3293 lines
3.9 MiB
Text
3293 lines
3.9 MiB
Text
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import sys\n",
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"import babeltrace\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from matplotlib import pyplot as plt\n",
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"\n",
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"from IPython.display import display, clear_output\n",
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"\n",
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"sys.path.append(\"/home/adlink/ros2-workspace/install/tracetools_read/lib/python3.8/site-packages/\")\n",
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"sys.path.append(\"/home/adlink/ros2-workspace/src/tracetools_analysis/tracetools_analysis/\")\n",
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"from tracetools_read.trace import *\n",
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"from tracetools_analysis.loading import load_file\n",
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"from tracetools_analysis.processor.ros2 import Ros2Handler\n",
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"from tracetools_analysis.utils.ros2 import Ros2DataModelUtil"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"found converted file: /home/adlink/.ros/tracing/autoware-trace/converted\n"
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]
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}
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],
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"source": [
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"path = os.path.expanduser(\"~/.ros/tracing/autoware-trace/\")\n",
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"file = load_file(path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" [100%] [Ros2Handler]\n"
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]
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}
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],
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"source": [
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"handler = Ros2Handler.process(file)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"====================ROS 2 DATA MODEL===================\n",
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"Contexts:\n",
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" timestamp pid version\n",
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"context_handle \n",
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"578889920 1652795531158728529 9807 2.3.0\n",
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"187651121439504 1652795532294150521 9844 2.3.0\n",
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"187651140920480 1652795532303522314 9846 2.3.0\n",
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"\n",
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"Nodes:\n",
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" timestamp tid rmw_handle name namespace\n",
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"node_handle \n",
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"578605264 1652795531205345494 9807 578658512 launch_ros_9807 /\n",
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"187651140946384 1652795532309089294 9846 187651141104576 system_monitor_container /system/system_monitor/system_monitor\n",
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"187651121479152 1652795532314980435 9844 187651121698368 robot_state_publisher /\n",
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"\n",
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"Publishers (rmw):\n",
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"Empty DataFrame\n",
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"Columns: []\n",
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"Index: []\n",
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"\n",
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"Publishers (rcl):\n",
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" timestamp node_handle rmw_handle topic_name depth\n",
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"publisher_handle \n",
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"281474154018016 1652795531205338094 578605264 580193376 /rosout 1000\n",
|
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"580257728 1652795531225678366 578605264 580723392 /parameter_events 1000\n",
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|
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"281474959216400 1652795532309077134 187651140946384 187651141067920 /rosout 1000\n",
|
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"\n",
|
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"Subscriptions (rmw):\n",
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"Empty DataFrame\n",
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"Columns: []\n",
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"Index: []\n",
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"\n",
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"Subscriptions (rcl):\n",
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|
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" timestamp node_handle rmw_handle topic_name depth\n",
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"subscription_handle \n",
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"187651141000448 1652795532311067501 187651140946384 187651141210960 /parameter_events 1000\n",
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"187651121908000 1652795532321661338 187651121479152 187651121979184 /parameter_events 1000\n",
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"187650864348208 1652795532328044561 187650863948976 187650864221216 /parameter_events 1000\n",
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"\n",
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"Subscription objects:\n",
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" timestamp subscription_handle\n",
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"subscription \n",
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"187651141131344 1652795532311098621 187651141000448\n",
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"187651121977600 1652795532321690459 187651121908000\n",
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|
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"187650864342320 1652795532328082681 187650864348208\n",
