{
"cells": [
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os.path as op\n",
"import os\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"from datetime import datetime\n",
"import matplotlib\n",
"\n",
"from definitions import RESULTS_FOLDER, FIGURE_FOLDER, ROOT_FOLDER\n",
"%matplotlib notebook"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [],
"source": [
"results_folder = op.join(RESULTS_FOLDER, 'plusmaze_deval')\n",
"figure_folder = op.join(ROOT_FOLDER, 'figures', 'plusmaze_deval')\n",
"if not op.exists(figure_folder):\n",
" os.makedirs(figure_folder)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"control_results = pd.read_csv(op.join(results_folder, 'control', 'summary.csv'))\n",
"control_results['group'] = 'control'\n",
"hpc_lesion_results = pd.read_csv(op.join(results_folder, 'inactivate_HPC', 'summary.csv'))\n",
"hpc_lesion_results['group'] = 'HPC'"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [],
"source": [
"def label_trial(row):\n",
" if row['trial'] == 27.:\n",
" return 'Non-deval'\n",
" elif row['trial'] == 29.:\n",
" return 'Deval'"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [],
"source": [
"control_results['condition'] = control_results.apply(lambda row: label_trial(row), axis=1)\n",
"hpc_lesion_results['condition'] = hpc_lesion_results.apply(lambda row: label_trial(row), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [
{
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" <td>Non-deval</td>\n",
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" <td>27</td>\n",
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" <td>Deval</td>\n",
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" <th>28</th>\n",
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" <td>14.0</td>\n",
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" <td>place</td>\n",
" <td>27.0</td>\n",
" <td>Non-deval</td>\n",
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" <th>29</th>\n",
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" <th>39</th>\n",
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],
"text/plain": [
" Unnamed: 0 agent group score trial condition\n",
"0 0 0.0 control place 27.0 Non-deval\n",
"1 1 0.0 control response 29.0 Deval\n",
"2 2 1.0 control place 27.0 Non-deval\n",
"3 3 1.0 control response 29.0 Deval\n",
"4 4 2.0 control response 27.0 Non-deval\n",
"5 5 2.0 control place 29.0 Deval\n",
"6 6 3.0 control place 27.0 Non-deval\n",
"7 7 3.0 control response 29.0 Deval\n",
"8 8 4.0 control response 27.0 Non-deval\n",
"9 9 4.0 control response 29.0 Deval\n",
"10 10 5.0 control place 27.0 Non-deval\n",
"11 11 5.0 control response 29.0 Deval\n",
"12 12 6.0 control place 27.0 Non-deval\n",
"13 13 6.0 control response 29.0 Deval\n",
"14 14 7.0 control response 27.0 Non-deval\n",
"15 15 7.0 control response 29.0 Deval\n",
"16 16 8.0 control response 27.0 Non-deval\n",
"17 17 8.0 control response 29.0 Deval\n",
"18 18 9.0 control response 27.0 Non-deval\n",
"19 19 9.0 control response 29.0 Deval\n",
"20 20 10.0 control place 27.0 Non-deval\n",
"21 21 10.0 control response 29.0 Deval\n",
"22 22 11.0 control response 27.0 Non-deval\n",
"23 23 11.0 control place 29.0 Deval\n",
"24 24 12.0 control response 27.0 Non-deval\n",
"25 25 12.0 control response 29.0 Deval\n",
"26 26 13.0 control response 27.0 Non-deval\n",
"27 27 13.0 control response 29.0 Deval\n",
"28 28 14.0 control place 27.0 Non-deval\n",
"29 29 14.0 control place 29.0 Deval\n",
"30 30 15.0 control place 27.0 Non-deval\n",
"31 31 15.0 control response 29.0 Deval\n",
"32 32 16.0 control response 27.0 Non-deval\n",
"33 33 16.0 control response 29.0 Deval\n",
"34 34 17.0 control place 27.0 Non-deval\n",
"35 35 17.0 control response 29.0 Deval\n",
"36 36 18.0 control place 27.0 Non-deval\n",
"37 37 18.0 control response 29.0 Deval\n",
"38 38 19.0 control place 27.0 Non-deval\n",
"39 39 19.0 control place 29.0 Deval"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"control_results"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [],
"source": [
"ctrl_numbers = control_results.pivot_table(index=['condition', 'score'],aggfunc=len).reset_index().sort_values(by='condition',ascending=False)\n",
"les_numbers = hpc_lesion_results.pivot_table(index=['condition', 'score'],aggfunc=len).reset_index().sort_values(by='condition',ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {},
"outputs": [
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" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"320\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"font = {'weight' : 'bold', 'size':12}\n",
"\n",
"matplotlib.rc('font', **font) # pass in the font dict as kwargs\n",
"matplotlib.rc('axes', linewidth=2)\n",
"\n",
"\n",
"def kosaki_style_plot(data):\n",
" fig, ax = plt.subplots(figsize=[3.2,4])\n",
" colpal = [(0.,0.,0.), (1.,1.,1.)][::-1]\n",
"\n",
" bar = sns.barplot(data=data, y='agent',x='condition', hue='score',palette=colpal, edgecolor='black')\n",
"\n",
" hatches = ['......', '......', '', '']\n",
"\n",
" for i,thisbar in enumerate(bar.patches):\n",
" # Set a different hatch for each bar\n",
" thisbar.set_hatch(hatches[i])\n",
"\n",
" plt.ylabel('Number of agents', fontweight='bold')\n",
" plt.ylim([0,20])\n",
" plt.legend()\n",
" plt.tight_layout()\n",
" plt.xlabel('')\n",
" ax.spines['right'].set_visible(False)\n",
" ax.spines['top'].set_visible(False)\n",
" \n",
" yint = range(0, 20+1,2)\n",
" plt.yticks(yint)\n",
" ax.get_legend().set_visible(False)\n",
"\n",
" \n",
" return ax\n",
" \n",
" \n",
"ax = kosaki_style_plot(ctrl_numbers)\n",
"plt.savefig(op.join(figure_folder, 'control.pdf'))"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" event.shiftKey = false;\n",
" // Send a \"J\" for go to next cell\n",
" event.which = 74;\n",
" event.keyCode = 74;\n",
" manager.command_mode();\n",
" manager.handle_keydown(event);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
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"output_type": "display_data"
},
{
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\" width=\"320\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = kosaki_style_plot(les_numbers)\n",
"plt.savefig(op.join(figure_folder, 'lesion.pdf'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}