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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "a0d39c3c",
"metadata": {},
"outputs": [],
"source": [
"%matplotlib notebook\n",
"# Switch to inline if debugging plotting.\n",
"# %matplotlib inline\n",
"\n",
"import os\n",
"import pickle\n",
"import sys\n",
"\n",
"from matplotlib import animation\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"id": "edc5a0af",
"metadata": {},
"source": [
"### Load evaluation results."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "569e209d",
"metadata": {},
"outputs": [],
"source": [
"# Replace with the directory containing evaluation outputs.\n",
"stats_dir = \"../scripts/eval_scripts/logs/planner_eval/run-1rdonjl7_0/\"\n",
"scene_type = \"safer_slow\"\n",
"risk_level = 1.0\n",
"num_samples = 256\n",
"\n",
"filepath = os.path.join(stats_dir, f\"{scene_type}_{num_samples}_samples_risk_level_{risk_level}_in_predictor.pkl\")\n",
"with open(filepath, \"rb\") as infile:\n",
" predictor_data = pickle.load(infile)\n",
"filepath = os.path.join(stats_dir, f\"{scene_type}_{num_samples}_samples_risk_level_{risk_level}_in_planner.pkl\")\n",
"with open(filepath, \"rb\") as infile:\n",
" planner_data = pickle.load(infile)"
]
},
{
"cell_type": "markdown",
"id": "6cdbb58a",
"metadata": {},
"source": [
"### Find the most relevant episodes."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "49b8859c",
"metadata": {},
"outputs": [],
"source": [
"def print_riskiest_episodes(data, max_ep_id=10000, num_to_print=10, key=\"interaction_risk\"):\n",
" episode_id_risk = []\n",
" for episode_id in range(max_ep_id):\n",
" if episode_id not in data:\n",
" break\n",
" episode_id_risk.append((episode_id, data[episode_id][key]))\n",
" episode_id_risk = sorted(episode_id_risk, key=lambda x: x[1], reverse=True)\n",
" \n",
" print(\"Riskiest episodes:\")\n",
" for ep_id, risk in episode_id_risk[:num_to_print]:\n",
" print(f\"episode id: {ep_id}\\trisk: {risk:0.4f}\")\n",
"\n",
"def print_largest_risk_difference_episodes(\n",
" data_a, \n",
" data_b, \n",
" max_ep_id=10000, \n",
" key=\"interaction_risk\", \n",
" num_to_print=10):\n",
" \"\"\"Plots the episodes where the risk of a is most larger than that of b.\"\"\"\n",
" risk_a = []\n",
" risk_b = []\n",
" for episode_id in range(max_ep_id):\n",
" if episode_id not in data_a or episode_id not in data_b:\n",
" break\n",
" risk_a.append(data_a[episode_id][key])\n",
" risk_b.append(data_b[episode_id][key])\n",
" risk_a = np.array(risk_a)\n",
" risk_b = np.array(risk_b)\n",
" \n",
" diff = risk_a - risk_b\n",
" indices = np.argsort(diff)[::-1]\n",
" \n",
" print(\"Episdoes where the first data is risker than the second\")\n",
" for episode_id in indices[:num_to_print]:\n",
" print(f\"episode_id: {episode_id}\\tfirst data risk: {risk_a[episode_id]:0.4f}\\tsecond data risk: {risk_b[episode_id]:0.4f}\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "1b7de380",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Riskiest episodes:\n",
"episode id: 66\trisk: 1.1713\n",
"episode id: 48\trisk: 1.0506\n",
"episode id: 32\trisk: 1.0245\n",
"episode id: 37\trisk: 1.0139\n",
"episode id: 39\trisk: 0.9978\n",
"episode id: 84\trisk: 0.9266\n",
"episode id: 24\trisk: 0.9190\n",
"episode id: 79\trisk: 0.8993\n",
"episode id: 75\trisk: 0.8989\n",
"episode id: 71\trisk: 0.8468\n"
]
}
],
"source": [
"print_riskiest_episodes(predictor_data)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "ba9bc377",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Episdoes where the first data is risker than the second\n",
"episode_id: 54\tfirst data risk: 1.2152\tsecond data risk: 1.0733\n",
"episode_id: 82\tfirst data risk: 0.5233\tsecond data risk: 0.3918\n",