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"\n",
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"Services:\n",
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" timestamp node_handle rmw_handle service_name\n",
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"service_handle \n",
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"1652795531227060891 1652795531227060891 578605264 578509024 /launch_ros_9807/describe_parameters\n",
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|
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"1652795531227590973 1652795531227590973 578605264 580692688 /launch_ros_9807/get_parameters\n",
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"1652795531228087295 1652795531228087295 578605264 580711008 /launch_ros_9807/get_parameter_types\n",
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"\n",
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"Clients:\n",
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" timestamp node_handle rmw_handle service_name\n",
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"client_handle \n",
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"281473303560864 1652795531234252437 578605264 580853584 /system/system_monitor/system_monitor/system_m...\n",
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"281473301140736 1652795531565506293 578605264 580919696 /map/map_container/_container/load_node\n",
|
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"281473301141184 1652795531618278041 578605264 580902560 /planning/mission_planning/mission_planning_co...\n",
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"\n",
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"Timers:\n",
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||
|
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" timestamp period tid\n",
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||
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"timer_handle \n",
|
||
|
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"281472359468464 1652795531230470704 1000000000 9832\n",
|
||
|
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"281472359470592 1652795532231528538 1000000000 9832\n",
|
||
|
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"187650864529200 1652795532337450114 100000000 9852\n",
|
||
|
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"\n",
|
||
|
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"Timer-node links:\n",
|
||
|
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" timestamp node_handle\n",
|
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|
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"timer_handle \n",
|
||
|
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"187650864529200 1652795532337464355 187650863948976\n",
|
||
|
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"187650830150352 1652795532361873201 187650829372592\n",
|
||
|
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"187650226411360 1652795532381128230 187650225769280\n",
|
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|
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"\n",
|
||
|
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"Callback objects:\n",
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|
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" timestamp callback_object\n",
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"reference \n",
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||
|
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"187651141131344 1652795532311101741 187651141131568\n",
|
||
|
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"187651141224224 1652795532312414306 187651141224112\n",
|
||
|
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"187651141044096 1652795532312704587 187651141279680\n",
|
||
|
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"\n",
|
||
|
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"Callback symbols:\n",
|
||
|
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" timestamp symbol\n",
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||
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"callback_object \n",
|
||
|
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"187651141131568 1652795532311126901 std::_Bind<void (rclcpp::TimeSource::*(rclcpp:...\n",
|
||
|
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"187651141224112 1652795532312433946 std::_Bind<void (rclcpp_components::ComponentM...\n",
|
||
|
|
"187651141279680 1652795532312719227 std::_Bind<void (rclcpp_components::ComponentM...\n",
|
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"\n",
|
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|
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"Callback instances:\n",
|
||
|
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" callback_object timestamp duration intra_process\n",
|
||
|
|
"0 187650864342544 2022-05-17 13:52:12.337689915 00:00:00.000010 False\n",
|
||
|
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"1 187650864342544 2022-05-17 13:52:12.337722115 00:00:00.000001 False\n",
|
||
|
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"2 187650864342544 2022-05-17 13:52:12.337747756 00:00:00.000001 False\n",
|
||
|
|
"\n",
|
||
|
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"Publish instances (rclcpp):\n",
|
||
|
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" timestamp message\n",
|
||
|
|
"0 1652795532321318177 281474880694112\n",
|
||
|
|
"1 1652795532321770739 281474880694704\n",
|
||
|
|
"2 1652795532321806579 281474880694704\n",
|
||
|
|
"\n",
|
||
|
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"Publish instances (rcl):\n",
|
||
|
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" publisher_handle timestamp message\n",
|
||
|
|
"0 580257728 1652795531226289729 580728240\n",
|
||
|
|
"1 187651121896880 1652795532321323857 281474880694112\n",
|
||
|
|
"2 187651121896880 1652795532321772379 281474880694704\n",
|
||
|
|
"\n",
|
||
|
|
"Publish instances (rmw):\n",
|
||
|
|
"Empty DataFrame\n",
|
||
|
|
"Columns: []\n",
|
||
|
|
"Index: []\n",
|
||
|
|
"\n",
|
||
|
|
"Take instances (rmw):\n",
|
||
|
|
"Empty DataFrame\n",
|
||
|
|
"Columns: []\n",
|
||
|
|
"Index: []\n",
|
||
|
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"\n",
|
||
|
|
"Take instances (rcl):\n",
|
||
|
|
"Empty DataFrame\n",
|
||
|
|
"Columns: []\n",
|
||
|
|
"Index: []\n",
|
||
|
|
"\n",
|
||
|
|
"Take instances (rclcpp):\n",
|
||
