - "# # description=['memory_cross']*len(memory_cross_start))\n", - "\n", - "# # wm_ref_data.set_annotations(my_annot)\n", - "# # events_from_annot, event_dict = mne.events_from_annotations(wm_ref_data)\n", - "\n", - "# # rm_baseline_epochs = mne.Epochs(wm_ref_data, events_from_annot, event_id=event_dict, baseline=None, tmin=-buf, tmax=0.5+buf, reject=None, preload=True)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "7d07b6f8", - "metadata": {}, - "outputs": [], - "source": [ - "# # baseline the LFP data in time: \n", - "\n", - "# buf_ix = int(buf*epochs.info['sfreq'])\n", - "\n", - "# time_baseline = baseline_epochs._data[:, :, buf_ix:-buf_ix]\n", - "\n", - "# epochs._data = lfp_preprocess_utils.mean_baseline_time(epochs._data, time_baseline, mode='mean')\n" - ] - }, - { - "cell_type": "markdown", - "id": "35d55949", - "metadata": {}, - "source": [ - "## Downsample and annotate the epoched data" - ] - }, - { - "cell_type": "markdown", - "id": "a40bbef6", - "metadata": {}, - "source": [ - "Resampling is the two-step process of applying a low-pass FIR filter and subselecting samples from the data.\n", - "\n", - "Using this function to resample data before forming mne.Epochs for final analysis is generally discouraged because doing so effectively loses precision of (and jitters) the event timings (see commented example below to prove this to yourself)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "c076d52f", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# \"\"\"\n", - "# An example where effecting jittering of triggers occurs when\n", - "# downsampling before epoching.\n", - "# \"\"\"\n", - "# import numpy as np\n", - "# import matplotlib.pyplot as plt\n", - "\n", - "# # 1 sec of data @ 1000 Hz\n", - "# fs = 1000. # Hz\n", - "# decim = 5\n", - "# n_samples = 1000\n", - "# freq = 20. # Hz\n", - "# t = np.arange(n_samples) / fs\n", - "# epoch_dur = 1. / freq # 2 cycles of our sinusoid\n", - "\n", - "# # we have a 10 Hz sinusoid signal\n", - "# raw_data = np.cos(2 * np.pi * freq * t)\n", - "\n", - "# # let's make events that should show the sinusoid moving out of phase\n", - "# # continuously\n", - "# n_events = 40\n", - "# event_times = np.linspace(0, 1. / freq, n_events, endpoint=False)\n", - "# event_samples = np.round(event_times * fs).astype(int)\n", - "# data_epoch = list()\n", - "# epoch_len = int(round(epoch_dur * fs))\n", - "# for event_time in event_times:\n", - "# start_idx = int(np.round(event_time * fs))\n", - "# data_epoch.append(raw_data[start_idx:start_idx + epoch_len])\n", - "# data_epoch = np.array(data_epoch)\n", - "# data_epoch_ds = data_epoch[:, ::decim]\n", - "\n", - "# # now let's try downsampling the raw data instead\n", - "# raw_data_ds = raw_data[::decim]\n", - "# fs_new = fs / decim\n", - "# data_ds_epoch = list()\n", - "# epoch_ds_len = int(round(epoch_dur * fs_new))\n", - "# for event_time in event_times:\n", - "# start_idx = int(np.round(event_time * fs_new))\n", - "# data_ds_epoch.append(raw_data_ds[start_idx:start_idx + epoch_ds_len])\n", - "# data_ds_epoch = np.array(data_ds_epoch)\n", - "\n", - "# # Look at the results\n", - "# assert data_ds_epoch.shape == data_epoch_ds.shape\n", - "# fig, axs = plt.subplots(1, 2)\n", - "# t_ds = np.arange(epoch_ds_len) / fs_new\n", - "# for di, (data_e_d, data_d_e) in enumerate(zip(data_epoch_ds, data_ds_epoch)):\n", - "# color = [di / float(n_events + 10)] * 3\n", - "# axs[0].plot(t_ds, data_e_d, color=color)\n", - "# axs[1].plot(t_ds, data_d_e, color=color)\n", - "# axs[0].set_ylabel('Epoch then downsample')\n", - "# axs[1].set_ylabel('Downsample then epoch')\n", - "# for ax in axs:\n", - "# ax.set_xlim(t_ds[[0, -1]])\n", - "# ax.set_xticks(t_ds[[0, -1]])\n", - "# fig.set_tight_layout(True)\n" - ] - }, - { - "cell_type": "markdown", - "id": "dedeca5e", - "metadata": {}, - "source": [ - "Given the above, we use the built-in downsample method of mne Epochs, which will do the filtering and sub-sampling for us. Pick a frequency that is a factor of the current sampling rate." - ] - }, - { - "cell_type": "markdown", - "id": "20413b5b", - "metadata": {}, - "source": [ - "Prelude: Some background on filtering: https://mark-kramer.github.io/Case-Studies-Python/06.html" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "52756278", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Number of events60
Eventsgamble_feedback: 60
Time range-2.000 – 2.498 sec
Baseline-1.000 – 0.000 sec
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Downsample \n", - "epochs.resample(sfreq=wm_ref_data.info['sfreq']/2)\n", - "# mb_baseline_epochs.resample(sfreq=wm_ref_data.info['sfreq']/2)\n", - "# rm_baseline_epochs.resample(sfreq=wm_ref_data.info['sfreq']/2)" - ] - }, - { - "cell_type": "markdown", - "id": "e463addc", - "metadata": {}, - "source": [ - "The following functions detects all of the IEDs on each channel, for each event, returning (samples, time(s)) for each IED. " - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "d5c42e21", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Setting up band-pass filter from 25 - 80 Hz\n", - "\n", - "FIR filter parameters\n", - "---------------------\n", - "Designing a one-pass, zero-phase, non-causal bandpass filter:\n", - "- Windowed time-domain design (firwin) method\n", - "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n", - "- Lower passband edge: 25.00\n", - "- Lower transition bandwidth: 6.25 Hz (-6 dB cutoff frequency: 21.88 Hz)\n", - "- Upper passband edge: 80.00 Hz\n", - "- Upper transition bandwidth: 20.00 Hz (-6 dB cutoff frequency: 90.00 Hz)\n", - "- Filter length: 271 samples (0.529 sec)\n", - "\n" - ] - } - ], - "source": [ - "IED_times_s = lfp_preprocess_utils.detect_IEDs(epochs, \n", - " peak_thresh=4, \n", - " closeness_thresh=0.5, \n", - " width_thresh=0.2)" - ] - }, - { - "cell_type": "markdown", - "id": "4372b930", - "metadata": {}, - "source": [ - "Add this into the metadata" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "d88fdded", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Adding metadata with 120 columns\n" - ] - } - ], - "source": [ - "# Let's make METADATA to assign each event some features\n", - "\n", - "event_metadata = pd.DataFrame(columns=['rt', 'reward', 'rpe', 'dprime']+list(IED_times_s.keys()))\n", - "\n", - "event_metadata['rt'] = mb_df['draw_rt'].tolist()\n", - "event_metadata['reward'] = mb_df['reward'].tolist()\n", - "event_metadata['dprime'] = mb_df['image_dprime'].tolist()\n", - "\n", - "for ch in list(IED_times_s.keys()):\n", - " for ev, val in IED_times_s[ch].items():\n", - " event_metadata[ch].loc[ev] = val\n", - " \n", - "epochs.metadata = event_metadata\n", - "# event_metadata" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "658ea06e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " rt reward rpe dprime lacas1-lmolf1 lacas10-lacas9 \\\n", - "0 12.598328 1.0 NaN 1.812729 NaN NaN \n", - "1 5.527587 1.0 NaN 2.668942 NaN NaN \n", - "2 8.326901 0.0 NaN 1.983692 NaN NaN \n", - "3 6.414634 1.0 NaN 1.713817 NaN NaN \n", - "4 29.968384 0.0 NaN 2.115791 NaN NaN \n", - "5 2.146740 1.0 NaN 2.206184 NaN NaN \n", - "6 3.129091 0.0 NaN 2.077757 NaN NaN \n", - "7 1.863495 0.0 NaN 1.851859 NaN NaN \n", - "8 0.532891 1.0 NaN 1.918059 NaN NaN \n", - "9 0.812089 1.0 NaN 2.718790 NaN NaN \n", - "10 2.527751 1.0 NaN 1.871871 NaN NaN \n", - "11 2.296167 1.0 NaN 1.805837 NaN NaN \n", - "12 1.861940 0.0 NaN 1.864361 NaN NaN \n", - "13 3.846403 0.0 NaN 1.975903 NaN NaN \n", - "14 1.463148 1.0 NaN 2.778578 NaN NaN \n", - "15 4.411011 1.0 NaN 2.271960 NaN NaN \n", - "16 3.898737 0.0 NaN 1.746685 NaN NaN \n", - "17 2.196505 1.0 NaN 2.052340 NaN NaN \n", - "18 10.325994 1.0 NaN 2.511274 NaN NaN \n", - "19 3.695989 1.0 NaN 2.188941 NaN NaN \n", - "20 3.130716 1.0 NaN 2.109963 NaN NaN \n", - "21 1.394408 1.0 NaN 1.741055 NaN NaN \n", - "22 1.730593 0.0 NaN 2.108043 NaN NaN \n", - "23 1.779117 0.0 NaN 2.337818 NaN NaN \n", - "24 0.764556 0.0 NaN 2.232522 NaN NaN \n", - "25 3.845023 1.0 NaN 1.814540 NaN NaN \n", - "26 6.212000 1.0 NaN 2.176351 NaN NaN \n", - "27 3.411712 0.0 NaN 2.256778 NaN NaN \n", - "28 2.348030 1.0 NaN 1.712533 NaN NaN \n", - "29 1.345093 0.0 NaN 1.955710 NaN NaN \n", - "30 2.812430 1.0 NaN 2.117272 NaN NaN \n", - "31 1.899916 1.0 NaN 2.003544 NaN NaN \n", - "32 4.080200 1.0 NaN 1.722404 NaN NaN \n", - "33 4.028931 1.0 NaN 1.977470 NaN NaN \n", - "34 1.830059 1.0 NaN 2.042904 NaN NaN \n", - "35 3.195039 0.0 NaN 2.762531 NaN NaN \n", - "36 1.262956 1.0 NaN 1.896968 NaN NaN \n", - "37 7.595123 1.0 NaN 2.641212 NaN NaN \n", - "38 6.144402 0.0 NaN 2.482130 NaN NaN \n", - "39 2.032223 0.0 NaN 2.034913 NaN NaN \n", - "40 5.075889 1.0 NaN 2.038333 NaN NaN \n", - "41 11.593547 0.0 NaN 1.910682 NaN NaN \n", - "42 0.780308 0.0 NaN 1.854114 NaN NaN \n", - "43 1.660040 1.0 NaN 2.516719 NaN NaN \n", - 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"7 NaN NaN NaN NaN ... \n", - "8 NaN NaN NaN NaN ... \n", - "9 NaN NaN NaN NaN ... \n", - "10 NaN NaN NaN NaN ... \n", - "11 NaN NaN NaN NaN ... \n", - "12 NaN NaN NaN NaN ... \n", - "13 NaN NaN [1251] NaN ... \n", - "14 NaN NaN NaN NaN ... \n", - "15 NaN NaN NaN NaN ... \n", - "16 NaN NaN NaN NaN ... \n", - "17 NaN NaN NaN NaN ... \n", - "18 NaN NaN NaN NaN ... \n", - "19 NaN NaN NaN NaN ... \n", - "20 NaN NaN NaN NaN ... \n", - "21 NaN NaN NaN NaN ... \n", - "22 NaN NaN NaN NaN ... \n", - "23 NaN NaN NaN NaN ... \n", - "24 NaN NaN NaN NaN ... \n", - "25 NaN NaN NaN NaN ... \n", - "26 NaN NaN NaN NaN ... \n", - "27 NaN NaN NaN NaN ... \n", - "28 NaN NaN NaN NaN ... \n", - "29 NaN NaN NaN NaN ... \n", - "30 NaN NaN NaN NaN ... \n", - "31 NaN [1144] NaN NaN ... \n", - "32 NaN NaN NaN NaN ... \n", - "33 [735] NaN NaN NaN ... \n", - "34 NaN NaN NaN NaN ... \n", - "35 NaN NaN NaN NaN ... \n", - "36 NaN NaN NaN NaN ... \n", - "37 