"episode_id: 30\tfirst data risk: 1.2241\tsecond data risk: 1.0988\n",
"episode_id: 29\tfirst data risk: 0.8171\tsecond data risk: 0.7078\n",
"episode_id: 48\tfirst data risk: 1.0183\tsecond data risk: 0.9133\n",
"episode_id: 18\tfirst data risk: 0.4989\tsecond data risk: 0.4065\n",
"episode_id: 95\tfirst data risk: 0.5201\tsecond data risk: 0.4409\n",
"episode_id: 4\tfirst data risk: 0.8029\tsecond data risk: 0.7406\n",
"episode_id: 23\tfirst data risk: 0.9542\tsecond data risk: 0.8920\n",
"episode_id: 72\tfirst data risk: 1.0446\tsecond data risk: 0.9828\n"
]
}
],
"source": [
"print_largest_risk_difference_episodes(predictor_data, planner_data)"
]
},
{
"cell_type": "markdown",
"id": "5fc3cfc3",
"metadata": {},
"source": [
"### Animate those episodes for the risk-sensitive predictor vs risk-sensitive planner."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "c03ae0f0",
"metadata": {},
"outputs": [],
"source": [
"def animate_episode(solver_infos, ado_positions, ado_predictions):\n",
" fig = plt.figure()\n",
" ax = plt.axes(xlim=(0, 100), ylim=(-4, 8))\n",
" \n",
" scatter = ax.scatter([], [])\n",
" text = ax.annotate(\"\", (2,7.5))\n",
" \n",
" def animate(t):\n",
" solver_iter = t // 45\n",
" timestep = t % 45\n",
" ado_position = np.array([[ado_positions[0, timestep, 0], ado_positions[0, timestep, 1]]]) \n",
" solver_info = solver_infos[solver_iter]\n",
" biased_predicted_ado_positions = solver_info[\"ado_state_future_samples\"][:, 0, timestep, :2].reshape([-1, 2])\n",
" predicted_ado_positions = ado_predictions[:num_samples, 0 , timestep, :2].reshape([-1, 2])\n",
" len_pred = len(predicted_ado_positions)\n",
" len_biased_pred = len(biased_predicted_ado_positions)\n",
" ego_positions = solver_info[\"ego_state_future\"][:, 0, timestep, :2]\n",
" positions = np.concatenate((ado_position, predicted_ado_positions, biased_predicted_ado_positions, ego_positions)) \n",
" scatter.set_offsets(positions)\n",
" scatter.set_alpha(0.5)\n",
" colors = np.ones(len(positions))*0.4\n",
" colors[0] = 0\n",
" colors[1:len_pred+1] = 0.6\n",
" colors[len_pred+1:len_biased_pred+len_pred+1] = 0.8\n",
" scatter.set_array(colors)\n",
" total_risk = solver_info[\"total_risk\"].mean()\n",
" tracking_cost = solver_info[\"tracking_cost\"].mean()\n",
" text_str = \"solver_iter: {}, timestep: {:02d}, total risk: {:0.2f}, tracking cost: {:0.2f}\".format(\n",
" solver_iter,\n",
" timestep, \n",
" total_risk,\n",
" tracking_cost,\n",
" )\n",
" text.set_text(text_str)\n",
" return scatter, text\n",
" \n",
" num_frames = 45 * 10\n",
" anim = animation.FuncAnimation(fig, animate, frames=num_frames, interval=20, blit=True, save_count=sys.maxsize)\n",
" \n",
" return anim"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "519e9eee",
"metadata": {},
"outputs": [],
"source": [
"episode_id = 66"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "dcc04f38",
"metadata": {},
"outputs": [
{
"data": {
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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.rubberband_canvas.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// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.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 }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\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 var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * 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 /* 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\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 icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\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(\"<div id='\" + id + \"'></div>\");\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 '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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 '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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 = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n buttonGroup.appendChild(button);\n }\n\n 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 = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\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",