|
|
"Empty DataFrame\n",
|
||
|
|
"Columns: []\n",
|
||
|
|
"Index: []\n",
|
||
|
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"\n",
|
||
|
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"Lifecycle state machines:\n",
|
||
|
|
"Empty DataFrame\n",
|
||
|
|
"Columns: []\n",
|
||
|
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"Index: []\n",
|
||
|
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"\n",
|
||
|
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"Lifecycle transitions:\n",
|
||
|
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"Empty DataFrame\n",
|
||
|
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"Columns: []\n",
|
||
|
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"Index: []\n",
|
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|
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"==================================================\n"
|
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]
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}
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],
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"source": [
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"\n",
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"self = handler.data\n",
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"n=3\n",
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"print('====================ROS 2 DATA MODEL===================')\n",
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"print('Contexts:')\n",
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"print(self.contexts[:n].to_string())\n",
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"print()\n",
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"print('Nodes:')\n",
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|
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"print(self.nodes[:n].to_string())\n",
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"print()\n",
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"print('Publishers (rmw):')\n",
|
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|
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"print(self.rmw_publishers[:n].to_string())\n",
|
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|
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"print()\n",
|
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|
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"print('Publishers (rcl):')\n",
|
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|
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"print(self.rcl_publishers[:n].to_string())\n",
|
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|
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"print()\n",
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|
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"print('Subscriptions (rmw):')\n",
|
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|
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"print(self.rmw_subscriptions[:n].to_string())\n",
|
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|
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"print()\n",
|
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|
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"print('Subscriptions (rcl):')\n",
|
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|
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"print(self.rcl_subscriptions[:n].to_string())\n",
|
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|
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"print()\n",
|
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|
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"print('Subscription objects:')\n",
|
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|
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"print(self.subscription_objects[:n].to_string())\n",
|
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|
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"print()\n",
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"print('Services:')\n",
|
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|
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"print(self.services[:n].to_string())\n",
|
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|
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"print()\n",
|
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"print('Clients:')\n",
|
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|
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"print(self.clients[:n].to_string())\n",
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"print()\n",
|
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|
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"print('Timers:')\n",
|
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|
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"print(self.timers[:n].to_string())\n",
|
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|
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"print()\n",
|
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|
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"print('Timer-node links:')\n",
|
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|
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"print(self.timer_node_links[:n].to_string())\n",
|
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|
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"print()\n",
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|
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"print('Callback objects:')\n",
|
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|
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"print(self.callback_objects[:n].to_string())\n",
|
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|
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"print()\n",
|
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|
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"print('Callback symbols:')\n",
|
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|
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"print(self.callback_symbols[:n].to_string())\n",
|
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|
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"print()\n",
|
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|
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"print('Callback instances:')\n",
|
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|
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"print(self.callback_instances[:n].to_string())\n",
|
||
|
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"print()\n",
|
||
|
|
"print('Publish instances (rclcpp):')\n",
|
||
|
|
"print(self.rclcpp_publish_instances[:n].to_string())\n",
|
||
|
|
"print()\n",
|
||
|
|
"print('Publish instances (rcl):')\n",
|
||
|
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"print(self.rcl_publish_instances[:n].to_string())\n",
|
||
|
|
"print()\n",
|
||
|
|
"print('Publish instances (rmw):')\n",
|
||
|
|
"print(self.rmw_publish_instances[:n].to_string())\n",
|
||
|
|
"print()\n",
|
||
|
|
"print('Take instances (rmw):')\n",
|
||
|
|
"print(self.rmw_take_instances[:n].to_string())\n",
|
||
|
|
"print()\n",
|
||
|
|
"print('Take instances (rcl):')\n",
|
||
|
|
"print(self.rcl_take_instances[:n].to_string())\n",
|
||
|
|
"print()\n",
|
||
|
|
"print('Take instances (rclcpp):')\n",
|
||
|
|
"print(self.rclcpp_take_instances[:n].to_string())\n",
|
||
|
|
"print()\n",
|
||
|
|
"print('Lifecycle state machines:')\n",
|
||
|
|
"print(self.lifecycle_state_machines[:n].to_string())\n",
|
||
|
|
"print()\n",
|
||
|
|
"print('Lifecycle transitions:')\n",
|
||
|
|
"print(self.lifecycle_transitions[:n].to_string())\n",
|
||
|
|
"print('==================================================')\n"
|
||
|
|
]
|
||
|
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},
|
||
|
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{
|
||
|
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"cell_type": "code",
|
||
|
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"execution_count": null,
|
||
|
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"metadata": {},
|
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|
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"outputs": [],
|
||
|
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"source": [
|
||
|
|
"nodes = {row.name: row.to_dict() for _, row in handler.data.nodes.iterrows()}\n",
|
||
|
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"\n",
|
||
|
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"df_pub = handler.data.rcl_publishers\n",
|
||
|
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"df_sub = handler.data.rcl_subscriptions\n",
|
||
|
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"topics_pub = {topic_name: {} for topic_name in df_pub['topic_name']}\n",
|
||
|
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"topics_sub = {topic_name: {} for topic_name in df_sub['topic_name']}\n",
|
||
|
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"topics = {**topics_pub, **topics_sub}\n",
|
||
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"\n",
|
||
|
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"for topic_name, topic_dict in topics.items():\n",
|
||
|
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" topic_dict[\"pubs\"] = [handle for handle in df_pub[df_pub['topic_name']==topic_name]['node_handle']]\n",
|
||
|
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" topic_dict[\"subs\"] = [handle for handle in df_sub[df_sub['topic_name']==topic_name]['node_handle']]\n",
|
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" \n",
|
||
|
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"# Aggregate topics that have the same pubs and subs\n",
|
||
|
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"topic_cohorts = {}\n",
|
||
|
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"for topic_name, topic_dict in topics.items():\n",
|
||
|
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" key = (frozenset({*topic_dict[\"pubs\"]}), frozenset({*topic_dict[\"subs\"]}))\n",
|
||
|
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" if key not in topic_cohorts:\n",
|
||
|
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" topic_cohorts[key] = []\n",
|
||
|
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" topic_cohorts[key].append(topic_name)\n",
|
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" \n",
|
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|
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"# Get Publisher frequencies\n",
|
||
|
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"df_publications = handler.data.rcl_publish_instances\n",
|
||
|
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"pub_stats = {}\n",
|
||
|
|
"for _, publication in df_publications.iterrows():\n",
|
||
|
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" hdl = publication['publisher_handle']\n",
|
||
|
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" if hdl not in pub_stats:\n",
|
||
|
|
" pub_stats[hdl] = {'times': []}\n",
|
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|
|
" pub_stats[hdl]['times'].append(publication['timestamp']*1e-9) # Nanoseconds to seconds float"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 27,
|
||
|
|
"metadata": {
|
||
|
|
"scrolled": true
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"<ipython-input-27-0c38c906b61b>:21: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
|
||
|
|
" fig = plt.figure(figsize=(15,5))\n",
|
||
|
|
"<ipython-input-27-0c38c906b61b>:4: RuntimeWarning: Mean of empty slice.\n",
|
||
|
|
" v['period'] = pub_time_diff.mean()\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"#Topics: 161; #Cohorts: 78\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1080x360 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1080x360 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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||
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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||
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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||
|
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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"metadata": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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|
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"metadata": {
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{
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"metadata": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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},
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"metadata": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"metadata": {
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{