NaN NaN NaN NaN ... \n", - "38 NaN NaN NaN NaN ... \n", - "39 NaN NaN NaN NaN ... \n", - "40 NaN NaN NaN NaN ... \n", - "41 NaN NaN NaN NaN ... \n", - "42 NaN NaN NaN NaN ... \n", - "43 NaN NaN NaN NaN ... \n", - "44 NaN NaN NaN NaN ... \n", - "45 NaN NaN NaN NaN ... \n", - "46 NaN NaN NaN NaN ... \n", - "47 NaN NaN NaN NaN ... \n", - "48 NaN NaN NaN NaN ... \n", - "49 NaN NaN NaN NaN ... \n", - "50 NaN NaN NaN NaN ... \n", - "51 NaN NaN NaN NaN ... \n", - "52 NaN NaN NaN NaN ... \n", - "53 NaN NaN NaN NaN ... \n", - "54 NaN NaN NaN NaN ... \n", - "55 NaN NaN NaN NaN ... \n", - "56 NaN NaN NaN NaN ... \n", - "57 NaN NaN NaN NaN ... \n", - "58 [254] [259] NaN NaN ... \n", - "59 NaN NaN [662] NaN ... \n", - "\n", - " rmtpt1-rmtpt3 rmtpt2-rmtpt3 rmtpt6-rmtpt4 rmtpt7-rmtpt4 rmtpt8-rmtpt4 \\\n", - "0 NaN NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN NaN \n", - "5 NaN NaN NaN NaN NaN \n", - "6 NaN NaN NaN NaN NaN \n", - "7 NaN NaN NaN [430] [433] \n", - "8 NaN NaN NaN NaN NaN \n", - "9 NaN NaN NaN NaN [135] \n", - "10 NaN NaN NaN NaN NaN \n", - "11 NaN NaN NaN NaN NaN \n", - "12 NaN NaN NaN NaN NaN \n", - "13 NaN NaN NaN NaN NaN \n", - "14 NaN NaN NaN NaN NaN \n", - "15 NaN NaN NaN NaN NaN \n", - "16 NaN NaN NaN NaN NaN \n", - "17 NaN NaN NaN NaN NaN \n", - "18 NaN NaN [2050] NaN NaN \n", - "19 NaN NaN NaN NaN NaN \n", - "20 NaN NaN NaN NaN NaN \n", - "21 NaN NaN NaN NaN NaN \n", - "22 NaN NaN NaN NaN NaN \n", - "23 NaN NaN NaN NaN NaN \n", - "24 NaN NaN NaN NaN NaN \n", - "25 NaN NaN NaN NaN NaN \n", - "26 NaN NaN [2169] NaN [417] \n", - "27 [304] NaN NaN NaN NaN \n", - "28 NaN NaN NaN NaN NaN \n", - "29 NaN NaN [1690] NaN NaN \n", - "30 NaN NaN NaN NaN NaN \n", - "31 NaN NaN [1013] [1014] [1022] \n", - "32 NaN NaN NaN NaN NaN \n", - "33 NaN NaN NaN NaN NaN \n", - "34 NaN NaN NaN NaN NaN \n", - "35 NaN NaN NaN NaN NaN \n", - "36 NaN NaN NaN NaN [441] \n", - "37 NaN NaN NaN NaN NaN \n", - "38 NaN NaN NaN NaN NaN \n", - "39 NaN NaN NaN NaN NaN \n", - "40 NaN NaN NaN NaN NaN \n", - "41 NaN NaN [434] NaN [431] \n", - "42 NaN NaN NaN NaN NaN \n", - "43 NaN NaN NaN NaN NaN \n", - "44 NaN NaN NaN NaN NaN \n", - "45 NaN NaN NaN NaN NaN \n", - "46 NaN NaN NaN NaN NaN \n", - "47 NaN NaN NaN NaN NaN \n", - "48 NaN NaN NaN NaN [932] \n", - "49 NaN NaN NaN NaN NaN \n", - "50 NaN NaN NaN NaN [535] \n", - "51 NaN NaN NaN NaN NaN \n", - "52 NaN NaN NaN [432] NaN \n", - "53 NaN NaN [725] NaN NaN \n", - "54 NaN NaN NaN NaN NaN \n", - "55 NaN NaN NaN [841] [839] \n", - "56 NaN NaN NaN NaN NaN \n", - "57 NaN NaN NaN NaN NaN \n", - "58 NaN NaN NaN NaN NaN \n", - "59 NaN NaN NaN NaN [866] \n", - "\n", - " rpcip1-rpcip5 rpcip11-rpcip8 rpcip2-rpcip5 rpcip7-rpcip6 rpcip9-rpcip8 \n", - "0 NaN NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN NaN \n", - "2 NaN [870] NaN NaN NaN \n", - "3 NaN NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN [901] \n", - "5 NaN NaN NaN NaN NaN \n", - "6 NaN NaN NaN NaN NaN \n", - "7 NaN NaN NaN NaN [564] \n", - "8 NaN NaN NaN NaN [183, 2234] \n", - "9 NaN NaN NaN NaN NaN \n", - "10 NaN NaN NaN NaN [1085] \n", - "11 NaN NaN NaN NaN [1802] \n", - "12 NaN NaN NaN NaN NaN \n", - "13 NaN NaN NaN NaN NaN \n", - "14 NaN NaN NaN NaN [1054] \n", - "15 NaN NaN NaN NaN [2050] \n", - "16 NaN NaN NaN NaN NaN \n", - "17 NaN NaN NaN NaN NaN \n", - "18 NaN NaN NaN NaN NaN \n", - "19 NaN NaN NaN NaN NaN \n", - "20 NaN NaN NaN NaN NaN \n", - "21 NaN NaN NaN NaN NaN \n", - "22 NaN NaN NaN NaN NaN \n", - "23 NaN NaN NaN NaN NaN \n", - "24 NaN NaN NaN NaN NaN \n", - "25 [1483] NaN NaN NaN NaN \n", - "26 NaN NaN NaN NaN [2097] \n", - "27 NaN NaN NaN NaN NaN \n", - "28 NaN NaN NaN NaN NaN \n", - "29 NaN NaN NaN NaN NaN \n", - "30 NaN NaN NaN NaN NaN \n", - "31 [1250] NaN NaN NaN NaN \n", - "32 NaN [2229] NaN NaN [2264] \n", - "33 NaN NaN NaN NaN NaN \n", - "34 [2274] [449] NaN NaN NaN \n", - "35 NaN NaN NaN NaN NaN \n", - "36 NaN NaN NaN NaN NaN \n", - "37 NaN NaN NaN NaN NaN \n", - "38 NaN NaN [1317] NaN NaN \n", - "39 NaN NaN NaN NaN NaN \n", - "40 NaN [923, 2107] NaN NaN [924] \n", - "41 NaN NaN NaN NaN NaN \n", - "42 NaN NaN NaN NaN [60] \n", - "43 [1064] NaN NaN NaN NaN \n", - "44 NaN [2085] NaN NaN [2084] \n", - "45 NaN NaN NaN NaN NaN \n", - "46 NaN NaN NaN NaN NaN \n", - "47 NaN NaN NaN NaN NaN \n", - "48 NaN NaN NaN NaN NaN \n", - "49 NaN NaN NaN NaN NaN \n", - "50 NaN [1395] NaN NaN NaN \n", - "51 NaN NaN NaN NaN NaN \n", - "52 NaN NaN NaN [213] NaN \n", - "53 NaN NaN NaN NaN NaN \n", - "54 [755] [1150] NaN NaN [760] \n", - "55 NaN NaN NaN NaN [820] \n", - "56 NaN NaN NaN NaN [1838] \n", - "57 NaN NaN NaN NaN NaN \n", - "58 NaN [1527] NaN NaN NaN \n", - "59 NaN NaN NaN NaN [767] \n", - "\n", - "[60 rows x 120 columns]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "event_metadata" - ] - }, - { - "cell_type": "markdown", - "id": "db4f82f1", - "metadata": {}, - "source": [ - "Now, we can plot the epochs and annotate them. Specifically, look for evidence of interictal discharges and seizure ramping during your behavioral epochs, as these should be used to exclude epochs (and potentially channels) from analysis. \n", - "\n", - "Resource: https://www.frontiersin.org/articles/10.3389/fnhum.2020.00044/full" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "93570caf", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\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(\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", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_device_pixel_ratio', {\n", - " device_pixel_ratio: fig.ratio,\n", - " });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = 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._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute('tabindex', '0');\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", - " 'z-index: 2;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\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 = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box;' +\n", - " 'pointer-events: none;' +\n", - " 'position: relative;' +\n", - " 'z-index: 0;'\n", - " );\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\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", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box;' +\n", - " 'left: 0;' +\n", - " 'pointer-events: none;' +\n", - " 'position: absolute;' +\n", - " 'top: 0;' +\n", - " 'z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.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", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\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", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " /* This rescales the canvas back to display pixels, so that it\n", - " * appears correct on HiDPI screens. */\n", - " canvas.style.width = width + 'px';\n", - " canvas.style.height = height + 'px';\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 (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " /* User Agent sniffing is bad, but WebKit is busted:\n", - " * https://bugs.webkit.org/show_bug.cgi?id=144526\n", - " * https://bugs.webkit.org/show_bug.cgi?id=181818\n", - " * The worst that happens here is that they get an extra browser\n", - " * selection when dragging, if this check fails to catch them.\n", - " */\n", - " var UA = navigator.userAgent;\n", - " var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n", - " if(isWebKit) {\n", - " return function (event) {\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We\n", - " * want to control all of the cursor setting manually through\n", - " * the 'cursor' event from matplotlib */\n", - " event.preventDefault()\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " } else {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'dblclick',\n", - " on_mouse_event_closure('dblclick')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " canvas_div.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " canvas_div.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\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", - " canvas_div.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\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", - " 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", - " /* 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", - "\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", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\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 = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\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", - " // 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", - "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", - "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], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\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", - " fig.canvas_div.style.cursor = msg['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.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", - " // 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", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\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", - " img.type = 'image/png';\n", - " }\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", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\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", - " 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(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\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(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * https://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", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " // from https://stackoverflow.com/q/1114465\n", - " var boundingRect = this.canvas.getBoundingClientRect();\n", - " var x = (event.clientX - boundingRect.left) * this.ratio;\n", - " var y = (event.clientY - boundingRect.top) * this.ratio;\n", - "\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", - " 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", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, 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", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n", - "\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.binaryType = comm.kernel.ws.binaryType;\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " function updateReadyState(_event) {\n", - " if (comm.kernel.ws) {\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " } else {\n", - " ws.readyState = 3; // Closed state.