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oMKphjNetD2akfrwww+jA5QObGqC4l/68eu23nrraKaw2MErfr2KmGY05BvPfhT7DPrvhU4tFWoA4+vc4mse1QyqaVEzGD89Xtusg79mv5K4q+HRLGKhB44XOsX29ttv22abbWb639xf9Z///OejQqh9qHGahcptROOZI/0o0Ou0n9UQqAEodr2eZrbUuKugKCe6YUrL/PnzTVlV46tZtnhRpjQLonUlWQoVH2VDMzb6LEcddVT0NjqtFe8r/ehJWrRUnJVpNR1quJM2gPmZzB930EEHRY1fodnm+LX6URbP2uoShdxnjKnp0H7QPii0aIZJ+zH3Lw7pddpf+uFR6AkFMtL3Wj9A9J3TbK1+PCqPaoLUgGpRg66GRbOR+mGgRl6Ngl6TdGnqFHBT3+f899WPNM3uxo2ETl0r2039/fV436uJ0gy0rl/M/Z4XOh7pB208W60zEvpRqh9wOi7qsgUdIzVDpuOQFv3YUMPWVAMYz7Bq3frO5S9qMPUDRDNy+hGiRddsavZTM7o6Vjd37G/qFLAmB/Rd0/Gw0KLv/S233NL4YzF+ja5h1ufNPTtUaHzS75J+WGj7te/iH/pJ81LOryulfpfz5/BsGw2gQ6+UAGnWQxdz6+YF/a8OuppNUsGL30dfSDVJ8aJf+/p1rlmBQgVMB0I1PzrQa1ZH76sDpU7rqGnUqUvNrOQumq3QTKEKiYrU7373u+g0bNIlSQOopvWll15a5zSPDsS6qUC/xDU7ogO7Dia5p06TbkP8On1mXfenWQkdLEtZkjaAKpCahY0XNW1qLjRzES/aZzpA6pRwUnedctc4FZTDDjusceamUIHVuvQ549Pn8XrVJOj0rK7X0Th56t/l/lkj5UvX/DT1122aMtMMh2bS1FAqXzrdqxmttmwAlRnNruT/SNKPAX0ONV1NFS1lWLN+akC1f/SDSA2l7DTbk7QBzM9k/rjJkydHM5ea8dF3WLOgamK0xK9V86x/9L3N//4Vy2hLGkC95wsvvNA4m6cfX8qVcqDvnbZZi2andPxR46AmVpdeqFFUw/qpT32q2KZF/725BrDQ91nZ1Iy0fjApT/pBoR9A+mGqRU2ovjNqegst+p7q2Kl9qaZRzWLuUuh4pB/S+rEQL8qU9pnykXtMjf97nLumGkD9yNQPEh07c09dx+P1HVSTH8/g6d/ruK1T05phVBPX1LFfr23pNYBpNID6saLjlJzVKHekpZT63ZE+d+5noQF07NlSA6SDcXw9kGa+dFeeCpYaNh1ASm0AdQDQr2kdwHTw1Z2AOl2mRQ2eZpkKnWrS6R8VgPgaN/1CTbokaQDV4Ok0qBrQ/EWzlDq94b24W7NNmuFU05V7fU/Sz5G0AVSRzPUpdLDONUnirm3UrIKaExVnfRbtRzU6hQqsiqiunVIjquKeu+j0sGY4WrsBzF2HTuNpRkUzn61xCljvXaiR8zSAmoXVzLpmIjVLqgZQM3/6nqlJS9oA5s9mFxqnmVGdhtSMkGZ+1HBodiR+rfKoGS1d26bTn6UsLTkFnPv+mnHSPtLsp2YEdW2YGiw1YPEF/bkzaGoU9e81M5Zkaa4BzLfTKVPNxmk2Sg2QjnHKuS6riB+1pGOf7JprANVg67ui2dP8a0yTHI/UiCkDOt61pAHUftR3s6UNoI55TR37dTq6pQ1gW58C1o89+eryGu33jvb3ckut30m+H+3tNTSAjj3mCZB+1apg6XoY/dPUKWCd+tHsS6FCpH+nX8S6CFgHch3EdYpHi4qTrt3S9L2anUJLazSAmgnRXaK6FiVe1KyoGdWDtptaPA2gmgfNiKnZyv1TfqXsykLb3dRNIKU0gEnc87dTM2ua4VDzrhkgeeo6pniJT0E98cQTUUEttMSngOPTvXqNGjY1Rrn/rhSj+LW6QF/FRnnTosZCudQPDC3x5Qe6Pi7JTSAaox9Cn/70p6PZOjUBWmSg6580k5J7ClhFUs2FZq4LjVu8eHH0gybXR6fi45sJWrsBzDXUKVfNNulYkPsd1ayaGhY1Lrmz+sX845tAdLNP/HiWeD82dRNIoffUjKj2vX5gaKYyvk4wnoGPx6gx03FIN58lWQrlU+MKfZ/V6OlUqJrPeNHlIbouL24AdVpbx75ip4CVN11+oVk85SBeSm0AdYzU5Qw6BRxfGnPjjTdG17c2NQMYH2d16ryUU8D6Luhz5jdO+cd+/WDQmRs1mqUs8U0gmhnVGQgt2kY5eW8C0cyf9o3eV/sm/4dnKdtZrq/11O9y/UylbhcNYKliOa8vJUAqwn/961+jg7Gu0dD/r2craYZJjZpO0+mOTx2I9YtVRVCzW03dBBJvhm6A0KkV3Z2lpis+5aRfnHoWmBoJXTithke/6FSQ9GtWMwOt0QDq82idOtDrgKpCrJkc/WrUzRE6sOrUpQqPmqP4ztGmGsBi1wDGp311s0DuDKNmFdU8JF0KbXdrNIDF3DX7ooZBp//V2OhArdOzKvZqaOMGR82zTl3pFJn+UVZ084GKqq5T0yyU8qSL/zUTGt8Eov+mU266xksNmRY1DlqSXAOo2SLNWGg/aFFTpVkTWWsmUItmJLXNan5UmJVdnSbTNYfNPasud99oW3STgnKuU7TKkGYz1ejptLuKcnzTlBpjNRE6DVxonHz0ndL3SN8hXSup4qsfRq09A6gGREVR+1Gn27Uenc7U9zn/R5pc1GyoCdP3VEuxawD1Gl27pRlG7VOdLtWpWy3KRrzkf09kplk/manZ0/dD26kZSi26TlB3jA4YMCCacVPTrWOPsqjrFLUPkixN5bPQ91n7TbnW7LEuC9GxR7OBaoDiBlA/5nRdqq7L000gOkWsz67r57Tkvq+aeu1j3eARPxi61AYwvglEPyC175QVvZeyqx968RMS8i00Q6nvm75beo1O7Wq/H3744Y03geg1+s6pYddxL74JpNixX2dy9F1S8xafDdKp5mLXAGob5aHjvhrb+DEwOrbHj4Ep9J3XmSf9o8ZRZ470Hdc1zsqOjqEaI1f9MNA1hrnNn842dJSllPrdUT5z/uegAXTs2VICpF9rKqS6WUHj9OXSgT0u0ppF0YFNdxfqwKKDdVOPgcl9jIWuf9PdnjpY6Muau6j5iy9QVsOgL6+aQl1UrOLbGg2grhHTjJUOeiqI8WNgVHy1bjUf+ne6I1Czk/FNLk01gMXuAtbpkvzPqc+sWZb4dHd8elHXsTU1+1lou1ujAdS2NOeuBknNk5o5HbjVMOsaIt1sEl+UrgZABV0zW/FjYHRwVwOmmwd0gNY4NdkqqLp+K952zdSpqOs1mgGLGyltV5K7gDWrp2IkOzWR2m8qEtrHuTMZauTjG2SURxXG+C5oravY3a56jfKuHw5yUH71GfR8Mp3G1QyYHHMfAxNnu9A4NcxqUvVDSHd7antUxFq7AdQ+UHGVpZpWGes6OjXzhWbp9SNO3zPdQalTmUlc4gdByyD3QdC5xTf/e6LPq/0hSzV58lTzmfvgXs0Kxjcq6UejTv1qNi33sTDF7jyNG9T8fDb1fda+VCb1OfRDRZmVR+5f27nnnnuiLOhHohpeHaPixjX/fdWsqFnVMUzHz1IbwLiR1ndMTZ++Ozp7oRln/f/KTqFF+0THLp3C1vdSzVL8+B+9vrnHwBQ79uvYrLMmOlZqv8R36SfZF3qigWpI7oOglYX4QdCFvvPyz3/0jD5D/PSF+GxCIYf4+O4om2UztJT6XTYb3cobQgPoACVADrw2HKoDmX5Vq6AUumi7DVcd7K2TPqA3rQ1UQ65TSCo2LO1HQD9M1RyU8him9vPpCm+pboTT7J1Ov5d60057/+xZ3n7qd4b/FnBrBJ8AtYZi67+H7qrVqRn9b1aWcmoAVUh1ilQzKs39Saqs7Jv28jl1uYmuR42fndhetrvU7dQsuq6d1rWHultXM2iabWvqOsRS35/Xtw8B6jcNoCupHS1Auh5Gp56bWjSjluSvGbhQGdwigXJqAFv0ARiEQEoCuiZalx7oOjidLtdNQnosk64lZcmOQEer3y3Zc5wCbonaf8d0tADpAuz4Ts9CLLoeJ/7TRg42hiKAAAIIIBBUoKPV75Zg0gC2RK2DNoAOCoYigAACCCDQbgRoADkF7AorAXLxMRgBBBBAAIEgAtRvGkBX8AiQi4/BCCCAAAIIBBGgftMAuoJHgFx8DEYAAQQQQCCIAPWbBtAVPALk4mMwAggggAACQQSo3zSAruARIBcfgxFAAAEEEAgiQP2mAXQFjwC5+BiMAAIIIIBAEAHqNw2gK3gEyMXHYAQQQAABBIIIUL9pAF3BI0AuPgYjgAACCCAQRID6TQPoCh4BcvExGAEEEEAAgSAC1G8aQFfwCJCLj8EIIIAAAggEEaB+0wC6gkeAXHwMRgABBBBAIIgA9ZsG0BU8AuTiYzACCCCAAAJBBKjfNICu4BEgFx+DEUAAAQQQCCJA/aYBdAWPALn4GIwAAggggEAQAeo3DaAreATIxcdgBBBAAAEEgghQv2kAXcEjQC4+BiOAAAIIIBBEgPpNA+gKHgFy8TEYAQQQQACBIALUbxpAV/AIkIuPwQgggAACCAQRoH7TALqCR4BcfAxGAAEEEEAgiAD1mwbQFTwC5OJjMAIIIIAAAkEEqN80gK7gESAXH4MRQAABBBAIIkD9pgF0BY8AufgYjAACCCCAQBAB6jcNoCt4BMjFx2AEEEAAAQSCCFC/aQBdwSNALj4GI4AAAgggEESA+k0D6AoeAXLxMRgBBBBAAIEgAtRvGkBX8AiQi4/BCCCAAAIIBBGgftMAuoJHgFx8DEYAAQQQQCCIAPWbBtAVPALk4mMwAggggAACQQSo3zSAruARIBcfgxFAAAEEEAgiQP2mAXQFjwC5+BiMAAIIIIBAEAHqNw2gK3gEyMXHYAQQQAABBIIIUL9pAF3BI0AuPgYjgAACCCAQRID6TQPoCh4BcvExGAEEEEAAgSAC1G8aQFfwCJCLj8EIIIAAAggEEaB+0wC6gkeAXHwMRgABBBBAIIgA9ZsG0BU8AuTiYzACCCCAAAJBBKjfNICu4BEgFx+DEUAAAQQQCCJA/c54Azhv3jw766yzbPLkyfbRRx/ZsGHD7KabbrKRI0cmCiQBSsTEixBAAAEEECgrAep3hhvAJUuW2E477WRjxoyxk046yTbccEN7/fXXbfPNN4/+SbIQoCRKvAYBBBBAAIHyEqB+Z7gBHD9+vD399NP25JNPtjiVBKjFdAxEAAEEEEAgmAD1O8MN4Lbbbmv77befvfPOOzZlyhQbNGiQnXzyyXbiiSc2Gci6ujrTP/GiANXU1Fhtba1VV1cHCzIrRgABBBBAAIHkAjSAGW4Au3XrFiXljDPOsMMOO8ymTZtm48aNs2uvvdbGjh1bMEUTJkywiRMnfuK/0QAm/9LxSgQQQAABBEIL0ABmuAHs0qVLdLPHM88805jD0047LWoEn3322YLZZAYw9FeW9SOAAAIIIOAXoAHMcAM4ZMgQ22effWzSpEmNSbrmmmvsvPPOM90dnGQhQEmUeA0CCCCAAALlJUD9znADeNRRR9ncuXPXuQnk9NNPt6lTp64zK9hcZAlQeX2h2RoEEEAAAQSSCFC/M9wA6lTv7rvvHl3Td/jhh9tzzz0X3QBy/fXX29FHH50kP0aAEjHxIgQQQAABBMpKgPqd4QZQSXzggQfs7LPPjp7/N3To0OiGkObuAs5PLwEqq+8zG4MAAggggEAiAep3xhvARClp5kUEyCvIeAQQQAABBNIXoH7TALpSR4BcfAxGAAEEEEAgiAD1mwbQFTwC5OJjMAIIIIAAAkEEqN80gK7gESAXH4MRQAABBBAIIkD9pgF0BY8AufgYjAACCCCAQBAB6jcNoCt4BMjFx2AEEEAAAQSCCFC/aQBdwSNALj4GI4AAAgggEESA+k0D6AoeAXLxMRgBBBBAAIEgAtRvGkBX8AiQi4/BCCCAAAIIBBGgftMAuoJHgFx8DEYAAQQQQCCIAPWbBtAVPALk4mMwAggggAACQQTWisUxAAAgAElEQVSo3zSAruARIBcfgxFAAAEEEAgiQP2mAXQFjwC5+BiMAAIIIIBAEAHqNw2gK3gEyMXHYAQQQAABBIIIUL9pAF3BI0AuPgYjgAACCCAQRID6TQPoCh4BcvExGAEEEEAAgSAC1G8aQFfwCJCLj8EIIIAAAggEEaB+0wC6gkeAXHwMRgABBBBAIIgA9ZsG0BU8AuTiYzACCCCAAAJBBKjfNICu4BEgFx+DEUAAAQQQCCJA/aYBdAWPALn4GIwAAggggEAQAeo3DaAreATIxcdgBBBAAAEEgghQv2kAXcEjQC4+BiOAAAIIIBBEgPpNA+gKHgFy8TEYAQQQQACBIALUbxpAV/AIkIuPwQgggAACCAQRoH7TALqCR4BcfAxGAAEEEEAgiAD1mwbQFTwC5OJjMAIIIIAAAkEEqN80gK7gESAXH4MRQAABBBAIIkD9pgF0BY8AufgYjAACCCCAQBAB6jcNoCt4BMjFx2AEEEAAAQSCCFC/aQBdwSNALj4GI4AAAgggEESA+k0D6AoeAXLxMRgBBBBAAIEgAtRvGkBX8AiQi4/BCCCAAAIIBBGgftMAuoJHgFx8DEYAAQQQQCCIAPWbBtAVPALk4mMwAggggAACQQSo3zSAruARIBcfgxFAAAEEEAgiQP2mAXQFjwC5+BiMAAIIIIBAEAHqNw2gK3gEyMXHYAQQQAABBIIIUL9pAF3BI0AuPgYjgAACCCAQRID6TQPoCh4BcvExGAEEEEAAgSAC1G8aQFfwCJCLj8EIIIAAAggEEaB+0wC6gkeAXHwMRgABBBBAIIgA9ZsG0BU8AuTiYzACCCCAAAJBBKjfNICu4BEgFx+DEUAAAQQQCCJA/aYBdAWPALn4GIwAAggggEAQAeo3DaAreATIxcdgBBBAAAEEgghQv2kAXcEjQC4+BiOAAAIIIBBEgPpNA+gKHgFy8TEYAQQQQACBIALUbxpAV/AIkIuPwQgggAACCAQRoH7TALqCR4BcfAxGAAEEEEAgiAD1mwbQFTwC5OJjMAIIIIAAAkEEqN80gI3Bu+iii+zss8+2cePG2WWXXZYokAQoERMvQgABBBBAoKwEqN80gFEgp02bZocffrhVV1fbmDFjaADL6mvKxiCAAAIIINC6AjSANIC2YsUK23nnne3qq6+28847z4YPH04D2LrfM94NAQQQQACBshKgAaQBtLFjx9oGG2xgl156qe299940gGX1FWVjEEAAAQQQaH0BGsCMN4C33367nX/++dEp4G7duhVtAOvq6kz/xIsCVFNTY7W1tdHpYxYEEEAAAQQQKH8BGsAMN4Bz5861kSNH2qOPPmo77LBDlNZiM4ATJkywiRMnfiLZNIDl/2VnCxFAAAEEEMidwOndu3emJ3AqGhoaGrIYifvuu88OOeQQq6qqavz4a9eutYqKCqusrIxm+nL/m17EDGAWk8JnRgABBBDoaALMAGZ4BnD58uX21ltvrZPp448/3rbeems766yzbPvtty+adwJUlIgXIIAAAgggUHYC1O8MN4CF0ljsFHD+GAJUdt9pNggBBBBAAIGiAtRvGsB1QkIDWPQ7wwsQQAABBBBo9wI0gDSArhATIBcfgxFAAAEEEAgiQP2mAXQFjwC5+BiMAAIIIIBAEAHqNw2gK3gEyMXHYAQQQAABBIIIUL9pAF3BI0AuPgYjgAACCCAQRID6TQPoCh4BcvExGAEEEEAAgSAC1G8aQFfwCJCLj8EIIIAAAggEEaB+0wC6gkeAXHwMRgABBBBAIIgA9ZsG0BU8AuTiYzACCCCAAAJBBKjfNICu4BEgFx+DEUAAAQQQCCJA/aYBdAWPALn4GIwAAggggEAQAeo3DaAreATIxcdgBBBAAAEEgghQv2kAXcEjQC4+BiOAAAIIIBBEgPpNA+gKHgFy8TEYAQQQQACBIALUbxpAV/AIkIuPwQgggAACCAQRoH7TALqCR4BcfAxGAAEEEEAgiAD1mwbQFTwC5OJjMAIIpCjwYe2H9t7cxdEa+9f0tZ69e6a4dlaFQHkJUL9pAF2JJEAuPgYjgEAKAnUr6+yZPz5vM5542WrfXx6tsXe/XvapPbe13Q8aaXVVq2xVwyrr06m3darqlMIWsQoEwgtQv2kAXSkkQC4+BiOAQBsLrF2z1h647hGb/teZVt23l/Xp3ztaY+2iWnt7wFxb+5lVVtf7Y2sws16d17Od1x9uhw462Lp16trGW8bbIxBWgPpNA+hKIAFy8TEYAQTaWGD29H/bHT+/z/oO3MB69OreuLbZw2bbW0PmWH1FvXXu3NmqKqpsra21CjMb2nNT+/5W36UJbON9w9uHFaB+0wC6EkiAXHwMRgCBNhZ48IZH7YVHXrJNt6tpXNOynsvshdHP29pOa61hTYNVdq60yspKq6qoNLMKa7B6O2DAvvaVmkPaeOt4ewTCCVC/aQBd6SNALj4GI4BAGwvcftG9Nudfc23g5hs3rulf2820+YPmq9ez6NxvhVnU+1mFVVqlNViDbdR1I7t4x59aRYVexIJAxxOgftMAulJNgFx8DEYAgTYWeOjGv9rzD0+3TbfbpHFNU3f9uy3rvew/DWC85PV53Su729UjLrVOldwU0sa7iLcPJED9pgF0RY8AufgYjAACbSzwxotzTLOAGwxY33pW94jWNuUzj9uqHqv+vwEsMMnXuaKzTdrlKqv8z9QgCwIdToD6TQPoCjUBcvExGAEE2lhAdwE/NOmv9sKjL1mvPj2tT/9qe/zAx80669zvf5cCDWCPiu523agr2njreHsEwglQv2kAXekjQC4+BiOAQAoCq+pW29QHX7CXprxsS95baq+e9nKzs3/apG4V3eyGUVemsHWsAoEwAtRvGkBX8giQi4/BCCCQosDKFSvtzTfetos+vqToWqusym4efV3R1/ECBNqrAPWbBtCVXQLk4mMwAgikLPDq0tftvFcvLrpWnRX+zehJRV/HCxBorwLUbxpAV3YJkIuPwQggkLLA79640x5+/5FEa/0tDWAiJ17UPgWo3zSAruQSIBcfgxFAIGWB46d+29bYmkRrpQFMxMSL2qkA9ZsG0BVdAuTiYzACCKQscMzUbyReIw1gYipe2A4FqN80gK7YEiAXH4MRQCBlARrAlMFZXdkKUL9pAF3hJEAuPgYjgEDKAjSAKYOzurIVoH7TALrCSYBcfAxGAIGUBeIGsIcttl8N+JdVVZk1NJhd+W6lTa/fo3Frdusx2k7+1Ikpbx2rQyA9Aeo3DaArbQTIxcdgBBBIWUAN4A0DnrBOncwqcv4CiJpA/fONd0ZYvfW087b+sQ3p/f9/PzjlzWR1CLS5APWbBtAVMgLk4mMwAgikKLB27VpbPW8b69y56ZXW15ud8M6e9uuR11jnqmZemOJ2syoE2kKA+k0D6MoVAXLxMRgBBFIU+NOsB+2A3qdHa8yd/cvdBM0C/uNds1EjXktxy1gVAukLUL9pAF2pI0AuPgYjgECKAktf29J69Sq+wrVrzboMpgEsLsUr2rMA9ZsG0JVfAuTiYzACCKQosPKtLa1Ll+IrpAEsbsQr2r8A9ZsG0JViAuTiYzACCKQo8PHbWzZ7/V+8KWvWmHWtYQYwxV3DqgIIUL9pAF2xI0AuPgYjgECKAq/N2NI271d8hSs/NltvKA1gcSle0Z4FqN80gK78EiAXH4MRQCBFAT0C5qaaJ5q8AUSboptAbl56jn1j2+NS3DJWhUD6AtRvGkBX6giQi4/BCCCQksCaNWvs+Be+bV/t+oTt37/wXcBq/hYvM1s7eIoNXG9ASlvGahAII0D9pgF0JY8AufgYjAACKQk8NOcR+/3CO6O1ja56wr414D9NoP6JHwI9eZHZXXV72i2jrrfKisqUtozVIBBGgPpNA+hKHgFy8TEYAQRSEjjjufG2qOH9ddZWYfXW2epttVVag/1/w/fb0ZNS2ipWg0A4Aeo3DaArfQTIxcdgBBBISeDbU0+zD+2jRGujAUzExIvauQD1mwbQFWEC5OJjMAIIpCTwnWmn27L65UXXVmVVdvPo64q+jhcg0N4FqN80gK4MEyAXH4MRQCAlgZOnnW7LEzSAxww6yvYd/NmUtorVIBBOgPpNA+hKHwFy8TEYAQRSEvj2tNPsw/rip4An7XSVde3SNaWtYjUIhBOgftMAutJHgFx8DEYAgZQEjpv6LVtra4uujev/ihLxgg4iQP2mAXRFmQC5+BiMAAIpCKxdu9ZOeP4kq7f6ZtfWpaKL3Tjq6hS2iFUgEF6A+k0D6EohAXLxMRgBBFIQWLqq1k6ffpatsTXNrm3Dzv3slztflMIWsQoEwgtQv2kAXSkkQC4+BiOAQAoCS1cttZ/MvMA+WP1Bs2vbu99n7Oubj01hi1gFAuEFqN8ZbwAvvPBCu+eee+yVV16x7t272+67724XX3yxbbXVVonSSYASMfEiBBAIKLC6frVdM3uSTV/yYpOzgF0ru9oPt/6BDe21acAtZdUIpCdA/c54A7j//vvbEUccYbvssovpb2Wec845NnPmTHv55ZetZ8+eRZNIgIoS8QIEECgDgX988E/7wzv326K6921V/arG6wErrdK6VHaxT/cbbcdsehR/Aq4M9hWbkI4A9TvjDWB+zBYtWmT9+/e3KVOm2J577lk0hQSoKBEvQACBMhBQ0/fXBY/bi0tn2NLVtVZXXxdtVc+qnjas12