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modaresimrmodaresimr
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new update aaai2023
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‎All copy.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-11-04T21:12:20.851007Z",
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"start_time": "2020-11-04T21:12:20.848232Z"
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}
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},
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"source": [
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"# Run Pipeline\n",
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"To run the Pipeline (all steps from Sensor events to Activity events) : Please execute following code"
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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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"ExecuteTime": {
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"end_time": "2021-11-23T10:55:41.517374Z",
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"start_time": "2021-11-23T10:55:39.391992Z"
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}
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "b3acd518f0114383924f626d0e92968a",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"interactive(children=(Dropdown(description='segmentation', options={\"SlidingEventActivityWindow [{'size': 25},…"
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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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"source": [
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"import general.utils as utils\n",
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"utils.reload()\n",
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"import general.libinstall\n",
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"import general.uiutils\n",
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"general.uiutils.runPipelineUI()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# GEM-Details\n",
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"You can select a specific run and it will show the evaluation information and display charts for precision, recall and f1 measure."
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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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"ExecuteTime": {
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"end_time": "2021-11-12T16:44:59.326471Z",
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"start_time": "2021-11-12T16:44:58.070360Z"
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}
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "e7fa501774ba409a8c1075d326e1e1dc",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"interactive(children=(Dropdown(description='file', options=(('220818_21-40-35-Aruba-s=1', '220818_21-40-35-Aru…"
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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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"source": [
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"import general.libinstall\n",
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"import general.uiutils\n",
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"%matplotlib inline\n",
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"general.uiutils.loadGemMetricUI()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Gem-Compare-Multi-Run\n",
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"You can select multiple run (by holding CTRL key) and it will show the f1 in evaluation information for those run for each activities and also it will shows the combination of activities."
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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": 4,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-11-15T17:10:28.980433Z",
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"start_time": "2021-11-15T17:10:28.932900Z"
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}
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 16/16 [00:00<00:00, 1531.08it/s]"
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]
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},
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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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"=================== The required libraries have been installed ===================\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "91f03ce4bbdf412d845fc5ba2bed643d",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"interactive(children=(Dropdown(description='dataset', options=('Aruba', 'Home1', 'Home2', 'A4H', 'ward', 'VanK…"
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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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"source": [
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"import general.libinstall\n",
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"import general.uiutils\n",
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"import general.utils as utils\n",
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"utils.reload()\n",
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"%matplotlib inline\n",
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"general.uiutils.loadGemMultiUI()"
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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": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-11-12T09:41:37.438665Z",
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"start_time": "2021-11-12T09:41:37.420944Z"
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}
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},
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"outputs": [],
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"source": [
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"import general.utils\n",
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"import result_analyse.resultloader\n",
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"import result_analyse.visualisation as vs\n",
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"\n",
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"from ipywidgets import interact, interactive, fixed, interact_manual,widgets\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"\n",
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"@interact_manual\n",
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"def result_selector(file=result_analyse.resultloader.get_runs()):\n",
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" if(file==None):return\n",
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" print('Analysing ',file)\n",
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" general.utils.convert2SED(file)\n",
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" print('finished')\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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.13"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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},
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"varInspector": {
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"cols": {
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"lenName": 16,
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"lenType": 16,
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"lenVar": 40
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},
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"kernels_config": {
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"python": {
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"delete_cmd_postfix": "",
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"delete_cmd_prefix": "del ",
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"library": "var_list.py",
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"varRefreshCmd": "print(var_dic_list())"
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},
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"r": {
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"delete_cmd_postfix": ") ",
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"delete_cmd_prefix": "rm(",
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"library": "var_list.r",
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"varRefreshCmd": "cat(var_dic_list()) "
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}
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},
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"types_to_exclude": [
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"module",
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"function",
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"builtin_function_or_method",
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"instance",
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"_Feature"
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],
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"window_display": false
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},
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"vscode": {
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"interpreter": {
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"hash": "0f91b223a9be7f82bfd164eb4b10e5e1c532d3ceac636b749a2ce370dda12911"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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