\n", - " }\n", - " }\n", - " comm.kernel.ws.addEventListener('open', updateReadyState);\n", - " comm.kernel.ws.addEventListener('close', updateReadyState);\n", - " comm.kernel.ws.addEventListener('error', updateReadyState);\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", - " var data = msg['content']['data'];\n", - " if (data['blob'] !== undefined) {\n", - " data = {\n", - " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", - " };\n", - " }\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(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 = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\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;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n", - " '';\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 / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\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 () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\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", - " 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) {\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 = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\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 = 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 = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\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 (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\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", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\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", - "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(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib notebook\n", - "fig = epochs.plot(n_epochs=1, n_channels=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "35dd7da0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dropped 1 epoch: 5\n", - "The following epochs were marked as bad and are dropped:\n", - "[5]\n", - "Channels marked as bad:\n", - "['laimm12-laimm11', 'lmolf4-laimm6']\n", - "Dropped 0 epochs: \n", - "The following epochs were marked as bad and are dropped:\n", - "[5]\n", - "Channels marked as bad:\n", - "['laimm12-laimm11', 'lmolf4-laimm6']\n" - ] - } - ], - "source": [ - "# Need this following line to save the annotations to the epochs object \n", - "fig.fake_keypress('a')" - ] - }, - { - "cell_type": "markdown", - "id": "d32f1414", - "metadata": {}, - "source": [ - "You can examine information about the epochs you dropped: " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "de5ebdb7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - ">" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Check the epoch annotations \n", - "epochs.get_annotations_per_epoch" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "b3e71a29", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "((),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " ('USER',),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " (),\n", - " ())" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "epochs.drop_log" - ] - }, - { - "cell_type": "markdown", - "id": "f5d9f52c", - "metadata": {}, - "source": [ - "## Save the epoched data and use for plots" - ] - }, - { - "cell_type": "markdown", - "id": "73e0e812", - "metadata": {}, - "source": [ - "Because most of your downstream analyses will operate on these epoched data, let's save them. Also, in the interest of reproducability, these will likely be the data you share (see: https://oir.nih.gov/sourcebook/intramural-program-oversight/intramural-data-sharing/2023-nih-data-management-sharing-policy)\n", - "\n", - "**Bad epochs will be dropped before saving the epochs to disk**" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "66b1842b", - "metadata": {}, - "outputs": [], - "source": [ - "epochs.save(f'{load_dir}/feedback-epo.fif', overwrite=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "a6e1fe56", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reading /sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/feedback-epo.fif ...\n", - " Found the data of interest:\n", - " t = -2000.00 ... 2498.05 ms\n", - " 0 CTF compensation matrices available\n", - "Adding metadata with 120 columns\n", - "60 matching events found\n", - "No baseline correction applied\n", - "0 projection items activated\n" - ] - } - ], - "source": [ - "epochs = mne.read_epochs(f'{load_dir}/feedback-epo.fif')" - ] - }, - { - "cell_type": "markdown", - "id": "4a8b691f", - "metadata": {}, - "source": [ - "Here, we will plot some basic TFRs of our behaviorally-locked analysis using a wavelet transform. Let's set some parameters first" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "b472adda", - "metadata": {}, - "outputs": [], - "source": [ - "# power parameters \n", - "freqs = np.logspace(*np.log10([4, 128]), num=20)\n", - "n_cycles = 4 \n", - "sr = epochs.info['sfreq']\n", - "buf = 1.0\n", - "buf_ix = int(buf*sr)\n", - "\n", - "good_chans = [x for x in epochs.ch_names if x not in epochs.info['bads']]\n", - "picks = [x for x in good_chans]" - ] - }, - { - "cell_type": "markdown", - "id": "18e10853", - "metadata": {}, - "source": [ - "Use the metadata to assign conditions to parse your epochs!" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "bcb2e968", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not setting metadata\n", - "Applying baseline correction (mode: zscore)\n", - "Not setting metadata\n", - "Applying baseline correction (mode: zscore)\n" - ] - } - ], - "source": [ - "data_parsing = ['reward==1',\n", - " 'reward==0'\n", - " ]\n", - "\n", - "\n", - "diff_pow_epochs = {f'{x}':np.nan for x in data_parsing}\n", - "\n", - "# baseline_pow = mne.time_frequency.tfr_morlet(baseline_epochs, picks=picks,\n", - "# freqs=freqs, n_cycles=n_cycles, use_fft=True,\n", - "# return_itc=False, n_jobs=-1, average=False)\n", - "\n", - "# Compute power without averaging over events\n", - "data_struct = np.ones([epochs._data.shape[0], epochs._data.shape[1], len(freqs), epochs._data.shape[-1]])\n", - "for parsing in data_parsing: \n", - " for ch_ix in np.arange(epochs._data.shape[1]): \n", - " ch_data = epochs[parsing]._data[:, ch_ix:ch_ix+1, :]\n", - " bad_epochs = np.where(epochs.metadata.query(parsing)[epochs.ch_names[ch_ix]].notnull())[0]\n", - " good_epochs = np.delete(np.arange(ch_data.shape[0]), bad_epochs)\n", - " ch_data = np.delete(ch_data, bad_epochs, axis=0)\n", - " ch_pow = mne.time_frequency.tfr_array_morlet(ch_data, sfreq=epochs.info['sfreq'], \n", - " freqs=freqs, n_cycles=n_cycles, zero_mean=True, \n", - " use_fft=True, output='power', \n", - " n_jobs=1)\n", - " \n", - " data_struct[good_epochs, ch_ix, :, :] = ch_pow[:, 0, :, :]\n", - " temp_pow = mne.time_frequency.EpochsTFR(epochs[parsing].info, data_struct, \n", - " epochs.times, freqs)\n", - "\n", - "# temp_pow = mne.time_frequency.tfr_morlet(epochs[parsing], picks=picks,\n", - "# freqs=freqs, n_cycles=n_cycles, use_fft=True,\n", - "# return_itc=False, n_jobs=-1, average=False)\n", - " # Baseline correct the TFRs\n", - " temp_pow = temp_pow.apply_baseline(baseline=(-feedback_pre, 0), mode='zscore')\n", - "# baseline_pow.crop(tmin=-baseline_pre, tmax=baseline_post)\n", - "# temp_pow.data = lfp_preprocess_utils.zscore_TFR_across_trials(temp_pow.data, baseline_pow.data)\n", - " temp_pow.crop(tmin=-feedback_pre, tmax=feedback_post)\n", - " diff_pow_epochs[parsing] = temp_pow\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "a2a34d7e", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "id": "e4fce377", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot: \n", - "\n", - "freqs = np.logspace(*np.log10([4, 128]), num=20)\n", - "\n", - "for parsing in data_parsing: \n", - " save_file = f'{load_dir}/gamble_TFR_{parsing}.pdf'\n", - " with PdfPages(save_file) as pdf:\n", - " for label in diff_pow_epochs[parsing].ch_names: \n", - " plot_ix = diff_pow_epochs[parsing].ch_names.index(label)\n", - " region_label = elec_df[elec_df.label==label].YBA_1.values[0]\n", - " if region_label == 'Unknown':\n", - " region_label = elec_df[elec_df.label==label]['Manual Examination'].values[0]\n", - "# plot_data = np.mean(diff_pow_epochs[parsing].data[:, plot_ix, :, :], axis=0)\n", - " plot_data = np.nanmean(diff_pow_epochs[parsing].data[:, plot_ix, :, :], axis=0)\n", - "\n", - "\n", - " f, tfr = plt.subplots(1, 1, figsize=[7, 4], dpi=300)\n", - "\n", - " tfr.imshow(plot_data, aspect='auto', interpolation='bicubic', cmap='RdBu_r', vmin=-3, vmax=3)\n", - " tfr.invert_yaxis()\n", - "\n", - " # neg_plot.vlines(3*buf_ix//4, 0, len(freqs)-1, 'k')\n", - " tfr.set_yticks(np.arange(0, len(freqs), 4))\n", - " tfr.set_yticklabels(np.round(freqs[np.arange(0, len(freqs), 4)]), fontsize=10)\n", - " tfr.set_xticks(np.linspace(0, plot_data.shape[-1], plot_data.shape[-1]//250))\n", - " tfr.set_xticklabels(np.linspace(-(feedback_pre*1000), feedback_post*1000, plot_data.shape[-1]//250))\n", - " tfr.set_xlabel('Time (ms)', fontsize=12)\n", - " tfr.set_ylabel('Frequency (Hz)', fontsize=12)\n", - " tfr.vlines((feedback_pre * diff_pow_epochs[parsing].info['sfreq']), 0, len(freqs)-1, 'k')\n", - "\n", - " f.suptitle(f'{region_label}')\n", - " f.tight_layout()\n", - " pdf.savefig()\n", - " plt.close(f)" - ] - }, - { - "cell_type": "markdown", - "id": "29622b22", - "metadata": {}, - "source": [ - "## Statistical Analyses:" - ] - }, - { - "cell_type": "markdown", - "id": "d362cdbd", - "metadata": {}, - "source": [ - "At this stage, you should **heavily** brainstorm the statistics you want to do, before you start writing any code. What is the goal of your analysis? What would actually allow you to show what you want to show?" - ] - }, - { - "cell_type": "markdown", - "id": "ff8e63d8", - "metadata": {}, - "source": [ - "As an example, let's say I want to compare the reward vs. no-reward conditions for every channel, and identify the timepoints and frequencies that exhibit significant differences between conditions. To do so, I would utilize a non-parametric cluster-permutation test." - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "id": "f96243ec", - "metadata": {}, - "outputs": [], - "source": [ - "# def stat_fun(*arg):\n", - " return(scipy.stats.ttest_ind(arg[0], arg[1], equal_var=True, nan_policy='omit')[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "12b81aad", - "metadata": {}, - "outputs": [], - "source": [ - "for label in diff_pow_epochs[parsing].ch_names[0:1]: \n", - " region_label = elec_df[elec_df.label==label].YBA_1.values[0]\n", - " if region_label == 'Unknown':\n", - " region_label = elec_df[elec_df.label==label]['Manual Examination'].values[0]\n", - "\n", - " plot_ix = diff_pow_epochs[parsing].ch_names.index(label)\n", - " \n", - " rdata = diff_pow_epochs['reward==1'].data[:, plot_ix, :, :]\n", - " nrdata = diff_pow_epochs['reward==0'].data[:, plot_ix, :, :]" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "86a00773", - "metadata": {}, - "outputs": [], - "source": [ - "non_nan_mask = np.unique(np.where(~((np.isnan(rdata)) | (np.isnan(nrdata))))[0])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "db5f8af3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", - " 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34])" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "non_nan_mask" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "52f5b79f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using a threshold of 3.986269\n", - "stat_fun(H1): min=0.000000 max=8.793878\n", - "Running initial clustering …\n", - "Found 45 clusters\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "bd83efaf1bd9455d9a3c3a61665473a7", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | Permuting : 0/249 [00:00 1\u001b[0m \u001b[43mfig\u001b[49m\n", - "\u001b[0;31mNameError\u001b[0m: name 'fig' is not defined" - ] - } - ], - "source": [ - "fig" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "id": "365e6432", - "metadata": {}, - "outputs": [], - "source": [ - "# # Create new stats image with only significant clusters\n", - "# fig, (ax, ax2) = plt.subplots(2, 1, figsize=(6, 4))\n", - "\n", - "# times = diff_pow_epochs['reward==0'].times\n", - "\n", - "# evoked_power_1 = X[0].mean(axis=0)\n", - "# evoked_power_2 = X[1].mean(axis=0)\n", - "# evoked_power_contrast = evoked_power_1 - evoked_power_2\n", - "# signs = np.sign(evoked_power_contrast)\n", - "\n", - "# F_obs_plot = np.nan * np.ones_like(F_obs)\n", - "# for c, p_val in zip(clusters, cluster_p_values):\n", - "# if p_val <= 0.05:\n", - "# F_obs_plot[c] = F_obs[c] * signs[c]\n", - "\n", - "# ax.imshow(F_obs,\n", - "# extent=[times[0], times[-1], freqs[0], freqs[-1]],\n", - "# aspect='auto', origin='lower', cmap='gray')\n", - "# max_F = np.nanmax(abs(F_obs_plot))\n", - "# ax.imshow(F_obs_plot,\n", - "# extent=[times[0], times[-1], freqs[0], freqs[-1]],\n", - "# aspect='auto', origin='lower', cmap='RdBu_r',\n", - "# vmin=-max_F, vmax=max_F)\n", - "\n", - "# ax.set_xlabel('Time (ms)')\n", - "# ax.set_ylabel('Frequency (Hz)')\n", - "# # ax.set_title(f'Induced power ({ch_name})')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1482f24d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "lfp_analysis", - "language": "python", - "name": "lfpanalysis" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/scripts/.ipynb_checkpoints/LFP_preprocessing_step_by_step-checkpoint.ipynb b/scripts/.ipynb_checkpoints/LFP_preprocessing_step_by_step-checkpoint.ipynb deleted file mode 100644 index 6c71e03..0000000 --- a/scripts/.ipynb_checkpoints/LFP_preprocessing_step_by_step-checkpoint.ipynb +++ /dev/null @@ -1,4835 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example preprocessing notebook" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this notebook we are going to walk through a single patient example. There are probably some patient-specific stuff in here that might change with other patients. Should be able to demonstrate the usage of different functions from the toolbox.\n", - "\n", - "1. Load raw data (.edf in this notebook) using mne\n", - "\n", - "2. Add in electrode information\n", - "\n", - "3. Notch filter line noise and cleaning out bad channels \n", - "\n", - "4. Re-reference the data \n", - "\n", - "5. Annotate data (i.e. artifact and IEDs) \n", - "\n", - "\n", - "Must read guides: \n", - "\n", - "https://www.sciencedirect.com/science/article/pii/S1053811922005559\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%reload_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import mne\n", - "from glob import glob\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.backends.backend_pdf import PdfPages\n", - "import seaborn as sns\n", - "from scipy.stats import zscore, linregress\n", - "import pandas as pd\n", - "import h5py\n", - "from mne.preprocessing.bads import _find_outliers" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from LFPAnalysis import lfp_preprocess_utils, sync_utils" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load raw data (.edf in this notebook) using mne" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It's a good idea to setup a sensible directory structure like below. Note that all my data lives on '/sc/arion' which is Minerva. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "mne: https://mne.tools/stable/index.html" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "base_dir = '/sc/arion' # this is the root directory for most un-archived data and results \n", - "\n", - "save_dir = f'{base_dir}/work/qasims01/MemoryBanditData/EMU/Subjects/MS007' # save intermediate results in the 'work' directory\n", - " \n", - "# I have saved most of my raw data in the 'projects directory'\n", - "behav_dir = f'{base_dir}/projects/guLab/Salman/EMU/MS007/behav/Day1'\n", - "neural_dir = f'{base_dir}/projects/guLab/Salman/EMU/MS007/neural/Day1'\n", - "anat_dir = f'{base_dir}/projects/guLab/Salman/EMU/MS007/anat'\n", - "edf_files = glob(f'{neural_dir}/*.edf')\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Try loading in the data into memory" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting EDF parameters from /sc/arion/projects/guLab/Salman/EMU/MS007/neural/Day1/MS007_MemBandit.edf...\n", - "EDF file detected\n", - "Setting channel info structure...\n", - "Creating raw.info structure...\n", - "Reading 0 ... 1867007 = 0.000 ... 1823.249 secs...\n" - ] - } - ], - "source": [ - "MS007_data = mne.io.read_raw_edf(edf_files[0], preload=True)\n", - "# If you try to preload, it will kill the kernel (at mem=4000). Probably need to request more memory in Minerva (mem=8000 seems to work)\n", - "\n", - "# # If not preloading: \n", - "# raw_data = MS007_data.get_data()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Sanity check\n", - "plt.plot(MS007_data._data[0,:4999])\n", - "plt.title(\"Raw iEEG, electrode 0, samples 0-4999\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Sanity check the photodiode\n", - "trig_ix = MS007_data.ch_names.index('DC1')\n", - "plt.plot(MS007_data._data[trig_ix, 10000:50000])\n", - "plt.title(\"Photodiode\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add in electrode information" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Load the electrode localization data and add it in\n", - "\n", - "csv_files = glob(f'{anat_dir}/*labels.csv')\n", - "elec_locs = pd.read_csv(csv_files[0])\n", - "\n", - "# Sometimes there's extra columns with no entries: \n", - "elec_locs = elec_locs[elec_locs.columns.drop(list(elec_locs.filter(regex='Unnamed')))]\n", - "\n", - "# # identify the bundles for re-referencing:\n", - "# loc_data['bundle'] = np.nan\n", - "# loc_data['bundle'] = loc_data.apply(lambda x: ''.join(i for i in x.label if not i.isdigit()), axis=1)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The electrode names read out of the edf file do not always match those \n", - "in the pdf (used for localization). This could be error on the side of the tech who input the labels, \n", - "or on the side of MNE reading the labels in. Usually there's a mixup between lowercase 'l' and capital 'I'.\n", - "\n", - "Sometimes, there's electrodes on the pdf that are NOT in the MNE data structure... let's identify those as well. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Could not find a match for rhplt9.\n" - ] - } - ], - "source": [ - "new_mne_names, unmatched_names, unmatched_seeg = lfp_preprocess_utils.match_elec_names(MS007_data.ch_names, elec_locs.label)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So we retun a new list of channel names for the mne data structure as well as a list of channels in the localization csv which are not found in the mne structure. Make sure that unmatched_seeg does not factor into any referencing schemes later - it's not in the MNE data" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized pointsNot available