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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"metadata": {
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{
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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||
|
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"<Figure size 1080x360 with 1 Axes>"
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||
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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||
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"metadata": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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||
|
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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|
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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"metadata": {
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{
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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"metadata": {
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{
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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"metadata": {
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{
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"metadata": {
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{
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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|
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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"metadata": {
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{
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 1080x360 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1080x360 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1080x360 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"os.makedirs(\"figs\", exist_ok=True)\n",
|
||
|
|
"for i, (k, v) in enumerate(sorted(pub_stats.items(), key=lambda kv: len(kv[1]['times']), reverse=True)):\n",
|
||
|
|
" pub_time_diff = np.diff(np.array(v['times']))\n",
|
||
|
|
" v['period'] = pub_time_diff.mean()\n",
|
||
|
|
" v['period_std'] = pub_time_diff.std()\n",
|
||
|
|
" v['frequency'] = 1 / v['period']\n",
|
||
|
|
" v['frequency_std'] = (1/pub_time_diff).std()\n",
|
||
|
|
"\n",
|
||
|
|
" try:\n",
|
||
|
|
" publisher_data = df_pub[df_pub.index==k]\n",
|
||
|
|
" publisher_node = publisher_data[\"node_handle\"].iloc[0]\n",
|
||
|
|
" topic_name = publisher_data[\"topic_name\"].iloc[0]\n",
|
||
|
|
" node_name = handler.data.nodes[handler.data.nodes.index==publisher_node][\"name\"].iloc[0]\n",
|
||
|
|
" node_scope = handler.data.nodes[handler.data.nodes.index==publisher_node][\"namespace\"].iloc[0]\n",
|
||
|
|
"\n",
|
||
|
|
" except Exception as e:\n",
|
||
|
|
" node_name=\"UNKNOWN\"\n",
|
||
|
|
" topic_name=\"UNKNOWN\"\n",
|
||
|
|
" node_scope=\"UNKNOWN\"\n",
|
||
|
|
" \n",
|
||
|
|
" fig = plt.figure(figsize=(15,5))\n",
|
||
|
|
" ax = fig.add_subplot()\n",
|
||
|
|
" ax.hist(1/pub_time_diff)\n",
|
||
|
|
" ax.set_xlabel(\"Publication Frequency [Hz]\")\n",
|
||
|
|
" ax.set_ylabel(\"#Publications\")\n",
|
||
|
|
" ax.set_title(f\"{os.path.join(node_scope, node_name)} =({v['frequency']:.2f}Hz)=> {topic_name}\")\n",
|
||
|
|
" plt.savefig(f\"figs/{i:06}{os.path.join(node_scope, node_name).replace('/','-')}__{topic_name.replace('/','-')}\")\n",
|
||
|
|
"\n",
|
||
|
|
" \n",
|
||
|
|
"print(f\"#Topics: {len(topics)}; #Cohorts: {len(topic_cohorts)}\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {
|
||
|
|
"scrolled": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"\n",
|
||
|
|
" <iframe\n",
|
||
|
|
" width=\"100%\"\n",
|
||
|
|
" height=\"750px\"\n",
|
||
|
|
" src=\"graph.html\"\n",
|
||
|
|
" frameborder=\"0\"\n",
|
||
|
|
" allowfullscreen\n",
|
||
|
|
" ></iframe>\n",
|
||
|
|
" "
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.lib.display.IFrame at 0xfffbf0ac43d0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"node_filters = [\"transform_listener_impl\", \"_monitor\"]\n",
|
||
|
|
"topic_filters = [\"/rosout\", \"/parameter_events\", \"/diagnostics\"]\n",
|
||
|
|
"\n",
|
||
|
|
"from pyvis.network import Network\n",
|
||
|
|
"net = Network(notebook=True, height='750px', width='100%', bgcolor='#ffffff', font_color='#000000')\n",
|
||
|
|
"\n",
|
||
|
|
"net.add_node(\"INPUT\", label=\"Input\", size=100, color=\"green\", physics=False, x=0, y=0)\n",
|
||
|
|
"net.add_node(\"OUTPUT\", label=\"Output\", size=100, color=\"red\", physics=False, x=6000, y=0)\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"for handle, node in nodes.items():\n",
|
||
|
|
" if any(f in node['name'] for f in node_filters): \n",
|
||
|
|
" continue\n",
|
||
|
|
" net.add_node(handle, label=node['name'], size=20, color=\"#333\")\n",
|
||
|
|
"\n",
|
||
|
|
"for cohort_key, cohort_topic_names in topic_cohorts.items():\n",
|
||
|
|
" cohort_topic_names = [name for name in cohort_topic_names if not any(f in name for f in topic_filters)]\n",
|
||
|
|
" if not cohort_topic_names: \n",
|
||
|
|
" continue\n",
|
||
|
|
" cohort_id=\"\\n\".join(cohort_topic_names)\n",
|
||
|
|
" cohort_weight=len(cohort_topic_names)\n",
|
||
|
|
" net.add_node(cohort_id, label=\" \", title=cohort_id, size=5, color=\"#333\")\n",
|
||
|
|
" \n",
|
||
|
|
" pubs = cohort_key[0]\n",
|
||
|
|
" subs = cohort_key[1]\n",
|
||
|
|
" n_pubs = len(pubs)\n",
|
||
|
|
" n_subs = len(subs)\n",
|
||
|
|
" \n",
|
||
|
|
" try:\n",
|
||
|
|
" if not n_pubs:\n",
|
||
|
|
" net.add_edge(\"INPUT\", cohort_id, arrows=\"to\", color=\"green\", weight=cohort_weight)\n",
|
||
|
|
" if not n_subs:\n",
|
||
|
|
" net.add_edge(cohort_id, \"OUTPUT\", arrows=\"to\", color=\"red\", weight=cohort_weight)\n",
|
||
|
|
"\n",
|
||
|
|
" for pub in pubs:\n",
|
||
|
|
" net.add_edge(pub, cohort_id, arrows=\"to\", color=\"green\", weight=cohort_weight)\n",
|
||
|
|
" for sub in subs:\n",
|
||
|
|
" net.add_edge(cohort_id, sub, arrows=\"to\", color=\"red\", weight=cohort_weight)\n",
|
||
|
|
" except:\n",
|
||
|
|
" continue\n",
|
||
|
|
"\n",
|
||
|
|
"net.toggle_physics(True)\n",
|
||
|
|
"net.show_buttons()\n",
|
||
|
|
"net.show(\"graph.html\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"interpreter": {
|
||
|
|
"hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
|
||
|
|
},
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.8.10"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|