b2xYEHWL+ufctgS9kEBNIRoH7TAK6TtNmzZ9sWW2xhM2bMsO233/4TKayrqzP9Ey8KUE1NjdXW1lp1dXU6qWUtCCCAQAsEdCp49oo3bNay12zZ6mXWpbKzbdlri+if6s69WvCODEGg/QrQANIANqa3vr7eDjroIFu6dKk99dRTBVM9YcIEmzhx4if+Gw1g+z0IsOUIZE2goaEhOgWs078VFRVZ+/h8XgQiARpAGsDGr8JJJ51kkydPjpq/wYMHF/yKMAPIkQMBBBBAAIH2L0ADSAMYpfiUU06x+++/35544gkbOnRo4mQToMRUvBABBBBAAIGyEaB+Z7wB1KmQU0891e699157/PHHo+v/SlkIUClavBYBBBBAAIHyEKB+Z7wBPPnkk+22226LZv9yn/3Xu3fv6LmAxRYCVEyI/44AAggggED5CVC/M94ANnUB9E033WTHHXdc0cQSoKJEvAABBBBAAIGyE6B+Z7wB9CaSAHkFGY8AAggggED6AtRvGkBX6giQi4/BCCCAAAIIBBGgftMAuoJHgFx8DEYAAQQQQCCIAPWbBtAVPALk4mMwAggggAACQQSo3zSAruARIBcfgxFAAAEEEAgiQP2mAXQFjwC5+BiMAAIIIIBAEAHqNw2gK3gEyMXHYAQQQAABBIIIUL9pAF3BI0AuPgYjgAACCCAQRID6TQPoCh4BcvExGAEEEEAAgSAC1G8aQFfwCJCLj8EIIIAAAggEEaB+0wC6gkeAXHwMRgABBBBAIIgA9ZsG0BU8AuTiYzACCCCAAAJBBKjfNICu4BEgFx+DEUAAAQQQCCJA/aYBdAWPALn4GIwAAggggEAQAeo3DaAreATIxcdgBBBAAAEEgghQv2kAXcEjQC4+BiOAAAIIIBBEgPpNA+gKHgFy8TEYAQQQQACBIALUbxpAV/AIkIuPwQgggAACCAQRoH7TALqCR4BcfAxGAAEEEEAgiAD1mwbQFTwC5OJjMAIIIIAAAkEEqN80gK7gESAXH4MRQAABBBAIIkD9pgF0BY8AufgYjAACCCCAQBAB6jcNoCt4BMjFx2AEEEAAAQSCCFC/aQBdwSNALj4GI4AAAgggEESA+k0D6AoeAXLxMRgBBBBAAIEgAtRvGkBX8AiQi4/BCCCAAAIIBBGgftMAuoJHgFx8DEYAAQQQQCCIAPWbBtAVPALk4mMwAggggAACQQSo3zSAruARIBcfgxFAAAEEEAgiQP2mAXQFjwC5+BiMAAIIIIBAEAHqNw2gK3gEyMXHYAQQQAABBIIIUL9pAF3BI0AuPgYjgAACCCAQRID6TQPoCh4BcvExGAEEEEAAgSAC1G8aQFfwCJCLj8EIIIAAAggEEaB+0wC6gkeAXHwMRgABBBBAIIgA9ZsG0BU8AuTiYzACCCCAAAJBBKjfNICu4BEgFx+DEUAAAQQQCCJA/aYBdAWPALn4GIwAAggggEAQAeo3DaAreATIxcdgBBBAAAEEgghQv2kAXcEjQC4+BiOAAAIIIBBEgPpNA+gKHgFy8TEYAQQQQACBIALUbxpAV/AIkIuPwQgggAACCAQRoH7TALqCR4BcfAxGAAEEEEAgiAD1mwbQFTwC5OJjMAIIIIAAAkEEqN80gK7gESAXH4MRQAABBBAIIkD9pgF0BY8AufgYjAACCCCAQBAB6jcNoCt4BMjFx2AEEEAAAQSCCFC/aQBdwSNALj4GI4AAAgggEESA+k0D6AoeAXLxMRgBBBBAAIEgAtRvGkBX8AiQi4/BCCCAAAIIBBGgftMAuoJHgFx8DEYAAQQQQCCIAPWbBtAVPALk4mMwAm0qsPijj+zFhQvsjSWLraHBbPMNNrAdNxpgvbt2tVnvL7KZ7y202o8/tj7dutt6XbvYW0uW2AOvzrI3ly6xNW26Zeu+ebfKShvWt68dOGwrO2irbWxgdXWKa2dVCGRTgPpNA2hXXXWVXXLJJbZgwQLbcccd7YorrrBRo0Yl+kYQoERMvAiB1AVeeX+R3T7zJVuwYoV17dQpWn/dmjXWt0d3W69LF5u/fIXVN9RbVUWFvbFkib334QpbvHJl6tuZu8IulZW23Yb97cw99rTRg2qCbgsrR6CjC1C/M94A3nHHHXbsscfatddea6NHj7bLLrvM7rrrLnv11Vetf//+RfNPgIoS8QIEUhf4YOVHdsVzfzfNAG7aZ32rrKiItqG+ocGeentO1Oh9ZpNNrV+PHvba4vft1fcX2dxly2xNQ33q25q/wu6dOtnIAQPtgs/tZ4OYCQy+P9iAjitA/c54A6imb5dddrErr7zyPwWivt5qamrs1FNPtfHjxxdNPgEqSsQLEEhd4G9z3rQ7Zs6wLfv2a2z+tBErV6+2p+e+bbV1K23nAQNtYK9qe2bu26aGcd7y5alvZ6EVdq6osA17rmen77q7Hbrt9mWxTWwEAh1RgPqd4QZw1apV1qNHD7v77rvt4IMPbsz32LFjbenSpXb//fd/IvN1dXWmf+JFAVLDWFtba9X8Wu+Ixwg+UzsUmPSP523GwoU2dP3119n69z780KbNf8cqzaIma9M+fey5ee/YBytX2gcfhz39G2+oTkmv37Wb7TdsS/vZZz/fDvXZZATahwANYIYbwPnz59ugQYPsmWeesd12260xsWeeeaZNmTLFpk6d+okUT5gwwSZOnPiJf08D2D6+8GxlNgRoALOxn/mUCHgEaABpAEtqAJkB9HzdGItAOgKcAk7HmbUg0J4FaAAz3AC25BRwftgJUHv++rPtHVUgvgnk/Y8+tKF9NuAmkI66o/lcCDgEqN8ZbgCVG90Eoke+6NEvWnQTyCabbGKnnHIKN4E4vlgMRSC0wDqPganqZFZh9vGaNdavHTwG5qw99rRRPAYmdIRYfwcXoAHMeAOox8Dopo/rrrsuagT1GJg777zTXnnlFdtoo42Kxp8AFSXiBQgEE9BjYF56b4HN/uCTD4J+ZfH7NmPhgv88CLp7d+vVpYvNWbLEHnxtlr25ZImtTnGru1dW2uZ9+9oXttjKvrQlD4JOkZ5VZViA+p3xBlDZ1yNg4gdBDx8+3C6//PJoZjDJQoCSKPEaBBBAAAEEykuA+k0D6EokAXLxMRgBBBBAAIEgAtRvGkBX8AiQi4/BCCCAAAIIBBGgftMAuoJHgFx8DEYAAQQQQCCIAPWbBtAVPALk4mMwAggggAACQQSo3zSAruARIBcfgxFAAAEEEAgiQP2mAXQFjwC5+BiMAAIIIIBAEAHqNw2gK3gEyMXHYAQQQAABBIIIUL9pAF3BI0AuPgYjgAACCCAQRID6TQPoCh4BcvExGAEEEEAAgSAC1G8aQFfwCJCLj8EIIIAAAggEEaB+0wC6gkeAXHwMRgABBBBAIIgA9ZsG0BU8AuTiYzACCCCAAAJBBKjfNICu4BEgFx+DEUAAAQQQCCJA/aYBdAWPALn4GIwAAggggEAQAeo3DaAreATIxcdgBBBAAAEEgghQv2kAXcEjQC4+BiOAAAIIIBBEgPpNA+gKHgFy8TEYAQQQQACBIALUbxpAV/AIkIuPwQgggAACCAQRoH7TALqCR4BcfAxGAAEEEEAgiAD1mwbQFTwC5OJjMAIIIIAAAkEEqN80gK7gESAXH4MRQAABBBAIIkD9pgF0BY8AufgYjAACCCCAQBAB6jcNoCt4BMjFx2AEEEAAAQSCCFC/aQBdwSNALj4GI4AAAgggEESA+k0D6AoeAXLxMRgBBBBAAIEgAtRvGkBX8AiQi4/BCCCAAAIIBBGgftMAuoJHgFx8DEYAAQQQQCCIAPWbBtAVPALk4mMwAggggAACQQSo3zSAruARIBcfgxFAAAEEEAgiQP2mAXQFjwC5+BiMAAIIIIBAEAHqNw2gK3gEyMXHYAQQQAABBIIIUL9pAF3BI0AuPgYjgAACCCAQRID6TQPoCh4BcvExGAEEEEAAgSAC1G8aQFfwCJCLj8EIIIAAAggEEaB+0wC6gkeAXHwMRgABBBBAIIgA9ZsG0BU8AuTiYzACCCCAAAJBBKjfNICu4BEgFx+DEUAAAQQQCCJA/aYBdAWPALn4GIwAAggggEAQAeo3DaAreATIxcdgBBBAAAEEgghQv2kAXcEjQC4+BiOAAAIIIBBEgPpNA+gKHgFy8TEYAQQQQACBIALUbxpAV/AIkIuPwQgggAACCAQRoH7TALqCR4BcfAxGAAEEEEAgiAD1mwbQFTwC5OJjMAIIIIAAAkEEqN80gK7gESAXH4MRQAABBBAIIkD9pgF0BY8AufgYjAACCCCAQBAB6jcNoCt4BMjFx2AEEEAAAQSCCFC/aQBdwSNALj4GI4AAAgggEESA+k0D6AoeAXLxMRgBBBBAAIEgAtRvGkBX8AiQi4/BCCCAAAIIBBGgftMAuoJHgFx8DEYAAQQQQCCIAPU7ow3gnDlz7Gc/+5k99thjtmDBAhs4cKB97Wtfsx/+8IfWpUuXxGEkQImpeCECCCCAAAJlI0D9zmgD+PDDD9sdd9xhRx55pA0bNsxmzpxpJ554oh1zzDH2i1/8InFACVBiKl6IAAIIIIBA2QhQvzPaABZK4CWXXGLXXHONvfnmm4kDSoASU/FCBBBAAAEEykaA+k0D2BjGc8891zQz+PzzzycOKAFKTMULEUAAAQQQKBsB6jcNYBTG2bNn24gRI6LTvzoV3NRSV1dn+ideFKCamhqrra216urqsgk2G4IAAggggAACTQvQAHawBnD8+PF28cUXN5v5WbNm2dZbb934mnnz5tlee+1le++9t02aNKnZsRMmTLCJEyd+4jU0gBxmEEAAAQQQaD8CNIAdrAFctGiRLV68uNkEbrbZZo13+s6fPz9q/HbddVe7+eabrbKystmxzAC2ny83W4oAAggggEBTAjSAHawBLCXqmvkbM2ZMdOr31ltvtaqqqlKGR68lQCWTMQABBBBAAIHgAtTvjDaAav408zdkyBC75ZZb1mn+Nt5448TBJECJqXghAggggAACZSNA/c5oA6jTvccff3zBIDY0NCQOKAFKTMULEUAAAQQQKBsB6ndGG8DWSiABai1J3gcBBBBAAIH0BKjfNICutBEgFx+DEUAAAQQQCCJA/aYBdAWPALn4GIwAAggggEAQAeo3DaAreATIxcdgBBBAAAEEgghQv2kAXcEjQC4+BiOAAAIIIBBEgPpNA+gKHgFy8TEYAQQQQACBIALUbxpAV/AIkIuPwQgggAACCAQRoH7TALqCR4BcfAxGAAEEEEAgiAD1mwbQFTwC5OJjMAIIIIAAAkEEqN80gK7gESAXH4MRQAABBBAIIkD9pgF0BY8AufgYjAACCCCAQBAB6jcNoCt4BMjFx2AEEEAAAQSCCFC/aQBdwSNALj4GI4AAAgggEESA+k0D6AoeAXLxMRgBBBBAAIEgAtRvGkBX8AiQi4/BCCCAAAIIBBGgftMAuoJHgFx8DEYAAQQQQCCIAPWbBtAVPALk4mMwAggggAACQQSo3zSAruARIBcfgxFAAAEEEAgiQP2mAXQFjwC5+BiMAAIIIIBAEAHqNw2gK3gEyMXHYAQQQAABBIIIUL9pAF3BI0AuPgYjgAACCCAQRID6TQPoCh4BcvExGAEEEEAAgSAC1G8aQFfwCJCLj8EIIIAAAggEEaB+0wC6gkeAXHwMRgABBBBAIIgA9ZsG0BU8AuTiYzACCCCAAAJBBKjfNICu4BEgFx+DEUAAAQQQCCJA/aYBdAWPALn4GIwAAggggEAQAeo3DaAreATIxcdgBBBAAAEEgghQv2kAXcEjQC4+BiOAAAIIIBBEgPpNA+gKHgFy8TEYAQQQQACBIALUbxpAV/AIkIuPwQgggAACCAQRoH7TALqCR4BcfAxGAAEEEEAgiAD1mwbQFTwC5OJjMAIIIIAAAkEEqN80gK7gESAXH4MRQAABBBAIIkD9pgF0BY8AufgYjAACCCCAQBAB6jcNoCt4BMjFx2AEEEAAAQSCCFC/aQBdwSNALj4GI4AAAgggEESA+k0D6AoeAXLxMRgBBBBAAIEgAtRvGkBX8AiQi4/BCCCAAAIIBBGgftMAuoJHgFx8DEYAAQQQQCCIAPWbBtAVPALk4mMwAggggAACQQSo3zSAruARIBcfgxFAAAEEEAgiQP2mAXQFjwC5+BiMAAIIIIBAEAHqNw2gK3gEyMXHYAQQQAABBIIIUL9pAF3BI0AuPgYjgAACCCAQRID6TQPoCh4BcvExGAEEEEAAgSAC1Ipcb4cAAAznSURBVG8aQFfwCJCLj8EIIIAAAggEEaB+0wC6gkeAXHwMRgABBBBAIIgA9ZsG0BU8AuTiYzACCCCAAAJBBKjfNICu4BEgFx+DEUAAAQQQCCJA/aYBdAWPALn4GIwAAggggEAQAeo3DaAreATIxcdgBBBAAAEEgghQv2kAra6uzkaPHm0vvviiTZ8+3YYPH544jAQoMRUvRAABBBBAoGwEqN80gDZu3Dh7/fXXbfLkyTSAZfPVZEMQQAABBBBoOwEawIw3gGr6zjjjDPvDH/5g2223HQ1g233XeGcEEEAAAQTKRoAGMMMN4MKFC23EiBF23333Wb9+/Wzo0KFFG0CdLtY/8VJbW2ubbLKJzZ0716qrq8sm2GwIAggggAACCDQtoAawpqbGli5dar17984kVUVDQ0ND1j65PvKBBx5on/70p+3cc8+1OXPmJGoAJ0yYYBMnTswaF58XAQQQQACBDinwxhtv2GabbdYhP1uxD9WhGsDx48fbxRdf3OxnnjVrlj3yyCN255132pQpU6yqqipxA5g/A6hfDkOGDLG33347s78gigUsrf8e/5pjNjYt8eK/rNkX4feFtoDvRnnsB/ZF+ewHbUl8Bm/JkiXWp0+f8tq4lLamQzWAixYtssWLFzdLp07/8MMPtz/96U9WUVHR+Nq1a9dGzeDRRx9tt9xySyJ+riFIxJTKi9gXqTAnWgn7IhFTai9if6RGXXRF7IuiRKm9gH2R0WsANWOnnR8v8+fPt/3228/uvvvu6JEwgwcPThRCApSIKZUXsS9SYU60EvZFIqbUXsT+SI266IrYF0WJUnsB+yKjDWB+wpJeA5g/jgCl9l0tuiL2RVGi1F7AvkiNOtGK2B+JmFJ5EfsiFeZEK2Ff0ABGQWlpA6hrAi+88EI7++yzrWvXrolCx4vaRoB90TauLXlX9kVL1NpuDPuj7WxLfWf2Ralibfd69gUNYNuli3dGAAEEEEAAAQTKVKBD3QRSpsZsFgIIIIAAAgggUFYCNIBltTvYGAQQQAABBBBAoO0FaADb3pg1IIAAAggggAACZSVAA1hWu4ONQQABBBBAAAEE2l6ABrCFxldddZVdcskltmDBAttxxx3tiiuusFGjRrXw3RiWREB3XN9zzz32yiuvWPfu3W333XeP/vLLVltt1Tj8448/tu9973t2++23R3+3Wc93vPrqq22jjTZKsgpe00KBiy66KLobfty4cXbZZZdF78K+aCFmC4fNmzfPzjrrLJs8ebJ99NFHNmzYMLvpppts5MiR0TvqT2D+5Cc/sRtuuCH6+6f6U5jXXHONbbHFFi1cI8MKCeiPCujPht56661RfRg4cKAdd9xx0Z8djf/4APuibbLzxBNPRHX5hRdesHfffdfuvfdeO/jggxtXlsT9gw8+sFNPPTX6YxGVlZV26KGH2q9+9Stbb7312majA74rDWAL8O+44w479thj7dprr40eHK2Cd9ddd9mrr75q/fv3b8E7MiSJwP77729HHHGE7bLLLrZmzRo755xzbObMmfbyyy9bz549o7c46aST7MEHH7Sbb745+vN8p5xySvQlfvrpp5Osgte0QGDatGnRX9eprq62MWPGNDaA7IsWYLZwiP6c1U477RT5y33DDTe0119/3TbffPPoHy36saQfUfpLR0OHDrUf/ehHNmPGjOj7061btxaumWH5AhdccIH98pe/jJy32247e/755+3444+3888/30477TT2RRtGRj9+dKwfMWKEffnLX/5EA5jkO3DAAQdEzeN1111nq1evjvadas5tt93Whlse5q1pAFvgrqZPgbjyyiuj0fX19VZTUxP9atDfI2ZJR0B/+k8Nt/6m85577hn9bUcVPn1Rv/KVr0QbodnCbbbZxp599lnbdddd09mwDK1lxYoVtvPOO0ezrOedd54NHz48agDZF+mGQMcdFb4nn3yy4Io186GZKM2Of//7349eo32kmXH9WNIPK5bWEfjiF78Yud54442Nb6hZJJ210Kwg+6J1nIu9i2Zbc2cAk7jPmjXLtt12W9OP2njm/OGHH7YDDzzQ3nnnneg71JEWGsAS9+aqVausR48e0Z+Ny51aHjt2bHRa5f777y/xHXl5SwVmz54dnb7SLMb2229vjz32mH3uc5+z/D/uPWTIEPvud79rp59+ektXxbgmBJT7DTbYwC699FLbe++9GxtA9kW6kVHR0uUOKlL6QTRo0CA7+eST7cQTT4w25M0334xmAqdPnx7to3jZa6+9ov9fp7hYWkdAM4DXX3+9PfLII7blllvaiy++aPvuu280K6i/Nc++aB3nYu+S3wAmcf/1r38d/UhSDYkXnW3SDLnO8h1yyCHFVtuu/jsNYIm7S383WAfXZ555xnbbbbfG0WeeeWZ04J06dWqJ78jLWyKgWdeDDjooarqfeuqp6C0086fpel37l7vo2kydGtP0P0vrCeg6S53W0q9lHSBzG0D2Res5J3mn+BTuGWecYYcddli0T3Q9pi5TUZOu45Wu+dPxa8CAAY1vqVP3KpS6rIWldQR0bNLlKT//+c+tqqrKdE2gvie6RlYL+6J1nIu9S34DmMRdzbtO3etyrtxFZ5omTpwYXV7RkRYawBL3Jg1giWBt9HJ9EXW9h5q/wYMH0wC2kXNTbzt37tzoFMmjjz5qO+ywQ/QyGsCUd0LO6rp06RLtDxW5eNH1ZmoEdflDkuIXbus71pr1w+gHP/hBdDOCrgH85z//GZ2B0AwgzXh6+5oGsLg1DWBxo3VewSngEsHa4OW6sUOn2nXHly5mjxdOO7YBdhNved9990WnQzTDES+a6dBBVzfd/PnPf7bPf/7znI5PaZfoMod99tnHJk2a1LhG3eGr6zJ1d3CS018pbWqHX42uB9c1md/5zncaP6v2g67/0zXJ7It0IsAp4OLONIDFjT7xCt0EotOKevSLFk35b7LJJtEdp9wE0gLQhEN0Ea9utNGFvY8//vgnHl8R33jw+9//Prp1X4um8rfeemtuAklonPRly5cvt7feemudl+v0u6z1KBIVQd2Qw75IKup73VFHHWWalc29CUTXvOqSFM3+xRfA6wYQXeOkZdmyZdFNVNwE4rPPH923b9+o8c49Xai7r/VIntdee4190brcTb5bUzeBNPcdiG8C0Z3bupNYi67l1BMouAkkpR1X7qvR9TKaytdt4moEddfjnXfeGf2643lzbbf3dFG7ri3T7F/us//0uBfdYadFB92HHnooKmp6LIkaRi25p8babguz/c65p4DZF+lmQad69VxMXaek6/qee+656AYQ3YygGw+06BpYPa8x9zEwL730Eo+BaeVdpWf+/eUvf4nqg04B68abb37zm3bCCSc0XofMvmhl9P++nZ5KoJsDteixSDrtruu/daOaJmmSuOsxMAsXLoyun40fA6PLK3gMTNvss3b5rnoETPwgaN1Fd/nll0fPBGRpO4H4Iar5a9Avax10tcQPH9bMU+6DoDfeeOO22zDeORLIbwDZF+kG44EHHohuNNDz/3RphG4Iie8C1pbED8FVU6ibp/bYY4/o8T26U5Wl9QQ0O65nLOpMxXvvvRc9OuTII4+0H//4x6ZrNdkXrWed/046M6SGL3/RhI0mBZJ8B/QgaJ3Ny30QtOo7D4Juu/3GOyOAAAIIIIAAAgikJMA1gClBsxoEEEAAAQQQQKBcBGgAy2VPsB0IIIAAAggggEBKAjSAKUGzGgQQQAABBBBAoFwEaADLZU+wHQgggAACCCCAQEoCNIApQbMaBBBAAAEEEECgXARoAMtlT7AdCCCAAAIIIIBASgI0gClBsxoEEEAAAQQQQKBcBGgAy2VPsB0IIIAAAggggEBKAjSAKUGzGgQQQAABBBBAoFwEaADLZU+wHQgggAACCCCAQEoCNIApQbMaBBBAAAEEEECgXARoAMtlT7AdCCCAAAIIIIBASgI0gClBsxoEEEAAAQQQQKBcBGgAy2VPsB0IIIAAAggggEBKAjSAKUGzGgQQQAABBBBAoFwEaADLZU+wHQgggAACCCCAQEoCNIApQbMaBBBAAAEEEECgXARoAMtlT7AdCCCAAAIIIIBASgI0gClBsxoEEEAAAQQQQKBcBGgAy2VPsB0IIIAAAggggEBKAjSAKUGzGgQQQAABBBBAoFwEaADLZU+wHQgggAACCCCAQEoCNIApQbMaBBBAAAEEEECgXARoAMtlT7AdCCCAAAIIIIBASgI0gClBsxoEEEAAAQQQQKBcBGgAy2VPsB0IIIAAAggggEBKAjSAKUGzGgQQQAABBBBAoFwEaADLZU+wHQgggAACCCCAQEoCNIApQbMaBBBAAAEEEECgXARoAMtlT7AdCCCAAAIIIIBASgI0gClBsxoEEEAAAQQQQKBcBP4P8ta9QDIyYDwAAAAASUVORK5CYII=\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"anim = animate_episode(predictor_data[episode_id][\"solver_info\"], predictor_data[episode_id][\"ado_position_future\"], predictor_data[episode_id][\"ado_unbiased_predictions\"])\n",
"anim.save(os.path.join(stats_dir, f\"ep_{episode_id}_predictor.mp4\"), writer=\"ffmpeg\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "39c17b02",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "144aec04",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
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