Good channels276 EEG
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Rename the mne data according to the localization data\n", - "new_name_dict = {x:y for (x,y) in zip(MS007_data.ch_names, new_mne_names)}\n", - "MS007_data.rename_channels(new_name_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['lmolf1',\n", - " 'lmolf2',\n", - " 'lmolf3',\n", - " 'lmolf4',\n", - " 'lmolf5',\n", - " 'lmolf6',\n", - " 'lmolf7',\n", - " 'lmolf8',\n", - " 'lmolf9',\n", - " 'rmolf1',\n", - " 'rmolf2',\n", - " 'rmolf3',\n", - " 'rmolf4',\n", - " 'rmolf5',\n", - " 'rmolf6',\n", - " 'rmolf7',\n", - " 'rmolf8',\n", - " 'rmolf9',\n", - " 'laimm1',\n", - " 'laimm2',\n", - " 'laimm3',\n", - " 'laimm4',\n", - " 'laimm5',\n", - " 'laimm6',\n", - " 'laimm7',\n", - " 'laimm8',\n", - " 'laimm9',\n", - " 'laimm10',\n", - " 'laimm11',\n", - " 'laimm12',\n", - " 'laimm13',\n", - " 'laimm14',\n", - " 'raimm1',\n", - " 'raimm2',\n", - " 'raimm3',\n", - " 'raimm4',\n", - " 'raimm5',\n", - " 'raimm6',\n", - " 'raimm7',\n", - " 'raimm8',\n", - " 'raimm9',\n", - " 'raimm10',\n", - " 'raimm11',\n", - " 'raimm12',\n", - " 'lcmfo1',\n", - " 'lcmfo2',\n", - " 'lcmfo3',\n", - " 'lcmfo4',\n", - " 'lcmfo5',\n", - " 'lcmfo6',\n", - " 'lcmfo7',\n", - " 'lcmfo8',\n", - " 'lcmfo9',\n", - " 'lcmfo10',\n", - " 'lcmfo11',\n", - " 'lcmfo12',\n", - " 'lcmfo13',\n", - " 'lcmfo14',\n", - " 'c59',\n", - " 'c60',\n", - " 'c61',\n", - " 'c62',\n", - " 'c63',\n", - " 'c64',\n", - " 'rcmfo1',\n", - " 'rcmfo2',\n", - " 'rcmfo3',\n", - " 'rcmfo4',\n", - " 'rcmfo5',\n", - " 'rcmfo6',\n", - " 'rcmfo7',\n", - " 'rcmfo8',\n", - " 'rcmfo9',\n", - " 'rcmfo10',\n", - " 'rcmfo11',\n", - " 'rcmfo12',\n", - " 'rcmfo13',\n", - " 'rcmfo14',\n", - " 'lacas1',\n", - " 'lacas2',\n", - " 'lacas3',\n", - " 'lacas4',\n", - " 'lacas5',\n", - " 'lacas6',\n", - " 'lacas7',\n", - " 'lacas8',\n", - " 'lacas9',\n", - " 'lacas10',\n", - " 'lacas11',\n", - " 'lacas12',\n", - " 'racas1',\n", - " 'racas2',\n", - " 'racas3',\n", - " 'racas4',\n", - " 'racas5',\n", - " 'racas6',\n", - " 'racas7',\n", - " 'racas8',\n", - " 'racas9',\n", - " 'racas10',\n", - " 'racas11',\n", - " 'racas12',\n", - " 'racas13',\n", - " 'racas14',\n", - " 'lmcms1',\n", - " 'lmcms2',\n", - " 'lmcms3',\n", - " 'lmcms4',\n", - " 'lmcms5',\n", - " 'lmcms6',\n", - " 'lmcms7',\n", - " 'lmcms8',\n", - " 'lmcms9',\n", - " 'lmcms10',\n", - " 'rmcms1',\n", - " 'rmcms2',\n", - " 'rmcms3',\n", - " 'rmcms4',\n", - " 'rmcms5',\n", - " 'rmcms6',\n", - " 'rmcms7',\n", - " 'rmcms8',\n", - " 'rmcms9',\n", - " 'rmcms10',\n", - " 'c125',\n", - " 'c126',\n", - " 'c127',\n", - " 'c128',\n", - " 'lpcip1',\n", - " 'lpcip2',\n", - " 'lpcip3',\n", - " 'lpcip4',\n", - " 'lpcip5',\n", - " 'lpcip6',\n", - " 'lpcip7',\n", - " 'lpcip8',\n", - " 'lpcip9',\n", - " 'lpcip10',\n", - " 'lpcip11',\n", - " 'lpcip12',\n", - " 'rpcip1',\n", - " 'rpcip2',\n", - " 'rpcip3',\n", - " 'rpcip4',\n", - " 'rpcip5',\n", - " 'rpcip6',\n", - " 'rpcip7',\n", - " 'rpcip8',\n", - " 'rpcip9',\n", - " 'rpcip10',\n", - " 'rpcip11',\n", - " 'rpcip12',\n", - " 'laglt1',\n", - " 'laglt2',\n", - " 'laglt3',\n", - " 'laglt4',\n", - " 'laglt5',\n", - " 'laglt6',\n", - " 'laglt7',\n", - " 'laglt8',\n", - " 'laglt9',\n", - " 'laglt10',\n", - " 'raglt1',\n", - " 'raglt2',\n", - " 'raglt3',\n", - " 'raglt4',\n", - " 'raglt5',\n", - " 'raglt6',\n", - " 'raglt7',\n", - " 'raglt8',\n", - " 'raglt9',\n", - " 'raglt10',\n", - " 'lhplt1',\n", - " 'lhplt2',\n", - " 'lhplt3',\n", - " 'lhplt4',\n", - " 'lhplt5',\n", - " 'lhplt6',\n", - " 'lhplt7',\n", - " 'lhplt8',\n", - " 'lhplt9',\n", - " 'lhplt10',\n", - " 'rhplt1',\n", - " 'rhplt2',\n", - " 'rhplt3',\n", - " 'rhplt4',\n", - " 'rhplt5',\n", - " 'rhplt6',\n", - " 'rhplt7',\n", - " 'rhplt8',\n", - " 'c191',\n", - " 'c192',\n", - " 'lmtpt1',\n", - " 'lmtpt2',\n", - " 'lmtpt3',\n", - " 'lmtpt4',\n", - " 'lmtpt5',\n", - " 'lmtpt6',\n", - " 'lmtpt7',\n", - " 'lmtpt8',\n", - " 'rmtpt1',\n", - " 'rmtpt2',\n", - " 'rmtpt3',\n", - " 'rmtpt4',\n", - " 'rmtpt5',\n", - " 'rmtpt6',\n", - " 'rmtpt7',\n", - " 'rmtpt8',\n", - " 'fp1',\n", - " 'f7',\n", - " 't3',\n", - " 't5',\n", - " 'o1',\n", - " 'f3',\n", - " 'c3',\n", - " 'p3',\n", - " 'fp2',\n", - " 'f8',\n", - " 't4',\n", - " 't6',\n", - " 'o2',\n", - " 'f4',\n", - " 'c4',\n", - " 'p4',\n", - " 'fz',\n", - " 'cz',\n", - " 'pz',\n", - " 'ekg1',\n", - " 'ekg2',\n", - " 'c230',\n", - " 'c231',\n", - " 'c232',\n", - " 'c233',\n", - " 'c234',\n", - " 'c235',\n", - " 'c236',\n", - " 'c237',\n", - " 'c238',\n", - " 'c239',\n", - " 'c240',\n", - " 'c241',\n", - " 'c242',\n", - " 'c243',\n", - " 'c244',\n", - " 'c245',\n", - " 'c246',\n", - " 'c247',\n", - " 'c248',\n", - " 'c249',\n", - " 'c250',\n", - " 'c251',\n", - " 'c252',\n", - " 'c253',\n", - " 'c254',\n", - " 'c255',\n", - " 'c256',\n", - " 'dc1',\n", - " 'dc2',\n", - " 'dc3',\n", - " 'dc4',\n", - " 'dc5',\n", - " 'dc6',\n", - " 'dc7',\n", - " 'dc8',\n", - " 'dc9',\n", - " 'dc10',\n", - " 'dc11',\n", - " 'dc12',\n", - " 'dc13',\n", - " 'dc14',\n", - " 'dc15',\n", - " 'dc16',\n", - " 'trig',\n", - " 'osat',\n", - " 'pr',\n", - " 'pleth']" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "MS007_data.ch_names" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "We have a total of 196 sEEG electrodes\n", - "We have a total of 19 EEG electrodes\n" - ] - } - ], - "source": [ - "# Note, there is surface EEG data that we should separately indicate from the sEEG:\n", - "right_seeg_names = [i for i in MS007_data.ch_names if i.startswith('r')]\n", - "left_seeg_names = [i for i in MS007_data.ch_names if i.startswith('l')]\n", - "# This is optional. I might want to look at scalp EEG at some point (lol) so might as well tag them here. \n", - "eeg_names = [\n", - " 'fp1',\n", - " 'f7',\n", - " 't3',\n", - " 't5',\n", - " 'o1',\n", - " 'f3',\n", - " 'c3',\n", - " 'p3',\n", - " 'fp2',\n", - " 'f8',\n", - " 't4',\n", - " 't6',\n", - " 'o2',\n", - " 'f4',\n", - " 'c4',\n", - " 'p4',\n", - " 'fz',\n", - " 'cz',\n", - " 'pz']\n", - "print(f'We have a total of {len(left_seeg_names) + len(right_seeg_names)} sEEG electrodes')\n", - "print(f'We have a total of {len(eeg_names)} EEG electrodes')\n", - "# MS007_data.set_channel_types()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized pointsNot available
Good channels216 EEG
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sEEG_mapping_dict = {f'{x}':'seeg' for x in left_seeg_names+right_seeg_names}\n", - "EEG_mapping_dict = {f'{x}':'eeg' for x in eeg_names}\n", - "trig_mapping_dict = {'dc1':'stim'}\n", - "# Drop random chans? \n", - "drop_chans = list(set(MS007_data.ch_names)^set(eeg_names+left_seeg_names+right_seeg_names+['dc1']))\n", - "MS007_data.drop_channels(drop_chans)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_46739/1830004009.py:4: RuntimeWarning: The unit for channel(s) dc1 has changed from V to NA.\n", - " MS007_data.set_channel_types(trig_mapping_dict)\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized pointsNot available
Good channels196 sEEG, 19 EEG, 1 Stimulus
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Set channel types:\n", - "MS007_data.set_channel_types(sEEG_mapping_dict)\n", - "MS007_data.set_channel_types(EEG_mapping_dict)\n", - "MS007_data.set_channel_types(trig_mapping_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_46739/3983129385.py:7: RuntimeWarning: Fiducial point nasion not found, assuming identity unknown to head transformation\n", - " MS007_data.set_montage(montage, match_case=False, on_missing='warn')\n", - "/tmp/ipykernel_46739/3983129385.py:7: RuntimeWarning: DigMontage is only a subset of info. There are 19 channel positions not present in the DigMontage. The required channels are:\n", - "\n", - "['fp1', 'f7', 't3', 't5', 'o1', 'f3', 'c3', 'p3', 'fp2', 'f8', 't4', 't6', 'o2', 'f4', 'c4', 'p4', 'fz', 'cz', 'pz'].\n", - "\n", - "Consider using inst.set_channel_types if these are not EEG channels, or use the on_missing parameter if the channel positions are allowed to be unknown in your analyses.\n", - " MS007_data.set_montage(montage, match_case=False, on_missing='warn')\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized points196 points
Good channels196 sEEG, 19 EEG, 1 Stimulus
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# make montage (convert mm to m)!! I'm not sure if we will ever use or need this step, but why not just do it\n", - "\n", - "montage = mne.channels.make_dig_montage(ch_pos=dict(zip(elec_locs.label, \n", - " elec_locs[['mni_x', 'mni_y', 'mni_z']].to_numpy(dtype=float)/1000)),\n", - " coord_frame='mni_tal')\n", - "\n", - "MS007_data.set_montage(montage, match_case=False, on_missing='warn')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Notch filter line noise and cleaning out bad channels \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We want to remove the line noise (60 Hz and harmonics in US data, 50 Hz and harmonics in EU data). \n", - "\n", - "To do so, we use a band-stop filter that removes a narrow band of frequencies. \n", - "\n", - "Maybe eventually we don't want to use filters, especially if interested in ERPs: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6456018/" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Setting up band-stop filter\n", - "\n", - "FIR filter parameters\n", - "---------------------\n", - "Designing a one-pass, zero-phase, non-causal bandstop filter:\n", - "- Windowed time-domain design (firwin) method\n", - "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n", - "- Lower transition bandwidth: 0.50 Hz\n", - "- Upper transition bandwidth: 0.50 Hz\n", - "- Filter length: 6759 samples (6.601 sec)\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", - "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.1s remaining: 0.0s\n", - "[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.2s remaining: 0.0s\n", - "[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 0.3s remaining: 0.0s\n", - "[Parallel(n_jobs=1)]: Done 215 out of 215 | elapsed: 13.2s finished\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized points196 points
Good channels196 sEEG, 19 EEG, 1 Stimulus
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Identify line noise\n", - "MS007_data.info['line_freq'] = 60\n", - "\n", - "# Notch out 60 Hz noise and harmonics \n", - "MS007_data.notch_filter(freqs=(60, 120, 180, 240))" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting existing file.\n", - "Writing /sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/photodiode.fif\n", - "Closing /sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/photodiode.fif\n", - "[done]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_46739/2725322655.py:2: RuntimeWarning: This filename (/sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/photodiode.fif) does not conform to MNE naming conventions. All raw files should end with raw.fif, raw_sss.fif, raw_tsss.fif, _meg.fif, _eeg.fif, _ieeg.fif, raw.fif.gz, raw_sss.fif.gz, raw_tsss.fif.gz, _meg.fif.gz, _eeg.fif.gz or _ieeg.fif.gz\n", - " MS007_data.save(f'{save_dir}/photodiode.fif', picks='dc1', overwrite=True)\n" - ] - } - ], - "source": [ - "# Save out the photodiode channel separately\n", - "MS007_data.save(f'{save_dir}/photodiode.fif', picks='dc1', overwrite=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Denote bad channels" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Clean up the MNE data \n", - "\n", - "bads = lfp_preprocess_utils.detect_bad_elecs(MS007_data, \n", - " sEEG_mapping_dict)\n", - "\n", - "MS007_data.info['bads'] = bads" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['laglt10',\n", - " 'laglt8',\n", - " 'laglt9',\n", - " 'lcmfo7',\n", - " 'lhplt1',\n", - " 'lhplt2',\n", - " 'lmcms1',\n", - " 'lmcms2',\n", - " 'lmcms9',\n", - " 'lmolf4',\n", - " 'lmolf5',\n", - " 'racas8',\n", - " 'rpcip7']" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bads" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Let's pick out any bad channels missed by automatic screening, or restore channels that were erroneously deemed bad" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When we plot the data using the interactive plotting, bad channels are grayed out. Can click them on the left to toggle this. We aren't annotating bad data time-windows yet! " - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using matplotlib as 2D backend.\n" - ] - }, - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\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(\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", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_device_pixel_ratio', {\n", - " device_pixel_ratio: fig.ratio,\n", - " });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = 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._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute('tabindex', '0');\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", - " 'z-index: 2;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\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 = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box;' +\n", - " 'pointer-events: none;' +\n", - " 'position: relative;' +\n", - " 'z-index: 0;'\n", - " );\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\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", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box;' +\n", - " 'left: 0;' +\n", - " 'pointer-events: none;' +\n", - " 'position: absolute;' +\n", - " 'top: 0;' +\n", - " 'z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.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", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\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", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " /* This rescales the canvas back to display pixels, so that it\n", - " * appears correct on HiDPI screens. */\n", - " canvas.style.width = width + 'px';\n", - " canvas.style.height = height + 'px';\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 (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " /* User Agent sniffing is bad, but WebKit is busted:\n", - " * https://bugs.webkit.org/show_bug.cgi?id=144526\n", - " * https://bugs.webkit.org/show_bug.cgi?id=181818\n", - " * The worst that happens here is that they get an extra browser\n", - " * selection when dragging, if this check fails to catch them.\n", - " */\n", - " var UA = navigator.userAgent;\n", - " var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n", - " if(isWebKit) {\n", - " return function (event) {\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We\n", - " * want to control all of the cursor setting manually through\n", - " * the 'cursor' event from matplotlib */\n", - " event.preventDefault()\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " } else {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'dblclick',\n", - " on_mouse_event_closure('dblclick')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " canvas_div.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " canvas_div.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\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", - " canvas_div.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\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", - " 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", - " /* 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", - "\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", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\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 = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\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", - " // 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", - "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", - "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], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\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", - " fig.canvas_div.style.cursor = msg['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.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", - " // 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", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\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", - " img.type = 'image/png';\n", - " }\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", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\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", - " 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(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\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(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * https://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", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " // from https://stackoverflow.com/q/1114465\n", - " var boundingRect = this.canvas.getBoundingClientRect();\n", - " var x = (event.clientX - boundingRect.left) * this.ratio;\n", - " var y = (event.clientY - boundingRect.top) * this.ratio;\n", - "\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", - " 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", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, 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", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n", - "\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.binaryType = comm.kernel.ws.binaryType;\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " function updateReadyState(_event) {\n", - " if (comm.kernel.ws) {\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " } else {\n", - " ws.readyState = 3; // Closed state.\n", - " }\n", - " }\n", - " comm.kernel.ws.addEventListener('open', updateReadyState);\n", - " comm.kernel.ws.addEventListener('close', updateReadyState);\n", - " comm.kernel.ws.addEventListener('error', updateReadyState);\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", - " var data = msg['content']['data'];\n", - " if (data['blob'] !== undefined) {\n", - " data = {\n", - " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", - " };\n", - " }\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(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 = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\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;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n", - " '';\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 / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\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 () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\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", - " 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) {\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 = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\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 = 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 = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\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 (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\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", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\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", - "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(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\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(\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", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_device_pixel_ratio', {\n", - " device_pixel_ratio: fig.ratio,\n", - " });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = 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._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute('tabindex', '0');\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", - " 'z-index: 2;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\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 = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box;' +\n", - " 'pointer-events: none;' +\n", - " 'position: relative;' +\n", - " 'z-index: 0;'\n", - " );\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\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", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box;' +\n", - " 'left: 0;' +\n", - " 'pointer-events: none;' +\n", - " 'position: absolute;' +\n", - " 'top: 0;' +\n", - " 'z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.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", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\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", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " /* This rescales the canvas back to display pixels, so that it\n", - " * appears correct on HiDPI screens. */\n", - " canvas.style.width = width + 'px';\n", - " canvas.style.height = height + 'px';\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 (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " /* User Agent sniffing is bad, but WebKit is busted:\n", - " * https://bugs.webkit.org/show_bug.cgi?id=144526\n", - " * https://bugs.webkit.org/show_bug.cgi?id=181818\n", - " * The worst that happens here is that they get an extra browser\n", - " * selection when dragging, if this check fails to catch them.\n", - " */\n", - " var UA = navigator.userAgent;\n", - " var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n", - " if(isWebKit) {\n", - " return function (event) {\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We\n", - " * want to control all of the cursor setting manually through\n", - " * the 'cursor' event from matplotlib */\n", - " event.preventDefault()\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " } else {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'dblclick',\n", - " on_mouse_event_closure('dblclick')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " canvas_div.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " canvas_div.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\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", - " canvas_div.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\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", - " 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", - " /* 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", - "\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", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\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 = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\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", - " // 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", - "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", - "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], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\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", - " fig.canvas_div.style.cursor = msg['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.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", - " // 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", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\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", - " img.type = 'image/png';\n", - " }\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", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\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", - " 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(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\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(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * https://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", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " // from https://stackoverflow.com/q/1114465\n", - " var boundingRect = this.canvas.getBoundingClientRect();\n", - " var x = (event.clientX - boundingRect.left) * this.ratio;\n", - " var y = (event.clientY - boundingRect.top) * this.ratio;\n", - "\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", - " 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", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, 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", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - 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"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n", - "\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. 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" el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\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", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\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", - "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", - 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] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['laglt10',\n", - " 'laglt8',\n", - " 'laglt9',\n", - " 'lcmfo7',\n", - " 'lmcms1',\n", - " 'lmcms2',\n", - " 'lmcms9',\n", - " 'lmolf4',\n", - " 'lmolf5',\n", - " 'racas8',\n", - " 'rpcip7',\n", - " 'laimm12',\n", - " 'lcmfo14',\n", - " 'lmolf7']" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "MS007_data.info['bads']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Re-reference the data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you're like me, you find the concept of re-referencing somewhat confusing. Isn't the data recorded relative to a ground and reference in the EMU (https://ahleighton.github.io/OE-ephys-course/EEA/theoryday3.html)? \n", - "\n", - "It is, but we do digital re-referencing of the recorded signal to clean up any remaining shared noise. \n", - "\n", - "**Re-referencing should be an EXTREMELY conscious choice as it changes the LFP signal dramatically!** In our case, we choose to do local white-matter re-referencing because electrodes in white matter should be fairly stable (low-variance) and not contain local, slow oscillations of interest. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's use the localization data to determine the gray vs. white matter electrodes. \n", - "Then, let's re-reference each gray matter electrode to the closest and most low-amplitude white matter electrode. \n", - "\n", - "Make sure 'bad' electrodes are not used in the re-referencing. Same with unmatched seeg electrodes (not present in the mne data structure)." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "anode_list, cathode_list, drop_wm_channels, oob_channels = lfp_preprocess_utils.wm_ref(mne_data=MS007_data, \n", - " loc_data=elec_locs, \n", - " bad_channels=MS007_data.info['bads'], \n", - " unmatched_seeg=unmatched_seeg,\n", - " site='MSSM')\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['lmolf1',\n", - " 'lacas9',\n", - " 'lacas9',\n", - " 'lmolf1',\n", - " 'lmolf1',\n", - " 'lacas8',\n", - " 'lacas8',\n", - " 'lacas8',\n", - " 'lacas8',\n", - " 'lhplt5',\n", - " 'laglt6',\n", - " 'lhplt5',\n", - " 'lhplt5',\n", - " 'lhplt6',\n", - " 'laglt6',\n", - " 'laglt6',\n", - " 'laglt5',\n", - " 'laimm11',\n", - " 'laimm11',\n", - " 'laimm6',\n", - " 'lmolf6',\n", - " 'laimm8',\n", - " 'laimm6',\n", - " 'laimm8',\n", - 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" Range : 0 ... 1867007 = 0.000 ... 1823.249 secs\n", - "Ready.\n", - "Added the following bipolar channels:\n", - "lacas1-lmolf1, lacas10-lacas9, lacas12-lacas9, lacas2-lmolf1, lacas3-lmolf1, lacas4-lacas8, lacas5-lacas8, lacas6-lacas8, lacas7-lacas8, laglt1-lhplt5, laglt10-laglt6, laglt2-lhplt5, laglt3-lhplt5, laglt7-lhplt6, laglt8-laglt6, laglt9-laglt6, laimm1-laglt5, laimm12-laimm11, laimm13-laimm11, laimm2-laimm6, laimm3-lmolf6, laimm4-laimm8, laimm5-laimm6, laimm7-laimm8, lcmfo1-lcmfo4, lcmfo12-lcmfo10, lcmfo13-lcmfo10, lcmfo2-lcmfo4, lcmfo3-lcmfo4, lcmfo7-lcmfo6, lcmfo8-lcmfo6, lhplt1-laglt5, lhplt10-lhplt8, lhplt2-laglt5, lhplt3-laglt4, lhplt4-lhplt6, lhplt9-lhplt8, lmcms1-lmcms5, lmcms2-lmcms5, lmcms3-lmcms5, lmcms4-lmcms5, lmcms9-lmcms8, lmolf2-lmolf6, lmolf3-laimm6, lmolf4-laimm6, lmolf5-laimm6, lmolf7-lmolf1, lmolf8-laimm8, lmtpt1-lhplt5, lmtpt2-lhplt5, lmtpt3-lhplt5, lmtpt4-lhplt5, lmtpt5-lhplt7, lmtpt6-lhplt8, lmtpt7-lhplt8, lmtpt8-lhplt8, lpcip1-lpcip4, lpcip11-lpcip10, lpcip2-lpcip5, racas1-rmolf4, racas11-racas10, racas2-rmolf4, racas4-racas6, racas7-racas5, racas8-racas10, raglt1-raglt4, raglt2-raglt4, raglt3-raglt4, raglt6-raglt5, raglt7-raglt8, raglt9-rhplt8, raimm1-raglt5, raimm11-racas12, raimm12-racas12, raimm2-raimm5, raimm3-rmolf8, raimm4-rmolf8, raimm6-rmolf8, raimm7-racas9, raimm8-racas10, rcmfo1-rcmfo5, rcmfo10-rcmfo5, rcmfo11-rcmfo5, rcmfo12-raimm5, rcmfo13-raimm5, rcmfo2-rcmfo5, rcmfo3-rcmfo5, rcmfo4-rcmfo5, rcmfo7-rcmfo5, rcmfo8-rcmfo5, rcmfo9-rcmfo5, rhplt1-raglt4, rhplt2-rhplt4, rhplt3-rhplt4, rmcms2-rmcms7, rmcms3-rmcms7, rmcms4-rmcms1, rmcms5-rmcms8, rmcms6-rmcms7, rmcms9-rmcms7, rmolf1-racas3, rmolf2-racas3, rmolf5-rmolf8, rmolf6-rmolf3, rmolf7-rmolf3, rmolf9-rmolf3, rmtpt1-rmtpt3, rmtpt2-rmtpt3, rmtpt6-rmtpt4, rmtpt7-rmtpt4, rmtpt8-rmtpt4, rpcip1-rpcip5, rpcip11-rpcip8, rpcip2-rpcip5, rpcip7-rpcip6, rpcip9-rpcip8\n" - ] - } - ], - "source": [ - "MS007_data_reref = mne.set_bipolar_reference(MS007_data, \n", - " anode=anode_list, \n", - " cathode=cathode_list,\n", - " copy=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized points196 points
Good channels127 sEEG
Bad channelslcmfo14
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "MS007_data_reref.drop_channels(drop_wm_channels)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['lacas11',\n", - " 'laimm10',\n", - " 'laimm9',\n", - " 'lcmfo11',\n", - " 'lcmfo5',\n", - " 'lcmfo9',\n", - " 'lmcms6',\n", - " 'lmcms7',\n", - " 'lpcip3',\n", - " 'lpcip6',\n", - " 'lpcip7',\n", - " 'lpcip8',\n", - " 'lpcip9',\n", - " 'raimm10',\n", - " 'raimm9',\n", - " 'rcmfo6',\n", - " 'rhplt5',\n", - " 'rhplt6',\n", - " 'rhplt7',\n", - " 'rmtpt5',\n", - " 'rpcip10',\n", - " 'rpcip3',\n", - " 'rpcip4']" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "drop_wm_channels" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized points196 points
Good channels116 sEEG
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "MS007_data_reref.drop_channels(oob_channels)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Measurement dateJanuary 01, 2001 16:05:56 GMT
ExperimenterUnknown
ParticipantUnknown
Digitized points196 points
Good channels116 sEEG
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
Sampling frequency1024.00 Hz
Highpass0.00 Hz
Lowpass512.00 Hz
FilenamesMS007_MemBandit.edf
Duration00:30:24 (HH:MM:SS)
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "right_seeg_names = [i for i in MS007_data_reref.ch_names if i.startswith('r')]\n", - "left_seeg_names = [i for i in MS007_data_reref.ch_names if i.startswith('l')]\n", - "sEEG_mapping_dict = {f'{x}':'seeg' for x in left_seeg_names+right_seeg_names}\n", - "MS007_data_reref.set_channel_types(sEEG_mapping_dict)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Annotate data? Optional" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "# We COULD annotate the bad timepoints post-referencing. However, my personal preference is to do so after EPOCHING so I am not sending \n", - "# too much subjective stuff to all my downstream analyses... \n", - "\n", - "# Furthermore, we only filter after epoching which definitely helps visualize bad epochs \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\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(\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", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_device_pixel_ratio', {\n", - " device_pixel_ratio: fig.ratio,\n", - " });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = 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._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute('tabindex', '0');\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", - " 'z-index: 2;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\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 = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box;' +\n", - " 'pointer-events: none;' +\n", - " 'position: relative;' +\n", - " 'z-index: 0;'\n", - " );\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\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", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box;' +\n", - " 'left: 0;' +\n", - " 'pointer-events: none;' +\n", - " 'position: absolute;' +\n", - " 'top: 0;' +\n", - " 'z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.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", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\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", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " /* This rescales the canvas back to display pixels, so that it\n", - " * appears correct on HiDPI screens. */\n", - " canvas.style.width = width + 'px';\n", - " canvas.style.height = height + 'px';\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 (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " /* User Agent sniffing is bad, but WebKit is busted:\n", - " * https://bugs.webkit.org/show_bug.cgi?id=144526\n", - " * https://bugs.webkit.org/show_bug.cgi?id=181818\n", - " * The worst that happens here is that they get an extra browser\n", - " * selection when dragging, if this check fails to catch them.\n", - " */\n", - " var UA = navigator.userAgent;\n", - " var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n", - " if(isWebKit) {\n", - " return function (event) {\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We\n", - " * want to control all of the cursor setting manually through\n", - " * the 'cursor' event from matplotlib */\n", - " event.preventDefault()\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " } else {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'dblclick',\n", - " on_mouse_event_closure('dblclick')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " canvas_div.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " canvas_div.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\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", - " canvas_div.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\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", - " 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", - " /* 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", - "\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", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\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 = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\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", - " // 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", - "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", - "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], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\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", - " fig.canvas_div.style.cursor = msg['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.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", - " // 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", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\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", - " img.type = 'image/png';\n", - " }\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", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\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", - " 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(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\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(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * https://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", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " // from https://stackoverflow.com/q/1114465\n", - " var boundingRect = this.canvas.getBoundingClientRect();\n", - " var x = (event.clientX - boundingRect.left) * this.ratio;\n", - " var y = (event.clientY - boundingRect.top) * this.ratio;\n", - "\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", - " 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", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, 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", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n", - "\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.binaryType = comm.kernel.ws.binaryType;\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " function updateReadyState(_event) {\n", - " if (comm.kernel.ws) {\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " } else {\n", - " ws.readyState = 3; // Closed state.\n", - " }\n", - " }\n", - " comm.kernel.ws.addEventListener('open', updateReadyState);\n", - " comm.kernel.ws.addEventListener('close', updateReadyState);\n", - " comm.kernel.ws.addEventListener('error', updateReadyState);\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", - " var data = msg['content']['data'];\n", - " if (data['blob'] !== undefined) {\n", - " data = {\n", - " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", - " };\n", - " }\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(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 = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\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;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n", - " '';\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 / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\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 () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\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", - " 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) {\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 = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\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 = 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 = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\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 (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\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", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\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", - "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(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib notebook\n", - "fig = MS007_data_reref.plot(start=0, duration=120, n_channels=8, scalings=MS007_data_reref._data.max()/20)\n", - "# fig.fake_keypress('a')" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Overwriting existing file.\n", - "Writing /sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/wm_ref_ieeg.fif\n", - "Closing /sc/arion/work/qasims01/MemoryBanditData/EMU/Subjects/MS007/wm_ref_ieeg.fif\n", - "[done]\n" - ] - } - ], - "source": [ - "# Save out the re-referenced data:\n", - "\n", - "MS007_data_reref.save(f'{save_dir}/wm_ref_ieeg.fif', overwrite=True)\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "lfp_analysis", - "language": "python", - "name": "lfpanalysis" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}